Date: 2019-12-26 00:56:23 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 13580 rows and 133 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] 13580 133
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:pam | 2 | 1.000 | 0.973 | 0.989 | ** | |
| MAD:kmeans | 2 | 1.000 | 0.957 | 0.983 | ** | |
| MAD:pam | 2 | 1.000 | 0.970 | 0.988 | ** | |
| MAD:mclust | 2 | 1.000 | 0.955 | 0.975 | ** | |
| SD:kmeans | 2 | 0.999 | 0.964 | 0.985 | ** | |
| ATC:pam | 6 | 0.970 | 0.928 | 0.967 | ** | 2,3,4,5 |
| CV:kmeans | 2 | 0.969 | 0.936 | 0.974 | ** | |
| CV:mclust | 3 | 0.962 | 0.924 | 0.972 | ** | 2 |
| ATC:mclust | 5 | 0.942 | 0.914 | 0.954 | * | 2 |
| SD:skmeans | 3 | 0.931 | 0.934 | 0.970 | * | 2 |
| CV:pam | 5 | 0.925 | 0.904 | 0.962 | * | 4 |
| ATC:skmeans | 3 | 0.923 | 0.929 | 0.966 | * | 2 |
| ATC:kmeans | 4 | 0.907 | 0.886 | 0.940 | * | 2,3 |
| CV:skmeans | 4 | 0.903 | 0.900 | 0.944 | * | 2,3 |
| MAD:skmeans | 3 | 0.902 | 0.927 | 0.968 | * | 2 |
| CV:NMF | 2 | 0.888 | 0.923 | 0.967 | ||
| ATC:NMF | 2 | 0.820 | 0.897 | 0.957 | ||
| CV:hclust | 2 | 0.777 | 0.906 | 0.954 | ||
| SD:NMF | 2 | 0.762 | 0.913 | 0.959 | ||
| SD:mclust | 5 | 0.758 | 0.844 | 0.900 | ||
| MAD:NMF | 2 | 0.743 | 0.879 | 0.947 | ||
| ATC:hclust | 2 | 0.620 | 0.895 | 0.944 | ||
| SD:hclust | 3 | 0.610 | 0.757 | 0.883 | ||
| MAD:hclust | 3 | 0.547 | 0.757 | 0.875 |
**: 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 0.762 0.913 0.959 0.488 0.514 0.514
#> CV:NMF 2 0.888 0.923 0.967 0.493 0.511 0.511
#> MAD:NMF 2 0.743 0.879 0.947 0.492 0.506 0.506
#> ATC:NMF 2 0.820 0.897 0.957 0.482 0.520 0.520
#> SD:skmeans 2 0.984 0.960 0.983 0.495 0.506 0.506
#> CV:skmeans 2 0.968 0.948 0.979 0.497 0.504 0.504
#> MAD:skmeans 2 1.000 0.953 0.982 0.495 0.506 0.506
#> ATC:skmeans 2 1.000 0.999 1.000 0.492 0.509 0.509
#> SD:mclust 2 0.570 0.886 0.941 0.317 0.702 0.702
#> CV:mclust 2 0.922 0.914 0.968 0.295 0.722 0.722
#> MAD:mclust 2 1.000 0.955 0.975 0.301 0.722 0.722
#> ATC:mclust 2 0.953 0.934 0.974 0.236 0.798 0.798
#> SD:kmeans 2 0.999 0.964 0.985 0.455 0.554 0.554
#> CV:kmeans 2 0.969 0.936 0.974 0.464 0.524 0.524
#> MAD:kmeans 2 1.000 0.957 0.983 0.457 0.549 0.549
#> ATC:kmeans 2 1.000 0.984 0.994 0.468 0.535 0.535
#> SD:pam 2 1.000 0.973 0.989 0.428 0.565 0.565
#> CV:pam 2 0.535 0.821 0.887 0.448 0.549 0.549
#> MAD:pam 2 1.000 0.970 0.988 0.430 0.570 0.570
#> ATC:pam 2 1.000 0.977 0.990 0.476 0.520 0.520
#> SD:hclust 2 0.696 0.876 0.940 0.391 0.632 0.632
#> CV:hclust 2 0.777 0.906 0.954 0.404 0.602 0.602
#> MAD:hclust 2 0.732 0.880 0.942 0.397 0.624 0.624
#> ATC:hclust 2 0.620 0.895 0.944 0.449 0.531 0.531
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.815 0.843 0.933 0.276 0.778 0.604
#> CV:NMF 3 0.732 0.819 0.922 0.332 0.725 0.512
#> MAD:NMF 3 0.774 0.829 0.923 0.273 0.795 0.620
#> ATC:NMF 3 0.426 0.564 0.774 0.349 0.702 0.488
#> SD:skmeans 3 0.931 0.934 0.970 0.341 0.805 0.625
#> CV:skmeans 3 0.958 0.929 0.971 0.335 0.763 0.559
#> MAD:skmeans 3 0.902 0.927 0.968 0.341 0.806 0.626
#> ATC:skmeans 3 0.923 0.929 0.966 0.316 0.811 0.637
#> SD:mclust 3 0.402 0.484 0.751 0.800 0.724 0.609
#> CV:mclust 3 0.962 0.924 0.972 1.064 0.657 0.530
#> MAD:mclust 3 0.425 0.686 0.785 0.864 0.643 0.507
#> ATC:mclust 3 0.794 0.785 0.915 1.565 0.537 0.431
#> SD:kmeans 3 0.859 0.875 0.936 0.390 0.721 0.535
#> CV:kmeans 3 0.798 0.891 0.938 0.405 0.658 0.436
#> MAD:kmeans 3 0.770 0.852 0.915 0.394 0.724 0.537
#> ATC:kmeans 3 1.000 0.959 0.983 0.386 0.675 0.463
#> SD:pam 3 0.767 0.865 0.935 0.487 0.667 0.468
#> CV:pam 3 0.819 0.890 0.944 0.423 0.730 0.542
#> MAD:pam 3 0.765 0.873 0.938 0.488 0.666 0.469
#> ATC:pam 3 1.000 0.984 0.993 0.361 0.682 0.466
#> SD:hclust 3 0.610 0.757 0.883 0.563 0.734 0.584
#> CV:hclust 3 0.620 0.839 0.889 0.183 0.952 0.921
#> MAD:hclust 3 0.547 0.757 0.875 0.558 0.722 0.563
#> ATC:hclust 3 0.496 0.728 0.817 0.403 0.789 0.607
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.610 0.675 0.841 0.1581 0.767 0.474
#> CV:NMF 4 0.619 0.699 0.827 0.1343 0.771 0.449
#> MAD:NMF 4 0.643 0.663 0.844 0.1511 0.807 0.538
#> ATC:NMF 4 0.534 0.571 0.779 0.1494 0.687 0.311
#> SD:skmeans 4 0.739 0.778 0.873 0.0934 0.894 0.701
#> CV:skmeans 4 0.903 0.900 0.944 0.1139 0.876 0.654
#> MAD:skmeans 4 0.800 0.854 0.916 0.1003 0.907 0.733
#> ATC:skmeans 4 0.841 0.824 0.925 0.0553 0.876 0.683
#> SD:mclust 4 0.588 0.710 0.786 0.1711 0.759 0.517
#> CV:mclust 4 0.627 0.611 0.823 0.1372 0.899 0.757
#> MAD:mclust 4 0.671 0.834 0.885 0.1962 0.842 0.632
#> ATC:mclust 4 0.784 0.772 0.900 0.1149 0.882 0.698
#> SD:kmeans 4 0.724 0.846 0.899 0.1747 0.798 0.508
#> CV:kmeans 4 0.695 0.772 0.879 0.1012 0.906 0.744
#> MAD:kmeans 4 0.724 0.843 0.897 0.1679 0.798 0.508
#> ATC:kmeans 4 0.907 0.886 0.940 0.1241 0.795 0.505
#> SD:pam 4 0.750 0.782 0.892 0.1509 0.778 0.476
#> CV:pam 4 0.908 0.908 0.955 0.1674 0.833 0.571
#> MAD:pam 4 0.809 0.770 0.900 0.1505 0.820 0.544
#> ATC:pam 4 0.950 0.920 0.969 0.1123 0.902 0.732
#> SD:hclust 4 0.648 0.740 0.874 0.1155 0.954 0.879
#> CV:hclust 4 0.569 0.720 0.816 0.3235 0.740 0.541
#> MAD:hclust 4 0.652 0.733 0.871 0.1112 0.952 0.872
#> ATC:hclust 4 0.606 0.725 0.843 0.1356 0.853 0.605
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.603 0.636 0.791 0.0753 0.847 0.523
#> CV:NMF 5 0.585 0.502 0.717 0.0724 0.837 0.475
#> MAD:NMF 5 0.642 0.679 0.821 0.0816 0.839 0.503
#> ATC:NMF 5 0.541 0.443 0.678 0.0618 0.870 0.557
#> SD:skmeans 5 0.786 0.709 0.866 0.0558 0.932 0.758
#> CV:skmeans 5 0.847 0.799 0.903 0.0488 0.935 0.764
#> MAD:skmeans 5 0.782 0.726 0.873 0.0514 0.906 0.681
#> ATC:skmeans 5 0.839 0.785 0.900 0.0518 0.888 0.689
#> SD:mclust 5 0.758 0.844 0.900 0.1380 0.810 0.486
#> CV:mclust 5 0.612 0.578 0.757 0.1110 0.767 0.403
#> MAD:mclust 5 0.758 0.866 0.909 0.1325 0.825 0.523
#> ATC:mclust 5 0.942 0.914 0.954 0.1255 0.872 0.614
#> SD:kmeans 5 0.716 0.703 0.827 0.0705 0.896 0.624
#> CV:kmeans 5 0.705 0.617 0.774 0.0810 0.844 0.529
#> MAD:kmeans 5 0.710 0.720 0.823 0.0650 0.917 0.689
#> ATC:kmeans 5 0.765 0.710 0.854 0.0754 0.906 0.679
#> SD:pam 5 0.731 0.781 0.879 0.0663 0.853 0.535
#> CV:pam 5 0.925 0.904 0.962 0.0373 0.971 0.887
#> MAD:pam 5 0.741 0.745 0.879 0.0495 0.951 0.816
#> ATC:pam 5 0.947 0.916 0.965 0.1084 0.908 0.683
#> SD:hclust 5 0.612 0.613 0.755 0.1014 0.867 0.623
#> CV:hclust 5 0.575 0.734 0.814 0.0763 0.918 0.764
#> MAD:hclust 5 0.628 0.576 0.734 0.0964 0.877 0.634
#> ATC:hclust 5 0.664 0.618 0.740 0.0801 0.864 0.558
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.651 0.668 0.796 0.0484 0.909 0.629
#> CV:NMF 6 0.703 0.663 0.794 0.0442 0.892 0.539
#> MAD:NMF 6 0.641 0.642 0.782 0.0457 0.919 0.662
#> ATC:NMF 6 0.593 0.513 0.707 0.0371 0.888 0.553
#> SD:skmeans 6 0.807 0.744 0.870 0.0472 0.898 0.612
#> CV:skmeans 6 0.839 0.685 0.851 0.0284 0.972 0.881
#> MAD:skmeans 6 0.809 0.763 0.873 0.0444 0.892 0.593
#> ATC:skmeans 6 0.842 0.807 0.908 0.0555 0.912 0.719
#> SD:mclust 6 0.797 0.775 0.868 0.0625 0.893 0.586
#> CV:mclust 6 0.702 0.624 0.795 0.0468 0.903 0.610
#> MAD:mclust 6 0.773 0.771 0.843 0.0628 0.887 0.568
#> ATC:mclust 6 0.719 0.732 0.797 0.0286 0.934 0.745
#> SD:kmeans 6 0.781 0.691 0.836 0.0389 0.929 0.680
#> CV:kmeans 6 0.711 0.579 0.763 0.0491 0.926 0.699
#> MAD:kmeans 6 0.778 0.728 0.842 0.0403 0.948 0.757
#> ATC:kmeans 6 0.762 0.736 0.850 0.0486 0.896 0.576
#> SD:pam 6 0.720 0.724 0.804 0.0525 0.910 0.619
#> CV:pam 6 0.819 0.792 0.888 0.0300 0.991 0.963
#> MAD:pam 6 0.714 0.622 0.817 0.0616 0.887 0.555
#> ATC:pam 6 0.970 0.928 0.967 0.0333 0.937 0.715
#> SD:hclust 6 0.683 0.666 0.722 0.0597 0.915 0.660
#> CV:hclust 6 0.635 0.733 0.825 0.0603 0.966 0.890
#> MAD:hclust 6 0.683 0.650 0.716 0.0592 0.904 0.613
#> ATC:hclust 6 0.745 0.780 0.868 0.0569 0.916 0.649
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 13580 rows and 133 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.696 0.876 0.940 0.3909 0.632 0.632
#> 3 3 0.610 0.757 0.883 0.5631 0.734 0.584
#> 4 4 0.648 0.740 0.874 0.1155 0.954 0.879
#> 5 5 0.612 0.613 0.755 0.1014 0.867 0.623
#> 6 6 0.683 0.666 0.722 0.0597 0.915 0.660
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
#> SRR1951667 2 0.0000 0.946 0.000 1.000
#> SRR1951666 1 0.9000 0.620 0.684 0.316
#> SRR1951665 2 0.0000 0.946 0.000 1.000
#> SRR1951664 1 0.0000 0.930 1.000 0.000
#> SRR1951663 1 0.0672 0.928 0.992 0.008
#> SRR1951662 2 0.0000 0.946 0.000 1.000
#> SRR1951661 1 0.8443 0.692 0.728 0.272
#> SRR1951660 1 0.0000 0.930 1.000 0.000
#> SRR1951659 2 0.0000 0.946 0.000 1.000
#> SRR1951658 2 0.0000 0.946 0.000 1.000
#> SRR1951657 1 0.0000 0.930 1.000 0.000
#> SRR1951656 1 0.0000 0.930 1.000 0.000
#> SRR1951655 1 0.0000 0.930 1.000 0.000
#> SRR1951654 1 0.0000 0.930 1.000 0.000
#> SRR1951653 1 0.0000 0.930 1.000 0.000
#> SRR1951652 2 0.4939 0.863 0.108 0.892
#> SRR1951651 1 0.0000 0.930 1.000 0.000
#> SRR1951650 1 0.1184 0.924 0.984 0.016
#> SRR1951648 1 0.8499 0.686 0.724 0.276
#> SRR1951649 1 0.0000 0.930 1.000 0.000
#> SRR1951647 1 0.5059 0.863 0.888 0.112
#> SRR1951646 1 0.0000 0.930 1.000 0.000
#> SRR1951645 1 0.0000 0.930 1.000 0.000
#> SRR1951644 1 0.0000 0.930 1.000 0.000
#> SRR1951643 1 0.0000 0.930 1.000 0.000
#> SRR1951641 2 0.0000 0.946 0.000 1.000
#> SRR1951642 1 0.0000 0.930 1.000 0.000
#> SRR1951694 1 0.0938 0.926 0.988 0.012
#> SRR1951731 1 0.5178 0.860 0.884 0.116
#> SRR1951730 2 0.5629 0.835 0.132 0.868
#> SRR1951752 1 0.8813 0.649 0.700 0.300
#> SRR1951751 1 0.0000 0.930 1.000 0.000
#> SRR1951749 2 1.0000 -0.139 0.500 0.500
#> SRR1951750 1 0.0000 0.930 1.000 0.000
#> SRR1951748 2 0.0000 0.946 0.000 1.000
#> SRR1951747 2 0.0000 0.946 0.000 1.000
#> SRR1951745 1 0.0672 0.928 0.992 0.008
#> SRR1951746 2 0.0000 0.946 0.000 1.000
#> SRR1951744 1 0.7815 0.745 0.768 0.232
#> SRR1951742 2 0.8386 0.608 0.268 0.732
#> SRR1951743 2 0.0000 0.946 0.000 1.000
#> SRR1951741 1 0.0000 0.930 1.000 0.000
#> SRR1951740 1 0.0000 0.930 1.000 0.000
#> SRR1951738 2 0.2043 0.930 0.032 0.968
#> SRR1951739 2 0.1184 0.939 0.016 0.984
#> SRR1951737 1 0.0000 0.930 1.000 0.000
#> SRR1951736 1 0.0000 0.930 1.000 0.000
#> SRR1951734 2 0.0000 0.946 0.000 1.000
#> SRR1951735 1 0.0672 0.928 0.992 0.008
#> SRR1951733 1 1.0000 0.118 0.504 0.496
#> SRR1951732 2 0.0000 0.946 0.000 1.000
#> SRR1951729 1 0.0000 0.930 1.000 0.000
#> SRR1951728 1 0.2603 0.910 0.956 0.044
#> SRR1951727 2 0.0000 0.946 0.000 1.000
#> SRR1951725 1 0.0000 0.930 1.000 0.000
#> SRR1951726 1 0.8081 0.725 0.752 0.248
#> SRR1951724 1 0.0000 0.930 1.000 0.000
#> SRR1951722 1 0.2423 0.912 0.960 0.040
#> SRR1951723 1 0.0000 0.930 1.000 0.000
#> SRR1951721 1 0.0938 0.926 0.988 0.012
#> SRR1951719 1 0.0000 0.930 1.000 0.000
#> SRR1951720 1 0.0000 0.930 1.000 0.000
#> SRR1951718 1 0.0000 0.930 1.000 0.000
#> SRR1951717 1 0.0000 0.930 1.000 0.000
#> SRR1951715 1 0.0938 0.926 0.988 0.012
#> SRR1951716 1 0.0000 0.930 1.000 0.000
#> SRR1951714 1 0.0000 0.930 1.000 0.000
#> SRR1951713 1 0.0000 0.930 1.000 0.000
#> SRR1951712 1 0.0000 0.930 1.000 0.000
#> SRR1951711 2 0.0000 0.946 0.000 1.000
#> SRR1951710 1 0.0376 0.929 0.996 0.004
#> SRR1951709 1 0.8661 0.668 0.712 0.288
#> SRR1951708 2 0.0000 0.946 0.000 1.000
#> SRR1951707 1 0.5629 0.847 0.868 0.132
#> SRR1951705 1 0.0000 0.930 1.000 0.000
#> SRR1951704 1 0.7815 0.745 0.768 0.232
#> SRR1951703 2 0.0000 0.946 0.000 1.000
#> SRR1951706 1 0.7883 0.740 0.764 0.236
#> SRR1951702 1 0.7815 0.745 0.768 0.232
#> SRR1951701 2 0.0000 0.946 0.000 1.000
#> SRR1951699 1 0.8443 0.692 0.728 0.272
#> SRR1951698 1 0.7219 0.781 0.800 0.200
#> SRR1951700 1 0.2236 0.914 0.964 0.036
#> SRR1951697 2 0.0000 0.946 0.000 1.000
#> SRR1951696 1 0.0000 0.930 1.000 0.000
#> SRR1951695 1 0.2236 0.914 0.964 0.036
#> SRR1951693 1 0.2236 0.914 0.964 0.036
#> SRR1951692 1 0.0000 0.930 1.000 0.000
#> SRR1951691 1 0.0000 0.930 1.000 0.000
#> SRR1951688 2 0.3431 0.907 0.064 0.936
#> SRR1951689 1 0.8861 0.642 0.696 0.304
#> SRR1951687 1 0.0000 0.930 1.000 0.000
#> SRR1951685 1 0.5629 0.847 0.868 0.132
#> SRR1951684 1 0.0000 0.930 1.000 0.000
#> SRR1951686 2 0.7528 0.706 0.216 0.784
#> SRR1951683 1 0.0000 0.930 1.000 0.000
#> SRR1951682 2 0.0000 0.946 0.000 1.000
#> SRR1951690 2 0.0000 0.946 0.000 1.000
#> SRR1951680 1 0.0000 0.930 1.000 0.000
#> SRR1951679 1 0.2603 0.909 0.956 0.044
#> SRR1951678 1 0.0000 0.930 1.000 0.000
#> SRR1951677 1 0.0000 0.930 1.000 0.000
#> SRR1951676 1 0.0938 0.926 0.988 0.012
#> SRR1951675 2 0.0000 0.946 0.000 1.000
#> SRR1951681 1 0.2423 0.913 0.960 0.040
#> SRR1951673 1 0.8861 0.642 0.696 0.304
#> SRR1951674 1 0.0672 0.928 0.992 0.008
#> SRR1951672 1 0.0000 0.930 1.000 0.000
#> SRR1951671 1 0.8909 0.635 0.692 0.308
#> SRR1951670 1 0.0000 0.930 1.000 0.000
#> SRR1951640 1 0.0000 0.930 1.000 0.000
#> SRR1951668 1 0.0000 0.930 1.000 0.000
#> SRR1951669 2 0.3431 0.907 0.064 0.936
#> SRR1951638 1 0.0000 0.930 1.000 0.000
#> SRR1951637 2 0.3733 0.900 0.072 0.928
#> SRR1951635 1 0.0000 0.930 1.000 0.000
#> SRR1951634 1 0.0000 0.930 1.000 0.000
#> SRR1951631 1 0.0000 0.930 1.000 0.000
#> SRR1951632 2 0.1184 0.939 0.016 0.984
#> SRR1951633 1 0.0000 0.930 1.000 0.000
#> SRR1951630 1 0.0000 0.930 1.000 0.000
#> SRR1951629 1 0.0000 0.930 1.000 0.000
#> SRR1951628 1 0.4298 0.881 0.912 0.088
#> SRR1951627 1 0.8661 0.668 0.712 0.288
#> SRR1951639 1 0.7219 0.781 0.800 0.200
#> SRR1951625 1 0.8016 0.731 0.756 0.244
#> SRR1951626 1 0.2423 0.913 0.960 0.040
#> SRR1951624 1 0.0000 0.930 1.000 0.000
#> SRR1951622 1 0.0000 0.930 1.000 0.000
#> SRR1951636 1 0.2423 0.913 0.960 0.040
#> SRR1951623 1 0.7674 0.754 0.776 0.224
#> SRR1951621 1 0.0000 0.930 1.000 0.000
#> SRR1951620 1 0.0000 0.930 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.2537 0.899 0.000 0.920 0.080
#> SRR1951666 3 0.2537 0.702 0.000 0.080 0.920
#> SRR1951665 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951664 1 0.1860 0.867 0.948 0.000 0.052
#> SRR1951663 3 0.6225 0.241 0.432 0.000 0.568
#> SRR1951662 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951661 3 0.1765 0.727 0.004 0.040 0.956
#> SRR1951660 1 0.2356 0.863 0.928 0.000 0.072
#> SRR1951659 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951657 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951656 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951655 1 0.0892 0.878 0.980 0.000 0.020
#> SRR1951654 1 0.1031 0.873 0.976 0.000 0.024
#> SRR1951653 1 0.1753 0.868 0.952 0.000 0.048
#> SRR1951652 2 0.4700 0.811 0.008 0.812 0.180
#> SRR1951651 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951650 1 0.3482 0.817 0.872 0.000 0.128
#> SRR1951648 3 0.1878 0.726 0.004 0.044 0.952
#> SRR1951649 1 0.3412 0.828 0.876 0.000 0.124
#> SRR1951647 3 0.5815 0.554 0.304 0.004 0.692
#> SRR1951646 1 0.1964 0.865 0.944 0.000 0.056
#> SRR1951645 1 0.0747 0.878 0.984 0.000 0.016
#> SRR1951644 1 0.3412 0.828 0.876 0.000 0.124
#> SRR1951643 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951641 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951642 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951694 3 0.6286 0.131 0.464 0.000 0.536
#> SRR1951731 3 0.5723 0.632 0.240 0.016 0.744
#> SRR1951730 2 0.5247 0.754 0.008 0.768 0.224
#> SRR1951752 3 0.2400 0.718 0.004 0.064 0.932
#> SRR1951751 1 0.2261 0.864 0.932 0.000 0.068
#> SRR1951749 3 0.5254 0.429 0.000 0.264 0.736
#> SRR1951750 1 0.2261 0.864 0.932 0.000 0.068
#> SRR1951748 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951745 3 0.6225 0.241 0.432 0.000 0.568
#> SRR1951746 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951744 3 0.1753 0.742 0.048 0.000 0.952
#> SRR1951742 3 0.6309 -0.269 0.000 0.496 0.504
#> SRR1951743 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951741 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951740 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951738 2 0.2955 0.900 0.008 0.912 0.080
#> SRR1951739 2 0.2959 0.888 0.000 0.900 0.100
#> SRR1951737 1 0.2448 0.855 0.924 0.000 0.076
#> SRR1951736 1 0.2261 0.864 0.932 0.000 0.068
#> SRR1951734 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951735 1 0.4750 0.723 0.784 0.000 0.216
#> SRR1951733 3 0.5443 0.437 0.004 0.260 0.736
#> SRR1951732 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951729 1 0.2625 0.850 0.916 0.000 0.084
#> SRR1951728 1 0.5678 0.558 0.684 0.000 0.316
#> SRR1951727 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951725 1 0.4974 0.696 0.764 0.000 0.236
#> SRR1951726 3 0.2152 0.739 0.036 0.016 0.948
#> SRR1951724 1 0.3267 0.838 0.884 0.000 0.116
#> SRR1951722 1 0.5560 0.596 0.700 0.000 0.300
#> SRR1951723 1 0.3412 0.828 0.876 0.000 0.124
#> SRR1951721 3 0.6215 0.247 0.428 0.000 0.572
#> SRR1951719 1 0.2625 0.850 0.916 0.000 0.084
#> SRR1951720 1 0.4931 0.702 0.768 0.000 0.232
#> SRR1951718 1 0.2356 0.863 0.928 0.000 0.072
#> SRR1951717 1 0.2261 0.864 0.932 0.000 0.068
#> SRR1951715 3 0.6215 0.247 0.428 0.000 0.572
#> SRR1951716 1 0.2356 0.863 0.928 0.000 0.072
#> SRR1951714 1 0.0747 0.879 0.984 0.000 0.016
#> SRR1951713 1 0.0747 0.878 0.984 0.000 0.016
#> SRR1951712 1 0.2261 0.864 0.932 0.000 0.068
#> SRR1951711 2 0.0237 0.941 0.000 0.996 0.004
#> SRR1951710 1 0.6291 0.124 0.532 0.000 0.468
#> SRR1951709 3 0.2200 0.722 0.004 0.056 0.940
#> SRR1951708 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951707 3 0.5848 0.605 0.268 0.012 0.720
#> SRR1951705 1 0.6252 0.201 0.556 0.000 0.444
#> SRR1951704 3 0.1529 0.741 0.040 0.000 0.960
#> SRR1951703 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951706 3 0.1647 0.740 0.036 0.004 0.960
#> SRR1951702 3 0.1529 0.741 0.040 0.000 0.960
#> SRR1951701 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951699 3 0.1765 0.727 0.004 0.040 0.956
#> SRR1951698 3 0.3193 0.742 0.100 0.004 0.896
#> SRR1951700 1 0.5465 0.612 0.712 0.000 0.288
#> SRR1951697 2 0.0747 0.936 0.000 0.984 0.016
#> SRR1951696 1 0.1964 0.865 0.944 0.000 0.056
#> SRR1951695 1 0.6045 0.391 0.620 0.000 0.380
#> SRR1951693 1 0.5465 0.612 0.712 0.000 0.288
#> SRR1951692 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951691 1 0.0747 0.879 0.984 0.000 0.016
#> SRR1951688 2 0.4033 0.858 0.008 0.856 0.136
#> SRR1951689 3 0.2496 0.715 0.004 0.068 0.928
#> SRR1951687 1 0.0237 0.877 0.996 0.000 0.004
#> SRR1951685 3 0.5848 0.605 0.268 0.012 0.720
#> SRR1951684 1 0.0237 0.877 0.996 0.000 0.004
#> SRR1951686 2 0.6180 0.443 0.000 0.584 0.416
#> SRR1951683 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951682 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951690 2 0.0000 0.942 0.000 1.000 0.000
#> SRR1951680 1 0.0747 0.878 0.984 0.000 0.016
#> SRR1951679 3 0.6295 0.115 0.472 0.000 0.528
#> SRR1951678 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951677 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951676 1 0.3686 0.809 0.860 0.000 0.140
#> SRR1951675 2 0.0237 0.941 0.000 0.996 0.004
#> SRR1951681 1 0.5560 0.594 0.700 0.000 0.300
#> SRR1951673 3 0.2496 0.715 0.004 0.068 0.928
#> SRR1951674 3 0.6225 0.241 0.432 0.000 0.568
#> SRR1951672 1 0.3412 0.828 0.876 0.000 0.124
#> SRR1951671 3 0.2356 0.710 0.000 0.072 0.928
#> SRR1951670 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951640 1 0.1964 0.865 0.944 0.000 0.056
#> SRR1951668 1 0.0747 0.878 0.984 0.000 0.016
#> SRR1951669 2 0.4033 0.858 0.008 0.856 0.136
#> SRR1951638 1 0.5497 0.599 0.708 0.000 0.292
#> SRR1951637 2 0.4164 0.851 0.008 0.848 0.144
#> SRR1951635 1 0.4974 0.696 0.764 0.000 0.236
#> SRR1951634 1 0.0747 0.878 0.984 0.000 0.016
#> SRR1951631 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951632 2 0.3038 0.885 0.000 0.896 0.104
#> SRR1951633 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951630 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951629 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951628 3 0.6264 0.382 0.380 0.004 0.616
#> SRR1951627 3 0.2200 0.722 0.004 0.056 0.940
#> SRR1951639 3 0.3573 0.739 0.120 0.004 0.876
#> SRR1951625 3 0.3337 0.742 0.060 0.032 0.908
#> SRR1951626 1 0.5560 0.594 0.700 0.000 0.300
#> SRR1951624 1 0.2625 0.850 0.916 0.000 0.084
#> SRR1951622 1 0.2356 0.863 0.928 0.000 0.072
#> SRR1951636 1 0.5560 0.594 0.700 0.000 0.300
#> SRR1951623 3 0.3482 0.721 0.128 0.000 0.872
#> SRR1951621 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1951620 1 0.1964 0.865 0.944 0.000 0.056
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.2149 0.8840 0.000 0.912 0.088 0.000
#> SRR1951666 3 0.1716 0.8172 0.000 0.064 0.936 0.000
#> SRR1951665 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951664 4 0.1824 0.7950 0.004 0.000 0.060 0.936
#> SRR1951663 1 0.0779 0.7586 0.980 0.000 0.016 0.004
#> SRR1951662 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.1004 0.8263 0.004 0.024 0.972 0.000
#> SRR1951660 4 0.3975 0.6605 0.240 0.000 0.000 0.760
#> SRR1951659 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951657 4 0.0000 0.8103 0.000 0.000 0.000 1.000
#> SRR1951656 4 0.0188 0.8101 0.004 0.000 0.000 0.996
#> SRR1951655 4 0.0817 0.8105 0.024 0.000 0.000 0.976
#> SRR1951654 4 0.2345 0.7696 0.100 0.000 0.000 0.900
#> SRR1951653 4 0.1661 0.7975 0.004 0.000 0.052 0.944
#> SRR1951652 2 0.4182 0.7884 0.024 0.796 0.180 0.000
#> SRR1951651 4 0.0000 0.8103 0.000 0.000 0.000 1.000
#> SRR1951650 4 0.3105 0.7445 0.004 0.000 0.140 0.856
#> SRR1951648 3 0.1109 0.8259 0.004 0.028 0.968 0.000
#> SRR1951649 4 0.4356 0.5878 0.292 0.000 0.000 0.708
#> SRR1951647 3 0.5366 0.4950 0.040 0.000 0.684 0.276
#> SRR1951646 4 0.1824 0.7943 0.004 0.000 0.060 0.936
#> SRR1951645 4 0.1389 0.8023 0.048 0.000 0.000 0.952
#> SRR1951644 4 0.4382 0.5813 0.296 0.000 0.000 0.704
#> SRR1951643 4 0.0000 0.8103 0.000 0.000 0.000 1.000
#> SRR1951641 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951642 4 0.0336 0.8108 0.008 0.000 0.000 0.992
#> SRR1951694 1 0.3708 0.7465 0.832 0.000 0.020 0.148
#> SRR1951731 3 0.4779 0.5984 0.028 0.004 0.756 0.212
#> SRR1951730 2 0.4542 0.7235 0.020 0.752 0.228 0.000
#> SRR1951752 3 0.1975 0.8208 0.016 0.048 0.936 0.000
#> SRR1951751 4 0.3975 0.6584 0.240 0.000 0.000 0.760
#> SRR1951749 3 0.4220 0.6048 0.004 0.248 0.748 0.000
#> SRR1951750 4 0.3942 0.6621 0.236 0.000 0.000 0.764
#> SRR1951748 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951745 1 0.0779 0.7586 0.980 0.000 0.016 0.004
#> SRR1951746 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951744 3 0.1820 0.8190 0.020 0.000 0.944 0.036
#> SRR1951742 3 0.4994 -0.0579 0.000 0.480 0.520 0.000
#> SRR1951743 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951741 4 0.0469 0.8104 0.012 0.000 0.000 0.988
#> SRR1951740 4 0.0188 0.8101 0.004 0.000 0.000 0.996
#> SRR1951738 2 0.2450 0.8923 0.016 0.912 0.072 0.000
#> SRR1951739 2 0.2469 0.8709 0.000 0.892 0.108 0.000
#> SRR1951737 4 0.2271 0.7845 0.008 0.000 0.076 0.916
#> SRR1951736 4 0.3942 0.6621 0.236 0.000 0.000 0.764
#> SRR1951734 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951735 4 0.4053 0.6511 0.004 0.000 0.228 0.768
#> SRR1951733 3 0.4453 0.6056 0.012 0.244 0.744 0.000
#> SRR1951732 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951729 4 0.2334 0.7797 0.004 0.000 0.088 0.908
#> SRR1951728 4 0.5812 0.4957 0.048 0.000 0.328 0.624
#> SRR1951727 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951725 4 0.4996 0.0586 0.484 0.000 0.000 0.516
#> SRR1951726 3 0.1871 0.8246 0.016 0.012 0.948 0.024
#> SRR1951724 4 0.4621 0.5989 0.284 0.000 0.008 0.708
#> SRR1951722 4 0.5994 0.5206 0.068 0.000 0.296 0.636
#> SRR1951723 4 0.4356 0.5878 0.292 0.000 0.000 0.708
#> SRR1951721 1 0.0188 0.7570 0.996 0.000 0.004 0.000
#> SRR1951719 4 0.2334 0.7797 0.004 0.000 0.088 0.908
#> SRR1951720 4 0.4985 0.1240 0.468 0.000 0.000 0.532
#> SRR1951718 4 0.3975 0.6605 0.240 0.000 0.000 0.760
#> SRR1951717 4 0.3975 0.6584 0.240 0.000 0.000 0.760
#> SRR1951715 1 0.0188 0.7570 0.996 0.000 0.004 0.000
#> SRR1951716 4 0.3907 0.6678 0.232 0.000 0.000 0.768
#> SRR1951714 4 0.0592 0.8108 0.016 0.000 0.000 0.984
#> SRR1951713 4 0.1389 0.8023 0.048 0.000 0.000 0.952
#> SRR1951712 4 0.3975 0.6584 0.240 0.000 0.000 0.760
#> SRR1951711 2 0.0188 0.9327 0.004 0.996 0.000 0.000
#> SRR1951710 1 0.4509 0.6050 0.708 0.000 0.004 0.288
#> SRR1951709 3 0.1398 0.8256 0.004 0.040 0.956 0.000
#> SRR1951708 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951707 3 0.5531 0.6056 0.128 0.000 0.732 0.140
#> SRR1951705 1 0.4819 0.4890 0.652 0.000 0.004 0.344
#> SRR1951704 3 0.1510 0.8209 0.016 0.000 0.956 0.028
#> SRR1951703 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951706 3 0.1406 0.8216 0.016 0.000 0.960 0.024
#> SRR1951702 3 0.1510 0.8209 0.016 0.000 0.956 0.028
#> SRR1951701 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951699 3 0.1004 0.8263 0.004 0.024 0.972 0.000
#> SRR1951698 3 0.2342 0.7932 0.008 0.000 0.912 0.080
#> SRR1951700 4 0.5769 0.5398 0.056 0.000 0.292 0.652
#> SRR1951697 2 0.0921 0.9235 0.000 0.972 0.028 0.000
#> SRR1951696 4 0.1824 0.7943 0.004 0.000 0.060 0.936
#> SRR1951695 4 0.5735 0.3998 0.032 0.000 0.392 0.576
#> SRR1951693 4 0.5769 0.5398 0.056 0.000 0.292 0.652
#> SRR1951692 4 0.0188 0.8101 0.004 0.000 0.000 0.996
#> SRR1951691 4 0.0592 0.8108 0.016 0.000 0.000 0.984
#> SRR1951688 2 0.3606 0.8376 0.020 0.840 0.140 0.000
#> SRR1951689 3 0.2060 0.8193 0.016 0.052 0.932 0.000
#> SRR1951687 4 0.1637 0.7948 0.060 0.000 0.000 0.940
#> SRR1951685 3 0.5531 0.6056 0.128 0.000 0.732 0.140
#> SRR1951684 4 0.0376 0.8096 0.004 0.000 0.004 0.992
#> SRR1951686 2 0.4933 0.3068 0.000 0.568 0.432 0.000
#> SRR1951683 4 0.0000 0.8103 0.000 0.000 0.000 1.000
#> SRR1951682 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9341 0.000 1.000 0.000 0.000
#> SRR1951680 4 0.1389 0.8023 0.048 0.000 0.000 0.952
#> SRR1951679 1 0.6194 0.6212 0.644 0.000 0.096 0.260
#> SRR1951678 4 0.0000 0.8103 0.000 0.000 0.000 1.000
#> SRR1951677 4 0.0000 0.8103 0.000 0.000 0.000 1.000
#> SRR1951676 4 0.3351 0.7346 0.008 0.000 0.148 0.844
#> SRR1951675 2 0.0188 0.9327 0.004 0.996 0.000 0.000
#> SRR1951681 4 0.4792 0.5523 0.008 0.000 0.312 0.680
#> SRR1951673 3 0.2060 0.8193 0.016 0.052 0.932 0.000
#> SRR1951674 1 0.0779 0.7586 0.980 0.000 0.016 0.004
#> SRR1951672 4 0.4356 0.5878 0.292 0.000 0.000 0.708
#> SRR1951671 3 0.2021 0.8187 0.012 0.056 0.932 0.000
#> SRR1951670 4 0.0592 0.8101 0.016 0.000 0.000 0.984
#> SRR1951640 4 0.1824 0.7943 0.004 0.000 0.060 0.936
#> SRR1951668 4 0.1389 0.8023 0.048 0.000 0.000 0.952
#> SRR1951669 2 0.3606 0.8376 0.020 0.840 0.140 0.000
#> SRR1951638 1 0.4406 0.5874 0.700 0.000 0.000 0.300
#> SRR1951637 2 0.3708 0.8293 0.020 0.832 0.148 0.000
#> SRR1951635 4 0.4996 0.0586 0.484 0.000 0.000 0.516
#> SRR1951634 4 0.1389 0.8023 0.048 0.000 0.000 0.952
#> SRR1951631 4 0.0000 0.8103 0.000 0.000 0.000 1.000
#> SRR1951632 2 0.2530 0.8675 0.000 0.888 0.112 0.000
#> SRR1951633 4 0.0524 0.8116 0.008 0.000 0.004 0.988
#> SRR1951630 4 0.0000 0.8103 0.000 0.000 0.000 1.000
#> SRR1951629 4 0.0000 0.8103 0.000 0.000 0.000 1.000
#> SRR1951628 3 0.7319 0.1891 0.220 0.000 0.532 0.248
#> SRR1951627 3 0.1398 0.8256 0.004 0.040 0.956 0.000
#> SRR1951639 3 0.2984 0.7788 0.028 0.000 0.888 0.084
#> SRR1951625 3 0.2920 0.8116 0.032 0.020 0.908 0.040
#> SRR1951626 4 0.4792 0.5523 0.008 0.000 0.312 0.680
#> SRR1951624 4 0.2480 0.7772 0.008 0.000 0.088 0.904
#> SRR1951622 4 0.3975 0.6605 0.240 0.000 0.000 0.760
#> SRR1951636 4 0.4792 0.5523 0.008 0.000 0.312 0.680
#> SRR1951623 3 0.2714 0.7598 0.004 0.000 0.884 0.112
#> SRR1951621 4 0.0469 0.8104 0.012 0.000 0.000 0.988
#> SRR1951620 4 0.1824 0.7943 0.004 0.000 0.060 0.936
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.2888 0.8761 0.036 0.888 0.056 0.000 0.020
#> SRR1951666 5 0.2665 0.7828 0.020 0.048 0.032 0.000 0.900
#> SRR1951665 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 3 0.4090 0.6636 0.000 0.000 0.716 0.268 0.016
#> SRR1951663 1 0.3002 0.8565 0.876 0.000 0.048 0.068 0.008
#> SRR1951662 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 5 0.1281 0.7966 0.000 0.012 0.032 0.000 0.956
#> SRR1951660 4 0.1018 0.5731 0.016 0.000 0.016 0.968 0.000
#> SRR1951659 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951657 4 0.4210 0.3685 0.000 0.000 0.412 0.588 0.000
#> SRR1951656 3 0.4305 -0.0869 0.000 0.000 0.512 0.488 0.000
#> SRR1951655 4 0.4183 0.5013 0.008 0.000 0.324 0.668 0.000
#> SRR1951654 4 0.5433 0.4786 0.092 0.000 0.288 0.620 0.000
#> SRR1951653 3 0.4173 0.6231 0.000 0.000 0.688 0.300 0.012
#> SRR1951652 2 0.4473 0.7678 0.012 0.764 0.056 0.000 0.168
#> SRR1951651 4 0.4310 0.4031 0.004 0.000 0.392 0.604 0.000
#> SRR1951650 3 0.5309 0.6668 0.012 0.000 0.676 0.236 0.076
#> SRR1951648 5 0.0912 0.7949 0.000 0.016 0.012 0.000 0.972
#> SRR1951649 4 0.2144 0.5501 0.068 0.000 0.020 0.912 0.000
#> SRR1951647 5 0.5834 0.5373 0.012 0.000 0.212 0.136 0.640
#> SRR1951646 3 0.4219 0.6636 0.004 0.000 0.716 0.264 0.016
#> SRR1951645 4 0.3586 0.5186 0.000 0.000 0.264 0.736 0.000
#> SRR1951644 4 0.1942 0.5472 0.068 0.000 0.012 0.920 0.000
#> SRR1951643 4 0.4310 0.4031 0.004 0.000 0.392 0.604 0.000
#> SRR1951641 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951642 4 0.4517 0.4100 0.012 0.000 0.388 0.600 0.000
#> SRR1951694 1 0.4817 0.7135 0.680 0.000 0.056 0.264 0.000
#> SRR1951731 5 0.6069 0.6196 0.048 0.000 0.200 0.100 0.652
#> SRR1951730 2 0.4974 0.7069 0.020 0.720 0.056 0.000 0.204
#> SRR1951752 5 0.3738 0.7644 0.044 0.024 0.096 0.000 0.836
#> SRR1951751 4 0.0912 0.5719 0.012 0.000 0.016 0.972 0.000
#> SRR1951749 5 0.5380 0.5791 0.036 0.224 0.052 0.000 0.688
#> SRR1951750 4 0.0807 0.5731 0.012 0.000 0.012 0.976 0.000
#> SRR1951748 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 1 0.3002 0.8565 0.876 0.000 0.048 0.068 0.008
#> SRR1951746 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 5 0.3047 0.7783 0.024 0.000 0.096 0.012 0.868
#> SRR1951742 5 0.6201 -0.0837 0.036 0.452 0.056 0.000 0.456
#> SRR1951743 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 4 0.4029 0.5022 0.004 0.000 0.316 0.680 0.000
#> SRR1951740 3 0.4306 -0.1034 0.000 0.000 0.508 0.492 0.000
#> SRR1951738 2 0.2694 0.8775 0.008 0.892 0.032 0.000 0.068
#> SRR1951739 2 0.3310 0.8655 0.036 0.868 0.056 0.000 0.040
#> SRR1951737 3 0.4080 0.6690 0.000 0.000 0.728 0.252 0.020
#> SRR1951736 4 0.0807 0.5731 0.012 0.000 0.012 0.976 0.000
#> SRR1951734 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 3 0.5721 0.6594 0.008 0.000 0.648 0.200 0.144
#> SRR1951733 5 0.5976 0.5713 0.044 0.224 0.084 0.000 0.648
#> SRR1951732 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 3 0.3970 0.6820 0.000 0.000 0.744 0.236 0.020
#> SRR1951728 3 0.6973 0.5606 0.024 0.000 0.496 0.236 0.244
#> SRR1951727 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 4 0.3967 0.2223 0.264 0.000 0.012 0.724 0.000
#> SRR1951726 5 0.2669 0.7834 0.020 0.000 0.104 0.000 0.876
#> SRR1951724 4 0.3916 0.4966 0.092 0.000 0.104 0.804 0.000
#> SRR1951722 3 0.7200 0.5378 0.032 0.000 0.464 0.276 0.228
#> SRR1951723 4 0.2144 0.5501 0.068 0.000 0.020 0.912 0.000
#> SRR1951721 1 0.2361 0.8539 0.892 0.000 0.012 0.096 0.000
#> SRR1951719 3 0.3970 0.6820 0.000 0.000 0.744 0.236 0.020
#> SRR1951720 4 0.3863 0.2469 0.248 0.000 0.012 0.740 0.000
#> SRR1951718 4 0.1018 0.5731 0.016 0.000 0.016 0.968 0.000
#> SRR1951717 4 0.0912 0.5719 0.012 0.000 0.016 0.972 0.000
#> SRR1951715 1 0.2361 0.8539 0.892 0.000 0.012 0.096 0.000
#> SRR1951716 4 0.1117 0.5728 0.016 0.000 0.020 0.964 0.000
#> SRR1951714 4 0.4151 0.4809 0.004 0.000 0.344 0.652 0.000
#> SRR1951713 4 0.3586 0.5186 0.000 0.000 0.264 0.736 0.000
#> SRR1951712 4 0.0912 0.5719 0.012 0.000 0.016 0.972 0.000
#> SRR1951711 2 0.0324 0.9239 0.004 0.992 0.004 0.000 0.000
#> SRR1951710 4 0.4559 -0.3817 0.480 0.000 0.008 0.512 0.000
#> SRR1951709 5 0.1211 0.7952 0.000 0.024 0.016 0.000 0.960
#> SRR1951708 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951707 5 0.5764 0.6289 0.028 0.000 0.168 0.128 0.676
#> SRR1951705 4 0.4696 -0.2577 0.428 0.000 0.016 0.556 0.000
#> SRR1951704 5 0.2561 0.7806 0.020 0.000 0.096 0.000 0.884
#> SRR1951703 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 5 0.2505 0.7822 0.020 0.000 0.092 0.000 0.888
#> SRR1951702 5 0.2561 0.7806 0.020 0.000 0.096 0.000 0.884
#> SRR1951701 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951699 5 0.1281 0.7966 0.000 0.012 0.032 0.000 0.956
#> SRR1951698 5 0.3010 0.7751 0.012 0.000 0.100 0.020 0.868
#> SRR1951700 3 0.6932 0.5601 0.020 0.000 0.488 0.272 0.220
#> SRR1951697 2 0.1377 0.9101 0.004 0.956 0.020 0.000 0.020
#> SRR1951696 3 0.4040 0.6637 0.000 0.000 0.724 0.260 0.016
#> SRR1951695 3 0.6625 0.4621 0.016 0.000 0.520 0.168 0.296
#> SRR1951693 3 0.6932 0.5601 0.020 0.000 0.488 0.272 0.220
#> SRR1951692 3 0.4306 -0.1024 0.000 0.000 0.508 0.492 0.000
#> SRR1951691 4 0.4151 0.4809 0.004 0.000 0.344 0.652 0.000
#> SRR1951688 2 0.4011 0.8182 0.012 0.808 0.056 0.000 0.124
#> SRR1951689 5 0.3770 0.7611 0.044 0.028 0.092 0.000 0.836
#> SRR1951687 4 0.4907 0.5041 0.052 0.000 0.292 0.656 0.000
#> SRR1951685 5 0.5764 0.6289 0.028 0.000 0.168 0.128 0.676
#> SRR1951684 3 0.4235 0.2701 0.000 0.000 0.576 0.424 0.000
#> SRR1951686 2 0.5759 0.3017 0.024 0.540 0.044 0.000 0.392
#> SRR1951683 4 0.4310 0.4031 0.004 0.000 0.392 0.604 0.000
#> SRR1951682 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9262 0.000 1.000 0.000 0.000 0.000
#> SRR1951680 4 0.3586 0.5186 0.000 0.000 0.264 0.736 0.000
#> SRR1951679 4 0.6855 -0.4868 0.428 0.000 0.084 0.428 0.060
#> SRR1951678 4 0.4341 0.3849 0.004 0.000 0.404 0.592 0.000
#> SRR1951677 4 0.4341 0.3849 0.004 0.000 0.404 0.592 0.000
#> SRR1951676 3 0.4863 0.6722 0.008 0.000 0.716 0.212 0.064
#> SRR1951675 2 0.0324 0.9239 0.004 0.992 0.004 0.000 0.000
#> SRR1951681 3 0.6350 0.6207 0.016 0.000 0.584 0.180 0.220
#> SRR1951673 5 0.3770 0.7611 0.044 0.028 0.092 0.000 0.836
#> SRR1951674 1 0.3002 0.8565 0.876 0.000 0.048 0.068 0.008
#> SRR1951672 4 0.2144 0.5501 0.068 0.000 0.020 0.912 0.000
#> SRR1951671 5 0.3640 0.7615 0.040 0.028 0.088 0.000 0.844
#> SRR1951670 4 0.3949 0.5147 0.004 0.000 0.300 0.696 0.000
#> SRR1951640 3 0.4194 0.6620 0.004 0.000 0.720 0.260 0.016
#> SRR1951668 4 0.3586 0.5186 0.000 0.000 0.264 0.736 0.000
#> SRR1951669 2 0.4011 0.8182 0.012 0.808 0.056 0.000 0.124
#> SRR1951638 1 0.5221 0.4814 0.552 0.000 0.048 0.400 0.000
#> SRR1951637 2 0.4103 0.8096 0.012 0.800 0.056 0.000 0.132
#> SRR1951635 4 0.3967 0.2223 0.264 0.000 0.012 0.724 0.000
#> SRR1951634 4 0.3586 0.5186 0.000 0.000 0.264 0.736 0.000
#> SRR1951631 4 0.4210 0.3752 0.000 0.000 0.412 0.588 0.000
#> SRR1951632 2 0.3385 0.8627 0.036 0.864 0.056 0.000 0.044
#> SRR1951633 4 0.4527 0.4062 0.012 0.000 0.392 0.596 0.000
#> SRR1951630 4 0.4341 0.3849 0.004 0.000 0.404 0.592 0.000
#> SRR1951629 4 0.4210 0.3752 0.000 0.000 0.412 0.588 0.000
#> SRR1951628 5 0.7109 0.2245 0.092 0.000 0.084 0.336 0.488
#> SRR1951627 5 0.1211 0.7952 0.000 0.024 0.016 0.000 0.960
#> SRR1951639 5 0.3609 0.7658 0.016 0.000 0.112 0.036 0.836
#> SRR1951625 5 0.3170 0.7795 0.008 0.012 0.068 0.036 0.876
#> SRR1951626 3 0.6350 0.6207 0.016 0.000 0.584 0.180 0.220
#> SRR1951624 3 0.4067 0.6830 0.004 0.000 0.748 0.228 0.020
#> SRR1951622 4 0.1018 0.5731 0.016 0.000 0.016 0.968 0.000
#> SRR1951636 3 0.6350 0.6207 0.016 0.000 0.584 0.180 0.220
#> SRR1951623 5 0.3870 0.7234 0.016 0.000 0.176 0.016 0.792
#> SRR1951621 4 0.4029 0.5022 0.004 0.000 0.316 0.680 0.000
#> SRR1951620 3 0.4194 0.6620 0.004 0.000 0.720 0.260 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 2 0.3048 0.85993 0.024 0.860 0.072 0.000 0.044 0.000
#> SRR1951666 5 0.2085 0.73176 0.008 0.024 0.056 0.000 0.912 0.000
#> SRR1951665 2 0.0000 0.91208 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 3 0.4337 0.70024 0.020 0.000 0.500 0.480 0.000 0.000
#> SRR1951663 6 0.0806 0.84163 0.008 0.000 0.020 0.000 0.000 0.972
#> SRR1951662 2 0.0000 0.91208 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 5 0.1701 0.74101 0.008 0.000 0.072 0.000 0.920 0.000
#> SRR1951660 1 0.3634 0.68987 0.644 0.000 0.000 0.356 0.000 0.000
#> SRR1951659 2 0.0260 0.91222 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1951658 2 0.0000 0.91208 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951657 4 0.1257 0.72351 0.028 0.000 0.020 0.952 0.000 0.000
#> SRR1951656 4 0.2100 0.53338 0.004 0.000 0.112 0.884 0.000 0.000
#> SRR1951655 4 0.3217 0.64329 0.224 0.000 0.008 0.768 0.000 0.000
#> SRR1951654 4 0.4617 0.50433 0.252 0.000 0.000 0.664 0.000 0.084
#> SRR1951653 4 0.4797 -0.64143 0.052 0.000 0.444 0.504 0.000 0.000
#> SRR1951652 2 0.4768 0.73972 0.028 0.724 0.064 0.000 0.176 0.008
#> SRR1951651 4 0.1010 0.73500 0.036 0.000 0.004 0.960 0.000 0.000
#> SRR1951650 3 0.4771 0.72252 0.008 0.000 0.544 0.412 0.036 0.000
#> SRR1951648 5 0.0790 0.73962 0.000 0.000 0.032 0.000 0.968 0.000
#> SRR1951649 1 0.4580 0.71043 0.612 0.000 0.000 0.336 0.000 0.052
#> SRR1951647 5 0.6923 0.30073 0.200 0.000 0.364 0.068 0.368 0.000
#> SRR1951646 3 0.4097 0.69535 0.008 0.000 0.504 0.488 0.000 0.000
#> SRR1951645 4 0.3595 0.51450 0.288 0.000 0.008 0.704 0.000 0.000
#> SRR1951644 1 0.4553 0.71180 0.620 0.000 0.000 0.328 0.000 0.052
#> SRR1951643 4 0.0865 0.73563 0.036 0.000 0.000 0.964 0.000 0.000
#> SRR1951641 2 0.0260 0.91222 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1951642 4 0.1340 0.73276 0.040 0.000 0.004 0.948 0.000 0.008
#> SRR1951694 6 0.4976 0.65367 0.252 0.000 0.072 0.020 0.000 0.656
#> SRR1951731 5 0.5575 0.57021 0.068 0.000 0.308 0.044 0.580 0.000
#> SRR1951730 2 0.5023 0.68476 0.024 0.684 0.080 0.000 0.208 0.004
#> SRR1951752 5 0.3517 0.70090 0.056 0.000 0.136 0.000 0.804 0.004
#> SRR1951751 1 0.3607 0.70437 0.652 0.000 0.000 0.348 0.000 0.000
#> SRR1951749 5 0.4575 0.57526 0.020 0.196 0.068 0.000 0.716 0.000
#> SRR1951750 1 0.3620 0.70086 0.648 0.000 0.000 0.352 0.000 0.000
#> SRR1951748 2 0.0000 0.91208 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.91208 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 6 0.0806 0.84163 0.008 0.000 0.020 0.000 0.000 0.972
#> SRR1951746 2 0.0000 0.91208 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951744 5 0.4514 0.69014 0.056 0.000 0.188 0.008 0.732 0.016
#> SRR1951742 5 0.5554 -0.01338 0.024 0.424 0.072 0.000 0.480 0.000
#> SRR1951743 2 0.0000 0.91208 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951741 4 0.2883 0.63643 0.212 0.000 0.000 0.788 0.000 0.000
#> SRR1951740 4 0.2118 0.55888 0.008 0.000 0.104 0.888 0.000 0.000
#> SRR1951738 2 0.3226 0.85472 0.016 0.852 0.040 0.000 0.084 0.008
#> SRR1951739 2 0.3468 0.84456 0.024 0.832 0.072 0.000 0.072 0.000
#> SRR1951737 3 0.4089 0.70908 0.008 0.000 0.524 0.468 0.000 0.000
#> SRR1951736 1 0.3620 0.70086 0.648 0.000 0.000 0.352 0.000 0.000
#> SRR1951734 2 0.0000 0.91208 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951735 3 0.5095 0.73882 0.056 0.000 0.576 0.352 0.016 0.000
#> SRR1951733 5 0.5291 0.56433 0.024 0.196 0.112 0.000 0.664 0.004
#> SRR1951732 2 0.0000 0.91208 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 3 0.4238 0.73064 0.016 0.000 0.540 0.444 0.000 0.000
#> SRR1951728 3 0.6545 0.64237 0.168 0.000 0.548 0.212 0.064 0.008
#> SRR1951727 2 0.0000 0.91208 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 1 0.4931 0.54610 0.648 0.000 0.000 0.136 0.000 0.216
#> SRR1951726 5 0.4173 0.69763 0.060 0.000 0.180 0.000 0.748 0.012
#> SRR1951724 1 0.5815 0.56140 0.544 0.000 0.064 0.332 0.000 0.060
#> SRR1951722 3 0.6672 0.60427 0.228 0.000 0.516 0.192 0.056 0.008
#> SRR1951723 1 0.4580 0.71043 0.612 0.000 0.000 0.336 0.000 0.052
#> SRR1951721 6 0.2412 0.83161 0.092 0.000 0.028 0.000 0.000 0.880
#> SRR1951719 3 0.4238 0.73064 0.016 0.000 0.540 0.444 0.000 0.000
#> SRR1951720 1 0.4801 0.56502 0.668 0.000 0.000 0.136 0.000 0.196
#> SRR1951718 1 0.3634 0.68987 0.644 0.000 0.000 0.356 0.000 0.000
#> SRR1951717 1 0.3607 0.70437 0.652 0.000 0.000 0.348 0.000 0.000
#> SRR1951715 6 0.2412 0.83161 0.092 0.000 0.028 0.000 0.000 0.880
#> SRR1951716 1 0.3620 0.69567 0.648 0.000 0.000 0.352 0.000 0.000
#> SRR1951714 4 0.2955 0.68495 0.172 0.000 0.008 0.816 0.000 0.004
#> SRR1951713 4 0.3595 0.51450 0.288 0.000 0.008 0.704 0.000 0.000
#> SRR1951712 1 0.3607 0.70437 0.652 0.000 0.000 0.348 0.000 0.000
#> SRR1951711 2 0.1293 0.90397 0.004 0.956 0.020 0.000 0.016 0.004
#> SRR1951710 1 0.4550 0.00595 0.544 0.000 0.000 0.036 0.000 0.420
#> SRR1951709 5 0.1340 0.73982 0.004 0.008 0.040 0.000 0.948 0.000
#> SRR1951708 2 0.0000 0.91208 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951707 5 0.5611 0.51749 0.152 0.000 0.308 0.000 0.536 0.004
#> SRR1951705 1 0.4828 0.16918 0.568 0.000 0.000 0.064 0.000 0.368
#> SRR1951704 5 0.4151 0.69551 0.052 0.000 0.192 0.000 0.744 0.012
#> SRR1951703 2 0.0000 0.91208 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 5 0.4089 0.69729 0.052 0.000 0.184 0.000 0.752 0.012
#> SRR1951702 5 0.4151 0.69551 0.052 0.000 0.192 0.000 0.744 0.012
#> SRR1951701 2 0.0260 0.91222 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1951699 5 0.1701 0.74101 0.008 0.000 0.072 0.000 0.920 0.000
#> SRR1951698 5 0.3078 0.71164 0.012 0.000 0.192 0.000 0.796 0.000
#> SRR1951700 3 0.6636 0.62506 0.212 0.000 0.512 0.220 0.048 0.008
#> SRR1951697 2 0.1542 0.89830 0.016 0.944 0.016 0.000 0.024 0.000
#> SRR1951696 3 0.4095 0.70197 0.008 0.000 0.512 0.480 0.000 0.000
#> SRR1951695 3 0.5988 0.61456 0.084 0.000 0.608 0.200 0.108 0.000
#> SRR1951693 3 0.6636 0.62506 0.212 0.000 0.512 0.220 0.048 0.008
#> SRR1951692 4 0.2053 0.54304 0.004 0.000 0.108 0.888 0.000 0.000
#> SRR1951691 4 0.2955 0.68495 0.172 0.000 0.008 0.816 0.000 0.004
#> SRR1951688 2 0.4362 0.79042 0.028 0.768 0.060 0.000 0.136 0.008
#> SRR1951689 5 0.3618 0.69733 0.056 0.004 0.132 0.000 0.804 0.004
#> SRR1951687 4 0.4014 0.57349 0.240 0.000 0.000 0.716 0.000 0.044
#> SRR1951685 5 0.5611 0.51749 0.152 0.000 0.308 0.000 0.536 0.004
#> SRR1951684 4 0.3725 -0.15969 0.008 0.000 0.316 0.676 0.000 0.000
#> SRR1951686 2 0.5364 0.22882 0.024 0.504 0.056 0.000 0.416 0.000
#> SRR1951683 4 0.0865 0.73563 0.036 0.000 0.000 0.964 0.000 0.000
#> SRR1951682 2 0.0260 0.91222 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1951690 2 0.0260 0.91222 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1951680 4 0.3595 0.51450 0.288 0.000 0.008 0.704 0.000 0.000
#> SRR1951679 1 0.6260 -0.18916 0.504 0.000 0.088 0.016 0.040 0.352
#> SRR1951678 4 0.0806 0.72005 0.020 0.000 0.008 0.972 0.000 0.000
#> SRR1951677 4 0.0806 0.72005 0.020 0.000 0.008 0.972 0.000 0.000
#> SRR1951676 3 0.4652 0.73100 0.012 0.000 0.560 0.404 0.024 0.000
#> SRR1951675 2 0.1293 0.90397 0.004 0.956 0.020 0.000 0.016 0.004
#> SRR1951681 3 0.5460 0.71869 0.060 0.000 0.612 0.276 0.052 0.000
#> SRR1951673 5 0.3618 0.69733 0.056 0.004 0.132 0.000 0.804 0.004
#> SRR1951674 6 0.0806 0.84163 0.008 0.000 0.020 0.000 0.000 0.972
#> SRR1951672 1 0.4580 0.71043 0.612 0.000 0.000 0.336 0.000 0.052
#> SRR1951671 5 0.3395 0.69971 0.056 0.004 0.124 0.000 0.816 0.000
#> SRR1951670 4 0.3076 0.60294 0.240 0.000 0.000 0.760 0.000 0.000
#> SRR1951640 3 0.4097 0.69029 0.008 0.000 0.500 0.492 0.000 0.000
#> SRR1951668 4 0.3534 0.53361 0.276 0.000 0.008 0.716 0.000 0.000
#> SRR1951669 2 0.4362 0.79042 0.028 0.768 0.060 0.000 0.136 0.008
#> SRR1951638 6 0.5069 0.34413 0.256 0.000 0.004 0.112 0.000 0.628
#> SRR1951637 2 0.4438 0.78134 0.028 0.760 0.060 0.000 0.144 0.008
#> SRR1951635 1 0.4931 0.54610 0.648 0.000 0.000 0.136 0.000 0.216
#> SRR1951634 4 0.3595 0.51450 0.288 0.000 0.008 0.704 0.000 0.000
#> SRR1951631 4 0.1408 0.72683 0.036 0.000 0.020 0.944 0.000 0.000
#> SRR1951632 2 0.3522 0.84137 0.024 0.828 0.072 0.000 0.076 0.000
#> SRR1951633 4 0.1453 0.73111 0.040 0.000 0.008 0.944 0.000 0.008
#> SRR1951630 4 0.0909 0.71802 0.020 0.000 0.012 0.968 0.000 0.000
#> SRR1951629 4 0.1408 0.72683 0.036 0.000 0.020 0.944 0.000 0.000
#> SRR1951628 1 0.7053 -0.14592 0.472 0.000 0.224 0.048 0.232 0.024
#> SRR1951627 5 0.1340 0.73982 0.004 0.008 0.040 0.000 0.948 0.000
#> SRR1951639 5 0.3315 0.71169 0.020 0.000 0.200 0.000 0.780 0.000
#> SRR1951625 5 0.5108 0.60203 0.164 0.000 0.208 0.000 0.628 0.000
#> SRR1951626 3 0.5460 0.71869 0.060 0.000 0.612 0.276 0.052 0.000
#> SRR1951624 3 0.4152 0.73326 0.012 0.000 0.548 0.440 0.000 0.000
#> SRR1951622 1 0.3634 0.68987 0.644 0.000 0.000 0.356 0.000 0.000
#> SRR1951636 3 0.5460 0.71869 0.060 0.000 0.612 0.276 0.052 0.000
#> SRR1951623 5 0.4847 0.61704 0.056 0.000 0.284 0.016 0.644 0.000
#> SRR1951621 4 0.2883 0.63643 0.212 0.000 0.000 0.788 0.000 0.000
#> SRR1951620 3 0.4097 0.69029 0.008 0.000 0.500 0.492 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "kmeans"]
# you can also extract it by
# res = res_list["SD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 13580 rows and 133 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.999 0.964 0.985 0.4547 0.554 0.554
#> 3 3 0.859 0.875 0.936 0.3896 0.721 0.535
#> 4 4 0.724 0.846 0.899 0.1747 0.798 0.508
#> 5 5 0.716 0.703 0.827 0.0705 0.896 0.624
#> 6 6 0.781 0.691 0.836 0.0389 0.929 0.680
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
#> SRR1951667 2 0.0000 0.996 0.000 1.000
#> SRR1951666 2 0.0000 0.996 0.000 1.000
#> SRR1951665 2 0.0000 0.996 0.000 1.000
#> SRR1951664 1 0.0000 0.978 1.000 0.000
#> SRR1951663 1 0.0000 0.978 1.000 0.000
#> SRR1951662 2 0.0000 0.996 0.000 1.000
#> SRR1951661 1 0.8713 0.606 0.708 0.292
#> SRR1951660 1 0.0000 0.978 1.000 0.000
#> SRR1951659 2 0.0000 0.996 0.000 1.000
#> SRR1951658 2 0.0000 0.996 0.000 1.000
#> SRR1951657 1 0.0000 0.978 1.000 0.000
#> SRR1951656 1 0.0000 0.978 1.000 0.000
#> SRR1951655 1 0.0000 0.978 1.000 0.000
#> SRR1951654 1 0.0000 0.978 1.000 0.000
#> SRR1951653 1 0.0000 0.978 1.000 0.000
#> SRR1951652 2 0.0000 0.996 0.000 1.000
#> SRR1951651 1 0.0000 0.978 1.000 0.000
#> SRR1951650 1 0.0000 0.978 1.000 0.000
#> SRR1951648 2 0.0000 0.996 0.000 1.000
#> SRR1951649 1 0.0000 0.978 1.000 0.000
#> SRR1951647 1 0.0000 0.978 1.000 0.000
#> SRR1951646 1 0.0000 0.978 1.000 0.000
#> SRR1951645 1 0.0000 0.978 1.000 0.000
#> SRR1951644 1 0.0000 0.978 1.000 0.000
#> SRR1951643 1 0.0000 0.978 1.000 0.000
#> SRR1951641 2 0.0000 0.996 0.000 1.000
#> SRR1951642 1 0.0000 0.978 1.000 0.000
#> SRR1951694 1 0.0000 0.978 1.000 0.000
#> SRR1951731 1 0.0000 0.978 1.000 0.000
#> SRR1951730 2 0.0000 0.996 0.000 1.000
#> SRR1951752 2 0.0938 0.985 0.012 0.988
#> SRR1951751 1 0.0000 0.978 1.000 0.000
#> SRR1951749 2 0.0000 0.996 0.000 1.000
#> SRR1951750 1 0.0000 0.978 1.000 0.000
#> SRR1951748 2 0.0000 0.996 0.000 1.000
#> SRR1951747 2 0.0000 0.996 0.000 1.000
#> SRR1951745 1 0.0000 0.978 1.000 0.000
#> SRR1951746 2 0.0000 0.996 0.000 1.000
#> SRR1951744 1 0.0000 0.978 1.000 0.000
#> SRR1951742 2 0.0000 0.996 0.000 1.000
#> SRR1951743 2 0.0000 0.996 0.000 1.000
#> SRR1951741 1 0.0000 0.978 1.000 0.000
#> SRR1951740 1 0.0000 0.978 1.000 0.000
#> SRR1951738 2 0.0000 0.996 0.000 1.000
#> SRR1951739 2 0.0000 0.996 0.000 1.000
#> SRR1951737 1 0.0000 0.978 1.000 0.000
#> SRR1951736 1 0.0000 0.978 1.000 0.000
#> SRR1951734 2 0.0000 0.996 0.000 1.000
#> SRR1951735 1 0.0000 0.978 1.000 0.000
#> SRR1951733 2 0.0000 0.996 0.000 1.000
#> SRR1951732 2 0.0000 0.996 0.000 1.000
#> SRR1951729 1 0.0000 0.978 1.000 0.000
#> SRR1951728 1 0.0000 0.978 1.000 0.000
#> SRR1951727 2 0.0000 0.996 0.000 1.000
#> SRR1951725 1 0.0000 0.978 1.000 0.000
#> SRR1951726 1 0.9635 0.396 0.612 0.388
#> SRR1951724 1 0.0000 0.978 1.000 0.000
#> SRR1951722 1 0.0000 0.978 1.000 0.000
#> SRR1951723 1 0.0000 0.978 1.000 0.000
#> SRR1951721 2 0.2043 0.966 0.032 0.968
#> SRR1951719 1 0.0000 0.978 1.000 0.000
#> SRR1951720 1 0.0000 0.978 1.000 0.000
#> SRR1951718 1 0.0000 0.978 1.000 0.000
#> SRR1951717 1 0.0000 0.978 1.000 0.000
#> SRR1951715 1 0.0000 0.978 1.000 0.000
#> SRR1951716 1 0.0000 0.978 1.000 0.000
#> SRR1951714 1 0.0000 0.978 1.000 0.000
#> SRR1951713 1 0.0000 0.978 1.000 0.000
#> SRR1951712 1 0.0000 0.978 1.000 0.000
#> SRR1951711 2 0.0000 0.996 0.000 1.000
#> SRR1951710 1 0.0000 0.978 1.000 0.000
#> SRR1951709 2 0.0000 0.996 0.000 1.000
#> SRR1951708 2 0.0000 0.996 0.000 1.000
#> SRR1951707 1 0.0000 0.978 1.000 0.000
#> SRR1951705 1 0.0000 0.978 1.000 0.000
#> SRR1951704 1 0.4022 0.905 0.920 0.080
#> SRR1951703 2 0.0000 0.996 0.000 1.000
#> SRR1951706 1 0.9710 0.366 0.600 0.400
#> SRR1951702 1 0.4161 0.900 0.916 0.084
#> SRR1951701 2 0.0000 0.996 0.000 1.000
#> SRR1951699 2 0.0000 0.996 0.000 1.000
#> SRR1951698 1 0.4161 0.900 0.916 0.084
#> SRR1951700 1 0.0000 0.978 1.000 0.000
#> SRR1951697 2 0.0000 0.996 0.000 1.000
#> SRR1951696 1 0.0000 0.978 1.000 0.000
#> SRR1951695 1 0.0000 0.978 1.000 0.000
#> SRR1951693 1 0.0000 0.978 1.000 0.000
#> SRR1951692 1 0.0000 0.978 1.000 0.000
#> SRR1951691 1 0.0000 0.978 1.000 0.000
#> SRR1951688 2 0.0000 0.996 0.000 1.000
#> SRR1951689 2 0.0000 0.996 0.000 1.000
#> SRR1951687 1 0.0000 0.978 1.000 0.000
#> SRR1951685 1 0.9710 0.335 0.600 0.400
#> SRR1951684 1 0.0000 0.978 1.000 0.000
#> SRR1951686 2 0.0000 0.996 0.000 1.000
#> SRR1951683 1 0.0000 0.978 1.000 0.000
#> SRR1951682 2 0.0000 0.996 0.000 1.000
#> SRR1951690 2 0.0000 0.996 0.000 1.000
#> SRR1951680 1 0.0000 0.978 1.000 0.000
#> SRR1951679 1 0.0000 0.978 1.000 0.000
#> SRR1951678 1 0.0000 0.978 1.000 0.000
#> SRR1951677 1 0.0000 0.978 1.000 0.000
#> SRR1951676 1 0.0000 0.978 1.000 0.000
#> SRR1951675 2 0.0000 0.996 0.000 1.000
#> SRR1951681 1 0.0000 0.978 1.000 0.000
#> SRR1951673 2 0.3114 0.941 0.056 0.944
#> SRR1951674 1 0.0000 0.978 1.000 0.000
#> SRR1951672 1 0.0000 0.978 1.000 0.000
#> SRR1951671 2 0.0000 0.996 0.000 1.000
#> SRR1951670 1 0.0000 0.978 1.000 0.000
#> SRR1951640 1 0.0000 0.978 1.000 0.000
#> SRR1951668 1 0.0000 0.978 1.000 0.000
#> SRR1951669 2 0.0000 0.996 0.000 1.000
#> SRR1951638 1 0.0000 0.978 1.000 0.000
#> SRR1951637 2 0.0000 0.996 0.000 1.000
#> SRR1951635 1 0.0000 0.978 1.000 0.000
#> SRR1951634 1 0.0000 0.978 1.000 0.000
#> SRR1951631 1 0.0000 0.978 1.000 0.000
#> SRR1951632 2 0.0000 0.996 0.000 1.000
#> SRR1951633 1 0.0000 0.978 1.000 0.000
#> SRR1951630 1 0.0000 0.978 1.000 0.000
#> SRR1951629 1 0.0000 0.978 1.000 0.000
#> SRR1951628 1 0.0000 0.978 1.000 0.000
#> SRR1951627 2 0.0000 0.996 0.000 1.000
#> SRR1951639 1 0.3879 0.909 0.924 0.076
#> SRR1951625 2 0.3274 0.937 0.060 0.940
#> SRR1951626 1 0.0000 0.978 1.000 0.000
#> SRR1951624 1 0.0000 0.978 1.000 0.000
#> SRR1951622 1 0.0000 0.978 1.000 0.000
#> SRR1951636 1 0.0000 0.978 1.000 0.000
#> SRR1951623 1 0.4022 0.905 0.920 0.080
#> SRR1951621 1 0.0000 0.978 1.000 0.000
#> SRR1951620 1 0.0000 0.978 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951666 3 0.4654 0.714 0.000 0.208 0.792
#> SRR1951665 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951664 1 0.0424 0.947 0.992 0.000 0.008
#> SRR1951663 3 0.1289 0.857 0.032 0.000 0.968
#> SRR1951662 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951661 3 0.2173 0.877 0.048 0.008 0.944
#> SRR1951660 1 0.1964 0.939 0.944 0.000 0.056
#> SRR1951659 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951657 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951656 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951655 1 0.1529 0.944 0.960 0.000 0.040
#> SRR1951654 1 0.1964 0.939 0.944 0.000 0.056
#> SRR1951653 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951652 3 0.2066 0.873 0.000 0.060 0.940
#> SRR1951651 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951650 1 0.5058 0.660 0.756 0.000 0.244
#> SRR1951648 3 0.2280 0.876 0.008 0.052 0.940
#> SRR1951649 1 0.1289 0.946 0.968 0.000 0.032
#> SRR1951647 3 0.2537 0.862 0.080 0.000 0.920
#> SRR1951646 1 0.3116 0.857 0.892 0.000 0.108
#> SRR1951645 1 0.1529 0.944 0.960 0.000 0.040
#> SRR1951644 1 0.1964 0.939 0.944 0.000 0.056
#> SRR1951643 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951641 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951642 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951694 3 0.4605 0.704 0.204 0.000 0.796
#> SRR1951731 3 0.1753 0.877 0.048 0.000 0.952
#> SRR1951730 3 0.2066 0.873 0.000 0.060 0.940
#> SRR1951752 3 0.1964 0.874 0.000 0.056 0.944
#> SRR1951751 1 0.1860 0.941 0.948 0.000 0.052
#> SRR1951749 3 0.2066 0.873 0.000 0.060 0.940
#> SRR1951750 1 0.1753 0.942 0.952 0.000 0.048
#> SRR1951748 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951745 3 0.0424 0.869 0.008 0.000 0.992
#> SRR1951746 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951744 3 0.5465 0.617 0.288 0.000 0.712
#> SRR1951742 2 0.5968 0.443 0.000 0.636 0.364
#> SRR1951743 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951741 1 0.1411 0.944 0.964 0.000 0.036
#> SRR1951740 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951738 2 0.1753 0.914 0.000 0.952 0.048
#> SRR1951739 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951737 1 0.1411 0.929 0.964 0.000 0.036
#> SRR1951736 1 0.1964 0.939 0.944 0.000 0.056
#> SRR1951734 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951735 1 0.3267 0.848 0.884 0.000 0.116
#> SRR1951733 3 0.2066 0.873 0.000 0.060 0.940
#> SRR1951732 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951729 1 0.1411 0.929 0.964 0.000 0.036
#> SRR1951728 3 0.1529 0.879 0.040 0.000 0.960
#> SRR1951727 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951725 1 0.6286 0.126 0.536 0.000 0.464
#> SRR1951726 3 0.0983 0.876 0.004 0.016 0.980
#> SRR1951724 1 0.1860 0.941 0.948 0.000 0.052
#> SRR1951722 3 0.5785 0.579 0.332 0.000 0.668
#> SRR1951723 1 0.0424 0.949 0.992 0.000 0.008
#> SRR1951721 3 0.0237 0.871 0.000 0.004 0.996
#> SRR1951719 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951720 1 0.1964 0.939 0.944 0.000 0.056
#> SRR1951718 1 0.1964 0.939 0.944 0.000 0.056
#> SRR1951717 1 0.1964 0.939 0.944 0.000 0.056
#> SRR1951715 3 0.4555 0.708 0.200 0.000 0.800
#> SRR1951716 1 0.1964 0.939 0.944 0.000 0.056
#> SRR1951714 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951713 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951712 1 0.1860 0.941 0.948 0.000 0.052
#> SRR1951711 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951710 3 0.6140 0.332 0.404 0.000 0.596
#> SRR1951709 3 0.2066 0.873 0.000 0.060 0.940
#> SRR1951708 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951707 3 0.1529 0.879 0.040 0.000 0.960
#> SRR1951705 1 0.1964 0.939 0.944 0.000 0.056
#> SRR1951704 3 0.1529 0.879 0.040 0.000 0.960
#> SRR1951703 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951706 3 0.2443 0.879 0.028 0.032 0.940
#> SRR1951702 3 0.1529 0.879 0.040 0.000 0.960
#> SRR1951701 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951699 3 0.2280 0.876 0.008 0.052 0.940
#> SRR1951698 3 0.1964 0.875 0.056 0.000 0.944
#> SRR1951700 1 0.0892 0.947 0.980 0.000 0.020
#> SRR1951697 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951696 1 0.1289 0.932 0.968 0.000 0.032
#> SRR1951695 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951693 1 0.0892 0.947 0.980 0.000 0.020
#> SRR1951692 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951691 1 0.1753 0.942 0.952 0.000 0.048
#> SRR1951688 2 0.5926 0.459 0.000 0.644 0.356
#> SRR1951689 3 0.2066 0.873 0.000 0.060 0.940
#> SRR1951687 1 0.1643 0.943 0.956 0.000 0.044
#> SRR1951685 3 0.1781 0.881 0.020 0.020 0.960
#> SRR1951684 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951686 2 0.1860 0.909 0.000 0.948 0.052
#> SRR1951683 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1951682 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951690 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951680 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951679 3 0.6225 0.247 0.432 0.000 0.568
#> SRR1951678 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951677 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951676 1 0.4555 0.731 0.800 0.000 0.200
#> SRR1951675 2 0.0000 0.950 0.000 1.000 0.000
#> SRR1951681 1 0.5497 0.568 0.708 0.000 0.292
#> SRR1951673 3 0.1964 0.874 0.000 0.056 0.944
#> SRR1951674 3 0.0424 0.869 0.008 0.000 0.992
#> SRR1951672 1 0.1964 0.939 0.944 0.000 0.056
#> SRR1951671 3 0.2066 0.873 0.000 0.060 0.940
#> SRR1951670 1 0.1411 0.944 0.964 0.000 0.036
#> SRR1951640 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951668 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951669 2 0.1753 0.914 0.000 0.952 0.048
#> SRR1951638 1 0.1964 0.939 0.944 0.000 0.056
#> SRR1951637 3 0.2165 0.870 0.000 0.064 0.936
#> SRR1951635 1 0.1964 0.939 0.944 0.000 0.056
#> SRR1951634 1 0.0000 0.949 1.000 0.000 0.000
#> SRR1951631 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951632 2 0.6079 0.387 0.000 0.612 0.388
#> SRR1951633 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951630 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951629 1 0.0237 0.949 0.996 0.000 0.004
#> SRR1951628 3 0.5785 0.507 0.332 0.000 0.668
#> SRR1951627 3 0.2383 0.878 0.016 0.044 0.940
#> SRR1951639 3 0.1964 0.875 0.056 0.000 0.944
#> SRR1951625 3 0.1964 0.874 0.000 0.056 0.944
#> SRR1951626 3 0.1964 0.875 0.056 0.000 0.944
#> SRR1951624 1 0.3941 0.796 0.844 0.000 0.156
#> SRR1951622 1 0.1964 0.939 0.944 0.000 0.056
#> SRR1951636 3 0.1964 0.875 0.056 0.000 0.944
#> SRR1951623 3 0.5138 0.685 0.252 0.000 0.748
#> SRR1951621 1 0.1411 0.944 0.964 0.000 0.036
#> SRR1951620 1 0.0237 0.949 0.996 0.000 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.0188 0.981 0.000 0.996 0.004 0.000
#> SRR1951666 3 0.1474 0.899 0.000 0.052 0.948 0.000
#> SRR1951665 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951664 4 0.2843 0.822 0.020 0.000 0.088 0.892
#> SRR1951663 1 0.2704 0.756 0.876 0.000 0.124 0.000
#> SRR1951662 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.0524 0.917 0.004 0.000 0.988 0.008
#> SRR1951660 1 0.2868 0.836 0.864 0.000 0.000 0.136
#> SRR1951659 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951658 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951657 4 0.0707 0.878 0.020 0.000 0.000 0.980
#> SRR1951656 4 0.0707 0.878 0.020 0.000 0.000 0.980
#> SRR1951655 1 0.4761 0.524 0.628 0.000 0.000 0.372
#> SRR1951654 1 0.2814 0.825 0.868 0.000 0.000 0.132
#> SRR1951653 4 0.1557 0.878 0.056 0.000 0.000 0.944
#> SRR1951652 3 0.1389 0.906 0.048 0.000 0.952 0.000
#> SRR1951651 4 0.2408 0.869 0.104 0.000 0.000 0.896
#> SRR1951650 4 0.4361 0.697 0.020 0.000 0.208 0.772
#> SRR1951648 3 0.0188 0.918 0.000 0.000 0.996 0.004
#> SRR1951649 1 0.4431 0.688 0.696 0.000 0.000 0.304
#> SRR1951647 3 0.3280 0.837 0.016 0.000 0.860 0.124
#> SRR1951646 4 0.2174 0.842 0.020 0.000 0.052 0.928
#> SRR1951645 1 0.4356 0.702 0.708 0.000 0.000 0.292
#> SRR1951644 1 0.2760 0.837 0.872 0.000 0.000 0.128
#> SRR1951643 4 0.2408 0.869 0.104 0.000 0.000 0.896
#> SRR1951641 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951642 4 0.2281 0.873 0.096 0.000 0.000 0.904
#> SRR1951694 1 0.2216 0.781 0.908 0.000 0.092 0.000
#> SRR1951731 3 0.1677 0.902 0.012 0.000 0.948 0.040
#> SRR1951730 3 0.1389 0.906 0.048 0.000 0.952 0.000
#> SRR1951752 3 0.1302 0.907 0.044 0.000 0.956 0.000
#> SRR1951751 1 0.3975 0.763 0.760 0.000 0.000 0.240
#> SRR1951749 3 0.0000 0.918 0.000 0.000 1.000 0.000
#> SRR1951750 4 0.4624 0.516 0.340 0.000 0.000 0.660
#> SRR1951748 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951745 1 0.3873 0.640 0.772 0.000 0.228 0.000
#> SRR1951746 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951744 1 0.7134 0.422 0.532 0.000 0.312 0.156
#> SRR1951742 3 0.3074 0.812 0.000 0.152 0.848 0.000
#> SRR1951743 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951741 4 0.3074 0.829 0.152 0.000 0.000 0.848
#> SRR1951740 4 0.1474 0.880 0.052 0.000 0.000 0.948
#> SRR1951738 2 0.3182 0.872 0.028 0.876 0.096 0.000
#> SRR1951739 2 0.1940 0.913 0.000 0.924 0.076 0.000
#> SRR1951737 4 0.2706 0.827 0.020 0.000 0.080 0.900
#> SRR1951736 1 0.2408 0.837 0.896 0.000 0.000 0.104
#> SRR1951734 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951735 4 0.3037 0.814 0.020 0.000 0.100 0.880
#> SRR1951733 3 0.0000 0.918 0.000 0.000 1.000 0.000
#> SRR1951732 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951729 4 0.2843 0.822 0.020 0.000 0.088 0.892
#> SRR1951728 3 0.2174 0.894 0.020 0.000 0.928 0.052
#> SRR1951727 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951725 1 0.0817 0.819 0.976 0.000 0.024 0.000
#> SRR1951726 3 0.1474 0.905 0.052 0.000 0.948 0.000
#> SRR1951724 1 0.2345 0.841 0.900 0.000 0.000 0.100
#> SRR1951722 1 0.5167 0.778 0.760 0.000 0.108 0.132
#> SRR1951723 1 0.4564 0.669 0.672 0.000 0.000 0.328
#> SRR1951721 1 0.3764 0.659 0.784 0.000 0.216 0.000
#> SRR1951719 4 0.0707 0.870 0.020 0.000 0.000 0.980
#> SRR1951720 1 0.2216 0.837 0.908 0.000 0.000 0.092
#> SRR1951718 1 0.2868 0.836 0.864 0.000 0.000 0.136
#> SRR1951717 1 0.2408 0.837 0.896 0.000 0.000 0.104
#> SRR1951715 1 0.2469 0.769 0.892 0.000 0.108 0.000
#> SRR1951716 1 0.2760 0.833 0.872 0.000 0.000 0.128
#> SRR1951714 4 0.2011 0.876 0.080 0.000 0.000 0.920
#> SRR1951713 4 0.1792 0.878 0.068 0.000 0.000 0.932
#> SRR1951712 1 0.3975 0.750 0.760 0.000 0.000 0.240
#> SRR1951711 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951710 1 0.2002 0.821 0.936 0.000 0.044 0.020
#> SRR1951709 3 0.0000 0.918 0.000 0.000 1.000 0.000
#> SRR1951708 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951707 3 0.1913 0.900 0.020 0.000 0.940 0.040
#> SRR1951705 1 0.1743 0.836 0.940 0.000 0.004 0.056
#> SRR1951704 3 0.0817 0.916 0.024 0.000 0.976 0.000
#> SRR1951703 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951706 3 0.0707 0.915 0.020 0.000 0.980 0.000
#> SRR1951702 3 0.0376 0.917 0.004 0.000 0.992 0.004
#> SRR1951701 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951699 3 0.0188 0.918 0.000 0.000 0.996 0.004
#> SRR1951698 3 0.2867 0.863 0.012 0.000 0.884 0.104
#> SRR1951700 1 0.4072 0.761 0.748 0.000 0.000 0.252
#> SRR1951697 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951696 4 0.2089 0.844 0.020 0.000 0.048 0.932
#> SRR1951695 4 0.4374 0.755 0.120 0.000 0.068 0.812
#> SRR1951693 1 0.4193 0.748 0.732 0.000 0.000 0.268
#> SRR1951692 4 0.1637 0.880 0.060 0.000 0.000 0.940
#> SRR1951691 1 0.4193 0.738 0.732 0.000 0.000 0.268
#> SRR1951688 3 0.4711 0.680 0.024 0.236 0.740 0.000
#> SRR1951689 3 0.1474 0.905 0.052 0.000 0.948 0.000
#> SRR1951687 4 0.4522 0.597 0.320 0.000 0.000 0.680
#> SRR1951685 3 0.0592 0.917 0.016 0.000 0.984 0.000
#> SRR1951684 4 0.0336 0.873 0.008 0.000 0.000 0.992
#> SRR1951686 3 0.4193 0.652 0.000 0.268 0.732 0.000
#> SRR1951683 4 0.2530 0.864 0.112 0.000 0.000 0.888
#> SRR1951682 2 0.0188 0.981 0.000 0.996 0.004 0.000
#> SRR1951690 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951680 4 0.1557 0.880 0.056 0.000 0.000 0.944
#> SRR1951679 1 0.2089 0.820 0.932 0.000 0.048 0.020
#> SRR1951678 4 0.2281 0.873 0.096 0.000 0.000 0.904
#> SRR1951677 4 0.2281 0.873 0.096 0.000 0.000 0.904
#> SRR1951676 4 0.3658 0.769 0.020 0.000 0.144 0.836
#> SRR1951675 2 0.0000 0.984 0.000 1.000 0.000 0.000
#> SRR1951681 4 0.4284 0.710 0.020 0.000 0.200 0.780
#> SRR1951673 3 0.1474 0.905 0.052 0.000 0.948 0.000
#> SRR1951674 1 0.3837 0.646 0.776 0.000 0.224 0.000
#> SRR1951672 1 0.2814 0.837 0.868 0.000 0.000 0.132
#> SRR1951671 3 0.0376 0.918 0.004 0.004 0.992 0.000
#> SRR1951670 4 0.3726 0.760 0.212 0.000 0.000 0.788
#> SRR1951640 4 0.1297 0.861 0.020 0.000 0.016 0.964
#> SRR1951668 4 0.2469 0.867 0.108 0.000 0.000 0.892
#> SRR1951669 2 0.3907 0.813 0.032 0.828 0.140 0.000
#> SRR1951638 1 0.1867 0.835 0.928 0.000 0.000 0.072
#> SRR1951637 3 0.3198 0.867 0.080 0.040 0.880 0.000
#> SRR1951635 1 0.1389 0.831 0.952 0.000 0.000 0.048
#> SRR1951634 4 0.2469 0.867 0.108 0.000 0.000 0.892
#> SRR1951631 4 0.2408 0.869 0.104 0.000 0.000 0.896
#> SRR1951632 3 0.4164 0.659 0.000 0.264 0.736 0.000
#> SRR1951633 4 0.1716 0.880 0.064 0.000 0.000 0.936
#> SRR1951630 4 0.2281 0.873 0.096 0.000 0.000 0.904
#> SRR1951629 4 0.1389 0.880 0.048 0.000 0.000 0.952
#> SRR1951628 1 0.3398 0.828 0.872 0.000 0.060 0.068
#> SRR1951627 3 0.0336 0.917 0.000 0.000 0.992 0.008
#> SRR1951639 3 0.2222 0.890 0.016 0.000 0.924 0.060
#> SRR1951625 3 0.0188 0.918 0.004 0.000 0.996 0.000
#> SRR1951626 3 0.2987 0.860 0.016 0.000 0.880 0.104
#> SRR1951624 4 0.3099 0.809 0.020 0.000 0.104 0.876
#> SRR1951622 1 0.2760 0.833 0.872 0.000 0.000 0.128
#> SRR1951636 3 0.2867 0.863 0.012 0.000 0.884 0.104
#> SRR1951623 3 0.5256 0.362 0.012 0.000 0.596 0.392
#> SRR1951621 4 0.3486 0.790 0.188 0.000 0.000 0.812
#> SRR1951620 4 0.1174 0.866 0.020 0.000 0.012 0.968
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.2690 0.8260 0.000 0.844 0.000 0.000 0.156
#> SRR1951666 5 0.1310 0.8436 0.000 0.024 0.020 0.000 0.956
#> SRR1951665 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 3 0.3741 0.6785 0.000 0.000 0.732 0.264 0.004
#> SRR1951663 1 0.5004 0.6480 0.692 0.000 0.216 0.000 0.092
#> SRR1951662 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 5 0.3913 0.6003 0.000 0.000 0.324 0.000 0.676
#> SRR1951660 1 0.2513 0.8001 0.876 0.000 0.008 0.116 0.000
#> SRR1951659 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000
#> SRR1951657 4 0.2561 0.7178 0.000 0.000 0.144 0.856 0.000
#> SRR1951656 4 0.2230 0.7428 0.000 0.000 0.116 0.884 0.000
#> SRR1951655 4 0.4165 0.4547 0.320 0.000 0.008 0.672 0.000
#> SRR1951654 1 0.4898 0.3817 0.592 0.000 0.032 0.376 0.000
#> SRR1951653 4 0.5341 -0.0529 0.052 0.000 0.444 0.504 0.000
#> SRR1951652 5 0.0703 0.8316 0.000 0.000 0.024 0.000 0.976
#> SRR1951651 4 0.0000 0.7942 0.000 0.000 0.000 1.000 0.000
#> SRR1951650 3 0.3847 0.7024 0.000 0.000 0.784 0.180 0.036
#> SRR1951648 5 0.2020 0.8416 0.000 0.000 0.100 0.000 0.900
#> SRR1951649 1 0.4852 0.6760 0.716 0.000 0.100 0.184 0.000
#> SRR1951647 5 0.4979 0.1317 0.000 0.000 0.480 0.028 0.492
#> SRR1951646 3 0.3508 0.6840 0.000 0.000 0.748 0.252 0.000
#> SRR1951645 1 0.4670 0.6752 0.724 0.000 0.076 0.200 0.000
#> SRR1951644 1 0.2616 0.8058 0.880 0.000 0.020 0.100 0.000
#> SRR1951643 4 0.0000 0.7942 0.000 0.000 0.000 1.000 0.000
#> SRR1951641 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000
#> SRR1951642 4 0.0000 0.7942 0.000 0.000 0.000 1.000 0.000
#> SRR1951694 1 0.2377 0.7617 0.872 0.000 0.128 0.000 0.000
#> SRR1951731 5 0.3966 0.5744 0.000 0.000 0.336 0.000 0.664
#> SRR1951730 5 0.0609 0.8334 0.000 0.000 0.020 0.000 0.980
#> SRR1951752 5 0.1410 0.8493 0.000 0.000 0.060 0.000 0.940
#> SRR1951751 1 0.3731 0.7482 0.800 0.000 0.040 0.160 0.000
#> SRR1951749 5 0.1671 0.8488 0.000 0.000 0.076 0.000 0.924
#> SRR1951750 4 0.3949 0.4374 0.332 0.000 0.000 0.668 0.000
#> SRR1951748 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 1 0.5258 0.6265 0.664 0.000 0.232 0.000 0.104
#> SRR1951746 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 3 0.4928 0.6523 0.064 0.000 0.768 0.072 0.096
#> SRR1951742 5 0.1197 0.8311 0.000 0.048 0.000 0.000 0.952
#> SRR1951743 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 4 0.2329 0.7429 0.124 0.000 0.000 0.876 0.000
#> SRR1951740 4 0.1965 0.7567 0.000 0.000 0.096 0.904 0.000
#> SRR1951738 2 0.4538 0.7101 0.004 0.724 0.044 0.000 0.228
#> SRR1951739 2 0.3837 0.6196 0.000 0.692 0.000 0.000 0.308
#> SRR1951737 3 0.3814 0.6661 0.000 0.000 0.720 0.276 0.004
#> SRR1951736 1 0.2462 0.8017 0.880 0.000 0.008 0.112 0.000
#> SRR1951734 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 3 0.3461 0.6972 0.000 0.000 0.772 0.224 0.004
#> SRR1951733 5 0.1544 0.8500 0.000 0.000 0.068 0.000 0.932
#> SRR1951732 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 3 0.3741 0.6785 0.000 0.000 0.732 0.264 0.004
#> SRR1951728 3 0.3949 0.3437 0.000 0.000 0.668 0.000 0.332
#> SRR1951727 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 1 0.2930 0.7419 0.832 0.000 0.164 0.000 0.004
#> SRR1951726 5 0.1410 0.8423 0.000 0.000 0.060 0.000 0.940
#> SRR1951724 1 0.3966 0.7462 0.796 0.000 0.132 0.072 0.000
#> SRR1951722 3 0.4848 0.1948 0.420 0.000 0.556 0.000 0.024
#> SRR1951723 1 0.6206 0.3886 0.532 0.000 0.296 0.172 0.000
#> SRR1951721 1 0.5638 0.5914 0.632 0.000 0.216 0.000 0.152
#> SRR1951719 3 0.4161 0.4582 0.000 0.000 0.608 0.392 0.000
#> SRR1951720 1 0.2124 0.8057 0.900 0.000 0.004 0.096 0.000
#> SRR1951718 1 0.2660 0.7933 0.864 0.000 0.008 0.128 0.000
#> SRR1951717 1 0.2286 0.8034 0.888 0.000 0.004 0.108 0.000
#> SRR1951715 1 0.3897 0.7045 0.768 0.000 0.204 0.000 0.028
#> SRR1951716 1 0.2612 0.7962 0.868 0.000 0.008 0.124 0.000
#> SRR1951714 4 0.2006 0.7745 0.012 0.000 0.072 0.916 0.000
#> SRR1951713 4 0.4670 0.6587 0.076 0.000 0.200 0.724 0.000
#> SRR1951712 4 0.4533 0.1161 0.448 0.000 0.008 0.544 0.000
#> SRR1951711 2 0.0880 0.9284 0.000 0.968 0.000 0.000 0.032
#> SRR1951710 1 0.1544 0.7886 0.932 0.000 0.068 0.000 0.000
#> SRR1951709 5 0.1608 0.8495 0.000 0.000 0.072 0.000 0.928
#> SRR1951708 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000
#> SRR1951707 3 0.4227 0.0540 0.000 0.000 0.580 0.000 0.420
#> SRR1951705 1 0.0807 0.8045 0.976 0.000 0.012 0.012 0.000
#> SRR1951704 5 0.3837 0.6427 0.000 0.000 0.308 0.000 0.692
#> SRR1951703 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 5 0.2280 0.8286 0.000 0.000 0.120 0.000 0.880
#> SRR1951702 5 0.3949 0.6044 0.000 0.000 0.332 0.000 0.668
#> SRR1951701 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000
#> SRR1951699 5 0.2074 0.8398 0.000 0.000 0.104 0.000 0.896
#> SRR1951698 3 0.3983 0.3417 0.000 0.000 0.660 0.000 0.340
#> SRR1951700 3 0.5604 -0.0527 0.460 0.000 0.468 0.072 0.000
#> SRR1951697 2 0.0880 0.9284 0.000 0.968 0.000 0.000 0.032
#> SRR1951696 3 0.3796 0.6388 0.000 0.000 0.700 0.300 0.000
#> SRR1951695 3 0.3989 0.6732 0.008 0.000 0.728 0.260 0.004
#> SRR1951693 3 0.5646 0.0077 0.444 0.000 0.480 0.076 0.000
#> SRR1951692 4 0.0290 0.7912 0.000 0.000 0.008 0.992 0.000
#> SRR1951691 1 0.4665 0.6437 0.692 0.000 0.048 0.260 0.000
#> SRR1951688 5 0.2208 0.7838 0.000 0.072 0.020 0.000 0.908
#> SRR1951689 5 0.0771 0.8325 0.004 0.000 0.020 0.000 0.976
#> SRR1951687 4 0.3861 0.5898 0.264 0.000 0.008 0.728 0.000
#> SRR1951685 5 0.2852 0.7950 0.000 0.000 0.172 0.000 0.828
#> SRR1951684 4 0.3730 0.4981 0.000 0.000 0.288 0.712 0.000
#> SRR1951686 5 0.2329 0.7695 0.000 0.124 0.000 0.000 0.876
#> SRR1951683 4 0.0703 0.7911 0.024 0.000 0.000 0.976 0.000
#> SRR1951682 2 0.1544 0.9047 0.000 0.932 0.000 0.000 0.068
#> SRR1951690 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000
#> SRR1951680 4 0.3177 0.6447 0.000 0.000 0.208 0.792 0.000
#> SRR1951679 1 0.0609 0.8014 0.980 0.000 0.020 0.000 0.000
#> SRR1951678 4 0.0000 0.7942 0.000 0.000 0.000 1.000 0.000
#> SRR1951677 4 0.0000 0.7942 0.000 0.000 0.000 1.000 0.000
#> SRR1951676 3 0.3970 0.6939 0.000 0.000 0.752 0.224 0.024
#> SRR1951675 2 0.0609 0.9338 0.000 0.980 0.000 0.000 0.020
#> SRR1951681 3 0.3882 0.7017 0.000 0.000 0.788 0.168 0.044
#> SRR1951673 5 0.1571 0.8485 0.004 0.000 0.060 0.000 0.936
#> SRR1951674 1 0.5258 0.6265 0.664 0.000 0.232 0.000 0.104
#> SRR1951672 1 0.2616 0.8058 0.880 0.000 0.020 0.100 0.000
#> SRR1951671 5 0.1430 0.8504 0.000 0.004 0.052 0.000 0.944
#> SRR1951670 4 0.3143 0.6521 0.204 0.000 0.000 0.796 0.000
#> SRR1951640 3 0.3752 0.6453 0.000 0.000 0.708 0.292 0.000
#> SRR1951668 4 0.1851 0.7677 0.088 0.000 0.000 0.912 0.000
#> SRR1951669 2 0.5496 0.2074 0.004 0.476 0.052 0.000 0.468
#> SRR1951638 1 0.1124 0.7997 0.960 0.000 0.036 0.004 0.000
#> SRR1951637 5 0.2970 0.7184 0.004 0.000 0.168 0.000 0.828
#> SRR1951635 1 0.1430 0.7945 0.944 0.000 0.052 0.004 0.000
#> SRR1951634 4 0.1410 0.7802 0.060 0.000 0.000 0.940 0.000
#> SRR1951631 4 0.0000 0.7942 0.000 0.000 0.000 1.000 0.000
#> SRR1951632 5 0.3106 0.7575 0.000 0.132 0.024 0.000 0.844
#> SRR1951633 4 0.1965 0.7262 0.000 0.000 0.096 0.904 0.000
#> SRR1951630 4 0.0000 0.7942 0.000 0.000 0.000 1.000 0.000
#> SRR1951629 4 0.2813 0.6933 0.000 0.000 0.168 0.832 0.000
#> SRR1951628 1 0.2472 0.8086 0.908 0.000 0.020 0.052 0.020
#> SRR1951627 5 0.2074 0.8398 0.000 0.000 0.104 0.000 0.896
#> SRR1951639 5 0.4242 0.3708 0.000 0.000 0.428 0.000 0.572
#> SRR1951625 5 0.1544 0.8500 0.000 0.000 0.068 0.000 0.932
#> SRR1951626 3 0.3837 0.4100 0.000 0.000 0.692 0.000 0.308
#> SRR1951624 3 0.3550 0.6946 0.000 0.000 0.760 0.236 0.004
#> SRR1951622 1 0.2389 0.8010 0.880 0.000 0.004 0.116 0.000
#> SRR1951636 3 0.3932 0.3688 0.000 0.000 0.672 0.000 0.328
#> SRR1951623 3 0.4850 0.5530 0.000 0.000 0.696 0.072 0.232
#> SRR1951621 4 0.2773 0.7025 0.164 0.000 0.000 0.836 0.000
#> SRR1951620 4 0.4015 0.3601 0.000 0.000 0.348 0.652 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 2 0.4798 0.54541 0.000 0.612 0.000 0.000 0.312 0.076
#> SRR1951666 5 0.1625 0.76969 0.000 0.000 0.012 0.000 0.928 0.060
#> SRR1951665 2 0.0000 0.92099 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 3 0.1841 0.77187 0.008 0.000 0.920 0.064 0.000 0.008
#> SRR1951663 6 0.3217 0.88397 0.224 0.000 0.000 0.000 0.008 0.768
#> SRR1951662 2 0.0000 0.92099 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 5 0.5327 0.33919 0.000 0.000 0.372 0.000 0.516 0.112
#> SRR1951660 1 0.0858 0.76623 0.968 0.000 0.004 0.028 0.000 0.000
#> SRR1951659 2 0.0547 0.91703 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1951658 2 0.0458 0.91820 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1951657 4 0.3263 0.75894 0.004 0.000 0.176 0.800 0.000 0.020
#> SRR1951656 4 0.2981 0.77835 0.000 0.000 0.160 0.820 0.000 0.020
#> SRR1951655 1 0.3744 0.57785 0.724 0.000 0.004 0.256 0.000 0.016
#> SRR1951654 4 0.4701 0.54218 0.148 0.000 0.000 0.684 0.000 0.168
#> SRR1951653 1 0.5944 0.21650 0.476 0.000 0.380 0.120 0.000 0.024
#> SRR1951652 5 0.1141 0.75226 0.000 0.000 0.000 0.000 0.948 0.052
#> SRR1951651 4 0.0000 0.86776 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951650 3 0.0935 0.77337 0.000 0.000 0.964 0.032 0.004 0.000
#> SRR1951648 5 0.3215 0.75395 0.000 0.000 0.072 0.000 0.828 0.100
#> SRR1951649 1 0.2765 0.74616 0.876 0.000 0.064 0.044 0.000 0.016
#> SRR1951647 3 0.5707 0.00131 0.008 0.000 0.508 0.004 0.364 0.116
#> SRR1951646 3 0.2039 0.76916 0.004 0.000 0.908 0.072 0.000 0.016
#> SRR1951645 1 0.2862 0.74655 0.872 0.000 0.056 0.052 0.000 0.020
#> SRR1951644 1 0.0909 0.76530 0.968 0.000 0.012 0.020 0.000 0.000
#> SRR1951643 4 0.0000 0.86776 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951641 2 0.0000 0.92099 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951642 4 0.0000 0.86776 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951694 1 0.3565 0.17180 0.692 0.000 0.004 0.000 0.000 0.304
#> SRR1951731 5 0.5491 0.36232 0.004 0.000 0.372 0.000 0.508 0.116
#> SRR1951730 5 0.1806 0.73631 0.000 0.000 0.004 0.000 0.908 0.088
#> SRR1951752 5 0.3149 0.76283 0.000 0.000 0.044 0.000 0.824 0.132
#> SRR1951751 1 0.2577 0.75446 0.888 0.000 0.056 0.040 0.000 0.016
#> SRR1951749 5 0.2712 0.76684 0.000 0.000 0.048 0.000 0.864 0.088
#> SRR1951750 4 0.4046 0.32718 0.368 0.000 0.004 0.620 0.000 0.008
#> SRR1951748 2 0.0000 0.92099 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.92099 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 6 0.3514 0.88256 0.208 0.000 0.004 0.000 0.020 0.768
#> SRR1951746 2 0.0000 0.92099 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951744 3 0.3547 0.70883 0.024 0.000 0.828 0.004 0.044 0.100
#> SRR1951742 5 0.1194 0.75092 0.000 0.008 0.004 0.000 0.956 0.032
#> SRR1951743 2 0.0000 0.92099 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951741 4 0.1075 0.84924 0.048 0.000 0.000 0.952 0.000 0.000
#> SRR1951740 4 0.2748 0.80291 0.000 0.000 0.128 0.848 0.000 0.024
#> SRR1951738 2 0.5587 0.46854 0.000 0.548 0.004 0.000 0.292 0.156
#> SRR1951739 5 0.5045 -0.05167 0.000 0.412 0.000 0.000 0.512 0.076
#> SRR1951737 3 0.2151 0.76867 0.008 0.000 0.904 0.072 0.000 0.016
#> SRR1951736 1 0.1219 0.76613 0.948 0.000 0.000 0.048 0.000 0.004
#> SRR1951734 2 0.0000 0.92099 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951735 3 0.1925 0.77651 0.008 0.000 0.920 0.060 0.004 0.008
#> SRR1951733 5 0.2318 0.77090 0.000 0.000 0.044 0.000 0.892 0.064
#> SRR1951732 2 0.0000 0.92099 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 3 0.1524 0.77452 0.008 0.000 0.932 0.060 0.000 0.000
#> SRR1951728 3 0.4301 0.57360 0.004 0.000 0.740 0.000 0.136 0.120
#> SRR1951727 2 0.0000 0.92099 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 6 0.3714 0.79182 0.340 0.000 0.000 0.000 0.004 0.656
#> SRR1951726 5 0.3602 0.75343 0.000 0.000 0.056 0.000 0.784 0.160
#> SRR1951724 1 0.1976 0.75575 0.916 0.000 0.060 0.016 0.000 0.008
#> SRR1951722 3 0.4750 0.18690 0.404 0.000 0.544 0.000 0.000 0.052
#> SRR1951723 1 0.3007 0.73272 0.860 0.000 0.080 0.040 0.000 0.020
#> SRR1951721 6 0.3122 0.85059 0.176 0.000 0.000 0.000 0.020 0.804
#> SRR1951719 3 0.2686 0.74203 0.008 0.000 0.868 0.100 0.000 0.024
#> SRR1951720 1 0.1124 0.76473 0.956 0.000 0.000 0.036 0.000 0.008
#> SRR1951718 1 0.1265 0.76853 0.948 0.000 0.008 0.044 0.000 0.000
#> SRR1951717 1 0.1124 0.76473 0.956 0.000 0.000 0.036 0.000 0.008
#> SRR1951715 6 0.3314 0.87054 0.256 0.000 0.000 0.000 0.004 0.740
#> SRR1951716 1 0.1141 0.76596 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR1951714 4 0.2308 0.83624 0.016 0.000 0.076 0.896 0.000 0.012
#> SRR1951713 1 0.6482 -0.00980 0.392 0.000 0.228 0.356 0.000 0.024
#> SRR1951712 1 0.2975 0.71138 0.840 0.000 0.012 0.132 0.000 0.016
#> SRR1951711 2 0.3534 0.79683 0.000 0.800 0.000 0.000 0.124 0.076
#> SRR1951710 1 0.3126 0.35162 0.752 0.000 0.000 0.000 0.000 0.248
#> SRR1951709 5 0.2812 0.76579 0.000 0.000 0.048 0.000 0.856 0.096
#> SRR1951708 2 0.0458 0.91820 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1951707 3 0.5083 0.35775 0.004 0.000 0.632 0.000 0.244 0.120
#> SRR1951705 1 0.0363 0.73871 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1951704 5 0.5531 0.43350 0.000 0.000 0.316 0.000 0.528 0.156
#> SRR1951703 2 0.0000 0.92099 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 5 0.4462 0.70013 0.000 0.000 0.136 0.000 0.712 0.152
#> SRR1951702 5 0.5605 0.34084 0.000 0.000 0.360 0.000 0.488 0.152
#> SRR1951701 2 0.0260 0.91970 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1951699 5 0.3261 0.75404 0.000 0.000 0.072 0.000 0.824 0.104
#> SRR1951698 3 0.3918 0.62054 0.000 0.000 0.768 0.000 0.124 0.108
#> SRR1951700 1 0.4565 0.08273 0.496 0.000 0.476 0.008 0.000 0.020
#> SRR1951697 2 0.3587 0.78905 0.000 0.792 0.000 0.000 0.140 0.068
#> SRR1951696 3 0.2203 0.76200 0.004 0.000 0.896 0.084 0.000 0.016
#> SRR1951695 3 0.1942 0.77513 0.008 0.000 0.916 0.064 0.000 0.012
#> SRR1951693 3 0.4653 -0.08976 0.476 0.000 0.492 0.012 0.000 0.020
#> SRR1951692 4 0.0508 0.86606 0.000 0.000 0.004 0.984 0.000 0.012
#> SRR1951691 1 0.3801 0.57353 0.740 0.000 0.012 0.232 0.000 0.016
#> SRR1951688 5 0.2537 0.71380 0.000 0.032 0.000 0.000 0.872 0.096
#> SRR1951689 5 0.2121 0.73439 0.000 0.000 0.012 0.000 0.892 0.096
#> SRR1951687 4 0.2260 0.77159 0.140 0.000 0.000 0.860 0.000 0.000
#> SRR1951685 5 0.5066 0.59720 0.004 0.000 0.248 0.000 0.632 0.116
#> SRR1951684 4 0.4315 0.49111 0.004 0.000 0.348 0.624 0.000 0.024
#> SRR1951686 5 0.1923 0.73547 0.000 0.016 0.004 0.000 0.916 0.064
#> SRR1951683 4 0.0603 0.86407 0.016 0.000 0.004 0.980 0.000 0.000
#> SRR1951682 2 0.3877 0.76344 0.000 0.764 0.000 0.000 0.160 0.076
#> SRR1951690 2 0.0547 0.91703 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1951680 4 0.3881 0.66638 0.004 0.000 0.252 0.720 0.000 0.024
#> SRR1951679 1 0.0820 0.73323 0.972 0.000 0.012 0.000 0.000 0.016
#> SRR1951678 4 0.0000 0.86776 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951677 4 0.0000 0.86776 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951676 3 0.1555 0.77593 0.000 0.000 0.932 0.060 0.004 0.004
#> SRR1951675 2 0.1549 0.89427 0.000 0.936 0.000 0.000 0.020 0.044
#> SRR1951681 3 0.1053 0.76771 0.000 0.000 0.964 0.020 0.004 0.012
#> SRR1951673 5 0.2164 0.75497 0.000 0.000 0.032 0.000 0.900 0.068
#> SRR1951674 6 0.3514 0.88256 0.208 0.000 0.004 0.000 0.020 0.768
#> SRR1951672 1 0.1053 0.76548 0.964 0.000 0.012 0.020 0.000 0.004
#> SRR1951671 5 0.1245 0.76514 0.000 0.000 0.032 0.000 0.952 0.016
#> SRR1951670 4 0.1588 0.83120 0.072 0.000 0.004 0.924 0.000 0.000
#> SRR1951640 3 0.2349 0.76234 0.008 0.000 0.892 0.080 0.000 0.020
#> SRR1951668 4 0.1511 0.85196 0.044 0.000 0.004 0.940 0.000 0.012
#> SRR1951669 5 0.5706 0.19103 0.000 0.304 0.004 0.000 0.524 0.168
#> SRR1951638 6 0.3857 0.53521 0.468 0.000 0.000 0.000 0.000 0.532
#> SRR1951637 5 0.3215 0.62689 0.000 0.000 0.004 0.000 0.756 0.240
#> SRR1951635 1 0.3563 0.05706 0.664 0.000 0.000 0.000 0.000 0.336
#> SRR1951634 4 0.1624 0.85676 0.040 0.000 0.004 0.936 0.000 0.020
#> SRR1951631 4 0.0692 0.86666 0.000 0.000 0.004 0.976 0.000 0.020
#> SRR1951632 5 0.2714 0.70236 0.000 0.060 0.004 0.000 0.872 0.064
#> SRR1951633 4 0.0547 0.86079 0.000 0.000 0.020 0.980 0.000 0.000
#> SRR1951630 4 0.0000 0.86776 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951629 4 0.3732 0.69474 0.004 0.000 0.228 0.744 0.000 0.024
#> SRR1951628 1 0.1053 0.73319 0.964 0.000 0.012 0.000 0.004 0.020
#> SRR1951627 5 0.3367 0.75099 0.000 0.000 0.080 0.000 0.816 0.104
#> SRR1951639 5 0.5326 0.18981 0.000 0.000 0.432 0.000 0.464 0.104
#> SRR1951625 5 0.2221 0.77031 0.000 0.000 0.072 0.000 0.896 0.032
#> SRR1951626 3 0.3464 0.65026 0.000 0.000 0.808 0.000 0.084 0.108
#> SRR1951624 3 0.1429 0.77678 0.004 0.000 0.940 0.052 0.004 0.000
#> SRR1951622 1 0.0858 0.76242 0.968 0.000 0.000 0.028 0.000 0.004
#> SRR1951636 3 0.3703 0.63897 0.000 0.000 0.788 0.000 0.104 0.108
#> SRR1951623 3 0.4447 0.53302 0.000 0.000 0.704 0.000 0.196 0.100
#> SRR1951621 4 0.1141 0.84667 0.052 0.000 0.000 0.948 0.000 0.000
#> SRR1951620 3 0.4463 -0.10458 0.004 0.000 0.508 0.468 0.000 0.020
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 13580 rows and 133 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 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 0.984 0.960 0.983 0.4952 0.506 0.506
#> 3 3 0.931 0.934 0.970 0.3408 0.805 0.625
#> 4 4 0.739 0.778 0.873 0.0934 0.894 0.701
#> 5 5 0.786 0.709 0.866 0.0558 0.932 0.758
#> 6 6 0.807 0.744 0.870 0.0472 0.898 0.612
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
#> SRR1951667 2 0.000 0.985 0.000 1.000
#> SRR1951666 2 0.000 0.985 0.000 1.000
#> SRR1951665 2 0.000 0.985 0.000 1.000
#> SRR1951664 1 0.000 0.981 1.000 0.000
#> SRR1951663 2 0.730 0.741 0.204 0.796
#> SRR1951662 2 0.000 0.985 0.000 1.000
#> SRR1951661 2 0.000 0.985 0.000 1.000
#> SRR1951660 1 0.000 0.981 1.000 0.000
#> SRR1951659 2 0.000 0.985 0.000 1.000
#> SRR1951658 2 0.000 0.985 0.000 1.000
#> SRR1951657 1 0.000 0.981 1.000 0.000
#> SRR1951656 1 0.000 0.981 1.000 0.000
#> SRR1951655 1 0.000 0.981 1.000 0.000
#> SRR1951654 1 0.000 0.981 1.000 0.000
#> SRR1951653 1 0.000 0.981 1.000 0.000
#> SRR1951652 2 0.000 0.985 0.000 1.000
#> SRR1951651 1 0.000 0.981 1.000 0.000
#> SRR1951650 1 0.000 0.981 1.000 0.000
#> SRR1951648 2 0.000 0.985 0.000 1.000
#> SRR1951649 1 0.000 0.981 1.000 0.000
#> SRR1951647 2 0.971 0.338 0.400 0.600
#> SRR1951646 1 0.000 0.981 1.000 0.000
#> SRR1951645 1 0.000 0.981 1.000 0.000
#> SRR1951644 1 0.000 0.981 1.000 0.000
#> SRR1951643 1 0.000 0.981 1.000 0.000
#> SRR1951641 2 0.000 0.985 0.000 1.000
#> SRR1951642 1 0.000 0.981 1.000 0.000
#> SRR1951694 1 0.000 0.981 1.000 0.000
#> SRR1951731 2 0.000 0.985 0.000 1.000
#> SRR1951730 2 0.000 0.985 0.000 1.000
#> SRR1951752 2 0.000 0.985 0.000 1.000
#> SRR1951751 1 0.000 0.981 1.000 0.000
#> SRR1951749 2 0.000 0.985 0.000 1.000
#> SRR1951750 1 0.000 0.981 1.000 0.000
#> SRR1951748 2 0.000 0.985 0.000 1.000
#> SRR1951747 2 0.000 0.985 0.000 1.000
#> SRR1951745 1 0.881 0.579 0.700 0.300
#> SRR1951746 2 0.000 0.985 0.000 1.000
#> SRR1951744 1 0.000 0.981 1.000 0.000
#> SRR1951742 2 0.000 0.985 0.000 1.000
#> SRR1951743 2 0.000 0.985 0.000 1.000
#> SRR1951741 1 0.000 0.981 1.000 0.000
#> SRR1951740 1 0.000 0.981 1.000 0.000
#> SRR1951738 2 0.000 0.985 0.000 1.000
#> SRR1951739 2 0.000 0.985 0.000 1.000
#> SRR1951737 1 0.000 0.981 1.000 0.000
#> SRR1951736 1 0.000 0.981 1.000 0.000
#> SRR1951734 2 0.000 0.985 0.000 1.000
#> SRR1951735 1 0.000 0.981 1.000 0.000
#> SRR1951733 2 0.000 0.985 0.000 1.000
#> SRR1951732 2 0.000 0.985 0.000 1.000
#> SRR1951729 1 0.000 0.981 1.000 0.000
#> SRR1951728 1 0.000 0.981 1.000 0.000
#> SRR1951727 2 0.000 0.985 0.000 1.000
#> SRR1951725 1 0.000 0.981 1.000 0.000
#> SRR1951726 2 0.000 0.985 0.000 1.000
#> SRR1951724 1 0.000 0.981 1.000 0.000
#> SRR1951722 1 0.000 0.981 1.000 0.000
#> SRR1951723 1 0.000 0.981 1.000 0.000
#> SRR1951721 2 0.000 0.985 0.000 1.000
#> SRR1951719 1 0.000 0.981 1.000 0.000
#> SRR1951720 1 0.000 0.981 1.000 0.000
#> SRR1951718 1 0.000 0.981 1.000 0.000
#> SRR1951717 1 0.000 0.981 1.000 0.000
#> SRR1951715 2 0.730 0.741 0.204 0.796
#> SRR1951716 1 0.000 0.981 1.000 0.000
#> SRR1951714 1 0.000 0.981 1.000 0.000
#> SRR1951713 1 0.000 0.981 1.000 0.000
#> SRR1951712 1 0.000 0.981 1.000 0.000
#> SRR1951711 2 0.000 0.985 0.000 1.000
#> SRR1951710 1 0.000 0.981 1.000 0.000
#> SRR1951709 2 0.000 0.985 0.000 1.000
#> SRR1951708 2 0.000 0.985 0.000 1.000
#> SRR1951707 2 0.000 0.985 0.000 1.000
#> SRR1951705 1 0.000 0.981 1.000 0.000
#> SRR1951704 2 0.000 0.985 0.000 1.000
#> SRR1951703 2 0.000 0.985 0.000 1.000
#> SRR1951706 2 0.000 0.985 0.000 1.000
#> SRR1951702 2 0.000 0.985 0.000 1.000
#> SRR1951701 2 0.000 0.985 0.000 1.000
#> SRR1951699 2 0.000 0.985 0.000 1.000
#> SRR1951698 2 0.000 0.985 0.000 1.000
#> SRR1951700 1 0.000 0.981 1.000 0.000
#> SRR1951697 2 0.000 0.985 0.000 1.000
#> SRR1951696 1 0.000 0.981 1.000 0.000
#> SRR1951695 1 0.000 0.981 1.000 0.000
#> SRR1951693 1 0.000 0.981 1.000 0.000
#> SRR1951692 1 0.000 0.981 1.000 0.000
#> SRR1951691 1 0.000 0.981 1.000 0.000
#> SRR1951688 2 0.000 0.985 0.000 1.000
#> SRR1951689 2 0.000 0.985 0.000 1.000
#> SRR1951687 1 0.000 0.981 1.000 0.000
#> SRR1951685 2 0.000 0.985 0.000 1.000
#> SRR1951684 1 0.000 0.981 1.000 0.000
#> SRR1951686 2 0.000 0.985 0.000 1.000
#> SRR1951683 1 0.000 0.981 1.000 0.000
#> SRR1951682 2 0.000 0.985 0.000 1.000
#> SRR1951690 2 0.000 0.985 0.000 1.000
#> SRR1951680 1 0.000 0.981 1.000 0.000
#> SRR1951679 1 0.000 0.981 1.000 0.000
#> SRR1951678 1 0.000 0.981 1.000 0.000
#> SRR1951677 1 0.000 0.981 1.000 0.000
#> SRR1951676 1 0.000 0.981 1.000 0.000
#> SRR1951675 2 0.000 0.985 0.000 1.000
#> SRR1951681 1 0.000 0.981 1.000 0.000
#> SRR1951673 2 0.000 0.985 0.000 1.000
#> SRR1951674 1 0.000 0.981 1.000 0.000
#> SRR1951672 1 0.000 0.981 1.000 0.000
#> SRR1951671 2 0.000 0.985 0.000 1.000
#> SRR1951670 1 0.000 0.981 1.000 0.000
#> SRR1951640 1 0.000 0.981 1.000 0.000
#> SRR1951668 1 0.000 0.981 1.000 0.000
#> SRR1951669 2 0.000 0.985 0.000 1.000
#> SRR1951638 1 0.000 0.981 1.000 0.000
#> SRR1951637 2 0.000 0.985 0.000 1.000
#> SRR1951635 1 0.000 0.981 1.000 0.000
#> SRR1951634 1 0.000 0.981 1.000 0.000
#> SRR1951631 1 0.000 0.981 1.000 0.000
#> SRR1951632 2 0.000 0.985 0.000 1.000
#> SRR1951633 1 0.000 0.981 1.000 0.000
#> SRR1951630 1 0.000 0.981 1.000 0.000
#> SRR1951629 1 0.000 0.981 1.000 0.000
#> SRR1951628 1 0.971 0.319 0.600 0.400
#> SRR1951627 2 0.000 0.985 0.000 1.000
#> SRR1951639 2 0.000 0.985 0.000 1.000
#> SRR1951625 2 0.000 0.985 0.000 1.000
#> SRR1951626 1 0.753 0.727 0.784 0.216
#> SRR1951624 1 0.000 0.981 1.000 0.000
#> SRR1951622 1 0.000 0.981 1.000 0.000
#> SRR1951636 1 0.861 0.614 0.716 0.284
#> SRR1951623 1 0.730 0.744 0.796 0.204
#> SRR1951621 1 0.000 0.981 1.000 0.000
#> SRR1951620 1 0.000 0.981 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951666 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951665 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951664 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951663 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951662 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951661 2 0.1529 0.9417 0.000 0.960 0.040
#> SRR1951660 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951659 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951657 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951656 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951655 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951654 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951653 3 0.4504 0.7884 0.196 0.000 0.804
#> SRR1951652 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951651 3 0.4452 0.7928 0.192 0.000 0.808
#> SRR1951650 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951648 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951649 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951647 2 0.6126 0.3827 0.000 0.600 0.400
#> SRR1951646 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951645 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951644 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951643 3 0.4452 0.7928 0.192 0.000 0.808
#> SRR1951641 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951642 3 0.0237 0.9374 0.004 0.000 0.996
#> SRR1951694 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951731 2 0.4555 0.7658 0.000 0.800 0.200
#> SRR1951730 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951752 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951751 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951749 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951750 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951748 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951745 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951746 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951744 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951742 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951743 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951741 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951740 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951738 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951739 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951737 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951736 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951734 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951735 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951733 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951732 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951729 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951728 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951727 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951725 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951726 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951724 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951722 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951723 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951721 2 0.1529 0.9426 0.040 0.960 0.000
#> SRR1951719 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951720 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951718 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951717 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951715 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951716 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951714 3 0.4399 0.7966 0.188 0.000 0.812
#> SRR1951713 3 0.4504 0.7884 0.196 0.000 0.804
#> SRR1951712 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951711 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951710 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951709 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951708 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951707 2 0.0237 0.9752 0.000 0.996 0.004
#> SRR1951705 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951704 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951703 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951706 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951702 2 0.5098 0.6616 0.000 0.752 0.248
#> SRR1951701 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951699 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951698 3 0.0237 0.9367 0.000 0.004 0.996
#> SRR1951700 1 0.6286 -0.0132 0.536 0.000 0.464
#> SRR1951697 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951696 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951695 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951693 3 0.6168 0.3883 0.412 0.000 0.588
#> SRR1951692 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951691 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951688 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951689 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951687 1 0.0592 0.9727 0.988 0.000 0.012
#> SRR1951685 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951684 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951686 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951683 3 0.4504 0.7884 0.196 0.000 0.804
#> SRR1951682 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951690 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951680 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951679 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951678 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951677 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951676 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951675 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951681 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951673 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951674 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951672 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951671 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951670 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951640 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951668 3 0.5926 0.5223 0.356 0.000 0.644
#> SRR1951669 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951638 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951637 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951635 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951634 3 0.4504 0.7884 0.196 0.000 0.804
#> SRR1951631 3 0.4452 0.7928 0.192 0.000 0.808
#> SRR1951632 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951633 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951630 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951629 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951628 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951627 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951639 2 0.4504 0.7709 0.000 0.804 0.196
#> SRR1951625 2 0.0000 0.9785 0.000 1.000 0.000
#> SRR1951626 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951624 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951622 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951636 3 0.0000 0.9396 0.000 0.000 1.000
#> SRR1951623 3 0.0237 0.9367 0.000 0.004 0.996
#> SRR1951621 1 0.0000 0.9854 1.000 0.000 0.000
#> SRR1951620 3 0.0000 0.9396 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951666 3 0.4830 0.563 0.000 0.392 0.608 0.000
#> SRR1951665 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951664 4 0.4431 0.625 0.000 0.000 0.304 0.696
#> SRR1951663 1 0.1637 0.729 0.940 0.000 0.060 0.000
#> SRR1951662 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.1940 0.756 0.000 0.076 0.924 0.000
#> SRR1951660 1 0.3219 0.815 0.836 0.000 0.000 0.164
#> SRR1951659 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951658 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951657 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> SRR1951656 4 0.1792 0.821 0.000 0.000 0.068 0.932
#> SRR1951655 1 0.4713 0.708 0.640 0.000 0.000 0.360
#> SRR1951654 1 0.3831 0.789 0.792 0.000 0.004 0.204
#> SRR1951653 4 0.0817 0.829 0.024 0.000 0.000 0.976
#> SRR1951652 2 0.0817 0.942 0.000 0.976 0.024 0.000
#> SRR1951651 4 0.0817 0.829 0.024 0.000 0.000 0.976
#> SRR1951650 4 0.4830 0.502 0.000 0.000 0.392 0.608
#> SRR1951648 3 0.4746 0.600 0.000 0.368 0.632 0.000
#> SRR1951649 1 0.4981 0.541 0.536 0.000 0.000 0.464
#> SRR1951647 2 0.6971 0.175 0.000 0.568 0.156 0.276
#> SRR1951646 4 0.3486 0.739 0.000 0.000 0.188 0.812
#> SRR1951645 1 0.4907 0.627 0.580 0.000 0.000 0.420
#> SRR1951644 1 0.2973 0.819 0.856 0.000 0.000 0.144
#> SRR1951643 4 0.0817 0.829 0.024 0.000 0.000 0.976
#> SRR1951641 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951642 4 0.0188 0.838 0.004 0.000 0.000 0.996
#> SRR1951694 1 0.0188 0.772 0.996 0.000 0.000 0.004
#> SRR1951731 3 0.6508 0.537 0.000 0.344 0.568 0.088
#> SRR1951730 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951752 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951751 1 0.4661 0.719 0.652 0.000 0.000 0.348
#> SRR1951749 3 0.4830 0.563 0.000 0.392 0.608 0.000
#> SRR1951750 1 0.4697 0.712 0.644 0.000 0.000 0.356
#> SRR1951748 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951745 1 0.2149 0.706 0.912 0.000 0.088 0.000
#> SRR1951746 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951744 4 0.3796 0.772 0.056 0.000 0.096 0.848
#> SRR1951742 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951743 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951741 1 0.4907 0.629 0.580 0.000 0.000 0.420
#> SRR1951740 4 0.0188 0.838 0.004 0.000 0.000 0.996
#> SRR1951738 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951739 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951737 4 0.4431 0.625 0.000 0.000 0.304 0.696
#> SRR1951736 1 0.2973 0.819 0.856 0.000 0.000 0.144
#> SRR1951734 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951735 4 0.4382 0.634 0.000 0.000 0.296 0.704
#> SRR1951733 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951732 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951729 4 0.4431 0.625 0.000 0.000 0.304 0.696
#> SRR1951728 3 0.4981 -0.153 0.000 0.000 0.536 0.464
#> SRR1951727 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951725 1 0.0188 0.767 0.996 0.000 0.004 0.000
#> SRR1951726 2 0.2334 0.862 0.004 0.908 0.088 0.000
#> SRR1951724 1 0.4431 0.748 0.696 0.000 0.000 0.304
#> SRR1951722 1 0.4382 0.756 0.704 0.000 0.000 0.296
#> SRR1951723 1 0.4989 0.522 0.528 0.000 0.000 0.472
#> SRR1951721 2 0.6176 0.307 0.368 0.572 0.060 0.000
#> SRR1951719 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> SRR1951720 1 0.2647 0.817 0.880 0.000 0.000 0.120
#> SRR1951718 1 0.3400 0.811 0.820 0.000 0.000 0.180
#> SRR1951717 1 0.2868 0.819 0.864 0.000 0.000 0.136
#> SRR1951715 1 0.0188 0.767 0.996 0.000 0.004 0.000
#> SRR1951716 1 0.2973 0.819 0.856 0.000 0.000 0.144
#> SRR1951714 4 0.0817 0.829 0.024 0.000 0.000 0.976
#> SRR1951713 4 0.0817 0.829 0.024 0.000 0.000 0.976
#> SRR1951712 1 0.4661 0.719 0.652 0.000 0.000 0.348
#> SRR1951711 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951710 1 0.0188 0.772 0.996 0.000 0.000 0.004
#> SRR1951709 3 0.4746 0.600 0.000 0.368 0.632 0.000
#> SRR1951708 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951707 3 0.4485 0.726 0.012 0.248 0.740 0.000
#> SRR1951705 1 0.1302 0.790 0.956 0.000 0.000 0.044
#> SRR1951704 3 0.3257 0.771 0.004 0.152 0.844 0.000
#> SRR1951703 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951706 3 0.3172 0.770 0.000 0.160 0.840 0.000
#> SRR1951702 3 0.2773 0.771 0.004 0.116 0.880 0.000
#> SRR1951701 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951699 3 0.3764 0.756 0.000 0.216 0.784 0.000
#> SRR1951698 3 0.1637 0.692 0.000 0.000 0.940 0.060
#> SRR1951700 4 0.4331 0.361 0.288 0.000 0.000 0.712
#> SRR1951697 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951696 4 0.4431 0.625 0.000 0.000 0.304 0.696
#> SRR1951695 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> SRR1951693 4 0.3266 0.638 0.168 0.000 0.000 0.832
#> SRR1951692 4 0.0336 0.839 0.000 0.000 0.008 0.992
#> SRR1951691 1 0.4866 0.654 0.596 0.000 0.000 0.404
#> SRR1951688 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951689 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951687 4 0.5000 -0.413 0.500 0.000 0.000 0.500
#> SRR1951685 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951684 4 0.2281 0.807 0.000 0.000 0.096 0.904
#> SRR1951686 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951683 4 0.0817 0.829 0.024 0.000 0.000 0.976
#> SRR1951682 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951690 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951680 4 0.0188 0.838 0.004 0.000 0.000 0.996
#> SRR1951679 1 0.0469 0.776 0.988 0.000 0.000 0.012
#> SRR1951678 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> SRR1951677 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> SRR1951676 4 0.4830 0.502 0.000 0.000 0.392 0.608
#> SRR1951675 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951681 4 0.4431 0.625 0.000 0.000 0.304 0.696
#> SRR1951673 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951674 1 0.2149 0.706 0.912 0.000 0.088 0.000
#> SRR1951672 1 0.2973 0.819 0.856 0.000 0.000 0.144
#> SRR1951671 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951670 1 0.4713 0.708 0.640 0.000 0.000 0.360
#> SRR1951640 4 0.2216 0.809 0.000 0.000 0.092 0.908
#> SRR1951668 4 0.1474 0.800 0.052 0.000 0.000 0.948
#> SRR1951669 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951638 1 0.0188 0.772 0.996 0.000 0.000 0.004
#> SRR1951637 2 0.1004 0.939 0.004 0.972 0.024 0.000
#> SRR1951635 1 0.0188 0.772 0.996 0.000 0.000 0.004
#> SRR1951634 4 0.0817 0.829 0.024 0.000 0.000 0.976
#> SRR1951631 4 0.0817 0.829 0.024 0.000 0.000 0.976
#> SRR1951632 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951633 4 0.1557 0.826 0.000 0.000 0.056 0.944
#> SRR1951630 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> SRR1951629 4 0.0000 0.839 0.000 0.000 0.000 1.000
#> SRR1951628 1 0.2739 0.781 0.904 0.036 0.000 0.060
#> SRR1951627 3 0.3764 0.756 0.000 0.216 0.784 0.000
#> SRR1951639 3 0.1929 0.723 0.000 0.036 0.940 0.024
#> SRR1951625 2 0.0000 0.968 0.000 1.000 0.000 0.000
#> SRR1951626 3 0.1637 0.692 0.000 0.000 0.940 0.060
#> SRR1951624 4 0.4431 0.625 0.000 0.000 0.304 0.696
#> SRR1951622 1 0.2281 0.809 0.904 0.000 0.000 0.096
#> SRR1951636 3 0.1637 0.692 0.000 0.000 0.940 0.060
#> SRR1951623 3 0.3907 0.432 0.000 0.000 0.768 0.232
#> SRR1951621 1 0.4776 0.690 0.624 0.000 0.000 0.376
#> SRR1951620 4 0.2216 0.809 0.000 0.000 0.092 0.908
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951666 3 0.3774 0.5857 0.000 0.296 0.704 0.000 0.000
#> SRR1951665 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 4 0.3809 0.6729 0.000 0.000 0.256 0.736 0.008
#> SRR1951663 5 0.0290 0.6612 0.008 0.000 0.000 0.000 0.992
#> SRR1951662 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 3 0.1341 0.6018 0.000 0.000 0.944 0.000 0.056
#> SRR1951660 1 0.0290 0.7511 0.992 0.000 0.000 0.008 0.000
#> SRR1951659 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951657 4 0.0404 0.8201 0.012 0.000 0.000 0.988 0.000
#> SRR1951656 4 0.0451 0.8168 0.000 0.000 0.004 0.988 0.008
#> SRR1951655 1 0.3305 0.6880 0.776 0.000 0.000 0.224 0.000
#> SRR1951654 1 0.6034 0.5268 0.572 0.000 0.000 0.256 0.172
#> SRR1951653 4 0.2890 0.7114 0.160 0.000 0.000 0.836 0.004
#> SRR1951652 2 0.1892 0.8842 0.000 0.916 0.004 0.000 0.080
#> SRR1951651 4 0.1671 0.8056 0.076 0.000 0.000 0.924 0.000
#> SRR1951650 4 0.4298 0.5590 0.000 0.000 0.352 0.640 0.008
#> SRR1951648 3 0.3586 0.6061 0.000 0.264 0.736 0.000 0.000
#> SRR1951649 1 0.0609 0.7510 0.980 0.000 0.000 0.020 0.000
#> SRR1951647 2 0.7213 -0.0573 0.016 0.460 0.252 0.264 0.008
#> SRR1951646 4 0.3809 0.6735 0.000 0.000 0.256 0.736 0.008
#> SRR1951645 1 0.3480 0.6770 0.752 0.000 0.000 0.248 0.000
#> SRR1951644 1 0.0000 0.7494 1.000 0.000 0.000 0.000 0.000
#> SRR1951643 4 0.1671 0.8056 0.076 0.000 0.000 0.924 0.000
#> SRR1951641 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951642 4 0.1608 0.8075 0.072 0.000 0.000 0.928 0.000
#> SRR1951694 1 0.4287 0.0248 0.540 0.000 0.000 0.000 0.460
#> SRR1951731 3 0.6073 0.2572 0.000 0.436 0.456 0.104 0.004
#> SRR1951730 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951752 2 0.0162 0.9717 0.000 0.996 0.000 0.000 0.004
#> SRR1951751 1 0.0162 0.7503 0.996 0.000 0.000 0.004 0.000
#> SRR1951749 3 0.3816 0.5779 0.000 0.304 0.696 0.000 0.000
#> SRR1951750 1 0.3177 0.6941 0.792 0.000 0.000 0.208 0.000
#> SRR1951748 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 5 0.0613 0.6603 0.004 0.000 0.008 0.004 0.984
#> SRR1951746 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 5 0.5679 0.1284 0.068 0.000 0.008 0.364 0.560
#> SRR1951742 2 0.1121 0.9281 0.000 0.956 0.044 0.000 0.000
#> SRR1951743 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 1 0.3752 0.6496 0.708 0.000 0.000 0.292 0.000
#> SRR1951740 4 0.0794 0.8201 0.028 0.000 0.000 0.972 0.000
#> SRR1951738 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951739 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951737 4 0.3809 0.6729 0.000 0.000 0.256 0.736 0.008
#> SRR1951736 1 0.0000 0.7494 1.000 0.000 0.000 0.000 0.000
#> SRR1951734 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 4 0.3809 0.6729 0.000 0.000 0.256 0.736 0.008
#> SRR1951733 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951732 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 4 0.3809 0.6729 0.000 0.000 0.256 0.736 0.008
#> SRR1951728 3 0.4700 -0.2107 0.004 0.000 0.516 0.472 0.008
#> SRR1951727 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 5 0.3837 0.4771 0.308 0.000 0.000 0.000 0.692
#> SRR1951726 5 0.4582 0.1585 0.000 0.416 0.012 0.000 0.572
#> SRR1951724 1 0.0290 0.7514 0.992 0.000 0.000 0.008 0.000
#> SRR1951722 1 0.0162 0.7483 0.996 0.000 0.000 0.004 0.000
#> SRR1951723 1 0.0703 0.7508 0.976 0.000 0.000 0.024 0.000
#> SRR1951721 5 0.0324 0.6606 0.004 0.004 0.000 0.000 0.992
#> SRR1951719 4 0.0162 0.8181 0.000 0.000 0.000 0.996 0.004
#> SRR1951720 1 0.0000 0.7494 1.000 0.000 0.000 0.000 0.000
#> SRR1951718 1 0.0404 0.7513 0.988 0.000 0.000 0.012 0.000
#> SRR1951717 1 0.0000 0.7494 1.000 0.000 0.000 0.000 0.000
#> SRR1951715 5 0.3966 0.4337 0.336 0.000 0.000 0.000 0.664
#> SRR1951716 1 0.0000 0.7494 1.000 0.000 0.000 0.000 0.000
#> SRR1951714 4 0.1851 0.7963 0.088 0.000 0.000 0.912 0.000
#> SRR1951713 1 0.4307 0.2259 0.504 0.000 0.000 0.496 0.000
#> SRR1951712 1 0.0510 0.7516 0.984 0.000 0.000 0.016 0.000
#> SRR1951711 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951710 1 0.4283 0.0345 0.544 0.000 0.000 0.000 0.456
#> SRR1951709 3 0.3707 0.5948 0.000 0.284 0.716 0.000 0.000
#> SRR1951708 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951707 3 0.4792 0.5613 0.052 0.172 0.752 0.020 0.004
#> SRR1951705 1 0.1732 0.6879 0.920 0.000 0.000 0.000 0.080
#> SRR1951704 3 0.4522 0.2986 0.000 0.008 0.552 0.000 0.440
#> SRR1951703 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 3 0.4392 0.3876 0.000 0.008 0.612 0.000 0.380
#> SRR1951702 3 0.4211 0.4102 0.000 0.004 0.636 0.000 0.360
#> SRR1951701 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951699 3 0.3534 0.6095 0.000 0.256 0.744 0.000 0.000
#> SRR1951698 3 0.0290 0.6117 0.000 0.000 0.992 0.008 0.000
#> SRR1951700 1 0.3932 0.5931 0.672 0.000 0.000 0.328 0.000
#> SRR1951697 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951696 4 0.3809 0.6729 0.000 0.000 0.256 0.736 0.008
#> SRR1951695 4 0.0794 0.8201 0.028 0.000 0.000 0.972 0.000
#> SRR1951693 1 0.4161 0.4956 0.608 0.000 0.000 0.392 0.000
#> SRR1951692 4 0.0404 0.8201 0.012 0.000 0.000 0.988 0.000
#> SRR1951691 1 0.3730 0.6522 0.712 0.000 0.000 0.288 0.000
#> SRR1951688 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951689 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951687 1 0.4029 0.6229 0.680 0.000 0.000 0.316 0.004
#> SRR1951685 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951684 4 0.1082 0.8120 0.000 0.000 0.028 0.964 0.008
#> SRR1951686 2 0.0162 0.9715 0.000 0.996 0.004 0.000 0.000
#> SRR1951683 4 0.2852 0.6929 0.172 0.000 0.000 0.828 0.000
#> SRR1951682 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951680 4 0.0566 0.8200 0.012 0.000 0.000 0.984 0.004
#> SRR1951679 1 0.2230 0.6584 0.884 0.000 0.000 0.000 0.116
#> SRR1951678 4 0.1671 0.8056 0.076 0.000 0.000 0.924 0.000
#> SRR1951677 4 0.1671 0.8056 0.076 0.000 0.000 0.924 0.000
#> SRR1951676 4 0.4298 0.5590 0.000 0.000 0.352 0.640 0.008
#> SRR1951675 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951681 4 0.3934 0.6520 0.000 0.000 0.276 0.716 0.008
#> SRR1951673 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951674 5 0.0613 0.6603 0.004 0.000 0.008 0.004 0.984
#> SRR1951672 1 0.0000 0.7494 1.000 0.000 0.000 0.000 0.000
#> SRR1951671 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951670 1 0.3561 0.6695 0.740 0.000 0.000 0.260 0.000
#> SRR1951640 4 0.1082 0.8120 0.000 0.000 0.028 0.964 0.008
#> SRR1951668 1 0.4287 0.3349 0.540 0.000 0.000 0.460 0.000
#> SRR1951669 2 0.0290 0.9679 0.000 0.992 0.000 0.000 0.008
#> SRR1951638 1 0.2561 0.6400 0.856 0.000 0.000 0.000 0.144
#> SRR1951637 2 0.2230 0.8485 0.000 0.884 0.000 0.000 0.116
#> SRR1951635 1 0.4287 0.0248 0.540 0.000 0.000 0.000 0.460
#> SRR1951634 4 0.4307 -0.2557 0.500 0.000 0.000 0.500 0.000
#> SRR1951631 4 0.1671 0.8056 0.076 0.000 0.000 0.924 0.000
#> SRR1951632 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951633 4 0.1043 0.8183 0.040 0.000 0.000 0.960 0.000
#> SRR1951630 4 0.1341 0.8141 0.056 0.000 0.000 0.944 0.000
#> SRR1951629 4 0.1270 0.8154 0.052 0.000 0.000 0.948 0.000
#> SRR1951628 1 0.4548 0.4376 0.716 0.052 0.000 0.000 0.232
#> SRR1951627 3 0.3039 0.6296 0.000 0.192 0.808 0.000 0.000
#> SRR1951639 3 0.0566 0.6094 0.000 0.000 0.984 0.012 0.004
#> SRR1951625 2 0.0000 0.9752 0.000 1.000 0.000 0.000 0.000
#> SRR1951626 3 0.0566 0.6094 0.000 0.000 0.984 0.012 0.004
#> SRR1951624 4 0.3835 0.6691 0.000 0.000 0.260 0.732 0.008
#> SRR1951622 1 0.1121 0.7191 0.956 0.000 0.000 0.000 0.044
#> SRR1951636 3 0.0290 0.6117 0.000 0.000 0.992 0.008 0.000
#> SRR1951623 3 0.5063 0.2891 0.000 0.000 0.632 0.312 0.056
#> SRR1951621 1 0.3636 0.6627 0.728 0.000 0.000 0.272 0.000
#> SRR1951620 4 0.1082 0.8120 0.000 0.000 0.028 0.964 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951666 5 0.0937 0.7498 0.000 0.040 0.000 0.000 0.960 0.000
#> SRR1951665 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 3 0.1610 0.7897 0.000 0.000 0.916 0.084 0.000 0.000
#> SRR1951663 6 0.0458 0.6674 0.016 0.000 0.000 0.000 0.000 0.984
#> SRR1951662 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 5 0.0458 0.7557 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1951660 1 0.0632 0.8189 0.976 0.000 0.000 0.024 0.000 0.000
#> SRR1951659 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951657 4 0.3126 0.5356 0.000 0.000 0.248 0.752 0.000 0.000
#> SRR1951656 3 0.3647 0.5993 0.000 0.000 0.640 0.360 0.000 0.000
#> SRR1951655 4 0.3634 0.4508 0.356 0.000 0.000 0.644 0.000 0.000
#> SRR1951654 4 0.4002 0.6670 0.068 0.000 0.000 0.744 0.000 0.188
#> SRR1951653 3 0.5814 0.5054 0.248 0.000 0.524 0.224 0.004 0.000
#> SRR1951652 2 0.2540 0.8539 0.000 0.872 0.004 0.000 0.104 0.020
#> SRR1951651 4 0.0603 0.8209 0.004 0.000 0.016 0.980 0.000 0.000
#> SRR1951650 3 0.2712 0.7796 0.000 0.000 0.864 0.088 0.048 0.000
#> SRR1951648 5 0.0547 0.7571 0.000 0.020 0.000 0.000 0.980 0.000
#> SRR1951649 1 0.1204 0.8177 0.944 0.000 0.000 0.056 0.000 0.000
#> SRR1951647 3 0.6956 0.0929 0.044 0.296 0.464 0.024 0.172 0.000
#> SRR1951646 3 0.2668 0.7889 0.000 0.000 0.828 0.168 0.004 0.000
#> SRR1951645 4 0.3868 0.0163 0.496 0.000 0.000 0.504 0.000 0.000
#> SRR1951644 1 0.0458 0.8169 0.984 0.000 0.000 0.016 0.000 0.000
#> SRR1951643 4 0.0777 0.8187 0.004 0.000 0.024 0.972 0.000 0.000
#> SRR1951641 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951642 4 0.0790 0.8149 0.000 0.000 0.032 0.968 0.000 0.000
#> SRR1951694 1 0.3592 0.3961 0.656 0.000 0.000 0.000 0.000 0.344
#> SRR1951731 5 0.6128 0.2709 0.000 0.296 0.268 0.004 0.432 0.000
#> SRR1951730 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951752 2 0.0291 0.9719 0.000 0.992 0.004 0.000 0.004 0.000
#> SRR1951751 1 0.3421 0.6542 0.736 0.000 0.008 0.256 0.000 0.000
#> SRR1951749 5 0.1444 0.7253 0.000 0.072 0.000 0.000 0.928 0.000
#> SRR1951750 4 0.3390 0.5747 0.296 0.000 0.000 0.704 0.000 0.000
#> SRR1951748 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 6 0.0405 0.6625 0.000 0.000 0.008 0.000 0.004 0.988
#> SRR1951746 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951744 6 0.4381 0.0360 0.000 0.000 0.016 0.456 0.004 0.524
#> SRR1951742 2 0.3371 0.5978 0.000 0.708 0.000 0.000 0.292 0.000
#> SRR1951743 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951741 4 0.1765 0.8047 0.096 0.000 0.000 0.904 0.000 0.000
#> SRR1951740 4 0.1765 0.7670 0.000 0.000 0.096 0.904 0.000 0.000
#> SRR1951738 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951739 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951737 3 0.1957 0.8000 0.000 0.000 0.888 0.112 0.000 0.000
#> SRR1951736 1 0.1141 0.8197 0.948 0.000 0.000 0.052 0.000 0.000
#> SRR1951734 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951735 3 0.1957 0.8000 0.000 0.000 0.888 0.112 0.000 0.000
#> SRR1951733 2 0.1663 0.8967 0.000 0.912 0.000 0.000 0.088 0.000
#> SRR1951732 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 3 0.1610 0.7897 0.000 0.000 0.916 0.084 0.000 0.000
#> SRR1951728 3 0.2331 0.6698 0.000 0.000 0.888 0.032 0.080 0.000
#> SRR1951727 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 6 0.3405 0.4688 0.272 0.000 0.000 0.004 0.000 0.724
#> SRR1951726 6 0.5407 0.2043 0.000 0.332 0.012 0.000 0.096 0.560
#> SRR1951724 1 0.3052 0.6921 0.780 0.000 0.004 0.216 0.000 0.000
#> SRR1951722 1 0.1313 0.8075 0.952 0.000 0.028 0.016 0.004 0.000
#> SRR1951723 1 0.1672 0.8176 0.932 0.000 0.016 0.048 0.004 0.000
#> SRR1951721 6 0.0547 0.6670 0.020 0.000 0.000 0.000 0.000 0.980
#> SRR1951719 3 0.3221 0.7118 0.000 0.000 0.736 0.264 0.000 0.000
#> SRR1951720 1 0.1444 0.8122 0.928 0.000 0.000 0.072 0.000 0.000
#> SRR1951718 1 0.0937 0.8204 0.960 0.000 0.000 0.040 0.000 0.000
#> SRR1951717 1 0.3390 0.5949 0.704 0.000 0.000 0.296 0.000 0.000
#> SRR1951715 6 0.3695 0.2676 0.376 0.000 0.000 0.000 0.000 0.624
#> SRR1951716 1 0.0937 0.8204 0.960 0.000 0.000 0.040 0.000 0.000
#> SRR1951714 4 0.0603 0.8216 0.004 0.000 0.016 0.980 0.000 0.000
#> SRR1951713 4 0.4166 0.7010 0.196 0.000 0.076 0.728 0.000 0.000
#> SRR1951712 1 0.3330 0.6083 0.716 0.000 0.000 0.284 0.000 0.000
#> SRR1951711 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951710 1 0.3390 0.4863 0.704 0.000 0.000 0.000 0.000 0.296
#> SRR1951709 5 0.0713 0.7552 0.000 0.028 0.000 0.000 0.972 0.000
#> SRR1951708 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951707 3 0.6203 -0.2140 0.068 0.068 0.460 0.004 0.400 0.000
#> SRR1951705 1 0.0000 0.8089 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951704 5 0.4482 0.3497 0.000 0.000 0.032 0.000 0.552 0.416
#> SRR1951703 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 5 0.3953 0.5024 0.000 0.000 0.016 0.000 0.656 0.328
#> SRR1951702 5 0.3938 0.5079 0.000 0.000 0.016 0.000 0.660 0.324
#> SRR1951701 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951699 5 0.0260 0.7567 0.000 0.008 0.000 0.000 0.992 0.000
#> SRR1951698 5 0.2003 0.7368 0.000 0.000 0.116 0.000 0.884 0.000
#> SRR1951700 4 0.3409 0.7061 0.192 0.000 0.028 0.780 0.000 0.000
#> SRR1951697 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951696 3 0.2260 0.7977 0.000 0.000 0.860 0.140 0.000 0.000
#> SRR1951695 4 0.2048 0.7609 0.000 0.000 0.120 0.880 0.000 0.000
#> SRR1951693 4 0.1910 0.8016 0.108 0.000 0.000 0.892 0.000 0.000
#> SRR1951692 4 0.3126 0.5345 0.000 0.000 0.248 0.752 0.000 0.000
#> SRR1951691 4 0.1765 0.8047 0.096 0.000 0.000 0.904 0.000 0.000
#> SRR1951688 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951689 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951687 4 0.2450 0.7875 0.116 0.000 0.000 0.868 0.000 0.016
#> SRR1951685 2 0.2431 0.8407 0.000 0.860 0.132 0.000 0.008 0.000
#> SRR1951684 3 0.2697 0.7814 0.000 0.000 0.812 0.188 0.000 0.000
#> SRR1951686 2 0.1501 0.9076 0.000 0.924 0.000 0.000 0.076 0.000
#> SRR1951683 4 0.0520 0.8222 0.008 0.000 0.008 0.984 0.000 0.000
#> SRR1951682 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951680 4 0.3601 0.4066 0.004 0.000 0.312 0.684 0.000 0.000
#> SRR1951679 1 0.0622 0.8010 0.980 0.000 0.012 0.000 0.000 0.008
#> SRR1951678 4 0.0790 0.8149 0.000 0.000 0.032 0.968 0.000 0.000
#> SRR1951677 4 0.0790 0.8149 0.000 0.000 0.032 0.968 0.000 0.000
#> SRR1951676 3 0.2712 0.7796 0.000 0.000 0.864 0.088 0.048 0.000
#> SRR1951675 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951681 3 0.2165 0.7985 0.000 0.000 0.884 0.108 0.008 0.000
#> SRR1951673 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951674 6 0.0405 0.6625 0.000 0.000 0.008 0.000 0.004 0.988
#> SRR1951672 1 0.1204 0.8185 0.944 0.000 0.000 0.056 0.000 0.000
#> SRR1951671 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951670 4 0.2378 0.7672 0.152 0.000 0.000 0.848 0.000 0.000
#> SRR1951640 3 0.3428 0.6874 0.000 0.000 0.696 0.304 0.000 0.000
#> SRR1951668 4 0.1444 0.8129 0.072 0.000 0.000 0.928 0.000 0.000
#> SRR1951669 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951638 1 0.5983 0.1757 0.388 0.000 0.000 0.228 0.000 0.384
#> SRR1951637 2 0.0937 0.9452 0.000 0.960 0.000 0.000 0.000 0.040
#> SRR1951635 1 0.3862 0.3049 0.608 0.000 0.000 0.004 0.000 0.388
#> SRR1951634 4 0.1970 0.8130 0.092 0.000 0.008 0.900 0.000 0.000
#> SRR1951631 4 0.0692 0.8199 0.004 0.000 0.020 0.976 0.000 0.000
#> SRR1951632 2 0.0000 0.9777 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951633 4 0.1075 0.8054 0.000 0.000 0.048 0.952 0.000 0.000
#> SRR1951630 4 0.1141 0.8034 0.000 0.000 0.052 0.948 0.000 0.000
#> SRR1951629 4 0.1444 0.7899 0.000 0.000 0.072 0.928 0.000 0.000
#> SRR1951628 1 0.1553 0.7715 0.944 0.012 0.008 0.000 0.004 0.032
#> SRR1951627 5 0.0260 0.7557 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1951639 5 0.2340 0.7122 0.000 0.000 0.148 0.000 0.852 0.000
#> SRR1951625 2 0.0146 0.9748 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1951626 5 0.3727 0.4471 0.000 0.000 0.388 0.000 0.612 0.000
#> SRR1951624 3 0.1556 0.7903 0.000 0.000 0.920 0.080 0.000 0.000
#> SRR1951622 1 0.0363 0.8149 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1951636 5 0.2697 0.7034 0.000 0.000 0.188 0.000 0.812 0.000
#> SRR1951623 5 0.5080 0.2806 0.000 0.000 0.380 0.056 0.552 0.012
#> SRR1951621 4 0.1910 0.7992 0.108 0.000 0.000 0.892 0.000 0.000
#> SRR1951620 3 0.3446 0.6819 0.000 0.000 0.692 0.308 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "pam"]
# you can also extract it by
# res = res_list["SD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 13580 rows and 133 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.973 0.989 0.4283 0.565 0.565
#> 3 3 0.767 0.865 0.935 0.4871 0.667 0.468
#> 4 4 0.750 0.782 0.892 0.1509 0.778 0.476
#> 5 5 0.731 0.781 0.879 0.0663 0.853 0.535
#> 6 6 0.720 0.724 0.804 0.0525 0.910 0.619
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
#> SRR1951667 2 0.0000 0.966 0.000 1.000
#> SRR1951666 2 0.0000 0.966 0.000 1.000
#> SRR1951665 2 0.0000 0.966 0.000 1.000
#> SRR1951664 1 0.0000 0.999 1.000 0.000
#> SRR1951663 1 0.0000 0.999 1.000 0.000
#> SRR1951662 2 0.0000 0.966 0.000 1.000
#> SRR1951661 1 0.0376 0.995 0.996 0.004
#> SRR1951660 1 0.0000 0.999 1.000 0.000
#> SRR1951659 2 0.0000 0.966 0.000 1.000
#> SRR1951658 2 0.0000 0.966 0.000 1.000
#> SRR1951657 1 0.0000 0.999 1.000 0.000
#> SRR1951656 1 0.0000 0.999 1.000 0.000
#> SRR1951655 1 0.0000 0.999 1.000 0.000
#> SRR1951654 1 0.0000 0.999 1.000 0.000
#> SRR1951653 1 0.0000 0.999 1.000 0.000
#> SRR1951652 2 0.0000 0.966 0.000 1.000
#> SRR1951651 1 0.0000 0.999 1.000 0.000
#> SRR1951650 1 0.0000 0.999 1.000 0.000
#> SRR1951648 2 0.9896 0.239 0.440 0.560
#> SRR1951649 1 0.0000 0.999 1.000 0.000
#> SRR1951647 1 0.0000 0.999 1.000 0.000
#> SRR1951646 1 0.0000 0.999 1.000 0.000
#> SRR1951645 1 0.0000 0.999 1.000 0.000
#> SRR1951644 1 0.0000 0.999 1.000 0.000
#> SRR1951643 1 0.0000 0.999 1.000 0.000
#> SRR1951641 2 0.0000 0.966 0.000 1.000
#> SRR1951642 1 0.0000 0.999 1.000 0.000
#> SRR1951694 1 0.0000 0.999 1.000 0.000
#> SRR1951731 1 0.0000 0.999 1.000 0.000
#> SRR1951730 2 0.0000 0.966 0.000 1.000
#> SRR1951752 1 0.0000 0.999 1.000 0.000
#> SRR1951751 1 0.0000 0.999 1.000 0.000
#> SRR1951749 2 0.0000 0.966 0.000 1.000
#> SRR1951750 1 0.0000 0.999 1.000 0.000
#> SRR1951748 2 0.0000 0.966 0.000 1.000
#> SRR1951747 2 0.0000 0.966 0.000 1.000
#> SRR1951745 1 0.0000 0.999 1.000 0.000
#> SRR1951746 2 0.0000 0.966 0.000 1.000
#> SRR1951744 1 0.0000 0.999 1.000 0.000
#> SRR1951742 2 0.0000 0.966 0.000 1.000
#> SRR1951743 2 0.0000 0.966 0.000 1.000
#> SRR1951741 1 0.0000 0.999 1.000 0.000
#> SRR1951740 1 0.0000 0.999 1.000 0.000
#> SRR1951738 2 0.0000 0.966 0.000 1.000
#> SRR1951739 2 0.0000 0.966 0.000 1.000
#> SRR1951737 1 0.0000 0.999 1.000 0.000
#> SRR1951736 1 0.0000 0.999 1.000 0.000
#> SRR1951734 2 0.0000 0.966 0.000 1.000
#> SRR1951735 1 0.0000 0.999 1.000 0.000
#> SRR1951733 2 0.0000 0.966 0.000 1.000
#> SRR1951732 2 0.0000 0.966 0.000 1.000
#> SRR1951729 1 0.0000 0.999 1.000 0.000
#> SRR1951728 1 0.0000 0.999 1.000 0.000
#> SRR1951727 2 0.0000 0.966 0.000 1.000
#> SRR1951725 1 0.0000 0.999 1.000 0.000
#> SRR1951726 1 0.0000 0.999 1.000 0.000
#> SRR1951724 1 0.0000 0.999 1.000 0.000
#> SRR1951722 1 0.0000 0.999 1.000 0.000
#> SRR1951723 1 0.0000 0.999 1.000 0.000
#> SRR1951721 1 0.0000 0.999 1.000 0.000
#> SRR1951719 1 0.0000 0.999 1.000 0.000
#> SRR1951720 1 0.0000 0.999 1.000 0.000
#> SRR1951718 1 0.0000 0.999 1.000 0.000
#> SRR1951717 1 0.0000 0.999 1.000 0.000
#> SRR1951715 1 0.0000 0.999 1.000 0.000
#> SRR1951716 1 0.0000 0.999 1.000 0.000
#> SRR1951714 1 0.0000 0.999 1.000 0.000
#> SRR1951713 1 0.0000 0.999 1.000 0.000
#> SRR1951712 1 0.0000 0.999 1.000 0.000
#> SRR1951711 2 0.0000 0.966 0.000 1.000
#> SRR1951710 1 0.0000 0.999 1.000 0.000
#> SRR1951709 2 0.0000 0.966 0.000 1.000
#> SRR1951708 2 0.0000 0.966 0.000 1.000
#> SRR1951707 1 0.0000 0.999 1.000 0.000
#> SRR1951705 1 0.0000 0.999 1.000 0.000
#> SRR1951704 1 0.0000 0.999 1.000 0.000
#> SRR1951703 2 0.0000 0.966 0.000 1.000
#> SRR1951706 1 0.3431 0.929 0.936 0.064
#> SRR1951702 1 0.0000 0.999 1.000 0.000
#> SRR1951701 2 0.0000 0.966 0.000 1.000
#> SRR1951699 2 0.0000 0.966 0.000 1.000
#> SRR1951698 1 0.0000 0.999 1.000 0.000
#> SRR1951700 1 0.0000 0.999 1.000 0.000
#> SRR1951697 2 0.0000 0.966 0.000 1.000
#> SRR1951696 1 0.0000 0.999 1.000 0.000
#> SRR1951695 1 0.0000 0.999 1.000 0.000
#> SRR1951693 1 0.0000 0.999 1.000 0.000
#> SRR1951692 1 0.0000 0.999 1.000 0.000
#> SRR1951691 1 0.0000 0.999 1.000 0.000
#> SRR1951688 2 0.0000 0.966 0.000 1.000
#> SRR1951689 2 0.7139 0.753 0.196 0.804
#> SRR1951687 1 0.0000 0.999 1.000 0.000
#> SRR1951685 1 0.0000 0.999 1.000 0.000
#> SRR1951684 1 0.0000 0.999 1.000 0.000
#> SRR1951686 2 0.0000 0.966 0.000 1.000
#> SRR1951683 1 0.0000 0.999 1.000 0.000
#> SRR1951682 2 0.0000 0.966 0.000 1.000
#> SRR1951690 2 0.0000 0.966 0.000 1.000
#> SRR1951680 1 0.0000 0.999 1.000 0.000
#> SRR1951679 1 0.0000 0.999 1.000 0.000
#> SRR1951678 1 0.0000 0.999 1.000 0.000
#> SRR1951677 1 0.0000 0.999 1.000 0.000
#> SRR1951676 1 0.0000 0.999 1.000 0.000
#> SRR1951675 2 0.0000 0.966 0.000 1.000
#> SRR1951681 1 0.0000 0.999 1.000 0.000
#> SRR1951673 2 0.9286 0.501 0.344 0.656
#> SRR1951674 1 0.0000 0.999 1.000 0.000
#> SRR1951672 1 0.0000 0.999 1.000 0.000
#> SRR1951671 2 0.0000 0.966 0.000 1.000
#> SRR1951670 1 0.0000 0.999 1.000 0.000
#> SRR1951640 1 0.0000 0.999 1.000 0.000
#> SRR1951668 1 0.0000 0.999 1.000 0.000
#> SRR1951669 2 0.0000 0.966 0.000 1.000
#> SRR1951638 1 0.0000 0.999 1.000 0.000
#> SRR1951637 2 0.0000 0.966 0.000 1.000
#> SRR1951635 1 0.0000 0.999 1.000 0.000
#> SRR1951634 1 0.0000 0.999 1.000 0.000
#> SRR1951631 1 0.0000 0.999 1.000 0.000
#> SRR1951632 2 0.0000 0.966 0.000 1.000
#> SRR1951633 1 0.0000 0.999 1.000 0.000
#> SRR1951630 1 0.0000 0.999 1.000 0.000
#> SRR1951629 1 0.0000 0.999 1.000 0.000
#> SRR1951628 1 0.0000 0.999 1.000 0.000
#> SRR1951627 2 0.0000 0.966 0.000 1.000
#> SRR1951639 1 0.0000 0.999 1.000 0.000
#> SRR1951625 2 0.9710 0.369 0.400 0.600
#> SRR1951626 1 0.0000 0.999 1.000 0.000
#> SRR1951624 1 0.0000 0.999 1.000 0.000
#> SRR1951622 1 0.0000 0.999 1.000 0.000
#> SRR1951636 1 0.0000 0.999 1.000 0.000
#> SRR1951623 1 0.0000 0.999 1.000 0.000
#> SRR1951621 1 0.0000 0.999 1.000 0.000
#> SRR1951620 1 0.0000 0.999 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951666 3 0.4062 0.7429 0.000 0.164 0.836
#> SRR1951665 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951664 1 0.2878 0.9045 0.904 0.000 0.096
#> SRR1951663 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951662 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951661 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951660 1 0.2066 0.9363 0.940 0.000 0.060
#> SRR1951659 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951657 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951656 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951655 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951654 1 0.3340 0.8423 0.880 0.000 0.120
#> SRR1951653 1 0.2066 0.9363 0.940 0.000 0.060
#> SRR1951652 3 0.2066 0.8329 0.000 0.060 0.940
#> SRR1951651 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951650 3 0.4702 0.7536 0.212 0.000 0.788
#> SRR1951648 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951649 1 0.2066 0.9363 0.940 0.000 0.060
#> SRR1951647 3 0.4291 0.7622 0.180 0.000 0.820
#> SRR1951646 1 0.2066 0.9363 0.940 0.000 0.060
#> SRR1951645 1 0.2066 0.9363 0.940 0.000 0.060
#> SRR1951644 1 0.2625 0.9166 0.916 0.000 0.084
#> SRR1951643 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951641 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951642 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951694 3 0.0237 0.8668 0.004 0.000 0.996
#> SRR1951731 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951730 3 0.2066 0.8329 0.000 0.060 0.940
#> SRR1951752 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951751 1 0.2066 0.9363 0.940 0.000 0.060
#> SRR1951749 3 0.2066 0.8329 0.000 0.060 0.940
#> SRR1951750 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951748 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951745 3 0.3619 0.8111 0.136 0.000 0.864
#> SRR1951746 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951744 3 0.3619 0.8111 0.136 0.000 0.864
#> SRR1951742 3 0.4291 0.7268 0.000 0.180 0.820
#> SRR1951743 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951741 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951740 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951738 2 0.2959 0.8603 0.000 0.900 0.100
#> SRR1951739 2 0.0592 0.9346 0.000 0.988 0.012
#> SRR1951737 1 0.2796 0.9076 0.908 0.000 0.092
#> SRR1951736 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951734 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951735 3 0.5497 0.6482 0.292 0.000 0.708
#> SRR1951733 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951732 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951729 1 0.5529 0.5748 0.704 0.000 0.296
#> SRR1951728 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951727 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951725 3 0.3619 0.8111 0.136 0.000 0.864
#> SRR1951726 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951724 3 0.4750 0.7516 0.216 0.000 0.784
#> SRR1951722 3 0.2959 0.8317 0.100 0.000 0.900
#> SRR1951723 1 0.2066 0.9363 0.940 0.000 0.060
#> SRR1951721 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951719 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951720 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951718 1 0.2066 0.9363 0.940 0.000 0.060
#> SRR1951717 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951715 3 0.0237 0.8668 0.004 0.000 0.996
#> SRR1951716 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951714 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951713 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951712 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951711 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951710 3 0.5785 0.5784 0.332 0.000 0.668
#> SRR1951709 3 0.2066 0.8329 0.000 0.060 0.940
#> SRR1951708 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951707 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951705 1 0.4702 0.7433 0.788 0.000 0.212
#> SRR1951704 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951703 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951706 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951702 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951701 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951699 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951698 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951700 1 0.4887 0.7092 0.772 0.000 0.228
#> SRR1951697 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951696 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951695 3 0.6260 0.2974 0.448 0.000 0.552
#> SRR1951693 3 0.5835 0.5631 0.340 0.000 0.660
#> SRR1951692 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951691 1 0.2066 0.9363 0.940 0.000 0.060
#> SRR1951688 2 0.5529 0.6182 0.000 0.704 0.296
#> SRR1951689 3 0.2066 0.8329 0.000 0.060 0.940
#> SRR1951687 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951685 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951684 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951686 2 0.6274 0.2122 0.000 0.544 0.456
#> SRR1951683 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951682 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951690 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951680 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951679 3 0.5785 0.5790 0.332 0.000 0.668
#> SRR1951678 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951677 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951676 3 0.5327 0.7094 0.272 0.000 0.728
#> SRR1951675 2 0.0000 0.9432 0.000 1.000 0.000
#> SRR1951681 3 0.4654 0.7574 0.208 0.000 0.792
#> SRR1951673 3 0.2066 0.8329 0.000 0.060 0.940
#> SRR1951674 3 0.3619 0.8111 0.136 0.000 0.864
#> SRR1951672 1 0.2066 0.9363 0.940 0.000 0.060
#> SRR1951671 3 0.2066 0.8329 0.000 0.060 0.940
#> SRR1951670 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951640 1 0.2066 0.9363 0.940 0.000 0.060
#> SRR1951668 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951669 2 0.3551 0.8297 0.000 0.868 0.132
#> SRR1951638 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951637 3 0.2066 0.8329 0.000 0.060 0.940
#> SRR1951635 1 0.2066 0.9363 0.940 0.000 0.060
#> SRR1951634 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951631 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951632 2 0.6235 0.2966 0.000 0.564 0.436
#> SRR1951633 1 0.0237 0.9580 0.996 0.000 0.004
#> SRR1951630 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951629 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951628 3 0.4702 0.7335 0.212 0.000 0.788
#> SRR1951627 3 0.2878 0.8112 0.000 0.096 0.904
#> SRR1951639 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951625 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951626 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951624 3 0.6309 0.0669 0.496 0.000 0.504
#> SRR1951622 1 0.2066 0.9363 0.940 0.000 0.060
#> SRR1951636 3 0.0000 0.8675 0.000 0.000 1.000
#> SRR1951623 3 0.3619 0.8111 0.136 0.000 0.864
#> SRR1951621 1 0.0000 0.9605 1.000 0.000 0.000
#> SRR1951620 1 0.0000 0.9605 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.3711 0.788 0.024 0.836 0.140 0.000
#> SRR1951666 3 0.4304 0.635 0.000 0.284 0.716 0.000
#> SRR1951665 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951664 4 0.3280 0.779 0.016 0.000 0.124 0.860
#> SRR1951663 1 0.1118 0.866 0.964 0.000 0.036 0.000
#> SRR1951662 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.0000 0.863 0.000 0.000 1.000 0.000
#> SRR1951660 1 0.0817 0.883 0.976 0.000 0.000 0.024
#> SRR1951659 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951658 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951657 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR1951656 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR1951655 4 0.1557 0.798 0.056 0.000 0.000 0.944
#> SRR1951654 1 0.2125 0.869 0.920 0.000 0.004 0.076
#> SRR1951653 4 0.0469 0.827 0.012 0.000 0.000 0.988
#> SRR1951652 3 0.3991 0.755 0.020 0.172 0.808 0.000
#> SRR1951651 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR1951650 4 0.5366 0.443 0.012 0.000 0.440 0.548
#> SRR1951648 3 0.0000 0.863 0.000 0.000 1.000 0.000
#> SRR1951649 4 0.1792 0.809 0.068 0.000 0.000 0.932
#> SRR1951647 3 0.0524 0.858 0.004 0.000 0.988 0.008
#> SRR1951646 4 0.5353 0.457 0.012 0.000 0.432 0.556
#> SRR1951645 4 0.1637 0.812 0.060 0.000 0.000 0.940
#> SRR1951644 1 0.4948 0.286 0.560 0.000 0.000 0.440
#> SRR1951643 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR1951641 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951642 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR1951694 1 0.0000 0.877 1.000 0.000 0.000 0.000
#> SRR1951731 3 0.0336 0.860 0.008 0.000 0.992 0.000
#> SRR1951730 3 0.5250 0.567 0.024 0.316 0.660 0.000
#> SRR1951752 3 0.0817 0.857 0.024 0.000 0.976 0.000
#> SRR1951751 4 0.2647 0.776 0.120 0.000 0.000 0.880
#> SRR1951749 3 0.0000 0.863 0.000 0.000 1.000 0.000
#> SRR1951750 4 0.3356 0.674 0.176 0.000 0.000 0.824
#> SRR1951748 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951745 1 0.1867 0.854 0.928 0.000 0.072 0.000
#> SRR1951746 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951744 4 0.5483 0.424 0.016 0.000 0.448 0.536
#> SRR1951742 3 0.4477 0.592 0.000 0.312 0.688 0.000
#> SRR1951743 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951741 4 0.3024 0.711 0.148 0.000 0.000 0.852
#> SRR1951740 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR1951738 2 0.1284 0.949 0.024 0.964 0.012 0.000
#> SRR1951739 2 0.5137 0.478 0.024 0.680 0.296 0.000
#> SRR1951737 4 0.5353 0.457 0.012 0.000 0.432 0.556
#> SRR1951736 1 0.1022 0.882 0.968 0.000 0.000 0.032
#> SRR1951734 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951735 4 0.5558 0.449 0.020 0.000 0.432 0.548
#> SRR1951733 3 0.0000 0.863 0.000 0.000 1.000 0.000
#> SRR1951732 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951729 4 0.5090 0.610 0.016 0.000 0.324 0.660
#> SRR1951728 3 0.0592 0.854 0.016 0.000 0.984 0.000
#> SRR1951727 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951725 1 0.0000 0.877 1.000 0.000 0.000 0.000
#> SRR1951726 3 0.0000 0.863 0.000 0.000 1.000 0.000
#> SRR1951724 1 0.5558 0.425 0.608 0.000 0.028 0.364
#> SRR1951722 4 0.5697 0.307 0.024 0.000 0.488 0.488
#> SRR1951723 4 0.1792 0.809 0.068 0.000 0.000 0.932
#> SRR1951721 1 0.3266 0.732 0.832 0.000 0.168 0.000
#> SRR1951719 4 0.1284 0.825 0.012 0.000 0.024 0.964
#> SRR1951720 1 0.1022 0.882 0.968 0.000 0.000 0.032
#> SRR1951718 1 0.3764 0.719 0.784 0.000 0.000 0.216
#> SRR1951717 1 0.2216 0.860 0.908 0.000 0.000 0.092
#> SRR1951715 1 0.0000 0.877 1.000 0.000 0.000 0.000
#> SRR1951716 1 0.1637 0.875 0.940 0.000 0.000 0.060
#> SRR1951714 4 0.0469 0.827 0.012 0.000 0.000 0.988
#> SRR1951713 4 0.0469 0.827 0.012 0.000 0.000 0.988
#> SRR1951712 4 0.3024 0.711 0.148 0.000 0.000 0.852
#> SRR1951711 2 0.0592 0.962 0.016 0.984 0.000 0.000
#> SRR1951710 1 0.0000 0.877 1.000 0.000 0.000 0.000
#> SRR1951709 3 0.0000 0.863 0.000 0.000 1.000 0.000
#> SRR1951708 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951707 3 0.1022 0.855 0.032 0.000 0.968 0.000
#> SRR1951705 1 0.0817 0.883 0.976 0.000 0.000 0.024
#> SRR1951704 3 0.0707 0.854 0.020 0.000 0.980 0.000
#> SRR1951703 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951706 3 0.0000 0.863 0.000 0.000 1.000 0.000
#> SRR1951702 3 0.0000 0.863 0.000 0.000 1.000 0.000
#> SRR1951701 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951699 3 0.0000 0.863 0.000 0.000 1.000 0.000
#> SRR1951698 3 0.0000 0.863 0.000 0.000 1.000 0.000
#> SRR1951700 4 0.2089 0.815 0.020 0.000 0.048 0.932
#> SRR1951697 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951696 4 0.0817 0.827 0.000 0.000 0.024 0.976
#> SRR1951695 4 0.5558 0.449 0.020 0.000 0.432 0.548
#> SRR1951693 4 0.4472 0.715 0.020 0.000 0.220 0.760
#> SRR1951692 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR1951691 4 0.1854 0.817 0.012 0.000 0.048 0.940
#> SRR1951688 3 0.5571 0.394 0.024 0.396 0.580 0.000
#> SRR1951689 3 0.4579 0.724 0.032 0.200 0.768 0.000
#> SRR1951687 1 0.4817 0.460 0.612 0.000 0.000 0.388
#> SRR1951685 3 0.1022 0.855 0.032 0.000 0.968 0.000
#> SRR1951684 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR1951686 3 0.5526 0.346 0.020 0.416 0.564 0.000
#> SRR1951683 4 0.0336 0.826 0.008 0.000 0.000 0.992
#> SRR1951682 2 0.1151 0.952 0.024 0.968 0.008 0.000
#> SRR1951690 2 0.0000 0.971 0.000 1.000 0.000 0.000
#> SRR1951680 4 0.0469 0.827 0.012 0.000 0.000 0.988
#> SRR1951679 1 0.0000 0.877 1.000 0.000 0.000 0.000
#> SRR1951678 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR1951677 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR1951676 4 0.5329 0.473 0.012 0.000 0.420 0.568
#> SRR1951675 2 0.0188 0.969 0.004 0.996 0.000 0.000
#> SRR1951681 4 0.5558 0.449 0.020 0.000 0.432 0.548
#> SRR1951673 3 0.4104 0.759 0.028 0.164 0.808 0.000
#> SRR1951674 1 0.2149 0.842 0.912 0.000 0.088 0.000
#> SRR1951672 4 0.4072 0.640 0.252 0.000 0.000 0.748
#> SRR1951671 3 0.4004 0.759 0.024 0.164 0.812 0.000
#> SRR1951670 4 0.3024 0.711 0.148 0.000 0.000 0.852
#> SRR1951640 4 0.4642 0.699 0.020 0.000 0.240 0.740
#> SRR1951668 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR1951669 2 0.1284 0.949 0.024 0.964 0.012 0.000
#> SRR1951638 1 0.2011 0.867 0.920 0.000 0.000 0.080
#> SRR1951637 3 0.5250 0.567 0.024 0.316 0.660 0.000
#> SRR1951635 1 0.0817 0.883 0.976 0.000 0.000 0.024
#> SRR1951634 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR1951631 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR1951632 3 0.5349 0.528 0.024 0.336 0.640 0.000
#> SRR1951633 4 0.0469 0.828 0.000 0.000 0.012 0.988
#> SRR1951630 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR1951629 4 0.0000 0.829 0.000 0.000 0.000 1.000
#> SRR1951628 1 0.4567 0.552 0.716 0.000 0.276 0.008
#> SRR1951627 3 0.0000 0.863 0.000 0.000 1.000 0.000
#> SRR1951639 3 0.0000 0.863 0.000 0.000 1.000 0.000
#> SRR1951625 3 0.1837 0.848 0.028 0.028 0.944 0.000
#> SRR1951626 3 0.0188 0.862 0.004 0.000 0.996 0.000
#> SRR1951624 4 0.5558 0.449 0.020 0.000 0.432 0.548
#> SRR1951622 1 0.0921 0.883 0.972 0.000 0.000 0.028
#> SRR1951636 3 0.0000 0.863 0.000 0.000 1.000 0.000
#> SRR1951623 3 0.4643 0.214 0.000 0.000 0.656 0.344
#> SRR1951621 4 0.3219 0.697 0.164 0.000 0.000 0.836
#> SRR1951620 4 0.2408 0.788 0.000 0.000 0.104 0.896
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 5 0.1478 0.778 0.000 0.064 0.000 0.000 0.936
#> SRR1951666 5 0.4836 0.585 0.000 0.032 0.356 0.000 0.612
#> SRR1951665 2 0.0000 0.906 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 4 0.4991 0.534 0.016 0.000 0.284 0.668 0.032
#> SRR1951663 1 0.3752 0.763 0.804 0.000 0.148 0.000 0.048
#> SRR1951662 2 0.0000 0.906 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 3 0.0000 0.847 0.000 0.000 1.000 0.000 0.000
#> SRR1951660 1 0.0404 0.834 0.988 0.000 0.000 0.000 0.012
#> SRR1951659 5 0.3534 0.592 0.000 0.256 0.000 0.000 0.744
#> SRR1951658 2 0.2020 0.863 0.000 0.900 0.000 0.000 0.100
#> SRR1951657 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951656 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951655 4 0.0880 0.886 0.032 0.000 0.000 0.968 0.000
#> SRR1951654 1 0.1831 0.814 0.920 0.000 0.004 0.076 0.000
#> SRR1951653 4 0.0865 0.891 0.004 0.000 0.000 0.972 0.024
#> SRR1951652 5 0.4210 0.515 0.000 0.000 0.412 0.000 0.588
#> SRR1951651 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951650 3 0.3231 0.774 0.000 0.000 0.800 0.196 0.004
#> SRR1951648 3 0.0162 0.848 0.000 0.000 0.996 0.000 0.004
#> SRR1951649 4 0.2595 0.847 0.080 0.000 0.000 0.888 0.032
#> SRR1951647 3 0.1756 0.838 0.016 0.000 0.940 0.008 0.036
#> SRR1951646 3 0.3266 0.772 0.000 0.000 0.796 0.200 0.004
#> SRR1951645 4 0.2020 0.856 0.100 0.000 0.000 0.900 0.000
#> SRR1951644 4 0.4623 0.609 0.304 0.000 0.000 0.664 0.032
#> SRR1951643 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951641 2 0.2329 0.845 0.000 0.876 0.000 0.000 0.124
#> SRR1951642 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951694 1 0.1851 0.818 0.912 0.000 0.000 0.000 0.088
#> SRR1951731 3 0.0324 0.848 0.004 0.000 0.992 0.000 0.004
#> SRR1951730 5 0.2824 0.787 0.000 0.032 0.096 0.000 0.872
#> SRR1951752 3 0.1410 0.822 0.000 0.000 0.940 0.000 0.060
#> SRR1951751 4 0.2583 0.834 0.132 0.000 0.000 0.864 0.004
#> SRR1951749 5 0.4249 0.487 0.000 0.000 0.432 0.000 0.568
#> SRR1951750 4 0.3480 0.690 0.248 0.000 0.000 0.752 0.000
#> SRR1951748 2 0.0000 0.906 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.906 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 1 0.4394 0.710 0.732 0.000 0.220 0.000 0.048
#> SRR1951746 2 0.0000 0.906 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 3 0.1732 0.836 0.000 0.000 0.920 0.080 0.000
#> SRR1951742 5 0.4010 0.740 0.000 0.032 0.208 0.000 0.760
#> SRR1951743 2 0.0000 0.906 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 4 0.2813 0.774 0.168 0.000 0.000 0.832 0.000
#> SRR1951740 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951738 5 0.2011 0.764 0.000 0.088 0.004 0.000 0.908
#> SRR1951739 5 0.1341 0.780 0.000 0.056 0.000 0.000 0.944
#> SRR1951737 3 0.3424 0.736 0.000 0.000 0.760 0.240 0.000
#> SRR1951736 1 0.0510 0.834 0.984 0.000 0.000 0.016 0.000
#> SRR1951734 2 0.0000 0.906 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 3 0.2233 0.827 0.004 0.000 0.892 0.104 0.000
#> SRR1951733 5 0.4294 0.390 0.000 0.000 0.468 0.000 0.532
#> SRR1951732 2 0.0000 0.906 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 3 0.4460 0.742 0.016 0.000 0.748 0.204 0.032
#> SRR1951728 3 0.1281 0.838 0.012 0.000 0.956 0.000 0.032
#> SRR1951727 2 0.0000 0.906 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 1 0.0000 0.835 1.000 0.000 0.000 0.000 0.000
#> SRR1951726 3 0.0703 0.841 0.000 0.000 0.976 0.000 0.024
#> SRR1951724 1 0.6200 0.623 0.628 0.000 0.136 0.204 0.032
#> SRR1951722 3 0.4416 0.746 0.016 0.000 0.776 0.056 0.152
#> SRR1951723 4 0.2595 0.847 0.080 0.000 0.000 0.888 0.032
#> SRR1951721 1 0.5526 0.652 0.648 0.000 0.152 0.000 0.200
#> SRR1951719 4 0.1478 0.866 0.000 0.000 0.064 0.936 0.000
#> SRR1951720 1 0.0510 0.834 0.984 0.000 0.000 0.016 0.000
#> SRR1951718 1 0.3039 0.703 0.808 0.000 0.000 0.192 0.000
#> SRR1951717 1 0.1732 0.812 0.920 0.000 0.000 0.080 0.000
#> SRR1951715 1 0.1341 0.827 0.944 0.000 0.000 0.000 0.056
#> SRR1951716 1 0.2813 0.733 0.832 0.000 0.000 0.168 0.000
#> SRR1951714 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951713 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951712 4 0.3242 0.729 0.216 0.000 0.000 0.784 0.000
#> SRR1951711 5 0.2280 0.740 0.000 0.120 0.000 0.000 0.880
#> SRR1951710 1 0.0703 0.833 0.976 0.000 0.000 0.000 0.024
#> SRR1951709 5 0.4249 0.487 0.000 0.000 0.432 0.000 0.568
#> SRR1951708 2 0.3305 0.766 0.000 0.776 0.000 0.000 0.224
#> SRR1951707 3 0.3141 0.740 0.016 0.000 0.832 0.000 0.152
#> SRR1951705 1 0.0000 0.835 1.000 0.000 0.000 0.000 0.000
#> SRR1951704 3 0.1410 0.822 0.000 0.000 0.940 0.000 0.060
#> SRR1951703 2 0.0000 0.906 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 3 0.1121 0.830 0.000 0.000 0.956 0.000 0.044
#> SRR1951702 3 0.0000 0.847 0.000 0.000 1.000 0.000 0.000
#> SRR1951701 2 0.3366 0.759 0.000 0.768 0.000 0.000 0.232
#> SRR1951699 3 0.0162 0.848 0.000 0.000 0.996 0.000 0.004
#> SRR1951698 3 0.0162 0.848 0.000 0.000 0.996 0.000 0.004
#> SRR1951700 4 0.3265 0.821 0.016 0.000 0.092 0.860 0.032
#> SRR1951697 5 0.3210 0.661 0.000 0.212 0.000 0.000 0.788
#> SRR1951696 4 0.1478 0.866 0.000 0.000 0.064 0.936 0.000
#> SRR1951695 3 0.4643 0.730 0.016 0.000 0.724 0.228 0.032
#> SRR1951693 3 0.5456 0.205 0.016 0.000 0.524 0.428 0.032
#> SRR1951692 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951691 4 0.1478 0.866 0.000 0.000 0.064 0.936 0.000
#> SRR1951688 5 0.1579 0.790 0.000 0.032 0.024 0.000 0.944
#> SRR1951689 5 0.1469 0.782 0.016 0.000 0.036 0.000 0.948
#> SRR1951687 1 0.4268 0.129 0.556 0.000 0.000 0.444 0.000
#> SRR1951685 3 0.3596 0.680 0.016 0.000 0.784 0.000 0.200
#> SRR1951684 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951686 5 0.3649 0.726 0.000 0.152 0.040 0.000 0.808
#> SRR1951683 4 0.0404 0.896 0.012 0.000 0.000 0.988 0.000
#> SRR1951682 5 0.1341 0.780 0.000 0.056 0.000 0.000 0.944
#> SRR1951690 2 0.3816 0.673 0.000 0.696 0.000 0.000 0.304
#> SRR1951680 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951679 1 0.4119 0.757 0.780 0.000 0.068 0.000 0.152
#> SRR1951678 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951677 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951676 3 0.3210 0.759 0.000 0.000 0.788 0.212 0.000
#> SRR1951675 2 0.4030 0.599 0.000 0.648 0.000 0.000 0.352
#> SRR1951681 3 0.3231 0.774 0.004 0.000 0.800 0.196 0.000
#> SRR1951673 5 0.2873 0.757 0.016 0.000 0.128 0.000 0.856
#> SRR1951674 1 0.4777 0.645 0.680 0.000 0.268 0.000 0.052
#> SRR1951672 4 0.5019 0.246 0.436 0.000 0.000 0.532 0.032
#> SRR1951671 5 0.2648 0.776 0.000 0.000 0.152 0.000 0.848
#> SRR1951670 4 0.3480 0.690 0.248 0.000 0.000 0.752 0.000
#> SRR1951640 4 0.4474 0.414 0.012 0.000 0.332 0.652 0.004
#> SRR1951668 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951669 5 0.1281 0.784 0.000 0.032 0.012 0.000 0.956
#> SRR1951638 1 0.1732 0.812 0.920 0.000 0.000 0.080 0.000
#> SRR1951637 5 0.1018 0.784 0.000 0.016 0.016 0.000 0.968
#> SRR1951635 1 0.0000 0.835 1.000 0.000 0.000 0.000 0.000
#> SRR1951634 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951631 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951632 5 0.1836 0.793 0.000 0.032 0.036 0.000 0.932
#> SRR1951633 4 0.0510 0.895 0.000 0.000 0.016 0.984 0.000
#> SRR1951630 4 0.0000 0.899 0.000 0.000 0.000 1.000 0.000
#> SRR1951629 4 0.0162 0.898 0.000 0.000 0.000 0.996 0.004
#> SRR1951628 1 0.5187 0.681 0.700 0.000 0.216 0.020 0.064
#> SRR1951627 3 0.0880 0.840 0.000 0.000 0.968 0.000 0.032
#> SRR1951639 3 0.0162 0.848 0.000 0.000 0.996 0.000 0.004
#> SRR1951625 5 0.3419 0.736 0.016 0.000 0.180 0.000 0.804
#> SRR1951626 3 0.0324 0.848 0.004 0.000 0.992 0.000 0.004
#> SRR1951624 3 0.3491 0.745 0.004 0.000 0.768 0.228 0.000
#> SRR1951622 1 0.0510 0.834 0.984 0.000 0.000 0.016 0.000
#> SRR1951636 3 0.0162 0.848 0.000 0.000 0.996 0.000 0.004
#> SRR1951623 3 0.1732 0.836 0.000 0.000 0.920 0.080 0.000
#> SRR1951621 4 0.3480 0.688 0.248 0.000 0.000 0.752 0.000
#> SRR1951620 4 0.2020 0.831 0.000 0.000 0.100 0.900 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 5 0.1444 0.765 0.000 0.072 0.000 0.000 0.928 0.000
#> SRR1951666 5 0.2994 0.753 0.000 0.000 0.208 0.000 0.788 0.004
#> SRR1951665 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 4 0.6075 -0.138 0.000 0.000 0.280 0.396 0.000 0.324
#> SRR1951663 1 0.6197 0.391 0.516 0.000 0.036 0.000 0.156 0.292
#> SRR1951662 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 3 0.0000 0.824 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951660 1 0.2300 0.744 0.856 0.000 0.000 0.000 0.000 0.144
#> SRR1951659 5 0.2793 0.634 0.000 0.200 0.000 0.000 0.800 0.000
#> SRR1951658 2 0.2664 0.828 0.000 0.816 0.000 0.000 0.184 0.000
#> SRR1951657 4 0.0000 0.883 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951656 4 0.0000 0.883 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951655 4 0.1444 0.844 0.072 0.000 0.000 0.928 0.000 0.000
#> SRR1951654 1 0.0000 0.799 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951653 4 0.3867 0.038 0.000 0.000 0.000 0.512 0.000 0.488
#> SRR1951652 5 0.3161 0.747 0.000 0.000 0.216 0.000 0.776 0.008
#> SRR1951651 4 0.0603 0.880 0.016 0.000 0.000 0.980 0.000 0.004
#> SRR1951650 3 0.2730 0.759 0.000 0.000 0.808 0.192 0.000 0.000
#> SRR1951648 3 0.0000 0.824 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951649 6 0.2883 0.673 0.000 0.000 0.000 0.212 0.000 0.788
#> SRR1951647 6 0.3547 0.605 0.000 0.000 0.332 0.000 0.000 0.668
#> SRR1951646 3 0.3012 0.753 0.000 0.000 0.796 0.196 0.000 0.008
#> SRR1951645 4 0.4757 0.458 0.280 0.000 0.000 0.636 0.000 0.084
#> SRR1951644 6 0.2896 0.639 0.160 0.000 0.000 0.016 0.000 0.824
#> SRR1951643 4 0.0603 0.880 0.016 0.000 0.000 0.980 0.000 0.004
#> SRR1951641 2 0.1141 0.899 0.000 0.948 0.000 0.000 0.052 0.000
#> SRR1951642 4 0.0291 0.882 0.004 0.000 0.000 0.992 0.000 0.004
#> SRR1951694 6 0.2378 0.630 0.152 0.000 0.000 0.000 0.000 0.848
#> SRR1951731 3 0.0260 0.822 0.000 0.000 0.992 0.000 0.000 0.008
#> SRR1951730 5 0.2738 0.764 0.000 0.000 0.176 0.000 0.820 0.004
#> SRR1951752 3 0.0520 0.818 0.000 0.000 0.984 0.000 0.008 0.008
#> SRR1951751 4 0.4003 0.703 0.152 0.000 0.000 0.756 0.000 0.092
#> SRR1951749 5 0.3695 0.624 0.000 0.000 0.376 0.000 0.624 0.000
#> SRR1951750 1 0.3330 0.587 0.716 0.000 0.000 0.284 0.000 0.000
#> SRR1951748 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 1 0.6463 0.279 0.464 0.000 0.048 0.000 0.156 0.332
#> SRR1951746 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951744 3 0.2703 0.770 0.000 0.000 0.824 0.172 0.000 0.004
#> SRR1951742 5 0.2994 0.753 0.000 0.000 0.208 0.000 0.788 0.004
#> SRR1951743 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951741 4 0.3023 0.652 0.212 0.000 0.000 0.784 0.000 0.004
#> SRR1951740 4 0.0000 0.883 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951738 5 0.1564 0.780 0.000 0.024 0.000 0.000 0.936 0.040
#> SRR1951739 5 0.0000 0.792 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951737 3 0.3862 0.232 0.000 0.000 0.524 0.476 0.000 0.000
#> SRR1951736 1 0.1075 0.791 0.952 0.000 0.000 0.000 0.000 0.048
#> SRR1951734 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951735 3 0.3254 0.759 0.000 0.000 0.820 0.124 0.000 0.056
#> SRR1951733 5 0.3872 0.530 0.000 0.000 0.392 0.000 0.604 0.004
#> SRR1951732 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 3 0.5046 0.459 0.000 0.000 0.620 0.124 0.000 0.256
#> SRR1951728 6 0.3309 0.655 0.000 0.000 0.280 0.000 0.000 0.720
#> SRR1951727 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 1 0.0458 0.797 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR1951726 3 0.2669 0.673 0.000 0.000 0.836 0.000 0.156 0.008
#> SRR1951724 6 0.4044 0.714 0.052 0.000 0.040 0.120 0.000 0.788
#> SRR1951722 6 0.3464 0.731 0.000 0.000 0.108 0.084 0.000 0.808
#> SRR1951723 6 0.2697 0.692 0.000 0.000 0.000 0.188 0.000 0.812
#> SRR1951721 6 0.6110 0.118 0.284 0.000 0.032 0.000 0.156 0.528
#> SRR1951719 4 0.1267 0.846 0.000 0.000 0.060 0.940 0.000 0.000
#> SRR1951720 1 0.0000 0.799 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951718 1 0.3449 0.729 0.808 0.000 0.000 0.116 0.000 0.076
#> SRR1951717 1 0.0000 0.799 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951715 1 0.3221 0.609 0.736 0.000 0.000 0.000 0.000 0.264
#> SRR1951716 1 0.2979 0.737 0.840 0.000 0.000 0.116 0.000 0.044
#> SRR1951714 4 0.0146 0.882 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1951713 4 0.1267 0.851 0.000 0.000 0.000 0.940 0.000 0.060
#> SRR1951712 4 0.3804 0.202 0.424 0.000 0.000 0.576 0.000 0.000
#> SRR1951711 5 0.2697 0.651 0.000 0.188 0.000 0.000 0.812 0.000
#> SRR1951710 1 0.1610 0.775 0.916 0.000 0.000 0.000 0.000 0.084
#> SRR1951709 5 0.3706 0.619 0.000 0.000 0.380 0.000 0.620 0.000
#> SRR1951708 2 0.2697 0.826 0.000 0.812 0.000 0.000 0.188 0.000
#> SRR1951707 6 0.3244 0.661 0.000 0.000 0.268 0.000 0.000 0.732
#> SRR1951705 1 0.0937 0.796 0.960 0.000 0.000 0.000 0.000 0.040
#> SRR1951704 3 0.1267 0.799 0.000 0.000 0.940 0.000 0.000 0.060
#> SRR1951703 2 0.0000 0.921 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 3 0.3290 0.601 0.000 0.000 0.776 0.000 0.208 0.016
#> SRR1951702 3 0.0146 0.823 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1951701 2 0.2969 0.798 0.000 0.776 0.000 0.000 0.224 0.000
#> SRR1951699 3 0.0000 0.824 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951698 3 0.0000 0.824 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951700 6 0.3345 0.701 0.000 0.000 0.028 0.184 0.000 0.788
#> SRR1951697 5 0.2730 0.667 0.000 0.192 0.000 0.000 0.808 0.000
#> SRR1951696 4 0.1327 0.843 0.000 0.000 0.064 0.936 0.000 0.000
#> SRR1951695 6 0.4308 0.680 0.000 0.000 0.120 0.152 0.000 0.728
#> SRR1951693 6 0.3542 0.712 0.000 0.000 0.052 0.160 0.000 0.788
#> SRR1951692 4 0.0146 0.883 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1951691 4 0.2416 0.769 0.000 0.000 0.000 0.844 0.000 0.156
#> SRR1951688 5 0.0260 0.792 0.000 0.000 0.000 0.000 0.992 0.008
#> SRR1951689 6 0.3868 0.113 0.000 0.000 0.000 0.000 0.496 0.504
#> SRR1951687 1 0.2003 0.756 0.884 0.000 0.000 0.116 0.000 0.000
#> SRR1951685 6 0.3974 0.684 0.000 0.000 0.224 0.000 0.048 0.728
#> SRR1951684 4 0.0000 0.883 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951686 5 0.1908 0.791 0.000 0.000 0.096 0.000 0.900 0.004
#> SRR1951683 4 0.0858 0.876 0.028 0.000 0.000 0.968 0.000 0.004
#> SRR1951682 5 0.0260 0.792 0.000 0.008 0.000 0.000 0.992 0.000
#> SRR1951690 2 0.3390 0.703 0.000 0.704 0.000 0.000 0.296 0.000
#> SRR1951680 4 0.0000 0.883 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951679 6 0.2048 0.653 0.120 0.000 0.000 0.000 0.000 0.880
#> SRR1951678 4 0.0603 0.880 0.016 0.000 0.000 0.980 0.000 0.004
#> SRR1951677 4 0.0603 0.880 0.016 0.000 0.000 0.980 0.000 0.004
#> SRR1951676 3 0.2883 0.745 0.000 0.000 0.788 0.212 0.000 0.000
#> SRR1951675 2 0.2969 0.798 0.000 0.776 0.000 0.000 0.224 0.000
#> SRR1951681 3 0.2838 0.761 0.000 0.000 0.808 0.188 0.000 0.004
#> SRR1951673 6 0.5193 0.394 0.000 0.000 0.104 0.000 0.344 0.552
#> SRR1951674 1 0.6532 0.249 0.448 0.000 0.052 0.000 0.156 0.344
#> SRR1951672 6 0.4991 0.181 0.404 0.000 0.000 0.072 0.000 0.524
#> SRR1951671 5 0.3531 0.680 0.000 0.000 0.328 0.000 0.672 0.000
#> SRR1951670 1 0.3330 0.587 0.716 0.000 0.000 0.284 0.000 0.000
#> SRR1951640 4 0.3652 0.699 0.000 0.000 0.044 0.768 0.000 0.188
#> SRR1951668 4 0.0000 0.883 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951669 5 0.1349 0.782 0.000 0.000 0.004 0.000 0.940 0.056
#> SRR1951638 1 0.0000 0.799 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951637 5 0.2053 0.765 0.000 0.000 0.004 0.000 0.888 0.108
#> SRR1951635 1 0.0458 0.797 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR1951634 4 0.0000 0.883 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951631 4 0.0000 0.883 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951632 5 0.1267 0.790 0.000 0.000 0.060 0.000 0.940 0.000
#> SRR1951633 4 0.1003 0.873 0.004 0.000 0.028 0.964 0.000 0.004
#> SRR1951630 4 0.0146 0.883 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1951629 4 0.0260 0.882 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1951628 6 0.3164 0.713 0.032 0.000 0.140 0.004 0.000 0.824
#> SRR1951627 3 0.0260 0.822 0.000 0.000 0.992 0.000 0.008 0.000
#> SRR1951639 3 0.0000 0.824 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951625 6 0.5635 0.401 0.000 0.000 0.208 0.000 0.256 0.536
#> SRR1951626 3 0.0363 0.821 0.000 0.000 0.988 0.000 0.000 0.012
#> SRR1951624 3 0.2941 0.737 0.000 0.000 0.780 0.220 0.000 0.000
#> SRR1951622 1 0.1267 0.790 0.940 0.000 0.000 0.000 0.000 0.060
#> SRR1951636 3 0.0000 0.824 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951623 3 0.2562 0.770 0.000 0.000 0.828 0.172 0.000 0.000
#> SRR1951621 1 0.3528 0.567 0.700 0.000 0.000 0.296 0.000 0.004
#> SRR1951620 4 0.1910 0.803 0.000 0.000 0.108 0.892 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "mclust"]
# you can also extract it by
# res = res_list["SD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 13580 rows and 133 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.570 0.886 0.941 0.3169 0.702 0.702
#> 3 3 0.402 0.484 0.751 0.8003 0.724 0.609
#> 4 4 0.588 0.710 0.786 0.1711 0.759 0.517
#> 5 5 0.758 0.844 0.900 0.1380 0.810 0.486
#> 6 6 0.797 0.775 0.868 0.0625 0.893 0.586
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1951667 2 0.4690 0.8923 0.100 0.900
#> SRR1951666 1 0.7674 0.7615 0.776 0.224
#> SRR1951665 2 0.0000 0.9059 0.000 1.000
#> SRR1951664 1 0.0376 0.9359 0.996 0.004
#> SRR1951663 1 0.0000 0.9368 1.000 0.000
#> SRR1951662 2 0.0000 0.9059 0.000 1.000
#> SRR1951661 1 0.6712 0.8123 0.824 0.176
#> SRR1951660 1 0.0000 0.9368 1.000 0.000
#> SRR1951659 2 0.4022 0.8969 0.080 0.920
#> SRR1951658 2 0.0000 0.9059 0.000 1.000
#> SRR1951657 1 0.0000 0.9368 1.000 0.000
#> SRR1951656 1 0.0376 0.9359 0.996 0.004
#> SRR1951655 1 0.0000 0.9368 1.000 0.000
#> SRR1951654 1 0.0000 0.9368 1.000 0.000
#> SRR1951653 1 0.0000 0.9368 1.000 0.000
#> SRR1951652 1 0.7674 0.7615 0.776 0.224
#> SRR1951651 1 0.0000 0.9368 1.000 0.000
#> SRR1951650 1 0.0376 0.9359 0.996 0.004
#> SRR1951648 1 0.7376 0.7805 0.792 0.208
#> SRR1951649 1 0.0000 0.9368 1.000 0.000
#> SRR1951647 1 0.0672 0.9341 0.992 0.008
#> SRR1951646 1 0.0376 0.9359 0.996 0.004
#> SRR1951645 1 0.0000 0.9368 1.000 0.000
#> SRR1951644 1 0.0000 0.9368 1.000 0.000
#> SRR1951643 1 0.0000 0.9368 1.000 0.000
#> SRR1951641 2 0.4690 0.8923 0.100 0.900
#> SRR1951642 1 0.0000 0.9368 1.000 0.000
#> SRR1951694 1 0.0000 0.9368 1.000 0.000
#> SRR1951731 1 0.5519 0.8534 0.872 0.128
#> SRR1951730 1 0.7674 0.7615 0.776 0.224
#> SRR1951752 1 0.7139 0.7937 0.804 0.196
#> SRR1951751 1 0.0000 0.9368 1.000 0.000
#> SRR1951749 1 0.7674 0.7615 0.776 0.224
#> SRR1951750 1 0.0000 0.9368 1.000 0.000
#> SRR1951748 2 0.0000 0.9059 0.000 1.000
#> SRR1951747 2 0.0000 0.9059 0.000 1.000
#> SRR1951745 1 0.0000 0.9368 1.000 0.000
#> SRR1951746 2 0.0000 0.9059 0.000 1.000
#> SRR1951744 1 0.0000 0.9368 1.000 0.000
#> SRR1951742 1 0.7674 0.7615 0.776 0.224
#> SRR1951743 2 0.0000 0.9059 0.000 1.000
#> SRR1951741 1 0.0000 0.9368 1.000 0.000
#> SRR1951740 1 0.0000 0.9368 1.000 0.000
#> SRR1951738 2 0.9815 0.2601 0.420 0.580
#> SRR1951739 2 0.5519 0.8620 0.128 0.872
#> SRR1951737 1 0.0376 0.9359 0.996 0.004
#> SRR1951736 1 0.0000 0.9368 1.000 0.000
#> SRR1951734 2 0.0000 0.9059 0.000 1.000
#> SRR1951735 1 0.0376 0.9359 0.996 0.004
#> SRR1951733 1 0.7139 0.7937 0.804 0.196
#> SRR1951732 2 0.0000 0.9059 0.000 1.000
#> SRR1951729 1 0.0376 0.9359 0.996 0.004
#> SRR1951728 1 0.2236 0.9183 0.964 0.036
#> SRR1951727 2 0.0000 0.9059 0.000 1.000
#> SRR1951725 1 0.0000 0.9368 1.000 0.000
#> SRR1951726 1 0.7056 0.7964 0.808 0.192
#> SRR1951724 1 0.0000 0.9368 1.000 0.000
#> SRR1951722 1 0.0376 0.9357 0.996 0.004
#> SRR1951723 1 0.0000 0.9368 1.000 0.000
#> SRR1951721 1 0.0000 0.9368 1.000 0.000
#> SRR1951719 1 0.0376 0.9359 0.996 0.004
#> SRR1951720 1 0.0000 0.9368 1.000 0.000
#> SRR1951718 1 0.0000 0.9368 1.000 0.000
#> SRR1951717 1 0.0000 0.9368 1.000 0.000
#> SRR1951715 1 0.0000 0.9368 1.000 0.000
#> SRR1951716 1 0.0000 0.9368 1.000 0.000
#> SRR1951714 1 0.0000 0.9368 1.000 0.000
#> SRR1951713 1 0.0000 0.9368 1.000 0.000
#> SRR1951712 1 0.0000 0.9368 1.000 0.000
#> SRR1951711 2 0.4690 0.8923 0.100 0.900
#> SRR1951710 1 0.0000 0.9368 1.000 0.000
#> SRR1951709 1 0.7674 0.7615 0.776 0.224
#> SRR1951708 2 0.0000 0.9059 0.000 1.000
#> SRR1951707 1 0.2948 0.9088 0.948 0.052
#> SRR1951705 1 0.0000 0.9368 1.000 0.000
#> SRR1951704 1 0.7139 0.7937 0.804 0.196
#> SRR1951703 2 0.0000 0.9059 0.000 1.000
#> SRR1951706 1 0.6148 0.8317 0.848 0.152
#> SRR1951702 1 0.7056 0.7979 0.808 0.192
#> SRR1951701 2 0.4690 0.8923 0.100 0.900
#> SRR1951699 1 0.7674 0.7615 0.776 0.224
#> SRR1951698 1 0.4298 0.8838 0.912 0.088
#> SRR1951700 1 0.0000 0.9368 1.000 0.000
#> SRR1951697 2 0.4690 0.8923 0.100 0.900
#> SRR1951696 1 0.0376 0.9359 0.996 0.004
#> SRR1951695 1 0.0000 0.9368 1.000 0.000
#> SRR1951693 1 0.0000 0.9368 1.000 0.000
#> SRR1951692 1 0.0376 0.9359 0.996 0.004
#> SRR1951691 1 0.0000 0.9368 1.000 0.000
#> SRR1951688 1 0.7674 0.7615 0.776 0.224
#> SRR1951689 1 0.7139 0.7937 0.804 0.196
#> SRR1951687 1 0.0000 0.9368 1.000 0.000
#> SRR1951685 1 0.7056 0.7979 0.808 0.192
#> SRR1951684 1 0.0376 0.9359 0.996 0.004
#> SRR1951686 1 0.7674 0.7615 0.776 0.224
#> SRR1951683 1 0.0376 0.9357 0.996 0.004
#> SRR1951682 2 0.4690 0.8923 0.100 0.900
#> SRR1951690 2 0.4815 0.8885 0.104 0.896
#> SRR1951680 1 0.0000 0.9368 1.000 0.000
#> SRR1951679 1 0.0000 0.9368 1.000 0.000
#> SRR1951678 1 0.0376 0.9357 0.996 0.004
#> SRR1951677 1 0.0000 0.9368 1.000 0.000
#> SRR1951676 1 0.0376 0.9359 0.996 0.004
#> SRR1951675 2 0.4690 0.8923 0.100 0.900
#> SRR1951681 1 0.0376 0.9359 0.996 0.004
#> SRR1951673 1 0.7139 0.7937 0.804 0.196
#> SRR1951674 1 0.0000 0.9368 1.000 0.000
#> SRR1951672 1 0.0000 0.9368 1.000 0.000
#> SRR1951671 1 0.7674 0.7615 0.776 0.224
#> SRR1951670 1 0.0000 0.9368 1.000 0.000
#> SRR1951640 1 0.0376 0.9359 0.996 0.004
#> SRR1951668 1 0.0000 0.9368 1.000 0.000
#> SRR1951669 1 0.7674 0.7615 0.776 0.224
#> SRR1951638 1 0.0000 0.9368 1.000 0.000
#> SRR1951637 1 0.7674 0.7615 0.776 0.224
#> SRR1951635 1 0.0000 0.9368 1.000 0.000
#> SRR1951634 1 0.0000 0.9368 1.000 0.000
#> SRR1951631 1 0.0000 0.9368 1.000 0.000
#> SRR1951632 2 0.9983 0.0444 0.476 0.524
#> SRR1951633 1 0.0000 0.9368 1.000 0.000
#> SRR1951630 1 0.0000 0.9368 1.000 0.000
#> SRR1951629 1 0.0000 0.9368 1.000 0.000
#> SRR1951628 1 0.0000 0.9368 1.000 0.000
#> SRR1951627 1 0.7674 0.7615 0.776 0.224
#> SRR1951639 1 0.7139 0.7937 0.804 0.196
#> SRR1951625 1 0.7139 0.7937 0.804 0.196
#> SRR1951626 1 0.2948 0.9090 0.948 0.052
#> SRR1951624 1 0.0376 0.9359 0.996 0.004
#> SRR1951622 1 0.0000 0.9368 1.000 0.000
#> SRR1951636 1 0.4161 0.8867 0.916 0.084
#> SRR1951623 1 0.0000 0.9368 1.000 0.000
#> SRR1951621 1 0.0000 0.9368 1.000 0.000
#> SRR1951620 1 0.0376 0.9359 0.996 0.004
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.5977 0.7507 0.252 0.728 0.020
#> SRR1951666 3 0.5882 0.4836 0.348 0.000 0.652
#> SRR1951665 2 0.0000 0.8602 0.000 1.000 0.000
#> SRR1951664 3 0.1163 0.6115 0.028 0.000 0.972
#> SRR1951663 1 0.5363 0.5839 0.724 0.000 0.276
#> SRR1951662 2 0.0000 0.8602 0.000 1.000 0.000
#> SRR1951661 3 0.5882 0.4836 0.348 0.000 0.652
#> SRR1951660 1 0.6008 0.6108 0.628 0.000 0.372
#> SRR1951659 2 0.4979 0.8175 0.168 0.812 0.020
#> SRR1951658 2 0.0000 0.8602 0.000 1.000 0.000
#> SRR1951657 3 0.0000 0.6198 0.000 0.000 1.000
#> SRR1951656 3 0.1163 0.6115 0.028 0.000 0.972
#> SRR1951655 3 0.6309 -0.5076 0.496 0.000 0.504
#> SRR1951654 1 0.6305 0.5068 0.516 0.000 0.484
#> SRR1951653 3 0.0592 0.6191 0.012 0.000 0.988
#> SRR1951652 1 0.6823 0.3141 0.668 0.036 0.296
#> SRR1951651 3 0.0592 0.6191 0.012 0.000 0.988
#> SRR1951650 3 0.4235 0.5657 0.176 0.000 0.824
#> SRR1951648 3 0.5835 0.4835 0.340 0.000 0.660
#> SRR1951649 1 0.6225 0.5622 0.568 0.000 0.432
#> SRR1951647 3 0.3482 0.5828 0.128 0.000 0.872
#> SRR1951646 3 0.1163 0.6115 0.028 0.000 0.972
#> SRR1951645 3 0.6309 -0.5076 0.496 0.000 0.504
#> SRR1951644 1 0.5988 0.6102 0.632 0.000 0.368
#> SRR1951643 3 0.0592 0.6191 0.012 0.000 0.988
#> SRR1951641 2 0.3183 0.8451 0.076 0.908 0.016
#> SRR1951642 3 0.0592 0.6191 0.012 0.000 0.988
#> SRR1951694 1 0.5968 0.5717 0.636 0.000 0.364
#> SRR1951731 3 0.5760 0.4936 0.328 0.000 0.672
#> SRR1951730 1 0.7190 0.1473 0.608 0.036 0.356
#> SRR1951752 3 0.6302 0.2831 0.480 0.000 0.520
#> SRR1951751 1 0.5988 0.6102 0.632 0.000 0.368
#> SRR1951749 3 0.5835 0.4835 0.340 0.000 0.660
#> SRR1951750 3 0.6309 -0.5076 0.496 0.000 0.504
#> SRR1951748 2 0.0000 0.8602 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.8602 0.000 1.000 0.000
#> SRR1951745 1 0.4235 0.5650 0.824 0.000 0.176
#> SRR1951746 2 0.0000 0.8602 0.000 1.000 0.000
#> SRR1951744 3 0.1643 0.6159 0.044 0.000 0.956
#> SRR1951742 3 0.7044 0.4509 0.348 0.032 0.620
#> SRR1951743 2 0.0000 0.8602 0.000 1.000 0.000
#> SRR1951741 3 0.6309 -0.5076 0.496 0.000 0.504
#> SRR1951740 3 0.0000 0.6198 0.000 0.000 1.000
#> SRR1951738 1 0.5831 0.4819 0.796 0.128 0.076
#> SRR1951739 2 0.6651 0.6522 0.340 0.640 0.020
#> SRR1951737 3 0.1163 0.6115 0.028 0.000 0.972
#> SRR1951736 1 0.6008 0.6108 0.628 0.000 0.372
#> SRR1951734 2 0.0000 0.8602 0.000 1.000 0.000
#> SRR1951735 3 0.0592 0.6168 0.012 0.000 0.988
#> SRR1951733 3 0.5882 0.4836 0.348 0.000 0.652
#> SRR1951732 2 0.0000 0.8602 0.000 1.000 0.000
#> SRR1951729 3 0.1163 0.6115 0.028 0.000 0.972
#> SRR1951728 3 0.3192 0.5904 0.112 0.000 0.888
#> SRR1951727 2 0.0000 0.8602 0.000 1.000 0.000
#> SRR1951725 1 0.6280 0.5298 0.540 0.000 0.460
#> SRR1951726 1 0.4605 0.5245 0.796 0.000 0.204
#> SRR1951724 3 0.6225 -0.4060 0.432 0.000 0.568
#> SRR1951722 3 0.3340 0.5214 0.120 0.000 0.880
#> SRR1951723 3 0.5706 -0.1081 0.320 0.000 0.680
#> SRR1951721 1 0.4121 0.5590 0.832 0.000 0.168
#> SRR1951719 3 0.1163 0.6115 0.028 0.000 0.972
#> SRR1951720 3 0.6309 -0.5076 0.496 0.000 0.504
#> SRR1951718 1 0.6008 0.6108 0.628 0.000 0.372
#> SRR1951717 3 0.6309 -0.5076 0.496 0.000 0.504
#> SRR1951715 1 0.5431 0.5841 0.716 0.000 0.284
#> SRR1951716 1 0.5988 0.6102 0.632 0.000 0.368
#> SRR1951714 3 0.0592 0.6191 0.012 0.000 0.988
#> SRR1951713 3 0.0747 0.6186 0.016 0.000 0.984
#> SRR1951712 1 0.6008 0.6108 0.628 0.000 0.372
#> SRR1951711 2 0.5092 0.8135 0.176 0.804 0.020
#> SRR1951710 1 0.6291 0.5235 0.532 0.000 0.468
#> SRR1951709 3 0.5882 0.4836 0.348 0.000 0.652
#> SRR1951708 2 0.0424 0.8595 0.008 0.992 0.000
#> SRR1951707 3 0.5678 0.4962 0.316 0.000 0.684
#> SRR1951705 1 0.5988 0.6102 0.632 0.000 0.368
#> SRR1951704 3 0.5882 0.4836 0.348 0.000 0.652
#> SRR1951703 2 0.0000 0.8602 0.000 1.000 0.000
#> SRR1951706 3 0.5882 0.4836 0.348 0.000 0.652
#> SRR1951702 3 0.5882 0.4836 0.348 0.000 0.652
#> SRR1951701 2 0.4921 0.8192 0.164 0.816 0.020
#> SRR1951699 3 0.5859 0.4839 0.344 0.000 0.656
#> SRR1951698 3 0.5810 0.4826 0.336 0.000 0.664
#> SRR1951700 3 0.2537 0.5403 0.080 0.000 0.920
#> SRR1951697 2 0.5036 0.8157 0.172 0.808 0.020
#> SRR1951696 3 0.1163 0.6115 0.028 0.000 0.972
#> SRR1951695 3 0.0592 0.6191 0.012 0.000 0.988
#> SRR1951693 3 0.0747 0.6165 0.016 0.000 0.984
#> SRR1951692 3 0.0237 0.6191 0.004 0.000 0.996
#> SRR1951691 3 0.6309 -0.5076 0.496 0.000 0.504
#> SRR1951688 1 0.7044 0.4861 0.724 0.108 0.168
#> SRR1951689 1 0.5470 0.5430 0.796 0.036 0.168
#> SRR1951687 3 0.6309 -0.5076 0.496 0.000 0.504
#> SRR1951685 3 0.5835 0.4880 0.340 0.000 0.660
#> SRR1951684 3 0.1163 0.6115 0.028 0.000 0.972
#> SRR1951686 3 0.7150 0.4463 0.348 0.036 0.616
#> SRR1951683 3 0.0747 0.6165 0.016 0.000 0.984
#> SRR1951682 2 0.5977 0.7507 0.252 0.728 0.020
#> SRR1951690 2 0.5200 0.8082 0.184 0.796 0.020
#> SRR1951680 3 0.0237 0.6199 0.004 0.000 0.996
#> SRR1951679 1 0.5859 0.6147 0.656 0.000 0.344
#> SRR1951678 3 0.0592 0.6191 0.012 0.000 0.988
#> SRR1951677 3 0.0592 0.6191 0.012 0.000 0.988
#> SRR1951676 3 0.1411 0.6180 0.036 0.000 0.964
#> SRR1951675 2 0.6357 0.6846 0.296 0.684 0.020
#> SRR1951681 3 0.1031 0.6197 0.024 0.000 0.976
#> SRR1951673 1 0.5470 0.5430 0.796 0.036 0.168
#> SRR1951674 1 0.4291 0.5665 0.820 0.000 0.180
#> SRR1951672 1 0.6045 0.6076 0.620 0.000 0.380
#> SRR1951671 3 0.7150 0.4463 0.348 0.036 0.616
#> SRR1951670 3 0.6309 -0.5076 0.496 0.000 0.504
#> SRR1951640 3 0.1163 0.6115 0.028 0.000 0.972
#> SRR1951668 3 0.5254 0.0981 0.264 0.000 0.736
#> SRR1951669 1 0.5470 0.5430 0.796 0.036 0.168
#> SRR1951638 3 0.6309 -0.5076 0.496 0.000 0.504
#> SRR1951637 1 0.4121 0.5590 0.832 0.000 0.168
#> SRR1951635 3 0.6309 -0.5076 0.496 0.000 0.504
#> SRR1951634 3 0.0592 0.6191 0.012 0.000 0.988
#> SRR1951631 3 0.0592 0.6191 0.012 0.000 0.988
#> SRR1951632 2 0.9959 0.1484 0.340 0.368 0.292
#> SRR1951633 3 0.1289 0.6178 0.032 0.000 0.968
#> SRR1951630 3 0.0424 0.6197 0.008 0.000 0.992
#> SRR1951629 3 0.0424 0.6197 0.008 0.000 0.992
#> SRR1951628 1 0.6286 0.5271 0.536 0.000 0.464
#> SRR1951627 3 0.5859 0.4839 0.344 0.000 0.656
#> SRR1951639 3 0.5810 0.4826 0.336 0.000 0.664
#> SRR1951625 3 0.5882 0.4836 0.348 0.000 0.652
#> SRR1951626 3 0.5760 0.4895 0.328 0.000 0.672
#> SRR1951624 3 0.1163 0.6115 0.028 0.000 0.972
#> SRR1951622 1 0.5988 0.6102 0.632 0.000 0.368
#> SRR1951636 3 0.5785 0.4858 0.332 0.000 0.668
#> SRR1951623 3 0.5882 0.4836 0.348 0.000 0.652
#> SRR1951621 3 0.6309 -0.5076 0.496 0.000 0.504
#> SRR1951620 3 0.1163 0.6115 0.028 0.000 0.972
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.3074 0.777 0.000 0.848 0.152 0.000
#> SRR1951666 3 0.0000 0.586 0.000 0.000 1.000 0.000
#> SRR1951665 2 0.1211 0.733 0.040 0.960 0.000 0.000
#> SRR1951664 3 0.6928 0.718 0.156 0.000 0.576 0.268
#> SRR1951663 1 0.3895 0.746 0.804 0.000 0.184 0.012
#> SRR1951662 2 0.1211 0.733 0.040 0.960 0.000 0.000
#> SRR1951661 3 0.0000 0.586 0.000 0.000 1.000 0.000
#> SRR1951660 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> SRR1951659 2 0.2760 0.777 0.000 0.872 0.128 0.000
#> SRR1951658 2 0.0000 0.735 0.000 1.000 0.000 0.000
#> SRR1951657 3 0.6553 0.718 0.100 0.000 0.584 0.316
#> SRR1951656 3 0.6652 0.714 0.108 0.000 0.576 0.316
#> SRR1951655 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> SRR1951654 1 0.6136 0.363 0.584 0.000 0.060 0.356
#> SRR1951653 3 0.6553 0.718 0.100 0.000 0.584 0.316
#> SRR1951652 2 0.4933 0.657 0.000 0.568 0.432 0.000
#> SRR1951651 3 0.6553 0.718 0.100 0.000 0.584 0.316
#> SRR1951650 3 0.6236 0.703 0.152 0.000 0.668 0.180
#> SRR1951648 3 0.0000 0.586 0.000 0.000 1.000 0.000
#> SRR1951649 4 0.0336 0.920 0.008 0.000 0.000 0.992
#> SRR1951647 3 0.2401 0.628 0.004 0.000 0.904 0.092
#> SRR1951646 3 0.6890 0.719 0.152 0.000 0.580 0.268
#> SRR1951645 4 0.0469 0.918 0.012 0.000 0.000 0.988
#> SRR1951644 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> SRR1951643 3 0.6553 0.718 0.100 0.000 0.584 0.316
#> SRR1951641 2 0.2345 0.770 0.000 0.900 0.100 0.000
#> SRR1951642 3 0.6501 0.718 0.096 0.000 0.588 0.316
#> SRR1951694 4 0.4919 0.517 0.152 0.000 0.076 0.772
#> SRR1951731 3 0.0000 0.586 0.000 0.000 1.000 0.000
#> SRR1951730 2 0.4933 0.657 0.000 0.568 0.432 0.000
#> SRR1951752 3 0.3444 0.254 0.000 0.184 0.816 0.000
#> SRR1951751 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> SRR1951749 3 0.0000 0.586 0.000 0.000 1.000 0.000
#> SRR1951750 4 0.0376 0.921 0.004 0.000 0.004 0.992
#> SRR1951748 2 0.1211 0.733 0.040 0.960 0.000 0.000
#> SRR1951747 2 0.1211 0.733 0.040 0.960 0.000 0.000
#> SRR1951745 1 0.3895 0.746 0.804 0.000 0.184 0.012
#> SRR1951746 2 0.1118 0.733 0.036 0.964 0.000 0.000
#> SRR1951744 3 0.3975 0.683 0.000 0.000 0.760 0.240
#> SRR1951742 2 0.4981 0.611 0.000 0.536 0.464 0.000
#> SRR1951743 2 0.0469 0.735 0.012 0.988 0.000 0.000
#> SRR1951741 4 0.1635 0.889 0.044 0.000 0.008 0.948
#> SRR1951740 3 0.6603 0.716 0.104 0.000 0.580 0.316
#> SRR1951738 2 0.4516 0.745 0.000 0.736 0.252 0.012
#> SRR1951739 2 0.3942 0.755 0.000 0.764 0.236 0.000
#> SRR1951737 3 0.6928 0.718 0.156 0.000 0.576 0.268
#> SRR1951736 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> SRR1951734 2 0.0592 0.735 0.016 0.984 0.000 0.000
#> SRR1951735 3 0.6890 0.719 0.152 0.000 0.580 0.268
#> SRR1951733 3 0.0000 0.586 0.000 0.000 1.000 0.000
#> SRR1951732 2 0.1211 0.733 0.040 0.960 0.000 0.000
#> SRR1951729 3 0.6928 0.718 0.156 0.000 0.576 0.268
#> SRR1951728 3 0.2412 0.626 0.008 0.000 0.908 0.084
#> SRR1951727 2 0.1211 0.733 0.040 0.960 0.000 0.000
#> SRR1951725 1 0.4669 0.743 0.796 0.000 0.104 0.100
#> SRR1951726 3 0.0336 0.585 0.000 0.000 0.992 0.008
#> SRR1951724 4 0.0657 0.916 0.004 0.000 0.012 0.984
#> SRR1951722 3 0.5137 0.533 0.004 0.000 0.544 0.452
#> SRR1951723 4 0.0937 0.908 0.012 0.000 0.012 0.976
#> SRR1951721 1 0.3895 0.746 0.804 0.000 0.184 0.012
#> SRR1951719 3 0.6603 0.716 0.104 0.000 0.580 0.316
#> SRR1951720 4 0.0672 0.917 0.008 0.000 0.008 0.984
#> SRR1951718 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> SRR1951717 4 0.0672 0.917 0.008 0.000 0.008 0.984
#> SRR1951715 1 0.5689 0.732 0.712 0.000 0.184 0.104
#> SRR1951716 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> SRR1951714 3 0.6553 0.718 0.100 0.000 0.584 0.316
#> SRR1951713 3 0.6659 0.606 0.088 0.000 0.512 0.400
#> SRR1951712 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> SRR1951711 2 0.2921 0.778 0.000 0.860 0.140 0.000
#> SRR1951710 4 0.4356 0.592 0.148 0.000 0.048 0.804
#> SRR1951709 3 0.0000 0.586 0.000 0.000 1.000 0.000
#> SRR1951708 2 0.0000 0.735 0.000 1.000 0.000 0.000
#> SRR1951707 3 0.0000 0.586 0.000 0.000 1.000 0.000
#> SRR1951705 4 0.0188 0.920 0.004 0.000 0.000 0.996
#> SRR1951704 3 0.0000 0.586 0.000 0.000 1.000 0.000
#> SRR1951703 2 0.1211 0.733 0.040 0.960 0.000 0.000
#> SRR1951706 3 0.0336 0.585 0.000 0.000 0.992 0.008
#> SRR1951702 3 0.0000 0.586 0.000 0.000 1.000 0.000
#> SRR1951701 2 0.2868 0.778 0.000 0.864 0.136 0.000
#> SRR1951699 3 0.0000 0.586 0.000 0.000 1.000 0.000
#> SRR1951698 3 0.0000 0.586 0.000 0.000 1.000 0.000
#> SRR1951700 3 0.5626 0.661 0.028 0.000 0.588 0.384
#> SRR1951697 2 0.2921 0.778 0.000 0.860 0.140 0.000
#> SRR1951696 3 0.6928 0.718 0.156 0.000 0.576 0.268
#> SRR1951695 3 0.6553 0.718 0.100 0.000 0.584 0.316
#> SRR1951693 3 0.6407 0.707 0.084 0.000 0.584 0.332
#> SRR1951692 3 0.6603 0.716 0.104 0.000 0.580 0.316
#> SRR1951691 4 0.1406 0.897 0.024 0.000 0.016 0.960
#> SRR1951688 2 0.4916 0.663 0.000 0.576 0.424 0.000
#> SRR1951689 2 0.5080 0.664 0.000 0.576 0.420 0.004
#> SRR1951687 4 0.5816 0.549 0.148 0.000 0.144 0.708
#> SRR1951685 3 0.0000 0.586 0.000 0.000 1.000 0.000
#> SRR1951684 3 0.6652 0.714 0.108 0.000 0.576 0.316
#> SRR1951686 2 0.4933 0.657 0.000 0.568 0.432 0.000
#> SRR1951683 3 0.6280 0.698 0.072 0.000 0.584 0.344
#> SRR1951682 2 0.2973 0.778 0.000 0.856 0.144 0.000
#> SRR1951690 2 0.2921 0.778 0.000 0.860 0.140 0.000
#> SRR1951680 3 0.6553 0.718 0.100 0.000 0.584 0.316
#> SRR1951679 4 0.0524 0.912 0.004 0.000 0.008 0.988
#> SRR1951678 3 0.6553 0.718 0.100 0.000 0.584 0.316
#> SRR1951677 3 0.6553 0.718 0.100 0.000 0.584 0.316
#> SRR1951676 3 0.5989 0.723 0.080 0.000 0.656 0.264
#> SRR1951675 2 0.3105 0.778 0.000 0.856 0.140 0.004
#> SRR1951681 3 0.6792 0.723 0.140 0.000 0.588 0.272
#> SRR1951673 2 0.5220 0.657 0.000 0.568 0.424 0.008
#> SRR1951674 1 0.3895 0.746 0.804 0.000 0.184 0.012
#> SRR1951672 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> SRR1951671 2 0.4933 0.657 0.000 0.568 0.432 0.000
#> SRR1951670 4 0.0524 0.920 0.008 0.000 0.004 0.988
#> SRR1951640 3 0.6890 0.719 0.152 0.000 0.580 0.268
#> SRR1951668 4 0.6022 0.245 0.084 0.000 0.260 0.656
#> SRR1951669 2 0.4894 0.739 0.008 0.720 0.260 0.012
#> SRR1951638 1 0.5497 0.155 0.524 0.000 0.016 0.460
#> SRR1951637 2 0.5919 0.654 0.020 0.564 0.404 0.012
#> SRR1951635 1 0.5313 0.425 0.608 0.000 0.016 0.376
#> SRR1951634 3 0.6716 0.596 0.092 0.000 0.504 0.404
#> SRR1951631 3 0.6553 0.718 0.100 0.000 0.584 0.316
#> SRR1951632 2 0.4898 0.668 0.000 0.584 0.416 0.000
#> SRR1951633 3 0.6280 0.718 0.080 0.000 0.604 0.316
#> SRR1951630 3 0.6553 0.718 0.100 0.000 0.584 0.316
#> SRR1951629 3 0.6553 0.718 0.100 0.000 0.584 0.316
#> SRR1951628 4 0.1557 0.852 0.000 0.000 0.056 0.944
#> SRR1951627 3 0.0000 0.586 0.000 0.000 1.000 0.000
#> SRR1951639 3 0.0000 0.586 0.000 0.000 1.000 0.000
#> SRR1951625 2 0.4933 0.657 0.000 0.568 0.432 0.000
#> SRR1951626 3 0.0469 0.590 0.012 0.000 0.988 0.000
#> SRR1951624 3 0.6928 0.718 0.156 0.000 0.576 0.268
#> SRR1951622 4 0.0000 0.923 0.000 0.000 0.000 1.000
#> SRR1951636 3 0.0336 0.590 0.008 0.000 0.992 0.000
#> SRR1951623 3 0.0336 0.590 0.008 0.000 0.992 0.000
#> SRR1951621 4 0.0927 0.915 0.016 0.000 0.008 0.976
#> SRR1951620 3 0.6928 0.718 0.156 0.000 0.576 0.268
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.2612 0.875 0.000 0.868 0.008 0.000 0.124
#> SRR1951666 5 0.1732 0.870 0.000 0.000 0.000 0.080 0.920
#> SRR1951665 2 0.0992 0.876 0.008 0.968 0.024 0.000 0.000
#> SRR1951664 4 0.0880 0.933 0.000 0.000 0.000 0.968 0.032
#> SRR1951663 3 0.0955 0.896 0.000 0.000 0.968 0.004 0.028
#> SRR1951662 2 0.0992 0.876 0.008 0.968 0.024 0.000 0.000
#> SRR1951661 5 0.2179 0.872 0.000 0.000 0.004 0.100 0.896
#> SRR1951660 1 0.0290 0.910 0.992 0.000 0.000 0.008 0.000
#> SRR1951659 2 0.2020 0.882 0.000 0.900 0.000 0.000 0.100
#> SRR1951658 2 0.1732 0.883 0.000 0.920 0.000 0.000 0.080
#> SRR1951657 4 0.1211 0.937 0.016 0.000 0.000 0.960 0.024
#> SRR1951656 4 0.1386 0.937 0.016 0.000 0.000 0.952 0.032
#> SRR1951655 1 0.0404 0.912 0.988 0.000 0.000 0.012 0.000
#> SRR1951654 1 0.5571 0.456 0.568 0.000 0.364 0.060 0.008
#> SRR1951653 4 0.1399 0.937 0.020 0.000 0.000 0.952 0.028
#> SRR1951652 5 0.2930 0.836 0.000 0.032 0.032 0.048 0.888
#> SRR1951651 4 0.0865 0.929 0.024 0.000 0.004 0.972 0.000
#> SRR1951650 4 0.0880 0.933 0.000 0.000 0.000 0.968 0.032
#> SRR1951648 5 0.2020 0.872 0.000 0.000 0.000 0.100 0.900
#> SRR1951649 1 0.0794 0.907 0.972 0.000 0.000 0.028 0.000
#> SRR1951647 5 0.3932 0.587 0.000 0.000 0.000 0.328 0.672
#> SRR1951646 4 0.0880 0.933 0.000 0.000 0.000 0.968 0.032
#> SRR1951645 1 0.1043 0.900 0.960 0.000 0.000 0.040 0.000
#> SRR1951644 1 0.0404 0.912 0.988 0.000 0.000 0.012 0.000
#> SRR1951643 4 0.0960 0.929 0.016 0.000 0.004 0.972 0.008
#> SRR1951641 2 0.2470 0.881 0.000 0.884 0.012 0.000 0.104
#> SRR1951642 4 0.1200 0.924 0.016 0.000 0.012 0.964 0.008
#> SRR1951694 1 0.1990 0.888 0.928 0.000 0.040 0.004 0.028
#> SRR1951731 5 0.2377 0.854 0.000 0.000 0.000 0.128 0.872
#> SRR1951730 5 0.1673 0.805 0.000 0.032 0.008 0.016 0.944
#> SRR1951752 5 0.2609 0.854 0.000 0.028 0.008 0.068 0.896
#> SRR1951751 1 0.0404 0.912 0.988 0.000 0.000 0.012 0.000
#> SRR1951749 5 0.1965 0.873 0.000 0.000 0.000 0.096 0.904
#> SRR1951750 1 0.1299 0.908 0.960 0.000 0.020 0.012 0.008
#> SRR1951748 2 0.0992 0.876 0.008 0.968 0.024 0.000 0.000
#> SRR1951747 2 0.0992 0.876 0.008 0.968 0.024 0.000 0.000
#> SRR1951745 3 0.0955 0.896 0.000 0.000 0.968 0.004 0.028
#> SRR1951746 2 0.0898 0.876 0.008 0.972 0.020 0.000 0.000
#> SRR1951744 4 0.1597 0.898 0.000 0.000 0.012 0.940 0.048
#> SRR1951742 5 0.1965 0.843 0.000 0.024 0.000 0.052 0.924
#> SRR1951743 2 0.0579 0.877 0.008 0.984 0.008 0.000 0.000
#> SRR1951741 1 0.4195 0.809 0.796 0.000 0.104 0.092 0.008
#> SRR1951740 4 0.1211 0.937 0.016 0.000 0.000 0.960 0.024
#> SRR1951738 2 0.4558 0.592 0.000 0.652 0.024 0.000 0.324
#> SRR1951739 2 0.4183 0.628 0.000 0.668 0.008 0.000 0.324
#> SRR1951737 4 0.0880 0.933 0.000 0.000 0.000 0.968 0.032
#> SRR1951736 1 0.0404 0.912 0.988 0.000 0.000 0.012 0.000
#> SRR1951734 2 0.0693 0.877 0.008 0.980 0.012 0.000 0.000
#> SRR1951735 4 0.0880 0.933 0.000 0.000 0.000 0.968 0.032
#> SRR1951733 5 0.1671 0.869 0.000 0.000 0.000 0.076 0.924
#> SRR1951732 2 0.0992 0.876 0.008 0.968 0.024 0.000 0.000
#> SRR1951729 4 0.0880 0.933 0.000 0.000 0.000 0.968 0.032
#> SRR1951728 5 0.3333 0.764 0.000 0.000 0.004 0.208 0.788
#> SRR1951727 2 0.0992 0.876 0.008 0.968 0.024 0.000 0.000
#> SRR1951725 3 0.2670 0.828 0.080 0.000 0.888 0.004 0.028
#> SRR1951726 5 0.3224 0.855 0.000 0.028 0.028 0.076 0.868
#> SRR1951724 1 0.2074 0.844 0.896 0.000 0.000 0.104 0.000
#> SRR1951722 1 0.4751 0.652 0.732 0.000 0.000 0.152 0.116
#> SRR1951723 1 0.1792 0.868 0.916 0.000 0.000 0.084 0.000
#> SRR1951721 3 0.0955 0.896 0.000 0.000 0.968 0.004 0.028
#> SRR1951719 4 0.1386 0.937 0.016 0.000 0.000 0.952 0.032
#> SRR1951720 1 0.2645 0.872 0.884 0.000 0.096 0.012 0.008
#> SRR1951718 1 0.0404 0.912 0.988 0.000 0.000 0.012 0.000
#> SRR1951717 1 0.1883 0.899 0.932 0.000 0.048 0.012 0.008
#> SRR1951715 3 0.1356 0.891 0.012 0.000 0.956 0.004 0.028
#> SRR1951716 1 0.0404 0.912 0.988 0.000 0.000 0.012 0.000
#> SRR1951714 4 0.0960 0.929 0.016 0.000 0.004 0.972 0.008
#> SRR1951713 4 0.3151 0.834 0.144 0.000 0.000 0.836 0.020
#> SRR1951712 1 0.0404 0.912 0.988 0.000 0.000 0.012 0.000
#> SRR1951711 2 0.2020 0.882 0.000 0.900 0.000 0.000 0.100
#> SRR1951710 1 0.2206 0.882 0.912 0.000 0.068 0.004 0.016
#> SRR1951709 5 0.1965 0.873 0.000 0.000 0.000 0.096 0.904
#> SRR1951708 2 0.1341 0.883 0.000 0.944 0.000 0.000 0.056
#> SRR1951707 5 0.2377 0.854 0.000 0.000 0.000 0.128 0.872
#> SRR1951705 1 0.0451 0.910 0.988 0.000 0.004 0.008 0.000
#> SRR1951704 5 0.2249 0.872 0.000 0.000 0.008 0.096 0.896
#> SRR1951703 2 0.0992 0.876 0.008 0.968 0.024 0.000 0.000
#> SRR1951706 5 0.2624 0.863 0.000 0.000 0.012 0.116 0.872
#> SRR1951702 5 0.2179 0.872 0.000 0.000 0.004 0.100 0.896
#> SRR1951701 2 0.2612 0.875 0.000 0.868 0.008 0.000 0.124
#> SRR1951699 5 0.2179 0.872 0.000 0.000 0.004 0.100 0.896
#> SRR1951698 5 0.2286 0.869 0.000 0.000 0.004 0.108 0.888
#> SRR1951700 4 0.4405 0.630 0.280 0.000 0.004 0.696 0.020
#> SRR1951697 2 0.2411 0.876 0.000 0.884 0.008 0.000 0.108
#> SRR1951696 4 0.0880 0.933 0.000 0.000 0.000 0.968 0.032
#> SRR1951695 4 0.1461 0.937 0.016 0.000 0.004 0.952 0.028
#> SRR1951693 4 0.2812 0.880 0.096 0.000 0.004 0.876 0.024
#> SRR1951692 4 0.1211 0.937 0.016 0.000 0.000 0.960 0.024
#> SRR1951691 1 0.3289 0.752 0.816 0.000 0.004 0.172 0.008
#> SRR1951688 5 0.4902 -0.183 0.000 0.468 0.024 0.000 0.508
#> SRR1951689 5 0.3170 0.720 0.000 0.120 0.012 0.016 0.852
#> SRR1951687 4 0.6021 0.406 0.284 0.000 0.124 0.584 0.008
#> SRR1951685 5 0.1892 0.868 0.000 0.000 0.004 0.080 0.916
#> SRR1951684 4 0.1386 0.937 0.016 0.000 0.000 0.952 0.032
#> SRR1951686 5 0.1913 0.799 0.000 0.044 0.008 0.016 0.932
#> SRR1951683 4 0.3211 0.792 0.164 0.000 0.004 0.824 0.008
#> SRR1951682 2 0.2612 0.875 0.000 0.868 0.008 0.000 0.124
#> SRR1951690 2 0.2612 0.875 0.000 0.868 0.008 0.000 0.124
#> SRR1951680 4 0.1211 0.937 0.016 0.000 0.000 0.960 0.024
#> SRR1951679 1 0.0613 0.906 0.984 0.000 0.008 0.004 0.004
#> SRR1951678 4 0.0960 0.929 0.016 0.000 0.004 0.972 0.008
#> SRR1951677 4 0.1200 0.924 0.016 0.000 0.012 0.964 0.008
#> SRR1951676 4 0.1710 0.935 0.016 0.000 0.004 0.940 0.040
#> SRR1951675 2 0.2813 0.866 0.000 0.868 0.024 0.000 0.108
#> SRR1951681 4 0.1121 0.929 0.000 0.000 0.000 0.956 0.044
#> SRR1951673 5 0.3209 0.705 0.000 0.120 0.028 0.004 0.848
#> SRR1951674 3 0.0955 0.896 0.000 0.000 0.968 0.004 0.028
#> SRR1951672 1 0.0290 0.910 0.992 0.000 0.000 0.008 0.000
#> SRR1951671 5 0.1168 0.782 0.000 0.032 0.008 0.000 0.960
#> SRR1951670 1 0.1588 0.905 0.948 0.000 0.028 0.016 0.008
#> SRR1951640 4 0.0880 0.933 0.000 0.000 0.000 0.968 0.032
#> SRR1951668 4 0.2970 0.804 0.168 0.000 0.004 0.828 0.000
#> SRR1951669 2 0.5379 0.543 0.000 0.648 0.108 0.000 0.244
#> SRR1951638 1 0.3774 0.782 0.780 0.000 0.200 0.012 0.008
#> SRR1951637 3 0.6598 0.254 0.000 0.260 0.464 0.000 0.276
#> SRR1951635 1 0.4156 0.665 0.700 0.000 0.288 0.004 0.008
#> SRR1951634 4 0.2648 0.830 0.152 0.000 0.000 0.848 0.000
#> SRR1951631 4 0.0960 0.934 0.016 0.000 0.004 0.972 0.008
#> SRR1951632 5 0.3835 0.486 0.000 0.260 0.008 0.000 0.732
#> SRR1951633 4 0.1200 0.924 0.016 0.000 0.012 0.964 0.008
#> SRR1951630 4 0.0960 0.929 0.016 0.000 0.004 0.972 0.008
#> SRR1951629 4 0.1278 0.937 0.016 0.000 0.004 0.960 0.020
#> SRR1951628 1 0.1012 0.902 0.968 0.000 0.000 0.012 0.020
#> SRR1951627 5 0.2179 0.872 0.000 0.000 0.004 0.100 0.896
#> SRR1951639 5 0.2127 0.869 0.000 0.000 0.000 0.108 0.892
#> SRR1951625 5 0.1772 0.804 0.000 0.032 0.008 0.020 0.940
#> SRR1951626 5 0.2424 0.852 0.000 0.000 0.000 0.132 0.868
#> SRR1951624 4 0.0880 0.933 0.000 0.000 0.000 0.968 0.032
#> SRR1951622 1 0.0404 0.912 0.988 0.000 0.000 0.012 0.000
#> SRR1951636 5 0.2233 0.870 0.000 0.000 0.004 0.104 0.892
#> SRR1951623 5 0.4356 0.549 0.000 0.000 0.012 0.340 0.648
#> SRR1951621 1 0.2990 0.865 0.868 0.000 0.100 0.024 0.008
#> SRR1951620 4 0.1041 0.934 0.004 0.000 0.000 0.964 0.032
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 2 0.0146 0.8500 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1951666 5 0.0291 0.9124 0.000 0.000 0.004 0.000 0.992 0.004
#> SRR1951665 2 0.4207 0.8139 0.004 0.748 0.144 0.000 0.000 0.104
#> SRR1951664 3 0.2340 0.7673 0.000 0.000 0.852 0.148 0.000 0.000
#> SRR1951663 6 0.2121 0.9923 0.000 0.000 0.000 0.012 0.096 0.892
#> SRR1951662 2 0.4207 0.8139 0.004 0.748 0.144 0.000 0.000 0.104
#> SRR1951661 5 0.0000 0.9134 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951660 1 0.0146 0.9082 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1951659 2 0.0000 0.8512 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951658 2 0.0146 0.8512 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1951657 4 0.3023 0.6774 0.000 0.000 0.232 0.768 0.000 0.000
#> SRR1951656 4 0.3023 0.6774 0.000 0.000 0.232 0.768 0.000 0.000
#> SRR1951655 1 0.1714 0.8358 0.908 0.000 0.000 0.092 0.000 0.000
#> SRR1951654 4 0.2313 0.7541 0.100 0.000 0.000 0.884 0.004 0.012
#> SRR1951653 3 0.4256 0.1293 0.016 0.000 0.520 0.464 0.000 0.000
#> SRR1951652 5 0.0653 0.9108 0.000 0.000 0.004 0.012 0.980 0.004
#> SRR1951651 4 0.0508 0.8420 0.004 0.000 0.012 0.984 0.000 0.000
#> SRR1951650 3 0.2513 0.7670 0.000 0.000 0.852 0.140 0.008 0.000
#> SRR1951648 5 0.0000 0.9134 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951649 1 0.0363 0.9070 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1951647 5 0.3974 0.5451 0.000 0.000 0.296 0.024 0.680 0.000
#> SRR1951646 3 0.2340 0.7673 0.000 0.000 0.852 0.148 0.000 0.000
#> SRR1951645 1 0.2003 0.8139 0.884 0.000 0.000 0.116 0.000 0.000
#> SRR1951644 1 0.0146 0.9082 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1951643 4 0.0508 0.8420 0.004 0.000 0.012 0.984 0.000 0.000
#> SRR1951641 2 0.0000 0.8512 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951642 4 0.0260 0.8407 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR1951694 1 0.3308 0.7385 0.828 0.000 0.000 0.004 0.096 0.072
#> SRR1951731 5 0.3081 0.6891 0.000 0.000 0.220 0.004 0.776 0.000
#> SRR1951730 5 0.0603 0.9121 0.000 0.016 0.004 0.000 0.980 0.000
#> SRR1951752 5 0.0508 0.9111 0.000 0.000 0.000 0.012 0.984 0.004
#> SRR1951751 1 0.0146 0.9082 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1951749 5 0.0000 0.9134 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951750 4 0.3547 0.4329 0.332 0.000 0.000 0.668 0.000 0.000
#> SRR1951748 2 0.4207 0.8139 0.004 0.748 0.144 0.000 0.000 0.104
#> SRR1951747 2 0.4207 0.8139 0.004 0.748 0.144 0.000 0.000 0.104
#> SRR1951745 6 0.2121 0.9923 0.000 0.000 0.000 0.012 0.096 0.892
#> SRR1951746 2 0.4048 0.8177 0.004 0.764 0.132 0.000 0.000 0.100
#> SRR1951744 3 0.4382 0.7259 0.000 0.000 0.728 0.164 0.104 0.004
#> SRR1951742 5 0.0291 0.9124 0.000 0.000 0.004 0.000 0.992 0.004
#> SRR1951743 2 0.2696 0.8340 0.000 0.856 0.116 0.000 0.000 0.028
#> SRR1951741 4 0.0603 0.8324 0.016 0.000 0.000 0.980 0.000 0.004
#> SRR1951740 4 0.3023 0.6774 0.000 0.000 0.232 0.768 0.000 0.000
#> SRR1951738 2 0.3790 0.5457 0.000 0.716 0.004 0.000 0.264 0.016
#> SRR1951739 2 0.3189 0.5893 0.000 0.760 0.004 0.000 0.236 0.000
#> SRR1951737 3 0.2340 0.7673 0.000 0.000 0.852 0.148 0.000 0.000
#> SRR1951736 1 0.0146 0.9082 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1951734 2 0.3370 0.8274 0.000 0.812 0.124 0.000 0.000 0.064
#> SRR1951735 3 0.2340 0.7673 0.000 0.000 0.852 0.148 0.000 0.000
#> SRR1951733 5 0.0458 0.9127 0.000 0.016 0.000 0.000 0.984 0.000
#> SRR1951732 2 0.4207 0.8139 0.004 0.748 0.144 0.000 0.000 0.104
#> SRR1951729 3 0.2340 0.7673 0.000 0.000 0.852 0.148 0.000 0.000
#> SRR1951728 3 0.3971 0.1629 0.000 0.000 0.548 0.004 0.448 0.000
#> SRR1951727 2 0.4207 0.8139 0.004 0.748 0.144 0.000 0.000 0.104
#> SRR1951725 1 0.5355 0.2307 0.548 0.000 0.000 0.016 0.076 0.360
#> SRR1951726 5 0.0508 0.9111 0.000 0.000 0.000 0.012 0.984 0.004
#> SRR1951724 1 0.0508 0.9046 0.984 0.000 0.012 0.004 0.000 0.000
#> SRR1951722 1 0.3663 0.7407 0.816 0.000 0.064 0.024 0.096 0.000
#> SRR1951723 1 0.0547 0.9032 0.980 0.000 0.000 0.020 0.000 0.000
#> SRR1951721 6 0.2214 0.9913 0.000 0.000 0.000 0.016 0.096 0.888
#> SRR1951719 3 0.3869 0.0122 0.000 0.000 0.500 0.500 0.000 0.000
#> SRR1951720 1 0.0603 0.9040 0.980 0.000 0.000 0.016 0.000 0.004
#> SRR1951718 1 0.0260 0.9077 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1951717 1 0.0603 0.9040 0.980 0.000 0.000 0.016 0.000 0.004
#> SRR1951715 6 0.2829 0.9720 0.024 0.000 0.000 0.016 0.096 0.864
#> SRR1951716 1 0.0146 0.9082 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1951714 4 0.3852 0.4815 0.012 0.000 0.324 0.664 0.000 0.000
#> SRR1951713 4 0.3916 0.5560 0.020 0.000 0.300 0.680 0.000 0.000
#> SRR1951712 1 0.0260 0.9081 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1951711 2 0.0146 0.8500 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1951710 1 0.0858 0.8962 0.968 0.000 0.000 0.004 0.000 0.028
#> SRR1951709 5 0.0146 0.9126 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR1951708 2 0.0146 0.8512 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1951707 5 0.3405 0.6178 0.000 0.000 0.272 0.004 0.724 0.000
#> SRR1951705 1 0.0146 0.9082 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1951704 5 0.0508 0.9111 0.000 0.000 0.000 0.012 0.984 0.004
#> SRR1951703 2 0.4207 0.8139 0.004 0.748 0.144 0.000 0.000 0.104
#> SRR1951706 5 0.0508 0.9111 0.000 0.000 0.000 0.012 0.984 0.004
#> SRR1951702 5 0.0551 0.9116 0.000 0.000 0.004 0.008 0.984 0.004
#> SRR1951701 2 0.0000 0.8512 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951699 5 0.0146 0.9126 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR1951698 3 0.3950 0.2380 0.000 0.000 0.564 0.004 0.432 0.000
#> SRR1951700 1 0.5467 0.1633 0.544 0.000 0.304 0.152 0.000 0.000
#> SRR1951697 2 0.0146 0.8500 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1951696 3 0.2340 0.7673 0.000 0.000 0.852 0.148 0.000 0.000
#> SRR1951695 3 0.4038 0.7472 0.008 0.000 0.764 0.156 0.072 0.000
#> SRR1951693 3 0.6017 0.2347 0.304 0.000 0.428 0.268 0.000 0.000
#> SRR1951692 4 0.0865 0.8354 0.000 0.000 0.036 0.964 0.000 0.000
#> SRR1951691 1 0.3564 0.5998 0.724 0.000 0.012 0.264 0.000 0.000
#> SRR1951688 5 0.2362 0.7947 0.000 0.136 0.004 0.000 0.860 0.000
#> SRR1951689 5 0.0603 0.9121 0.000 0.016 0.004 0.000 0.980 0.000
#> SRR1951687 4 0.0692 0.8303 0.020 0.000 0.000 0.976 0.000 0.004
#> SRR1951685 5 0.2361 0.8228 0.000 0.012 0.104 0.004 0.880 0.000
#> SRR1951684 4 0.3860 0.0310 0.000 0.000 0.472 0.528 0.000 0.000
#> SRR1951686 5 0.2278 0.7978 0.000 0.128 0.004 0.000 0.868 0.000
#> SRR1951683 4 0.0725 0.8411 0.012 0.000 0.012 0.976 0.000 0.000
#> SRR1951682 2 0.0291 0.8479 0.000 0.992 0.004 0.000 0.004 0.000
#> SRR1951690 2 0.0000 0.8512 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951680 4 0.3371 0.5800 0.000 0.000 0.292 0.708 0.000 0.000
#> SRR1951679 1 0.0260 0.9069 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1951678 4 0.0405 0.8420 0.004 0.000 0.008 0.988 0.000 0.000
#> SRR1951677 4 0.0363 0.8415 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1951676 3 0.3967 0.7421 0.000 0.000 0.760 0.148 0.092 0.000
#> SRR1951675 2 0.0000 0.8512 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951681 3 0.3551 0.7561 0.000 0.000 0.792 0.148 0.060 0.000
#> SRR1951673 5 0.0458 0.9127 0.000 0.016 0.000 0.000 0.984 0.000
#> SRR1951674 6 0.2121 0.9923 0.000 0.000 0.000 0.012 0.096 0.892
#> SRR1951672 1 0.0146 0.9082 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1951671 5 0.0603 0.9121 0.000 0.016 0.004 0.000 0.980 0.000
#> SRR1951670 4 0.0777 0.8270 0.024 0.000 0.000 0.972 0.000 0.004
#> SRR1951640 3 0.2340 0.7673 0.000 0.000 0.852 0.148 0.000 0.000
#> SRR1951668 4 0.0725 0.8411 0.012 0.000 0.012 0.976 0.000 0.000
#> SRR1951669 2 0.4483 0.4631 0.000 0.656 0.004 0.012 0.304 0.024
#> SRR1951638 1 0.0820 0.9019 0.972 0.000 0.000 0.016 0.000 0.012
#> SRR1951637 5 0.2214 0.8343 0.000 0.000 0.004 0.012 0.892 0.092
#> SRR1951635 1 0.1461 0.8856 0.940 0.000 0.000 0.016 0.000 0.044
#> SRR1951634 4 0.1074 0.8402 0.012 0.000 0.028 0.960 0.000 0.000
#> SRR1951631 4 0.0777 0.8404 0.004 0.000 0.024 0.972 0.000 0.000
#> SRR1951632 5 0.2191 0.8157 0.000 0.120 0.004 0.000 0.876 0.000
#> SRR1951633 4 0.0291 0.8368 0.000 0.000 0.004 0.992 0.000 0.004
#> SRR1951630 4 0.0458 0.8410 0.000 0.000 0.016 0.984 0.000 0.000
#> SRR1951629 4 0.3076 0.6663 0.000 0.000 0.240 0.760 0.000 0.000
#> SRR1951628 1 0.1237 0.8891 0.956 0.000 0.000 0.004 0.020 0.020
#> SRR1951627 5 0.0146 0.9126 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR1951639 5 0.3163 0.6740 0.000 0.000 0.232 0.004 0.764 0.000
#> SRR1951625 5 0.0458 0.9127 0.000 0.016 0.000 0.000 0.984 0.000
#> SRR1951626 3 0.3290 0.5792 0.000 0.000 0.744 0.004 0.252 0.000
#> SRR1951624 3 0.2340 0.7673 0.000 0.000 0.852 0.148 0.000 0.000
#> SRR1951622 1 0.0146 0.9082 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1951636 3 0.3337 0.5699 0.000 0.000 0.736 0.004 0.260 0.000
#> SRR1951623 3 0.4586 0.3962 0.000 0.000 0.564 0.032 0.400 0.004
#> SRR1951621 4 0.0692 0.8303 0.020 0.000 0.000 0.976 0.000 0.004
#> SRR1951620 3 0.2416 0.7607 0.000 0.000 0.844 0.156 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "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 13580 rows and 133 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 0.762 0.913 0.959 0.4882 0.514 0.514
#> 3 3 0.815 0.843 0.933 0.2762 0.778 0.604
#> 4 4 0.610 0.675 0.841 0.1581 0.767 0.474
#> 5 5 0.603 0.636 0.791 0.0753 0.847 0.523
#> 6 6 0.651 0.668 0.796 0.0484 0.909 0.629
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
#> SRR1951667 1 0.2236 0.929 0.964 0.036
#> SRR1951666 1 0.0000 0.953 1.000 0.000
#> SRR1951665 2 0.2043 0.939 0.032 0.968
#> SRR1951664 1 0.0000 0.953 1.000 0.000
#> SRR1951663 2 0.0000 0.959 0.000 1.000
#> SRR1951662 2 0.7376 0.751 0.208 0.792
#> SRR1951661 1 0.0000 0.953 1.000 0.000
#> SRR1951660 2 0.0000 0.959 0.000 1.000
#> SRR1951659 1 0.0000 0.953 1.000 0.000
#> SRR1951658 1 0.0000 0.953 1.000 0.000
#> SRR1951657 1 0.0000 0.953 1.000 0.000
#> SRR1951656 1 0.0000 0.953 1.000 0.000
#> SRR1951655 2 0.0938 0.953 0.012 0.988
#> SRR1951654 2 0.0000 0.959 0.000 1.000
#> SRR1951653 1 0.5294 0.862 0.880 0.120
#> SRR1951652 1 0.8713 0.572 0.708 0.292
#> SRR1951651 1 0.0000 0.953 1.000 0.000
#> SRR1951650 1 0.0000 0.953 1.000 0.000
#> SRR1951648 1 0.0000 0.953 1.000 0.000
#> SRR1951649 1 0.7453 0.765 0.788 0.212
#> SRR1951647 1 0.0000 0.953 1.000 0.000
#> SRR1951646 1 0.0000 0.953 1.000 0.000
#> SRR1951645 1 0.9661 0.423 0.608 0.392
#> SRR1951644 2 0.0000 0.959 0.000 1.000
#> SRR1951643 1 0.0000 0.953 1.000 0.000
#> SRR1951641 2 0.0000 0.959 0.000 1.000
#> SRR1951642 1 0.0000 0.953 1.000 0.000
#> SRR1951694 2 0.0000 0.959 0.000 1.000
#> SRR1951731 1 0.0000 0.953 1.000 0.000
#> SRR1951730 2 0.8813 0.550 0.300 0.700
#> SRR1951752 1 0.8144 0.651 0.748 0.252
#> SRR1951751 2 0.2423 0.933 0.040 0.960
#> SRR1951749 1 0.0000 0.953 1.000 0.000
#> SRR1951750 2 0.0376 0.957 0.004 0.996
#> SRR1951748 2 0.4815 0.874 0.104 0.896
#> SRR1951747 2 0.0938 0.953 0.012 0.988
#> SRR1951745 2 0.0000 0.959 0.000 1.000
#> SRR1951746 1 0.0376 0.950 0.996 0.004
#> SRR1951744 1 0.0000 0.953 1.000 0.000
#> SRR1951742 1 0.0000 0.953 1.000 0.000
#> SRR1951743 2 0.0000 0.959 0.000 1.000
#> SRR1951741 2 0.2423 0.933 0.040 0.960
#> SRR1951740 1 0.0000 0.953 1.000 0.000
#> SRR1951738 2 0.0000 0.959 0.000 1.000
#> SRR1951739 1 0.0000 0.953 1.000 0.000
#> SRR1951737 1 0.0000 0.953 1.000 0.000
#> SRR1951736 2 0.0000 0.959 0.000 1.000
#> SRR1951734 2 0.2423 0.934 0.040 0.960
#> SRR1951735 1 0.0000 0.953 1.000 0.000
#> SRR1951733 1 0.0000 0.953 1.000 0.000
#> SRR1951732 1 0.0000 0.953 1.000 0.000
#> SRR1951729 1 0.0000 0.953 1.000 0.000
#> SRR1951728 1 0.0000 0.953 1.000 0.000
#> SRR1951727 2 0.7219 0.761 0.200 0.800
#> SRR1951725 2 0.0000 0.959 0.000 1.000
#> SRR1951726 2 0.5178 0.869 0.116 0.884
#> SRR1951724 2 0.9881 0.167 0.436 0.564
#> SRR1951722 1 0.7950 0.725 0.760 0.240
#> SRR1951723 1 0.7219 0.779 0.800 0.200
#> SRR1951721 2 0.0000 0.959 0.000 1.000
#> SRR1951719 1 0.0000 0.953 1.000 0.000
#> SRR1951720 2 0.0000 0.959 0.000 1.000
#> SRR1951718 2 0.0376 0.957 0.004 0.996
#> SRR1951717 2 0.0000 0.959 0.000 1.000
#> SRR1951715 2 0.0000 0.959 0.000 1.000
#> SRR1951716 2 0.0000 0.959 0.000 1.000
#> SRR1951714 1 0.0376 0.950 0.996 0.004
#> SRR1951713 1 0.6887 0.799 0.816 0.184
#> SRR1951712 2 0.0938 0.953 0.012 0.988
#> SRR1951711 2 0.0000 0.959 0.000 1.000
#> SRR1951710 2 0.0000 0.959 0.000 1.000
#> SRR1951709 1 0.0000 0.953 1.000 0.000
#> SRR1951708 2 0.0000 0.959 0.000 1.000
#> SRR1951707 1 0.7219 0.779 0.800 0.200
#> SRR1951705 2 0.0000 0.959 0.000 1.000
#> SRR1951704 1 0.0000 0.953 1.000 0.000
#> SRR1951703 2 0.7139 0.766 0.196 0.804
#> SRR1951706 1 0.0000 0.953 1.000 0.000
#> SRR1951702 1 0.0000 0.953 1.000 0.000
#> SRR1951701 1 0.0000 0.953 1.000 0.000
#> SRR1951699 1 0.0000 0.953 1.000 0.000
#> SRR1951698 1 0.0000 0.953 1.000 0.000
#> SRR1951700 1 0.7674 0.748 0.776 0.224
#> SRR1951697 1 0.0000 0.953 1.000 0.000
#> SRR1951696 1 0.0000 0.953 1.000 0.000
#> SRR1951695 1 0.0000 0.953 1.000 0.000
#> SRR1951693 1 0.6623 0.812 0.828 0.172
#> SRR1951692 1 0.0000 0.953 1.000 0.000
#> SRR1951691 2 0.6247 0.804 0.156 0.844
#> SRR1951688 2 0.1633 0.945 0.024 0.976
#> SRR1951689 2 0.0000 0.959 0.000 1.000
#> SRR1951687 2 0.3584 0.907 0.068 0.932
#> SRR1951685 1 0.6712 0.808 0.824 0.176
#> SRR1951684 1 0.0000 0.953 1.000 0.000
#> SRR1951686 1 0.0000 0.953 1.000 0.000
#> SRR1951683 1 0.7056 0.789 0.808 0.192
#> SRR1951682 2 0.0000 0.959 0.000 1.000
#> SRR1951690 1 0.6801 0.803 0.820 0.180
#> SRR1951680 1 0.0000 0.953 1.000 0.000
#> SRR1951679 2 0.0000 0.959 0.000 1.000
#> SRR1951678 1 0.0000 0.953 1.000 0.000
#> SRR1951677 1 0.0000 0.953 1.000 0.000
#> SRR1951676 1 0.0000 0.953 1.000 0.000
#> SRR1951675 2 0.0000 0.959 0.000 1.000
#> SRR1951681 1 0.0000 0.953 1.000 0.000
#> SRR1951673 2 0.0376 0.957 0.004 0.996
#> SRR1951674 2 0.0000 0.959 0.000 1.000
#> SRR1951672 2 0.0000 0.959 0.000 1.000
#> SRR1951671 1 0.0000 0.953 1.000 0.000
#> SRR1951670 2 0.0376 0.957 0.004 0.996
#> SRR1951640 1 0.0000 0.953 1.000 0.000
#> SRR1951668 1 0.6148 0.832 0.848 0.152
#> SRR1951669 2 0.0000 0.959 0.000 1.000
#> SRR1951638 2 0.0000 0.959 0.000 1.000
#> SRR1951637 2 0.0000 0.959 0.000 1.000
#> SRR1951635 2 0.0000 0.959 0.000 1.000
#> SRR1951634 1 0.6148 0.832 0.848 0.152
#> SRR1951631 1 0.0376 0.950 0.996 0.004
#> SRR1951632 1 0.0000 0.953 1.000 0.000
#> SRR1951633 1 0.0000 0.953 1.000 0.000
#> SRR1951630 1 0.0000 0.953 1.000 0.000
#> SRR1951629 1 0.0000 0.953 1.000 0.000
#> SRR1951628 2 0.0000 0.959 0.000 1.000
#> SRR1951627 1 0.0000 0.953 1.000 0.000
#> SRR1951639 1 0.0000 0.953 1.000 0.000
#> SRR1951625 1 0.4022 0.895 0.920 0.080
#> SRR1951626 1 0.0000 0.953 1.000 0.000
#> SRR1951624 1 0.0000 0.953 1.000 0.000
#> SRR1951622 2 0.0000 0.959 0.000 1.000
#> SRR1951636 1 0.0000 0.953 1.000 0.000
#> SRR1951623 1 0.0000 0.953 1.000 0.000
#> SRR1951621 2 0.0376 0.957 0.004 0.996
#> SRR1951620 1 0.0000 0.953 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 3 0.1753 0.8944 0.048 0.000 0.952
#> SRR1951666 3 0.1411 0.8968 0.000 0.036 0.964
#> SRR1951665 2 0.0000 0.9270 0.000 1.000 0.000
#> SRR1951664 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951663 2 0.3267 0.8357 0.116 0.884 0.000
#> SRR1951662 2 0.0237 0.9265 0.000 0.996 0.004
#> SRR1951661 3 0.1289 0.8993 0.000 0.032 0.968
#> SRR1951660 1 0.0000 0.9334 1.000 0.000 0.000
#> SRR1951659 3 0.2448 0.8645 0.000 0.076 0.924
#> SRR1951658 2 0.1643 0.9052 0.000 0.956 0.044
#> SRR1951657 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951656 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951655 1 0.0424 0.9325 0.992 0.000 0.008
#> SRR1951654 1 0.4555 0.7309 0.800 0.200 0.000
#> SRR1951653 3 0.4291 0.7769 0.180 0.000 0.820
#> SRR1951652 2 0.0237 0.9265 0.000 0.996 0.004
#> SRR1951651 3 0.1289 0.9039 0.032 0.000 0.968
#> SRR1951650 3 0.0000 0.9154 0.000 0.000 1.000
#> SRR1951648 3 0.0237 0.9142 0.000 0.004 0.996
#> SRR1951649 1 0.2165 0.8881 0.936 0.000 0.064
#> SRR1951647 3 0.2066 0.8850 0.060 0.000 0.940
#> SRR1951646 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951645 1 0.2066 0.8920 0.940 0.000 0.060
#> SRR1951644 1 0.0237 0.9338 0.996 0.000 0.004
#> SRR1951643 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951641 1 0.0000 0.9334 1.000 0.000 0.000
#> SRR1951642 3 0.0237 0.9142 0.000 0.004 0.996
#> SRR1951694 1 0.0237 0.9318 0.996 0.004 0.000
#> SRR1951731 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951730 3 0.9974 -0.1194 0.300 0.340 0.360
#> SRR1951752 3 0.6516 0.0854 0.004 0.480 0.516
#> SRR1951751 1 0.0747 0.9284 0.984 0.000 0.016
#> SRR1951749 3 0.0237 0.9142 0.000 0.004 0.996
#> SRR1951750 1 0.0237 0.9338 0.996 0.000 0.004
#> SRR1951748 2 0.0000 0.9270 0.000 1.000 0.000
#> SRR1951747 2 0.0237 0.9260 0.004 0.996 0.000
#> SRR1951745 2 0.0237 0.9260 0.004 0.996 0.000
#> SRR1951746 2 0.1753 0.9011 0.000 0.952 0.048
#> SRR1951744 3 0.5968 0.4487 0.000 0.364 0.636
#> SRR1951742 3 0.1964 0.8825 0.000 0.056 0.944
#> SRR1951743 2 0.3412 0.8235 0.124 0.876 0.000
#> SRR1951741 1 0.0237 0.9335 0.996 0.000 0.004
#> SRR1951740 3 0.0592 0.9136 0.012 0.000 0.988
#> SRR1951738 1 0.1753 0.9055 0.952 0.048 0.000
#> SRR1951739 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951737 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951736 1 0.0000 0.9334 1.000 0.000 0.000
#> SRR1951734 2 0.0237 0.9263 0.004 0.996 0.000
#> SRR1951735 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951733 3 0.0237 0.9142 0.000 0.004 0.996
#> SRR1951732 2 0.0592 0.9235 0.000 0.988 0.012
#> SRR1951729 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951728 3 0.1031 0.9086 0.024 0.000 0.976
#> SRR1951727 2 0.0237 0.9265 0.000 0.996 0.004
#> SRR1951725 1 0.1411 0.9143 0.964 0.036 0.000
#> SRR1951726 2 0.0592 0.9227 0.012 0.988 0.000
#> SRR1951724 1 0.1289 0.9170 0.968 0.000 0.032
#> SRR1951722 1 0.4235 0.7479 0.824 0.000 0.176
#> SRR1951723 1 0.2959 0.8496 0.900 0.000 0.100
#> SRR1951721 1 0.4235 0.7636 0.824 0.176 0.000
#> SRR1951719 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951720 1 0.0237 0.9318 0.996 0.004 0.000
#> SRR1951718 1 0.0237 0.9338 0.996 0.000 0.004
#> SRR1951717 1 0.0237 0.9318 0.996 0.004 0.000
#> SRR1951715 1 0.0747 0.9267 0.984 0.016 0.000
#> SRR1951716 1 0.0237 0.9338 0.996 0.000 0.004
#> SRR1951714 3 0.1525 0.9049 0.032 0.004 0.964
#> SRR1951713 3 0.6008 0.4544 0.372 0.000 0.628
#> SRR1951712 1 0.0592 0.9306 0.988 0.000 0.012
#> SRR1951711 2 0.6305 0.0208 0.484 0.516 0.000
#> SRR1951710 1 0.0237 0.9318 0.996 0.004 0.000
#> SRR1951709 3 0.0592 0.9107 0.000 0.012 0.988
#> SRR1951708 2 0.0237 0.9260 0.004 0.996 0.000
#> SRR1951707 3 0.4887 0.7144 0.228 0.000 0.772
#> SRR1951705 1 0.0000 0.9334 1.000 0.000 0.000
#> SRR1951704 3 0.3193 0.8476 0.004 0.100 0.896
#> SRR1951703 2 0.0000 0.9270 0.000 1.000 0.000
#> SRR1951706 2 0.1643 0.9049 0.000 0.956 0.044
#> SRR1951702 3 0.4605 0.7337 0.000 0.204 0.796
#> SRR1951701 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951699 3 0.0592 0.9107 0.000 0.012 0.988
#> SRR1951698 3 0.0237 0.9142 0.000 0.004 0.996
#> SRR1951700 1 0.6260 0.1081 0.552 0.000 0.448
#> SRR1951697 2 0.1289 0.9125 0.000 0.968 0.032
#> SRR1951696 3 0.0000 0.9154 0.000 0.000 1.000
#> SRR1951695 3 0.0424 0.9150 0.008 0.000 0.992
#> SRR1951693 3 0.5810 0.5371 0.336 0.000 0.664
#> SRR1951692 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951691 1 0.0892 0.9256 0.980 0.000 0.020
#> SRR1951688 2 0.0000 0.9270 0.000 1.000 0.000
#> SRR1951689 1 0.0237 0.9338 0.996 0.000 0.004
#> SRR1951687 1 0.4399 0.8354 0.864 0.044 0.092
#> SRR1951685 3 0.5988 0.4658 0.368 0.000 0.632
#> SRR1951684 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951686 2 0.6295 0.0357 0.000 0.528 0.472
#> SRR1951683 3 0.4931 0.7089 0.232 0.000 0.768
#> SRR1951682 1 0.0237 0.9338 0.996 0.000 0.004
#> SRR1951690 3 0.6274 0.2156 0.456 0.000 0.544
#> SRR1951680 3 0.1964 0.8879 0.056 0.000 0.944
#> SRR1951679 1 0.0000 0.9334 1.000 0.000 0.000
#> SRR1951678 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951677 3 0.0237 0.9142 0.000 0.004 0.996
#> SRR1951676 3 0.0237 0.9142 0.000 0.004 0.996
#> SRR1951675 1 0.1643 0.9085 0.956 0.044 0.000
#> SRR1951681 3 0.0000 0.9154 0.000 0.000 1.000
#> SRR1951673 1 0.0592 0.9307 0.988 0.000 0.012
#> SRR1951674 2 0.0000 0.9270 0.000 1.000 0.000
#> SRR1951672 1 0.0237 0.9338 0.996 0.000 0.004
#> SRR1951671 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951670 1 0.0237 0.9338 0.996 0.000 0.004
#> SRR1951640 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951668 1 0.6299 0.0216 0.524 0.000 0.476
#> SRR1951669 2 0.1411 0.9072 0.036 0.964 0.000
#> SRR1951638 1 0.1289 0.9172 0.968 0.032 0.000
#> SRR1951637 1 0.2537 0.8753 0.920 0.080 0.000
#> SRR1951635 1 0.0592 0.9288 0.988 0.012 0.000
#> SRR1951634 3 0.5678 0.5725 0.316 0.000 0.684
#> SRR1951631 3 0.1753 0.8941 0.048 0.000 0.952
#> SRR1951632 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951633 3 0.0747 0.9088 0.000 0.016 0.984
#> SRR1951630 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951629 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951628 1 0.0237 0.9338 0.996 0.000 0.004
#> SRR1951627 3 0.0424 0.9126 0.000 0.008 0.992
#> SRR1951639 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951625 3 0.3340 0.8359 0.120 0.000 0.880
#> SRR1951626 3 0.0000 0.9154 0.000 0.000 1.000
#> SRR1951624 3 0.0237 0.9163 0.004 0.000 0.996
#> SRR1951622 1 0.0000 0.9334 1.000 0.000 0.000
#> SRR1951636 3 0.0237 0.9142 0.000 0.004 0.996
#> SRR1951623 3 0.2165 0.8785 0.000 0.064 0.936
#> SRR1951621 1 0.0000 0.9334 1.000 0.000 0.000
#> SRR1951620 3 0.0237 0.9163 0.004 0.000 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 4 0.1940 0.72907 0.000 0.000 0.076 0.924
#> SRR1951666 3 0.0524 0.85582 0.000 0.008 0.988 0.004
#> SRR1951665 2 0.0336 0.80527 0.008 0.992 0.000 0.000
#> SRR1951664 4 0.4948 0.29002 0.000 0.000 0.440 0.560
#> SRR1951663 1 0.3933 0.62691 0.792 0.200 0.000 0.008
#> SRR1951662 2 0.0000 0.80511 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.0524 0.85571 0.000 0.004 0.988 0.008
#> SRR1951660 1 0.1978 0.84540 0.928 0.000 0.004 0.068
#> SRR1951659 3 0.4793 0.66332 0.000 0.204 0.756 0.040
#> SRR1951658 2 0.5503 0.04156 0.000 0.516 0.468 0.016
#> SRR1951657 3 0.0336 0.85496 0.000 0.000 0.992 0.008
#> SRR1951656 3 0.1716 0.84181 0.000 0.000 0.936 0.064
#> SRR1951655 1 0.1807 0.85097 0.940 0.000 0.008 0.052
#> SRR1951654 1 0.1884 0.83327 0.948 0.020 0.016 0.016
#> SRR1951653 4 0.5130 0.54777 0.020 0.000 0.312 0.668
#> SRR1951652 3 0.5606 -0.04582 0.020 0.480 0.500 0.000
#> SRR1951651 3 0.0469 0.85558 0.000 0.000 0.988 0.012
#> SRR1951650 3 0.0707 0.85517 0.000 0.000 0.980 0.020
#> SRR1951648 3 0.1211 0.84985 0.000 0.000 0.960 0.040
#> SRR1951649 4 0.5184 0.50221 0.304 0.000 0.024 0.672
#> SRR1951647 4 0.5055 0.43906 0.008 0.000 0.368 0.624
#> SRR1951646 3 0.1902 0.83722 0.004 0.000 0.932 0.064
#> SRR1951645 4 0.5657 0.21022 0.436 0.000 0.024 0.540
#> SRR1951644 1 0.4998 -0.00888 0.512 0.000 0.000 0.488
#> SRR1951643 3 0.0376 0.85591 0.004 0.000 0.992 0.004
#> SRR1951641 4 0.4817 0.31683 0.388 0.000 0.000 0.612
#> SRR1951642 3 0.0376 0.85553 0.000 0.004 0.992 0.004
#> SRR1951694 1 0.0707 0.85609 0.980 0.000 0.000 0.020
#> SRR1951731 4 0.4134 0.64300 0.000 0.000 0.260 0.740
#> SRR1951730 2 0.6007 0.67613 0.040 0.740 0.088 0.132
#> SRR1951752 2 0.5627 0.60785 0.012 0.704 0.240 0.044
#> SRR1951751 4 0.2081 0.70834 0.084 0.000 0.000 0.916
#> SRR1951749 3 0.4406 0.55392 0.000 0.000 0.700 0.300
#> SRR1951750 1 0.2973 0.78439 0.856 0.000 0.000 0.144
#> SRR1951748 2 0.0000 0.80511 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0336 0.80527 0.008 0.992 0.000 0.000
#> SRR1951745 1 0.5167 -0.10903 0.508 0.488 0.000 0.004
#> SRR1951746 2 0.3757 0.71685 0.000 0.828 0.020 0.152
#> SRR1951744 3 0.2311 0.81817 0.004 0.076 0.916 0.004
#> SRR1951742 3 0.1820 0.84645 0.000 0.036 0.944 0.020
#> SRR1951743 2 0.1975 0.78692 0.016 0.936 0.000 0.048
#> SRR1951741 1 0.2363 0.82341 0.920 0.000 0.056 0.024
#> SRR1951740 3 0.3801 0.67687 0.000 0.000 0.780 0.220
#> SRR1951738 1 0.0707 0.85609 0.980 0.000 0.000 0.020
#> SRR1951739 4 0.2402 0.72498 0.000 0.012 0.076 0.912
#> SRR1951737 3 0.3569 0.70643 0.000 0.000 0.804 0.196
#> SRR1951736 1 0.4222 0.62043 0.728 0.000 0.000 0.272
#> SRR1951734 2 0.3808 0.72081 0.004 0.824 0.012 0.160
#> SRR1951735 3 0.2530 0.79911 0.000 0.000 0.888 0.112
#> SRR1951733 3 0.1452 0.85421 0.000 0.008 0.956 0.036
#> SRR1951732 2 0.0000 0.80511 0.000 1.000 0.000 0.000
#> SRR1951729 4 0.4804 0.39995 0.000 0.000 0.384 0.616
#> SRR1951728 4 0.4643 0.48828 0.000 0.000 0.344 0.656
#> SRR1951727 2 0.0188 0.80531 0.004 0.996 0.000 0.000
#> SRR1951725 1 0.0188 0.85281 0.996 0.000 0.000 0.004
#> SRR1951726 3 0.7589 -0.00837 0.400 0.196 0.404 0.000
#> SRR1951724 4 0.5168 -0.02352 0.496 0.000 0.004 0.500
#> SRR1951722 4 0.4018 0.61932 0.224 0.000 0.004 0.772
#> SRR1951723 4 0.3377 0.68922 0.140 0.000 0.012 0.848
#> SRR1951721 1 0.1388 0.83879 0.960 0.028 0.000 0.012
#> SRR1951719 4 0.4454 0.55124 0.000 0.000 0.308 0.692
#> SRR1951720 1 0.0592 0.85580 0.984 0.000 0.000 0.016
#> SRR1951718 1 0.3975 0.67384 0.760 0.000 0.000 0.240
#> SRR1951717 1 0.1389 0.85337 0.952 0.000 0.000 0.048
#> SRR1951715 1 0.0707 0.85506 0.980 0.000 0.000 0.020
#> SRR1951716 1 0.3074 0.76888 0.848 0.000 0.000 0.152
#> SRR1951714 3 0.2483 0.82882 0.032 0.000 0.916 0.052
#> SRR1951713 4 0.2089 0.73403 0.020 0.000 0.048 0.932
#> SRR1951712 4 0.2647 0.69091 0.120 0.000 0.000 0.880
#> SRR1951711 2 0.5248 0.66372 0.088 0.748 0.000 0.164
#> SRR1951710 1 0.1302 0.85348 0.956 0.000 0.000 0.044
#> SRR1951709 3 0.0336 0.85545 0.000 0.000 0.992 0.008
#> SRR1951708 2 0.0469 0.80473 0.012 0.988 0.000 0.000
#> SRR1951707 4 0.2101 0.72996 0.012 0.000 0.060 0.928
#> SRR1951705 1 0.4331 0.58685 0.712 0.000 0.000 0.288
#> SRR1951704 3 0.5428 0.65362 0.004 0.164 0.744 0.088
#> SRR1951703 2 0.0469 0.80473 0.012 0.988 0.000 0.000
#> SRR1951706 2 0.5290 0.10067 0.008 0.516 0.476 0.000
#> SRR1951702 3 0.3545 0.73108 0.000 0.164 0.828 0.008
#> SRR1951701 4 0.3356 0.71160 0.000 0.000 0.176 0.824
#> SRR1951699 3 0.0921 0.84980 0.000 0.000 0.972 0.028
#> SRR1951698 3 0.1302 0.84841 0.000 0.000 0.956 0.044
#> SRR1951700 4 0.4072 0.71534 0.052 0.000 0.120 0.828
#> SRR1951697 2 0.4770 0.54823 0.012 0.700 0.288 0.000
#> SRR1951696 3 0.1022 0.85249 0.000 0.000 0.968 0.032
#> SRR1951695 3 0.5028 0.31525 0.004 0.000 0.596 0.400
#> SRR1951693 4 0.4630 0.70272 0.036 0.000 0.196 0.768
#> SRR1951692 3 0.1118 0.85091 0.000 0.000 0.964 0.036
#> SRR1951691 1 0.2214 0.84661 0.928 0.000 0.028 0.044
#> SRR1951688 2 0.0524 0.80391 0.004 0.988 0.000 0.008
#> SRR1951689 4 0.2011 0.71007 0.080 0.000 0.000 0.920
#> SRR1951687 1 0.4869 0.49992 0.708 0.012 0.276 0.004
#> SRR1951685 4 0.2142 0.72922 0.016 0.000 0.056 0.928
#> SRR1951684 3 0.4250 0.58338 0.000 0.000 0.724 0.276
#> SRR1951686 3 0.5268 0.26283 0.000 0.396 0.592 0.012
#> SRR1951683 3 0.3435 0.77549 0.100 0.000 0.864 0.036
#> SRR1951682 4 0.3356 0.64925 0.176 0.000 0.000 0.824
#> SRR1951690 4 0.1520 0.72596 0.020 0.000 0.024 0.956
#> SRR1951680 4 0.3907 0.68029 0.000 0.000 0.232 0.768
#> SRR1951679 4 0.4040 0.55586 0.248 0.000 0.000 0.752
#> SRR1951678 3 0.0524 0.85579 0.004 0.000 0.988 0.008
#> SRR1951677 3 0.0524 0.85545 0.004 0.000 0.988 0.008
#> SRR1951676 3 0.0707 0.85262 0.000 0.000 0.980 0.020
#> SRR1951675 1 0.0817 0.85485 0.976 0.000 0.000 0.024
#> SRR1951681 3 0.0188 0.85531 0.000 0.000 0.996 0.004
#> SRR1951673 4 0.5112 0.19792 0.436 0.000 0.004 0.560
#> SRR1951674 2 0.5332 0.12080 0.480 0.512 0.004 0.004
#> SRR1951672 1 0.2081 0.82887 0.916 0.000 0.000 0.084
#> SRR1951671 4 0.3400 0.71049 0.000 0.000 0.180 0.820
#> SRR1951670 1 0.0707 0.85601 0.980 0.000 0.000 0.020
#> SRR1951640 3 0.2814 0.79726 0.000 0.000 0.868 0.132
#> SRR1951668 3 0.7513 0.05665 0.296 0.000 0.488 0.216
#> SRR1951669 2 0.4564 0.48072 0.328 0.672 0.000 0.000
#> SRR1951638 1 0.0592 0.85436 0.984 0.000 0.000 0.016
#> SRR1951637 1 0.0469 0.84857 0.988 0.012 0.000 0.000
#> SRR1951635 1 0.0336 0.85419 0.992 0.000 0.000 0.008
#> SRR1951634 4 0.5661 0.65898 0.080 0.000 0.220 0.700
#> SRR1951631 3 0.2611 0.82833 0.008 0.000 0.896 0.096
#> SRR1951632 3 0.5099 0.32848 0.000 0.008 0.612 0.380
#> SRR1951633 3 0.0564 0.85600 0.004 0.004 0.988 0.004
#> SRR1951630 3 0.1004 0.85426 0.004 0.000 0.972 0.024
#> SRR1951629 3 0.0376 0.85599 0.004 0.000 0.992 0.004
#> SRR1951628 1 0.5151 0.11454 0.532 0.000 0.004 0.464
#> SRR1951627 3 0.0188 0.85531 0.000 0.000 0.996 0.004
#> SRR1951639 3 0.2868 0.79504 0.000 0.000 0.864 0.136
#> SRR1951625 4 0.2976 0.72771 0.008 0.000 0.120 0.872
#> SRR1951626 3 0.1211 0.84649 0.000 0.000 0.960 0.040
#> SRR1951624 3 0.4761 0.38815 0.000 0.000 0.628 0.372
#> SRR1951622 1 0.1474 0.84928 0.948 0.000 0.000 0.052
#> SRR1951636 3 0.0188 0.85531 0.000 0.000 0.996 0.004
#> SRR1951623 3 0.0524 0.85582 0.000 0.008 0.988 0.004
#> SRR1951621 1 0.1256 0.85337 0.964 0.000 0.008 0.028
#> SRR1951620 3 0.1637 0.84635 0.000 0.000 0.940 0.060
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 3 0.0290 0.76968 0.000 0.000 0.992 0.000 0.008
#> SRR1951666 4 0.0451 0.77850 0.000 0.004 0.000 0.988 0.008
#> SRR1951665 2 0.0000 0.87515 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 5 0.6779 0.37560 0.000 0.000 0.304 0.304 0.392
#> SRR1951663 1 0.0609 0.79763 0.980 0.020 0.000 0.000 0.000
#> SRR1951662 2 0.0162 0.87479 0.000 0.996 0.000 0.000 0.004
#> SRR1951661 4 0.1891 0.76536 0.000 0.016 0.016 0.936 0.032
#> SRR1951660 1 0.4630 0.46291 0.572 0.000 0.008 0.004 0.416
#> SRR1951659 5 0.6602 0.14672 0.000 0.212 0.000 0.384 0.404
#> SRR1951658 2 0.3476 0.69622 0.000 0.804 0.000 0.176 0.020
#> SRR1951657 4 0.0963 0.77796 0.000 0.000 0.000 0.964 0.036
#> SRR1951656 5 0.4307 0.00545 0.000 0.000 0.000 0.500 0.500
#> SRR1951655 1 0.3885 0.70459 0.724 0.000 0.008 0.000 0.268
#> SRR1951654 1 0.1043 0.79650 0.960 0.000 0.000 0.040 0.000
#> SRR1951653 5 0.4664 0.64022 0.004 0.000 0.096 0.152 0.748
#> SRR1951652 4 0.4648 0.64272 0.036 0.148 0.008 0.776 0.032
#> SRR1951651 4 0.1544 0.77097 0.000 0.000 0.000 0.932 0.068
#> SRR1951650 4 0.2280 0.74058 0.000 0.000 0.000 0.880 0.120
#> SRR1951648 4 0.3636 0.54447 0.000 0.000 0.000 0.728 0.272
#> SRR1951649 5 0.5673 0.59578 0.100 0.000 0.176 0.036 0.688
#> SRR1951647 5 0.4449 0.63643 0.000 0.000 0.080 0.168 0.752
#> SRR1951646 5 0.4307 0.03052 0.000 0.000 0.000 0.496 0.504
#> SRR1951645 5 0.6151 0.60306 0.128 0.000 0.164 0.052 0.656
#> SRR1951644 5 0.5831 0.44970 0.220 0.000 0.172 0.000 0.608
#> SRR1951643 4 0.0992 0.77759 0.000 0.000 0.008 0.968 0.024
#> SRR1951641 5 0.4855 0.59615 0.112 0.000 0.168 0.000 0.720
#> SRR1951642 4 0.0992 0.77283 0.000 0.000 0.008 0.968 0.024
#> SRR1951694 1 0.2230 0.79514 0.884 0.000 0.000 0.000 0.116
#> SRR1951731 3 0.4114 0.67424 0.000 0.000 0.732 0.024 0.244
#> SRR1951730 2 0.4712 0.59972 0.016 0.708 0.252 0.020 0.004
#> SRR1951752 3 0.7626 0.21045 0.000 0.260 0.432 0.060 0.248
#> SRR1951751 3 0.0609 0.76713 0.000 0.000 0.980 0.000 0.020
#> SRR1951749 4 0.4722 0.26475 0.000 0.000 0.368 0.608 0.024
#> SRR1951750 1 0.3530 0.71870 0.784 0.000 0.204 0.000 0.012
#> SRR1951748 2 0.0000 0.87515 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.87515 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 1 0.3927 0.68720 0.820 0.124 0.004 0.032 0.020
#> SRR1951746 2 0.4133 0.77600 0.000 0.768 0.052 0.000 0.180
#> SRR1951744 4 0.2625 0.75815 0.020 0.016 0.012 0.908 0.044
#> SRR1951742 4 0.3812 0.73188 0.000 0.092 0.000 0.812 0.096
#> SRR1951743 2 0.3844 0.79130 0.000 0.792 0.044 0.000 0.164
#> SRR1951741 1 0.2628 0.78385 0.884 0.000 0.000 0.028 0.088
#> SRR1951740 4 0.6301 0.29087 0.000 0.000 0.308 0.512 0.180
#> SRR1951738 1 0.2648 0.78055 0.848 0.000 0.000 0.000 0.152
#> SRR1951739 3 0.3741 0.67307 0.000 0.000 0.732 0.004 0.264
#> SRR1951737 4 0.5748 0.39245 0.000 0.000 0.300 0.584 0.116
#> SRR1951736 1 0.5870 0.55716 0.584 0.000 0.276 0.000 0.140
#> SRR1951734 2 0.5642 0.62748 0.000 0.636 0.184 0.000 0.180
#> SRR1951735 4 0.3550 0.67858 0.000 0.000 0.020 0.796 0.184
#> SRR1951733 4 0.4392 0.52148 0.000 0.000 0.008 0.612 0.380
#> SRR1951732 2 0.0162 0.87479 0.000 0.996 0.000 0.000 0.004
#> SRR1951729 3 0.4503 0.57653 0.000 0.000 0.696 0.268 0.036
#> SRR1951728 3 0.3326 0.70806 0.000 0.000 0.824 0.152 0.024
#> SRR1951727 2 0.0000 0.87515 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 1 0.0000 0.80140 1.000 0.000 0.000 0.000 0.000
#> SRR1951726 1 0.5960 0.39222 0.580 0.072 0.004 0.328 0.016
#> SRR1951724 1 0.6750 0.26503 0.412 0.000 0.368 0.004 0.216
#> SRR1951722 3 0.2805 0.73127 0.032 0.000 0.888 0.012 0.068
#> SRR1951723 3 0.4272 0.61240 0.020 0.000 0.752 0.016 0.212
#> SRR1951721 1 0.0671 0.80000 0.980 0.016 0.000 0.000 0.004
#> SRR1951719 3 0.2873 0.73765 0.000 0.000 0.860 0.120 0.020
#> SRR1951720 1 0.1197 0.80555 0.952 0.000 0.000 0.000 0.048
#> SRR1951718 5 0.4965 0.40717 0.292 0.000 0.024 0.020 0.664
#> SRR1951717 1 0.1764 0.79862 0.928 0.000 0.064 0.000 0.008
#> SRR1951715 1 0.0703 0.80516 0.976 0.000 0.000 0.000 0.024
#> SRR1951716 1 0.5104 0.61513 0.648 0.000 0.068 0.000 0.284
#> SRR1951714 4 0.5200 0.50279 0.068 0.000 0.000 0.628 0.304
#> SRR1951713 3 0.1845 0.76019 0.000 0.000 0.928 0.016 0.056
#> SRR1951712 3 0.0771 0.76619 0.004 0.000 0.976 0.000 0.020
#> SRR1951711 3 0.4065 0.58096 0.016 0.224 0.752 0.000 0.008
#> SRR1951710 1 0.3152 0.78094 0.840 0.000 0.024 0.000 0.136
#> SRR1951709 4 0.1270 0.77536 0.000 0.000 0.000 0.948 0.052
#> SRR1951708 2 0.0798 0.86689 0.016 0.976 0.000 0.008 0.000
#> SRR1951707 3 0.0703 0.76779 0.000 0.000 0.976 0.000 0.024
#> SRR1951705 1 0.6110 0.52770 0.568 0.000 0.216 0.000 0.216
#> SRR1951704 4 0.6380 0.46609 0.016 0.088 0.228 0.632 0.036
#> SRR1951703 2 0.0162 0.87417 0.004 0.996 0.000 0.000 0.000
#> SRR1951706 4 0.5183 0.59667 0.016 0.224 0.020 0.708 0.032
#> SRR1951702 4 0.3478 0.70995 0.004 0.096 0.016 0.852 0.032
#> SRR1951701 5 0.5678 0.50021 0.000 0.000 0.284 0.116 0.600
#> SRR1951699 4 0.1547 0.76933 0.000 0.004 0.016 0.948 0.032
#> SRR1951698 4 0.2891 0.67936 0.000 0.000 0.000 0.824 0.176
#> SRR1951700 3 0.1364 0.76903 0.000 0.000 0.952 0.012 0.036
#> SRR1951697 4 0.4865 0.25027 0.000 0.444 0.004 0.536 0.016
#> SRR1951696 4 0.3480 0.60827 0.000 0.000 0.000 0.752 0.248
#> SRR1951695 3 0.4982 0.65458 0.000 0.000 0.692 0.088 0.220
#> SRR1951693 3 0.2158 0.76610 0.008 0.000 0.920 0.052 0.020
#> SRR1951692 4 0.4101 0.37516 0.000 0.000 0.000 0.628 0.372
#> SRR1951691 1 0.3806 0.75573 0.792 0.000 0.016 0.012 0.180
#> SRR1951688 2 0.6220 0.58665 0.244 0.600 0.004 0.012 0.140
#> SRR1951689 3 0.1041 0.76540 0.004 0.000 0.964 0.000 0.032
#> SRR1951687 1 0.3707 0.61479 0.768 0.008 0.004 0.220 0.000
#> SRR1951685 3 0.0609 0.76901 0.000 0.000 0.980 0.000 0.020
#> SRR1951684 3 0.5929 0.10776 0.000 0.000 0.464 0.432 0.104
#> SRR1951686 4 0.3856 0.67749 0.000 0.140 0.016 0.812 0.032
#> SRR1951683 4 0.2824 0.74547 0.028 0.000 0.068 0.888 0.016
#> SRR1951682 5 0.4430 0.55594 0.036 0.000 0.256 0.000 0.708
#> SRR1951690 5 0.3983 0.46930 0.000 0.000 0.340 0.000 0.660
#> SRR1951680 3 0.4238 0.66508 0.000 0.000 0.776 0.088 0.136
#> SRR1951679 3 0.1992 0.74911 0.032 0.000 0.924 0.000 0.044
#> SRR1951678 4 0.1544 0.76922 0.000 0.000 0.000 0.932 0.068
#> SRR1951677 4 0.1732 0.76621 0.000 0.000 0.000 0.920 0.080
#> SRR1951676 4 0.1300 0.76944 0.000 0.000 0.016 0.956 0.028
#> SRR1951675 1 0.2927 0.80160 0.872 0.000 0.060 0.000 0.068
#> SRR1951681 4 0.1197 0.77611 0.000 0.000 0.000 0.952 0.048
#> SRR1951673 5 0.5301 0.52135 0.192 0.000 0.120 0.004 0.684
#> SRR1951674 1 0.4846 0.62979 0.760 0.140 0.004 0.076 0.020
#> SRR1951672 1 0.3888 0.76045 0.796 0.000 0.056 0.000 0.148
#> SRR1951671 3 0.2983 0.74679 0.000 0.000 0.864 0.096 0.040
#> SRR1951670 1 0.1851 0.80340 0.912 0.000 0.000 0.000 0.088
#> SRR1951640 5 0.4434 0.14227 0.000 0.000 0.004 0.460 0.536
#> SRR1951668 3 0.7140 0.45783 0.140 0.000 0.560 0.204 0.096
#> SRR1951669 1 0.4030 0.45776 0.648 0.352 0.000 0.000 0.000
#> SRR1951638 1 0.0693 0.80088 0.980 0.000 0.012 0.000 0.008
#> SRR1951637 1 0.0162 0.80096 0.996 0.004 0.000 0.000 0.000
#> SRR1951635 1 0.0794 0.80541 0.972 0.000 0.000 0.000 0.028
#> SRR1951634 5 0.5198 0.62614 0.012 0.000 0.164 0.112 0.712
#> SRR1951631 4 0.4930 0.08958 0.000 0.000 0.028 0.548 0.424
#> SRR1951632 5 0.4640 0.42978 0.000 0.000 0.048 0.256 0.696
#> SRR1951633 4 0.0566 0.77622 0.000 0.000 0.004 0.984 0.012
#> SRR1951630 4 0.2690 0.72133 0.000 0.000 0.000 0.844 0.156
#> SRR1951629 4 0.2011 0.76765 0.000 0.000 0.004 0.908 0.088
#> SRR1951628 5 0.4303 0.53142 0.192 0.000 0.056 0.000 0.752
#> SRR1951627 4 0.0963 0.77856 0.000 0.000 0.000 0.964 0.036
#> SRR1951639 5 0.4425 0.16089 0.000 0.000 0.004 0.452 0.544
#> SRR1951625 5 0.4121 0.59994 0.008 0.000 0.208 0.024 0.760
#> SRR1951626 4 0.1485 0.77691 0.000 0.000 0.032 0.948 0.020
#> SRR1951624 3 0.4708 0.25442 0.000 0.000 0.548 0.436 0.016
#> SRR1951622 1 0.3835 0.70339 0.732 0.000 0.008 0.000 0.260
#> SRR1951636 4 0.0963 0.77776 0.000 0.000 0.000 0.964 0.036
#> SRR1951623 4 0.0566 0.77819 0.000 0.004 0.000 0.984 0.012
#> SRR1951621 1 0.3106 0.78154 0.840 0.000 0.000 0.020 0.140
#> SRR1951620 4 0.4273 0.13236 0.000 0.000 0.000 0.552 0.448
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 3 0.1267 0.746 0.000 0.000 0.940 0.000 0.000 0.060
#> SRR1951666 5 0.1478 0.825 0.000 0.000 0.004 0.032 0.944 0.020
#> SRR1951665 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 3 0.6193 0.227 0.000 0.000 0.440 0.260 0.292 0.008
#> SRR1951663 1 0.0935 0.763 0.964 0.000 0.000 0.000 0.004 0.032
#> SRR1951662 2 0.0146 0.986 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1951661 5 0.1312 0.819 0.000 0.008 0.012 0.004 0.956 0.020
#> SRR1951660 4 0.5054 0.139 0.352 0.000 0.008 0.580 0.004 0.056
#> SRR1951659 4 0.6381 0.419 0.000 0.264 0.000 0.496 0.204 0.036
#> SRR1951658 2 0.1692 0.918 0.000 0.932 0.000 0.048 0.012 0.008
#> SRR1951657 5 0.1745 0.825 0.000 0.000 0.000 0.056 0.924 0.020
#> SRR1951656 4 0.4634 0.555 0.000 0.000 0.000 0.656 0.264 0.080
#> SRR1951655 1 0.5357 0.582 0.600 0.000 0.000 0.264 0.008 0.128
#> SRR1951654 1 0.1630 0.770 0.940 0.000 0.000 0.016 0.024 0.020
#> SRR1951653 4 0.2100 0.710 0.000 0.000 0.036 0.916 0.032 0.016
#> SRR1951652 5 0.3988 0.708 0.132 0.012 0.004 0.012 0.796 0.044
#> SRR1951651 5 0.2213 0.819 0.004 0.000 0.000 0.048 0.904 0.044
#> SRR1951650 5 0.2531 0.794 0.000 0.000 0.000 0.132 0.856 0.012
#> SRR1951648 5 0.3725 0.538 0.000 0.000 0.000 0.316 0.676 0.008
#> SRR1951649 4 0.4308 0.676 0.052 0.000 0.076 0.788 0.008 0.076
#> SRR1951647 4 0.1950 0.707 0.000 0.000 0.032 0.924 0.016 0.028
#> SRR1951646 4 0.4272 0.539 0.000 0.000 0.000 0.668 0.288 0.044
#> SRR1951645 4 0.4220 0.677 0.068 0.000 0.068 0.800 0.016 0.048
#> SRR1951644 4 0.2944 0.680 0.072 0.000 0.068 0.856 0.000 0.004
#> SRR1951643 5 0.1555 0.826 0.000 0.000 0.008 0.040 0.940 0.012
#> SRR1951641 4 0.2915 0.691 0.036 0.000 0.048 0.872 0.000 0.044
#> SRR1951642 5 0.0912 0.821 0.004 0.000 0.004 0.012 0.972 0.008
#> SRR1951694 1 0.3868 0.738 0.772 0.000 0.012 0.172 0.000 0.044
#> SRR1951731 6 0.3331 0.685 0.000 0.000 0.160 0.012 0.020 0.808
#> SRR1951730 3 0.6678 0.574 0.040 0.192 0.612 0.036 0.080 0.040
#> SRR1951752 6 0.2630 0.708 0.004 0.024 0.064 0.000 0.020 0.888
#> SRR1951751 3 0.0806 0.754 0.000 0.000 0.972 0.008 0.000 0.020
#> SRR1951749 5 0.4949 0.399 0.000 0.000 0.308 0.016 0.620 0.056
#> SRR1951750 1 0.5061 0.112 0.500 0.000 0.444 0.024 0.000 0.032
#> SRR1951748 2 0.0146 0.986 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1951747 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 1 0.2461 0.742 0.904 0.004 0.004 0.016 0.036 0.036
#> SRR1951746 6 0.3871 0.494 0.000 0.308 0.016 0.000 0.000 0.676
#> SRR1951744 5 0.3126 0.782 0.064 0.004 0.000 0.012 0.856 0.064
#> SRR1951742 5 0.4727 0.717 0.000 0.044 0.000 0.080 0.732 0.144
#> SRR1951743 6 0.4792 0.261 0.000 0.416 0.044 0.004 0.000 0.536
#> SRR1951741 1 0.3867 0.716 0.784 0.000 0.000 0.044 0.020 0.152
#> SRR1951740 6 0.4910 0.657 0.000 0.000 0.048 0.100 0.132 0.720
#> SRR1951738 1 0.3612 0.741 0.780 0.000 0.000 0.168 0.000 0.052
#> SRR1951739 6 0.3078 0.657 0.000 0.000 0.192 0.012 0.000 0.796
#> SRR1951737 6 0.6400 0.265 0.000 0.000 0.072 0.104 0.352 0.472
#> SRR1951736 3 0.5096 0.336 0.292 0.000 0.596 0.112 0.000 0.000
#> SRR1951734 6 0.4270 0.625 0.000 0.156 0.100 0.004 0.000 0.740
#> SRR1951735 5 0.4783 0.529 0.000 0.000 0.028 0.300 0.640 0.032
#> SRR1951733 6 0.3607 0.685 0.000 0.000 0.000 0.092 0.112 0.796
#> SRR1951732 2 0.0146 0.986 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1951729 3 0.4156 0.625 0.000 0.000 0.728 0.028 0.224 0.020
#> SRR1951728 3 0.3603 0.718 0.000 0.000 0.816 0.036 0.116 0.032
#> SRR1951727 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 1 0.1321 0.777 0.952 0.004 0.020 0.024 0.000 0.000
#> SRR1951726 1 0.5351 0.131 0.508 0.004 0.012 0.016 0.428 0.032
#> SRR1951724 3 0.5274 0.620 0.116 0.000 0.684 0.164 0.008 0.028
#> SRR1951722 3 0.2719 0.733 0.040 0.000 0.876 0.072 0.000 0.012
#> SRR1951723 3 0.4908 0.242 0.008 0.000 0.520 0.428 0.000 0.044
#> SRR1951721 1 0.0964 0.769 0.968 0.004 0.012 0.000 0.000 0.016
#> SRR1951719 3 0.2517 0.731 0.000 0.000 0.876 0.008 0.100 0.016
#> SRR1951720 1 0.1682 0.780 0.928 0.000 0.020 0.052 0.000 0.000
#> SRR1951718 4 0.2361 0.686 0.104 0.000 0.012 0.880 0.000 0.004
#> SRR1951717 1 0.3466 0.637 0.760 0.000 0.224 0.008 0.000 0.008
#> SRR1951715 1 0.1949 0.780 0.924 0.000 0.020 0.036 0.000 0.020
#> SRR1951716 1 0.5148 0.483 0.560 0.000 0.036 0.372 0.000 0.032
#> SRR1951714 6 0.4428 0.662 0.048 0.000 0.000 0.064 0.128 0.760
#> SRR1951713 3 0.2492 0.737 0.000 0.000 0.876 0.100 0.004 0.020
#> SRR1951712 3 0.0964 0.757 0.012 0.000 0.968 0.016 0.000 0.004
#> SRR1951711 3 0.3635 0.705 0.040 0.080 0.832 0.008 0.000 0.040
#> SRR1951710 1 0.3942 0.734 0.768 0.000 0.036 0.176 0.000 0.020
#> SRR1951709 5 0.2762 0.803 0.000 0.000 0.000 0.092 0.860 0.048
#> SRR1951708 2 0.0291 0.981 0.000 0.992 0.000 0.004 0.000 0.004
#> SRR1951707 3 0.1225 0.756 0.000 0.000 0.952 0.000 0.012 0.036
#> SRR1951705 1 0.6223 0.323 0.428 0.000 0.296 0.268 0.000 0.008
#> SRR1951704 5 0.5392 0.469 0.016 0.004 0.260 0.016 0.640 0.064
#> SRR1951703 2 0.0000 0.987 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 5 0.3197 0.786 0.012 0.032 0.020 0.016 0.872 0.048
#> SRR1951702 5 0.2450 0.798 0.016 0.016 0.020 0.016 0.912 0.020
#> SRR1951701 4 0.4094 0.622 0.000 0.000 0.160 0.768 0.044 0.028
#> SRR1951699 5 0.1346 0.821 0.000 0.000 0.016 0.008 0.952 0.024
#> SRR1951698 5 0.2968 0.764 0.000 0.000 0.000 0.168 0.816 0.016
#> SRR1951700 3 0.2216 0.749 0.012 0.000 0.912 0.012 0.052 0.012
#> SRR1951697 5 0.4562 0.404 0.004 0.388 0.008 0.004 0.584 0.012
#> SRR1951696 5 0.4452 0.509 0.000 0.000 0.000 0.316 0.636 0.048
#> SRR1951695 6 0.4386 0.583 0.000 0.000 0.260 0.004 0.052 0.684
#> SRR1951693 3 0.1592 0.762 0.004 0.000 0.944 0.016 0.024 0.012
#> SRR1951692 4 0.5634 0.388 0.000 0.000 0.000 0.512 0.316 0.172
#> SRR1951691 6 0.3328 0.658 0.124 0.000 0.012 0.020 0.012 0.832
#> SRR1951688 6 0.4117 0.625 0.100 0.128 0.000 0.000 0.008 0.764
#> SRR1951689 3 0.1245 0.753 0.000 0.000 0.952 0.016 0.000 0.032
#> SRR1951687 1 0.4110 0.516 0.680 0.000 0.004 0.008 0.296 0.012
#> SRR1951685 3 0.1349 0.745 0.000 0.000 0.940 0.004 0.000 0.056
#> SRR1951684 6 0.7099 0.315 0.000 0.000 0.164 0.116 0.300 0.420
#> SRR1951686 5 0.2228 0.804 0.000 0.012 0.024 0.016 0.916 0.032
#> SRR1951683 5 0.3423 0.738 0.008 0.000 0.152 0.012 0.812 0.016
#> SRR1951682 4 0.2285 0.701 0.028 0.000 0.064 0.900 0.000 0.008
#> SRR1951690 4 0.3134 0.640 0.000 0.000 0.168 0.808 0.000 0.024
#> SRR1951680 3 0.4449 0.621 0.000 0.000 0.708 0.228 0.020 0.044
#> SRR1951679 3 0.1599 0.753 0.028 0.000 0.940 0.024 0.000 0.008
#> SRR1951678 5 0.3270 0.780 0.000 0.000 0.000 0.060 0.820 0.120
#> SRR1951677 5 0.3525 0.763 0.012 0.000 0.000 0.032 0.800 0.156
#> SRR1951676 5 0.1078 0.816 0.000 0.000 0.016 0.008 0.964 0.012
#> SRR1951675 1 0.4368 0.749 0.760 0.000 0.092 0.120 0.000 0.028
#> SRR1951681 5 0.2145 0.821 0.000 0.000 0.000 0.072 0.900 0.028
#> SRR1951673 4 0.3413 0.680 0.072 0.000 0.024 0.836 0.000 0.068
#> SRR1951674 1 0.3703 0.683 0.820 0.008 0.004 0.016 0.112 0.040
#> SRR1951672 1 0.5491 0.643 0.652 0.000 0.076 0.220 0.008 0.044
#> SRR1951671 3 0.4915 0.656 0.000 0.000 0.716 0.144 0.044 0.096
#> SRR1951670 1 0.2404 0.779 0.896 0.000 0.020 0.064 0.000 0.020
#> SRR1951640 4 0.4823 0.579 0.000 0.000 0.000 0.660 0.216 0.124
#> SRR1951668 3 0.8100 0.252 0.196 0.000 0.420 0.128 0.076 0.180
#> SRR1951669 1 0.3450 0.666 0.780 0.188 0.000 0.000 0.000 0.032
#> SRR1951638 1 0.2294 0.757 0.912 0.004 0.032 0.012 0.004 0.036
#> SRR1951637 1 0.1138 0.769 0.960 0.004 0.000 0.012 0.000 0.024
#> SRR1951635 1 0.1483 0.779 0.944 0.000 0.012 0.036 0.000 0.008
#> SRR1951634 4 0.3818 0.695 0.024 0.000 0.084 0.824 0.024 0.044
#> SRR1951631 4 0.4232 0.451 0.000 0.000 0.012 0.640 0.336 0.012
#> SRR1951632 6 0.3784 0.666 0.000 0.000 0.000 0.144 0.080 0.776
#> SRR1951633 5 0.0767 0.823 0.004 0.000 0.000 0.012 0.976 0.008
#> SRR1951630 5 0.5454 0.355 0.008 0.000 0.000 0.316 0.560 0.116
#> SRR1951629 5 0.3772 0.739 0.000 0.000 0.000 0.160 0.772 0.068
#> SRR1951628 4 0.3787 0.645 0.072 0.000 0.012 0.796 0.000 0.120
#> SRR1951627 5 0.2474 0.806 0.000 0.000 0.000 0.040 0.880 0.080
#> SRR1951639 4 0.4479 0.585 0.000 0.000 0.000 0.684 0.236 0.080
#> SRR1951625 4 0.2452 0.705 0.000 0.000 0.044 0.892 0.008 0.056
#> SRR1951626 5 0.1528 0.825 0.000 0.000 0.012 0.016 0.944 0.028
#> SRR1951624 3 0.4456 0.434 0.000 0.000 0.608 0.008 0.360 0.024
#> SRR1951622 1 0.4540 0.609 0.644 0.000 0.008 0.308 0.000 0.040
#> SRR1951636 5 0.1720 0.826 0.000 0.000 0.000 0.040 0.928 0.032
#> SRR1951623 5 0.1088 0.825 0.000 0.000 0.000 0.024 0.960 0.016
#> SRR1951621 1 0.4542 0.713 0.728 0.000 0.000 0.140 0.012 0.120
#> SRR1951620 4 0.5442 0.368 0.000 0.000 0.000 0.528 0.336 0.136
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 13580 rows and 133 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.777 0.906 0.954 0.4038 0.602 0.602
#> 3 3 0.620 0.839 0.889 0.1827 0.952 0.921
#> 4 4 0.569 0.720 0.816 0.3235 0.740 0.541
#> 5 5 0.575 0.734 0.814 0.0763 0.918 0.764
#> 6 6 0.635 0.733 0.825 0.0603 0.966 0.890
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
#> SRR1951667 2 0.0000 0.957 0.000 1.000
#> SRR1951666 2 0.0000 0.957 0.000 1.000
#> SRR1951665 2 0.0000 0.957 0.000 1.000
#> SRR1951664 2 0.0376 0.956 0.004 0.996
#> SRR1951663 1 0.0376 0.934 0.996 0.004
#> SRR1951662 2 0.0000 0.957 0.000 1.000
#> SRR1951661 2 0.0000 0.957 0.000 1.000
#> SRR1951660 1 0.5059 0.901 0.888 0.112
#> SRR1951659 2 0.0000 0.957 0.000 1.000
#> SRR1951658 2 0.0000 0.957 0.000 1.000
#> SRR1951657 2 0.2948 0.922 0.052 0.948
#> SRR1951656 2 0.3733 0.904 0.072 0.928
#> SRR1951655 1 0.5059 0.901 0.888 0.112
#> SRR1951654 1 0.0000 0.932 1.000 0.000
#> SRR1951653 2 0.0672 0.955 0.008 0.992
#> SRR1951652 2 0.0938 0.952 0.012 0.988
#> SRR1951651 1 0.9732 0.338 0.596 0.404
#> SRR1951650 2 0.0672 0.954 0.008 0.992
#> SRR1951648 2 0.0000 0.957 0.000 1.000
#> SRR1951649 2 0.0376 0.956 0.004 0.996
#> SRR1951647 2 0.0000 0.957 0.000 1.000
#> SRR1951646 2 0.2043 0.938 0.032 0.968
#> SRR1951645 2 0.6801 0.777 0.180 0.820
#> SRR1951644 1 0.6247 0.857 0.844 0.156
#> SRR1951643 1 0.2778 0.933 0.952 0.048
#> SRR1951641 2 0.0000 0.957 0.000 1.000
#> SRR1951642 1 0.2236 0.936 0.964 0.036
#> SRR1951694 1 0.3879 0.922 0.924 0.076
#> SRR1951731 2 0.0000 0.957 0.000 1.000
#> SRR1951730 2 0.0000 0.957 0.000 1.000
#> SRR1951752 2 0.0000 0.957 0.000 1.000
#> SRR1951751 1 0.7674 0.764 0.776 0.224
#> SRR1951749 2 0.0000 0.957 0.000 1.000
#> SRR1951750 1 0.3584 0.926 0.932 0.068
#> SRR1951748 2 0.0000 0.957 0.000 1.000
#> SRR1951747 2 0.0000 0.957 0.000 1.000
#> SRR1951745 1 0.0000 0.932 1.000 0.000
#> SRR1951746 2 0.0000 0.957 0.000 1.000
#> SRR1951744 2 0.8207 0.668 0.256 0.744
#> SRR1951742 2 0.0000 0.957 0.000 1.000
#> SRR1951743 2 0.0000 0.957 0.000 1.000
#> SRR1951741 1 0.0000 0.932 1.000 0.000
#> SRR1951740 2 0.2948 0.920 0.052 0.948
#> SRR1951738 2 0.0000 0.957 0.000 1.000
#> SRR1951739 2 0.0000 0.957 0.000 1.000
#> SRR1951737 2 0.0672 0.954 0.008 0.992
#> SRR1951736 1 0.3584 0.926 0.932 0.068
#> SRR1951734 2 0.0000 0.957 0.000 1.000
#> SRR1951735 2 0.0000 0.957 0.000 1.000
#> SRR1951733 2 0.0000 0.957 0.000 1.000
#> SRR1951732 2 0.0000 0.957 0.000 1.000
#> SRR1951729 2 0.0376 0.956 0.004 0.996
#> SRR1951728 2 0.0376 0.956 0.004 0.996
#> SRR1951727 2 0.0000 0.957 0.000 1.000
#> SRR1951725 1 0.2778 0.934 0.952 0.048
#> SRR1951726 2 0.0938 0.952 0.012 0.988
#> SRR1951724 2 0.1633 0.944 0.024 0.976
#> SRR1951722 2 0.0376 0.956 0.004 0.996
#> SRR1951723 2 0.0000 0.957 0.000 1.000
#> SRR1951721 1 0.0938 0.937 0.988 0.012
#> SRR1951719 2 0.0376 0.956 0.004 0.996
#> SRR1951720 1 0.1414 0.938 0.980 0.020
#> SRR1951718 1 0.5059 0.901 0.888 0.112
#> SRR1951717 1 0.1184 0.938 0.984 0.016
#> SRR1951715 1 0.0938 0.937 0.988 0.012
#> SRR1951716 1 0.0672 0.935 0.992 0.008
#> SRR1951714 2 0.8813 0.586 0.300 0.700
#> SRR1951713 2 0.0672 0.955 0.008 0.992
#> SRR1951712 1 0.7674 0.764 0.776 0.224
#> SRR1951711 2 0.0000 0.957 0.000 1.000
#> SRR1951710 1 0.5629 0.883 0.868 0.132
#> SRR1951709 2 0.0000 0.957 0.000 1.000
#> SRR1951708 2 0.0000 0.957 0.000 1.000
#> SRR1951707 2 0.0000 0.957 0.000 1.000
#> SRR1951705 1 0.5737 0.879 0.864 0.136
#> SRR1951704 2 0.0376 0.956 0.004 0.996
#> SRR1951703 2 0.0000 0.957 0.000 1.000
#> SRR1951706 2 0.9358 0.472 0.352 0.648
#> SRR1951702 2 0.0376 0.956 0.004 0.996
#> SRR1951701 2 0.0000 0.957 0.000 1.000
#> SRR1951699 2 0.0000 0.957 0.000 1.000
#> SRR1951698 2 0.0000 0.957 0.000 1.000
#> SRR1951700 2 0.0376 0.956 0.004 0.996
#> SRR1951697 2 0.0000 0.957 0.000 1.000
#> SRR1951696 2 0.3733 0.904 0.072 0.928
#> SRR1951695 2 0.0000 0.957 0.000 1.000
#> SRR1951693 2 0.0376 0.956 0.004 0.996
#> SRR1951692 2 0.9909 0.199 0.444 0.556
#> SRR1951691 2 0.6531 0.792 0.168 0.832
#> SRR1951688 2 0.0000 0.957 0.000 1.000
#> SRR1951689 2 0.0000 0.957 0.000 1.000
#> SRR1951687 1 0.0000 0.932 1.000 0.000
#> SRR1951685 2 0.0000 0.957 0.000 1.000
#> SRR1951684 2 0.0672 0.954 0.008 0.992
#> SRR1951686 2 0.0000 0.957 0.000 1.000
#> SRR1951683 1 0.1184 0.937 0.984 0.016
#> SRR1951682 2 0.0000 0.957 0.000 1.000
#> SRR1951690 2 0.0000 0.957 0.000 1.000
#> SRR1951680 2 0.0672 0.955 0.008 0.992
#> SRR1951679 2 0.0376 0.956 0.004 0.996
#> SRR1951678 1 0.1843 0.937 0.972 0.028
#> SRR1951677 1 0.2043 0.937 0.968 0.032
#> SRR1951676 2 0.6438 0.800 0.164 0.836
#> SRR1951675 2 0.0000 0.957 0.000 1.000
#> SRR1951681 2 0.0672 0.954 0.008 0.992
#> SRR1951673 2 0.0000 0.957 0.000 1.000
#> SRR1951674 1 0.0000 0.932 1.000 0.000
#> SRR1951672 2 0.4298 0.885 0.088 0.912
#> SRR1951671 2 0.0000 0.957 0.000 1.000
#> SRR1951670 1 0.1414 0.938 0.980 0.020
#> SRR1951640 2 0.0672 0.955 0.008 0.992
#> SRR1951668 2 0.7376 0.737 0.208 0.792
#> SRR1951669 2 0.0938 0.952 0.012 0.988
#> SRR1951638 1 0.0376 0.934 0.996 0.004
#> SRR1951637 2 0.0938 0.952 0.012 0.988
#> SRR1951635 1 0.0672 0.935 0.992 0.008
#> SRR1951634 2 0.6973 0.765 0.188 0.812
#> SRR1951631 2 0.9944 0.163 0.456 0.544
#> SRR1951632 2 0.0000 0.957 0.000 1.000
#> SRR1951633 1 0.2236 0.936 0.964 0.036
#> SRR1951630 1 0.1843 0.937 0.972 0.028
#> SRR1951629 2 0.6531 0.793 0.168 0.832
#> SRR1951628 2 0.0376 0.956 0.004 0.996
#> SRR1951627 2 0.0000 0.957 0.000 1.000
#> SRR1951639 2 0.0000 0.957 0.000 1.000
#> SRR1951625 2 0.0000 0.957 0.000 1.000
#> SRR1951626 2 0.0000 0.957 0.000 1.000
#> SRR1951624 2 0.0672 0.954 0.008 0.992
#> SRR1951622 1 0.5059 0.901 0.888 0.112
#> SRR1951636 2 0.0000 0.957 0.000 1.000
#> SRR1951623 2 0.9608 0.380 0.384 0.616
#> SRR1951621 1 0.0000 0.932 1.000 0.000
#> SRR1951620 2 0.0672 0.954 0.008 0.992
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951666 3 0.3272 0.8958 0.004 0.104 0.892
#> SRR1951665 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951664 3 0.3607 0.8924 0.008 0.112 0.880
#> SRR1951663 1 0.2165 0.8218 0.936 0.064 0.000
#> SRR1951662 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951661 3 0.3500 0.8909 0.004 0.116 0.880
#> SRR1951660 1 0.2711 0.8541 0.912 0.000 0.088
#> SRR1951659 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951658 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951657 3 0.4514 0.8621 0.012 0.156 0.832
#> SRR1951656 3 0.5147 0.8335 0.020 0.180 0.800
#> SRR1951655 1 0.2711 0.8541 0.912 0.000 0.088
#> SRR1951654 2 0.5016 0.7522 0.240 0.760 0.000
#> SRR1951653 3 0.3618 0.8946 0.012 0.104 0.884
#> SRR1951652 3 0.1129 0.9141 0.020 0.004 0.976
#> SRR1951651 2 0.6839 0.3526 0.044 0.684 0.272
#> SRR1951650 3 0.3755 0.8868 0.008 0.120 0.872
#> SRR1951648 3 0.3272 0.8958 0.004 0.104 0.892
#> SRR1951649 3 0.1182 0.9169 0.012 0.012 0.976
#> SRR1951647 3 0.3112 0.8983 0.004 0.096 0.900
#> SRR1951646 3 0.4164 0.8731 0.008 0.144 0.848
#> SRR1951645 3 0.6151 0.7658 0.180 0.056 0.764
#> SRR1951644 1 0.3851 0.7893 0.860 0.004 0.136
#> SRR1951643 2 0.4683 0.7754 0.140 0.836 0.024
#> SRR1951641 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951642 2 0.3983 0.7845 0.144 0.852 0.004
#> SRR1951694 1 0.2200 0.8651 0.940 0.004 0.056
#> SRR1951731 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951730 3 0.0424 0.9172 0.000 0.008 0.992
#> SRR1951752 3 0.0237 0.9167 0.000 0.004 0.996
#> SRR1951751 1 0.5305 0.6587 0.788 0.020 0.192
#> SRR1951749 3 0.0747 0.9164 0.000 0.016 0.984
#> SRR1951750 1 0.2492 0.8642 0.936 0.016 0.048
#> SRR1951748 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951747 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951745 2 0.5254 0.7350 0.264 0.736 0.000
#> SRR1951746 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951744 3 0.7401 0.5624 0.048 0.340 0.612
#> SRR1951742 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951743 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951741 2 0.5178 0.7411 0.256 0.744 0.000
#> SRR1951740 3 0.3375 0.8993 0.044 0.048 0.908
#> SRR1951738 3 0.1182 0.9124 0.012 0.012 0.976
#> SRR1951739 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951737 3 0.3755 0.8868 0.008 0.120 0.872
#> SRR1951736 1 0.2492 0.8642 0.936 0.016 0.048
#> SRR1951734 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951735 3 0.2945 0.9014 0.004 0.088 0.908
#> SRR1951733 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951732 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951729 3 0.3425 0.8926 0.004 0.112 0.884
#> SRR1951728 3 0.1832 0.9143 0.008 0.036 0.956
#> SRR1951727 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951725 1 0.1751 0.8604 0.960 0.012 0.028
#> SRR1951726 3 0.1482 0.9157 0.020 0.012 0.968
#> SRR1951724 3 0.3765 0.8981 0.028 0.084 0.888
#> SRR1951722 3 0.1585 0.9155 0.008 0.028 0.964
#> SRR1951723 3 0.0424 0.9173 0.008 0.000 0.992
#> SRR1951721 1 0.1031 0.8399 0.976 0.024 0.000
#> SRR1951719 3 0.3500 0.8909 0.004 0.116 0.880
#> SRR1951720 1 0.1529 0.8406 0.960 0.040 0.000
#> SRR1951718 1 0.2711 0.8541 0.912 0.000 0.088
#> SRR1951717 1 0.0892 0.8439 0.980 0.020 0.000
#> SRR1951715 1 0.1031 0.8399 0.976 0.024 0.000
#> SRR1951716 1 0.1411 0.8352 0.964 0.036 0.000
#> SRR1951714 3 0.8392 0.5673 0.176 0.200 0.624
#> SRR1951713 3 0.1950 0.9139 0.008 0.040 0.952
#> SRR1951712 1 0.5305 0.6587 0.788 0.020 0.192
#> SRR1951711 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951710 1 0.3349 0.8299 0.888 0.004 0.108
#> SRR1951709 3 0.3349 0.8942 0.004 0.108 0.888
#> SRR1951708 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951707 3 0.0475 0.9178 0.004 0.004 0.992
#> SRR1951705 1 0.3425 0.8243 0.884 0.004 0.112
#> SRR1951704 3 0.2063 0.9135 0.008 0.044 0.948
#> SRR1951703 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951706 3 0.7890 0.3147 0.056 0.432 0.512
#> SRR1951702 3 0.2063 0.9135 0.008 0.044 0.948
#> SRR1951701 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951699 3 0.3272 0.8957 0.004 0.104 0.892
#> SRR1951698 3 0.3500 0.8909 0.004 0.116 0.880
#> SRR1951700 3 0.1585 0.9155 0.008 0.028 0.964
#> SRR1951697 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951696 3 0.5147 0.8335 0.020 0.180 0.800
#> SRR1951695 3 0.1525 0.9144 0.004 0.032 0.964
#> SRR1951693 3 0.1950 0.9141 0.008 0.040 0.952
#> SRR1951692 2 0.7526 -0.0485 0.040 0.536 0.424
#> SRR1951691 3 0.5970 0.7822 0.160 0.060 0.780
#> SRR1951688 3 0.0237 0.9167 0.000 0.004 0.996
#> SRR1951689 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951687 2 0.4654 0.7646 0.208 0.792 0.000
#> SRR1951685 3 0.0000 0.9170 0.000 0.000 1.000
#> SRR1951684 3 0.3755 0.8868 0.008 0.120 0.872
#> SRR1951686 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951683 2 0.4291 0.7821 0.180 0.820 0.000
#> SRR1951682 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951690 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951680 3 0.1950 0.9139 0.008 0.040 0.952
#> SRR1951679 3 0.0424 0.9171 0.008 0.000 0.992
#> SRR1951678 2 0.4047 0.7883 0.148 0.848 0.004
#> SRR1951677 2 0.4110 0.7869 0.152 0.844 0.004
#> SRR1951676 3 0.5919 0.7237 0.012 0.276 0.712
#> SRR1951675 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951681 3 0.3682 0.8891 0.008 0.116 0.876
#> SRR1951673 3 0.0661 0.9171 0.008 0.004 0.988
#> SRR1951674 2 0.5254 0.7350 0.264 0.736 0.000
#> SRR1951672 3 0.3459 0.8659 0.096 0.012 0.892
#> SRR1951671 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951670 1 0.1529 0.8406 0.960 0.040 0.000
#> SRR1951640 3 0.2063 0.9134 0.008 0.044 0.948
#> SRR1951668 3 0.6746 0.7278 0.192 0.076 0.732
#> SRR1951669 3 0.1129 0.9141 0.020 0.004 0.976
#> SRR1951638 1 0.2165 0.8218 0.936 0.064 0.000
#> SRR1951637 3 0.1129 0.9141 0.020 0.004 0.976
#> SRR1951635 1 0.2959 0.7919 0.900 0.100 0.000
#> SRR1951634 3 0.6254 0.7543 0.188 0.056 0.756
#> SRR1951631 3 0.9399 0.0981 0.176 0.372 0.452
#> SRR1951632 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951633 2 0.3983 0.7845 0.144 0.852 0.004
#> SRR1951630 2 0.4047 0.7883 0.148 0.848 0.004
#> SRR1951629 3 0.6561 0.7699 0.144 0.100 0.756
#> SRR1951628 3 0.1337 0.9110 0.016 0.012 0.972
#> SRR1951627 3 0.3425 0.8927 0.004 0.112 0.884
#> SRR1951639 3 0.3193 0.8970 0.004 0.100 0.896
#> SRR1951625 3 0.0592 0.9162 0.000 0.012 0.988
#> SRR1951626 3 0.3193 0.8970 0.004 0.100 0.896
#> SRR1951624 3 0.3755 0.8868 0.008 0.120 0.872
#> SRR1951622 1 0.2711 0.8541 0.912 0.000 0.088
#> SRR1951636 3 0.3500 0.8909 0.004 0.116 0.880
#> SRR1951623 3 0.7188 0.2147 0.024 0.488 0.488
#> SRR1951621 2 0.5178 0.7411 0.256 0.744 0.000
#> SRR1951620 3 0.3607 0.8912 0.008 0.112 0.880
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951666 3 0.4624 0.7818 0.000 0.340 0.660 0.000
#> SRR1951665 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951664 3 0.4655 0.7985 0.004 0.312 0.684 0.000
#> SRR1951663 1 0.3249 0.8532 0.852 0.000 0.008 0.140
#> SRR1951662 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.4500 0.7960 0.000 0.316 0.684 0.000
#> SRR1951660 1 0.1936 0.8985 0.940 0.032 0.028 0.000
#> SRR1951659 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951658 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951657 3 0.4452 0.7887 0.000 0.260 0.732 0.008
#> SRR1951656 3 0.3945 0.7630 0.000 0.216 0.780 0.004
#> SRR1951655 1 0.1936 0.8985 0.940 0.032 0.028 0.000
#> SRR1951654 4 0.1022 0.8369 0.032 0.000 0.000 0.968
#> SRR1951653 3 0.4936 0.7928 0.012 0.316 0.672 0.000
#> SRR1951652 2 0.1975 0.8195 0.016 0.936 0.048 0.000
#> SRR1951651 3 0.5386 -0.2978 0.008 0.012 0.636 0.344
#> SRR1951650 3 0.4382 0.7991 0.000 0.296 0.704 0.000
#> SRR1951648 3 0.4661 0.7748 0.000 0.348 0.652 0.000
#> SRR1951649 2 0.2271 0.7953 0.008 0.916 0.076 0.000
#> SRR1951647 3 0.4855 0.7015 0.000 0.400 0.600 0.000
#> SRR1951646 3 0.4401 0.7944 0.000 0.272 0.724 0.004
#> SRR1951645 3 0.7495 0.5115 0.180 0.392 0.428 0.000
#> SRR1951644 1 0.3013 0.8497 0.888 0.080 0.032 0.000
#> SRR1951643 4 0.4898 0.8531 0.004 0.016 0.264 0.716
#> SRR1951641 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951642 4 0.4331 0.8560 0.000 0.000 0.288 0.712
#> SRR1951694 1 0.1229 0.9030 0.968 0.020 0.008 0.004
#> SRR1951731 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951730 2 0.4018 0.5621 0.004 0.772 0.224 0.000
#> SRR1951752 2 0.0469 0.8452 0.000 0.988 0.012 0.000
#> SRR1951751 1 0.4277 0.7700 0.824 0.116 0.056 0.004
#> SRR1951749 2 0.1474 0.8181 0.000 0.948 0.052 0.000
#> SRR1951750 1 0.1114 0.8978 0.972 0.004 0.016 0.008
#> SRR1951748 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951745 4 0.2944 0.7601 0.128 0.000 0.004 0.868
#> SRR1951746 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951744 3 0.6283 0.6261 0.016 0.172 0.696 0.116
#> SRR1951742 2 0.0336 0.8466 0.000 0.992 0.008 0.000
#> SRR1951743 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951741 4 0.1489 0.8371 0.044 0.000 0.004 0.952
#> SRR1951740 2 0.6022 -0.3223 0.032 0.504 0.460 0.004
#> SRR1951738 2 0.0895 0.8273 0.020 0.976 0.004 0.000
#> SRR1951739 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951737 3 0.4356 0.7987 0.000 0.292 0.708 0.000
#> SRR1951736 1 0.1114 0.8978 0.972 0.004 0.016 0.008
#> SRR1951734 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951735 3 0.4916 0.6478 0.000 0.424 0.576 0.000
#> SRR1951733 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951732 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951729 3 0.4605 0.7840 0.000 0.336 0.664 0.000
#> SRR1951728 2 0.4920 0.1282 0.004 0.628 0.368 0.000
#> SRR1951727 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951725 1 0.1943 0.9040 0.944 0.008 0.016 0.032
#> SRR1951726 2 0.2522 0.7939 0.016 0.908 0.076 0.000
#> SRR1951724 3 0.5660 0.6781 0.028 0.396 0.576 0.000
#> SRR1951722 2 0.2654 0.7605 0.004 0.888 0.108 0.000
#> SRR1951723 2 0.1902 0.8097 0.004 0.932 0.064 0.000
#> SRR1951721 1 0.2053 0.8901 0.924 0.000 0.004 0.072
#> SRR1951719 3 0.4585 0.7860 0.000 0.332 0.668 0.000
#> SRR1951720 1 0.2125 0.8907 0.920 0.000 0.004 0.076
#> SRR1951718 1 0.1936 0.8985 0.940 0.032 0.028 0.000
#> SRR1951717 1 0.1824 0.8970 0.936 0.000 0.004 0.060
#> SRR1951715 1 0.2053 0.8901 0.924 0.000 0.004 0.072
#> SRR1951716 1 0.2401 0.8831 0.904 0.000 0.004 0.092
#> SRR1951714 3 0.6835 0.6130 0.144 0.164 0.664 0.028
#> SRR1951713 2 0.5016 0.0144 0.004 0.600 0.396 0.000
#> SRR1951712 1 0.4277 0.7700 0.824 0.116 0.056 0.004
#> SRR1951711 2 0.0188 0.8456 0.004 0.996 0.000 0.000
#> SRR1951710 1 0.2466 0.8789 0.916 0.056 0.028 0.000
#> SRR1951709 3 0.4564 0.7905 0.000 0.328 0.672 0.000
#> SRR1951708 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951707 2 0.4511 0.4591 0.008 0.724 0.268 0.000
#> SRR1951705 1 0.2565 0.8774 0.912 0.056 0.032 0.000
#> SRR1951704 2 0.5004 0.0255 0.004 0.604 0.392 0.000
#> SRR1951703 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951706 3 0.5477 0.4239 0.012 0.080 0.752 0.156
#> SRR1951702 2 0.5004 0.0255 0.004 0.604 0.392 0.000
#> SRR1951701 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951699 3 0.4605 0.7851 0.000 0.336 0.664 0.000
#> SRR1951698 3 0.4661 0.7729 0.000 0.348 0.652 0.000
#> SRR1951700 2 0.2654 0.7605 0.004 0.888 0.108 0.000
#> SRR1951697 2 0.0469 0.8442 0.000 0.988 0.012 0.000
#> SRR1951696 3 0.3945 0.7630 0.000 0.216 0.780 0.004
#> SRR1951695 2 0.4543 0.2953 0.000 0.676 0.324 0.000
#> SRR1951693 2 0.4950 0.0977 0.004 0.620 0.376 0.000
#> SRR1951692 3 0.4745 0.1628 0.000 0.036 0.756 0.208
#> SRR1951691 2 0.7477 -0.4632 0.152 0.424 0.420 0.004
#> SRR1951688 2 0.0469 0.8452 0.000 0.988 0.012 0.000
#> SRR1951689 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951687 4 0.1059 0.8473 0.012 0.000 0.016 0.972
#> SRR1951685 2 0.0895 0.8420 0.004 0.976 0.020 0.000
#> SRR1951684 3 0.4304 0.7978 0.000 0.284 0.716 0.000
#> SRR1951686 2 0.1211 0.8281 0.000 0.960 0.040 0.000
#> SRR1951683 4 0.4728 0.8635 0.032 0.000 0.216 0.752
#> SRR1951682 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951690 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951680 2 0.5016 0.0144 0.004 0.600 0.396 0.000
#> SRR1951679 2 0.1356 0.8352 0.008 0.960 0.032 0.000
#> SRR1951678 4 0.3975 0.8701 0.000 0.000 0.240 0.760
#> SRR1951677 4 0.3975 0.8692 0.000 0.000 0.240 0.760
#> SRR1951676 3 0.5716 0.7212 0.000 0.212 0.700 0.088
#> SRR1951675 2 0.0188 0.8456 0.004 0.996 0.000 0.000
#> SRR1951681 3 0.4406 0.7992 0.000 0.300 0.700 0.000
#> SRR1951673 2 0.1824 0.8133 0.004 0.936 0.060 0.000
#> SRR1951674 4 0.2944 0.7601 0.128 0.000 0.004 0.868
#> SRR1951672 2 0.5615 0.4994 0.096 0.716 0.188 0.000
#> SRR1951671 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951670 1 0.2125 0.8907 0.920 0.000 0.004 0.076
#> SRR1951640 2 0.5158 -0.3222 0.004 0.524 0.472 0.000
#> SRR1951668 3 0.7566 0.5966 0.184 0.324 0.488 0.004
#> SRR1951669 2 0.1888 0.8222 0.016 0.940 0.044 0.000
#> SRR1951638 1 0.3196 0.8555 0.856 0.000 0.008 0.136
#> SRR1951637 2 0.1888 0.8222 0.016 0.940 0.044 0.000
#> SRR1951635 1 0.3591 0.8267 0.824 0.000 0.008 0.168
#> SRR1951634 3 0.7459 0.6014 0.188 0.336 0.476 0.000
#> SRR1951631 3 0.7061 0.2839 0.144 0.068 0.672 0.116
#> SRR1951632 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951633 4 0.4356 0.8558 0.000 0.000 0.292 0.708
#> SRR1951630 4 0.3975 0.8701 0.000 0.000 0.240 0.760
#> SRR1951629 3 0.7301 0.6761 0.140 0.316 0.536 0.008
#> SRR1951628 2 0.1151 0.8247 0.024 0.968 0.008 0.000
#> SRR1951627 3 0.4522 0.7943 0.000 0.320 0.680 0.000
#> SRR1951639 3 0.4697 0.7652 0.000 0.356 0.644 0.000
#> SRR1951625 2 0.0000 0.8488 0.000 1.000 0.000 0.000
#> SRR1951626 3 0.4679 0.7701 0.000 0.352 0.648 0.000
#> SRR1951624 3 0.4331 0.7982 0.000 0.288 0.712 0.000
#> SRR1951622 1 0.1936 0.8985 0.940 0.032 0.028 0.000
#> SRR1951636 3 0.4500 0.7960 0.000 0.316 0.684 0.000
#> SRR1951623 3 0.5910 0.3633 0.000 0.104 0.688 0.208
#> SRR1951621 4 0.1489 0.8371 0.044 0.000 0.004 0.952
#> SRR1951620 3 0.4431 0.7988 0.000 0.304 0.696 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951666 3 0.3177 0.797250 0.000 0.208 0.792 0.000 0.000
#> SRR1951665 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 3 0.2929 0.798136 0.000 0.180 0.820 0.000 0.000
#> SRR1951663 1 0.5015 0.671718 0.648 0.000 0.032 0.012 0.308
#> SRR1951662 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 3 0.2891 0.795486 0.000 0.176 0.824 0.000 0.000
#> SRR1951660 1 0.1588 0.857632 0.948 0.028 0.016 0.000 0.008
#> SRR1951659 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951657 3 0.3678 0.774818 0.000 0.140 0.816 0.040 0.004
#> SRR1951656 3 0.3120 0.719634 0.000 0.084 0.864 0.048 0.004
#> SRR1951655 1 0.1588 0.857632 0.948 0.028 0.016 0.000 0.008
#> SRR1951654 5 0.3728 0.841000 0.008 0.000 0.000 0.244 0.748
#> SRR1951653 3 0.3821 0.795827 0.008 0.200 0.780 0.008 0.004
#> SRR1951652 2 0.2125 0.855040 0.024 0.920 0.052 0.000 0.004
#> SRR1951651 4 0.4516 0.299887 0.004 0.000 0.416 0.576 0.004
#> SRR1951650 3 0.2806 0.787642 0.000 0.152 0.844 0.000 0.004
#> SRR1951648 3 0.3336 0.791270 0.000 0.228 0.772 0.000 0.000
#> SRR1951649 2 0.3851 0.758285 0.016 0.824 0.108 0.000 0.052
#> SRR1951647 3 0.4029 0.718260 0.000 0.316 0.680 0.000 0.004
#> SRR1951646 3 0.3264 0.778482 0.000 0.140 0.836 0.020 0.004
#> SRR1951645 3 0.7367 0.598658 0.184 0.272 0.484 0.000 0.060
#> SRR1951644 1 0.2554 0.814224 0.896 0.076 0.020 0.000 0.008
#> SRR1951643 4 0.1603 0.758433 0.004 0.012 0.032 0.948 0.004
#> SRR1951641 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951642 4 0.1043 0.764887 0.000 0.000 0.040 0.960 0.000
#> SRR1951694 1 0.2664 0.846214 0.884 0.020 0.004 0.000 0.092
#> SRR1951731 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951730 2 0.4469 0.464252 0.012 0.704 0.268 0.000 0.016
#> SRR1951752 2 0.0960 0.885502 0.004 0.972 0.008 0.000 0.016
#> SRR1951751 1 0.4299 0.759040 0.820 0.048 0.060 0.008 0.064
#> SRR1951749 2 0.1792 0.834102 0.000 0.916 0.084 0.000 0.000
#> SRR1951750 1 0.1306 0.851979 0.960 0.000 0.016 0.008 0.016
#> SRR1951748 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 5 0.2843 0.816927 0.008 0.000 0.000 0.144 0.848
#> SRR1951746 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 3 0.5702 0.542388 0.016 0.080 0.664 0.232 0.008
#> SRR1951742 2 0.0510 0.888548 0.000 0.984 0.016 0.000 0.000
#> SRR1951743 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 5 0.5351 0.804772 0.028 0.000 0.024 0.344 0.604
#> SRR1951740 3 0.6447 0.547884 0.040 0.360 0.536 0.012 0.052
#> SRR1951738 2 0.0703 0.876204 0.024 0.976 0.000 0.000 0.000
#> SRR1951739 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951737 3 0.2763 0.785551 0.000 0.148 0.848 0.000 0.004
#> SRR1951736 1 0.1306 0.851979 0.960 0.000 0.016 0.008 0.016
#> SRR1951734 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 3 0.4236 0.696904 0.004 0.328 0.664 0.000 0.004
#> SRR1951733 2 0.0162 0.893144 0.000 0.996 0.004 0.000 0.000
#> SRR1951732 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 3 0.3353 0.799150 0.000 0.196 0.796 0.000 0.008
#> SRR1951728 2 0.5549 -0.319206 0.008 0.476 0.468 0.000 0.048
#> SRR1951727 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 1 0.1651 0.859669 0.944 0.008 0.012 0.000 0.036
#> SRR1951726 2 0.2642 0.825544 0.024 0.888 0.084 0.000 0.004
#> SRR1951724 3 0.5012 0.723214 0.032 0.288 0.664 0.000 0.016
#> SRR1951722 2 0.4100 0.705807 0.008 0.792 0.148 0.000 0.052
#> SRR1951723 2 0.3556 0.774804 0.012 0.840 0.104 0.000 0.044
#> SRR1951721 1 0.3525 0.808276 0.816 0.000 0.024 0.004 0.156
#> SRR1951719 3 0.3318 0.798829 0.000 0.192 0.800 0.000 0.008
#> SRR1951720 1 0.2378 0.846310 0.908 0.000 0.016 0.012 0.064
#> SRR1951718 1 0.1588 0.857632 0.948 0.028 0.016 0.000 0.008
#> SRR1951717 1 0.2162 0.852252 0.916 0.000 0.012 0.008 0.064
#> SRR1951715 1 0.3525 0.808276 0.816 0.000 0.024 0.004 0.156
#> SRR1951716 1 0.3767 0.802753 0.800 0.000 0.024 0.008 0.168
#> SRR1951714 3 0.7279 0.521086 0.148 0.084 0.596 0.148 0.024
#> SRR1951713 3 0.5658 0.334957 0.008 0.460 0.476 0.000 0.056
#> SRR1951712 1 0.4299 0.759040 0.820 0.048 0.060 0.008 0.064
#> SRR1951711 2 0.0324 0.892840 0.004 0.992 0.004 0.000 0.000
#> SRR1951710 1 0.2270 0.841980 0.916 0.052 0.020 0.000 0.012
#> SRR1951709 3 0.3074 0.799020 0.000 0.196 0.804 0.000 0.000
#> SRR1951708 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951707 2 0.4910 0.252645 0.012 0.628 0.340 0.000 0.020
#> SRR1951705 1 0.2158 0.840372 0.920 0.052 0.020 0.000 0.008
#> SRR1951704 3 0.5542 0.382486 0.008 0.448 0.496 0.000 0.048
#> SRR1951703 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 3 0.4846 0.328122 0.012 0.012 0.640 0.332 0.004
#> SRR1951702 3 0.5542 0.382486 0.008 0.448 0.496 0.000 0.048
#> SRR1951701 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951699 3 0.3143 0.798476 0.000 0.204 0.796 0.000 0.000
#> SRR1951698 3 0.3461 0.793784 0.000 0.224 0.772 0.000 0.004
#> SRR1951700 2 0.4100 0.705807 0.008 0.792 0.148 0.000 0.052
#> SRR1951697 2 0.0510 0.888771 0.000 0.984 0.016 0.000 0.000
#> SRR1951696 3 0.3120 0.719634 0.000 0.084 0.864 0.048 0.004
#> SRR1951695 2 0.5383 -0.104156 0.004 0.540 0.408 0.000 0.048
#> SRR1951693 2 0.5548 -0.316917 0.008 0.480 0.464 0.000 0.048
#> SRR1951692 3 0.4538 0.000731 0.000 0.004 0.564 0.428 0.004
#> SRR1951691 3 0.7440 0.591731 0.160 0.280 0.496 0.008 0.056
#> SRR1951688 2 0.0960 0.885502 0.004 0.972 0.008 0.000 0.016
#> SRR1951689 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951687 5 0.5272 0.767631 0.004 0.000 0.060 0.312 0.624
#> SRR1951685 2 0.1588 0.874443 0.008 0.948 0.028 0.000 0.016
#> SRR1951684 3 0.2674 0.781005 0.000 0.140 0.856 0.000 0.004
#> SRR1951686 2 0.1478 0.854569 0.000 0.936 0.064 0.000 0.000
#> SRR1951683 4 0.1648 0.721405 0.020 0.000 0.040 0.940 0.000
#> SRR1951682 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951680 3 0.5658 0.334957 0.008 0.460 0.476 0.000 0.056
#> SRR1951679 2 0.1989 0.866802 0.020 0.932 0.032 0.000 0.016
#> SRR1951678 4 0.2932 0.725823 0.000 0.000 0.032 0.864 0.104
#> SRR1951677 4 0.2932 0.728090 0.000 0.000 0.032 0.864 0.104
#> SRR1951676 3 0.4905 0.679676 0.000 0.116 0.728 0.152 0.004
#> SRR1951675 2 0.0324 0.892840 0.004 0.992 0.004 0.000 0.000
#> SRR1951681 3 0.2848 0.789889 0.000 0.156 0.840 0.000 0.004
#> SRR1951673 2 0.3504 0.779093 0.012 0.844 0.100 0.000 0.044
#> SRR1951674 5 0.2843 0.816927 0.008 0.000 0.000 0.144 0.848
#> SRR1951672 2 0.6475 0.322902 0.104 0.604 0.236 0.000 0.056
#> SRR1951671 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951670 1 0.2482 0.845762 0.904 0.000 0.016 0.016 0.064
#> SRR1951640 3 0.5320 0.547440 0.008 0.384 0.568 0.000 0.040
#> SRR1951668 3 0.7265 0.632329 0.192 0.184 0.556 0.012 0.056
#> SRR1951669 2 0.2053 0.858297 0.024 0.924 0.048 0.000 0.004
#> SRR1951638 1 0.5106 0.678770 0.656 0.000 0.036 0.016 0.292
#> SRR1951637 2 0.2053 0.858297 0.024 0.924 0.048 0.000 0.004
#> SRR1951635 1 0.5272 0.649695 0.636 0.000 0.036 0.020 0.308
#> SRR1951634 3 0.7060 0.649322 0.196 0.204 0.544 0.000 0.056
#> SRR1951631 3 0.7018 0.135725 0.140 0.008 0.512 0.308 0.032
#> SRR1951632 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951633 4 0.1282 0.765981 0.000 0.000 0.044 0.952 0.004
#> SRR1951630 4 0.2932 0.725823 0.000 0.000 0.032 0.864 0.104
#> SRR1951629 3 0.6905 0.701661 0.140 0.180 0.612 0.024 0.044
#> SRR1951628 2 0.1116 0.873338 0.028 0.964 0.004 0.000 0.004
#> SRR1951627 3 0.2929 0.796398 0.000 0.180 0.820 0.000 0.000
#> SRR1951639 3 0.3395 0.786400 0.000 0.236 0.764 0.000 0.000
#> SRR1951625 2 0.0000 0.894806 0.000 1.000 0.000 0.000 0.000
#> SRR1951626 3 0.3366 0.788865 0.000 0.232 0.768 0.000 0.000
#> SRR1951624 3 0.2719 0.783203 0.000 0.144 0.852 0.000 0.004
#> SRR1951622 1 0.1588 0.857632 0.948 0.028 0.016 0.000 0.008
#> SRR1951636 3 0.2891 0.795486 0.000 0.176 0.824 0.000 0.000
#> SRR1951623 3 0.5543 0.278561 0.000 0.068 0.572 0.356 0.004
#> SRR1951621 5 0.5351 0.804772 0.028 0.000 0.024 0.344 0.604
#> SRR1951620 3 0.2890 0.791998 0.000 0.160 0.836 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951666 3 0.1910 0.7497 0.000 0.108 0.892 0.000 0.000 0.000
#> SRR1951665 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 3 0.1663 0.7498 0.000 0.088 0.912 0.000 0.000 0.000
#> SRR1951663 5 0.3570 0.7290 0.144 0.000 0.000 0.000 0.792 0.064
#> SRR1951662 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 3 0.1444 0.7434 0.000 0.072 0.928 0.000 0.000 0.000
#> SRR1951660 1 0.1180 0.8644 0.960 0.004 0.008 0.000 0.024 0.004
#> SRR1951659 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951657 3 0.2883 0.7168 0.004 0.040 0.884 0.044 0.016 0.012
#> SRR1951656 3 0.2179 0.6872 0.000 0.016 0.908 0.064 0.004 0.008
#> SRR1951655 1 0.1180 0.8644 0.960 0.004 0.008 0.000 0.024 0.004
#> SRR1951654 6 0.3161 0.8117 0.004 0.000 0.000 0.080 0.076 0.840
#> SRR1951653 3 0.3262 0.7357 0.020 0.148 0.820 0.004 0.008 0.000
#> SRR1951652 2 0.2244 0.8758 0.032 0.912 0.036 0.000 0.016 0.004
#> SRR1951651 4 0.5280 0.3560 0.004 0.000 0.320 0.596 0.024 0.056
#> SRR1951650 3 0.1621 0.7330 0.000 0.048 0.936 0.004 0.008 0.004
#> SRR1951648 3 0.2135 0.7478 0.000 0.128 0.872 0.000 0.000 0.000
#> SRR1951649 2 0.4094 0.7724 0.036 0.796 0.080 0.000 0.084 0.004
#> SRR1951647 3 0.3703 0.6572 0.004 0.304 0.688 0.000 0.004 0.000
#> SRR1951646 3 0.1965 0.7250 0.000 0.040 0.924 0.024 0.004 0.008
#> SRR1951645 3 0.6982 0.5329 0.204 0.204 0.488 0.004 0.100 0.000
#> SRR1951644 1 0.1969 0.8106 0.920 0.052 0.004 0.000 0.020 0.004
#> SRR1951643 4 0.1414 0.7908 0.012 0.000 0.020 0.952 0.004 0.012
#> SRR1951641 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951642 4 0.0653 0.7907 0.004 0.000 0.012 0.980 0.000 0.004
#> SRR1951694 5 0.4103 0.6374 0.448 0.004 0.004 0.000 0.544 0.000
#> SRR1951731 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951730 2 0.4680 0.4750 0.028 0.672 0.264 0.000 0.036 0.000
#> SRR1951752 2 0.0891 0.9036 0.008 0.968 0.000 0.000 0.024 0.000
#> SRR1951751 1 0.3007 0.7343 0.836 0.008 0.012 0.004 0.140 0.000
#> SRR1951749 2 0.2454 0.7687 0.000 0.840 0.160 0.000 0.000 0.000
#> SRR1951750 1 0.1872 0.8438 0.920 0.000 0.004 0.004 0.064 0.008
#> SRR1951748 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 6 0.2595 0.7787 0.004 0.000 0.000 0.000 0.160 0.836
#> SRR1951746 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951744 3 0.6559 0.4420 0.016 0.044 0.588 0.244 0.052 0.056
#> SRR1951742 2 0.0547 0.9057 0.000 0.980 0.020 0.000 0.000 0.000
#> SRR1951743 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951741 6 0.5492 0.7331 0.012 0.000 0.000 0.252 0.140 0.596
#> SRR1951740 3 0.6292 0.5520 0.060 0.296 0.544 0.012 0.088 0.000
#> SRR1951738 2 0.0935 0.8931 0.032 0.964 0.004 0.000 0.000 0.000
#> SRR1951739 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951737 3 0.1296 0.7308 0.000 0.044 0.948 0.004 0.000 0.004
#> SRR1951736 1 0.1872 0.8438 0.920 0.000 0.004 0.004 0.064 0.008
#> SRR1951734 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951735 3 0.3596 0.6960 0.008 0.244 0.740 0.000 0.008 0.000
#> SRR1951733 2 0.0146 0.9122 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1951732 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 3 0.2386 0.7509 0.000 0.112 0.876 0.004 0.004 0.004
#> SRR1951728 3 0.5544 0.3674 0.020 0.400 0.500 0.000 0.080 0.000
#> SRR1951727 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 1 0.1956 0.8357 0.908 0.000 0.004 0.000 0.080 0.008
#> SRR1951726 2 0.2713 0.8458 0.024 0.880 0.076 0.000 0.016 0.004
#> SRR1951724 3 0.4803 0.6756 0.048 0.240 0.680 0.000 0.032 0.000
#> SRR1951722 2 0.4317 0.7151 0.024 0.760 0.132 0.000 0.084 0.000
#> SRR1951723 2 0.3868 0.7883 0.032 0.812 0.076 0.000 0.076 0.004
#> SRR1951721 5 0.3634 0.7871 0.356 0.000 0.000 0.000 0.644 0.000
#> SRR1951719 3 0.2292 0.7510 0.000 0.104 0.884 0.004 0.004 0.004
#> SRR1951720 1 0.2750 0.7732 0.844 0.000 0.000 0.000 0.136 0.020
#> SRR1951718 1 0.1180 0.8644 0.960 0.004 0.008 0.000 0.024 0.004
#> SRR1951717 1 0.2420 0.7893 0.864 0.000 0.000 0.004 0.128 0.004
#> SRR1951715 5 0.3634 0.7871 0.356 0.000 0.000 0.000 0.644 0.000
#> SRR1951716 5 0.3714 0.7812 0.340 0.000 0.000 0.000 0.656 0.004
#> SRR1951714 3 0.6791 0.4976 0.164 0.068 0.568 0.164 0.036 0.000
#> SRR1951713 3 0.5789 0.3703 0.020 0.388 0.484 0.000 0.108 0.000
#> SRR1951712 1 0.3007 0.7343 0.836 0.008 0.012 0.004 0.140 0.000
#> SRR1951711 2 0.0551 0.9102 0.008 0.984 0.004 0.000 0.004 0.000
#> SRR1951710 1 0.1629 0.8504 0.940 0.028 0.004 0.000 0.024 0.004
#> SRR1951709 3 0.1863 0.7516 0.000 0.104 0.896 0.000 0.000 0.000
#> SRR1951708 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951707 2 0.5147 0.2683 0.028 0.592 0.332 0.000 0.048 0.000
#> SRR1951705 1 0.1546 0.8489 0.944 0.028 0.004 0.000 0.020 0.004
#> SRR1951704 3 0.5389 0.4734 0.016 0.356 0.548 0.000 0.080 0.000
#> SRR1951703 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 3 0.5991 0.2314 0.008 0.000 0.528 0.348 0.056 0.060
#> SRR1951702 3 0.5389 0.4734 0.016 0.356 0.548 0.000 0.080 0.000
#> SRR1951701 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951699 3 0.1910 0.7515 0.000 0.108 0.892 0.000 0.000 0.000
#> SRR1951698 3 0.2288 0.7510 0.004 0.116 0.876 0.000 0.004 0.000
#> SRR1951700 2 0.4317 0.7151 0.024 0.760 0.132 0.000 0.084 0.000
#> SRR1951697 2 0.0937 0.8937 0.000 0.960 0.040 0.000 0.000 0.000
#> SRR1951696 3 0.2179 0.6872 0.000 0.016 0.908 0.064 0.004 0.008
#> SRR1951695 2 0.5533 -0.1665 0.016 0.476 0.424 0.000 0.084 0.000
#> SRR1951693 3 0.5484 0.3459 0.016 0.412 0.492 0.000 0.080 0.000
#> SRR1951692 3 0.5314 -0.0587 0.000 0.000 0.484 0.440 0.020 0.056
#> SRR1951691 3 0.7101 0.5385 0.188 0.220 0.496 0.012 0.080 0.004
#> SRR1951688 2 0.0891 0.9036 0.008 0.968 0.000 0.000 0.024 0.000
#> SRR1951689 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951687 6 0.3351 0.7441 0.000 0.000 0.004 0.152 0.036 0.808
#> SRR1951685 2 0.1970 0.8817 0.028 0.920 0.008 0.000 0.044 0.000
#> SRR1951684 3 0.1155 0.7263 0.000 0.036 0.956 0.004 0.000 0.004
#> SRR1951686 2 0.1910 0.8329 0.000 0.892 0.108 0.000 0.000 0.000
#> SRR1951683 4 0.2326 0.7558 0.020 0.000 0.016 0.912 0.020 0.032
#> SRR1951682 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951680 3 0.5789 0.3703 0.020 0.388 0.484 0.000 0.108 0.000
#> SRR1951679 2 0.2334 0.8740 0.040 0.904 0.008 0.000 0.044 0.004
#> SRR1951678 4 0.2613 0.7449 0.000 0.000 0.012 0.848 0.000 0.140
#> SRR1951677 4 0.2655 0.7458 0.004 0.000 0.008 0.848 0.000 0.140
#> SRR1951676 3 0.4779 0.5869 0.000 0.028 0.740 0.152 0.020 0.060
#> SRR1951675 2 0.0551 0.9102 0.008 0.984 0.004 0.000 0.004 0.000
#> SRR1951681 3 0.1573 0.7359 0.000 0.052 0.936 0.004 0.004 0.004
#> SRR1951673 2 0.3741 0.7962 0.028 0.820 0.072 0.000 0.076 0.004
#> SRR1951674 6 0.2595 0.7787 0.004 0.000 0.000 0.000 0.160 0.836
#> SRR1951672 2 0.6433 0.3632 0.124 0.572 0.200 0.000 0.100 0.004
#> SRR1951671 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951670 1 0.2892 0.7728 0.840 0.000 0.000 0.004 0.136 0.020
#> SRR1951640 3 0.4849 0.6187 0.012 0.280 0.644 0.000 0.064 0.000
#> SRR1951668 3 0.6498 0.5569 0.216 0.092 0.580 0.020 0.092 0.000
#> SRR1951669 2 0.2171 0.8787 0.032 0.916 0.032 0.000 0.016 0.004
#> SRR1951638 5 0.3307 0.7400 0.148 0.000 0.000 0.000 0.808 0.044
#> SRR1951637 2 0.2171 0.8787 0.032 0.916 0.032 0.000 0.016 0.004
#> SRR1951635 5 0.3946 0.7358 0.168 0.000 0.000 0.000 0.756 0.076
#> SRR1951634 3 0.6490 0.5727 0.216 0.140 0.556 0.004 0.084 0.000
#> SRR1951631 3 0.6386 0.1469 0.144 0.000 0.476 0.332 0.048 0.000
#> SRR1951632 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951633 4 0.0717 0.7918 0.000 0.000 0.016 0.976 0.000 0.008
#> SRR1951630 4 0.2613 0.7449 0.000 0.000 0.012 0.848 0.000 0.140
#> SRR1951629 3 0.5683 0.6468 0.152 0.084 0.680 0.028 0.056 0.000
#> SRR1951628 2 0.1219 0.8879 0.048 0.948 0.004 0.000 0.000 0.000
#> SRR1951627 3 0.1501 0.7447 0.000 0.076 0.924 0.000 0.000 0.000
#> SRR1951639 3 0.2219 0.7473 0.000 0.136 0.864 0.000 0.000 0.000
#> SRR1951625 2 0.0000 0.9139 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951626 3 0.2178 0.7474 0.000 0.132 0.868 0.000 0.000 0.000
#> SRR1951624 3 0.1226 0.7288 0.000 0.040 0.952 0.004 0.000 0.004
#> SRR1951622 1 0.1180 0.8644 0.960 0.004 0.008 0.000 0.024 0.004
#> SRR1951636 3 0.1444 0.7434 0.000 0.072 0.928 0.000 0.000 0.000
#> SRR1951623 3 0.5535 0.1713 0.000 0.012 0.544 0.368 0.020 0.056
#> SRR1951621 6 0.5492 0.7331 0.012 0.000 0.000 0.252 0.140 0.596
#> SRR1951620 3 0.1759 0.7424 0.000 0.064 0.924 0.004 0.004 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 13580 rows and 133 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 0.969 0.936 0.974 0.4639 0.524 0.524
#> 3 3 0.798 0.891 0.938 0.4051 0.658 0.436
#> 4 4 0.695 0.772 0.879 0.1012 0.906 0.744
#> 5 5 0.705 0.617 0.774 0.0810 0.844 0.529
#> 6 6 0.711 0.579 0.763 0.0491 0.926 0.699
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
#> SRR1951667 2 0.0000 0.998 0.000 1.000
#> SRR1951666 2 0.0000 0.998 0.000 1.000
#> SRR1951665 2 0.0000 0.998 0.000 1.000
#> SRR1951664 2 0.0000 0.998 0.000 1.000
#> SRR1951663 1 0.0000 0.932 1.000 0.000
#> SRR1951662 2 0.0000 0.998 0.000 1.000
#> SRR1951661 2 0.0000 0.998 0.000 1.000
#> SRR1951660 1 0.0000 0.932 1.000 0.000
#> SRR1951659 2 0.0000 0.998 0.000 1.000
#> SRR1951658 2 0.0000 0.998 0.000 1.000
#> SRR1951657 1 0.9881 0.312 0.564 0.436
#> SRR1951656 1 0.9881 0.312 0.564 0.436
#> SRR1951655 1 0.0000 0.932 1.000 0.000
#> SRR1951654 1 0.0000 0.932 1.000 0.000
#> SRR1951653 2 0.0672 0.990 0.008 0.992
#> SRR1951652 2 0.0000 0.998 0.000 1.000
#> SRR1951651 1 0.0000 0.932 1.000 0.000
#> SRR1951650 2 0.0000 0.998 0.000 1.000
#> SRR1951648 2 0.0000 0.998 0.000 1.000
#> SRR1951649 2 0.0000 0.998 0.000 1.000
#> SRR1951647 2 0.0000 0.998 0.000 1.000
#> SRR1951646 1 0.9988 0.179 0.520 0.480
#> SRR1951645 2 0.4939 0.869 0.108 0.892
#> SRR1951644 1 0.9129 0.535 0.672 0.328
#> SRR1951643 1 0.0000 0.932 1.000 0.000
#> SRR1951641 2 0.0000 0.998 0.000 1.000
#> SRR1951642 1 0.0000 0.932 1.000 0.000
#> SRR1951694 1 0.0000 0.932 1.000 0.000
#> SRR1951731 2 0.0000 0.998 0.000 1.000
#> SRR1951730 2 0.0000 0.998 0.000 1.000
#> SRR1951752 2 0.0000 0.998 0.000 1.000
#> SRR1951751 1 0.0000 0.932 1.000 0.000
#> SRR1951749 2 0.0000 0.998 0.000 1.000
#> SRR1951750 1 0.0000 0.932 1.000 0.000
#> SRR1951748 2 0.0000 0.998 0.000 1.000
#> SRR1951747 2 0.0000 0.998 0.000 1.000
#> SRR1951745 1 0.0000 0.932 1.000 0.000
#> SRR1951746 2 0.0000 0.998 0.000 1.000
#> SRR1951744 1 0.2778 0.898 0.952 0.048
#> SRR1951742 2 0.0000 0.998 0.000 1.000
#> SRR1951743 2 0.0000 0.998 0.000 1.000
#> SRR1951741 1 0.0000 0.932 1.000 0.000
#> SRR1951740 2 0.2043 0.964 0.032 0.968
#> SRR1951738 2 0.0000 0.998 0.000 1.000
#> SRR1951739 2 0.0000 0.998 0.000 1.000
#> SRR1951737 2 0.0000 0.998 0.000 1.000
#> SRR1951736 1 0.0000 0.932 1.000 0.000
#> SRR1951734 2 0.0000 0.998 0.000 1.000
#> SRR1951735 2 0.0000 0.998 0.000 1.000
#> SRR1951733 2 0.0000 0.998 0.000 1.000
#> SRR1951732 2 0.0000 0.998 0.000 1.000
#> SRR1951729 2 0.0000 0.998 0.000 1.000
#> SRR1951728 2 0.0000 0.998 0.000 1.000
#> SRR1951727 2 0.0000 0.998 0.000 1.000
#> SRR1951725 1 0.0000 0.932 1.000 0.000
#> SRR1951726 2 0.0000 0.998 0.000 1.000
#> SRR1951724 1 0.2778 0.899 0.952 0.048
#> SRR1951722 2 0.0000 0.998 0.000 1.000
#> SRR1951723 2 0.0000 0.998 0.000 1.000
#> SRR1951721 1 0.0000 0.932 1.000 0.000
#> SRR1951719 2 0.0000 0.998 0.000 1.000
#> SRR1951720 1 0.0000 0.932 1.000 0.000
#> SRR1951718 1 0.0000 0.932 1.000 0.000
#> SRR1951717 1 0.0000 0.932 1.000 0.000
#> SRR1951715 1 0.0000 0.932 1.000 0.000
#> SRR1951716 1 0.0000 0.932 1.000 0.000
#> SRR1951714 1 0.0000 0.932 1.000 0.000
#> SRR1951713 2 0.0000 0.998 0.000 1.000
#> SRR1951712 1 0.0000 0.932 1.000 0.000
#> SRR1951711 2 0.0000 0.998 0.000 1.000
#> SRR1951710 1 0.0000 0.932 1.000 0.000
#> SRR1951709 2 0.0000 0.998 0.000 1.000
#> SRR1951708 2 0.0000 0.998 0.000 1.000
#> SRR1951707 2 0.0000 0.998 0.000 1.000
#> SRR1951705 1 0.0376 0.930 0.996 0.004
#> SRR1951704 2 0.0000 0.998 0.000 1.000
#> SRR1951703 2 0.0000 0.998 0.000 1.000
#> SRR1951706 1 0.0000 0.932 1.000 0.000
#> SRR1951702 2 0.0000 0.998 0.000 1.000
#> SRR1951701 2 0.0000 0.998 0.000 1.000
#> SRR1951699 2 0.0000 0.998 0.000 1.000
#> SRR1951698 2 0.0000 0.998 0.000 1.000
#> SRR1951700 2 0.0000 0.998 0.000 1.000
#> SRR1951697 2 0.0000 0.998 0.000 1.000
#> SRR1951696 2 0.0672 0.990 0.008 0.992
#> SRR1951695 2 0.0000 0.998 0.000 1.000
#> SRR1951693 2 0.0000 0.998 0.000 1.000
#> SRR1951692 1 0.0000 0.932 1.000 0.000
#> SRR1951691 1 0.9977 0.176 0.528 0.472
#> SRR1951688 2 0.0000 0.998 0.000 1.000
#> SRR1951689 2 0.0000 0.998 0.000 1.000
#> SRR1951687 1 0.0000 0.932 1.000 0.000
#> SRR1951685 2 0.0000 0.998 0.000 1.000
#> SRR1951684 2 0.0672 0.990 0.008 0.992
#> SRR1951686 2 0.0000 0.998 0.000 1.000
#> SRR1951683 1 0.0000 0.932 1.000 0.000
#> SRR1951682 2 0.0000 0.998 0.000 1.000
#> SRR1951690 2 0.0000 0.998 0.000 1.000
#> SRR1951680 2 0.0000 0.998 0.000 1.000
#> SRR1951679 2 0.0000 0.998 0.000 1.000
#> SRR1951678 1 0.0000 0.932 1.000 0.000
#> SRR1951677 1 0.0000 0.932 1.000 0.000
#> SRR1951676 1 0.9881 0.312 0.564 0.436
#> SRR1951675 2 0.0000 0.998 0.000 1.000
#> SRR1951681 2 0.0672 0.990 0.008 0.992
#> SRR1951673 2 0.0000 0.998 0.000 1.000
#> SRR1951674 1 0.0000 0.932 1.000 0.000
#> SRR1951672 2 0.0000 0.998 0.000 1.000
#> SRR1951671 2 0.0000 0.998 0.000 1.000
#> SRR1951670 1 0.0000 0.932 1.000 0.000
#> SRR1951640 2 0.0000 0.998 0.000 1.000
#> SRR1951668 1 0.0000 0.932 1.000 0.000
#> SRR1951669 2 0.0000 0.998 0.000 1.000
#> SRR1951638 1 0.0000 0.932 1.000 0.000
#> SRR1951637 2 0.0000 0.998 0.000 1.000
#> SRR1951635 1 0.0000 0.932 1.000 0.000
#> SRR1951634 1 0.5519 0.824 0.872 0.128
#> SRR1951631 1 0.0000 0.932 1.000 0.000
#> SRR1951632 2 0.0000 0.998 0.000 1.000
#> SRR1951633 1 0.0000 0.932 1.000 0.000
#> SRR1951630 1 0.0000 0.932 1.000 0.000
#> SRR1951629 1 0.9881 0.312 0.564 0.436
#> SRR1951628 2 0.0000 0.998 0.000 1.000
#> SRR1951627 2 0.0000 0.998 0.000 1.000
#> SRR1951639 2 0.0000 0.998 0.000 1.000
#> SRR1951625 2 0.0000 0.998 0.000 1.000
#> SRR1951626 2 0.0000 0.998 0.000 1.000
#> SRR1951624 2 0.0000 0.998 0.000 1.000
#> SRR1951622 1 0.0000 0.932 1.000 0.000
#> SRR1951636 2 0.0000 0.998 0.000 1.000
#> SRR1951623 1 0.3114 0.892 0.944 0.056
#> SRR1951621 1 0.0000 0.932 1.000 0.000
#> SRR1951620 2 0.0672 0.990 0.008 0.992
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951666 3 0.4842 0.777 0.000 0.224 0.776
#> SRR1951665 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951664 3 0.0892 0.885 0.000 0.020 0.980
#> SRR1951663 1 0.0592 0.939 0.988 0.000 0.012
#> SRR1951662 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951661 3 0.0892 0.885 0.000 0.020 0.980
#> SRR1951660 1 0.0592 0.939 0.988 0.000 0.012
#> SRR1951659 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951657 3 0.0424 0.881 0.000 0.008 0.992
#> SRR1951656 3 0.0661 0.881 0.004 0.008 0.988
#> SRR1951655 1 0.0592 0.939 0.988 0.000 0.012
#> SRR1951654 1 0.0424 0.936 0.992 0.000 0.008
#> SRR1951653 3 0.0747 0.884 0.000 0.016 0.984
#> SRR1951652 3 0.4702 0.789 0.000 0.212 0.788
#> SRR1951651 1 0.6140 0.463 0.596 0.000 0.404
#> SRR1951650 3 0.0892 0.885 0.000 0.020 0.980
#> SRR1951648 3 0.6274 0.352 0.000 0.456 0.544
#> SRR1951649 3 0.5016 0.761 0.000 0.240 0.760
#> SRR1951647 2 0.0424 0.988 0.000 0.992 0.008
#> SRR1951646 3 0.0424 0.881 0.000 0.008 0.992
#> SRR1951645 3 0.0661 0.878 0.008 0.004 0.988
#> SRR1951644 3 0.5363 0.650 0.276 0.000 0.724
#> SRR1951643 1 0.0747 0.934 0.984 0.000 0.016
#> SRR1951641 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951642 1 0.4654 0.792 0.792 0.000 0.208
#> SRR1951694 1 0.0592 0.939 0.988 0.000 0.012
#> SRR1951731 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951730 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951752 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951751 3 0.5291 0.662 0.268 0.000 0.732
#> SRR1951749 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951750 1 0.0592 0.939 0.988 0.000 0.012
#> SRR1951748 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951745 1 0.0424 0.936 0.992 0.000 0.008
#> SRR1951746 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951744 3 0.0237 0.877 0.004 0.000 0.996
#> SRR1951742 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951743 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951741 1 0.0424 0.936 0.992 0.000 0.008
#> SRR1951740 3 0.0747 0.884 0.000 0.016 0.984
#> SRR1951738 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951739 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951737 3 0.0892 0.885 0.000 0.020 0.980
#> SRR1951736 1 0.0592 0.939 0.988 0.000 0.012
#> SRR1951734 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951735 3 0.4796 0.781 0.000 0.220 0.780
#> SRR1951733 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951732 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951729 3 0.1289 0.883 0.000 0.032 0.968
#> SRR1951728 3 0.4555 0.799 0.000 0.200 0.800
#> SRR1951727 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951725 1 0.0592 0.939 0.988 0.000 0.012
#> SRR1951726 3 0.4291 0.814 0.000 0.180 0.820
#> SRR1951724 3 0.0424 0.877 0.008 0.000 0.992
#> SRR1951722 3 0.5178 0.744 0.000 0.256 0.744
#> SRR1951723 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951721 1 0.0592 0.939 0.988 0.000 0.012
#> SRR1951719 3 0.0892 0.885 0.000 0.020 0.980
#> SRR1951720 1 0.0592 0.939 0.988 0.000 0.012
#> SRR1951718 3 0.5098 0.684 0.248 0.000 0.752
#> SRR1951717 1 0.0592 0.939 0.988 0.000 0.012
#> SRR1951715 1 0.0592 0.939 0.988 0.000 0.012
#> SRR1951716 1 0.0592 0.939 0.988 0.000 0.012
#> SRR1951714 3 0.0747 0.871 0.016 0.000 0.984
#> SRR1951713 3 0.2537 0.864 0.000 0.080 0.920
#> SRR1951712 3 0.5327 0.656 0.272 0.000 0.728
#> SRR1951711 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951710 1 0.0592 0.939 0.988 0.000 0.012
#> SRR1951709 3 0.6215 0.425 0.000 0.428 0.572
#> SRR1951708 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951707 3 0.5327 0.722 0.000 0.272 0.728
#> SRR1951705 3 0.5431 0.638 0.284 0.000 0.716
#> SRR1951704 3 0.4346 0.812 0.000 0.184 0.816
#> SRR1951703 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951706 3 0.0424 0.877 0.008 0.000 0.992
#> SRR1951702 3 0.0892 0.885 0.000 0.020 0.980
#> SRR1951701 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951699 3 0.4796 0.781 0.000 0.220 0.780
#> SRR1951698 3 0.0892 0.885 0.000 0.020 0.980
#> SRR1951700 3 0.4504 0.801 0.000 0.196 0.804
#> SRR1951697 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951696 3 0.0747 0.884 0.000 0.016 0.984
#> SRR1951695 3 0.4399 0.809 0.000 0.188 0.812
#> SRR1951693 3 0.0747 0.884 0.000 0.016 0.984
#> SRR1951692 3 0.0592 0.876 0.012 0.000 0.988
#> SRR1951691 3 0.0747 0.873 0.016 0.000 0.984
#> SRR1951688 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951689 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951687 1 0.0424 0.936 0.992 0.000 0.008
#> SRR1951685 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951684 3 0.0892 0.885 0.000 0.020 0.980
#> SRR1951686 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951683 1 0.0424 0.936 0.992 0.000 0.008
#> SRR1951682 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951690 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951680 3 0.0892 0.885 0.000 0.020 0.980
#> SRR1951679 2 0.0237 0.992 0.000 0.996 0.004
#> SRR1951678 1 0.5016 0.755 0.760 0.000 0.240
#> SRR1951677 1 0.4654 0.792 0.792 0.000 0.208
#> SRR1951676 3 0.0661 0.881 0.004 0.008 0.988
#> SRR1951675 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951681 3 0.0892 0.885 0.000 0.020 0.980
#> SRR1951673 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951674 1 0.0424 0.936 0.992 0.000 0.008
#> SRR1951672 3 0.5420 0.748 0.008 0.240 0.752
#> SRR1951671 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951670 1 0.0000 0.937 1.000 0.000 0.000
#> SRR1951640 3 0.1411 0.881 0.000 0.036 0.964
#> SRR1951668 3 0.0424 0.877 0.008 0.000 0.992
#> SRR1951669 2 0.0661 0.984 0.004 0.988 0.008
#> SRR1951638 1 0.0237 0.938 0.996 0.000 0.004
#> SRR1951637 2 0.0661 0.984 0.004 0.988 0.008
#> SRR1951635 1 0.0237 0.938 0.996 0.000 0.004
#> SRR1951634 3 0.0424 0.877 0.008 0.000 0.992
#> SRR1951631 1 0.5016 0.767 0.760 0.000 0.240
#> SRR1951632 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951633 1 0.5058 0.750 0.756 0.000 0.244
#> SRR1951630 1 0.4654 0.792 0.792 0.000 0.208
#> SRR1951629 3 0.0424 0.881 0.000 0.008 0.992
#> SRR1951628 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951627 3 0.0892 0.885 0.000 0.020 0.980
#> SRR1951639 2 0.2796 0.886 0.000 0.908 0.092
#> SRR1951625 2 0.0000 0.997 0.000 1.000 0.000
#> SRR1951626 3 0.6260 0.375 0.000 0.448 0.552
#> SRR1951624 3 0.0892 0.885 0.000 0.020 0.980
#> SRR1951622 1 0.0592 0.939 0.988 0.000 0.012
#> SRR1951636 3 0.0892 0.885 0.000 0.020 0.980
#> SRR1951623 3 0.0592 0.876 0.012 0.000 0.988
#> SRR1951621 1 0.0424 0.936 0.992 0.000 0.008
#> SRR1951620 3 0.0892 0.885 0.000 0.020 0.980
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951666 3 0.2611 0.834 0.008 0.096 0.896 0.000
#> SRR1951665 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951664 3 0.0188 0.856 0.000 0.000 0.996 0.004
#> SRR1951663 1 0.4382 0.580 0.704 0.000 0.000 0.296
#> SRR1951662 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.0188 0.856 0.000 0.000 0.996 0.004
#> SRR1951660 1 0.1211 0.722 0.960 0.000 0.000 0.040
#> SRR1951659 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951658 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951657 3 0.1557 0.828 0.000 0.000 0.944 0.056
#> SRR1951656 3 0.3024 0.753 0.000 0.000 0.852 0.148
#> SRR1951655 1 0.2011 0.730 0.920 0.000 0.000 0.080
#> SRR1951654 4 0.3074 0.723 0.152 0.000 0.000 0.848
#> SRR1951653 3 0.1978 0.854 0.068 0.000 0.928 0.004
#> SRR1951652 3 0.5988 0.702 0.224 0.100 0.676 0.000
#> SRR1951651 4 0.3577 0.705 0.012 0.000 0.156 0.832
#> SRR1951650 3 0.0188 0.856 0.000 0.000 0.996 0.004
#> SRR1951648 3 0.3464 0.821 0.032 0.108 0.860 0.000
#> SRR1951649 3 0.5994 0.633 0.296 0.068 0.636 0.000
#> SRR1951647 2 0.4332 0.735 0.032 0.792 0.176 0.000
#> SRR1951646 3 0.1716 0.823 0.000 0.000 0.936 0.064
#> SRR1951645 3 0.4876 0.639 0.320 0.004 0.672 0.004
#> SRR1951644 1 0.2593 0.659 0.892 0.000 0.104 0.004
#> SRR1951643 4 0.2408 0.761 0.044 0.000 0.036 0.920
#> SRR1951641 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951642 4 0.2676 0.765 0.012 0.000 0.092 0.896
#> SRR1951694 1 0.2081 0.730 0.916 0.000 0.000 0.084
#> SRR1951731 2 0.0336 0.946 0.008 0.992 0.000 0.000
#> SRR1951730 2 0.2565 0.889 0.056 0.912 0.032 0.000
#> SRR1951752 2 0.1724 0.917 0.032 0.948 0.020 0.000
#> SRR1951751 1 0.2831 0.645 0.876 0.000 0.120 0.004
#> SRR1951749 2 0.0336 0.946 0.008 0.992 0.000 0.000
#> SRR1951750 1 0.4356 0.536 0.708 0.000 0.000 0.292
#> SRR1951748 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951745 4 0.3123 0.719 0.156 0.000 0.000 0.844
#> SRR1951746 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951744 3 0.4312 0.774 0.132 0.000 0.812 0.056
#> SRR1951742 2 0.0336 0.946 0.008 0.992 0.000 0.000
#> SRR1951743 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951741 4 0.3074 0.723 0.152 0.000 0.000 0.848
#> SRR1951740 3 0.1406 0.858 0.024 0.000 0.960 0.016
#> SRR1951738 2 0.0188 0.947 0.004 0.996 0.000 0.000
#> SRR1951739 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951737 3 0.0188 0.856 0.000 0.000 0.996 0.004
#> SRR1951736 1 0.2081 0.730 0.916 0.000 0.000 0.084
#> SRR1951734 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951735 3 0.3342 0.827 0.032 0.100 0.868 0.000
#> SRR1951733 2 0.1022 0.932 0.032 0.968 0.000 0.000
#> SRR1951732 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951729 3 0.1833 0.858 0.032 0.024 0.944 0.000
#> SRR1951728 3 0.3542 0.836 0.076 0.060 0.864 0.000
#> SRR1951727 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951725 1 0.2081 0.730 0.916 0.000 0.000 0.084
#> SRR1951726 3 0.5397 0.734 0.220 0.064 0.716 0.000
#> SRR1951724 3 0.4855 0.599 0.352 0.000 0.644 0.004
#> SRR1951722 3 0.4235 0.815 0.084 0.092 0.824 0.000
#> SRR1951723 2 0.3128 0.864 0.076 0.884 0.040 0.000
#> SRR1951721 1 0.2469 0.724 0.892 0.000 0.000 0.108
#> SRR1951719 3 0.0817 0.858 0.024 0.000 0.976 0.000
#> SRR1951720 1 0.4746 0.415 0.632 0.000 0.000 0.368
#> SRR1951718 1 0.2714 0.654 0.884 0.000 0.112 0.004
#> SRR1951717 1 0.3975 0.612 0.760 0.000 0.000 0.240
#> SRR1951715 1 0.2469 0.724 0.892 0.000 0.000 0.108
#> SRR1951716 1 0.4164 0.586 0.736 0.000 0.000 0.264
#> SRR1951714 3 0.6745 0.536 0.176 0.000 0.612 0.212
#> SRR1951713 3 0.3051 0.845 0.088 0.028 0.884 0.000
#> SRR1951712 1 0.2831 0.645 0.876 0.000 0.120 0.004
#> SRR1951711 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951710 1 0.0921 0.720 0.972 0.000 0.000 0.028
#> SRR1951709 3 0.3404 0.824 0.032 0.104 0.864 0.000
#> SRR1951708 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951707 3 0.3959 0.828 0.092 0.068 0.840 0.000
#> SRR1951705 1 0.2401 0.667 0.904 0.000 0.092 0.004
#> SRR1951704 3 0.3245 0.841 0.064 0.056 0.880 0.000
#> SRR1951703 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951706 3 0.5995 0.586 0.096 0.000 0.672 0.232
#> SRR1951702 3 0.1022 0.858 0.032 0.000 0.968 0.000
#> SRR1951701 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951699 3 0.3279 0.829 0.032 0.096 0.872 0.000
#> SRR1951698 3 0.0188 0.856 0.000 0.000 0.996 0.004
#> SRR1951700 3 0.5726 0.674 0.280 0.048 0.668 0.004
#> SRR1951697 2 0.0336 0.946 0.008 0.992 0.000 0.000
#> SRR1951696 3 0.2149 0.808 0.000 0.000 0.912 0.088
#> SRR1951695 3 0.3542 0.836 0.076 0.060 0.864 0.000
#> SRR1951693 3 0.2999 0.833 0.132 0.000 0.864 0.004
#> SRR1951692 3 0.3982 0.662 0.004 0.000 0.776 0.220
#> SRR1951691 3 0.5320 0.457 0.416 0.000 0.572 0.012
#> SRR1951688 2 0.1022 0.932 0.032 0.968 0.000 0.000
#> SRR1951689 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951687 4 0.2868 0.730 0.136 0.000 0.000 0.864
#> SRR1951685 2 0.2101 0.905 0.060 0.928 0.012 0.000
#> SRR1951684 3 0.0188 0.856 0.000 0.000 0.996 0.004
#> SRR1951686 2 0.0336 0.946 0.008 0.992 0.000 0.000
#> SRR1951683 4 0.2530 0.742 0.100 0.000 0.004 0.896
#> SRR1951682 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951690 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951680 3 0.1474 0.856 0.052 0.000 0.948 0.000
#> SRR1951679 2 0.5884 0.443 0.364 0.592 0.044 0.000
#> SRR1951678 4 0.2805 0.760 0.012 0.000 0.100 0.888
#> SRR1951677 4 0.2676 0.765 0.012 0.000 0.092 0.896
#> SRR1951676 3 0.2345 0.796 0.000 0.000 0.900 0.100
#> SRR1951675 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951681 3 0.0188 0.856 0.000 0.000 0.996 0.004
#> SRR1951673 2 0.1022 0.932 0.032 0.968 0.000 0.000
#> SRR1951674 4 0.3123 0.719 0.156 0.000 0.000 0.844
#> SRR1951672 1 0.7151 -0.208 0.448 0.132 0.420 0.000
#> SRR1951671 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951670 4 0.4679 0.409 0.352 0.000 0.000 0.648
#> SRR1951640 3 0.1488 0.859 0.032 0.012 0.956 0.000
#> SRR1951668 3 0.4891 0.632 0.308 0.000 0.680 0.012
#> SRR1951669 2 0.5511 0.493 0.352 0.620 0.028 0.000
#> SRR1951638 1 0.4977 0.243 0.540 0.000 0.000 0.460
#> SRR1951637 2 0.5699 0.427 0.380 0.588 0.032 0.000
#> SRR1951635 1 0.4967 0.263 0.548 0.000 0.000 0.452
#> SRR1951634 3 0.3448 0.800 0.168 0.000 0.828 0.004
#> SRR1951631 1 0.7509 0.119 0.452 0.000 0.188 0.360
#> SRR1951632 2 0.0000 0.948 0.000 1.000 0.000 0.000
#> SRR1951633 4 0.2654 0.756 0.004 0.000 0.108 0.888
#> SRR1951630 4 0.2676 0.765 0.012 0.000 0.092 0.896
#> SRR1951629 3 0.0188 0.856 0.000 0.000 0.996 0.004
#> SRR1951628 2 0.4508 0.752 0.184 0.780 0.036 0.000
#> SRR1951627 3 0.0188 0.856 0.000 0.000 0.996 0.004
#> SRR1951639 3 0.4332 0.753 0.032 0.176 0.792 0.000
#> SRR1951625 2 0.0336 0.946 0.008 0.992 0.000 0.000
#> SRR1951626 3 0.3404 0.824 0.032 0.104 0.864 0.000
#> SRR1951624 3 0.0000 0.856 0.000 0.000 1.000 0.000
#> SRR1951622 1 0.1940 0.731 0.924 0.000 0.000 0.076
#> SRR1951636 3 0.0188 0.856 0.000 0.000 0.996 0.004
#> SRR1951623 4 0.5161 0.129 0.004 0.000 0.476 0.520
#> SRR1951621 4 0.3074 0.723 0.152 0.000 0.000 0.848
#> SRR1951620 3 0.0188 0.856 0.000 0.000 0.996 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951666 3 0.3696 0.7045 0.000 0.016 0.772 0.000 0.212
#> SRR1951665 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 3 0.1121 0.7983 0.000 0.000 0.956 0.000 0.044
#> SRR1951663 1 0.1018 0.6506 0.968 0.000 0.000 0.016 0.016
#> SRR1951662 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 3 0.1197 0.7990 0.000 0.000 0.952 0.000 0.048
#> SRR1951660 5 0.4283 0.1367 0.348 0.000 0.000 0.008 0.644
#> SRR1951659 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951657 3 0.2707 0.6812 0.000 0.000 0.876 0.100 0.024
#> SRR1951656 3 0.4558 0.1874 0.000 0.000 0.652 0.324 0.024
#> SRR1951655 5 0.4522 -0.1185 0.440 0.000 0.000 0.008 0.552
#> SRR1951654 1 0.4304 0.1642 0.516 0.000 0.000 0.484 0.000
#> SRR1951653 3 0.3756 0.5227 0.000 0.000 0.744 0.008 0.248
#> SRR1951652 5 0.4578 0.4716 0.004 0.040 0.244 0.000 0.712
#> SRR1951651 4 0.4141 0.6449 0.000 0.000 0.236 0.736 0.028
#> SRR1951650 3 0.0162 0.7875 0.000 0.000 0.996 0.000 0.004
#> SRR1951648 3 0.3852 0.6919 0.000 0.020 0.760 0.000 0.220
#> SRR1951649 5 0.3109 0.5331 0.000 0.000 0.200 0.000 0.800
#> SRR1951647 2 0.6528 0.1380 0.000 0.480 0.236 0.000 0.284
#> SRR1951646 3 0.1106 0.7644 0.000 0.000 0.964 0.012 0.024
#> SRR1951645 5 0.2966 0.5441 0.000 0.000 0.184 0.000 0.816
#> SRR1951644 5 0.3963 0.3123 0.256 0.000 0.004 0.008 0.732
#> SRR1951643 4 0.0510 0.6706 0.016 0.000 0.000 0.984 0.000
#> SRR1951641 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951642 4 0.0510 0.6848 0.000 0.000 0.016 0.984 0.000
#> SRR1951694 1 0.4067 0.5999 0.692 0.000 0.000 0.008 0.300
#> SRR1951731 2 0.1768 0.8736 0.000 0.924 0.004 0.000 0.072
#> SRR1951730 2 0.4088 0.5990 0.000 0.688 0.008 0.000 0.304
#> SRR1951752 2 0.3949 0.6116 0.000 0.696 0.004 0.000 0.300
#> SRR1951751 5 0.3968 0.2871 0.276 0.000 0.004 0.004 0.716
#> SRR1951749 2 0.0162 0.9291 0.000 0.996 0.000 0.000 0.004
#> SRR1951750 1 0.3967 0.6333 0.724 0.000 0.000 0.012 0.264
#> SRR1951748 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 1 0.4161 0.3071 0.608 0.000 0.000 0.392 0.000
#> SRR1951746 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 3 0.6296 0.1376 0.000 0.000 0.528 0.200 0.272
#> SRR1951742 2 0.0162 0.9291 0.000 0.996 0.000 0.000 0.004
#> SRR1951743 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 1 0.4300 0.1786 0.524 0.000 0.000 0.476 0.000
#> SRR1951740 3 0.4135 0.2965 0.000 0.000 0.656 0.004 0.340
#> SRR1951738 2 0.0794 0.9152 0.000 0.972 0.000 0.000 0.028
#> SRR1951739 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951737 3 0.1121 0.7983 0.000 0.000 0.956 0.000 0.044
#> SRR1951736 1 0.4067 0.6008 0.692 0.000 0.000 0.008 0.300
#> SRR1951734 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 3 0.3715 0.6578 0.000 0.004 0.736 0.000 0.260
#> SRR1951733 2 0.3333 0.7345 0.000 0.788 0.004 0.000 0.208
#> SRR1951732 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 3 0.2179 0.7807 0.000 0.000 0.888 0.000 0.112
#> SRR1951728 3 0.4235 0.3627 0.000 0.000 0.576 0.000 0.424
#> SRR1951727 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 1 0.4067 0.6008 0.692 0.000 0.000 0.008 0.300
#> SRR1951726 5 0.3508 0.4758 0.000 0.000 0.252 0.000 0.748
#> SRR1951724 5 0.3642 0.5173 0.000 0.000 0.232 0.008 0.760
#> SRR1951722 5 0.4270 0.3511 0.000 0.012 0.320 0.000 0.668
#> SRR1951723 5 0.4449 -0.1137 0.000 0.484 0.004 0.000 0.512
#> SRR1951721 1 0.3612 0.6303 0.732 0.000 0.000 0.000 0.268
#> SRR1951719 3 0.1341 0.7972 0.000 0.000 0.944 0.000 0.056
#> SRR1951720 1 0.2069 0.6661 0.912 0.000 0.000 0.012 0.076
#> SRR1951718 5 0.4039 0.2932 0.268 0.000 0.004 0.008 0.720
#> SRR1951717 1 0.2970 0.6724 0.828 0.000 0.000 0.004 0.168
#> SRR1951715 1 0.3612 0.6303 0.732 0.000 0.000 0.000 0.268
#> SRR1951716 1 0.2813 0.6729 0.832 0.000 0.000 0.000 0.168
#> SRR1951714 4 0.6549 0.4613 0.000 0.000 0.280 0.476 0.244
#> SRR1951713 5 0.3913 0.3567 0.000 0.000 0.324 0.000 0.676
#> SRR1951712 5 0.4088 0.2831 0.276 0.000 0.004 0.008 0.712
#> SRR1951711 2 0.0510 0.9220 0.000 0.984 0.000 0.000 0.016
#> SRR1951710 5 0.4542 -0.1405 0.456 0.000 0.000 0.008 0.536
#> SRR1951709 3 0.3789 0.7015 0.000 0.020 0.768 0.000 0.212
#> SRR1951708 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951707 5 0.4287 -0.0638 0.000 0.000 0.460 0.000 0.540
#> SRR1951705 5 0.4178 0.2587 0.292 0.000 0.004 0.008 0.696
#> SRR1951704 3 0.3983 0.5417 0.000 0.000 0.660 0.000 0.340
#> SRR1951703 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 4 0.6316 0.4493 0.000 0.000 0.356 0.480 0.164
#> SRR1951702 3 0.2179 0.7807 0.000 0.000 0.888 0.000 0.112
#> SRR1951701 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951699 3 0.3696 0.7045 0.000 0.016 0.772 0.000 0.212
#> SRR1951698 3 0.1197 0.7990 0.000 0.000 0.952 0.000 0.048
#> SRR1951700 5 0.3177 0.5260 0.000 0.000 0.208 0.000 0.792
#> SRR1951697 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951696 3 0.1725 0.7386 0.000 0.000 0.936 0.044 0.020
#> SRR1951695 5 0.4138 0.2089 0.000 0.000 0.384 0.000 0.616
#> SRR1951693 5 0.3796 0.3993 0.000 0.000 0.300 0.000 0.700
#> SRR1951692 4 0.4746 0.5219 0.000 0.000 0.376 0.600 0.024
#> SRR1951691 5 0.1444 0.5643 0.012 0.000 0.040 0.000 0.948
#> SRR1951688 2 0.3579 0.6970 0.000 0.756 0.004 0.000 0.240
#> SRR1951689 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951687 4 0.4307 -0.2384 0.500 0.000 0.000 0.500 0.000
#> SRR1951685 2 0.3906 0.6244 0.000 0.704 0.004 0.000 0.292
#> SRR1951684 3 0.0324 0.7815 0.000 0.000 0.992 0.004 0.004
#> SRR1951686 2 0.0404 0.9250 0.000 0.988 0.000 0.000 0.012
#> SRR1951683 4 0.0912 0.6603 0.016 0.000 0.000 0.972 0.012
#> SRR1951682 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951680 3 0.3395 0.6641 0.000 0.000 0.764 0.000 0.236
#> SRR1951679 5 0.2681 0.5439 0.012 0.108 0.004 0.000 0.876
#> SRR1951678 4 0.0510 0.6848 0.000 0.000 0.016 0.984 0.000
#> SRR1951677 4 0.0510 0.6848 0.000 0.000 0.016 0.984 0.000
#> SRR1951676 3 0.3602 0.5566 0.000 0.000 0.796 0.180 0.024
#> SRR1951675 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951681 3 0.0290 0.7822 0.000 0.000 0.992 0.000 0.008
#> SRR1951673 2 0.3550 0.7022 0.000 0.760 0.004 0.000 0.236
#> SRR1951674 1 0.4161 0.3071 0.608 0.000 0.000 0.392 0.000
#> SRR1951672 5 0.2788 0.5870 0.008 0.040 0.064 0.000 0.888
#> SRR1951671 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951670 1 0.5091 0.5327 0.672 0.000 0.000 0.244 0.084
#> SRR1951640 3 0.2966 0.7411 0.000 0.000 0.816 0.000 0.184
#> SRR1951668 5 0.4934 0.3467 0.000 0.000 0.364 0.036 0.600
#> SRR1951669 5 0.3752 0.5040 0.016 0.200 0.004 0.000 0.780
#> SRR1951638 1 0.1043 0.6371 0.960 0.000 0.000 0.040 0.000
#> SRR1951637 5 0.3612 0.5156 0.016 0.184 0.004 0.000 0.796
#> SRR1951635 1 0.1043 0.6371 0.960 0.000 0.000 0.040 0.000
#> SRR1951634 5 0.4731 0.2299 0.000 0.000 0.456 0.016 0.528
#> SRR1951631 4 0.7031 0.5159 0.040 0.000 0.220 0.524 0.216
#> SRR1951632 2 0.0000 0.9310 0.000 1.000 0.000 0.000 0.000
#> SRR1951633 4 0.0703 0.6829 0.000 0.000 0.024 0.976 0.000
#> SRR1951630 4 0.0510 0.6848 0.000 0.000 0.016 0.984 0.000
#> SRR1951629 3 0.1750 0.7525 0.000 0.000 0.936 0.028 0.036
#> SRR1951628 5 0.4264 0.2498 0.000 0.376 0.004 0.000 0.620
#> SRR1951627 3 0.1197 0.7990 0.000 0.000 0.952 0.000 0.048
#> SRR1951639 3 0.3789 0.7015 0.000 0.020 0.768 0.000 0.212
#> SRR1951625 2 0.0510 0.9227 0.000 0.984 0.000 0.000 0.016
#> SRR1951626 3 0.3789 0.7015 0.000 0.020 0.768 0.000 0.212
#> SRR1951624 3 0.1341 0.7972 0.000 0.000 0.944 0.000 0.056
#> SRR1951622 1 0.4546 0.2988 0.532 0.000 0.000 0.008 0.460
#> SRR1951636 3 0.1197 0.7990 0.000 0.000 0.952 0.000 0.048
#> SRR1951623 4 0.4668 0.5586 0.000 0.000 0.352 0.624 0.024
#> SRR1951621 1 0.4307 0.1441 0.504 0.000 0.000 0.496 0.000
#> SRR1951620 3 0.0000 0.7860 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951666 3 0.3346 0.69930 0.000 0.008 0.816 0.000 0.036 0.140
#> SRR1951665 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 3 0.0508 0.76522 0.000 0.000 0.984 0.000 0.004 0.012
#> SRR1951663 1 0.3695 0.14504 0.624 0.000 0.000 0.000 0.000 0.376
#> SRR1951662 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 3 0.1092 0.76878 0.000 0.000 0.960 0.000 0.020 0.020
#> SRR1951660 1 0.4691 0.46213 0.616 0.000 0.000 0.008 0.332 0.044
#> SRR1951659 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951657 3 0.4572 0.54925 0.000 0.000 0.672 0.020 0.036 0.272
#> SRR1951656 3 0.5271 0.47105 0.000 0.000 0.624 0.076 0.028 0.272
#> SRR1951655 1 0.3730 0.61108 0.740 0.000 0.000 0.008 0.236 0.016
#> SRR1951654 6 0.5322 0.81660 0.104 0.000 0.000 0.424 0.000 0.472
#> SRR1951653 3 0.6197 0.12472 0.004 0.000 0.412 0.004 0.360 0.220
#> SRR1951652 5 0.5179 0.61067 0.000 0.024 0.096 0.004 0.672 0.204
#> SRR1951651 4 0.5039 0.51865 0.000 0.000 0.088 0.640 0.012 0.260
#> SRR1951650 3 0.1204 0.74985 0.000 0.000 0.944 0.000 0.000 0.056
#> SRR1951648 3 0.3705 0.68086 0.000 0.008 0.792 0.000 0.056 0.144
#> SRR1951649 5 0.2036 0.63519 0.008 0.000 0.064 0.000 0.912 0.016
#> SRR1951647 5 0.7402 0.38987 0.000 0.196 0.204 0.000 0.408 0.192
#> SRR1951646 3 0.4078 0.57576 0.000 0.000 0.700 0.008 0.024 0.268
#> SRR1951645 5 0.2664 0.60330 0.040 0.000 0.056 0.000 0.884 0.020
#> SRR1951644 5 0.4529 0.00787 0.388 0.000 0.000 0.008 0.580 0.024
#> SRR1951643 4 0.0622 0.51302 0.000 0.000 0.000 0.980 0.008 0.012
#> SRR1951641 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951642 4 0.0779 0.51681 0.000 0.000 0.008 0.976 0.008 0.008
#> SRR1951694 1 0.1075 0.71034 0.952 0.000 0.000 0.000 0.048 0.000
#> SRR1951731 2 0.3490 0.75092 0.000 0.784 0.000 0.000 0.040 0.176
#> SRR1951730 5 0.6582 0.29097 0.000 0.320 0.044 0.000 0.444 0.192
#> SRR1951752 5 0.6189 0.48633 0.000 0.228 0.036 0.000 0.540 0.196
#> SRR1951751 5 0.4399 -0.16847 0.460 0.000 0.000 0.000 0.516 0.024
#> SRR1951749 2 0.2872 0.79592 0.000 0.836 0.000 0.000 0.024 0.140
#> SRR1951750 1 0.1225 0.70996 0.952 0.000 0.000 0.000 0.036 0.012
#> SRR1951748 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 6 0.5867 0.84187 0.240 0.000 0.000 0.280 0.000 0.480
#> SRR1951746 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951744 5 0.7796 -0.05224 0.016 0.000 0.192 0.152 0.344 0.296
#> SRR1951742 2 0.3175 0.77504 0.000 0.808 0.000 0.000 0.028 0.164
#> SRR1951743 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951741 6 0.5353 0.82204 0.108 0.000 0.000 0.420 0.000 0.472
#> SRR1951740 5 0.5787 0.30849 0.000 0.000 0.212 0.004 0.528 0.256
#> SRR1951738 2 0.3227 0.79191 0.000 0.824 0.000 0.000 0.060 0.116
#> SRR1951739 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951737 3 0.0909 0.76877 0.000 0.000 0.968 0.000 0.020 0.012
#> SRR1951736 1 0.1265 0.71077 0.948 0.000 0.000 0.000 0.044 0.008
#> SRR1951734 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951735 3 0.3857 0.66318 0.000 0.004 0.776 0.000 0.072 0.148
#> SRR1951733 2 0.5278 0.46685 0.000 0.604 0.000 0.000 0.204 0.192
#> SRR1951732 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 3 0.1572 0.76344 0.000 0.000 0.936 0.000 0.028 0.036
#> SRR1951728 5 0.5638 0.20327 0.000 0.000 0.384 0.000 0.464 0.152
#> SRR1951727 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 1 0.1082 0.71089 0.956 0.000 0.000 0.000 0.040 0.004
#> SRR1951726 5 0.4396 0.63669 0.008 0.004 0.100 0.004 0.756 0.128
#> SRR1951724 5 0.4160 0.53131 0.084 0.000 0.052 0.000 0.788 0.076
#> SRR1951722 5 0.4809 0.59375 0.000 0.004 0.128 0.000 0.680 0.188
#> SRR1951723 5 0.5146 0.59531 0.000 0.140 0.016 0.000 0.664 0.180
#> SRR1951721 1 0.1257 0.70394 0.952 0.000 0.000 0.000 0.028 0.020
#> SRR1951719 3 0.0858 0.76903 0.000 0.000 0.968 0.000 0.028 0.004
#> SRR1951720 1 0.2823 0.50332 0.796 0.000 0.000 0.000 0.000 0.204
#> SRR1951718 5 0.5547 0.10124 0.324 0.000 0.000 0.008 0.544 0.124
#> SRR1951717 1 0.1151 0.68832 0.956 0.000 0.000 0.000 0.012 0.032
#> SRR1951715 1 0.1168 0.70494 0.956 0.000 0.000 0.000 0.028 0.016
#> SRR1951716 1 0.0632 0.68920 0.976 0.000 0.000 0.000 0.000 0.024
#> SRR1951714 4 0.7420 0.42102 0.024 0.000 0.076 0.412 0.196 0.292
#> SRR1951713 5 0.2869 0.62149 0.000 0.000 0.148 0.000 0.832 0.020
#> SRR1951712 5 0.4401 -0.17179 0.464 0.000 0.000 0.000 0.512 0.024
#> SRR1951711 2 0.1845 0.86388 0.000 0.920 0.000 0.000 0.028 0.052
#> SRR1951710 1 0.2902 0.65287 0.800 0.000 0.000 0.000 0.196 0.004
#> SRR1951709 3 0.3606 0.68782 0.000 0.008 0.800 0.000 0.052 0.140
#> SRR1951708 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951707 5 0.5202 0.52846 0.000 0.000 0.196 0.000 0.616 0.188
#> SRR1951705 1 0.4264 0.18421 0.496 0.000 0.000 0.000 0.488 0.016
#> SRR1951704 3 0.5439 -0.02832 0.000 0.000 0.472 0.000 0.408 0.120
#> SRR1951703 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 4 0.7324 0.44614 0.012 0.000 0.172 0.436 0.108 0.272
#> SRR1951702 3 0.1720 0.76256 0.000 0.000 0.928 0.000 0.040 0.032
#> SRR1951701 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951699 3 0.3593 0.69028 0.000 0.004 0.800 0.000 0.064 0.132
#> SRR1951698 3 0.0935 0.76414 0.000 0.000 0.964 0.000 0.004 0.032
#> SRR1951700 5 0.2114 0.63578 0.008 0.000 0.076 0.000 0.904 0.012
#> SRR1951697 2 0.0993 0.88936 0.000 0.964 0.000 0.000 0.012 0.024
#> SRR1951696 3 0.4076 0.59890 0.000 0.000 0.724 0.016 0.024 0.236
#> SRR1951695 5 0.4860 0.57821 0.000 0.000 0.160 0.000 0.664 0.176
#> SRR1951693 5 0.2830 0.62173 0.000 0.000 0.144 0.000 0.836 0.020
#> SRR1951692 4 0.5930 0.48372 0.000 0.000 0.176 0.548 0.020 0.256
#> SRR1951691 5 0.2501 0.59445 0.036 0.000 0.016 0.008 0.900 0.040
#> SRR1951688 2 0.5869 -0.07892 0.000 0.416 0.000 0.000 0.388 0.196
#> SRR1951689 2 0.0405 0.90164 0.000 0.988 0.000 0.000 0.008 0.004
#> SRR1951687 4 0.5243 -0.79593 0.080 0.000 0.000 0.464 0.004 0.452
#> SRR1951685 5 0.5940 0.13662 0.000 0.384 0.004 0.000 0.428 0.184
#> SRR1951684 3 0.2060 0.72855 0.000 0.000 0.900 0.000 0.016 0.084
#> SRR1951686 2 0.3283 0.76998 0.000 0.804 0.000 0.000 0.036 0.160
#> SRR1951683 4 0.0881 0.50391 0.012 0.000 0.000 0.972 0.008 0.008
#> SRR1951682 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951680 3 0.4176 0.20584 0.000 0.000 0.580 0.000 0.404 0.016
#> SRR1951679 5 0.2955 0.62628 0.016 0.036 0.000 0.000 0.860 0.088
#> SRR1951678 4 0.0405 0.51913 0.000 0.000 0.008 0.988 0.004 0.000
#> SRR1951677 4 0.0405 0.51913 0.000 0.000 0.008 0.988 0.004 0.000
#> SRR1951676 3 0.5227 0.48458 0.000 0.000 0.628 0.080 0.024 0.268
#> SRR1951675 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951681 3 0.1812 0.73584 0.000 0.000 0.912 0.000 0.008 0.080
#> SRR1951673 2 0.5774 0.04701 0.000 0.456 0.000 0.000 0.364 0.180
#> SRR1951674 6 0.5867 0.84187 0.240 0.000 0.000 0.280 0.000 0.480
#> SRR1951672 5 0.2051 0.60365 0.044 0.012 0.020 0.004 0.920 0.000
#> SRR1951671 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951670 1 0.5426 0.10937 0.596 0.000 0.000 0.220 0.004 0.180
#> SRR1951640 3 0.4198 0.51391 0.000 0.000 0.708 0.000 0.232 0.060
#> SRR1951668 5 0.5796 0.41250 0.056 0.000 0.064 0.020 0.636 0.224
#> SRR1951669 5 0.4479 0.61795 0.008 0.080 0.000 0.004 0.728 0.180
#> SRR1951638 1 0.3857 -0.13717 0.532 0.000 0.000 0.000 0.000 0.468
#> SRR1951637 5 0.4281 0.62238 0.008 0.072 0.000 0.004 0.748 0.168
#> SRR1951635 1 0.3864 -0.16186 0.520 0.000 0.000 0.000 0.000 0.480
#> SRR1951634 5 0.5674 0.40215 0.028 0.000 0.124 0.004 0.616 0.228
#> SRR1951631 4 0.7093 0.43359 0.040 0.000 0.036 0.468 0.188 0.268
#> SRR1951632 2 0.0000 0.90698 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951633 4 0.0405 0.51722 0.000 0.000 0.008 0.988 0.004 0.000
#> SRR1951630 4 0.0405 0.51913 0.000 0.000 0.008 0.988 0.004 0.000
#> SRR1951629 3 0.5032 0.52731 0.000 0.000 0.636 0.008 0.096 0.260
#> SRR1951628 5 0.4646 0.61474 0.012 0.100 0.000 0.004 0.724 0.160
#> SRR1951627 3 0.1003 0.76897 0.000 0.000 0.964 0.000 0.016 0.020
#> SRR1951639 3 0.3645 0.68467 0.000 0.008 0.796 0.000 0.052 0.144
#> SRR1951625 2 0.3706 0.73723 0.000 0.772 0.000 0.000 0.056 0.172
#> SRR1951626 3 0.3645 0.68467 0.000 0.008 0.796 0.000 0.052 0.144
#> SRR1951624 3 0.0713 0.76900 0.000 0.000 0.972 0.000 0.028 0.000
#> SRR1951622 1 0.2668 0.66470 0.828 0.000 0.000 0.004 0.168 0.000
#> SRR1951636 3 0.1003 0.76863 0.000 0.000 0.964 0.000 0.020 0.016
#> SRR1951623 4 0.5780 0.49044 0.000 0.000 0.160 0.560 0.016 0.264
#> SRR1951621 4 0.5318 -0.80882 0.088 0.000 0.000 0.460 0.004 0.448
#> SRR1951620 3 0.2653 0.69527 0.000 0.000 0.844 0.000 0.012 0.144
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 13580 rows and 133 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.968 0.948 0.979 0.4974 0.504 0.504
#> 3 3 0.958 0.929 0.971 0.3353 0.763 0.559
#> 4 4 0.903 0.900 0.944 0.1139 0.876 0.654
#> 5 5 0.847 0.799 0.903 0.0488 0.935 0.764
#> 6 6 0.839 0.685 0.851 0.0284 0.972 0.881
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1951667 2 0.0000 0.971 0.000 1.000
#> SRR1951666 2 0.0000 0.971 0.000 1.000
#> SRR1951665 2 0.0000 0.971 0.000 1.000
#> SRR1951664 2 0.0000 0.971 0.000 1.000
#> SRR1951663 1 0.0000 0.986 1.000 0.000
#> SRR1951662 2 0.0000 0.971 0.000 1.000
#> SRR1951661 2 0.0000 0.971 0.000 1.000
#> SRR1951660 1 0.0000 0.986 1.000 0.000
#> SRR1951659 2 0.0000 0.971 0.000 1.000
#> SRR1951658 2 0.0000 0.971 0.000 1.000
#> SRR1951657 1 0.0000 0.986 1.000 0.000
#> SRR1951656 1 0.0000 0.986 1.000 0.000
#> SRR1951655 1 0.0000 0.986 1.000 0.000
#> SRR1951654 1 0.0000 0.986 1.000 0.000
#> SRR1951653 1 0.0000 0.986 1.000 0.000
#> SRR1951652 2 0.0376 0.968 0.004 0.996
#> SRR1951651 1 0.0000 0.986 1.000 0.000
#> SRR1951650 2 0.7219 0.754 0.200 0.800
#> SRR1951648 2 0.0000 0.971 0.000 1.000
#> SRR1951649 2 0.0000 0.971 0.000 1.000
#> SRR1951647 2 0.0000 0.971 0.000 1.000
#> SRR1951646 1 0.0000 0.986 1.000 0.000
#> SRR1951645 1 0.0000 0.986 1.000 0.000
#> SRR1951644 1 0.0000 0.986 1.000 0.000
#> SRR1951643 1 0.0000 0.986 1.000 0.000
#> SRR1951641 2 0.0000 0.971 0.000 1.000
#> SRR1951642 1 0.0000 0.986 1.000 0.000
#> SRR1951694 1 0.0000 0.986 1.000 0.000
#> SRR1951731 2 0.0000 0.971 0.000 1.000
#> SRR1951730 2 0.0000 0.971 0.000 1.000
#> SRR1951752 2 0.0000 0.971 0.000 1.000
#> SRR1951751 1 0.0000 0.986 1.000 0.000
#> SRR1951749 2 0.0000 0.971 0.000 1.000
#> SRR1951750 1 0.0000 0.986 1.000 0.000
#> SRR1951748 2 0.0000 0.971 0.000 1.000
#> SRR1951747 2 0.0000 0.971 0.000 1.000
#> SRR1951745 1 0.0000 0.986 1.000 0.000
#> SRR1951746 2 0.0000 0.971 0.000 1.000
#> SRR1951744 1 0.0000 0.986 1.000 0.000
#> SRR1951742 2 0.0000 0.971 0.000 1.000
#> SRR1951743 2 0.0000 0.971 0.000 1.000
#> SRR1951741 1 0.0000 0.986 1.000 0.000
#> SRR1951740 1 0.0000 0.986 1.000 0.000
#> SRR1951738 2 0.0000 0.971 0.000 1.000
#> SRR1951739 2 0.0000 0.971 0.000 1.000
#> SRR1951737 2 0.0000 0.971 0.000 1.000
#> SRR1951736 1 0.0000 0.986 1.000 0.000
#> SRR1951734 2 0.0000 0.971 0.000 1.000
#> SRR1951735 2 0.0000 0.971 0.000 1.000
#> SRR1951733 2 0.0000 0.971 0.000 1.000
#> SRR1951732 2 0.0000 0.971 0.000 1.000
#> SRR1951729 2 0.0000 0.971 0.000 1.000
#> SRR1951728 2 0.0000 0.971 0.000 1.000
#> SRR1951727 2 0.0000 0.971 0.000 1.000
#> SRR1951725 1 0.0000 0.986 1.000 0.000
#> SRR1951726 1 0.9044 0.508 0.680 0.320
#> SRR1951724 1 0.0000 0.986 1.000 0.000
#> SRR1951722 2 0.0000 0.971 0.000 1.000
#> SRR1951723 2 0.0000 0.971 0.000 1.000
#> SRR1951721 1 0.0000 0.986 1.000 0.000
#> SRR1951719 2 0.7139 0.759 0.196 0.804
#> SRR1951720 1 0.0000 0.986 1.000 0.000
#> SRR1951718 1 0.0000 0.986 1.000 0.000
#> SRR1951717 1 0.0000 0.986 1.000 0.000
#> SRR1951715 1 0.0000 0.986 1.000 0.000
#> SRR1951716 1 0.0000 0.986 1.000 0.000
#> SRR1951714 1 0.0000 0.986 1.000 0.000
#> SRR1951713 2 0.0000 0.971 0.000 1.000
#> SRR1951712 1 0.0000 0.986 1.000 0.000
#> SRR1951711 2 0.0000 0.971 0.000 1.000
#> SRR1951710 1 0.0000 0.986 1.000 0.000
#> SRR1951709 2 0.0000 0.971 0.000 1.000
#> SRR1951708 2 0.0000 0.971 0.000 1.000
#> SRR1951707 2 0.0000 0.971 0.000 1.000
#> SRR1951705 1 0.0000 0.986 1.000 0.000
#> SRR1951704 2 0.0000 0.971 0.000 1.000
#> SRR1951703 2 0.0000 0.971 0.000 1.000
#> SRR1951706 1 0.0000 0.986 1.000 0.000
#> SRR1951702 2 0.2043 0.944 0.032 0.968
#> SRR1951701 2 0.0000 0.971 0.000 1.000
#> SRR1951699 2 0.0000 0.971 0.000 1.000
#> SRR1951698 2 0.0000 0.971 0.000 1.000
#> SRR1951700 2 0.3584 0.910 0.068 0.932
#> SRR1951697 2 0.0000 0.971 0.000 1.000
#> SRR1951696 1 0.1843 0.959 0.972 0.028
#> SRR1951695 2 0.0000 0.971 0.000 1.000
#> SRR1951693 1 0.0000 0.986 1.000 0.000
#> SRR1951692 1 0.0000 0.986 1.000 0.000
#> SRR1951691 1 0.0000 0.986 1.000 0.000
#> SRR1951688 2 0.0000 0.971 0.000 1.000
#> SRR1951689 2 0.0000 0.971 0.000 1.000
#> SRR1951687 1 0.0000 0.986 1.000 0.000
#> SRR1951685 2 0.0000 0.971 0.000 1.000
#> SRR1951684 2 0.9686 0.370 0.396 0.604
#> SRR1951686 2 0.0000 0.971 0.000 1.000
#> SRR1951683 1 0.0000 0.986 1.000 0.000
#> SRR1951682 2 0.0000 0.971 0.000 1.000
#> SRR1951690 2 0.0000 0.971 0.000 1.000
#> SRR1951680 2 0.7219 0.754 0.200 0.800
#> SRR1951679 2 0.0000 0.971 0.000 1.000
#> SRR1951678 1 0.0000 0.986 1.000 0.000
#> SRR1951677 1 0.0000 0.986 1.000 0.000
#> SRR1951676 1 0.0000 0.986 1.000 0.000
#> SRR1951675 2 0.0000 0.971 0.000 1.000
#> SRR1951681 2 0.9754 0.337 0.408 0.592
#> SRR1951673 2 0.0000 0.971 0.000 1.000
#> SRR1951674 1 0.0000 0.986 1.000 0.000
#> SRR1951672 1 0.9661 0.325 0.608 0.392
#> SRR1951671 2 0.0000 0.971 0.000 1.000
#> SRR1951670 1 0.0000 0.986 1.000 0.000
#> SRR1951640 2 0.0000 0.971 0.000 1.000
#> SRR1951668 1 0.0000 0.986 1.000 0.000
#> SRR1951669 2 0.6343 0.802 0.160 0.840
#> SRR1951638 1 0.0000 0.986 1.000 0.000
#> SRR1951637 2 0.2423 0.937 0.040 0.960
#> SRR1951635 1 0.0000 0.986 1.000 0.000
#> SRR1951634 1 0.0000 0.986 1.000 0.000
#> SRR1951631 1 0.0000 0.986 1.000 0.000
#> SRR1951632 2 0.0000 0.971 0.000 1.000
#> SRR1951633 1 0.0000 0.986 1.000 0.000
#> SRR1951630 1 0.0000 0.986 1.000 0.000
#> SRR1951629 1 0.0000 0.986 1.000 0.000
#> SRR1951628 2 0.0000 0.971 0.000 1.000
#> SRR1951627 2 0.0000 0.971 0.000 1.000
#> SRR1951639 2 0.0000 0.971 0.000 1.000
#> SRR1951625 2 0.0000 0.971 0.000 1.000
#> SRR1951626 2 0.0000 0.971 0.000 1.000
#> SRR1951624 2 0.0000 0.971 0.000 1.000
#> SRR1951622 1 0.0000 0.986 1.000 0.000
#> SRR1951636 2 0.0000 0.971 0.000 1.000
#> SRR1951623 1 0.0000 0.986 1.000 0.000
#> SRR1951621 1 0.0000 0.986 1.000 0.000
#> SRR1951620 2 0.9608 0.400 0.384 0.616
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951666 3 0.0424 0.9325 0.000 0.008 0.992
#> SRR1951665 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951664 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951663 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951662 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951661 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951660 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951659 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951657 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951656 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951655 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951654 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951653 3 0.2448 0.8727 0.076 0.000 0.924
#> SRR1951652 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951651 1 0.0237 0.9744 0.996 0.000 0.004
#> SRR1951650 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951648 3 0.6309 0.0892 0.000 0.500 0.500
#> SRR1951649 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951647 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951646 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951645 1 0.1643 0.9387 0.956 0.000 0.044
#> SRR1951644 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951643 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951641 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951642 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951694 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951731 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951730 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951752 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951751 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951749 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951750 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951748 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951745 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951746 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951744 1 0.1031 0.9580 0.976 0.000 0.024
#> SRR1951742 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951743 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951741 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951740 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951738 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951739 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951737 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951736 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951734 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951735 3 0.5650 0.5758 0.000 0.312 0.688
#> SRR1951733 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951732 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951729 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951728 3 0.3412 0.8337 0.000 0.124 0.876
#> SRR1951727 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951725 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951726 1 0.5882 0.4752 0.652 0.348 0.000
#> SRR1951724 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951722 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951723 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951721 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951719 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951720 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951718 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951717 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951715 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951716 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951714 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951713 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951712 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951711 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951710 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951709 3 0.6045 0.4381 0.000 0.380 0.620
#> SRR1951708 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951707 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951705 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951704 3 0.1031 0.9221 0.000 0.024 0.976
#> SRR1951703 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951706 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951702 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951701 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951699 3 0.0892 0.9250 0.000 0.020 0.980
#> SRR1951698 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951700 2 0.5254 0.6218 0.264 0.736 0.000
#> SRR1951697 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951696 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951695 2 0.6062 0.3301 0.000 0.616 0.384
#> SRR1951693 3 0.3879 0.7890 0.152 0.000 0.848
#> SRR1951692 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951691 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951688 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951689 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951687 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951685 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951684 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951686 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951683 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951682 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951690 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951680 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951679 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951678 1 0.0237 0.9744 0.996 0.000 0.004
#> SRR1951677 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951676 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951675 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951681 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951673 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951674 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951672 1 0.6215 0.2686 0.572 0.428 0.000
#> SRR1951671 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951670 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951640 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951668 1 0.0237 0.9744 0.996 0.000 0.004
#> SRR1951669 2 0.0592 0.9724 0.012 0.988 0.000
#> SRR1951638 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951637 2 0.0237 0.9806 0.004 0.996 0.000
#> SRR1951635 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951634 1 0.2448 0.9049 0.924 0.000 0.076
#> SRR1951631 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951632 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951633 1 0.1289 0.9510 0.968 0.000 0.032
#> SRR1951630 1 0.0237 0.9744 0.996 0.000 0.004
#> SRR1951629 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951628 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951627 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951639 3 0.6280 0.2252 0.000 0.460 0.540
#> SRR1951625 2 0.0000 0.9847 0.000 1.000 0.000
#> SRR1951626 3 0.3941 0.8005 0.000 0.156 0.844
#> SRR1951624 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951622 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951636 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951623 3 0.0000 0.9367 0.000 0.000 1.000
#> SRR1951621 1 0.0000 0.9770 1.000 0.000 0.000
#> SRR1951620 3 0.0000 0.9367 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951666 3 0.0188 0.964 0.000 0.004 0.996 0.000
#> SRR1951665 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951664 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> SRR1951663 1 0.0469 0.928 0.988 0.000 0.000 0.012
#> SRR1951662 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> SRR1951660 1 0.0707 0.926 0.980 0.000 0.000 0.020
#> SRR1951659 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951658 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951657 4 0.2921 0.831 0.000 0.000 0.140 0.860
#> SRR1951656 4 0.3444 0.791 0.000 0.000 0.184 0.816
#> SRR1951655 1 0.0336 0.929 0.992 0.000 0.000 0.008
#> SRR1951654 4 0.4040 0.717 0.248 0.000 0.000 0.752
#> SRR1951653 4 0.5530 0.447 0.032 0.000 0.336 0.632
#> SRR1951652 2 0.0188 0.970 0.000 0.996 0.000 0.004
#> SRR1951651 4 0.1716 0.888 0.064 0.000 0.000 0.936
#> SRR1951650 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> SRR1951648 3 0.0707 0.954 0.000 0.020 0.980 0.000
#> SRR1951649 2 0.5142 0.684 0.192 0.744 0.000 0.064
#> SRR1951647 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951646 4 0.3486 0.787 0.000 0.000 0.188 0.812
#> SRR1951645 1 0.1867 0.899 0.928 0.000 0.000 0.072
#> SRR1951644 1 0.1716 0.899 0.936 0.000 0.000 0.064
#> SRR1951643 4 0.1716 0.888 0.064 0.000 0.000 0.936
#> SRR1951641 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951642 4 0.1716 0.888 0.064 0.000 0.000 0.936
#> SRR1951694 1 0.0188 0.929 0.996 0.000 0.000 0.004
#> SRR1951731 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951730 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951752 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951751 1 0.1716 0.899 0.936 0.000 0.000 0.064
#> SRR1951749 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951750 1 0.1118 0.917 0.964 0.000 0.000 0.036
#> SRR1951748 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951745 1 0.2704 0.838 0.876 0.000 0.000 0.124
#> SRR1951746 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951744 4 0.1824 0.888 0.060 0.000 0.004 0.936
#> SRR1951742 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951743 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951741 4 0.4222 0.679 0.272 0.000 0.000 0.728
#> SRR1951740 4 0.2345 0.855 0.000 0.000 0.100 0.900
#> SRR1951738 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951739 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951737 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> SRR1951736 1 0.0188 0.929 0.996 0.000 0.000 0.004
#> SRR1951734 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951735 3 0.0592 0.958 0.000 0.016 0.984 0.000
#> SRR1951733 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951732 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951729 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> SRR1951728 3 0.2197 0.887 0.000 0.080 0.916 0.004
#> SRR1951727 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951725 1 0.0469 0.929 0.988 0.000 0.000 0.012
#> SRR1951726 1 0.6238 0.556 0.632 0.276 0.000 0.092
#> SRR1951724 1 0.4040 0.706 0.752 0.000 0.000 0.248
#> SRR1951722 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951723 2 0.0469 0.965 0.000 0.988 0.000 0.012
#> SRR1951721 1 0.0188 0.929 0.996 0.000 0.000 0.004
#> SRR1951719 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> SRR1951720 1 0.0469 0.929 0.988 0.000 0.000 0.012
#> SRR1951718 1 0.0707 0.926 0.980 0.000 0.000 0.020
#> SRR1951717 1 0.0188 0.929 0.996 0.000 0.000 0.004
#> SRR1951715 1 0.0000 0.928 1.000 0.000 0.000 0.000
#> SRR1951716 1 0.0188 0.929 0.996 0.000 0.000 0.004
#> SRR1951714 4 0.1716 0.888 0.064 0.000 0.000 0.936
#> SRR1951713 3 0.1902 0.917 0.004 0.000 0.932 0.064
#> SRR1951712 1 0.1716 0.899 0.936 0.000 0.000 0.064
#> SRR1951711 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951710 1 0.0469 0.925 0.988 0.000 0.000 0.012
#> SRR1951709 3 0.0592 0.958 0.000 0.016 0.984 0.000
#> SRR1951708 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951707 2 0.1474 0.931 0.000 0.948 0.000 0.052
#> SRR1951705 1 0.1716 0.899 0.936 0.000 0.000 0.064
#> SRR1951704 3 0.0336 0.962 0.000 0.008 0.992 0.000
#> SRR1951703 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951706 4 0.1716 0.888 0.064 0.000 0.000 0.936
#> SRR1951702 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> SRR1951701 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951699 3 0.0336 0.962 0.000 0.008 0.992 0.000
#> SRR1951698 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> SRR1951700 1 0.5835 0.566 0.656 0.280 0.000 0.064
#> SRR1951697 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951696 3 0.1637 0.913 0.000 0.000 0.940 0.060
#> SRR1951695 3 0.4509 0.598 0.000 0.288 0.708 0.004
#> SRR1951693 3 0.4022 0.820 0.096 0.000 0.836 0.068
#> SRR1951692 4 0.1867 0.870 0.000 0.000 0.072 0.928
#> SRR1951691 1 0.1302 0.916 0.956 0.000 0.000 0.044
#> SRR1951688 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951689 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951687 4 0.2760 0.849 0.128 0.000 0.000 0.872
#> SRR1951685 2 0.0817 0.955 0.000 0.976 0.000 0.024
#> SRR1951684 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> SRR1951686 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951683 4 0.2408 0.866 0.104 0.000 0.000 0.896
#> SRR1951682 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951690 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951680 3 0.1637 0.923 0.000 0.000 0.940 0.060
#> SRR1951679 2 0.4188 0.806 0.112 0.824 0.000 0.064
#> SRR1951678 4 0.1716 0.888 0.064 0.000 0.000 0.936
#> SRR1951677 4 0.1716 0.888 0.064 0.000 0.000 0.936
#> SRR1951676 4 0.4992 0.233 0.000 0.000 0.476 0.524
#> SRR1951675 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951681 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> SRR1951673 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951674 1 0.2868 0.825 0.864 0.000 0.000 0.136
#> SRR1951672 1 0.3547 0.843 0.864 0.072 0.000 0.064
#> SRR1951671 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951670 1 0.1118 0.917 0.964 0.000 0.000 0.036
#> SRR1951640 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> SRR1951668 4 0.0000 0.865 0.000 0.000 0.000 1.000
#> SRR1951669 2 0.4477 0.549 0.312 0.688 0.000 0.000
#> SRR1951638 1 0.0469 0.929 0.988 0.000 0.000 0.012
#> SRR1951637 2 0.4746 0.422 0.368 0.632 0.000 0.000
#> SRR1951635 1 0.0469 0.929 0.988 0.000 0.000 0.012
#> SRR1951634 4 0.1624 0.858 0.020 0.000 0.028 0.952
#> SRR1951631 4 0.1716 0.888 0.064 0.000 0.000 0.936
#> SRR1951632 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951633 4 0.1716 0.888 0.064 0.000 0.000 0.936
#> SRR1951630 4 0.1716 0.888 0.064 0.000 0.000 0.936
#> SRR1951629 4 0.3074 0.821 0.000 0.000 0.152 0.848
#> SRR1951628 2 0.0336 0.967 0.008 0.992 0.000 0.000
#> SRR1951627 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> SRR1951639 3 0.0592 0.958 0.000 0.016 0.984 0.000
#> SRR1951625 2 0.0000 0.973 0.000 1.000 0.000 0.000
#> SRR1951626 3 0.0469 0.960 0.000 0.012 0.988 0.000
#> SRR1951624 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> SRR1951622 1 0.0188 0.929 0.996 0.000 0.000 0.004
#> SRR1951636 3 0.0000 0.965 0.000 0.000 1.000 0.000
#> SRR1951623 4 0.1792 0.871 0.000 0.000 0.068 0.932
#> SRR1951621 4 0.3942 0.733 0.236 0.000 0.000 0.764
#> SRR1951620 3 0.0000 0.965 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
#> SRR1951667 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951666 3 0.0000 0.9097 0.000 0.000 1.000 0.000 0.000
#> SRR1951665 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 3 0.1484 0.9005 0.000 0.000 0.944 0.008 0.048
#> SRR1951663 1 0.0000 0.8363 1.000 0.000 0.000 0.000 0.000
#> SRR1951662 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 3 0.0000 0.9097 0.000 0.000 1.000 0.000 0.000
#> SRR1951660 1 0.0693 0.8362 0.980 0.000 0.000 0.008 0.012
#> SRR1951659 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951657 4 0.2110 0.8024 0.000 0.000 0.016 0.912 0.072
#> SRR1951656 4 0.3336 0.7527 0.000 0.000 0.060 0.844 0.096
#> SRR1951655 1 0.0404 0.8359 0.988 0.000 0.000 0.000 0.012
#> SRR1951654 1 0.3636 0.6482 0.728 0.000 0.000 0.272 0.000
#> SRR1951653 4 0.5408 0.5575 0.000 0.000 0.120 0.652 0.228
#> SRR1951652 2 0.1483 0.9271 0.028 0.952 0.012 0.000 0.008
#> SRR1951651 4 0.1043 0.8453 0.040 0.000 0.000 0.960 0.000
#> SRR1951650 3 0.2905 0.8521 0.000 0.000 0.868 0.036 0.096
#> SRR1951648 3 0.0404 0.9038 0.000 0.012 0.988 0.000 0.000
#> SRR1951649 5 0.3148 0.7333 0.072 0.060 0.000 0.004 0.864
#> SRR1951647 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951646 4 0.3759 0.7290 0.000 0.000 0.092 0.816 0.092
#> SRR1951645 5 0.2674 0.7410 0.140 0.000 0.000 0.004 0.856
#> SRR1951644 1 0.4294 -0.0182 0.532 0.000 0.000 0.000 0.468
#> SRR1951643 4 0.2280 0.7895 0.120 0.000 0.000 0.880 0.000
#> SRR1951641 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951642 4 0.1043 0.8453 0.040 0.000 0.000 0.960 0.000
#> SRR1951694 1 0.0404 0.8359 0.988 0.000 0.000 0.000 0.012
#> SRR1951731 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951730 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951752 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951751 5 0.3003 0.7132 0.188 0.000 0.000 0.000 0.812
#> SRR1951749 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951750 1 0.2189 0.8074 0.904 0.000 0.000 0.084 0.012
#> SRR1951748 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 1 0.2329 0.7819 0.876 0.000 0.000 0.124 0.000
#> SRR1951746 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 4 0.1410 0.8386 0.060 0.000 0.000 0.940 0.000
#> SRR1951742 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951743 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 1 0.3534 0.6708 0.744 0.000 0.000 0.256 0.000
#> SRR1951740 4 0.4276 0.4334 0.000 0.000 0.004 0.616 0.380
#> SRR1951738 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951739 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951737 3 0.1430 0.9004 0.000 0.000 0.944 0.004 0.052
#> SRR1951736 1 0.0609 0.8320 0.980 0.000 0.000 0.000 0.020
#> SRR1951734 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 3 0.0162 0.9086 0.000 0.004 0.996 0.000 0.000
#> SRR1951733 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951732 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 3 0.0865 0.9068 0.000 0.000 0.972 0.004 0.024
#> SRR1951728 3 0.2610 0.8423 0.000 0.028 0.892 0.004 0.076
#> SRR1951727 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 1 0.0162 0.8365 0.996 0.000 0.000 0.000 0.004
#> SRR1951726 1 0.6938 0.3661 0.524 0.236 0.012 0.216 0.012
#> SRR1951724 1 0.3970 0.7382 0.788 0.000 0.000 0.156 0.056
#> SRR1951722 2 0.0794 0.9450 0.000 0.972 0.000 0.000 0.028
#> SRR1951723 2 0.1608 0.9033 0.000 0.928 0.000 0.000 0.072
#> SRR1951721 1 0.0000 0.8363 1.000 0.000 0.000 0.000 0.000
#> SRR1951719 3 0.1704 0.8948 0.000 0.000 0.928 0.004 0.068
#> SRR1951720 1 0.0404 0.8359 0.988 0.000 0.000 0.000 0.012
#> SRR1951718 1 0.0404 0.8359 0.988 0.000 0.000 0.000 0.012
#> SRR1951717 1 0.0290 0.8364 0.992 0.000 0.000 0.000 0.008
#> SRR1951715 1 0.1341 0.8025 0.944 0.000 0.000 0.000 0.056
#> SRR1951716 1 0.0404 0.8359 0.988 0.000 0.000 0.000 0.012
#> SRR1951714 4 0.1043 0.8453 0.040 0.000 0.000 0.960 0.000
#> SRR1951713 5 0.1168 0.6970 0.000 0.000 0.032 0.008 0.960
#> SRR1951712 5 0.2966 0.7172 0.184 0.000 0.000 0.000 0.816
#> SRR1951711 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951710 1 0.1341 0.8025 0.944 0.000 0.000 0.000 0.056
#> SRR1951709 3 0.0162 0.9086 0.000 0.004 0.996 0.000 0.000
#> SRR1951708 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951707 2 0.3561 0.6526 0.000 0.740 0.000 0.000 0.260
#> SRR1951705 1 0.4300 -0.0483 0.524 0.000 0.000 0.000 0.476
#> SRR1951704 3 0.0451 0.9073 0.000 0.000 0.988 0.004 0.008
#> SRR1951703 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 4 0.1410 0.8386 0.060 0.000 0.000 0.940 0.000
#> SRR1951702 3 0.0000 0.9097 0.000 0.000 1.000 0.000 0.000
#> SRR1951701 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951699 3 0.0000 0.9097 0.000 0.000 1.000 0.000 0.000
#> SRR1951698 3 0.1195 0.9022 0.000 0.000 0.960 0.012 0.028
#> SRR1951700 5 0.2020 0.7462 0.100 0.000 0.000 0.000 0.900
#> SRR1951697 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951696 3 0.5334 0.5901 0.000 0.000 0.652 0.244 0.104
#> SRR1951695 3 0.6792 -0.0896 0.000 0.228 0.404 0.004 0.364
#> SRR1951693 5 0.3949 0.4564 0.000 0.000 0.300 0.004 0.696
#> SRR1951692 4 0.1768 0.8069 0.000 0.000 0.004 0.924 0.072
#> SRR1951691 5 0.4559 0.1157 0.480 0.000 0.000 0.008 0.512
#> SRR1951688 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951689 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951687 1 0.4283 0.2516 0.544 0.000 0.000 0.456 0.000
#> SRR1951685 2 0.2773 0.7947 0.000 0.836 0.000 0.000 0.164
#> SRR1951684 3 0.3386 0.8369 0.000 0.000 0.832 0.040 0.128
#> SRR1951686 2 0.0162 0.9642 0.000 0.996 0.004 0.000 0.000
#> SRR1951683 4 0.3003 0.7086 0.188 0.000 0.000 0.812 0.000
#> SRR1951682 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951680 5 0.4874 0.2569 0.000 0.000 0.328 0.040 0.632
#> SRR1951679 5 0.3274 0.5882 0.000 0.220 0.000 0.000 0.780
#> SRR1951678 4 0.1043 0.8453 0.040 0.000 0.000 0.960 0.000
#> SRR1951677 4 0.1043 0.8453 0.040 0.000 0.000 0.960 0.000
#> SRR1951676 4 0.5638 0.1108 0.000 0.000 0.432 0.492 0.076
#> SRR1951675 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951681 3 0.2473 0.8694 0.000 0.000 0.896 0.032 0.072
#> SRR1951673 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951674 1 0.2690 0.7601 0.844 0.000 0.000 0.156 0.000
#> SRR1951672 5 0.2890 0.7317 0.160 0.004 0.000 0.000 0.836
#> SRR1951671 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951670 1 0.2305 0.8035 0.896 0.000 0.000 0.092 0.012
#> SRR1951640 3 0.3771 0.8075 0.000 0.000 0.796 0.040 0.164
#> SRR1951668 4 0.4908 0.4040 0.036 0.000 0.000 0.608 0.356
#> SRR1951669 2 0.4356 0.4721 0.340 0.648 0.000 0.000 0.012
#> SRR1951638 1 0.0000 0.8363 1.000 0.000 0.000 0.000 0.000
#> SRR1951637 2 0.4851 0.4302 0.340 0.624 0.000 0.000 0.036
#> SRR1951635 1 0.0000 0.8363 1.000 0.000 0.000 0.000 0.000
#> SRR1951634 5 0.4126 0.1484 0.000 0.000 0.000 0.380 0.620
#> SRR1951631 4 0.1671 0.8262 0.076 0.000 0.000 0.924 0.000
#> SRR1951632 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951633 4 0.1043 0.8453 0.040 0.000 0.000 0.960 0.000
#> SRR1951630 4 0.1043 0.8453 0.040 0.000 0.000 0.960 0.000
#> SRR1951629 4 0.1914 0.8099 0.000 0.000 0.016 0.924 0.060
#> SRR1951628 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951627 3 0.0000 0.9097 0.000 0.000 1.000 0.000 0.000
#> SRR1951639 3 0.0162 0.9086 0.000 0.004 0.996 0.000 0.000
#> SRR1951625 2 0.0000 0.9676 0.000 1.000 0.000 0.000 0.000
#> SRR1951626 3 0.0000 0.9097 0.000 0.000 1.000 0.000 0.000
#> SRR1951624 3 0.1571 0.8980 0.000 0.000 0.936 0.004 0.060
#> SRR1951622 1 0.0880 0.8225 0.968 0.000 0.000 0.000 0.032
#> SRR1951636 3 0.0000 0.9097 0.000 0.000 1.000 0.000 0.000
#> SRR1951623 4 0.0451 0.8317 0.000 0.000 0.004 0.988 0.008
#> SRR1951621 1 0.3661 0.6450 0.724 0.000 0.000 0.276 0.000
#> SRR1951620 3 0.3911 0.8041 0.000 0.000 0.796 0.060 0.144
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951666 5 0.0363 0.8369 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1951665 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 5 0.3240 0.6745 0.000 0.000 0.244 0.004 0.752 0.000
#> SRR1951663 1 0.1152 0.8118 0.952 0.000 0.044 0.004 0.000 0.000
#> SRR1951662 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 5 0.0000 0.8374 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951660 1 0.1225 0.8204 0.952 0.000 0.036 0.012 0.000 0.000
#> SRR1951659 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951657 4 0.3840 0.4655 0.000 0.000 0.284 0.696 0.020 0.000
#> SRR1951656 4 0.4408 0.3059 0.000 0.000 0.356 0.608 0.036 0.000
#> SRR1951655 1 0.1124 0.8195 0.956 0.000 0.036 0.008 0.000 0.000
#> SRR1951654 1 0.4552 0.4798 0.592 0.000 0.044 0.364 0.000 0.000
#> SRR1951653 3 0.6086 0.0354 0.024 0.000 0.484 0.396 0.028 0.068
#> SRR1951652 2 0.5689 0.3838 0.024 0.552 0.364 0.016 0.024 0.020
#> SRR1951651 4 0.0146 0.7452 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951650 5 0.3584 0.5278 0.000 0.000 0.308 0.004 0.688 0.000
#> SRR1951648 5 0.0363 0.8369 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1951649 6 0.1850 0.6909 0.016 0.008 0.052 0.000 0.000 0.924
#> SRR1951647 2 0.0508 0.9344 0.000 0.984 0.012 0.000 0.004 0.000
#> SRR1951646 4 0.4795 0.3033 0.000 0.000 0.324 0.604 0.072 0.000
#> SRR1951645 6 0.3006 0.6890 0.064 0.000 0.092 0.000 0.000 0.844
#> SRR1951644 1 0.4735 0.0753 0.520 0.000 0.048 0.000 0.000 0.432
#> SRR1951643 4 0.1610 0.7022 0.084 0.000 0.000 0.916 0.000 0.000
#> SRR1951641 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951642 4 0.0146 0.7452 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951694 1 0.0935 0.8198 0.964 0.000 0.032 0.004 0.000 0.000
#> SRR1951731 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951730 2 0.1074 0.9185 0.000 0.960 0.028 0.000 0.000 0.012
#> SRR1951752 2 0.0146 0.9420 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1951751 6 0.2020 0.6907 0.096 0.000 0.008 0.000 0.000 0.896
#> SRR1951749 2 0.0146 0.9418 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1951750 1 0.2814 0.7483 0.820 0.000 0.008 0.172 0.000 0.000
#> SRR1951748 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 1 0.3964 0.6701 0.724 0.000 0.044 0.232 0.000 0.000
#> SRR1951746 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951744 4 0.3278 0.6361 0.040 0.000 0.152 0.808 0.000 0.000
#> SRR1951742 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951743 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951741 1 0.4026 0.5380 0.636 0.000 0.016 0.348 0.000 0.000
#> SRR1951740 3 0.5686 0.1053 0.000 0.000 0.472 0.364 0.000 0.164
#> SRR1951738 2 0.1531 0.8942 0.000 0.928 0.068 0.000 0.000 0.004
#> SRR1951739 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951737 5 0.2883 0.7078 0.000 0.000 0.212 0.000 0.788 0.000
#> SRR1951736 1 0.1080 0.8170 0.960 0.000 0.032 0.004 0.000 0.004
#> SRR1951734 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951735 5 0.0458 0.8375 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1951733 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951732 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 5 0.1714 0.8038 0.000 0.000 0.092 0.000 0.908 0.000
#> SRR1951728 5 0.3077 0.7122 0.000 0.032 0.040 0.000 0.860 0.068
#> SRR1951727 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 1 0.0603 0.8207 0.980 0.000 0.016 0.004 0.000 0.000
#> SRR1951726 3 0.8209 -0.1493 0.276 0.112 0.360 0.204 0.028 0.020
#> SRR1951724 1 0.4833 0.6777 0.704 0.000 0.048 0.196 0.000 0.052
#> SRR1951722 2 0.1434 0.9019 0.000 0.940 0.012 0.000 0.000 0.048
#> SRR1951723 2 0.1866 0.8724 0.000 0.908 0.008 0.000 0.000 0.084
#> SRR1951721 1 0.1010 0.8163 0.960 0.000 0.036 0.000 0.000 0.004
#> SRR1951719 5 0.3189 0.6797 0.000 0.000 0.236 0.000 0.760 0.004
#> SRR1951720 1 0.0405 0.8227 0.988 0.000 0.004 0.008 0.000 0.000
#> SRR1951718 1 0.1225 0.8204 0.952 0.000 0.036 0.012 0.000 0.000
#> SRR1951717 1 0.0405 0.8216 0.988 0.000 0.008 0.004 0.000 0.000
#> SRR1951715 1 0.1152 0.8120 0.952 0.000 0.044 0.000 0.000 0.004
#> SRR1951716 1 0.1049 0.8201 0.960 0.000 0.032 0.008 0.000 0.000
#> SRR1951714 4 0.0146 0.7452 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951713 6 0.2730 0.6072 0.000 0.000 0.192 0.000 0.000 0.808
#> SRR1951712 6 0.2118 0.6869 0.104 0.000 0.008 0.000 0.000 0.888
#> SRR1951711 2 0.0260 0.9397 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1951710 1 0.1713 0.7974 0.928 0.000 0.044 0.000 0.000 0.028
#> SRR1951709 5 0.0260 0.8372 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1951708 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951707 2 0.4305 0.5939 0.000 0.692 0.048 0.000 0.004 0.256
#> SRR1951705 1 0.4756 -0.0392 0.488 0.000 0.048 0.000 0.000 0.464
#> SRR1951704 5 0.1285 0.8115 0.000 0.000 0.052 0.000 0.944 0.004
#> SRR1951703 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 4 0.3066 0.6556 0.044 0.000 0.124 0.832 0.000 0.000
#> SRR1951702 5 0.1007 0.8192 0.000 0.000 0.044 0.000 0.956 0.000
#> SRR1951701 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951699 5 0.0146 0.8375 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1951698 5 0.2838 0.7138 0.000 0.000 0.188 0.004 0.808 0.000
#> SRR1951700 6 0.0748 0.6942 0.016 0.004 0.004 0.000 0.000 0.976
#> SRR1951697 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951696 3 0.5838 0.1449 0.000 0.000 0.412 0.188 0.400 0.000
#> SRR1951695 6 0.7620 0.0766 0.000 0.228 0.228 0.000 0.204 0.340
#> SRR1951693 6 0.6115 0.1716 0.016 0.000 0.284 0.000 0.204 0.496
#> SRR1951692 4 0.3101 0.5460 0.000 0.000 0.244 0.756 0.000 0.000
#> SRR1951691 6 0.6062 0.2435 0.356 0.000 0.116 0.036 0.000 0.492
#> SRR1951688 2 0.0865 0.9223 0.000 0.964 0.036 0.000 0.000 0.000
#> SRR1951689 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951687 4 0.4177 -0.1508 0.468 0.000 0.012 0.520 0.000 0.000
#> SRR1951685 2 0.3046 0.7497 0.000 0.800 0.012 0.000 0.000 0.188
#> SRR1951684 5 0.4095 0.0741 0.000 0.000 0.480 0.000 0.512 0.008
#> SRR1951686 2 0.0146 0.9418 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1951683 4 0.2135 0.6628 0.128 0.000 0.000 0.872 0.000 0.000
#> SRR1951682 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951680 3 0.5586 -0.0948 0.000 0.000 0.440 0.000 0.140 0.420
#> SRR1951679 6 0.2151 0.6526 0.008 0.072 0.016 0.000 0.000 0.904
#> SRR1951678 4 0.0146 0.7452 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951677 4 0.0146 0.7452 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951676 4 0.5983 -0.2101 0.000 0.000 0.232 0.412 0.356 0.000
#> SRR1951675 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951681 5 0.3789 0.5630 0.000 0.000 0.260 0.024 0.716 0.000
#> SRR1951673 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951674 1 0.4107 0.6419 0.700 0.000 0.044 0.256 0.000 0.000
#> SRR1951672 6 0.2401 0.6958 0.060 0.004 0.044 0.000 0.000 0.892
#> SRR1951671 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951670 1 0.3012 0.7320 0.796 0.000 0.008 0.196 0.000 0.000
#> SRR1951640 3 0.4704 -0.1074 0.000 0.000 0.488 0.000 0.468 0.044
#> SRR1951668 4 0.4671 0.3919 0.004 0.000 0.060 0.640 0.000 0.296
#> SRR1951669 2 0.6225 0.1924 0.172 0.464 0.340 0.000 0.000 0.024
#> SRR1951638 1 0.0508 0.8214 0.984 0.000 0.012 0.004 0.000 0.000
#> SRR1951637 2 0.6658 0.0616 0.204 0.416 0.336 0.000 0.000 0.044
#> SRR1951635 1 0.0508 0.8214 0.984 0.000 0.012 0.004 0.000 0.000
#> SRR1951634 6 0.6031 0.0577 0.000 0.000 0.268 0.312 0.000 0.420
#> SRR1951631 4 0.1007 0.7294 0.044 0.000 0.000 0.956 0.000 0.000
#> SRR1951632 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951633 4 0.0146 0.7452 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951630 4 0.0146 0.7452 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951629 4 0.3539 0.5570 0.000 0.000 0.220 0.756 0.024 0.000
#> SRR1951628 2 0.1644 0.8893 0.000 0.920 0.076 0.000 0.000 0.004
#> SRR1951627 5 0.0260 0.8373 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1951639 5 0.0458 0.8375 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1951625 2 0.0000 0.9439 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951626 5 0.0363 0.8369 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1951624 5 0.2871 0.7315 0.000 0.000 0.192 0.000 0.804 0.004
#> SRR1951622 1 0.1155 0.8160 0.956 0.000 0.036 0.004 0.000 0.004
#> SRR1951636 5 0.0000 0.8374 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951623 4 0.1204 0.7202 0.000 0.000 0.056 0.944 0.000 0.000
#> SRR1951621 1 0.4037 0.4801 0.608 0.000 0.012 0.380 0.000 0.000
#> SRR1951620 3 0.4699 -0.0869 0.000 0.000 0.496 0.028 0.468 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 13580 rows and 133 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.535 0.821 0.887 0.4479 0.549 0.549
#> 3 3 0.819 0.890 0.944 0.4234 0.730 0.542
#> 4 4 0.908 0.908 0.955 0.1674 0.833 0.571
#> 5 5 0.925 0.904 0.962 0.0373 0.971 0.887
#> 6 6 0.819 0.792 0.888 0.0300 0.991 0.963
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] 4
There is also optional best \(k\) = 4 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1951667 2 0.0000 0.9600 0.000 1.000
#> SRR1951666 2 0.3584 0.8820 0.068 0.932
#> SRR1951665 2 0.0000 0.9600 0.000 1.000
#> SRR1951664 1 0.9000 0.7362 0.684 0.316
#> SRR1951663 1 0.0000 0.8193 1.000 0.000
#> SRR1951662 2 0.0000 0.9600 0.000 1.000
#> SRR1951661 1 0.9087 0.7272 0.676 0.324
#> SRR1951660 1 0.0938 0.8229 0.988 0.012
#> SRR1951659 2 0.0000 0.9600 0.000 1.000
#> SRR1951658 2 0.0000 0.9600 0.000 1.000
#> SRR1951657 1 0.3584 0.8277 0.932 0.068
#> SRR1951656 1 0.2948 0.8290 0.948 0.052
#> SRR1951655 1 0.0000 0.8193 1.000 0.000
#> SRR1951654 1 0.0000 0.8193 1.000 0.000
#> SRR1951653 1 0.2948 0.8290 0.948 0.052
#> SRR1951652 1 0.9393 0.6845 0.644 0.356
#> SRR1951651 1 0.2948 0.8290 0.948 0.052
#> SRR1951650 1 0.9000 0.7362 0.684 0.316
#> SRR1951648 2 0.0000 0.9600 0.000 1.000
#> SRR1951649 1 0.9000 0.7362 0.684 0.316
#> SRR1951647 2 0.0672 0.9519 0.008 0.992
#> SRR1951646 1 0.6048 0.8087 0.852 0.148
#> SRR1951645 1 0.8608 0.7548 0.716 0.284
#> SRR1951644 1 0.0672 0.8219 0.992 0.008
#> SRR1951643 1 0.0000 0.8193 1.000 0.000
#> SRR1951641 2 0.0000 0.9600 0.000 1.000
#> SRR1951642 1 0.2423 0.8281 0.960 0.040
#> SRR1951694 1 0.0000 0.8193 1.000 0.000
#> SRR1951731 2 0.0000 0.9600 0.000 1.000
#> SRR1951730 2 0.0000 0.9600 0.000 1.000
#> SRR1951752 2 0.9732 -0.0136 0.404 0.596
#> SRR1951751 1 0.2043 0.8273 0.968 0.032
#> SRR1951749 2 0.0000 0.9600 0.000 1.000
#> SRR1951750 1 0.0000 0.8193 1.000 0.000
#> SRR1951748 2 0.0000 0.9600 0.000 1.000
#> SRR1951747 2 0.0000 0.9600 0.000 1.000
#> SRR1951745 1 0.0000 0.8193 1.000 0.000
#> SRR1951746 2 0.0000 0.9600 0.000 1.000
#> SRR1951744 1 0.8386 0.7627 0.732 0.268
#> SRR1951742 2 0.0000 0.9600 0.000 1.000
#> SRR1951743 2 0.0000 0.9600 0.000 1.000
#> SRR1951741 1 0.0000 0.8193 1.000 0.000
#> SRR1951740 1 0.3431 0.8282 0.936 0.064
#> SRR1951738 2 0.0000 0.9600 0.000 1.000
#> SRR1951739 2 0.0000 0.9600 0.000 1.000
#> SRR1951737 1 0.9000 0.7362 0.684 0.316
#> SRR1951736 1 0.0000 0.8193 1.000 0.000
#> SRR1951734 2 0.0000 0.9600 0.000 1.000
#> SRR1951735 1 0.9661 0.6244 0.608 0.392
#> SRR1951733 2 0.0000 0.9600 0.000 1.000
#> SRR1951732 2 0.0000 0.9600 0.000 1.000
#> SRR1951729 1 0.9000 0.7362 0.684 0.316
#> SRR1951728 1 0.9000 0.7362 0.684 0.316
#> SRR1951727 2 0.0000 0.9600 0.000 1.000
#> SRR1951725 1 0.0000 0.8193 1.000 0.000
#> SRR1951726 1 0.9000 0.7362 0.684 0.316
#> SRR1951724 1 0.8955 0.7386 0.688 0.312
#> SRR1951722 1 0.9000 0.7362 0.684 0.316
#> SRR1951723 1 0.9635 0.6339 0.612 0.388
#> SRR1951721 1 0.0000 0.8193 1.000 0.000
#> SRR1951719 1 0.9000 0.7362 0.684 0.316
#> SRR1951720 1 0.0000 0.8193 1.000 0.000
#> SRR1951718 1 0.2603 0.8285 0.956 0.044
#> SRR1951717 1 0.0000 0.8193 1.000 0.000
#> SRR1951715 1 0.0000 0.8193 1.000 0.000
#> SRR1951716 1 0.0000 0.8193 1.000 0.000
#> SRR1951714 1 0.2948 0.8290 0.948 0.052
#> SRR1951713 1 0.9000 0.7362 0.684 0.316
#> SRR1951712 1 0.1843 0.8266 0.972 0.028
#> SRR1951711 2 0.0000 0.9600 0.000 1.000
#> SRR1951710 1 0.0376 0.8205 0.996 0.004
#> SRR1951709 2 0.0000 0.9600 0.000 1.000
#> SRR1951708 2 0.0000 0.9600 0.000 1.000
#> SRR1951707 1 0.9635 0.6339 0.612 0.388
#> SRR1951705 1 0.6531 0.7924 0.832 0.168
#> SRR1951704 1 0.9000 0.7362 0.684 0.316
#> SRR1951703 2 0.0000 0.9600 0.000 1.000
#> SRR1951706 1 0.5059 0.8186 0.888 0.112
#> SRR1951702 1 0.9000 0.7362 0.684 0.316
#> SRR1951701 2 0.0000 0.9600 0.000 1.000
#> SRR1951699 1 0.9850 0.5510 0.572 0.428
#> SRR1951698 1 0.9129 0.7224 0.672 0.328
#> SRR1951700 1 0.9000 0.7362 0.684 0.316
#> SRR1951697 2 0.0000 0.9600 0.000 1.000
#> SRR1951696 1 0.3114 0.8288 0.944 0.056
#> SRR1951695 1 0.9000 0.7362 0.684 0.316
#> SRR1951693 1 0.9000 0.7362 0.684 0.316
#> SRR1951692 1 0.2948 0.8290 0.948 0.052
#> SRR1951691 1 0.8327 0.7645 0.736 0.264
#> SRR1951688 2 0.0000 0.9600 0.000 1.000
#> SRR1951689 2 0.0000 0.9600 0.000 1.000
#> SRR1951687 1 0.0000 0.8193 1.000 0.000
#> SRR1951685 2 0.9044 0.3480 0.320 0.680
#> SRR1951684 1 0.9000 0.7362 0.684 0.316
#> SRR1951686 2 0.0000 0.9600 0.000 1.000
#> SRR1951683 1 0.0000 0.8193 1.000 0.000
#> SRR1951682 2 0.0000 0.9600 0.000 1.000
#> SRR1951690 2 0.0000 0.9600 0.000 1.000
#> SRR1951680 1 0.8909 0.7412 0.692 0.308
#> SRR1951679 1 0.9635 0.6339 0.612 0.388
#> SRR1951678 1 0.1843 0.8265 0.972 0.028
#> SRR1951677 1 0.1633 0.8257 0.976 0.024
#> SRR1951676 1 0.6048 0.8088 0.852 0.148
#> SRR1951675 2 0.0000 0.9600 0.000 1.000
#> SRR1951681 1 0.9000 0.7362 0.684 0.316
#> SRR1951673 2 0.0000 0.9600 0.000 1.000
#> SRR1951674 1 0.0000 0.8193 1.000 0.000
#> SRR1951672 1 0.9000 0.7362 0.684 0.316
#> SRR1951671 2 0.0000 0.9600 0.000 1.000
#> SRR1951670 1 0.0000 0.8193 1.000 0.000
#> SRR1951640 1 0.9000 0.7362 0.684 0.316
#> SRR1951668 1 0.2948 0.8290 0.948 0.052
#> SRR1951669 2 0.6712 0.7165 0.176 0.824
#> SRR1951638 1 0.0000 0.8193 1.000 0.000
#> SRR1951637 1 0.9209 0.7123 0.664 0.336
#> SRR1951635 1 0.0000 0.8193 1.000 0.000
#> SRR1951634 1 0.2948 0.8290 0.948 0.052
#> SRR1951631 1 0.0938 0.8230 0.988 0.012
#> SRR1951632 2 0.0000 0.9600 0.000 1.000
#> SRR1951633 1 0.2948 0.8290 0.948 0.052
#> SRR1951630 1 0.0000 0.8193 1.000 0.000
#> SRR1951629 1 0.3584 0.8277 0.932 0.068
#> SRR1951628 2 0.5519 0.7992 0.128 0.872
#> SRR1951627 1 0.9393 0.6845 0.644 0.356
#> SRR1951639 2 0.0000 0.9600 0.000 1.000
#> SRR1951625 2 0.0000 0.9600 0.000 1.000
#> SRR1951626 2 0.7815 0.5848 0.232 0.768
#> SRR1951624 1 0.9000 0.7362 0.684 0.316
#> SRR1951622 1 0.0000 0.8193 1.000 0.000
#> SRR1951636 1 0.9393 0.6845 0.644 0.356
#> SRR1951623 1 0.2948 0.8290 0.948 0.052
#> SRR1951621 1 0.0000 0.8193 1.000 0.000
#> SRR1951620 1 0.9000 0.7362 0.684 0.316
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951666 3 0.4504 0.808 0.000 0.196 0.804
#> SRR1951665 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951664 3 0.2066 0.906 0.000 0.060 0.940
#> SRR1951663 1 0.1163 0.934 0.972 0.000 0.028
#> SRR1951662 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951661 3 0.2711 0.895 0.000 0.088 0.912
#> SRR1951660 3 0.5678 0.479 0.316 0.000 0.684
#> SRR1951659 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951657 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951656 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951655 1 0.3267 0.881 0.884 0.000 0.116
#> SRR1951654 1 0.0000 0.940 1.000 0.000 0.000
#> SRR1951653 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951652 3 0.6307 0.182 0.000 0.488 0.512
#> SRR1951651 3 0.1163 0.897 0.028 0.000 0.972
#> SRR1951650 3 0.1163 0.912 0.000 0.028 0.972
#> SRR1951648 3 0.5397 0.694 0.000 0.280 0.720
#> SRR1951649 3 0.2448 0.900 0.000 0.076 0.924
#> SRR1951647 2 0.5098 0.635 0.000 0.752 0.248
#> SRR1951646 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951645 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951644 3 0.5191 0.830 0.112 0.060 0.828
#> SRR1951643 1 0.4750 0.778 0.784 0.000 0.216
#> SRR1951641 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951642 3 0.5138 0.614 0.252 0.000 0.748
#> SRR1951694 1 0.0237 0.940 0.996 0.000 0.004
#> SRR1951731 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951730 2 0.2356 0.899 0.000 0.928 0.072
#> SRR1951752 2 0.1411 0.937 0.000 0.964 0.036
#> SRR1951751 3 0.1129 0.913 0.004 0.020 0.976
#> SRR1951749 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951750 1 0.2066 0.916 0.940 0.000 0.060
#> SRR1951748 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951745 1 0.0000 0.940 1.000 0.000 0.000
#> SRR1951746 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951744 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951742 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951743 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951741 1 0.0000 0.940 1.000 0.000 0.000
#> SRR1951740 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951738 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951739 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951737 3 0.1031 0.913 0.000 0.024 0.976
#> SRR1951736 1 0.0000 0.940 1.000 0.000 0.000
#> SRR1951734 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951735 3 0.3686 0.859 0.000 0.140 0.860
#> SRR1951733 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951732 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951729 3 0.2356 0.902 0.000 0.072 0.928
#> SRR1951728 3 0.2711 0.895 0.000 0.088 0.912
#> SRR1951727 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951725 1 0.1163 0.934 0.972 0.000 0.028
#> SRR1951726 3 0.4702 0.780 0.000 0.212 0.788
#> SRR1951724 3 0.2356 0.902 0.000 0.072 0.928
#> SRR1951722 3 0.2878 0.891 0.000 0.096 0.904
#> SRR1951723 2 0.2796 0.881 0.000 0.908 0.092
#> SRR1951721 1 0.1163 0.934 0.972 0.000 0.028
#> SRR1951719 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951720 1 0.0000 0.940 1.000 0.000 0.000
#> SRR1951718 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951717 1 0.0000 0.940 1.000 0.000 0.000
#> SRR1951715 1 0.1163 0.934 0.972 0.000 0.028
#> SRR1951716 1 0.0000 0.940 1.000 0.000 0.000
#> SRR1951714 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951713 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951712 3 0.0475 0.912 0.004 0.004 0.992
#> SRR1951711 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951710 1 0.2537 0.905 0.920 0.000 0.080
#> SRR1951709 3 0.5591 0.656 0.000 0.304 0.696
#> SRR1951708 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951707 3 0.2711 0.895 0.000 0.088 0.912
#> SRR1951705 1 0.4346 0.794 0.816 0.000 0.184
#> SRR1951704 3 0.2711 0.895 0.000 0.088 0.912
#> SRR1951703 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951706 3 0.0237 0.912 0.000 0.004 0.996
#> SRR1951702 3 0.2448 0.900 0.000 0.076 0.924
#> SRR1951701 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951699 3 0.3816 0.852 0.000 0.148 0.852
#> SRR1951698 3 0.2066 0.904 0.000 0.060 0.940
#> SRR1951700 3 0.2356 0.902 0.000 0.072 0.928
#> SRR1951697 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951696 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951695 3 0.1643 0.910 0.000 0.044 0.956
#> SRR1951693 3 0.1643 0.910 0.000 0.044 0.956
#> SRR1951692 3 0.0237 0.910 0.004 0.000 0.996
#> SRR1951691 3 0.0237 0.912 0.000 0.004 0.996
#> SRR1951688 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951689 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951687 1 0.0000 0.940 1.000 0.000 0.000
#> SRR1951685 2 0.3192 0.861 0.000 0.888 0.112
#> SRR1951684 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951686 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951683 1 0.0237 0.940 0.996 0.000 0.004
#> SRR1951682 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951690 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951680 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951679 2 0.2878 0.877 0.000 0.904 0.096
#> SRR1951678 1 0.5810 0.582 0.664 0.000 0.336
#> SRR1951677 1 0.5835 0.574 0.660 0.000 0.340
#> SRR1951676 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951675 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951681 3 0.1163 0.912 0.000 0.028 0.972
#> SRR1951673 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951674 1 0.0000 0.940 1.000 0.000 0.000
#> SRR1951672 3 0.5016 0.743 0.000 0.240 0.760
#> SRR1951671 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951670 1 0.0000 0.940 1.000 0.000 0.000
#> SRR1951640 3 0.1643 0.910 0.000 0.044 0.956
#> SRR1951668 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951669 2 0.0592 0.958 0.000 0.988 0.012
#> SRR1951638 1 0.0000 0.940 1.000 0.000 0.000
#> SRR1951637 2 0.2448 0.898 0.000 0.924 0.076
#> SRR1951635 1 0.0000 0.940 1.000 0.000 0.000
#> SRR1951634 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951631 3 0.0592 0.906 0.012 0.000 0.988
#> SRR1951632 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951633 3 0.4654 0.691 0.208 0.000 0.792
#> SRR1951630 1 0.2711 0.900 0.912 0.000 0.088
#> SRR1951629 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951628 2 0.0747 0.954 0.000 0.984 0.016
#> SRR1951627 3 0.3192 0.880 0.000 0.112 0.888
#> SRR1951639 2 0.6180 0.201 0.000 0.584 0.416
#> SRR1951625 2 0.0000 0.967 0.000 1.000 0.000
#> SRR1951626 3 0.5560 0.650 0.000 0.300 0.700
#> SRR1951624 3 0.1643 0.910 0.000 0.044 0.956
#> SRR1951622 1 0.1163 0.934 0.972 0.000 0.028
#> SRR1951636 3 0.3192 0.880 0.000 0.112 0.888
#> SRR1951623 3 0.0000 0.911 0.000 0.000 1.000
#> SRR1951621 1 0.0000 0.940 1.000 0.000 0.000
#> SRR1951620 3 0.0237 0.912 0.000 0.004 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951666 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1951665 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951664 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1951663 1 0.1118 0.902 0.964 0.000 0.000 0.036
#> SRR1951662 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1951660 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1951659 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951658 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951657 4 0.2973 0.848 0.000 0.000 0.144 0.856
#> SRR1951656 3 0.0817 0.954 0.000 0.000 0.976 0.024
#> SRR1951655 4 0.0188 0.939 0.004 0.000 0.000 0.996
#> SRR1951654 1 0.0000 0.913 1.000 0.000 0.000 0.000
#> SRR1951653 4 0.1022 0.947 0.000 0.000 0.032 0.968
#> SRR1951652 4 0.3448 0.790 0.000 0.168 0.004 0.828
#> SRR1951651 4 0.1118 0.916 0.036 0.000 0.000 0.964
#> SRR1951650 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1951648 3 0.0188 0.967 0.000 0.004 0.996 0.000
#> SRR1951649 4 0.1118 0.947 0.000 0.000 0.036 0.964
#> SRR1951647 2 0.4697 0.437 0.000 0.644 0.356 0.000
#> SRR1951646 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1951645 4 0.1118 0.947 0.000 0.000 0.036 0.964
#> SRR1951644 4 0.0895 0.940 0.020 0.000 0.004 0.976
#> SRR1951643 1 0.4888 0.421 0.588 0.000 0.000 0.412
#> SRR1951641 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951642 4 0.2868 0.802 0.136 0.000 0.000 0.864
#> SRR1951694 1 0.0817 0.907 0.976 0.000 0.000 0.024
#> SRR1951731 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951730 2 0.0188 0.965 0.000 0.996 0.004 0.000
#> SRR1951752 2 0.5028 0.285 0.000 0.596 0.004 0.400
#> SRR1951751 4 0.1022 0.947 0.000 0.000 0.032 0.968
#> SRR1951749 2 0.0188 0.965 0.000 0.996 0.004 0.000
#> SRR1951750 1 0.4776 0.473 0.624 0.000 0.000 0.376
#> SRR1951748 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951745 1 0.0000 0.913 1.000 0.000 0.000 0.000
#> SRR1951746 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951744 3 0.3400 0.780 0.000 0.000 0.820 0.180
#> SRR1951742 2 0.0188 0.965 0.000 0.996 0.004 0.000
#> SRR1951743 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951741 1 0.0000 0.913 1.000 0.000 0.000 0.000
#> SRR1951740 4 0.0469 0.944 0.000 0.000 0.012 0.988
#> SRR1951738 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951739 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951737 3 0.0188 0.967 0.000 0.000 0.996 0.004
#> SRR1951736 1 0.0000 0.913 1.000 0.000 0.000 0.000
#> SRR1951734 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951735 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1951733 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951732 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951729 3 0.0188 0.967 0.000 0.000 0.996 0.004
#> SRR1951728 4 0.1118 0.947 0.000 0.000 0.036 0.964
#> SRR1951727 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951725 1 0.1118 0.902 0.964 0.000 0.000 0.036
#> SRR1951726 4 0.1297 0.942 0.000 0.020 0.016 0.964
#> SRR1951724 4 0.1118 0.947 0.000 0.000 0.036 0.964
#> SRR1951722 4 0.1118 0.947 0.000 0.000 0.036 0.964
#> SRR1951723 4 0.1489 0.926 0.000 0.044 0.004 0.952
#> SRR1951721 1 0.4500 0.540 0.684 0.000 0.000 0.316
#> SRR1951719 3 0.0817 0.949 0.000 0.000 0.976 0.024
#> SRR1951720 1 0.0000 0.913 1.000 0.000 0.000 0.000
#> SRR1951718 4 0.0188 0.941 0.000 0.000 0.004 0.996
#> SRR1951717 1 0.0000 0.913 1.000 0.000 0.000 0.000
#> SRR1951715 1 0.1118 0.902 0.964 0.000 0.000 0.036
#> SRR1951716 1 0.0000 0.913 1.000 0.000 0.000 0.000
#> SRR1951714 4 0.2647 0.840 0.000 0.000 0.120 0.880
#> SRR1951713 4 0.1022 0.947 0.000 0.000 0.032 0.968
#> SRR1951712 4 0.0817 0.947 0.000 0.000 0.024 0.976
#> SRR1951711 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951710 4 0.2530 0.866 0.112 0.000 0.000 0.888
#> SRR1951709 3 0.0707 0.951 0.000 0.020 0.980 0.000
#> SRR1951708 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951707 4 0.1118 0.947 0.000 0.000 0.036 0.964
#> SRR1951705 4 0.1118 0.933 0.036 0.000 0.000 0.964
#> SRR1951704 4 0.1118 0.947 0.000 0.000 0.036 0.964
#> SRR1951703 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951706 4 0.0336 0.943 0.000 0.000 0.008 0.992
#> SRR1951702 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1951701 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951699 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1951698 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1951700 4 0.1118 0.947 0.000 0.000 0.036 0.964
#> SRR1951697 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951696 3 0.0188 0.967 0.000 0.000 0.996 0.004
#> SRR1951695 4 0.1118 0.947 0.000 0.000 0.036 0.964
#> SRR1951693 4 0.1118 0.947 0.000 0.000 0.036 0.964
#> SRR1951692 3 0.1118 0.944 0.000 0.000 0.964 0.036
#> SRR1951691 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1951688 2 0.0188 0.965 0.000 0.996 0.004 0.000
#> SRR1951689 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951687 1 0.0000 0.913 1.000 0.000 0.000 0.000
#> SRR1951685 4 0.5004 0.363 0.000 0.392 0.004 0.604
#> SRR1951684 3 0.0188 0.967 0.000 0.000 0.996 0.004
#> SRR1951686 2 0.0188 0.965 0.000 0.996 0.004 0.000
#> SRR1951683 1 0.0817 0.908 0.976 0.000 0.000 0.024
#> SRR1951682 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951690 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951680 4 0.1022 0.947 0.000 0.000 0.032 0.968
#> SRR1951679 4 0.1118 0.932 0.000 0.036 0.000 0.964
#> SRR1951678 1 0.4842 0.760 0.760 0.000 0.048 0.192
#> SRR1951677 1 0.5222 0.654 0.688 0.000 0.032 0.280
#> SRR1951676 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1951675 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951681 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1951673 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951674 1 0.0000 0.913 1.000 0.000 0.000 0.000
#> SRR1951672 4 0.1256 0.937 0.000 0.028 0.008 0.964
#> SRR1951671 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951670 1 0.0000 0.913 1.000 0.000 0.000 0.000
#> SRR1951640 4 0.1118 0.947 0.000 0.000 0.036 0.964
#> SRR1951668 4 0.0707 0.946 0.000 0.000 0.020 0.980
#> SRR1951669 2 0.3219 0.789 0.000 0.836 0.000 0.164
#> SRR1951638 1 0.0000 0.913 1.000 0.000 0.000 0.000
#> SRR1951637 4 0.2999 0.835 0.000 0.132 0.004 0.864
#> SRR1951635 1 0.0000 0.913 1.000 0.000 0.000 0.000
#> SRR1951634 4 0.0817 0.947 0.000 0.000 0.024 0.976
#> SRR1951631 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> SRR1951632 2 0.0000 0.967 0.000 1.000 0.000 0.000
#> SRR1951633 3 0.5728 0.358 0.364 0.000 0.600 0.036
#> SRR1951630 1 0.3308 0.827 0.872 0.000 0.092 0.036
#> SRR1951629 3 0.0188 0.967 0.000 0.000 0.996 0.004
#> SRR1951628 2 0.3157 0.811 0.000 0.852 0.004 0.144
#> SRR1951627 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1951639 3 0.2149 0.879 0.000 0.088 0.912 0.000
#> SRR1951625 2 0.0188 0.965 0.000 0.996 0.004 0.000
#> SRR1951626 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1951624 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1951622 1 0.1118 0.902 0.964 0.000 0.000 0.036
#> SRR1951636 3 0.0000 0.969 0.000 0.000 1.000 0.000
#> SRR1951623 3 0.1022 0.947 0.000 0.000 0.968 0.032
#> SRR1951621 1 0.0707 0.907 0.980 0.000 0.000 0.020
#> SRR1951620 3 0.0188 0.967 0.000 0.000 0.996 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951666 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951665 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951663 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000
#> SRR1951662 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951660 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951659 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951657 5 0.2329 0.823 0.000 0.000 0.124 0.000 0.876
#> SRR1951656 3 0.3949 0.561 0.000 0.000 0.668 0.332 0.000
#> SRR1951655 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951654 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000
#> SRR1951653 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951652 5 0.2890 0.764 0.000 0.160 0.004 0.000 0.836
#> SRR1951651 4 0.0000 0.929 0.000 0.000 0.000 1.000 0.000
#> SRR1951650 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951648 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951649 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951647 2 0.4030 0.450 0.000 0.648 0.352 0.000 0.000
#> SRR1951646 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951645 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951644 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951643 4 0.3452 0.663 0.000 0.000 0.000 0.756 0.244
#> SRR1951641 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951642 4 0.0000 0.929 0.000 0.000 0.000 1.000 0.000
#> SRR1951694 1 0.0794 0.902 0.972 0.000 0.000 0.000 0.028
#> SRR1951731 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951730 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951752 2 0.4182 0.297 0.000 0.600 0.000 0.000 0.400
#> SRR1951751 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951749 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951750 1 0.4182 0.351 0.600 0.000 0.000 0.000 0.400
#> SRR1951748 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000
#> SRR1951746 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 3 0.2732 0.748 0.000 0.000 0.840 0.000 0.160
#> SRR1951742 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951743 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000
#> SRR1951740 5 0.1965 0.871 0.000 0.000 0.000 0.096 0.904
#> SRR1951738 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951739 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951737 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951736 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000
#> SRR1951734 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951733 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951732 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 3 0.0162 0.947 0.000 0.000 0.996 0.000 0.004
#> SRR1951728 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951727 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000
#> SRR1951726 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951724 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951722 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951723 5 0.0404 0.945 0.000 0.012 0.000 0.000 0.988
#> SRR1951721 1 0.3876 0.479 0.684 0.000 0.000 0.000 0.316
#> SRR1951719 3 0.0703 0.930 0.000 0.000 0.976 0.000 0.024
#> SRR1951720 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000
#> SRR1951718 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951717 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000
#> SRR1951715 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000
#> SRR1951716 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000
#> SRR1951714 5 0.3983 0.495 0.000 0.000 0.000 0.340 0.660
#> SRR1951713 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951712 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951711 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951710 5 0.1908 0.868 0.092 0.000 0.000 0.000 0.908
#> SRR1951709 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951708 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951707 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951705 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951704 5 0.0404 0.946 0.000 0.000 0.012 0.000 0.988
#> SRR1951703 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 5 0.0404 0.946 0.000 0.000 0.012 0.000 0.988
#> SRR1951702 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951701 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951699 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951698 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951700 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951697 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951696 3 0.2424 0.844 0.000 0.000 0.868 0.132 0.000
#> SRR1951695 5 0.0404 0.946 0.000 0.000 0.012 0.000 0.988
#> SRR1951693 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951692 4 0.0000 0.929 0.000 0.000 0.000 1.000 0.000
#> SRR1951691 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951688 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951689 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951687 1 0.3039 0.716 0.808 0.000 0.000 0.192 0.000
#> SRR1951685 5 0.4074 0.405 0.000 0.364 0.000 0.000 0.636
#> SRR1951684 3 0.2852 0.801 0.000 0.000 0.828 0.172 0.000
#> SRR1951686 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951683 4 0.3659 0.673 0.220 0.000 0.000 0.768 0.012
#> SRR1951682 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951680 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951679 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951678 4 0.0000 0.929 0.000 0.000 0.000 1.000 0.000
#> SRR1951677 4 0.0000 0.929 0.000 0.000 0.000 1.000 0.000
#> SRR1951676 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951675 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951681 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951673 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951674 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000
#> SRR1951672 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951671 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951670 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000
#> SRR1951640 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951668 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951669 2 0.2773 0.762 0.000 0.836 0.000 0.000 0.164
#> SRR1951638 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000
#> SRR1951637 5 0.2230 0.827 0.000 0.116 0.000 0.000 0.884
#> SRR1951635 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000
#> SRR1951634 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951631 5 0.0000 0.955 0.000 0.000 0.000 0.000 1.000
#> SRR1951632 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951633 4 0.0000 0.929 0.000 0.000 0.000 1.000 0.000
#> SRR1951630 4 0.0000 0.929 0.000 0.000 0.000 1.000 0.000
#> SRR1951629 3 0.0510 0.938 0.000 0.000 0.984 0.000 0.016
#> SRR1951628 2 0.2561 0.791 0.000 0.856 0.000 0.000 0.144
#> SRR1951627 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951639 3 0.1965 0.841 0.000 0.096 0.904 0.000 0.000
#> SRR1951625 2 0.0000 0.965 0.000 1.000 0.000 0.000 0.000
#> SRR1951626 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951624 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951622 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000
#> SRR1951636 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
#> SRR1951623 3 0.3684 0.649 0.000 0.000 0.720 0.280 0.000
#> SRR1951621 1 0.0000 0.931 1.000 0.000 0.000 0.000 0.000
#> SRR1951620 3 0.0000 0.950 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951666 3 0.1075 0.887 0.000 0.000 0.952 0.000 0.000 0.048
#> SRR1951665 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 3 0.0000 0.908 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951663 6 0.3151 0.957 0.252 0.000 0.000 0.000 0.000 0.748
#> SRR1951662 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 3 0.0000 0.908 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951660 5 0.3309 0.625 0.280 0.000 0.000 0.000 0.720 0.000
#> SRR1951659 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951658 2 0.0260 0.868 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1951657 5 0.2597 0.744 0.000 0.000 0.176 0.000 0.824 0.000
#> SRR1951656 3 0.3684 0.488 0.000 0.000 0.628 0.372 0.000 0.000
#> SRR1951655 5 0.1765 0.824 0.096 0.000 0.000 0.000 0.904 0.000
#> SRR1951654 6 0.3023 0.980 0.232 0.000 0.000 0.000 0.000 0.768
#> SRR1951653 5 0.1141 0.858 0.000 0.000 0.052 0.000 0.948 0.000
#> SRR1951652 5 0.5321 0.480 0.000 0.156 0.004 0.000 0.608 0.232
#> SRR1951651 4 0.0000 0.911 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951650 3 0.0000 0.908 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951648 3 0.3023 0.706 0.000 0.000 0.768 0.000 0.000 0.232
#> SRR1951649 5 0.0000 0.881 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951647 2 0.5971 0.282 0.000 0.424 0.344 0.000 0.000 0.232
#> SRR1951646 3 0.0508 0.905 0.000 0.000 0.984 0.012 0.004 0.000
#> SRR1951645 5 0.0000 0.881 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951644 5 0.3244 0.643 0.268 0.000 0.000 0.000 0.732 0.000
#> SRR1951643 4 0.3126 0.565 0.000 0.000 0.000 0.752 0.248 0.000
#> SRR1951641 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951642 4 0.0000 0.911 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951694 1 0.0632 0.696 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR1951731 2 0.3023 0.816 0.000 0.768 0.000 0.000 0.000 0.232
#> SRR1951730 2 0.3023 0.816 0.000 0.768 0.000 0.000 0.000 0.232
#> SRR1951752 2 0.5481 0.585 0.000 0.568 0.000 0.000 0.200 0.232
#> SRR1951751 5 0.0146 0.881 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1951749 2 0.3023 0.816 0.000 0.768 0.000 0.000 0.000 0.232
#> SRR1951750 1 0.4649 0.187 0.572 0.000 0.000 0.000 0.380 0.048
#> SRR1951748 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 6 0.3023 0.980 0.232 0.000 0.000 0.000 0.000 0.768
#> SRR1951746 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951744 3 0.2697 0.730 0.000 0.000 0.812 0.000 0.188 0.000
#> SRR1951742 2 0.3023 0.816 0.000 0.768 0.000 0.000 0.000 0.232
#> SRR1951743 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951741 6 0.3198 0.949 0.260 0.000 0.000 0.000 0.000 0.740
#> SRR1951740 5 0.1765 0.822 0.000 0.000 0.000 0.096 0.904 0.000
#> SRR1951738 2 0.2854 0.824 0.000 0.792 0.000 0.000 0.000 0.208
#> SRR1951739 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951737 3 0.0000 0.908 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951736 1 0.0000 0.712 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951734 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951735 3 0.1444 0.872 0.000 0.000 0.928 0.000 0.000 0.072
#> SRR1951733 2 0.3023 0.816 0.000 0.768 0.000 0.000 0.000 0.232
#> SRR1951732 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 3 0.0146 0.907 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1951728 5 0.0622 0.877 0.000 0.000 0.008 0.000 0.980 0.012
#> SRR1951727 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 1 0.1075 0.717 0.952 0.000 0.000 0.000 0.000 0.048
#> SRR1951726 5 0.0000 0.881 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951724 5 0.0000 0.881 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951722 5 0.0000 0.881 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951723 5 0.3457 0.670 0.000 0.016 0.000 0.000 0.752 0.232
#> SRR1951721 1 0.3023 0.451 0.784 0.000 0.000 0.000 0.212 0.004
#> SRR1951719 3 0.0632 0.896 0.000 0.000 0.976 0.000 0.024 0.000
#> SRR1951720 1 0.1075 0.717 0.952 0.000 0.000 0.000 0.000 0.048
#> SRR1951718 5 0.0363 0.878 0.012 0.000 0.000 0.000 0.988 0.000
#> SRR1951717 1 0.1075 0.717 0.952 0.000 0.000 0.000 0.000 0.048
#> SRR1951715 1 0.0000 0.712 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951716 1 0.1075 0.717 0.952 0.000 0.000 0.000 0.000 0.048
#> SRR1951714 5 0.3706 0.400 0.000 0.000 0.000 0.380 0.620 0.000
#> SRR1951713 5 0.0146 0.881 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1951712 5 0.0146 0.881 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1951711 2 0.3023 0.816 0.000 0.768 0.000 0.000 0.000 0.232
#> SRR1951710 5 0.3647 0.493 0.360 0.000 0.000 0.000 0.640 0.000
#> SRR1951709 3 0.2738 0.767 0.000 0.004 0.820 0.000 0.000 0.176
#> SRR1951708 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951707 5 0.2743 0.753 0.000 0.000 0.008 0.000 0.828 0.164
#> SRR1951705 5 0.3330 0.621 0.284 0.000 0.000 0.000 0.716 0.000
#> SRR1951704 5 0.0363 0.878 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1951703 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 5 0.0363 0.878 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1951702 3 0.0000 0.908 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951701 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951699 3 0.0000 0.908 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951698 3 0.0000 0.908 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951700 5 0.0000 0.881 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951697 2 0.3023 0.816 0.000 0.768 0.000 0.000 0.000 0.232
#> SRR1951696 3 0.2178 0.822 0.000 0.000 0.868 0.132 0.000 0.000
#> SRR1951695 5 0.0458 0.878 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1951693 5 0.0000 0.881 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951692 4 0.0000 0.911 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951691 5 0.0000 0.881 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951688 2 0.3023 0.816 0.000 0.768 0.000 0.000 0.000 0.232
#> SRR1951689 2 0.2793 0.826 0.000 0.800 0.000 0.000 0.000 0.200
#> SRR1951687 1 0.5618 0.116 0.500 0.000 0.000 0.160 0.000 0.340
#> SRR1951685 5 0.5681 0.396 0.000 0.212 0.004 0.000 0.552 0.232
#> SRR1951684 3 0.2527 0.785 0.000 0.000 0.832 0.168 0.000 0.000
#> SRR1951686 2 0.3023 0.816 0.000 0.768 0.000 0.000 0.000 0.232
#> SRR1951683 4 0.3572 0.649 0.204 0.000 0.000 0.764 0.032 0.000
#> SRR1951682 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951680 5 0.1141 0.858 0.000 0.000 0.052 0.000 0.948 0.000
#> SRR1951679 5 0.0146 0.881 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1951678 4 0.0000 0.911 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951677 4 0.0000 0.911 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951676 3 0.1007 0.892 0.000 0.000 0.956 0.044 0.000 0.000
#> SRR1951675 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951681 3 0.0000 0.908 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951673 2 0.3023 0.816 0.000 0.768 0.000 0.000 0.000 0.232
#> SRR1951674 6 0.3023 0.980 0.232 0.000 0.000 0.000 0.000 0.768
#> SRR1951672 5 0.0000 0.881 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951671 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951670 1 0.3892 0.532 0.740 0.000 0.000 0.212 0.000 0.048
#> SRR1951640 5 0.1141 0.858 0.000 0.000 0.052 0.000 0.948 0.000
#> SRR1951668 5 0.0146 0.881 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1951669 2 0.5217 0.647 0.000 0.608 0.000 0.000 0.160 0.232
#> SRR1951638 1 0.3330 0.404 0.716 0.000 0.000 0.000 0.000 0.284
#> SRR1951637 5 0.4771 0.559 0.000 0.116 0.000 0.000 0.664 0.220
#> SRR1951635 1 0.3309 0.414 0.720 0.000 0.000 0.000 0.000 0.280
#> SRR1951634 5 0.0146 0.881 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1951631 5 0.0000 0.881 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951632 2 0.0000 0.870 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951633 4 0.0000 0.911 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951630 4 0.0000 0.911 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951629 3 0.1267 0.876 0.000 0.000 0.940 0.000 0.060 0.000
#> SRR1951628 2 0.5092 0.670 0.000 0.624 0.000 0.000 0.144 0.232
#> SRR1951627 3 0.0000 0.908 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951639 3 0.4407 0.604 0.000 0.076 0.692 0.000 0.000 0.232
#> SRR1951625 2 0.3023 0.816 0.000 0.768 0.000 0.000 0.000 0.232
#> SRR1951626 3 0.0713 0.898 0.000 0.000 0.972 0.000 0.000 0.028
#> SRR1951624 3 0.0000 0.908 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951622 1 0.0000 0.712 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951636 3 0.0000 0.908 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951623 3 0.3499 0.583 0.000 0.000 0.680 0.320 0.000 0.000
#> SRR1951621 1 0.4050 0.500 0.716 0.000 0.000 0.236 0.000 0.048
#> SRR1951620 3 0.0000 0.908 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 13580 rows and 133 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 0.922 0.914 0.968 0.2948 0.722 0.722
#> 3 3 0.962 0.924 0.972 1.0640 0.657 0.530
#> 4 4 0.627 0.611 0.823 0.1372 0.899 0.757
#> 5 5 0.612 0.578 0.757 0.1110 0.767 0.403
#> 6 6 0.702 0.624 0.795 0.0468 0.903 0.610
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
#> SRR1951667 2 0.9661 0.376 0.392 0.608
#> SRR1951666 1 0.0000 0.970 1.000 0.000
#> SRR1951665 2 0.0000 0.939 0.000 1.000
#> SRR1951664 1 0.0000 0.970 1.000 0.000
#> SRR1951663 1 0.0000 0.970 1.000 0.000
#> SRR1951662 2 0.0000 0.939 0.000 1.000
#> SRR1951661 1 0.0000 0.970 1.000 0.000
#> SRR1951660 1 0.0000 0.970 1.000 0.000
#> SRR1951659 2 0.0000 0.939 0.000 1.000
#> SRR1951658 2 0.0000 0.939 0.000 1.000
#> SRR1951657 1 0.0000 0.970 1.000 0.000
#> SRR1951656 1 0.0000 0.970 1.000 0.000
#> SRR1951655 1 0.0000 0.970 1.000 0.000
#> SRR1951654 1 0.0000 0.970 1.000 0.000
#> SRR1951653 1 0.0000 0.970 1.000 0.000
#> SRR1951652 1 0.0000 0.970 1.000 0.000
#> SRR1951651 1 0.0000 0.970 1.000 0.000
#> SRR1951650 1 0.0000 0.970 1.000 0.000
#> SRR1951648 1 0.0000 0.970 1.000 0.000
#> SRR1951649 1 0.0000 0.970 1.000 0.000
#> SRR1951647 1 0.0000 0.970 1.000 0.000
#> SRR1951646 1 0.0000 0.970 1.000 0.000
#> SRR1951645 1 0.0000 0.970 1.000 0.000
#> SRR1951644 1 0.0000 0.970 1.000 0.000
#> SRR1951643 1 0.0000 0.970 1.000 0.000
#> SRR1951641 2 0.0000 0.939 0.000 1.000
#> SRR1951642 1 0.0000 0.970 1.000 0.000
#> SRR1951694 1 0.0000 0.970 1.000 0.000
#> SRR1951731 1 0.0000 0.970 1.000 0.000
#> SRR1951730 1 0.0000 0.970 1.000 0.000
#> SRR1951752 1 0.0000 0.970 1.000 0.000
#> SRR1951751 1 0.0000 0.970 1.000 0.000
#> SRR1951749 1 0.6247 0.787 0.844 0.156
#> SRR1951750 1 0.0000 0.970 1.000 0.000
#> SRR1951748 2 0.0000 0.939 0.000 1.000
#> SRR1951747 2 0.0000 0.939 0.000 1.000
#> SRR1951745 1 0.0000 0.970 1.000 0.000
#> SRR1951746 2 0.0000 0.939 0.000 1.000
#> SRR1951744 1 0.0000 0.970 1.000 0.000
#> SRR1951742 1 0.0000 0.970 1.000 0.000
#> SRR1951743 2 0.0000 0.939 0.000 1.000
#> SRR1951741 1 0.0000 0.970 1.000 0.000
#> SRR1951740 1 0.0000 0.970 1.000 0.000
#> SRR1951738 1 0.9635 0.331 0.612 0.388
#> SRR1951739 2 0.0000 0.939 0.000 1.000
#> SRR1951737 1 0.0000 0.970 1.000 0.000
#> SRR1951736 1 0.0000 0.970 1.000 0.000
#> SRR1951734 2 0.0000 0.939 0.000 1.000
#> SRR1951735 1 0.0000 0.970 1.000 0.000
#> SRR1951733 1 0.0000 0.970 1.000 0.000
#> SRR1951732 2 0.0000 0.939 0.000 1.000
#> SRR1951729 1 0.0000 0.970 1.000 0.000
#> SRR1951728 1 0.0000 0.970 1.000 0.000
#> SRR1951727 2 0.0000 0.939 0.000 1.000
#> SRR1951725 1 0.0000 0.970 1.000 0.000
#> SRR1951726 1 0.0000 0.970 1.000 0.000
#> SRR1951724 1 0.0000 0.970 1.000 0.000
#> SRR1951722 1 0.0000 0.970 1.000 0.000
#> SRR1951723 1 0.0000 0.970 1.000 0.000
#> SRR1951721 1 0.0000 0.970 1.000 0.000
#> SRR1951719 1 0.0000 0.970 1.000 0.000
#> SRR1951720 1 0.0000 0.970 1.000 0.000
#> SRR1951718 1 0.0000 0.970 1.000 0.000
#> SRR1951717 1 0.0000 0.970 1.000 0.000
#> SRR1951715 1 0.0000 0.970 1.000 0.000
#> SRR1951716 1 0.0000 0.970 1.000 0.000
#> SRR1951714 1 0.0000 0.970 1.000 0.000
#> SRR1951713 1 0.0000 0.970 1.000 0.000
#> SRR1951712 1 0.0000 0.970 1.000 0.000
#> SRR1951711 1 0.9896 0.172 0.560 0.440
#> SRR1951710 1 0.0000 0.970 1.000 0.000
#> SRR1951709 1 0.0000 0.970 1.000 0.000
#> SRR1951708 2 0.0000 0.939 0.000 1.000
#> SRR1951707 1 0.0000 0.970 1.000 0.000
#> SRR1951705 1 0.0000 0.970 1.000 0.000
#> SRR1951704 1 0.0000 0.970 1.000 0.000
#> SRR1951703 2 0.0000 0.939 0.000 1.000
#> SRR1951706 1 0.0000 0.970 1.000 0.000
#> SRR1951702 1 0.0000 0.970 1.000 0.000
#> SRR1951701 2 0.0672 0.934 0.008 0.992
#> SRR1951699 1 0.0000 0.970 1.000 0.000
#> SRR1951698 1 0.0000 0.970 1.000 0.000
#> SRR1951700 1 0.0000 0.970 1.000 0.000
#> SRR1951697 2 0.7745 0.704 0.228 0.772
#> SRR1951696 1 0.0000 0.970 1.000 0.000
#> SRR1951695 1 0.0000 0.970 1.000 0.000
#> SRR1951693 1 0.0000 0.970 1.000 0.000
#> SRR1951692 1 0.0000 0.970 1.000 0.000
#> SRR1951691 1 0.0000 0.970 1.000 0.000
#> SRR1951688 1 0.9323 0.433 0.652 0.348
#> SRR1951689 1 0.9909 0.159 0.556 0.444
#> SRR1951687 1 0.0000 0.970 1.000 0.000
#> SRR1951685 1 0.0000 0.970 1.000 0.000
#> SRR1951684 1 0.0000 0.970 1.000 0.000
#> SRR1951686 1 0.9686 0.308 0.604 0.396
#> SRR1951683 1 0.0000 0.970 1.000 0.000
#> SRR1951682 2 0.0000 0.939 0.000 1.000
#> SRR1951690 2 0.7056 0.754 0.192 0.808
#> SRR1951680 1 0.0000 0.970 1.000 0.000
#> SRR1951679 1 0.0000 0.970 1.000 0.000
#> SRR1951678 1 0.0000 0.970 1.000 0.000
#> SRR1951677 1 0.0000 0.970 1.000 0.000
#> SRR1951676 1 0.0000 0.970 1.000 0.000
#> SRR1951675 1 0.9909 0.159 0.556 0.444
#> SRR1951681 1 0.0000 0.970 1.000 0.000
#> SRR1951673 1 0.0000 0.970 1.000 0.000
#> SRR1951674 1 0.0000 0.970 1.000 0.000
#> SRR1951672 1 0.0000 0.970 1.000 0.000
#> SRR1951671 2 0.9608 0.398 0.384 0.616
#> SRR1951670 1 0.0000 0.970 1.000 0.000
#> SRR1951640 1 0.0000 0.970 1.000 0.000
#> SRR1951668 1 0.0000 0.970 1.000 0.000
#> SRR1951669 1 0.0000 0.970 1.000 0.000
#> SRR1951638 1 0.0000 0.970 1.000 0.000
#> SRR1951637 1 0.0000 0.970 1.000 0.000
#> SRR1951635 1 0.0000 0.970 1.000 0.000
#> SRR1951634 1 0.0000 0.970 1.000 0.000
#> SRR1951631 1 0.0000 0.970 1.000 0.000
#> SRR1951632 2 0.0000 0.939 0.000 1.000
#> SRR1951633 1 0.0000 0.970 1.000 0.000
#> SRR1951630 1 0.0000 0.970 1.000 0.000
#> SRR1951629 1 0.0000 0.970 1.000 0.000
#> SRR1951628 1 0.0000 0.970 1.000 0.000
#> SRR1951627 1 0.0000 0.970 1.000 0.000
#> SRR1951639 1 0.0000 0.970 1.000 0.000
#> SRR1951625 1 0.9881 0.186 0.564 0.436
#> SRR1951626 1 0.0000 0.970 1.000 0.000
#> SRR1951624 1 0.0000 0.970 1.000 0.000
#> SRR1951622 1 0.0000 0.970 1.000 0.000
#> SRR1951636 1 0.0000 0.970 1.000 0.000
#> SRR1951623 1 0.0000 0.970 1.000 0.000
#> SRR1951621 1 0.0000 0.970 1.000 0.000
#> SRR1951620 1 0.0000 0.970 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.5650 0.536 0.000 0.688 0.312
#> SRR1951666 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951665 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951664 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951663 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951662 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951661 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951660 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951659 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951657 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951656 3 0.6225 0.221 0.432 0.000 0.568
#> SRR1951655 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951654 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951653 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951652 3 0.0892 0.945 0.020 0.000 0.980
#> SRR1951651 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951650 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951648 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951649 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951647 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951646 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951645 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951644 3 0.6168 0.294 0.412 0.000 0.588
#> SRR1951643 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951641 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951642 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951694 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951731 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951730 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951752 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951751 1 0.0592 0.954 0.988 0.000 0.012
#> SRR1951749 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951750 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951748 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951745 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951746 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951744 1 0.0592 0.954 0.988 0.000 0.012
#> SRR1951742 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951743 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951741 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951740 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951738 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951739 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951737 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951736 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951734 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951735 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951733 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951732 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951729 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951728 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951727 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951725 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951726 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951724 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951722 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951723 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951721 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951719 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951720 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951718 3 0.6267 0.169 0.452 0.000 0.548
#> SRR1951717 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951715 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951716 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951714 1 0.4452 0.724 0.808 0.000 0.192
#> SRR1951713 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951712 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951711 3 0.3412 0.838 0.000 0.124 0.876
#> SRR1951710 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951709 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951708 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951707 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951705 1 0.1163 0.937 0.972 0.000 0.028
#> SRR1951704 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951703 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951706 1 0.0424 0.958 0.992 0.000 0.008
#> SRR1951702 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951701 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951699 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951698 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951700 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951697 3 0.2878 0.872 0.000 0.096 0.904
#> SRR1951696 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951695 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951693 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951692 1 0.0592 0.954 0.988 0.000 0.012
#> SRR1951691 3 0.0424 0.957 0.008 0.000 0.992
#> SRR1951688 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951689 3 0.2165 0.905 0.000 0.064 0.936
#> SRR1951687 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951685 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951684 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951686 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951683 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951682 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951690 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951680 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951679 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951678 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951677 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951676 1 0.6062 0.370 0.616 0.000 0.384
#> SRR1951675 3 0.6062 0.363 0.000 0.384 0.616
#> SRR1951681 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951673 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951674 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951672 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951671 2 0.2711 0.883 0.000 0.912 0.088
#> SRR1951670 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951640 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951668 1 0.1964 0.904 0.944 0.000 0.056
#> SRR1951669 3 0.4346 0.754 0.184 0.000 0.816
#> SRR1951638 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951637 3 0.1643 0.921 0.044 0.000 0.956
#> SRR1951635 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951634 1 0.6225 0.235 0.568 0.000 0.432
#> SRR1951631 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951632 2 0.0000 0.976 0.000 1.000 0.000
#> SRR1951633 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951630 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951629 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951628 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951627 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951639 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951625 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951626 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951624 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951622 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951636 3 0.0000 0.964 0.000 0.000 1.000
#> SRR1951623 1 0.0592 0.954 0.988 0.000 0.012
#> SRR1951621 1 0.0000 0.964 1.000 0.000 0.000
#> SRR1951620 3 0.0000 0.964 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.4677 0.6935 0.000 0.776 0.048 0.176
#> SRR1951666 3 0.4730 0.7858 0.000 0.000 0.636 0.364
#> SRR1951665 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951664 4 0.4621 0.3598 0.008 0.000 0.284 0.708
#> SRR1951663 1 0.0000 0.8610 1.000 0.000 0.000 0.000
#> SRR1951662 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.4585 0.7930 0.000 0.000 0.668 0.332
#> SRR1951660 1 0.5486 0.7737 0.720 0.000 0.200 0.080
#> SRR1951659 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951658 2 0.1118 0.9414 0.000 0.964 0.036 0.000
#> SRR1951657 4 0.4647 0.3558 0.008 0.000 0.288 0.704
#> SRR1951656 4 0.5569 0.2913 0.044 0.000 0.296 0.660
#> SRR1951655 1 0.4755 0.7959 0.760 0.000 0.200 0.040
#> SRR1951654 1 0.0000 0.8610 1.000 0.000 0.000 0.000
#> SRR1951653 4 0.5041 0.3939 0.040 0.000 0.232 0.728
#> SRR1951652 4 0.4164 0.3586 0.264 0.000 0.000 0.736
#> SRR1951651 1 0.1297 0.8542 0.964 0.000 0.020 0.016
#> SRR1951650 4 0.4543 0.3026 0.000 0.000 0.324 0.676
#> SRR1951648 4 0.4955 -0.4235 0.000 0.000 0.444 0.556
#> SRR1951649 4 0.0188 0.5638 0.000 0.000 0.004 0.996
#> SRR1951647 4 0.2647 0.5177 0.000 0.000 0.120 0.880
#> SRR1951646 4 0.4770 0.3515 0.012 0.000 0.288 0.700
#> SRR1951645 4 0.1182 0.5607 0.016 0.000 0.016 0.968
#> SRR1951644 1 0.5839 0.7529 0.696 0.000 0.200 0.104
#> SRR1951643 1 0.0188 0.8604 0.996 0.000 0.000 0.004
#> SRR1951641 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951642 1 0.0895 0.8559 0.976 0.000 0.020 0.004
#> SRR1951694 1 0.3219 0.8242 0.836 0.000 0.164 0.000
#> SRR1951731 4 0.2921 0.5098 0.000 0.000 0.140 0.860
#> SRR1951730 4 0.2814 0.5173 0.000 0.000 0.132 0.868
#> SRR1951752 4 0.2921 0.5098 0.000 0.000 0.140 0.860
#> SRR1951751 1 0.5669 0.7642 0.708 0.000 0.200 0.092
#> SRR1951749 4 0.7133 -0.1500 0.000 0.172 0.280 0.548
#> SRR1951750 1 0.2760 0.8372 0.872 0.000 0.128 0.000
#> SRR1951748 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951745 1 0.0000 0.8610 1.000 0.000 0.000 0.000
#> SRR1951746 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951744 1 0.5378 0.1476 0.540 0.000 0.012 0.448
#> SRR1951742 4 0.6985 -0.1749 0.000 0.140 0.312 0.548
#> SRR1951743 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951741 1 0.0000 0.8610 1.000 0.000 0.000 0.000
#> SRR1951740 4 0.4621 0.3598 0.008 0.000 0.284 0.708
#> SRR1951738 4 0.2704 0.5226 0.000 0.000 0.124 0.876
#> SRR1951739 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951737 4 0.4483 0.3622 0.004 0.000 0.284 0.712
#> SRR1951736 1 0.3610 0.8086 0.800 0.000 0.200 0.000
#> SRR1951734 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951735 4 0.2149 0.5229 0.000 0.000 0.088 0.912
#> SRR1951733 4 0.2973 0.5056 0.000 0.000 0.144 0.856
#> SRR1951732 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951729 4 0.4331 0.3688 0.000 0.000 0.288 0.712
#> SRR1951728 4 0.0921 0.5566 0.000 0.000 0.028 0.972
#> SRR1951727 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951725 1 0.1389 0.8564 0.952 0.000 0.048 0.000
#> SRR1951726 4 0.0592 0.5653 0.016 0.000 0.000 0.984
#> SRR1951724 4 0.4483 0.3165 0.284 0.000 0.004 0.712
#> SRR1951722 4 0.0817 0.5587 0.000 0.000 0.024 0.976
#> SRR1951723 4 0.1792 0.5534 0.000 0.000 0.068 0.932
#> SRR1951721 1 0.0000 0.8610 1.000 0.000 0.000 0.000
#> SRR1951719 4 0.4456 0.3656 0.004 0.000 0.280 0.716
#> SRR1951720 1 0.0000 0.8610 1.000 0.000 0.000 0.000
#> SRR1951718 1 0.5404 0.6238 0.700 0.000 0.052 0.248
#> SRR1951717 1 0.1867 0.8519 0.928 0.000 0.072 0.000
#> SRR1951715 1 0.1716 0.8536 0.936 0.000 0.064 0.000
#> SRR1951716 1 0.3024 0.8306 0.852 0.000 0.148 0.000
#> SRR1951714 4 0.5453 0.1948 0.388 0.000 0.020 0.592
#> SRR1951713 4 0.0895 0.5606 0.004 0.000 0.020 0.976
#> SRR1951712 1 0.5548 0.7708 0.716 0.000 0.200 0.084
#> SRR1951711 4 0.4605 0.4422 0.000 0.072 0.132 0.796
#> SRR1951710 1 0.4919 0.7923 0.752 0.000 0.200 0.048
#> SRR1951709 3 0.4790 0.7249 0.000 0.000 0.620 0.380
#> SRR1951708 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951707 4 0.0188 0.5644 0.004 0.000 0.000 0.996
#> SRR1951705 1 0.5669 0.7642 0.708 0.000 0.200 0.092
#> SRR1951704 4 0.1474 0.5526 0.000 0.000 0.052 0.948
#> SRR1951703 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951706 1 0.5289 0.3893 0.636 0.000 0.020 0.344
#> SRR1951702 4 0.3975 0.4114 0.000 0.000 0.240 0.760
#> SRR1951701 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951699 3 0.4925 0.6767 0.000 0.000 0.572 0.428
#> SRR1951698 3 0.4679 0.7829 0.000 0.000 0.648 0.352
#> SRR1951700 4 0.0000 0.5641 0.000 0.000 0.000 1.000
#> SRR1951697 4 0.7192 -0.1056 0.000 0.388 0.140 0.472
#> SRR1951696 4 0.5137 0.3254 0.024 0.000 0.296 0.680
#> SRR1951695 4 0.0817 0.5587 0.000 0.000 0.024 0.976
#> SRR1951693 4 0.1398 0.5530 0.004 0.000 0.040 0.956
#> SRR1951692 1 0.6135 0.2570 0.568 0.000 0.056 0.376
#> SRR1951691 4 0.4222 0.3333 0.272 0.000 0.000 0.728
#> SRR1951688 4 0.2814 0.5173 0.000 0.000 0.132 0.868
#> SRR1951689 4 0.4312 0.4625 0.000 0.056 0.132 0.812
#> SRR1951687 1 0.0000 0.8610 1.000 0.000 0.000 0.000
#> SRR1951685 4 0.2408 0.5365 0.000 0.000 0.104 0.896
#> SRR1951684 4 0.4988 0.3422 0.020 0.000 0.288 0.692
#> SRR1951686 4 0.7227 -0.1119 0.000 0.228 0.224 0.548
#> SRR1951683 1 0.0000 0.8610 1.000 0.000 0.000 0.000
#> SRR1951682 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951690 2 0.0188 0.9672 0.000 0.996 0.000 0.004
#> SRR1951680 4 0.4343 0.3811 0.004 0.000 0.264 0.732
#> SRR1951679 4 0.3812 0.4825 0.140 0.000 0.028 0.832
#> SRR1951678 1 0.0895 0.8559 0.976 0.000 0.020 0.004
#> SRR1951677 1 0.0895 0.8559 0.976 0.000 0.020 0.004
#> SRR1951676 4 0.7668 0.0309 0.252 0.000 0.288 0.460
#> SRR1951675 4 0.5533 0.3481 0.000 0.136 0.132 0.732
#> SRR1951681 4 0.4543 0.3175 0.000 0.000 0.324 0.676
#> SRR1951673 4 0.2868 0.5136 0.000 0.000 0.136 0.864
#> SRR1951674 1 0.0000 0.8610 1.000 0.000 0.000 0.000
#> SRR1951672 4 0.1305 0.5591 0.036 0.000 0.004 0.960
#> SRR1951671 2 0.5507 0.6617 0.000 0.732 0.112 0.156
#> SRR1951670 1 0.0336 0.8608 0.992 0.000 0.008 0.000
#> SRR1951640 4 0.3688 0.4441 0.000 0.000 0.208 0.792
#> SRR1951668 1 0.6104 0.6940 0.680 0.000 0.140 0.180
#> SRR1951669 4 0.4500 0.2951 0.316 0.000 0.000 0.684
#> SRR1951638 1 0.0000 0.8610 1.000 0.000 0.000 0.000
#> SRR1951637 4 0.4502 0.3855 0.236 0.000 0.016 0.748
#> SRR1951635 1 0.0000 0.8610 1.000 0.000 0.000 0.000
#> SRR1951634 1 0.6202 0.4845 0.612 0.000 0.076 0.312
#> SRR1951631 1 0.2722 0.8403 0.904 0.000 0.032 0.064
#> SRR1951632 2 0.0000 0.9705 0.000 1.000 0.000 0.000
#> SRR1951633 1 0.0895 0.8559 0.976 0.000 0.020 0.004
#> SRR1951630 1 0.0895 0.8559 0.976 0.000 0.020 0.004
#> SRR1951629 4 0.4621 0.3598 0.008 0.000 0.284 0.708
#> SRR1951628 4 0.1474 0.5597 0.000 0.000 0.052 0.948
#> SRR1951627 3 0.4624 0.7845 0.000 0.000 0.660 0.340
#> SRR1951639 3 0.4999 0.5269 0.000 0.000 0.508 0.492
#> SRR1951625 4 0.2921 0.5098 0.000 0.000 0.140 0.860
#> SRR1951626 4 0.4972 -0.4605 0.000 0.000 0.456 0.544
#> SRR1951624 4 0.4456 0.3656 0.004 0.000 0.280 0.716
#> SRR1951622 1 0.4175 0.8039 0.784 0.000 0.200 0.016
#> SRR1951636 3 0.4605 0.7981 0.000 0.000 0.664 0.336
#> SRR1951623 1 0.5271 0.3968 0.640 0.000 0.020 0.340
#> SRR1951621 1 0.0000 0.8610 1.000 0.000 0.000 0.000
#> SRR1951620 4 0.4431 0.3470 0.000 0.000 0.304 0.696
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.3355 0.73606 0.000 0.804 0.012 0.000 0.184
#> SRR1951666 3 0.5690 0.48511 0.152 0.000 0.624 0.000 0.224
#> SRR1951665 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 3 0.0510 0.67445 0.000 0.000 0.984 0.000 0.016
#> SRR1951663 4 0.2110 0.77222 0.072 0.000 0.000 0.912 0.016
#> SRR1951662 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 3 0.4872 0.54275 0.120 0.000 0.720 0.000 0.160
#> SRR1951660 1 0.4138 0.61259 0.780 0.000 0.072 0.148 0.000
#> SRR1951659 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951658 2 0.3395 0.70234 0.000 0.764 0.000 0.000 0.236
#> SRR1951657 3 0.0510 0.67445 0.000 0.000 0.984 0.000 0.016
#> SRR1951656 3 0.0955 0.66354 0.000 0.000 0.968 0.028 0.004
#> SRR1951655 1 0.4355 0.59972 0.732 0.000 0.044 0.224 0.000
#> SRR1951654 4 0.0510 0.80604 0.000 0.000 0.000 0.984 0.016
#> SRR1951653 3 0.2234 0.66359 0.044 0.000 0.916 0.004 0.036
#> SRR1951652 3 0.8101 0.02361 0.228 0.000 0.388 0.112 0.272
#> SRR1951651 4 0.4409 0.68463 0.148 0.000 0.080 0.768 0.004
#> SRR1951650 3 0.2006 0.65457 0.072 0.000 0.916 0.000 0.012
#> SRR1951648 5 0.5975 0.03776 0.124 0.000 0.344 0.000 0.532
#> SRR1951649 5 0.6772 0.14527 0.308 0.000 0.296 0.000 0.396
#> SRR1951647 5 0.3231 0.75975 0.004 0.000 0.196 0.000 0.800
#> SRR1951646 3 0.0671 0.67419 0.000 0.000 0.980 0.004 0.016
#> SRR1951645 3 0.6660 0.06766 0.380 0.000 0.392 0.000 0.228
#> SRR1951644 1 0.3966 0.61602 0.796 0.000 0.072 0.132 0.000
#> SRR1951643 4 0.2067 0.79172 0.048 0.000 0.032 0.920 0.000
#> SRR1951641 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951642 4 0.3087 0.77477 0.064 0.000 0.064 0.868 0.004
#> SRR1951694 1 0.4211 0.44510 0.636 0.000 0.004 0.360 0.000
#> SRR1951731 5 0.3048 0.76926 0.000 0.004 0.176 0.000 0.820
#> SRR1951730 5 0.3209 0.77208 0.000 0.008 0.180 0.000 0.812
#> SRR1951752 5 0.3209 0.77208 0.000 0.008 0.180 0.000 0.812
#> SRR1951751 1 0.4522 0.61419 0.736 0.000 0.068 0.196 0.000
#> SRR1951749 5 0.3633 0.67269 0.012 0.116 0.040 0.000 0.832
#> SRR1951750 4 0.4299 0.35813 0.388 0.000 0.004 0.608 0.000
#> SRR1951748 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 4 0.0510 0.80604 0.000 0.000 0.000 0.984 0.016
#> SRR1951746 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 3 0.4877 0.02759 0.016 0.000 0.524 0.456 0.004
#> SRR1951742 5 0.3627 0.66915 0.020 0.100 0.040 0.000 0.840
#> SRR1951743 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 4 0.0510 0.80604 0.000 0.000 0.000 0.984 0.016
#> SRR1951740 3 0.1117 0.67335 0.016 0.000 0.964 0.000 0.020
#> SRR1951738 5 0.3209 0.77208 0.000 0.008 0.180 0.000 0.812
#> SRR1951739 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951737 3 0.0510 0.67445 0.000 0.000 0.984 0.000 0.016
#> SRR1951736 1 0.4166 0.47154 0.648 0.000 0.004 0.348 0.000
#> SRR1951734 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 3 0.5230 -0.11089 0.044 0.000 0.504 0.000 0.452
#> SRR1951733 5 0.3521 0.76417 0.012 0.008 0.172 0.000 0.808
#> SRR1951732 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 3 0.1704 0.65366 0.004 0.000 0.928 0.000 0.068
#> SRR1951728 3 0.4375 0.04057 0.004 0.000 0.576 0.000 0.420
#> SRR1951727 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 1 0.4182 0.44364 0.644 0.000 0.004 0.352 0.000
#> SRR1951726 3 0.6955 0.01505 0.208 0.000 0.444 0.016 0.332
#> SRR1951724 1 0.6376 0.10881 0.448 0.000 0.444 0.076 0.032
#> SRR1951722 5 0.4359 0.42434 0.004 0.000 0.412 0.000 0.584
#> SRR1951723 5 0.3550 0.71904 0.004 0.000 0.236 0.000 0.760
#> SRR1951721 4 0.4291 -0.05548 0.464 0.000 0.000 0.536 0.000
#> SRR1951719 3 0.0510 0.67445 0.000 0.000 0.984 0.000 0.016
#> SRR1951720 4 0.1908 0.76649 0.092 0.000 0.000 0.908 0.000
#> SRR1951718 1 0.5240 0.60410 0.716 0.000 0.144 0.124 0.016
#> SRR1951717 4 0.4015 0.40120 0.348 0.000 0.000 0.652 0.000
#> SRR1951715 1 0.4294 0.17691 0.532 0.000 0.000 0.468 0.000
#> SRR1951716 4 0.3752 0.51254 0.292 0.000 0.000 0.708 0.000
#> SRR1951714 3 0.6392 0.04348 0.272 0.000 0.532 0.192 0.004
#> SRR1951713 3 0.5080 0.25980 0.056 0.000 0.628 0.000 0.316
#> SRR1951712 1 0.4587 0.61189 0.728 0.000 0.068 0.204 0.000
#> SRR1951711 5 0.3209 0.77208 0.000 0.008 0.180 0.000 0.812
#> SRR1951710 1 0.4430 0.58011 0.708 0.000 0.036 0.256 0.000
#> SRR1951709 3 0.6292 0.22269 0.152 0.000 0.448 0.000 0.400
#> SRR1951708 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951707 5 0.4905 0.23168 0.024 0.000 0.476 0.000 0.500
#> SRR1951705 1 0.4465 0.61230 0.736 0.000 0.060 0.204 0.000
#> SRR1951704 3 0.4138 0.16954 0.000 0.000 0.616 0.000 0.384
#> SRR1951703 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 4 0.4084 0.44501 0.000 0.000 0.328 0.668 0.004
#> SRR1951702 3 0.2124 0.64147 0.004 0.000 0.900 0.000 0.096
#> SRR1951701 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951699 3 0.6294 0.22170 0.152 0.000 0.444 0.000 0.404
#> SRR1951698 3 0.4872 0.54275 0.120 0.000 0.720 0.000 0.160
#> SRR1951700 3 0.6653 -0.08713 0.228 0.000 0.408 0.000 0.364
#> SRR1951697 5 0.3616 0.65745 0.000 0.164 0.032 0.000 0.804
#> SRR1951696 3 0.0865 0.66557 0.000 0.000 0.972 0.024 0.004
#> SRR1951695 3 0.4403 -0.01938 0.004 0.000 0.560 0.000 0.436
#> SRR1951693 3 0.4558 0.46536 0.060 0.000 0.724 0.000 0.216
#> SRR1951692 3 0.4908 0.09237 0.020 0.000 0.560 0.416 0.004
#> SRR1951691 1 0.6451 0.54855 0.624 0.000 0.204 0.100 0.072
#> SRR1951688 5 0.3209 0.77208 0.000 0.008 0.180 0.000 0.812
#> SRR1951689 5 0.3209 0.77208 0.000 0.008 0.180 0.000 0.812
#> SRR1951687 4 0.0510 0.80604 0.000 0.000 0.000 0.984 0.016
#> SRR1951685 5 0.3300 0.75408 0.004 0.000 0.204 0.000 0.792
#> SRR1951684 3 0.0451 0.67384 0.000 0.000 0.988 0.004 0.008
#> SRR1951686 5 0.3729 0.67126 0.012 0.124 0.040 0.000 0.824
#> SRR1951683 4 0.1914 0.79435 0.060 0.000 0.016 0.924 0.000
#> SRR1951682 2 0.0162 0.97170 0.000 0.996 0.000 0.000 0.004
#> SRR1951690 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951680 3 0.1386 0.66934 0.016 0.000 0.952 0.000 0.032
#> SRR1951679 1 0.6942 0.19814 0.480 0.000 0.168 0.028 0.324
#> SRR1951678 4 0.3151 0.77161 0.064 0.000 0.068 0.864 0.004
#> SRR1951677 4 0.2954 0.77882 0.064 0.000 0.056 0.876 0.004
#> SRR1951676 3 0.3491 0.49649 0.000 0.000 0.768 0.228 0.004
#> SRR1951675 5 0.3209 0.77208 0.000 0.008 0.180 0.000 0.812
#> SRR1951681 3 0.2110 0.65322 0.072 0.000 0.912 0.000 0.016
#> SRR1951673 5 0.3209 0.77208 0.000 0.008 0.180 0.000 0.812
#> SRR1951674 4 0.0510 0.80604 0.000 0.000 0.000 0.984 0.016
#> SRR1951672 1 0.6342 0.06575 0.476 0.000 0.168 0.000 0.356
#> SRR1951671 5 0.4367 0.14192 0.000 0.416 0.004 0.000 0.580
#> SRR1951670 4 0.1121 0.79689 0.044 0.000 0.000 0.956 0.000
#> SRR1951640 3 0.2629 0.60408 0.004 0.000 0.860 0.000 0.136
#> SRR1951668 1 0.6209 0.20233 0.456 0.000 0.440 0.088 0.016
#> SRR1951669 1 0.8311 0.14651 0.324 0.000 0.280 0.128 0.268
#> SRR1951638 4 0.1211 0.79947 0.024 0.000 0.000 0.960 0.016
#> SRR1951637 1 0.7997 -0.00464 0.324 0.000 0.276 0.080 0.320
#> SRR1951635 4 0.0671 0.80520 0.004 0.000 0.000 0.980 0.016
#> SRR1951634 3 0.5923 0.08946 0.372 0.000 0.544 0.064 0.020
#> SRR1951631 4 0.6049 0.32409 0.300 0.000 0.132 0.564 0.004
#> SRR1951632 2 0.0000 0.97485 0.000 1.000 0.000 0.000 0.000
#> SRR1951633 4 0.3021 0.77582 0.060 0.000 0.064 0.872 0.004
#> SRR1951630 4 0.2585 0.78785 0.064 0.000 0.036 0.896 0.004
#> SRR1951629 3 0.0671 0.67451 0.000 0.000 0.980 0.004 0.016
#> SRR1951628 5 0.3890 0.69148 0.012 0.000 0.252 0.000 0.736
#> SRR1951627 3 0.4872 0.54275 0.120 0.000 0.720 0.000 0.160
#> SRR1951639 5 0.6042 -0.06309 0.120 0.000 0.396 0.000 0.484
#> SRR1951625 5 0.3209 0.77208 0.000 0.008 0.180 0.000 0.812
#> SRR1951626 5 0.5930 -0.02181 0.112 0.000 0.372 0.000 0.516
#> SRR1951624 3 0.0510 0.67445 0.000 0.000 0.984 0.000 0.016
#> SRR1951622 1 0.4232 0.51954 0.676 0.000 0.012 0.312 0.000
#> SRR1951636 3 0.4909 0.54203 0.120 0.000 0.716 0.000 0.164
#> SRR1951623 4 0.4067 0.58036 0.020 0.000 0.228 0.748 0.004
#> SRR1951621 4 0.0510 0.80604 0.000 0.000 0.000 0.984 0.016
#> SRR1951620 3 0.1522 0.66621 0.044 0.000 0.944 0.000 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 2 0.3050 0.64605 0.000 0.764 0.000 0.000 0.236 0.000
#> SRR1951666 6 0.3606 0.75051 0.000 0.000 0.256 0.000 0.016 0.728
#> SRR1951665 2 0.0000 0.96694 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 3 0.0622 0.68462 0.000 0.000 0.980 0.000 0.008 0.012
#> SRR1951663 4 0.5510 0.41169 0.248 0.000 0.000 0.560 0.000 0.192
#> SRR1951662 2 0.0000 0.96694 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 3 0.3508 0.41282 0.000 0.000 0.704 0.000 0.004 0.292
#> SRR1951660 1 0.1887 0.72973 0.924 0.000 0.048 0.012 0.016 0.000
#> SRR1951659 2 0.0000 0.96694 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951658 2 0.3023 0.67417 0.000 0.768 0.000 0.000 0.232 0.000
#> SRR1951657 3 0.0982 0.68977 0.004 0.000 0.968 0.004 0.020 0.004
#> SRR1951656 3 0.1555 0.66580 0.000 0.000 0.932 0.060 0.004 0.004
#> SRR1951655 1 0.0665 0.74429 0.980 0.000 0.008 0.008 0.000 0.004
#> SRR1951654 4 0.3763 0.68707 0.060 0.000 0.000 0.768 0.000 0.172
#> SRR1951653 3 0.2400 0.68116 0.040 0.000 0.900 0.004 0.048 0.008
#> SRR1951652 5 0.7337 0.17744 0.216 0.000 0.248 0.104 0.424 0.008
#> SRR1951651 4 0.3232 0.67962 0.020 0.000 0.140 0.824 0.016 0.000
#> SRR1951650 3 0.2743 0.58813 0.000 0.000 0.828 0.000 0.008 0.164
#> SRR1951648 6 0.4871 0.80261 0.000 0.000 0.124 0.000 0.224 0.652
#> SRR1951649 5 0.6283 0.18287 0.220 0.000 0.300 0.004 0.464 0.012
#> SRR1951647 5 0.0547 0.73665 0.000 0.000 0.020 0.000 0.980 0.000
#> SRR1951646 3 0.1340 0.67830 0.000 0.000 0.948 0.040 0.008 0.004
#> SRR1951645 3 0.6257 0.32119 0.224 0.000 0.476 0.004 0.284 0.012
#> SRR1951644 1 0.1536 0.73349 0.940 0.000 0.040 0.004 0.016 0.000
#> SRR1951643 4 0.3571 0.69122 0.048 0.000 0.124 0.812 0.016 0.000
#> SRR1951641 2 0.0000 0.96694 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951642 4 0.2468 0.70181 0.008 0.000 0.096 0.880 0.016 0.000
#> SRR1951694 1 0.3608 0.66710 0.788 0.000 0.000 0.148 0.000 0.064
#> SRR1951731 5 0.0692 0.73374 0.000 0.004 0.020 0.000 0.976 0.000
#> SRR1951730 5 0.0458 0.73745 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1951752 5 0.0458 0.73745 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1951751 1 0.1010 0.74016 0.960 0.000 0.036 0.000 0.004 0.000
#> SRR1951749 5 0.1760 0.68928 0.000 0.020 0.004 0.000 0.928 0.048
#> SRR1951750 1 0.4183 0.47352 0.668 0.000 0.000 0.296 0.000 0.036
#> SRR1951748 2 0.0000 0.96694 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.96694 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 4 0.3796 0.68538 0.060 0.000 0.000 0.764 0.000 0.176
#> SRR1951746 2 0.0000 0.96694 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951744 3 0.5852 0.20091 0.112 0.000 0.528 0.332 0.028 0.000
#> SRR1951742 5 0.1732 0.67871 0.000 0.004 0.004 0.000 0.920 0.072
#> SRR1951743 2 0.0000 0.96694 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951741 4 0.3763 0.68707 0.060 0.000 0.000 0.768 0.000 0.172
#> SRR1951740 3 0.1321 0.68949 0.020 0.000 0.952 0.000 0.024 0.004
#> SRR1951738 5 0.0363 0.73605 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1951739 2 0.0000 0.96694 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951737 3 0.0891 0.68018 0.000 0.000 0.968 0.000 0.008 0.024
#> SRR1951736 1 0.2451 0.70943 0.884 0.000 0.000 0.056 0.000 0.060
#> SRR1951734 2 0.0000 0.96694 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951735 5 0.5233 0.09787 0.000 0.000 0.404 0.000 0.500 0.096
#> SRR1951733 5 0.0692 0.73342 0.000 0.000 0.020 0.000 0.976 0.004
#> SRR1951732 2 0.0000 0.96694 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 3 0.2170 0.65430 0.000 0.000 0.888 0.000 0.100 0.012
#> SRR1951728 3 0.3982 0.14846 0.004 0.000 0.536 0.000 0.460 0.000
#> SRR1951727 2 0.0000 0.96694 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 1 0.3978 0.63082 0.744 0.000 0.000 0.192 0.000 0.064
#> SRR1951726 3 0.7162 0.18442 0.160 0.000 0.400 0.104 0.332 0.004
#> SRR1951724 3 0.5511 0.46058 0.288 0.000 0.592 0.004 0.100 0.016
#> SRR1951722 5 0.4108 0.55684 0.060 0.000 0.184 0.000 0.748 0.008
#> SRR1951723 5 0.0922 0.73436 0.004 0.000 0.024 0.000 0.968 0.004
#> SRR1951721 1 0.4495 0.55947 0.672 0.000 0.000 0.256 0.000 0.072
#> SRR1951719 3 0.0692 0.68943 0.004 0.000 0.976 0.000 0.020 0.000
#> SRR1951720 4 0.5219 0.39625 0.316 0.000 0.000 0.568 0.000 0.116
#> SRR1951718 1 0.3073 0.65396 0.832 0.000 0.140 0.004 0.020 0.004
#> SRR1951717 1 0.4573 0.54380 0.672 0.000 0.000 0.244 0.000 0.084
#> SRR1951715 1 0.4332 0.59402 0.700 0.000 0.000 0.228 0.000 0.072
#> SRR1951716 1 0.4738 0.49034 0.640 0.000 0.000 0.276 0.000 0.084
#> SRR1951714 3 0.5518 0.47760 0.200 0.000 0.628 0.152 0.016 0.004
#> SRR1951713 3 0.5564 0.39905 0.128 0.000 0.568 0.000 0.292 0.012
#> SRR1951712 1 0.0935 0.74108 0.964 0.000 0.032 0.000 0.004 0.000
#> SRR1951711 5 0.0458 0.73745 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1951710 1 0.1078 0.74248 0.964 0.000 0.008 0.012 0.000 0.016
#> SRR1951709 6 0.4172 0.83452 0.000 0.000 0.204 0.000 0.072 0.724
#> SRR1951708 2 0.0000 0.96694 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951707 5 0.4776 0.43991 0.052 0.000 0.300 0.000 0.636 0.012
#> SRR1951705 1 0.1080 0.74204 0.960 0.000 0.032 0.004 0.004 0.000
#> SRR1951704 3 0.4155 0.40029 0.020 0.000 0.616 0.000 0.364 0.000
#> SRR1951703 2 0.0000 0.96694 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 4 0.5216 0.27999 0.052 0.000 0.396 0.532 0.020 0.000
#> SRR1951702 3 0.2911 0.63319 0.000 0.000 0.832 0.000 0.144 0.024
#> SRR1951701 2 0.0000 0.96694 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951699 6 0.4215 0.83971 0.000 0.000 0.196 0.000 0.080 0.724
#> SRR1951698 3 0.3636 0.34627 0.000 0.000 0.676 0.000 0.004 0.320
#> SRR1951700 5 0.6141 -0.00142 0.180 0.000 0.380 0.004 0.428 0.008
#> SRR1951697 5 0.0937 0.70808 0.000 0.040 0.000 0.000 0.960 0.000
#> SRR1951696 3 0.1555 0.66580 0.000 0.000 0.932 0.060 0.004 0.004
#> SRR1951695 5 0.4980 0.01535 0.048 0.000 0.452 0.000 0.492 0.008
#> SRR1951693 3 0.5476 0.47504 0.128 0.000 0.604 0.000 0.252 0.016
#> SRR1951692 4 0.4095 0.01200 0.000 0.000 0.480 0.512 0.008 0.000
#> SRR1951691 1 0.5596 0.33072 0.588 0.000 0.204 0.004 0.200 0.004
#> SRR1951688 5 0.0458 0.73745 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1951689 5 0.0458 0.73745 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1951687 4 0.3763 0.68707 0.060 0.000 0.000 0.768 0.000 0.172
#> SRR1951685 5 0.0713 0.73482 0.000 0.000 0.028 0.000 0.972 0.000
#> SRR1951684 3 0.0665 0.68718 0.008 0.000 0.980 0.004 0.008 0.000
#> SRR1951686 5 0.1562 0.69880 0.000 0.032 0.004 0.000 0.940 0.024
#> SRR1951683 4 0.3907 0.69173 0.104 0.000 0.088 0.792 0.016 0.000
#> SRR1951682 2 0.0260 0.96045 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1951690 2 0.0632 0.94452 0.000 0.976 0.000 0.000 0.024 0.000
#> SRR1951680 3 0.2418 0.67279 0.016 0.000 0.884 0.000 0.092 0.008
#> SRR1951679 5 0.5113 0.13777 0.464 0.000 0.048 0.004 0.476 0.008
#> SRR1951678 4 0.2468 0.70181 0.008 0.000 0.096 0.880 0.016 0.000
#> SRR1951677 4 0.2468 0.70181 0.008 0.000 0.096 0.880 0.016 0.000
#> SRR1951676 3 0.3586 0.45728 0.000 0.000 0.712 0.280 0.004 0.004
#> SRR1951675 5 0.0458 0.73745 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1951681 3 0.2768 0.59715 0.000 0.000 0.832 0.000 0.012 0.156
#> SRR1951673 5 0.0458 0.73745 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1951674 4 0.3763 0.68707 0.060 0.000 0.000 0.768 0.000 0.172
#> SRR1951672 1 0.5388 -0.08968 0.496 0.000 0.084 0.004 0.412 0.004
#> SRR1951671 5 0.3578 0.28157 0.000 0.340 0.000 0.000 0.660 0.000
#> SRR1951670 4 0.4219 0.50836 0.304 0.000 0.000 0.660 0.000 0.036
#> SRR1951640 3 0.2706 0.62466 0.000 0.000 0.832 0.000 0.160 0.008
#> SRR1951668 3 0.5364 0.44529 0.288 0.000 0.616 0.064 0.024 0.008
#> SRR1951669 5 0.6293 0.10582 0.388 0.000 0.048 0.092 0.464 0.008
#> SRR1951638 4 0.4503 0.63305 0.084 0.000 0.000 0.684 0.000 0.232
#> SRR1951637 5 0.5765 0.19978 0.392 0.000 0.048 0.044 0.508 0.008
#> SRR1951635 4 0.4223 0.64950 0.060 0.000 0.000 0.704 0.000 0.236
#> SRR1951634 3 0.4414 0.55804 0.204 0.000 0.732 0.024 0.032 0.008
#> SRR1951631 4 0.6387 0.16711 0.240 0.000 0.372 0.372 0.016 0.000
#> SRR1951632 2 0.0000 0.96694 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951633 4 0.2468 0.70181 0.008 0.000 0.096 0.880 0.016 0.000
#> SRR1951630 4 0.2468 0.70181 0.008 0.000 0.096 0.880 0.016 0.000
#> SRR1951629 3 0.0922 0.69004 0.000 0.000 0.968 0.004 0.024 0.004
#> SRR1951628 5 0.1708 0.71275 0.024 0.000 0.040 0.000 0.932 0.004
#> SRR1951627 3 0.3668 0.33205 0.000 0.000 0.668 0.000 0.004 0.328
#> SRR1951639 6 0.5165 0.80900 0.000 0.000 0.156 0.000 0.228 0.616
#> SRR1951625 5 0.0458 0.73745 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1951626 6 0.5270 0.78886 0.000 0.000 0.216 0.000 0.180 0.604
#> SRR1951624 3 0.0717 0.68429 0.000 0.000 0.976 0.000 0.008 0.016
#> SRR1951622 1 0.1074 0.73725 0.960 0.000 0.000 0.012 0.000 0.028
#> SRR1951636 3 0.3684 0.32069 0.000 0.000 0.664 0.000 0.004 0.332
#> SRR1951623 4 0.2814 0.66358 0.000 0.000 0.172 0.820 0.008 0.000
#> SRR1951621 4 0.3763 0.68707 0.060 0.000 0.000 0.768 0.000 0.172
#> SRR1951620 3 0.2218 0.63418 0.000 0.000 0.884 0.000 0.012 0.104
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 13580 rows and 133 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 0.888 0.923 0.967 0.4930 0.511 0.511
#> 3 3 0.732 0.819 0.922 0.3320 0.725 0.512
#> 4 4 0.619 0.699 0.827 0.1343 0.771 0.449
#> 5 5 0.585 0.502 0.717 0.0724 0.837 0.475
#> 6 6 0.703 0.663 0.794 0.0442 0.892 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
#> SRR1951667 2 0.0000 0.9567 0.000 1.000
#> SRR1951666 2 0.0000 0.9567 0.000 1.000
#> SRR1951665 2 0.9963 0.1764 0.464 0.536
#> SRR1951664 2 0.0000 0.9567 0.000 1.000
#> SRR1951663 1 0.0000 0.9786 1.000 0.000
#> SRR1951662 2 0.0000 0.9567 0.000 1.000
#> SRR1951661 2 0.0000 0.9567 0.000 1.000
#> SRR1951660 1 0.0000 0.9786 1.000 0.000
#> SRR1951659 2 0.0000 0.9567 0.000 1.000
#> SRR1951658 2 0.0000 0.9567 0.000 1.000
#> SRR1951657 2 0.0000 0.9567 0.000 1.000
#> SRR1951656 2 0.0000 0.9567 0.000 1.000
#> SRR1951655 1 0.0000 0.9786 1.000 0.000
#> SRR1951654 1 0.0000 0.9786 1.000 0.000
#> SRR1951653 2 0.0000 0.9567 0.000 1.000
#> SRR1951652 1 0.0000 0.9786 1.000 0.000
#> SRR1951651 2 0.0000 0.9567 0.000 1.000
#> SRR1951650 2 0.0000 0.9567 0.000 1.000
#> SRR1951648 2 0.0000 0.9567 0.000 1.000
#> SRR1951649 1 0.0000 0.9786 1.000 0.000
#> SRR1951647 2 0.0000 0.9567 0.000 1.000
#> SRR1951646 2 0.0000 0.9567 0.000 1.000
#> SRR1951645 1 0.2043 0.9518 0.968 0.032
#> SRR1951644 1 0.0000 0.9786 1.000 0.000
#> SRR1951643 1 0.4690 0.8784 0.900 0.100
#> SRR1951641 1 0.0000 0.9786 1.000 0.000
#> SRR1951642 2 0.0000 0.9567 0.000 1.000
#> SRR1951694 1 0.0000 0.9786 1.000 0.000
#> SRR1951731 2 0.0000 0.9567 0.000 1.000
#> SRR1951730 2 0.5629 0.8468 0.132 0.868
#> SRR1951752 2 0.8144 0.6788 0.252 0.748
#> SRR1951751 1 0.0000 0.9786 1.000 0.000
#> SRR1951749 2 0.0000 0.9567 0.000 1.000
#> SRR1951750 1 0.0000 0.9786 1.000 0.000
#> SRR1951748 1 0.5737 0.8351 0.864 0.136
#> SRR1951747 1 0.0000 0.9786 1.000 0.000
#> SRR1951745 1 0.0000 0.9786 1.000 0.000
#> SRR1951746 2 0.0000 0.9567 0.000 1.000
#> SRR1951744 2 0.6973 0.7746 0.188 0.812
#> SRR1951742 2 0.0000 0.9567 0.000 1.000
#> SRR1951743 1 0.1843 0.9556 0.972 0.028
#> SRR1951741 1 0.0000 0.9786 1.000 0.000
#> SRR1951740 2 0.0000 0.9567 0.000 1.000
#> SRR1951738 1 0.0000 0.9786 1.000 0.000
#> SRR1951739 2 0.0000 0.9567 0.000 1.000
#> SRR1951737 2 0.0000 0.9567 0.000 1.000
#> SRR1951736 1 0.0000 0.9786 1.000 0.000
#> SRR1951734 2 0.3879 0.8988 0.076 0.924
#> SRR1951735 2 0.0000 0.9567 0.000 1.000
#> SRR1951733 2 0.0000 0.9567 0.000 1.000
#> SRR1951732 2 0.0000 0.9567 0.000 1.000
#> SRR1951729 2 0.0000 0.9567 0.000 1.000
#> SRR1951728 2 0.0000 0.9567 0.000 1.000
#> SRR1951727 2 0.2778 0.9238 0.048 0.952
#> SRR1951725 1 0.0000 0.9786 1.000 0.000
#> SRR1951726 1 0.0376 0.9756 0.996 0.004
#> SRR1951724 1 0.4562 0.8832 0.904 0.096
#> SRR1951722 2 0.9580 0.4276 0.380 0.620
#> SRR1951723 1 0.0000 0.9786 1.000 0.000
#> SRR1951721 1 0.0000 0.9786 1.000 0.000
#> SRR1951719 2 0.0000 0.9567 0.000 1.000
#> SRR1951720 1 0.0000 0.9786 1.000 0.000
#> SRR1951718 1 0.0000 0.9786 1.000 0.000
#> SRR1951717 1 0.0000 0.9786 1.000 0.000
#> SRR1951715 1 0.0000 0.9786 1.000 0.000
#> SRR1951716 1 0.0000 0.9786 1.000 0.000
#> SRR1951714 2 0.9635 0.4006 0.388 0.612
#> SRR1951713 2 0.0000 0.9567 0.000 1.000
#> SRR1951712 1 0.0000 0.9786 1.000 0.000
#> SRR1951711 1 0.0000 0.9786 1.000 0.000
#> SRR1951710 1 0.0000 0.9786 1.000 0.000
#> SRR1951709 2 0.0000 0.9567 0.000 1.000
#> SRR1951708 2 0.7815 0.7121 0.232 0.768
#> SRR1951707 2 0.5059 0.8632 0.112 0.888
#> SRR1951705 1 0.0000 0.9786 1.000 0.000
#> SRR1951704 2 0.0000 0.9567 0.000 1.000
#> SRR1951703 1 0.9988 0.0159 0.520 0.480
#> SRR1951706 2 0.4298 0.8893 0.088 0.912
#> SRR1951702 2 0.0000 0.9567 0.000 1.000
#> SRR1951701 2 0.0000 0.9567 0.000 1.000
#> SRR1951699 2 0.0000 0.9567 0.000 1.000
#> SRR1951698 2 0.0000 0.9567 0.000 1.000
#> SRR1951700 1 0.0000 0.9786 1.000 0.000
#> SRR1951697 2 0.0000 0.9567 0.000 1.000
#> SRR1951696 2 0.0000 0.9567 0.000 1.000
#> SRR1951695 2 0.0000 0.9567 0.000 1.000
#> SRR1951693 2 0.2948 0.9206 0.052 0.948
#> SRR1951692 2 0.0000 0.9567 0.000 1.000
#> SRR1951691 1 0.0000 0.9786 1.000 0.000
#> SRR1951688 1 0.0000 0.9786 1.000 0.000
#> SRR1951689 1 0.0000 0.9786 1.000 0.000
#> SRR1951687 1 0.0000 0.9786 1.000 0.000
#> SRR1951685 1 0.6973 0.7566 0.812 0.188
#> SRR1951684 2 0.0000 0.9567 0.000 1.000
#> SRR1951686 2 0.0000 0.9567 0.000 1.000
#> SRR1951683 2 0.9909 0.2445 0.444 0.556
#> SRR1951682 2 0.7139 0.7609 0.196 0.804
#> SRR1951690 2 0.0000 0.9567 0.000 1.000
#> SRR1951680 2 0.0000 0.9567 0.000 1.000
#> SRR1951679 1 0.0000 0.9786 1.000 0.000
#> SRR1951678 2 0.0000 0.9567 0.000 1.000
#> SRR1951677 2 0.4431 0.8851 0.092 0.908
#> SRR1951676 2 0.0000 0.9567 0.000 1.000
#> SRR1951675 1 0.0000 0.9786 1.000 0.000
#> SRR1951681 2 0.0000 0.9567 0.000 1.000
#> SRR1951673 1 0.0376 0.9756 0.996 0.004
#> SRR1951674 1 0.0000 0.9786 1.000 0.000
#> SRR1951672 1 0.0000 0.9786 1.000 0.000
#> SRR1951671 2 0.0000 0.9567 0.000 1.000
#> SRR1951670 1 0.0000 0.9786 1.000 0.000
#> SRR1951640 2 0.0000 0.9567 0.000 1.000
#> SRR1951668 2 0.2236 0.9319 0.036 0.964
#> SRR1951669 1 0.0000 0.9786 1.000 0.000
#> SRR1951638 1 0.0000 0.9786 1.000 0.000
#> SRR1951637 1 0.0000 0.9786 1.000 0.000
#> SRR1951635 1 0.0000 0.9786 1.000 0.000
#> SRR1951634 2 0.0000 0.9567 0.000 1.000
#> SRR1951631 1 0.1414 0.9626 0.980 0.020
#> SRR1951632 2 0.0000 0.9567 0.000 1.000
#> SRR1951633 2 0.0000 0.9567 0.000 1.000
#> SRR1951630 2 0.0000 0.9567 0.000 1.000
#> SRR1951629 2 0.0000 0.9567 0.000 1.000
#> SRR1951628 1 0.0000 0.9786 1.000 0.000
#> SRR1951627 2 0.0000 0.9567 0.000 1.000
#> SRR1951639 2 0.0000 0.9567 0.000 1.000
#> SRR1951625 2 0.3584 0.9057 0.068 0.932
#> SRR1951626 2 0.0000 0.9567 0.000 1.000
#> SRR1951624 2 0.0000 0.9567 0.000 1.000
#> SRR1951622 1 0.0000 0.9786 1.000 0.000
#> SRR1951636 2 0.0000 0.9567 0.000 1.000
#> SRR1951623 2 0.0000 0.9567 0.000 1.000
#> SRR1951621 1 0.0000 0.9786 1.000 0.000
#> SRR1951620 2 0.0000 0.9567 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.0592 0.8780 0.000 0.988 0.012
#> SRR1951666 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951665 2 0.4007 0.8176 0.084 0.880 0.036
#> SRR1951664 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951663 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951662 3 0.4750 0.7175 0.000 0.216 0.784
#> SRR1951661 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951660 1 0.0592 0.9031 0.988 0.012 0.000
#> SRR1951659 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951658 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951657 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951656 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951655 1 0.0592 0.9031 0.988 0.012 0.000
#> SRR1951654 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951653 2 0.4062 0.7655 0.000 0.836 0.164
#> SRR1951652 1 0.0237 0.9062 0.996 0.000 0.004
#> SRR1951651 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951650 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951648 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951649 2 0.0237 0.8807 0.004 0.996 0.000
#> SRR1951647 3 0.4796 0.7137 0.000 0.220 0.780
#> SRR1951646 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951645 2 0.0237 0.8807 0.004 0.996 0.000
#> SRR1951644 2 0.2165 0.8434 0.064 0.936 0.000
#> SRR1951643 1 0.3192 0.8129 0.888 0.000 0.112
#> SRR1951641 2 0.0000 0.8817 0.000 1.000 0.000
#> SRR1951642 3 0.2537 0.8710 0.080 0.000 0.920
#> SRR1951694 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951731 3 0.5882 0.4802 0.000 0.348 0.652
#> SRR1951730 2 0.0000 0.8817 0.000 1.000 0.000
#> SRR1951752 2 0.5327 0.5999 0.000 0.728 0.272
#> SRR1951751 2 0.0000 0.8817 0.000 1.000 0.000
#> SRR1951749 3 0.0237 0.9281 0.000 0.004 0.996
#> SRR1951750 2 0.6260 0.1508 0.448 0.552 0.000
#> SRR1951748 2 0.0661 0.8788 0.008 0.988 0.004
#> SRR1951747 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951745 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951746 2 0.6267 0.1459 0.000 0.548 0.452
#> SRR1951744 3 0.5905 0.4661 0.352 0.000 0.648
#> SRR1951742 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951743 2 0.0000 0.8817 0.000 1.000 0.000
#> SRR1951741 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951740 3 0.5650 0.5577 0.000 0.312 0.688
#> SRR1951738 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951739 3 0.5098 0.6693 0.000 0.248 0.752
#> SRR1951737 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951736 2 0.6095 0.3138 0.392 0.608 0.000
#> SRR1951734 2 0.4346 0.7324 0.000 0.816 0.184
#> SRR1951735 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951733 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951732 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951729 3 0.0237 0.9281 0.000 0.004 0.996
#> SRR1951728 3 0.2959 0.8522 0.000 0.100 0.900
#> SRR1951727 3 0.4796 0.7124 0.220 0.000 0.780
#> SRR1951725 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951726 1 0.0237 0.9062 0.996 0.000 0.004
#> SRR1951724 2 0.2339 0.8538 0.048 0.940 0.012
#> SRR1951722 2 0.4808 0.7330 0.008 0.804 0.188
#> SRR1951723 2 0.0000 0.8817 0.000 1.000 0.000
#> SRR1951721 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951719 3 0.4399 0.7560 0.000 0.188 0.812
#> SRR1951720 1 0.0237 0.9067 0.996 0.004 0.000
#> SRR1951718 1 0.1163 0.8939 0.972 0.028 0.000
#> SRR1951717 1 0.3116 0.8322 0.892 0.108 0.000
#> SRR1951715 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951716 1 0.3941 0.7849 0.844 0.156 0.000
#> SRR1951714 1 0.6026 0.3762 0.624 0.000 0.376
#> SRR1951713 2 0.0000 0.8817 0.000 1.000 0.000
#> SRR1951712 2 0.0000 0.8817 0.000 1.000 0.000
#> SRR1951711 2 0.0237 0.8807 0.004 0.996 0.000
#> SRR1951710 1 0.5835 0.4800 0.660 0.340 0.000
#> SRR1951709 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951708 1 0.9930 0.0559 0.380 0.280 0.340
#> SRR1951707 2 0.0000 0.8817 0.000 1.000 0.000
#> SRR1951705 2 0.2066 0.8456 0.060 0.940 0.000
#> SRR1951704 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951703 1 0.6079 0.3350 0.612 0.000 0.388
#> SRR1951706 3 0.5254 0.6412 0.264 0.000 0.736
#> SRR1951702 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951701 2 0.5760 0.4953 0.000 0.672 0.328
#> SRR1951699 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951698 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951700 2 0.0000 0.8817 0.000 1.000 0.000
#> SRR1951697 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951696 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951695 3 0.5835 0.4980 0.000 0.340 0.660
#> SRR1951693 2 0.0892 0.8753 0.000 0.980 0.020
#> SRR1951692 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951691 1 0.4452 0.7383 0.808 0.192 0.000
#> SRR1951688 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951689 2 0.0000 0.8817 0.000 1.000 0.000
#> SRR1951687 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951685 2 0.0000 0.8817 0.000 1.000 0.000
#> SRR1951684 3 0.0424 0.9255 0.000 0.008 0.992
#> SRR1951686 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951683 3 0.7213 0.5120 0.060 0.272 0.668
#> SRR1951682 2 0.0000 0.8817 0.000 1.000 0.000
#> SRR1951690 2 0.0000 0.8817 0.000 1.000 0.000
#> SRR1951680 2 0.0424 0.8796 0.000 0.992 0.008
#> SRR1951679 2 0.0237 0.8807 0.004 0.996 0.000
#> SRR1951678 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951677 3 0.5254 0.6393 0.264 0.000 0.736
#> SRR1951676 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951675 2 0.6295 0.0683 0.472 0.528 0.000
#> SRR1951681 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951673 2 0.0237 0.8807 0.004 0.996 0.000
#> SRR1951674 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951672 2 0.4750 0.6682 0.216 0.784 0.000
#> SRR1951671 2 0.5465 0.5748 0.000 0.712 0.288
#> SRR1951670 1 0.4178 0.7659 0.828 0.172 0.000
#> SRR1951640 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951668 2 0.0892 0.8741 0.000 0.980 0.020
#> SRR1951669 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951638 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951637 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951635 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951634 2 0.0000 0.8817 0.000 1.000 0.000
#> SRR1951631 1 0.1529 0.8833 0.960 0.000 0.040
#> SRR1951632 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951633 3 0.1643 0.9011 0.044 0.000 0.956
#> SRR1951630 3 0.1860 0.8949 0.052 0.000 0.948
#> SRR1951629 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951628 1 0.4974 0.6715 0.764 0.236 0.000
#> SRR1951627 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951639 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951625 2 0.2711 0.8329 0.000 0.912 0.088
#> SRR1951626 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951624 3 0.0237 0.9281 0.000 0.004 0.996
#> SRR1951622 1 0.2066 0.8715 0.940 0.060 0.000
#> SRR1951636 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951623 3 0.0000 0.9302 0.000 0.000 1.000
#> SRR1951621 1 0.0000 0.9082 1.000 0.000 0.000
#> SRR1951620 3 0.0000 0.9302 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.2704 0.8117 0.000 0.876 0.000 0.124
#> SRR1951666 3 0.0336 0.8565 0.000 0.008 0.992 0.000
#> SRR1951665 2 0.3725 0.7533 0.120 0.848 0.004 0.028
#> SRR1951664 3 0.0336 0.8567 0.000 0.008 0.992 0.000
#> SRR1951663 1 0.1389 0.7921 0.952 0.000 0.000 0.048
#> SRR1951662 2 0.3634 0.7388 0.096 0.856 0.048 0.000
#> SRR1951661 3 0.1411 0.8491 0.020 0.020 0.960 0.000
#> SRR1951660 4 0.3751 0.7021 0.196 0.004 0.000 0.800
#> SRR1951659 3 0.3335 0.7991 0.020 0.120 0.860 0.000
#> SRR1951658 3 0.6635 0.5184 0.152 0.228 0.620 0.000
#> SRR1951657 3 0.1302 0.8530 0.000 0.000 0.956 0.044
#> SRR1951656 3 0.2271 0.8396 0.000 0.008 0.916 0.076
#> SRR1951655 4 0.3486 0.7065 0.188 0.000 0.000 0.812
#> SRR1951654 1 0.1211 0.7939 0.960 0.000 0.000 0.040
#> SRR1951653 4 0.4889 0.3367 0.000 0.004 0.360 0.636
#> SRR1951652 1 0.2255 0.7820 0.920 0.068 0.012 0.000
#> SRR1951651 3 0.0779 0.8574 0.000 0.004 0.980 0.016
#> SRR1951650 3 0.0000 0.8571 0.000 0.000 1.000 0.000
#> SRR1951648 3 0.0188 0.8570 0.000 0.004 0.996 0.000
#> SRR1951649 4 0.1792 0.7374 0.000 0.068 0.000 0.932
#> SRR1951647 3 0.3219 0.7812 0.000 0.000 0.836 0.164
#> SRR1951646 3 0.0592 0.8575 0.000 0.000 0.984 0.016
#> SRR1951645 4 0.1302 0.7448 0.000 0.044 0.000 0.956
#> SRR1951644 4 0.1833 0.7519 0.032 0.024 0.000 0.944
#> SRR1951643 3 0.7818 -0.0851 0.356 0.000 0.388 0.256
#> SRR1951641 4 0.1557 0.7399 0.000 0.056 0.000 0.944
#> SRR1951642 3 0.3569 0.7134 0.196 0.000 0.804 0.000
#> SRR1951694 4 0.4543 0.5783 0.324 0.000 0.000 0.676
#> SRR1951731 2 0.4868 0.6464 0.000 0.748 0.212 0.040
#> SRR1951730 2 0.3160 0.8094 0.040 0.892 0.008 0.060
#> SRR1951752 2 0.2441 0.7736 0.068 0.916 0.004 0.012
#> SRR1951751 2 0.3356 0.7893 0.000 0.824 0.000 0.176
#> SRR1951749 3 0.5050 0.2856 0.004 0.408 0.588 0.000
#> SRR1951750 4 0.4608 0.7146 0.104 0.096 0.000 0.800
#> SRR1951748 2 0.3037 0.7673 0.100 0.880 0.000 0.020
#> SRR1951747 1 0.3428 0.7458 0.844 0.144 0.012 0.000
#> SRR1951745 1 0.0592 0.7968 0.984 0.000 0.000 0.016
#> SRR1951746 2 0.1509 0.7992 0.008 0.960 0.020 0.012
#> SRR1951744 1 0.4591 0.7183 0.800 0.116 0.084 0.000
#> SRR1951742 3 0.2197 0.8430 0.004 0.080 0.916 0.000
#> SRR1951743 2 0.1406 0.8079 0.016 0.960 0.000 0.024
#> SRR1951741 1 0.4049 0.6362 0.780 0.008 0.000 0.212
#> SRR1951740 3 0.5051 0.7607 0.000 0.100 0.768 0.132
#> SRR1951738 1 0.4989 -0.1155 0.528 0.000 0.000 0.472
#> SRR1951739 2 0.4719 0.6803 0.000 0.772 0.180 0.048
#> SRR1951737 3 0.3972 0.7626 0.004 0.164 0.816 0.016
#> SRR1951736 4 0.2983 0.7466 0.068 0.040 0.000 0.892
#> SRR1951734 2 0.1913 0.7935 0.040 0.940 0.000 0.020
#> SRR1951735 3 0.0469 0.8576 0.000 0.000 0.988 0.012
#> SRR1951733 3 0.3320 0.8320 0.000 0.068 0.876 0.056
#> SRR1951732 3 0.5159 0.4499 0.012 0.364 0.624 0.000
#> SRR1951729 3 0.2081 0.8267 0.000 0.084 0.916 0.000
#> SRR1951728 2 0.5119 0.7656 0.004 0.768 0.148 0.080
#> SRR1951727 1 0.6412 0.4049 0.592 0.320 0.088 0.000
#> SRR1951725 1 0.1792 0.7845 0.932 0.000 0.000 0.068
#> SRR1951726 1 0.1970 0.7860 0.932 0.060 0.008 0.000
#> SRR1951724 2 0.5472 0.2769 0.016 0.544 0.000 0.440
#> SRR1951722 4 0.2586 0.7382 0.000 0.048 0.040 0.912
#> SRR1951723 4 0.2647 0.7086 0.000 0.120 0.000 0.880
#> SRR1951721 1 0.1940 0.7796 0.924 0.000 0.000 0.076
#> SRR1951719 2 0.3672 0.7627 0.000 0.824 0.164 0.012
#> SRR1951720 4 0.4331 0.6255 0.288 0.000 0.000 0.712
#> SRR1951718 4 0.3636 0.7109 0.172 0.000 0.008 0.820
#> SRR1951717 1 0.4679 0.3606 0.648 0.000 0.000 0.352
#> SRR1951715 4 0.4888 0.4056 0.412 0.000 0.000 0.588
#> SRR1951716 4 0.3486 0.7133 0.188 0.000 0.000 0.812
#> SRR1951714 3 0.5857 0.6998 0.044 0.044 0.732 0.180
#> SRR1951713 2 0.3649 0.7676 0.000 0.796 0.000 0.204
#> SRR1951712 2 0.3764 0.7534 0.000 0.784 0.000 0.216
#> SRR1951711 2 0.2530 0.8140 0.000 0.888 0.000 0.112
#> SRR1951710 4 0.3925 0.7140 0.176 0.016 0.000 0.808
#> SRR1951709 3 0.0779 0.8543 0.004 0.016 0.980 0.000
#> SRR1951708 2 0.5680 0.5389 0.264 0.684 0.044 0.008
#> SRR1951707 2 0.3219 0.7966 0.000 0.836 0.000 0.164
#> SRR1951705 4 0.3402 0.6872 0.004 0.164 0.000 0.832
#> SRR1951704 2 0.6958 0.4414 0.232 0.584 0.184 0.000
#> SRR1951703 1 0.4552 0.7202 0.800 0.128 0.072 0.000
#> SRR1951706 1 0.4840 0.7037 0.784 0.116 0.100 0.000
#> SRR1951702 3 0.7756 0.0965 0.328 0.248 0.424 0.000
#> SRR1951701 2 0.3587 0.8144 0.000 0.860 0.052 0.088
#> SRR1951699 3 0.5309 0.6049 0.044 0.256 0.700 0.000
#> SRR1951698 3 0.0336 0.8574 0.000 0.000 0.992 0.008
#> SRR1951700 2 0.3311 0.7919 0.000 0.828 0.000 0.172
#> SRR1951697 2 0.7878 0.0225 0.340 0.376 0.284 0.000
#> SRR1951696 3 0.0469 0.8576 0.000 0.000 0.988 0.012
#> SRR1951695 2 0.5061 0.6692 0.004 0.752 0.196 0.048
#> SRR1951693 2 0.2773 0.8149 0.000 0.880 0.004 0.116
#> SRR1951692 3 0.2385 0.8430 0.000 0.028 0.920 0.052
#> SRR1951691 4 0.4804 0.6917 0.148 0.072 0.000 0.780
#> SRR1951688 1 0.3384 0.7618 0.860 0.116 0.000 0.024
#> SRR1951689 2 0.2973 0.8054 0.000 0.856 0.000 0.144
#> SRR1951687 1 0.1637 0.7880 0.940 0.000 0.000 0.060
#> SRR1951685 2 0.2868 0.8082 0.000 0.864 0.000 0.136
#> SRR1951684 3 0.3245 0.8254 0.000 0.100 0.872 0.028
#> SRR1951686 3 0.4036 0.7701 0.076 0.088 0.836 0.000
#> SRR1951683 4 0.6126 0.2000 0.028 0.012 0.416 0.544
#> SRR1951682 2 0.3266 0.7923 0.000 0.832 0.000 0.168
#> SRR1951690 2 0.3123 0.8017 0.000 0.844 0.000 0.156
#> SRR1951680 2 0.3810 0.7912 0.000 0.804 0.008 0.188
#> SRR1951679 4 0.4477 0.4690 0.000 0.312 0.000 0.688
#> SRR1951678 3 0.1888 0.8490 0.000 0.016 0.940 0.044
#> SRR1951677 1 0.5514 0.5975 0.712 0.040 0.236 0.012
#> SRR1951676 3 0.1042 0.8528 0.008 0.020 0.972 0.000
#> SRR1951675 4 0.4104 0.7254 0.080 0.088 0.000 0.832
#> SRR1951681 3 0.0188 0.8570 0.000 0.004 0.996 0.000
#> SRR1951673 4 0.4406 0.4800 0.000 0.300 0.000 0.700
#> SRR1951674 1 0.0524 0.7970 0.988 0.008 0.000 0.004
#> SRR1951672 4 0.2313 0.7527 0.044 0.032 0.000 0.924
#> SRR1951671 2 0.1610 0.8103 0.000 0.952 0.032 0.016
#> SRR1951670 4 0.3751 0.7116 0.196 0.004 0.000 0.800
#> SRR1951640 3 0.3441 0.8055 0.000 0.024 0.856 0.120
#> SRR1951668 2 0.3402 0.7965 0.000 0.832 0.004 0.164
#> SRR1951669 1 0.1356 0.7953 0.960 0.032 0.000 0.008
#> SRR1951638 1 0.2647 0.7468 0.880 0.000 0.000 0.120
#> SRR1951637 1 0.2589 0.7501 0.884 0.000 0.000 0.116
#> SRR1951635 1 0.3444 0.6726 0.816 0.000 0.000 0.184
#> SRR1951634 4 0.3942 0.5617 0.000 0.236 0.000 0.764
#> SRR1951631 4 0.4465 0.6965 0.144 0.000 0.056 0.800
#> SRR1951632 3 0.3745 0.8163 0.000 0.060 0.852 0.088
#> SRR1951633 3 0.4990 0.4273 0.352 0.008 0.640 0.000
#> SRR1951630 3 0.3280 0.8074 0.000 0.016 0.860 0.124
#> SRR1951629 3 0.0707 0.8572 0.000 0.000 0.980 0.020
#> SRR1951628 4 0.3308 0.7264 0.092 0.036 0.000 0.872
#> SRR1951627 3 0.0657 0.8551 0.004 0.012 0.984 0.000
#> SRR1951639 3 0.0592 0.8576 0.000 0.000 0.984 0.016
#> SRR1951625 4 0.6352 0.4497 0.000 0.108 0.260 0.632
#> SRR1951626 3 0.0524 0.8580 0.000 0.004 0.988 0.008
#> SRR1951624 3 0.4948 0.1655 0.000 0.440 0.560 0.000
#> SRR1951622 4 0.3569 0.7054 0.196 0.000 0.000 0.804
#> SRR1951636 3 0.0657 0.8551 0.004 0.012 0.984 0.000
#> SRR1951623 3 0.0188 0.8572 0.004 0.000 0.996 0.000
#> SRR1951621 4 0.4907 0.3832 0.420 0.000 0.000 0.580
#> SRR1951620 3 0.2915 0.8297 0.000 0.028 0.892 0.080
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.5559 0.4520 0.344 0.572 0.000 0.084 0.000
#> SRR1951666 3 0.0000 0.8312 0.000 0.000 1.000 0.000 0.000
#> SRR1951665 2 0.2853 0.5475 0.016 0.888 0.012 0.008 0.076
#> SRR1951664 3 0.0566 0.8305 0.000 0.004 0.984 0.012 0.000
#> SRR1951663 5 0.1331 0.7298 0.000 0.040 0.000 0.008 0.952
#> SRR1951662 2 0.2473 0.5662 0.000 0.896 0.000 0.072 0.032
#> SRR1951661 3 0.0404 0.8300 0.000 0.012 0.988 0.000 0.000
#> SRR1951660 1 0.6474 0.2610 0.496 0.000 0.000 0.264 0.240
#> SRR1951659 2 0.6630 0.1414 0.000 0.504 0.280 0.208 0.008
#> SRR1951658 3 0.5201 0.3201 0.000 0.424 0.532 0.000 0.044
#> SRR1951657 3 0.2471 0.7455 0.000 0.000 0.864 0.136 0.000
#> SRR1951656 3 0.4114 0.3150 0.000 0.000 0.624 0.376 0.000
#> SRR1951655 4 0.6443 0.2399 0.248 0.000 0.000 0.504 0.248
#> SRR1951654 5 0.0807 0.7254 0.000 0.012 0.000 0.012 0.976
#> SRR1951653 4 0.6485 0.3630 0.344 0.000 0.196 0.460 0.000
#> SRR1951652 5 0.3039 0.6819 0.000 0.192 0.000 0.000 0.808
#> SRR1951651 3 0.0451 0.8311 0.004 0.000 0.988 0.008 0.000
#> SRR1951650 3 0.0000 0.8312 0.000 0.000 1.000 0.000 0.000
#> SRR1951648 3 0.0290 0.8303 0.000 0.000 0.992 0.008 0.000
#> SRR1951649 4 0.5246 0.4140 0.288 0.020 0.000 0.652 0.040
#> SRR1951647 4 0.5297 0.2613 0.036 0.008 0.404 0.552 0.000
#> SRR1951646 3 0.2329 0.7568 0.000 0.000 0.876 0.124 0.000
#> SRR1951645 4 0.5267 0.4240 0.288 0.028 0.000 0.652 0.032
#> SRR1951644 1 0.4238 0.5165 0.776 0.000 0.000 0.136 0.088
#> SRR1951643 5 0.6843 -0.0627 0.068 0.000 0.076 0.388 0.468
#> SRR1951641 4 0.4697 0.3706 0.388 0.020 0.000 0.592 0.000
#> SRR1951642 3 0.2522 0.7721 0.000 0.000 0.880 0.012 0.108
#> SRR1951694 1 0.5405 0.3951 0.596 0.000 0.000 0.076 0.328
#> SRR1951731 2 0.4830 0.4423 0.020 0.492 0.000 0.488 0.000
#> SRR1951730 1 0.5080 0.2590 0.596 0.372 0.016 0.004 0.012
#> SRR1951752 2 0.4317 0.5517 0.004 0.668 0.000 0.320 0.008
#> SRR1951751 1 0.4923 0.3226 0.700 0.212 0.000 0.088 0.000
#> SRR1951749 3 0.4114 0.5480 0.000 0.272 0.712 0.016 0.000
#> SRR1951750 1 0.3078 0.5819 0.872 0.056 0.000 0.008 0.064
#> SRR1951748 2 0.2068 0.5457 0.000 0.904 0.000 0.004 0.092
#> SRR1951747 5 0.4297 0.3699 0.000 0.472 0.000 0.000 0.528
#> SRR1951745 5 0.1282 0.7300 0.000 0.044 0.000 0.004 0.952
#> SRR1951746 2 0.3949 0.5492 0.000 0.668 0.000 0.332 0.000
#> SRR1951744 5 0.3779 0.6384 0.000 0.236 0.012 0.000 0.752
#> SRR1951742 3 0.6180 0.3665 0.000 0.224 0.556 0.220 0.000
#> SRR1951743 2 0.1484 0.6047 0.008 0.944 0.000 0.048 0.000
#> SRR1951741 5 0.3386 0.6390 0.040 0.000 0.000 0.128 0.832
#> SRR1951740 4 0.2786 0.4431 0.012 0.084 0.020 0.884 0.000
#> SRR1951738 5 0.5255 0.4141 0.244 0.008 0.000 0.076 0.672
#> SRR1951739 2 0.4583 0.4464 0.004 0.528 0.004 0.464 0.000
#> SRR1951737 2 0.6436 0.4190 0.008 0.444 0.136 0.412 0.000
#> SRR1951736 1 0.2171 0.5791 0.912 0.000 0.000 0.024 0.064
#> SRR1951734 2 0.3508 0.5682 0.000 0.748 0.000 0.252 0.000
#> SRR1951735 3 0.2127 0.7772 0.000 0.000 0.892 0.108 0.000
#> SRR1951733 4 0.3593 0.4604 0.000 0.116 0.060 0.824 0.000
#> SRR1951732 2 0.4732 0.4709 0.000 0.716 0.208 0.076 0.000
#> SRR1951729 3 0.0451 0.8300 0.004 0.008 0.988 0.000 0.000
#> SRR1951728 2 0.7783 0.3898 0.164 0.428 0.308 0.100 0.000
#> SRR1951727 2 0.3491 0.3603 0.000 0.768 0.000 0.004 0.228
#> SRR1951725 5 0.2005 0.7084 0.056 0.016 0.000 0.004 0.924
#> SRR1951726 5 0.3247 0.7053 0.000 0.136 0.016 0.008 0.840
#> SRR1951724 1 0.3348 0.5389 0.836 0.140 0.008 0.012 0.004
#> SRR1951722 1 0.3667 0.5502 0.848 0.000 0.064 0.048 0.040
#> SRR1951723 1 0.4354 0.1591 0.624 0.008 0.000 0.368 0.000
#> SRR1951721 5 0.0798 0.7267 0.008 0.016 0.000 0.000 0.976
#> SRR1951719 2 0.7729 0.3817 0.140 0.428 0.324 0.108 0.000
#> SRR1951720 1 0.5678 0.2871 0.524 0.000 0.000 0.084 0.392
#> SRR1951718 4 0.6586 0.0159 0.384 0.000 0.000 0.408 0.208
#> SRR1951717 5 0.4613 0.2327 0.360 0.000 0.000 0.020 0.620
#> SRR1951715 1 0.5345 0.2884 0.540 0.000 0.000 0.056 0.404
#> SRR1951716 1 0.5348 0.4720 0.656 0.000 0.000 0.112 0.232
#> SRR1951714 4 0.4316 0.5480 0.020 0.004 0.056 0.800 0.120
#> SRR1951713 2 0.6408 0.5020 0.272 0.508 0.000 0.220 0.000
#> SRR1951712 1 0.4948 0.3470 0.708 0.184 0.000 0.108 0.000
#> SRR1951711 1 0.4218 0.3643 0.668 0.324 0.000 0.004 0.004
#> SRR1951710 1 0.3863 0.5508 0.796 0.000 0.000 0.052 0.152
#> SRR1951709 3 0.0162 0.8313 0.000 0.004 0.996 0.000 0.000
#> SRR1951708 2 0.2694 0.5092 0.004 0.864 0.000 0.004 0.128
#> SRR1951707 1 0.4208 0.4522 0.760 0.204 0.016 0.020 0.000
#> SRR1951705 1 0.1568 0.5783 0.944 0.000 0.000 0.020 0.036
#> SRR1951704 2 0.6226 0.4860 0.012 0.660 0.160 0.032 0.136
#> SRR1951703 5 0.4670 0.4168 0.000 0.440 0.008 0.004 0.548
#> SRR1951706 5 0.4789 0.5474 0.000 0.324 0.028 0.004 0.644
#> SRR1951702 3 0.5165 0.5698 0.000 0.240 0.676 0.004 0.080
#> SRR1951701 2 0.5631 0.5909 0.084 0.672 0.028 0.216 0.000
#> SRR1951699 3 0.2629 0.7676 0.000 0.136 0.860 0.004 0.000
#> SRR1951698 3 0.0609 0.8272 0.000 0.000 0.980 0.020 0.000
#> SRR1951700 1 0.6081 -0.2140 0.496 0.376 0.000 0.128 0.000
#> SRR1951697 3 0.6027 0.2062 0.000 0.420 0.476 0.004 0.100
#> SRR1951696 3 0.1043 0.8192 0.000 0.000 0.960 0.040 0.000
#> SRR1951695 2 0.5509 0.4603 0.064 0.472 0.000 0.464 0.000
#> SRR1951693 1 0.4971 0.2255 0.628 0.332 0.004 0.036 0.000
#> SRR1951692 4 0.3999 0.3562 0.000 0.000 0.344 0.656 0.000
#> SRR1951691 4 0.3040 0.4876 0.012 0.068 0.000 0.876 0.044
#> SRR1951688 5 0.6808 0.1192 0.000 0.324 0.000 0.308 0.368
#> SRR1951689 2 0.5996 0.3959 0.388 0.496 0.000 0.116 0.000
#> SRR1951687 5 0.1153 0.7191 0.024 0.000 0.008 0.004 0.964
#> SRR1951685 2 0.5825 0.4319 0.360 0.536 0.000 0.104 0.000
#> SRR1951684 4 0.6477 -0.3816 0.004 0.388 0.160 0.448 0.000
#> SRR1951686 3 0.2707 0.7741 0.000 0.100 0.876 0.000 0.024
#> SRR1951683 1 0.4821 0.0175 0.516 0.000 0.464 0.020 0.000
#> SRR1951682 2 0.5776 0.4399 0.160 0.632 0.004 0.204 0.000
#> SRR1951690 2 0.6118 0.5139 0.288 0.548 0.000 0.164 0.000
#> SRR1951680 2 0.6824 0.4539 0.188 0.476 0.016 0.320 0.000
#> SRR1951679 1 0.2079 0.5579 0.916 0.064 0.000 0.020 0.000
#> SRR1951678 3 0.5673 0.0649 0.000 0.000 0.500 0.420 0.080
#> SRR1951677 5 0.5266 0.5593 0.000 0.008 0.096 0.208 0.688
#> SRR1951676 3 0.0324 0.8309 0.000 0.004 0.992 0.004 0.000
#> SRR1951675 1 0.3043 0.5809 0.872 0.020 0.000 0.020 0.088
#> SRR1951681 3 0.0162 0.8309 0.000 0.000 0.996 0.004 0.000
#> SRR1951673 4 0.6605 0.0253 0.164 0.328 0.000 0.496 0.012
#> SRR1951674 5 0.1341 0.7285 0.000 0.056 0.000 0.000 0.944
#> SRR1951672 4 0.6498 0.2623 0.304 0.024 0.000 0.544 0.128
#> SRR1951671 2 0.4522 0.6135 0.080 0.744 0.000 0.176 0.000
#> SRR1951670 1 0.6288 0.3795 0.516 0.000 0.000 0.180 0.304
#> SRR1951640 4 0.3932 0.5084 0.000 0.064 0.140 0.796 0.000
#> SRR1951668 2 0.6224 0.5415 0.220 0.548 0.000 0.232 0.000
#> SRR1951669 5 0.2813 0.6980 0.000 0.168 0.000 0.000 0.832
#> SRR1951638 5 0.2358 0.6666 0.104 0.000 0.000 0.008 0.888
#> SRR1951637 5 0.1597 0.7010 0.012 0.000 0.000 0.048 0.940
#> SRR1951635 5 0.3307 0.6343 0.104 0.000 0.000 0.052 0.844
#> SRR1951634 4 0.6211 0.2166 0.264 0.192 0.000 0.544 0.000
#> SRR1951631 4 0.7300 0.3285 0.156 0.000 0.072 0.508 0.264
#> SRR1951632 4 0.4266 0.4934 0.000 0.104 0.120 0.776 0.000
#> SRR1951633 3 0.3562 0.6857 0.000 0.000 0.788 0.016 0.196
#> SRR1951630 4 0.5524 0.1936 0.000 0.000 0.416 0.516 0.068
#> SRR1951629 3 0.4290 0.4723 0.000 0.000 0.680 0.304 0.016
#> SRR1951628 4 0.4297 0.4699 0.236 0.000 0.000 0.728 0.036
#> SRR1951627 3 0.0162 0.8313 0.000 0.004 0.996 0.000 0.000
#> SRR1951639 3 0.1671 0.7989 0.000 0.000 0.924 0.076 0.000
#> SRR1951625 4 0.5732 0.5212 0.224 0.076 0.036 0.664 0.000
#> SRR1951626 3 0.0000 0.8312 0.000 0.000 1.000 0.000 0.000
#> SRR1951624 3 0.3163 0.7481 0.012 0.092 0.864 0.032 0.000
#> SRR1951622 1 0.6155 0.3594 0.560 0.000 0.000 0.212 0.228
#> SRR1951636 3 0.0000 0.8312 0.000 0.000 1.000 0.000 0.000
#> SRR1951623 3 0.0290 0.8304 0.000 0.000 0.992 0.008 0.000
#> SRR1951621 5 0.5541 0.4114 0.164 0.000 0.000 0.188 0.648
#> SRR1951620 4 0.3728 0.4999 0.000 0.008 0.244 0.748 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 3 0.2149 0.7736 0.080 0.016 0.900 0.000 0.004 0.000
#> SRR1951666 5 0.0291 0.8678 0.000 0.004 0.000 0.004 0.992 0.000
#> SRR1951665 2 0.1282 0.8356 0.004 0.956 0.024 0.004 0.000 0.012
#> SRR1951664 5 0.1604 0.8656 0.008 0.024 0.008 0.016 0.944 0.000
#> SRR1951663 6 0.1692 0.8246 0.012 0.048 0.000 0.008 0.000 0.932
#> SRR1951662 2 0.1655 0.8366 0.000 0.932 0.052 0.008 0.000 0.008
#> SRR1951661 5 0.0520 0.8673 0.000 0.008 0.008 0.000 0.984 0.000
#> SRR1951660 1 0.4314 0.5724 0.720 0.000 0.000 0.184 0.000 0.096
#> SRR1951659 2 0.2290 0.8217 0.000 0.908 0.012 0.044 0.032 0.004
#> SRR1951658 2 0.2361 0.7906 0.000 0.880 0.012 0.004 0.104 0.000
#> SRR1951657 5 0.3911 0.4235 0.000 0.000 0.000 0.368 0.624 0.008
#> SRR1951656 4 0.4224 0.2806 0.004 0.008 0.004 0.600 0.384 0.000
#> SRR1951655 4 0.5046 0.4506 0.256 0.000 0.000 0.620 0.000 0.124
#> SRR1951654 6 0.0603 0.8224 0.000 0.016 0.000 0.004 0.000 0.980
#> SRR1951653 4 0.5838 0.4736 0.304 0.032 0.012 0.580 0.068 0.004
#> SRR1951652 6 0.2135 0.7885 0.000 0.128 0.000 0.000 0.000 0.872
#> SRR1951651 5 0.1275 0.8603 0.000 0.000 0.016 0.016 0.956 0.012
#> SRR1951650 5 0.1649 0.8625 0.000 0.016 0.008 0.040 0.936 0.000
#> SRR1951648 5 0.1893 0.8602 0.004 0.024 0.008 0.036 0.928 0.000
#> SRR1951649 4 0.5855 0.2874 0.112 0.004 0.372 0.496 0.000 0.016
#> SRR1951647 4 0.5644 0.5582 0.064 0.056 0.016 0.652 0.212 0.000
#> SRR1951646 5 0.4053 0.4352 0.004 0.004 0.004 0.360 0.628 0.000
#> SRR1951645 4 0.5996 0.2156 0.136 0.008 0.396 0.452 0.000 0.008
#> SRR1951644 1 0.2177 0.7077 0.908 0.000 0.008 0.052 0.000 0.032
#> SRR1951643 6 0.4941 0.2621 0.032 0.000 0.000 0.368 0.024 0.576
#> SRR1951641 4 0.5982 0.4687 0.288 0.124 0.024 0.556 0.000 0.008
#> SRR1951642 5 0.3883 0.6711 0.004 0.004 0.012 0.012 0.752 0.216
#> SRR1951694 1 0.2988 0.6802 0.824 0.000 0.000 0.024 0.000 0.152
#> SRR1951731 3 0.4389 0.5741 0.000 0.052 0.660 0.288 0.000 0.000
#> SRR1951730 1 0.5917 0.4990 0.584 0.220 0.160 0.000 0.036 0.000
#> SRR1951752 2 0.5396 0.4446 0.000 0.564 0.284 0.152 0.000 0.000
#> SRR1951751 3 0.2673 0.7541 0.132 0.012 0.852 0.000 0.000 0.004
#> SRR1951749 5 0.2326 0.8555 0.004 0.040 0.028 0.020 0.908 0.000
#> SRR1951750 1 0.2809 0.6831 0.824 0.004 0.168 0.000 0.000 0.004
#> SRR1951748 2 0.1633 0.8358 0.000 0.932 0.044 0.000 0.000 0.024
#> SRR1951747 2 0.2135 0.7831 0.000 0.872 0.000 0.000 0.000 0.128
#> SRR1951745 6 0.0937 0.8219 0.000 0.040 0.000 0.000 0.000 0.960
#> SRR1951746 2 0.3961 0.7627 0.000 0.764 0.112 0.124 0.000 0.000
#> SRR1951744 6 0.3209 0.7540 0.000 0.156 0.012 0.000 0.016 0.816
#> SRR1951742 2 0.3819 0.7315 0.000 0.764 0.000 0.172 0.064 0.000
#> SRR1951743 2 0.2009 0.8294 0.000 0.904 0.084 0.008 0.000 0.004
#> SRR1951741 6 0.2480 0.7804 0.024 0.000 0.000 0.104 0.000 0.872
#> SRR1951740 4 0.2956 0.6038 0.000 0.040 0.120 0.840 0.000 0.000
#> SRR1951738 1 0.6541 0.1700 0.396 0.316 0.000 0.024 0.000 0.264
#> SRR1951739 2 0.5054 0.5010 0.000 0.572 0.092 0.336 0.000 0.000
#> SRR1951737 3 0.4944 0.6640 0.004 0.028 0.712 0.152 0.104 0.000
#> SRR1951736 1 0.1531 0.7212 0.928 0.000 0.068 0.000 0.000 0.004
#> SRR1951734 2 0.2837 0.8073 0.000 0.856 0.056 0.088 0.000 0.000
#> SRR1951735 5 0.4476 0.6911 0.004 0.044 0.024 0.200 0.728 0.000
#> SRR1951733 4 0.2474 0.6306 0.000 0.080 0.040 0.880 0.000 0.000
#> SRR1951732 2 0.2466 0.8156 0.000 0.896 0.024 0.028 0.052 0.000
#> SRR1951729 5 0.2545 0.8258 0.004 0.020 0.084 0.008 0.884 0.000
#> SRR1951728 3 0.3259 0.7693 0.024 0.044 0.844 0.000 0.088 0.000
#> SRR1951727 2 0.1616 0.8305 0.000 0.932 0.020 0.000 0.000 0.048
#> SRR1951725 6 0.1500 0.8185 0.052 0.012 0.000 0.000 0.000 0.936
#> SRR1951726 6 0.1949 0.8088 0.000 0.088 0.004 0.000 0.004 0.904
#> SRR1951724 1 0.3019 0.6928 0.840 0.020 0.128 0.000 0.012 0.000
#> SRR1951722 1 0.2071 0.7176 0.916 0.000 0.044 0.012 0.028 0.000
#> SRR1951723 4 0.5414 0.2468 0.420 0.004 0.100 0.476 0.000 0.000
#> SRR1951721 6 0.1788 0.8220 0.040 0.028 0.000 0.004 0.000 0.928
#> SRR1951719 3 0.2001 0.7793 0.008 0.012 0.912 0.000 0.068 0.000
#> SRR1951720 1 0.4319 0.2735 0.576 0.000 0.000 0.024 0.000 0.400
#> SRR1951718 1 0.4979 0.1693 0.556 0.000 0.004 0.376 0.000 0.064
#> SRR1951717 6 0.3091 0.7396 0.148 0.000 0.024 0.004 0.000 0.824
#> SRR1951715 1 0.3374 0.6406 0.772 0.000 0.000 0.020 0.000 0.208
#> SRR1951716 1 0.2542 0.6987 0.876 0.000 0.000 0.044 0.000 0.080
#> SRR1951714 4 0.2700 0.6483 0.008 0.032 0.020 0.896 0.008 0.036
#> SRR1951713 3 0.2819 0.7836 0.032 0.032 0.876 0.060 0.000 0.000
#> SRR1951712 3 0.2340 0.7388 0.148 0.000 0.852 0.000 0.000 0.000
#> SRR1951711 1 0.4871 0.5691 0.660 0.196 0.144 0.000 0.000 0.000
#> SRR1951710 1 0.1693 0.7196 0.932 0.000 0.004 0.020 0.000 0.044
#> SRR1951709 5 0.0767 0.8683 0.000 0.012 0.008 0.004 0.976 0.000
#> SRR1951708 2 0.1716 0.8339 0.000 0.932 0.028 0.004 0.000 0.036
#> SRR1951707 1 0.4474 0.5819 0.704 0.044 0.232 0.000 0.020 0.000
#> SRR1951705 1 0.1297 0.7239 0.948 0.000 0.040 0.000 0.000 0.012
#> SRR1951704 3 0.5542 0.6161 0.000 0.088 0.660 0.000 0.172 0.080
#> SRR1951703 2 0.1901 0.8163 0.004 0.912 0.000 0.008 0.000 0.076
#> SRR1951706 6 0.5368 0.4711 0.004 0.284 0.004 0.000 0.116 0.592
#> SRR1951702 5 0.2278 0.8323 0.000 0.052 0.012 0.000 0.904 0.032
#> SRR1951701 3 0.4492 0.6658 0.004 0.236 0.700 0.052 0.008 0.000
#> SRR1951699 5 0.1350 0.8600 0.000 0.020 0.020 0.000 0.952 0.008
#> SRR1951698 5 0.2544 0.8397 0.004 0.028 0.008 0.072 0.888 0.000
#> SRR1951700 3 0.1657 0.7835 0.056 0.016 0.928 0.000 0.000 0.000
#> SRR1951697 2 0.3944 0.6640 0.000 0.744 0.016 0.000 0.216 0.024
#> SRR1951696 5 0.2587 0.8227 0.004 0.020 0.000 0.108 0.868 0.000
#> SRR1951695 3 0.3279 0.7134 0.000 0.028 0.796 0.176 0.000 0.000
#> SRR1951693 3 0.5397 0.2243 0.376 0.028 0.544 0.000 0.048 0.004
#> SRR1951692 4 0.2992 0.6507 0.000 0.016 0.016 0.852 0.112 0.004
#> SRR1951691 4 0.4548 0.5755 0.004 0.040 0.128 0.756 0.000 0.072
#> SRR1951688 2 0.4661 0.6727 0.000 0.696 0.028 0.228 0.000 0.048
#> SRR1951689 3 0.2230 0.7785 0.084 0.024 0.892 0.000 0.000 0.000
#> SRR1951687 6 0.2610 0.8011 0.048 0.004 0.000 0.004 0.060 0.884
#> SRR1951685 3 0.1812 0.7766 0.080 0.008 0.912 0.000 0.000 0.000
#> SRR1951684 3 0.4029 0.7160 0.004 0.016 0.772 0.164 0.044 0.000
#> SRR1951686 5 0.1957 0.8472 0.000 0.072 0.008 0.000 0.912 0.008
#> SRR1951683 1 0.5090 0.0922 0.492 0.000 0.028 0.016 0.456 0.008
#> SRR1951682 2 0.2722 0.8032 0.004 0.872 0.032 0.088 0.004 0.000
#> SRR1951690 3 0.4759 0.7243 0.064 0.172 0.720 0.044 0.000 0.000
#> SRR1951680 3 0.2838 0.7802 0.024 0.032 0.872 0.072 0.000 0.000
#> SRR1951679 1 0.2378 0.6936 0.848 0.000 0.152 0.000 0.000 0.000
#> SRR1951678 4 0.6309 0.2940 0.000 0.000 0.016 0.444 0.240 0.300
#> SRR1951677 6 0.4335 0.6849 0.000 0.012 0.016 0.176 0.044 0.752
#> SRR1951676 5 0.0767 0.8656 0.000 0.004 0.012 0.000 0.976 0.008
#> SRR1951675 1 0.1426 0.7252 0.948 0.016 0.028 0.000 0.000 0.008
#> SRR1951681 5 0.0291 0.8664 0.000 0.000 0.004 0.004 0.992 0.000
#> SRR1951673 4 0.6388 0.1405 0.056 0.120 0.384 0.440 0.000 0.000
#> SRR1951674 6 0.1007 0.8211 0.000 0.044 0.000 0.000 0.000 0.956
#> SRR1951672 3 0.7715 0.0582 0.136 0.016 0.384 0.220 0.000 0.244
#> SRR1951671 3 0.3088 0.7395 0.000 0.172 0.808 0.020 0.000 0.000
#> SRR1951670 6 0.5542 0.3340 0.312 0.000 0.016 0.108 0.000 0.564
#> SRR1951640 4 0.3402 0.6038 0.000 0.012 0.168 0.800 0.020 0.000
#> SRR1951668 3 0.2515 0.7817 0.016 0.024 0.888 0.072 0.000 0.000
#> SRR1951669 6 0.3528 0.5797 0.004 0.296 0.000 0.000 0.000 0.700
#> SRR1951638 6 0.1732 0.8100 0.072 0.004 0.000 0.004 0.000 0.920
#> SRR1951637 6 0.1515 0.8190 0.028 0.008 0.000 0.020 0.000 0.944
#> SRR1951635 6 0.2572 0.7619 0.136 0.000 0.000 0.012 0.000 0.852
#> SRR1951634 3 0.3934 0.7208 0.048 0.028 0.788 0.136 0.000 0.000
#> SRR1951631 4 0.5636 0.5375 0.092 0.000 0.004 0.640 0.052 0.212
#> SRR1951632 4 0.3888 0.5192 0.004 0.212 0.020 0.752 0.012 0.000
#> SRR1951633 5 0.3329 0.6892 0.000 0.000 0.004 0.008 0.768 0.220
#> SRR1951630 4 0.4131 0.6066 0.000 0.000 0.004 0.744 0.180 0.072
#> SRR1951629 5 0.4958 0.0577 0.000 0.004 0.004 0.452 0.496 0.044
#> SRR1951628 4 0.4593 0.5362 0.252 0.024 0.020 0.692 0.000 0.012
#> SRR1951627 5 0.0000 0.8670 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951639 5 0.2907 0.8242 0.004 0.028 0.008 0.100 0.860 0.000
#> SRR1951625 4 0.6560 0.2180 0.160 0.360 0.028 0.440 0.012 0.000
#> SRR1951626 5 0.0912 0.8677 0.004 0.008 0.004 0.012 0.972 0.000
#> SRR1951624 5 0.2737 0.7616 0.004 0.004 0.160 0.000 0.832 0.000
#> SRR1951622 1 0.3472 0.6587 0.808 0.000 0.000 0.100 0.000 0.092
#> SRR1951636 5 0.0291 0.8677 0.000 0.004 0.004 0.000 0.992 0.000
#> SRR1951623 5 0.0870 0.8647 0.000 0.004 0.000 0.012 0.972 0.012
#> SRR1951621 6 0.4600 0.6437 0.152 0.004 0.000 0.136 0.000 0.708
#> SRR1951620 4 0.1719 0.6620 0.000 0.008 0.008 0.928 0.056 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 13580 rows and 133 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 0.732 0.880 0.942 0.3973 0.624 0.624
#> 3 3 0.547 0.757 0.875 0.5576 0.722 0.563
#> 4 4 0.652 0.733 0.871 0.1112 0.952 0.872
#> 5 5 0.628 0.576 0.734 0.0964 0.877 0.634
#> 6 6 0.683 0.650 0.716 0.0592 0.904 0.613
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
#> SRR1951667 2 0.0672 0.946 0.008 0.992
#> SRR1951666 1 0.9393 0.541 0.644 0.356
#> SRR1951665 2 0.0000 0.948 0.000 1.000
#> SRR1951664 1 0.0000 0.931 1.000 0.000
#> SRR1951663 1 0.0938 0.928 0.988 0.012
#> SRR1951662 2 0.0000 0.948 0.000 1.000
#> SRR1951661 1 0.8443 0.691 0.728 0.272
#> SRR1951660 1 0.0000 0.931 1.000 0.000
#> SRR1951659 2 0.0000 0.948 0.000 1.000
#> SRR1951658 2 0.0000 0.948 0.000 1.000
#> SRR1951657 1 0.0000 0.931 1.000 0.000
#> SRR1951656 1 0.0000 0.931 1.000 0.000
#> SRR1951655 1 0.0000 0.931 1.000 0.000
#> SRR1951654 1 0.0000 0.931 1.000 0.000
#> SRR1951653 1 0.0000 0.931 1.000 0.000
#> SRR1951652 2 0.3114 0.915 0.056 0.944
#> SRR1951651 1 0.0000 0.931 1.000 0.000
#> SRR1951650 1 0.2043 0.917 0.968 0.032
#> SRR1951648 1 0.8861 0.642 0.696 0.304
#> SRR1951649 1 0.0000 0.931 1.000 0.000
#> SRR1951647 1 0.3879 0.888 0.924 0.076
#> SRR1951646 1 0.0000 0.931 1.000 0.000
#> SRR1951645 1 0.0000 0.931 1.000 0.000
#> SRR1951644 1 0.0000 0.931 1.000 0.000
#> SRR1951643 1 0.0000 0.931 1.000 0.000
#> SRR1951641 2 0.0000 0.948 0.000 1.000
#> SRR1951642 1 0.0000 0.931 1.000 0.000
#> SRR1951694 1 0.0376 0.930 0.996 0.004
#> SRR1951731 1 0.6438 0.817 0.836 0.164
#> SRR1951730 2 0.2778 0.922 0.048 0.952
#> SRR1951752 1 0.8661 0.667 0.712 0.288
#> SRR1951751 1 0.0000 0.931 1.000 0.000
#> SRR1951749 2 0.9710 0.258 0.400 0.600
#> SRR1951750 1 0.0000 0.931 1.000 0.000
#> SRR1951748 2 0.0000 0.948 0.000 1.000
#> SRR1951747 2 0.0000 0.948 0.000 1.000
#> SRR1951745 1 0.0938 0.928 0.988 0.012
#> SRR1951746 2 0.0000 0.948 0.000 1.000
#> SRR1951744 1 0.7815 0.743 0.768 0.232
#> SRR1951742 2 0.8327 0.607 0.264 0.736
#> SRR1951743 2 0.0000 0.948 0.000 1.000
#> SRR1951741 1 0.0000 0.931 1.000 0.000
#> SRR1951740 1 0.0000 0.931 1.000 0.000
#> SRR1951738 2 0.2236 0.931 0.036 0.964
#> SRR1951739 2 0.0672 0.946 0.008 0.992
#> SRR1951737 1 0.0000 0.931 1.000 0.000
#> SRR1951736 1 0.0000 0.931 1.000 0.000
#> SRR1951734 2 0.0000 0.948 0.000 1.000
#> SRR1951735 1 0.0672 0.929 0.992 0.008
#> SRR1951733 2 0.9850 0.173 0.428 0.572
#> SRR1951732 2 0.0000 0.948 0.000 1.000
#> SRR1951729 1 0.0000 0.931 1.000 0.000
#> SRR1951728 1 0.2043 0.917 0.968 0.032
#> SRR1951727 2 0.0000 0.948 0.000 1.000
#> SRR1951725 1 0.0000 0.931 1.000 0.000
#> SRR1951726 1 0.7815 0.743 0.768 0.232
#> SRR1951724 1 0.0000 0.931 1.000 0.000
#> SRR1951722 1 0.1633 0.922 0.976 0.024
#> SRR1951723 1 0.0000 0.931 1.000 0.000
#> SRR1951721 1 0.0376 0.930 0.996 0.004
#> SRR1951719 1 0.0000 0.931 1.000 0.000
#> SRR1951720 1 0.0000 0.931 1.000 0.000
#> SRR1951718 1 0.0000 0.931 1.000 0.000
#> SRR1951717 1 0.0000 0.931 1.000 0.000
#> SRR1951715 1 0.0376 0.930 0.996 0.004
#> SRR1951716 1 0.0000 0.931 1.000 0.000
#> SRR1951714 1 0.0000 0.931 1.000 0.000
#> SRR1951713 1 0.0000 0.931 1.000 0.000
#> SRR1951712 1 0.0000 0.931 1.000 0.000
#> SRR1951711 2 0.0000 0.948 0.000 1.000
#> SRR1951710 1 0.0376 0.930 0.996 0.004
#> SRR1951709 1 0.8955 0.628 0.688 0.312
#> SRR1951708 2 0.0000 0.948 0.000 1.000
#> SRR1951707 1 0.6887 0.796 0.816 0.184
#> SRR1951705 1 0.0376 0.930 0.996 0.004
#> SRR1951704 1 0.7815 0.743 0.768 0.232
#> SRR1951703 2 0.0000 0.948 0.000 1.000
#> SRR1951706 1 0.8327 0.702 0.736 0.264
#> SRR1951702 1 0.7815 0.743 0.768 0.232
#> SRR1951701 2 0.0000 0.948 0.000 1.000
#> SRR1951699 1 0.8813 0.648 0.700 0.300
#> SRR1951698 1 0.7745 0.749 0.772 0.228
#> SRR1951700 1 0.1414 0.924 0.980 0.020
#> SRR1951697 2 0.0672 0.946 0.008 0.992
#> SRR1951696 1 0.0000 0.931 1.000 0.000
#> SRR1951695 1 0.3431 0.897 0.936 0.064
#> SRR1951693 1 0.1414 0.924 0.980 0.020
#> SRR1951692 1 0.0000 0.931 1.000 0.000
#> SRR1951691 1 0.0000 0.931 1.000 0.000
#> SRR1951688 2 0.2603 0.926 0.044 0.956
#> SRR1951689 1 0.8813 0.647 0.700 0.300
#> SRR1951687 1 0.0000 0.931 1.000 0.000
#> SRR1951685 1 0.6887 0.796 0.816 0.184
#> SRR1951684 1 0.0000 0.931 1.000 0.000
#> SRR1951686 2 0.3733 0.897 0.072 0.928
#> SRR1951683 1 0.0000 0.931 1.000 0.000
#> SRR1951682 2 0.0000 0.948 0.000 1.000
#> SRR1951690 2 0.0000 0.948 0.000 1.000
#> SRR1951680 1 0.0000 0.931 1.000 0.000
#> SRR1951679 1 0.0672 0.929 0.992 0.008
#> SRR1951678 1 0.0000 0.931 1.000 0.000
#> SRR1951677 1 0.0000 0.931 1.000 0.000
#> SRR1951676 1 0.5178 0.855 0.884 0.116
#> SRR1951675 2 0.0000 0.948 0.000 1.000
#> SRR1951681 1 0.2043 0.918 0.968 0.032
#> SRR1951673 1 0.8813 0.647 0.700 0.300
#> SRR1951674 1 0.0938 0.928 0.988 0.012
#> SRR1951672 1 0.0000 0.931 1.000 0.000
#> SRR1951671 1 0.9129 0.599 0.672 0.328
#> SRR1951670 1 0.0000 0.931 1.000 0.000
#> SRR1951640 1 0.0000 0.931 1.000 0.000
#> SRR1951668 1 0.0000 0.931 1.000 0.000
#> SRR1951669 2 0.2423 0.929 0.040 0.960
#> SRR1951638 1 0.0000 0.931 1.000 0.000
#> SRR1951637 2 0.2423 0.929 0.040 0.960
#> SRR1951635 1 0.0000 0.931 1.000 0.000
#> SRR1951634 1 0.0000 0.931 1.000 0.000
#> SRR1951631 1 0.0000 0.931 1.000 0.000
#> SRR1951632 2 0.0672 0.946 0.008 0.992
#> SRR1951633 1 0.0000 0.931 1.000 0.000
#> SRR1951630 1 0.0000 0.931 1.000 0.000
#> SRR1951629 1 0.0000 0.931 1.000 0.000
#> SRR1951628 1 0.1633 0.922 0.976 0.024
#> SRR1951627 1 0.9044 0.614 0.680 0.320
#> SRR1951639 1 0.7453 0.768 0.788 0.212
#> SRR1951625 1 0.8081 0.724 0.752 0.248
#> SRR1951626 1 0.2043 0.918 0.968 0.032
#> SRR1951624 1 0.0000 0.931 1.000 0.000
#> SRR1951622 1 0.0000 0.931 1.000 0.000
#> SRR1951636 1 0.2236 0.916 0.964 0.036
#> SRR1951623 1 0.7745 0.750 0.772 0.228
#> SRR1951621 1 0.0000 0.931 1.000 0.000
#> SRR1951620 1 0.0000 0.931 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.1163 0.946 0.000 0.972 0.028
#> SRR1951666 3 0.3116 0.701 0.000 0.108 0.892
#> SRR1951665 2 0.0000 0.954 0.000 1.000 0.000
#> SRR1951664 1 0.2066 0.847 0.940 0.000 0.060
#> SRR1951663 3 0.5733 0.536 0.324 0.000 0.676
#> SRR1951662 2 0.0000 0.954 0.000 1.000 0.000
#> SRR1951661 3 0.2313 0.756 0.024 0.032 0.944
#> SRR1951660 1 0.3412 0.824 0.876 0.000 0.124
#> SRR1951659 2 0.0000 0.954 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.954 0.000 1.000 0.000
#> SRR1951657 1 0.0000 0.857 1.000 0.000 0.000
#> SRR1951656 1 0.0000 0.857 1.000 0.000 0.000
#> SRR1951655 1 0.0892 0.859 0.980 0.000 0.020
#> SRR1951654 1 0.3038 0.836 0.896 0.000 0.104
#> SRR1951653 1 0.1753 0.849 0.952 0.000 0.048
#> SRR1951652 2 0.4121 0.851 0.000 0.832 0.168
#> SRR1951651 1 0.0000 0.857 1.000 0.000 0.000
#> SRR1951650 1 0.3715 0.803 0.868 0.004 0.128
#> SRR1951648 3 0.2400 0.737 0.004 0.064 0.932
#> SRR1951649 1 0.3551 0.818 0.868 0.000 0.132
#> SRR1951647 3 0.5623 0.613 0.280 0.004 0.716
#> SRR1951646 1 0.2261 0.843 0.932 0.000 0.068
#> SRR1951645 1 0.0424 0.858 0.992 0.000 0.008
#> SRR1951644 1 0.3551 0.818 0.868 0.000 0.132
#> SRR1951643 1 0.0000 0.857 1.000 0.000 0.000
#> SRR1951641 2 0.0000 0.954 0.000 1.000 0.000
#> SRR1951642 1 0.0000 0.857 1.000 0.000 0.000
#> SRR1951694 3 0.5968 0.461 0.364 0.000 0.636
#> SRR1951731 3 0.4744 0.744 0.136 0.028 0.836
#> SRR1951730 2 0.4062 0.852 0.000 0.836 0.164
#> SRR1951752 3 0.2845 0.742 0.012 0.068 0.920
#> SRR1951751 1 0.3551 0.819 0.868 0.000 0.132
#> SRR1951749 3 0.5988 0.253 0.000 0.368 0.632
#> SRR1951750 1 0.2796 0.839 0.908 0.000 0.092
#> SRR1951748 2 0.0000 0.954 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.954 0.000 1.000 0.000
#> SRR1951745 3 0.5733 0.536 0.324 0.000 0.676
#> SRR1951746 2 0.0000 0.954 0.000 1.000 0.000
#> SRR1951744 3 0.2165 0.769 0.064 0.000 0.936
#> SRR1951742 3 0.6309 -0.194 0.000 0.496 0.504
#> SRR1951743 2 0.0000 0.954 0.000 1.000 0.000
#> SRR1951741 1 0.0237 0.858 0.996 0.000 0.004
#> SRR1951740 1 0.0000 0.857 1.000 0.000 0.000
#> SRR1951738 2 0.3482 0.886 0.000 0.872 0.128
#> SRR1951739 2 0.1643 0.939 0.000 0.956 0.044
#> SRR1951737 1 0.3340 0.815 0.880 0.000 0.120
#> SRR1951736 1 0.3267 0.828 0.884 0.000 0.116
#> SRR1951734 2 0.0000 0.954 0.000 1.000 0.000
#> SRR1951735 1 0.5397 0.623 0.720 0.000 0.280
#> SRR1951733 3 0.6470 0.288 0.012 0.356 0.632
#> SRR1951732 2 0.0000 0.954 0.000 1.000 0.000
#> SRR1951729 1 0.3482 0.808 0.872 0.000 0.128
#> SRR1951728 1 0.6140 0.358 0.596 0.000 0.404
#> SRR1951727 2 0.0000 0.954 0.000 1.000 0.000
#> SRR1951725 1 0.5650 0.571 0.688 0.000 0.312
#> SRR1951726 3 0.2165 0.769 0.064 0.000 0.936
#> SRR1951724 1 0.3941 0.800 0.844 0.000 0.156
#> SRR1951722 1 0.6062 0.410 0.616 0.000 0.384
#> SRR1951723 1 0.3551 0.818 0.868 0.000 0.132
#> SRR1951721 3 0.5785 0.522 0.332 0.000 0.668
#> SRR1951719 1 0.3482 0.808 0.872 0.000 0.128
#> SRR1951720 1 0.5591 0.585 0.696 0.000 0.304
#> SRR1951718 1 0.3412 0.824 0.876 0.000 0.124
#> SRR1951717 1 0.3551 0.819 0.868 0.000 0.132
#> SRR1951715 3 0.5785 0.522 0.332 0.000 0.668
#> SRR1951716 1 0.3192 0.830 0.888 0.000 0.112
#> SRR1951714 1 0.0892 0.859 0.980 0.000 0.020
#> SRR1951713 1 0.0237 0.858 0.996 0.000 0.004
#> SRR1951712 1 0.3551 0.819 0.868 0.000 0.132
#> SRR1951711 2 0.0592 0.951 0.000 0.988 0.012
#> SRR1951710 3 0.6140 0.368 0.404 0.000 0.596
#> SRR1951709 3 0.2400 0.736 0.004 0.064 0.932
#> SRR1951708 2 0.0000 0.954 0.000 1.000 0.000
#> SRR1951707 3 0.4063 0.757 0.112 0.020 0.868
#> SRR1951705 3 0.6168 0.346 0.412 0.000 0.588
#> SRR1951704 3 0.2165 0.769 0.064 0.000 0.936
#> SRR1951703 2 0.0000 0.954 0.000 1.000 0.000
#> SRR1951706 3 0.2176 0.759 0.032 0.020 0.948
#> SRR1951702 3 0.2165 0.769 0.064 0.000 0.936
#> SRR1951701 2 0.0000 0.954 0.000 1.000 0.000
#> SRR1951699 3 0.2280 0.742 0.008 0.052 0.940
#> SRR1951698 3 0.3272 0.752 0.104 0.004 0.892
#> SRR1951700 1 0.6026 0.432 0.624 0.000 0.376
#> SRR1951697 2 0.1753 0.937 0.000 0.952 0.048
#> SRR1951696 1 0.1964 0.848 0.944 0.000 0.056
#> SRR1951695 1 0.6260 0.218 0.552 0.000 0.448
#> SRR1951693 1 0.6008 0.443 0.628 0.000 0.372
#> SRR1951692 1 0.0000 0.857 1.000 0.000 0.000
#> SRR1951691 1 0.0892 0.859 0.980 0.000 0.020
#> SRR1951688 2 0.3941 0.864 0.000 0.844 0.156
#> SRR1951689 3 0.3207 0.734 0.012 0.084 0.904
#> SRR1951687 1 0.0237 0.858 0.996 0.000 0.004
#> SRR1951685 3 0.4063 0.757 0.112 0.020 0.868
#> SRR1951684 1 0.0592 0.855 0.988 0.000 0.012
#> SRR1951686 2 0.5098 0.752 0.000 0.752 0.248
#> SRR1951683 1 0.0000 0.857 1.000 0.000 0.000
#> SRR1951682 2 0.0237 0.953 0.000 0.996 0.004
#> SRR1951690 2 0.0000 0.954 0.000 1.000 0.000
#> SRR1951680 1 0.0237 0.858 0.996 0.000 0.004
#> SRR1951679 3 0.6095 0.400 0.392 0.000 0.608
#> SRR1951678 1 0.0000 0.857 1.000 0.000 0.000
#> SRR1951677 1 0.0000 0.857 1.000 0.000 0.000
#> SRR1951676 1 0.5363 0.622 0.724 0.000 0.276
#> SRR1951675 2 0.0592 0.951 0.000 0.988 0.012
#> SRR1951681 1 0.6062 0.423 0.616 0.000 0.384
#> SRR1951673 3 0.3207 0.734 0.012 0.084 0.904
#> SRR1951674 3 0.5733 0.536 0.324 0.000 0.676
#> SRR1951672 1 0.3551 0.818 0.868 0.000 0.132
#> SRR1951671 3 0.2878 0.720 0.000 0.096 0.904
#> SRR1951670 1 0.0592 0.856 0.988 0.000 0.012
#> SRR1951640 1 0.2261 0.843 0.932 0.000 0.068
#> SRR1951668 1 0.0424 0.858 0.992 0.000 0.008
#> SRR1951669 2 0.3879 0.868 0.000 0.848 0.152
#> SRR1951638 3 0.6302 0.114 0.480 0.000 0.520
#> SRR1951637 2 0.3879 0.868 0.000 0.848 0.152
#> SRR1951635 1 0.5650 0.571 0.688 0.000 0.312
#> SRR1951634 1 0.0424 0.858 0.992 0.000 0.008
#> SRR1951631 1 0.0000 0.857 1.000 0.000 0.000
#> SRR1951632 2 0.1643 0.939 0.000 0.956 0.044
#> SRR1951633 1 0.0000 0.857 1.000 0.000 0.000
#> SRR1951630 1 0.0000 0.857 1.000 0.000 0.000
#> SRR1951629 1 0.0000 0.857 1.000 0.000 0.000
#> SRR1951628 3 0.6081 0.504 0.344 0.004 0.652
#> SRR1951627 3 0.2356 0.730 0.000 0.072 0.928
#> SRR1951639 3 0.3610 0.763 0.096 0.016 0.888
#> SRR1951625 3 0.3692 0.766 0.056 0.048 0.896
#> SRR1951626 1 0.6062 0.423 0.616 0.000 0.384
#> SRR1951624 1 0.3482 0.808 0.872 0.000 0.128
#> SRR1951622 1 0.3412 0.824 0.876 0.000 0.124
#> SRR1951636 1 0.6079 0.414 0.612 0.000 0.388
#> SRR1951623 3 0.5244 0.614 0.240 0.004 0.756
#> SRR1951621 1 0.0237 0.858 0.996 0.000 0.004
#> SRR1951620 1 0.2261 0.843 0.932 0.000 0.068
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.1109 0.9359 0.004 0.968 0.028 0.000
#> SRR1951666 3 0.2081 0.8140 0.000 0.084 0.916 0.000
#> SRR1951665 2 0.0000 0.9459 0.000 1.000 0.000 0.000
#> SRR1951664 4 0.1978 0.7747 0.004 0.000 0.068 0.928
#> SRR1951663 1 0.0672 0.7273 0.984 0.000 0.008 0.008
#> SRR1951662 2 0.0000 0.9459 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.0992 0.8315 0.004 0.012 0.976 0.008
#> SRR1951660 4 0.4040 0.6102 0.248 0.000 0.000 0.752
#> SRR1951659 2 0.0000 0.9459 0.000 1.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9459 0.000 1.000 0.000 0.000
#> SRR1951657 4 0.0000 0.7953 0.000 0.000 0.000 1.000
#> SRR1951656 4 0.0000 0.7953 0.000 0.000 0.000 1.000
#> SRR1951655 4 0.0817 0.7912 0.024 0.000 0.000 0.976
#> SRR1951654 4 0.3837 0.6368 0.224 0.000 0.000 0.776
#> SRR1951653 4 0.2256 0.7779 0.020 0.000 0.056 0.924
#> SRR1951652 2 0.3958 0.8238 0.024 0.816 0.160 0.000
#> SRR1951651 4 0.0000 0.7953 0.000 0.000 0.000 1.000
#> SRR1951650 4 0.3157 0.7304 0.004 0.000 0.144 0.852
#> SRR1951648 3 0.1211 0.8318 0.000 0.040 0.960 0.000
#> SRR1951649 4 0.4103 0.5978 0.256 0.000 0.000 0.744
#> SRR1951647 3 0.5631 0.5632 0.072 0.000 0.696 0.232
#> SRR1951646 4 0.2053 0.7716 0.004 0.000 0.072 0.924
#> SRR1951645 4 0.0469 0.7950 0.012 0.000 0.000 0.988
#> SRR1951644 4 0.4103 0.5978 0.256 0.000 0.000 0.744
#> SRR1951643 4 0.0000 0.7953 0.000 0.000 0.000 1.000
#> SRR1951641 2 0.0000 0.9459 0.000 1.000 0.000 0.000
#> SRR1951642 4 0.0336 0.7954 0.008 0.000 0.000 0.992
#> SRR1951694 1 0.3764 0.7578 0.816 0.000 0.012 0.172
#> SRR1951731 3 0.4432 0.7655 0.056 0.012 0.824 0.108
#> SRR1951730 2 0.3836 0.8221 0.016 0.816 0.168 0.000
#> SRR1951752 3 0.3091 0.8243 0.048 0.048 0.896 0.008
#> SRR1951751 4 0.4103 0.5995 0.256 0.000 0.000 0.744
#> SRR1951749 3 0.4955 0.4422 0.008 0.344 0.648 0.000
#> SRR1951750 4 0.3764 0.6442 0.216 0.000 0.000 0.784
#> SRR1951748 2 0.0000 0.9459 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9459 0.000 1.000 0.000 0.000
#> SRR1951745 1 0.0672 0.7273 0.984 0.000 0.008 0.008
#> SRR1951746 2 0.0000 0.9459 0.000 1.000 0.000 0.000
#> SRR1951744 3 0.1798 0.8286 0.016 0.000 0.944 0.040
#> SRR1951742 3 0.5288 0.0289 0.008 0.472 0.520 0.000
#> SRR1951743 2 0.0000 0.9459 0.000 1.000 0.000 0.000
#> SRR1951741 4 0.0336 0.7948 0.008 0.000 0.000 0.992
#> SRR1951740 4 0.0188 0.7951 0.004 0.000 0.000 0.996
#> SRR1951738 2 0.3384 0.8666 0.024 0.860 0.116 0.000
#> SRR1951739 2 0.1489 0.9284 0.004 0.952 0.044 0.000
#> SRR1951737 4 0.2944 0.7381 0.004 0.000 0.128 0.868
#> SRR1951736 4 0.3975 0.6189 0.240 0.000 0.000 0.760
#> SRR1951734 2 0.0000 0.9459 0.000 1.000 0.000 0.000
#> SRR1951735 4 0.4535 0.5726 0.004 0.000 0.292 0.704
#> SRR1951733 3 0.5752 0.4556 0.028 0.336 0.628 0.008
#> SRR1951732 2 0.0000 0.9459 0.000 1.000 0.000 0.000
#> SRR1951729 4 0.3052 0.7315 0.004 0.000 0.136 0.860
#> SRR1951728 4 0.5933 0.3448 0.040 0.000 0.408 0.552
#> SRR1951727 2 0.0000 0.9459 0.000 1.000 0.000 0.000
#> SRR1951725 1 0.4972 0.2312 0.544 0.000 0.000 0.456
#> SRR1951726 3 0.1798 0.8286 0.016 0.000 0.944 0.040
#> SRR1951724 4 0.4406 0.5267 0.300 0.000 0.000 0.700
#> SRR1951722 4 0.6090 0.3877 0.052 0.000 0.384 0.564
#> SRR1951723 4 0.4103 0.5978 0.256 0.000 0.000 0.744
#> SRR1951721 1 0.0336 0.7304 0.992 0.000 0.000 0.008
#> SRR1951719 4 0.3052 0.7315 0.004 0.000 0.136 0.860
#> SRR1951720 4 0.4996 -0.0823 0.484 0.000 0.000 0.516
#> SRR1951718 4 0.4040 0.6102 0.248 0.000 0.000 0.752
#> SRR1951717 4 0.4103 0.5995 0.256 0.000 0.000 0.744
#> SRR1951715 1 0.0336 0.7304 0.992 0.000 0.000 0.008
#> SRR1951716 4 0.3942 0.6235 0.236 0.000 0.000 0.764
#> SRR1951714 4 0.0817 0.7912 0.024 0.000 0.000 0.976
#> SRR1951713 4 0.0188 0.7954 0.004 0.000 0.000 0.996
#> SRR1951712 4 0.4103 0.5995 0.256 0.000 0.000 0.744
#> SRR1951711 2 0.0469 0.9430 0.000 0.988 0.012 0.000
#> SRR1951710 1 0.3444 0.7576 0.816 0.000 0.000 0.184
#> SRR1951709 3 0.1211 0.8307 0.000 0.040 0.960 0.000
#> SRR1951708 2 0.0000 0.9459 0.000 1.000 0.000 0.000
#> SRR1951707 3 0.3732 0.8006 0.068 0.004 0.860 0.068
#> SRR1951705 1 0.4134 0.6834 0.740 0.000 0.000 0.260
#> SRR1951704 3 0.1798 0.8286 0.016 0.000 0.944 0.040
#> SRR1951703 2 0.0000 0.9459 0.000 1.000 0.000 0.000
#> SRR1951706 3 0.1229 0.8318 0.004 0.008 0.968 0.020
#> SRR1951702 3 0.1798 0.8286 0.016 0.000 0.944 0.040
#> SRR1951701 2 0.0000 0.9459 0.000 1.000 0.000 0.000
#> SRR1951699 3 0.1209 0.8324 0.000 0.032 0.964 0.004
#> SRR1951698 3 0.2266 0.7958 0.004 0.000 0.912 0.084
#> SRR1951700 4 0.6067 0.4050 0.052 0.000 0.376 0.572
#> SRR1951697 2 0.1970 0.9188 0.008 0.932 0.060 0.000
#> SRR1951696 4 0.1902 0.7751 0.004 0.000 0.064 0.932
#> SRR1951695 4 0.6137 0.2077 0.048 0.000 0.448 0.504
#> SRR1951693 4 0.6302 0.4008 0.068 0.000 0.368 0.564
#> SRR1951692 4 0.0000 0.7953 0.000 0.000 0.000 1.000
#> SRR1951691 4 0.0817 0.7912 0.024 0.000 0.000 0.976
#> SRR1951688 2 0.3813 0.8377 0.024 0.828 0.148 0.000
#> SRR1951689 3 0.3411 0.8191 0.048 0.064 0.880 0.008
#> SRR1951687 4 0.0469 0.7947 0.012 0.000 0.000 0.988
#> SRR1951685 3 0.3732 0.8006 0.068 0.004 0.860 0.068
#> SRR1951684 4 0.0895 0.7895 0.004 0.000 0.020 0.976
#> SRR1951686 2 0.4452 0.6953 0.008 0.732 0.260 0.000
#> SRR1951683 4 0.0000 0.7953 0.000 0.000 0.000 1.000
#> SRR1951682 2 0.0188 0.9450 0.000 0.996 0.004 0.000
#> SRR1951690 2 0.0000 0.9459 0.000 1.000 0.000 0.000
#> SRR1951680 4 0.0188 0.7954 0.004 0.000 0.000 0.996
#> SRR1951679 1 0.4319 0.7203 0.760 0.000 0.012 0.228
#> SRR1951678 4 0.0000 0.7953 0.000 0.000 0.000 1.000
#> SRR1951677 4 0.0000 0.7953 0.000 0.000 0.000 1.000
#> SRR1951676 4 0.4535 0.5649 0.004 0.000 0.292 0.704
#> SRR1951675 2 0.0469 0.9430 0.000 0.988 0.012 0.000
#> SRR1951681 4 0.5150 0.4327 0.008 0.000 0.396 0.596
#> SRR1951673 3 0.3411 0.8191 0.048 0.064 0.880 0.008
#> SRR1951674 1 0.0672 0.7273 0.984 0.000 0.008 0.008
#> SRR1951672 4 0.4103 0.5978 0.256 0.000 0.000 0.744
#> SRR1951671 3 0.2773 0.8164 0.028 0.072 0.900 0.000
#> SRR1951670 4 0.0707 0.7926 0.020 0.000 0.000 0.980
#> SRR1951640 4 0.2053 0.7716 0.004 0.000 0.072 0.924
#> SRR1951668 4 0.0336 0.7955 0.008 0.000 0.000 0.992
#> SRR1951669 2 0.3763 0.8420 0.024 0.832 0.144 0.000
#> SRR1951638 1 0.3764 0.7260 0.784 0.000 0.000 0.216
#> SRR1951637 2 0.3763 0.8420 0.024 0.832 0.144 0.000
#> SRR1951635 1 0.4972 0.2312 0.544 0.000 0.000 0.456
#> SRR1951634 4 0.0469 0.7950 0.012 0.000 0.000 0.988
#> SRR1951631 4 0.0000 0.7953 0.000 0.000 0.000 1.000
#> SRR1951632 2 0.1489 0.9284 0.004 0.952 0.044 0.000
#> SRR1951633 4 0.0336 0.7954 0.008 0.000 0.000 0.992
#> SRR1951630 4 0.0000 0.7953 0.000 0.000 0.000 1.000
#> SRR1951629 4 0.0000 0.7953 0.000 0.000 0.000 1.000
#> SRR1951628 3 0.7453 0.1353 0.300 0.000 0.496 0.204
#> SRR1951627 3 0.1389 0.8290 0.000 0.048 0.952 0.000
#> SRR1951639 3 0.2602 0.8076 0.008 0.008 0.908 0.076
#> SRR1951625 3 0.3606 0.8180 0.080 0.028 0.872 0.020
#> SRR1951626 4 0.5150 0.4327 0.008 0.000 0.396 0.596
#> SRR1951624 4 0.3052 0.7315 0.004 0.000 0.136 0.860
#> SRR1951622 4 0.4040 0.6102 0.248 0.000 0.000 0.752
#> SRR1951636 4 0.5161 0.4238 0.008 0.000 0.400 0.592
#> SRR1951623 3 0.3982 0.6094 0.004 0.000 0.776 0.220
#> SRR1951621 4 0.0336 0.7948 0.008 0.000 0.000 0.992
#> SRR1951620 4 0.2053 0.7716 0.004 0.000 0.072 0.924
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.1278 0.9312 0.016 0.960 0.004 0.000 0.020
#> SRR1951666 5 0.3075 0.7700 0.008 0.072 0.004 0.040 0.876
#> SRR1951665 2 0.0000 0.9421 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 4 0.2520 0.5262 0.096 0.000 0.004 0.888 0.012
#> SRR1951663 3 0.1124 0.7809 0.036 0.000 0.960 0.000 0.004
#> SRR1951662 2 0.0000 0.9421 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 5 0.3410 0.7824 0.016 0.012 0.016 0.100 0.856
#> SRR1951660 1 0.3602 0.6776 0.796 0.000 0.024 0.180 0.000
#> SRR1951659 2 0.0000 0.9421 0.000 1.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9421 0.000 1.000 0.000 0.000 0.000
#> SRR1951657 4 0.4171 0.2665 0.396 0.000 0.000 0.604 0.000
#> SRR1951656 4 0.3857 0.3679 0.312 0.000 0.000 0.688 0.000
#> SRR1951655 1 0.4249 0.3090 0.568 0.000 0.000 0.432 0.000
#> SRR1951654 1 0.6210 0.4676 0.540 0.000 0.184 0.276 0.000
#> SRR1951653 4 0.2813 0.5029 0.168 0.000 0.000 0.832 0.000
#> SRR1951652 2 0.4231 0.8214 0.052 0.796 0.020 0.000 0.132
#> SRR1951651 4 0.4182 0.2587 0.400 0.000 0.000 0.600 0.000
#> SRR1951650 4 0.3320 0.5161 0.068 0.000 0.012 0.860 0.060
#> SRR1951648 5 0.2116 0.7879 0.008 0.028 0.000 0.040 0.924
#> SRR1951649 1 0.3732 0.6766 0.792 0.000 0.032 0.176 0.000
#> SRR1951647 5 0.5616 0.6021 0.112 0.000 0.008 0.232 0.648
#> SRR1951646 4 0.2358 0.5264 0.104 0.000 0.000 0.888 0.008
#> SRR1951645 1 0.4278 0.2786 0.548 0.000 0.000 0.452 0.000
#> SRR1951644 1 0.3732 0.6766 0.792 0.000 0.032 0.176 0.000
#> SRR1951643 4 0.4210 0.2327 0.412 0.000 0.000 0.588 0.000
#> SRR1951641 2 0.0000 0.9421 0.000 1.000 0.000 0.000 0.000
#> SRR1951642 4 0.4367 0.2197 0.416 0.000 0.004 0.580 0.000
#> SRR1951694 3 0.5402 0.7099 0.316 0.000 0.612 0.068 0.004
#> SRR1951731 5 0.4248 0.7466 0.052 0.004 0.016 0.128 0.800
#> SRR1951730 2 0.4155 0.8204 0.048 0.796 0.016 0.000 0.140
#> SRR1951752 5 0.3574 0.7705 0.056 0.036 0.024 0.020 0.864
#> SRR1951751 1 0.3488 0.6770 0.808 0.000 0.024 0.168 0.000
#> SRR1951749 5 0.4742 0.4386 0.020 0.324 0.008 0.000 0.648
#> SRR1951750 1 0.3789 0.6533 0.768 0.000 0.020 0.212 0.000
#> SRR1951748 2 0.0000 0.9421 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9421 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 3 0.1124 0.7809 0.036 0.000 0.960 0.000 0.004
#> SRR1951746 2 0.0000 0.9421 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 5 0.4410 0.7577 0.036 0.000 0.028 0.160 0.776
#> SRR1951742 5 0.5131 0.0402 0.016 0.452 0.008 0.004 0.520
#> SRR1951743 2 0.0000 0.9421 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 4 0.4306 -0.0753 0.492 0.000 0.000 0.508 0.000
#> SRR1951740 4 0.3966 0.3364 0.336 0.000 0.000 0.664 0.000
#> SRR1951738 2 0.3666 0.8614 0.048 0.840 0.020 0.000 0.092
#> SRR1951739 2 0.1756 0.9232 0.016 0.940 0.008 0.000 0.036
#> SRR1951737 4 0.2198 0.5249 0.048 0.000 0.020 0.920 0.012
#> SRR1951736 1 0.3513 0.6760 0.800 0.000 0.020 0.180 0.000
#> SRR1951734 2 0.0000 0.9421 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 4 0.3599 0.4641 0.016 0.000 0.020 0.824 0.140
#> SRR1951733 5 0.5876 0.4370 0.048 0.316 0.020 0.012 0.604
#> SRR1951732 2 0.0000 0.9421 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 4 0.2047 0.5240 0.040 0.000 0.020 0.928 0.012
#> SRR1951728 4 0.5563 0.2499 0.072 0.000 0.020 0.652 0.256
#> SRR1951727 2 0.0000 0.9421 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 1 0.5708 0.0394 0.528 0.000 0.384 0.088 0.000
#> SRR1951726 5 0.4410 0.7577 0.036 0.000 0.028 0.160 0.776
#> SRR1951724 1 0.4605 0.6123 0.732 0.000 0.076 0.192 0.000
#> SRR1951722 4 0.6157 0.2781 0.116 0.000 0.028 0.616 0.240
#> SRR1951723 1 0.3732 0.6766 0.792 0.000 0.032 0.176 0.000
#> SRR1951721 3 0.2966 0.8029 0.184 0.000 0.816 0.000 0.000
#> SRR1951719 4 0.2047 0.5240 0.040 0.000 0.020 0.928 0.012
#> SRR1951720 1 0.5674 0.2168 0.576 0.000 0.324 0.100 0.000
#> SRR1951718 1 0.3602 0.6776 0.796 0.000 0.024 0.180 0.000
#> SRR1951717 1 0.3488 0.6770 0.808 0.000 0.024 0.168 0.000
#> SRR1951715 3 0.2966 0.8029 0.184 0.000 0.816 0.000 0.000
#> SRR1951716 1 0.3550 0.6739 0.796 0.000 0.020 0.184 0.000
#> SRR1951714 1 0.4273 0.2741 0.552 0.000 0.000 0.448 0.000
#> SRR1951713 1 0.4307 0.1338 0.500 0.000 0.000 0.500 0.000
#> SRR1951712 1 0.3488 0.6770 0.808 0.000 0.024 0.168 0.000
#> SRR1951711 2 0.0566 0.9386 0.004 0.984 0.000 0.000 0.012
#> SRR1951710 3 0.5513 0.6211 0.408 0.000 0.524 0.068 0.000
#> SRR1951709 5 0.1990 0.7853 0.000 0.028 0.004 0.040 0.928
#> SRR1951708 2 0.0000 0.9421 0.000 1.000 0.000 0.000 0.000
#> SRR1951707 5 0.4184 0.7597 0.080 0.000 0.028 0.080 0.812
#> SRR1951705 1 0.5736 -0.5409 0.468 0.000 0.448 0.084 0.000
#> SRR1951704 5 0.4410 0.7577 0.036 0.000 0.028 0.160 0.776
#> SRR1951703 2 0.0000 0.9421 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 5 0.4279 0.7664 0.028 0.008 0.028 0.136 0.800
#> SRR1951702 5 0.4410 0.7577 0.036 0.000 0.028 0.160 0.776
#> SRR1951701 2 0.0000 0.9421 0.000 1.000 0.000 0.000 0.000
#> SRR1951699 5 0.2929 0.7870 0.004 0.020 0.016 0.076 0.884
#> SRR1951698 5 0.3678 0.7515 0.020 0.000 0.008 0.168 0.804
#> SRR1951700 4 0.5933 0.2882 0.100 0.000 0.028 0.640 0.232
#> SRR1951697 2 0.2102 0.9104 0.012 0.916 0.004 0.000 0.068
#> SRR1951696 4 0.2408 0.5256 0.096 0.000 0.004 0.892 0.008
#> SRR1951695 4 0.5639 0.1157 0.048 0.000 0.032 0.628 0.292
#> SRR1951693 4 0.6166 0.2789 0.124 0.000 0.028 0.620 0.228
#> SRR1951692 4 0.3913 0.3560 0.324 0.000 0.000 0.676 0.000
#> SRR1951691 1 0.4273 0.2741 0.552 0.000 0.000 0.448 0.000
#> SRR1951688 2 0.4093 0.8348 0.052 0.808 0.020 0.000 0.120
#> SRR1951689 5 0.3595 0.7581 0.060 0.044 0.024 0.012 0.860
#> SRR1951687 4 0.4448 -0.0330 0.480 0.000 0.004 0.516 0.000
#> SRR1951685 5 0.4184 0.7597 0.080 0.000 0.028 0.080 0.812
#> SRR1951684 4 0.3544 0.4645 0.200 0.000 0.004 0.788 0.008
#> SRR1951686 2 0.4405 0.6829 0.020 0.712 0.008 0.000 0.260
#> SRR1951683 4 0.4210 0.2327 0.412 0.000 0.000 0.588 0.000
#> SRR1951682 2 0.0162 0.9411 0.000 0.996 0.000 0.000 0.004
#> SRR1951690 2 0.0000 0.9421 0.000 1.000 0.000 0.000 0.000
#> SRR1951680 1 0.4307 0.1338 0.500 0.000 0.000 0.500 0.000
#> SRR1951679 3 0.5747 0.5533 0.460 0.000 0.464 0.072 0.004
#> SRR1951678 4 0.4210 0.2327 0.412 0.000 0.000 0.588 0.000
#> SRR1951677 4 0.4210 0.2327 0.412 0.000 0.000 0.588 0.000
#> SRR1951676 4 0.4280 0.4523 0.040 0.000 0.024 0.788 0.148
#> SRR1951675 2 0.0566 0.9386 0.004 0.984 0.000 0.000 0.012
#> SRR1951681 4 0.4514 0.3483 0.020 0.000 0.020 0.728 0.232
#> SRR1951673 5 0.3595 0.7581 0.060 0.044 0.024 0.012 0.860
#> SRR1951674 3 0.1124 0.7809 0.036 0.000 0.960 0.000 0.004
#> SRR1951672 1 0.3732 0.6766 0.792 0.000 0.032 0.176 0.000
#> SRR1951671 5 0.2536 0.7635 0.032 0.052 0.012 0.000 0.904
#> SRR1951670 1 0.4448 0.1198 0.516 0.000 0.004 0.480 0.000
#> SRR1951640 4 0.2411 0.5260 0.108 0.000 0.000 0.884 0.008
#> SRR1951668 1 0.4294 0.2360 0.532 0.000 0.000 0.468 0.000
#> SRR1951669 2 0.4045 0.8384 0.052 0.812 0.020 0.000 0.116
#> SRR1951638 3 0.4065 0.6579 0.264 0.000 0.720 0.016 0.000
#> SRR1951637 2 0.4045 0.8384 0.052 0.812 0.020 0.000 0.116
#> SRR1951635 1 0.5708 0.0394 0.528 0.000 0.384 0.088 0.000
#> SRR1951634 1 0.4278 0.2786 0.548 0.000 0.000 0.452 0.000
#> SRR1951631 4 0.4171 0.2665 0.396 0.000 0.000 0.604 0.000
#> SRR1951632 2 0.1756 0.9232 0.016 0.940 0.008 0.000 0.036
#> SRR1951633 4 0.4367 0.2197 0.416 0.000 0.004 0.580 0.000
#> SRR1951630 4 0.4210 0.2327 0.412 0.000 0.000 0.588 0.000
#> SRR1951629 4 0.4171 0.2665 0.396 0.000 0.000 0.604 0.000
#> SRR1951628 5 0.7192 0.1621 0.356 0.000 0.068 0.116 0.460
#> SRR1951627 5 0.2157 0.7839 0.000 0.036 0.004 0.040 0.920
#> SRR1951639 5 0.3398 0.7697 0.024 0.000 0.004 0.144 0.828
#> SRR1951625 5 0.4396 0.7627 0.096 0.020 0.024 0.048 0.812
#> SRR1951626 4 0.4514 0.3483 0.020 0.000 0.020 0.728 0.232
#> SRR1951624 4 0.2047 0.5240 0.040 0.000 0.020 0.928 0.012
#> SRR1951622 1 0.3565 0.6777 0.800 0.000 0.024 0.176 0.000
#> SRR1951636 4 0.4601 0.3406 0.024 0.000 0.020 0.724 0.232
#> SRR1951623 5 0.5337 0.5658 0.028 0.000 0.024 0.340 0.608
#> SRR1951621 4 0.4306 -0.0753 0.492 0.000 0.000 0.508 0.000
#> SRR1951620 4 0.2411 0.5260 0.108 0.000 0.000 0.884 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 2 0.1448 0.9149 0.012 0.948 0.016 0.000 0.024 0.000
#> SRR1951666 5 0.2647 0.7322 0.000 0.044 0.088 0.000 0.868 0.000
#> SRR1951665 2 0.0000 0.9286 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 3 0.3860 0.5917 0.000 0.000 0.528 0.472 0.000 0.000
#> SRR1951663 6 0.0260 0.8289 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1951662 2 0.0000 0.9286 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 5 0.3050 0.7388 0.028 0.004 0.136 0.000 0.832 0.000
#> SRR1951660 1 0.3515 0.7018 0.676 0.000 0.000 0.324 0.000 0.000
#> SRR1951659 2 0.0000 0.9286 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9286 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951657 4 0.0713 0.7048 0.000 0.000 0.028 0.972 0.000 0.000
#> SRR1951656 4 0.2219 0.5183 0.000 0.000 0.136 0.864 0.000 0.000
#> SRR1951655 4 0.4018 0.4388 0.324 0.000 0.020 0.656 0.000 0.000
#> SRR1951654 4 0.5655 0.1003 0.272 0.000 0.000 0.528 0.000 0.200
#> SRR1951653 4 0.4852 -0.5173 0.056 0.000 0.452 0.492 0.000 0.000
#> SRR1951652 2 0.4473 0.7793 0.024 0.748 0.068 0.000 0.156 0.004
#> SRR1951651 4 0.0777 0.7115 0.004 0.000 0.024 0.972 0.000 0.000
#> SRR1951650 3 0.4649 0.6554 0.004 0.000 0.560 0.400 0.036 0.000
#> SRR1951648 5 0.2056 0.7466 0.004 0.012 0.080 0.000 0.904 0.000
#> SRR1951649 1 0.3601 0.7119 0.684 0.000 0.000 0.312 0.000 0.004
#> SRR1951647 5 0.6878 0.3968 0.196 0.000 0.268 0.080 0.456 0.000
#> SRR1951646 3 0.4127 0.5733 0.004 0.000 0.508 0.484 0.004 0.000
#> SRR1951645 4 0.3898 0.5027 0.296 0.000 0.020 0.684 0.000 0.000
#> SRR1951644 1 0.3601 0.7119 0.684 0.000 0.000 0.312 0.000 0.004
#> SRR1951643 4 0.0260 0.7333 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1951641 2 0.0000 0.9286 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951642 4 0.0508 0.7333 0.012 0.000 0.000 0.984 0.000 0.004
#> SRR1951694 6 0.5878 0.6216 0.272 0.000 0.092 0.040 0.008 0.588
#> SRR1951731 5 0.4368 0.6950 0.076 0.000 0.140 0.028 0.756 0.000
#> SRR1951730 2 0.4429 0.7784 0.020 0.748 0.068 0.000 0.160 0.004
#> SRR1951752 5 0.3406 0.7165 0.072 0.012 0.076 0.000 0.836 0.004
#> SRR1951751 1 0.3428 0.7119 0.696 0.000 0.000 0.304 0.000 0.000
#> SRR1951749 5 0.4706 0.4619 0.012 0.284 0.052 0.000 0.652 0.000
#> SRR1951750 1 0.3717 0.6113 0.616 0.000 0.000 0.384 0.000 0.000
#> SRR1951748 2 0.0000 0.9286 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9286 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 6 0.0260 0.8289 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1951746 2 0.0000 0.9286 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951744 5 0.4529 0.6630 0.064 0.000 0.256 0.000 0.676 0.004
#> SRR1951742 5 0.4852 0.0905 0.004 0.420 0.048 0.000 0.528 0.000
#> SRR1951743 2 0.0000 0.9286 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951741 4 0.2378 0.6942 0.152 0.000 0.000 0.848 0.000 0.000
#> SRR1951740 4 0.2858 0.5852 0.032 0.000 0.124 0.844 0.000 0.000
#> SRR1951738 2 0.3877 0.8263 0.020 0.800 0.056 0.000 0.120 0.004
#> SRR1951739 2 0.1908 0.9052 0.012 0.924 0.020 0.000 0.044 0.000
#> SRR1951737 3 0.3993 0.6689 0.008 0.000 0.592 0.400 0.000 0.000
#> SRR1951736 1 0.3499 0.7046 0.680 0.000 0.000 0.320 0.000 0.000
#> SRR1951734 2 0.0000 0.9286 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951735 3 0.5012 0.7037 0.028 0.000 0.652 0.260 0.060 0.000
#> SRR1951733 5 0.5173 0.4527 0.024 0.280 0.060 0.000 0.632 0.004
#> SRR1951732 2 0.0000 0.9286 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 3 0.3975 0.6756 0.008 0.000 0.600 0.392 0.000 0.000
#> SRR1951728 3 0.6229 0.5578 0.112 0.000 0.608 0.132 0.144 0.004
#> SRR1951727 2 0.0000 0.9286 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 1 0.5412 0.3001 0.496 0.000 0.000 0.120 0.000 0.384
#> SRR1951726 5 0.4529 0.6630 0.064 0.000 0.256 0.000 0.676 0.004
#> SRR1951724 1 0.4999 0.6325 0.628 0.000 0.024 0.296 0.000 0.052
#> SRR1951722 3 0.6557 0.5438 0.156 0.000 0.568 0.148 0.124 0.004
#> SRR1951723 1 0.3601 0.7119 0.684 0.000 0.000 0.312 0.000 0.004
#> SRR1951721 6 0.3530 0.8077 0.152 0.000 0.056 0.000 0.000 0.792
#> SRR1951719 3 0.3975 0.6756 0.008 0.000 0.600 0.392 0.000 0.000
#> SRR1951720 1 0.5580 0.3998 0.516 0.000 0.000 0.160 0.000 0.324
#> SRR1951718 1 0.3515 0.7018 0.676 0.000 0.000 0.324 0.000 0.000
#> SRR1951717 1 0.3515 0.7008 0.676 0.000 0.000 0.324 0.000 0.000
#> SRR1951715 6 0.3530 0.8077 0.152 0.000 0.056 0.000 0.000 0.792
#> SRR1951716 1 0.3547 0.6923 0.668 0.000 0.000 0.332 0.000 0.000
#> SRR1951714 4 0.3799 0.5394 0.276 0.000 0.020 0.704 0.000 0.000
#> SRR1951713 4 0.4038 0.5870 0.244 0.000 0.044 0.712 0.000 0.000
#> SRR1951712 1 0.3428 0.7119 0.696 0.000 0.000 0.304 0.000 0.000
#> SRR1951711 2 0.0964 0.9218 0.004 0.968 0.016 0.000 0.012 0.000
#> SRR1951710 1 0.4928 -0.3505 0.480 0.000 0.016 0.032 0.000 0.472
#> SRR1951709 5 0.1951 0.7439 0.000 0.016 0.076 0.000 0.908 0.000
#> SRR1951708 2 0.0000 0.9286 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951707 5 0.4174 0.6972 0.092 0.000 0.172 0.000 0.736 0.000
#> SRR1951705 1 0.5140 -0.1830 0.536 0.000 0.016 0.052 0.000 0.396
#> SRR1951704 5 0.4529 0.6630 0.064 0.000 0.256 0.000 0.676 0.004
#> SRR1951703 2 0.0000 0.9286 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 5 0.4202 0.6806 0.064 0.000 0.224 0.000 0.712 0.000
#> SRR1951702 5 0.4529 0.6630 0.064 0.000 0.256 0.000 0.676 0.004
#> SRR1951701 2 0.0000 0.9286 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951699 5 0.2611 0.7453 0.008 0.012 0.116 0.000 0.864 0.000
#> SRR1951698 5 0.3852 0.6888 0.032 0.000 0.224 0.004 0.740 0.000
#> SRR1951700 3 0.6546 0.5627 0.152 0.000 0.568 0.160 0.116 0.004
#> SRR1951697 2 0.2362 0.8875 0.012 0.892 0.016 0.000 0.080 0.000
#> SRR1951696 3 0.3862 0.5872 0.000 0.000 0.524 0.476 0.000 0.000
#> SRR1951695 3 0.6148 0.5191 0.084 0.000 0.608 0.128 0.176 0.004
#> SRR1951693 3 0.6624 0.5441 0.176 0.000 0.556 0.148 0.116 0.004
#> SRR1951692 4 0.2048 0.5529 0.000 0.000 0.120 0.880 0.000 0.000
#> SRR1951691 4 0.3799 0.5394 0.276 0.000 0.020 0.704 0.000 0.000
#> SRR1951688 2 0.4322 0.7921 0.020 0.760 0.068 0.000 0.148 0.004
#> SRR1951689 5 0.3380 0.7056 0.060 0.016 0.080 0.000 0.840 0.004
#> SRR1951687 4 0.2320 0.7065 0.132 0.000 0.000 0.864 0.000 0.004
#> SRR1951685 5 0.4141 0.6980 0.092 0.000 0.168 0.000 0.740 0.000
#> SRR1951684 4 0.3563 -0.1284 0.000 0.000 0.336 0.664 0.000 0.000
#> SRR1951686 2 0.4570 0.6385 0.012 0.672 0.048 0.000 0.268 0.000
#> SRR1951683 4 0.0260 0.7333 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1951682 2 0.0146 0.9278 0.000 0.996 0.004 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9286 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951680 4 0.4038 0.5870 0.244 0.000 0.044 0.712 0.000 0.000
#> SRR1951679 1 0.5464 -0.2757 0.520 0.000 0.028 0.036 0.012 0.404
#> SRR1951678 4 0.0260 0.7333 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1951677 4 0.0260 0.7333 0.008 0.000 0.000 0.992 0.000 0.000
#> SRR1951676 3 0.5824 0.6424 0.032 0.000 0.548 0.312 0.108 0.000
#> SRR1951675 2 0.0964 0.9218 0.004 0.968 0.016 0.000 0.012 0.000
#> SRR1951681 3 0.5171 0.6500 0.032 0.000 0.680 0.168 0.120 0.000
#> SRR1951673 5 0.3380 0.7056 0.060 0.016 0.080 0.000 0.840 0.004
#> SRR1951674 6 0.0260 0.8289 0.000 0.000 0.008 0.000 0.000 0.992
#> SRR1951672 1 0.3601 0.7119 0.684 0.000 0.000 0.312 0.000 0.004
#> SRR1951671 5 0.3031 0.7178 0.048 0.020 0.072 0.000 0.860 0.000
#> SRR1951670 4 0.2793 0.6473 0.200 0.000 0.000 0.800 0.000 0.000
#> SRR1951640 3 0.4128 0.5663 0.004 0.000 0.504 0.488 0.004 0.000
#> SRR1951668 4 0.3695 0.5491 0.272 0.000 0.016 0.712 0.000 0.000
#> SRR1951669 2 0.4231 0.7992 0.020 0.768 0.064 0.000 0.144 0.004
#> SRR1951638 6 0.3802 0.5901 0.208 0.000 0.000 0.044 0.000 0.748
#> SRR1951637 2 0.4231 0.7992 0.020 0.768 0.064 0.000 0.144 0.004
#> SRR1951635 1 0.5380 0.2926 0.500 0.000 0.000 0.116 0.000 0.384
#> SRR1951634 4 0.3898 0.5027 0.296 0.000 0.020 0.684 0.000 0.000
#> SRR1951631 4 0.0713 0.7048 0.000 0.000 0.028 0.972 0.000 0.000
#> SRR1951632 2 0.1908 0.9052 0.012 0.924 0.020 0.000 0.044 0.000
#> SRR1951633 4 0.0508 0.7333 0.012 0.000 0.000 0.984 0.000 0.004
#> SRR1951630 4 0.0405 0.7313 0.008 0.000 0.004 0.988 0.000 0.000
#> SRR1951629 4 0.0713 0.7048 0.000 0.000 0.028 0.972 0.000 0.000
#> SRR1951628 1 0.6891 -0.2115 0.452 0.000 0.212 0.044 0.280 0.012
#> SRR1951627 5 0.2039 0.7429 0.000 0.020 0.076 0.000 0.904 0.000
#> SRR1951639 5 0.3301 0.7132 0.008 0.000 0.216 0.004 0.772 0.000
#> SRR1951625 5 0.5329 0.6085 0.172 0.008 0.176 0.000 0.640 0.004
#> SRR1951626 3 0.5171 0.6500 0.032 0.000 0.680 0.168 0.120 0.000
#> SRR1951624 3 0.3975 0.6756 0.008 0.000 0.600 0.392 0.000 0.000
#> SRR1951622 1 0.3499 0.7051 0.680 0.000 0.000 0.320 0.000 0.000
#> SRR1951636 3 0.5238 0.6481 0.036 0.000 0.676 0.168 0.120 0.000
#> SRR1951623 5 0.5531 0.3152 0.064 0.000 0.424 0.028 0.484 0.000
#> SRR1951621 4 0.2378 0.6942 0.152 0.000 0.000 0.848 0.000 0.000
#> SRR1951620 3 0.4128 0.5663 0.004 0.000 0.504 0.488 0.004 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "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 13580 rows and 133 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 0.957 0.983 0.4573 0.549 0.549
#> 3 3 0.770 0.852 0.915 0.3939 0.724 0.537
#> 4 4 0.724 0.843 0.897 0.1679 0.798 0.508
#> 5 5 0.710 0.720 0.823 0.0650 0.917 0.689
#> 6 6 0.778 0.728 0.842 0.0403 0.948 0.757
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
#> SRR1951667 2 0.0000 0.9889 0.000 1.000
#> SRR1951666 2 0.0000 0.9889 0.000 1.000
#> SRR1951665 2 0.0000 0.9889 0.000 1.000
#> SRR1951664 1 0.0000 0.9798 1.000 0.000
#> SRR1951663 1 0.0000 0.9798 1.000 0.000
#> SRR1951662 2 0.0000 0.9889 0.000 1.000
#> SRR1951661 1 0.9661 0.3714 0.608 0.392
#> SRR1951660 1 0.0000 0.9798 1.000 0.000
#> SRR1951659 2 0.0000 0.9889 0.000 1.000
#> SRR1951658 2 0.0000 0.9889 0.000 1.000
#> SRR1951657 1 0.0000 0.9798 1.000 0.000
#> SRR1951656 1 0.0000 0.9798 1.000 0.000
#> SRR1951655 1 0.0000 0.9798 1.000 0.000
#> SRR1951654 1 0.0000 0.9798 1.000 0.000
#> SRR1951653 1 0.0000 0.9798 1.000 0.000
#> SRR1951652 2 0.0000 0.9889 0.000 1.000
#> SRR1951651 1 0.0000 0.9798 1.000 0.000
#> SRR1951650 1 0.0000 0.9798 1.000 0.000
#> SRR1951648 2 0.0000 0.9889 0.000 1.000
#> SRR1951649 1 0.0000 0.9798 1.000 0.000
#> SRR1951647 1 0.0000 0.9798 1.000 0.000
#> SRR1951646 1 0.0000 0.9798 1.000 0.000
#> SRR1951645 1 0.0000 0.9798 1.000 0.000
#> SRR1951644 1 0.0000 0.9798 1.000 0.000
#> SRR1951643 1 0.0000 0.9798 1.000 0.000
#> SRR1951641 2 0.0000 0.9889 0.000 1.000
#> SRR1951642 1 0.0000 0.9798 1.000 0.000
#> SRR1951694 1 0.0000 0.9798 1.000 0.000
#> SRR1951731 1 0.0000 0.9798 1.000 0.000
#> SRR1951730 2 0.0000 0.9889 0.000 1.000
#> SRR1951752 2 0.0000 0.9889 0.000 1.000
#> SRR1951751 1 0.0000 0.9798 1.000 0.000
#> SRR1951749 2 0.0000 0.9889 0.000 1.000
#> SRR1951750 1 0.0000 0.9798 1.000 0.000
#> SRR1951748 2 0.0000 0.9889 0.000 1.000
#> SRR1951747 2 0.0000 0.9889 0.000 1.000
#> SRR1951745 1 0.0000 0.9798 1.000 0.000
#> SRR1951746 2 0.0000 0.9889 0.000 1.000
#> SRR1951744 1 0.0000 0.9798 1.000 0.000
#> SRR1951742 2 0.0000 0.9889 0.000 1.000
#> SRR1951743 2 0.0000 0.9889 0.000 1.000
#> SRR1951741 1 0.0000 0.9798 1.000 0.000
#> SRR1951740 1 0.0000 0.9798 1.000 0.000
#> SRR1951738 2 0.0000 0.9889 0.000 1.000
#> SRR1951739 2 0.0000 0.9889 0.000 1.000
#> SRR1951737 1 0.0000 0.9798 1.000 0.000
#> SRR1951736 1 0.0000 0.9798 1.000 0.000
#> SRR1951734 2 0.0000 0.9889 0.000 1.000
#> SRR1951735 1 0.0000 0.9798 1.000 0.000
#> SRR1951733 2 0.0000 0.9889 0.000 1.000
#> SRR1951732 2 0.0000 0.9889 0.000 1.000
#> SRR1951729 1 0.0000 0.9798 1.000 0.000
#> SRR1951728 1 0.0000 0.9798 1.000 0.000
#> SRR1951727 2 0.0000 0.9889 0.000 1.000
#> SRR1951725 1 0.0000 0.9798 1.000 0.000
#> SRR1951726 1 0.9954 0.1695 0.540 0.460
#> SRR1951724 1 0.0000 0.9798 1.000 0.000
#> SRR1951722 1 0.0000 0.9798 1.000 0.000
#> SRR1951723 1 0.0000 0.9798 1.000 0.000
#> SRR1951721 2 0.7056 0.7586 0.192 0.808
#> SRR1951719 1 0.0000 0.9798 1.000 0.000
#> SRR1951720 1 0.0000 0.9798 1.000 0.000
#> SRR1951718 1 0.0000 0.9798 1.000 0.000
#> SRR1951717 1 0.0000 0.9798 1.000 0.000
#> SRR1951715 1 0.0000 0.9798 1.000 0.000
#> SRR1951716 1 0.0000 0.9798 1.000 0.000
#> SRR1951714 1 0.0000 0.9798 1.000 0.000
#> SRR1951713 1 0.0000 0.9798 1.000 0.000
#> SRR1951712 1 0.0000 0.9798 1.000 0.000
#> SRR1951711 2 0.0000 0.9889 0.000 1.000
#> SRR1951710 1 0.0000 0.9798 1.000 0.000
#> SRR1951709 2 0.0000 0.9889 0.000 1.000
#> SRR1951708 2 0.0000 0.9889 0.000 1.000
#> SRR1951707 1 0.0000 0.9798 1.000 0.000
#> SRR1951705 1 0.0000 0.9798 1.000 0.000
#> SRR1951704 1 0.0672 0.9727 0.992 0.008
#> SRR1951703 2 0.0000 0.9889 0.000 1.000
#> SRR1951706 1 1.0000 0.0274 0.500 0.500
#> SRR1951702 1 0.1414 0.9617 0.980 0.020
#> SRR1951701 2 0.0000 0.9889 0.000 1.000
#> SRR1951699 2 0.0000 0.9889 0.000 1.000
#> SRR1951698 1 0.6531 0.7921 0.832 0.168
#> SRR1951700 1 0.0000 0.9798 1.000 0.000
#> SRR1951697 2 0.0000 0.9889 0.000 1.000
#> SRR1951696 1 0.0000 0.9798 1.000 0.000
#> SRR1951695 1 0.0000 0.9798 1.000 0.000
#> SRR1951693 1 0.0000 0.9798 1.000 0.000
#> SRR1951692 1 0.0000 0.9798 1.000 0.000
#> SRR1951691 1 0.0000 0.9798 1.000 0.000
#> SRR1951688 2 0.0000 0.9889 0.000 1.000
#> SRR1951689 2 0.0000 0.9889 0.000 1.000
#> SRR1951687 1 0.0000 0.9798 1.000 0.000
#> SRR1951685 2 0.8608 0.6032 0.284 0.716
#> SRR1951684 1 0.0000 0.9798 1.000 0.000
#> SRR1951686 2 0.0000 0.9889 0.000 1.000
#> SRR1951683 1 0.0000 0.9798 1.000 0.000
#> SRR1951682 2 0.0000 0.9889 0.000 1.000
#> SRR1951690 2 0.0000 0.9889 0.000 1.000
#> SRR1951680 1 0.0000 0.9798 1.000 0.000
#> SRR1951679 1 0.0000 0.9798 1.000 0.000
#> SRR1951678 1 0.0000 0.9798 1.000 0.000
#> SRR1951677 1 0.0000 0.9798 1.000 0.000
#> SRR1951676 1 0.0000 0.9798 1.000 0.000
#> SRR1951675 2 0.0000 0.9889 0.000 1.000
#> SRR1951681 1 0.0000 0.9798 1.000 0.000
#> SRR1951673 2 0.0000 0.9889 0.000 1.000
#> SRR1951674 1 0.0000 0.9798 1.000 0.000
#> SRR1951672 1 0.0000 0.9798 1.000 0.000
#> SRR1951671 2 0.0000 0.9889 0.000 1.000
#> SRR1951670 1 0.0000 0.9798 1.000 0.000
#> SRR1951640 1 0.0000 0.9798 1.000 0.000
#> SRR1951668 1 0.0000 0.9798 1.000 0.000
#> SRR1951669 2 0.0000 0.9889 0.000 1.000
#> SRR1951638 1 0.0000 0.9798 1.000 0.000
#> SRR1951637 2 0.0000 0.9889 0.000 1.000
#> SRR1951635 1 0.0000 0.9798 1.000 0.000
#> SRR1951634 1 0.0000 0.9798 1.000 0.000
#> SRR1951631 1 0.0000 0.9798 1.000 0.000
#> SRR1951632 2 0.0000 0.9889 0.000 1.000
#> SRR1951633 1 0.0000 0.9798 1.000 0.000
#> SRR1951630 1 0.0000 0.9798 1.000 0.000
#> SRR1951629 1 0.0000 0.9798 1.000 0.000
#> SRR1951628 1 0.0000 0.9798 1.000 0.000
#> SRR1951627 2 0.0000 0.9889 0.000 1.000
#> SRR1951639 1 0.6623 0.7866 0.828 0.172
#> SRR1951625 2 0.0000 0.9889 0.000 1.000
#> SRR1951626 1 0.0000 0.9798 1.000 0.000
#> SRR1951624 1 0.0000 0.9798 1.000 0.000
#> SRR1951622 1 0.0000 0.9798 1.000 0.000
#> SRR1951636 1 0.0000 0.9798 1.000 0.000
#> SRR1951623 1 0.0938 0.9691 0.988 0.012
#> SRR1951621 1 0.0000 0.9798 1.000 0.000
#> SRR1951620 1 0.0000 0.9798 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951666 3 0.5397 0.6328 0.000 0.280 0.720
#> SRR1951665 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951664 1 0.4750 0.7118 0.784 0.000 0.216
#> SRR1951663 3 0.4178 0.7346 0.172 0.000 0.828
#> SRR1951662 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951661 3 0.2774 0.8540 0.072 0.008 0.920
#> SRR1951660 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951659 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951657 1 0.0424 0.9042 0.992 0.000 0.008
#> SRR1951656 1 0.0424 0.9042 0.992 0.000 0.008
#> SRR1951655 1 0.2448 0.8994 0.924 0.000 0.076
#> SRR1951654 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951653 1 0.0424 0.9042 0.992 0.000 0.008
#> SRR1951652 3 0.2959 0.8421 0.000 0.100 0.900
#> SRR1951651 1 0.0424 0.9042 0.992 0.000 0.008
#> SRR1951650 1 0.5678 0.5415 0.684 0.000 0.316
#> SRR1951648 3 0.2625 0.8500 0.000 0.084 0.916
#> SRR1951649 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951647 3 0.2356 0.8551 0.072 0.000 0.928
#> SRR1951646 1 0.4796 0.7066 0.780 0.000 0.220
#> SRR1951645 1 0.2448 0.8994 0.924 0.000 0.076
#> SRR1951644 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951643 1 0.0424 0.9042 0.992 0.000 0.008
#> SRR1951641 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951642 1 0.0424 0.9042 0.992 0.000 0.008
#> SRR1951694 3 0.5098 0.6385 0.248 0.000 0.752
#> SRR1951731 3 0.2165 0.8566 0.064 0.000 0.936
#> SRR1951730 3 0.3686 0.8104 0.000 0.140 0.860
#> SRR1951752 3 0.2448 0.8522 0.000 0.076 0.924
#> SRR1951751 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951749 3 0.2711 0.8480 0.000 0.088 0.912
#> SRR1951750 1 0.2448 0.8994 0.924 0.000 0.076
#> SRR1951748 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951745 3 0.1860 0.8269 0.052 0.000 0.948
#> SRR1951746 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951744 3 0.5363 0.6252 0.276 0.000 0.724
#> SRR1951742 2 0.4504 0.7479 0.000 0.804 0.196
#> SRR1951743 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951741 1 0.2165 0.9003 0.936 0.000 0.064
#> SRR1951740 1 0.0424 0.9042 0.992 0.000 0.008
#> SRR1951738 2 0.0892 0.9638 0.000 0.980 0.020
#> SRR1951739 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951737 1 0.4796 0.7066 0.780 0.000 0.220
#> SRR1951736 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951734 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951735 1 0.4974 0.6846 0.764 0.000 0.236
#> SRR1951733 3 0.2711 0.8480 0.000 0.088 0.912
#> SRR1951732 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951729 1 0.4796 0.7066 0.780 0.000 0.220
#> SRR1951728 3 0.2165 0.8566 0.064 0.000 0.936
#> SRR1951727 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951725 1 0.6302 0.0856 0.520 0.000 0.480
#> SRR1951726 3 0.1289 0.8552 0.000 0.032 0.968
#> SRR1951724 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951722 3 0.5216 0.6783 0.260 0.000 0.740
#> SRR1951723 1 0.2625 0.8997 0.916 0.000 0.084
#> SRR1951721 3 0.1860 0.8269 0.052 0.000 0.948
#> SRR1951719 1 0.0424 0.9042 0.992 0.000 0.008
#> SRR1951720 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951718 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951717 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951715 3 0.5098 0.6385 0.248 0.000 0.752
#> SRR1951716 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951714 1 0.0424 0.9042 0.992 0.000 0.008
#> SRR1951713 1 0.0237 0.9046 0.996 0.000 0.004
#> SRR1951712 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951711 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951710 3 0.6008 0.4013 0.372 0.000 0.628
#> SRR1951709 3 0.2711 0.8480 0.000 0.088 0.912
#> SRR1951708 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951707 3 0.2165 0.8566 0.064 0.000 0.936
#> SRR1951705 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951704 3 0.2165 0.8566 0.064 0.000 0.936
#> SRR1951703 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951706 3 0.3134 0.8572 0.032 0.052 0.916
#> SRR1951702 3 0.2165 0.8566 0.064 0.000 0.936
#> SRR1951701 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951699 3 0.2772 0.8516 0.004 0.080 0.916
#> SRR1951698 3 0.2537 0.8516 0.080 0.000 0.920
#> SRR1951700 1 0.1289 0.9044 0.968 0.000 0.032
#> SRR1951697 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951696 1 0.4796 0.7066 0.780 0.000 0.220
#> SRR1951695 1 0.2537 0.8575 0.920 0.000 0.080
#> SRR1951693 1 0.1031 0.9037 0.976 0.000 0.024
#> SRR1951692 1 0.0424 0.9042 0.992 0.000 0.008
#> SRR1951691 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951688 2 0.4654 0.7288 0.000 0.792 0.208
#> SRR1951689 3 0.2959 0.8421 0.000 0.100 0.900
#> SRR1951687 1 0.2165 0.9003 0.936 0.000 0.064
#> SRR1951685 3 0.2492 0.8590 0.016 0.048 0.936
#> SRR1951684 1 0.0892 0.8985 0.980 0.000 0.020
#> SRR1951686 2 0.0892 0.9632 0.000 0.980 0.020
#> SRR1951683 1 0.0237 0.9046 0.996 0.000 0.004
#> SRR1951682 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951690 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951680 1 0.0424 0.9042 0.992 0.000 0.008
#> SRR1951679 3 0.6008 0.4013 0.372 0.000 0.628
#> SRR1951678 1 0.0424 0.9042 0.992 0.000 0.008
#> SRR1951677 1 0.0424 0.9042 0.992 0.000 0.008
#> SRR1951676 1 0.5098 0.6650 0.752 0.000 0.248
#> SRR1951675 2 0.0000 0.9783 0.000 1.000 0.000
#> SRR1951681 1 0.5785 0.5076 0.668 0.000 0.332
#> SRR1951673 3 0.2537 0.8512 0.000 0.080 0.920
#> SRR1951674 3 0.1860 0.8269 0.052 0.000 0.948
#> SRR1951672 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951671 3 0.3038 0.8396 0.000 0.104 0.896
#> SRR1951670 1 0.2165 0.9003 0.936 0.000 0.064
#> SRR1951640 1 0.1860 0.8783 0.948 0.000 0.052
#> SRR1951668 1 0.1289 0.9041 0.968 0.000 0.032
#> SRR1951669 2 0.0892 0.9638 0.000 0.980 0.020
#> SRR1951638 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951637 3 0.3038 0.8397 0.000 0.104 0.896
#> SRR1951635 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951634 1 0.0237 0.9046 0.996 0.000 0.004
#> SRR1951631 1 0.0424 0.9042 0.992 0.000 0.008
#> SRR1951632 2 0.2356 0.9145 0.000 0.928 0.072
#> SRR1951633 1 0.0592 0.9027 0.988 0.000 0.012
#> SRR1951630 1 0.0424 0.9042 0.992 0.000 0.008
#> SRR1951629 1 0.0424 0.9042 0.992 0.000 0.008
#> SRR1951628 3 0.5859 0.4662 0.344 0.000 0.656
#> SRR1951627 3 0.3083 0.8564 0.024 0.060 0.916
#> SRR1951639 3 0.2537 0.8516 0.080 0.000 0.920
#> SRR1951625 3 0.2711 0.8480 0.000 0.088 0.912
#> SRR1951626 3 0.2537 0.8516 0.080 0.000 0.920
#> SRR1951624 1 0.5016 0.6774 0.760 0.000 0.240
#> SRR1951622 1 0.2537 0.8986 0.920 0.000 0.080
#> SRR1951636 3 0.2537 0.8516 0.080 0.000 0.920
#> SRR1951623 3 0.5363 0.6425 0.276 0.000 0.724
#> SRR1951621 1 0.2165 0.9003 0.936 0.000 0.064
#> SRR1951620 1 0.1860 0.8783 0.948 0.000 0.052
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951666 3 0.2011 0.863 0.000 0.080 0.920 0.000
#> SRR1951665 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951664 4 0.3428 0.767 0.012 0.000 0.144 0.844
#> SRR1951663 1 0.2814 0.769 0.868 0.000 0.132 0.000
#> SRR1951662 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.0707 0.897 0.000 0.000 0.980 0.020
#> SRR1951660 1 0.2469 0.878 0.892 0.000 0.000 0.108
#> SRR1951659 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951658 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951657 4 0.0469 0.854 0.012 0.000 0.000 0.988
#> SRR1951656 4 0.0592 0.854 0.016 0.000 0.000 0.984
#> SRR1951655 1 0.3569 0.804 0.804 0.000 0.000 0.196
#> SRR1951654 1 0.2921 0.848 0.860 0.000 0.000 0.140
#> SRR1951653 4 0.1792 0.856 0.068 0.000 0.000 0.932
#> SRR1951652 3 0.1557 0.886 0.056 0.000 0.944 0.000
#> SRR1951651 4 0.2814 0.842 0.132 0.000 0.000 0.868
#> SRR1951650 4 0.4019 0.709 0.012 0.000 0.196 0.792
#> SRR1951648 3 0.0000 0.900 0.000 0.000 1.000 0.000
#> SRR1951649 1 0.3400 0.832 0.820 0.000 0.000 0.180
#> SRR1951647 3 0.3271 0.821 0.012 0.000 0.856 0.132
#> SRR1951646 4 0.2480 0.809 0.008 0.000 0.088 0.904
#> SRR1951645 1 0.3486 0.825 0.812 0.000 0.000 0.188
#> SRR1951644 1 0.2469 0.878 0.892 0.000 0.000 0.108
#> SRR1951643 4 0.2814 0.842 0.132 0.000 0.000 0.868
#> SRR1951641 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951642 4 0.2345 0.854 0.100 0.000 0.000 0.900
#> SRR1951694 1 0.1716 0.831 0.936 0.000 0.064 0.000
#> SRR1951731 3 0.1722 0.883 0.008 0.000 0.944 0.048
#> SRR1951730 3 0.2142 0.879 0.056 0.016 0.928 0.000
#> SRR1951752 3 0.1389 0.889 0.048 0.000 0.952 0.000
#> SRR1951751 1 0.2921 0.864 0.860 0.000 0.000 0.140
#> SRR1951749 3 0.0000 0.900 0.000 0.000 1.000 0.000
#> SRR1951750 4 0.4804 0.448 0.384 0.000 0.000 0.616
#> SRR1951748 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951745 1 0.3764 0.673 0.784 0.000 0.216 0.000
#> SRR1951746 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951744 1 0.7119 0.300 0.508 0.000 0.352 0.140
#> SRR1951742 3 0.3975 0.691 0.000 0.240 0.760 0.000
#> SRR1951743 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951741 4 0.3649 0.780 0.204 0.000 0.000 0.796
#> SRR1951740 4 0.2345 0.851 0.100 0.000 0.000 0.900
#> SRR1951738 2 0.3598 0.833 0.028 0.848 0.124 0.000
#> SRR1951739 2 0.0336 0.979 0.000 0.992 0.008 0.000
#> SRR1951737 4 0.3271 0.777 0.012 0.000 0.132 0.856
#> SRR1951736 1 0.2408 0.876 0.896 0.000 0.000 0.104
#> SRR1951734 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951735 4 0.3672 0.745 0.012 0.000 0.164 0.824
#> SRR1951733 3 0.0000 0.900 0.000 0.000 1.000 0.000
#> SRR1951732 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951729 4 0.3428 0.767 0.012 0.000 0.144 0.844
#> SRR1951728 3 0.2473 0.865 0.012 0.000 0.908 0.080
#> SRR1951727 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951725 1 0.1118 0.844 0.964 0.000 0.036 0.000
#> SRR1951726 3 0.1716 0.887 0.064 0.000 0.936 0.000
#> SRR1951724 1 0.2216 0.879 0.908 0.000 0.000 0.092
#> SRR1951722 1 0.5248 0.763 0.748 0.000 0.164 0.088
#> SRR1951723 1 0.3726 0.821 0.788 0.000 0.000 0.212
#> SRR1951721 1 0.3688 0.683 0.792 0.000 0.208 0.000
#> SRR1951719 4 0.0188 0.850 0.004 0.000 0.000 0.996
#> SRR1951720 1 0.2345 0.877 0.900 0.000 0.000 0.100
#> SRR1951718 1 0.2589 0.876 0.884 0.000 0.000 0.116
#> SRR1951717 1 0.2345 0.877 0.900 0.000 0.000 0.100
#> SRR1951715 1 0.1867 0.827 0.928 0.000 0.072 0.000
#> SRR1951716 1 0.2408 0.876 0.896 0.000 0.000 0.104
#> SRR1951714 4 0.2704 0.844 0.124 0.000 0.000 0.876
#> SRR1951713 4 0.2704 0.842 0.124 0.000 0.000 0.876
#> SRR1951712 1 0.2814 0.863 0.868 0.000 0.000 0.132
#> SRR1951711 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951710 1 0.2021 0.842 0.932 0.000 0.056 0.012
#> SRR1951709 3 0.0000 0.900 0.000 0.000 1.000 0.000
#> SRR1951708 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951707 3 0.0937 0.897 0.012 0.000 0.976 0.012
#> SRR1951705 1 0.1637 0.873 0.940 0.000 0.000 0.060
#> SRR1951704 3 0.0469 0.900 0.012 0.000 0.988 0.000
#> SRR1951703 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951706 3 0.0469 0.900 0.012 0.000 0.988 0.000
#> SRR1951702 3 0.0524 0.899 0.008 0.000 0.988 0.004
#> SRR1951701 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951699 3 0.0000 0.900 0.000 0.000 1.000 0.000
#> SRR1951698 3 0.3271 0.830 0.012 0.000 0.856 0.132
#> SRR1951700 1 0.3172 0.858 0.840 0.000 0.000 0.160
#> SRR1951697 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951696 4 0.2334 0.811 0.004 0.000 0.088 0.908
#> SRR1951695 4 0.5551 0.661 0.160 0.000 0.112 0.728
#> SRR1951693 1 0.3649 0.827 0.796 0.000 0.000 0.204
#> SRR1951692 4 0.1302 0.858 0.044 0.000 0.000 0.956
#> SRR1951691 1 0.2760 0.871 0.872 0.000 0.000 0.128
#> SRR1951688 3 0.5010 0.616 0.024 0.276 0.700 0.000
#> SRR1951689 3 0.1743 0.885 0.056 0.004 0.940 0.000
#> SRR1951687 4 0.3907 0.749 0.232 0.000 0.000 0.768
#> SRR1951685 3 0.0707 0.900 0.020 0.000 0.980 0.000
#> SRR1951684 4 0.0188 0.850 0.000 0.000 0.004 0.996
#> SRR1951686 3 0.4624 0.523 0.000 0.340 0.660 0.000
#> SRR1951683 4 0.3024 0.832 0.148 0.000 0.000 0.852
#> SRR1951682 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951690 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951680 4 0.2345 0.851 0.100 0.000 0.000 0.900
#> SRR1951679 1 0.2101 0.840 0.928 0.000 0.060 0.012
#> SRR1951678 4 0.2814 0.842 0.132 0.000 0.000 0.868
#> SRR1951677 4 0.2345 0.854 0.100 0.000 0.000 0.900
#> SRR1951676 4 0.3271 0.778 0.012 0.000 0.132 0.856
#> SRR1951675 2 0.0000 0.986 0.000 1.000 0.000 0.000
#> SRR1951681 4 0.3852 0.728 0.012 0.000 0.180 0.808
#> SRR1951673 3 0.1743 0.885 0.056 0.004 0.940 0.000
#> SRR1951674 1 0.3764 0.673 0.784 0.000 0.216 0.000
#> SRR1951672 1 0.2469 0.878 0.892 0.000 0.000 0.108
#> SRR1951671 3 0.0937 0.898 0.012 0.012 0.976 0.000
#> SRR1951670 4 0.4164 0.702 0.264 0.000 0.000 0.736
#> SRR1951640 4 0.0779 0.845 0.004 0.000 0.016 0.980
#> SRR1951668 4 0.3074 0.829 0.152 0.000 0.000 0.848
#> SRR1951669 2 0.4037 0.805 0.040 0.824 0.136 0.000
#> SRR1951638 1 0.1792 0.875 0.932 0.000 0.000 0.068
#> SRR1951637 3 0.3810 0.823 0.092 0.060 0.848 0.000
#> SRR1951635 1 0.1474 0.872 0.948 0.000 0.000 0.052
#> SRR1951634 4 0.3024 0.832 0.148 0.000 0.000 0.852
#> SRR1951631 4 0.2814 0.842 0.132 0.000 0.000 0.868
#> SRR1951632 3 0.4643 0.519 0.000 0.344 0.656 0.000
#> SRR1951633 4 0.1489 0.857 0.044 0.000 0.004 0.952
#> SRR1951630 4 0.2345 0.854 0.100 0.000 0.000 0.900
#> SRR1951629 4 0.1211 0.857 0.040 0.000 0.000 0.960
#> SRR1951628 1 0.3081 0.853 0.888 0.000 0.064 0.048
#> SRR1951627 3 0.0592 0.898 0.000 0.000 0.984 0.016
#> SRR1951639 3 0.2466 0.860 0.004 0.000 0.900 0.096
#> SRR1951625 3 0.0469 0.900 0.012 0.000 0.988 0.000
#> SRR1951626 3 0.3271 0.830 0.012 0.000 0.856 0.132
#> SRR1951624 4 0.3672 0.745 0.012 0.000 0.164 0.824
#> SRR1951622 1 0.2408 0.876 0.896 0.000 0.000 0.104
#> SRR1951636 3 0.3271 0.830 0.012 0.000 0.856 0.132
#> SRR1951623 3 0.5383 0.168 0.012 0.000 0.536 0.452
#> SRR1951621 4 0.3873 0.753 0.228 0.000 0.000 0.772
#> SRR1951620 4 0.0779 0.845 0.004 0.000 0.016 0.980
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.1571 0.9161 0.000 0.936 0.004 0.000 0.060
#> SRR1951666 5 0.1992 0.8257 0.000 0.032 0.044 0.000 0.924
#> SRR1951665 2 0.0000 0.9584 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 3 0.4675 0.7900 0.004 0.000 0.704 0.248 0.044
#> SRR1951663 1 0.5124 0.6113 0.644 0.000 0.288 0.000 0.068
#> SRR1951662 2 0.0000 0.9584 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 5 0.3730 0.6365 0.000 0.000 0.288 0.000 0.712
#> SRR1951660 1 0.2773 0.7489 0.836 0.000 0.000 0.164 0.000
#> SRR1951659 2 0.0000 0.9584 0.000 1.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9584 0.000 1.000 0.000 0.000 0.000
#> SRR1951657 4 0.2424 0.7096 0.000 0.000 0.132 0.868 0.000
#> SRR1951656 4 0.1965 0.7501 0.000 0.000 0.096 0.904 0.000
#> SRR1951655 4 0.4299 0.2909 0.388 0.000 0.004 0.608 0.000
#> SRR1951654 4 0.5071 0.1550 0.424 0.000 0.036 0.540 0.000
#> SRR1951653 4 0.5641 0.1224 0.088 0.000 0.356 0.556 0.000
#> SRR1951652 5 0.0510 0.8186 0.000 0.000 0.016 0.000 0.984
#> SRR1951651 4 0.0162 0.8035 0.004 0.000 0.000 0.996 0.000
#> SRR1951650 3 0.4891 0.7920 0.004 0.000 0.704 0.224 0.068
#> SRR1951648 5 0.2074 0.8146 0.000 0.000 0.104 0.000 0.896
#> SRR1951649 1 0.3921 0.7272 0.784 0.000 0.044 0.172 0.000
#> SRR1951647 5 0.4872 0.1979 0.000 0.000 0.436 0.024 0.540
#> SRR1951646 3 0.4162 0.7269 0.004 0.000 0.680 0.312 0.004
#> SRR1951645 1 0.4083 0.6806 0.744 0.000 0.028 0.228 0.000
#> SRR1951644 1 0.2439 0.7645 0.876 0.000 0.004 0.120 0.000
#> SRR1951643 4 0.0000 0.8034 0.000 0.000 0.000 1.000 0.000
#> SRR1951641 2 0.0000 0.9584 0.000 1.000 0.000 0.000 0.000
#> SRR1951642 4 0.0404 0.7997 0.000 0.000 0.012 0.988 0.000
#> SRR1951694 1 0.2964 0.7225 0.856 0.000 0.120 0.000 0.024
#> SRR1951731 5 0.3895 0.5566 0.000 0.000 0.320 0.000 0.680
#> SRR1951730 5 0.0771 0.8165 0.000 0.004 0.020 0.000 0.976
#> SRR1951752 5 0.0609 0.8200 0.000 0.000 0.020 0.000 0.980
#> SRR1951751 1 0.3535 0.7392 0.808 0.000 0.028 0.164 0.000
#> SRR1951749 5 0.1544 0.8271 0.000 0.000 0.068 0.000 0.932
#> SRR1951750 4 0.4029 0.4564 0.316 0.000 0.004 0.680 0.000
#> SRR1951748 2 0.0000 0.9584 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9584 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 1 0.5868 0.5524 0.576 0.000 0.292 0.000 0.132
#> SRR1951746 2 0.0000 0.9584 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 3 0.5574 0.6498 0.100 0.000 0.712 0.052 0.136
#> SRR1951742 5 0.2230 0.7737 0.000 0.116 0.000 0.000 0.884
#> SRR1951743 2 0.0000 0.9584 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 4 0.2674 0.7388 0.140 0.000 0.004 0.856 0.000
#> SRR1951740 4 0.1410 0.7821 0.000 0.000 0.060 0.940 0.000
#> SRR1951738 2 0.4549 0.6748 0.008 0.716 0.032 0.000 0.244
#> SRR1951739 2 0.3010 0.7982 0.000 0.824 0.004 0.000 0.172
#> SRR1951737 3 0.4701 0.7876 0.004 0.000 0.700 0.252 0.044
#> SRR1951736 1 0.2690 0.7535 0.844 0.000 0.000 0.156 0.000
#> SRR1951734 2 0.0000 0.9584 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 3 0.4635 0.7923 0.004 0.000 0.716 0.232 0.048
#> SRR1951733 5 0.1341 0.8288 0.000 0.000 0.056 0.000 0.944
#> SRR1951732 2 0.0000 0.9584 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 3 0.4675 0.7900 0.004 0.000 0.704 0.248 0.044
#> SRR1951728 3 0.4264 0.3058 0.000 0.000 0.620 0.004 0.376
#> SRR1951727 2 0.0000 0.9584 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 1 0.3696 0.6848 0.772 0.000 0.212 0.000 0.016
#> SRR1951726 5 0.0963 0.8183 0.000 0.000 0.036 0.000 0.964
#> SRR1951724 1 0.4002 0.7371 0.796 0.000 0.084 0.120 0.000
#> SRR1951722 1 0.5657 0.0983 0.480 0.000 0.456 0.008 0.056
#> SRR1951723 1 0.3991 0.7290 0.780 0.000 0.048 0.172 0.000
#> SRR1951721 1 0.6188 0.4914 0.540 0.000 0.284 0.000 0.176
#> SRR1951719 3 0.4359 0.5335 0.004 0.000 0.584 0.412 0.000
#> SRR1951720 1 0.2629 0.7610 0.860 0.000 0.004 0.136 0.000
#> SRR1951718 1 0.3462 0.7223 0.792 0.000 0.012 0.196 0.000
#> SRR1951717 1 0.2536 0.7627 0.868 0.000 0.004 0.128 0.000
#> SRR1951715 1 0.4527 0.6415 0.700 0.000 0.260 0.000 0.040
#> SRR1951716 1 0.2852 0.7452 0.828 0.000 0.000 0.172 0.000
#> SRR1951714 4 0.2067 0.7970 0.032 0.000 0.048 0.920 0.000
#> SRR1951713 4 0.4437 0.7170 0.140 0.000 0.100 0.760 0.000
#> SRR1951712 1 0.3857 0.5668 0.688 0.000 0.000 0.312 0.000
#> SRR1951711 2 0.0324 0.9549 0.000 0.992 0.004 0.000 0.004
#> SRR1951710 1 0.2519 0.7327 0.884 0.000 0.100 0.000 0.016
#> SRR1951709 5 0.1544 0.8271 0.000 0.000 0.068 0.000 0.932
#> SRR1951708 2 0.0000 0.9584 0.000 1.000 0.000 0.000 0.000
#> SRR1951707 5 0.4302 0.1878 0.000 0.000 0.480 0.000 0.520
#> SRR1951705 1 0.0865 0.7605 0.972 0.000 0.004 0.024 0.000
#> SRR1951704 5 0.3612 0.6773 0.000 0.000 0.268 0.000 0.732
#> SRR1951703 2 0.0000 0.9584 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 5 0.2471 0.7968 0.000 0.000 0.136 0.000 0.864
#> SRR1951702 5 0.3774 0.6308 0.000 0.000 0.296 0.000 0.704
#> SRR1951701 2 0.0000 0.9584 0.000 1.000 0.000 0.000 0.000
#> SRR1951699 5 0.2127 0.8126 0.000 0.000 0.108 0.000 0.892
#> SRR1951698 3 0.4060 0.3870 0.000 0.000 0.640 0.000 0.360
#> SRR1951700 1 0.5505 0.4815 0.588 0.000 0.328 0.084 0.000
#> SRR1951697 2 0.0162 0.9567 0.000 0.996 0.000 0.000 0.004
#> SRR1951696 3 0.4084 0.7072 0.000 0.000 0.668 0.328 0.004
#> SRR1951695 3 0.5248 0.7625 0.052 0.000 0.700 0.216 0.032
#> SRR1951693 1 0.6054 0.3147 0.496 0.000 0.380 0.124 0.000
#> SRR1951692 4 0.0609 0.7952 0.000 0.000 0.020 0.980 0.000
#> SRR1951691 1 0.4150 0.6968 0.748 0.000 0.036 0.216 0.000
#> SRR1951688 5 0.3106 0.7246 0.000 0.140 0.020 0.000 0.840
#> SRR1951689 5 0.0865 0.8151 0.004 0.000 0.024 0.000 0.972
#> SRR1951687 4 0.2763 0.7324 0.148 0.000 0.004 0.848 0.000
#> SRR1951685 5 0.1908 0.8262 0.000 0.000 0.092 0.000 0.908
#> SRR1951684 4 0.4060 0.2169 0.000 0.000 0.360 0.640 0.000
#> SRR1951686 5 0.3074 0.7007 0.000 0.196 0.000 0.000 0.804
#> SRR1951683 4 0.0794 0.8015 0.028 0.000 0.000 0.972 0.000
#> SRR1951682 2 0.1571 0.9161 0.000 0.936 0.004 0.000 0.060
#> SRR1951690 2 0.0000 0.9584 0.000 1.000 0.000 0.000 0.000
#> SRR1951680 4 0.2127 0.7456 0.000 0.000 0.108 0.892 0.000
#> SRR1951679 1 0.1043 0.7515 0.960 0.000 0.040 0.000 0.000
#> SRR1951678 4 0.0000 0.8034 0.000 0.000 0.000 1.000 0.000
#> SRR1951677 4 0.0290 0.8012 0.000 0.000 0.008 0.992 0.000
#> SRR1951676 3 0.4384 0.7103 0.000 0.000 0.660 0.324 0.016
#> SRR1951675 2 0.0162 0.9568 0.000 0.996 0.004 0.000 0.000
#> SRR1951681 3 0.4712 0.7910 0.004 0.000 0.720 0.216 0.060
#> SRR1951673 5 0.0865 0.8151 0.004 0.000 0.024 0.000 0.972
#> SRR1951674 1 0.5868 0.5524 0.576 0.000 0.292 0.000 0.132
#> SRR1951672 1 0.2536 0.7631 0.868 0.000 0.004 0.128 0.000
#> SRR1951671 5 0.1357 0.8294 0.000 0.004 0.048 0.000 0.948
#> SRR1951670 4 0.3196 0.6709 0.192 0.000 0.004 0.804 0.000
#> SRR1951640 3 0.4558 0.6902 0.024 0.000 0.652 0.324 0.000
#> SRR1951668 4 0.2179 0.7673 0.112 0.000 0.000 0.888 0.000
#> SRR1951669 2 0.5001 0.5053 0.004 0.620 0.036 0.000 0.340
#> SRR1951638 1 0.2674 0.7299 0.856 0.000 0.140 0.004 0.000
#> SRR1951637 5 0.3757 0.6977 0.012 0.024 0.156 0.000 0.808
#> SRR1951635 1 0.2286 0.7388 0.888 0.000 0.108 0.004 0.000
#> SRR1951634 4 0.1608 0.7886 0.072 0.000 0.000 0.928 0.000
#> SRR1951631 4 0.0000 0.8034 0.000 0.000 0.000 1.000 0.000
#> SRR1951632 5 0.2966 0.7144 0.000 0.184 0.000 0.000 0.816
#> SRR1951633 4 0.1965 0.7223 0.000 0.000 0.096 0.904 0.000
#> SRR1951630 4 0.0290 0.8012 0.000 0.000 0.008 0.992 0.000
#> SRR1951629 4 0.1671 0.7718 0.000 0.000 0.076 0.924 0.000
#> SRR1951628 1 0.2765 0.7572 0.896 0.000 0.024 0.036 0.044
#> SRR1951627 5 0.2127 0.8126 0.000 0.000 0.108 0.000 0.892
#> SRR1951639 5 0.4114 0.4359 0.000 0.000 0.376 0.000 0.624
#> SRR1951625 5 0.1270 0.8291 0.000 0.000 0.052 0.000 0.948
#> SRR1951626 3 0.4015 0.4103 0.000 0.000 0.652 0.000 0.348
#> SRR1951624 3 0.4717 0.7912 0.004 0.000 0.704 0.244 0.048
#> SRR1951622 1 0.2329 0.7637 0.876 0.000 0.000 0.124 0.000
#> SRR1951636 3 0.4045 0.3922 0.000 0.000 0.644 0.000 0.356
#> SRR1951623 3 0.5223 0.6644 0.000 0.000 0.672 0.108 0.220
#> SRR1951621 4 0.2806 0.7258 0.152 0.000 0.004 0.844 0.000
#> SRR1951620 4 0.4060 0.2169 0.000 0.000 0.360 0.640 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 2 0.4303 0.7383 0.000 0.740 0.004 0.000 0.132 0.124
#> SRR1951666 5 0.1168 0.8073 0.000 0.000 0.016 0.000 0.956 0.028
#> SRR1951665 2 0.0146 0.9008 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1951664 3 0.1333 0.7913 0.000 0.000 0.944 0.048 0.000 0.008
#> SRR1951663 6 0.3600 0.8660 0.192 0.000 0.000 0.012 0.020 0.776
#> SRR1951662 2 0.0146 0.9008 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1951661 5 0.4382 0.5775 0.000 0.000 0.264 0.000 0.676 0.060
#> SRR1951660 1 0.1686 0.7955 0.924 0.000 0.000 0.064 0.000 0.012
#> SRR1951659 2 0.0692 0.8981 0.000 0.976 0.004 0.000 0.000 0.020
#> SRR1951658 2 0.0547 0.8990 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1951657 4 0.3405 0.6842 0.000 0.000 0.272 0.724 0.000 0.004
#> SRR1951656 4 0.3323 0.7237 0.000 0.000 0.240 0.752 0.000 0.008
#> SRR1951655 1 0.3052 0.6581 0.780 0.000 0.000 0.216 0.000 0.004
#> SRR1951654 4 0.3274 0.7536 0.096 0.000 0.000 0.824 0.000 0.080
#> SRR1951653 3 0.5702 -0.0455 0.408 0.000 0.472 0.104 0.000 0.016
#> SRR1951652 5 0.2219 0.7866 0.000 0.000 0.000 0.000 0.864 0.136
#> SRR1951651 4 0.0858 0.8735 0.004 0.000 0.028 0.968 0.000 0.000
#> SRR1951650 3 0.1268 0.7908 0.000 0.000 0.952 0.036 0.004 0.008
#> SRR1951648 5 0.2201 0.7904 0.000 0.000 0.076 0.000 0.896 0.028
#> SRR1951649 1 0.2414 0.7893 0.896 0.000 0.036 0.056 0.000 0.012
#> SRR1951647 3 0.5015 -0.1568 0.012 0.000 0.480 0.008 0.472 0.028
#> SRR1951646 3 0.1625 0.7871 0.000 0.000 0.928 0.060 0.000 0.012
#> SRR1951645 1 0.2476 0.7785 0.880 0.000 0.024 0.092 0.000 0.004
#> SRR1951644 1 0.1168 0.7879 0.956 0.000 0.000 0.028 0.000 0.016
#> SRR1951643 4 0.0865 0.8741 0.000 0.000 0.036 0.964 0.000 0.000
#> SRR1951641 2 0.0000 0.9006 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951642 4 0.0865 0.8741 0.000 0.000 0.036 0.964 0.000 0.000
#> SRR1951694 1 0.3476 0.3902 0.732 0.000 0.004 0.004 0.000 0.260
#> SRR1951731 5 0.4625 0.5236 0.008 0.000 0.328 0.004 0.628 0.032
#> SRR1951730 5 0.2653 0.7777 0.000 0.000 0.012 0.000 0.844 0.144
#> SRR1951752 5 0.2288 0.8036 0.004 0.000 0.028 0.000 0.896 0.072
#> SRR1951751 1 0.2146 0.7937 0.908 0.000 0.024 0.060 0.000 0.008
#> SRR1951749 5 0.1124 0.8047 0.000 0.000 0.036 0.000 0.956 0.008
#> SRR1951750 4 0.3742 0.3764 0.348 0.000 0.000 0.648 0.000 0.004
#> SRR1951748 2 0.0146 0.9008 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1951747 2 0.0146 0.9008 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1951745 6 0.3667 0.8614 0.164 0.000 0.000 0.012 0.036 0.788
#> SRR1951746 2 0.0146 0.9008 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1951744 3 0.4067 0.6934 0.012 0.000 0.788 0.012 0.064 0.124
#> SRR1951742 5 0.2274 0.7895 0.000 0.012 0.008 0.000 0.892 0.088
#> SRR1951743 2 0.0146 0.9008 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1951741 4 0.1531 0.8356 0.068 0.000 0.000 0.928 0.000 0.004
#> SRR1951740 4 0.2883 0.7589 0.000 0.000 0.212 0.788 0.000 0.000
#> SRR1951738 2 0.5848 0.5569 0.000 0.580 0.016 0.004 0.204 0.196
#> SRR1951739 2 0.5090 0.6141 0.000 0.636 0.004 0.000 0.232 0.128
#> SRR1951737 3 0.1219 0.7914 0.000 0.000 0.948 0.048 0.000 0.004
#> SRR1951736 1 0.1411 0.7946 0.936 0.000 0.000 0.060 0.000 0.004
#> SRR1951734 2 0.0146 0.9008 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1951735 3 0.1218 0.7870 0.004 0.000 0.956 0.028 0.000 0.012
#> SRR1951733 5 0.1176 0.8086 0.000 0.000 0.020 0.000 0.956 0.024
#> SRR1951732 2 0.0146 0.9008 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1951729 3 0.1075 0.7916 0.000 0.000 0.952 0.048 0.000 0.000
#> SRR1951728 3 0.4940 0.3689 0.016 0.000 0.632 0.004 0.300 0.048
#> SRR1951727 2 0.0146 0.9008 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1951725 6 0.3668 0.7825 0.328 0.000 0.000 0.004 0.000 0.668
#> SRR1951726 5 0.3098 0.7821 0.004 0.000 0.040 0.000 0.836 0.120
#> SRR1951724 1 0.1708 0.7863 0.932 0.000 0.040 0.024 0.000 0.004
#> SRR1951722 1 0.5124 0.3700 0.596 0.000 0.328 0.004 0.012 0.060
#> SRR1951723 1 0.2414 0.7893 0.896 0.000 0.036 0.056 0.000 0.012
#> SRR1951721 6 0.3317 0.8445 0.156 0.000 0.004 0.000 0.032 0.808
#> SRR1951719 3 0.1863 0.7583 0.000 0.000 0.896 0.104 0.000 0.000
#> SRR1951720 1 0.1285 0.7922 0.944 0.000 0.000 0.052 0.000 0.004
#> SRR1951718 1 0.1858 0.7939 0.912 0.000 0.000 0.076 0.000 0.012
#> SRR1951717 1 0.1285 0.7922 0.944 0.000 0.000 0.052 0.000 0.004
#> SRR1951715 6 0.3672 0.8290 0.276 0.000 0.004 0.000 0.008 0.712
#> SRR1951716 1 0.1531 0.7937 0.928 0.000 0.000 0.068 0.000 0.004
#> SRR1951714 4 0.3048 0.8116 0.020 0.000 0.152 0.824 0.000 0.004
#> SRR1951713 1 0.6047 0.1828 0.448 0.000 0.228 0.320 0.000 0.004
#> SRR1951712 1 0.2113 0.7850 0.896 0.000 0.008 0.092 0.000 0.004
#> SRR1951711 2 0.2501 0.8464 0.000 0.872 0.004 0.000 0.016 0.108
#> SRR1951710 1 0.3151 0.4265 0.748 0.000 0.000 0.000 0.000 0.252
#> SRR1951709 5 0.1049 0.8048 0.000 0.000 0.032 0.000 0.960 0.008
#> SRR1951708 2 0.0547 0.8990 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1951707 5 0.5373 0.2699 0.012 0.000 0.424 0.004 0.496 0.064
#> SRR1951705 1 0.0858 0.7635 0.968 0.000 0.000 0.004 0.000 0.028
#> SRR1951704 5 0.4983 0.6320 0.004 0.000 0.220 0.000 0.652 0.124
#> SRR1951703 2 0.0146 0.9008 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1951706 5 0.3876 0.7489 0.000 0.000 0.108 0.000 0.772 0.120
#> SRR1951702 5 0.5201 0.5645 0.004 0.000 0.268 0.000 0.608 0.120
#> SRR1951701 2 0.0547 0.8990 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1951699 5 0.2255 0.7884 0.000 0.000 0.080 0.000 0.892 0.028
#> SRR1951698 3 0.4423 0.5107 0.000 0.000 0.668 0.000 0.272 0.060
#> SRR1951700 1 0.4885 0.3728 0.576 0.000 0.372 0.024 0.000 0.028
#> SRR1951697 2 0.2504 0.8498 0.000 0.880 0.004 0.000 0.028 0.088
#> SRR1951696 3 0.1745 0.7828 0.000 0.000 0.920 0.068 0.000 0.012
#> SRR1951695 3 0.1599 0.7833 0.008 0.000 0.940 0.028 0.000 0.024
#> SRR1951693 1 0.4974 0.2701 0.528 0.000 0.420 0.024 0.000 0.028
#> SRR1951692 4 0.1082 0.8719 0.000 0.000 0.040 0.956 0.000 0.004
#> SRR1951691 1 0.2734 0.7345 0.840 0.000 0.004 0.148 0.000 0.008
#> SRR1951688 5 0.3537 0.7481 0.000 0.024 0.016 0.000 0.796 0.164
#> SRR1951689 5 0.3176 0.7716 0.008 0.000 0.016 0.004 0.824 0.148
#> SRR1951687 4 0.1471 0.8390 0.064 0.000 0.000 0.932 0.000 0.004
#> SRR1951685 5 0.3505 0.7794 0.012 0.000 0.132 0.004 0.816 0.036
#> SRR1951684 3 0.3714 0.3601 0.000 0.000 0.656 0.340 0.000 0.004
#> SRR1951686 5 0.2821 0.7718 0.000 0.040 0.004 0.000 0.860 0.096
#> SRR1951683 4 0.0909 0.8698 0.012 0.000 0.020 0.968 0.000 0.000
#> SRR1951682 2 0.4008 0.7649 0.000 0.768 0.004 0.000 0.100 0.128
#> SRR1951690 2 0.0692 0.8981 0.000 0.976 0.004 0.000 0.000 0.020
#> SRR1951680 4 0.3428 0.6301 0.000 0.000 0.304 0.696 0.000 0.000
#> SRR1951679 1 0.1411 0.7289 0.936 0.000 0.000 0.004 0.000 0.060
#> SRR1951678 4 0.0865 0.8741 0.000 0.000 0.036 0.964 0.000 0.000
#> SRR1951677 4 0.1010 0.8735 0.000 0.000 0.036 0.960 0.000 0.004
#> SRR1951676 3 0.1411 0.7886 0.000 0.000 0.936 0.060 0.000 0.004
#> SRR1951675 2 0.1285 0.8865 0.000 0.944 0.004 0.000 0.000 0.052
#> SRR1951681 3 0.1408 0.7785 0.000 0.000 0.944 0.020 0.000 0.036
#> SRR1951673 5 0.3047 0.7763 0.008 0.000 0.012 0.004 0.832 0.144
#> SRR1951674 6 0.3667 0.8614 0.164 0.000 0.000 0.012 0.036 0.788
#> SRR1951672 1 0.1124 0.7911 0.956 0.000 0.000 0.036 0.000 0.008
#> SRR1951671 5 0.2070 0.7976 0.000 0.000 0.012 0.000 0.896 0.092
#> SRR1951670 4 0.1588 0.8325 0.072 0.000 0.000 0.924 0.000 0.004
#> SRR1951640 3 0.1584 0.7849 0.000 0.000 0.928 0.064 0.000 0.008
#> SRR1951668 4 0.1493 0.8458 0.056 0.000 0.004 0.936 0.000 0.004
#> SRR1951669 2 0.6177 0.3622 0.000 0.480 0.016 0.000 0.292 0.212
#> SRR1951638 6 0.4429 0.5795 0.424 0.000 0.000 0.028 0.000 0.548
#> SRR1951637 5 0.3998 0.6104 0.000 0.000 0.016 0.000 0.644 0.340
#> SRR1951635 1 0.3674 0.3498 0.716 0.000 0.000 0.016 0.000 0.268
#> SRR1951634 4 0.1196 0.8573 0.040 0.000 0.008 0.952 0.000 0.000
#> SRR1951631 4 0.1082 0.8743 0.004 0.000 0.040 0.956 0.000 0.000
#> SRR1951632 5 0.3663 0.7270 0.000 0.072 0.004 0.000 0.796 0.128
#> SRR1951633 4 0.1471 0.8585 0.000 0.000 0.064 0.932 0.000 0.004
#> SRR1951630 4 0.1010 0.8735 0.000 0.000 0.036 0.960 0.000 0.004
#> SRR1951629 4 0.3023 0.7370 0.000 0.000 0.232 0.768 0.000 0.000
#> SRR1951628 1 0.1655 0.7484 0.936 0.000 0.004 0.012 0.004 0.044
#> SRR1951627 5 0.2361 0.7848 0.000 0.000 0.088 0.000 0.884 0.028
#> SRR1951639 5 0.4616 0.3423 0.004 0.000 0.384 0.000 0.576 0.036
#> SRR1951625 5 0.2933 0.8000 0.008 0.000 0.032 0.004 0.860 0.096
#> SRR1951626 3 0.3915 0.6057 0.004 0.000 0.756 0.000 0.188 0.052
#> SRR1951624 3 0.1075 0.7916 0.000 0.000 0.952 0.048 0.000 0.000
#> SRR1951622 1 0.1500 0.7940 0.936 0.000 0.000 0.052 0.000 0.012
#> SRR1951636 3 0.4353 0.5471 0.004 0.000 0.696 0.000 0.244 0.056
#> SRR1951623 3 0.3974 0.6470 0.000 0.000 0.752 0.004 0.188 0.056
#> SRR1951621 4 0.1531 0.8356 0.068 0.000 0.000 0.928 0.000 0.004
#> SRR1951620 3 0.3619 0.4187 0.000 0.000 0.680 0.316 0.000 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 13580 rows and 133 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 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 0.953 0.982 0.4950 0.506 0.506
#> 3 3 0.902 0.927 0.968 0.3405 0.806 0.626
#> 4 4 0.800 0.854 0.916 0.1003 0.907 0.733
#> 5 5 0.782 0.726 0.873 0.0514 0.906 0.681
#> 6 6 0.809 0.763 0.873 0.0444 0.892 0.593
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
#> SRR1951667 2 0.0000 0.982 0.000 1.000
#> SRR1951666 2 0.0000 0.982 0.000 1.000
#> SRR1951665 2 0.0000 0.982 0.000 1.000
#> SRR1951664 1 0.0000 0.980 1.000 0.000
#> SRR1951663 2 0.7453 0.723 0.212 0.788
#> SRR1951662 2 0.0000 0.982 0.000 1.000
#> SRR1951661 2 0.0000 0.982 0.000 1.000
#> SRR1951660 1 0.0000 0.980 1.000 0.000
#> SRR1951659 2 0.0000 0.982 0.000 1.000
#> SRR1951658 2 0.0000 0.982 0.000 1.000
#> SRR1951657 1 0.0000 0.980 1.000 0.000
#> SRR1951656 1 0.0000 0.980 1.000 0.000
#> SRR1951655 1 0.0000 0.980 1.000 0.000
#> SRR1951654 1 0.0000 0.980 1.000 0.000
#> SRR1951653 1 0.0000 0.980 1.000 0.000
#> SRR1951652 2 0.0000 0.982 0.000 1.000
#> SRR1951651 1 0.0000 0.980 1.000 0.000
#> SRR1951650 1 0.0000 0.980 1.000 0.000
#> SRR1951648 2 0.0000 0.982 0.000 1.000
#> SRR1951649 1 0.0000 0.980 1.000 0.000
#> SRR1951647 2 0.9710 0.337 0.400 0.600
#> SRR1951646 1 0.0000 0.980 1.000 0.000
#> SRR1951645 1 0.0000 0.980 1.000 0.000
#> SRR1951644 1 0.0000 0.980 1.000 0.000
#> SRR1951643 1 0.0000 0.980 1.000 0.000
#> SRR1951641 2 0.0000 0.982 0.000 1.000
#> SRR1951642 1 0.0000 0.980 1.000 0.000
#> SRR1951694 1 0.0000 0.980 1.000 0.000
#> SRR1951731 2 0.0000 0.982 0.000 1.000
#> SRR1951730 2 0.0000 0.982 0.000 1.000
#> SRR1951752 2 0.0000 0.982 0.000 1.000
#> SRR1951751 1 0.0000 0.980 1.000 0.000
#> SRR1951749 2 0.0000 0.982 0.000 1.000
#> SRR1951750 1 0.0000 0.980 1.000 0.000
#> SRR1951748 2 0.0000 0.982 0.000 1.000
#> SRR1951747 2 0.0000 0.982 0.000 1.000
#> SRR1951745 1 0.2948 0.929 0.948 0.052
#> SRR1951746 2 0.0000 0.982 0.000 1.000
#> SRR1951744 1 0.0000 0.980 1.000 0.000
#> SRR1951742 2 0.0000 0.982 0.000 1.000
#> SRR1951743 2 0.0000 0.982 0.000 1.000
#> SRR1951741 1 0.0000 0.980 1.000 0.000
#> SRR1951740 1 0.0000 0.980 1.000 0.000
#> SRR1951738 2 0.0000 0.982 0.000 1.000
#> SRR1951739 2 0.0000 0.982 0.000 1.000
#> SRR1951737 1 0.0000 0.980 1.000 0.000
#> SRR1951736 1 0.0000 0.980 1.000 0.000
#> SRR1951734 2 0.0000 0.982 0.000 1.000
#> SRR1951735 1 0.0000 0.980 1.000 0.000
#> SRR1951733 2 0.0000 0.982 0.000 1.000
#> SRR1951732 2 0.0000 0.982 0.000 1.000
#> SRR1951729 1 0.0000 0.980 1.000 0.000
#> SRR1951728 1 0.0000 0.980 1.000 0.000
#> SRR1951727 2 0.0000 0.982 0.000 1.000
#> SRR1951725 1 0.0000 0.980 1.000 0.000
#> SRR1951726 2 0.0000 0.982 0.000 1.000
#> SRR1951724 1 0.0000 0.980 1.000 0.000
#> SRR1951722 1 0.0000 0.980 1.000 0.000
#> SRR1951723 1 0.0000 0.980 1.000 0.000
#> SRR1951721 2 0.0000 0.982 0.000 1.000
#> SRR1951719 1 0.0000 0.980 1.000 0.000
#> SRR1951720 1 0.0000 0.980 1.000 0.000
#> SRR1951718 1 0.0000 0.980 1.000 0.000
#> SRR1951717 1 0.0000 0.980 1.000 0.000
#> SRR1951715 2 0.9427 0.438 0.360 0.640
#> SRR1951716 1 0.0000 0.980 1.000 0.000
#> SRR1951714 1 0.0000 0.980 1.000 0.000
#> SRR1951713 1 0.0000 0.980 1.000 0.000
#> SRR1951712 1 0.0000 0.980 1.000 0.000
#> SRR1951711 2 0.0000 0.982 0.000 1.000
#> SRR1951710 1 0.0000 0.980 1.000 0.000
#> SRR1951709 2 0.0000 0.982 0.000 1.000
#> SRR1951708 2 0.0000 0.982 0.000 1.000
#> SRR1951707 2 0.0000 0.982 0.000 1.000
#> SRR1951705 1 0.0000 0.980 1.000 0.000
#> SRR1951704 2 0.0000 0.982 0.000 1.000
#> SRR1951703 2 0.0000 0.982 0.000 1.000
#> SRR1951706 2 0.0000 0.982 0.000 1.000
#> SRR1951702 2 0.0000 0.982 0.000 1.000
#> SRR1951701 2 0.0000 0.982 0.000 1.000
#> SRR1951699 2 0.0000 0.982 0.000 1.000
#> SRR1951698 2 0.0938 0.970 0.012 0.988
#> SRR1951700 1 0.0000 0.980 1.000 0.000
#> SRR1951697 2 0.0000 0.982 0.000 1.000
#> SRR1951696 1 0.0000 0.980 1.000 0.000
#> SRR1951695 1 0.0000 0.980 1.000 0.000
#> SRR1951693 1 0.0000 0.980 1.000 0.000
#> SRR1951692 1 0.0000 0.980 1.000 0.000
#> SRR1951691 1 0.0000 0.980 1.000 0.000
#> SRR1951688 2 0.0000 0.982 0.000 1.000
#> SRR1951689 2 0.0000 0.982 0.000 1.000
#> SRR1951687 1 0.0000 0.980 1.000 0.000
#> SRR1951685 2 0.0000 0.982 0.000 1.000
#> SRR1951684 1 0.0000 0.980 1.000 0.000
#> SRR1951686 2 0.0000 0.982 0.000 1.000
#> SRR1951683 1 0.0000 0.980 1.000 0.000
#> SRR1951682 2 0.0000 0.982 0.000 1.000
#> SRR1951690 2 0.0000 0.982 0.000 1.000
#> SRR1951680 1 0.0000 0.980 1.000 0.000
#> SRR1951679 1 0.0000 0.980 1.000 0.000
#> SRR1951678 1 0.0000 0.980 1.000 0.000
#> SRR1951677 1 0.0000 0.980 1.000 0.000
#> SRR1951676 1 0.0000 0.980 1.000 0.000
#> SRR1951675 2 0.0000 0.982 0.000 1.000
#> SRR1951681 1 0.0000 0.980 1.000 0.000
#> SRR1951673 2 0.0000 0.982 0.000 1.000
#> SRR1951674 1 0.0000 0.980 1.000 0.000
#> SRR1951672 1 0.0000 0.980 1.000 0.000
#> SRR1951671 2 0.0000 0.982 0.000 1.000
#> SRR1951670 1 0.0000 0.980 1.000 0.000
#> SRR1951640 1 0.0000 0.980 1.000 0.000
#> SRR1951668 1 0.0000 0.980 1.000 0.000
#> SRR1951669 2 0.0000 0.982 0.000 1.000
#> SRR1951638 1 0.0000 0.980 1.000 0.000
#> SRR1951637 2 0.0000 0.982 0.000 1.000
#> SRR1951635 1 0.0000 0.980 1.000 0.000
#> SRR1951634 1 0.0000 0.980 1.000 0.000
#> SRR1951631 1 0.0000 0.980 1.000 0.000
#> SRR1951632 2 0.0000 0.982 0.000 1.000
#> SRR1951633 1 0.0000 0.980 1.000 0.000
#> SRR1951630 1 0.0000 0.980 1.000 0.000
#> SRR1951629 1 0.0000 0.980 1.000 0.000
#> SRR1951628 1 0.9710 0.314 0.600 0.400
#> SRR1951627 2 0.0000 0.982 0.000 1.000
#> SRR1951639 2 0.0000 0.982 0.000 1.000
#> SRR1951625 2 0.0000 0.982 0.000 1.000
#> SRR1951626 1 0.9608 0.384 0.616 0.384
#> SRR1951624 1 0.0000 0.980 1.000 0.000
#> SRR1951622 1 0.0000 0.980 1.000 0.000
#> SRR1951636 1 0.9710 0.342 0.600 0.400
#> SRR1951623 1 0.7528 0.717 0.784 0.216
#> SRR1951621 1 0.0000 0.980 1.000 0.000
#> SRR1951620 1 0.0000 0.980 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951666 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951665 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951664 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951663 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951662 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951661 2 0.2066 0.9227 0.000 0.940 0.060
#> SRR1951660 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951659 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951657 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951656 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951655 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951654 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951653 3 0.3941 0.8214 0.156 0.000 0.844
#> SRR1951652 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951651 3 0.3752 0.8325 0.144 0.000 0.856
#> SRR1951650 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951648 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951649 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951647 2 0.6154 0.3600 0.000 0.592 0.408
#> SRR1951646 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951645 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951644 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951643 3 0.3752 0.8325 0.144 0.000 0.856
#> SRR1951641 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951642 3 0.0237 0.9304 0.004 0.000 0.996
#> SRR1951694 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951731 2 0.4121 0.8094 0.000 0.832 0.168
#> SRR1951730 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951752 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951751 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951749 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951750 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951748 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951745 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951746 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951744 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951742 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951743 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951741 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951740 3 0.0237 0.9304 0.004 0.000 0.996
#> SRR1951738 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951739 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951737 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951736 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951734 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951735 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951733 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951732 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951729 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951728 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951727 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951725 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951726 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951724 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951722 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951723 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951721 2 0.3686 0.8258 0.140 0.860 0.000
#> SRR1951719 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951720 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951718 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951717 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951715 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951716 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951714 3 0.4605 0.7709 0.204 0.000 0.796
#> SRR1951713 3 0.4605 0.7709 0.204 0.000 0.796
#> SRR1951712 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951711 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951710 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951709 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951708 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951707 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951705 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951704 2 0.0237 0.9757 0.000 0.996 0.004
#> SRR1951703 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951706 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951702 2 0.4750 0.7160 0.000 0.784 0.216
#> SRR1951701 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951699 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951698 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951700 3 0.6302 0.1942 0.480 0.000 0.520
#> SRR1951697 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951696 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951695 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951693 3 0.6168 0.3936 0.412 0.000 0.588
#> SRR1951692 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951691 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951688 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951689 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951687 1 0.6252 0.0522 0.556 0.000 0.444
#> SRR1951685 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951684 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951686 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951683 3 0.4555 0.7756 0.200 0.000 0.800
#> SRR1951682 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951690 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951680 3 0.0424 0.9282 0.008 0.000 0.992
#> SRR1951679 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951678 3 0.0237 0.9304 0.004 0.000 0.996
#> SRR1951677 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951676 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951675 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951681 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951673 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951674 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951672 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951671 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951670 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951640 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951668 3 0.6168 0.3936 0.412 0.000 0.588
#> SRR1951669 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951638 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951637 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951635 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951634 3 0.4555 0.7756 0.200 0.000 0.800
#> SRR1951631 3 0.3752 0.8325 0.144 0.000 0.856
#> SRR1951632 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951633 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951630 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951629 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951628 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951627 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951639 2 0.2959 0.8874 0.000 0.900 0.100
#> SRR1951625 2 0.0000 0.9791 0.000 1.000 0.000
#> SRR1951626 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951624 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951622 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951636 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951623 3 0.0000 0.9321 0.000 0.000 1.000
#> SRR1951621 1 0.0000 0.9858 1.000 0.000 0.000
#> SRR1951620 3 0.0000 0.9321 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951666 3 0.3907 0.763 0.000 0.232 0.768 0.000
#> SRR1951665 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951664 4 0.3400 0.785 0.000 0.000 0.180 0.820
#> SRR1951663 1 0.0592 0.863 0.984 0.000 0.016 0.000
#> SRR1951662 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.0592 0.832 0.000 0.016 0.984 0.000
#> SRR1951660 1 0.2469 0.898 0.892 0.000 0.000 0.108
#> SRR1951659 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951658 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951657 4 0.0000 0.889 0.000 0.000 0.000 1.000
#> SRR1951656 4 0.1118 0.877 0.000 0.000 0.036 0.964
#> SRR1951655 1 0.3569 0.874 0.804 0.000 0.000 0.196
#> SRR1951654 1 0.3219 0.886 0.836 0.000 0.000 0.164
#> SRR1951653 4 0.0188 0.888 0.004 0.000 0.000 0.996
#> SRR1951652 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951651 4 0.0188 0.888 0.004 0.000 0.000 0.996
#> SRR1951650 4 0.4643 0.597 0.000 0.000 0.344 0.656
#> SRR1951648 3 0.3569 0.795 0.000 0.196 0.804 0.000
#> SRR1951649 1 0.4477 0.741 0.688 0.000 0.000 0.312
#> SRR1951647 2 0.6265 0.297 0.000 0.588 0.072 0.340
#> SRR1951646 4 0.2408 0.840 0.000 0.000 0.104 0.896
#> SRR1951645 1 0.4477 0.741 0.688 0.000 0.000 0.312
#> SRR1951644 1 0.2216 0.898 0.908 0.000 0.000 0.092
#> SRR1951643 4 0.0188 0.888 0.004 0.000 0.000 0.996
#> SRR1951641 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951642 4 0.0000 0.889 0.000 0.000 0.000 1.000
#> SRR1951694 1 0.0000 0.870 1.000 0.000 0.000 0.000
#> SRR1951731 3 0.5220 0.385 0.000 0.424 0.568 0.008
#> SRR1951730 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951752 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951751 1 0.3400 0.883 0.820 0.000 0.000 0.180
#> SRR1951749 3 0.4564 0.636 0.000 0.328 0.672 0.000
#> SRR1951750 1 0.3569 0.874 0.804 0.000 0.000 0.196
#> SRR1951748 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951745 1 0.0592 0.863 0.984 0.000 0.016 0.000
#> SRR1951746 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951744 4 0.3547 0.785 0.016 0.000 0.144 0.840
#> SRR1951742 2 0.1716 0.898 0.000 0.936 0.064 0.000
#> SRR1951743 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951741 1 0.3801 0.854 0.780 0.000 0.000 0.220
#> SRR1951740 4 0.0000 0.889 0.000 0.000 0.000 1.000
#> SRR1951738 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951739 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951737 4 0.3400 0.785 0.000 0.000 0.180 0.820
#> SRR1951736 1 0.2281 0.899 0.904 0.000 0.000 0.096
#> SRR1951734 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951735 4 0.3356 0.788 0.000 0.000 0.176 0.824
#> SRR1951733 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951732 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951729 4 0.3400 0.785 0.000 0.000 0.180 0.820
#> SRR1951728 4 0.4998 0.226 0.000 0.000 0.488 0.512
#> SRR1951727 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951725 1 0.0000 0.870 1.000 0.000 0.000 0.000
#> SRR1951726 2 0.2345 0.855 0.000 0.900 0.100 0.000
#> SRR1951724 1 0.3400 0.883 0.820 0.000 0.000 0.180
#> SRR1951722 1 0.3219 0.888 0.836 0.000 0.000 0.164
#> SRR1951723 1 0.4431 0.753 0.696 0.000 0.000 0.304
#> SRR1951721 2 0.5253 0.424 0.360 0.624 0.016 0.000
#> SRR1951719 4 0.0000 0.889 0.000 0.000 0.000 1.000
#> SRR1951720 1 0.2216 0.898 0.908 0.000 0.000 0.092
#> SRR1951718 1 0.3123 0.891 0.844 0.000 0.000 0.156
#> SRR1951717 1 0.2216 0.898 0.908 0.000 0.000 0.092
#> SRR1951715 1 0.0000 0.870 1.000 0.000 0.000 0.000
#> SRR1951716 1 0.2281 0.899 0.904 0.000 0.000 0.096
#> SRR1951714 4 0.0188 0.888 0.004 0.000 0.000 0.996
#> SRR1951713 4 0.0188 0.888 0.004 0.000 0.000 0.996
#> SRR1951712 1 0.3400 0.883 0.820 0.000 0.000 0.180
#> SRR1951711 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951710 1 0.0000 0.870 1.000 0.000 0.000 0.000
#> SRR1951709 3 0.3569 0.795 0.000 0.196 0.804 0.000
#> SRR1951708 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951707 3 0.4304 0.690 0.000 0.284 0.716 0.000
#> SRR1951705 1 0.0469 0.875 0.988 0.000 0.000 0.012
#> SRR1951704 3 0.1867 0.853 0.000 0.072 0.928 0.000
#> SRR1951703 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951706 3 0.1867 0.853 0.000 0.072 0.928 0.000
#> SRR1951702 3 0.1637 0.851 0.000 0.060 0.940 0.000
#> SRR1951701 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951699 3 0.2149 0.854 0.000 0.088 0.912 0.000
#> SRR1951698 3 0.0592 0.818 0.000 0.000 0.984 0.016
#> SRR1951700 4 0.4661 0.326 0.348 0.000 0.000 0.652
#> SRR1951697 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951696 4 0.3400 0.785 0.000 0.000 0.180 0.820
#> SRR1951695 4 0.0000 0.889 0.000 0.000 0.000 1.000
#> SRR1951693 4 0.3311 0.712 0.172 0.000 0.000 0.828
#> SRR1951692 4 0.0000 0.889 0.000 0.000 0.000 1.000
#> SRR1951691 1 0.3569 0.874 0.804 0.000 0.000 0.196
#> SRR1951688 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951689 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951687 4 0.4643 0.333 0.344 0.000 0.000 0.656
#> SRR1951685 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951684 4 0.1557 0.869 0.000 0.000 0.056 0.944
#> SRR1951686 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951683 4 0.0188 0.888 0.004 0.000 0.000 0.996
#> SRR1951682 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951690 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951680 4 0.0000 0.889 0.000 0.000 0.000 1.000
#> SRR1951679 1 0.0000 0.870 1.000 0.000 0.000 0.000
#> SRR1951678 4 0.0000 0.889 0.000 0.000 0.000 1.000
#> SRR1951677 4 0.0000 0.889 0.000 0.000 0.000 1.000
#> SRR1951676 4 0.4643 0.597 0.000 0.000 0.344 0.656
#> SRR1951675 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951681 4 0.3726 0.759 0.000 0.000 0.212 0.788
#> SRR1951673 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951674 1 0.0592 0.863 0.984 0.000 0.016 0.000
#> SRR1951672 1 0.2216 0.898 0.908 0.000 0.000 0.092
#> SRR1951671 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951670 1 0.3569 0.874 0.804 0.000 0.000 0.196
#> SRR1951640 4 0.1211 0.876 0.000 0.000 0.040 0.960
#> SRR1951668 4 0.0336 0.885 0.008 0.000 0.000 0.992
#> SRR1951669 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951638 1 0.0000 0.870 1.000 0.000 0.000 0.000
#> SRR1951637 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951635 1 0.0000 0.870 1.000 0.000 0.000 0.000
#> SRR1951634 4 0.0188 0.888 0.004 0.000 0.000 0.996
#> SRR1951631 4 0.0188 0.888 0.004 0.000 0.000 0.996
#> SRR1951632 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951633 4 0.0469 0.886 0.000 0.000 0.012 0.988
#> SRR1951630 4 0.0000 0.889 0.000 0.000 0.000 1.000
#> SRR1951629 4 0.0000 0.889 0.000 0.000 0.000 1.000
#> SRR1951628 1 0.1888 0.850 0.940 0.044 0.000 0.016
#> SRR1951627 3 0.2149 0.854 0.000 0.088 0.912 0.000
#> SRR1951639 3 0.0657 0.827 0.000 0.012 0.984 0.004
#> SRR1951625 2 0.0000 0.970 0.000 1.000 0.000 0.000
#> SRR1951626 3 0.0592 0.818 0.000 0.000 0.984 0.016
#> SRR1951624 4 0.3400 0.785 0.000 0.000 0.180 0.820
#> SRR1951622 1 0.1302 0.886 0.956 0.000 0.000 0.044
#> SRR1951636 3 0.0592 0.818 0.000 0.000 0.984 0.016
#> SRR1951623 3 0.4277 0.423 0.000 0.000 0.720 0.280
#> SRR1951621 1 0.3610 0.871 0.800 0.000 0.000 0.200
#> SRR1951620 4 0.1474 0.871 0.000 0.000 0.052 0.948
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951666 3 0.3336 0.6801 0.000 0.228 0.772 0.000 0.000
#> SRR1951665 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 4 0.3305 0.7434 0.000 0.000 0.224 0.776 0.000
#> SRR1951663 5 0.0000 0.5565 0.000 0.000 0.000 0.000 1.000
#> SRR1951662 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 3 0.1043 0.6841 0.000 0.000 0.960 0.000 0.040
#> SRR1951660 1 0.0162 0.7402 0.996 0.000 0.000 0.000 0.004
#> SRR1951659 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951657 4 0.0000 0.8538 0.000 0.000 0.000 1.000 0.000
#> SRR1951656 4 0.0703 0.8522 0.000 0.000 0.024 0.976 0.000
#> SRR1951655 1 0.2732 0.7148 0.840 0.000 0.000 0.160 0.000
#> SRR1951654 1 0.5792 0.5463 0.612 0.000 0.000 0.224 0.164
#> SRR1951653 4 0.2732 0.7410 0.160 0.000 0.000 0.840 0.000
#> SRR1951652 2 0.1831 0.8840 0.000 0.920 0.004 0.000 0.076
#> SRR1951651 4 0.1732 0.8342 0.080 0.000 0.000 0.920 0.000
#> SRR1951650 4 0.4030 0.5929 0.000 0.000 0.352 0.648 0.000
#> SRR1951648 3 0.3305 0.6831 0.000 0.224 0.776 0.000 0.000
#> SRR1951649 1 0.0162 0.7402 0.996 0.000 0.000 0.000 0.004
#> SRR1951647 2 0.7178 0.0841 0.036 0.484 0.252 0.228 0.000
#> SRR1951646 4 0.3305 0.7434 0.000 0.000 0.224 0.776 0.000
#> SRR1951645 1 0.3177 0.6984 0.792 0.000 0.000 0.208 0.000
#> SRR1951644 1 0.0162 0.7402 0.996 0.000 0.000 0.000 0.004
#> SRR1951643 4 0.1671 0.8370 0.076 0.000 0.000 0.924 0.000
#> SRR1951641 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951642 4 0.1478 0.8437 0.064 0.000 0.000 0.936 0.000
#> SRR1951694 5 0.4307 0.2642 0.496 0.000 0.000 0.000 0.504
#> SRR1951731 2 0.4564 0.3111 0.000 0.612 0.372 0.016 0.000
#> SRR1951730 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951752 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951751 1 0.0162 0.7412 0.996 0.000 0.000 0.004 0.000
#> SRR1951749 3 0.3876 0.5887 0.000 0.316 0.684 0.000 0.000
#> SRR1951750 1 0.2852 0.7114 0.828 0.000 0.000 0.172 0.000
#> SRR1951748 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 5 0.0162 0.5563 0.004 0.000 0.000 0.000 0.996
#> SRR1951746 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 5 0.5683 -0.1866 0.068 0.000 0.004 0.428 0.500
#> SRR1951742 2 0.0794 0.9382 0.000 0.972 0.028 0.000 0.000
#> SRR1951743 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 1 0.3336 0.6882 0.772 0.000 0.000 0.228 0.000
#> SRR1951740 4 0.0609 0.8543 0.020 0.000 0.000 0.980 0.000
#> SRR1951738 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951739 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951737 4 0.3305 0.7434 0.000 0.000 0.224 0.776 0.000
#> SRR1951736 1 0.0162 0.7402 0.996 0.000 0.000 0.000 0.004
#> SRR1951734 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 4 0.3305 0.7434 0.000 0.000 0.224 0.776 0.000
#> SRR1951733 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951732 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 4 0.3305 0.7434 0.000 0.000 0.224 0.776 0.000
#> SRR1951728 3 0.4242 -0.0369 0.000 0.000 0.572 0.428 0.000
#> SRR1951727 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 5 0.3561 0.5700 0.260 0.000 0.000 0.000 0.740
#> SRR1951726 5 0.4653 -0.1118 0.000 0.472 0.012 0.000 0.516
#> SRR1951724 1 0.0510 0.7418 0.984 0.000 0.000 0.016 0.000
#> SRR1951722 1 0.0162 0.7402 0.996 0.000 0.000 0.000 0.004
#> SRR1951723 1 0.0162 0.7402 0.996 0.000 0.000 0.000 0.004
#> SRR1951721 5 0.0162 0.5546 0.000 0.004 0.000 0.000 0.996
#> SRR1951719 4 0.0000 0.8538 0.000 0.000 0.000 1.000 0.000
#> SRR1951720 1 0.0162 0.7402 0.996 0.000 0.000 0.000 0.004
#> SRR1951718 1 0.0162 0.7402 0.996 0.000 0.000 0.000 0.004
#> SRR1951717 1 0.0000 0.7405 1.000 0.000 0.000 0.000 0.000
#> SRR1951715 5 0.3561 0.5700 0.260 0.000 0.000 0.000 0.740
#> SRR1951716 1 0.0162 0.7402 0.996 0.000 0.000 0.000 0.004
#> SRR1951714 4 0.1965 0.8192 0.096 0.000 0.000 0.904 0.000
#> SRR1951713 1 0.4273 0.4052 0.552 0.000 0.000 0.448 0.000
#> SRR1951712 1 0.0510 0.7418 0.984 0.000 0.000 0.016 0.000
#> SRR1951711 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951710 5 0.4307 0.2568 0.500 0.000 0.000 0.000 0.500
#> SRR1951709 3 0.3305 0.6831 0.000 0.224 0.776 0.000 0.000
#> SRR1951708 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951707 3 0.3730 0.5755 0.000 0.288 0.712 0.000 0.000
#> SRR1951705 1 0.1792 0.6542 0.916 0.000 0.000 0.000 0.084
#> SRR1951704 3 0.4015 0.5249 0.000 0.000 0.652 0.000 0.348
#> SRR1951703 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 3 0.3876 0.5566 0.000 0.000 0.684 0.000 0.316
#> SRR1951702 3 0.3508 0.6041 0.000 0.000 0.748 0.000 0.252
#> SRR1951701 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951699 3 0.3305 0.6831 0.000 0.224 0.776 0.000 0.000
#> SRR1951698 3 0.0000 0.6870 0.000 0.000 1.000 0.000 0.000
#> SRR1951700 1 0.4015 0.5876 0.652 0.000 0.000 0.348 0.000
#> SRR1951697 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951696 4 0.3305 0.7434 0.000 0.000 0.224 0.776 0.000
#> SRR1951695 4 0.1043 0.8517 0.040 0.000 0.000 0.960 0.000
#> SRR1951693 1 0.4235 0.4599 0.576 0.000 0.000 0.424 0.000
#> SRR1951692 4 0.0000 0.8538 0.000 0.000 0.000 1.000 0.000
#> SRR1951691 1 0.3305 0.6908 0.776 0.000 0.000 0.224 0.000
#> SRR1951688 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951689 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951687 1 0.4161 0.5259 0.608 0.000 0.000 0.392 0.000
#> SRR1951685 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951684 4 0.1043 0.8486 0.000 0.000 0.040 0.960 0.000
#> SRR1951686 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951683 4 0.2852 0.7167 0.172 0.000 0.000 0.828 0.000
#> SRR1951682 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951680 4 0.0404 0.8544 0.012 0.000 0.000 0.988 0.000
#> SRR1951679 1 0.3895 0.2112 0.680 0.000 0.000 0.000 0.320
#> SRR1951678 4 0.1608 0.8394 0.072 0.000 0.000 0.928 0.000
#> SRR1951677 4 0.1608 0.8394 0.072 0.000 0.000 0.928 0.000
#> SRR1951676 4 0.4030 0.5929 0.000 0.000 0.352 0.648 0.000
#> SRR1951675 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951681 4 0.3336 0.7401 0.000 0.000 0.228 0.772 0.000
#> SRR1951673 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951674 5 0.0162 0.5563 0.004 0.000 0.000 0.000 0.996
#> SRR1951672 1 0.0162 0.7402 0.996 0.000 0.000 0.000 0.004
#> SRR1951671 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951670 1 0.3305 0.6908 0.776 0.000 0.000 0.224 0.000
#> SRR1951640 4 0.0880 0.8508 0.000 0.000 0.032 0.968 0.000
#> SRR1951668 1 0.4088 0.5649 0.632 0.000 0.000 0.368 0.000
#> SRR1951669 2 0.0162 0.9622 0.000 0.996 0.000 0.000 0.004
#> SRR1951638 1 0.3999 0.1430 0.656 0.000 0.000 0.000 0.344
#> SRR1951637 2 0.2852 0.7766 0.000 0.828 0.000 0.000 0.172
#> SRR1951635 5 0.4307 0.2568 0.500 0.000 0.000 0.000 0.500
#> SRR1951634 1 0.4287 0.3653 0.540 0.000 0.000 0.460 0.000
#> SRR1951631 4 0.1732 0.8342 0.080 0.000 0.000 0.920 0.000
#> SRR1951632 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951633 4 0.1197 0.8497 0.048 0.000 0.000 0.952 0.000
#> SRR1951630 4 0.1341 0.8473 0.056 0.000 0.000 0.944 0.000
#> SRR1951629 4 0.1341 0.8473 0.056 0.000 0.000 0.944 0.000
#> SRR1951628 1 0.4786 0.1664 0.652 0.040 0.000 0.000 0.308
#> SRR1951627 3 0.3003 0.6957 0.000 0.188 0.812 0.000 0.000
#> SRR1951639 3 0.0000 0.6870 0.000 0.000 1.000 0.000 0.000
#> SRR1951625 2 0.0000 0.9659 0.000 1.000 0.000 0.000 0.000
#> SRR1951626 3 0.0162 0.6851 0.000 0.000 0.996 0.004 0.000
#> SRR1951624 4 0.3305 0.7434 0.000 0.000 0.224 0.776 0.000
#> SRR1951622 1 0.1341 0.6851 0.944 0.000 0.000 0.000 0.056
#> SRR1951636 3 0.0000 0.6870 0.000 0.000 1.000 0.000 0.000
#> SRR1951623 3 0.5026 0.1542 0.000 0.000 0.588 0.372 0.040
#> SRR1951621 1 0.3305 0.6908 0.776 0.000 0.000 0.224 0.000
#> SRR1951620 4 0.0880 0.8508 0.000 0.000 0.032 0.968 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951666 5 0.0458 0.7674 0.000 0.016 0.000 0.000 0.984 0.000
#> SRR1951665 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 3 0.0865 0.8224 0.000 0.000 0.964 0.036 0.000 0.000
#> SRR1951663 6 0.1327 0.7647 0.064 0.000 0.000 0.000 0.000 0.936
#> SRR1951662 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 5 0.0858 0.7642 0.000 0.000 0.028 0.000 0.968 0.004
#> SRR1951660 1 0.1753 0.8363 0.912 0.000 0.000 0.084 0.004 0.000
#> SRR1951659 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951657 4 0.3446 0.4888 0.000 0.000 0.308 0.692 0.000 0.000
#> SRR1951656 3 0.3266 0.6966 0.000 0.000 0.728 0.272 0.000 0.000
#> SRR1951655 4 0.3737 0.2034 0.392 0.000 0.000 0.608 0.000 0.000
#> SRR1951654 4 0.3202 0.7155 0.040 0.000 0.000 0.816 0.000 0.144
#> SRR1951653 3 0.5557 0.4612 0.248 0.000 0.552 0.200 0.000 0.000
#> SRR1951652 2 0.2312 0.8449 0.000 0.876 0.000 0.000 0.112 0.012
#> SRR1951651 4 0.1908 0.8002 0.004 0.000 0.096 0.900 0.000 0.000
#> SRR1951650 3 0.2003 0.8122 0.000 0.000 0.912 0.044 0.044 0.000
#> SRR1951648 5 0.0458 0.7674 0.000 0.016 0.000 0.000 0.984 0.000
#> SRR1951649 1 0.1610 0.8361 0.916 0.000 0.000 0.084 0.000 0.000
#> SRR1951647 3 0.6336 0.1068 0.032 0.240 0.524 0.004 0.200 0.000
#> SRR1951646 3 0.1714 0.8306 0.000 0.000 0.908 0.092 0.000 0.000
#> SRR1951645 4 0.3717 0.2478 0.384 0.000 0.000 0.616 0.000 0.000
#> SRR1951644 1 0.1444 0.8344 0.928 0.000 0.000 0.072 0.000 0.000
#> SRR1951643 4 0.1908 0.8002 0.004 0.000 0.096 0.900 0.000 0.000
#> SRR1951641 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951642 4 0.1814 0.7967 0.000 0.000 0.100 0.900 0.000 0.000
#> SRR1951694 1 0.3163 0.5433 0.764 0.000 0.000 0.000 0.004 0.232
#> SRR1951731 2 0.5946 0.0663 0.000 0.472 0.204 0.004 0.320 0.000
#> SRR1951730 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951752 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951751 1 0.3288 0.6999 0.724 0.000 0.000 0.276 0.000 0.000
#> SRR1951749 5 0.1501 0.7238 0.000 0.076 0.000 0.000 0.924 0.000
#> SRR1951750 4 0.3023 0.6035 0.232 0.000 0.000 0.768 0.000 0.000
#> SRR1951748 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 6 0.0291 0.7507 0.004 0.000 0.000 0.000 0.004 0.992
#> SRR1951746 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951744 4 0.4467 0.1367 0.000 0.000 0.020 0.496 0.004 0.480
#> SRR1951742 2 0.3371 0.6044 0.000 0.708 0.000 0.000 0.292 0.000
#> SRR1951743 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951741 4 0.1387 0.7946 0.068 0.000 0.000 0.932 0.000 0.000
#> SRR1951740 4 0.1910 0.7916 0.000 0.000 0.108 0.892 0.000 0.000
#> SRR1951738 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951739 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951737 3 0.1387 0.8330 0.000 0.000 0.932 0.068 0.000 0.000
#> SRR1951736 1 0.1863 0.8332 0.896 0.000 0.000 0.104 0.000 0.000
#> SRR1951734 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951735 3 0.1501 0.8330 0.000 0.000 0.924 0.076 0.000 0.000
#> SRR1951733 2 0.1007 0.9281 0.000 0.956 0.000 0.000 0.044 0.000
#> SRR1951732 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 3 0.0937 0.8248 0.000 0.000 0.960 0.040 0.000 0.000
#> SRR1951728 3 0.2357 0.7007 0.004 0.000 0.888 0.012 0.092 0.004
#> SRR1951727 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 6 0.3808 0.6034 0.284 0.000 0.000 0.012 0.004 0.700
#> SRR1951726 6 0.4594 0.3872 0.000 0.232 0.000 0.000 0.092 0.676
#> SRR1951724 1 0.3390 0.6807 0.704 0.000 0.000 0.296 0.000 0.000
#> SRR1951722 1 0.1757 0.8335 0.916 0.000 0.008 0.076 0.000 0.000
#> SRR1951723 1 0.1501 0.8355 0.924 0.000 0.000 0.076 0.000 0.000
#> SRR1951721 6 0.1387 0.7649 0.068 0.000 0.000 0.000 0.000 0.932
#> SRR1951719 3 0.3390 0.6391 0.000 0.000 0.704 0.296 0.000 0.000
#> SRR1951720 1 0.2146 0.8298 0.880 0.000 0.000 0.116 0.004 0.000
#> SRR1951718 1 0.1908 0.8359 0.900 0.000 0.000 0.096 0.004 0.000
#> SRR1951717 1 0.3351 0.6883 0.712 0.000 0.000 0.288 0.000 0.000
#> SRR1951715 6 0.3819 0.4708 0.372 0.000 0.000 0.000 0.004 0.624
#> SRR1951716 1 0.1714 0.8354 0.908 0.000 0.000 0.092 0.000 0.000
#> SRR1951714 4 0.1387 0.8053 0.000 0.000 0.068 0.932 0.000 0.000
#> SRR1951713 4 0.2573 0.7774 0.112 0.000 0.024 0.864 0.000 0.000
#> SRR1951712 1 0.3464 0.6592 0.688 0.000 0.000 0.312 0.000 0.000
#> SRR1951711 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951710 1 0.2964 0.5875 0.792 0.000 0.000 0.000 0.004 0.204
#> SRR1951709 5 0.0458 0.7674 0.000 0.016 0.000 0.000 0.984 0.000
#> SRR1951708 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951707 5 0.5992 0.3893 0.008 0.220 0.240 0.004 0.528 0.000
#> SRR1951705 1 0.0508 0.7987 0.984 0.000 0.000 0.012 0.004 0.000
#> SRR1951704 5 0.4066 0.4150 0.000 0.000 0.012 0.000 0.596 0.392
#> SRR1951703 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 5 0.4034 0.5026 0.000 0.004 0.012 0.000 0.648 0.336
#> SRR1951702 5 0.3586 0.5852 0.000 0.000 0.012 0.000 0.720 0.268
#> SRR1951701 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951699 5 0.0508 0.7678 0.000 0.012 0.004 0.000 0.984 0.000
#> SRR1951698 5 0.2300 0.7353 0.000 0.000 0.144 0.000 0.856 0.000
#> SRR1951700 4 0.2048 0.7562 0.120 0.000 0.000 0.880 0.000 0.000
#> SRR1951697 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951696 3 0.1444 0.8330 0.000 0.000 0.928 0.072 0.000 0.000
#> SRR1951695 4 0.2135 0.7819 0.000 0.000 0.128 0.872 0.000 0.000
#> SRR1951693 4 0.1387 0.7969 0.068 0.000 0.000 0.932 0.000 0.000
#> SRR1951692 4 0.3409 0.5060 0.000 0.000 0.300 0.700 0.000 0.000
#> SRR1951691 4 0.1610 0.7849 0.084 0.000 0.000 0.916 0.000 0.000
#> SRR1951688 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951689 2 0.0260 0.9575 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1951687 4 0.1444 0.7930 0.072 0.000 0.000 0.928 0.000 0.000
#> SRR1951685 2 0.1816 0.9067 0.004 0.928 0.048 0.004 0.016 0.000
#> SRR1951684 3 0.2300 0.8068 0.000 0.000 0.856 0.144 0.000 0.000
#> SRR1951686 2 0.1444 0.8998 0.000 0.928 0.000 0.000 0.072 0.000
#> SRR1951683 4 0.1951 0.8057 0.016 0.000 0.076 0.908 0.000 0.000
#> SRR1951682 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951680 4 0.3405 0.5728 0.004 0.000 0.272 0.724 0.000 0.000
#> SRR1951679 1 0.0713 0.7695 0.972 0.000 0.000 0.000 0.000 0.028
#> SRR1951678 4 0.1814 0.7967 0.000 0.000 0.100 0.900 0.000 0.000
#> SRR1951677 4 0.1814 0.7967 0.000 0.000 0.100 0.900 0.000 0.000
#> SRR1951676 3 0.2070 0.8151 0.000 0.000 0.908 0.048 0.044 0.000
#> SRR1951675 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951681 3 0.1471 0.8319 0.000 0.000 0.932 0.064 0.004 0.000
#> SRR1951673 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951674 6 0.0291 0.7507 0.004 0.000 0.000 0.000 0.004 0.992
#> SRR1951672 1 0.1910 0.8326 0.892 0.000 0.000 0.108 0.000 0.000
#> SRR1951671 2 0.0146 0.9603 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1951670 4 0.1814 0.7722 0.100 0.000 0.000 0.900 0.000 0.000
#> SRR1951640 3 0.3126 0.7307 0.000 0.000 0.752 0.248 0.000 0.000
#> SRR1951668 4 0.1444 0.7930 0.072 0.000 0.000 0.928 0.000 0.000
#> SRR1951669 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951638 1 0.5956 0.3284 0.440 0.000 0.000 0.236 0.000 0.324
#> SRR1951637 2 0.2631 0.7692 0.000 0.820 0.000 0.000 0.000 0.180
#> SRR1951635 1 0.3648 0.5364 0.740 0.000 0.000 0.016 0.004 0.240
#> SRR1951634 4 0.1471 0.7990 0.064 0.000 0.004 0.932 0.000 0.000
#> SRR1951631 4 0.1908 0.8002 0.004 0.000 0.096 0.900 0.000 0.000
#> SRR1951632 2 0.0000 0.9630 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951633 4 0.1910 0.7909 0.000 0.000 0.108 0.892 0.000 0.000
#> SRR1951630 4 0.1863 0.7943 0.000 0.000 0.104 0.896 0.000 0.000
#> SRR1951629 4 0.1863 0.7943 0.000 0.000 0.104 0.896 0.000 0.000
#> SRR1951628 1 0.1096 0.7694 0.964 0.004 0.008 0.000 0.004 0.020
#> SRR1951627 5 0.0508 0.7678 0.000 0.012 0.004 0.000 0.984 0.000
#> SRR1951639 5 0.2664 0.7058 0.000 0.000 0.184 0.000 0.816 0.000
#> SRR1951625 2 0.0508 0.9523 0.000 0.984 0.012 0.000 0.004 0.000
#> SRR1951626 5 0.3756 0.5616 0.000 0.000 0.352 0.000 0.644 0.004
#> SRR1951624 3 0.0937 0.8248 0.000 0.000 0.960 0.040 0.000 0.000
#> SRR1951622 1 0.1285 0.8249 0.944 0.000 0.000 0.052 0.004 0.000
#> SRR1951636 5 0.3081 0.7019 0.000 0.000 0.220 0.000 0.776 0.004
#> SRR1951623 5 0.4866 0.1623 0.000 0.000 0.476 0.016 0.480 0.028
#> SRR1951621 4 0.1501 0.7900 0.076 0.000 0.000 0.924 0.000 0.000
#> SRR1951620 3 0.3101 0.7356 0.000 0.000 0.756 0.244 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "pam"]
# you can also extract it by
# res = res_list["MAD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 13580 rows and 133 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.970 0.988 0.4298 0.570 0.570
#> 3 3 0.765 0.873 0.938 0.4881 0.666 0.469
#> 4 4 0.809 0.770 0.900 0.1505 0.820 0.544
#> 5 5 0.741 0.745 0.879 0.0495 0.951 0.816
#> 6 6 0.714 0.622 0.817 0.0616 0.887 0.555
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
#> SRR1951667 2 0.0000 0.979 0.000 1.000
#> SRR1951666 2 0.0000 0.979 0.000 1.000
#> SRR1951665 2 0.0000 0.979 0.000 1.000
#> SRR1951664 1 0.0000 0.991 1.000 0.000
#> SRR1951663 1 0.0000 0.991 1.000 0.000
#> SRR1951662 2 0.0000 0.979 0.000 1.000
#> SRR1951661 1 0.4562 0.887 0.904 0.096
#> SRR1951660 1 0.0000 0.991 1.000 0.000
#> SRR1951659 2 0.0000 0.979 0.000 1.000
#> SRR1951658 2 0.0000 0.979 0.000 1.000
#> SRR1951657 1 0.0000 0.991 1.000 0.000
#> SRR1951656 1 0.0000 0.991 1.000 0.000
#> SRR1951655 1 0.0000 0.991 1.000 0.000
#> SRR1951654 1 0.0000 0.991 1.000 0.000
#> SRR1951653 1 0.0000 0.991 1.000 0.000
#> SRR1951652 2 0.0000 0.979 0.000 1.000
#> SRR1951651 1 0.0000 0.991 1.000 0.000
#> SRR1951650 1 0.0000 0.991 1.000 0.000
#> SRR1951648 1 0.9850 0.245 0.572 0.428
#> SRR1951649 1 0.0000 0.991 1.000 0.000
#> SRR1951647 1 0.0000 0.991 1.000 0.000
#> SRR1951646 1 0.0000 0.991 1.000 0.000
#> SRR1951645 1 0.0000 0.991 1.000 0.000
#> SRR1951644 1 0.0000 0.991 1.000 0.000
#> SRR1951643 1 0.0000 0.991 1.000 0.000
#> SRR1951641 2 0.0000 0.979 0.000 1.000
#> SRR1951642 1 0.0000 0.991 1.000 0.000
#> SRR1951694 1 0.0000 0.991 1.000 0.000
#> SRR1951731 1 0.0000 0.991 1.000 0.000
#> SRR1951730 2 0.0000 0.979 0.000 1.000
#> SRR1951752 1 0.0672 0.984 0.992 0.008
#> SRR1951751 1 0.0000 0.991 1.000 0.000
#> SRR1951749 2 0.0000 0.979 0.000 1.000
#> SRR1951750 1 0.0000 0.991 1.000 0.000
#> SRR1951748 2 0.0000 0.979 0.000 1.000
#> SRR1951747 2 0.0000 0.979 0.000 1.000
#> SRR1951745 1 0.0000 0.991 1.000 0.000
#> SRR1951746 2 0.0000 0.979 0.000 1.000
#> SRR1951744 1 0.0000 0.991 1.000 0.000
#> SRR1951742 2 0.0000 0.979 0.000 1.000
#> SRR1951743 2 0.0000 0.979 0.000 1.000
#> SRR1951741 1 0.0000 0.991 1.000 0.000
#> SRR1951740 1 0.0000 0.991 1.000 0.000
#> SRR1951738 2 0.0000 0.979 0.000 1.000
#> SRR1951739 2 0.0000 0.979 0.000 1.000
#> SRR1951737 1 0.0000 0.991 1.000 0.000
#> SRR1951736 1 0.0000 0.991 1.000 0.000
#> SRR1951734 2 0.0000 0.979 0.000 1.000
#> SRR1951735 1 0.0000 0.991 1.000 0.000
#> SRR1951733 2 0.0000 0.979 0.000 1.000
#> SRR1951732 2 0.0000 0.979 0.000 1.000
#> SRR1951729 1 0.0000 0.991 1.000 0.000
#> SRR1951728 1 0.0000 0.991 1.000 0.000
#> SRR1951727 2 0.0000 0.979 0.000 1.000
#> SRR1951725 1 0.0000 0.991 1.000 0.000
#> SRR1951726 1 0.0376 0.988 0.996 0.004
#> SRR1951724 1 0.0000 0.991 1.000 0.000
#> SRR1951722 1 0.0000 0.991 1.000 0.000
#> SRR1951723 1 0.0000 0.991 1.000 0.000
#> SRR1951721 1 0.0672 0.984 0.992 0.008
#> SRR1951719 1 0.0000 0.991 1.000 0.000
#> SRR1951720 1 0.0000 0.991 1.000 0.000
#> SRR1951718 1 0.0000 0.991 1.000 0.000
#> SRR1951717 1 0.0000 0.991 1.000 0.000
#> SRR1951715 1 0.0000 0.991 1.000 0.000
#> SRR1951716 1 0.0000 0.991 1.000 0.000
#> SRR1951714 1 0.0000 0.991 1.000 0.000
#> SRR1951713 1 0.0000 0.991 1.000 0.000
#> SRR1951712 1 0.0000 0.991 1.000 0.000
#> SRR1951711 2 0.0000 0.979 0.000 1.000
#> SRR1951710 1 0.0000 0.991 1.000 0.000
#> SRR1951709 2 0.0000 0.979 0.000 1.000
#> SRR1951708 2 0.0000 0.979 0.000 1.000
#> SRR1951707 1 0.0000 0.991 1.000 0.000
#> SRR1951705 1 0.0000 0.991 1.000 0.000
#> SRR1951704 1 0.0000 0.991 1.000 0.000
#> SRR1951703 2 0.0000 0.979 0.000 1.000
#> SRR1951706 1 0.7674 0.705 0.776 0.224
#> SRR1951702 1 0.0000 0.991 1.000 0.000
#> SRR1951701 2 0.0000 0.979 0.000 1.000
#> SRR1951699 2 0.0000 0.979 0.000 1.000
#> SRR1951698 1 0.0376 0.988 0.996 0.004
#> SRR1951700 1 0.0000 0.991 1.000 0.000
#> SRR1951697 2 0.0000 0.979 0.000 1.000
#> SRR1951696 1 0.0000 0.991 1.000 0.000
#> SRR1951695 1 0.0000 0.991 1.000 0.000
#> SRR1951693 1 0.0000 0.991 1.000 0.000
#> SRR1951692 1 0.0000 0.991 1.000 0.000
#> SRR1951691 1 0.0000 0.991 1.000 0.000
#> SRR1951688 2 0.0000 0.979 0.000 1.000
#> SRR1951689 2 0.5178 0.859 0.116 0.884
#> SRR1951687 1 0.0000 0.991 1.000 0.000
#> SRR1951685 1 0.0376 0.988 0.996 0.004
#> SRR1951684 1 0.0000 0.991 1.000 0.000
#> SRR1951686 2 0.0000 0.979 0.000 1.000
#> SRR1951683 1 0.0000 0.991 1.000 0.000
#> SRR1951682 2 0.0000 0.979 0.000 1.000
#> SRR1951690 2 0.0000 0.979 0.000 1.000
#> SRR1951680 1 0.0000 0.991 1.000 0.000
#> SRR1951679 1 0.0000 0.991 1.000 0.000
#> SRR1951678 1 0.0000 0.991 1.000 0.000
#> SRR1951677 1 0.0000 0.991 1.000 0.000
#> SRR1951676 1 0.0000 0.991 1.000 0.000
#> SRR1951675 2 0.0000 0.979 0.000 1.000
#> SRR1951681 1 0.0000 0.991 1.000 0.000
#> SRR1951673 2 0.9087 0.532 0.324 0.676
#> SRR1951674 1 0.0000 0.991 1.000 0.000
#> SRR1951672 1 0.0000 0.991 1.000 0.000
#> SRR1951671 2 0.0000 0.979 0.000 1.000
#> SRR1951670 1 0.0000 0.991 1.000 0.000
#> SRR1951640 1 0.0000 0.991 1.000 0.000
#> SRR1951668 1 0.0000 0.991 1.000 0.000
#> SRR1951669 2 0.0000 0.979 0.000 1.000
#> SRR1951638 1 0.0000 0.991 1.000 0.000
#> SRR1951637 2 0.0000 0.979 0.000 1.000
#> SRR1951635 1 0.0000 0.991 1.000 0.000
#> SRR1951634 1 0.0000 0.991 1.000 0.000
#> SRR1951631 1 0.0000 0.991 1.000 0.000
#> SRR1951632 2 0.0000 0.979 0.000 1.000
#> SRR1951633 1 0.0000 0.991 1.000 0.000
#> SRR1951630 1 0.0000 0.991 1.000 0.000
#> SRR1951629 1 0.0000 0.991 1.000 0.000
#> SRR1951628 1 0.0000 0.991 1.000 0.000
#> SRR1951627 2 0.0000 0.979 0.000 1.000
#> SRR1951639 1 0.0376 0.988 0.996 0.004
#> SRR1951625 2 0.9710 0.351 0.400 0.600
#> SRR1951626 1 0.0000 0.991 1.000 0.000
#> SRR1951624 1 0.0000 0.991 1.000 0.000
#> SRR1951622 1 0.0000 0.991 1.000 0.000
#> SRR1951636 1 0.0000 0.991 1.000 0.000
#> SRR1951623 1 0.0376 0.988 0.996 0.004
#> SRR1951621 1 0.0000 0.991 1.000 0.000
#> SRR1951620 1 0.0000 0.991 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951666 3 0.3192 0.808 0.000 0.112 0.888
#> SRR1951665 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951664 1 0.3941 0.840 0.844 0.000 0.156
#> SRR1951663 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951662 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951661 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951660 1 0.2537 0.920 0.920 0.000 0.080
#> SRR1951659 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951657 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951656 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951655 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951654 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951653 1 0.2537 0.920 0.920 0.000 0.080
#> SRR1951652 3 0.2537 0.832 0.000 0.080 0.920
#> SRR1951651 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951650 3 0.5138 0.694 0.252 0.000 0.748
#> SRR1951648 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951649 1 0.2537 0.920 0.920 0.000 0.080
#> SRR1951647 3 0.3340 0.817 0.120 0.000 0.880
#> SRR1951646 1 0.2537 0.920 0.920 0.000 0.080
#> SRR1951645 1 0.2537 0.920 0.920 0.000 0.080
#> SRR1951644 1 0.3038 0.898 0.896 0.000 0.104
#> SRR1951643 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951641 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951642 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951694 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951731 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951730 3 0.2537 0.832 0.000 0.080 0.920
#> SRR1951752 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951751 1 0.2537 0.920 0.920 0.000 0.080
#> SRR1951749 3 0.2537 0.832 0.000 0.080 0.920
#> SRR1951750 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951748 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951745 3 0.3038 0.840 0.104 0.000 0.896
#> SRR1951746 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951744 3 0.3038 0.840 0.104 0.000 0.896
#> SRR1951742 3 0.6062 0.372 0.000 0.384 0.616
#> SRR1951743 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951741 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951740 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951738 2 0.4121 0.796 0.000 0.832 0.168
#> SRR1951739 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951737 1 0.2537 0.920 0.920 0.000 0.080
#> SRR1951736 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951734 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951735 3 0.3941 0.801 0.156 0.000 0.844
#> SRR1951733 3 0.0424 0.878 0.000 0.008 0.992
#> SRR1951732 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951729 1 0.6095 0.360 0.608 0.000 0.392
#> SRR1951728 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951727 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951725 3 0.3038 0.840 0.104 0.000 0.896
#> SRR1951726 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951724 3 0.3267 0.833 0.116 0.000 0.884
#> SRR1951722 3 0.1753 0.868 0.048 0.000 0.952
#> SRR1951723 1 0.2537 0.920 0.920 0.000 0.080
#> SRR1951721 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951719 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951720 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951718 1 0.2537 0.920 0.920 0.000 0.080
#> SRR1951717 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951715 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951716 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951714 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951713 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951712 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951711 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951710 3 0.5497 0.625 0.292 0.000 0.708
#> SRR1951709 3 0.2537 0.832 0.000 0.080 0.920
#> SRR1951708 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951707 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951705 1 0.5291 0.661 0.732 0.000 0.268
#> SRR1951704 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951703 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951706 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951702 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951701 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951699 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951698 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951700 1 0.4842 0.732 0.776 0.000 0.224
#> SRR1951697 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951696 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951695 3 0.6267 0.244 0.452 0.000 0.548
#> SRR1951693 3 0.5291 0.669 0.268 0.000 0.732
#> SRR1951692 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951691 1 0.2537 0.920 0.920 0.000 0.080
#> SRR1951688 2 0.5327 0.669 0.000 0.728 0.272
#> SRR1951689 3 0.2537 0.832 0.000 0.080 0.920
#> SRR1951687 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951685 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951684 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951686 2 0.6140 0.340 0.000 0.596 0.404
#> SRR1951683 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951682 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951690 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951680 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951679 3 0.5591 0.605 0.304 0.000 0.696
#> SRR1951678 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951677 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951676 3 0.5785 0.612 0.332 0.000 0.668
#> SRR1951675 2 0.0000 0.951 0.000 1.000 0.000
#> SRR1951681 3 0.5138 0.694 0.252 0.000 0.748
#> SRR1951673 3 0.2537 0.832 0.000 0.080 0.920
#> SRR1951674 3 0.3038 0.840 0.104 0.000 0.896
#> SRR1951672 1 0.2537 0.920 0.920 0.000 0.080
#> SRR1951671 3 0.2959 0.817 0.000 0.100 0.900
#> SRR1951670 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951640 1 0.2537 0.920 0.920 0.000 0.080
#> SRR1951668 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951669 2 0.4178 0.792 0.000 0.828 0.172
#> SRR1951638 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951637 3 0.2537 0.832 0.000 0.080 0.920
#> SRR1951635 1 0.2537 0.920 0.920 0.000 0.080
#> SRR1951634 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951631 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951632 2 0.4702 0.755 0.000 0.788 0.212
#> SRR1951633 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951630 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951629 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951628 3 0.4605 0.736 0.204 0.000 0.796
#> SRR1951627 3 0.1031 0.873 0.000 0.024 0.976
#> SRR1951639 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951625 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951626 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951624 3 0.5363 0.656 0.276 0.000 0.724
#> SRR1951622 1 0.2537 0.920 0.920 0.000 0.080
#> SRR1951636 3 0.0000 0.881 0.000 0.000 1.000
#> SRR1951623 3 0.3038 0.840 0.104 0.000 0.896
#> SRR1951621 1 0.0000 0.954 1.000 0.000 0.000
#> SRR1951620 1 0.0000 0.954 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.0469 0.8931 0.000 0.988 0.012 0.000
#> SRR1951666 3 0.4888 0.0384 0.000 0.412 0.588 0.000
#> SRR1951665 2 0.0000 0.8970 0.000 1.000 0.000 0.000
#> SRR1951664 4 0.4549 0.7090 0.036 0.000 0.188 0.776
#> SRR1951663 1 0.1118 0.8840 0.964 0.000 0.036 0.000
#> SRR1951662 2 0.0000 0.8970 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.0000 0.7805 0.000 0.000 1.000 0.000
#> SRR1951660 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1951659 2 0.0188 0.8962 0.000 0.996 0.004 0.000
#> SRR1951658 2 0.0000 0.8970 0.000 1.000 0.000 0.000
#> SRR1951657 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951656 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951655 4 0.0188 0.9356 0.004 0.000 0.000 0.996
#> SRR1951654 1 0.1940 0.8733 0.924 0.000 0.000 0.076
#> SRR1951653 4 0.1118 0.9242 0.036 0.000 0.000 0.964
#> SRR1951652 3 0.4679 0.2275 0.000 0.352 0.648 0.000
#> SRR1951651 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951650 3 0.5213 0.5306 0.020 0.000 0.652 0.328
#> SRR1951648 3 0.0000 0.7805 0.000 0.000 1.000 0.000
#> SRR1951649 4 0.2281 0.8879 0.096 0.000 0.000 0.904
#> SRR1951647 3 0.0592 0.7794 0.016 0.000 0.984 0.000
#> SRR1951646 3 0.5213 0.5306 0.020 0.000 0.652 0.328
#> SRR1951645 4 0.2011 0.8983 0.080 0.000 0.000 0.920
#> SRR1951644 4 0.4948 0.2049 0.440 0.000 0.000 0.560
#> SRR1951643 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951641 2 0.0000 0.8970 0.000 1.000 0.000 0.000
#> SRR1951642 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951694 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1951731 3 0.0592 0.7794 0.016 0.000 0.984 0.000
#> SRR1951730 2 0.4933 0.4048 0.000 0.568 0.432 0.000
#> SRR1951752 3 0.0188 0.7815 0.004 0.000 0.996 0.000
#> SRR1951751 4 0.2216 0.8923 0.092 0.000 0.000 0.908
#> SRR1951749 3 0.0000 0.7805 0.000 0.000 1.000 0.000
#> SRR1951750 4 0.0817 0.9255 0.024 0.000 0.000 0.976
#> SRR1951748 2 0.0000 0.8970 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.8970 0.000 1.000 0.000 0.000
#> SRR1951745 1 0.1474 0.8742 0.948 0.000 0.052 0.000
#> SRR1951746 2 0.0000 0.8970 0.000 1.000 0.000 0.000
#> SRR1951744 3 0.5473 0.5297 0.032 0.000 0.644 0.324
#> SRR1951742 2 0.4925 0.4135 0.000 0.572 0.428 0.000
#> SRR1951743 2 0.0000 0.8970 0.000 1.000 0.000 0.000
#> SRR1951741 4 0.0469 0.9323 0.012 0.000 0.000 0.988
#> SRR1951740 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951738 2 0.1302 0.8746 0.000 0.956 0.044 0.000
#> SRR1951739 2 0.1389 0.8719 0.000 0.952 0.048 0.000
#> SRR1951737 3 0.5615 0.4745 0.032 0.000 0.612 0.356
#> SRR1951736 1 0.0592 0.8967 0.984 0.000 0.000 0.016
#> SRR1951734 2 0.0000 0.8970 0.000 1.000 0.000 0.000
#> SRR1951735 3 0.5599 0.4827 0.032 0.000 0.616 0.352
#> SRR1951733 3 0.0000 0.7805 0.000 0.000 1.000 0.000
#> SRR1951732 2 0.0000 0.8970 0.000 1.000 0.000 0.000
#> SRR1951729 3 0.5615 0.4745 0.032 0.000 0.612 0.356
#> SRR1951728 3 0.1118 0.7726 0.036 0.000 0.964 0.000
#> SRR1951727 2 0.0000 0.8970 0.000 1.000 0.000 0.000
#> SRR1951725 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1951726 3 0.0188 0.7815 0.004 0.000 0.996 0.000
#> SRR1951724 1 0.5596 0.4141 0.632 0.000 0.036 0.332
#> SRR1951722 3 0.5334 0.5802 0.036 0.000 0.680 0.284
#> SRR1951723 4 0.2281 0.8879 0.096 0.000 0.000 0.904
#> SRR1951721 1 0.1302 0.8797 0.956 0.000 0.044 0.000
#> SRR1951719 4 0.1624 0.9178 0.020 0.000 0.028 0.952
#> SRR1951720 1 0.0707 0.8956 0.980 0.000 0.000 0.020
#> SRR1951718 1 0.3873 0.6804 0.772 0.000 0.000 0.228
#> SRR1951717 1 0.2281 0.8590 0.904 0.000 0.000 0.096
#> SRR1951715 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1951716 1 0.1557 0.8817 0.944 0.000 0.000 0.056
#> SRR1951714 4 0.0707 0.9305 0.020 0.000 0.000 0.980
#> SRR1951713 4 0.0707 0.9305 0.020 0.000 0.000 0.980
#> SRR1951712 4 0.0469 0.9323 0.012 0.000 0.000 0.988
#> SRR1951711 2 0.0188 0.8962 0.000 0.996 0.004 0.000
#> SRR1951710 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1951709 3 0.0000 0.7805 0.000 0.000 1.000 0.000
#> SRR1951708 2 0.0000 0.8970 0.000 1.000 0.000 0.000
#> SRR1951707 3 0.0592 0.7794 0.016 0.000 0.984 0.000
#> SRR1951705 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1951704 3 0.0707 0.7778 0.020 0.000 0.980 0.000
#> SRR1951703 2 0.0000 0.8970 0.000 1.000 0.000 0.000
#> SRR1951706 3 0.0188 0.7815 0.004 0.000 0.996 0.000
#> SRR1951702 3 0.0188 0.7815 0.004 0.000 0.996 0.000
#> SRR1951701 2 0.0000 0.8970 0.000 1.000 0.000 0.000
#> SRR1951699 3 0.0000 0.7805 0.000 0.000 1.000 0.000
#> SRR1951698 3 0.0188 0.7815 0.004 0.000 0.996 0.000
#> SRR1951700 4 0.2739 0.8807 0.036 0.000 0.060 0.904
#> SRR1951697 2 0.0188 0.8962 0.000 0.996 0.004 0.000
#> SRR1951696 4 0.1637 0.9016 0.000 0.000 0.060 0.940
#> SRR1951695 3 0.5682 0.4787 0.036 0.000 0.612 0.352
#> SRR1951693 4 0.5113 0.5852 0.036 0.000 0.252 0.712
#> SRR1951692 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951691 4 0.2443 0.8899 0.024 0.000 0.060 0.916
#> SRR1951688 2 0.4843 0.4745 0.000 0.604 0.396 0.000
#> SRR1951689 3 0.5396 -0.1495 0.012 0.464 0.524 0.000
#> SRR1951687 4 0.4697 0.3971 0.356 0.000 0.000 0.644
#> SRR1951685 3 0.0592 0.7794 0.016 0.000 0.984 0.000
#> SRR1951684 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951686 2 0.4804 0.4959 0.000 0.616 0.384 0.000
#> SRR1951683 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951682 2 0.1302 0.8746 0.000 0.956 0.044 0.000
#> SRR1951690 2 0.0000 0.8970 0.000 1.000 0.000 0.000
#> SRR1951680 4 0.0707 0.9305 0.020 0.000 0.000 0.980
#> SRR1951679 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1951678 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951677 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951676 3 0.5355 0.4822 0.020 0.000 0.620 0.360
#> SRR1951675 2 0.0000 0.8970 0.000 1.000 0.000 0.000
#> SRR1951681 3 0.5492 0.5244 0.032 0.000 0.640 0.328
#> SRR1951673 3 0.4319 0.5053 0.012 0.228 0.760 0.000
#> SRR1951674 1 0.1716 0.8643 0.936 0.000 0.064 0.000
#> SRR1951672 1 0.5000 -0.0865 0.504 0.000 0.000 0.496
#> SRR1951671 3 0.4817 0.1208 0.000 0.388 0.612 0.000
#> SRR1951670 4 0.0469 0.9323 0.012 0.000 0.000 0.988
#> SRR1951640 4 0.3082 0.8595 0.032 0.000 0.084 0.884
#> SRR1951668 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951669 2 0.0188 0.8962 0.000 0.996 0.004 0.000
#> SRR1951638 1 0.1940 0.8733 0.924 0.000 0.000 0.076
#> SRR1951637 2 0.4933 0.4048 0.000 0.568 0.432 0.000
#> SRR1951635 1 0.0000 0.8995 1.000 0.000 0.000 0.000
#> SRR1951634 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951631 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951632 2 0.4994 0.2796 0.000 0.520 0.480 0.000
#> SRR1951633 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951630 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951629 4 0.0000 0.9369 0.000 0.000 0.000 1.000
#> SRR1951628 1 0.4406 0.5446 0.700 0.000 0.300 0.000
#> SRR1951627 3 0.0000 0.7805 0.000 0.000 1.000 0.000
#> SRR1951639 3 0.0188 0.7815 0.004 0.000 0.996 0.000
#> SRR1951625 3 0.1356 0.7571 0.008 0.032 0.960 0.000
#> SRR1951626 3 0.0188 0.7815 0.004 0.000 0.996 0.000
#> SRR1951624 3 0.5548 0.5050 0.032 0.000 0.628 0.340
#> SRR1951622 1 0.0469 0.8975 0.988 0.000 0.000 0.012
#> SRR1951636 3 0.0188 0.7815 0.004 0.000 0.996 0.000
#> SRR1951623 3 0.4454 0.5711 0.000 0.000 0.692 0.308
#> SRR1951621 4 0.1716 0.8956 0.064 0.000 0.000 0.936
#> SRR1951620 4 0.0336 0.9341 0.000 0.000 0.008 0.992
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 5 0.1168 0.7653 0.000 0.032 0.008 0.000 0.960
#> SRR1951666 3 0.3696 0.5592 0.000 0.016 0.772 0.000 0.212
#> SRR1951665 2 0.0000 0.9283 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 4 0.4436 0.6505 0.012 0.000 0.224 0.736 0.028
#> SRR1951663 1 0.1386 0.8458 0.952 0.000 0.032 0.000 0.016
#> SRR1951662 2 0.0000 0.9283 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 3 0.0000 0.7601 0.000 0.000 1.000 0.000 0.000
#> SRR1951660 1 0.0000 0.8593 1.000 0.000 0.000 0.000 0.000
#> SRR1951659 5 0.3636 0.5827 0.000 0.272 0.000 0.000 0.728
#> SRR1951658 2 0.4294 -0.0598 0.000 0.532 0.000 0.000 0.468
#> SRR1951657 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951656 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951655 4 0.0609 0.9187 0.020 0.000 0.000 0.980 0.000
#> SRR1951654 1 0.1908 0.8202 0.908 0.000 0.000 0.092 0.000
#> SRR1951653 4 0.1195 0.9093 0.012 0.000 0.000 0.960 0.028
#> SRR1951652 3 0.4300 -0.1529 0.000 0.000 0.524 0.000 0.476
#> SRR1951651 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951650 3 0.3796 0.6201 0.000 0.000 0.700 0.300 0.000
#> SRR1951648 3 0.0000 0.7601 0.000 0.000 1.000 0.000 0.000
#> SRR1951649 4 0.2685 0.8613 0.092 0.000 0.000 0.880 0.028
#> SRR1951647 3 0.1195 0.7498 0.012 0.000 0.960 0.000 0.028
#> SRR1951646 3 0.3796 0.6201 0.000 0.000 0.700 0.300 0.000
#> SRR1951645 4 0.1965 0.8761 0.096 0.000 0.000 0.904 0.000
#> SRR1951644 4 0.4917 0.2354 0.416 0.000 0.000 0.556 0.028
#> SRR1951643 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951641 2 0.2732 0.7234 0.000 0.840 0.000 0.000 0.160
#> SRR1951642 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951694 1 0.0963 0.8485 0.964 0.000 0.000 0.000 0.036
#> SRR1951731 3 0.0290 0.7589 0.000 0.000 0.992 0.000 0.008
#> SRR1951730 5 0.4275 0.5954 0.000 0.020 0.284 0.000 0.696
#> SRR1951752 3 0.2929 0.6246 0.000 0.000 0.820 0.000 0.180
#> SRR1951751 4 0.2464 0.8683 0.096 0.000 0.000 0.888 0.016
#> SRR1951749 3 0.3109 0.5860 0.000 0.000 0.800 0.000 0.200
#> SRR1951750 4 0.1043 0.9088 0.040 0.000 0.000 0.960 0.000
#> SRR1951748 2 0.0000 0.9283 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9283 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 1 0.2305 0.8121 0.896 0.000 0.092 0.000 0.012
#> SRR1951746 2 0.0000 0.9283 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 3 0.3957 0.6349 0.000 0.000 0.712 0.280 0.008
#> SRR1951742 5 0.4815 0.3016 0.000 0.020 0.456 0.000 0.524
#> SRR1951743 2 0.0000 0.9283 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 4 0.0963 0.9108 0.036 0.000 0.000 0.964 0.000
#> SRR1951740 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951738 5 0.2017 0.7467 0.000 0.080 0.008 0.000 0.912
#> SRR1951739 5 0.1082 0.7660 0.000 0.028 0.008 0.000 0.964
#> SRR1951737 3 0.4147 0.5984 0.000 0.000 0.676 0.316 0.008
#> SRR1951736 1 0.0404 0.8585 0.988 0.000 0.000 0.012 0.000
#> SRR1951734 2 0.0000 0.9283 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 3 0.4046 0.6204 0.000 0.000 0.696 0.296 0.008
#> SRR1951733 3 0.3274 0.5683 0.000 0.000 0.780 0.000 0.220
#> SRR1951732 2 0.0000 0.9283 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 3 0.4900 0.5981 0.012 0.000 0.660 0.300 0.028
#> SRR1951728 3 0.1195 0.7498 0.012 0.000 0.960 0.000 0.028
#> SRR1951727 2 0.0000 0.9283 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 1 0.0000 0.8593 1.000 0.000 0.000 0.000 0.000
#> SRR1951726 3 0.0404 0.7570 0.000 0.000 0.988 0.000 0.012
#> SRR1951724 1 0.5445 0.4040 0.612 0.000 0.032 0.328 0.028
#> SRR1951722 3 0.6159 0.5789 0.012 0.000 0.604 0.196 0.188
#> SRR1951723 4 0.2685 0.8613 0.092 0.000 0.000 0.880 0.028
#> SRR1951721 1 0.3769 0.7214 0.788 0.000 0.032 0.000 0.180
#> SRR1951719 4 0.1732 0.8742 0.000 0.000 0.080 0.920 0.000
#> SRR1951720 1 0.0404 0.8585 0.988 0.000 0.000 0.012 0.000
#> SRR1951718 1 0.3424 0.6592 0.760 0.000 0.000 0.240 0.000
#> SRR1951717 1 0.1908 0.8202 0.908 0.000 0.000 0.092 0.000
#> SRR1951715 1 0.0162 0.8586 0.996 0.000 0.000 0.000 0.004
#> SRR1951716 1 0.0880 0.8538 0.968 0.000 0.000 0.032 0.000
#> SRR1951714 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951713 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951712 4 0.0703 0.9171 0.024 0.000 0.000 0.976 0.000
#> SRR1951711 5 0.1851 0.7441 0.000 0.088 0.000 0.000 0.912
#> SRR1951710 1 0.0000 0.8593 1.000 0.000 0.000 0.000 0.000
#> SRR1951709 3 0.3109 0.5860 0.000 0.000 0.800 0.000 0.200
#> SRR1951708 5 0.4235 0.3015 0.000 0.424 0.000 0.000 0.576
#> SRR1951707 3 0.3462 0.6132 0.012 0.000 0.792 0.000 0.196
#> SRR1951705 1 0.0000 0.8593 1.000 0.000 0.000 0.000 0.000
#> SRR1951704 3 0.0162 0.7592 0.000 0.000 0.996 0.000 0.004
#> SRR1951703 2 0.0000 0.9283 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 3 0.0404 0.7570 0.000 0.000 0.988 0.000 0.012
#> SRR1951702 3 0.0000 0.7601 0.000 0.000 1.000 0.000 0.000
#> SRR1951701 5 0.4201 0.3388 0.000 0.408 0.000 0.000 0.592
#> SRR1951699 3 0.0000 0.7601 0.000 0.000 1.000 0.000 0.000
#> SRR1951698 3 0.0000 0.7601 0.000 0.000 1.000 0.000 0.000
#> SRR1951700 4 0.3079 0.8421 0.012 0.000 0.092 0.868 0.028
#> SRR1951697 5 0.3366 0.6284 0.000 0.232 0.000 0.000 0.768
#> SRR1951696 4 0.2230 0.8431 0.000 0.000 0.116 0.884 0.000
#> SRR1951695 3 0.4975 0.5792 0.012 0.000 0.644 0.316 0.028
#> SRR1951693 4 0.4957 0.4523 0.012 0.000 0.312 0.648 0.028
#> SRR1951692 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951691 4 0.1732 0.8742 0.000 0.000 0.080 0.920 0.000
#> SRR1951688 5 0.0898 0.7656 0.000 0.020 0.008 0.000 0.972
#> SRR1951689 5 0.0566 0.7516 0.012 0.000 0.004 0.000 0.984
#> SRR1951687 4 0.4126 0.3662 0.380 0.000 0.000 0.620 0.000
#> SRR1951685 3 0.4016 0.5195 0.012 0.000 0.716 0.000 0.272
#> SRR1951684 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951686 5 0.5296 0.6280 0.000 0.180 0.144 0.000 0.676
#> SRR1951683 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951682 5 0.1082 0.7660 0.000 0.028 0.008 0.000 0.964
#> SRR1951690 5 0.4201 0.3388 0.000 0.408 0.000 0.000 0.592
#> SRR1951680 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951679 1 0.3562 0.7169 0.788 0.000 0.016 0.000 0.196
#> SRR1951678 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951677 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951676 3 0.3857 0.6068 0.000 0.000 0.688 0.312 0.000
#> SRR1951675 5 0.3480 0.5833 0.000 0.248 0.000 0.000 0.752
#> SRR1951681 3 0.4067 0.6165 0.000 0.000 0.692 0.300 0.008
#> SRR1951673 5 0.2818 0.7001 0.012 0.000 0.132 0.000 0.856
#> SRR1951674 1 0.2574 0.7939 0.876 0.000 0.112 0.000 0.012
#> SRR1951672 1 0.4937 0.1414 0.544 0.000 0.000 0.428 0.028
#> SRR1951671 5 0.3949 0.5825 0.000 0.004 0.300 0.000 0.696
#> SRR1951670 4 0.1043 0.9088 0.040 0.000 0.000 0.960 0.000
#> SRR1951640 4 0.3063 0.8328 0.012 0.000 0.104 0.864 0.020
#> SRR1951668 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951669 5 0.0898 0.7656 0.000 0.020 0.008 0.000 0.972
#> SRR1951638 1 0.1908 0.8202 0.908 0.000 0.000 0.092 0.000
#> SRR1951637 5 0.0566 0.7612 0.000 0.004 0.012 0.000 0.984
#> SRR1951635 1 0.0000 0.8593 1.000 0.000 0.000 0.000 0.000
#> SRR1951634 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951631 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951632 5 0.0912 0.7653 0.000 0.016 0.012 0.000 0.972
#> SRR1951633 4 0.0162 0.9254 0.000 0.000 0.004 0.996 0.000
#> SRR1951630 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951629 4 0.0000 0.9268 0.000 0.000 0.000 1.000 0.000
#> SRR1951628 1 0.4735 0.5459 0.668 0.000 0.296 0.004 0.032
#> SRR1951627 3 0.0162 0.7591 0.000 0.000 0.996 0.000 0.004
#> SRR1951639 3 0.0000 0.7601 0.000 0.000 1.000 0.000 0.000
#> SRR1951625 5 0.4086 0.5657 0.012 0.000 0.284 0.000 0.704
#> SRR1951626 3 0.0000 0.7601 0.000 0.000 1.000 0.000 0.000
#> SRR1951624 3 0.4127 0.6033 0.000 0.000 0.680 0.312 0.008
#> SRR1951622 1 0.0404 0.8585 0.988 0.000 0.000 0.012 0.000
#> SRR1951636 3 0.0000 0.7601 0.000 0.000 1.000 0.000 0.000
#> SRR1951623 3 0.3684 0.6377 0.000 0.000 0.720 0.280 0.000
#> SRR1951621 4 0.1792 0.8778 0.084 0.000 0.000 0.916 0.000
#> SRR1951620 4 0.0290 0.9240 0.000 0.000 0.008 0.992 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 6 0.1500 0.7081 0.000 0.052 0.012 0.000 0.000 0.936
#> SRR1951666 6 0.3828 0.2792 0.000 0.000 0.000 0.000 0.440 0.560
#> SRR1951665 2 0.0000 0.8909 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 4 0.6076 -0.2079 0.000 0.000 0.344 0.384 0.272 0.000
#> SRR1951663 1 0.5158 0.2471 0.496 0.000 0.436 0.000 0.012 0.056
#> SRR1951662 2 0.0000 0.8909 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 5 0.0622 0.7239 0.000 0.000 0.008 0.000 0.980 0.012
#> SRR1951660 1 0.1610 0.7305 0.916 0.000 0.084 0.000 0.000 0.000
#> SRR1951659 6 0.2260 0.6482 0.000 0.140 0.000 0.000 0.000 0.860
#> SRR1951658 2 0.3592 0.5317 0.000 0.656 0.000 0.000 0.000 0.344
#> SRR1951657 4 0.0000 0.8822 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951656 4 0.0000 0.8822 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951655 4 0.2793 0.6836 0.200 0.000 0.000 0.800 0.000 0.000
#> SRR1951654 1 0.0000 0.7402 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951653 3 0.3857 0.1593 0.000 0.000 0.532 0.468 0.000 0.000
#> SRR1951652 6 0.4066 0.3758 0.000 0.000 0.012 0.000 0.392 0.596
#> SRR1951651 4 0.0146 0.8810 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951650 5 0.3309 0.6215 0.000 0.000 0.000 0.280 0.720 0.000
#> SRR1951648 5 0.0260 0.7240 0.000 0.000 0.000 0.000 0.992 0.008
#> SRR1951649 3 0.3050 0.6631 0.000 0.000 0.764 0.236 0.000 0.000
#> SRR1951647 3 0.3684 0.5389 0.000 0.000 0.628 0.000 0.372 0.000
#> SRR1951646 5 0.3309 0.6215 0.000 0.000 0.000 0.280 0.720 0.000
#> SRR1951645 4 0.4143 0.6492 0.180 0.000 0.084 0.736 0.000 0.000
#> SRR1951644 3 0.3333 0.5432 0.192 0.000 0.784 0.024 0.000 0.000
#> SRR1951643 4 0.0146 0.8810 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951641 2 0.2416 0.7485 0.000 0.844 0.000 0.000 0.000 0.156
#> SRR1951642 4 0.0000 0.8822 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951694 3 0.2135 0.5696 0.128 0.000 0.872 0.000 0.000 0.000
#> SRR1951731 5 0.0790 0.7172 0.000 0.000 0.032 0.000 0.968 0.000
#> SRR1951730 6 0.3518 0.6034 0.000 0.000 0.012 0.000 0.256 0.732
#> SRR1951752 5 0.1829 0.6974 0.000 0.000 0.024 0.000 0.920 0.056
#> SRR1951751 4 0.4772 0.5600 0.124 0.000 0.208 0.668 0.000 0.000
#> SRR1951749 5 0.3860 -0.1168 0.000 0.000 0.000 0.000 0.528 0.472
#> SRR1951750 1 0.3797 0.2933 0.580 0.000 0.000 0.420 0.000 0.000
#> SRR1951748 2 0.0000 0.8909 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.8909 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 1 0.5240 0.2226 0.488 0.000 0.440 0.000 0.016 0.056
#> SRR1951746 2 0.0000 0.8909 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951744 5 0.3421 0.6328 0.000 0.000 0.008 0.256 0.736 0.000
#> SRR1951742 6 0.3531 0.5071 0.000 0.000 0.000 0.000 0.328 0.672
#> SRR1951743 2 0.0000 0.8909 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951741 4 0.3464 0.4203 0.312 0.000 0.000 0.688 0.000 0.000
#> SRR1951740 4 0.0000 0.8822 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951738 6 0.1563 0.7060 0.000 0.056 0.012 0.000 0.000 0.932
#> SRR1951739 6 0.0993 0.7204 0.000 0.024 0.012 0.000 0.000 0.964
#> SRR1951737 5 0.3847 0.3047 0.000 0.000 0.000 0.456 0.544 0.000
#> SRR1951736 1 0.1556 0.7306 0.920 0.000 0.080 0.000 0.000 0.000
#> SRR1951734 2 0.0000 0.8909 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951735 5 0.3794 0.6211 0.000 0.000 0.028 0.248 0.724 0.000
#> SRR1951733 5 0.3864 -0.1256 0.000 0.000 0.000 0.000 0.520 0.480
#> SRR1951732 2 0.0000 0.8909 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 5 0.5611 0.3311 0.000 0.000 0.232 0.224 0.544 0.000
#> SRR1951728 3 0.3428 0.6075 0.000 0.000 0.696 0.000 0.304 0.000
#> SRR1951727 2 0.0000 0.8909 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 1 0.0146 0.7396 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1951726 5 0.1657 0.7017 0.000 0.000 0.016 0.000 0.928 0.056
#> SRR1951724 3 0.3453 0.6818 0.028 0.000 0.788 0.180 0.004 0.000
#> SRR1951722 3 0.3552 0.7044 0.000 0.000 0.800 0.116 0.084 0.000
#> SRR1951723 3 0.2941 0.6754 0.000 0.000 0.780 0.220 0.000 0.000
#> SRR1951721 3 0.4667 -0.0206 0.380 0.000 0.576 0.000 0.004 0.040
#> SRR1951719 4 0.1556 0.8150 0.000 0.000 0.000 0.920 0.080 0.000
#> SRR1951720 1 0.0000 0.7402 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951718 1 0.4273 0.6032 0.716 0.000 0.080 0.204 0.000 0.000
#> SRR1951717 1 0.0000 0.7402 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951715 1 0.3843 0.2753 0.548 0.000 0.452 0.000 0.000 0.000
#> SRR1951716 1 0.1913 0.7288 0.908 0.000 0.080 0.012 0.000 0.000
#> SRR1951714 4 0.0146 0.8806 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1951713 4 0.1556 0.8232 0.000 0.000 0.080 0.920 0.000 0.000
#> SRR1951712 4 0.3797 0.1410 0.420 0.000 0.000 0.580 0.000 0.000
#> SRR1951711 6 0.2446 0.6578 0.000 0.124 0.012 0.000 0.000 0.864
#> SRR1951710 1 0.1663 0.7038 0.912 0.000 0.088 0.000 0.000 0.000
#> SRR1951709 5 0.3851 -0.0921 0.000 0.000 0.000 0.000 0.540 0.460
#> SRR1951708 2 0.3672 0.4860 0.000 0.632 0.000 0.000 0.000 0.368
#> SRR1951707 3 0.3309 0.6219 0.000 0.000 0.720 0.000 0.280 0.000
#> SRR1951705 1 0.1556 0.7317 0.920 0.000 0.080 0.000 0.000 0.000
#> SRR1951704 5 0.1556 0.6898 0.000 0.000 0.080 0.000 0.920 0.000
#> SRR1951703 2 0.0000 0.8909 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 5 0.3284 0.6016 0.000 0.000 0.032 0.000 0.800 0.168
#> SRR1951702 5 0.0260 0.7247 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1951701 2 0.3563 0.5466 0.000 0.664 0.000 0.000 0.000 0.336
#> SRR1951699 5 0.0865 0.7161 0.000 0.000 0.000 0.000 0.964 0.036
#> SRR1951698 5 0.0000 0.7247 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951700 3 0.2941 0.6754 0.000 0.000 0.780 0.220 0.000 0.000
#> SRR1951697 6 0.2762 0.6053 0.000 0.196 0.000 0.000 0.000 0.804
#> SRR1951696 4 0.2048 0.7773 0.000 0.000 0.000 0.880 0.120 0.000
#> SRR1951695 3 0.4490 0.6490 0.000 0.000 0.700 0.196 0.104 0.000
#> SRR1951693 3 0.2994 0.6810 0.000 0.000 0.788 0.208 0.004 0.000
#> SRR1951692 4 0.0000 0.8822 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951691 4 0.3620 0.3960 0.000 0.000 0.352 0.648 0.000 0.000
#> SRR1951688 6 0.0363 0.7230 0.000 0.000 0.012 0.000 0.000 0.988
#> SRR1951689 6 0.3737 0.1912 0.000 0.000 0.392 0.000 0.000 0.608
#> SRR1951687 1 0.2562 0.6674 0.828 0.000 0.000 0.172 0.000 0.000
#> SRR1951685 3 0.4223 0.6271 0.000 0.000 0.720 0.000 0.204 0.076
#> SRR1951684 4 0.0000 0.8822 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951686 6 0.2278 0.6965 0.000 0.004 0.000 0.000 0.128 0.868
#> SRR1951683 4 0.0146 0.8810 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951682 6 0.0993 0.7204 0.000 0.024 0.012 0.000 0.000 0.964
#> SRR1951690 6 0.3868 -0.2476 0.000 0.496 0.000 0.000 0.000 0.504
#> SRR1951680 4 0.0000 0.8822 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951679 3 0.0790 0.6200 0.032 0.000 0.968 0.000 0.000 0.000
#> SRR1951678 4 0.0146 0.8810 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951677 4 0.0146 0.8810 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951676 5 0.3309 0.6215 0.000 0.000 0.000 0.280 0.720 0.000
#> SRR1951675 6 0.4185 -0.2440 0.000 0.492 0.012 0.000 0.000 0.496
#> SRR1951681 5 0.3426 0.6222 0.000 0.000 0.004 0.276 0.720 0.000
#> SRR1951673 3 0.5327 0.2502 0.000 0.000 0.516 0.000 0.112 0.372
#> SRR1951674 1 0.5247 0.1777 0.468 0.000 0.460 0.000 0.016 0.056
#> SRR1951672 1 0.4212 0.2537 0.560 0.000 0.424 0.016 0.000 0.000
#> SRR1951671 6 0.3330 0.5831 0.000 0.000 0.000 0.000 0.284 0.716
#> SRR1951670 1 0.3797 0.2933 0.580 0.000 0.000 0.420 0.000 0.000
#> SRR1951640 4 0.2814 0.7237 0.000 0.000 0.172 0.820 0.008 0.000
#> SRR1951668 4 0.0000 0.8822 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951669 6 0.0547 0.7232 0.000 0.000 0.020 0.000 0.000 0.980
#> SRR1951638 1 0.0000 0.7402 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951637 6 0.2838 0.6574 0.000 0.000 0.188 0.000 0.004 0.808
#> SRR1951635 1 0.0146 0.7396 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1951634 4 0.0000 0.8822 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951631 4 0.0000 0.8822 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951632 6 0.0000 0.7234 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1951633 4 0.0146 0.8806 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1951630 4 0.0000 0.8822 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951629 4 0.0000 0.8822 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951628 3 0.3333 0.6632 0.024 0.000 0.784 0.000 0.192 0.000
#> SRR1951627 5 0.0865 0.7169 0.000 0.000 0.000 0.000 0.964 0.036
#> SRR1951639 5 0.0000 0.7247 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951625 3 0.5579 0.4056 0.000 0.000 0.544 0.000 0.264 0.192
#> SRR1951626 5 0.0458 0.7222 0.000 0.000 0.016 0.000 0.984 0.000
#> SRR1951624 5 0.3309 0.6215 0.000 0.000 0.000 0.280 0.720 0.000
#> SRR1951622 1 0.1610 0.7305 0.916 0.000 0.084 0.000 0.000 0.000
#> SRR1951636 5 0.0260 0.7247 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1951623 5 0.3421 0.6328 0.000 0.000 0.008 0.256 0.736 0.000
#> SRR1951621 1 0.3727 0.3659 0.612 0.000 0.000 0.388 0.000 0.000
#> SRR1951620 4 0.0363 0.8753 0.000 0.000 0.000 0.988 0.012 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 13580 rows and 133 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.955 0.975 0.3007 0.722 0.722
#> 3 3 0.425 0.686 0.785 0.8642 0.643 0.507
#> 4 4 0.671 0.834 0.885 0.1962 0.842 0.632
#> 5 5 0.758 0.866 0.909 0.1325 0.825 0.523
#> 6 6 0.773 0.771 0.843 0.0628 0.887 0.568
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
#> SRR1951667 2 0.0938 0.991 0.012 0.988
#> SRR1951666 1 0.3879 0.936 0.924 0.076
#> SRR1951665 2 0.0000 0.994 0.000 1.000
#> SRR1951664 1 0.0000 0.970 1.000 0.000
#> SRR1951663 1 0.0000 0.970 1.000 0.000
#> SRR1951662 2 0.0000 0.994 0.000 1.000
#> SRR1951661 1 0.3431 0.942 0.936 0.064
#> SRR1951660 1 0.0000 0.970 1.000 0.000
#> SRR1951659 2 0.0672 0.993 0.008 0.992
#> SRR1951658 2 0.0000 0.994 0.000 1.000
#> SRR1951657 1 0.0000 0.970 1.000 0.000
#> SRR1951656 1 0.0000 0.970 1.000 0.000
#> SRR1951655 1 0.0000 0.970 1.000 0.000
#> SRR1951654 1 0.0000 0.970 1.000 0.000
#> SRR1951653 1 0.0000 0.970 1.000 0.000
#> SRR1951652 1 0.3879 0.936 0.924 0.076
#> SRR1951651 1 0.0000 0.970 1.000 0.000
#> SRR1951650 1 0.0000 0.970 1.000 0.000
#> SRR1951648 1 0.3879 0.936 0.924 0.076
#> SRR1951649 1 0.0000 0.970 1.000 0.000
#> SRR1951647 1 0.1843 0.960 0.972 0.028
#> SRR1951646 1 0.0000 0.970 1.000 0.000
#> SRR1951645 1 0.0000 0.970 1.000 0.000
#> SRR1951644 1 0.0000 0.970 1.000 0.000
#> SRR1951643 1 0.0000 0.970 1.000 0.000
#> SRR1951641 2 0.0672 0.993 0.008 0.992
#> SRR1951642 1 0.0000 0.970 1.000 0.000
#> SRR1951694 1 0.0000 0.970 1.000 0.000
#> SRR1951731 1 0.3879 0.936 0.924 0.076
#> SRR1951730 1 0.3879 0.936 0.924 0.076
#> SRR1951752 1 0.3879 0.936 0.924 0.076
#> SRR1951751 1 0.0000 0.970 1.000 0.000
#> SRR1951749 1 0.3879 0.936 0.924 0.076
#> SRR1951750 1 0.0000 0.970 1.000 0.000
#> SRR1951748 2 0.0000 0.994 0.000 1.000
#> SRR1951747 2 0.0000 0.994 0.000 1.000
#> SRR1951745 1 0.0000 0.970 1.000 0.000
#> SRR1951746 2 0.0000 0.994 0.000 1.000
#> SRR1951744 1 0.0000 0.970 1.000 0.000
#> SRR1951742 1 0.3879 0.936 0.924 0.076
#> SRR1951743 2 0.0000 0.994 0.000 1.000
#> SRR1951741 1 0.0000 0.970 1.000 0.000
#> SRR1951740 1 0.0000 0.970 1.000 0.000
#> SRR1951738 1 0.9909 0.278 0.556 0.444
#> SRR1951739 2 0.1414 0.984 0.020 0.980
#> SRR1951737 1 0.0000 0.970 1.000 0.000
#> SRR1951736 1 0.0000 0.970 1.000 0.000
#> SRR1951734 2 0.0000 0.994 0.000 1.000
#> SRR1951735 1 0.0376 0.969 0.996 0.004
#> SRR1951733 1 0.3879 0.936 0.924 0.076
#> SRR1951732 2 0.0000 0.994 0.000 1.000
#> SRR1951729 1 0.0000 0.970 1.000 0.000
#> SRR1951728 1 0.2043 0.958 0.968 0.032
#> SRR1951727 2 0.0000 0.994 0.000 1.000
#> SRR1951725 1 0.0000 0.970 1.000 0.000
#> SRR1951726 1 0.3879 0.936 0.924 0.076
#> SRR1951724 1 0.0000 0.970 1.000 0.000
#> SRR1951722 1 0.1633 0.961 0.976 0.024
#> SRR1951723 1 0.0000 0.970 1.000 0.000
#> SRR1951721 1 0.0000 0.970 1.000 0.000
#> SRR1951719 1 0.0000 0.970 1.000 0.000
#> SRR1951720 1 0.0000 0.970 1.000 0.000
#> SRR1951718 1 0.0000 0.970 1.000 0.000
#> SRR1951717 1 0.0000 0.970 1.000 0.000
#> SRR1951715 1 0.0000 0.970 1.000 0.000
#> SRR1951716 1 0.0000 0.970 1.000 0.000
#> SRR1951714 1 0.0000 0.970 1.000 0.000
#> SRR1951713 1 0.0000 0.970 1.000 0.000
#> SRR1951712 1 0.0000 0.970 1.000 0.000
#> SRR1951711 2 0.0672 0.993 0.008 0.992
#> SRR1951710 1 0.0000 0.970 1.000 0.000
#> SRR1951709 1 0.3879 0.936 0.924 0.076
#> SRR1951708 2 0.0000 0.994 0.000 1.000
#> SRR1951707 1 0.3879 0.936 0.924 0.076
#> SRR1951705 1 0.0000 0.970 1.000 0.000
#> SRR1951704 1 0.3879 0.936 0.924 0.076
#> SRR1951703 2 0.0000 0.994 0.000 1.000
#> SRR1951706 1 0.2603 0.952 0.956 0.044
#> SRR1951702 1 0.3879 0.936 0.924 0.076
#> SRR1951701 2 0.0938 0.991 0.012 0.988
#> SRR1951699 1 0.3879 0.936 0.924 0.076
#> SRR1951698 1 0.2778 0.951 0.952 0.048
#> SRR1951700 1 0.0000 0.970 1.000 0.000
#> SRR1951697 2 0.0938 0.991 0.012 0.988
#> SRR1951696 1 0.0000 0.970 1.000 0.000
#> SRR1951695 1 0.0000 0.970 1.000 0.000
#> SRR1951693 1 0.0000 0.970 1.000 0.000
#> SRR1951692 1 0.0000 0.970 1.000 0.000
#> SRR1951691 1 0.0000 0.970 1.000 0.000
#> SRR1951688 1 0.3879 0.936 0.924 0.076
#> SRR1951689 1 0.3879 0.936 0.924 0.076
#> SRR1951687 1 0.0000 0.970 1.000 0.000
#> SRR1951685 1 0.3879 0.936 0.924 0.076
#> SRR1951684 1 0.0000 0.970 1.000 0.000
#> SRR1951686 1 0.3879 0.936 0.924 0.076
#> SRR1951683 1 0.1633 0.961 0.976 0.024
#> SRR1951682 2 0.0938 0.991 0.012 0.988
#> SRR1951690 2 0.1184 0.988 0.016 0.984
#> SRR1951680 1 0.0000 0.970 1.000 0.000
#> SRR1951679 1 0.0000 0.970 1.000 0.000
#> SRR1951678 1 0.1633 0.961 0.976 0.024
#> SRR1951677 1 0.0000 0.970 1.000 0.000
#> SRR1951676 1 0.0000 0.970 1.000 0.000
#> SRR1951675 2 0.0938 0.991 0.012 0.988
#> SRR1951681 1 0.0000 0.970 1.000 0.000
#> SRR1951673 1 0.3879 0.936 0.924 0.076
#> SRR1951674 1 0.0000 0.970 1.000 0.000
#> SRR1951672 1 0.0000 0.970 1.000 0.000
#> SRR1951671 1 0.3879 0.936 0.924 0.076
#> SRR1951670 1 0.0000 0.970 1.000 0.000
#> SRR1951640 1 0.0000 0.970 1.000 0.000
#> SRR1951668 1 0.0000 0.970 1.000 0.000
#> SRR1951669 1 0.3879 0.936 0.924 0.076
#> SRR1951638 1 0.0000 0.970 1.000 0.000
#> SRR1951637 1 0.3879 0.936 0.924 0.076
#> SRR1951635 1 0.0000 0.970 1.000 0.000
#> SRR1951634 1 0.0000 0.970 1.000 0.000
#> SRR1951631 1 0.0000 0.970 1.000 0.000
#> SRR1951632 1 0.9922 0.266 0.552 0.448
#> SRR1951633 1 0.0000 0.970 1.000 0.000
#> SRR1951630 1 0.0376 0.969 0.996 0.004
#> SRR1951629 1 0.0000 0.970 1.000 0.000
#> SRR1951628 1 0.0000 0.970 1.000 0.000
#> SRR1951627 1 0.3879 0.936 0.924 0.076
#> SRR1951639 1 0.3879 0.936 0.924 0.076
#> SRR1951625 1 0.3879 0.936 0.924 0.076
#> SRR1951626 1 0.2948 0.949 0.948 0.052
#> SRR1951624 1 0.0000 0.970 1.000 0.000
#> SRR1951622 1 0.0000 0.970 1.000 0.000
#> SRR1951636 1 0.2948 0.949 0.948 0.052
#> SRR1951623 1 0.0000 0.970 1.000 0.000
#> SRR1951621 1 0.0000 0.970 1.000 0.000
#> SRR1951620 1 0.0000 0.970 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.4409 0.855 0.172 0.824 0.004
#> SRR1951666 3 0.6309 0.684 0.496 0.000 0.504
#> SRR1951665 2 0.0000 0.897 0.000 1.000 0.000
#> SRR1951664 3 0.4931 0.803 0.232 0.000 0.768
#> SRR1951663 1 0.2796 0.631 0.908 0.000 0.092
#> SRR1951662 2 0.0000 0.897 0.000 1.000 0.000
#> SRR1951661 3 0.6309 0.684 0.496 0.000 0.504
#> SRR1951660 1 0.6295 0.639 0.528 0.000 0.472
#> SRR1951659 2 0.3482 0.875 0.128 0.872 0.000
#> SRR1951658 2 0.0747 0.897 0.016 0.984 0.000
#> SRR1951657 3 0.5016 0.805 0.240 0.000 0.760
#> SRR1951656 3 0.4931 0.803 0.232 0.000 0.768
#> SRR1951655 1 0.5254 0.682 0.736 0.000 0.264
#> SRR1951654 1 0.4887 0.681 0.772 0.000 0.228
#> SRR1951653 3 0.5016 0.805 0.240 0.000 0.760
#> SRR1951652 1 0.1774 0.532 0.960 0.016 0.024
#> SRR1951651 3 0.5016 0.805 0.240 0.000 0.760
#> SRR1951650 3 0.5810 0.774 0.336 0.000 0.664
#> SRR1951648 3 0.6309 0.684 0.496 0.000 0.504
#> SRR1951649 1 0.6295 0.639 0.528 0.000 0.472
#> SRR1951647 3 0.5465 0.796 0.288 0.000 0.712
#> SRR1951646 3 0.4974 0.805 0.236 0.000 0.764
#> SRR1951645 1 0.5178 0.681 0.744 0.000 0.256
#> SRR1951644 1 0.6295 0.639 0.528 0.000 0.472
#> SRR1951643 3 0.5016 0.805 0.240 0.000 0.760
#> SRR1951641 2 0.2448 0.887 0.076 0.924 0.000
#> SRR1951642 3 0.5016 0.805 0.240 0.000 0.760
#> SRR1951694 1 0.4702 0.676 0.788 0.000 0.212
#> SRR1951731 3 0.6154 0.742 0.408 0.000 0.592
#> SRR1951730 1 0.5115 0.116 0.796 0.016 0.188
#> SRR1951752 1 0.2200 0.495 0.940 0.004 0.056
#> SRR1951751 1 0.6295 0.639 0.528 0.000 0.472
#> SRR1951749 3 0.6309 0.684 0.496 0.000 0.504
#> SRR1951750 1 0.5178 0.681 0.744 0.000 0.256
#> SRR1951748 2 0.0000 0.897 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.897 0.000 1.000 0.000
#> SRR1951745 1 0.2878 0.626 0.904 0.000 0.096
#> SRR1951746 2 0.0000 0.897 0.000 1.000 0.000
#> SRR1951744 3 0.5968 0.765 0.364 0.000 0.636
#> SRR1951742 1 0.6955 -0.677 0.496 0.016 0.488
#> SRR1951743 2 0.0000 0.897 0.000 1.000 0.000
#> SRR1951741 1 0.5178 0.681 0.744 0.000 0.256
#> SRR1951740 3 0.5016 0.805 0.240 0.000 0.760
#> SRR1951738 1 0.3752 0.467 0.856 0.144 0.000
#> SRR1951739 2 0.5404 0.783 0.256 0.740 0.004
#> SRR1951737 3 0.4931 0.803 0.232 0.000 0.768
#> SRR1951736 1 0.6295 0.639 0.528 0.000 0.472
#> SRR1951734 2 0.0000 0.897 0.000 1.000 0.000
#> SRR1951735 3 0.5058 0.806 0.244 0.000 0.756
#> SRR1951733 3 0.6309 0.684 0.496 0.000 0.504
#> SRR1951732 2 0.0000 0.897 0.000 1.000 0.000
#> SRR1951729 3 0.4931 0.803 0.232 0.000 0.768
#> SRR1951728 3 0.5678 0.787 0.316 0.000 0.684
#> SRR1951727 2 0.0000 0.897 0.000 1.000 0.000
#> SRR1951725 1 0.4121 0.666 0.832 0.000 0.168
#> SRR1951726 1 0.0747 0.554 0.984 0.000 0.016
#> SRR1951724 1 0.5216 0.677 0.740 0.000 0.260
#> SRR1951722 3 0.6295 0.189 0.472 0.000 0.528
#> SRR1951723 1 0.5327 0.663 0.728 0.000 0.272
#> SRR1951721 1 0.2356 0.620 0.928 0.000 0.072
#> SRR1951719 3 0.4974 0.804 0.236 0.000 0.764
#> SRR1951720 1 0.5178 0.681 0.744 0.000 0.256
#> SRR1951718 1 0.6295 0.639 0.528 0.000 0.472
#> SRR1951717 1 0.5178 0.681 0.744 0.000 0.256
#> SRR1951715 1 0.2878 0.633 0.904 0.000 0.096
#> SRR1951716 1 0.6295 0.639 0.528 0.000 0.472
#> SRR1951714 3 0.5016 0.805 0.240 0.000 0.760
#> SRR1951713 3 0.5785 0.651 0.332 0.000 0.668
#> SRR1951712 1 0.6295 0.639 0.528 0.000 0.472
#> SRR1951711 2 0.4235 0.855 0.176 0.824 0.000
#> SRR1951710 1 0.5098 0.682 0.752 0.000 0.248
#> SRR1951709 3 0.6309 0.684 0.496 0.000 0.504
#> SRR1951708 2 0.1031 0.896 0.024 0.976 0.000
#> SRR1951707 3 0.6126 0.747 0.400 0.000 0.600
#> SRR1951705 1 0.6295 0.639 0.528 0.000 0.472
#> SRR1951704 3 0.6308 0.688 0.492 0.000 0.508
#> SRR1951703 2 0.0000 0.897 0.000 1.000 0.000
#> SRR1951706 3 0.6309 0.684 0.496 0.000 0.504
#> SRR1951702 3 0.6308 0.688 0.492 0.000 0.508
#> SRR1951701 2 0.3112 0.883 0.096 0.900 0.004
#> SRR1951699 3 0.6309 0.684 0.496 0.000 0.504
#> SRR1951698 3 0.6308 0.688 0.492 0.000 0.508
#> SRR1951700 3 0.6215 0.372 0.428 0.000 0.572
#> SRR1951697 2 0.4351 0.857 0.168 0.828 0.004
#> SRR1951696 3 0.4931 0.803 0.232 0.000 0.768
#> SRR1951695 3 0.5016 0.805 0.240 0.000 0.760
#> SRR1951693 3 0.5431 0.745 0.284 0.000 0.716
#> SRR1951692 3 0.5016 0.805 0.240 0.000 0.760
#> SRR1951691 1 0.5178 0.681 0.744 0.000 0.256
#> SRR1951688 1 0.3551 0.454 0.868 0.132 0.000
#> SRR1951689 1 0.0747 0.558 0.984 0.016 0.000
#> SRR1951687 1 0.5178 0.681 0.744 0.000 0.256
#> SRR1951685 1 0.6267 -0.620 0.548 0.000 0.452
#> SRR1951684 3 0.4931 0.803 0.232 0.000 0.768
#> SRR1951686 3 0.7295 0.665 0.484 0.028 0.488
#> SRR1951683 3 0.5138 0.792 0.252 0.000 0.748
#> SRR1951682 2 0.5058 0.800 0.244 0.756 0.000
#> SRR1951690 2 0.4178 0.857 0.172 0.828 0.000
#> SRR1951680 3 0.5016 0.805 0.240 0.000 0.760
#> SRR1951679 1 0.6295 0.639 0.528 0.000 0.472
#> SRR1951678 3 0.5016 0.805 0.240 0.000 0.760
#> SRR1951677 3 0.5016 0.805 0.240 0.000 0.760
#> SRR1951676 3 0.6008 0.761 0.372 0.000 0.628
#> SRR1951675 2 0.5882 0.653 0.348 0.652 0.000
#> SRR1951681 3 0.5138 0.806 0.252 0.000 0.748
#> SRR1951673 1 0.0747 0.558 0.984 0.016 0.000
#> SRR1951674 1 0.3116 0.634 0.892 0.000 0.108
#> SRR1951672 1 0.6252 0.648 0.556 0.000 0.444
#> SRR1951671 1 0.6955 -0.677 0.496 0.016 0.488
#> SRR1951670 1 0.5178 0.681 0.744 0.000 0.256
#> SRR1951640 3 0.4931 0.803 0.232 0.000 0.768
#> SRR1951668 1 0.5327 0.663 0.728 0.000 0.272
#> SRR1951669 1 0.0747 0.558 0.984 0.016 0.000
#> SRR1951638 1 0.5178 0.681 0.744 0.000 0.256
#> SRR1951637 1 0.0237 0.565 0.996 0.004 0.000
#> SRR1951635 1 0.5178 0.681 0.744 0.000 0.256
#> SRR1951634 3 0.5650 0.694 0.312 0.000 0.688
#> SRR1951631 3 0.5016 0.805 0.240 0.000 0.760
#> SRR1951632 2 0.8765 0.409 0.380 0.504 0.116
#> SRR1951633 3 0.5254 0.803 0.264 0.000 0.736
#> SRR1951630 3 0.5016 0.805 0.240 0.000 0.760
#> SRR1951629 3 0.5016 0.805 0.240 0.000 0.760
#> SRR1951628 1 0.5138 0.684 0.748 0.000 0.252
#> SRR1951627 3 0.6309 0.684 0.496 0.000 0.504
#> SRR1951639 3 0.6305 0.694 0.484 0.000 0.516
#> SRR1951625 3 0.6683 0.680 0.492 0.008 0.500
#> SRR1951626 3 0.6215 0.731 0.428 0.000 0.572
#> SRR1951624 3 0.5016 0.805 0.240 0.000 0.760
#> SRR1951622 1 0.6295 0.639 0.528 0.000 0.472
#> SRR1951636 3 0.6302 0.696 0.480 0.000 0.520
#> SRR1951623 3 0.6305 0.694 0.484 0.000 0.516
#> SRR1951621 1 0.5178 0.681 0.744 0.000 0.256
#> SRR1951620 3 0.4931 0.803 0.232 0.000 0.768
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.1302 0.881 0.000 0.956 0.044 0.000
#> SRR1951666 3 0.1302 0.799 0.000 0.044 0.956 0.000
#> SRR1951665 2 0.1474 0.870 0.052 0.948 0.000 0.000
#> SRR1951664 3 0.4070 0.852 0.044 0.000 0.824 0.132
#> SRR1951663 1 0.2739 0.884 0.904 0.036 0.060 0.000
#> SRR1951662 2 0.1474 0.870 0.052 0.948 0.000 0.000
#> SRR1951661 3 0.1118 0.805 0.000 0.036 0.964 0.000
#> SRR1951660 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> SRR1951659 2 0.1004 0.876 0.024 0.972 0.004 0.000
#> SRR1951658 2 0.1118 0.871 0.036 0.964 0.000 0.000
#> SRR1951657 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951656 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951655 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> SRR1951654 1 0.4160 0.705 0.792 0.012 0.004 0.192
#> SRR1951653 3 0.3444 0.849 0.000 0.000 0.816 0.184
#> SRR1951652 2 0.3400 0.819 0.000 0.820 0.180 0.000
#> SRR1951651 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951650 3 0.2284 0.823 0.036 0.012 0.932 0.020
#> SRR1951648 3 0.1118 0.805 0.000 0.036 0.964 0.000
#> SRR1951649 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> SRR1951647 3 0.3279 0.842 0.000 0.032 0.872 0.096
#> SRR1951646 3 0.4017 0.852 0.044 0.000 0.828 0.128
#> SRR1951645 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> SRR1951644 4 0.0188 0.956 0.004 0.000 0.000 0.996
#> SRR1951643 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951641 2 0.1004 0.876 0.024 0.972 0.004 0.000
#> SRR1951642 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951694 4 0.0524 0.947 0.004 0.000 0.008 0.988
#> SRR1951731 3 0.1305 0.807 0.000 0.036 0.960 0.004
#> SRR1951730 2 0.3400 0.819 0.000 0.820 0.180 0.000
#> SRR1951752 3 0.4977 -0.169 0.000 0.460 0.540 0.000
#> SRR1951751 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> SRR1951749 3 0.1118 0.805 0.000 0.036 0.964 0.000
#> SRR1951750 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> SRR1951748 2 0.1474 0.870 0.052 0.948 0.000 0.000
#> SRR1951747 2 0.1474 0.870 0.052 0.948 0.000 0.000
#> SRR1951745 1 0.2739 0.884 0.904 0.036 0.060 0.000
#> SRR1951746 2 0.1211 0.871 0.040 0.960 0.000 0.000
#> SRR1951744 3 0.3557 0.844 0.000 0.036 0.856 0.108
#> SRR1951742 2 0.3975 0.743 0.000 0.760 0.240 0.000
#> SRR1951743 2 0.1211 0.871 0.040 0.960 0.000 0.000
#> SRR1951741 4 0.0524 0.948 0.004 0.000 0.008 0.988
#> SRR1951740 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951738 2 0.2704 0.855 0.000 0.876 0.124 0.000
#> SRR1951739 2 0.1389 0.880 0.000 0.952 0.048 0.000
#> SRR1951737 3 0.4070 0.852 0.044 0.000 0.824 0.132
#> SRR1951736 4 0.0188 0.956 0.004 0.000 0.000 0.996
#> SRR1951734 2 0.1211 0.871 0.040 0.960 0.000 0.000
#> SRR1951735 3 0.4070 0.852 0.044 0.000 0.824 0.132
#> SRR1951733 3 0.1118 0.805 0.000 0.036 0.964 0.000
#> SRR1951732 2 0.1474 0.870 0.052 0.948 0.000 0.000
#> SRR1951729 3 0.4070 0.852 0.044 0.000 0.824 0.132
#> SRR1951728 3 0.2739 0.831 0.000 0.036 0.904 0.060
#> SRR1951727 2 0.1474 0.870 0.052 0.948 0.000 0.000
#> SRR1951725 1 0.3805 0.790 0.832 0.012 0.008 0.148
#> SRR1951726 3 0.1118 0.805 0.000 0.036 0.964 0.000
#> SRR1951724 4 0.0188 0.953 0.000 0.000 0.004 0.996
#> SRR1951722 3 0.5039 0.557 0.000 0.004 0.592 0.404
#> SRR1951723 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> SRR1951721 1 0.2816 0.881 0.900 0.036 0.064 0.000
#> SRR1951719 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951720 4 0.0336 0.955 0.008 0.000 0.000 0.992
#> SRR1951718 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> SRR1951717 4 0.0336 0.955 0.008 0.000 0.000 0.992
#> SRR1951715 1 0.5994 0.777 0.724 0.036 0.060 0.180
#> SRR1951716 4 0.0188 0.956 0.004 0.000 0.000 0.996
#> SRR1951714 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951713 3 0.4907 0.519 0.000 0.000 0.580 0.420
#> SRR1951712 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> SRR1951711 2 0.1211 0.882 0.000 0.960 0.040 0.000
#> SRR1951710 4 0.0672 0.946 0.008 0.000 0.008 0.984
#> SRR1951709 3 0.1118 0.805 0.000 0.036 0.964 0.000
#> SRR1951708 2 0.1305 0.872 0.036 0.960 0.004 0.000
#> SRR1951707 3 0.1452 0.807 0.000 0.036 0.956 0.008
#> SRR1951705 4 0.0188 0.956 0.004 0.000 0.000 0.996
#> SRR1951704 3 0.1118 0.805 0.000 0.036 0.964 0.000
#> SRR1951703 2 0.1389 0.870 0.048 0.952 0.000 0.000
#> SRR1951706 3 0.1118 0.805 0.000 0.036 0.964 0.000
#> SRR1951702 3 0.1118 0.805 0.000 0.036 0.964 0.000
#> SRR1951701 2 0.1004 0.876 0.024 0.972 0.004 0.000
#> SRR1951699 3 0.1118 0.805 0.000 0.036 0.964 0.000
#> SRR1951698 3 0.1118 0.805 0.000 0.036 0.964 0.000
#> SRR1951700 3 0.4454 0.731 0.000 0.000 0.692 0.308
#> SRR1951697 2 0.1118 0.882 0.000 0.964 0.036 0.000
#> SRR1951696 3 0.4070 0.852 0.044 0.000 0.824 0.132
#> SRR1951695 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951693 3 0.3444 0.851 0.000 0.000 0.816 0.184
#> SRR1951692 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951691 4 0.0336 0.950 0.000 0.000 0.008 0.992
#> SRR1951688 2 0.3123 0.836 0.000 0.844 0.156 0.000
#> SRR1951689 2 0.3356 0.822 0.000 0.824 0.176 0.000
#> SRR1951687 3 0.4607 0.769 0.004 0.004 0.716 0.276
#> SRR1951685 3 0.1584 0.801 0.000 0.036 0.952 0.012
#> SRR1951684 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951686 2 0.2760 0.853 0.000 0.872 0.128 0.000
#> SRR1951683 3 0.3569 0.843 0.000 0.000 0.804 0.196
#> SRR1951682 2 0.1389 0.880 0.000 0.952 0.048 0.000
#> SRR1951690 2 0.1211 0.882 0.000 0.960 0.040 0.000
#> SRR1951680 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951679 4 0.0376 0.952 0.004 0.000 0.004 0.992
#> SRR1951678 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951677 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951676 3 0.1677 0.830 0.000 0.012 0.948 0.040
#> SRR1951675 2 0.1302 0.881 0.000 0.956 0.044 0.000
#> SRR1951681 3 0.3279 0.850 0.032 0.000 0.872 0.096
#> SRR1951673 2 0.3852 0.812 0.000 0.808 0.180 0.012
#> SRR1951674 1 0.2739 0.884 0.904 0.036 0.060 0.000
#> SRR1951672 4 0.0188 0.956 0.004 0.000 0.000 0.996
#> SRR1951671 2 0.3400 0.819 0.000 0.820 0.180 0.000
#> SRR1951670 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> SRR1951640 3 0.4070 0.852 0.044 0.000 0.824 0.132
#> SRR1951668 4 0.1211 0.906 0.000 0.000 0.040 0.960
#> SRR1951669 2 0.2714 0.859 0.004 0.884 0.112 0.000
#> SRR1951638 4 0.5161 0.020 0.476 0.004 0.000 0.520
#> SRR1951637 2 0.6324 0.651 0.168 0.660 0.172 0.000
#> SRR1951635 4 0.4925 0.197 0.428 0.000 0.000 0.572
#> SRR1951634 3 0.4776 0.603 0.000 0.000 0.624 0.376
#> SRR1951631 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951632 2 0.3356 0.822 0.000 0.824 0.176 0.000
#> SRR1951633 3 0.3539 0.854 0.000 0.004 0.820 0.176
#> SRR1951630 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951629 3 0.3356 0.854 0.000 0.000 0.824 0.176
#> SRR1951628 4 0.0524 0.947 0.004 0.000 0.008 0.988
#> SRR1951627 3 0.1118 0.805 0.000 0.036 0.964 0.000
#> SRR1951639 3 0.1118 0.805 0.000 0.036 0.964 0.000
#> SRR1951625 2 0.4643 0.562 0.000 0.656 0.344 0.000
#> SRR1951626 3 0.1118 0.805 0.000 0.036 0.964 0.000
#> SRR1951624 3 0.3850 0.852 0.044 0.000 0.840 0.116
#> SRR1951622 4 0.0188 0.956 0.004 0.000 0.000 0.996
#> SRR1951636 3 0.1118 0.805 0.000 0.036 0.964 0.000
#> SRR1951623 3 0.1118 0.805 0.000 0.036 0.964 0.000
#> SRR1951621 4 0.0188 0.955 0.004 0.000 0.000 0.996
#> SRR1951620 3 0.4070 0.852 0.044 0.000 0.824 0.132
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.2338 0.889 0.000 0.884 0.004 0.000 0.112
#> SRR1951666 5 0.2286 0.901 0.000 0.000 0.004 0.108 0.888
#> SRR1951665 2 0.0880 0.882 0.000 0.968 0.032 0.000 0.000
#> SRR1951664 4 0.0510 0.944 0.000 0.000 0.000 0.984 0.016
#> SRR1951663 3 0.1106 0.879 0.024 0.000 0.964 0.000 0.012
#> SRR1951662 2 0.0880 0.882 0.000 0.968 0.032 0.000 0.000
#> SRR1951661 5 0.2286 0.901 0.000 0.000 0.004 0.108 0.888
#> SRR1951660 1 0.0703 0.921 0.976 0.000 0.000 0.024 0.000
#> SRR1951659 2 0.1851 0.895 0.000 0.912 0.000 0.000 0.088
#> SRR1951658 2 0.1851 0.895 0.000 0.912 0.000 0.000 0.088
#> SRR1951657 4 0.0579 0.946 0.008 0.000 0.000 0.984 0.008
#> SRR1951656 4 0.0693 0.946 0.008 0.000 0.000 0.980 0.012
#> SRR1951655 1 0.0703 0.921 0.976 0.000 0.000 0.024 0.000
#> SRR1951654 1 0.5611 0.550 0.616 0.000 0.284 0.096 0.004
#> SRR1951653 4 0.1469 0.943 0.036 0.000 0.000 0.948 0.016
#> SRR1951652 5 0.2906 0.879 0.000 0.012 0.028 0.080 0.880
#> SRR1951651 4 0.1270 0.929 0.052 0.000 0.000 0.948 0.000
#> SRR1951650 4 0.0510 0.944 0.000 0.000 0.000 0.984 0.016
#> SRR1951648 5 0.2127 0.902 0.000 0.000 0.000 0.108 0.892
#> SRR1951649 1 0.0703 0.921 0.976 0.000 0.000 0.024 0.000
#> SRR1951647 5 0.3876 0.647 0.000 0.000 0.000 0.316 0.684
#> SRR1951646 4 0.0510 0.944 0.000 0.000 0.000 0.984 0.016
#> SRR1951645 1 0.0880 0.917 0.968 0.000 0.000 0.032 0.000
#> SRR1951644 1 0.0703 0.921 0.976 0.000 0.000 0.024 0.000
#> SRR1951643 4 0.1041 0.941 0.032 0.000 0.000 0.964 0.004
#> SRR1951641 2 0.2011 0.894 0.000 0.908 0.004 0.000 0.088
#> SRR1951642 4 0.1243 0.942 0.028 0.000 0.004 0.960 0.008
#> SRR1951694 1 0.1522 0.891 0.944 0.000 0.044 0.000 0.012
#> SRR1951731 5 0.2280 0.897 0.000 0.000 0.000 0.120 0.880
#> SRR1951730 5 0.1106 0.824 0.000 0.012 0.000 0.024 0.964
#> SRR1951752 5 0.2464 0.890 0.000 0.012 0.004 0.092 0.892
#> SRR1951751 1 0.0703 0.921 0.976 0.000 0.000 0.024 0.000
#> SRR1951749 5 0.2127 0.902 0.000 0.000 0.000 0.108 0.892
#> SRR1951750 1 0.1564 0.916 0.948 0.000 0.024 0.024 0.004
#> SRR1951748 2 0.0880 0.882 0.000 0.968 0.032 0.000 0.000
#> SRR1951747 2 0.0880 0.882 0.000 0.968 0.032 0.000 0.000
#> SRR1951745 3 0.1106 0.879 0.024 0.000 0.964 0.000 0.012
#> SRR1951746 2 0.0880 0.882 0.000 0.968 0.032 0.000 0.000
#> SRR1951744 4 0.0671 0.942 0.000 0.000 0.004 0.980 0.016
#> SRR1951742 5 0.1197 0.859 0.000 0.000 0.000 0.048 0.952
#> SRR1951743 2 0.0880 0.882 0.000 0.968 0.032 0.000 0.000
#> SRR1951741 1 0.4228 0.799 0.788 0.000 0.108 0.100 0.004
#> SRR1951740 4 0.0955 0.944 0.028 0.000 0.000 0.968 0.004
#> SRR1951738 2 0.4445 0.629 0.000 0.676 0.024 0.000 0.300
#> SRR1951739 2 0.2629 0.873 0.000 0.860 0.004 0.000 0.136
#> SRR1951737 4 0.0510 0.944 0.000 0.000 0.000 0.984 0.016
#> SRR1951736 1 0.0703 0.921 0.976 0.000 0.000 0.024 0.000
#> SRR1951734 2 0.0880 0.882 0.000 0.968 0.032 0.000 0.000
#> SRR1951735 4 0.0798 0.946 0.008 0.000 0.000 0.976 0.016
#> SRR1951733 5 0.2020 0.900 0.000 0.000 0.000 0.100 0.900
#> SRR1951732 2 0.0880 0.882 0.000 0.968 0.032 0.000 0.000
#> SRR1951729 4 0.0510 0.944 0.000 0.000 0.000 0.984 0.016
#> SRR1951728 5 0.2929 0.839 0.000 0.000 0.000 0.180 0.820
#> SRR1951727 2 0.0880 0.882 0.000 0.968 0.032 0.000 0.000
#> SRR1951725 3 0.3690 0.653 0.224 0.000 0.764 0.000 0.012
#> SRR1951726 5 0.2570 0.885 0.000 0.000 0.028 0.084 0.888
#> SRR1951724 1 0.2280 0.829 0.880 0.000 0.000 0.120 0.000
#> SRR1951722 1 0.4349 0.682 0.756 0.000 0.000 0.176 0.068
#> SRR1951723 1 0.0703 0.921 0.976 0.000 0.000 0.024 0.000
#> SRR1951721 3 0.1106 0.879 0.024 0.000 0.964 0.000 0.012
#> SRR1951719 4 0.0798 0.946 0.008 0.000 0.000 0.976 0.016
#> SRR1951720 1 0.2125 0.906 0.920 0.000 0.052 0.024 0.004
#> SRR1951718 1 0.0703 0.921 0.976 0.000 0.000 0.024 0.000
#> SRR1951717 1 0.2053 0.908 0.924 0.000 0.048 0.024 0.004
#> SRR1951715 3 0.1195 0.877 0.028 0.000 0.960 0.000 0.012
#> SRR1951716 1 0.0703 0.921 0.976 0.000 0.000 0.024 0.000
#> SRR1951714 4 0.1331 0.938 0.040 0.000 0.000 0.952 0.008
#> SRR1951713 4 0.2848 0.829 0.156 0.000 0.000 0.840 0.004
#> SRR1951712 1 0.0703 0.921 0.976 0.000 0.000 0.024 0.000
#> SRR1951711 2 0.1851 0.895 0.000 0.912 0.000 0.000 0.088
#> SRR1951710 1 0.1628 0.886 0.936 0.000 0.056 0.000 0.008
#> SRR1951709 5 0.2127 0.902 0.000 0.000 0.000 0.108 0.892
#> SRR1951708 2 0.1851 0.895 0.000 0.912 0.000 0.000 0.088
#> SRR1951707 5 0.2230 0.899 0.000 0.000 0.000 0.116 0.884
#> SRR1951705 1 0.0000 0.908 1.000 0.000 0.000 0.000 0.000
#> SRR1951704 5 0.2286 0.901 0.000 0.000 0.004 0.108 0.888
#> SRR1951703 2 0.0880 0.882 0.000 0.968 0.032 0.000 0.000
#> SRR1951706 5 0.2416 0.898 0.000 0.000 0.012 0.100 0.888
#> SRR1951702 5 0.2286 0.901 0.000 0.000 0.004 0.108 0.888
#> SRR1951701 2 0.2338 0.889 0.000 0.884 0.004 0.000 0.112
#> SRR1951699 5 0.2286 0.901 0.000 0.000 0.004 0.108 0.888
#> SRR1951698 5 0.2230 0.899 0.000 0.000 0.000 0.116 0.884
#> SRR1951700 4 0.3607 0.709 0.244 0.000 0.000 0.752 0.004
#> SRR1951697 2 0.1908 0.894 0.000 0.908 0.000 0.000 0.092
#> SRR1951696 4 0.0510 0.944 0.000 0.000 0.000 0.984 0.016
#> SRR1951695 4 0.0798 0.946 0.008 0.000 0.000 0.976 0.016
#> SRR1951693 4 0.1892 0.907 0.080 0.000 0.000 0.916 0.004
#> SRR1951692 4 0.0451 0.945 0.008 0.000 0.000 0.988 0.004
#> SRR1951691 1 0.2741 0.804 0.860 0.000 0.004 0.132 0.004
#> SRR1951688 2 0.4465 0.623 0.000 0.672 0.024 0.000 0.304
#> SRR1951689 5 0.3722 0.640 0.000 0.176 0.004 0.024 0.796
#> SRR1951687 4 0.4781 0.682 0.188 0.000 0.080 0.728 0.004
#> SRR1951685 5 0.2286 0.901 0.000 0.000 0.004 0.108 0.888
#> SRR1951684 4 0.0798 0.946 0.008 0.000 0.000 0.976 0.016
#> SRR1951686 5 0.1661 0.809 0.000 0.036 0.000 0.024 0.940
#> SRR1951683 4 0.2719 0.839 0.144 0.000 0.000 0.852 0.004
#> SRR1951682 2 0.2338 0.889 0.000 0.884 0.004 0.000 0.112
#> SRR1951690 2 0.2338 0.889 0.000 0.884 0.004 0.000 0.112
#> SRR1951680 4 0.1082 0.945 0.028 0.000 0.000 0.964 0.008
#> SRR1951679 1 0.0290 0.903 0.992 0.000 0.000 0.000 0.008
#> SRR1951678 4 0.1116 0.941 0.028 0.000 0.004 0.964 0.004
#> SRR1951677 4 0.0932 0.944 0.020 0.000 0.004 0.972 0.004
#> SRR1951676 4 0.0955 0.939 0.000 0.000 0.004 0.968 0.028
#> SRR1951675 2 0.2628 0.884 0.000 0.884 0.028 0.000 0.088
#> SRR1951681 4 0.0703 0.941 0.000 0.000 0.000 0.976 0.024
#> SRR1951673 5 0.3645 0.624 0.000 0.168 0.024 0.004 0.804
#> SRR1951674 3 0.1106 0.879 0.024 0.000 0.964 0.000 0.012
#> SRR1951672 1 0.0703 0.921 0.976 0.000 0.000 0.024 0.000
#> SRR1951671 5 0.0566 0.794 0.000 0.012 0.004 0.000 0.984
#> SRR1951670 1 0.1739 0.914 0.940 0.000 0.032 0.024 0.004
#> SRR1951640 4 0.0510 0.944 0.000 0.000 0.000 0.984 0.016
#> SRR1951668 4 0.2852 0.810 0.172 0.000 0.000 0.828 0.000
#> SRR1951669 2 0.5227 0.570 0.000 0.676 0.116 0.000 0.208
#> SRR1951638 1 0.2629 0.828 0.860 0.000 0.136 0.000 0.004
#> SRR1951637 3 0.6635 0.284 0.004 0.284 0.484 0.000 0.228
#> SRR1951635 1 0.3861 0.627 0.712 0.000 0.284 0.000 0.004
#> SRR1951634 4 0.2629 0.852 0.136 0.000 0.000 0.860 0.004
#> SRR1951631 4 0.1205 0.939 0.040 0.000 0.000 0.956 0.004
#> SRR1951632 5 0.3884 0.406 0.000 0.288 0.004 0.000 0.708
#> SRR1951633 4 0.0740 0.945 0.008 0.000 0.004 0.980 0.008
#> SRR1951630 4 0.0865 0.944 0.024 0.000 0.000 0.972 0.004
#> SRR1951629 4 0.0671 0.946 0.016 0.000 0.000 0.980 0.004
#> SRR1951628 1 0.0290 0.903 0.992 0.000 0.000 0.000 0.008
#> SRR1951627 5 0.2286 0.901 0.000 0.000 0.004 0.108 0.888
#> SRR1951639 5 0.2230 0.899 0.000 0.000 0.000 0.116 0.884
#> SRR1951625 5 0.1267 0.822 0.000 0.012 0.004 0.024 0.960
#> SRR1951626 5 0.2424 0.889 0.000 0.000 0.000 0.132 0.868
#> SRR1951624 4 0.0510 0.944 0.000 0.000 0.000 0.984 0.016
#> SRR1951622 1 0.0162 0.911 0.996 0.000 0.000 0.004 0.000
#> SRR1951636 5 0.2179 0.901 0.000 0.000 0.000 0.112 0.888
#> SRR1951623 5 0.4350 0.445 0.000 0.000 0.004 0.408 0.588
#> SRR1951621 1 0.2751 0.889 0.888 0.000 0.056 0.052 0.004
#> SRR1951620 4 0.0510 0.944 0.000 0.000 0.000 0.984 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 2 0.0146 0.8392 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1951666 5 0.0146 0.8641 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1951665 2 0.4111 0.8065 0.000 0.748 0.108 0.000 0.000 0.144
#> SRR1951664 3 0.1910 0.8024 0.000 0.000 0.892 0.108 0.000 0.000
#> SRR1951663 6 0.3026 0.9789 0.024 0.000 0.000 0.092 0.028 0.856
#> SRR1951662 2 0.4111 0.8065 0.000 0.748 0.108 0.000 0.000 0.144
#> SRR1951661 5 0.0692 0.8633 0.000 0.000 0.020 0.004 0.976 0.000
#> SRR1951660 1 0.0632 0.8895 0.976 0.000 0.000 0.024 0.000 0.000
#> SRR1951659 2 0.0146 0.8392 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1951658 2 0.0146 0.8392 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1951657 4 0.3547 0.6167 0.000 0.000 0.332 0.668 0.000 0.000
#> SRR1951656 4 0.3547 0.6167 0.000 0.000 0.332 0.668 0.000 0.000
#> SRR1951655 1 0.1204 0.8761 0.944 0.000 0.000 0.056 0.000 0.000
#> SRR1951654 4 0.0951 0.7771 0.020 0.000 0.000 0.968 0.008 0.004
#> SRR1951653 3 0.4648 0.3153 0.040 0.000 0.584 0.372 0.004 0.000
#> SRR1951652 5 0.1714 0.8424 0.000 0.000 0.000 0.092 0.908 0.000
#> SRR1951651 4 0.1863 0.8272 0.000 0.000 0.104 0.896 0.000 0.000
#> SRR1951650 3 0.2006 0.8020 0.000 0.000 0.892 0.104 0.004 0.000
#> SRR1951648 5 0.0146 0.8641 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1951649 1 0.0790 0.8874 0.968 0.000 0.000 0.032 0.000 0.000
#> SRR1951647 5 0.3828 0.2709 0.000 0.000 0.440 0.000 0.560 0.000
#> SRR1951646 3 0.1910 0.8024 0.000 0.000 0.892 0.108 0.000 0.000
#> SRR1951645 1 0.2527 0.7656 0.832 0.000 0.000 0.168 0.000 0.000
#> SRR1951644 1 0.0547 0.8884 0.980 0.000 0.000 0.020 0.000 0.000
#> SRR1951643 4 0.1863 0.8272 0.000 0.000 0.104 0.896 0.000 0.000
#> SRR1951641 2 0.0146 0.8392 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1951642 4 0.1531 0.8205 0.000 0.000 0.068 0.928 0.004 0.000
#> SRR1951694 1 0.2924 0.8180 0.868 0.000 0.000 0.068 0.024 0.040
#> SRR1951731 5 0.3499 0.5541 0.000 0.000 0.320 0.000 0.680 0.000
#> SRR1951730 5 0.2121 0.8589 0.000 0.096 0.000 0.012 0.892 0.000
#> SRR1951752 5 0.2176 0.8535 0.000 0.024 0.000 0.080 0.896 0.000
#> SRR1951751 1 0.0632 0.8895 0.976 0.000 0.000 0.024 0.000 0.000
#> SRR1951749 5 0.0291 0.8639 0.000 0.004 0.004 0.000 0.992 0.000
#> SRR1951750 4 0.3151 0.5215 0.252 0.000 0.000 0.748 0.000 0.000
#> SRR1951748 2 0.4111 0.8065 0.000 0.748 0.108 0.000 0.000 0.144
#> SRR1951747 2 0.4111 0.8065 0.000 0.748 0.108 0.000 0.000 0.144
#> SRR1951745 6 0.3026 0.9789 0.024 0.000 0.000 0.092 0.028 0.856
#> SRR1951746 2 0.4067 0.8074 0.000 0.752 0.104 0.000 0.000 0.144
#> SRR1951744 3 0.3586 0.7291 0.000 0.000 0.756 0.216 0.028 0.000
#> SRR1951742 5 0.0146 0.8634 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951743 2 0.4030 0.8079 0.000 0.756 0.104 0.000 0.000 0.140
#> SRR1951741 4 0.0692 0.7787 0.020 0.000 0.000 0.976 0.004 0.000
#> SRR1951740 4 0.3547 0.6167 0.000 0.000 0.332 0.668 0.000 0.000
#> SRR1951738 2 0.3314 0.5888 0.000 0.764 0.000 0.012 0.224 0.000
#> SRR1951739 2 0.2454 0.6832 0.000 0.840 0.000 0.000 0.160 0.000
#> SRR1951737 3 0.1910 0.8024 0.000 0.000 0.892 0.108 0.000 0.000
#> SRR1951736 1 0.0632 0.8895 0.976 0.000 0.000 0.024 0.000 0.000
#> SRR1951734 2 0.4067 0.8074 0.000 0.752 0.104 0.000 0.000 0.144
#> SRR1951735 3 0.2135 0.7953 0.000 0.000 0.872 0.128 0.000 0.000
#> SRR1951733 5 0.1908 0.8617 0.000 0.096 0.004 0.000 0.900 0.000
#> SRR1951732 2 0.4111 0.8065 0.000 0.748 0.108 0.000 0.000 0.144
#> SRR1951729 3 0.1910 0.8024 0.000 0.000 0.892 0.108 0.000 0.000
#> SRR1951728 3 0.3765 0.2236 0.000 0.000 0.596 0.000 0.404 0.000
#> SRR1951727 2 0.4111 0.8065 0.000 0.748 0.108 0.000 0.000 0.144
#> SRR1951725 1 0.5062 0.4735 0.640 0.000 0.000 0.092 0.012 0.256
#> SRR1951726 5 0.1958 0.8394 0.000 0.000 0.000 0.100 0.896 0.004
#> SRR1951724 1 0.2333 0.8231 0.884 0.000 0.092 0.024 0.000 0.000
#> SRR1951722 1 0.3196 0.7596 0.828 0.000 0.108 0.000 0.064 0.000
#> SRR1951723 1 0.0632 0.8895 0.976 0.000 0.000 0.024 0.000 0.000
#> SRR1951721 6 0.3026 0.9789 0.024 0.000 0.000 0.092 0.028 0.856
#> SRR1951719 3 0.3765 0.2715 0.000 0.000 0.596 0.404 0.000 0.000
#> SRR1951720 1 0.2003 0.8530 0.884 0.000 0.000 0.116 0.000 0.000
#> SRR1951718 1 0.1387 0.8678 0.932 0.000 0.000 0.068 0.000 0.000
#> SRR1951717 1 0.2146 0.8508 0.880 0.000 0.000 0.116 0.004 0.000
#> SRR1951715 6 0.4151 0.9115 0.100 0.000 0.000 0.092 0.028 0.780
#> SRR1951716 1 0.0632 0.8895 0.976 0.000 0.000 0.024 0.000 0.000
#> SRR1951714 4 0.4009 0.5373 0.004 0.000 0.356 0.632 0.008 0.000
#> SRR1951713 4 0.4101 0.6251 0.028 0.000 0.308 0.664 0.000 0.000
#> SRR1951712 1 0.0632 0.8895 0.976 0.000 0.000 0.024 0.000 0.000
#> SRR1951711 2 0.0146 0.8392 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1951710 1 0.1788 0.8576 0.928 0.000 0.000 0.028 0.004 0.040
#> SRR1951709 5 0.0146 0.8641 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1951708 2 0.0291 0.8390 0.000 0.992 0.000 0.000 0.004 0.004
#> SRR1951707 5 0.2768 0.7760 0.000 0.000 0.156 0.012 0.832 0.000
#> SRR1951705 1 0.0000 0.8786 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951704 5 0.2129 0.8469 0.000 0.000 0.040 0.056 0.904 0.000
#> SRR1951703 2 0.4111 0.8065 0.000 0.748 0.108 0.000 0.000 0.144
#> SRR1951706 5 0.1858 0.8421 0.000 0.000 0.004 0.092 0.904 0.000
#> SRR1951702 5 0.2129 0.8425 0.000 0.000 0.056 0.040 0.904 0.000
#> SRR1951701 2 0.0146 0.8392 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1951699 5 0.0146 0.8641 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1951698 3 0.3288 0.5778 0.000 0.000 0.724 0.000 0.276 0.000
#> SRR1951700 1 0.5740 0.2694 0.548 0.000 0.264 0.180 0.008 0.000
#> SRR1951697 2 0.0146 0.8392 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1951696 3 0.1910 0.8024 0.000 0.000 0.892 0.108 0.000 0.000
#> SRR1951695 3 0.2851 0.7908 0.004 0.000 0.844 0.132 0.020 0.000
#> SRR1951693 3 0.6329 0.0178 0.328 0.000 0.360 0.304 0.008 0.000
#> SRR1951692 4 0.2178 0.8180 0.000 0.000 0.132 0.868 0.000 0.000
#> SRR1951691 1 0.3710 0.7536 0.788 0.000 0.064 0.144 0.004 0.000
#> SRR1951688 5 0.3342 0.7375 0.000 0.228 0.000 0.012 0.760 0.000
#> SRR1951689 5 0.2121 0.8589 0.000 0.096 0.000 0.012 0.892 0.000
#> SRR1951687 4 0.0748 0.7775 0.016 0.000 0.000 0.976 0.004 0.004
#> SRR1951685 5 0.2214 0.8619 0.000 0.092 0.004 0.012 0.892 0.000
#> SRR1951684 3 0.3499 0.5169 0.000 0.000 0.680 0.320 0.000 0.000
#> SRR1951686 5 0.2664 0.7656 0.000 0.184 0.000 0.000 0.816 0.000
#> SRR1951683 4 0.2070 0.8279 0.012 0.000 0.092 0.896 0.000 0.000
#> SRR1951682 2 0.0603 0.8293 0.000 0.980 0.000 0.004 0.016 0.000
#> SRR1951690 2 0.0146 0.8392 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1951680 4 0.3547 0.6167 0.000 0.000 0.332 0.668 0.000 0.000
#> SRR1951679 1 0.0260 0.8775 0.992 0.000 0.000 0.000 0.000 0.008
#> SRR1951678 4 0.1765 0.8287 0.000 0.000 0.096 0.904 0.000 0.000
#> SRR1951677 4 0.1501 0.8251 0.000 0.000 0.076 0.924 0.000 0.000
#> SRR1951676 3 0.2872 0.7894 0.000 0.000 0.836 0.140 0.024 0.000
#> SRR1951675 2 0.0146 0.8392 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1951681 3 0.2450 0.8006 0.000 0.000 0.868 0.116 0.016 0.000
#> SRR1951673 5 0.2121 0.8589 0.000 0.096 0.000 0.012 0.892 0.000
#> SRR1951674 6 0.3026 0.9789 0.024 0.000 0.000 0.092 0.028 0.856
#> SRR1951672 1 0.0632 0.8895 0.976 0.000 0.000 0.024 0.000 0.000
#> SRR1951671 5 0.1765 0.8601 0.000 0.096 0.000 0.000 0.904 0.000
#> SRR1951670 4 0.1141 0.7619 0.052 0.000 0.000 0.948 0.000 0.000
#> SRR1951640 3 0.2048 0.7993 0.000 0.000 0.880 0.120 0.000 0.000
#> SRR1951668 4 0.2333 0.8258 0.024 0.000 0.092 0.884 0.000 0.000
#> SRR1951669 2 0.5103 0.4174 0.000 0.612 0.000 0.104 0.280 0.004
#> SRR1951638 1 0.2604 0.8321 0.872 0.000 0.000 0.100 0.008 0.020
#> SRR1951637 5 0.4351 0.7265 0.012 0.012 0.000 0.104 0.768 0.104
#> SRR1951635 1 0.2813 0.8154 0.864 0.000 0.000 0.092 0.008 0.036
#> SRR1951634 4 0.2383 0.8266 0.024 0.000 0.096 0.880 0.000 0.000
#> SRR1951631 4 0.2003 0.8244 0.000 0.000 0.116 0.884 0.000 0.000
#> SRR1951632 5 0.3023 0.7616 0.000 0.212 0.000 0.004 0.784 0.000
#> SRR1951633 4 0.1398 0.8113 0.000 0.000 0.052 0.940 0.008 0.000
#> SRR1951630 4 0.1863 0.8272 0.000 0.000 0.104 0.896 0.000 0.000
#> SRR1951629 4 0.3531 0.6205 0.000 0.000 0.328 0.672 0.000 0.000
#> SRR1951628 1 0.1124 0.8665 0.956 0.000 0.000 0.000 0.008 0.036
#> SRR1951627 5 0.0146 0.8641 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1951639 5 0.3428 0.5837 0.000 0.000 0.304 0.000 0.696 0.000
#> SRR1951625 5 0.2121 0.8589 0.000 0.096 0.000 0.012 0.892 0.000
#> SRR1951626 3 0.2597 0.6851 0.000 0.000 0.824 0.000 0.176 0.000
#> SRR1951624 3 0.1910 0.8024 0.000 0.000 0.892 0.108 0.000 0.000
#> SRR1951622 1 0.0000 0.8786 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951636 3 0.2697 0.6752 0.000 0.000 0.812 0.000 0.188 0.000
#> SRR1951623 3 0.4595 0.5676 0.000 0.000 0.668 0.084 0.248 0.000
#> SRR1951621 4 0.0692 0.7787 0.020 0.000 0.000 0.976 0.004 0.000
#> SRR1951620 3 0.2178 0.7926 0.000 0.000 0.868 0.132 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "NMF"]
# you can also extract it by
# res = res_list["MAD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 13580 rows and 133 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 0.743 0.879 0.947 0.4915 0.506 0.506
#> 3 3 0.774 0.829 0.923 0.2735 0.795 0.620
#> 4 4 0.643 0.663 0.844 0.1511 0.807 0.538
#> 5 5 0.642 0.679 0.821 0.0816 0.839 0.503
#> 6 6 0.641 0.642 0.782 0.0457 0.919 0.662
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
#> SRR1951667 1 0.3879 0.892 0.924 0.076
#> SRR1951666 1 0.0000 0.946 1.000 0.000
#> SRR1951665 2 0.5059 0.842 0.112 0.888
#> SRR1951664 1 0.0000 0.946 1.000 0.000
#> SRR1951663 2 0.0000 0.936 0.000 1.000
#> SRR1951662 2 0.9427 0.482 0.360 0.640
#> SRR1951661 1 0.0000 0.946 1.000 0.000
#> SRR1951660 2 0.0000 0.936 0.000 1.000
#> SRR1951659 1 0.0000 0.946 1.000 0.000
#> SRR1951658 1 0.0000 0.946 1.000 0.000
#> SRR1951657 1 0.0000 0.946 1.000 0.000
#> SRR1951656 1 0.0000 0.946 1.000 0.000
#> SRR1951655 2 0.0000 0.936 0.000 1.000
#> SRR1951654 2 0.0000 0.936 0.000 1.000
#> SRR1951653 1 0.6973 0.783 0.812 0.188
#> SRR1951652 1 0.7815 0.667 0.768 0.232
#> SRR1951651 1 0.1414 0.934 0.980 0.020
#> SRR1951650 1 0.0000 0.946 1.000 0.000
#> SRR1951648 1 0.0000 0.946 1.000 0.000
#> SRR1951649 2 0.9933 0.126 0.452 0.548
#> SRR1951647 1 0.0376 0.944 0.996 0.004
#> SRR1951646 1 0.0000 0.946 1.000 0.000
#> SRR1951645 2 0.8443 0.606 0.272 0.728
#> SRR1951644 2 0.0000 0.936 0.000 1.000
#> SRR1951643 1 0.0376 0.944 0.996 0.004
#> SRR1951641 2 0.0000 0.936 0.000 1.000
#> SRR1951642 1 0.0000 0.946 1.000 0.000
#> SRR1951694 2 0.0000 0.936 0.000 1.000
#> SRR1951731 1 0.0000 0.946 1.000 0.000
#> SRR1951730 2 0.9775 0.264 0.412 0.588
#> SRR1951752 2 0.9977 0.168 0.472 0.528
#> SRR1951751 2 0.0000 0.936 0.000 1.000
#> SRR1951749 1 0.0000 0.946 1.000 0.000
#> SRR1951750 2 0.0000 0.936 0.000 1.000
#> SRR1951748 2 0.4161 0.869 0.084 0.916
#> SRR1951747 2 0.1414 0.922 0.020 0.980
#> SRR1951745 2 0.0000 0.936 0.000 1.000
#> SRR1951746 1 0.0000 0.946 1.000 0.000
#> SRR1951744 1 0.0000 0.946 1.000 0.000
#> SRR1951742 1 0.0000 0.946 1.000 0.000
#> SRR1951743 2 0.0000 0.936 0.000 1.000
#> SRR1951741 2 0.0000 0.936 0.000 1.000
#> SRR1951740 1 0.0000 0.946 1.000 0.000
#> SRR1951738 2 0.0000 0.936 0.000 1.000
#> SRR1951739 1 0.0000 0.946 1.000 0.000
#> SRR1951737 1 0.0000 0.946 1.000 0.000
#> SRR1951736 2 0.0000 0.936 0.000 1.000
#> SRR1951734 2 0.0000 0.936 0.000 1.000
#> SRR1951735 1 0.0000 0.946 1.000 0.000
#> SRR1951733 1 0.0000 0.946 1.000 0.000
#> SRR1951732 1 0.0000 0.946 1.000 0.000
#> SRR1951729 1 0.0000 0.946 1.000 0.000
#> SRR1951728 1 0.1633 0.931 0.976 0.024
#> SRR1951727 2 0.7745 0.706 0.228 0.772
#> SRR1951725 2 0.0000 0.936 0.000 1.000
#> SRR1951726 2 0.4431 0.867 0.092 0.908
#> SRR1951724 2 0.9608 0.345 0.384 0.616
#> SRR1951722 1 0.9635 0.416 0.612 0.388
#> SRR1951723 1 0.9552 0.447 0.624 0.376
#> SRR1951721 2 0.0000 0.936 0.000 1.000
#> SRR1951719 1 0.0000 0.946 1.000 0.000
#> SRR1951720 2 0.0000 0.936 0.000 1.000
#> SRR1951718 2 0.0000 0.936 0.000 1.000
#> SRR1951717 2 0.0000 0.936 0.000 1.000
#> SRR1951715 2 0.0000 0.936 0.000 1.000
#> SRR1951716 2 0.0000 0.936 0.000 1.000
#> SRR1951714 1 0.4815 0.868 0.896 0.104
#> SRR1951713 1 0.7299 0.764 0.796 0.204
#> SRR1951712 2 0.0000 0.936 0.000 1.000
#> SRR1951711 2 0.0000 0.936 0.000 1.000
#> SRR1951710 2 0.0000 0.936 0.000 1.000
#> SRR1951709 1 0.0000 0.946 1.000 0.000
#> SRR1951708 2 0.0376 0.933 0.004 0.996
#> SRR1951707 1 0.7219 0.769 0.800 0.200
#> SRR1951705 2 0.0000 0.936 0.000 1.000
#> SRR1951704 1 0.0000 0.946 1.000 0.000
#> SRR1951703 2 0.7528 0.722 0.216 0.784
#> SRR1951706 1 0.0000 0.946 1.000 0.000
#> SRR1951702 1 0.0000 0.946 1.000 0.000
#> SRR1951701 1 0.0000 0.946 1.000 0.000
#> SRR1951699 1 0.0000 0.946 1.000 0.000
#> SRR1951698 1 0.0000 0.946 1.000 0.000
#> SRR1951700 1 0.9000 0.580 0.684 0.316
#> SRR1951697 1 0.0000 0.946 1.000 0.000
#> SRR1951696 1 0.0000 0.946 1.000 0.000
#> SRR1951695 1 0.0000 0.946 1.000 0.000
#> SRR1951693 1 0.7453 0.754 0.788 0.212
#> SRR1951692 1 0.0000 0.946 1.000 0.000
#> SRR1951691 2 0.2236 0.908 0.036 0.964
#> SRR1951688 2 0.0000 0.936 0.000 1.000
#> SRR1951689 2 0.0000 0.936 0.000 1.000
#> SRR1951687 2 0.6801 0.755 0.180 0.820
#> SRR1951685 1 0.7602 0.743 0.780 0.220
#> SRR1951684 1 0.0000 0.946 1.000 0.000
#> SRR1951686 1 0.0000 0.946 1.000 0.000
#> SRR1951683 1 0.7139 0.774 0.804 0.196
#> SRR1951682 2 0.0000 0.936 0.000 1.000
#> SRR1951690 1 0.7219 0.769 0.800 0.200
#> SRR1951680 1 0.0938 0.939 0.988 0.012
#> SRR1951679 2 0.0000 0.936 0.000 1.000
#> SRR1951678 1 0.0000 0.946 1.000 0.000
#> SRR1951677 1 0.0000 0.946 1.000 0.000
#> SRR1951676 1 0.0000 0.946 1.000 0.000
#> SRR1951675 2 0.0000 0.936 0.000 1.000
#> SRR1951681 1 0.0000 0.946 1.000 0.000
#> SRR1951673 2 0.0000 0.936 0.000 1.000
#> SRR1951674 2 0.0000 0.936 0.000 1.000
#> SRR1951672 2 0.0000 0.936 0.000 1.000
#> SRR1951671 1 0.0000 0.946 1.000 0.000
#> SRR1951670 2 0.0000 0.936 0.000 1.000
#> SRR1951640 1 0.0000 0.946 1.000 0.000
#> SRR1951668 1 0.8267 0.682 0.740 0.260
#> SRR1951669 2 0.0000 0.936 0.000 1.000
#> SRR1951638 2 0.0000 0.936 0.000 1.000
#> SRR1951637 2 0.0000 0.936 0.000 1.000
#> SRR1951635 2 0.0000 0.936 0.000 1.000
#> SRR1951634 1 0.7139 0.774 0.804 0.196
#> SRR1951631 1 0.3879 0.892 0.924 0.076
#> SRR1951632 1 0.0000 0.946 1.000 0.000
#> SRR1951633 1 0.0000 0.946 1.000 0.000
#> SRR1951630 1 0.0000 0.946 1.000 0.000
#> SRR1951629 1 0.0000 0.946 1.000 0.000
#> SRR1951628 2 0.0000 0.936 0.000 1.000
#> SRR1951627 1 0.0000 0.946 1.000 0.000
#> SRR1951639 1 0.0000 0.946 1.000 0.000
#> SRR1951625 1 0.6973 0.783 0.812 0.188
#> SRR1951626 1 0.0000 0.946 1.000 0.000
#> SRR1951624 1 0.0000 0.946 1.000 0.000
#> SRR1951622 2 0.0000 0.936 0.000 1.000
#> SRR1951636 1 0.0000 0.946 1.000 0.000
#> SRR1951623 1 0.0000 0.946 1.000 0.000
#> SRR1951621 2 0.0000 0.936 0.000 1.000
#> SRR1951620 1 0.0000 0.946 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 3 0.2066 0.8928 0.060 0.000 0.940
#> SRR1951666 3 0.1860 0.8828 0.000 0.052 0.948
#> SRR1951665 2 0.0000 0.8637 0.000 1.000 0.000
#> SRR1951664 3 0.0747 0.9130 0.016 0.000 0.984
#> SRR1951663 2 0.3941 0.7550 0.156 0.844 0.000
#> SRR1951662 2 0.0237 0.8635 0.000 0.996 0.004
#> SRR1951661 3 0.2711 0.8549 0.000 0.088 0.912
#> SRR1951660 1 0.0237 0.9292 0.996 0.000 0.004
#> SRR1951659 3 0.2165 0.8737 0.000 0.064 0.936
#> SRR1951658 2 0.2625 0.8290 0.000 0.916 0.084
#> SRR1951657 3 0.0000 0.9128 0.000 0.000 1.000
#> SRR1951656 3 0.0892 0.9123 0.020 0.000 0.980
#> SRR1951655 1 0.0747 0.9253 0.984 0.000 0.016
#> SRR1951654 1 0.4235 0.7603 0.824 0.176 0.000
#> SRR1951653 3 0.4654 0.7548 0.208 0.000 0.792
#> SRR1951652 2 0.0424 0.8631 0.000 0.992 0.008
#> SRR1951651 3 0.1860 0.8977 0.052 0.000 0.948
#> SRR1951650 3 0.0000 0.9128 0.000 0.000 1.000
#> SRR1951648 3 0.0000 0.9128 0.000 0.000 1.000
#> SRR1951649 1 0.2261 0.8829 0.932 0.000 0.068
#> SRR1951647 3 0.2448 0.8809 0.076 0.000 0.924
#> SRR1951646 3 0.0000 0.9128 0.000 0.000 1.000
#> SRR1951645 1 0.2165 0.8867 0.936 0.000 0.064
#> SRR1951644 1 0.0592 0.9270 0.988 0.000 0.012
#> SRR1951643 3 0.0592 0.9138 0.012 0.000 0.988
#> SRR1951641 1 0.0237 0.9292 0.996 0.000 0.004
#> SRR1951642 3 0.0592 0.9075 0.000 0.012 0.988
#> SRR1951694 1 0.0424 0.9261 0.992 0.008 0.000
#> SRR1951731 3 0.1031 0.9110 0.024 0.000 0.976
#> SRR1951730 2 0.9398 0.1737 0.172 0.428 0.400
#> SRR1951752 2 0.8595 0.5641 0.180 0.604 0.216
#> SRR1951751 1 0.1031 0.9205 0.976 0.000 0.024
#> SRR1951749 3 0.0000 0.9128 0.000 0.000 1.000
#> SRR1951750 1 0.0424 0.9285 0.992 0.000 0.008
#> SRR1951748 2 0.0000 0.8637 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.8637 0.000 1.000 0.000
#> SRR1951745 2 0.0747 0.8609 0.016 0.984 0.000
#> SRR1951746 2 0.1643 0.8536 0.000 0.956 0.044
#> SRR1951744 2 0.6308 0.0322 0.000 0.508 0.492
#> SRR1951742 3 0.2356 0.8671 0.000 0.072 0.928
#> SRR1951743 2 0.4346 0.7193 0.184 0.816 0.000
#> SRR1951741 1 0.0237 0.9278 0.996 0.004 0.000
#> SRR1951740 3 0.1163 0.9095 0.028 0.000 0.972
#> SRR1951738 1 0.1860 0.8979 0.948 0.052 0.000
#> SRR1951739 3 0.0892 0.9123 0.020 0.000 0.980
#> SRR1951737 3 0.0000 0.9128 0.000 0.000 1.000
#> SRR1951736 1 0.0000 0.9288 1.000 0.000 0.000
#> SRR1951734 2 0.2261 0.8330 0.068 0.932 0.000
#> SRR1951735 3 0.0892 0.9123 0.020 0.000 0.980
#> SRR1951733 3 0.0000 0.9128 0.000 0.000 1.000
#> SRR1951732 2 0.1289 0.8569 0.000 0.968 0.032
#> SRR1951729 3 0.0592 0.9134 0.012 0.000 0.988
#> SRR1951728 3 0.1643 0.9023 0.044 0.000 0.956
#> SRR1951727 2 0.0000 0.8637 0.000 1.000 0.000
#> SRR1951725 1 0.1643 0.9041 0.956 0.044 0.000
#> SRR1951726 2 0.1529 0.8515 0.040 0.960 0.000
#> SRR1951724 1 0.1529 0.9085 0.960 0.000 0.040
#> SRR1951722 1 0.3879 0.7824 0.848 0.000 0.152
#> SRR1951723 1 0.2356 0.8791 0.928 0.000 0.072
#> SRR1951721 1 0.5216 0.6257 0.740 0.260 0.000
#> SRR1951719 3 0.0892 0.9123 0.020 0.000 0.980
#> SRR1951720 1 0.0424 0.9261 0.992 0.008 0.000
#> SRR1951718 1 0.0892 0.9231 0.980 0.000 0.020
#> SRR1951717 1 0.0424 0.9261 0.992 0.008 0.000
#> SRR1951715 1 0.1289 0.9120 0.968 0.032 0.000
#> SRR1951716 1 0.0000 0.9288 1.000 0.000 0.000
#> SRR1951714 3 0.3412 0.8371 0.124 0.000 0.876
#> SRR1951713 3 0.6244 0.2938 0.440 0.000 0.560
#> SRR1951712 1 0.0892 0.9231 0.980 0.000 0.020
#> SRR1951711 2 0.6252 0.1748 0.444 0.556 0.000
#> SRR1951710 1 0.0237 0.9278 0.996 0.004 0.000
#> SRR1951709 3 0.0892 0.9040 0.000 0.020 0.980
#> SRR1951708 2 0.0747 0.8610 0.016 0.984 0.000
#> SRR1951707 3 0.4931 0.7255 0.232 0.000 0.768
#> SRR1951705 1 0.0000 0.9288 1.000 0.000 0.000
#> SRR1951704 3 0.4842 0.6966 0.000 0.224 0.776
#> SRR1951703 2 0.0000 0.8637 0.000 1.000 0.000
#> SRR1951706 2 0.1860 0.8494 0.000 0.948 0.052
#> SRR1951702 3 0.5016 0.6711 0.000 0.240 0.760
#> SRR1951701 3 0.0237 0.9132 0.004 0.000 0.996
#> SRR1951699 3 0.1411 0.8946 0.000 0.036 0.964
#> SRR1951698 3 0.0237 0.9113 0.000 0.004 0.996
#> SRR1951700 1 0.6295 -0.0152 0.528 0.000 0.472
#> SRR1951697 2 0.1753 0.8514 0.000 0.952 0.048
#> SRR1951696 3 0.0000 0.9128 0.000 0.000 1.000
#> SRR1951695 3 0.1031 0.9112 0.024 0.000 0.976
#> SRR1951693 3 0.5760 0.5726 0.328 0.000 0.672
#> SRR1951692 3 0.0747 0.9130 0.016 0.000 0.984
#> SRR1951691 1 0.0747 0.9258 0.984 0.000 0.016
#> SRR1951688 2 0.0747 0.8608 0.016 0.984 0.000
#> SRR1951689 1 0.0424 0.9285 0.992 0.000 0.008
#> SRR1951687 1 0.6295 0.6299 0.728 0.036 0.236
#> SRR1951685 3 0.6192 0.3559 0.420 0.000 0.580
#> SRR1951684 3 0.0892 0.9123 0.020 0.000 0.980
#> SRR1951686 2 0.6225 0.2532 0.000 0.568 0.432
#> SRR1951683 3 0.4887 0.7296 0.228 0.000 0.772
#> SRR1951682 1 0.0424 0.9285 0.992 0.000 0.008
#> SRR1951690 3 0.6291 0.2016 0.468 0.000 0.532
#> SRR1951680 3 0.2356 0.8841 0.072 0.000 0.928
#> SRR1951679 1 0.0000 0.9288 1.000 0.000 0.000
#> SRR1951678 3 0.0747 0.9130 0.016 0.000 0.984
#> SRR1951677 3 0.0000 0.9128 0.000 0.000 1.000
#> SRR1951676 3 0.0747 0.9064 0.000 0.016 0.984
#> SRR1951675 1 0.1643 0.9046 0.956 0.044 0.000
#> SRR1951681 3 0.0000 0.9128 0.000 0.000 1.000
#> SRR1951673 1 0.0237 0.9292 0.996 0.000 0.004
#> SRR1951674 2 0.0237 0.8632 0.004 0.996 0.000
#> SRR1951672 1 0.0237 0.9292 0.996 0.000 0.004
#> SRR1951671 3 0.0747 0.9134 0.016 0.000 0.984
#> SRR1951670 1 0.0000 0.9288 1.000 0.000 0.000
#> SRR1951640 3 0.1163 0.9095 0.028 0.000 0.972
#> SRR1951668 1 0.5254 0.6217 0.736 0.000 0.264
#> SRR1951669 2 0.2261 0.8336 0.068 0.932 0.000
#> SRR1951638 1 0.1411 0.9097 0.964 0.036 0.000
#> SRR1951637 1 0.3267 0.8346 0.884 0.116 0.000
#> SRR1951635 1 0.1289 0.9120 0.968 0.032 0.000
#> SRR1951634 3 0.6079 0.4364 0.388 0.000 0.612
#> SRR1951631 3 0.2711 0.8721 0.088 0.000 0.912
#> SRR1951632 3 0.0000 0.9128 0.000 0.000 1.000
#> SRR1951633 3 0.1411 0.8944 0.000 0.036 0.964
#> SRR1951630 3 0.0000 0.9128 0.000 0.000 1.000
#> SRR1951629 3 0.0892 0.9123 0.020 0.000 0.980
#> SRR1951628 1 0.0237 0.9292 0.996 0.000 0.004
#> SRR1951627 3 0.1163 0.8994 0.000 0.028 0.972
#> SRR1951639 3 0.0000 0.9128 0.000 0.000 1.000
#> SRR1951625 3 0.4750 0.7455 0.216 0.000 0.784
#> SRR1951626 3 0.0000 0.9128 0.000 0.000 1.000
#> SRR1951624 3 0.0000 0.9128 0.000 0.000 1.000
#> SRR1951622 1 0.0000 0.9288 1.000 0.000 0.000
#> SRR1951636 3 0.0000 0.9128 0.000 0.000 1.000
#> SRR1951623 3 0.2625 0.8583 0.000 0.084 0.916
#> SRR1951621 1 0.0000 0.9288 1.000 0.000 0.000
#> SRR1951620 3 0.0592 0.9134 0.012 0.000 0.988
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 4 0.1109 0.7317 0.004 0.000 0.028 0.968
#> SRR1951666 3 0.0469 0.8604 0.000 0.012 0.988 0.000
#> SRR1951665 2 0.0336 0.8176 0.008 0.992 0.000 0.000
#> SRR1951664 3 0.4761 0.3925 0.000 0.000 0.628 0.372
#> SRR1951663 1 0.3688 0.5990 0.792 0.208 0.000 0.000
#> SRR1951662 2 0.0188 0.8166 0.000 0.996 0.000 0.004
#> SRR1951661 3 0.1610 0.8501 0.000 0.032 0.952 0.016
#> SRR1951660 1 0.1284 0.8071 0.964 0.000 0.012 0.024
#> SRR1951659 3 0.3962 0.7489 0.000 0.152 0.820 0.028
#> SRR1951658 3 0.5363 0.3770 0.004 0.372 0.612 0.012
#> SRR1951657 3 0.0469 0.8621 0.000 0.000 0.988 0.012
#> SRR1951656 3 0.1474 0.8519 0.000 0.000 0.948 0.052
#> SRR1951655 1 0.1174 0.8077 0.968 0.000 0.012 0.020
#> SRR1951654 1 0.1767 0.7850 0.944 0.044 0.012 0.000
#> SRR1951653 4 0.5620 0.3134 0.024 0.000 0.416 0.560
#> SRR1951652 2 0.5070 0.3080 0.004 0.580 0.416 0.000
#> SRR1951651 3 0.0707 0.8607 0.000 0.000 0.980 0.020
#> SRR1951650 3 0.0336 0.8615 0.000 0.000 0.992 0.008
#> SRR1951648 3 0.0469 0.8610 0.000 0.000 0.988 0.012
#> SRR1951649 4 0.5432 0.4746 0.316 0.000 0.032 0.652
#> SRR1951647 4 0.5466 0.2554 0.016 0.000 0.436 0.548
#> SRR1951646 3 0.0921 0.8535 0.000 0.000 0.972 0.028
#> SRR1951645 1 0.6389 -0.0575 0.488 0.000 0.064 0.448
#> SRR1951644 4 0.5277 0.1149 0.460 0.000 0.008 0.532
#> SRR1951643 3 0.0336 0.8622 0.000 0.000 0.992 0.008
#> SRR1951641 4 0.4761 0.4236 0.332 0.000 0.004 0.664
#> SRR1951642 3 0.0672 0.8605 0.000 0.008 0.984 0.008
#> SRR1951694 1 0.0469 0.8089 0.988 0.000 0.000 0.012
#> SRR1951731 4 0.3105 0.7093 0.004 0.000 0.140 0.856
#> SRR1951730 2 0.4122 0.7619 0.008 0.840 0.096 0.056
#> SRR1951752 2 0.4314 0.7671 0.032 0.844 0.060 0.064
#> SRR1951751 4 0.1211 0.7289 0.040 0.000 0.000 0.960
#> SRR1951749 3 0.5108 0.5790 0.000 0.020 0.672 0.308
#> SRR1951750 1 0.3074 0.7316 0.848 0.000 0.000 0.152
#> SRR1951748 2 0.0336 0.8176 0.008 0.992 0.000 0.000
#> SRR1951747 2 0.0336 0.8176 0.008 0.992 0.000 0.000
#> SRR1951745 1 0.4977 -0.0127 0.540 0.460 0.000 0.000
#> SRR1951746 2 0.4079 0.7093 0.000 0.800 0.020 0.180
#> SRR1951744 3 0.2402 0.8260 0.000 0.076 0.912 0.012
#> SRR1951742 3 0.1209 0.8542 0.000 0.032 0.964 0.004
#> SRR1951743 2 0.3300 0.7521 0.008 0.848 0.000 0.144
#> SRR1951741 1 0.0657 0.8077 0.984 0.004 0.012 0.000
#> SRR1951740 3 0.4018 0.6905 0.004 0.000 0.772 0.224
#> SRR1951738 1 0.0937 0.8084 0.976 0.012 0.000 0.012
#> SRR1951739 4 0.1302 0.7311 0.000 0.000 0.044 0.956
#> SRR1951737 3 0.3831 0.7216 0.000 0.004 0.792 0.204
#> SRR1951736 1 0.4500 0.5174 0.684 0.000 0.000 0.316
#> SRR1951734 2 0.4431 0.6584 0.004 0.740 0.004 0.252
#> SRR1951735 3 0.2081 0.8272 0.000 0.000 0.916 0.084
#> SRR1951733 3 0.1661 0.8488 0.000 0.004 0.944 0.052
#> SRR1951732 2 0.0524 0.8159 0.000 0.988 0.008 0.004
#> SRR1951729 3 0.5097 0.2968 0.000 0.004 0.568 0.428
#> SRR1951728 3 0.5334 0.1127 0.004 0.004 0.508 0.484
#> SRR1951727 2 0.0000 0.8171 0.000 1.000 0.000 0.000
#> SRR1951725 1 0.0524 0.8060 0.988 0.008 0.000 0.004
#> SRR1951726 1 0.7077 0.2622 0.536 0.148 0.316 0.000
#> SRR1951724 1 0.5483 0.1562 0.536 0.000 0.016 0.448
#> SRR1951722 4 0.4088 0.5878 0.232 0.000 0.004 0.764
#> SRR1951723 4 0.2197 0.7189 0.080 0.000 0.004 0.916
#> SRR1951721 1 0.1902 0.7753 0.932 0.064 0.000 0.004
#> SRR1951719 4 0.4925 0.1368 0.000 0.000 0.428 0.572
#> SRR1951720 1 0.0188 0.8083 0.996 0.000 0.000 0.004
#> SRR1951718 1 0.3962 0.7104 0.820 0.000 0.028 0.152
#> SRR1951717 1 0.1557 0.8006 0.944 0.000 0.000 0.056
#> SRR1951715 1 0.0469 0.8082 0.988 0.000 0.000 0.012
#> SRR1951716 1 0.3498 0.7078 0.832 0.000 0.008 0.160
#> SRR1951714 3 0.2222 0.8250 0.060 0.000 0.924 0.016
#> SRR1951713 4 0.1356 0.7350 0.032 0.000 0.008 0.960
#> SRR1951712 4 0.2011 0.7102 0.080 0.000 0.000 0.920
#> SRR1951711 2 0.4833 0.6613 0.032 0.740 0.000 0.228
#> SRR1951710 1 0.1022 0.8082 0.968 0.000 0.000 0.032
#> SRR1951709 3 0.0336 0.8610 0.000 0.008 0.992 0.000
#> SRR1951708 2 0.1022 0.8112 0.032 0.968 0.000 0.000
#> SRR1951707 4 0.1209 0.7328 0.004 0.000 0.032 0.964
#> SRR1951705 1 0.4193 0.5863 0.732 0.000 0.000 0.268
#> SRR1951704 3 0.5906 0.4482 0.000 0.292 0.644 0.064
#> SRR1951703 2 0.1059 0.8167 0.016 0.972 0.012 0.000
#> SRR1951706 2 0.5138 0.3769 0.000 0.600 0.392 0.008
#> SRR1951702 3 0.3300 0.7549 0.000 0.144 0.848 0.008
#> SRR1951701 4 0.4134 0.6107 0.000 0.000 0.260 0.740
#> SRR1951699 3 0.1520 0.8530 0.000 0.020 0.956 0.024
#> SRR1951698 3 0.0469 0.8591 0.000 0.000 0.988 0.012
#> SRR1951700 4 0.2867 0.7032 0.012 0.000 0.104 0.884
#> SRR1951697 2 0.4188 0.6477 0.000 0.752 0.244 0.004
#> SRR1951696 3 0.0592 0.8612 0.000 0.000 0.984 0.016
#> SRR1951695 3 0.5168 0.0815 0.004 0.000 0.500 0.496
#> SRR1951693 4 0.4500 0.6768 0.032 0.000 0.192 0.776
#> SRR1951692 3 0.0707 0.8607 0.000 0.000 0.980 0.020
#> SRR1951691 1 0.2053 0.7931 0.924 0.000 0.004 0.072
#> SRR1951688 2 0.0336 0.8176 0.008 0.992 0.000 0.000
#> SRR1951689 4 0.1302 0.7279 0.044 0.000 0.000 0.956
#> SRR1951687 1 0.5771 0.0786 0.504 0.020 0.472 0.004
#> SRR1951685 4 0.1004 0.7316 0.004 0.000 0.024 0.972
#> SRR1951684 3 0.4313 0.6471 0.000 0.004 0.736 0.260
#> SRR1951686 3 0.5404 -0.0421 0.000 0.476 0.512 0.012
#> SRR1951683 3 0.1584 0.8499 0.036 0.000 0.952 0.012
#> SRR1951682 4 0.4137 0.6098 0.208 0.000 0.012 0.780
#> SRR1951690 4 0.1488 0.7339 0.012 0.000 0.032 0.956
#> SRR1951680 4 0.4594 0.5746 0.008 0.000 0.280 0.712
#> SRR1951679 4 0.3942 0.5466 0.236 0.000 0.000 0.764
#> SRR1951678 3 0.0188 0.8616 0.000 0.000 0.996 0.004
#> SRR1951677 3 0.0188 0.8616 0.000 0.000 0.996 0.004
#> SRR1951676 3 0.1059 0.8577 0.000 0.016 0.972 0.012
#> SRR1951675 1 0.1938 0.8027 0.936 0.012 0.000 0.052
#> SRR1951681 3 0.0376 0.8615 0.000 0.004 0.992 0.004
#> SRR1951673 4 0.5290 0.0701 0.476 0.000 0.008 0.516
#> SRR1951674 2 0.5696 0.1051 0.484 0.492 0.024 0.000
#> SRR1951672 1 0.1489 0.8044 0.952 0.000 0.004 0.044
#> SRR1951671 4 0.3945 0.6455 0.004 0.000 0.216 0.780
#> SRR1951670 1 0.0804 0.8090 0.980 0.000 0.008 0.012
#> SRR1951640 3 0.2589 0.8123 0.000 0.000 0.884 0.116
#> SRR1951668 1 0.7799 -0.1383 0.384 0.000 0.368 0.248
#> SRR1951669 2 0.4431 0.5311 0.304 0.696 0.000 0.000
#> SRR1951638 1 0.1042 0.8080 0.972 0.008 0.000 0.020
#> SRR1951637 1 0.0707 0.8027 0.980 0.020 0.000 0.000
#> SRR1951635 1 0.0336 0.8057 0.992 0.008 0.000 0.000
#> SRR1951634 4 0.6843 0.4219 0.112 0.000 0.356 0.532
#> SRR1951631 3 0.1743 0.8481 0.004 0.000 0.940 0.056
#> SRR1951632 3 0.4776 0.3670 0.000 0.000 0.624 0.376
#> SRR1951633 3 0.0376 0.8615 0.000 0.004 0.992 0.004
#> SRR1951630 3 0.0336 0.8605 0.000 0.000 0.992 0.008
#> SRR1951629 3 0.0469 0.8621 0.000 0.000 0.988 0.012
#> SRR1951628 1 0.5281 0.1041 0.528 0.000 0.008 0.464
#> SRR1951627 3 0.0657 0.8601 0.000 0.012 0.984 0.004
#> SRR1951639 3 0.2760 0.8020 0.000 0.000 0.872 0.128
#> SRR1951625 4 0.2149 0.7290 0.000 0.000 0.088 0.912
#> SRR1951626 3 0.1452 0.8578 0.000 0.008 0.956 0.036
#> SRR1951624 3 0.4422 0.6641 0.000 0.008 0.736 0.256
#> SRR1951622 1 0.1022 0.8078 0.968 0.000 0.000 0.032
#> SRR1951636 3 0.0376 0.8615 0.000 0.004 0.992 0.004
#> SRR1951623 3 0.0779 0.8594 0.000 0.016 0.980 0.004
#> SRR1951621 1 0.0657 0.8087 0.984 0.000 0.012 0.004
#> SRR1951620 3 0.1211 0.8555 0.000 0.000 0.960 0.040
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 5 0.0162 0.8194 0.000 0.000 0.000 0.004 0.996
#> SRR1951666 3 0.0566 0.8033 0.000 0.004 0.984 0.012 0.000
#> SRR1951665 2 0.0000 0.8613 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 4 0.6375 0.4424 0.000 0.000 0.316 0.496 0.188
#> SRR1951663 1 0.1211 0.8173 0.960 0.016 0.000 0.024 0.000
#> SRR1951662 2 0.0000 0.8613 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 3 0.0992 0.7974 0.000 0.008 0.968 0.024 0.000
#> SRR1951660 1 0.4302 0.2863 0.520 0.000 0.000 0.480 0.000
#> SRR1951659 4 0.6526 0.4508 0.000 0.260 0.256 0.484 0.000
#> SRR1951658 2 0.3410 0.7364 0.000 0.840 0.092 0.068 0.000
#> SRR1951657 3 0.0794 0.8021 0.000 0.000 0.972 0.028 0.000
#> SRR1951656 4 0.4150 0.4496 0.000 0.000 0.388 0.612 0.000
#> SRR1951655 1 0.3424 0.7364 0.760 0.000 0.000 0.240 0.000
#> SRR1951654 1 0.0404 0.8291 0.988 0.000 0.000 0.012 0.000
#> SRR1951653 4 0.3375 0.7228 0.000 0.000 0.104 0.840 0.056
#> SRR1951652 3 0.4131 0.7049 0.044 0.076 0.820 0.060 0.000
#> SRR1951651 3 0.0880 0.8023 0.000 0.000 0.968 0.032 0.000
#> SRR1951650 3 0.2690 0.7197 0.000 0.000 0.844 0.156 0.000
#> SRR1951648 3 0.4045 0.3582 0.000 0.000 0.644 0.356 0.000
#> SRR1951649 4 0.4455 0.6661 0.096 0.000 0.004 0.768 0.132
#> SRR1951647 4 0.3339 0.7225 0.000 0.000 0.124 0.836 0.040
#> SRR1951646 4 0.4126 0.4650 0.000 0.000 0.380 0.620 0.000
#> SRR1951645 4 0.4496 0.6399 0.156 0.000 0.008 0.764 0.072
#> SRR1951644 4 0.4840 0.6050 0.152 0.000 0.000 0.724 0.124
#> SRR1951643 3 0.0880 0.8026 0.000 0.000 0.968 0.032 0.000
#> SRR1951641 4 0.3754 0.6830 0.084 0.000 0.000 0.816 0.100
#> SRR1951642 3 0.1124 0.7939 0.004 0.000 0.960 0.036 0.000
#> SRR1951694 1 0.2127 0.8230 0.892 0.000 0.000 0.108 0.000
#> SRR1951731 5 0.3061 0.7855 0.000 0.000 0.020 0.136 0.844
#> SRR1951730 2 0.4342 0.6724 0.008 0.760 0.032 0.004 0.196
#> SRR1951752 5 0.6926 0.3326 0.004 0.300 0.048 0.116 0.532
#> SRR1951751 5 0.0162 0.8192 0.000 0.000 0.000 0.004 0.996
#> SRR1951749 3 0.4908 0.2982 0.000 0.000 0.608 0.036 0.356
#> SRR1951750 1 0.3427 0.7472 0.796 0.000 0.000 0.012 0.192
#> SRR1951748 2 0.0000 0.8613 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.8613 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 1 0.3213 0.7687 0.872 0.052 0.012 0.060 0.004
#> SRR1951746 2 0.3003 0.8095 0.000 0.864 0.000 0.092 0.044
#> SRR1951744 3 0.2199 0.7733 0.016 0.008 0.916 0.060 0.000
#> SRR1951742 3 0.3427 0.7453 0.000 0.056 0.836 0.108 0.000
#> SRR1951743 2 0.3535 0.7899 0.000 0.832 0.000 0.088 0.080
#> SRR1951741 1 0.1484 0.8307 0.944 0.000 0.008 0.048 0.000
#> SRR1951740 3 0.6510 0.1018 0.000 0.000 0.444 0.196 0.360
#> SRR1951738 1 0.2471 0.8119 0.864 0.000 0.000 0.136 0.000
#> SRR1951739 5 0.2230 0.7913 0.000 0.000 0.000 0.116 0.884
#> SRR1951737 3 0.5390 0.3686 0.000 0.000 0.600 0.076 0.324
#> SRR1951736 1 0.5555 0.6481 0.636 0.000 0.000 0.132 0.232
#> SRR1951734 2 0.5309 0.5374 0.000 0.644 0.000 0.092 0.264
#> SRR1951735 3 0.3596 0.6552 0.000 0.000 0.776 0.212 0.012
#> SRR1951733 3 0.4151 0.5254 0.000 0.000 0.652 0.344 0.004
#> SRR1951732 2 0.0000 0.8613 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 5 0.5128 0.4424 0.000 0.000 0.344 0.052 0.604
#> SRR1951728 5 0.3829 0.7045 0.000 0.000 0.196 0.028 0.776
#> SRR1951727 2 0.0000 0.8613 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 1 0.0290 0.8278 0.992 0.000 0.000 0.008 0.000
#> SRR1951726 1 0.4848 0.6079 0.724 0.016 0.208 0.052 0.000
#> SRR1951724 1 0.6605 0.4389 0.496 0.000 0.004 0.252 0.248
#> SRR1951722 5 0.2270 0.7873 0.020 0.000 0.000 0.076 0.904
#> SRR1951723 5 0.4109 0.5353 0.012 0.000 0.000 0.288 0.700
#> SRR1951721 1 0.0798 0.8238 0.976 0.016 0.000 0.008 0.000
#> SRR1951719 5 0.3236 0.7558 0.000 0.000 0.152 0.020 0.828
#> SRR1951720 1 0.1043 0.8329 0.960 0.000 0.000 0.040 0.000
#> SRR1951718 4 0.3318 0.6111 0.192 0.000 0.008 0.800 0.000
#> SRR1951717 1 0.2470 0.7974 0.884 0.000 0.000 0.012 0.104
#> SRR1951715 1 0.0404 0.8318 0.988 0.000 0.000 0.012 0.000
#> SRR1951716 1 0.4371 0.5862 0.644 0.000 0.000 0.344 0.012
#> SRR1951714 3 0.5737 0.3517 0.120 0.000 0.592 0.288 0.000
#> SRR1951713 5 0.1043 0.8155 0.000 0.000 0.000 0.040 0.960
#> SRR1951712 5 0.0162 0.8192 0.000 0.000 0.000 0.004 0.996
#> SRR1951711 5 0.3403 0.7006 0.012 0.160 0.000 0.008 0.820
#> SRR1951710 1 0.2329 0.8168 0.876 0.000 0.000 0.124 0.000
#> SRR1951709 3 0.1410 0.7914 0.000 0.000 0.940 0.060 0.000
#> SRR1951708 2 0.0290 0.8584 0.008 0.992 0.000 0.000 0.000
#> SRR1951707 5 0.0510 0.8186 0.000 0.000 0.000 0.016 0.984
#> SRR1951705 1 0.5430 0.6793 0.660 0.000 0.000 0.192 0.148
#> SRR1951704 3 0.5685 0.6082 0.008 0.060 0.712 0.064 0.156
#> SRR1951703 2 0.0000 0.8613 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 3 0.3831 0.7134 0.012 0.092 0.832 0.060 0.004
#> SRR1951702 3 0.2464 0.7660 0.000 0.044 0.904 0.048 0.004
#> SRR1951701 4 0.4836 0.6412 0.000 0.000 0.096 0.716 0.188
#> SRR1951699 3 0.0865 0.7985 0.000 0.004 0.972 0.024 0.000
#> SRR1951698 3 0.3305 0.6363 0.000 0.000 0.776 0.224 0.000
#> SRR1951700 5 0.0898 0.8167 0.000 0.000 0.008 0.020 0.972
#> SRR1951697 2 0.5190 -0.0294 0.004 0.496 0.468 0.032 0.000
#> SRR1951696 3 0.3612 0.5720 0.000 0.000 0.732 0.268 0.000
#> SRR1951695 5 0.3697 0.7662 0.000 0.000 0.080 0.100 0.820
#> SRR1951693 5 0.1901 0.8150 0.004 0.000 0.056 0.012 0.928
#> SRR1951692 3 0.4201 0.2033 0.000 0.000 0.592 0.408 0.000
#> SRR1951691 1 0.3291 0.8113 0.840 0.000 0.000 0.120 0.040
#> SRR1951688 2 0.5054 0.6453 0.216 0.696 0.000 0.084 0.004
#> SRR1951689 5 0.0290 0.8191 0.000 0.000 0.000 0.008 0.992
#> SRR1951687 1 0.4252 0.5324 0.700 0.000 0.280 0.020 0.000
#> SRR1951685 5 0.0000 0.8189 0.000 0.000 0.000 0.000 1.000
#> SRR1951684 5 0.5650 0.0609 0.000 0.000 0.456 0.076 0.468
#> SRR1951686 3 0.2770 0.7483 0.000 0.076 0.880 0.044 0.000
#> SRR1951683 3 0.1356 0.7977 0.028 0.000 0.956 0.004 0.012
#> SRR1951682 4 0.3485 0.6849 0.048 0.000 0.000 0.828 0.124
#> SRR1951690 4 0.4009 0.4952 0.000 0.000 0.004 0.684 0.312
#> SRR1951680 5 0.3983 0.7024 0.000 0.000 0.052 0.164 0.784
#> SRR1951679 5 0.1310 0.8104 0.024 0.000 0.000 0.020 0.956
#> SRR1951678 3 0.0880 0.8022 0.000 0.000 0.968 0.032 0.000
#> SRR1951677 3 0.1043 0.8036 0.000 0.000 0.960 0.040 0.000
#> SRR1951676 3 0.1282 0.7909 0.000 0.004 0.952 0.044 0.000
#> SRR1951675 1 0.2473 0.8304 0.896 0.000 0.000 0.072 0.032
#> SRR1951681 3 0.1043 0.7993 0.000 0.000 0.960 0.040 0.000
#> SRR1951673 4 0.4707 0.5245 0.228 0.000 0.000 0.708 0.064
#> SRR1951674 1 0.4167 0.7316 0.824 0.068 0.044 0.060 0.004
#> SRR1951672 1 0.2825 0.8155 0.860 0.000 0.000 0.124 0.016
#> SRR1951671 5 0.2989 0.7886 0.000 0.000 0.072 0.060 0.868
#> SRR1951670 1 0.1410 0.8319 0.940 0.000 0.000 0.060 0.000
#> SRR1951640 4 0.4193 0.5788 0.000 0.000 0.304 0.684 0.012
#> SRR1951668 5 0.6304 0.5360 0.240 0.000 0.116 0.036 0.608
#> SRR1951669 1 0.3949 0.5011 0.668 0.332 0.000 0.000 0.000
#> SRR1951638 1 0.1399 0.8195 0.952 0.000 0.000 0.028 0.020
#> SRR1951637 1 0.0162 0.8301 0.996 0.000 0.000 0.004 0.000
#> SRR1951635 1 0.0609 0.8323 0.980 0.000 0.000 0.020 0.000
#> SRR1951634 4 0.3980 0.7208 0.024 0.000 0.080 0.824 0.072
#> SRR1951631 4 0.4510 0.3431 0.000 0.000 0.432 0.560 0.008
#> SRR1951632 4 0.4125 0.6543 0.000 0.000 0.172 0.772 0.056
#> SRR1951633 3 0.0671 0.8007 0.004 0.000 0.980 0.016 0.000
#> SRR1951630 3 0.3177 0.6648 0.000 0.000 0.792 0.208 0.000
#> SRR1951629 3 0.1792 0.7788 0.000 0.000 0.916 0.084 0.000
#> SRR1951628 4 0.3655 0.6326 0.160 0.000 0.000 0.804 0.036
#> SRR1951627 3 0.0510 0.8030 0.000 0.000 0.984 0.016 0.000
#> SRR1951639 4 0.4211 0.4943 0.000 0.000 0.360 0.636 0.004
#> SRR1951625 4 0.3229 0.6961 0.000 0.000 0.032 0.840 0.128
#> SRR1951626 3 0.0898 0.8038 0.000 0.000 0.972 0.020 0.008
#> SRR1951624 3 0.4653 -0.0355 0.000 0.000 0.516 0.012 0.472
#> SRR1951622 1 0.3534 0.7206 0.744 0.000 0.000 0.256 0.000
#> SRR1951636 3 0.0510 0.8030 0.000 0.000 0.984 0.016 0.000
#> SRR1951623 3 0.0566 0.8010 0.000 0.004 0.984 0.012 0.000
#> SRR1951621 1 0.2230 0.8195 0.884 0.000 0.000 0.116 0.000
#> SRR1951620 4 0.4297 0.2202 0.000 0.000 0.472 0.528 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 3 0.1320 0.7474 0.000 0.000 0.948 0.016 0.000 0.036
#> SRR1951666 5 0.1716 0.8003 0.000 0.000 0.004 0.028 0.932 0.036
#> SRR1951665 2 0.0000 0.9210 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 3 0.6608 0.1449 0.000 0.000 0.396 0.288 0.288 0.028
#> SRR1951663 1 0.1349 0.7574 0.940 0.000 0.000 0.000 0.004 0.056
#> SRR1951662 2 0.0000 0.9210 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 5 0.1628 0.7893 0.000 0.008 0.012 0.004 0.940 0.036
#> SRR1951660 4 0.4807 0.0893 0.392 0.000 0.000 0.556 0.004 0.048
#> SRR1951659 4 0.6104 0.5012 0.000 0.204 0.000 0.592 0.128 0.076
#> SRR1951658 2 0.2279 0.8218 0.000 0.904 0.000 0.056 0.024 0.016
#> SRR1951657 5 0.2499 0.7974 0.000 0.000 0.000 0.048 0.880 0.072
#> SRR1951656 4 0.4666 0.5755 0.000 0.000 0.000 0.676 0.216 0.108
#> SRR1951655 1 0.5103 0.5719 0.612 0.000 0.000 0.280 0.004 0.104
#> SRR1951654 1 0.2556 0.7599 0.888 0.000 0.000 0.012 0.052 0.048
#> SRR1951653 4 0.2026 0.7061 0.004 0.000 0.024 0.924 0.028 0.020
#> SRR1951652 5 0.3813 0.7115 0.084 0.012 0.000 0.000 0.796 0.108
#> SRR1951651 5 0.2507 0.7954 0.004 0.000 0.000 0.040 0.884 0.072
#> SRR1951650 5 0.3456 0.7592 0.000 0.000 0.004 0.156 0.800 0.040
#> SRR1951648 5 0.4834 0.4600 0.000 0.000 0.004 0.340 0.596 0.060
#> SRR1951649 4 0.4452 0.6506 0.060 0.000 0.068 0.764 0.000 0.108
#> SRR1951647 4 0.1262 0.7073 0.000 0.000 0.016 0.956 0.020 0.008
#> SRR1951646 4 0.4548 0.5527 0.000 0.000 0.000 0.672 0.248 0.080
#> SRR1951645 4 0.4606 0.6464 0.088 0.000 0.044 0.756 0.004 0.108
#> SRR1951644 4 0.3258 0.6730 0.092 0.000 0.064 0.836 0.000 0.008
#> SRR1951643 5 0.3093 0.7966 0.012 0.000 0.012 0.064 0.864 0.048
#> SRR1951641 4 0.1874 0.7002 0.028 0.000 0.028 0.928 0.000 0.016
#> SRR1951642 5 0.1036 0.8004 0.008 0.000 0.004 0.000 0.964 0.024
#> SRR1951694 1 0.2760 0.7568 0.856 0.000 0.004 0.116 0.000 0.024
#> SRR1951731 6 0.3852 0.6103 0.000 0.000 0.256 0.016 0.008 0.720
#> SRR1951730 3 0.7793 0.5104 0.060 0.180 0.524 0.036 0.100 0.100
#> SRR1951752 6 0.3367 0.6642 0.016 0.008 0.108 0.004 0.024 0.840
#> SRR1951751 3 0.0909 0.7506 0.000 0.000 0.968 0.012 0.000 0.020
#> SRR1951749 5 0.4839 0.4575 0.000 0.000 0.300 0.004 0.624 0.072
#> SRR1951750 1 0.5522 0.3452 0.580 0.000 0.324 0.016 0.016 0.064
#> SRR1951748 2 0.0000 0.9210 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9210 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 1 0.2894 0.7293 0.852 0.004 0.000 0.000 0.036 0.108
#> SRR1951746 6 0.4747 0.2769 0.000 0.376 0.056 0.000 0.000 0.568
#> SRR1951744 5 0.3265 0.7608 0.032 0.004 0.012 0.000 0.836 0.116
#> SRR1951742 5 0.4598 0.7243 0.000 0.032 0.000 0.092 0.740 0.136
#> SRR1951743 2 0.5031 -0.0997 0.000 0.476 0.060 0.004 0.000 0.460
#> SRR1951741 1 0.3168 0.7361 0.820 0.000 0.000 0.028 0.004 0.148
#> SRR1951740 6 0.5261 0.6565 0.000 0.000 0.100 0.060 0.152 0.688
#> SRR1951738 1 0.3236 0.7465 0.820 0.000 0.004 0.140 0.000 0.036
#> SRR1951739 6 0.3916 0.5707 0.000 0.000 0.300 0.020 0.000 0.680
#> SRR1951737 6 0.6630 0.3082 0.000 0.000 0.144 0.072 0.316 0.468
#> SRR1951736 1 0.5503 0.1180 0.456 0.000 0.416 0.128 0.000 0.000
#> SRR1951734 6 0.5173 0.5334 0.000 0.180 0.200 0.000 0.000 0.620
#> SRR1951735 5 0.5239 0.5715 0.000 0.000 0.052 0.276 0.628 0.044
#> SRR1951733 6 0.4481 0.6063 0.000 0.000 0.004 0.120 0.156 0.720
#> SRR1951732 2 0.0000 0.9210 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 3 0.4342 0.5514 0.000 0.000 0.688 0.024 0.268 0.020
#> SRR1951728 3 0.4852 0.6553 0.008 0.000 0.712 0.020 0.180 0.080
#> SRR1951727 2 0.0000 0.9210 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 1 0.0551 0.7693 0.984 0.000 0.004 0.000 0.004 0.008
#> SRR1951726 1 0.5372 0.3821 0.576 0.004 0.008 0.000 0.320 0.092
#> SRR1951724 3 0.6610 0.5219 0.164 0.000 0.560 0.204 0.040 0.032
#> SRR1951722 3 0.3544 0.7234 0.068 0.000 0.828 0.076 0.000 0.028
#> SRR1951723 3 0.4635 0.1472 0.008 0.000 0.492 0.476 0.000 0.024
#> SRR1951721 1 0.0632 0.7662 0.976 0.000 0.000 0.000 0.000 0.024
#> SRR1951719 3 0.2826 0.7177 0.000 0.000 0.856 0.008 0.112 0.024
#> SRR1951720 1 0.1429 0.7733 0.940 0.000 0.004 0.052 0.000 0.004
#> SRR1951718 4 0.2009 0.6939 0.084 0.000 0.004 0.904 0.000 0.008
#> SRR1951717 1 0.2572 0.7244 0.852 0.000 0.136 0.000 0.000 0.012
#> SRR1951715 1 0.1167 0.7738 0.960 0.000 0.012 0.020 0.000 0.008
#> SRR1951716 1 0.4457 0.3521 0.544 0.000 0.008 0.432 0.000 0.016
#> SRR1951714 6 0.5167 0.5646 0.036 0.000 0.000 0.116 0.164 0.684
#> SRR1951713 3 0.2688 0.7320 0.000 0.000 0.868 0.068 0.000 0.064
#> SRR1951712 3 0.1714 0.7572 0.024 0.000 0.936 0.016 0.000 0.024
#> SRR1951711 3 0.3405 0.7091 0.060 0.064 0.844 0.004 0.000 0.028
#> SRR1951710 1 0.2799 0.7570 0.852 0.000 0.012 0.124 0.000 0.012
#> SRR1951709 5 0.2511 0.7938 0.000 0.000 0.000 0.064 0.880 0.056
#> SRR1951708 2 0.0000 0.9210 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951707 3 0.2179 0.7572 0.008 0.000 0.916 0.040 0.012 0.024
#> SRR1951705 1 0.6128 0.3495 0.484 0.000 0.268 0.236 0.000 0.012
#> SRR1951704 5 0.5158 0.5778 0.020 0.004 0.172 0.000 0.680 0.124
#> SRR1951703 2 0.0000 0.9210 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 5 0.2723 0.7666 0.016 0.008 0.008 0.000 0.872 0.096
#> SRR1951702 5 0.1914 0.7842 0.000 0.008 0.016 0.000 0.920 0.056
#> SRR1951701 4 0.3593 0.6142 0.000 0.000 0.172 0.788 0.012 0.028
#> SRR1951699 5 0.1768 0.7927 0.000 0.004 0.020 0.004 0.932 0.040
#> SRR1951698 5 0.3810 0.7016 0.000 0.000 0.004 0.208 0.752 0.036
#> SRR1951700 3 0.2594 0.7451 0.036 0.000 0.888 0.000 0.056 0.020
#> SRR1951697 5 0.4276 0.3204 0.000 0.416 0.000 0.000 0.564 0.020
#> SRR1951696 5 0.5065 0.4234 0.000 0.000 0.000 0.340 0.568 0.092
#> SRR1951695 6 0.4282 0.5676 0.000 0.000 0.304 0.000 0.040 0.656
#> SRR1951693 3 0.4123 0.7415 0.044 0.000 0.808 0.020 0.056 0.072
#> SRR1951692 4 0.5803 0.2583 0.000 0.000 0.000 0.472 0.332 0.196
#> SRR1951691 6 0.3773 0.5614 0.192 0.000 0.020 0.020 0.000 0.768
#> SRR1951688 6 0.4992 0.4648 0.112 0.208 0.000 0.000 0.012 0.668
#> SRR1951689 3 0.1672 0.7451 0.004 0.000 0.932 0.016 0.000 0.048
#> SRR1951687 1 0.4399 0.3531 0.592 0.000 0.004 0.004 0.384 0.016
#> SRR1951685 3 0.0937 0.7411 0.000 0.000 0.960 0.000 0.000 0.040
#> SRR1951684 6 0.6908 0.4226 0.000 0.000 0.236 0.068 0.264 0.432
#> SRR1951686 5 0.2339 0.7809 0.000 0.012 0.020 0.000 0.896 0.072
#> SRR1951683 5 0.3867 0.7539 0.048 0.000 0.088 0.008 0.816 0.040
#> SRR1951682 4 0.1708 0.7000 0.024 0.000 0.040 0.932 0.000 0.004
#> SRR1951690 4 0.3314 0.5395 0.000 0.000 0.224 0.764 0.000 0.012
#> SRR1951680 3 0.4568 0.6182 0.000 0.000 0.704 0.212 0.012 0.072
#> SRR1951679 3 0.2624 0.7419 0.080 0.000 0.880 0.016 0.000 0.024
#> SRR1951678 5 0.3999 0.7383 0.004 0.000 0.000 0.072 0.760 0.164
#> SRR1951677 5 0.3705 0.7442 0.008 0.000 0.000 0.036 0.776 0.180
#> SRR1951676 5 0.1552 0.7877 0.000 0.004 0.020 0.000 0.940 0.036
#> SRR1951675 1 0.3490 0.7583 0.832 0.004 0.064 0.084 0.000 0.016
#> SRR1951681 5 0.2571 0.7966 0.000 0.000 0.000 0.060 0.876 0.064
#> SRR1951673 4 0.3575 0.6688 0.092 0.000 0.028 0.824 0.000 0.056
#> SRR1951674 1 0.3337 0.7127 0.824 0.004 0.000 0.000 0.064 0.108
#> SRR1951672 1 0.5009 0.6812 0.708 0.000 0.044 0.140 0.000 0.108
#> SRR1951671 3 0.4871 0.6080 0.000 0.000 0.692 0.108 0.016 0.184
#> SRR1951670 1 0.1668 0.7726 0.928 0.000 0.004 0.060 0.000 0.008
#> SRR1951640 4 0.4566 0.6042 0.000 0.000 0.000 0.700 0.160 0.140
#> SRR1951668 1 0.7110 0.1274 0.408 0.000 0.304 0.024 0.036 0.228
#> SRR1951669 1 0.3293 0.7095 0.812 0.140 0.000 0.000 0.000 0.048
#> SRR1951638 1 0.2380 0.7531 0.892 0.000 0.036 0.000 0.004 0.068
#> SRR1951637 1 0.1007 0.7617 0.956 0.000 0.000 0.000 0.000 0.044
#> SRR1951635 1 0.1003 0.7730 0.964 0.000 0.004 0.028 0.000 0.004
#> SRR1951634 4 0.4120 0.6794 0.032 0.000 0.036 0.800 0.024 0.108
#> SRR1951631 4 0.4445 0.4971 0.004 0.000 0.012 0.676 0.280 0.028
#> SRR1951632 6 0.4220 0.6204 0.000 0.000 0.016 0.168 0.064 0.752
#> SRR1951633 5 0.0603 0.7976 0.000 0.000 0.000 0.004 0.980 0.016
#> SRR1951630 5 0.5304 0.4599 0.000 0.000 0.000 0.276 0.580 0.144
#> SRR1951629 5 0.4454 0.7324 0.004 0.000 0.008 0.144 0.740 0.104
#> SRR1951628 4 0.3317 0.6631 0.072 0.000 0.012 0.836 0.000 0.080
#> SRR1951627 5 0.2527 0.7825 0.000 0.000 0.000 0.024 0.868 0.108
#> SRR1951639 4 0.4614 0.5646 0.000 0.000 0.000 0.676 0.228 0.096
#> SRR1951625 4 0.1864 0.7051 0.000 0.000 0.032 0.924 0.004 0.040
#> SRR1951626 5 0.2095 0.7989 0.000 0.000 0.016 0.004 0.904 0.076
#> SRR1951624 5 0.4467 0.0971 0.000 0.000 0.464 0.000 0.508 0.028
#> SRR1951622 1 0.4035 0.6032 0.680 0.000 0.004 0.296 0.000 0.020
#> SRR1951636 5 0.2094 0.7965 0.000 0.000 0.000 0.020 0.900 0.080
#> SRR1951623 5 0.1367 0.8004 0.000 0.000 0.000 0.012 0.944 0.044
#> SRR1951621 1 0.4008 0.7242 0.768 0.000 0.000 0.128 0.004 0.100
#> SRR1951620 4 0.5454 0.3910 0.000 0.000 0.000 0.548 0.300 0.152
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 13580 rows and 133 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.620 0.895 0.944 0.4486 0.531 0.531
#> 3 3 0.496 0.728 0.817 0.4033 0.789 0.607
#> 4 4 0.606 0.725 0.843 0.1356 0.853 0.605
#> 5 5 0.664 0.618 0.740 0.0801 0.864 0.558
#> 6 6 0.745 0.780 0.868 0.0569 0.916 0.649
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
#> SRR1951667 2 0.0000 0.8943 0.000 1.000
#> SRR1951666 2 0.8081 0.7676 0.248 0.752
#> SRR1951665 2 0.0000 0.8943 0.000 1.000
#> SRR1951664 1 0.1633 0.9470 0.976 0.024
#> SRR1951663 1 0.0000 0.9605 1.000 0.000
#> SRR1951662 2 0.0000 0.8943 0.000 1.000
#> SRR1951661 1 0.9393 0.3996 0.644 0.356
#> SRR1951660 1 0.0000 0.9605 1.000 0.000
#> SRR1951659 2 0.0000 0.8943 0.000 1.000
#> SRR1951658 2 0.0000 0.8943 0.000 1.000
#> SRR1951657 1 0.0000 0.9605 1.000 0.000
#> SRR1951656 1 0.0000 0.9605 1.000 0.000
#> SRR1951655 1 0.0000 0.9605 1.000 0.000
#> SRR1951654 1 0.0000 0.9605 1.000 0.000
#> SRR1951653 1 0.0938 0.9549 0.988 0.012
#> SRR1951652 2 0.7674 0.7990 0.224 0.776
#> SRR1951651 1 0.0000 0.9605 1.000 0.000
#> SRR1951650 1 0.0672 0.9572 0.992 0.008
#> SRR1951648 2 0.7674 0.7990 0.224 0.776
#> SRR1951649 1 0.0000 0.9605 1.000 0.000
#> SRR1951647 2 0.6801 0.8435 0.180 0.820
#> SRR1951646 1 0.0672 0.9572 0.992 0.008
#> SRR1951645 1 0.0000 0.9605 1.000 0.000
#> SRR1951644 1 0.4562 0.8800 0.904 0.096
#> SRR1951643 1 0.0000 0.9605 1.000 0.000
#> SRR1951641 2 0.0000 0.8943 0.000 1.000
#> SRR1951642 1 0.0000 0.9605 1.000 0.000
#> SRR1951694 1 0.0000 0.9605 1.000 0.000
#> SRR1951731 2 0.6247 0.8628 0.156 0.844
#> SRR1951730 2 0.7674 0.7990 0.224 0.776
#> SRR1951752 2 0.6247 0.8628 0.156 0.844
#> SRR1951751 1 0.0000 0.9605 1.000 0.000
#> SRR1951749 2 0.6247 0.8628 0.156 0.844
#> SRR1951750 1 0.0000 0.9605 1.000 0.000
#> SRR1951748 2 0.0000 0.8943 0.000 1.000
#> SRR1951747 2 0.0000 0.8943 0.000 1.000
#> SRR1951745 1 0.0000 0.9605 1.000 0.000
#> SRR1951746 2 0.0000 0.8943 0.000 1.000
#> SRR1951744 1 0.0000 0.9605 1.000 0.000
#> SRR1951742 2 0.0000 0.8943 0.000 1.000
#> SRR1951743 2 0.0000 0.8943 0.000 1.000
#> SRR1951741 1 0.0000 0.9605 1.000 0.000
#> SRR1951740 1 0.0000 0.9605 1.000 0.000
#> SRR1951738 2 0.0000 0.8943 0.000 1.000
#> SRR1951739 2 0.0000 0.8943 0.000 1.000
#> SRR1951737 1 0.0672 0.9572 0.992 0.008
#> SRR1951736 1 0.0000 0.9605 1.000 0.000
#> SRR1951734 2 0.0000 0.8943 0.000 1.000
#> SRR1951735 1 0.4815 0.8715 0.896 0.104
#> SRR1951733 2 0.6247 0.8628 0.156 0.844
#> SRR1951732 2 0.0000 0.8943 0.000 1.000
#> SRR1951729 1 0.0672 0.9572 0.992 0.008
#> SRR1951728 1 0.5294 0.8540 0.880 0.120
#> SRR1951727 2 0.0000 0.8943 0.000 1.000
#> SRR1951725 1 0.0000 0.9605 1.000 0.000
#> SRR1951726 1 0.5178 0.8587 0.884 0.116
#> SRR1951724 1 0.0000 0.9605 1.000 0.000
#> SRR1951722 1 0.5294 0.8540 0.880 0.120
#> SRR1951723 1 0.5178 0.8574 0.884 0.116
#> SRR1951721 1 0.2236 0.9359 0.964 0.036
#> SRR1951719 1 0.0376 0.9590 0.996 0.004
#> SRR1951720 1 0.0000 0.9605 1.000 0.000
#> SRR1951718 1 0.0376 0.9590 0.996 0.004
#> SRR1951717 1 0.0000 0.9605 1.000 0.000
#> SRR1951715 1 0.0000 0.9605 1.000 0.000
#> SRR1951716 1 0.0000 0.9605 1.000 0.000
#> SRR1951714 1 0.0000 0.9605 1.000 0.000
#> SRR1951713 1 0.0376 0.9590 0.996 0.004
#> SRR1951712 1 0.0000 0.9605 1.000 0.000
#> SRR1951711 2 0.0000 0.8943 0.000 1.000
#> SRR1951710 1 0.0000 0.9605 1.000 0.000
#> SRR1951709 2 0.7674 0.7990 0.224 0.776
#> SRR1951708 2 0.0000 0.8943 0.000 1.000
#> SRR1951707 1 0.9922 0.0738 0.552 0.448
#> SRR1951705 1 0.0000 0.9605 1.000 0.000
#> SRR1951704 1 0.5178 0.8587 0.884 0.116
#> SRR1951703 2 0.0000 0.8943 0.000 1.000
#> SRR1951706 1 0.5178 0.8587 0.884 0.116
#> SRR1951702 1 0.8081 0.6560 0.752 0.248
#> SRR1951701 2 0.0000 0.8943 0.000 1.000
#> SRR1951699 2 0.8327 0.7445 0.264 0.736
#> SRR1951698 2 0.8443 0.7316 0.272 0.728
#> SRR1951700 1 0.1414 0.9496 0.980 0.020
#> SRR1951697 2 0.0000 0.8943 0.000 1.000
#> SRR1951696 1 0.0938 0.9549 0.988 0.012
#> SRR1951695 1 0.3879 0.9009 0.924 0.076
#> SRR1951693 1 0.1414 0.9496 0.980 0.020
#> SRR1951692 1 0.0000 0.9605 1.000 0.000
#> SRR1951691 1 0.1184 0.9522 0.984 0.016
#> SRR1951688 2 0.6247 0.8628 0.156 0.844
#> SRR1951689 2 0.6247 0.8628 0.156 0.844
#> SRR1951687 1 0.0000 0.9605 1.000 0.000
#> SRR1951685 2 0.6247 0.8628 0.156 0.844
#> SRR1951684 1 0.0000 0.9605 1.000 0.000
#> SRR1951686 2 0.0000 0.8943 0.000 1.000
#> SRR1951683 1 0.0000 0.9605 1.000 0.000
#> SRR1951682 2 0.0000 0.8943 0.000 1.000
#> SRR1951690 2 0.0000 0.8943 0.000 1.000
#> SRR1951680 1 0.0376 0.9590 0.996 0.004
#> SRR1951679 1 0.5178 0.8574 0.884 0.116
#> SRR1951678 1 0.0000 0.9605 1.000 0.000
#> SRR1951677 1 0.0000 0.9605 1.000 0.000
#> SRR1951676 1 0.0000 0.9605 1.000 0.000
#> SRR1951675 2 0.0000 0.8943 0.000 1.000
#> SRR1951681 1 0.0376 0.9590 0.996 0.004
#> SRR1951673 2 0.6247 0.8628 0.156 0.844
#> SRR1951674 1 0.0000 0.9605 1.000 0.000
#> SRR1951672 1 0.0000 0.9605 1.000 0.000
#> SRR1951671 2 0.6247 0.8628 0.156 0.844
#> SRR1951670 1 0.0000 0.9605 1.000 0.000
#> SRR1951640 1 0.0672 0.9572 0.992 0.008
#> SRR1951668 1 0.0000 0.9605 1.000 0.000
#> SRR1951669 2 0.6247 0.8628 0.156 0.844
#> SRR1951638 1 0.0000 0.9605 1.000 0.000
#> SRR1951637 2 0.6247 0.8628 0.156 0.844
#> SRR1951635 1 0.0000 0.9605 1.000 0.000
#> SRR1951634 1 0.0000 0.9605 1.000 0.000
#> SRR1951631 1 0.0000 0.9605 1.000 0.000
#> SRR1951632 2 0.0000 0.8943 0.000 1.000
#> SRR1951633 1 0.0000 0.9605 1.000 0.000
#> SRR1951630 1 0.0000 0.9605 1.000 0.000
#> SRR1951629 1 0.0000 0.9605 1.000 0.000
#> SRR1951628 2 0.6531 0.8535 0.168 0.832
#> SRR1951627 2 0.9922 0.3162 0.448 0.552
#> SRR1951639 2 0.8386 0.7380 0.268 0.732
#> SRR1951625 2 0.6247 0.8628 0.156 0.844
#> SRR1951626 1 0.8499 0.5994 0.724 0.276
#> SRR1951624 1 0.0672 0.9572 0.992 0.008
#> SRR1951622 1 0.0000 0.9605 1.000 0.000
#> SRR1951636 1 0.8081 0.6560 0.752 0.248
#> SRR1951623 1 0.0000 0.9605 1.000 0.000
#> SRR1951621 1 0.0000 0.9605 1.000 0.000
#> SRR1951620 1 0.0000 0.9605 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951666 2 0.6225 0.649 0.000 0.568 0.432
#> SRR1951665 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951664 3 0.4002 0.812 0.160 0.000 0.840
#> SRR1951663 3 0.4346 0.810 0.184 0.000 0.816
#> SRR1951662 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951661 3 0.4178 0.475 0.000 0.172 0.828
#> SRR1951660 3 0.4702 0.797 0.212 0.000 0.788
#> SRR1951659 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951657 1 0.3482 0.806 0.872 0.000 0.128
#> SRR1951656 1 0.3482 0.806 0.872 0.000 0.128
#> SRR1951655 1 0.5016 0.741 0.760 0.000 0.240
#> SRR1951654 1 0.0000 0.773 1.000 0.000 0.000
#> SRR1951653 3 0.4178 0.812 0.172 0.000 0.828
#> SRR1951652 2 0.6154 0.682 0.000 0.592 0.408
#> SRR1951651 1 0.3038 0.805 0.896 0.000 0.104
#> SRR1951650 3 0.5098 0.754 0.248 0.000 0.752
#> SRR1951648 2 0.6154 0.682 0.000 0.592 0.408
#> SRR1951649 3 0.6140 0.387 0.404 0.000 0.596
#> SRR1951647 2 0.5968 0.731 0.000 0.636 0.364
#> SRR1951646 3 0.5254 0.734 0.264 0.000 0.736
#> SRR1951645 3 0.6140 0.387 0.404 0.000 0.596
#> SRR1951644 3 0.2711 0.782 0.088 0.000 0.912
#> SRR1951643 1 0.0000 0.773 1.000 0.000 0.000
#> SRR1951641 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951642 1 0.0000 0.773 1.000 0.000 0.000
#> SRR1951694 3 0.4555 0.804 0.200 0.000 0.800
#> SRR1951731 2 0.5835 0.752 0.000 0.660 0.340
#> SRR1951730 2 0.6154 0.682 0.000 0.592 0.408
#> SRR1951752 2 0.5835 0.752 0.000 0.660 0.340
#> SRR1951751 1 0.4842 0.760 0.776 0.000 0.224
#> SRR1951749 2 0.5835 0.752 0.000 0.660 0.340
#> SRR1951750 1 0.3038 0.805 0.896 0.000 0.104
#> SRR1951748 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951745 3 0.4399 0.809 0.188 0.000 0.812
#> SRR1951746 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951744 1 0.6225 0.318 0.568 0.000 0.432
#> SRR1951742 2 0.0424 0.826 0.000 0.992 0.008
#> SRR1951743 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951741 1 0.0000 0.773 1.000 0.000 0.000
#> SRR1951740 1 0.6286 0.173 0.536 0.000 0.464
#> SRR1951738 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951739 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951737 3 0.4235 0.812 0.176 0.000 0.824
#> SRR1951736 1 0.4796 0.763 0.780 0.000 0.220
#> SRR1951734 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951735 3 0.2860 0.778 0.084 0.004 0.912
#> SRR1951733 2 0.5835 0.752 0.000 0.660 0.340
#> SRR1951732 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951729 3 0.4235 0.812 0.176 0.000 0.824
#> SRR1951728 3 0.2496 0.769 0.068 0.004 0.928
#> SRR1951727 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951725 1 0.6126 0.420 0.600 0.000 0.400
#> SRR1951726 3 0.2261 0.771 0.068 0.000 0.932
#> SRR1951724 1 0.6244 0.287 0.560 0.000 0.440
#> SRR1951722 3 0.2496 0.769 0.068 0.004 0.928
#> SRR1951723 3 0.3359 0.770 0.084 0.016 0.900
#> SRR1951721 3 0.3816 0.809 0.148 0.000 0.852
#> SRR1951719 3 0.5591 0.672 0.304 0.000 0.696
#> SRR1951720 1 0.4796 0.763 0.780 0.000 0.220
#> SRR1951718 3 0.4654 0.800 0.208 0.000 0.792
#> SRR1951717 1 0.4842 0.759 0.776 0.000 0.224
#> SRR1951715 3 0.4346 0.810 0.184 0.000 0.816
#> SRR1951716 1 0.4796 0.763 0.780 0.000 0.220
#> SRR1951714 1 0.6260 0.258 0.552 0.000 0.448
#> SRR1951713 3 0.5706 0.641 0.320 0.000 0.680
#> SRR1951712 1 0.4842 0.760 0.776 0.000 0.224
#> SRR1951711 2 0.0237 0.826 0.000 0.996 0.004
#> SRR1951710 3 0.4346 0.810 0.184 0.000 0.816
#> SRR1951709 2 0.6154 0.682 0.000 0.592 0.408
#> SRR1951708 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951707 3 0.5254 0.224 0.000 0.264 0.736
#> SRR1951705 3 0.4702 0.797 0.212 0.000 0.788
#> SRR1951704 3 0.2261 0.771 0.068 0.000 0.932
#> SRR1951703 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951706 3 0.2261 0.771 0.068 0.000 0.932
#> SRR1951702 3 0.2496 0.648 0.004 0.068 0.928
#> SRR1951701 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951699 2 0.6260 0.622 0.000 0.552 0.448
#> SRR1951698 2 0.6274 0.610 0.000 0.544 0.456
#> SRR1951700 3 0.5058 0.748 0.244 0.000 0.756
#> SRR1951697 2 0.0424 0.826 0.000 0.992 0.008
#> SRR1951696 3 0.4178 0.812 0.172 0.000 0.828
#> SRR1951695 3 0.3192 0.795 0.112 0.000 0.888
#> SRR1951693 3 0.5098 0.744 0.248 0.000 0.752
#> SRR1951692 1 0.3482 0.806 0.872 0.000 0.128
#> SRR1951691 3 0.5216 0.725 0.260 0.000 0.740
#> SRR1951688 2 0.5835 0.752 0.000 0.660 0.340
#> SRR1951689 2 0.5835 0.752 0.000 0.660 0.340
#> SRR1951687 1 0.0000 0.773 1.000 0.000 0.000
#> SRR1951685 2 0.5835 0.752 0.000 0.660 0.340
#> SRR1951684 1 0.5926 0.516 0.644 0.000 0.356
#> SRR1951686 2 0.0424 0.826 0.000 0.992 0.008
#> SRR1951683 1 0.0000 0.773 1.000 0.000 0.000
#> SRR1951682 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951690 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951680 3 0.5706 0.641 0.320 0.000 0.680
#> SRR1951679 3 0.3359 0.770 0.084 0.016 0.900
#> SRR1951678 1 0.0000 0.773 1.000 0.000 0.000
#> SRR1951677 1 0.0000 0.773 1.000 0.000 0.000
#> SRR1951676 1 0.3116 0.805 0.892 0.000 0.108
#> SRR1951675 2 0.0000 0.826 0.000 1.000 0.000
#> SRR1951681 3 0.5529 0.688 0.296 0.000 0.704
#> SRR1951673 2 0.5835 0.752 0.000 0.660 0.340
#> SRR1951674 3 0.4399 0.809 0.188 0.000 0.812
#> SRR1951672 1 0.6295 0.142 0.528 0.000 0.472
#> SRR1951671 2 0.5835 0.752 0.000 0.660 0.340
#> SRR1951670 1 0.0000 0.773 1.000 0.000 0.000
#> SRR1951640 3 0.5254 0.734 0.264 0.000 0.736
#> SRR1951668 1 0.3412 0.806 0.876 0.000 0.124
#> SRR1951669 2 0.5835 0.752 0.000 0.660 0.340
#> SRR1951638 3 0.4399 0.809 0.188 0.000 0.812
#> SRR1951637 2 0.5835 0.752 0.000 0.660 0.340
#> SRR1951635 1 0.4796 0.763 0.780 0.000 0.220
#> SRR1951634 1 0.3412 0.806 0.876 0.000 0.124
#> SRR1951631 1 0.4750 0.764 0.784 0.000 0.216
#> SRR1951632 2 0.0424 0.826 0.000 0.992 0.008
#> SRR1951633 1 0.0000 0.773 1.000 0.000 0.000
#> SRR1951630 1 0.0000 0.773 1.000 0.000 0.000
#> SRR1951629 1 0.4750 0.764 0.784 0.000 0.216
#> SRR1951628 2 0.5905 0.740 0.000 0.648 0.352
#> SRR1951627 3 0.5988 -0.170 0.000 0.368 0.632
#> SRR1951639 2 0.6267 0.617 0.000 0.548 0.452
#> SRR1951625 2 0.5835 0.752 0.000 0.660 0.340
#> SRR1951626 3 0.2796 0.621 0.000 0.092 0.908
#> SRR1951624 3 0.4235 0.812 0.176 0.000 0.824
#> SRR1951622 3 0.4654 0.800 0.208 0.000 0.792
#> SRR1951636 3 0.2496 0.648 0.004 0.068 0.928
#> SRR1951623 1 0.3619 0.803 0.864 0.000 0.136
#> SRR1951621 1 0.0000 0.773 1.000 0.000 0.000
#> SRR1951620 1 0.3816 0.800 0.852 0.000 0.148
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951666 3 0.1042 0.8606 0.000 0.020 0.972 0.008
#> SRR1951665 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951664 4 0.3123 0.7603 0.000 0.000 0.156 0.844
#> SRR1951663 4 0.0188 0.7488 0.000 0.000 0.004 0.996
#> SRR1951662 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.4948 -0.0720 0.000 0.000 0.560 0.440
#> SRR1951660 4 0.1624 0.7467 0.028 0.000 0.020 0.952
#> SRR1951659 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951657 1 0.4164 0.7478 0.736 0.000 0.000 0.264
#> SRR1951656 1 0.4164 0.7478 0.736 0.000 0.000 0.264
#> SRR1951655 1 0.4898 0.5703 0.584 0.000 0.000 0.416
#> SRR1951654 1 0.0000 0.7336 1.000 0.000 0.000 0.000
#> SRR1951653 4 0.2921 0.7620 0.000 0.000 0.140 0.860
#> SRR1951652 3 0.1489 0.8755 0.000 0.044 0.952 0.004
#> SRR1951651 1 0.3873 0.7571 0.772 0.000 0.000 0.228
#> SRR1951650 4 0.3966 0.7526 0.072 0.000 0.088 0.840
#> SRR1951648 3 0.1489 0.8755 0.000 0.044 0.952 0.004
#> SRR1951649 4 0.3982 0.5703 0.220 0.000 0.004 0.776
#> SRR1951647 3 0.2081 0.8832 0.000 0.084 0.916 0.000
#> SRR1951646 4 0.4171 0.7446 0.088 0.000 0.084 0.828
#> SRR1951645 4 0.3982 0.5703 0.220 0.000 0.004 0.776
#> SRR1951644 4 0.4454 0.6575 0.000 0.000 0.308 0.692
#> SRR1951643 1 0.0000 0.7336 1.000 0.000 0.000 0.000
#> SRR1951641 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951642 1 0.0000 0.7336 1.000 0.000 0.000 0.000
#> SRR1951694 4 0.0779 0.7443 0.016 0.000 0.004 0.980
#> SRR1951731 3 0.2469 0.8851 0.000 0.108 0.892 0.000
#> SRR1951730 3 0.1489 0.8755 0.000 0.044 0.952 0.004
#> SRR1951752 3 0.2469 0.8851 0.000 0.108 0.892 0.000
#> SRR1951751 1 0.4830 0.6223 0.608 0.000 0.000 0.392
#> SRR1951749 3 0.2530 0.8829 0.000 0.112 0.888 0.000
#> SRR1951750 1 0.3873 0.7571 0.772 0.000 0.000 0.228
#> SRR1951748 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951745 4 0.0376 0.7485 0.004 0.000 0.004 0.992
#> SRR1951746 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951744 4 0.5004 0.1171 0.392 0.000 0.004 0.604
#> SRR1951742 2 0.4382 0.6048 0.000 0.704 0.296 0.000
#> SRR1951743 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951741 1 0.0000 0.7336 1.000 0.000 0.000 0.000
#> SRR1951740 4 0.5682 0.2605 0.352 0.000 0.036 0.612
#> SRR1951738 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951739 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951737 4 0.2868 0.7624 0.000 0.000 0.136 0.864
#> SRR1951736 1 0.4746 0.6561 0.632 0.000 0.000 0.368
#> SRR1951734 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951735 4 0.4543 0.6433 0.000 0.000 0.324 0.676
#> SRR1951733 3 0.2469 0.8851 0.000 0.108 0.892 0.000
#> SRR1951732 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951729 4 0.2868 0.7624 0.000 0.000 0.136 0.864
#> SRR1951728 4 0.4164 0.6999 0.000 0.000 0.264 0.736
#> SRR1951727 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951725 4 0.4916 -0.0369 0.424 0.000 0.000 0.576
#> SRR1951726 4 0.4134 0.7032 0.000 0.000 0.260 0.740
#> SRR1951724 4 0.4978 0.1540 0.384 0.000 0.004 0.612
#> SRR1951722 4 0.4164 0.6999 0.000 0.000 0.264 0.736
#> SRR1951723 4 0.4564 0.6271 0.000 0.000 0.328 0.672
#> SRR1951721 4 0.1474 0.7535 0.000 0.000 0.052 0.948
#> SRR1951719 4 0.5332 0.7046 0.128 0.000 0.124 0.748
#> SRR1951720 1 0.4746 0.6561 0.632 0.000 0.000 0.368
#> SRR1951718 4 0.1733 0.7493 0.028 0.000 0.024 0.948
#> SRR1951717 1 0.4804 0.6340 0.616 0.000 0.000 0.384
#> SRR1951715 4 0.0188 0.7488 0.000 0.000 0.004 0.996
#> SRR1951716 1 0.4746 0.6561 0.632 0.000 0.000 0.368
#> SRR1951714 4 0.5400 0.1962 0.372 0.000 0.020 0.608
#> SRR1951713 4 0.5528 0.6867 0.144 0.000 0.124 0.732
#> SRR1951712 1 0.4830 0.6223 0.608 0.000 0.000 0.392
#> SRR1951711 2 0.2469 0.8527 0.000 0.892 0.108 0.000
#> SRR1951710 4 0.0707 0.7532 0.000 0.000 0.020 0.980
#> SRR1951709 3 0.1489 0.8755 0.000 0.044 0.952 0.004
#> SRR1951708 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951707 3 0.4522 0.3336 0.000 0.000 0.680 0.320
#> SRR1951705 4 0.1624 0.7467 0.028 0.000 0.020 0.952
#> SRR1951704 4 0.4134 0.7032 0.000 0.000 0.260 0.740
#> SRR1951703 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951706 4 0.4134 0.7032 0.000 0.000 0.260 0.740
#> SRR1951702 4 0.4967 0.3549 0.000 0.000 0.452 0.548
#> SRR1951701 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951699 3 0.3550 0.8125 0.000 0.044 0.860 0.096
#> SRR1951698 3 0.0592 0.8437 0.000 0.000 0.984 0.016
#> SRR1951700 4 0.4168 0.7532 0.080 0.000 0.092 0.828
#> SRR1951697 2 0.4382 0.6048 0.000 0.704 0.296 0.000
#> SRR1951696 4 0.2921 0.7620 0.000 0.000 0.140 0.860
#> SRR1951695 4 0.3945 0.7368 0.004 0.000 0.216 0.780
#> SRR1951693 4 0.4171 0.7514 0.084 0.000 0.088 0.828
#> SRR1951692 1 0.4164 0.7478 0.736 0.000 0.000 0.264
#> SRR1951691 4 0.4030 0.7459 0.092 0.000 0.072 0.836
#> SRR1951688 3 0.2469 0.8851 0.000 0.108 0.892 0.000
#> SRR1951689 3 0.2530 0.8829 0.000 0.112 0.888 0.000
#> SRR1951687 1 0.0000 0.7336 1.000 0.000 0.000 0.000
#> SRR1951685 3 0.2469 0.8851 0.000 0.108 0.892 0.000
#> SRR1951684 4 0.4994 -0.2438 0.480 0.000 0.000 0.520
#> SRR1951686 2 0.4382 0.6048 0.000 0.704 0.296 0.000
#> SRR1951683 1 0.0000 0.7336 1.000 0.000 0.000 0.000
#> SRR1951682 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951680 4 0.5528 0.6867 0.144 0.000 0.124 0.732
#> SRR1951679 4 0.4564 0.6271 0.000 0.000 0.328 0.672
#> SRR1951678 1 0.0000 0.7336 1.000 0.000 0.000 0.000
#> SRR1951677 1 0.0000 0.7336 1.000 0.000 0.000 0.000
#> SRR1951676 1 0.3975 0.7542 0.760 0.000 0.000 0.240
#> SRR1951675 2 0.0000 0.9457 0.000 1.000 0.000 0.000
#> SRR1951681 4 0.4458 0.7243 0.116 0.000 0.076 0.808
#> SRR1951673 3 0.2469 0.8851 0.000 0.108 0.892 0.000
#> SRR1951674 4 0.0376 0.7485 0.004 0.000 0.004 0.992
#> SRR1951672 4 0.4819 0.2589 0.344 0.000 0.004 0.652
#> SRR1951671 3 0.2530 0.8829 0.000 0.112 0.888 0.000
#> SRR1951670 1 0.0000 0.7336 1.000 0.000 0.000 0.000
#> SRR1951640 4 0.4171 0.7444 0.088 0.000 0.084 0.828
#> SRR1951668 1 0.4193 0.7471 0.732 0.000 0.000 0.268
#> SRR1951669 3 0.2469 0.8851 0.000 0.108 0.892 0.000
#> SRR1951638 4 0.0376 0.7485 0.004 0.000 0.004 0.992
#> SRR1951637 3 0.2469 0.8851 0.000 0.108 0.892 0.000
#> SRR1951635 1 0.4746 0.6561 0.632 0.000 0.000 0.368
#> SRR1951634 1 0.4193 0.7471 0.732 0.000 0.000 0.268
#> SRR1951631 1 0.4804 0.6355 0.616 0.000 0.000 0.384
#> SRR1951632 2 0.3873 0.7107 0.000 0.772 0.228 0.000
#> SRR1951633 1 0.0000 0.7336 1.000 0.000 0.000 0.000
#> SRR1951630 1 0.0000 0.7336 1.000 0.000 0.000 0.000
#> SRR1951629 1 0.4804 0.6355 0.616 0.000 0.000 0.384
#> SRR1951628 3 0.2281 0.8848 0.000 0.096 0.904 0.000
#> SRR1951627 3 0.3975 0.5370 0.000 0.000 0.760 0.240
#> SRR1951639 3 0.0336 0.8475 0.000 0.000 0.992 0.008
#> SRR1951625 3 0.2530 0.8829 0.000 0.112 0.888 0.000
#> SRR1951626 4 0.4998 0.2637 0.000 0.000 0.488 0.512
#> SRR1951624 4 0.2868 0.7624 0.000 0.000 0.136 0.864
#> SRR1951622 4 0.1520 0.7484 0.024 0.000 0.020 0.956
#> SRR1951636 4 0.4967 0.3549 0.000 0.000 0.452 0.548
#> SRR1951623 1 0.4193 0.7429 0.732 0.000 0.000 0.268
#> SRR1951621 1 0.0000 0.7336 1.000 0.000 0.000 0.000
#> SRR1951620 1 0.4331 0.7333 0.712 0.000 0.000 0.288
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951666 5 0.2689 0.825 0.000 0.016 0.012 0.084 0.888
#> SRR1951665 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 3 0.7622 0.442 0.296 0.000 0.360 0.300 0.044
#> SRR1951663 3 0.0693 0.516 0.012 0.000 0.980 0.008 0.000
#> SRR1951662 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 5 0.7154 -0.120 0.024 0.000 0.280 0.248 0.448
#> SRR1951660 3 0.3399 0.474 0.172 0.000 0.812 0.012 0.004
#> SRR1951659 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951657 1 0.2648 0.454 0.848 0.000 0.000 0.152 0.000
#> SRR1951656 1 0.2648 0.454 0.848 0.000 0.000 0.152 0.000
#> SRR1951655 1 0.0510 0.592 0.984 0.000 0.016 0.000 0.000
#> SRR1951654 4 0.3837 1.000 0.308 0.000 0.000 0.692 0.000
#> SRR1951653 3 0.7409 0.421 0.312 0.000 0.360 0.300 0.028
#> SRR1951652 5 0.2521 0.843 0.000 0.024 0.008 0.068 0.900
#> SRR1951651 1 0.2966 0.375 0.816 0.000 0.000 0.184 0.000
#> SRR1951650 1 0.6930 -0.263 0.408 0.000 0.340 0.244 0.008
#> SRR1951648 5 0.2521 0.843 0.000 0.024 0.008 0.068 0.900
#> SRR1951649 1 0.5142 0.184 0.564 0.000 0.392 0.044 0.000
#> SRR1951647 5 0.0693 0.851 0.000 0.008 0.000 0.012 0.980
#> SRR1951646 1 0.6886 -0.211 0.428 0.000 0.328 0.236 0.008
#> SRR1951645 1 0.5142 0.184 0.564 0.000 0.392 0.044 0.000
#> SRR1951644 3 0.8275 0.527 0.168 0.000 0.392 0.236 0.204
#> SRR1951643 4 0.3837 1.000 0.308 0.000 0.000 0.692 0.000
#> SRR1951641 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951642 4 0.3837 1.000 0.308 0.000 0.000 0.692 0.000
#> SRR1951694 3 0.2193 0.484 0.092 0.000 0.900 0.008 0.000
#> SRR1951731 5 0.0880 0.861 0.000 0.032 0.000 0.000 0.968
#> SRR1951730 5 0.2521 0.843 0.000 0.024 0.008 0.068 0.900
#> SRR1951752 5 0.0880 0.861 0.000 0.032 0.000 0.000 0.968
#> SRR1951751 1 0.1012 0.583 0.968 0.000 0.012 0.020 0.000
#> SRR1951749 5 0.0963 0.859 0.000 0.036 0.000 0.000 0.964
#> SRR1951750 1 0.2929 0.382 0.820 0.000 0.000 0.180 0.000
#> SRR1951748 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 3 0.0798 0.514 0.016 0.000 0.976 0.008 0.000
#> SRR1951746 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 1 0.4073 0.531 0.752 0.000 0.216 0.032 0.000
#> SRR1951742 2 0.3774 0.627 0.000 0.704 0.000 0.000 0.296
#> SRR1951743 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 4 0.3837 1.000 0.308 0.000 0.000 0.692 0.000
#> SRR1951740 1 0.5008 0.464 0.708 0.000 0.140 0.152 0.000
#> SRR1951738 2 0.0162 0.944 0.000 0.996 0.000 0.000 0.004
#> SRR1951739 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951737 3 0.7348 0.414 0.316 0.000 0.360 0.300 0.024
#> SRR1951736 1 0.1522 0.560 0.944 0.000 0.012 0.044 0.000
#> SRR1951734 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 3 0.8339 0.522 0.164 0.000 0.368 0.256 0.212
#> SRR1951733 5 0.0880 0.861 0.000 0.032 0.000 0.000 0.968
#> SRR1951732 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 3 0.7348 0.414 0.316 0.000 0.360 0.300 0.024
#> SRR1951728 3 0.8065 0.558 0.164 0.000 0.432 0.240 0.164
#> SRR1951727 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 1 0.3675 0.564 0.788 0.000 0.188 0.024 0.000
#> SRR1951726 3 0.7942 0.563 0.156 0.000 0.456 0.228 0.160
#> SRR1951724 1 0.4150 0.521 0.748 0.000 0.216 0.036 0.000
#> SRR1951722 3 0.8065 0.558 0.164 0.000 0.432 0.240 0.164
#> SRR1951723 3 0.8237 0.528 0.148 0.000 0.392 0.236 0.224
#> SRR1951721 3 0.2395 0.533 0.012 0.000 0.912 0.040 0.036
#> SRR1951719 1 0.7120 -0.115 0.456 0.000 0.236 0.284 0.024
#> SRR1951720 1 0.1522 0.560 0.944 0.000 0.012 0.044 0.000
#> SRR1951718 3 0.3360 0.479 0.168 0.000 0.816 0.012 0.004
#> SRR1951717 1 0.1195 0.573 0.960 0.000 0.012 0.028 0.000
#> SRR1951715 3 0.1557 0.506 0.052 0.000 0.940 0.008 0.000
#> SRR1951716 1 0.1522 0.560 0.944 0.000 0.012 0.044 0.000
#> SRR1951714 1 0.4514 0.504 0.740 0.000 0.188 0.072 0.000
#> SRR1951713 1 0.7030 -0.061 0.476 0.000 0.216 0.284 0.024
#> SRR1951712 1 0.1012 0.583 0.968 0.000 0.012 0.020 0.000
#> SRR1951711 2 0.2127 0.859 0.000 0.892 0.000 0.000 0.108
#> SRR1951710 3 0.2976 0.500 0.132 0.000 0.852 0.012 0.004
#> SRR1951709 5 0.2521 0.843 0.000 0.024 0.008 0.068 0.900
#> SRR1951708 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951707 5 0.6150 0.244 0.004 0.000 0.244 0.176 0.576
#> SRR1951705 3 0.3320 0.476 0.164 0.000 0.820 0.012 0.004
#> SRR1951704 3 0.7942 0.563 0.156 0.000 0.456 0.228 0.160
#> SRR1951703 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 3 0.7924 0.564 0.156 0.000 0.460 0.224 0.160
#> SRR1951702 3 0.7333 0.372 0.028 0.000 0.384 0.248 0.340
#> SRR1951701 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951699 5 0.3578 0.767 0.008 0.008 0.020 0.132 0.832
#> SRR1951698 5 0.2522 0.799 0.000 0.000 0.012 0.108 0.880
#> SRR1951700 3 0.7062 0.282 0.380 0.000 0.380 0.224 0.016
#> SRR1951697 2 0.3774 0.627 0.000 0.704 0.000 0.000 0.296
#> SRR1951696 3 0.7409 0.421 0.312 0.000 0.360 0.300 0.028
#> SRR1951695 3 0.8108 0.521 0.224 0.000 0.392 0.268 0.116
#> SRR1951693 1 0.7035 -0.305 0.388 0.000 0.380 0.216 0.016
#> SRR1951692 1 0.2648 0.454 0.848 0.000 0.000 0.152 0.000
#> SRR1951691 3 0.6881 0.237 0.400 0.000 0.404 0.180 0.016
#> SRR1951688 5 0.0880 0.861 0.000 0.032 0.000 0.000 0.968
#> SRR1951689 5 0.0963 0.859 0.000 0.036 0.000 0.000 0.964
#> SRR1951687 4 0.3837 1.000 0.308 0.000 0.000 0.692 0.000
#> SRR1951685 5 0.0880 0.861 0.000 0.032 0.000 0.000 0.968
#> SRR1951684 1 0.4035 0.588 0.784 0.000 0.060 0.156 0.000
#> SRR1951686 2 0.3774 0.627 0.000 0.704 0.000 0.000 0.296
#> SRR1951683 4 0.3837 1.000 0.308 0.000 0.000 0.692 0.000
#> SRR1951682 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951680 1 0.7030 -0.061 0.476 0.000 0.216 0.284 0.024
#> SRR1951679 3 0.8237 0.528 0.148 0.000 0.392 0.236 0.224
#> SRR1951678 4 0.3837 1.000 0.308 0.000 0.000 0.692 0.000
#> SRR1951677 4 0.3837 1.000 0.308 0.000 0.000 0.692 0.000
#> SRR1951676 1 0.2891 0.403 0.824 0.000 0.000 0.176 0.000
#> SRR1951675 2 0.0000 0.946 0.000 1.000 0.000 0.000 0.000
#> SRR1951681 1 0.6809 -0.107 0.464 0.000 0.288 0.240 0.008
#> SRR1951673 5 0.0880 0.861 0.000 0.032 0.000 0.000 0.968
#> SRR1951674 3 0.0798 0.514 0.016 0.000 0.976 0.008 0.000
#> SRR1951672 1 0.4547 0.472 0.704 0.000 0.252 0.044 0.000
#> SRR1951671 5 0.0963 0.859 0.000 0.036 0.000 0.000 0.964
#> SRR1951670 4 0.3837 1.000 0.308 0.000 0.000 0.692 0.000
#> SRR1951640 1 0.6886 -0.209 0.428 0.000 0.328 0.236 0.008
#> SRR1951668 1 0.2516 0.467 0.860 0.000 0.000 0.140 0.000
#> SRR1951669 5 0.0880 0.861 0.000 0.032 0.000 0.000 0.968
#> SRR1951638 3 0.0798 0.514 0.016 0.000 0.976 0.008 0.000
#> SRR1951637 5 0.0880 0.861 0.000 0.032 0.000 0.000 0.968
#> SRR1951635 1 0.1522 0.560 0.944 0.000 0.012 0.044 0.000
#> SRR1951634 1 0.2516 0.467 0.860 0.000 0.000 0.140 0.000
#> SRR1951631 1 0.1670 0.585 0.936 0.000 0.012 0.052 0.000
#> SRR1951632 2 0.3336 0.726 0.000 0.772 0.000 0.000 0.228
#> SRR1951633 4 0.3837 1.000 0.308 0.000 0.000 0.692 0.000
#> SRR1951630 4 0.3837 1.000 0.308 0.000 0.000 0.692 0.000
#> SRR1951629 1 0.1670 0.585 0.936 0.000 0.012 0.052 0.000
#> SRR1951628 5 0.1012 0.856 0.000 0.020 0.000 0.012 0.968
#> SRR1951627 5 0.5816 0.451 0.012 0.000 0.160 0.180 0.648
#> SRR1951639 5 0.2416 0.806 0.000 0.000 0.012 0.100 0.888
#> SRR1951625 5 0.0963 0.859 0.000 0.036 0.000 0.000 0.964
#> SRR1951626 5 0.7204 -0.344 0.020 0.000 0.356 0.248 0.376
#> SRR1951624 3 0.7348 0.414 0.316 0.000 0.360 0.300 0.024
#> SRR1951622 3 0.3280 0.480 0.160 0.000 0.824 0.012 0.004
#> SRR1951636 3 0.7333 0.372 0.028 0.000 0.384 0.248 0.340
#> SRR1951623 1 0.3355 0.438 0.804 0.000 0.012 0.184 0.000
#> SRR1951621 4 0.3837 1.000 0.308 0.000 0.000 0.692 0.000
#> SRR1951620 1 0.2471 0.490 0.864 0.000 0.000 0.136 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951666 5 0.2234 0.8450 0.000 0.000 0.124 0.000 0.872 0.004
#> SRR1951665 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 3 0.0922 0.7512 0.024 0.000 0.968 0.000 0.004 0.004
#> SRR1951663 6 0.0653 0.8567 0.004 0.000 0.012 0.004 0.000 0.980
#> SRR1951662 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 3 0.4237 0.3221 0.000 0.000 0.584 0.000 0.396 0.020
#> SRR1951660 6 0.3706 0.8364 0.172 0.000 0.056 0.000 0.000 0.772
#> SRR1951659 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951657 1 0.4061 0.7429 0.748 0.000 0.088 0.164 0.000 0.000
#> SRR1951656 1 0.4061 0.7429 0.748 0.000 0.088 0.164 0.000 0.000
#> SRR1951655 1 0.0891 0.7660 0.968 0.000 0.024 0.000 0.000 0.008
#> SRR1951654 4 0.0146 1.0000 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951653 3 0.0865 0.7516 0.036 0.000 0.964 0.000 0.000 0.000
#> SRR1951652 5 0.1908 0.8680 0.000 0.000 0.096 0.000 0.900 0.004
#> SRR1951651 1 0.3043 0.7290 0.792 0.000 0.008 0.200 0.000 0.000
#> SRR1951650 3 0.2831 0.6989 0.136 0.000 0.840 0.000 0.000 0.024
#> SRR1951648 5 0.1908 0.8680 0.000 0.000 0.096 0.000 0.900 0.004
#> SRR1951649 1 0.5536 0.3052 0.536 0.000 0.164 0.000 0.000 0.300
#> SRR1951647 5 0.0790 0.8906 0.000 0.000 0.032 0.000 0.968 0.000
#> SRR1951646 3 0.3102 0.6790 0.156 0.000 0.816 0.000 0.000 0.028
#> SRR1951645 1 0.5536 0.3052 0.536 0.000 0.164 0.000 0.000 0.300
#> SRR1951644 3 0.3425 0.7106 0.008 0.000 0.800 0.000 0.164 0.028
#> SRR1951643 4 0.0146 1.0000 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951641 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951642 4 0.0146 1.0000 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951694 6 0.2199 0.8683 0.088 0.000 0.020 0.000 0.000 0.892
#> SRR1951731 5 0.0146 0.9000 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951730 5 0.1908 0.8680 0.000 0.000 0.096 0.000 0.900 0.004
#> SRR1951752 5 0.0146 0.9000 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951751 1 0.0665 0.7677 0.980 0.000 0.004 0.008 0.000 0.008
#> SRR1951749 5 0.0260 0.8988 0.000 0.008 0.000 0.000 0.992 0.000
#> SRR1951750 1 0.3012 0.7321 0.796 0.000 0.008 0.196 0.000 0.000
#> SRR1951748 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 6 0.0551 0.8560 0.004 0.000 0.008 0.004 0.000 0.984
#> SRR1951746 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951744 1 0.4433 0.6421 0.724 0.000 0.160 0.004 0.000 0.112
#> SRR1951742 2 0.3409 0.6277 0.000 0.700 0.000 0.000 0.300 0.000
#> SRR1951743 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951741 4 0.0146 1.0000 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951740 1 0.4413 0.1722 0.492 0.000 0.488 0.008 0.000 0.012
#> SRR1951738 2 0.0146 0.9387 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1951739 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951737 3 0.1152 0.7510 0.044 0.000 0.952 0.000 0.000 0.004
#> SRR1951736 1 0.1196 0.7610 0.952 0.000 0.000 0.040 0.000 0.008
#> SRR1951734 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951735 3 0.2814 0.7155 0.008 0.000 0.820 0.000 0.172 0.000
#> SRR1951733 5 0.0146 0.9000 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951732 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 3 0.1152 0.7510 0.044 0.000 0.952 0.000 0.000 0.004
#> SRR1951728 3 0.4180 0.6949 0.004 0.000 0.752 0.000 0.132 0.112
#> SRR1951727 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 1 0.4097 0.6676 0.760 0.000 0.128 0.004 0.000 0.108
#> SRR1951726 3 0.4381 0.6802 0.004 0.000 0.732 0.000 0.128 0.136
#> SRR1951724 1 0.4606 0.6093 0.692 0.000 0.212 0.004 0.000 0.092
#> SRR1951722 3 0.4093 0.6995 0.004 0.000 0.760 0.000 0.132 0.104
#> SRR1951723 3 0.3594 0.6972 0.008 0.000 0.780 0.000 0.184 0.028
#> SRR1951721 6 0.4047 0.4629 0.000 0.000 0.296 0.000 0.028 0.676
#> SRR1951719 3 0.3166 0.6456 0.184 0.000 0.800 0.008 0.000 0.008
#> SRR1951720 1 0.1196 0.7610 0.952 0.000 0.000 0.040 0.000 0.008
#> SRR1951718 6 0.3946 0.8288 0.168 0.000 0.076 0.000 0.000 0.756
#> SRR1951717 1 0.0717 0.7624 0.976 0.000 0.000 0.016 0.000 0.008
#> SRR1951715 6 0.1367 0.8660 0.044 0.000 0.012 0.000 0.000 0.944
#> SRR1951716 1 0.1196 0.7610 0.952 0.000 0.000 0.040 0.000 0.008
#> SRR1951714 1 0.4272 0.5446 0.668 0.000 0.288 0.000 0.000 0.044
#> SRR1951713 3 0.3323 0.6194 0.204 0.000 0.780 0.008 0.000 0.008
#> SRR1951712 1 0.0665 0.7677 0.980 0.000 0.004 0.008 0.000 0.008
#> SRR1951711 2 0.1910 0.8520 0.000 0.892 0.000 0.000 0.108 0.000
#> SRR1951710 6 0.3254 0.8574 0.136 0.000 0.048 0.000 0.000 0.816
#> SRR1951709 5 0.1908 0.8680 0.000 0.000 0.096 0.000 0.900 0.004
#> SRR1951708 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951707 5 0.3991 0.0819 0.000 0.000 0.472 0.000 0.524 0.004
#> SRR1951705 6 0.3612 0.8418 0.168 0.000 0.052 0.000 0.000 0.780
#> SRR1951704 3 0.4343 0.6828 0.004 0.000 0.736 0.000 0.128 0.132
#> SRR1951703 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 3 0.4454 0.6737 0.004 0.000 0.724 0.000 0.128 0.144
#> SRR1951702 3 0.4291 0.5487 0.000 0.000 0.664 0.000 0.292 0.044
#> SRR1951701 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951699 5 0.2772 0.7824 0.000 0.000 0.180 0.000 0.816 0.004
#> SRR1951698 5 0.2595 0.8151 0.000 0.000 0.160 0.000 0.836 0.004
#> SRR1951700 3 0.4397 0.5574 0.284 0.000 0.672 0.000 0.012 0.032
#> SRR1951697 2 0.3409 0.6277 0.000 0.700 0.000 0.000 0.300 0.000
#> SRR1951696 3 0.0865 0.7516 0.036 0.000 0.964 0.000 0.000 0.000
#> SRR1951695 3 0.3616 0.7480 0.040 0.000 0.824 0.000 0.088 0.048
#> SRR1951693 3 0.4626 0.5372 0.292 0.000 0.652 0.000 0.012 0.044
#> SRR1951692 1 0.4061 0.7429 0.748 0.000 0.088 0.164 0.000 0.000
#> SRR1951691 3 0.5089 0.4347 0.328 0.000 0.592 0.000 0.012 0.068
#> SRR1951688 5 0.0146 0.9000 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951689 5 0.0260 0.8988 0.000 0.008 0.000 0.000 0.992 0.000
#> SRR1951687 4 0.0146 1.0000 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951685 5 0.0146 0.9000 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951684 1 0.4694 0.4593 0.572 0.000 0.376 0.052 0.000 0.000
#> SRR1951686 2 0.3409 0.6277 0.000 0.700 0.000 0.000 0.300 0.000
#> SRR1951683 4 0.0146 1.0000 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951682 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951680 3 0.3323 0.6194 0.204 0.000 0.780 0.008 0.000 0.008
#> SRR1951679 3 0.3594 0.6972 0.008 0.000 0.780 0.000 0.184 0.028
#> SRR1951678 4 0.0146 1.0000 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951677 4 0.0146 1.0000 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951676 1 0.4002 0.7282 0.744 0.000 0.068 0.188 0.000 0.000
#> SRR1951675 2 0.0000 0.9417 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951681 3 0.3514 0.6215 0.208 0.000 0.768 0.004 0.000 0.020
#> SRR1951673 5 0.0146 0.9000 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951674 6 0.0551 0.8560 0.004 0.000 0.008 0.004 0.000 0.984
#> SRR1951672 1 0.4767 0.5841 0.676 0.000 0.168 0.000 0.000 0.156
#> SRR1951671 5 0.0260 0.8988 0.000 0.008 0.000 0.000 0.992 0.000
#> SRR1951670 4 0.0146 1.0000 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951640 3 0.2981 0.6779 0.160 0.000 0.820 0.000 0.000 0.020
#> SRR1951668 1 0.2790 0.7639 0.840 0.000 0.020 0.140 0.000 0.000
#> SRR1951669 5 0.0146 0.9000 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951638 6 0.0551 0.8560 0.004 0.000 0.008 0.004 0.000 0.984
#> SRR1951637 5 0.0146 0.9000 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951635 1 0.1196 0.7610 0.952 0.000 0.000 0.040 0.000 0.008
#> SRR1951634 1 0.2790 0.7639 0.840 0.000 0.020 0.140 0.000 0.000
#> SRR1951631 1 0.2629 0.7788 0.868 0.000 0.092 0.040 0.000 0.000
#> SRR1951632 2 0.3023 0.7262 0.000 0.768 0.000 0.000 0.232 0.000
#> SRR1951633 4 0.0146 1.0000 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951630 4 0.0146 1.0000 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951629 1 0.2629 0.7788 0.868 0.000 0.092 0.040 0.000 0.000
#> SRR1951628 5 0.0547 0.8942 0.000 0.000 0.020 0.000 0.980 0.000
#> SRR1951627 5 0.3881 0.3598 0.000 0.000 0.396 0.000 0.600 0.004
#> SRR1951639 5 0.2520 0.8236 0.000 0.000 0.152 0.000 0.844 0.004
#> SRR1951625 5 0.0260 0.8988 0.000 0.008 0.000 0.000 0.992 0.000
#> SRR1951626 3 0.3652 0.5005 0.000 0.000 0.672 0.000 0.324 0.004
#> SRR1951624 3 0.1152 0.7510 0.044 0.000 0.952 0.000 0.000 0.004
#> SRR1951622 6 0.3578 0.8449 0.164 0.000 0.052 0.000 0.000 0.784
#> SRR1951636 3 0.4291 0.5487 0.000 0.000 0.664 0.000 0.292 0.044
#> SRR1951623 1 0.4422 0.7306 0.720 0.000 0.096 0.180 0.000 0.004
#> SRR1951621 4 0.0146 1.0000 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951620 1 0.4117 0.7476 0.748 0.000 0.112 0.140 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "kmeans"]
# you can also extract it by
# res = res_list["ATC:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 13580 rows and 133 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 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 0.984 0.994 0.4684 0.535 0.535
#> 3 3 1.000 0.959 0.983 0.3862 0.675 0.463
#> 4 4 0.907 0.886 0.940 0.1241 0.795 0.505
#> 5 5 0.765 0.710 0.854 0.0754 0.906 0.679
#> 6 6 0.762 0.736 0.850 0.0486 0.896 0.576
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1951667 2 0.00 1.0000 0.000 1.000
#> SRR1951666 2 0.00 1.0000 0.000 1.000
#> SRR1951665 2 0.00 1.0000 0.000 1.000
#> SRR1951664 1 0.00 0.9909 1.000 0.000
#> SRR1951663 1 0.00 0.9909 1.000 0.000
#> SRR1951662 2 0.00 1.0000 0.000 1.000
#> SRR1951661 1 0.00 0.9909 1.000 0.000
#> SRR1951660 1 0.00 0.9909 1.000 0.000
#> SRR1951659 2 0.00 1.0000 0.000 1.000
#> SRR1951658 2 0.00 1.0000 0.000 1.000
#> SRR1951657 1 0.00 0.9909 1.000 0.000
#> SRR1951656 1 0.00 0.9909 1.000 0.000
#> SRR1951655 1 0.00 0.9909 1.000 0.000
#> SRR1951654 1 0.00 0.9909 1.000 0.000
#> SRR1951653 1 0.00 0.9909 1.000 0.000
#> SRR1951652 2 0.00 1.0000 0.000 1.000
#> SRR1951651 1 0.00 0.9909 1.000 0.000
#> SRR1951650 1 0.00 0.9909 1.000 0.000
#> SRR1951648 2 0.00 1.0000 0.000 1.000
#> SRR1951649 1 0.00 0.9909 1.000 0.000
#> SRR1951647 2 0.00 1.0000 0.000 1.000
#> SRR1951646 1 0.00 0.9909 1.000 0.000
#> SRR1951645 1 0.00 0.9909 1.000 0.000
#> SRR1951644 1 0.00 0.9909 1.000 0.000
#> SRR1951643 1 0.00 0.9909 1.000 0.000
#> SRR1951641 2 0.00 1.0000 0.000 1.000
#> SRR1951642 1 0.00 0.9909 1.000 0.000
#> SRR1951694 1 0.00 0.9909 1.000 0.000
#> SRR1951731 2 0.00 1.0000 0.000 1.000
#> SRR1951730 2 0.00 1.0000 0.000 1.000
#> SRR1951752 2 0.00 1.0000 0.000 1.000
#> SRR1951751 1 0.00 0.9909 1.000 0.000
#> SRR1951749 2 0.00 1.0000 0.000 1.000
#> SRR1951750 1 0.00 0.9909 1.000 0.000
#> SRR1951748 2 0.00 1.0000 0.000 1.000
#> SRR1951747 2 0.00 1.0000 0.000 1.000
#> SRR1951745 1 0.00 0.9909 1.000 0.000
#> SRR1951746 2 0.00 1.0000 0.000 1.000
#> SRR1951744 1 0.00 0.9909 1.000 0.000
#> SRR1951742 2 0.00 1.0000 0.000 1.000
#> SRR1951743 2 0.00 1.0000 0.000 1.000
#> SRR1951741 1 0.00 0.9909 1.000 0.000
#> SRR1951740 1 0.00 0.9909 1.000 0.000
#> SRR1951738 2 0.00 1.0000 0.000 1.000
#> SRR1951739 2 0.00 1.0000 0.000 1.000
#> SRR1951737 1 0.00 0.9909 1.000 0.000
#> SRR1951736 1 0.00 0.9909 1.000 0.000
#> SRR1951734 2 0.00 1.0000 0.000 1.000
#> SRR1951735 1 0.00 0.9909 1.000 0.000
#> SRR1951733 2 0.00 1.0000 0.000 1.000
#> SRR1951732 2 0.00 1.0000 0.000 1.000
#> SRR1951729 1 0.00 0.9909 1.000 0.000
#> SRR1951728 1 0.00 0.9909 1.000 0.000
#> SRR1951727 2 0.00 1.0000 0.000 1.000
#> SRR1951725 1 0.00 0.9909 1.000 0.000
#> SRR1951726 1 0.00 0.9909 1.000 0.000
#> SRR1951724 1 0.00 0.9909 1.000 0.000
#> SRR1951722 1 0.00 0.9909 1.000 0.000
#> SRR1951723 1 0.00 0.9909 1.000 0.000
#> SRR1951721 1 0.00 0.9909 1.000 0.000
#> SRR1951719 1 0.00 0.9909 1.000 0.000
#> SRR1951720 1 0.00 0.9909 1.000 0.000
#> SRR1951718 1 0.00 0.9909 1.000 0.000
#> SRR1951717 1 0.00 0.9909 1.000 0.000
#> SRR1951715 1 0.00 0.9909 1.000 0.000
#> SRR1951716 1 0.00 0.9909 1.000 0.000
#> SRR1951714 1 0.00 0.9909 1.000 0.000
#> SRR1951713 1 0.00 0.9909 1.000 0.000
#> SRR1951712 1 0.00 0.9909 1.000 0.000
#> SRR1951711 2 0.00 1.0000 0.000 1.000
#> SRR1951710 1 0.00 0.9909 1.000 0.000
#> SRR1951709 2 0.00 1.0000 0.000 1.000
#> SRR1951708 2 0.00 1.0000 0.000 1.000
#> SRR1951707 2 0.00 1.0000 0.000 1.000
#> SRR1951705 1 0.00 0.9909 1.000 0.000
#> SRR1951704 1 0.00 0.9909 1.000 0.000
#> SRR1951703 2 0.00 1.0000 0.000 1.000
#> SRR1951706 1 0.00 0.9909 1.000 0.000
#> SRR1951702 1 0.00 0.9909 1.000 0.000
#> SRR1951701 2 0.00 1.0000 0.000 1.000
#> SRR1951699 2 0.00 1.0000 0.000 1.000
#> SRR1951698 1 0.85 0.6193 0.724 0.276
#> SRR1951700 1 0.00 0.9909 1.000 0.000
#> SRR1951697 2 0.00 1.0000 0.000 1.000
#> SRR1951696 1 0.00 0.9909 1.000 0.000
#> SRR1951695 1 0.00 0.9909 1.000 0.000
#> SRR1951693 1 0.00 0.9909 1.000 0.000
#> SRR1951692 1 0.00 0.9909 1.000 0.000
#> SRR1951691 1 0.00 0.9909 1.000 0.000
#> SRR1951688 2 0.00 1.0000 0.000 1.000
#> SRR1951689 2 0.00 1.0000 0.000 1.000
#> SRR1951687 1 0.00 0.9909 1.000 0.000
#> SRR1951685 2 0.00 1.0000 0.000 1.000
#> SRR1951684 1 0.00 0.9909 1.000 0.000
#> SRR1951686 2 0.00 1.0000 0.000 1.000
#> SRR1951683 1 0.00 0.9909 1.000 0.000
#> SRR1951682 2 0.00 1.0000 0.000 1.000
#> SRR1951690 2 0.00 1.0000 0.000 1.000
#> SRR1951680 1 0.00 0.9909 1.000 0.000
#> SRR1951679 1 0.00 0.9909 1.000 0.000
#> SRR1951678 1 0.00 0.9909 1.000 0.000
#> SRR1951677 1 0.00 0.9909 1.000 0.000
#> SRR1951676 1 0.00 0.9909 1.000 0.000
#> SRR1951675 2 0.00 1.0000 0.000 1.000
#> SRR1951681 1 0.00 0.9909 1.000 0.000
#> SRR1951673 2 0.00 1.0000 0.000 1.000
#> SRR1951674 1 0.00 0.9909 1.000 0.000
#> SRR1951672 1 0.00 0.9909 1.000 0.000
#> SRR1951671 2 0.00 1.0000 0.000 1.000
#> SRR1951670 1 0.00 0.9909 1.000 0.000
#> SRR1951640 1 0.00 0.9909 1.000 0.000
#> SRR1951668 1 0.00 0.9909 1.000 0.000
#> SRR1951669 2 0.00 1.0000 0.000 1.000
#> SRR1951638 1 0.00 0.9909 1.000 0.000
#> SRR1951637 2 0.00 1.0000 0.000 1.000
#> SRR1951635 1 0.00 0.9909 1.000 0.000
#> SRR1951634 1 0.00 0.9909 1.000 0.000
#> SRR1951631 1 0.00 0.9909 1.000 0.000
#> SRR1951632 2 0.00 1.0000 0.000 1.000
#> SRR1951633 1 0.00 0.9909 1.000 0.000
#> SRR1951630 1 0.00 0.9909 1.000 0.000
#> SRR1951629 1 0.00 0.9909 1.000 0.000
#> SRR1951628 2 0.00 1.0000 0.000 1.000
#> SRR1951627 1 1.00 0.0512 0.512 0.488
#> SRR1951639 2 0.00 1.0000 0.000 1.000
#> SRR1951625 2 0.00 1.0000 0.000 1.000
#> SRR1951626 1 0.00 0.9909 1.000 0.000
#> SRR1951624 1 0.00 0.9909 1.000 0.000
#> SRR1951622 1 0.00 0.9909 1.000 0.000
#> SRR1951636 1 0.00 0.9909 1.000 0.000
#> SRR1951623 1 0.00 0.9909 1.000 0.000
#> SRR1951621 1 0.00 0.9909 1.000 0.000
#> SRR1951620 1 0.00 0.9909 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951666 3 0.2448 0.914 0.000 0.076 0.924
#> SRR1951665 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951664 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951663 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951662 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951661 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951660 3 0.0892 0.969 0.020 0.000 0.980
#> SRR1951659 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951657 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951656 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951655 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951654 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951653 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951652 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951651 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951650 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951648 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951649 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951647 3 0.5397 0.625 0.000 0.280 0.720
#> SRR1951646 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951645 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951644 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951643 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951641 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951642 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951694 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951731 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951730 3 0.4504 0.763 0.000 0.196 0.804
#> SRR1951752 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951751 3 0.0892 0.969 0.020 0.000 0.980
#> SRR1951749 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951750 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951748 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951745 1 0.0592 0.950 0.988 0.000 0.012
#> SRR1951746 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951744 3 0.0892 0.969 0.020 0.000 0.980
#> SRR1951742 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951743 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951741 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951740 1 0.6225 0.272 0.568 0.000 0.432
#> SRR1951738 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951739 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951737 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951736 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951734 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951735 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951733 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951732 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951729 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951728 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951727 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951725 1 0.0592 0.950 0.988 0.000 0.012
#> SRR1951726 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951724 3 0.1163 0.962 0.028 0.000 0.972
#> SRR1951722 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951723 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951721 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951719 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951720 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951718 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951717 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951715 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951716 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951714 1 0.6274 0.198 0.544 0.000 0.456
#> SRR1951713 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951712 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951711 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951710 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951709 3 0.3879 0.825 0.000 0.152 0.848
#> SRR1951708 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951707 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951705 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951704 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951703 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951706 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951702 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951701 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951699 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951698 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951700 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951697 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951696 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951695 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951693 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951692 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951691 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951688 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951689 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951687 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951685 3 0.0424 0.978 0.000 0.008 0.992
#> SRR1951684 1 0.5948 0.452 0.640 0.000 0.360
#> SRR1951686 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951683 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951682 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951690 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951680 3 0.1289 0.959 0.032 0.000 0.968
#> SRR1951679 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951678 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951677 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951676 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951675 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951681 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951673 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951674 1 0.0592 0.950 0.988 0.000 0.012
#> SRR1951672 3 0.1163 0.962 0.028 0.000 0.972
#> SRR1951671 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951670 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951640 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951668 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951669 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951638 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951637 2 0.1529 0.954 0.000 0.960 0.040
#> SRR1951635 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951634 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951631 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951632 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951633 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951630 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951629 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951628 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951627 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951639 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951625 2 0.0000 0.999 0.000 1.000 0.000
#> SRR1951626 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951624 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951622 3 0.0892 0.969 0.020 0.000 0.980
#> SRR1951636 3 0.0000 0.984 0.000 0.000 1.000
#> SRR1951623 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951621 1 0.0000 0.960 1.000 0.000 0.000
#> SRR1951620 1 0.0000 0.960 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951666 3 0.0000 0.9558 0.000 0.000 1.000 0.000
#> SRR1951665 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951664 4 0.1940 0.9091 0.000 0.000 0.076 0.924
#> SRR1951663 4 0.2647 0.8278 0.000 0.000 0.120 0.880
#> SRR1951662 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.0000 0.9558 0.000 0.000 1.000 0.000
#> SRR1951660 4 0.0000 0.9001 0.000 0.000 0.000 1.000
#> SRR1951659 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951657 1 0.4008 0.6994 0.756 0.000 0.000 0.244
#> SRR1951656 1 0.2973 0.8319 0.856 0.000 0.000 0.144
#> SRR1951655 1 0.4972 0.2837 0.544 0.000 0.000 0.456
#> SRR1951654 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951653 4 0.1940 0.9091 0.000 0.000 0.076 0.924
#> SRR1951652 3 0.0000 0.9558 0.000 0.000 1.000 0.000
#> SRR1951651 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951650 4 0.1940 0.9091 0.000 0.000 0.076 0.924
#> SRR1951648 3 0.0000 0.9558 0.000 0.000 1.000 0.000
#> SRR1951649 4 0.0000 0.9001 0.000 0.000 0.000 1.000
#> SRR1951647 3 0.0000 0.9558 0.000 0.000 1.000 0.000
#> SRR1951646 4 0.1940 0.9091 0.000 0.000 0.076 0.924
#> SRR1951645 4 0.0000 0.9001 0.000 0.000 0.000 1.000
#> SRR1951644 4 0.0000 0.9001 0.000 0.000 0.000 1.000
#> SRR1951643 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951641 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951642 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951694 4 0.0000 0.9001 0.000 0.000 0.000 1.000
#> SRR1951731 3 0.0000 0.9558 0.000 0.000 1.000 0.000
#> SRR1951730 3 0.0000 0.9558 0.000 0.000 1.000 0.000
#> SRR1951752 3 0.0000 0.9558 0.000 0.000 1.000 0.000
#> SRR1951751 4 0.0000 0.9001 0.000 0.000 0.000 1.000
#> SRR1951749 3 0.1940 0.9314 0.000 0.076 0.924 0.000
#> SRR1951750 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951748 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951745 4 0.4605 0.4484 0.336 0.000 0.000 0.664
#> SRR1951746 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951744 4 0.0000 0.9001 0.000 0.000 0.000 1.000
#> SRR1951742 3 0.2589 0.8951 0.000 0.116 0.884 0.000
#> SRR1951743 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951741 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951740 4 0.0524 0.9006 0.008 0.000 0.004 0.988
#> SRR1951738 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951739 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951737 4 0.1940 0.9091 0.000 0.000 0.076 0.924
#> SRR1951736 1 0.2216 0.8904 0.908 0.000 0.000 0.092
#> SRR1951734 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951735 4 0.2281 0.9010 0.000 0.000 0.096 0.904
#> SRR1951733 3 0.1792 0.9348 0.000 0.068 0.932 0.000
#> SRR1951732 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951729 4 0.1940 0.9091 0.000 0.000 0.076 0.924
#> SRR1951728 4 0.3024 0.8628 0.000 0.000 0.148 0.852
#> SRR1951727 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951725 4 0.0000 0.9001 0.000 0.000 0.000 1.000
#> SRR1951726 4 0.3801 0.7864 0.000 0.000 0.220 0.780
#> SRR1951724 4 0.0000 0.9001 0.000 0.000 0.000 1.000
#> SRR1951722 4 0.2149 0.9049 0.000 0.000 0.088 0.912
#> SRR1951723 4 0.2281 0.9010 0.000 0.000 0.096 0.904
#> SRR1951721 4 0.2973 0.8024 0.000 0.000 0.144 0.856
#> SRR1951719 4 0.1940 0.9091 0.000 0.000 0.076 0.924
#> SRR1951720 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951718 4 0.0000 0.9001 0.000 0.000 0.000 1.000
#> SRR1951717 1 0.2281 0.8874 0.904 0.000 0.000 0.096
#> SRR1951715 4 0.0000 0.9001 0.000 0.000 0.000 1.000
#> SRR1951716 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951714 4 0.0000 0.9001 0.000 0.000 0.000 1.000
#> SRR1951713 4 0.1867 0.9094 0.000 0.000 0.072 0.928
#> SRR1951712 1 0.2647 0.8706 0.880 0.000 0.000 0.120
#> SRR1951711 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951710 4 0.0000 0.9001 0.000 0.000 0.000 1.000
#> SRR1951709 3 0.0000 0.9558 0.000 0.000 1.000 0.000
#> SRR1951708 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951707 3 0.0000 0.9558 0.000 0.000 1.000 0.000
#> SRR1951705 4 0.0000 0.9001 0.000 0.000 0.000 1.000
#> SRR1951704 4 0.2011 0.9080 0.000 0.000 0.080 0.920
#> SRR1951703 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951706 4 0.2081 0.9070 0.000 0.000 0.084 0.916
#> SRR1951702 4 0.2281 0.9013 0.000 0.000 0.096 0.904
#> SRR1951701 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951699 3 0.0000 0.9558 0.000 0.000 1.000 0.000
#> SRR1951698 3 0.0000 0.9558 0.000 0.000 1.000 0.000
#> SRR1951700 4 0.1867 0.9094 0.000 0.000 0.072 0.928
#> SRR1951697 2 0.4817 0.3099 0.000 0.612 0.388 0.000
#> SRR1951696 4 0.1940 0.9091 0.000 0.000 0.076 0.924
#> SRR1951695 4 0.1940 0.9091 0.000 0.000 0.076 0.924
#> SRR1951693 4 0.1867 0.9094 0.000 0.000 0.072 0.928
#> SRR1951692 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951691 4 0.0188 0.9009 0.000 0.000 0.004 0.996
#> SRR1951688 3 0.1940 0.9314 0.000 0.076 0.924 0.000
#> SRR1951689 3 0.1940 0.9314 0.000 0.076 0.924 0.000
#> SRR1951687 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951685 3 0.0000 0.9558 0.000 0.000 1.000 0.000
#> SRR1951684 4 0.2256 0.8895 0.056 0.000 0.020 0.924
#> SRR1951686 3 0.2345 0.9113 0.000 0.100 0.900 0.000
#> SRR1951683 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951682 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951680 4 0.1867 0.9094 0.000 0.000 0.072 0.928
#> SRR1951679 4 0.2149 0.9049 0.000 0.000 0.088 0.912
#> SRR1951678 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951677 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951676 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951675 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951681 4 0.1940 0.9091 0.000 0.000 0.076 0.924
#> SRR1951673 3 0.1940 0.9314 0.000 0.076 0.924 0.000
#> SRR1951674 4 0.4605 0.4484 0.336 0.000 0.000 0.664
#> SRR1951672 4 0.0000 0.9001 0.000 0.000 0.000 1.000
#> SRR1951671 3 0.1940 0.9314 0.000 0.076 0.924 0.000
#> SRR1951670 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951640 4 0.1940 0.9091 0.000 0.000 0.076 0.924
#> SRR1951668 1 0.0707 0.9256 0.980 0.000 0.000 0.020
#> SRR1951669 3 0.2345 0.9113 0.000 0.100 0.900 0.000
#> SRR1951638 4 0.4998 -0.0490 0.488 0.000 0.000 0.512
#> SRR1951637 3 0.1940 0.9314 0.000 0.076 0.924 0.000
#> SRR1951635 1 0.1940 0.8938 0.924 0.000 0.000 0.076
#> SRR1951634 1 0.2149 0.8833 0.912 0.000 0.000 0.088
#> SRR1951631 4 0.4898 0.2549 0.416 0.000 0.000 0.584
#> SRR1951632 2 0.0000 0.9820 0.000 1.000 0.000 0.000
#> SRR1951633 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951630 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951629 1 0.4164 0.6685 0.736 0.000 0.000 0.264
#> SRR1951628 3 0.0000 0.9558 0.000 0.000 1.000 0.000
#> SRR1951627 3 0.0000 0.9558 0.000 0.000 1.000 0.000
#> SRR1951639 3 0.0000 0.9558 0.000 0.000 1.000 0.000
#> SRR1951625 3 0.1940 0.9314 0.000 0.076 0.924 0.000
#> SRR1951626 4 0.4222 0.7133 0.000 0.000 0.272 0.728
#> SRR1951624 4 0.1940 0.9091 0.000 0.000 0.076 0.924
#> SRR1951622 4 0.0000 0.9001 0.000 0.000 0.000 1.000
#> SRR1951636 4 0.2345 0.8988 0.000 0.000 0.100 0.900
#> SRR1951623 1 0.1637 0.9037 0.940 0.000 0.000 0.060
#> SRR1951621 1 0.0000 0.9339 1.000 0.000 0.000 0.000
#> SRR1951620 4 0.4999 -0.0141 0.492 0.000 0.000 0.508
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.1478 0.93920 0.064 0.936 0.000 0.000 0.000
#> SRR1951666 5 0.0807 0.92426 0.012 0.000 0.012 0.000 0.976
#> SRR1951665 2 0.0000 0.94938 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 3 0.0451 0.71719 0.008 0.000 0.988 0.000 0.004
#> SRR1951663 1 0.4565 0.43620 0.580 0.000 0.408 0.000 0.012
#> SRR1951662 2 0.0000 0.94938 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 5 0.4613 0.44969 0.020 0.000 0.360 0.000 0.620
#> SRR1951660 1 0.3774 0.64526 0.704 0.000 0.296 0.000 0.000
#> SRR1951659 2 0.1410 0.94060 0.060 0.940 0.000 0.000 0.000
#> SRR1951658 2 0.1410 0.94060 0.060 0.940 0.000 0.000 0.000
#> SRR1951657 3 0.6485 0.09938 0.344 0.000 0.460 0.196 0.000
#> SRR1951656 3 0.6799 -0.06551 0.332 0.000 0.372 0.296 0.000
#> SRR1951655 1 0.5312 0.46518 0.652 0.000 0.248 0.100 0.000
#> SRR1951654 4 0.0000 0.82698 0.000 0.000 0.000 1.000 0.000
#> SRR1951653 3 0.0510 0.72100 0.016 0.000 0.984 0.000 0.000
#> SRR1951652 5 0.0807 0.92426 0.012 0.000 0.012 0.000 0.976
#> SRR1951651 4 0.0290 0.82347 0.008 0.000 0.000 0.992 0.000
#> SRR1951650 3 0.0510 0.72100 0.016 0.000 0.984 0.000 0.000
#> SRR1951648 5 0.0807 0.92426 0.012 0.000 0.012 0.000 0.976
#> SRR1951649 3 0.3336 0.57935 0.228 0.000 0.772 0.000 0.000
#> SRR1951647 5 0.0510 0.92604 0.016 0.000 0.000 0.000 0.984
#> SRR1951646 3 0.0510 0.72100 0.016 0.000 0.984 0.000 0.000
#> SRR1951645 3 0.3561 0.53898 0.260 0.000 0.740 0.000 0.000
#> SRR1951644 3 0.1608 0.70576 0.072 0.000 0.928 0.000 0.000
#> SRR1951643 4 0.0000 0.82698 0.000 0.000 0.000 1.000 0.000
#> SRR1951641 2 0.0000 0.94938 0.000 1.000 0.000 0.000 0.000
#> SRR1951642 4 0.0000 0.82698 0.000 0.000 0.000 1.000 0.000
#> SRR1951694 1 0.3395 0.68064 0.764 0.000 0.236 0.000 0.000
#> SRR1951731 5 0.0693 0.92518 0.012 0.000 0.008 0.000 0.980
#> SRR1951730 5 0.0000 0.92706 0.000 0.000 0.000 0.000 1.000
#> SRR1951752 5 0.0807 0.92426 0.012 0.000 0.012 0.000 0.976
#> SRR1951751 3 0.3966 0.43523 0.336 0.000 0.664 0.000 0.000
#> SRR1951749 5 0.0703 0.92467 0.024 0.000 0.000 0.000 0.976
#> SRR1951750 4 0.0000 0.82698 0.000 0.000 0.000 1.000 0.000
#> SRR1951748 2 0.0000 0.94938 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.94938 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 1 0.2928 0.67911 0.872 0.000 0.064 0.064 0.000
#> SRR1951746 2 0.0000 0.94938 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 3 0.4297 0.18729 0.472 0.000 0.528 0.000 0.000
#> SRR1951742 5 0.3239 0.83890 0.080 0.068 0.000 0.000 0.852
#> SRR1951743 2 0.0000 0.94938 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 4 0.0000 0.82698 0.000 0.000 0.000 1.000 0.000
#> SRR1951740 3 0.4030 0.40347 0.352 0.000 0.648 0.000 0.000
#> SRR1951738 2 0.2423 0.91888 0.080 0.896 0.000 0.000 0.024
#> SRR1951739 2 0.1341 0.94146 0.056 0.944 0.000 0.000 0.000
#> SRR1951737 3 0.0000 0.72010 0.000 0.000 1.000 0.000 0.000
#> SRR1951736 4 0.5161 0.32901 0.444 0.000 0.040 0.516 0.000
#> SRR1951734 2 0.0000 0.94938 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 3 0.2569 0.67177 0.040 0.000 0.892 0.000 0.068
#> SRR1951733 5 0.0510 0.92604 0.016 0.000 0.000 0.000 0.984
#> SRR1951732 2 0.0000 0.94938 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 3 0.0290 0.71808 0.008 0.000 0.992 0.000 0.000
#> SRR1951728 3 0.3307 0.63826 0.052 0.000 0.844 0.000 0.104
#> SRR1951727 2 0.0000 0.94938 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 1 0.2179 0.69097 0.888 0.000 0.112 0.000 0.000
#> SRR1951726 3 0.6049 0.17917 0.164 0.000 0.564 0.000 0.272
#> SRR1951724 3 0.3949 0.42495 0.332 0.000 0.668 0.000 0.000
#> SRR1951722 3 0.2795 0.66855 0.056 0.000 0.880 0.000 0.064
#> SRR1951723 3 0.2726 0.67180 0.052 0.000 0.884 0.000 0.064
#> SRR1951721 1 0.4682 0.41046 0.564 0.000 0.420 0.000 0.016
#> SRR1951719 3 0.0510 0.72100 0.016 0.000 0.984 0.000 0.000
#> SRR1951720 4 0.1478 0.79637 0.064 0.000 0.000 0.936 0.000
#> SRR1951718 3 0.2813 0.64112 0.168 0.000 0.832 0.000 0.000
#> SRR1951717 1 0.2891 0.52037 0.824 0.000 0.000 0.176 0.000
#> SRR1951715 1 0.3707 0.64279 0.716 0.000 0.284 0.000 0.000
#> SRR1951716 4 0.3796 0.59735 0.300 0.000 0.000 0.700 0.000
#> SRR1951714 3 0.4192 0.32095 0.404 0.000 0.596 0.000 0.000
#> SRR1951713 3 0.2648 0.62570 0.152 0.000 0.848 0.000 0.000
#> SRR1951712 4 0.6356 0.25610 0.364 0.000 0.168 0.468 0.000
#> SRR1951711 2 0.2331 0.92135 0.080 0.900 0.000 0.000 0.020
#> SRR1951710 1 0.4126 0.51255 0.620 0.000 0.380 0.000 0.000
#> SRR1951709 5 0.0000 0.92706 0.000 0.000 0.000 0.000 1.000
#> SRR1951708 2 0.1410 0.94060 0.060 0.940 0.000 0.000 0.000
#> SRR1951707 5 0.2677 0.83969 0.016 0.000 0.112 0.000 0.872
#> SRR1951705 1 0.3983 0.60070 0.660 0.000 0.340 0.000 0.000
#> SRR1951704 3 0.2124 0.69449 0.056 0.000 0.916 0.000 0.028
#> SRR1951703 2 0.0000 0.94938 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 3 0.3513 0.57012 0.180 0.000 0.800 0.000 0.020
#> SRR1951702 3 0.2790 0.66736 0.052 0.000 0.880 0.000 0.068
#> SRR1951701 2 0.0000 0.94938 0.000 1.000 0.000 0.000 0.000
#> SRR1951699 5 0.0807 0.92426 0.012 0.000 0.012 0.000 0.976
#> SRR1951698 5 0.3098 0.80089 0.016 0.000 0.148 0.000 0.836
#> SRR1951700 3 0.0963 0.71796 0.036 0.000 0.964 0.000 0.000
#> SRR1951697 2 0.5677 0.18487 0.080 0.496 0.000 0.000 0.424
#> SRR1951696 3 0.0794 0.71829 0.028 0.000 0.972 0.000 0.000
#> SRR1951695 3 0.1341 0.71228 0.056 0.000 0.944 0.000 0.000
#> SRR1951693 3 0.1270 0.71302 0.052 0.000 0.948 0.000 0.000
#> SRR1951692 4 0.2886 0.74377 0.148 0.000 0.008 0.844 0.000
#> SRR1951691 3 0.1608 0.70518 0.072 0.000 0.928 0.000 0.000
#> SRR1951688 5 0.0794 0.92349 0.028 0.000 0.000 0.000 0.972
#> SRR1951689 5 0.0609 0.92559 0.020 0.000 0.000 0.000 0.980
#> SRR1951687 4 0.0000 0.82698 0.000 0.000 0.000 1.000 0.000
#> SRR1951685 5 0.0162 0.92709 0.004 0.000 0.000 0.000 0.996
#> SRR1951684 3 0.3983 0.42080 0.340 0.000 0.660 0.000 0.000
#> SRR1951686 5 0.3239 0.83890 0.080 0.068 0.000 0.000 0.852
#> SRR1951683 4 0.0000 0.82698 0.000 0.000 0.000 1.000 0.000
#> SRR1951682 2 0.2423 0.91888 0.080 0.896 0.000 0.000 0.024
#> SRR1951690 2 0.0000 0.94938 0.000 1.000 0.000 0.000 0.000
#> SRR1951680 3 0.3857 0.45849 0.312 0.000 0.688 0.000 0.000
#> SRR1951679 3 0.2922 0.67898 0.072 0.000 0.872 0.000 0.056
#> SRR1951678 4 0.0000 0.82698 0.000 0.000 0.000 1.000 0.000
#> SRR1951677 4 0.0000 0.82698 0.000 0.000 0.000 1.000 0.000
#> SRR1951676 4 0.4150 0.66670 0.216 0.000 0.036 0.748 0.000
#> SRR1951675 2 0.1410 0.94060 0.060 0.940 0.000 0.000 0.000
#> SRR1951681 3 0.0510 0.72100 0.016 0.000 0.984 0.000 0.000
#> SRR1951673 5 0.0609 0.92559 0.020 0.000 0.000 0.000 0.980
#> SRR1951674 1 0.2928 0.67911 0.872 0.000 0.064 0.064 0.000
#> SRR1951672 1 0.4060 0.38836 0.640 0.000 0.360 0.000 0.000
#> SRR1951671 5 0.1608 0.90228 0.072 0.000 0.000 0.000 0.928
#> SRR1951670 4 0.0000 0.82698 0.000 0.000 0.000 1.000 0.000
#> SRR1951640 3 0.0510 0.72100 0.016 0.000 0.984 0.000 0.000
#> SRR1951668 4 0.5382 0.46551 0.336 0.000 0.072 0.592 0.000
#> SRR1951669 5 0.3239 0.83890 0.080 0.068 0.000 0.000 0.852
#> SRR1951638 1 0.2974 0.66078 0.868 0.000 0.052 0.080 0.000
#> SRR1951637 5 0.0609 0.92559 0.020 0.000 0.000 0.000 0.980
#> SRR1951635 1 0.2891 0.51870 0.824 0.000 0.000 0.176 0.000
#> SRR1951634 4 0.6456 0.26007 0.340 0.000 0.192 0.468 0.000
#> SRR1951631 3 0.5518 0.23116 0.384 0.000 0.544 0.072 0.000
#> SRR1951632 2 0.2511 0.91598 0.080 0.892 0.000 0.000 0.028
#> SRR1951633 4 0.0000 0.82698 0.000 0.000 0.000 1.000 0.000
#> SRR1951630 4 0.0000 0.82698 0.000 0.000 0.000 1.000 0.000
#> SRR1951629 3 0.6680 0.00703 0.348 0.000 0.412 0.240 0.000
#> SRR1951628 5 0.0451 0.92651 0.008 0.000 0.004 0.000 0.988
#> SRR1951627 5 0.3527 0.74270 0.016 0.000 0.192 0.000 0.792
#> SRR1951639 5 0.0807 0.92426 0.012 0.000 0.012 0.000 0.976
#> SRR1951625 5 0.1608 0.90228 0.072 0.000 0.000 0.000 0.928
#> SRR1951626 3 0.3409 0.58454 0.024 0.000 0.816 0.000 0.160
#> SRR1951624 3 0.0162 0.72065 0.004 0.000 0.996 0.000 0.000
#> SRR1951622 1 0.3508 0.67778 0.748 0.000 0.252 0.000 0.000
#> SRR1951636 3 0.2813 0.66057 0.040 0.000 0.876 0.000 0.084
#> SRR1951623 4 0.6746 0.07395 0.344 0.000 0.264 0.392 0.000
#> SRR1951621 4 0.0000 0.82698 0.000 0.000 0.000 1.000 0.000
#> SRR1951620 3 0.5559 0.30218 0.344 0.000 0.572 0.084 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 2 0.2872 0.8985 0.052 0.864 0.000 0.000 0.008 0.076
#> SRR1951666 5 0.1219 0.8722 0.000 0.000 0.048 0.000 0.948 0.004
#> SRR1951665 2 0.0000 0.9293 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 3 0.1806 0.8215 0.088 0.000 0.908 0.000 0.000 0.004
#> SRR1951663 6 0.2845 0.6915 0.004 0.000 0.172 0.000 0.004 0.820
#> SRR1951662 2 0.0000 0.9293 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 3 0.3323 0.6194 0.000 0.000 0.752 0.000 0.240 0.008
#> SRR1951660 6 0.5527 0.3779 0.380 0.000 0.136 0.000 0.000 0.484
#> SRR1951659 2 0.2506 0.9059 0.052 0.880 0.000 0.000 0.000 0.068
#> SRR1951658 2 0.2506 0.9059 0.052 0.880 0.000 0.000 0.000 0.068
#> SRR1951657 1 0.2750 0.7091 0.844 0.000 0.136 0.020 0.000 0.000
#> SRR1951656 1 0.2798 0.7112 0.852 0.000 0.112 0.036 0.000 0.000
#> SRR1951655 1 0.2152 0.6616 0.904 0.000 0.024 0.004 0.000 0.068
#> SRR1951654 4 0.0000 0.9201 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951653 3 0.3460 0.6851 0.220 0.000 0.760 0.000 0.000 0.020
#> SRR1951652 5 0.1477 0.8704 0.004 0.000 0.048 0.000 0.940 0.008
#> SRR1951651 4 0.0790 0.9002 0.032 0.000 0.000 0.968 0.000 0.000
#> SRR1951650 3 0.2163 0.8178 0.092 0.000 0.892 0.000 0.000 0.016
#> SRR1951648 5 0.1333 0.8704 0.000 0.000 0.048 0.000 0.944 0.008
#> SRR1951649 1 0.5814 0.1961 0.448 0.000 0.364 0.000 0.000 0.188
#> SRR1951647 5 0.0806 0.8796 0.000 0.000 0.020 0.000 0.972 0.008
#> SRR1951646 3 0.3541 0.6675 0.232 0.000 0.748 0.000 0.000 0.020
#> SRR1951645 1 0.5775 0.2395 0.480 0.000 0.328 0.000 0.000 0.192
#> SRR1951644 3 0.4546 0.5807 0.204 0.000 0.692 0.000 0.000 0.104
#> SRR1951643 4 0.0000 0.9201 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951641 2 0.0000 0.9293 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951642 4 0.0000 0.9201 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951694 6 0.3790 0.7202 0.116 0.000 0.104 0.000 0.000 0.780
#> SRR1951731 5 0.1219 0.8722 0.000 0.000 0.048 0.000 0.948 0.004
#> SRR1951730 5 0.0260 0.8790 0.000 0.000 0.008 0.000 0.992 0.000
#> SRR1951752 5 0.1477 0.8704 0.004 0.000 0.048 0.000 0.940 0.008
#> SRR1951751 1 0.5367 0.3791 0.588 0.000 0.224 0.000 0.000 0.188
#> SRR1951749 5 0.1524 0.8727 0.008 0.000 0.000 0.000 0.932 0.060
#> SRR1951750 4 0.0858 0.9002 0.028 0.000 0.000 0.968 0.000 0.004
#> SRR1951748 2 0.0000 0.9293 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9293 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 6 0.3168 0.6832 0.192 0.000 0.000 0.016 0.000 0.792
#> SRR1951746 2 0.0000 0.9293 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951744 1 0.5062 0.4944 0.636 0.000 0.196 0.000 0.000 0.168
#> SRR1951742 5 0.3536 0.8090 0.060 0.004 0.000 0.000 0.804 0.132
#> SRR1951743 2 0.0000 0.9293 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951741 4 0.0000 0.9201 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951740 1 0.2416 0.7035 0.844 0.000 0.156 0.000 0.000 0.000
#> SRR1951738 2 0.4472 0.8244 0.060 0.756 0.000 0.000 0.052 0.132
#> SRR1951739 2 0.2328 0.9092 0.052 0.892 0.000 0.000 0.000 0.056
#> SRR1951737 3 0.1858 0.8199 0.092 0.000 0.904 0.000 0.000 0.004
#> SRR1951736 1 0.3537 0.5761 0.808 0.000 0.004 0.116 0.000 0.072
#> SRR1951734 2 0.0000 0.9293 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951735 3 0.0508 0.8225 0.000 0.000 0.984 0.000 0.012 0.004
#> SRR1951733 5 0.1524 0.8727 0.008 0.000 0.000 0.000 0.932 0.060
#> SRR1951732 2 0.0000 0.9293 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 3 0.1663 0.8215 0.088 0.000 0.912 0.000 0.000 0.000
#> SRR1951728 3 0.1686 0.7888 0.000 0.000 0.924 0.000 0.064 0.012
#> SRR1951727 2 0.0000 0.9293 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 6 0.3670 0.6487 0.284 0.000 0.012 0.000 0.000 0.704
#> SRR1951726 3 0.3670 0.7003 0.012 0.000 0.808 0.000 0.100 0.080
#> SRR1951724 1 0.3352 0.6711 0.792 0.000 0.176 0.000 0.000 0.032
#> SRR1951722 3 0.0777 0.8210 0.000 0.000 0.972 0.000 0.004 0.024
#> SRR1951723 3 0.1341 0.8066 0.024 0.000 0.948 0.000 0.000 0.028
#> SRR1951721 6 0.3479 0.6584 0.012 0.000 0.212 0.000 0.008 0.768
#> SRR1951719 3 0.2664 0.7897 0.136 0.000 0.848 0.000 0.000 0.016
#> SRR1951720 4 0.2609 0.8133 0.096 0.000 0.000 0.868 0.000 0.036
#> SRR1951718 3 0.5970 -0.1466 0.356 0.000 0.416 0.000 0.000 0.228
#> SRR1951717 1 0.4401 -0.2194 0.512 0.000 0.000 0.024 0.000 0.464
#> SRR1951715 6 0.3149 0.7146 0.044 0.000 0.132 0.000 0.000 0.824
#> SRR1951716 4 0.4620 0.1949 0.428 0.000 0.000 0.532 0.000 0.040
#> SRR1951714 1 0.1701 0.7073 0.920 0.000 0.072 0.000 0.000 0.008
#> SRR1951713 1 0.4377 0.2555 0.540 0.000 0.436 0.000 0.000 0.024
#> SRR1951712 1 0.2377 0.6637 0.892 0.000 0.008 0.076 0.000 0.024
#> SRR1951711 2 0.4147 0.8407 0.060 0.776 0.000 0.000 0.032 0.132
#> SRR1951710 6 0.3907 0.6939 0.068 0.000 0.176 0.000 0.000 0.756
#> SRR1951709 5 0.0632 0.8785 0.000 0.000 0.024 0.000 0.976 0.000
#> SRR1951708 2 0.2506 0.9059 0.052 0.880 0.000 0.000 0.000 0.068
#> SRR1951707 5 0.4098 -0.0485 0.000 0.000 0.496 0.000 0.496 0.008
#> SRR1951705 6 0.5671 0.4470 0.312 0.000 0.180 0.000 0.000 0.508
#> SRR1951704 3 0.0837 0.8232 0.004 0.000 0.972 0.000 0.004 0.020
#> SRR1951703 2 0.0000 0.9293 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 3 0.1531 0.8051 0.000 0.000 0.928 0.000 0.004 0.068
#> SRR1951702 3 0.0767 0.8216 0.004 0.000 0.976 0.000 0.012 0.008
#> SRR1951701 2 0.0000 0.9293 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951699 5 0.1333 0.8704 0.000 0.000 0.048 0.000 0.944 0.008
#> SRR1951698 3 0.3993 0.3015 0.000 0.000 0.592 0.000 0.400 0.008
#> SRR1951700 3 0.1594 0.8259 0.052 0.000 0.932 0.000 0.000 0.016
#> SRR1951697 5 0.6119 0.4622 0.060 0.236 0.000 0.000 0.572 0.132
#> SRR1951696 3 0.2146 0.8089 0.116 0.000 0.880 0.000 0.000 0.004
#> SRR1951695 3 0.1461 0.8268 0.044 0.000 0.940 0.000 0.000 0.016
#> SRR1951693 3 0.1644 0.8244 0.040 0.000 0.932 0.000 0.000 0.028
#> SRR1951692 4 0.3860 0.1463 0.472 0.000 0.000 0.528 0.000 0.000
#> SRR1951691 3 0.2389 0.8036 0.060 0.000 0.888 0.000 0.000 0.052
#> SRR1951688 5 0.2308 0.8589 0.040 0.000 0.000 0.000 0.892 0.068
#> SRR1951689 5 0.1584 0.8716 0.008 0.000 0.000 0.000 0.928 0.064
#> SRR1951687 4 0.0000 0.9201 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951685 5 0.0937 0.8758 0.000 0.000 0.040 0.000 0.960 0.000
#> SRR1951684 1 0.2454 0.7015 0.840 0.000 0.160 0.000 0.000 0.000
#> SRR1951686 5 0.3536 0.8090 0.060 0.004 0.000 0.000 0.804 0.132
#> SRR1951683 4 0.0000 0.9201 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951682 2 0.4472 0.8244 0.060 0.756 0.000 0.000 0.052 0.132
#> SRR1951690 2 0.0000 0.9293 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951680 1 0.2946 0.6887 0.812 0.000 0.176 0.000 0.000 0.012
#> SRR1951679 3 0.2145 0.7870 0.028 0.000 0.900 0.000 0.000 0.072
#> SRR1951678 4 0.0000 0.9201 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951677 4 0.0000 0.9201 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951676 1 0.3986 0.2476 0.608 0.000 0.004 0.384 0.000 0.004
#> SRR1951675 2 0.2506 0.9059 0.052 0.880 0.000 0.000 0.000 0.068
#> SRR1951681 3 0.2311 0.8125 0.104 0.000 0.880 0.000 0.000 0.016
#> SRR1951673 5 0.1686 0.8711 0.012 0.000 0.000 0.000 0.924 0.064
#> SRR1951674 6 0.3168 0.6832 0.192 0.000 0.000 0.016 0.000 0.792
#> SRR1951672 1 0.5171 0.1359 0.560 0.000 0.104 0.000 0.000 0.336
#> SRR1951671 5 0.3254 0.8199 0.056 0.000 0.000 0.000 0.820 0.124
#> SRR1951670 4 0.0000 0.9201 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951640 3 0.3541 0.6669 0.232 0.000 0.748 0.000 0.000 0.020
#> SRR1951668 1 0.2748 0.6201 0.848 0.000 0.000 0.128 0.000 0.024
#> SRR1951669 5 0.3593 0.8078 0.064 0.004 0.000 0.000 0.800 0.132
#> SRR1951638 6 0.3470 0.6434 0.248 0.000 0.000 0.012 0.000 0.740
#> SRR1951637 5 0.1297 0.8757 0.012 0.000 0.000 0.000 0.948 0.040
#> SRR1951635 6 0.3855 0.6364 0.272 0.000 0.000 0.024 0.000 0.704
#> SRR1951634 1 0.2159 0.6778 0.904 0.000 0.012 0.072 0.000 0.012
#> SRR1951631 1 0.2001 0.7074 0.912 0.000 0.068 0.008 0.000 0.012
#> SRR1951632 2 0.4531 0.8205 0.060 0.752 0.000 0.000 0.056 0.132
#> SRR1951633 4 0.0000 0.9201 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951630 4 0.0000 0.9201 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951629 1 0.2285 0.7074 0.900 0.000 0.064 0.028 0.000 0.008
#> SRR1951628 5 0.0922 0.8781 0.004 0.000 0.024 0.000 0.968 0.004
#> SRR1951627 3 0.4032 0.2475 0.000 0.000 0.572 0.000 0.420 0.008
#> SRR1951639 5 0.1219 0.8722 0.000 0.000 0.048 0.000 0.948 0.004
#> SRR1951625 5 0.3254 0.8199 0.056 0.000 0.000 0.000 0.820 0.124
#> SRR1951626 3 0.1757 0.7828 0.000 0.000 0.916 0.000 0.076 0.008
#> SRR1951624 3 0.1858 0.8199 0.092 0.000 0.904 0.000 0.000 0.004
#> SRR1951622 6 0.5395 0.4581 0.356 0.000 0.124 0.000 0.000 0.520
#> SRR1951636 3 0.1167 0.8192 0.012 0.000 0.960 0.000 0.020 0.008
#> SRR1951623 1 0.2888 0.6995 0.860 0.000 0.068 0.068 0.000 0.004
#> SRR1951621 4 0.0000 0.9201 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951620 1 0.2520 0.7054 0.844 0.000 0.152 0.004 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 13580 rows and 133 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.999 1.000 0.4916 0.509 0.509
#> 3 3 0.923 0.929 0.966 0.3161 0.811 0.637
#> 4 4 0.841 0.824 0.925 0.0553 0.876 0.683
#> 5 5 0.839 0.785 0.900 0.0518 0.888 0.689
#> 6 6 0.842 0.807 0.908 0.0555 0.912 0.719
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
#> SRR1951667 2 0.0000 0.999 0.000 1.000
#> SRR1951666 2 0.0000 0.999 0.000 1.000
#> SRR1951665 2 0.0000 0.999 0.000 1.000
#> SRR1951664 1 0.0000 1.000 1.000 0.000
#> SRR1951663 2 0.0672 0.991 0.008 0.992
#> SRR1951662 2 0.0000 0.999 0.000 1.000
#> SRR1951661 2 0.0000 0.999 0.000 1.000
#> SRR1951660 1 0.0000 1.000 1.000 0.000
#> SRR1951659 2 0.0000 0.999 0.000 1.000
#> SRR1951658 2 0.0000 0.999 0.000 1.000
#> SRR1951657 1 0.0000 1.000 1.000 0.000
#> SRR1951656 1 0.0000 1.000 1.000 0.000
#> SRR1951655 1 0.0000 1.000 1.000 0.000
#> SRR1951654 1 0.0000 1.000 1.000 0.000
#> SRR1951653 1 0.0000 1.000 1.000 0.000
#> SRR1951652 2 0.0000 0.999 0.000 1.000
#> SRR1951651 1 0.0000 1.000 1.000 0.000
#> SRR1951650 1 0.0000 1.000 1.000 0.000
#> SRR1951648 2 0.0000 0.999 0.000 1.000
#> SRR1951649 1 0.0000 1.000 1.000 0.000
#> SRR1951647 2 0.0000 0.999 0.000 1.000
#> SRR1951646 1 0.0000 1.000 1.000 0.000
#> SRR1951645 1 0.0000 1.000 1.000 0.000
#> SRR1951644 1 0.0000 1.000 1.000 0.000
#> SRR1951643 1 0.0000 1.000 1.000 0.000
#> SRR1951641 2 0.0000 0.999 0.000 1.000
#> SRR1951642 1 0.0000 1.000 1.000 0.000
#> SRR1951694 1 0.0000 1.000 1.000 0.000
#> SRR1951731 2 0.0000 0.999 0.000 1.000
#> SRR1951730 2 0.0000 0.999 0.000 1.000
#> SRR1951752 2 0.0000 0.999 0.000 1.000
#> SRR1951751 1 0.0000 1.000 1.000 0.000
#> SRR1951749 2 0.0000 0.999 0.000 1.000
#> SRR1951750 1 0.0000 1.000 1.000 0.000
#> SRR1951748 2 0.0000 0.999 0.000 1.000
#> SRR1951747 2 0.0000 0.999 0.000 1.000
#> SRR1951745 1 0.0000 1.000 1.000 0.000
#> SRR1951746 2 0.0000 0.999 0.000 1.000
#> SRR1951744 1 0.0000 1.000 1.000 0.000
#> SRR1951742 2 0.0000 0.999 0.000 1.000
#> SRR1951743 2 0.0000 0.999 0.000 1.000
#> SRR1951741 1 0.0000 1.000 1.000 0.000
#> SRR1951740 1 0.0000 1.000 1.000 0.000
#> SRR1951738 2 0.0000 0.999 0.000 1.000
#> SRR1951739 2 0.0000 0.999 0.000 1.000
#> SRR1951737 1 0.0000 1.000 1.000 0.000
#> SRR1951736 1 0.0000 1.000 1.000 0.000
#> SRR1951734 2 0.0000 0.999 0.000 1.000
#> SRR1951735 1 0.0000 1.000 1.000 0.000
#> SRR1951733 2 0.0000 0.999 0.000 1.000
#> SRR1951732 2 0.0000 0.999 0.000 1.000
#> SRR1951729 1 0.0000 1.000 1.000 0.000
#> SRR1951728 2 0.0000 0.999 0.000 1.000
#> SRR1951727 2 0.0000 0.999 0.000 1.000
#> SRR1951725 1 0.0000 1.000 1.000 0.000
#> SRR1951726 2 0.0000 0.999 0.000 1.000
#> SRR1951724 1 0.0000 1.000 1.000 0.000
#> SRR1951722 1 0.0000 1.000 1.000 0.000
#> SRR1951723 1 0.0000 1.000 1.000 0.000
#> SRR1951721 2 0.0000 0.999 0.000 1.000
#> SRR1951719 1 0.0000 1.000 1.000 0.000
#> SRR1951720 1 0.0000 1.000 1.000 0.000
#> SRR1951718 1 0.0000 1.000 1.000 0.000
#> SRR1951717 1 0.0000 1.000 1.000 0.000
#> SRR1951715 1 0.0000 1.000 1.000 0.000
#> SRR1951716 1 0.0000 1.000 1.000 0.000
#> SRR1951714 1 0.0000 1.000 1.000 0.000
#> SRR1951713 1 0.0000 1.000 1.000 0.000
#> SRR1951712 1 0.0000 1.000 1.000 0.000
#> SRR1951711 2 0.0000 0.999 0.000 1.000
#> SRR1951710 1 0.0000 1.000 1.000 0.000
#> SRR1951709 2 0.0000 0.999 0.000 1.000
#> SRR1951708 2 0.0000 0.999 0.000 1.000
#> SRR1951707 2 0.0000 0.999 0.000 1.000
#> SRR1951705 1 0.0000 1.000 1.000 0.000
#> SRR1951704 1 0.0000 1.000 1.000 0.000
#> SRR1951703 2 0.0000 0.999 0.000 1.000
#> SRR1951706 1 0.0000 1.000 1.000 0.000
#> SRR1951702 1 0.0000 1.000 1.000 0.000
#> SRR1951701 2 0.0000 0.999 0.000 1.000
#> SRR1951699 2 0.0000 0.999 0.000 1.000
#> SRR1951698 2 0.0000 0.999 0.000 1.000
#> SRR1951700 1 0.0000 1.000 1.000 0.000
#> SRR1951697 2 0.0000 0.999 0.000 1.000
#> SRR1951696 1 0.0000 1.000 1.000 0.000
#> SRR1951695 1 0.0000 1.000 1.000 0.000
#> SRR1951693 1 0.0000 1.000 1.000 0.000
#> SRR1951692 1 0.0000 1.000 1.000 0.000
#> SRR1951691 1 0.0000 1.000 1.000 0.000
#> SRR1951688 2 0.0000 0.999 0.000 1.000
#> SRR1951689 2 0.0000 0.999 0.000 1.000
#> SRR1951687 1 0.0000 1.000 1.000 0.000
#> SRR1951685 2 0.0000 0.999 0.000 1.000
#> SRR1951684 1 0.0000 1.000 1.000 0.000
#> SRR1951686 2 0.0000 0.999 0.000 1.000
#> SRR1951683 1 0.0000 1.000 1.000 0.000
#> SRR1951682 2 0.0000 0.999 0.000 1.000
#> SRR1951690 2 0.0000 0.999 0.000 1.000
#> SRR1951680 1 0.0000 1.000 1.000 0.000
#> SRR1951679 1 0.0000 1.000 1.000 0.000
#> SRR1951678 1 0.0000 1.000 1.000 0.000
#> SRR1951677 1 0.0000 1.000 1.000 0.000
#> SRR1951676 1 0.0000 1.000 1.000 0.000
#> SRR1951675 2 0.0000 0.999 0.000 1.000
#> SRR1951681 1 0.0000 1.000 1.000 0.000
#> SRR1951673 2 0.0000 0.999 0.000 1.000
#> SRR1951674 1 0.0000 1.000 1.000 0.000
#> SRR1951672 1 0.0000 1.000 1.000 0.000
#> SRR1951671 2 0.0000 0.999 0.000 1.000
#> SRR1951670 1 0.0000 1.000 1.000 0.000
#> SRR1951640 1 0.0000 1.000 1.000 0.000
#> SRR1951668 1 0.0000 1.000 1.000 0.000
#> SRR1951669 2 0.0000 0.999 0.000 1.000
#> SRR1951638 1 0.0000 1.000 1.000 0.000
#> SRR1951637 2 0.0000 0.999 0.000 1.000
#> SRR1951635 1 0.0000 1.000 1.000 0.000
#> SRR1951634 1 0.0000 1.000 1.000 0.000
#> SRR1951631 1 0.0000 1.000 1.000 0.000
#> SRR1951632 2 0.0000 0.999 0.000 1.000
#> SRR1951633 1 0.0000 1.000 1.000 0.000
#> SRR1951630 1 0.0000 1.000 1.000 0.000
#> SRR1951629 1 0.0000 1.000 1.000 0.000
#> SRR1951628 2 0.0000 0.999 0.000 1.000
#> SRR1951627 2 0.0000 0.999 0.000 1.000
#> SRR1951639 2 0.0000 0.999 0.000 1.000
#> SRR1951625 2 0.0000 0.999 0.000 1.000
#> SRR1951626 2 0.2603 0.954 0.044 0.956
#> SRR1951624 1 0.0000 1.000 1.000 0.000
#> SRR1951622 1 0.0000 1.000 1.000 0.000
#> SRR1951636 1 0.0000 1.000 1.000 0.000
#> SRR1951623 1 0.0000 1.000 1.000 0.000
#> SRR1951621 1 0.0000 1.000 1.000 0.000
#> SRR1951620 1 0.0000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951666 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951665 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951664 3 0.0000 0.877 0.000 0.000 1.000
#> SRR1951663 1 0.5216 0.590 0.740 0.260 0.000
#> SRR1951662 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951661 2 0.3482 0.850 0.000 0.872 0.128
#> SRR1951660 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951659 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951657 3 0.6126 0.484 0.400 0.000 0.600
#> SRR1951656 3 0.6126 0.484 0.400 0.000 0.600
#> SRR1951655 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951654 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951653 3 0.0747 0.873 0.016 0.000 0.984
#> SRR1951652 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951651 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951650 3 0.0237 0.877 0.004 0.000 0.996
#> SRR1951648 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951649 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951647 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951646 3 0.6154 0.467 0.408 0.000 0.592
#> SRR1951645 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951644 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951643 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951641 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951642 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951694 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951731 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951730 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951752 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951751 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951749 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951750 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951748 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951745 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951746 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951744 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951742 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951743 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951741 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951740 3 0.6026 0.525 0.376 0.000 0.624
#> SRR1951738 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951739 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951737 3 0.0000 0.877 0.000 0.000 1.000
#> SRR1951736 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951734 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951735 3 0.0000 0.877 0.000 0.000 1.000
#> SRR1951733 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951732 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951729 3 0.0000 0.877 0.000 0.000 1.000
#> SRR1951728 3 0.4399 0.709 0.000 0.188 0.812
#> SRR1951727 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951725 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951726 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951724 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951722 3 0.0424 0.876 0.008 0.000 0.992
#> SRR1951723 3 0.0424 0.876 0.008 0.000 0.992
#> SRR1951721 2 0.4750 0.708 0.216 0.784 0.000
#> SRR1951719 3 0.0237 0.877 0.004 0.000 0.996
#> SRR1951720 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951718 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951717 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951715 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951716 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951714 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951713 3 0.0424 0.876 0.008 0.000 0.992
#> SRR1951712 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951711 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951710 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951709 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951708 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951707 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951705 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951704 3 0.0000 0.877 0.000 0.000 1.000
#> SRR1951703 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951706 1 0.0892 0.973 0.980 0.000 0.020
#> SRR1951702 3 0.0000 0.877 0.000 0.000 1.000
#> SRR1951701 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951699 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951698 2 0.3038 0.878 0.000 0.896 0.104
#> SRR1951700 3 0.0237 0.877 0.004 0.000 0.996
#> SRR1951697 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951696 3 0.0000 0.877 0.000 0.000 1.000
#> SRR1951695 3 0.0237 0.877 0.004 0.000 0.996
#> SRR1951693 3 0.2448 0.842 0.076 0.000 0.924
#> SRR1951692 1 0.2959 0.866 0.900 0.000 0.100
#> SRR1951691 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951688 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951689 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951687 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951685 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951684 3 0.0237 0.877 0.004 0.000 0.996
#> SRR1951686 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951683 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951682 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951690 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951680 3 0.0424 0.876 0.008 0.000 0.992
#> SRR1951679 3 0.4654 0.713 0.208 0.000 0.792
#> SRR1951678 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951677 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951676 3 0.6299 0.286 0.476 0.000 0.524
#> SRR1951675 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951681 3 0.6126 0.484 0.400 0.000 0.600
#> SRR1951673 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951674 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951672 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951671 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951670 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951640 3 0.3752 0.795 0.144 0.000 0.856
#> SRR1951668 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951669 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951638 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951637 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951635 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951634 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951631 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951632 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951633 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951630 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951629 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951628 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951627 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951639 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951625 2 0.0000 0.991 0.000 1.000 0.000
#> SRR1951626 3 0.0000 0.877 0.000 0.000 1.000
#> SRR1951624 3 0.0000 0.877 0.000 0.000 1.000
#> SRR1951622 1 0.0000 0.987 1.000 0.000 0.000
#> SRR1951636 3 0.0000 0.877 0.000 0.000 1.000
#> SRR1951623 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951621 1 0.0237 0.988 0.996 0.000 0.004
#> SRR1951620 3 0.6126 0.484 0.400 0.000 0.600
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951666 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951665 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951664 3 0.0000 0.6657 0.000 0.000 1.000 0.000
#> SRR1951663 1 0.0376 0.8429 0.992 0.004 0.000 0.004
#> SRR1951662 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951661 3 0.5155 -0.0903 0.004 0.468 0.528 0.000
#> SRR1951660 4 0.2589 0.8554 0.116 0.000 0.000 0.884
#> SRR1951659 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951657 4 0.0336 0.9202 0.000 0.000 0.008 0.992
#> SRR1951656 4 0.0592 0.9157 0.000 0.000 0.016 0.984
#> SRR1951655 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951654 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951653 3 0.5112 0.5126 0.008 0.000 0.608 0.384
#> SRR1951652 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951651 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951650 3 0.4222 0.6078 0.000 0.000 0.728 0.272
#> SRR1951648 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951649 4 0.2345 0.8696 0.100 0.000 0.000 0.900
#> SRR1951647 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951646 4 0.0707 0.9133 0.000 0.000 0.020 0.980
#> SRR1951645 4 0.1022 0.9112 0.032 0.000 0.000 0.968
#> SRR1951644 4 0.2530 0.8590 0.112 0.000 0.000 0.888
#> SRR1951643 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951641 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951642 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951694 1 0.2973 0.7389 0.856 0.000 0.000 0.144
#> SRR1951731 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951730 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951752 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951751 4 0.1557 0.8976 0.056 0.000 0.000 0.944
#> SRR1951749 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951750 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951748 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951745 1 0.1389 0.8406 0.952 0.000 0.000 0.048
#> SRR1951746 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951744 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951742 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951743 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951741 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951740 4 0.0469 0.9179 0.000 0.000 0.012 0.988
#> SRR1951738 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951739 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951737 3 0.0000 0.6657 0.000 0.000 1.000 0.000
#> SRR1951736 4 0.1557 0.8967 0.056 0.000 0.000 0.944
#> SRR1951734 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951735 3 0.0188 0.6648 0.004 0.000 0.996 0.000
#> SRR1951733 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951732 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951729 3 0.0000 0.6657 0.000 0.000 1.000 0.000
#> SRR1951728 3 0.2593 0.5685 0.004 0.104 0.892 0.000
#> SRR1951727 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951725 4 0.4972 0.1096 0.456 0.000 0.000 0.544
#> SRR1951726 1 0.2408 0.7437 0.896 0.104 0.000 0.000
#> SRR1951724 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951722 3 0.2999 0.6507 0.004 0.000 0.864 0.132
#> SRR1951723 3 0.2408 0.6558 0.036 0.000 0.920 0.044
#> SRR1951721 1 0.0336 0.8400 0.992 0.008 0.000 0.000
#> SRR1951719 3 0.4776 0.5485 0.000 0.000 0.624 0.376
#> SRR1951720 4 0.1792 0.8887 0.068 0.000 0.000 0.932
#> SRR1951718 4 0.2589 0.8554 0.116 0.000 0.000 0.884
#> SRR1951717 4 0.1792 0.8893 0.068 0.000 0.000 0.932
#> SRR1951715 1 0.0188 0.8424 0.996 0.000 0.000 0.004
#> SRR1951716 4 0.1716 0.8915 0.064 0.000 0.000 0.936
#> SRR1951714 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951713 3 0.4961 0.4335 0.000 0.000 0.552 0.448
#> SRR1951712 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951711 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951710 1 0.0188 0.8424 0.996 0.000 0.000 0.004
#> SRR1951709 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951708 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951707 2 0.4677 0.5687 0.004 0.680 0.316 0.000
#> SRR1951705 4 0.4605 0.5027 0.336 0.000 0.000 0.664
#> SRR1951704 3 0.5248 0.5874 0.088 0.000 0.748 0.164
#> SRR1951703 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951706 1 0.3523 0.7565 0.856 0.000 0.032 0.112
#> SRR1951702 3 0.0188 0.6640 0.004 0.000 0.996 0.000
#> SRR1951701 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951699 2 0.0188 0.9739 0.004 0.996 0.000 0.000
#> SRR1951698 2 0.5004 0.4112 0.004 0.604 0.392 0.000
#> SRR1951700 3 0.4972 0.4193 0.000 0.000 0.544 0.456
#> SRR1951697 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951696 3 0.0000 0.6657 0.000 0.000 1.000 0.000
#> SRR1951695 3 0.4961 0.4361 0.000 0.000 0.552 0.448
#> SRR1951693 4 0.4008 0.5579 0.000 0.000 0.244 0.756
#> SRR1951692 4 0.0188 0.9221 0.000 0.000 0.004 0.996
#> SRR1951691 4 0.1211 0.9061 0.040 0.000 0.000 0.960
#> SRR1951688 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951689 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951687 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951685 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951684 3 0.4933 0.4639 0.000 0.000 0.568 0.432
#> SRR1951686 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951683 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951682 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951680 3 0.4961 0.4335 0.000 0.000 0.552 0.448
#> SRR1951679 3 0.7375 0.2735 0.336 0.000 0.488 0.176
#> SRR1951678 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951677 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951676 4 0.0188 0.9221 0.000 0.000 0.004 0.996
#> SRR1951675 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951681 4 0.1022 0.9038 0.000 0.000 0.032 0.968
#> SRR1951673 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951674 1 0.1389 0.8406 0.952 0.000 0.000 0.048
#> SRR1951672 4 0.2011 0.8830 0.080 0.000 0.000 0.920
#> SRR1951671 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951670 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951640 4 0.4331 0.4578 0.000 0.000 0.288 0.712
#> SRR1951668 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951669 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951638 1 0.4989 0.0665 0.528 0.000 0.000 0.472
#> SRR1951637 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951635 4 0.4977 0.0961 0.460 0.000 0.000 0.540
#> SRR1951634 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951631 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951632 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951633 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951630 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951629 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951628 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951627 2 0.4741 0.5477 0.004 0.668 0.328 0.000
#> SRR1951639 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951625 2 0.0000 0.9775 0.000 1.000 0.000 0.000
#> SRR1951626 3 0.0188 0.6640 0.004 0.000 0.996 0.000
#> SRR1951624 3 0.0000 0.6657 0.000 0.000 1.000 0.000
#> SRR1951622 4 0.4605 0.5027 0.336 0.000 0.000 0.664
#> SRR1951636 3 0.0188 0.6640 0.004 0.000 0.996 0.000
#> SRR1951623 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951621 4 0.0000 0.9242 0.000 0.000 0.000 1.000
#> SRR1951620 4 0.0592 0.9157 0.000 0.000 0.016 0.984
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951666 2 0.0162 0.9934 0.000 0.996 0.004 0.000 0.000
#> SRR1951665 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 3 0.2891 0.7172 0.176 0.000 0.824 0.000 0.000
#> SRR1951663 5 0.0000 0.7830 0.000 0.000 0.000 0.000 1.000
#> SRR1951662 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 3 0.1197 0.6945 0.000 0.048 0.952 0.000 0.000
#> SRR1951660 1 0.4029 0.6737 0.680 0.000 0.000 0.316 0.004
#> SRR1951659 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951657 4 0.1410 0.8128 0.060 0.000 0.000 0.940 0.000
#> SRR1951656 4 0.1410 0.8128 0.060 0.000 0.000 0.940 0.000
#> SRR1951655 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951654 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951653 1 0.2291 0.5435 0.908 0.000 0.036 0.056 0.000
#> SRR1951652 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951651 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951650 4 0.6219 0.4070 0.260 0.000 0.196 0.544 0.000
#> SRR1951648 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951649 1 0.3790 0.7171 0.724 0.000 0.000 0.272 0.004
#> SRR1951647 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951646 4 0.1270 0.8168 0.052 0.000 0.000 0.948 0.000
#> SRR1951645 4 0.4434 -0.1503 0.460 0.000 0.000 0.536 0.004
#> SRR1951644 1 0.3635 0.7262 0.748 0.000 0.000 0.248 0.004
#> SRR1951643 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951641 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951642 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951694 5 0.5569 0.2235 0.364 0.000 0.000 0.080 0.556
#> SRR1951731 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951730 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951752 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951751 4 0.4029 0.5555 0.316 0.000 0.000 0.680 0.004
#> SRR1951749 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951750 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951748 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 5 0.0000 0.7830 0.000 0.000 0.000 0.000 1.000
#> SRR1951746 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 4 0.0162 0.8341 0.000 0.000 0.000 0.996 0.004
#> SRR1951742 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951743 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951740 4 0.3508 0.6692 0.252 0.000 0.000 0.748 0.000
#> SRR1951738 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951739 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951737 3 0.3586 0.6817 0.264 0.000 0.736 0.000 0.000
#> SRR1951736 4 0.2338 0.7412 0.112 0.000 0.000 0.884 0.004
#> SRR1951734 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 3 0.3684 0.6725 0.280 0.000 0.720 0.000 0.000
#> SRR1951733 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951732 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 3 0.3274 0.7068 0.220 0.000 0.780 0.000 0.000
#> SRR1951728 3 0.0404 0.7121 0.000 0.012 0.988 0.000 0.000
#> SRR1951727 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 4 0.4897 0.0546 0.024 0.000 0.000 0.516 0.460
#> SRR1951726 5 0.0162 0.7795 0.000 0.004 0.000 0.000 0.996
#> SRR1951724 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951722 4 0.6717 0.1435 0.336 0.000 0.256 0.408 0.000
#> SRR1951723 1 0.0794 0.5460 0.972 0.000 0.028 0.000 0.000
#> SRR1951721 5 0.0000 0.7830 0.000 0.000 0.000 0.000 1.000
#> SRR1951719 4 0.5570 0.5122 0.288 0.000 0.104 0.608 0.000
#> SRR1951720 4 0.1430 0.7983 0.052 0.000 0.000 0.944 0.004
#> SRR1951718 1 0.3884 0.7045 0.708 0.000 0.000 0.288 0.004
#> SRR1951717 4 0.1485 0.8055 0.032 0.000 0.000 0.948 0.020
#> SRR1951715 5 0.3508 0.5632 0.252 0.000 0.000 0.000 0.748
#> SRR1951716 4 0.2389 0.7368 0.116 0.000 0.000 0.880 0.004
#> SRR1951714 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951713 4 0.4990 0.5451 0.324 0.000 0.048 0.628 0.000
#> SRR1951712 4 0.0703 0.8274 0.024 0.000 0.000 0.976 0.000
#> SRR1951711 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951710 1 0.3816 0.3540 0.696 0.000 0.000 0.000 0.304
#> SRR1951709 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951708 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951707 3 0.4182 0.3687 0.000 0.400 0.600 0.000 0.000
#> SRR1951705 1 0.4649 0.7128 0.720 0.000 0.000 0.212 0.068
#> SRR1951704 3 0.6682 0.1735 0.048 0.000 0.524 0.096 0.332
#> SRR1951703 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 5 0.2329 0.7045 0.000 0.000 0.124 0.000 0.876
#> SRR1951702 3 0.0290 0.7146 0.008 0.000 0.992 0.000 0.000
#> SRR1951701 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951699 2 0.2179 0.8661 0.000 0.888 0.112 0.000 0.000
#> SRR1951698 3 0.3730 0.4987 0.000 0.288 0.712 0.000 0.000
#> SRR1951700 4 0.4822 0.5845 0.288 0.000 0.048 0.664 0.000
#> SRR1951697 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951696 3 0.3579 0.6975 0.240 0.000 0.756 0.004 0.000
#> SRR1951695 4 0.5104 0.5671 0.284 0.000 0.068 0.648 0.000
#> SRR1951693 4 0.4206 0.6326 0.272 0.000 0.020 0.708 0.000
#> SRR1951692 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951691 4 0.2848 0.7496 0.156 0.000 0.000 0.840 0.004
#> SRR1951688 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951689 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951687 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951685 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951684 4 0.5285 0.5458 0.288 0.000 0.080 0.632 0.000
#> SRR1951686 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951683 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951682 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951680 4 0.4843 0.5806 0.292 0.000 0.048 0.660 0.000
#> SRR1951679 1 0.0898 0.5605 0.972 0.000 0.008 0.000 0.020
#> SRR1951678 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951677 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951676 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951675 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951681 4 0.1205 0.8209 0.040 0.000 0.004 0.956 0.000
#> SRR1951673 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951674 5 0.0000 0.7830 0.000 0.000 0.000 0.000 1.000
#> SRR1951672 4 0.3550 0.5895 0.236 0.000 0.000 0.760 0.004
#> SRR1951671 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951670 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951640 4 0.4141 0.6539 0.248 0.000 0.024 0.728 0.000
#> SRR1951668 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951669 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951638 5 0.4268 0.1153 0.000 0.000 0.000 0.444 0.556
#> SRR1951637 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951635 4 0.5334 0.0643 0.052 0.000 0.000 0.512 0.436
#> SRR1951634 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951631 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951632 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951633 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951630 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951629 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951628 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951627 3 0.3366 0.5419 0.000 0.232 0.768 0.000 0.000
#> SRR1951639 2 0.0162 0.9934 0.000 0.996 0.004 0.000 0.000
#> SRR1951625 2 0.0000 0.9972 0.000 1.000 0.000 0.000 0.000
#> SRR1951626 3 0.0000 0.7129 0.000 0.000 1.000 0.000 0.000
#> SRR1951624 3 0.3424 0.6991 0.240 0.000 0.760 0.000 0.000
#> SRR1951622 1 0.4679 0.7125 0.716 0.000 0.000 0.216 0.068
#> SRR1951636 3 0.0000 0.7129 0.000 0.000 1.000 0.000 0.000
#> SRR1951623 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951621 4 0.0000 0.8359 0.000 0.000 0.000 1.000 0.000
#> SRR1951620 4 0.1410 0.8128 0.060 0.000 0.000 0.940 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951666 2 0.1863 0.8795 0.000 0.896 0.000 0.000 0.104 0.000
#> SRR1951665 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 5 0.4246 0.0349 0.016 0.000 0.452 0.000 0.532 0.000
#> SRR1951663 6 0.0146 0.9043 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR1951662 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 5 0.0146 0.8114 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951660 1 0.1814 0.7089 0.900 0.000 0.000 0.100 0.000 0.000
#> SRR1951659 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951657 4 0.3409 0.4781 0.000 0.000 0.300 0.700 0.000 0.000
#> SRR1951656 4 0.3727 0.2518 0.000 0.000 0.388 0.612 0.000 0.000
#> SRR1951655 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951654 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951653 3 0.3894 0.3960 0.324 0.000 0.664 0.008 0.004 0.000
#> SRR1951652 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951651 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951650 3 0.3287 0.7581 0.000 0.000 0.768 0.220 0.012 0.000
#> SRR1951648 2 0.0363 0.9756 0.000 0.988 0.000 0.000 0.012 0.000
#> SRR1951649 1 0.0865 0.7425 0.964 0.000 0.000 0.036 0.000 0.000
#> SRR1951647 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951646 4 0.3706 0.2789 0.000 0.000 0.380 0.620 0.000 0.000
#> SRR1951645 1 0.3515 0.4809 0.676 0.000 0.000 0.324 0.000 0.000
#> SRR1951644 1 0.1625 0.7100 0.928 0.000 0.060 0.012 0.000 0.000
#> SRR1951643 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951641 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951642 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951694 1 0.4441 0.5518 0.700 0.000 0.000 0.092 0.000 0.208
#> SRR1951731 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951730 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951752 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951751 1 0.6093 0.1456 0.380 0.000 0.284 0.336 0.000 0.000
#> SRR1951749 2 0.0146 0.9826 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1951750 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951748 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951745 6 0.0146 0.9043 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR1951746 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951744 4 0.0146 0.8805 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1951742 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951743 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951741 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951740 3 0.3330 0.6996 0.000 0.000 0.716 0.284 0.000 0.000
#> SRR1951738 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951739 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951737 3 0.2278 0.6899 0.004 0.000 0.868 0.000 0.128 0.000
#> SRR1951736 4 0.2260 0.7746 0.140 0.000 0.000 0.860 0.000 0.000
#> SRR1951734 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951735 3 0.2527 0.6537 0.040 0.000 0.876 0.000 0.084 0.000
#> SRR1951733 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951732 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 3 0.3729 0.4942 0.012 0.000 0.692 0.000 0.296 0.000
#> SRR1951728 5 0.0508 0.8144 0.000 0.004 0.012 0.000 0.984 0.000
#> SRR1951727 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 4 0.4265 0.6525 0.100 0.000 0.000 0.728 0.000 0.172
#> SRR1951726 6 0.0146 0.9027 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1951724 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951722 3 0.2322 0.7565 0.024 0.000 0.896 0.072 0.008 0.000
#> SRR1951723 1 0.3717 0.4092 0.616 0.000 0.384 0.000 0.000 0.000
#> SRR1951721 6 0.0291 0.9039 0.004 0.000 0.004 0.000 0.000 0.992
#> SRR1951719 3 0.2070 0.7783 0.000 0.000 0.896 0.092 0.012 0.000
#> SRR1951720 4 0.2178 0.7815 0.132 0.000 0.000 0.868 0.000 0.000
#> SRR1951718 1 0.0790 0.7430 0.968 0.000 0.000 0.032 0.000 0.000
#> SRR1951717 4 0.1663 0.8199 0.088 0.000 0.000 0.912 0.000 0.000
#> SRR1951715 1 0.3817 0.2093 0.568 0.000 0.000 0.000 0.000 0.432
#> SRR1951716 4 0.2597 0.7366 0.176 0.000 0.000 0.824 0.000 0.000
#> SRR1951714 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951713 3 0.1908 0.7829 0.004 0.000 0.900 0.096 0.000 0.000
#> SRR1951712 4 0.0622 0.8729 0.012 0.000 0.008 0.980 0.000 0.000
#> SRR1951711 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951710 1 0.0790 0.7170 0.968 0.000 0.000 0.000 0.000 0.032
#> SRR1951709 2 0.0260 0.9791 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1951708 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951707 5 0.2340 0.6820 0.000 0.148 0.000 0.000 0.852 0.000
#> SRR1951705 1 0.0790 0.7430 0.968 0.000 0.000 0.032 0.000 0.000
#> SRR1951704 6 0.6909 0.2444 0.004 0.000 0.260 0.060 0.228 0.448
#> SRR1951703 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951706 6 0.0508 0.8965 0.000 0.000 0.004 0.000 0.012 0.984
#> SRR1951702 5 0.2122 0.7913 0.008 0.000 0.084 0.000 0.900 0.008
#> SRR1951701 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951699 2 0.3684 0.3989 0.000 0.628 0.000 0.000 0.372 0.000
#> SRR1951698 5 0.1814 0.7548 0.000 0.100 0.000 0.000 0.900 0.000
#> SRR1951700 3 0.3052 0.7657 0.004 0.000 0.780 0.216 0.000 0.000
#> SRR1951697 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951696 3 0.4162 0.5426 0.020 0.000 0.700 0.016 0.264 0.000
#> SRR1951695 3 0.3293 0.7779 0.004 0.000 0.788 0.196 0.008 0.004
#> SRR1951693 3 0.3081 0.7645 0.004 0.000 0.776 0.220 0.000 0.000
#> SRR1951692 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951691 4 0.4756 0.4741 0.064 0.000 0.280 0.648 0.000 0.008
#> SRR1951688 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951689 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951687 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951685 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951684 3 0.2300 0.7934 0.000 0.000 0.856 0.144 0.000 0.000
#> SRR1951686 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951683 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951682 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951690 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951680 3 0.2378 0.7927 0.000 0.000 0.848 0.152 0.000 0.000
#> SRR1951679 1 0.3221 0.5861 0.736 0.000 0.264 0.000 0.000 0.000
#> SRR1951678 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951677 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951676 4 0.0363 0.8743 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1951675 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951681 4 0.3314 0.5678 0.000 0.000 0.256 0.740 0.004 0.000
#> SRR1951673 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951674 6 0.0146 0.9043 0.004 0.000 0.000 0.000 0.000 0.996
#> SRR1951672 4 0.3634 0.4536 0.356 0.000 0.000 0.644 0.000 0.000
#> SRR1951671 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951670 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951640 3 0.3330 0.6988 0.000 0.000 0.716 0.284 0.000 0.000
#> SRR1951668 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951669 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951638 4 0.3953 0.5331 0.016 0.000 0.000 0.656 0.000 0.328
#> SRR1951637 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951635 4 0.4634 0.5947 0.164 0.000 0.000 0.692 0.000 0.144
#> SRR1951634 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951631 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951632 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951633 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951630 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951629 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951628 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951627 5 0.1387 0.7848 0.000 0.068 0.000 0.000 0.932 0.000
#> SRR1951639 2 0.2135 0.8487 0.000 0.872 0.000 0.000 0.128 0.000
#> SRR1951625 2 0.0000 0.9858 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951626 5 0.1152 0.8107 0.004 0.000 0.044 0.000 0.952 0.000
#> SRR1951624 3 0.2402 0.6821 0.004 0.000 0.856 0.000 0.140 0.000
#> SRR1951622 1 0.0790 0.7430 0.968 0.000 0.000 0.032 0.000 0.000
#> SRR1951636 5 0.1531 0.8043 0.004 0.000 0.068 0.000 0.928 0.000
#> SRR1951623 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951621 4 0.0000 0.8821 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951620 4 0.3756 0.2119 0.000 0.000 0.400 0.600 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "pam"]
# you can also extract it by
# res = res_list["ATC:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 13580 rows and 133 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.977 0.990 0.4756 0.520 0.520
#> 3 3 1.000 0.984 0.993 0.3608 0.682 0.466
#> 4 4 0.950 0.920 0.969 0.1123 0.902 0.732
#> 5 5 0.947 0.916 0.965 0.1084 0.908 0.683
#> 6 6 0.970 0.928 0.967 0.0333 0.937 0.715
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 4 5
There is also optional best \(k\) = 2 3 4 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
#> SRR1951667 2 0.000 0.975 0.000 1.000
#> SRR1951666 2 0.000 0.975 0.000 1.000
#> SRR1951665 2 0.000 0.975 0.000 1.000
#> SRR1951664 1 0.000 1.000 1.000 0.000
#> SRR1951663 1 0.000 1.000 1.000 0.000
#> SRR1951662 2 0.000 0.975 0.000 1.000
#> SRR1951661 2 0.861 0.622 0.284 0.716
#> SRR1951660 1 0.000 1.000 1.000 0.000
#> SRR1951659 2 0.000 0.975 0.000 1.000
#> SRR1951658 2 0.000 0.975 0.000 1.000
#> SRR1951657 1 0.000 1.000 1.000 0.000
#> SRR1951656 1 0.000 1.000 1.000 0.000
#> SRR1951655 1 0.000 1.000 1.000 0.000
#> SRR1951654 1 0.000 1.000 1.000 0.000
#> SRR1951653 1 0.000 1.000 1.000 0.000
#> SRR1951652 2 0.000 0.975 0.000 1.000
#> SRR1951651 1 0.000 1.000 1.000 0.000
#> SRR1951650 1 0.000 1.000 1.000 0.000
#> SRR1951648 2 0.000 0.975 0.000 1.000
#> SRR1951649 1 0.000 1.000 1.000 0.000
#> SRR1951647 2 0.000 0.975 0.000 1.000
#> SRR1951646 1 0.000 1.000 1.000 0.000
#> SRR1951645 1 0.000 1.000 1.000 0.000
#> SRR1951644 1 0.000 1.000 1.000 0.000
#> SRR1951643 1 0.000 1.000 1.000 0.000
#> SRR1951641 2 0.000 0.975 0.000 1.000
#> SRR1951642 1 0.000 1.000 1.000 0.000
#> SRR1951694 1 0.000 1.000 1.000 0.000
#> SRR1951731 2 0.000 0.975 0.000 1.000
#> SRR1951730 2 0.000 0.975 0.000 1.000
#> SRR1951752 2 0.000 0.975 0.000 1.000
#> SRR1951751 1 0.000 1.000 1.000 0.000
#> SRR1951749 2 0.000 0.975 0.000 1.000
#> SRR1951750 1 0.000 1.000 1.000 0.000
#> SRR1951748 2 0.000 0.975 0.000 1.000
#> SRR1951747 2 0.000 0.975 0.000 1.000
#> SRR1951745 1 0.000 1.000 1.000 0.000
#> SRR1951746 2 0.000 0.975 0.000 1.000
#> SRR1951744 1 0.000 1.000 1.000 0.000
#> SRR1951742 2 0.000 0.975 0.000 1.000
#> SRR1951743 2 0.000 0.975 0.000 1.000
#> SRR1951741 1 0.000 1.000 1.000 0.000
#> SRR1951740 1 0.000 1.000 1.000 0.000
#> SRR1951738 2 0.000 0.975 0.000 1.000
#> SRR1951739 2 0.000 0.975 0.000 1.000
#> SRR1951737 1 0.000 1.000 1.000 0.000
#> SRR1951736 1 0.000 1.000 1.000 0.000
#> SRR1951734 2 0.000 0.975 0.000 1.000
#> SRR1951735 1 0.000 1.000 1.000 0.000
#> SRR1951733 2 0.000 0.975 0.000 1.000
#> SRR1951732 2 0.000 0.975 0.000 1.000
#> SRR1951729 1 0.000 1.000 1.000 0.000
#> SRR1951728 1 0.000 1.000 1.000 0.000
#> SRR1951727 2 0.000 0.975 0.000 1.000
#> SRR1951725 1 0.000 1.000 1.000 0.000
#> SRR1951726 1 0.000 1.000 1.000 0.000
#> SRR1951724 1 0.000 1.000 1.000 0.000
#> SRR1951722 1 0.000 1.000 1.000 0.000
#> SRR1951723 1 0.000 1.000 1.000 0.000
#> SRR1951721 2 0.998 0.147 0.472 0.528
#> SRR1951719 1 0.000 1.000 1.000 0.000
#> SRR1951720 1 0.000 1.000 1.000 0.000
#> SRR1951718 1 0.000 1.000 1.000 0.000
#> SRR1951717 1 0.000 1.000 1.000 0.000
#> SRR1951715 1 0.000 1.000 1.000 0.000
#> SRR1951716 1 0.000 1.000 1.000 0.000
#> SRR1951714 1 0.000 1.000 1.000 0.000
#> SRR1951713 1 0.000 1.000 1.000 0.000
#> SRR1951712 1 0.000 1.000 1.000 0.000
#> SRR1951711 2 0.000 0.975 0.000 1.000
#> SRR1951710 1 0.000 1.000 1.000 0.000
#> SRR1951709 2 0.000 0.975 0.000 1.000
#> SRR1951708 2 0.000 0.975 0.000 1.000
#> SRR1951707 2 0.482 0.876 0.104 0.896
#> SRR1951705 1 0.000 1.000 1.000 0.000
#> SRR1951704 1 0.000 1.000 1.000 0.000
#> SRR1951703 2 0.000 0.975 0.000 1.000
#> SRR1951706 1 0.000 1.000 1.000 0.000
#> SRR1951702 1 0.000 1.000 1.000 0.000
#> SRR1951701 2 0.000 0.975 0.000 1.000
#> SRR1951699 2 0.000 0.975 0.000 1.000
#> SRR1951698 2 0.671 0.790 0.176 0.824
#> SRR1951700 1 0.000 1.000 1.000 0.000
#> SRR1951697 2 0.000 0.975 0.000 1.000
#> SRR1951696 1 0.000 1.000 1.000 0.000
#> SRR1951695 1 0.000 1.000 1.000 0.000
#> SRR1951693 1 0.000 1.000 1.000 0.000
#> SRR1951692 1 0.000 1.000 1.000 0.000
#> SRR1951691 1 0.000 1.000 1.000 0.000
#> SRR1951688 2 0.000 0.975 0.000 1.000
#> SRR1951689 2 0.000 0.975 0.000 1.000
#> SRR1951687 1 0.000 1.000 1.000 0.000
#> SRR1951685 2 0.000 0.975 0.000 1.000
#> SRR1951684 1 0.000 1.000 1.000 0.000
#> SRR1951686 2 0.000 0.975 0.000 1.000
#> SRR1951683 1 0.000 1.000 1.000 0.000
#> SRR1951682 2 0.000 0.975 0.000 1.000
#> SRR1951690 2 0.000 0.975 0.000 1.000
#> SRR1951680 1 0.000 1.000 1.000 0.000
#> SRR1951679 1 0.000 1.000 1.000 0.000
#> SRR1951678 1 0.000 1.000 1.000 0.000
#> SRR1951677 1 0.000 1.000 1.000 0.000
#> SRR1951676 1 0.000 1.000 1.000 0.000
#> SRR1951675 2 0.000 0.975 0.000 1.000
#> SRR1951681 1 0.000 1.000 1.000 0.000
#> SRR1951673 2 0.000 0.975 0.000 1.000
#> SRR1951674 1 0.000 1.000 1.000 0.000
#> SRR1951672 1 0.000 1.000 1.000 0.000
#> SRR1951671 2 0.000 0.975 0.000 1.000
#> SRR1951670 1 0.000 1.000 1.000 0.000
#> SRR1951640 1 0.000 1.000 1.000 0.000
#> SRR1951668 1 0.000 1.000 1.000 0.000
#> SRR1951669 2 0.000 0.975 0.000 1.000
#> SRR1951638 1 0.000 1.000 1.000 0.000
#> SRR1951637 2 0.000 0.975 0.000 1.000
#> SRR1951635 1 0.000 1.000 1.000 0.000
#> SRR1951634 1 0.000 1.000 1.000 0.000
#> SRR1951631 1 0.000 1.000 1.000 0.000
#> SRR1951632 2 0.000 0.975 0.000 1.000
#> SRR1951633 1 0.000 1.000 1.000 0.000
#> SRR1951630 1 0.000 1.000 1.000 0.000
#> SRR1951629 1 0.000 1.000 1.000 0.000
#> SRR1951628 2 0.000 0.975 0.000 1.000
#> SRR1951627 2 0.788 0.704 0.236 0.764
#> SRR1951639 2 0.000 0.975 0.000 1.000
#> SRR1951625 2 0.000 0.975 0.000 1.000
#> SRR1951626 1 0.000 1.000 1.000 0.000
#> SRR1951624 1 0.000 1.000 1.000 0.000
#> SRR1951622 1 0.000 1.000 1.000 0.000
#> SRR1951636 1 0.000 1.000 1.000 0.000
#> SRR1951623 1 0.000 1.000 1.000 0.000
#> SRR1951621 1 0.000 1.000 1.000 0.000
#> SRR1951620 1 0.000 1.000 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951666 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951665 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951664 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951663 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951662 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951661 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951660 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951659 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951657 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951656 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951655 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951654 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951653 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951652 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951651 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951650 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951648 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951649 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951647 2 0.1753 0.938 0.000 0.952 0.048
#> SRR1951646 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951645 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951644 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951643 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951641 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951642 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951694 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951731 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951730 2 0.2711 0.894 0.000 0.912 0.088
#> SRR1951752 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951751 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951749 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951750 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951748 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951747 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951745 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951746 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951744 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951742 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951743 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951741 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951740 1 0.1964 0.934 0.944 0.000 0.056
#> SRR1951738 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951739 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951737 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951736 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951734 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951735 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951733 2 0.0237 0.981 0.000 0.996 0.004
#> SRR1951732 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951729 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951728 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951727 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951725 3 0.2959 0.887 0.100 0.000 0.900
#> SRR1951726 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951724 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951722 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951723 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951721 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951719 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951720 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951718 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951717 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951715 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951716 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951714 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951713 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951712 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951711 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951710 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951709 3 0.5098 0.666 0.000 0.248 0.752
#> SRR1951708 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951707 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951705 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951704 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951703 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951706 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951702 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951701 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951699 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951698 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951700 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951697 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951696 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951695 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951693 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951692 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951691 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951688 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951689 2 0.0237 0.981 0.000 0.996 0.004
#> SRR1951687 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951685 3 0.0237 0.990 0.000 0.004 0.996
#> SRR1951684 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951686 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951683 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951682 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951690 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951680 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951679 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951678 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951677 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951676 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951675 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951681 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951673 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951674 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951672 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951671 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951670 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951640 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951668 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951669 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951638 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951637 2 0.1964 0.929 0.000 0.944 0.056
#> SRR1951635 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951634 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951631 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951632 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951633 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951630 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951629 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951628 2 0.5591 0.575 0.000 0.696 0.304
#> SRR1951627 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951639 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951625 2 0.0000 0.984 0.000 1.000 0.000
#> SRR1951626 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951624 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951622 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951636 3 0.0000 0.994 0.000 0.000 1.000
#> SRR1951623 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951621 1 0.0000 0.998 1.000 0.000 0.000
#> SRR1951620 1 0.0000 0.998 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.3486 0.7592 0.000 0.812 0.188 0.000
#> SRR1951666 3 0.3311 0.7168 0.000 0.000 0.828 0.172
#> SRR1951665 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951664 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951663 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951662 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951661 4 0.3528 0.7628 0.000 0.000 0.192 0.808
#> SRR1951660 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951659 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951658 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951657 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951656 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951655 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951654 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951653 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951652 3 0.4999 -0.0587 0.000 0.000 0.508 0.492
#> SRR1951651 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951650 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951648 4 0.4998 0.0804 0.000 0.000 0.488 0.512
#> SRR1951649 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951647 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951646 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951645 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951644 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951643 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951641 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951642 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951694 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951731 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951730 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951752 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951751 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951749 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951750 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951748 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951745 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951746 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951744 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951742 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951743 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951741 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951740 1 0.4008 0.6631 0.756 0.000 0.000 0.244
#> SRR1951738 3 0.4972 0.1171 0.000 0.456 0.544 0.000
#> SRR1951739 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951737 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951736 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951734 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951735 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951733 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951732 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951729 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951728 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951727 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951725 4 0.2081 0.8803 0.084 0.000 0.000 0.916
#> SRR1951726 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951724 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951722 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951723 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951721 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951719 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951720 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951718 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951717 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951715 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951716 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951714 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951713 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951712 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951711 2 0.4382 0.5558 0.000 0.704 0.296 0.000
#> SRR1951710 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951709 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951708 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951707 4 0.3975 0.6944 0.000 0.000 0.240 0.760
#> SRR1951705 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951704 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951703 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951706 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951702 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951701 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951699 4 0.4761 0.4312 0.000 0.000 0.372 0.628
#> SRR1951698 4 0.3569 0.7575 0.000 0.000 0.196 0.804
#> SRR1951700 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951697 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951696 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951695 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951693 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951692 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951691 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951688 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951689 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951687 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951685 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951684 1 0.0707 0.9667 0.980 0.000 0.000 0.020
#> SRR1951686 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951683 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951682 3 0.3610 0.6956 0.000 0.200 0.800 0.000
#> SRR1951690 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951680 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951679 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951678 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951677 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951676 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951675 2 0.0000 0.9728 0.000 1.000 0.000 0.000
#> SRR1951681 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951673 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951674 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951672 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951671 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951670 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951640 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951668 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951669 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951638 1 0.1302 0.9382 0.956 0.000 0.000 0.044
#> SRR1951637 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951635 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951634 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951631 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951632 3 0.3400 0.7243 0.000 0.180 0.820 0.000
#> SRR1951633 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951630 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951629 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951628 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951627 4 0.3569 0.7575 0.000 0.000 0.196 0.804
#> SRR1951639 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951625 3 0.0000 0.9225 0.000 0.000 1.000 0.000
#> SRR1951626 4 0.1022 0.9372 0.000 0.000 0.032 0.968
#> SRR1951624 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951622 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951636 4 0.0000 0.9642 0.000 0.000 0.000 1.000
#> SRR1951623 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951621 1 0.0000 0.9884 1.000 0.000 0.000 0.000
#> SRR1951620 1 0.0000 0.9884 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 2 0.3109 0.7486 0.000 0.800 0.000 0.000 0.200
#> SRR1951666 5 0.2966 0.7472 0.000 0.000 0.184 0.000 0.816
#> SRR1951665 2 0.0000 0.9699 0.000 1.000 0.000 0.000 0.000
#> SRR1951664 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951663 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951662 2 0.0000 0.9699 0.000 1.000 0.000 0.000 0.000
#> SRR1951661 3 0.0000 0.9332 0.000 0.000 1.000 0.000 0.000
#> SRR1951660 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951659 2 0.0290 0.9672 0.000 0.992 0.000 0.000 0.008
#> SRR1951658 2 0.0290 0.9672 0.000 0.992 0.000 0.000 0.008
#> SRR1951657 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951656 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951655 4 0.0162 0.9790 0.004 0.000 0.000 0.996 0.000
#> SRR1951654 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951653 1 0.1043 0.9315 0.960 0.000 0.040 0.000 0.000
#> SRR1951652 3 0.4306 0.0875 0.000 0.000 0.508 0.000 0.492
#> SRR1951651 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951650 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951648 3 0.4297 0.1579 0.000 0.000 0.528 0.000 0.472
#> SRR1951649 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951647 5 0.0290 0.9556 0.000 0.000 0.008 0.000 0.992
#> SRR1951646 1 0.4294 0.1065 0.532 0.000 0.468 0.000 0.000
#> SRR1951645 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951644 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951643 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951641 2 0.0000 0.9699 0.000 1.000 0.000 0.000 0.000
#> SRR1951642 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951694 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951731 5 0.0290 0.9556 0.000 0.000 0.008 0.000 0.992
#> SRR1951730 5 0.0290 0.9556 0.000 0.000 0.008 0.000 0.992
#> SRR1951752 5 0.0290 0.9556 0.000 0.000 0.008 0.000 0.992
#> SRR1951751 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951749 5 0.0000 0.9538 0.000 0.000 0.000 0.000 1.000
#> SRR1951750 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951748 2 0.0000 0.9699 0.000 1.000 0.000 0.000 0.000
#> SRR1951747 2 0.0000 0.9699 0.000 1.000 0.000 0.000 0.000
#> SRR1951745 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951746 2 0.0000 0.9699 0.000 1.000 0.000 0.000 0.000
#> SRR1951744 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951742 5 0.0000 0.9538 0.000 0.000 0.000 0.000 1.000
#> SRR1951743 2 0.0000 0.9699 0.000 1.000 0.000 0.000 0.000
#> SRR1951741 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951740 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951738 5 0.4262 0.1763 0.000 0.440 0.000 0.000 0.560
#> SRR1951739 2 0.0290 0.9672 0.000 0.992 0.000 0.000 0.008
#> SRR1951737 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951736 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951734 2 0.0000 0.9699 0.000 1.000 0.000 0.000 0.000
#> SRR1951735 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951733 5 0.0290 0.9556 0.000 0.000 0.008 0.000 0.992
#> SRR1951732 2 0.0000 0.9699 0.000 1.000 0.000 0.000 0.000
#> SRR1951729 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951728 3 0.0000 0.9332 0.000 0.000 1.000 0.000 0.000
#> SRR1951727 2 0.0000 0.9699 0.000 1.000 0.000 0.000 0.000
#> SRR1951725 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951726 3 0.0000 0.9332 0.000 0.000 1.000 0.000 0.000
#> SRR1951724 3 0.2648 0.7999 0.152 0.000 0.848 0.000 0.000
#> SRR1951722 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951723 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951721 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951719 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951720 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951718 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951717 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951715 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951716 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951714 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951713 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951712 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951711 2 0.3857 0.5310 0.000 0.688 0.000 0.000 0.312
#> SRR1951710 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951709 5 0.0290 0.9556 0.000 0.000 0.008 0.000 0.992
#> SRR1951708 2 0.0290 0.9672 0.000 0.992 0.000 0.000 0.008
#> SRR1951707 3 0.2813 0.7847 0.000 0.000 0.832 0.000 0.168
#> SRR1951705 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951704 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951703 2 0.0000 0.9699 0.000 1.000 0.000 0.000 0.000
#> SRR1951706 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951702 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951701 2 0.0000 0.9699 0.000 1.000 0.000 0.000 0.000
#> SRR1951699 3 0.3752 0.5956 0.000 0.000 0.708 0.000 0.292
#> SRR1951698 3 0.2127 0.8469 0.000 0.000 0.892 0.000 0.108
#> SRR1951700 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951697 5 0.0000 0.9538 0.000 0.000 0.000 0.000 1.000
#> SRR1951696 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951695 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951693 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951692 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951691 3 0.3074 0.7294 0.196 0.000 0.804 0.000 0.000
#> SRR1951688 5 0.0290 0.9556 0.000 0.000 0.008 0.000 0.992
#> SRR1951689 5 0.0290 0.9556 0.000 0.000 0.008 0.000 0.992
#> SRR1951687 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951685 5 0.0290 0.9556 0.000 0.000 0.008 0.000 0.992
#> SRR1951684 4 0.2891 0.7856 0.176 0.000 0.000 0.824 0.000
#> SRR1951686 5 0.0000 0.9538 0.000 0.000 0.000 0.000 1.000
#> SRR1951683 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951682 5 0.2561 0.8049 0.000 0.144 0.000 0.000 0.856
#> SRR1951690 2 0.0000 0.9699 0.000 1.000 0.000 0.000 0.000
#> SRR1951680 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951679 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951678 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951677 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951676 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951675 2 0.0290 0.9672 0.000 0.992 0.000 0.000 0.008
#> SRR1951681 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951673 5 0.0290 0.9556 0.000 0.000 0.008 0.000 0.992
#> SRR1951674 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951672 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951671 5 0.0000 0.9538 0.000 0.000 0.000 0.000 1.000
#> SRR1951670 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951640 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951668 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951669 5 0.0000 0.9538 0.000 0.000 0.000 0.000 1.000
#> SRR1951638 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951637 5 0.0290 0.9556 0.000 0.000 0.008 0.000 0.992
#> SRR1951635 4 0.0290 0.9755 0.008 0.000 0.000 0.992 0.000
#> SRR1951634 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951631 4 0.4060 0.4554 0.360 0.000 0.000 0.640 0.000
#> SRR1951632 5 0.2020 0.8577 0.000 0.100 0.000 0.000 0.900
#> SRR1951633 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951630 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951629 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951628 5 0.0290 0.9556 0.000 0.000 0.008 0.000 0.992
#> SRR1951627 3 0.0703 0.9186 0.000 0.000 0.976 0.000 0.024
#> SRR1951639 5 0.0290 0.9556 0.000 0.000 0.008 0.000 0.992
#> SRR1951625 5 0.0000 0.9538 0.000 0.000 0.000 0.000 1.000
#> SRR1951626 3 0.0000 0.9332 0.000 0.000 1.000 0.000 0.000
#> SRR1951624 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951622 1 0.0000 0.9724 1.000 0.000 0.000 0.000 0.000
#> SRR1951636 3 0.0290 0.9373 0.008 0.000 0.992 0.000 0.000
#> SRR1951623 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951621 4 0.0000 0.9825 0.000 0.000 0.000 1.000 0.000
#> SRR1951620 4 0.0000 0.9825 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
#> SRR1951667 2 0.0993 0.9517 0.000 0.964 0.000 0.000 0.012 0.024
#> SRR1951666 5 0.0363 0.9078 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1951665 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1951664 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951663 3 0.0547 0.9622 0.000 0.020 0.980 0.000 0.000 0.000
#> SRR1951662 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1951661 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951660 1 0.0000 0.9686 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951659 2 0.0865 0.9457 0.000 0.964 0.000 0.000 0.000 0.036
#> SRR1951658 2 0.0790 0.9480 0.000 0.968 0.000 0.000 0.000 0.032
#> SRR1951657 4 0.0000 0.9760 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951656 4 0.0000 0.9760 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951655 4 0.0146 0.9742 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1951654 4 0.0363 0.9757 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1951653 1 0.0937 0.9278 0.960 0.000 0.040 0.000 0.000 0.000
#> SRR1951652 5 0.0363 0.9078 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1951651 4 0.0363 0.9757 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1951650 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951648 5 0.0363 0.9078 0.000 0.000 0.012 0.000 0.988 0.000
#> SRR1951649 1 0.0000 0.9686 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951647 5 0.0000 0.9111 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951646 1 0.3862 0.0549 0.524 0.000 0.476 0.000 0.000 0.000
#> SRR1951645 1 0.0000 0.9686 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951644 1 0.0000 0.9686 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951643 4 0.0363 0.9757 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1951641 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1951642 4 0.0363 0.9757 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1951694 1 0.0000 0.9686 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951731 5 0.0000 0.9111 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951730 5 0.0000 0.9111 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951752 5 0.0000 0.9111 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951751 1 0.0000 0.9686 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951749 5 0.0363 0.9060 0.000 0.012 0.000 0.000 0.988 0.000
#> SRR1951750 4 0.0363 0.9757 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1951748 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1951747 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1951745 3 0.0909 0.9538 0.000 0.020 0.968 0.012 0.000 0.000
#> SRR1951746 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1951744 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951742 2 0.1910 0.8814 0.000 0.892 0.000 0.000 0.108 0.000
#> SRR1951743 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1951741 4 0.0363 0.9757 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1951740 1 0.0363 0.9602 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1951738 2 0.0909 0.9542 0.000 0.968 0.000 0.000 0.020 0.012
#> SRR1951739 2 0.0865 0.9457 0.000 0.964 0.000 0.000 0.000 0.036
#> SRR1951737 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951736 4 0.0000 0.9760 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951734 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1951735 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951733 5 0.0146 0.9097 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951732 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1951729 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951728 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951727 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1951725 1 0.0260 0.9632 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1951726 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951724 3 0.2482 0.8142 0.148 0.000 0.848 0.004 0.000 0.000
#> SRR1951722 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951723 1 0.0000 0.9686 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951721 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951719 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951720 4 0.0146 0.9760 0.000 0.004 0.000 0.996 0.000 0.000
#> SRR1951718 1 0.0000 0.9686 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951717 4 0.0000 0.9760 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951715 1 0.0000 0.9686 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951716 4 0.0000 0.9760 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951714 1 0.0146 0.9661 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1951713 1 0.0000 0.9686 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951712 4 0.0000 0.9760 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951711 2 0.0909 0.9529 0.000 0.968 0.000 0.000 0.012 0.020
#> SRR1951710 1 0.0000 0.9686 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951709 5 0.0000 0.9111 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951708 2 0.0790 0.9480 0.000 0.968 0.000 0.000 0.000 0.032
#> SRR1951707 5 0.0790 0.8952 0.000 0.000 0.032 0.000 0.968 0.000
#> SRR1951705 1 0.0000 0.9686 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951704 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951703 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1951706 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951702 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951701 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1951699 5 0.2003 0.8217 0.000 0.000 0.116 0.000 0.884 0.000
#> SRR1951698 5 0.0713 0.8975 0.000 0.000 0.028 0.000 0.972 0.000
#> SRR1951700 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951697 2 0.0790 0.9525 0.000 0.968 0.000 0.000 0.032 0.000
#> SRR1951696 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951695 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951693 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951692 4 0.0000 0.9760 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951691 3 0.2697 0.7651 0.188 0.000 0.812 0.000 0.000 0.000
#> SRR1951688 5 0.3607 0.4487 0.000 0.348 0.000 0.000 0.652 0.000
#> SRR1951689 5 0.3482 0.5169 0.000 0.316 0.000 0.000 0.684 0.000
#> SRR1951687 4 0.0363 0.9757 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1951685 5 0.0000 0.9111 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951684 4 0.2491 0.8000 0.164 0.000 0.000 0.836 0.000 0.000
#> SRR1951686 2 0.0790 0.9525 0.000 0.968 0.000 0.000 0.032 0.000
#> SRR1951683 4 0.0363 0.9757 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1951682 2 0.0891 0.9541 0.000 0.968 0.000 0.000 0.024 0.008
#> SRR1951690 6 0.0000 1.0000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1951680 1 0.0363 0.9602 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1951679 1 0.0000 0.9686 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951678 4 0.0363 0.9757 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1951677 4 0.0363 0.9757 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1951676 4 0.0000 0.9760 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951675 2 0.0790 0.9480 0.000 0.968 0.000 0.000 0.000 0.032
#> SRR1951681 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951673 2 0.3695 0.3840 0.000 0.624 0.000 0.000 0.376 0.000
#> SRR1951674 3 0.0909 0.9538 0.000 0.020 0.968 0.012 0.000 0.000
#> SRR1951672 1 0.0000 0.9686 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951671 2 0.0790 0.9525 0.000 0.968 0.000 0.000 0.032 0.000
#> SRR1951670 4 0.0363 0.9757 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1951640 1 0.0000 0.9686 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951668 4 0.0000 0.9760 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951669 2 0.0790 0.9525 0.000 0.968 0.000 0.000 0.032 0.000
#> SRR1951638 4 0.0547 0.9646 0.000 0.020 0.000 0.980 0.000 0.000
#> SRR1951637 5 0.2416 0.7739 0.000 0.156 0.000 0.000 0.844 0.000
#> SRR1951635 4 0.0692 0.9621 0.004 0.020 0.000 0.976 0.000 0.000
#> SRR1951634 4 0.0000 0.9760 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951631 4 0.3634 0.4587 0.356 0.000 0.000 0.644 0.000 0.000
#> SRR1951632 2 0.0858 0.9536 0.000 0.968 0.000 0.000 0.028 0.004
#> SRR1951633 4 0.0363 0.9757 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1951630 4 0.0363 0.9757 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1951629 4 0.0000 0.9760 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951628 5 0.0000 0.9111 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951627 5 0.3765 0.2898 0.000 0.000 0.404 0.000 0.596 0.000
#> SRR1951639 5 0.0000 0.9111 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1951625 2 0.0790 0.9525 0.000 0.968 0.000 0.000 0.032 0.000
#> SRR1951626 3 0.2969 0.7045 0.000 0.000 0.776 0.000 0.224 0.000
#> SRR1951624 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951622 1 0.0000 0.9686 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1951636 3 0.0000 0.9745 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951623 4 0.0000 0.9760 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1951621 4 0.0363 0.9757 0.000 0.012 0.000 0.988 0.000 0.000
#> SRR1951620 4 0.0000 0.9760 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["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 13580 rows and 133 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.953 0.934 0.974 0.2363 0.798 0.798
#> 3 3 0.794 0.785 0.915 1.5647 0.537 0.431
#> 4 4 0.784 0.772 0.900 0.1149 0.882 0.698
#> 5 5 0.942 0.914 0.954 0.1255 0.872 0.614
#> 6 6 0.719 0.732 0.797 0.0286 0.934 0.745
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
#> SRR1951667 1 0.9686 0.371 0.604 0.396
#> SRR1951666 1 0.0000 0.971 1.000 0.000
#> SRR1951665 2 0.0000 0.988 0.000 1.000
#> SRR1951664 1 0.0000 0.971 1.000 0.000
#> SRR1951663 1 0.0000 0.971 1.000 0.000
#> SRR1951662 2 0.0000 0.988 0.000 1.000
#> SRR1951661 1 0.0000 0.971 1.000 0.000
#> SRR1951660 1 0.0000 0.971 1.000 0.000
#> SRR1951659 2 0.0000 0.988 0.000 1.000
#> SRR1951658 2 0.0000 0.988 0.000 1.000
#> SRR1951657 1 0.0000 0.971 1.000 0.000
#> SRR1951656 1 0.0000 0.971 1.000 0.000
#> SRR1951655 1 0.0000 0.971 1.000 0.000
#> SRR1951654 1 0.0000 0.971 1.000 0.000
#> SRR1951653 1 0.0000 0.971 1.000 0.000
#> SRR1951652 1 0.0000 0.971 1.000 0.000
#> SRR1951651 1 0.0000 0.971 1.000 0.000
#> SRR1951650 1 0.0000 0.971 1.000 0.000
#> SRR1951648 1 0.0000 0.971 1.000 0.000
#> SRR1951649 1 0.0000 0.971 1.000 0.000
#> SRR1951647 1 0.0000 0.971 1.000 0.000
#> SRR1951646 1 0.0000 0.971 1.000 0.000
#> SRR1951645 1 0.0000 0.971 1.000 0.000
#> SRR1951644 1 0.0000 0.971 1.000 0.000
#> SRR1951643 1 0.0000 0.971 1.000 0.000
#> SRR1951641 2 0.0000 0.988 0.000 1.000
#> SRR1951642 1 0.0000 0.971 1.000 0.000
#> SRR1951694 1 0.0000 0.971 1.000 0.000
#> SRR1951731 1 0.0000 0.971 1.000 0.000
#> SRR1951730 1 0.0000 0.971 1.000 0.000
#> SRR1951752 1 0.0000 0.971 1.000 0.000
#> SRR1951751 1 0.0000 0.971 1.000 0.000
#> SRR1951749 1 0.0000 0.971 1.000 0.000
#> SRR1951750 1 0.0000 0.971 1.000 0.000
#> SRR1951748 2 0.0000 0.988 0.000 1.000
#> SRR1951747 2 0.0000 0.988 0.000 1.000
#> SRR1951745 1 0.0000 0.971 1.000 0.000
#> SRR1951746 2 0.0000 0.988 0.000 1.000
#> SRR1951744 1 0.0000 0.971 1.000 0.000
#> SRR1951742 1 0.0000 0.971 1.000 0.000
#> SRR1951743 2 0.0000 0.988 0.000 1.000
#> SRR1951741 1 0.0000 0.971 1.000 0.000
#> SRR1951740 1 0.0000 0.971 1.000 0.000
#> SRR1951738 1 0.5519 0.836 0.872 0.128
#> SRR1951739 1 0.9754 0.341 0.592 0.408
#> SRR1951737 1 0.0000 0.971 1.000 0.000
#> SRR1951736 1 0.0000 0.971 1.000 0.000
#> SRR1951734 2 0.0000 0.988 0.000 1.000
#> SRR1951735 1 0.0000 0.971 1.000 0.000
#> SRR1951733 1 0.0000 0.971 1.000 0.000
#> SRR1951732 2 0.0000 0.988 0.000 1.000
#> SRR1951729 1 0.0000 0.971 1.000 0.000
#> SRR1951728 1 0.0000 0.971 1.000 0.000
#> SRR1951727 2 0.0000 0.988 0.000 1.000
#> SRR1951725 1 0.0000 0.971 1.000 0.000
#> SRR1951726 1 0.0000 0.971 1.000 0.000
#> SRR1951724 1 0.0000 0.971 1.000 0.000
#> SRR1951722 1 0.0000 0.971 1.000 0.000
#> SRR1951723 1 0.0000 0.971 1.000 0.000
#> SRR1951721 1 0.0000 0.971 1.000 0.000
#> SRR1951719 1 0.0000 0.971 1.000 0.000
#> SRR1951720 1 0.0000 0.971 1.000 0.000
#> SRR1951718 1 0.0000 0.971 1.000 0.000
#> SRR1951717 1 0.0000 0.971 1.000 0.000
#> SRR1951715 1 0.0000 0.971 1.000 0.000
#> SRR1951716 1 0.0000 0.971 1.000 0.000
#> SRR1951714 1 0.0000 0.971 1.000 0.000
#> SRR1951713 1 0.0000 0.971 1.000 0.000
#> SRR1951712 1 0.0000 0.971 1.000 0.000
#> SRR1951711 2 0.6148 0.811 0.152 0.848
#> SRR1951710 1 0.0000 0.971 1.000 0.000
#> SRR1951709 1 0.0000 0.971 1.000 0.000
#> SRR1951708 2 0.0938 0.977 0.012 0.988
#> SRR1951707 1 0.0000 0.971 1.000 0.000
#> SRR1951705 1 0.0000 0.971 1.000 0.000
#> SRR1951704 1 0.0000 0.971 1.000 0.000
#> SRR1951703 2 0.0000 0.988 0.000 1.000
#> SRR1951706 1 0.0000 0.971 1.000 0.000
#> SRR1951702 1 0.0000 0.971 1.000 0.000
#> SRR1951701 1 0.9922 0.227 0.552 0.448
#> SRR1951699 1 0.0000 0.971 1.000 0.000
#> SRR1951698 1 0.0000 0.971 1.000 0.000
#> SRR1951700 1 0.0000 0.971 1.000 0.000
#> SRR1951697 1 0.9170 0.515 0.668 0.332
#> SRR1951696 1 0.0000 0.971 1.000 0.000
#> SRR1951695 1 0.0000 0.971 1.000 0.000
#> SRR1951693 1 0.0000 0.971 1.000 0.000
#> SRR1951692 1 0.0000 0.971 1.000 0.000
#> SRR1951691 1 0.0000 0.971 1.000 0.000
#> SRR1951688 1 0.0000 0.971 1.000 0.000
#> SRR1951689 1 0.0000 0.971 1.000 0.000
#> SRR1951687 1 0.0000 0.971 1.000 0.000
#> SRR1951685 1 0.0000 0.971 1.000 0.000
#> SRR1951684 1 0.0000 0.971 1.000 0.000
#> SRR1951686 1 0.0000 0.971 1.000 0.000
#> SRR1951683 1 0.0000 0.971 1.000 0.000
#> SRR1951682 1 0.9552 0.420 0.624 0.376
#> SRR1951690 1 0.9922 0.227 0.552 0.448
#> SRR1951680 1 0.0000 0.971 1.000 0.000
#> SRR1951679 1 0.0000 0.971 1.000 0.000
#> SRR1951678 1 0.0000 0.971 1.000 0.000
#> SRR1951677 1 0.0000 0.971 1.000 0.000
#> SRR1951676 1 0.0000 0.971 1.000 0.000
#> SRR1951675 1 0.9754 0.341 0.592 0.408
#> SRR1951681 1 0.0000 0.971 1.000 0.000
#> SRR1951673 1 0.0000 0.971 1.000 0.000
#> SRR1951674 1 0.0000 0.971 1.000 0.000
#> SRR1951672 1 0.0000 0.971 1.000 0.000
#> SRR1951671 1 0.0000 0.971 1.000 0.000
#> SRR1951670 1 0.0000 0.971 1.000 0.000
#> SRR1951640 1 0.0000 0.971 1.000 0.000
#> SRR1951668 1 0.0000 0.971 1.000 0.000
#> SRR1951669 1 0.0000 0.971 1.000 0.000
#> SRR1951638 1 0.0000 0.971 1.000 0.000
#> SRR1951637 1 0.0000 0.971 1.000 0.000
#> SRR1951635 1 0.0000 0.971 1.000 0.000
#> SRR1951634 1 0.0000 0.971 1.000 0.000
#> SRR1951631 1 0.0000 0.971 1.000 0.000
#> SRR1951632 1 0.9460 0.447 0.636 0.364
#> SRR1951633 1 0.0000 0.971 1.000 0.000
#> SRR1951630 1 0.0000 0.971 1.000 0.000
#> SRR1951629 1 0.0000 0.971 1.000 0.000
#> SRR1951628 1 0.0000 0.971 1.000 0.000
#> SRR1951627 1 0.0000 0.971 1.000 0.000
#> SRR1951639 1 0.0000 0.971 1.000 0.000
#> SRR1951625 1 0.0000 0.971 1.000 0.000
#> SRR1951626 1 0.0000 0.971 1.000 0.000
#> SRR1951624 1 0.0000 0.971 1.000 0.000
#> SRR1951622 1 0.0000 0.971 1.000 0.000
#> SRR1951636 1 0.0000 0.971 1.000 0.000
#> SRR1951623 1 0.0000 0.971 1.000 0.000
#> SRR1951621 1 0.0000 0.971 1.000 0.000
#> SRR1951620 1 0.0000 0.971 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 2 0.6309 0.2026 0.000 0.500 0.500
#> SRR1951666 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951665 2 0.0000 0.7753 0.000 1.000 0.000
#> SRR1951664 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951663 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951662 2 0.0000 0.7753 0.000 1.000 0.000
#> SRR1951661 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951660 1 0.0237 0.8857 0.996 0.000 0.004
#> SRR1951659 2 0.0000 0.7753 0.000 1.000 0.000
#> SRR1951658 2 0.0000 0.7753 0.000 1.000 0.000
#> SRR1951657 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951656 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951655 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951654 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951653 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951652 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951651 3 0.6308 -0.0443 0.492 0.000 0.508
#> SRR1951650 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951648 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951649 1 0.5733 0.5229 0.676 0.000 0.324
#> SRR1951647 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951646 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951645 1 0.5760 0.5228 0.672 0.000 0.328
#> SRR1951644 1 0.6280 0.1996 0.540 0.000 0.460
#> SRR1951643 1 0.5363 0.6007 0.724 0.000 0.276
#> SRR1951641 2 0.0000 0.7753 0.000 1.000 0.000
#> SRR1951642 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951694 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951731 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951730 3 0.0424 0.9354 0.008 0.000 0.992
#> SRR1951752 3 0.4002 0.7379 0.160 0.000 0.840
#> SRR1951751 1 0.6274 0.2214 0.544 0.000 0.456
#> SRR1951749 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951750 1 0.0892 0.8932 0.980 0.000 0.020
#> SRR1951748 2 0.0000 0.7753 0.000 1.000 0.000
#> SRR1951747 2 0.0237 0.7740 0.000 0.996 0.004
#> SRR1951745 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951746 2 0.0000 0.7753 0.000 1.000 0.000
#> SRR1951744 1 0.1643 0.8750 0.956 0.000 0.044
#> SRR1951742 3 0.3340 0.7985 0.000 0.120 0.880
#> SRR1951743 2 0.0000 0.7753 0.000 1.000 0.000
#> SRR1951741 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951740 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951738 1 0.6948 -0.0216 0.512 0.472 0.016
#> SRR1951739 2 0.6309 0.2140 0.000 0.504 0.496
#> SRR1951737 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951736 1 0.0424 0.8891 0.992 0.000 0.008
#> SRR1951734 2 0.0000 0.7753 0.000 1.000 0.000
#> SRR1951735 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951733 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951732 2 0.0000 0.7753 0.000 1.000 0.000
#> SRR1951729 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951728 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951727 2 0.0000 0.7753 0.000 1.000 0.000
#> SRR1951725 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951726 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951724 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951722 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951723 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951721 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951719 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951720 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951718 1 0.1860 0.8593 0.948 0.000 0.052
#> SRR1951717 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951715 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951716 1 0.0237 0.8857 0.996 0.000 0.004
#> SRR1951714 3 0.5835 0.4396 0.340 0.000 0.660
#> SRR1951713 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951712 1 0.5968 0.4624 0.636 0.000 0.364
#> SRR1951711 2 0.0661 0.7699 0.004 0.988 0.008
#> SRR1951710 1 0.0000 0.8818 1.000 0.000 0.000
#> SRR1951709 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951708 2 0.0237 0.7739 0.000 0.996 0.004
#> SRR1951707 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951705 1 0.0000 0.8818 1.000 0.000 0.000
#> SRR1951704 3 0.0592 0.9313 0.012 0.000 0.988
#> SRR1951703 2 0.0000 0.7753 0.000 1.000 0.000
#> SRR1951706 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951702 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951701 2 0.6309 0.2140 0.000 0.504 0.496
#> SRR1951699 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951698 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951700 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951697 2 0.7979 0.3026 0.060 0.500 0.440
#> SRR1951696 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951695 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951693 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951692 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951691 3 0.1163 0.9140 0.028 0.000 0.972
#> SRR1951688 2 0.9568 0.2809 0.336 0.456 0.208
#> SRR1951689 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951687 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951685 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951684 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951686 3 0.6505 -0.1503 0.004 0.468 0.528
#> SRR1951683 1 0.2537 0.8399 0.920 0.000 0.080
#> SRR1951682 2 0.6954 0.2364 0.016 0.500 0.484
#> SRR1951690 2 0.7484 0.2826 0.036 0.504 0.460
#> SRR1951680 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951679 1 0.6299 0.1638 0.524 0.000 0.476
#> SRR1951678 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951677 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951676 3 0.6267 0.1089 0.452 0.000 0.548
#> SRR1951675 2 0.6823 0.0614 0.484 0.504 0.012
#> SRR1951681 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951673 1 0.2165 0.8578 0.936 0.000 0.064
#> SRR1951674 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951672 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951671 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951670 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951640 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951668 1 0.6267 0.2199 0.548 0.000 0.452
#> SRR1951669 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951638 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951637 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951635 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951634 3 0.5138 0.6002 0.252 0.000 0.748
#> SRR1951631 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951632 2 0.6309 0.2026 0.000 0.500 0.500
#> SRR1951633 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951630 1 0.1289 0.8847 0.968 0.000 0.032
#> SRR1951629 3 0.1860 0.8863 0.052 0.000 0.948
#> SRR1951628 1 0.0000 0.8818 1.000 0.000 0.000
#> SRR1951627 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951639 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951625 3 0.1753 0.8928 0.000 0.048 0.952
#> SRR1951626 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951624 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951622 1 0.0000 0.8818 1.000 0.000 0.000
#> SRR1951636 3 0.0000 0.9431 0.000 0.000 1.000
#> SRR1951623 1 0.0892 0.8931 0.980 0.000 0.020
#> SRR1951621 1 0.0747 0.8954 0.984 0.000 0.016
#> SRR1951620 3 0.0000 0.9431 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 2 0.4985 0.3777 0.000 0.532 0.468 0.000
#> SRR1951666 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951665 2 0.0707 0.6353 0.000 0.980 0.000 0.020
#> SRR1951664 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951663 1 0.1022 0.9088 0.968 0.000 0.000 0.032
#> SRR1951662 2 0.0707 0.6353 0.000 0.980 0.000 0.020
#> SRR1951661 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951660 4 0.1118 0.9245 0.036 0.000 0.000 0.964
#> SRR1951659 2 0.0469 0.6410 0.000 0.988 0.012 0.000
#> SRR1951658 2 0.0707 0.6427 0.000 0.980 0.020 0.000
#> SRR1951657 3 0.0188 0.9303 0.000 0.000 0.996 0.004
#> SRR1951656 3 0.0336 0.9279 0.000 0.000 0.992 0.008
#> SRR1951655 1 0.7815 -0.0343 0.392 0.000 0.256 0.352
#> SRR1951654 1 0.0469 0.9087 0.988 0.000 0.000 0.012
#> SRR1951653 3 0.0592 0.9241 0.000 0.000 0.984 0.016
#> SRR1951652 1 0.2101 0.8715 0.928 0.000 0.060 0.012
#> SRR1951651 3 0.5406 0.0660 0.480 0.000 0.508 0.012
#> SRR1951650 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951648 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951649 4 0.1174 0.9165 0.020 0.000 0.012 0.968
#> SRR1951647 3 0.0804 0.9209 0.000 0.008 0.980 0.012
#> SRR1951646 3 0.0188 0.9303 0.000 0.000 0.996 0.004
#> SRR1951645 4 0.1297 0.9151 0.020 0.000 0.016 0.964
#> SRR1951644 4 0.1888 0.8988 0.016 0.000 0.044 0.940
#> SRR1951643 1 0.3610 0.6757 0.800 0.000 0.200 0.000
#> SRR1951641 2 0.0707 0.6353 0.000 0.980 0.000 0.020
#> SRR1951642 1 0.0000 0.9075 1.000 0.000 0.000 0.000
#> SRR1951694 1 0.1211 0.9054 0.960 0.000 0.000 0.040
#> SRR1951731 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951730 3 0.4643 0.2875 0.000 0.344 0.656 0.000
#> SRR1951752 3 0.0707 0.9172 0.000 0.020 0.980 0.000
#> SRR1951751 4 0.3032 0.8220 0.008 0.000 0.124 0.868
#> SRR1951749 3 0.0707 0.9172 0.000 0.020 0.980 0.000
#> SRR1951750 1 0.3547 0.7619 0.840 0.000 0.144 0.016
#> SRR1951748 2 0.0707 0.6353 0.000 0.980 0.000 0.020
#> SRR1951747 2 0.0592 0.6423 0.000 0.984 0.016 0.000
#> SRR1951745 1 0.0817 0.9108 0.976 0.000 0.000 0.024
#> SRR1951746 2 0.0707 0.6353 0.000 0.980 0.000 0.020
#> SRR1951744 1 0.2179 0.8642 0.924 0.000 0.064 0.012
#> SRR1951742 2 0.4992 0.3582 0.000 0.524 0.476 0.000
#> SRR1951743 2 0.0707 0.6353 0.000 0.980 0.000 0.020
#> SRR1951741 1 0.0000 0.9075 1.000 0.000 0.000 0.000
#> SRR1951740 3 0.0336 0.9279 0.000 0.000 0.992 0.008
#> SRR1951738 2 0.6439 0.2374 0.416 0.528 0.044 0.012
#> SRR1951739 2 0.4981 0.3869 0.000 0.536 0.464 0.000
#> SRR1951737 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951736 4 0.1118 0.9245 0.036 0.000 0.000 0.964
#> SRR1951734 2 0.0707 0.6353 0.000 0.980 0.000 0.020
#> SRR1951735 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951733 3 0.0707 0.9172 0.000 0.020 0.980 0.000
#> SRR1951732 2 0.0707 0.6353 0.000 0.980 0.000 0.020
#> SRR1951729 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951728 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951727 2 0.0707 0.6353 0.000 0.980 0.000 0.020
#> SRR1951725 1 0.0921 0.9100 0.972 0.000 0.000 0.028
#> SRR1951726 1 0.0817 0.9108 0.976 0.000 0.000 0.024
#> SRR1951724 3 0.0804 0.9190 0.008 0.000 0.980 0.012
#> SRR1951722 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951723 3 0.4866 0.3293 0.000 0.000 0.596 0.404
#> SRR1951721 1 0.0817 0.9108 0.976 0.000 0.000 0.024
#> SRR1951719 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951720 1 0.1211 0.9054 0.960 0.000 0.000 0.040
#> SRR1951718 4 0.0707 0.9180 0.020 0.000 0.000 0.980
#> SRR1951717 1 0.1022 0.9088 0.968 0.000 0.000 0.032
#> SRR1951715 1 0.1211 0.9054 0.960 0.000 0.000 0.040
#> SRR1951716 4 0.1118 0.9245 0.036 0.000 0.000 0.964
#> SRR1951714 3 0.1388 0.8995 0.028 0.000 0.960 0.012
#> SRR1951713 3 0.0469 0.9248 0.000 0.000 0.988 0.012
#> SRR1951712 4 0.5615 0.4190 0.032 0.000 0.356 0.612
#> SRR1951711 2 0.1211 0.6408 0.000 0.960 0.040 0.000
#> SRR1951710 4 0.1118 0.9245 0.036 0.000 0.000 0.964
#> SRR1951709 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951708 2 0.0707 0.6427 0.000 0.980 0.020 0.000
#> SRR1951707 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951705 4 0.1118 0.9245 0.036 0.000 0.000 0.964
#> SRR1951704 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951703 2 0.1059 0.6398 0.000 0.972 0.012 0.016
#> SRR1951706 1 0.0817 0.9108 0.976 0.000 0.000 0.024
#> SRR1951702 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951701 2 0.4977 0.3914 0.000 0.540 0.460 0.000
#> SRR1951699 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951698 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951700 3 0.0188 0.9303 0.000 0.000 0.996 0.004
#> SRR1951697 2 0.4981 0.3869 0.000 0.536 0.464 0.000
#> SRR1951696 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951695 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951693 3 0.0336 0.9279 0.000 0.000 0.992 0.008
#> SRR1951692 3 0.0524 0.9259 0.004 0.000 0.988 0.008
#> SRR1951691 3 0.0336 0.9279 0.000 0.000 0.992 0.008
#> SRR1951688 2 0.5404 0.3487 0.012 0.512 0.476 0.000
#> SRR1951689 3 0.4697 0.2465 0.000 0.356 0.644 0.000
#> SRR1951687 1 0.0000 0.9075 1.000 0.000 0.000 0.000
#> SRR1951685 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951684 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951686 2 0.4992 0.3587 0.000 0.524 0.476 0.000
#> SRR1951683 1 0.3074 0.7493 0.848 0.000 0.152 0.000
#> SRR1951682 2 0.4981 0.3869 0.000 0.536 0.464 0.000
#> SRR1951690 2 0.5143 0.3966 0.004 0.540 0.456 0.000
#> SRR1951680 3 0.0336 0.9279 0.000 0.000 0.992 0.008
#> SRR1951679 4 0.2647 0.8281 0.000 0.000 0.120 0.880
#> SRR1951678 1 0.0000 0.9075 1.000 0.000 0.000 0.000
#> SRR1951677 1 0.0000 0.9075 1.000 0.000 0.000 0.000
#> SRR1951676 1 0.5110 0.4147 0.636 0.000 0.352 0.012
#> SRR1951675 2 0.6251 0.2454 0.416 0.536 0.040 0.008
#> SRR1951681 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951673 2 0.6293 0.3683 0.024 0.500 0.456 0.020
#> SRR1951674 1 0.0817 0.9108 0.976 0.000 0.000 0.024
#> SRR1951672 4 0.2011 0.8910 0.080 0.000 0.000 0.920
#> SRR1951671 3 0.4605 0.3131 0.000 0.336 0.664 0.000
#> SRR1951670 1 0.0000 0.9075 1.000 0.000 0.000 0.000
#> SRR1951640 3 0.0188 0.9303 0.000 0.000 0.996 0.004
#> SRR1951668 3 0.4826 0.5613 0.264 0.000 0.716 0.020
#> SRR1951669 2 0.6599 0.1649 0.440 0.500 0.040 0.020
#> SRR1951638 1 0.0817 0.9108 0.976 0.000 0.000 0.024
#> SRR1951637 1 0.3886 0.8113 0.860 0.080 0.040 0.020
#> SRR1951635 1 0.1022 0.9088 0.968 0.000 0.000 0.032
#> SRR1951634 3 0.2483 0.8560 0.032 0.000 0.916 0.052
#> SRR1951631 3 0.0657 0.9219 0.004 0.000 0.984 0.012
#> SRR1951632 2 0.4981 0.3869 0.000 0.536 0.464 0.000
#> SRR1951633 1 0.0000 0.9075 1.000 0.000 0.000 0.000
#> SRR1951630 1 0.2081 0.8376 0.916 0.000 0.084 0.000
#> SRR1951629 3 0.1284 0.9038 0.024 0.000 0.964 0.012
#> SRR1951628 4 0.1118 0.9245 0.036 0.000 0.000 0.964
#> SRR1951627 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951639 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951625 3 0.4697 0.2378 0.000 0.356 0.644 0.000
#> SRR1951626 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951624 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951622 4 0.1118 0.9245 0.036 0.000 0.000 0.964
#> SRR1951636 3 0.0000 0.9318 0.000 0.000 1.000 0.000
#> SRR1951623 1 0.0895 0.9105 0.976 0.000 0.004 0.020
#> SRR1951621 1 0.0000 0.9075 1.000 0.000 0.000 0.000
#> SRR1951620 3 0.0000 0.9318 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
#> SRR1951667 5 0.0000 0.942 0.000 0.000 0.000 0.000 1.000
#> SRR1951666 3 0.4734 0.299 0.004 0.008 0.588 0.004 0.396
#> SRR1951665 2 0.1410 0.983 0.000 0.940 0.000 0.000 0.060
#> SRR1951664 3 0.0000 0.962 0.000 0.000 1.000 0.000 0.000
#> SRR1951663 4 0.1885 0.950 0.020 0.044 0.004 0.932 0.000
#> SRR1951662 2 0.1410 0.983 0.000 0.940 0.000 0.000 0.060
#> SRR1951661 3 0.0290 0.959 0.000 0.008 0.992 0.000 0.000
#> SRR1951660 1 0.0671 0.915 0.980 0.000 0.004 0.016 0.000
#> SRR1951659 5 0.0162 0.942 0.000 0.004 0.000 0.000 0.996
#> SRR1951658 5 0.0162 0.942 0.000 0.004 0.000 0.000 0.996
#> SRR1951657 3 0.0162 0.961 0.000 0.000 0.996 0.004 0.000
#> SRR1951656 3 0.0162 0.962 0.000 0.004 0.996 0.000 0.000
#> SRR1951655 4 0.4333 0.460 0.352 0.004 0.004 0.640 0.000
#> SRR1951654 4 0.0510 0.958 0.016 0.000 0.000 0.984 0.000
#> SRR1951653 3 0.0324 0.960 0.004 0.004 0.992 0.000 0.000
#> SRR1951652 5 0.3779 0.668 0.024 0.000 0.000 0.200 0.776
#> SRR1951651 4 0.0404 0.954 0.000 0.000 0.012 0.988 0.000
#> SRR1951650 3 0.0000 0.962 0.000 0.000 1.000 0.000 0.000
#> SRR1951648 3 0.0162 0.960 0.004 0.000 0.996 0.000 0.000
#> SRR1951649 1 0.0771 0.909 0.976 0.004 0.020 0.000 0.000
#> SRR1951647 3 0.4945 0.162 0.020 0.004 0.536 0.000 0.440
#> SRR1951646 3 0.0162 0.962 0.000 0.004 0.996 0.000 0.000
#> SRR1951645 1 0.0865 0.907 0.972 0.004 0.024 0.000 0.000
#> SRR1951644 1 0.0992 0.906 0.968 0.008 0.024 0.000 0.000
#> SRR1951643 4 0.0162 0.958 0.000 0.000 0.004 0.996 0.000
#> SRR1951641 2 0.2773 0.868 0.000 0.836 0.000 0.000 0.164
#> SRR1951642 4 0.0162 0.958 0.000 0.000 0.004 0.996 0.000
#> SRR1951694 4 0.2078 0.946 0.036 0.036 0.004 0.924 0.000
#> SRR1951731 3 0.0162 0.960 0.004 0.000 0.996 0.000 0.000
#> SRR1951730 5 0.0566 0.936 0.004 0.000 0.012 0.000 0.984
#> SRR1951752 5 0.1282 0.903 0.004 0.000 0.044 0.000 0.952
#> SRR1951751 1 0.2193 0.858 0.900 0.008 0.092 0.000 0.000
#> SRR1951749 5 0.1704 0.872 0.004 0.000 0.068 0.000 0.928
#> SRR1951750 4 0.0451 0.959 0.008 0.000 0.004 0.988 0.000
#> SRR1951748 2 0.1410 0.983 0.000 0.940 0.000 0.000 0.060
#> SRR1951747 5 0.4161 0.272 0.000 0.392 0.000 0.000 0.608
#> SRR1951745 4 0.1885 0.950 0.020 0.044 0.004 0.932 0.000
#> SRR1951746 2 0.1410 0.983 0.000 0.940 0.000 0.000 0.060
#> SRR1951744 4 0.0955 0.949 0.004 0.000 0.028 0.968 0.000
#> SRR1951742 5 0.0162 0.942 0.000 0.000 0.004 0.000 0.996
#> SRR1951743 2 0.1410 0.983 0.000 0.940 0.000 0.000 0.060
#> SRR1951741 4 0.0000 0.957 0.000 0.000 0.000 1.000 0.000
#> SRR1951740 3 0.0162 0.962 0.000 0.004 0.996 0.000 0.000
#> SRR1951738 5 0.0162 0.942 0.000 0.000 0.000 0.004 0.996
#> SRR1951739 5 0.0000 0.942 0.000 0.000 0.000 0.000 1.000
#> SRR1951737 3 0.0000 0.962 0.000 0.000 1.000 0.000 0.000
#> SRR1951736 1 0.1041 0.909 0.964 0.000 0.004 0.032 0.000
#> SRR1951734 2 0.1410 0.983 0.000 0.940 0.000 0.000 0.060
#> SRR1951735 3 0.0162 0.962 0.000 0.004 0.996 0.000 0.000
#> SRR1951733 5 0.0566 0.936 0.004 0.000 0.012 0.000 0.984
#> SRR1951732 2 0.1410 0.983 0.000 0.940 0.000 0.000 0.060
#> SRR1951729 3 0.0000 0.962 0.000 0.000 1.000 0.000 0.000
#> SRR1951728 3 0.0000 0.962 0.000 0.000 1.000 0.000 0.000
#> SRR1951727 2 0.1410 0.983 0.000 0.940 0.000 0.000 0.060
#> SRR1951725 4 0.1646 0.954 0.020 0.032 0.004 0.944 0.000
#> SRR1951726 4 0.2101 0.949 0.020 0.024 0.016 0.932 0.008
#> SRR1951724 3 0.0324 0.960 0.000 0.004 0.992 0.004 0.000
#> SRR1951722 3 0.0000 0.962 0.000 0.000 1.000 0.000 0.000
#> SRR1951723 1 0.3266 0.721 0.796 0.004 0.200 0.000 0.000
#> SRR1951721 4 0.1885 0.950 0.020 0.044 0.004 0.932 0.000
#> SRR1951719 3 0.0162 0.962 0.000 0.004 0.996 0.000 0.000
#> SRR1951720 4 0.1924 0.938 0.064 0.008 0.004 0.924 0.000
#> SRR1951718 1 0.0324 0.909 0.992 0.004 0.004 0.000 0.000
#> SRR1951717 4 0.1278 0.956 0.020 0.016 0.004 0.960 0.000
#> SRR1951715 4 0.2152 0.945 0.032 0.044 0.004 0.920 0.000
#> SRR1951716 1 0.2763 0.787 0.848 0.000 0.004 0.148 0.000
#> SRR1951714 3 0.1638 0.900 0.000 0.004 0.932 0.064 0.000
#> SRR1951713 3 0.0324 0.960 0.004 0.004 0.992 0.000 0.000
#> SRR1951712 1 0.4746 0.739 0.744 0.004 0.132 0.120 0.000
#> SRR1951711 5 0.0162 0.942 0.000 0.004 0.000 0.000 0.996
#> SRR1951710 1 0.0671 0.915 0.980 0.000 0.004 0.016 0.000
#> SRR1951709 5 0.3906 0.543 0.004 0.000 0.292 0.000 0.704
#> SRR1951708 5 0.0162 0.942 0.000 0.004 0.000 0.000 0.996
#> SRR1951707 3 0.0162 0.960 0.004 0.000 0.996 0.000 0.000
#> SRR1951705 1 0.0671 0.915 0.980 0.000 0.004 0.016 0.000
#> SRR1951704 3 0.1410 0.906 0.000 0.000 0.940 0.060 0.000
#> SRR1951703 2 0.1671 0.969 0.000 0.924 0.000 0.000 0.076
#> SRR1951706 4 0.0898 0.958 0.020 0.000 0.008 0.972 0.000
#> SRR1951702 3 0.0000 0.962 0.000 0.000 1.000 0.000 0.000
#> SRR1951701 5 0.0162 0.942 0.000 0.004 0.000 0.000 0.996
#> SRR1951699 3 0.0613 0.954 0.004 0.008 0.984 0.000 0.004
#> SRR1951698 3 0.0162 0.960 0.004 0.000 0.996 0.000 0.000
#> SRR1951700 3 0.0000 0.962 0.000 0.000 1.000 0.000 0.000
#> SRR1951697 5 0.0162 0.942 0.000 0.000 0.000 0.004 0.996
#> SRR1951696 3 0.0000 0.962 0.000 0.000 1.000 0.000 0.000
#> SRR1951695 3 0.0000 0.962 0.000 0.000 1.000 0.000 0.000
#> SRR1951693 3 0.0162 0.962 0.000 0.004 0.996 0.000 0.000
#> SRR1951692 3 0.0162 0.962 0.000 0.004 0.996 0.000 0.000
#> SRR1951691 3 0.0451 0.957 0.008 0.004 0.988 0.000 0.000
#> SRR1951688 5 0.0324 0.942 0.004 0.000 0.000 0.004 0.992
#> SRR1951689 5 0.0451 0.939 0.004 0.000 0.008 0.000 0.988
#> SRR1951687 4 0.0162 0.958 0.000 0.000 0.004 0.996 0.000
#> SRR1951685 5 0.2806 0.761 0.004 0.000 0.152 0.000 0.844
#> SRR1951684 3 0.0162 0.962 0.000 0.004 0.996 0.000 0.000
#> SRR1951686 5 0.0162 0.942 0.000 0.000 0.000 0.004 0.996
#> SRR1951683 4 0.0162 0.958 0.000 0.000 0.004 0.996 0.000
#> SRR1951682 5 0.0000 0.942 0.000 0.000 0.000 0.000 1.000
#> SRR1951690 5 0.0162 0.942 0.000 0.004 0.000 0.000 0.996
#> SRR1951680 3 0.0162 0.962 0.000 0.004 0.996 0.000 0.000
#> SRR1951679 1 0.2136 0.861 0.904 0.008 0.088 0.000 0.000
#> SRR1951678 4 0.0162 0.958 0.000 0.000 0.004 0.996 0.000
#> SRR1951677 4 0.0162 0.958 0.000 0.000 0.004 0.996 0.000
#> SRR1951676 4 0.1121 0.936 0.000 0.000 0.044 0.956 0.000
#> SRR1951675 5 0.0162 0.942 0.000 0.000 0.000 0.004 0.996
#> SRR1951681 3 0.0000 0.962 0.000 0.000 1.000 0.000 0.000
#> SRR1951673 5 0.0324 0.942 0.004 0.000 0.000 0.004 0.992
#> SRR1951674 4 0.1885 0.950 0.020 0.044 0.004 0.932 0.000
#> SRR1951672 1 0.1282 0.901 0.952 0.000 0.004 0.044 0.000
#> SRR1951671 5 0.0324 0.941 0.004 0.000 0.004 0.000 0.992
#> SRR1951670 4 0.0162 0.958 0.000 0.000 0.004 0.996 0.000
#> SRR1951640 3 0.0162 0.962 0.000 0.004 0.996 0.000 0.000
#> SRR1951668 4 0.2623 0.864 0.016 0.004 0.096 0.884 0.000
#> SRR1951669 5 0.0324 0.942 0.004 0.000 0.000 0.004 0.992
#> SRR1951638 4 0.1560 0.955 0.020 0.028 0.004 0.948 0.000
#> SRR1951637 5 0.0451 0.940 0.004 0.000 0.000 0.008 0.988
#> SRR1951635 4 0.1885 0.950 0.020 0.044 0.004 0.932 0.000
#> SRR1951634 3 0.4320 0.723 0.120 0.004 0.780 0.096 0.000
#> SRR1951631 3 0.0162 0.962 0.000 0.004 0.996 0.000 0.000
#> SRR1951632 5 0.0000 0.942 0.000 0.000 0.000 0.000 1.000
#> SRR1951633 4 0.0162 0.958 0.000 0.000 0.004 0.996 0.000
#> SRR1951630 4 0.0162 0.958 0.000 0.000 0.004 0.996 0.000
#> SRR1951629 3 0.2280 0.836 0.000 0.000 0.880 0.120 0.000
#> SRR1951628 1 0.0510 0.913 0.984 0.000 0.000 0.016 0.000
#> SRR1951627 3 0.0613 0.954 0.004 0.008 0.984 0.004 0.000
#> SRR1951639 3 0.0162 0.960 0.004 0.000 0.996 0.000 0.000
#> SRR1951625 5 0.0162 0.942 0.004 0.000 0.000 0.000 0.996
#> SRR1951626 3 0.0000 0.962 0.000 0.000 1.000 0.000 0.000
#> SRR1951624 3 0.0000 0.962 0.000 0.000 1.000 0.000 0.000
#> SRR1951622 1 0.0671 0.915 0.980 0.000 0.004 0.016 0.000
#> SRR1951636 3 0.0000 0.962 0.000 0.000 1.000 0.000 0.000
#> SRR1951623 4 0.0912 0.958 0.016 0.000 0.012 0.972 0.000
#> SRR1951621 4 0.0000 0.957 0.000 0.000 0.000 1.000 0.000
#> SRR1951620 3 0.0162 0.962 0.000 0.004 0.996 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 5 0.0146 0.8042 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951666 3 0.6234 0.5390 0.256 0.000 0.536 0.164 0.044 0.000
#> SRR1951665 2 0.0000 0.9202 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951664 3 0.0363 0.7878 0.000 0.000 0.988 0.012 0.000 0.000
#> SRR1951663 6 0.0291 0.7043 0.004 0.000 0.004 0.000 0.000 0.992
#> SRR1951662 2 0.0000 0.9202 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951661 3 0.2632 0.7311 0.000 0.000 0.832 0.164 0.004 0.000
#> SRR1951660 1 0.3337 0.8727 0.736 0.000 0.004 0.000 0.000 0.260
#> SRR1951659 5 0.0146 0.8042 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951658 5 0.0146 0.8042 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951657 3 0.3266 0.6393 0.000 0.000 0.728 0.000 0.000 0.272
#> SRR1951656 3 0.3641 0.6518 0.000 0.000 0.732 0.020 0.000 0.248
#> SRR1951655 1 0.4379 0.6663 0.576 0.000 0.020 0.004 0.000 0.400
#> SRR1951654 6 0.3797 -0.3704 0.000 0.000 0.000 0.420 0.000 0.580
#> SRR1951653 3 0.1003 0.7856 0.016 0.000 0.964 0.020 0.000 0.000
#> SRR1951652 5 0.6167 0.6504 0.256 0.000 0.004 0.036 0.552 0.152
#> SRR1951651 6 0.4594 -0.2184 0.000 0.000 0.036 0.476 0.000 0.488
#> SRR1951650 3 0.0363 0.7880 0.000 0.000 0.988 0.012 0.000 0.000
#> SRR1951648 3 0.4632 0.6474 0.256 0.000 0.668 0.072 0.004 0.000
#> SRR1951649 1 0.4327 0.8724 0.708 0.000 0.020 0.032 0.000 0.240
#> SRR1951647 5 0.6103 0.2344 0.300 0.000 0.320 0.000 0.380 0.000
#> SRR1951646 3 0.0547 0.7859 0.000 0.000 0.980 0.020 0.000 0.000
#> SRR1951645 1 0.4395 0.8713 0.704 0.000 0.020 0.036 0.000 0.240
#> SRR1951644 1 0.4715 0.8588 0.696 0.000 0.040 0.040 0.000 0.224
#> SRR1951643 4 0.3198 0.9898 0.000 0.000 0.000 0.740 0.000 0.260
#> SRR1951641 2 0.1610 0.8578 0.000 0.916 0.000 0.000 0.084 0.000
#> SRR1951642 4 0.3198 0.9898 0.000 0.000 0.000 0.740 0.000 0.260
#> SRR1951694 6 0.1219 0.6609 0.048 0.000 0.004 0.000 0.000 0.948
#> SRR1951731 3 0.4959 0.6334 0.256 0.000 0.660 0.048 0.036 0.000
#> SRR1951730 5 0.3314 0.8236 0.256 0.000 0.000 0.004 0.740 0.000
#> SRR1951752 5 0.3421 0.8214 0.256 0.000 0.000 0.008 0.736 0.000
#> SRR1951751 1 0.5667 0.7874 0.612 0.000 0.096 0.048 0.000 0.244
#> SRR1951749 5 0.4781 0.7709 0.256 0.000 0.012 0.068 0.664 0.000
#> SRR1951750 4 0.3482 0.8770 0.000 0.000 0.000 0.684 0.000 0.316
#> SRR1951748 2 0.0000 0.9202 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951747 2 0.3765 0.2514 0.000 0.596 0.000 0.000 0.404 0.000
#> SRR1951745 6 0.1501 0.6827 0.000 0.000 0.000 0.076 0.000 0.924
#> SRR1951746 2 0.0000 0.9202 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951744 6 0.5091 0.1296 0.000 0.000 0.416 0.080 0.000 0.504
#> SRR1951742 5 0.2883 0.8268 0.212 0.000 0.000 0.000 0.788 0.000
#> SRR1951743 2 0.0000 0.9202 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951741 4 0.3198 0.9898 0.000 0.000 0.000 0.740 0.000 0.260
#> SRR1951740 3 0.3617 0.6555 0.000 0.000 0.736 0.020 0.000 0.244
#> SRR1951738 5 0.0146 0.8042 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951739 5 0.0146 0.8042 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951737 3 0.0000 0.7878 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1951736 1 0.3288 0.8681 0.724 0.000 0.000 0.000 0.000 0.276
#> SRR1951734 2 0.0000 0.9202 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951735 3 0.0458 0.7863 0.000 0.000 0.984 0.016 0.000 0.000
#> SRR1951733 5 0.3314 0.8236 0.256 0.000 0.000 0.004 0.740 0.000
#> SRR1951732 2 0.0000 0.9202 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951729 3 0.0260 0.7880 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR1951728 3 0.1219 0.7816 0.000 0.000 0.948 0.048 0.004 0.000
#> SRR1951727 2 0.0000 0.9202 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1951725 6 0.0260 0.7050 0.000 0.000 0.000 0.008 0.000 0.992
#> SRR1951726 6 0.2631 0.6741 0.000 0.000 0.044 0.076 0.004 0.876
#> SRR1951724 3 0.4301 0.6367 0.000 0.000 0.696 0.064 0.000 0.240
#> SRR1951722 3 0.0865 0.7870 0.000 0.000 0.964 0.036 0.000 0.000
#> SRR1951723 3 0.3840 0.5135 0.284 0.000 0.696 0.020 0.000 0.000
#> SRR1951721 6 0.0146 0.7051 0.000 0.000 0.004 0.000 0.000 0.996
#> SRR1951719 3 0.0458 0.7863 0.000 0.000 0.984 0.016 0.000 0.000
#> SRR1951720 6 0.2537 0.6471 0.096 0.000 0.000 0.032 0.000 0.872
#> SRR1951718 1 0.4257 0.8725 0.712 0.000 0.020 0.028 0.000 0.240
#> SRR1951717 6 0.1501 0.6827 0.000 0.000 0.000 0.076 0.000 0.924
#> SRR1951715 6 0.1700 0.6132 0.080 0.000 0.004 0.000 0.000 0.916
#> SRR1951716 1 0.3371 0.8546 0.708 0.000 0.000 0.000 0.000 0.292
#> SRR1951714 3 0.4539 0.6076 0.004 0.000 0.668 0.060 0.000 0.268
#> SRR1951713 3 0.3834 0.6848 0.020 0.000 0.760 0.020 0.000 0.200
#> SRR1951712 1 0.5783 0.6919 0.568 0.000 0.152 0.020 0.000 0.260
#> SRR1951711 5 0.0146 0.8042 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951710 1 0.3383 0.8711 0.728 0.000 0.004 0.000 0.000 0.268
#> SRR1951709 3 0.7073 -0.0156 0.256 0.000 0.372 0.072 0.300 0.000
#> SRR1951708 5 0.0146 0.8042 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951707 3 0.4376 0.6618 0.248 0.000 0.692 0.056 0.004 0.000
#> SRR1951705 1 0.3360 0.8722 0.732 0.000 0.004 0.000 0.000 0.264
#> SRR1951704 3 0.3654 0.6640 0.000 0.000 0.792 0.060 0.004 0.144
#> SRR1951703 2 0.2730 0.7840 0.000 0.808 0.000 0.000 0.192 0.000
#> SRR1951706 6 0.3410 0.6316 0.000 0.000 0.100 0.076 0.004 0.820
#> SRR1951702 3 0.1531 0.7763 0.000 0.000 0.928 0.068 0.004 0.000
#> SRR1951701 5 0.1219 0.7873 0.004 0.048 0.000 0.000 0.948 0.000
#> SRR1951699 3 0.5600 0.5760 0.256 0.000 0.572 0.164 0.008 0.000
#> SRR1951698 3 0.2294 0.7711 0.036 0.000 0.892 0.072 0.000 0.000
#> SRR1951700 3 0.0937 0.7857 0.000 0.000 0.960 0.040 0.000 0.000
#> SRR1951697 5 0.0692 0.8086 0.020 0.004 0.000 0.000 0.976 0.000
#> SRR1951696 3 0.0458 0.7874 0.000 0.000 0.984 0.016 0.000 0.000
#> SRR1951695 3 0.0790 0.7870 0.000 0.000 0.968 0.032 0.000 0.000
#> SRR1951693 3 0.1204 0.7803 0.000 0.000 0.944 0.056 0.000 0.000
#> SRR1951692 3 0.3582 0.6507 0.000 0.000 0.732 0.016 0.000 0.252
#> SRR1951691 3 0.2358 0.7745 0.012 0.000 0.900 0.048 0.000 0.040
#> SRR1951688 5 0.3175 0.8251 0.256 0.000 0.000 0.000 0.744 0.000
#> SRR1951689 5 0.3175 0.8251 0.256 0.000 0.000 0.000 0.744 0.000
#> SRR1951687 4 0.3198 0.9898 0.000 0.000 0.000 0.740 0.000 0.260
#> SRR1951685 5 0.5073 0.7436 0.256 0.000 0.072 0.024 0.648 0.000
#> SRR1951684 3 0.3534 0.6577 0.000 0.000 0.740 0.016 0.000 0.244
#> SRR1951686 5 0.3076 0.8271 0.240 0.000 0.000 0.000 0.760 0.000
#> SRR1951683 4 0.3198 0.9898 0.000 0.000 0.000 0.740 0.000 0.260
#> SRR1951682 5 0.0146 0.8042 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951690 5 0.1219 0.7873 0.004 0.048 0.000 0.000 0.948 0.000
#> SRR1951680 3 0.3403 0.6827 0.000 0.000 0.768 0.020 0.000 0.212
#> SRR1951679 1 0.5823 0.7532 0.612 0.000 0.140 0.048 0.000 0.200
#> SRR1951678 4 0.3198 0.9898 0.000 0.000 0.000 0.740 0.000 0.260
#> SRR1951677 4 0.3198 0.9898 0.000 0.000 0.000 0.740 0.000 0.260
#> SRR1951676 6 0.5087 0.1354 0.000 0.000 0.412 0.080 0.000 0.508
#> SRR1951675 5 0.0146 0.8042 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951681 3 0.0363 0.7884 0.000 0.000 0.988 0.012 0.000 0.000
#> SRR1951673 5 0.3314 0.8259 0.256 0.004 0.000 0.000 0.740 0.000
#> SRR1951674 6 0.1501 0.6827 0.000 0.000 0.000 0.076 0.000 0.924
#> SRR1951672 1 0.3724 0.8717 0.716 0.000 0.012 0.004 0.000 0.268
#> SRR1951671 5 0.3175 0.8251 0.256 0.000 0.000 0.000 0.744 0.000
#> SRR1951670 4 0.3198 0.9898 0.000 0.000 0.000 0.740 0.000 0.260
#> SRR1951640 3 0.0547 0.7859 0.000 0.000 0.980 0.020 0.000 0.000
#> SRR1951668 3 0.4706 0.2394 0.012 0.000 0.520 0.024 0.000 0.444
#> SRR1951669 5 0.3314 0.8259 0.256 0.004 0.000 0.000 0.740 0.000
#> SRR1951638 6 0.0146 0.7052 0.000 0.000 0.000 0.004 0.000 0.996
#> SRR1951637 5 0.3314 0.8259 0.256 0.004 0.000 0.000 0.740 0.000
#> SRR1951635 6 0.0000 0.7046 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1951634 3 0.4996 0.5573 0.064 0.000 0.640 0.020 0.000 0.276
#> SRR1951631 3 0.3641 0.6518 0.000 0.000 0.732 0.020 0.000 0.248
#> SRR1951632 5 0.0146 0.8042 0.000 0.004 0.000 0.000 0.996 0.000
#> SRR1951633 4 0.3198 0.9898 0.000 0.000 0.000 0.740 0.000 0.260
#> SRR1951630 4 0.3198 0.9898 0.000 0.000 0.000 0.740 0.000 0.260
#> SRR1951629 3 0.3607 0.5323 0.000 0.000 0.652 0.000 0.000 0.348
#> SRR1951628 1 0.0458 0.5069 0.984 0.000 0.000 0.000 0.000 0.016
#> SRR1951627 3 0.4873 0.6664 0.140 0.000 0.688 0.164 0.004 0.004
#> SRR1951639 3 0.4305 0.6595 0.256 0.000 0.692 0.048 0.004 0.000
#> SRR1951625 5 0.3175 0.8251 0.256 0.000 0.000 0.000 0.744 0.000
#> SRR1951626 3 0.1075 0.7822 0.000 0.000 0.952 0.048 0.000 0.000
#> SRR1951624 3 0.0458 0.7874 0.000 0.000 0.984 0.016 0.000 0.000
#> SRR1951622 1 0.3383 0.8711 0.728 0.000 0.004 0.000 0.000 0.268
#> SRR1951636 3 0.1327 0.7786 0.000 0.000 0.936 0.064 0.000 0.000
#> SRR1951623 6 0.5059 0.1984 0.000 0.000 0.392 0.080 0.000 0.528
#> SRR1951621 4 0.3198 0.9898 0.000 0.000 0.000 0.740 0.000 0.260
#> SRR1951620 3 0.3534 0.6577 0.000 0.000 0.740 0.016 0.000 0.244
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 13580 rows and 133 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.820 0.897 0.957 0.4825 0.520 0.520
#> 3 3 0.426 0.564 0.774 0.3486 0.702 0.488
#> 4 4 0.534 0.571 0.779 0.1494 0.687 0.311
#> 5 5 0.541 0.443 0.678 0.0618 0.870 0.557
#> 6 6 0.593 0.513 0.707 0.0371 0.888 0.553
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
#> SRR1951667 1 0.0000 0.9492 1.000 0.000
#> SRR1951666 1 0.0000 0.9492 1.000 0.000
#> SRR1951665 2 0.0000 0.9604 0.000 1.000
#> SRR1951664 1 0.0000 0.9492 1.000 0.000
#> SRR1951663 2 0.0000 0.9604 0.000 1.000
#> SRR1951662 2 0.0000 0.9604 0.000 1.000
#> SRR1951661 1 0.0000 0.9492 1.000 0.000
#> SRR1951660 2 0.0000 0.9604 0.000 1.000
#> SRR1951659 1 0.0000 0.9492 1.000 0.000
#> SRR1951658 2 0.9608 0.3526 0.384 0.616
#> SRR1951657 1 0.0000 0.9492 1.000 0.000
#> SRR1951656 1 0.0000 0.9492 1.000 0.000
#> SRR1951655 2 0.2043 0.9363 0.032 0.968
#> SRR1951654 2 0.0000 0.9604 0.000 1.000
#> SRR1951653 1 0.0000 0.9492 1.000 0.000
#> SRR1951652 2 0.0000 0.9604 0.000 1.000
#> SRR1951651 1 0.3114 0.9120 0.944 0.056
#> SRR1951650 1 0.0000 0.9492 1.000 0.000
#> SRR1951648 1 0.0000 0.9492 1.000 0.000
#> SRR1951649 1 0.3733 0.8990 0.928 0.072
#> SRR1951647 1 0.0000 0.9492 1.000 0.000
#> SRR1951646 1 0.0000 0.9492 1.000 0.000
#> SRR1951645 1 0.8016 0.6936 0.756 0.244
#> SRR1951644 1 0.4939 0.8667 0.892 0.108
#> SRR1951643 1 0.5059 0.8624 0.888 0.112
#> SRR1951641 2 0.8555 0.5952 0.280 0.720
#> SRR1951642 2 0.6623 0.7746 0.172 0.828
#> SRR1951694 2 0.0000 0.9604 0.000 1.000
#> SRR1951731 1 0.0000 0.9492 1.000 0.000
#> SRR1951730 1 0.5059 0.8613 0.888 0.112
#> SRR1951752 2 0.9248 0.4718 0.340 0.660
#> SRR1951751 1 0.5629 0.8367 0.868 0.132
#> SRR1951749 1 0.0000 0.9492 1.000 0.000
#> SRR1951750 2 0.4939 0.8577 0.108 0.892
#> SRR1951748 2 0.0000 0.9604 0.000 1.000
#> SRR1951747 2 0.0000 0.9604 0.000 1.000
#> SRR1951745 2 0.0000 0.9604 0.000 1.000
#> SRR1951746 1 0.0000 0.9492 1.000 0.000
#> SRR1951744 2 0.1414 0.9468 0.020 0.980
#> SRR1951742 1 0.0000 0.9492 1.000 0.000
#> SRR1951743 2 0.0000 0.9604 0.000 1.000
#> SRR1951741 2 0.0000 0.9604 0.000 1.000
#> SRR1951740 1 0.0000 0.9492 1.000 0.000
#> SRR1951738 2 0.0000 0.9604 0.000 1.000
#> SRR1951739 1 0.0000 0.9492 1.000 0.000
#> SRR1951737 1 0.0000 0.9492 1.000 0.000
#> SRR1951736 2 0.0376 0.9587 0.004 0.996
#> SRR1951734 1 0.0000 0.9492 1.000 0.000
#> SRR1951735 1 0.0000 0.9492 1.000 0.000
#> SRR1951733 1 0.0000 0.9492 1.000 0.000
#> SRR1951732 1 0.8955 0.5688 0.688 0.312
#> SRR1951729 1 0.0000 0.9492 1.000 0.000
#> SRR1951728 1 0.0000 0.9492 1.000 0.000
#> SRR1951727 2 0.0000 0.9604 0.000 1.000
#> SRR1951725 2 0.0000 0.9604 0.000 1.000
#> SRR1951726 2 0.0000 0.9604 0.000 1.000
#> SRR1951724 1 0.3114 0.9119 0.944 0.056
#> SRR1951722 1 0.0938 0.9421 0.988 0.012
#> SRR1951723 1 0.0000 0.9492 1.000 0.000
#> SRR1951721 2 0.0000 0.9604 0.000 1.000
#> SRR1951719 1 0.0000 0.9492 1.000 0.000
#> SRR1951720 2 0.0000 0.9604 0.000 1.000
#> SRR1951718 2 0.9922 0.1394 0.448 0.552
#> SRR1951717 2 0.0000 0.9604 0.000 1.000
#> SRR1951715 2 0.0000 0.9604 0.000 1.000
#> SRR1951716 2 0.0000 0.9604 0.000 1.000
#> SRR1951714 1 0.9922 0.2300 0.552 0.448
#> SRR1951713 1 0.0000 0.9492 1.000 0.000
#> SRR1951712 1 0.4022 0.8906 0.920 0.080
#> SRR1951711 2 0.2236 0.9325 0.036 0.964
#> SRR1951710 2 0.0000 0.9604 0.000 1.000
#> SRR1951709 1 0.0000 0.9492 1.000 0.000
#> SRR1951708 2 0.0000 0.9604 0.000 1.000
#> SRR1951707 1 0.0000 0.9492 1.000 0.000
#> SRR1951705 2 0.0000 0.9604 0.000 1.000
#> SRR1951704 1 0.2423 0.9241 0.960 0.040
#> SRR1951703 2 0.0000 0.9604 0.000 1.000
#> SRR1951706 2 0.0672 0.9561 0.008 0.992
#> SRR1951702 1 0.0000 0.9492 1.000 0.000
#> SRR1951701 1 0.0000 0.9492 1.000 0.000
#> SRR1951699 1 0.0000 0.9492 1.000 0.000
#> SRR1951698 1 0.0000 0.9492 1.000 0.000
#> SRR1951700 1 0.0000 0.9492 1.000 0.000
#> SRR1951697 1 0.0376 0.9470 0.996 0.004
#> SRR1951696 1 0.0000 0.9492 1.000 0.000
#> SRR1951695 1 0.0000 0.9492 1.000 0.000
#> SRR1951693 1 0.0000 0.9492 1.000 0.000
#> SRR1951692 1 0.0000 0.9492 1.000 0.000
#> SRR1951691 1 0.8207 0.6739 0.744 0.256
#> SRR1951688 2 0.0376 0.9587 0.004 0.996
#> SRR1951689 1 0.0000 0.9492 1.000 0.000
#> SRR1951687 2 0.0000 0.9604 0.000 1.000
#> SRR1951685 1 0.0000 0.9492 1.000 0.000
#> SRR1951684 1 0.0000 0.9492 1.000 0.000
#> SRR1951686 1 0.0000 0.9492 1.000 0.000
#> SRR1951683 1 0.8267 0.6682 0.740 0.260
#> SRR1951682 1 0.0000 0.9492 1.000 0.000
#> SRR1951690 1 0.0000 0.9492 1.000 0.000
#> SRR1951680 1 0.0000 0.9492 1.000 0.000
#> SRR1951679 1 0.0376 0.9470 0.996 0.004
#> SRR1951678 2 0.0938 0.9530 0.012 0.988
#> SRR1951677 2 0.0376 0.9587 0.004 0.996
#> SRR1951676 1 0.0000 0.9492 1.000 0.000
#> SRR1951675 2 0.0000 0.9604 0.000 1.000
#> SRR1951681 1 0.0000 0.9492 1.000 0.000
#> SRR1951673 2 0.0376 0.9587 0.004 0.996
#> SRR1951674 2 0.0000 0.9604 0.000 1.000
#> SRR1951672 2 0.0376 0.9587 0.004 0.996
#> SRR1951671 1 0.0000 0.9492 1.000 0.000
#> SRR1951670 2 0.0376 0.9587 0.004 0.996
#> SRR1951640 1 0.0000 0.9492 1.000 0.000
#> SRR1951668 1 0.5946 0.8270 0.856 0.144
#> SRR1951669 2 0.0000 0.9604 0.000 1.000
#> SRR1951638 2 0.0000 0.9604 0.000 1.000
#> SRR1951637 2 0.0000 0.9604 0.000 1.000
#> SRR1951635 2 0.0000 0.9604 0.000 1.000
#> SRR1951634 1 0.0376 0.9470 0.996 0.004
#> SRR1951631 1 0.1414 0.9370 0.980 0.020
#> SRR1951632 1 0.0000 0.9492 1.000 0.000
#> SRR1951633 2 0.0376 0.9587 0.004 0.996
#> SRR1951630 1 0.9963 0.1773 0.536 0.464
#> SRR1951629 1 0.2948 0.9150 0.948 0.052
#> SRR1951628 1 0.9993 0.0998 0.516 0.484
#> SRR1951627 1 0.0000 0.9492 1.000 0.000
#> SRR1951639 1 0.0000 0.9492 1.000 0.000
#> SRR1951625 1 0.0000 0.9492 1.000 0.000
#> SRR1951626 1 0.0000 0.9492 1.000 0.000
#> SRR1951624 1 0.0000 0.9492 1.000 0.000
#> SRR1951622 2 0.0000 0.9604 0.000 1.000
#> SRR1951636 1 0.0000 0.9492 1.000 0.000
#> SRR1951623 1 0.9710 0.3680 0.600 0.400
#> SRR1951621 2 0.0000 0.9604 0.000 1.000
#> SRR1951620 1 0.0000 0.9492 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1951667 3 0.2356 0.7807 0.000 0.072 0.928
#> SRR1951666 2 0.6154 0.0852 0.000 0.592 0.408
#> SRR1951665 2 0.6235 0.1905 0.436 0.564 0.000
#> SRR1951664 3 0.3116 0.7799 0.000 0.108 0.892
#> SRR1951663 1 0.4796 0.5619 0.780 0.220 0.000
#> SRR1951662 2 0.4750 0.5232 0.216 0.784 0.000
#> SRR1951661 3 0.6309 0.2228 0.000 0.496 0.504
#> SRR1951660 1 0.2066 0.6664 0.940 0.000 0.060
#> SRR1951659 3 0.4912 0.7457 0.008 0.196 0.796
#> SRR1951658 2 0.9082 0.2183 0.392 0.468 0.140
#> SRR1951657 3 0.5650 0.6206 0.000 0.312 0.688
#> SRR1951656 3 0.3530 0.7795 0.032 0.068 0.900
#> SRR1951655 1 0.3181 0.6703 0.912 0.024 0.064
#> SRR1951654 2 0.6045 0.3088 0.380 0.620 0.000
#> SRR1951653 3 0.4062 0.6571 0.164 0.000 0.836
#> SRR1951652 2 0.2301 0.6346 0.060 0.936 0.004
#> SRR1951651 3 0.6676 0.2525 0.008 0.476 0.516
#> SRR1951650 3 0.5363 0.6672 0.000 0.276 0.724
#> SRR1951648 3 0.4862 0.7672 0.020 0.160 0.820
#> SRR1951649 1 0.6045 0.4465 0.620 0.000 0.380
#> SRR1951647 3 0.5363 0.4834 0.276 0.000 0.724
#> SRR1951646 3 0.4836 0.7684 0.072 0.080 0.848
#> SRR1951645 1 0.5948 0.4801 0.640 0.000 0.360
#> SRR1951644 1 0.5926 0.4873 0.644 0.000 0.356
#> SRR1951643 2 0.4931 0.5450 0.004 0.784 0.212
#> SRR1951641 1 0.5529 0.5652 0.704 0.000 0.296
#> SRR1951642 2 0.2774 0.6565 0.008 0.920 0.072
#> SRR1951694 1 0.3482 0.6293 0.872 0.128 0.000
#> SRR1951731 3 0.1999 0.7653 0.036 0.012 0.952
#> SRR1951730 2 0.7807 -0.0615 0.052 0.516 0.432
#> SRR1951752 1 0.9463 0.2640 0.500 0.256 0.244
#> SRR1951751 1 0.6819 0.2617 0.512 0.012 0.476
#> SRR1951749 3 0.3879 0.7669 0.000 0.152 0.848
#> SRR1951750 2 0.7276 0.2173 0.404 0.564 0.032
#> SRR1951748 2 0.5926 0.3527 0.356 0.644 0.000
#> SRR1951747 2 0.5529 0.4398 0.296 0.704 0.000
#> SRR1951745 2 0.6111 0.2721 0.396 0.604 0.000
#> SRR1951746 3 0.3551 0.7750 0.000 0.132 0.868
#> SRR1951744 2 0.2176 0.6528 0.020 0.948 0.032
#> SRR1951742 3 0.5254 0.6775 0.000 0.264 0.736
#> SRR1951743 1 0.5325 0.5341 0.748 0.248 0.004
#> SRR1951741 1 0.6267 0.1374 0.548 0.452 0.000
#> SRR1951740 3 0.1170 0.7741 0.008 0.016 0.976
#> SRR1951738 1 0.0424 0.6641 0.992 0.008 0.000
#> SRR1951739 3 0.1832 0.7643 0.036 0.008 0.956
#> SRR1951737 3 0.3482 0.7757 0.000 0.128 0.872
#> SRR1951736 1 0.2682 0.6653 0.920 0.004 0.076
#> SRR1951734 3 0.5365 0.6820 0.004 0.252 0.744
#> SRR1951735 3 0.2261 0.7462 0.068 0.000 0.932
#> SRR1951733 3 0.3551 0.7749 0.000 0.132 0.868
#> SRR1951732 2 0.3192 0.6407 0.000 0.888 0.112
#> SRR1951729 3 0.3192 0.7789 0.000 0.112 0.888
#> SRR1951728 3 0.3816 0.7686 0.000 0.148 0.852
#> SRR1951727 2 0.2804 0.6370 0.060 0.924 0.016
#> SRR1951725 1 0.5650 0.4483 0.688 0.312 0.000
#> SRR1951726 2 0.5678 0.4165 0.316 0.684 0.000
#> SRR1951724 3 0.4526 0.7739 0.040 0.104 0.856
#> SRR1951722 3 0.2173 0.7802 0.008 0.048 0.944
#> SRR1951723 3 0.5948 0.2717 0.360 0.000 0.640
#> SRR1951721 1 0.6111 0.2873 0.604 0.396 0.000
#> SRR1951719 3 0.3116 0.7795 0.000 0.108 0.892
#> SRR1951720 1 0.3192 0.6371 0.888 0.112 0.000
#> SRR1951718 1 0.5785 0.5604 0.696 0.004 0.300
#> SRR1951717 1 0.5465 0.4825 0.712 0.288 0.000
#> SRR1951715 1 0.3412 0.6323 0.876 0.124 0.000
#> SRR1951716 1 0.0829 0.6661 0.984 0.004 0.012
#> SRR1951714 1 0.7477 0.5151 0.648 0.068 0.284
#> SRR1951713 3 0.3267 0.7063 0.116 0.000 0.884
#> SRR1951712 3 0.6540 0.0750 0.408 0.008 0.584
#> SRR1951711 2 0.7140 0.4043 0.328 0.632 0.040
#> SRR1951710 1 0.1989 0.6678 0.948 0.004 0.048
#> SRR1951709 3 0.5621 0.6234 0.000 0.308 0.692
#> SRR1951708 1 0.2959 0.6418 0.900 0.100 0.000
#> SRR1951707 3 0.3038 0.7799 0.000 0.104 0.896
#> SRR1951705 1 0.4452 0.6308 0.808 0.000 0.192
#> SRR1951704 2 0.4346 0.5741 0.000 0.816 0.184
#> SRR1951703 1 0.3207 0.6525 0.904 0.084 0.012
#> SRR1951706 2 0.2796 0.6539 0.000 0.908 0.092
#> SRR1951702 2 0.6126 0.1149 0.000 0.600 0.400
#> SRR1951701 3 0.3038 0.7196 0.104 0.000 0.896
#> SRR1951699 3 0.6225 0.3927 0.000 0.432 0.568
#> SRR1951698 3 0.4702 0.7276 0.000 0.212 0.788
#> SRR1951700 3 0.4605 0.7379 0.000 0.204 0.796
#> SRR1951697 2 0.4555 0.5559 0.000 0.800 0.200
#> SRR1951696 3 0.4605 0.7365 0.000 0.204 0.796
#> SRR1951695 3 0.5465 0.6588 0.000 0.288 0.712
#> SRR1951693 3 0.2152 0.7678 0.036 0.016 0.948
#> SRR1951692 3 0.5109 0.7314 0.008 0.212 0.780
#> SRR1951691 1 0.7072 0.2495 0.504 0.020 0.476
#> SRR1951688 2 0.4883 0.5414 0.208 0.788 0.004
#> SRR1951689 3 0.3618 0.7224 0.104 0.012 0.884
#> SRR1951687 2 0.4605 0.5388 0.204 0.796 0.000
#> SRR1951685 3 0.2187 0.7728 0.024 0.028 0.948
#> SRR1951684 3 0.3551 0.7744 0.000 0.132 0.868
#> SRR1951686 2 0.5882 0.2661 0.000 0.652 0.348
#> SRR1951683 2 0.4963 0.5650 0.008 0.792 0.200
#> SRR1951682 1 0.6307 0.1901 0.512 0.000 0.488
#> SRR1951690 3 0.4504 0.6161 0.196 0.000 0.804
#> SRR1951680 3 0.1163 0.7657 0.028 0.000 0.972
#> SRR1951679 1 0.6235 0.3481 0.564 0.000 0.436
#> SRR1951678 2 0.2866 0.6329 0.076 0.916 0.008
#> SRR1951677 2 0.2955 0.6306 0.080 0.912 0.008
#> SRR1951676 2 0.5291 0.4469 0.000 0.732 0.268
#> SRR1951675 1 0.2448 0.6497 0.924 0.076 0.000
#> SRR1951681 3 0.6286 0.3150 0.000 0.464 0.536
#> SRR1951673 1 0.4842 0.6172 0.776 0.000 0.224
#> SRR1951674 2 0.5835 0.3782 0.340 0.660 0.000
#> SRR1951672 1 0.3752 0.6648 0.884 0.020 0.096
#> SRR1951671 3 0.3116 0.7144 0.108 0.000 0.892
#> SRR1951670 1 0.5678 0.4367 0.684 0.316 0.000
#> SRR1951640 3 0.2066 0.7544 0.060 0.000 0.940
#> SRR1951668 3 0.5858 0.5230 0.240 0.020 0.740
#> SRR1951669 1 0.4750 0.5653 0.784 0.216 0.000
#> SRR1951638 1 0.5785 0.4199 0.668 0.332 0.000
#> SRR1951637 1 0.3412 0.6320 0.876 0.124 0.000
#> SRR1951635 1 0.4062 0.6057 0.836 0.164 0.000
#> SRR1951634 3 0.4702 0.5950 0.212 0.000 0.788
#> SRR1951631 3 0.3207 0.7457 0.084 0.012 0.904
#> SRR1951632 3 0.3116 0.7151 0.108 0.000 0.892
#> SRR1951633 2 0.1774 0.6491 0.024 0.960 0.016
#> SRR1951630 2 0.4056 0.6547 0.032 0.876 0.092
#> SRR1951629 3 0.6553 0.4181 0.008 0.412 0.580
#> SRR1951628 1 0.5733 0.5306 0.676 0.000 0.324
#> SRR1951627 2 0.6267 -0.0817 0.000 0.548 0.452
#> SRR1951639 3 0.1860 0.7581 0.052 0.000 0.948
#> SRR1951625 3 0.4291 0.6378 0.180 0.000 0.820
#> SRR1951626 3 0.4654 0.7339 0.000 0.208 0.792
#> SRR1951624 3 0.4974 0.7094 0.000 0.236 0.764
#> SRR1951622 1 0.3686 0.6478 0.860 0.000 0.140
#> SRR1951636 3 0.5835 0.5768 0.000 0.340 0.660
#> SRR1951623 2 0.3532 0.6484 0.008 0.884 0.108
#> SRR1951621 1 0.6225 0.1842 0.568 0.432 0.000
#> SRR1951620 3 0.4353 0.7662 0.008 0.156 0.836
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1951667 4 0.0859 0.8402 0.008 0.004 0.008 0.980
#> SRR1951666 3 0.1356 0.7623 0.000 0.032 0.960 0.008
#> SRR1951665 3 0.5407 0.6160 0.152 0.108 0.740 0.000
#> SRR1951664 4 0.7188 0.3532 0.000 0.292 0.172 0.536
#> SRR1951663 1 0.2335 0.6702 0.920 0.060 0.000 0.020
#> SRR1951662 1 0.4406 0.6432 0.808 0.004 0.144 0.044
#> SRR1951661 3 0.4230 0.6995 0.004 0.008 0.776 0.212
#> SRR1951660 2 0.3873 0.4882 0.228 0.772 0.000 0.000
#> SRR1951659 3 0.5282 0.5946 0.000 0.276 0.688 0.036
#> SRR1951658 3 0.5636 0.2814 0.024 0.424 0.552 0.000
#> SRR1951657 3 0.4920 0.6933 0.000 0.052 0.756 0.192
#> SRR1951656 2 0.7119 -0.2110 0.000 0.440 0.432 0.128
#> SRR1951655 2 0.5039 0.1956 0.404 0.592 0.000 0.004
#> SRR1951654 1 0.5047 0.5093 0.668 0.016 0.316 0.000
#> SRR1951653 2 0.4525 0.5565 0.000 0.804 0.080 0.116
#> SRR1951652 3 0.5297 0.1385 0.444 0.004 0.548 0.004
#> SRR1951651 3 0.2561 0.7560 0.004 0.068 0.912 0.016
#> SRR1951650 3 0.5970 0.6178 0.000 0.088 0.668 0.244
#> SRR1951648 3 0.6347 0.5523 0.000 0.276 0.624 0.100
#> SRR1951649 2 0.3367 0.5801 0.108 0.864 0.000 0.028
#> SRR1951647 2 0.3009 0.5877 0.000 0.892 0.052 0.056
#> SRR1951646 2 0.5856 -0.1765 0.000 0.504 0.464 0.032
#> SRR1951645 2 0.3224 0.5701 0.120 0.864 0.000 0.016
#> SRR1951644 2 0.2949 0.5873 0.088 0.888 0.000 0.024
#> SRR1951643 3 0.0779 0.7625 0.016 0.004 0.980 0.000
#> SRR1951641 2 0.2266 0.5868 0.084 0.912 0.000 0.004
#> SRR1951642 3 0.2011 0.7333 0.080 0.000 0.920 0.000
#> SRR1951694 1 0.5050 0.2847 0.588 0.408 0.004 0.000
#> SRR1951731 4 0.0707 0.8415 0.000 0.020 0.000 0.980
#> SRR1951730 3 0.2531 0.7675 0.024 0.032 0.924 0.020
#> SRR1951752 4 0.5147 0.1540 0.460 0.004 0.000 0.536
#> SRR1951751 4 0.3658 0.7591 0.144 0.020 0.000 0.836
#> SRR1951749 4 0.1151 0.8416 0.000 0.008 0.024 0.968
#> SRR1951750 1 0.2796 0.6872 0.892 0.016 0.092 0.000
#> SRR1951748 1 0.2839 0.6772 0.884 0.004 0.108 0.004
#> SRR1951747 1 0.3808 0.6409 0.808 0.004 0.184 0.004
#> SRR1951745 1 0.2401 0.6827 0.904 0.000 0.092 0.004
#> SRR1951746 4 0.0992 0.8386 0.012 0.004 0.008 0.976
#> SRR1951744 1 0.6128 0.1276 0.536 0.004 0.420 0.040
#> SRR1951742 3 0.5307 0.6668 0.000 0.188 0.736 0.076
#> SRR1951743 1 0.4313 0.5334 0.736 0.004 0.000 0.260
#> SRR1951741 1 0.3239 0.6805 0.880 0.068 0.052 0.000
#> SRR1951740 4 0.3107 0.8209 0.000 0.080 0.036 0.884
#> SRR1951738 2 0.5028 0.2049 0.400 0.596 0.000 0.004
#> SRR1951739 4 0.1042 0.8421 0.000 0.020 0.008 0.972
#> SRR1951737 4 0.2411 0.8340 0.000 0.040 0.040 0.920
#> SRR1951736 2 0.4950 0.2563 0.376 0.620 0.000 0.004
#> SRR1951734 4 0.3676 0.7237 0.172 0.004 0.004 0.820
#> SRR1951735 4 0.3812 0.7863 0.000 0.140 0.028 0.832
#> SRR1951733 4 0.4300 0.7745 0.000 0.088 0.092 0.820
#> SRR1951732 3 0.1474 0.7475 0.052 0.000 0.948 0.000
#> SRR1951729 4 0.3621 0.8071 0.000 0.072 0.068 0.860
#> SRR1951728 4 0.2494 0.8306 0.000 0.036 0.048 0.916
#> SRR1951727 3 0.5214 0.3977 0.336 0.004 0.648 0.012
#> SRR1951725 1 0.2287 0.6777 0.924 0.060 0.012 0.004
#> SRR1951726 1 0.3626 0.6514 0.812 0.004 0.184 0.000
#> SRR1951724 3 0.7445 0.1950 0.004 0.412 0.436 0.148
#> SRR1951722 4 0.1484 0.8400 0.020 0.016 0.004 0.960
#> SRR1951723 4 0.4826 0.6192 0.020 0.264 0.000 0.716
#> SRR1951721 1 0.1404 0.6868 0.964 0.012 0.012 0.012
#> SRR1951719 4 0.0967 0.8417 0.004 0.004 0.016 0.976
#> SRR1951720 1 0.4941 0.2191 0.564 0.436 0.000 0.000
#> SRR1951718 2 0.1557 0.5906 0.056 0.944 0.000 0.000
#> SRR1951717 1 0.1929 0.6794 0.940 0.024 0.000 0.036
#> SRR1951715 1 0.4212 0.5625 0.772 0.216 0.000 0.012
#> SRR1951716 2 0.4406 0.4006 0.300 0.700 0.000 0.000
#> SRR1951714 2 0.3612 0.5619 0.012 0.840 0.144 0.004
#> SRR1951713 4 0.2156 0.8315 0.008 0.060 0.004 0.928
#> SRR1951712 4 0.3542 0.7768 0.120 0.028 0.000 0.852
#> SRR1951711 1 0.5500 0.1521 0.564 0.004 0.012 0.420
#> SRR1951710 2 0.5168 -0.0856 0.496 0.500 0.000 0.004
#> SRR1951709 3 0.5031 0.6994 0.000 0.092 0.768 0.140
#> SRR1951708 2 0.5212 0.1414 0.420 0.572 0.008 0.000
#> SRR1951707 4 0.3081 0.8244 0.000 0.064 0.048 0.888
#> SRR1951705 2 0.4328 0.4634 0.244 0.748 0.000 0.008
#> SRR1951704 4 0.7973 0.0347 0.348 0.004 0.256 0.392
#> SRR1951703 2 0.5857 0.3564 0.056 0.636 0.308 0.000
#> SRR1951706 3 0.6465 0.3543 0.332 0.004 0.588 0.076
#> SRR1951702 3 0.3587 0.7562 0.032 0.004 0.860 0.104
#> SRR1951701 2 0.6392 -0.1155 0.000 0.484 0.064 0.452
#> SRR1951699 4 0.3494 0.8132 0.028 0.016 0.080 0.876
#> SRR1951698 3 0.5596 0.6220 0.000 0.236 0.696 0.068
#> SRR1951700 4 0.2207 0.8222 0.056 0.004 0.012 0.928
#> SRR1951697 3 0.1677 0.7558 0.040 0.000 0.948 0.012
#> SRR1951696 3 0.6479 0.5824 0.000 0.224 0.636 0.140
#> SRR1951695 4 0.2777 0.7922 0.104 0.004 0.004 0.888
#> SRR1951693 4 0.1191 0.8423 0.004 0.024 0.004 0.968
#> SRR1951692 3 0.4877 0.6667 0.000 0.204 0.752 0.044
#> SRR1951691 4 0.6075 0.4973 0.288 0.076 0.000 0.636
#> SRR1951688 1 0.4781 0.4409 0.660 0.000 0.336 0.004
#> SRR1951689 4 0.1724 0.8299 0.032 0.020 0.000 0.948
#> SRR1951687 3 0.2814 0.6996 0.132 0.000 0.868 0.000
#> SRR1951685 4 0.1637 0.8181 0.060 0.000 0.000 0.940
#> SRR1951684 4 0.2926 0.8250 0.000 0.048 0.056 0.896
#> SRR1951686 3 0.5099 0.6865 0.048 0.004 0.748 0.200
#> SRR1951683 3 0.0895 0.7616 0.020 0.004 0.976 0.000
#> SRR1951682 2 0.1247 0.5953 0.004 0.968 0.012 0.016
#> SRR1951690 2 0.5756 0.1611 0.000 0.592 0.036 0.372
#> SRR1951680 4 0.3279 0.8163 0.000 0.096 0.032 0.872
#> SRR1951679 4 0.4344 0.7563 0.076 0.108 0.000 0.816
#> SRR1951678 3 0.2647 0.7095 0.120 0.000 0.880 0.000
#> SRR1951677 3 0.2868 0.6965 0.136 0.000 0.864 0.000
#> SRR1951676 3 0.5100 0.6975 0.088 0.004 0.772 0.136
#> SRR1951675 1 0.5168 0.0476 0.504 0.492 0.000 0.004
#> SRR1951681 3 0.2739 0.7549 0.000 0.060 0.904 0.036
#> SRR1951673 2 0.5161 0.3883 0.300 0.676 0.000 0.024
#> SRR1951674 1 0.2469 0.6797 0.892 0.000 0.108 0.000
#> SRR1951672 1 0.5149 0.4022 0.648 0.336 0.000 0.016
#> SRR1951671 4 0.1452 0.8374 0.008 0.036 0.000 0.956
#> SRR1951670 1 0.5106 0.5541 0.720 0.240 0.040 0.000
#> SRR1951640 2 0.7122 0.1457 0.000 0.516 0.144 0.340
#> SRR1951668 1 0.7430 0.0574 0.456 0.148 0.004 0.392
#> SRR1951669 1 0.2466 0.6572 0.900 0.096 0.000 0.004
#> SRR1951638 1 0.1975 0.6763 0.936 0.016 0.000 0.048
#> SRR1951637 1 0.4978 0.3328 0.612 0.384 0.004 0.000
#> SRR1951635 1 0.4372 0.5201 0.728 0.268 0.004 0.000
#> SRR1951634 2 0.3581 0.5768 0.000 0.852 0.032 0.116
#> SRR1951631 2 0.5417 0.4609 0.000 0.732 0.180 0.088
#> SRR1951632 4 0.5254 0.5700 0.000 0.300 0.028 0.672
#> SRR1951633 3 0.2408 0.7186 0.104 0.000 0.896 0.000
#> SRR1951630 3 0.1297 0.7622 0.016 0.020 0.964 0.000
#> SRR1951629 3 0.5739 0.6838 0.056 0.016 0.716 0.212
#> SRR1951628 2 0.2546 0.5837 0.092 0.900 0.000 0.008
#> SRR1951627 3 0.1356 0.7661 0.000 0.008 0.960 0.032
#> SRR1951639 2 0.7036 0.0466 0.000 0.492 0.124 0.384
#> SRR1951625 2 0.4920 0.5018 0.000 0.756 0.052 0.192
#> SRR1951626 4 0.4415 0.7503 0.000 0.056 0.140 0.804
#> SRR1951624 4 0.3056 0.8194 0.000 0.040 0.072 0.888
#> SRR1951622 2 0.4372 0.4382 0.268 0.728 0.000 0.004
#> SRR1951636 3 0.5067 0.6816 0.000 0.048 0.736 0.216
#> SRR1951623 3 0.1211 0.7524 0.040 0.000 0.960 0.000
#> SRR1951621 1 0.6426 0.4937 0.628 0.256 0.116 0.000
#> SRR1951620 3 0.6640 0.5361 0.000 0.268 0.604 0.128
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1951667 3 0.1864 0.66306 0.000 0.004 0.924 0.004 0.068
#> SRR1951666 4 0.0727 0.77199 0.004 0.012 0.000 0.980 0.004
#> SRR1951665 4 0.5972 0.56887 0.184 0.108 0.000 0.664 0.044
#> SRR1951664 3 0.6075 0.36710 0.264 0.000 0.588 0.140 0.008
#> SRR1951663 2 0.1914 0.62143 0.060 0.924 0.000 0.000 0.016
#> SRR1951662 2 0.5680 0.27795 0.000 0.508 0.012 0.052 0.428
#> SRR1951661 4 0.5406 0.60824 0.000 0.052 0.208 0.696 0.044
#> SRR1951660 1 0.4258 0.48978 0.744 0.032 0.000 0.004 0.220
#> SRR1951659 1 0.8692 0.03847 0.296 0.092 0.292 0.292 0.028
#> SRR1951658 4 0.5552 0.41700 0.328 0.000 0.000 0.584 0.088
#> SRR1951657 3 0.6669 0.08247 0.072 0.020 0.492 0.392 0.024
#> SRR1951656 1 0.6759 0.01805 0.436 0.000 0.196 0.360 0.008
#> SRR1951655 2 0.5481 0.12917 0.400 0.544 0.048 0.000 0.008
#> SRR1951654 2 0.5964 0.46767 0.008 0.592 0.000 0.280 0.120
#> SRR1951653 1 0.5900 0.29914 0.628 0.016 0.276 0.068 0.012
#> SRR1951652 2 0.4618 0.55735 0.000 0.748 0.024 0.192 0.036
#> SRR1951651 4 0.1525 0.76644 0.036 0.000 0.004 0.948 0.012
#> SRR1951650 4 0.4289 0.70253 0.064 0.000 0.120 0.796 0.020
#> SRR1951648 4 0.4754 0.60939 0.232 0.000 0.048 0.712 0.008
#> SRR1951649 1 0.4684 0.43515 0.664 0.016 0.012 0.000 0.308
#> SRR1951647 1 0.3955 0.47248 0.804 0.000 0.128 0.064 0.004
#> SRR1951646 1 0.5956 -0.03277 0.516 0.004 0.064 0.404 0.012
#> SRR1951645 1 0.5124 0.35907 0.580 0.016 0.012 0.004 0.388
#> SRR1951644 1 0.3699 0.52101 0.836 0.104 0.028 0.000 0.032
#> SRR1951643 4 0.2709 0.75975 0.004 0.084 0.004 0.888 0.020
#> SRR1951641 1 0.4527 0.45890 0.748 0.200 0.040 0.004 0.008
#> SRR1951642 4 0.2563 0.74195 0.000 0.120 0.000 0.872 0.008
#> SRR1951694 1 0.6871 0.17736 0.388 0.256 0.000 0.004 0.352
#> SRR1951731 3 0.4004 0.58831 0.016 0.000 0.748 0.004 0.232
#> SRR1951730 4 0.5652 0.15310 0.064 0.000 0.004 0.472 0.460
#> SRR1951752 5 0.3632 0.48916 0.004 0.020 0.176 0.000 0.800
#> SRR1951751 5 0.4486 0.40882 0.020 0.012 0.256 0.000 0.712
#> SRR1951749 3 0.1059 0.66156 0.000 0.008 0.968 0.004 0.020
#> SRR1951750 5 0.3375 0.43741 0.048 0.020 0.000 0.072 0.860
#> SRR1951748 2 0.4477 0.54585 0.000 0.708 0.000 0.040 0.252
#> SRR1951747 2 0.4883 0.56159 0.000 0.708 0.000 0.092 0.200
#> SRR1951745 2 0.2438 0.63639 0.000 0.900 0.000 0.060 0.040
#> SRR1951746 5 0.5134 -0.00710 0.012 0.000 0.424 0.020 0.544
#> SRR1951744 5 0.5760 -0.02867 0.000 0.052 0.016 0.420 0.512
#> SRR1951742 4 0.6585 0.43045 0.220 0.004 0.180 0.576 0.020
#> SRR1951743 5 0.4545 0.42539 0.004 0.156 0.084 0.000 0.756
#> SRR1951741 2 0.4247 0.62876 0.056 0.808 0.000 0.036 0.100
#> SRR1951740 3 0.1808 0.66717 0.040 0.000 0.936 0.004 0.020
#> SRR1951738 1 0.5916 0.27792 0.528 0.372 0.004 0.000 0.096
#> SRR1951739 3 0.1299 0.66582 0.012 0.008 0.960 0.000 0.020
#> SRR1951737 3 0.5579 0.49601 0.036 0.000 0.632 0.040 0.292
#> SRR1951736 1 0.5680 0.44827 0.628 0.160 0.000 0.000 0.212
#> SRR1951734 3 0.4820 0.47720 0.000 0.232 0.708 0.008 0.052
#> SRR1951735 3 0.4094 0.58482 0.164 0.024 0.792 0.004 0.016
#> SRR1951733 3 0.4773 0.57310 0.132 0.028 0.780 0.040 0.020
#> SRR1951732 4 0.2017 0.76102 0.000 0.080 0.000 0.912 0.008
#> SRR1951729 3 0.3833 0.65840 0.032 0.000 0.828 0.032 0.108
#> SRR1951728 3 0.3732 0.64099 0.008 0.000 0.812 0.032 0.148
#> SRR1951727 2 0.5087 0.36716 0.000 0.616 0.012 0.344 0.028
#> SRR1951725 2 0.6315 0.38866 0.116 0.536 0.000 0.016 0.332
#> SRR1951726 5 0.6474 -0.11830 0.012 0.128 0.000 0.424 0.436
#> SRR1951724 4 0.6977 0.05554 0.224 0.000 0.012 0.396 0.368
#> SRR1951722 3 0.1990 0.66409 0.000 0.008 0.920 0.004 0.068
#> SRR1951723 3 0.5009 0.50583 0.236 0.044 0.700 0.000 0.020
#> SRR1951721 2 0.2720 0.63049 0.020 0.880 0.000 0.004 0.096
#> SRR1951719 3 0.3525 0.62337 0.008 0.000 0.800 0.008 0.184
#> SRR1951720 1 0.6762 0.25289 0.452 0.256 0.000 0.004 0.288
#> SRR1951718 1 0.2420 0.53037 0.896 0.008 0.000 0.008 0.088
#> SRR1951717 5 0.4769 -0.20504 0.012 0.440 0.000 0.004 0.544
#> SRR1951715 2 0.6665 0.16580 0.252 0.436 0.000 0.000 0.312
#> SRR1951716 1 0.4444 0.50169 0.748 0.072 0.000 0.000 0.180
#> SRR1951714 1 0.5772 0.43295 0.704 0.152 0.044 0.092 0.008
#> SRR1951713 3 0.3141 0.65401 0.040 0.000 0.852 0.000 0.108
#> SRR1951712 5 0.4598 0.39390 0.028 0.008 0.264 0.000 0.700
#> SRR1951711 2 0.4879 0.42702 0.000 0.688 0.264 0.016 0.032
#> SRR1951710 1 0.6311 0.34576 0.504 0.176 0.000 0.000 0.320
#> SRR1951709 4 0.2751 0.75082 0.052 0.000 0.056 0.888 0.004
#> SRR1951708 1 0.6194 0.30526 0.508 0.032 0.000 0.064 0.396
#> SRR1951707 3 0.2290 0.64717 0.044 0.004 0.920 0.016 0.016
#> SRR1951705 1 0.5067 0.44054 0.648 0.064 0.000 0.000 0.288
#> SRR1951704 5 0.5952 0.43242 0.000 0.024 0.156 0.168 0.652
#> SRR1951703 1 0.6415 0.19990 0.532 0.032 0.000 0.344 0.092
#> SRR1951706 2 0.6398 0.13348 0.000 0.476 0.040 0.416 0.068
#> SRR1951702 4 0.3227 0.75082 0.004 0.016 0.028 0.868 0.084
#> SRR1951701 3 0.5908 0.10834 0.448 0.000 0.480 0.040 0.032
#> SRR1951699 3 0.6837 0.11909 0.000 0.008 0.452 0.236 0.304
#> SRR1951698 4 0.6525 0.37388 0.264 0.000 0.172 0.548 0.016
#> SRR1951700 3 0.2784 0.65257 0.000 0.016 0.872 0.004 0.108
#> SRR1951697 4 0.2037 0.76235 0.000 0.064 0.004 0.920 0.012
#> SRR1951696 3 0.7506 0.12515 0.232 0.012 0.424 0.308 0.024
#> SRR1951695 5 0.5544 0.00799 0.004 0.028 0.440 0.016 0.512
#> SRR1951693 3 0.5060 0.44234 0.032 0.000 0.616 0.008 0.344
#> SRR1951692 4 0.3474 0.71588 0.148 0.000 0.020 0.824 0.008
#> SRR1951691 2 0.6355 0.05877 0.140 0.448 0.408 0.000 0.004
#> SRR1951688 2 0.3410 0.61653 0.012 0.856 0.016 0.100 0.016
#> SRR1951689 3 0.4194 0.55105 0.016 0.004 0.720 0.000 0.260
#> SRR1951687 4 0.3479 0.74022 0.016 0.056 0.000 0.852 0.076
#> SRR1951685 3 0.4402 0.38730 0.004 0.004 0.620 0.000 0.372
#> SRR1951684 3 0.4562 0.62300 0.028 0.000 0.764 0.040 0.168
#> SRR1951686 3 0.7447 0.16147 0.012 0.244 0.452 0.268 0.024
#> SRR1951683 4 0.1652 0.77150 0.008 0.040 0.004 0.944 0.004
#> SRR1951682 1 0.2769 0.52478 0.892 0.000 0.020 0.024 0.064
#> SRR1951690 1 0.5530 0.01320 0.532 0.004 0.420 0.020 0.024
#> SRR1951680 3 0.2152 0.66828 0.044 0.000 0.920 0.004 0.032
#> SRR1951679 3 0.6605 0.36102 0.104 0.056 0.580 0.000 0.260
#> SRR1951678 4 0.3247 0.74384 0.012 0.052 0.000 0.864 0.072
#> SRR1951677 4 0.3817 0.71833 0.012 0.056 0.000 0.824 0.108
#> SRR1951676 4 0.4711 0.68961 0.000 0.044 0.052 0.772 0.132
#> SRR1951675 1 0.6386 0.29404 0.492 0.320 0.000 0.000 0.188
#> SRR1951681 4 0.2060 0.76577 0.036 0.000 0.012 0.928 0.024
#> SRR1951673 1 0.6073 0.37946 0.592 0.288 0.020 0.000 0.100
#> SRR1951674 2 0.2325 0.63573 0.000 0.904 0.000 0.068 0.028
#> SRR1951672 5 0.7113 -0.22324 0.348 0.244 0.016 0.000 0.392
#> SRR1951671 3 0.5195 0.27490 0.044 0.000 0.536 0.000 0.420
#> SRR1951670 5 0.7326 -0.01123 0.288 0.120 0.000 0.092 0.500
#> SRR1951640 1 0.6768 0.21845 0.520 0.000 0.264 0.196 0.020
#> SRR1951668 5 0.5215 0.43835 0.124 0.028 0.104 0.004 0.740
#> SRR1951669 2 0.3649 0.60236 0.088 0.824 0.000 0.000 0.088
#> SRR1951638 2 0.1739 0.62532 0.032 0.940 0.024 0.004 0.000
#> SRR1951637 2 0.3527 0.54471 0.172 0.804 0.000 0.000 0.024
#> SRR1951635 2 0.6542 0.25936 0.264 0.508 0.000 0.004 0.224
#> SRR1951634 1 0.5869 0.38981 0.596 0.000 0.016 0.084 0.304
#> SRR1951631 1 0.7246 0.18880 0.508 0.068 0.324 0.084 0.016
#> SRR1951632 3 0.4929 0.51060 0.216 0.044 0.720 0.004 0.016
#> SRR1951633 4 0.2563 0.74285 0.000 0.120 0.000 0.872 0.008
#> SRR1951630 4 0.3495 0.74483 0.024 0.096 0.004 0.852 0.024
#> SRR1951629 4 0.4116 0.72728 0.000 0.040 0.100 0.816 0.044
#> SRR1951628 1 0.4046 0.52102 0.808 0.116 0.012 0.000 0.064
#> SRR1951627 4 0.1314 0.77097 0.000 0.012 0.012 0.960 0.016
#> SRR1951639 1 0.6522 -0.03774 0.452 0.000 0.408 0.124 0.016
#> SRR1951625 1 0.6328 0.03917 0.504 0.052 0.404 0.028 0.012
#> SRR1951626 3 0.3564 0.61239 0.008 0.000 0.820 0.148 0.024
#> SRR1951624 3 0.4159 0.61607 0.000 0.000 0.776 0.068 0.156
#> SRR1951622 1 0.5457 0.45997 0.668 0.196 0.004 0.000 0.132
#> SRR1951636 4 0.3365 0.74586 0.028 0.000 0.052 0.864 0.056
#> SRR1951623 4 0.1502 0.76637 0.000 0.056 0.000 0.940 0.004
#> SRR1951621 4 0.8459 -0.07684 0.244 0.172 0.000 0.328 0.256
#> SRR1951620 4 0.3716 0.69716 0.172 0.000 0.020 0.800 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1951667 3 0.2272 0.7008 0.000 0.040 0.900 0.000 0.056 0.004
#> SRR1951666 4 0.2373 0.7652 0.000 0.016 0.012 0.908 0.040 0.024
#> SRR1951665 4 0.6799 0.4819 0.108 0.052 0.000 0.576 0.072 0.192
#> SRR1951664 5 0.5355 0.4656 0.004 0.004 0.276 0.104 0.608 0.004
#> SRR1951663 6 0.3290 0.6580 0.132 0.044 0.004 0.000 0.000 0.820
#> SRR1951662 6 0.4161 0.4169 0.000 0.376 0.000 0.012 0.004 0.608
#> SRR1951661 4 0.6818 0.3974 0.000 0.032 0.228 0.528 0.044 0.168
#> SRR1951660 1 0.4072 0.6739 0.772 0.148 0.000 0.020 0.060 0.000
#> SRR1951659 5 0.8661 0.3977 0.068 0.040 0.112 0.168 0.400 0.212
#> SRR1951658 4 0.7071 0.0997 0.084 0.116 0.000 0.428 0.352 0.020
#> SRR1951657 3 0.6680 -0.0228 0.000 0.024 0.428 0.404 0.080 0.064
#> SRR1951656 5 0.4785 0.4919 0.024 0.004 0.036 0.272 0.664 0.000
#> SRR1951655 1 0.5878 0.4928 0.684 0.040 0.072 0.008 0.056 0.140
#> SRR1951654 6 0.5166 0.5591 0.036 0.112 0.000 0.152 0.004 0.696
#> SRR1951653 5 0.3107 0.6176 0.056 0.004 0.068 0.008 0.860 0.004
#> SRR1951652 6 0.1408 0.6456 0.000 0.020 0.000 0.036 0.000 0.944
#> SRR1951651 4 0.1552 0.7636 0.020 0.004 0.000 0.940 0.036 0.000
#> SRR1951650 4 0.5234 0.5832 0.000 0.016 0.080 0.676 0.208 0.020
#> SRR1951648 4 0.4057 0.6284 0.008 0.004 0.036 0.744 0.208 0.000
#> SRR1951649 1 0.5484 0.5211 0.588 0.272 0.000 0.012 0.128 0.000
#> SRR1951647 5 0.2631 0.5809 0.068 0.044 0.008 0.000 0.880 0.000
#> SRR1951646 5 0.4768 0.5317 0.040 0.012 0.004 0.240 0.692 0.012
#> SRR1951645 2 0.6634 -0.1309 0.352 0.396 0.012 0.016 0.224 0.000
#> SRR1951644 1 0.4035 0.5820 0.708 0.008 0.004 0.000 0.264 0.016
#> SRR1951643 4 0.2977 0.7469 0.004 0.008 0.008 0.872 0.060 0.048
#> SRR1951641 1 0.5678 0.5446 0.672 0.052 0.008 0.008 0.168 0.092
#> SRR1951642 4 0.1642 0.7712 0.004 0.004 0.012 0.944 0.012 0.024
#> SRR1951694 1 0.4009 0.6670 0.764 0.184 0.000 0.024 0.004 0.024
#> SRR1951731 3 0.3406 0.6816 0.004 0.112 0.824 0.004 0.056 0.000
#> SRR1951730 2 0.5503 0.0120 0.056 0.468 0.004 0.452 0.016 0.004
#> SRR1951752 2 0.4592 0.4502 0.024 0.712 0.228 0.004 0.016 0.016
#> SRR1951751 2 0.4364 0.4267 0.020 0.712 0.240 0.000 0.016 0.012
#> SRR1951749 3 0.2312 0.6914 0.000 0.012 0.896 0.004 0.080 0.008
#> SRR1951750 2 0.5121 0.4217 0.156 0.680 0.008 0.148 0.004 0.004
#> SRR1951748 6 0.3871 0.5376 0.004 0.288 0.000 0.008 0.004 0.696
#> SRR1951747 6 0.3861 0.5896 0.004 0.196 0.000 0.036 0.004 0.760
#> SRR1951745 6 0.1461 0.6609 0.016 0.044 0.000 0.000 0.000 0.940
#> SRR1951746 3 0.4570 0.3956 0.004 0.380 0.588 0.020 0.008 0.000
#> SRR1951744 2 0.6670 0.2083 0.000 0.452 0.028 0.264 0.008 0.248
#> SRR1951742 4 0.6055 -0.0620 0.004 0.008 0.068 0.448 0.436 0.036
#> SRR1951743 2 0.4864 0.3768 0.020 0.700 0.080 0.000 0.004 0.196
#> SRR1951741 6 0.5419 0.5500 0.296 0.080 0.000 0.028 0.000 0.596
#> SRR1951740 3 0.1644 0.6985 0.000 0.004 0.920 0.000 0.076 0.000
#> SRR1951738 1 0.3278 0.6496 0.848 0.056 0.000 0.000 0.032 0.064
#> SRR1951739 3 0.1219 0.6988 0.000 0.004 0.948 0.000 0.048 0.000
#> SRR1951737 3 0.4922 0.5794 0.000 0.228 0.680 0.040 0.052 0.000
#> SRR1951736 1 0.2833 0.6979 0.860 0.104 0.000 0.012 0.024 0.000
#> SRR1951734 3 0.3165 0.6638 0.012 0.032 0.860 0.000 0.020 0.076
#> SRR1951735 3 0.4975 0.0969 0.004 0.032 0.492 0.000 0.460 0.012
#> SRR1951733 3 0.5252 0.5197 0.004 0.040 0.712 0.016 0.152 0.076
#> SRR1951732 4 0.3167 0.7493 0.000 0.016 0.004 0.840 0.020 0.120
#> SRR1951729 3 0.4120 0.6677 0.000 0.048 0.744 0.012 0.196 0.000
#> SRR1951728 3 0.3255 0.6881 0.000 0.076 0.848 0.044 0.032 0.000
#> SRR1951727 6 0.2639 0.6163 0.000 0.032 0.000 0.084 0.008 0.876
#> SRR1951725 6 0.6471 0.2231 0.320 0.268 0.000 0.012 0.004 0.396
#> SRR1951726 4 0.7035 -0.0699 0.044 0.308 0.000 0.400 0.012 0.236
#> SRR1951724 4 0.6429 0.1436 0.200 0.272 0.008 0.496 0.024 0.000
#> SRR1951722 3 0.3079 0.6719 0.068 0.020 0.868 0.024 0.020 0.000
#> SRR1951723 3 0.5505 0.2086 0.068 0.008 0.500 0.000 0.412 0.012
#> SRR1951721 6 0.4821 0.6120 0.240 0.096 0.000 0.000 0.004 0.660
#> SRR1951719 3 0.4603 0.6474 0.000 0.144 0.708 0.000 0.144 0.004
#> SRR1951720 1 0.4171 0.6732 0.764 0.172 0.000 0.016 0.012 0.036
#> SRR1951718 1 0.4783 0.4853 0.624 0.044 0.000 0.008 0.320 0.004
#> SRR1951717 2 0.5675 0.0403 0.096 0.556 0.020 0.000 0.004 0.324
#> SRR1951715 1 0.4440 0.6139 0.716 0.200 0.000 0.000 0.008 0.076
#> SRR1951716 1 0.3919 0.6877 0.796 0.120 0.000 0.040 0.044 0.000
#> SRR1951714 1 0.6787 0.4574 0.604 0.032 0.012 0.128 0.136 0.088
#> SRR1951713 3 0.3972 0.6736 0.012 0.068 0.776 0.000 0.144 0.000
#> SRR1951712 2 0.5044 0.2924 0.052 0.616 0.312 0.004 0.016 0.000
#> SRR1951711 3 0.6396 0.4447 0.144 0.072 0.636 0.016 0.020 0.112
#> SRR1951710 1 0.3971 0.6673 0.748 0.208 0.000 0.000 0.024 0.020
#> SRR1951709 4 0.4084 0.6785 0.000 0.024 0.028 0.760 0.184 0.004
#> SRR1951708 2 0.7796 0.0618 0.240 0.440 0.000 0.068 0.164 0.088
#> SRR1951707 3 0.2858 0.6764 0.000 0.008 0.864 0.028 0.096 0.004
#> SRR1951705 1 0.4640 0.6259 0.692 0.204 0.000 0.004 0.100 0.000
#> SRR1951704 2 0.6434 0.2802 0.000 0.452 0.248 0.280 0.008 0.012
#> SRR1951703 4 0.6702 0.1022 0.360 0.056 0.000 0.448 0.124 0.012
#> SRR1951706 6 0.4713 0.4667 0.000 0.044 0.028 0.224 0.004 0.700
#> SRR1951702 4 0.4658 0.7024 0.000 0.052 0.024 0.760 0.036 0.128
#> SRR1951701 5 0.3008 0.5933 0.016 0.052 0.072 0.000 0.860 0.000
#> SRR1951699 3 0.6973 0.2060 0.000 0.320 0.464 0.136 0.044 0.036
#> SRR1951698 5 0.5188 0.3959 0.004 0.008 0.040 0.312 0.616 0.020
#> SRR1951700 3 0.1699 0.6972 0.000 0.040 0.936 0.004 0.008 0.012
#> SRR1951697 4 0.2957 0.7569 0.000 0.028 0.008 0.868 0.016 0.080
#> SRR1951696 5 0.6948 0.2907 0.000 0.016 0.184 0.332 0.424 0.044
#> SRR1951695 3 0.4088 0.5178 0.000 0.308 0.668 0.020 0.004 0.000
#> SRR1951693 3 0.5260 0.5227 0.020 0.272 0.628 0.004 0.076 0.000
#> SRR1951692 4 0.1901 0.7546 0.004 0.000 0.008 0.912 0.076 0.000
#> SRR1951691 6 0.7919 0.1753 0.208 0.056 0.300 0.000 0.080 0.356
#> SRR1951688 6 0.8201 0.2866 0.296 0.044 0.152 0.084 0.032 0.392
#> SRR1951689 3 0.2791 0.6714 0.016 0.124 0.852 0.000 0.008 0.000
#> SRR1951687 4 0.2572 0.7564 0.024 0.028 0.000 0.900 0.016 0.032
#> SRR1951685 3 0.4194 0.4964 0.004 0.320 0.656 0.000 0.016 0.004
#> SRR1951684 3 0.3325 0.6825 0.000 0.100 0.836 0.044 0.020 0.000
#> SRR1951686 3 0.7232 0.0618 0.000 0.028 0.392 0.136 0.072 0.372
#> SRR1951683 4 0.2094 0.7570 0.008 0.000 0.000 0.908 0.068 0.016
#> SRR1951682 5 0.5147 0.0533 0.392 0.076 0.000 0.004 0.528 0.000
#> SRR1951690 5 0.3219 0.5847 0.032 0.060 0.056 0.000 0.852 0.000
#> SRR1951680 3 0.3139 0.6882 0.000 0.032 0.816 0.000 0.152 0.000
#> SRR1951679 3 0.6170 0.3494 0.220 0.168 0.572 0.000 0.028 0.012
#> SRR1951678 4 0.1647 0.7643 0.016 0.032 0.000 0.940 0.004 0.008
#> SRR1951677 4 0.1767 0.7617 0.020 0.036 0.000 0.932 0.000 0.012
#> SRR1951676 4 0.4904 0.6736 0.000 0.088 0.068 0.752 0.020 0.072
#> SRR1951675 1 0.1971 0.6943 0.928 0.024 0.000 0.016 0.008 0.024
#> SRR1951681 4 0.0862 0.7673 0.000 0.016 0.008 0.972 0.004 0.000
#> SRR1951673 1 0.6104 0.3503 0.520 0.076 0.012 0.000 0.348 0.044
#> SRR1951674 6 0.0964 0.6608 0.012 0.016 0.000 0.004 0.000 0.968
#> SRR1951672 1 0.5567 0.5891 0.632 0.260 0.028 0.000 0.044 0.036
#> SRR1951671 5 0.6269 -0.1228 0.008 0.344 0.260 0.000 0.388 0.000
#> SRR1951670 1 0.6111 0.3288 0.496 0.328 0.000 0.156 0.008 0.012
#> SRR1951640 5 0.6712 0.3521 0.044 0.024 0.120 0.340 0.472 0.000
#> SRR1951668 2 0.5080 0.5204 0.056 0.728 0.112 0.000 0.092 0.012
#> SRR1951669 6 0.4174 0.6405 0.172 0.092 0.000 0.000 0.000 0.736
#> SRR1951638 6 0.3964 0.6380 0.124 0.036 0.028 0.000 0.012 0.800
#> SRR1951637 6 0.4963 0.3961 0.364 0.044 0.000 0.000 0.016 0.576
#> SRR1951635 1 0.4218 0.6290 0.748 0.136 0.000 0.004 0.000 0.112
#> SRR1951634 5 0.6513 0.0337 0.200 0.316 0.004 0.028 0.452 0.000
#> SRR1951631 5 0.7689 0.5233 0.140 0.024 0.104 0.064 0.536 0.132
#> SRR1951632 3 0.5732 0.4324 0.048 0.052 0.628 0.000 0.248 0.024
#> SRR1951633 4 0.1788 0.7676 0.000 0.000 0.004 0.916 0.004 0.076
#> SRR1951630 4 0.3707 0.7231 0.012 0.008 0.004 0.824 0.084 0.068
#> SRR1951629 4 0.3238 0.6925 0.004 0.008 0.148 0.824 0.008 0.008
#> SRR1951628 1 0.4077 0.6327 0.772 0.048 0.008 0.000 0.160 0.012
#> SRR1951627 4 0.1464 0.7669 0.000 0.016 0.036 0.944 0.004 0.000
#> SRR1951639 5 0.3103 0.6140 0.016 0.020 0.076 0.024 0.864 0.000
#> SRR1951625 5 0.5381 0.5819 0.108 0.012 0.124 0.008 0.708 0.040
#> SRR1951626 3 0.3147 0.6286 0.000 0.008 0.816 0.160 0.016 0.000
#> SRR1951624 3 0.3297 0.6602 0.000 0.068 0.820 0.112 0.000 0.000
#> SRR1951622 1 0.1448 0.6990 0.948 0.016 0.000 0.000 0.024 0.012
#> SRR1951636 4 0.3338 0.7405 0.000 0.024 0.076 0.852 0.032 0.016
#> SRR1951623 4 0.0909 0.7683 0.000 0.000 0.000 0.968 0.012 0.020
#> SRR1951621 1 0.5817 0.2452 0.492 0.068 0.000 0.404 0.020 0.016
#> SRR1951620 4 0.1554 0.7646 0.004 0.004 0.008 0.940 0.044 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
sessionInfo()
#> R version 3.6.0 (2019-04-26)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS: /usr/lib64/libblas.so.3.4.2
#> LAPACK: /usr/lib64/liblapack.so.3.4.2
#>
#> locale:
#> [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8
#> [4] LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
#> [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] grid stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] genefilter_1.66.0 ComplexHeatmap_2.3.1 markdown_1.1 knitr_1.26
#> [5] GetoptLong_0.1.7 cola_1.3.2
#>
#> loaded via a namespace (and not attached):
#> [1] circlize_0.4.8 shape_1.4.4 xfun_0.11 slam_0.1-46
#> [5] lattice_0.20-38 splines_3.6.0 colorspace_1.4-1 vctrs_0.2.0
#> [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