Date: 2019-12-25 22:37:46 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 17380 rows and 102 columns.
#> Top rows are extracted by 'SD, CV, MAD, ATC' methods.
#> Subgroups are detected by 'hclust, kmeans, skmeans, pam, mclust, NMF' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 30000 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots" "collect_stats"
#> [5] "colnames" "functional_enrichment" "get_anno_col" "get_anno"
#> [9] "get_classes" "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol" "nrow"
#> [17] "rownames" "show" "suggest_best_k" "test_to_known_factors"
#> [21] "top_rows_heatmap" "top_rows_overlap"
#>
#> You can get result for a single method by, e.g. object["SD", "hclust"] or object["SD:hclust"]
#> or a subset of methods by object[c("SD", "CV")], c("hclust", "kmeans")]
The call of run_all_consensus_partition_methods() was:
#> run_all_consensus_partition_methods(data = mat, mc.cores = 4)
Dimension of the input matrix:
mat = get_matrix(res_list)
dim(mat)
#> [1] 17380 102
The density distribution for each sample is visualized as in one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_list)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| SD:kmeans | 2 | 1.000 | 1.000 | 1.000 | ** | |
| SD:skmeans | 2 | 1.000 | 0.963 | 0.984 | ** | |
| CV:kmeans | 2 | 1.000 | 0.996 | 0.998 | ** | |
| CV:skmeans | 2 | 1.000 | 0.976 | 0.989 | ** | |
| CV:pam | 2 | 1.000 | 0.981 | 0.993 | ** | |
| MAD:kmeans | 2 | 1.000 | 0.965 | 0.987 | ** | |
| ATC:hclust | 2 | 1.000 | 0.968 | 0.988 | ** | |
| ATC:kmeans | 2 | 1.000 | 0.997 | 0.999 | ** | |
| ATC:mclust | 4 | 1.000 | 0.939 | 0.979 | ** | 2 |
| ATC:NMF | 2 | 1.000 | 0.957 | 0.982 | ** | |
| CV:hclust | 3 | 0.996 | 0.972 | 0.987 | ** | 2 |
| ATC:skmeans | 5 | 0.980 | 0.933 | 0.976 | ** | 2 |
| MAD:pam | 2 | 0.980 | 0.957 | 0.982 | ** | |
| SD:pam | 2 | 0.979 | 0.927 | 0.972 | ** | |
| MAD:NMF | 2 | 0.979 | 0.953 | 0.981 | ** | |
| SD:NMF | 2 | 0.959 | 0.961 | 0.983 | ** | |
| ATC:pam | 4 | 0.945 | 0.923 | 0.968 | * | 2 |
| SD:hclust | 2 | 0.939 | 0.943 | 0.971 | * | |
| MAD:skmeans | 6 | 0.913 | 0.940 | 0.956 | * | 2,5 |
| SD:mclust | 5 | 0.906 | 0.891 | 0.934 | * | 2 |
| CV:NMF | 2 | 0.791 | 0.885 | 0.952 | ||
| MAD:mclust | 2 | 0.621 | 0.816 | 0.912 | ||
| CV:mclust | 3 | 0.416 | 0.708 | 0.806 | ||
| MAD:hclust | 3 | 0.356 | 0.741 | 0.807 |
**: 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 0.959 0.961 0.983 0.334 0.670 0.670
#> CV:NMF 2 0.791 0.885 0.952 0.405 0.616 0.616
#> MAD:NMF 2 0.979 0.953 0.981 0.362 0.637 0.637
#> ATC:NMF 2 1.000 0.957 0.982 0.423 0.581 0.581
#> SD:skmeans 2 1.000 0.963 0.984 0.455 0.539 0.539
#> CV:skmeans 2 1.000 0.976 0.989 0.399 0.598 0.598
#> MAD:skmeans 2 0.919 0.911 0.966 0.465 0.539 0.539
#> ATC:skmeans 2 1.000 0.965 0.986 0.459 0.539 0.539
#> SD:mclust 2 1.000 0.963 0.983 0.336 0.682 0.682
#> CV:mclust 2 0.815 0.940 0.974 0.295 0.719 0.719
#> MAD:mclust 2 0.621 0.816 0.912 0.422 0.533 0.533
#> ATC:mclust 2 0.979 0.949 0.977 0.497 0.498 0.498
#> SD:kmeans 2 1.000 1.000 1.000 0.294 0.706 0.706
#> CV:kmeans 2 1.000 0.996 0.998 0.166 0.838 0.838
#> MAD:kmeans 2 1.000 0.965 0.987 0.306 0.706 0.706
#> ATC:kmeans 2 1.000 0.997 0.999 0.283 0.719 0.719
#> SD:pam 2 0.979 0.927 0.972 0.245 0.761 0.761
#> CV:pam 2 1.000 0.981 0.993 0.151 0.854 0.854
#> MAD:pam 2 0.980 0.957 0.982 0.240 0.761 0.761
#> ATC:pam 2 1.000 0.948 0.980 0.239 0.790 0.790
#> SD:hclust 2 0.939 0.943 0.971 0.218 0.821 0.821
#> CV:hclust 2 1.000 0.990 0.994 0.141 0.854 0.854
#> MAD:hclust 2 0.881 0.926 0.968 0.240 0.806 0.806
#> ATC:hclust 2 1.000 0.968 0.988 0.296 0.694 0.694
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.573 0.817 0.897 0.6745 0.689 0.557
#> CV:NMF 3 0.691 0.776 0.896 0.3569 0.790 0.673
#> MAD:NMF 3 0.739 0.823 0.923 0.7269 0.644 0.473
#> ATC:NMF 3 0.675 0.829 0.923 0.3228 0.841 0.730
#> SD:skmeans 3 0.721 0.878 0.936 0.4068 0.743 0.557
#> CV:skmeans 3 0.774 0.852 0.927 0.6427 0.710 0.526
#> MAD:skmeans 3 0.851 0.893 0.950 0.4206 0.704 0.493
#> ATC:skmeans 3 0.872 0.860 0.943 0.3248 0.815 0.668
#> SD:mclust 3 0.479 0.566 0.827 0.4312 0.880 0.825
#> CV:mclust 3 0.416 0.708 0.806 0.8018 0.722 0.622
#> MAD:mclust 3 0.515 0.734 0.803 0.1141 0.788 0.679
#> ATC:mclust 3 0.631 0.859 0.929 0.0771 0.726 0.570
#> SD:kmeans 3 0.640 0.852 0.914 0.9726 0.594 0.465
#> CV:kmeans 3 0.400 0.638 0.785 1.8043 0.768 0.724
#> MAD:kmeans 3 0.703 0.795 0.908 0.9081 0.609 0.480
#> ATC:kmeans 3 0.889 0.866 0.949 1.1609 0.616 0.485
#> SD:pam 3 0.683 0.852 0.926 1.4437 0.593 0.482
#> CV:pam 3 0.352 0.808 0.868 2.5118 0.599 0.531
#> MAD:pam 3 0.683 0.857 0.934 1.4870 0.585 0.476
#> ATC:pam 3 0.857 0.926 0.971 1.4886 0.596 0.494
#> SD:hclust 3 0.607 0.877 0.915 0.8042 0.827 0.789
#> CV:hclust 3 0.996 0.972 0.987 0.4775 0.947 0.938
#> MAD:hclust 3 0.356 0.741 0.807 1.1181 0.705 0.634
#> ATC:hclust 3 0.524 0.442 0.692 0.5990 0.854 0.793
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.632 0.800 0.896 0.0855 0.894 0.768
#> CV:NMF 4 0.425 0.482 0.750 0.2283 0.713 0.452
#> MAD:NMF 4 0.541 0.620 0.814 0.0864 0.952 0.871
#> ATC:NMF 4 0.526 0.657 0.829 0.1499 0.883 0.751
#> SD:skmeans 4 0.829 0.855 0.923 0.1051 0.873 0.680
#> CV:skmeans 4 0.645 0.763 0.861 0.0773 0.903 0.733
#> MAD:skmeans 4 0.788 0.797 0.891 0.1049 0.918 0.760
#> ATC:skmeans 4 0.850 0.880 0.942 0.0936 0.871 0.697
#> SD:mclust 4 0.411 0.539 0.747 0.1148 0.815 0.722
#> CV:mclust 4 0.535 0.666 0.820 0.0740 0.691 0.482
#> MAD:mclust 4 0.455 0.381 0.751 0.2579 0.813 0.701
#> ATC:mclust 4 1.000 0.939 0.979 0.1365 0.859 0.726
#> SD:kmeans 4 0.602 0.759 0.843 0.1818 0.921 0.809
#> CV:kmeans 4 0.375 0.750 0.761 0.2715 0.661 0.471
#> MAD:kmeans 4 0.485 0.513 0.751 0.1920 0.729 0.454
#> ATC:kmeans 4 0.898 0.887 0.939 0.1142 0.843 0.636
#> SD:pam 4 0.769 0.894 0.949 0.1134 0.800 0.574
#> CV:pam 4 0.477 0.580 0.805 0.2083 0.871 0.723
#> MAD:pam 4 0.772 0.837 0.930 0.1362 0.787 0.549
#> ATC:pam 4 0.945 0.923 0.968 0.2008 0.772 0.501
#> SD:hclust 4 0.507 0.691 0.823 0.2228 0.945 0.916
#> CV:hclust 4 0.935 0.955 0.969 0.1260 0.997 0.996
#> MAD:hclust 4 0.460 0.791 0.849 0.1435 0.885 0.776
#> ATC:hclust 4 0.806 0.852 0.932 0.3448 0.639 0.442
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.526 0.477 0.747 0.1603 0.922 0.809
#> CV:NMF 5 0.545 0.675 0.807 0.1097 0.824 0.498
#> MAD:NMF 5 0.546 0.621 0.746 0.0949 0.825 0.526
#> ATC:NMF 5 0.464 0.587 0.756 0.0877 0.928 0.818
#> SD:skmeans 5 0.816 0.869 0.889 0.1055 0.881 0.621
#> CV:skmeans 5 0.791 0.801 0.897 0.0835 0.882 0.636
#> MAD:skmeans 5 0.951 0.932 0.962 0.0829 0.868 0.569
#> ATC:skmeans 5 0.980 0.933 0.976 0.0876 0.930 0.789
#> SD:mclust 5 0.906 0.891 0.934 0.1739 0.789 0.643
#> CV:mclust 5 0.408 0.560 0.721 0.2010 0.834 0.637
#> MAD:mclust 5 0.655 0.764 0.873 0.0539 0.707 0.506
#> ATC:mclust 5 0.819 0.882 0.934 0.2429 0.824 0.554
#> SD:kmeans 5 0.675 0.590 0.789 0.0877 0.845 0.573
#> CV:kmeans 5 0.588 0.773 0.853 0.1572 0.977 0.930
#> MAD:kmeans 5 0.673 0.742 0.838 0.0949 0.793 0.446
#> ATC:kmeans 5 0.688 0.646 0.828 0.0826 0.886 0.693
#> SD:pam 5 0.735 0.703 0.865 0.1158 0.876 0.645
#> CV:pam 5 0.588 0.701 0.811 0.0774 0.969 0.912
#> MAD:pam 5 0.732 0.801 0.896 0.0836 0.952 0.853
#> ATC:pam 5 0.857 0.819 0.928 0.0336 0.913 0.721
#> SD:hclust 5 0.432 0.580 0.693 0.2234 0.684 0.503
#> CV:hclust 5 0.881 0.942 0.967 0.1859 0.965 0.957
#> MAD:hclust 5 0.483 0.749 0.832 0.0629 1.000 1.000
#> ATC:hclust 5 0.700 0.733 0.866 0.0586 0.979 0.941
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.514 0.567 0.732 0.089831 0.761 0.397
#> CV:NMF 6 0.628 0.583 0.795 0.051867 0.965 0.847
#> MAD:NMF 6 0.626 0.627 0.787 0.058487 0.873 0.544
#> ATC:NMF 6 0.467 0.447 0.675 0.064611 0.967 0.905
#> SD:skmeans 6 0.885 0.831 0.891 0.045620 0.957 0.802
#> CV:skmeans 6 0.744 0.758 0.856 0.040416 0.969 0.874
#> MAD:skmeans 6 0.913 0.940 0.956 0.037181 0.961 0.815
#> ATC:skmeans 6 0.857 0.822 0.890 0.038382 1.000 1.000
#> SD:mclust 6 0.605 0.558 0.778 0.181800 0.824 0.566
#> CV:mclust 6 0.444 0.353 0.679 0.093361 0.904 0.733
#> MAD:mclust 6 0.729 0.749 0.844 0.253059 0.793 0.496
#> ATC:mclust 6 0.808 0.796 0.894 0.000749 0.878 0.606
#> SD:kmeans 6 0.698 0.763 0.832 0.067666 0.901 0.623
#> CV:kmeans 6 0.643 0.632 0.784 0.089956 0.877 0.622
#> MAD:kmeans 6 0.806 0.816 0.874 0.055456 0.946 0.775
#> ATC:kmeans 6 0.672 0.578 0.741 0.066898 0.887 0.652
#> SD:pam 6 0.687 0.664 0.806 0.064686 0.899 0.615
#> CV:pam 6 0.595 0.692 0.803 0.022638 0.996 0.988
#> MAD:pam 6 0.728 0.718 0.854 0.068576 0.913 0.713
#> ATC:pam 6 0.770 0.813 0.906 0.041674 0.954 0.822
#> SD:hclust 6 0.496 0.586 0.741 0.105272 0.795 0.539
#> CV:hclust 6 0.845 0.928 0.967 0.006623 1.000 0.999
#> MAD:hclust 6 0.540 0.733 0.796 0.176937 0.835 0.584
#> ATC:hclust 6 0.674 0.534 0.781 0.057949 0.861 0.622
Following heatmap plots the partition for each combination of methods and the lightness correspond to the silhouette scores for samples in each method. On top the consensus subgroup is inferred from all methods by taking the mean silhouette scores as weight.
collect_stats(res_list, k = 2)
collect_stats(res_list, k = 3)
collect_stats(res_list, k = 4)
collect_stats(res_list, k = 5)
collect_stats(res_list, k = 6)
Collect partitions from all methods:
collect_classes(res_list, k = 2)
collect_classes(res_list, k = 3)
collect_classes(res_list, k = 4)
collect_classes(res_list, k = 5)
collect_classes(res_list, k = 6)
Overlap of top rows from different top-row methods:
top_rows_overlap(res_list, top_n = 1000, method = "euler")
top_rows_overlap(res_list, top_n = 2000, method = "euler")
top_rows_overlap(res_list, top_n = 3000, method = "euler")
top_rows_overlap(res_list, top_n = 4000, method = "euler")
top_rows_overlap(res_list, top_n = 5000, method = "euler")
Also visualize the correspondance of rankings between different top-row methods:
top_rows_overlap(res_list, top_n = 1000, method = "correspondance")
top_rows_overlap(res_list, top_n = 2000, method = "correspondance")
top_rows_overlap(res_list, top_n = 3000, method = "correspondance")
top_rows_overlap(res_list, top_n = 4000, method = "correspondance")
top_rows_overlap(res_list, top_n = 5000, method = "correspondance")
Heatmaps of the top rows:
top_rows_heatmap(res_list, top_n = 1000)
top_rows_heatmap(res_list, top_n = 2000)
top_rows_heatmap(res_list, top_n = 3000)
top_rows_heatmap(res_list, top_n = 4000)
top_rows_heatmap(res_list, top_n = 5000)
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "hclust"]
# you can also extract it by
# res = res_list["SD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17380 rows and 102 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.939 0.943 0.971 0.218 0.821 0.821
#> 3 3 0.607 0.877 0.915 0.804 0.827 0.789
#> 4 4 0.507 0.691 0.823 0.223 0.945 0.916
#> 5 5 0.432 0.580 0.693 0.223 0.684 0.503
#> 6 6 0.496 0.586 0.741 0.105 0.795 0.539
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
#> SRR612587 2 0.8327 0.688 0.264 0.736
#> SRR1458769 2 0.0000 0.968 0.000 1.000
#> SRR613162 1 0.0000 1.000 1.000 0.000
#> SRR1352481 1 0.0000 1.000 1.000 0.000
#> SRR1468876 2 0.1633 0.958 0.024 0.976
#> SRR1399223 2 0.0000 0.968 0.000 1.000
#> SRR660030 2 0.1184 0.962 0.016 0.984
#> SRR1333609 2 0.0938 0.964 0.012 0.988
#> SRR1471612 2 0.0000 0.968 0.000 1.000
#> SRR1413998 2 0.0000 0.968 0.000 1.000
#> SRR1122940 2 0.0000 0.968 0.000 1.000
#> SRR1402563 2 0.0938 0.964 0.012 0.988
#> SRR1398393 2 0.0000 0.968 0.000 1.000
#> SRR657961 2 0.0000 0.968 0.000 1.000
#> SRR1471135 2 0.1184 0.962 0.016 0.984
#> SRR1430001 2 0.1414 0.960 0.020 0.980
#> SRR662775 1 0.0000 1.000 1.000 0.000
#> SRR1474182 2 0.0000 0.968 0.000 1.000
#> SRR607190 1 0.0000 1.000 1.000 0.000
#> SRR612467 2 0.0000 0.968 0.000 1.000
#> SRR1465959 2 0.0000 0.968 0.000 1.000
#> SRR1446132 2 0.0000 0.968 0.000 1.000
#> SRR1416933 2 0.0000 0.968 0.000 1.000
#> SRR1102538 2 0.0000 0.968 0.000 1.000
#> SRR1098636 2 0.0000 0.968 0.000 1.000
#> SRR1072998 2 0.0000 0.968 0.000 1.000
#> SRR627443 1 0.0000 1.000 1.000 0.000
#> SRR656131 1 0.0000 1.000 1.000 0.000
#> SRR823991 2 0.0000 0.968 0.000 1.000
#> SRR1089158 2 0.0000 0.968 0.000 1.000
#> SRR1469036 2 0.1414 0.960 0.020 0.980
#> SRR824039 2 0.0000 0.968 0.000 1.000
#> SRR1339047 2 0.0000 0.968 0.000 1.000
#> SRR1443049 2 0.0000 0.968 0.000 1.000
#> SRR1122885 2 0.0000 0.968 0.000 1.000
#> SRR602895 2 0.1633 0.953 0.024 0.976
#> SRR1409837 2 0.0000 0.968 0.000 1.000
#> SRR1388959 2 0.0000 0.968 0.000 1.000
#> SRR659863 1 0.0000 1.000 1.000 0.000
#> SRR1089877 2 0.0000 0.968 0.000 1.000
#> SRR1123775 2 0.1184 0.962 0.016 0.984
#> SRR658909 2 0.7950 0.723 0.240 0.760
#> SRR1140510 2 0.0000 0.968 0.000 1.000
#> SRR607562 2 0.1633 0.953 0.024 0.976
#> SRR1122913 2 0.0000 0.968 0.000 1.000
#> SRR598042 2 0.0000 0.968 0.000 1.000
#> SRR1467340 2 0.1184 0.962 0.016 0.984
#> SRR1072321 2 0.0000 0.968 0.000 1.000
#> SRR1094580 2 0.0000 0.968 0.000 1.000
#> SRR1076608 2 0.0000 0.968 0.000 1.000
#> SRR1395462 2 0.0000 0.968 0.000 1.000
#> SRR1489220 2 0.2948 0.936 0.052 0.948
#> SRR614371 2 0.7950 0.723 0.240 0.760
#> SRR615455 2 0.9775 0.383 0.412 0.588
#> SRR1070573 2 0.0000 0.968 0.000 1.000
#> SRR598749 2 0.0000 0.968 0.000 1.000
#> SRR1365556 2 0.0000 0.968 0.000 1.000
#> SRR1350023 2 0.0000 0.968 0.000 1.000
#> SRR1446582 2 0.1184 0.962 0.016 0.984
#> SRR1439763 2 0.1184 0.962 0.016 0.984
#> SRR1343986 2 0.1184 0.962 0.016 0.984
#> SRR807463 2 0.0000 0.968 0.000 1.000
#> SRR660390 1 0.0000 1.000 1.000 0.000
#> SRR1367672 2 0.0000 0.968 0.000 1.000
#> SRR613294 2 0.8327 0.688 0.264 0.736
#> SRR824015 2 0.0000 0.968 0.000 1.000
#> SRR1078924 2 0.0000 0.968 0.000 1.000
#> SRR662221 2 0.8207 0.700 0.256 0.744
#> SRR655017 1 0.0000 1.000 1.000 0.000
#> SRR1338450 2 0.1633 0.958 0.024 0.976
#> SRR663741 2 0.8207 0.700 0.256 0.744
#> SRR1396057 2 0.0000 0.968 0.000 1.000
#> SRR1083800 2 0.0672 0.965 0.008 0.992
#> SRR1445789 2 0.0000 0.968 0.000 1.000
#> SRR1387355 2 0.1414 0.960 0.020 0.980
#> SRR1388855 2 0.0000 0.968 0.000 1.000
#> SRR1445449 2 0.1633 0.958 0.024 0.976
#> SRR1380740 2 0.0938 0.964 0.012 0.988
#> SRR659995 2 0.8207 0.700 0.256 0.744
#> SRR1489524 2 0.0000 0.968 0.000 1.000
#> SRR1444662 2 0.0000 0.968 0.000 1.000
#> SRR1383652 2 0.1184 0.962 0.016 0.984
#> SRR1361243 2 0.0938 0.964 0.012 0.988
#> SRR1490337 2 0.1184 0.962 0.016 0.984
#> SRR823967 2 0.0000 0.968 0.000 1.000
#> SRR660127 1 0.0000 1.000 1.000 0.000
#> SRR1366627 2 0.0000 0.968 0.000 1.000
#> SRR1361219 2 0.0000 0.968 0.000 1.000
#> SRR1393510 2 0.0000 0.968 0.000 1.000
#> SRR662558 2 0.7950 0.723 0.240 0.760
#> SRR1077334 2 0.0000 0.968 0.000 1.000
#> SRR807438 2 0.1633 0.958 0.024 0.976
#> SRR1459078 2 0.0938 0.964 0.012 0.988
#> SRR1329704 2 0.0000 0.968 0.000 1.000
#> SRR1468072 2 0.0938 0.964 0.012 0.988
#> SRR1376196 2 0.0000 0.968 0.000 1.000
#> SRR1442909 2 0.1184 0.962 0.016 0.984
#> SRR1414269 2 0.1184 0.962 0.016 0.984
#> SRR1381913 2 0.0000 0.968 0.000 1.000
#> SRR1340157 2 0.0000 0.968 0.000 1.000
#> SRR1407583 2 0.0000 0.968 0.000 1.000
#> SRR615826 2 0.0000 0.968 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 3 0.4399 0.668 0.188 0.000 0.812
#> SRR1458769 2 0.0424 0.925 0.000 0.992 0.008
#> SRR613162 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1352481 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1468876 2 0.3918 0.879 0.004 0.856 0.140
#> SRR1399223 2 0.2165 0.924 0.000 0.936 0.064
#> SRR660030 2 0.3412 0.895 0.000 0.876 0.124
#> SRR1333609 2 0.2959 0.910 0.000 0.900 0.100
#> SRR1471612 2 0.0237 0.926 0.000 0.996 0.004
#> SRR1413998 2 0.1411 0.917 0.000 0.964 0.036
#> SRR1122940 2 0.0237 0.926 0.000 0.996 0.004
#> SRR1402563 2 0.2959 0.910 0.000 0.900 0.100
#> SRR1398393 2 0.1031 0.927 0.000 0.976 0.024
#> SRR657961 2 0.4121 0.832 0.000 0.832 0.168
#> SRR1471135 2 0.3412 0.895 0.000 0.876 0.124
#> SRR1430001 2 0.3619 0.887 0.000 0.864 0.136
#> SRR662775 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1474182 2 0.0237 0.926 0.000 0.996 0.004
#> SRR607190 1 0.0000 1.000 1.000 0.000 0.000
#> SRR612467 2 0.4121 0.832 0.000 0.832 0.168
#> SRR1465959 2 0.0237 0.926 0.000 0.996 0.004
#> SRR1446132 2 0.1411 0.917 0.000 0.964 0.036
#> SRR1416933 2 0.0424 0.925 0.000 0.992 0.008
#> SRR1102538 2 0.0592 0.925 0.000 0.988 0.012
#> SRR1098636 2 0.1163 0.926 0.000 0.972 0.028
#> SRR1072998 2 0.0592 0.925 0.000 0.988 0.012
#> SRR627443 1 0.0000 1.000 1.000 0.000 0.000
#> SRR656131 1 0.0000 1.000 1.000 0.000 0.000
#> SRR823991 2 0.1163 0.926 0.000 0.972 0.028
#> SRR1089158 2 0.0237 0.926 0.000 0.996 0.004
#> SRR1469036 2 0.3619 0.887 0.000 0.864 0.136
#> SRR824039 2 0.1163 0.926 0.000 0.972 0.028
#> SRR1339047 2 0.1411 0.917 0.000 0.964 0.036
#> SRR1443049 2 0.0237 0.926 0.000 0.996 0.004
#> SRR1122885 2 0.0237 0.926 0.000 0.996 0.004
#> SRR602895 2 0.6168 0.367 0.000 0.588 0.412
#> SRR1409837 2 0.0237 0.926 0.000 0.996 0.004
#> SRR1388959 2 0.1411 0.917 0.000 0.964 0.036
#> SRR659863 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1089877 2 0.1031 0.927 0.000 0.976 0.024
#> SRR1123775 2 0.3412 0.895 0.000 0.876 0.124
#> SRR658909 3 0.8331 0.652 0.164 0.208 0.628
#> SRR1140510 2 0.0892 0.925 0.000 0.980 0.020
#> SRR607562 2 0.6168 0.367 0.000 0.588 0.412
#> SRR1122913 2 0.0237 0.926 0.000 0.996 0.004
#> SRR598042 2 0.4121 0.832 0.000 0.832 0.168
#> SRR1467340 2 0.2796 0.911 0.000 0.908 0.092
#> SRR1072321 2 0.0237 0.926 0.000 0.996 0.004
#> SRR1094580 2 0.0237 0.926 0.000 0.996 0.004
#> SRR1076608 2 0.1289 0.916 0.000 0.968 0.032
#> SRR1395462 2 0.4121 0.832 0.000 0.832 0.168
#> SRR1489220 2 0.5902 0.614 0.004 0.680 0.316
#> SRR614371 3 0.8331 0.652 0.164 0.208 0.628
#> SRR615455 3 0.5810 0.461 0.336 0.000 0.664
#> SRR1070573 2 0.0237 0.926 0.000 0.996 0.004
#> SRR598749 3 0.3192 0.672 0.000 0.112 0.888
#> SRR1365556 2 0.2165 0.924 0.000 0.936 0.064
#> SRR1350023 2 0.1411 0.917 0.000 0.964 0.036
#> SRR1446582 2 0.3412 0.895 0.000 0.876 0.124
#> SRR1439763 2 0.3038 0.906 0.000 0.896 0.104
#> SRR1343986 2 0.3038 0.906 0.000 0.896 0.104
#> SRR807463 2 0.0592 0.925 0.000 0.988 0.012
#> SRR660390 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1367672 2 0.0237 0.926 0.000 0.996 0.004
#> SRR613294 3 0.4399 0.668 0.188 0.000 0.812
#> SRR824015 2 0.2066 0.924 0.000 0.940 0.060
#> SRR1078924 2 0.0237 0.926 0.000 0.996 0.004
#> SRR662221 3 0.4521 0.696 0.180 0.004 0.816
#> SRR655017 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1338450 2 0.3918 0.879 0.004 0.856 0.140
#> SRR663741 3 0.7058 0.710 0.180 0.100 0.720
#> SRR1396057 2 0.0424 0.925 0.000 0.992 0.008
#> SRR1083800 2 0.1964 0.924 0.000 0.944 0.056
#> SRR1445789 2 0.1411 0.917 0.000 0.964 0.036
#> SRR1387355 2 0.3551 0.889 0.000 0.868 0.132
#> SRR1388855 2 0.1289 0.919 0.000 0.968 0.032
#> SRR1445449 2 0.3918 0.879 0.004 0.856 0.140
#> SRR1380740 2 0.2959 0.910 0.000 0.900 0.100
#> SRR659995 3 0.4521 0.696 0.180 0.004 0.816
#> SRR1489524 2 0.1411 0.917 0.000 0.964 0.036
#> SRR1444662 2 0.2165 0.924 0.000 0.936 0.064
#> SRR1383652 2 0.3412 0.895 0.000 0.876 0.124
#> SRR1361243 2 0.2959 0.910 0.000 0.900 0.100
#> SRR1490337 2 0.3412 0.895 0.000 0.876 0.124
#> SRR823967 2 0.1163 0.926 0.000 0.972 0.028
#> SRR660127 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1366627 2 0.2165 0.924 0.000 0.936 0.064
#> SRR1361219 2 0.0424 0.925 0.000 0.992 0.008
#> SRR1393510 2 0.2356 0.923 0.000 0.928 0.072
#> SRR662558 3 0.8331 0.652 0.164 0.208 0.628
#> SRR1077334 2 0.0237 0.926 0.000 0.996 0.004
#> SRR807438 2 0.3918 0.879 0.004 0.856 0.140
#> SRR1459078 2 0.2959 0.910 0.000 0.900 0.100
#> SRR1329704 2 0.0424 0.925 0.000 0.992 0.008
#> SRR1468072 2 0.2959 0.910 0.000 0.900 0.100
#> SRR1376196 2 0.0237 0.926 0.000 0.996 0.004
#> SRR1442909 2 0.3412 0.895 0.000 0.876 0.124
#> SRR1414269 2 0.3412 0.895 0.000 0.876 0.124
#> SRR1381913 2 0.4121 0.832 0.000 0.832 0.168
#> SRR1340157 2 0.0237 0.926 0.000 0.996 0.004
#> SRR1407583 2 0.0424 0.925 0.000 0.992 0.008
#> SRR615826 3 0.3192 0.672 0.000 0.112 0.888
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.4454 0.677 0.000 0.000 0.308 0.692
#> SRR1458769 2 0.1118 0.801 0.000 0.964 0.000 0.036
#> SRR613162 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1468876 2 0.4800 0.660 0.000 0.656 0.340 0.004
#> SRR1399223 2 0.3004 0.801 0.000 0.892 0.060 0.048
#> SRR660030 2 0.4522 0.688 0.000 0.680 0.320 0.000
#> SRR1333609 2 0.4697 0.710 0.000 0.696 0.296 0.008
#> SRR1471612 2 0.0657 0.807 0.000 0.984 0.004 0.012
#> SRR1413998 2 0.2271 0.777 0.000 0.916 0.008 0.076
#> SRR1122940 2 0.1488 0.811 0.000 0.956 0.032 0.012
#> SRR1402563 2 0.4697 0.710 0.000 0.696 0.296 0.008
#> SRR1398393 2 0.1302 0.813 0.000 0.956 0.044 0.000
#> SRR657961 2 0.6571 0.540 0.000 0.612 0.264 0.124
#> SRR1471135 2 0.4543 0.685 0.000 0.676 0.324 0.000
#> SRR1430001 2 0.4624 0.666 0.000 0.660 0.340 0.000
#> SRR662775 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1474182 2 0.0336 0.806 0.000 0.992 0.000 0.008
#> SRR607190 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR612467 2 0.6595 0.535 0.000 0.608 0.268 0.124
#> SRR1465959 2 0.0336 0.805 0.000 0.992 0.000 0.008
#> SRR1446132 2 0.2271 0.777 0.000 0.916 0.008 0.076
#> SRR1416933 2 0.1109 0.804 0.000 0.968 0.004 0.028
#> SRR1102538 2 0.0927 0.806 0.000 0.976 0.008 0.016
#> SRR1098636 2 0.3219 0.780 0.000 0.836 0.164 0.000
#> SRR1072998 2 0.0927 0.806 0.000 0.976 0.008 0.016
#> SRR627443 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR823991 2 0.1389 0.812 0.000 0.952 0.048 0.000
#> SRR1089158 2 0.0672 0.807 0.000 0.984 0.008 0.008
#> SRR1469036 2 0.4624 0.666 0.000 0.660 0.340 0.000
#> SRR824039 2 0.1389 0.812 0.000 0.952 0.048 0.000
#> SRR1339047 2 0.2198 0.780 0.000 0.920 0.008 0.072
#> SRR1443049 2 0.0336 0.805 0.000 0.992 0.000 0.008
#> SRR1122885 2 0.0336 0.805 0.000 0.992 0.000 0.008
#> SRR602895 3 0.7128 0.166 0.000 0.372 0.492 0.136
#> SRR1409837 2 0.0336 0.806 0.000 0.992 0.000 0.008
#> SRR1388959 2 0.2271 0.777 0.000 0.916 0.008 0.076
#> SRR659863 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1089877 2 0.1211 0.812 0.000 0.960 0.040 0.000
#> SRR1123775 2 0.4522 0.688 0.000 0.680 0.320 0.000
#> SRR658909 3 0.1488 0.126 0.000 0.012 0.956 0.032
#> SRR1140510 2 0.2002 0.803 0.000 0.936 0.020 0.044
#> SRR607562 3 0.7128 0.166 0.000 0.372 0.492 0.136
#> SRR1122913 2 0.1488 0.811 0.000 0.956 0.032 0.012
#> SRR598042 2 0.6595 0.535 0.000 0.608 0.268 0.124
#> SRR1467340 2 0.4356 0.713 0.000 0.708 0.292 0.000
#> SRR1072321 2 0.0524 0.807 0.000 0.988 0.004 0.008
#> SRR1094580 2 0.0672 0.809 0.000 0.984 0.008 0.008
#> SRR1076608 2 0.1978 0.780 0.000 0.928 0.004 0.068
#> SRR1395462 2 0.6547 0.548 0.000 0.616 0.260 0.124
#> SRR1489220 3 0.5510 -0.314 0.000 0.480 0.504 0.016
#> SRR614371 3 0.1488 0.126 0.000 0.012 0.956 0.032
#> SRR615455 4 0.5696 0.448 0.024 0.000 0.480 0.496
#> SRR1070573 2 0.2198 0.809 0.000 0.920 0.072 0.008
#> SRR598749 4 0.5655 0.576 0.000 0.084 0.212 0.704
#> SRR1365556 2 0.3071 0.802 0.000 0.888 0.068 0.044
#> SRR1350023 2 0.2271 0.777 0.000 0.916 0.008 0.076
#> SRR1446582 2 0.4543 0.685 0.000 0.676 0.324 0.000
#> SRR1439763 2 0.4431 0.703 0.000 0.696 0.304 0.000
#> SRR1343986 2 0.4454 0.700 0.000 0.692 0.308 0.000
#> SRR807463 2 0.0927 0.806 0.000 0.976 0.008 0.016
#> SRR660390 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1367672 2 0.0469 0.804 0.000 0.988 0.000 0.012
#> SRR613294 4 0.4454 0.677 0.000 0.000 0.308 0.692
#> SRR824015 2 0.2892 0.805 0.000 0.896 0.068 0.036
#> SRR1078924 2 0.1488 0.811 0.000 0.956 0.032 0.012
#> SRR662221 3 0.4746 -0.484 0.000 0.000 0.632 0.368
#> SRR655017 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1338450 2 0.4800 0.660 0.000 0.656 0.340 0.004
#> SRR663741 3 0.3975 -0.261 0.000 0.000 0.760 0.240
#> SRR1396057 2 0.1109 0.804 0.000 0.968 0.004 0.028
#> SRR1083800 2 0.3610 0.764 0.000 0.800 0.200 0.000
#> SRR1445789 2 0.2198 0.780 0.000 0.920 0.008 0.072
#> SRR1387355 2 0.4605 0.671 0.000 0.664 0.336 0.000
#> SRR1388855 2 0.2198 0.780 0.000 0.920 0.008 0.072
#> SRR1445449 2 0.4781 0.664 0.000 0.660 0.336 0.004
#> SRR1380740 2 0.4697 0.710 0.000 0.696 0.296 0.008
#> SRR659995 3 0.4746 -0.484 0.000 0.000 0.632 0.368
#> SRR1489524 2 0.2271 0.777 0.000 0.916 0.008 0.076
#> SRR1444662 2 0.4046 0.795 0.000 0.828 0.124 0.048
#> SRR1383652 2 0.4543 0.685 0.000 0.676 0.324 0.000
#> SRR1361243 2 0.4697 0.710 0.000 0.696 0.296 0.008
#> SRR1490337 2 0.4543 0.685 0.000 0.676 0.324 0.000
#> SRR823967 2 0.1389 0.812 0.000 0.952 0.048 0.000
#> SRR660127 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1366627 2 0.3004 0.801 0.000 0.892 0.060 0.048
#> SRR1361219 2 0.0592 0.803 0.000 0.984 0.000 0.016
#> SRR1393510 2 0.4322 0.783 0.000 0.804 0.152 0.044
#> SRR662558 3 0.1488 0.126 0.000 0.012 0.956 0.032
#> SRR1077334 2 0.0672 0.807 0.000 0.984 0.008 0.008
#> SRR807438 2 0.4800 0.660 0.000 0.656 0.340 0.004
#> SRR1459078 2 0.4697 0.710 0.000 0.696 0.296 0.008
#> SRR1329704 2 0.3182 0.803 0.000 0.876 0.096 0.028
#> SRR1468072 2 0.4539 0.727 0.000 0.720 0.272 0.008
#> SRR1376196 2 0.2048 0.811 0.000 0.928 0.064 0.008
#> SRR1442909 2 0.4543 0.685 0.000 0.676 0.324 0.000
#> SRR1414269 2 0.4543 0.685 0.000 0.676 0.324 0.000
#> SRR1381913 2 0.6595 0.535 0.000 0.608 0.268 0.124
#> SRR1340157 2 0.0469 0.804 0.000 0.988 0.000 0.012
#> SRR1407583 2 0.1256 0.805 0.000 0.964 0.008 0.028
#> SRR615826 4 0.5655 0.576 0.000 0.084 0.212 0.704
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR612587 4 0.5032 0.262 0.000 0.000 0.032 0.520 0.448
#> SRR1458769 2 0.2359 0.702 0.000 0.904 0.060 0.000 0.036
#> SRR613162 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1468876 3 0.4630 0.710 0.000 0.396 0.588 0.016 0.000
#> SRR1399223 2 0.4708 0.431 0.000 0.668 0.292 0.000 0.040
#> SRR660030 3 0.4201 0.708 0.000 0.408 0.592 0.000 0.000
#> SRR1333609 3 0.4410 0.678 0.000 0.440 0.556 0.000 0.004
#> SRR1471612 2 0.1502 0.700 0.000 0.940 0.056 0.000 0.004
#> SRR1413998 2 0.5222 0.548 0.000 0.716 0.112 0.016 0.156
#> SRR1122940 2 0.1341 0.694 0.000 0.944 0.056 0.000 0.000
#> SRR1402563 3 0.4410 0.678 0.000 0.440 0.556 0.000 0.004
#> SRR1398393 2 0.3586 0.411 0.000 0.736 0.264 0.000 0.000
#> SRR657961 3 0.5736 0.466 0.000 0.400 0.512 0.000 0.088
#> SRR1471135 3 0.4192 0.712 0.000 0.404 0.596 0.000 0.000
#> SRR1430001 3 0.4150 0.713 0.000 0.388 0.612 0.000 0.000
#> SRR662775 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.1270 0.699 0.000 0.948 0.052 0.000 0.000
#> SRR607190 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR612467 3 0.5729 0.473 0.000 0.396 0.516 0.000 0.088
#> SRR1465959 2 0.1121 0.700 0.000 0.956 0.044 0.000 0.000
#> SRR1446132 2 0.5222 0.548 0.000 0.716 0.112 0.016 0.156
#> SRR1416933 2 0.2144 0.696 0.000 0.912 0.068 0.000 0.020
#> SRR1102538 2 0.1952 0.684 0.000 0.912 0.084 0.000 0.004
#> SRR1098636 2 0.4210 -0.247 0.000 0.588 0.412 0.000 0.000
#> SRR1072998 2 0.1952 0.684 0.000 0.912 0.084 0.000 0.004
#> SRR627443 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR823991 2 0.3774 0.276 0.000 0.704 0.296 0.000 0.000
#> SRR1089158 2 0.1341 0.698 0.000 0.944 0.056 0.000 0.000
#> SRR1469036 3 0.4150 0.713 0.000 0.388 0.612 0.000 0.000
#> SRR824039 2 0.3774 0.276 0.000 0.704 0.296 0.000 0.000
#> SRR1339047 2 0.5089 0.563 0.000 0.728 0.104 0.016 0.152
#> SRR1443049 2 0.1270 0.699 0.000 0.948 0.052 0.000 0.000
#> SRR1122885 2 0.1121 0.700 0.000 0.956 0.044 0.000 0.000
#> SRR602895 3 0.4990 0.212 0.000 0.152 0.732 0.012 0.104
#> SRR1409837 2 0.1270 0.699 0.000 0.948 0.052 0.000 0.000
#> SRR1388959 2 0.5222 0.548 0.000 0.716 0.112 0.016 0.156
#> SRR659863 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1089877 2 0.3730 0.305 0.000 0.712 0.288 0.000 0.000
#> SRR1123775 3 0.4201 0.708 0.000 0.408 0.592 0.000 0.000
#> SRR658909 3 0.4738 -0.568 0.000 0.000 0.520 0.464 0.016
#> SRR1140510 2 0.3616 0.625 0.000 0.804 0.164 0.000 0.032
#> SRR607562 3 0.4990 0.212 0.000 0.152 0.732 0.012 0.104
#> SRR1122913 2 0.1341 0.694 0.000 0.944 0.056 0.000 0.000
#> SRR598042 3 0.5729 0.473 0.000 0.396 0.516 0.000 0.088
#> SRR1467340 3 0.4278 0.666 0.000 0.452 0.548 0.000 0.000
#> SRR1072321 2 0.1410 0.695 0.000 0.940 0.060 0.000 0.000
#> SRR1094580 2 0.1478 0.693 0.000 0.936 0.064 0.000 0.000
#> SRR1076608 2 0.4416 0.595 0.000 0.776 0.084 0.008 0.132
#> SRR1395462 3 0.5742 0.466 0.000 0.404 0.508 0.000 0.088
#> SRR1489220 3 0.4820 0.543 0.000 0.232 0.712 0.040 0.016
#> SRR614371 3 0.4738 -0.568 0.000 0.000 0.520 0.464 0.016
#> SRR615455 4 0.1117 0.508 0.016 0.000 0.000 0.964 0.020
#> SRR1070573 2 0.2377 0.630 0.000 0.872 0.128 0.000 0.000
#> SRR598749 5 0.3710 1.000 0.000 0.048 0.144 0.000 0.808
#> SRR1365556 2 0.4805 0.321 0.000 0.648 0.312 0.000 0.040
#> SRR1350023 2 0.5222 0.548 0.000 0.716 0.112 0.016 0.156
#> SRR1446582 3 0.4192 0.711 0.000 0.404 0.596 0.000 0.000
#> SRR1439763 3 0.4249 0.689 0.000 0.432 0.568 0.000 0.000
#> SRR1343986 3 0.4242 0.694 0.000 0.428 0.572 0.000 0.000
#> SRR807463 2 0.1952 0.684 0.000 0.912 0.084 0.000 0.004
#> SRR660390 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1367672 2 0.0609 0.703 0.000 0.980 0.020 0.000 0.000
#> SRR613294 4 0.5032 0.262 0.000 0.000 0.032 0.520 0.448
#> SRR824015 2 0.4836 0.265 0.000 0.628 0.336 0.000 0.036
#> SRR1078924 2 0.1341 0.694 0.000 0.944 0.056 0.000 0.000
#> SRR662221 4 0.5177 0.629 0.000 0.000 0.180 0.688 0.132
#> SRR655017 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1338450 3 0.4630 0.710 0.000 0.396 0.588 0.016 0.000
#> SRR663741 4 0.3752 0.506 0.000 0.000 0.292 0.708 0.000
#> SRR1396057 2 0.2144 0.696 0.000 0.912 0.068 0.000 0.020
#> SRR1083800 2 0.4138 -0.157 0.000 0.616 0.384 0.000 0.000
#> SRR1445789 2 0.5089 0.563 0.000 0.728 0.104 0.016 0.152
#> SRR1387355 3 0.4161 0.713 0.000 0.392 0.608 0.000 0.000
#> SRR1388855 2 0.4990 0.570 0.000 0.732 0.104 0.012 0.152
#> SRR1445449 3 0.4640 0.708 0.000 0.400 0.584 0.016 0.000
#> SRR1380740 3 0.4410 0.678 0.000 0.440 0.556 0.000 0.004
#> SRR659995 4 0.5177 0.629 0.000 0.000 0.180 0.688 0.132
#> SRR1489524 2 0.5222 0.548 0.000 0.716 0.112 0.016 0.156
#> SRR1444662 2 0.4990 0.131 0.000 0.600 0.360 0.000 0.040
#> SRR1383652 3 0.4192 0.712 0.000 0.404 0.596 0.000 0.000
#> SRR1361243 3 0.4410 0.678 0.000 0.440 0.556 0.000 0.004
#> SRR1490337 3 0.4192 0.712 0.000 0.404 0.596 0.000 0.000
#> SRR823967 2 0.3774 0.276 0.000 0.704 0.296 0.000 0.000
#> SRR660127 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1366627 2 0.4708 0.390 0.000 0.668 0.292 0.000 0.040
#> SRR1361219 2 0.1628 0.699 0.000 0.936 0.056 0.000 0.008
#> SRR1393510 2 0.5118 -0.178 0.000 0.548 0.412 0.000 0.040
#> SRR662558 3 0.4738 -0.568 0.000 0.000 0.520 0.464 0.016
#> SRR1077334 2 0.1341 0.698 0.000 0.944 0.056 0.000 0.000
#> SRR807438 3 0.4630 0.710 0.000 0.396 0.588 0.016 0.000
#> SRR1459078 3 0.4410 0.678 0.000 0.440 0.556 0.000 0.004
#> SRR1329704 2 0.3399 0.586 0.000 0.812 0.168 0.000 0.020
#> SRR1468072 3 0.4437 0.625 0.000 0.464 0.532 0.000 0.004
#> SRR1376196 2 0.2280 0.638 0.000 0.880 0.120 0.000 0.000
#> SRR1442909 3 0.4192 0.712 0.000 0.404 0.596 0.000 0.000
#> SRR1414269 3 0.4192 0.711 0.000 0.404 0.596 0.000 0.000
#> SRR1381913 3 0.5729 0.473 0.000 0.396 0.516 0.000 0.088
#> SRR1340157 2 0.0609 0.703 0.000 0.980 0.020 0.000 0.000
#> SRR1407583 2 0.2669 0.684 0.000 0.876 0.104 0.000 0.020
#> SRR615826 5 0.3710 1.000 0.000 0.048 0.144 0.000 0.808
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 5 0.5729 0.3201 0.000 0.000 0.000 0.176 0.476 0.348
#> SRR1458769 2 0.4078 0.6681 0.000 0.656 0.320 0.024 0.000 0.000
#> SRR613162 1 0.0405 0.9912 0.988 0.004 0.000 0.008 0.000 0.000
#> SRR1352481 1 0.0405 0.9912 0.988 0.004 0.000 0.008 0.000 0.000
#> SRR1468876 3 0.1462 0.7332 0.000 0.008 0.936 0.056 0.000 0.000
#> SRR1399223 3 0.5358 -0.1428 0.000 0.392 0.496 0.112 0.000 0.000
#> SRR660030 3 0.0909 0.7418 0.000 0.012 0.968 0.020 0.000 0.000
#> SRR1333609 3 0.0777 0.7362 0.000 0.024 0.972 0.004 0.000 0.000
#> SRR1471612 2 0.3620 0.6641 0.000 0.648 0.352 0.000 0.000 0.000
#> SRR1413998 2 0.2831 0.3852 0.000 0.840 0.024 0.136 0.000 0.000
#> SRR1122940 2 0.4200 0.6267 0.000 0.592 0.392 0.004 0.012 0.000
#> SRR1402563 3 0.0777 0.7362 0.000 0.024 0.972 0.004 0.000 0.000
#> SRR1398393 3 0.4461 -0.0436 0.000 0.404 0.564 0.032 0.000 0.000
#> SRR657961 3 0.4340 0.5742 0.000 0.064 0.720 0.008 0.208 0.000
#> SRR1471135 3 0.0806 0.7425 0.000 0.008 0.972 0.020 0.000 0.000
#> SRR1430001 3 0.0935 0.7379 0.000 0.004 0.964 0.032 0.000 0.000
#> SRR662775 1 0.0000 0.9962 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.3769 0.6642 0.000 0.640 0.356 0.004 0.000 0.000
#> SRR607190 1 0.0000 0.9962 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR612467 3 0.4286 0.5769 0.000 0.060 0.724 0.008 0.208 0.000
#> SRR1465959 2 0.4093 0.6841 0.000 0.656 0.324 0.008 0.012 0.000
#> SRR1446132 2 0.2831 0.3852 0.000 0.840 0.024 0.136 0.000 0.000
#> SRR1416933 2 0.4193 0.6530 0.000 0.624 0.352 0.024 0.000 0.000
#> SRR1102538 2 0.4601 0.6616 0.000 0.644 0.308 0.020 0.028 0.000
#> SRR1098636 3 0.3025 0.5966 0.000 0.156 0.820 0.024 0.000 0.000
#> SRR1072998 2 0.4601 0.6616 0.000 0.644 0.308 0.020 0.028 0.000
#> SRR627443 1 0.0000 0.9962 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 0.9962 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR823991 3 0.3819 0.3893 0.000 0.280 0.700 0.020 0.000 0.000
#> SRR1089158 2 0.4411 0.6636 0.000 0.628 0.340 0.020 0.012 0.000
#> SRR1469036 3 0.0935 0.7379 0.000 0.004 0.964 0.032 0.000 0.000
#> SRR824039 3 0.3819 0.3893 0.000 0.280 0.700 0.020 0.000 0.000
#> SRR1339047 2 0.3508 0.4566 0.000 0.800 0.068 0.132 0.000 0.000
#> SRR1443049 2 0.4257 0.6847 0.000 0.652 0.320 0.016 0.012 0.000
#> SRR1122885 2 0.4093 0.6841 0.000 0.656 0.324 0.008 0.012 0.000
#> SRR602895 3 0.6514 -0.0413 0.000 0.036 0.436 0.332 0.196 0.000
#> SRR1409837 2 0.3756 0.6663 0.000 0.644 0.352 0.004 0.000 0.000
#> SRR1388959 2 0.2831 0.3852 0.000 0.840 0.024 0.136 0.000 0.000
#> SRR659863 1 0.0000 0.9962 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1089877 3 0.4105 0.1760 0.000 0.348 0.632 0.020 0.000 0.000
#> SRR1123775 3 0.0909 0.7418 0.000 0.012 0.968 0.020 0.000 0.000
#> SRR658909 4 0.2491 0.7208 0.000 0.000 0.164 0.836 0.000 0.000
#> SRR1140510 2 0.5242 0.4530 0.000 0.492 0.412 0.096 0.000 0.000
#> SRR607562 3 0.6514 -0.0413 0.000 0.036 0.436 0.332 0.196 0.000
#> SRR1122913 2 0.4200 0.6267 0.000 0.592 0.392 0.004 0.012 0.000
#> SRR598042 3 0.4286 0.5769 0.000 0.060 0.724 0.008 0.208 0.000
#> SRR1467340 3 0.1007 0.7301 0.000 0.044 0.956 0.000 0.000 0.000
#> SRR1072321 2 0.3737 0.6271 0.000 0.608 0.392 0.000 0.000 0.000
#> SRR1094580 2 0.3881 0.6236 0.000 0.600 0.396 0.004 0.000 0.000
#> SRR1076608 2 0.3921 0.5320 0.000 0.768 0.116 0.116 0.000 0.000
#> SRR1395462 3 0.4233 0.5780 0.000 0.064 0.724 0.004 0.208 0.000
#> SRR1489220 3 0.3607 0.4041 0.000 0.000 0.652 0.348 0.000 0.000
#> SRR614371 4 0.2491 0.7208 0.000 0.000 0.164 0.836 0.000 0.000
#> SRR615455 6 0.0000 0.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1070573 2 0.3869 0.4051 0.000 0.500 0.500 0.000 0.000 0.000
#> SRR598749 5 0.0000 0.4785 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1365556 3 0.5147 0.0919 0.000 0.316 0.576 0.108 0.000 0.000
#> SRR1350023 2 0.2831 0.3852 0.000 0.840 0.024 0.136 0.000 0.000
#> SRR1446582 3 0.0806 0.7421 0.000 0.008 0.972 0.020 0.000 0.000
#> SRR1439763 3 0.0692 0.7387 0.000 0.020 0.976 0.004 0.000 0.000
#> SRR1343986 3 0.0603 0.7397 0.000 0.016 0.980 0.004 0.000 0.000
#> SRR807463 2 0.4585 0.6643 0.000 0.648 0.304 0.020 0.028 0.000
#> SRR660390 1 0.0405 0.9912 0.988 0.004 0.000 0.008 0.000 0.000
#> SRR1367672 2 0.4004 0.6844 0.000 0.656 0.328 0.004 0.012 0.000
#> SRR613294 5 0.5729 0.3201 0.000 0.000 0.000 0.176 0.476 0.348
#> SRR824015 3 0.5084 0.2140 0.000 0.264 0.612 0.124 0.000 0.000
#> SRR1078924 2 0.4200 0.6267 0.000 0.592 0.392 0.004 0.012 0.000
#> SRR662221 4 0.6319 0.5301 0.000 0.000 0.072 0.548 0.132 0.248
#> SRR655017 1 0.0000 0.9962 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1338450 3 0.1462 0.7332 0.000 0.008 0.936 0.056 0.000 0.000
#> SRR663741 4 0.5123 0.6463 0.000 0.000 0.140 0.616 0.000 0.244
#> SRR1396057 2 0.4193 0.6530 0.000 0.624 0.352 0.024 0.000 0.000
#> SRR1083800 3 0.3161 0.5322 0.000 0.216 0.776 0.008 0.000 0.000
#> SRR1445789 2 0.3616 0.4679 0.000 0.792 0.076 0.132 0.000 0.000
#> SRR1387355 3 0.0858 0.7391 0.000 0.004 0.968 0.028 0.000 0.000
#> SRR1388855 2 0.3616 0.4649 0.000 0.792 0.076 0.132 0.000 0.000
#> SRR1445449 3 0.1563 0.7336 0.000 0.012 0.932 0.056 0.000 0.000
#> SRR1380740 3 0.0777 0.7362 0.000 0.024 0.972 0.004 0.000 0.000
#> SRR659995 4 0.6319 0.5301 0.000 0.000 0.072 0.548 0.132 0.248
#> SRR1489524 2 0.2831 0.3852 0.000 0.840 0.024 0.136 0.000 0.000
#> SRR1444662 3 0.4691 0.3753 0.000 0.220 0.672 0.108 0.000 0.000
#> SRR1383652 3 0.0806 0.7425 0.000 0.008 0.972 0.020 0.000 0.000
#> SRR1361243 3 0.0777 0.7362 0.000 0.024 0.972 0.004 0.000 0.000
#> SRR1490337 3 0.0806 0.7425 0.000 0.008 0.972 0.020 0.000 0.000
#> SRR823967 3 0.3819 0.3893 0.000 0.280 0.700 0.020 0.000 0.000
#> SRR660127 1 0.0000 0.9962 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1366627 3 0.5303 -0.0539 0.000 0.360 0.528 0.112 0.000 0.000
#> SRR1361219 2 0.3861 0.6628 0.000 0.640 0.352 0.008 0.000 0.000
#> SRR1393510 3 0.3782 0.5678 0.000 0.124 0.780 0.096 0.000 0.000
#> SRR662558 4 0.2527 0.7174 0.000 0.000 0.168 0.832 0.000 0.000
#> SRR1077334 2 0.4411 0.6636 0.000 0.628 0.340 0.020 0.012 0.000
#> SRR807438 3 0.1462 0.7332 0.000 0.008 0.936 0.056 0.000 0.000
#> SRR1459078 3 0.0777 0.7362 0.000 0.024 0.972 0.004 0.000 0.000
#> SRR1329704 3 0.4570 -0.2864 0.000 0.436 0.528 0.036 0.000 0.000
#> SRR1468072 3 0.1616 0.7148 0.000 0.048 0.932 0.020 0.000 0.000
#> SRR1376196 2 0.3866 0.4511 0.000 0.516 0.484 0.000 0.000 0.000
#> SRR1442909 3 0.0806 0.7425 0.000 0.008 0.972 0.020 0.000 0.000
#> SRR1414269 3 0.0806 0.7421 0.000 0.008 0.972 0.020 0.000 0.000
#> SRR1381913 3 0.4286 0.5769 0.000 0.060 0.724 0.008 0.208 0.000
#> SRR1340157 2 0.3912 0.6811 0.000 0.648 0.340 0.000 0.012 0.000
#> SRR1407583 2 0.4573 0.6051 0.000 0.584 0.372 0.044 0.000 0.000
#> SRR615826 5 0.0000 0.4785 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)
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 17380 rows and 102 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.2940 0.706 0.706
#> 3 3 0.640 0.852 0.914 0.9726 0.594 0.465
#> 4 4 0.602 0.759 0.843 0.1818 0.921 0.809
#> 5 5 0.675 0.590 0.789 0.0877 0.845 0.573
#> 6 6 0.698 0.763 0.832 0.0677 0.901 0.623
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
#> SRR612587 1 0.000 0.999 1.00 0.00
#> SRR1458769 2 0.000 1.000 0.00 1.00
#> SRR613162 1 0.000 0.999 1.00 0.00
#> SRR1352481 1 0.000 0.999 1.00 0.00
#> SRR1468876 2 0.000 1.000 0.00 1.00
#> SRR1399223 2 0.000 1.000 0.00 1.00
#> SRR660030 2 0.000 1.000 0.00 1.00
#> SRR1333609 2 0.000 1.000 0.00 1.00
#> SRR1471612 2 0.000 1.000 0.00 1.00
#> SRR1413998 2 0.000 1.000 0.00 1.00
#> SRR1122940 2 0.000 1.000 0.00 1.00
#> SRR1402563 2 0.000 1.000 0.00 1.00
#> SRR1398393 2 0.000 1.000 0.00 1.00
#> SRR657961 2 0.000 1.000 0.00 1.00
#> SRR1471135 2 0.000 1.000 0.00 1.00
#> SRR1430001 2 0.000 1.000 0.00 1.00
#> SRR662775 1 0.000 0.999 1.00 0.00
#> SRR1474182 2 0.000 1.000 0.00 1.00
#> SRR607190 1 0.000 0.999 1.00 0.00
#> SRR612467 2 0.000 1.000 0.00 1.00
#> SRR1465959 2 0.000 1.000 0.00 1.00
#> SRR1446132 2 0.000 1.000 0.00 1.00
#> SRR1416933 2 0.000 1.000 0.00 1.00
#> SRR1102538 2 0.000 1.000 0.00 1.00
#> SRR1098636 2 0.000 1.000 0.00 1.00
#> SRR1072998 2 0.000 1.000 0.00 1.00
#> SRR627443 1 0.000 0.999 1.00 0.00
#> SRR656131 1 0.000 0.999 1.00 0.00
#> SRR823991 2 0.000 1.000 0.00 1.00
#> SRR1089158 2 0.000 1.000 0.00 1.00
#> SRR1469036 2 0.000 1.000 0.00 1.00
#> SRR824039 2 0.000 1.000 0.00 1.00
#> SRR1339047 2 0.000 1.000 0.00 1.00
#> SRR1443049 2 0.000 1.000 0.00 1.00
#> SRR1122885 2 0.000 1.000 0.00 1.00
#> SRR602895 2 0.000 1.000 0.00 1.00
#> SRR1409837 2 0.000 1.000 0.00 1.00
#> SRR1388959 2 0.000 1.000 0.00 1.00
#> SRR659863 1 0.000 0.999 1.00 0.00
#> SRR1089877 2 0.000 1.000 0.00 1.00
#> SRR1123775 2 0.000 1.000 0.00 1.00
#> SRR658909 1 0.000 0.999 1.00 0.00
#> SRR1140510 2 0.000 1.000 0.00 1.00
#> SRR607562 2 0.000 1.000 0.00 1.00
#> SRR1122913 2 0.000 1.000 0.00 1.00
#> SRR598042 2 0.000 1.000 0.00 1.00
#> SRR1467340 2 0.000 1.000 0.00 1.00
#> SRR1072321 2 0.000 1.000 0.00 1.00
#> SRR1094580 2 0.000 1.000 0.00 1.00
#> SRR1076608 2 0.000 1.000 0.00 1.00
#> SRR1395462 2 0.000 1.000 0.00 1.00
#> SRR1489220 2 0.000 1.000 0.00 1.00
#> SRR614371 1 0.000 0.999 1.00 0.00
#> SRR615455 1 0.000 0.999 1.00 0.00
#> SRR1070573 2 0.000 1.000 0.00 1.00
#> SRR598749 2 0.000 1.000 0.00 1.00
#> SRR1365556 2 0.000 1.000 0.00 1.00
#> SRR1350023 2 0.000 1.000 0.00 1.00
#> SRR1446582 2 0.000 1.000 0.00 1.00
#> SRR1439763 2 0.000 1.000 0.00 1.00
#> SRR1343986 2 0.000 1.000 0.00 1.00
#> SRR807463 2 0.000 1.000 0.00 1.00
#> SRR660390 1 0.000 0.999 1.00 0.00
#> SRR1367672 2 0.000 1.000 0.00 1.00
#> SRR613294 1 0.000 0.999 1.00 0.00
#> SRR824015 2 0.000 1.000 0.00 1.00
#> SRR1078924 2 0.000 1.000 0.00 1.00
#> SRR662221 1 0.000 0.999 1.00 0.00
#> SRR655017 1 0.000 0.999 1.00 0.00
#> SRR1338450 2 0.000 1.000 0.00 1.00
#> SRR663741 1 0.000 0.999 1.00 0.00
#> SRR1396057 2 0.000 1.000 0.00 1.00
#> SRR1083800 2 0.000 1.000 0.00 1.00
#> SRR1445789 2 0.000 1.000 0.00 1.00
#> SRR1387355 2 0.000 1.000 0.00 1.00
#> SRR1388855 2 0.000 1.000 0.00 1.00
#> SRR1445449 2 0.000 1.000 0.00 1.00
#> SRR1380740 2 0.000 1.000 0.00 1.00
#> SRR659995 1 0.141 0.980 0.98 0.02
#> SRR1489524 2 0.000 1.000 0.00 1.00
#> SRR1444662 2 0.000 1.000 0.00 1.00
#> SRR1383652 2 0.000 1.000 0.00 1.00
#> SRR1361243 2 0.000 1.000 0.00 1.00
#> SRR1490337 2 0.000 1.000 0.00 1.00
#> SRR823967 2 0.000 1.000 0.00 1.00
#> SRR660127 1 0.000 0.999 1.00 0.00
#> SRR1366627 2 0.000 1.000 0.00 1.00
#> SRR1361219 2 0.000 1.000 0.00 1.00
#> SRR1393510 2 0.000 1.000 0.00 1.00
#> SRR662558 2 0.000 1.000 0.00 1.00
#> SRR1077334 2 0.000 1.000 0.00 1.00
#> SRR807438 2 0.000 1.000 0.00 1.00
#> SRR1459078 2 0.000 1.000 0.00 1.00
#> SRR1329704 2 0.000 1.000 0.00 1.00
#> SRR1468072 2 0.000 1.000 0.00 1.00
#> SRR1376196 2 0.000 1.000 0.00 1.00
#> SRR1442909 2 0.000 1.000 0.00 1.00
#> SRR1414269 2 0.000 1.000 0.00 1.00
#> SRR1381913 2 0.000 1.000 0.00 1.00
#> SRR1340157 2 0.000 1.000 0.00 1.00
#> SRR1407583 2 0.000 1.000 0.00 1.00
#> SRR615826 2 0.000 1.000 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 3 0.6260 -0.0346 0.448 0.000 0.552
#> SRR1458769 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR613162 1 0.0000 0.9964 1.000 0.000 0.000
#> SRR1352481 1 0.0000 0.9964 1.000 0.000 0.000
#> SRR1468876 3 0.3619 0.8296 0.000 0.136 0.864
#> SRR1399223 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR660030 3 0.2165 0.8093 0.000 0.064 0.936
#> SRR1333609 3 0.5216 0.7801 0.000 0.260 0.740
#> SRR1471612 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1413998 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1122940 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1402563 3 0.5291 0.7717 0.000 0.268 0.732
#> SRR1398393 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR657961 3 0.3412 0.8227 0.000 0.124 0.876
#> SRR1471135 3 0.5291 0.7717 0.000 0.268 0.732
#> SRR1430001 3 0.3619 0.8296 0.000 0.136 0.864
#> SRR662775 1 0.0000 0.9964 1.000 0.000 0.000
#> SRR1474182 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR607190 1 0.0000 0.9964 1.000 0.000 0.000
#> SRR612467 3 0.3816 0.8173 0.000 0.148 0.852
#> SRR1465959 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1446132 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1416933 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1102538 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1098636 3 0.2165 0.8093 0.000 0.064 0.936
#> SRR1072998 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR627443 1 0.0000 0.9964 1.000 0.000 0.000
#> SRR656131 1 0.0000 0.9964 1.000 0.000 0.000
#> SRR823991 3 0.6252 0.4652 0.000 0.444 0.556
#> SRR1089158 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1469036 3 0.3619 0.8296 0.000 0.136 0.864
#> SRR824039 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1339047 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1443049 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1122885 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR602895 3 0.1163 0.7854 0.000 0.028 0.972
#> SRR1409837 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1388959 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR659863 1 0.0000 0.9964 1.000 0.000 0.000
#> SRR1089877 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1123775 3 0.3752 0.8294 0.000 0.144 0.856
#> SRR658909 3 0.5678 0.3761 0.316 0.000 0.684
#> SRR1140510 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR607562 3 0.1163 0.7854 0.000 0.028 0.972
#> SRR1122913 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR598042 3 0.2165 0.8093 0.000 0.064 0.936
#> SRR1467340 2 0.6244 -0.0847 0.000 0.560 0.440
#> SRR1072321 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1094580 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1076608 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1395462 3 0.5497 0.7410 0.000 0.292 0.708
#> SRR1489220 3 0.1289 0.7885 0.000 0.032 0.968
#> SRR614371 3 0.6299 -0.0622 0.476 0.000 0.524
#> SRR615455 1 0.1860 0.9634 0.948 0.000 0.052
#> SRR1070573 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR598749 3 0.0000 0.7592 0.000 0.000 1.000
#> SRR1365556 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1350023 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1446582 3 0.3752 0.8294 0.000 0.144 0.856
#> SRR1439763 3 0.4452 0.8179 0.000 0.192 0.808
#> SRR1343986 3 0.5138 0.7857 0.000 0.252 0.748
#> SRR807463 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR660390 1 0.0000 0.9964 1.000 0.000 0.000
#> SRR1367672 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR613294 3 0.6260 -0.0346 0.448 0.000 0.552
#> SRR824015 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1078924 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR662221 3 0.0000 0.7592 0.000 0.000 1.000
#> SRR655017 1 0.0000 0.9964 1.000 0.000 0.000
#> SRR1338450 3 0.2448 0.8145 0.000 0.076 0.924
#> SRR663741 3 0.6260 -0.0346 0.448 0.000 0.552
#> SRR1396057 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1083800 3 0.6168 0.5389 0.000 0.412 0.588
#> SRR1445789 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1387355 3 0.3619 0.8296 0.000 0.136 0.864
#> SRR1388855 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1445449 3 0.3619 0.8296 0.000 0.136 0.864
#> SRR1380740 3 0.5138 0.7857 0.000 0.252 0.748
#> SRR659995 3 0.0424 0.7553 0.008 0.000 0.992
#> SRR1489524 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1444662 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1383652 3 0.4452 0.8179 0.000 0.192 0.808
#> SRR1361243 3 0.5291 0.7717 0.000 0.268 0.732
#> SRR1490337 3 0.3752 0.8294 0.000 0.144 0.856
#> SRR823967 3 0.4504 0.8164 0.000 0.196 0.804
#> SRR660127 1 0.0000 0.9964 1.000 0.000 0.000
#> SRR1366627 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1361219 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1393510 3 0.5138 0.7857 0.000 0.252 0.748
#> SRR662558 3 0.1163 0.7854 0.000 0.028 0.972
#> SRR1077334 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR807438 3 0.2066 0.8071 0.000 0.060 0.940
#> SRR1459078 3 0.5216 0.7801 0.000 0.260 0.740
#> SRR1329704 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1468072 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1376196 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1442909 3 0.4399 0.8199 0.000 0.188 0.812
#> SRR1414269 3 0.4555 0.8148 0.000 0.200 0.800
#> SRR1381913 3 0.2356 0.8128 0.000 0.072 0.928
#> SRR1340157 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR1407583 2 0.0000 0.9879 0.000 1.000 0.000
#> SRR615826 3 0.0000 0.7592 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.5926 0.723 0.192 0.000 0.116 0.692
#> SRR1458769 2 0.0188 0.828 0.000 0.996 0.000 0.004
#> SRR613162 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1468876 3 0.3198 0.789 0.000 0.040 0.880 0.080
#> SRR1399223 2 0.0921 0.816 0.000 0.972 0.028 0.000
#> SRR660030 3 0.1398 0.807 0.000 0.004 0.956 0.040
#> SRR1333609 3 0.1389 0.810 0.000 0.048 0.952 0.000
#> SRR1471612 2 0.5062 0.806 0.000 0.752 0.064 0.184
#> SRR1413998 2 0.0188 0.828 0.000 0.996 0.000 0.004
#> SRR1122940 2 0.5615 0.788 0.000 0.716 0.096 0.188
#> SRR1402563 3 0.1389 0.810 0.000 0.048 0.952 0.000
#> SRR1398393 2 0.6284 0.740 0.000 0.664 0.172 0.164
#> SRR657961 3 0.6116 0.419 0.000 0.068 0.612 0.320
#> SRR1471135 3 0.1724 0.800 0.000 0.020 0.948 0.032
#> SRR1430001 3 0.3198 0.789 0.000 0.040 0.880 0.080
#> SRR662775 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1474182 2 0.3542 0.831 0.000 0.852 0.028 0.120
#> SRR607190 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR612467 3 0.7156 0.269 0.000 0.140 0.492 0.368
#> SRR1465959 2 0.4636 0.813 0.000 0.772 0.040 0.188
#> SRR1446132 2 0.0188 0.828 0.000 0.996 0.000 0.004
#> SRR1416933 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> SRR1102538 2 0.6428 0.717 0.000 0.624 0.112 0.264
#> SRR1098636 3 0.2773 0.749 0.000 0.004 0.880 0.116
#> SRR1072998 2 0.6273 0.729 0.000 0.636 0.100 0.264
#> SRR627443 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR823991 3 0.5369 0.584 0.000 0.164 0.740 0.096
#> SRR1089158 2 0.6112 0.745 0.000 0.656 0.096 0.248
#> SRR1469036 3 0.3198 0.789 0.000 0.040 0.880 0.080
#> SRR824039 2 0.7276 0.633 0.000 0.540 0.224 0.236
#> SRR1339047 2 0.0188 0.828 0.000 0.996 0.000 0.004
#> SRR1443049 2 0.3707 0.830 0.000 0.840 0.028 0.132
#> SRR1122885 2 0.5615 0.788 0.000 0.716 0.096 0.188
#> SRR602895 3 0.4543 0.449 0.000 0.000 0.676 0.324
#> SRR1409837 2 0.3542 0.831 0.000 0.852 0.028 0.120
#> SRR1388959 2 0.0188 0.828 0.000 0.996 0.000 0.004
#> SRR659863 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1089877 2 0.6656 0.711 0.000 0.624 0.188 0.188
#> SRR1123775 3 0.1938 0.810 0.000 0.012 0.936 0.052
#> SRR658909 4 0.6548 0.654 0.104 0.000 0.304 0.592
#> SRR1140510 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> SRR607562 3 0.4830 0.396 0.000 0.000 0.608 0.392
#> SRR1122913 2 0.5556 0.790 0.000 0.720 0.092 0.188
#> SRR598042 3 0.4720 0.508 0.000 0.004 0.672 0.324
#> SRR1467340 3 0.2843 0.785 0.000 0.088 0.892 0.020
#> SRR1072321 2 0.4153 0.828 0.000 0.820 0.048 0.132
#> SRR1094580 2 0.4955 0.814 0.000 0.772 0.084 0.144
#> SRR1076608 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> SRR1395462 3 0.6663 0.397 0.000 0.144 0.612 0.244
#> SRR1489220 3 0.2647 0.763 0.000 0.000 0.880 0.120
#> SRR614371 4 0.6791 0.596 0.316 0.000 0.120 0.564
#> SRR615455 1 0.5050 0.260 0.588 0.000 0.004 0.408
#> SRR1070573 2 0.5556 0.790 0.000 0.720 0.092 0.188
#> SRR598749 4 0.3172 0.656 0.000 0.000 0.160 0.840
#> SRR1365556 2 0.3726 0.645 0.000 0.788 0.212 0.000
#> SRR1350023 2 0.0188 0.828 0.000 0.996 0.000 0.004
#> SRR1446582 3 0.0895 0.803 0.000 0.004 0.976 0.020
#> SRR1439763 3 0.1118 0.811 0.000 0.036 0.964 0.000
#> SRR1343986 3 0.2586 0.807 0.000 0.048 0.912 0.040
#> SRR807463 2 0.4459 0.815 0.000 0.780 0.032 0.188
#> SRR660390 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1367672 2 0.5615 0.788 0.000 0.716 0.096 0.188
#> SRR613294 4 0.5926 0.723 0.192 0.000 0.116 0.692
#> SRR824015 2 0.4214 0.648 0.000 0.780 0.204 0.016
#> SRR1078924 2 0.4801 0.810 0.000 0.764 0.048 0.188
#> SRR662221 4 0.4072 0.727 0.000 0.000 0.252 0.748
#> SRR655017 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1338450 3 0.3198 0.789 0.000 0.040 0.880 0.080
#> SRR663741 4 0.6075 0.724 0.192 0.000 0.128 0.680
#> SRR1396057 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> SRR1083800 3 0.4057 0.676 0.000 0.028 0.812 0.160
#> SRR1445789 2 0.0188 0.828 0.000 0.996 0.000 0.004
#> SRR1387355 3 0.3198 0.789 0.000 0.040 0.880 0.080
#> SRR1388855 2 0.0188 0.828 0.000 0.996 0.000 0.004
#> SRR1445449 3 0.3198 0.789 0.000 0.040 0.880 0.080
#> SRR1380740 3 0.2759 0.805 0.000 0.052 0.904 0.044
#> SRR659995 4 0.4630 0.734 0.016 0.000 0.252 0.732
#> SRR1489524 2 0.0188 0.828 0.000 0.996 0.000 0.004
#> SRR1444662 2 0.3726 0.645 0.000 0.788 0.212 0.000
#> SRR1383652 3 0.1174 0.806 0.000 0.020 0.968 0.012
#> SRR1361243 3 0.1474 0.809 0.000 0.052 0.948 0.000
#> SRR1490337 3 0.0779 0.810 0.000 0.016 0.980 0.004
#> SRR823967 3 0.2413 0.786 0.000 0.020 0.916 0.064
#> SRR660127 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> SRR1366627 2 0.2704 0.741 0.000 0.876 0.124 0.000
#> SRR1361219 2 0.1820 0.833 0.000 0.944 0.020 0.036
#> SRR1393510 3 0.3088 0.795 0.000 0.060 0.888 0.052
#> SRR662558 3 0.4679 0.391 0.000 0.000 0.648 0.352
#> SRR1077334 2 0.6273 0.735 0.000 0.644 0.108 0.248
#> SRR807438 3 0.2987 0.779 0.000 0.016 0.880 0.104
#> SRR1459078 3 0.2908 0.801 0.000 0.064 0.896 0.040
#> SRR1329704 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> SRR1468072 2 0.4103 0.590 0.000 0.744 0.256 0.000
#> SRR1376196 2 0.4282 0.828 0.000 0.816 0.060 0.124
#> SRR1442909 3 0.2060 0.790 0.000 0.016 0.932 0.052
#> SRR1414269 3 0.1913 0.797 0.000 0.020 0.940 0.040
#> SRR1381913 3 0.4632 0.534 0.000 0.004 0.688 0.308
#> SRR1340157 2 0.3037 0.834 0.000 0.880 0.020 0.100
#> SRR1407583 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> SRR615826 4 0.2647 0.608 0.000 0.000 0.120 0.880
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR612587 4 0.0798 0.79403 0.016 0.000 0.008 0.976 0.000
#> SRR1458769 2 0.0162 0.66612 0.000 0.996 0.000 0.004 0.000
#> SRR613162 1 0.0486 0.99137 0.988 0.000 0.004 0.004 0.004
#> SRR1352481 1 0.0486 0.99137 0.988 0.000 0.004 0.004 0.004
#> SRR1468876 3 0.0960 0.87468 0.000 0.008 0.972 0.016 0.004
#> SRR1399223 2 0.0898 0.65412 0.000 0.972 0.020 0.000 0.008
#> SRR660030 3 0.1764 0.86551 0.000 0.000 0.928 0.008 0.064
#> SRR1333609 3 0.0807 0.87809 0.000 0.012 0.976 0.000 0.012
#> SRR1471612 2 0.4674 -0.01849 0.000 0.568 0.016 0.000 0.416
#> SRR1413998 2 0.0162 0.66612 0.000 0.996 0.000 0.004 0.000
#> SRR1122940 5 0.5148 0.36724 0.000 0.432 0.040 0.000 0.528
#> SRR1402563 3 0.0693 0.87857 0.000 0.012 0.980 0.000 0.008
#> SRR1398393 5 0.6191 0.30551 0.000 0.400 0.120 0.004 0.476
#> SRR657961 5 0.2642 0.36567 0.000 0.008 0.104 0.008 0.880
#> SRR1471135 3 0.2464 0.85177 0.000 0.012 0.892 0.004 0.092
#> SRR1430001 3 0.0798 0.87490 0.000 0.008 0.976 0.016 0.000
#> SRR662775 1 0.0000 0.99490 1.000 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.4470 0.13548 0.000 0.616 0.012 0.000 0.372
#> SRR607190 1 0.0000 0.99490 1.000 0.000 0.000 0.000 0.000
#> SRR612467 5 0.2907 0.36855 0.000 0.016 0.092 0.016 0.876
#> SRR1465959 2 0.4641 -0.12942 0.000 0.532 0.012 0.000 0.456
#> SRR1446132 2 0.0162 0.66612 0.000 0.996 0.000 0.004 0.000
#> SRR1416933 2 0.0579 0.66444 0.000 0.984 0.008 0.000 0.008
#> SRR1102538 5 0.4298 0.46180 0.000 0.352 0.008 0.000 0.640
#> SRR1098636 3 0.4781 0.48507 0.000 0.000 0.552 0.020 0.428
#> SRR1072998 5 0.4425 0.43363 0.000 0.392 0.008 0.000 0.600
#> SRR627443 1 0.0671 0.98319 0.980 0.000 0.004 0.000 0.016
#> SRR656131 1 0.0000 0.99490 1.000 0.000 0.000 0.000 0.000
#> SRR823991 3 0.4974 0.61577 0.000 0.048 0.660 0.004 0.288
#> SRR1089158 5 0.4557 0.42584 0.000 0.404 0.012 0.000 0.584
#> SRR1469036 3 0.0798 0.87490 0.000 0.008 0.976 0.016 0.000
#> SRR824039 5 0.5828 0.44972 0.000 0.260 0.128 0.004 0.608
#> SRR1339047 2 0.0000 0.66567 0.000 1.000 0.000 0.000 0.000
#> SRR1443049 2 0.4565 0.03985 0.000 0.580 0.012 0.000 0.408
#> SRR1122885 5 0.4622 0.37226 0.000 0.440 0.012 0.000 0.548
#> SRR602895 3 0.6659 -0.09865 0.000 0.000 0.436 0.248 0.316
#> SRR1409837 2 0.4470 0.13548 0.000 0.616 0.012 0.000 0.372
#> SRR1388959 2 0.0162 0.66612 0.000 0.996 0.000 0.004 0.000
#> SRR659863 1 0.0000 0.99490 1.000 0.000 0.000 0.000 0.000
#> SRR1089877 5 0.5968 0.43134 0.000 0.308 0.120 0.004 0.568
#> SRR1123775 3 0.1843 0.87036 0.000 0.008 0.932 0.008 0.052
#> SRR658909 4 0.4215 0.70423 0.004 0.000 0.172 0.772 0.052
#> SRR1140510 2 0.0579 0.66444 0.000 0.984 0.008 0.000 0.008
#> SRR607562 5 0.6579 -0.33914 0.000 0.000 0.372 0.208 0.420
#> SRR1122913 5 0.5028 0.34902 0.000 0.444 0.032 0.000 0.524
#> SRR598042 5 0.4161 0.11603 0.000 0.000 0.280 0.016 0.704
#> SRR1467340 3 0.0912 0.87751 0.000 0.016 0.972 0.000 0.012
#> SRR1072321 2 0.4655 -0.21916 0.000 0.512 0.012 0.000 0.476
#> SRR1094580 5 0.4650 0.30860 0.000 0.468 0.012 0.000 0.520
#> SRR1076608 2 0.0290 0.66500 0.000 0.992 0.000 0.000 0.008
#> SRR1395462 5 0.2733 0.37464 0.000 0.012 0.112 0.004 0.872
#> SRR1489220 3 0.1399 0.85867 0.000 0.000 0.952 0.028 0.020
#> SRR614371 4 0.4892 0.72232 0.088 0.000 0.096 0.768 0.048
#> SRR615455 4 0.3815 0.57607 0.220 0.000 0.004 0.764 0.012
#> SRR1070573 5 0.4648 0.31409 0.000 0.464 0.012 0.000 0.524
#> SRR598749 4 0.4218 0.63901 0.000 0.000 0.008 0.660 0.332
#> SRR1365556 2 0.4283 0.29080 0.000 0.644 0.348 0.000 0.008
#> SRR1350023 2 0.0162 0.66612 0.000 0.996 0.000 0.004 0.000
#> SRR1446582 3 0.2124 0.84928 0.000 0.000 0.900 0.004 0.096
#> SRR1439763 3 0.1522 0.86779 0.000 0.012 0.944 0.000 0.044
#> SRR1343986 3 0.0968 0.87767 0.000 0.012 0.972 0.004 0.012
#> SRR807463 2 0.4574 0.00229 0.000 0.576 0.012 0.000 0.412
#> SRR660390 1 0.0486 0.99137 0.988 0.000 0.004 0.004 0.004
#> SRR1367672 5 0.4627 0.36430 0.000 0.444 0.012 0.000 0.544
#> SRR613294 4 0.0798 0.79403 0.016 0.000 0.008 0.976 0.000
#> SRR824015 2 0.4422 0.32638 0.000 0.680 0.300 0.004 0.016
#> SRR1078924 2 0.4650 -0.17469 0.000 0.520 0.012 0.000 0.468
#> SRR662221 4 0.0510 0.79700 0.000 0.000 0.016 0.984 0.000
#> SRR655017 1 0.0000 0.99490 1.000 0.000 0.000 0.000 0.000
#> SRR1338450 3 0.1059 0.87295 0.000 0.008 0.968 0.020 0.004
#> SRR663741 4 0.0960 0.79418 0.016 0.000 0.008 0.972 0.004
#> SRR1396057 2 0.0579 0.66444 0.000 0.984 0.008 0.000 0.008
#> SRR1083800 3 0.3961 0.72194 0.000 0.016 0.736 0.000 0.248
#> SRR1445789 2 0.0162 0.66612 0.000 0.996 0.000 0.004 0.000
#> SRR1387355 3 0.1059 0.87295 0.000 0.008 0.968 0.020 0.004
#> SRR1388855 2 0.0162 0.66612 0.000 0.996 0.000 0.004 0.000
#> SRR1445449 3 0.1059 0.87295 0.000 0.008 0.968 0.020 0.004
#> SRR1380740 3 0.0854 0.87763 0.000 0.012 0.976 0.008 0.004
#> SRR659995 4 0.0510 0.79700 0.000 0.000 0.016 0.984 0.000
#> SRR1489524 2 0.0162 0.66612 0.000 0.996 0.000 0.004 0.000
#> SRR1444662 2 0.4313 0.25160 0.000 0.636 0.356 0.000 0.008
#> SRR1383652 3 0.1877 0.86524 0.000 0.012 0.924 0.000 0.064
#> SRR1361243 3 0.0807 0.87809 0.000 0.012 0.976 0.000 0.012
#> SRR1490337 3 0.2179 0.86191 0.000 0.008 0.912 0.008 0.072
#> SRR823967 3 0.3996 0.74841 0.000 0.012 0.752 0.008 0.228
#> SRR660127 1 0.0000 0.99490 1.000 0.000 0.000 0.000 0.000
#> SRR1366627 2 0.3013 0.49479 0.000 0.832 0.160 0.000 0.008
#> SRR1361219 2 0.3807 0.40293 0.000 0.748 0.012 0.000 0.240
#> SRR1393510 3 0.0960 0.87746 0.000 0.016 0.972 0.008 0.004
#> SRR662558 4 0.5351 0.35623 0.000 0.000 0.380 0.560 0.060
#> SRR1077334 5 0.4564 0.45528 0.000 0.372 0.016 0.000 0.612
#> SRR807438 3 0.1026 0.87012 0.000 0.004 0.968 0.024 0.004
#> SRR1459078 3 0.1074 0.87674 0.000 0.012 0.968 0.004 0.016
#> SRR1329704 2 0.1012 0.65664 0.000 0.968 0.012 0.000 0.020
#> SRR1468072 3 0.4420 0.56101 0.000 0.280 0.692 0.000 0.028
#> SRR1376196 2 0.5403 -0.26544 0.000 0.488 0.056 0.000 0.456
#> SRR1442909 3 0.3858 0.75328 0.000 0.008 0.760 0.008 0.224
#> SRR1414269 3 0.3807 0.76475 0.000 0.012 0.776 0.008 0.204
#> SRR1381913 5 0.3849 0.19211 0.000 0.000 0.232 0.016 0.752
#> SRR1340157 2 0.4517 0.10382 0.000 0.600 0.012 0.000 0.388
#> SRR1407583 2 0.0693 0.66245 0.000 0.980 0.012 0.000 0.008
#> SRR615826 4 0.4151 0.63130 0.000 0.000 0.004 0.652 0.344
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 4 0.1390 0.750 0.000 0.000 0.004 0.948 0.016 0.032
#> SRR1458769 6 0.3791 0.868 0.000 0.180 0.004 0.000 0.048 0.768
#> SRR613162 1 0.0806 0.981 0.972 0.000 0.000 0.000 0.008 0.020
#> SRR1352481 1 0.0806 0.981 0.972 0.000 0.000 0.000 0.008 0.020
#> SRR1468876 3 0.1408 0.805 0.000 0.000 0.944 0.000 0.036 0.020
#> SRR1399223 6 0.3730 0.839 0.000 0.160 0.048 0.000 0.008 0.784
#> SRR660030 3 0.2876 0.767 0.000 0.016 0.844 0.000 0.132 0.008
#> SRR1333609 3 0.1434 0.815 0.000 0.028 0.948 0.000 0.012 0.012
#> SRR1471612 2 0.2404 0.825 0.000 0.872 0.000 0.000 0.016 0.112
#> SRR1413998 6 0.4024 0.867 0.000 0.180 0.004 0.000 0.064 0.752
#> SRR1122940 2 0.1390 0.869 0.000 0.948 0.032 0.000 0.004 0.016
#> SRR1402563 3 0.1777 0.814 0.000 0.024 0.932 0.000 0.032 0.012
#> SRR1398393 2 0.6049 0.491 0.000 0.572 0.044 0.000 0.156 0.228
#> SRR657961 5 0.3403 0.738 0.000 0.212 0.020 0.000 0.768 0.000
#> SRR1471135 3 0.4204 0.650 0.000 0.040 0.696 0.000 0.260 0.004
#> SRR1430001 3 0.0363 0.811 0.000 0.000 0.988 0.000 0.000 0.012
#> SRR662775 1 0.0146 0.988 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1474182 2 0.2340 0.794 0.000 0.852 0.000 0.000 0.000 0.148
#> SRR607190 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR612467 5 0.3315 0.737 0.000 0.200 0.020 0.000 0.780 0.000
#> SRR1465959 2 0.1204 0.868 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1446132 6 0.4024 0.867 0.000 0.180 0.004 0.000 0.064 0.752
#> SRR1416933 6 0.3109 0.855 0.000 0.224 0.004 0.000 0.000 0.772
#> SRR1102538 2 0.2006 0.800 0.000 0.904 0.000 0.000 0.080 0.016
#> SRR1098636 5 0.5178 0.462 0.000 0.064 0.256 0.000 0.644 0.036
#> SRR1072998 2 0.1141 0.843 0.000 0.948 0.000 0.000 0.052 0.000
#> SRR627443 1 0.0984 0.972 0.968 0.000 0.000 0.008 0.012 0.012
#> SRR656131 1 0.0146 0.988 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR823991 3 0.6528 0.354 0.000 0.172 0.492 0.000 0.280 0.056
#> SRR1089158 2 0.1007 0.849 0.000 0.956 0.000 0.000 0.044 0.000
#> SRR1469036 3 0.0363 0.811 0.000 0.000 0.988 0.000 0.000 0.012
#> SRR824039 2 0.4905 0.577 0.000 0.704 0.044 0.000 0.184 0.068
#> SRR1339047 6 0.2845 0.868 0.000 0.172 0.004 0.000 0.004 0.820
#> SRR1443049 2 0.1700 0.857 0.000 0.916 0.000 0.000 0.004 0.080
#> SRR1122885 2 0.0405 0.872 0.000 0.988 0.000 0.000 0.008 0.004
#> SRR602895 5 0.5594 0.377 0.000 0.000 0.276 0.112 0.588 0.024
#> SRR1409837 2 0.2340 0.794 0.000 0.852 0.000 0.000 0.000 0.148
#> SRR1388959 6 0.4024 0.867 0.000 0.180 0.004 0.000 0.064 0.752
#> SRR659863 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1089877 2 0.4371 0.686 0.000 0.768 0.048 0.000 0.112 0.072
#> SRR1123775 3 0.2612 0.779 0.000 0.016 0.868 0.000 0.108 0.008
#> SRR658909 4 0.4717 0.615 0.000 0.000 0.152 0.724 0.096 0.028
#> SRR1140510 6 0.3564 0.855 0.000 0.200 0.020 0.000 0.008 0.772
#> SRR607562 5 0.4654 0.611 0.000 0.020 0.124 0.076 0.756 0.024
#> SRR1122913 2 0.1320 0.869 0.000 0.948 0.036 0.000 0.000 0.016
#> SRR598042 5 0.3501 0.752 0.000 0.116 0.080 0.000 0.804 0.000
#> SRR1467340 3 0.1426 0.815 0.000 0.028 0.948 0.000 0.008 0.016
#> SRR1072321 2 0.1010 0.876 0.000 0.960 0.000 0.000 0.004 0.036
#> SRR1094580 2 0.0547 0.877 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1076608 6 0.3109 0.857 0.000 0.224 0.004 0.000 0.000 0.772
#> SRR1395462 5 0.3745 0.720 0.000 0.240 0.028 0.000 0.732 0.000
#> SRR1489220 3 0.2632 0.767 0.000 0.000 0.880 0.012 0.076 0.032
#> SRR614371 4 0.4895 0.665 0.060 0.000 0.088 0.756 0.068 0.028
#> SRR615455 4 0.3209 0.694 0.088 0.000 0.000 0.840 0.008 0.064
#> SRR1070573 2 0.0748 0.877 0.000 0.976 0.004 0.000 0.004 0.016
#> SRR598749 4 0.4907 0.188 0.000 0.012 0.000 0.488 0.464 0.036
#> SRR1365556 6 0.5186 0.408 0.000 0.072 0.344 0.000 0.012 0.572
#> SRR1350023 6 0.4024 0.867 0.000 0.180 0.004 0.000 0.064 0.752
#> SRR1446582 3 0.3950 0.655 0.000 0.024 0.708 0.000 0.264 0.004
#> SRR1439763 3 0.1168 0.817 0.000 0.028 0.956 0.000 0.016 0.000
#> SRR1343986 3 0.1078 0.815 0.000 0.016 0.964 0.000 0.008 0.012
#> SRR807463 2 0.2053 0.847 0.000 0.888 0.000 0.000 0.004 0.108
#> SRR660390 1 0.0806 0.981 0.972 0.000 0.000 0.000 0.008 0.020
#> SRR1367672 2 0.0508 0.876 0.000 0.984 0.000 0.000 0.004 0.012
#> SRR613294 4 0.1390 0.750 0.000 0.000 0.004 0.948 0.016 0.032
#> SRR824015 6 0.4935 0.681 0.000 0.080 0.124 0.000 0.072 0.724
#> SRR1078924 2 0.1265 0.872 0.000 0.948 0.008 0.000 0.000 0.044
#> SRR662221 4 0.0260 0.755 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR655017 1 0.0000 0.988 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1338450 3 0.1408 0.805 0.000 0.000 0.944 0.000 0.036 0.020
#> SRR663741 4 0.1262 0.750 0.000 0.000 0.008 0.956 0.020 0.016
#> SRR1396057 6 0.3109 0.855 0.000 0.224 0.004 0.000 0.000 0.772
#> SRR1083800 3 0.4657 0.598 0.000 0.220 0.684 0.000 0.092 0.004
#> SRR1445789 6 0.4024 0.867 0.000 0.180 0.004 0.000 0.064 0.752
#> SRR1387355 3 0.0717 0.810 0.000 0.000 0.976 0.000 0.008 0.016
#> SRR1388855 6 0.4024 0.867 0.000 0.180 0.004 0.000 0.064 0.752
#> SRR1445449 3 0.1723 0.803 0.000 0.000 0.928 0.000 0.036 0.036
#> SRR1380740 3 0.0725 0.813 0.000 0.012 0.976 0.000 0.000 0.012
#> SRR659995 4 0.0260 0.755 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1489524 6 0.4024 0.867 0.000 0.180 0.004 0.000 0.064 0.752
#> SRR1444662 6 0.3944 0.713 0.000 0.060 0.164 0.000 0.008 0.768
#> SRR1383652 3 0.3744 0.671 0.000 0.016 0.724 0.000 0.256 0.004
#> SRR1361243 3 0.1434 0.815 0.000 0.028 0.948 0.000 0.012 0.012
#> SRR1490337 3 0.4103 0.650 0.000 0.008 0.684 0.000 0.288 0.020
#> SRR823967 3 0.5743 0.433 0.000 0.084 0.536 0.000 0.344 0.036
#> SRR660127 1 0.0146 0.988 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1366627 6 0.3886 0.811 0.000 0.124 0.084 0.000 0.008 0.784
#> SRR1361219 2 0.2823 0.718 0.000 0.796 0.000 0.000 0.000 0.204
#> SRR1393510 3 0.1983 0.781 0.000 0.012 0.916 0.000 0.012 0.060
#> SRR662558 4 0.6071 0.356 0.000 0.000 0.272 0.548 0.140 0.040
#> SRR1077334 2 0.1838 0.814 0.000 0.916 0.000 0.000 0.068 0.016
#> SRR807438 3 0.1480 0.804 0.000 0.000 0.940 0.000 0.040 0.020
#> SRR1459078 3 0.1346 0.811 0.000 0.024 0.952 0.000 0.008 0.016
#> SRR1329704 6 0.4312 0.569 0.000 0.396 0.012 0.000 0.008 0.584
#> SRR1468072 3 0.4077 0.624 0.000 0.060 0.752 0.000 0.008 0.180
#> SRR1376196 2 0.2680 0.824 0.000 0.868 0.076 0.000 0.000 0.056
#> SRR1442909 3 0.5419 0.454 0.000 0.052 0.548 0.000 0.364 0.036
#> SRR1414269 3 0.5294 0.537 0.000 0.068 0.604 0.000 0.300 0.028
#> SRR1381913 5 0.3641 0.759 0.000 0.140 0.072 0.000 0.788 0.000
#> SRR1340157 2 0.1588 0.859 0.000 0.924 0.000 0.000 0.004 0.072
#> SRR1407583 6 0.3357 0.850 0.000 0.224 0.004 0.000 0.008 0.764
#> SRR615826 4 0.5059 0.168 0.000 0.020 0.000 0.480 0.464 0.036
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "skmeans"]
# you can also extract it by
# res = res_list["SD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17380 rows and 102 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.963 0.984 0.4548 0.539 0.539
#> 3 3 0.721 0.878 0.936 0.4068 0.743 0.557
#> 4 4 0.829 0.855 0.923 0.1051 0.873 0.680
#> 5 5 0.816 0.869 0.889 0.1055 0.881 0.621
#> 6 6 0.885 0.831 0.891 0.0456 0.957 0.802
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
#> SRR612587 1 0.0000 0.960 1.000 0.000
#> SRR1458769 2 0.0000 0.996 0.000 1.000
#> SRR613162 1 0.0000 0.960 1.000 0.000
#> SRR1352481 1 0.0000 0.960 1.000 0.000
#> SRR1468876 1 0.0000 0.960 1.000 0.000
#> SRR1399223 2 0.0000 0.996 0.000 1.000
#> SRR660030 1 0.0000 0.960 1.000 0.000
#> SRR1333609 1 0.9795 0.333 0.584 0.416
#> SRR1471612 2 0.0000 0.996 0.000 1.000
#> SRR1413998 2 0.0000 0.996 0.000 1.000
#> SRR1122940 2 0.0000 0.996 0.000 1.000
#> SRR1402563 2 0.0000 0.996 0.000 1.000
#> SRR1398393 2 0.0000 0.996 0.000 1.000
#> SRR657961 2 0.0000 0.996 0.000 1.000
#> SRR1471135 2 0.0000 0.996 0.000 1.000
#> SRR1430001 1 0.0000 0.960 1.000 0.000
#> SRR662775 1 0.0000 0.960 1.000 0.000
#> SRR1474182 2 0.0000 0.996 0.000 1.000
#> SRR607190 1 0.0000 0.960 1.000 0.000
#> SRR612467 2 0.0000 0.996 0.000 1.000
#> SRR1465959 2 0.0000 0.996 0.000 1.000
#> SRR1446132 2 0.0000 0.996 0.000 1.000
#> SRR1416933 2 0.0000 0.996 0.000 1.000
#> SRR1102538 2 0.0000 0.996 0.000 1.000
#> SRR1098636 2 0.2043 0.963 0.032 0.968
#> SRR1072998 2 0.0000 0.996 0.000 1.000
#> SRR627443 1 0.0000 0.960 1.000 0.000
#> SRR656131 1 0.0000 0.960 1.000 0.000
#> SRR823991 2 0.0000 0.996 0.000 1.000
#> SRR1089158 2 0.0000 0.996 0.000 1.000
#> SRR1469036 1 0.0000 0.960 1.000 0.000
#> SRR824039 2 0.0000 0.996 0.000 1.000
#> SRR1339047 2 0.0000 0.996 0.000 1.000
#> SRR1443049 2 0.0000 0.996 0.000 1.000
#> SRR1122885 2 0.0000 0.996 0.000 1.000
#> SRR602895 1 0.0000 0.960 1.000 0.000
#> SRR1409837 2 0.0000 0.996 0.000 1.000
#> SRR1388959 2 0.0000 0.996 0.000 1.000
#> SRR659863 1 0.0000 0.960 1.000 0.000
#> SRR1089877 2 0.0000 0.996 0.000 1.000
#> SRR1123775 2 0.0376 0.992 0.004 0.996
#> SRR658909 1 0.0000 0.960 1.000 0.000
#> SRR1140510 2 0.0000 0.996 0.000 1.000
#> SRR607562 1 0.5294 0.851 0.880 0.120
#> SRR1122913 2 0.0000 0.996 0.000 1.000
#> SRR598042 2 0.0672 0.988 0.008 0.992
#> SRR1467340 2 0.0000 0.996 0.000 1.000
#> SRR1072321 2 0.0000 0.996 0.000 1.000
#> SRR1094580 2 0.0000 0.996 0.000 1.000
#> SRR1076608 2 0.0000 0.996 0.000 1.000
#> SRR1395462 2 0.0000 0.996 0.000 1.000
#> SRR1489220 1 0.0000 0.960 1.000 0.000
#> SRR614371 1 0.0000 0.960 1.000 0.000
#> SRR615455 1 0.0000 0.960 1.000 0.000
#> SRR1070573 2 0.0000 0.996 0.000 1.000
#> SRR598749 1 0.7219 0.752 0.800 0.200
#> SRR1365556 2 0.0000 0.996 0.000 1.000
#> SRR1350023 2 0.0000 0.996 0.000 1.000
#> SRR1446582 2 0.0000 0.996 0.000 1.000
#> SRR1439763 2 0.0000 0.996 0.000 1.000
#> SRR1343986 2 0.0000 0.996 0.000 1.000
#> SRR807463 2 0.0000 0.996 0.000 1.000
#> SRR660390 1 0.0000 0.960 1.000 0.000
#> SRR1367672 2 0.0000 0.996 0.000 1.000
#> SRR613294 1 0.0000 0.960 1.000 0.000
#> SRR824015 2 0.0000 0.996 0.000 1.000
#> SRR1078924 2 0.0000 0.996 0.000 1.000
#> SRR662221 1 0.0000 0.960 1.000 0.000
#> SRR655017 1 0.0000 0.960 1.000 0.000
#> SRR1338450 1 0.0000 0.960 1.000 0.000
#> SRR663741 1 0.0000 0.960 1.000 0.000
#> SRR1396057 2 0.0000 0.996 0.000 1.000
#> SRR1083800 2 0.0000 0.996 0.000 1.000
#> SRR1445789 2 0.0000 0.996 0.000 1.000
#> SRR1387355 1 0.0000 0.960 1.000 0.000
#> SRR1388855 2 0.0000 0.996 0.000 1.000
#> SRR1445449 1 0.0000 0.960 1.000 0.000
#> SRR1380740 1 0.0000 0.960 1.000 0.000
#> SRR659995 1 0.0000 0.960 1.000 0.000
#> SRR1489524 2 0.0000 0.996 0.000 1.000
#> SRR1444662 2 0.0000 0.996 0.000 1.000
#> SRR1383652 2 0.0000 0.996 0.000 1.000
#> SRR1361243 2 0.0376 0.992 0.004 0.996
#> SRR1490337 1 0.7219 0.754 0.800 0.200
#> SRR823967 2 0.0000 0.996 0.000 1.000
#> SRR660127 1 0.0000 0.960 1.000 0.000
#> SRR1366627 2 0.0000 0.996 0.000 1.000
#> SRR1361219 2 0.0000 0.996 0.000 1.000
#> SRR1393510 1 0.2423 0.929 0.960 0.040
#> SRR662558 1 0.0000 0.960 1.000 0.000
#> SRR1077334 2 0.0000 0.996 0.000 1.000
#> SRR807438 1 0.0000 0.960 1.000 0.000
#> SRR1459078 1 0.9732 0.366 0.596 0.404
#> SRR1329704 2 0.0000 0.996 0.000 1.000
#> SRR1468072 2 0.0000 0.996 0.000 1.000
#> SRR1376196 2 0.0000 0.996 0.000 1.000
#> SRR1442909 2 0.0000 0.996 0.000 1.000
#> SRR1414269 2 0.0000 0.996 0.000 1.000
#> SRR1381913 2 0.0000 0.996 0.000 1.000
#> SRR1340157 2 0.0000 0.996 0.000 1.000
#> SRR1407583 2 0.0000 0.996 0.000 1.000
#> SRR615826 2 0.7376 0.723 0.208 0.792
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1458769 2 0.0000 0.891 0.000 1.000 0.000
#> SRR613162 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1352481 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1468876 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1399223 2 0.0000 0.891 0.000 1.000 0.000
#> SRR660030 3 0.0000 0.918 0.000 0.000 1.000
#> SRR1333609 2 0.5408 0.734 0.136 0.812 0.052
#> SRR1471612 2 0.4452 0.813 0.000 0.808 0.192
#> SRR1413998 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1122940 2 0.4062 0.834 0.000 0.836 0.164
#> SRR1402563 2 0.3551 0.799 0.000 0.868 0.132
#> SRR1398393 2 0.5016 0.755 0.000 0.760 0.240
#> SRR657961 3 0.0000 0.918 0.000 0.000 1.000
#> SRR1471135 3 0.0000 0.918 0.000 0.000 1.000
#> SRR1430001 1 0.0000 0.990 1.000 0.000 0.000
#> SRR662775 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1474182 2 0.4062 0.834 0.000 0.836 0.164
#> SRR607190 1 0.0000 0.990 1.000 0.000 0.000
#> SRR612467 3 0.0000 0.918 0.000 0.000 1.000
#> SRR1465959 2 0.4062 0.834 0.000 0.836 0.164
#> SRR1446132 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1416933 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1102538 3 0.2261 0.881 0.000 0.068 0.932
#> SRR1098636 3 0.0000 0.918 0.000 0.000 1.000
#> SRR1072998 2 0.5497 0.680 0.000 0.708 0.292
#> SRR627443 1 0.0000 0.990 1.000 0.000 0.000
#> SRR656131 1 0.0000 0.990 1.000 0.000 0.000
#> SRR823991 3 0.3482 0.827 0.000 0.128 0.872
#> SRR1089158 2 0.5098 0.745 0.000 0.752 0.248
#> SRR1469036 1 0.0000 0.990 1.000 0.000 0.000
#> SRR824039 3 0.3482 0.827 0.000 0.128 0.872
#> SRR1339047 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1443049 2 0.4062 0.834 0.000 0.836 0.164
#> SRR1122885 2 0.4121 0.830 0.000 0.832 0.168
#> SRR602895 1 0.2066 0.927 0.940 0.000 0.060
#> SRR1409837 2 0.4062 0.834 0.000 0.836 0.164
#> SRR1388959 2 0.0000 0.891 0.000 1.000 0.000
#> SRR659863 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1089877 2 0.5497 0.686 0.000 0.708 0.292
#> SRR1123775 3 0.2261 0.882 0.000 0.068 0.932
#> SRR658909 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1140510 2 0.0000 0.891 0.000 1.000 0.000
#> SRR607562 3 0.3267 0.842 0.116 0.000 0.884
#> SRR1122913 2 0.4062 0.834 0.000 0.836 0.164
#> SRR598042 3 0.0000 0.918 0.000 0.000 1.000
#> SRR1467340 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1072321 2 0.3879 0.839 0.000 0.848 0.152
#> SRR1094580 2 0.4062 0.834 0.000 0.836 0.164
#> SRR1076608 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1395462 3 0.0000 0.918 0.000 0.000 1.000
#> SRR1489220 1 0.0000 0.990 1.000 0.000 0.000
#> SRR614371 1 0.0000 0.990 1.000 0.000 0.000
#> SRR615455 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1070573 2 0.4062 0.834 0.000 0.836 0.164
#> SRR598749 3 0.4555 0.760 0.200 0.000 0.800
#> SRR1365556 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1350023 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1446582 3 0.0000 0.918 0.000 0.000 1.000
#> SRR1439763 3 0.3879 0.796 0.000 0.152 0.848
#> SRR1343986 2 0.0000 0.891 0.000 1.000 0.000
#> SRR807463 2 0.4062 0.834 0.000 0.836 0.164
#> SRR660390 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1367672 2 0.4121 0.830 0.000 0.832 0.168
#> SRR613294 1 0.0000 0.990 1.000 0.000 0.000
#> SRR824015 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1078924 2 0.4062 0.834 0.000 0.836 0.164
#> SRR662221 1 0.0000 0.990 1.000 0.000 0.000
#> SRR655017 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1338450 1 0.0000 0.990 1.000 0.000 0.000
#> SRR663741 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1396057 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1083800 3 0.6062 0.267 0.000 0.384 0.616
#> SRR1445789 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1387355 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1388855 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1445449 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1380740 1 0.4291 0.773 0.820 0.180 0.000
#> SRR659995 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1489524 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1444662 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1383652 3 0.0000 0.918 0.000 0.000 1.000
#> SRR1361243 2 0.0424 0.888 0.000 0.992 0.008
#> SRR1490337 3 0.4062 0.741 0.164 0.000 0.836
#> SRR823967 3 0.0000 0.918 0.000 0.000 1.000
#> SRR660127 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1366627 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1361219 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1393510 2 0.6299 0.041 0.476 0.524 0.000
#> SRR662558 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1077334 2 0.5560 0.673 0.000 0.700 0.300
#> SRR807438 1 0.0000 0.990 1.000 0.000 0.000
#> SRR1459078 2 0.3619 0.772 0.136 0.864 0.000
#> SRR1329704 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1468072 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1376196 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1442909 3 0.0000 0.918 0.000 0.000 1.000
#> SRR1414269 3 0.0000 0.918 0.000 0.000 1.000
#> SRR1381913 3 0.0000 0.918 0.000 0.000 1.000
#> SRR1340157 2 0.0000 0.891 0.000 1.000 0.000
#> SRR1407583 2 0.0000 0.891 0.000 1.000 0.000
#> SRR615826 3 0.4555 0.760 0.200 0.000 0.800
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 1 0.0707 0.974 0.980 0.000 0.000 0.020
#> SRR1458769 2 0.1637 0.882 0.000 0.940 0.000 0.060
#> SRR613162 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR1468876 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR1399223 2 0.3024 0.827 0.000 0.852 0.000 0.148
#> SRR660030 3 0.3569 0.779 0.000 0.000 0.804 0.196
#> SRR1333609 4 0.1388 0.833 0.000 0.028 0.012 0.960
#> SRR1471612 2 0.1489 0.877 0.000 0.952 0.044 0.004
#> SRR1413998 2 0.2081 0.874 0.000 0.916 0.000 0.084
#> SRR1122940 2 0.2983 0.853 0.000 0.892 0.040 0.068
#> SRR1402563 4 0.2021 0.822 0.000 0.024 0.040 0.936
#> SRR1398393 2 0.0592 0.884 0.000 0.984 0.000 0.016
#> SRR657961 3 0.0000 0.902 0.000 0.000 1.000 0.000
#> SRR1471135 3 0.0188 0.903 0.000 0.000 0.996 0.004
#> SRR1430001 4 0.3172 0.739 0.160 0.000 0.000 0.840
#> SRR662775 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR1474182 2 0.0000 0.884 0.000 1.000 0.000 0.000
#> SRR607190 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR612467 3 0.0707 0.897 0.000 0.000 0.980 0.020
#> SRR1465959 2 0.2483 0.866 0.000 0.916 0.032 0.052
#> SRR1446132 2 0.2760 0.844 0.000 0.872 0.000 0.128
#> SRR1416933 2 0.1637 0.882 0.000 0.940 0.000 0.060
#> SRR1102538 3 0.6090 0.286 0.000 0.384 0.564 0.052
#> SRR1098636 3 0.0000 0.902 0.000 0.000 1.000 0.000
#> SRR1072998 2 0.2844 0.857 0.000 0.900 0.048 0.052
#> SRR627443 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR823991 2 0.5313 0.347 0.000 0.608 0.376 0.016
#> SRR1089158 2 0.2844 0.857 0.000 0.900 0.048 0.052
#> SRR1469036 4 0.3172 0.739 0.160 0.000 0.000 0.840
#> SRR824039 2 0.5792 0.249 0.000 0.552 0.416 0.032
#> SRR1339047 2 0.2081 0.874 0.000 0.916 0.000 0.084
#> SRR1443049 2 0.1474 0.876 0.000 0.948 0.000 0.052
#> SRR1122885 2 0.2670 0.861 0.000 0.908 0.040 0.052
#> SRR602895 1 0.3801 0.700 0.780 0.000 0.220 0.000
#> SRR1409837 2 0.0000 0.884 0.000 1.000 0.000 0.000
#> SRR1388959 2 0.1637 0.882 0.000 0.940 0.000 0.060
#> SRR659863 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR1089877 2 0.1807 0.875 0.000 0.940 0.008 0.052
#> SRR1123775 3 0.3569 0.779 0.000 0.000 0.804 0.196
#> SRR658909 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR1140510 2 0.2081 0.874 0.000 0.916 0.000 0.084
#> SRR607562 3 0.2048 0.865 0.064 0.000 0.928 0.008
#> SRR1122913 2 0.2483 0.866 0.000 0.916 0.032 0.052
#> SRR598042 3 0.0188 0.903 0.000 0.000 0.996 0.004
#> SRR1467340 4 0.4955 0.142 0.000 0.444 0.000 0.556
#> SRR1072321 2 0.1474 0.876 0.000 0.948 0.000 0.052
#> SRR1094580 2 0.0592 0.884 0.000 0.984 0.016 0.000
#> SRR1076608 2 0.2081 0.874 0.000 0.916 0.000 0.084
#> SRR1395462 3 0.0188 0.903 0.000 0.000 0.996 0.004
#> SRR1489220 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR614371 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR615455 1 0.0707 0.974 0.980 0.000 0.000 0.020
#> SRR1070573 2 0.2483 0.866 0.000 0.916 0.032 0.052
#> SRR598749 3 0.4035 0.750 0.176 0.000 0.804 0.020
#> SRR1365556 2 0.3024 0.827 0.000 0.852 0.000 0.148
#> SRR1350023 2 0.2081 0.874 0.000 0.916 0.000 0.084
#> SRR1446582 3 0.0188 0.903 0.000 0.000 0.996 0.004
#> SRR1439763 4 0.2271 0.807 0.000 0.076 0.008 0.916
#> SRR1343986 4 0.1302 0.835 0.000 0.044 0.000 0.956
#> SRR807463 2 0.1610 0.878 0.000 0.952 0.032 0.016
#> SRR660390 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR1367672 2 0.2500 0.866 0.000 0.916 0.044 0.040
#> SRR613294 1 0.0707 0.974 0.980 0.000 0.000 0.020
#> SRR824015 2 0.2466 0.871 0.000 0.900 0.004 0.096
#> SRR1078924 2 0.2483 0.866 0.000 0.916 0.032 0.052
#> SRR662221 1 0.0707 0.974 0.980 0.000 0.000 0.020
#> SRR655017 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR1338450 1 0.0336 0.981 0.992 0.000 0.000 0.008
#> SRR663741 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR1396057 2 0.1637 0.882 0.000 0.940 0.000 0.060
#> SRR1083800 2 0.6176 0.175 0.000 0.524 0.424 0.052
#> SRR1445789 2 0.2081 0.874 0.000 0.916 0.000 0.084
#> SRR1387355 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR1388855 2 0.2081 0.874 0.000 0.916 0.000 0.084
#> SRR1445449 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR1380740 4 0.1474 0.819 0.052 0.000 0.000 0.948
#> SRR659995 1 0.0707 0.974 0.980 0.000 0.000 0.020
#> SRR1489524 2 0.1637 0.882 0.000 0.940 0.000 0.060
#> SRR1444662 2 0.3024 0.827 0.000 0.852 0.000 0.148
#> SRR1383652 3 0.0188 0.903 0.000 0.000 0.996 0.004
#> SRR1361243 4 0.1256 0.835 0.000 0.028 0.008 0.964
#> SRR1490337 3 0.1489 0.871 0.044 0.000 0.952 0.004
#> SRR823967 3 0.2443 0.844 0.000 0.060 0.916 0.024
#> SRR660127 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR1366627 2 0.3266 0.806 0.000 0.832 0.000 0.168
#> SRR1361219 2 0.0000 0.884 0.000 1.000 0.000 0.000
#> SRR1393510 4 0.5067 0.689 0.048 0.216 0.000 0.736
#> SRR662558 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR1077334 2 0.2928 0.854 0.000 0.896 0.052 0.052
#> SRR807438 1 0.0000 0.985 1.000 0.000 0.000 0.000
#> SRR1459078 4 0.0817 0.832 0.000 0.024 0.000 0.976
#> SRR1329704 2 0.1637 0.882 0.000 0.940 0.000 0.060
#> SRR1468072 4 0.3610 0.725 0.000 0.200 0.000 0.800
#> SRR1376196 2 0.1474 0.876 0.000 0.948 0.000 0.052
#> SRR1442909 3 0.0188 0.903 0.000 0.000 0.996 0.004
#> SRR1414269 3 0.0592 0.896 0.000 0.000 0.984 0.016
#> SRR1381913 3 0.0188 0.903 0.000 0.000 0.996 0.004
#> SRR1340157 2 0.1302 0.879 0.000 0.956 0.000 0.044
#> SRR1407583 2 0.1637 0.882 0.000 0.940 0.000 0.060
#> SRR615826 3 0.4035 0.750 0.176 0.000 0.804 0.020
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR612587 1 0.3471 0.847 0.836 0.072 0.000 0.092 0.000
#> SRR1458769 2 0.2648 0.971 0.000 0.848 0.152 0.000 0.000
#> SRR613162 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR1468876 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR1399223 2 0.3012 0.954 0.000 0.852 0.124 0.024 0.000
#> SRR660030 5 0.4323 0.809 0.000 0.020 0.024 0.196 0.760
#> SRR1333609 4 0.1908 0.868 0.000 0.000 0.092 0.908 0.000
#> SRR1471612 3 0.3307 0.824 0.000 0.104 0.844 0.000 0.052
#> SRR1413998 2 0.2648 0.971 0.000 0.848 0.152 0.000 0.000
#> SRR1122940 3 0.0912 0.887 0.000 0.016 0.972 0.012 0.000
#> SRR1402563 4 0.2438 0.842 0.000 0.000 0.040 0.900 0.060
#> SRR1398393 3 0.4604 0.384 0.000 0.428 0.560 0.000 0.012
#> SRR657961 5 0.0703 0.899 0.000 0.000 0.024 0.000 0.976
#> SRR1471135 5 0.0290 0.901 0.000 0.000 0.008 0.000 0.992
#> SRR1430001 4 0.1965 0.820 0.096 0.000 0.000 0.904 0.000
#> SRR662775 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR1474182 3 0.2773 0.788 0.000 0.164 0.836 0.000 0.000
#> SRR607190 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR612467 5 0.4110 0.846 0.000 0.064 0.028 0.092 0.816
#> SRR1465959 3 0.0703 0.890 0.000 0.024 0.976 0.000 0.000
#> SRR1446132 2 0.2648 0.971 0.000 0.848 0.152 0.000 0.000
#> SRR1416933 2 0.2648 0.971 0.000 0.848 0.152 0.000 0.000
#> SRR1102538 3 0.1571 0.835 0.000 0.004 0.936 0.000 0.060
#> SRR1098636 5 0.2300 0.866 0.000 0.072 0.024 0.000 0.904
#> SRR1072998 3 0.0000 0.883 0.000 0.000 1.000 0.000 0.000
#> SRR627443 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR823991 3 0.4548 0.710 0.000 0.124 0.752 0.000 0.124
#> SRR1089158 3 0.0000 0.883 0.000 0.000 1.000 0.000 0.000
#> SRR1469036 4 0.1908 0.822 0.092 0.000 0.000 0.908 0.000
#> SRR824039 3 0.3644 0.747 0.000 0.080 0.824 0.000 0.096
#> SRR1339047 2 0.2561 0.967 0.000 0.856 0.144 0.000 0.000
#> SRR1443049 3 0.0880 0.889 0.000 0.032 0.968 0.000 0.000
#> SRR1122885 3 0.0609 0.890 0.000 0.020 0.980 0.000 0.000
#> SRR602895 1 0.4970 0.250 0.580 0.020 0.000 0.008 0.392
#> SRR1409837 3 0.2732 0.793 0.000 0.160 0.840 0.000 0.000
#> SRR1388959 2 0.2648 0.971 0.000 0.848 0.152 0.000 0.000
#> SRR659863 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR1089877 3 0.2390 0.811 0.000 0.084 0.896 0.000 0.020
#> SRR1123775 5 0.4655 0.785 0.000 0.016 0.080 0.140 0.764
#> SRR658909 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR1140510 2 0.2648 0.971 0.000 0.848 0.152 0.000 0.000
#> SRR607562 5 0.3865 0.853 0.040 0.028 0.008 0.084 0.840
#> SRR1122913 3 0.0703 0.890 0.000 0.024 0.976 0.000 0.000
#> SRR598042 5 0.0290 0.901 0.000 0.000 0.008 0.000 0.992
#> SRR1467340 4 0.4165 0.590 0.000 0.008 0.320 0.672 0.000
#> SRR1072321 3 0.0880 0.889 0.000 0.032 0.968 0.000 0.000
#> SRR1094580 3 0.2377 0.827 0.000 0.128 0.872 0.000 0.000
#> SRR1076608 2 0.2690 0.968 0.000 0.844 0.156 0.000 0.000
#> SRR1395462 5 0.0290 0.901 0.000 0.000 0.008 0.000 0.992
#> SRR1489220 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR614371 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR615455 1 0.3033 0.865 0.864 0.052 0.000 0.084 0.000
#> SRR1070573 3 0.0609 0.890 0.000 0.020 0.980 0.000 0.000
#> SRR598749 5 0.5452 0.801 0.064 0.072 0.020 0.092 0.752
#> SRR1365556 2 0.2964 0.950 0.000 0.856 0.120 0.024 0.000
#> SRR1350023 2 0.2648 0.971 0.000 0.848 0.152 0.000 0.000
#> SRR1446582 5 0.0290 0.901 0.000 0.000 0.008 0.000 0.992
#> SRR1439763 4 0.2886 0.836 0.000 0.008 0.148 0.844 0.000
#> SRR1343986 4 0.1908 0.868 0.000 0.000 0.092 0.908 0.000
#> SRR807463 3 0.1043 0.888 0.000 0.040 0.960 0.000 0.000
#> SRR660390 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR1367672 3 0.0880 0.890 0.000 0.032 0.968 0.000 0.000
#> SRR613294 1 0.3471 0.847 0.836 0.072 0.000 0.092 0.000
#> SRR824015 2 0.1408 0.822 0.000 0.948 0.044 0.000 0.008
#> SRR1078924 3 0.0703 0.890 0.000 0.024 0.976 0.000 0.000
#> SRR662221 1 0.3471 0.847 0.836 0.072 0.000 0.092 0.000
#> SRR655017 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR1338450 1 0.0451 0.944 0.988 0.008 0.000 0.004 0.000
#> SRR663741 1 0.0162 0.948 0.996 0.000 0.000 0.004 0.000
#> SRR1396057 2 0.2648 0.971 0.000 0.848 0.152 0.000 0.000
#> SRR1083800 3 0.1502 0.848 0.000 0.004 0.940 0.000 0.056
#> SRR1445789 2 0.2648 0.971 0.000 0.848 0.152 0.000 0.000
#> SRR1387355 1 0.0162 0.947 0.996 0.000 0.000 0.004 0.000
#> SRR1388855 2 0.2648 0.971 0.000 0.848 0.152 0.000 0.000
#> SRR1445449 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR1380740 4 0.2300 0.853 0.052 0.000 0.040 0.908 0.000
#> SRR659995 1 0.3471 0.847 0.836 0.072 0.000 0.092 0.000
#> SRR1489524 2 0.2648 0.971 0.000 0.848 0.152 0.000 0.000
#> SRR1444662 2 0.3012 0.954 0.000 0.852 0.124 0.024 0.000
#> SRR1383652 5 0.0290 0.901 0.000 0.000 0.008 0.000 0.992
#> SRR1361243 4 0.2011 0.869 0.000 0.000 0.088 0.908 0.004
#> SRR1490337 5 0.1608 0.872 0.000 0.072 0.000 0.000 0.928
#> SRR823967 3 0.5456 0.400 0.000 0.080 0.592 0.000 0.328
#> SRR660127 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR1366627 2 0.3012 0.954 0.000 0.852 0.124 0.024 0.000
#> SRR1361219 3 0.2773 0.788 0.000 0.164 0.836 0.000 0.000
#> SRR1393510 2 0.2642 0.777 0.024 0.888 0.004 0.084 0.000
#> SRR662558 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR1077334 3 0.0324 0.879 0.000 0.004 0.992 0.000 0.004
#> SRR807438 1 0.0000 0.950 1.000 0.000 0.000 0.000 0.000
#> SRR1459078 4 0.2130 0.868 0.000 0.012 0.080 0.908 0.000
#> SRR1329704 2 0.2852 0.950 0.000 0.828 0.172 0.000 0.000
#> SRR1468072 4 0.5119 0.289 0.000 0.360 0.048 0.592 0.000
#> SRR1376196 3 0.0880 0.889 0.000 0.032 0.968 0.000 0.000
#> SRR1442909 5 0.1608 0.872 0.000 0.072 0.000 0.000 0.928
#> SRR1414269 5 0.3579 0.815 0.000 0.072 0.100 0.000 0.828
#> SRR1381913 5 0.0290 0.901 0.000 0.000 0.008 0.000 0.992
#> SRR1340157 3 0.1478 0.875 0.000 0.064 0.936 0.000 0.000
#> SRR1407583 2 0.2648 0.971 0.000 0.848 0.152 0.000 0.000
#> SRR615826 5 0.5561 0.803 0.060 0.072 0.028 0.092 0.748
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 1 0.3986 0.580 0.608 0.000 0.004 0.384 0.004 0.000
#> SRR1458769 6 0.0405 0.982 0.000 0.008 0.000 0.004 0.000 0.988
#> SRR613162 1 0.0000 0.925 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 0.925 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1468876 1 0.0146 0.923 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1399223 6 0.0000 0.985 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR660030 5 0.4986 0.685 0.000 0.016 0.060 0.304 0.620 0.000
#> SRR1333609 3 0.0146 0.846 0.004 0.000 0.996 0.000 0.000 0.000
#> SRR1471612 2 0.3658 0.796 0.000 0.800 0.000 0.004 0.104 0.092
#> SRR1413998 6 0.0146 0.986 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR1122940 2 0.0972 0.923 0.000 0.964 0.028 0.000 0.000 0.008
#> SRR1402563 3 0.1010 0.826 0.000 0.004 0.960 0.000 0.036 0.000
#> SRR1398393 4 0.4824 0.384 0.000 0.048 0.000 0.588 0.008 0.356
#> SRR657961 5 0.1408 0.723 0.000 0.020 0.000 0.036 0.944 0.000
#> SRR1471135 5 0.0458 0.742 0.000 0.000 0.000 0.016 0.984 0.000
#> SRR1430001 3 0.0713 0.830 0.028 0.000 0.972 0.000 0.000 0.000
#> SRR662775 1 0.0000 0.925 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.2442 0.838 0.000 0.852 0.000 0.004 0.000 0.144
#> SRR607190 1 0.0000 0.925 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR612467 5 0.3547 0.711 0.000 0.004 0.000 0.300 0.696 0.000
#> SRR1465959 2 0.1297 0.929 0.000 0.948 0.012 0.000 0.000 0.040
#> SRR1446132 6 0.0000 0.985 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1416933 6 0.0405 0.982 0.000 0.008 0.000 0.004 0.000 0.988
#> SRR1102538 2 0.0260 0.913 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR1098636 4 0.4367 0.591 0.000 0.032 0.000 0.604 0.364 0.000
#> SRR1072998 2 0.0260 0.913 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR627443 1 0.0000 0.925 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 0.925 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR823991 4 0.4832 0.684 0.000 0.324 0.000 0.608 0.064 0.004
#> SRR1089158 2 0.0260 0.913 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR1469036 3 0.0363 0.842 0.012 0.000 0.988 0.000 0.000 0.000
#> SRR824039 4 0.4408 0.646 0.000 0.356 0.000 0.608 0.036 0.000
#> SRR1339047 6 0.0000 0.985 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1443049 2 0.1500 0.925 0.000 0.936 0.012 0.000 0.000 0.052
#> SRR1122885 2 0.0405 0.924 0.000 0.988 0.004 0.000 0.000 0.008
#> SRR602895 5 0.5598 0.389 0.356 0.000 0.000 0.152 0.492 0.000
#> SRR1409837 2 0.2402 0.843 0.000 0.856 0.000 0.004 0.000 0.140
#> SRR1388959 6 0.0146 0.986 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR659863 1 0.0000 0.925 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1089877 4 0.3747 0.574 0.000 0.396 0.000 0.604 0.000 0.000
#> SRR1123775 5 0.5108 0.672 0.000 0.036 0.152 0.120 0.692 0.000
#> SRR658909 1 0.0146 0.923 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1140510 6 0.0146 0.986 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR607562 5 0.3641 0.725 0.020 0.000 0.000 0.248 0.732 0.000
#> SRR1122913 2 0.1644 0.926 0.000 0.932 0.028 0.000 0.000 0.040
#> SRR598042 5 0.0000 0.746 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1467340 3 0.3955 0.132 0.000 0.436 0.560 0.000 0.000 0.004
#> SRR1072321 2 0.0508 0.926 0.000 0.984 0.004 0.000 0.000 0.012
#> SRR1094580 2 0.1387 0.915 0.000 0.932 0.000 0.000 0.000 0.068
#> SRR1076608 6 0.0146 0.986 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR1395462 5 0.0363 0.742 0.000 0.000 0.000 0.012 0.988 0.000
#> SRR1489220 1 0.0000 0.925 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR614371 1 0.0777 0.911 0.972 0.000 0.004 0.024 0.000 0.000
#> SRR615455 1 0.2996 0.748 0.772 0.000 0.000 0.228 0.000 0.000
#> SRR1070573 2 0.0717 0.925 0.000 0.976 0.016 0.000 0.000 0.008
#> SRR598749 5 0.3765 0.662 0.000 0.000 0.000 0.404 0.596 0.000
#> SRR1365556 6 0.0000 0.985 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1350023 6 0.0146 0.986 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR1446582 5 0.0146 0.746 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR1439763 3 0.3062 0.667 0.000 0.160 0.816 0.024 0.000 0.000
#> SRR1343986 3 0.0146 0.844 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR807463 2 0.0790 0.929 0.000 0.968 0.000 0.000 0.000 0.032
#> SRR660390 1 0.0000 0.925 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1367672 2 0.0632 0.929 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR613294 1 0.3852 0.586 0.612 0.000 0.004 0.384 0.000 0.000
#> SRR824015 6 0.1257 0.939 0.000 0.020 0.000 0.028 0.000 0.952
#> SRR1078924 2 0.1644 0.926 0.000 0.932 0.028 0.000 0.000 0.040
#> SRR662221 1 0.3852 0.586 0.612 0.000 0.004 0.384 0.000 0.000
#> SRR655017 1 0.0000 0.925 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1338450 1 0.0405 0.921 0.988 0.000 0.004 0.008 0.000 0.000
#> SRR663741 1 0.0146 0.923 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1396057 6 0.0405 0.982 0.000 0.008 0.000 0.004 0.000 0.988
#> SRR1083800 2 0.0653 0.918 0.000 0.980 0.012 0.004 0.004 0.000
#> SRR1445789 6 0.0146 0.986 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR1387355 1 0.0146 0.923 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1388855 6 0.0146 0.986 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR1445449 1 0.0146 0.923 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1380740 3 0.0146 0.846 0.004 0.000 0.996 0.000 0.000 0.000
#> SRR659995 1 0.3852 0.586 0.612 0.000 0.004 0.384 0.000 0.000
#> SRR1489524 6 0.0146 0.986 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR1444662 6 0.0000 0.985 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1383652 5 0.0363 0.744 0.000 0.000 0.000 0.012 0.988 0.000
#> SRR1361243 3 0.0146 0.844 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1490337 4 0.3789 0.552 0.000 0.000 0.000 0.584 0.416 0.000
#> SRR823967 4 0.5231 0.699 0.000 0.224 0.000 0.608 0.168 0.000
#> SRR660127 1 0.0000 0.925 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1366627 6 0.0000 0.985 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1361219 2 0.2442 0.838 0.000 0.852 0.000 0.004 0.000 0.144
#> SRR1393510 6 0.0935 0.951 0.000 0.000 0.004 0.032 0.000 0.964
#> SRR662558 1 0.0405 0.920 0.988 0.000 0.004 0.008 0.000 0.000
#> SRR1077334 2 0.0632 0.901 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR807438 1 0.0146 0.923 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1459078 3 0.0146 0.846 0.004 0.000 0.996 0.000 0.000 0.000
#> SRR1329704 6 0.1765 0.872 0.000 0.096 0.000 0.000 0.000 0.904
#> SRR1468072 3 0.3797 0.244 0.000 0.000 0.580 0.000 0.000 0.420
#> SRR1376196 2 0.1644 0.926 0.000 0.932 0.028 0.000 0.000 0.040
#> SRR1442909 4 0.3797 0.549 0.000 0.000 0.000 0.580 0.420 0.000
#> SRR1414269 4 0.5231 0.686 0.000 0.168 0.000 0.608 0.224 0.000
#> SRR1381913 5 0.0363 0.742 0.000 0.000 0.000 0.012 0.988 0.000
#> SRR1340157 2 0.1858 0.897 0.000 0.904 0.004 0.000 0.000 0.092
#> SRR1407583 6 0.0291 0.983 0.000 0.004 0.000 0.004 0.000 0.992
#> SRR615826 5 0.3782 0.658 0.000 0.000 0.000 0.412 0.588 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "pam"]
# you can also extract it by
# res = res_list["SD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17380 rows and 102 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 0.979 0.927 0.972 0.2449 0.761 0.761
#> 3 3 0.683 0.852 0.926 1.4437 0.593 0.482
#> 4 4 0.769 0.894 0.949 0.1134 0.800 0.574
#> 5 5 0.735 0.703 0.865 0.1158 0.876 0.645
#> 6 6 0.687 0.664 0.806 0.0647 0.899 0.615
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
#> SRR612587 2 0.978 0.216 0.412 0.588
#> SRR1458769 2 0.000 0.978 0.000 1.000
#> SRR613162 1 0.000 0.904 1.000 0.000
#> SRR1352481 1 0.000 0.904 1.000 0.000
#> SRR1468876 2 0.000 0.978 0.000 1.000
#> SRR1399223 2 0.000 0.978 0.000 1.000
#> SRR660030 2 0.000 0.978 0.000 1.000
#> SRR1333609 2 0.000 0.978 0.000 1.000
#> SRR1471612 2 0.000 0.978 0.000 1.000
#> SRR1413998 2 0.000 0.978 0.000 1.000
#> SRR1122940 2 0.000 0.978 0.000 1.000
#> SRR1402563 2 0.000 0.978 0.000 1.000
#> SRR1398393 2 0.000 0.978 0.000 1.000
#> SRR657961 2 0.000 0.978 0.000 1.000
#> SRR1471135 2 0.000 0.978 0.000 1.000
#> SRR1430001 2 0.000 0.978 0.000 1.000
#> SRR662775 1 0.000 0.904 1.000 0.000
#> SRR1474182 2 0.000 0.978 0.000 1.000
#> SRR607190 1 0.000 0.904 1.000 0.000
#> SRR612467 2 0.000 0.978 0.000 1.000
#> SRR1465959 2 0.000 0.978 0.000 1.000
#> SRR1446132 2 0.000 0.978 0.000 1.000
#> SRR1416933 2 0.000 0.978 0.000 1.000
#> SRR1102538 2 0.000 0.978 0.000 1.000
#> SRR1098636 2 0.000 0.978 0.000 1.000
#> SRR1072998 2 0.000 0.978 0.000 1.000
#> SRR627443 1 0.000 0.904 1.000 0.000
#> SRR656131 1 0.000 0.904 1.000 0.000
#> SRR823991 2 0.000 0.978 0.000 1.000
#> SRR1089158 2 0.000 0.978 0.000 1.000
#> SRR1469036 2 0.000 0.978 0.000 1.000
#> SRR824039 2 0.000 0.978 0.000 1.000
#> SRR1339047 2 0.000 0.978 0.000 1.000
#> SRR1443049 2 0.000 0.978 0.000 1.000
#> SRR1122885 2 0.000 0.978 0.000 1.000
#> SRR602895 2 0.402 0.895 0.080 0.920
#> SRR1409837 2 0.000 0.978 0.000 1.000
#> SRR1388959 2 0.000 0.978 0.000 1.000
#> SRR659863 1 0.000 0.904 1.000 0.000
#> SRR1089877 2 0.000 0.978 0.000 1.000
#> SRR1123775 2 0.000 0.978 0.000 1.000
#> SRR658909 1 0.966 0.420 0.608 0.392
#> SRR1140510 2 0.000 0.978 0.000 1.000
#> SRR607562 2 0.000 0.978 0.000 1.000
#> SRR1122913 2 0.000 0.978 0.000 1.000
#> SRR598042 2 0.000 0.978 0.000 1.000
#> SRR1467340 2 0.000 0.978 0.000 1.000
#> SRR1072321 2 0.000 0.978 0.000 1.000
#> SRR1094580 2 0.000 0.978 0.000 1.000
#> SRR1076608 2 0.000 0.978 0.000 1.000
#> SRR1395462 2 0.000 0.978 0.000 1.000
#> SRR1489220 2 0.118 0.963 0.016 0.984
#> SRR614371 1 0.971 0.400 0.600 0.400
#> SRR615455 1 0.000 0.904 1.000 0.000
#> SRR1070573 2 0.000 0.978 0.000 1.000
#> SRR598749 2 0.327 0.917 0.060 0.940
#> SRR1365556 2 0.000 0.978 0.000 1.000
#> SRR1350023 2 0.000 0.978 0.000 1.000
#> SRR1446582 2 0.000 0.978 0.000 1.000
#> SRR1439763 2 0.000 0.978 0.000 1.000
#> SRR1343986 2 0.000 0.978 0.000 1.000
#> SRR807463 2 0.000 0.978 0.000 1.000
#> SRR660390 1 0.000 0.904 1.000 0.000
#> SRR1367672 2 0.000 0.978 0.000 1.000
#> SRR613294 2 0.963 0.294 0.388 0.612
#> SRR824015 2 0.000 0.978 0.000 1.000
#> SRR1078924 2 0.000 0.978 0.000 1.000
#> SRR662221 2 0.802 0.643 0.244 0.756
#> SRR655017 1 0.000 0.904 1.000 0.000
#> SRR1338450 2 0.000 0.978 0.000 1.000
#> SRR663741 1 0.966 0.420 0.608 0.392
#> SRR1396057 2 0.000 0.978 0.000 1.000
#> SRR1083800 2 0.000 0.978 0.000 1.000
#> SRR1445789 2 0.000 0.978 0.000 1.000
#> SRR1387355 2 0.000 0.978 0.000 1.000
#> SRR1388855 2 0.000 0.978 0.000 1.000
#> SRR1445449 2 0.000 0.978 0.000 1.000
#> SRR1380740 2 0.000 0.978 0.000 1.000
#> SRR659995 2 0.958 0.318 0.380 0.620
#> SRR1489524 2 0.000 0.978 0.000 1.000
#> SRR1444662 2 0.000 0.978 0.000 1.000
#> SRR1383652 2 0.000 0.978 0.000 1.000
#> SRR1361243 2 0.000 0.978 0.000 1.000
#> SRR1490337 2 0.000 0.978 0.000 1.000
#> SRR823967 2 0.000 0.978 0.000 1.000
#> SRR660127 1 0.000 0.904 1.000 0.000
#> SRR1366627 2 0.000 0.978 0.000 1.000
#> SRR1361219 2 0.000 0.978 0.000 1.000
#> SRR1393510 2 0.000 0.978 0.000 1.000
#> SRR662558 2 0.327 0.917 0.060 0.940
#> SRR1077334 2 0.000 0.978 0.000 1.000
#> SRR807438 2 0.000 0.978 0.000 1.000
#> SRR1459078 2 0.000 0.978 0.000 1.000
#> SRR1329704 2 0.000 0.978 0.000 1.000
#> SRR1468072 2 0.000 0.978 0.000 1.000
#> SRR1376196 2 0.000 0.978 0.000 1.000
#> SRR1442909 2 0.000 0.978 0.000 1.000
#> SRR1414269 2 0.000 0.978 0.000 1.000
#> SRR1381913 2 0.000 0.978 0.000 1.000
#> SRR1340157 2 0.000 0.978 0.000 1.000
#> SRR1407583 2 0.000 0.978 0.000 1.000
#> SRR615826 2 0.327 0.917 0.060 0.940
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 3 0.4974 0.6765 0.236 0.000 0.764
#> SRR1458769 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR613162 1 0.0000 0.9531 1.000 0.000 0.000
#> SRR1352481 1 0.0000 0.9531 1.000 0.000 0.000
#> SRR1468876 3 0.0237 0.8815 0.000 0.004 0.996
#> SRR1399223 2 0.4291 0.8270 0.000 0.820 0.180
#> SRR660030 3 0.0237 0.8815 0.000 0.004 0.996
#> SRR1333609 3 0.0237 0.8815 0.000 0.004 0.996
#> SRR1471612 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR1413998 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR1122940 2 0.5058 0.7787 0.000 0.756 0.244
#> SRR1402563 3 0.1964 0.8647 0.000 0.056 0.944
#> SRR1398393 2 0.0892 0.9265 0.000 0.980 0.020
#> SRR657961 2 0.2448 0.8997 0.000 0.924 0.076
#> SRR1471135 3 0.4974 0.7242 0.000 0.236 0.764
#> SRR1430001 3 0.1289 0.8751 0.000 0.032 0.968
#> SRR662775 1 0.0000 0.9531 1.000 0.000 0.000
#> SRR1474182 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR607190 1 0.0000 0.9531 1.000 0.000 0.000
#> SRR612467 2 0.4702 0.8071 0.000 0.788 0.212
#> SRR1465959 2 0.2537 0.9005 0.000 0.920 0.080
#> SRR1446132 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR1416933 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR1102538 2 0.1860 0.9118 0.000 0.948 0.052
#> SRR1098636 3 0.3038 0.8362 0.000 0.104 0.896
#> SRR1072998 2 0.2959 0.8945 0.000 0.900 0.100
#> SRR627443 1 0.0000 0.9531 1.000 0.000 0.000
#> SRR656131 1 0.0000 0.9531 1.000 0.000 0.000
#> SRR823991 3 0.5650 0.6102 0.000 0.312 0.688
#> SRR1089158 2 0.1964 0.9103 0.000 0.944 0.056
#> SRR1469036 3 0.0424 0.8812 0.000 0.008 0.992
#> SRR824039 2 0.1860 0.9118 0.000 0.948 0.052
#> SRR1339047 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR1443049 2 0.2448 0.9026 0.000 0.924 0.076
#> SRR1122885 2 0.2165 0.9103 0.000 0.936 0.064
#> SRR602895 3 0.0237 0.8815 0.000 0.004 0.996
#> SRR1409837 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR1388959 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR659863 1 0.0000 0.9531 1.000 0.000 0.000
#> SRR1089877 2 0.1860 0.9118 0.000 0.948 0.052
#> SRR1123775 3 0.0237 0.8815 0.000 0.004 0.996
#> SRR658909 3 0.4974 0.6757 0.236 0.000 0.764
#> SRR1140510 2 0.1529 0.9191 0.000 0.960 0.040
#> SRR607562 3 0.0237 0.8815 0.000 0.004 0.996
#> SRR1122913 2 0.4291 0.8270 0.000 0.820 0.180
#> SRR598042 3 0.6252 0.0631 0.000 0.444 0.556
#> SRR1467340 3 0.2356 0.8597 0.000 0.072 0.928
#> SRR1072321 2 0.0892 0.9275 0.000 0.980 0.020
#> SRR1094580 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR1076608 2 0.4235 0.8310 0.000 0.824 0.176
#> SRR1395462 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR1489220 3 0.0237 0.8815 0.000 0.004 0.996
#> SRR614371 3 0.5016 0.6704 0.240 0.000 0.760
#> SRR615455 1 0.6192 0.1737 0.580 0.000 0.420
#> SRR1070573 2 0.4605 0.8174 0.000 0.796 0.204
#> SRR598749 3 0.0000 0.8796 0.000 0.000 1.000
#> SRR1365556 2 0.4291 0.8077 0.000 0.820 0.180
#> SRR1350023 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR1446582 3 0.1753 0.8696 0.000 0.048 0.952
#> SRR1439763 3 0.0237 0.8815 0.000 0.004 0.996
#> SRR1343986 3 0.0237 0.8815 0.000 0.004 0.996
#> SRR807463 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR660390 1 0.0000 0.9531 1.000 0.000 0.000
#> SRR1367672 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR613294 3 0.4887 0.6869 0.228 0.000 0.772
#> SRR824015 3 0.4452 0.7548 0.000 0.192 0.808
#> SRR1078924 2 0.4178 0.8344 0.000 0.828 0.172
#> SRR662221 3 0.0000 0.8796 0.000 0.000 1.000
#> SRR655017 1 0.0000 0.9531 1.000 0.000 0.000
#> SRR1338450 3 0.0237 0.8815 0.000 0.004 0.996
#> SRR663741 3 0.5016 0.6704 0.240 0.000 0.760
#> SRR1396057 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR1083800 3 0.0237 0.8815 0.000 0.004 0.996
#> SRR1445789 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR1387355 3 0.0237 0.8815 0.000 0.004 0.996
#> SRR1388855 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR1445449 3 0.2625 0.8597 0.000 0.084 0.916
#> SRR1380740 3 0.0237 0.8815 0.000 0.004 0.996
#> SRR659995 3 0.0000 0.8796 0.000 0.000 1.000
#> SRR1489524 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR1444662 3 0.5098 0.7117 0.000 0.248 0.752
#> SRR1383652 3 0.3551 0.8302 0.000 0.132 0.868
#> SRR1361243 3 0.1964 0.8647 0.000 0.056 0.944
#> SRR1490337 3 0.1860 0.8674 0.000 0.052 0.948
#> SRR823967 3 0.1643 0.8714 0.000 0.044 0.956
#> SRR660127 1 0.0000 0.9531 1.000 0.000 0.000
#> SRR1366627 2 0.3038 0.8772 0.000 0.896 0.104
#> SRR1361219 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR1393510 3 0.2878 0.8540 0.000 0.096 0.904
#> SRR662558 3 0.0000 0.8796 0.000 0.000 1.000
#> SRR1077334 2 0.3038 0.8920 0.000 0.896 0.104
#> SRR807438 3 0.0237 0.8815 0.000 0.004 0.996
#> SRR1459078 3 0.1964 0.8647 0.000 0.056 0.944
#> SRR1329704 2 0.0237 0.9314 0.000 0.996 0.004
#> SRR1468072 3 0.2261 0.8610 0.000 0.068 0.932
#> SRR1376196 2 0.4291 0.8270 0.000 0.820 0.180
#> SRR1442909 3 0.3686 0.8143 0.000 0.140 0.860
#> SRR1414269 3 0.1031 0.8792 0.000 0.024 0.976
#> SRR1381913 3 0.5650 0.5982 0.000 0.312 0.688
#> SRR1340157 2 0.1163 0.9241 0.000 0.972 0.028
#> SRR1407583 2 0.0000 0.9320 0.000 1.000 0.000
#> SRR615826 3 0.6168 0.1915 0.000 0.412 0.588
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.0000 0.970 0 0.000 0.000 1.000
#> SRR1458769 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR613162 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1352481 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1468876 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR1399223 3 0.0188 0.901 0 0.004 0.996 0.000
#> SRR660030 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR1333609 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR1471612 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR1413998 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR1122940 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR1402563 3 0.0188 0.901 0 0.004 0.996 0.000
#> SRR1398393 2 0.0188 0.950 0 0.996 0.004 0.000
#> SRR657961 2 0.0336 0.948 0 0.992 0.008 0.000
#> SRR1471135 3 0.2921 0.845 0 0.140 0.860 0.000
#> SRR1430001 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR662775 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1474182 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR607190 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR612467 2 0.3837 0.728 0 0.776 0.224 0.000
#> SRR1465959 2 0.3356 0.782 0 0.824 0.176 0.000
#> SRR1446132 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR1416933 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR1102538 2 0.0188 0.950 0 0.996 0.004 0.000
#> SRR1098636 3 0.3873 0.767 0 0.228 0.772 0.000
#> SRR1072998 2 0.2868 0.825 0 0.864 0.136 0.000
#> SRR627443 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR656131 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR823991 3 0.4888 0.454 0 0.412 0.588 0.000
#> SRR1089158 2 0.0188 0.950 0 0.996 0.004 0.000
#> SRR1469036 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR824039 2 0.0188 0.950 0 0.996 0.004 0.000
#> SRR1339047 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR1443049 2 0.3266 0.791 0 0.832 0.168 0.000
#> SRR1122885 2 0.0921 0.934 0 0.972 0.028 0.000
#> SRR602895 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR1409837 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR1388959 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR659863 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1089877 2 0.0188 0.950 0 0.996 0.004 0.000
#> SRR1123775 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR658909 4 0.0188 0.965 0 0.000 0.004 0.996
#> SRR1140510 3 0.4543 0.640 0 0.324 0.676 0.000
#> SRR607562 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR1122913 3 0.0592 0.896 0 0.016 0.984 0.000
#> SRR598042 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR1467340 3 0.0188 0.901 0 0.004 0.996 0.000
#> SRR1072321 2 0.0817 0.934 0 0.976 0.024 0.000
#> SRR1094580 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR1076608 3 0.0817 0.895 0 0.024 0.976 0.000
#> SRR1395462 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR1489220 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR614371 4 0.0000 0.970 0 0.000 0.000 1.000
#> SRR615455 4 0.0000 0.970 0 0.000 0.000 1.000
#> SRR1070573 3 0.3528 0.749 0 0.192 0.808 0.000
#> SRR598749 4 0.0000 0.970 0 0.000 0.000 1.000
#> SRR1365556 3 0.3024 0.839 0 0.148 0.852 0.000
#> SRR1350023 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR1446582 3 0.2530 0.859 0 0.112 0.888 0.000
#> SRR1439763 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR1343986 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR807463 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR660390 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1367672 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR613294 4 0.0000 0.970 0 0.000 0.000 1.000
#> SRR824015 3 0.4817 0.509 0 0.388 0.612 0.000
#> SRR1078924 2 0.4790 0.450 0 0.620 0.380 0.000
#> SRR662221 4 0.0000 0.970 0 0.000 0.000 1.000
#> SRR655017 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1338450 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR663741 4 0.0000 0.970 0 0.000 0.000 1.000
#> SRR1396057 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR1083800 3 0.0188 0.901 0 0.004 0.996 0.000
#> SRR1445789 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR1387355 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR1388855 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR1445449 3 0.2704 0.853 0 0.124 0.876 0.000
#> SRR1380740 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR659995 4 0.0000 0.970 0 0.000 0.000 1.000
#> SRR1489524 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR1444662 3 0.3123 0.835 0 0.156 0.844 0.000
#> SRR1383652 3 0.2921 0.845 0 0.140 0.860 0.000
#> SRR1361243 3 0.0188 0.901 0 0.004 0.996 0.000
#> SRR1490337 3 0.2868 0.846 0 0.136 0.864 0.000
#> SRR823967 3 0.2589 0.858 0 0.116 0.884 0.000
#> SRR660127 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1366627 3 0.3356 0.817 0 0.176 0.824 0.000
#> SRR1361219 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR1393510 3 0.2868 0.846 0 0.136 0.864 0.000
#> SRR662558 4 0.3610 0.693 0 0.000 0.200 0.800
#> SRR1077334 2 0.2921 0.821 0 0.860 0.140 0.000
#> SRR807438 3 0.0000 0.901 0 0.000 1.000 0.000
#> SRR1459078 3 0.0188 0.901 0 0.004 0.996 0.000
#> SRR1329704 2 0.0336 0.947 0 0.992 0.008 0.000
#> SRR1468072 3 0.0188 0.901 0 0.004 0.996 0.000
#> SRR1376196 3 0.0188 0.901 0 0.004 0.996 0.000
#> SRR1442909 3 0.3486 0.805 0 0.188 0.812 0.000
#> SRR1414269 3 0.0921 0.894 0 0.028 0.972 0.000
#> SRR1381913 3 0.4941 0.383 0 0.436 0.564 0.000
#> SRR1340157 2 0.1389 0.914 0 0.952 0.048 0.000
#> SRR1407583 2 0.0000 0.952 0 1.000 0.000 0.000
#> SRR615826 4 0.0000 0.970 0 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
#> SRR612587 4 0.0000 0.9133 0 0.000 0.000 1.000 0.000
#> SRR1458769 2 0.0000 0.9184 0 1.000 0.000 0.000 0.000
#> SRR613162 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000
#> SRR1468876 3 0.4045 0.2781 0 0.000 0.644 0.000 0.356
#> SRR1399223 3 0.0162 0.8124 0 0.004 0.996 0.000 0.000
#> SRR660030 3 0.4283 -0.1796 0 0.000 0.544 0.000 0.456
#> SRR1333609 3 0.0000 0.8125 0 0.000 1.000 0.000 0.000
#> SRR1471612 2 0.0000 0.9184 0 1.000 0.000 0.000 0.000
#> SRR1413998 2 0.0000 0.9184 0 1.000 0.000 0.000 0.000
#> SRR1122940 3 0.0162 0.8116 0 0.000 0.996 0.000 0.004
#> SRR1402563 3 0.0510 0.8087 0 0.000 0.984 0.000 0.016
#> SRR1398393 2 0.4030 0.2992 0 0.648 0.000 0.000 0.352
#> SRR657961 5 0.1965 0.4110 0 0.096 0.000 0.000 0.904
#> SRR1471135 3 0.3888 0.7133 0 0.136 0.800 0.000 0.064
#> SRR1430001 3 0.0000 0.8125 0 0.000 1.000 0.000 0.000
#> SRR662775 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.1478 0.9046 0 0.936 0.000 0.000 0.064
#> SRR607190 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000
#> SRR612467 5 0.4369 0.2541 0 0.208 0.052 0.000 0.740
#> SRR1465959 2 0.3354 0.8422 0 0.844 0.088 0.000 0.068
#> SRR1446132 2 0.0000 0.9184 0 1.000 0.000 0.000 0.000
#> SRR1416933 2 0.0000 0.9184 0 1.000 0.000 0.000 0.000
#> SRR1102538 2 0.2516 0.8541 0 0.860 0.000 0.000 0.140
#> SRR1098636 5 0.5454 0.3288 0 0.064 0.404 0.000 0.532
#> SRR1072998 2 0.2424 0.8609 0 0.868 0.000 0.000 0.132
#> SRR627443 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000
#> SRR823991 5 0.5976 0.3293 0 0.112 0.400 0.000 0.488
#> SRR1089158 2 0.2605 0.8459 0 0.852 0.000 0.000 0.148
#> SRR1469036 3 0.0000 0.8125 0 0.000 1.000 0.000 0.000
#> SRR824039 5 0.4300 -0.0382 0 0.476 0.000 0.000 0.524
#> SRR1339047 2 0.0000 0.9184 0 1.000 0.000 0.000 0.000
#> SRR1443049 2 0.3464 0.8351 0 0.836 0.096 0.000 0.068
#> SRR1122885 2 0.2377 0.8644 0 0.872 0.000 0.000 0.128
#> SRR602895 5 0.4249 0.0268 0 0.000 0.432 0.000 0.568
#> SRR1409837 2 0.0609 0.9181 0 0.980 0.000 0.000 0.020
#> SRR1388959 2 0.0000 0.9184 0 1.000 0.000 0.000 0.000
#> SRR659863 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000
#> SRR1089877 5 0.4300 -0.0382 0 0.476 0.000 0.000 0.524
#> SRR1123775 3 0.0000 0.8125 0 0.000 1.000 0.000 0.000
#> SRR658909 4 0.1704 0.8566 0 0.000 0.004 0.928 0.068
#> SRR1140510 3 0.3895 0.4942 0 0.320 0.680 0.000 0.000
#> SRR607562 5 0.1544 0.4060 0 0.000 0.068 0.000 0.932
#> SRR1122913 3 0.2806 0.6580 0 0.152 0.844 0.000 0.004
#> SRR598042 5 0.1478 0.4054 0 0.000 0.064 0.000 0.936
#> SRR1467340 3 0.0162 0.8124 0 0.004 0.996 0.000 0.000
#> SRR1072321 2 0.2409 0.8906 0 0.900 0.032 0.000 0.068
#> SRR1094580 2 0.0000 0.9184 0 1.000 0.000 0.000 0.000
#> SRR1076608 3 0.1205 0.8013 0 0.040 0.956 0.000 0.004
#> SRR1395462 2 0.1410 0.8706 0 0.940 0.000 0.000 0.060
#> SRR1489220 3 0.1671 0.7755 0 0.000 0.924 0.000 0.076
#> SRR614371 4 0.0162 0.9107 0 0.000 0.004 0.996 0.000
#> SRR615455 4 0.0000 0.9133 0 0.000 0.000 1.000 0.000
#> SRR1070573 3 0.5490 0.2371 0 0.324 0.592 0.000 0.084
#> SRR598749 4 0.4182 0.6347 0 0.000 0.000 0.600 0.400
#> SRR1365556 3 0.2516 0.7411 0 0.140 0.860 0.000 0.000
#> SRR1350023 2 0.0510 0.9184 0 0.984 0.000 0.000 0.016
#> SRR1446582 3 0.5202 0.2498 0 0.056 0.596 0.000 0.348
#> SRR1439763 3 0.1043 0.7956 0 0.000 0.960 0.000 0.040
#> SRR1343986 3 0.0000 0.8125 0 0.000 1.000 0.000 0.000
#> SRR807463 2 0.1544 0.9024 0 0.932 0.000 0.000 0.068
#> SRR660390 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000
#> SRR1367672 2 0.1544 0.9024 0 0.932 0.000 0.000 0.068
#> SRR613294 4 0.0000 0.9133 0 0.000 0.000 1.000 0.000
#> SRR824015 5 0.6097 0.2948 0 0.124 0.420 0.000 0.456
#> SRR1078924 2 0.3906 0.5882 0 0.704 0.292 0.000 0.004
#> SRR662221 4 0.0000 0.9133 0 0.000 0.000 1.000 0.000
#> SRR655017 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000
#> SRR1338450 5 0.4294 0.2570 0 0.000 0.468 0.000 0.532
#> SRR663741 4 0.0000 0.9133 0 0.000 0.000 1.000 0.000
#> SRR1396057 2 0.0000 0.9184 0 1.000 0.000 0.000 0.000
#> SRR1083800 3 0.3003 0.6981 0 0.000 0.812 0.000 0.188
#> SRR1445789 2 0.0000 0.9184 0 1.000 0.000 0.000 0.000
#> SRR1387355 3 0.1410 0.7869 0 0.000 0.940 0.000 0.060
#> SRR1388855 2 0.0000 0.9184 0 1.000 0.000 0.000 0.000
#> SRR1445449 3 0.3586 0.7355 0 0.096 0.828 0.000 0.076
#> SRR1380740 3 0.0000 0.8125 0 0.000 1.000 0.000 0.000
#> SRR659995 4 0.0000 0.9133 0 0.000 0.000 1.000 0.000
#> SRR1489524 2 0.0963 0.9148 0 0.964 0.000 0.000 0.036
#> SRR1444662 3 0.3691 0.7110 0 0.156 0.804 0.000 0.040
#> SRR1383652 3 0.4025 0.7073 0 0.132 0.792 0.000 0.076
#> SRR1361243 3 0.0000 0.8125 0 0.000 1.000 0.000 0.000
#> SRR1490337 5 0.5454 0.3288 0 0.064 0.404 0.000 0.532
#> SRR823967 5 0.4192 0.3348 0 0.000 0.404 0.000 0.596
#> SRR660127 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000
#> SRR1366627 3 0.2813 0.7151 0 0.168 0.832 0.000 0.000
#> SRR1361219 2 0.1121 0.9127 0 0.956 0.000 0.000 0.044
#> SRR1393510 3 0.2179 0.7603 0 0.112 0.888 0.000 0.000
#> SRR662558 5 0.5499 0.1317 0 0.000 0.068 0.400 0.532
#> SRR1077334 5 0.4300 -0.0382 0 0.476 0.000 0.000 0.524
#> SRR807438 5 0.4294 0.2570 0 0.000 0.468 0.000 0.532
#> SRR1459078 3 0.0000 0.8125 0 0.000 1.000 0.000 0.000
#> SRR1329704 2 0.0404 0.9135 0 0.988 0.012 0.000 0.000
#> SRR1468072 3 0.0290 0.8123 0 0.008 0.992 0.000 0.000
#> SRR1376196 3 0.0162 0.8116 0 0.000 0.996 0.000 0.004
#> SRR1442909 5 0.5499 0.3290 0 0.068 0.400 0.000 0.532
#> SRR1414269 3 0.4641 -0.1981 0 0.012 0.532 0.000 0.456
#> SRR1381913 5 0.1544 0.3990 0 0.068 0.000 0.000 0.932
#> SRR1340157 2 0.2046 0.8971 0 0.916 0.016 0.000 0.068
#> SRR1407583 2 0.0000 0.9184 0 1.000 0.000 0.000 0.000
#> SRR615826 4 0.3966 0.6899 0 0.000 0.000 0.664 0.336
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 4 0.0000 0.8934 0 0.000 0.000 1.000 0.000 0.000
#> SRR1458769 6 0.3620 0.2212 0 0.352 0.000 0.000 0.000 0.648
#> SRR613162 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR1468876 5 0.3804 0.3030 0 0.000 0.424 0.000 0.576 0.000
#> SRR1399223 3 0.1007 0.8295 0 0.000 0.956 0.000 0.000 0.044
#> SRR660030 5 0.3843 0.4368 0 0.000 0.452 0.000 0.548 0.000
#> SRR1333609 3 0.0000 0.8485 0 0.000 1.000 0.000 0.000 0.000
#> SRR1471612 2 0.3923 0.4787 0 0.580 0.000 0.000 0.004 0.416
#> SRR1413998 6 0.0000 0.7084 0 0.000 0.000 0.000 0.000 1.000
#> SRR1122940 3 0.2941 0.5928 0 0.220 0.780 0.000 0.000 0.000
#> SRR1402563 3 0.1349 0.8324 0 0.004 0.940 0.000 0.056 0.000
#> SRR1398393 5 0.5866 0.2078 0 0.292 0.008 0.000 0.516 0.184
#> SRR657961 5 0.2697 0.4731 0 0.188 0.000 0.000 0.812 0.000
#> SRR1471135 3 0.3152 0.7302 0 0.008 0.792 0.000 0.196 0.004
#> SRR1430001 3 0.0000 0.8485 0 0.000 1.000 0.000 0.000 0.000
#> SRR662775 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.3126 0.7445 0 0.752 0.000 0.000 0.000 0.248
#> SRR607190 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR612467 2 0.3528 0.1608 0 0.700 0.004 0.000 0.296 0.000
#> SRR1465959 2 0.3189 0.7532 0 0.760 0.004 0.000 0.000 0.236
#> SRR1446132 6 0.0000 0.7084 0 0.000 0.000 0.000 0.000 1.000
#> SRR1416933 6 0.5617 0.1357 0 0.356 0.136 0.000 0.004 0.504
#> SRR1102538 2 0.3109 0.7491 0 0.772 0.000 0.000 0.004 0.224
#> SRR1098636 5 0.3470 0.6230 0 0.012 0.248 0.000 0.740 0.000
#> SRR1072998 2 0.3109 0.7491 0 0.772 0.000 0.000 0.004 0.224
#> SRR627443 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR823991 5 0.6176 0.5714 0 0.060 0.248 0.000 0.560 0.132
#> SRR1089158 2 0.3190 0.7461 0 0.772 0.000 0.000 0.008 0.220
#> SRR1469036 3 0.0000 0.8485 0 0.000 1.000 0.000 0.000 0.000
#> SRR824039 5 0.3843 0.2103 0 0.452 0.000 0.000 0.548 0.000
#> SRR1339047 6 0.0000 0.7084 0 0.000 0.000 0.000 0.000 1.000
#> SRR1443049 2 0.3189 0.7532 0 0.760 0.004 0.000 0.000 0.236
#> SRR1122885 2 0.3189 0.7527 0 0.760 0.000 0.000 0.004 0.236
#> SRR602895 5 0.5731 0.2095 0 0.224 0.260 0.000 0.516 0.000
#> SRR1409837 2 0.3737 0.5382 0 0.608 0.000 0.000 0.000 0.392
#> SRR1388959 6 0.0146 0.7073 0 0.004 0.000 0.000 0.000 0.996
#> SRR659863 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR1089877 5 0.4300 0.2627 0 0.432 0.020 0.000 0.548 0.000
#> SRR1123775 3 0.0000 0.8485 0 0.000 1.000 0.000 0.000 0.000
#> SRR658909 4 0.2762 0.6931 0 0.000 0.000 0.804 0.196 0.000
#> SRR1140510 3 0.3947 0.6604 0 0.036 0.732 0.000 0.004 0.228
#> SRR607562 5 0.2969 0.3805 0 0.224 0.000 0.000 0.776 0.000
#> SRR1122913 3 0.2624 0.7127 0 0.124 0.856 0.000 0.000 0.020
#> SRR598042 5 0.3288 0.3440 0 0.276 0.000 0.000 0.724 0.000
#> SRR1467340 3 0.0000 0.8485 0 0.000 1.000 0.000 0.000 0.000
#> SRR1072321 2 0.3050 0.7527 0 0.764 0.000 0.000 0.000 0.236
#> SRR1094580 2 0.4261 0.4595 0 0.572 0.020 0.000 0.000 0.408
#> SRR1076608 3 0.2070 0.8025 0 0.008 0.892 0.000 0.000 0.100
#> SRR1395462 2 0.5419 0.5028 0 0.580 0.000 0.000 0.200 0.220
#> SRR1489220 3 0.2793 0.7304 0 0.000 0.800 0.000 0.200 0.000
#> SRR614371 4 0.1075 0.8765 0 0.048 0.000 0.952 0.000 0.000
#> SRR615455 4 0.0000 0.8934 0 0.000 0.000 1.000 0.000 0.000
#> SRR1070573 2 0.4628 0.2905 0 0.632 0.312 0.000 0.004 0.052
#> SRR598749 4 0.5579 0.5769 0 0.204 0.000 0.548 0.248 0.000
#> SRR1365556 3 0.2631 0.7652 0 0.008 0.840 0.000 0.000 0.152
#> SRR1350023 6 0.0363 0.7029 0 0.012 0.000 0.000 0.000 0.988
#> SRR1446582 3 0.3890 0.3095 0 0.004 0.596 0.000 0.400 0.000
#> SRR1439763 3 0.0146 0.8475 0 0.000 0.996 0.000 0.004 0.000
#> SRR1343986 3 0.0000 0.8485 0 0.000 1.000 0.000 0.000 0.000
#> SRR807463 2 0.3076 0.7520 0 0.760 0.000 0.000 0.000 0.240
#> SRR660390 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR1367672 2 0.3050 0.7527 0 0.764 0.000 0.000 0.000 0.236
#> SRR613294 4 0.0000 0.8934 0 0.000 0.000 1.000 0.000 0.000
#> SRR824015 5 0.6195 0.5583 0 0.056 0.264 0.000 0.548 0.132
#> SRR1078924 2 0.5421 0.4867 0 0.580 0.216 0.000 0.000 0.204
#> SRR662221 4 0.0000 0.8934 0 0.000 0.000 1.000 0.000 0.000
#> SRR655017 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR1338450 5 0.3126 0.6207 0 0.000 0.248 0.000 0.752 0.000
#> SRR663741 4 0.0000 0.8934 0 0.000 0.000 1.000 0.000 0.000
#> SRR1396057 6 0.4543 0.2002 0 0.356 0.036 0.000 0.004 0.604
#> SRR1083800 3 0.2902 0.7351 0 0.004 0.800 0.000 0.196 0.000
#> SRR1445789 6 0.0000 0.7084 0 0.000 0.000 0.000 0.000 1.000
#> SRR1387355 3 0.2320 0.7873 0 0.004 0.864 0.000 0.132 0.000
#> SRR1388855 6 0.0865 0.6936 0 0.036 0.000 0.000 0.000 0.964
#> SRR1445449 3 0.3103 0.7190 0 0.008 0.784 0.000 0.208 0.000
#> SRR1380740 3 0.0000 0.8485 0 0.000 1.000 0.000 0.000 0.000
#> SRR659995 4 0.0000 0.8934 0 0.000 0.000 1.000 0.000 0.000
#> SRR1489524 6 0.1141 0.6627 0 0.052 0.000 0.000 0.000 0.948
#> SRR1444662 3 0.3658 0.7182 0 0.008 0.772 0.000 0.028 0.192
#> SRR1383652 3 0.3073 0.7228 0 0.008 0.788 0.000 0.204 0.000
#> SRR1361243 3 0.0146 0.8479 0 0.004 0.996 0.000 0.000 0.000
#> SRR1490337 5 0.3265 0.6197 0 0.004 0.248 0.000 0.748 0.000
#> SRR823967 5 0.3865 0.6249 0 0.032 0.248 0.000 0.720 0.000
#> SRR660127 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR1366627 3 0.3349 0.6826 0 0.008 0.748 0.000 0.000 0.244
#> SRR1361219 2 0.3756 0.5251 0 0.600 0.000 0.000 0.000 0.400
#> SRR1393510 3 0.2389 0.7851 0 0.008 0.864 0.000 0.000 0.128
#> SRR662558 5 0.3490 0.4032 0 0.000 0.008 0.268 0.724 0.000
#> SRR1077334 2 0.4244 0.2927 0 0.652 0.020 0.000 0.320 0.008
#> SRR807438 5 0.3151 0.6193 0 0.000 0.252 0.000 0.748 0.000
#> SRR1459078 3 0.0000 0.8485 0 0.000 1.000 0.000 0.000 0.000
#> SRR1329704 6 0.5899 0.0643 0 0.360 0.208 0.000 0.000 0.432
#> SRR1468072 3 0.0000 0.8485 0 0.000 1.000 0.000 0.000 0.000
#> SRR1376196 3 0.0000 0.8485 0 0.000 1.000 0.000 0.000 0.000
#> SRR1442909 5 0.3265 0.6197 0 0.004 0.248 0.000 0.748 0.000
#> SRR1414269 5 0.4652 0.4896 0 0.012 0.404 0.000 0.560 0.024
#> SRR1381913 5 0.2823 0.3969 0 0.204 0.000 0.000 0.796 0.000
#> SRR1340157 2 0.3189 0.7532 0 0.760 0.004 0.000 0.000 0.236
#> SRR1407583 6 0.6053 0.0607 0 0.356 0.216 0.000 0.004 0.424
#> SRR615826 4 0.4198 0.7145 0 0.060 0.000 0.708 0.232 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "mclust"]
# you can also extract it by
# res = res_list["SD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17380 rows and 102 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 1.000 0.963 0.983 0.336 0.682 0.682
#> 3 3 0.479 0.566 0.827 0.431 0.880 0.825
#> 4 4 0.411 0.539 0.747 0.115 0.815 0.722
#> 5 5 0.906 0.891 0.934 0.174 0.789 0.643
#> 6 6 0.605 0.558 0.778 0.182 0.824 0.566
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
#> SRR612587 1 0.000 0.997 1.000 0.000
#> SRR1458769 2 0.000 0.979 0.000 1.000
#> SRR613162 1 0.000 0.997 1.000 0.000
#> SRR1352481 1 0.000 0.997 1.000 0.000
#> SRR1468876 2 0.224 0.955 0.036 0.964
#> SRR1399223 2 0.000 0.979 0.000 1.000
#> SRR660030 2 0.000 0.979 0.000 1.000
#> SRR1333609 2 0.000 0.979 0.000 1.000
#> SRR1471612 2 0.000 0.979 0.000 1.000
#> SRR1413998 2 0.000 0.979 0.000 1.000
#> SRR1122940 2 0.000 0.979 0.000 1.000
#> SRR1402563 2 0.000 0.979 0.000 1.000
#> SRR1398393 2 0.278 0.946 0.048 0.952
#> SRR657961 2 0.000 0.979 0.000 1.000
#> SRR1471135 2 0.000 0.979 0.000 1.000
#> SRR1430001 2 0.000 0.979 0.000 1.000
#> SRR662775 1 0.000 0.997 1.000 0.000
#> SRR1474182 2 0.000 0.979 0.000 1.000
#> SRR607190 1 0.000 0.997 1.000 0.000
#> SRR612467 2 0.788 0.691 0.236 0.764
#> SRR1465959 2 0.000 0.979 0.000 1.000
#> SRR1446132 2 0.000 0.979 0.000 1.000
#> SRR1416933 2 0.000 0.979 0.000 1.000
#> SRR1102538 2 0.000 0.979 0.000 1.000
#> SRR1098636 2 0.295 0.943 0.052 0.948
#> SRR1072998 2 0.000 0.979 0.000 1.000
#> SRR627443 1 0.000 0.997 1.000 0.000
#> SRR656131 1 0.000 0.997 1.000 0.000
#> SRR823991 2 0.278 0.946 0.048 0.952
#> SRR1089158 2 0.000 0.979 0.000 1.000
#> SRR1469036 2 0.000 0.979 0.000 1.000
#> SRR824039 2 0.278 0.946 0.048 0.952
#> SRR1339047 2 0.278 0.946 0.048 0.952
#> SRR1443049 2 0.000 0.979 0.000 1.000
#> SRR1122885 2 0.000 0.979 0.000 1.000
#> SRR602895 2 0.000 0.979 0.000 1.000
#> SRR1409837 2 0.000 0.979 0.000 1.000
#> SRR1388959 2 0.000 0.979 0.000 1.000
#> SRR659863 1 0.000 0.997 1.000 0.000
#> SRR1089877 2 0.278 0.946 0.048 0.952
#> SRR1123775 2 0.000 0.979 0.000 1.000
#> SRR658909 1 0.311 0.938 0.944 0.056
#> SRR1140510 2 0.000 0.979 0.000 1.000
#> SRR607562 2 0.000 0.979 0.000 1.000
#> SRR1122913 2 0.000 0.979 0.000 1.000
#> SRR598042 2 0.000 0.979 0.000 1.000
#> SRR1467340 2 0.000 0.979 0.000 1.000
#> SRR1072321 2 0.000 0.979 0.000 1.000
#> SRR1094580 2 0.000 0.979 0.000 1.000
#> SRR1076608 2 0.000 0.979 0.000 1.000
#> SRR1395462 2 0.000 0.979 0.000 1.000
#> SRR1489220 2 0.000 0.979 0.000 1.000
#> SRR614371 1 0.000 0.997 1.000 0.000
#> SRR615455 1 0.000 0.997 1.000 0.000
#> SRR1070573 2 0.000 0.979 0.000 1.000
#> SRR598749 1 0.000 0.997 1.000 0.000
#> SRR1365556 2 0.000 0.979 0.000 1.000
#> SRR1350023 2 0.000 0.979 0.000 1.000
#> SRR1446582 2 0.000 0.979 0.000 1.000
#> SRR1439763 2 0.000 0.979 0.000 1.000
#> SRR1343986 2 0.000 0.979 0.000 1.000
#> SRR807463 2 0.000 0.979 0.000 1.000
#> SRR660390 1 0.000 0.997 1.000 0.000
#> SRR1367672 2 0.000 0.979 0.000 1.000
#> SRR613294 1 0.000 0.997 1.000 0.000
#> SRR824015 2 0.971 0.367 0.400 0.600
#> SRR1078924 2 0.000 0.979 0.000 1.000
#> SRR662221 1 0.000 0.997 1.000 0.000
#> SRR655017 1 0.000 0.997 1.000 0.000
#> SRR1338450 2 0.278 0.946 0.048 0.952
#> SRR663741 1 0.000 0.997 1.000 0.000
#> SRR1396057 2 0.000 0.979 0.000 1.000
#> SRR1083800 2 0.000 0.979 0.000 1.000
#> SRR1445789 2 0.000 0.979 0.000 1.000
#> SRR1387355 2 0.278 0.946 0.048 0.952
#> SRR1388855 2 0.000 0.979 0.000 1.000
#> SRR1445449 2 0.278 0.946 0.048 0.952
#> SRR1380740 2 0.000 0.979 0.000 1.000
#> SRR659995 1 0.000 0.997 1.000 0.000
#> SRR1489524 2 0.000 0.979 0.000 1.000
#> SRR1444662 2 0.000 0.979 0.000 1.000
#> SRR1383652 2 0.000 0.979 0.000 1.000
#> SRR1361243 2 0.000 0.979 0.000 1.000
#> SRR1490337 2 0.000 0.979 0.000 1.000
#> SRR823967 2 0.260 0.949 0.044 0.956
#> SRR660127 1 0.000 0.997 1.000 0.000
#> SRR1366627 2 0.000 0.979 0.000 1.000
#> SRR1361219 2 0.000 0.979 0.000 1.000
#> SRR1393510 2 0.278 0.946 0.048 0.952
#> SRR662558 2 0.969 0.377 0.396 0.604
#> SRR1077334 2 0.000 0.979 0.000 1.000
#> SRR807438 2 0.278 0.946 0.048 0.952
#> SRR1459078 2 0.000 0.979 0.000 1.000
#> SRR1329704 2 0.000 0.979 0.000 1.000
#> SRR1468072 2 0.000 0.979 0.000 1.000
#> SRR1376196 2 0.000 0.979 0.000 1.000
#> SRR1442909 2 0.000 0.979 0.000 1.000
#> SRR1414269 2 0.000 0.979 0.000 1.000
#> SRR1381913 2 0.000 0.979 0.000 1.000
#> SRR1340157 2 0.000 0.979 0.000 1.000
#> SRR1407583 2 0.000 0.979 0.000 1.000
#> SRR615826 1 0.000 0.997 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 1 0.0000 0.813631 1.000 0.000 0.000
#> SRR1458769 2 0.5706 0.221206 0.000 0.680 0.320
#> SRR613162 1 0.5882 0.856912 0.652 0.000 0.348
#> SRR1352481 1 0.5882 0.856912 0.652 0.000 0.348
#> SRR1468876 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR1399223 2 0.4931 0.207947 0.000 0.768 0.232
#> SRR660030 2 0.1163 0.681993 0.000 0.972 0.028
#> SRR1333609 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR1471612 2 0.4235 0.554803 0.000 0.824 0.176
#> SRR1413998 3 0.5968 0.862628 0.000 0.364 0.636
#> SRR1122940 2 0.5397 0.343796 0.000 0.720 0.280
#> SRR1402563 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR1398393 2 0.1751 0.681922 0.012 0.960 0.028
#> SRR657961 2 0.8440 0.033384 0.196 0.620 0.184
#> SRR1471135 2 0.0237 0.692666 0.000 0.996 0.004
#> SRR1430001 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR662775 1 0.5882 0.856912 0.652 0.000 0.348
#> SRR1474182 2 0.5529 0.303804 0.000 0.704 0.296
#> SRR607190 1 0.5882 0.856912 0.652 0.000 0.348
#> SRR612467 2 0.7519 -0.070160 0.388 0.568 0.044
#> SRR1465959 2 0.5529 0.303804 0.000 0.704 0.296
#> SRR1446132 3 0.6079 0.837902 0.000 0.388 0.612
#> SRR1416933 2 0.5529 0.300113 0.000 0.704 0.296
#> SRR1102538 2 0.4178 0.554836 0.000 0.828 0.172
#> SRR1098636 2 0.6588 0.261113 0.208 0.732 0.060
#> SRR1072998 2 0.4121 0.572740 0.000 0.832 0.168
#> SRR627443 1 0.5882 0.856912 0.652 0.000 0.348
#> SRR656131 1 0.5882 0.856912 0.652 0.000 0.348
#> SRR823991 2 0.0892 0.689326 0.000 0.980 0.020
#> SRR1089158 2 0.1753 0.678340 0.000 0.952 0.048
#> SRR1469036 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR824039 2 0.2845 0.660359 0.012 0.920 0.068
#> SRR1339047 2 0.5882 -0.186474 0.000 0.652 0.348
#> SRR1443049 2 0.5560 0.291177 0.000 0.700 0.300
#> SRR1122885 2 0.5497 0.312462 0.000 0.708 0.292
#> SRR602895 2 0.7143 -0.088306 0.396 0.576 0.028
#> SRR1409837 2 0.5529 0.303804 0.000 0.704 0.296
#> SRR1388959 3 0.5968 0.862628 0.000 0.364 0.636
#> SRR659863 1 0.5882 0.856912 0.652 0.000 0.348
#> SRR1089877 2 0.5061 0.457834 0.008 0.784 0.208
#> SRR1123775 2 0.0424 0.688191 0.000 0.992 0.008
#> SRR658909 1 0.6565 0.548602 0.720 0.232 0.048
#> SRR1140510 2 0.0592 0.686917 0.000 0.988 0.012
#> SRR607562 2 0.7128 0.152492 0.252 0.684 0.064
#> SRR1122913 2 0.5291 0.370234 0.000 0.732 0.268
#> SRR598042 2 0.8896 -0.140189 0.292 0.552 0.156
#> SRR1467340 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR1072321 2 0.5529 0.303804 0.000 0.704 0.296
#> SRR1094580 2 0.5497 0.312462 0.000 0.708 0.292
#> SRR1076608 2 0.5497 0.257770 0.000 0.708 0.292
#> SRR1395462 2 0.4062 0.570915 0.000 0.836 0.164
#> SRR1489220 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR614371 1 0.7606 0.511268 0.664 0.244 0.092
#> SRR615455 1 0.3551 0.837302 0.868 0.000 0.132
#> SRR1070573 2 0.5497 0.312462 0.000 0.708 0.292
#> SRR598749 1 0.1031 0.807296 0.976 0.024 0.000
#> SRR1365556 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR1350023 3 0.5968 0.862628 0.000 0.364 0.636
#> SRR1446582 2 0.0592 0.691058 0.000 0.988 0.012
#> SRR1439763 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR1343986 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR807463 2 0.4291 0.543415 0.000 0.820 0.180
#> SRR660390 1 0.5882 0.856912 0.652 0.000 0.348
#> SRR1367672 2 0.5497 0.312462 0.000 0.708 0.292
#> SRR613294 1 0.0000 0.813631 1.000 0.000 0.000
#> SRR824015 2 0.9054 -0.186977 0.360 0.496 0.144
#> SRR1078924 2 0.5497 0.312462 0.000 0.708 0.292
#> SRR662221 1 0.0000 0.813631 1.000 0.000 0.000
#> SRR655017 1 0.5882 0.856912 0.652 0.000 0.348
#> SRR1338450 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR663741 1 0.4068 0.835853 0.864 0.016 0.120
#> SRR1396057 2 0.5529 0.303804 0.000 0.704 0.296
#> SRR1083800 2 0.1163 0.685588 0.000 0.972 0.028
#> SRR1445789 3 0.6302 0.774546 0.000 0.480 0.520
#> SRR1387355 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR1388855 3 0.6305 0.706278 0.000 0.484 0.516
#> SRR1445449 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR1380740 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR659995 1 0.0000 0.813631 1.000 0.000 0.000
#> SRR1489524 3 0.6267 0.791652 0.000 0.452 0.548
#> SRR1444662 2 0.0592 0.689830 0.000 0.988 0.012
#> SRR1383652 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR1361243 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR1490337 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR823967 2 0.0892 0.688355 0.000 0.980 0.020
#> SRR660127 1 0.5882 0.856912 0.652 0.000 0.348
#> SRR1366627 2 0.4842 0.235354 0.000 0.776 0.224
#> SRR1361219 2 0.5529 0.303804 0.000 0.704 0.296
#> SRR1393510 2 0.0237 0.690736 0.000 0.996 0.004
#> SRR662558 1 0.6543 0.278914 0.640 0.344 0.016
#> SRR1077334 2 0.3482 0.608753 0.000 0.872 0.128
#> SRR807438 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR1459078 2 0.0000 0.692704 0.000 1.000 0.000
#> SRR1329704 2 0.1289 0.681480 0.000 0.968 0.032
#> SRR1468072 2 0.0237 0.690736 0.000 0.996 0.004
#> SRR1376196 2 0.3686 0.591311 0.000 0.860 0.140
#> SRR1442909 2 0.1411 0.684223 0.000 0.964 0.036
#> SRR1414269 2 0.0747 0.689751 0.000 0.984 0.016
#> SRR1381913 2 0.8444 -0.000797 0.236 0.612 0.152
#> SRR1340157 2 0.5529 0.303804 0.000 0.704 0.296
#> SRR1407583 2 0.3192 0.620086 0.000 0.888 0.112
#> SRR615826 1 0.0424 0.812345 0.992 0.008 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.0000 0.6147 0.000 0.000 0.000 1.000
#> SRR1458769 2 0.2843 0.6434 0.020 0.892 0.088 0.000
#> SRR613162 1 0.5132 0.7668 0.548 0.000 0.004 0.448
#> SRR1352481 1 0.4961 0.7696 0.552 0.000 0.000 0.448
#> SRR1468876 2 0.3668 0.6344 0.004 0.808 0.188 0.000
#> SRR1399223 2 0.5977 0.2015 0.216 0.680 0.104 0.000
#> SRR660030 2 0.3142 0.6617 0.008 0.860 0.132 0.000
#> SRR1333609 2 0.3494 0.6427 0.004 0.824 0.172 0.000
#> SRR1471612 2 0.0657 0.6800 0.004 0.984 0.012 0.000
#> SRR1413998 2 0.7357 -0.1339 0.216 0.524 0.260 0.000
#> SRR1122940 2 0.2759 0.6543 0.044 0.904 0.052 0.000
#> SRR1402563 2 0.3266 0.6444 0.000 0.832 0.168 0.000
#> SRR1398393 2 0.4285 0.6429 0.040 0.804 0.156 0.000
#> SRR657961 2 0.5904 -0.2571 0.008 0.648 0.300 0.044
#> SRR1471135 2 0.1305 0.6835 0.004 0.960 0.036 0.000
#> SRR1430001 2 0.3494 0.6427 0.004 0.824 0.172 0.000
#> SRR662775 1 0.4304 0.9130 0.716 0.000 0.000 0.284
#> SRR1474182 2 0.3547 0.6336 0.064 0.864 0.072 0.000
#> SRR607190 1 0.4304 0.9130 0.716 0.000 0.000 0.284
#> SRR612467 2 0.7724 -0.6868 0.008 0.484 0.316 0.192
#> SRR1465959 2 0.3229 0.6371 0.048 0.880 0.072 0.000
#> SRR1446132 2 0.7357 -0.1339 0.216 0.524 0.260 0.000
#> SRR1416933 2 0.2271 0.6509 0.008 0.916 0.076 0.000
#> SRR1102538 2 0.4018 0.4219 0.004 0.772 0.224 0.000
#> SRR1098636 2 0.6315 -0.3034 0.004 0.480 0.468 0.048
#> SRR1072998 2 0.4770 0.3736 0.004 0.780 0.168 0.048
#> SRR627443 1 0.4382 0.9024 0.704 0.000 0.000 0.296
#> SRR656131 1 0.4304 0.9130 0.716 0.000 0.000 0.284
#> SRR823991 2 0.4378 0.6396 0.040 0.796 0.164 0.000
#> SRR1089158 2 0.2300 0.6591 0.016 0.920 0.064 0.000
#> SRR1469036 2 0.3494 0.6427 0.004 0.824 0.172 0.000
#> SRR824039 2 0.4378 0.6419 0.040 0.796 0.164 0.000
#> SRR1339047 2 0.7135 0.1825 0.200 0.560 0.240 0.000
#> SRR1443049 2 0.2197 0.6705 0.024 0.928 0.048 0.000
#> SRR1122885 2 0.3144 0.6399 0.044 0.884 0.072 0.000
#> SRR602895 3 0.7041 0.8635 0.008 0.432 0.468 0.092
#> SRR1409837 2 0.3056 0.6426 0.040 0.888 0.072 0.000
#> SRR1388959 2 0.7377 -0.1439 0.216 0.520 0.264 0.000
#> SRR659863 1 0.4304 0.9130 0.716 0.000 0.000 0.284
#> SRR1089877 2 0.4423 0.6460 0.040 0.792 0.168 0.000
#> SRR1123775 2 0.3907 0.6704 0.044 0.836 0.120 0.000
#> SRR658909 4 0.7672 0.2687 0.016 0.184 0.264 0.536
#> SRR1140510 2 0.2563 0.6849 0.020 0.908 0.072 0.000
#> SRR607562 3 0.6900 0.8613 0.008 0.444 0.468 0.080
#> SRR1122913 2 0.3156 0.6403 0.048 0.884 0.068 0.000
#> SRR598042 3 0.7098 0.8145 0.020 0.368 0.532 0.080
#> SRR1467340 2 0.1109 0.6836 0.004 0.968 0.028 0.000
#> SRR1072321 2 0.3229 0.6371 0.048 0.880 0.072 0.000
#> SRR1094580 2 0.3229 0.6371 0.048 0.880 0.072 0.000
#> SRR1076608 2 0.2843 0.6434 0.020 0.892 0.088 0.000
#> SRR1395462 2 0.6196 -0.2659 0.016 0.616 0.328 0.040
#> SRR1489220 2 0.4535 0.2537 0.004 0.704 0.292 0.000
#> SRR614371 4 0.6985 0.3747 0.016 0.108 0.276 0.600
#> SRR615455 4 0.2988 0.4651 0.112 0.000 0.012 0.876
#> SRR1070573 2 0.3229 0.6371 0.048 0.880 0.072 0.000
#> SRR598749 4 0.1716 0.5979 0.000 0.000 0.064 0.936
#> SRR1365556 2 0.3539 0.6464 0.004 0.820 0.176 0.000
#> SRR1350023 2 0.7357 -0.1339 0.216 0.524 0.260 0.000
#> SRR1446582 2 0.1109 0.6815 0.004 0.968 0.028 0.000
#> SRR1439763 2 0.3525 0.6803 0.040 0.860 0.100 0.000
#> SRR1343986 2 0.2266 0.6801 0.004 0.912 0.084 0.000
#> SRR807463 2 0.2751 0.6552 0.040 0.904 0.056 0.000
#> SRR660390 1 0.5132 0.7668 0.548 0.000 0.004 0.448
#> SRR1367672 2 0.2926 0.6499 0.048 0.896 0.056 0.000
#> SRR613294 4 0.0000 0.6147 0.000 0.000 0.000 1.000
#> SRR824015 4 0.7644 0.0205 0.004 0.236 0.260 0.500
#> SRR1078924 2 0.3229 0.6371 0.048 0.880 0.072 0.000
#> SRR662221 4 0.0000 0.6147 0.000 0.000 0.000 1.000
#> SRR655017 1 0.4304 0.9130 0.716 0.000 0.000 0.284
#> SRR1338450 2 0.3710 0.6313 0.004 0.804 0.192 0.000
#> SRR663741 4 0.3323 0.5406 0.060 0.000 0.064 0.876
#> SRR1396057 2 0.2635 0.6481 0.020 0.904 0.076 0.000
#> SRR1083800 2 0.0657 0.6834 0.004 0.984 0.012 0.000
#> SRR1445789 2 0.7357 -0.1339 0.216 0.524 0.260 0.000
#> SRR1387355 2 0.3710 0.6313 0.004 0.804 0.192 0.000
#> SRR1388855 2 0.7357 -0.1339 0.216 0.524 0.260 0.000
#> SRR1445449 2 0.3870 0.6155 0.004 0.788 0.208 0.000
#> SRR1380740 2 0.3052 0.6648 0.004 0.860 0.136 0.000
#> SRR659995 4 0.0000 0.6147 0.000 0.000 0.000 1.000
#> SRR1489524 2 0.7357 -0.1339 0.216 0.524 0.260 0.000
#> SRR1444662 2 0.3706 0.6552 0.040 0.848 0.112 0.000
#> SRR1383652 2 0.1209 0.6825 0.004 0.964 0.032 0.000
#> SRR1361243 2 0.3494 0.6427 0.004 0.824 0.172 0.000
#> SRR1490337 2 0.2334 0.6813 0.004 0.908 0.088 0.000
#> SRR823967 2 0.4511 0.6335 0.040 0.784 0.176 0.000
#> SRR660127 1 0.4304 0.9130 0.716 0.000 0.000 0.284
#> SRR1366627 2 0.7135 0.1878 0.200 0.560 0.240 0.000
#> SRR1361219 2 0.3229 0.6371 0.048 0.880 0.072 0.000
#> SRR1393510 2 0.3831 0.6199 0.004 0.792 0.204 0.000
#> SRR662558 4 0.7894 -0.1237 0.004 0.264 0.292 0.440
#> SRR1077334 2 0.1890 0.6751 0.056 0.936 0.008 0.000
#> SRR807438 2 0.3751 0.6349 0.004 0.800 0.196 0.000
#> SRR1459078 2 0.3355 0.6512 0.004 0.836 0.160 0.000
#> SRR1329704 2 0.1042 0.6815 0.020 0.972 0.008 0.000
#> SRR1468072 2 0.2342 0.6838 0.008 0.912 0.080 0.000
#> SRR1376196 2 0.0524 0.6809 0.004 0.988 0.008 0.000
#> SRR1442909 2 0.2053 0.6874 0.004 0.924 0.072 0.000
#> SRR1414269 2 0.4467 0.6375 0.040 0.788 0.172 0.000
#> SRR1381913 3 0.7049 0.7884 0.020 0.452 0.460 0.068
#> SRR1340157 2 0.2871 0.6440 0.032 0.896 0.072 0.000
#> SRR1407583 2 0.2563 0.6900 0.020 0.908 0.072 0.000
#> SRR615826 4 0.1716 0.5979 0.000 0.000 0.064 0.936
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR612587 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1458769 3 0.1469 0.930 0.000 0.036 0.948 0.000 0.016
#> SRR613162 4 0.3452 0.714 0.244 0.000 0.000 0.756 0.000
#> SRR1352481 4 0.3707 0.658 0.284 0.000 0.000 0.716 0.000
#> SRR1468876 3 0.0794 0.934 0.000 0.000 0.972 0.000 0.028
#> SRR1399223 2 0.0609 0.973 0.000 0.980 0.020 0.000 0.000
#> SRR660030 3 0.4201 0.302 0.000 0.000 0.592 0.000 0.408
#> SRR1333609 3 0.0794 0.934 0.000 0.000 0.972 0.000 0.028
#> SRR1471612 3 0.0798 0.936 0.000 0.008 0.976 0.000 0.016
#> SRR1413998 2 0.0000 0.994 0.000 1.000 0.000 0.000 0.000
#> SRR1122940 3 0.0992 0.936 0.000 0.008 0.968 0.000 0.024
#> SRR1402563 3 0.0794 0.934 0.000 0.000 0.972 0.000 0.028
#> SRR1398393 3 0.0798 0.936 0.000 0.008 0.976 0.000 0.016
#> SRR657961 5 0.1725 0.846 0.000 0.000 0.020 0.044 0.936
#> SRR1471135 3 0.0510 0.936 0.000 0.000 0.984 0.000 0.016
#> SRR1430001 3 0.0955 0.934 0.000 0.004 0.968 0.000 0.028
#> SRR662775 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1474182 3 0.1168 0.934 0.000 0.008 0.960 0.000 0.032
#> SRR607190 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR612467 5 0.1725 0.846 0.000 0.000 0.020 0.044 0.936
#> SRR1465959 3 0.1168 0.934 0.000 0.008 0.960 0.000 0.032
#> SRR1446132 2 0.0000 0.994 0.000 1.000 0.000 0.000 0.000
#> SRR1416933 3 0.1018 0.936 0.000 0.016 0.968 0.000 0.016
#> SRR1102538 3 0.3937 0.670 0.000 0.008 0.736 0.004 0.252
#> SRR1098636 5 0.4588 0.743 0.000 0.000 0.116 0.136 0.748
#> SRR1072998 3 0.4668 0.577 0.000 0.008 0.688 0.028 0.276
#> SRR627443 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR823991 3 0.0000 0.937 0.000 0.000 1.000 0.000 0.000
#> SRR1089158 3 0.3455 0.744 0.000 0.008 0.784 0.000 0.208
#> SRR1469036 3 0.1082 0.934 0.000 0.008 0.964 0.000 0.028
#> SRR824039 3 0.3093 0.799 0.000 0.008 0.824 0.000 0.168
#> SRR1339047 2 0.0000 0.994 0.000 1.000 0.000 0.000 0.000
#> SRR1443049 3 0.1018 0.936 0.000 0.016 0.968 0.000 0.016
#> SRR1122885 3 0.0798 0.936 0.000 0.008 0.976 0.000 0.016
#> SRR602895 5 0.1626 0.844 0.000 0.000 0.016 0.044 0.940
#> SRR1409837 3 0.0992 0.935 0.000 0.008 0.968 0.000 0.024
#> SRR1388959 2 0.0000 0.994 0.000 1.000 0.000 0.000 0.000
#> SRR659863 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1089877 3 0.0992 0.936 0.000 0.008 0.968 0.000 0.024
#> SRR1123775 3 0.0510 0.936 0.000 0.000 0.984 0.000 0.016
#> SRR658909 5 0.4252 0.708 0.000 0.000 0.020 0.280 0.700
#> SRR1140510 3 0.0963 0.933 0.000 0.036 0.964 0.000 0.000
#> SRR607562 5 0.1725 0.846 0.000 0.000 0.020 0.044 0.936
#> SRR1122913 3 0.1041 0.935 0.000 0.004 0.964 0.000 0.032
#> SRR598042 5 0.1725 0.846 0.000 0.000 0.020 0.044 0.936
#> SRR1467340 3 0.0510 0.936 0.000 0.000 0.984 0.000 0.016
#> SRR1072321 3 0.1168 0.934 0.000 0.008 0.960 0.000 0.032
#> SRR1094580 3 0.1168 0.934 0.000 0.008 0.960 0.000 0.032
#> SRR1076608 3 0.1704 0.918 0.000 0.068 0.928 0.000 0.004
#> SRR1395462 5 0.1668 0.829 0.000 0.000 0.032 0.028 0.940
#> SRR1489220 5 0.4779 0.346 0.000 0.000 0.388 0.024 0.588
#> SRR614371 5 0.3819 0.755 0.000 0.000 0.016 0.228 0.756
#> SRR615455 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1070573 3 0.1168 0.934 0.000 0.008 0.960 0.000 0.032
#> SRR598749 4 0.0404 0.912 0.000 0.000 0.000 0.988 0.012
#> SRR1365556 3 0.0807 0.937 0.000 0.012 0.976 0.000 0.012
#> SRR1350023 2 0.0000 0.994 0.000 1.000 0.000 0.000 0.000
#> SRR1446582 3 0.0609 0.936 0.000 0.000 0.980 0.000 0.020
#> SRR1439763 3 0.0510 0.936 0.000 0.000 0.984 0.000 0.016
#> SRR1343986 3 0.0955 0.934 0.000 0.004 0.968 0.000 0.028
#> SRR807463 3 0.0798 0.936 0.000 0.008 0.976 0.016 0.000
#> SRR660390 4 0.3452 0.714 0.244 0.000 0.000 0.756 0.000
#> SRR1367672 3 0.1168 0.934 0.000 0.008 0.960 0.000 0.032
#> SRR613294 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR824015 4 0.1018 0.897 0.000 0.016 0.016 0.968 0.000
#> SRR1078924 3 0.1168 0.934 0.000 0.008 0.960 0.000 0.032
#> SRR662221 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR655017 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1338450 3 0.0794 0.934 0.000 0.000 0.972 0.000 0.028
#> SRR663741 4 0.0324 0.911 0.000 0.000 0.004 0.992 0.004
#> SRR1396057 3 0.1211 0.934 0.000 0.016 0.960 0.000 0.024
#> SRR1083800 3 0.0510 0.936 0.000 0.000 0.984 0.000 0.016
#> SRR1445789 2 0.0000 0.994 0.000 1.000 0.000 0.000 0.000
#> SRR1387355 3 0.1082 0.934 0.000 0.008 0.964 0.000 0.028
#> SRR1388855 2 0.0000 0.994 0.000 1.000 0.000 0.000 0.000
#> SRR1445449 3 0.1082 0.934 0.000 0.008 0.964 0.000 0.028
#> SRR1380740 3 0.1082 0.934 0.000 0.008 0.964 0.000 0.028
#> SRR659995 4 0.0000 0.915 0.000 0.000 0.000 1.000 0.000
#> SRR1489524 2 0.0000 0.994 0.000 1.000 0.000 0.000 0.000
#> SRR1444662 3 0.3300 0.740 0.000 0.204 0.792 0.000 0.004
#> SRR1383652 3 0.0510 0.936 0.000 0.000 0.984 0.000 0.016
#> SRR1361243 3 0.0794 0.934 0.000 0.000 0.972 0.000 0.028
#> SRR1490337 3 0.4150 0.395 0.000 0.000 0.612 0.000 0.388
#> SRR823967 3 0.1341 0.917 0.000 0.000 0.944 0.000 0.056
#> SRR660127 1 0.0000 1.000 1.000 0.000 0.000 0.000 0.000
#> SRR1366627 2 0.0703 0.973 0.000 0.976 0.024 0.000 0.000
#> SRR1361219 3 0.1168 0.934 0.000 0.008 0.960 0.000 0.032
#> SRR1393510 3 0.1582 0.926 0.000 0.028 0.944 0.000 0.028
#> SRR662558 5 0.4599 0.711 0.000 0.000 0.040 0.272 0.688
#> SRR1077334 3 0.2077 0.893 0.000 0.008 0.908 0.000 0.084
#> SRR807438 3 0.0794 0.934 0.000 0.000 0.972 0.000 0.028
#> SRR1459078 3 0.1582 0.926 0.000 0.028 0.944 0.000 0.028
#> SRR1329704 3 0.1469 0.931 0.000 0.036 0.948 0.000 0.016
#> SRR1468072 3 0.1195 0.930 0.000 0.028 0.960 0.000 0.012
#> SRR1376196 3 0.0671 0.937 0.000 0.004 0.980 0.000 0.016
#> SRR1442909 3 0.2561 0.830 0.000 0.000 0.856 0.000 0.144
#> SRR1414269 3 0.0510 0.936 0.000 0.000 0.984 0.000 0.016
#> SRR1381913 5 0.1725 0.846 0.000 0.000 0.020 0.044 0.936
#> SRR1340157 3 0.1168 0.934 0.000 0.008 0.960 0.000 0.032
#> SRR1407583 3 0.1018 0.936 0.000 0.016 0.968 0.000 0.016
#> SRR615826 4 0.0404 0.912 0.000 0.000 0.000 0.988 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 4 0.0146 0.8448 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1458769 2 0.4731 0.2630 0.000 0.524 0.428 0.000 0.000 0.048
#> SRR613162 4 0.3778 0.6493 0.288 0.016 0.000 0.696 0.000 0.000
#> SRR1352481 4 0.3927 0.5615 0.344 0.012 0.000 0.644 0.000 0.000
#> SRR1468876 3 0.0520 0.5921 0.000 0.008 0.984 0.000 0.008 0.000
#> SRR1399223 6 0.3207 0.7652 0.000 0.044 0.124 0.004 0.000 0.828
#> SRR660030 3 0.4145 0.3798 0.000 0.048 0.700 0.000 0.252 0.000
#> SRR1333609 3 0.0260 0.5897 0.000 0.008 0.992 0.000 0.000 0.000
#> SRR1471612 3 0.3899 -0.5087 0.000 0.404 0.592 0.000 0.004 0.000
#> SRR1413998 6 0.0000 0.9348 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1122940 3 0.3854 -0.5885 0.000 0.464 0.536 0.000 0.000 0.000
#> SRR1402563 3 0.0405 0.5893 0.000 0.004 0.988 0.000 0.008 0.000
#> SRR1398393 3 0.3668 -0.2484 0.000 0.328 0.668 0.000 0.000 0.004
#> SRR657961 5 0.0458 0.7557 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1471135 3 0.2538 0.5407 0.000 0.016 0.860 0.000 0.124 0.000
#> SRR1430001 3 0.2912 0.5522 0.000 0.216 0.784 0.000 0.000 0.000
#> SRR662775 1 0.0000 0.9983 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.3684 0.7323 0.000 0.628 0.372 0.000 0.000 0.000
#> SRR607190 1 0.0000 0.9983 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR612467 5 0.2126 0.7396 0.000 0.072 0.020 0.004 0.904 0.000
#> SRR1465959 2 0.3659 0.7349 0.000 0.636 0.364 0.000 0.000 0.000
#> SRR1446132 6 0.0000 0.9348 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1416933 2 0.3997 0.3297 0.000 0.508 0.488 0.000 0.000 0.004
#> SRR1102538 5 0.5751 0.3093 0.000 0.288 0.208 0.000 0.504 0.000
#> SRR1098636 5 0.2006 0.7326 0.000 0.000 0.080 0.016 0.904 0.000
#> SRR1072998 5 0.5718 0.3286 0.000 0.284 0.204 0.000 0.512 0.000
#> SRR627443 1 0.0146 0.9957 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR656131 1 0.0146 0.9957 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR823991 3 0.2191 0.5011 0.000 0.120 0.876 0.000 0.004 0.000
#> SRR1089158 2 0.5582 0.5011 0.000 0.480 0.424 0.016 0.076 0.004
#> SRR1469036 3 0.2912 0.5522 0.000 0.216 0.784 0.000 0.000 0.000
#> SRR824039 3 0.4701 -0.3899 0.000 0.436 0.524 0.000 0.036 0.004
#> SRR1339047 6 0.0260 0.9286 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1443049 3 0.4045 -0.5430 0.000 0.428 0.564 0.000 0.000 0.008
#> SRR1122885 2 0.3866 0.6252 0.000 0.516 0.484 0.000 0.000 0.000
#> SRR602895 5 0.0146 0.7516 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR1409837 2 0.3774 0.7402 0.000 0.592 0.408 0.000 0.000 0.000
#> SRR1388959 6 0.0000 0.9348 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR659863 1 0.0000 0.9983 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1089877 3 0.4147 -0.4138 0.000 0.436 0.552 0.000 0.000 0.012
#> SRR1123775 3 0.1088 0.5788 0.000 0.024 0.960 0.000 0.016 0.000
#> SRR658909 5 0.3647 0.4885 0.000 0.000 0.000 0.360 0.640 0.000
#> SRR1140510 3 0.3476 0.5309 0.000 0.260 0.732 0.004 0.000 0.004
#> SRR607562 5 0.0146 0.7516 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR1122913 3 0.3867 -0.6403 0.000 0.488 0.512 0.000 0.000 0.000
#> SRR598042 5 0.0000 0.7512 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1467340 3 0.0713 0.5889 0.000 0.028 0.972 0.000 0.000 0.000
#> SRR1072321 2 0.3659 0.7349 0.000 0.636 0.364 0.000 0.000 0.000
#> SRR1094580 2 0.3810 0.7320 0.000 0.572 0.428 0.000 0.000 0.000
#> SRR1076608 3 0.5825 0.1016 0.000 0.344 0.460 0.000 0.000 0.196
#> SRR1395462 5 0.3125 0.7035 0.000 0.084 0.080 0.000 0.836 0.000
#> SRR1489220 3 0.3890 0.2688 0.000 0.000 0.596 0.004 0.400 0.000
#> SRR614371 5 0.3592 0.5100 0.000 0.000 0.000 0.344 0.656 0.000
#> SRR615455 4 0.0291 0.8445 0.000 0.004 0.000 0.992 0.004 0.000
#> SRR1070573 2 0.3833 0.7116 0.000 0.556 0.444 0.000 0.000 0.000
#> SRR598749 4 0.2527 0.7691 0.000 0.000 0.000 0.832 0.168 0.000
#> SRR1365556 3 0.3429 0.5320 0.000 0.252 0.740 0.004 0.000 0.004
#> SRR1350023 6 0.0000 0.9348 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1446582 3 0.2263 0.5448 0.000 0.016 0.884 0.000 0.100 0.000
#> SRR1439763 3 0.0632 0.5763 0.000 0.024 0.976 0.000 0.000 0.000
#> SRR1343986 3 0.0363 0.5896 0.000 0.012 0.988 0.000 0.000 0.000
#> SRR807463 3 0.5277 -0.5591 0.000 0.384 0.528 0.000 0.080 0.008
#> SRR660390 4 0.3778 0.6493 0.288 0.016 0.000 0.696 0.000 0.000
#> SRR1367672 2 0.3867 0.6194 0.000 0.512 0.488 0.000 0.000 0.000
#> SRR613294 4 0.0146 0.8448 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR824015 4 0.3304 0.6598 0.000 0.008 0.172 0.804 0.004 0.012
#> SRR1078924 2 0.3659 0.7349 0.000 0.636 0.364 0.000 0.000 0.000
#> SRR662221 4 0.0146 0.8448 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR655017 1 0.0000 0.9983 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1338450 3 0.0520 0.5921 0.000 0.008 0.984 0.000 0.008 0.000
#> SRR663741 4 0.0717 0.8410 0.000 0.016 0.000 0.976 0.008 0.000
#> SRR1396057 2 0.3996 0.6495 0.000 0.512 0.484 0.000 0.000 0.004
#> SRR1083800 3 0.0790 0.5702 0.000 0.032 0.968 0.000 0.000 0.000
#> SRR1445789 6 0.0000 0.9348 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1387355 3 0.2912 0.5522 0.000 0.216 0.784 0.000 0.000 0.000
#> SRR1388855 6 0.0146 0.9325 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR1445449 3 0.2912 0.5522 0.000 0.216 0.784 0.000 0.000 0.000
#> SRR1380740 3 0.3103 0.5534 0.000 0.208 0.784 0.000 0.008 0.000
#> SRR659995 4 0.0146 0.8448 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1489524 6 0.0000 0.9348 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1444662 3 0.3536 0.5299 0.000 0.252 0.736 0.004 0.000 0.008
#> SRR1383652 3 0.2060 0.5544 0.000 0.016 0.900 0.000 0.084 0.000
#> SRR1361243 3 0.0937 0.5927 0.000 0.040 0.960 0.000 0.000 0.000
#> SRR1490337 3 0.3741 0.4047 0.000 0.008 0.672 0.000 0.320 0.000
#> SRR823967 3 0.1563 0.5717 0.000 0.012 0.932 0.000 0.056 0.000
#> SRR660127 1 0.0000 0.9983 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1366627 6 0.3662 0.6966 0.000 0.044 0.172 0.004 0.000 0.780
#> SRR1361219 2 0.3706 0.7386 0.000 0.620 0.380 0.000 0.000 0.000
#> SRR1393510 3 0.3136 0.5472 0.000 0.228 0.768 0.000 0.000 0.004
#> SRR662558 5 0.5590 0.4176 0.000 0.000 0.152 0.352 0.496 0.000
#> SRR1077334 2 0.4205 0.5963 0.000 0.564 0.420 0.000 0.016 0.000
#> SRR807438 3 0.0865 0.5863 0.000 0.000 0.964 0.000 0.036 0.000
#> SRR1459078 3 0.2854 0.5542 0.000 0.208 0.792 0.000 0.000 0.000
#> SRR1329704 2 0.4080 0.1628 0.000 0.536 0.456 0.000 0.000 0.008
#> SRR1468072 3 0.3429 0.5320 0.000 0.252 0.740 0.004 0.000 0.004
#> SRR1376196 3 0.2762 0.2344 0.000 0.196 0.804 0.000 0.000 0.000
#> SRR1442909 3 0.3695 0.4494 0.000 0.016 0.712 0.000 0.272 0.000
#> SRR1414269 3 0.0935 0.5700 0.000 0.032 0.964 0.000 0.004 0.000
#> SRR1381913 5 0.0146 0.7535 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1340157 2 0.3923 0.7337 0.000 0.580 0.416 0.000 0.000 0.004
#> SRR1407583 3 0.3615 -0.0831 0.000 0.292 0.700 0.000 0.000 0.008
#> SRR615826 4 0.2491 0.7716 0.000 0.000 0.000 0.836 0.164 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "NMF"]
# you can also extract it by
# res = res_list["SD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17380 rows and 102 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 0.959 0.961 0.983 0.3345 0.670 0.670
#> 3 3 0.573 0.817 0.897 0.6745 0.689 0.557
#> 4 4 0.632 0.800 0.896 0.0855 0.894 0.768
#> 5 5 0.526 0.477 0.747 0.1603 0.922 0.809
#> 6 6 0.514 0.567 0.732 0.0898 0.761 0.397
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
#> SRR612587 1 0.0000 0.964 1.000 0.000
#> SRR1458769 2 0.0000 0.986 0.000 1.000
#> SRR613162 1 0.0000 0.964 1.000 0.000
#> SRR1352481 1 0.0000 0.964 1.000 0.000
#> SRR1468876 2 0.0000 0.986 0.000 1.000
#> SRR1399223 2 0.0000 0.986 0.000 1.000
#> SRR660030 2 0.0000 0.986 0.000 1.000
#> SRR1333609 2 0.0000 0.986 0.000 1.000
#> SRR1471612 2 0.0000 0.986 0.000 1.000
#> SRR1413998 2 0.0000 0.986 0.000 1.000
#> SRR1122940 2 0.0000 0.986 0.000 1.000
#> SRR1402563 2 0.0000 0.986 0.000 1.000
#> SRR1398393 2 0.0000 0.986 0.000 1.000
#> SRR657961 2 0.0000 0.986 0.000 1.000
#> SRR1471135 2 0.0000 0.986 0.000 1.000
#> SRR1430001 2 0.1843 0.960 0.028 0.972
#> SRR662775 1 0.0000 0.964 1.000 0.000
#> SRR1474182 2 0.0000 0.986 0.000 1.000
#> SRR607190 1 0.0000 0.964 1.000 0.000
#> SRR612467 2 0.0000 0.986 0.000 1.000
#> SRR1465959 2 0.0000 0.986 0.000 1.000
#> SRR1446132 2 0.0000 0.986 0.000 1.000
#> SRR1416933 2 0.0000 0.986 0.000 1.000
#> SRR1102538 2 0.0000 0.986 0.000 1.000
#> SRR1098636 2 0.0000 0.986 0.000 1.000
#> SRR1072998 2 0.0000 0.986 0.000 1.000
#> SRR627443 1 0.0000 0.964 1.000 0.000
#> SRR656131 1 0.0000 0.964 1.000 0.000
#> SRR823991 2 0.0000 0.986 0.000 1.000
#> SRR1089158 2 0.0000 0.986 0.000 1.000
#> SRR1469036 2 0.0938 0.975 0.012 0.988
#> SRR824039 2 0.0000 0.986 0.000 1.000
#> SRR1339047 2 0.0000 0.986 0.000 1.000
#> SRR1443049 2 0.0000 0.986 0.000 1.000
#> SRR1122885 2 0.0000 0.986 0.000 1.000
#> SRR602895 1 0.6801 0.785 0.820 0.180
#> SRR1409837 2 0.0000 0.986 0.000 1.000
#> SRR1388959 2 0.0000 0.986 0.000 1.000
#> SRR659863 1 0.0000 0.964 1.000 0.000
#> SRR1089877 2 0.0000 0.986 0.000 1.000
#> SRR1123775 2 0.0000 0.986 0.000 1.000
#> SRR658909 1 0.0000 0.964 1.000 0.000
#> SRR1140510 2 0.0000 0.986 0.000 1.000
#> SRR607562 2 0.0000 0.986 0.000 1.000
#> SRR1122913 2 0.0000 0.986 0.000 1.000
#> SRR598042 2 0.0000 0.986 0.000 1.000
#> SRR1467340 2 0.0000 0.986 0.000 1.000
#> SRR1072321 2 0.0000 0.986 0.000 1.000
#> SRR1094580 2 0.0000 0.986 0.000 1.000
#> SRR1076608 2 0.0000 0.986 0.000 1.000
#> SRR1395462 2 0.0000 0.986 0.000 1.000
#> SRR1489220 1 0.9170 0.522 0.668 0.332
#> SRR614371 1 0.0000 0.964 1.000 0.000
#> SRR615455 1 0.0000 0.964 1.000 0.000
#> SRR1070573 2 0.0000 0.986 0.000 1.000
#> SRR598749 2 0.7528 0.719 0.216 0.784
#> SRR1365556 2 0.0000 0.986 0.000 1.000
#> SRR1350023 2 0.0000 0.986 0.000 1.000
#> SRR1446582 2 0.0000 0.986 0.000 1.000
#> SRR1439763 2 0.0000 0.986 0.000 1.000
#> SRR1343986 2 0.0000 0.986 0.000 1.000
#> SRR807463 2 0.0000 0.986 0.000 1.000
#> SRR660390 1 0.0000 0.964 1.000 0.000
#> SRR1367672 2 0.0000 0.986 0.000 1.000
#> SRR613294 1 0.0000 0.964 1.000 0.000
#> SRR824015 2 0.0000 0.986 0.000 1.000
#> SRR1078924 2 0.0000 0.986 0.000 1.000
#> SRR662221 1 0.0000 0.964 1.000 0.000
#> SRR655017 1 0.0000 0.964 1.000 0.000
#> SRR1338450 2 0.0000 0.986 0.000 1.000
#> SRR663741 1 0.0000 0.964 1.000 0.000
#> SRR1396057 2 0.0000 0.986 0.000 1.000
#> SRR1083800 2 0.0000 0.986 0.000 1.000
#> SRR1445789 2 0.0000 0.986 0.000 1.000
#> SRR1387355 2 0.9044 0.517 0.320 0.680
#> SRR1388855 2 0.0000 0.986 0.000 1.000
#> SRR1445449 2 0.5629 0.841 0.132 0.868
#> SRR1380740 2 0.0000 0.986 0.000 1.000
#> SRR659995 1 0.0000 0.964 1.000 0.000
#> SRR1489524 2 0.0000 0.986 0.000 1.000
#> SRR1444662 2 0.0000 0.986 0.000 1.000
#> SRR1383652 2 0.0000 0.986 0.000 1.000
#> SRR1361243 2 0.0000 0.986 0.000 1.000
#> SRR1490337 2 0.0000 0.986 0.000 1.000
#> SRR823967 2 0.0000 0.986 0.000 1.000
#> SRR660127 1 0.0000 0.964 1.000 0.000
#> SRR1366627 2 0.0000 0.986 0.000 1.000
#> SRR1361219 2 0.0000 0.986 0.000 1.000
#> SRR1393510 2 0.0000 0.986 0.000 1.000
#> SRR662558 1 0.7056 0.768 0.808 0.192
#> SRR1077334 2 0.0000 0.986 0.000 1.000
#> SRR807438 2 0.7056 0.757 0.192 0.808
#> SRR1459078 2 0.0000 0.986 0.000 1.000
#> SRR1329704 2 0.0000 0.986 0.000 1.000
#> SRR1468072 2 0.0000 0.986 0.000 1.000
#> SRR1376196 2 0.0000 0.986 0.000 1.000
#> SRR1442909 2 0.0000 0.986 0.000 1.000
#> SRR1414269 2 0.0000 0.986 0.000 1.000
#> SRR1381913 2 0.0000 0.986 0.000 1.000
#> SRR1340157 2 0.0000 0.986 0.000 1.000
#> SRR1407583 2 0.0000 0.986 0.000 1.000
#> SRR615826 2 0.6148 0.814 0.152 0.848
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 3 0.3941 0.670 0.156 0.000 0.844
#> SRR1458769 2 0.0237 0.930 0.000 0.996 0.004
#> SRR613162 1 0.0424 0.886 0.992 0.000 0.008
#> SRR1352481 1 0.0424 0.886 0.992 0.000 0.008
#> SRR1468876 2 0.2301 0.890 0.060 0.936 0.004
#> SRR1399223 2 0.0237 0.929 0.000 0.996 0.004
#> SRR660030 3 0.3482 0.827 0.000 0.128 0.872
#> SRR1333609 2 0.0592 0.926 0.000 0.988 0.012
#> SRR1471612 2 0.4399 0.764 0.000 0.812 0.188
#> SRR1413998 2 0.0237 0.930 0.000 0.996 0.004
#> SRR1122940 3 0.6111 0.532 0.000 0.396 0.604
#> SRR1402563 2 0.3267 0.838 0.000 0.884 0.116
#> SRR1398393 2 0.1031 0.920 0.000 0.976 0.024
#> SRR657961 3 0.3267 0.829 0.000 0.116 0.884
#> SRR1471135 3 0.5098 0.725 0.000 0.248 0.752
#> SRR1430001 2 0.5968 0.429 0.364 0.636 0.000
#> SRR662775 1 0.0000 0.888 1.000 0.000 0.000
#> SRR1474182 2 0.0237 0.930 0.000 0.996 0.004
#> SRR607190 1 0.0000 0.888 1.000 0.000 0.000
#> SRR612467 3 0.2711 0.817 0.000 0.088 0.912
#> SRR1465959 2 0.1411 0.909 0.000 0.964 0.036
#> SRR1446132 2 0.0424 0.929 0.000 0.992 0.008
#> SRR1416933 2 0.0237 0.930 0.000 0.996 0.004
#> SRR1102538 3 0.3412 0.827 0.000 0.124 0.876
#> SRR1098636 3 0.3267 0.829 0.000 0.116 0.884
#> SRR1072998 3 0.6126 0.528 0.000 0.400 0.600
#> SRR627443 1 0.0000 0.888 1.000 0.000 0.000
#> SRR656131 1 0.0000 0.888 1.000 0.000 0.000
#> SRR823991 2 0.3116 0.846 0.000 0.892 0.108
#> SRR1089158 3 0.4974 0.760 0.000 0.236 0.764
#> SRR1469036 2 0.3272 0.850 0.104 0.892 0.004
#> SRR824039 2 0.0424 0.929 0.000 0.992 0.008
#> SRR1339047 2 0.0237 0.930 0.000 0.996 0.004
#> SRR1443049 2 0.0000 0.930 0.000 1.000 0.000
#> SRR1122885 2 0.6280 -0.108 0.000 0.540 0.460
#> SRR602895 3 0.3607 0.742 0.112 0.008 0.880
#> SRR1409837 2 0.0237 0.930 0.000 0.996 0.004
#> SRR1388959 2 0.0237 0.930 0.000 0.996 0.004
#> SRR659863 1 0.0000 0.888 1.000 0.000 0.000
#> SRR1089877 2 0.0000 0.930 0.000 1.000 0.000
#> SRR1123775 3 0.5058 0.754 0.000 0.244 0.756
#> SRR658909 1 0.3192 0.804 0.888 0.000 0.112
#> SRR1140510 2 0.0237 0.930 0.000 0.996 0.004
#> SRR607562 3 0.3116 0.826 0.000 0.108 0.892
#> SRR1122913 2 0.0747 0.923 0.000 0.984 0.016
#> SRR598042 3 0.3267 0.829 0.000 0.116 0.884
#> SRR1467340 2 0.0000 0.930 0.000 1.000 0.000
#> SRR1072321 2 0.0000 0.930 0.000 1.000 0.000
#> SRR1094580 2 0.1964 0.895 0.000 0.944 0.056
#> SRR1076608 2 0.0000 0.930 0.000 1.000 0.000
#> SRR1395462 3 0.3267 0.829 0.000 0.116 0.884
#> SRR1489220 1 0.5863 0.719 0.796 0.084 0.120
#> SRR614371 3 0.3340 0.732 0.120 0.000 0.880
#> SRR615455 1 0.2959 0.846 0.900 0.000 0.100
#> SRR1070573 2 0.0237 0.929 0.000 0.996 0.004
#> SRR598749 3 0.1964 0.733 0.056 0.000 0.944
#> SRR1365556 2 0.0237 0.930 0.000 0.996 0.004
#> SRR1350023 2 0.0237 0.929 0.000 0.996 0.004
#> SRR1446582 3 0.3267 0.829 0.000 0.116 0.884
#> SRR1439763 2 0.0000 0.930 0.000 1.000 0.000
#> SRR1343986 2 0.0000 0.930 0.000 1.000 0.000
#> SRR807463 2 0.0237 0.930 0.000 0.996 0.004
#> SRR660390 1 0.1529 0.875 0.960 0.000 0.040
#> SRR1367672 3 0.6274 0.340 0.000 0.456 0.544
#> SRR613294 3 0.3551 0.689 0.132 0.000 0.868
#> SRR824015 2 0.0892 0.919 0.000 0.980 0.020
#> SRR1078924 2 0.3816 0.785 0.000 0.852 0.148
#> SRR662221 3 0.4555 0.620 0.200 0.000 0.800
#> SRR655017 1 0.0000 0.888 1.000 0.000 0.000
#> SRR1338450 2 0.5678 0.541 0.316 0.684 0.000
#> SRR663741 1 0.2959 0.846 0.900 0.000 0.100
#> SRR1396057 2 0.0747 0.925 0.000 0.984 0.016
#> SRR1083800 2 0.5529 0.499 0.000 0.704 0.296
#> SRR1445789 2 0.0237 0.929 0.000 0.996 0.004
#> SRR1387355 1 0.8457 0.211 0.512 0.396 0.092
#> SRR1388855 2 0.0000 0.930 0.000 1.000 0.000
#> SRR1445449 2 0.3918 0.815 0.140 0.856 0.004
#> SRR1380740 2 0.0237 0.929 0.000 0.996 0.004
#> SRR659995 3 0.3551 0.689 0.132 0.000 0.868
#> SRR1489524 2 0.0237 0.929 0.000 0.996 0.004
#> SRR1444662 2 0.0237 0.930 0.000 0.996 0.004
#> SRR1383652 3 0.4291 0.792 0.000 0.180 0.820
#> SRR1361243 2 0.3038 0.848 0.000 0.896 0.104
#> SRR1490337 2 0.6857 0.598 0.052 0.696 0.252
#> SRR823967 2 0.4178 0.778 0.000 0.828 0.172
#> SRR660127 1 0.0000 0.888 1.000 0.000 0.000
#> SRR1366627 2 0.0237 0.930 0.000 0.996 0.004
#> SRR1361219 2 0.0237 0.930 0.000 0.996 0.004
#> SRR1393510 2 0.0237 0.930 0.000 0.996 0.004
#> SRR662558 3 0.6313 0.763 0.148 0.084 0.768
#> SRR1077334 2 0.1643 0.902 0.000 0.956 0.044
#> SRR807438 1 0.7446 0.495 0.664 0.260 0.076
#> SRR1459078 2 0.0592 0.925 0.000 0.988 0.012
#> SRR1329704 2 0.0000 0.930 0.000 1.000 0.000
#> SRR1468072 2 0.0000 0.930 0.000 1.000 0.000
#> SRR1376196 2 0.0000 0.930 0.000 1.000 0.000
#> SRR1442909 3 0.4346 0.789 0.000 0.184 0.816
#> SRR1414269 2 0.5397 0.611 0.000 0.720 0.280
#> SRR1381913 3 0.3267 0.829 0.000 0.116 0.884
#> SRR1340157 2 0.0000 0.930 0.000 1.000 0.000
#> SRR1407583 2 0.0237 0.930 0.000 0.996 0.004
#> SRR615826 3 0.3551 0.689 0.132 0.000 0.868
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.2563 0.8655 0.072 0.000 0.020 0.908
#> SRR1458769 2 0.0000 0.9244 0.000 1.000 0.000 0.000
#> SRR613162 1 0.2281 0.8003 0.904 0.000 0.000 0.096
#> SRR1352481 1 0.1743 0.8296 0.940 0.000 0.056 0.004
#> SRR1468876 2 0.4640 0.8253 0.092 0.820 0.068 0.020
#> SRR1399223 2 0.0188 0.9244 0.000 0.996 0.000 0.004
#> SRR660030 3 0.1890 0.8164 0.000 0.056 0.936 0.008
#> SRR1333609 2 0.2124 0.8960 0.000 0.924 0.068 0.008
#> SRR1471612 3 0.3311 0.7641 0.000 0.172 0.828 0.000
#> SRR1413998 2 0.0000 0.9244 0.000 1.000 0.000 0.000
#> SRR1122940 3 0.5773 0.5596 0.000 0.320 0.632 0.048
#> SRR1402563 2 0.2973 0.8419 0.000 0.856 0.144 0.000
#> SRR1398393 2 0.2635 0.8671 0.000 0.904 0.076 0.020
#> SRR657961 3 0.1557 0.8174 0.000 0.056 0.944 0.000
#> SRR1471135 3 0.1902 0.8187 0.000 0.064 0.932 0.004
#> SRR1430001 2 0.6127 0.5536 0.288 0.644 0.060 0.008
#> SRR662775 1 0.0000 0.8500 1.000 0.000 0.000 0.000
#> SRR1474182 2 0.0000 0.9244 0.000 1.000 0.000 0.000
#> SRR607190 1 0.0000 0.8500 1.000 0.000 0.000 0.000
#> SRR612467 3 0.3160 0.7883 0.000 0.108 0.872 0.020
#> SRR1465959 2 0.1970 0.8983 0.000 0.932 0.060 0.008
#> SRR1446132 2 0.0000 0.9244 0.000 1.000 0.000 0.000
#> SRR1416933 2 0.0000 0.9244 0.000 1.000 0.000 0.000
#> SRR1102538 3 0.1940 0.8142 0.000 0.076 0.924 0.000
#> SRR1098636 3 0.2142 0.8154 0.000 0.056 0.928 0.016
#> SRR1072998 3 0.4797 0.6684 0.000 0.260 0.720 0.020
#> SRR627443 1 0.0000 0.8500 1.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 0.8500 1.000 0.000 0.000 0.000
#> SRR823991 2 0.2376 0.8806 0.000 0.916 0.068 0.016
#> SRR1089158 3 0.4399 0.7214 0.000 0.212 0.768 0.020
#> SRR1469036 2 0.3769 0.8633 0.072 0.864 0.052 0.012
#> SRR824039 2 0.1975 0.9079 0.000 0.936 0.048 0.016
#> SRR1339047 2 0.0188 0.9239 0.000 0.996 0.000 0.004
#> SRR1443049 2 0.0336 0.9239 0.000 0.992 0.000 0.008
#> SRR1122885 3 0.4819 0.5761 0.000 0.344 0.652 0.004
#> SRR602895 3 0.2153 0.7720 0.036 0.020 0.936 0.008
#> SRR1409837 2 0.0336 0.9240 0.000 0.992 0.008 0.000
#> SRR1388959 2 0.0000 0.9244 0.000 1.000 0.000 0.000
#> SRR659863 1 0.0000 0.8500 1.000 0.000 0.000 0.000
#> SRR1089877 2 0.2413 0.8932 0.000 0.916 0.064 0.020
#> SRR1123775 3 0.3208 0.7671 0.000 0.148 0.848 0.004
#> SRR658909 1 0.4136 0.6425 0.788 0.000 0.196 0.016
#> SRR1140510 2 0.0000 0.9244 0.000 1.000 0.000 0.000
#> SRR607562 3 0.1743 0.8162 0.000 0.056 0.940 0.004
#> SRR1122913 2 0.3351 0.8303 0.000 0.844 0.148 0.008
#> SRR598042 3 0.1557 0.8174 0.000 0.056 0.944 0.000
#> SRR1467340 2 0.1970 0.9009 0.000 0.932 0.060 0.008
#> SRR1072321 2 0.0927 0.9215 0.000 0.976 0.016 0.008
#> SRR1094580 2 0.2469 0.8597 0.000 0.892 0.108 0.000
#> SRR1076608 2 0.0336 0.9239 0.000 0.992 0.000 0.008
#> SRR1395462 3 0.1557 0.8174 0.000 0.056 0.944 0.000
#> SRR1489220 1 0.5908 0.5641 0.728 0.104 0.152 0.016
#> SRR614371 3 0.2675 0.7077 0.100 0.000 0.892 0.008
#> SRR615455 4 0.2281 0.8432 0.096 0.000 0.000 0.904
#> SRR1070573 2 0.2799 0.8697 0.000 0.884 0.108 0.008
#> SRR598749 3 0.4964 0.5398 0.068 0.000 0.764 0.168
#> SRR1365556 2 0.0000 0.9244 0.000 1.000 0.000 0.000
#> SRR1350023 2 0.0000 0.9244 0.000 1.000 0.000 0.000
#> SRR1446582 3 0.1557 0.8174 0.000 0.056 0.944 0.000
#> SRR1439763 2 0.2271 0.8926 0.000 0.916 0.076 0.008
#> SRR1343986 2 0.1722 0.9078 0.000 0.944 0.048 0.008
#> SRR807463 2 0.0336 0.9240 0.000 0.992 0.008 0.000
#> SRR660390 1 0.2589 0.7859 0.884 0.000 0.000 0.116
#> SRR1367672 3 0.3688 0.7322 0.000 0.208 0.792 0.000
#> SRR613294 4 0.2450 0.8654 0.072 0.000 0.016 0.912
#> SRR824015 2 0.0336 0.9232 0.000 0.992 0.000 0.008
#> SRR1078924 2 0.3768 0.7487 0.000 0.808 0.184 0.008
#> SRR662221 3 0.6074 0.3955 0.104 0.000 0.668 0.228
#> SRR655017 1 0.0000 0.8500 1.000 0.000 0.000 0.000
#> SRR1338450 2 0.5584 0.6212 0.264 0.692 0.024 0.020
#> SRR663741 1 0.4382 0.5506 0.704 0.000 0.000 0.296
#> SRR1396057 2 0.1022 0.9117 0.000 0.968 0.032 0.000
#> SRR1083800 2 0.5151 0.0529 0.000 0.532 0.464 0.004
#> SRR1445789 2 0.0188 0.9244 0.000 0.996 0.000 0.004
#> SRR1387355 2 0.4972 0.1790 0.456 0.544 0.000 0.000
#> SRR1388855 2 0.0188 0.9244 0.000 0.996 0.000 0.004
#> SRR1445449 2 0.2563 0.8804 0.072 0.908 0.000 0.020
#> SRR1380740 2 0.0336 0.9239 0.000 0.992 0.000 0.008
#> SRR659995 3 0.6337 0.1323 0.072 0.000 0.568 0.360
#> SRR1489524 2 0.0000 0.9244 0.000 1.000 0.000 0.000
#> SRR1444662 2 0.0592 0.9215 0.000 0.984 0.000 0.016
#> SRR1383652 3 0.1792 0.8187 0.000 0.068 0.932 0.000
#> SRR1361243 2 0.2053 0.8952 0.000 0.924 0.072 0.004
#> SRR1490337 3 0.2485 0.8152 0.004 0.064 0.916 0.016
#> SRR823967 3 0.5313 0.4535 0.000 0.376 0.608 0.016
#> SRR660127 1 0.0188 0.8478 0.996 0.000 0.004 0.000
#> SRR1366627 2 0.0188 0.9239 0.000 0.996 0.000 0.004
#> SRR1361219 2 0.0000 0.9244 0.000 1.000 0.000 0.000
#> SRR1393510 2 0.0779 0.9194 0.000 0.980 0.016 0.004
#> SRR662558 3 0.2838 0.8078 0.020 0.056 0.908 0.016
#> SRR1077334 2 0.2466 0.8720 0.000 0.900 0.096 0.004
#> SRR807438 1 0.5870 0.4622 0.700 0.224 0.064 0.012
#> SRR1459078 2 0.0524 0.9233 0.000 0.988 0.004 0.008
#> SRR1329704 2 0.0188 0.9244 0.000 0.996 0.000 0.004
#> SRR1468072 2 0.0000 0.9244 0.000 1.000 0.000 0.000
#> SRR1376196 2 0.2124 0.8972 0.000 0.924 0.068 0.008
#> SRR1442909 3 0.2222 0.8161 0.000 0.060 0.924 0.016
#> SRR1414269 3 0.4907 0.3441 0.000 0.420 0.580 0.000
#> SRR1381913 3 0.1557 0.8174 0.000 0.056 0.944 0.000
#> SRR1340157 2 0.0336 0.9239 0.000 0.992 0.000 0.008
#> SRR1407583 2 0.0592 0.9215 0.000 0.984 0.000 0.016
#> SRR615826 4 0.5716 0.6443 0.068 0.000 0.252 0.680
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR612587 4 0.0162 0.8452 0.004 0.000 0.000 0.996 0.000
#> SRR1458769 2 0.1043 0.6038 0.000 0.960 0.000 0.000 0.040
#> SRR613162 1 0.2149 0.7077 0.916 0.000 0.000 0.036 0.048
#> SRR1352481 1 0.3398 0.6569 0.780 0.000 0.004 0.000 0.216
#> SRR1468876 2 0.7155 0.1821 0.372 0.416 0.032 0.000 0.180
#> SRR1399223 2 0.0880 0.6402 0.000 0.968 0.000 0.000 0.032
#> SRR660030 3 0.2806 0.5814 0.000 0.000 0.844 0.004 0.152
#> SRR1333609 2 0.4009 0.6416 0.000 0.684 0.004 0.000 0.312
#> SRR1471612 2 0.5334 -0.4313 0.000 0.512 0.436 0.000 0.052
#> SRR1413998 2 0.0609 0.6185 0.000 0.980 0.000 0.000 0.020
#> SRR1122940 3 0.6667 0.1677 0.000 0.248 0.432 0.000 0.320
#> SRR1402563 2 0.5950 0.5320 0.000 0.592 0.188 0.000 0.220
#> SRR1398393 2 0.5845 -0.4389 0.000 0.540 0.108 0.000 0.352
#> SRR657961 3 0.2077 0.5802 0.000 0.008 0.908 0.000 0.084
#> SRR1471135 3 0.3675 0.3471 0.000 0.188 0.788 0.000 0.024
#> SRR1430001 2 0.5608 0.5984 0.084 0.596 0.004 0.000 0.316
#> SRR662775 1 0.0510 0.7258 0.984 0.000 0.000 0.000 0.016
#> SRR1474182 2 0.1270 0.5992 0.000 0.948 0.000 0.000 0.052
#> SRR607190 1 0.0880 0.7220 0.968 0.000 0.000 0.000 0.032
#> SRR612467 3 0.0960 0.6033 0.000 0.008 0.972 0.004 0.016
#> SRR1465959 2 0.5449 0.5987 0.000 0.636 0.108 0.000 0.256
#> SRR1446132 2 0.0162 0.6321 0.000 0.996 0.000 0.000 0.004
#> SRR1416933 2 0.0290 0.6262 0.000 0.992 0.000 0.000 0.008
#> SRR1102538 3 0.2660 0.5929 0.000 0.008 0.864 0.000 0.128
#> SRR1098636 3 0.4108 0.2692 0.000 0.008 0.684 0.000 0.308
#> SRR1072998 3 0.5322 0.3361 0.000 0.056 0.552 0.000 0.392
#> SRR627443 1 0.1410 0.7118 0.940 0.000 0.000 0.000 0.060
#> SRR656131 1 0.0000 0.7281 1.000 0.000 0.000 0.000 0.000
#> SRR823991 2 0.5719 -0.4009 0.000 0.552 0.096 0.000 0.352
#> SRR1089158 3 0.6204 0.2998 0.000 0.228 0.584 0.008 0.180
#> SRR1469036 2 0.4522 0.6343 0.024 0.660 0.000 0.000 0.316
#> SRR824039 5 0.6818 0.0000 0.000 0.336 0.312 0.000 0.352
#> SRR1339047 2 0.3003 0.4111 0.000 0.812 0.000 0.000 0.188
#> SRR1443049 2 0.3837 0.6444 0.000 0.692 0.000 0.000 0.308
#> SRR1122885 3 0.4879 0.4628 0.000 0.124 0.720 0.000 0.156
#> SRR602895 3 0.2206 0.5911 0.016 0.000 0.912 0.004 0.068
#> SRR1409837 2 0.1012 0.6154 0.000 0.968 0.012 0.000 0.020
#> SRR1388959 2 0.0290 0.6262 0.000 0.992 0.000 0.000 0.008
#> SRR659863 1 0.0000 0.7281 1.000 0.000 0.000 0.000 0.000
#> SRR1089877 2 0.4811 0.3668 0.000 0.528 0.020 0.000 0.452
#> SRR1123775 3 0.2293 0.6065 0.000 0.016 0.900 0.000 0.084
#> SRR658909 1 0.5408 0.5008 0.652 0.000 0.228 0.000 0.120
#> SRR1140510 2 0.0162 0.6285 0.000 0.996 0.000 0.000 0.004
#> SRR607562 3 0.0955 0.6043 0.000 0.004 0.968 0.000 0.028
#> SRR1122913 2 0.6246 0.4807 0.000 0.528 0.180 0.000 0.292
#> SRR598042 3 0.0955 0.6043 0.000 0.004 0.968 0.000 0.028
#> SRR1467340 2 0.4009 0.6416 0.000 0.684 0.004 0.000 0.312
#> SRR1072321 2 0.3949 0.6454 0.000 0.696 0.004 0.000 0.300
#> SRR1094580 2 0.3201 0.6441 0.000 0.852 0.052 0.000 0.096
#> SRR1076608 2 0.3684 0.6512 0.000 0.720 0.000 0.000 0.280
#> SRR1395462 3 0.1557 0.5947 0.000 0.008 0.940 0.000 0.052
#> SRR1489220 1 0.6215 0.2339 0.508 0.000 0.336 0.000 0.156
#> SRR614371 3 0.3535 0.5420 0.088 0.000 0.832 0.000 0.080
#> SRR615455 4 0.0671 0.8431 0.004 0.000 0.000 0.980 0.016
#> SRR1070573 2 0.5422 0.5954 0.000 0.616 0.088 0.000 0.296
#> SRR598749 3 0.2429 0.5845 0.004 0.000 0.900 0.076 0.020
#> SRR1365556 2 0.1341 0.5898 0.000 0.944 0.000 0.000 0.056
#> SRR1350023 2 0.0794 0.6401 0.000 0.972 0.000 0.000 0.028
#> SRR1446582 3 0.1310 0.6067 0.000 0.020 0.956 0.000 0.024
#> SRR1439763 2 0.5584 0.5645 0.000 0.592 0.096 0.000 0.312
#> SRR1343986 2 0.4823 0.6198 0.000 0.644 0.040 0.000 0.316
#> SRR807463 2 0.5180 0.4902 0.000 0.624 0.064 0.000 0.312
#> SRR660390 1 0.3381 0.6216 0.808 0.000 0.000 0.176 0.016
#> SRR1367672 3 0.4768 0.2069 0.000 0.304 0.656 0.000 0.040
#> SRR613294 4 0.0566 0.8507 0.004 0.000 0.012 0.984 0.000
#> SRR824015 2 0.3534 0.2760 0.000 0.744 0.000 0.000 0.256
#> SRR1078924 2 0.6691 0.2867 0.000 0.428 0.260 0.000 0.312
#> SRR662221 4 0.7496 0.5020 0.096 0.000 0.192 0.512 0.200
#> SRR655017 1 0.0000 0.7281 1.000 0.000 0.000 0.000 0.000
#> SRR1338450 1 0.6602 0.2279 0.548 0.312 0.056 0.000 0.084
#> SRR663741 1 0.5296 0.0244 0.484 0.000 0.000 0.468 0.048
#> SRR1396057 2 0.3835 0.2718 0.000 0.744 0.012 0.000 0.244
#> SRR1083800 3 0.6674 0.1144 0.000 0.304 0.436 0.000 0.260
#> SRR1445789 2 0.0404 0.6340 0.000 0.988 0.000 0.000 0.012
#> SRR1387355 1 0.4294 0.0960 0.532 0.468 0.000 0.000 0.000
#> SRR1388855 2 0.0000 0.6305 0.000 1.000 0.000 0.000 0.000
#> SRR1445449 2 0.6245 -0.2428 0.220 0.544 0.000 0.000 0.236
#> SRR1380740 2 0.3857 0.6432 0.000 0.688 0.000 0.000 0.312
#> SRR659995 4 0.2921 0.8093 0.004 0.000 0.148 0.844 0.004
#> SRR1489524 2 0.0162 0.6285 0.000 0.996 0.000 0.000 0.004
#> SRR1444662 2 0.3003 0.4105 0.000 0.812 0.000 0.000 0.188
#> SRR1383652 3 0.4419 0.0606 0.000 0.312 0.668 0.000 0.020
#> SRR1361243 2 0.3774 0.6475 0.000 0.704 0.000 0.000 0.296
#> SRR1490337 3 0.6842 -0.6283 0.008 0.300 0.452 0.000 0.240
#> SRR823967 3 0.5086 0.1602 0.000 0.060 0.636 0.000 0.304
#> SRR660127 1 0.0162 0.7280 0.996 0.000 0.004 0.000 0.000
#> SRR1366627 2 0.0162 0.6285 0.000 0.996 0.000 0.000 0.004
#> SRR1361219 2 0.0000 0.6305 0.000 1.000 0.000 0.000 0.000
#> SRR1393510 2 0.3209 0.4162 0.008 0.812 0.000 0.000 0.180
#> SRR662558 3 0.4832 0.1379 0.024 0.004 0.616 0.000 0.356
#> SRR1077334 2 0.6674 0.1955 0.000 0.428 0.324 0.000 0.248
#> SRR807438 1 0.4808 0.5764 0.736 0.040 0.196 0.000 0.028
#> SRR1459078 2 0.3876 0.6418 0.000 0.684 0.000 0.000 0.316
#> SRR1329704 2 0.3039 0.6582 0.000 0.808 0.000 0.000 0.192
#> SRR1468072 2 0.3143 0.6580 0.000 0.796 0.000 0.000 0.204
#> SRR1376196 2 0.4318 0.6429 0.000 0.688 0.020 0.000 0.292
#> SRR1442909 3 0.5513 0.1415 0.000 0.180 0.652 0.000 0.168
#> SRR1414269 3 0.3741 0.5505 0.000 0.076 0.816 0.000 0.108
#> SRR1381913 3 0.1831 0.5851 0.000 0.004 0.920 0.000 0.076
#> SRR1340157 2 0.3857 0.6432 0.000 0.688 0.000 0.000 0.312
#> SRR1407583 2 0.4067 0.1247 0.000 0.692 0.008 0.000 0.300
#> SRR615826 4 0.2124 0.8399 0.004 0.000 0.096 0.900 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 4 0.1663 0.74568 0.000 0.000 0.000 0.912 0.088 0.000
#> SRR1458769 2 0.2106 0.82852 0.000 0.904 0.032 0.000 0.000 0.064
#> SRR613162 1 0.3525 0.72256 0.816 0.000 0.008 0.080 0.000 0.096
#> SRR1352481 1 0.3796 0.68043 0.764 0.000 0.060 0.000 0.000 0.176
#> SRR1468876 3 0.7082 -0.02331 0.088 0.056 0.456 0.000 0.060 0.340
#> SRR1399223 2 0.2121 0.79445 0.000 0.892 0.096 0.000 0.000 0.012
#> SRR660030 3 0.4439 0.30934 0.000 0.000 0.540 0.000 0.432 0.028
#> SRR1333609 3 0.3767 0.66984 0.004 0.276 0.708 0.000 0.000 0.012
#> SRR1471612 2 0.4045 0.44951 0.000 0.664 0.000 0.000 0.312 0.024
#> SRR1413998 2 0.0405 0.86636 0.000 0.988 0.004 0.000 0.000 0.008
#> SRR1122940 3 0.4236 0.59528 0.000 0.024 0.736 0.000 0.204 0.036
#> SRR1402563 3 0.6115 0.47634 0.004 0.392 0.456 0.000 0.124 0.024
#> SRR1398393 6 0.5184 0.47380 0.000 0.316 0.008 0.000 0.088 0.588
#> SRR657961 5 0.3164 0.57453 0.000 0.004 0.032 0.000 0.824 0.140
#> SRR1471135 5 0.4527 0.36365 0.000 0.332 0.004 0.000 0.624 0.040
#> SRR1430001 3 0.5700 0.63746 0.092 0.212 0.644 0.000 0.024 0.028
#> SRR662775 1 0.0777 0.78645 0.972 0.000 0.004 0.000 0.000 0.024
#> SRR1474182 2 0.0508 0.86500 0.000 0.984 0.000 0.000 0.004 0.012
#> SRR607190 1 0.1866 0.76591 0.908 0.000 0.008 0.000 0.000 0.084
#> SRR612467 5 0.1036 0.64284 0.000 0.004 0.008 0.000 0.964 0.024
#> SRR1465959 3 0.5734 0.64635 0.000 0.188 0.580 0.000 0.216 0.016
#> SRR1446132 2 0.0914 0.86107 0.000 0.968 0.016 0.000 0.000 0.016
#> SRR1416933 2 0.0551 0.86534 0.000 0.984 0.008 0.000 0.004 0.004
#> SRR1102538 5 0.5737 -0.15102 0.000 0.004 0.416 0.000 0.436 0.144
#> SRR1098636 6 0.4933 0.45158 0.000 0.016 0.056 0.000 0.304 0.624
#> SRR1072998 3 0.5069 0.49295 0.000 0.004 0.624 0.000 0.264 0.108
#> SRR627443 1 0.2165 0.75516 0.884 0.000 0.008 0.000 0.000 0.108
#> SRR656131 1 0.0291 0.79142 0.992 0.000 0.004 0.000 0.000 0.004
#> SRR823991 6 0.6108 0.54390 0.016 0.216 0.068 0.000 0.088 0.612
#> SRR1089158 3 0.5200 0.43193 0.000 0.028 0.556 0.000 0.372 0.044
#> SRR1469036 3 0.4822 0.64956 0.060 0.252 0.668 0.000 0.000 0.020
#> SRR824039 6 0.5793 0.51270 0.000 0.044 0.124 0.000 0.228 0.604
#> SRR1339047 2 0.2092 0.79607 0.000 0.876 0.000 0.000 0.000 0.124
#> SRR1443049 3 0.4527 0.67349 0.000 0.256 0.680 0.000 0.008 0.056
#> SRR1122885 3 0.5778 0.30794 0.000 0.036 0.468 0.000 0.420 0.076
#> SRR602895 5 0.2872 0.58603 0.028 0.000 0.024 0.000 0.868 0.080
#> SRR1409837 2 0.1767 0.84943 0.000 0.932 0.036 0.000 0.020 0.012
#> SRR1388959 2 0.0405 0.86574 0.000 0.988 0.004 0.000 0.000 0.008
#> SRR659863 1 0.0260 0.79133 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1089877 6 0.5862 0.15867 0.000 0.116 0.396 0.000 0.020 0.468
#> SRR1123775 5 0.5259 0.34293 0.000 0.012 0.300 0.000 0.596 0.092
#> SRR658909 1 0.5998 0.07022 0.492 0.000 0.012 0.000 0.180 0.316
#> SRR1140510 2 0.0622 0.86591 0.000 0.980 0.012 0.000 0.000 0.008
#> SRR607562 5 0.0405 0.64239 0.000 0.004 0.008 0.000 0.988 0.000
#> SRR1122913 3 0.4805 0.65654 0.000 0.116 0.696 0.000 0.176 0.012
#> SRR598042 5 0.0405 0.64239 0.000 0.004 0.008 0.000 0.988 0.000
#> SRR1467340 3 0.3633 0.67679 0.000 0.252 0.732 0.000 0.012 0.004
#> SRR1072321 3 0.4957 0.61087 0.000 0.304 0.624 0.000 0.020 0.052
#> SRR1094580 2 0.4988 0.40209 0.000 0.672 0.228 0.000 0.072 0.028
#> SRR1076608 3 0.4333 0.56719 0.000 0.376 0.596 0.000 0.000 0.028
#> SRR1395462 5 0.1674 0.63367 0.000 0.004 0.004 0.000 0.924 0.068
#> SRR1489220 3 0.6586 0.17369 0.308 0.000 0.432 0.000 0.224 0.036
#> SRR614371 5 0.4239 0.43735 0.180 0.000 0.008 0.000 0.740 0.072
#> SRR615455 4 0.0146 0.69430 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1070573 3 0.5217 0.64892 0.000 0.188 0.648 0.000 0.152 0.012
#> SRR598749 5 0.2006 0.57738 0.000 0.000 0.004 0.104 0.892 0.000
#> SRR1365556 2 0.0858 0.86452 0.000 0.968 0.004 0.000 0.000 0.028
#> SRR1350023 2 0.0909 0.86164 0.000 0.968 0.020 0.000 0.000 0.012
#> SRR1446582 5 0.2188 0.62574 0.000 0.032 0.036 0.000 0.912 0.020
#> SRR1439763 3 0.4081 0.65964 0.000 0.088 0.776 0.000 0.120 0.016
#> SRR1343986 3 0.4053 0.67969 0.000 0.176 0.760 0.000 0.048 0.016
#> SRR807463 3 0.6814 0.31002 0.000 0.328 0.424 0.000 0.068 0.180
#> SRR660390 1 0.3194 0.65240 0.808 0.000 0.004 0.168 0.000 0.020
#> SRR1367672 5 0.3543 0.44381 0.000 0.272 0.004 0.000 0.720 0.004
#> SRR613294 4 0.1957 0.74953 0.000 0.000 0.000 0.888 0.112 0.000
#> SRR824015 2 0.4945 -0.11639 0.000 0.484 0.064 0.000 0.000 0.452
#> SRR1078924 3 0.4174 0.64352 0.000 0.084 0.732 0.000 0.184 0.000
#> SRR662221 6 0.7304 0.00981 0.128 0.000 0.012 0.340 0.124 0.396
#> SRR655017 1 0.0717 0.78905 0.976 0.000 0.008 0.000 0.000 0.016
#> SRR1338450 6 0.7778 0.40176 0.260 0.128 0.144 0.000 0.044 0.424
#> SRR663741 4 0.4728 -0.07782 0.480 0.000 0.008 0.488 0.012 0.012
#> SRR1396057 2 0.1434 0.85289 0.000 0.940 0.000 0.000 0.012 0.048
#> SRR1083800 3 0.5429 0.52911 0.000 0.068 0.616 0.000 0.272 0.044
#> SRR1445789 2 0.1913 0.81935 0.000 0.908 0.080 0.000 0.000 0.012
#> SRR1387355 1 0.4845 0.12035 0.528 0.428 0.020 0.000 0.000 0.024
#> SRR1388855 2 0.0717 0.86429 0.000 0.976 0.008 0.000 0.000 0.016
#> SRR1445449 6 0.6105 0.25101 0.180 0.396 0.012 0.000 0.000 0.412
#> SRR1380740 3 0.3957 0.66477 0.004 0.280 0.696 0.000 0.000 0.020
#> SRR659995 4 0.3564 0.64459 0.000 0.000 0.000 0.724 0.264 0.012
#> SRR1489524 2 0.1720 0.84844 0.000 0.928 0.040 0.000 0.000 0.032
#> SRR1444662 2 0.1204 0.85065 0.000 0.944 0.000 0.000 0.000 0.056
#> SRR1383652 5 0.4375 0.25783 0.004 0.412 0.004 0.000 0.568 0.012
#> SRR1361243 3 0.4344 0.52953 0.000 0.412 0.568 0.000 0.008 0.012
#> SRR1490337 5 0.6760 -0.03307 0.068 0.136 0.008 0.000 0.472 0.316
#> SRR823967 6 0.5964 0.42269 0.000 0.024 0.156 0.000 0.280 0.540
#> SRR660127 1 0.0146 0.79060 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1366627 2 0.0717 0.86173 0.000 0.976 0.016 0.000 0.000 0.008
#> SRR1361219 2 0.1829 0.84412 0.000 0.928 0.036 0.000 0.008 0.028
#> SRR1393510 2 0.1745 0.84882 0.000 0.924 0.020 0.000 0.000 0.056
#> SRR662558 6 0.5663 0.45444 0.040 0.016 0.052 0.000 0.292 0.600
#> SRR1077334 3 0.5449 0.45806 0.000 0.020 0.580 0.000 0.308 0.092
#> SRR807438 3 0.7253 0.04199 0.356 0.016 0.392 0.000 0.144 0.092
#> SRR1459078 3 0.4062 0.61633 0.000 0.316 0.660 0.000 0.000 0.024
#> SRR1329704 2 0.2214 0.81796 0.000 0.888 0.096 0.000 0.000 0.016
#> SRR1468072 2 0.3969 0.36694 0.000 0.668 0.312 0.000 0.000 0.020
#> SRR1376196 3 0.4244 0.67762 0.000 0.280 0.680 0.000 0.036 0.004
#> SRR1442909 5 0.5229 0.38829 0.000 0.124 0.012 0.000 0.636 0.228
#> SRR1414269 5 0.6601 0.14210 0.000 0.052 0.228 0.000 0.484 0.236
#> SRR1381913 5 0.2051 0.62100 0.000 0.004 0.004 0.000 0.896 0.096
#> SRR1340157 3 0.3586 0.67099 0.000 0.280 0.712 0.000 0.004 0.004
#> SRR1407583 2 0.2266 0.79671 0.000 0.880 0.000 0.000 0.012 0.108
#> SRR615826 4 0.3409 0.62012 0.000 0.000 0.000 0.700 0.300 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "hclust"]
# you can also extract it by
# res = res_list["CV:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17380 rows and 102 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 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.990 0.994 0.14123 0.854 0.854
#> 3 3 0.996 0.972 0.987 0.47747 0.947 0.938
#> 4 4 0.935 0.955 0.969 0.12600 0.997 0.996
#> 5 5 0.881 0.942 0.967 0.18589 0.965 0.957
#> 6 6 0.845 0.928 0.967 0.00662 1.000 0.999
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
#> SRR612587 1 0.0000 0.936 1.000 0.000
#> SRR1458769 2 0.0000 0.999 0.000 1.000
#> SRR613162 1 0.6048 0.875 0.852 0.148
#> SRR1352481 2 0.2043 0.966 0.032 0.968
#> SRR1468876 2 0.0000 0.999 0.000 1.000
#> SRR1399223 2 0.0000 0.999 0.000 1.000
#> SRR660030 2 0.0000 0.999 0.000 1.000
#> SRR1333609 2 0.0000 0.999 0.000 1.000
#> SRR1471612 2 0.0000 0.999 0.000 1.000
#> SRR1413998 2 0.0000 0.999 0.000 1.000
#> SRR1122940 2 0.0000 0.999 0.000 1.000
#> SRR1402563 2 0.0000 0.999 0.000 1.000
#> SRR1398393 2 0.0000 0.999 0.000 1.000
#> SRR657961 2 0.0000 0.999 0.000 1.000
#> SRR1471135 2 0.0000 0.999 0.000 1.000
#> SRR1430001 2 0.0000 0.999 0.000 1.000
#> SRR662775 2 0.0000 0.999 0.000 1.000
#> SRR1474182 2 0.0000 0.999 0.000 1.000
#> SRR607190 2 0.0000 0.999 0.000 1.000
#> SRR612467 2 0.0672 0.991 0.008 0.992
#> SRR1465959 2 0.0000 0.999 0.000 1.000
#> SRR1446132 2 0.0000 0.999 0.000 1.000
#> SRR1416933 2 0.0000 0.999 0.000 1.000
#> SRR1102538 2 0.0000 0.999 0.000 1.000
#> SRR1098636 2 0.0000 0.999 0.000 1.000
#> SRR1072998 2 0.0000 0.999 0.000 1.000
#> SRR627443 2 0.0000 0.999 0.000 1.000
#> SRR656131 2 0.0000 0.999 0.000 1.000
#> SRR823991 2 0.0000 0.999 0.000 1.000
#> SRR1089158 2 0.0672 0.991 0.008 0.992
#> SRR1469036 2 0.0000 0.999 0.000 1.000
#> SRR824039 2 0.0000 0.999 0.000 1.000
#> SRR1339047 2 0.0000 0.999 0.000 1.000
#> SRR1443049 2 0.0000 0.999 0.000 1.000
#> SRR1122885 2 0.0000 0.999 0.000 1.000
#> SRR602895 2 0.0000 0.999 0.000 1.000
#> SRR1409837 2 0.0000 0.999 0.000 1.000
#> SRR1388959 2 0.0000 0.999 0.000 1.000
#> SRR659863 2 0.0000 0.999 0.000 1.000
#> SRR1089877 2 0.0000 0.999 0.000 1.000
#> SRR1123775 2 0.0000 0.999 0.000 1.000
#> SRR658909 2 0.0000 0.999 0.000 1.000
#> SRR1140510 2 0.0000 0.999 0.000 1.000
#> SRR607562 2 0.0000 0.999 0.000 1.000
#> SRR1122913 2 0.0000 0.999 0.000 1.000
#> SRR598042 2 0.0000 0.999 0.000 1.000
#> SRR1467340 2 0.0000 0.999 0.000 1.000
#> SRR1072321 2 0.0000 0.999 0.000 1.000
#> SRR1094580 2 0.0000 0.999 0.000 1.000
#> SRR1076608 2 0.0000 0.999 0.000 1.000
#> SRR1395462 2 0.0000 0.999 0.000 1.000
#> SRR1489220 2 0.0000 0.999 0.000 1.000
#> SRR614371 2 0.0000 0.999 0.000 1.000
#> SRR615455 1 0.0000 0.936 1.000 0.000
#> SRR1070573 2 0.0000 0.999 0.000 1.000
#> SRR598749 1 0.0000 0.936 1.000 0.000
#> SRR1365556 2 0.0000 0.999 0.000 1.000
#> SRR1350023 2 0.0000 0.999 0.000 1.000
#> SRR1446582 2 0.0000 0.999 0.000 1.000
#> SRR1439763 2 0.0000 0.999 0.000 1.000
#> SRR1343986 2 0.0000 0.999 0.000 1.000
#> SRR807463 2 0.0000 0.999 0.000 1.000
#> SRR660390 1 0.6048 0.875 0.852 0.148
#> SRR1367672 2 0.0000 0.999 0.000 1.000
#> SRR613294 1 0.0000 0.936 1.000 0.000
#> SRR824015 2 0.0672 0.991 0.008 0.992
#> SRR1078924 2 0.0000 0.999 0.000 1.000
#> SRR662221 2 0.2603 0.953 0.044 0.956
#> SRR655017 2 0.0000 0.999 0.000 1.000
#> SRR1338450 2 0.0000 0.999 0.000 1.000
#> SRR663741 2 0.2043 0.966 0.032 0.968
#> SRR1396057 2 0.0000 0.999 0.000 1.000
#> SRR1083800 2 0.0000 0.999 0.000 1.000
#> SRR1445789 2 0.0000 0.999 0.000 1.000
#> SRR1387355 2 0.0000 0.999 0.000 1.000
#> SRR1388855 2 0.0000 0.999 0.000 1.000
#> SRR1445449 2 0.0000 0.999 0.000 1.000
#> SRR1380740 2 0.0000 0.999 0.000 1.000
#> SRR659995 1 0.6048 0.875 0.852 0.148
#> SRR1489524 2 0.0000 0.999 0.000 1.000
#> SRR1444662 2 0.0000 0.999 0.000 1.000
#> SRR1383652 2 0.0000 0.999 0.000 1.000
#> SRR1361243 2 0.0000 0.999 0.000 1.000
#> SRR1490337 2 0.0000 0.999 0.000 1.000
#> SRR823967 2 0.0000 0.999 0.000 1.000
#> SRR660127 2 0.0000 0.999 0.000 1.000
#> SRR1366627 2 0.0000 0.999 0.000 1.000
#> SRR1361219 2 0.0000 0.999 0.000 1.000
#> SRR1393510 2 0.0000 0.999 0.000 1.000
#> SRR662558 2 0.0000 0.999 0.000 1.000
#> SRR1077334 2 0.0000 0.999 0.000 1.000
#> SRR807438 2 0.0000 0.999 0.000 1.000
#> SRR1459078 2 0.0000 0.999 0.000 1.000
#> SRR1329704 2 0.0000 0.999 0.000 1.000
#> SRR1468072 2 0.0000 0.999 0.000 1.000
#> SRR1376196 2 0.0000 0.999 0.000 1.000
#> SRR1442909 2 0.0000 0.999 0.000 1.000
#> SRR1414269 2 0.0000 0.999 0.000 1.000
#> SRR1381913 2 0.0000 0.999 0.000 1.000
#> SRR1340157 2 0.0000 0.999 0.000 1.000
#> SRR1407583 2 0.0000 0.999 0.000 1.000
#> SRR615826 1 0.0000 0.936 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 1 0.000 0.912 1.000 0.000 0.000
#> SRR1458769 2 0.000 0.993 0.000 1.000 0.000
#> SRR613162 1 0.445 0.835 0.808 0.000 0.192
#> SRR1352481 3 0.103 0.879 0.000 0.024 0.976
#> SRR1468876 2 0.000 0.993 0.000 1.000 0.000
#> SRR1399223 2 0.000 0.993 0.000 1.000 0.000
#> SRR660030 2 0.000 0.993 0.000 1.000 0.000
#> SRR1333609 2 0.000 0.993 0.000 1.000 0.000
#> SRR1471612 2 0.000 0.993 0.000 1.000 0.000
#> SRR1413998 2 0.000 0.993 0.000 1.000 0.000
#> SRR1122940 2 0.000 0.993 0.000 1.000 0.000
#> SRR1402563 2 0.000 0.993 0.000 1.000 0.000
#> SRR1398393 2 0.000 0.993 0.000 1.000 0.000
#> SRR657961 2 0.000 0.993 0.000 1.000 0.000
#> SRR1471135 2 0.000 0.993 0.000 1.000 0.000
#> SRR1430001 2 0.000 0.993 0.000 1.000 0.000
#> SRR662775 2 0.000 0.993 0.000 1.000 0.000
#> SRR1474182 2 0.000 0.993 0.000 1.000 0.000
#> SRR607190 2 0.000 0.993 0.000 1.000 0.000
#> SRR612467 2 0.369 0.833 0.000 0.860 0.140
#> SRR1465959 2 0.000 0.993 0.000 1.000 0.000
#> SRR1446132 2 0.000 0.993 0.000 1.000 0.000
#> SRR1416933 2 0.000 0.993 0.000 1.000 0.000
#> SRR1102538 2 0.000 0.993 0.000 1.000 0.000
#> SRR1098636 2 0.000 0.993 0.000 1.000 0.000
#> SRR1072998 2 0.000 0.993 0.000 1.000 0.000
#> SRR627443 2 0.000 0.993 0.000 1.000 0.000
#> SRR656131 2 0.000 0.993 0.000 1.000 0.000
#> SRR823991 2 0.000 0.993 0.000 1.000 0.000
#> SRR1089158 2 0.502 0.677 0.000 0.760 0.240
#> SRR1469036 2 0.000 0.993 0.000 1.000 0.000
#> SRR824039 2 0.000 0.993 0.000 1.000 0.000
#> SRR1339047 2 0.000 0.993 0.000 1.000 0.000
#> SRR1443049 2 0.000 0.993 0.000 1.000 0.000
#> SRR1122885 2 0.000 0.993 0.000 1.000 0.000
#> SRR602895 2 0.000 0.993 0.000 1.000 0.000
#> SRR1409837 2 0.000 0.993 0.000 1.000 0.000
#> SRR1388959 2 0.000 0.993 0.000 1.000 0.000
#> SRR659863 2 0.000 0.993 0.000 1.000 0.000
#> SRR1089877 2 0.000 0.993 0.000 1.000 0.000
#> SRR1123775 2 0.000 0.993 0.000 1.000 0.000
#> SRR658909 2 0.000 0.993 0.000 1.000 0.000
#> SRR1140510 2 0.000 0.993 0.000 1.000 0.000
#> SRR607562 2 0.000 0.993 0.000 1.000 0.000
#> SRR1122913 2 0.000 0.993 0.000 1.000 0.000
#> SRR598042 2 0.000 0.993 0.000 1.000 0.000
#> SRR1467340 2 0.000 0.993 0.000 1.000 0.000
#> SRR1072321 2 0.000 0.993 0.000 1.000 0.000
#> SRR1094580 2 0.000 0.993 0.000 1.000 0.000
#> SRR1076608 2 0.000 0.993 0.000 1.000 0.000
#> SRR1395462 2 0.000 0.993 0.000 1.000 0.000
#> SRR1489220 2 0.000 0.993 0.000 1.000 0.000
#> SRR614371 2 0.000 0.993 0.000 1.000 0.000
#> SRR615455 1 0.000 0.912 1.000 0.000 0.000
#> SRR1070573 2 0.000 0.993 0.000 1.000 0.000
#> SRR598749 1 0.000 0.912 1.000 0.000 0.000
#> SRR1365556 2 0.000 0.993 0.000 1.000 0.000
#> SRR1350023 2 0.000 0.993 0.000 1.000 0.000
#> SRR1446582 2 0.000 0.993 0.000 1.000 0.000
#> SRR1439763 2 0.000 0.993 0.000 1.000 0.000
#> SRR1343986 2 0.000 0.993 0.000 1.000 0.000
#> SRR807463 2 0.000 0.993 0.000 1.000 0.000
#> SRR660390 1 0.445 0.835 0.808 0.000 0.192
#> SRR1367672 2 0.000 0.993 0.000 1.000 0.000
#> SRR613294 1 0.000 0.912 1.000 0.000 0.000
#> SRR824015 2 0.502 0.677 0.000 0.760 0.240
#> SRR1078924 2 0.000 0.993 0.000 1.000 0.000
#> SRR662221 3 0.000 0.840 0.000 0.000 1.000
#> SRR655017 2 0.000 0.993 0.000 1.000 0.000
#> SRR1338450 2 0.000 0.993 0.000 1.000 0.000
#> SRR663741 3 0.236 0.804 0.000 0.072 0.928
#> SRR1396057 2 0.000 0.993 0.000 1.000 0.000
#> SRR1083800 2 0.000 0.993 0.000 1.000 0.000
#> SRR1445789 2 0.000 0.993 0.000 1.000 0.000
#> SRR1387355 2 0.000 0.993 0.000 1.000 0.000
#> SRR1388855 2 0.000 0.993 0.000 1.000 0.000
#> SRR1445449 2 0.000 0.993 0.000 1.000 0.000
#> SRR1380740 2 0.000 0.993 0.000 1.000 0.000
#> SRR659995 1 0.445 0.835 0.808 0.000 0.192
#> SRR1489524 2 0.000 0.993 0.000 1.000 0.000
#> SRR1444662 2 0.000 0.993 0.000 1.000 0.000
#> SRR1383652 2 0.000 0.993 0.000 1.000 0.000
#> SRR1361243 2 0.000 0.993 0.000 1.000 0.000
#> SRR1490337 2 0.000 0.993 0.000 1.000 0.000
#> SRR823967 2 0.000 0.993 0.000 1.000 0.000
#> SRR660127 2 0.000 0.993 0.000 1.000 0.000
#> SRR1366627 2 0.000 0.993 0.000 1.000 0.000
#> SRR1361219 2 0.000 0.993 0.000 1.000 0.000
#> SRR1393510 2 0.000 0.993 0.000 1.000 0.000
#> SRR662558 2 0.000 0.993 0.000 1.000 0.000
#> SRR1077334 2 0.000 0.993 0.000 1.000 0.000
#> SRR807438 2 0.000 0.993 0.000 1.000 0.000
#> SRR1459078 2 0.000 0.993 0.000 1.000 0.000
#> SRR1329704 2 0.000 0.993 0.000 1.000 0.000
#> SRR1468072 2 0.000 0.993 0.000 1.000 0.000
#> SRR1376196 2 0.000 0.993 0.000 1.000 0.000
#> SRR1442909 2 0.000 0.993 0.000 1.000 0.000
#> SRR1414269 2 0.000 0.993 0.000 1.000 0.000
#> SRR1381913 2 0.000 0.993 0.000 1.000 0.000
#> SRR1340157 2 0.000 0.993 0.000 1.000 0.000
#> SRR1407583 2 0.000 0.993 0.000 1.000 0.000
#> SRR615826 1 0.000 0.912 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1458769 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR613162 1 0.3356 1.000 0.824 0.000 0.000 0.176
#> SRR1352481 3 0.0000 0.771 0.000 0.000 1.000 0.000
#> SRR1468876 2 0.0592 0.969 0.000 0.984 0.016 0.000
#> SRR1399223 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR660030 2 0.0592 0.968 0.016 0.984 0.000 0.000
#> SRR1333609 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1471612 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1413998 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1122940 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1402563 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1398393 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR657961 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1471135 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1430001 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR662775 2 0.1792 0.931 0.068 0.932 0.000 0.000
#> SRR1474182 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR607190 2 0.2011 0.921 0.080 0.920 0.000 0.000
#> SRR612467 2 0.4312 0.800 0.056 0.812 0.132 0.000
#> SRR1465959 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1446132 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1416933 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1102538 2 0.0336 0.973 0.000 0.992 0.008 0.000
#> SRR1098636 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1072998 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR627443 2 0.3219 0.835 0.164 0.836 0.000 0.000
#> SRR656131 2 0.1302 0.950 0.044 0.956 0.000 0.000
#> SRR823991 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1089158 2 0.5113 0.625 0.032 0.704 0.264 0.000
#> SRR1469036 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR824039 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1339047 2 0.1118 0.956 0.036 0.964 0.000 0.000
#> SRR1443049 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1122885 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR602895 2 0.1302 0.950 0.044 0.956 0.000 0.000
#> SRR1409837 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1388959 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR659863 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1089877 2 0.0817 0.964 0.000 0.976 0.024 0.000
#> SRR1123775 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR658909 2 0.2149 0.915 0.088 0.912 0.000 0.000
#> SRR1140510 2 0.0817 0.964 0.000 0.976 0.024 0.000
#> SRR607562 2 0.2011 0.921 0.080 0.920 0.000 0.000
#> SRR1122913 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR598042 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1467340 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1072321 2 0.0469 0.971 0.000 0.988 0.012 0.000
#> SRR1094580 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1076608 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1395462 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1489220 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR614371 2 0.2469 0.896 0.108 0.892 0.000 0.000
#> SRR615455 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1070573 2 0.0336 0.973 0.000 0.992 0.008 0.000
#> SRR598749 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR1365556 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1350023 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1446582 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1439763 2 0.0707 0.967 0.000 0.980 0.020 0.000
#> SRR1343986 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR807463 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR660390 1 0.3356 1.000 0.824 0.000 0.000 0.176
#> SRR1367672 2 0.2149 0.915 0.088 0.912 0.000 0.000
#> SRR613294 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR824015 2 0.5113 0.625 0.032 0.704 0.264 0.000
#> SRR1078924 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR662221 3 0.3610 0.798 0.200 0.000 0.800 0.000
#> SRR655017 2 0.3219 0.835 0.164 0.836 0.000 0.000
#> SRR1338450 2 0.0707 0.967 0.000 0.980 0.020 0.000
#> SRR663741 3 0.4671 0.781 0.220 0.028 0.752 0.000
#> SRR1396057 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1083800 2 0.0707 0.967 0.000 0.980 0.020 0.000
#> SRR1445789 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1387355 2 0.0188 0.975 0.000 0.996 0.004 0.000
#> SRR1388855 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1445449 2 0.2149 0.915 0.088 0.912 0.000 0.000
#> SRR1380740 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR659995 1 0.3356 1.000 0.824 0.000 0.000 0.176
#> SRR1489524 2 0.0707 0.967 0.000 0.980 0.020 0.000
#> SRR1444662 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1383652 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1361243 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1490337 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR823967 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR660127 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1366627 2 0.1118 0.956 0.036 0.964 0.000 0.000
#> SRR1361219 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1393510 2 0.1118 0.956 0.036 0.964 0.000 0.000
#> SRR662558 2 0.0188 0.975 0.000 0.996 0.004 0.000
#> SRR1077334 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR807438 2 0.0336 0.973 0.000 0.992 0.008 0.000
#> SRR1459078 2 0.0817 0.964 0.000 0.976 0.024 0.000
#> SRR1329704 2 0.0817 0.964 0.000 0.976 0.024 0.000
#> SRR1468072 2 0.0817 0.964 0.000 0.976 0.024 0.000
#> SRR1376196 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1442909 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1414269 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1381913 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1340157 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR1407583 2 0.0000 0.976 0.000 1.000 0.000 0.000
#> SRR615826 4 0.0000 1.000 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
#> SRR612587 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1458769 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR613162 1 0.0404 0.995 0.988 0.000 0.000 0.012 0.000
#> SRR1352481 2 0.0000 0.768 0.000 1.000 0.000 0.000 0.000
#> SRR1468876 3 0.0510 0.962 0.000 0.016 0.984 0.000 0.000
#> SRR1399223 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR660030 3 0.0510 0.961 0.000 0.000 0.984 0.000 0.016
#> SRR1333609 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1471612 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1413998 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1122940 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1402563 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1398393 3 0.0162 0.967 0.000 0.000 0.996 0.000 0.004
#> SRR657961 3 0.0162 0.967 0.000 0.000 0.996 0.000 0.004
#> SRR1471135 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1430001 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR662775 3 0.2230 0.881 0.000 0.000 0.884 0.000 0.116
#> SRR1474182 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR607190 3 0.2690 0.839 0.000 0.000 0.844 0.000 0.156
#> SRR612467 3 0.4535 0.748 0.012 0.128 0.772 0.000 0.088
#> SRR1465959 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1446132 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1416933 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1102538 3 0.0290 0.966 0.000 0.008 0.992 0.000 0.000
#> SRR1098636 3 0.0162 0.967 0.000 0.000 0.996 0.000 0.004
#> SRR1072998 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR627443 5 0.0162 1.000 0.000 0.000 0.004 0.000 0.996
#> SRR656131 3 0.2074 0.892 0.000 0.000 0.896 0.000 0.104
#> SRR823991 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1089158 3 0.4404 0.631 0.000 0.264 0.704 0.000 0.032
#> SRR1469036 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR824039 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1339047 3 0.1197 0.938 0.000 0.000 0.952 0.000 0.048
#> SRR1443049 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1122885 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR602895 3 0.2074 0.892 0.000 0.000 0.896 0.000 0.104
#> SRR1409837 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1388959 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR659863 3 0.0703 0.955 0.000 0.000 0.976 0.000 0.024
#> SRR1089877 3 0.0703 0.957 0.000 0.024 0.976 0.000 0.000
#> SRR1123775 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR658909 3 0.3452 0.723 0.000 0.000 0.756 0.000 0.244
#> SRR1140510 3 0.0703 0.957 0.000 0.024 0.976 0.000 0.000
#> SRR607562 3 0.2648 0.842 0.000 0.000 0.848 0.000 0.152
#> SRR1122913 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR598042 3 0.0162 0.967 0.000 0.000 0.996 0.000 0.004
#> SRR1467340 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1072321 3 0.0404 0.964 0.000 0.012 0.988 0.000 0.000
#> SRR1094580 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1076608 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1395462 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1489220 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR614371 3 0.3796 0.632 0.000 0.000 0.700 0.000 0.300
#> SRR615455 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1070573 3 0.0290 0.966 0.000 0.008 0.992 0.000 0.000
#> SRR598749 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR1365556 3 0.0162 0.967 0.000 0.000 0.996 0.000 0.004
#> SRR1350023 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1446582 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1439763 3 0.0609 0.960 0.000 0.020 0.980 0.000 0.000
#> SRR1343986 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR807463 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR660390 1 0.0609 0.989 0.980 0.000 0.000 0.020 0.000
#> SRR1367672 3 0.2732 0.834 0.000 0.000 0.840 0.000 0.160
#> SRR613294 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR824015 3 0.4404 0.631 0.000 0.264 0.704 0.000 0.032
#> SRR1078924 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR662221 2 0.3109 0.815 0.200 0.800 0.000 0.000 0.000
#> SRR655017 5 0.0162 1.000 0.000 0.000 0.004 0.000 0.996
#> SRR1338450 3 0.0609 0.960 0.000 0.020 0.980 0.000 0.000
#> SRR663741 2 0.4681 0.807 0.180 0.748 0.016 0.000 0.056
#> SRR1396057 3 0.0162 0.967 0.000 0.000 0.996 0.000 0.004
#> SRR1083800 3 0.0609 0.960 0.000 0.020 0.980 0.000 0.000
#> SRR1445789 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1387355 3 0.0162 0.967 0.000 0.000 0.996 0.000 0.004
#> SRR1388855 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1445449 3 0.2732 0.834 0.000 0.000 0.840 0.000 0.160
#> SRR1380740 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR659995 1 0.0404 0.995 0.988 0.000 0.000 0.012 0.000
#> SRR1489524 3 0.0609 0.960 0.000 0.020 0.980 0.000 0.000
#> SRR1444662 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1383652 3 0.0162 0.967 0.000 0.000 0.996 0.000 0.004
#> SRR1361243 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1490337 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR823967 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR660127 3 0.0703 0.955 0.000 0.000 0.976 0.000 0.024
#> SRR1366627 3 0.1197 0.938 0.000 0.000 0.952 0.000 0.048
#> SRR1361219 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1393510 3 0.1197 0.938 0.000 0.000 0.952 0.000 0.048
#> SRR662558 3 0.0162 0.967 0.000 0.000 0.996 0.000 0.004
#> SRR1077334 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR807438 3 0.0290 0.966 0.000 0.008 0.992 0.000 0.000
#> SRR1459078 3 0.0703 0.957 0.000 0.024 0.976 0.000 0.000
#> SRR1329704 3 0.0703 0.957 0.000 0.024 0.976 0.000 0.000
#> SRR1468072 3 0.0703 0.957 0.000 0.024 0.976 0.000 0.000
#> SRR1376196 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1442909 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1414269 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1381913 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1340157 3 0.0000 0.969 0.000 0.000 1.000 0.000 0.000
#> SRR1407583 3 0.0162 0.967 0.000 0.000 0.996 0.000 0.004
#> SRR615826 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 5 0.0260 0.970 0.000 0.000 0.000 0.000 0.992 0.008
#> SRR1458769 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR613162 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1352481 6 0.2340 0.000 0.000 0.000 0.000 0.148 0.000 0.852
#> SRR1468876 2 0.0458 0.961 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1399223 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR660030 2 0.0458 0.960 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR1333609 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1471612 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1413998 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1122940 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1402563 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1398393 2 0.0146 0.967 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR657961 2 0.0146 0.967 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1471135 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1430001 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR662775 2 0.2003 0.881 0.000 0.884 0.116 0.000 0.000 0.000
#> SRR1474182 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR607190 2 0.2558 0.835 0.000 0.840 0.156 0.004 0.000 0.000
#> SRR612467 2 0.3789 0.730 0.000 0.760 0.040 0.196 0.000 0.004
#> SRR1465959 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1446132 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1416933 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1102538 2 0.0260 0.965 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1098636 2 0.0146 0.967 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1072998 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR627443 3 0.0146 1.000 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR656131 2 0.1863 0.892 0.000 0.896 0.104 0.000 0.000 0.000
#> SRR823991 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1089158 2 0.3950 0.618 0.000 0.696 0.000 0.276 0.000 0.028
#> SRR1469036 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR824039 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1339047 2 0.1075 0.938 0.000 0.952 0.048 0.000 0.000 0.000
#> SRR1443049 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1122885 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR602895 2 0.1863 0.892 0.000 0.896 0.104 0.000 0.000 0.000
#> SRR1409837 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1388959 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR659863 2 0.0632 0.955 0.000 0.976 0.024 0.000 0.000 0.000
#> SRR1089877 2 0.0632 0.957 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR1123775 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR658909 2 0.3240 0.717 0.000 0.752 0.244 0.004 0.000 0.000
#> SRR1140510 2 0.0632 0.957 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR607562 2 0.2520 0.838 0.000 0.844 0.152 0.004 0.000 0.000
#> SRR1122913 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR598042 2 0.0146 0.967 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1467340 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1072321 2 0.0363 0.963 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1094580 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1076608 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1395462 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1489220 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR614371 2 0.3634 0.625 0.000 0.696 0.296 0.008 0.000 0.000
#> SRR615455 5 0.0000 0.970 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1070573 2 0.0260 0.965 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR598749 5 0.1957 0.890 0.000 0.000 0.000 0.000 0.888 0.112
#> SRR1365556 2 0.0146 0.967 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1350023 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1446582 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1439763 2 0.0547 0.959 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1343986 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR807463 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR660390 1 0.0260 0.989 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1367672 2 0.2454 0.833 0.000 0.840 0.160 0.000 0.000 0.000
#> SRR613294 5 0.0260 0.970 0.000 0.000 0.000 0.000 0.992 0.008
#> SRR824015 2 0.3950 0.618 0.000 0.696 0.000 0.276 0.000 0.028
#> SRR1078924 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR662221 4 0.2772 0.657 0.000 0.000 0.004 0.816 0.000 0.180
#> SRR655017 3 0.0146 1.000 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1338450 2 0.0547 0.959 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR663741 4 0.0696 0.690 0.004 0.004 0.008 0.980 0.000 0.004
#> SRR1396057 2 0.0146 0.967 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1083800 2 0.0547 0.959 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1445789 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1387355 2 0.0146 0.967 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1388855 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1445449 2 0.2454 0.833 0.000 0.840 0.160 0.000 0.000 0.000
#> SRR1380740 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR659995 1 0.0000 0.994 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1489524 2 0.0547 0.959 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1444662 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1383652 2 0.0146 0.967 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1361243 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1490337 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR823967 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR660127 2 0.0632 0.955 0.000 0.976 0.024 0.000 0.000 0.000
#> SRR1366627 2 0.1075 0.938 0.000 0.952 0.048 0.000 0.000 0.000
#> SRR1361219 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1393510 2 0.1075 0.938 0.000 0.952 0.048 0.000 0.000 0.000
#> SRR662558 2 0.0146 0.967 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1077334 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR807438 2 0.0260 0.965 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1459078 2 0.0632 0.957 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR1329704 2 0.0632 0.957 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR1468072 2 0.0632 0.957 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR1376196 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1442909 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1414269 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1381913 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1340157 2 0.0000 0.968 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1407583 2 0.0146 0.967 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR615826 5 0.0000 0.970 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)
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 17380 rows and 102 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.996 0.998 0.166 0.838 0.838
#> 3 3 0.400 0.638 0.785 1.804 0.768 0.724
#> 4 4 0.375 0.750 0.761 0.271 0.661 0.471
#> 5 5 0.588 0.773 0.853 0.157 0.977 0.930
#> 6 6 0.643 0.632 0.784 0.090 0.877 0.622
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
#> SRR612587 1 0.00 1.000 1.00 0.00
#> SRR1458769 2 0.00 0.998 0.00 1.00
#> SRR613162 1 0.00 1.000 1.00 0.00
#> SRR1352481 2 0.00 0.998 0.00 1.00
#> SRR1468876 2 0.00 0.998 0.00 1.00
#> SRR1399223 2 0.00 0.998 0.00 1.00
#> SRR660030 2 0.00 0.998 0.00 1.00
#> SRR1333609 2 0.00 0.998 0.00 1.00
#> SRR1471612 2 0.00 0.998 0.00 1.00
#> SRR1413998 2 0.00 0.998 0.00 1.00
#> SRR1122940 2 0.00 0.998 0.00 1.00
#> SRR1402563 2 0.00 0.998 0.00 1.00
#> SRR1398393 2 0.00 0.998 0.00 1.00
#> SRR657961 2 0.00 0.998 0.00 1.00
#> SRR1471135 2 0.00 0.998 0.00 1.00
#> SRR1430001 2 0.00 0.998 0.00 1.00
#> SRR662775 2 0.00 0.998 0.00 1.00
#> SRR1474182 2 0.00 0.998 0.00 1.00
#> SRR607190 2 0.00 0.998 0.00 1.00
#> SRR612467 2 0.00 0.998 0.00 1.00
#> SRR1465959 2 0.00 0.998 0.00 1.00
#> SRR1446132 2 0.00 0.998 0.00 1.00
#> SRR1416933 2 0.00 0.998 0.00 1.00
#> SRR1102538 2 0.00 0.998 0.00 1.00
#> SRR1098636 2 0.00 0.998 0.00 1.00
#> SRR1072998 2 0.00 0.998 0.00 1.00
#> SRR627443 2 0.00 0.998 0.00 1.00
#> SRR656131 2 0.00 0.998 0.00 1.00
#> SRR823991 2 0.00 0.998 0.00 1.00
#> SRR1089158 2 0.00 0.998 0.00 1.00
#> SRR1469036 2 0.00 0.998 0.00 1.00
#> SRR824039 2 0.00 0.998 0.00 1.00
#> SRR1339047 2 0.00 0.998 0.00 1.00
#> SRR1443049 2 0.00 0.998 0.00 1.00
#> SRR1122885 2 0.00 0.998 0.00 1.00
#> SRR602895 2 0.00 0.998 0.00 1.00
#> SRR1409837 2 0.00 0.998 0.00 1.00
#> SRR1388959 2 0.00 0.998 0.00 1.00
#> SRR659863 2 0.00 0.998 0.00 1.00
#> SRR1089877 2 0.00 0.998 0.00 1.00
#> SRR1123775 2 0.00 0.998 0.00 1.00
#> SRR658909 2 0.00 0.998 0.00 1.00
#> SRR1140510 2 0.00 0.998 0.00 1.00
#> SRR607562 2 0.00 0.998 0.00 1.00
#> SRR1122913 2 0.00 0.998 0.00 1.00
#> SRR598042 2 0.00 0.998 0.00 1.00
#> SRR1467340 2 0.00 0.998 0.00 1.00
#> SRR1072321 2 0.00 0.998 0.00 1.00
#> SRR1094580 2 0.00 0.998 0.00 1.00
#> SRR1076608 2 0.00 0.998 0.00 1.00
#> SRR1395462 2 0.00 0.998 0.00 1.00
#> SRR1489220 2 0.00 0.998 0.00 1.00
#> SRR614371 2 0.00 0.998 0.00 1.00
#> SRR615455 1 0.00 1.000 1.00 0.00
#> SRR1070573 2 0.00 0.998 0.00 1.00
#> SRR598749 1 0.00 1.000 1.00 0.00
#> SRR1365556 2 0.00 0.998 0.00 1.00
#> SRR1350023 2 0.00 0.998 0.00 1.00
#> SRR1446582 2 0.00 0.998 0.00 1.00
#> SRR1439763 2 0.00 0.998 0.00 1.00
#> SRR1343986 2 0.00 0.998 0.00 1.00
#> SRR807463 2 0.00 0.998 0.00 1.00
#> SRR660390 1 0.00 1.000 1.00 0.00
#> SRR1367672 2 0.00 0.998 0.00 1.00
#> SRR613294 1 0.00 1.000 1.00 0.00
#> SRR824015 2 0.00 0.998 0.00 1.00
#> SRR1078924 2 0.00 0.998 0.00 1.00
#> SRR662221 1 0.00 1.000 1.00 0.00
#> SRR655017 2 0.00 0.998 0.00 1.00
#> SRR1338450 2 0.00 0.998 0.00 1.00
#> SRR663741 2 0.68 0.780 0.18 0.82
#> SRR1396057 2 0.00 0.998 0.00 1.00
#> SRR1083800 2 0.00 0.998 0.00 1.00
#> SRR1445789 2 0.00 0.998 0.00 1.00
#> SRR1387355 2 0.00 0.998 0.00 1.00
#> SRR1388855 2 0.00 0.998 0.00 1.00
#> SRR1445449 2 0.00 0.998 0.00 1.00
#> SRR1380740 2 0.00 0.998 0.00 1.00
#> SRR659995 1 0.00 1.000 1.00 0.00
#> SRR1489524 2 0.00 0.998 0.00 1.00
#> SRR1444662 2 0.00 0.998 0.00 1.00
#> SRR1383652 2 0.00 0.998 0.00 1.00
#> SRR1361243 2 0.00 0.998 0.00 1.00
#> SRR1490337 2 0.00 0.998 0.00 1.00
#> SRR823967 2 0.00 0.998 0.00 1.00
#> SRR660127 2 0.00 0.998 0.00 1.00
#> SRR1366627 2 0.00 0.998 0.00 1.00
#> SRR1361219 2 0.00 0.998 0.00 1.00
#> SRR1393510 2 0.00 0.998 0.00 1.00
#> SRR662558 2 0.00 0.998 0.00 1.00
#> SRR1077334 2 0.00 0.998 0.00 1.00
#> SRR807438 2 0.00 0.998 0.00 1.00
#> SRR1459078 2 0.00 0.998 0.00 1.00
#> SRR1329704 2 0.00 0.998 0.00 1.00
#> SRR1468072 2 0.00 0.998 0.00 1.00
#> SRR1376196 2 0.00 0.998 0.00 1.00
#> SRR1442909 2 0.00 0.998 0.00 1.00
#> SRR1414269 2 0.00 0.998 0.00 1.00
#> SRR1381913 2 0.00 0.998 0.00 1.00
#> SRR1340157 2 0.00 0.998 0.00 1.00
#> SRR1407583 2 0.00 0.998 0.00 1.00
#> SRR615826 1 0.00 1.000 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 3 0.5529 0.9749 0.296 0.000 0.704
#> SRR1458769 2 0.5465 0.6879 0.000 0.712 0.288
#> SRR613162 3 0.6305 0.8303 0.484 0.000 0.516
#> SRR1352481 1 0.5591 0.4687 0.696 0.304 0.000
#> SRR1468876 2 0.2165 0.6777 0.064 0.936 0.000
#> SRR1399223 2 0.0424 0.7048 0.008 0.992 0.000
#> SRR660030 2 0.3941 0.5877 0.156 0.844 0.000
#> SRR1333609 2 0.5178 0.6975 0.000 0.744 0.256
#> SRR1471612 2 0.5928 0.6778 0.008 0.696 0.296
#> SRR1413998 2 0.5754 0.6809 0.004 0.700 0.296
#> SRR1122940 2 0.2796 0.6572 0.092 0.908 0.000
#> SRR1402563 2 0.5529 0.6839 0.000 0.704 0.296
#> SRR1398393 2 0.5254 0.6955 0.000 0.736 0.264
#> SRR657961 2 0.6823 0.6475 0.036 0.668 0.296
#> SRR1471135 2 0.5928 0.6778 0.008 0.696 0.296
#> SRR1430001 2 0.2918 0.7055 0.044 0.924 0.032
#> SRR662775 1 0.8622 0.6818 0.572 0.132 0.296
#> SRR1474182 2 0.5529 0.6839 0.000 0.704 0.296
#> SRR607190 1 0.8674 0.6794 0.568 0.136 0.296
#> SRR612467 1 0.6497 0.5243 0.648 0.336 0.016
#> SRR1465959 2 0.2496 0.7221 0.004 0.928 0.068
#> SRR1446132 2 0.2448 0.7226 0.000 0.924 0.076
#> SRR1416933 2 0.5529 0.6839 0.000 0.704 0.296
#> SRR1102538 2 0.2261 0.7224 0.000 0.932 0.068
#> SRR1098636 2 0.5754 0.6809 0.004 0.700 0.296
#> SRR1072998 2 0.3116 0.6434 0.108 0.892 0.000
#> SRR627443 1 0.8082 0.6696 0.608 0.096 0.296
#> SRR656131 1 0.8543 0.6856 0.580 0.128 0.292
#> SRR823991 2 0.5986 0.6990 0.024 0.736 0.240
#> SRR1089158 1 0.6309 0.2923 0.500 0.500 0.000
#> SRR1469036 2 0.3644 0.6296 0.124 0.872 0.004
#> SRR824039 2 0.1411 0.6943 0.036 0.964 0.000
#> SRR1339047 2 0.8685 0.5292 0.192 0.596 0.212
#> SRR1443049 2 0.3551 0.6187 0.132 0.868 0.000
#> SRR1122885 2 0.2261 0.7222 0.000 0.932 0.068
#> SRR602895 1 0.9431 0.5461 0.496 0.212 0.292
#> SRR1409837 2 0.5529 0.6839 0.000 0.704 0.296
#> SRR1388959 2 0.5529 0.6839 0.000 0.704 0.296
#> SRR659863 2 0.5928 0.6778 0.008 0.696 0.296
#> SRR1089877 2 0.0747 0.7007 0.016 0.984 0.000
#> SRR1123775 2 0.3192 0.6396 0.112 0.888 0.000
#> SRR658909 1 0.7481 0.6663 0.640 0.064 0.296
#> SRR1140510 2 0.2066 0.6803 0.060 0.940 0.000
#> SRR607562 1 0.8674 0.6780 0.568 0.136 0.296
#> SRR1122913 2 0.0000 0.7069 0.000 1.000 0.000
#> SRR598042 2 0.5928 0.6778 0.008 0.696 0.296
#> SRR1467340 2 0.2261 0.7222 0.000 0.932 0.068
#> SRR1072321 2 0.0237 0.7055 0.004 0.996 0.000
#> SRR1094580 2 0.5497 0.6860 0.000 0.708 0.292
#> SRR1076608 2 0.0424 0.7048 0.008 0.992 0.000
#> SRR1395462 2 0.5928 0.6778 0.008 0.696 0.296
#> SRR1489220 2 0.2537 0.7225 0.000 0.920 0.080
#> SRR614371 1 0.7308 0.6651 0.648 0.056 0.296
#> SRR615455 3 0.5529 0.9749 0.296 0.000 0.704
#> SRR1070573 2 0.0237 0.7055 0.004 0.996 0.000
#> SRR598749 3 0.5529 0.9749 0.296 0.000 0.704
#> SRR1365556 2 0.5529 0.6839 0.000 0.704 0.296
#> SRR1350023 2 0.1399 0.7004 0.028 0.968 0.004
#> SRR1446582 2 0.5465 0.6879 0.000 0.712 0.288
#> SRR1439763 2 0.3816 0.5996 0.148 0.852 0.000
#> SRR1343986 2 0.3482 0.6224 0.128 0.872 0.000
#> SRR807463 2 0.5497 0.6860 0.000 0.708 0.292
#> SRR660390 3 0.5621 0.9717 0.308 0.000 0.692
#> SRR1367672 2 0.9911 0.0932 0.304 0.400 0.296
#> SRR613294 3 0.5529 0.9749 0.296 0.000 0.704
#> SRR824015 1 0.5591 0.4687 0.696 0.304 0.000
#> SRR1078924 2 0.0424 0.7048 0.008 0.992 0.000
#> SRR662221 1 0.6309 -0.8457 0.504 0.000 0.496
#> SRR655017 1 0.7218 0.6525 0.652 0.052 0.296
#> SRR1338450 2 0.3816 0.5996 0.148 0.852 0.000
#> SRR663741 1 0.5138 0.4454 0.748 0.252 0.000
#> SRR1396057 2 0.5928 0.6778 0.008 0.696 0.296
#> SRR1083800 2 0.0237 0.7055 0.004 0.996 0.000
#> SRR1445789 2 0.0237 0.7062 0.004 0.996 0.000
#> SRR1387355 2 0.6274 -0.2567 0.456 0.544 0.000
#> SRR1388855 2 0.2261 0.7222 0.000 0.932 0.068
#> SRR1445449 1 0.9319 0.5816 0.508 0.196 0.296
#> SRR1380740 2 0.3686 0.6095 0.140 0.860 0.000
#> SRR659995 3 0.5650 0.9702 0.312 0.000 0.688
#> SRR1489524 2 0.0424 0.7045 0.008 0.992 0.000
#> SRR1444662 2 0.5529 0.6839 0.000 0.704 0.296
#> SRR1383652 2 0.5928 0.6778 0.008 0.696 0.296
#> SRR1361243 2 0.5529 0.6839 0.000 0.704 0.296
#> SRR1490337 2 0.5529 0.6839 0.000 0.704 0.296
#> SRR823967 2 0.4679 0.7185 0.020 0.832 0.148
#> SRR660127 2 0.5928 0.6778 0.008 0.696 0.296
#> SRR1366627 2 0.6051 0.2993 0.292 0.696 0.012
#> SRR1361219 2 0.5465 0.6879 0.000 0.712 0.288
#> SRR1393510 2 0.6284 0.2594 0.304 0.680 0.016
#> SRR662558 2 0.6260 -0.2385 0.448 0.552 0.000
#> SRR1077334 2 0.2261 0.6751 0.068 0.932 0.000
#> SRR807438 2 0.3425 0.6419 0.112 0.884 0.004
#> SRR1459078 2 0.3816 0.5996 0.148 0.852 0.000
#> SRR1329704 2 0.3816 0.5996 0.148 0.852 0.000
#> SRR1468072 2 0.3816 0.5996 0.148 0.852 0.000
#> SRR1376196 2 0.4346 0.7122 0.000 0.816 0.184
#> SRR1442909 2 0.5529 0.6839 0.000 0.704 0.296
#> SRR1414269 2 0.5016 0.7016 0.000 0.760 0.240
#> SRR1381913 2 0.5928 0.6778 0.008 0.696 0.296
#> SRR1340157 2 0.0000 0.7069 0.000 1.000 0.000
#> SRR1407583 2 0.5928 0.6778 0.008 0.696 0.296
#> SRR615826 3 0.5529 0.9749 0.296 0.000 0.704
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.0188 0.8828 0.000 0.004 0.000 0.996
#> SRR1458769 2 0.4222 0.9018 0.000 0.728 0.272 0.000
#> SRR613162 4 0.7226 0.6885 0.232 0.220 0.000 0.548
#> SRR1352481 3 0.7706 -0.1350 0.364 0.224 0.412 0.000
#> SRR1468876 3 0.1059 0.8094 0.012 0.016 0.972 0.000
#> SRR1399223 3 0.4319 0.6030 0.012 0.228 0.760 0.000
#> SRR660030 3 0.0804 0.8090 0.012 0.008 0.980 0.000
#> SRR1333609 2 0.4585 0.8296 0.000 0.668 0.332 0.000
#> SRR1471612 2 0.4328 0.9159 0.008 0.748 0.244 0.000
#> SRR1413998 2 0.4008 0.9181 0.000 0.756 0.244 0.000
#> SRR1122940 3 0.0937 0.8096 0.012 0.012 0.976 0.000
#> SRR1402563 2 0.4008 0.9181 0.000 0.756 0.244 0.000
#> SRR1398393 2 0.4746 0.8697 0.008 0.688 0.304 0.000
#> SRR657961 2 0.6868 0.6301 0.152 0.584 0.264 0.000
#> SRR1471135 2 0.4328 0.9159 0.008 0.748 0.244 0.000
#> SRR1430001 3 0.4453 0.5410 0.012 0.244 0.744 0.000
#> SRR662775 1 0.5574 0.8139 0.728 0.148 0.124 0.000
#> SRR1474182 2 0.4008 0.9181 0.000 0.756 0.244 0.000
#> SRR607190 1 0.5594 0.8040 0.724 0.164 0.112 0.000
#> SRR612467 1 0.5989 0.5399 0.556 0.044 0.400 0.000
#> SRR1465959 3 0.3024 0.7318 0.000 0.148 0.852 0.000
#> SRR1446132 2 0.4855 0.7164 0.000 0.600 0.400 0.000
#> SRR1416933 2 0.4008 0.9181 0.000 0.756 0.244 0.000
#> SRR1102538 2 0.5310 0.6669 0.012 0.576 0.412 0.000
#> SRR1098636 2 0.4382 0.8675 0.000 0.704 0.296 0.000
#> SRR1072998 3 0.0469 0.8073 0.012 0.000 0.988 0.000
#> SRR627443 1 0.4841 0.7850 0.780 0.140 0.080 0.000
#> SRR656131 1 0.5708 0.8119 0.716 0.124 0.160 0.000
#> SRR823991 3 0.3975 0.5747 0.000 0.240 0.760 0.000
#> SRR1089158 3 0.5436 0.5146 0.176 0.092 0.732 0.000
#> SRR1469036 3 0.0804 0.8090 0.012 0.008 0.980 0.000
#> SRR824039 3 0.1474 0.8027 0.000 0.052 0.948 0.000
#> SRR1339047 3 0.5257 0.5760 0.144 0.104 0.752 0.000
#> SRR1443049 3 0.0804 0.8090 0.012 0.008 0.980 0.000
#> SRR1122885 2 0.5070 0.6736 0.004 0.580 0.416 0.000
#> SRR602895 1 0.6414 0.7674 0.636 0.124 0.240 0.000
#> SRR1409837 2 0.4072 0.9148 0.000 0.748 0.252 0.000
#> SRR1388959 2 0.4008 0.9181 0.000 0.756 0.244 0.000
#> SRR659863 2 0.5351 0.7826 0.104 0.744 0.152 0.000
#> SRR1089877 3 0.2255 0.7915 0.012 0.068 0.920 0.000
#> SRR1123775 3 0.0804 0.8090 0.012 0.008 0.980 0.000
#> SRR658909 1 0.4948 0.8022 0.776 0.124 0.100 0.000
#> SRR1140510 3 0.1284 0.8098 0.012 0.024 0.964 0.000
#> SRR607562 1 0.5944 0.8116 0.696 0.140 0.164 0.000
#> SRR1122913 3 0.3852 0.6689 0.008 0.192 0.800 0.000
#> SRR598042 2 0.4328 0.9159 0.008 0.748 0.244 0.000
#> SRR1467340 3 0.5093 0.2322 0.012 0.348 0.640 0.000
#> SRR1072321 3 0.3047 0.7620 0.012 0.116 0.872 0.000
#> SRR1094580 2 0.4406 0.8632 0.000 0.700 0.300 0.000
#> SRR1076608 3 0.3852 0.6872 0.012 0.180 0.808 0.000
#> SRR1395462 2 0.4328 0.9159 0.008 0.748 0.244 0.000
#> SRR1489220 3 0.5112 0.0618 0.008 0.384 0.608 0.000
#> SRR614371 1 0.4591 0.7872 0.800 0.116 0.084 0.000
#> SRR615455 4 0.0000 0.8831 0.000 0.000 0.000 1.000
#> SRR1070573 3 0.3105 0.7608 0.012 0.120 0.868 0.000
#> SRR598749 4 0.0000 0.8831 0.000 0.000 0.000 1.000
#> SRR1365556 2 0.4188 0.9172 0.004 0.752 0.244 0.000
#> SRR1350023 3 0.2101 0.8013 0.012 0.060 0.928 0.000
#> SRR1446582 2 0.4222 0.9018 0.000 0.728 0.272 0.000
#> SRR1439763 3 0.0592 0.8061 0.016 0.000 0.984 0.000
#> SRR1343986 3 0.0804 0.8090 0.012 0.008 0.980 0.000
#> SRR807463 2 0.4103 0.9127 0.000 0.744 0.256 0.000
#> SRR660390 4 0.3638 0.8576 0.032 0.120 0.000 0.848
#> SRR1367672 1 0.7259 0.5522 0.492 0.156 0.352 0.000
#> SRR613294 4 0.0188 0.8828 0.000 0.004 0.000 0.996
#> SRR824015 3 0.7153 0.1282 0.248 0.196 0.556 0.000
#> SRR1078924 3 0.2918 0.7622 0.008 0.116 0.876 0.000
#> SRR662221 4 0.7430 0.6666 0.260 0.228 0.000 0.512
#> SRR655017 1 0.4552 0.7678 0.800 0.128 0.072 0.000
#> SRR1338450 3 0.0592 0.8061 0.016 0.000 0.984 0.000
#> SRR663741 1 0.5392 0.2203 0.720 0.224 0.052 0.004
#> SRR1396057 2 0.4420 0.9129 0.012 0.748 0.240 0.000
#> SRR1083800 3 0.2988 0.7652 0.012 0.112 0.876 0.000
#> SRR1445789 3 0.4546 0.5397 0.012 0.256 0.732 0.000
#> SRR1387355 3 0.2048 0.7593 0.064 0.008 0.928 0.000
#> SRR1388855 2 0.5137 0.5853 0.004 0.544 0.452 0.000
#> SRR1445449 1 0.6373 0.7905 0.652 0.148 0.200 0.000
#> SRR1380740 3 0.1059 0.8086 0.016 0.012 0.972 0.000
#> SRR659995 4 0.4139 0.8479 0.040 0.144 0.000 0.816
#> SRR1489524 3 0.2988 0.7652 0.012 0.112 0.876 0.000
#> SRR1444662 2 0.4008 0.9181 0.000 0.756 0.244 0.000
#> SRR1383652 2 0.4420 0.9129 0.012 0.748 0.240 0.000
#> SRR1361243 2 0.4008 0.9181 0.000 0.756 0.244 0.000
#> SRR1490337 2 0.4008 0.9181 0.000 0.756 0.244 0.000
#> SRR823967 3 0.2281 0.7826 0.000 0.096 0.904 0.000
#> SRR660127 2 0.5351 0.7826 0.104 0.744 0.152 0.000
#> SRR1366627 3 0.4405 0.6268 0.152 0.048 0.800 0.000
#> SRR1361219 2 0.4222 0.9018 0.000 0.728 0.272 0.000
#> SRR1393510 3 0.4713 0.5819 0.172 0.052 0.776 0.000
#> SRR662558 3 0.1637 0.7694 0.060 0.000 0.940 0.000
#> SRR1077334 3 0.0937 0.8093 0.012 0.012 0.976 0.000
#> SRR807438 3 0.1706 0.8091 0.016 0.036 0.948 0.000
#> SRR1459078 3 0.0592 0.8061 0.016 0.000 0.984 0.000
#> SRR1329704 3 0.0592 0.8061 0.016 0.000 0.984 0.000
#> SRR1468072 3 0.1059 0.8080 0.016 0.012 0.972 0.000
#> SRR1376196 2 0.4920 0.7663 0.004 0.628 0.368 0.000
#> SRR1442909 2 0.4008 0.9181 0.000 0.756 0.244 0.000
#> SRR1414269 3 0.4925 -0.1363 0.000 0.428 0.572 0.000
#> SRR1381913 2 0.4328 0.9159 0.008 0.748 0.244 0.000
#> SRR1340157 3 0.4123 0.6200 0.008 0.220 0.772 0.000
#> SRR1407583 2 0.4420 0.9129 0.012 0.748 0.240 0.000
#> SRR615826 4 0.0000 0.8831 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
#> SRR612587 4 0.000 0.84905 0.000 0.000 0.000 1.000 0.000
#> SRR1458769 2 0.189 0.90702 0.000 0.916 0.080 0.000 0.004
#> SRR613162 5 0.586 0.09237 0.060 0.032 0.000 0.300 0.608
#> SRR1352481 5 0.204 0.45386 0.036 0.008 0.028 0.000 0.928
#> SRR1468876 3 0.363 0.81266 0.004 0.020 0.800 0.000 0.176
#> SRR1399223 3 0.333 0.75952 0.008 0.120 0.844 0.000 0.028
#> SRR660030 3 0.181 0.80596 0.020 0.004 0.936 0.000 0.040
#> SRR1333609 2 0.297 0.84411 0.000 0.828 0.168 0.000 0.004
#> SRR1471612 2 0.152 0.91542 0.012 0.944 0.044 0.000 0.000
#> SRR1413998 2 0.112 0.91824 0.000 0.956 0.044 0.000 0.000
#> SRR1122940 3 0.160 0.81577 0.012 0.012 0.948 0.000 0.028
#> SRR1402563 2 0.112 0.91824 0.000 0.956 0.044 0.000 0.000
#> SRR1398393 2 0.340 0.83112 0.012 0.820 0.160 0.000 0.008
#> SRR657961 2 0.421 0.77596 0.140 0.788 0.064 0.000 0.008
#> SRR1471135 2 0.152 0.91542 0.012 0.944 0.044 0.000 0.000
#> SRR1430001 3 0.353 0.74902 0.016 0.112 0.840 0.000 0.032
#> SRR662775 1 0.200 0.80100 0.932 0.024 0.028 0.000 0.016
#> SRR1474182 2 0.112 0.91824 0.000 0.956 0.044 0.000 0.000
#> SRR607190 1 0.169 0.80052 0.944 0.028 0.020 0.000 0.008
#> SRR612467 1 0.508 0.36790 0.588 0.000 0.368 0.000 0.044
#> SRR1465959 3 0.331 0.81336 0.000 0.104 0.844 0.000 0.052
#> SRR1446132 2 0.395 0.66354 0.000 0.696 0.300 0.000 0.004
#> SRR1416933 2 0.112 0.91824 0.000 0.956 0.044 0.000 0.000
#> SRR1102538 2 0.440 0.68040 0.000 0.696 0.276 0.000 0.028
#> SRR1098636 2 0.281 0.87263 0.000 0.868 0.108 0.000 0.024
#> SRR1072998 3 0.157 0.81932 0.012 0.008 0.948 0.000 0.032
#> SRR627443 1 0.176 0.79337 0.940 0.036 0.012 0.000 0.012
#> SRR656131 1 0.233 0.79472 0.916 0.020 0.044 0.000 0.020
#> SRR823991 3 0.385 0.76367 0.004 0.172 0.792 0.000 0.032
#> SRR1089158 3 0.473 0.40687 0.016 0.000 0.532 0.000 0.452
#> SRR1469036 3 0.173 0.80727 0.020 0.004 0.940 0.000 0.036
#> SRR824039 3 0.277 0.83391 0.004 0.036 0.884 0.000 0.076
#> SRR1339047 3 0.462 0.68867 0.184 0.024 0.752 0.000 0.040
#> SRR1443049 3 0.181 0.80596 0.020 0.004 0.936 0.000 0.040
#> SRR1122885 2 0.405 0.64521 0.000 0.676 0.320 0.000 0.004
#> SRR602895 1 0.520 0.52030 0.652 0.020 0.292 0.000 0.036
#> SRR1409837 2 0.183 0.90860 0.000 0.920 0.076 0.000 0.004
#> SRR1388959 2 0.112 0.91824 0.000 0.956 0.044 0.000 0.000
#> SRR659863 2 0.221 0.87881 0.052 0.916 0.028 0.000 0.004
#> SRR1089877 3 0.369 0.81429 0.000 0.032 0.796 0.000 0.172
#> SRR1123775 3 0.176 0.80953 0.016 0.008 0.940 0.000 0.036
#> SRR658909 1 0.130 0.79833 0.960 0.020 0.012 0.000 0.008
#> SRR1140510 3 0.356 0.81526 0.004 0.020 0.808 0.000 0.168
#> SRR607562 1 0.200 0.80005 0.932 0.020 0.028 0.000 0.020
#> SRR1122913 3 0.223 0.79604 0.000 0.104 0.892 0.000 0.004
#> SRR598042 2 0.152 0.91542 0.012 0.944 0.044 0.000 0.000
#> SRR1467340 3 0.361 0.73867 0.008 0.144 0.820 0.000 0.028
#> SRR1072321 3 0.369 0.81708 0.000 0.040 0.804 0.000 0.156
#> SRR1094580 2 0.320 0.84535 0.000 0.836 0.140 0.000 0.024
#> SRR1076608 3 0.252 0.80472 0.008 0.068 0.900 0.000 0.024
#> SRR1395462 2 0.152 0.91542 0.012 0.944 0.044 0.000 0.000
#> SRR1489220 3 0.337 0.69611 0.004 0.180 0.808 0.000 0.008
#> SRR614371 1 0.130 0.79833 0.960 0.020 0.012 0.000 0.008
#> SRR615455 4 0.000 0.84905 0.000 0.000 0.000 1.000 0.000
#> SRR1070573 3 0.375 0.81937 0.000 0.048 0.804 0.000 0.148
#> SRR598749 4 0.000 0.84905 0.000 0.000 0.000 1.000 0.000
#> SRR1365556 2 0.156 0.91605 0.008 0.940 0.052 0.000 0.000
#> SRR1350023 3 0.400 0.81860 0.004 0.056 0.796 0.000 0.144
#> SRR1446582 2 0.189 0.90702 0.000 0.916 0.080 0.000 0.004
#> SRR1439763 3 0.386 0.81372 0.008 0.020 0.784 0.000 0.188
#> SRR1343986 3 0.181 0.80596 0.020 0.004 0.936 0.000 0.040
#> SRR807463 2 0.225 0.90019 0.000 0.900 0.088 0.000 0.012
#> SRR660390 4 0.518 0.49339 0.020 0.024 0.000 0.616 0.340
#> SRR1367672 1 0.540 0.52719 0.660 0.052 0.264 0.000 0.024
#> SRR613294 4 0.000 0.84905 0.000 0.000 0.000 1.000 0.000
#> SRR824015 5 0.454 -0.00203 0.016 0.000 0.364 0.000 0.620
#> SRR1078924 3 0.190 0.81763 0.004 0.040 0.932 0.000 0.024
#> SRR662221 5 0.379 0.31113 0.020 0.000 0.000 0.212 0.768
#> SRR655017 1 0.148 0.79568 0.952 0.028 0.012 0.000 0.008
#> SRR1338450 3 0.393 0.81038 0.008 0.020 0.776 0.000 0.196
#> SRR663741 5 0.342 0.40912 0.204 0.000 0.008 0.000 0.788
#> SRR1396057 2 0.152 0.91542 0.012 0.944 0.044 0.000 0.000
#> SRR1083800 3 0.369 0.81708 0.000 0.040 0.804 0.000 0.156
#> SRR1445789 3 0.343 0.74917 0.008 0.136 0.832 0.000 0.024
#> SRR1387355 3 0.257 0.80319 0.028 0.000 0.888 0.000 0.084
#> SRR1388855 2 0.407 0.67580 0.000 0.692 0.300 0.000 0.008
#> SRR1445449 1 0.333 0.74655 0.856 0.020 0.096 0.000 0.028
#> SRR1380740 3 0.359 0.81418 0.004 0.020 0.804 0.000 0.172
#> SRR659995 4 0.527 0.44540 0.020 0.024 0.000 0.588 0.368
#> SRR1489524 3 0.380 0.81753 0.000 0.044 0.796 0.000 0.160
#> SRR1444662 2 0.127 0.91678 0.000 0.948 0.052 0.000 0.000
#> SRR1383652 2 0.152 0.91542 0.012 0.944 0.044 0.000 0.000
#> SRR1361243 2 0.127 0.91678 0.000 0.948 0.052 0.000 0.000
#> SRR1490337 2 0.112 0.91824 0.000 0.956 0.044 0.000 0.000
#> SRR823967 3 0.348 0.79488 0.004 0.136 0.828 0.000 0.032
#> SRR660127 2 0.207 0.88401 0.044 0.924 0.028 0.000 0.004
#> SRR1366627 3 0.426 0.69993 0.192 0.004 0.760 0.000 0.044
#> SRR1361219 2 0.189 0.90702 0.000 0.916 0.080 0.000 0.004
#> SRR1393510 3 0.425 0.68833 0.200 0.004 0.756 0.000 0.040
#> SRR662558 3 0.228 0.79929 0.032 0.000 0.908 0.000 0.060
#> SRR1077334 3 0.287 0.82866 0.000 0.020 0.860 0.000 0.120
#> SRR807438 3 0.369 0.81973 0.004 0.028 0.804 0.000 0.164
#> SRR1459078 3 0.393 0.81038 0.008 0.020 0.776 0.000 0.196
#> SRR1329704 3 0.393 0.81038 0.008 0.020 0.776 0.000 0.196
#> SRR1468072 3 0.371 0.80924 0.004 0.020 0.792 0.000 0.184
#> SRR1376196 2 0.325 0.82800 0.000 0.808 0.184 0.000 0.008
#> SRR1442909 2 0.112 0.91824 0.000 0.956 0.044 0.000 0.000
#> SRR1414269 3 0.531 0.51941 0.004 0.336 0.604 0.000 0.056
#> SRR1381913 2 0.152 0.91542 0.012 0.944 0.044 0.000 0.000
#> SRR1340157 3 0.239 0.78806 0.000 0.116 0.880 0.000 0.004
#> SRR1407583 2 0.152 0.91542 0.012 0.944 0.044 0.000 0.000
#> SRR615826 4 0.000 0.84905 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 5 0.0436 0.9919 0.004 0.004 0.000 0.000 0.988 0.004
#> SRR1458769 2 0.1863 0.8214 0.000 0.896 0.104 0.000 0.000 0.000
#> SRR613162 4 0.6000 0.4230 0.036 0.000 0.000 0.572 0.220 0.172
#> SRR1352481 4 0.1788 0.5705 0.004 0.000 0.028 0.928 0.000 0.040
#> SRR1468876 3 0.0603 0.6911 0.000 0.000 0.980 0.004 0.000 0.016
#> SRR1399223 6 0.5238 0.6278 0.000 0.096 0.408 0.000 0.000 0.496
#> SRR660030 6 0.3756 0.6963 0.000 0.000 0.352 0.004 0.000 0.644
#> SRR1333609 2 0.3641 0.6870 0.000 0.748 0.224 0.000 0.000 0.028
#> SRR1471612 2 0.0260 0.8683 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1413998 2 0.0458 0.8675 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR1122940 6 0.3867 0.6040 0.000 0.000 0.488 0.000 0.000 0.512
#> SRR1402563 2 0.0260 0.8683 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1398393 2 0.4389 0.6459 0.000 0.728 0.084 0.008 0.000 0.180
#> SRR657961 2 0.5009 0.5955 0.160 0.704 0.020 0.008 0.000 0.108
#> SRR1471135 2 0.0260 0.8683 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1430001 6 0.5057 0.6785 0.000 0.096 0.324 0.000 0.000 0.580
#> SRR662775 1 0.3511 0.7412 0.780 0.008 0.008 0.008 0.000 0.196
#> SRR1474182 2 0.0458 0.8675 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR607190 1 0.1643 0.7497 0.924 0.008 0.000 0.000 0.000 0.068
#> SRR612467 1 0.6129 0.3961 0.452 0.000 0.196 0.012 0.000 0.340
#> SRR1465959 3 0.2442 0.6184 0.000 0.048 0.884 0.000 0.000 0.068
#> SRR1446132 2 0.4389 0.1640 0.000 0.512 0.468 0.004 0.000 0.016
#> SRR1416933 2 0.0260 0.8683 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1102538 3 0.4538 0.2834 0.000 0.340 0.612 0.000 0.000 0.048
#> SRR1098636 2 0.4252 0.5994 0.000 0.676 0.288 0.008 0.000 0.028
#> SRR1072998 3 0.3828 -0.4676 0.000 0.000 0.560 0.000 0.000 0.440
#> SRR627443 1 0.1542 0.7192 0.936 0.008 0.000 0.004 0.000 0.052
#> SRR656131 1 0.3982 0.7260 0.740 0.008 0.020 0.008 0.000 0.224
#> SRR823991 3 0.2549 0.6580 0.000 0.036 0.884 0.008 0.000 0.072
#> SRR1089158 3 0.5368 0.0101 0.000 0.000 0.488 0.400 0.000 0.112
#> SRR1469036 6 0.3756 0.7138 0.000 0.000 0.400 0.000 0.000 0.600
#> SRR824039 3 0.2009 0.6700 0.000 0.004 0.904 0.008 0.000 0.084
#> SRR1339047 6 0.5890 0.5006 0.124 0.004 0.340 0.016 0.000 0.516
#> SRR1443049 6 0.3737 0.7159 0.000 0.000 0.392 0.000 0.000 0.608
#> SRR1122885 3 0.4978 0.0403 0.000 0.432 0.500 0.000 0.000 0.068
#> SRR602895 1 0.5971 0.5505 0.516 0.004 0.156 0.012 0.000 0.312
#> SRR1409837 2 0.1007 0.8564 0.000 0.956 0.044 0.000 0.000 0.000
#> SRR1388959 2 0.0458 0.8675 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR659863 2 0.1693 0.8246 0.044 0.932 0.004 0.000 0.000 0.020
#> SRR1089877 3 0.1010 0.6803 0.000 0.000 0.960 0.004 0.000 0.036
#> SRR1123775 6 0.3782 0.7045 0.000 0.000 0.412 0.000 0.000 0.588
#> SRR658909 1 0.0508 0.7381 0.984 0.004 0.000 0.000 0.000 0.012
#> SRR1140510 3 0.1152 0.6892 0.000 0.000 0.952 0.004 0.000 0.044
#> SRR607562 1 0.2951 0.7562 0.844 0.004 0.020 0.004 0.000 0.128
#> SRR1122913 3 0.4130 0.1596 0.000 0.044 0.696 0.000 0.000 0.260
#> SRR598042 2 0.0260 0.8683 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1467340 6 0.5498 0.6157 0.000 0.132 0.380 0.000 0.000 0.488
#> SRR1072321 3 0.1003 0.6792 0.000 0.004 0.964 0.004 0.000 0.028
#> SRR1094580 3 0.4788 0.0208 0.000 0.424 0.532 0.008 0.000 0.036
#> SRR1076608 6 0.4535 0.6022 0.000 0.032 0.484 0.000 0.000 0.484
#> SRR1395462 2 0.0260 0.8683 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1489220 6 0.5552 0.5819 0.000 0.136 0.404 0.000 0.000 0.460
#> SRR614371 1 0.0291 0.7368 0.992 0.004 0.000 0.000 0.000 0.004
#> SRR615455 5 0.0000 0.9946 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1070573 3 0.1364 0.6612 0.000 0.004 0.944 0.004 0.000 0.048
#> SRR598749 5 0.0000 0.9946 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1365556 2 0.0653 0.8655 0.000 0.980 0.012 0.004 0.000 0.004
#> SRR1350023 3 0.1333 0.6836 0.000 0.000 0.944 0.008 0.000 0.048
#> SRR1446582 2 0.3791 0.6068 0.000 0.688 0.300 0.004 0.000 0.008
#> SRR1439763 3 0.1471 0.6693 0.000 0.000 0.932 0.004 0.000 0.064
#> SRR1343986 6 0.3737 0.7159 0.000 0.000 0.392 0.000 0.000 0.608
#> SRR807463 2 0.3955 0.4481 0.000 0.608 0.384 0.000 0.000 0.008
#> SRR660390 4 0.5724 0.1902 0.000 0.000 0.000 0.424 0.412 0.164
#> SRR1367672 1 0.5885 0.5772 0.596 0.024 0.224 0.008 0.000 0.148
#> SRR613294 5 0.0436 0.9919 0.004 0.004 0.000 0.000 0.988 0.004
#> SRR824015 4 0.5359 0.1929 0.004 0.000 0.376 0.520 0.000 0.100
#> SRR1078924 3 0.4089 -0.5822 0.000 0.008 0.524 0.000 0.000 0.468
#> SRR662221 4 0.0692 0.5831 0.004 0.000 0.000 0.976 0.020 0.000
#> SRR655017 1 0.0653 0.7320 0.980 0.004 0.000 0.004 0.000 0.012
#> SRR1338450 3 0.1644 0.6663 0.000 0.000 0.920 0.004 0.000 0.076
#> SRR663741 4 0.0603 0.5831 0.016 0.000 0.000 0.980 0.000 0.004
#> SRR1396057 2 0.0260 0.8683 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1083800 3 0.1080 0.6797 0.000 0.004 0.960 0.004 0.000 0.032
#> SRR1445789 6 0.5350 0.5999 0.000 0.108 0.416 0.000 0.000 0.476
#> SRR1387355 6 0.4058 0.6396 0.004 0.000 0.320 0.016 0.000 0.660
#> SRR1388855 2 0.4903 0.0675 0.000 0.472 0.468 0.000 0.000 0.060
#> SRR1445449 1 0.4343 0.7196 0.748 0.004 0.088 0.008 0.000 0.152
#> SRR1380740 3 0.1531 0.6795 0.000 0.000 0.928 0.004 0.000 0.068
#> SRR659995 4 0.5716 0.2289 0.000 0.000 0.000 0.444 0.392 0.164
#> SRR1489524 3 0.1003 0.6792 0.000 0.004 0.964 0.004 0.000 0.028
#> SRR1444662 2 0.0458 0.8675 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR1383652 2 0.0260 0.8683 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1361243 2 0.0458 0.8670 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR1490337 2 0.0260 0.8683 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR823967 3 0.2420 0.6661 0.000 0.032 0.892 0.008 0.000 0.068
#> SRR660127 2 0.1478 0.8335 0.032 0.944 0.004 0.000 0.000 0.020
#> SRR1366627 6 0.5768 0.5040 0.124 0.000 0.344 0.016 0.000 0.516
#> SRR1361219 2 0.2092 0.8075 0.000 0.876 0.124 0.000 0.000 0.000
#> SRR1393510 6 0.5682 0.5415 0.124 0.000 0.312 0.016 0.000 0.548
#> SRR662558 6 0.3988 0.6487 0.004 0.000 0.324 0.012 0.000 0.660
#> SRR1077334 3 0.1152 0.6821 0.000 0.000 0.952 0.004 0.000 0.044
#> SRR807438 3 0.1349 0.6837 0.000 0.000 0.940 0.004 0.000 0.056
#> SRR1459078 3 0.1471 0.6693 0.000 0.000 0.932 0.004 0.000 0.064
#> SRR1329704 3 0.1700 0.6664 0.000 0.000 0.916 0.004 0.000 0.080
#> SRR1468072 3 0.1753 0.6640 0.000 0.000 0.912 0.004 0.000 0.084
#> SRR1376196 2 0.3566 0.7310 0.000 0.800 0.104 0.000 0.000 0.096
#> SRR1442909 2 0.0260 0.8683 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1414269 3 0.3318 0.5685 0.000 0.124 0.824 0.008 0.000 0.044
#> SRR1381913 2 0.0260 0.8683 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1340157 3 0.4249 0.1498 0.000 0.052 0.688 0.000 0.000 0.260
#> SRR1407583 2 0.0767 0.8639 0.000 0.976 0.012 0.004 0.000 0.008
#> SRR615826 5 0.0000 0.9946 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)
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 17380 rows and 102 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.976 0.989 0.3987 0.598 0.598
#> 3 3 0.774 0.852 0.927 0.6427 0.710 0.526
#> 4 4 0.645 0.763 0.861 0.0773 0.903 0.733
#> 5 5 0.791 0.801 0.897 0.0835 0.882 0.636
#> 6 6 0.744 0.758 0.856 0.0404 0.969 0.874
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
#> SRR612587 1 0.000 0.971 1.000 0.000
#> SRR1458769 2 0.000 0.995 0.000 1.000
#> SRR613162 1 0.000 0.971 1.000 0.000
#> SRR1352481 1 0.000 0.971 1.000 0.000
#> SRR1468876 2 0.000 0.995 0.000 1.000
#> SRR1399223 2 0.000 0.995 0.000 1.000
#> SRR660030 2 0.634 0.806 0.160 0.840
#> SRR1333609 2 0.000 0.995 0.000 1.000
#> SRR1471612 2 0.000 0.995 0.000 1.000
#> SRR1413998 2 0.000 0.995 0.000 1.000
#> SRR1122940 2 0.000 0.995 0.000 1.000
#> SRR1402563 2 0.000 0.995 0.000 1.000
#> SRR1398393 2 0.000 0.995 0.000 1.000
#> SRR657961 2 0.000 0.995 0.000 1.000
#> SRR1471135 2 0.000 0.995 0.000 1.000
#> SRR1430001 2 0.000 0.995 0.000 1.000
#> SRR662775 1 0.000 0.971 1.000 0.000
#> SRR1474182 2 0.000 0.995 0.000 1.000
#> SRR607190 1 0.913 0.531 0.672 0.328
#> SRR612467 1 0.000 0.971 1.000 0.000
#> SRR1465959 2 0.000 0.995 0.000 1.000
#> SRR1446132 2 0.000 0.995 0.000 1.000
#> SRR1416933 2 0.000 0.995 0.000 1.000
#> SRR1102538 2 0.000 0.995 0.000 1.000
#> SRR1098636 2 0.000 0.995 0.000 1.000
#> SRR1072998 2 0.000 0.995 0.000 1.000
#> SRR627443 1 0.327 0.920 0.940 0.060
#> SRR656131 1 0.000 0.971 1.000 0.000
#> SRR823991 2 0.000 0.995 0.000 1.000
#> SRR1089158 1 0.000 0.971 1.000 0.000
#> SRR1469036 2 0.000 0.995 0.000 1.000
#> SRR824039 2 0.000 0.995 0.000 1.000
#> SRR1339047 2 0.469 0.887 0.100 0.900
#> SRR1443049 2 0.000 0.995 0.000 1.000
#> SRR1122885 2 0.000 0.995 0.000 1.000
#> SRR602895 1 0.000 0.971 1.000 0.000
#> SRR1409837 2 0.000 0.995 0.000 1.000
#> SRR1388959 2 0.000 0.995 0.000 1.000
#> SRR659863 2 0.000 0.995 0.000 1.000
#> SRR1089877 2 0.000 0.995 0.000 1.000
#> SRR1123775 2 0.000 0.995 0.000 1.000
#> SRR658909 1 0.000 0.971 1.000 0.000
#> SRR1140510 2 0.000 0.995 0.000 1.000
#> SRR607562 1 0.000 0.971 1.000 0.000
#> SRR1122913 2 0.000 0.995 0.000 1.000
#> SRR598042 2 0.000 0.995 0.000 1.000
#> SRR1467340 2 0.000 0.995 0.000 1.000
#> SRR1072321 2 0.000 0.995 0.000 1.000
#> SRR1094580 2 0.000 0.995 0.000 1.000
#> SRR1076608 2 0.000 0.995 0.000 1.000
#> SRR1395462 2 0.000 0.995 0.000 1.000
#> SRR1489220 2 0.000 0.995 0.000 1.000
#> SRR614371 1 0.000 0.971 1.000 0.000
#> SRR615455 1 0.000 0.971 1.000 0.000
#> SRR1070573 2 0.000 0.995 0.000 1.000
#> SRR598749 1 0.000 0.971 1.000 0.000
#> SRR1365556 2 0.000 0.995 0.000 1.000
#> SRR1350023 2 0.000 0.995 0.000 1.000
#> SRR1446582 2 0.000 0.995 0.000 1.000
#> SRR1439763 2 0.000 0.995 0.000 1.000
#> SRR1343986 2 0.000 0.995 0.000 1.000
#> SRR807463 2 0.000 0.995 0.000 1.000
#> SRR660390 1 0.000 0.971 1.000 0.000
#> SRR1367672 2 0.358 0.924 0.068 0.932
#> SRR613294 1 0.000 0.971 1.000 0.000
#> SRR824015 1 0.000 0.971 1.000 0.000
#> SRR1078924 2 0.000 0.995 0.000 1.000
#> SRR662221 1 0.000 0.971 1.000 0.000
#> SRR655017 1 0.000 0.971 1.000 0.000
#> SRR1338450 2 0.000 0.995 0.000 1.000
#> SRR663741 1 0.000 0.971 1.000 0.000
#> SRR1396057 2 0.000 0.995 0.000 1.000
#> SRR1083800 2 0.000 0.995 0.000 1.000
#> SRR1445789 2 0.000 0.995 0.000 1.000
#> SRR1387355 1 0.000 0.971 1.000 0.000
#> SRR1388855 2 0.000 0.995 0.000 1.000
#> SRR1445449 1 0.000 0.971 1.000 0.000
#> SRR1380740 2 0.000 0.995 0.000 1.000
#> SRR659995 1 0.000 0.971 1.000 0.000
#> SRR1489524 2 0.000 0.995 0.000 1.000
#> SRR1444662 2 0.000 0.995 0.000 1.000
#> SRR1383652 2 0.000 0.995 0.000 1.000
#> SRR1361243 2 0.000 0.995 0.000 1.000
#> SRR1490337 2 0.000 0.995 0.000 1.000
#> SRR823967 2 0.000 0.995 0.000 1.000
#> SRR660127 2 0.000 0.995 0.000 1.000
#> SRR1366627 1 0.876 0.591 0.704 0.296
#> SRR1361219 2 0.000 0.995 0.000 1.000
#> SRR1393510 1 0.430 0.892 0.912 0.088
#> SRR662558 1 0.000 0.971 1.000 0.000
#> SRR1077334 2 0.000 0.995 0.000 1.000
#> SRR807438 2 0.000 0.995 0.000 1.000
#> SRR1459078 2 0.000 0.995 0.000 1.000
#> SRR1329704 2 0.000 0.995 0.000 1.000
#> SRR1468072 2 0.000 0.995 0.000 1.000
#> SRR1376196 2 0.000 0.995 0.000 1.000
#> SRR1442909 2 0.000 0.995 0.000 1.000
#> SRR1414269 2 0.000 0.995 0.000 1.000
#> SRR1381913 2 0.000 0.995 0.000 1.000
#> SRR1340157 2 0.000 0.995 0.000 1.000
#> SRR1407583 2 0.000 0.995 0.000 1.000
#> SRR615826 1 0.000 0.971 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 1 0.0000 0.912 1.000 0.000 0.000
#> SRR1458769 3 0.0424 0.949 0.000 0.008 0.992
#> SRR613162 1 0.0000 0.912 1.000 0.000 0.000
#> SRR1352481 1 0.0000 0.912 1.000 0.000 0.000
#> SRR1468876 2 0.0592 0.897 0.000 0.988 0.012
#> SRR1399223 2 0.2165 0.893 0.000 0.936 0.064
#> SRR660030 2 0.2878 0.881 0.000 0.904 0.096
#> SRR1333609 3 0.4842 0.651 0.000 0.224 0.776
#> SRR1471612 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1413998 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1122940 2 0.0592 0.896 0.000 0.988 0.012
#> SRR1402563 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1398393 3 0.0237 0.950 0.000 0.004 0.996
#> SRR657961 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1471135 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1430001 2 0.3038 0.876 0.000 0.896 0.104
#> SRR662775 1 0.6299 0.145 0.524 0.000 0.476
#> SRR1474182 3 0.0237 0.950 0.000 0.004 0.996
#> SRR607190 3 0.5882 0.398 0.348 0.000 0.652
#> SRR612467 1 0.0000 0.912 1.000 0.000 0.000
#> SRR1465959 2 0.5465 0.686 0.000 0.712 0.288
#> SRR1446132 3 0.4702 0.675 0.000 0.212 0.788
#> SRR1416933 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1102538 2 0.4399 0.797 0.000 0.812 0.188
#> SRR1098636 3 0.1031 0.940 0.000 0.024 0.976
#> SRR1072998 2 0.0000 0.894 0.000 1.000 0.000
#> SRR627443 1 0.6026 0.423 0.624 0.000 0.376
#> SRR656131 1 0.0237 0.912 0.996 0.000 0.004
#> SRR823991 3 0.1289 0.936 0.000 0.032 0.968
#> SRR1089158 1 0.0424 0.908 0.992 0.008 0.000
#> SRR1469036 2 0.1860 0.896 0.000 0.948 0.052
#> SRR824039 2 0.5327 0.693 0.000 0.728 0.272
#> SRR1339047 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1443049 2 0.1031 0.898 0.000 0.976 0.024
#> SRR1122885 2 0.5529 0.686 0.000 0.704 0.296
#> SRR602895 1 0.6095 0.394 0.608 0.000 0.392
#> SRR1409837 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1388959 3 0.0237 0.950 0.000 0.004 0.996
#> SRR659863 3 0.0000 0.948 0.000 0.000 1.000
#> SRR1089877 2 0.0592 0.897 0.000 0.988 0.012
#> SRR1123775 2 0.1411 0.898 0.000 0.964 0.036
#> SRR658909 1 0.0237 0.912 0.996 0.000 0.004
#> SRR1140510 2 0.5465 0.633 0.000 0.712 0.288
#> SRR607562 1 0.0237 0.912 0.996 0.000 0.004
#> SRR1122913 2 0.1163 0.900 0.000 0.972 0.028
#> SRR598042 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1467340 2 0.3038 0.876 0.000 0.896 0.104
#> SRR1072321 2 0.0747 0.897 0.000 0.984 0.016
#> SRR1094580 3 0.1031 0.940 0.000 0.024 0.976
#> SRR1076608 2 0.1289 0.898 0.000 0.968 0.032
#> SRR1395462 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1489220 2 0.2537 0.888 0.000 0.920 0.080
#> SRR614371 1 0.0237 0.912 0.996 0.000 0.004
#> SRR615455 1 0.0000 0.912 1.000 0.000 0.000
#> SRR1070573 2 0.0892 0.897 0.000 0.980 0.020
#> SRR598749 1 0.0000 0.912 1.000 0.000 0.000
#> SRR1365556 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1350023 3 0.5178 0.639 0.000 0.256 0.744
#> SRR1446582 3 0.0592 0.947 0.000 0.012 0.988
#> SRR1439763 2 0.0000 0.894 0.000 1.000 0.000
#> SRR1343986 2 0.1411 0.898 0.000 0.964 0.036
#> SRR807463 3 0.1031 0.941 0.000 0.024 0.976
#> SRR660390 1 0.0000 0.912 1.000 0.000 0.000
#> SRR1367672 3 0.0892 0.938 0.000 0.020 0.980
#> SRR613294 1 0.0000 0.912 1.000 0.000 0.000
#> SRR824015 1 0.0000 0.912 1.000 0.000 0.000
#> SRR1078924 2 0.0892 0.898 0.000 0.980 0.020
#> SRR662221 1 0.0000 0.912 1.000 0.000 0.000
#> SRR655017 1 0.0237 0.912 0.996 0.000 0.004
#> SRR1338450 2 0.0237 0.891 0.004 0.996 0.000
#> SRR663741 1 0.0000 0.912 1.000 0.000 0.000
#> SRR1396057 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1083800 2 0.0747 0.897 0.000 0.984 0.016
#> SRR1445789 2 0.2537 0.889 0.000 0.920 0.080
#> SRR1387355 1 0.3192 0.823 0.888 0.112 0.000
#> SRR1388855 2 0.5058 0.757 0.000 0.756 0.244
#> SRR1445449 3 0.6126 0.254 0.400 0.000 0.600
#> SRR1380740 2 0.0000 0.894 0.000 1.000 0.000
#> SRR659995 1 0.0000 0.912 1.000 0.000 0.000
#> SRR1489524 2 0.1031 0.897 0.000 0.976 0.024
#> SRR1444662 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1383652 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1361243 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1490337 3 0.0237 0.950 0.000 0.004 0.996
#> SRR823967 3 0.1643 0.928 0.000 0.044 0.956
#> SRR660127 3 0.0000 0.948 0.000 0.000 1.000
#> SRR1366627 1 0.7974 0.472 0.604 0.084 0.312
#> SRR1361219 3 0.0592 0.947 0.000 0.012 0.988
#> SRR1393510 1 0.6810 0.675 0.720 0.068 0.212
#> SRR662558 1 0.1643 0.883 0.956 0.044 0.000
#> SRR1077334 2 0.0000 0.894 0.000 1.000 0.000
#> SRR807438 2 0.5968 0.500 0.000 0.636 0.364
#> SRR1459078 2 0.0000 0.894 0.000 1.000 0.000
#> SRR1329704 2 0.0000 0.894 0.000 1.000 0.000
#> SRR1468072 2 0.5733 0.555 0.000 0.676 0.324
#> SRR1376196 2 0.5859 0.610 0.000 0.656 0.344
#> SRR1442909 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1414269 3 0.1529 0.928 0.000 0.040 0.960
#> SRR1381913 3 0.0237 0.950 0.000 0.004 0.996
#> SRR1340157 2 0.1411 0.900 0.000 0.964 0.036
#> SRR1407583 3 0.0237 0.950 0.000 0.004 0.996
#> SRR615826 1 0.0000 0.912 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.0000 0.9358 0.000 0.000 0.000 1.000
#> SRR1458769 2 0.0469 0.8904 0.000 0.988 0.012 0.000
#> SRR613162 4 0.0000 0.9358 0.000 0.000 0.000 1.000
#> SRR1352481 4 0.0000 0.9358 0.000 0.000 0.000 1.000
#> SRR1468876 3 0.1118 0.7481 0.000 0.036 0.964 0.000
#> SRR1399223 3 0.6279 0.7331 0.156 0.180 0.664 0.000
#> SRR660030 3 0.6719 0.7096 0.180 0.204 0.616 0.000
#> SRR1333609 2 0.3647 0.7112 0.016 0.832 0.152 0.000
#> SRR1471612 2 0.0000 0.8933 0.000 1.000 0.000 0.000
#> SRR1413998 2 0.0000 0.8933 0.000 1.000 0.000 0.000
#> SRR1122940 3 0.4436 0.7517 0.148 0.052 0.800 0.000
#> SRR1402563 2 0.0188 0.8921 0.004 0.996 0.000 0.000
#> SRR1398393 2 0.0376 0.8907 0.004 0.992 0.004 0.000
#> SRR657961 2 0.4746 0.3542 0.368 0.632 0.000 0.000
#> SRR1471135 2 0.0000 0.8933 0.000 1.000 0.000 0.000
#> SRR1430001 3 0.6745 0.7056 0.176 0.212 0.612 0.000
#> SRR662775 1 0.3399 0.8544 0.868 0.092 0.000 0.040
#> SRR1474182 2 0.0000 0.8933 0.000 1.000 0.000 0.000
#> SRR607190 1 0.3612 0.8526 0.856 0.100 0.000 0.044
#> SRR612467 4 0.0000 0.9358 0.000 0.000 0.000 1.000
#> SRR1465959 3 0.5607 0.1929 0.020 0.484 0.496 0.000
#> SRR1446132 2 0.1545 0.8723 0.008 0.952 0.040 0.000
#> SRR1416933 2 0.0000 0.8933 0.000 1.000 0.000 0.000
#> SRR1102538 2 0.5366 0.1091 0.012 0.548 0.440 0.000
#> SRR1098636 2 0.1389 0.8729 0.000 0.952 0.048 0.000
#> SRR1072998 3 0.2081 0.7457 0.084 0.000 0.916 0.000
#> SRR627443 1 0.3907 0.8700 0.836 0.044 0.000 0.120
#> SRR656131 1 0.2921 0.8583 0.860 0.000 0.000 0.140
#> SRR823991 2 0.2011 0.8536 0.000 0.920 0.080 0.000
#> SRR1089158 4 0.0592 0.9209 0.000 0.000 0.016 0.984
#> SRR1469036 3 0.6179 0.6453 0.320 0.072 0.608 0.000
#> SRR824039 3 0.5550 0.2917 0.020 0.428 0.552 0.000
#> SRR1339047 2 0.4456 0.5696 0.280 0.716 0.004 0.000
#> SRR1443049 3 0.5325 0.7517 0.160 0.096 0.744 0.000
#> SRR1122885 2 0.5746 0.0333 0.032 0.572 0.396 0.000
#> SRR602895 1 0.3474 0.8654 0.868 0.068 0.000 0.064
#> SRR1409837 2 0.0592 0.8894 0.000 0.984 0.016 0.000
#> SRR1388959 2 0.0000 0.8933 0.000 1.000 0.000 0.000
#> SRR659863 2 0.3219 0.7552 0.164 0.836 0.000 0.000
#> SRR1089877 3 0.1022 0.7482 0.000 0.032 0.968 0.000
#> SRR1123775 3 0.5742 0.7460 0.168 0.120 0.712 0.000
#> SRR658909 1 0.3356 0.8548 0.824 0.000 0.000 0.176
#> SRR1140510 3 0.4961 0.2072 0.000 0.448 0.552 0.000
#> SRR607562 1 0.3400 0.8526 0.820 0.000 0.000 0.180
#> SRR1122913 3 0.5222 0.7680 0.112 0.132 0.756 0.000
#> SRR598042 2 0.0000 0.8933 0.000 1.000 0.000 0.000
#> SRR1467340 3 0.6703 0.7023 0.156 0.232 0.612 0.000
#> SRR1072321 3 0.1792 0.7420 0.000 0.068 0.932 0.000
#> SRR1094580 2 0.2011 0.8506 0.000 0.920 0.080 0.000
#> SRR1076608 3 0.5578 0.7529 0.144 0.128 0.728 0.000
#> SRR1395462 2 0.0000 0.8933 0.000 1.000 0.000 0.000
#> SRR1489220 3 0.6621 0.7073 0.140 0.244 0.616 0.000
#> SRR614371 1 0.3400 0.8526 0.820 0.000 0.000 0.180
#> SRR615455 4 0.0000 0.9358 0.000 0.000 0.000 1.000
#> SRR1070573 3 0.1890 0.7531 0.008 0.056 0.936 0.000
#> SRR598749 4 0.0000 0.9358 0.000 0.000 0.000 1.000
#> SRR1365556 2 0.0188 0.8921 0.004 0.996 0.000 0.000
#> SRR1350023 2 0.4500 0.5453 0.000 0.684 0.316 0.000
#> SRR1446582 2 0.1211 0.8794 0.000 0.960 0.040 0.000
#> SRR1439763 3 0.0000 0.7432 0.000 0.000 1.000 0.000
#> SRR1343986 3 0.5874 0.7412 0.176 0.124 0.700 0.000
#> SRR807463 2 0.1474 0.8717 0.000 0.948 0.052 0.000
#> SRR660390 4 0.0000 0.9358 0.000 0.000 0.000 1.000
#> SRR1367672 1 0.4456 0.6430 0.716 0.280 0.004 0.000
#> SRR613294 4 0.0000 0.9358 0.000 0.000 0.000 1.000
#> SRR824015 4 0.0000 0.9358 0.000 0.000 0.000 1.000
#> SRR1078924 3 0.4920 0.7590 0.136 0.088 0.776 0.000
#> SRR662221 4 0.0000 0.9358 0.000 0.000 0.000 1.000
#> SRR655017 1 0.3356 0.8548 0.824 0.000 0.000 0.176
#> SRR1338450 3 0.0000 0.7432 0.000 0.000 1.000 0.000
#> SRR663741 4 0.0000 0.9358 0.000 0.000 0.000 1.000
#> SRR1396057 2 0.0000 0.8933 0.000 1.000 0.000 0.000
#> SRR1083800 3 0.1557 0.7449 0.000 0.056 0.944 0.000
#> SRR1445789 3 0.6538 0.7111 0.140 0.232 0.628 0.000
#> SRR1387355 4 0.4718 0.7435 0.116 0.000 0.092 0.792
#> SRR1388855 3 0.5508 0.2886 0.016 0.476 0.508 0.000
#> SRR1445449 1 0.4215 0.8527 0.824 0.104 0.000 0.072
#> SRR1380740 3 0.1229 0.7457 0.008 0.020 0.968 0.004
#> SRR659995 4 0.0000 0.9358 0.000 0.000 0.000 1.000
#> SRR1489524 3 0.2011 0.7425 0.000 0.080 0.920 0.000
#> SRR1444662 2 0.0000 0.8933 0.000 1.000 0.000 0.000
#> SRR1383652 2 0.0000 0.8933 0.000 1.000 0.000 0.000
#> SRR1361243 2 0.0188 0.8922 0.004 0.996 0.000 0.000
#> SRR1490337 2 0.0000 0.8933 0.000 1.000 0.000 0.000
#> SRR823967 2 0.2469 0.8326 0.000 0.892 0.108 0.000
#> SRR660127 2 0.3024 0.7737 0.148 0.852 0.000 0.000
#> SRR1366627 4 0.8566 0.3644 0.204 0.144 0.116 0.536
#> SRR1361219 2 0.1302 0.8772 0.000 0.956 0.044 0.000
#> SRR1393510 1 0.5495 0.7099 0.776 0.040 0.076 0.108
#> SRR662558 4 0.4746 0.7294 0.168 0.000 0.056 0.776
#> SRR1077334 3 0.0469 0.7450 0.012 0.000 0.988 0.000
#> SRR807438 3 0.6805 0.4585 0.148 0.260 0.592 0.000
#> SRR1459078 3 0.0000 0.7432 0.000 0.000 1.000 0.000
#> SRR1329704 3 0.0188 0.7438 0.004 0.000 0.996 0.000
#> SRR1468072 3 0.4790 0.3074 0.000 0.380 0.620 0.000
#> SRR1376196 2 0.5951 0.2619 0.064 0.636 0.300 0.000
#> SRR1442909 2 0.0000 0.8933 0.000 1.000 0.000 0.000
#> SRR1414269 2 0.3024 0.7920 0.000 0.852 0.148 0.000
#> SRR1381913 2 0.0000 0.8933 0.000 1.000 0.000 0.000
#> SRR1340157 3 0.5266 0.7681 0.108 0.140 0.752 0.000
#> SRR1407583 2 0.0000 0.8933 0.000 1.000 0.000 0.000
#> SRR615826 4 0.0000 0.9358 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
#> SRR612587 4 0.0162 0.962 0.004 0.000 0.000 0.996 0.000
#> SRR1458769 2 0.0451 0.897 0.000 0.988 0.008 0.000 0.004
#> SRR613162 4 0.0162 0.962 0.004 0.000 0.000 0.996 0.000
#> SRR1352481 4 0.0162 0.957 0.004 0.000 0.000 0.996 0.000
#> SRR1468876 3 0.0794 0.883 0.000 0.000 0.972 0.000 0.028
#> SRR1399223 5 0.1830 0.824 0.000 0.028 0.040 0.000 0.932
#> SRR660030 5 0.1059 0.816 0.008 0.004 0.020 0.000 0.968
#> SRR1333609 2 0.1571 0.869 0.000 0.936 0.004 0.000 0.060
#> SRR1471612 2 0.0162 0.898 0.000 0.996 0.000 0.000 0.004
#> SRR1413998 2 0.0162 0.898 0.000 0.996 0.000 0.000 0.004
#> SRR1122940 5 0.2011 0.814 0.000 0.004 0.088 0.000 0.908
#> SRR1402563 2 0.0162 0.898 0.000 0.996 0.000 0.000 0.004
#> SRR1398393 2 0.1282 0.886 0.000 0.952 0.004 0.000 0.044
#> SRR657961 2 0.4276 0.391 0.380 0.616 0.004 0.000 0.000
#> SRR1471135 2 0.0162 0.898 0.000 0.996 0.000 0.000 0.004
#> SRR1430001 5 0.0727 0.816 0.004 0.004 0.012 0.000 0.980
#> SRR662775 1 0.1012 0.911 0.968 0.012 0.000 0.000 0.020
#> SRR1474182 2 0.0000 0.897 0.000 1.000 0.000 0.000 0.000
#> SRR607190 1 0.1377 0.910 0.956 0.020 0.000 0.004 0.020
#> SRR612467 4 0.0162 0.962 0.004 0.000 0.000 0.996 0.000
#> SRR1465959 2 0.5527 0.105 0.004 0.512 0.428 0.000 0.056
#> SRR1446132 2 0.1668 0.881 0.000 0.940 0.028 0.000 0.032
#> SRR1416933 2 0.0000 0.897 0.000 1.000 0.000 0.000 0.000
#> SRR1102538 3 0.5351 0.334 0.000 0.380 0.560 0.000 0.060
#> SRR1098636 2 0.0912 0.893 0.012 0.972 0.016 0.000 0.000
#> SRR1072998 5 0.4074 0.502 0.000 0.000 0.364 0.000 0.636
#> SRR627443 1 0.1202 0.916 0.960 0.004 0.000 0.032 0.004
#> SRR656131 1 0.1399 0.913 0.952 0.000 0.000 0.020 0.028
#> SRR823991 2 0.2270 0.859 0.016 0.908 0.072 0.000 0.004
#> SRR1089158 4 0.0162 0.957 0.004 0.000 0.000 0.996 0.000
#> SRR1469036 5 0.2074 0.797 0.060 0.004 0.016 0.000 0.920
#> SRR824039 2 0.6756 -0.135 0.012 0.408 0.408 0.000 0.172
#> SRR1339047 2 0.6448 0.363 0.292 0.564 0.032 0.000 0.112
#> SRR1443049 5 0.0865 0.819 0.000 0.004 0.024 0.000 0.972
#> SRR1122885 2 0.4355 0.700 0.000 0.760 0.076 0.000 0.164
#> SRR602895 1 0.1267 0.910 0.960 0.012 0.004 0.000 0.024
#> SRR1409837 2 0.0451 0.896 0.004 0.988 0.008 0.000 0.000
#> SRR1388959 2 0.0290 0.898 0.000 0.992 0.000 0.000 0.008
#> SRR659863 2 0.3495 0.759 0.160 0.812 0.000 0.000 0.028
#> SRR1089877 3 0.0703 0.883 0.000 0.000 0.976 0.000 0.024
#> SRR1123775 5 0.0955 0.820 0.000 0.004 0.028 0.000 0.968
#> SRR658909 1 0.1043 0.914 0.960 0.000 0.000 0.040 0.000
#> SRR1140510 3 0.4059 0.707 0.000 0.172 0.776 0.000 0.052
#> SRR607562 1 0.0794 0.916 0.972 0.000 0.000 0.028 0.000
#> SRR1122913 5 0.5810 0.487 0.000 0.124 0.296 0.000 0.580
#> SRR598042 2 0.0324 0.898 0.004 0.992 0.000 0.000 0.004
#> SRR1467340 5 0.1992 0.820 0.000 0.044 0.032 0.000 0.924
#> SRR1072321 3 0.1168 0.883 0.000 0.008 0.960 0.000 0.032
#> SRR1094580 2 0.1469 0.885 0.016 0.948 0.036 0.000 0.000
#> SRR1076608 5 0.2233 0.818 0.000 0.016 0.080 0.000 0.904
#> SRR1395462 2 0.0162 0.898 0.000 0.996 0.000 0.000 0.004
#> SRR1489220 5 0.4210 0.733 0.004 0.140 0.072 0.000 0.784
#> SRR614371 1 0.1270 0.908 0.948 0.000 0.000 0.052 0.000
#> SRR615455 4 0.0162 0.962 0.004 0.000 0.000 0.996 0.000
#> SRR1070573 3 0.2344 0.860 0.000 0.032 0.904 0.000 0.064
#> SRR598749 4 0.0162 0.962 0.004 0.000 0.000 0.996 0.000
#> SRR1365556 2 0.0404 0.897 0.000 0.988 0.000 0.000 0.012
#> SRR1350023 3 0.4231 0.652 0.012 0.232 0.740 0.000 0.016
#> SRR1446582 2 0.0992 0.892 0.008 0.968 0.024 0.000 0.000
#> SRR1439763 3 0.1365 0.880 0.004 0.000 0.952 0.004 0.040
#> SRR1343986 5 0.0566 0.816 0.000 0.004 0.012 0.000 0.984
#> SRR807463 2 0.1281 0.887 0.012 0.956 0.032 0.000 0.000
#> SRR660390 4 0.0162 0.962 0.004 0.000 0.000 0.996 0.000
#> SRR1367672 1 0.2984 0.779 0.856 0.124 0.016 0.000 0.004
#> SRR613294 4 0.0162 0.962 0.004 0.000 0.000 0.996 0.000
#> SRR824015 4 0.0162 0.957 0.004 0.000 0.000 0.996 0.000
#> SRR1078924 5 0.2966 0.787 0.000 0.016 0.136 0.000 0.848
#> SRR662221 4 0.0162 0.962 0.004 0.000 0.000 0.996 0.000
#> SRR655017 1 0.1197 0.911 0.952 0.000 0.000 0.048 0.000
#> SRR1338450 3 0.0865 0.881 0.000 0.000 0.972 0.004 0.024
#> SRR663741 4 0.0162 0.962 0.004 0.000 0.000 0.996 0.000
#> SRR1396057 2 0.0162 0.898 0.000 0.996 0.000 0.000 0.004
#> SRR1083800 3 0.0865 0.884 0.000 0.004 0.972 0.000 0.024
#> SRR1445789 5 0.3558 0.771 0.000 0.108 0.064 0.000 0.828
#> SRR1387355 4 0.5990 0.130 0.020 0.000 0.064 0.508 0.408
#> SRR1388855 2 0.5240 0.535 0.000 0.660 0.244 0.000 0.096
#> SRR1445449 1 0.0902 0.913 0.976 0.008 0.004 0.008 0.004
#> SRR1380740 3 0.1652 0.882 0.008 0.004 0.944 0.004 0.040
#> SRR659995 4 0.0162 0.962 0.004 0.000 0.000 0.996 0.000
#> SRR1489524 3 0.1300 0.884 0.000 0.016 0.956 0.000 0.028
#> SRR1444662 2 0.0324 0.897 0.000 0.992 0.004 0.000 0.004
#> SRR1383652 2 0.0162 0.898 0.000 0.996 0.000 0.000 0.004
#> SRR1361243 2 0.0404 0.897 0.000 0.988 0.000 0.000 0.012
#> SRR1490337 2 0.0162 0.898 0.000 0.996 0.000 0.000 0.004
#> SRR823967 2 0.2554 0.853 0.008 0.896 0.076 0.000 0.020
#> SRR660127 2 0.3327 0.776 0.144 0.828 0.000 0.000 0.028
#> SRR1366627 5 0.8629 0.049 0.156 0.068 0.068 0.328 0.380
#> SRR1361219 2 0.0703 0.894 0.000 0.976 0.024 0.000 0.000
#> SRR1393510 1 0.6602 0.179 0.464 0.020 0.036 0.048 0.432
#> SRR662558 5 0.4733 0.517 0.032 0.000 0.012 0.256 0.700
#> SRR1077334 3 0.2536 0.807 0.000 0.000 0.868 0.004 0.128
#> SRR807438 3 0.3890 0.801 0.080 0.072 0.828 0.000 0.020
#> SRR1459078 3 0.0932 0.881 0.004 0.000 0.972 0.004 0.020
#> SRR1329704 3 0.1026 0.880 0.004 0.000 0.968 0.004 0.024
#> SRR1468072 3 0.1186 0.870 0.008 0.020 0.964 0.000 0.008
#> SRR1376196 2 0.4106 0.625 0.000 0.724 0.020 0.000 0.256
#> SRR1442909 2 0.0162 0.898 0.000 0.996 0.000 0.000 0.004
#> SRR1414269 2 0.2953 0.798 0.012 0.844 0.144 0.000 0.000
#> SRR1381913 2 0.0162 0.898 0.000 0.996 0.000 0.000 0.004
#> SRR1340157 5 0.6005 0.484 0.000 0.156 0.276 0.000 0.568
#> SRR1407583 2 0.0404 0.897 0.000 0.988 0.000 0.000 0.012
#> SRR615826 4 0.0162 0.962 0.004 0.000 0.000 0.996 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 4 0.0000 0.9968 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1458769 2 0.2452 0.8308 0.000 0.892 0.044 0.000 0.056 0.008
#> SRR613162 4 0.0000 0.9968 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1352481 4 0.0260 0.9911 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1468876 3 0.1218 0.7656 0.004 0.000 0.956 0.000 0.028 0.012
#> SRR1399223 6 0.1346 0.8103 0.000 0.024 0.008 0.000 0.016 0.952
#> SRR660030 6 0.3144 0.6779 0.000 0.016 0.004 0.000 0.172 0.808
#> SRR1333609 2 0.3160 0.7878 0.000 0.840 0.008 0.000 0.048 0.104
#> SRR1471612 2 0.0146 0.8445 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1413998 2 0.1003 0.8471 0.000 0.964 0.004 0.000 0.028 0.004
#> SRR1122940 6 0.1168 0.8072 0.000 0.000 0.028 0.000 0.016 0.956
#> SRR1402563 2 0.0508 0.8453 0.000 0.984 0.000 0.000 0.004 0.012
#> SRR1398393 2 0.3278 0.7486 0.000 0.808 0.000 0.000 0.152 0.040
#> SRR657961 2 0.4254 0.5262 0.272 0.680 0.000 0.000 0.048 0.000
#> SRR1471135 2 0.0146 0.8445 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1430001 6 0.1367 0.7955 0.000 0.012 0.000 0.000 0.044 0.944
#> SRR662775 1 0.1152 0.8959 0.952 0.000 0.000 0.000 0.044 0.004
#> SRR1474182 2 0.0508 0.8466 0.000 0.984 0.004 0.000 0.012 0.000
#> SRR607190 1 0.0405 0.9072 0.988 0.000 0.000 0.004 0.008 0.000
#> SRR612467 4 0.0146 0.9935 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1465959 2 0.7174 -0.0854 0.000 0.376 0.344 0.000 0.140 0.140
#> SRR1446132 2 0.4090 0.7760 0.000 0.784 0.044 0.000 0.124 0.048
#> SRR1416933 2 0.0146 0.8445 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1102538 3 0.6596 0.1203 0.000 0.380 0.424 0.000 0.120 0.076
#> SRR1098636 2 0.2712 0.8184 0.000 0.864 0.048 0.000 0.088 0.000
#> SRR1072998 6 0.4728 0.5285 0.004 0.000 0.256 0.000 0.080 0.660
#> SRR627443 1 0.0291 0.9077 0.992 0.000 0.000 0.004 0.004 0.000
#> SRR656131 1 0.2520 0.8160 0.844 0.000 0.000 0.004 0.152 0.000
#> SRR823991 2 0.4324 0.7092 0.000 0.724 0.080 0.000 0.192 0.004
#> SRR1089158 4 0.0458 0.9835 0.000 0.000 0.000 0.984 0.016 0.000
#> SRR1469036 6 0.1977 0.7923 0.032 0.000 0.008 0.000 0.040 0.920
#> SRR824039 3 0.7468 0.1633 0.004 0.300 0.344 0.000 0.240 0.112
#> SRR1339047 5 0.5315 0.4519 0.048 0.280 0.000 0.000 0.620 0.052
#> SRR1443049 6 0.1501 0.7740 0.000 0.000 0.000 0.000 0.076 0.924
#> SRR1122885 2 0.5962 0.5006 0.000 0.588 0.080 0.000 0.084 0.248
#> SRR602895 1 0.2838 0.7776 0.808 0.000 0.000 0.000 0.188 0.004
#> SRR1409837 2 0.1909 0.8365 0.000 0.920 0.024 0.000 0.052 0.004
#> SRR1388959 2 0.0508 0.8455 0.000 0.984 0.000 0.000 0.004 0.012
#> SRR659863 2 0.3121 0.7434 0.148 0.824 0.000 0.000 0.020 0.008
#> SRR1089877 3 0.0806 0.7617 0.000 0.000 0.972 0.000 0.020 0.008
#> SRR1123775 6 0.0935 0.7988 0.000 0.000 0.004 0.000 0.032 0.964
#> SRR658909 1 0.0260 0.9082 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1140510 3 0.6099 0.4893 0.000 0.148 0.588 0.000 0.200 0.064
#> SRR607562 1 0.0820 0.9062 0.972 0.000 0.000 0.012 0.016 0.000
#> SRR1122913 6 0.5307 0.5560 0.000 0.080 0.212 0.000 0.048 0.660
#> SRR598042 2 0.0260 0.8451 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1467340 6 0.1194 0.8077 0.000 0.032 0.004 0.000 0.008 0.956
#> SRR1072321 3 0.2630 0.7534 0.000 0.004 0.872 0.000 0.092 0.032
#> SRR1094580 2 0.3375 0.7838 0.000 0.816 0.096 0.000 0.088 0.000
#> SRR1076608 6 0.1148 0.8105 0.000 0.016 0.020 0.000 0.004 0.960
#> SRR1395462 2 0.0146 0.8445 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1489220 6 0.3831 0.6889 0.008 0.136 0.020 0.000 0.036 0.800
#> SRR614371 1 0.0458 0.9055 0.984 0.000 0.000 0.016 0.000 0.000
#> SRR615455 4 0.0000 0.9968 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1070573 3 0.4196 0.7065 0.000 0.036 0.780 0.000 0.084 0.100
#> SRR598749 4 0.0000 0.9968 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1365556 2 0.1829 0.8264 0.000 0.920 0.000 0.000 0.056 0.024
#> SRR1350023 3 0.5486 0.5536 0.004 0.148 0.632 0.000 0.200 0.016
#> SRR1446582 2 0.3784 0.7626 0.000 0.776 0.080 0.000 0.144 0.000
#> SRR1439763 3 0.2448 0.7473 0.000 0.000 0.884 0.000 0.064 0.052
#> SRR1343986 6 0.1327 0.7823 0.000 0.000 0.000 0.000 0.064 0.936
#> SRR807463 2 0.3301 0.8023 0.000 0.828 0.068 0.000 0.100 0.004
#> SRR660390 4 0.0000 0.9968 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1367672 1 0.4403 0.6440 0.740 0.100 0.012 0.000 0.148 0.000
#> SRR613294 4 0.0000 0.9968 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR824015 4 0.0260 0.9911 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1078924 6 0.1838 0.8017 0.000 0.012 0.040 0.000 0.020 0.928
#> SRR662221 4 0.0000 0.9968 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR655017 1 0.0260 0.9082 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1338450 3 0.2798 0.7373 0.000 0.000 0.852 0.000 0.112 0.036
#> SRR663741 4 0.0000 0.9968 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1396057 2 0.0547 0.8438 0.000 0.980 0.000 0.000 0.020 0.000
#> SRR1083800 3 0.0820 0.7649 0.000 0.000 0.972 0.000 0.016 0.012
#> SRR1445789 6 0.3642 0.6743 0.000 0.144 0.016 0.000 0.040 0.800
#> SRR1387355 5 0.6179 0.5740 0.000 0.000 0.052 0.244 0.556 0.148
#> SRR1388855 2 0.6309 0.4709 0.000 0.568 0.204 0.000 0.080 0.148
#> SRR1445449 1 0.2377 0.8323 0.868 0.004 0.000 0.004 0.124 0.000
#> SRR1380740 3 0.3189 0.7242 0.000 0.000 0.796 0.000 0.184 0.020
#> SRR659995 4 0.0000 0.9968 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1489524 3 0.2103 0.7624 0.000 0.012 0.912 0.000 0.056 0.020
#> SRR1444662 2 0.1269 0.8436 0.000 0.956 0.012 0.000 0.020 0.012
#> SRR1383652 2 0.0458 0.8436 0.000 0.984 0.000 0.000 0.016 0.000
#> SRR1361243 2 0.1261 0.8461 0.000 0.952 0.000 0.000 0.024 0.024
#> SRR1490337 2 0.0291 0.8451 0.000 0.992 0.000 0.000 0.004 0.004
#> SRR823967 2 0.6236 0.4419 0.004 0.532 0.164 0.000 0.268 0.032
#> SRR660127 2 0.2959 0.7639 0.124 0.844 0.000 0.000 0.024 0.008
#> SRR1366627 5 0.5804 0.6519 0.040 0.044 0.016 0.084 0.700 0.116
#> SRR1361219 2 0.2865 0.8209 0.000 0.868 0.064 0.000 0.056 0.012
#> SRR1393510 5 0.5807 0.5685 0.160 0.016 0.000 0.028 0.636 0.160
#> SRR662558 5 0.6096 0.3341 0.020 0.000 0.008 0.116 0.448 0.408
#> SRR1077334 3 0.4612 0.6349 0.004 0.000 0.704 0.000 0.120 0.172
#> SRR807438 3 0.4535 0.6811 0.084 0.048 0.776 0.000 0.076 0.016
#> SRR1459078 3 0.2301 0.7464 0.000 0.000 0.884 0.000 0.096 0.020
#> SRR1329704 3 0.2431 0.7423 0.000 0.000 0.860 0.000 0.132 0.008
#> SRR1468072 3 0.2260 0.7413 0.000 0.000 0.860 0.000 0.140 0.000
#> SRR1376196 2 0.4629 0.4094 0.000 0.596 0.012 0.000 0.028 0.364
#> SRR1442909 2 0.0291 0.8451 0.000 0.992 0.000 0.000 0.004 0.004
#> SRR1414269 2 0.5113 0.6144 0.004 0.644 0.188 0.000 0.164 0.000
#> SRR1381913 2 0.0146 0.8445 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1340157 6 0.5991 0.4558 0.000 0.172 0.172 0.000 0.056 0.600
#> SRR1407583 2 0.0937 0.8400 0.000 0.960 0.000 0.000 0.040 0.000
#> SRR615826 4 0.0000 0.9968 0.000 0.000 0.000 1.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "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 17380 rows and 102 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 1.000 0.981 0.993 0.1513 0.854 0.854
#> 3 3 0.352 0.808 0.868 2.5118 0.599 0.531
#> 4 4 0.477 0.580 0.805 0.2083 0.871 0.723
#> 5 5 0.588 0.701 0.811 0.0774 0.969 0.912
#> 6 6 0.595 0.692 0.803 0.0226 0.996 0.988
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
#> SRR612587 1 0.000 0.9705 1.000 0.000
#> SRR1458769 2 0.000 0.9947 0.000 1.000
#> SRR613162 1 0.730 0.7408 0.796 0.204
#> SRR1352481 2 0.000 0.9947 0.000 1.000
#> SRR1468876 2 0.000 0.9947 0.000 1.000
#> SRR1399223 2 0.000 0.9947 0.000 1.000
#> SRR660030 2 0.000 0.9947 0.000 1.000
#> SRR1333609 2 0.000 0.9947 0.000 1.000
#> SRR1471612 2 0.000 0.9947 0.000 1.000
#> SRR1413998 2 0.000 0.9947 0.000 1.000
#> SRR1122940 2 0.000 0.9947 0.000 1.000
#> SRR1402563 2 0.000 0.9947 0.000 1.000
#> SRR1398393 2 0.000 0.9947 0.000 1.000
#> SRR657961 2 0.000 0.9947 0.000 1.000
#> SRR1471135 2 0.000 0.9947 0.000 1.000
#> SRR1430001 2 0.000 0.9947 0.000 1.000
#> SRR662775 2 0.000 0.9947 0.000 1.000
#> SRR1474182 2 0.000 0.9947 0.000 1.000
#> SRR607190 2 0.000 0.9947 0.000 1.000
#> SRR612467 2 0.000 0.9947 0.000 1.000
#> SRR1465959 2 0.000 0.9947 0.000 1.000
#> SRR1446132 2 0.000 0.9947 0.000 1.000
#> SRR1416933 2 0.000 0.9947 0.000 1.000
#> SRR1102538 2 0.000 0.9947 0.000 1.000
#> SRR1098636 2 0.000 0.9947 0.000 1.000
#> SRR1072998 2 0.000 0.9947 0.000 1.000
#> SRR627443 2 0.000 0.9947 0.000 1.000
#> SRR656131 2 0.000 0.9947 0.000 1.000
#> SRR823991 2 0.000 0.9947 0.000 1.000
#> SRR1089158 2 0.000 0.9947 0.000 1.000
#> SRR1469036 2 0.000 0.9947 0.000 1.000
#> SRR824039 2 0.000 0.9947 0.000 1.000
#> SRR1339047 2 0.000 0.9947 0.000 1.000
#> SRR1443049 2 0.000 0.9947 0.000 1.000
#> SRR1122885 2 0.000 0.9947 0.000 1.000
#> SRR602895 2 0.000 0.9947 0.000 1.000
#> SRR1409837 2 0.000 0.9947 0.000 1.000
#> SRR1388959 2 0.000 0.9947 0.000 1.000
#> SRR659863 2 0.000 0.9947 0.000 1.000
#> SRR1089877 2 0.000 0.9947 0.000 1.000
#> SRR1123775 2 0.000 0.9947 0.000 1.000
#> SRR658909 2 0.000 0.9947 0.000 1.000
#> SRR1140510 2 0.000 0.9947 0.000 1.000
#> SRR607562 2 0.000 0.9947 0.000 1.000
#> SRR1122913 2 0.000 0.9947 0.000 1.000
#> SRR598042 2 0.000 0.9947 0.000 1.000
#> SRR1467340 2 0.000 0.9947 0.000 1.000
#> SRR1072321 2 0.000 0.9947 0.000 1.000
#> SRR1094580 2 0.000 0.9947 0.000 1.000
#> SRR1076608 2 0.000 0.9947 0.000 1.000
#> SRR1395462 2 0.000 0.9947 0.000 1.000
#> SRR1489220 2 0.000 0.9947 0.000 1.000
#> SRR614371 2 0.000 0.9947 0.000 1.000
#> SRR615455 1 0.000 0.9705 1.000 0.000
#> SRR1070573 2 0.000 0.9947 0.000 1.000
#> SRR598749 1 0.000 0.9705 1.000 0.000
#> SRR1365556 2 0.000 0.9947 0.000 1.000
#> SRR1350023 2 0.000 0.9947 0.000 1.000
#> SRR1446582 2 0.000 0.9947 0.000 1.000
#> SRR1439763 2 0.000 0.9947 0.000 1.000
#> SRR1343986 2 0.000 0.9947 0.000 1.000
#> SRR807463 2 0.000 0.9947 0.000 1.000
#> SRR660390 1 0.000 0.9705 1.000 0.000
#> SRR1367672 2 0.000 0.9947 0.000 1.000
#> SRR613294 1 0.000 0.9705 1.000 0.000
#> SRR824015 2 0.000 0.9947 0.000 1.000
#> SRR1078924 2 0.000 0.9947 0.000 1.000
#> SRR662221 2 0.999 0.0251 0.480 0.520
#> SRR655017 2 0.000 0.9947 0.000 1.000
#> SRR1338450 2 0.000 0.9947 0.000 1.000
#> SRR663741 2 0.000 0.9947 0.000 1.000
#> SRR1396057 2 0.000 0.9947 0.000 1.000
#> SRR1083800 2 0.000 0.9947 0.000 1.000
#> SRR1445789 2 0.000 0.9947 0.000 1.000
#> SRR1387355 2 0.000 0.9947 0.000 1.000
#> SRR1388855 2 0.000 0.9947 0.000 1.000
#> SRR1445449 2 0.000 0.9947 0.000 1.000
#> SRR1380740 2 0.000 0.9947 0.000 1.000
#> SRR659995 1 0.000 0.9705 1.000 0.000
#> SRR1489524 2 0.000 0.9947 0.000 1.000
#> SRR1444662 2 0.000 0.9947 0.000 1.000
#> SRR1383652 2 0.000 0.9947 0.000 1.000
#> SRR1361243 2 0.000 0.9947 0.000 1.000
#> SRR1490337 2 0.000 0.9947 0.000 1.000
#> SRR823967 2 0.000 0.9947 0.000 1.000
#> SRR660127 2 0.000 0.9947 0.000 1.000
#> SRR1366627 2 0.000 0.9947 0.000 1.000
#> SRR1361219 2 0.000 0.9947 0.000 1.000
#> SRR1393510 2 0.000 0.9947 0.000 1.000
#> SRR662558 2 0.000 0.9947 0.000 1.000
#> SRR1077334 2 0.000 0.9947 0.000 1.000
#> SRR807438 2 0.000 0.9947 0.000 1.000
#> SRR1459078 2 0.000 0.9947 0.000 1.000
#> SRR1329704 2 0.000 0.9947 0.000 1.000
#> SRR1468072 2 0.000 0.9947 0.000 1.000
#> SRR1376196 2 0.000 0.9947 0.000 1.000
#> SRR1442909 2 0.000 0.9947 0.000 1.000
#> SRR1414269 2 0.000 0.9947 0.000 1.000
#> SRR1381913 2 0.000 0.9947 0.000 1.000
#> SRR1340157 2 0.000 0.9947 0.000 1.000
#> SRR1407583 2 0.000 0.9947 0.000 1.000
#> SRR615826 1 0.000 0.9705 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 1 0.0000 0.939 1.000 0.000 0.000
#> SRR1458769 3 0.0000 0.893 0.000 0.000 1.000
#> SRR613162 1 0.6111 0.433 0.604 0.396 0.000
#> SRR1352481 2 0.0237 0.807 0.000 0.996 0.004
#> SRR1468876 2 0.4399 0.846 0.000 0.812 0.188
#> SRR1399223 2 0.4452 0.826 0.000 0.808 0.192
#> SRR660030 2 0.3340 0.841 0.000 0.880 0.120
#> SRR1333609 3 0.1163 0.872 0.000 0.028 0.972
#> SRR1471612 3 0.0000 0.893 0.000 0.000 1.000
#> SRR1413998 3 0.0237 0.892 0.000 0.004 0.996
#> SRR1122940 2 0.4062 0.838 0.000 0.836 0.164
#> SRR1402563 3 0.0237 0.891 0.000 0.004 0.996
#> SRR1398393 2 0.5650 0.683 0.000 0.688 0.312
#> SRR657961 3 0.5988 0.386 0.000 0.368 0.632
#> SRR1471135 3 0.0000 0.893 0.000 0.000 1.000
#> SRR1430001 2 0.4605 0.817 0.000 0.796 0.204
#> SRR662775 2 0.2066 0.835 0.000 0.940 0.060
#> SRR1474182 3 0.0000 0.893 0.000 0.000 1.000
#> SRR607190 2 0.4750 0.831 0.000 0.784 0.216
#> SRR612467 2 0.2165 0.838 0.000 0.936 0.064
#> SRR1465959 2 0.4702 0.834 0.000 0.788 0.212
#> SRR1446132 2 0.6302 0.451 0.000 0.520 0.480
#> SRR1416933 3 0.0237 0.892 0.000 0.004 0.996
#> SRR1102538 3 0.0892 0.879 0.000 0.020 0.980
#> SRR1098636 3 0.2066 0.852 0.000 0.060 0.940
#> SRR1072998 2 0.3941 0.839 0.000 0.844 0.156
#> SRR627443 3 0.5216 0.661 0.000 0.260 0.740
#> SRR656131 2 0.3267 0.837 0.000 0.884 0.116
#> SRR823991 2 0.5810 0.734 0.000 0.664 0.336
#> SRR1089158 2 0.2066 0.830 0.000 0.940 0.060
#> SRR1469036 2 0.4121 0.837 0.000 0.832 0.168
#> SRR824039 2 0.4654 0.838 0.000 0.792 0.208
#> SRR1339047 2 0.3551 0.830 0.000 0.868 0.132
#> SRR1443049 2 0.3686 0.839 0.000 0.860 0.140
#> SRR1122885 2 0.5733 0.722 0.000 0.676 0.324
#> SRR602895 2 0.3941 0.833 0.000 0.844 0.156
#> SRR1409837 3 0.0000 0.893 0.000 0.000 1.000
#> SRR1388959 3 0.0000 0.893 0.000 0.000 1.000
#> SRR659863 3 0.0424 0.891 0.000 0.008 0.992
#> SRR1089877 2 0.5098 0.810 0.000 0.752 0.248
#> SRR1123775 2 0.3816 0.840 0.000 0.852 0.148
#> SRR658909 2 0.2356 0.837 0.000 0.928 0.072
#> SRR1140510 2 0.5363 0.747 0.000 0.724 0.276
#> SRR607562 2 0.3551 0.830 0.000 0.868 0.132
#> SRR1122913 2 0.5905 0.633 0.000 0.648 0.352
#> SRR598042 3 0.0237 0.892 0.000 0.004 0.996
#> SRR1467340 2 0.4452 0.826 0.000 0.808 0.192
#> SRR1072321 2 0.5760 0.760 0.000 0.672 0.328
#> SRR1094580 3 0.3619 0.809 0.000 0.136 0.864
#> SRR1076608 2 0.5810 0.652 0.000 0.664 0.336
#> SRR1395462 3 0.0000 0.893 0.000 0.000 1.000
#> SRR1489220 2 0.4452 0.826 0.000 0.808 0.192
#> SRR614371 2 0.3482 0.829 0.000 0.872 0.128
#> SRR615455 1 0.0000 0.939 1.000 0.000 0.000
#> SRR1070573 2 0.4452 0.836 0.000 0.808 0.192
#> SRR598749 1 0.0000 0.939 1.000 0.000 0.000
#> SRR1365556 3 0.5810 0.293 0.000 0.336 0.664
#> SRR1350023 2 0.4702 0.822 0.000 0.788 0.212
#> SRR1446582 3 0.4002 0.760 0.000 0.160 0.840
#> SRR1439763 2 0.1860 0.837 0.000 0.948 0.052
#> SRR1343986 2 0.3686 0.839 0.000 0.860 0.140
#> SRR807463 3 0.2261 0.846 0.000 0.068 0.932
#> SRR660390 1 0.0000 0.939 1.000 0.000 0.000
#> SRR1367672 2 0.3941 0.833 0.000 0.844 0.156
#> SRR613294 1 0.0000 0.939 1.000 0.000 0.000
#> SRR824015 2 0.2356 0.830 0.000 0.928 0.072
#> SRR1078924 2 0.4291 0.832 0.000 0.820 0.180
#> SRR662221 2 0.2796 0.764 0.092 0.908 0.000
#> SRR655017 2 0.4796 0.749 0.000 0.780 0.220
#> SRR1338450 2 0.3340 0.837 0.000 0.880 0.120
#> SRR663741 2 0.0592 0.810 0.000 0.988 0.012
#> SRR1396057 3 0.0237 0.892 0.000 0.004 0.996
#> SRR1083800 3 0.4178 0.767 0.000 0.172 0.828
#> SRR1445789 2 0.4399 0.829 0.000 0.812 0.188
#> SRR1387355 2 0.2537 0.835 0.000 0.920 0.080
#> SRR1388855 3 0.5650 0.502 0.000 0.312 0.688
#> SRR1445449 2 0.4702 0.769 0.000 0.788 0.212
#> SRR1380740 2 0.3192 0.850 0.000 0.888 0.112
#> SRR659995 1 0.0000 0.939 1.000 0.000 0.000
#> SRR1489524 3 0.5291 0.628 0.000 0.268 0.732
#> SRR1444662 3 0.0237 0.891 0.000 0.004 0.996
#> SRR1383652 3 0.0237 0.892 0.000 0.004 0.996
#> SRR1361243 3 0.5650 0.348 0.000 0.312 0.688
#> SRR1490337 3 0.0000 0.893 0.000 0.000 1.000
#> SRR823967 2 0.5397 0.811 0.000 0.720 0.280
#> SRR660127 3 0.0237 0.892 0.000 0.004 0.996
#> SRR1366627 2 0.3340 0.834 0.000 0.880 0.120
#> SRR1361219 3 0.0000 0.893 0.000 0.000 1.000
#> SRR1393510 2 0.2625 0.839 0.000 0.916 0.084
#> SRR662558 2 0.2537 0.835 0.000 0.920 0.080
#> SRR1077334 2 0.4121 0.840 0.000 0.832 0.168
#> SRR807438 2 0.4842 0.830 0.000 0.776 0.224
#> SRR1459078 2 0.3412 0.846 0.000 0.876 0.124
#> SRR1329704 2 0.3941 0.836 0.000 0.844 0.156
#> SRR1468072 2 0.4002 0.834 0.000 0.840 0.160
#> SRR1376196 3 0.5098 0.645 0.000 0.248 0.752
#> SRR1442909 3 0.0000 0.893 0.000 0.000 1.000
#> SRR1414269 2 0.5327 0.813 0.000 0.728 0.272
#> SRR1381913 3 0.0000 0.893 0.000 0.000 1.000
#> SRR1340157 2 0.5905 0.633 0.000 0.648 0.352
#> SRR1407583 3 0.0237 0.892 0.000 0.004 0.996
#> SRR615826 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
#> SRR612587 4 0.0000 0.9683 0.000 0.000 0.000 1.000
#> SRR1458769 3 0.0188 0.8652 0.000 0.004 0.996 0.000
#> SRR613162 1 0.3356 0.0241 0.824 0.000 0.000 0.176
#> SRR1352481 1 0.4790 0.3711 0.620 0.380 0.000 0.000
#> SRR1468876 2 0.5938 0.4621 0.232 0.676 0.092 0.000
#> SRR1399223 2 0.0592 0.6399 0.000 0.984 0.016 0.000
#> SRR660030 2 0.3166 0.5950 0.116 0.868 0.016 0.000
#> SRR1333609 3 0.3893 0.7097 0.008 0.196 0.796 0.000
#> SRR1471612 3 0.0336 0.8633 0.000 0.008 0.992 0.000
#> SRR1413998 3 0.0000 0.8651 0.000 0.000 1.000 0.000
#> SRR1122940 2 0.0592 0.6399 0.000 0.984 0.016 0.000
#> SRR1402563 3 0.0336 0.8633 0.000 0.008 0.992 0.000
#> SRR1398393 2 0.6245 0.4173 0.168 0.668 0.164 0.000
#> SRR657961 3 0.4767 0.5310 0.020 0.256 0.724 0.000
#> SRR1471135 3 0.0188 0.8651 0.000 0.004 0.996 0.000
#> SRR1430001 2 0.0592 0.6399 0.000 0.984 0.016 0.000
#> SRR662775 2 0.5231 0.3958 0.296 0.676 0.028 0.000
#> SRR1474182 3 0.0000 0.8651 0.000 0.000 1.000 0.000
#> SRR607190 1 0.7747 0.2839 0.388 0.380 0.232 0.000
#> SRR612467 2 0.5526 0.1572 0.416 0.564 0.020 0.000
#> SRR1465959 2 0.3051 0.6347 0.088 0.884 0.028 0.000
#> SRR1446132 2 0.5003 0.3931 0.016 0.676 0.308 0.000
#> SRR1416933 3 0.0188 0.8651 0.000 0.004 0.996 0.000
#> SRR1102538 3 0.3501 0.7638 0.020 0.132 0.848 0.000
#> SRR1098636 3 0.1867 0.8247 0.072 0.000 0.928 0.000
#> SRR1072998 2 0.0592 0.6399 0.000 0.984 0.016 0.000
#> SRR627443 3 0.4967 0.2434 0.452 0.000 0.548 0.000
#> SRR656131 2 0.6351 0.2892 0.332 0.588 0.080 0.000
#> SRR823991 2 0.7282 0.1932 0.172 0.512 0.316 0.000
#> SRR1089158 1 0.4804 0.3537 0.616 0.384 0.000 0.000
#> SRR1469036 2 0.0592 0.6399 0.000 0.984 0.016 0.000
#> SRR824039 2 0.3286 0.6347 0.080 0.876 0.044 0.000
#> SRR1339047 2 0.6523 0.2535 0.348 0.564 0.088 0.000
#> SRR1443049 2 0.0592 0.6399 0.000 0.984 0.016 0.000
#> SRR1122885 2 0.2676 0.6231 0.012 0.896 0.092 0.000
#> SRR602895 2 0.6688 0.0475 0.420 0.492 0.088 0.000
#> SRR1409837 3 0.0000 0.8651 0.000 0.000 1.000 0.000
#> SRR1388959 3 0.0188 0.8651 0.000 0.004 0.996 0.000
#> SRR659863 3 0.0188 0.8644 0.000 0.004 0.996 0.000
#> SRR1089877 2 0.6538 0.4023 0.232 0.628 0.140 0.000
#> SRR1123775 2 0.0779 0.6394 0.004 0.980 0.016 0.000
#> SRR658909 1 0.5277 0.5635 0.668 0.304 0.028 0.000
#> SRR1140510 2 0.6791 0.2737 0.316 0.564 0.120 0.000
#> SRR607562 1 0.5970 0.6083 0.668 0.244 0.088 0.000
#> SRR1122913 2 0.0707 0.6390 0.000 0.980 0.020 0.000
#> SRR598042 3 0.0000 0.8651 0.000 0.000 1.000 0.000
#> SRR1467340 2 0.0592 0.6399 0.000 0.984 0.016 0.000
#> SRR1072321 2 0.6031 0.4954 0.168 0.688 0.144 0.000
#> SRR1094580 3 0.4312 0.6991 0.132 0.056 0.812 0.000
#> SRR1076608 2 0.0592 0.6399 0.000 0.984 0.016 0.000
#> SRR1395462 3 0.0188 0.8651 0.000 0.004 0.996 0.000
#> SRR1489220 2 0.0592 0.6399 0.000 0.984 0.016 0.000
#> SRR614371 1 0.6027 0.6098 0.664 0.244 0.092 0.000
#> SRR615455 4 0.0000 0.9683 0.000 0.000 0.000 1.000
#> SRR1070573 2 0.3280 0.6210 0.124 0.860 0.016 0.000
#> SRR598749 4 0.0000 0.9683 0.000 0.000 0.000 1.000
#> SRR1365556 3 0.5947 0.1939 0.044 0.384 0.572 0.000
#> SRR1350023 2 0.7585 0.0666 0.304 0.472 0.224 0.000
#> SRR1446582 3 0.1716 0.8270 0.064 0.000 0.936 0.000
#> SRR1439763 2 0.4836 0.3855 0.320 0.672 0.008 0.000
#> SRR1343986 2 0.0592 0.6399 0.000 0.984 0.016 0.000
#> SRR807463 3 0.2197 0.8144 0.080 0.004 0.916 0.000
#> SRR660390 4 0.2814 0.9231 0.132 0.000 0.000 0.868
#> SRR1367672 1 0.6585 0.4978 0.584 0.312 0.104 0.000
#> SRR613294 4 0.0000 0.9683 0.000 0.000 0.000 1.000
#> SRR824015 1 0.4643 0.4496 0.656 0.344 0.000 0.000
#> SRR1078924 2 0.0592 0.6399 0.000 0.984 0.016 0.000
#> SRR662221 1 0.1510 0.3794 0.956 0.016 0.000 0.028
#> SRR655017 1 0.6346 0.6076 0.656 0.192 0.152 0.000
#> SRR1338450 2 0.6627 0.0743 0.412 0.504 0.084 0.000
#> SRR663741 1 0.3024 0.5932 0.852 0.148 0.000 0.000
#> SRR1396057 3 0.0000 0.8651 0.000 0.000 1.000 0.000
#> SRR1083800 3 0.6916 0.3730 0.176 0.236 0.588 0.000
#> SRR1445789 2 0.0592 0.6399 0.000 0.984 0.016 0.000
#> SRR1387355 2 0.4134 0.4677 0.260 0.740 0.000 0.000
#> SRR1388855 2 0.5000 -0.1852 0.000 0.504 0.496 0.000
#> SRR1445449 1 0.6215 0.6125 0.668 0.192 0.140 0.000
#> SRR1380740 2 0.4576 0.5208 0.232 0.748 0.020 0.000
#> SRR659995 4 0.3311 0.9015 0.172 0.000 0.000 0.828
#> SRR1489524 3 0.6969 0.3918 0.224 0.192 0.584 0.000
#> SRR1444662 3 0.0592 0.8604 0.000 0.016 0.984 0.000
#> SRR1383652 3 0.0188 0.8642 0.004 0.000 0.996 0.000
#> SRR1361243 3 0.4585 0.3999 0.000 0.332 0.668 0.000
#> SRR1490337 3 0.0336 0.8633 0.000 0.008 0.992 0.000
#> SRR823967 2 0.4352 0.6041 0.080 0.816 0.104 0.000
#> SRR660127 3 0.0000 0.8651 0.000 0.000 1.000 0.000
#> SRR1366627 2 0.6495 0.2400 0.356 0.560 0.084 0.000
#> SRR1361219 3 0.0000 0.8651 0.000 0.000 1.000 0.000
#> SRR1393510 2 0.5905 0.3709 0.304 0.636 0.060 0.000
#> SRR662558 2 0.3444 0.5111 0.184 0.816 0.000 0.000
#> SRR1077334 2 0.2845 0.6371 0.076 0.896 0.028 0.000
#> SRR807438 2 0.6984 0.3372 0.236 0.580 0.184 0.000
#> SRR1459078 2 0.6426 0.2125 0.352 0.568 0.080 0.000
#> SRR1329704 2 0.6683 0.0570 0.416 0.496 0.088 0.000
#> SRR1468072 2 0.6733 0.0716 0.416 0.492 0.092 0.000
#> SRR1376196 3 0.4998 0.2499 0.000 0.488 0.512 0.000
#> SRR1442909 3 0.0336 0.8633 0.000 0.008 0.992 0.000
#> SRR1414269 2 0.5582 0.5448 0.136 0.728 0.136 0.000
#> SRR1381913 3 0.0000 0.8651 0.000 0.000 1.000 0.000
#> SRR1340157 2 0.0592 0.6399 0.000 0.984 0.016 0.000
#> SRR1407583 3 0.0188 0.8642 0.004 0.000 0.996 0.000
#> SRR615826 4 0.0000 0.9683 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
#> SRR612587 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000
#> SRR1458769 2 0.0162 0.855 0.000 0.996 0.004 0.000 0.000
#> SRR613162 1 0.3035 0.297 0.856 0.000 0.000 0.032 0.112
#> SRR1352481 5 0.1282 0.731 0.004 0.000 0.044 0.000 0.952
#> SRR1468876 3 0.4968 0.689 0.052 0.016 0.708 0.000 0.224
#> SRR1399223 3 0.0000 0.746 0.000 0.000 1.000 0.000 0.000
#> SRR660030 3 0.1768 0.742 0.004 0.000 0.924 0.000 0.072
#> SRR1333609 2 0.3475 0.720 0.004 0.804 0.180 0.000 0.012
#> SRR1471612 2 0.0162 0.856 0.000 0.996 0.004 0.000 0.000
#> SRR1413998 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000
#> SRR1122940 3 0.0000 0.746 0.000 0.000 1.000 0.000 0.000
#> SRR1402563 2 0.0162 0.856 0.000 0.996 0.004 0.000 0.000
#> SRR1398393 3 0.6251 0.612 0.052 0.128 0.644 0.000 0.176
#> SRR657961 2 0.5133 0.483 0.056 0.664 0.272 0.000 0.008
#> SRR1471135 2 0.0162 0.856 0.000 0.996 0.004 0.000 0.000
#> SRR1430001 3 0.0000 0.746 0.000 0.000 1.000 0.000 0.000
#> SRR662775 3 0.6151 0.573 0.156 0.008 0.584 0.000 0.252
#> SRR1474182 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000
#> SRR607190 1 0.7222 0.427 0.532 0.068 0.200 0.000 0.200
#> SRR612467 3 0.6078 0.534 0.116 0.004 0.524 0.000 0.356
#> SRR1465959 3 0.2853 0.755 0.052 0.000 0.876 0.000 0.072
#> SRR1446132 3 0.4318 0.527 0.000 0.292 0.688 0.000 0.020
#> SRR1416933 2 0.0162 0.856 0.000 0.996 0.004 0.000 0.000
#> SRR1102538 2 0.3134 0.761 0.000 0.848 0.120 0.000 0.032
#> SRR1098636 2 0.1990 0.812 0.008 0.920 0.004 0.000 0.068
#> SRR1072998 3 0.0290 0.747 0.000 0.000 0.992 0.000 0.008
#> SRR627443 1 0.4589 0.667 0.724 0.064 0.000 0.000 0.212
#> SRR656131 3 0.6448 0.547 0.104 0.028 0.532 0.000 0.336
#> SRR823991 3 0.7019 0.493 0.064 0.252 0.544 0.000 0.140
#> SRR1089158 5 0.2193 0.725 0.008 0.000 0.092 0.000 0.900
#> SRR1469036 3 0.0000 0.746 0.000 0.000 1.000 0.000 0.000
#> SRR824039 3 0.3062 0.755 0.048 0.004 0.868 0.000 0.080
#> SRR1339047 3 0.6051 0.621 0.092 0.020 0.584 0.000 0.304
#> SRR1443049 3 0.0000 0.746 0.000 0.000 1.000 0.000 0.000
#> SRR1122885 3 0.1857 0.738 0.004 0.060 0.928 0.000 0.008
#> SRR602895 3 0.6402 0.445 0.100 0.020 0.456 0.000 0.424
#> SRR1409837 2 0.0162 0.855 0.000 0.996 0.004 0.000 0.000
#> SRR1388959 2 0.0162 0.856 0.000 0.996 0.004 0.000 0.000
#> SRR659863 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000
#> SRR1089877 3 0.5651 0.665 0.048 0.056 0.672 0.000 0.224
#> SRR1123775 3 0.0000 0.746 0.000 0.000 1.000 0.000 0.000
#> SRR658909 1 0.3579 0.730 0.756 0.000 0.004 0.000 0.240
#> SRR1140510 3 0.5982 0.628 0.028 0.076 0.600 0.000 0.296
#> SRR607562 1 0.4272 0.712 0.752 0.000 0.052 0.000 0.196
#> SRR1122913 3 0.0451 0.746 0.000 0.008 0.988 0.000 0.004
#> SRR598042 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000
#> SRR1467340 3 0.0000 0.746 0.000 0.000 1.000 0.000 0.000
#> SRR1072321 3 0.5623 0.677 0.032 0.140 0.696 0.000 0.132
#> SRR1094580 2 0.4014 0.711 0.020 0.816 0.056 0.000 0.108
#> SRR1076608 3 0.0000 0.746 0.000 0.000 1.000 0.000 0.000
#> SRR1395462 2 0.0162 0.856 0.000 0.996 0.004 0.000 0.000
#> SRR1489220 3 0.0000 0.746 0.000 0.000 1.000 0.000 0.000
#> SRR614371 1 0.3579 0.730 0.756 0.000 0.004 0.000 0.240
#> SRR615455 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000
#> SRR1070573 3 0.3507 0.747 0.052 0.000 0.828 0.000 0.120
#> SRR598749 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000
#> SRR1365556 2 0.5284 0.149 0.004 0.532 0.424 0.000 0.040
#> SRR1350023 3 0.7104 0.530 0.060 0.132 0.504 0.000 0.304
#> SRR1446582 2 0.2722 0.787 0.040 0.892 0.008 0.000 0.060
#> SRR1439763 3 0.5071 0.658 0.048 0.004 0.640 0.000 0.308
#> SRR1343986 3 0.0000 0.746 0.000 0.000 1.000 0.000 0.000
#> SRR807463 2 0.1928 0.810 0.004 0.920 0.004 0.000 0.072
#> SRR660390 4 0.4325 0.783 0.220 0.000 0.000 0.736 0.044
#> SRR1367672 1 0.6151 0.433 0.580 0.004 0.192 0.000 0.224
#> SRR613294 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000
#> SRR824015 5 0.2068 0.719 0.004 0.000 0.092 0.000 0.904
#> SRR1078924 3 0.0000 0.746 0.000 0.000 1.000 0.000 0.000
#> SRR662221 5 0.3715 0.480 0.260 0.000 0.000 0.004 0.736
#> SRR655017 1 0.3579 0.730 0.756 0.000 0.004 0.000 0.240
#> SRR1338450 3 0.5769 0.552 0.048 0.020 0.524 0.000 0.408
#> SRR663741 5 0.2127 0.647 0.108 0.000 0.000 0.000 0.892
#> SRR1396057 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000
#> SRR1083800 2 0.6083 0.428 0.004 0.588 0.232 0.000 0.176
#> SRR1445789 3 0.0000 0.746 0.000 0.000 1.000 0.000 0.000
#> SRR1387355 3 0.4800 0.676 0.052 0.000 0.676 0.000 0.272
#> SRR1388855 2 0.4268 0.315 0.000 0.556 0.444 0.000 0.000
#> SRR1445449 1 0.4665 0.698 0.724 0.012 0.040 0.000 0.224
#> SRR1380740 3 0.4461 0.697 0.052 0.000 0.728 0.000 0.220
#> SRR659995 4 0.5263 0.724 0.240 0.000 0.000 0.660 0.100
#> SRR1489524 2 0.6975 0.360 0.052 0.552 0.188 0.000 0.208
#> SRR1444662 2 0.0404 0.853 0.000 0.988 0.012 0.000 0.000
#> SRR1383652 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000
#> SRR1361243 2 0.4045 0.418 0.000 0.644 0.356 0.000 0.000
#> SRR1490337 2 0.0162 0.856 0.000 0.996 0.004 0.000 0.000
#> SRR823967 3 0.3575 0.755 0.056 0.020 0.848 0.000 0.076
#> SRR660127 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000
#> SRR1366627 3 0.6051 0.615 0.088 0.020 0.576 0.000 0.316
#> SRR1361219 2 0.0162 0.855 0.000 0.996 0.004 0.000 0.000
#> SRR1393510 3 0.5909 0.639 0.100 0.016 0.612 0.000 0.272
#> SRR662558 3 0.3318 0.684 0.008 0.000 0.800 0.000 0.192
#> SRR1077334 3 0.3301 0.754 0.048 0.008 0.856 0.000 0.088
#> SRR807438 3 0.6063 0.660 0.056 0.076 0.644 0.000 0.224
#> SRR1459078 3 0.5409 0.642 0.044 0.020 0.628 0.000 0.308
#> SRR1329704 3 0.5799 0.568 0.052 0.020 0.536 0.000 0.392
#> SRR1468072 3 0.6029 0.542 0.068 0.020 0.512 0.000 0.400
#> SRR1376196 2 0.4300 0.317 0.000 0.524 0.476 0.000 0.000
#> SRR1442909 2 0.0162 0.856 0.000 0.996 0.004 0.000 0.000
#> SRR1414269 3 0.5227 0.729 0.092 0.056 0.744 0.000 0.108
#> SRR1381913 2 0.0000 0.855 0.000 1.000 0.000 0.000 0.000
#> SRR1340157 3 0.0290 0.747 0.000 0.000 0.992 0.000 0.008
#> SRR1407583 2 0.0963 0.836 0.036 0.964 0.000 0.000 0.000
#> SRR615826 4 0.0000 0.917 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1458769 2 0.0405 0.827 0.000 0.988 0.004 0.000 0.000 0.008
#> SRR613162 4 0.1204 0.880 0.056 0.000 0.000 0.944 0.000 0.000
#> SRR1352481 6 0.2003 0.599 0.044 0.000 0.000 0.044 0.000 0.912
#> SRR1468876 3 0.5161 0.660 0.124 0.004 0.620 0.000 0.000 0.252
#> SRR1399223 3 0.0000 0.738 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR660030 3 0.1268 0.733 0.008 0.000 0.952 0.004 0.000 0.036
#> SRR1333609 2 0.2946 0.723 0.012 0.824 0.160 0.000 0.000 0.004
#> SRR1471612 2 0.0146 0.828 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1413998 2 0.0000 0.828 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1122940 3 0.0000 0.738 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1402563 2 0.0146 0.828 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1398393 3 0.5412 0.665 0.140 0.048 0.696 0.016 0.000 0.100
#> SRR657961 2 0.5492 0.370 0.140 0.580 0.272 0.000 0.000 0.008
#> SRR1471135 2 0.0146 0.828 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1430001 3 0.0000 0.738 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR662775 3 0.5150 0.633 0.244 0.012 0.652 0.008 0.000 0.084
#> SRR1474182 2 0.0260 0.827 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR607190 1 0.3984 0.437 0.736 0.028 0.224 0.000 0.000 0.012
#> SRR612467 3 0.6006 0.597 0.268 0.004 0.548 0.020 0.000 0.160
#> SRR1465959 3 0.2909 0.746 0.028 0.000 0.836 0.000 0.000 0.136
#> SRR1446132 3 0.4947 0.558 0.000 0.244 0.636 0.000 0.000 0.120
#> SRR1416933 2 0.0146 0.828 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1102538 2 0.3701 0.700 0.000 0.788 0.112 0.000 0.000 0.100
#> SRR1098636 2 0.2830 0.758 0.068 0.864 0.004 0.000 0.000 0.064
#> SRR1072998 3 0.0363 0.740 0.000 0.000 0.988 0.000 0.000 0.012
#> SRR627443 1 0.0632 0.754 0.976 0.024 0.000 0.000 0.000 0.000
#> SRR656131 3 0.5416 0.647 0.228 0.004 0.636 0.020 0.000 0.112
#> SRR823991 3 0.6354 0.473 0.140 0.264 0.532 0.000 0.000 0.064
#> SRR1089158 6 0.4575 0.605 0.080 0.000 0.100 0.064 0.000 0.756
#> SRR1469036 3 0.0000 0.738 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR824039 3 0.3504 0.734 0.024 0.004 0.776 0.000 0.000 0.196
#> SRR1339047 3 0.6039 0.642 0.160 0.008 0.560 0.020 0.000 0.252
#> SRR1443049 3 0.0000 0.738 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1122885 3 0.1686 0.727 0.000 0.064 0.924 0.000 0.000 0.012
#> SRR602895 3 0.6352 0.570 0.264 0.008 0.504 0.020 0.000 0.204
#> SRR1409837 2 0.0405 0.827 0.000 0.988 0.004 0.000 0.000 0.008
#> SRR1388959 2 0.0146 0.828 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR659863 2 0.0000 0.828 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1089877 3 0.5954 0.623 0.120 0.048 0.576 0.000 0.000 0.256
#> SRR1123775 3 0.0000 0.738 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR658909 1 0.0146 0.775 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1140510 3 0.5955 0.668 0.104 0.024 0.588 0.020 0.000 0.264
#> SRR607562 1 0.0000 0.774 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1122913 3 0.0363 0.737 0.000 0.012 0.988 0.000 0.000 0.000
#> SRR598042 2 0.0000 0.828 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1467340 3 0.0000 0.738 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1072321 3 0.5379 0.684 0.064 0.064 0.652 0.000 0.000 0.220
#> SRR1094580 2 0.4975 0.587 0.092 0.716 0.056 0.000 0.000 0.136
#> SRR1076608 3 0.0000 0.738 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1395462 2 0.0146 0.828 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1489220 3 0.0000 0.738 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR614371 1 0.0000 0.774 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR615455 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1070573 3 0.3950 0.715 0.040 0.000 0.720 0.000 0.000 0.240
#> SRR598749 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1365556 2 0.5009 0.154 0.012 0.524 0.428 0.012 0.000 0.024
#> SRR1350023 3 0.6624 0.567 0.136 0.084 0.492 0.000 0.000 0.288
#> SRR1446582 2 0.3471 0.674 0.020 0.784 0.008 0.000 0.000 0.188
#> SRR1439763 3 0.5367 0.638 0.124 0.004 0.572 0.000 0.000 0.300
#> SRR1343986 3 0.0000 0.738 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR807463 2 0.2146 0.789 0.060 0.908 0.008 0.000 0.000 0.024
#> SRR660390 4 0.1957 0.913 0.000 0.000 0.000 0.888 0.112 0.000
#> SRR1367672 1 0.5335 0.224 0.612 0.004 0.200 0.000 0.000 0.184
#> SRR613294 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR824015 6 0.4120 0.600 0.068 0.000 0.100 0.044 0.000 0.788
#> SRR1078924 3 0.0000 0.738 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR662221 6 0.3864 -0.162 0.000 0.000 0.000 0.480 0.000 0.520
#> SRR655017 1 0.0146 0.775 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1338450 3 0.5756 0.589 0.124 0.008 0.508 0.004 0.000 0.356
#> SRR663741 6 0.5096 0.440 0.292 0.000 0.000 0.112 0.000 0.596
#> SRR1396057 2 0.0000 0.828 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1083800 2 0.6963 0.189 0.100 0.464 0.192 0.000 0.000 0.244
#> SRR1445789 3 0.0000 0.738 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1387355 3 0.4717 0.683 0.032 0.000 0.628 0.020 0.000 0.320
#> SRR1388855 2 0.3838 0.319 0.000 0.552 0.448 0.000 0.000 0.000
#> SRR1445449 1 0.1863 0.655 0.896 0.000 0.000 0.000 0.000 0.104
#> SRR1380740 3 0.5005 0.663 0.124 0.000 0.628 0.000 0.000 0.248
#> SRR659995 4 0.1556 0.929 0.000 0.000 0.000 0.920 0.080 0.000
#> SRR1489524 2 0.6746 0.269 0.120 0.500 0.124 0.000 0.000 0.256
#> SRR1444662 2 0.0692 0.823 0.000 0.976 0.020 0.000 0.000 0.004
#> SRR1383652 2 0.0146 0.827 0.004 0.996 0.000 0.000 0.000 0.000
#> SRR1361243 2 0.3874 0.400 0.000 0.636 0.356 0.000 0.000 0.008
#> SRR1490337 2 0.0146 0.828 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR823967 3 0.3194 0.749 0.032 0.008 0.828 0.000 0.000 0.132
#> SRR660127 2 0.0146 0.827 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1366627 3 0.5958 0.640 0.128 0.008 0.552 0.020 0.000 0.292
#> SRR1361219 2 0.0508 0.826 0.000 0.984 0.004 0.000 0.000 0.012
#> SRR1393510 3 0.5725 0.656 0.180 0.004 0.608 0.020 0.000 0.188
#> SRR662558 3 0.2520 0.707 0.008 0.000 0.872 0.012 0.000 0.108
#> SRR1077334 3 0.3202 0.742 0.024 0.000 0.800 0.000 0.000 0.176
#> SRR807438 3 0.5628 0.649 0.128 0.024 0.596 0.000 0.000 0.252
#> SRR1459078 3 0.5149 0.677 0.128 0.008 0.640 0.000 0.000 0.224
#> SRR1329704 3 0.5756 0.589 0.124 0.008 0.508 0.004 0.000 0.356
#> SRR1468072 3 0.5779 0.589 0.128 0.008 0.508 0.004 0.000 0.352
#> SRR1376196 2 0.3851 0.356 0.000 0.540 0.460 0.000 0.000 0.000
#> SRR1442909 2 0.0146 0.828 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1414269 3 0.5071 0.713 0.176 0.032 0.688 0.000 0.000 0.104
#> SRR1381913 2 0.0000 0.828 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1340157 3 0.0146 0.738 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1407583 2 0.2234 0.744 0.124 0.872 0.000 0.000 0.000 0.004
#> SRR615826 5 0.0000 1.000 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)
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 17380 rows and 102 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.815 0.940 0.974 0.2949 0.719 0.719
#> 3 3 0.416 0.708 0.806 0.8018 0.722 0.622
#> 4 4 0.535 0.666 0.820 0.0740 0.691 0.482
#> 5 5 0.408 0.560 0.721 0.2010 0.834 0.637
#> 6 6 0.444 0.353 0.679 0.0934 0.904 0.733
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
#> SRR612587 1 0.000 0.948 1.000 0.000
#> SRR1458769 2 0.000 0.976 0.000 1.000
#> SRR613162 1 0.000 0.948 1.000 0.000
#> SRR1352481 1 0.000 0.948 1.000 0.000
#> SRR1468876 2 0.000 0.976 0.000 1.000
#> SRR1399223 2 0.000 0.976 0.000 1.000
#> SRR660030 2 0.000 0.976 0.000 1.000
#> SRR1333609 2 0.000 0.976 0.000 1.000
#> SRR1471612 2 0.000 0.976 0.000 1.000
#> SRR1413998 2 0.000 0.976 0.000 1.000
#> SRR1122940 2 0.000 0.976 0.000 1.000
#> SRR1402563 2 0.000 0.976 0.000 1.000
#> SRR1398393 2 0.000 0.976 0.000 1.000
#> SRR657961 2 0.000 0.976 0.000 1.000
#> SRR1471135 2 0.000 0.976 0.000 1.000
#> SRR1430001 2 0.000 0.976 0.000 1.000
#> SRR662775 2 0.722 0.749 0.200 0.800
#> SRR1474182 2 0.000 0.976 0.000 1.000
#> SRR607190 2 0.722 0.749 0.200 0.800
#> SRR612467 2 0.855 0.590 0.280 0.720
#> SRR1465959 2 0.000 0.976 0.000 1.000
#> SRR1446132 2 0.000 0.976 0.000 1.000
#> SRR1416933 2 0.000 0.976 0.000 1.000
#> SRR1102538 2 0.000 0.976 0.000 1.000
#> SRR1098636 2 0.000 0.976 0.000 1.000
#> SRR1072998 2 0.000 0.976 0.000 1.000
#> SRR627443 1 0.730 0.759 0.796 0.204
#> SRR656131 2 0.722 0.749 0.200 0.800
#> SRR823991 2 0.000 0.976 0.000 1.000
#> SRR1089158 1 0.722 0.767 0.800 0.200
#> SRR1469036 2 0.000 0.976 0.000 1.000
#> SRR824039 2 0.000 0.976 0.000 1.000
#> SRR1339047 2 0.000 0.976 0.000 1.000
#> SRR1443049 2 0.000 0.976 0.000 1.000
#> SRR1122885 2 0.000 0.976 0.000 1.000
#> SRR602895 2 0.000 0.976 0.000 1.000
#> SRR1409837 2 0.000 0.976 0.000 1.000
#> SRR1388959 2 0.000 0.976 0.000 1.000
#> SRR659863 2 0.722 0.749 0.200 0.800
#> SRR1089877 2 0.311 0.922 0.056 0.944
#> SRR1123775 2 0.000 0.976 0.000 1.000
#> SRR658909 1 0.722 0.764 0.800 0.200
#> SRR1140510 2 0.000 0.976 0.000 1.000
#> SRR607562 2 0.584 0.828 0.140 0.860
#> SRR1122913 2 0.000 0.976 0.000 1.000
#> SRR598042 2 0.000 0.976 0.000 1.000
#> SRR1467340 2 0.000 0.976 0.000 1.000
#> SRR1072321 2 0.000 0.976 0.000 1.000
#> SRR1094580 2 0.000 0.976 0.000 1.000
#> SRR1076608 2 0.000 0.976 0.000 1.000
#> SRR1395462 2 0.000 0.976 0.000 1.000
#> SRR1489220 2 0.000 0.976 0.000 1.000
#> SRR614371 1 0.118 0.939 0.984 0.016
#> SRR615455 1 0.000 0.948 1.000 0.000
#> SRR1070573 2 0.000 0.976 0.000 1.000
#> SRR598749 1 0.000 0.948 1.000 0.000
#> SRR1365556 2 0.000 0.976 0.000 1.000
#> SRR1350023 2 0.000 0.976 0.000 1.000
#> SRR1446582 2 0.000 0.976 0.000 1.000
#> SRR1439763 2 0.000 0.976 0.000 1.000
#> SRR1343986 2 0.000 0.976 0.000 1.000
#> SRR807463 2 0.000 0.976 0.000 1.000
#> SRR660390 1 0.000 0.948 1.000 0.000
#> SRR1367672 2 0.000 0.976 0.000 1.000
#> SRR613294 1 0.000 0.948 1.000 0.000
#> SRR824015 1 0.634 0.817 0.840 0.160
#> SRR1078924 2 0.000 0.976 0.000 1.000
#> SRR662221 1 0.000 0.948 1.000 0.000
#> SRR655017 1 0.000 0.948 1.000 0.000
#> SRR1338450 2 0.000 0.976 0.000 1.000
#> SRR663741 1 0.000 0.948 1.000 0.000
#> SRR1396057 2 0.000 0.976 0.000 1.000
#> SRR1083800 2 0.000 0.976 0.000 1.000
#> SRR1445789 2 0.000 0.976 0.000 1.000
#> SRR1387355 2 0.987 0.235 0.432 0.568
#> SRR1388855 2 0.000 0.976 0.000 1.000
#> SRR1445449 2 0.000 0.976 0.000 1.000
#> SRR1380740 2 0.000 0.976 0.000 1.000
#> SRR659995 1 0.000 0.948 1.000 0.000
#> SRR1489524 2 0.000 0.976 0.000 1.000
#> SRR1444662 2 0.000 0.976 0.000 1.000
#> SRR1383652 2 0.000 0.976 0.000 1.000
#> SRR1361243 2 0.000 0.976 0.000 1.000
#> SRR1490337 2 0.000 0.976 0.000 1.000
#> SRR823967 2 0.000 0.976 0.000 1.000
#> SRR660127 2 0.722 0.749 0.200 0.800
#> SRR1366627 2 0.000 0.976 0.000 1.000
#> SRR1361219 2 0.000 0.976 0.000 1.000
#> SRR1393510 2 0.000 0.976 0.000 1.000
#> SRR662558 2 0.000 0.976 0.000 1.000
#> SRR1077334 2 0.000 0.976 0.000 1.000
#> SRR807438 2 0.000 0.976 0.000 1.000
#> SRR1459078 2 0.000 0.976 0.000 1.000
#> SRR1329704 2 0.000 0.976 0.000 1.000
#> SRR1468072 2 0.000 0.976 0.000 1.000
#> SRR1376196 2 0.000 0.976 0.000 1.000
#> SRR1442909 2 0.000 0.976 0.000 1.000
#> SRR1414269 2 0.000 0.976 0.000 1.000
#> SRR1381913 2 0.000 0.976 0.000 1.000
#> SRR1340157 2 0.000 0.976 0.000 1.000
#> SRR1407583 2 0.000 0.976 0.000 1.000
#> SRR615826 1 0.000 0.948 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1458769 2 0.1643 0.8137 0.000 0.956 0.044
#> SRR613162 1 0.2625 0.9431 0.916 0.000 0.084
#> SRR1352481 1 0.3454 0.9322 0.888 0.008 0.104
#> SRR1468876 2 0.0000 0.8158 0.000 1.000 0.000
#> SRR1399223 2 0.3551 0.7616 0.000 0.868 0.132
#> SRR660030 3 0.5926 0.6610 0.000 0.356 0.644
#> SRR1333609 2 0.3941 0.7645 0.000 0.844 0.156
#> SRR1471612 2 0.6095 0.5762 0.000 0.608 0.392
#> SRR1413998 2 0.6045 0.5878 0.000 0.620 0.380
#> SRR1122940 2 0.0237 0.8141 0.000 0.996 0.004
#> SRR1402563 2 0.4399 0.7442 0.000 0.812 0.188
#> SRR1398393 2 0.5882 0.4091 0.000 0.652 0.348
#> SRR657961 3 0.4399 0.6155 0.000 0.188 0.812
#> SRR1471135 2 0.6111 0.5703 0.000 0.604 0.396
#> SRR1430001 3 0.5926 0.6610 0.000 0.356 0.644
#> SRR662775 3 0.7569 0.6289 0.200 0.116 0.684
#> SRR1474182 2 0.3619 0.7874 0.000 0.864 0.136
#> SRR607190 3 0.7272 0.6083 0.204 0.096 0.700
#> SRR612467 3 0.6158 0.5444 0.188 0.052 0.760
#> SRR1465959 2 0.0424 0.8173 0.000 0.992 0.008
#> SRR1446132 2 0.0424 0.8173 0.000 0.992 0.008
#> SRR1416933 2 0.4555 0.7399 0.000 0.800 0.200
#> SRR1102538 2 0.0000 0.8158 0.000 1.000 0.000
#> SRR1098636 2 0.5988 0.6005 0.000 0.632 0.368
#> SRR1072998 2 0.0237 0.8141 0.000 0.996 0.004
#> SRR627443 3 0.6912 0.2369 0.344 0.028 0.628
#> SRR656131 3 0.7569 0.6289 0.200 0.116 0.684
#> SRR823991 2 0.1289 0.8148 0.000 0.968 0.032
#> SRR1089158 1 0.4768 0.8713 0.848 0.052 0.100
#> SRR1469036 3 0.6308 0.3722 0.000 0.492 0.508
#> SRR824039 2 0.0424 0.8173 0.000 0.992 0.008
#> SRR1339047 3 0.6154 0.5238 0.000 0.408 0.592
#> SRR1443049 3 0.5926 0.6610 0.000 0.356 0.644
#> SRR1122885 2 0.0592 0.8180 0.000 0.988 0.012
#> SRR602895 3 0.5760 0.6593 0.000 0.328 0.672
#> SRR1409837 2 0.3619 0.7683 0.000 0.864 0.136
#> SRR1388959 2 0.5178 0.7080 0.000 0.744 0.256
#> SRR659863 2 0.8802 0.4284 0.200 0.584 0.216
#> SRR1089877 2 0.7755 -0.0690 0.460 0.492 0.048
#> SRR1123775 3 0.6286 0.4806 0.000 0.464 0.536
#> SRR658909 3 0.5816 0.4799 0.224 0.024 0.752
#> SRR1140510 2 0.0424 0.8173 0.000 0.992 0.008
#> SRR607562 3 0.2682 0.6096 0.004 0.076 0.920
#> SRR1122913 2 0.0000 0.8158 0.000 1.000 0.000
#> SRR598042 2 0.6154 0.5507 0.000 0.592 0.408
#> SRR1467340 2 0.3551 0.7616 0.000 0.868 0.132
#> SRR1072321 2 0.0000 0.8158 0.000 1.000 0.000
#> SRR1094580 2 0.4452 0.7123 0.000 0.808 0.192
#> SRR1076608 2 0.0592 0.8152 0.000 0.988 0.012
#> SRR1395462 2 0.6095 0.5762 0.000 0.608 0.392
#> SRR1489220 2 0.3340 0.7699 0.000 0.880 0.120
#> SRR614371 3 0.6651 0.2869 0.320 0.024 0.656
#> SRR615455 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1070573 2 0.0000 0.8158 0.000 1.000 0.000
#> SRR598749 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1365556 2 0.4346 0.7429 0.000 0.816 0.184
#> SRR1350023 2 0.0424 0.8173 0.000 0.992 0.008
#> SRR1446582 2 0.1031 0.8163 0.000 0.976 0.024
#> SRR1439763 2 0.0000 0.8158 0.000 1.000 0.000
#> SRR1343986 3 0.5905 0.6639 0.000 0.352 0.648
#> SRR807463 2 0.5291 0.6626 0.000 0.732 0.268
#> SRR660390 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1367672 3 0.5502 0.5307 0.008 0.248 0.744
#> SRR613294 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR824015 1 0.3454 0.9322 0.888 0.008 0.104
#> SRR1078924 2 0.0000 0.8158 0.000 1.000 0.000
#> SRR662221 1 0.2625 0.9432 0.916 0.000 0.084
#> SRR655017 3 0.6215 -0.0249 0.428 0.000 0.572
#> SRR1338450 2 0.0000 0.8158 0.000 1.000 0.000
#> SRR663741 1 0.2878 0.9389 0.904 0.000 0.096
#> SRR1396057 2 0.6095 0.5762 0.000 0.608 0.392
#> SRR1083800 2 0.0000 0.8158 0.000 1.000 0.000
#> SRR1445789 2 0.2625 0.7919 0.000 0.916 0.084
#> SRR1387355 3 0.7259 0.4409 0.248 0.072 0.680
#> SRR1388855 2 0.0237 0.8167 0.000 0.996 0.004
#> SRR1445449 3 0.4629 0.6157 0.004 0.188 0.808
#> SRR1380740 2 0.0000 0.8158 0.000 1.000 0.000
#> SRR659995 1 0.0000 0.9550 1.000 0.000 0.000
#> SRR1489524 2 0.0000 0.8158 0.000 1.000 0.000
#> SRR1444662 2 0.4399 0.7442 0.000 0.812 0.188
#> SRR1383652 2 0.6095 0.5762 0.000 0.608 0.392
#> SRR1361243 2 0.4291 0.7488 0.000 0.820 0.180
#> SRR1490337 2 0.6079 0.5812 0.000 0.612 0.388
#> SRR823967 2 0.1289 0.8159 0.000 0.968 0.032
#> SRR660127 2 0.8765 0.4327 0.200 0.588 0.212
#> SRR1366627 3 0.5968 0.6383 0.000 0.364 0.636
#> SRR1361219 2 0.1163 0.8166 0.000 0.972 0.028
#> SRR1393510 3 0.5733 0.6628 0.000 0.324 0.676
#> SRR662558 3 0.5859 0.6680 0.000 0.344 0.656
#> SRR1077334 2 0.0000 0.8158 0.000 1.000 0.000
#> SRR807438 2 0.2066 0.8065 0.000 0.940 0.060
#> SRR1459078 2 0.0000 0.8158 0.000 1.000 0.000
#> SRR1329704 2 0.0000 0.8158 0.000 1.000 0.000
#> SRR1468072 2 0.0424 0.8173 0.000 0.992 0.008
#> SRR1376196 2 0.4002 0.7578 0.000 0.840 0.160
#> SRR1442909 2 0.5397 0.6897 0.000 0.720 0.280
#> SRR1414269 2 0.0747 0.8172 0.000 0.984 0.016
#> SRR1381913 2 0.6095 0.5762 0.000 0.608 0.392
#> SRR1340157 2 0.0000 0.8158 0.000 1.000 0.000
#> SRR1407583 2 0.6045 0.5878 0.000 0.620 0.380
#> SRR615826 1 0.0000 0.9550 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.0188 0.90197 0.000 0.000 0.004 0.996
#> SRR1458769 2 0.0927 0.83898 0.016 0.976 0.008 0.000
#> SRR613162 4 0.7058 0.19948 0.136 0.000 0.344 0.520
#> SRR1352481 1 0.4936 -0.00236 0.700 0.020 0.000 0.280
#> SRR1468876 2 0.0804 0.84173 0.008 0.980 0.012 0.000
#> SRR1399223 2 0.2335 0.82661 0.020 0.920 0.060 0.000
#> SRR660030 2 0.5792 0.50547 0.168 0.708 0.124 0.000
#> SRR1333609 2 0.0921 0.83076 0.000 0.972 0.028 0.000
#> SRR1471612 3 0.4304 0.78632 0.000 0.284 0.716 0.000
#> SRR1413998 2 0.4713 -0.11776 0.000 0.640 0.360 0.000
#> SRR1122940 2 0.1854 0.83174 0.012 0.940 0.048 0.000
#> SRR1402563 3 0.5147 0.77030 0.004 0.460 0.536 0.000
#> SRR1398393 2 0.2928 0.79993 0.052 0.896 0.052 0.000
#> SRR657961 2 0.7264 -0.01766 0.148 0.460 0.392 0.000
#> SRR1471135 3 0.4356 0.80398 0.000 0.292 0.708 0.000
#> SRR1430001 2 0.5380 0.59967 0.136 0.744 0.120 0.000
#> SRR662775 1 0.6178 0.64693 0.564 0.016 0.392 0.028
#> SRR1474182 2 0.3280 0.69912 0.016 0.860 0.124 0.000
#> SRR607190 1 0.5798 0.64308 0.584 0.004 0.384 0.028
#> SRR612467 1 0.6099 0.64637 0.564 0.016 0.396 0.024
#> SRR1465959 2 0.0707 0.83976 0.020 0.980 0.000 0.000
#> SRR1446132 2 0.0927 0.84388 0.008 0.976 0.016 0.000
#> SRR1416933 3 0.4916 0.81206 0.000 0.424 0.576 0.000
#> SRR1102538 2 0.2002 0.83740 0.020 0.936 0.044 0.000
#> SRR1098636 2 0.2271 0.79781 0.008 0.916 0.076 0.000
#> SRR1072998 2 0.2245 0.83613 0.020 0.932 0.040 0.008
#> SRR627443 1 0.5957 0.62872 0.588 0.000 0.364 0.048
#> SRR656131 1 0.6178 0.64660 0.564 0.016 0.392 0.028
#> SRR823991 2 0.0707 0.83976 0.020 0.980 0.000 0.000
#> SRR1089158 1 0.3948 0.36484 0.840 0.064 0.000 0.096
#> SRR1469036 2 0.2996 0.79325 0.064 0.892 0.044 0.000
#> SRR824039 2 0.0895 0.83968 0.020 0.976 0.004 0.000
#> SRR1339047 2 0.3858 0.75846 0.100 0.844 0.056 0.000
#> SRR1443049 2 0.4224 0.71181 0.144 0.812 0.044 0.000
#> SRR1122885 2 0.1411 0.84224 0.020 0.960 0.020 0.000
#> SRR602895 2 0.6956 0.16856 0.288 0.564 0.148 0.000
#> SRR1409837 2 0.3978 0.52298 0.012 0.796 0.192 0.000
#> SRR1388959 3 0.5088 0.80957 0.004 0.424 0.572 0.000
#> SRR659863 1 0.8607 -0.11771 0.356 0.308 0.308 0.028
#> SRR1089877 2 0.7276 -0.09132 0.440 0.464 0.044 0.052
#> SRR1123775 2 0.3439 0.78229 0.084 0.868 0.048 0.000
#> SRR658909 1 0.5626 0.64033 0.588 0.000 0.384 0.028
#> SRR1140510 2 0.0188 0.84173 0.000 0.996 0.004 0.000
#> SRR607562 1 0.4888 0.63543 0.588 0.000 0.412 0.000
#> SRR1122913 2 0.1398 0.83628 0.004 0.956 0.040 0.000
#> SRR598042 3 0.3718 0.48657 0.012 0.168 0.820 0.000
#> SRR1467340 2 0.2843 0.80890 0.020 0.892 0.088 0.000
#> SRR1072321 2 0.2214 0.83519 0.028 0.928 0.044 0.000
#> SRR1094580 2 0.1256 0.83384 0.008 0.964 0.028 0.000
#> SRR1076608 2 0.1545 0.83551 0.008 0.952 0.040 0.000
#> SRR1395462 3 0.4222 0.77111 0.000 0.272 0.728 0.000
#> SRR1489220 2 0.1489 0.83665 0.004 0.952 0.044 0.000
#> SRR614371 1 0.5769 0.63689 0.588 0.000 0.376 0.036
#> SRR615455 4 0.0000 0.90282 0.000 0.000 0.000 1.000
#> SRR1070573 2 0.1854 0.83741 0.012 0.940 0.048 0.000
#> SRR598749 4 0.0188 0.90262 0.000 0.000 0.004 0.996
#> SRR1365556 2 0.2840 0.79515 0.044 0.900 0.056 0.000
#> SRR1350023 2 0.0895 0.83968 0.020 0.976 0.004 0.000
#> SRR1446582 2 0.0592 0.84036 0.016 0.984 0.000 0.000
#> SRR1439763 2 0.0524 0.84164 0.008 0.988 0.004 0.000
#> SRR1343986 2 0.7344 0.10535 0.268 0.524 0.208 0.000
#> SRR807463 2 0.1302 0.82582 0.000 0.956 0.044 0.000
#> SRR660390 4 0.0188 0.90262 0.000 0.000 0.004 0.996
#> SRR1367672 1 0.6635 0.04016 0.524 0.388 0.088 0.000
#> SRR613294 4 0.0000 0.90282 0.000 0.000 0.000 1.000
#> SRR824015 1 0.3658 0.30716 0.836 0.020 0.000 0.144
#> SRR1078924 2 0.1452 0.83749 0.008 0.956 0.036 0.000
#> SRR662221 4 0.4452 0.68771 0.260 0.000 0.008 0.732
#> SRR655017 1 0.6966 0.56646 0.532 0.000 0.340 0.128
#> SRR1338450 2 0.0524 0.84164 0.008 0.988 0.004 0.000
#> SRR663741 1 0.7537 0.48443 0.456 0.000 0.348 0.196
#> SRR1396057 3 0.4914 0.82086 0.012 0.312 0.676 0.000
#> SRR1083800 2 0.2089 0.83435 0.020 0.932 0.048 0.000
#> SRR1445789 2 0.1975 0.83103 0.016 0.936 0.048 0.000
#> SRR1387355 1 0.6838 0.29387 0.596 0.312 0.064 0.028
#> SRR1388855 2 0.1406 0.84236 0.016 0.960 0.024 0.000
#> SRR1445449 1 0.6489 0.09710 0.548 0.372 0.080 0.000
#> SRR1380740 2 0.0524 0.84164 0.008 0.988 0.004 0.000
#> SRR659995 4 0.0336 0.90119 0.000 0.000 0.008 0.992
#> SRR1489524 2 0.1520 0.84199 0.024 0.956 0.020 0.000
#> SRR1444662 3 0.5147 0.76448 0.004 0.460 0.536 0.000
#> SRR1383652 3 0.4957 0.82591 0.012 0.320 0.668 0.000
#> SRR1361243 2 0.1109 0.83207 0.004 0.968 0.028 0.000
#> SRR1490337 3 0.4916 0.81206 0.000 0.424 0.576 0.000
#> SRR823967 2 0.0817 0.84121 0.024 0.976 0.000 0.000
#> SRR660127 1 0.8622 -0.16289 0.336 0.316 0.320 0.028
#> SRR1366627 2 0.4401 0.69425 0.112 0.812 0.076 0.000
#> SRR1361219 2 0.1411 0.83975 0.020 0.960 0.020 0.000
#> SRR1393510 2 0.7301 0.02107 0.160 0.484 0.356 0.000
#> SRR662558 1 0.7550 0.50763 0.464 0.204 0.332 0.000
#> SRR1077334 2 0.0657 0.84128 0.012 0.984 0.004 0.000
#> SRR807438 2 0.0657 0.84320 0.012 0.984 0.004 0.000
#> SRR1459078 2 0.0524 0.84164 0.008 0.988 0.004 0.000
#> SRR1329704 2 0.0524 0.84220 0.008 0.988 0.004 0.000
#> SRR1468072 2 0.0376 0.84184 0.004 0.992 0.004 0.000
#> SRR1376196 2 0.1661 0.82102 0.004 0.944 0.052 0.000
#> SRR1442909 3 0.4916 0.81206 0.000 0.424 0.576 0.000
#> SRR1414269 2 0.1042 0.83939 0.020 0.972 0.008 0.000
#> SRR1381913 3 0.4522 0.82898 0.000 0.320 0.680 0.000
#> SRR1340157 2 0.1635 0.83574 0.008 0.948 0.044 0.000
#> SRR1407583 2 0.5402 -0.59195 0.012 0.516 0.472 0.000
#> SRR615826 4 0.0000 0.90282 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
#> SRR612587 4 0.0000 0.7707 0.000 0.000 0.000 1.000 0.000
#> SRR1458769 3 0.4199 0.6752 0.000 0.160 0.772 0.000 0.068
#> SRR613162 4 0.4306 0.6578 0.172 0.012 0.000 0.772 0.044
#> SRR1352481 4 0.8825 0.2333 0.260 0.024 0.240 0.340 0.136
#> SRR1468876 3 0.3318 0.5586 0.000 0.008 0.800 0.000 0.192
#> SRR1399223 3 0.5434 0.6270 0.016 0.336 0.604 0.000 0.044
#> SRR660030 1 0.6701 0.4805 0.464 0.004 0.300 0.000 0.232
#> SRR1333609 3 0.3081 0.6080 0.000 0.156 0.832 0.000 0.012
#> SRR1471612 2 0.5855 0.7803 0.028 0.620 0.280 0.000 0.072
#> SRR1413998 2 0.4522 0.6152 0.000 0.552 0.440 0.000 0.008
#> SRR1122940 3 0.5156 0.6272 0.004 0.276 0.656 0.000 0.064
#> SRR1402563 2 0.4242 0.6586 0.000 0.572 0.428 0.000 0.000
#> SRR1398393 3 0.5686 0.4681 0.284 0.064 0.628 0.000 0.024
#> SRR657961 1 0.5880 0.4865 0.632 0.092 0.252 0.000 0.024
#> SRR1471135 2 0.5855 0.7803 0.028 0.620 0.280 0.000 0.072
#> SRR1430001 3 0.8558 -0.1638 0.220 0.268 0.296 0.000 0.216
#> SRR662775 1 0.2112 0.5833 0.908 0.000 0.004 0.004 0.084
#> SRR1474182 3 0.3123 0.6032 0.000 0.160 0.828 0.000 0.012
#> SRR607190 1 0.1116 0.5785 0.964 0.000 0.004 0.004 0.028
#> SRR612467 1 0.2439 0.5797 0.876 0.004 0.000 0.000 0.120
#> SRR1465959 3 0.0451 0.6847 0.000 0.008 0.988 0.000 0.004
#> SRR1446132 3 0.4478 0.6617 0.000 0.144 0.756 0.000 0.100
#> SRR1416933 2 0.4171 0.7074 0.000 0.604 0.396 0.000 0.000
#> SRR1102538 3 0.5063 0.6304 0.000 0.312 0.632 0.000 0.056
#> SRR1098636 3 0.3965 0.6315 0.064 0.076 0.828 0.000 0.032
#> SRR1072998 3 0.2723 0.7014 0.000 0.124 0.864 0.000 0.012
#> SRR627443 1 0.1788 0.5531 0.932 0.004 0.000 0.008 0.056
#> SRR656131 1 0.2787 0.5970 0.880 0.000 0.028 0.004 0.088
#> SRR823991 3 0.0451 0.6833 0.000 0.008 0.988 0.000 0.004
#> SRR1089158 1 0.8781 -0.0798 0.364 0.024 0.260 0.208 0.144
#> SRR1469036 3 0.5326 0.3150 0.360 0.024 0.592 0.000 0.024
#> SRR824039 3 0.0404 0.6825 0.000 0.012 0.988 0.000 0.000
#> SRR1339047 1 0.5910 0.2192 0.492 0.020 0.432 0.000 0.056
#> SRR1443049 1 0.7722 0.4603 0.384 0.060 0.240 0.000 0.316
#> SRR1122885 3 0.3704 0.6416 0.000 0.092 0.820 0.000 0.088
#> SRR602895 1 0.4708 0.5943 0.712 0.000 0.220 0.000 0.068
#> SRR1409837 3 0.5037 0.6179 0.000 0.376 0.584 0.000 0.040
#> SRR1388959 2 0.4182 0.7025 0.000 0.600 0.400 0.000 0.000
#> SRR659863 2 0.6149 -0.1347 0.420 0.492 0.064 0.004 0.020
#> SRR1089877 3 0.8082 0.3386 0.164 0.272 0.452 0.016 0.096
#> SRR1123775 3 0.8222 0.1288 0.132 0.248 0.380 0.000 0.240
#> SRR658909 1 0.1843 0.5552 0.932 0.008 0.000 0.008 0.052
#> SRR1140510 3 0.0162 0.6833 0.000 0.000 0.996 0.000 0.004
#> SRR607562 1 0.0960 0.5691 0.972 0.008 0.000 0.004 0.016
#> SRR1122913 3 0.4412 0.6915 0.000 0.164 0.756 0.000 0.080
#> SRR598042 2 0.6311 0.7434 0.084 0.612 0.248 0.000 0.056
#> SRR1467340 3 0.5365 0.6247 0.028 0.348 0.600 0.000 0.024
#> SRR1072321 3 0.5062 0.6216 0.000 0.276 0.656 0.000 0.068
#> SRR1094580 3 0.1990 0.6797 0.004 0.040 0.928 0.000 0.028
#> SRR1076608 3 0.5433 0.6413 0.000 0.288 0.620 0.000 0.092
#> SRR1395462 2 0.5855 0.7803 0.028 0.620 0.280 0.000 0.072
#> SRR1489220 3 0.4028 0.6925 0.000 0.176 0.776 0.000 0.048
#> SRR614371 1 0.1857 0.5516 0.928 0.004 0.000 0.008 0.060
#> SRR615455 4 0.0000 0.7707 0.000 0.000 0.000 1.000 0.000
#> SRR1070573 3 0.4752 0.6363 0.004 0.272 0.684 0.000 0.040
#> SRR598749 4 0.0000 0.7707 0.000 0.000 0.000 1.000 0.000
#> SRR1365556 3 0.6092 0.5749 0.088 0.356 0.540 0.000 0.016
#> SRR1350023 3 0.0162 0.6841 0.000 0.000 0.996 0.000 0.004
#> SRR1446582 3 0.2448 0.6626 0.000 0.088 0.892 0.000 0.020
#> SRR1439763 3 0.2605 0.5821 0.000 0.000 0.852 0.000 0.148
#> SRR1343986 1 0.8540 0.2620 0.296 0.212 0.280 0.000 0.212
#> SRR807463 3 0.3368 0.6530 0.028 0.080 0.860 0.000 0.032
#> SRR660390 4 0.0290 0.7695 0.000 0.008 0.000 0.992 0.000
#> SRR1367672 1 0.6356 0.1623 0.592 0.056 0.276 0.000 0.076
#> SRR613294 4 0.0000 0.7707 0.000 0.000 0.000 1.000 0.000
#> SRR824015 4 0.8900 0.1777 0.288 0.024 0.244 0.300 0.144
#> SRR1078924 3 0.4138 0.6971 0.000 0.148 0.780 0.000 0.072
#> SRR662221 4 0.6177 0.5484 0.164 0.000 0.000 0.532 0.304
#> SRR655017 1 0.5261 0.0848 0.628 0.012 0.000 0.316 0.044
#> SRR1338450 3 0.2690 0.5716 0.000 0.000 0.844 0.000 0.156
#> SRR663741 4 0.6929 0.3491 0.312 0.004 0.000 0.380 0.304
#> SRR1396057 2 0.6096 0.7639 0.084 0.612 0.268 0.000 0.036
#> SRR1083800 3 0.4735 0.6250 0.000 0.272 0.680 0.000 0.048
#> SRR1445789 3 0.5639 0.6107 0.000 0.340 0.568 0.000 0.092
#> SRR1387355 1 0.5209 0.5563 0.684 0.024 0.036 0.004 0.252
#> SRR1388855 3 0.5382 0.6208 0.000 0.352 0.580 0.000 0.068
#> SRR1445449 1 0.5673 0.3938 0.692 0.052 0.180 0.000 0.076
#> SRR1380740 3 0.2648 0.5766 0.000 0.000 0.848 0.000 0.152
#> SRR659995 4 0.0290 0.7695 0.000 0.008 0.000 0.992 0.000
#> SRR1489524 3 0.4425 0.6429 0.000 0.244 0.716 0.000 0.040
#> SRR1444662 3 0.4562 -0.4897 0.000 0.492 0.500 0.000 0.008
#> SRR1383652 2 0.6096 0.7639 0.084 0.612 0.268 0.000 0.036
#> SRR1361243 3 0.3318 0.5764 0.000 0.180 0.808 0.000 0.012
#> SRR1490337 2 0.5538 0.7725 0.000 0.588 0.324 0.000 0.088
#> SRR823967 3 0.0740 0.6861 0.008 0.008 0.980 0.000 0.004
#> SRR660127 2 0.6126 -0.1680 0.432 0.480 0.068 0.004 0.016
#> SRR1366627 1 0.6024 0.4558 0.532 0.000 0.336 0.000 0.132
#> SRR1361219 3 0.5418 0.6166 0.000 0.364 0.568 0.000 0.068
#> SRR1393510 1 0.5776 0.5394 0.588 0.000 0.288 0.000 0.124
#> SRR662558 1 0.5243 0.5703 0.672 0.004 0.088 0.000 0.236
#> SRR1077334 3 0.0703 0.6815 0.000 0.000 0.976 0.000 0.024
#> SRR807438 3 0.1588 0.6840 0.016 0.008 0.948 0.000 0.028
#> SRR1459078 3 0.3039 0.5569 0.000 0.000 0.808 0.000 0.192
#> SRR1329704 3 0.1792 0.6472 0.000 0.000 0.916 0.000 0.084
#> SRR1468072 3 0.0000 0.6834 0.000 0.000 1.000 0.000 0.000
#> SRR1376196 3 0.3454 0.6064 0.000 0.156 0.816 0.000 0.028
#> SRR1442909 2 0.4570 0.7563 0.000 0.632 0.348 0.000 0.020
#> SRR1414269 3 0.0693 0.6829 0.000 0.008 0.980 0.000 0.012
#> SRR1381913 2 0.5663 0.7802 0.016 0.620 0.292 0.000 0.072
#> SRR1340157 3 0.4343 0.6728 0.000 0.136 0.768 0.000 0.096
#> SRR1407583 2 0.6199 0.6392 0.088 0.476 0.420 0.000 0.016
#> SRR615826 4 0.0000 0.7707 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 4 0.0000 0.820431 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1458769 2 0.5211 -0.333646 0.000 0.580 0.088 0.000 0.324 0.008
#> SRR613162 4 0.3323 0.710581 0.056 0.000 0.016 0.848 0.008 0.072
#> SRR1352481 4 0.7628 -0.538423 0.068 0.148 0.016 0.452 0.036 0.280
#> SRR1468876 2 0.5129 0.360640 0.000 0.704 0.088 0.000 0.140 0.068
#> SRR1399223 2 0.5911 -0.440216 0.060 0.520 0.068 0.000 0.352 0.000
#> SRR660030 1 0.6987 0.428195 0.524 0.216 0.040 0.000 0.168 0.052
#> SRR1333609 2 0.4490 0.204562 0.000 0.700 0.196 0.000 0.104 0.000
#> SRR1471612 3 0.2673 0.771191 0.012 0.132 0.852 0.000 0.000 0.004
#> SRR1413998 3 0.5161 0.620010 0.000 0.376 0.540 0.000 0.080 0.004
#> SRR1122940 2 0.5360 0.062409 0.020 0.644 0.040 0.000 0.260 0.036
#> SRR1402563 3 0.4747 0.703435 0.000 0.288 0.632 0.000 0.080 0.000
#> SRR1398393 2 0.6374 0.048472 0.204 0.568 0.164 0.000 0.056 0.008
#> SRR657961 1 0.6372 0.381809 0.584 0.152 0.168 0.000 0.004 0.092
#> SRR1471135 3 0.3043 0.771332 0.020 0.140 0.832 0.000 0.000 0.008
#> SRR1430001 1 0.7518 0.348039 0.384 0.204 0.044 0.000 0.316 0.052
#> SRR662775 1 0.1478 0.454923 0.944 0.020 0.000 0.004 0.000 0.032
#> SRR1474182 2 0.4614 0.180169 0.000 0.676 0.228 0.000 0.096 0.000
#> SRR607190 1 0.2320 0.410744 0.864 0.000 0.000 0.004 0.000 0.132
#> SRR612467 1 0.3143 0.430133 0.836 0.000 0.004 0.008 0.024 0.128
#> SRR1465959 2 0.1218 0.444238 0.000 0.956 0.012 0.000 0.028 0.004
#> SRR1446132 2 0.4808 -0.725127 0.000 0.480 0.052 0.000 0.468 0.000
#> SRR1416933 3 0.4357 0.761260 0.000 0.224 0.700 0.000 0.076 0.000
#> SRR1102538 2 0.4479 -0.196632 0.000 0.624 0.036 0.000 0.336 0.004
#> SRR1098636 2 0.5422 0.333430 0.052 0.704 0.132 0.000 0.024 0.088
#> SRR1072998 2 0.3942 0.319692 0.000 0.768 0.020 0.000 0.176 0.036
#> SRR627443 1 0.4501 0.260977 0.684 0.000 0.004 0.052 0.004 0.256
#> SRR656131 1 0.1414 0.461068 0.952 0.020 0.000 0.004 0.012 0.012
#> SRR823991 2 0.2302 0.445330 0.016 0.912 0.036 0.000 0.024 0.012
#> SRR1089158 6 0.7905 0.834757 0.152 0.184 0.000 0.220 0.036 0.408
#> SRR1469036 2 0.6190 0.110937 0.280 0.548 0.132 0.000 0.024 0.016
#> SRR824039 2 0.2461 0.435053 0.000 0.888 0.064 0.000 0.044 0.004
#> SRR1339047 1 0.6294 0.164251 0.456 0.404 0.088 0.000 0.024 0.028
#> SRR1443049 1 0.7898 0.359438 0.392 0.160 0.064 0.000 0.292 0.092
#> SRR1122885 2 0.4783 -0.606937 0.000 0.520 0.052 0.000 0.428 0.000
#> SRR602895 1 0.4339 0.469723 0.776 0.088 0.012 0.000 0.020 0.104
#> SRR1409837 2 0.5895 0.084985 0.000 0.592 0.228 0.000 0.136 0.044
#> SRR1388959 3 0.4625 0.753253 0.000 0.216 0.680 0.000 0.104 0.000
#> SRR659863 1 0.5135 0.123211 0.480 0.008 0.464 0.004 0.040 0.004
#> SRR1089877 2 0.8615 0.017106 0.104 0.424 0.056 0.060 0.172 0.184
#> SRR1123775 1 0.8242 0.201998 0.296 0.256 0.080 0.000 0.284 0.084
#> SRR658909 1 0.4224 0.275490 0.688 0.000 0.004 0.028 0.004 0.276
#> SRR1140510 2 0.1167 0.465155 0.000 0.960 0.008 0.000 0.020 0.012
#> SRR607562 1 0.3265 0.383551 0.748 0.000 0.004 0.000 0.000 0.248
#> SRR1122913 2 0.4463 -0.703378 0.000 0.516 0.028 0.000 0.456 0.000
#> SRR598042 3 0.4103 0.732165 0.084 0.136 0.768 0.000 0.000 0.012
#> SRR1467340 2 0.6142 -0.284410 0.084 0.552 0.084 0.000 0.280 0.000
#> SRR1072321 2 0.5491 0.014765 0.000 0.596 0.052 0.000 0.296 0.056
#> SRR1094580 2 0.3557 0.432536 0.024 0.844 0.052 0.000 0.024 0.056
#> SRR1076608 5 0.4756 0.851863 0.000 0.408 0.052 0.000 0.540 0.000
#> SRR1395462 3 0.2673 0.771191 0.012 0.132 0.852 0.000 0.000 0.004
#> SRR1489220 2 0.5279 -0.461210 0.004 0.544 0.096 0.000 0.356 0.000
#> SRR614371 1 0.4087 0.276305 0.692 0.000 0.000 0.028 0.004 0.276
#> SRR615455 4 0.0000 0.820431 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1070573 2 0.4025 0.051319 0.000 0.668 0.016 0.000 0.312 0.004
#> SRR598749 4 0.0000 0.820431 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1365556 2 0.6904 -0.094303 0.156 0.492 0.224 0.000 0.128 0.000
#> SRR1350023 2 0.0993 0.465315 0.000 0.964 0.000 0.000 0.012 0.024
#> SRR1446582 2 0.2680 0.350652 0.000 0.860 0.032 0.000 0.108 0.000
#> SRR1439763 2 0.3316 0.412987 0.000 0.812 0.000 0.000 0.136 0.052
#> SRR1343986 1 0.7424 0.375381 0.416 0.204 0.040 0.000 0.288 0.052
#> SRR807463 2 0.5254 0.329279 0.028 0.708 0.148 0.000 0.028 0.088
#> SRR660390 4 0.0551 0.817114 0.000 0.000 0.008 0.984 0.004 0.004
#> SRR1367672 1 0.7343 -0.000737 0.412 0.172 0.040 0.000 0.052 0.324
#> SRR613294 4 0.0000 0.820431 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR824015 6 0.7708 0.814385 0.092 0.164 0.004 0.288 0.036 0.416
#> SRR1078924 5 0.4594 0.768431 0.000 0.476 0.036 0.000 0.488 0.000
#> SRR662221 4 0.4711 0.615866 0.048 0.000 0.024 0.740 0.024 0.164
#> SRR655017 1 0.5971 -0.267804 0.476 0.000 0.008 0.372 0.008 0.136
#> SRR1338450 2 0.3557 0.409673 0.000 0.800 0.004 0.000 0.140 0.056
#> SRR663741 4 0.6002 0.390494 0.140 0.000 0.024 0.620 0.028 0.188
#> SRR1396057 3 0.4053 0.737230 0.080 0.136 0.772 0.000 0.000 0.012
#> SRR1083800 2 0.5033 0.248833 0.000 0.692 0.056 0.000 0.192 0.060
#> SRR1445789 5 0.4996 0.824707 0.004 0.384 0.064 0.000 0.548 0.000
#> SRR1387355 1 0.6646 0.338229 0.516 0.004 0.048 0.004 0.224 0.204
#> SRR1388855 2 0.4553 -0.389490 0.000 0.580 0.032 0.000 0.384 0.004
#> SRR1445449 1 0.6969 0.117684 0.476 0.084 0.064 0.000 0.048 0.328
#> SRR1380740 2 0.3546 0.417096 0.000 0.808 0.008 0.000 0.128 0.056
#> SRR659995 4 0.0551 0.817114 0.000 0.000 0.008 0.984 0.004 0.004
#> SRR1489524 2 0.4444 0.289403 0.000 0.736 0.084 0.000 0.164 0.016
#> SRR1444662 3 0.5457 0.596827 0.000 0.264 0.588 0.000 0.140 0.008
#> SRR1383652 3 0.4002 0.738549 0.076 0.136 0.776 0.000 0.000 0.012
#> SRR1361243 2 0.5003 0.098176 0.000 0.608 0.288 0.000 0.104 0.000
#> SRR1490337 3 0.3304 0.718402 0.004 0.080 0.840 0.000 0.068 0.008
#> SRR823967 2 0.3039 0.437336 0.020 0.868 0.064 0.000 0.040 0.008
#> SRR660127 1 0.5154 0.181173 0.524 0.020 0.416 0.004 0.036 0.000
#> SRR1366627 1 0.6057 0.427345 0.584 0.272 0.056 0.000 0.072 0.016
#> SRR1361219 2 0.5405 -0.102252 0.000 0.604 0.148 0.000 0.240 0.008
#> SRR1393510 1 0.5050 0.455351 0.668 0.240 0.008 0.000 0.064 0.020
#> SRR662558 1 0.6666 0.436098 0.592 0.108 0.032 0.000 0.160 0.108
#> SRR1077334 2 0.2356 0.454819 0.000 0.884 0.004 0.000 0.096 0.016
#> SRR807438 2 0.2876 0.457351 0.056 0.880 0.012 0.000 0.032 0.020
#> SRR1459078 2 0.3662 0.414936 0.000 0.800 0.008 0.000 0.128 0.064
#> SRR1329704 2 0.3248 0.429258 0.000 0.828 0.004 0.000 0.116 0.052
#> SRR1468072 2 0.1633 0.464892 0.000 0.932 0.000 0.000 0.044 0.024
#> SRR1376196 2 0.5135 0.067786 0.000 0.608 0.260 0.000 0.132 0.000
#> SRR1442909 3 0.3806 0.776325 0.000 0.164 0.768 0.000 0.068 0.000
#> SRR1414269 2 0.1353 0.466584 0.000 0.952 0.012 0.000 0.012 0.024
#> SRR1381913 3 0.3073 0.772872 0.016 0.152 0.824 0.000 0.000 0.008
#> SRR1340157 5 0.4529 0.785245 0.000 0.460 0.032 0.000 0.508 0.000
#> SRR1407583 3 0.5460 0.638574 0.068 0.316 0.588 0.000 0.016 0.012
#> SRR615826 4 0.0000 0.820431 0.000 0.000 0.000 1.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "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 17380 rows and 102 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 0.791 0.885 0.952 0.4045 0.616 0.616
#> 3 3 0.691 0.776 0.896 0.3569 0.790 0.673
#> 4 4 0.425 0.482 0.750 0.2283 0.713 0.452
#> 5 5 0.545 0.675 0.807 0.1097 0.824 0.498
#> 6 6 0.628 0.583 0.795 0.0519 0.965 0.847
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
#> SRR612587 1 0.0000 0.9498 1.000 0.000
#> SRR1458769 2 0.0000 0.9471 0.000 1.000
#> SRR613162 1 0.0000 0.9498 1.000 0.000
#> SRR1352481 1 0.7219 0.7466 0.800 0.200
#> SRR1468876 2 0.0000 0.9471 0.000 1.000
#> SRR1399223 2 0.0000 0.9471 0.000 1.000
#> SRR660030 2 0.2603 0.9134 0.044 0.956
#> SRR1333609 2 0.0000 0.9471 0.000 1.000
#> SRR1471612 2 0.0000 0.9471 0.000 1.000
#> SRR1413998 2 0.0000 0.9471 0.000 1.000
#> SRR1122940 2 0.0000 0.9471 0.000 1.000
#> SRR1402563 2 0.0000 0.9471 0.000 1.000
#> SRR1398393 2 0.8267 0.6696 0.260 0.740
#> SRR657961 2 0.9977 0.1686 0.472 0.528
#> SRR1471135 2 0.2043 0.9227 0.032 0.968
#> SRR1430001 2 0.0000 0.9471 0.000 1.000
#> SRR662775 1 0.0000 0.9498 1.000 0.000
#> SRR1474182 2 0.0000 0.9471 0.000 1.000
#> SRR607190 1 0.0000 0.9498 1.000 0.000
#> SRR612467 1 0.0000 0.9498 1.000 0.000
#> SRR1465959 2 0.0000 0.9471 0.000 1.000
#> SRR1446132 2 0.0000 0.9471 0.000 1.000
#> SRR1416933 2 0.0000 0.9471 0.000 1.000
#> SRR1102538 2 0.0000 0.9471 0.000 1.000
#> SRR1098636 2 0.0000 0.9471 0.000 1.000
#> SRR1072998 2 0.0000 0.9471 0.000 1.000
#> SRR627443 1 0.0000 0.9498 1.000 0.000
#> SRR656131 1 0.0000 0.9498 1.000 0.000
#> SRR823991 2 0.0000 0.9471 0.000 1.000
#> SRR1089158 2 0.9983 0.0260 0.476 0.524
#> SRR1469036 2 0.0000 0.9471 0.000 1.000
#> SRR824039 2 0.0000 0.9471 0.000 1.000
#> SRR1339047 2 0.9998 0.0955 0.492 0.508
#> SRR1443049 2 0.0000 0.9471 0.000 1.000
#> SRR1122885 2 0.0000 0.9471 0.000 1.000
#> SRR602895 2 0.9286 0.5161 0.344 0.656
#> SRR1409837 2 0.0000 0.9471 0.000 1.000
#> SRR1388959 2 0.0000 0.9471 0.000 1.000
#> SRR659863 2 0.7219 0.7539 0.200 0.800
#> SRR1089877 2 0.0000 0.9471 0.000 1.000
#> SRR1123775 2 0.0000 0.9471 0.000 1.000
#> SRR658909 1 0.0000 0.9498 1.000 0.000
#> SRR1140510 2 0.0000 0.9471 0.000 1.000
#> SRR607562 1 0.0000 0.9498 1.000 0.000
#> SRR1122913 2 0.0000 0.9471 0.000 1.000
#> SRR598042 2 0.7219 0.7539 0.200 0.800
#> SRR1467340 2 0.0000 0.9471 0.000 1.000
#> SRR1072321 2 0.0000 0.9471 0.000 1.000
#> SRR1094580 2 0.0000 0.9471 0.000 1.000
#> SRR1076608 2 0.0000 0.9471 0.000 1.000
#> SRR1395462 2 0.0000 0.9471 0.000 1.000
#> SRR1489220 2 0.0000 0.9471 0.000 1.000
#> SRR614371 1 0.0000 0.9498 1.000 0.000
#> SRR615455 1 0.0000 0.9498 1.000 0.000
#> SRR1070573 2 0.0000 0.9471 0.000 1.000
#> SRR598749 1 0.0000 0.9498 1.000 0.000
#> SRR1365556 2 0.4022 0.8812 0.080 0.920
#> SRR1350023 2 0.0000 0.9471 0.000 1.000
#> SRR1446582 2 0.0000 0.9471 0.000 1.000
#> SRR1439763 2 0.0000 0.9471 0.000 1.000
#> SRR1343986 2 0.0000 0.9471 0.000 1.000
#> SRR807463 2 0.0000 0.9471 0.000 1.000
#> SRR660390 1 0.0000 0.9498 1.000 0.000
#> SRR1367672 2 0.9087 0.5572 0.324 0.676
#> SRR613294 1 0.0000 0.9498 1.000 0.000
#> SRR824015 1 0.7815 0.7055 0.768 0.232
#> SRR1078924 2 0.0000 0.9471 0.000 1.000
#> SRR662221 1 0.0000 0.9498 1.000 0.000
#> SRR655017 1 0.0000 0.9498 1.000 0.000
#> SRR1338450 2 0.0000 0.9471 0.000 1.000
#> SRR663741 1 0.0000 0.9498 1.000 0.000
#> SRR1396057 2 0.7219 0.7539 0.200 0.800
#> SRR1083800 2 0.0000 0.9471 0.000 1.000
#> SRR1445789 2 0.0000 0.9471 0.000 1.000
#> SRR1387355 1 0.9866 0.2308 0.568 0.432
#> SRR1388855 2 0.0000 0.9471 0.000 1.000
#> SRR1445449 1 0.0672 0.9445 0.992 0.008
#> SRR1380740 2 0.0000 0.9471 0.000 1.000
#> SRR659995 1 0.0000 0.9498 1.000 0.000
#> SRR1489524 2 0.0000 0.9471 0.000 1.000
#> SRR1444662 2 0.0000 0.9471 0.000 1.000
#> SRR1383652 2 0.7219 0.7539 0.200 0.800
#> SRR1361243 2 0.0000 0.9471 0.000 1.000
#> SRR1490337 2 0.0000 0.9471 0.000 1.000
#> SRR823967 2 0.0000 0.9471 0.000 1.000
#> SRR660127 2 0.7219 0.7539 0.200 0.800
#> SRR1366627 1 0.6887 0.7535 0.816 0.184
#> SRR1361219 2 0.0000 0.9471 0.000 1.000
#> SRR1393510 1 0.0376 0.9473 0.996 0.004
#> SRR662558 1 0.4939 0.8599 0.892 0.108
#> SRR1077334 2 0.0000 0.9471 0.000 1.000
#> SRR807438 2 0.0000 0.9471 0.000 1.000
#> SRR1459078 2 0.0000 0.9471 0.000 1.000
#> SRR1329704 2 0.0000 0.9471 0.000 1.000
#> SRR1468072 2 0.0000 0.9471 0.000 1.000
#> SRR1376196 2 0.0000 0.9471 0.000 1.000
#> SRR1442909 2 0.0000 0.9471 0.000 1.000
#> SRR1414269 2 0.0000 0.9471 0.000 1.000
#> SRR1381913 2 0.0000 0.9471 0.000 1.000
#> SRR1340157 2 0.0000 0.9471 0.000 1.000
#> SRR1407583 2 0.6973 0.7683 0.188 0.812
#> SRR615826 1 0.0000 0.9498 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 3 0.1529 0.7962 0.040 0.000 0.960
#> SRR1458769 2 0.0747 0.9198 0.016 0.984 0.000
#> SRR613162 1 0.6252 -0.0148 0.556 0.000 0.444
#> SRR1352481 3 0.1163 0.7809 0.000 0.028 0.972
#> SRR1468876 2 0.1031 0.9191 0.000 0.976 0.024
#> SRR1399223 2 0.1529 0.9164 0.000 0.960 0.040
#> SRR660030 2 0.6924 0.2997 0.020 0.580 0.400
#> SRR1333609 2 0.1453 0.9196 0.024 0.968 0.008
#> SRR1471612 2 0.5926 0.4562 0.356 0.644 0.000
#> SRR1413998 2 0.2711 0.8782 0.088 0.912 0.000
#> SRR1122940 2 0.1529 0.9164 0.000 0.960 0.040
#> SRR1402563 2 0.1877 0.9172 0.032 0.956 0.012
#> SRR1398393 2 0.7656 0.3012 0.052 0.572 0.376
#> SRR657961 1 0.4002 0.6693 0.840 0.160 0.000
#> SRR1471135 1 0.6302 0.0287 0.520 0.480 0.000
#> SRR1430001 2 0.3845 0.8630 0.012 0.872 0.116
#> SRR662775 1 0.4178 0.6144 0.828 0.000 0.172
#> SRR1474182 2 0.1529 0.9111 0.040 0.960 0.000
#> SRR607190 1 0.0592 0.7649 0.988 0.000 0.012
#> SRR612467 3 0.1860 0.7957 0.052 0.000 0.948
#> SRR1465959 2 0.0747 0.9198 0.016 0.984 0.000
#> SRR1446132 2 0.1411 0.9178 0.000 0.964 0.036
#> SRR1416933 2 0.3267 0.8522 0.116 0.884 0.000
#> SRR1102538 2 0.0661 0.9220 0.008 0.988 0.004
#> SRR1098636 2 0.5591 0.5680 0.304 0.696 0.000
#> SRR1072998 2 0.1529 0.9164 0.000 0.960 0.040
#> SRR627443 1 0.0747 0.7633 0.984 0.000 0.016
#> SRR656131 1 0.5905 0.2619 0.648 0.000 0.352
#> SRR823991 2 0.1289 0.9150 0.032 0.968 0.000
#> SRR1089158 3 0.4326 0.7246 0.012 0.144 0.844
#> SRR1469036 2 0.1905 0.9193 0.028 0.956 0.016
#> SRR824039 2 0.1337 0.9212 0.016 0.972 0.012
#> SRR1339047 3 0.8404 0.0516 0.084 0.452 0.464
#> SRR1443049 2 0.3038 0.8718 0.000 0.896 0.104
#> SRR1122885 2 0.1337 0.9215 0.012 0.972 0.016
#> SRR602895 1 0.1878 0.7687 0.952 0.044 0.004
#> SRR1409837 2 0.1411 0.9132 0.036 0.964 0.000
#> SRR1388959 2 0.1163 0.9166 0.028 0.972 0.000
#> SRR659863 1 0.1031 0.7683 0.976 0.024 0.000
#> SRR1089877 2 0.1031 0.9191 0.000 0.976 0.024
#> SRR1123775 2 0.1753 0.9145 0.000 0.952 0.048
#> SRR658909 1 0.1289 0.7558 0.968 0.000 0.032
#> SRR1140510 2 0.0892 0.9200 0.000 0.980 0.020
#> SRR607562 1 0.1163 0.7579 0.972 0.000 0.028
#> SRR1122913 2 0.1411 0.9178 0.000 0.964 0.036
#> SRR598042 1 0.1753 0.7672 0.952 0.048 0.000
#> SRR1467340 2 0.1491 0.9209 0.016 0.968 0.016
#> SRR1072321 2 0.0892 0.9200 0.000 0.980 0.020
#> SRR1094580 2 0.2796 0.8755 0.092 0.908 0.000
#> SRR1076608 2 0.1529 0.9164 0.000 0.960 0.040
#> SRR1395462 2 0.6079 0.3755 0.388 0.612 0.000
#> SRR1489220 2 0.1620 0.9216 0.012 0.964 0.024
#> SRR614371 1 0.1289 0.7558 0.968 0.000 0.032
#> SRR615455 3 0.1753 0.7965 0.048 0.000 0.952
#> SRR1070573 2 0.1163 0.9187 0.000 0.972 0.028
#> SRR598749 3 0.6168 0.3296 0.412 0.000 0.588
#> SRR1365556 2 0.5798 0.7476 0.040 0.776 0.184
#> SRR1350023 2 0.0424 0.9217 0.000 0.992 0.008
#> SRR1446582 2 0.1163 0.9166 0.028 0.972 0.000
#> SRR1439763 2 0.1529 0.9164 0.000 0.960 0.040
#> SRR1343986 2 0.3879 0.8226 0.000 0.848 0.152
#> SRR807463 2 0.1753 0.9068 0.048 0.952 0.000
#> SRR660390 3 0.4887 0.6596 0.228 0.000 0.772
#> SRR1367672 1 0.1964 0.7652 0.944 0.056 0.000
#> SRR613294 3 0.1643 0.7966 0.044 0.000 0.956
#> SRR824015 3 0.5201 0.6009 0.004 0.236 0.760
#> SRR1078924 2 0.1411 0.9178 0.000 0.964 0.036
#> SRR662221 3 0.1163 0.7960 0.028 0.000 0.972
#> SRR655017 1 0.1411 0.7529 0.964 0.000 0.036
#> SRR1338450 2 0.1643 0.9152 0.000 0.956 0.044
#> SRR663741 3 0.1753 0.7965 0.048 0.000 0.952
#> SRR1396057 1 0.3267 0.7202 0.884 0.116 0.000
#> SRR1083800 2 0.0892 0.9200 0.000 0.980 0.020
#> SRR1445789 2 0.1529 0.9164 0.000 0.960 0.040
#> SRR1387355 3 0.3752 0.7127 0.000 0.144 0.856
#> SRR1388855 2 0.0475 0.9223 0.004 0.992 0.004
#> SRR1445449 1 0.0661 0.7674 0.988 0.004 0.008
#> SRR1380740 2 0.1289 0.9183 0.000 0.968 0.032
#> SRR659995 3 0.6045 0.4157 0.380 0.000 0.620
#> SRR1489524 2 0.0892 0.9200 0.000 0.980 0.020
#> SRR1444662 2 0.1289 0.9150 0.032 0.968 0.000
#> SRR1383652 1 0.2448 0.7549 0.924 0.076 0.000
#> SRR1361243 2 0.1411 0.9132 0.036 0.964 0.000
#> SRR1490337 2 0.3412 0.8398 0.124 0.876 0.000
#> SRR823967 2 0.1031 0.9179 0.024 0.976 0.000
#> SRR660127 1 0.7174 0.0607 0.516 0.460 0.024
#> SRR1366627 3 0.4937 0.6951 0.028 0.148 0.824
#> SRR1361219 2 0.0892 0.9190 0.020 0.980 0.000
#> SRR1393510 3 0.2269 0.7970 0.040 0.016 0.944
#> SRR662558 3 0.3192 0.7463 0.000 0.112 0.888
#> SRR1077334 2 0.1529 0.9164 0.000 0.960 0.040
#> SRR807438 2 0.1031 0.9179 0.024 0.976 0.000
#> SRR1459078 2 0.1289 0.9183 0.000 0.968 0.032
#> SRR1329704 2 0.1163 0.9187 0.000 0.972 0.028
#> SRR1468072 2 0.0475 0.9219 0.004 0.992 0.004
#> SRR1376196 2 0.1491 0.9209 0.016 0.968 0.016
#> SRR1442909 2 0.2165 0.8961 0.064 0.936 0.000
#> SRR1414269 2 0.1163 0.9166 0.028 0.972 0.000
#> SRR1381913 2 0.6111 0.3532 0.396 0.604 0.000
#> SRR1340157 2 0.1411 0.9178 0.000 0.964 0.036
#> SRR1407583 1 0.4931 0.5878 0.768 0.232 0.000
#> SRR615826 3 0.1860 0.7957 0.052 0.000 0.948
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.0000 0.746484 0.000 0.000 0.000 1.000
#> SRR1458769 2 0.5000 -0.017314 0.000 0.500 0.500 0.000
#> SRR613162 1 0.5000 -0.210143 0.504 0.000 0.000 0.496
#> SRR1352481 2 0.4925 -0.159839 0.000 0.572 0.000 0.428
#> SRR1468876 2 0.0188 0.667761 0.000 0.996 0.004 0.000
#> SRR1399223 3 0.2845 0.563290 0.000 0.076 0.896 0.028
#> SRR660030 3 0.4722 0.250139 0.000 0.008 0.692 0.300
#> SRR1333609 3 0.5010 0.475812 0.024 0.276 0.700 0.000
#> SRR1471612 3 0.6202 0.447555 0.312 0.076 0.612 0.000
#> SRR1413998 3 0.6494 0.342786 0.088 0.340 0.572 0.000
#> SRR1122940 3 0.4761 0.328557 0.000 0.332 0.664 0.004
#> SRR1402563 3 0.3545 0.556260 0.008 0.164 0.828 0.000
#> SRR1398393 3 0.5184 0.448026 0.000 0.024 0.672 0.304
#> SRR657961 3 0.5586 0.190385 0.452 0.020 0.528 0.000
#> SRR1471135 3 0.6052 0.431681 0.320 0.064 0.616 0.000
#> SRR1430001 3 0.2775 0.549948 0.000 0.020 0.896 0.084
#> SRR662775 1 0.2915 0.644722 0.892 0.004 0.016 0.088
#> SRR1474182 3 0.5728 0.346271 0.036 0.364 0.600 0.000
#> SRR607190 1 0.0895 0.725434 0.976 0.004 0.020 0.000
#> SRR612467 4 0.1042 0.747246 0.020 0.000 0.008 0.972
#> SRR1465959 2 0.4454 0.540238 0.000 0.692 0.308 0.000
#> SRR1446132 3 0.4967 0.175986 0.000 0.452 0.548 0.000
#> SRR1416933 3 0.5778 0.350627 0.040 0.356 0.604 0.000
#> SRR1102538 2 0.3311 0.702842 0.000 0.828 0.172 0.000
#> SRR1098636 2 0.6845 0.392889 0.128 0.564 0.308 0.000
#> SRR1072998 3 0.4509 0.356144 0.000 0.288 0.708 0.004
#> SRR627443 1 0.0895 0.725434 0.976 0.004 0.020 0.000
#> SRR656131 4 0.6001 0.596785 0.176 0.004 0.120 0.700
#> SRR823991 3 0.5626 0.310727 0.028 0.384 0.588 0.000
#> SRR1089158 4 0.5250 0.444665 0.000 0.316 0.024 0.660
#> SRR1469036 3 0.2021 0.570413 0.012 0.056 0.932 0.000
#> SRR824039 3 0.4999 0.047826 0.000 0.492 0.508 0.000
#> SRR1339047 3 0.5161 0.430391 0.000 0.024 0.676 0.300
#> SRR1443049 3 0.4872 0.391825 0.000 0.028 0.728 0.244
#> SRR1122885 3 0.4454 0.438625 0.000 0.308 0.692 0.000
#> SRR602895 3 0.4456 0.442290 0.280 0.004 0.716 0.000
#> SRR1409837 2 0.4776 0.389968 0.000 0.624 0.376 0.000
#> SRR1388959 3 0.4817 0.310746 0.000 0.388 0.612 0.000
#> SRR659863 3 0.5147 -0.000859 0.460 0.004 0.536 0.000
#> SRR1089877 2 0.0188 0.667761 0.000 0.996 0.004 0.000
#> SRR1123775 3 0.3307 0.547415 0.000 0.028 0.868 0.104
#> SRR658909 1 0.0188 0.728129 0.996 0.000 0.004 0.000
#> SRR1140510 2 0.4584 0.578348 0.000 0.696 0.300 0.004
#> SRR607562 1 0.0469 0.728815 0.988 0.000 0.012 0.000
#> SRR1122913 2 0.4907 0.322211 0.000 0.580 0.420 0.000
#> SRR598042 1 0.4992 -0.006022 0.524 0.000 0.476 0.000
#> SRR1467340 3 0.1211 0.576062 0.000 0.040 0.960 0.000
#> SRR1072321 2 0.1389 0.699630 0.000 0.952 0.048 0.000
#> SRR1094580 2 0.4228 0.667289 0.008 0.760 0.232 0.000
#> SRR1076608 3 0.4509 0.407692 0.000 0.288 0.708 0.004
#> SRR1395462 3 0.6141 0.442579 0.312 0.072 0.616 0.000
#> SRR1489220 3 0.3801 0.476926 0.000 0.220 0.780 0.000
#> SRR614371 1 0.0188 0.726571 0.996 0.000 0.000 0.004
#> SRR615455 4 0.1302 0.738907 0.044 0.000 0.000 0.956
#> SRR1070573 2 0.1557 0.704629 0.000 0.944 0.056 0.000
#> SRR598749 4 0.4925 0.264100 0.428 0.000 0.000 0.572
#> SRR1365556 3 0.1042 0.576529 0.000 0.020 0.972 0.008
#> SRR1350023 2 0.3123 0.709688 0.000 0.844 0.156 0.000
#> SRR1446582 3 0.4972 0.123431 0.000 0.456 0.544 0.000
#> SRR1439763 2 0.1302 0.692151 0.000 0.956 0.044 0.000
#> SRR1343986 3 0.4121 0.457759 0.000 0.020 0.796 0.184
#> SRR807463 2 0.3975 0.657598 0.000 0.760 0.240 0.000
#> SRR660390 4 0.3649 0.624278 0.204 0.000 0.000 0.796
#> SRR1367672 1 0.1724 0.709961 0.948 0.020 0.032 0.000
#> SRR613294 4 0.0469 0.745895 0.012 0.000 0.000 0.988
#> SRR824015 2 0.3486 0.455687 0.000 0.812 0.000 0.188
#> SRR1078924 3 0.4072 0.447208 0.000 0.252 0.748 0.000
#> SRR662221 4 0.0921 0.744023 0.000 0.000 0.028 0.972
#> SRR655017 1 0.1118 0.700402 0.964 0.000 0.000 0.036
#> SRR1338450 2 0.2125 0.710327 0.000 0.920 0.076 0.004
#> SRR663741 4 0.0000 0.746484 0.000 0.000 0.000 1.000
#> SRR1396057 1 0.5594 -0.087947 0.520 0.020 0.460 0.000
#> SRR1083800 2 0.0921 0.687510 0.000 0.972 0.028 0.000
#> SRR1445789 3 0.2859 0.549471 0.000 0.112 0.880 0.008
#> SRR1387355 4 0.5699 0.554830 0.000 0.032 0.380 0.588
#> SRR1388855 2 0.4040 0.636394 0.000 0.752 0.248 0.000
#> SRR1445449 1 0.0336 0.728580 0.992 0.000 0.008 0.000
#> SRR1380740 2 0.1637 0.706234 0.000 0.940 0.060 0.000
#> SRR659995 4 0.4103 0.563993 0.256 0.000 0.000 0.744
#> SRR1489524 2 0.2814 0.715067 0.000 0.868 0.132 0.000
#> SRR1444662 3 0.5161 0.297369 0.008 0.400 0.592 0.000
#> SRR1383652 3 0.5511 0.099841 0.484 0.016 0.500 0.000
#> SRR1361243 3 0.5400 0.341202 0.020 0.372 0.608 0.000
#> SRR1490337 3 0.6071 0.387470 0.064 0.324 0.612 0.000
#> SRR823967 3 0.5387 0.294893 0.016 0.400 0.584 0.000
#> SRR660127 3 0.4835 0.476183 0.208 0.004 0.756 0.032
#> SRR1366627 4 0.4781 0.602136 0.000 0.004 0.336 0.660
#> SRR1361219 2 0.4992 0.061723 0.000 0.524 0.476 0.000
#> SRR1393510 4 0.4746 0.583574 0.000 0.000 0.368 0.632
#> SRR662558 4 0.5268 0.549735 0.000 0.012 0.396 0.592
#> SRR1077334 2 0.4193 0.608701 0.000 0.732 0.268 0.000
#> SRR807438 2 0.3390 0.714463 0.016 0.852 0.132 0.000
#> SRR1459078 2 0.0188 0.667761 0.000 0.996 0.004 0.000
#> SRR1329704 2 0.3591 0.702479 0.000 0.824 0.168 0.008
#> SRR1468072 2 0.3688 0.685591 0.000 0.792 0.208 0.000
#> SRR1376196 3 0.2281 0.570319 0.000 0.096 0.904 0.000
#> SRR1442909 3 0.5645 0.349141 0.032 0.364 0.604 0.000
#> SRR1414269 2 0.4679 0.460224 0.000 0.648 0.352 0.000
#> SRR1381913 3 0.6141 0.442579 0.312 0.072 0.616 0.000
#> SRR1340157 3 0.4605 0.335913 0.000 0.336 0.664 0.000
#> SRR1407583 1 0.5671 0.050028 0.572 0.028 0.400 0.000
#> SRR615826 4 0.1389 0.737350 0.048 0.000 0.000 0.952
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR612587 4 0.1205 0.84854 0.040 0.000 0.000 0.956 0.004
#> SRR1458769 2 0.2929 0.72811 0.000 0.840 0.152 0.000 0.008
#> SRR613162 4 0.3790 0.72306 0.272 0.000 0.000 0.724 0.004
#> SRR1352481 3 0.3062 0.68800 0.004 0.000 0.868 0.048 0.080
#> SRR1468876 3 0.0162 0.77974 0.000 0.004 0.996 0.000 0.000
#> SRR1399223 5 0.3246 0.81203 0.000 0.184 0.000 0.008 0.808
#> SRR660030 5 0.5447 0.75396 0.004 0.200 0.000 0.128 0.668
#> SRR1333609 2 0.5111 0.00696 0.000 0.552 0.040 0.000 0.408
#> SRR1471612 2 0.0609 0.75784 0.020 0.980 0.000 0.000 0.000
#> SRR1413998 2 0.2852 0.70754 0.000 0.828 0.172 0.000 0.000
#> SRR1122940 5 0.4091 0.79879 0.000 0.124 0.076 0.004 0.796
#> SRR1402563 2 0.2220 0.76909 0.004 0.920 0.052 0.016 0.008
#> SRR1398393 2 0.5099 0.44001 0.016 0.596 0.000 0.368 0.020
#> SRR657961 2 0.2471 0.70335 0.136 0.864 0.000 0.000 0.000
#> SRR1471135 2 0.0703 0.75750 0.024 0.976 0.000 0.000 0.000
#> SRR1430001 5 0.2416 0.77220 0.000 0.100 0.000 0.012 0.888
#> SRR662775 1 0.4645 0.66503 0.724 0.000 0.000 0.072 0.204
#> SRR1474182 2 0.0703 0.76800 0.000 0.976 0.024 0.000 0.000
#> SRR607190 1 0.4313 0.74835 0.760 0.068 0.000 0.000 0.172
#> SRR612467 4 0.1478 0.85221 0.064 0.000 0.000 0.936 0.000
#> SRR1465959 3 0.5815 0.27398 0.000 0.396 0.508 0.000 0.096
#> SRR1446132 2 0.4963 0.38562 0.000 0.608 0.352 0.000 0.040
#> SRR1416933 2 0.0794 0.76890 0.000 0.972 0.028 0.000 0.000
#> SRR1102538 3 0.3561 0.68120 0.000 0.260 0.740 0.000 0.000
#> SRR1098636 2 0.4630 0.26074 0.016 0.588 0.396 0.000 0.000
#> SRR1072998 5 0.4132 0.77082 0.000 0.260 0.020 0.000 0.720
#> SRR627443 1 0.4749 0.74208 0.736 0.088 0.000 0.004 0.172
#> SRR656131 4 0.4577 0.65418 0.028 0.012 0.000 0.716 0.244
#> SRR823991 2 0.1205 0.77309 0.000 0.956 0.040 0.004 0.000
#> SRR1089158 4 0.4964 0.53999 0.000 0.096 0.204 0.700 0.000
#> SRR1469036 5 0.2020 0.77989 0.000 0.100 0.000 0.000 0.900
#> SRR824039 2 0.5974 0.34400 0.000 0.548 0.320 0.000 0.132
#> SRR1339047 2 0.5687 0.21021 0.012 0.500 0.000 0.436 0.052
#> SRR1443049 5 0.3495 0.81164 0.000 0.152 0.000 0.032 0.816
#> SRR1122885 2 0.4000 0.56664 0.000 0.748 0.024 0.000 0.228
#> SRR602895 2 0.4219 0.46875 0.004 0.716 0.000 0.016 0.264
#> SRR1409837 2 0.4210 0.26993 0.000 0.588 0.412 0.000 0.000
#> SRR1388959 2 0.1851 0.76663 0.000 0.912 0.088 0.000 0.000
#> SRR659863 2 0.5037 0.38707 0.088 0.684 0.000 0.000 0.228
#> SRR1089877 3 0.0162 0.77974 0.000 0.004 0.996 0.000 0.000
#> SRR1123775 5 0.3280 0.81320 0.000 0.176 0.000 0.012 0.812
#> SRR658909 1 0.0898 0.75916 0.972 0.020 0.000 0.008 0.000
#> SRR1140510 3 0.4961 0.64526 0.008 0.276 0.672 0.044 0.000
#> SRR607562 1 0.1831 0.77533 0.920 0.076 0.000 0.004 0.000
#> SRR1122913 5 0.5841 0.68145 0.000 0.212 0.180 0.000 0.608
#> SRR598042 2 0.2824 0.70690 0.116 0.864 0.000 0.000 0.020
#> SRR1467340 5 0.3561 0.77542 0.000 0.260 0.000 0.000 0.740
#> SRR1072321 3 0.0880 0.79111 0.000 0.032 0.968 0.000 0.000
#> SRR1094580 3 0.4074 0.51410 0.000 0.364 0.636 0.000 0.000
#> SRR1076608 5 0.4096 0.80531 0.000 0.176 0.052 0.000 0.772
#> SRR1395462 2 0.0609 0.75784 0.020 0.980 0.000 0.000 0.000
#> SRR1489220 5 0.3681 0.76501 0.000 0.148 0.044 0.000 0.808
#> SRR614371 1 0.2570 0.74370 0.888 0.000 0.000 0.028 0.084
#> SRR615455 4 0.1671 0.85176 0.076 0.000 0.000 0.924 0.000
#> SRR1070573 3 0.0566 0.78557 0.000 0.012 0.984 0.000 0.004
#> SRR598749 4 0.3949 0.63179 0.332 0.000 0.000 0.668 0.000
#> SRR1365556 2 0.2976 0.71926 0.012 0.880 0.000 0.064 0.044
#> SRR1350023 3 0.3274 0.71740 0.000 0.220 0.780 0.000 0.000
#> SRR1446582 2 0.2462 0.75686 0.000 0.880 0.112 0.000 0.008
#> SRR1439763 3 0.1195 0.77969 0.000 0.012 0.960 0.000 0.028
#> SRR1343986 5 0.2932 0.78366 0.000 0.104 0.000 0.032 0.864
#> SRR807463 3 0.3983 0.55857 0.000 0.340 0.660 0.000 0.000
#> SRR660390 4 0.1965 0.84666 0.096 0.000 0.000 0.904 0.000
#> SRR1367672 1 0.2389 0.76132 0.880 0.116 0.000 0.004 0.000
#> SRR613294 4 0.1502 0.85111 0.056 0.000 0.000 0.940 0.004
#> SRR824015 3 0.0404 0.77206 0.000 0.000 0.988 0.012 0.000
#> SRR1078924 5 0.4615 0.76489 0.000 0.252 0.048 0.000 0.700
#> SRR662221 4 0.1405 0.82123 0.016 0.008 0.000 0.956 0.020
#> SRR655017 1 0.0510 0.74566 0.984 0.000 0.000 0.016 0.000
#> SRR1338450 3 0.0955 0.79129 0.000 0.028 0.968 0.000 0.004
#> SRR663741 4 0.0324 0.83331 0.004 0.000 0.000 0.992 0.004
#> SRR1396057 1 0.4304 0.09645 0.516 0.484 0.000 0.000 0.000
#> SRR1083800 3 0.0162 0.77974 0.000 0.004 0.996 0.000 0.000
#> SRR1445789 5 0.3807 0.78804 0.000 0.240 0.012 0.000 0.748
#> SRR1387355 5 0.4987 0.36722 0.016 0.016 0.000 0.360 0.608
#> SRR1388855 3 0.4235 0.56238 0.000 0.336 0.656 0.000 0.008
#> SRR1445449 1 0.1502 0.77427 0.940 0.056 0.000 0.004 0.000
#> SRR1380740 3 0.0807 0.78265 0.000 0.012 0.976 0.000 0.012
#> SRR659995 4 0.2813 0.81151 0.168 0.000 0.000 0.832 0.000
#> SRR1489524 3 0.2732 0.75808 0.000 0.160 0.840 0.000 0.000
#> SRR1444662 2 0.2329 0.75380 0.000 0.876 0.124 0.000 0.000
#> SRR1383652 2 0.3752 0.50045 0.292 0.708 0.000 0.000 0.000
#> SRR1361243 2 0.2825 0.75781 0.000 0.860 0.124 0.000 0.016
#> SRR1490337 2 0.0703 0.76810 0.000 0.976 0.024 0.000 0.000
#> SRR823967 2 0.2170 0.77052 0.000 0.904 0.088 0.004 0.004
#> SRR660127 2 0.4054 0.50584 0.008 0.744 0.000 0.012 0.236
#> SRR1366627 4 0.4706 0.64909 0.016 0.112 0.000 0.764 0.108
#> SRR1361219 2 0.3274 0.66804 0.000 0.780 0.220 0.000 0.000
#> SRR1393510 5 0.5818 0.36551 0.012 0.068 0.000 0.380 0.540
#> SRR662558 5 0.4295 0.59596 0.012 0.020 0.000 0.228 0.740
#> SRR1077334 3 0.4306 0.57210 0.000 0.328 0.660 0.000 0.012
#> SRR807438 3 0.1792 0.78813 0.000 0.084 0.916 0.000 0.000
#> SRR1459078 3 0.0162 0.77974 0.000 0.004 0.996 0.000 0.000
#> SRR1329704 3 0.4256 0.74703 0.004 0.148 0.788 0.052 0.008
#> SRR1468072 3 0.3885 0.67601 0.000 0.268 0.724 0.008 0.000
#> SRR1376196 5 0.4425 0.56649 0.000 0.392 0.008 0.000 0.600
#> SRR1442909 2 0.2020 0.76553 0.000 0.900 0.100 0.000 0.000
#> SRR1414269 2 0.3807 0.63698 0.000 0.748 0.240 0.000 0.012
#> SRR1381913 2 0.0510 0.75939 0.016 0.984 0.000 0.000 0.000
#> SRR1340157 5 0.5252 0.67986 0.000 0.292 0.076 0.000 0.632
#> SRR1407583 1 0.4182 0.34623 0.600 0.400 0.000 0.000 0.000
#> SRR615826 4 0.1671 0.85176 0.076 0.000 0.000 0.924 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 4 0.0291 0.83178 0.000 0.000 0.004 0.992 0.000 0.004
#> SRR1458769 5 0.3419 0.70909 0.000 0.116 0.008 0.000 0.820 0.056
#> SRR613162 4 0.2300 0.73836 0.144 0.000 0.000 0.856 0.000 0.000
#> SRR1352481 2 0.5248 0.45789 0.004 0.680 0.116 0.020 0.004 0.176
#> SRR1468876 2 0.0551 0.76378 0.000 0.984 0.004 0.000 0.008 0.004
#> SRR1399223 3 0.1149 0.83978 0.000 0.008 0.960 0.000 0.024 0.008
#> SRR660030 3 0.3794 0.77330 0.000 0.000 0.796 0.016 0.128 0.060
#> SRR1333609 3 0.4057 0.40741 0.000 0.012 0.600 0.000 0.388 0.000
#> SRR1471612 5 0.0725 0.72911 0.012 0.000 0.012 0.000 0.976 0.000
#> SRR1413998 5 0.3161 0.64105 0.008 0.216 0.000 0.000 0.776 0.000
#> SRR1122940 3 0.1053 0.83920 0.000 0.012 0.964 0.000 0.020 0.004
#> SRR1402563 5 0.1173 0.73107 0.000 0.008 0.016 0.000 0.960 0.016
#> SRR1398393 5 0.5269 0.45975 0.000 0.000 0.012 0.096 0.600 0.292
#> SRR657961 5 0.2446 0.69906 0.124 0.000 0.012 0.000 0.864 0.000
#> SRR1471135 5 0.0725 0.72911 0.012 0.000 0.012 0.000 0.976 0.000
#> SRR1430001 3 0.1168 0.81913 0.000 0.000 0.956 0.000 0.016 0.028
#> SRR662775 6 0.6195 -0.20536 0.288 0.000 0.008 0.264 0.000 0.440
#> SRR1474182 5 0.1082 0.74327 0.004 0.040 0.000 0.000 0.956 0.000
#> SRR607190 1 0.4951 0.29858 0.516 0.000 0.012 0.020 0.012 0.440
#> SRR612467 4 0.0291 0.83178 0.000 0.000 0.004 0.992 0.000 0.004
#> SRR1465959 2 0.6079 0.11255 0.000 0.404 0.220 0.000 0.372 0.004
#> SRR1446132 5 0.4778 0.56918 0.000 0.240 0.016 0.000 0.676 0.068
#> SRR1416933 5 0.1296 0.74361 0.004 0.044 0.000 0.000 0.948 0.004
#> SRR1102538 2 0.3398 0.62063 0.000 0.740 0.008 0.000 0.252 0.000
#> SRR1098636 5 0.4096 -0.01259 0.008 0.484 0.000 0.000 0.508 0.000
#> SRR1072998 3 0.2376 0.83970 0.000 0.012 0.884 0.000 0.096 0.008
#> SRR627443 6 0.6276 -0.28772 0.392 0.000 0.004 0.020 0.160 0.424
#> SRR656131 4 0.4719 0.18343 0.008 0.000 0.016 0.496 0.008 0.472
#> SRR823991 5 0.2237 0.74245 0.004 0.080 0.000 0.000 0.896 0.020
#> SRR1089158 4 0.4303 0.42196 0.000 0.292 0.000 0.672 0.020 0.016
#> SRR1469036 3 0.0622 0.83252 0.000 0.008 0.980 0.000 0.012 0.000
#> SRR824039 5 0.5073 0.30025 0.000 0.364 0.052 0.000 0.568 0.016
#> SRR1339047 5 0.6913 0.12924 0.004 0.004 0.124 0.088 0.460 0.320
#> SRR1443049 3 0.1194 0.81003 0.000 0.000 0.956 0.004 0.008 0.032
#> SRR1122885 5 0.4300 0.12799 0.000 0.020 0.432 0.000 0.548 0.000
#> SRR602895 6 0.4533 -0.02244 0.004 0.000 0.024 0.000 0.468 0.504
#> SRR1409837 5 0.3867 0.00306 0.000 0.488 0.000 0.000 0.512 0.000
#> SRR1388959 5 0.1075 0.74397 0.000 0.048 0.000 0.000 0.952 0.000
#> SRR659863 5 0.4627 -0.02340 0.020 0.000 0.012 0.000 0.532 0.436
#> SRR1089877 2 0.0291 0.76413 0.000 0.992 0.004 0.000 0.004 0.000
#> SRR1123775 3 0.0692 0.83117 0.000 0.000 0.976 0.004 0.020 0.000
#> SRR658909 1 0.0363 0.67005 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1140510 2 0.5588 0.28906 0.004 0.508 0.000 0.004 0.372 0.112
#> SRR607562 1 0.0767 0.67162 0.976 0.000 0.000 0.012 0.008 0.004
#> SRR1122913 3 0.3502 0.77535 0.000 0.076 0.812 0.000 0.108 0.004
#> SRR598042 5 0.2890 0.66974 0.032 0.000 0.012 0.000 0.860 0.096
#> SRR1467340 3 0.2118 0.83485 0.000 0.008 0.888 0.000 0.104 0.000
#> SRR1072321 2 0.0603 0.76823 0.000 0.980 0.004 0.000 0.016 0.000
#> SRR1094580 2 0.3950 0.24170 0.004 0.564 0.000 0.000 0.432 0.000
#> SRR1076608 3 0.1296 0.84184 0.000 0.012 0.952 0.000 0.032 0.004
#> SRR1395462 5 0.0725 0.72911 0.012 0.000 0.012 0.000 0.976 0.000
#> SRR1489220 3 0.2535 0.82391 0.000 0.012 0.888 0.000 0.036 0.064
#> SRR614371 1 0.5765 0.17082 0.420 0.000 0.000 0.172 0.000 0.408
#> SRR615455 4 0.0260 0.83341 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1070573 2 0.0748 0.75771 0.000 0.976 0.016 0.000 0.004 0.004
#> SRR598749 4 0.1007 0.82478 0.044 0.000 0.000 0.956 0.000 0.000
#> SRR1365556 5 0.3419 0.65001 0.000 0.000 0.028 0.004 0.792 0.176
#> SRR1350023 2 0.3357 0.65899 0.004 0.764 0.000 0.000 0.224 0.008
#> SRR1446582 5 0.3680 0.71392 0.008 0.084 0.008 0.000 0.816 0.084
#> SRR1439763 2 0.1152 0.74464 0.000 0.952 0.044 0.000 0.000 0.004
#> SRR1343986 3 0.1148 0.81905 0.000 0.000 0.960 0.004 0.016 0.020
#> SRR807463 2 0.3706 0.40131 0.000 0.620 0.000 0.000 0.380 0.000
#> SRR660390 4 0.0865 0.82825 0.036 0.000 0.000 0.964 0.000 0.000
#> SRR1367672 1 0.0692 0.66808 0.976 0.000 0.000 0.004 0.020 0.000
#> SRR613294 4 0.0405 0.83061 0.000 0.000 0.004 0.988 0.000 0.008
#> SRR824015 2 0.0260 0.76458 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1078924 3 0.2110 0.83942 0.000 0.012 0.900 0.000 0.084 0.004
#> SRR662221 4 0.4318 0.28204 0.000 0.000 0.020 0.532 0.000 0.448
#> SRR655017 1 0.0603 0.66852 0.980 0.000 0.000 0.016 0.000 0.004
#> SRR1338450 2 0.1275 0.76272 0.000 0.956 0.012 0.000 0.016 0.016
#> SRR663741 4 0.0891 0.82153 0.000 0.000 0.008 0.968 0.000 0.024
#> SRR1396057 1 0.3944 0.11037 0.568 0.000 0.004 0.000 0.428 0.000
#> SRR1083800 2 0.0000 0.76225 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1445789 3 0.2214 0.83664 0.000 0.012 0.892 0.000 0.092 0.004
#> SRR1387355 6 0.4861 0.29350 0.000 0.000 0.392 0.052 0.004 0.552
#> SRR1388855 2 0.4101 0.54061 0.000 0.664 0.028 0.000 0.308 0.000
#> SRR1445449 1 0.0405 0.67162 0.988 0.000 0.000 0.004 0.008 0.000
#> SRR1380740 2 0.1863 0.74174 0.004 0.924 0.008 0.000 0.008 0.056
#> SRR659995 4 0.1285 0.82144 0.052 0.000 0.000 0.944 0.000 0.004
#> SRR1489524 2 0.1958 0.74840 0.000 0.896 0.000 0.000 0.100 0.004
#> SRR1444662 5 0.2668 0.69531 0.000 0.168 0.000 0.000 0.828 0.004
#> SRR1383652 5 0.3862 0.34820 0.388 0.000 0.004 0.000 0.608 0.000
#> SRR1361243 5 0.3344 0.72190 0.000 0.120 0.020 0.000 0.828 0.032
#> SRR1490337 5 0.0806 0.73639 0.000 0.020 0.008 0.000 0.972 0.000
#> SRR823967 5 0.3426 0.71865 0.004 0.116 0.000 0.000 0.816 0.064
#> SRR660127 5 0.4538 -0.00271 0.008 0.000 0.020 0.000 0.536 0.436
#> SRR1366627 6 0.6970 0.17977 0.000 0.000 0.148 0.296 0.112 0.444
#> SRR1361219 5 0.3163 0.63175 0.000 0.232 0.000 0.000 0.764 0.004
#> SRR1393510 6 0.6576 0.22138 0.004 0.000 0.396 0.128 0.056 0.416
#> SRR662558 6 0.4475 0.19054 0.000 0.000 0.448 0.016 0.008 0.528
#> SRR1077334 2 0.3855 0.58748 0.000 0.704 0.024 0.000 0.272 0.000
#> SRR807438 2 0.0508 0.76749 0.000 0.984 0.004 0.000 0.012 0.000
#> SRR1459078 2 0.0436 0.76366 0.000 0.988 0.004 0.000 0.004 0.004
#> SRR1329704 2 0.4299 0.63539 0.000 0.772 0.072 0.004 0.028 0.124
#> SRR1468072 2 0.3690 0.56910 0.000 0.700 0.000 0.000 0.288 0.012
#> SRR1376196 3 0.3555 0.60663 0.000 0.008 0.712 0.000 0.280 0.000
#> SRR1442909 5 0.1668 0.74598 0.000 0.060 0.008 0.000 0.928 0.004
#> SRR1414269 5 0.4330 0.49358 0.004 0.308 0.008 0.000 0.660 0.020
#> SRR1381913 5 0.0725 0.72911 0.012 0.000 0.012 0.000 0.976 0.000
#> SRR1340157 3 0.4074 0.70298 0.000 0.108 0.752 0.000 0.140 0.000
#> SRR1407583 1 0.3390 0.40839 0.704 0.000 0.000 0.000 0.296 0.000
#> SRR615826 4 0.0260 0.83341 0.008 0.000 0.000 0.992 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "hclust"]
# you can also extract it by
# res = res_list["MAD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17380 rows and 102 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.881 0.926 0.968 0.2400 0.806 0.806
#> 3 3 0.356 0.741 0.807 1.1181 0.705 0.634
#> 4 4 0.460 0.791 0.849 0.1435 0.885 0.776
#> 5 5 0.483 0.749 0.832 0.0629 1.000 1.000
#> 6 6 0.540 0.733 0.796 0.1769 0.835 0.584
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
#> SRR612587 2 0.955 0.461 0.376 0.624
#> SRR1458769 2 0.000 0.962 0.000 1.000
#> SRR613162 1 0.000 0.998 1.000 0.000
#> SRR1352481 1 0.000 0.998 1.000 0.000
#> SRR1468876 2 0.000 0.962 0.000 1.000
#> SRR1399223 2 0.000 0.962 0.000 1.000
#> SRR660030 2 0.000 0.962 0.000 1.000
#> SRR1333609 2 0.000 0.962 0.000 1.000
#> SRR1471612 2 0.000 0.962 0.000 1.000
#> SRR1413998 2 0.000 0.962 0.000 1.000
#> SRR1122940 2 0.000 0.962 0.000 1.000
#> SRR1402563 2 0.000 0.962 0.000 1.000
#> SRR1398393 2 0.000 0.962 0.000 1.000
#> SRR657961 2 0.000 0.962 0.000 1.000
#> SRR1471135 2 0.000 0.962 0.000 1.000
#> SRR1430001 2 0.000 0.962 0.000 1.000
#> SRR662775 1 0.000 0.998 1.000 0.000
#> SRR1474182 2 0.000 0.962 0.000 1.000
#> SRR607190 1 0.000 0.998 1.000 0.000
#> SRR612467 2 0.000 0.962 0.000 1.000
#> SRR1465959 2 0.000 0.962 0.000 1.000
#> SRR1446132 2 0.000 0.962 0.000 1.000
#> SRR1416933 2 0.000 0.962 0.000 1.000
#> SRR1102538 2 0.000 0.962 0.000 1.000
#> SRR1098636 2 0.000 0.962 0.000 1.000
#> SRR1072998 2 0.000 0.962 0.000 1.000
#> SRR627443 1 0.000 0.998 1.000 0.000
#> SRR656131 1 0.000 0.998 1.000 0.000
#> SRR823991 2 0.000 0.962 0.000 1.000
#> SRR1089158 2 0.000 0.962 0.000 1.000
#> SRR1469036 2 0.000 0.962 0.000 1.000
#> SRR824039 2 0.000 0.962 0.000 1.000
#> SRR1339047 2 0.000 0.962 0.000 1.000
#> SRR1443049 2 0.000 0.962 0.000 1.000
#> SRR1122885 2 0.000 0.962 0.000 1.000
#> SRR602895 2 0.584 0.827 0.140 0.860
#> SRR1409837 2 0.000 0.962 0.000 1.000
#> SRR1388959 2 0.000 0.962 0.000 1.000
#> SRR659863 1 0.000 0.998 1.000 0.000
#> SRR1089877 2 0.000 0.962 0.000 1.000
#> SRR1123775 2 0.000 0.962 0.000 1.000
#> SRR658909 2 0.891 0.594 0.308 0.692
#> SRR1140510 2 0.000 0.962 0.000 1.000
#> SRR607562 2 0.584 0.827 0.140 0.860
#> SRR1122913 2 0.000 0.962 0.000 1.000
#> SRR598042 2 0.000 0.962 0.000 1.000
#> SRR1467340 2 0.000 0.962 0.000 1.000
#> SRR1072321 2 0.000 0.962 0.000 1.000
#> SRR1094580 2 0.000 0.962 0.000 1.000
#> SRR1076608 2 0.000 0.962 0.000 1.000
#> SRR1395462 2 0.000 0.962 0.000 1.000
#> SRR1489220 2 0.788 0.706 0.236 0.764
#> SRR614371 2 0.891 0.594 0.308 0.692
#> SRR615455 1 0.118 0.982 0.984 0.016
#> SRR1070573 2 0.000 0.962 0.000 1.000
#> SRR598749 2 0.000 0.962 0.000 1.000
#> SRR1365556 2 0.000 0.962 0.000 1.000
#> SRR1350023 2 0.000 0.962 0.000 1.000
#> SRR1446582 2 0.000 0.962 0.000 1.000
#> SRR1439763 2 0.000 0.962 0.000 1.000
#> SRR1343986 2 0.000 0.962 0.000 1.000
#> SRR807463 2 0.000 0.962 0.000 1.000
#> SRR660390 1 0.000 0.998 1.000 0.000
#> SRR1367672 2 0.000 0.962 0.000 1.000
#> SRR613294 2 0.955 0.461 0.376 0.624
#> SRR824015 2 0.000 0.962 0.000 1.000
#> SRR1078924 2 0.000 0.962 0.000 1.000
#> SRR662221 2 0.952 0.470 0.372 0.628
#> SRR655017 1 0.000 0.998 1.000 0.000
#> SRR1338450 2 0.000 0.962 0.000 1.000
#> SRR663741 2 0.943 0.495 0.360 0.640
#> SRR1396057 2 0.000 0.962 0.000 1.000
#> SRR1083800 2 0.000 0.962 0.000 1.000
#> SRR1445789 2 0.000 0.962 0.000 1.000
#> SRR1387355 2 0.000 0.962 0.000 1.000
#> SRR1388855 2 0.000 0.962 0.000 1.000
#> SRR1445449 2 0.000 0.962 0.000 1.000
#> SRR1380740 2 0.000 0.962 0.000 1.000
#> SRR659995 2 0.952 0.470 0.372 0.628
#> SRR1489524 2 0.000 0.962 0.000 1.000
#> SRR1444662 2 0.000 0.962 0.000 1.000
#> SRR1383652 2 0.000 0.962 0.000 1.000
#> SRR1361243 2 0.000 0.962 0.000 1.000
#> SRR1490337 2 0.000 0.962 0.000 1.000
#> SRR823967 2 0.000 0.962 0.000 1.000
#> SRR660127 1 0.000 0.998 1.000 0.000
#> SRR1366627 2 0.000 0.962 0.000 1.000
#> SRR1361219 2 0.000 0.962 0.000 1.000
#> SRR1393510 2 0.000 0.962 0.000 1.000
#> SRR662558 2 0.891 0.594 0.308 0.692
#> SRR1077334 2 0.000 0.962 0.000 1.000
#> SRR807438 2 0.000 0.962 0.000 1.000
#> SRR1459078 2 0.000 0.962 0.000 1.000
#> SRR1329704 2 0.000 0.962 0.000 1.000
#> SRR1468072 2 0.000 0.962 0.000 1.000
#> SRR1376196 2 0.000 0.962 0.000 1.000
#> SRR1442909 2 0.000 0.962 0.000 1.000
#> SRR1414269 2 0.000 0.962 0.000 1.000
#> SRR1381913 2 0.000 0.962 0.000 1.000
#> SRR1340157 2 0.000 0.962 0.000 1.000
#> SRR1407583 2 0.000 0.962 0.000 1.000
#> SRR615826 2 0.000 0.962 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 3 0.8518 0.380 0.356 0.104 0.540
#> SRR1458769 2 0.5760 0.768 0.000 0.672 0.328
#> SRR613162 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1352481 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1468876 3 0.2066 0.774 0.000 0.060 0.940
#> SRR1399223 2 0.4291 0.849 0.000 0.820 0.180
#> SRR660030 3 0.2165 0.775 0.000 0.064 0.936
#> SRR1333609 3 0.2959 0.761 0.000 0.100 0.900
#> SRR1471612 3 0.5397 0.554 0.000 0.280 0.720
#> SRR1413998 2 0.4235 0.848 0.000 0.824 0.176
#> SRR1122940 3 0.3752 0.732 0.000 0.144 0.856
#> SRR1402563 3 0.2959 0.761 0.000 0.100 0.900
#> SRR1398393 3 0.4121 0.715 0.000 0.168 0.832
#> SRR657961 3 0.3752 0.709 0.000 0.144 0.856
#> SRR1471135 3 0.2261 0.774 0.000 0.068 0.932
#> SRR1430001 3 0.2625 0.770 0.000 0.084 0.916
#> SRR662775 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1474182 2 0.6295 0.490 0.000 0.528 0.472
#> SRR607190 1 0.0000 0.999 1.000 0.000 0.000
#> SRR612467 3 0.3686 0.712 0.000 0.140 0.860
#> SRR1465959 3 0.4002 0.718 0.000 0.160 0.840
#> SRR1446132 2 0.4235 0.848 0.000 0.824 0.176
#> SRR1416933 2 0.5835 0.754 0.000 0.660 0.340
#> SRR1102538 3 0.4121 0.724 0.000 0.168 0.832
#> SRR1098636 3 0.2165 0.772 0.000 0.064 0.936
#> SRR1072998 3 0.4121 0.724 0.000 0.168 0.832
#> SRR627443 1 0.0000 0.999 1.000 0.000 0.000
#> SRR656131 1 0.0000 0.999 1.000 0.000 0.000
#> SRR823991 3 0.2261 0.776 0.000 0.068 0.932
#> SRR1089158 3 0.4121 0.724 0.000 0.168 0.832
#> SRR1469036 3 0.2625 0.770 0.000 0.084 0.916
#> SRR824039 3 0.2261 0.776 0.000 0.068 0.932
#> SRR1339047 2 0.4235 0.848 0.000 0.824 0.176
#> SRR1443049 3 0.4062 0.717 0.000 0.164 0.836
#> SRR1122885 3 0.4002 0.718 0.000 0.160 0.840
#> SRR602895 3 0.7026 0.607 0.120 0.152 0.728
#> SRR1409837 2 0.6280 0.511 0.000 0.540 0.460
#> SRR1388959 2 0.4235 0.848 0.000 0.824 0.176
#> SRR659863 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1089877 3 0.2878 0.773 0.000 0.096 0.904
#> SRR1123775 3 0.2165 0.775 0.000 0.064 0.936
#> SRR658909 3 0.8345 0.477 0.288 0.116 0.596
#> SRR1140510 2 0.5621 0.783 0.000 0.692 0.308
#> SRR607562 3 0.7026 0.607 0.120 0.152 0.728
#> SRR1122913 3 0.3752 0.732 0.000 0.144 0.856
#> SRR598042 3 0.3752 0.709 0.000 0.144 0.856
#> SRR1467340 3 0.2796 0.764 0.000 0.092 0.908
#> SRR1072321 3 0.3619 0.731 0.000 0.136 0.864
#> SRR1094580 3 0.3816 0.724 0.000 0.148 0.852
#> SRR1076608 2 0.4291 0.849 0.000 0.820 0.180
#> SRR1395462 3 0.3686 0.710 0.000 0.140 0.860
#> SRR1489220 3 0.7145 0.573 0.236 0.072 0.692
#> SRR614371 3 0.8345 0.477 0.288 0.116 0.596
#> SRR615455 1 0.0747 0.987 0.984 0.016 0.000
#> SRR1070573 3 0.3619 0.731 0.000 0.136 0.864
#> SRR598749 3 0.3941 0.695 0.000 0.156 0.844
#> SRR1365556 3 0.5291 0.537 0.000 0.268 0.732
#> SRR1350023 2 0.4235 0.848 0.000 0.824 0.176
#> SRR1446582 3 0.1964 0.777 0.000 0.056 0.944
#> SRR1439763 3 0.2356 0.770 0.000 0.072 0.928
#> SRR1343986 3 0.2878 0.762 0.000 0.096 0.904
#> SRR807463 3 0.4002 0.718 0.000 0.160 0.840
#> SRR660390 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1367672 3 0.3941 0.724 0.000 0.156 0.844
#> SRR613294 3 0.8518 0.380 0.356 0.104 0.540
#> SRR824015 2 0.5706 0.764 0.000 0.680 0.320
#> SRR1078924 3 0.3752 0.732 0.000 0.144 0.856
#> SRR662221 3 0.8503 0.384 0.352 0.104 0.544
#> SRR655017 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1338450 3 0.2066 0.774 0.000 0.060 0.940
#> SRR663741 3 0.8571 0.395 0.340 0.112 0.548
#> SRR1396057 2 0.5835 0.754 0.000 0.660 0.340
#> SRR1083800 3 0.3619 0.744 0.000 0.136 0.864
#> SRR1445789 2 0.4291 0.849 0.000 0.820 0.180
#> SRR1387355 3 0.2625 0.770 0.000 0.084 0.916
#> SRR1388855 2 0.4291 0.849 0.000 0.820 0.180
#> SRR1445449 3 0.4062 0.694 0.000 0.164 0.836
#> SRR1380740 3 0.2959 0.761 0.000 0.100 0.900
#> SRR659995 3 0.8503 0.384 0.352 0.104 0.544
#> SRR1489524 2 0.4235 0.848 0.000 0.824 0.176
#> SRR1444662 2 0.4750 0.828 0.000 0.784 0.216
#> SRR1383652 3 0.2261 0.774 0.000 0.068 0.932
#> SRR1361243 3 0.2959 0.761 0.000 0.100 0.900
#> SRR1490337 3 0.2165 0.775 0.000 0.064 0.936
#> SRR823967 3 0.2261 0.776 0.000 0.068 0.932
#> SRR660127 1 0.0000 0.999 1.000 0.000 0.000
#> SRR1366627 2 0.4291 0.849 0.000 0.820 0.180
#> SRR1361219 2 0.6280 0.521 0.000 0.540 0.460
#> SRR1393510 3 0.5291 0.537 0.000 0.268 0.732
#> SRR662558 3 0.8345 0.477 0.288 0.116 0.596
#> SRR1077334 3 0.4121 0.726 0.000 0.168 0.832
#> SRR807438 3 0.2066 0.774 0.000 0.060 0.940
#> SRR1459078 3 0.2959 0.761 0.000 0.100 0.900
#> SRR1329704 2 0.6235 0.608 0.000 0.564 0.436
#> SRR1468072 3 0.2959 0.761 0.000 0.100 0.900
#> SRR1376196 3 0.3816 0.730 0.000 0.148 0.852
#> SRR1442909 3 0.2165 0.775 0.000 0.064 0.936
#> SRR1414269 3 0.2066 0.779 0.000 0.060 0.940
#> SRR1381913 3 0.3752 0.709 0.000 0.144 0.856
#> SRR1340157 3 0.3941 0.718 0.000 0.156 0.844
#> SRR1407583 2 0.5859 0.760 0.000 0.656 0.344
#> SRR615826 3 0.3941 0.695 0.000 0.156 0.844
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.147 0.860 0.004 0.012 0.024 0.960
#> SRR1458769 2 0.456 0.749 0.000 0.672 0.328 0.000
#> SRR613162 1 0.000 0.962 1.000 0.000 0.000 0.000
#> SRR1352481 1 0.000 0.962 1.000 0.000 0.000 0.000
#> SRR1468876 3 0.247 0.831 0.000 0.056 0.916 0.028
#> SRR1399223 2 0.322 0.828 0.000 0.836 0.164 0.000
#> SRR660030 3 0.228 0.834 0.000 0.052 0.924 0.024
#> SRR1333609 3 0.241 0.824 0.000 0.084 0.908 0.008
#> SRR1471612 3 0.413 0.619 0.000 0.260 0.740 0.000
#> SRR1413998 2 0.317 0.826 0.000 0.840 0.160 0.000
#> SRR1122940 3 0.265 0.799 0.000 0.120 0.880 0.000
#> SRR1402563 3 0.241 0.824 0.000 0.084 0.908 0.008
#> SRR1398393 3 0.340 0.772 0.000 0.164 0.832 0.004
#> SRR657961 3 0.397 0.722 0.000 0.076 0.840 0.084
#> SRR1471135 3 0.240 0.826 0.000 0.048 0.920 0.032
#> SRR1430001 3 0.238 0.831 0.000 0.068 0.916 0.016
#> SRR662775 1 0.000 0.962 1.000 0.000 0.000 0.000
#> SRR1474182 2 0.500 0.420 0.000 0.508 0.492 0.000
#> SRR607190 1 0.000 0.962 1.000 0.000 0.000 0.000
#> SRR612467 3 0.390 0.727 0.000 0.072 0.844 0.084
#> SRR1465959 3 0.297 0.783 0.000 0.144 0.856 0.000
#> SRR1446132 2 0.317 0.826 0.000 0.840 0.160 0.000
#> SRR1416933 2 0.462 0.735 0.000 0.660 0.340 0.000
#> SRR1102538 3 0.307 0.786 0.000 0.152 0.848 0.000
#> SRR1098636 3 0.230 0.823 0.000 0.048 0.924 0.028
#> SRR1072998 3 0.307 0.786 0.000 0.152 0.848 0.000
#> SRR627443 1 0.104 0.946 0.972 0.008 0.000 0.020
#> SRR656131 1 0.000 0.962 1.000 0.000 0.000 0.000
#> SRR823991 3 0.218 0.837 0.000 0.064 0.924 0.012
#> SRR1089158 3 0.307 0.786 0.000 0.152 0.848 0.000
#> SRR1469036 3 0.238 0.831 0.000 0.068 0.916 0.016
#> SRR824039 3 0.218 0.837 0.000 0.064 0.924 0.012
#> SRR1339047 2 0.317 0.826 0.000 0.840 0.160 0.000
#> SRR1443049 3 0.302 0.782 0.000 0.148 0.852 0.000
#> SRR1122885 3 0.297 0.783 0.000 0.144 0.856 0.000
#> SRR602895 4 0.512 0.679 0.000 0.044 0.232 0.724
#> SRR1409837 2 0.499 0.445 0.000 0.520 0.480 0.000
#> SRR1388959 2 0.317 0.826 0.000 0.840 0.160 0.000
#> SRR659863 1 0.000 0.962 1.000 0.000 0.000 0.000
#> SRR1089877 3 0.233 0.836 0.000 0.088 0.908 0.004
#> SRR1123775 3 0.228 0.834 0.000 0.052 0.924 0.024
#> SRR658909 4 0.220 0.865 0.000 0.008 0.072 0.920
#> SRR1140510 2 0.443 0.768 0.000 0.696 0.304 0.000
#> SRR607562 4 0.566 0.568 0.000 0.044 0.312 0.644
#> SRR1122913 3 0.265 0.799 0.000 0.120 0.880 0.000
#> SRR598042 3 0.404 0.719 0.000 0.076 0.836 0.088
#> SRR1467340 3 0.227 0.827 0.000 0.076 0.916 0.008
#> SRR1072321 3 0.253 0.799 0.000 0.112 0.888 0.000
#> SRR1094580 3 0.270 0.792 0.000 0.124 0.876 0.000
#> SRR1076608 2 0.322 0.828 0.000 0.836 0.164 0.000
#> SRR1395462 3 0.390 0.724 0.000 0.072 0.844 0.084
#> SRR1489220 3 0.513 0.360 0.000 0.008 0.604 0.388
#> SRR614371 4 0.220 0.865 0.000 0.008 0.072 0.920
#> SRR615455 1 0.620 0.490 0.612 0.076 0.000 0.312
#> SRR1070573 3 0.253 0.799 0.000 0.112 0.888 0.000
#> SRR598749 3 0.433 0.705 0.000 0.072 0.816 0.112
#> SRR1365556 3 0.440 0.561 0.000 0.272 0.724 0.004
#> SRR1350023 2 0.317 0.826 0.000 0.840 0.160 0.000
#> SRR1446582 3 0.212 0.833 0.000 0.040 0.932 0.028
#> SRR1439763 3 0.249 0.830 0.000 0.068 0.912 0.020
#> SRR1343986 3 0.234 0.824 0.000 0.080 0.912 0.008
#> SRR807463 3 0.297 0.783 0.000 0.144 0.856 0.000
#> SRR660390 1 0.000 0.962 1.000 0.000 0.000 0.000
#> SRR1367672 3 0.281 0.791 0.000 0.132 0.868 0.000
#> SRR613294 4 0.147 0.860 0.004 0.012 0.024 0.960
#> SRR824015 2 0.450 0.746 0.000 0.684 0.316 0.000
#> SRR1078924 3 0.265 0.799 0.000 0.120 0.880 0.000
#> SRR662221 4 0.134 0.863 0.004 0.008 0.024 0.964
#> SRR655017 1 0.000 0.962 1.000 0.000 0.000 0.000
#> SRR1338450 3 0.247 0.831 0.000 0.056 0.916 0.028
#> SRR663741 4 0.111 0.864 0.004 0.000 0.028 0.968
#> SRR1396057 2 0.462 0.735 0.000 0.660 0.340 0.000
#> SRR1083800 3 0.265 0.812 0.000 0.108 0.888 0.004
#> SRR1445789 2 0.322 0.828 0.000 0.836 0.164 0.000
#> SRR1387355 3 0.238 0.831 0.000 0.068 0.916 0.016
#> SRR1388855 2 0.322 0.828 0.000 0.836 0.164 0.000
#> SRR1445449 3 0.354 0.739 0.000 0.164 0.828 0.008
#> SRR1380740 3 0.241 0.824 0.000 0.084 0.908 0.008
#> SRR659995 4 0.134 0.863 0.004 0.008 0.024 0.964
#> SRR1489524 2 0.317 0.826 0.000 0.840 0.160 0.000
#> SRR1444662 2 0.373 0.809 0.000 0.788 0.212 0.000
#> SRR1383652 3 0.240 0.826 0.000 0.048 0.920 0.032
#> SRR1361243 3 0.241 0.824 0.000 0.084 0.908 0.008
#> SRR1490337 3 0.231 0.828 0.000 0.044 0.924 0.032
#> SRR823967 3 0.218 0.837 0.000 0.064 0.924 0.012
#> SRR660127 1 0.000 0.962 1.000 0.000 0.000 0.000
#> SRR1366627 2 0.322 0.828 0.000 0.836 0.164 0.000
#> SRR1361219 2 0.499 0.449 0.000 0.520 0.480 0.000
#> SRR1393510 3 0.440 0.561 0.000 0.272 0.724 0.004
#> SRR662558 4 0.220 0.865 0.000 0.008 0.072 0.920
#> SRR1077334 3 0.307 0.790 0.000 0.152 0.848 0.000
#> SRR807438 3 0.247 0.831 0.000 0.056 0.916 0.028
#> SRR1459078 3 0.241 0.824 0.000 0.084 0.908 0.008
#> SRR1329704 2 0.495 0.563 0.000 0.556 0.444 0.000
#> SRR1468072 3 0.241 0.824 0.000 0.084 0.908 0.008
#> SRR1376196 3 0.289 0.802 0.000 0.124 0.872 0.004
#> SRR1442909 3 0.231 0.828 0.000 0.044 0.924 0.032
#> SRR1414269 3 0.220 0.839 0.000 0.048 0.928 0.024
#> SRR1381913 3 0.397 0.722 0.000 0.076 0.840 0.084
#> SRR1340157 3 0.292 0.784 0.000 0.140 0.860 0.000
#> SRR1407583 2 0.462 0.745 0.000 0.660 0.340 0.000
#> SRR615826 3 0.433 0.705 0.000 0.072 0.816 0.112
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR612587 4 0.0671 0.832 0.000 0.004 0.016 0.980 NA
#> SRR1458769 2 0.3480 0.741 0.000 0.752 0.248 0.000 NA
#> SRR613162 1 0.0000 0.916 1.000 0.000 0.000 0.000 NA
#> SRR1352481 1 0.0000 0.916 1.000 0.000 0.000 0.000 NA
#> SRR1468876 3 0.2012 0.797 0.000 0.060 0.920 0.020 NA
#> SRR1399223 2 0.1608 0.801 0.000 0.928 0.072 0.000 NA
#> SRR660030 3 0.1901 0.800 0.000 0.056 0.928 0.012 NA
#> SRR1333609 3 0.2416 0.789 0.000 0.100 0.888 0.012 NA
#> SRR1471612 3 0.4473 0.569 0.000 0.324 0.656 0.000 NA
#> SRR1413998 2 0.1341 0.792 0.000 0.944 0.056 0.000 NA
#> SRR1122940 3 0.3710 0.737 0.000 0.192 0.784 0.000 NA
#> SRR1402563 3 0.2416 0.789 0.000 0.100 0.888 0.012 NA
#> SRR1398393 3 0.3328 0.747 0.000 0.176 0.812 0.004 NA
#> SRR657961 3 0.4869 0.694 0.000 0.028 0.752 0.068 NA
#> SRR1471135 3 0.2124 0.795 0.000 0.044 0.924 0.012 NA
#> SRR1430001 3 0.2233 0.796 0.000 0.080 0.904 0.016 NA
#> SRR662775 1 0.0000 0.916 1.000 0.000 0.000 0.000 NA
#> SRR1474182 2 0.4574 0.417 0.000 0.576 0.412 0.000 NA
#> SRR607190 1 0.0000 0.916 1.000 0.000 0.000 0.000 NA
#> SRR612467 3 0.4945 0.697 0.000 0.036 0.752 0.068 NA
#> SRR1465959 3 0.4033 0.720 0.000 0.212 0.760 0.004 NA
#> SRR1446132 2 0.1341 0.792 0.000 0.944 0.056 0.000 NA
#> SRR1416933 2 0.3561 0.728 0.000 0.740 0.260 0.000 NA
#> SRR1102538 3 0.4125 0.719 0.000 0.224 0.748 0.004 NA
#> SRR1098636 3 0.1780 0.789 0.000 0.024 0.940 0.008 NA
#> SRR1072998 3 0.4125 0.719 0.000 0.224 0.748 0.004 NA
#> SRR627443 1 0.4547 0.591 0.588 0.000 0.000 0.012 NA
#> SRR656131 1 0.0000 0.916 1.000 0.000 0.000 0.000 NA
#> SRR823991 3 0.1644 0.802 0.000 0.048 0.940 0.004 NA
#> SRR1089158 3 0.3970 0.720 0.000 0.224 0.752 0.000 NA
#> SRR1469036 3 0.2233 0.796 0.000 0.080 0.904 0.016 NA
#> SRR824039 3 0.1644 0.802 0.000 0.048 0.940 0.004 NA
#> SRR1339047 2 0.1732 0.799 0.000 0.920 0.080 0.000 NA
#> SRR1443049 3 0.4064 0.719 0.000 0.216 0.756 0.004 NA
#> SRR1122885 3 0.4001 0.720 0.000 0.208 0.764 0.004 NA
#> SRR602895 4 0.5781 0.692 0.000 0.000 0.116 0.576 NA
#> SRR1409837 2 0.4547 0.441 0.000 0.588 0.400 0.000 NA
#> SRR1388959 2 0.1341 0.792 0.000 0.944 0.056 0.000 NA
#> SRR659863 1 0.0000 0.916 1.000 0.000 0.000 0.000 NA
#> SRR1089877 3 0.2237 0.803 0.000 0.084 0.904 0.004 NA
#> SRR1123775 3 0.1901 0.800 0.000 0.056 0.928 0.012 NA
#> SRR658909 4 0.3988 0.825 0.000 0.000 0.036 0.768 NA
#> SRR1140510 2 0.3274 0.761 0.000 0.780 0.220 0.000 NA
#> SRR607562 4 0.6399 0.578 0.000 0.000 0.196 0.496 NA
#> SRR1122913 3 0.3710 0.737 0.000 0.192 0.784 0.000 NA
#> SRR598042 3 0.5094 0.672 0.000 0.028 0.728 0.068 NA
#> SRR1467340 3 0.2305 0.792 0.000 0.092 0.896 0.012 NA
#> SRR1072321 3 0.3639 0.738 0.000 0.184 0.792 0.000 NA
#> SRR1094580 3 0.3745 0.730 0.000 0.196 0.780 0.000 NA
#> SRR1076608 2 0.1608 0.801 0.000 0.928 0.072 0.000 NA
#> SRR1395462 3 0.4745 0.698 0.000 0.024 0.760 0.068 NA
#> SRR1489220 3 0.6098 0.353 0.000 0.000 0.568 0.236 NA
#> SRR614371 4 0.3910 0.824 0.000 0.000 0.032 0.772 NA
#> SRR615455 1 0.6961 0.228 0.412 0.008 0.000 0.312 NA
#> SRR1070573 3 0.3639 0.738 0.000 0.184 0.792 0.000 NA
#> SRR598749 3 0.5390 0.681 0.000 0.044 0.724 0.096 NA
#> SRR1365556 3 0.3884 0.569 0.000 0.288 0.708 0.004 NA
#> SRR1350023 2 0.1341 0.792 0.000 0.944 0.056 0.000 NA
#> SRR1446582 3 0.1808 0.800 0.000 0.044 0.936 0.012 NA
#> SRR1439763 3 0.2172 0.795 0.000 0.076 0.908 0.016 NA
#> SRR1343986 3 0.2361 0.789 0.000 0.096 0.892 0.012 NA
#> SRR807463 3 0.4064 0.718 0.000 0.216 0.756 0.004 NA
#> SRR660390 1 0.0000 0.916 1.000 0.000 0.000 0.000 NA
#> SRR1367672 3 0.3877 0.725 0.000 0.212 0.764 0.000 NA
#> SRR613294 4 0.0671 0.832 0.000 0.004 0.016 0.980 NA
#> SRR824015 2 0.3796 0.661 0.000 0.700 0.300 0.000 NA
#> SRR1078924 3 0.3710 0.737 0.000 0.192 0.784 0.000 NA
#> SRR662221 4 0.0703 0.837 0.000 0.000 0.024 0.976 NA
#> SRR655017 1 0.0000 0.916 1.000 0.000 0.000 0.000 NA
#> SRR1338450 3 0.2012 0.797 0.000 0.060 0.920 0.020 NA
#> SRR663741 4 0.1281 0.839 0.000 0.000 0.032 0.956 NA
#> SRR1396057 2 0.3561 0.728 0.000 0.740 0.260 0.000 NA
#> SRR1083800 3 0.3343 0.761 0.000 0.172 0.812 0.000 NA
#> SRR1445789 2 0.1608 0.801 0.000 0.928 0.072 0.000 NA
#> SRR1387355 3 0.2233 0.796 0.000 0.080 0.904 0.016 NA
#> SRR1388855 2 0.1608 0.801 0.000 0.928 0.072 0.000 NA
#> SRR1445449 3 0.3209 0.709 0.000 0.180 0.812 0.008 NA
#> SRR1380740 3 0.2416 0.789 0.000 0.100 0.888 0.012 NA
#> SRR659995 4 0.0703 0.837 0.000 0.000 0.024 0.976 NA
#> SRR1489524 2 0.1341 0.792 0.000 0.944 0.056 0.000 NA
#> SRR1444662 2 0.3074 0.722 0.000 0.804 0.196 0.000 NA
#> SRR1383652 3 0.2124 0.795 0.000 0.044 0.924 0.012 NA
#> SRR1361243 3 0.2416 0.789 0.000 0.100 0.888 0.012 NA
#> SRR1490337 3 0.2047 0.796 0.000 0.040 0.928 0.012 NA
#> SRR823967 3 0.1644 0.802 0.000 0.048 0.940 0.004 NA
#> SRR660127 1 0.0000 0.916 1.000 0.000 0.000 0.000 NA
#> SRR1366627 2 0.1608 0.801 0.000 0.928 0.072 0.000 NA
#> SRR1361219 2 0.4341 0.451 0.000 0.592 0.404 0.000 NA
#> SRR1393510 3 0.3884 0.569 0.000 0.288 0.708 0.004 NA
#> SRR662558 4 0.3988 0.825 0.000 0.000 0.036 0.768 NA
#> SRR1077334 3 0.3970 0.724 0.000 0.224 0.752 0.000 NA
#> SRR807438 3 0.2012 0.797 0.000 0.060 0.920 0.020 NA
#> SRR1459078 3 0.2416 0.789 0.000 0.100 0.888 0.012 NA
#> SRR1329704 2 0.4211 0.572 0.000 0.636 0.360 0.000 NA
#> SRR1468072 3 0.2416 0.789 0.000 0.100 0.888 0.012 NA
#> SRR1376196 3 0.3353 0.744 0.000 0.196 0.796 0.000 NA
#> SRR1442909 3 0.2047 0.796 0.000 0.040 0.928 0.012 NA
#> SRR1414269 3 0.1830 0.805 0.000 0.052 0.932 0.012 NA
#> SRR1381913 3 0.4869 0.694 0.000 0.028 0.752 0.068 NA
#> SRR1340157 3 0.4001 0.721 0.000 0.208 0.764 0.004 NA
#> SRR1407583 2 0.3636 0.732 0.000 0.728 0.272 0.000 NA
#> SRR615826 3 0.5390 0.681 0.000 0.044 0.724 0.096 NA
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 4 0.3321 0.7596 0.000 0.016 0.000 0.796 0.180 0.008
#> SRR1458769 6 0.4402 0.7088 0.000 0.116 0.168 0.000 0.000 0.716
#> SRR613162 1 0.0000 0.9241 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 0.9241 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1468876 3 0.0405 0.8803 0.000 0.000 0.988 0.004 0.000 0.008
#> SRR1399223 6 0.1663 0.7974 0.000 0.000 0.088 0.000 0.000 0.912
#> SRR660030 3 0.0725 0.8805 0.000 0.012 0.976 0.000 0.000 0.012
#> SRR1333609 3 0.1204 0.8754 0.000 0.000 0.944 0.000 0.000 0.056
#> SRR1471612 2 0.5778 0.5311 0.000 0.492 0.204 0.000 0.000 0.304
#> SRR1413998 6 0.1267 0.7833 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR1122940 2 0.5278 0.7734 0.000 0.604 0.204 0.000 0.000 0.192
#> SRR1402563 3 0.1204 0.8754 0.000 0.000 0.944 0.000 0.000 0.056
#> SRR1398393 3 0.3530 0.7590 0.000 0.056 0.792 0.000 0.000 0.152
#> SRR657961 2 0.2983 0.5833 0.000 0.860 0.092 0.032 0.004 0.012
#> SRR1471135 3 0.1606 0.8576 0.000 0.056 0.932 0.008 0.000 0.004
#> SRR1430001 3 0.0935 0.8796 0.000 0.000 0.964 0.004 0.000 0.032
#> SRR662775 1 0.0000 0.9241 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1474182 6 0.5561 0.3702 0.000 0.244 0.204 0.000 0.000 0.552
#> SRR607190 1 0.0000 0.9241 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR612467 2 0.2565 0.5623 0.000 0.888 0.072 0.024 0.004 0.012
#> SRR1465959 2 0.5191 0.7814 0.000 0.616 0.172 0.000 0.000 0.212
#> SRR1446132 6 0.1267 0.7833 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR1416933 6 0.4520 0.6981 0.000 0.128 0.168 0.000 0.000 0.704
#> SRR1102538 2 0.5015 0.7793 0.000 0.640 0.152 0.000 0.000 0.208
#> SRR1098636 3 0.2225 0.8240 0.000 0.092 0.892 0.008 0.000 0.008
#> SRR1072998 2 0.5015 0.7793 0.000 0.640 0.152 0.000 0.000 0.208
#> SRR627443 1 0.3782 0.1318 0.588 0.000 0.000 0.000 0.412 0.000
#> SRR656131 1 0.0000 0.9241 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR823991 3 0.2221 0.8402 0.000 0.072 0.896 0.000 0.000 0.032
#> SRR1089158 2 0.5032 0.7806 0.000 0.636 0.148 0.000 0.000 0.216
#> SRR1469036 3 0.0935 0.8796 0.000 0.000 0.964 0.004 0.000 0.032
#> SRR824039 3 0.2221 0.8402 0.000 0.072 0.896 0.000 0.000 0.032
#> SRR1339047 6 0.1714 0.7944 0.000 0.000 0.092 0.000 0.000 0.908
#> SRR1443049 2 0.5150 0.7775 0.000 0.620 0.160 0.000 0.000 0.220
#> SRR1122885 2 0.5156 0.7806 0.000 0.620 0.164 0.000 0.000 0.216
#> SRR602895 4 0.3511 0.6043 0.000 0.216 0.024 0.760 0.000 0.000
#> SRR1409837 6 0.5524 0.4033 0.000 0.236 0.204 0.000 0.000 0.560
#> SRR1388959 6 0.1267 0.7833 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR659863 1 0.0000 0.9241 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1089877 3 0.2389 0.8531 0.000 0.052 0.888 0.000 0.000 0.060
#> SRR1123775 3 0.0725 0.8805 0.000 0.012 0.976 0.000 0.000 0.012
#> SRR658909 4 0.1408 0.7666 0.000 0.020 0.036 0.944 0.000 0.000
#> SRR1140510 6 0.4218 0.7273 0.000 0.108 0.156 0.000 0.000 0.736
#> SRR607562 4 0.3916 0.4799 0.000 0.300 0.020 0.680 0.000 0.000
#> SRR1122913 2 0.5278 0.7734 0.000 0.604 0.204 0.000 0.000 0.192
#> SRR598042 2 0.3064 0.5499 0.000 0.860 0.068 0.056 0.004 0.012
#> SRR1467340 3 0.2070 0.8582 0.000 0.044 0.908 0.000 0.000 0.048
#> SRR1072321 2 0.5348 0.7620 0.000 0.592 0.216 0.000 0.000 0.192
#> SRR1094580 2 0.5328 0.7659 0.000 0.596 0.204 0.000 0.000 0.200
#> SRR1076608 6 0.1663 0.7974 0.000 0.000 0.088 0.000 0.000 0.912
#> SRR1395462 2 0.3128 0.5817 0.000 0.848 0.104 0.032 0.004 0.012
#> SRR1489220 3 0.4261 0.2746 0.000 0.020 0.572 0.408 0.000 0.000
#> SRR614371 4 0.0909 0.7636 0.000 0.020 0.012 0.968 0.000 0.000
#> SRR615455 5 0.4341 0.0000 0.412 0.000 0.000 0.024 0.564 0.000
#> SRR1070573 2 0.5348 0.7620 0.000 0.592 0.216 0.000 0.000 0.192
#> SRR598749 2 0.3378 0.3917 0.000 0.856 0.040 0.028 0.024 0.052
#> SRR1365556 3 0.3101 0.6614 0.000 0.000 0.756 0.000 0.000 0.244
#> SRR1350023 6 0.1267 0.7833 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR1446582 3 0.0922 0.8729 0.000 0.024 0.968 0.004 0.000 0.004
#> SRR1439763 3 0.0713 0.8799 0.000 0.000 0.972 0.000 0.000 0.028
#> SRR1343986 3 0.1141 0.8759 0.000 0.000 0.948 0.000 0.000 0.052
#> SRR807463 2 0.5150 0.7787 0.000 0.620 0.160 0.000 0.000 0.220
#> SRR660390 1 0.0000 0.9241 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1367672 2 0.5223 0.7797 0.000 0.612 0.180 0.000 0.000 0.208
#> SRR613294 4 0.3321 0.7596 0.000 0.016 0.000 0.796 0.180 0.008
#> SRR824015 6 0.3592 0.5314 0.000 0.000 0.344 0.000 0.000 0.656
#> SRR1078924 2 0.5278 0.7734 0.000 0.604 0.204 0.000 0.000 0.192
#> SRR662221 4 0.3705 0.7730 0.000 0.008 0.036 0.776 0.180 0.000
#> SRR655017 1 0.0000 0.9241 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1338450 3 0.0405 0.8803 0.000 0.000 0.988 0.004 0.000 0.008
#> SRR663741 4 0.3354 0.7768 0.000 0.000 0.036 0.796 0.168 0.000
#> SRR1396057 6 0.4520 0.6981 0.000 0.128 0.168 0.000 0.000 0.704
#> SRR1083800 3 0.5618 -0.2054 0.000 0.340 0.500 0.000 0.000 0.160
#> SRR1445789 6 0.1663 0.7974 0.000 0.000 0.088 0.000 0.000 0.912
#> SRR1387355 3 0.0935 0.8796 0.000 0.000 0.964 0.004 0.000 0.032
#> SRR1388855 6 0.1501 0.7915 0.000 0.000 0.076 0.000 0.000 0.924
#> SRR1445449 3 0.2219 0.8053 0.000 0.000 0.864 0.000 0.000 0.136
#> SRR1380740 3 0.1204 0.8754 0.000 0.000 0.944 0.000 0.000 0.056
#> SRR659995 4 0.3705 0.7730 0.000 0.008 0.036 0.776 0.180 0.000
#> SRR1489524 6 0.1267 0.7833 0.000 0.000 0.060 0.000 0.000 0.940
#> SRR1444662 6 0.3076 0.6698 0.000 0.000 0.240 0.000 0.000 0.760
#> SRR1383652 3 0.1606 0.8576 0.000 0.056 0.932 0.008 0.000 0.004
#> SRR1361243 3 0.1204 0.8754 0.000 0.000 0.944 0.000 0.000 0.056
#> SRR1490337 3 0.1542 0.8600 0.000 0.052 0.936 0.008 0.000 0.004
#> SRR823967 3 0.2221 0.8402 0.000 0.072 0.896 0.000 0.000 0.032
#> SRR660127 1 0.0000 0.9241 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1366627 6 0.1714 0.7978 0.000 0.000 0.092 0.000 0.000 0.908
#> SRR1361219 6 0.5504 0.4155 0.000 0.232 0.204 0.000 0.000 0.564
#> SRR1393510 3 0.3101 0.6614 0.000 0.000 0.756 0.000 0.000 0.244
#> SRR662558 4 0.1408 0.7666 0.000 0.020 0.036 0.944 0.000 0.000
#> SRR1077334 2 0.5273 0.7713 0.000 0.604 0.184 0.000 0.000 0.212
#> SRR807438 3 0.0405 0.8803 0.000 0.000 0.988 0.004 0.000 0.008
#> SRR1459078 3 0.1204 0.8754 0.000 0.000 0.944 0.000 0.000 0.056
#> SRR1329704 6 0.5228 0.5456 0.000 0.192 0.196 0.000 0.000 0.612
#> SRR1468072 3 0.1204 0.8754 0.000 0.000 0.944 0.000 0.000 0.056
#> SRR1376196 3 0.5528 0.0968 0.000 0.252 0.556 0.000 0.000 0.192
#> SRR1442909 3 0.1542 0.8600 0.000 0.052 0.936 0.008 0.000 0.004
#> SRR1414269 3 0.1003 0.8772 0.000 0.020 0.964 0.000 0.000 0.016
#> SRR1381913 2 0.2983 0.5833 0.000 0.860 0.092 0.032 0.004 0.012
#> SRR1340157 2 0.5214 0.7803 0.000 0.612 0.172 0.000 0.000 0.216
#> SRR1407583 6 0.4695 0.6991 0.000 0.116 0.208 0.000 0.000 0.676
#> SRR615826 2 0.3378 0.3917 0.000 0.856 0.040 0.028 0.024 0.052
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "kmeans"]
# you can also extract it by
# res = res_list["MAD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17380 rows and 102 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 0.965 0.987 0.3061 0.706 0.706
#> 3 3 0.703 0.795 0.908 0.9081 0.609 0.480
#> 4 4 0.485 0.513 0.751 0.1920 0.729 0.454
#> 5 5 0.673 0.742 0.838 0.0949 0.793 0.446
#> 6 6 0.806 0.816 0.874 0.0555 0.946 0.775
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
#> SRR612587 1 0.000 0.988 1.000 0.000
#> SRR1458769 2 0.000 0.985 0.000 1.000
#> SRR613162 1 0.000 0.988 1.000 0.000
#> SRR1352481 1 0.000 0.988 1.000 0.000
#> SRR1468876 2 0.000 0.985 0.000 1.000
#> SRR1399223 2 0.000 0.985 0.000 1.000
#> SRR660030 2 0.000 0.985 0.000 1.000
#> SRR1333609 2 0.000 0.985 0.000 1.000
#> SRR1471612 2 0.000 0.985 0.000 1.000
#> SRR1413998 2 0.000 0.985 0.000 1.000
#> SRR1122940 2 0.000 0.985 0.000 1.000
#> SRR1402563 2 0.000 0.985 0.000 1.000
#> SRR1398393 2 0.000 0.985 0.000 1.000
#> SRR657961 2 0.000 0.985 0.000 1.000
#> SRR1471135 2 0.000 0.985 0.000 1.000
#> SRR1430001 2 0.000 0.985 0.000 1.000
#> SRR662775 1 0.000 0.988 1.000 0.000
#> SRR1474182 2 0.000 0.985 0.000 1.000
#> SRR607190 1 0.000 0.988 1.000 0.000
#> SRR612467 2 0.000 0.985 0.000 1.000
#> SRR1465959 2 0.000 0.985 0.000 1.000
#> SRR1446132 2 0.000 0.985 0.000 1.000
#> SRR1416933 2 0.000 0.985 0.000 1.000
#> SRR1102538 2 0.000 0.985 0.000 1.000
#> SRR1098636 2 0.000 0.985 0.000 1.000
#> SRR1072998 2 0.000 0.985 0.000 1.000
#> SRR627443 1 0.000 0.988 1.000 0.000
#> SRR656131 1 0.000 0.988 1.000 0.000
#> SRR823991 2 0.000 0.985 0.000 1.000
#> SRR1089158 2 0.000 0.985 0.000 1.000
#> SRR1469036 2 0.000 0.985 0.000 1.000
#> SRR824039 2 0.000 0.985 0.000 1.000
#> SRR1339047 2 0.000 0.985 0.000 1.000
#> SRR1443049 2 0.000 0.985 0.000 1.000
#> SRR1122885 2 0.000 0.985 0.000 1.000
#> SRR602895 2 0.952 0.407 0.372 0.628
#> SRR1409837 2 0.000 0.985 0.000 1.000
#> SRR1388959 2 0.000 0.985 0.000 1.000
#> SRR659863 1 0.000 0.988 1.000 0.000
#> SRR1089877 2 0.000 0.985 0.000 1.000
#> SRR1123775 2 0.000 0.985 0.000 1.000
#> SRR658909 1 0.000 0.988 1.000 0.000
#> SRR1140510 2 0.000 0.985 0.000 1.000
#> SRR607562 2 0.000 0.985 0.000 1.000
#> SRR1122913 2 0.000 0.985 0.000 1.000
#> SRR598042 2 0.000 0.985 0.000 1.000
#> SRR1467340 2 0.000 0.985 0.000 1.000
#> SRR1072321 2 0.000 0.985 0.000 1.000
#> SRR1094580 2 0.000 0.985 0.000 1.000
#> SRR1076608 2 0.000 0.985 0.000 1.000
#> SRR1395462 2 0.000 0.985 0.000 1.000
#> SRR1489220 2 0.971 0.335 0.400 0.600
#> SRR614371 1 0.000 0.988 1.000 0.000
#> SRR615455 1 0.000 0.988 1.000 0.000
#> SRR1070573 2 0.000 0.985 0.000 1.000
#> SRR598749 2 0.000 0.985 0.000 1.000
#> SRR1365556 2 0.000 0.985 0.000 1.000
#> SRR1350023 2 0.000 0.985 0.000 1.000
#> SRR1446582 2 0.000 0.985 0.000 1.000
#> SRR1439763 2 0.000 0.985 0.000 1.000
#> SRR1343986 2 0.000 0.985 0.000 1.000
#> SRR807463 2 0.000 0.985 0.000 1.000
#> SRR660390 1 0.000 0.988 1.000 0.000
#> SRR1367672 2 0.000 0.985 0.000 1.000
#> SRR613294 1 0.000 0.988 1.000 0.000
#> SRR824015 2 0.000 0.985 0.000 1.000
#> SRR1078924 2 0.000 0.985 0.000 1.000
#> SRR662221 1 0.000 0.988 1.000 0.000
#> SRR655017 1 0.000 0.988 1.000 0.000
#> SRR1338450 2 0.000 0.985 0.000 1.000
#> SRR663741 1 0.000 0.988 1.000 0.000
#> SRR1396057 2 0.000 0.985 0.000 1.000
#> SRR1083800 2 0.000 0.985 0.000 1.000
#> SRR1445789 2 0.000 0.985 0.000 1.000
#> SRR1387355 2 0.000 0.985 0.000 1.000
#> SRR1388855 2 0.000 0.985 0.000 1.000
#> SRR1445449 2 0.000 0.985 0.000 1.000
#> SRR1380740 2 0.000 0.985 0.000 1.000
#> SRR659995 1 0.706 0.755 0.808 0.192
#> SRR1489524 2 0.000 0.985 0.000 1.000
#> SRR1444662 2 0.000 0.985 0.000 1.000
#> SRR1383652 2 0.000 0.985 0.000 1.000
#> SRR1361243 2 0.000 0.985 0.000 1.000
#> SRR1490337 2 0.000 0.985 0.000 1.000
#> SRR823967 2 0.000 0.985 0.000 1.000
#> SRR660127 1 0.000 0.988 1.000 0.000
#> SRR1366627 2 0.000 0.985 0.000 1.000
#> SRR1361219 2 0.000 0.985 0.000 1.000
#> SRR1393510 2 0.000 0.985 0.000 1.000
#> SRR662558 2 0.971 0.335 0.400 0.600
#> SRR1077334 2 0.000 0.985 0.000 1.000
#> SRR807438 2 0.000 0.985 0.000 1.000
#> SRR1459078 2 0.000 0.985 0.000 1.000
#> SRR1329704 2 0.000 0.985 0.000 1.000
#> SRR1468072 2 0.000 0.985 0.000 1.000
#> SRR1376196 2 0.000 0.985 0.000 1.000
#> SRR1442909 2 0.000 0.985 0.000 1.000
#> SRR1414269 2 0.000 0.985 0.000 1.000
#> SRR1381913 2 0.000 0.985 0.000 1.000
#> SRR1340157 2 0.000 0.985 0.000 1.000
#> SRR1407583 2 0.000 0.985 0.000 1.000
#> SRR615826 2 0.000 0.985 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 3 0.5560 0.447 0.300 0.000 0.700
#> SRR1458769 2 0.0000 0.927 0.000 1.000 0.000
#> SRR613162 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1352481 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1468876 3 0.2625 0.786 0.000 0.084 0.916
#> SRR1399223 2 0.0237 0.927 0.000 0.996 0.004
#> SRR660030 3 0.0592 0.794 0.000 0.012 0.988
#> SRR1333609 3 0.6225 0.337 0.000 0.432 0.568
#> SRR1471612 2 0.0592 0.922 0.000 0.988 0.012
#> SRR1413998 2 0.0000 0.927 0.000 1.000 0.000
#> SRR1122940 2 0.1964 0.907 0.000 0.944 0.056
#> SRR1402563 3 0.6225 0.337 0.000 0.432 0.568
#> SRR1398393 2 0.0237 0.927 0.000 0.996 0.004
#> SRR657961 3 0.0892 0.795 0.000 0.020 0.980
#> SRR1471135 3 0.5650 0.592 0.000 0.312 0.688
#> SRR1430001 3 0.4974 0.706 0.000 0.236 0.764
#> SRR662775 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1474182 2 0.0000 0.927 0.000 1.000 0.000
#> SRR607190 1 0.0000 0.998 1.000 0.000 0.000
#> SRR612467 3 0.0237 0.789 0.000 0.004 0.996
#> SRR1465959 2 0.0592 0.922 0.000 0.988 0.012
#> SRR1446132 2 0.0237 0.927 0.000 0.996 0.004
#> SRR1416933 2 0.0000 0.927 0.000 1.000 0.000
#> SRR1102538 2 0.2711 0.879 0.000 0.912 0.088
#> SRR1098636 3 0.0424 0.792 0.000 0.008 0.992
#> SRR1072998 2 0.1964 0.907 0.000 0.944 0.056
#> SRR627443 1 0.0000 0.998 1.000 0.000 0.000
#> SRR656131 1 0.0000 0.998 1.000 0.000 0.000
#> SRR823991 2 0.6215 0.178 0.000 0.572 0.428
#> SRR1089158 2 0.1964 0.907 0.000 0.944 0.056
#> SRR1469036 3 0.5138 0.687 0.000 0.252 0.748
#> SRR824039 2 0.4931 0.686 0.000 0.768 0.232
#> SRR1339047 2 0.0237 0.927 0.000 0.996 0.004
#> SRR1443049 2 0.0000 0.927 0.000 1.000 0.000
#> SRR1122885 2 0.1964 0.907 0.000 0.944 0.056
#> SRR602895 3 0.0000 0.788 0.000 0.000 1.000
#> SRR1409837 2 0.0000 0.927 0.000 1.000 0.000
#> SRR1388959 2 0.0000 0.927 0.000 1.000 0.000
#> SRR659863 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1089877 2 0.1753 0.910 0.000 0.952 0.048
#> SRR1123775 3 0.1289 0.797 0.000 0.032 0.968
#> SRR658909 3 0.5706 0.408 0.320 0.000 0.680
#> SRR1140510 2 0.0237 0.927 0.000 0.996 0.004
#> SRR607562 3 0.0237 0.789 0.000 0.004 0.996
#> SRR1122913 2 0.1964 0.907 0.000 0.944 0.056
#> SRR598042 3 0.0237 0.789 0.000 0.004 0.996
#> SRR1467340 2 0.5591 0.533 0.000 0.696 0.304
#> SRR1072321 2 0.0000 0.927 0.000 1.000 0.000
#> SRR1094580 2 0.1643 0.910 0.000 0.956 0.044
#> SRR1076608 2 0.0237 0.927 0.000 0.996 0.004
#> SRR1395462 3 0.1643 0.796 0.000 0.044 0.956
#> SRR1489220 3 0.0747 0.781 0.016 0.000 0.984
#> SRR614371 3 0.5560 0.447 0.300 0.000 0.700
#> SRR615455 1 0.1163 0.975 0.972 0.000 0.028
#> SRR1070573 2 0.1964 0.907 0.000 0.944 0.056
#> SRR598749 3 0.0237 0.789 0.000 0.004 0.996
#> SRR1365556 2 0.0237 0.927 0.000 0.996 0.004
#> SRR1350023 2 0.0000 0.927 0.000 1.000 0.000
#> SRR1446582 3 0.1529 0.797 0.000 0.040 0.960
#> SRR1439763 3 0.5138 0.687 0.000 0.252 0.748
#> SRR1343986 3 0.6225 0.337 0.000 0.432 0.568
#> SRR807463 2 0.0592 0.922 0.000 0.988 0.012
#> SRR660390 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1367672 2 0.1964 0.907 0.000 0.944 0.056
#> SRR613294 3 0.5497 0.462 0.292 0.000 0.708
#> SRR824015 2 0.0237 0.927 0.000 0.996 0.004
#> SRR1078924 2 0.1964 0.907 0.000 0.944 0.056
#> SRR662221 3 0.1860 0.755 0.052 0.000 0.948
#> SRR655017 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1338450 3 0.1411 0.796 0.000 0.036 0.964
#> SRR663741 3 0.5706 0.408 0.320 0.000 0.680
#> SRR1396057 2 0.0000 0.927 0.000 1.000 0.000
#> SRR1083800 3 0.6192 0.358 0.000 0.420 0.580
#> SRR1445789 2 0.0237 0.927 0.000 0.996 0.004
#> SRR1387355 3 0.4399 0.744 0.000 0.188 0.812
#> SRR1388855 2 0.0000 0.927 0.000 1.000 0.000
#> SRR1445449 3 0.4974 0.706 0.000 0.236 0.764
#> SRR1380740 3 0.6225 0.337 0.000 0.432 0.568
#> SRR659995 3 0.1643 0.760 0.044 0.000 0.956
#> SRR1489524 2 0.0000 0.927 0.000 1.000 0.000
#> SRR1444662 2 0.0237 0.927 0.000 0.996 0.004
#> SRR1383652 3 0.4291 0.746 0.000 0.180 0.820
#> SRR1361243 2 0.6168 0.235 0.000 0.588 0.412
#> SRR1490337 3 0.1289 0.797 0.000 0.032 0.968
#> SRR823967 3 0.4931 0.704 0.000 0.232 0.768
#> SRR660127 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1366627 2 0.0237 0.927 0.000 0.996 0.004
#> SRR1361219 2 0.0000 0.927 0.000 1.000 0.000
#> SRR1393510 2 0.6126 0.242 0.000 0.600 0.400
#> SRR662558 3 0.1163 0.773 0.028 0.000 0.972
#> SRR1077334 2 0.2711 0.879 0.000 0.912 0.088
#> SRR807438 3 0.0892 0.793 0.000 0.020 0.980
#> SRR1459078 2 0.6111 0.285 0.000 0.604 0.396
#> SRR1329704 2 0.0000 0.927 0.000 1.000 0.000
#> SRR1468072 2 0.0237 0.927 0.000 0.996 0.004
#> SRR1376196 2 0.1643 0.910 0.000 0.956 0.044
#> SRR1442909 3 0.1411 0.797 0.000 0.036 0.964
#> SRR1414269 3 0.4605 0.732 0.000 0.204 0.796
#> SRR1381913 3 0.0592 0.792 0.000 0.012 0.988
#> SRR1340157 2 0.0000 0.927 0.000 1.000 0.000
#> SRR1407583 2 0.0237 0.927 0.000 0.996 0.004
#> SRR615826 3 0.0237 0.789 0.000 0.004 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.3160 0.69875 0.108 0.000 0.020 0.872
#> SRR1458769 2 0.0000 0.80456 0.000 1.000 0.000 0.000
#> SRR613162 1 0.0000 0.97878 1.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 0.97878 1.000 0.000 0.000 0.000
#> SRR1468876 3 0.6020 0.39327 0.000 0.048 0.568 0.384
#> SRR1399223 2 0.0000 0.80456 0.000 1.000 0.000 0.000
#> SRR660030 3 0.4356 0.46901 0.000 0.000 0.708 0.292
#> SRR1333609 3 0.6214 0.49536 0.000 0.092 0.636 0.272
#> SRR1471612 2 0.4907 0.47577 0.000 0.580 0.420 0.000
#> SRR1413998 2 0.0000 0.80456 0.000 1.000 0.000 0.000
#> SRR1122940 3 0.4746 -0.00394 0.000 0.368 0.632 0.000
#> SRR1402563 3 0.6133 0.49887 0.000 0.088 0.644 0.268
#> SRR1398393 2 0.5004 0.38601 0.000 0.604 0.392 0.004
#> SRR657961 3 0.4382 0.14091 0.000 0.000 0.704 0.296
#> SRR1471135 3 0.2867 0.45551 0.000 0.012 0.884 0.104
#> SRR1430001 3 0.6023 0.45773 0.000 0.060 0.612 0.328
#> SRR662775 1 0.0000 0.97878 1.000 0.000 0.000 0.000
#> SRR1474182 2 0.3610 0.71576 0.000 0.800 0.200 0.000
#> SRR607190 1 0.0000 0.97878 1.000 0.000 0.000 0.000
#> SRR612467 3 0.4866 -0.10286 0.000 0.000 0.596 0.404
#> SRR1465959 2 0.4933 0.45356 0.000 0.568 0.432 0.000
#> SRR1446132 2 0.0000 0.80456 0.000 1.000 0.000 0.000
#> SRR1416933 2 0.0000 0.80456 0.000 1.000 0.000 0.000
#> SRR1102538 3 0.5277 0.11535 0.000 0.304 0.668 0.028
#> SRR1098636 4 0.4948 0.02736 0.000 0.000 0.440 0.560
#> SRR1072998 3 0.5626 -0.09894 0.000 0.384 0.588 0.028
#> SRR627443 1 0.0188 0.97659 0.996 0.000 0.004 0.000
#> SRR656131 1 0.0000 0.97878 1.000 0.000 0.000 0.000
#> SRR823991 3 0.5788 0.50281 0.000 0.084 0.688 0.228
#> SRR1089158 3 0.5070 -0.14147 0.000 0.416 0.580 0.004
#> SRR1469036 3 0.6052 0.46267 0.000 0.064 0.616 0.320
#> SRR824039 3 0.3479 0.43828 0.000 0.148 0.840 0.012
#> SRR1339047 2 0.0000 0.80456 0.000 1.000 0.000 0.000
#> SRR1443049 2 0.4103 0.67244 0.000 0.744 0.256 0.000
#> SRR1122885 3 0.4925 -0.16774 0.000 0.428 0.572 0.000
#> SRR602895 4 0.1557 0.69244 0.000 0.000 0.056 0.944
#> SRR1409837 2 0.3907 0.69258 0.000 0.768 0.232 0.000
#> SRR1388959 2 0.0000 0.80456 0.000 1.000 0.000 0.000
#> SRR659863 1 0.0000 0.97878 1.000 0.000 0.000 0.000
#> SRR1089877 3 0.5018 0.27980 0.000 0.332 0.656 0.012
#> SRR1123775 3 0.4456 0.47540 0.000 0.004 0.716 0.280
#> SRR658909 4 0.4332 0.70322 0.112 0.000 0.072 0.816
#> SRR1140510 2 0.0000 0.80456 0.000 1.000 0.000 0.000
#> SRR607562 4 0.4331 0.53647 0.000 0.000 0.288 0.712
#> SRR1122913 3 0.4830 -0.05000 0.000 0.392 0.608 0.000
#> SRR598042 3 0.4994 -0.19748 0.000 0.000 0.520 0.480
#> SRR1467340 3 0.6414 0.49989 0.000 0.124 0.636 0.240
#> SRR1072321 2 0.4522 0.59183 0.000 0.680 0.320 0.000
#> SRR1094580 2 0.4998 0.29057 0.000 0.512 0.488 0.000
#> SRR1076608 2 0.0000 0.80456 0.000 1.000 0.000 0.000
#> SRR1395462 3 0.3873 0.25019 0.000 0.000 0.772 0.228
#> SRR1489220 4 0.4996 -0.17903 0.000 0.000 0.484 0.516
#> SRR614371 4 0.3160 0.69875 0.108 0.000 0.020 0.872
#> SRR615455 1 0.3688 0.75167 0.792 0.000 0.000 0.208
#> SRR1070573 3 0.4948 -0.18254 0.000 0.440 0.560 0.000
#> SRR598749 4 0.4250 0.53405 0.000 0.000 0.276 0.724
#> SRR1365556 2 0.3837 0.56410 0.000 0.776 0.224 0.000
#> SRR1350023 2 0.0000 0.80456 0.000 1.000 0.000 0.000
#> SRR1446582 3 0.3528 0.38447 0.000 0.000 0.808 0.192
#> SRR1439763 3 0.5549 0.49473 0.000 0.048 0.672 0.280
#> SRR1343986 3 0.6158 0.49698 0.000 0.088 0.640 0.272
#> SRR807463 2 0.4830 0.51872 0.000 0.608 0.392 0.000
#> SRR660390 1 0.0000 0.97878 1.000 0.000 0.000 0.000
#> SRR1367672 3 0.4925 -0.16774 0.000 0.428 0.572 0.000
#> SRR613294 4 0.3160 0.69875 0.108 0.000 0.020 0.872
#> SRR824015 2 0.3945 0.57210 0.000 0.780 0.216 0.004
#> SRR1078924 3 0.5000 -0.32537 0.000 0.496 0.504 0.000
#> SRR662221 4 0.1867 0.68949 0.000 0.000 0.072 0.928
#> SRR655017 1 0.0000 0.97878 1.000 0.000 0.000 0.000
#> SRR1338450 3 0.5827 0.38077 0.000 0.036 0.568 0.396
#> SRR663741 4 0.4274 0.70574 0.108 0.000 0.072 0.820
#> SRR1396057 2 0.0000 0.80456 0.000 1.000 0.000 0.000
#> SRR1083800 3 0.0804 0.43452 0.000 0.012 0.980 0.008
#> SRR1445789 2 0.0000 0.80456 0.000 1.000 0.000 0.000
#> SRR1387355 3 0.6201 0.40197 0.000 0.060 0.564 0.376
#> SRR1388855 2 0.0000 0.80456 0.000 1.000 0.000 0.000
#> SRR1445449 3 0.6386 0.38788 0.000 0.072 0.552 0.376
#> SRR1380740 3 0.6238 0.49255 0.000 0.092 0.632 0.276
#> SRR659995 4 0.1004 0.71199 0.004 0.000 0.024 0.972
#> SRR1489524 2 0.0000 0.80456 0.000 1.000 0.000 0.000
#> SRR1444662 2 0.3610 0.60033 0.000 0.800 0.200 0.000
#> SRR1383652 3 0.4511 0.48539 0.000 0.008 0.724 0.268
#> SRR1361243 3 0.6214 0.49536 0.000 0.092 0.636 0.272
#> SRR1490337 3 0.5626 0.40159 0.000 0.028 0.588 0.384
#> SRR823967 3 0.5182 0.48745 0.000 0.028 0.684 0.288
#> SRR660127 1 0.0000 0.97878 1.000 0.000 0.000 0.000
#> SRR1366627 2 0.1118 0.78123 0.000 0.964 0.036 0.000
#> SRR1361219 2 0.3172 0.73976 0.000 0.840 0.160 0.000
#> SRR1393510 3 0.7372 0.36245 0.000 0.240 0.524 0.236
#> SRR662558 4 0.1940 0.68739 0.000 0.000 0.076 0.924
#> SRR1077334 3 0.4313 0.24547 0.000 0.260 0.736 0.004
#> SRR807438 3 0.4933 0.32169 0.000 0.000 0.568 0.432
#> SRR1459078 3 0.6476 0.48144 0.000 0.112 0.616 0.272
#> SRR1329704 2 0.1474 0.78480 0.000 0.948 0.052 0.000
#> SRR1468072 2 0.5007 0.35359 0.000 0.636 0.356 0.008
#> SRR1376196 2 0.5147 0.26813 0.000 0.536 0.460 0.004
#> SRR1442909 3 0.4456 0.47553 0.000 0.004 0.716 0.280
#> SRR1414269 3 0.4482 0.48623 0.000 0.008 0.728 0.264
#> SRR1381913 3 0.4989 -0.17997 0.000 0.000 0.528 0.472
#> SRR1340157 2 0.4008 0.68261 0.000 0.756 0.244 0.000
#> SRR1407583 2 0.0000 0.80456 0.000 1.000 0.000 0.000
#> SRR615826 4 0.4877 0.38027 0.000 0.000 0.408 0.592
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR612587 4 0.1934 0.79580 0.008 0.000 0.040 0.932 0.020
#> SRR1458769 2 0.0000 0.94752 0.000 1.000 0.000 0.000 0.000
#> SRR613162 1 0.0162 0.95918 0.996 0.000 0.000 0.000 0.004
#> SRR1352481 1 0.0162 0.95918 0.996 0.000 0.000 0.000 0.004
#> SRR1468876 3 0.1444 0.85040 0.000 0.012 0.948 0.040 0.000
#> SRR1399223 2 0.0727 0.93991 0.000 0.980 0.012 0.004 0.004
#> SRR660030 3 0.2416 0.85314 0.000 0.000 0.888 0.012 0.100
#> SRR1333609 3 0.1774 0.86956 0.000 0.016 0.932 0.000 0.052
#> SRR1471612 5 0.3863 0.70537 0.000 0.248 0.012 0.000 0.740
#> SRR1413998 2 0.0000 0.94752 0.000 1.000 0.000 0.000 0.000
#> SRR1122940 5 0.4238 0.72666 0.000 0.136 0.088 0.000 0.776
#> SRR1402563 3 0.1774 0.86956 0.000 0.016 0.932 0.000 0.052
#> SRR1398393 3 0.7475 -0.12204 0.000 0.232 0.376 0.040 0.352
#> SRR657961 5 0.2362 0.53202 0.000 0.000 0.076 0.024 0.900
#> SRR1471135 3 0.3006 0.81735 0.000 0.004 0.836 0.004 0.156
#> SRR1430001 3 0.0693 0.86029 0.000 0.012 0.980 0.000 0.008
#> SRR662775 1 0.0000 0.95977 1.000 0.000 0.000 0.000 0.000
#> SRR1474182 5 0.4617 0.49399 0.000 0.436 0.012 0.000 0.552
#> SRR607190 1 0.0162 0.95895 0.996 0.000 0.000 0.000 0.004
#> SRR612467 5 0.4155 0.36262 0.000 0.000 0.076 0.144 0.780
#> SRR1465959 5 0.4058 0.71374 0.000 0.236 0.024 0.000 0.740
#> SRR1446132 2 0.0162 0.94746 0.000 0.996 0.004 0.000 0.000
#> SRR1416933 2 0.0000 0.94752 0.000 1.000 0.000 0.000 0.000
#> SRR1102538 5 0.3201 0.70424 0.000 0.096 0.052 0.000 0.852
#> SRR1098636 3 0.5810 0.52241 0.000 0.000 0.612 0.176 0.212
#> SRR1072998 5 0.3165 0.71345 0.000 0.116 0.036 0.000 0.848
#> SRR627443 1 0.0807 0.94805 0.976 0.000 0.000 0.012 0.012
#> SRR656131 1 0.0000 0.95977 1.000 0.000 0.000 0.000 0.000
#> SRR823991 3 0.4221 0.80899 0.000 0.016 0.780 0.036 0.168
#> SRR1089158 5 0.3691 0.73394 0.000 0.156 0.040 0.000 0.804
#> SRR1469036 3 0.1106 0.86299 0.000 0.012 0.964 0.000 0.024
#> SRR824039 5 0.5841 0.00981 0.000 0.032 0.440 0.036 0.492
#> SRR1339047 2 0.0486 0.94515 0.000 0.988 0.004 0.004 0.004
#> SRR1443049 5 0.4582 0.52698 0.000 0.416 0.012 0.000 0.572
#> SRR1122885 5 0.3731 0.73490 0.000 0.160 0.040 0.000 0.800
#> SRR602895 4 0.4412 0.74910 0.000 0.000 0.080 0.756 0.164
#> SRR1409837 5 0.4610 0.50107 0.000 0.432 0.012 0.000 0.556
#> SRR1388959 2 0.0000 0.94752 0.000 1.000 0.000 0.000 0.000
#> SRR659863 1 0.0162 0.95895 0.996 0.000 0.000 0.000 0.004
#> SRR1089877 5 0.6148 -0.05663 0.000 0.052 0.452 0.036 0.460
#> SRR1123775 3 0.2574 0.84830 0.000 0.000 0.876 0.012 0.112
#> SRR658909 4 0.3163 0.77589 0.012 0.000 0.164 0.824 0.000
#> SRR1140510 2 0.0486 0.94515 0.000 0.988 0.004 0.004 0.004
#> SRR607562 4 0.5325 0.66352 0.000 0.000 0.076 0.616 0.308
#> SRR1122913 5 0.4558 0.73441 0.000 0.168 0.088 0.000 0.744
#> SRR598042 5 0.5959 -0.39627 0.000 0.000 0.108 0.420 0.472
#> SRR1467340 3 0.1774 0.86956 0.000 0.016 0.932 0.000 0.052
#> SRR1072321 5 0.4714 0.64784 0.000 0.324 0.032 0.000 0.644
#> SRR1094580 5 0.4462 0.73134 0.000 0.196 0.064 0.000 0.740
#> SRR1076608 2 0.0162 0.94746 0.000 0.996 0.004 0.000 0.000
#> SRR1395462 5 0.2331 0.53469 0.000 0.000 0.080 0.020 0.900
#> SRR1489220 3 0.1544 0.82820 0.000 0.000 0.932 0.068 0.000
#> SRR614371 4 0.1934 0.79580 0.008 0.000 0.040 0.932 0.020
#> SRR615455 1 0.4538 0.46007 0.636 0.000 0.012 0.348 0.004
#> SRR1070573 5 0.4409 0.73640 0.000 0.176 0.072 0.000 0.752
#> SRR598749 4 0.4193 0.68896 0.000 0.000 0.012 0.684 0.304
#> SRR1365556 2 0.3243 0.73069 0.000 0.812 0.180 0.004 0.004
#> SRR1350023 2 0.0000 0.94752 0.000 1.000 0.000 0.000 0.000
#> SRR1446582 3 0.3491 0.75068 0.000 0.000 0.768 0.004 0.228
#> SRR1439763 3 0.1670 0.86942 0.000 0.012 0.936 0.000 0.052
#> SRR1343986 3 0.1774 0.86956 0.000 0.016 0.932 0.000 0.052
#> SRR807463 5 0.4371 0.61537 0.000 0.344 0.012 0.000 0.644
#> SRR660390 1 0.0162 0.95918 0.996 0.000 0.000 0.000 0.004
#> SRR1367672 5 0.3731 0.73490 0.000 0.160 0.040 0.000 0.800
#> SRR613294 4 0.1934 0.79580 0.008 0.000 0.040 0.932 0.020
#> SRR824015 2 0.3543 0.76484 0.000 0.828 0.128 0.040 0.004
#> SRR1078924 5 0.4429 0.73284 0.000 0.192 0.064 0.000 0.744
#> SRR662221 4 0.2648 0.78039 0.000 0.000 0.152 0.848 0.000
#> SRR655017 1 0.0000 0.95977 1.000 0.000 0.000 0.000 0.000
#> SRR1338450 3 0.1444 0.85040 0.000 0.012 0.948 0.040 0.000
#> SRR663741 4 0.3013 0.77704 0.008 0.000 0.160 0.832 0.000
#> SRR1396057 2 0.0000 0.94752 0.000 1.000 0.000 0.000 0.000
#> SRR1083800 3 0.4341 0.38106 0.000 0.004 0.592 0.000 0.404
#> SRR1445789 2 0.0162 0.94746 0.000 0.996 0.004 0.000 0.000
#> SRR1387355 3 0.0968 0.85219 0.000 0.012 0.972 0.012 0.004
#> SRR1388855 2 0.0000 0.94752 0.000 1.000 0.000 0.000 0.000
#> SRR1445449 3 0.1682 0.84784 0.000 0.012 0.940 0.044 0.004
#> SRR1380740 3 0.1626 0.86874 0.000 0.016 0.940 0.000 0.044
#> SRR659995 4 0.1965 0.79842 0.000 0.000 0.096 0.904 0.000
#> SRR1489524 2 0.0000 0.94752 0.000 1.000 0.000 0.000 0.000
#> SRR1444662 2 0.2741 0.79728 0.000 0.860 0.132 0.004 0.004
#> SRR1383652 3 0.2411 0.85195 0.000 0.000 0.884 0.008 0.108
#> SRR1361243 3 0.1774 0.86956 0.000 0.016 0.932 0.000 0.052
#> SRR1490337 3 0.1651 0.85510 0.000 0.012 0.944 0.036 0.008
#> SRR823967 3 0.3565 0.81902 0.000 0.000 0.816 0.040 0.144
#> SRR660127 1 0.0000 0.95977 1.000 0.000 0.000 0.000 0.000
#> SRR1366627 2 0.0833 0.93636 0.000 0.976 0.016 0.004 0.004
#> SRR1361219 5 0.4656 0.40096 0.000 0.480 0.012 0.000 0.508
#> SRR1393510 3 0.2678 0.81057 0.000 0.100 0.880 0.016 0.004
#> SRR662558 4 0.2852 0.77210 0.000 0.000 0.172 0.828 0.000
#> SRR1077334 5 0.4489 0.68923 0.000 0.092 0.140 0.004 0.764
#> SRR807438 3 0.1484 0.84779 0.000 0.008 0.944 0.048 0.000
#> SRR1459078 3 0.1893 0.86667 0.000 0.024 0.928 0.000 0.048
#> SRR1329704 2 0.2130 0.83170 0.000 0.908 0.012 0.000 0.080
#> SRR1468072 3 0.4033 0.69085 0.000 0.212 0.760 0.004 0.024
#> SRR1376196 5 0.6074 0.35114 0.000 0.128 0.372 0.000 0.500
#> SRR1442909 3 0.3615 0.81132 0.000 0.000 0.808 0.036 0.156
#> SRR1414269 3 0.3488 0.81916 0.000 0.000 0.808 0.024 0.168
#> SRR1381913 5 0.5983 -0.31775 0.000 0.000 0.116 0.380 0.504
#> SRR1340157 5 0.4574 0.53318 0.000 0.412 0.012 0.000 0.576
#> SRR1407583 2 0.0324 0.94611 0.000 0.992 0.000 0.004 0.004
#> SRR615826 4 0.4436 0.58425 0.000 0.000 0.008 0.596 0.396
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 4 0.2051 0.830 0.004 0.004 0.000 0.896 0.096 0.000
#> SRR1458769 6 0.2744 0.884 0.000 0.052 0.000 0.012 0.060 0.876
#> SRR613162 1 0.0260 0.994 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1352481 1 0.0260 0.994 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1468876 3 0.1895 0.854 0.000 0.000 0.912 0.072 0.016 0.000
#> SRR1399223 6 0.1036 0.871 0.000 0.024 0.008 0.004 0.000 0.964
#> SRR660030 3 0.1434 0.870 0.000 0.020 0.948 0.000 0.024 0.008
#> SRR1333609 3 0.1492 0.873 0.000 0.036 0.940 0.000 0.000 0.024
#> SRR1471612 2 0.1082 0.863 0.000 0.956 0.000 0.000 0.004 0.040
#> SRR1413998 6 0.3368 0.880 0.000 0.052 0.000 0.012 0.108 0.828
#> SRR1122940 2 0.0779 0.867 0.000 0.976 0.008 0.000 0.008 0.008
#> SRR1402563 3 0.1492 0.873 0.000 0.036 0.940 0.000 0.000 0.024
#> SRR1398393 6 0.8178 -0.094 0.000 0.296 0.228 0.084 0.076 0.316
#> SRR657961 5 0.4308 0.699 0.000 0.280 0.040 0.004 0.676 0.000
#> SRR1471135 3 0.2478 0.849 0.000 0.024 0.888 0.000 0.076 0.012
#> SRR1430001 3 0.0777 0.871 0.000 0.004 0.972 0.000 0.000 0.024
#> SRR662775 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.2260 0.808 0.000 0.860 0.000 0.000 0.000 0.140
#> SRR607190 1 0.0146 0.995 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR612467 5 0.3628 0.770 0.000 0.184 0.036 0.004 0.776 0.000
#> SRR1465959 2 0.0748 0.869 0.000 0.976 0.004 0.000 0.004 0.016
#> SRR1446132 6 0.3368 0.880 0.000 0.052 0.000 0.012 0.108 0.828
#> SRR1416933 6 0.1327 0.880 0.000 0.064 0.000 0.000 0.000 0.936
#> SRR1102538 2 0.1523 0.827 0.000 0.940 0.008 0.000 0.044 0.008
#> SRR1098636 3 0.6239 0.236 0.000 0.028 0.460 0.108 0.392 0.012
#> SRR1072998 2 0.1226 0.843 0.000 0.952 0.004 0.000 0.040 0.004
#> SRR627443 1 0.0405 0.991 0.988 0.000 0.000 0.000 0.004 0.008
#> SRR656131 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR823991 3 0.6502 0.631 0.000 0.172 0.612 0.088 0.080 0.048
#> SRR1089158 2 0.0665 0.867 0.000 0.980 0.004 0.000 0.008 0.008
#> SRR1469036 3 0.1341 0.872 0.000 0.028 0.948 0.000 0.000 0.024
#> SRR824039 2 0.6863 0.368 0.000 0.548 0.240 0.084 0.076 0.052
#> SRR1339047 6 0.3270 0.883 0.000 0.052 0.000 0.016 0.092 0.840
#> SRR1443049 2 0.1663 0.843 0.000 0.912 0.000 0.000 0.000 0.088
#> SRR1122885 2 0.0665 0.867 0.000 0.980 0.004 0.000 0.008 0.008
#> SRR602895 5 0.3740 0.690 0.000 0.000 0.032 0.228 0.740 0.000
#> SRR1409837 2 0.2260 0.808 0.000 0.860 0.000 0.000 0.000 0.140
#> SRR1388959 6 0.3368 0.880 0.000 0.052 0.000 0.012 0.108 0.828
#> SRR659863 1 0.0146 0.995 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1089877 2 0.6915 0.378 0.000 0.552 0.228 0.080 0.076 0.064
#> SRR1123775 3 0.1434 0.870 0.000 0.020 0.948 0.000 0.024 0.008
#> SRR658909 4 0.1793 0.852 0.004 0.000 0.036 0.928 0.032 0.000
#> SRR1140510 6 0.1285 0.881 0.000 0.052 0.000 0.004 0.000 0.944
#> SRR607562 5 0.4025 0.788 0.000 0.048 0.040 0.124 0.788 0.000
#> SRR1122913 2 0.0622 0.869 0.000 0.980 0.008 0.000 0.000 0.012
#> SRR598042 5 0.4109 0.799 0.000 0.072 0.040 0.100 0.788 0.000
#> SRR1467340 3 0.1572 0.872 0.000 0.036 0.936 0.000 0.000 0.028
#> SRR1072321 2 0.1471 0.855 0.000 0.932 0.004 0.000 0.000 0.064
#> SRR1094580 2 0.0692 0.869 0.000 0.976 0.004 0.000 0.000 0.020
#> SRR1076608 6 0.1141 0.881 0.000 0.052 0.000 0.000 0.000 0.948
#> SRR1395462 5 0.4291 0.684 0.000 0.292 0.044 0.000 0.664 0.000
#> SRR1489220 3 0.2019 0.852 0.000 0.000 0.900 0.088 0.012 0.000
#> SRR614371 4 0.2278 0.820 0.004 0.000 0.000 0.868 0.128 0.000
#> SRR615455 4 0.4715 0.363 0.372 0.004 0.000 0.588 0.024 0.012
#> SRR1070573 2 0.0622 0.869 0.000 0.980 0.008 0.000 0.000 0.012
#> SRR598749 5 0.4319 0.702 0.000 0.052 0.004 0.248 0.696 0.000
#> SRR1365556 6 0.2533 0.801 0.000 0.004 0.052 0.008 0.044 0.892
#> SRR1350023 6 0.3368 0.880 0.000 0.052 0.000 0.012 0.108 0.828
#> SRR1446582 3 0.2933 0.831 0.000 0.056 0.860 0.000 0.076 0.008
#> SRR1439763 3 0.0865 0.874 0.000 0.036 0.964 0.000 0.000 0.000
#> SRR1343986 3 0.1492 0.873 0.000 0.036 0.940 0.000 0.000 0.024
#> SRR807463 2 0.0935 0.865 0.000 0.964 0.000 0.000 0.004 0.032
#> SRR660390 1 0.0260 0.994 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1367672 2 0.0665 0.867 0.000 0.980 0.004 0.000 0.008 0.008
#> SRR613294 4 0.2051 0.830 0.004 0.004 0.000 0.896 0.096 0.000
#> SRR824015 6 0.3009 0.780 0.000 0.004 0.040 0.016 0.076 0.864
#> SRR1078924 2 0.0767 0.869 0.000 0.976 0.008 0.000 0.004 0.012
#> SRR662221 4 0.0632 0.861 0.000 0.000 0.024 0.976 0.000 0.000
#> SRR655017 1 0.0146 0.995 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1338450 3 0.1951 0.853 0.000 0.000 0.908 0.076 0.016 0.000
#> SRR663741 4 0.0858 0.860 0.004 0.000 0.028 0.968 0.000 0.000
#> SRR1396057 6 0.1327 0.880 0.000 0.064 0.000 0.000 0.000 0.936
#> SRR1083800 3 0.3693 0.650 0.000 0.280 0.708 0.000 0.004 0.008
#> SRR1445789 6 0.3368 0.880 0.000 0.052 0.000 0.012 0.108 0.828
#> SRR1387355 3 0.0547 0.872 0.000 0.000 0.980 0.000 0.000 0.020
#> SRR1388855 6 0.3368 0.880 0.000 0.052 0.000 0.012 0.108 0.828
#> SRR1445449 3 0.4278 0.794 0.000 0.000 0.776 0.080 0.044 0.100
#> SRR1380740 3 0.1418 0.873 0.000 0.032 0.944 0.000 0.000 0.024
#> SRR659995 4 0.0891 0.863 0.000 0.000 0.008 0.968 0.024 0.000
#> SRR1489524 6 0.3368 0.880 0.000 0.052 0.000 0.012 0.108 0.828
#> SRR1444662 6 0.1124 0.847 0.000 0.000 0.036 0.008 0.000 0.956
#> SRR1383652 3 0.2478 0.849 0.000 0.024 0.888 0.000 0.076 0.012
#> SRR1361243 3 0.1492 0.873 0.000 0.036 0.940 0.000 0.000 0.024
#> SRR1490337 3 0.3572 0.824 0.000 0.008 0.824 0.072 0.088 0.008
#> SRR823967 3 0.5164 0.755 0.000 0.060 0.720 0.084 0.124 0.012
#> SRR660127 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1366627 6 0.1053 0.869 0.000 0.020 0.012 0.004 0.000 0.964
#> SRR1361219 2 0.2340 0.802 0.000 0.852 0.000 0.000 0.000 0.148
#> SRR1393510 3 0.3016 0.811 0.000 0.000 0.836 0.012 0.016 0.136
#> SRR662558 4 0.1575 0.852 0.000 0.000 0.032 0.936 0.032 0.000
#> SRR1077334 2 0.1297 0.824 0.000 0.948 0.040 0.000 0.000 0.012
#> SRR807438 3 0.1983 0.853 0.000 0.000 0.908 0.072 0.020 0.000
#> SRR1459078 3 0.1723 0.870 0.000 0.036 0.928 0.000 0.000 0.036
#> SRR1329704 2 0.3857 0.178 0.000 0.532 0.000 0.000 0.000 0.468
#> SRR1468072 3 0.2957 0.810 0.000 0.016 0.836 0.008 0.000 0.140
#> SRR1376196 2 0.2932 0.728 0.000 0.820 0.164 0.000 0.000 0.016
#> SRR1442909 3 0.4238 0.804 0.000 0.028 0.784 0.072 0.108 0.008
#> SRR1414269 3 0.2706 0.854 0.000 0.056 0.876 0.000 0.060 0.008
#> SRR1381913 5 0.4391 0.788 0.000 0.080 0.068 0.080 0.772 0.000
#> SRR1340157 2 0.1610 0.846 0.000 0.916 0.000 0.000 0.000 0.084
#> SRR1407583 6 0.1398 0.880 0.000 0.052 0.000 0.008 0.000 0.940
#> SRR615826 5 0.4431 0.718 0.000 0.076 0.000 0.236 0.688 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "skmeans"]
# you can also extract it by
# res = res_list["MAD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17380 rows and 102 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.919 0.911 0.966 0.4650 0.539 0.539
#> 3 3 0.851 0.893 0.950 0.4206 0.704 0.493
#> 4 4 0.788 0.797 0.891 0.1049 0.918 0.760
#> 5 5 0.951 0.932 0.962 0.0829 0.868 0.569
#> 6 6 0.913 0.940 0.956 0.0372 0.961 0.815
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 5
There is also optional best \(k\) = 2 5 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
#> SRR612587 1 0.0000 0.957 1.000 0.000
#> SRR1458769 2 0.0000 0.967 0.000 1.000
#> SRR613162 1 0.0000 0.957 1.000 0.000
#> SRR1352481 1 0.0000 0.957 1.000 0.000
#> SRR1468876 1 0.0000 0.957 1.000 0.000
#> SRR1399223 2 0.0000 0.967 0.000 1.000
#> SRR660030 1 0.0000 0.957 1.000 0.000
#> SRR1333609 1 0.9963 0.147 0.536 0.464
#> SRR1471612 2 0.0000 0.967 0.000 1.000
#> SRR1413998 2 0.0000 0.967 0.000 1.000
#> SRR1122940 2 0.0000 0.967 0.000 1.000
#> SRR1402563 2 0.0000 0.967 0.000 1.000
#> SRR1398393 2 0.0000 0.967 0.000 1.000
#> SRR657961 2 0.0000 0.967 0.000 1.000
#> SRR1471135 2 0.0000 0.967 0.000 1.000
#> SRR1430001 1 0.0000 0.957 1.000 0.000
#> SRR662775 1 0.0000 0.957 1.000 0.000
#> SRR1474182 2 0.0000 0.967 0.000 1.000
#> SRR607190 1 0.0000 0.957 1.000 0.000
#> SRR612467 2 0.6531 0.780 0.168 0.832
#> SRR1465959 2 0.0000 0.967 0.000 1.000
#> SRR1446132 2 0.0000 0.967 0.000 1.000
#> SRR1416933 2 0.0000 0.967 0.000 1.000
#> SRR1102538 2 0.0000 0.967 0.000 1.000
#> SRR1098636 1 0.0000 0.957 1.000 0.000
#> SRR1072998 2 0.0000 0.967 0.000 1.000
#> SRR627443 1 0.0000 0.957 1.000 0.000
#> SRR656131 1 0.0000 0.957 1.000 0.000
#> SRR823991 2 0.0000 0.967 0.000 1.000
#> SRR1089158 2 0.0000 0.967 0.000 1.000
#> SRR1469036 1 0.0000 0.957 1.000 0.000
#> SRR824039 2 0.0000 0.967 0.000 1.000
#> SRR1339047 2 0.0000 0.967 0.000 1.000
#> SRR1443049 2 0.0000 0.967 0.000 1.000
#> SRR1122885 2 0.0000 0.967 0.000 1.000
#> SRR602895 1 0.0000 0.957 1.000 0.000
#> SRR1409837 2 0.0000 0.967 0.000 1.000
#> SRR1388959 2 0.0000 0.967 0.000 1.000
#> SRR659863 1 0.0000 0.957 1.000 0.000
#> SRR1089877 2 0.0000 0.967 0.000 1.000
#> SRR1123775 2 0.1414 0.949 0.020 0.980
#> SRR658909 1 0.0000 0.957 1.000 0.000
#> SRR1140510 2 0.0000 0.967 0.000 1.000
#> SRR607562 1 0.0672 0.950 0.992 0.008
#> SRR1122913 2 0.0000 0.967 0.000 1.000
#> SRR598042 2 0.9732 0.317 0.404 0.596
#> SRR1467340 2 0.0000 0.967 0.000 1.000
#> SRR1072321 2 0.0000 0.967 0.000 1.000
#> SRR1094580 2 0.0000 0.967 0.000 1.000
#> SRR1076608 2 0.0000 0.967 0.000 1.000
#> SRR1395462 2 0.0000 0.967 0.000 1.000
#> SRR1489220 1 0.0000 0.957 1.000 0.000
#> SRR614371 1 0.0000 0.957 1.000 0.000
#> SRR615455 1 0.0000 0.957 1.000 0.000
#> SRR1070573 2 0.0000 0.967 0.000 1.000
#> SRR598749 1 0.9661 0.328 0.608 0.392
#> SRR1365556 2 0.0000 0.967 0.000 1.000
#> SRR1350023 2 0.0000 0.967 0.000 1.000
#> SRR1446582 2 0.1633 0.946 0.024 0.976
#> SRR1439763 2 0.9732 0.286 0.404 0.596
#> SRR1343986 2 0.0000 0.967 0.000 1.000
#> SRR807463 2 0.0000 0.967 0.000 1.000
#> SRR660390 1 0.0000 0.957 1.000 0.000
#> SRR1367672 2 0.0000 0.967 0.000 1.000
#> SRR613294 1 0.0000 0.957 1.000 0.000
#> SRR824015 2 0.0000 0.967 0.000 1.000
#> SRR1078924 2 0.0000 0.967 0.000 1.000
#> SRR662221 1 0.0000 0.957 1.000 0.000
#> SRR655017 1 0.0000 0.957 1.000 0.000
#> SRR1338450 1 0.0000 0.957 1.000 0.000
#> SRR663741 1 0.0000 0.957 1.000 0.000
#> SRR1396057 2 0.0000 0.967 0.000 1.000
#> SRR1083800 2 0.0000 0.967 0.000 1.000
#> SRR1445789 2 0.0000 0.967 0.000 1.000
#> SRR1387355 1 0.0000 0.957 1.000 0.000
#> SRR1388855 2 0.0000 0.967 0.000 1.000
#> SRR1445449 1 0.0000 0.957 1.000 0.000
#> SRR1380740 1 0.6801 0.759 0.820 0.180
#> SRR659995 1 0.0000 0.957 1.000 0.000
#> SRR1489524 2 0.0000 0.967 0.000 1.000
#> SRR1444662 2 0.0000 0.967 0.000 1.000
#> SRR1383652 2 0.0000 0.967 0.000 1.000
#> SRR1361243 2 0.1184 0.953 0.016 0.984
#> SRR1490337 1 0.0000 0.957 1.000 0.000
#> SRR823967 2 0.0000 0.967 0.000 1.000
#> SRR660127 1 0.0000 0.957 1.000 0.000
#> SRR1366627 2 0.0000 0.967 0.000 1.000
#> SRR1361219 2 0.0000 0.967 0.000 1.000
#> SRR1393510 1 0.9635 0.371 0.612 0.388
#> SRR662558 1 0.0000 0.957 1.000 0.000
#> SRR1077334 2 0.0000 0.967 0.000 1.000
#> SRR807438 1 0.0000 0.957 1.000 0.000
#> SRR1459078 2 0.9732 0.286 0.404 0.596
#> SRR1329704 2 0.0000 0.967 0.000 1.000
#> SRR1468072 2 0.0000 0.967 0.000 1.000
#> SRR1376196 2 0.0000 0.967 0.000 1.000
#> SRR1442909 2 0.0000 0.967 0.000 1.000
#> SRR1414269 2 0.0000 0.967 0.000 1.000
#> SRR1381913 2 0.9491 0.411 0.368 0.632
#> SRR1340157 2 0.0000 0.967 0.000 1.000
#> SRR1407583 2 0.0000 0.967 0.000 1.000
#> SRR615826 2 0.7219 0.735 0.200 0.800
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1458769 2 0.0000 0.972 0.000 1.000 0.000
#> SRR613162 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1352481 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1468876 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1399223 2 0.0000 0.972 0.000 1.000 0.000
#> SRR660030 3 0.0000 0.863 0.000 0.000 1.000
#> SRR1333609 2 0.0983 0.954 0.016 0.980 0.004
#> SRR1471612 3 0.6126 0.515 0.000 0.400 0.600
#> SRR1413998 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1122940 3 0.6126 0.515 0.000 0.400 0.600
#> SRR1402563 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1398393 2 0.0000 0.972 0.000 1.000 0.000
#> SRR657961 3 0.0000 0.863 0.000 0.000 1.000
#> SRR1471135 3 0.0747 0.859 0.000 0.016 0.984
#> SRR1430001 1 0.0000 0.992 1.000 0.000 0.000
#> SRR662775 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1474182 2 0.0000 0.972 0.000 1.000 0.000
#> SRR607190 1 0.0000 0.992 1.000 0.000 0.000
#> SRR612467 3 0.0000 0.863 0.000 0.000 1.000
#> SRR1465959 3 0.6126 0.515 0.000 0.400 0.600
#> SRR1446132 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1416933 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1102538 3 0.0000 0.863 0.000 0.000 1.000
#> SRR1098636 3 0.0000 0.863 0.000 0.000 1.000
#> SRR1072998 3 0.0592 0.861 0.000 0.012 0.988
#> SRR627443 1 0.0000 0.992 1.000 0.000 0.000
#> SRR656131 1 0.0000 0.992 1.000 0.000 0.000
#> SRR823991 2 0.6140 0.289 0.000 0.596 0.404
#> SRR1089158 3 0.0892 0.858 0.000 0.020 0.980
#> SRR1469036 1 0.0000 0.992 1.000 0.000 0.000
#> SRR824039 3 0.0000 0.863 0.000 0.000 1.000
#> SRR1339047 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1443049 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1122885 3 0.6095 0.527 0.000 0.392 0.608
#> SRR602895 1 0.1289 0.960 0.968 0.000 0.032
#> SRR1409837 2 0.1860 0.913 0.000 0.948 0.052
#> SRR1388959 2 0.0000 0.972 0.000 1.000 0.000
#> SRR659863 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1089877 2 0.4235 0.745 0.000 0.824 0.176
#> SRR1123775 3 0.0000 0.863 0.000 0.000 1.000
#> SRR658909 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1140510 2 0.0000 0.972 0.000 1.000 0.000
#> SRR607562 3 0.0000 0.863 0.000 0.000 1.000
#> SRR1122913 3 0.6126 0.515 0.000 0.400 0.600
#> SRR598042 3 0.0000 0.863 0.000 0.000 1.000
#> SRR1467340 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1072321 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1094580 3 0.6168 0.490 0.000 0.412 0.588
#> SRR1076608 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1395462 3 0.0000 0.863 0.000 0.000 1.000
#> SRR1489220 1 0.0000 0.992 1.000 0.000 0.000
#> SRR614371 1 0.0000 0.992 1.000 0.000 0.000
#> SRR615455 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1070573 3 0.6126 0.515 0.000 0.400 0.600
#> SRR598749 3 0.0000 0.863 0.000 0.000 1.000
#> SRR1365556 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1350023 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1446582 3 0.0000 0.863 0.000 0.000 1.000
#> SRR1439763 3 0.0237 0.862 0.000 0.004 0.996
#> SRR1343986 2 0.0000 0.972 0.000 1.000 0.000
#> SRR807463 3 0.6126 0.515 0.000 0.400 0.600
#> SRR660390 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1367672 3 0.5810 0.598 0.000 0.336 0.664
#> SRR613294 1 0.0000 0.992 1.000 0.000 0.000
#> SRR824015 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1078924 3 0.6126 0.515 0.000 0.400 0.600
#> SRR662221 1 0.0000 0.992 1.000 0.000 0.000
#> SRR655017 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1338450 1 0.0000 0.992 1.000 0.000 0.000
#> SRR663741 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1396057 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1083800 3 0.0592 0.861 0.000 0.012 0.988
#> SRR1445789 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1387355 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1388855 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1445449 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1380740 2 0.5058 0.664 0.244 0.756 0.000
#> SRR659995 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1489524 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1444662 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1383652 3 0.0424 0.862 0.000 0.008 0.992
#> SRR1361243 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1490337 1 0.4399 0.779 0.812 0.000 0.188
#> SRR823967 3 0.0000 0.863 0.000 0.000 1.000
#> SRR660127 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1366627 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1361219 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1393510 2 0.0000 0.972 0.000 1.000 0.000
#> SRR662558 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1077334 3 0.0592 0.861 0.000 0.012 0.988
#> SRR807438 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1459078 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1329704 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1468072 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1376196 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1442909 3 0.0000 0.863 0.000 0.000 1.000
#> SRR1414269 3 0.0000 0.863 0.000 0.000 1.000
#> SRR1381913 3 0.0000 0.863 0.000 0.000 1.000
#> SRR1340157 2 0.0000 0.972 0.000 1.000 0.000
#> SRR1407583 2 0.0000 0.972 0.000 1.000 0.000
#> SRR615826 3 0.0000 0.863 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1458769 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> SRR613162 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1468876 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1399223 2 0.0188 0.896 0.000 0.996 0.000 0.004
#> SRR660030 3 0.0000 0.740 0.000 0.000 1.000 0.000
#> SRR1333609 4 0.1356 0.872 0.008 0.032 0.000 0.960
#> SRR1471612 2 0.6559 -0.266 0.000 0.468 0.456 0.076
#> SRR1413998 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> SRR1122940 3 0.7594 0.533 0.000 0.260 0.480 0.260
#> SRR1402563 4 0.1022 0.870 0.000 0.032 0.000 0.968
#> SRR1398393 2 0.1022 0.881 0.000 0.968 0.000 0.032
#> SRR657961 3 0.0000 0.740 0.000 0.000 1.000 0.000
#> SRR1471135 3 0.0000 0.740 0.000 0.000 1.000 0.000
#> SRR1430001 4 0.4134 0.639 0.260 0.000 0.000 0.740
#> SRR662775 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1474182 2 0.1940 0.857 0.000 0.924 0.000 0.076
#> SRR607190 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR612467 3 0.0000 0.740 0.000 0.000 1.000 0.000
#> SRR1465959 3 0.7702 0.495 0.000 0.288 0.452 0.260
#> SRR1446132 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> SRR1416933 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> SRR1102538 3 0.4134 0.651 0.000 0.000 0.740 0.260
#> SRR1098636 3 0.0000 0.740 0.000 0.000 1.000 0.000
#> SRR1072998 3 0.5416 0.643 0.000 0.048 0.692 0.260
#> SRR627443 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR823991 2 0.6429 0.385 0.000 0.588 0.324 0.088
#> SRR1089158 3 0.6452 0.618 0.000 0.116 0.624 0.260
#> SRR1469036 4 0.4103 0.647 0.256 0.000 0.000 0.744
#> SRR824039 3 0.6269 0.535 0.000 0.272 0.632 0.096
#> SRR1339047 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> SRR1443049 2 0.4134 0.681 0.000 0.740 0.000 0.260
#> SRR1122885 3 0.7594 0.533 0.000 0.260 0.480 0.260
#> SRR602895 1 0.3649 0.728 0.796 0.000 0.204 0.000
#> SRR1409837 2 0.1940 0.857 0.000 0.924 0.000 0.076
#> SRR1388959 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> SRR659863 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1089877 2 0.4252 0.686 0.000 0.744 0.004 0.252
#> SRR1123775 3 0.0000 0.740 0.000 0.000 1.000 0.000
#> SRR658909 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1140510 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> SRR607562 3 0.0000 0.740 0.000 0.000 1.000 0.000
#> SRR1122913 3 0.7688 0.502 0.000 0.284 0.456 0.260
#> SRR598042 3 0.0000 0.740 0.000 0.000 1.000 0.000
#> SRR1467340 4 0.1474 0.858 0.000 0.052 0.000 0.948
#> SRR1072321 2 0.4134 0.681 0.000 0.740 0.000 0.260
#> SRR1094580 3 0.7792 0.420 0.000 0.324 0.416 0.260
#> SRR1076608 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> SRR1395462 3 0.0000 0.740 0.000 0.000 1.000 0.000
#> SRR1489220 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR614371 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR615455 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1070573 3 0.7688 0.502 0.000 0.284 0.456 0.260
#> SRR598749 3 0.0000 0.740 0.000 0.000 1.000 0.000
#> SRR1365556 2 0.0188 0.896 0.000 0.996 0.000 0.004
#> SRR1350023 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> SRR1446582 3 0.0000 0.740 0.000 0.000 1.000 0.000
#> SRR1439763 4 0.0000 0.837 0.000 0.000 0.000 1.000
#> SRR1343986 4 0.1022 0.870 0.000 0.032 0.000 0.968
#> SRR807463 3 0.7702 0.495 0.000 0.288 0.452 0.260
#> SRR660390 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1367672 3 0.7576 0.536 0.000 0.256 0.484 0.260
#> SRR613294 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR824015 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> SRR1078924 3 0.7688 0.502 0.000 0.284 0.456 0.260
#> SRR662221 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR655017 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1338450 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR663741 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1396057 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> SRR1083800 3 0.4313 0.651 0.000 0.004 0.736 0.260
#> SRR1445789 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> SRR1387355 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1388855 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> SRR1445449 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1380740 4 0.2319 0.861 0.040 0.036 0.000 0.924
#> SRR659995 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1489524 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> SRR1444662 2 0.0188 0.896 0.000 0.996 0.000 0.004
#> SRR1383652 3 0.0000 0.740 0.000 0.000 1.000 0.000
#> SRR1361243 4 0.1211 0.871 0.000 0.040 0.000 0.960
#> SRR1490337 1 0.3873 0.693 0.772 0.000 0.228 0.000
#> SRR823967 3 0.1940 0.725 0.000 0.000 0.924 0.076
#> SRR660127 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1366627 2 0.0188 0.896 0.000 0.996 0.000 0.004
#> SRR1361219 2 0.1716 0.865 0.000 0.936 0.000 0.064
#> SRR1393510 2 0.2739 0.802 0.036 0.904 0.000 0.060
#> SRR662558 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1077334 3 0.6352 0.622 0.000 0.108 0.632 0.260
#> SRR807438 1 0.0000 0.980 1.000 0.000 0.000 0.000
#> SRR1459078 4 0.1940 0.856 0.000 0.076 0.000 0.924
#> SRR1329704 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> SRR1468072 4 0.4134 0.651 0.000 0.260 0.000 0.740
#> SRR1376196 2 0.4134 0.681 0.000 0.740 0.000 0.260
#> SRR1442909 3 0.0000 0.740 0.000 0.000 1.000 0.000
#> SRR1414269 3 0.1211 0.732 0.000 0.000 0.960 0.040
#> SRR1381913 3 0.0000 0.740 0.000 0.000 1.000 0.000
#> SRR1340157 2 0.4134 0.681 0.000 0.740 0.000 0.260
#> SRR1407583 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> SRR615826 3 0.0000 0.740 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR612587 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR1458769 2 0.0162 0.993 0.000 0.996 0.004 0.000 0.000
#> SRR613162 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR1468876 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR1399223 2 0.0000 0.992 0.000 1.000 0.000 0.000 0.000
#> SRR660030 5 0.3051 0.837 0.000 0.000 0.028 0.120 0.852
#> SRR1333609 4 0.0579 0.968 0.000 0.008 0.008 0.984 0.000
#> SRR1471612 3 0.2852 0.801 0.000 0.172 0.828 0.000 0.000
#> SRR1413998 2 0.0162 0.993 0.000 0.996 0.004 0.000 0.000
#> SRR1122940 3 0.0566 0.919 0.000 0.012 0.984 0.004 0.000
#> SRR1402563 4 0.0510 0.966 0.000 0.000 0.016 0.984 0.000
#> SRR1398393 2 0.1498 0.948 0.000 0.952 0.008 0.016 0.024
#> SRR657961 5 0.0794 0.938 0.000 0.000 0.028 0.000 0.972
#> SRR1471135 5 0.0703 0.939 0.000 0.000 0.024 0.000 0.976
#> SRR1430001 4 0.0609 0.955 0.020 0.000 0.000 0.980 0.000
#> SRR662775 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR1474182 3 0.3143 0.780 0.000 0.204 0.796 0.000 0.000
#> SRR607190 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR612467 5 0.0703 0.939 0.000 0.000 0.024 0.000 0.976
#> SRR1465959 3 0.0566 0.919 0.000 0.012 0.984 0.004 0.000
#> SRR1446132 2 0.0162 0.993 0.000 0.996 0.004 0.000 0.000
#> SRR1416933 2 0.0290 0.990 0.000 0.992 0.008 0.000 0.000
#> SRR1102538 3 0.0404 0.910 0.000 0.000 0.988 0.000 0.012
#> SRR1098636 5 0.1018 0.908 0.000 0.000 0.016 0.016 0.968
#> SRR1072998 3 0.0290 0.913 0.000 0.000 0.992 0.000 0.008
#> SRR627443 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR823991 3 0.5181 0.405 0.000 0.368 0.592 0.016 0.024
#> SRR1089158 3 0.0290 0.917 0.000 0.008 0.992 0.000 0.000
#> SRR1469036 4 0.0510 0.959 0.016 0.000 0.000 0.984 0.000
#> SRR824039 3 0.1498 0.893 0.000 0.008 0.952 0.016 0.024
#> SRR1339047 2 0.0000 0.992 0.000 1.000 0.000 0.000 0.000
#> SRR1443049 3 0.1124 0.912 0.000 0.036 0.960 0.004 0.000
#> SRR1122885 3 0.0566 0.919 0.000 0.012 0.984 0.004 0.000
#> SRR602895 5 0.3990 0.565 0.308 0.000 0.004 0.000 0.688
#> SRR1409837 3 0.3143 0.780 0.000 0.204 0.796 0.000 0.000
#> SRR1388959 2 0.0162 0.993 0.000 0.996 0.004 0.000 0.000
#> SRR659863 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR1089877 3 0.4766 0.612 0.000 0.272 0.688 0.016 0.024
#> SRR1123775 5 0.1043 0.930 0.000 0.000 0.040 0.000 0.960
#> SRR658909 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR1140510 2 0.0162 0.993 0.000 0.996 0.004 0.000 0.000
#> SRR607562 5 0.0703 0.939 0.000 0.000 0.024 0.000 0.976
#> SRR1122913 3 0.0566 0.919 0.000 0.012 0.984 0.004 0.000
#> SRR598042 5 0.0703 0.939 0.000 0.000 0.024 0.000 0.976
#> SRR1467340 4 0.1331 0.941 0.000 0.008 0.040 0.952 0.000
#> SRR1072321 3 0.1124 0.912 0.000 0.036 0.960 0.004 0.000
#> SRR1094580 3 0.0566 0.919 0.000 0.012 0.984 0.004 0.000
#> SRR1076608 2 0.0162 0.993 0.000 0.996 0.004 0.000 0.000
#> SRR1395462 5 0.0703 0.939 0.000 0.000 0.024 0.000 0.976
#> SRR1489220 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR614371 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR615455 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR1070573 3 0.0566 0.919 0.000 0.012 0.984 0.004 0.000
#> SRR598749 5 0.0794 0.938 0.000 0.000 0.028 0.000 0.972
#> SRR1365556 2 0.0000 0.992 0.000 1.000 0.000 0.000 0.000
#> SRR1350023 2 0.0162 0.993 0.000 0.996 0.004 0.000 0.000
#> SRR1446582 5 0.0703 0.939 0.000 0.000 0.024 0.000 0.976
#> SRR1439763 4 0.0290 0.965 0.000 0.000 0.008 0.992 0.000
#> SRR1343986 4 0.0510 0.966 0.000 0.000 0.016 0.984 0.000
#> SRR807463 3 0.0566 0.919 0.000 0.012 0.984 0.004 0.000
#> SRR660390 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR1367672 3 0.0566 0.919 0.000 0.012 0.984 0.004 0.000
#> SRR613294 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR824015 2 0.0451 0.982 0.000 0.988 0.008 0.000 0.004
#> SRR1078924 3 0.0566 0.919 0.000 0.012 0.984 0.004 0.000
#> SRR662221 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR655017 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR1338450 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR663741 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR1396057 2 0.0162 0.993 0.000 0.996 0.004 0.000 0.000
#> SRR1083800 3 0.0566 0.911 0.000 0.000 0.984 0.004 0.012
#> SRR1445789 2 0.0162 0.993 0.000 0.996 0.004 0.000 0.000
#> SRR1387355 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR1388855 2 0.0162 0.993 0.000 0.996 0.004 0.000 0.000
#> SRR1445449 1 0.0162 0.986 0.996 0.004 0.000 0.000 0.000
#> SRR1380740 4 0.0579 0.968 0.000 0.008 0.008 0.984 0.000
#> SRR659995 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR1489524 2 0.0162 0.993 0.000 0.996 0.004 0.000 0.000
#> SRR1444662 2 0.0000 0.992 0.000 1.000 0.000 0.000 0.000
#> SRR1383652 5 0.0703 0.939 0.000 0.000 0.024 0.000 0.976
#> SRR1361243 4 0.0579 0.968 0.000 0.008 0.008 0.984 0.000
#> SRR1490337 1 0.4004 0.681 0.748 0.000 0.004 0.016 0.232
#> SRR823967 3 0.2060 0.879 0.000 0.008 0.924 0.016 0.052
#> SRR660127 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR1366627 2 0.0000 0.992 0.000 1.000 0.000 0.000 0.000
#> SRR1361219 3 0.3143 0.780 0.000 0.204 0.796 0.000 0.000
#> SRR1393510 2 0.0290 0.985 0.000 0.992 0.000 0.008 0.000
#> SRR662558 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR1077334 3 0.0000 0.913 0.000 0.000 1.000 0.000 0.000
#> SRR807438 1 0.0000 0.990 1.000 0.000 0.000 0.000 0.000
#> SRR1459078 4 0.0579 0.968 0.000 0.008 0.008 0.984 0.000
#> SRR1329704 2 0.0794 0.968 0.000 0.972 0.028 0.000 0.000
#> SRR1468072 4 0.2852 0.792 0.000 0.172 0.000 0.828 0.000
#> SRR1376196 3 0.1124 0.912 0.000 0.036 0.960 0.004 0.000
#> SRR1442909 5 0.0671 0.913 0.000 0.000 0.004 0.016 0.980
#> SRR1414269 5 0.4114 0.618 0.000 0.000 0.272 0.016 0.712
#> SRR1381913 5 0.0703 0.939 0.000 0.000 0.024 0.000 0.976
#> SRR1340157 3 0.1124 0.912 0.000 0.036 0.960 0.004 0.000
#> SRR1407583 2 0.0162 0.993 0.000 0.996 0.004 0.000 0.000
#> SRR615826 5 0.0880 0.937 0.000 0.000 0.032 0.000 0.968
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 1 0.2633 0.881 0.864 0.000 0.004 0.020 0.112 0.000
#> SRR1458769 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR613162 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1468876 1 0.0146 0.974 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1399223 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR660030 5 0.1333 0.885 0.000 0.000 0.008 0.048 0.944 0.000
#> SRR1333609 3 0.0146 0.965 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1471612 2 0.2118 0.865 0.000 0.888 0.000 0.008 0.000 0.104
#> SRR1413998 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1122940 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1402563 3 0.0146 0.965 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1398393 4 0.3023 0.684 0.000 0.000 0.000 0.768 0.000 0.232
#> SRR657961 5 0.2378 0.902 0.000 0.000 0.000 0.152 0.848 0.000
#> SRR1471135 5 0.2260 0.904 0.000 0.000 0.000 0.140 0.860 0.000
#> SRR1430001 3 0.0146 0.962 0.004 0.000 0.996 0.000 0.000 0.000
#> SRR662775 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.2003 0.857 0.000 0.884 0.000 0.000 0.000 0.116
#> SRR607190 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR612467 5 0.0458 0.905 0.000 0.000 0.000 0.016 0.984 0.000
#> SRR1465959 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1446132 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1416933 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1102538 2 0.1007 0.933 0.000 0.956 0.000 0.044 0.000 0.000
#> SRR1098636 4 0.0547 0.837 0.000 0.000 0.000 0.980 0.020 0.000
#> SRR1072998 2 0.0146 0.963 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR627443 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR823991 4 0.2667 0.855 0.000 0.128 0.000 0.852 0.000 0.020
#> SRR1089158 2 0.0146 0.963 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1469036 3 0.0146 0.962 0.004 0.000 0.996 0.000 0.000 0.000
#> SRR824039 4 0.2416 0.839 0.000 0.156 0.000 0.844 0.000 0.000
#> SRR1339047 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1443049 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1122885 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR602895 5 0.1946 0.830 0.072 0.000 0.004 0.012 0.912 0.000
#> SRR1409837 2 0.1957 0.861 0.000 0.888 0.000 0.000 0.000 0.112
#> SRR1388959 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR659863 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1089877 4 0.2805 0.835 0.000 0.160 0.000 0.828 0.000 0.012
#> SRR1123775 5 0.0858 0.904 0.000 0.004 0.000 0.028 0.968 0.000
#> SRR658909 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1140510 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR607562 5 0.0000 0.903 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1122913 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR598042 5 0.2048 0.907 0.000 0.000 0.000 0.120 0.880 0.000
#> SRR1467340 3 0.1863 0.849 0.000 0.104 0.896 0.000 0.000 0.000
#> SRR1072321 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1094580 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1076608 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1395462 5 0.2260 0.904 0.000 0.000 0.000 0.140 0.860 0.000
#> SRR1489220 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR614371 1 0.1003 0.957 0.964 0.000 0.000 0.016 0.020 0.000
#> SRR615455 1 0.0291 0.973 0.992 0.000 0.004 0.004 0.000 0.000
#> SRR1070573 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR598749 5 0.0777 0.897 0.000 0.000 0.004 0.024 0.972 0.000
#> SRR1365556 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1350023 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1446582 5 0.2260 0.904 0.000 0.000 0.000 0.140 0.860 0.000
#> SRR1439763 3 0.0363 0.957 0.000 0.000 0.988 0.012 0.000 0.000
#> SRR1343986 3 0.0146 0.965 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR807463 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR660390 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1367672 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR613294 1 0.2633 0.881 0.864 0.000 0.004 0.020 0.112 0.000
#> SRR824015 6 0.0260 0.991 0.000 0.000 0.000 0.008 0.000 0.992
#> SRR1078924 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR662221 1 0.2587 0.885 0.868 0.000 0.004 0.020 0.108 0.000
#> SRR655017 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1338450 1 0.0405 0.972 0.988 0.000 0.004 0.008 0.000 0.000
#> SRR663741 1 0.0146 0.974 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1396057 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1083800 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1445789 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1387355 1 0.0146 0.974 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1388855 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1445449 1 0.0146 0.974 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1380740 3 0.0146 0.965 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR659995 1 0.2633 0.881 0.864 0.000 0.004 0.020 0.112 0.000
#> SRR1489524 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1444662 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1383652 5 0.2260 0.904 0.000 0.000 0.000 0.140 0.860 0.000
#> SRR1361243 3 0.0146 0.965 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1490337 4 0.1779 0.812 0.016 0.000 0.000 0.920 0.064 0.000
#> SRR823967 4 0.1908 0.865 0.000 0.096 0.000 0.900 0.004 0.000
#> SRR660127 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1366627 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1361219 2 0.2003 0.857 0.000 0.884 0.000 0.000 0.000 0.116
#> SRR1393510 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR662558 1 0.0603 0.967 0.980 0.000 0.004 0.016 0.000 0.000
#> SRR1077334 2 0.1075 0.930 0.000 0.952 0.000 0.048 0.000 0.000
#> SRR807438 1 0.0000 0.975 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1459078 3 0.0146 0.965 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1329704 6 0.0458 0.979 0.000 0.016 0.000 0.000 0.000 0.984
#> SRR1468072 3 0.2260 0.796 0.000 0.000 0.860 0.000 0.000 0.140
#> SRR1376196 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1442909 4 0.1663 0.802 0.000 0.000 0.000 0.912 0.088 0.000
#> SRR1414269 4 0.1984 0.859 0.000 0.056 0.000 0.912 0.032 0.000
#> SRR1381913 5 0.2260 0.904 0.000 0.000 0.000 0.140 0.860 0.000
#> SRR1340157 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1407583 6 0.0000 0.999 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR615826 5 0.1219 0.887 0.000 0.000 0.004 0.048 0.948 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "pam"]
# you can also extract it by
# res = res_list["MAD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17380 rows and 102 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 0.980 0.957 0.982 0.2401 0.761 0.761
#> 3 3 0.683 0.857 0.934 1.4870 0.585 0.476
#> 4 4 0.772 0.837 0.930 0.1362 0.787 0.549
#> 5 5 0.732 0.801 0.896 0.0836 0.952 0.853
#> 6 6 0.728 0.718 0.854 0.0686 0.913 0.713
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR612587 2 0.839 0.619 0.268 0.732
#> SRR1458769 2 0.000 0.988 0.000 1.000
#> SRR613162 1 0.000 0.931 1.000 0.000
#> SRR1352481 1 0.000 0.931 1.000 0.000
#> SRR1468876 2 0.000 0.988 0.000 1.000
#> SRR1399223 2 0.000 0.988 0.000 1.000
#> SRR660030 2 0.000 0.988 0.000 1.000
#> SRR1333609 2 0.000 0.988 0.000 1.000
#> SRR1471612 2 0.000 0.988 0.000 1.000
#> SRR1413998 2 0.000 0.988 0.000 1.000
#> SRR1122940 2 0.000 0.988 0.000 1.000
#> SRR1402563 2 0.000 0.988 0.000 1.000
#> SRR1398393 2 0.000 0.988 0.000 1.000
#> SRR657961 2 0.000 0.988 0.000 1.000
#> SRR1471135 2 0.000 0.988 0.000 1.000
#> SRR1430001 2 0.000 0.988 0.000 1.000
#> SRR662775 1 0.000 0.931 1.000 0.000
#> SRR1474182 2 0.000 0.988 0.000 1.000
#> SRR607190 1 0.000 0.931 1.000 0.000
#> SRR612467 2 0.000 0.988 0.000 1.000
#> SRR1465959 2 0.000 0.988 0.000 1.000
#> SRR1446132 2 0.000 0.988 0.000 1.000
#> SRR1416933 2 0.000 0.988 0.000 1.000
#> SRR1102538 2 0.000 0.988 0.000 1.000
#> SRR1098636 2 0.000 0.988 0.000 1.000
#> SRR1072998 2 0.000 0.988 0.000 1.000
#> SRR627443 1 0.000 0.931 1.000 0.000
#> SRR656131 1 0.000 0.931 1.000 0.000
#> SRR823991 2 0.000 0.988 0.000 1.000
#> SRR1089158 2 0.000 0.988 0.000 1.000
#> SRR1469036 2 0.000 0.988 0.000 1.000
#> SRR824039 2 0.000 0.988 0.000 1.000
#> SRR1339047 2 0.000 0.988 0.000 1.000
#> SRR1443049 2 0.000 0.988 0.000 1.000
#> SRR1122885 2 0.000 0.988 0.000 1.000
#> SRR602895 2 0.260 0.946 0.044 0.956
#> SRR1409837 2 0.000 0.988 0.000 1.000
#> SRR1388959 2 0.000 0.988 0.000 1.000
#> SRR659863 1 0.000 0.931 1.000 0.000
#> SRR1089877 2 0.000 0.988 0.000 1.000
#> SRR1123775 2 0.000 0.988 0.000 1.000
#> SRR658909 1 0.295 0.893 0.948 0.052
#> SRR1140510 2 0.000 0.988 0.000 1.000
#> SRR607562 2 0.000 0.988 0.000 1.000
#> SRR1122913 2 0.000 0.988 0.000 1.000
#> SRR598042 2 0.000 0.988 0.000 1.000
#> SRR1467340 2 0.000 0.988 0.000 1.000
#> SRR1072321 2 0.000 0.988 0.000 1.000
#> SRR1094580 2 0.000 0.988 0.000 1.000
#> SRR1076608 2 0.000 0.988 0.000 1.000
#> SRR1395462 2 0.000 0.988 0.000 1.000
#> SRR1489220 2 0.000 0.988 0.000 1.000
#> SRR614371 1 0.973 0.356 0.596 0.404
#> SRR615455 1 0.000 0.931 1.000 0.000
#> SRR1070573 2 0.000 0.988 0.000 1.000
#> SRR598749 2 0.260 0.946 0.044 0.956
#> SRR1365556 2 0.000 0.988 0.000 1.000
#> SRR1350023 2 0.000 0.988 0.000 1.000
#> SRR1446582 2 0.000 0.988 0.000 1.000
#> SRR1439763 2 0.000 0.988 0.000 1.000
#> SRR1343986 2 0.000 0.988 0.000 1.000
#> SRR807463 2 0.000 0.988 0.000 1.000
#> SRR660390 1 0.000 0.931 1.000 0.000
#> SRR1367672 2 0.000 0.988 0.000 1.000
#> SRR613294 2 0.689 0.768 0.184 0.816
#> SRR824015 2 0.000 0.988 0.000 1.000
#> SRR1078924 2 0.000 0.988 0.000 1.000
#> SRR662221 2 0.552 0.848 0.128 0.872
#> SRR655017 1 0.000 0.931 1.000 0.000
#> SRR1338450 2 0.000 0.988 0.000 1.000
#> SRR663741 1 0.973 0.356 0.596 0.404
#> SRR1396057 2 0.000 0.988 0.000 1.000
#> SRR1083800 2 0.000 0.988 0.000 1.000
#> SRR1445789 2 0.000 0.988 0.000 1.000
#> SRR1387355 2 0.000 0.988 0.000 1.000
#> SRR1388855 2 0.000 0.988 0.000 1.000
#> SRR1445449 2 0.000 0.988 0.000 1.000
#> SRR1380740 2 0.000 0.988 0.000 1.000
#> SRR659995 2 0.615 0.815 0.152 0.848
#> SRR1489524 2 0.000 0.988 0.000 1.000
#> SRR1444662 2 0.000 0.988 0.000 1.000
#> SRR1383652 2 0.000 0.988 0.000 1.000
#> SRR1361243 2 0.000 0.988 0.000 1.000
#> SRR1490337 2 0.000 0.988 0.000 1.000
#> SRR823967 2 0.000 0.988 0.000 1.000
#> SRR660127 1 0.000 0.931 1.000 0.000
#> SRR1366627 2 0.000 0.988 0.000 1.000
#> SRR1361219 2 0.000 0.988 0.000 1.000
#> SRR1393510 2 0.000 0.988 0.000 1.000
#> SRR662558 2 0.494 0.873 0.108 0.892
#> SRR1077334 2 0.000 0.988 0.000 1.000
#> SRR807438 2 0.000 0.988 0.000 1.000
#> SRR1459078 2 0.000 0.988 0.000 1.000
#> SRR1329704 2 0.000 0.988 0.000 1.000
#> SRR1468072 2 0.000 0.988 0.000 1.000
#> SRR1376196 2 0.000 0.988 0.000 1.000
#> SRR1442909 2 0.000 0.988 0.000 1.000
#> SRR1414269 2 0.000 0.988 0.000 1.000
#> SRR1381913 2 0.000 0.988 0.000 1.000
#> SRR1340157 2 0.000 0.988 0.000 1.000
#> SRR1407583 2 0.000 0.988 0.000 1.000
#> SRR615826 2 0.260 0.946 0.044 0.956
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 3 0.4235 0.757 0.176 0.000 0.824
#> SRR1458769 2 0.0000 0.923 0.000 1.000 0.000
#> SRR613162 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1352481 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1468876 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1399223 2 0.4235 0.819 0.000 0.824 0.176
#> SRR660030 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1333609 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1471612 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1413998 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1122940 2 0.4235 0.819 0.000 0.824 0.176
#> SRR1402563 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1398393 2 0.0747 0.914 0.000 0.984 0.016
#> SRR657961 2 0.4974 0.680 0.000 0.764 0.236
#> SRR1471135 3 0.5882 0.430 0.000 0.348 0.652
#> SRR1430001 3 0.0000 0.896 0.000 0.000 1.000
#> SRR662775 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1474182 2 0.0000 0.923 0.000 1.000 0.000
#> SRR607190 1 0.0000 1.000 1.000 0.000 0.000
#> SRR612467 2 0.4504 0.801 0.000 0.804 0.196
#> SRR1465959 2 0.4121 0.825 0.000 0.832 0.168
#> SRR1446132 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1416933 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1102538 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1098636 3 0.3941 0.771 0.000 0.156 0.844
#> SRR1072998 2 0.0000 0.923 0.000 1.000 0.000
#> SRR627443 1 0.0000 1.000 1.000 0.000 0.000
#> SRR656131 1 0.0000 1.000 1.000 0.000 0.000
#> SRR823991 3 0.4235 0.750 0.000 0.176 0.824
#> SRR1089158 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1469036 3 0.0000 0.896 0.000 0.000 1.000
#> SRR824039 2 0.1411 0.902 0.000 0.964 0.036
#> SRR1339047 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1443049 2 0.3267 0.862 0.000 0.884 0.116
#> SRR1122885 2 0.0000 0.923 0.000 1.000 0.000
#> SRR602895 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1409837 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1388959 2 0.0000 0.923 0.000 1.000 0.000
#> SRR659863 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1089877 2 0.4605 0.727 0.000 0.796 0.204
#> SRR1123775 3 0.0000 0.896 0.000 0.000 1.000
#> SRR658909 3 0.4235 0.757 0.176 0.000 0.824
#> SRR1140510 2 0.0000 0.923 0.000 1.000 0.000
#> SRR607562 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1122913 2 0.4235 0.819 0.000 0.824 0.176
#> SRR598042 3 0.5650 0.506 0.000 0.312 0.688
#> SRR1467340 3 0.1860 0.868 0.000 0.052 0.948
#> SRR1072321 2 0.4178 0.822 0.000 0.828 0.172
#> SRR1094580 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1076608 2 0.4235 0.819 0.000 0.824 0.176
#> SRR1395462 2 0.0747 0.914 0.000 0.984 0.016
#> SRR1489220 3 0.0000 0.896 0.000 0.000 1.000
#> SRR614371 3 0.4235 0.757 0.176 0.000 0.824
#> SRR615455 3 0.6267 0.247 0.452 0.000 0.548
#> SRR1070573 2 0.3551 0.851 0.000 0.868 0.132
#> SRR598749 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1365556 2 0.4504 0.789 0.000 0.804 0.196
#> SRR1350023 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1446582 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1439763 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1343986 3 0.0747 0.889 0.000 0.016 0.984
#> SRR807463 2 0.0000 0.923 0.000 1.000 0.000
#> SRR660390 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1367672 2 0.0000 0.923 0.000 1.000 0.000
#> SRR613294 3 0.3551 0.802 0.132 0.000 0.868
#> SRR824015 3 0.6192 0.358 0.000 0.420 0.580
#> SRR1078924 2 0.4235 0.819 0.000 0.824 0.176
#> SRR662221 3 0.0000 0.896 0.000 0.000 1.000
#> SRR655017 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1338450 3 0.0000 0.896 0.000 0.000 1.000
#> SRR663741 3 0.4235 0.757 0.176 0.000 0.824
#> SRR1396057 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1083800 3 0.3267 0.805 0.000 0.116 0.884
#> SRR1445789 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1387355 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1388855 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1445449 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1380740 3 0.0747 0.889 0.000 0.016 0.984
#> SRR659995 3 0.2537 0.846 0.080 0.000 0.920
#> SRR1489524 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1444662 3 0.6244 0.216 0.000 0.440 0.560
#> SRR1383652 3 0.2356 0.848 0.000 0.072 0.928
#> SRR1361243 3 0.0424 0.893 0.000 0.008 0.992
#> SRR1490337 3 0.0000 0.896 0.000 0.000 1.000
#> SRR823967 3 0.0000 0.896 0.000 0.000 1.000
#> SRR660127 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1366627 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1361219 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1393510 3 0.0592 0.891 0.000 0.012 0.988
#> SRR662558 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1077334 2 0.0424 0.920 0.000 0.992 0.008
#> SRR807438 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1459078 3 0.0747 0.889 0.000 0.016 0.984
#> SRR1329704 2 0.0000 0.923 0.000 1.000 0.000
#> SRR1468072 3 0.1964 0.863 0.000 0.056 0.944
#> SRR1376196 2 0.4235 0.819 0.000 0.824 0.176
#> SRR1442909 3 0.0747 0.890 0.000 0.016 0.984
#> SRR1414269 3 0.0000 0.896 0.000 0.000 1.000
#> SRR1381913 3 0.5621 0.608 0.000 0.308 0.692
#> SRR1340157 2 0.0892 0.915 0.000 0.980 0.020
#> SRR1407583 2 0.0000 0.923 0.000 1.000 0.000
#> SRR615826 2 0.5098 0.647 0.000 0.752 0.248
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.0188 0.820 0.004 0.000 0.000 0.996
#> SRR1458769 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR613162 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1468876 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR1399223 3 0.0707 0.902 0.000 0.020 0.980 0.000
#> SRR660030 3 0.0188 0.913 0.000 0.000 0.996 0.004
#> SRR1333609 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR1471612 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR1413998 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR1122940 3 0.1474 0.878 0.000 0.052 0.948 0.000
#> SRR1402563 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR1398393 2 0.0188 0.903 0.000 0.996 0.000 0.004
#> SRR657961 2 0.5412 0.679 0.000 0.736 0.096 0.168
#> SRR1471135 3 0.0336 0.911 0.000 0.008 0.992 0.000
#> SRR1430001 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR662775 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1474182 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR607190 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR612467 2 0.6946 0.478 0.000 0.580 0.252 0.168
#> SRR1465959 2 0.3486 0.743 0.000 0.812 0.188 0.000
#> SRR1446132 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR1416933 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR1102538 2 0.0188 0.903 0.000 0.996 0.000 0.004
#> SRR1098636 3 0.6957 0.342 0.000 0.172 0.580 0.248
#> SRR1072998 2 0.0188 0.903 0.000 0.996 0.000 0.004
#> SRR627443 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR823991 3 0.4428 0.607 0.000 0.276 0.720 0.004
#> SRR1089158 2 0.0188 0.903 0.000 0.996 0.000 0.004
#> SRR1469036 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR824039 2 0.1209 0.884 0.000 0.964 0.032 0.004
#> SRR1339047 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR1443049 2 0.2868 0.798 0.000 0.864 0.136 0.000
#> SRR1122885 2 0.0188 0.903 0.000 0.996 0.004 0.000
#> SRR602895 4 0.0817 0.819 0.000 0.000 0.024 0.976
#> SRR1409837 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR1388959 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR659863 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1089877 2 0.3908 0.687 0.000 0.784 0.212 0.004
#> SRR1123775 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR658909 4 0.3708 0.752 0.148 0.000 0.020 0.832
#> SRR1140510 2 0.4948 0.147 0.000 0.560 0.440 0.000
#> SRR607562 4 0.4941 0.213 0.000 0.000 0.436 0.564
#> SRR1122913 3 0.4500 0.497 0.000 0.316 0.684 0.000
#> SRR598042 3 0.4149 0.720 0.000 0.028 0.804 0.168
#> SRR1467340 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR1072321 2 0.3873 0.697 0.000 0.772 0.228 0.000
#> SRR1094580 2 0.0921 0.888 0.000 0.972 0.028 0.000
#> SRR1076608 3 0.2011 0.857 0.000 0.080 0.920 0.000
#> SRR1395462 2 0.0188 0.903 0.000 0.996 0.000 0.004
#> SRR1489220 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR614371 4 0.0188 0.820 0.004 0.000 0.000 0.996
#> SRR615455 4 0.3708 0.752 0.148 0.000 0.020 0.832
#> SRR1070573 2 0.3688 0.724 0.000 0.792 0.208 0.000
#> SRR598749 4 0.0000 0.821 0.000 0.000 0.000 1.000
#> SRR1365556 3 0.2469 0.828 0.000 0.108 0.892 0.000
#> SRR1350023 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR1446582 3 0.0188 0.913 0.000 0.000 0.996 0.004
#> SRR1439763 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR1343986 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR807463 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR660390 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1367672 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR613294 4 0.0707 0.826 0.000 0.000 0.020 0.980
#> SRR824015 3 0.4655 0.558 0.000 0.312 0.684 0.004
#> SRR1078924 2 0.4454 0.587 0.000 0.692 0.308 0.000
#> SRR662221 4 0.3172 0.789 0.000 0.000 0.160 0.840
#> SRR655017 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1338450 3 0.0188 0.913 0.000 0.000 0.996 0.004
#> SRR663741 4 0.3498 0.787 0.008 0.000 0.160 0.832
#> SRR1396057 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR1083800 3 0.0336 0.910 0.000 0.008 0.992 0.000
#> SRR1445789 2 0.0469 0.898 0.000 0.988 0.012 0.000
#> SRR1387355 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR1388855 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR1445449 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR1380740 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR659995 4 0.3172 0.789 0.000 0.000 0.160 0.840
#> SRR1489524 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR1444662 3 0.2149 0.847 0.000 0.088 0.912 0.000
#> SRR1383652 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR1361243 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR1490337 3 0.0188 0.913 0.000 0.000 0.996 0.004
#> SRR823967 3 0.0188 0.913 0.000 0.000 0.996 0.004
#> SRR660127 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1366627 3 0.4454 0.578 0.000 0.308 0.692 0.000
#> SRR1361219 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR1393510 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR662558 4 0.3219 0.785 0.000 0.000 0.164 0.836
#> SRR1077334 3 0.5088 0.299 0.000 0.424 0.572 0.004
#> SRR807438 3 0.0188 0.913 0.000 0.000 0.996 0.004
#> SRR1459078 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR1329704 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR1468072 3 0.0000 0.914 0.000 0.000 1.000 0.000
#> SRR1376196 3 0.0707 0.902 0.000 0.020 0.980 0.000
#> SRR1442909 3 0.0895 0.902 0.000 0.020 0.976 0.004
#> SRR1414269 3 0.0188 0.913 0.000 0.000 0.996 0.004
#> SRR1381913 2 0.7099 0.407 0.000 0.552 0.280 0.168
#> SRR1340157 2 0.0817 0.892 0.000 0.976 0.024 0.000
#> SRR1407583 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> SRR615826 4 0.0000 0.821 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
#> SRR612587 4 0.0703 0.964 0 0.000 0.000 0.976 0.024
#> SRR1458769 2 0.0000 0.889 0 1.000 0.000 0.000 0.000
#> SRR613162 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> SRR1468876 3 0.2036 0.830 0 0.000 0.920 0.024 0.056
#> SRR1399223 3 0.1121 0.824 0 0.044 0.956 0.000 0.000
#> SRR660030 3 0.3274 0.741 0 0.000 0.780 0.000 0.220
#> SRR1333609 3 0.0000 0.842 0 0.000 1.000 0.000 0.000
#> SRR1471612 2 0.0290 0.888 0 0.992 0.000 0.000 0.008
#> SRR1413998 2 0.0000 0.889 0 1.000 0.000 0.000 0.000
#> SRR1122940 3 0.0609 0.836 0 0.000 0.980 0.000 0.020
#> SRR1402563 3 0.0000 0.842 0 0.000 1.000 0.000 0.000
#> SRR1398393 2 0.3214 0.799 0 0.844 0.036 0.000 0.120
#> SRR657961 5 0.0000 0.736 0 0.000 0.000 0.000 1.000
#> SRR1471135 3 0.4164 0.758 0 0.096 0.784 0.000 0.120
#> SRR1430001 3 0.0000 0.842 0 0.000 1.000 0.000 0.000
#> SRR662775 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.0510 0.886 0 0.984 0.000 0.000 0.016
#> SRR607190 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> SRR612467 5 0.2754 0.667 0 0.080 0.040 0.000 0.880
#> SRR1465959 2 0.2561 0.835 0 0.884 0.096 0.000 0.020
#> SRR1446132 2 0.0000 0.889 0 1.000 0.000 0.000 0.000
#> SRR1416933 2 0.0000 0.889 0 1.000 0.000 0.000 0.000
#> SRR1102538 2 0.3305 0.738 0 0.776 0.000 0.000 0.224
#> SRR1098636 5 0.6586 0.146 0 0.000 0.208 0.384 0.408
#> SRR1072998 2 0.2280 0.834 0 0.880 0.000 0.000 0.120
#> SRR627443 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> SRR823991 3 0.6152 0.601 0 0.128 0.616 0.024 0.232
#> SRR1089158 2 0.2516 0.817 0 0.860 0.000 0.000 0.140
#> SRR1469036 3 0.0000 0.842 0 0.000 1.000 0.000 0.000
#> SRR824039 2 0.4352 0.663 0 0.720 0.036 0.000 0.244
#> SRR1339047 2 0.0000 0.889 0 1.000 0.000 0.000 0.000
#> SRR1443049 2 0.3016 0.809 0 0.848 0.132 0.000 0.020
#> SRR1122885 2 0.2171 0.861 0 0.912 0.024 0.000 0.064
#> SRR602895 5 0.3508 0.586 0 0.000 0.000 0.252 0.748
#> SRR1409837 2 0.0290 0.888 0 0.992 0.000 0.000 0.008
#> SRR1388959 2 0.0000 0.889 0 1.000 0.000 0.000 0.000
#> SRR659863 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> SRR1089877 2 0.5775 0.476 0 0.608 0.148 0.000 0.244
#> SRR1123775 3 0.0000 0.842 0 0.000 1.000 0.000 0.000
#> SRR658909 4 0.0162 0.984 0 0.000 0.000 0.996 0.004
#> SRR1140510 2 0.4297 -0.136 0 0.528 0.472 0.000 0.000
#> SRR607562 5 0.0609 0.725 0 0.000 0.020 0.000 0.980
#> SRR1122913 3 0.3074 0.666 0 0.196 0.804 0.000 0.000
#> SRR598042 5 0.0000 0.736 0 0.000 0.000 0.000 1.000
#> SRR1467340 3 0.0000 0.842 0 0.000 1.000 0.000 0.000
#> SRR1072321 2 0.3821 0.724 0 0.764 0.216 0.000 0.020
#> SRR1094580 2 0.1399 0.878 0 0.952 0.028 0.000 0.020
#> SRR1076608 3 0.3039 0.729 0 0.192 0.808 0.000 0.000
#> SRR1395462 2 0.3586 0.693 0 0.736 0.000 0.000 0.264
#> SRR1489220 3 0.1893 0.831 0 0.000 0.928 0.024 0.048
#> SRR614371 4 0.0794 0.961 0 0.000 0.000 0.972 0.028
#> SRR615455 4 0.0000 0.987 0 0.000 0.000 1.000 0.000
#> SRR1070573 2 0.3550 0.756 0 0.796 0.184 0.000 0.020
#> SRR598749 5 0.4114 0.378 0 0.000 0.000 0.376 0.624
#> SRR1365556 3 0.2020 0.810 0 0.100 0.900 0.000 0.000
#> SRR1350023 2 0.0000 0.889 0 1.000 0.000 0.000 0.000
#> SRR1446582 3 0.3707 0.701 0 0.000 0.716 0.000 0.284
#> SRR1439763 3 0.0000 0.842 0 0.000 1.000 0.000 0.000
#> SRR1343986 3 0.0000 0.842 0 0.000 1.000 0.000 0.000
#> SRR807463 2 0.0609 0.885 0 0.980 0.000 0.000 0.020
#> SRR660390 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> SRR1367672 2 0.0609 0.885 0 0.980 0.000 0.000 0.020
#> SRR613294 4 0.0000 0.987 0 0.000 0.000 1.000 0.000
#> SRR824015 3 0.6553 0.533 0 0.192 0.572 0.024 0.212
#> SRR1078924 2 0.4380 0.590 0 0.676 0.304 0.000 0.020
#> SRR662221 4 0.0000 0.987 0 0.000 0.000 1.000 0.000
#> SRR655017 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> SRR1338450 3 0.4338 0.694 0 0.000 0.696 0.024 0.280
#> SRR663741 4 0.0000 0.987 0 0.000 0.000 1.000 0.000
#> SRR1396057 2 0.0000 0.889 0 1.000 0.000 0.000 0.000
#> SRR1083800 3 0.1792 0.824 0 0.000 0.916 0.000 0.084
#> SRR1445789 2 0.0000 0.889 0 1.000 0.000 0.000 0.000
#> SRR1387355 3 0.1818 0.832 0 0.000 0.932 0.024 0.044
#> SRR1388855 2 0.0000 0.889 0 1.000 0.000 0.000 0.000
#> SRR1445449 3 0.2053 0.831 0 0.004 0.924 0.024 0.048
#> SRR1380740 3 0.0000 0.842 0 0.000 1.000 0.000 0.000
#> SRR659995 4 0.0000 0.987 0 0.000 0.000 1.000 0.000
#> SRR1489524 2 0.0000 0.889 0 1.000 0.000 0.000 0.000
#> SRR1444662 3 0.3388 0.732 0 0.200 0.792 0.000 0.008
#> SRR1383652 3 0.1478 0.833 0 0.000 0.936 0.000 0.064
#> SRR1361243 3 0.0000 0.842 0 0.000 1.000 0.000 0.000
#> SRR1490337 3 0.4360 0.690 0 0.000 0.692 0.024 0.284
#> SRR823967 3 0.4360 0.690 0 0.000 0.692 0.024 0.284
#> SRR660127 1 0.0000 1.000 1 0.000 0.000 0.000 0.000
#> SRR1366627 3 0.3932 0.577 0 0.328 0.672 0.000 0.000
#> SRR1361219 2 0.0290 0.888 0 0.992 0.000 0.000 0.008
#> SRR1393510 3 0.0992 0.840 0 0.008 0.968 0.024 0.000
#> SRR662558 4 0.0404 0.974 0 0.000 0.000 0.988 0.012
#> SRR1077334 3 0.6592 0.291 0 0.300 0.460 0.000 0.240
#> SRR807438 3 0.4315 0.697 0 0.000 0.700 0.024 0.276
#> SRR1459078 3 0.0000 0.842 0 0.000 1.000 0.000 0.000
#> SRR1329704 2 0.0290 0.888 0 0.992 0.008 0.000 0.000
#> SRR1468072 3 0.0000 0.842 0 0.000 1.000 0.000 0.000
#> SRR1376196 3 0.0000 0.842 0 0.000 1.000 0.000 0.000
#> SRR1442909 3 0.4668 0.609 0 0.000 0.624 0.024 0.352
#> SRR1414269 3 0.3366 0.735 0 0.000 0.768 0.000 0.232
#> SRR1381913 5 0.0000 0.736 0 0.000 0.000 0.000 1.000
#> SRR1340157 2 0.1648 0.874 0 0.940 0.040 0.000 0.020
#> SRR1407583 2 0.0000 0.889 0 1.000 0.000 0.000 0.000
#> SRR615826 5 0.4114 0.378 0 0.000 0.000 0.376 0.624
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 4 0.0632 0.9611 0 0.000 0.000 0.976 0.024 0.000
#> SRR1458769 2 0.3563 0.5047 0 0.664 0.000 0.000 0.000 0.336
#> SRR613162 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR1468876 3 0.2383 0.7621 0 0.000 0.880 0.024 0.096 0.000
#> SRR1399223 3 0.3244 0.5523 0 0.000 0.732 0.000 0.000 0.268
#> SRR660030 3 0.3126 0.6704 0 0.000 0.752 0.000 0.248 0.000
#> SRR1333609 3 0.0000 0.7787 0 0.000 1.000 0.000 0.000 0.000
#> SRR1471612 2 0.1663 0.7493 0 0.912 0.000 0.000 0.000 0.088
#> SRR1413998 6 0.0363 0.9573 0 0.012 0.000 0.000 0.000 0.988
#> SRR1122940 3 0.3868 0.0102 0 0.492 0.508 0.000 0.000 0.000
#> SRR1402563 3 0.0000 0.7787 0 0.000 1.000 0.000 0.000 0.000
#> SRR1398393 2 0.5909 0.4784 0 0.584 0.224 0.000 0.156 0.036
#> SRR657961 5 0.2416 0.6326 0 0.156 0.000 0.000 0.844 0.000
#> SRR1471135 3 0.3024 0.7480 0 0.032 0.844 0.000 0.116 0.008
#> SRR1430001 3 0.0000 0.7787 0 0.000 1.000 0.000 0.000 0.000
#> SRR662775 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.0363 0.7679 0 0.988 0.000 0.000 0.000 0.012
#> SRR607190 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR612467 5 0.3481 0.5752 0 0.228 0.004 0.000 0.756 0.012
#> SRR1465959 2 0.0713 0.7663 0 0.972 0.028 0.000 0.000 0.000
#> SRR1446132 6 0.0363 0.9573 0 0.012 0.000 0.000 0.000 0.988
#> SRR1416933 2 0.3563 0.5047 0 0.664 0.000 0.000 0.000 0.336
#> SRR1102538 2 0.2454 0.6525 0 0.840 0.000 0.000 0.160 0.000
#> SRR1098636 5 0.5900 0.1410 0 0.000 0.204 0.384 0.412 0.000
#> SRR1072998 2 0.0363 0.7651 0 0.988 0.000 0.000 0.012 0.000
#> SRR627443 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR823991 3 0.4961 0.5758 0 0.036 0.588 0.024 0.352 0.000
#> SRR1089158 2 0.1501 0.7295 0 0.924 0.000 0.000 0.076 0.000
#> SRR1469036 3 0.0000 0.7787 0 0.000 1.000 0.000 0.000 0.000
#> SRR824039 2 0.6020 -0.0371 0 0.408 0.344 0.000 0.248 0.000
#> SRR1339047 6 0.0363 0.9573 0 0.012 0.000 0.000 0.000 0.988
#> SRR1443049 2 0.0865 0.7655 0 0.964 0.036 0.000 0.000 0.000
#> SRR1122885 2 0.0000 0.7665 0 1.000 0.000 0.000 0.000 0.000
#> SRR602895 5 0.3171 0.5867 0 0.000 0.000 0.204 0.784 0.012
#> SRR1409837 2 0.1714 0.7473 0 0.908 0.000 0.000 0.000 0.092
#> SRR1388959 6 0.0363 0.9573 0 0.012 0.000 0.000 0.000 0.988
#> SRR659863 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR1089877 3 0.5570 0.5187 0 0.172 0.564 0.004 0.260 0.000
#> SRR1123775 3 0.0000 0.7787 0 0.000 1.000 0.000 0.000 0.000
#> SRR658909 4 0.0146 0.9813 0 0.000 0.000 0.996 0.004 0.000
#> SRR1140510 3 0.5675 -0.0108 0 0.400 0.444 0.000 0.000 0.156
#> SRR607562 5 0.0363 0.6912 0 0.000 0.000 0.000 0.988 0.012
#> SRR1122913 2 0.3843 0.3571 0 0.548 0.452 0.000 0.000 0.000
#> SRR598042 5 0.0363 0.6912 0 0.000 0.000 0.000 0.988 0.012
#> SRR1467340 3 0.0000 0.7787 0 0.000 1.000 0.000 0.000 0.000
#> SRR1072321 2 0.1501 0.7575 0 0.924 0.076 0.000 0.000 0.000
#> SRR1094580 2 0.3927 0.4057 0 0.644 0.344 0.000 0.000 0.012
#> SRR1076608 3 0.4438 0.4388 0 0.044 0.628 0.000 0.000 0.328
#> SRR1395462 2 0.2260 0.7088 0 0.860 0.000 0.000 0.140 0.000
#> SRR1489220 3 0.2282 0.7634 0 0.000 0.888 0.024 0.088 0.000
#> SRR614371 4 0.0993 0.9545 0 0.000 0.000 0.964 0.024 0.012
#> SRR615455 4 0.0000 0.9843 0 0.000 0.000 1.000 0.000 0.000
#> SRR1070573 2 0.0713 0.7664 0 0.972 0.028 0.000 0.000 0.000
#> SRR598749 5 0.3862 0.3183 0 0.000 0.000 0.388 0.608 0.004
#> SRR1365556 3 0.1616 0.7640 0 0.020 0.932 0.000 0.000 0.048
#> SRR1350023 6 0.0363 0.9573 0 0.012 0.000 0.000 0.000 0.988
#> SRR1446582 3 0.3309 0.6795 0 0.000 0.720 0.000 0.280 0.000
#> SRR1439763 3 0.0000 0.7787 0 0.000 1.000 0.000 0.000 0.000
#> SRR1343986 3 0.0000 0.7787 0 0.000 1.000 0.000 0.000 0.000
#> SRR807463 2 0.0000 0.7665 0 1.000 0.000 0.000 0.000 0.000
#> SRR660390 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR1367672 2 0.0000 0.7665 0 1.000 0.000 0.000 0.000 0.000
#> SRR613294 4 0.0000 0.9843 0 0.000 0.000 1.000 0.000 0.000
#> SRR824015 3 0.7126 0.3017 0 0.036 0.412 0.024 0.248 0.280
#> SRR1078924 2 0.2454 0.6761 0 0.840 0.160 0.000 0.000 0.000
#> SRR662221 4 0.0000 0.9843 0 0.000 0.000 1.000 0.000 0.000
#> SRR655017 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR1338450 3 0.4206 0.6033 0 0.000 0.620 0.024 0.356 0.000
#> SRR663741 4 0.0000 0.9843 0 0.000 0.000 1.000 0.000 0.000
#> SRR1396057 2 0.3563 0.5047 0 0.664 0.000 0.000 0.000 0.336
#> SRR1083800 3 0.2629 0.7543 0 0.060 0.872 0.000 0.068 0.000
#> SRR1445789 6 0.0363 0.9573 0 0.012 0.000 0.000 0.000 0.988
#> SRR1387355 3 0.2230 0.7646 0 0.000 0.892 0.024 0.084 0.000
#> SRR1388855 6 0.2631 0.7542 0 0.180 0.000 0.000 0.000 0.820
#> SRR1445449 3 0.2282 0.7634 0 0.000 0.888 0.024 0.088 0.000
#> SRR1380740 3 0.0000 0.7787 0 0.000 1.000 0.000 0.000 0.000
#> SRR659995 4 0.0000 0.9843 0 0.000 0.000 1.000 0.000 0.000
#> SRR1489524 6 0.0363 0.9573 0 0.012 0.000 0.000 0.000 0.988
#> SRR1444662 3 0.5446 0.3285 0 0.120 0.540 0.000 0.004 0.336
#> SRR1383652 3 0.1814 0.7661 0 0.000 0.900 0.000 0.100 0.000
#> SRR1361243 3 0.0000 0.7787 0 0.000 1.000 0.000 0.000 0.000
#> SRR1490337 3 0.4218 0.5997 0 0.000 0.616 0.024 0.360 0.000
#> SRR823967 3 0.4218 0.5997 0 0.000 0.616 0.024 0.360 0.000
#> SRR660127 1 0.0000 1.0000 1 0.000 0.000 0.000 0.000 0.000
#> SRR1366627 6 0.1719 0.8735 0 0.016 0.060 0.000 0.000 0.924
#> SRR1361219 2 0.2762 0.6748 0 0.804 0.000 0.000 0.000 0.196
#> SRR1393510 3 0.0777 0.7749 0 0.000 0.972 0.024 0.000 0.004
#> SRR662558 4 0.0547 0.9619 0 0.000 0.000 0.980 0.020 0.000
#> SRR1077334 3 0.5827 0.3547 0 0.316 0.476 0.000 0.208 0.000
#> SRR807438 3 0.4167 0.6132 0 0.000 0.632 0.024 0.344 0.000
#> SRR1459078 3 0.0000 0.7787 0 0.000 1.000 0.000 0.000 0.000
#> SRR1329704 2 0.4641 0.5907 0 0.664 0.248 0.000 0.000 0.088
#> SRR1468072 3 0.0000 0.7787 0 0.000 1.000 0.000 0.000 0.000
#> SRR1376196 3 0.0000 0.7787 0 0.000 1.000 0.000 0.000 0.000
#> SRR1442909 3 0.4301 0.5630 0 0.000 0.584 0.024 0.392 0.000
#> SRR1414269 3 0.3371 0.6541 0 0.000 0.708 0.000 0.292 0.000
#> SRR1381913 5 0.0000 0.6876 0 0.000 0.000 0.000 1.000 0.000
#> SRR1340157 2 0.1141 0.7585 0 0.948 0.052 0.000 0.000 0.000
#> SRR1407583 2 0.4332 0.5487 0 0.664 0.048 0.000 0.000 0.288
#> SRR615826 5 0.3737 0.3132 0 0.000 0.000 0.392 0.608 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "mclust"]
# you can also extract it by
# res = res_list["MAD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17380 rows and 102 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.621 0.816 0.912 0.4225 0.533 0.533
#> 3 3 0.515 0.734 0.803 0.1141 0.788 0.679
#> 4 4 0.455 0.381 0.751 0.2579 0.813 0.701
#> 5 5 0.655 0.764 0.873 0.0539 0.707 0.506
#> 6 6 0.729 0.749 0.844 0.2531 0.793 0.496
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
#> SRR612587 1 0.0000 0.7796 1.000 0.000
#> SRR1458769 2 0.0000 0.9561 0.000 1.000
#> SRR613162 1 0.0000 0.7796 1.000 0.000
#> SRR1352481 1 0.0000 0.7796 1.000 0.000
#> SRR1468876 1 0.9710 0.5709 0.600 0.400
#> SRR1399223 2 0.0000 0.9561 0.000 1.000
#> SRR660030 2 0.0000 0.9561 0.000 1.000
#> SRR1333609 2 0.0000 0.9561 0.000 1.000
#> SRR1471612 2 0.0000 0.9561 0.000 1.000
#> SRR1413998 2 0.0000 0.9561 0.000 1.000
#> SRR1122940 2 0.0000 0.9561 0.000 1.000
#> SRR1402563 2 0.4022 0.8615 0.080 0.920
#> SRR1398393 1 0.9710 0.5709 0.600 0.400
#> SRR657961 2 0.0000 0.9561 0.000 1.000
#> SRR1471135 2 0.0000 0.9561 0.000 1.000
#> SRR1430001 2 0.7602 0.6289 0.220 0.780
#> SRR662775 1 0.0000 0.7796 1.000 0.000
#> SRR1474182 2 0.0000 0.9561 0.000 1.000
#> SRR607190 1 0.0000 0.7796 1.000 0.000
#> SRR612467 2 0.4690 0.8289 0.100 0.900
#> SRR1465959 2 0.0000 0.9561 0.000 1.000
#> SRR1446132 2 0.0000 0.9561 0.000 1.000
#> SRR1416933 2 0.0000 0.9561 0.000 1.000
#> SRR1102538 2 0.0000 0.9561 0.000 1.000
#> SRR1098636 1 0.9710 0.5709 0.600 0.400
#> SRR1072998 2 0.0000 0.9561 0.000 1.000
#> SRR627443 1 0.0000 0.7796 1.000 0.000
#> SRR656131 1 0.0000 0.7796 1.000 0.000
#> SRR823991 1 0.9710 0.5709 0.600 0.400
#> SRR1089158 2 0.0000 0.9561 0.000 1.000
#> SRR1469036 2 0.7602 0.6289 0.220 0.780
#> SRR824039 1 0.9710 0.5709 0.600 0.400
#> SRR1339047 1 0.9710 0.5709 0.600 0.400
#> SRR1443049 2 0.0000 0.9561 0.000 1.000
#> SRR1122885 2 0.0000 0.9561 0.000 1.000
#> SRR602895 2 0.7674 0.6230 0.224 0.776
#> SRR1409837 2 0.0000 0.9561 0.000 1.000
#> SRR1388959 2 0.0000 0.9561 0.000 1.000
#> SRR659863 1 0.0000 0.7796 1.000 0.000
#> SRR1089877 1 0.9710 0.5709 0.600 0.400
#> SRR1123775 2 0.0000 0.9561 0.000 1.000
#> SRR658909 1 0.0000 0.7796 1.000 0.000
#> SRR1140510 2 0.0000 0.9561 0.000 1.000
#> SRR607562 2 0.0000 0.9561 0.000 1.000
#> SRR1122913 2 0.0000 0.9561 0.000 1.000
#> SRR598042 2 0.0000 0.9561 0.000 1.000
#> SRR1467340 2 0.0000 0.9561 0.000 1.000
#> SRR1072321 2 0.0000 0.9561 0.000 1.000
#> SRR1094580 2 0.0000 0.9561 0.000 1.000
#> SRR1076608 2 0.0000 0.9561 0.000 1.000
#> SRR1395462 2 0.0000 0.9561 0.000 1.000
#> SRR1489220 1 0.9635 0.5815 0.612 0.388
#> SRR614371 1 0.0000 0.7796 1.000 0.000
#> SRR615455 1 0.0000 0.7796 1.000 0.000
#> SRR1070573 2 0.0000 0.9561 0.000 1.000
#> SRR598749 1 0.9661 0.3370 0.608 0.392
#> SRR1365556 2 0.9815 -0.0204 0.420 0.580
#> SRR1350023 2 0.0000 0.9561 0.000 1.000
#> SRR1446582 2 0.0000 0.9561 0.000 1.000
#> SRR1439763 2 0.0000 0.9561 0.000 1.000
#> SRR1343986 2 0.0000 0.9561 0.000 1.000
#> SRR807463 2 0.0000 0.9561 0.000 1.000
#> SRR660390 1 0.0000 0.7796 1.000 0.000
#> SRR1367672 2 0.0000 0.9561 0.000 1.000
#> SRR613294 1 0.0000 0.7796 1.000 0.000
#> SRR824015 1 0.7950 0.6856 0.760 0.240
#> SRR1078924 2 0.0000 0.9561 0.000 1.000
#> SRR662221 1 0.0000 0.7796 1.000 0.000
#> SRR655017 1 0.0000 0.7796 1.000 0.000
#> SRR1338450 1 0.9710 0.5709 0.600 0.400
#> SRR663741 1 0.0000 0.7796 1.000 0.000
#> SRR1396057 2 0.0000 0.9561 0.000 1.000
#> SRR1083800 2 0.0000 0.9561 0.000 1.000
#> SRR1445789 2 0.0000 0.9561 0.000 1.000
#> SRR1387355 1 0.9710 0.5709 0.600 0.400
#> SRR1388855 2 0.0000 0.9561 0.000 1.000
#> SRR1445449 1 0.9710 0.5709 0.600 0.400
#> SRR1380740 2 0.0000 0.9561 0.000 1.000
#> SRR659995 1 0.0000 0.7796 1.000 0.000
#> SRR1489524 2 0.0000 0.9561 0.000 1.000
#> SRR1444662 2 0.0376 0.9520 0.004 0.996
#> SRR1383652 2 0.0000 0.9561 0.000 1.000
#> SRR1361243 2 0.0000 0.9561 0.000 1.000
#> SRR1490337 1 0.9850 0.5089 0.572 0.428
#> SRR823967 1 0.9710 0.5709 0.600 0.400
#> SRR660127 1 0.0000 0.7796 1.000 0.000
#> SRR1366627 2 0.0000 0.9561 0.000 1.000
#> SRR1361219 2 0.0000 0.9561 0.000 1.000
#> SRR1393510 1 0.9710 0.5709 0.600 0.400
#> SRR662558 1 0.0000 0.7796 1.000 0.000
#> SRR1077334 2 0.0000 0.9561 0.000 1.000
#> SRR807438 1 0.9710 0.5709 0.600 0.400
#> SRR1459078 2 0.7139 0.6753 0.196 0.804
#> SRR1329704 2 0.0000 0.9561 0.000 1.000
#> SRR1468072 2 0.0000 0.9561 0.000 1.000
#> SRR1376196 2 0.0000 0.9561 0.000 1.000
#> SRR1442909 2 0.9977 -0.2341 0.472 0.528
#> SRR1414269 2 0.6801 0.6939 0.180 0.820
#> SRR1381913 2 0.0000 0.9561 0.000 1.000
#> SRR1340157 2 0.0000 0.9561 0.000 1.000
#> SRR1407583 2 0.0000 0.9561 0.000 1.000
#> SRR615826 1 0.7528 0.6151 0.784 0.216
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 3 0.5733 0.4129 0.324 0.000 0.676
#> SRR1458769 2 0.2711 0.8463 0.000 0.912 0.088
#> SRR613162 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1352481 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1468876 2 0.5291 0.5730 0.000 0.732 0.268
#> SRR1399223 2 0.4974 0.7359 0.000 0.764 0.236
#> SRR660030 2 0.1860 0.8517 0.000 0.948 0.052
#> SRR1333609 2 0.0424 0.8601 0.000 0.992 0.008
#> SRR1471612 2 0.0424 0.8613 0.000 0.992 0.008
#> SRR1413998 2 0.5497 0.6832 0.000 0.708 0.292
#> SRR1122940 2 0.0424 0.8613 0.000 0.992 0.008
#> SRR1402563 2 0.0892 0.8569 0.000 0.980 0.020
#> SRR1398393 3 0.6204 0.4802 0.000 0.424 0.576
#> SRR657961 2 0.5536 0.7527 0.144 0.804 0.052
#> SRR1471135 2 0.0000 0.8610 0.000 1.000 0.000
#> SRR1430001 2 0.1289 0.8520 0.000 0.968 0.032
#> SRR662775 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1474182 2 0.1753 0.8568 0.000 0.952 0.048
#> SRR607190 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR612467 2 0.6781 0.6213 0.244 0.704 0.052
#> SRR1465959 2 0.0424 0.8613 0.000 0.992 0.008
#> SRR1446132 2 0.5497 0.6832 0.000 0.708 0.292
#> SRR1416933 2 0.2066 0.8525 0.000 0.940 0.060
#> SRR1102538 2 0.0424 0.8613 0.000 0.992 0.008
#> SRR1098636 3 0.7325 0.5228 0.036 0.388 0.576
#> SRR1072998 2 0.2878 0.8095 0.096 0.904 0.000
#> SRR627443 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR656131 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR823991 3 0.6204 0.4802 0.000 0.424 0.576
#> SRR1089158 2 0.0892 0.8609 0.000 0.980 0.020
#> SRR1469036 2 0.1289 0.8520 0.000 0.968 0.032
#> SRR824039 3 0.6204 0.4802 0.000 0.424 0.576
#> SRR1339047 3 0.4002 0.4916 0.000 0.160 0.840
#> SRR1443049 2 0.1753 0.8567 0.000 0.952 0.048
#> SRR1122885 2 0.0424 0.8613 0.000 0.992 0.008
#> SRR602895 2 0.6742 0.6289 0.240 0.708 0.052
#> SRR1409837 2 0.0424 0.8613 0.000 0.992 0.008
#> SRR1388959 2 0.5497 0.6832 0.000 0.708 0.292
#> SRR659863 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1089877 3 0.6204 0.4802 0.000 0.424 0.576
#> SRR1123775 2 0.1860 0.8517 0.000 0.948 0.052
#> SRR658909 3 0.6126 0.3258 0.400 0.000 0.600
#> SRR1140510 2 0.1964 0.8518 0.000 0.944 0.056
#> SRR607562 2 0.6400 0.6736 0.208 0.740 0.052
#> SRR1122913 2 0.0424 0.8613 0.000 0.992 0.008
#> SRR598042 2 0.6588 0.6720 0.208 0.732 0.060
#> SRR1467340 2 0.0000 0.8610 0.000 1.000 0.000
#> SRR1072321 2 0.0424 0.8613 0.000 0.992 0.008
#> SRR1094580 2 0.0424 0.8613 0.000 0.992 0.008
#> SRR1076608 2 0.2537 0.8470 0.000 0.920 0.080
#> SRR1395462 2 0.2066 0.8525 0.000 0.940 0.060
#> SRR1489220 2 0.6007 0.6805 0.184 0.768 0.048
#> SRR614371 2 0.8587 0.1349 0.400 0.500 0.100
#> SRR615455 3 0.5968 0.3452 0.364 0.000 0.636
#> SRR1070573 2 0.0424 0.8613 0.000 0.992 0.008
#> SRR598749 2 0.8043 0.3535 0.324 0.592 0.084
#> SRR1365556 2 0.2796 0.8373 0.000 0.908 0.092
#> SRR1350023 2 0.5497 0.6832 0.000 0.708 0.292
#> SRR1446582 2 0.0000 0.8610 0.000 1.000 0.000
#> SRR1439763 2 0.0000 0.8610 0.000 1.000 0.000
#> SRR1343986 2 0.0000 0.8610 0.000 1.000 0.000
#> SRR807463 2 0.0000 0.8610 0.000 1.000 0.000
#> SRR660390 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1367672 2 0.0424 0.8613 0.000 0.992 0.008
#> SRR613294 3 0.5733 0.4129 0.324 0.000 0.676
#> SRR824015 3 0.6886 0.5007 0.184 0.088 0.728
#> SRR1078924 2 0.0424 0.8613 0.000 0.992 0.008
#> SRR662221 3 0.5733 0.4129 0.324 0.000 0.676
#> SRR655017 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1338450 2 0.5859 0.4161 0.000 0.656 0.344
#> SRR663741 3 0.5733 0.4129 0.324 0.000 0.676
#> SRR1396057 2 0.2066 0.8525 0.000 0.940 0.060
#> SRR1083800 2 0.0000 0.8610 0.000 1.000 0.000
#> SRR1445789 2 0.5363 0.7013 0.000 0.724 0.276
#> SRR1387355 2 0.2537 0.8425 0.000 0.920 0.080
#> SRR1388855 2 0.5363 0.7013 0.000 0.724 0.276
#> SRR1445449 3 0.8349 0.5579 0.128 0.264 0.608
#> SRR1380740 2 0.1289 0.8520 0.000 0.968 0.032
#> SRR659995 3 0.5733 0.4129 0.324 0.000 0.676
#> SRR1489524 2 0.5431 0.6925 0.000 0.716 0.284
#> SRR1444662 2 0.4209 0.8191 0.016 0.856 0.128
#> SRR1383652 2 0.0000 0.8610 0.000 1.000 0.000
#> SRR1361243 2 0.1163 0.8538 0.000 0.972 0.028
#> SRR1490337 2 0.3038 0.7957 0.000 0.896 0.104
#> SRR823967 3 0.6225 0.4600 0.000 0.432 0.568
#> SRR660127 1 0.0000 1.0000 1.000 0.000 0.000
#> SRR1366627 2 0.5560 0.6811 0.000 0.700 0.300
#> SRR1361219 2 0.0892 0.8618 0.000 0.980 0.020
#> SRR1393510 2 0.4261 0.8179 0.012 0.848 0.140
#> SRR662558 3 0.6906 0.4338 0.324 0.032 0.644
#> SRR1077334 2 0.0000 0.8610 0.000 1.000 0.000
#> SRR807438 2 0.6180 -0.0123 0.000 0.584 0.416
#> SRR1459078 2 0.1289 0.8520 0.000 0.968 0.032
#> SRR1329704 2 0.1860 0.8517 0.000 0.948 0.052
#> SRR1468072 2 0.2625 0.8484 0.000 0.916 0.084
#> SRR1376196 2 0.0000 0.8610 0.000 1.000 0.000
#> SRR1442909 2 0.4178 0.6750 0.000 0.828 0.172
#> SRR1414269 2 0.1529 0.8571 0.000 0.960 0.040
#> SRR1381913 2 0.6107 0.7046 0.184 0.764 0.052
#> SRR1340157 2 0.0424 0.8613 0.000 0.992 0.008
#> SRR1407583 2 0.1860 0.8517 0.000 0.948 0.052
#> SRR615826 2 0.9833 -0.2197 0.324 0.416 0.260
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.2759 0.6236 0.044 0.000 0.052 0.904
#> SRR1458769 2 0.2281 0.5583 0.000 0.904 0.096 0.000
#> SRR613162 1 0.0817 0.9584 0.976 0.000 0.000 0.024
#> SRR1352481 1 0.0336 0.9689 0.992 0.000 0.000 0.008
#> SRR1468876 2 0.6915 -0.5210 0.000 0.476 0.416 0.108
#> SRR1399223 2 0.4679 0.3400 0.000 0.648 0.352 0.000
#> SRR660030 2 0.1970 0.5814 0.000 0.932 0.060 0.008
#> SRR1333609 2 0.4877 -0.1670 0.000 0.592 0.408 0.000
#> SRR1471612 2 0.0336 0.6162 0.000 0.992 0.008 0.000
#> SRR1413998 2 0.4981 0.2490 0.000 0.536 0.464 0.000
#> SRR1122940 2 0.0336 0.6162 0.000 0.992 0.008 0.000
#> SRR1402563 2 0.4888 -0.1782 0.000 0.588 0.412 0.000
#> SRR1398393 2 0.7683 -0.7003 0.000 0.400 0.384 0.216
#> SRR657961 2 0.5113 0.2916 0.000 0.684 0.024 0.292
#> SRR1471135 2 0.1302 0.5996 0.000 0.956 0.044 0.000
#> SRR1430001 2 0.5300 -0.1979 0.000 0.580 0.408 0.012
#> SRR662775 1 0.0000 0.9720 1.000 0.000 0.000 0.000
#> SRR1474182 2 0.0188 0.6157 0.000 0.996 0.004 0.000
#> SRR607190 1 0.0000 0.9720 1.000 0.000 0.000 0.000
#> SRR612467 2 0.5681 0.1582 0.000 0.568 0.028 0.404
#> SRR1465959 2 0.0336 0.6162 0.000 0.992 0.008 0.000
#> SRR1446132 2 0.4999 0.2163 0.000 0.508 0.492 0.000
#> SRR1416933 2 0.0000 0.6158 0.000 1.000 0.000 0.000
#> SRR1102538 2 0.1305 0.6022 0.000 0.960 0.004 0.036
#> SRR1098636 4 0.7900 -0.6170 0.000 0.300 0.332 0.368
#> SRR1072998 2 0.4606 0.3470 0.000 0.724 0.012 0.264
#> SRR627443 1 0.2589 0.7967 0.884 0.000 0.000 0.116
#> SRR656131 1 0.0000 0.9720 1.000 0.000 0.000 0.000
#> SRR823991 3 0.7648 0.6715 0.000 0.392 0.400 0.208
#> SRR1089158 2 0.0657 0.6160 0.000 0.984 0.012 0.004
#> SRR1469036 2 0.5408 -0.2137 0.000 0.576 0.408 0.016
#> SRR824039 2 0.7684 -0.7047 0.000 0.396 0.388 0.216
#> SRR1339047 3 0.5143 0.0533 0.000 0.036 0.708 0.256
#> SRR1443049 2 0.0188 0.6157 0.000 0.996 0.004 0.000
#> SRR1122885 2 0.0469 0.6160 0.000 0.988 0.012 0.000
#> SRR602895 4 0.5963 0.3268 0.008 0.252 0.064 0.676
#> SRR1409837 2 0.0188 0.6157 0.000 0.996 0.004 0.000
#> SRR1388959 2 0.4981 0.2490 0.000 0.536 0.464 0.000
#> SRR659863 1 0.0000 0.9720 1.000 0.000 0.000 0.000
#> SRR1089877 2 0.7644 -0.6895 0.000 0.412 0.380 0.208
#> SRR1123775 2 0.1722 0.5919 0.000 0.944 0.048 0.008
#> SRR658909 4 0.5035 0.5276 0.196 0.000 0.056 0.748
#> SRR1140510 2 0.0592 0.6121 0.000 0.984 0.016 0.000
#> SRR607562 2 0.6226 0.1271 0.008 0.548 0.040 0.404
#> SRR1122913 2 0.0336 0.6162 0.000 0.992 0.008 0.000
#> SRR598042 2 0.5896 0.1482 0.008 0.564 0.024 0.404
#> SRR1467340 2 0.0921 0.6071 0.000 0.972 0.028 0.000
#> SRR1072321 2 0.0188 0.6157 0.000 0.996 0.004 0.000
#> SRR1094580 2 0.0000 0.6158 0.000 1.000 0.000 0.000
#> SRR1076608 2 0.2868 0.5342 0.000 0.864 0.136 0.000
#> SRR1395462 2 0.2489 0.5746 0.000 0.912 0.020 0.068
#> SRR1489220 4 0.8632 -0.3291 0.032 0.264 0.344 0.360
#> SRR614371 4 0.6591 0.5865 0.116 0.088 0.084 0.712
#> SRR615455 4 0.6153 0.3159 0.328 0.000 0.068 0.604
#> SRR1070573 2 0.0336 0.6162 0.000 0.992 0.008 0.000
#> SRR598749 4 0.7312 0.3592 0.044 0.352 0.064 0.540
#> SRR1365556 2 0.6994 -0.5313 0.000 0.472 0.412 0.116
#> SRR1350023 2 0.4981 0.2490 0.000 0.536 0.464 0.000
#> SRR1446582 2 0.1489 0.5983 0.000 0.952 0.044 0.004
#> SRR1439763 2 0.4741 0.0257 0.000 0.668 0.328 0.004
#> SRR1343986 2 0.4679 -0.0197 0.000 0.648 0.352 0.000
#> SRR807463 2 0.0469 0.6160 0.000 0.988 0.012 0.000
#> SRR660390 1 0.0817 0.9584 0.976 0.000 0.000 0.024
#> SRR1367672 2 0.0336 0.6162 0.000 0.992 0.008 0.000
#> SRR613294 4 0.2111 0.6250 0.044 0.000 0.024 0.932
#> SRR824015 3 0.7710 0.2222 0.040 0.104 0.524 0.332
#> SRR1078924 2 0.0336 0.6162 0.000 0.992 0.008 0.000
#> SRR662221 4 0.2578 0.6153 0.052 0.000 0.036 0.912
#> SRR655017 1 0.0000 0.9720 1.000 0.000 0.000 0.000
#> SRR1338450 3 0.7454 0.6846 0.000 0.376 0.448 0.176
#> SRR663741 4 0.3652 0.6205 0.052 0.000 0.092 0.856
#> SRR1396057 2 0.0336 0.6149 0.000 0.992 0.008 0.000
#> SRR1083800 2 0.0921 0.6071 0.000 0.972 0.028 0.000
#> SRR1445789 2 0.4977 0.2518 0.000 0.540 0.460 0.000
#> SRR1387355 2 0.7396 -0.6227 0.000 0.432 0.404 0.164
#> SRR1388855 2 0.4981 0.2490 0.000 0.536 0.464 0.000
#> SRR1445449 3 0.8485 0.6573 0.032 0.312 0.424 0.232
#> SRR1380740 2 0.4877 -0.1670 0.000 0.592 0.408 0.000
#> SRR659995 4 0.2840 0.6260 0.044 0.000 0.056 0.900
#> SRR1489524 2 0.4981 0.2490 0.000 0.536 0.464 0.000
#> SRR1444662 3 0.5511 0.0700 0.000 0.484 0.500 0.016
#> SRR1383652 2 0.2216 0.5557 0.000 0.908 0.092 0.000
#> SRR1361243 2 0.4888 -0.1782 0.000 0.588 0.412 0.000
#> SRR1490337 2 0.5638 -0.2075 0.000 0.584 0.388 0.028
#> SRR823967 3 0.7519 0.6743 0.000 0.392 0.424 0.184
#> SRR660127 1 0.0000 0.9720 1.000 0.000 0.000 0.000
#> SRR1366627 2 0.4804 0.3088 0.000 0.616 0.384 0.000
#> SRR1361219 2 0.0188 0.6157 0.000 0.996 0.004 0.000
#> SRR1393510 3 0.7436 0.6686 0.000 0.364 0.460 0.176
#> SRR662558 4 0.5243 0.6202 0.052 0.080 0.072 0.796
#> SRR1077334 2 0.0000 0.6158 0.000 1.000 0.000 0.000
#> SRR807438 3 0.7497 0.6665 0.000 0.396 0.424 0.180
#> SRR1459078 2 0.4877 -0.1670 0.000 0.592 0.408 0.000
#> SRR1329704 2 0.1211 0.5980 0.000 0.960 0.040 0.000
#> SRR1468072 2 0.2281 0.5474 0.000 0.904 0.096 0.000
#> SRR1376196 2 0.0336 0.6162 0.000 0.992 0.008 0.000
#> SRR1442909 2 0.5482 -0.1582 0.000 0.608 0.368 0.024
#> SRR1414269 2 0.4936 -0.1068 0.000 0.624 0.372 0.004
#> SRR1381913 2 0.5436 0.2146 0.000 0.620 0.024 0.356
#> SRR1340157 2 0.0336 0.6162 0.000 0.992 0.008 0.000
#> SRR1407583 2 0.2814 0.4851 0.000 0.868 0.132 0.000
#> SRR615826 4 0.7290 0.3877 0.044 0.328 0.068 0.560
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR612587 4 0.2377 0.65966 0.000 0.000 0.000 0.872 0.128
#> SRR1458769 3 0.3183 0.84953 0.000 0.156 0.828 0.000 0.016
#> SRR613162 1 0.1197 0.95551 0.952 0.000 0.000 0.048 0.000
#> SRR1352481 1 0.0510 0.97644 0.984 0.000 0.000 0.016 0.000
#> SRR1468876 3 0.1168 0.86077 0.000 0.000 0.960 0.032 0.008
#> SRR1399223 2 0.3932 0.39996 0.000 0.672 0.328 0.000 0.000
#> SRR660030 3 0.1970 0.84633 0.000 0.004 0.924 0.012 0.060
#> SRR1333609 3 0.0609 0.86874 0.000 0.000 0.980 0.000 0.020
#> SRR1471612 3 0.3043 0.87455 0.000 0.080 0.864 0.000 0.056
#> SRR1413998 2 0.0162 0.82775 0.000 0.996 0.004 0.000 0.000
#> SRR1122940 3 0.2946 0.87495 0.000 0.088 0.868 0.000 0.044
#> SRR1402563 3 0.0609 0.86874 0.000 0.000 0.980 0.000 0.020
#> SRR1398393 3 0.3915 0.79777 0.000 0.012 0.792 0.172 0.024
#> SRR657961 5 0.5125 0.59220 0.000 0.072 0.112 0.064 0.752
#> SRR1471135 3 0.0290 0.87064 0.000 0.000 0.992 0.000 0.008
#> SRR1430001 3 0.0609 0.86874 0.000 0.000 0.980 0.000 0.020
#> SRR662775 1 0.0000 0.98239 1.000 0.000 0.000 0.000 0.000
#> SRR1474182 3 0.3090 0.87313 0.000 0.088 0.860 0.000 0.052
#> SRR607190 1 0.0000 0.98239 1.000 0.000 0.000 0.000 0.000
#> SRR612467 5 0.5138 0.59485 0.000 0.068 0.108 0.072 0.752
#> SRR1465959 3 0.3090 0.87313 0.000 0.088 0.860 0.000 0.052
#> SRR1446132 2 0.0162 0.82775 0.000 0.996 0.004 0.000 0.000
#> SRR1416933 3 0.2962 0.87447 0.000 0.084 0.868 0.000 0.048
#> SRR1102538 3 0.5268 0.63997 0.000 0.084 0.664 0.004 0.248
#> SRR1098636 3 0.6642 -0.08515 0.000 0.000 0.420 0.352 0.228
#> SRR1072998 5 0.7031 0.14134 0.000 0.084 0.400 0.076 0.440
#> SRR627443 1 0.0865 0.95742 0.972 0.000 0.000 0.024 0.004
#> SRR656131 1 0.0000 0.98239 1.000 0.000 0.000 0.000 0.000
#> SRR823991 3 0.2890 0.80067 0.000 0.000 0.836 0.160 0.004
#> SRR1089158 3 0.2903 0.87580 0.000 0.080 0.872 0.000 0.048
#> SRR1469036 3 0.0609 0.86874 0.000 0.000 0.980 0.000 0.020
#> SRR824039 3 0.4306 0.79382 0.000 0.012 0.772 0.172 0.044
#> SRR1339047 2 0.2548 0.66502 0.000 0.876 0.004 0.116 0.004
#> SRR1443049 3 0.3090 0.87313 0.000 0.088 0.860 0.000 0.052
#> SRR1122885 3 0.3102 0.87315 0.000 0.084 0.860 0.000 0.056
#> SRR602895 5 0.2170 0.60214 0.000 0.004 0.004 0.088 0.904
#> SRR1409837 3 0.3090 0.87313 0.000 0.088 0.860 0.000 0.052
#> SRR1388959 2 0.0162 0.82775 0.000 0.996 0.004 0.000 0.000
#> SRR659863 1 0.0000 0.98239 1.000 0.000 0.000 0.000 0.000
#> SRR1089877 3 0.4476 0.79063 0.000 0.016 0.764 0.172 0.048
#> SRR1123775 3 0.0290 0.87064 0.000 0.000 0.992 0.000 0.008
#> SRR658909 4 0.2964 0.65471 0.004 0.000 0.004 0.840 0.152
#> SRR1140510 3 0.2707 0.87488 0.000 0.100 0.876 0.000 0.024
#> SRR607562 5 0.1768 0.60787 0.000 0.004 0.000 0.072 0.924
#> SRR1122913 3 0.2903 0.87557 0.000 0.080 0.872 0.000 0.048
#> SRR598042 5 0.1768 0.60787 0.000 0.004 0.000 0.072 0.924
#> SRR1467340 3 0.0290 0.87064 0.000 0.000 0.992 0.000 0.008
#> SRR1072321 3 0.3090 0.87313 0.000 0.088 0.860 0.000 0.052
#> SRR1094580 3 0.3090 0.87313 0.000 0.088 0.860 0.000 0.052
#> SRR1076608 3 0.3370 0.85235 0.000 0.148 0.824 0.000 0.028
#> SRR1395462 5 0.5055 0.49834 0.000 0.072 0.208 0.012 0.708
#> SRR1489220 3 0.6261 -0.20275 0.000 0.000 0.456 0.396 0.148
#> SRR614371 4 0.4059 0.54943 0.004 0.000 0.004 0.700 0.292
#> SRR615455 4 0.3707 0.47169 0.284 0.000 0.000 0.716 0.000
#> SRR1070573 3 0.3090 0.87313 0.000 0.088 0.860 0.000 0.052
#> SRR598749 5 0.4171 0.28379 0.000 0.000 0.000 0.396 0.604
#> SRR1365556 3 0.2740 0.82437 0.000 0.096 0.876 0.028 0.000
#> SRR1350023 2 0.0162 0.82775 0.000 0.996 0.004 0.000 0.000
#> SRR1446582 3 0.1331 0.87741 0.000 0.040 0.952 0.000 0.008
#> SRR1439763 3 0.0290 0.87064 0.000 0.000 0.992 0.000 0.008
#> SRR1343986 3 0.0290 0.87064 0.000 0.000 0.992 0.000 0.008
#> SRR807463 3 0.3033 0.87399 0.000 0.084 0.864 0.000 0.052
#> SRR660390 1 0.1197 0.95551 0.952 0.000 0.000 0.048 0.000
#> SRR1367672 3 0.3102 0.87315 0.000 0.084 0.860 0.000 0.056
#> SRR613294 4 0.2074 0.68020 0.000 0.000 0.000 0.896 0.104
#> SRR824015 4 0.6794 0.00179 0.000 0.244 0.320 0.432 0.004
#> SRR1078924 3 0.3033 0.87434 0.000 0.084 0.864 0.000 0.052
#> SRR662221 4 0.0000 0.71470 0.000 0.000 0.000 1.000 0.000
#> SRR655017 1 0.0000 0.98239 1.000 0.000 0.000 0.000 0.000
#> SRR1338450 3 0.1484 0.85330 0.000 0.000 0.944 0.048 0.008
#> SRR663741 4 0.1121 0.70641 0.000 0.000 0.044 0.956 0.000
#> SRR1396057 3 0.2962 0.87447 0.000 0.084 0.868 0.000 0.048
#> SRR1083800 3 0.1830 0.87813 0.000 0.068 0.924 0.000 0.008
#> SRR1445789 2 0.0162 0.82775 0.000 0.996 0.004 0.000 0.000
#> SRR1387355 3 0.1082 0.86246 0.000 0.000 0.964 0.028 0.008
#> SRR1388855 2 0.0162 0.82775 0.000 0.996 0.004 0.000 0.000
#> SRR1445449 3 0.3059 0.79908 0.000 0.016 0.856 0.120 0.008
#> SRR1380740 3 0.0609 0.86874 0.000 0.000 0.980 0.000 0.020
#> SRR659995 4 0.0000 0.71470 0.000 0.000 0.000 1.000 0.000
#> SRR1489524 2 0.0162 0.82775 0.000 0.996 0.004 0.000 0.000
#> SRR1444662 3 0.4656 0.08431 0.000 0.480 0.508 0.012 0.000
#> SRR1383652 3 0.0290 0.87064 0.000 0.000 0.992 0.000 0.008
#> SRR1361243 3 0.0609 0.86874 0.000 0.000 0.980 0.000 0.020
#> SRR1490337 3 0.1310 0.86091 0.000 0.000 0.956 0.024 0.020
#> SRR823967 3 0.2358 0.81433 0.000 0.000 0.888 0.104 0.008
#> SRR660127 1 0.0000 0.98239 1.000 0.000 0.000 0.000 0.000
#> SRR1366627 2 0.4084 0.40100 0.000 0.668 0.328 0.000 0.004
#> SRR1361219 3 0.3090 0.87313 0.000 0.088 0.860 0.000 0.052
#> SRR1393510 3 0.1774 0.85251 0.000 0.016 0.932 0.052 0.000
#> SRR662558 4 0.3201 0.59008 0.000 0.000 0.096 0.852 0.052
#> SRR1077334 3 0.2903 0.87580 0.000 0.080 0.872 0.000 0.048
#> SRR807438 3 0.2358 0.81433 0.000 0.000 0.888 0.104 0.008
#> SRR1459078 3 0.0609 0.86874 0.000 0.000 0.980 0.000 0.020
#> SRR1329704 3 0.3058 0.87218 0.000 0.096 0.860 0.000 0.044
#> SRR1468072 3 0.1608 0.87873 0.000 0.072 0.928 0.000 0.000
#> SRR1376196 3 0.2511 0.87781 0.000 0.080 0.892 0.000 0.028
#> SRR1442909 3 0.0798 0.86747 0.000 0.000 0.976 0.016 0.008
#> SRR1414269 3 0.0290 0.87064 0.000 0.000 0.992 0.000 0.008
#> SRR1381913 5 0.2419 0.62172 0.000 0.004 0.028 0.064 0.904
#> SRR1340157 3 0.3090 0.87313 0.000 0.088 0.860 0.000 0.052
#> SRR1407583 3 0.2632 0.87887 0.000 0.072 0.888 0.000 0.040
#> SRR615826 5 0.4219 0.24450 0.000 0.000 0.000 0.416 0.584
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 4 0.2378 0.6003 0.000 0.000 0.000 0.848 0.152 0.000
#> SRR1458769 2 0.4920 0.3730 0.000 0.580 0.064 0.000 0.004 0.352
#> SRR613162 1 0.1387 0.9368 0.932 0.000 0.000 0.068 0.000 0.000
#> SRR1352481 1 0.0547 0.9704 0.980 0.000 0.000 0.020 0.000 0.000
#> SRR1468876 3 0.3878 0.7930 0.000 0.056 0.764 0.176 0.004 0.000
#> SRR1399223 6 0.0603 0.9226 0.000 0.000 0.016 0.000 0.004 0.980
#> SRR660030 3 0.5301 0.6803 0.000 0.168 0.628 0.008 0.196 0.000
#> SRR1333609 3 0.1625 0.7852 0.000 0.060 0.928 0.000 0.012 0.000
#> SRR1471612 2 0.0363 0.8540 0.000 0.988 0.012 0.000 0.000 0.000
#> SRR1413998 6 0.0146 0.9317 0.000 0.000 0.000 0.000 0.004 0.996
#> SRR1122940 2 0.0363 0.8537 0.000 0.988 0.012 0.000 0.000 0.000
#> SRR1402563 3 0.1625 0.7852 0.000 0.060 0.928 0.000 0.012 0.000
#> SRR1398393 3 0.5902 0.6070 0.000 0.268 0.540 0.176 0.000 0.016
#> SRR657961 5 0.0767 0.7841 0.000 0.004 0.008 0.012 0.976 0.000
#> SRR1471135 3 0.3394 0.7762 0.000 0.236 0.752 0.000 0.012 0.000
#> SRR1430001 3 0.0146 0.7699 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR662775 1 0.0000 0.9781 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.0363 0.8534 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR607190 1 0.0000 0.9781 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR612467 5 0.0748 0.7858 0.000 0.004 0.004 0.016 0.976 0.000
#> SRR1465959 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1446132 6 0.0291 0.9315 0.000 0.000 0.004 0.000 0.004 0.992
#> SRR1416933 2 0.3417 0.7706 0.000 0.812 0.132 0.000 0.004 0.052
#> SRR1102538 2 0.3615 0.5873 0.000 0.700 0.008 0.000 0.292 0.000
#> SRR1098636 5 0.5788 0.1038 0.000 0.004 0.292 0.188 0.516 0.000
#> SRR1072998 2 0.4637 0.2839 0.000 0.556 0.008 0.028 0.408 0.000
#> SRR627443 1 0.0820 0.9594 0.972 0.000 0.000 0.016 0.012 0.000
#> SRR656131 1 0.0000 0.9781 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR823991 3 0.4807 0.7775 0.000 0.112 0.700 0.176 0.004 0.008
#> SRR1089158 2 0.2126 0.8147 0.000 0.904 0.020 0.004 0.072 0.000
#> SRR1469036 3 0.0146 0.7699 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR824039 2 0.6297 0.0679 0.000 0.492 0.304 0.176 0.024 0.004
#> SRR1339047 6 0.2879 0.6953 0.000 0.000 0.004 0.176 0.004 0.816
#> SRR1443049 2 0.0508 0.8533 0.000 0.984 0.004 0.000 0.000 0.012
#> SRR1122885 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR602895 5 0.2260 0.7303 0.000 0.000 0.000 0.140 0.860 0.000
#> SRR1409837 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1388959 6 0.0146 0.9317 0.000 0.000 0.000 0.000 0.004 0.996
#> SRR659863 1 0.0000 0.9781 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1089877 2 0.6035 -0.1026 0.000 0.468 0.344 0.176 0.000 0.012
#> SRR1123775 3 0.3394 0.7764 0.000 0.236 0.752 0.000 0.012 0.000
#> SRR658909 4 0.1387 0.7449 0.000 0.000 0.000 0.932 0.068 0.000
#> SRR1140510 3 0.4901 0.5260 0.000 0.316 0.608 0.000 0.004 0.072
#> SRR607562 5 0.1387 0.7725 0.000 0.000 0.000 0.068 0.932 0.000
#> SRR1122913 2 0.0260 0.8545 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR598042 5 0.0547 0.7841 0.000 0.000 0.000 0.020 0.980 0.000
#> SRR1467340 3 0.3023 0.7797 0.000 0.232 0.768 0.000 0.000 0.000
#> SRR1072321 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1094580 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1076608 6 0.3894 0.7055 0.000 0.152 0.072 0.000 0.004 0.772
#> SRR1395462 5 0.2501 0.6841 0.000 0.108 0.016 0.004 0.872 0.000
#> SRR1489220 3 0.4832 0.5867 0.000 0.004 0.608 0.324 0.064 0.000
#> SRR614371 4 0.2941 0.5487 0.000 0.000 0.000 0.780 0.220 0.000
#> SRR615455 4 0.3175 0.5574 0.256 0.000 0.000 0.744 0.000 0.000
#> SRR1070573 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR598749 5 0.3464 0.5447 0.000 0.000 0.000 0.312 0.688 0.000
#> SRR1365556 3 0.4013 0.7457 0.000 0.000 0.756 0.172 0.004 0.068
#> SRR1350023 6 0.0146 0.9317 0.000 0.000 0.000 0.000 0.004 0.996
#> SRR1446582 3 0.3511 0.7860 0.000 0.216 0.760 0.000 0.024 0.000
#> SRR1439763 3 0.3050 0.7773 0.000 0.236 0.764 0.000 0.000 0.000
#> SRR1343986 3 0.3050 0.7773 0.000 0.236 0.764 0.000 0.000 0.000
#> SRR807463 2 0.1269 0.8464 0.000 0.956 0.012 0.000 0.012 0.020
#> SRR660390 1 0.1387 0.9368 0.932 0.000 0.000 0.068 0.000 0.000
#> SRR1367672 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR613294 4 0.0713 0.7632 0.000 0.000 0.000 0.972 0.028 0.000
#> SRR824015 4 0.5452 -0.2746 0.000 0.000 0.436 0.444 0.000 0.120
#> SRR1078924 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR662221 4 0.0000 0.7740 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR655017 1 0.0000 0.9781 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1338450 3 0.4084 0.7915 0.000 0.056 0.756 0.176 0.012 0.000
#> SRR663741 4 0.0000 0.7740 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1396057 2 0.3355 0.7731 0.000 0.816 0.132 0.000 0.004 0.048
#> SRR1083800 3 0.3050 0.7773 0.000 0.236 0.764 0.000 0.000 0.000
#> SRR1445789 6 0.0146 0.9310 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1387355 3 0.2989 0.7766 0.000 0.004 0.812 0.176 0.008 0.000
#> SRR1388855 6 0.0146 0.9310 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1445449 3 0.3296 0.7646 0.000 0.000 0.792 0.188 0.008 0.012
#> SRR1380740 3 0.0146 0.7699 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR659995 4 0.0000 0.7740 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1489524 6 0.0146 0.9317 0.000 0.000 0.000 0.000 0.004 0.996
#> SRR1444662 3 0.3862 0.4388 0.000 0.000 0.608 0.000 0.004 0.388
#> SRR1383652 3 0.3394 0.7762 0.000 0.236 0.752 0.000 0.012 0.000
#> SRR1361243 3 0.1524 0.7870 0.000 0.060 0.932 0.000 0.008 0.000
#> SRR1490337 3 0.3903 0.7954 0.000 0.060 0.764 0.172 0.004 0.000
#> SRR823967 3 0.4141 0.7920 0.000 0.060 0.752 0.176 0.012 0.000
#> SRR660127 1 0.0000 0.9781 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1366627 6 0.1588 0.8618 0.000 0.000 0.072 0.000 0.004 0.924
#> SRR1361219 2 0.0260 0.8540 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1393510 3 0.3236 0.7674 0.000 0.000 0.796 0.180 0.000 0.024
#> SRR662558 4 0.1141 0.7571 0.000 0.000 0.000 0.948 0.052 0.000
#> SRR1077334 2 0.0909 0.8482 0.000 0.968 0.012 0.000 0.020 0.000
#> SRR807438 3 0.3878 0.7930 0.000 0.056 0.764 0.176 0.004 0.000
#> SRR1459078 3 0.0146 0.7699 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1329704 2 0.3355 0.7731 0.000 0.816 0.132 0.000 0.004 0.048
#> SRR1468072 3 0.3293 0.7234 0.000 0.008 0.788 0.004 0.004 0.196
#> SRR1376196 2 0.2491 0.7399 0.000 0.836 0.164 0.000 0.000 0.000
#> SRR1442909 3 0.3819 0.7962 0.000 0.064 0.764 0.172 0.000 0.000
#> SRR1414269 3 0.4137 0.8077 0.000 0.132 0.756 0.108 0.004 0.000
#> SRR1381913 5 0.0622 0.7849 0.000 0.000 0.008 0.012 0.980 0.000
#> SRR1340157 2 0.0000 0.8545 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1407583 2 0.5605 0.2725 0.000 0.544 0.360 0.048 0.004 0.044
#> SRR615826 5 0.3499 0.5334 0.000 0.000 0.000 0.320 0.680 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "NMF"]
# you can also extract it by
# res = res_list["MAD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17380 rows and 102 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 0.979 0.953 0.981 0.3616 0.637 0.637
#> 3 3 0.739 0.823 0.923 0.7269 0.644 0.473
#> 4 4 0.541 0.620 0.814 0.0864 0.952 0.871
#> 5 5 0.546 0.621 0.746 0.0949 0.825 0.526
#> 6 6 0.626 0.627 0.787 0.0585 0.873 0.544
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
#> SRR612587 1 0.0000 0.951 1.000 0.000
#> SRR1458769 2 0.0000 0.988 0.000 1.000
#> SRR613162 1 0.0000 0.951 1.000 0.000
#> SRR1352481 1 0.0000 0.951 1.000 0.000
#> SRR1468876 2 0.0938 0.976 0.012 0.988
#> SRR1399223 2 0.0000 0.988 0.000 1.000
#> SRR660030 2 0.0000 0.988 0.000 1.000
#> SRR1333609 2 0.0000 0.988 0.000 1.000
#> SRR1471612 2 0.0000 0.988 0.000 1.000
#> SRR1413998 2 0.0000 0.988 0.000 1.000
#> SRR1122940 2 0.0000 0.988 0.000 1.000
#> SRR1402563 2 0.0000 0.988 0.000 1.000
#> SRR1398393 2 0.0000 0.988 0.000 1.000
#> SRR657961 2 0.0000 0.988 0.000 1.000
#> SRR1471135 2 0.0000 0.988 0.000 1.000
#> SRR1430001 2 0.7602 0.705 0.220 0.780
#> SRR662775 1 0.0000 0.951 1.000 0.000
#> SRR1474182 2 0.0000 0.988 0.000 1.000
#> SRR607190 1 0.0000 0.951 1.000 0.000
#> SRR612467 2 0.0000 0.988 0.000 1.000
#> SRR1465959 2 0.0000 0.988 0.000 1.000
#> SRR1446132 2 0.0000 0.988 0.000 1.000
#> SRR1416933 2 0.0000 0.988 0.000 1.000
#> SRR1102538 2 0.0000 0.988 0.000 1.000
#> SRR1098636 2 0.0000 0.988 0.000 1.000
#> SRR1072998 2 0.0000 0.988 0.000 1.000
#> SRR627443 1 0.0000 0.951 1.000 0.000
#> SRR656131 1 0.0000 0.951 1.000 0.000
#> SRR823991 2 0.0000 0.988 0.000 1.000
#> SRR1089158 2 0.0000 0.988 0.000 1.000
#> SRR1469036 2 0.5946 0.822 0.144 0.856
#> SRR824039 2 0.0000 0.988 0.000 1.000
#> SRR1339047 2 0.0000 0.988 0.000 1.000
#> SRR1443049 2 0.0000 0.988 0.000 1.000
#> SRR1122885 2 0.0000 0.988 0.000 1.000
#> SRR602895 1 0.3114 0.905 0.944 0.056
#> SRR1409837 2 0.0000 0.988 0.000 1.000
#> SRR1388959 2 0.0000 0.988 0.000 1.000
#> SRR659863 1 0.0000 0.951 1.000 0.000
#> SRR1089877 2 0.0000 0.988 0.000 1.000
#> SRR1123775 2 0.0000 0.988 0.000 1.000
#> SRR658909 1 0.0000 0.951 1.000 0.000
#> SRR1140510 2 0.0000 0.988 0.000 1.000
#> SRR607562 2 0.8267 0.636 0.260 0.740
#> SRR1122913 2 0.0000 0.988 0.000 1.000
#> SRR598042 2 0.0000 0.988 0.000 1.000
#> SRR1467340 2 0.0000 0.988 0.000 1.000
#> SRR1072321 2 0.0000 0.988 0.000 1.000
#> SRR1094580 2 0.0000 0.988 0.000 1.000
#> SRR1076608 2 0.0000 0.988 0.000 1.000
#> SRR1395462 2 0.0000 0.988 0.000 1.000
#> SRR1489220 1 0.0000 0.951 1.000 0.000
#> SRR614371 1 0.0000 0.951 1.000 0.000
#> SRR615455 1 0.0000 0.951 1.000 0.000
#> SRR1070573 2 0.0000 0.988 0.000 1.000
#> SRR598749 2 0.7376 0.725 0.208 0.792
#> SRR1365556 2 0.0000 0.988 0.000 1.000
#> SRR1350023 2 0.0000 0.988 0.000 1.000
#> SRR1446582 2 0.0000 0.988 0.000 1.000
#> SRR1439763 2 0.0000 0.988 0.000 1.000
#> SRR1343986 2 0.0000 0.988 0.000 1.000
#> SRR807463 2 0.0000 0.988 0.000 1.000
#> SRR660390 1 0.0000 0.951 1.000 0.000
#> SRR1367672 2 0.0000 0.988 0.000 1.000
#> SRR613294 1 0.0000 0.951 1.000 0.000
#> SRR824015 2 0.0000 0.988 0.000 1.000
#> SRR1078924 2 0.0000 0.988 0.000 1.000
#> SRR662221 1 0.0000 0.951 1.000 0.000
#> SRR655017 1 0.0000 0.951 1.000 0.000
#> SRR1338450 2 0.3114 0.931 0.056 0.944
#> SRR663741 1 0.0000 0.951 1.000 0.000
#> SRR1396057 2 0.0000 0.988 0.000 1.000
#> SRR1083800 2 0.0000 0.988 0.000 1.000
#> SRR1445789 2 0.0000 0.988 0.000 1.000
#> SRR1387355 1 0.8327 0.653 0.736 0.264
#> SRR1388855 2 0.0000 0.988 0.000 1.000
#> SRR1445449 1 0.9963 0.173 0.536 0.464
#> SRR1380740 2 0.0000 0.988 0.000 1.000
#> SRR659995 1 0.0000 0.951 1.000 0.000
#> SRR1489524 2 0.0000 0.988 0.000 1.000
#> SRR1444662 2 0.0000 0.988 0.000 1.000
#> SRR1383652 2 0.0000 0.988 0.000 1.000
#> SRR1361243 2 0.0000 0.988 0.000 1.000
#> SRR1490337 2 0.0000 0.988 0.000 1.000
#> SRR823967 2 0.0000 0.988 0.000 1.000
#> SRR660127 1 0.0000 0.951 1.000 0.000
#> SRR1366627 2 0.0000 0.988 0.000 1.000
#> SRR1361219 2 0.0000 0.988 0.000 1.000
#> SRR1393510 2 0.0000 0.988 0.000 1.000
#> SRR662558 1 0.0000 0.951 1.000 0.000
#> SRR1077334 2 0.0000 0.988 0.000 1.000
#> SRR807438 1 0.8861 0.584 0.696 0.304
#> SRR1459078 2 0.0000 0.988 0.000 1.000
#> SRR1329704 2 0.0000 0.988 0.000 1.000
#> SRR1468072 2 0.0000 0.988 0.000 1.000
#> SRR1376196 2 0.0000 0.988 0.000 1.000
#> SRR1442909 2 0.0000 0.988 0.000 1.000
#> SRR1414269 2 0.0000 0.988 0.000 1.000
#> SRR1381913 2 0.0000 0.988 0.000 1.000
#> SRR1340157 2 0.0000 0.988 0.000 1.000
#> SRR1407583 2 0.0000 0.988 0.000 1.000
#> SRR615826 2 0.0000 0.988 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 3 0.4291 0.7359 0.180 0.000 0.820
#> SRR1458769 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR613162 1 0.0000 0.8561 1.000 0.000 0.000
#> SRR1352481 1 0.0000 0.8561 1.000 0.000 0.000
#> SRR1468876 1 0.6111 0.4020 0.604 0.396 0.000
#> SRR1399223 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR660030 3 0.0892 0.8687 0.000 0.020 0.980
#> SRR1333609 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1471612 3 0.5810 0.5421 0.000 0.336 0.664
#> SRR1413998 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1122940 3 0.4452 0.7539 0.000 0.192 0.808
#> SRR1402563 2 0.3267 0.8385 0.000 0.884 0.116
#> SRR1398393 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR657961 3 0.0892 0.8687 0.000 0.020 0.980
#> SRR1471135 3 0.0892 0.8687 0.000 0.020 0.980
#> SRR1430001 1 0.5363 0.6341 0.724 0.276 0.000
#> SRR662775 1 0.0000 0.8561 1.000 0.000 0.000
#> SRR1474182 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR607190 1 0.0000 0.8561 1.000 0.000 0.000
#> SRR612467 3 0.0747 0.8666 0.000 0.016 0.984
#> SRR1465959 2 0.4887 0.6546 0.000 0.772 0.228
#> SRR1446132 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1416933 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1102538 3 0.0892 0.8687 0.000 0.020 0.980
#> SRR1098636 3 0.0892 0.8687 0.000 0.020 0.980
#> SRR1072998 3 0.2261 0.8545 0.000 0.068 0.932
#> SRR627443 1 0.0000 0.8561 1.000 0.000 0.000
#> SRR656131 1 0.0000 0.8561 1.000 0.000 0.000
#> SRR823991 2 0.0592 0.9484 0.000 0.988 0.012
#> SRR1089158 3 0.3551 0.8132 0.000 0.132 0.868
#> SRR1469036 2 0.6180 0.1772 0.416 0.584 0.000
#> SRR824039 2 0.2261 0.8941 0.000 0.932 0.068
#> SRR1339047 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1443049 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1122885 3 0.3412 0.8210 0.000 0.124 0.876
#> SRR602895 3 0.0892 0.8547 0.020 0.000 0.980
#> SRR1409837 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1388959 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR659863 1 0.0000 0.8561 1.000 0.000 0.000
#> SRR1089877 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1123775 3 0.2356 0.8526 0.000 0.072 0.928
#> SRR658909 1 0.1031 0.8451 0.976 0.000 0.024
#> SRR1140510 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR607562 3 0.0892 0.8547 0.020 0.000 0.980
#> SRR1122913 2 0.6274 -0.0102 0.000 0.544 0.456
#> SRR598042 3 0.0892 0.8687 0.000 0.020 0.980
#> SRR1467340 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1072321 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1094580 2 0.1860 0.9102 0.000 0.948 0.052
#> SRR1076608 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1395462 3 0.0892 0.8687 0.000 0.020 0.980
#> SRR1489220 1 0.0237 0.8547 0.996 0.000 0.004
#> SRR614371 3 0.2796 0.8222 0.092 0.000 0.908
#> SRR615455 1 0.0892 0.8501 0.980 0.000 0.020
#> SRR1070573 2 0.4842 0.6619 0.000 0.776 0.224
#> SRR598749 3 0.0000 0.8553 0.000 0.000 1.000
#> SRR1365556 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1350023 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1446582 3 0.0892 0.8687 0.000 0.020 0.980
#> SRR1439763 2 0.0424 0.9520 0.000 0.992 0.008
#> SRR1343986 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR807463 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR660390 1 0.0000 0.8561 1.000 0.000 0.000
#> SRR1367672 3 0.1860 0.8608 0.000 0.052 0.948
#> SRR613294 3 0.4291 0.7359 0.180 0.000 0.820
#> SRR824015 2 0.0892 0.9382 0.000 0.980 0.020
#> SRR1078924 3 0.6008 0.5050 0.000 0.372 0.628
#> SRR662221 1 0.5905 0.4427 0.648 0.000 0.352
#> SRR655017 1 0.0000 0.8561 1.000 0.000 0.000
#> SRR1338450 1 0.4887 0.6944 0.772 0.228 0.000
#> SRR663741 1 0.0892 0.8501 0.980 0.000 0.020
#> SRR1396057 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1083800 3 0.3686 0.8084 0.000 0.140 0.860
#> SRR1445789 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1387355 1 0.4291 0.7357 0.820 0.180 0.000
#> SRR1388855 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1445449 1 0.6309 0.1083 0.504 0.496 0.000
#> SRR1380740 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR659995 3 0.3619 0.7781 0.136 0.000 0.864
#> SRR1489524 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1444662 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1383652 3 0.0892 0.8687 0.000 0.020 0.980
#> SRR1361243 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1490337 3 0.5253 0.6952 0.188 0.020 0.792
#> SRR823967 3 0.6295 0.1751 0.000 0.472 0.528
#> SRR660127 1 0.0000 0.8561 1.000 0.000 0.000
#> SRR1366627 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1361219 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1393510 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR662558 1 0.6111 0.2349 0.604 0.000 0.396
#> SRR1077334 3 0.6302 0.2013 0.000 0.480 0.520
#> SRR807438 1 0.4094 0.7803 0.872 0.028 0.100
#> SRR1459078 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1329704 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1468072 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1376196 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1442909 3 0.0892 0.8687 0.000 0.020 0.980
#> SRR1414269 3 0.4399 0.7543 0.000 0.188 0.812
#> SRR1381913 3 0.0892 0.8687 0.000 0.020 0.980
#> SRR1340157 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR1407583 2 0.0000 0.9587 0.000 1.000 0.000
#> SRR615826 3 0.0237 0.8582 0.000 0.004 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.4724 0.64138 0.076 0.000 0.136 0.788
#> SRR1458769 2 0.0336 0.82895 0.000 0.992 0.000 0.008
#> SRR613162 1 0.0817 0.71535 0.976 0.000 0.000 0.024
#> SRR1352481 1 0.0817 0.71539 0.976 0.000 0.000 0.024
#> SRR1468876 1 0.7952 0.27736 0.532 0.308 0.076 0.084
#> SRR1399223 2 0.0336 0.83074 0.000 0.992 0.000 0.008
#> SRR660030 3 0.1118 0.62590 0.000 0.000 0.964 0.036
#> SRR1333609 2 0.6229 0.74443 0.028 0.704 0.188 0.080
#> SRR1471612 3 0.5968 0.48889 0.000 0.236 0.672 0.092
#> SRR1413998 2 0.0188 0.82992 0.000 0.996 0.000 0.004
#> SRR1122940 3 0.5063 0.48749 0.000 0.124 0.768 0.108
#> SRR1402563 2 0.5055 0.60845 0.000 0.624 0.368 0.008
#> SRR1398393 2 0.3972 0.73548 0.000 0.840 0.080 0.080
#> SRR657961 3 0.3400 0.62322 0.000 0.000 0.820 0.180
#> SRR1471135 3 0.4735 0.63112 0.000 0.068 0.784 0.148
#> SRR1430001 1 0.8957 -0.00317 0.412 0.328 0.180 0.080
#> SRR662775 1 0.0592 0.71740 0.984 0.000 0.000 0.016
#> SRR1474182 2 0.0188 0.82992 0.000 0.996 0.000 0.004
#> SRR607190 1 0.0817 0.71353 0.976 0.000 0.000 0.024
#> SRR612467 3 0.3801 0.60190 0.000 0.000 0.780 0.220
#> SRR1465959 2 0.6520 0.52146 0.000 0.552 0.364 0.084
#> SRR1446132 2 0.0000 0.83059 0.000 1.000 0.000 0.000
#> SRR1416933 2 0.0000 0.83059 0.000 1.000 0.000 0.000
#> SRR1102538 3 0.0336 0.62424 0.000 0.000 0.992 0.008
#> SRR1098636 3 0.3486 0.62333 0.000 0.000 0.812 0.188
#> SRR1072998 3 0.2266 0.58926 0.000 0.004 0.912 0.084
#> SRR627443 1 0.1637 0.69463 0.940 0.000 0.000 0.060
#> SRR656131 1 0.0000 0.71971 1.000 0.000 0.000 0.000
#> SRR823991 2 0.3320 0.77494 0.000 0.876 0.056 0.068
#> SRR1089158 3 0.1661 0.62000 0.000 0.004 0.944 0.052
#> SRR1469036 2 0.8483 0.52288 0.216 0.528 0.176 0.080
#> SRR824039 2 0.6160 0.55641 0.000 0.612 0.316 0.072
#> SRR1339047 2 0.1716 0.80294 0.000 0.936 0.000 0.064
#> SRR1443049 2 0.5250 0.76448 0.000 0.744 0.176 0.080
#> SRR1122885 3 0.2845 0.57848 0.000 0.076 0.896 0.028
#> SRR602895 3 0.4134 0.56902 0.000 0.000 0.740 0.260
#> SRR1409837 2 0.1118 0.82758 0.000 0.964 0.036 0.000
#> SRR1388959 2 0.0000 0.83059 0.000 1.000 0.000 0.000
#> SRR659863 1 0.0000 0.71971 1.000 0.000 0.000 0.000
#> SRR1089877 2 0.6016 0.74307 0.000 0.680 0.208 0.112
#> SRR1123775 3 0.3933 0.62808 0.000 0.008 0.792 0.200
#> SRR658909 1 0.1510 0.70635 0.956 0.000 0.016 0.028
#> SRR1140510 2 0.0188 0.82992 0.000 0.996 0.000 0.004
#> SRR607562 3 0.4193 0.55498 0.000 0.000 0.732 0.268
#> SRR1122913 3 0.6419 -0.16975 0.000 0.420 0.512 0.068
#> SRR598042 3 0.3975 0.58564 0.000 0.000 0.760 0.240
#> SRR1467340 2 0.5332 0.76007 0.000 0.736 0.184 0.080
#> SRR1072321 2 0.5448 0.75367 0.000 0.724 0.196 0.080
#> SRR1094580 2 0.4382 0.70422 0.000 0.704 0.296 0.000
#> SRR1076608 2 0.2635 0.81604 0.000 0.904 0.020 0.076
#> SRR1395462 3 0.3311 0.62612 0.000 0.000 0.828 0.172
#> SRR1489220 1 0.3372 0.62541 0.868 0.000 0.096 0.036
#> SRR614371 3 0.7155 0.22227 0.164 0.000 0.536 0.300
#> SRR615455 4 0.4888 0.07336 0.412 0.000 0.000 0.588
#> SRR1070573 2 0.6529 0.47538 0.000 0.532 0.388 0.080
#> SRR598749 3 0.4500 0.48953 0.000 0.000 0.684 0.316
#> SRR1365556 2 0.0469 0.82802 0.000 0.988 0.000 0.012
#> SRR1350023 2 0.0000 0.83059 0.000 1.000 0.000 0.000
#> SRR1446582 3 0.3444 0.62823 0.000 0.000 0.816 0.184
#> SRR1439763 2 0.6026 0.70826 0.004 0.672 0.244 0.080
#> SRR1343986 2 0.5332 0.76007 0.000 0.736 0.184 0.080
#> SRR807463 2 0.4936 0.68436 0.000 0.672 0.316 0.012
#> SRR660390 1 0.0817 0.71539 0.976 0.000 0.000 0.024
#> SRR1367672 3 0.1820 0.63550 0.000 0.020 0.944 0.036
#> SRR613294 4 0.4740 0.64505 0.080 0.000 0.132 0.788
#> SRR824015 2 0.2676 0.77682 0.012 0.896 0.000 0.092
#> SRR1078924 3 0.6356 0.19175 0.000 0.320 0.596 0.084
#> SRR662221 4 0.7680 0.28809 0.232 0.000 0.324 0.444
#> SRR655017 1 0.0000 0.71971 1.000 0.000 0.000 0.000
#> SRR1338450 1 0.4198 0.48874 0.768 0.224 0.004 0.004
#> SRR663741 1 0.4134 0.44958 0.740 0.000 0.000 0.260
#> SRR1396057 2 0.0469 0.82802 0.000 0.988 0.000 0.012
#> SRR1083800 3 0.4939 0.42709 0.000 0.220 0.740 0.040
#> SRR1445789 2 0.0000 0.83059 0.000 1.000 0.000 0.000
#> SRR1387355 1 0.4356 0.43732 0.708 0.292 0.000 0.000
#> SRR1388855 2 0.0000 0.83059 0.000 1.000 0.000 0.000
#> SRR1445449 2 0.6107 0.41186 0.264 0.648 0.000 0.088
#> SRR1380740 2 0.5332 0.76007 0.000 0.736 0.184 0.080
#> SRR659995 3 0.6615 0.13180 0.084 0.000 0.512 0.404
#> SRR1489524 2 0.0188 0.82992 0.000 0.996 0.000 0.004
#> SRR1444662 2 0.0707 0.82501 0.000 0.980 0.000 0.020
#> SRR1383652 3 0.4541 0.63447 0.000 0.060 0.796 0.144
#> SRR1361243 2 0.5218 0.75966 0.000 0.736 0.200 0.064
#> SRR1490337 3 0.4715 0.47050 0.188 0.012 0.776 0.024
#> SRR823967 3 0.3219 0.55940 0.000 0.112 0.868 0.020
#> SRR660127 1 0.0000 0.71971 1.000 0.000 0.000 0.000
#> SRR1366627 2 0.0336 0.82895 0.000 0.992 0.000 0.008
#> SRR1361219 2 0.0000 0.83059 0.000 1.000 0.000 0.000
#> SRR1393510 2 0.1902 0.80087 0.004 0.932 0.000 0.064
#> SRR662558 1 0.7833 -0.42636 0.376 0.000 0.364 0.260
#> SRR1077334 3 0.6023 0.18814 0.000 0.328 0.612 0.060
#> SRR807438 1 0.4213 0.58212 0.824 0.028 0.136 0.012
#> SRR1459078 2 0.5250 0.76437 0.000 0.744 0.176 0.080
#> SRR1329704 2 0.0000 0.83059 0.000 1.000 0.000 0.000
#> SRR1468072 2 0.2859 0.81249 0.000 0.880 0.112 0.008
#> SRR1376196 2 0.5661 0.73485 0.000 0.700 0.220 0.080
#> SRR1442909 3 0.3172 0.63372 0.000 0.000 0.840 0.160
#> SRR1414269 3 0.2976 0.55867 0.000 0.120 0.872 0.008
#> SRR1381913 3 0.3400 0.62076 0.000 0.000 0.820 0.180
#> SRR1340157 2 0.5332 0.76007 0.000 0.736 0.184 0.080
#> SRR1407583 2 0.1557 0.80929 0.000 0.944 0.000 0.056
#> SRR615826 3 0.5902 0.08176 0.008 0.020 0.488 0.484
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR612587 4 0.2249 0.7805 0.000 0.000 0.008 0.896 0.096
#> SRR1458769 2 0.1544 0.7629 0.000 0.932 0.068 0.000 0.000
#> SRR613162 1 0.3102 0.7650 0.860 0.000 0.084 0.056 0.000
#> SRR1352481 1 0.0510 0.8153 0.984 0.000 0.016 0.000 0.000
#> SRR1468876 1 0.6156 0.4590 0.608 0.044 0.288 0.008 0.052
#> SRR1399223 2 0.2690 0.7356 0.000 0.844 0.156 0.000 0.000
#> SRR660030 5 0.3366 0.6159 0.000 0.000 0.232 0.000 0.768
#> SRR1333609 3 0.4455 0.7529 0.008 0.284 0.692 0.000 0.016
#> SRR1471612 5 0.5271 0.1676 0.000 0.432 0.048 0.000 0.520
#> SRR1413998 2 0.2230 0.7627 0.000 0.884 0.116 0.000 0.000
#> SRR1122940 3 0.5122 0.4150 0.000 0.060 0.628 0.000 0.312
#> SRR1402563 3 0.5717 0.6588 0.000 0.324 0.572 0.000 0.104
#> SRR1398393 2 0.5588 0.4512 0.000 0.704 0.096 0.044 0.156
#> SRR657961 5 0.1270 0.6969 0.000 0.000 0.052 0.000 0.948
#> SRR1471135 5 0.4616 0.4372 0.000 0.288 0.036 0.000 0.676
#> SRR1430001 3 0.6187 0.6357 0.172 0.204 0.608 0.000 0.016
#> SRR662775 1 0.0510 0.8153 0.984 0.000 0.016 0.000 0.000
#> SRR1474182 2 0.2392 0.7652 0.000 0.888 0.104 0.004 0.004
#> SRR607190 1 0.1557 0.8029 0.940 0.000 0.052 0.008 0.000
#> SRR612467 5 0.1386 0.6791 0.000 0.000 0.016 0.032 0.952
#> SRR1465959 3 0.5690 0.7333 0.000 0.224 0.624 0.000 0.152
#> SRR1446132 2 0.2424 0.7557 0.000 0.868 0.132 0.000 0.000
#> SRR1416933 2 0.2377 0.7577 0.000 0.872 0.128 0.000 0.000
#> SRR1102538 5 0.3123 0.6505 0.000 0.004 0.184 0.000 0.812
#> SRR1098636 5 0.3498 0.6566 0.000 0.016 0.092 0.044 0.848
#> SRR1072998 3 0.4705 -0.1982 0.000 0.008 0.504 0.004 0.484
#> SRR627443 1 0.3035 0.7604 0.856 0.000 0.112 0.032 0.000
#> SRR656131 1 0.0451 0.8161 0.988 0.000 0.008 0.004 0.000
#> SRR823991 2 0.5763 0.4609 0.008 0.704 0.100 0.040 0.148
#> SRR1089158 5 0.5281 0.2207 0.000 0.044 0.388 0.004 0.564
#> SRR1469036 3 0.5499 0.6894 0.112 0.232 0.652 0.000 0.004
#> SRR824039 2 0.6820 -0.1040 0.000 0.484 0.116 0.040 0.360
#> SRR1339047 2 0.0771 0.7351 0.000 0.976 0.020 0.004 0.000
#> SRR1443049 3 0.3884 0.7474 0.000 0.288 0.708 0.000 0.004
#> SRR1122885 5 0.4851 0.3799 0.000 0.036 0.340 0.000 0.624
#> SRR602895 5 0.3241 0.6162 0.008 0.000 0.100 0.036 0.856
#> SRR1409837 2 0.4054 0.7029 0.000 0.800 0.116 0.004 0.080
#> SRR1388959 2 0.2648 0.7390 0.000 0.848 0.152 0.000 0.000
#> SRR659863 1 0.0324 0.8163 0.992 0.000 0.004 0.004 0.000
#> SRR1089877 3 0.6169 0.4735 0.000 0.348 0.552 0.040 0.060
#> SRR1123775 5 0.0854 0.6988 0.000 0.012 0.008 0.004 0.976
#> SRR658909 1 0.2204 0.7866 0.920 0.000 0.036 0.036 0.008
#> SRR1140510 2 0.2127 0.7642 0.000 0.892 0.108 0.000 0.000
#> SRR607562 5 0.1211 0.6816 0.000 0.000 0.016 0.024 0.960
#> SRR1122913 3 0.5775 0.6524 0.000 0.148 0.608 0.000 0.244
#> SRR598042 5 0.0912 0.6881 0.000 0.000 0.016 0.012 0.972
#> SRR1467340 3 0.3684 0.7482 0.000 0.280 0.720 0.000 0.000
#> SRR1072321 3 0.4173 0.7452 0.000 0.300 0.688 0.000 0.012
#> SRR1094580 2 0.6309 -0.2696 0.000 0.472 0.368 0.000 0.160
#> SRR1076608 3 0.4278 0.5157 0.000 0.452 0.548 0.000 0.000
#> SRR1395462 5 0.0671 0.6970 0.000 0.000 0.016 0.004 0.980
#> SRR1489220 1 0.5006 0.6117 0.708 0.000 0.136 0.000 0.156
#> SRR614371 5 0.6263 0.2368 0.220 0.000 0.124 0.036 0.620
#> SRR615455 4 0.2880 0.6683 0.108 0.004 0.020 0.868 0.000
#> SRR1070573 3 0.5327 0.7481 0.000 0.216 0.664 0.000 0.120
#> SRR598749 5 0.3098 0.5522 0.000 0.000 0.016 0.148 0.836
#> SRR1365556 2 0.0794 0.7548 0.000 0.972 0.028 0.000 0.000
#> SRR1350023 2 0.3508 0.5590 0.000 0.748 0.252 0.000 0.000
#> SRR1446582 5 0.0451 0.6947 0.000 0.000 0.008 0.004 0.988
#> SRR1439763 3 0.5111 0.7552 0.004 0.224 0.688 0.000 0.084
#> SRR1343986 3 0.3878 0.7506 0.000 0.236 0.748 0.000 0.016
#> SRR807463 3 0.5913 0.5541 0.000 0.236 0.608 0.004 0.152
#> SRR660390 1 0.0510 0.8153 0.984 0.000 0.016 0.000 0.000
#> SRR1367672 5 0.3117 0.6784 0.000 0.036 0.100 0.004 0.860
#> SRR613294 4 0.2124 0.7825 0.000 0.000 0.004 0.900 0.096
#> SRR824015 2 0.2464 0.6916 0.004 0.904 0.044 0.048 0.000
#> SRR1078924 3 0.5305 0.6905 0.000 0.132 0.672 0.000 0.196
#> SRR662221 4 0.6910 0.6882 0.108 0.028 0.052 0.616 0.196
#> SRR655017 1 0.0162 0.8164 0.996 0.000 0.004 0.000 0.000
#> SRR1338450 1 0.4553 0.6850 0.784 0.068 0.020 0.004 0.124
#> SRR663741 1 0.4714 0.2771 0.576 0.000 0.012 0.408 0.004
#> SRR1396057 2 0.0579 0.7437 0.000 0.984 0.008 0.000 0.008
#> SRR1083800 5 0.5631 -0.0239 0.000 0.076 0.424 0.000 0.500
#> SRR1445789 2 0.2891 0.7086 0.000 0.824 0.176 0.000 0.000
#> SRR1387355 1 0.3895 0.4612 0.680 0.320 0.000 0.000 0.000
#> SRR1388855 2 0.2516 0.7495 0.000 0.860 0.140 0.000 0.000
#> SRR1445449 2 0.5240 0.4314 0.204 0.708 0.048 0.040 0.000
#> SRR1380740 3 0.3928 0.7430 0.004 0.296 0.700 0.000 0.000
#> SRR659995 4 0.4526 0.7016 0.028 0.000 0.000 0.672 0.300
#> SRR1489524 2 0.2424 0.7557 0.000 0.868 0.132 0.000 0.000
#> SRR1444662 2 0.0404 0.7413 0.000 0.988 0.012 0.000 0.000
#> SRR1383652 5 0.3789 0.5136 0.000 0.224 0.016 0.000 0.760
#> SRR1361243 3 0.4517 0.6362 0.000 0.388 0.600 0.000 0.012
#> SRR1490337 5 0.6748 0.4647 0.160 0.124 0.040 0.036 0.640
#> SRR823967 5 0.4848 0.6383 0.000 0.052 0.152 0.040 0.756
#> SRR660127 1 0.0000 0.8164 1.000 0.000 0.000 0.000 0.000
#> SRR1366627 2 0.2230 0.7627 0.000 0.884 0.116 0.000 0.000
#> SRR1361219 2 0.2690 0.7344 0.000 0.844 0.156 0.000 0.000
#> SRR1393510 2 0.0867 0.7370 0.008 0.976 0.008 0.008 0.000
#> SRR662558 5 0.7835 0.2994 0.176 0.128 0.104 0.040 0.552
#> SRR1077334 3 0.5957 0.6013 0.000 0.148 0.572 0.000 0.280
#> SRR807438 1 0.4505 0.6207 0.752 0.004 0.068 0.000 0.176
#> SRR1459078 3 0.4400 0.7271 0.020 0.308 0.672 0.000 0.000
#> SRR1329704 2 0.3876 0.3721 0.000 0.684 0.316 0.000 0.000
#> SRR1468072 3 0.4304 0.4362 0.000 0.484 0.516 0.000 0.000
#> SRR1376196 3 0.4907 0.7492 0.000 0.292 0.656 0.000 0.052
#> SRR1442909 5 0.2960 0.6744 0.000 0.008 0.080 0.036 0.876
#> SRR1414269 5 0.3016 0.6853 0.000 0.020 0.132 0.000 0.848
#> SRR1381913 5 0.0955 0.6952 0.000 0.000 0.028 0.004 0.968
#> SRR1340157 3 0.3816 0.7390 0.000 0.304 0.696 0.000 0.000
#> SRR1407583 2 0.1845 0.7030 0.000 0.928 0.056 0.000 0.016
#> SRR615826 4 0.4354 0.6016 0.000 0.000 0.008 0.624 0.368
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 4 0.2454 0.75662 0.000 0.000 0.000 0.840 0.160 0.000
#> SRR1458769 6 0.1219 0.85304 0.000 0.000 0.048 0.000 0.004 0.948
#> SRR613162 1 0.3874 0.69943 0.760 0.000 0.172 0.068 0.000 0.000
#> SRR1352481 1 0.2013 0.77593 0.908 0.008 0.076 0.008 0.000 0.000
#> SRR1468876 2 0.6164 0.24532 0.292 0.504 0.184 0.000 0.016 0.004
#> SRR1399223 6 0.1411 0.83977 0.000 0.060 0.004 0.000 0.000 0.936
#> SRR660030 2 0.4481 0.59612 0.000 0.648 0.056 0.000 0.296 0.000
#> SRR1333609 2 0.2722 0.74993 0.008 0.872 0.008 0.004 0.008 0.100
#> SRR1471612 6 0.3838 0.13240 0.000 0.000 0.000 0.000 0.448 0.552
#> SRR1413998 6 0.0260 0.87059 0.000 0.008 0.000 0.000 0.000 0.992
#> SRR1122940 2 0.2261 0.74438 0.000 0.884 0.008 0.000 0.104 0.004
#> SRR1402563 2 0.5021 0.69250 0.008 0.700 0.020 0.000 0.100 0.172
#> SRR1398393 3 0.5264 0.25730 0.000 0.000 0.520 0.000 0.104 0.376
#> SRR657961 5 0.2094 0.70013 0.000 0.020 0.080 0.000 0.900 0.000
#> SRR1471135 5 0.3910 0.34889 0.000 0.004 0.008 0.000 0.660 0.328
#> SRR1430001 2 0.5141 0.51866 0.260 0.644 0.012 0.000 0.008 0.076
#> SRR662775 1 0.1387 0.77970 0.932 0.000 0.068 0.000 0.000 0.000
#> SRR1474182 6 0.0964 0.86791 0.000 0.004 0.012 0.000 0.016 0.968
#> SRR607190 1 0.2562 0.73372 0.828 0.000 0.172 0.000 0.000 0.000
#> SRR612467 5 0.1605 0.72191 0.000 0.016 0.012 0.032 0.940 0.000
#> SRR1465959 2 0.3928 0.73506 0.000 0.764 0.008 0.000 0.176 0.052
#> SRR1446132 6 0.0458 0.86884 0.000 0.016 0.000 0.000 0.000 0.984
#> SRR1416933 6 0.0603 0.87024 0.000 0.016 0.000 0.000 0.004 0.980
#> SRR1102538 2 0.5762 0.19563 0.000 0.428 0.172 0.000 0.400 0.000
#> SRR1098636 3 0.4661 0.45645 0.000 0.036 0.620 0.000 0.332 0.012
#> SRR1072998 2 0.4344 0.66428 0.000 0.716 0.096 0.000 0.188 0.000
#> SRR627443 1 0.3357 0.69157 0.764 0.000 0.224 0.008 0.004 0.000
#> SRR656131 1 0.0547 0.78696 0.980 0.000 0.020 0.000 0.000 0.000
#> SRR823991 3 0.6217 0.54550 0.068 0.060 0.636 0.000 0.176 0.060
#> SRR1089158 2 0.4091 0.68115 0.000 0.720 0.056 0.000 0.224 0.000
#> SRR1469036 2 0.4437 0.67156 0.132 0.748 0.012 0.004 0.000 0.104
#> SRR824039 3 0.5544 0.51649 0.000 0.136 0.596 0.000 0.252 0.016
#> SRR1339047 6 0.2377 0.79286 0.000 0.004 0.124 0.000 0.004 0.868
#> SRR1443049 2 0.3269 0.75147 0.000 0.832 0.052 0.000 0.008 0.108
#> SRR1122885 2 0.4503 0.65312 0.000 0.684 0.084 0.000 0.232 0.000
#> SRR602895 5 0.4047 0.58371 0.040 0.016 0.132 0.020 0.792 0.000
#> SRR1409837 6 0.0865 0.86016 0.000 0.000 0.000 0.000 0.036 0.964
#> SRR1388959 6 0.0363 0.87066 0.000 0.012 0.000 0.000 0.000 0.988
#> SRR659863 1 0.0547 0.78752 0.980 0.000 0.020 0.000 0.000 0.000
#> SRR1089877 3 0.5100 0.08106 0.000 0.416 0.524 0.000 0.028 0.032
#> SRR1123775 5 0.2009 0.71076 0.000 0.068 0.024 0.000 0.908 0.000
#> SRR658909 1 0.4376 0.40072 0.604 0.004 0.368 0.000 0.024 0.000
#> SRR1140510 6 0.0405 0.87069 0.000 0.004 0.008 0.000 0.000 0.988
#> SRR607562 5 0.1325 0.72337 0.004 0.016 0.012 0.012 0.956 0.000
#> SRR1122913 2 0.3144 0.73250 0.000 0.808 0.004 0.000 0.172 0.016
#> SRR598042 5 0.0748 0.72906 0.000 0.016 0.004 0.004 0.976 0.000
#> SRR1467340 2 0.2212 0.74765 0.000 0.880 0.008 0.000 0.000 0.112
#> SRR1072321 2 0.3603 0.74572 0.000 0.808 0.072 0.000 0.008 0.112
#> SRR1094580 2 0.5926 0.33498 0.000 0.460 0.036 0.000 0.092 0.412
#> SRR1076608 2 0.3742 0.54158 0.000 0.648 0.004 0.000 0.000 0.348
#> SRR1395462 5 0.0891 0.72696 0.000 0.008 0.024 0.000 0.968 0.000
#> SRR1489220 1 0.4513 0.51657 0.712 0.204 0.012 0.000 0.072 0.000
#> SRR614371 5 0.5693 0.21737 0.216 0.008 0.188 0.004 0.584 0.000
#> SRR615455 4 0.1321 0.58305 0.024 0.000 0.020 0.952 0.000 0.004
#> SRR1070573 2 0.2793 0.75274 0.000 0.856 0.004 0.000 0.112 0.028
#> SRR598749 5 0.2773 0.58020 0.000 0.008 0.004 0.152 0.836 0.000
#> SRR1365556 6 0.1196 0.86567 0.000 0.008 0.040 0.000 0.000 0.952
#> SRR1350023 6 0.0937 0.85723 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR1446582 5 0.0984 0.72901 0.008 0.012 0.012 0.000 0.968 0.000
#> SRR1439763 2 0.2675 0.74833 0.004 0.880 0.024 0.000 0.080 0.012
#> SRR1343986 2 0.2365 0.75157 0.000 0.892 0.008 0.004 0.012 0.084
#> SRR807463 2 0.5533 0.64350 0.000 0.656 0.180 0.000 0.064 0.100
#> SRR660390 1 0.1285 0.78283 0.944 0.000 0.052 0.004 0.000 0.000
#> SRR1367672 5 0.1890 0.71136 0.000 0.060 0.000 0.000 0.916 0.024
#> SRR613294 4 0.2597 0.76120 0.000 0.000 0.000 0.824 0.176 0.000
#> SRR824015 6 0.4911 0.00734 0.000 0.012 0.456 0.028 0.004 0.500
#> SRR1078924 2 0.2566 0.74702 0.000 0.868 0.008 0.000 0.112 0.012
#> SRR662221 3 0.7210 -0.03294 0.156 0.000 0.372 0.340 0.132 0.000
#> SRR655017 1 0.0458 0.78738 0.984 0.000 0.016 0.000 0.000 0.000
#> SRR1338450 1 0.5652 0.30030 0.540 0.048 0.368 0.000 0.016 0.028
#> SRR663741 1 0.4326 0.35202 0.572 0.000 0.024 0.404 0.000 0.000
#> SRR1396057 6 0.1807 0.83962 0.000 0.000 0.060 0.000 0.020 0.920
#> SRR1083800 2 0.3943 0.70268 0.000 0.756 0.056 0.000 0.184 0.004
#> SRR1445789 6 0.2300 0.73517 0.000 0.144 0.000 0.000 0.000 0.856
#> SRR1387355 1 0.4496 0.49532 0.696 0.036 0.024 0.000 0.000 0.244
#> SRR1388855 6 0.0363 0.86997 0.000 0.012 0.000 0.000 0.000 0.988
#> SRR1445449 3 0.6034 0.20819 0.180 0.004 0.444 0.000 0.004 0.368
#> SRR1380740 2 0.2234 0.74359 0.000 0.872 0.000 0.004 0.000 0.124
#> SRR659995 4 0.4537 0.56191 0.020 0.000 0.012 0.584 0.384 0.000
#> SRR1489524 6 0.0717 0.86870 0.000 0.008 0.016 0.000 0.000 0.976
#> SRR1444662 6 0.1843 0.83023 0.000 0.004 0.080 0.000 0.004 0.912
#> SRR1383652 5 0.3898 0.37997 0.000 0.020 0.000 0.000 0.684 0.296
#> SRR1361243 2 0.3591 0.70304 0.004 0.772 0.012 0.004 0.004 0.204
#> SRR1490337 5 0.5258 0.36458 0.200 0.000 0.128 0.000 0.652 0.020
#> SRR823967 3 0.5789 0.38936 0.000 0.216 0.496 0.000 0.288 0.000
#> SRR660127 1 0.0260 0.78720 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1366627 6 0.0363 0.86981 0.000 0.012 0.000 0.000 0.000 0.988
#> SRR1361219 6 0.0717 0.86991 0.000 0.016 0.000 0.000 0.008 0.976
#> SRR1393510 6 0.1674 0.84294 0.000 0.004 0.068 0.000 0.004 0.924
#> SRR662558 3 0.5452 0.45814 0.160 0.004 0.620 0.000 0.208 0.008
#> SRR1077334 2 0.4390 0.66701 0.000 0.720 0.148 0.000 0.132 0.000
#> SRR807438 2 0.6161 0.25000 0.376 0.476 0.088 0.000 0.060 0.000
#> SRR1459078 2 0.3708 0.67688 0.016 0.760 0.008 0.004 0.000 0.212
#> SRR1329704 6 0.0937 0.85763 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR1468072 6 0.4128 -0.23283 0.000 0.492 0.004 0.004 0.000 0.500
#> SRR1376196 2 0.3181 0.75569 0.000 0.840 0.028 0.000 0.020 0.112
#> SRR1442909 5 0.2425 0.69176 0.000 0.004 0.088 0.000 0.884 0.024
#> SRR1414269 5 0.5878 0.08939 0.012 0.264 0.188 0.000 0.536 0.000
#> SRR1381913 5 0.1219 0.71919 0.000 0.004 0.048 0.000 0.948 0.000
#> SRR1340157 2 0.2587 0.74512 0.000 0.864 0.008 0.004 0.004 0.120
#> SRR1407583 6 0.2540 0.79895 0.000 0.004 0.104 0.000 0.020 0.872
#> SRR615826 4 0.3915 0.55128 0.000 0.000 0.004 0.584 0.412 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "hclust"]
# you can also extract it by
# res = res_list["ATC:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17380 rows and 102 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.968 0.988 0.2964 0.694 0.694
#> 3 3 0.524 0.442 0.692 0.5990 0.854 0.793
#> 4 4 0.806 0.852 0.932 0.3448 0.639 0.442
#> 5 5 0.700 0.733 0.866 0.0586 0.979 0.941
#> 6 6 0.674 0.534 0.781 0.0579 0.861 0.622
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
#> SRR612587 1 0.0000 0.9440 1.000 0.000
#> SRR1458769 2 0.0000 0.9971 0.000 1.000
#> SRR613162 1 0.0000 0.9440 1.000 0.000
#> SRR1352481 1 0.0000 0.9440 1.000 0.000
#> SRR1468876 2 0.1843 0.9720 0.028 0.972
#> SRR1399223 2 0.0000 0.9971 0.000 1.000
#> SRR660030 2 0.0000 0.9971 0.000 1.000
#> SRR1333609 2 0.0000 0.9971 0.000 1.000
#> SRR1471612 2 0.0000 0.9971 0.000 1.000
#> SRR1413998 2 0.0000 0.9971 0.000 1.000
#> SRR1122940 2 0.0000 0.9971 0.000 1.000
#> SRR1402563 2 0.0000 0.9971 0.000 1.000
#> SRR1398393 2 0.0000 0.9971 0.000 1.000
#> SRR657961 2 0.0000 0.9971 0.000 1.000
#> SRR1471135 2 0.0000 0.9971 0.000 1.000
#> SRR1430001 2 0.0000 0.9971 0.000 1.000
#> SRR662775 1 0.0000 0.9440 1.000 0.000
#> SRR1474182 2 0.0000 0.9971 0.000 1.000
#> SRR607190 1 0.0000 0.9440 1.000 0.000
#> SRR612467 2 0.0000 0.9971 0.000 1.000
#> SRR1465959 2 0.0000 0.9971 0.000 1.000
#> SRR1446132 2 0.0000 0.9971 0.000 1.000
#> SRR1416933 2 0.0000 0.9971 0.000 1.000
#> SRR1102538 2 0.0000 0.9971 0.000 1.000
#> SRR1098636 2 0.0000 0.9971 0.000 1.000
#> SRR1072998 2 0.0000 0.9971 0.000 1.000
#> SRR627443 1 0.0000 0.9440 1.000 0.000
#> SRR656131 1 0.0000 0.9440 1.000 0.000
#> SRR823991 2 0.0000 0.9971 0.000 1.000
#> SRR1089158 2 0.0000 0.9971 0.000 1.000
#> SRR1469036 2 0.0000 0.9971 0.000 1.000
#> SRR824039 2 0.0000 0.9971 0.000 1.000
#> SRR1339047 2 0.0000 0.9971 0.000 1.000
#> SRR1443049 2 0.0000 0.9971 0.000 1.000
#> SRR1122885 2 0.0000 0.9971 0.000 1.000
#> SRR602895 2 0.1414 0.9794 0.020 0.980
#> SRR1409837 2 0.0000 0.9971 0.000 1.000
#> SRR1388959 2 0.0000 0.9971 0.000 1.000
#> SRR659863 1 0.0000 0.9440 1.000 0.000
#> SRR1089877 2 0.0000 0.9971 0.000 1.000
#> SRR1123775 2 0.0000 0.9971 0.000 1.000
#> SRR658909 1 0.0000 0.9440 1.000 0.000
#> SRR1140510 2 0.0000 0.9971 0.000 1.000
#> SRR607562 2 0.0000 0.9971 0.000 1.000
#> SRR1122913 2 0.0000 0.9971 0.000 1.000
#> SRR598042 2 0.0000 0.9971 0.000 1.000
#> SRR1467340 2 0.0000 0.9971 0.000 1.000
#> SRR1072321 2 0.0000 0.9971 0.000 1.000
#> SRR1094580 2 0.0000 0.9971 0.000 1.000
#> SRR1076608 2 0.0000 0.9971 0.000 1.000
#> SRR1395462 2 0.0000 0.9971 0.000 1.000
#> SRR1489220 2 0.1843 0.9720 0.028 0.972
#> SRR614371 1 0.0000 0.9440 1.000 0.000
#> SRR615455 1 0.0000 0.9440 1.000 0.000
#> SRR1070573 2 0.0000 0.9971 0.000 1.000
#> SRR598749 2 0.0000 0.9971 0.000 1.000
#> SRR1365556 2 0.0000 0.9971 0.000 1.000
#> SRR1350023 2 0.0000 0.9971 0.000 1.000
#> SRR1446582 2 0.0000 0.9971 0.000 1.000
#> SRR1439763 2 0.0000 0.9971 0.000 1.000
#> SRR1343986 2 0.0000 0.9971 0.000 1.000
#> SRR807463 2 0.0000 0.9971 0.000 1.000
#> SRR660390 1 0.0000 0.9440 1.000 0.000
#> SRR1367672 2 0.0000 0.9971 0.000 1.000
#> SRR613294 1 0.0000 0.9440 1.000 0.000
#> SRR824015 2 0.0000 0.9971 0.000 1.000
#> SRR1078924 2 0.0000 0.9971 0.000 1.000
#> SRR662221 1 0.0000 0.9440 1.000 0.000
#> SRR655017 1 0.0000 0.9440 1.000 0.000
#> SRR1338450 2 0.1843 0.9720 0.028 0.972
#> SRR663741 1 0.0000 0.9440 1.000 0.000
#> SRR1396057 2 0.0000 0.9971 0.000 1.000
#> SRR1083800 2 0.0000 0.9971 0.000 1.000
#> SRR1445789 2 0.0000 0.9971 0.000 1.000
#> SRR1387355 2 0.1843 0.9720 0.028 0.972
#> SRR1388855 2 0.0000 0.9971 0.000 1.000
#> SRR1445449 2 0.1843 0.9720 0.028 0.972
#> SRR1380740 2 0.0000 0.9971 0.000 1.000
#> SRR659995 1 1.0000 0.0587 0.504 0.496
#> SRR1489524 2 0.0000 0.9971 0.000 1.000
#> SRR1444662 2 0.0000 0.9971 0.000 1.000
#> SRR1383652 2 0.0000 0.9971 0.000 1.000
#> SRR1361243 2 0.0000 0.9971 0.000 1.000
#> SRR1490337 2 0.1843 0.9720 0.028 0.972
#> SRR823967 2 0.0000 0.9971 0.000 1.000
#> SRR660127 1 0.0000 0.9440 1.000 0.000
#> SRR1366627 2 0.0000 0.9971 0.000 1.000
#> SRR1361219 2 0.0000 0.9971 0.000 1.000
#> SRR1393510 2 0.0376 0.9937 0.004 0.996
#> SRR662558 1 1.0000 0.0587 0.504 0.496
#> SRR1077334 2 0.0000 0.9971 0.000 1.000
#> SRR807438 2 0.1843 0.9720 0.028 0.972
#> SRR1459078 2 0.0000 0.9971 0.000 1.000
#> SRR1329704 2 0.0000 0.9971 0.000 1.000
#> SRR1468072 2 0.0000 0.9971 0.000 1.000
#> SRR1376196 2 0.0000 0.9971 0.000 1.000
#> SRR1442909 2 0.0000 0.9971 0.000 1.000
#> SRR1414269 2 0.0000 0.9971 0.000 1.000
#> SRR1381913 2 0.0000 0.9971 0.000 1.000
#> SRR1340157 2 0.0000 0.9971 0.000 1.000
#> SRR1407583 2 0.0000 0.9971 0.000 1.000
#> SRR615826 2 0.0000 0.9971 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 3 0.6309 -0.538 0.496 0.000 0.504
#> SRR1458769 2 0.0000 0.633 0.000 1.000 0.000
#> SRR613162 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1352481 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1468876 3 0.6299 -0.379 0.000 0.476 0.524
#> SRR1399223 2 0.5497 0.566 0.000 0.708 0.292
#> SRR660030 2 0.6309 0.406 0.000 0.504 0.496
#> SRR1333609 2 0.6309 0.406 0.000 0.504 0.496
#> SRR1471612 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1413998 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1122940 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1402563 2 0.6309 0.406 0.000 0.504 0.496
#> SRR1398393 2 0.6267 0.466 0.000 0.548 0.452
#> SRR657961 2 0.5733 0.550 0.000 0.676 0.324
#> SRR1471135 2 0.6309 0.406 0.000 0.504 0.496
#> SRR1430001 2 0.6309 0.406 0.000 0.504 0.496
#> SRR662775 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1474182 2 0.0000 0.633 0.000 1.000 0.000
#> SRR607190 1 0.0000 0.992 1.000 0.000 0.000
#> SRR612467 2 0.1289 0.631 0.000 0.968 0.032
#> SRR1465959 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1446132 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1416933 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1102538 2 0.0237 0.632 0.000 0.996 0.004
#> SRR1098636 2 0.6309 0.406 0.000 0.504 0.496
#> SRR1072998 2 0.0000 0.633 0.000 1.000 0.000
#> SRR627443 1 0.0000 0.992 1.000 0.000 0.000
#> SRR656131 1 0.0000 0.992 1.000 0.000 0.000
#> SRR823991 2 0.6267 0.466 0.000 0.548 0.452
#> SRR1089158 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1469036 2 0.6309 0.406 0.000 0.504 0.496
#> SRR824039 2 0.6267 0.466 0.000 0.548 0.452
#> SRR1339047 2 0.3038 0.617 0.000 0.896 0.104
#> SRR1443049 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1122885 2 0.0000 0.633 0.000 1.000 0.000
#> SRR602895 3 0.6305 -0.400 0.000 0.484 0.516
#> SRR1409837 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1388959 2 0.0000 0.633 0.000 1.000 0.000
#> SRR659863 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1089877 2 0.6204 0.488 0.000 0.576 0.424
#> SRR1123775 2 0.6309 0.406 0.000 0.504 0.496
#> SRR658909 3 0.6309 -0.538 0.496 0.000 0.504
#> SRR1140510 2 0.5016 0.585 0.000 0.760 0.240
#> SRR607562 2 0.6267 0.466 0.000 0.548 0.452
#> SRR1122913 2 0.0000 0.633 0.000 1.000 0.000
#> SRR598042 2 0.6267 0.466 0.000 0.548 0.452
#> SRR1467340 2 0.6309 0.406 0.000 0.504 0.496
#> SRR1072321 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1094580 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1076608 2 0.1163 0.631 0.000 0.972 0.028
#> SRR1395462 2 0.5733 0.550 0.000 0.676 0.324
#> SRR1489220 3 0.6299 -0.379 0.000 0.476 0.524
#> SRR614371 3 0.6309 -0.538 0.496 0.000 0.504
#> SRR615455 1 0.3038 0.911 0.896 0.000 0.104
#> SRR1070573 2 0.0000 0.633 0.000 1.000 0.000
#> SRR598749 2 0.6267 0.466 0.000 0.548 0.452
#> SRR1365556 2 0.6168 0.496 0.000 0.588 0.412
#> SRR1350023 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1446582 2 0.6309 0.406 0.000 0.504 0.496
#> SRR1439763 2 0.6309 0.406 0.000 0.504 0.496
#> SRR1343986 2 0.6309 0.406 0.000 0.504 0.496
#> SRR807463 2 0.0000 0.633 0.000 1.000 0.000
#> SRR660390 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1367672 2 0.0000 0.633 0.000 1.000 0.000
#> SRR613294 3 0.6309 -0.538 0.496 0.000 0.504
#> SRR824015 2 0.6286 0.451 0.000 0.536 0.464
#> SRR1078924 2 0.0000 0.633 0.000 1.000 0.000
#> SRR662221 3 0.6309 -0.538 0.496 0.000 0.504
#> SRR655017 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1338450 3 0.6299 -0.379 0.000 0.476 0.524
#> SRR663741 3 0.6309 -0.538 0.496 0.000 0.504
#> SRR1396057 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1083800 2 0.6309 0.406 0.000 0.504 0.496
#> SRR1445789 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1387355 3 0.6299 -0.379 0.000 0.476 0.524
#> SRR1388855 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1445449 3 0.6299 -0.379 0.000 0.476 0.524
#> SRR1380740 2 0.6309 0.406 0.000 0.504 0.496
#> SRR659995 3 0.0000 0.169 0.000 0.000 1.000
#> SRR1489524 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1444662 2 0.5926 0.533 0.000 0.644 0.356
#> SRR1383652 2 0.6309 0.406 0.000 0.504 0.496
#> SRR1361243 2 0.6309 0.406 0.000 0.504 0.496
#> SRR1490337 3 0.6299 -0.379 0.000 0.476 0.524
#> SRR823967 2 0.6267 0.466 0.000 0.548 0.452
#> SRR660127 1 0.0000 0.992 1.000 0.000 0.000
#> SRR1366627 2 0.5560 0.563 0.000 0.700 0.300
#> SRR1361219 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1393510 2 0.6299 0.434 0.000 0.524 0.476
#> SRR662558 3 0.0000 0.169 0.000 0.000 1.000
#> SRR1077334 2 0.4452 0.599 0.000 0.808 0.192
#> SRR807438 3 0.6299 -0.379 0.000 0.476 0.524
#> SRR1459078 2 0.6309 0.406 0.000 0.504 0.496
#> SRR1329704 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1468072 2 0.6309 0.406 0.000 0.504 0.496
#> SRR1376196 2 0.1163 0.631 0.000 0.972 0.028
#> SRR1442909 2 0.6274 0.461 0.000 0.544 0.456
#> SRR1414269 2 0.6274 0.461 0.000 0.544 0.456
#> SRR1381913 2 0.6309 0.406 0.000 0.504 0.496
#> SRR1340157 2 0.0000 0.633 0.000 1.000 0.000
#> SRR1407583 2 0.4931 0.588 0.000 0.768 0.232
#> SRR615826 2 0.6267 0.466 0.000 0.548 0.452
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.0000 0.9285 0.0 0.000 0.000 1.000
#> SRR1458769 2 0.1022 0.9145 0.0 0.968 0.032 0.000
#> SRR613162 1 0.0000 1.0000 1.0 0.000 0.000 0.000
#> SRR1352481 1 0.0000 1.0000 1.0 0.000 0.000 0.000
#> SRR1468876 3 0.0336 0.8899 0.0 0.000 0.992 0.008
#> SRR1399223 3 0.4898 0.3289 0.0 0.416 0.584 0.000
#> SRR660030 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR1333609 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR1471612 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR1413998 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR1122940 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR1402563 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR1398393 3 0.1716 0.8954 0.0 0.064 0.936 0.000
#> SRR657961 3 0.3801 0.7441 0.0 0.220 0.780 0.000
#> SRR1471135 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR1430001 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR662775 1 0.0000 1.0000 1.0 0.000 0.000 0.000
#> SRR1474182 2 0.1022 0.9145 0.0 0.968 0.032 0.000
#> SRR607190 1 0.0000 1.0000 1.0 0.000 0.000 0.000
#> SRR612467 2 0.3837 0.6970 0.0 0.776 0.224 0.000
#> SRR1465959 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR1446132 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR1416933 2 0.1118 0.9125 0.0 0.964 0.036 0.000
#> SRR1102538 2 0.0188 0.9176 0.0 0.996 0.004 0.000
#> SRR1098636 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR1072998 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR627443 1 0.0000 1.0000 1.0 0.000 0.000 0.000
#> SRR656131 1 0.0000 1.0000 1.0 0.000 0.000 0.000
#> SRR823991 3 0.1716 0.8954 0.0 0.064 0.936 0.000
#> SRR1089158 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR1469036 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR824039 3 0.1716 0.8954 0.0 0.064 0.936 0.000
#> SRR1339047 2 0.4040 0.6782 0.0 0.752 0.248 0.000
#> SRR1443049 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR1122885 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR602895 3 0.0000 0.8923 0.0 0.000 1.000 0.000
#> SRR1409837 2 0.1022 0.9145 0.0 0.968 0.032 0.000
#> SRR1388959 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR659863 1 0.0000 1.0000 1.0 0.000 0.000 0.000
#> SRR1089877 3 0.2408 0.8665 0.0 0.104 0.896 0.000
#> SRR1123775 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR658909 4 0.0000 0.9285 0.0 0.000 0.000 1.000
#> SRR1140510 2 0.4713 0.4236 0.0 0.640 0.360 0.000
#> SRR607562 3 0.1716 0.8954 0.0 0.064 0.936 0.000
#> SRR1122913 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR598042 3 0.1716 0.8954 0.0 0.064 0.936 0.000
#> SRR1467340 3 0.4454 0.5640 0.0 0.308 0.692 0.000
#> SRR1072321 2 0.1022 0.9145 0.0 0.968 0.032 0.000
#> SRR1094580 2 0.1022 0.9145 0.0 0.968 0.032 0.000
#> SRR1076608 2 0.1867 0.8831 0.0 0.928 0.072 0.000
#> SRR1395462 3 0.3801 0.7441 0.0 0.220 0.780 0.000
#> SRR1489220 3 0.0336 0.8899 0.0 0.000 0.992 0.008
#> SRR614371 4 0.0000 0.9285 0.0 0.000 0.000 1.000
#> SRR615455 4 0.4855 0.2960 0.4 0.000 0.000 0.600
#> SRR1070573 2 0.1022 0.9145 0.0 0.968 0.032 0.000
#> SRR598749 3 0.1716 0.8954 0.0 0.064 0.936 0.000
#> SRR1365556 3 0.2469 0.8608 0.0 0.108 0.892 0.000
#> SRR1350023 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR1446582 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR1439763 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR1343986 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR807463 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR660390 1 0.0000 1.0000 1.0 0.000 0.000 0.000
#> SRR1367672 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR613294 4 0.0000 0.9285 0.0 0.000 0.000 1.000
#> SRR824015 3 0.1474 0.8990 0.0 0.052 0.948 0.000
#> SRR1078924 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR662221 4 0.0000 0.9285 0.0 0.000 0.000 1.000
#> SRR655017 1 0.0000 1.0000 1.0 0.000 0.000 0.000
#> SRR1338450 3 0.0336 0.8899 0.0 0.000 0.992 0.008
#> SRR663741 4 0.0000 0.9285 0.0 0.000 0.000 1.000
#> SRR1396057 2 0.1118 0.9125 0.0 0.964 0.036 0.000
#> SRR1083800 3 0.4454 0.5640 0.0 0.308 0.692 0.000
#> SRR1445789 2 0.2081 0.8707 0.0 0.916 0.084 0.000
#> SRR1387355 3 0.0336 0.8899 0.0 0.000 0.992 0.008
#> SRR1388855 2 0.1022 0.9145 0.0 0.968 0.032 0.000
#> SRR1445449 3 0.0336 0.8899 0.0 0.000 0.992 0.008
#> SRR1380740 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR659995 3 0.4996 0.0584 0.0 0.000 0.516 0.484
#> SRR1489524 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR1444662 3 0.3219 0.8044 0.0 0.164 0.836 0.000
#> SRR1383652 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR1361243 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR1490337 3 0.0336 0.8899 0.0 0.000 0.992 0.008
#> SRR823967 3 0.1716 0.8954 0.0 0.064 0.936 0.000
#> SRR660127 1 0.0000 1.0000 1.0 0.000 0.000 0.000
#> SRR1366627 3 0.4877 0.3529 0.0 0.408 0.592 0.000
#> SRR1361219 2 0.0188 0.9182 0.0 0.996 0.004 0.000
#> SRR1393510 3 0.1211 0.9015 0.0 0.040 0.960 0.000
#> SRR662558 3 0.4996 0.0584 0.0 0.000 0.516 0.484
#> SRR1077334 2 0.4713 0.4261 0.0 0.640 0.360 0.000
#> SRR807438 3 0.0336 0.8899 0.0 0.000 0.992 0.008
#> SRR1459078 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR1329704 2 0.1118 0.9125 0.0 0.964 0.036 0.000
#> SRR1468072 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR1376196 2 0.3024 0.8044 0.0 0.852 0.148 0.000
#> SRR1442909 3 0.1637 0.8969 0.0 0.060 0.940 0.000
#> SRR1414269 3 0.1637 0.8969 0.0 0.060 0.940 0.000
#> SRR1381913 3 0.0707 0.9046 0.0 0.020 0.980 0.000
#> SRR1340157 2 0.0000 0.9182 0.0 1.000 0.000 0.000
#> SRR1407583 2 0.4661 0.4550 0.0 0.652 0.348 0.000
#> SRR615826 3 0.1716 0.8954 0.0 0.064 0.936 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR612587 4 0.4302 0.482 0.00 0.000 0.000 0.520 0.480
#> SRR1458769 2 0.1270 0.905 0.00 0.948 0.052 0.000 0.000
#> SRR613162 1 0.0000 0.950 1.00 0.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 0.950 1.00 0.000 0.000 0.000 0.000
#> SRR1468876 3 0.4045 0.528 0.00 0.000 0.644 0.000 0.356
#> SRR1399223 3 0.5195 0.292 0.00 0.388 0.564 0.000 0.048
#> SRR660030 3 0.0162 0.832 0.00 0.000 0.996 0.000 0.004
#> SRR1333609 3 0.0162 0.832 0.00 0.000 0.996 0.000 0.004
#> SRR1471612 2 0.0609 0.906 0.00 0.980 0.020 0.000 0.000
#> SRR1413998 2 0.0963 0.873 0.00 0.964 0.000 0.000 0.036
#> SRR1122940 2 0.0609 0.906 0.00 0.980 0.020 0.000 0.000
#> SRR1402563 3 0.0162 0.832 0.00 0.000 0.996 0.000 0.004
#> SRR1398393 3 0.2149 0.823 0.00 0.036 0.916 0.000 0.048
#> SRR657961 3 0.3876 0.708 0.00 0.192 0.776 0.000 0.032
#> SRR1471135 3 0.0162 0.832 0.00 0.000 0.996 0.000 0.004
#> SRR1430001 3 0.1043 0.820 0.00 0.000 0.960 0.000 0.040
#> SRR662775 1 0.0000 0.950 1.00 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.1270 0.905 0.00 0.948 0.052 0.000 0.000
#> SRR607190 1 0.0000 0.950 1.00 0.000 0.000 0.000 0.000
#> SRR612467 2 0.3728 0.681 0.00 0.748 0.244 0.000 0.008
#> SRR1465959 2 0.0609 0.906 0.00 0.980 0.020 0.000 0.000
#> SRR1446132 2 0.0963 0.873 0.00 0.964 0.000 0.000 0.036
#> SRR1416933 2 0.1341 0.903 0.00 0.944 0.056 0.000 0.000
#> SRR1102538 2 0.0703 0.906 0.00 0.976 0.024 0.000 0.000
#> SRR1098636 3 0.1908 0.801 0.00 0.000 0.908 0.000 0.092
#> SRR1072998 2 0.0963 0.873 0.00 0.964 0.000 0.000 0.036
#> SRR627443 1 0.4302 0.445 0.52 0.000 0.000 0.480 0.000
#> SRR656131 1 0.0000 0.950 1.00 0.000 0.000 0.000 0.000
#> SRR823991 3 0.2074 0.824 0.00 0.036 0.920 0.000 0.044
#> SRR1089158 2 0.0609 0.906 0.00 0.980 0.020 0.000 0.000
#> SRR1469036 3 0.1043 0.820 0.00 0.000 0.960 0.000 0.040
#> SRR824039 3 0.2149 0.823 0.00 0.036 0.916 0.000 0.048
#> SRR1339047 2 0.4453 0.679 0.00 0.724 0.228 0.000 0.048
#> SRR1443049 2 0.0609 0.906 0.00 0.980 0.020 0.000 0.000
#> SRR1122885 2 0.0609 0.906 0.00 0.980 0.020 0.000 0.000
#> SRR602895 3 0.4030 0.534 0.00 0.000 0.648 0.000 0.352
#> SRR1409837 2 0.1270 0.905 0.00 0.948 0.052 0.000 0.000
#> SRR1388959 2 0.0963 0.873 0.00 0.964 0.000 0.000 0.036
#> SRR659863 1 0.0000 0.950 1.00 0.000 0.000 0.000 0.000
#> SRR1089877 3 0.2843 0.798 0.00 0.076 0.876 0.000 0.048
#> SRR1123775 3 0.0162 0.832 0.00 0.000 0.996 0.000 0.004
#> SRR658909 5 0.4256 -0.446 0.00 0.000 0.000 0.436 0.564
#> SRR1140510 2 0.5002 0.449 0.00 0.612 0.344 0.000 0.044
#> SRR607562 3 0.2149 0.823 0.00 0.036 0.916 0.000 0.048
#> SRR1122913 2 0.0609 0.906 0.00 0.980 0.020 0.000 0.000
#> SRR598042 3 0.2149 0.823 0.00 0.036 0.916 0.000 0.048
#> SRR1467340 3 0.3884 0.522 0.00 0.288 0.708 0.000 0.004
#> SRR1072321 2 0.1270 0.905 0.00 0.948 0.052 0.000 0.000
#> SRR1094580 2 0.1270 0.905 0.00 0.948 0.052 0.000 0.000
#> SRR1076608 2 0.2248 0.874 0.00 0.900 0.088 0.000 0.012
#> SRR1395462 3 0.3876 0.708 0.00 0.192 0.776 0.000 0.032
#> SRR1489220 3 0.4045 0.528 0.00 0.000 0.644 0.000 0.356
#> SRR614371 4 0.4302 0.482 0.00 0.000 0.000 0.520 0.480
#> SRR615455 4 0.5646 0.212 0.40 0.000 0.000 0.520 0.080
#> SRR1070573 2 0.1270 0.905 0.00 0.948 0.052 0.000 0.000
#> SRR598749 3 0.2149 0.823 0.00 0.036 0.916 0.000 0.048
#> SRR1365556 3 0.2889 0.796 0.00 0.084 0.872 0.000 0.044
#> SRR1350023 2 0.0963 0.873 0.00 0.964 0.000 0.000 0.036
#> SRR1446582 3 0.0162 0.832 0.00 0.000 0.996 0.000 0.004
#> SRR1439763 3 0.0162 0.832 0.00 0.000 0.996 0.000 0.004
#> SRR1343986 3 0.0162 0.832 0.00 0.000 0.996 0.000 0.004
#> SRR807463 2 0.0963 0.873 0.00 0.964 0.000 0.000 0.036
#> SRR660390 1 0.0000 0.950 1.00 0.000 0.000 0.000 0.000
#> SRR1367672 2 0.0609 0.906 0.00 0.980 0.020 0.000 0.000
#> SRR613294 4 0.4302 0.482 0.00 0.000 0.000 0.520 0.480
#> SRR824015 3 0.1493 0.829 0.00 0.028 0.948 0.000 0.024
#> SRR1078924 2 0.0609 0.906 0.00 0.980 0.020 0.000 0.000
#> SRR662221 5 0.4256 -0.446 0.00 0.000 0.000 0.436 0.564
#> SRR655017 1 0.0000 0.950 1.00 0.000 0.000 0.000 0.000
#> SRR1338450 3 0.4045 0.528 0.00 0.000 0.644 0.000 0.356
#> SRR663741 5 0.4256 -0.446 0.00 0.000 0.000 0.436 0.564
#> SRR1396057 2 0.1341 0.903 0.00 0.944 0.056 0.000 0.000
#> SRR1083800 3 0.3884 0.522 0.00 0.288 0.708 0.000 0.004
#> SRR1445789 2 0.2358 0.859 0.00 0.888 0.104 0.000 0.008
#> SRR1387355 3 0.4045 0.528 0.00 0.000 0.644 0.000 0.356
#> SRR1388855 2 0.1270 0.905 0.00 0.948 0.052 0.000 0.000
#> SRR1445449 3 0.4030 0.534 0.00 0.000 0.648 0.000 0.352
#> SRR1380740 3 0.0162 0.832 0.00 0.000 0.996 0.000 0.004
#> SRR659995 5 0.2773 0.300 0.00 0.000 0.164 0.000 0.836
#> SRR1489524 2 0.0963 0.873 0.00 0.964 0.000 0.000 0.036
#> SRR1444662 3 0.3595 0.751 0.00 0.140 0.816 0.000 0.044
#> SRR1383652 3 0.0162 0.832 0.00 0.000 0.996 0.000 0.004
#> SRR1361243 3 0.0162 0.832 0.00 0.000 0.996 0.000 0.004
#> SRR1490337 3 0.4045 0.528 0.00 0.000 0.644 0.000 0.356
#> SRR823967 3 0.2074 0.824 0.00 0.036 0.920 0.000 0.044
#> SRR660127 1 0.0000 0.950 1.00 0.000 0.000 0.000 0.000
#> SRR1366627 3 0.5176 0.316 0.00 0.380 0.572 0.000 0.048
#> SRR1361219 2 0.0703 0.907 0.00 0.976 0.024 0.000 0.000
#> SRR1393510 3 0.2036 0.828 0.00 0.024 0.920 0.000 0.056
#> SRR662558 5 0.2773 0.300 0.00 0.000 0.164 0.000 0.836
#> SRR1077334 2 0.4564 0.414 0.00 0.612 0.372 0.000 0.016
#> SRR807438 3 0.4045 0.528 0.00 0.000 0.644 0.000 0.356
#> SRR1459078 3 0.0162 0.832 0.00 0.000 0.996 0.000 0.004
#> SRR1329704 2 0.1628 0.899 0.00 0.936 0.056 0.000 0.008
#> SRR1468072 3 0.0162 0.832 0.00 0.000 0.996 0.000 0.004
#> SRR1376196 2 0.3163 0.794 0.00 0.824 0.164 0.000 0.012
#> SRR1442909 3 0.1997 0.825 0.00 0.036 0.924 0.000 0.040
#> SRR1414269 3 0.1997 0.825 0.00 0.036 0.924 0.000 0.040
#> SRR1381913 3 0.0963 0.830 0.00 0.000 0.964 0.000 0.036
#> SRR1340157 2 0.0609 0.906 0.00 0.980 0.020 0.000 0.000
#> SRR1407583 2 0.4957 0.478 0.00 0.624 0.332 0.000 0.044
#> SRR615826 3 0.2149 0.823 0.00 0.036 0.916 0.000 0.048
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 4 0.2416 0.81094 0.000 0.000 0.000 0.844 0.000 0.156
#> SRR1458769 2 0.0790 0.86947 0.000 0.968 0.032 0.000 0.000 0.000
#> SRR613162 1 0.0000 1.00000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 1.00000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1468876 3 0.1866 0.40779 0.000 0.000 0.908 0.008 0.084 0.000
#> SRR1399223 2 0.6105 -0.29045 0.000 0.408 0.200 0.000 0.384 0.008
#> SRR660030 3 0.4336 -0.00451 0.000 0.020 0.504 0.000 0.476 0.000
#> SRR1333609 3 0.4310 0.10316 0.000 0.020 0.540 0.000 0.440 0.000
#> SRR1471612 2 0.0000 0.86984 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1413998 2 0.1890 0.82362 0.000 0.916 0.000 0.000 0.060 0.024
#> SRR1122940 2 0.0000 0.86984 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1402563 3 0.4310 0.10316 0.000 0.020 0.540 0.000 0.440 0.000
#> SRR1398393 5 0.4829 0.41804 0.000 0.056 0.356 0.000 0.584 0.004
#> SRR657961 5 0.3568 0.36972 0.000 0.212 0.016 0.000 0.764 0.008
#> SRR1471135 5 0.4305 0.12427 0.000 0.020 0.436 0.000 0.544 0.000
#> SRR1430001 3 0.4209 0.17135 0.000 0.020 0.596 0.000 0.384 0.000
#> SRR662775 1 0.0000 1.00000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.0790 0.86947 0.000 0.968 0.032 0.000 0.000 0.000
#> SRR607190 1 0.0000 1.00000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR612467 2 0.3023 0.67478 0.000 0.768 0.000 0.000 0.232 0.000
#> SRR1465959 2 0.0000 0.86984 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1446132 2 0.1890 0.82362 0.000 0.916 0.000 0.000 0.060 0.024
#> SRR1416933 2 0.0865 0.86787 0.000 0.964 0.036 0.000 0.000 0.000
#> SRR1102538 2 0.0146 0.87008 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1098636 3 0.4254 0.06529 0.000 0.020 0.576 0.000 0.404 0.000
#> SRR1072998 2 0.1890 0.82362 0.000 0.916 0.000 0.000 0.060 0.024
#> SRR627443 6 0.2730 0.00000 0.192 0.000 0.000 0.000 0.000 0.808
#> SRR656131 1 0.0000 1.00000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR823991 5 0.4861 0.40866 0.000 0.056 0.368 0.000 0.572 0.004
#> SRR1089158 2 0.0000 0.86984 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1469036 3 0.4209 0.17135 0.000 0.020 0.596 0.000 0.384 0.000
#> SRR824039 5 0.4829 0.41804 0.000 0.056 0.356 0.000 0.584 0.004
#> SRR1339047 2 0.4332 0.66172 0.000 0.744 0.120 0.000 0.128 0.008
#> SRR1443049 2 0.0000 0.86984 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1122885 2 0.0000 0.86984 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR602895 3 0.3684 0.11763 0.000 0.000 0.628 0.000 0.372 0.000
#> SRR1409837 2 0.0790 0.86947 0.000 0.968 0.032 0.000 0.000 0.000
#> SRR1388959 2 0.1890 0.82362 0.000 0.916 0.000 0.000 0.060 0.024
#> SRR659863 1 0.0000 1.00000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1089877 5 0.5266 0.44006 0.000 0.096 0.316 0.000 0.580 0.008
#> SRR1123775 5 0.4305 0.12427 0.000 0.020 0.436 0.000 0.544 0.000
#> SRR658909 4 0.0291 0.82483 0.000 0.000 0.004 0.992 0.000 0.004
#> SRR1140510 2 0.4633 0.42914 0.000 0.632 0.044 0.000 0.316 0.008
#> SRR607562 5 0.2197 0.43084 0.000 0.056 0.044 0.000 0.900 0.000
#> SRR1122913 2 0.0000 0.86984 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR598042 5 0.2058 0.43086 0.000 0.056 0.036 0.000 0.908 0.000
#> SRR1467340 5 0.6024 0.25291 0.000 0.308 0.268 0.000 0.424 0.000
#> SRR1072321 2 0.0790 0.86947 0.000 0.968 0.032 0.000 0.000 0.000
#> SRR1094580 2 0.0790 0.86947 0.000 0.968 0.032 0.000 0.000 0.000
#> SRR1076608 2 0.1938 0.84488 0.000 0.920 0.036 0.000 0.040 0.004
#> SRR1395462 5 0.3568 0.36972 0.000 0.212 0.016 0.000 0.764 0.008
#> SRR1489220 3 0.1757 0.40672 0.000 0.000 0.916 0.008 0.076 0.000
#> SRR614371 4 0.1556 0.82569 0.000 0.000 0.000 0.920 0.000 0.080
#> SRR615455 4 0.6814 0.14714 0.328 0.000 0.068 0.444 0.004 0.156
#> SRR1070573 2 0.0790 0.86947 0.000 0.968 0.032 0.000 0.000 0.000
#> SRR598749 5 0.2058 0.43086 0.000 0.056 0.036 0.000 0.908 0.000
#> SRR1365556 5 0.5395 0.37166 0.000 0.104 0.376 0.000 0.516 0.004
#> SRR1350023 2 0.1890 0.82362 0.000 0.916 0.000 0.000 0.060 0.024
#> SRR1446582 5 0.4305 0.12427 0.000 0.020 0.436 0.000 0.544 0.000
#> SRR1439763 3 0.4338 -0.02219 0.000 0.020 0.496 0.000 0.484 0.000
#> SRR1343986 3 0.4310 0.10316 0.000 0.020 0.540 0.000 0.440 0.000
#> SRR807463 2 0.1890 0.82362 0.000 0.916 0.000 0.000 0.060 0.024
#> SRR660390 1 0.0000 1.00000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1367672 2 0.0000 0.86984 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR613294 4 0.2416 0.81094 0.000 0.000 0.000 0.844 0.000 0.156
#> SRR824015 5 0.4852 0.27460 0.000 0.048 0.420 0.000 0.528 0.004
#> SRR1078924 2 0.0000 0.86984 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR662221 4 0.0291 0.82483 0.000 0.000 0.004 0.992 0.000 0.004
#> SRR655017 1 0.0000 1.00000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1338450 3 0.1812 0.40764 0.000 0.000 0.912 0.008 0.080 0.000
#> SRR663741 4 0.0146 0.82562 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1396057 2 0.0865 0.86787 0.000 0.964 0.036 0.000 0.000 0.000
#> SRR1083800 5 0.6024 0.25291 0.000 0.308 0.268 0.000 0.424 0.000
#> SRR1445789 2 0.1913 0.83018 0.000 0.908 0.080 0.000 0.012 0.000
#> SRR1387355 3 0.1757 0.40672 0.000 0.000 0.916 0.008 0.076 0.000
#> SRR1388855 2 0.0790 0.86947 0.000 0.968 0.032 0.000 0.000 0.000
#> SRR1445449 3 0.1866 0.40719 0.000 0.000 0.908 0.008 0.084 0.000
#> SRR1380740 3 0.4310 0.10316 0.000 0.020 0.540 0.000 0.440 0.000
#> SRR659995 3 0.3864 -0.38742 0.000 0.000 0.520 0.480 0.000 0.000
#> SRR1489524 2 0.1890 0.82362 0.000 0.916 0.000 0.000 0.060 0.024
#> SRR1444662 5 0.5806 0.34179 0.000 0.160 0.376 0.000 0.460 0.004
#> SRR1383652 5 0.4305 0.12427 0.000 0.020 0.436 0.000 0.544 0.000
#> SRR1361243 3 0.4310 0.10316 0.000 0.020 0.540 0.000 0.440 0.000
#> SRR1490337 3 0.1866 0.40779 0.000 0.000 0.908 0.008 0.084 0.000
#> SRR823967 5 0.4861 0.40866 0.000 0.056 0.368 0.000 0.572 0.004
#> SRR660127 1 0.0000 1.00000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1366627 2 0.6106 -0.31520 0.000 0.400 0.200 0.000 0.392 0.008
#> SRR1361219 2 0.0146 0.87033 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1393510 3 0.4603 0.01384 0.000 0.040 0.544 0.000 0.416 0.000
#> SRR662558 3 0.3864 -0.38742 0.000 0.000 0.520 0.480 0.000 0.000
#> SRR1077334 2 0.4755 0.41910 0.000 0.632 0.056 0.000 0.304 0.008
#> SRR807438 3 0.1757 0.40672 0.000 0.000 0.916 0.008 0.076 0.000
#> SRR1459078 3 0.4310 0.10316 0.000 0.020 0.540 0.000 0.440 0.000
#> SRR1329704 2 0.1124 0.86517 0.000 0.956 0.036 0.000 0.008 0.000
#> SRR1468072 3 0.4310 0.10316 0.000 0.020 0.540 0.000 0.440 0.000
#> SRR1376196 2 0.3089 0.78233 0.000 0.844 0.060 0.000 0.092 0.004
#> SRR1442909 5 0.4769 0.42880 0.000 0.056 0.336 0.000 0.604 0.004
#> SRR1414269 5 0.4769 0.42880 0.000 0.056 0.336 0.000 0.604 0.004
#> SRR1381913 5 0.3189 0.34180 0.000 0.020 0.184 0.000 0.796 0.000
#> SRR1340157 2 0.0000 0.86984 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1407583 2 0.4540 0.45865 0.000 0.644 0.040 0.000 0.308 0.008
#> SRR615826 5 0.2058 0.43086 0.000 0.056 0.036 0.000 0.908 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "kmeans"]
# you can also extract it by
# res = res_list["ATC:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17380 rows and 102 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.997 0.999 0.2831 0.719 0.719
#> 3 3 0.889 0.866 0.949 1.1609 0.616 0.485
#> 4 4 0.898 0.887 0.939 0.1142 0.843 0.636
#> 5 5 0.688 0.646 0.828 0.0826 0.886 0.693
#> 6 6 0.672 0.578 0.741 0.0669 0.887 0.652
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
#> SRR612587 1 0.000 1.000 1.00 0.00
#> SRR1458769 2 0.000 0.998 0.00 1.00
#> SRR613162 1 0.000 1.000 1.00 0.00
#> SRR1352481 1 0.000 1.000 1.00 0.00
#> SRR1468876 2 0.000 0.998 0.00 1.00
#> SRR1399223 2 0.000 0.998 0.00 1.00
#> SRR660030 2 0.000 0.998 0.00 1.00
#> SRR1333609 2 0.000 0.998 0.00 1.00
#> SRR1471612 2 0.000 0.998 0.00 1.00
#> SRR1413998 2 0.000 0.998 0.00 1.00
#> SRR1122940 2 0.000 0.998 0.00 1.00
#> SRR1402563 2 0.000 0.998 0.00 1.00
#> SRR1398393 2 0.000 0.998 0.00 1.00
#> SRR657961 2 0.000 0.998 0.00 1.00
#> SRR1471135 2 0.000 0.998 0.00 1.00
#> SRR1430001 2 0.000 0.998 0.00 1.00
#> SRR662775 1 0.000 1.000 1.00 0.00
#> SRR1474182 2 0.000 0.998 0.00 1.00
#> SRR607190 1 0.000 1.000 1.00 0.00
#> SRR612467 2 0.000 0.998 0.00 1.00
#> SRR1465959 2 0.000 0.998 0.00 1.00
#> SRR1446132 2 0.000 0.998 0.00 1.00
#> SRR1416933 2 0.000 0.998 0.00 1.00
#> SRR1102538 2 0.000 0.998 0.00 1.00
#> SRR1098636 2 0.000 0.998 0.00 1.00
#> SRR1072998 2 0.000 0.998 0.00 1.00
#> SRR627443 1 0.000 1.000 1.00 0.00
#> SRR656131 1 0.000 1.000 1.00 0.00
#> SRR823991 2 0.000 0.998 0.00 1.00
#> SRR1089158 2 0.000 0.998 0.00 1.00
#> SRR1469036 2 0.000 0.998 0.00 1.00
#> SRR824039 2 0.000 0.998 0.00 1.00
#> SRR1339047 2 0.000 0.998 0.00 1.00
#> SRR1443049 2 0.000 0.998 0.00 1.00
#> SRR1122885 2 0.000 0.998 0.00 1.00
#> SRR602895 2 0.000 0.998 0.00 1.00
#> SRR1409837 2 0.000 0.998 0.00 1.00
#> SRR1388959 2 0.000 0.998 0.00 1.00
#> SRR659863 1 0.000 1.000 1.00 0.00
#> SRR1089877 2 0.000 0.998 0.00 1.00
#> SRR1123775 2 0.000 0.998 0.00 1.00
#> SRR658909 1 0.000 1.000 1.00 0.00
#> SRR1140510 2 0.000 0.998 0.00 1.00
#> SRR607562 2 0.000 0.998 0.00 1.00
#> SRR1122913 2 0.000 0.998 0.00 1.00
#> SRR598042 2 0.000 0.998 0.00 1.00
#> SRR1467340 2 0.000 0.998 0.00 1.00
#> SRR1072321 2 0.000 0.998 0.00 1.00
#> SRR1094580 2 0.000 0.998 0.00 1.00
#> SRR1076608 2 0.000 0.998 0.00 1.00
#> SRR1395462 2 0.000 0.998 0.00 1.00
#> SRR1489220 2 0.000 0.998 0.00 1.00
#> SRR614371 1 0.000 1.000 1.00 0.00
#> SRR615455 1 0.000 1.000 1.00 0.00
#> SRR1070573 2 0.000 0.998 0.00 1.00
#> SRR598749 2 0.000 0.998 0.00 1.00
#> SRR1365556 2 0.000 0.998 0.00 1.00
#> SRR1350023 2 0.000 0.998 0.00 1.00
#> SRR1446582 2 0.000 0.998 0.00 1.00
#> SRR1439763 2 0.000 0.998 0.00 1.00
#> SRR1343986 2 0.000 0.998 0.00 1.00
#> SRR807463 2 0.000 0.998 0.00 1.00
#> SRR660390 1 0.000 1.000 1.00 0.00
#> SRR1367672 2 0.000 0.998 0.00 1.00
#> SRR613294 1 0.000 1.000 1.00 0.00
#> SRR824015 2 0.000 0.998 0.00 1.00
#> SRR1078924 2 0.000 0.998 0.00 1.00
#> SRR662221 1 0.000 1.000 1.00 0.00
#> SRR655017 1 0.000 1.000 1.00 0.00
#> SRR1338450 2 0.000 0.998 0.00 1.00
#> SRR663741 1 0.000 1.000 1.00 0.00
#> SRR1396057 2 0.000 0.998 0.00 1.00
#> SRR1083800 2 0.000 0.998 0.00 1.00
#> SRR1445789 2 0.000 0.998 0.00 1.00
#> SRR1387355 2 0.000 0.998 0.00 1.00
#> SRR1388855 2 0.000 0.998 0.00 1.00
#> SRR1445449 2 0.000 0.998 0.00 1.00
#> SRR1380740 2 0.000 0.998 0.00 1.00
#> SRR659995 2 0.584 0.837 0.14 0.86
#> SRR1489524 2 0.000 0.998 0.00 1.00
#> SRR1444662 2 0.000 0.998 0.00 1.00
#> SRR1383652 2 0.000 0.998 0.00 1.00
#> SRR1361243 2 0.000 0.998 0.00 1.00
#> SRR1490337 2 0.000 0.998 0.00 1.00
#> SRR823967 2 0.000 0.998 0.00 1.00
#> SRR660127 1 0.000 1.000 1.00 0.00
#> SRR1366627 2 0.000 0.998 0.00 1.00
#> SRR1361219 2 0.000 0.998 0.00 1.00
#> SRR1393510 2 0.000 0.998 0.00 1.00
#> SRR662558 2 0.000 0.998 0.00 1.00
#> SRR1077334 2 0.000 0.998 0.00 1.00
#> SRR807438 2 0.000 0.998 0.00 1.00
#> SRR1459078 2 0.000 0.998 0.00 1.00
#> SRR1329704 2 0.000 0.998 0.00 1.00
#> SRR1468072 2 0.000 0.998 0.00 1.00
#> SRR1376196 2 0.000 0.998 0.00 1.00
#> SRR1442909 2 0.000 0.998 0.00 1.00
#> SRR1414269 2 0.000 0.998 0.00 1.00
#> SRR1381913 2 0.000 0.998 0.00 1.00
#> SRR1340157 2 0.000 0.998 0.00 1.00
#> SRR1407583 2 0.000 0.998 0.00 1.00
#> SRR615826 2 0.000 0.998 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 1 0.6235 0.3348 0.564 0.000 0.436
#> SRR1458769 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR613162 1 0.0000 0.9105 1.000 0.000 0.000
#> SRR1352481 1 0.0000 0.9105 1.000 0.000 0.000
#> SRR1468876 3 0.0000 0.8855 0.000 0.000 1.000
#> SRR1399223 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR660030 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR1333609 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR1471612 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1413998 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1122940 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1402563 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR1398393 3 0.6235 0.3061 0.000 0.436 0.564
#> SRR657961 3 0.6244 0.2953 0.000 0.440 0.560
#> SRR1471135 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR1430001 3 0.0000 0.8855 0.000 0.000 1.000
#> SRR662775 1 0.0000 0.9105 1.000 0.000 0.000
#> SRR1474182 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR607190 1 0.0000 0.9105 1.000 0.000 0.000
#> SRR612467 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1465959 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1446132 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1416933 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1102538 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1098636 3 0.0000 0.8855 0.000 0.000 1.000
#> SRR1072998 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR627443 1 0.0000 0.9105 1.000 0.000 0.000
#> SRR656131 1 0.0000 0.9105 1.000 0.000 0.000
#> SRR823991 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR1089158 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1469036 3 0.0000 0.8855 0.000 0.000 1.000
#> SRR824039 3 0.6235 0.3061 0.000 0.436 0.564
#> SRR1339047 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1443049 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1122885 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR602895 3 0.0000 0.8855 0.000 0.000 1.000
#> SRR1409837 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1388959 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR659863 1 0.0000 0.9105 1.000 0.000 0.000
#> SRR1089877 3 0.6308 0.1346 0.000 0.492 0.508
#> SRR1123775 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR658909 1 0.4931 0.6952 0.768 0.000 0.232
#> SRR1140510 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR607562 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR1122913 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR598042 3 0.6235 0.3061 0.000 0.436 0.564
#> SRR1467340 3 0.1964 0.8495 0.000 0.056 0.944
#> SRR1072321 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1094580 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1076608 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1395462 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1489220 3 0.0000 0.8855 0.000 0.000 1.000
#> SRR614371 1 0.6235 0.3348 0.564 0.000 0.436
#> SRR615455 1 0.0000 0.9105 1.000 0.000 0.000
#> SRR1070573 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR598749 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR1365556 3 0.1411 0.8680 0.000 0.036 0.964
#> SRR1350023 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1446582 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR1439763 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR1343986 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR807463 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR660390 1 0.0000 0.9105 1.000 0.000 0.000
#> SRR1367672 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR613294 3 0.5948 0.2903 0.360 0.000 0.640
#> SRR824015 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR1078924 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR662221 3 0.4555 0.6412 0.200 0.000 0.800
#> SRR655017 1 0.0000 0.9105 1.000 0.000 0.000
#> SRR1338450 3 0.0000 0.8855 0.000 0.000 1.000
#> SRR663741 3 0.6204 0.0816 0.424 0.000 0.576
#> SRR1396057 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1083800 3 0.1964 0.8495 0.000 0.056 0.944
#> SRR1445789 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1387355 3 0.0000 0.8855 0.000 0.000 1.000
#> SRR1388855 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1445449 3 0.0000 0.8855 0.000 0.000 1.000
#> SRR1380740 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR659995 3 0.0000 0.8855 0.000 0.000 1.000
#> SRR1489524 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1444662 3 0.1411 0.8680 0.000 0.036 0.964
#> SRR1383652 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR1361243 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR1490337 3 0.0000 0.8855 0.000 0.000 1.000
#> SRR823967 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR660127 1 0.0000 0.9105 1.000 0.000 0.000
#> SRR1366627 2 0.2261 0.9105 0.000 0.932 0.068
#> SRR1361219 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1393510 3 0.0000 0.8855 0.000 0.000 1.000
#> SRR662558 3 0.0000 0.8855 0.000 0.000 1.000
#> SRR1077334 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR807438 3 0.0000 0.8855 0.000 0.000 1.000
#> SRR1459078 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR1329704 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1468072 3 0.1163 0.8744 0.000 0.028 0.972
#> SRR1376196 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1442909 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR1414269 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR1381913 3 0.0424 0.8884 0.000 0.008 0.992
#> SRR1340157 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR1407583 2 0.0000 0.9978 0.000 1.000 0.000
#> SRR615826 3 0.6235 0.3061 0.000 0.436 0.564
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.3301 0.839 0.076 0.000 0.048 0.876
#> SRR1458769 2 0.1211 0.958 0.000 0.960 0.000 0.040
#> SRR613162 1 0.0000 0.993 1.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 0.993 1.000 0.000 0.000 0.000
#> SRR1468876 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR1399223 3 0.5000 0.109 0.000 0.496 0.504 0.000
#> SRR660030 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR1333609 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR1471612 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR1413998 2 0.1637 0.951 0.000 0.940 0.000 0.060
#> SRR1122940 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR1402563 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR1398393 3 0.2149 0.847 0.000 0.088 0.912 0.000
#> SRR657961 3 0.2908 0.851 0.000 0.040 0.896 0.064
#> SRR1471135 3 0.0188 0.911 0.000 0.000 0.996 0.004
#> SRR1430001 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR662775 1 0.0000 0.993 1.000 0.000 0.000 0.000
#> SRR1474182 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR607190 1 0.0000 0.993 1.000 0.000 0.000 0.000
#> SRR612467 2 0.1557 0.926 0.000 0.944 0.000 0.056
#> SRR1465959 2 0.1211 0.958 0.000 0.960 0.000 0.040
#> SRR1446132 2 0.1637 0.951 0.000 0.940 0.000 0.060
#> SRR1416933 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR1102538 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR1098636 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR1072998 2 0.1637 0.951 0.000 0.940 0.000 0.060
#> SRR627443 1 0.0000 0.993 1.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 0.993 1.000 0.000 0.000 0.000
#> SRR823991 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR1089158 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR1469036 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR824039 3 0.1389 0.879 0.000 0.048 0.952 0.000
#> SRR1339047 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR1443049 2 0.1637 0.951 0.000 0.940 0.000 0.060
#> SRR1122885 2 0.1389 0.956 0.000 0.952 0.000 0.048
#> SRR602895 4 0.4331 0.759 0.000 0.000 0.288 0.712
#> SRR1409837 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR1388959 2 0.1637 0.951 0.000 0.940 0.000 0.060
#> SRR659863 1 0.0000 0.993 1.000 0.000 0.000 0.000
#> SRR1089877 3 0.2216 0.843 0.000 0.092 0.908 0.000
#> SRR1123775 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR658909 4 0.3243 0.822 0.088 0.000 0.036 0.876
#> SRR1140510 3 0.4985 0.203 0.000 0.468 0.532 0.000
#> SRR607562 3 0.1716 0.880 0.000 0.000 0.936 0.064
#> SRR1122913 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR598042 3 0.1716 0.880 0.000 0.000 0.936 0.064
#> SRR1467340 3 0.0336 0.908 0.000 0.008 0.992 0.000
#> SRR1072321 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR1094580 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR1076608 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR1395462 3 0.5785 0.548 0.000 0.272 0.664 0.064
#> SRR1489220 4 0.3873 0.831 0.000 0.000 0.228 0.772
#> SRR614371 4 0.3301 0.839 0.076 0.000 0.048 0.876
#> SRR615455 1 0.1867 0.928 0.928 0.000 0.000 0.072
#> SRR1070573 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR598749 3 0.1716 0.880 0.000 0.000 0.936 0.064
#> SRR1365556 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR1350023 2 0.1637 0.951 0.000 0.940 0.000 0.060
#> SRR1446582 3 0.0188 0.911 0.000 0.000 0.996 0.004
#> SRR1439763 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR1343986 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR807463 2 0.1637 0.951 0.000 0.940 0.000 0.060
#> SRR660390 1 0.0000 0.993 1.000 0.000 0.000 0.000
#> SRR1367672 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR613294 4 0.3320 0.847 0.068 0.000 0.056 0.876
#> SRR824015 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR1078924 2 0.1211 0.958 0.000 0.960 0.000 0.040
#> SRR662221 4 0.3312 0.854 0.052 0.000 0.072 0.876
#> SRR655017 1 0.0000 0.993 1.000 0.000 0.000 0.000
#> SRR1338450 3 0.3764 0.603 0.000 0.000 0.784 0.216
#> SRR663741 4 0.3320 0.847 0.068 0.000 0.056 0.876
#> SRR1396057 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR1083800 3 0.0524 0.908 0.000 0.004 0.988 0.008
#> SRR1445789 2 0.0592 0.963 0.000 0.984 0.000 0.016
#> SRR1387355 4 0.3873 0.831 0.000 0.000 0.228 0.772
#> SRR1388855 2 0.0592 0.963 0.000 0.984 0.000 0.016
#> SRR1445449 4 0.4164 0.794 0.000 0.000 0.264 0.736
#> SRR1380740 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR659995 4 0.2704 0.863 0.000 0.000 0.124 0.876
#> SRR1489524 2 0.1637 0.951 0.000 0.940 0.000 0.060
#> SRR1444662 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR1383652 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR1361243 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR1490337 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR823967 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR660127 1 0.0000 0.993 1.000 0.000 0.000 0.000
#> SRR1366627 3 0.2760 0.804 0.000 0.128 0.872 0.000
#> SRR1361219 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR1393510 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR662558 4 0.2704 0.863 0.000 0.000 0.124 0.876
#> SRR1077334 2 0.4164 0.599 0.000 0.736 0.264 0.000
#> SRR807438 4 0.3873 0.831 0.000 0.000 0.228 0.772
#> SRR1459078 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR1329704 2 0.0000 0.964 0.000 1.000 0.000 0.000
#> SRR1468072 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR1376196 2 0.2345 0.854 0.000 0.900 0.100 0.000
#> SRR1442909 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR1414269 3 0.0000 0.912 0.000 0.000 1.000 0.000
#> SRR1381913 3 0.1716 0.880 0.000 0.000 0.936 0.064
#> SRR1340157 2 0.1474 0.954 0.000 0.948 0.000 0.052
#> SRR1407583 3 0.5000 0.109 0.000 0.496 0.504 0.000
#> SRR615826 3 0.1716 0.880 0.000 0.000 0.936 0.064
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR612587 4 0.0290 0.96909 0.000 0.000 0.000 0.992 0.008
#> SRR1458769 2 0.2074 0.77292 0.000 0.896 0.000 0.000 0.104
#> SRR613162 1 0.0162 0.94796 0.996 0.000 0.000 0.000 0.004
#> SRR1352481 1 0.0000 0.94824 1.000 0.000 0.000 0.000 0.000
#> SRR1468876 3 0.2011 0.66691 0.000 0.000 0.908 0.004 0.088
#> SRR1399223 2 0.5599 0.33517 0.000 0.620 0.120 0.000 0.260
#> SRR660030 3 0.0162 0.69479 0.000 0.000 0.996 0.000 0.004
#> SRR1333609 3 0.0162 0.69479 0.000 0.000 0.996 0.000 0.004
#> SRR1471612 2 0.0703 0.78011 0.000 0.976 0.000 0.000 0.024
#> SRR1413998 2 0.3857 0.68640 0.000 0.688 0.000 0.000 0.312
#> SRR1122940 2 0.0703 0.78011 0.000 0.976 0.000 0.000 0.024
#> SRR1402563 3 0.1341 0.67766 0.000 0.000 0.944 0.000 0.056
#> SRR1398393 3 0.5359 0.15129 0.000 0.080 0.616 0.000 0.304
#> SRR657961 5 0.5821 0.67243 0.000 0.096 0.400 0.000 0.504
#> SRR1471135 3 0.2020 0.64463 0.000 0.000 0.900 0.000 0.100
#> SRR1430001 3 0.1608 0.67127 0.000 0.000 0.928 0.000 0.072
#> SRR662775 1 0.0162 0.94796 0.996 0.000 0.000 0.000 0.004
#> SRR1474182 2 0.0162 0.78322 0.000 0.996 0.000 0.000 0.004
#> SRR607190 1 0.0000 0.94824 1.000 0.000 0.000 0.000 0.000
#> SRR612467 2 0.3707 0.49908 0.000 0.716 0.000 0.000 0.284
#> SRR1465959 2 0.2773 0.75572 0.000 0.836 0.000 0.000 0.164
#> SRR1446132 2 0.3857 0.68640 0.000 0.688 0.000 0.000 0.312
#> SRR1416933 2 0.0794 0.78029 0.000 0.972 0.000 0.000 0.028
#> SRR1102538 2 0.0162 0.78330 0.000 0.996 0.000 0.000 0.004
#> SRR1098636 3 0.2286 0.66648 0.000 0.000 0.888 0.004 0.108
#> SRR1072998 2 0.3837 0.68652 0.000 0.692 0.000 0.000 0.308
#> SRR627443 1 0.0963 0.92820 0.964 0.000 0.000 0.000 0.036
#> SRR656131 1 0.0162 0.94796 0.996 0.000 0.000 0.000 0.004
#> SRR823991 3 0.1965 0.66794 0.000 0.000 0.904 0.000 0.096
#> SRR1089158 2 0.0162 0.78330 0.000 0.996 0.000 0.000 0.004
#> SRR1469036 3 0.1608 0.67127 0.000 0.000 0.928 0.000 0.072
#> SRR824039 3 0.5200 0.19120 0.000 0.068 0.628 0.000 0.304
#> SRR1339047 2 0.3424 0.62423 0.000 0.760 0.000 0.000 0.240
#> SRR1443049 2 0.3837 0.68652 0.000 0.692 0.000 0.000 0.308
#> SRR1122885 2 0.3074 0.74354 0.000 0.804 0.000 0.000 0.196
#> SRR602895 3 0.6485 0.01131 0.000 0.000 0.488 0.224 0.288
#> SRR1409837 2 0.0404 0.78332 0.000 0.988 0.000 0.000 0.012
#> SRR1388959 2 0.3857 0.68640 0.000 0.688 0.000 0.000 0.312
#> SRR659863 1 0.0000 0.94824 1.000 0.000 0.000 0.000 0.000
#> SRR1089877 3 0.5409 0.13940 0.000 0.084 0.612 0.000 0.304
#> SRR1123775 3 0.1043 0.68532 0.000 0.000 0.960 0.000 0.040
#> SRR658909 4 0.0000 0.97037 0.000 0.000 0.000 1.000 0.000
#> SRR1140510 2 0.5557 0.34248 0.000 0.624 0.116 0.000 0.260
#> SRR607562 3 0.4047 0.07638 0.000 0.004 0.676 0.000 0.320
#> SRR1122913 2 0.0794 0.77904 0.000 0.972 0.000 0.000 0.028
#> SRR598042 5 0.5694 0.64465 0.000 0.080 0.456 0.000 0.464
#> SRR1467340 3 0.3424 0.46559 0.000 0.000 0.760 0.000 0.240
#> SRR1072321 2 0.0794 0.77904 0.000 0.972 0.000 0.000 0.028
#> SRR1094580 2 0.3074 0.66023 0.000 0.804 0.000 0.000 0.196
#> SRR1076608 2 0.3143 0.65946 0.000 0.796 0.000 0.000 0.204
#> SRR1395462 5 0.6140 0.34939 0.000 0.356 0.140 0.000 0.504
#> SRR1489220 3 0.5507 0.33264 0.000 0.000 0.596 0.316 0.088
#> SRR614371 4 0.0290 0.96909 0.000 0.000 0.000 0.992 0.008
#> SRR615455 1 0.4738 0.15661 0.520 0.000 0.000 0.464 0.016
#> SRR1070573 2 0.2280 0.72401 0.000 0.880 0.000 0.000 0.120
#> SRR598749 3 0.4401 0.00977 0.000 0.016 0.656 0.000 0.328
#> SRR1365556 3 0.3636 0.44742 0.000 0.000 0.728 0.000 0.272
#> SRR1350023 2 0.3857 0.68640 0.000 0.688 0.000 0.000 0.312
#> SRR1446582 3 0.1043 0.68532 0.000 0.000 0.960 0.000 0.040
#> SRR1439763 3 0.0162 0.69479 0.000 0.000 0.996 0.000 0.004
#> SRR1343986 3 0.1270 0.68017 0.000 0.000 0.948 0.000 0.052
#> SRR807463 2 0.3837 0.68652 0.000 0.692 0.000 0.000 0.308
#> SRR660390 1 0.0000 0.94824 1.000 0.000 0.000 0.000 0.000
#> SRR1367672 2 0.0703 0.78011 0.000 0.976 0.000 0.000 0.024
#> SRR613294 4 0.0290 0.96909 0.000 0.000 0.000 0.992 0.008
#> SRR824015 3 0.2424 0.64964 0.000 0.000 0.868 0.000 0.132
#> SRR1078924 2 0.2773 0.75572 0.000 0.836 0.000 0.000 0.164
#> SRR662221 4 0.0000 0.97037 0.000 0.000 0.000 1.000 0.000
#> SRR655017 1 0.0000 0.94824 1.000 0.000 0.000 0.000 0.000
#> SRR1338450 3 0.2011 0.66691 0.000 0.000 0.908 0.004 0.088
#> SRR663741 4 0.0000 0.97037 0.000 0.000 0.000 1.000 0.000
#> SRR1396057 2 0.0880 0.77930 0.000 0.968 0.000 0.000 0.032
#> SRR1083800 3 0.3388 0.48335 0.000 0.008 0.792 0.000 0.200
#> SRR1445789 2 0.1851 0.77605 0.000 0.912 0.000 0.000 0.088
#> SRR1387355 3 0.5538 0.32197 0.000 0.000 0.588 0.324 0.088
#> SRR1388855 2 0.1792 0.77659 0.000 0.916 0.000 0.000 0.084
#> SRR1445449 3 0.5115 0.43835 0.000 0.000 0.676 0.232 0.092
#> SRR1380740 3 0.0162 0.69479 0.000 0.000 0.996 0.000 0.004
#> SRR659995 4 0.1544 0.92750 0.000 0.000 0.000 0.932 0.068
#> SRR1489524 2 0.3857 0.68640 0.000 0.688 0.000 0.000 0.312
#> SRR1444662 3 0.3636 0.44742 0.000 0.000 0.728 0.000 0.272
#> SRR1383652 3 0.0794 0.68964 0.000 0.000 0.972 0.000 0.028
#> SRR1361243 3 0.1341 0.67766 0.000 0.000 0.944 0.000 0.056
#> SRR1490337 3 0.2011 0.66691 0.000 0.000 0.908 0.004 0.088
#> SRR823967 3 0.1270 0.69160 0.000 0.000 0.948 0.000 0.052
#> SRR660127 1 0.0162 0.94796 0.996 0.000 0.000 0.000 0.004
#> SRR1366627 3 0.6645 -0.29189 0.000 0.292 0.448 0.000 0.260
#> SRR1361219 2 0.0880 0.77930 0.000 0.968 0.000 0.000 0.032
#> SRR1393510 3 0.1608 0.68884 0.000 0.000 0.928 0.000 0.072
#> SRR662558 4 0.2331 0.89791 0.000 0.000 0.020 0.900 0.080
#> SRR1077334 2 0.5082 0.43200 0.000 0.664 0.076 0.000 0.260
#> SRR807438 3 0.5538 0.32197 0.000 0.000 0.588 0.324 0.088
#> SRR1459078 3 0.0510 0.69248 0.000 0.000 0.984 0.000 0.016
#> SRR1329704 2 0.3109 0.66016 0.000 0.800 0.000 0.000 0.200
#> SRR1468072 3 0.3424 0.47408 0.000 0.000 0.760 0.000 0.240
#> SRR1376196 2 0.4930 0.46842 0.000 0.684 0.072 0.000 0.244
#> SRR1442909 3 0.1341 0.69029 0.000 0.000 0.944 0.000 0.056
#> SRR1414269 3 0.0963 0.69251 0.000 0.000 0.964 0.000 0.036
#> SRR1381913 3 0.4084 0.06393 0.000 0.004 0.668 0.000 0.328
#> SRR1340157 2 0.3143 0.74052 0.000 0.796 0.000 0.000 0.204
#> SRR1407583 2 0.5472 0.36335 0.000 0.632 0.108 0.000 0.260
#> SRR615826 5 0.5692 0.65837 0.000 0.080 0.448 0.000 0.472
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 4 0.0458 0.8765 0.000 0.000 0.000 0.984 0.000 0.016
#> SRR1458769 2 0.1910 0.5961 0.000 0.892 0.000 0.000 0.000 0.108
#> SRR613162 1 0.0260 0.9866 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1352481 1 0.0363 0.9883 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1468876 3 0.4405 0.5843 0.000 0.000 0.688 0.000 0.240 0.072
#> SRR1399223 5 0.5778 0.3269 0.000 0.404 0.124 0.000 0.460 0.012
#> SRR660030 3 0.0000 0.6557 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1333609 3 0.0000 0.6557 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1471612 2 0.0000 0.7016 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1413998 6 0.3823 0.9866 0.000 0.436 0.000 0.000 0.000 0.564
#> SRR1122940 2 0.0000 0.7016 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1402563 3 0.2092 0.5614 0.000 0.000 0.876 0.000 0.124 0.000
#> SRR1398393 5 0.5267 0.4785 0.000 0.120 0.260 0.000 0.612 0.008
#> SRR657961 5 0.6926 0.3351 0.000 0.080 0.204 0.000 0.444 0.272
#> SRR1471135 3 0.2562 0.5044 0.000 0.000 0.828 0.000 0.172 0.000
#> SRR1430001 3 0.2872 0.6281 0.000 0.000 0.836 0.000 0.140 0.024
#> SRR662775 1 0.0260 0.9866 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1474182 2 0.0260 0.6980 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR607190 1 0.0363 0.9883 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR612467 2 0.5316 0.1702 0.000 0.592 0.000 0.000 0.168 0.240
#> SRR1465959 2 0.2730 0.3361 0.000 0.808 0.000 0.000 0.000 0.192
#> SRR1446132 6 0.3823 0.9866 0.000 0.436 0.000 0.000 0.000 0.564
#> SRR1416933 2 0.0363 0.7000 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1102538 2 0.0363 0.6952 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1098636 3 0.4913 0.5176 0.000 0.000 0.564 0.000 0.364 0.072
#> SRR1072998 6 0.3847 0.9681 0.000 0.456 0.000 0.000 0.000 0.544
#> SRR627443 1 0.1155 0.9620 0.956 0.000 0.000 0.004 0.004 0.036
#> SRR656131 1 0.0363 0.9852 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR823991 3 0.3984 0.3967 0.000 0.000 0.648 0.000 0.336 0.016
#> SRR1089158 2 0.0363 0.6952 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1469036 3 0.2872 0.6281 0.000 0.000 0.836 0.000 0.140 0.024
#> SRR824039 5 0.5231 0.4674 0.000 0.112 0.268 0.000 0.612 0.008
#> SRR1339047 2 0.4199 0.2762 0.000 0.600 0.000 0.000 0.380 0.020
#> SRR1443049 2 0.3607 -0.3971 0.000 0.652 0.000 0.000 0.000 0.348
#> SRR1122885 2 0.2823 0.2916 0.000 0.796 0.000 0.000 0.000 0.204
#> SRR602895 3 0.6720 0.3961 0.000 0.000 0.492 0.092 0.268 0.148
#> SRR1409837 2 0.0260 0.6980 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1388959 6 0.3828 0.9875 0.000 0.440 0.000 0.000 0.000 0.560
#> SRR659863 1 0.0363 0.9883 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1089877 5 0.5440 0.4921 0.000 0.136 0.248 0.000 0.604 0.012
#> SRR1123775 3 0.0547 0.6530 0.000 0.000 0.980 0.000 0.020 0.000
#> SRR658909 4 0.0146 0.8778 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1140510 5 0.5723 0.3107 0.000 0.412 0.116 0.000 0.460 0.012
#> SRR607562 3 0.6131 0.0543 0.000 0.008 0.460 0.000 0.244 0.288
#> SRR1122913 2 0.0632 0.7007 0.000 0.976 0.000 0.000 0.024 0.000
#> SRR598042 5 0.6955 0.2637 0.000 0.060 0.264 0.000 0.388 0.288
#> SRR1467340 3 0.3807 0.1065 0.000 0.004 0.628 0.000 0.368 0.000
#> SRR1072321 2 0.0547 0.7017 0.000 0.980 0.000 0.000 0.020 0.000
#> SRR1094580 2 0.2762 0.5772 0.000 0.804 0.000 0.000 0.196 0.000
#> SRR1076608 2 0.3259 0.5584 0.000 0.772 0.000 0.000 0.216 0.012
#> SRR1395462 5 0.6758 0.3327 0.000 0.260 0.048 0.000 0.428 0.264
#> SRR1489220 3 0.6413 0.4820 0.000 0.000 0.544 0.160 0.224 0.072
#> SRR614371 4 0.0458 0.8765 0.000 0.000 0.000 0.984 0.000 0.016
#> SRR615455 4 0.4504 0.2660 0.392 0.000 0.000 0.576 0.004 0.028
#> SRR1070573 2 0.1663 0.6611 0.000 0.912 0.000 0.000 0.088 0.000
#> SRR598749 3 0.6230 0.0374 0.000 0.012 0.452 0.000 0.248 0.288
#> SRR1365556 5 0.3937 0.1789 0.000 0.000 0.424 0.000 0.572 0.004
#> SRR1350023 6 0.3823 0.9866 0.000 0.436 0.000 0.000 0.000 0.564
#> SRR1446582 3 0.0458 0.6522 0.000 0.000 0.984 0.000 0.016 0.000
#> SRR1439763 3 0.0000 0.6557 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1343986 3 0.1910 0.5777 0.000 0.000 0.892 0.000 0.108 0.000
#> SRR807463 6 0.3838 0.9785 0.000 0.448 0.000 0.000 0.000 0.552
#> SRR660390 1 0.0363 0.9883 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1367672 2 0.0000 0.7016 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR613294 4 0.0458 0.8765 0.000 0.000 0.000 0.984 0.000 0.016
#> SRR824015 3 0.4219 0.2772 0.000 0.000 0.592 0.000 0.388 0.020
#> SRR1078924 2 0.2730 0.3361 0.000 0.808 0.000 0.000 0.000 0.192
#> SRR662221 4 0.0146 0.8778 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR655017 1 0.0363 0.9883 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1338450 3 0.4405 0.5843 0.000 0.000 0.688 0.000 0.240 0.072
#> SRR663741 4 0.0146 0.8778 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1396057 2 0.0993 0.6988 0.000 0.964 0.000 0.000 0.024 0.012
#> SRR1083800 3 0.3888 0.2076 0.000 0.016 0.672 0.000 0.312 0.000
#> SRR1445789 2 0.1910 0.5984 0.000 0.892 0.000 0.000 0.000 0.108
#> SRR1387355 3 0.6306 0.4912 0.000 0.000 0.564 0.160 0.204 0.072
#> SRR1388855 2 0.1863 0.6048 0.000 0.896 0.000 0.000 0.000 0.104
#> SRR1445449 3 0.5913 0.5280 0.000 0.000 0.592 0.088 0.248 0.072
#> SRR1380740 3 0.0000 0.6557 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR659995 4 0.3681 0.7538 0.000 0.000 0.000 0.780 0.156 0.064
#> SRR1489524 6 0.3828 0.9875 0.000 0.440 0.000 0.000 0.000 0.560
#> SRR1444662 5 0.3995 0.1174 0.000 0.000 0.480 0.000 0.516 0.004
#> SRR1383652 3 0.0146 0.6545 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1361243 3 0.2048 0.5645 0.000 0.000 0.880 0.000 0.120 0.000
#> SRR1490337 3 0.4473 0.5802 0.000 0.000 0.676 0.000 0.252 0.072
#> SRR823967 3 0.3674 0.5047 0.000 0.000 0.716 0.000 0.268 0.016
#> SRR660127 1 0.0260 0.9866 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1366627 5 0.6187 0.4881 0.000 0.228 0.300 0.000 0.460 0.012
#> SRR1361219 2 0.0717 0.7020 0.000 0.976 0.000 0.000 0.016 0.008
#> SRR1393510 3 0.1967 0.6430 0.000 0.000 0.904 0.000 0.084 0.012
#> SRR662558 4 0.4085 0.7207 0.000 0.000 0.000 0.736 0.192 0.072
#> SRR1077334 2 0.4653 0.1287 0.000 0.588 0.052 0.000 0.360 0.000
#> SRR807438 3 0.6413 0.4820 0.000 0.000 0.544 0.160 0.224 0.072
#> SRR1459078 3 0.0000 0.6557 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1329704 2 0.3012 0.5748 0.000 0.796 0.000 0.000 0.196 0.008
#> SRR1468072 3 0.3634 0.1496 0.000 0.000 0.644 0.000 0.356 0.000
#> SRR1376196 2 0.4782 0.1687 0.000 0.600 0.056 0.000 0.340 0.004
#> SRR1442909 3 0.2805 0.6109 0.000 0.000 0.828 0.000 0.160 0.012
#> SRR1414269 3 0.1152 0.6534 0.000 0.000 0.952 0.000 0.044 0.004
#> SRR1381913 3 0.6145 0.0510 0.000 0.008 0.456 0.000 0.248 0.288
#> SRR1340157 2 0.3198 0.0717 0.000 0.740 0.000 0.000 0.000 0.260
#> SRR1407583 5 0.5343 0.2125 0.000 0.444 0.072 0.000 0.472 0.012
#> SRR615826 5 0.6955 0.2637 0.000 0.060 0.264 0.000 0.388 0.288
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "skmeans"]
# you can also extract it by
# res = res_list["ATC:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17380 rows and 102 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.965 0.986 0.4592 0.539 0.539
#> 3 3 0.872 0.860 0.943 0.3248 0.815 0.668
#> 4 4 0.850 0.880 0.942 0.0936 0.871 0.697
#> 5 5 0.980 0.933 0.976 0.0876 0.930 0.789
#> 6 6 0.857 0.822 0.890 0.0384 1.000 1.000
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
#> SRR612587 1 0.000 0.973 1.000 0.000
#> SRR1458769 2 0.000 0.992 0.000 1.000
#> SRR613162 1 0.000 0.973 1.000 0.000
#> SRR1352481 1 0.000 0.973 1.000 0.000
#> SRR1468876 1 0.000 0.973 1.000 0.000
#> SRR1399223 2 0.000 0.992 0.000 1.000
#> SRR660030 1 0.000 0.973 1.000 0.000
#> SRR1333609 1 0.000 0.973 1.000 0.000
#> SRR1471612 2 0.000 0.992 0.000 1.000
#> SRR1413998 2 0.000 0.992 0.000 1.000
#> SRR1122940 2 0.000 0.992 0.000 1.000
#> SRR1402563 2 0.000 0.992 0.000 1.000
#> SRR1398393 2 0.000 0.992 0.000 1.000
#> SRR657961 2 0.000 0.992 0.000 1.000
#> SRR1471135 2 0.000 0.992 0.000 1.000
#> SRR1430001 1 0.000 0.973 1.000 0.000
#> SRR662775 1 0.000 0.973 1.000 0.000
#> SRR1474182 2 0.000 0.992 0.000 1.000
#> SRR607190 1 0.000 0.973 1.000 0.000
#> SRR612467 2 0.000 0.992 0.000 1.000
#> SRR1465959 2 0.000 0.992 0.000 1.000
#> SRR1446132 2 0.000 0.992 0.000 1.000
#> SRR1416933 2 0.000 0.992 0.000 1.000
#> SRR1102538 2 0.000 0.992 0.000 1.000
#> SRR1098636 1 0.000 0.973 1.000 0.000
#> SRR1072998 2 0.000 0.992 0.000 1.000
#> SRR627443 1 0.000 0.973 1.000 0.000
#> SRR656131 1 0.000 0.973 1.000 0.000
#> SRR823991 2 0.000 0.992 0.000 1.000
#> SRR1089158 2 0.000 0.992 0.000 1.000
#> SRR1469036 1 0.000 0.973 1.000 0.000
#> SRR824039 2 0.000 0.992 0.000 1.000
#> SRR1339047 2 0.000 0.992 0.000 1.000
#> SRR1443049 2 0.000 0.992 0.000 1.000
#> SRR1122885 2 0.000 0.992 0.000 1.000
#> SRR602895 1 0.000 0.973 1.000 0.000
#> SRR1409837 2 0.000 0.992 0.000 1.000
#> SRR1388959 2 0.000 0.992 0.000 1.000
#> SRR659863 1 0.000 0.973 1.000 0.000
#> SRR1089877 2 0.000 0.992 0.000 1.000
#> SRR1123775 2 0.000 0.992 0.000 1.000
#> SRR658909 1 0.000 0.973 1.000 0.000
#> SRR1140510 2 0.000 0.992 0.000 1.000
#> SRR607562 2 0.909 0.512 0.324 0.676
#> SRR1122913 2 0.000 0.992 0.000 1.000
#> SRR598042 2 0.000 0.992 0.000 1.000
#> SRR1467340 2 0.000 0.992 0.000 1.000
#> SRR1072321 2 0.000 0.992 0.000 1.000
#> SRR1094580 2 0.000 0.992 0.000 1.000
#> SRR1076608 2 0.000 0.992 0.000 1.000
#> SRR1395462 2 0.000 0.992 0.000 1.000
#> SRR1489220 1 0.000 0.973 1.000 0.000
#> SRR614371 1 0.000 0.973 1.000 0.000
#> SRR615455 1 0.000 0.973 1.000 0.000
#> SRR1070573 2 0.000 0.992 0.000 1.000
#> SRR598749 2 0.000 0.992 0.000 1.000
#> SRR1365556 2 0.000 0.992 0.000 1.000
#> SRR1350023 2 0.000 0.992 0.000 1.000
#> SRR1446582 2 0.000 0.992 0.000 1.000
#> SRR1439763 1 0.929 0.497 0.656 0.344
#> SRR1343986 2 0.000 0.992 0.000 1.000
#> SRR807463 2 0.000 0.992 0.000 1.000
#> SRR660390 1 0.000 0.973 1.000 0.000
#> SRR1367672 2 0.000 0.992 0.000 1.000
#> SRR613294 1 0.000 0.973 1.000 0.000
#> SRR824015 2 0.714 0.748 0.196 0.804
#> SRR1078924 2 0.000 0.992 0.000 1.000
#> SRR662221 1 0.000 0.973 1.000 0.000
#> SRR655017 1 0.000 0.973 1.000 0.000
#> SRR1338450 1 0.000 0.973 1.000 0.000
#> SRR663741 1 0.000 0.973 1.000 0.000
#> SRR1396057 2 0.000 0.992 0.000 1.000
#> SRR1083800 2 0.000 0.992 0.000 1.000
#> SRR1445789 2 0.000 0.992 0.000 1.000
#> SRR1387355 1 0.000 0.973 1.000 0.000
#> SRR1388855 2 0.000 0.992 0.000 1.000
#> SRR1445449 1 0.000 0.973 1.000 0.000
#> SRR1380740 1 0.722 0.748 0.800 0.200
#> SRR659995 1 0.000 0.973 1.000 0.000
#> SRR1489524 2 0.000 0.992 0.000 1.000
#> SRR1444662 2 0.000 0.992 0.000 1.000
#> SRR1383652 2 0.000 0.992 0.000 1.000
#> SRR1361243 2 0.000 0.992 0.000 1.000
#> SRR1490337 1 0.000 0.973 1.000 0.000
#> SRR823967 2 0.000 0.992 0.000 1.000
#> SRR660127 1 0.000 0.973 1.000 0.000
#> SRR1366627 2 0.000 0.992 0.000 1.000
#> SRR1361219 2 0.000 0.992 0.000 1.000
#> SRR1393510 1 0.000 0.973 1.000 0.000
#> SRR662558 1 0.000 0.973 1.000 0.000
#> SRR1077334 2 0.000 0.992 0.000 1.000
#> SRR807438 1 0.000 0.973 1.000 0.000
#> SRR1459078 1 0.971 0.361 0.600 0.400
#> SRR1329704 2 0.000 0.992 0.000 1.000
#> SRR1468072 2 0.000 0.992 0.000 1.000
#> SRR1376196 2 0.000 0.992 0.000 1.000
#> SRR1442909 2 0.000 0.992 0.000 1.000
#> SRR1414269 2 0.000 0.992 0.000 1.000
#> SRR1381913 2 0.000 0.992 0.000 1.000
#> SRR1340157 2 0.000 0.992 0.000 1.000
#> SRR1407583 2 0.000 0.992 0.000 1.000
#> SRR615826 2 0.000 0.992 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1458769 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR613162 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1352481 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1468876 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1399223 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR660030 3 0.4887 0.6136 0.228 0.000 0.772
#> SRR1333609 1 0.0747 0.9778 0.984 0.000 0.016
#> SRR1471612 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1413998 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1122940 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1402563 2 0.0592 0.9230 0.000 0.988 0.012
#> SRR1398393 2 0.5291 0.5648 0.000 0.732 0.268
#> SRR657961 3 0.4346 0.7687 0.000 0.184 0.816
#> SRR1471135 2 0.6286 -0.1246 0.000 0.536 0.464
#> SRR1430001 1 0.0592 0.9805 0.988 0.000 0.012
#> SRR662775 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1474182 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR607190 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR612467 2 0.6062 0.1720 0.000 0.616 0.384
#> SRR1465959 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1446132 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1416933 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1102538 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1098636 3 0.2165 0.7715 0.064 0.000 0.936
#> SRR1072998 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR627443 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR656131 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR823991 3 0.6286 0.1411 0.000 0.464 0.536
#> SRR1089158 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1469036 1 0.0592 0.9805 0.988 0.000 0.012
#> SRR824039 2 0.6308 -0.0561 0.000 0.508 0.492
#> SRR1339047 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1443049 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1122885 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR602895 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1409837 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1388959 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR659863 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1089877 2 0.5706 0.4640 0.000 0.680 0.320
#> SRR1123775 3 0.4931 0.7300 0.000 0.232 0.768
#> SRR658909 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1140510 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR607562 3 0.1774 0.8034 0.024 0.016 0.960
#> SRR1122913 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR598042 3 0.1643 0.8158 0.000 0.044 0.956
#> SRR1467340 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1072321 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1094580 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1076608 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1395462 3 0.6168 0.4650 0.000 0.412 0.588
#> SRR1489220 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR614371 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR615455 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1070573 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR598749 3 0.1643 0.8158 0.000 0.044 0.956
#> SRR1365556 2 0.0424 0.9267 0.000 0.992 0.008
#> SRR1350023 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1446582 3 0.5678 0.6408 0.000 0.316 0.684
#> SRR1439763 3 0.0000 0.7985 0.000 0.000 1.000
#> SRR1343986 2 0.0592 0.9230 0.000 0.988 0.012
#> SRR807463 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR660390 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1367672 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR613294 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR824015 2 0.6625 0.4316 0.024 0.660 0.316
#> SRR1078924 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR662221 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR655017 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1338450 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR663741 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1396057 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1083800 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1445789 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1387355 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1388855 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1445449 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1380740 1 0.5219 0.6622 0.788 0.196 0.016
#> SRR659995 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1489524 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1444662 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1383652 3 0.5785 0.6160 0.000 0.332 0.668
#> SRR1361243 2 0.0592 0.9230 0.000 0.988 0.012
#> SRR1490337 1 0.0237 0.9862 0.996 0.000 0.004
#> SRR823967 3 0.2261 0.8062 0.000 0.068 0.932
#> SRR660127 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1366627 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1361219 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1393510 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR662558 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1077334 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR807438 1 0.0000 0.9895 1.000 0.000 0.000
#> SRR1459078 2 0.6701 0.2360 0.412 0.576 0.012
#> SRR1329704 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1468072 2 0.0592 0.9230 0.000 0.988 0.012
#> SRR1376196 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1442909 3 0.0592 0.8060 0.000 0.012 0.988
#> SRR1414269 3 0.0592 0.8060 0.000 0.012 0.988
#> SRR1381913 3 0.0747 0.8081 0.000 0.016 0.984
#> SRR1340157 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR1407583 2 0.0000 0.9337 0.000 1.000 0.000
#> SRR615826 3 0.5621 0.6515 0.000 0.308 0.692
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1458769 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR613162 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1468876 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1399223 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR660030 3 0.4312 0.7356 0.056 0.000 0.812 0.132
#> SRR1333609 3 0.1637 0.8082 0.060 0.000 0.940 0.000
#> SRR1471612 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1413998 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1122940 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1402563 3 0.2530 0.8306 0.000 0.112 0.888 0.000
#> SRR1398393 2 0.4388 0.7653 0.000 0.808 0.060 0.132
#> SRR657961 4 0.4134 0.6903 0.000 0.260 0.000 0.740
#> SRR1471135 4 0.3172 0.7932 0.000 0.160 0.000 0.840
#> SRR1430001 1 0.4134 0.6023 0.740 0.000 0.260 0.000
#> SRR662775 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1474182 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR607190 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR612467 4 0.4972 0.3842 0.000 0.456 0.000 0.544
#> SRR1465959 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1446132 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1416933 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1102538 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1098636 1 0.6967 0.0305 0.456 0.000 0.112 0.432
#> SRR1072998 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR627443 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR823991 2 0.5462 0.6719 0.000 0.736 0.112 0.152
#> SRR1089158 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1469036 3 0.4967 0.1907 0.452 0.000 0.548 0.000
#> SRR824039 2 0.5321 0.6888 0.000 0.748 0.112 0.140
#> SRR1339047 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1443049 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1122885 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR602895 1 0.0707 0.9478 0.980 0.000 0.000 0.020
#> SRR1409837 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1388959 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR659863 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1089877 2 0.5272 0.6941 0.000 0.752 0.112 0.136
#> SRR1123775 4 0.1716 0.8037 0.000 0.064 0.000 0.936
#> SRR658909 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1140510 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR607562 4 0.2124 0.7798 0.028 0.040 0.000 0.932
#> SRR1122913 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR598042 4 0.1940 0.8103 0.000 0.076 0.000 0.924
#> SRR1467340 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1072321 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1094580 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1076608 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1395462 4 0.4454 0.6440 0.000 0.308 0.000 0.692
#> SRR1489220 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR614371 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR615455 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1070573 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR598749 4 0.1867 0.8088 0.000 0.072 0.000 0.928
#> SRR1365556 2 0.2411 0.8871 0.000 0.920 0.040 0.040
#> SRR1350023 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1446582 4 0.2868 0.8089 0.000 0.136 0.000 0.864
#> SRR1439763 3 0.0188 0.7910 0.000 0.000 0.996 0.004
#> SRR1343986 3 0.2530 0.8306 0.000 0.112 0.888 0.000
#> SRR807463 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR660390 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1367672 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR613294 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR824015 2 0.6399 0.6378 0.040 0.712 0.112 0.136
#> SRR1078924 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR662221 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR655017 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1338450 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR663741 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1396057 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1083800 2 0.0188 0.9568 0.000 0.996 0.000 0.004
#> SRR1445789 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1387355 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1388855 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1445449 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1380740 3 0.1854 0.8190 0.048 0.012 0.940 0.000
#> SRR659995 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1489524 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1444662 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1383652 4 0.2868 0.8089 0.000 0.136 0.000 0.864
#> SRR1361243 3 0.2530 0.8306 0.000 0.112 0.888 0.000
#> SRR1490337 1 0.1118 0.9316 0.964 0.000 0.036 0.000
#> SRR823967 2 0.6147 0.5478 0.000 0.664 0.112 0.224
#> SRR660127 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1366627 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1361219 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1393510 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR662558 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1077334 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR807438 1 0.0000 0.9676 1.000 0.000 0.000 0.000
#> SRR1459078 3 0.3013 0.8360 0.032 0.080 0.888 0.000
#> SRR1329704 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1468072 3 0.2530 0.8306 0.000 0.112 0.888 0.000
#> SRR1376196 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1442909 4 0.2530 0.6942 0.000 0.000 0.112 0.888
#> SRR1414269 4 0.2530 0.6942 0.000 0.000 0.112 0.888
#> SRR1381913 4 0.0000 0.7417 0.000 0.000 0.000 1.000
#> SRR1340157 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR1407583 2 0.0000 0.9605 0.000 1.000 0.000 0.000
#> SRR615826 4 0.2814 0.8102 0.000 0.132 0.000 0.868
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR612587 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1458769 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR613162 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1468876 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1399223 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR660030 3 0.2773 0.736 0.000 0.000 0.836 0.000 0.164
#> SRR1333609 3 0.0000 0.915 0.000 0.000 1.000 0.000 0.000
#> SRR1471612 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1413998 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1122940 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1402563 3 0.0000 0.915 0.000 0.000 1.000 0.000 0.000
#> SRR1398393 4 0.0000 0.940 0.000 0.000 0.000 1.000 0.000
#> SRR657961 5 0.3932 0.548 0.000 0.328 0.000 0.000 0.672
#> SRR1471135 5 0.0000 0.841 0.000 0.000 0.000 0.000 1.000
#> SRR1430001 1 0.3177 0.735 0.792 0.000 0.208 0.000 0.000
#> SRR662775 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR607190 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR612467 5 0.4114 0.486 0.000 0.376 0.000 0.000 0.624
#> SRR1465959 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1446132 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1416933 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1102538 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1098636 4 0.0162 0.936 0.004 0.000 0.000 0.996 0.000
#> SRR1072998 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR627443 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR656131 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR823991 4 0.0000 0.940 0.000 0.000 0.000 1.000 0.000
#> SRR1089158 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1469036 3 0.4182 0.297 0.400 0.000 0.600 0.000 0.000
#> SRR824039 4 0.0000 0.940 0.000 0.000 0.000 1.000 0.000
#> SRR1339047 2 0.0162 0.996 0.000 0.996 0.000 0.004 0.000
#> SRR1443049 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1122885 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR602895 1 0.2966 0.774 0.816 0.000 0.000 0.000 0.184
#> SRR1409837 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1388959 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR659863 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1089877 4 0.0000 0.940 0.000 0.000 0.000 1.000 0.000
#> SRR1123775 5 0.0000 0.841 0.000 0.000 0.000 0.000 1.000
#> SRR658909 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1140510 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR607562 5 0.0000 0.841 0.000 0.000 0.000 0.000 1.000
#> SRR1122913 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR598042 5 0.0000 0.841 0.000 0.000 0.000 0.000 1.000
#> SRR1467340 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1072321 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1094580 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1076608 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1395462 5 0.3932 0.548 0.000 0.328 0.000 0.000 0.672
#> SRR1489220 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR614371 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR615455 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1070573 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR598749 5 0.0000 0.841 0.000 0.000 0.000 0.000 1.000
#> SRR1365556 4 0.3837 0.447 0.000 0.308 0.000 0.692 0.000
#> SRR1350023 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1446582 5 0.0000 0.841 0.000 0.000 0.000 0.000 1.000
#> SRR1439763 3 0.0000 0.915 0.000 0.000 1.000 0.000 0.000
#> SRR1343986 3 0.0000 0.915 0.000 0.000 1.000 0.000 0.000
#> SRR807463 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR660390 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1367672 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR613294 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR824015 4 0.0000 0.940 0.000 0.000 0.000 1.000 0.000
#> SRR1078924 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR662221 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR655017 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1338450 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR663741 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1396057 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1083800 2 0.0404 0.986 0.000 0.988 0.000 0.000 0.012
#> SRR1445789 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1387355 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1388855 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1445449 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1380740 3 0.0000 0.915 0.000 0.000 1.000 0.000 0.000
#> SRR659995 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1489524 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1444662 2 0.0162 0.996 0.000 0.996 0.000 0.004 0.000
#> SRR1383652 5 0.0000 0.841 0.000 0.000 0.000 0.000 1.000
#> SRR1361243 3 0.0000 0.915 0.000 0.000 1.000 0.000 0.000
#> SRR1490337 1 0.2561 0.832 0.856 0.000 0.000 0.144 0.000
#> SRR823967 4 0.0000 0.940 0.000 0.000 0.000 1.000 0.000
#> SRR660127 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1366627 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1361219 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1393510 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR662558 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1077334 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR807438 1 0.0000 0.980 1.000 0.000 0.000 0.000 0.000
#> SRR1459078 3 0.0000 0.915 0.000 0.000 1.000 0.000 0.000
#> SRR1329704 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1468072 3 0.0000 0.915 0.000 0.000 1.000 0.000 0.000
#> SRR1376196 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1442909 4 0.0162 0.938 0.000 0.000 0.000 0.996 0.004
#> SRR1414269 4 0.0162 0.938 0.000 0.000 0.000 0.996 0.004
#> SRR1381913 5 0.0000 0.841 0.000 0.000 0.000 0.000 1.000
#> SRR1340157 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR1407583 2 0.0000 0.999 0.000 1.000 0.000 0.000 0.000
#> SRR615826 5 0.0000 0.841 0.000 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 1 0.0000 0.9145 1.000 0.000 0.000 NA 0.000 0.000
#> SRR1458769 2 0.1765 0.9015 0.000 0.904 0.000 NA 0.000 0.000
#> SRR613162 1 0.0000 0.9145 1.000 0.000 0.000 NA 0.000 0.000
#> SRR1352481 1 0.0000 0.9145 1.000 0.000 0.000 NA 0.000 0.000
#> SRR1468876 1 0.2664 0.8454 0.816 0.000 0.000 NA 0.000 0.000
#> SRR1399223 2 0.3499 0.6894 0.000 0.680 0.000 NA 0.000 0.000
#> SRR660030 3 0.3198 0.6200 0.000 0.000 0.740 NA 0.260 0.000
#> SRR1333609 3 0.0000 0.8909 0.000 0.000 1.000 NA 0.000 0.000
#> SRR1471612 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1413998 2 0.2135 0.8865 0.000 0.872 0.000 NA 0.000 0.000
#> SRR1122940 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1402563 3 0.0000 0.8909 0.000 0.000 1.000 NA 0.000 0.000
#> SRR1398393 6 0.0603 0.8313 0.000 0.016 0.000 NA 0.000 0.980
#> SRR657961 5 0.3175 0.5283 0.000 0.256 0.000 NA 0.744 0.000
#> SRR1471135 5 0.3930 0.6653 0.000 0.004 0.000 NA 0.576 0.000
#> SRR1430001 1 0.3141 0.7033 0.788 0.000 0.200 NA 0.000 0.000
#> SRR662775 1 0.0000 0.9145 1.000 0.000 0.000 NA 0.000 0.000
#> SRR1474182 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR607190 1 0.0000 0.9145 1.000 0.000 0.000 NA 0.000 0.000
#> SRR612467 5 0.3737 0.3945 0.000 0.392 0.000 NA 0.608 0.000
#> SRR1465959 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1446132 2 0.2135 0.8865 0.000 0.872 0.000 NA 0.000 0.000
#> SRR1416933 2 0.1765 0.9015 0.000 0.904 0.000 NA 0.000 0.000
#> SRR1102538 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1098636 6 0.2060 0.7945 0.016 0.000 0.000 NA 0.000 0.900
#> SRR1072998 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR627443 1 0.0000 0.9145 1.000 0.000 0.000 NA 0.000 0.000
#> SRR656131 1 0.0000 0.9145 1.000 0.000 0.000 NA 0.000 0.000
#> SRR823991 6 0.0000 0.8395 0.000 0.000 0.000 NA 0.000 1.000
#> SRR1089158 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1469036 3 0.4179 0.0639 0.472 0.000 0.516 NA 0.000 0.000
#> SRR824039 6 0.0000 0.8395 0.000 0.000 0.000 NA 0.000 1.000
#> SRR1339047 2 0.4026 0.5935 0.000 0.612 0.000 NA 0.000 0.012
#> SRR1443049 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1122885 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR602895 1 0.2219 0.8049 0.864 0.000 0.000 NA 0.136 0.000
#> SRR1409837 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1388959 2 0.0363 0.9253 0.000 0.988 0.000 NA 0.000 0.000
#> SRR659863 1 0.0000 0.9145 1.000 0.000 0.000 NA 0.000 0.000
#> SRR1089877 6 0.0000 0.8395 0.000 0.000 0.000 NA 0.000 1.000
#> SRR1123775 5 0.3804 0.6635 0.000 0.000 0.000 NA 0.576 0.000
#> SRR658909 1 0.2340 0.8654 0.852 0.000 0.000 NA 0.000 0.000
#> SRR1140510 2 0.2135 0.8865 0.000 0.872 0.000 NA 0.000 0.000
#> SRR607562 5 0.0000 0.6623 0.000 0.000 0.000 NA 1.000 0.000
#> SRR1122913 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR598042 5 0.0000 0.6623 0.000 0.000 0.000 NA 1.000 0.000
#> SRR1467340 2 0.0713 0.9219 0.000 0.972 0.000 NA 0.000 0.000
#> SRR1072321 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1094580 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1076608 2 0.2135 0.8865 0.000 0.872 0.000 NA 0.000 0.000
#> SRR1395462 5 0.3945 0.4185 0.000 0.380 0.000 NA 0.612 0.000
#> SRR1489220 1 0.2178 0.8722 0.868 0.000 0.000 NA 0.000 0.000
#> SRR614371 1 0.0000 0.9145 1.000 0.000 0.000 NA 0.000 0.000
#> SRR615455 1 0.0000 0.9145 1.000 0.000 0.000 NA 0.000 0.000
#> SRR1070573 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR598749 5 0.0000 0.6623 0.000 0.000 0.000 NA 1.000 0.000
#> SRR1365556 6 0.5810 0.2926 0.000 0.184 0.000 NA 0.000 0.436
#> SRR1350023 2 0.1863 0.8981 0.000 0.896 0.000 NA 0.000 0.000
#> SRR1446582 5 0.3797 0.6658 0.000 0.000 0.000 NA 0.580 0.000
#> SRR1439763 3 0.0000 0.8909 0.000 0.000 1.000 NA 0.000 0.000
#> SRR1343986 3 0.0000 0.8909 0.000 0.000 1.000 NA 0.000 0.000
#> SRR807463 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR660390 1 0.0000 0.9145 1.000 0.000 0.000 NA 0.000 0.000
#> SRR1367672 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR613294 1 0.0000 0.9145 1.000 0.000 0.000 NA 0.000 0.000
#> SRR824015 6 0.3076 0.6997 0.000 0.000 0.000 NA 0.000 0.760
#> SRR1078924 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR662221 1 0.2340 0.8654 0.852 0.000 0.000 NA 0.000 0.000
#> SRR655017 1 0.0000 0.9145 1.000 0.000 0.000 NA 0.000 0.000
#> SRR1338450 1 0.2527 0.8551 0.832 0.000 0.000 NA 0.000 0.000
#> SRR663741 1 0.0000 0.9145 1.000 0.000 0.000 NA 0.000 0.000
#> SRR1396057 2 0.1765 0.9015 0.000 0.904 0.000 NA 0.000 0.000
#> SRR1083800 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1445789 2 0.2135 0.8865 0.000 0.872 0.000 NA 0.000 0.000
#> SRR1387355 1 0.0260 0.9128 0.992 0.000 0.000 NA 0.000 0.000
#> SRR1388855 2 0.2135 0.8865 0.000 0.872 0.000 NA 0.000 0.000
#> SRR1445449 1 0.2597 0.8503 0.824 0.000 0.000 NA 0.000 0.000
#> SRR1380740 3 0.0260 0.8884 0.000 0.000 0.992 NA 0.000 0.000
#> SRR659995 1 0.0146 0.9138 0.996 0.000 0.000 NA 0.000 0.000
#> SRR1489524 2 0.1765 0.9015 0.000 0.904 0.000 NA 0.000 0.000
#> SRR1444662 2 0.4037 0.5871 0.000 0.608 0.000 NA 0.000 0.012
#> SRR1383652 5 0.3797 0.6658 0.000 0.000 0.000 NA 0.580 0.000
#> SRR1361243 3 0.0000 0.8909 0.000 0.000 1.000 NA 0.000 0.000
#> SRR1490337 1 0.5504 0.5134 0.560 0.000 0.000 NA 0.000 0.252
#> SRR823967 6 0.0000 0.8395 0.000 0.000 0.000 NA 0.000 1.000
#> SRR660127 1 0.0000 0.9145 1.000 0.000 0.000 NA 0.000 0.000
#> SRR1366627 2 0.3695 0.6114 0.000 0.624 0.000 NA 0.000 0.000
#> SRR1361219 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1393510 1 0.3823 0.5650 0.564 0.000 0.000 NA 0.000 0.000
#> SRR662558 1 0.2562 0.8527 0.828 0.000 0.000 NA 0.000 0.000
#> SRR1077334 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR807438 1 0.2378 0.8633 0.848 0.000 0.000 NA 0.000 0.000
#> SRR1459078 3 0.0363 0.8865 0.000 0.000 0.988 NA 0.000 0.000
#> SRR1329704 2 0.1075 0.9167 0.000 0.952 0.000 NA 0.000 0.000
#> SRR1468072 3 0.0146 0.8883 0.000 0.000 0.996 NA 0.000 0.000
#> SRR1376196 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1442909 6 0.3217 0.7170 0.000 0.000 0.000 NA 0.008 0.768
#> SRR1414269 6 0.3245 0.7135 0.000 0.000 0.000 NA 0.008 0.764
#> SRR1381913 5 0.3797 0.6658 0.000 0.000 0.000 NA 0.580 0.000
#> SRR1340157 2 0.0000 0.9273 0.000 1.000 0.000 NA 0.000 0.000
#> SRR1407583 2 0.2178 0.8840 0.000 0.868 0.000 NA 0.000 0.000
#> SRR615826 5 0.0146 0.6628 0.000 0.000 0.000 NA 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)
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 17380 rows and 102 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 0.948 0.980 0.2393 0.790 0.790
#> 3 3 0.857 0.926 0.971 1.4886 0.596 0.494
#> 4 4 0.945 0.923 0.968 0.2008 0.772 0.501
#> 5 5 0.857 0.819 0.928 0.0336 0.913 0.721
#> 6 6 0.770 0.813 0.906 0.0417 0.954 0.822
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
#> SRR612587 2 0.994 0.200 0.456 0.544
#> SRR1458769 2 0.000 0.977 0.000 1.000
#> SRR613162 1 0.000 1.000 1.000 0.000
#> SRR1352481 1 0.000 1.000 1.000 0.000
#> SRR1468876 2 0.000 0.977 0.000 1.000
#> SRR1399223 2 0.000 0.977 0.000 1.000
#> SRR660030 2 0.000 0.977 0.000 1.000
#> SRR1333609 2 0.000 0.977 0.000 1.000
#> SRR1471612 2 0.000 0.977 0.000 1.000
#> SRR1413998 2 0.000 0.977 0.000 1.000
#> SRR1122940 2 0.000 0.977 0.000 1.000
#> SRR1402563 2 0.000 0.977 0.000 1.000
#> SRR1398393 2 0.000 0.977 0.000 1.000
#> SRR657961 2 0.000 0.977 0.000 1.000
#> SRR1471135 2 0.000 0.977 0.000 1.000
#> SRR1430001 2 0.000 0.977 0.000 1.000
#> SRR662775 1 0.000 1.000 1.000 0.000
#> SRR1474182 2 0.000 0.977 0.000 1.000
#> SRR607190 1 0.000 1.000 1.000 0.000
#> SRR612467 2 0.000 0.977 0.000 1.000
#> SRR1465959 2 0.000 0.977 0.000 1.000
#> SRR1446132 2 0.000 0.977 0.000 1.000
#> SRR1416933 2 0.000 0.977 0.000 1.000
#> SRR1102538 2 0.000 0.977 0.000 1.000
#> SRR1098636 2 0.000 0.977 0.000 1.000
#> SRR1072998 2 0.000 0.977 0.000 1.000
#> SRR627443 1 0.000 1.000 1.000 0.000
#> SRR656131 1 0.000 1.000 1.000 0.000
#> SRR823991 2 0.000 0.977 0.000 1.000
#> SRR1089158 2 0.000 0.977 0.000 1.000
#> SRR1469036 2 0.000 0.977 0.000 1.000
#> SRR824039 2 0.000 0.977 0.000 1.000
#> SRR1339047 2 0.000 0.977 0.000 1.000
#> SRR1443049 2 0.000 0.977 0.000 1.000
#> SRR1122885 2 0.000 0.977 0.000 1.000
#> SRR602895 2 0.000 0.977 0.000 1.000
#> SRR1409837 2 0.000 0.977 0.000 1.000
#> SRR1388959 2 0.000 0.977 0.000 1.000
#> SRR659863 1 0.000 1.000 1.000 0.000
#> SRR1089877 2 0.000 0.977 0.000 1.000
#> SRR1123775 2 0.000 0.977 0.000 1.000
#> SRR658909 1 0.000 1.000 1.000 0.000
#> SRR1140510 2 0.000 0.977 0.000 1.000
#> SRR607562 2 0.000 0.977 0.000 1.000
#> SRR1122913 2 0.000 0.977 0.000 1.000
#> SRR598042 2 0.000 0.977 0.000 1.000
#> SRR1467340 2 0.000 0.977 0.000 1.000
#> SRR1072321 2 0.000 0.977 0.000 1.000
#> SRR1094580 2 0.000 0.977 0.000 1.000
#> SRR1076608 2 0.000 0.977 0.000 1.000
#> SRR1395462 2 0.000 0.977 0.000 1.000
#> SRR1489220 2 0.000 0.977 0.000 1.000
#> SRR614371 2 0.994 0.200 0.456 0.544
#> SRR615455 1 0.000 1.000 1.000 0.000
#> SRR1070573 2 0.000 0.977 0.000 1.000
#> SRR598749 2 0.000 0.977 0.000 1.000
#> SRR1365556 2 0.000 0.977 0.000 1.000
#> SRR1350023 2 0.000 0.977 0.000 1.000
#> SRR1446582 2 0.000 0.977 0.000 1.000
#> SRR1439763 2 0.000 0.977 0.000 1.000
#> SRR1343986 2 0.000 0.977 0.000 1.000
#> SRR807463 2 0.000 0.977 0.000 1.000
#> SRR660390 1 0.000 1.000 1.000 0.000
#> SRR1367672 2 0.000 0.977 0.000 1.000
#> SRR613294 2 0.969 0.367 0.396 0.604
#> SRR824015 2 0.000 0.977 0.000 1.000
#> SRR1078924 2 0.000 0.977 0.000 1.000
#> SRR662221 2 0.827 0.648 0.260 0.740
#> SRR655017 1 0.000 1.000 1.000 0.000
#> SRR1338450 2 0.000 0.977 0.000 1.000
#> SRR663741 2 0.994 0.200 0.456 0.544
#> SRR1396057 2 0.000 0.977 0.000 1.000
#> SRR1083800 2 0.000 0.977 0.000 1.000
#> SRR1445789 2 0.000 0.977 0.000 1.000
#> SRR1387355 2 0.000 0.977 0.000 1.000
#> SRR1388855 2 0.000 0.977 0.000 1.000
#> SRR1445449 2 0.000 0.977 0.000 1.000
#> SRR1380740 2 0.000 0.977 0.000 1.000
#> SRR659995 2 0.000 0.977 0.000 1.000
#> SRR1489524 2 0.000 0.977 0.000 1.000
#> SRR1444662 2 0.000 0.977 0.000 1.000
#> SRR1383652 2 0.000 0.977 0.000 1.000
#> SRR1361243 2 0.000 0.977 0.000 1.000
#> SRR1490337 2 0.000 0.977 0.000 1.000
#> SRR823967 2 0.000 0.977 0.000 1.000
#> SRR660127 1 0.000 1.000 1.000 0.000
#> SRR1366627 2 0.000 0.977 0.000 1.000
#> SRR1361219 2 0.000 0.977 0.000 1.000
#> SRR1393510 2 0.000 0.977 0.000 1.000
#> SRR662558 2 0.000 0.977 0.000 1.000
#> SRR1077334 2 0.000 0.977 0.000 1.000
#> SRR807438 2 0.000 0.977 0.000 1.000
#> SRR1459078 2 0.000 0.977 0.000 1.000
#> SRR1329704 2 0.000 0.977 0.000 1.000
#> SRR1468072 2 0.000 0.977 0.000 1.000
#> SRR1376196 2 0.000 0.977 0.000 1.000
#> SRR1442909 2 0.000 0.977 0.000 1.000
#> SRR1414269 2 0.000 0.977 0.000 1.000
#> SRR1381913 2 0.000 0.977 0.000 1.000
#> SRR1340157 2 0.000 0.977 0.000 1.000
#> SRR1407583 2 0.000 0.977 0.000 1.000
#> SRR615826 2 0.000 0.977 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 3 0.0592 0.958 0.012 0.000 0.988
#> SRR1458769 2 0.0000 0.946 0.000 1.000 0.000
#> SRR613162 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1352481 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1468876 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1399223 2 0.0000 0.946 0.000 1.000 0.000
#> SRR660030 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1333609 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1471612 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1413998 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1122940 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1402563 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1398393 2 0.5591 0.597 0.000 0.696 0.304
#> SRR657961 2 0.5591 0.597 0.000 0.696 0.304
#> SRR1471135 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1430001 3 0.0000 0.970 0.000 0.000 1.000
#> SRR662775 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1474182 2 0.0000 0.946 0.000 1.000 0.000
#> SRR607190 1 0.0000 1.000 1.000 0.000 0.000
#> SRR612467 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1465959 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1446132 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1416933 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1102538 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1098636 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1072998 2 0.0000 0.946 0.000 1.000 0.000
#> SRR627443 1 0.0000 1.000 1.000 0.000 0.000
#> SRR656131 1 0.0000 1.000 1.000 0.000 0.000
#> SRR823991 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1089158 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1469036 3 0.0000 0.970 0.000 0.000 1.000
#> SRR824039 3 0.6180 0.238 0.000 0.416 0.584
#> SRR1339047 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1443049 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1122885 2 0.0000 0.946 0.000 1.000 0.000
#> SRR602895 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1409837 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1388959 2 0.0000 0.946 0.000 1.000 0.000
#> SRR659863 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1089877 2 0.5591 0.597 0.000 0.696 0.304
#> SRR1123775 3 0.0000 0.970 0.000 0.000 1.000
#> SRR658909 3 0.4504 0.729 0.196 0.000 0.804
#> SRR1140510 2 0.0000 0.946 0.000 1.000 0.000
#> SRR607562 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1122913 2 0.0000 0.946 0.000 1.000 0.000
#> SRR598042 2 0.5591 0.597 0.000 0.696 0.304
#> SRR1467340 3 0.4702 0.698 0.000 0.212 0.788
#> SRR1072321 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1094580 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1076608 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1395462 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1489220 3 0.0000 0.970 0.000 0.000 1.000
#> SRR614371 3 0.0000 0.970 0.000 0.000 1.000
#> SRR615455 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1070573 2 0.0000 0.946 0.000 1.000 0.000
#> SRR598749 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1365556 3 0.0237 0.965 0.000 0.004 0.996
#> SRR1350023 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1446582 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1439763 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1343986 3 0.0000 0.970 0.000 0.000 1.000
#> SRR807463 2 0.0000 0.946 0.000 1.000 0.000
#> SRR660390 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1367672 2 0.0000 0.946 0.000 1.000 0.000
#> SRR613294 3 0.0000 0.970 0.000 0.000 1.000
#> SRR824015 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1078924 2 0.0000 0.946 0.000 1.000 0.000
#> SRR662221 3 0.0000 0.970 0.000 0.000 1.000
#> SRR655017 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1338450 3 0.0000 0.970 0.000 0.000 1.000
#> SRR663741 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1396057 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1083800 3 0.5327 0.603 0.000 0.272 0.728
#> SRR1445789 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1387355 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1388855 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1445449 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1380740 3 0.0000 0.970 0.000 0.000 1.000
#> SRR659995 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1489524 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1444662 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1383652 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1361243 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1490337 3 0.0000 0.970 0.000 0.000 1.000
#> SRR823967 3 0.0000 0.970 0.000 0.000 1.000
#> SRR660127 1 0.0000 1.000 1.000 0.000 0.000
#> SRR1366627 2 0.5591 0.597 0.000 0.696 0.304
#> SRR1361219 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1393510 3 0.0000 0.970 0.000 0.000 1.000
#> SRR662558 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1077334 2 0.0000 0.946 0.000 1.000 0.000
#> SRR807438 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1459078 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1329704 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1468072 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1376196 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1442909 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1414269 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1381913 3 0.0000 0.970 0.000 0.000 1.000
#> SRR1340157 2 0.0000 0.946 0.000 1.000 0.000
#> SRR1407583 2 0.0000 0.946 0.000 1.000 0.000
#> SRR615826 2 0.5591 0.597 0.000 0.696 0.304
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR1458769 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR613162 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1352481 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1468876 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR1399223 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR660030 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1333609 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1471612 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1413998 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1122940 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1402563 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1398393 3 0.3486 0.782 0 0.188 0.812 0.000
#> SRR657961 3 0.1022 0.929 0 0.032 0.968 0.000
#> SRR1471135 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1430001 4 0.2081 0.863 0 0.000 0.084 0.916
#> SRR662775 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1474182 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR607190 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR612467 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1465959 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1446132 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1416933 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1102538 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1098636 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR1072998 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR627443 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR656131 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR823991 3 0.2081 0.900 0 0.000 0.916 0.084
#> SRR1089158 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1469036 4 0.2081 0.863 0 0.000 0.084 0.916
#> SRR824039 3 0.3486 0.782 0 0.188 0.812 0.000
#> SRR1339047 2 0.1557 0.913 0 0.944 0.056 0.000
#> SRR1443049 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1122885 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR602895 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR1409837 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1388959 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR659863 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1089877 3 0.3074 0.822 0 0.152 0.848 0.000
#> SRR1123775 3 0.2011 0.903 0 0.000 0.920 0.080
#> SRR658909 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR1140510 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR607562 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1122913 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR598042 3 0.0469 0.942 0 0.012 0.988 0.000
#> SRR1467340 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1072321 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1094580 2 0.1557 0.913 0 0.944 0.056 0.000
#> SRR1076608 2 0.1302 0.926 0 0.956 0.044 0.000
#> SRR1395462 2 0.4933 0.173 0 0.568 0.432 0.000
#> SRR1489220 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR614371 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR615455 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1070573 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR598749 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1365556 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1350023 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1446582 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1439763 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1343986 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR807463 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR660390 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1367672 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR613294 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR824015 4 0.4866 0.286 0 0.000 0.404 0.596
#> SRR1078924 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR662221 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR655017 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1338450 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR663741 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR1396057 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1083800 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1445789 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1387355 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR1388855 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1445449 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR1380740 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR659995 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR1489524 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1444662 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1383652 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1361243 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1490337 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR823967 3 0.2081 0.900 0 0.000 0.916 0.084
#> SRR660127 1 0.0000 1.000 1 0.000 0.000 0.000
#> SRR1366627 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1361219 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1393510 4 0.4356 0.613 0 0.000 0.292 0.708
#> SRR662558 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR1077334 3 0.3356 0.796 0 0.176 0.824 0.000
#> SRR807438 4 0.0000 0.943 0 0.000 0.000 1.000
#> SRR1459078 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1329704 2 0.4250 0.626 0 0.724 0.276 0.000
#> SRR1468072 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1376196 3 0.0000 0.949 0 0.000 1.000 0.000
#> SRR1442909 3 0.2149 0.897 0 0.000 0.912 0.088
#> SRR1414269 3 0.2081 0.900 0 0.000 0.916 0.084
#> SRR1381913 3 0.2081 0.900 0 0.000 0.916 0.084
#> SRR1340157 2 0.0000 0.968 0 1.000 0.000 0.000
#> SRR1407583 3 0.4382 0.617 0 0.296 0.704 0.000
#> SRR615826 3 0.0000 0.949 0 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR612587 4 0.1544 0.9800 0.000 0.000 0.000 0.932 0.068
#> SRR1458769 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR613162 1 0.0000 0.9848 1.000 0.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 0.9848 1.000 0.000 0.000 0.000 0.000
#> SRR1468876 5 0.0000 0.7031 0.000 0.000 0.000 0.000 1.000
#> SRR1399223 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR660030 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR1333609 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR1471612 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1413998 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1122940 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1402563 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR1398393 3 0.3074 0.6951 0.000 0.196 0.804 0.000 0.000
#> SRR657961 3 0.0880 0.8615 0.000 0.032 0.968 0.000 0.000
#> SRR1471135 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR1430001 3 0.4306 0.0325 0.000 0.000 0.508 0.000 0.492
#> SRR662775 1 0.0000 0.9848 1.000 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR607190 1 0.0000 0.9848 1.000 0.000 0.000 0.000 0.000
#> SRR612467 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1465959 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1446132 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1416933 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1102538 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1098636 5 0.0000 0.7031 0.000 0.000 0.000 0.000 1.000
#> SRR1072998 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR627443 1 0.2424 0.8446 0.868 0.000 0.000 0.132 0.000
#> SRR656131 1 0.0000 0.9848 1.000 0.000 0.000 0.000 0.000
#> SRR823991 5 0.4306 0.2612 0.000 0.000 0.492 0.000 0.508
#> SRR1089158 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1469036 3 0.4306 0.0325 0.000 0.000 0.508 0.000 0.492
#> SRR824039 3 0.3074 0.6951 0.000 0.196 0.804 0.000 0.000
#> SRR1339047 2 0.1197 0.9191 0.000 0.952 0.048 0.000 0.000
#> SRR1443049 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1122885 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR602895 5 0.1544 0.6720 0.000 0.000 0.000 0.068 0.932
#> SRR1409837 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1388959 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR659863 1 0.0000 0.9848 1.000 0.000 0.000 0.000 0.000
#> SRR1089877 3 0.2648 0.7452 0.000 0.152 0.848 0.000 0.000
#> SRR1123775 3 0.2377 0.7546 0.000 0.000 0.872 0.000 0.128
#> SRR658909 4 0.1544 0.9800 0.000 0.000 0.000 0.932 0.068
#> SRR1140510 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR607562 3 0.1704 0.8454 0.000 0.000 0.928 0.068 0.004
#> SRR1122913 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR598042 3 0.1704 0.8462 0.000 0.004 0.928 0.068 0.000
#> SRR1467340 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR1072321 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1094580 2 0.1197 0.9191 0.000 0.952 0.048 0.000 0.000
#> SRR1076608 2 0.0963 0.9317 0.000 0.964 0.036 0.000 0.000
#> SRR1395462 2 0.4249 0.1564 0.000 0.568 0.432 0.000 0.000
#> SRR1489220 5 0.0000 0.7031 0.000 0.000 0.000 0.000 1.000
#> SRR614371 4 0.1544 0.9800 0.000 0.000 0.000 0.932 0.068
#> SRR615455 4 0.1965 0.8676 0.096 0.000 0.000 0.904 0.000
#> SRR1070573 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR598749 3 0.1544 0.8477 0.000 0.000 0.932 0.068 0.000
#> SRR1365556 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR1350023 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1446582 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR1439763 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR1343986 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR807463 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR660390 1 0.0000 0.9848 1.000 0.000 0.000 0.000 0.000
#> SRR1367672 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR613294 4 0.1544 0.9800 0.000 0.000 0.000 0.932 0.068
#> SRR824015 5 0.3305 0.5881 0.000 0.000 0.224 0.000 0.776
#> SRR1078924 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR662221 4 0.1544 0.9800 0.000 0.000 0.000 0.932 0.068
#> SRR655017 1 0.0000 0.9848 1.000 0.000 0.000 0.000 0.000
#> SRR1338450 5 0.0000 0.7031 0.000 0.000 0.000 0.000 1.000
#> SRR663741 4 0.1544 0.9800 0.000 0.000 0.000 0.932 0.068
#> SRR1396057 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1083800 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR1445789 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1387355 5 0.0000 0.7031 0.000 0.000 0.000 0.000 1.000
#> SRR1388855 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1445449 5 0.0000 0.7031 0.000 0.000 0.000 0.000 1.000
#> SRR1380740 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR659995 5 0.3003 0.4853 0.000 0.000 0.000 0.188 0.812
#> SRR1489524 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1444662 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR1383652 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR1361243 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR1490337 5 0.0000 0.7031 0.000 0.000 0.000 0.000 1.000
#> SRR823967 5 0.4306 0.2612 0.000 0.000 0.492 0.000 0.508
#> SRR660127 1 0.0000 0.9848 1.000 0.000 0.000 0.000 0.000
#> SRR1366627 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR1361219 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1393510 3 0.4307 0.0184 0.000 0.000 0.504 0.000 0.496
#> SRR662558 5 0.0794 0.6819 0.000 0.000 0.000 0.028 0.972
#> SRR1077334 3 0.2966 0.7094 0.000 0.184 0.816 0.000 0.000
#> SRR807438 5 0.0000 0.7031 0.000 0.000 0.000 0.000 1.000
#> SRR1459078 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR1329704 2 0.3661 0.6035 0.000 0.724 0.276 0.000 0.000
#> SRR1468072 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR1376196 3 0.0000 0.8815 0.000 0.000 1.000 0.000 0.000
#> SRR1442909 5 0.4306 0.2612 0.000 0.000 0.492 0.000 0.508
#> SRR1414269 5 0.4306 0.2612 0.000 0.000 0.492 0.000 0.508
#> SRR1381913 5 0.5534 0.3157 0.000 0.000 0.424 0.068 0.508
#> SRR1340157 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1407583 3 0.3796 0.5363 0.000 0.300 0.700 0.000 0.000
#> SRR615826 3 0.1544 0.8477 0.000 0.000 0.932 0.068 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 4 0.000 0.9890 0.00 0.000 0.000 1.000 0.000 0.000
#> SRR1458769 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR613162 1 0.000 0.9817 1.00 0.000 0.000 0.000 0.000 0.000
#> SRR1352481 1 0.000 0.9817 1.00 0.000 0.000 0.000 0.000 0.000
#> SRR1468876 5 0.000 0.7073 0.00 0.000 0.000 0.000 1.000 0.000
#> SRR1399223 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR660030 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR1333609 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR1471612 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR1413998 6 0.317 1.0000 0.00 0.256 0.000 0.000 0.000 0.744
#> SRR1122940 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR1402563 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR1398393 3 0.282 0.7181 0.00 0.204 0.796 0.000 0.000 0.000
#> SRR657961 3 0.266 0.7372 0.00 0.184 0.816 0.000 0.000 0.000
#> SRR1471135 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR1430001 3 0.380 0.0996 0.00 0.000 0.576 0.000 0.424 0.000
#> SRR662775 1 0.000 0.9817 1.00 0.000 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR607190 1 0.000 0.9817 1.00 0.000 0.000 0.000 0.000 0.000
#> SRR612467 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR1465959 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR1446132 6 0.317 1.0000 0.00 0.256 0.000 0.000 0.000 0.744
#> SRR1416933 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR1102538 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR1098636 5 0.000 0.7073 0.00 0.000 0.000 0.000 1.000 0.000
#> SRR1072998 6 0.317 1.0000 0.00 0.256 0.000 0.000 0.000 0.744
#> SRR627443 1 0.245 0.8067 0.84 0.000 0.000 0.160 0.000 0.000
#> SRR656131 1 0.000 0.9817 1.00 0.000 0.000 0.000 0.000 0.000
#> SRR823991 5 0.380 0.3710 0.00 0.000 0.424 0.000 0.576 0.000
#> SRR1089158 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR1469036 3 0.380 0.0996 0.00 0.000 0.576 0.000 0.424 0.000
#> SRR824039 3 0.282 0.7181 0.00 0.204 0.796 0.000 0.000 0.000
#> SRR1339047 2 0.079 0.9131 0.00 0.968 0.032 0.000 0.000 0.000
#> SRR1443049 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR1122885 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR602895 5 0.317 0.5297 0.00 0.000 0.000 0.000 0.744 0.256
#> SRR1409837 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR1388959 6 0.317 1.0000 0.00 0.256 0.000 0.000 0.000 0.744
#> SRR659863 1 0.000 0.9817 1.00 0.000 0.000 0.000 0.000 0.000
#> SRR1089877 3 0.273 0.7300 0.00 0.192 0.808 0.000 0.000 0.000
#> SRR1123775 3 0.279 0.6776 0.00 0.000 0.800 0.000 0.200 0.000
#> SRR658909 4 0.000 0.9890 0.00 0.000 0.000 1.000 0.000 0.000
#> SRR1140510 3 0.186 0.7977 0.00 0.104 0.896 0.000 0.000 0.000
#> SRR607562 3 0.331 0.7028 0.00 0.000 0.740 0.000 0.004 0.256
#> SRR1122913 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR598042 3 0.317 0.7064 0.00 0.000 0.744 0.000 0.000 0.256
#> SRR1467340 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR1072321 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR1094580 2 0.079 0.9131 0.00 0.968 0.032 0.000 0.000 0.000
#> SRR1076608 2 0.079 0.9131 0.00 0.968 0.032 0.000 0.000 0.000
#> SRR1395462 2 0.382 0.0965 0.00 0.568 0.432 0.000 0.000 0.000
#> SRR1489220 5 0.000 0.7073 0.00 0.000 0.000 0.000 1.000 0.000
#> SRR614371 4 0.000 0.9890 0.00 0.000 0.000 1.000 0.000 0.000
#> SRR615455 4 0.127 0.9304 0.06 0.000 0.000 0.940 0.000 0.000
#> SRR1070573 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR598749 3 0.317 0.7064 0.00 0.000 0.744 0.000 0.000 0.256
#> SRR1365556 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR1350023 6 0.317 1.0000 0.00 0.256 0.000 0.000 0.000 0.744
#> SRR1446582 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR1439763 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR1343986 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR807463 6 0.317 1.0000 0.00 0.256 0.000 0.000 0.000 0.744
#> SRR660390 1 0.000 0.9817 1.00 0.000 0.000 0.000 0.000 0.000
#> SRR1367672 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR613294 4 0.000 0.9890 0.00 0.000 0.000 1.000 0.000 0.000
#> SRR824015 5 0.297 0.6252 0.00 0.000 0.224 0.000 0.776 0.000
#> SRR1078924 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR662221 4 0.000 0.9890 0.00 0.000 0.000 1.000 0.000 0.000
#> SRR655017 1 0.000 0.9817 1.00 0.000 0.000 0.000 0.000 0.000
#> SRR1338450 5 0.000 0.7073 0.00 0.000 0.000 0.000 1.000 0.000
#> SRR663741 4 0.000 0.9890 0.00 0.000 0.000 1.000 0.000 0.000
#> SRR1396057 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR1083800 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR1445789 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR1387355 5 0.000 0.7073 0.00 0.000 0.000 0.000 1.000 0.000
#> SRR1388855 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR1445449 5 0.000 0.7073 0.00 0.000 0.000 0.000 1.000 0.000
#> SRR1380740 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR659995 5 0.300 0.4866 0.00 0.000 0.000 0.228 0.772 0.000
#> SRR1489524 6 0.317 1.0000 0.00 0.256 0.000 0.000 0.000 0.744
#> SRR1444662 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR1383652 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR1361243 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR1490337 5 0.000 0.7073 0.00 0.000 0.000 0.000 1.000 0.000
#> SRR823967 5 0.380 0.3710 0.00 0.000 0.424 0.000 0.576 0.000
#> SRR660127 1 0.000 0.9817 1.00 0.000 0.000 0.000 0.000 0.000
#> SRR1366627 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR1361219 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR1393510 5 0.375 0.2541 0.00 0.000 0.396 0.000 0.604 0.000
#> SRR662558 5 0.127 0.6688 0.00 0.000 0.000 0.060 0.940 0.000
#> SRR1077334 3 0.285 0.7141 0.00 0.208 0.792 0.000 0.000 0.000
#> SRR807438 5 0.000 0.7073 0.00 0.000 0.000 0.000 1.000 0.000
#> SRR1459078 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR1329704 2 0.218 0.7445 0.00 0.868 0.132 0.000 0.000 0.000
#> SRR1468072 3 0.000 0.8513 0.00 0.000 1.000 0.000 0.000 0.000
#> SRR1376196 3 0.245 0.7574 0.00 0.160 0.840 0.000 0.000 0.000
#> SRR1442909 5 0.380 0.3710 0.00 0.000 0.424 0.000 0.576 0.000
#> SRR1414269 5 0.380 0.3710 0.00 0.000 0.424 0.000 0.576 0.000
#> SRR1381913 5 0.380 0.3710 0.00 0.000 0.424 0.000 0.576 0.000
#> SRR1340157 2 0.000 0.9541 0.00 1.000 0.000 0.000 0.000 0.000
#> SRR1407583 3 0.348 0.5518 0.00 0.316 0.684 0.000 0.000 0.000
#> SRR615826 3 0.313 0.7139 0.00 0.000 0.752 0.000 0.000 0.248
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "mclust"]
# you can also extract it by
# res = res_list["ATC:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 17380 rows and 102 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.979 0.949 0.977 0.497191 0.498 0.498
#> 3 3 0.631 0.859 0.929 0.077078 0.726 0.570
#> 4 4 1.000 0.939 0.979 0.136457 0.859 0.726
#> 5 5 0.819 0.882 0.934 0.242890 0.824 0.554
#> 6 6 0.808 0.796 0.894 0.000749 0.878 0.606
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
#> SRR612587 1 0.0376 0.959 0.996 0.004
#> SRR1458769 2 0.0000 0.991 0.000 1.000
#> SRR613162 1 0.0000 0.958 1.000 0.000
#> SRR1352481 1 0.0000 0.958 1.000 0.000
#> SRR1468876 1 0.0672 0.959 0.992 0.008
#> SRR1399223 2 0.0000 0.991 0.000 1.000
#> SRR660030 2 0.0000 0.991 0.000 1.000
#> SRR1333609 2 0.0000 0.991 0.000 1.000
#> SRR1471612 2 0.0000 0.991 0.000 1.000
#> SRR1413998 2 0.0000 0.991 0.000 1.000
#> SRR1122940 2 0.0000 0.991 0.000 1.000
#> SRR1402563 2 0.0000 0.991 0.000 1.000
#> SRR1398393 1 0.0672 0.959 0.992 0.008
#> SRR657961 1 0.0672 0.959 0.992 0.008
#> SRR1471135 2 0.0000 0.991 0.000 1.000
#> SRR1430001 2 0.7299 0.731 0.204 0.796
#> SRR662775 1 0.0000 0.958 1.000 0.000
#> SRR1474182 2 0.0000 0.991 0.000 1.000
#> SRR607190 1 0.0000 0.958 1.000 0.000
#> SRR612467 1 0.0376 0.959 0.996 0.004
#> SRR1465959 2 0.0000 0.991 0.000 1.000
#> SRR1446132 1 0.9754 0.362 0.592 0.408
#> SRR1416933 2 0.0000 0.991 0.000 1.000
#> SRR1102538 2 0.4161 0.903 0.084 0.916
#> SRR1098636 1 0.0672 0.959 0.992 0.008
#> SRR1072998 2 0.0000 0.991 0.000 1.000
#> SRR627443 1 0.0000 0.958 1.000 0.000
#> SRR656131 1 0.0000 0.958 1.000 0.000
#> SRR823991 1 0.0672 0.959 0.992 0.008
#> SRR1089158 2 0.0000 0.991 0.000 1.000
#> SRR1469036 2 0.1184 0.977 0.016 0.984
#> SRR824039 1 0.0672 0.959 0.992 0.008
#> SRR1339047 1 0.9732 0.372 0.596 0.404
#> SRR1443049 2 0.0000 0.991 0.000 1.000
#> SRR1122885 2 0.0000 0.991 0.000 1.000
#> SRR602895 1 0.0376 0.959 0.996 0.004
#> SRR1409837 2 0.0000 0.991 0.000 1.000
#> SRR1388959 2 0.0000 0.991 0.000 1.000
#> SRR659863 1 0.0000 0.958 1.000 0.000
#> SRR1089877 1 0.0672 0.959 0.992 0.008
#> SRR1123775 2 0.0000 0.991 0.000 1.000
#> SRR658909 1 0.0376 0.959 0.996 0.004
#> SRR1140510 2 0.0000 0.991 0.000 1.000
#> SRR607562 1 0.0376 0.959 0.996 0.004
#> SRR1122913 2 0.0000 0.991 0.000 1.000
#> SRR598042 1 0.0376 0.959 0.996 0.004
#> SRR1467340 2 0.0000 0.991 0.000 1.000
#> SRR1072321 2 0.0000 0.991 0.000 1.000
#> SRR1094580 2 0.0000 0.991 0.000 1.000
#> SRR1076608 2 0.0000 0.991 0.000 1.000
#> SRR1395462 1 0.9754 0.362 0.592 0.408
#> SRR1489220 1 0.0672 0.959 0.992 0.008
#> SRR614371 1 0.0376 0.959 0.996 0.004
#> SRR615455 1 0.0000 0.958 1.000 0.000
#> SRR1070573 2 0.0000 0.991 0.000 1.000
#> SRR598749 1 0.0376 0.959 0.996 0.004
#> SRR1365556 1 0.7299 0.751 0.796 0.204
#> SRR1350023 2 0.0000 0.991 0.000 1.000
#> SRR1446582 2 0.0000 0.991 0.000 1.000
#> SRR1439763 2 0.0000 0.991 0.000 1.000
#> SRR1343986 2 0.0000 0.991 0.000 1.000
#> SRR807463 2 0.0000 0.991 0.000 1.000
#> SRR660390 1 0.0000 0.958 1.000 0.000
#> SRR1367672 2 0.0000 0.991 0.000 1.000
#> SRR613294 1 0.0376 0.959 0.996 0.004
#> SRR824015 1 0.0672 0.959 0.992 0.008
#> SRR1078924 2 0.0000 0.991 0.000 1.000
#> SRR662221 1 0.0672 0.959 0.992 0.008
#> SRR655017 1 0.0000 0.958 1.000 0.000
#> SRR1338450 1 0.1184 0.953 0.984 0.016
#> SRR663741 1 0.0376 0.959 0.996 0.004
#> SRR1396057 2 0.0000 0.991 0.000 1.000
#> SRR1083800 2 0.0000 0.991 0.000 1.000
#> SRR1445789 2 0.0000 0.991 0.000 1.000
#> SRR1387355 1 0.3431 0.911 0.936 0.064
#> SRR1388855 2 0.0000 0.991 0.000 1.000
#> SRR1445449 1 0.0672 0.959 0.992 0.008
#> SRR1380740 2 0.0000 0.991 0.000 1.000
#> SRR659995 1 0.0376 0.959 0.996 0.004
#> SRR1489524 2 0.0000 0.991 0.000 1.000
#> SRR1444662 2 0.4690 0.884 0.100 0.900
#> SRR1383652 2 0.0000 0.991 0.000 1.000
#> SRR1361243 2 0.0000 0.991 0.000 1.000
#> SRR1490337 1 0.0672 0.959 0.992 0.008
#> SRR823967 1 0.0672 0.959 0.992 0.008
#> SRR660127 1 0.0000 0.958 1.000 0.000
#> SRR1366627 2 0.0000 0.991 0.000 1.000
#> SRR1361219 2 0.0000 0.991 0.000 1.000
#> SRR1393510 2 0.0376 0.988 0.004 0.996
#> SRR662558 1 0.0672 0.959 0.992 0.008
#> SRR1077334 2 0.0672 0.984 0.008 0.992
#> SRR807438 1 0.0672 0.959 0.992 0.008
#> SRR1459078 2 0.1633 0.969 0.024 0.976
#> SRR1329704 2 0.0000 0.991 0.000 1.000
#> SRR1468072 2 0.0000 0.991 0.000 1.000
#> SRR1376196 2 0.0000 0.991 0.000 1.000
#> SRR1442909 1 0.0672 0.959 0.992 0.008
#> SRR1414269 1 0.7950 0.699 0.760 0.240
#> SRR1381913 1 0.0376 0.959 0.996 0.004
#> SRR1340157 2 0.0000 0.991 0.000 1.000
#> SRR1407583 2 0.0000 0.991 0.000 1.000
#> SRR615826 1 0.0376 0.959 0.996 0.004
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 2 0.6527 0.462 0.404 0.588 0.008
#> SRR1458769 2 0.0000 0.905 0.000 1.000 0.000
#> SRR613162 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1352481 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1468876 3 0.0237 0.933 0.000 0.004 0.996
#> SRR1399223 2 0.3412 0.850 0.000 0.876 0.124
#> SRR660030 2 0.0237 0.904 0.000 0.996 0.004
#> SRR1333609 2 0.5291 0.643 0.000 0.732 0.268
#> SRR1471612 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1413998 2 0.3412 0.850 0.000 0.876 0.124
#> SRR1122940 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1402563 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1398393 3 0.0237 0.933 0.000 0.004 0.996
#> SRR657961 2 0.3826 0.843 0.124 0.868 0.008
#> SRR1471135 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1430001 2 0.5363 0.633 0.000 0.724 0.276
#> SRR662775 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1474182 2 0.0000 0.905 0.000 1.000 0.000
#> SRR607190 1 0.0000 0.998 1.000 0.000 0.000
#> SRR612467 2 0.3896 0.840 0.128 0.864 0.008
#> SRR1465959 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1446132 2 0.3482 0.848 0.000 0.872 0.128
#> SRR1416933 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1102538 2 0.0237 0.904 0.000 0.996 0.004
#> SRR1098636 3 0.0237 0.933 0.000 0.004 0.996
#> SRR1072998 2 0.0000 0.905 0.000 1.000 0.000
#> SRR627443 1 0.0237 0.995 0.996 0.000 0.004
#> SRR656131 1 0.0237 0.995 0.996 0.000 0.004
#> SRR823991 3 0.0237 0.933 0.000 0.004 0.996
#> SRR1089158 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1469036 2 0.5327 0.637 0.000 0.728 0.272
#> SRR824039 3 0.0237 0.933 0.000 0.004 0.996
#> SRR1339047 3 0.5733 0.460 0.000 0.324 0.676
#> SRR1443049 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1122885 2 0.0000 0.905 0.000 1.000 0.000
#> SRR602895 2 0.3896 0.840 0.128 0.864 0.008
#> SRR1409837 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1388959 2 0.0000 0.905 0.000 1.000 0.000
#> SRR659863 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1089877 3 0.0237 0.933 0.000 0.004 0.996
#> SRR1123775 2 0.0000 0.905 0.000 1.000 0.000
#> SRR658909 3 0.0475 0.930 0.004 0.004 0.992
#> SRR1140510 2 0.0000 0.905 0.000 1.000 0.000
#> SRR607562 2 0.3896 0.840 0.128 0.864 0.008
#> SRR1122913 2 0.0000 0.905 0.000 1.000 0.000
#> SRR598042 2 0.3896 0.840 0.128 0.864 0.008
#> SRR1467340 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1072321 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1094580 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1076608 2 0.3412 0.850 0.000 0.876 0.124
#> SRR1395462 2 0.0424 0.903 0.000 0.992 0.008
#> SRR1489220 3 0.0237 0.933 0.000 0.004 0.996
#> SRR614371 2 0.6527 0.462 0.404 0.588 0.008
#> SRR615455 1 0.0592 0.988 0.988 0.000 0.012
#> SRR1070573 2 0.0000 0.905 0.000 1.000 0.000
#> SRR598749 2 0.3896 0.840 0.128 0.864 0.008
#> SRR1365556 3 0.0237 0.933 0.000 0.004 0.996
#> SRR1350023 2 0.3412 0.850 0.000 0.876 0.124
#> SRR1446582 2 0.0237 0.904 0.000 0.996 0.004
#> SRR1439763 2 0.6095 0.488 0.000 0.608 0.392
#> SRR1343986 2 0.0000 0.905 0.000 1.000 0.000
#> SRR807463 2 0.0000 0.905 0.000 1.000 0.000
#> SRR660390 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1367672 2 0.0000 0.905 0.000 1.000 0.000
#> SRR613294 2 0.6513 0.471 0.400 0.592 0.008
#> SRR824015 3 0.0237 0.933 0.000 0.004 0.996
#> SRR1078924 2 0.0000 0.905 0.000 1.000 0.000
#> SRR662221 3 0.0475 0.930 0.004 0.004 0.992
#> SRR655017 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1338450 3 0.0237 0.933 0.000 0.004 0.996
#> SRR663741 3 0.3715 0.778 0.128 0.004 0.868
#> SRR1396057 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1083800 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1445789 2 0.3412 0.850 0.000 0.876 0.124
#> SRR1387355 3 0.6045 0.241 0.000 0.380 0.620
#> SRR1388855 2 0.3412 0.850 0.000 0.876 0.124
#> SRR1445449 3 0.0237 0.933 0.000 0.004 0.996
#> SRR1380740 2 0.5291 0.643 0.000 0.732 0.268
#> SRR659995 3 0.3644 0.782 0.124 0.004 0.872
#> SRR1489524 2 0.0747 0.901 0.000 0.984 0.016
#> SRR1444662 2 0.3551 0.845 0.000 0.868 0.132
#> SRR1383652 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1361243 2 0.1643 0.886 0.000 0.956 0.044
#> SRR1490337 3 0.0237 0.933 0.000 0.004 0.996
#> SRR823967 3 0.0237 0.933 0.000 0.004 0.996
#> SRR660127 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1366627 2 0.3412 0.850 0.000 0.876 0.124
#> SRR1361219 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1393510 2 0.6140 0.463 0.000 0.596 0.404
#> SRR662558 3 0.0237 0.933 0.000 0.004 0.996
#> SRR1077334 2 0.5560 0.657 0.000 0.700 0.300
#> SRR807438 3 0.0237 0.933 0.000 0.004 0.996
#> SRR1459078 2 0.5327 0.637 0.000 0.728 0.272
#> SRR1329704 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1468072 2 0.1289 0.896 0.000 0.968 0.032
#> SRR1376196 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1442909 3 0.0237 0.933 0.000 0.004 0.996
#> SRR1414269 3 0.0592 0.925 0.000 0.012 0.988
#> SRR1381913 2 0.3896 0.840 0.128 0.864 0.008
#> SRR1340157 2 0.0000 0.905 0.000 1.000 0.000
#> SRR1407583 2 0.3267 0.855 0.000 0.884 0.116
#> SRR615826 2 0.3896 0.840 0.128 0.864 0.008
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 4 0.0000 0.903 0.000 0.000 0.000 1.000
#> SRR1458769 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR613162 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1468876 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR1399223 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR660030 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1333609 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1471612 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1413998 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1122940 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1402563 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1398393 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR657961 4 0.5582 0.370 0.000 0.348 0.032 0.620
#> SRR1471135 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1430001 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR662775 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1474182 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR607190 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR612467 4 0.0000 0.903 0.000 0.000 0.000 1.000
#> SRR1465959 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1446132 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1416933 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1102538 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1098636 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR1072998 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR627443 4 0.4888 0.275 0.412 0.000 0.000 0.588
#> SRR656131 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR823991 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR1089158 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1469036 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR824039 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR1339047 2 0.4804 0.344 0.000 0.616 0.384 0.000
#> SRR1443049 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1122885 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR602895 4 0.0000 0.903 0.000 0.000 0.000 1.000
#> SRR1409837 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1388959 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR659863 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1089877 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR1123775 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR658909 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR1140510 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR607562 4 0.0000 0.903 0.000 0.000 0.000 1.000
#> SRR1122913 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR598042 4 0.0000 0.903 0.000 0.000 0.000 1.000
#> SRR1467340 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1072321 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1094580 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1076608 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1395462 2 0.0707 0.969 0.000 0.980 0.000 0.020
#> SRR1489220 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR614371 4 0.0000 0.903 0.000 0.000 0.000 1.000
#> SRR615455 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1070573 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR598749 4 0.0000 0.903 0.000 0.000 0.000 1.000
#> SRR1365556 3 0.4454 0.483 0.000 0.308 0.692 0.000
#> SRR1350023 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1446582 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1439763 2 0.3266 0.783 0.000 0.832 0.168 0.000
#> SRR1343986 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR807463 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR660390 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1367672 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR613294 4 0.0000 0.903 0.000 0.000 0.000 1.000
#> SRR824015 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR1078924 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR662221 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR655017 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1338450 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR663741 3 0.0188 0.940 0.004 0.000 0.996 0.000
#> SRR1396057 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1083800 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1445789 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1387355 3 0.4843 0.347 0.000 0.396 0.604 0.000
#> SRR1388855 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1445449 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR1380740 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR659995 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR1489524 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1444662 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1383652 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1361243 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1490337 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR823967 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR660127 1 0.0000 1.000 1.000 0.000 0.000 0.000
#> SRR1366627 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1361219 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1393510 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR662558 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR1077334 2 0.0592 0.972 0.000 0.984 0.016 0.000
#> SRR807438 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR1459078 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1329704 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1468072 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1376196 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1442909 3 0.0000 0.943 0.000 0.000 1.000 0.000
#> SRR1414269 3 0.1302 0.886 0.000 0.044 0.956 0.000
#> SRR1381913 4 0.0000 0.903 0.000 0.000 0.000 1.000
#> SRR1340157 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR1407583 2 0.0000 0.989 0.000 1.000 0.000 0.000
#> SRR615826 4 0.0000 0.903 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
#> SRR612587 5 0.0000 0.9310 0.000 0.000 0.000 0.000 1.000
#> SRR1458769 2 0.2561 0.8439 0.000 0.856 0.144 0.000 0.000
#> SRR613162 1 0.0162 0.9967 0.996 0.004 0.000 0.000 0.000
#> SRR1352481 1 0.0000 0.9992 1.000 0.000 0.000 0.000 0.000
#> SRR1468876 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR1399223 2 0.1608 0.8242 0.000 0.928 0.072 0.000 0.000
#> SRR660030 3 0.0290 0.9588 0.000 0.008 0.992 0.000 0.000
#> SRR1333609 2 0.4210 0.5814 0.000 0.588 0.412 0.000 0.000
#> SRR1471612 3 0.0000 0.9578 0.000 0.000 1.000 0.000 0.000
#> SRR1413998 2 0.2127 0.8441 0.000 0.892 0.108 0.000 0.000
#> SRR1122940 3 0.0000 0.9578 0.000 0.000 1.000 0.000 0.000
#> SRR1402563 3 0.3177 0.6404 0.000 0.208 0.792 0.000 0.000
#> SRR1398393 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR657961 5 0.2233 0.8221 0.000 0.004 0.104 0.000 0.892
#> SRR1471135 3 0.0290 0.9588 0.000 0.008 0.992 0.000 0.000
#> SRR1430001 2 0.3366 0.7957 0.000 0.768 0.232 0.000 0.000
#> SRR662775 1 0.0000 0.9992 1.000 0.000 0.000 0.000 0.000
#> SRR1474182 3 0.0290 0.9588 0.000 0.008 0.992 0.000 0.000
#> SRR607190 1 0.0000 0.9992 1.000 0.000 0.000 0.000 0.000
#> SRR612467 5 0.0000 0.9310 0.000 0.000 0.000 0.000 1.000
#> SRR1465959 3 0.0000 0.9578 0.000 0.000 1.000 0.000 0.000
#> SRR1446132 2 0.2127 0.8441 0.000 0.892 0.108 0.000 0.000
#> SRR1416933 2 0.3452 0.8007 0.000 0.756 0.244 0.000 0.000
#> SRR1102538 3 0.0000 0.9578 0.000 0.000 1.000 0.000 0.000
#> SRR1098636 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR1072998 3 0.0000 0.9578 0.000 0.000 1.000 0.000 0.000
#> SRR627443 5 0.3521 0.6893 0.232 0.004 0.000 0.000 0.764
#> SRR656131 1 0.0162 0.9967 0.996 0.004 0.000 0.000 0.000
#> SRR823991 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR1089158 3 0.0000 0.9578 0.000 0.000 1.000 0.000 0.000
#> SRR1469036 2 0.3816 0.7232 0.000 0.696 0.304 0.000 0.000
#> SRR824039 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR1339047 2 0.0451 0.7600 0.000 0.988 0.004 0.008 0.000
#> SRR1443049 3 0.0290 0.9588 0.000 0.008 0.992 0.000 0.000
#> SRR1122885 3 0.0000 0.9578 0.000 0.000 1.000 0.000 0.000
#> SRR602895 5 0.0000 0.9310 0.000 0.000 0.000 0.000 1.000
#> SRR1409837 3 0.0290 0.9588 0.000 0.008 0.992 0.000 0.000
#> SRR1388959 2 0.2929 0.8379 0.000 0.820 0.180 0.000 0.000
#> SRR659863 1 0.0000 0.9992 1.000 0.000 0.000 0.000 0.000
#> SRR1089877 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR1123775 3 0.0290 0.9588 0.000 0.008 0.992 0.000 0.000
#> SRR658909 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR1140510 2 0.2732 0.8421 0.000 0.840 0.160 0.000 0.000
#> SRR607562 5 0.0000 0.9310 0.000 0.000 0.000 0.000 1.000
#> SRR1122913 3 0.0000 0.9578 0.000 0.000 1.000 0.000 0.000
#> SRR598042 5 0.0000 0.9310 0.000 0.000 0.000 0.000 1.000
#> SRR1467340 3 0.0290 0.9588 0.000 0.008 0.992 0.000 0.000
#> SRR1072321 3 0.0290 0.9588 0.000 0.008 0.992 0.000 0.000
#> SRR1094580 3 0.0290 0.9588 0.000 0.008 0.992 0.000 0.000
#> SRR1076608 2 0.1121 0.8028 0.000 0.956 0.044 0.000 0.000
#> SRR1395462 5 0.3242 0.6764 0.000 0.000 0.216 0.000 0.784
#> SRR1489220 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR614371 5 0.0000 0.9310 0.000 0.000 0.000 0.000 1.000
#> SRR615455 1 0.0000 0.9992 1.000 0.000 0.000 0.000 0.000
#> SRR1070573 3 0.0162 0.9585 0.000 0.004 0.996 0.000 0.000
#> SRR598749 5 0.0000 0.9310 0.000 0.000 0.000 0.000 1.000
#> SRR1365556 2 0.3949 0.4014 0.000 0.668 0.000 0.332 0.000
#> SRR1350023 2 0.2127 0.8441 0.000 0.892 0.108 0.000 0.000
#> SRR1446582 3 0.0290 0.9588 0.000 0.008 0.992 0.000 0.000
#> SRR1439763 3 0.3421 0.6853 0.000 0.008 0.788 0.204 0.000
#> SRR1343986 3 0.4227 -0.0819 0.000 0.420 0.580 0.000 0.000
#> SRR807463 3 0.0000 0.9578 0.000 0.000 1.000 0.000 0.000
#> SRR660390 1 0.0000 0.9992 1.000 0.000 0.000 0.000 0.000
#> SRR1367672 3 0.0000 0.9578 0.000 0.000 1.000 0.000 0.000
#> SRR613294 5 0.0000 0.9310 0.000 0.000 0.000 0.000 1.000
#> SRR824015 4 0.1478 0.9212 0.000 0.064 0.000 0.936 0.000
#> SRR1078924 3 0.0000 0.9578 0.000 0.000 1.000 0.000 0.000
#> SRR662221 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR655017 1 0.0000 0.9992 1.000 0.000 0.000 0.000 0.000
#> SRR1338450 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR663741 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR1396057 3 0.0794 0.9402 0.000 0.028 0.972 0.000 0.000
#> SRR1083800 3 0.0290 0.9588 0.000 0.008 0.992 0.000 0.000
#> SRR1445789 2 0.1197 0.8064 0.000 0.952 0.048 0.000 0.000
#> SRR1387355 4 0.4390 0.2038 0.000 0.428 0.004 0.568 0.000
#> SRR1388855 2 0.2127 0.8441 0.000 0.892 0.108 0.000 0.000
#> SRR1445449 4 0.1410 0.9243 0.000 0.060 0.000 0.940 0.000
#> SRR1380740 2 0.4171 0.6137 0.000 0.604 0.396 0.000 0.000
#> SRR659995 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR1489524 2 0.2127 0.8441 0.000 0.892 0.108 0.000 0.000
#> SRR1444662 2 0.0451 0.7600 0.000 0.988 0.004 0.008 0.000
#> SRR1383652 3 0.0290 0.9588 0.000 0.008 0.992 0.000 0.000
#> SRR1361243 2 0.4138 0.6355 0.000 0.616 0.384 0.000 0.000
#> SRR1490337 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR823967 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR660127 1 0.0000 0.9992 1.000 0.000 0.000 0.000 0.000
#> SRR1366627 2 0.2127 0.8441 0.000 0.892 0.108 0.000 0.000
#> SRR1361219 3 0.0290 0.9588 0.000 0.008 0.992 0.000 0.000
#> SRR1393510 2 0.1918 0.7731 0.000 0.928 0.036 0.036 0.000
#> SRR662558 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR1077334 3 0.1549 0.9108 0.000 0.016 0.944 0.040 0.000
#> SRR807438 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR1459078 2 0.3661 0.7538 0.000 0.724 0.276 0.000 0.000
#> SRR1329704 3 0.0510 0.9523 0.000 0.016 0.984 0.000 0.000
#> SRR1468072 2 0.2852 0.8407 0.000 0.828 0.172 0.000 0.000
#> SRR1376196 3 0.0290 0.9588 0.000 0.008 0.992 0.000 0.000
#> SRR1442909 4 0.0000 0.9680 0.000 0.000 0.000 1.000 0.000
#> SRR1414269 4 0.0290 0.9589 0.000 0.008 0.000 0.992 0.000
#> SRR1381913 5 0.1430 0.8839 0.000 0.004 0.052 0.000 0.944
#> SRR1340157 3 0.0000 0.9578 0.000 0.000 1.000 0.000 0.000
#> SRR1407583 2 0.4101 0.6437 0.000 0.628 0.372 0.000 0.000
#> SRR615826 5 0.0000 0.9310 0.000 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 5 0.0458 0.9055 0.000 0.000 0.000 0.016 0.984 0.000
#> SRR1458769 2 0.3823 0.3801 0.000 0.564 0.000 0.000 0.000 0.436
#> SRR613162 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1352481 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1468876 3 0.0260 0.9139 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR1399223 6 0.0146 0.8649 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR660030 2 0.3424 0.7987 0.000 0.772 0.024 0.204 0.000 0.000
#> SRR1333609 2 0.3756 0.7241 0.000 0.644 0.000 0.352 0.000 0.004
#> SRR1471612 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1413998 6 0.0935 0.8615 0.000 0.004 0.000 0.032 0.000 0.964
#> SRR1122940 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1402563 2 0.2996 0.7973 0.000 0.772 0.000 0.228 0.000 0.000
#> SRR1398393 3 0.0363 0.9140 0.000 0.000 0.988 0.012 0.000 0.000
#> SRR657961 5 0.1082 0.8329 0.000 0.040 0.004 0.000 0.956 0.000
#> SRR1471135 2 0.2823 0.8050 0.000 0.796 0.000 0.204 0.000 0.000
#> SRR1430001 2 0.4684 0.6846 0.000 0.592 0.000 0.352 0.000 0.056
#> SRR662775 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1474182 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR607190 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR612467 5 0.0000 0.9183 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1465959 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1446132 6 0.0935 0.8615 0.000 0.004 0.000 0.032 0.000 0.964
#> SRR1416933 2 0.3634 0.5450 0.000 0.644 0.000 0.000 0.000 0.356
#> SRR1102538 2 0.0458 0.8274 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR1098636 3 0.0790 0.9086 0.000 0.000 0.968 0.032 0.000 0.000
#> SRR1072998 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR627443 4 0.5443 0.0000 0.184 0.000 0.000 0.572 0.244 0.000
#> SRR656131 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR823991 3 0.0363 0.9140 0.000 0.000 0.988 0.012 0.000 0.000
#> SRR1089158 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1469036 2 0.4518 0.6935 0.000 0.604 0.000 0.352 0.000 0.044
#> SRR824039 3 0.0790 0.9086 0.000 0.000 0.968 0.032 0.000 0.000
#> SRR1339047 6 0.0146 0.8595 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1443049 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1122885 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR602895 5 0.0000 0.9183 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1409837 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1388959 6 0.1814 0.7722 0.000 0.100 0.000 0.000 0.000 0.900
#> SRR659863 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1089877 3 0.0790 0.9086 0.000 0.000 0.968 0.032 0.000 0.000
#> SRR1123775 2 0.2823 0.8050 0.000 0.796 0.000 0.204 0.000 0.000
#> SRR658909 3 0.0790 0.9086 0.000 0.000 0.968 0.032 0.000 0.000
#> SRR1140510 2 0.4779 0.4812 0.000 0.572 0.000 0.060 0.000 0.368
#> SRR607562 5 0.0000 0.9183 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1122913 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR598042 5 0.0000 0.9183 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1467340 2 0.2883 0.8026 0.000 0.788 0.000 0.212 0.000 0.000
#> SRR1072321 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1094580 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1076608 6 0.2300 0.6949 0.000 0.144 0.000 0.000 0.000 0.856
#> SRR1395462 5 0.3330 0.2567 0.000 0.284 0.000 0.000 0.716 0.000
#> SRR1489220 3 0.0260 0.9139 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR614371 5 0.0146 0.9163 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR615455 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1070573 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR598749 5 0.0000 0.9183 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1365556 3 0.5615 -0.0161 0.000 0.112 0.448 0.008 0.000 0.432
#> SRR1350023 6 0.0935 0.8615 0.000 0.004 0.000 0.032 0.000 0.964
#> SRR1446582 2 0.2933 0.8054 0.000 0.796 0.000 0.200 0.004 0.000
#> SRR1439763 2 0.4495 0.6270 0.000 0.660 0.276 0.064 0.000 0.000
#> SRR1343986 2 0.3620 0.7267 0.000 0.648 0.000 0.352 0.000 0.000
#> SRR807463 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR660390 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1367672 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR613294 5 0.0146 0.9163 0.000 0.000 0.000 0.004 0.996 0.000
#> SRR824015 3 0.0603 0.9125 0.000 0.000 0.980 0.016 0.000 0.004
#> SRR1078924 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR662221 3 0.0790 0.9086 0.000 0.000 0.968 0.032 0.000 0.000
#> SRR655017 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1338450 3 0.0260 0.9139 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR663741 3 0.0146 0.9140 0.004 0.000 0.996 0.000 0.000 0.000
#> SRR1396057 2 0.1387 0.8168 0.000 0.932 0.000 0.000 0.000 0.068
#> SRR1083800 2 0.2823 0.8050 0.000 0.796 0.000 0.204 0.000 0.000
#> SRR1445789 6 0.0000 0.8612 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1387355 2 0.3995 0.2329 0.000 0.516 0.480 0.004 0.000 0.000
#> SRR1388855 6 0.0508 0.8651 0.000 0.012 0.000 0.004 0.000 0.984
#> SRR1445449 3 0.0260 0.9144 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR1380740 2 0.3954 0.7190 0.000 0.636 0.000 0.352 0.000 0.012
#> SRR659995 3 0.0146 0.9142 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1489524 6 0.0865 0.8484 0.000 0.036 0.000 0.000 0.000 0.964
#> SRR1444662 6 0.0291 0.8632 0.000 0.004 0.000 0.004 0.000 0.992
#> SRR1383652 2 0.2823 0.8050 0.000 0.796 0.000 0.204 0.000 0.000
#> SRR1361243 2 0.4264 0.7069 0.000 0.620 0.000 0.352 0.000 0.028
#> SRR1490337 3 0.0260 0.9139 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR823967 3 0.0260 0.9139 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR660127 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1366627 6 0.0146 0.8649 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR1361219 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1393510 2 0.6660 0.5700 0.000 0.532 0.108 0.176 0.000 0.184
#> SRR662558 3 0.0790 0.9086 0.000 0.000 0.968 0.032 0.000 0.000
#> SRR1077334 2 0.3215 0.6959 0.000 0.756 0.240 0.004 0.000 0.000
#> SRR807438 3 0.0260 0.9139 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR1459078 2 0.5564 0.5527 0.000 0.500 0.000 0.352 0.000 0.148
#> SRR1329704 2 0.1265 0.8298 0.000 0.948 0.000 0.044 0.000 0.008
#> SRR1468072 6 0.5943 -0.1991 0.000 0.380 0.000 0.216 0.000 0.404
#> SRR1376196 2 0.2260 0.8188 0.000 0.860 0.000 0.140 0.000 0.000
#> SRR1442909 3 0.0260 0.9139 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR1414269 3 0.4062 -0.0164 0.000 0.440 0.552 0.008 0.000 0.000
#> SRR1381913 5 0.0000 0.9183 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1340157 2 0.0000 0.8303 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1407583 2 0.3612 0.8031 0.000 0.780 0.000 0.168 0.000 0.052
#> SRR615826 5 0.0000 0.9183 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)
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 17380 rows and 102 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.957 0.982 0.4230 0.581 0.581
#> 3 3 0.675 0.829 0.923 0.3228 0.841 0.730
#> 4 4 0.526 0.657 0.829 0.1499 0.883 0.751
#> 5 5 0.464 0.587 0.756 0.0877 0.928 0.818
#> 6 6 0.467 0.447 0.675 0.0646 0.967 0.905
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
#> SRR612587 1 0.0000 0.977 1.000 0.000
#> SRR1458769 2 0.0000 0.983 0.000 1.000
#> SRR613162 1 0.0000 0.977 1.000 0.000
#> SRR1352481 1 0.0000 0.977 1.000 0.000
#> SRR1468876 1 0.3114 0.924 0.944 0.056
#> SRR1399223 2 0.0000 0.983 0.000 1.000
#> SRR660030 2 0.8081 0.677 0.248 0.752
#> SRR1333609 2 0.6887 0.779 0.184 0.816
#> SRR1471612 2 0.0000 0.983 0.000 1.000
#> SRR1413998 2 0.0000 0.983 0.000 1.000
#> SRR1122940 2 0.0000 0.983 0.000 1.000
#> SRR1402563 2 0.0000 0.983 0.000 1.000
#> SRR1398393 2 0.0000 0.983 0.000 1.000
#> SRR657961 2 0.0000 0.983 0.000 1.000
#> SRR1471135 2 0.0000 0.983 0.000 1.000
#> SRR1430001 1 0.0000 0.977 1.000 0.000
#> SRR662775 1 0.0000 0.977 1.000 0.000
#> SRR1474182 2 0.0000 0.983 0.000 1.000
#> SRR607190 1 0.0000 0.977 1.000 0.000
#> SRR612467 2 0.0000 0.983 0.000 1.000
#> SRR1465959 2 0.0000 0.983 0.000 1.000
#> SRR1446132 2 0.0000 0.983 0.000 1.000
#> SRR1416933 2 0.0000 0.983 0.000 1.000
#> SRR1102538 2 0.0000 0.983 0.000 1.000
#> SRR1098636 1 0.9933 0.147 0.548 0.452
#> SRR1072998 2 0.0000 0.983 0.000 1.000
#> SRR627443 1 0.0000 0.977 1.000 0.000
#> SRR656131 1 0.0000 0.977 1.000 0.000
#> SRR823991 2 0.0672 0.977 0.008 0.992
#> SRR1089158 2 0.0000 0.983 0.000 1.000
#> SRR1469036 1 0.0000 0.977 1.000 0.000
#> SRR824039 2 0.0000 0.983 0.000 1.000
#> SRR1339047 2 0.0000 0.983 0.000 1.000
#> SRR1443049 2 0.0000 0.983 0.000 1.000
#> SRR1122885 2 0.0000 0.983 0.000 1.000
#> SRR602895 1 0.0000 0.977 1.000 0.000
#> SRR1409837 2 0.0000 0.983 0.000 1.000
#> SRR1388959 2 0.0000 0.983 0.000 1.000
#> SRR659863 1 0.0000 0.977 1.000 0.000
#> SRR1089877 2 0.0000 0.983 0.000 1.000
#> SRR1123775 2 0.0672 0.977 0.008 0.992
#> SRR658909 1 0.0000 0.977 1.000 0.000
#> SRR1140510 2 0.0000 0.983 0.000 1.000
#> SRR607562 2 0.6247 0.817 0.156 0.844
#> SRR1122913 2 0.0000 0.983 0.000 1.000
#> SRR598042 2 0.0000 0.983 0.000 1.000
#> SRR1467340 2 0.0000 0.983 0.000 1.000
#> SRR1072321 2 0.0000 0.983 0.000 1.000
#> SRR1094580 2 0.0000 0.983 0.000 1.000
#> SRR1076608 2 0.0000 0.983 0.000 1.000
#> SRR1395462 2 0.0000 0.983 0.000 1.000
#> SRR1489220 1 0.0000 0.977 1.000 0.000
#> SRR614371 1 0.0000 0.977 1.000 0.000
#> SRR615455 1 0.0000 0.977 1.000 0.000
#> SRR1070573 2 0.0000 0.983 0.000 1.000
#> SRR598749 2 0.0000 0.983 0.000 1.000
#> SRR1365556 2 0.0000 0.983 0.000 1.000
#> SRR1350023 2 0.0000 0.983 0.000 1.000
#> SRR1446582 2 0.0000 0.983 0.000 1.000
#> SRR1439763 2 0.2043 0.956 0.032 0.968
#> SRR1343986 2 0.0000 0.983 0.000 1.000
#> SRR807463 2 0.0000 0.983 0.000 1.000
#> SRR660390 1 0.0000 0.977 1.000 0.000
#> SRR1367672 2 0.0000 0.983 0.000 1.000
#> SRR613294 1 0.0000 0.977 1.000 0.000
#> SRR824015 2 0.3114 0.933 0.056 0.944
#> SRR1078924 2 0.0000 0.983 0.000 1.000
#> SRR662221 1 0.0000 0.977 1.000 0.000
#> SRR655017 1 0.0000 0.977 1.000 0.000
#> SRR1338450 1 0.0000 0.977 1.000 0.000
#> SRR663741 1 0.0000 0.977 1.000 0.000
#> SRR1396057 2 0.0000 0.983 0.000 1.000
#> SRR1083800 2 0.0000 0.983 0.000 1.000
#> SRR1445789 2 0.0000 0.983 0.000 1.000
#> SRR1387355 1 0.0000 0.977 1.000 0.000
#> SRR1388855 2 0.0000 0.983 0.000 1.000
#> SRR1445449 1 0.0000 0.977 1.000 0.000
#> SRR1380740 2 0.4022 0.909 0.080 0.920
#> SRR659995 1 0.0000 0.977 1.000 0.000
#> SRR1489524 2 0.0000 0.983 0.000 1.000
#> SRR1444662 2 0.0000 0.983 0.000 1.000
#> SRR1383652 2 0.0000 0.983 0.000 1.000
#> SRR1361243 2 0.0000 0.983 0.000 1.000
#> SRR1490337 1 0.5629 0.836 0.868 0.132
#> SRR823967 2 0.0938 0.974 0.012 0.988
#> SRR660127 1 0.0000 0.977 1.000 0.000
#> SRR1366627 2 0.0000 0.983 0.000 1.000
#> SRR1361219 2 0.0000 0.983 0.000 1.000
#> SRR1393510 2 0.9580 0.394 0.380 0.620
#> SRR662558 1 0.0000 0.977 1.000 0.000
#> SRR1077334 2 0.0000 0.983 0.000 1.000
#> SRR807438 1 0.0000 0.977 1.000 0.000
#> SRR1459078 2 0.1184 0.970 0.016 0.984
#> SRR1329704 2 0.0000 0.983 0.000 1.000
#> SRR1468072 2 0.0000 0.983 0.000 1.000
#> SRR1376196 2 0.0000 0.983 0.000 1.000
#> SRR1442909 2 0.0672 0.977 0.008 0.992
#> SRR1414269 2 0.0376 0.980 0.004 0.996
#> SRR1381913 2 0.0000 0.983 0.000 1.000
#> SRR1340157 2 0.0000 0.983 0.000 1.000
#> SRR1407583 2 0.0000 0.983 0.000 1.000
#> SRR615826 2 0.0000 0.983 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR612587 3 0.2165 0.7660 0.064 0.000 0.936
#> SRR1458769 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR613162 1 0.2165 0.8869 0.936 0.000 0.064
#> SRR1352481 1 0.0237 0.9080 0.996 0.000 0.004
#> SRR1468876 1 0.3340 0.8138 0.880 0.120 0.000
#> SRR1399223 2 0.0237 0.9236 0.000 0.996 0.004
#> SRR660030 3 0.5945 0.7741 0.024 0.236 0.740
#> SRR1333609 2 0.3412 0.8045 0.124 0.876 0.000
#> SRR1471612 2 0.0592 0.9189 0.000 0.988 0.012
#> SRR1413998 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1122940 2 0.5291 0.5779 0.000 0.732 0.268
#> SRR1402563 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1398393 2 0.0237 0.9236 0.000 0.996 0.004
#> SRR657961 3 0.3752 0.8642 0.000 0.144 0.856
#> SRR1471135 2 0.5835 0.4143 0.000 0.660 0.340
#> SRR1430001 1 0.2959 0.8404 0.900 0.100 0.000
#> SRR662775 1 0.1411 0.8987 0.964 0.000 0.036
#> SRR1474182 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR607190 1 0.0000 0.9084 1.000 0.000 0.000
#> SRR612467 3 0.3752 0.8642 0.000 0.144 0.856
#> SRR1465959 2 0.0592 0.9189 0.000 0.988 0.012
#> SRR1446132 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1416933 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1102538 3 0.6302 0.1913 0.000 0.480 0.520
#> SRR1098636 1 0.9207 -0.0985 0.456 0.152 0.392
#> SRR1072998 2 0.0424 0.9213 0.000 0.992 0.008
#> SRR627443 1 0.2796 0.8731 0.908 0.000 0.092
#> SRR656131 1 0.0000 0.9084 1.000 0.000 0.000
#> SRR823991 2 0.0237 0.9236 0.000 0.996 0.004
#> SRR1089158 2 0.5706 0.4651 0.000 0.680 0.320
#> SRR1469036 1 0.2959 0.8404 0.900 0.100 0.000
#> SRR824039 2 0.0237 0.9236 0.000 0.996 0.004
#> SRR1339047 2 0.1529 0.8950 0.000 0.960 0.040
#> SRR1443049 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1122885 2 0.4399 0.7184 0.000 0.812 0.188
#> SRR602895 3 0.1860 0.7723 0.052 0.000 0.948
#> SRR1409837 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1388959 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR659863 1 0.0424 0.9076 0.992 0.000 0.008
#> SRR1089877 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1123775 2 0.6235 0.0956 0.000 0.564 0.436
#> SRR658909 1 0.0237 0.9078 0.996 0.000 0.004
#> SRR1140510 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR607562 3 0.3325 0.8379 0.020 0.076 0.904
#> SRR1122913 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR598042 3 0.3752 0.8642 0.000 0.144 0.856
#> SRR1467340 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1072321 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1094580 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1076608 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1395462 3 0.4931 0.7890 0.000 0.232 0.768
#> SRR1489220 1 0.3031 0.8607 0.912 0.076 0.012
#> SRR614371 3 0.2066 0.7700 0.060 0.000 0.940
#> SRR615455 1 0.1643 0.8951 0.956 0.000 0.044
#> SRR1070573 2 0.1163 0.9070 0.000 0.972 0.028
#> SRR598749 3 0.3030 0.8469 0.004 0.092 0.904
#> SRR1365556 2 0.0237 0.9236 0.000 0.996 0.004
#> SRR1350023 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1446582 3 0.3941 0.8566 0.000 0.156 0.844
#> SRR1439763 2 0.1753 0.8885 0.048 0.952 0.000
#> SRR1343986 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR807463 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR660390 1 0.0000 0.9084 1.000 0.000 0.000
#> SRR1367672 2 0.3267 0.8164 0.000 0.884 0.116
#> SRR613294 3 0.2165 0.7688 0.064 0.000 0.936
#> SRR824015 2 0.3484 0.8412 0.048 0.904 0.048
#> SRR1078924 2 0.0592 0.9189 0.000 0.988 0.012
#> SRR662221 1 0.0000 0.9084 1.000 0.000 0.000
#> SRR655017 1 0.0000 0.9084 1.000 0.000 0.000
#> SRR1338450 1 0.3038 0.8355 0.896 0.104 0.000
#> SRR663741 1 0.0000 0.9084 1.000 0.000 0.000
#> SRR1396057 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1083800 2 0.6111 0.2477 0.000 0.604 0.396
#> SRR1445789 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1387355 1 0.0475 0.9081 0.992 0.004 0.004
#> SRR1388855 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1445449 1 0.1989 0.8926 0.948 0.004 0.048
#> SRR1380740 2 0.2959 0.8335 0.100 0.900 0.000
#> SRR659995 1 0.5948 0.4916 0.640 0.000 0.360
#> SRR1489524 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1444662 2 0.1031 0.9091 0.000 0.976 0.024
#> SRR1383652 2 0.6111 0.2472 0.000 0.604 0.396
#> SRR1361243 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1490337 1 0.3340 0.8138 0.880 0.120 0.000
#> SRR823967 2 0.0237 0.9233 0.004 0.996 0.000
#> SRR660127 1 0.0000 0.9084 1.000 0.000 0.000
#> SRR1366627 2 0.0237 0.9236 0.000 0.996 0.004
#> SRR1361219 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1393510 2 0.5553 0.5551 0.272 0.724 0.004
#> SRR662558 1 0.0424 0.9075 0.992 0.000 0.008
#> SRR1077334 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR807438 1 0.1753 0.8862 0.952 0.048 0.000
#> SRR1459078 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1329704 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1468072 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1376196 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1442909 2 0.5882 0.3928 0.000 0.652 0.348
#> SRR1414269 2 0.0747 0.9164 0.000 0.984 0.016
#> SRR1381913 3 0.3918 0.8631 0.004 0.140 0.856
#> SRR1340157 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR1407583 2 0.0000 0.9254 0.000 1.000 0.000
#> SRR615826 3 0.3752 0.8642 0.000 0.144 0.856
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR612587 3 0.3271 0.7304 0.012 0.000 0.856 0.132
#> SRR1458769 2 0.1637 0.7989 0.000 0.940 0.000 0.060
#> SRR613162 1 0.3157 0.7674 0.852 0.000 0.004 0.144
#> SRR1352481 1 0.0657 0.8157 0.984 0.000 0.004 0.012
#> SRR1468876 1 0.2773 0.7057 0.880 0.116 0.000 0.004
#> SRR1399223 2 0.2011 0.7973 0.000 0.920 0.000 0.080
#> SRR660030 2 0.9327 -0.2134 0.148 0.392 0.316 0.144
#> SRR1333609 2 0.5109 0.5236 0.196 0.744 0.000 0.060
#> SRR1471612 2 0.1724 0.8111 0.000 0.948 0.032 0.020
#> SRR1413998 2 0.2216 0.7947 0.000 0.908 0.000 0.092
#> SRR1122940 2 0.4144 0.7444 0.000 0.828 0.104 0.068
#> SRR1402563 2 0.2227 0.7950 0.000 0.928 0.036 0.036
#> SRR1398393 4 0.6950 0.4962 0.000 0.272 0.156 0.572
#> SRR657961 3 0.5770 0.5748 0.000 0.148 0.712 0.140
#> SRR1471135 2 0.4328 0.5933 0.000 0.748 0.244 0.008
#> SRR1430001 1 0.5233 0.2945 0.648 0.332 0.000 0.020
#> SRR662775 1 0.1867 0.8053 0.928 0.000 0.000 0.072
#> SRR1474182 2 0.1978 0.8027 0.000 0.928 0.004 0.068
#> SRR607190 1 0.2053 0.8026 0.924 0.000 0.004 0.072
#> SRR612467 3 0.2494 0.7545 0.000 0.036 0.916 0.048
#> SRR1465959 2 0.1584 0.8052 0.000 0.952 0.012 0.036
#> SRR1446132 2 0.2281 0.7935 0.000 0.904 0.000 0.096
#> SRR1416933 2 0.1792 0.8003 0.000 0.932 0.000 0.068
#> SRR1102538 2 0.6990 0.1376 0.000 0.552 0.304 0.144
#> SRR1098636 3 0.7782 0.4089 0.068 0.088 0.560 0.284
#> SRR1072998 2 0.4359 0.7014 0.000 0.816 0.084 0.100
#> SRR627443 1 0.3681 0.7461 0.816 0.000 0.008 0.176
#> SRR656131 1 0.0657 0.8147 0.984 0.000 0.004 0.012
#> SRR823991 2 0.7344 0.0826 0.048 0.548 0.064 0.340
#> SRR1089158 2 0.4844 0.6863 0.000 0.784 0.108 0.108
#> SRR1469036 1 0.6091 0.2089 0.596 0.344 0.000 0.060
#> SRR824039 4 0.7264 0.3455 0.000 0.392 0.148 0.460
#> SRR1339047 4 0.4996 0.2184 0.000 0.484 0.000 0.516
#> SRR1443049 2 0.1576 0.8092 0.000 0.948 0.004 0.048
#> SRR1122885 2 0.4424 0.7017 0.000 0.812 0.088 0.100
#> SRR602895 3 0.3208 0.7301 0.004 0.000 0.848 0.148
#> SRR1409837 2 0.1807 0.8078 0.000 0.940 0.008 0.052
#> SRR1388959 2 0.2216 0.7991 0.000 0.908 0.000 0.092
#> SRR659863 1 0.1118 0.8123 0.964 0.000 0.000 0.036
#> SRR1089877 2 0.4139 0.7506 0.000 0.816 0.040 0.144
#> SRR1123775 3 0.6951 0.2921 0.024 0.272 0.612 0.092
#> SRR658909 1 0.4088 0.6743 0.764 0.000 0.004 0.232
#> SRR1140510 2 0.1557 0.8004 0.000 0.944 0.000 0.056
#> SRR607562 3 0.1743 0.7628 0.004 0.000 0.940 0.056
#> SRR1122913 2 0.2300 0.7967 0.000 0.924 0.028 0.048
#> SRR598042 3 0.0804 0.7681 0.000 0.008 0.980 0.012
#> SRR1467340 2 0.0188 0.8073 0.000 0.996 0.000 0.004
#> SRR1072321 2 0.1890 0.8012 0.000 0.936 0.008 0.056
#> SRR1094580 2 0.3051 0.7752 0.000 0.884 0.028 0.088
#> SRR1076608 2 0.1389 0.8021 0.000 0.952 0.000 0.048
#> SRR1395462 3 0.2443 0.7436 0.000 0.060 0.916 0.024
#> SRR1489220 1 0.3961 0.7630 0.852 0.012 0.048 0.088
#> SRR614371 3 0.4415 0.7114 0.056 0.000 0.804 0.140
#> SRR615455 1 0.2011 0.8041 0.920 0.000 0.000 0.080
#> SRR1070573 2 0.3081 0.7840 0.000 0.888 0.048 0.064
#> SRR598749 3 0.1302 0.7652 0.000 0.000 0.956 0.044
#> SRR1365556 2 0.2300 0.7966 0.016 0.920 0.000 0.064
#> SRR1350023 2 0.1940 0.7998 0.000 0.924 0.000 0.076
#> SRR1446582 3 0.3335 0.6962 0.000 0.120 0.860 0.020
#> SRR1439763 2 0.3547 0.7460 0.112 0.860 0.020 0.008
#> SRR1343986 2 0.1732 0.8004 0.004 0.948 0.008 0.040
#> SRR807463 2 0.3342 0.7668 0.000 0.868 0.032 0.100
#> SRR660390 1 0.1489 0.8104 0.952 0.000 0.004 0.044
#> SRR1367672 2 0.2965 0.7875 0.000 0.892 0.072 0.036
#> SRR613294 3 0.4462 0.7125 0.064 0.000 0.804 0.132
#> SRR824015 4 0.7145 0.3654 0.252 0.192 0.000 0.556
#> SRR1078924 2 0.2670 0.7871 0.000 0.904 0.024 0.072
#> SRR662221 1 0.7485 0.1918 0.472 0.000 0.192 0.336
#> SRR655017 1 0.1305 0.8120 0.960 0.000 0.004 0.036
#> SRR1338450 1 0.2699 0.7927 0.904 0.028 0.000 0.068
#> SRR663741 1 0.1867 0.8053 0.928 0.000 0.000 0.072
#> SRR1396057 2 0.3074 0.7269 0.000 0.848 0.000 0.152
#> SRR1083800 2 0.4037 0.7357 0.000 0.824 0.136 0.040
#> SRR1445789 2 0.1637 0.8018 0.000 0.940 0.000 0.060
#> SRR1387355 1 0.1510 0.8096 0.956 0.028 0.000 0.016
#> SRR1388855 2 0.2081 0.7954 0.000 0.916 0.000 0.084
#> SRR1445449 1 0.5000 0.2307 0.500 0.000 0.000 0.500
#> SRR1380740 2 0.4746 0.5985 0.168 0.776 0.000 0.056
#> SRR659995 3 0.5511 0.3493 0.352 0.000 0.620 0.028
#> SRR1489524 2 0.1637 0.8023 0.000 0.940 0.000 0.060
#> SRR1444662 2 0.5298 0.1378 0.016 0.612 0.000 0.372
#> SRR1383652 2 0.4201 0.6784 0.012 0.788 0.196 0.004
#> SRR1361243 2 0.1867 0.7936 0.000 0.928 0.000 0.072
#> SRR1490337 1 0.6554 0.5148 0.676 0.092 0.204 0.028
#> SRR823967 2 0.7828 0.0870 0.036 0.540 0.140 0.284
#> SRR660127 1 0.0188 0.8150 0.996 0.000 0.000 0.004
#> SRR1366627 2 0.2408 0.7906 0.000 0.896 0.000 0.104
#> SRR1361219 2 0.1474 0.8034 0.000 0.948 0.000 0.052
#> SRR1393510 2 0.6134 0.4108 0.216 0.668 0.000 0.116
#> SRR662558 4 0.7607 0.0035 0.292 0.000 0.236 0.472
#> SRR1077334 2 0.2741 0.7792 0.000 0.892 0.012 0.096
#> SRR807438 1 0.1004 0.8163 0.972 0.004 0.000 0.024
#> SRR1459078 2 0.3168 0.7616 0.056 0.884 0.000 0.060
#> SRR1329704 2 0.1389 0.8043 0.000 0.952 0.000 0.048
#> SRR1468072 2 0.1716 0.8018 0.000 0.936 0.000 0.064
#> SRR1376196 2 0.1118 0.8051 0.000 0.964 0.000 0.036
#> SRR1442909 3 0.7570 0.3321 0.036 0.220 0.592 0.152
#> SRR1414269 2 0.4552 0.7177 0.076 0.828 0.072 0.024
#> SRR1381913 3 0.1389 0.7600 0.000 0.000 0.952 0.048
#> SRR1340157 2 0.1211 0.8061 0.000 0.960 0.000 0.040
#> SRR1407583 2 0.5004 0.1160 0.000 0.604 0.004 0.392
#> SRR615826 3 0.0657 0.7674 0.000 0.012 0.984 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR612587 5 0.170 0.76894 0.008 0.000 0.044 0.008 0.940
#> SRR1458769 2 0.287 0.69968 0.000 0.856 0.016 0.128 0.000
#> SRR613162 1 0.374 0.72285 0.840 0.000 0.024 0.064 0.072
#> SRR1352481 1 0.184 0.76496 0.936 0.000 0.012 0.040 0.012
#> SRR1468876 1 0.541 0.47661 0.648 0.060 0.276 0.016 0.000
#> SRR1399223 2 0.229 0.71195 0.000 0.888 0.004 0.108 0.000
#> SRR660030 3 0.812 0.35927 0.200 0.272 0.440 0.060 0.028
#> SRR1333609 2 0.557 0.45918 0.264 0.652 0.044 0.040 0.000
#> SRR1471612 2 0.279 0.74788 0.000 0.884 0.072 0.040 0.004
#> SRR1413998 2 0.207 0.71792 0.000 0.896 0.000 0.104 0.000
#> SRR1122940 2 0.417 0.56957 0.000 0.648 0.348 0.004 0.000
#> SRR1402563 2 0.391 0.72590 0.020 0.812 0.136 0.032 0.000
#> SRR1398393 4 0.660 0.16685 0.000 0.128 0.376 0.476 0.020
#> SRR657961 3 0.509 0.44859 0.000 0.100 0.732 0.020 0.148
#> SRR1471135 2 0.597 0.54427 0.000 0.656 0.112 0.036 0.196
#> SRR1430001 1 0.579 0.30295 0.608 0.308 0.040 0.044 0.000
#> SRR662775 1 0.213 0.74897 0.892 0.000 0.000 0.108 0.000
#> SRR1474182 2 0.394 0.71756 0.000 0.788 0.160 0.052 0.000
#> SRR607190 1 0.235 0.75567 0.916 0.000 0.020 0.036 0.028
#> SRR612467 5 0.520 0.58289 0.000 0.072 0.292 0.000 0.636
#> SRR1465959 2 0.312 0.71285 0.000 0.812 0.184 0.004 0.000
#> SRR1446132 2 0.258 0.70802 0.000 0.864 0.004 0.132 0.000
#> SRR1416933 2 0.257 0.71485 0.000 0.880 0.016 0.104 0.000
#> SRR1102538 3 0.447 0.25193 0.000 0.372 0.616 0.000 0.012
#> SRR1098636 3 0.528 0.44671 0.048 0.060 0.772 0.072 0.048
#> SRR1072998 2 0.418 0.49295 0.000 0.644 0.352 0.004 0.000
#> SRR627443 1 0.537 0.62725 0.708 0.000 0.028 0.088 0.176
#> SRR656131 1 0.180 0.76243 0.928 0.000 0.004 0.064 0.004
#> SRR823991 3 0.712 0.21204 0.032 0.312 0.464 0.192 0.000
#> SRR1089158 2 0.419 0.46443 0.000 0.596 0.404 0.000 0.000
#> SRR1469036 1 0.575 0.22771 0.572 0.356 0.028 0.044 0.000
#> SRR824039 3 0.515 0.45308 0.000 0.184 0.700 0.112 0.004
#> SRR1339047 4 0.514 0.18562 0.000 0.424 0.040 0.536 0.000
#> SRR1443049 2 0.293 0.71894 0.000 0.832 0.164 0.004 0.000
#> SRR1122885 2 0.447 0.42032 0.000 0.596 0.396 0.004 0.004
#> SRR602895 5 0.225 0.76932 0.016 0.000 0.036 0.028 0.920
#> SRR1409837 2 0.295 0.74874 0.000 0.868 0.088 0.044 0.000
#> SRR1388959 2 0.214 0.74213 0.000 0.916 0.052 0.032 0.000
#> SRR659863 1 0.127 0.76064 0.948 0.000 0.000 0.052 0.000
#> SRR1089877 2 0.569 0.33470 0.000 0.572 0.328 0.100 0.000
#> SRR1123775 3 0.661 0.43100 0.016 0.144 0.636 0.044 0.160
#> SRR658909 1 0.473 0.60019 0.720 0.000 0.200 0.080 0.000
#> SRR1140510 2 0.225 0.71944 0.000 0.896 0.008 0.096 0.000
#> SRR607562 5 0.256 0.79279 0.000 0.008 0.120 0.000 0.872
#> SRR1122913 2 0.392 0.67788 0.000 0.732 0.256 0.012 0.000
#> SRR598042 5 0.362 0.77491 0.000 0.032 0.164 0.000 0.804
#> SRR1467340 2 0.223 0.74389 0.000 0.904 0.080 0.016 0.000
#> SRR1072321 2 0.321 0.70055 0.000 0.788 0.212 0.000 0.000
#> SRR1094580 2 0.373 0.64395 0.000 0.712 0.288 0.000 0.000
#> SRR1076608 2 0.141 0.73956 0.000 0.948 0.008 0.044 0.000
#> SRR1395462 5 0.553 0.48890 0.000 0.040 0.392 0.016 0.552
#> SRR1489220 1 0.551 0.48055 0.636 0.016 0.284 0.064 0.000
#> SRR614371 5 0.216 0.75870 0.036 0.000 0.024 0.016 0.924
#> SRR615455 1 0.508 0.35076 0.568 0.000 0.000 0.392 0.040
#> SRR1070573 2 0.397 0.65798 0.000 0.716 0.276 0.004 0.004
#> SRR598749 5 0.239 0.79245 0.000 0.004 0.116 0.000 0.880
#> SRR1365556 2 0.277 0.70081 0.000 0.860 0.012 0.128 0.000
#> SRR1350023 2 0.189 0.73011 0.000 0.920 0.008 0.072 0.000
#> SRR1446582 5 0.616 0.41080 0.000 0.120 0.332 0.008 0.540
#> SRR1439763 2 0.601 0.55826 0.180 0.652 0.144 0.020 0.004
#> SRR1343986 2 0.367 0.73055 0.036 0.840 0.096 0.028 0.000
#> SRR807463 2 0.355 0.66979 0.000 0.760 0.236 0.004 0.000
#> SRR660390 1 0.162 0.76212 0.948 0.000 0.016 0.016 0.020
#> SRR1367672 2 0.417 0.70890 0.000 0.760 0.204 0.028 0.008
#> SRR613294 5 0.150 0.74869 0.024 0.000 0.016 0.008 0.952
#> SRR824015 4 0.640 0.48321 0.128 0.108 0.100 0.660 0.004
#> SRR1078924 2 0.403 0.60653 0.000 0.680 0.316 0.004 0.000
#> SRR662221 1 0.688 0.20985 0.480 0.000 0.328 0.168 0.024
#> SRR655017 1 0.152 0.76265 0.952 0.000 0.016 0.012 0.020
#> SRR1338450 1 0.398 0.69831 0.796 0.016 0.028 0.160 0.000
#> SRR663741 1 0.260 0.74370 0.872 0.000 0.004 0.120 0.004
#> SRR1396057 2 0.384 0.63497 0.000 0.780 0.032 0.188 0.000
#> SRR1083800 2 0.424 0.63837 0.000 0.700 0.284 0.004 0.012
#> SRR1445789 2 0.183 0.72995 0.000 0.920 0.004 0.076 0.000
#> SRR1387355 1 0.215 0.74993 0.916 0.040 0.000 0.044 0.000
#> SRR1388855 2 0.271 0.70394 0.000 0.860 0.008 0.132 0.000
#> SRR1445449 4 0.564 0.33129 0.260 0.028 0.064 0.648 0.000
#> SRR1380740 2 0.559 0.43772 0.284 0.636 0.052 0.028 0.000
#> SRR659995 3 0.623 0.23289 0.308 0.000 0.560 0.016 0.116
#> SRR1489524 2 0.202 0.72548 0.000 0.912 0.008 0.080 0.000
#> SRR1444662 2 0.488 -0.00956 0.000 0.536 0.024 0.440 0.000
#> SRR1383652 2 0.572 0.64597 0.012 0.716 0.124 0.040 0.108
#> SRR1361243 2 0.368 0.72745 0.040 0.836 0.104 0.020 0.000
#> SRR1490337 3 0.660 0.27876 0.352 0.060 0.532 0.044 0.012
#> SRR823967 3 0.524 0.51374 0.052 0.212 0.708 0.020 0.008
#> SRR660127 1 0.107 0.76523 0.968 0.000 0.012 0.016 0.004
#> SRR1366627 2 0.337 0.67957 0.000 0.820 0.024 0.156 0.000
#> SRR1361219 2 0.201 0.73149 0.000 0.916 0.012 0.072 0.000
#> SRR1393510 2 0.599 0.33515 0.144 0.620 0.012 0.224 0.000
#> SRR662558 3 0.723 0.00330 0.184 0.000 0.508 0.252 0.056
#> SRR1077334 2 0.381 0.65740 0.000 0.736 0.256 0.008 0.000
#> SRR807438 1 0.216 0.75813 0.920 0.004 0.040 0.036 0.000
#> SRR1459078 2 0.420 0.67435 0.088 0.812 0.032 0.068 0.000
#> SRR1329704 2 0.241 0.73549 0.000 0.900 0.032 0.068 0.000
#> SRR1468072 2 0.170 0.74079 0.000 0.932 0.008 0.060 0.000
#> SRR1376196 2 0.309 0.71998 0.000 0.824 0.168 0.008 0.000
#> SRR1442909 3 0.466 0.51566 0.020 0.092 0.800 0.036 0.052
#> SRR1414269 2 0.491 0.57434 0.028 0.640 0.324 0.000 0.008
#> SRR1381913 5 0.444 0.61358 0.000 0.004 0.384 0.004 0.608
#> SRR1340157 2 0.302 0.72991 0.000 0.848 0.132 0.020 0.000
#> SRR1407583 2 0.621 -0.12380 0.000 0.460 0.140 0.400 0.000
#> SRR615826 5 0.336 0.78401 0.000 0.008 0.156 0.012 0.824
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR612587 5 0.2393 0.726071 0.028 0.000 0.008 NA 0.904 0.012
#> SRR1458769 2 0.2952 0.586932 0.000 0.864 0.068 NA 0.000 0.052
#> SRR613162 1 0.3220 0.683062 0.856 0.000 0.004 NA 0.040 0.032
#> SRR1352481 1 0.1092 0.710399 0.960 0.000 0.000 NA 0.000 0.020
#> SRR1468876 1 0.6754 0.367976 0.528 0.016 0.224 NA 0.000 0.172
#> SRR1399223 2 0.3065 0.573532 0.000 0.852 0.012 NA 0.000 0.048
#> SRR660030 3 0.6880 0.418362 0.156 0.216 0.544 NA 0.012 0.016
#> SRR1333609 2 0.6118 0.328229 0.272 0.572 0.084 NA 0.000 0.008
#> SRR1471612 2 0.4427 0.558262 0.000 0.752 0.144 NA 0.012 0.008
#> SRR1413998 2 0.2401 0.583688 0.000 0.892 0.004 NA 0.000 0.060
#> SRR1122940 2 0.4822 0.304428 0.000 0.548 0.400 NA 0.004 0.000
#> SRR1402563 2 0.4711 0.549427 0.008 0.708 0.180 NA 0.000 0.004
#> SRR1398393 6 0.5550 0.478510 0.000 0.116 0.192 NA 0.008 0.652
#> SRR657961 3 0.4004 0.450638 0.000 0.116 0.796 NA 0.052 0.004
#> SRR1471135 2 0.8013 0.015883 0.008 0.384 0.136 NA 0.144 0.036
#> SRR1430001 1 0.6620 0.127602 0.452 0.352 0.048 NA 0.000 0.008
#> SRR662775 1 0.2146 0.697277 0.908 0.000 0.008 NA 0.000 0.060
#> SRR1474182 2 0.3409 0.562423 0.000 0.780 0.192 NA 0.000 0.028
#> SRR607190 1 0.2123 0.700808 0.908 0.000 0.000 NA 0.008 0.020
#> SRR612467 5 0.5148 0.393759 0.000 0.076 0.336 NA 0.580 0.004
#> SRR1465959 2 0.3772 0.459524 0.000 0.672 0.320 NA 0.004 0.000
#> SRR1446132 2 0.3356 0.559502 0.000 0.824 0.004 NA 0.000 0.072
#> SRR1416933 2 0.3421 0.556690 0.000 0.804 0.016 NA 0.000 0.020
#> SRR1102538 3 0.3955 0.313460 0.000 0.316 0.668 NA 0.004 0.000
#> SRR1098636 3 0.5445 0.231202 0.016 0.044 0.676 NA 0.016 0.216
#> SRR1072998 2 0.4111 0.188032 0.000 0.536 0.456 NA 0.004 0.004
#> SRR627443 1 0.5506 0.573260 0.688 0.000 0.016 NA 0.100 0.052
#> SRR656131 1 0.1944 0.710122 0.924 0.000 0.016 NA 0.000 0.036
#> SRR823991 3 0.7068 0.368254 0.032 0.280 0.432 NA 0.000 0.228
#> SRR1089158 3 0.4468 -0.194902 0.000 0.484 0.492 NA 0.004 0.000
#> SRR1469036 1 0.5976 0.163751 0.496 0.380 0.044 NA 0.000 0.004
#> SRR824039 3 0.5935 0.412272 0.000 0.200 0.572 NA 0.000 0.200
#> SRR1339047 6 0.5773 0.325722 0.000 0.328 0.044 NA 0.004 0.556
#> SRR1443049 2 0.3802 0.453286 0.000 0.676 0.312 NA 0.000 0.012
#> SRR1122885 2 0.4208 0.212157 0.000 0.536 0.452 NA 0.004 0.000
#> SRR602895 5 0.2645 0.727178 0.008 0.000 0.044 NA 0.888 0.008
#> SRR1409837 2 0.4359 0.561807 0.000 0.748 0.168 NA 0.004 0.016
#> SRR1388959 2 0.3406 0.555234 0.000 0.792 0.180 NA 0.000 0.020
#> SRR659863 1 0.1341 0.707827 0.948 0.000 0.000 NA 0.000 0.024
#> SRR1089877 3 0.6500 0.273555 0.000 0.352 0.432 NA 0.000 0.176
#> SRR1123775 3 0.8774 0.030460 0.016 0.136 0.324 NA 0.116 0.128
#> SRR658909 1 0.5199 0.544417 0.680 0.000 0.116 NA 0.000 0.168
#> SRR1140510 2 0.2781 0.583839 0.000 0.880 0.040 NA 0.000 0.044
#> SRR607562 5 0.2679 0.747437 0.004 0.004 0.092 NA 0.876 0.004
#> SRR1122913 2 0.5034 0.413964 0.000 0.588 0.328 NA 0.000 0.004
#> SRR598042 5 0.2964 0.734828 0.000 0.012 0.140 NA 0.836 0.000
#> SRR1467340 2 0.2383 0.595733 0.000 0.880 0.096 NA 0.000 0.000
#> SRR1072321 2 0.3629 0.506878 0.000 0.724 0.260 NA 0.000 0.000
#> SRR1094580 2 0.5094 0.316301 0.000 0.536 0.400 NA 0.000 0.016
#> SRR1076608 2 0.2901 0.593069 0.000 0.872 0.056 NA 0.000 0.040
#> SRR1395462 5 0.6896 0.393372 0.000 0.080 0.348 NA 0.448 0.016
#> SRR1489220 1 0.6139 0.378902 0.524 0.000 0.320 NA 0.000 0.068
#> SRR614371 5 0.2375 0.700778 0.068 0.000 0.004 NA 0.896 0.004
#> SRR615455 1 0.6256 0.271791 0.528 0.000 0.016 NA 0.064 0.328
#> SRR1070573 2 0.5004 0.394112 0.000 0.576 0.348 NA 0.000 0.004
#> SRR598749 5 0.1615 0.747277 0.000 0.004 0.064 NA 0.928 0.004
#> SRR1365556 2 0.3844 0.533299 0.000 0.788 0.024 NA 0.000 0.148
#> SRR1350023 2 0.2016 0.599683 0.000 0.920 0.016 NA 0.000 0.024
#> SRR1446582 5 0.8135 0.173068 0.000 0.140 0.248 NA 0.292 0.036
#> SRR1439763 2 0.6682 0.222508 0.176 0.484 0.288 NA 0.000 0.012
#> SRR1343986 2 0.4753 0.556767 0.052 0.740 0.148 NA 0.000 0.008
#> SRR807463 2 0.3979 0.382163 0.000 0.628 0.360 NA 0.000 0.012
#> SRR660390 1 0.1765 0.703884 0.924 0.000 0.000 NA 0.000 0.024
#> SRR1367672 2 0.5269 0.430832 0.000 0.604 0.292 NA 0.016 0.000
#> SRR613294 5 0.2401 0.719439 0.024 0.000 0.000 NA 0.900 0.028
#> SRR824015 6 0.5255 0.582048 0.072 0.096 0.076 NA 0.000 0.728
#> SRR1078924 2 0.4294 0.299818 0.000 0.552 0.428 NA 0.000 0.000
#> SRR662221 1 0.6374 0.278132 0.532 0.000 0.176 NA 0.012 0.252
#> SRR655017 1 0.1549 0.705596 0.936 0.000 0.000 NA 0.000 0.020
#> SRR1338450 1 0.4596 0.557464 0.696 0.008 0.020 NA 0.000 0.244
#> SRR663741 1 0.2748 0.687264 0.872 0.000 0.012 NA 0.004 0.092
#> SRR1396057 2 0.5400 0.486879 0.000 0.692 0.084 NA 0.004 0.128
#> SRR1083800 2 0.4570 0.397220 0.000 0.608 0.352 NA 0.008 0.000
#> SRR1445789 2 0.3872 0.553832 0.000 0.788 0.144 NA 0.000 0.044
#> SRR1387355 1 0.2823 0.695040 0.876 0.020 0.004 NA 0.000 0.072
#> SRR1388855 2 0.3211 0.558361 0.000 0.824 0.000 NA 0.000 0.056
#> SRR1445449 6 0.4685 0.478740 0.212 0.044 0.012 NA 0.000 0.712
#> SRR1380740 2 0.5765 0.327212 0.284 0.584 0.092 NA 0.000 0.004
#> SRR659995 1 0.7368 0.008788 0.372 0.000 0.372 NA 0.044 0.160
#> SRR1489524 2 0.2831 0.585013 0.000 0.872 0.064 NA 0.000 0.048
#> SRR1444662 2 0.6125 0.001313 0.016 0.496 0.012 NA 0.000 0.348
#> SRR1383652 2 0.7965 -0.000975 0.008 0.376 0.148 NA 0.112 0.040
#> SRR1361243 2 0.4798 0.558694 0.044 0.736 0.156 NA 0.000 0.012
#> SRR1490337 3 0.7679 -0.171814 0.288 0.024 0.396 NA 0.008 0.208
#> SRR823967 3 0.5981 0.451386 0.028 0.180 0.624 NA 0.000 0.144
#> SRR660127 1 0.0717 0.709948 0.976 0.000 0.000 NA 0.000 0.008
#> SRR1366627 2 0.4253 0.512633 0.000 0.728 0.004 NA 0.000 0.072
#> SRR1361219 2 0.2128 0.601570 0.000 0.908 0.032 NA 0.000 0.004
#> SRR1393510 2 0.6450 0.119218 0.096 0.544 0.008 NA 0.000 0.268
#> SRR662558 6 0.6874 0.324233 0.212 0.000 0.280 NA 0.020 0.456
#> SRR1077334 2 0.4830 0.231995 0.000 0.540 0.412 NA 0.000 0.008
#> SRR807438 1 0.3253 0.688987 0.848 0.004 0.088 NA 0.000 0.040
#> SRR1459078 2 0.5298 0.499182 0.112 0.696 0.036 NA 0.000 0.012
#> SRR1329704 2 0.4245 0.511592 0.000 0.716 0.048 NA 0.000 0.008
#> SRR1468072 2 0.3323 0.598184 0.008 0.852 0.044 NA 0.000 0.028
#> SRR1376196 2 0.3794 0.519599 0.000 0.724 0.248 NA 0.000 0.000
#> SRR1442909 3 0.7410 -0.002314 0.000 0.056 0.452 NA 0.044 0.232
#> SRR1414269 2 0.7125 0.142090 0.008 0.420 0.332 NA 0.012 0.048
#> SRR1381913 5 0.5970 0.569053 0.000 0.012 0.280 NA 0.564 0.020
#> SRR1340157 2 0.3592 0.516170 0.000 0.740 0.240 NA 0.000 0.000
#> SRR1407583 2 0.6457 0.018180 0.000 0.464 0.096 NA 0.000 0.356
#> SRR615826 5 0.2358 0.747972 0.000 0.004 0.076 NA 0.896 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)
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