Date: 2019-12-25 22:49:43 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 17611 rows and 118 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] 17611 118
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 | ||
|---|---|---|---|---|---|
| SD:mclust | 2 | 1.000 | 0.975 | 0.988 | ** |
| ATC:kmeans | 2 | 1.000 | 0.971 | 0.988 | ** |
| ATC:skmeans | 2 | 1.000 | 0.986 | 0.994 | ** |
| MAD:mclust | 2 | 1.000 | 0.978 | 0.990 | ** |
| ATC:pam | 3 | 0.900 | 0.904 | 0.962 | * |
| SD:skmeans | 2 | 0.820 | 0.898 | 0.958 | |
| MAD:skmeans | 2 | 0.787 | 0.898 | 0.957 | |
| SD:kmeans | 2 | 0.740 | 0.845 | 0.940 | |
| CV:NMF | 2 | 0.739 | 0.885 | 0.949 | |
| CV:skmeans | 2 | 0.653 | 0.827 | 0.921 | |
| CV:pam | 3 | 0.639 | 0.824 | 0.913 | |
| MAD:kmeans | 5 | 0.639 | 0.653 | 0.786 | |
| MAD:pam | 4 | 0.595 | 0.673 | 0.817 | |
| MAD:NMF | 4 | 0.576 | 0.707 | 0.827 | |
| CV:kmeans | 2 | 0.569 | 0.764 | 0.901 | |
| SD:NMF | 3 | 0.555 | 0.774 | 0.870 | |
| CV:mclust | 2 | 0.512 | 0.861 | 0.895 | |
| SD:pam | 3 | 0.463 | 0.684 | 0.808 | |
| ATC:hclust | 3 | 0.424 | 0.711 | 0.828 | |
| ATC:NMF | 3 | 0.374 | 0.708 | 0.816 | |
| SD:hclust | 3 | 0.219 | 0.607 | 0.774 | |
| MAD:hclust | 3 | 0.212 | 0.588 | 0.706 | |
| ATC:mclust | 2 | 0.209 | 0.736 | 0.800 | |
| CV:hclust | 3 | 0.080 | 0.517 | 0.709 |
**: 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 0.896 0.919 0.967 0.411 0.594 0.594
#> CV:NMF 2 0.739 0.885 0.949 0.451 0.554 0.554
#> MAD:NMF 2 0.895 0.928 0.971 0.411 0.594 0.594
#> ATC:NMF 2 0.488 0.818 0.898 0.402 0.618 0.618
#> SD:skmeans 2 0.820 0.898 0.958 0.496 0.503 0.503
#> CV:skmeans 2 0.653 0.827 0.921 0.494 0.501 0.501
#> MAD:skmeans 2 0.787 0.898 0.957 0.496 0.503 0.503
#> ATC:skmeans 2 1.000 0.986 0.994 0.502 0.499 0.499
#> SD:mclust 2 1.000 0.975 0.988 0.408 0.586 0.586
#> CV:mclust 2 0.512 0.861 0.895 0.427 0.572 0.572
#> MAD:mclust 2 1.000 0.978 0.990 0.402 0.594 0.594
#> ATC:mclust 2 0.209 0.736 0.800 0.440 0.498 0.498
#> SD:kmeans 2 0.740 0.845 0.940 0.437 0.566 0.566
#> CV:kmeans 2 0.569 0.764 0.901 0.442 0.560 0.560
#> MAD:kmeans 2 0.658 0.823 0.929 0.440 0.579 0.579
#> ATC:kmeans 2 1.000 0.971 0.988 0.472 0.533 0.533
#> SD:pam 2 0.825 0.908 0.956 0.393 0.618 0.618
#> CV:pam 2 0.492 0.857 0.890 0.381 0.644 0.644
#> MAD:pam 2 0.852 0.897 0.956 0.391 0.618 0.618
#> ATC:pam 2 0.564 0.773 0.894 0.452 0.560 0.560
#> SD:hclust 2 0.206 0.488 0.756 0.310 0.609 0.609
#> CV:hclust 2 0.272 0.790 0.849 0.212 0.950 0.950
#> MAD:hclust 2 0.190 0.676 0.777 0.359 0.524 0.524
#> ATC:hclust 2 0.350 0.833 0.884 0.414 0.566 0.566
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.5549 0.774 0.870 0.540 0.700 0.526
#> CV:NMF 3 0.6686 0.816 0.909 0.341 0.797 0.653
#> MAD:NMF 3 0.4720 0.656 0.818 0.585 0.682 0.497
#> ATC:NMF 3 0.3742 0.708 0.816 0.487 0.733 0.590
#> SD:skmeans 3 0.6128 0.701 0.863 0.339 0.788 0.603
#> CV:skmeans 3 0.5175 0.504 0.730 0.342 0.689 0.455
#> MAD:skmeans 3 0.5859 0.659 0.845 0.340 0.770 0.574
#> ATC:skmeans 3 0.7428 0.899 0.915 0.300 0.794 0.606
#> SD:mclust 3 0.5598 0.505 0.752 0.529 0.794 0.651
#> CV:mclust 3 0.3588 0.669 0.807 0.414 0.787 0.632
#> MAD:mclust 3 0.5758 0.809 0.874 0.606 0.740 0.562
#> ATC:mclust 3 0.2511 0.341 0.657 0.358 0.692 0.496
#> SD:kmeans 3 0.4484 0.644 0.789 0.434 0.702 0.511
#> CV:kmeans 3 0.3760 0.476 0.728 0.401 0.829 0.705
#> MAD:kmeans 3 0.4599 0.651 0.810 0.444 0.714 0.529
#> ATC:kmeans 3 0.6052 0.802 0.884 0.336 0.746 0.560
#> SD:pam 3 0.4632 0.684 0.808 0.634 0.656 0.476
#> CV:pam 3 0.6390 0.824 0.913 0.593 0.720 0.582
#> MAD:pam 3 0.4410 0.647 0.832 0.639 0.684 0.511
#> ATC:pam 3 0.9003 0.904 0.962 0.427 0.728 0.544
#> SD:hclust 3 0.2186 0.607 0.774 0.709 0.638 0.490
#> CV:hclust 3 0.0803 0.517 0.709 1.458 0.544 0.520
#> MAD:hclust 3 0.2124 0.588 0.706 0.541 0.767 0.614
#> ATC:hclust 3 0.4245 0.711 0.828 0.486 0.780 0.616
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.549 0.635 0.791 0.1701 0.803 0.519
#> CV:NMF 4 0.488 0.448 0.705 0.1935 0.862 0.681
#> MAD:NMF 4 0.576 0.707 0.827 0.1415 0.825 0.549
#> ATC:NMF 4 0.445 0.486 0.725 0.2041 0.728 0.438
#> SD:skmeans 4 0.763 0.816 0.905 0.1303 0.828 0.556
#> CV:skmeans 4 0.515 0.596 0.762 0.1291 0.822 0.530
#> MAD:skmeans 4 0.742 0.811 0.903 0.1292 0.829 0.552
#> ATC:skmeans 4 0.579 0.572 0.784 0.1195 0.874 0.659
#> SD:mclust 4 0.496 0.596 0.739 0.1055 0.833 0.595
#> CV:mclust 4 0.434 0.651 0.745 0.1211 0.935 0.839
#> MAD:mclust 4 0.484 0.519 0.711 0.0824 0.864 0.642
#> ATC:mclust 4 0.288 0.454 0.638 0.1344 0.705 0.405
#> SD:kmeans 4 0.511 0.484 0.678 0.1472 0.748 0.416
#> CV:kmeans 4 0.428 0.466 0.681 0.1568 0.802 0.578
#> MAD:kmeans 4 0.549 0.595 0.753 0.1443 0.868 0.648
#> ATC:kmeans 4 0.585 0.597 0.789 0.1566 0.862 0.644
#> SD:pam 4 0.504 0.560 0.793 0.1467 0.771 0.457
#> CV:pam 4 0.628 0.716 0.833 0.1730 0.848 0.645
#> MAD:pam 4 0.595 0.673 0.817 0.1506 0.812 0.535
#> ATC:pam 4 0.801 0.851 0.922 0.1564 0.857 0.623
#> SD:hclust 4 0.287 0.622 0.717 0.2241 0.817 0.613
#> CV:hclust 4 0.125 0.433 0.662 0.1563 0.920 0.843
#> MAD:hclust 4 0.264 0.442 0.640 0.1822 0.873 0.725
#> ATC:hclust 4 0.490 0.477 0.702 0.1617 0.856 0.630
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.586 0.556 0.766 0.0616 0.843 0.485
#> CV:NMF 5 0.534 0.556 0.734 0.0862 0.762 0.382
#> MAD:NMF 5 0.600 0.529 0.767 0.0574 0.819 0.428
#> ATC:NMF 5 0.482 0.435 0.645 0.0853 0.722 0.285
#> SD:skmeans 5 0.704 0.635 0.803 0.0588 0.864 0.541
#> CV:skmeans 5 0.550 0.509 0.702 0.0659 0.934 0.748
#> MAD:skmeans 5 0.701 0.642 0.799 0.0602 0.887 0.595
#> ATC:skmeans 5 0.630 0.495 0.745 0.0634 0.845 0.533
#> SD:mclust 5 0.514 0.557 0.732 0.0891 0.867 0.582
#> CV:mclust 5 0.572 0.734 0.791 0.0663 0.927 0.809
#> MAD:mclust 5 0.523 0.420 0.649 0.0657 0.826 0.499
#> ATC:mclust 5 0.482 0.401 0.726 0.0779 0.795 0.462
#> SD:kmeans 5 0.645 0.648 0.801 0.0774 0.845 0.526
#> CV:kmeans 5 0.506 0.520 0.700 0.0755 0.867 0.607
#> MAD:kmeans 5 0.639 0.653 0.786 0.0647 0.932 0.766
#> ATC:kmeans 5 0.595 0.558 0.738 0.0736 0.887 0.623
#> SD:pam 5 0.572 0.495 0.736 0.0634 0.917 0.714
#> CV:pam 5 0.725 0.770 0.865 0.0981 0.879 0.614
#> MAD:pam 5 0.583 0.498 0.734 0.0651 0.948 0.810
#> ATC:pam 5 0.732 0.747 0.869 0.0541 0.946 0.794
#> SD:hclust 5 0.346 0.587 0.706 0.0785 0.972 0.909
#> CV:hclust 5 0.204 0.443 0.617 0.1178 0.873 0.724
#> MAD:hclust 5 0.355 0.581 0.689 0.0894 0.864 0.643
#> ATC:hclust 5 0.496 0.400 0.621 0.0515 0.921 0.747
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.581 0.405 0.651 0.0429 0.859 0.472
#> CV:NMF 6 0.601 0.505 0.711 0.0511 0.875 0.509
#> MAD:NMF 6 0.579 0.427 0.692 0.0423 0.882 0.536
#> ATC:NMF 6 0.574 0.542 0.720 0.0491 0.833 0.407
#> SD:skmeans 6 0.695 0.590 0.768 0.0403 0.932 0.707
#> CV:skmeans 6 0.619 0.477 0.683 0.0423 0.915 0.639
#> MAD:skmeans 6 0.702 0.627 0.777 0.0398 0.926 0.678
#> ATC:skmeans 6 0.703 0.571 0.765 0.0458 0.888 0.576
#> SD:mclust 6 0.558 0.554 0.704 0.0408 0.897 0.636
#> CV:mclust 6 0.619 0.606 0.767 0.0460 0.996 0.989
#> MAD:mclust 6 0.565 0.417 0.660 0.0483 0.832 0.451
#> ATC:mclust 6 0.535 0.427 0.682 0.0600 0.919 0.721
#> SD:kmeans 6 0.658 0.553 0.694 0.0481 0.888 0.595
#> CV:kmeans 6 0.578 0.509 0.691 0.0493 0.951 0.795
#> MAD:kmeans 6 0.662 0.551 0.704 0.0494 0.896 0.605
#> ATC:kmeans 6 0.647 0.476 0.667 0.0470 0.930 0.703
#> SD:pam 6 0.653 0.576 0.761 0.0330 0.945 0.774
#> CV:pam 6 0.694 0.624 0.794 0.0277 0.951 0.783
#> MAD:pam 6 0.662 0.483 0.739 0.0364 0.917 0.676
#> ATC:pam 6 0.763 0.716 0.825 0.0342 0.956 0.805
#> SD:hclust 6 0.425 0.597 0.720 0.0565 0.947 0.824
#> CV:hclust 6 0.308 0.335 0.588 0.0772 0.920 0.780
#> MAD:hclust 6 0.426 0.580 0.698 0.0364 0.989 0.961
#> ATC:hclust 6 0.518 0.482 0.595 0.0445 0.869 0.554
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 17611 rows and 118 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 0.206 0.488 0.756 0.3103 0.609 0.609
#> 3 3 0.219 0.607 0.774 0.7089 0.638 0.490
#> 4 4 0.287 0.622 0.717 0.2241 0.817 0.613
#> 5 5 0.346 0.587 0.706 0.0785 0.972 0.909
#> 6 6 0.425 0.597 0.720 0.0565 0.947 0.824
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1396765 1 0.0000 0.7010 1.000 0.000
#> SRR1429287 1 0.1633 0.6999 0.976 0.024
#> SRR1359238 1 0.9170 0.2065 0.668 0.332
#> SRR1309597 1 0.5059 0.6914 0.888 0.112
#> SRR1441398 2 0.9983 0.6616 0.476 0.524
#> SRR1084055 1 0.0000 0.7010 1.000 0.000
#> SRR1417566 1 0.7376 0.5754 0.792 0.208
#> SRR1351857 1 0.8813 0.1602 0.700 0.300
#> SRR1487485 1 0.4939 0.6929 0.892 0.108
#> SRR1335875 1 0.6148 0.6683 0.848 0.152
#> SRR1073947 2 0.9988 0.6785 0.480 0.520
#> SRR1443483 1 0.5629 0.6824 0.868 0.132
#> SRR1346794 1 0.9209 0.2388 0.664 0.336
#> SRR1405245 2 0.9996 0.6523 0.488 0.512
#> SRR1409677 1 0.2948 0.7016 0.948 0.052
#> SRR1095549 1 0.9635 -0.1068 0.612 0.388
#> SRR1323788 1 0.9710 -0.1522 0.600 0.400
#> SRR1314054 1 0.0000 0.7010 1.000 0.000
#> SRR1077944 1 0.9998 -0.5928 0.508 0.492
#> SRR1480587 1 0.2043 0.7067 0.968 0.032
#> SRR1311205 2 0.9988 0.6735 0.480 0.520
#> SRR1076369 1 0.9896 -0.2526 0.560 0.440
#> SRR1453549 1 0.3431 0.7107 0.936 0.064
#> SRR1345782 2 0.9963 0.6878 0.464 0.536
#> SRR1447850 1 0.0938 0.7007 0.988 0.012
#> SRR1391553 1 0.6048 0.6731 0.852 0.148
#> SRR1444156 1 0.0672 0.6959 0.992 0.008
#> SRR1471731 1 0.5294 0.6918 0.880 0.120
#> SRR1120987 1 0.3879 0.7037 0.924 0.076
#> SRR1477363 2 0.9993 0.6540 0.484 0.516
#> SRR1391961 2 0.1414 0.3594 0.020 0.980
#> SRR1373879 1 0.5178 0.6918 0.884 0.116
#> SRR1318732 1 0.5629 0.6918 0.868 0.132
#> SRR1091404 2 0.9977 0.6839 0.472 0.528
#> SRR1402109 1 0.5178 0.6918 0.884 0.116
#> SRR1407336 1 0.6343 0.6661 0.840 0.160
#> SRR1097417 1 0.5842 0.6790 0.860 0.140
#> SRR1396227 1 0.9775 -0.3139 0.588 0.412
#> SRR1400775 1 0.0000 0.7010 1.000 0.000
#> SRR1392861 1 0.1843 0.7039 0.972 0.028
#> SRR1472929 2 0.1184 0.3493 0.016 0.984
#> SRR1436740 1 0.1843 0.7039 0.972 0.028
#> SRR1477057 1 0.8713 0.3039 0.708 0.292
#> SRR1311980 1 0.6048 0.6731 0.852 0.148
#> SRR1069400 1 0.6343 0.6661 0.840 0.160
#> SRR1351016 2 0.9988 0.6735 0.480 0.520
#> SRR1096291 1 0.4690 0.7033 0.900 0.100
#> SRR1418145 1 0.4298 0.6985 0.912 0.088
#> SRR1488111 1 0.3879 0.7037 0.924 0.076
#> SRR1370495 1 0.9460 -0.0188 0.636 0.364
#> SRR1352639 1 0.6531 0.6592 0.832 0.168
#> SRR1348911 1 0.5737 0.6828 0.864 0.136
#> SRR1467386 1 0.9983 -0.5753 0.524 0.476
#> SRR1415956 2 0.9933 0.6822 0.452 0.548
#> SRR1500495 2 0.9983 0.6616 0.476 0.524
#> SRR1405099 2 0.9933 0.6822 0.452 0.548
#> SRR1345585 1 0.5059 0.6989 0.888 0.112
#> SRR1093196 1 0.5294 0.6918 0.880 0.120
#> SRR1466006 1 0.8144 0.4487 0.748 0.252
#> SRR1351557 1 0.0000 0.7010 1.000 0.000
#> SRR1382687 1 0.9933 -0.4713 0.548 0.452
#> SRR1375549 1 0.9775 -0.2381 0.588 0.412
#> SRR1101765 1 0.9896 -0.2526 0.560 0.440
#> SRR1334461 2 0.0672 0.3530 0.008 0.992
#> SRR1094073 1 0.0000 0.7010 1.000 0.000
#> SRR1077549 2 0.9993 0.6718 0.484 0.516
#> SRR1440332 1 0.9970 -0.5166 0.532 0.468
#> SRR1454177 1 0.1843 0.7039 0.972 0.028
#> SRR1082447 2 0.9988 0.6662 0.480 0.520
#> SRR1420043 1 0.3431 0.7107 0.936 0.064
#> SRR1432500 2 1.0000 0.6296 0.500 0.500
#> SRR1378045 1 0.0672 0.6959 0.992 0.008
#> SRR1334200 2 0.6801 0.3493 0.180 0.820
#> SRR1069539 1 0.4690 0.7033 0.900 0.100
#> SRR1343031 1 0.6343 0.6661 0.840 0.160
#> SRR1319690 1 0.9996 -0.5798 0.512 0.488
#> SRR1310604 1 0.0376 0.6994 0.996 0.004
#> SRR1327747 1 0.9000 0.3303 0.684 0.316
#> SRR1072456 1 0.0376 0.6994 0.996 0.004
#> SRR1367896 1 0.5842 0.6790 0.860 0.140
#> SRR1480107 2 0.9963 0.6878 0.464 0.536
#> SRR1377756 1 0.9996 -0.6112 0.512 0.488
#> SRR1435272 1 0.4298 0.6777 0.912 0.088
#> SRR1089230 1 0.2423 0.7061 0.960 0.040
#> SRR1389522 1 0.6048 0.6748 0.852 0.148
#> SRR1080600 1 0.8144 0.4487 0.748 0.252
#> SRR1086935 1 0.1184 0.6991 0.984 0.016
#> SRR1344060 2 0.6531 0.3564 0.168 0.832
#> SRR1467922 1 0.0672 0.6959 0.992 0.008
#> SRR1090984 1 0.7883 0.5295 0.764 0.236
#> SRR1456991 2 0.9963 0.6878 0.464 0.536
#> SRR1085039 2 0.9977 0.6807 0.472 0.528
#> SRR1069303 2 0.9993 0.6748 0.484 0.516
#> SRR1091500 1 0.1633 0.7060 0.976 0.024
#> SRR1075198 1 0.4022 0.7038 0.920 0.080
#> SRR1086915 1 0.5946 0.6063 0.856 0.144
#> SRR1499503 1 0.0376 0.6994 0.996 0.004
#> SRR1094312 1 0.0000 0.7010 1.000 0.000
#> SRR1352437 2 0.9993 0.6748 0.484 0.516
#> SRR1436323 1 0.5294 0.6918 0.880 0.120
#> SRR1073507 2 0.9993 0.6718 0.484 0.516
#> SRR1401972 2 0.9993 0.6748 0.484 0.516
#> SRR1415510 1 0.0376 0.6994 0.996 0.004
#> SRR1327279 2 0.9988 0.6785 0.480 0.520
#> SRR1086983 1 0.8813 0.1602 0.700 0.300
#> SRR1105174 2 0.9933 0.6822 0.452 0.548
#> SRR1468893 2 0.9977 0.6853 0.472 0.528
#> SRR1362555 1 0.4022 0.7038 0.920 0.080
#> SRR1074526 2 0.3879 0.3624 0.076 0.924
#> SRR1326225 1 0.0000 0.7010 1.000 0.000
#> SRR1401933 1 0.9795 -0.3275 0.584 0.416
#> SRR1324062 1 0.9944 -0.5068 0.544 0.456
#> SRR1102296 1 0.9922 -0.4452 0.552 0.448
#> SRR1085087 2 0.9993 0.6718 0.484 0.516
#> SRR1079046 1 0.9775 -0.2461 0.588 0.412
#> SRR1328339 1 0.7674 0.5562 0.776 0.224
#> SRR1079782 1 0.4022 0.7038 0.920 0.080
#> SRR1092257 1 0.3879 0.7037 0.924 0.076
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 3 0.3998 0.6164 0.056 0.060 0.884
#> SRR1429287 3 0.4636 0.6574 0.116 0.036 0.848
#> SRR1359238 1 0.5384 0.6544 0.788 0.024 0.188
#> SRR1309597 3 0.6835 0.5885 0.284 0.040 0.676
#> SRR1441398 1 0.2879 0.8094 0.924 0.052 0.024
#> SRR1084055 3 0.4194 0.6214 0.064 0.060 0.876
#> SRR1417566 1 0.7059 -0.1573 0.520 0.020 0.460
#> SRR1351857 1 0.5894 0.6423 0.752 0.028 0.220
#> SRR1487485 3 0.6735 0.6101 0.260 0.044 0.696
#> SRR1335875 3 0.7065 0.5322 0.352 0.032 0.616
#> SRR1073947 1 0.0661 0.8138 0.988 0.008 0.004
#> SRR1443483 3 0.7246 0.5914 0.276 0.060 0.664
#> SRR1346794 1 0.6546 0.5927 0.716 0.044 0.240
#> SRR1405245 1 0.2414 0.8118 0.940 0.040 0.020
#> SRR1409677 3 0.7283 0.2714 0.460 0.028 0.512
#> SRR1095549 1 0.4615 0.7179 0.836 0.020 0.144
#> SRR1323788 1 0.5058 0.7182 0.820 0.032 0.148
#> SRR1314054 3 0.5004 0.6311 0.088 0.072 0.840
#> SRR1077944 1 0.2773 0.8097 0.928 0.024 0.048
#> SRR1480587 3 0.4745 0.6193 0.068 0.080 0.852
#> SRR1311205 1 0.0848 0.8155 0.984 0.008 0.008
#> SRR1076369 1 0.7337 0.6500 0.708 0.140 0.152
#> SRR1453549 3 0.7411 0.4151 0.416 0.036 0.548
#> SRR1345782 1 0.0892 0.8110 0.980 0.020 0.000
#> SRR1447850 3 0.5608 0.6445 0.120 0.072 0.808
#> SRR1391553 3 0.7279 0.4852 0.376 0.036 0.588
#> SRR1444156 3 0.2711 0.5617 0.000 0.088 0.912
#> SRR1471731 3 0.7480 0.3274 0.456 0.036 0.508
#> SRR1120987 3 0.5012 0.6487 0.204 0.008 0.788
#> SRR1477363 1 0.2187 0.8118 0.948 0.024 0.028
#> SRR1391961 2 0.4291 0.9081 0.152 0.840 0.008
#> SRR1373879 3 0.7395 0.4787 0.380 0.040 0.580
#> SRR1318732 3 0.7878 0.4389 0.392 0.060 0.548
#> SRR1091404 1 0.1170 0.8155 0.976 0.016 0.008
#> SRR1402109 3 0.7395 0.4787 0.380 0.040 0.580
#> SRR1407336 3 0.7974 0.3627 0.436 0.060 0.504
#> SRR1097417 3 0.7259 0.6044 0.248 0.072 0.680
#> SRR1396227 1 0.3682 0.7710 0.876 0.008 0.116
#> SRR1400775 3 0.4737 0.6276 0.084 0.064 0.852
#> SRR1392861 3 0.7295 0.1986 0.480 0.028 0.492
#> SRR1472929 2 0.4059 0.9017 0.128 0.860 0.012
#> SRR1436740 3 0.7295 0.1986 0.480 0.028 0.492
#> SRR1477057 3 0.7013 0.3164 0.432 0.020 0.548
#> SRR1311980 3 0.7311 0.4700 0.384 0.036 0.580
#> SRR1069400 3 0.7742 0.5215 0.356 0.060 0.584
#> SRR1351016 1 0.0848 0.8155 0.984 0.008 0.008
#> SRR1096291 3 0.7357 0.5677 0.332 0.048 0.620
#> SRR1418145 3 0.6448 0.5185 0.352 0.012 0.636
#> SRR1488111 3 0.5012 0.6487 0.204 0.008 0.788
#> SRR1370495 1 0.6512 0.4626 0.676 0.024 0.300
#> SRR1352639 3 0.6313 0.5907 0.308 0.016 0.676
#> SRR1348911 3 0.6744 0.5866 0.300 0.032 0.668
#> SRR1467386 1 0.1860 0.8128 0.948 0.000 0.052
#> SRR1415956 1 0.1289 0.8065 0.968 0.032 0.000
#> SRR1500495 1 0.2879 0.8094 0.924 0.052 0.024
#> SRR1405099 1 0.1289 0.8065 0.968 0.032 0.000
#> SRR1345585 3 0.7285 0.5795 0.320 0.048 0.632
#> SRR1093196 3 0.7476 0.3390 0.452 0.036 0.512
#> SRR1466006 3 0.6016 0.4207 0.020 0.256 0.724
#> SRR1351557 3 0.4469 0.6150 0.060 0.076 0.864
#> SRR1382687 1 0.4063 0.7809 0.868 0.020 0.112
#> SRR1375549 1 0.5618 0.7285 0.796 0.048 0.156
#> SRR1101765 1 0.7337 0.6500 0.708 0.140 0.152
#> SRR1334461 2 0.3983 0.9063 0.144 0.852 0.004
#> SRR1094073 3 0.4469 0.6150 0.060 0.076 0.864
#> SRR1077549 1 0.0848 0.8154 0.984 0.008 0.008
#> SRR1440332 1 0.2845 0.7977 0.920 0.012 0.068
#> SRR1454177 3 0.7295 0.1986 0.480 0.028 0.492
#> SRR1082447 1 0.1919 0.8169 0.956 0.020 0.024
#> SRR1420043 3 0.7411 0.4151 0.416 0.036 0.548
#> SRR1432500 1 0.1267 0.8163 0.972 0.004 0.024
#> SRR1378045 3 0.3832 0.6053 0.036 0.076 0.888
#> SRR1334200 2 0.6865 0.8478 0.104 0.736 0.160
#> SRR1069539 3 0.7334 0.5731 0.328 0.048 0.624
#> SRR1343031 3 0.7878 0.4624 0.392 0.060 0.548
#> SRR1319690 1 0.3554 0.8017 0.900 0.036 0.064
#> SRR1310604 3 0.4095 0.6155 0.056 0.064 0.880
#> SRR1327747 1 0.6781 0.5533 0.704 0.052 0.244
#> SRR1072456 3 0.4095 0.6155 0.056 0.064 0.880
#> SRR1367896 3 0.7259 0.6044 0.248 0.072 0.680
#> SRR1480107 1 0.0892 0.8110 0.980 0.020 0.000
#> SRR1377756 1 0.2636 0.8110 0.932 0.020 0.048
#> SRR1435272 1 0.7248 0.0176 0.536 0.028 0.436
#> SRR1089230 1 0.7295 -0.1792 0.492 0.028 0.480
#> SRR1389522 3 0.7451 0.5757 0.304 0.060 0.636
#> SRR1080600 3 0.6016 0.4207 0.020 0.256 0.724
#> SRR1086935 3 0.7319 0.3614 0.420 0.032 0.548
#> SRR1344060 2 0.6693 0.8601 0.104 0.748 0.148
#> SRR1467922 3 0.2711 0.5617 0.000 0.088 0.912
#> SRR1090984 1 0.7152 -0.0948 0.532 0.024 0.444
#> SRR1456991 1 0.0892 0.8110 0.980 0.020 0.000
#> SRR1085039 1 0.1482 0.8156 0.968 0.020 0.012
#> SRR1069303 1 0.0848 0.8147 0.984 0.008 0.008
#> SRR1091500 3 0.6031 0.6337 0.116 0.096 0.788
#> SRR1075198 3 0.5414 0.6458 0.212 0.016 0.772
#> SRR1086915 1 0.7067 0.2638 0.596 0.028 0.376
#> SRR1499503 3 0.4095 0.6155 0.056 0.064 0.880
#> SRR1094312 3 0.4737 0.6276 0.084 0.064 0.852
#> SRR1352437 1 0.0848 0.8147 0.984 0.008 0.008
#> SRR1436323 1 0.7493 -0.2629 0.484 0.036 0.480
#> SRR1073507 1 0.0848 0.8154 0.984 0.008 0.008
#> SRR1401972 1 0.0848 0.8147 0.984 0.008 0.008
#> SRR1415510 3 0.4290 0.6231 0.064 0.064 0.872
#> SRR1327279 1 0.1015 0.8153 0.980 0.008 0.012
#> SRR1086983 1 0.5894 0.6423 0.752 0.028 0.220
#> SRR1105174 1 0.1163 0.8078 0.972 0.028 0.000
#> SRR1468893 1 0.1129 0.8162 0.976 0.020 0.004
#> SRR1362555 3 0.5414 0.6458 0.212 0.016 0.772
#> SRR1074526 2 0.5235 0.9091 0.152 0.812 0.036
#> SRR1326225 3 0.4194 0.6214 0.064 0.060 0.876
#> SRR1401933 1 0.3845 0.7722 0.872 0.012 0.116
#> SRR1324062 1 0.3349 0.7819 0.888 0.004 0.108
#> SRR1102296 1 0.5597 0.5890 0.764 0.020 0.216
#> SRR1085087 1 0.1170 0.8162 0.976 0.008 0.016
#> SRR1079046 1 0.6990 0.6386 0.728 0.108 0.164
#> SRR1328339 3 0.7063 0.3248 0.464 0.020 0.516
#> SRR1079782 3 0.5414 0.6458 0.212 0.016 0.772
#> SRR1092257 3 0.5012 0.6487 0.204 0.008 0.788
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.3808 0.6986 0.012 0.812 0.176 0.000
#> SRR1429287 2 0.5881 0.4905 0.028 0.656 0.296 0.020
#> SRR1359238 1 0.5618 0.5326 0.672 0.028 0.288 0.012
#> SRR1309597 3 0.4238 0.6023 0.108 0.060 0.828 0.004
#> SRR1441398 1 0.2611 0.8059 0.896 0.000 0.096 0.008
#> SRR1084055 2 0.3808 0.7005 0.012 0.824 0.160 0.004
#> SRR1417566 3 0.6123 0.4618 0.372 0.056 0.572 0.000
#> SRR1351857 1 0.7651 0.3068 0.552 0.180 0.248 0.020
#> SRR1487485 3 0.4965 0.5824 0.112 0.100 0.784 0.004
#> SRR1335875 3 0.5434 0.6174 0.188 0.084 0.728 0.000
#> SRR1073947 1 0.1661 0.8149 0.944 0.000 0.052 0.004
#> SRR1443483 3 0.4504 0.5746 0.088 0.064 0.828 0.020
#> SRR1346794 1 0.6017 0.5179 0.664 0.040 0.276 0.020
#> SRR1405245 1 0.2593 0.8114 0.892 0.000 0.104 0.004
#> SRR1409677 3 0.8210 0.4674 0.264 0.260 0.456 0.020
#> SRR1095549 1 0.4963 0.6774 0.740 0.024 0.228 0.008
#> SRR1323788 1 0.4931 0.6904 0.760 0.028 0.200 0.012
#> SRR1314054 2 0.3774 0.6915 0.008 0.820 0.168 0.004
#> SRR1077944 1 0.2384 0.8131 0.916 0.008 0.072 0.004
#> SRR1480587 2 0.4434 0.6846 0.016 0.772 0.208 0.004
#> SRR1311205 1 0.2401 0.8083 0.904 0.000 0.092 0.004
#> SRR1076369 1 0.6887 0.5984 0.676 0.048 0.156 0.120
#> SRR1453549 3 0.7681 0.4976 0.188 0.260 0.536 0.016
#> SRR1345782 1 0.1109 0.8125 0.968 0.000 0.028 0.004
#> SRR1447850 2 0.4869 0.6187 0.028 0.780 0.172 0.020
#> SRR1391553 3 0.5464 0.6436 0.212 0.072 0.716 0.000
#> SRR1444156 2 0.5231 0.5683 0.000 0.676 0.296 0.028
#> SRR1471731 3 0.5646 0.6521 0.272 0.056 0.672 0.000
#> SRR1120987 2 0.6982 0.4474 0.108 0.584 0.296 0.012
#> SRR1477363 1 0.2635 0.8169 0.908 0.004 0.072 0.016
#> SRR1391961 4 0.2748 0.8817 0.072 0.004 0.020 0.904
#> SRR1373879 3 0.4974 0.6659 0.224 0.040 0.736 0.000
#> SRR1318732 3 0.6216 0.6204 0.272 0.080 0.644 0.004
#> SRR1091404 1 0.1209 0.8165 0.964 0.004 0.032 0.000
#> SRR1402109 3 0.4974 0.6659 0.224 0.040 0.736 0.000
#> SRR1407336 3 0.5636 0.6613 0.248 0.036 0.700 0.016
#> SRR1097417 3 0.4744 0.5305 0.056 0.088 0.820 0.036
#> SRR1396227 1 0.4625 0.7531 0.804 0.044 0.140 0.012
#> SRR1400775 2 0.3400 0.6970 0.012 0.856 0.128 0.004
#> SRR1392861 3 0.8120 0.4546 0.240 0.264 0.476 0.020
#> SRR1472929 4 0.2810 0.8564 0.008 0.008 0.088 0.896
#> SRR1436740 3 0.8120 0.4546 0.240 0.264 0.476 0.020
#> SRR1477057 2 0.7666 0.0949 0.392 0.448 0.148 0.012
#> SRR1311980 3 0.5363 0.6472 0.216 0.064 0.720 0.000
#> SRR1069400 3 0.5044 0.6359 0.156 0.044 0.780 0.020
#> SRR1351016 1 0.2401 0.8083 0.904 0.000 0.092 0.004
#> SRR1096291 3 0.7596 0.4929 0.168 0.240 0.568 0.024
#> SRR1418145 2 0.8140 0.0404 0.228 0.440 0.316 0.016
#> SRR1488111 2 0.6982 0.4474 0.108 0.584 0.296 0.012
#> SRR1370495 1 0.7084 0.4585 0.616 0.216 0.152 0.016
#> SRR1352639 2 0.7916 0.2709 0.232 0.484 0.272 0.012
#> SRR1348911 3 0.4998 0.5892 0.128 0.088 0.780 0.004
#> SRR1467386 1 0.3350 0.8013 0.864 0.016 0.116 0.004
#> SRR1415956 1 0.1174 0.8055 0.968 0.000 0.020 0.012
#> SRR1500495 1 0.2611 0.8059 0.896 0.000 0.096 0.008
#> SRR1405099 1 0.0804 0.8090 0.980 0.000 0.008 0.012
#> SRR1345585 3 0.5437 0.6367 0.144 0.104 0.748 0.004
#> SRR1093196 3 0.5592 0.6549 0.264 0.056 0.680 0.000
#> SRR1466006 2 0.7392 0.3219 0.000 0.472 0.356 0.172
#> SRR1351557 2 0.3863 0.6921 0.008 0.812 0.176 0.004
#> SRR1382687 1 0.4132 0.7636 0.804 0.008 0.176 0.012
#> SRR1375549 1 0.5345 0.7088 0.776 0.076 0.124 0.024
#> SRR1101765 1 0.6844 0.6042 0.680 0.048 0.152 0.120
#> SRR1334461 4 0.2521 0.8780 0.060 0.004 0.020 0.916
#> SRR1094073 2 0.3907 0.6908 0.008 0.808 0.180 0.004
#> SRR1077549 1 0.1661 0.8179 0.944 0.000 0.052 0.004
#> SRR1440332 1 0.3632 0.7626 0.832 0.008 0.156 0.004
#> SRR1454177 3 0.8120 0.4546 0.240 0.264 0.476 0.020
#> SRR1082447 1 0.1489 0.8204 0.952 0.004 0.044 0.000
#> SRR1420043 3 0.7681 0.4976 0.188 0.260 0.536 0.016
#> SRR1432500 1 0.3006 0.8060 0.888 0.012 0.092 0.008
#> SRR1378045 3 0.5842 -0.3128 0.000 0.448 0.520 0.032
#> SRR1334200 4 0.5748 0.8208 0.024 0.176 0.064 0.736
#> SRR1069539 3 0.7620 0.4868 0.168 0.244 0.564 0.024
#> SRR1343031 3 0.5460 0.6549 0.204 0.040 0.736 0.020
#> SRR1319690 1 0.3623 0.7947 0.856 0.016 0.116 0.012
#> SRR1310604 2 0.3990 0.6993 0.012 0.808 0.176 0.004
#> SRR1327747 1 0.6068 0.4441 0.648 0.032 0.296 0.024
#> SRR1072456 2 0.4088 0.6982 0.012 0.808 0.172 0.008
#> SRR1367896 3 0.4744 0.5305 0.056 0.088 0.820 0.036
#> SRR1480107 1 0.1109 0.8125 0.968 0.000 0.028 0.004
#> SRR1377756 1 0.2989 0.8117 0.884 0.004 0.100 0.012
#> SRR1435272 3 0.8295 0.4183 0.300 0.252 0.428 0.020
#> SRR1089230 3 0.8142 0.4570 0.256 0.252 0.472 0.020
#> SRR1389522 3 0.4959 0.6015 0.124 0.060 0.796 0.020
#> SRR1080600 2 0.7392 0.3219 0.000 0.472 0.356 0.172
#> SRR1086935 3 0.8001 0.4266 0.184 0.296 0.496 0.024
#> SRR1344060 4 0.5561 0.8302 0.024 0.172 0.056 0.748
#> SRR1467922 2 0.5231 0.5683 0.000 0.676 0.296 0.028
#> SRR1090984 3 0.6449 0.4206 0.380 0.056 0.556 0.008
#> SRR1456991 1 0.1109 0.8125 0.968 0.000 0.028 0.004
#> SRR1085039 1 0.1305 0.8193 0.960 0.004 0.036 0.000
#> SRR1069303 1 0.1994 0.8140 0.936 0.008 0.052 0.004
#> SRR1091500 2 0.4592 0.6297 0.028 0.812 0.132 0.028
#> SRR1075198 2 0.7325 0.4231 0.112 0.552 0.316 0.020
#> SRR1086915 1 0.8320 -0.2041 0.404 0.240 0.336 0.020
#> SRR1499503 2 0.4088 0.6982 0.012 0.808 0.172 0.008
#> SRR1094312 2 0.3400 0.6970 0.012 0.856 0.128 0.004
#> SRR1352437 1 0.1994 0.8140 0.936 0.008 0.052 0.004
#> SRR1436323 3 0.5546 0.6247 0.292 0.044 0.664 0.000
#> SRR1073507 1 0.1661 0.8179 0.944 0.000 0.052 0.004
#> SRR1401972 1 0.1994 0.8140 0.936 0.008 0.052 0.004
#> SRR1415510 2 0.4279 0.6871 0.012 0.780 0.204 0.004
#> SRR1327279 1 0.1902 0.8140 0.932 0.000 0.064 0.004
#> SRR1086983 1 0.7651 0.3068 0.552 0.180 0.248 0.020
#> SRR1105174 1 0.0937 0.8102 0.976 0.000 0.012 0.012
#> SRR1468893 1 0.1798 0.8190 0.944 0.000 0.040 0.016
#> SRR1362555 2 0.7325 0.4231 0.112 0.552 0.316 0.020
#> SRR1074526 4 0.5239 0.8751 0.084 0.088 0.036 0.792
#> SRR1326225 2 0.3808 0.7005 0.012 0.824 0.160 0.004
#> SRR1401933 1 0.4574 0.7511 0.808 0.044 0.136 0.012
#> SRR1324062 1 0.4121 0.7516 0.796 0.020 0.184 0.000
#> SRR1102296 1 0.5720 0.4945 0.652 0.052 0.296 0.000
#> SRR1085087 1 0.2125 0.8182 0.932 0.012 0.052 0.004
#> SRR1079046 1 0.6396 0.6220 0.720 0.136 0.072 0.072
#> SRR1328339 3 0.6404 0.5648 0.296 0.096 0.608 0.000
#> SRR1079782 2 0.7325 0.4231 0.112 0.552 0.316 0.020
#> SRR1092257 2 0.6982 0.4474 0.108 0.584 0.296 0.012
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.2700 0.6837 0.004 0.884 0.088 0.024 0.000
#> SRR1429287 2 0.6438 0.4803 0.020 0.568 0.152 0.260 0.000
#> SRR1359238 1 0.5691 0.5154 0.648 0.008 0.212 0.132 0.000
#> SRR1309597 3 0.2012 0.6168 0.060 0.020 0.920 0.000 0.000
#> SRR1441398 1 0.3193 0.7624 0.840 0.000 0.132 0.028 0.000
#> SRR1084055 2 0.2052 0.6865 0.004 0.912 0.080 0.004 0.000
#> SRR1417566 3 0.6489 0.3481 0.332 0.044 0.540 0.084 0.000
#> SRR1351857 1 0.7129 -0.1091 0.528 0.104 0.092 0.276 0.000
#> SRR1487485 3 0.3518 0.6140 0.064 0.044 0.856 0.036 0.000
#> SRR1335875 3 0.4598 0.6076 0.152 0.056 0.768 0.024 0.000
#> SRR1073947 1 0.1725 0.7916 0.936 0.000 0.044 0.020 0.000
#> SRR1443483 3 0.2165 0.5978 0.036 0.024 0.924 0.000 0.016
#> SRR1346794 1 0.6052 0.5273 0.644 0.028 0.184 0.144 0.000
#> SRR1405245 1 0.2864 0.7761 0.852 0.000 0.136 0.012 0.000
#> SRR1409677 4 0.8275 0.7881 0.232 0.152 0.232 0.384 0.000
#> SRR1095549 1 0.5045 0.6575 0.720 0.008 0.164 0.108 0.000
#> SRR1323788 1 0.4704 0.6843 0.748 0.008 0.160 0.084 0.000
#> SRR1314054 2 0.3526 0.6684 0.000 0.832 0.072 0.096 0.000
#> SRR1077944 1 0.2166 0.7950 0.912 0.004 0.072 0.012 0.000
#> SRR1480587 2 0.3452 0.6757 0.008 0.840 0.124 0.024 0.004
#> SRR1311205 1 0.2482 0.7844 0.892 0.000 0.084 0.024 0.000
#> SRR1076369 1 0.6182 0.5467 0.656 0.016 0.100 0.200 0.028
#> SRR1453549 3 0.8126 -0.4467 0.168 0.148 0.396 0.288 0.000
#> SRR1345782 1 0.0880 0.7918 0.968 0.000 0.032 0.000 0.000
#> SRR1447850 2 0.5115 0.5531 0.020 0.696 0.052 0.232 0.000
#> SRR1391553 3 0.5006 0.6005 0.168 0.048 0.740 0.044 0.000
#> SRR1444156 2 0.5122 0.4985 0.000 0.688 0.200 0.112 0.000
#> SRR1471731 3 0.6542 0.3853 0.216 0.028 0.580 0.176 0.000
#> SRR1120987 2 0.7144 0.5031 0.096 0.564 0.180 0.160 0.000
#> SRR1477363 1 0.2536 0.7910 0.900 0.004 0.052 0.044 0.000
#> SRR1391961 5 0.2787 0.8231 0.028 0.000 0.004 0.088 0.880
#> SRR1373879 3 0.4702 0.6027 0.172 0.020 0.752 0.056 0.000
#> SRR1318732 3 0.6267 0.4683 0.240 0.064 0.628 0.064 0.004
#> SRR1091404 1 0.1251 0.7950 0.956 0.000 0.036 0.008 0.000
#> SRR1402109 3 0.4702 0.6027 0.172 0.020 0.752 0.056 0.000
#> SRR1407336 3 0.5925 0.5278 0.188 0.020 0.676 0.100 0.016
#> SRR1097417 3 0.3005 0.5829 0.028 0.048 0.884 0.000 0.040
#> SRR1396227 1 0.4314 0.7169 0.796 0.024 0.060 0.120 0.000
#> SRR1400775 2 0.2529 0.6825 0.004 0.900 0.056 0.040 0.000
#> SRR1392861 4 0.8128 0.9140 0.208 0.164 0.200 0.428 0.000
#> SRR1472929 5 0.1990 0.7912 0.000 0.008 0.068 0.004 0.920
#> SRR1436740 4 0.8128 0.9140 0.208 0.164 0.200 0.428 0.000
#> SRR1477057 2 0.7340 0.0241 0.392 0.400 0.056 0.152 0.000
#> SRR1311980 3 0.5042 0.5973 0.172 0.044 0.736 0.048 0.000
#> SRR1069400 3 0.4499 0.6059 0.108 0.012 0.796 0.068 0.016
#> SRR1351016 1 0.2482 0.7844 0.892 0.000 0.084 0.024 0.000
#> SRR1096291 3 0.8404 0.0321 0.148 0.180 0.436 0.220 0.016
#> SRR1418145 2 0.8214 0.1207 0.208 0.432 0.204 0.152 0.004
#> SRR1488111 2 0.7144 0.5031 0.096 0.564 0.180 0.160 0.000
#> SRR1370495 1 0.6833 0.4026 0.608 0.164 0.076 0.148 0.004
#> SRR1352639 2 0.7790 0.3793 0.212 0.492 0.180 0.112 0.004
#> SRR1348911 3 0.3019 0.6165 0.088 0.048 0.864 0.000 0.000
#> SRR1467386 1 0.3354 0.7668 0.844 0.000 0.068 0.088 0.000
#> SRR1415956 1 0.1579 0.7768 0.944 0.000 0.024 0.032 0.000
#> SRR1500495 1 0.3193 0.7624 0.840 0.000 0.132 0.028 0.000
#> SRR1405099 1 0.1300 0.7803 0.956 0.000 0.016 0.028 0.000
#> SRR1345585 3 0.5433 0.5894 0.120 0.068 0.736 0.072 0.004
#> SRR1093196 3 0.6521 0.3943 0.208 0.028 0.584 0.180 0.000
#> SRR1466006 2 0.7563 0.2731 0.000 0.428 0.252 0.268 0.052
#> SRR1351557 2 0.2654 0.6757 0.000 0.884 0.084 0.032 0.000
#> SRR1382687 1 0.4088 0.7442 0.792 0.004 0.140 0.064 0.000
#> SRR1375549 1 0.4989 0.6705 0.752 0.044 0.064 0.140 0.000
#> SRR1101765 1 0.6211 0.5452 0.652 0.016 0.100 0.204 0.028
#> SRR1334461 5 0.0865 0.8098 0.024 0.000 0.000 0.004 0.972
#> SRR1094073 2 0.2769 0.6737 0.000 0.876 0.092 0.032 0.000
#> SRR1077549 1 0.1661 0.7919 0.940 0.000 0.036 0.024 0.000
#> SRR1440332 1 0.3871 0.7359 0.808 0.004 0.132 0.056 0.000
#> SRR1454177 4 0.8128 0.9140 0.208 0.164 0.200 0.428 0.000
#> SRR1082447 1 0.1893 0.7981 0.928 0.000 0.048 0.024 0.000
#> SRR1420043 3 0.8126 -0.4467 0.168 0.148 0.396 0.288 0.000
#> SRR1432500 1 0.3073 0.7708 0.872 0.008 0.068 0.052 0.000
#> SRR1378045 3 0.5911 -0.1244 0.000 0.408 0.488 0.104 0.000
#> SRR1334200 5 0.7062 0.7511 0.004 0.140 0.044 0.296 0.516
#> SRR1069539 3 0.8426 0.0377 0.148 0.184 0.432 0.220 0.016
#> SRR1343031 3 0.5064 0.5843 0.148 0.012 0.748 0.076 0.016
#> SRR1319690 1 0.3689 0.7655 0.820 0.004 0.128 0.048 0.000
#> SRR1310604 2 0.2946 0.6844 0.004 0.876 0.088 0.028 0.004
#> SRR1327747 1 0.6101 0.4715 0.624 0.020 0.208 0.148 0.000
#> SRR1072456 2 0.2767 0.6821 0.004 0.884 0.088 0.020 0.004
#> SRR1367896 3 0.3005 0.5829 0.028 0.048 0.884 0.000 0.040
#> SRR1480107 1 0.0880 0.7918 0.968 0.000 0.032 0.000 0.000
#> SRR1377756 1 0.2940 0.7880 0.876 0.004 0.072 0.048 0.000
#> SRR1435272 4 0.8189 0.8271 0.276 0.156 0.176 0.392 0.000
#> SRR1089230 4 0.8073 0.8972 0.228 0.148 0.192 0.432 0.000
#> SRR1389522 3 0.2959 0.6151 0.072 0.024 0.884 0.004 0.016
#> SRR1080600 2 0.7563 0.2731 0.000 0.428 0.252 0.268 0.052
#> SRR1086935 4 0.7902 0.8288 0.152 0.176 0.204 0.468 0.000
#> SRR1344060 5 0.6607 0.7708 0.004 0.136 0.028 0.260 0.572
#> SRR1467922 2 0.5122 0.4985 0.000 0.688 0.200 0.112 0.000
#> SRR1090984 3 0.6425 0.3102 0.356 0.036 0.524 0.084 0.000
#> SRR1456991 1 0.0880 0.7918 0.968 0.000 0.032 0.000 0.000
#> SRR1085039 1 0.1741 0.7962 0.936 0.000 0.040 0.024 0.000
#> SRR1069303 1 0.1907 0.7867 0.928 0.000 0.028 0.044 0.000
#> SRR1091500 2 0.4078 0.6009 0.020 0.792 0.028 0.160 0.000
#> SRR1075198 2 0.7269 0.4909 0.092 0.556 0.208 0.140 0.004
#> SRR1086915 1 0.8006 -0.6589 0.380 0.144 0.140 0.336 0.000
#> SRR1499503 2 0.2767 0.6821 0.004 0.884 0.088 0.020 0.004
#> SRR1094312 2 0.2529 0.6825 0.004 0.900 0.056 0.040 0.000
#> SRR1352437 1 0.1907 0.7867 0.928 0.000 0.028 0.044 0.000
#> SRR1436323 3 0.6521 0.3341 0.240 0.020 0.564 0.176 0.000
#> SRR1073507 1 0.1661 0.7919 0.940 0.000 0.036 0.024 0.000
#> SRR1401972 1 0.1907 0.7867 0.928 0.000 0.028 0.044 0.000
#> SRR1415510 2 0.3280 0.6750 0.004 0.848 0.120 0.024 0.004
#> SRR1327279 1 0.2193 0.7883 0.912 0.000 0.060 0.028 0.000
#> SRR1086983 1 0.7129 -0.1091 0.528 0.104 0.092 0.276 0.000
#> SRR1105174 1 0.1399 0.7815 0.952 0.000 0.020 0.028 0.000
#> SRR1468893 1 0.1915 0.7962 0.928 0.000 0.032 0.040 0.000
#> SRR1362555 2 0.7269 0.4909 0.092 0.556 0.208 0.140 0.004
#> SRR1074526 5 0.6165 0.8017 0.036 0.028 0.020 0.360 0.556
#> SRR1326225 2 0.2052 0.6865 0.004 0.912 0.080 0.004 0.000
#> SRR1401933 1 0.4406 0.7159 0.788 0.024 0.060 0.128 0.000
#> SRR1324062 1 0.4514 0.7016 0.756 0.016 0.184 0.044 0.000
#> SRR1102296 1 0.5180 0.4796 0.628 0.032 0.324 0.016 0.000
#> SRR1085087 1 0.2251 0.7945 0.916 0.008 0.052 0.024 0.000
#> SRR1079046 1 0.5808 0.5441 0.692 0.084 0.032 0.180 0.012
#> SRR1328339 3 0.6017 0.4717 0.264 0.060 0.624 0.052 0.000
#> SRR1079782 2 0.7269 0.4909 0.092 0.556 0.208 0.140 0.004
#> SRR1092257 2 0.7144 0.5031 0.096 0.564 0.180 0.160 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.167 0.6538 0.000 0.928 0.060 0.004 0.000 0.008
#> SRR1429287 2 0.697 0.4735 0.004 0.484 0.108 0.248 0.000 0.156
#> SRR1359238 1 0.591 0.3951 0.548 0.000 0.204 0.232 0.000 0.016
#> SRR1309597 3 0.115 0.6736 0.028 0.004 0.960 0.004 0.000 0.004
#> SRR1441398 1 0.345 0.6981 0.792 0.000 0.164 0.044 0.000 0.000
#> SRR1084055 2 0.186 0.6587 0.000 0.920 0.060 0.016 0.000 0.004
#> SRR1417566 3 0.583 0.4103 0.304 0.020 0.560 0.108 0.000 0.008
#> SRR1351857 4 0.525 0.2916 0.440 0.032 0.036 0.492 0.000 0.000
#> SRR1487485 3 0.296 0.6718 0.028 0.036 0.872 0.060 0.000 0.004
#> SRR1335875 3 0.393 0.6651 0.108 0.036 0.808 0.036 0.000 0.012
#> SRR1073947 1 0.256 0.7467 0.868 0.000 0.028 0.104 0.000 0.000
#> SRR1443483 3 0.176 0.6596 0.016 0.008 0.936 0.008 0.000 0.032
#> SRR1346794 1 0.594 0.4968 0.616 0.012 0.180 0.160 0.000 0.032
#> SRR1405245 1 0.325 0.7250 0.808 0.000 0.156 0.036 0.000 0.000
#> SRR1409677 4 0.587 0.6681 0.176 0.052 0.120 0.640 0.000 0.012
#> SRR1095549 1 0.541 0.5913 0.632 0.000 0.140 0.208 0.000 0.020
#> SRR1323788 1 0.467 0.6555 0.724 0.000 0.144 0.112 0.000 0.020
#> SRR1314054 2 0.380 0.6343 0.000 0.808 0.052 0.104 0.000 0.036
#> SRR1077944 1 0.229 0.7634 0.892 0.000 0.072 0.036 0.000 0.000
#> SRR1480587 2 0.254 0.6463 0.000 0.880 0.088 0.008 0.000 0.024
#> SRR1311205 1 0.321 0.7368 0.828 0.000 0.072 0.100 0.000 0.000
#> SRR1076369 1 0.595 0.5185 0.640 0.008 0.068 0.096 0.004 0.184
#> SRR1453549 4 0.661 0.3264 0.128 0.048 0.344 0.468 0.000 0.012
#> SRR1345782 1 0.126 0.7612 0.952 0.000 0.028 0.020 0.000 0.000
#> SRR1447850 2 0.557 0.5328 0.004 0.624 0.048 0.252 0.000 0.072
#> SRR1391553 3 0.429 0.6620 0.104 0.020 0.780 0.084 0.000 0.012
#> SRR1444156 2 0.504 0.4382 0.000 0.716 0.116 0.096 0.000 0.072
#> SRR1471731 3 0.551 0.4068 0.112 0.008 0.532 0.348 0.000 0.000
#> SRR1120987 2 0.764 0.5185 0.080 0.504 0.132 0.140 0.000 0.144
#> SRR1477363 1 0.285 0.7560 0.868 0.000 0.048 0.072 0.000 0.012
#> SRR1391961 5 0.228 0.7494 0.000 0.000 0.004 0.000 0.868 0.128
#> SRR1373879 3 0.433 0.6596 0.108 0.008 0.760 0.116 0.000 0.008
#> SRR1318732 3 0.604 0.5455 0.216 0.048 0.620 0.092 0.000 0.024
#> SRR1091404 1 0.142 0.7641 0.944 0.000 0.032 0.024 0.000 0.000
#> SRR1402109 3 0.433 0.6596 0.108 0.008 0.760 0.116 0.000 0.008
#> SRR1407336 3 0.570 0.5529 0.100 0.008 0.632 0.220 0.000 0.040
#> SRR1097417 3 0.277 0.6435 0.008 0.032 0.892 0.008 0.020 0.040
#> SRR1396227 1 0.475 0.6605 0.708 0.016 0.036 0.216 0.000 0.024
#> SRR1400775 2 0.283 0.6550 0.000 0.876 0.048 0.048 0.000 0.028
#> SRR1392861 4 0.438 0.7246 0.112 0.052 0.068 0.768 0.000 0.000
#> SRR1472929 5 0.233 0.7529 0.000 0.008 0.048 0.004 0.904 0.036
#> SRR1436740 4 0.438 0.7246 0.112 0.052 0.068 0.768 0.000 0.000
#> SRR1477057 1 0.752 -0.0315 0.384 0.356 0.048 0.136 0.000 0.076
#> SRR1311980 3 0.435 0.6577 0.104 0.016 0.772 0.096 0.000 0.012
#> SRR1069400 3 0.448 0.6544 0.068 0.008 0.768 0.116 0.000 0.040
#> SRR1351016 1 0.321 0.7368 0.828 0.000 0.072 0.100 0.000 0.000
#> SRR1096291 3 0.788 0.0606 0.096 0.140 0.352 0.348 0.000 0.064
#> SRR1418145 2 0.844 0.3203 0.144 0.388 0.140 0.180 0.000 0.148
#> SRR1488111 2 0.764 0.5185 0.080 0.504 0.132 0.140 0.000 0.144
#> SRR1370495 1 0.709 0.4021 0.548 0.124 0.052 0.188 0.000 0.088
#> SRR1352639 2 0.806 0.4124 0.204 0.440 0.124 0.112 0.000 0.120
#> SRR1348911 3 0.234 0.6730 0.056 0.036 0.900 0.004 0.000 0.004
#> SRR1467386 1 0.382 0.6975 0.760 0.000 0.044 0.192 0.000 0.004
#> SRR1415956 1 0.168 0.7350 0.928 0.000 0.020 0.052 0.000 0.000
#> SRR1500495 1 0.345 0.6981 0.792 0.000 0.164 0.044 0.000 0.000
#> SRR1405099 1 0.143 0.7389 0.940 0.000 0.012 0.048 0.000 0.000
#> SRR1345585 3 0.529 0.6569 0.088 0.048 0.716 0.124 0.000 0.024
#> SRR1093196 3 0.551 0.4061 0.112 0.008 0.532 0.348 0.000 0.000
#> SRR1466006 2 0.660 0.1280 0.000 0.416 0.132 0.068 0.000 0.384
#> SRR1351557 2 0.208 0.6472 0.000 0.912 0.060 0.016 0.000 0.012
#> SRR1382687 1 0.421 0.7095 0.764 0.000 0.128 0.092 0.000 0.016
#> SRR1375549 1 0.504 0.6391 0.732 0.024 0.048 0.136 0.000 0.060
#> SRR1101765 1 0.603 0.5146 0.632 0.008 0.068 0.104 0.004 0.184
#> SRR1334461 5 0.026 0.8133 0.000 0.000 0.000 0.000 0.992 0.008
#> SRR1094073 2 0.219 0.6448 0.000 0.904 0.068 0.016 0.000 0.012
#> SRR1077549 1 0.253 0.7449 0.868 0.000 0.024 0.108 0.000 0.000
#> SRR1440332 1 0.415 0.6738 0.744 0.000 0.112 0.144 0.000 0.000
#> SRR1454177 4 0.438 0.7246 0.112 0.052 0.068 0.768 0.000 0.000
#> SRR1082447 1 0.163 0.7659 0.932 0.000 0.044 0.024 0.000 0.000
#> SRR1420043 4 0.661 0.3264 0.128 0.048 0.344 0.468 0.000 0.012
#> SRR1432500 1 0.357 0.7081 0.796 0.004 0.052 0.148 0.000 0.000
#> SRR1378045 2 0.636 0.0929 0.000 0.424 0.412 0.088 0.000 0.076
#> SRR1334200 6 0.503 0.7679 0.000 0.116 0.012 0.004 0.188 0.680
#> SRR1069539 3 0.789 0.0660 0.096 0.144 0.352 0.344 0.000 0.064
#> SRR1343031 3 0.506 0.6264 0.096 0.008 0.716 0.140 0.000 0.040
#> SRR1319690 1 0.376 0.7182 0.792 0.000 0.140 0.056 0.000 0.012
#> SRR1310604 2 0.191 0.6547 0.000 0.920 0.060 0.004 0.004 0.012
#> SRR1327747 1 0.578 0.4382 0.596 0.000 0.208 0.168 0.000 0.028
#> SRR1072456 2 0.170 0.6528 0.000 0.928 0.060 0.004 0.004 0.004
#> SRR1367896 3 0.277 0.6435 0.008 0.032 0.892 0.008 0.020 0.040
#> SRR1480107 1 0.126 0.7612 0.952 0.000 0.028 0.020 0.000 0.000
#> SRR1377756 1 0.313 0.7554 0.852 0.000 0.060 0.072 0.000 0.016
#> SRR1435272 4 0.486 0.7190 0.184 0.052 0.056 0.708 0.000 0.000
#> SRR1089230 4 0.435 0.7287 0.132 0.048 0.056 0.764 0.000 0.000
#> SRR1389522 3 0.250 0.6728 0.044 0.012 0.900 0.012 0.000 0.032
#> SRR1080600 2 0.660 0.1280 0.000 0.416 0.132 0.068 0.000 0.384
#> SRR1086935 4 0.382 0.6576 0.056 0.056 0.064 0.820 0.000 0.004
#> SRR1344060 6 0.527 0.7587 0.000 0.112 0.012 0.000 0.256 0.620
#> SRR1467922 2 0.504 0.4382 0.000 0.716 0.116 0.096 0.000 0.072
#> SRR1090984 3 0.600 0.3764 0.316 0.016 0.540 0.112 0.000 0.016
#> SRR1456991 1 0.126 0.7612 0.952 0.000 0.028 0.020 0.000 0.000
#> SRR1085039 1 0.149 0.7640 0.940 0.000 0.036 0.024 0.000 0.000
#> SRR1069303 1 0.258 0.7314 0.848 0.000 0.004 0.144 0.000 0.004
#> SRR1091500 2 0.476 0.5833 0.004 0.732 0.032 0.144 0.000 0.088
#> SRR1075198 2 0.761 0.5101 0.072 0.504 0.152 0.124 0.000 0.148
#> SRR1086915 4 0.520 0.6419 0.296 0.052 0.036 0.616 0.000 0.000
#> SRR1499503 2 0.170 0.6528 0.000 0.928 0.060 0.004 0.004 0.004
#> SRR1094312 2 0.283 0.6550 0.000 0.876 0.048 0.048 0.000 0.028
#> SRR1352437 1 0.258 0.7314 0.848 0.000 0.004 0.144 0.000 0.004
#> SRR1436323 3 0.561 0.3614 0.124 0.008 0.520 0.348 0.000 0.000
#> SRR1073507 1 0.253 0.7449 0.868 0.000 0.024 0.108 0.000 0.000
#> SRR1401972 1 0.258 0.7314 0.848 0.000 0.004 0.144 0.000 0.004
#> SRR1415510 2 0.226 0.6451 0.000 0.892 0.092 0.004 0.004 0.008
#> SRR1327279 1 0.293 0.7378 0.844 0.000 0.044 0.112 0.000 0.000
#> SRR1086983 4 0.525 0.2916 0.440 0.032 0.036 0.492 0.000 0.000
#> SRR1105174 1 0.146 0.7401 0.940 0.000 0.016 0.044 0.000 0.000
#> SRR1468893 1 0.245 0.7666 0.888 0.000 0.020 0.080 0.000 0.012
#> SRR1362555 2 0.761 0.5101 0.072 0.504 0.152 0.124 0.000 0.148
#> SRR1074526 6 0.334 0.5749 0.000 0.000 0.004 0.000 0.260 0.736
#> SRR1326225 2 0.186 0.6587 0.000 0.920 0.060 0.016 0.000 0.004
#> SRR1401933 1 0.466 0.6821 0.732 0.016 0.040 0.184 0.000 0.028
#> SRR1324062 1 0.499 0.6351 0.676 0.000 0.180 0.132 0.000 0.012
#> SRR1102296 1 0.509 0.4942 0.604 0.028 0.328 0.036 0.000 0.004
#> SRR1085087 1 0.298 0.7458 0.844 0.004 0.036 0.116 0.000 0.000
#> SRR1079046 1 0.589 0.5232 0.660 0.052 0.028 0.152 0.000 0.108
#> SRR1328339 3 0.539 0.5259 0.224 0.036 0.656 0.076 0.000 0.008
#> SRR1079782 2 0.761 0.5101 0.072 0.504 0.152 0.124 0.000 0.148
#> SRR1092257 2 0.764 0.5185 0.080 0.504 0.132 0.140 0.000 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["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 17611 rows and 118 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 0.740 0.845 0.940 0.4372 0.566 0.566
#> 3 3 0.448 0.644 0.789 0.4338 0.702 0.511
#> 4 4 0.511 0.484 0.678 0.1472 0.748 0.416
#> 5 5 0.645 0.648 0.801 0.0774 0.845 0.526
#> 6 6 0.658 0.553 0.694 0.0481 0.888 0.595
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
#> SRR1396765 2 0.0000 0.9067 0.000 1.000
#> SRR1429287 2 0.0000 0.9067 0.000 1.000
#> SRR1359238 1 0.0000 0.9419 1.000 0.000
#> SRR1309597 1 0.8861 0.5735 0.696 0.304
#> SRR1441398 1 0.0000 0.9419 1.000 0.000
#> SRR1084055 2 0.0000 0.9067 0.000 1.000
#> SRR1417566 1 0.6531 0.7824 0.832 0.168
#> SRR1351857 1 0.0000 0.9419 1.000 0.000
#> SRR1487485 2 0.9998 -0.0164 0.492 0.508
#> SRR1335875 1 0.6343 0.7916 0.840 0.160
#> SRR1073947 1 0.0000 0.9419 1.000 0.000
#> SRR1443483 1 0.8861 0.5735 0.696 0.304
#> SRR1346794 1 0.0000 0.9419 1.000 0.000
#> SRR1405245 1 0.0000 0.9419 1.000 0.000
#> SRR1409677 1 0.0000 0.9419 1.000 0.000
#> SRR1095549 1 0.0000 0.9419 1.000 0.000
#> SRR1323788 1 0.0000 0.9419 1.000 0.000
#> SRR1314054 2 0.0000 0.9067 0.000 1.000
#> SRR1077944 1 0.0000 0.9419 1.000 0.000
#> SRR1480587 2 0.0000 0.9067 0.000 1.000
#> SRR1311205 1 0.0000 0.9419 1.000 0.000
#> SRR1076369 1 0.0000 0.9419 1.000 0.000
#> SRR1453549 1 0.0000 0.9419 1.000 0.000
#> SRR1345782 1 0.0000 0.9419 1.000 0.000
#> SRR1447850 2 0.0000 0.9067 0.000 1.000
#> SRR1391553 2 0.9977 0.0601 0.472 0.528
#> SRR1444156 2 0.0000 0.9067 0.000 1.000
#> SRR1471731 1 0.6531 0.7824 0.832 0.168
#> SRR1120987 1 0.0000 0.9419 1.000 0.000
#> SRR1477363 1 0.0000 0.9419 1.000 0.000
#> SRR1391961 2 0.9963 0.2047 0.464 0.536
#> SRR1373879 1 0.0000 0.9419 1.000 0.000
#> SRR1318732 1 0.7883 0.6903 0.764 0.236
#> SRR1091404 1 0.0000 0.9419 1.000 0.000
#> SRR1402109 1 0.0000 0.9419 1.000 0.000
#> SRR1407336 1 0.1414 0.9265 0.980 0.020
#> SRR1097417 2 0.2236 0.8821 0.036 0.964
#> SRR1396227 1 0.0000 0.9419 1.000 0.000
#> SRR1400775 2 0.0000 0.9067 0.000 1.000
#> SRR1392861 1 0.0000 0.9419 1.000 0.000
#> SRR1472929 2 0.1414 0.8943 0.020 0.980
#> SRR1436740 1 0.0000 0.9419 1.000 0.000
#> SRR1477057 2 0.0000 0.9067 0.000 1.000
#> SRR1311980 1 0.6343 0.7916 0.840 0.160
#> SRR1069400 1 0.8813 0.5812 0.700 0.300
#> SRR1351016 1 0.0000 0.9419 1.000 0.000
#> SRR1096291 1 0.0000 0.9419 1.000 0.000
#> SRR1418145 1 0.0000 0.9419 1.000 0.000
#> SRR1488111 2 0.8861 0.5334 0.304 0.696
#> SRR1370495 1 0.0000 0.9419 1.000 0.000
#> SRR1352639 1 0.0000 0.9419 1.000 0.000
#> SRR1348911 1 0.9000 0.5497 0.684 0.316
#> SRR1467386 1 0.0000 0.9419 1.000 0.000
#> SRR1415956 1 0.0000 0.9419 1.000 0.000
#> SRR1500495 1 0.0000 0.9419 1.000 0.000
#> SRR1405099 1 0.0000 0.9419 1.000 0.000
#> SRR1345585 2 0.9998 -0.0164 0.492 0.508
#> SRR1093196 1 0.6531 0.7824 0.832 0.168
#> SRR1466006 2 0.0000 0.9067 0.000 1.000
#> SRR1351557 2 0.0000 0.9067 0.000 1.000
#> SRR1382687 1 0.0000 0.9419 1.000 0.000
#> SRR1375549 1 0.0000 0.9419 1.000 0.000
#> SRR1101765 1 0.0000 0.9419 1.000 0.000
#> SRR1334461 2 0.9996 0.1322 0.488 0.512
#> SRR1094073 2 0.0000 0.9067 0.000 1.000
#> SRR1077549 1 0.0000 0.9419 1.000 0.000
#> SRR1440332 1 0.0000 0.9419 1.000 0.000
#> SRR1454177 1 0.0000 0.9419 1.000 0.000
#> SRR1082447 1 0.0000 0.9419 1.000 0.000
#> SRR1420043 1 0.0000 0.9419 1.000 0.000
#> SRR1432500 1 0.0000 0.9419 1.000 0.000
#> SRR1378045 2 0.0000 0.9067 0.000 1.000
#> SRR1334200 2 0.1184 0.8971 0.016 0.984
#> SRR1069539 1 0.9922 0.1947 0.552 0.448
#> SRR1343031 1 0.0000 0.9419 1.000 0.000
#> SRR1319690 1 0.0000 0.9419 1.000 0.000
#> SRR1310604 2 0.0000 0.9067 0.000 1.000
#> SRR1327747 1 0.0000 0.9419 1.000 0.000
#> SRR1072456 2 0.0000 0.9067 0.000 1.000
#> SRR1367896 1 0.9000 0.5497 0.684 0.316
#> SRR1480107 1 0.0000 0.9419 1.000 0.000
#> SRR1377756 1 0.0000 0.9419 1.000 0.000
#> SRR1435272 1 0.0000 0.9419 1.000 0.000
#> SRR1089230 1 0.0000 0.9419 1.000 0.000
#> SRR1389522 1 0.2603 0.9064 0.956 0.044
#> SRR1080600 2 0.0000 0.9067 0.000 1.000
#> SRR1086935 1 0.8555 0.6159 0.720 0.280
#> SRR1344060 2 0.2948 0.8710 0.052 0.948
#> SRR1467922 2 0.0000 0.9067 0.000 1.000
#> SRR1090984 1 0.0000 0.9419 1.000 0.000
#> SRR1456991 1 0.0000 0.9419 1.000 0.000
#> SRR1085039 1 0.0000 0.9419 1.000 0.000
#> SRR1069303 1 0.0000 0.9419 1.000 0.000
#> SRR1091500 2 0.0000 0.9067 0.000 1.000
#> SRR1075198 2 0.0000 0.9067 0.000 1.000
#> SRR1086915 1 0.0000 0.9419 1.000 0.000
#> SRR1499503 2 0.0000 0.9067 0.000 1.000
#> SRR1094312 2 0.0000 0.9067 0.000 1.000
#> SRR1352437 1 0.0000 0.9419 1.000 0.000
#> SRR1436323 1 0.0000 0.9419 1.000 0.000
#> SRR1073507 1 0.0000 0.9419 1.000 0.000
#> SRR1401972 1 0.0000 0.9419 1.000 0.000
#> SRR1415510 2 0.0000 0.9067 0.000 1.000
#> SRR1327279 1 0.0000 0.9419 1.000 0.000
#> SRR1086983 1 0.0000 0.9419 1.000 0.000
#> SRR1105174 1 0.0000 0.9419 1.000 0.000
#> SRR1468893 1 0.0000 0.9419 1.000 0.000
#> SRR1362555 2 0.0376 0.9045 0.004 0.996
#> SRR1074526 2 0.7299 0.7090 0.204 0.796
#> SRR1326225 2 0.0000 0.9067 0.000 1.000
#> SRR1401933 1 0.0000 0.9419 1.000 0.000
#> SRR1324062 1 0.0000 0.9419 1.000 0.000
#> SRR1102296 1 0.0000 0.9419 1.000 0.000
#> SRR1085087 1 0.0000 0.9419 1.000 0.000
#> SRR1079046 1 0.9996 -0.0859 0.512 0.488
#> SRR1328339 1 0.7139 0.7479 0.804 0.196
#> SRR1079782 2 0.0000 0.9067 0.000 1.000
#> SRR1092257 2 0.0000 0.9067 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.0424 0.9368 0.000 0.992 0.008
#> SRR1429287 2 0.2165 0.9217 0.000 0.936 0.064
#> SRR1359238 1 0.6307 -0.2875 0.512 0.000 0.488
#> SRR1309597 3 0.7536 0.6221 0.304 0.064 0.632
#> SRR1441398 1 0.3941 0.6262 0.844 0.000 0.156
#> SRR1084055 2 0.1411 0.9289 0.000 0.964 0.036
#> SRR1417566 1 0.7389 -0.2084 0.504 0.032 0.464
#> SRR1351857 1 0.5291 0.4986 0.732 0.000 0.268
#> SRR1487485 3 0.6981 0.6173 0.068 0.228 0.704
#> SRR1335875 3 0.6772 0.6744 0.304 0.032 0.664
#> SRR1073947 1 0.3412 0.6772 0.876 0.000 0.124
#> SRR1443483 3 0.7015 0.6941 0.240 0.064 0.696
#> SRR1346794 1 0.4605 0.5922 0.796 0.000 0.204
#> SRR1405245 1 0.4121 0.6126 0.832 0.000 0.168
#> SRR1409677 3 0.6215 0.4761 0.428 0.000 0.572
#> SRR1095549 1 0.4887 0.5440 0.772 0.000 0.228
#> SRR1323788 1 0.3686 0.6406 0.860 0.000 0.140
#> SRR1314054 2 0.0237 0.9373 0.000 0.996 0.004
#> SRR1077944 1 0.0892 0.7137 0.980 0.000 0.020
#> SRR1480587 2 0.0424 0.9368 0.000 0.992 0.008
#> SRR1311205 1 0.2537 0.6916 0.920 0.000 0.080
#> SRR1076369 1 0.6081 0.4329 0.652 0.004 0.344
#> SRR1453549 3 0.4796 0.7370 0.220 0.000 0.780
#> SRR1345782 1 0.2165 0.7042 0.936 0.000 0.064
#> SRR1447850 2 0.1860 0.9161 0.000 0.948 0.052
#> SRR1391553 3 0.6096 0.5405 0.016 0.280 0.704
#> SRR1444156 2 0.0592 0.9370 0.000 0.988 0.012
#> SRR1471731 3 0.5503 0.7400 0.208 0.020 0.772
#> SRR1120987 1 0.6664 -0.0254 0.528 0.008 0.464
#> SRR1477363 1 0.1031 0.7147 0.976 0.000 0.024
#> SRR1391961 1 0.8608 0.3955 0.604 0.192 0.204
#> SRR1373879 3 0.4887 0.7358 0.228 0.000 0.772
#> SRR1318732 1 0.8070 -0.3021 0.468 0.064 0.468
#> SRR1091404 1 0.0892 0.7148 0.980 0.000 0.020
#> SRR1402109 3 0.5016 0.7347 0.240 0.000 0.760
#> SRR1407336 3 0.5202 0.7401 0.220 0.008 0.772
#> SRR1097417 3 0.6254 0.4703 0.056 0.188 0.756
#> SRR1396227 1 0.2356 0.7058 0.928 0.000 0.072
#> SRR1400775 2 0.0237 0.9373 0.000 0.996 0.004
#> SRR1392861 3 0.5058 0.7287 0.244 0.000 0.756
#> SRR1472929 2 0.7801 0.6719 0.088 0.636 0.276
#> SRR1436740 3 0.6192 0.4877 0.420 0.000 0.580
#> SRR1477057 2 0.2165 0.9222 0.000 0.936 0.064
#> SRR1311980 3 0.5643 0.7363 0.220 0.020 0.760
#> SRR1069400 3 0.6764 0.7079 0.224 0.060 0.716
#> SRR1351016 1 0.3116 0.6934 0.892 0.000 0.108
#> SRR1096291 3 0.6033 0.6386 0.336 0.004 0.660
#> SRR1418145 1 0.6483 0.0215 0.544 0.004 0.452
#> SRR1488111 3 0.6952 0.3345 0.024 0.376 0.600
#> SRR1370495 1 0.2774 0.6880 0.920 0.008 0.072
#> SRR1352639 1 0.2301 0.7156 0.936 0.004 0.060
#> SRR1348911 3 0.7053 0.6844 0.244 0.064 0.692
#> SRR1467386 1 0.4062 0.6454 0.836 0.000 0.164
#> SRR1415956 1 0.2165 0.6996 0.936 0.000 0.064
#> SRR1500495 1 0.3879 0.6307 0.848 0.000 0.152
#> SRR1405099 1 0.0747 0.7142 0.984 0.000 0.016
#> SRR1345585 3 0.7263 0.6183 0.084 0.224 0.692
#> SRR1093196 3 0.5597 0.7411 0.216 0.020 0.764
#> SRR1466006 2 0.0747 0.9372 0.000 0.984 0.016
#> SRR1351557 2 0.0424 0.9373 0.000 0.992 0.008
#> SRR1382687 1 0.4346 0.6341 0.816 0.000 0.184
#> SRR1375549 1 0.2066 0.7004 0.940 0.000 0.060
#> SRR1101765 1 0.3038 0.6738 0.896 0.000 0.104
#> SRR1334461 1 0.8608 0.3955 0.604 0.192 0.204
#> SRR1094073 2 0.0592 0.9370 0.000 0.988 0.012
#> SRR1077549 3 0.6307 0.3149 0.488 0.000 0.512
#> SRR1440332 3 0.6126 0.5271 0.400 0.000 0.600
#> SRR1454177 3 0.5926 0.6058 0.356 0.000 0.644
#> SRR1082447 1 0.0424 0.7152 0.992 0.000 0.008
#> SRR1420043 3 0.5016 0.7332 0.240 0.000 0.760
#> SRR1432500 1 0.5363 0.4735 0.724 0.000 0.276
#> SRR1378045 2 0.4887 0.6935 0.000 0.772 0.228
#> SRR1334200 2 0.7613 0.7155 0.116 0.680 0.204
#> SRR1069539 3 0.6910 0.6844 0.144 0.120 0.736
#> SRR1343031 3 0.4974 0.7329 0.236 0.000 0.764
#> SRR1319690 1 0.6140 0.0242 0.596 0.000 0.404
#> SRR1310604 2 0.1411 0.9327 0.000 0.964 0.036
#> SRR1327747 3 0.6359 0.5235 0.404 0.004 0.592
#> SRR1072456 2 0.0592 0.9372 0.000 0.988 0.012
#> SRR1367896 3 0.6542 0.7006 0.204 0.060 0.736
#> SRR1480107 1 0.0424 0.7153 0.992 0.000 0.008
#> SRR1377756 1 0.2537 0.6973 0.920 0.000 0.080
#> SRR1435272 3 0.6008 0.5897 0.372 0.000 0.628
#> SRR1089230 3 0.6307 0.2988 0.488 0.000 0.512
#> SRR1389522 3 0.6818 0.6030 0.348 0.024 0.628
#> SRR1080600 2 0.1643 0.9322 0.000 0.956 0.044
#> SRR1086935 3 0.7878 0.6457 0.160 0.172 0.668
#> SRR1344060 2 0.8981 0.5476 0.228 0.564 0.208
#> SRR1467922 2 0.0592 0.9370 0.000 0.988 0.012
#> SRR1090984 1 0.4605 0.5770 0.796 0.000 0.204
#> SRR1456991 1 0.0892 0.7148 0.980 0.000 0.020
#> SRR1085039 1 0.0592 0.7156 0.988 0.000 0.012
#> SRR1069303 1 0.3941 0.6583 0.844 0.000 0.156
#> SRR1091500 2 0.1163 0.9323 0.000 0.972 0.028
#> SRR1075198 2 0.1529 0.9317 0.000 0.960 0.040
#> SRR1086915 1 0.5905 0.3108 0.648 0.000 0.352
#> SRR1499503 2 0.0424 0.9368 0.000 0.992 0.008
#> SRR1094312 2 0.0237 0.9373 0.000 0.996 0.004
#> SRR1352437 1 0.4796 0.5930 0.780 0.000 0.220
#> SRR1436323 3 0.5016 0.7336 0.240 0.000 0.760
#> SRR1073507 1 0.4062 0.6454 0.836 0.000 0.164
#> SRR1401972 1 0.3941 0.6583 0.844 0.000 0.156
#> SRR1415510 2 0.0424 0.9368 0.000 0.992 0.008
#> SRR1327279 1 0.5835 0.3346 0.660 0.000 0.340
#> SRR1086983 1 0.5706 0.3876 0.680 0.000 0.320
#> SRR1105174 1 0.0892 0.7148 0.980 0.000 0.020
#> SRR1468893 1 0.0747 0.7147 0.984 0.000 0.016
#> SRR1362555 2 0.2982 0.9100 0.024 0.920 0.056
#> SRR1074526 1 0.9442 0.1918 0.496 0.288 0.216
#> SRR1326225 2 0.0592 0.9370 0.000 0.988 0.012
#> SRR1401933 1 0.3619 0.6747 0.864 0.000 0.136
#> SRR1324062 1 0.5733 0.3803 0.676 0.000 0.324
#> SRR1102296 1 0.1411 0.7157 0.964 0.000 0.036
#> SRR1085087 1 0.4346 0.6302 0.816 0.000 0.184
#> SRR1079046 1 0.5348 0.5705 0.796 0.028 0.176
#> SRR1328339 1 0.5899 0.4934 0.736 0.020 0.244
#> SRR1079782 2 0.1964 0.9239 0.000 0.944 0.056
#> SRR1092257 2 0.2261 0.9179 0.000 0.932 0.068
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.0188 0.9664 0.004 0.996 0.000 0.000
#> SRR1429287 2 0.3272 0.9240 0.080 0.884 0.024 0.012
#> SRR1359238 4 0.3547 0.5463 0.016 0.000 0.144 0.840
#> SRR1309597 3 0.2716 0.7280 0.052 0.008 0.912 0.028
#> SRR1441398 1 0.7812 0.4097 0.396 0.000 0.256 0.348
#> SRR1084055 2 0.0672 0.9644 0.008 0.984 0.008 0.000
#> SRR1417566 3 0.5574 0.5134 0.124 0.000 0.728 0.148
#> SRR1351857 4 0.3037 0.5471 0.020 0.000 0.100 0.880
#> SRR1487485 3 0.4068 0.7144 0.004 0.092 0.840 0.064
#> SRR1335875 3 0.3117 0.7049 0.028 0.000 0.880 0.092
#> SRR1073947 4 0.5496 0.1976 0.232 0.000 0.064 0.704
#> SRR1443483 3 0.2433 0.7363 0.012 0.008 0.920 0.060
#> SRR1346794 1 0.7795 0.4058 0.420 0.000 0.268 0.312
#> SRR1405245 1 0.7916 0.3721 0.352 0.000 0.336 0.312
#> SRR1409677 4 0.5136 0.4142 0.048 0.000 0.224 0.728
#> SRR1095549 1 0.7919 0.3115 0.352 0.000 0.324 0.324
#> SRR1323788 1 0.7782 0.4006 0.396 0.000 0.244 0.360
#> SRR1314054 2 0.0524 0.9640 0.004 0.988 0.008 0.000
#> SRR1077944 4 0.6452 -0.3845 0.464 0.000 0.068 0.468
#> SRR1480587 2 0.0188 0.9664 0.004 0.996 0.000 0.000
#> SRR1311205 4 0.7314 -0.4077 0.424 0.000 0.152 0.424
#> SRR1076369 1 0.6973 0.3406 0.556 0.000 0.300 0.144
#> SRR1453549 3 0.3528 0.6942 0.000 0.000 0.808 0.192
#> SRR1345782 4 0.7154 -0.4019 0.432 0.000 0.132 0.436
#> SRR1447850 2 0.2700 0.9266 0.044 0.916 0.020 0.020
#> SRR1391553 3 0.4165 0.6762 0.012 0.140 0.824 0.024
#> SRR1444156 2 0.0188 0.9657 0.004 0.996 0.000 0.000
#> SRR1471731 3 0.4677 0.5902 0.004 0.000 0.680 0.316
#> SRR1120987 4 0.4872 0.4976 0.076 0.000 0.148 0.776
#> SRR1477363 4 0.6332 -0.3520 0.452 0.000 0.060 0.488
#> SRR1391961 1 0.5270 0.3346 0.788 0.108 0.036 0.068
#> SRR1373879 3 0.2408 0.7281 0.000 0.000 0.896 0.104
#> SRR1318732 3 0.5953 0.5447 0.172 0.012 0.716 0.100
#> SRR1091404 1 0.6268 0.3887 0.496 0.000 0.056 0.448
#> SRR1402109 3 0.3688 0.6825 0.000 0.000 0.792 0.208
#> SRR1407336 3 0.4356 0.6142 0.000 0.000 0.708 0.292
#> SRR1097417 3 0.4462 0.6069 0.164 0.044 0.792 0.000
#> SRR1396227 4 0.5805 -0.1645 0.388 0.000 0.036 0.576
#> SRR1400775 2 0.0336 0.9650 0.000 0.992 0.008 0.000
#> SRR1392861 4 0.5869 0.0843 0.044 0.000 0.360 0.596
#> SRR1472929 1 0.6398 -0.1349 0.576 0.344 0.080 0.000
#> SRR1436740 4 0.4998 0.4398 0.052 0.000 0.200 0.748
#> SRR1477057 2 0.2945 0.9362 0.056 0.904 0.024 0.016
#> SRR1311980 3 0.2984 0.7264 0.028 0.000 0.888 0.084
#> SRR1069400 3 0.2053 0.7345 0.004 0.000 0.924 0.072
#> SRR1351016 4 0.6407 -0.1232 0.332 0.000 0.084 0.584
#> SRR1096291 4 0.5810 0.3251 0.064 0.000 0.276 0.660
#> SRR1418145 4 0.4938 0.4952 0.080 0.000 0.148 0.772
#> SRR1488111 4 0.8835 0.0774 0.076 0.216 0.240 0.468
#> SRR1370495 1 0.5957 0.3949 0.588 0.000 0.048 0.364
#> SRR1352639 4 0.6417 -0.1617 0.388 0.000 0.072 0.540
#> SRR1348911 3 0.2790 0.7239 0.012 0.012 0.904 0.072
#> SRR1467386 4 0.2412 0.4890 0.084 0.000 0.008 0.908
#> SRR1415956 1 0.6919 0.4249 0.500 0.000 0.112 0.388
#> SRR1500495 1 0.7803 0.4098 0.396 0.000 0.252 0.352
#> SRR1405099 1 0.6265 0.3896 0.500 0.000 0.056 0.444
#> SRR1345585 3 0.3400 0.7257 0.008 0.068 0.880 0.044
#> SRR1093196 3 0.4406 0.6088 0.000 0.000 0.700 0.300
#> SRR1466006 2 0.0188 0.9664 0.004 0.996 0.000 0.000
#> SRR1351557 2 0.0188 0.9664 0.004 0.996 0.000 0.000
#> SRR1382687 4 0.3485 0.4424 0.116 0.000 0.028 0.856
#> SRR1375549 1 0.5597 0.3346 0.516 0.000 0.020 0.464
#> SRR1101765 1 0.5213 0.3936 0.652 0.000 0.020 0.328
#> SRR1334461 1 0.5270 0.3346 0.788 0.108 0.036 0.068
#> SRR1094073 2 0.0000 0.9661 0.000 1.000 0.000 0.000
#> SRR1077549 4 0.3591 0.5316 0.008 0.000 0.168 0.824
#> SRR1440332 4 0.5213 0.3544 0.020 0.000 0.328 0.652
#> SRR1454177 4 0.5472 0.3025 0.044 0.000 0.280 0.676
#> SRR1082447 1 0.6268 0.3887 0.496 0.000 0.056 0.448
#> SRR1420043 3 0.4356 0.6132 0.000 0.000 0.708 0.292
#> SRR1432500 4 0.3312 0.5224 0.072 0.000 0.052 0.876
#> SRR1378045 3 0.4936 0.3373 0.004 0.372 0.624 0.000
#> SRR1334200 1 0.6069 -0.1536 0.600 0.352 0.040 0.008
#> SRR1069539 3 0.7257 0.2655 0.068 0.032 0.500 0.400
#> SRR1343031 3 0.3726 0.6796 0.000 0.000 0.788 0.212
#> SRR1319690 3 0.6835 0.2662 0.252 0.000 0.592 0.156
#> SRR1310604 2 0.2021 0.9473 0.040 0.936 0.024 0.000
#> SRR1327747 3 0.6523 0.5725 0.156 0.000 0.636 0.208
#> SRR1072456 2 0.0188 0.9664 0.004 0.996 0.000 0.000
#> SRR1367896 3 0.2099 0.7335 0.012 0.008 0.936 0.044
#> SRR1480107 1 0.6277 0.3605 0.476 0.000 0.056 0.468
#> SRR1377756 4 0.4546 0.2339 0.256 0.000 0.012 0.732
#> SRR1435272 4 0.5235 0.3901 0.048 0.000 0.236 0.716
#> SRR1089230 4 0.4544 0.4881 0.048 0.000 0.164 0.788
#> SRR1389522 3 0.2660 0.7195 0.056 0.000 0.908 0.036
#> SRR1080600 2 0.2521 0.9339 0.064 0.912 0.024 0.000
#> SRR1086935 4 0.6895 0.1693 0.056 0.040 0.300 0.604
#> SRR1344060 1 0.5988 -0.0697 0.628 0.324 0.036 0.012
#> SRR1467922 2 0.0188 0.9657 0.004 0.996 0.000 0.000
#> SRR1090984 3 0.7646 -0.2171 0.292 0.000 0.464 0.244
#> SRR1456991 1 0.6337 0.3606 0.476 0.000 0.060 0.464
#> SRR1085039 4 0.6229 -0.2836 0.416 0.000 0.056 0.528
#> SRR1069303 4 0.4199 0.3889 0.164 0.000 0.032 0.804
#> SRR1091500 2 0.0804 0.9634 0.012 0.980 0.008 0.000
#> SRR1075198 2 0.2075 0.9472 0.044 0.936 0.016 0.004
#> SRR1086915 4 0.3634 0.5428 0.048 0.000 0.096 0.856
#> SRR1499503 2 0.0188 0.9664 0.004 0.996 0.000 0.000
#> SRR1094312 2 0.0336 0.9650 0.000 0.992 0.008 0.000
#> SRR1352437 4 0.3383 0.4972 0.076 0.000 0.052 0.872
#> SRR1436323 3 0.4905 0.5189 0.004 0.000 0.632 0.364
#> SRR1073507 4 0.2611 0.4793 0.096 0.000 0.008 0.896
#> SRR1401972 4 0.4199 0.3889 0.164 0.000 0.032 0.804
#> SRR1415510 2 0.0188 0.9664 0.004 0.996 0.000 0.000
#> SRR1327279 4 0.4890 0.4982 0.080 0.000 0.144 0.776
#> SRR1086983 4 0.2805 0.5474 0.012 0.000 0.100 0.888
#> SRR1105174 1 0.6268 0.3887 0.496 0.000 0.056 0.448
#> SRR1468893 4 0.5503 -0.3103 0.468 0.000 0.016 0.516
#> SRR1362555 2 0.3143 0.9207 0.080 0.888 0.024 0.008
#> SRR1074526 1 0.5412 0.3169 0.768 0.140 0.068 0.024
#> SRR1326225 2 0.0336 0.9650 0.000 0.992 0.008 0.000
#> SRR1401933 4 0.4281 0.3835 0.180 0.000 0.028 0.792
#> SRR1324062 4 0.3754 0.5197 0.064 0.000 0.084 0.852
#> SRR1102296 4 0.6906 -0.3305 0.408 0.000 0.108 0.484
#> SRR1085087 4 0.1867 0.4956 0.072 0.000 0.000 0.928
#> SRR1079046 1 0.4606 0.4165 0.724 0.000 0.012 0.264
#> SRR1328339 3 0.7486 -0.1156 0.272 0.000 0.500 0.228
#> SRR1079782 2 0.3398 0.9163 0.080 0.880 0.020 0.020
#> SRR1092257 2 0.4008 0.8991 0.092 0.852 0.024 0.032
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.0162 0.90426 0.000 0.996 0.000 0.000 0.004
#> SRR1429287 2 0.4747 0.77085 0.000 0.744 0.004 0.128 0.124
#> SRR1359238 4 0.4080 0.65032 0.212 0.000 0.012 0.760 0.016
#> SRR1309597 3 0.2253 0.75255 0.028 0.000 0.920 0.036 0.016
#> SRR1441398 1 0.3830 0.65512 0.820 0.000 0.124 0.016 0.040
#> SRR1084055 2 0.0955 0.90406 0.000 0.968 0.004 0.000 0.028
#> SRR1417566 3 0.4967 0.57999 0.204 0.000 0.716 0.012 0.068
#> SRR1351857 4 0.3446 0.74143 0.144 0.000 0.016 0.828 0.012
#> SRR1487485 3 0.2270 0.75234 0.000 0.012 0.916 0.052 0.020
#> SRR1335875 3 0.1787 0.74250 0.032 0.000 0.940 0.012 0.016
#> SRR1073947 1 0.5377 0.49246 0.664 0.000 0.024 0.260 0.052
#> SRR1443483 3 0.2228 0.75360 0.008 0.000 0.916 0.056 0.020
#> SRR1346794 1 0.5371 0.56402 0.700 0.000 0.200 0.032 0.068
#> SRR1405245 1 0.4054 0.64620 0.800 0.000 0.144 0.016 0.040
#> SRR1409677 4 0.2308 0.80606 0.048 0.000 0.036 0.912 0.004
#> SRR1095549 1 0.5720 0.54750 0.672 0.000 0.212 0.076 0.040
#> SRR1323788 1 0.4233 0.66501 0.804 0.000 0.108 0.024 0.064
#> SRR1314054 2 0.0955 0.90038 0.000 0.968 0.004 0.000 0.028
#> SRR1077944 1 0.0854 0.71054 0.976 0.000 0.004 0.008 0.012
#> SRR1480587 2 0.0703 0.90302 0.000 0.976 0.000 0.000 0.024
#> SRR1311205 1 0.2677 0.70604 0.896 0.000 0.064 0.020 0.020
#> SRR1076369 1 0.7318 0.11581 0.436 0.000 0.200 0.040 0.324
#> SRR1453549 3 0.3475 0.70323 0.004 0.000 0.804 0.180 0.012
#> SRR1345782 1 0.2204 0.70940 0.920 0.000 0.048 0.016 0.016
#> SRR1447850 2 0.4051 0.79235 0.000 0.816 0.020 0.096 0.068
#> SRR1391553 3 0.2555 0.72876 0.004 0.024 0.908 0.016 0.048
#> SRR1444156 2 0.0566 0.90321 0.000 0.984 0.004 0.000 0.012
#> SRR1471731 3 0.5010 0.44180 0.008 0.000 0.592 0.376 0.024
#> SRR1120987 4 0.1948 0.78282 0.024 0.000 0.008 0.932 0.036
#> SRR1477363 1 0.1412 0.71003 0.952 0.000 0.004 0.008 0.036
#> SRR1391961 5 0.4619 0.88659 0.148 0.068 0.012 0.004 0.768
#> SRR1373879 3 0.2517 0.75010 0.004 0.000 0.884 0.104 0.008
#> SRR1318732 3 0.5330 0.53857 0.248 0.000 0.668 0.012 0.072
#> SRR1091404 1 0.1483 0.70586 0.952 0.000 0.012 0.008 0.028
#> SRR1402109 3 0.4159 0.62235 0.008 0.000 0.716 0.268 0.008
#> SRR1407336 3 0.4813 0.45677 0.004 0.000 0.600 0.376 0.020
#> SRR1097417 3 0.3236 0.66684 0.004 0.004 0.844 0.016 0.132
#> SRR1396227 1 0.4254 0.69100 0.808 0.000 0.032 0.068 0.092
#> SRR1400775 2 0.0451 0.90358 0.000 0.988 0.004 0.000 0.008
#> SRR1392861 4 0.2352 0.76841 0.004 0.000 0.092 0.896 0.008
#> SRR1472929 5 0.4360 0.86139 0.024 0.148 0.036 0.004 0.788
#> SRR1436740 4 0.2347 0.80371 0.056 0.000 0.016 0.912 0.016
#> SRR1477057 2 0.4390 0.81445 0.008 0.792 0.016 0.048 0.136
#> SRR1311980 3 0.2273 0.74289 0.024 0.000 0.920 0.032 0.024
#> SRR1069400 3 0.2228 0.75360 0.008 0.000 0.916 0.056 0.020
#> SRR1351016 1 0.4160 0.65542 0.804 0.000 0.024 0.124 0.048
#> SRR1096291 4 0.2625 0.77003 0.012 0.000 0.048 0.900 0.040
#> SRR1418145 4 0.2456 0.76583 0.024 0.000 0.008 0.904 0.064
#> SRR1488111 4 0.5203 0.58212 0.004 0.112 0.052 0.752 0.080
#> SRR1370495 1 0.4584 0.54113 0.716 0.000 0.000 0.056 0.228
#> SRR1352639 1 0.3812 0.66836 0.824 0.000 0.008 0.076 0.092
#> SRR1348911 3 0.0854 0.74770 0.012 0.000 0.976 0.008 0.004
#> SRR1467386 1 0.4890 0.19884 0.524 0.000 0.000 0.452 0.024
#> SRR1415956 1 0.1808 0.70237 0.936 0.000 0.012 0.008 0.044
#> SRR1500495 1 0.3531 0.67093 0.844 0.000 0.100 0.016 0.040
#> SRR1405099 1 0.1124 0.70604 0.960 0.000 0.004 0.000 0.036
#> SRR1345585 3 0.1893 0.74694 0.000 0.012 0.936 0.024 0.028
#> SRR1093196 3 0.4824 0.43392 0.004 0.000 0.596 0.380 0.020
#> SRR1466006 2 0.1410 0.89319 0.000 0.940 0.000 0.000 0.060
#> SRR1351557 2 0.0000 0.90465 0.000 1.000 0.000 0.000 0.000
#> SRR1382687 1 0.4636 0.65175 0.756 0.000 0.016 0.168 0.060
#> SRR1375549 1 0.2429 0.69931 0.900 0.000 0.004 0.020 0.076
#> SRR1101765 1 0.6065 0.24688 0.560 0.000 0.004 0.132 0.304
#> SRR1334461 5 0.4619 0.88659 0.148 0.068 0.012 0.004 0.768
#> SRR1094073 2 0.0451 0.90397 0.000 0.988 0.004 0.000 0.008
#> SRR1077549 4 0.4121 0.64116 0.208 0.000 0.008 0.760 0.024
#> SRR1440332 1 0.6796 0.06530 0.428 0.000 0.140 0.408 0.024
#> SRR1454177 4 0.2054 0.80030 0.028 0.000 0.052 0.920 0.000
#> SRR1082447 1 0.1644 0.70615 0.940 0.000 0.004 0.008 0.048
#> SRR1420043 3 0.4541 0.46096 0.004 0.000 0.608 0.380 0.008
#> SRR1432500 1 0.4999 0.10740 0.504 0.000 0.008 0.472 0.016
#> SRR1378045 3 0.4007 0.55219 0.000 0.220 0.756 0.004 0.020
#> SRR1334200 5 0.3864 0.87501 0.028 0.132 0.008 0.012 0.820
#> SRR1069539 4 0.5025 0.48741 0.004 0.000 0.212 0.700 0.084
#> SRR1343031 3 0.4420 0.62624 0.012 0.000 0.712 0.260 0.016
#> SRR1319690 3 0.5937 0.21600 0.408 0.000 0.512 0.020 0.060
#> SRR1310604 2 0.2959 0.85283 0.000 0.864 0.000 0.036 0.100
#> SRR1327747 3 0.7612 0.41960 0.264 0.000 0.444 0.228 0.064
#> SRR1072456 2 0.0963 0.90028 0.000 0.964 0.000 0.000 0.036
#> SRR1367896 3 0.1646 0.74998 0.004 0.000 0.944 0.032 0.020
#> SRR1480107 1 0.1405 0.70740 0.956 0.000 0.008 0.016 0.020
#> SRR1377756 1 0.3670 0.70742 0.832 0.000 0.008 0.100 0.060
#> SRR1435272 4 0.1997 0.80486 0.036 0.000 0.040 0.924 0.000
#> SRR1089230 4 0.2095 0.80256 0.060 0.000 0.012 0.920 0.008
#> SRR1389522 3 0.2263 0.74768 0.036 0.000 0.920 0.024 0.020
#> SRR1080600 2 0.3506 0.83125 0.000 0.824 0.000 0.044 0.132
#> SRR1086935 4 0.2663 0.78657 0.012 0.008 0.064 0.900 0.016
#> SRR1344060 5 0.3875 0.88748 0.048 0.120 0.008 0.004 0.820
#> SRR1467922 2 0.0566 0.90321 0.000 0.984 0.004 0.000 0.012
#> SRR1090984 1 0.5809 0.00443 0.468 0.000 0.456 0.008 0.068
#> SRR1456991 1 0.1314 0.70853 0.960 0.000 0.016 0.012 0.012
#> SRR1085039 1 0.2027 0.71107 0.928 0.000 0.008 0.040 0.024
#> SRR1069303 1 0.6056 0.34134 0.560 0.000 0.028 0.344 0.068
#> SRR1091500 2 0.0865 0.89997 0.000 0.972 0.004 0.000 0.024
#> SRR1075198 2 0.3934 0.81566 0.000 0.800 0.000 0.076 0.124
#> SRR1086915 4 0.2050 0.80046 0.064 0.000 0.008 0.920 0.008
#> SRR1499503 2 0.0671 0.90258 0.000 0.980 0.004 0.000 0.016
#> SRR1094312 2 0.0324 0.90403 0.000 0.992 0.004 0.000 0.004
#> SRR1352437 1 0.6200 0.17868 0.488 0.000 0.028 0.416 0.068
#> SRR1436323 4 0.5368 -0.21638 0.008 0.000 0.476 0.480 0.036
#> SRR1073507 1 0.5040 0.17067 0.516 0.000 0.004 0.456 0.024
#> SRR1401972 1 0.6056 0.34134 0.560 0.000 0.028 0.344 0.068
#> SRR1415510 2 0.0771 0.90261 0.000 0.976 0.004 0.000 0.020
#> SRR1327279 1 0.5903 0.16464 0.500 0.000 0.056 0.424 0.020
#> SRR1086983 4 0.3449 0.71056 0.164 0.000 0.000 0.812 0.024
#> SRR1105174 1 0.1682 0.70461 0.940 0.000 0.012 0.004 0.044
#> SRR1468893 1 0.2046 0.70945 0.916 0.000 0.000 0.016 0.068
#> SRR1362555 2 0.4138 0.79911 0.000 0.780 0.000 0.072 0.148
#> SRR1074526 5 0.4339 0.88376 0.116 0.052 0.016 0.012 0.804
#> SRR1326225 2 0.0579 0.90373 0.000 0.984 0.008 0.000 0.008
#> SRR1401933 1 0.6239 0.37445 0.536 0.000 0.028 0.356 0.080
#> SRR1324062 1 0.6169 0.13567 0.464 0.000 0.028 0.444 0.064
#> SRR1102296 1 0.3160 0.69485 0.876 0.000 0.040 0.032 0.052
#> SRR1085087 4 0.5549 -0.14698 0.472 0.000 0.008 0.472 0.048
#> SRR1079046 1 0.4570 0.36477 0.648 0.000 0.004 0.016 0.332
#> SRR1328339 3 0.5289 0.25506 0.400 0.000 0.556 0.008 0.036
#> SRR1079782 2 0.4478 0.77134 0.004 0.764 0.000 0.144 0.088
#> SRR1092257 2 0.5358 0.68464 0.004 0.692 0.008 0.200 0.096
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.0748 0.8017 0.000 0.976 0.000 0.016 0.004 0.004
#> SRR1429287 2 0.5451 0.4803 0.000 0.464 0.000 0.452 0.056 0.028
#> SRR1359238 6 0.5696 0.0973 0.116 0.000 0.032 0.260 0.000 0.592
#> SRR1309597 3 0.1553 0.7795 0.008 0.000 0.944 0.032 0.012 0.004
#> SRR1441398 1 0.2344 0.5957 0.896 0.000 0.068 0.000 0.008 0.028
#> SRR1084055 2 0.1275 0.8039 0.000 0.956 0.000 0.016 0.012 0.016
#> SRR1417566 3 0.6731 0.0768 0.380 0.000 0.444 0.044 0.032 0.100
#> SRR1351857 6 0.4873 -0.3436 0.036 0.000 0.016 0.376 0.000 0.572
#> SRR1487485 3 0.1636 0.7825 0.000 0.004 0.936 0.036 0.000 0.024
#> SRR1335875 3 0.3414 0.7291 0.052 0.000 0.852 0.020 0.024 0.052
#> SRR1073947 6 0.4687 0.2909 0.440 0.000 0.004 0.012 0.016 0.528
#> SRR1443483 3 0.1230 0.7801 0.000 0.000 0.956 0.028 0.008 0.008
#> SRR1346794 1 0.4772 0.5377 0.748 0.000 0.128 0.048 0.012 0.064
#> SRR1405245 1 0.3025 0.5930 0.860 0.000 0.068 0.004 0.008 0.060
#> SRR1409677 4 0.4771 0.6542 0.004 0.000 0.032 0.524 0.004 0.436
#> SRR1095549 1 0.6191 0.4359 0.568 0.000 0.168 0.028 0.012 0.224
#> SRR1323788 1 0.3060 0.5821 0.868 0.000 0.060 0.016 0.012 0.044
#> SRR1314054 2 0.1890 0.7866 0.000 0.916 0.000 0.060 0.024 0.000
#> SRR1077944 1 0.2504 0.5659 0.856 0.000 0.000 0.004 0.004 0.136
#> SRR1480587 2 0.1173 0.8013 0.000 0.960 0.000 0.016 0.008 0.016
#> SRR1311205 1 0.4234 0.4359 0.708 0.000 0.024 0.008 0.008 0.252
#> SRR1076369 1 0.7401 0.3973 0.528 0.000 0.172 0.108 0.116 0.076
#> SRR1453549 3 0.4195 0.7486 0.024 0.000 0.776 0.132 0.004 0.064
#> SRR1345782 1 0.4301 0.4353 0.696 0.000 0.024 0.008 0.008 0.264
#> SRR1447850 2 0.4608 0.6543 0.000 0.712 0.000 0.208 0.036 0.044
#> SRR1391553 3 0.4679 0.7047 0.044 0.008 0.776 0.048 0.024 0.100
#> SRR1444156 2 0.0458 0.8022 0.000 0.984 0.000 0.016 0.000 0.000
#> SRR1471731 3 0.6051 0.5286 0.020 0.000 0.552 0.232 0.004 0.192
#> SRR1120987 4 0.3967 0.5910 0.000 0.000 0.008 0.668 0.008 0.316
#> SRR1477363 1 0.2504 0.5503 0.856 0.000 0.000 0.004 0.004 0.136
#> SRR1391961 5 0.1798 0.9382 0.020 0.020 0.000 0.000 0.932 0.028
#> SRR1373879 3 0.2492 0.7752 0.000 0.000 0.888 0.068 0.008 0.036
#> SRR1318732 1 0.6189 0.0231 0.464 0.004 0.416 0.048 0.016 0.052
#> SRR1091404 1 0.3122 0.5281 0.804 0.000 0.000 0.000 0.020 0.176
#> SRR1402109 3 0.3912 0.7163 0.000 0.000 0.776 0.148 0.008 0.068
#> SRR1407336 3 0.5016 0.6091 0.000 0.000 0.664 0.192 0.008 0.136
#> SRR1097417 3 0.3246 0.6571 0.000 0.000 0.812 0.012 0.160 0.016
#> SRR1396227 1 0.4937 0.2247 0.564 0.000 0.008 0.024 0.016 0.388
#> SRR1400775 2 0.1349 0.7943 0.000 0.940 0.000 0.056 0.004 0.000
#> SRR1392861 4 0.5013 0.6400 0.000 0.000 0.060 0.508 0.004 0.428
#> SRR1472929 5 0.2395 0.9100 0.000 0.072 0.004 0.012 0.896 0.016
#> SRR1436740 4 0.4666 0.6264 0.004 0.000 0.024 0.492 0.004 0.476
#> SRR1477057 2 0.6259 0.4965 0.000 0.488 0.000 0.340 0.048 0.124
#> SRR1311980 3 0.3969 0.7243 0.044 0.000 0.816 0.032 0.024 0.084
#> SRR1069400 3 0.1483 0.7806 0.000 0.000 0.944 0.036 0.008 0.012
#> SRR1351016 1 0.4477 0.0992 0.564 0.000 0.004 0.012 0.008 0.412
#> SRR1096291 4 0.4723 0.5941 0.000 0.000 0.064 0.636 0.004 0.296
#> SRR1418145 4 0.3455 0.5254 0.000 0.000 0.004 0.776 0.020 0.200
#> SRR1488111 4 0.4259 0.4420 0.000 0.060 0.016 0.788 0.028 0.108
#> SRR1370495 1 0.7220 0.1736 0.384 0.000 0.008 0.332 0.080 0.196
#> SRR1352639 1 0.6728 0.1614 0.460 0.000 0.008 0.268 0.036 0.228
#> SRR1348911 3 0.2162 0.7535 0.016 0.000 0.920 0.020 0.016 0.028
#> SRR1467386 6 0.4884 0.5868 0.324 0.000 0.008 0.060 0.000 0.608
#> SRR1415956 1 0.1124 0.5985 0.956 0.000 0.000 0.000 0.008 0.036
#> SRR1500495 1 0.2848 0.5941 0.872 0.000 0.060 0.004 0.008 0.056
#> SRR1405099 1 0.2346 0.5611 0.868 0.000 0.000 0.000 0.008 0.124
#> SRR1345585 3 0.2151 0.7680 0.012 0.004 0.916 0.032 0.000 0.036
#> SRR1093196 3 0.5167 0.5868 0.000 0.000 0.632 0.216 0.004 0.148
#> SRR1466006 2 0.1861 0.7941 0.000 0.928 0.000 0.036 0.020 0.016
#> SRR1351557 2 0.0458 0.8046 0.000 0.984 0.000 0.016 0.000 0.000
#> SRR1382687 1 0.3570 0.4410 0.752 0.000 0.004 0.016 0.000 0.228
#> SRR1375549 1 0.4660 0.5312 0.736 0.000 0.000 0.124 0.032 0.108
#> SRR1101765 1 0.6185 0.3971 0.572 0.000 0.000 0.208 0.160 0.060
#> SRR1334461 5 0.1874 0.9332 0.028 0.016 0.000 0.000 0.928 0.028
#> SRR1094073 2 0.0260 0.8024 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR1077549 6 0.5065 0.2288 0.080 0.000 0.036 0.184 0.004 0.696
#> SRR1440332 6 0.7009 0.2143 0.340 0.000 0.172 0.092 0.000 0.396
#> SRR1454177 4 0.4958 0.6425 0.004 0.000 0.044 0.496 0.004 0.452
#> SRR1082447 1 0.2737 0.5846 0.868 0.000 0.000 0.012 0.024 0.096
#> SRR1420043 3 0.4919 0.6063 0.000 0.000 0.664 0.204 0.004 0.128
#> SRR1432500 6 0.5490 0.5239 0.364 0.000 0.016 0.088 0.000 0.532
#> SRR1378045 2 0.6368 -0.0890 0.016 0.440 0.436 0.052 0.020 0.036
#> SRR1334200 5 0.2512 0.9309 0.008 0.048 0.000 0.040 0.896 0.008
#> SRR1069539 4 0.5065 0.4942 0.000 0.000 0.168 0.680 0.020 0.132
#> SRR1343031 3 0.3852 0.7230 0.000 0.000 0.784 0.136 0.008 0.072
#> SRR1319690 1 0.4971 0.4743 0.676 0.000 0.244 0.020 0.016 0.044
#> SRR1310604 2 0.4667 0.6392 0.000 0.664 0.004 0.280 0.036 0.016
#> SRR1327747 1 0.6968 0.1156 0.456 0.000 0.320 0.088 0.012 0.124
#> SRR1072456 2 0.1173 0.8013 0.000 0.960 0.000 0.016 0.008 0.016
#> SRR1367896 3 0.0717 0.7749 0.000 0.000 0.976 0.000 0.016 0.008
#> SRR1480107 1 0.4063 0.4028 0.692 0.000 0.000 0.008 0.020 0.280
#> SRR1377756 1 0.2699 0.5798 0.856 0.000 0.000 0.008 0.012 0.124
#> SRR1435272 4 0.4842 0.6487 0.004 0.000 0.036 0.508 0.004 0.448
#> SRR1089230 4 0.4694 0.6361 0.008 0.000 0.020 0.496 0.004 0.472
#> SRR1389522 3 0.1140 0.7762 0.008 0.000 0.964 0.008 0.012 0.008
#> SRR1080600 2 0.4854 0.6184 0.000 0.636 0.004 0.304 0.036 0.020
#> SRR1086935 4 0.4912 0.6499 0.004 0.008 0.020 0.512 0.008 0.448
#> SRR1344060 5 0.1836 0.9388 0.008 0.048 0.000 0.012 0.928 0.004
#> SRR1467922 2 0.0458 0.8022 0.000 0.984 0.000 0.016 0.000 0.000
#> SRR1090984 1 0.5974 0.4018 0.592 0.000 0.276 0.040 0.028 0.064
#> SRR1456991 1 0.3829 0.4280 0.720 0.000 0.004 0.008 0.008 0.260
#> SRR1085039 1 0.4008 0.3368 0.672 0.000 0.000 0.004 0.016 0.308
#> SRR1069303 6 0.4604 0.5867 0.232 0.000 0.008 0.028 0.028 0.704
#> SRR1091500 2 0.1867 0.7851 0.000 0.916 0.000 0.064 0.020 0.000
#> SRR1075198 2 0.4897 0.5930 0.000 0.600 0.004 0.344 0.040 0.012
#> SRR1086915 4 0.4412 0.6218 0.008 0.000 0.012 0.500 0.000 0.480
#> SRR1499503 2 0.0862 0.8011 0.000 0.972 0.000 0.016 0.004 0.008
#> SRR1094312 2 0.1141 0.7968 0.000 0.948 0.000 0.052 0.000 0.000
#> SRR1352437 6 0.4322 0.6117 0.204 0.000 0.008 0.036 0.016 0.736
#> SRR1436323 3 0.6806 0.3234 0.048 0.000 0.448 0.232 0.004 0.268
#> SRR1073507 6 0.4703 0.6174 0.280 0.000 0.008 0.060 0.000 0.652
#> SRR1401972 6 0.4604 0.5867 0.232 0.000 0.008 0.028 0.028 0.704
#> SRR1415510 2 0.1078 0.8012 0.000 0.964 0.000 0.016 0.008 0.012
#> SRR1327279 6 0.5994 0.4966 0.332 0.000 0.068 0.060 0.004 0.536
#> SRR1086983 6 0.4529 -0.2694 0.028 0.000 0.012 0.332 0.000 0.628
#> SRR1105174 1 0.2263 0.5746 0.884 0.000 0.000 0.000 0.016 0.100
#> SRR1468893 1 0.2920 0.5840 0.844 0.000 0.000 0.008 0.020 0.128
#> SRR1362555 2 0.5546 0.5437 0.004 0.536 0.004 0.380 0.044 0.032
#> SRR1074526 5 0.2214 0.9235 0.012 0.012 0.000 0.028 0.916 0.032
#> SRR1326225 2 0.0547 0.8019 0.000 0.980 0.000 0.020 0.000 0.000
#> SRR1401933 1 0.5959 0.1220 0.472 0.000 0.012 0.108 0.012 0.396
#> SRR1324062 6 0.4623 0.5998 0.240 0.000 0.008 0.040 0.016 0.696
#> SRR1102296 1 0.5588 0.2196 0.532 0.000 0.028 0.020 0.036 0.384
#> SRR1085087 6 0.3782 0.6268 0.224 0.000 0.000 0.036 0.000 0.740
#> SRR1079046 1 0.5980 0.4530 0.616 0.000 0.000 0.136 0.168 0.080
#> SRR1328339 1 0.6168 0.2177 0.504 0.000 0.368 0.036 0.024 0.068
#> SRR1079782 2 0.5256 0.4978 0.000 0.492 0.004 0.444 0.032 0.028
#> SRR1092257 4 0.5456 -0.4150 0.000 0.416 0.000 0.500 0.044 0.040
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 17611 rows and 118 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.820 0.898 0.958 0.4960 0.503 0.503
#> 3 3 0.613 0.701 0.863 0.3388 0.788 0.603
#> 4 4 0.763 0.816 0.905 0.1303 0.828 0.556
#> 5 5 0.704 0.635 0.803 0.0588 0.864 0.541
#> 6 6 0.695 0.590 0.768 0.0403 0.932 0.707
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
#> SRR1396765 2 0.0000 0.943 0.000 1.000
#> SRR1429287 2 0.0000 0.943 0.000 1.000
#> SRR1359238 1 0.0000 0.964 1.000 0.000
#> SRR1309597 2 0.5294 0.843 0.120 0.880
#> SRR1441398 1 0.0000 0.964 1.000 0.000
#> SRR1084055 2 0.0000 0.943 0.000 1.000
#> SRR1417566 2 0.0672 0.938 0.008 0.992
#> SRR1351857 1 0.0000 0.964 1.000 0.000
#> SRR1487485 2 0.0376 0.941 0.004 0.996
#> SRR1335875 2 0.0000 0.943 0.000 1.000
#> SRR1073947 1 0.0000 0.964 1.000 0.000
#> SRR1443483 2 0.4690 0.863 0.100 0.900
#> SRR1346794 1 0.0000 0.964 1.000 0.000
#> SRR1405245 1 0.0000 0.964 1.000 0.000
#> SRR1409677 1 0.0000 0.964 1.000 0.000
#> SRR1095549 1 0.0000 0.964 1.000 0.000
#> SRR1323788 1 0.0000 0.964 1.000 0.000
#> SRR1314054 2 0.0000 0.943 0.000 1.000
#> SRR1077944 1 0.0000 0.964 1.000 0.000
#> SRR1480587 2 0.0000 0.943 0.000 1.000
#> SRR1311205 1 0.0000 0.964 1.000 0.000
#> SRR1076369 1 0.9815 0.187 0.580 0.420
#> SRR1453549 1 0.1184 0.950 0.984 0.016
#> SRR1345782 1 0.0000 0.964 1.000 0.000
#> SRR1447850 2 0.0000 0.943 0.000 1.000
#> SRR1391553 2 0.0000 0.943 0.000 1.000
#> SRR1444156 2 0.0000 0.943 0.000 1.000
#> SRR1471731 2 0.9732 0.383 0.404 0.596
#> SRR1120987 1 0.3114 0.913 0.944 0.056
#> SRR1477363 1 0.0000 0.964 1.000 0.000
#> SRR1391961 1 0.9833 0.281 0.576 0.424
#> SRR1373879 1 0.2948 0.916 0.948 0.052
#> SRR1318732 2 0.7139 0.754 0.196 0.804
#> SRR1091404 1 0.0000 0.964 1.000 0.000
#> SRR1402109 1 0.0000 0.964 1.000 0.000
#> SRR1407336 2 0.9732 0.383 0.404 0.596
#> SRR1097417 2 0.0000 0.943 0.000 1.000
#> SRR1396227 1 0.0000 0.964 1.000 0.000
#> SRR1400775 2 0.0000 0.943 0.000 1.000
#> SRR1392861 1 0.3274 0.908 0.940 0.060
#> SRR1472929 2 0.0000 0.943 0.000 1.000
#> SRR1436740 1 0.0000 0.964 1.000 0.000
#> SRR1477057 2 0.0000 0.943 0.000 1.000
#> SRR1311980 2 0.0938 0.936 0.012 0.988
#> SRR1069400 2 0.5629 0.830 0.132 0.868
#> SRR1351016 1 0.0000 0.964 1.000 0.000
#> SRR1096291 2 0.9909 0.274 0.444 0.556
#> SRR1418145 1 0.0000 0.964 1.000 0.000
#> SRR1488111 2 0.0000 0.943 0.000 1.000
#> SRR1370495 1 0.1184 0.951 0.984 0.016
#> SRR1352639 1 0.7376 0.726 0.792 0.208
#> SRR1348911 2 0.0000 0.943 0.000 1.000
#> SRR1467386 1 0.0000 0.964 1.000 0.000
#> SRR1415956 1 0.0000 0.964 1.000 0.000
#> SRR1500495 1 0.0000 0.964 1.000 0.000
#> SRR1405099 1 0.0000 0.964 1.000 0.000
#> SRR1345585 2 0.0000 0.943 0.000 1.000
#> SRR1093196 2 0.9732 0.383 0.404 0.596
#> SRR1466006 2 0.0000 0.943 0.000 1.000
#> SRR1351557 2 0.0000 0.943 0.000 1.000
#> SRR1382687 1 0.0000 0.964 1.000 0.000
#> SRR1375549 1 0.0000 0.964 1.000 0.000
#> SRR1101765 1 0.0000 0.964 1.000 0.000
#> SRR1334461 1 0.9732 0.336 0.596 0.404
#> SRR1094073 2 0.0000 0.943 0.000 1.000
#> SRR1077549 1 0.0000 0.964 1.000 0.000
#> SRR1440332 1 0.0000 0.964 1.000 0.000
#> SRR1454177 1 0.0000 0.964 1.000 0.000
#> SRR1082447 1 0.0000 0.964 1.000 0.000
#> SRR1420043 1 0.0000 0.964 1.000 0.000
#> SRR1432500 1 0.0000 0.964 1.000 0.000
#> SRR1378045 2 0.0000 0.943 0.000 1.000
#> SRR1334200 2 0.0000 0.943 0.000 1.000
#> SRR1069539 2 0.0000 0.943 0.000 1.000
#> SRR1343031 1 0.0000 0.964 1.000 0.000
#> SRR1319690 1 0.0000 0.964 1.000 0.000
#> SRR1310604 2 0.0000 0.943 0.000 1.000
#> SRR1327747 1 0.0000 0.964 1.000 0.000
#> SRR1072456 2 0.0000 0.943 0.000 1.000
#> SRR1367896 2 0.0000 0.943 0.000 1.000
#> SRR1480107 1 0.0000 0.964 1.000 0.000
#> SRR1377756 1 0.0000 0.964 1.000 0.000
#> SRR1435272 1 0.0000 0.964 1.000 0.000
#> SRR1089230 1 0.0000 0.964 1.000 0.000
#> SRR1389522 2 0.4161 0.877 0.084 0.916
#> SRR1080600 2 0.0000 0.943 0.000 1.000
#> SRR1086935 2 0.9732 0.383 0.404 0.596
#> SRR1344060 2 0.0000 0.943 0.000 1.000
#> SRR1467922 2 0.0000 0.943 0.000 1.000
#> SRR1090984 1 0.0672 0.957 0.992 0.008
#> SRR1456991 1 0.0000 0.964 1.000 0.000
#> SRR1085039 1 0.0000 0.964 1.000 0.000
#> SRR1069303 1 0.0000 0.964 1.000 0.000
#> SRR1091500 2 0.0000 0.943 0.000 1.000
#> SRR1075198 2 0.0000 0.943 0.000 1.000
#> SRR1086915 1 0.0000 0.964 1.000 0.000
#> SRR1499503 2 0.0000 0.943 0.000 1.000
#> SRR1094312 2 0.0000 0.943 0.000 1.000
#> SRR1352437 1 0.0000 0.964 1.000 0.000
#> SRR1436323 1 0.0000 0.964 1.000 0.000
#> SRR1073507 1 0.0000 0.964 1.000 0.000
#> SRR1401972 1 0.0000 0.964 1.000 0.000
#> SRR1415510 2 0.0000 0.943 0.000 1.000
#> SRR1327279 1 0.0000 0.964 1.000 0.000
#> SRR1086983 1 0.0000 0.964 1.000 0.000
#> SRR1105174 1 0.0000 0.964 1.000 0.000
#> SRR1468893 1 0.0000 0.964 1.000 0.000
#> SRR1362555 2 0.0000 0.943 0.000 1.000
#> SRR1074526 2 0.0672 0.938 0.008 0.992
#> SRR1326225 2 0.0000 0.943 0.000 1.000
#> SRR1401933 1 0.0000 0.964 1.000 0.000
#> SRR1324062 1 0.0000 0.964 1.000 0.000
#> SRR1102296 1 0.5737 0.823 0.864 0.136
#> SRR1085087 1 0.0000 0.964 1.000 0.000
#> SRR1079046 1 0.9552 0.406 0.624 0.376
#> SRR1328339 2 0.0000 0.943 0.000 1.000
#> SRR1079782 2 0.0000 0.943 0.000 1.000
#> SRR1092257 2 0.0000 0.943 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1429287 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1359238 1 0.5363 0.6503 0.724 0.000 0.276
#> SRR1309597 3 0.3213 0.7890 0.060 0.028 0.912
#> SRR1441398 1 0.6204 0.0720 0.576 0.000 0.424
#> SRR1084055 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1417566 3 0.7146 0.6065 0.264 0.060 0.676
#> SRR1351857 1 0.5178 0.6665 0.744 0.000 0.256
#> SRR1487485 3 0.1964 0.7971 0.000 0.056 0.944
#> SRR1335875 3 0.5860 0.6850 0.024 0.228 0.748
#> SRR1073947 1 0.2261 0.7585 0.932 0.000 0.068
#> SRR1443483 3 0.1774 0.8008 0.016 0.024 0.960
#> SRR1346794 1 0.6225 0.0462 0.568 0.000 0.432
#> SRR1405245 1 0.6309 -0.1653 0.504 0.000 0.496
#> SRR1409677 1 0.6204 0.4553 0.576 0.000 0.424
#> SRR1095549 1 0.6235 0.0704 0.564 0.000 0.436
#> SRR1323788 1 0.6126 0.1404 0.600 0.000 0.400
#> SRR1314054 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1077944 1 0.0000 0.7594 1.000 0.000 0.000
#> SRR1480587 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1311205 1 0.1529 0.7417 0.960 0.000 0.040
#> SRR1076369 1 0.6309 -0.1421 0.504 0.000 0.496
#> SRR1453549 3 0.0000 0.7990 0.000 0.000 1.000
#> SRR1345782 1 0.0892 0.7520 0.980 0.000 0.020
#> SRR1447850 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1391553 3 0.5465 0.6195 0.000 0.288 0.712
#> SRR1444156 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1471731 3 0.0000 0.7990 0.000 0.000 1.000
#> SRR1120987 1 0.9557 0.4078 0.484 0.248 0.268
#> SRR1477363 1 0.0000 0.7594 1.000 0.000 0.000
#> SRR1391961 1 0.6295 0.1885 0.528 0.472 0.000
#> SRR1373879 3 0.0000 0.7990 0.000 0.000 1.000
#> SRR1318732 3 0.6402 0.6458 0.236 0.040 0.724
#> SRR1091404 1 0.0000 0.7594 1.000 0.000 0.000
#> SRR1402109 3 0.0000 0.7990 0.000 0.000 1.000
#> SRR1407336 3 0.0000 0.7990 0.000 0.000 1.000
#> SRR1097417 3 0.5621 0.5928 0.000 0.308 0.692
#> SRR1396227 1 0.0424 0.7602 0.992 0.000 0.008
#> SRR1400775 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1392861 3 0.0592 0.7911 0.012 0.000 0.988
#> SRR1472929 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1436740 1 0.6168 0.4756 0.588 0.000 0.412
#> SRR1477057 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1311980 3 0.0983 0.7975 0.016 0.004 0.980
#> SRR1069400 3 0.0424 0.8003 0.000 0.008 0.992
#> SRR1351016 1 0.2165 0.7592 0.936 0.000 0.064
#> SRR1096291 2 0.8496 0.3104 0.112 0.564 0.324
#> SRR1418145 1 0.9364 0.4463 0.512 0.220 0.268
#> SRR1488111 2 0.1031 0.9495 0.000 0.976 0.024
#> SRR1370495 1 0.5216 0.5868 0.740 0.260 0.000
#> SRR1352639 1 0.6111 0.3729 0.604 0.396 0.000
#> SRR1348911 3 0.4931 0.6888 0.000 0.232 0.768
#> SRR1467386 1 0.2959 0.7504 0.900 0.000 0.100
#> SRR1415956 1 0.1411 0.7440 0.964 0.000 0.036
#> SRR1500495 1 0.6192 0.0847 0.580 0.000 0.420
#> SRR1405099 1 0.0000 0.7594 1.000 0.000 0.000
#> SRR1345585 3 0.3752 0.7613 0.000 0.144 0.856
#> SRR1093196 3 0.0000 0.7990 0.000 0.000 1.000
#> SRR1466006 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1351557 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1382687 1 0.1289 0.7607 0.968 0.000 0.032
#> SRR1375549 1 0.0000 0.7594 1.000 0.000 0.000
#> SRR1101765 1 0.0000 0.7594 1.000 0.000 0.000
#> SRR1334461 1 0.6045 0.4173 0.620 0.380 0.000
#> SRR1094073 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1077549 1 0.5465 0.6390 0.712 0.000 0.288
#> SRR1440332 1 0.6204 0.4809 0.576 0.000 0.424
#> SRR1454177 3 0.6291 -0.2572 0.468 0.000 0.532
#> SRR1082447 1 0.0000 0.7594 1.000 0.000 0.000
#> SRR1420043 3 0.0000 0.7990 0.000 0.000 1.000
#> SRR1432500 1 0.4974 0.6798 0.764 0.000 0.236
#> SRR1378045 3 0.5621 0.5926 0.000 0.308 0.692
#> SRR1334200 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1069539 2 0.5138 0.6274 0.000 0.748 0.252
#> SRR1343031 3 0.0000 0.7990 0.000 0.000 1.000
#> SRR1319690 3 0.5397 0.6039 0.280 0.000 0.720
#> SRR1310604 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1327747 3 0.3482 0.7526 0.128 0.000 0.872
#> SRR1072456 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1367896 3 0.1860 0.7973 0.000 0.052 0.948
#> SRR1480107 1 0.0000 0.7594 1.000 0.000 0.000
#> SRR1377756 1 0.0000 0.7594 1.000 0.000 0.000
#> SRR1435272 1 0.6295 0.3628 0.528 0.000 0.472
#> SRR1089230 1 0.5591 0.6228 0.696 0.000 0.304
#> SRR1389522 3 0.3030 0.7744 0.092 0.004 0.904
#> SRR1080600 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1086935 3 0.7471 0.0292 0.036 0.448 0.516
#> SRR1344060 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1467922 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1090984 3 0.6260 0.2849 0.448 0.000 0.552
#> SRR1456991 1 0.0000 0.7594 1.000 0.000 0.000
#> SRR1085039 1 0.0000 0.7594 1.000 0.000 0.000
#> SRR1069303 1 0.2261 0.7584 0.932 0.000 0.068
#> SRR1091500 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1075198 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1086915 1 0.5178 0.6665 0.744 0.000 0.256
#> SRR1499503 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1094312 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1352437 1 0.3116 0.7480 0.892 0.000 0.108
#> SRR1436323 3 0.0424 0.7971 0.008 0.000 0.992
#> SRR1073507 1 0.3038 0.7493 0.896 0.000 0.104
#> SRR1401972 1 0.2261 0.7584 0.932 0.000 0.068
#> SRR1415510 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1327279 1 0.5254 0.6604 0.736 0.000 0.264
#> SRR1086983 1 0.5178 0.6665 0.744 0.000 0.256
#> SRR1105174 1 0.0000 0.7594 1.000 0.000 0.000
#> SRR1468893 1 0.0000 0.7594 1.000 0.000 0.000
#> SRR1362555 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1074526 2 0.0237 0.9693 0.004 0.996 0.000
#> SRR1326225 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1401933 1 0.2165 0.7596 0.936 0.000 0.064
#> SRR1324062 1 0.4702 0.6964 0.788 0.000 0.212
#> SRR1102296 1 0.0592 0.7589 0.988 0.012 0.000
#> SRR1085087 1 0.3116 0.7480 0.892 0.000 0.108
#> SRR1079046 1 0.3879 0.6705 0.848 0.152 0.000
#> SRR1328339 3 0.7250 0.3885 0.396 0.032 0.572
#> SRR1079782 2 0.0000 0.9737 0.000 1.000 0.000
#> SRR1092257 2 0.0000 0.9737 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.0188 0.9590 0.000 0.996 0.004 0.000
#> SRR1429287 2 0.0000 0.9585 0.000 1.000 0.000 0.000
#> SRR1359238 4 0.0336 0.8591 0.008 0.000 0.000 0.992
#> SRR1309597 3 0.0376 0.8882 0.004 0.000 0.992 0.004
#> SRR1441398 1 0.1557 0.8510 0.944 0.000 0.056 0.000
#> SRR1084055 2 0.0188 0.9590 0.000 0.996 0.004 0.000
#> SRR1417566 3 0.4539 0.5992 0.272 0.008 0.720 0.000
#> SRR1351857 4 0.0188 0.8596 0.004 0.000 0.000 0.996
#> SRR1487485 3 0.0376 0.8883 0.000 0.004 0.992 0.004
#> SRR1335875 3 0.0376 0.8871 0.004 0.004 0.992 0.000
#> SRR1073947 1 0.5155 -0.1347 0.528 0.000 0.004 0.468
#> SRR1443483 3 0.0469 0.8886 0.000 0.000 0.988 0.012
#> SRR1346794 1 0.2796 0.8267 0.892 0.000 0.092 0.016
#> SRR1405245 1 0.2081 0.8393 0.916 0.000 0.084 0.000
#> SRR1409677 4 0.0336 0.8554 0.000 0.000 0.008 0.992
#> SRR1095549 1 0.4379 0.7329 0.792 0.000 0.172 0.036
#> SRR1323788 1 0.1474 0.8528 0.948 0.000 0.052 0.000
#> SRR1314054 2 0.0000 0.9585 0.000 1.000 0.000 0.000
#> SRR1077944 1 0.0000 0.8640 1.000 0.000 0.000 0.000
#> SRR1480587 2 0.0188 0.9590 0.000 0.996 0.004 0.000
#> SRR1311205 1 0.0469 0.8615 0.988 0.000 0.000 0.012
#> SRR1076369 1 0.4579 0.6994 0.768 0.000 0.200 0.032
#> SRR1453549 3 0.1940 0.8700 0.000 0.000 0.924 0.076
#> SRR1345782 1 0.0188 0.8636 0.996 0.000 0.000 0.004
#> SRR1447850 2 0.0000 0.9585 0.000 1.000 0.000 0.000
#> SRR1391553 3 0.0336 0.8861 0.000 0.008 0.992 0.000
#> SRR1444156 2 0.0188 0.9590 0.000 0.996 0.004 0.000
#> SRR1471731 3 0.3942 0.7466 0.000 0.000 0.764 0.236
#> SRR1120987 4 0.0657 0.8579 0.004 0.012 0.000 0.984
#> SRR1477363 1 0.0000 0.8640 1.000 0.000 0.000 0.000
#> SRR1391961 1 0.4762 0.5609 0.692 0.300 0.004 0.004
#> SRR1373879 3 0.1637 0.8769 0.000 0.000 0.940 0.060
#> SRR1318732 3 0.4522 0.6197 0.264 0.004 0.728 0.004
#> SRR1091404 1 0.0000 0.8640 1.000 0.000 0.000 0.000
#> SRR1402109 3 0.2704 0.8492 0.000 0.000 0.876 0.124
#> SRR1407336 3 0.3649 0.7913 0.000 0.000 0.796 0.204
#> SRR1097417 3 0.0336 0.8884 0.000 0.000 0.992 0.008
#> SRR1396227 1 0.2401 0.8011 0.904 0.000 0.004 0.092
#> SRR1400775 2 0.0000 0.9585 0.000 1.000 0.000 0.000
#> SRR1392861 4 0.1022 0.8405 0.000 0.000 0.032 0.968
#> SRR1472929 2 0.1706 0.9231 0.016 0.948 0.036 0.000
#> SRR1436740 4 0.0000 0.8580 0.000 0.000 0.000 1.000
#> SRR1477057 2 0.0000 0.9585 0.000 1.000 0.000 0.000
#> SRR1311980 3 0.0188 0.8875 0.000 0.004 0.996 0.000
#> SRR1069400 3 0.0469 0.8886 0.000 0.000 0.988 0.012
#> SRR1351016 1 0.4252 0.5553 0.744 0.000 0.004 0.252
#> SRR1096291 4 0.0188 0.8570 0.000 0.000 0.004 0.996
#> SRR1418145 4 0.0524 0.8589 0.004 0.008 0.000 0.988
#> SRR1488111 2 0.1867 0.9006 0.000 0.928 0.000 0.072
#> SRR1370495 1 0.4720 0.6192 0.720 0.264 0.000 0.016
#> SRR1352639 2 0.7634 0.0314 0.352 0.436 0.000 0.212
#> SRR1348911 3 0.0000 0.8874 0.000 0.000 1.000 0.000
#> SRR1467386 4 0.3801 0.7791 0.220 0.000 0.000 0.780
#> SRR1415956 1 0.0188 0.8640 0.996 0.000 0.004 0.000
#> SRR1500495 1 0.1389 0.8543 0.952 0.000 0.048 0.000
#> SRR1405099 1 0.0000 0.8640 1.000 0.000 0.000 0.000
#> SRR1345585 3 0.0188 0.8873 0.000 0.004 0.996 0.000
#> SRR1093196 3 0.3726 0.7833 0.000 0.000 0.788 0.212
#> SRR1466006 2 0.0188 0.9590 0.000 0.996 0.004 0.000
#> SRR1351557 2 0.0000 0.9585 0.000 1.000 0.000 0.000
#> SRR1382687 4 0.5150 0.5114 0.396 0.000 0.008 0.596
#> SRR1375549 1 0.0336 0.8636 0.992 0.000 0.000 0.008
#> SRR1101765 1 0.2345 0.8181 0.900 0.000 0.000 0.100
#> SRR1334461 1 0.4687 0.5827 0.704 0.288 0.004 0.004
#> SRR1094073 2 0.0188 0.9590 0.000 0.996 0.004 0.000
#> SRR1077549 4 0.0469 0.8591 0.012 0.000 0.000 0.988
#> SRR1440332 4 0.4297 0.7827 0.096 0.000 0.084 0.820
#> SRR1454177 4 0.0469 0.8540 0.000 0.000 0.012 0.988
#> SRR1082447 1 0.0188 0.8639 0.996 0.000 0.000 0.004
#> SRR1420043 3 0.3569 0.7969 0.000 0.000 0.804 0.196
#> SRR1432500 4 0.3266 0.8181 0.168 0.000 0.000 0.832
#> SRR1378045 3 0.1389 0.8638 0.000 0.048 0.952 0.000
#> SRR1334200 2 0.0524 0.9551 0.000 0.988 0.008 0.004
#> SRR1069539 2 0.6444 0.4980 0.000 0.612 0.104 0.284
#> SRR1343031 3 0.2814 0.8442 0.000 0.000 0.868 0.132
#> SRR1319690 1 0.4978 0.3760 0.612 0.000 0.384 0.004
#> SRR1310604 2 0.0188 0.9590 0.000 0.996 0.004 0.000
#> SRR1327747 3 0.6752 0.5609 0.280 0.000 0.588 0.132
#> SRR1072456 2 0.0188 0.9590 0.000 0.996 0.004 0.000
#> SRR1367896 3 0.0336 0.8884 0.000 0.000 0.992 0.008
#> SRR1480107 1 0.0336 0.8627 0.992 0.000 0.000 0.008
#> SRR1377756 4 0.4977 0.3591 0.460 0.000 0.000 0.540
#> SRR1435272 4 0.0336 0.8554 0.000 0.000 0.008 0.992
#> SRR1089230 4 0.0000 0.8580 0.000 0.000 0.000 1.000
#> SRR1389522 3 0.0524 0.8885 0.004 0.000 0.988 0.008
#> SRR1080600 2 0.0188 0.9590 0.000 0.996 0.004 0.000
#> SRR1086935 4 0.0657 0.8552 0.000 0.004 0.012 0.984
#> SRR1344060 2 0.0524 0.9551 0.000 0.988 0.008 0.004
#> SRR1467922 2 0.0188 0.9590 0.000 0.996 0.004 0.000
#> SRR1090984 1 0.3266 0.7723 0.832 0.000 0.168 0.000
#> SRR1456991 1 0.0336 0.8627 0.992 0.000 0.000 0.008
#> SRR1085039 1 0.3123 0.7240 0.844 0.000 0.000 0.156
#> SRR1069303 4 0.4535 0.6868 0.292 0.000 0.004 0.704
#> SRR1091500 2 0.0000 0.9585 0.000 1.000 0.000 0.000
#> SRR1075198 2 0.0188 0.9590 0.000 0.996 0.004 0.000
#> SRR1086915 4 0.0336 0.8602 0.008 0.000 0.000 0.992
#> SRR1499503 2 0.0188 0.9590 0.000 0.996 0.004 0.000
#> SRR1094312 2 0.0000 0.9585 0.000 1.000 0.000 0.000
#> SRR1352437 4 0.3490 0.8198 0.156 0.004 0.004 0.836
#> SRR1436323 3 0.4866 0.4595 0.000 0.000 0.596 0.404
#> SRR1073507 4 0.3356 0.8118 0.176 0.000 0.000 0.824
#> SRR1401972 4 0.4535 0.6868 0.292 0.000 0.004 0.704
#> SRR1415510 2 0.0188 0.9590 0.000 0.996 0.004 0.000
#> SRR1327279 4 0.4417 0.8093 0.160 0.000 0.044 0.796
#> SRR1086983 4 0.0336 0.8602 0.008 0.000 0.000 0.992
#> SRR1105174 1 0.0000 0.8640 1.000 0.000 0.000 0.000
#> SRR1468893 1 0.0592 0.8624 0.984 0.000 0.000 0.016
#> SRR1362555 2 0.0000 0.9585 0.000 1.000 0.000 0.000
#> SRR1074526 2 0.3380 0.8049 0.136 0.852 0.008 0.004
#> SRR1326225 2 0.0188 0.9590 0.000 0.996 0.004 0.000
#> SRR1401933 4 0.5055 0.4696 0.368 0.000 0.008 0.624
#> SRR1324062 4 0.4126 0.7775 0.216 0.004 0.004 0.776
#> SRR1102296 1 0.1229 0.8598 0.968 0.004 0.020 0.008
#> SRR1085087 4 0.3402 0.8181 0.164 0.004 0.000 0.832
#> SRR1079046 1 0.0672 0.8628 0.984 0.008 0.000 0.008
#> SRR1328339 1 0.3801 0.7167 0.780 0.000 0.220 0.000
#> SRR1079782 2 0.0000 0.9585 0.000 1.000 0.000 0.000
#> SRR1092257 2 0.0188 0.9565 0.000 0.996 0.000 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.0162 0.96629 0.000 0.996 0.000 0.000 0.004
#> SRR1429287 2 0.1121 0.94523 0.000 0.956 0.000 0.000 0.044
#> SRR1359238 4 0.1764 0.76614 0.064 0.000 0.008 0.928 0.000
#> SRR1309597 3 0.0162 0.78472 0.000 0.000 0.996 0.000 0.004
#> SRR1441398 1 0.3612 0.61643 0.800 0.000 0.028 0.000 0.172
#> SRR1084055 2 0.0000 0.96669 0.000 1.000 0.000 0.000 0.000
#> SRR1417566 3 0.6107 0.48379 0.116 0.020 0.604 0.000 0.260
#> SRR1351857 4 0.1282 0.77825 0.044 0.000 0.000 0.952 0.004
#> SRR1487485 3 0.0566 0.78482 0.000 0.000 0.984 0.004 0.012
#> SRR1335875 3 0.2266 0.76771 0.008 0.016 0.912 0.000 0.064
#> SRR1073947 1 0.3794 0.59846 0.800 0.000 0.000 0.152 0.048
#> SRR1443483 3 0.0451 0.78489 0.000 0.000 0.988 0.004 0.008
#> SRR1346794 5 0.5291 0.15805 0.348 0.000 0.052 0.004 0.596
#> SRR1405245 1 0.4096 0.60392 0.772 0.000 0.052 0.000 0.176
#> SRR1409677 4 0.0898 0.78606 0.000 0.000 0.020 0.972 0.008
#> SRR1095549 1 0.6635 0.06637 0.444 0.000 0.064 0.060 0.432
#> SRR1323788 1 0.4181 0.58490 0.732 0.000 0.020 0.004 0.244
#> SRR1314054 2 0.0000 0.96669 0.000 1.000 0.000 0.000 0.000
#> SRR1077944 1 0.1792 0.65865 0.916 0.000 0.000 0.000 0.084
#> SRR1480587 2 0.0162 0.96629 0.000 0.996 0.000 0.000 0.004
#> SRR1311205 1 0.0613 0.66416 0.984 0.000 0.004 0.004 0.008
#> SRR1076369 5 0.2848 0.57091 0.104 0.000 0.028 0.000 0.868
#> SRR1453549 3 0.2616 0.77222 0.000 0.000 0.880 0.100 0.020
#> SRR1345782 1 0.0451 0.66286 0.988 0.000 0.004 0.000 0.008
#> SRR1447850 2 0.0510 0.95497 0.000 0.984 0.000 0.016 0.000
#> SRR1391553 3 0.3181 0.73957 0.000 0.072 0.856 0.000 0.072
#> SRR1444156 2 0.0000 0.96669 0.000 1.000 0.000 0.000 0.000
#> SRR1471731 3 0.4909 0.48060 0.000 0.000 0.588 0.380 0.032
#> SRR1120987 4 0.0794 0.78775 0.000 0.000 0.000 0.972 0.028
#> SRR1477363 1 0.2424 0.64132 0.868 0.000 0.000 0.000 0.132
#> SRR1391961 5 0.5384 0.64520 0.140 0.196 0.000 0.000 0.664
#> SRR1373879 3 0.2017 0.77405 0.000 0.000 0.912 0.080 0.008
#> SRR1318732 3 0.5798 0.45400 0.156 0.000 0.608 0.000 0.236
#> SRR1091404 1 0.4138 0.26418 0.616 0.000 0.000 0.000 0.384
#> SRR1402109 3 0.3551 0.68416 0.000 0.000 0.772 0.220 0.008
#> SRR1407336 3 0.4551 0.50087 0.000 0.000 0.616 0.368 0.016
#> SRR1097417 3 0.2470 0.73519 0.000 0.012 0.884 0.000 0.104
#> SRR1396227 1 0.5110 0.57985 0.680 0.000 0.000 0.096 0.224
#> SRR1400775 2 0.0000 0.96669 0.000 1.000 0.000 0.000 0.000
#> SRR1392861 4 0.0794 0.78276 0.000 0.000 0.028 0.972 0.000
#> SRR1472929 5 0.4863 0.44639 0.008 0.384 0.016 0.000 0.592
#> SRR1436740 4 0.0000 0.79153 0.000 0.000 0.000 1.000 0.000
#> SRR1477057 2 0.0000 0.96669 0.000 1.000 0.000 0.000 0.000
#> SRR1311980 3 0.1638 0.77407 0.000 0.004 0.932 0.000 0.064
#> SRR1069400 3 0.0671 0.78381 0.000 0.000 0.980 0.004 0.016
#> SRR1351016 1 0.2588 0.64424 0.892 0.000 0.000 0.060 0.048
#> SRR1096291 4 0.2142 0.75924 0.000 0.004 0.028 0.920 0.048
#> SRR1418145 4 0.0794 0.78622 0.000 0.000 0.000 0.972 0.028
#> SRR1488111 2 0.3495 0.75978 0.000 0.812 0.000 0.160 0.028
#> SRR1370495 5 0.5167 0.61451 0.200 0.116 0.000 0.000 0.684
#> SRR1352639 1 0.6188 0.31573 0.620 0.248 0.000 0.048 0.084
#> SRR1348911 3 0.0992 0.78022 0.000 0.008 0.968 0.000 0.024
#> SRR1467386 1 0.4415 0.14578 0.552 0.000 0.000 0.444 0.004
#> SRR1415956 1 0.3424 0.59004 0.760 0.000 0.000 0.000 0.240
#> SRR1500495 1 0.3242 0.62391 0.816 0.000 0.012 0.000 0.172
#> SRR1405099 1 0.2561 0.63858 0.856 0.000 0.000 0.000 0.144
#> SRR1345585 3 0.0703 0.78269 0.000 0.000 0.976 0.000 0.024
#> SRR1093196 3 0.4341 0.51247 0.000 0.000 0.628 0.364 0.008
#> SRR1466006 2 0.0404 0.96315 0.000 0.988 0.000 0.000 0.012
#> SRR1351557 2 0.0162 0.96629 0.000 0.996 0.000 0.000 0.004
#> SRR1382687 1 0.5572 0.61060 0.644 0.000 0.000 0.164 0.192
#> SRR1375549 5 0.3210 0.51834 0.212 0.000 0.000 0.000 0.788
#> SRR1101765 5 0.3283 0.56577 0.140 0.000 0.000 0.028 0.832
#> SRR1334461 5 0.5440 0.64026 0.156 0.184 0.000 0.000 0.660
#> SRR1094073 2 0.0000 0.96669 0.000 1.000 0.000 0.000 0.000
#> SRR1077549 4 0.3047 0.67608 0.160 0.000 0.004 0.832 0.004
#> SRR1440332 1 0.5615 0.38164 0.628 0.000 0.080 0.280 0.012
#> SRR1454177 4 0.0290 0.79150 0.000 0.000 0.008 0.992 0.000
#> SRR1082447 1 0.4287 0.22540 0.540 0.000 0.000 0.000 0.460
#> SRR1420043 3 0.4380 0.48774 0.000 0.000 0.616 0.376 0.008
#> SRR1432500 1 0.4375 0.20228 0.576 0.000 0.000 0.420 0.004
#> SRR1378045 3 0.3242 0.71099 0.000 0.116 0.844 0.000 0.040
#> SRR1334200 5 0.4045 0.50152 0.000 0.356 0.000 0.000 0.644
#> SRR1069539 4 0.6219 0.38063 0.000 0.248 0.064 0.620 0.068
#> SRR1343031 3 0.4173 0.67097 0.012 0.000 0.748 0.224 0.016
#> SRR1319690 3 0.6651 0.13751 0.312 0.000 0.440 0.000 0.248
#> SRR1310604 2 0.0794 0.95512 0.000 0.972 0.000 0.000 0.028
#> SRR1327747 3 0.8194 0.31304 0.164 0.000 0.412 0.200 0.224
#> SRR1072456 2 0.0162 0.96629 0.000 0.996 0.000 0.000 0.004
#> SRR1367896 3 0.0510 0.78331 0.000 0.000 0.984 0.000 0.016
#> SRR1480107 1 0.0807 0.66147 0.976 0.000 0.000 0.012 0.012
#> SRR1377756 1 0.5440 0.61115 0.660 0.000 0.000 0.156 0.184
#> SRR1435272 4 0.0162 0.79182 0.000 0.000 0.004 0.996 0.000
#> SRR1089230 4 0.0162 0.79172 0.000 0.000 0.000 0.996 0.004
#> SRR1389522 3 0.0609 0.78342 0.000 0.000 0.980 0.000 0.020
#> SRR1080600 2 0.2773 0.80816 0.000 0.836 0.000 0.000 0.164
#> SRR1086935 4 0.0693 0.78861 0.000 0.012 0.000 0.980 0.008
#> SRR1344060 5 0.4045 0.50151 0.000 0.356 0.000 0.000 0.644
#> SRR1467922 2 0.0000 0.96669 0.000 1.000 0.000 0.000 0.000
#> SRR1090984 5 0.6458 0.07423 0.292 0.000 0.216 0.000 0.492
#> SRR1456991 1 0.0404 0.66167 0.988 0.000 0.000 0.000 0.012
#> SRR1085039 1 0.3536 0.65556 0.832 0.000 0.000 0.084 0.084
#> SRR1069303 1 0.5844 0.25678 0.528 0.000 0.000 0.368 0.104
#> SRR1091500 2 0.0000 0.96669 0.000 1.000 0.000 0.000 0.000
#> SRR1075198 2 0.1408 0.94183 0.000 0.948 0.000 0.008 0.044
#> SRR1086915 4 0.0000 0.79153 0.000 0.000 0.000 1.000 0.000
#> SRR1499503 2 0.0162 0.96629 0.000 0.996 0.000 0.000 0.004
#> SRR1094312 2 0.0000 0.96669 0.000 1.000 0.000 0.000 0.000
#> SRR1352437 4 0.5599 -0.00975 0.444 0.000 0.000 0.484 0.072
#> SRR1436323 4 0.5491 -0.23941 0.004 0.000 0.452 0.492 0.052
#> SRR1073507 4 0.4452 -0.03642 0.496 0.000 0.000 0.500 0.004
#> SRR1401972 1 0.5844 0.25678 0.528 0.000 0.000 0.368 0.104
#> SRR1415510 2 0.0290 0.96502 0.000 0.992 0.000 0.000 0.008
#> SRR1327279 1 0.5436 0.37502 0.636 0.000 0.056 0.292 0.016
#> SRR1086983 4 0.1410 0.76810 0.060 0.000 0.000 0.940 0.000
#> SRR1105174 1 0.2732 0.63188 0.840 0.000 0.000 0.000 0.160
#> SRR1468893 1 0.4233 0.62115 0.748 0.000 0.000 0.044 0.208
#> SRR1362555 2 0.2127 0.88484 0.000 0.892 0.000 0.000 0.108
#> SRR1074526 5 0.4275 0.60748 0.020 0.284 0.000 0.000 0.696
#> SRR1326225 2 0.0000 0.96669 0.000 1.000 0.000 0.000 0.000
#> SRR1401933 4 0.6846 0.04424 0.224 0.000 0.008 0.436 0.332
#> SRR1324062 1 0.5352 0.20867 0.536 0.000 0.000 0.408 0.056
#> SRR1102296 1 0.3036 0.63951 0.868 0.012 0.008 0.008 0.104
#> SRR1085087 4 0.4818 0.04120 0.460 0.000 0.000 0.520 0.020
#> SRR1079046 5 0.2852 0.56214 0.172 0.000 0.000 0.000 0.828
#> SRR1328339 1 0.7057 -0.05472 0.344 0.008 0.316 0.000 0.332
#> SRR1079782 2 0.1661 0.93491 0.000 0.940 0.000 0.024 0.036
#> SRR1092257 2 0.1579 0.93349 0.000 0.944 0.000 0.032 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.0458 0.8865 0.000 0.984 0.000 0.000 0.016 0.000
#> SRR1429287 2 0.3526 0.8027 0.000 0.820 0.000 0.012 0.080 0.088
#> SRR1359238 4 0.3529 0.6921 0.176 0.000 0.028 0.788 0.000 0.008
#> SRR1309597 3 0.0547 0.7641 0.000 0.000 0.980 0.000 0.000 0.020
#> SRR1441398 1 0.4410 0.0902 0.560 0.000 0.028 0.000 0.000 0.412
#> SRR1084055 2 0.0405 0.8864 0.000 0.988 0.000 0.000 0.008 0.004
#> SRR1417566 6 0.4651 0.5124 0.036 0.024 0.140 0.000 0.044 0.756
#> SRR1351857 4 0.2515 0.7721 0.104 0.000 0.008 0.876 0.004 0.008
#> SRR1487485 3 0.2261 0.7447 0.000 0.000 0.884 0.004 0.008 0.104
#> SRR1335875 3 0.5104 0.5631 0.024 0.012 0.640 0.000 0.040 0.284
#> SRR1073947 1 0.2433 0.5318 0.884 0.000 0.000 0.072 0.000 0.044
#> SRR1443483 3 0.0000 0.7636 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1346794 6 0.5211 0.5066 0.180 0.000 0.016 0.004 0.132 0.668
#> SRR1405245 1 0.4453 0.1071 0.568 0.000 0.032 0.000 0.000 0.400
#> SRR1409677 4 0.1320 0.8157 0.000 0.000 0.036 0.948 0.000 0.016
#> SRR1095549 6 0.7476 0.3136 0.308 0.000 0.116 0.068 0.068 0.440
#> SRR1323788 6 0.4507 0.1183 0.432 0.000 0.008 0.004 0.012 0.544
#> SRR1314054 2 0.0291 0.8870 0.000 0.992 0.000 0.000 0.004 0.004
#> SRR1077944 1 0.2823 0.4327 0.796 0.000 0.000 0.000 0.000 0.204
#> SRR1480587 2 0.0458 0.8865 0.000 0.984 0.000 0.000 0.016 0.000
#> SRR1311205 1 0.1738 0.5231 0.928 0.000 0.016 0.004 0.000 0.052
#> SRR1076369 5 0.4623 0.5534 0.048 0.000 0.004 0.004 0.652 0.292
#> SRR1453549 3 0.4154 0.7331 0.004 0.000 0.772 0.080 0.012 0.132
#> SRR1345782 1 0.1682 0.5203 0.928 0.000 0.020 0.000 0.000 0.052
#> SRR1447850 2 0.1777 0.8664 0.000 0.932 0.000 0.024 0.032 0.012
#> SRR1391553 3 0.6584 0.2701 0.000 0.180 0.408 0.000 0.044 0.368
#> SRR1444156 2 0.0000 0.8873 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1471731 3 0.6259 0.2641 0.000 0.000 0.380 0.360 0.008 0.252
#> SRR1120987 4 0.3489 0.7531 0.012 0.004 0.000 0.828 0.060 0.096
#> SRR1477363 1 0.3101 0.3986 0.756 0.000 0.000 0.000 0.000 0.244
#> SRR1391961 5 0.2775 0.8028 0.040 0.104 0.000 0.000 0.856 0.000
#> SRR1373879 3 0.1075 0.7618 0.000 0.000 0.952 0.048 0.000 0.000
#> SRR1318732 6 0.4838 0.5452 0.072 0.008 0.216 0.000 0.012 0.692
#> SRR1091404 1 0.5202 0.2431 0.612 0.000 0.000 0.000 0.224 0.164
#> SRR1402109 3 0.2416 0.7147 0.000 0.000 0.844 0.156 0.000 0.000
#> SRR1407336 3 0.3445 0.6345 0.000 0.000 0.744 0.244 0.000 0.012
#> SRR1097417 3 0.3351 0.7098 0.000 0.016 0.832 0.000 0.104 0.048
#> SRR1396227 1 0.6057 0.1693 0.480 0.000 0.000 0.092 0.048 0.380
#> SRR1400775 2 0.0146 0.8869 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1392861 4 0.0858 0.8168 0.000 0.000 0.028 0.968 0.004 0.000
#> SRR1472929 5 0.2909 0.7757 0.004 0.156 0.012 0.000 0.828 0.000
#> SRR1436740 4 0.0405 0.8214 0.000 0.000 0.004 0.988 0.000 0.008
#> SRR1477057 2 0.1549 0.8694 0.000 0.936 0.000 0.000 0.020 0.044
#> SRR1311980 3 0.4390 0.6085 0.000 0.004 0.668 0.000 0.044 0.284
#> SRR1069400 3 0.0260 0.7634 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR1351016 1 0.2333 0.5321 0.896 0.000 0.004 0.040 0.000 0.060
#> SRR1096291 4 0.4901 0.6901 0.004 0.004 0.104 0.744 0.068 0.076
#> SRR1418145 4 0.3410 0.7432 0.004 0.000 0.000 0.820 0.076 0.100
#> SRR1488111 2 0.5701 0.6179 0.000 0.644 0.000 0.176 0.080 0.100
#> SRR1370495 5 0.2834 0.7374 0.060 0.008 0.000 0.008 0.876 0.048
#> SRR1352639 1 0.6507 0.3061 0.604 0.108 0.000 0.024 0.152 0.112
#> SRR1348911 3 0.3737 0.6804 0.000 0.008 0.772 0.000 0.036 0.184
#> SRR1467386 1 0.4199 0.4219 0.640 0.000 0.000 0.336 0.004 0.020
#> SRR1415956 1 0.4057 0.0982 0.556 0.000 0.000 0.000 0.008 0.436
#> SRR1500495 1 0.4362 0.1435 0.584 0.000 0.028 0.000 0.000 0.388
#> SRR1405099 1 0.3426 0.3693 0.720 0.000 0.000 0.000 0.004 0.276
#> SRR1345585 3 0.3376 0.6699 0.000 0.000 0.764 0.000 0.016 0.220
#> SRR1093196 3 0.4755 0.5329 0.000 0.000 0.632 0.304 0.008 0.056
#> SRR1466006 2 0.0790 0.8833 0.000 0.968 0.000 0.000 0.032 0.000
#> SRR1351557 2 0.0363 0.8870 0.000 0.988 0.000 0.000 0.012 0.000
#> SRR1382687 6 0.5830 -0.1603 0.416 0.000 0.000 0.160 0.004 0.420
#> SRR1375549 5 0.4986 0.5206 0.096 0.000 0.000 0.004 0.628 0.272
#> SRR1101765 5 0.4379 0.6166 0.052 0.000 0.000 0.008 0.700 0.240
#> SRR1334461 5 0.2837 0.7979 0.056 0.088 0.000 0.000 0.856 0.000
#> SRR1094073 2 0.0146 0.8875 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1077549 4 0.4737 0.4246 0.308 0.000 0.044 0.636 0.004 0.008
#> SRR1440332 1 0.5504 0.3396 0.624 0.000 0.216 0.136 0.000 0.024
#> SRR1454177 4 0.0146 0.8216 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1082447 1 0.5565 0.0934 0.508 0.000 0.000 0.000 0.152 0.340
#> SRR1420043 3 0.3198 0.6292 0.000 0.000 0.740 0.260 0.000 0.000
#> SRR1432500 1 0.3411 0.4912 0.756 0.000 0.008 0.232 0.000 0.004
#> SRR1378045 2 0.6819 -0.2978 0.000 0.336 0.320 0.000 0.040 0.304
#> SRR1334200 5 0.2048 0.7981 0.000 0.120 0.000 0.000 0.880 0.000
#> SRR1069539 4 0.6913 0.4788 0.000 0.060 0.184 0.572 0.096 0.088
#> SRR1343031 3 0.2070 0.7400 0.012 0.000 0.896 0.092 0.000 0.000
#> SRR1319690 6 0.5537 0.5334 0.212 0.000 0.188 0.000 0.008 0.592
#> SRR1310604 2 0.1956 0.8607 0.000 0.908 0.000 0.004 0.080 0.008
#> SRR1327747 6 0.6699 0.3774 0.060 0.000 0.268 0.148 0.012 0.512
#> SRR1072456 2 0.0632 0.8845 0.000 0.976 0.000 0.000 0.024 0.000
#> SRR1367896 3 0.0806 0.7636 0.000 0.000 0.972 0.000 0.008 0.020
#> SRR1480107 1 0.0291 0.5340 0.992 0.000 0.000 0.000 0.004 0.004
#> SRR1377756 1 0.5750 0.2409 0.512 0.000 0.000 0.132 0.012 0.344
#> SRR1435272 4 0.0146 0.8216 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1089230 4 0.0837 0.8173 0.000 0.000 0.004 0.972 0.004 0.020
#> SRR1389522 3 0.0000 0.7636 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1080600 2 0.4049 0.6766 0.000 0.708 0.000 0.004 0.256 0.032
#> SRR1086935 4 0.1413 0.8113 0.000 0.008 0.004 0.948 0.004 0.036
#> SRR1344060 5 0.2178 0.7949 0.000 0.132 0.000 0.000 0.868 0.000
#> SRR1467922 2 0.0000 0.8873 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1090984 6 0.4656 0.5764 0.112 0.000 0.064 0.000 0.076 0.748
#> SRR1456991 1 0.0692 0.5307 0.976 0.000 0.004 0.000 0.000 0.020
#> SRR1085039 1 0.3544 0.5178 0.820 0.000 0.000 0.064 0.016 0.100
#> SRR1069303 1 0.6186 0.3791 0.532 0.000 0.000 0.272 0.040 0.156
#> SRR1091500 2 0.0146 0.8869 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1075198 2 0.3699 0.7938 0.000 0.796 0.000 0.004 0.112 0.088
#> SRR1086915 4 0.0405 0.8201 0.008 0.000 0.000 0.988 0.000 0.004
#> SRR1499503 2 0.0547 0.8856 0.000 0.980 0.000 0.000 0.020 0.000
#> SRR1094312 2 0.0146 0.8869 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1352437 1 0.6113 0.2779 0.480 0.000 0.000 0.344 0.024 0.152
#> SRR1436323 4 0.6597 -0.0395 0.012 0.000 0.280 0.444 0.016 0.248
#> SRR1073507 1 0.3847 0.4004 0.644 0.000 0.000 0.348 0.000 0.008
#> SRR1401972 1 0.6171 0.3815 0.536 0.000 0.000 0.268 0.040 0.156
#> SRR1415510 2 0.0547 0.8856 0.000 0.980 0.000 0.000 0.020 0.000
#> SRR1327279 1 0.4622 0.4472 0.712 0.000 0.160 0.120 0.000 0.008
#> SRR1086983 4 0.2454 0.7622 0.104 0.000 0.000 0.876 0.004 0.016
#> SRR1105174 1 0.3766 0.3234 0.684 0.000 0.000 0.000 0.012 0.304
#> SRR1468893 1 0.5210 0.2596 0.576 0.000 0.000 0.040 0.036 0.348
#> SRR1362555 2 0.4905 0.6158 0.000 0.640 0.000 0.004 0.264 0.092
#> SRR1074526 5 0.2389 0.7978 0.000 0.128 0.000 0.000 0.864 0.008
#> SRR1326225 2 0.0146 0.8869 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1401933 6 0.5808 0.2373 0.060 0.000 0.000 0.348 0.060 0.532
#> SRR1324062 1 0.6013 0.3807 0.516 0.000 0.000 0.300 0.020 0.164
#> SRR1102296 1 0.4190 0.4103 0.692 0.000 0.000 0.000 0.048 0.260
#> SRR1085087 1 0.5105 0.2726 0.540 0.000 0.000 0.388 0.008 0.064
#> SRR1079046 5 0.3381 0.7126 0.044 0.000 0.000 0.000 0.800 0.156
#> SRR1328339 6 0.5734 0.5567 0.128 0.012 0.124 0.000 0.068 0.668
#> SRR1079782 2 0.4247 0.7754 0.000 0.776 0.000 0.036 0.096 0.092
#> SRR1092257 2 0.4571 0.7506 0.000 0.756 0.000 0.072 0.068 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["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 17611 rows and 118 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 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 0.825 0.908 0.956 0.3928 0.618 0.618
#> 3 3 0.463 0.684 0.808 0.6335 0.656 0.476
#> 4 4 0.504 0.560 0.793 0.1467 0.771 0.457
#> 5 5 0.572 0.495 0.736 0.0634 0.917 0.714
#> 6 6 0.653 0.576 0.761 0.0330 0.945 0.774
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
#> SRR1396765 2 0.0000 0.9382 0.000 1.000
#> SRR1429287 2 0.0376 0.9374 0.004 0.996
#> SRR1359238 1 0.0376 0.9577 0.996 0.004
#> SRR1309597 1 0.1633 0.9560 0.976 0.024
#> SRR1441398 1 0.0000 0.9575 1.000 0.000
#> SRR1084055 2 0.0000 0.9382 0.000 1.000
#> SRR1417566 1 0.1633 0.9560 0.976 0.024
#> SRR1351857 1 0.1633 0.9560 0.976 0.024
#> SRR1487485 1 0.6712 0.7922 0.824 0.176
#> SRR1335875 1 0.1633 0.9560 0.976 0.024
#> SRR1073947 1 0.0000 0.9575 1.000 0.000
#> SRR1443483 1 0.1633 0.9560 0.976 0.024
#> SRR1346794 1 0.1633 0.9560 0.976 0.024
#> SRR1405245 1 0.0000 0.9575 1.000 0.000
#> SRR1409677 1 0.1633 0.9560 0.976 0.024
#> SRR1095549 1 0.1414 0.9567 0.980 0.020
#> SRR1323788 1 0.1633 0.9560 0.976 0.024
#> SRR1314054 2 0.0938 0.9347 0.012 0.988
#> SRR1077944 1 0.0000 0.9575 1.000 0.000
#> SRR1480587 2 0.0000 0.9382 0.000 1.000
#> SRR1311205 1 0.0000 0.9575 1.000 0.000
#> SRR1076369 1 0.0000 0.9575 1.000 0.000
#> SRR1453549 1 0.1633 0.9560 0.976 0.024
#> SRR1345782 1 0.0000 0.9575 1.000 0.000
#> SRR1447850 2 0.4161 0.8880 0.084 0.916
#> SRR1391553 2 0.5059 0.8666 0.112 0.888
#> SRR1444156 2 0.0000 0.9382 0.000 1.000
#> SRR1471731 1 0.1633 0.9560 0.976 0.024
#> SRR1120987 1 0.8555 0.6111 0.720 0.280
#> SRR1477363 1 0.0000 0.9575 1.000 0.000
#> SRR1391961 1 0.3584 0.9078 0.932 0.068
#> SRR1373879 1 0.1633 0.9560 0.976 0.024
#> SRR1318732 1 0.2603 0.9440 0.956 0.044
#> SRR1091404 1 0.0376 0.9562 0.996 0.004
#> SRR1402109 1 0.1633 0.9560 0.976 0.024
#> SRR1407336 1 0.1633 0.9560 0.976 0.024
#> SRR1097417 2 0.9833 0.2820 0.424 0.576
#> SRR1396227 1 0.0000 0.9575 1.000 0.000
#> SRR1400775 2 0.0000 0.9382 0.000 1.000
#> SRR1392861 1 0.1633 0.9560 0.976 0.024
#> SRR1472929 1 0.9129 0.5038 0.672 0.328
#> SRR1436740 1 0.1633 0.9560 0.976 0.024
#> SRR1477057 2 0.6247 0.8401 0.156 0.844
#> SRR1311980 1 0.0000 0.9575 1.000 0.000
#> SRR1069400 1 0.1633 0.9560 0.976 0.024
#> SRR1351016 1 0.0000 0.9575 1.000 0.000
#> SRR1096291 1 0.1633 0.9560 0.976 0.024
#> SRR1418145 1 0.1414 0.9567 0.980 0.020
#> SRR1488111 2 0.8327 0.6732 0.264 0.736
#> SRR1370495 1 0.0672 0.9545 0.992 0.008
#> SRR1352639 1 0.0938 0.9525 0.988 0.012
#> SRR1348911 1 0.0672 0.9576 0.992 0.008
#> SRR1467386 1 0.0000 0.9575 1.000 0.000
#> SRR1415956 1 0.0000 0.9575 1.000 0.000
#> SRR1500495 1 0.0000 0.9575 1.000 0.000
#> SRR1405099 1 0.0000 0.9575 1.000 0.000
#> SRR1345585 1 0.2423 0.9459 0.960 0.040
#> SRR1093196 1 0.1633 0.9560 0.976 0.024
#> SRR1466006 2 0.0000 0.9382 0.000 1.000
#> SRR1351557 2 0.0000 0.9382 0.000 1.000
#> SRR1382687 1 0.1414 0.9567 0.980 0.020
#> SRR1375549 1 0.0672 0.9545 0.992 0.008
#> SRR1101765 1 0.1633 0.9560 0.976 0.024
#> SRR1334461 1 0.4815 0.8663 0.896 0.104
#> SRR1094073 2 0.0000 0.9382 0.000 1.000
#> SRR1077549 1 0.0000 0.9575 1.000 0.000
#> SRR1440332 1 0.0000 0.9575 1.000 0.000
#> SRR1454177 1 0.1633 0.9560 0.976 0.024
#> SRR1082447 1 0.0000 0.9575 1.000 0.000
#> SRR1420043 1 0.1633 0.9560 0.976 0.024
#> SRR1432500 1 0.0000 0.9575 1.000 0.000
#> SRR1378045 2 0.1184 0.9330 0.016 0.984
#> SRR1334200 1 0.9552 0.4271 0.624 0.376
#> SRR1069539 1 0.1843 0.9547 0.972 0.028
#> SRR1343031 1 0.0376 0.9576 0.996 0.004
#> SRR1319690 1 0.1633 0.9560 0.976 0.024
#> SRR1310604 2 0.0000 0.9382 0.000 1.000
#> SRR1327747 1 0.1633 0.9560 0.976 0.024
#> SRR1072456 2 0.1633 0.9239 0.024 0.976
#> SRR1367896 1 0.0672 0.9576 0.992 0.008
#> SRR1480107 1 0.0000 0.9575 1.000 0.000
#> SRR1377756 1 0.1414 0.9567 0.980 0.020
#> SRR1435272 1 0.1633 0.9560 0.976 0.024
#> SRR1089230 1 0.1633 0.9560 0.976 0.024
#> SRR1389522 1 0.0672 0.9545 0.992 0.008
#> SRR1080600 2 0.5737 0.8326 0.136 0.864
#> SRR1086935 2 0.8327 0.6715 0.264 0.736
#> SRR1344060 1 1.0000 -0.0631 0.500 0.500
#> SRR1467922 2 0.0000 0.9382 0.000 1.000
#> SRR1090984 1 0.0000 0.9575 1.000 0.000
#> SRR1456991 1 0.0000 0.9575 1.000 0.000
#> SRR1085039 1 0.0000 0.9575 1.000 0.000
#> SRR1069303 1 0.0000 0.9575 1.000 0.000
#> SRR1091500 2 0.0938 0.9319 0.012 0.988
#> SRR1075198 2 0.0000 0.9382 0.000 1.000
#> SRR1086915 1 0.1633 0.9560 0.976 0.024
#> SRR1499503 2 0.0000 0.9382 0.000 1.000
#> SRR1094312 2 0.0000 0.9382 0.000 1.000
#> SRR1352437 1 0.7883 0.6631 0.764 0.236
#> SRR1436323 1 0.1633 0.9560 0.976 0.024
#> SRR1073507 1 0.0000 0.9575 1.000 0.000
#> SRR1401972 1 0.0000 0.9575 1.000 0.000
#> SRR1415510 2 0.0672 0.9362 0.008 0.992
#> SRR1327279 1 0.0000 0.9575 1.000 0.000
#> SRR1086983 1 0.1633 0.9560 0.976 0.024
#> SRR1105174 1 0.0000 0.9575 1.000 0.000
#> SRR1468893 1 0.0000 0.9575 1.000 0.000
#> SRR1362555 1 0.8016 0.6720 0.756 0.244
#> SRR1074526 2 0.7139 0.7971 0.196 0.804
#> SRR1326225 2 0.0000 0.9382 0.000 1.000
#> SRR1401933 1 0.0000 0.9575 1.000 0.000
#> SRR1324062 1 0.0000 0.9575 1.000 0.000
#> SRR1102296 1 0.3584 0.9042 0.932 0.068
#> SRR1085087 1 0.0000 0.9575 1.000 0.000
#> SRR1079046 1 0.5946 0.8242 0.856 0.144
#> SRR1328339 1 0.0000 0.9575 1.000 0.000
#> SRR1079782 2 0.0000 0.9382 0.000 1.000
#> SRR1092257 2 0.1184 0.9327 0.016 0.984
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.0000 0.8946 0.000 1.000 0.000
#> SRR1429287 2 0.4654 0.7594 0.000 0.792 0.208
#> SRR1359238 1 0.6215 0.2724 0.572 0.000 0.428
#> SRR1309597 1 0.6168 0.3425 0.588 0.000 0.412
#> SRR1441398 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1084055 2 0.0000 0.8946 0.000 1.000 0.000
#> SRR1417566 3 0.4887 0.7214 0.228 0.000 0.772
#> SRR1351857 3 0.6309 -0.2764 0.496 0.000 0.504
#> SRR1487485 3 0.2356 0.7657 0.072 0.000 0.928
#> SRR1335875 3 0.5785 0.6351 0.332 0.000 0.668
#> SRR1073947 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1443483 1 0.6252 0.2675 0.556 0.000 0.444
#> SRR1346794 1 0.5465 0.5353 0.712 0.000 0.288
#> SRR1405245 1 0.4750 0.6654 0.784 0.000 0.216
#> SRR1409677 3 0.1753 0.7585 0.048 0.000 0.952
#> SRR1095549 1 0.3879 0.7105 0.848 0.000 0.152
#> SRR1323788 3 0.4974 0.7148 0.236 0.000 0.764
#> SRR1314054 2 0.3412 0.8058 0.000 0.876 0.124
#> SRR1077944 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1480587 2 0.0000 0.8946 0.000 1.000 0.000
#> SRR1311205 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1076369 1 0.3686 0.7179 0.860 0.000 0.140
#> SRR1453549 3 0.5431 0.6781 0.284 0.000 0.716
#> SRR1345782 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1447850 2 0.4605 0.7232 0.000 0.796 0.204
#> SRR1391553 3 0.7274 0.4830 0.052 0.304 0.644
#> SRR1444156 2 0.0000 0.8946 0.000 1.000 0.000
#> SRR1471731 3 0.2356 0.7657 0.072 0.000 0.928
#> SRR1120987 3 0.5327 0.6353 0.272 0.000 0.728
#> SRR1477363 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1391961 1 0.1643 0.7977 0.956 0.044 0.000
#> SRR1373879 3 0.2356 0.7657 0.072 0.000 0.928
#> SRR1318732 3 0.3686 0.7461 0.140 0.000 0.860
#> SRR1091404 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1402109 3 0.3267 0.7539 0.116 0.000 0.884
#> SRR1407336 3 0.2356 0.7657 0.072 0.000 0.928
#> SRR1097417 3 0.3764 0.7616 0.068 0.040 0.892
#> SRR1396227 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1400775 2 0.0000 0.8946 0.000 1.000 0.000
#> SRR1392861 3 0.0000 0.7550 0.000 0.000 1.000
#> SRR1472929 1 0.6111 0.3542 0.604 0.396 0.000
#> SRR1436740 3 0.0000 0.7550 0.000 0.000 1.000
#> SRR1477057 2 0.6887 0.6047 0.060 0.704 0.236
#> SRR1311980 3 0.6235 0.4700 0.436 0.000 0.564
#> SRR1069400 3 0.4555 0.6874 0.200 0.000 0.800
#> SRR1351016 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1096291 3 0.4750 0.6209 0.216 0.000 0.784
#> SRR1418145 1 0.5465 0.6277 0.712 0.000 0.288
#> SRR1488111 3 0.7273 0.6894 0.156 0.132 0.712
#> SRR1370495 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1352639 1 0.3619 0.7372 0.864 0.000 0.136
#> SRR1348911 3 0.5926 0.6048 0.356 0.000 0.644
#> SRR1467386 1 0.5785 0.3537 0.668 0.000 0.332
#> SRR1415956 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1500495 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1405099 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1345585 3 0.2537 0.7663 0.080 0.000 0.920
#> SRR1093196 3 0.2356 0.7657 0.072 0.000 0.928
#> SRR1466006 2 0.0237 0.8932 0.000 0.996 0.004
#> SRR1351557 2 0.0000 0.8946 0.000 1.000 0.000
#> SRR1382687 3 0.5810 0.6306 0.336 0.000 0.664
#> SRR1375549 1 0.2356 0.7850 0.928 0.000 0.072
#> SRR1101765 1 0.6309 -0.0526 0.504 0.000 0.496
#> SRR1334461 1 0.1964 0.7926 0.944 0.056 0.000
#> SRR1094073 2 0.0000 0.8946 0.000 1.000 0.000
#> SRR1077549 3 0.5835 0.6273 0.340 0.000 0.660
#> SRR1440332 1 0.3816 0.7093 0.852 0.000 0.148
#> SRR1454177 3 0.0000 0.7550 0.000 0.000 1.000
#> SRR1082447 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1420043 3 0.1163 0.7627 0.028 0.000 0.972
#> SRR1432500 1 0.4796 0.6926 0.780 0.000 0.220
#> SRR1378045 2 0.5948 0.4180 0.000 0.640 0.360
#> SRR1334200 3 0.9156 0.5422 0.256 0.204 0.540
#> SRR1069539 3 0.0000 0.7550 0.000 0.000 1.000
#> SRR1343031 3 0.5988 0.3766 0.368 0.000 0.632
#> SRR1319690 3 0.5431 0.6816 0.284 0.000 0.716
#> SRR1310604 2 0.2711 0.8506 0.000 0.912 0.088
#> SRR1327747 3 0.5678 0.4046 0.316 0.000 0.684
#> SRR1072456 2 0.0000 0.8946 0.000 1.000 0.000
#> SRR1367896 3 0.4399 0.7089 0.188 0.000 0.812
#> SRR1480107 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1377756 1 0.5178 0.6577 0.744 0.000 0.256
#> SRR1435272 3 0.0000 0.7550 0.000 0.000 1.000
#> SRR1089230 3 0.0000 0.7550 0.000 0.000 1.000
#> SRR1389522 1 0.4887 0.6206 0.772 0.000 0.228
#> SRR1080600 3 0.8518 -0.0886 0.092 0.436 0.472
#> SRR1086935 3 0.0000 0.7550 0.000 0.000 1.000
#> SRR1344060 2 0.5905 0.4025 0.352 0.648 0.000
#> SRR1467922 2 0.0000 0.8946 0.000 1.000 0.000
#> SRR1090984 3 0.6062 0.5700 0.384 0.000 0.616
#> SRR1456991 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1085039 1 0.2356 0.7850 0.928 0.000 0.072
#> SRR1069303 1 0.1860 0.7960 0.948 0.000 0.052
#> SRR1091500 2 0.0000 0.8946 0.000 1.000 0.000
#> SRR1075198 2 0.3340 0.8202 0.000 0.880 0.120
#> SRR1086915 3 0.3941 0.7253 0.156 0.000 0.844
#> SRR1499503 2 0.0000 0.8946 0.000 1.000 0.000
#> SRR1094312 2 0.0000 0.8946 0.000 1.000 0.000
#> SRR1352437 3 0.7424 0.6136 0.300 0.060 0.640
#> SRR1436323 3 0.2356 0.7657 0.072 0.000 0.928
#> SRR1073507 1 0.2356 0.7850 0.928 0.000 0.072
#> SRR1401972 1 0.3412 0.7313 0.876 0.000 0.124
#> SRR1415510 2 0.5431 0.5727 0.000 0.716 0.284
#> SRR1327279 1 0.3816 0.7093 0.852 0.000 0.148
#> SRR1086983 3 0.5216 0.6469 0.260 0.000 0.740
#> SRR1105174 1 0.2356 0.7850 0.928 0.000 0.072
#> SRR1468893 1 0.0424 0.8132 0.992 0.000 0.008
#> SRR1362555 1 0.6225 0.2649 0.568 0.432 0.000
#> SRR1074526 1 0.9767 -0.2368 0.404 0.232 0.364
#> SRR1326225 2 0.0000 0.8946 0.000 1.000 0.000
#> SRR1401933 1 0.5560 0.4377 0.700 0.000 0.300
#> SRR1324062 3 0.6215 0.4842 0.428 0.000 0.572
#> SRR1102296 1 0.5207 0.7166 0.824 0.052 0.124
#> SRR1085087 1 0.2448 0.7850 0.924 0.000 0.076
#> SRR1079046 1 0.4569 0.7554 0.860 0.068 0.072
#> SRR1328339 1 0.0000 0.8140 1.000 0.000 0.000
#> SRR1079782 2 0.2959 0.8464 0.000 0.900 0.100
#> SRR1092257 2 0.2165 0.8637 0.000 0.936 0.064
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.0592 0.8555 0.000 0.984 0.000 0.016
#> SRR1429287 4 0.5877 0.4834 0.000 0.276 0.068 0.656
#> SRR1359238 4 0.6049 0.6784 0.184 0.000 0.132 0.684
#> SRR1309597 3 0.6910 0.2977 0.324 0.000 0.548 0.128
#> SRR1441398 1 0.1022 0.7192 0.968 0.000 0.000 0.032
#> SRR1084055 2 0.0000 0.8578 0.000 1.000 0.000 0.000
#> SRR1417566 3 0.5039 0.1044 0.404 0.000 0.592 0.004
#> SRR1351857 4 0.3333 0.7188 0.088 0.000 0.040 0.872
#> SRR1487485 3 0.0000 0.6832 0.000 0.000 1.000 0.000
#> SRR1335875 3 0.5000 -0.1495 0.500 0.000 0.500 0.000
#> SRR1073947 1 0.0000 0.7244 1.000 0.000 0.000 0.000
#> SRR1443483 3 0.6950 0.3150 0.272 0.000 0.572 0.156
#> SRR1346794 1 0.7545 -0.0488 0.440 0.000 0.368 0.192
#> SRR1405245 1 0.4737 0.5442 0.728 0.000 0.252 0.020
#> SRR1409677 4 0.3401 0.7035 0.008 0.000 0.152 0.840
#> SRR1095549 1 0.3324 0.6402 0.852 0.000 0.136 0.012
#> SRR1323788 3 0.4877 0.3127 0.328 0.000 0.664 0.008
#> SRR1314054 2 0.2973 0.7540 0.000 0.856 0.144 0.000
#> SRR1077944 1 0.0188 0.7244 0.996 0.000 0.000 0.004
#> SRR1480587 2 0.0592 0.8555 0.000 0.984 0.000 0.016
#> SRR1311205 1 0.0188 0.7242 0.996 0.000 0.000 0.004
#> SRR1076369 1 0.7818 0.0672 0.416 0.000 0.292 0.292
#> SRR1453549 3 0.4977 -0.0482 0.460 0.000 0.540 0.000
#> SRR1345782 1 0.0000 0.7244 1.000 0.000 0.000 0.000
#> SRR1447850 2 0.3837 0.6643 0.000 0.776 0.224 0.000
#> SRR1391553 3 0.6079 -0.0460 0.044 0.464 0.492 0.000
#> SRR1444156 2 0.0000 0.8578 0.000 1.000 0.000 0.000
#> SRR1471731 3 0.0000 0.6832 0.000 0.000 1.000 0.000
#> SRR1120987 4 0.1635 0.7170 0.044 0.000 0.008 0.948
#> SRR1477363 1 0.0817 0.7208 0.976 0.000 0.000 0.024
#> SRR1391961 1 0.2610 0.6905 0.900 0.012 0.000 0.088
#> SRR1373879 3 0.0000 0.6832 0.000 0.000 1.000 0.000
#> SRR1318732 3 0.3768 0.5587 0.008 0.000 0.808 0.184
#> SRR1091404 1 0.0188 0.7244 0.996 0.000 0.000 0.004
#> SRR1402109 3 0.1302 0.6768 0.044 0.000 0.956 0.000
#> SRR1407336 3 0.3024 0.5712 0.000 0.000 0.852 0.148
#> SRR1097417 3 0.1042 0.6773 0.000 0.020 0.972 0.008
#> SRR1396227 1 0.0188 0.7242 0.996 0.000 0.000 0.004
#> SRR1400775 2 0.0000 0.8578 0.000 1.000 0.000 0.000
#> SRR1392861 3 0.0000 0.6832 0.000 0.000 1.000 0.000
#> SRR1472929 1 0.6974 0.1070 0.488 0.396 0.000 0.116
#> SRR1436740 4 0.4431 0.5865 0.000 0.000 0.304 0.696
#> SRR1477057 2 0.7004 0.5027 0.160 0.628 0.196 0.016
#> SRR1311980 1 0.4888 0.2838 0.588 0.000 0.412 0.000
#> SRR1069400 3 0.2342 0.6606 0.080 0.000 0.912 0.008
#> SRR1351016 1 0.0000 0.7244 1.000 0.000 0.000 0.000
#> SRR1096291 4 0.5010 0.6807 0.108 0.000 0.120 0.772
#> SRR1418145 4 0.1610 0.7165 0.016 0.000 0.032 0.952
#> SRR1488111 3 0.7521 0.1410 0.352 0.096 0.520 0.032
#> SRR1370495 1 0.2469 0.6877 0.892 0.000 0.000 0.108
#> SRR1352639 1 0.7872 -0.1076 0.376 0.000 0.344 0.280
#> SRR1348911 1 0.5000 0.0966 0.500 0.000 0.500 0.000
#> SRR1467386 1 0.4401 0.5346 0.724 0.000 0.272 0.004
#> SRR1415956 1 0.0817 0.7208 0.976 0.000 0.000 0.024
#> SRR1500495 1 0.1209 0.7184 0.964 0.000 0.004 0.032
#> SRR1405099 1 0.0817 0.7208 0.976 0.000 0.000 0.024
#> SRR1345585 3 0.0895 0.6807 0.004 0.000 0.976 0.020
#> SRR1093196 3 0.0336 0.6818 0.000 0.000 0.992 0.008
#> SRR1466006 2 0.0657 0.8556 0.000 0.984 0.004 0.012
#> SRR1351557 2 0.0000 0.8578 0.000 1.000 0.000 0.000
#> SRR1382687 1 0.4977 0.2029 0.540 0.000 0.460 0.000
#> SRR1375549 4 0.3528 0.6804 0.192 0.000 0.000 0.808
#> SRR1101765 4 0.3577 0.6927 0.156 0.000 0.012 0.832
#> SRR1334461 1 0.3307 0.6761 0.868 0.028 0.000 0.104
#> SRR1094073 2 0.0000 0.8578 0.000 1.000 0.000 0.000
#> SRR1077549 1 0.5161 0.1763 0.520 0.000 0.476 0.004
#> SRR1440332 1 0.3554 0.6615 0.844 0.000 0.020 0.136
#> SRR1454177 3 0.4967 -0.1684 0.000 0.000 0.548 0.452
#> SRR1082447 1 0.0921 0.7216 0.972 0.000 0.000 0.028
#> SRR1420043 3 0.0000 0.6832 0.000 0.000 1.000 0.000
#> SRR1432500 4 0.6031 0.6570 0.168 0.000 0.144 0.688
#> SRR1378045 2 0.4406 0.5410 0.000 0.700 0.300 0.000
#> SRR1334200 4 0.9135 -0.0832 0.296 0.072 0.256 0.376
#> SRR1069539 4 0.3975 0.6322 0.000 0.000 0.240 0.760
#> SRR1343031 3 0.3172 0.6017 0.160 0.000 0.840 0.000
#> SRR1319690 3 0.4406 0.5839 0.192 0.000 0.780 0.028
#> SRR1310604 2 0.5140 0.7234 0.000 0.760 0.096 0.144
#> SRR1327747 4 0.6474 0.5157 0.120 0.000 0.256 0.624
#> SRR1072456 2 0.0592 0.8555 0.000 0.984 0.000 0.016
#> SRR1367896 3 0.2149 0.6557 0.088 0.000 0.912 0.000
#> SRR1480107 1 0.0188 0.7244 0.996 0.000 0.000 0.004
#> SRR1377756 4 0.3873 0.7182 0.096 0.000 0.060 0.844
#> SRR1435272 4 0.4454 0.5831 0.000 0.000 0.308 0.692
#> SRR1089230 4 0.1716 0.7167 0.000 0.000 0.064 0.936
#> SRR1389522 1 0.5931 -0.0271 0.504 0.000 0.460 0.036
#> SRR1080600 3 0.7824 0.0707 0.004 0.216 0.424 0.356
#> SRR1086935 4 0.5000 0.1820 0.000 0.000 0.500 0.500
#> SRR1344060 2 0.7301 0.4549 0.236 0.536 0.000 0.228
#> SRR1467922 2 0.0000 0.8578 0.000 1.000 0.000 0.000
#> SRR1090984 1 0.4985 0.1794 0.532 0.000 0.468 0.000
#> SRR1456991 1 0.0188 0.7244 0.996 0.000 0.000 0.004
#> SRR1085039 1 0.3610 0.5798 0.800 0.000 0.000 0.200
#> SRR1069303 1 0.3123 0.6371 0.844 0.000 0.000 0.156
#> SRR1091500 2 0.0000 0.8578 0.000 1.000 0.000 0.000
#> SRR1075198 2 0.6269 0.5856 0.000 0.632 0.096 0.272
#> SRR1086915 4 0.2888 0.7086 0.004 0.000 0.124 0.872
#> SRR1499503 2 0.0592 0.8555 0.000 0.984 0.000 0.016
#> SRR1094312 2 0.0000 0.8578 0.000 1.000 0.000 0.000
#> SRR1352437 1 0.5236 0.2586 0.560 0.008 0.432 0.000
#> SRR1436323 3 0.0336 0.6822 0.000 0.000 0.992 0.008
#> SRR1073507 1 0.4250 0.4613 0.724 0.000 0.000 0.276
#> SRR1401972 1 0.3052 0.6538 0.860 0.000 0.136 0.004
#> SRR1415510 2 0.3443 0.7654 0.000 0.848 0.136 0.016
#> SRR1327279 1 0.3495 0.6583 0.844 0.000 0.016 0.140
#> SRR1086983 4 0.4781 0.6521 0.036 0.000 0.212 0.752
#> SRR1105174 4 0.4356 0.6215 0.292 0.000 0.000 0.708
#> SRR1468893 4 0.4941 0.3815 0.436 0.000 0.000 0.564
#> SRR1362555 2 0.7377 0.3939 0.216 0.520 0.000 0.264
#> SRR1074526 1 0.7824 0.4739 0.608 0.184 0.108 0.100
#> SRR1326225 2 0.0000 0.8578 0.000 1.000 0.000 0.000
#> SRR1401933 1 0.5772 0.5770 0.708 0.000 0.116 0.176
#> SRR1324062 1 0.4866 0.3067 0.596 0.000 0.404 0.000
#> SRR1102296 1 0.4057 0.6332 0.816 0.032 0.152 0.000
#> SRR1085087 1 0.4790 0.3868 0.620 0.000 0.000 0.380
#> SRR1079046 4 0.3626 0.6817 0.184 0.004 0.000 0.812
#> SRR1328339 1 0.0188 0.7244 0.996 0.000 0.000 0.004
#> SRR1079782 4 0.3074 0.6073 0.000 0.152 0.000 0.848
#> SRR1092257 2 0.4134 0.6530 0.000 0.740 0.000 0.260
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.2852 0.70127 0.000 0.828 0.000 0.000 0.172
#> SRR1429287 4 0.6963 0.21602 0.000 0.288 0.028 0.496 0.188
#> SRR1359238 4 0.3047 0.74819 0.044 0.000 0.084 0.868 0.004
#> SRR1309597 5 0.7291 0.03698 0.340 0.000 0.152 0.056 0.452
#> SRR1441398 1 0.4349 0.51763 0.756 0.000 0.000 0.068 0.176
#> SRR1084055 2 0.0794 0.77487 0.000 0.972 0.000 0.000 0.028
#> SRR1417566 3 0.3047 0.60556 0.160 0.000 0.832 0.004 0.004
#> SRR1351857 4 0.1478 0.75513 0.000 0.000 0.064 0.936 0.000
#> SRR1487485 3 0.0000 0.66588 0.000 0.000 1.000 0.000 0.000
#> SRR1335875 3 0.3967 0.51813 0.264 0.000 0.724 0.000 0.012
#> SRR1073947 1 0.0000 0.63995 1.000 0.000 0.000 0.000 0.000
#> SRR1443483 3 0.7688 -0.20292 0.324 0.000 0.332 0.048 0.296
#> SRR1346794 1 0.7646 0.01657 0.432 0.000 0.312 0.076 0.180
#> SRR1405245 1 0.6553 0.36462 0.592 0.000 0.200 0.036 0.172
#> SRR1409677 4 0.3058 0.74917 0.000 0.000 0.096 0.860 0.044
#> SRR1095549 1 0.6179 0.36727 0.664 0.000 0.108 0.076 0.152
#> SRR1323788 3 0.4928 0.59373 0.072 0.000 0.768 0.064 0.096
#> SRR1314054 2 0.2548 0.69232 0.000 0.876 0.116 0.004 0.004
#> SRR1077944 1 0.0771 0.63847 0.976 0.000 0.000 0.020 0.004
#> SRR1480587 2 0.1478 0.76377 0.000 0.936 0.000 0.000 0.064
#> SRR1311205 1 0.0290 0.63988 0.992 0.000 0.000 0.008 0.000
#> SRR1076369 1 0.7657 -0.08047 0.456 0.000 0.128 0.116 0.300
#> SRR1453549 3 0.3039 0.58589 0.192 0.000 0.808 0.000 0.000
#> SRR1345782 1 0.0000 0.63995 1.000 0.000 0.000 0.000 0.000
#> SRR1447850 2 0.2921 0.65977 0.000 0.844 0.148 0.004 0.004
#> SRR1391553 3 0.4143 0.47222 0.008 0.260 0.724 0.004 0.004
#> SRR1444156 2 0.0000 0.78035 0.000 1.000 0.000 0.000 0.000
#> SRR1471731 3 0.0324 0.66597 0.000 0.000 0.992 0.004 0.004
#> SRR1120987 4 0.3248 0.74275 0.064 0.000 0.020 0.868 0.048
#> SRR1477363 1 0.4162 0.52704 0.768 0.000 0.000 0.056 0.176
#> SRR1391961 1 0.4211 0.29586 0.636 0.000 0.000 0.004 0.360
#> SRR1373879 3 0.0404 0.66465 0.000 0.000 0.988 0.000 0.012
#> SRR1318732 3 0.5187 0.51048 0.020 0.000 0.700 0.064 0.216
#> SRR1091404 1 0.0771 0.63847 0.976 0.000 0.000 0.020 0.004
#> SRR1402109 3 0.3732 0.56521 0.032 0.000 0.792 0.000 0.176
#> SRR1407336 3 0.4797 0.45158 0.004 0.000 0.676 0.040 0.280
#> SRR1097417 3 0.5036 0.43509 0.000 0.052 0.628 0.000 0.320
#> SRR1396227 1 0.1410 0.62953 0.940 0.000 0.000 0.060 0.000
#> SRR1400775 2 0.0000 0.78035 0.000 1.000 0.000 0.000 0.000
#> SRR1392861 3 0.0000 0.66588 0.000 0.000 1.000 0.000 0.000
#> SRR1472929 5 0.3495 0.41899 0.028 0.160 0.000 0.000 0.812
#> SRR1436740 4 0.2329 0.74149 0.000 0.000 0.124 0.876 0.000
#> SRR1477057 2 0.6101 0.41515 0.128 0.660 0.176 0.008 0.028
#> SRR1311980 1 0.4273 0.08967 0.552 0.000 0.448 0.000 0.000
#> SRR1069400 3 0.5000 0.46139 0.068 0.000 0.688 0.004 0.240
#> SRR1351016 1 0.0000 0.63995 1.000 0.000 0.000 0.000 0.000
#> SRR1096291 4 0.4904 0.67808 0.044 0.000 0.132 0.760 0.064
#> SRR1418145 4 0.2972 0.74283 0.004 0.000 0.040 0.872 0.084
#> SRR1488111 3 0.5444 0.53355 0.112 0.016 0.720 0.012 0.140
#> SRR1370495 1 0.3343 0.52739 0.812 0.000 0.000 0.016 0.172
#> SRR1352639 1 0.7220 0.12257 0.496 0.000 0.048 0.192 0.264
#> SRR1348911 3 0.4201 0.27335 0.408 0.000 0.592 0.000 0.000
#> SRR1467386 1 0.3752 0.57451 0.812 0.000 0.124 0.064 0.000
#> SRR1415956 1 0.4226 0.52410 0.764 0.000 0.000 0.060 0.176
#> SRR1500495 1 0.4349 0.51763 0.756 0.000 0.000 0.068 0.176
#> SRR1405099 1 0.4162 0.52704 0.768 0.000 0.000 0.056 0.176
#> SRR1345585 3 0.1525 0.65785 0.004 0.000 0.948 0.036 0.012
#> SRR1093196 3 0.0693 0.66505 0.000 0.000 0.980 0.008 0.012
#> SRR1466006 2 0.2848 0.71301 0.000 0.840 0.004 0.000 0.156
#> SRR1351557 2 0.0290 0.77854 0.000 0.992 0.000 0.000 0.008
#> SRR1382687 3 0.4350 0.28025 0.408 0.000 0.588 0.004 0.000
#> SRR1375549 4 0.3075 0.70937 0.092 0.000 0.000 0.860 0.048
#> SRR1101765 4 0.2595 0.71665 0.080 0.000 0.000 0.888 0.032
#> SRR1334461 1 0.5868 -0.00911 0.472 0.052 0.000 0.020 0.456
#> SRR1094073 2 0.0000 0.78035 0.000 1.000 0.000 0.000 0.000
#> SRR1077549 1 0.5625 0.21485 0.564 0.000 0.368 0.056 0.012
#> SRR1440332 1 0.2162 0.62601 0.916 0.000 0.012 0.064 0.008
#> SRR1454177 4 0.4171 0.43918 0.000 0.000 0.396 0.604 0.000
#> SRR1082447 1 0.2068 0.61987 0.904 0.000 0.000 0.092 0.004
#> SRR1420043 3 0.0000 0.66588 0.000 0.000 1.000 0.000 0.000
#> SRR1432500 4 0.3675 0.67090 0.188 0.000 0.024 0.788 0.000
#> SRR1378045 2 0.4196 0.35537 0.000 0.640 0.356 0.000 0.004
#> SRR1334200 5 0.7372 0.38122 0.060 0.072 0.208 0.072 0.588
#> SRR1069539 4 0.6374 0.30755 0.000 0.000 0.196 0.504 0.300
#> SRR1343031 3 0.5949 0.28998 0.172 0.000 0.588 0.000 0.240
#> SRR1319690 3 0.5379 0.51006 0.056 0.000 0.712 0.052 0.180
#> SRR1310604 2 0.5932 0.12329 0.000 0.496 0.072 0.012 0.420
#> SRR1327747 3 0.7420 0.14742 0.120 0.000 0.488 0.292 0.100
#> SRR1072456 2 0.2852 0.70127 0.000 0.828 0.000 0.000 0.172
#> SRR1367896 3 0.4602 0.48574 0.052 0.000 0.708 0.000 0.240
#> SRR1480107 1 0.0771 0.63847 0.976 0.000 0.000 0.020 0.004
#> SRR1377756 4 0.3375 0.75428 0.020 0.000 0.072 0.860 0.048
#> SRR1435272 4 0.2966 0.71493 0.000 0.000 0.184 0.816 0.000
#> SRR1089230 4 0.1478 0.75513 0.000 0.000 0.064 0.936 0.000
#> SRR1389522 1 0.7309 -0.17436 0.412 0.000 0.176 0.044 0.368
#> SRR1080600 5 0.6514 0.36859 0.000 0.160 0.128 0.080 0.632
#> SRR1086935 3 0.3876 0.43235 0.000 0.000 0.684 0.316 0.000
#> SRR1344060 5 0.5816 -0.12130 0.040 0.420 0.000 0.028 0.512
#> SRR1467922 2 0.0162 0.77990 0.000 0.996 0.000 0.000 0.004
#> SRR1090984 1 0.4268 0.10603 0.556 0.000 0.444 0.000 0.000
#> SRR1456991 1 0.0771 0.63847 0.976 0.000 0.000 0.020 0.004
#> SRR1085039 1 0.4339 0.36837 0.652 0.000 0.000 0.336 0.012
#> SRR1069303 1 0.4138 0.32292 0.616 0.000 0.000 0.384 0.000
#> SRR1091500 2 0.0000 0.78035 0.000 1.000 0.000 0.000 0.000
#> SRR1075198 2 0.6803 0.34884 0.000 0.544 0.104 0.060 0.292
#> SRR1086915 4 0.2867 0.75176 0.004 0.000 0.072 0.880 0.044
#> SRR1499503 2 0.2852 0.70127 0.000 0.828 0.000 0.000 0.172
#> SRR1094312 2 0.0000 0.78035 0.000 1.000 0.000 0.000 0.000
#> SRR1352437 1 0.4497 0.14766 0.568 0.008 0.424 0.000 0.000
#> SRR1436323 3 0.1638 0.64546 0.000 0.000 0.932 0.064 0.004
#> SRR1073507 4 0.4307 -0.07122 0.500 0.000 0.000 0.500 0.000
#> SRR1401972 1 0.2079 0.62625 0.916 0.000 0.020 0.064 0.000
#> SRR1415510 2 0.6352 0.18153 0.000 0.488 0.336 0.000 0.176
#> SRR1327279 1 0.2433 0.62053 0.908 0.000 0.024 0.012 0.056
#> SRR1086983 4 0.2569 0.73568 0.068 0.000 0.040 0.892 0.000
#> SRR1105174 4 0.5339 0.52186 0.152 0.000 0.000 0.672 0.176
#> SRR1468893 4 0.3953 0.59088 0.168 0.000 0.000 0.784 0.048
#> SRR1362555 1 0.8024 -0.28427 0.356 0.328 0.000 0.096 0.220
#> SRR1074526 5 0.6728 0.04831 0.396 0.024 0.084 0.016 0.480
#> SRR1326225 2 0.0000 0.78035 0.000 1.000 0.000 0.000 0.000
#> SRR1401933 1 0.4359 0.56364 0.776 0.000 0.092 0.128 0.004
#> SRR1324062 1 0.4249 0.13999 0.568 0.000 0.432 0.000 0.000
#> SRR1102296 1 0.2331 0.61311 0.900 0.020 0.080 0.000 0.000
#> SRR1085087 1 0.4262 0.20577 0.560 0.000 0.000 0.440 0.000
#> SRR1079046 4 0.3455 0.69950 0.084 0.004 0.000 0.844 0.068
#> SRR1328339 1 0.0566 0.63951 0.984 0.000 0.000 0.012 0.004
#> SRR1079782 4 0.5789 0.43178 0.000 0.124 0.004 0.612 0.260
#> SRR1092257 2 0.4683 0.52759 0.000 0.732 0.000 0.176 0.092
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.2165 0.7833 0.000 0.884 0.000 0.000 0.108 0.008
#> SRR1429287 4 0.7106 0.3157 0.000 0.228 0.008 0.484 0.164 0.116
#> SRR1359238 4 0.2237 0.7765 0.036 0.000 0.068 0.896 0.000 0.000
#> SRR1309597 6 0.3618 0.4043 0.176 0.000 0.000 0.048 0.000 0.776
#> SRR1441398 1 0.4000 0.5468 0.724 0.000 0.000 0.048 0.000 0.228
#> SRR1084055 2 0.0692 0.8175 0.000 0.976 0.000 0.000 0.020 0.004
#> SRR1417566 3 0.2704 0.6851 0.140 0.000 0.844 0.000 0.000 0.016
#> SRR1351857 4 0.1349 0.7801 0.000 0.000 0.056 0.940 0.000 0.004
#> SRR1487485 3 0.0790 0.7123 0.000 0.000 0.968 0.000 0.000 0.032
#> SRR1335875 3 0.2902 0.6511 0.196 0.000 0.800 0.000 0.000 0.004
#> SRR1073947 1 0.0146 0.6926 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1443483 6 0.5512 0.5672 0.152 0.000 0.208 0.020 0.000 0.620
#> SRR1346794 1 0.6696 0.2224 0.476 0.000 0.240 0.060 0.000 0.224
#> SRR1405245 1 0.5897 0.4251 0.576 0.000 0.116 0.044 0.000 0.264
#> SRR1409677 4 0.2685 0.7735 0.000 0.000 0.072 0.868 0.000 0.060
#> SRR1095549 1 0.4969 0.4914 0.712 0.000 0.064 0.068 0.000 0.156
#> SRR1323788 3 0.3671 0.6936 0.036 0.000 0.820 0.056 0.000 0.088
#> SRR1314054 2 0.1908 0.7655 0.000 0.900 0.096 0.004 0.000 0.000
#> SRR1077944 1 0.0000 0.6926 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1480587 2 0.1682 0.8086 0.000 0.928 0.000 0.000 0.052 0.020
#> SRR1311205 1 0.0405 0.6928 0.988 0.000 0.000 0.008 0.000 0.004
#> SRR1076369 6 0.6260 0.4476 0.272 0.000 0.072 0.080 0.012 0.564
#> SRR1453549 3 0.2048 0.7041 0.120 0.000 0.880 0.000 0.000 0.000
#> SRR1345782 1 0.0000 0.6926 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1447850 2 0.2445 0.7453 0.000 0.868 0.120 0.004 0.000 0.008
#> SRR1391553 3 0.3263 0.6386 0.020 0.176 0.800 0.004 0.000 0.000
#> SRR1444156 2 0.0291 0.8186 0.000 0.992 0.000 0.000 0.004 0.004
#> SRR1471731 3 0.0458 0.7109 0.000 0.000 0.984 0.000 0.000 0.016
#> SRR1120987 4 0.2604 0.7702 0.056 0.000 0.032 0.888 0.000 0.024
#> SRR1477363 1 0.3938 0.5496 0.728 0.000 0.000 0.044 0.000 0.228
#> SRR1391961 5 0.2882 0.7258 0.180 0.000 0.000 0.008 0.812 0.000
#> SRR1373879 3 0.0790 0.7123 0.000 0.000 0.968 0.000 0.000 0.032
#> SRR1318732 3 0.4651 0.5632 0.016 0.000 0.676 0.052 0.000 0.256
#> SRR1091404 1 0.0000 0.6926 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1402109 3 0.3647 -0.0389 0.000 0.000 0.640 0.000 0.000 0.360
#> SRR1407336 6 0.4489 0.5592 0.008 0.000 0.404 0.020 0.000 0.568
#> SRR1097417 6 0.5790 0.4819 0.000 0.048 0.396 0.000 0.064 0.492
#> SRR1396227 1 0.1719 0.6795 0.924 0.000 0.000 0.060 0.000 0.016
#> SRR1400775 2 0.0000 0.8183 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1392861 3 0.0000 0.7156 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1472929 5 0.4354 0.5636 0.000 0.080 0.000 0.000 0.704 0.216
#> SRR1436740 4 0.1958 0.7725 0.000 0.000 0.100 0.896 0.000 0.004
#> SRR1477057 2 0.4927 0.5309 0.188 0.700 0.092 0.008 0.008 0.004
#> SRR1311980 1 0.3868 -0.0207 0.508 0.000 0.492 0.000 0.000 0.000
#> SRR1069400 6 0.4315 0.5234 0.008 0.000 0.460 0.008 0.000 0.524
#> SRR1351016 1 0.0146 0.6926 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR1096291 4 0.4289 0.7115 0.028 0.000 0.100 0.768 0.000 0.104
#> SRR1418145 4 0.2414 0.7623 0.000 0.000 0.012 0.896 0.036 0.056
#> SRR1488111 3 0.4460 0.6671 0.048 0.016 0.796 0.016 0.080 0.044
#> SRR1370495 1 0.2605 0.6383 0.876 0.000 0.000 0.020 0.092 0.012
#> SRR1352639 1 0.7082 0.3064 0.536 0.000 0.032 0.192 0.132 0.108
#> SRR1348911 3 0.3515 0.4822 0.324 0.000 0.676 0.000 0.000 0.000
#> SRR1467386 1 0.3953 0.5589 0.744 0.000 0.196 0.060 0.000 0.000
#> SRR1415956 1 0.3938 0.5496 0.728 0.000 0.000 0.044 0.000 0.228
#> SRR1500495 1 0.4138 0.5439 0.720 0.000 0.004 0.048 0.000 0.228
#> SRR1405099 1 0.3938 0.5496 0.728 0.000 0.000 0.044 0.000 0.228
#> SRR1345585 3 0.1794 0.7085 0.000 0.000 0.924 0.036 0.000 0.040
#> SRR1093196 3 0.1411 0.6936 0.000 0.000 0.936 0.004 0.000 0.060
#> SRR1466006 2 0.4175 0.6801 0.000 0.740 0.000 0.000 0.104 0.156
#> SRR1351557 2 0.1524 0.8018 0.000 0.932 0.000 0.000 0.008 0.060
#> SRR1382687 3 0.4141 0.3464 0.388 0.000 0.596 0.000 0.000 0.016
#> SRR1375549 4 0.2339 0.7525 0.072 0.000 0.000 0.896 0.012 0.020
#> SRR1101765 4 0.1719 0.7576 0.060 0.000 0.000 0.924 0.000 0.016
#> SRR1334461 5 0.2742 0.7708 0.076 0.044 0.000 0.008 0.872 0.000
#> SRR1094073 2 0.0260 0.8186 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1077549 1 0.5159 0.1046 0.524 0.000 0.408 0.052 0.000 0.016
#> SRR1440332 1 0.1461 0.6849 0.940 0.000 0.000 0.044 0.000 0.016
#> SRR1454177 4 0.4224 0.5161 0.000 0.000 0.340 0.632 0.000 0.028
#> SRR1082447 1 0.1387 0.6722 0.932 0.000 0.000 0.068 0.000 0.000
#> SRR1420043 3 0.0790 0.7123 0.000 0.000 0.968 0.000 0.000 0.032
#> SRR1432500 4 0.3558 0.7126 0.168 0.000 0.012 0.792 0.000 0.028
#> SRR1378045 2 0.4245 0.3605 0.000 0.604 0.376 0.000 0.016 0.004
#> SRR1334200 5 0.5554 0.6141 0.008 0.052 0.184 0.012 0.680 0.064
#> SRR1069539 6 0.5005 0.4866 0.000 0.000 0.136 0.156 0.020 0.688
#> SRR1343031 6 0.4147 0.5398 0.012 0.000 0.436 0.000 0.000 0.552
#> SRR1319690 3 0.4772 0.5585 0.024 0.000 0.668 0.048 0.000 0.260
#> SRR1310604 6 0.5328 0.2909 0.000 0.184 0.016 0.012 0.120 0.668
#> SRR1327747 3 0.6884 0.2439 0.140 0.000 0.496 0.224 0.000 0.140
#> SRR1072456 2 0.2165 0.7833 0.000 0.884 0.000 0.000 0.108 0.008
#> SRR1367896 6 0.3838 0.5209 0.000 0.000 0.448 0.000 0.000 0.552
#> SRR1480107 1 0.0000 0.6926 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1377756 4 0.2918 0.7799 0.020 0.000 0.064 0.868 0.000 0.048
#> SRR1435272 4 0.2482 0.7605 0.000 0.000 0.148 0.848 0.000 0.004
#> SRR1089230 4 0.1204 0.7796 0.000 0.000 0.056 0.944 0.000 0.000
#> SRR1389522 6 0.5152 0.4640 0.192 0.000 0.072 0.044 0.004 0.688
#> SRR1080600 6 0.5140 0.3894 0.000 0.032 0.044 0.040 0.172 0.712
#> SRR1086935 3 0.3198 0.5345 0.000 0.000 0.740 0.260 0.000 0.000
#> SRR1344060 5 0.2213 0.7428 0.000 0.100 0.000 0.008 0.888 0.004
#> SRR1467922 2 0.0603 0.8179 0.000 0.980 0.000 0.000 0.016 0.004
#> SRR1090984 1 0.3868 -0.0325 0.508 0.000 0.492 0.000 0.000 0.000
#> SRR1456991 1 0.0000 0.6926 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1085039 1 0.3737 0.2094 0.608 0.000 0.000 0.392 0.000 0.000
#> SRR1069303 1 0.4184 0.2593 0.576 0.000 0.000 0.408 0.000 0.016
#> SRR1091500 2 0.0000 0.8183 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1075198 2 0.7333 0.2992 0.000 0.444 0.056 0.040 0.176 0.284
#> SRR1086915 4 0.2433 0.7777 0.000 0.000 0.072 0.884 0.000 0.044
#> SRR1499503 2 0.2266 0.7828 0.000 0.880 0.000 0.000 0.108 0.012
#> SRR1094312 2 0.0260 0.8186 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1352437 1 0.4384 0.0475 0.520 0.000 0.460 0.004 0.000 0.016
#> SRR1436323 3 0.1387 0.7041 0.000 0.000 0.932 0.068 0.000 0.000
#> SRR1073507 4 0.3860 0.0171 0.472 0.000 0.000 0.528 0.000 0.000
#> SRR1401972 1 0.1779 0.6790 0.920 0.000 0.000 0.064 0.000 0.016
#> SRR1415510 2 0.5743 0.1009 0.000 0.456 0.420 0.000 0.108 0.016
#> SRR1327279 1 0.1267 0.6845 0.940 0.000 0.000 0.000 0.000 0.060
#> SRR1086983 4 0.1719 0.7651 0.060 0.000 0.016 0.924 0.000 0.000
#> SRR1105174 4 0.4979 0.5207 0.136 0.000 0.000 0.640 0.000 0.224
#> SRR1468893 4 0.3909 0.5157 0.244 0.000 0.000 0.720 0.000 0.036
#> SRR1362555 1 0.8559 -0.2054 0.308 0.180 0.000 0.080 0.236 0.196
#> SRR1074526 5 0.3377 0.7354 0.148 0.000 0.028 0.012 0.812 0.000
#> SRR1326225 2 0.0146 0.8180 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1401933 1 0.4269 0.5595 0.724 0.000 0.184 0.092 0.000 0.000
#> SRR1324062 1 0.4124 0.0289 0.516 0.000 0.476 0.004 0.000 0.004
#> SRR1102296 1 0.1970 0.6745 0.912 0.028 0.060 0.000 0.000 0.000
#> SRR1085087 1 0.3998 0.0401 0.504 0.000 0.000 0.492 0.000 0.004
#> SRR1079046 4 0.3453 0.7075 0.064 0.000 0.000 0.824 0.012 0.100
#> SRR1328339 1 0.0000 0.6926 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1079782 4 0.6733 0.3730 0.000 0.092 0.004 0.528 0.176 0.200
#> SRR1092257 2 0.4914 0.6317 0.000 0.728 0.000 0.112 0.080 0.080
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17611 rows and 118 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.975 0.988 0.4075 0.586 0.586
#> 3 3 0.560 0.505 0.752 0.5286 0.794 0.651
#> 4 4 0.496 0.596 0.739 0.1055 0.833 0.595
#> 5 5 0.514 0.557 0.732 0.0891 0.867 0.582
#> 6 6 0.558 0.554 0.704 0.0408 0.897 0.636
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
#> SRR1396765 2 0.0000 0.966 0.000 1.000
#> SRR1429287 2 0.0000 0.966 0.000 1.000
#> SRR1359238 1 0.0000 0.996 1.000 0.000
#> SRR1309597 1 0.0376 0.995 0.996 0.004
#> SRR1441398 1 0.0000 0.996 1.000 0.000
#> SRR1084055 2 0.0000 0.966 0.000 1.000
#> SRR1417566 1 0.0376 0.995 0.996 0.004
#> SRR1351857 1 0.0000 0.996 1.000 0.000
#> SRR1487485 1 0.0376 0.995 0.996 0.004
#> SRR1335875 1 0.0376 0.995 0.996 0.004
#> SRR1073947 1 0.0000 0.996 1.000 0.000
#> SRR1443483 1 0.0376 0.995 0.996 0.004
#> SRR1346794 1 0.0000 0.996 1.000 0.000
#> SRR1405245 1 0.0000 0.996 1.000 0.000
#> SRR1409677 1 0.0000 0.996 1.000 0.000
#> SRR1095549 1 0.0000 0.996 1.000 0.000
#> SRR1323788 1 0.0000 0.996 1.000 0.000
#> SRR1314054 2 0.0000 0.966 0.000 1.000
#> SRR1077944 1 0.0000 0.996 1.000 0.000
#> SRR1480587 2 0.0000 0.966 0.000 1.000
#> SRR1311205 1 0.0000 0.996 1.000 0.000
#> SRR1076369 1 0.0376 0.995 0.996 0.004
#> SRR1453549 1 0.0376 0.995 0.996 0.004
#> SRR1345782 1 0.0000 0.996 1.000 0.000
#> SRR1447850 2 0.0000 0.966 0.000 1.000
#> SRR1391553 1 0.0672 0.992 0.992 0.008
#> SRR1444156 2 0.0000 0.966 0.000 1.000
#> SRR1471731 1 0.0000 0.996 1.000 0.000
#> SRR1120987 1 0.0376 0.995 0.996 0.004
#> SRR1477363 1 0.0000 0.996 1.000 0.000
#> SRR1391961 2 0.0376 0.963 0.004 0.996
#> SRR1373879 1 0.0376 0.995 0.996 0.004
#> SRR1318732 1 0.0376 0.995 0.996 0.004
#> SRR1091404 1 0.0000 0.996 1.000 0.000
#> SRR1402109 1 0.0000 0.996 1.000 0.000
#> SRR1407336 1 0.0376 0.995 0.996 0.004
#> SRR1097417 2 0.9686 0.371 0.396 0.604
#> SRR1396227 1 0.0000 0.996 1.000 0.000
#> SRR1400775 2 0.0000 0.966 0.000 1.000
#> SRR1392861 1 0.0000 0.996 1.000 0.000
#> SRR1472929 2 0.0000 0.966 0.000 1.000
#> SRR1436740 1 0.0000 0.996 1.000 0.000
#> SRR1477057 2 0.0000 0.966 0.000 1.000
#> SRR1311980 1 0.0376 0.995 0.996 0.004
#> SRR1069400 1 0.0376 0.995 0.996 0.004
#> SRR1351016 1 0.0000 0.996 1.000 0.000
#> SRR1096291 1 0.0376 0.995 0.996 0.004
#> SRR1418145 1 0.0376 0.995 0.996 0.004
#> SRR1488111 1 0.2778 0.950 0.952 0.048
#> SRR1370495 1 0.0376 0.995 0.996 0.004
#> SRR1352639 1 0.0376 0.995 0.996 0.004
#> SRR1348911 1 0.0376 0.995 0.996 0.004
#> SRR1467386 1 0.0000 0.996 1.000 0.000
#> SRR1415956 1 0.0000 0.996 1.000 0.000
#> SRR1500495 1 0.0000 0.996 1.000 0.000
#> SRR1405099 1 0.0000 0.996 1.000 0.000
#> SRR1345585 1 0.0376 0.995 0.996 0.004
#> SRR1093196 1 0.0000 0.996 1.000 0.000
#> SRR1466006 2 0.0000 0.966 0.000 1.000
#> SRR1351557 2 0.0000 0.966 0.000 1.000
#> SRR1382687 1 0.0000 0.996 1.000 0.000
#> SRR1375549 1 0.0376 0.995 0.996 0.004
#> SRR1101765 1 0.5059 0.869 0.888 0.112
#> SRR1334461 2 0.0000 0.966 0.000 1.000
#> SRR1094073 2 0.0000 0.966 0.000 1.000
#> SRR1077549 1 0.0000 0.996 1.000 0.000
#> SRR1440332 1 0.0000 0.996 1.000 0.000
#> SRR1454177 1 0.0000 0.996 1.000 0.000
#> SRR1082447 1 0.0000 0.996 1.000 0.000
#> SRR1420043 1 0.0000 0.996 1.000 0.000
#> SRR1432500 1 0.0000 0.996 1.000 0.000
#> SRR1378045 2 0.8144 0.681 0.252 0.748
#> SRR1334200 2 0.0000 0.966 0.000 1.000
#> SRR1069539 1 0.0376 0.995 0.996 0.004
#> SRR1343031 1 0.0000 0.996 1.000 0.000
#> SRR1319690 1 0.0000 0.996 1.000 0.000
#> SRR1310604 2 0.0000 0.966 0.000 1.000
#> SRR1327747 1 0.0000 0.996 1.000 0.000
#> SRR1072456 2 0.0000 0.966 0.000 1.000
#> SRR1367896 1 0.0376 0.995 0.996 0.004
#> SRR1480107 1 0.0000 0.996 1.000 0.000
#> SRR1377756 1 0.0000 0.996 1.000 0.000
#> SRR1435272 1 0.0000 0.996 1.000 0.000
#> SRR1089230 1 0.0000 0.996 1.000 0.000
#> SRR1389522 1 0.0376 0.995 0.996 0.004
#> SRR1080600 2 0.0000 0.966 0.000 1.000
#> SRR1086935 1 0.0376 0.995 0.996 0.004
#> SRR1344060 2 0.0000 0.966 0.000 1.000
#> SRR1467922 2 0.0000 0.966 0.000 1.000
#> SRR1090984 1 0.0376 0.995 0.996 0.004
#> SRR1456991 1 0.0000 0.996 1.000 0.000
#> SRR1085039 1 0.0000 0.996 1.000 0.000
#> SRR1069303 1 0.0000 0.996 1.000 0.000
#> SRR1091500 2 0.0000 0.966 0.000 1.000
#> SRR1075198 2 0.0000 0.966 0.000 1.000
#> SRR1086915 1 0.0000 0.996 1.000 0.000
#> SRR1499503 2 0.0000 0.966 0.000 1.000
#> SRR1094312 2 0.0000 0.966 0.000 1.000
#> SRR1352437 1 0.0000 0.996 1.000 0.000
#> SRR1436323 1 0.0000 0.996 1.000 0.000
#> SRR1073507 1 0.0376 0.995 0.996 0.004
#> SRR1401972 1 0.0000 0.996 1.000 0.000
#> SRR1415510 2 0.0000 0.966 0.000 1.000
#> SRR1327279 1 0.0000 0.996 1.000 0.000
#> SRR1086983 1 0.0000 0.996 1.000 0.000
#> SRR1105174 1 0.0000 0.996 1.000 0.000
#> SRR1468893 1 0.0000 0.996 1.000 0.000
#> SRR1362555 2 0.0000 0.966 0.000 1.000
#> SRR1074526 2 0.3733 0.906 0.072 0.928
#> SRR1326225 2 0.0000 0.966 0.000 1.000
#> SRR1401933 1 0.0000 0.996 1.000 0.000
#> SRR1324062 1 0.0000 0.996 1.000 0.000
#> SRR1102296 1 0.0376 0.995 0.996 0.004
#> SRR1085087 1 0.0000 0.996 1.000 0.000
#> SRR1079046 2 0.5946 0.832 0.144 0.856
#> SRR1328339 1 0.0376 0.995 0.996 0.004
#> SRR1079782 2 0.7815 0.713 0.232 0.768
#> SRR1092257 2 0.0000 0.966 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.0592 0.970076 0.000 0.988 0.012
#> SRR1429287 2 0.0000 0.971503 0.000 1.000 0.000
#> SRR1359238 1 0.2959 0.505684 0.900 0.000 0.100
#> SRR1309597 3 0.5760 0.651089 0.328 0.000 0.672
#> SRR1441398 1 0.6302 -0.408739 0.520 0.000 0.480
#> SRR1084055 2 0.0592 0.970076 0.000 0.988 0.012
#> SRR1417566 3 0.5926 0.000765 0.356 0.000 0.644
#> SRR1351857 1 0.0237 0.582982 0.996 0.000 0.004
#> SRR1487485 3 0.5785 0.651901 0.332 0.000 0.668
#> SRR1335875 3 0.6308 0.468732 0.492 0.000 0.508
#> SRR1073947 1 0.0237 0.583241 0.996 0.000 0.004
#> SRR1443483 3 0.5785 0.651901 0.332 0.000 0.668
#> SRR1346794 1 0.6126 -0.191606 0.600 0.000 0.400
#> SRR1405245 1 0.6299 -0.397380 0.524 0.000 0.476
#> SRR1409677 1 0.1529 0.559225 0.960 0.000 0.040
#> SRR1095549 1 0.6111 -0.191722 0.604 0.000 0.396
#> SRR1323788 1 0.6204 -0.276213 0.576 0.000 0.424
#> SRR1314054 2 0.0000 0.971503 0.000 1.000 0.000
#> SRR1077944 1 0.2261 0.551146 0.932 0.000 0.068
#> SRR1480587 2 0.0000 0.971503 0.000 1.000 0.000
#> SRR1311205 1 0.6140 -0.205492 0.596 0.000 0.404
#> SRR1076369 3 0.6008 0.056314 0.372 0.000 0.628
#> SRR1453549 1 0.6154 -0.238951 0.592 0.000 0.408
#> SRR1345782 1 0.6215 -0.272172 0.572 0.000 0.428
#> SRR1447850 2 0.0000 0.971503 0.000 1.000 0.000
#> SRR1391553 3 0.5696 0.322199 0.148 0.056 0.796
#> SRR1444156 2 0.0592 0.970076 0.000 0.988 0.012
#> SRR1471731 3 0.6305 0.495714 0.484 0.000 0.516
#> SRR1120987 1 0.6102 0.411806 0.672 0.008 0.320
#> SRR1477363 1 0.2165 0.554668 0.936 0.000 0.064
#> SRR1391961 2 0.0000 0.971503 0.000 1.000 0.000
#> SRR1373879 3 0.6126 0.615039 0.400 0.000 0.600
#> SRR1318732 3 0.5760 0.650112 0.328 0.000 0.672
#> SRR1091404 1 0.5706 0.424174 0.680 0.000 0.320
#> SRR1402109 3 0.6299 0.509135 0.476 0.000 0.524
#> SRR1407336 3 0.6244 0.572209 0.440 0.000 0.560
#> SRR1097417 2 0.7178 0.364200 0.024 0.512 0.464
#> SRR1396227 1 0.3816 0.513692 0.852 0.000 0.148
#> SRR1400775 2 0.0592 0.970076 0.000 0.988 0.012
#> SRR1392861 1 0.1643 0.560361 0.956 0.000 0.044
#> SRR1472929 2 0.0000 0.971503 0.000 1.000 0.000
#> SRR1436740 1 0.0000 0.583334 1.000 0.000 0.000
#> SRR1477057 2 0.0000 0.971503 0.000 1.000 0.000
#> SRR1311980 1 0.6260 -0.353046 0.552 0.000 0.448
#> SRR1069400 3 0.5785 0.651901 0.332 0.000 0.668
#> SRR1351016 1 0.2625 0.529173 0.916 0.000 0.084
#> SRR1096291 1 0.6168 0.342147 0.588 0.000 0.412
#> SRR1418145 1 0.5760 0.413384 0.672 0.000 0.328
#> SRR1488111 1 0.8827 0.227756 0.496 0.120 0.384
#> SRR1370495 1 0.8486 0.315606 0.548 0.104 0.348
#> SRR1352639 1 0.5926 0.398968 0.644 0.000 0.356
#> SRR1348911 3 0.5706 0.639334 0.320 0.000 0.680
#> SRR1467386 1 0.1753 0.558765 0.952 0.000 0.048
#> SRR1415956 1 0.6079 -0.163747 0.612 0.000 0.388
#> SRR1500495 1 0.6295 -0.389467 0.528 0.000 0.472
#> SRR1405099 1 0.2537 0.539866 0.920 0.000 0.080
#> SRR1345585 3 0.5785 0.651901 0.332 0.000 0.668
#> SRR1093196 3 0.6126 0.613515 0.400 0.000 0.600
#> SRR1466006 2 0.0592 0.970076 0.000 0.988 0.012
#> SRR1351557 2 0.0592 0.970076 0.000 0.988 0.012
#> SRR1382687 1 0.3340 0.477812 0.880 0.000 0.120
#> SRR1375549 1 0.5733 0.418287 0.676 0.000 0.324
#> SRR1101765 1 0.9484 0.224859 0.472 0.200 0.328
#> SRR1334461 2 0.0237 0.969666 0.000 0.996 0.004
#> SRR1094073 2 0.0592 0.970076 0.000 0.988 0.012
#> SRR1077549 1 0.1964 0.551535 0.944 0.000 0.056
#> SRR1440332 1 0.6154 -0.242964 0.592 0.000 0.408
#> SRR1454177 1 0.0000 0.583334 1.000 0.000 0.000
#> SRR1082447 1 0.3116 0.542370 0.892 0.000 0.108
#> SRR1420043 3 0.6307 0.487532 0.488 0.000 0.512
#> SRR1432500 1 0.1031 0.574519 0.976 0.000 0.024
#> SRR1378045 3 0.6291 -0.310704 0.000 0.468 0.532
#> SRR1334200 2 0.0000 0.971503 0.000 1.000 0.000
#> SRR1069539 3 0.6308 -0.263710 0.492 0.000 0.508
#> SRR1343031 1 0.6295 -0.413336 0.528 0.000 0.472
#> SRR1319690 3 0.6280 0.518600 0.460 0.000 0.540
#> SRR1310604 2 0.0000 0.971503 0.000 1.000 0.000
#> SRR1327747 1 0.6252 -0.319657 0.556 0.000 0.444
#> SRR1072456 2 0.0000 0.971503 0.000 1.000 0.000
#> SRR1367896 3 0.5785 0.651901 0.332 0.000 0.668
#> SRR1480107 1 0.0592 0.582225 0.988 0.000 0.012
#> SRR1377756 1 0.0237 0.583241 0.996 0.000 0.004
#> SRR1435272 1 0.0592 0.578758 0.988 0.000 0.012
#> SRR1089230 1 0.0000 0.583334 1.000 0.000 0.000
#> SRR1389522 3 0.5733 0.649186 0.324 0.000 0.676
#> SRR1080600 2 0.0592 0.970076 0.000 0.988 0.012
#> SRR1086935 1 0.7190 0.388561 0.636 0.044 0.320
#> SRR1344060 2 0.0000 0.971503 0.000 1.000 0.000
#> SRR1467922 2 0.0592 0.970076 0.000 0.988 0.012
#> SRR1090984 1 0.6252 0.011638 0.556 0.000 0.444
#> SRR1456991 1 0.5138 0.230119 0.748 0.000 0.252
#> SRR1085039 1 0.0424 0.582951 0.992 0.000 0.008
#> SRR1069303 1 0.5650 0.421249 0.688 0.000 0.312
#> SRR1091500 2 0.0000 0.971503 0.000 1.000 0.000
#> SRR1075198 2 0.0000 0.971503 0.000 1.000 0.000
#> SRR1086915 1 0.0000 0.583334 1.000 0.000 0.000
#> SRR1499503 2 0.0592 0.970076 0.000 0.988 0.012
#> SRR1094312 2 0.0592 0.970076 0.000 0.988 0.012
#> SRR1352437 1 0.5621 0.423872 0.692 0.000 0.308
#> SRR1436323 1 0.6111 -0.206613 0.604 0.000 0.396
#> SRR1073507 1 0.0000 0.583334 1.000 0.000 0.000
#> SRR1401972 1 0.5650 0.421249 0.688 0.000 0.312
#> SRR1415510 2 0.0000 0.971503 0.000 1.000 0.000
#> SRR1327279 1 0.3816 0.431393 0.852 0.000 0.148
#> SRR1086983 1 0.0000 0.583334 1.000 0.000 0.000
#> SRR1105174 1 0.0892 0.580297 0.980 0.000 0.020
#> SRR1468893 1 0.0424 0.582346 0.992 0.000 0.008
#> SRR1362555 2 0.0000 0.971503 0.000 1.000 0.000
#> SRR1074526 2 0.0592 0.961985 0.012 0.988 0.000
#> SRR1326225 2 0.0592 0.970076 0.000 0.988 0.012
#> SRR1401933 1 0.0237 0.582982 0.996 0.000 0.004
#> SRR1324062 1 0.0000 0.583334 1.000 0.000 0.000
#> SRR1102296 1 0.5810 0.408748 0.664 0.000 0.336
#> SRR1085087 1 0.5397 0.438928 0.720 0.000 0.280
#> SRR1079046 2 0.4531 0.729364 0.168 0.824 0.008
#> SRR1328339 3 0.6737 0.130496 0.272 0.040 0.688
#> SRR1079782 2 0.3889 0.870231 0.032 0.884 0.084
#> SRR1092257 2 0.0000 0.971503 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.0937 0.8863 0.012 0.976 0.012 0.000
#> SRR1429287 2 0.2101 0.8809 0.060 0.928 0.012 0.000
#> SRR1359238 4 0.3498 0.6263 0.008 0.000 0.160 0.832
#> SRR1309597 3 0.3105 0.6495 0.004 0.000 0.856 0.140
#> SRR1441398 3 0.5543 0.5205 0.020 0.000 0.556 0.424
#> SRR1084055 2 0.0937 0.8863 0.012 0.976 0.012 0.000
#> SRR1417566 1 0.7402 0.6156 0.500 0.000 0.308 0.192
#> SRR1351857 4 0.1388 0.6860 0.012 0.000 0.028 0.960
#> SRR1487485 3 0.3351 0.6569 0.008 0.000 0.844 0.148
#> SRR1335875 3 0.5800 0.4774 0.032 0.000 0.548 0.420
#> SRR1073947 4 0.0937 0.6749 0.012 0.000 0.012 0.976
#> SRR1443483 3 0.3105 0.6495 0.004 0.000 0.856 0.140
#> SRR1346794 4 0.5358 0.3967 0.048 0.000 0.252 0.700
#> SRR1405245 3 0.5581 0.4793 0.020 0.000 0.532 0.448
#> SRR1409677 4 0.2796 0.6782 0.016 0.000 0.092 0.892
#> SRR1095549 4 0.5511 0.1492 0.028 0.000 0.352 0.620
#> SRR1323788 4 0.5597 -0.3060 0.020 0.000 0.464 0.516
#> SRR1314054 2 0.1635 0.8869 0.044 0.948 0.008 0.000
#> SRR1077944 4 0.1833 0.6736 0.024 0.000 0.032 0.944
#> SRR1480587 2 0.0469 0.8875 0.012 0.988 0.000 0.000
#> SRR1311205 4 0.5428 0.0138 0.020 0.000 0.380 0.600
#> SRR1076369 1 0.7309 0.6989 0.504 0.000 0.172 0.324
#> SRR1453549 4 0.5132 -0.2103 0.004 0.000 0.448 0.548
#> SRR1345782 4 0.5543 -0.1567 0.020 0.000 0.424 0.556
#> SRR1447850 2 0.4397 0.8396 0.168 0.800 0.020 0.012
#> SRR1391553 3 0.6793 -0.3156 0.428 0.020 0.500 0.052
#> SRR1444156 2 0.0937 0.8863 0.012 0.976 0.012 0.000
#> SRR1471731 3 0.5417 0.5529 0.016 0.000 0.572 0.412
#> SRR1120987 1 0.6340 0.7908 0.620 0.000 0.096 0.284
#> SRR1477363 4 0.2089 0.6782 0.020 0.000 0.048 0.932
#> SRR1391961 2 0.6871 0.6856 0.316 0.580 0.092 0.012
#> SRR1373879 3 0.4950 0.6111 0.004 0.000 0.620 0.376
#> SRR1318732 3 0.4776 0.6569 0.024 0.000 0.732 0.244
#> SRR1091404 1 0.5933 0.6949 0.552 0.000 0.040 0.408
#> SRR1402109 3 0.5203 0.5541 0.008 0.000 0.576 0.416
#> SRR1407336 3 0.5016 0.5964 0.004 0.000 0.600 0.396
#> SRR1097417 1 0.8352 0.4952 0.520 0.276 0.120 0.084
#> SRR1396227 4 0.3900 0.4935 0.164 0.000 0.020 0.816
#> SRR1400775 2 0.1854 0.8823 0.048 0.940 0.012 0.000
#> SRR1392861 4 0.6005 0.2316 0.060 0.000 0.324 0.616
#> SRR1472929 2 0.5528 0.7771 0.144 0.732 0.124 0.000
#> SRR1436740 4 0.4245 0.6309 0.064 0.000 0.116 0.820
#> SRR1477057 2 0.4210 0.8521 0.152 0.816 0.012 0.020
#> SRR1311980 3 0.5793 0.5876 0.040 0.000 0.600 0.360
#> SRR1069400 3 0.3448 0.6631 0.004 0.000 0.828 0.168
#> SRR1351016 4 0.1767 0.6831 0.012 0.000 0.044 0.944
#> SRR1096291 1 0.7152 0.7882 0.544 0.000 0.172 0.284
#> SRR1418145 1 0.6585 0.7955 0.584 0.000 0.104 0.312
#> SRR1488111 1 0.7741 0.7694 0.592 0.060 0.124 0.224
#> SRR1370495 1 0.6920 0.7993 0.596 0.012 0.108 0.284
#> SRR1352639 1 0.6739 0.7970 0.576 0.000 0.120 0.304
#> SRR1348911 3 0.6705 0.5219 0.148 0.000 0.608 0.244
#> SRR1467386 4 0.3105 0.6574 0.004 0.000 0.140 0.856
#> SRR1415956 4 0.4163 0.5178 0.020 0.000 0.188 0.792
#> SRR1500495 3 0.5594 0.4479 0.020 0.000 0.520 0.460
#> SRR1405099 4 0.1929 0.6738 0.024 0.000 0.036 0.940
#> SRR1345585 3 0.3743 0.6532 0.016 0.000 0.824 0.160
#> SRR1093196 3 0.4920 0.6109 0.004 0.000 0.628 0.368
#> SRR1466006 2 0.0937 0.8863 0.012 0.976 0.012 0.000
#> SRR1351557 2 0.0657 0.8870 0.004 0.984 0.012 0.000
#> SRR1382687 4 0.4194 0.5194 0.008 0.000 0.228 0.764
#> SRR1375549 1 0.5666 0.7678 0.616 0.000 0.036 0.348
#> SRR1101765 1 0.6149 0.7627 0.596 0.016 0.032 0.356
#> SRR1334461 2 0.6889 0.6815 0.320 0.576 0.092 0.012
#> SRR1094073 2 0.0937 0.8863 0.012 0.976 0.012 0.000
#> SRR1077549 4 0.4194 0.6136 0.028 0.000 0.172 0.800
#> SRR1440332 4 0.5257 -0.2220 0.008 0.000 0.444 0.548
#> SRR1454177 4 0.5218 0.5344 0.064 0.000 0.200 0.736
#> SRR1082447 4 0.3047 0.6149 0.116 0.000 0.012 0.872
#> SRR1420043 3 0.5724 0.5041 0.028 0.000 0.548 0.424
#> SRR1432500 4 0.3032 0.6589 0.008 0.000 0.124 0.868
#> SRR1378045 1 0.7149 0.3878 0.552 0.264 0.184 0.000
#> SRR1334200 2 0.2402 0.8811 0.076 0.912 0.012 0.000
#> SRR1069539 1 0.6915 0.7590 0.592 0.000 0.212 0.196
#> SRR1343031 3 0.4972 0.4880 0.000 0.000 0.544 0.456
#> SRR1319690 3 0.5526 0.5379 0.020 0.000 0.564 0.416
#> SRR1310604 2 0.1677 0.8841 0.040 0.948 0.012 0.000
#> SRR1327747 4 0.5506 -0.3164 0.016 0.000 0.472 0.512
#> SRR1072456 2 0.1256 0.8860 0.028 0.964 0.008 0.000
#> SRR1367896 3 0.3708 0.6518 0.020 0.000 0.832 0.148
#> SRR1480107 4 0.1297 0.6739 0.020 0.000 0.016 0.964
#> SRR1377756 4 0.1576 0.6881 0.004 0.000 0.048 0.948
#> SRR1435272 4 0.5213 0.5191 0.052 0.000 0.224 0.724
#> SRR1089230 4 0.3128 0.6734 0.040 0.000 0.076 0.884
#> SRR1389522 3 0.3791 0.6634 0.004 0.000 0.796 0.200
#> SRR1080600 2 0.1677 0.8841 0.040 0.948 0.012 0.000
#> SRR1086935 1 0.6914 0.7566 0.624 0.028 0.088 0.260
#> SRR1344060 2 0.6107 0.7391 0.264 0.648 0.088 0.000
#> SRR1467922 2 0.0937 0.8863 0.012 0.976 0.012 0.000
#> SRR1090984 4 0.7544 -0.3846 0.352 0.000 0.196 0.452
#> SRR1456991 4 0.2521 0.6643 0.024 0.000 0.064 0.912
#> SRR1085039 4 0.0779 0.6765 0.016 0.000 0.004 0.980
#> SRR1069303 4 0.5295 -0.5553 0.488 0.000 0.008 0.504
#> SRR1091500 2 0.3217 0.8600 0.128 0.860 0.012 0.000
#> SRR1075198 2 0.3047 0.8551 0.116 0.872 0.012 0.000
#> SRR1086915 4 0.2256 0.6826 0.020 0.000 0.056 0.924
#> SRR1499503 2 0.0469 0.8875 0.012 0.988 0.000 0.000
#> SRR1094312 2 0.0937 0.8863 0.012 0.976 0.012 0.000
#> SRR1352437 1 0.5294 0.5417 0.508 0.000 0.008 0.484
#> SRR1436323 4 0.5268 -0.2301 0.008 0.000 0.452 0.540
#> SRR1073507 4 0.2048 0.6854 0.008 0.000 0.064 0.928
#> SRR1401972 1 0.5277 0.6014 0.532 0.000 0.008 0.460
#> SRR1415510 2 0.1677 0.8841 0.040 0.948 0.012 0.000
#> SRR1327279 4 0.3810 0.5917 0.008 0.000 0.188 0.804
#> SRR1086983 4 0.3439 0.6683 0.048 0.000 0.084 0.868
#> SRR1105174 4 0.1724 0.6767 0.020 0.000 0.032 0.948
#> SRR1468893 4 0.0779 0.6763 0.016 0.000 0.004 0.980
#> SRR1362555 2 0.3276 0.8649 0.064 0.888 0.012 0.036
#> SRR1074526 2 0.6957 0.6907 0.312 0.576 0.100 0.012
#> SRR1326225 2 0.0937 0.8863 0.012 0.976 0.012 0.000
#> SRR1401933 4 0.2131 0.6857 0.032 0.000 0.036 0.932
#> SRR1324062 4 0.3245 0.6721 0.056 0.000 0.064 0.880
#> SRR1102296 1 0.6383 0.7997 0.612 0.000 0.096 0.292
#> SRR1085087 4 0.5007 -0.1308 0.356 0.000 0.008 0.636
#> SRR1079046 2 0.8059 0.5948 0.336 0.500 0.104 0.060
#> SRR1328339 1 0.7147 0.7566 0.560 0.000 0.216 0.224
#> SRR1079782 2 0.6671 0.3183 0.372 0.552 0.012 0.064
#> SRR1092257 2 0.3662 0.8632 0.148 0.836 0.012 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.0404 0.8030 0.000 0.988 0.000 0.000 0.012
#> SRR1429287 2 0.3682 0.7540 0.000 0.820 0.000 0.072 0.108
#> SRR1359238 1 0.4568 0.6772 0.788 0.000 0.088 0.036 0.088
#> SRR1309597 3 0.2267 0.6794 0.048 0.000 0.916 0.028 0.008
#> SRR1441398 1 0.3659 0.5588 0.768 0.000 0.220 0.000 0.012
#> SRR1084055 2 0.0510 0.8017 0.000 0.984 0.000 0.000 0.016
#> SRR1417566 4 0.6465 0.1143 0.140 0.000 0.412 0.440 0.008
#> SRR1351857 1 0.5212 0.6725 0.748 0.000 0.068 0.096 0.088
#> SRR1487485 3 0.1780 0.6805 0.024 0.000 0.940 0.028 0.008
#> SRR1335875 3 0.5850 0.4903 0.272 0.004 0.600 0.124 0.000
#> SRR1073947 1 0.3231 0.6506 0.800 0.000 0.000 0.196 0.004
#> SRR1443483 3 0.2228 0.6819 0.040 0.000 0.920 0.028 0.012
#> SRR1346794 1 0.3579 0.5104 0.756 0.000 0.240 0.004 0.000
#> SRR1405245 1 0.4088 0.3999 0.688 0.000 0.304 0.000 0.008
#> SRR1409677 1 0.7055 0.1949 0.440 0.000 0.280 0.264 0.016
#> SRR1095549 1 0.3923 0.6733 0.812 0.000 0.132 0.016 0.040
#> SRR1323788 1 0.5385 0.6121 0.692 0.000 0.208 0.024 0.076
#> SRR1314054 2 0.3620 0.7568 0.000 0.824 0.000 0.068 0.108
#> SRR1077944 1 0.1282 0.6748 0.952 0.000 0.044 0.004 0.000
#> SRR1480587 2 0.1043 0.8036 0.000 0.960 0.000 0.000 0.040
#> SRR1311205 1 0.2074 0.6628 0.896 0.000 0.104 0.000 0.000
#> SRR1076369 4 0.6571 0.3631 0.392 0.000 0.204 0.404 0.000
#> SRR1453549 3 0.4588 0.6824 0.220 0.000 0.720 0.060 0.000
#> SRR1345782 1 0.2179 0.6627 0.888 0.000 0.112 0.000 0.000
#> SRR1447850 2 0.5136 0.6245 0.000 0.688 0.000 0.196 0.116
#> SRR1391553 3 0.6342 0.1084 0.012 0.088 0.564 0.320 0.016
#> SRR1444156 2 0.0404 0.8030 0.000 0.988 0.000 0.000 0.012
#> SRR1471731 3 0.4400 0.6656 0.108 0.000 0.788 0.088 0.016
#> SRR1120987 4 0.2625 0.5056 0.108 0.000 0.016 0.876 0.000
#> SRR1477363 1 0.2074 0.6725 0.920 0.000 0.036 0.044 0.000
#> SRR1391961 5 0.3010 0.9276 0.004 0.172 0.000 0.000 0.824
#> SRR1373879 3 0.3822 0.7001 0.152 0.000 0.808 0.020 0.020
#> SRR1318732 3 0.2548 0.6758 0.072 0.000 0.896 0.028 0.004
#> SRR1091404 1 0.5036 -0.1129 0.560 0.000 0.036 0.404 0.000
#> SRR1402109 3 0.4159 0.6922 0.160 0.000 0.788 0.032 0.020
#> SRR1407336 3 0.4023 0.6975 0.164 0.000 0.792 0.028 0.016
#> SRR1097417 4 0.8573 0.1149 0.060 0.180 0.328 0.372 0.060
#> SRR1396227 1 0.4181 0.5002 0.676 0.000 0.004 0.316 0.004
#> SRR1400775 2 0.0609 0.8070 0.000 0.980 0.000 0.000 0.020
#> SRR1392861 3 0.6530 0.1867 0.196 0.000 0.424 0.380 0.000
#> SRR1472929 5 0.5008 0.7208 0.024 0.344 0.012 0.000 0.620
#> SRR1436740 4 0.6642 -0.0532 0.232 0.000 0.340 0.428 0.000
#> SRR1477057 2 0.5672 0.6025 0.016 0.672 0.000 0.156 0.156
#> SRR1311980 3 0.5083 0.6614 0.160 0.000 0.700 0.140 0.000
#> SRR1069400 3 0.2692 0.6896 0.092 0.000 0.884 0.016 0.008
#> SRR1351016 1 0.3584 0.6997 0.848 0.000 0.040 0.084 0.028
#> SRR1096291 4 0.6521 0.3978 0.244 0.000 0.272 0.484 0.000
#> SRR1418145 4 0.3106 0.5093 0.132 0.000 0.024 0.844 0.000
#> SRR1488111 4 0.5060 0.3706 0.072 0.100 0.040 0.772 0.016
#> SRR1370495 4 0.5359 0.3762 0.412 0.000 0.056 0.532 0.000
#> SRR1352639 4 0.6697 0.4077 0.364 0.048 0.068 0.512 0.008
#> SRR1348911 3 0.5597 0.4326 0.160 0.000 0.640 0.200 0.000
#> SRR1467386 1 0.4656 0.6547 0.740 0.000 0.076 0.180 0.004
#> SRR1415956 1 0.1704 0.6641 0.928 0.000 0.068 0.004 0.000
#> SRR1500495 1 0.3246 0.6043 0.808 0.000 0.184 0.000 0.008
#> SRR1405099 1 0.2221 0.6719 0.912 0.000 0.036 0.052 0.000
#> SRR1345585 3 0.2308 0.6637 0.036 0.000 0.912 0.048 0.004
#> SRR1093196 3 0.3911 0.6807 0.100 0.000 0.824 0.056 0.020
#> SRR1466006 2 0.0404 0.8030 0.000 0.988 0.000 0.000 0.012
#> SRR1351557 2 0.0290 0.8060 0.000 0.992 0.000 0.000 0.008
#> SRR1382687 1 0.6280 0.4923 0.600 0.000 0.268 0.044 0.088
#> SRR1375549 4 0.5148 0.3516 0.432 0.000 0.040 0.528 0.000
#> SRR1101765 4 0.5910 0.3366 0.452 0.000 0.040 0.476 0.032
#> SRR1334461 5 0.3318 0.9158 0.008 0.192 0.000 0.000 0.800
#> SRR1094073 2 0.0404 0.8030 0.000 0.988 0.000 0.000 0.012
#> SRR1077549 1 0.5307 0.5721 0.676 0.000 0.168 0.156 0.000
#> SRR1440332 1 0.5680 0.5935 0.672 0.000 0.212 0.032 0.084
#> SRR1454177 4 0.6682 -0.1223 0.236 0.000 0.368 0.396 0.000
#> SRR1082447 1 0.2983 0.6561 0.864 0.000 0.040 0.096 0.000
#> SRR1420043 3 0.4305 0.6886 0.152 0.000 0.784 0.044 0.020
#> SRR1432500 1 0.5059 0.6760 0.760 0.000 0.072 0.084 0.084
#> SRR1378045 2 0.7998 -0.0991 0.004 0.364 0.200 0.348 0.084
#> SRR1334200 2 0.4367 0.3805 0.000 0.620 0.000 0.008 0.372
#> SRR1069539 4 0.5788 0.1091 0.076 0.004 0.440 0.480 0.000
#> SRR1343031 3 0.5070 0.5498 0.316 0.000 0.640 0.028 0.016
#> SRR1319690 3 0.4249 0.4181 0.432 0.000 0.568 0.000 0.000
#> SRR1310604 2 0.2249 0.7886 0.000 0.896 0.000 0.008 0.096
#> SRR1327747 3 0.4210 0.4477 0.412 0.000 0.588 0.000 0.000
#> SRR1072456 2 0.1430 0.8008 0.000 0.944 0.000 0.004 0.052
#> SRR1367896 3 0.2193 0.6659 0.028 0.000 0.920 0.044 0.008
#> SRR1480107 1 0.1774 0.6813 0.932 0.000 0.016 0.052 0.000
#> SRR1377756 1 0.4378 0.6907 0.800 0.000 0.032 0.088 0.080
#> SRR1435272 4 0.6718 -0.1477 0.248 0.000 0.368 0.384 0.000
#> SRR1089230 1 0.6467 0.2072 0.432 0.000 0.140 0.420 0.008
#> SRR1389522 3 0.4647 0.4918 0.352 0.000 0.628 0.016 0.004
#> SRR1080600 2 0.2124 0.7895 0.000 0.900 0.000 0.004 0.096
#> SRR1086935 4 0.4343 0.4290 0.072 0.056 0.064 0.808 0.000
#> SRR1344060 5 0.3160 0.9223 0.000 0.188 0.000 0.004 0.808
#> SRR1467922 2 0.0404 0.8030 0.000 0.988 0.000 0.000 0.012
#> SRR1090984 1 0.6016 -0.0661 0.548 0.000 0.140 0.312 0.000
#> SRR1456991 1 0.2074 0.6743 0.920 0.000 0.036 0.044 0.000
#> SRR1085039 1 0.2644 0.6868 0.888 0.000 0.012 0.088 0.012
#> SRR1069303 4 0.4359 0.2553 0.412 0.000 0.000 0.584 0.004
#> SRR1091500 2 0.2873 0.7270 0.000 0.856 0.000 0.016 0.128
#> SRR1075198 2 0.3112 0.7738 0.000 0.856 0.000 0.044 0.100
#> SRR1086915 1 0.6529 0.5145 0.592 0.000 0.076 0.256 0.076
#> SRR1499503 2 0.1043 0.8062 0.000 0.960 0.000 0.000 0.040
#> SRR1094312 2 0.0000 0.8055 0.000 1.000 0.000 0.000 0.000
#> SRR1352437 4 0.3491 0.4396 0.228 0.000 0.000 0.768 0.004
#> SRR1436323 3 0.5589 0.6040 0.188 0.000 0.676 0.120 0.016
#> SRR1073507 1 0.4444 0.6536 0.748 0.000 0.072 0.180 0.000
#> SRR1401972 4 0.4288 0.3149 0.384 0.000 0.000 0.612 0.004
#> SRR1415510 2 0.2124 0.7895 0.000 0.900 0.000 0.004 0.096
#> SRR1327279 1 0.4458 0.6790 0.796 0.000 0.084 0.036 0.084
#> SRR1086983 1 0.4732 0.6306 0.716 0.000 0.076 0.208 0.000
#> SRR1105174 1 0.2304 0.6737 0.908 0.000 0.044 0.048 0.000
#> SRR1468893 1 0.1764 0.6899 0.928 0.000 0.008 0.064 0.000
#> SRR1362555 2 0.6262 0.4473 0.156 0.652 0.000 0.064 0.128
#> SRR1074526 5 0.3047 0.9219 0.004 0.160 0.004 0.000 0.832
#> SRR1326225 2 0.0404 0.8030 0.000 0.988 0.000 0.000 0.012
#> SRR1401933 1 0.5232 0.6723 0.744 0.000 0.060 0.112 0.084
#> SRR1324062 1 0.4985 0.5734 0.680 0.000 0.076 0.244 0.000
#> SRR1102296 4 0.5578 0.4338 0.308 0.008 0.056 0.620 0.008
#> SRR1085087 4 0.4586 0.0623 0.468 0.000 0.004 0.524 0.004
#> SRR1079046 5 0.3600 0.9122 0.008 0.152 0.008 0.012 0.820
#> SRR1328339 4 0.7230 0.2761 0.212 0.004 0.312 0.448 0.024
#> SRR1079782 2 0.6180 0.1789 0.008 0.456 0.000 0.432 0.104
#> SRR1092257 2 0.6001 0.4492 0.000 0.580 0.000 0.244 0.176
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.0000 0.80831 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1429287 2 0.5856 0.64160 0.000 0.636 0.040 0.224 0.060 0.040
#> SRR1359238 1 0.4840 0.63763 0.732 0.000 0.004 0.060 0.060 0.144
#> SRR1309597 3 0.1950 0.66980 0.032 0.000 0.924 0.016 0.000 0.028
#> SRR1441398 1 0.2371 0.60974 0.900 0.000 0.032 0.052 0.016 0.000
#> SRR1084055 2 0.1556 0.75719 0.000 0.920 0.000 0.000 0.080 0.000
#> SRR1417566 6 0.6109 0.36969 0.284 0.004 0.204 0.004 0.004 0.500
#> SRR1351857 1 0.5888 0.58775 0.560 0.000 0.004 0.064 0.060 0.312
#> SRR1487485 3 0.1861 0.66412 0.020 0.000 0.928 0.016 0.000 0.036
#> SRR1335875 1 0.6672 -0.17654 0.416 0.016 0.376 0.028 0.000 0.164
#> SRR1073947 1 0.4467 0.55787 0.564 0.000 0.004 0.024 0.000 0.408
#> SRR1443483 3 0.1806 0.64925 0.044 0.000 0.928 0.020 0.000 0.008
#> SRR1346794 1 0.3096 0.60125 0.868 0.000 0.052 0.016 0.016 0.048
#> SRR1405245 1 0.2384 0.60634 0.900 0.000 0.044 0.040 0.016 0.000
#> SRR1409677 1 0.6994 0.30685 0.456 0.000 0.096 0.220 0.000 0.228
#> SRR1095549 1 0.1793 0.64130 0.932 0.000 0.004 0.040 0.016 0.008
#> SRR1323788 1 0.4587 0.59593 0.780 0.000 0.044 0.084 0.052 0.040
#> SRR1314054 2 0.5043 0.67921 0.000 0.688 0.040 0.208 0.060 0.004
#> SRR1077944 1 0.1232 0.64366 0.956 0.000 0.004 0.016 0.000 0.024
#> SRR1480587 2 0.1168 0.80233 0.000 0.956 0.000 0.016 0.028 0.000
#> SRR1311205 1 0.1218 0.63233 0.956 0.000 0.012 0.028 0.000 0.004
#> SRR1076369 6 0.5859 0.48031 0.408 0.000 0.088 0.008 0.020 0.476
#> SRR1453549 1 0.6510 0.11802 0.540 0.000 0.224 0.144 0.000 0.092
#> SRR1345782 1 0.1218 0.62944 0.956 0.000 0.012 0.028 0.004 0.000
#> SRR1447850 2 0.6539 0.55801 0.000 0.572 0.040 0.240 0.056 0.092
#> SRR1391553 3 0.6487 0.34818 0.036 0.068 0.568 0.048 0.008 0.272
#> SRR1444156 2 0.0000 0.80831 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1471731 4 0.6401 0.61767 0.176 0.000 0.240 0.528 0.000 0.056
#> SRR1120987 6 0.3078 0.50245 0.032 0.000 0.028 0.084 0.000 0.856
#> SRR1477363 1 0.2592 0.65010 0.864 0.000 0.004 0.016 0.000 0.116
#> SRR1391961 5 0.1700 0.90621 0.004 0.080 0.000 0.000 0.916 0.000
#> SRR1373879 1 0.6857 -0.45798 0.348 0.000 0.308 0.300 0.000 0.044
#> SRR1318732 3 0.2644 0.67662 0.060 0.000 0.880 0.000 0.008 0.052
#> SRR1091404 6 0.5164 0.45695 0.416 0.000 0.024 0.032 0.004 0.524
#> SRR1402109 4 0.6953 0.44838 0.300 0.000 0.276 0.368 0.000 0.056
#> SRR1407336 3 0.6773 -0.36581 0.320 0.000 0.404 0.228 0.000 0.048
#> SRR1097417 6 0.7919 0.11048 0.048 0.224 0.268 0.024 0.040 0.396
#> SRR1396227 1 0.4446 0.52171 0.532 0.000 0.004 0.020 0.000 0.444
#> SRR1400775 2 0.1074 0.80640 0.000 0.960 0.000 0.012 0.028 0.000
#> SRR1392861 4 0.5680 0.66595 0.048 0.000 0.088 0.600 0.000 0.264
#> SRR1472929 5 0.4379 0.61356 0.024 0.336 0.000 0.008 0.632 0.000
#> SRR1436740 4 0.5604 0.64281 0.048 0.000 0.072 0.592 0.000 0.288
#> SRR1477057 2 0.6520 0.58172 0.000 0.576 0.036 0.224 0.116 0.048
#> SRR1311980 1 0.6911 -0.19853 0.404 0.000 0.360 0.120 0.000 0.116
#> SRR1069400 3 0.2476 0.66794 0.072 0.000 0.888 0.008 0.000 0.032
#> SRR1351016 1 0.2734 0.66276 0.860 0.000 0.004 0.016 0.004 0.116
#> SRR1096291 6 0.6391 0.45459 0.228 0.000 0.188 0.044 0.004 0.536
#> SRR1418145 6 0.3590 0.51341 0.052 0.000 0.044 0.076 0.000 0.828
#> SRR1488111 6 0.6617 0.46198 0.036 0.072 0.060 0.220 0.016 0.596
#> SRR1370495 6 0.5681 0.57851 0.200 0.008 0.068 0.064 0.004 0.656
#> SRR1352639 6 0.5993 0.59099 0.204 0.048 0.076 0.028 0.004 0.640
#> SRR1348911 3 0.5144 0.41278 0.100 0.004 0.624 0.004 0.000 0.268
#> SRR1467386 1 0.5498 0.57236 0.556 0.000 0.004 0.116 0.004 0.320
#> SRR1415956 1 0.1642 0.62754 0.936 0.000 0.004 0.032 0.000 0.028
#> SRR1500495 1 0.2084 0.61727 0.916 0.000 0.024 0.044 0.016 0.000
#> SRR1405099 1 0.3203 0.61955 0.812 0.000 0.004 0.024 0.000 0.160
#> SRR1345585 3 0.2201 0.67922 0.036 0.000 0.904 0.004 0.000 0.056
#> SRR1093196 4 0.6273 0.61553 0.164 0.000 0.240 0.544 0.000 0.052
#> SRR1466006 2 0.0146 0.80833 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1351557 2 0.0458 0.80923 0.000 0.984 0.000 0.000 0.016 0.000
#> SRR1382687 1 0.6655 0.58067 0.592 0.000 0.056 0.108 0.060 0.184
#> SRR1375549 6 0.5164 0.58750 0.216 0.000 0.052 0.052 0.004 0.676
#> SRR1101765 6 0.6393 0.57187 0.256 0.016 0.040 0.072 0.028 0.588
#> SRR1334461 5 0.1908 0.90123 0.004 0.096 0.000 0.000 0.900 0.000
#> SRR1094073 2 0.0000 0.80831 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077549 1 0.6137 0.49180 0.524 0.000 0.052 0.092 0.004 0.328
#> SRR1440332 1 0.5335 0.57285 0.728 0.000 0.052 0.092 0.060 0.068
#> SRR1454177 4 0.5457 0.66066 0.036 0.000 0.084 0.616 0.000 0.264
#> SRR1082447 1 0.3780 0.57607 0.744 0.000 0.004 0.028 0.000 0.224
#> SRR1420043 4 0.6315 0.61284 0.176 0.000 0.212 0.552 0.000 0.060
#> SRR1432500 1 0.6081 0.59315 0.584 0.000 0.004 0.108 0.060 0.244
#> SRR1378045 3 0.7721 0.04422 0.004 0.232 0.388 0.028 0.080 0.268
#> SRR1334200 2 0.4486 0.45650 0.000 0.584 0.004 0.028 0.384 0.000
#> SRR1069539 6 0.6187 0.41793 0.092 0.000 0.232 0.088 0.004 0.584
#> SRR1343031 1 0.6022 0.28479 0.588 0.000 0.208 0.152 0.000 0.052
#> SRR1319690 1 0.4148 0.48206 0.748 0.000 0.188 0.048 0.016 0.000
#> SRR1310604 2 0.4129 0.74190 0.000 0.780 0.032 0.028 0.148 0.012
#> SRR1327747 1 0.3781 0.49671 0.772 0.000 0.184 0.028 0.016 0.000
#> SRR1072456 2 0.1391 0.80070 0.000 0.944 0.000 0.016 0.040 0.000
#> SRR1367896 3 0.2007 0.67990 0.036 0.000 0.916 0.004 0.000 0.044
#> SRR1480107 1 0.3541 0.59956 0.748 0.000 0.000 0.020 0.000 0.232
#> SRR1377756 1 0.5261 0.61845 0.652 0.000 0.004 0.040 0.060 0.244
#> SRR1435272 4 0.5534 0.66182 0.044 0.000 0.080 0.612 0.000 0.264
#> SRR1089230 1 0.6615 0.36286 0.444 0.000 0.040 0.156 0.008 0.352
#> SRR1389522 3 0.4676 0.28616 0.384 0.000 0.572 0.004 0.000 0.040
#> SRR1080600 2 0.2425 0.78733 0.000 0.884 0.004 0.024 0.088 0.000
#> SRR1086935 6 0.4416 0.34326 0.076 0.000 0.000 0.212 0.004 0.708
#> SRR1344060 5 0.2100 0.89960 0.000 0.112 0.000 0.000 0.884 0.004
#> SRR1467922 2 0.0000 0.80831 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1090984 6 0.5161 0.46109 0.452 0.000 0.072 0.000 0.004 0.472
#> SRR1456991 1 0.2492 0.61139 0.876 0.000 0.004 0.020 0.000 0.100
#> SRR1085039 1 0.3965 0.61119 0.716 0.000 0.004 0.020 0.004 0.256
#> SRR1069303 6 0.3236 0.36837 0.180 0.000 0.000 0.024 0.000 0.796
#> SRR1091500 2 0.4444 0.55393 0.000 0.676 0.000 0.068 0.256 0.000
#> SRR1075198 2 0.5955 0.66999 0.000 0.660 0.032 0.116 0.136 0.056
#> SRR1086915 1 0.6089 0.55569 0.560 0.000 0.012 0.096 0.040 0.292
#> SRR1499503 2 0.1074 0.80328 0.000 0.960 0.000 0.012 0.028 0.000
#> SRR1094312 2 0.0146 0.80870 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1352437 6 0.3408 0.40049 0.152 0.000 0.000 0.048 0.000 0.800
#> SRR1436323 4 0.7295 0.55066 0.276 0.000 0.232 0.380 0.000 0.112
#> SRR1073507 1 0.5175 0.56268 0.572 0.000 0.000 0.092 0.004 0.332
#> SRR1401972 6 0.2624 0.43952 0.124 0.000 0.000 0.020 0.000 0.856
#> SRR1415510 2 0.3171 0.77048 0.000 0.844 0.028 0.024 0.104 0.000
#> SRR1327279 1 0.4608 0.63674 0.752 0.000 0.000 0.080 0.060 0.108
#> SRR1086983 1 0.5399 0.51360 0.528 0.000 0.000 0.108 0.004 0.360
#> SRR1105174 1 0.3409 0.59946 0.788 0.000 0.004 0.024 0.000 0.184
#> SRR1468893 1 0.3930 0.63998 0.728 0.000 0.004 0.032 0.000 0.236
#> SRR1362555 2 0.6535 0.56066 0.012 0.588 0.028 0.076 0.236 0.060
#> SRR1074526 5 0.2306 0.90712 0.004 0.096 0.004 0.008 0.888 0.000
#> SRR1326225 2 0.0000 0.80831 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1401933 1 0.5724 0.62242 0.652 0.000 0.012 0.084 0.060 0.192
#> SRR1324062 1 0.5958 0.49137 0.516 0.000 0.040 0.100 0.000 0.344
#> SRR1102296 6 0.5116 0.59217 0.120 0.020 0.052 0.060 0.008 0.740
#> SRR1085087 6 0.3950 0.09324 0.276 0.000 0.000 0.028 0.000 0.696
#> SRR1079046 5 0.3029 0.88990 0.004 0.076 0.032 0.008 0.868 0.012
#> SRR1328339 6 0.6216 0.45460 0.212 0.012 0.208 0.008 0.008 0.552
#> SRR1079782 6 0.7723 0.00211 0.000 0.256 0.032 0.256 0.084 0.372
#> SRR1092257 2 0.6333 0.58724 0.000 0.592 0.036 0.228 0.100 0.044
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17611 rows and 118 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 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 0.896 0.919 0.967 0.4108 0.594 0.594
#> 3 3 0.555 0.774 0.870 0.5404 0.700 0.526
#> 4 4 0.549 0.635 0.791 0.1701 0.803 0.519
#> 5 5 0.586 0.556 0.766 0.0616 0.843 0.485
#> 6 6 0.581 0.405 0.651 0.0429 0.859 0.472
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
#> SRR1396765 2 0.0000 0.94718 0.000 1.000
#> SRR1429287 2 0.0000 0.94718 0.000 1.000
#> SRR1359238 1 0.0000 0.97136 1.000 0.000
#> SRR1309597 1 0.0000 0.97136 1.000 0.000
#> SRR1441398 1 0.0000 0.97136 1.000 0.000
#> SRR1084055 2 0.0000 0.94718 0.000 1.000
#> SRR1417566 1 0.4161 0.89448 0.916 0.084
#> SRR1351857 1 0.0000 0.97136 1.000 0.000
#> SRR1487485 2 0.7299 0.73689 0.204 0.796
#> SRR1335875 1 0.2778 0.92899 0.952 0.048
#> SRR1073947 1 0.0000 0.97136 1.000 0.000
#> SRR1443483 1 0.0000 0.97136 1.000 0.000
#> SRR1346794 1 0.0000 0.97136 1.000 0.000
#> SRR1405245 1 0.0000 0.97136 1.000 0.000
#> SRR1409677 1 0.0000 0.97136 1.000 0.000
#> SRR1095549 1 0.0000 0.97136 1.000 0.000
#> SRR1323788 1 0.0000 0.97136 1.000 0.000
#> SRR1314054 2 0.0000 0.94718 0.000 1.000
#> SRR1077944 1 0.0000 0.97136 1.000 0.000
#> SRR1480587 2 0.0000 0.94718 0.000 1.000
#> SRR1311205 1 0.0000 0.97136 1.000 0.000
#> SRR1076369 1 0.0000 0.97136 1.000 0.000
#> SRR1453549 1 0.0000 0.97136 1.000 0.000
#> SRR1345782 1 0.0000 0.97136 1.000 0.000
#> SRR1447850 2 0.0000 0.94718 0.000 1.000
#> SRR1391553 2 0.0000 0.94718 0.000 1.000
#> SRR1444156 2 0.0000 0.94718 0.000 1.000
#> SRR1471731 1 0.0938 0.96166 0.988 0.012
#> SRR1120987 1 0.4431 0.88338 0.908 0.092
#> SRR1477363 1 0.0000 0.97136 1.000 0.000
#> SRR1391961 1 0.6801 0.77318 0.820 0.180
#> SRR1373879 1 0.0000 0.97136 1.000 0.000
#> SRR1318732 1 0.1633 0.95135 0.976 0.024
#> SRR1091404 1 0.0000 0.97136 1.000 0.000
#> SRR1402109 1 0.0000 0.97136 1.000 0.000
#> SRR1407336 1 0.0000 0.97136 1.000 0.000
#> SRR1097417 2 0.0000 0.94718 0.000 1.000
#> SRR1396227 1 0.0000 0.97136 1.000 0.000
#> SRR1400775 2 0.0000 0.94718 0.000 1.000
#> SRR1392861 1 0.0000 0.97136 1.000 0.000
#> SRR1472929 2 0.0376 0.94407 0.004 0.996
#> SRR1436740 1 0.0000 0.97136 1.000 0.000
#> SRR1477057 2 0.0000 0.94718 0.000 1.000
#> SRR1311980 1 0.6148 0.81248 0.848 0.152
#> SRR1069400 1 0.0000 0.97136 1.000 0.000
#> SRR1351016 1 0.0000 0.97136 1.000 0.000
#> SRR1096291 1 0.0000 0.97136 1.000 0.000
#> SRR1418145 1 0.0000 0.97136 1.000 0.000
#> SRR1488111 2 0.8955 0.54664 0.312 0.688
#> SRR1370495 1 0.0000 0.97136 1.000 0.000
#> SRR1352639 1 0.0000 0.97136 1.000 0.000
#> SRR1348911 1 0.1843 0.94829 0.972 0.028
#> SRR1467386 1 0.0000 0.97136 1.000 0.000
#> SRR1415956 1 0.0000 0.97136 1.000 0.000
#> SRR1500495 1 0.0000 0.97136 1.000 0.000
#> SRR1405099 1 0.0000 0.97136 1.000 0.000
#> SRR1345585 2 0.8813 0.58598 0.300 0.700
#> SRR1093196 1 0.0000 0.97136 1.000 0.000
#> SRR1466006 2 0.0000 0.94718 0.000 1.000
#> SRR1351557 2 0.0000 0.94718 0.000 1.000
#> SRR1382687 1 0.0000 0.97136 1.000 0.000
#> SRR1375549 1 0.0000 0.97136 1.000 0.000
#> SRR1101765 1 0.0000 0.97136 1.000 0.000
#> SRR1334461 1 0.0672 0.96501 0.992 0.008
#> SRR1094073 2 0.0000 0.94718 0.000 1.000
#> SRR1077549 1 0.0000 0.97136 1.000 0.000
#> SRR1440332 1 0.0000 0.97136 1.000 0.000
#> SRR1454177 1 0.0000 0.97136 1.000 0.000
#> SRR1082447 1 0.0000 0.97136 1.000 0.000
#> SRR1420043 1 0.0000 0.97136 1.000 0.000
#> SRR1432500 1 0.0000 0.97136 1.000 0.000
#> SRR1378045 2 0.0000 0.94718 0.000 1.000
#> SRR1334200 1 0.9686 0.33913 0.604 0.396
#> SRR1069539 1 0.8763 0.56461 0.704 0.296
#> SRR1343031 1 0.0000 0.97136 1.000 0.000
#> SRR1319690 1 0.0000 0.97136 1.000 0.000
#> SRR1310604 2 0.0000 0.94718 0.000 1.000
#> SRR1327747 1 0.0000 0.97136 1.000 0.000
#> SRR1072456 2 0.0000 0.94718 0.000 1.000
#> SRR1367896 1 0.7745 0.70754 0.772 0.228
#> SRR1480107 1 0.0000 0.97136 1.000 0.000
#> SRR1377756 1 0.0000 0.97136 1.000 0.000
#> SRR1435272 1 0.0000 0.97136 1.000 0.000
#> SRR1089230 1 0.0000 0.97136 1.000 0.000
#> SRR1389522 1 0.0000 0.97136 1.000 0.000
#> SRR1080600 2 0.0000 0.94718 0.000 1.000
#> SRR1086935 2 0.9993 0.10059 0.484 0.516
#> SRR1344060 1 1.0000 -0.00414 0.504 0.496
#> SRR1467922 2 0.0000 0.94718 0.000 1.000
#> SRR1090984 1 0.0000 0.97136 1.000 0.000
#> SRR1456991 1 0.0000 0.97136 1.000 0.000
#> SRR1085039 1 0.0000 0.97136 1.000 0.000
#> SRR1069303 1 0.0000 0.97136 1.000 0.000
#> SRR1091500 2 0.0000 0.94718 0.000 1.000
#> SRR1075198 2 0.0000 0.94718 0.000 1.000
#> SRR1086915 1 0.0000 0.97136 1.000 0.000
#> SRR1499503 2 0.0000 0.94718 0.000 1.000
#> SRR1094312 2 0.0000 0.94718 0.000 1.000
#> SRR1352437 1 0.0000 0.97136 1.000 0.000
#> SRR1436323 1 0.0000 0.97136 1.000 0.000
#> SRR1073507 1 0.0000 0.97136 1.000 0.000
#> SRR1401972 1 0.0000 0.97136 1.000 0.000
#> SRR1415510 2 0.0000 0.94718 0.000 1.000
#> SRR1327279 1 0.0000 0.97136 1.000 0.000
#> SRR1086983 1 0.0000 0.97136 1.000 0.000
#> SRR1105174 1 0.0000 0.97136 1.000 0.000
#> SRR1468893 1 0.0000 0.97136 1.000 0.000
#> SRR1362555 2 0.8909 0.55422 0.308 0.692
#> SRR1074526 1 0.7674 0.70836 0.776 0.224
#> SRR1326225 2 0.0000 0.94718 0.000 1.000
#> SRR1401933 1 0.0000 0.97136 1.000 0.000
#> SRR1324062 1 0.0000 0.97136 1.000 0.000
#> SRR1102296 1 0.0000 0.97136 1.000 0.000
#> SRR1085087 1 0.0000 0.97136 1.000 0.000
#> SRR1079046 1 0.0000 0.97136 1.000 0.000
#> SRR1328339 1 0.0000 0.97136 1.000 0.000
#> SRR1079782 2 0.0000 0.94718 0.000 1.000
#> SRR1092257 2 0.0000 0.94718 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.1163 0.921 0.028 0.972 0.000
#> SRR1429287 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1359238 3 0.2066 0.837 0.060 0.000 0.940
#> SRR1309597 3 0.3752 0.793 0.144 0.000 0.856
#> SRR1441398 3 0.5560 0.634 0.300 0.000 0.700
#> SRR1084055 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1417566 2 0.7164 0.468 0.044 0.640 0.316
#> SRR1351857 3 0.3412 0.808 0.124 0.000 0.876
#> SRR1487485 3 0.6373 0.281 0.004 0.408 0.588
#> SRR1335875 3 0.4281 0.805 0.072 0.056 0.872
#> SRR1073947 1 0.5948 0.481 0.640 0.000 0.360
#> SRR1443483 3 0.2959 0.808 0.100 0.000 0.900
#> SRR1346794 3 0.5810 0.568 0.336 0.000 0.664
#> SRR1405245 3 0.5138 0.701 0.252 0.000 0.748
#> SRR1409677 3 0.2066 0.834 0.060 0.000 0.940
#> SRR1095549 3 0.4504 0.759 0.196 0.000 0.804
#> SRR1323788 3 0.3038 0.823 0.104 0.000 0.896
#> SRR1314054 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1077944 3 0.6168 0.460 0.412 0.000 0.588
#> SRR1480587 2 0.1643 0.914 0.044 0.956 0.000
#> SRR1311205 3 0.4842 0.733 0.224 0.000 0.776
#> SRR1076369 1 0.2356 0.801 0.928 0.000 0.072
#> SRR1453549 3 0.0747 0.836 0.016 0.000 0.984
#> SRR1345782 3 0.5529 0.641 0.296 0.000 0.704
#> SRR1447850 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1391553 2 0.1753 0.902 0.000 0.952 0.048
#> SRR1444156 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1471731 3 0.1031 0.831 0.000 0.024 0.976
#> SRR1120987 3 0.6756 0.608 0.232 0.056 0.712
#> SRR1477363 3 0.5785 0.603 0.332 0.000 0.668
#> SRR1391961 1 0.0829 0.823 0.984 0.004 0.012
#> SRR1373879 3 0.0747 0.832 0.016 0.000 0.984
#> SRR1318732 3 0.6143 0.676 0.256 0.024 0.720
#> SRR1091404 1 0.0424 0.824 0.992 0.000 0.008
#> SRR1402109 3 0.0892 0.832 0.020 0.000 0.980
#> SRR1407336 3 0.1031 0.831 0.024 0.000 0.976
#> SRR1097417 2 0.6027 0.724 0.164 0.776 0.060
#> SRR1396227 3 0.6045 0.457 0.380 0.000 0.620
#> SRR1400775 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1392861 3 0.2066 0.834 0.060 0.000 0.940
#> SRR1472929 1 0.3213 0.788 0.912 0.028 0.060
#> SRR1436740 3 0.2165 0.833 0.064 0.000 0.936
#> SRR1477057 2 0.0424 0.927 0.008 0.992 0.000
#> SRR1311980 3 0.2681 0.825 0.040 0.028 0.932
#> SRR1069400 3 0.3192 0.803 0.112 0.000 0.888
#> SRR1351016 3 0.4887 0.772 0.228 0.000 0.772
#> SRR1096291 3 0.1964 0.835 0.056 0.000 0.944
#> SRR1418145 3 0.4346 0.738 0.184 0.000 0.816
#> SRR1488111 2 0.3539 0.841 0.012 0.888 0.100
#> SRR1370495 1 0.0592 0.824 0.988 0.000 0.012
#> SRR1352639 1 0.4346 0.736 0.816 0.000 0.184
#> SRR1348911 3 0.6039 0.735 0.104 0.108 0.788
#> SRR1467386 3 0.2448 0.833 0.076 0.000 0.924
#> SRR1415956 1 0.2261 0.809 0.932 0.000 0.068
#> SRR1500495 3 0.5706 0.601 0.320 0.000 0.680
#> SRR1405099 1 0.1753 0.825 0.952 0.000 0.048
#> SRR1345585 3 0.6379 0.586 0.032 0.256 0.712
#> SRR1093196 3 0.0747 0.832 0.016 0.000 0.984
#> SRR1466006 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1351557 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1382687 3 0.2356 0.835 0.072 0.000 0.928
#> SRR1375549 1 0.2625 0.802 0.916 0.000 0.084
#> SRR1101765 1 0.2261 0.812 0.932 0.000 0.068
#> SRR1334461 1 0.0592 0.824 0.988 0.000 0.012
#> SRR1094073 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1077549 3 0.2066 0.834 0.060 0.000 0.940
#> SRR1440332 3 0.1411 0.836 0.036 0.000 0.964
#> SRR1454177 3 0.2066 0.834 0.060 0.000 0.940
#> SRR1082447 1 0.3551 0.778 0.868 0.000 0.132
#> SRR1420043 3 0.0424 0.834 0.008 0.000 0.992
#> SRR1432500 3 0.2356 0.833 0.072 0.000 0.928
#> SRR1378045 2 0.2280 0.894 0.008 0.940 0.052
#> SRR1334200 1 0.3918 0.727 0.856 0.140 0.004
#> SRR1069539 2 0.6264 0.387 0.004 0.616 0.380
#> SRR1343031 3 0.2959 0.808 0.100 0.000 0.900
#> SRR1319690 3 0.4887 0.728 0.228 0.000 0.772
#> SRR1310604 2 0.2878 0.884 0.096 0.904 0.000
#> SRR1327747 3 0.2959 0.815 0.100 0.000 0.900
#> SRR1072456 2 0.2261 0.903 0.068 0.932 0.000
#> SRR1367896 3 0.6653 0.692 0.112 0.136 0.752
#> SRR1480107 1 0.2537 0.812 0.920 0.000 0.080
#> SRR1377756 3 0.3038 0.829 0.104 0.000 0.896
#> SRR1435272 3 0.2066 0.834 0.060 0.000 0.940
#> SRR1089230 3 0.2165 0.833 0.064 0.000 0.936
#> SRR1389522 3 0.5138 0.704 0.252 0.000 0.748
#> SRR1080600 2 0.3412 0.856 0.124 0.876 0.000
#> SRR1086935 3 0.4920 0.774 0.052 0.108 0.840
#> SRR1344060 1 0.4062 0.698 0.836 0.164 0.000
#> SRR1467922 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1090984 1 0.5948 0.416 0.640 0.000 0.360
#> SRR1456991 1 0.2356 0.807 0.928 0.000 0.072
#> SRR1085039 1 0.6192 0.291 0.580 0.000 0.420
#> SRR1069303 1 0.6079 0.447 0.612 0.000 0.388
#> SRR1091500 2 0.2625 0.876 0.084 0.916 0.000
#> SRR1075198 2 0.1031 0.923 0.024 0.976 0.000
#> SRR1086915 3 0.2356 0.833 0.072 0.000 0.928
#> SRR1499503 2 0.1163 0.921 0.028 0.972 0.000
#> SRR1094312 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1352437 3 0.3340 0.804 0.120 0.000 0.880
#> SRR1436323 3 0.0237 0.834 0.004 0.000 0.996
#> SRR1073507 3 0.3192 0.823 0.112 0.000 0.888
#> SRR1401972 1 0.6252 0.306 0.556 0.000 0.444
#> SRR1415510 2 0.3192 0.865 0.112 0.888 0.000
#> SRR1327279 3 0.2796 0.839 0.092 0.000 0.908
#> SRR1086983 3 0.2165 0.833 0.064 0.000 0.936
#> SRR1105174 1 0.1411 0.824 0.964 0.000 0.036
#> SRR1468893 1 0.6225 0.245 0.568 0.000 0.432
#> SRR1362555 1 0.4399 0.664 0.812 0.188 0.000
#> SRR1074526 1 0.3995 0.759 0.868 0.116 0.016
#> SRR1326225 2 0.0000 0.928 0.000 1.000 0.000
#> SRR1401933 3 0.2625 0.832 0.084 0.000 0.916
#> SRR1324062 3 0.2165 0.833 0.064 0.000 0.936
#> SRR1102296 1 0.3539 0.804 0.888 0.012 0.100
#> SRR1085087 3 0.4235 0.768 0.176 0.000 0.824
#> SRR1079046 1 0.1163 0.822 0.972 0.000 0.028
#> SRR1328339 1 0.2261 0.803 0.932 0.000 0.068
#> SRR1079782 2 0.0237 0.927 0.000 0.996 0.004
#> SRR1092257 2 0.1711 0.908 0.008 0.960 0.032
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.0524 0.8650 0.008 0.988 0.004 0.000
#> SRR1429287 2 0.0657 0.8656 0.004 0.984 0.000 0.012
#> SRR1359238 4 0.3486 0.6419 0.000 0.000 0.188 0.812
#> SRR1309597 3 0.3448 0.7199 0.004 0.000 0.828 0.168
#> SRR1441398 3 0.4638 0.7299 0.152 0.000 0.788 0.060
#> SRR1084055 2 0.1004 0.8715 0.004 0.972 0.024 0.000
#> SRR1417566 2 0.7473 0.1538 0.132 0.496 0.360 0.012
#> SRR1351857 4 0.2699 0.7560 0.068 0.000 0.028 0.904
#> SRR1487485 3 0.5590 0.6611 0.000 0.064 0.692 0.244
#> SRR1335875 3 0.3893 0.6459 0.008 0.000 0.796 0.196
#> SRR1073947 1 0.2089 0.7480 0.932 0.000 0.020 0.048
#> SRR1443483 3 0.3311 0.7173 0.000 0.000 0.828 0.172
#> SRR1346794 3 0.5894 0.6931 0.200 0.000 0.692 0.108
#> SRR1405245 3 0.4688 0.7437 0.128 0.000 0.792 0.080
#> SRR1409677 4 0.0336 0.7666 0.000 0.000 0.008 0.992
#> SRR1095549 3 0.6155 0.6956 0.176 0.000 0.676 0.148
#> SRR1323788 3 0.7252 0.1966 0.144 0.000 0.436 0.420
#> SRR1314054 2 0.3591 0.8592 0.000 0.824 0.168 0.008
#> SRR1077944 1 0.7601 0.0698 0.472 0.000 0.296 0.232
#> SRR1480587 2 0.1722 0.8542 0.008 0.944 0.048 0.000
#> SRR1311205 3 0.6078 0.6977 0.152 0.000 0.684 0.164
#> SRR1076369 3 0.6127 0.5723 0.180 0.116 0.696 0.008
#> SRR1453549 4 0.3907 0.5804 0.000 0.000 0.232 0.768
#> SRR1345782 3 0.5050 0.7349 0.152 0.000 0.764 0.084
#> SRR1447850 2 0.5582 0.7978 0.000 0.724 0.168 0.108
#> SRR1391553 2 0.4284 0.8276 0.000 0.764 0.224 0.012
#> SRR1444156 2 0.3266 0.8603 0.000 0.832 0.168 0.000
#> SRR1471731 4 0.1302 0.7601 0.000 0.000 0.044 0.956
#> SRR1120987 4 0.2559 0.7390 0.016 0.048 0.016 0.920
#> SRR1477363 4 0.7836 0.0571 0.348 0.000 0.264 0.388
#> SRR1391961 1 0.1867 0.7454 0.928 0.072 0.000 0.000
#> SRR1373879 3 0.4955 0.3626 0.000 0.000 0.556 0.444
#> SRR1318732 3 0.4057 0.7185 0.152 0.000 0.816 0.032
#> SRR1091404 1 0.0921 0.7500 0.972 0.000 0.028 0.000
#> SRR1402109 3 0.4925 0.4162 0.000 0.000 0.572 0.428
#> SRR1407336 4 0.4967 -0.1204 0.000 0.000 0.452 0.548
#> SRR1097417 3 0.3626 0.4445 0.004 0.184 0.812 0.000
#> SRR1396227 4 0.5060 0.2780 0.412 0.000 0.004 0.584
#> SRR1400775 2 0.3219 0.8618 0.000 0.836 0.164 0.000
#> SRR1392861 4 0.0336 0.7662 0.000 0.000 0.008 0.992
#> SRR1472929 1 0.7681 0.1709 0.404 0.216 0.380 0.000
#> SRR1436740 4 0.0188 0.7657 0.000 0.000 0.004 0.996
#> SRR1477057 2 0.3805 0.8670 0.008 0.832 0.148 0.012
#> SRR1311980 3 0.5459 0.1194 0.000 0.016 0.552 0.432
#> SRR1069400 3 0.3356 0.7149 0.000 0.000 0.824 0.176
#> SRR1351016 1 0.7741 0.0434 0.440 0.000 0.264 0.296
#> SRR1096291 4 0.1743 0.7549 0.000 0.004 0.056 0.940
#> SRR1418145 4 0.1890 0.7525 0.008 0.056 0.000 0.936
#> SRR1488111 4 0.5688 -0.0588 0.000 0.464 0.024 0.512
#> SRR1370495 1 0.3266 0.7010 0.832 0.168 0.000 0.000
#> SRR1352639 1 0.6475 0.6445 0.668 0.168 0.008 0.156
#> SRR1348911 3 0.1452 0.7002 0.000 0.008 0.956 0.036
#> SRR1467386 4 0.4378 0.6866 0.164 0.000 0.040 0.796
#> SRR1415956 1 0.3528 0.6126 0.808 0.000 0.192 0.000
#> SRR1500495 3 0.4499 0.7231 0.160 0.000 0.792 0.048
#> SRR1405099 1 0.2048 0.7352 0.928 0.000 0.064 0.008
#> SRR1345585 3 0.4308 0.7348 0.032 0.012 0.820 0.136
#> SRR1093196 4 0.4164 0.4927 0.000 0.000 0.264 0.736
#> SRR1466006 2 0.0672 0.8639 0.008 0.984 0.008 0.000
#> SRR1351557 2 0.0524 0.8680 0.004 0.988 0.008 0.000
#> SRR1382687 4 0.5102 0.6505 0.100 0.000 0.136 0.764
#> SRR1375549 1 0.1488 0.7577 0.956 0.012 0.000 0.032
#> SRR1101765 1 0.0817 0.7553 0.976 0.024 0.000 0.000
#> SRR1334461 1 0.2868 0.7187 0.864 0.136 0.000 0.000
#> SRR1094073 2 0.2760 0.8712 0.000 0.872 0.128 0.000
#> SRR1077549 4 0.0707 0.7664 0.000 0.000 0.020 0.980
#> SRR1440332 4 0.5396 -0.1348 0.012 0.000 0.464 0.524
#> SRR1454177 4 0.0188 0.7666 0.000 0.000 0.004 0.996
#> SRR1082447 1 0.1059 0.7535 0.972 0.000 0.012 0.016
#> SRR1420043 4 0.4164 0.5127 0.000 0.000 0.264 0.736
#> SRR1432500 4 0.2760 0.7079 0.000 0.000 0.128 0.872
#> SRR1378045 3 0.4804 -0.1350 0.000 0.384 0.616 0.000
#> SRR1334200 1 0.3831 0.6698 0.792 0.204 0.004 0.000
#> SRR1069539 4 0.7890 -0.0316 0.004 0.372 0.228 0.396
#> SRR1343031 3 0.4522 0.6125 0.000 0.000 0.680 0.320
#> SRR1319690 3 0.4764 0.7457 0.124 0.000 0.788 0.088
#> SRR1310604 2 0.3501 0.7916 0.020 0.848 0.132 0.000
#> SRR1327747 3 0.4993 0.6735 0.028 0.000 0.712 0.260
#> SRR1072456 2 0.1890 0.8554 0.008 0.936 0.056 0.000
#> SRR1367896 3 0.3300 0.7231 0.000 0.008 0.848 0.144
#> SRR1480107 1 0.0804 0.7532 0.980 0.000 0.012 0.008
#> SRR1377756 4 0.3355 0.7078 0.160 0.000 0.004 0.836
#> SRR1435272 4 0.0000 0.7660 0.000 0.000 0.000 1.000
#> SRR1089230 4 0.0188 0.7668 0.004 0.000 0.000 0.996
#> SRR1389522 3 0.4626 0.7395 0.064 0.036 0.828 0.072
#> SRR1080600 2 0.4345 0.7300 0.020 0.788 0.188 0.004
#> SRR1086935 4 0.2500 0.7336 0.000 0.044 0.040 0.916
#> SRR1344060 1 0.3726 0.6650 0.788 0.212 0.000 0.000
#> SRR1467922 2 0.3219 0.8618 0.000 0.836 0.164 0.000
#> SRR1090984 3 0.5792 0.2462 0.456 0.008 0.520 0.016
#> SRR1456991 1 0.4356 0.4414 0.708 0.000 0.292 0.000
#> SRR1085039 1 0.3959 0.6947 0.840 0.000 0.068 0.092
#> SRR1069303 1 0.5143 0.1536 0.540 0.000 0.004 0.456
#> SRR1091500 2 0.4517 0.8538 0.036 0.792 0.168 0.004
#> SRR1075198 2 0.1059 0.8611 0.012 0.972 0.016 0.000
#> SRR1086915 4 0.0779 0.7681 0.016 0.000 0.004 0.980
#> SRR1499503 2 0.1792 0.8522 0.000 0.932 0.068 0.000
#> SRR1094312 2 0.2589 0.8730 0.000 0.884 0.116 0.000
#> SRR1352437 4 0.5006 0.6718 0.136 0.016 0.060 0.788
#> SRR1436323 4 0.1022 0.7626 0.000 0.000 0.032 0.968
#> SRR1073507 4 0.3486 0.7019 0.188 0.000 0.000 0.812
#> SRR1401972 1 0.5155 0.1150 0.528 0.000 0.004 0.468
#> SRR1415510 2 0.2271 0.8583 0.008 0.916 0.076 0.000
#> SRR1327279 4 0.5453 0.4099 0.036 0.000 0.304 0.660
#> SRR1086983 4 0.0592 0.7686 0.016 0.000 0.000 0.984
#> SRR1105174 1 0.1792 0.7328 0.932 0.000 0.068 0.000
#> SRR1468893 1 0.4277 0.5210 0.720 0.000 0.000 0.280
#> SRR1362555 1 0.4720 0.5994 0.720 0.264 0.016 0.000
#> SRR1074526 1 0.2976 0.7097 0.872 0.008 0.120 0.000
#> SRR1326225 2 0.3123 0.8646 0.000 0.844 0.156 0.000
#> SRR1401933 4 0.2714 0.7410 0.112 0.000 0.004 0.884
#> SRR1324062 4 0.1833 0.7621 0.024 0.000 0.032 0.944
#> SRR1102296 1 0.1739 0.7556 0.952 0.008 0.024 0.016
#> SRR1085087 4 0.4072 0.5949 0.252 0.000 0.000 0.748
#> SRR1079046 1 0.1022 0.7540 0.968 0.032 0.000 0.000
#> SRR1328339 3 0.5630 0.4390 0.360 0.032 0.608 0.000
#> SRR1079782 2 0.1909 0.8485 0.008 0.940 0.004 0.048
#> SRR1092257 2 0.5857 0.7985 0.008 0.720 0.164 0.108
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 5 0.4046 0.51145 0.000 0.296 0.008 0.000 0.696
#> SRR1429287 5 0.3928 0.62453 0.004 0.156 0.004 0.036 0.800
#> SRR1359238 4 0.1502 0.80077 0.000 0.000 0.056 0.940 0.004
#> SRR1309597 3 0.0912 0.64854 0.000 0.000 0.972 0.016 0.012
#> SRR1441398 3 0.4354 0.45576 0.368 0.000 0.624 0.008 0.000
#> SRR1084055 5 0.3246 0.60436 0.000 0.184 0.008 0.000 0.808
#> SRR1417566 3 0.6483 0.34200 0.160 0.396 0.440 0.004 0.000
#> SRR1351857 4 0.2470 0.78407 0.012 0.000 0.000 0.884 0.104
#> SRR1487485 3 0.3063 0.64828 0.000 0.020 0.864 0.104 0.012
#> SRR1335875 3 0.6271 0.52357 0.276 0.084 0.596 0.044 0.000
#> SRR1073947 1 0.1329 0.70759 0.956 0.000 0.032 0.004 0.008
#> SRR1443483 3 0.1493 0.64476 0.000 0.000 0.948 0.028 0.024
#> SRR1346794 3 0.5526 0.34956 0.404 0.000 0.540 0.012 0.044
#> SRR1405245 3 0.4642 0.52308 0.308 0.000 0.660 0.032 0.000
#> SRR1409677 4 0.0880 0.81026 0.000 0.000 0.000 0.968 0.032
#> SRR1095549 3 0.5299 0.50146 0.296 0.000 0.640 0.052 0.012
#> SRR1323788 3 0.5501 0.30009 0.444 0.000 0.492 0.064 0.000
#> SRR1314054 2 0.0833 0.77341 0.004 0.976 0.000 0.004 0.016
#> SRR1077944 1 0.3196 0.59658 0.804 0.000 0.192 0.004 0.000
#> SRR1480587 5 0.6080 0.34613 0.000 0.344 0.136 0.000 0.520
#> SRR1311205 3 0.5043 0.45663 0.356 0.000 0.600 0.044 0.000
#> SRR1076369 5 0.5531 0.54587 0.168 0.000 0.164 0.004 0.664
#> SRR1453549 3 0.5579 0.42444 0.064 0.004 0.540 0.392 0.000
#> SRR1345782 3 0.3949 0.53119 0.300 0.000 0.696 0.000 0.004
#> SRR1447850 2 0.0486 0.77011 0.004 0.988 0.000 0.004 0.004
#> SRR1391553 2 0.0510 0.76237 0.000 0.984 0.016 0.000 0.000
#> SRR1444156 2 0.0290 0.77158 0.000 0.992 0.000 0.000 0.008
#> SRR1471731 4 0.2233 0.76657 0.000 0.004 0.104 0.892 0.000
#> SRR1120987 4 0.2011 0.78922 0.004 0.000 0.000 0.908 0.088
#> SRR1477363 1 0.4372 0.48913 0.712 0.000 0.260 0.024 0.004
#> SRR1391961 1 0.4640 0.26443 0.584 0.000 0.016 0.000 0.400
#> SRR1373879 3 0.4663 0.44793 0.020 0.000 0.604 0.376 0.000
#> SRR1318732 3 0.2780 0.64983 0.112 0.004 0.872 0.008 0.004
#> SRR1091404 1 0.3883 0.63032 0.780 0.000 0.036 0.000 0.184
#> SRR1402109 3 0.4480 0.37895 0.004 0.000 0.592 0.400 0.004
#> SRR1407336 4 0.2522 0.76465 0.000 0.000 0.108 0.880 0.012
#> SRR1097417 3 0.3991 0.50097 0.000 0.048 0.780 0.000 0.172
#> SRR1396227 1 0.2053 0.69530 0.924 0.000 0.048 0.024 0.004
#> SRR1400775 2 0.0566 0.77327 0.004 0.984 0.000 0.000 0.012
#> SRR1392861 4 0.0404 0.81191 0.000 0.000 0.012 0.988 0.000
#> SRR1472929 5 0.4430 0.60933 0.036 0.000 0.256 0.000 0.708
#> SRR1436740 4 0.0162 0.81335 0.000 0.000 0.000 0.996 0.004
#> SRR1477057 2 0.5000 0.55897 0.068 0.688 0.000 0.004 0.240
#> SRR1311980 3 0.7581 0.39335 0.204 0.348 0.392 0.056 0.000
#> SRR1069400 3 0.2569 0.60603 0.000 0.000 0.892 0.040 0.068
#> SRR1351016 1 0.4142 0.42278 0.684 0.000 0.308 0.004 0.004
#> SRR1096291 4 0.2864 0.75942 0.000 0.000 0.012 0.852 0.136
#> SRR1418145 4 0.3700 0.62699 0.008 0.000 0.000 0.752 0.240
#> SRR1488111 4 0.5086 0.53434 0.004 0.096 0.000 0.700 0.200
#> SRR1370495 5 0.2011 0.63532 0.088 0.000 0.000 0.004 0.908
#> SRR1352639 5 0.5460 0.32936 0.364 0.000 0.016 0.040 0.580
#> SRR1348911 3 0.4690 0.62671 0.108 0.140 0.748 0.004 0.000
#> SRR1467386 4 0.5088 0.16776 0.436 0.000 0.036 0.528 0.000
#> SRR1415956 1 0.3424 0.54011 0.760 0.000 0.240 0.000 0.000
#> SRR1500495 3 0.4354 0.45195 0.368 0.000 0.624 0.008 0.000
#> SRR1405099 1 0.2179 0.67150 0.888 0.000 0.112 0.000 0.000
#> SRR1345585 3 0.1074 0.64855 0.000 0.004 0.968 0.016 0.012
#> SRR1093196 4 0.2338 0.75491 0.000 0.000 0.112 0.884 0.004
#> SRR1466006 5 0.4879 0.57846 0.000 0.228 0.076 0.000 0.696
#> SRR1351557 2 0.4443 0.00421 0.004 0.524 0.000 0.000 0.472
#> SRR1382687 3 0.6534 0.24923 0.388 0.000 0.416 0.196 0.000
#> SRR1375549 1 0.3484 0.65582 0.824 0.000 0.004 0.028 0.144
#> SRR1101765 5 0.6121 0.06223 0.376 0.000 0.004 0.116 0.504
#> SRR1334461 1 0.4746 0.05662 0.504 0.000 0.016 0.000 0.480
#> SRR1094073 2 0.3837 0.48099 0.000 0.692 0.000 0.000 0.308
#> SRR1077549 4 0.0771 0.81143 0.004 0.000 0.020 0.976 0.000
#> SRR1440332 3 0.5211 0.33960 0.044 0.000 0.524 0.432 0.000
#> SRR1454177 4 0.0162 0.81318 0.000 0.000 0.004 0.996 0.000
#> SRR1082447 1 0.0898 0.70644 0.972 0.000 0.008 0.000 0.020
#> SRR1420043 4 0.3123 0.65354 0.000 0.000 0.184 0.812 0.004
#> SRR1432500 4 0.1282 0.80549 0.004 0.000 0.044 0.952 0.000
#> SRR1378045 2 0.3966 0.39312 0.000 0.664 0.336 0.000 0.000
#> SRR1334200 5 0.1764 0.64163 0.064 0.000 0.000 0.008 0.928
#> SRR1069539 5 0.4302 0.52069 0.000 0.000 0.032 0.248 0.720
#> SRR1343031 3 0.4430 0.21209 0.000 0.000 0.540 0.456 0.004
#> SRR1319690 3 0.2984 0.65143 0.108 0.000 0.860 0.032 0.000
#> SRR1310604 5 0.2522 0.66425 0.000 0.012 0.108 0.000 0.880
#> SRR1327747 3 0.4903 0.56927 0.028 0.000 0.712 0.228 0.032
#> SRR1072456 5 0.6130 0.47499 0.000 0.264 0.180 0.000 0.556
#> SRR1367896 3 0.1475 0.64635 0.004 0.012 0.956 0.012 0.016
#> SRR1480107 1 0.0992 0.70725 0.968 0.000 0.024 0.000 0.008
#> SRR1377756 1 0.5055 0.16011 0.544 0.000 0.016 0.428 0.012
#> SRR1435272 4 0.0162 0.81318 0.000 0.000 0.004 0.996 0.000
#> SRR1089230 4 0.1943 0.80315 0.020 0.000 0.000 0.924 0.056
#> SRR1389522 3 0.1012 0.64897 0.020 0.000 0.968 0.000 0.012
#> SRR1080600 5 0.3163 0.65728 0.000 0.012 0.164 0.000 0.824
#> SRR1086935 4 0.1596 0.80943 0.012 0.012 0.000 0.948 0.028
#> SRR1344060 5 0.2719 0.60173 0.144 0.000 0.004 0.000 0.852
#> SRR1467922 2 0.0794 0.77308 0.000 0.972 0.000 0.000 0.028
#> SRR1090984 1 0.4430 -0.06826 0.540 0.004 0.456 0.000 0.000
#> SRR1456991 1 0.3949 0.44640 0.696 0.000 0.300 0.000 0.004
#> SRR1085039 1 0.3012 0.70108 0.872 0.000 0.072 0.004 0.052
#> SRR1069303 1 0.1915 0.69883 0.928 0.000 0.000 0.040 0.032
#> SRR1091500 2 0.1892 0.73920 0.004 0.916 0.000 0.000 0.080
#> SRR1075198 5 0.4535 0.64838 0.000 0.124 0.084 0.016 0.776
#> SRR1086915 4 0.1331 0.80859 0.008 0.000 0.000 0.952 0.040
#> SRR1499503 5 0.6066 0.51378 0.000 0.240 0.188 0.000 0.572
#> SRR1094312 2 0.3231 0.66235 0.004 0.800 0.000 0.000 0.196
#> SRR1352437 1 0.5153 0.55051 0.684 0.112 0.000 0.204 0.000
#> SRR1436323 4 0.1591 0.80405 0.004 0.000 0.052 0.940 0.004
#> SRR1073507 4 0.4067 0.53426 0.300 0.000 0.008 0.692 0.000
#> SRR1401972 1 0.1588 0.70454 0.948 0.008 0.000 0.028 0.016
#> SRR1415510 5 0.6303 0.49914 0.004 0.212 0.228 0.000 0.556
#> SRR1327279 4 0.6191 -0.29136 0.136 0.000 0.424 0.440 0.000
#> SRR1086983 4 0.1251 0.80938 0.036 0.000 0.008 0.956 0.000
#> SRR1105174 1 0.2669 0.68317 0.876 0.000 0.104 0.000 0.020
#> SRR1468893 1 0.1612 0.70605 0.948 0.000 0.012 0.024 0.016
#> SRR1362555 5 0.1569 0.65881 0.044 0.000 0.004 0.008 0.944
#> SRR1074526 1 0.4531 0.20223 0.568 0.004 0.000 0.004 0.424
#> SRR1326225 2 0.1908 0.74856 0.000 0.908 0.000 0.000 0.092
#> SRR1401933 4 0.5121 0.22659 0.416 0.000 0.012 0.552 0.020
#> SRR1324062 4 0.5251 0.27174 0.372 0.012 0.032 0.584 0.000
#> SRR1102296 1 0.2331 0.69910 0.912 0.060 0.020 0.004 0.004
#> SRR1085087 1 0.4644 0.32550 0.604 0.000 0.012 0.380 0.004
#> SRR1079046 1 0.2536 0.66720 0.868 0.000 0.004 0.000 0.128
#> SRR1328339 1 0.4425 -0.03099 0.544 0.004 0.452 0.000 0.000
#> SRR1079782 5 0.6032 0.44057 0.004 0.256 0.004 0.136 0.600
#> SRR1092257 2 0.5745 0.37027 0.004 0.592 0.000 0.100 0.304
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.4832 0.5090 0.000 0.672 0.224 0.000 0.096 0.008
#> SRR1429287 2 0.1969 0.6459 0.000 0.920 0.004 0.020 0.004 0.052
#> SRR1359238 4 0.2540 0.6815 0.104 0.004 0.000 0.872 0.000 0.020
#> SRR1309597 1 0.4420 0.4324 0.624 0.008 0.000 0.012 0.008 0.348
#> SRR1441398 1 0.1950 0.4453 0.912 0.000 0.000 0.000 0.024 0.064
#> SRR1084055 2 0.5978 0.4411 0.000 0.532 0.152 0.000 0.292 0.024
#> SRR1417566 1 0.4512 0.2210 0.676 0.000 0.028 0.000 0.024 0.272
#> SRR1351857 4 0.5081 0.5480 0.000 0.020 0.000 0.676 0.180 0.124
#> SRR1487485 1 0.5708 0.4082 0.548 0.008 0.004 0.112 0.004 0.324
#> SRR1335875 1 0.2846 0.4725 0.872 0.000 0.080 0.004 0.016 0.028
#> SRR1073947 5 0.4372 0.4521 0.184 0.000 0.000 0.008 0.728 0.080
#> SRR1443483 1 0.5355 0.4224 0.580 0.028 0.000 0.032 0.016 0.344
#> SRR1346794 6 0.5374 0.4518 0.372 0.052 0.000 0.000 0.032 0.544
#> SRR1405245 1 0.1668 0.4564 0.928 0.000 0.000 0.004 0.008 0.060
#> SRR1409677 4 0.2687 0.6718 0.008 0.044 0.000 0.876 0.000 0.072
#> SRR1095549 1 0.7055 0.0490 0.356 0.000 0.000 0.080 0.208 0.356
#> SRR1323788 1 0.4684 -0.0753 0.604 0.000 0.000 0.008 0.040 0.348
#> SRR1314054 3 0.1769 0.7691 0.000 0.060 0.924 0.012 0.004 0.000
#> SRR1077944 1 0.5569 -0.0346 0.540 0.000 0.000 0.000 0.280 0.180
#> SRR1480587 2 0.5281 0.5510 0.016 0.616 0.080 0.000 0.004 0.284
#> SRR1311205 1 0.3331 0.4045 0.816 0.000 0.000 0.004 0.136 0.044
#> SRR1076369 6 0.4537 0.1507 0.012 0.316 0.000 0.000 0.032 0.640
#> SRR1453549 4 0.4524 0.3121 0.452 0.000 0.000 0.520 0.004 0.024
#> SRR1345782 1 0.4344 0.4477 0.748 0.000 0.000 0.028 0.168 0.056
#> SRR1447850 3 0.1010 0.7513 0.000 0.000 0.960 0.036 0.004 0.000
#> SRR1391553 3 0.0858 0.7514 0.028 0.000 0.968 0.000 0.004 0.000
#> SRR1444156 3 0.0146 0.7564 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1471731 4 0.3826 0.6390 0.124 0.004 0.000 0.784 0.000 0.088
#> SRR1120987 4 0.3786 0.5424 0.000 0.240 0.004 0.736 0.004 0.016
#> SRR1477363 1 0.5437 0.0839 0.596 0.000 0.000 0.004 0.204 0.196
#> SRR1391961 5 0.2179 0.4230 0.000 0.064 0.000 0.000 0.900 0.036
#> SRR1373879 4 0.5069 0.2352 0.472 0.004 0.000 0.476 0.020 0.028
#> SRR1318732 1 0.4428 0.1912 0.528 0.012 0.004 0.000 0.004 0.452
#> SRR1091404 5 0.4437 0.4090 0.120 0.016 0.000 0.000 0.744 0.120
#> SRR1402109 4 0.5164 0.2906 0.444 0.004 0.000 0.496 0.016 0.040
#> SRR1407336 4 0.2980 0.6759 0.116 0.004 0.000 0.848 0.004 0.028
#> SRR1097417 1 0.7059 0.1981 0.456 0.036 0.048 0.008 0.348 0.104
#> SRR1396227 6 0.6118 0.2382 0.380 0.000 0.000 0.004 0.232 0.384
#> SRR1400775 3 0.1700 0.7660 0.000 0.080 0.916 0.000 0.004 0.000
#> SRR1392861 4 0.0582 0.6827 0.000 0.004 0.004 0.984 0.004 0.004
#> SRR1472929 2 0.5310 0.4438 0.004 0.480 0.000 0.000 0.428 0.088
#> SRR1436740 4 0.1536 0.6741 0.000 0.040 0.004 0.940 0.000 0.016
#> SRR1477057 3 0.6991 0.2810 0.008 0.280 0.460 0.004 0.192 0.056
#> SRR1311980 1 0.4946 0.3985 0.700 0.000 0.200 0.064 0.008 0.028
#> SRR1069400 1 0.6537 0.3829 0.524 0.052 0.000 0.076 0.036 0.312
#> SRR1351016 1 0.5293 -0.2470 0.484 0.000 0.000 0.008 0.432 0.076
#> SRR1096291 4 0.3374 0.5824 0.000 0.208 0.000 0.772 0.000 0.020
#> SRR1418145 2 0.4654 0.1026 0.000 0.544 0.000 0.412 0.000 0.044
#> SRR1488111 4 0.4924 0.3097 0.000 0.368 0.016 0.580 0.004 0.032
#> SRR1370495 2 0.2191 0.6571 0.000 0.876 0.000 0.000 0.120 0.004
#> SRR1352639 2 0.6156 0.3952 0.132 0.616 0.000 0.008 0.164 0.080
#> SRR1348911 1 0.5637 0.2934 0.608 0.000 0.288 0.032 0.040 0.032
#> SRR1467386 4 0.5855 0.4178 0.084 0.000 0.000 0.632 0.164 0.120
#> SRR1415956 1 0.5134 0.0780 0.620 0.000 0.000 0.000 0.152 0.228
#> SRR1500495 1 0.1926 0.4482 0.912 0.000 0.000 0.000 0.020 0.068
#> SRR1405099 1 0.5837 -0.1898 0.452 0.000 0.000 0.000 0.352 0.196
#> SRR1345585 1 0.4528 0.4006 0.564 0.028 0.000 0.000 0.004 0.404
#> SRR1093196 4 0.1464 0.6906 0.036 0.004 0.000 0.944 0.000 0.016
#> SRR1466006 2 0.3273 0.6440 0.000 0.776 0.008 0.000 0.004 0.212
#> SRR1351557 2 0.3733 0.4509 0.000 0.700 0.288 0.000 0.008 0.004
#> SRR1382687 1 0.4928 0.0295 0.628 0.000 0.000 0.028 0.040 0.304
#> SRR1375549 6 0.6707 0.4364 0.084 0.284 0.000 0.012 0.108 0.512
#> SRR1101765 6 0.6151 0.3196 0.000 0.340 0.000 0.040 0.124 0.496
#> SRR1334461 5 0.2060 0.4226 0.000 0.084 0.000 0.000 0.900 0.016
#> SRR1094073 3 0.3841 0.3583 0.000 0.380 0.616 0.000 0.004 0.000
#> SRR1077549 4 0.1649 0.6870 0.040 0.000 0.000 0.936 0.008 0.016
#> SRR1440332 4 0.4698 0.3243 0.436 0.000 0.000 0.528 0.012 0.024
#> SRR1454177 4 0.0653 0.6848 0.012 0.000 0.000 0.980 0.004 0.004
#> SRR1082447 6 0.5871 0.3857 0.320 0.000 0.000 0.000 0.216 0.464
#> SRR1420043 4 0.3333 0.6293 0.192 0.000 0.000 0.784 0.000 0.024
#> SRR1432500 4 0.3410 0.6670 0.128 0.004 0.000 0.824 0.016 0.028
#> SRR1378045 3 0.5646 0.2508 0.296 0.004 0.552 0.000 0.004 0.144
#> SRR1334200 2 0.5420 0.3492 0.000 0.572 0.000 0.000 0.172 0.256
#> SRR1069539 2 0.3041 0.6263 0.000 0.832 0.000 0.128 0.000 0.040
#> SRR1343031 4 0.5280 0.4635 0.320 0.004 0.000 0.596 0.024 0.056
#> SRR1319690 1 0.2762 0.4453 0.804 0.000 0.000 0.000 0.000 0.196
#> SRR1310604 2 0.4634 0.6192 0.000 0.688 0.000 0.000 0.188 0.124
#> SRR1327747 6 0.5740 -0.0184 0.288 0.108 0.000 0.032 0.000 0.572
#> SRR1072456 2 0.5792 0.5482 0.008 0.600 0.112 0.000 0.028 0.252
#> SRR1367896 1 0.5866 0.4207 0.580 0.020 0.000 0.064 0.036 0.300
#> SRR1480107 5 0.5156 0.3292 0.272 0.000 0.000 0.000 0.600 0.128
#> SRR1377756 6 0.6230 0.4747 0.344 0.016 0.000 0.076 0.048 0.516
#> SRR1435272 4 0.0291 0.6828 0.000 0.004 0.000 0.992 0.000 0.004
#> SRR1089230 4 0.5933 0.1131 0.000 0.220 0.000 0.432 0.000 0.348
#> SRR1389522 1 0.5427 0.4342 0.604 0.016 0.000 0.040 0.032 0.308
#> SRR1080600 2 0.2730 0.6480 0.000 0.808 0.000 0.000 0.000 0.192
#> SRR1086935 4 0.5535 0.4493 0.000 0.124 0.016 0.612 0.004 0.244
#> SRR1344060 5 0.4852 -0.3695 0.000 0.452 0.000 0.000 0.492 0.056
#> SRR1467922 3 0.1987 0.7637 0.004 0.080 0.908 0.000 0.004 0.004
#> SRR1090984 1 0.4186 0.2955 0.756 0.000 0.016 0.000 0.064 0.164
#> SRR1456991 5 0.5116 0.2275 0.444 0.000 0.000 0.004 0.484 0.068
#> SRR1085039 5 0.4418 0.4156 0.140 0.000 0.000 0.012 0.740 0.108
#> SRR1069303 5 0.7120 0.0838 0.284 0.012 0.008 0.036 0.420 0.240
#> SRR1091500 3 0.1700 0.7507 0.000 0.024 0.928 0.000 0.048 0.000
#> SRR1075198 2 0.0935 0.6636 0.000 0.964 0.000 0.004 0.000 0.032
#> SRR1086915 4 0.4344 0.5585 0.000 0.188 0.000 0.716 0.000 0.096
#> SRR1499503 2 0.6190 0.5168 0.012 0.588 0.160 0.000 0.040 0.200
#> SRR1094312 3 0.3756 0.6183 0.000 0.240 0.736 0.000 0.016 0.008
#> SRR1352437 5 0.8327 0.1990 0.128 0.000 0.144 0.264 0.364 0.100
#> SRR1436323 4 0.5404 0.4165 0.100 0.020 0.000 0.604 0.000 0.276
#> SRR1073507 4 0.3648 0.6332 0.024 0.000 0.000 0.808 0.128 0.040
#> SRR1401972 5 0.6933 -0.1020 0.316 0.004 0.008 0.024 0.352 0.296
#> SRR1415510 2 0.5015 0.5448 0.016 0.600 0.044 0.000 0.004 0.336
#> SRR1327279 4 0.6504 0.3423 0.292 0.000 0.000 0.468 0.200 0.040
#> SRR1086983 4 0.1515 0.6798 0.000 0.020 0.000 0.944 0.008 0.028
#> SRR1105174 1 0.5819 -0.3779 0.420 0.000 0.000 0.000 0.184 0.396
#> SRR1468893 6 0.5639 0.4314 0.348 0.004 0.000 0.004 0.124 0.520
#> SRR1362555 2 0.1152 0.6624 0.000 0.952 0.000 0.000 0.044 0.004
#> SRR1074526 5 0.5400 0.1857 0.000 0.092 0.032 0.000 0.628 0.248
#> SRR1326225 3 0.2730 0.6848 0.000 0.192 0.808 0.000 0.000 0.000
#> SRR1401933 6 0.7033 0.4891 0.236 0.100 0.000 0.108 0.032 0.524
#> SRR1324062 4 0.6047 0.1440 0.316 0.000 0.012 0.544 0.032 0.096
#> SRR1102296 5 0.5781 0.2612 0.324 0.000 0.016 0.000 0.528 0.132
#> SRR1085087 4 0.7406 -0.0845 0.180 0.016 0.000 0.420 0.284 0.100
#> SRR1079046 6 0.6885 0.4143 0.136 0.188 0.000 0.000 0.172 0.504
#> SRR1328339 1 0.3301 0.4027 0.828 0.000 0.004 0.000 0.100 0.068
#> SRR1079782 2 0.2544 0.6438 0.000 0.896 0.036 0.044 0.004 0.020
#> SRR1092257 2 0.6616 0.2044 0.000 0.520 0.304 0.092 0.052 0.032
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 17611 rows and 118 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.2719 0.790 0.849 0.2116 0.950 0.950
#> 3 3 0.0803 0.517 0.709 1.4581 0.544 0.520
#> 4 4 0.1253 0.433 0.662 0.1563 0.920 0.843
#> 5 5 0.2040 0.443 0.617 0.1178 0.873 0.724
#> 6 6 0.3083 0.335 0.588 0.0772 0.920 0.780
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
#> SRR1396765 1 0.7674 0.799 0.776 0.224
#> SRR1429287 1 0.7219 0.811 0.800 0.200
#> SRR1359238 1 0.2236 0.818 0.964 0.036
#> SRR1309597 1 0.0672 0.808 0.992 0.008
#> SRR1441398 1 0.3274 0.767 0.940 0.060
#> SRR1084055 1 0.8207 0.777 0.744 0.256
#> SRR1417566 1 0.8763 0.745 0.704 0.296
#> SRR1351857 1 0.3733 0.827 0.928 0.072
#> SRR1487485 1 0.8763 0.750 0.704 0.296
#> SRR1335875 1 0.9044 0.727 0.680 0.320
#> SRR1073947 1 0.8813 0.741 0.700 0.300
#> SRR1443483 1 0.1414 0.808 0.980 0.020
#> SRR1346794 1 0.0672 0.809 0.992 0.008
#> SRR1405245 1 0.1633 0.813 0.976 0.024
#> SRR1409677 1 0.1633 0.814 0.976 0.024
#> SRR1095549 1 0.4161 0.829 0.916 0.084
#> SRR1323788 1 0.1633 0.813 0.976 0.024
#> SRR1314054 1 0.8267 0.775 0.740 0.260
#> SRR1077944 1 0.3733 0.829 0.928 0.072
#> SRR1480587 1 0.7376 0.805 0.792 0.208
#> SRR1311205 1 0.1184 0.804 0.984 0.016
#> SRR1076369 1 0.0938 0.806 0.988 0.012
#> SRR1453549 1 0.5946 0.820 0.856 0.144
#> SRR1345782 1 0.0672 0.808 0.992 0.008
#> SRR1447850 1 0.9608 0.655 0.616 0.384
#> SRR1391553 1 0.9170 0.715 0.668 0.332
#> SRR1444156 1 0.8016 0.784 0.756 0.244
#> SRR1471731 1 0.7376 0.807 0.792 0.208
#> SRR1120987 1 0.5519 0.828 0.872 0.128
#> SRR1477363 1 0.0938 0.806 0.988 0.012
#> SRR1391961 2 0.9710 0.939 0.400 0.600
#> SRR1373879 1 0.3584 0.826 0.932 0.068
#> SRR1318732 1 0.1843 0.815 0.972 0.028
#> SRR1091404 1 0.3584 0.829 0.932 0.068
#> SRR1402109 1 0.2778 0.818 0.952 0.048
#> SRR1407336 1 0.1184 0.809 0.984 0.016
#> SRR1097417 1 0.8955 0.735 0.688 0.312
#> SRR1396227 1 0.8713 0.750 0.708 0.292
#> SRR1400775 1 0.9087 0.729 0.676 0.324
#> SRR1392861 1 0.7745 0.799 0.772 0.228
#> SRR1472929 2 0.9608 0.971 0.384 0.616
#> SRR1436740 1 0.7376 0.806 0.792 0.208
#> SRR1477057 1 0.9710 0.618 0.600 0.400
#> SRR1311980 1 0.9170 0.715 0.668 0.332
#> SRR1069400 1 0.0938 0.806 0.988 0.012
#> SRR1351016 1 0.3584 0.829 0.932 0.068
#> SRR1096291 1 0.3584 0.827 0.932 0.068
#> SRR1418145 1 0.3584 0.821 0.932 0.068
#> SRR1488111 1 0.7376 0.808 0.792 0.208
#> SRR1370495 1 0.5408 0.701 0.876 0.124
#> SRR1352639 1 0.3431 0.815 0.936 0.064
#> SRR1348911 1 0.8713 0.751 0.708 0.292
#> SRR1467386 1 0.4161 0.829 0.916 0.084
#> SRR1415956 1 0.3274 0.767 0.940 0.060
#> SRR1500495 1 0.1184 0.804 0.984 0.016
#> SRR1405099 1 0.3274 0.767 0.940 0.060
#> SRR1345585 1 0.6531 0.817 0.832 0.168
#> SRR1093196 1 0.6887 0.815 0.816 0.184
#> SRR1466006 1 0.7376 0.804 0.792 0.208
#> SRR1351557 1 0.7299 0.806 0.796 0.204
#> SRR1382687 1 0.1843 0.815 0.972 0.028
#> SRR1375549 1 0.4161 0.736 0.916 0.084
#> SRR1101765 1 0.0938 0.806 0.988 0.012
#> SRR1334461 2 0.9608 0.971 0.384 0.616
#> SRR1094073 1 0.8608 0.759 0.716 0.284
#> SRR1077549 1 0.5178 0.830 0.884 0.116
#> SRR1440332 1 0.0938 0.806 0.988 0.012
#> SRR1454177 1 0.7745 0.799 0.772 0.228
#> SRR1082447 1 0.4431 0.830 0.908 0.092
#> SRR1420043 1 0.3733 0.830 0.928 0.072
#> SRR1432500 1 0.0938 0.806 0.988 0.012
#> SRR1378045 1 0.9000 0.731 0.684 0.316
#> SRR1334200 1 0.4161 0.821 0.916 0.084
#> SRR1069539 1 0.3584 0.827 0.932 0.068
#> SRR1343031 1 0.0938 0.806 0.988 0.012
#> SRR1319690 1 0.0672 0.807 0.992 0.008
#> SRR1310604 1 0.7056 0.809 0.808 0.192
#> SRR1327747 1 0.1184 0.810 0.984 0.016
#> SRR1072456 1 0.7674 0.794 0.776 0.224
#> SRR1367896 1 0.9044 0.730 0.680 0.320
#> SRR1480107 1 0.0672 0.808 0.992 0.008
#> SRR1377756 1 0.1633 0.813 0.976 0.024
#> SRR1435272 1 0.7745 0.799 0.772 0.228
#> SRR1089230 1 0.2043 0.815 0.968 0.032
#> SRR1389522 1 0.1414 0.801 0.980 0.020
#> SRR1080600 1 0.2603 0.820 0.956 0.044
#> SRR1086935 1 0.9286 0.705 0.656 0.344
#> SRR1344060 1 0.8713 0.352 0.708 0.292
#> SRR1467922 1 0.8713 0.753 0.708 0.292
#> SRR1090984 1 0.8713 0.749 0.708 0.292
#> SRR1456991 1 0.0672 0.808 0.992 0.008
#> SRR1085039 1 0.2778 0.815 0.952 0.048
#> SRR1069303 1 0.9170 0.712 0.668 0.332
#> SRR1091500 1 0.8267 0.775 0.740 0.260
#> SRR1075198 1 0.3733 0.820 0.928 0.072
#> SRR1086915 1 0.3274 0.825 0.940 0.060
#> SRR1499503 1 0.7056 0.809 0.808 0.192
#> SRR1094312 1 0.8267 0.775 0.740 0.260
#> SRR1352437 1 0.9209 0.710 0.664 0.336
#> SRR1436323 1 0.7139 0.811 0.804 0.196
#> SRR1073507 1 0.4298 0.830 0.912 0.088
#> SRR1401972 1 0.9170 0.712 0.668 0.332
#> SRR1415510 1 0.7056 0.809 0.808 0.192
#> SRR1327279 1 0.1843 0.812 0.972 0.028
#> SRR1086983 1 0.6887 0.818 0.816 0.184
#> SRR1105174 1 0.0672 0.807 0.992 0.008
#> SRR1468893 1 0.1633 0.813 0.976 0.024
#> SRR1362555 1 0.3733 0.817 0.928 0.072
#> SRR1074526 1 0.7674 0.792 0.776 0.224
#> SRR1326225 1 0.8207 0.777 0.744 0.256
#> SRR1401933 1 0.1633 0.813 0.976 0.024
#> SRR1324062 1 0.9044 0.727 0.680 0.320
#> SRR1102296 1 0.9129 0.720 0.672 0.328
#> SRR1085087 1 0.2236 0.816 0.964 0.036
#> SRR1079046 1 0.4690 0.721 0.900 0.100
#> SRR1328339 1 0.8861 0.740 0.696 0.304
#> SRR1079782 1 0.3879 0.818 0.924 0.076
#> SRR1092257 1 0.7674 0.800 0.776 0.224
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.5117 0.609272 0.108 0.832 0.060
#> SRR1429287 2 0.4821 0.541847 0.064 0.848 0.088
#> SRR1359238 1 0.6562 0.572816 0.700 0.264 0.036
#> SRR1309597 1 0.4465 0.673426 0.820 0.176 0.004
#> SRR1441398 1 0.2200 0.634612 0.940 0.004 0.056
#> SRR1084055 2 0.4556 0.615526 0.080 0.860 0.060
#> SRR1417566 2 0.9105 0.319598 0.348 0.500 0.152
#> SRR1351857 1 0.6446 0.640910 0.736 0.212 0.052
#> SRR1487485 2 0.8105 0.549258 0.196 0.648 0.156
#> SRR1335875 2 0.9361 0.218488 0.396 0.436 0.168
#> SRR1073947 1 0.8067 0.409466 0.652 0.160 0.188
#> SRR1443483 1 0.4915 0.666769 0.804 0.184 0.012
#> SRR1346794 1 0.6090 0.566420 0.716 0.264 0.020
#> SRR1405245 1 0.3583 0.664222 0.900 0.056 0.044
#> SRR1409677 1 0.6570 0.499979 0.668 0.308 0.024
#> SRR1095549 1 0.4768 0.695212 0.848 0.100 0.052
#> SRR1323788 1 0.3583 0.664222 0.900 0.056 0.044
#> SRR1314054 2 0.2663 0.586944 0.024 0.932 0.044
#> SRR1077944 1 0.3797 0.669669 0.892 0.052 0.056
#> SRR1480587 2 0.5905 0.623145 0.184 0.772 0.044
#> SRR1311205 1 0.2152 0.678595 0.948 0.036 0.016
#> SRR1076369 1 0.6984 0.261104 0.560 0.420 0.020
#> SRR1453549 1 0.6936 0.586050 0.704 0.232 0.064
#> SRR1345782 1 0.0592 0.656765 0.988 0.000 0.012
#> SRR1447850 2 0.6723 0.523567 0.048 0.704 0.248
#> SRR1391553 2 0.8938 0.420972 0.284 0.552 0.164
#> SRR1444156 2 0.1399 0.569498 0.004 0.968 0.028
#> SRR1471731 1 0.8685 0.377230 0.548 0.328 0.124
#> SRR1120987 2 0.7770 0.308631 0.384 0.560 0.056
#> SRR1477363 1 0.1585 0.670402 0.964 0.028 0.008
#> SRR1391961 3 0.7402 0.942018 0.324 0.052 0.624
#> SRR1373879 1 0.5852 0.677856 0.776 0.180 0.044
#> SRR1318732 1 0.4818 0.671317 0.844 0.108 0.048
#> SRR1091404 1 0.3375 0.666149 0.908 0.044 0.048
#> SRR1402109 1 0.5467 0.682630 0.792 0.176 0.032
#> SRR1407336 1 0.4755 0.668891 0.808 0.184 0.008
#> SRR1097417 2 0.8743 0.459133 0.268 0.576 0.156
#> SRR1396227 1 0.7975 0.420288 0.660 0.160 0.180
#> SRR1400775 2 0.7265 0.592908 0.160 0.712 0.128
#> SRR1392861 2 0.9028 -0.055388 0.432 0.436 0.132
#> SRR1472929 3 0.6769 0.971967 0.320 0.028 0.652
#> SRR1436740 1 0.8925 0.111081 0.464 0.412 0.124
#> SRR1477057 1 0.9686 -0.000224 0.452 0.308 0.240
#> SRR1311980 2 0.9174 0.361879 0.332 0.504 0.164
#> SRR1069400 1 0.4700 0.668773 0.812 0.180 0.008
#> SRR1351016 1 0.3375 0.666149 0.908 0.044 0.048
#> SRR1096291 1 0.7480 0.133204 0.508 0.456 0.036
#> SRR1418145 2 0.7555 0.219472 0.440 0.520 0.040
#> SRR1488111 2 0.6937 0.545780 0.272 0.680 0.048
#> SRR1370495 1 0.8287 0.373191 0.616 0.256 0.128
#> SRR1352639 1 0.7208 0.338341 0.620 0.340 0.040
#> SRR1348911 2 0.9199 0.377743 0.328 0.504 0.168
#> SRR1467386 1 0.4768 0.695212 0.848 0.100 0.052
#> SRR1415956 1 0.2301 0.635637 0.936 0.004 0.060
#> SRR1500495 1 0.1999 0.678410 0.952 0.036 0.012
#> SRR1405099 1 0.2200 0.634612 0.940 0.004 0.056
#> SRR1345585 1 0.7724 0.469996 0.620 0.308 0.072
#> SRR1093196 1 0.8543 0.448389 0.580 0.292 0.128
#> SRR1466006 2 0.5875 0.508565 0.072 0.792 0.136
#> SRR1351557 2 0.5637 0.626895 0.172 0.788 0.040
#> SRR1382687 1 0.3983 0.665401 0.884 0.068 0.048
#> SRR1375549 1 0.7227 0.507202 0.704 0.200 0.096
#> SRR1101765 1 0.6994 0.249917 0.556 0.424 0.020
#> SRR1334461 3 0.6769 0.971967 0.320 0.028 0.652
#> SRR1094073 2 0.3030 0.544061 0.004 0.904 0.092
#> SRR1077549 1 0.5407 0.661690 0.820 0.104 0.076
#> SRR1440332 1 0.4353 0.681594 0.836 0.156 0.008
#> SRR1454177 2 0.9028 -0.055388 0.432 0.436 0.132
#> SRR1082447 1 0.5093 0.683423 0.836 0.088 0.076
#> SRR1420043 1 0.6188 0.645974 0.744 0.216 0.040
#> SRR1432500 1 0.3532 0.699724 0.884 0.108 0.008
#> SRR1378045 2 0.7672 0.573703 0.156 0.684 0.160
#> SRR1334200 2 0.6662 0.465404 0.192 0.736 0.072
#> SRR1069539 1 0.7480 0.133204 0.508 0.456 0.036
#> SRR1343031 1 0.4531 0.675869 0.824 0.168 0.008
#> SRR1319690 1 0.4755 0.661204 0.808 0.184 0.008
#> SRR1310604 2 0.5951 0.617194 0.196 0.764 0.040
#> SRR1327747 1 0.4968 0.675720 0.800 0.188 0.012
#> SRR1072456 2 0.6710 0.607435 0.196 0.732 0.072
#> SRR1367896 2 0.8958 0.424704 0.280 0.552 0.168
#> SRR1480107 1 0.0829 0.658387 0.984 0.004 0.012
#> SRR1377756 1 0.3583 0.664222 0.900 0.056 0.044
#> SRR1435272 1 0.9028 0.032433 0.436 0.432 0.132
#> SRR1089230 1 0.7379 0.358292 0.584 0.376 0.040
#> SRR1389522 1 0.4663 0.681013 0.828 0.156 0.016
#> SRR1080600 2 0.6662 0.432379 0.252 0.704 0.044
#> SRR1086935 2 0.7666 0.569811 0.128 0.680 0.192
#> SRR1344060 2 0.9198 0.120023 0.192 0.528 0.280
#> SRR1467922 2 0.3644 0.529572 0.004 0.872 0.124
#> SRR1090984 2 0.9129 0.284942 0.372 0.480 0.148
#> SRR1456991 1 0.0829 0.658387 0.984 0.004 0.012
#> SRR1085039 1 0.3742 0.699866 0.892 0.072 0.036
#> SRR1069303 1 0.8437 0.334753 0.620 0.200 0.180
#> SRR1091500 2 0.2663 0.586944 0.024 0.932 0.044
#> SRR1075198 2 0.7610 0.265202 0.420 0.536 0.044
#> SRR1086915 1 0.7600 0.477047 0.612 0.328 0.060
#> SRR1499503 2 0.5951 0.617194 0.196 0.764 0.040
#> SRR1094312 2 0.3267 0.592826 0.044 0.912 0.044
#> SRR1352437 1 0.8442 0.340751 0.620 0.188 0.192
#> SRR1436323 1 0.8614 0.424301 0.568 0.304 0.128
#> SRR1073507 1 0.4749 0.680138 0.852 0.072 0.076
#> SRR1401972 1 0.8437 0.334753 0.620 0.200 0.180
#> SRR1415510 2 0.5407 0.623890 0.156 0.804 0.040
#> SRR1327279 1 0.3933 0.701152 0.880 0.092 0.028
#> SRR1086983 1 0.7548 0.564611 0.684 0.204 0.112
#> SRR1105174 1 0.4589 0.669775 0.820 0.172 0.008
#> SRR1468893 1 0.3583 0.664222 0.900 0.056 0.044
#> SRR1362555 2 0.7708 0.259638 0.424 0.528 0.048
#> SRR1074526 2 0.7564 0.523163 0.156 0.692 0.152
#> SRR1326225 2 0.3083 0.581807 0.024 0.916 0.060
#> SRR1401933 1 0.3875 0.670986 0.888 0.068 0.044
#> SRR1324062 2 0.9299 0.197624 0.408 0.432 0.160
#> SRR1102296 1 0.9358 0.135742 0.496 0.312 0.192
#> SRR1085087 1 0.4056 0.706254 0.876 0.092 0.032
#> SRR1079046 1 0.7635 0.482129 0.676 0.212 0.112
#> SRR1328339 2 0.9243 0.286043 0.368 0.472 0.160
#> SRR1079782 2 0.7690 0.274069 0.416 0.536 0.048
#> SRR1092257 2 0.6982 0.596164 0.220 0.708 0.072
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.725 0.2180 0.088 0.628 0.056 0.228
#> SRR1429287 4 0.630 0.4689 0.016 0.408 0.032 0.544
#> SRR1359238 1 0.639 0.5711 0.652 0.160 0.000 0.188
#> SRR1309597 1 0.395 0.6266 0.780 0.216 0.004 0.000
#> SRR1441398 1 0.277 0.6630 0.908 0.024 0.060 0.008
#> SRR1084055 2 0.622 0.2210 0.056 0.664 0.020 0.260
#> SRR1417566 2 0.554 0.3780 0.280 0.680 0.008 0.032
#> SRR1351857 1 0.599 0.6272 0.692 0.164 0.000 0.144
#> SRR1487485 2 0.471 0.4224 0.144 0.796 0.008 0.052
#> SRR1335875 2 0.608 0.3296 0.320 0.628 0.016 0.036
#> SRR1073947 1 0.685 0.3782 0.608 0.296 0.032 0.064
#> SRR1443483 1 0.461 0.6207 0.764 0.212 0.008 0.016
#> SRR1346794 1 0.693 0.5240 0.616 0.132 0.012 0.240
#> SRR1405245 1 0.507 0.6360 0.780 0.028 0.036 0.156
#> SRR1409677 1 0.720 0.4719 0.568 0.180 0.004 0.248
#> SRR1095549 1 0.436 0.6679 0.812 0.124 0.000 0.064
#> SRR1323788 1 0.474 0.6445 0.796 0.028 0.024 0.152
#> SRR1314054 2 0.463 0.1486 0.000 0.720 0.012 0.268
#> SRR1077944 1 0.377 0.6652 0.864 0.084 0.020 0.032
#> SRR1480587 2 0.783 0.3210 0.156 0.596 0.064 0.184
#> SRR1311205 1 0.232 0.6802 0.928 0.048 0.012 0.012
#> SRR1076369 1 0.746 0.2222 0.460 0.136 0.008 0.396
#> SRR1453549 1 0.578 0.5621 0.676 0.272 0.012 0.040
#> SRR1345782 1 0.114 0.6736 0.972 0.008 0.012 0.008
#> SRR1447850 2 0.665 -0.0988 0.004 0.616 0.116 0.264
#> SRR1391553 2 0.515 0.4410 0.208 0.744 0.008 0.040
#> SRR1444156 2 0.469 0.1538 0.000 0.724 0.016 0.260
#> SRR1471731 1 0.718 0.3054 0.488 0.412 0.020 0.080
#> SRR1120987 2 0.796 0.2479 0.316 0.460 0.012 0.212
#> SRR1477363 1 0.196 0.6809 0.944 0.020 0.008 0.028
#> SRR1391961 3 0.558 0.9335 0.152 0.028 0.756 0.064
#> SRR1373879 1 0.514 0.6362 0.740 0.216 0.008 0.036
#> SRR1318732 1 0.627 0.6179 0.712 0.084 0.036 0.168
#> SRR1091404 1 0.326 0.6667 0.888 0.072 0.020 0.020
#> SRR1402109 1 0.485 0.6448 0.760 0.200 0.004 0.036
#> SRR1407336 1 0.443 0.6241 0.764 0.220 0.004 0.012
#> SRR1097417 2 0.587 0.4266 0.196 0.716 0.016 0.072
#> SRR1396227 1 0.721 0.3491 0.580 0.304 0.036 0.080
#> SRR1400775 2 0.669 0.2907 0.120 0.668 0.024 0.188
#> SRR1392861 2 0.786 -0.0498 0.368 0.428 0.008 0.196
#> SRR1472929 3 0.458 0.9677 0.156 0.016 0.800 0.028
#> SRR1436740 2 0.816 -0.1204 0.388 0.396 0.020 0.196
#> SRR1477057 1 0.891 -0.1032 0.384 0.376 0.088 0.152
#> SRR1311980 2 0.560 0.4366 0.252 0.696 0.008 0.044
#> SRR1069400 1 0.439 0.6246 0.768 0.216 0.004 0.012
#> SRR1351016 1 0.326 0.6667 0.888 0.072 0.020 0.020
#> SRR1096291 1 0.789 0.1021 0.444 0.372 0.016 0.168
#> SRR1418145 2 0.860 0.1691 0.380 0.388 0.048 0.184
#> SRR1488111 2 0.769 0.3102 0.232 0.528 0.012 0.228
#> SRR1370495 1 0.862 0.2877 0.524 0.116 0.136 0.224
#> SRR1352639 1 0.761 0.2717 0.564 0.288 0.044 0.104
#> SRR1348911 2 0.531 0.4388 0.256 0.708 0.012 0.024
#> SRR1467386 1 0.436 0.6679 0.812 0.124 0.000 0.064
#> SRR1415956 1 0.280 0.6650 0.908 0.020 0.060 0.012
#> SRR1500495 1 0.219 0.6789 0.932 0.048 0.008 0.012
#> SRR1405099 1 0.267 0.6641 0.912 0.020 0.060 0.008
#> SRR1345585 1 0.568 0.4097 0.580 0.396 0.016 0.008
#> SRR1093196 1 0.714 0.3768 0.512 0.384 0.016 0.088
#> SRR1466006 4 0.615 0.6046 0.012 0.296 0.052 0.640
#> SRR1351557 2 0.765 0.3134 0.144 0.612 0.060 0.184
#> SRR1382687 1 0.552 0.6287 0.756 0.044 0.036 0.164
#> SRR1375549 1 0.757 0.4534 0.616 0.084 0.088 0.212
#> SRR1101765 1 0.734 0.2171 0.460 0.136 0.004 0.400
#> SRR1334461 3 0.458 0.9677 0.156 0.016 0.800 0.028
#> SRR1094073 2 0.578 0.0305 0.000 0.664 0.064 0.272
#> SRR1077549 1 0.477 0.6469 0.800 0.136 0.016 0.048
#> SRR1440332 1 0.408 0.6419 0.800 0.184 0.004 0.012
#> SRR1454177 2 0.786 -0.0498 0.368 0.428 0.008 0.196
#> SRR1082447 1 0.485 0.6621 0.800 0.124 0.016 0.060
#> SRR1420043 1 0.519 0.6045 0.712 0.256 0.008 0.024
#> SRR1432500 1 0.373 0.6765 0.856 0.096 0.004 0.044
#> SRR1378045 2 0.418 0.4060 0.116 0.832 0.008 0.044
#> SRR1334200 4 0.643 0.6357 0.096 0.184 0.028 0.692
#> SRR1069539 1 0.789 0.1021 0.444 0.372 0.016 0.168
#> SRR1343031 1 0.428 0.6354 0.788 0.192 0.004 0.016
#> SRR1319690 1 0.512 0.6362 0.756 0.080 0.000 0.164
#> SRR1310604 2 0.781 0.3210 0.164 0.596 0.060 0.180
#> SRR1327747 1 0.538 0.6446 0.748 0.160 0.004 0.088
#> SRR1072456 2 0.825 0.2924 0.164 0.568 0.092 0.176
#> SRR1367896 2 0.534 0.4328 0.212 0.736 0.016 0.036
#> SRR1480107 1 0.127 0.6750 0.968 0.012 0.012 0.008
#> SRR1377756 1 0.517 0.6341 0.772 0.028 0.036 0.164
#> SRR1435272 2 0.782 -0.0619 0.376 0.428 0.008 0.188
#> SRR1089230 1 0.761 0.3704 0.488 0.168 0.008 0.336
#> SRR1389522 1 0.430 0.6368 0.788 0.192 0.008 0.012
#> SRR1080600 4 0.740 0.5635 0.180 0.260 0.008 0.552
#> SRR1086935 2 0.605 0.2499 0.068 0.692 0.016 0.224
#> SRR1344060 4 0.851 0.4298 0.100 0.136 0.236 0.528
#> SRR1467922 2 0.624 -0.0319 0.000 0.636 0.096 0.268
#> SRR1090984 2 0.539 0.3674 0.300 0.672 0.012 0.016
#> SRR1456991 1 0.127 0.6750 0.968 0.012 0.012 0.008
#> SRR1085039 1 0.362 0.6845 0.864 0.084 0.004 0.048
#> SRR1069303 1 0.709 0.2540 0.548 0.344 0.016 0.092
#> SRR1091500 2 0.454 0.1470 0.000 0.720 0.008 0.272
#> SRR1075198 2 0.865 0.1573 0.360 0.404 0.052 0.184
#> SRR1086915 1 0.742 0.4718 0.540 0.232 0.004 0.224
#> SRR1499503 2 0.781 0.3210 0.164 0.596 0.060 0.180
#> SRR1094312 2 0.533 0.1701 0.024 0.692 0.008 0.276
#> SRR1352437 1 0.723 0.2583 0.540 0.352 0.028 0.080
#> SRR1436323 1 0.726 0.3505 0.500 0.392 0.020 0.088
#> SRR1073507 1 0.441 0.6638 0.828 0.104 0.016 0.052
#> SRR1401972 1 0.709 0.2540 0.548 0.344 0.016 0.092
#> SRR1415510 2 0.745 0.2846 0.120 0.628 0.060 0.192
#> SRR1327279 1 0.392 0.6775 0.840 0.124 0.008 0.028
#> SRR1086983 1 0.652 0.5364 0.644 0.252 0.012 0.092
#> SRR1105174 1 0.490 0.6445 0.772 0.072 0.000 0.156
#> SRR1468893 1 0.512 0.6363 0.776 0.028 0.036 0.160
#> SRR1362555 2 0.868 0.1396 0.364 0.396 0.052 0.188
#> SRR1074526 4 0.693 0.4377 0.076 0.180 0.072 0.672
#> SRR1326225 2 0.491 0.1217 0.000 0.704 0.020 0.276
#> SRR1401933 1 0.525 0.6339 0.764 0.028 0.036 0.172
#> SRR1324062 2 0.636 0.3066 0.340 0.596 0.012 0.052
#> SRR1102296 2 0.697 -0.0015 0.416 0.504 0.032 0.048
#> SRR1085087 1 0.425 0.6834 0.832 0.116 0.016 0.036
#> SRR1079046 1 0.808 0.3627 0.564 0.088 0.108 0.240
#> SRR1328339 2 0.522 0.3781 0.292 0.684 0.008 0.016
#> SRR1079782 2 0.867 0.1531 0.356 0.404 0.052 0.188
#> SRR1092257 2 0.798 0.2437 0.180 0.528 0.032 0.260
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.415 0.56444 0.080 0.828 0.024 0.016 0.052
#> SRR1429287 4 0.597 0.37154 0.004 0.424 0.036 0.504 0.032
#> SRR1359238 1 0.664 0.50953 0.628 0.104 0.176 0.088 0.004
#> SRR1309597 1 0.493 0.54160 0.744 0.108 0.132 0.016 0.000
#> SRR1441398 1 0.365 0.57984 0.836 0.000 0.100 0.012 0.052
#> SRR1084055 2 0.479 0.52689 0.048 0.796 0.076 0.060 0.020
#> SRR1417566 3 0.714 0.44643 0.216 0.336 0.428 0.004 0.016
#> SRR1351857 1 0.605 0.51377 0.656 0.068 0.216 0.056 0.004
#> SRR1487485 2 0.663 -0.21131 0.144 0.500 0.340 0.004 0.012
#> SRR1335875 3 0.700 0.51497 0.236 0.312 0.440 0.004 0.008
#> SRR1073947 3 0.560 0.27537 0.464 0.052 0.476 0.008 0.000
#> SRR1443483 1 0.468 0.53904 0.756 0.104 0.132 0.008 0.000
#> SRR1346794 1 0.757 0.46515 0.552 0.096 0.180 0.156 0.016
#> SRR1405245 1 0.592 0.49667 0.656 0.008 0.236 0.060 0.040
#> SRR1409677 1 0.785 0.42446 0.516 0.128 0.180 0.164 0.012
#> SRR1095549 1 0.407 0.52652 0.748 0.020 0.228 0.004 0.000
#> SRR1323788 1 0.538 0.52840 0.704 0.008 0.208 0.052 0.028
#> SRR1314054 2 0.308 0.49204 0.000 0.872 0.064 0.056 0.008
#> SRR1077944 1 0.334 0.53941 0.812 0.016 0.172 0.000 0.000
#> SRR1480587 2 0.505 0.57716 0.132 0.760 0.056 0.008 0.044
#> SRR1311205 1 0.203 0.60817 0.924 0.020 0.052 0.004 0.000
#> SRR1076369 1 0.823 0.25042 0.420 0.108 0.184 0.276 0.012
#> SRR1453549 1 0.559 0.39215 0.652 0.136 0.208 0.004 0.000
#> SRR1345782 1 0.183 0.59289 0.920 0.000 0.076 0.004 0.000
#> SRR1447850 2 0.752 0.11087 0.000 0.524 0.188 0.152 0.136
#> SRR1391553 3 0.689 0.39577 0.140 0.396 0.440 0.008 0.016
#> SRR1444156 2 0.136 0.52559 0.004 0.960 0.016 0.012 0.008
#> SRR1471731 1 0.715 -0.07672 0.432 0.172 0.368 0.020 0.008
#> SRR1120987 2 0.836 0.21526 0.272 0.420 0.156 0.136 0.016
#> SRR1477363 1 0.227 0.61457 0.912 0.012 0.064 0.012 0.000
#> SRR1391961 5 0.509 0.92618 0.120 0.044 0.020 0.048 0.768
#> SRR1373879 1 0.541 0.52810 0.696 0.096 0.192 0.008 0.008
#> SRR1318732 1 0.682 0.45543 0.600 0.048 0.252 0.056 0.044
#> SRR1091404 1 0.300 0.55309 0.840 0.012 0.148 0.000 0.000
#> SRR1402109 1 0.501 0.55111 0.724 0.096 0.172 0.004 0.004
#> SRR1407336 1 0.455 0.54204 0.760 0.100 0.136 0.004 0.000
#> SRR1097417 3 0.765 0.36844 0.144 0.384 0.408 0.040 0.024
#> SRR1396227 3 0.561 0.32650 0.416 0.048 0.524 0.012 0.000
#> SRR1400775 2 0.633 0.36017 0.060 0.632 0.244 0.044 0.020
#> SRR1392861 3 0.782 0.30139 0.308 0.188 0.424 0.076 0.004
#> SRR1472929 5 0.394 0.96391 0.132 0.036 0.000 0.020 0.812
#> SRR1436740 3 0.784 0.25403 0.324 0.164 0.420 0.088 0.004
#> SRR1477057 3 0.848 0.35781 0.264 0.180 0.432 0.048 0.076
#> SRR1311980 3 0.710 0.46490 0.180 0.356 0.440 0.008 0.016
#> SRR1069400 1 0.451 0.54368 0.764 0.100 0.132 0.004 0.000
#> SRR1351016 1 0.300 0.55309 0.840 0.012 0.148 0.000 0.000
#> SRR1096291 1 0.783 0.14809 0.420 0.348 0.124 0.100 0.008
#> SRR1418145 2 0.794 0.28186 0.344 0.436 0.056 0.124 0.040
#> SRR1488111 2 0.776 0.39995 0.200 0.540 0.120 0.116 0.024
#> SRR1370495 1 0.902 0.21174 0.448 0.132 0.128 0.156 0.136
#> SRR1352639 1 0.729 0.27971 0.532 0.292 0.060 0.092 0.024
#> SRR1348911 3 0.703 0.45289 0.204 0.364 0.416 0.004 0.012
#> SRR1467386 1 0.407 0.52652 0.748 0.020 0.228 0.004 0.000
#> SRR1415956 1 0.354 0.58224 0.844 0.000 0.092 0.012 0.052
#> SRR1500495 1 0.189 0.60856 0.932 0.020 0.044 0.004 0.000
#> SRR1405099 1 0.348 0.58236 0.848 0.000 0.088 0.012 0.052
#> SRR1345585 1 0.637 0.21183 0.564 0.192 0.236 0.004 0.004
#> SRR1093196 1 0.714 0.04658 0.452 0.156 0.356 0.032 0.004
#> SRR1466006 4 0.547 0.51701 0.000 0.332 0.008 0.600 0.060
#> SRR1351557 2 0.483 0.57960 0.124 0.776 0.052 0.008 0.040
#> SRR1382687 1 0.611 0.49373 0.656 0.016 0.224 0.060 0.044
#> SRR1375549 1 0.833 0.37305 0.512 0.072 0.152 0.172 0.092
#> SRR1101765 1 0.822 0.24691 0.420 0.108 0.180 0.280 0.012
#> SRR1334461 5 0.394 0.96391 0.132 0.036 0.000 0.020 0.812
#> SRR1094073 2 0.300 0.47318 0.004 0.884 0.016 0.032 0.064
#> SRR1077549 1 0.460 0.49626 0.748 0.040 0.196 0.012 0.004
#> SRR1440332 1 0.426 0.56510 0.784 0.088 0.124 0.004 0.000
#> SRR1454177 3 0.786 0.29932 0.308 0.188 0.420 0.080 0.004
#> SRR1082447 1 0.447 0.52645 0.748 0.028 0.208 0.012 0.004
#> SRR1420043 1 0.532 0.47300 0.680 0.116 0.200 0.004 0.000
#> SRR1432500 1 0.345 0.60394 0.840 0.048 0.108 0.004 0.000
#> SRR1378045 2 0.631 -0.13452 0.116 0.528 0.344 0.004 0.008
#> SRR1334200 4 0.425 0.52182 0.056 0.096 0.020 0.816 0.012
#> SRR1069539 1 0.783 0.14809 0.420 0.348 0.124 0.100 0.008
#> SRR1343031 1 0.426 0.55724 0.784 0.088 0.124 0.004 0.000
#> SRR1319690 1 0.536 0.58617 0.744 0.052 0.120 0.076 0.008
#> SRR1310604 2 0.502 0.57562 0.140 0.756 0.056 0.004 0.044
#> SRR1327747 1 0.545 0.58010 0.732 0.088 0.124 0.052 0.004
#> SRR1072456 2 0.546 0.56122 0.140 0.728 0.052 0.004 0.076
#> SRR1367896 3 0.706 0.41285 0.156 0.384 0.432 0.008 0.020
#> SRR1480107 1 0.196 0.59286 0.916 0.000 0.076 0.008 0.000
#> SRR1377756 1 0.582 0.50407 0.672 0.008 0.220 0.060 0.040
#> SRR1435272 3 0.779 0.29924 0.316 0.188 0.420 0.072 0.004
#> SRR1089230 1 0.826 0.34831 0.432 0.112 0.228 0.216 0.012
#> SRR1389522 1 0.460 0.54621 0.760 0.088 0.144 0.008 0.000
#> SRR1080600 4 0.690 0.43449 0.152 0.264 0.036 0.544 0.004
#> SRR1086935 3 0.711 0.14222 0.048 0.400 0.448 0.092 0.012
#> SRR1344060 4 0.670 0.34331 0.060 0.080 0.020 0.620 0.220
#> SRR1467922 2 0.348 0.43791 0.004 0.852 0.016 0.032 0.096
#> SRR1090984 3 0.717 0.48596 0.248 0.328 0.408 0.008 0.008
#> SRR1456991 1 0.196 0.59286 0.916 0.000 0.076 0.008 0.000
#> SRR1085039 1 0.342 0.60648 0.844 0.020 0.120 0.012 0.004
#> SRR1069303 3 0.644 0.41766 0.384 0.100 0.496 0.012 0.008
#> SRR1091500 2 0.315 0.49158 0.000 0.868 0.064 0.060 0.008
#> SRR1075198 2 0.781 0.29922 0.328 0.460 0.048 0.124 0.040
#> SRR1086915 1 0.787 0.39562 0.500 0.164 0.200 0.128 0.008
#> SRR1499503 2 0.502 0.57562 0.140 0.756 0.056 0.004 0.044
#> SRR1094312 2 0.351 0.51351 0.016 0.860 0.056 0.060 0.008
#> SRR1352437 3 0.581 0.42128 0.376 0.076 0.540 0.008 0.000
#> SRR1436323 1 0.715 -0.00514 0.440 0.156 0.368 0.032 0.004
#> SRR1073507 1 0.407 0.53028 0.776 0.016 0.192 0.012 0.004
#> SRR1401972 3 0.644 0.41766 0.384 0.100 0.496 0.012 0.008
#> SRR1415510 2 0.458 0.57553 0.112 0.796 0.036 0.012 0.044
#> SRR1327279 1 0.365 0.59419 0.816 0.036 0.144 0.004 0.000
#> SRR1086983 1 0.612 0.25046 0.576 0.080 0.320 0.020 0.004
#> SRR1105174 1 0.509 0.59284 0.764 0.048 0.108 0.072 0.008
#> SRR1468893 1 0.579 0.50695 0.676 0.008 0.216 0.060 0.040
#> SRR1362555 2 0.782 0.28953 0.336 0.452 0.048 0.124 0.040
#> SRR1074526 4 0.549 0.31749 0.040 0.016 0.184 0.716 0.044
#> SRR1326225 2 0.346 0.48387 0.000 0.856 0.060 0.064 0.020
#> SRR1401933 1 0.606 0.48815 0.644 0.008 0.240 0.068 0.040
#> SRR1324062 3 0.729 0.52248 0.284 0.300 0.396 0.004 0.016
#> SRR1102296 3 0.633 0.54627 0.276 0.164 0.552 0.008 0.000
#> SRR1085087 1 0.405 0.58706 0.788 0.040 0.164 0.008 0.000
#> SRR1079046 1 0.875 0.26604 0.460 0.076 0.156 0.196 0.112
#> SRR1328339 3 0.688 0.48969 0.224 0.332 0.436 0.004 0.004
#> SRR1079782 2 0.783 0.29908 0.324 0.460 0.048 0.128 0.040
#> SRR1092257 2 0.725 0.45831 0.148 0.612 0.076 0.124 0.040
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.469 0.5762 0.064 0.776 0.060 0.044 0.056 0.000
#> SRR1429287 6 0.664 0.3882 0.000 0.336 0.044 0.120 0.020 0.480
#> SRR1359238 1 0.599 -0.1571 0.596 0.024 0.076 0.264 0.000 0.040
#> SRR1309597 1 0.421 0.4235 0.740 0.024 0.200 0.036 0.000 0.000
#> SRR1441398 1 0.395 0.4480 0.804 0.000 0.060 0.080 0.056 0.000
#> SRR1084055 2 0.475 0.5291 0.020 0.776 0.092 0.060 0.020 0.032
#> SRR1417566 3 0.629 0.4627 0.188 0.204 0.552 0.056 0.000 0.000
#> SRR1351857 1 0.593 0.1380 0.620 0.020 0.128 0.204 0.000 0.028
#> SRR1487485 3 0.599 0.2887 0.140 0.348 0.496 0.004 0.008 0.004
#> SRR1335875 3 0.621 0.5061 0.200 0.180 0.580 0.024 0.008 0.008
#> SRR1073947 3 0.612 0.3242 0.300 0.008 0.484 0.204 0.004 0.000
#> SRR1443483 1 0.392 0.4330 0.756 0.016 0.204 0.020 0.000 0.004
#> SRR1346794 1 0.583 -0.4481 0.528 0.028 0.036 0.372 0.000 0.036
#> SRR1405245 1 0.551 0.2011 0.592 0.000 0.052 0.300 0.056 0.000
#> SRR1409677 1 0.660 -0.5341 0.488 0.044 0.072 0.352 0.000 0.044
#> SRR1095549 1 0.467 0.3673 0.696 0.004 0.180 0.120 0.000 0.000
#> SRR1323788 1 0.486 0.2261 0.644 0.000 0.028 0.288 0.040 0.000
#> SRR1314054 2 0.324 0.5098 0.004 0.856 0.084 0.024 0.008 0.024
#> SRR1077944 1 0.470 0.4235 0.716 0.000 0.144 0.128 0.008 0.004
#> SRR1480587 2 0.573 0.5778 0.084 0.696 0.076 0.096 0.048 0.000
#> SRR1311205 1 0.234 0.4908 0.896 0.000 0.048 0.052 0.004 0.000
#> SRR1076369 4 0.687 0.7504 0.392 0.060 0.024 0.416 0.000 0.108
#> SRR1453549 1 0.551 0.3495 0.656 0.088 0.188 0.068 0.000 0.000
#> SRR1345782 1 0.337 0.4627 0.824 0.000 0.080 0.092 0.004 0.000
#> SRR1447850 2 0.788 0.0502 0.000 0.468 0.192 0.120 0.100 0.120
#> SRR1391553 3 0.553 0.4457 0.112 0.212 0.644 0.024 0.004 0.004
#> SRR1444156 2 0.151 0.5408 0.012 0.944 0.036 0.004 0.004 0.000
#> SRR1471731 3 0.654 0.0907 0.384 0.060 0.420 0.136 0.000 0.000
#> SRR1120987 2 0.813 0.0683 0.240 0.340 0.148 0.240 0.004 0.028
#> SRR1477363 1 0.198 0.4719 0.912 0.000 0.016 0.068 0.004 0.000
#> SRR1391961 5 0.445 0.9274 0.112 0.032 0.004 0.040 0.784 0.028
#> SRR1373879 1 0.463 0.4320 0.700 0.016 0.240 0.032 0.004 0.008
#> SRR1318732 1 0.647 0.1281 0.540 0.020 0.100 0.284 0.056 0.000
#> SRR1091404 1 0.442 0.4422 0.744 0.000 0.136 0.108 0.008 0.004
#> SRR1402109 1 0.423 0.4465 0.728 0.012 0.224 0.028 0.000 0.008
#> SRR1407336 1 0.377 0.4349 0.760 0.012 0.204 0.024 0.000 0.000
#> SRR1097417 3 0.649 0.4332 0.132 0.204 0.584 0.016 0.004 0.060
#> SRR1396227 3 0.602 0.3427 0.228 0.004 0.500 0.264 0.004 0.000
#> SRR1400775 2 0.625 0.3841 0.024 0.576 0.260 0.112 0.008 0.020
#> SRR1392861 3 0.781 0.1760 0.268 0.092 0.380 0.228 0.004 0.028
#> SRR1472929 5 0.347 0.9646 0.124 0.032 0.000 0.024 0.820 0.000
#> SRR1436740 3 0.773 0.1284 0.280 0.072 0.360 0.256 0.004 0.028
#> SRR1477057 3 0.857 0.2258 0.144 0.108 0.416 0.220 0.072 0.040
#> SRR1311980 3 0.598 0.4780 0.144 0.196 0.612 0.040 0.004 0.004
#> SRR1069400 1 0.374 0.4351 0.764 0.012 0.200 0.024 0.000 0.000
#> SRR1351016 1 0.442 0.4422 0.744 0.000 0.136 0.108 0.008 0.004
#> SRR1096291 1 0.754 -0.2983 0.392 0.232 0.112 0.252 0.000 0.012
#> SRR1418145 2 0.800 0.0189 0.272 0.340 0.076 0.268 0.032 0.012
#> SRR1488111 2 0.803 0.3597 0.148 0.436 0.172 0.196 0.012 0.036
#> SRR1370495 1 0.788 -0.5243 0.376 0.072 0.028 0.360 0.124 0.040
#> SRR1352639 1 0.725 -0.2225 0.468 0.220 0.060 0.228 0.016 0.008
#> SRR1348911 3 0.612 0.4637 0.184 0.228 0.560 0.020 0.004 0.004
#> SRR1467386 1 0.467 0.3673 0.696 0.004 0.180 0.120 0.000 0.000
#> SRR1415956 1 0.401 0.4444 0.796 0.000 0.048 0.100 0.056 0.000
#> SRR1500495 1 0.187 0.4879 0.924 0.000 0.036 0.036 0.004 0.000
#> SRR1405099 1 0.395 0.4434 0.800 0.000 0.044 0.100 0.056 0.000
#> SRR1345585 1 0.569 0.2660 0.572 0.100 0.296 0.032 0.000 0.000
#> SRR1093196 3 0.655 -0.0241 0.400 0.032 0.408 0.152 0.004 0.004
#> SRR1466006 6 0.665 0.5043 0.000 0.244 0.008 0.168 0.060 0.520
#> SRR1351557 2 0.545 0.5813 0.076 0.720 0.072 0.084 0.048 0.000
#> SRR1382687 1 0.553 0.1804 0.596 0.008 0.036 0.304 0.056 0.000
#> SRR1375549 1 0.670 -0.5443 0.436 0.016 0.024 0.412 0.072 0.040
#> SRR1101765 4 0.690 0.7500 0.392 0.060 0.024 0.412 0.000 0.112
#> SRR1334461 5 0.347 0.9646 0.124 0.032 0.000 0.024 0.820 0.000
#> SRR1094073 2 0.286 0.5025 0.012 0.884 0.024 0.008 0.060 0.012
#> SRR1077549 1 0.524 0.3878 0.664 0.004 0.180 0.140 0.004 0.008
#> SRR1440332 1 0.363 0.4552 0.796 0.012 0.160 0.028 0.004 0.000
#> SRR1454177 3 0.782 0.1738 0.268 0.092 0.376 0.232 0.004 0.028
#> SRR1082447 1 0.516 0.3985 0.676 0.004 0.156 0.152 0.004 0.008
#> SRR1420043 1 0.486 0.4176 0.676 0.032 0.240 0.052 0.000 0.000
#> SRR1432500 1 0.314 0.4557 0.848 0.012 0.084 0.056 0.000 0.000
#> SRR1378045 3 0.568 0.2184 0.108 0.392 0.488 0.008 0.000 0.004
#> SRR1334200 6 0.323 0.5053 0.012 0.004 0.004 0.176 0.000 0.804
#> SRR1069539 1 0.754 -0.2983 0.392 0.232 0.112 0.252 0.000 0.012
#> SRR1343031 1 0.358 0.4464 0.784 0.012 0.180 0.024 0.000 0.000
#> SRR1319690 1 0.419 0.2052 0.740 0.016 0.016 0.212 0.000 0.016
#> SRR1310604 2 0.578 0.5767 0.092 0.692 0.076 0.092 0.048 0.000
#> SRR1327747 1 0.485 0.3540 0.720 0.012 0.124 0.132 0.000 0.012
#> SRR1072456 2 0.610 0.5640 0.096 0.668 0.068 0.092 0.076 0.000
#> SRR1367896 3 0.576 0.4584 0.148 0.204 0.620 0.012 0.004 0.012
#> SRR1480107 1 0.341 0.4632 0.820 0.000 0.080 0.096 0.004 0.000
#> SRR1377756 1 0.516 0.1993 0.612 0.000 0.028 0.304 0.056 0.000
#> SRR1435272 3 0.775 0.1829 0.276 0.092 0.380 0.224 0.004 0.024
#> SRR1089230 4 0.689 0.6363 0.404 0.048 0.060 0.416 0.000 0.072
#> SRR1389522 1 0.401 0.4494 0.756 0.016 0.196 0.028 0.004 0.000
#> SRR1080600 6 0.746 0.3455 0.136 0.176 0.008 0.232 0.004 0.444
#> SRR1086935 3 0.773 0.2016 0.048 0.296 0.392 0.208 0.008 0.048
#> SRR1344060 6 0.552 0.3504 0.016 0.000 0.000 0.164 0.208 0.612
#> SRR1467922 2 0.346 0.4758 0.012 0.848 0.028 0.012 0.084 0.016
#> SRR1090984 3 0.613 0.4866 0.212 0.200 0.552 0.036 0.000 0.000
#> SRR1456991 1 0.341 0.4632 0.820 0.000 0.080 0.096 0.004 0.000
#> SRR1085039 1 0.333 0.4846 0.840 0.004 0.088 0.056 0.000 0.012
#> SRR1069303 3 0.640 0.3619 0.196 0.040 0.528 0.232 0.004 0.000
#> SRR1091500 2 0.332 0.5082 0.004 0.852 0.084 0.024 0.008 0.028
#> SRR1075198 2 0.791 0.0860 0.260 0.356 0.068 0.272 0.032 0.012
#> SRR1086915 1 0.711 -0.3828 0.464 0.072 0.096 0.324 0.000 0.044
#> SRR1499503 2 0.578 0.5767 0.092 0.692 0.076 0.092 0.048 0.000
#> SRR1094312 2 0.382 0.5302 0.016 0.828 0.088 0.032 0.008 0.028
#> SRR1352437 3 0.604 0.3792 0.200 0.020 0.556 0.220 0.004 0.000
#> SRR1436323 3 0.664 0.0118 0.392 0.036 0.404 0.160 0.004 0.004
#> SRR1073507 1 0.492 0.4076 0.692 0.000 0.152 0.144 0.004 0.008
#> SRR1401972 3 0.640 0.3619 0.196 0.040 0.528 0.232 0.004 0.000
#> SRR1415510 2 0.528 0.5797 0.088 0.732 0.076 0.056 0.048 0.000
#> SRR1327279 1 0.348 0.4919 0.808 0.000 0.132 0.056 0.004 0.000
#> SRR1086983 1 0.641 0.2097 0.524 0.036 0.276 0.156 0.004 0.004
#> SRR1105174 1 0.391 0.2410 0.760 0.016 0.008 0.200 0.000 0.016
#> SRR1468893 1 0.514 0.2082 0.616 0.000 0.028 0.300 0.056 0.000
#> SRR1362555 2 0.790 0.0783 0.268 0.356 0.064 0.268 0.028 0.016
#> SRR1074526 6 0.476 0.3117 0.008 0.012 0.060 0.180 0.012 0.728
#> SRR1326225 2 0.367 0.5023 0.004 0.836 0.084 0.024 0.020 0.032
#> SRR1401933 1 0.543 0.1427 0.576 0.000 0.040 0.328 0.056 0.000
#> SRR1324062 3 0.651 0.5128 0.268 0.144 0.528 0.052 0.004 0.004
#> SRR1102296 3 0.571 0.5018 0.192 0.052 0.640 0.112 0.004 0.000
#> SRR1085087 1 0.441 0.4595 0.752 0.016 0.144 0.084 0.004 0.000
#> SRR1079046 4 0.724 0.4521 0.392 0.016 0.024 0.404 0.092 0.072
#> SRR1328339 3 0.586 0.4894 0.188 0.200 0.584 0.028 0.000 0.000
#> SRR1079782 2 0.798 0.0962 0.256 0.356 0.068 0.272 0.032 0.016
#> SRR1092257 2 0.777 0.4192 0.116 0.512 0.124 0.176 0.024 0.048
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 17611 rows and 118 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 0.569 0.764 0.901 0.4422 0.560 0.560
#> 3 3 0.376 0.476 0.728 0.4011 0.829 0.705
#> 4 4 0.428 0.466 0.681 0.1568 0.802 0.578
#> 5 5 0.506 0.520 0.700 0.0755 0.867 0.607
#> 6 6 0.578 0.509 0.691 0.0493 0.951 0.795
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
#> SRR1396765 2 0.0000 0.82493 0.000 1.000
#> SRR1429287 2 0.0000 0.82493 0.000 1.000
#> SRR1359238 1 0.0000 0.90002 1.000 0.000
#> SRR1309597 1 0.1414 0.89156 0.980 0.020
#> SRR1441398 1 0.0000 0.90002 1.000 0.000
#> SRR1084055 2 0.0000 0.82493 0.000 1.000
#> SRR1417566 1 0.8713 0.58299 0.708 0.292
#> SRR1351857 1 0.0000 0.90002 1.000 0.000
#> SRR1487485 2 0.9775 0.25205 0.412 0.588
#> SRR1335875 1 0.8661 0.58978 0.712 0.288
#> SRR1073947 1 0.0000 0.90002 1.000 0.000
#> SRR1443483 1 0.1414 0.89156 0.980 0.020
#> SRR1346794 1 0.0000 0.90002 1.000 0.000
#> SRR1405245 1 0.0000 0.90002 1.000 0.000
#> SRR1409677 1 0.3733 0.85407 0.928 0.072
#> SRR1095549 1 0.0000 0.90002 1.000 0.000
#> SRR1323788 1 0.0000 0.90002 1.000 0.000
#> SRR1314054 2 0.0000 0.82493 0.000 1.000
#> SRR1077944 1 0.0000 0.90002 1.000 0.000
#> SRR1480587 2 0.0000 0.82493 0.000 1.000
#> SRR1311205 1 0.0000 0.90002 1.000 0.000
#> SRR1076369 1 0.2603 0.87644 0.956 0.044
#> SRR1453549 1 0.3114 0.87168 0.944 0.056
#> SRR1345782 1 0.0000 0.90002 1.000 0.000
#> SRR1447850 2 0.0000 0.82493 0.000 1.000
#> SRR1391553 2 0.9170 0.45544 0.332 0.668
#> SRR1444156 2 0.0000 0.82493 0.000 1.000
#> SRR1471731 1 0.1414 0.89156 0.980 0.020
#> SRR1120987 2 0.8713 0.62711 0.292 0.708
#> SRR1477363 1 0.0000 0.90002 1.000 0.000
#> SRR1391961 2 0.8861 0.54901 0.304 0.696
#> SRR1373879 1 0.1414 0.89156 0.980 0.020
#> SRR1318732 1 0.0938 0.89528 0.988 0.012
#> SRR1091404 1 0.0000 0.90002 1.000 0.000
#> SRR1402109 1 0.0000 0.90002 1.000 0.000
#> SRR1407336 1 0.1414 0.89156 0.980 0.020
#> SRR1097417 2 0.9732 0.32681 0.404 0.596
#> SRR1396227 1 0.0000 0.90002 1.000 0.000
#> SRR1400775 2 0.0000 0.82493 0.000 1.000
#> SRR1392861 1 0.8499 0.62518 0.724 0.276
#> SRR1472929 2 1.0000 0.20811 0.500 0.500
#> SRR1436740 1 0.0672 0.89670 0.992 0.008
#> SRR1477057 2 0.3431 0.79391 0.064 0.936
#> SRR1311980 1 0.8386 0.60955 0.732 0.268
#> SRR1069400 1 0.1414 0.89156 0.980 0.020
#> SRR1351016 1 0.0000 0.90002 1.000 0.000
#> SRR1096291 1 0.3879 0.85112 0.924 0.076
#> SRR1418145 2 0.8267 0.65504 0.260 0.740
#> SRR1488111 2 0.0000 0.82493 0.000 1.000
#> SRR1370495 1 0.9710 0.11754 0.600 0.400
#> SRR1352639 1 0.0000 0.90002 1.000 0.000
#> SRR1348911 1 0.8713 0.58299 0.708 0.292
#> SRR1467386 1 0.0000 0.90002 1.000 0.000
#> SRR1415956 1 0.0000 0.90002 1.000 0.000
#> SRR1500495 1 0.0000 0.90002 1.000 0.000
#> SRR1405099 1 0.0000 0.90002 1.000 0.000
#> SRR1345585 1 0.4562 0.83138 0.904 0.096
#> SRR1093196 1 0.2948 0.87128 0.948 0.052
#> SRR1466006 2 0.0000 0.82493 0.000 1.000
#> SRR1351557 2 0.0000 0.82493 0.000 1.000
#> SRR1382687 1 0.0000 0.90002 1.000 0.000
#> SRR1375549 1 0.6048 0.73971 0.852 0.148
#> SRR1101765 1 0.3733 0.85407 0.928 0.072
#> SRR1334461 1 0.9896 -0.02614 0.560 0.440
#> SRR1094073 2 0.0000 0.82493 0.000 1.000
#> SRR1077549 1 0.0000 0.90002 1.000 0.000
#> SRR1440332 1 0.0000 0.90002 1.000 0.000
#> SRR1454177 1 0.3879 0.85112 0.924 0.076
#> SRR1082447 1 0.0000 0.90002 1.000 0.000
#> SRR1420043 1 0.0000 0.90002 1.000 0.000
#> SRR1432500 1 0.0000 0.90002 1.000 0.000
#> SRR1378045 2 0.9358 0.41102 0.352 0.648
#> SRR1334200 2 0.9850 0.37183 0.428 0.572
#> SRR1069539 1 0.9933 -0.00699 0.548 0.452
#> SRR1343031 1 0.0000 0.90002 1.000 0.000
#> SRR1319690 1 0.0000 0.90002 1.000 0.000
#> SRR1310604 2 0.7883 0.67879 0.236 0.764
#> SRR1327747 1 0.0000 0.90002 1.000 0.000
#> SRR1072456 2 0.0000 0.82493 0.000 1.000
#> SRR1367896 1 0.8661 0.58978 0.712 0.288
#> SRR1480107 1 0.0000 0.90002 1.000 0.000
#> SRR1377756 1 0.0000 0.90002 1.000 0.000
#> SRR1435272 1 0.3879 0.85112 0.924 0.076
#> SRR1089230 1 0.3733 0.85407 0.928 0.072
#> SRR1389522 1 0.0000 0.90002 1.000 0.000
#> SRR1080600 2 0.8081 0.66654 0.248 0.752
#> SRR1086935 2 0.9815 0.22902 0.420 0.580
#> SRR1344060 2 0.9552 0.48541 0.376 0.624
#> SRR1467922 2 0.0000 0.82493 0.000 1.000
#> SRR1090984 1 0.8267 0.62177 0.740 0.260
#> SRR1456991 1 0.0000 0.90002 1.000 0.000
#> SRR1085039 1 0.0000 0.90002 1.000 0.000
#> SRR1069303 1 0.8267 0.62177 0.740 0.260
#> SRR1091500 2 0.0000 0.82493 0.000 1.000
#> SRR1075198 2 0.8016 0.67099 0.244 0.756
#> SRR1086915 1 0.3584 0.85757 0.932 0.068
#> SRR1499503 2 0.0000 0.82493 0.000 1.000
#> SRR1094312 2 0.0000 0.82493 0.000 1.000
#> SRR1352437 1 0.8267 0.62177 0.740 0.260
#> SRR1436323 1 0.0000 0.90002 1.000 0.000
#> SRR1073507 1 0.0000 0.90002 1.000 0.000
#> SRR1401972 1 0.8267 0.62177 0.740 0.260
#> SRR1415510 2 0.0000 0.82493 0.000 1.000
#> SRR1327279 1 0.0000 0.90002 1.000 0.000
#> SRR1086983 1 0.0000 0.90002 1.000 0.000
#> SRR1105174 1 0.0000 0.90002 1.000 0.000
#> SRR1468893 1 0.0000 0.90002 1.000 0.000
#> SRR1362555 2 0.8016 0.67099 0.244 0.756
#> SRR1074526 2 0.6973 0.69425 0.188 0.812
#> SRR1326225 2 0.0000 0.82493 0.000 1.000
#> SRR1401933 1 0.0000 0.90002 1.000 0.000
#> SRR1324062 1 0.7745 0.66598 0.772 0.228
#> SRR1102296 1 0.8267 0.62177 0.740 0.260
#> SRR1085087 1 0.0000 0.90002 1.000 0.000
#> SRR1079046 1 0.9954 -0.09854 0.540 0.460
#> SRR1328339 1 0.5737 0.78538 0.864 0.136
#> SRR1079782 2 0.6801 0.72718 0.180 0.820
#> SRR1092257 2 0.0000 0.82493 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.3116 0.74223 0.000 0.892 0.108
#> SRR1429287 2 0.4346 0.71013 0.000 0.816 0.184
#> SRR1359238 1 0.0592 0.66877 0.988 0.000 0.012
#> SRR1309597 1 0.1860 0.66653 0.948 0.000 0.052
#> SRR1441398 1 0.6204 0.29122 0.576 0.000 0.424
#> SRR1084055 2 0.2796 0.73573 0.000 0.908 0.092
#> SRR1417566 1 0.9865 -0.16418 0.404 0.264 0.332
#> SRR1351857 1 0.1643 0.66457 0.956 0.000 0.044
#> SRR1487485 2 0.9266 -0.05427 0.420 0.424 0.156
#> SRR1335875 1 0.9815 -0.15194 0.420 0.256 0.324
#> SRR1073947 1 0.6192 0.28817 0.580 0.000 0.420
#> SRR1443483 1 0.1529 0.66782 0.960 0.000 0.040
#> SRR1346794 1 0.2711 0.65814 0.912 0.000 0.088
#> SRR1405245 1 0.5926 0.46364 0.644 0.000 0.356
#> SRR1409677 1 0.3472 0.63648 0.904 0.040 0.056
#> SRR1095549 1 0.1411 0.67062 0.964 0.000 0.036
#> SRR1323788 1 0.2959 0.66191 0.900 0.000 0.100
#> SRR1314054 2 0.1031 0.73232 0.000 0.976 0.024
#> SRR1077944 1 0.5968 0.37570 0.636 0.000 0.364
#> SRR1480587 2 0.3752 0.73420 0.000 0.856 0.144
#> SRR1311205 1 0.6045 0.34041 0.620 0.000 0.380
#> SRR1076369 1 0.5992 0.45151 0.716 0.016 0.268
#> SRR1453549 1 0.3573 0.60559 0.876 0.004 0.120
#> SRR1345782 1 0.5560 0.45960 0.700 0.000 0.300
#> SRR1447850 2 0.3038 0.65872 0.000 0.896 0.104
#> SRR1391553 2 0.8649 0.21725 0.232 0.596 0.172
#> SRR1444156 2 0.0592 0.74794 0.000 0.988 0.012
#> SRR1471731 1 0.4521 0.56897 0.816 0.004 0.180
#> SRR1120987 2 0.8521 -0.00928 0.440 0.468 0.092
#> SRR1477363 1 0.3038 0.65878 0.896 0.000 0.104
#> SRR1391961 3 0.6337 0.30686 0.028 0.264 0.708
#> SRR1373879 1 0.1529 0.66679 0.960 0.000 0.040
#> SRR1318732 1 0.5244 0.59127 0.756 0.004 0.240
#> SRR1091404 1 0.6215 0.28486 0.572 0.000 0.428
#> SRR1402109 1 0.1289 0.66823 0.968 0.000 0.032
#> SRR1407336 1 0.0747 0.66717 0.984 0.000 0.016
#> SRR1097417 3 0.9952 0.23547 0.332 0.292 0.376
#> SRR1396227 3 0.6260 -0.06680 0.448 0.000 0.552
#> SRR1400775 2 0.1411 0.74062 0.000 0.964 0.036
#> SRR1392861 1 0.8105 0.28860 0.648 0.196 0.156
#> SRR1472929 3 0.5970 0.40394 0.060 0.160 0.780
#> SRR1436740 1 0.3502 0.65376 0.896 0.020 0.084
#> SRR1477057 2 0.6192 0.19165 0.000 0.580 0.420
#> SRR1311980 3 0.9657 0.33186 0.300 0.240 0.460
#> SRR1069400 1 0.1411 0.66820 0.964 0.000 0.036
#> SRR1351016 1 0.6126 0.31729 0.600 0.000 0.400
#> SRR1096291 1 0.4206 0.61616 0.872 0.040 0.088
#> SRR1418145 2 0.8957 0.44348 0.192 0.564 0.244
#> SRR1488111 2 0.1170 0.74681 0.008 0.976 0.016
#> SRR1370495 3 0.6438 0.43770 0.100 0.136 0.764
#> SRR1352639 3 0.6286 -0.09714 0.464 0.000 0.536
#> SRR1348911 1 0.9934 -0.19960 0.388 0.292 0.320
#> SRR1467386 1 0.4796 0.55646 0.780 0.000 0.220
#> SRR1415956 1 0.6252 0.26268 0.556 0.000 0.444
#> SRR1500495 1 0.5591 0.47219 0.696 0.000 0.304
#> SRR1405099 1 0.6215 0.28121 0.572 0.000 0.428
#> SRR1345585 1 0.4035 0.62662 0.880 0.040 0.080
#> SRR1093196 1 0.2269 0.66217 0.944 0.016 0.040
#> SRR1466006 2 0.5678 0.63032 0.000 0.684 0.316
#> SRR1351557 2 0.2448 0.74766 0.000 0.924 0.076
#> SRR1382687 1 0.4452 0.62130 0.808 0.000 0.192
#> SRR1375549 3 0.5365 0.37035 0.252 0.004 0.744
#> SRR1101765 1 0.6867 0.37179 0.672 0.040 0.288
#> SRR1334461 3 0.6313 0.42659 0.084 0.148 0.768
#> SRR1094073 2 0.0592 0.74794 0.000 0.988 0.012
#> SRR1077549 1 0.2356 0.66186 0.928 0.000 0.072
#> SRR1440332 1 0.0747 0.67023 0.984 0.000 0.016
#> SRR1454177 1 0.4289 0.62058 0.868 0.040 0.092
#> SRR1082447 1 0.6192 0.31385 0.580 0.000 0.420
#> SRR1420043 1 0.1031 0.66677 0.976 0.000 0.024
#> SRR1432500 1 0.0892 0.66971 0.980 0.000 0.020
#> SRR1378045 2 0.8576 0.22473 0.240 0.600 0.160
#> SRR1334200 3 0.9183 0.17365 0.324 0.168 0.508
#> SRR1069539 1 0.8423 0.19656 0.616 0.156 0.228
#> SRR1343031 1 0.0747 0.66934 0.984 0.000 0.016
#> SRR1319690 1 0.2711 0.66109 0.912 0.000 0.088
#> SRR1310604 2 0.7338 0.59857 0.060 0.652 0.288
#> SRR1327747 1 0.1964 0.66212 0.944 0.000 0.056
#> SRR1072456 2 0.4062 0.72662 0.000 0.836 0.164
#> SRR1367896 1 0.9736 -0.12400 0.436 0.240 0.324
#> SRR1480107 1 0.6204 0.28697 0.576 0.000 0.424
#> SRR1377756 1 0.4178 0.62826 0.828 0.000 0.172
#> SRR1435272 1 0.2926 0.64859 0.924 0.040 0.036
#> SRR1089230 1 0.4449 0.61389 0.860 0.040 0.100
#> SRR1389522 1 0.4931 0.53698 0.768 0.000 0.232
#> SRR1080600 2 0.7334 0.56253 0.048 0.624 0.328
#> SRR1086935 1 0.9494 -0.10206 0.412 0.404 0.184
#> SRR1344060 3 0.5678 0.35965 0.032 0.192 0.776
#> SRR1467922 2 0.0592 0.74794 0.000 0.988 0.012
#> SRR1090984 1 0.9651 -0.16057 0.400 0.208 0.392
#> SRR1456991 1 0.6154 0.29653 0.592 0.000 0.408
#> SRR1085039 1 0.2711 0.65922 0.912 0.000 0.088
#> SRR1069303 3 0.8659 0.44977 0.176 0.228 0.596
#> SRR1091500 2 0.1289 0.74219 0.000 0.968 0.032
#> SRR1075198 2 0.7091 0.61179 0.064 0.688 0.248
#> SRR1086915 1 0.4449 0.61389 0.860 0.040 0.100
#> SRR1499503 2 0.3551 0.73839 0.000 0.868 0.132
#> SRR1094312 2 0.1163 0.74317 0.000 0.972 0.028
#> SRR1352437 3 0.9489 0.37075 0.280 0.228 0.492
#> SRR1436323 1 0.2066 0.66509 0.940 0.000 0.060
#> SRR1073507 1 0.5948 0.38140 0.640 0.000 0.360
#> SRR1401972 3 0.9151 0.41493 0.228 0.228 0.544
#> SRR1415510 2 0.2959 0.74594 0.000 0.900 0.100
#> SRR1327279 1 0.2356 0.66353 0.928 0.000 0.072
#> SRR1086983 1 0.2356 0.66614 0.928 0.000 0.072
#> SRR1105174 1 0.4842 0.59557 0.776 0.000 0.224
#> SRR1468893 1 0.6291 0.25509 0.532 0.000 0.468
#> SRR1362555 2 0.7660 0.55977 0.064 0.612 0.324
#> SRR1074526 3 0.9887 0.11347 0.268 0.336 0.396
#> SRR1326225 2 0.0237 0.74318 0.000 0.996 0.004
#> SRR1401933 1 0.6008 0.41832 0.628 0.000 0.372
#> SRR1324062 1 0.8398 0.24738 0.624 0.184 0.192
#> SRR1102296 3 0.9263 0.39115 0.252 0.220 0.528
#> SRR1085087 1 0.6168 0.30355 0.588 0.000 0.412
#> SRR1079046 3 0.5603 0.42509 0.060 0.136 0.804
#> SRR1328339 3 0.8025 0.05093 0.420 0.064 0.516
#> SRR1079782 2 0.7015 0.61639 0.064 0.696 0.240
#> SRR1092257 2 0.0237 0.74318 0.000 0.996 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.0336 0.83968 0.008 0.992 0.000 0.000
#> SRR1429287 2 0.4553 0.74936 0.180 0.780 0.040 0.000
#> SRR1359238 4 0.1059 0.62514 0.012 0.000 0.016 0.972
#> SRR1309597 4 0.3862 0.57595 0.024 0.000 0.152 0.824
#> SRR1441398 4 0.7879 -0.07429 0.288 0.000 0.332 0.380
#> SRR1084055 2 0.1624 0.83897 0.020 0.952 0.028 0.000
#> SRR1417566 3 0.4326 0.57113 0.036 0.036 0.840 0.088
#> SRR1351857 4 0.1302 0.62072 0.044 0.000 0.000 0.956
#> SRR1487485 4 0.8072 -0.08067 0.024 0.168 0.372 0.436
#> SRR1335875 3 0.5146 0.56158 0.004 0.076 0.764 0.156
#> SRR1073947 3 0.7773 -0.05969 0.284 0.000 0.432 0.284
#> SRR1443483 4 0.3271 0.58491 0.012 0.000 0.132 0.856
#> SRR1346794 4 0.5383 0.56281 0.160 0.000 0.100 0.740
#> SRR1405245 3 0.7883 -0.00751 0.316 0.000 0.384 0.300
#> SRR1409677 4 0.2708 0.60211 0.076 0.016 0.004 0.904
#> SRR1095549 4 0.4127 0.60638 0.052 0.000 0.124 0.824
#> SRR1323788 4 0.6883 0.45841 0.212 0.000 0.192 0.596
#> SRR1314054 2 0.1792 0.82390 0.000 0.932 0.068 0.000
#> SRR1077944 4 0.7792 -0.00537 0.260 0.000 0.324 0.416
#> SRR1480587 2 0.1118 0.83493 0.036 0.964 0.000 0.000
#> SRR1311205 4 0.7792 0.01741 0.260 0.000 0.324 0.416
#> SRR1076369 4 0.5483 0.18630 0.448 0.000 0.016 0.536
#> SRR1453549 4 0.4252 0.45522 0.004 0.000 0.252 0.744
#> SRR1345782 4 0.7438 0.20955 0.188 0.000 0.328 0.484
#> SRR1447850 2 0.2976 0.78183 0.008 0.872 0.120 0.000
#> SRR1391553 3 0.6216 0.40335 0.004 0.272 0.644 0.080
#> SRR1444156 2 0.0895 0.83986 0.004 0.976 0.020 0.000
#> SRR1471731 3 0.5495 0.30902 0.028 0.000 0.624 0.348
#> SRR1120987 4 0.8312 0.30109 0.112 0.192 0.132 0.564
#> SRR1477363 4 0.5947 0.48413 0.112 0.000 0.200 0.688
#> SRR1391961 1 0.6178 0.47230 0.688 0.208 0.092 0.012
#> SRR1373879 4 0.2704 0.59771 0.000 0.000 0.124 0.876
#> SRR1318732 3 0.8054 0.11467 0.300 0.008 0.424 0.268
#> SRR1091404 4 0.7917 -0.16046 0.340 0.000 0.312 0.348
#> SRR1402109 4 0.2530 0.60319 0.000 0.000 0.112 0.888
#> SRR1407336 4 0.1635 0.62107 0.008 0.000 0.044 0.948
#> SRR1097417 3 0.7144 0.49597 0.068 0.092 0.656 0.184
#> SRR1396227 3 0.5859 0.25715 0.284 0.000 0.652 0.064
#> SRR1400775 2 0.2142 0.82957 0.016 0.928 0.056 0.000
#> SRR1392861 4 0.5382 0.34886 0.016 0.016 0.280 0.688
#> SRR1472929 1 0.5862 0.54140 0.752 0.124 0.080 0.044
#> SRR1436740 4 0.5874 0.48774 0.064 0.008 0.240 0.688
#> SRR1477057 2 0.7379 0.15289 0.364 0.468 0.168 0.000
#> SRR1311980 3 0.2546 0.56096 0.008 0.028 0.920 0.044
#> SRR1069400 4 0.2125 0.61326 0.004 0.000 0.076 0.920
#> SRR1351016 3 0.7874 -0.13359 0.284 0.000 0.380 0.336
#> SRR1096291 4 0.3494 0.58267 0.116 0.016 0.008 0.860
#> SRR1418145 2 0.7812 0.24003 0.244 0.460 0.004 0.292
#> SRR1488111 2 0.2421 0.83296 0.020 0.924 0.048 0.008
#> SRR1370495 1 0.6190 0.55398 0.736 0.060 0.088 0.116
#> SRR1352639 1 0.7583 0.26121 0.480 0.000 0.280 0.240
#> SRR1348911 3 0.6022 0.54512 0.012 0.132 0.716 0.140
#> SRR1467386 4 0.6248 0.42055 0.100 0.000 0.260 0.640
#> SRR1415956 3 0.7908 -0.19499 0.336 0.000 0.360 0.304
#> SRR1500495 4 0.7357 0.22364 0.180 0.000 0.320 0.500
#> SRR1405099 1 0.7923 0.12131 0.344 0.000 0.328 0.328
#> SRR1345585 4 0.5243 0.42474 0.012 0.016 0.276 0.696
#> SRR1093196 4 0.3791 0.60131 0.032 0.008 0.108 0.852
#> SRR1466006 2 0.4262 0.71801 0.236 0.756 0.008 0.000
#> SRR1351557 2 0.0336 0.83968 0.008 0.992 0.000 0.000
#> SRR1382687 4 0.7577 0.22163 0.216 0.000 0.316 0.468
#> SRR1375549 1 0.4411 0.55151 0.824 0.012 0.052 0.112
#> SRR1101765 4 0.5895 0.21880 0.392 0.016 0.016 0.576
#> SRR1334461 1 0.6374 0.55200 0.728 0.084 0.092 0.096
#> SRR1094073 2 0.0895 0.83986 0.004 0.976 0.020 0.000
#> SRR1077549 4 0.5148 0.52649 0.056 0.000 0.208 0.736
#> SRR1440332 4 0.1520 0.62707 0.020 0.000 0.024 0.956
#> SRR1454177 4 0.4217 0.53673 0.016 0.016 0.152 0.816
#> SRR1082447 1 0.7836 0.15402 0.408 0.000 0.288 0.304
#> SRR1420043 4 0.1661 0.61900 0.004 0.000 0.052 0.944
#> SRR1432500 4 0.1284 0.62571 0.012 0.000 0.024 0.964
#> SRR1378045 3 0.6258 0.31617 0.000 0.324 0.600 0.076
#> SRR1334200 1 0.5206 0.41357 0.752 0.044 0.012 0.192
#> SRR1069539 4 0.5327 0.45026 0.208 0.056 0.004 0.732
#> SRR1343031 4 0.1452 0.62344 0.008 0.000 0.036 0.956
#> SRR1319690 4 0.4297 0.60216 0.084 0.000 0.096 0.820
#> SRR1310604 2 0.5964 0.63706 0.244 0.676 0.004 0.076
#> SRR1327747 4 0.2385 0.62154 0.052 0.000 0.028 0.920
#> SRR1072456 2 0.1398 0.83265 0.040 0.956 0.004 0.000
#> SRR1367896 3 0.4709 0.55143 0.008 0.024 0.768 0.200
#> SRR1480107 4 0.7919 -0.15290 0.324 0.000 0.324 0.352
#> SRR1377756 4 0.6921 0.41525 0.260 0.000 0.160 0.580
#> SRR1435272 4 0.2310 0.61401 0.020 0.016 0.032 0.932
#> SRR1089230 4 0.3873 0.56785 0.144 0.016 0.008 0.832
#> SRR1389522 4 0.6701 0.33281 0.120 0.000 0.296 0.584
#> SRR1080600 2 0.7062 0.42164 0.360 0.528 0.008 0.104
#> SRR1086935 4 0.7566 -0.03532 0.028 0.100 0.388 0.484
#> SRR1344060 1 0.3899 0.52312 0.840 0.108 0.052 0.000
#> SRR1467922 2 0.0895 0.83986 0.004 0.976 0.020 0.000
#> SRR1090984 3 0.4241 0.56108 0.056 0.016 0.840 0.088
#> SRR1456991 4 0.7916 -0.15049 0.312 0.000 0.336 0.352
#> SRR1085039 4 0.4514 0.56410 0.056 0.000 0.148 0.796
#> SRR1069303 3 0.4834 0.40955 0.200 0.024 0.764 0.012
#> SRR1091500 2 0.1488 0.83905 0.012 0.956 0.032 0.000
#> SRR1075198 2 0.6116 0.61442 0.248 0.664 0.004 0.084
#> SRR1086915 4 0.3717 0.57593 0.132 0.016 0.008 0.844
#> SRR1499503 2 0.0592 0.83877 0.016 0.984 0.000 0.000
#> SRR1094312 2 0.1356 0.83948 0.008 0.960 0.032 0.000
#> SRR1352437 3 0.4877 0.48791 0.096 0.012 0.800 0.092
#> SRR1436323 4 0.4462 0.59071 0.044 0.000 0.164 0.792
#> SRR1073507 4 0.7747 0.04072 0.252 0.000 0.316 0.432
#> SRR1401972 3 0.4968 0.46202 0.148 0.024 0.788 0.040
#> SRR1415510 2 0.0336 0.83968 0.008 0.992 0.000 0.000
#> SRR1327279 4 0.3863 0.58201 0.028 0.000 0.144 0.828
#> SRR1086983 4 0.5710 0.53939 0.100 0.000 0.192 0.708
#> SRR1105174 4 0.6881 0.34887 0.236 0.000 0.172 0.592
#> SRR1468893 1 0.7347 0.25062 0.528 0.000 0.244 0.228
#> SRR1362555 2 0.6391 0.56590 0.292 0.620 0.004 0.084
#> SRR1074526 1 0.8424 0.25663 0.544 0.128 0.108 0.220
#> SRR1326225 2 0.1389 0.83449 0.000 0.952 0.048 0.000
#> SRR1401933 1 0.7536 0.22412 0.492 0.000 0.244 0.264
#> SRR1324062 3 0.4785 0.55425 0.008 0.024 0.760 0.208
#> SRR1102296 3 0.4129 0.48738 0.104 0.012 0.840 0.044
#> SRR1085087 4 0.7641 0.01483 0.208 0.000 0.376 0.416
#> SRR1079046 1 0.4300 0.55664 0.844 0.076 0.036 0.044
#> SRR1328339 3 0.3421 0.53621 0.044 0.000 0.868 0.088
#> SRR1079782 2 0.5798 0.65328 0.208 0.704 0.004 0.084
#> SRR1092257 2 0.1389 0.83449 0.000 0.952 0.048 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.1965 0.7930 0.024 0.924 0.000 0.000 0.052
#> SRR1429287 2 0.6093 0.4777 0.016 0.604 0.088 0.008 0.284
#> SRR1359238 4 0.1544 0.6127 0.068 0.000 0.000 0.932 0.000
#> SRR1309597 4 0.6082 0.4277 0.116 0.000 0.216 0.636 0.032
#> SRR1441398 1 0.7199 0.5680 0.508 0.000 0.108 0.296 0.088
#> SRR1084055 2 0.1739 0.7935 0.004 0.940 0.024 0.000 0.032
#> SRR1417566 3 0.3529 0.6869 0.072 0.028 0.860 0.032 0.008
#> SRR1351857 4 0.1885 0.6219 0.032 0.000 0.012 0.936 0.020
#> SRR1487485 3 0.6715 0.0307 0.032 0.068 0.448 0.436 0.016
#> SRR1335875 3 0.5005 0.6926 0.096 0.040 0.756 0.108 0.000
#> SRR1073947 1 0.4435 0.6406 0.768 0.000 0.056 0.164 0.012
#> SRR1443483 4 0.4712 0.5344 0.080 0.000 0.180 0.736 0.004
#> SRR1346794 4 0.6510 0.3240 0.204 0.000 0.080 0.620 0.096
#> SRR1405245 1 0.7946 0.2489 0.400 0.000 0.320 0.160 0.120
#> SRR1409677 4 0.2354 0.6194 0.008 0.012 0.012 0.916 0.052
#> SRR1095549 4 0.4262 0.4575 0.252 0.000 0.016 0.724 0.008
#> SRR1323788 4 0.7851 0.0570 0.320 0.000 0.160 0.412 0.108
#> SRR1314054 2 0.1117 0.7953 0.000 0.964 0.020 0.000 0.016
#> SRR1077944 1 0.4054 0.6632 0.744 0.000 0.008 0.236 0.012
#> SRR1480587 2 0.2325 0.7901 0.028 0.904 0.000 0.000 0.068
#> SRR1311205 1 0.5340 0.6438 0.648 0.000 0.028 0.288 0.036
#> SRR1076369 4 0.7567 0.0666 0.088 0.000 0.136 0.416 0.360
#> SRR1453549 4 0.4681 0.5071 0.040 0.000 0.260 0.696 0.004
#> SRR1345782 1 0.5207 0.5860 0.620 0.000 0.044 0.328 0.008
#> SRR1447850 2 0.2351 0.7558 0.000 0.896 0.088 0.000 0.016
#> SRR1391553 3 0.4055 0.6124 0.008 0.192 0.776 0.020 0.004
#> SRR1444156 2 0.0865 0.7996 0.024 0.972 0.000 0.000 0.004
#> SRR1471731 3 0.4579 0.5943 0.048 0.004 0.740 0.204 0.004
#> SRR1120987 4 0.8003 0.3259 0.116 0.136 0.048 0.544 0.156
#> SRR1477363 4 0.6207 -0.1929 0.388 0.000 0.056 0.516 0.040
#> SRR1391961 5 0.5750 0.6775 0.244 0.080 0.020 0.004 0.652
#> SRR1373879 4 0.4269 0.5822 0.076 0.000 0.140 0.780 0.004
#> SRR1318732 3 0.8477 0.0494 0.264 0.016 0.400 0.192 0.128
#> SRR1091404 1 0.4116 0.6867 0.756 0.000 0.004 0.212 0.028
#> SRR1402109 4 0.3839 0.5900 0.072 0.000 0.108 0.816 0.004
#> SRR1407336 4 0.2074 0.6256 0.036 0.000 0.044 0.920 0.000
#> SRR1097417 3 0.5715 0.6773 0.064 0.068 0.736 0.104 0.028
#> SRR1396227 1 0.4846 0.0178 0.644 0.000 0.324 0.012 0.020
#> SRR1400775 2 0.1310 0.7959 0.000 0.956 0.024 0.000 0.020
#> SRR1392861 4 0.5053 0.4355 0.008 0.032 0.264 0.684 0.012
#> SRR1472929 5 0.4466 0.7139 0.200 0.016 0.016 0.012 0.756
#> SRR1436740 4 0.6039 0.3938 0.244 0.004 0.112 0.624 0.016
#> SRR1477057 2 0.7992 -0.1988 0.244 0.372 0.088 0.000 0.296
#> SRR1311980 3 0.3795 0.6773 0.184 0.024 0.788 0.004 0.000
#> SRR1069400 4 0.3449 0.6007 0.064 0.000 0.088 0.844 0.004
#> SRR1351016 1 0.4446 0.6715 0.752 0.000 0.040 0.196 0.012
#> SRR1096291 4 0.3716 0.6002 0.032 0.016 0.024 0.852 0.076
#> SRR1418145 5 0.7144 0.1020 0.000 0.316 0.012 0.328 0.344
#> SRR1488111 2 0.3160 0.7653 0.000 0.876 0.032 0.040 0.052
#> SRR1370495 5 0.4798 0.7063 0.212 0.008 0.016 0.032 0.732
#> SRR1352639 1 0.5582 0.3570 0.624 0.000 0.008 0.084 0.284
#> SRR1348911 3 0.5664 0.6891 0.112 0.068 0.720 0.096 0.004
#> SRR1467386 1 0.4637 0.4270 0.568 0.000 0.004 0.420 0.008
#> SRR1415956 1 0.6334 0.6668 0.636 0.000 0.076 0.200 0.088
#> SRR1500495 1 0.7065 0.4300 0.444 0.000 0.128 0.380 0.048
#> SRR1405099 1 0.6108 0.6695 0.648 0.000 0.056 0.208 0.088
#> SRR1345585 4 0.5026 0.4107 0.024 0.008 0.336 0.628 0.004
#> SRR1093196 4 0.3971 0.6161 0.068 0.008 0.092 0.824 0.008
#> SRR1466006 2 0.6157 0.4150 0.036 0.552 0.064 0.000 0.348
#> SRR1351557 2 0.1893 0.7944 0.024 0.928 0.000 0.000 0.048
#> SRR1382687 4 0.8035 0.1139 0.244 0.000 0.264 0.392 0.100
#> SRR1375549 5 0.5225 0.5876 0.288 0.000 0.016 0.044 0.652
#> SRR1101765 4 0.6759 0.0500 0.036 0.008 0.084 0.468 0.404
#> SRR1334461 5 0.4904 0.7043 0.216 0.008 0.016 0.036 0.724
#> SRR1094073 2 0.0992 0.7996 0.024 0.968 0.000 0.000 0.008
#> SRR1077549 4 0.5119 0.2637 0.360 0.000 0.048 0.592 0.000
#> SRR1440332 4 0.2463 0.6011 0.100 0.000 0.008 0.888 0.004
#> SRR1454177 4 0.4229 0.5822 0.012 0.016 0.184 0.776 0.012
#> SRR1082447 1 0.5959 0.6170 0.664 0.000 0.072 0.200 0.064
#> SRR1420043 4 0.2664 0.6142 0.064 0.000 0.040 0.892 0.004
#> SRR1432500 4 0.2911 0.5675 0.136 0.000 0.004 0.852 0.008
#> SRR1378045 3 0.4323 0.5790 0.000 0.240 0.728 0.028 0.004
#> SRR1334200 5 0.4127 0.6409 0.052 0.008 0.076 0.036 0.828
#> SRR1069539 4 0.4765 0.4585 0.008 0.016 0.016 0.704 0.256
#> SRR1343031 4 0.3081 0.6039 0.072 0.000 0.056 0.868 0.004
#> SRR1319690 4 0.5889 0.4669 0.132 0.000 0.120 0.688 0.060
#> SRR1310604 2 0.5854 0.3868 0.012 0.556 0.016 0.040 0.376
#> SRR1327747 4 0.2777 0.6178 0.040 0.000 0.036 0.896 0.028
#> SRR1072456 2 0.3317 0.7623 0.032 0.848 0.008 0.000 0.112
#> SRR1367896 3 0.4882 0.6861 0.100 0.024 0.756 0.120 0.000
#> SRR1480107 1 0.4110 0.6800 0.776 0.000 0.012 0.184 0.028
#> SRR1377756 4 0.7794 0.1538 0.272 0.000 0.144 0.456 0.128
#> SRR1435272 4 0.1877 0.6279 0.004 0.016 0.024 0.940 0.016
#> SRR1089230 4 0.4207 0.5919 0.044 0.016 0.028 0.824 0.088
#> SRR1389522 4 0.6908 -0.1197 0.328 0.000 0.200 0.456 0.016
#> SRR1080600 5 0.7001 0.0942 0.008 0.336 0.072 0.072 0.512
#> SRR1086935 4 0.6156 0.1145 0.012 0.052 0.392 0.524 0.020
#> SRR1344060 5 0.3716 0.7203 0.176 0.008 0.012 0.004 0.800
#> SRR1467922 2 0.0992 0.7996 0.024 0.968 0.000 0.000 0.008
#> SRR1090984 3 0.4587 0.6504 0.088 0.020 0.800 0.072 0.020
#> SRR1456991 1 0.4446 0.6800 0.752 0.000 0.012 0.196 0.040
#> SRR1085039 4 0.4774 0.1672 0.328 0.000 0.012 0.644 0.016
#> SRR1069303 3 0.5563 0.3896 0.452 0.024 0.500 0.004 0.020
#> SRR1091500 2 0.1211 0.7971 0.000 0.960 0.016 0.000 0.024
#> SRR1075198 2 0.5455 0.3873 0.000 0.572 0.012 0.044 0.372
#> SRR1086915 4 0.3933 0.5970 0.044 0.012 0.028 0.840 0.076
#> SRR1499503 2 0.2104 0.7925 0.024 0.916 0.000 0.000 0.060
#> SRR1094312 2 0.1117 0.7976 0.000 0.964 0.016 0.000 0.020
#> SRR1352437 3 0.5579 0.4332 0.420 0.016 0.524 0.040 0.000
#> SRR1436323 4 0.4288 0.5825 0.136 0.000 0.072 0.784 0.008
#> SRR1073507 1 0.4083 0.6586 0.728 0.000 0.008 0.256 0.008
#> SRR1401972 3 0.5282 0.4242 0.440 0.024 0.524 0.004 0.008
#> SRR1415510 2 0.2264 0.7919 0.024 0.912 0.000 0.004 0.060
#> SRR1327279 4 0.4754 0.4133 0.232 0.000 0.048 0.712 0.008
#> SRR1086983 4 0.5179 0.4305 0.252 0.000 0.048 0.680 0.020
#> SRR1105174 1 0.7078 0.2979 0.428 0.000 0.084 0.408 0.080
#> SRR1468893 1 0.7409 0.4570 0.536 0.000 0.116 0.168 0.180
#> SRR1362555 2 0.6187 0.1865 0.016 0.476 0.020 0.044 0.444
#> SRR1074526 5 0.7403 0.4971 0.036 0.152 0.128 0.088 0.596
#> SRR1326225 2 0.1012 0.7962 0.000 0.968 0.020 0.000 0.012
#> SRR1401933 1 0.7633 0.3493 0.504 0.000 0.144 0.212 0.140
#> SRR1324062 3 0.5025 0.6829 0.132 0.020 0.740 0.108 0.000
#> SRR1102296 3 0.5143 0.4076 0.456 0.012 0.516 0.012 0.004
#> SRR1085087 1 0.5374 0.6069 0.652 0.000 0.076 0.264 0.008
#> SRR1079046 5 0.4078 0.7114 0.152 0.000 0.020 0.032 0.796
#> SRR1328339 3 0.4352 0.6337 0.244 0.000 0.720 0.036 0.000
#> SRR1079782 2 0.5355 0.4441 0.000 0.604 0.012 0.044 0.340
#> SRR1092257 2 0.1211 0.7940 0.000 0.960 0.024 0.000 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.272 0.7416 0.008 0.884 0.012 0.000 0.032 0.064
#> SRR1429287 2 0.679 0.1388 0.012 0.440 0.032 0.000 0.216 0.300
#> SRR1359238 4 0.249 0.6268 0.100 0.000 0.000 0.876 0.004 0.020
#> SRR1309597 4 0.658 0.4013 0.140 0.000 0.196 0.556 0.004 0.104
#> SRR1441398 1 0.668 0.4881 0.564 0.000 0.060 0.196 0.028 0.152
#> SRR1084055 2 0.143 0.7490 0.000 0.948 0.024 0.000 0.008 0.020
#> SRR1417566 3 0.311 0.6689 0.052 0.000 0.832 0.000 0.000 0.116
#> SRR1351857 4 0.212 0.6163 0.024 0.000 0.004 0.912 0.004 0.056
#> SRR1487485 3 0.627 -0.0998 0.012 0.044 0.436 0.424 0.000 0.084
#> SRR1335875 3 0.365 0.6912 0.064 0.024 0.832 0.068 0.000 0.012
#> SRR1073947 1 0.366 0.6036 0.832 0.000 0.072 0.060 0.016 0.020
#> SRR1443483 4 0.508 0.5704 0.092 0.000 0.160 0.704 0.004 0.040
#> SRR1346794 4 0.716 -0.2493 0.324 0.000 0.008 0.352 0.056 0.260
#> SRR1405245 6 0.682 0.4108 0.316 0.000 0.148 0.076 0.004 0.456
#> SRR1409677 4 0.308 0.6021 0.028 0.000 0.000 0.860 0.056 0.056
#> SRR1095549 4 0.530 0.1786 0.364 0.000 0.020 0.552 0.000 0.064
#> SRR1323788 6 0.666 0.5114 0.248 0.000 0.064 0.200 0.000 0.488
#> SRR1314054 2 0.201 0.7386 0.000 0.908 0.068 0.000 0.000 0.024
#> SRR1077944 1 0.327 0.6376 0.832 0.000 0.008 0.108 0.000 0.052
#> SRR1480587 2 0.322 0.7329 0.008 0.848 0.008 0.000 0.048 0.088
#> SRR1311205 1 0.460 0.6534 0.716 0.000 0.020 0.216 0.016 0.032
#> SRR1076369 6 0.717 -0.0236 0.064 0.000 0.020 0.204 0.256 0.456
#> SRR1453549 4 0.412 0.5566 0.024 0.000 0.244 0.716 0.000 0.016
#> SRR1345782 1 0.475 0.6273 0.680 0.000 0.028 0.252 0.004 0.036
#> SRR1447850 2 0.315 0.7167 0.012 0.848 0.080 0.000 0.000 0.060
#> SRR1391553 3 0.284 0.6776 0.004 0.064 0.876 0.016 0.000 0.040
#> SRR1444156 2 0.246 0.7427 0.008 0.892 0.048 0.000 0.000 0.052
#> SRR1471731 3 0.416 0.6166 0.016 0.000 0.756 0.168 0.000 0.060
#> SRR1120987 4 0.750 0.3287 0.100 0.104 0.048 0.580 0.084 0.084
#> SRR1477363 1 0.621 0.3825 0.496 0.000 0.020 0.328 0.008 0.148
#> SRR1391961 5 0.413 0.6511 0.124 0.048 0.008 0.004 0.792 0.024
#> SRR1373879 4 0.461 0.5912 0.060 0.000 0.176 0.728 0.000 0.036
#> SRR1318732 6 0.663 0.5349 0.180 0.000 0.224 0.068 0.004 0.524
#> SRR1091404 1 0.319 0.6747 0.840 0.000 0.000 0.112 0.024 0.024
#> SRR1402109 4 0.438 0.6053 0.072 0.000 0.132 0.760 0.000 0.036
#> SRR1407336 4 0.200 0.6422 0.028 0.000 0.044 0.920 0.004 0.004
#> SRR1097417 3 0.416 0.6756 0.040 0.036 0.808 0.088 0.004 0.024
#> SRR1396227 1 0.528 0.1123 0.620 0.000 0.292 0.008 0.024 0.056
#> SRR1400775 2 0.155 0.7454 0.000 0.940 0.036 0.000 0.004 0.020
#> SRR1392861 4 0.374 0.5363 0.008 0.000 0.184 0.772 0.000 0.036
#> SRR1472929 5 0.295 0.6777 0.104 0.016 0.004 0.004 0.860 0.012
#> SRR1436740 4 0.575 0.3866 0.256 0.000 0.076 0.608 0.004 0.056
#> SRR1477057 2 0.816 -0.1425 0.188 0.348 0.088 0.000 0.292 0.084
#> SRR1311980 3 0.331 0.6885 0.128 0.000 0.820 0.004 0.000 0.048
#> SRR1069400 4 0.433 0.6130 0.080 0.000 0.104 0.776 0.004 0.036
#> SRR1351016 1 0.359 0.6427 0.832 0.000 0.048 0.088 0.012 0.020
#> SRR1096291 4 0.345 0.5718 0.032 0.000 0.000 0.836 0.072 0.060
#> SRR1418145 5 0.803 0.2127 0.012 0.212 0.012 0.284 0.336 0.144
#> SRR1488111 2 0.559 0.6188 0.004 0.716 0.076 0.080 0.048 0.076
#> SRR1370495 5 0.427 0.6664 0.116 0.036 0.004 0.004 0.784 0.056
#> SRR1352639 1 0.546 0.4229 0.672 0.012 0.008 0.040 0.212 0.056
#> SRR1348911 3 0.389 0.6878 0.068 0.032 0.820 0.064 0.000 0.016
#> SRR1467386 1 0.405 0.5792 0.664 0.000 0.000 0.312 0.000 0.024
#> SRR1415956 1 0.584 0.5919 0.668 0.000 0.044 0.140 0.036 0.112
#> SRR1500495 1 0.665 0.4467 0.528 0.000 0.060 0.264 0.016 0.132
#> SRR1405099 1 0.547 0.6036 0.692 0.000 0.024 0.136 0.036 0.112
#> SRR1345585 4 0.557 0.3891 0.028 0.000 0.344 0.556 0.004 0.068
#> SRR1093196 4 0.309 0.6339 0.032 0.000 0.060 0.860 0.000 0.048
#> SRR1466006 2 0.682 0.1290 0.012 0.388 0.024 0.000 0.248 0.328
#> SRR1351557 2 0.289 0.7399 0.008 0.872 0.012 0.000 0.032 0.076
#> SRR1382687 6 0.696 0.5778 0.176 0.000 0.128 0.184 0.004 0.508
#> SRR1375549 5 0.550 0.5286 0.224 0.000 0.008 0.020 0.636 0.112
#> SRR1101765 6 0.722 -0.1888 0.040 0.000 0.020 0.272 0.324 0.344
#> SRR1334461 5 0.292 0.6754 0.120 0.016 0.004 0.004 0.852 0.004
#> SRR1094073 2 0.246 0.7427 0.008 0.892 0.048 0.000 0.000 0.052
#> SRR1077549 4 0.480 0.2597 0.400 0.000 0.020 0.556 0.000 0.024
#> SRR1440332 4 0.372 0.6110 0.128 0.000 0.024 0.808 0.004 0.036
#> SRR1454177 4 0.321 0.5880 0.016 0.000 0.116 0.836 0.000 0.032
#> SRR1082447 1 0.447 0.4796 0.740 0.000 0.012 0.068 0.008 0.172
#> SRR1420043 4 0.261 0.6404 0.036 0.000 0.052 0.888 0.000 0.024
#> SRR1432500 4 0.388 0.5491 0.200 0.000 0.012 0.760 0.004 0.024
#> SRR1378045 3 0.410 0.6076 0.008 0.140 0.780 0.016 0.000 0.056
#> SRR1334200 5 0.472 0.5497 0.020 0.000 0.020 0.012 0.656 0.292
#> SRR1069539 4 0.529 0.4054 0.012 0.012 0.004 0.672 0.200 0.100
#> SRR1343031 4 0.404 0.6196 0.096 0.000 0.076 0.796 0.004 0.028
#> SRR1319690 4 0.685 0.2319 0.204 0.000 0.080 0.512 0.008 0.196
#> SRR1310604 2 0.574 0.2564 0.008 0.496 0.008 0.004 0.396 0.088
#> SRR1327747 4 0.398 0.5739 0.068 0.000 0.008 0.792 0.012 0.120
#> SRR1072456 2 0.381 0.7089 0.004 0.800 0.008 0.000 0.100 0.088
#> SRR1367896 3 0.371 0.6728 0.060 0.004 0.816 0.100 0.000 0.020
#> SRR1480107 1 0.294 0.6704 0.848 0.000 0.004 0.112 0.036 0.000
#> SRR1377756 6 0.660 0.5515 0.200 0.000 0.052 0.236 0.004 0.508
#> SRR1435272 4 0.172 0.6264 0.004 0.000 0.032 0.932 0.000 0.032
#> SRR1089230 4 0.483 0.4989 0.052 0.000 0.004 0.736 0.076 0.132
#> SRR1389522 4 0.688 -0.1267 0.360 0.000 0.188 0.396 0.008 0.048
#> SRR1080600 5 0.701 0.2824 0.008 0.212 0.020 0.020 0.408 0.332
#> SRR1086935 4 0.625 0.0747 0.020 0.024 0.388 0.468 0.000 0.100
#> SRR1344060 5 0.239 0.6832 0.064 0.004 0.004 0.000 0.896 0.032
#> SRR1467922 2 0.246 0.7427 0.008 0.892 0.048 0.000 0.000 0.052
#> SRR1090984 3 0.445 0.5509 0.084 0.000 0.728 0.012 0.000 0.176
#> SRR1456991 1 0.376 0.6747 0.808 0.000 0.024 0.128 0.032 0.008
#> SRR1085039 4 0.517 -0.1461 0.448 0.000 0.016 0.492 0.004 0.040
#> SRR1069303 3 0.541 0.4886 0.364 0.000 0.552 0.004 0.024 0.056
#> SRR1091500 2 0.148 0.7458 0.000 0.940 0.040 0.000 0.000 0.020
#> SRR1075198 2 0.622 0.2076 0.008 0.460 0.012 0.008 0.388 0.124
#> SRR1086915 4 0.418 0.5377 0.044 0.000 0.000 0.784 0.076 0.096
#> SRR1499503 2 0.256 0.7412 0.008 0.888 0.004 0.000 0.032 0.068
#> SRR1094312 2 0.109 0.7489 0.000 0.960 0.020 0.000 0.000 0.020
#> SRR1352437 3 0.530 0.5080 0.360 0.000 0.560 0.040 0.000 0.040
#> SRR1436323 4 0.473 0.5662 0.160 0.000 0.052 0.728 0.000 0.060
#> SRR1073507 1 0.345 0.6423 0.800 0.000 0.012 0.164 0.000 0.024
#> SRR1401972 3 0.532 0.5021 0.356 0.000 0.564 0.004 0.020 0.056
#> SRR1415510 2 0.361 0.7343 0.012 0.836 0.040 0.000 0.036 0.076
#> SRR1327279 4 0.471 0.3927 0.284 0.000 0.028 0.656 0.000 0.032
#> SRR1086983 4 0.496 0.4740 0.156 0.000 0.028 0.700 0.000 0.116
#> SRR1105174 1 0.632 0.3234 0.500 0.000 0.016 0.260 0.008 0.216
#> SRR1468893 6 0.600 0.3604 0.388 0.000 0.032 0.056 0.024 0.500
#> SRR1362555 2 0.594 0.1318 0.008 0.436 0.008 0.004 0.436 0.108
#> SRR1074526 5 0.722 0.4460 0.020 0.108 0.060 0.028 0.496 0.288
#> SRR1326225 2 0.166 0.7436 0.000 0.928 0.056 0.000 0.000 0.016
#> SRR1401933 6 0.630 0.4911 0.324 0.000 0.048 0.096 0.012 0.520
#> SRR1324062 3 0.420 0.6830 0.084 0.000 0.784 0.084 0.000 0.048
#> SRR1102296 3 0.467 0.4612 0.412 0.000 0.548 0.004 0.000 0.036
#> SRR1085087 1 0.558 0.5522 0.648 0.000 0.108 0.204 0.012 0.028
#> SRR1079046 5 0.348 0.6673 0.056 0.004 0.012 0.000 0.828 0.100
#> SRR1328339 3 0.334 0.6696 0.208 0.000 0.776 0.004 0.000 0.012
#> SRR1079782 2 0.603 0.3026 0.008 0.508 0.012 0.004 0.348 0.120
#> SRR1092257 2 0.191 0.7443 0.000 0.924 0.044 0.000 0.012 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["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 17611 rows and 118 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.653 0.827 0.921 0.4937 0.501 0.501
#> 3 3 0.518 0.504 0.730 0.3423 0.689 0.455
#> 4 4 0.515 0.596 0.762 0.1291 0.822 0.530
#> 5 5 0.550 0.509 0.702 0.0659 0.934 0.748
#> 6 6 0.619 0.477 0.683 0.0423 0.915 0.639
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1396765 2 0.0000 0.8754 0.000 1.000
#> SRR1429287 2 0.0000 0.8754 0.000 1.000
#> SRR1359238 1 0.0000 0.9373 1.000 0.000
#> SRR1309597 1 0.1184 0.9298 0.984 0.016
#> SRR1441398 1 0.0000 0.9373 1.000 0.000
#> SRR1084055 2 0.0000 0.8754 0.000 1.000
#> SRR1417566 2 0.2043 0.8663 0.032 0.968
#> SRR1351857 1 0.0000 0.9373 1.000 0.000
#> SRR1487485 2 0.0000 0.8754 0.000 1.000
#> SRR1335875 2 0.8555 0.6488 0.280 0.720
#> SRR1073947 1 0.0000 0.9373 1.000 0.000
#> SRR1443483 1 0.1414 0.9278 0.980 0.020
#> SRR1346794 1 0.0000 0.9373 1.000 0.000
#> SRR1405245 1 0.0000 0.9373 1.000 0.000
#> SRR1409677 1 0.3274 0.9025 0.940 0.060
#> SRR1095549 1 0.0000 0.9373 1.000 0.000
#> SRR1323788 1 0.0000 0.9373 1.000 0.000
#> SRR1314054 2 0.0000 0.8754 0.000 1.000
#> SRR1077944 1 0.0000 0.9373 1.000 0.000
#> SRR1480587 2 0.0000 0.8754 0.000 1.000
#> SRR1311205 1 0.0000 0.9373 1.000 0.000
#> SRR1076369 1 0.8555 0.5974 0.720 0.280
#> SRR1453549 1 0.8016 0.6716 0.756 0.244
#> SRR1345782 1 0.0000 0.9373 1.000 0.000
#> SRR1447850 2 0.0000 0.8754 0.000 1.000
#> SRR1391553 2 0.0000 0.8754 0.000 1.000
#> SRR1444156 2 0.0000 0.8754 0.000 1.000
#> SRR1471731 1 0.4815 0.8650 0.896 0.104
#> SRR1120987 2 0.1184 0.8709 0.016 0.984
#> SRR1477363 1 0.0000 0.9373 1.000 0.000
#> SRR1391961 2 0.3274 0.8521 0.060 0.940
#> SRR1373879 1 0.2236 0.9188 0.964 0.036
#> SRR1318732 1 0.9686 0.3266 0.604 0.396
#> SRR1091404 1 0.0000 0.9373 1.000 0.000
#> SRR1402109 1 0.0000 0.9373 1.000 0.000
#> SRR1407336 1 0.3274 0.9025 0.940 0.060
#> SRR1097417 2 0.1184 0.8711 0.016 0.984
#> SRR1396227 1 0.3733 0.8759 0.928 0.072
#> SRR1400775 2 0.0000 0.8754 0.000 1.000
#> SRR1392861 1 0.8327 0.6453 0.736 0.264
#> SRR1472929 2 0.5408 0.8262 0.124 0.876
#> SRR1436740 1 0.2778 0.9105 0.952 0.048
#> SRR1477057 2 0.1184 0.8709 0.016 0.984
#> SRR1311980 2 0.8861 0.6197 0.304 0.696
#> SRR1069400 1 0.2603 0.9137 0.956 0.044
#> SRR1351016 1 0.0000 0.9373 1.000 0.000
#> SRR1096291 1 0.3584 0.8965 0.932 0.068
#> SRR1418145 2 0.7056 0.7391 0.192 0.808
#> SRR1488111 2 0.0000 0.8754 0.000 1.000
#> SRR1370495 2 0.8499 0.6700 0.276 0.724
#> SRR1352639 1 0.0376 0.9354 0.996 0.004
#> SRR1348911 2 0.7815 0.7098 0.232 0.768
#> SRR1467386 1 0.0000 0.9373 1.000 0.000
#> SRR1415956 1 0.0000 0.9373 1.000 0.000
#> SRR1500495 1 0.0000 0.9373 1.000 0.000
#> SRR1405099 1 0.0000 0.9373 1.000 0.000
#> SRR1345585 1 0.9988 0.0808 0.520 0.480
#> SRR1093196 1 0.3274 0.9025 0.940 0.060
#> SRR1466006 2 0.0000 0.8754 0.000 1.000
#> SRR1351557 2 0.0000 0.8754 0.000 1.000
#> SRR1382687 1 0.0376 0.9351 0.996 0.004
#> SRR1375549 1 0.7950 0.6348 0.760 0.240
#> SRR1101765 1 0.8661 0.5818 0.712 0.288
#> SRR1334461 2 0.8386 0.6843 0.268 0.732
#> SRR1094073 2 0.0000 0.8754 0.000 1.000
#> SRR1077549 1 0.0000 0.9373 1.000 0.000
#> SRR1440332 1 0.0000 0.9373 1.000 0.000
#> SRR1454177 1 0.3431 0.9007 0.936 0.064
#> SRR1082447 1 0.0000 0.9373 1.000 0.000
#> SRR1420043 1 0.0000 0.9373 1.000 0.000
#> SRR1432500 1 0.0000 0.9373 1.000 0.000
#> SRR1378045 2 0.0000 0.8754 0.000 1.000
#> SRR1334200 2 0.3274 0.8498 0.060 0.940
#> SRR1069539 2 0.9896 0.2226 0.440 0.560
#> SRR1343031 1 0.0000 0.9373 1.000 0.000
#> SRR1319690 1 0.0000 0.9373 1.000 0.000
#> SRR1310604 2 0.4022 0.8380 0.080 0.920
#> SRR1327747 1 0.0376 0.9356 0.996 0.004
#> SRR1072456 2 0.0000 0.8754 0.000 1.000
#> SRR1367896 2 0.8555 0.6488 0.280 0.720
#> SRR1480107 1 0.0000 0.9373 1.000 0.000
#> SRR1377756 1 0.0000 0.9373 1.000 0.000
#> SRR1435272 1 0.3274 0.9025 0.940 0.060
#> SRR1089230 1 0.3274 0.9025 0.940 0.060
#> SRR1389522 1 0.0000 0.9373 1.000 0.000
#> SRR1080600 2 0.7139 0.7344 0.196 0.804
#> SRR1086935 2 0.0000 0.8754 0.000 1.000
#> SRR1344060 2 0.3274 0.8521 0.060 0.940
#> SRR1467922 2 0.0000 0.8754 0.000 1.000
#> SRR1090984 2 0.9248 0.5615 0.340 0.660
#> SRR1456991 1 0.0000 0.9373 1.000 0.000
#> SRR1085039 1 0.0000 0.9373 1.000 0.000
#> SRR1069303 2 0.9850 0.3737 0.428 0.572
#> SRR1091500 2 0.0000 0.8754 0.000 1.000
#> SRR1075198 2 0.6801 0.7522 0.180 0.820
#> SRR1086915 1 0.3274 0.9025 0.940 0.060
#> SRR1499503 2 0.0000 0.8754 0.000 1.000
#> SRR1094312 2 0.0000 0.8754 0.000 1.000
#> SRR1352437 2 0.9896 0.3412 0.440 0.560
#> SRR1436323 1 0.0000 0.9373 1.000 0.000
#> SRR1073507 1 0.0000 0.9373 1.000 0.000
#> SRR1401972 2 0.9850 0.3737 0.428 0.572
#> SRR1415510 2 0.0000 0.8754 0.000 1.000
#> SRR1327279 1 0.0000 0.9373 1.000 0.000
#> SRR1086983 1 0.0000 0.9373 1.000 0.000
#> SRR1105174 1 0.0000 0.9373 1.000 0.000
#> SRR1468893 1 0.0000 0.9373 1.000 0.000
#> SRR1362555 2 0.7139 0.7344 0.196 0.804
#> SRR1074526 2 0.0000 0.8754 0.000 1.000
#> SRR1326225 2 0.0000 0.8754 0.000 1.000
#> SRR1401933 1 0.0000 0.9373 1.000 0.000
#> SRR1324062 1 0.9775 0.1802 0.588 0.412
#> SRR1102296 2 0.9795 0.4040 0.416 0.584
#> SRR1085087 1 0.2043 0.9143 0.968 0.032
#> SRR1079046 2 0.3274 0.8521 0.060 0.940
#> SRR1328339 2 0.9732 0.4327 0.404 0.596
#> SRR1079782 2 0.3733 0.8430 0.072 0.928
#> SRR1092257 2 0.0000 0.8754 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.0000 0.8453 0.000 1.000 0.000
#> SRR1429287 2 0.0000 0.8453 0.000 1.000 0.000
#> SRR1359238 3 0.6244 0.5884 0.440 0.000 0.560
#> SRR1309597 3 0.6460 0.5878 0.440 0.004 0.556
#> SRR1441398 1 0.0000 0.6157 1.000 0.000 0.000
#> SRR1084055 2 0.0424 0.8455 0.000 0.992 0.008
#> SRR1417566 2 0.9805 0.1439 0.240 0.396 0.364
#> SRR1351857 3 0.6244 0.5884 0.440 0.000 0.560
#> SRR1487485 3 0.6305 -0.2537 0.000 0.484 0.516
#> SRR1335875 3 0.9836 -0.1929 0.252 0.344 0.404
#> SRR1073947 1 0.3752 0.5631 0.856 0.000 0.144
#> SRR1443483 3 0.6244 0.5884 0.440 0.000 0.560
#> SRR1346794 1 0.6180 -0.3740 0.584 0.000 0.416
#> SRR1405245 1 0.3879 0.5657 0.848 0.000 0.152
#> SRR1409677 3 0.6045 0.5766 0.380 0.000 0.620
#> SRR1095549 3 0.6309 0.5260 0.500 0.000 0.500
#> SRR1323788 1 0.6302 -0.5172 0.520 0.000 0.480
#> SRR1314054 2 0.1643 0.8319 0.000 0.956 0.044
#> SRR1077944 1 0.0000 0.6157 1.000 0.000 0.000
#> SRR1480587 2 0.0000 0.8453 0.000 1.000 0.000
#> SRR1311205 1 0.0424 0.6115 0.992 0.000 0.008
#> SRR1076369 3 0.6587 0.5608 0.352 0.016 0.632
#> SRR1453549 3 0.2448 0.3990 0.000 0.076 0.924
#> SRR1345782 1 0.1031 0.5996 0.976 0.000 0.024
#> SRR1447850 2 0.5178 0.6566 0.000 0.744 0.256
#> SRR1391553 2 0.6209 0.5252 0.004 0.628 0.368
#> SRR1444156 2 0.0747 0.8444 0.000 0.984 0.016
#> SRR1471731 3 0.3618 0.4296 0.104 0.012 0.884
#> SRR1120987 2 0.5529 0.5833 0.000 0.704 0.296
#> SRR1477363 1 0.5058 0.1779 0.756 0.000 0.244
#> SRR1391961 2 0.6280 0.1926 0.460 0.540 0.000
#> SRR1373879 3 0.6062 0.5840 0.384 0.000 0.616
#> SRR1318732 3 0.9268 -0.0322 0.348 0.168 0.484
#> SRR1091404 1 0.0000 0.6157 1.000 0.000 0.000
#> SRR1402109 3 0.6244 0.5884 0.440 0.000 0.560
#> SRR1407336 3 0.6204 0.5918 0.424 0.000 0.576
#> SRR1097417 2 0.6008 0.5262 0.000 0.628 0.372
#> SRR1396227 1 0.5926 0.4340 0.644 0.000 0.356
#> SRR1400775 2 0.0747 0.8444 0.000 0.984 0.016
#> SRR1392861 3 0.2448 0.3990 0.000 0.076 0.924
#> SRR1472929 2 0.8249 0.2267 0.424 0.500 0.076
#> SRR1436740 3 0.4452 0.4354 0.192 0.000 0.808
#> SRR1477057 2 0.5461 0.6551 0.216 0.768 0.016
#> SRR1311980 1 0.8784 0.3627 0.512 0.120 0.368
#> SRR1069400 3 0.6244 0.5884 0.440 0.000 0.560
#> SRR1351016 1 0.0892 0.6132 0.980 0.000 0.020
#> SRR1096291 3 0.5968 0.5701 0.364 0.000 0.636
#> SRR1418145 2 0.2625 0.8147 0.000 0.916 0.084
#> SRR1488111 2 0.0592 0.8453 0.000 0.988 0.012
#> SRR1370495 1 0.8273 -0.1282 0.476 0.448 0.076
#> SRR1352639 1 0.4658 0.5627 0.856 0.068 0.076
#> SRR1348911 3 0.9811 -0.2403 0.240 0.376 0.384
#> SRR1467386 1 0.4974 0.1753 0.764 0.000 0.236
#> SRR1415956 1 0.0000 0.6157 1.000 0.000 0.000
#> SRR1500495 1 0.1860 0.5761 0.948 0.000 0.052
#> SRR1405099 1 0.0000 0.6157 1.000 0.000 0.000
#> SRR1345585 3 0.7495 0.3220 0.120 0.188 0.692
#> SRR1093196 3 0.4605 0.5138 0.204 0.000 0.796
#> SRR1466006 2 0.2448 0.8180 0.000 0.924 0.076
#> SRR1351557 2 0.0237 0.8456 0.000 0.996 0.004
#> SRR1382687 3 0.4750 0.4394 0.216 0.000 0.784
#> SRR1375549 1 0.6324 0.4942 0.764 0.160 0.076
#> SRR1101765 3 0.6696 0.5605 0.348 0.020 0.632
#> SRR1334461 1 0.8249 -0.0628 0.500 0.424 0.076
#> SRR1094073 2 0.0747 0.8444 0.000 0.984 0.016
#> SRR1077549 3 0.6274 0.4109 0.456 0.000 0.544
#> SRR1440332 3 0.6244 0.5884 0.440 0.000 0.560
#> SRR1454177 3 0.3267 0.4657 0.116 0.000 0.884
#> SRR1082447 1 0.0000 0.6157 1.000 0.000 0.000
#> SRR1420043 3 0.6204 0.5910 0.424 0.000 0.576
#> SRR1432500 3 0.6252 0.5852 0.444 0.000 0.556
#> SRR1378045 2 0.6247 0.5149 0.004 0.620 0.376
#> SRR1334200 2 0.2860 0.8138 0.004 0.912 0.084
#> SRR1069539 3 0.6345 0.1618 0.004 0.400 0.596
#> SRR1343031 3 0.6244 0.5884 0.440 0.000 0.560
#> SRR1319690 3 0.6244 0.5884 0.440 0.000 0.560
#> SRR1310604 2 0.2448 0.8180 0.000 0.924 0.076
#> SRR1327747 3 0.6244 0.5884 0.440 0.000 0.560
#> SRR1072456 2 0.0000 0.8453 0.000 1.000 0.000
#> SRR1367896 3 0.9808 -0.1553 0.280 0.288 0.432
#> SRR1480107 1 0.0000 0.6157 1.000 0.000 0.000
#> SRR1377756 3 0.6168 0.4895 0.412 0.000 0.588
#> SRR1435272 3 0.6215 0.5911 0.428 0.000 0.572
#> SRR1089230 3 0.5968 0.5701 0.364 0.000 0.636
#> SRR1389522 1 0.2066 0.5704 0.940 0.000 0.060
#> SRR1080600 2 0.2448 0.8180 0.000 0.924 0.076
#> SRR1086935 3 0.3272 0.3795 0.004 0.104 0.892
#> SRR1344060 2 0.8102 0.3553 0.368 0.556 0.076
#> SRR1467922 2 0.0747 0.8444 0.000 0.984 0.016
#> SRR1090984 1 0.8087 0.4052 0.560 0.076 0.364
#> SRR1456991 1 0.0000 0.6157 1.000 0.000 0.000
#> SRR1085039 1 0.5785 -0.1154 0.668 0.000 0.332
#> SRR1069303 1 0.8087 0.4052 0.560 0.076 0.364
#> SRR1091500 2 0.0592 0.8453 0.000 0.988 0.012
#> SRR1075198 2 0.2448 0.8180 0.000 0.924 0.076
#> SRR1086915 3 0.5968 0.5701 0.364 0.000 0.636
#> SRR1499503 2 0.0000 0.8453 0.000 1.000 0.000
#> SRR1094312 2 0.0592 0.8453 0.000 0.988 0.012
#> SRR1352437 1 0.8087 0.4052 0.560 0.076 0.364
#> SRR1436323 3 0.4931 0.4850 0.232 0.000 0.768
#> SRR1073507 1 0.0747 0.6047 0.984 0.000 0.016
#> SRR1401972 1 0.8087 0.4052 0.560 0.076 0.364
#> SRR1415510 2 0.0237 0.8456 0.000 0.996 0.004
#> SRR1327279 1 0.6140 -0.3176 0.596 0.000 0.404
#> SRR1086983 3 0.6307 0.5269 0.488 0.000 0.512
#> SRR1105174 1 0.4346 0.3427 0.816 0.000 0.184
#> SRR1468893 1 0.2796 0.5818 0.908 0.000 0.092
#> SRR1362555 2 0.2448 0.8180 0.000 0.924 0.076
#> SRR1074526 2 0.2486 0.8257 0.008 0.932 0.060
#> SRR1326225 2 0.0592 0.8453 0.000 0.988 0.012
#> SRR1401933 1 0.4346 0.4703 0.816 0.000 0.184
#> SRR1324062 3 0.7970 -0.0810 0.300 0.088 0.612
#> SRR1102296 1 0.8087 0.4052 0.560 0.076 0.364
#> SRR1085087 1 0.4994 0.5449 0.816 0.024 0.160
#> SRR1079046 2 0.8273 0.1651 0.448 0.476 0.076
#> SRR1328339 1 0.7693 0.4132 0.580 0.056 0.364
#> SRR1079782 2 0.2448 0.8180 0.000 0.924 0.076
#> SRR1092257 2 0.0592 0.8453 0.000 0.988 0.012
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.1211 0.872 0.000 0.960 0.040 0.000
#> SRR1429287 2 0.0707 0.866 0.000 0.980 0.020 0.000
#> SRR1359238 4 0.1398 0.723 0.040 0.000 0.004 0.956
#> SRR1309597 4 0.6524 0.530 0.120 0.000 0.264 0.616
#> SRR1441398 1 0.5361 0.619 0.724 0.000 0.068 0.208
#> SRR1084055 2 0.1940 0.872 0.000 0.924 0.076 0.000
#> SRR1417566 3 0.2586 0.710 0.012 0.068 0.912 0.008
#> SRR1351857 4 0.1488 0.722 0.032 0.000 0.012 0.956
#> SRR1487485 3 0.7188 0.416 0.000 0.244 0.552 0.204
#> SRR1335875 3 0.2895 0.713 0.032 0.044 0.908 0.016
#> SRR1073947 1 0.5376 0.542 0.736 0.000 0.176 0.088
#> SRR1443483 4 0.5056 0.638 0.044 0.000 0.224 0.732
#> SRR1346794 4 0.7102 -0.177 0.448 0.020 0.072 0.460
#> SRR1405245 1 0.6750 0.125 0.540 0.000 0.356 0.104
#> SRR1409677 4 0.2521 0.719 0.060 0.004 0.020 0.916
#> SRR1095549 4 0.5519 0.481 0.264 0.000 0.052 0.684
#> SRR1323788 1 0.7500 -0.171 0.416 0.000 0.180 0.404
#> SRR1314054 2 0.2921 0.831 0.000 0.860 0.140 0.000
#> SRR1077944 1 0.3557 0.640 0.856 0.000 0.036 0.108
#> SRR1480587 2 0.1211 0.872 0.000 0.960 0.040 0.000
#> SRR1311205 1 0.4764 0.632 0.748 0.000 0.032 0.220
#> SRR1076369 4 0.6537 0.531 0.264 0.036 0.052 0.648
#> SRR1453549 4 0.5595 0.355 0.012 0.008 0.404 0.576
#> SRR1345782 1 0.5648 0.595 0.684 0.000 0.064 0.252
#> SRR1447850 2 0.4477 0.586 0.000 0.688 0.312 0.000
#> SRR1391553 3 0.2760 0.689 0.000 0.128 0.872 0.000
#> SRR1444156 2 0.2149 0.867 0.000 0.912 0.088 0.000
#> SRR1471731 3 0.4456 0.624 0.044 0.004 0.804 0.148
#> SRR1120987 2 0.7493 0.518 0.092 0.608 0.064 0.236
#> SRR1477363 1 0.6189 0.431 0.568 0.000 0.060 0.372
#> SRR1391961 1 0.6177 0.140 0.488 0.468 0.040 0.004
#> SRR1373879 4 0.4839 0.674 0.052 0.000 0.184 0.764
#> SRR1318732 3 0.7196 0.355 0.308 0.020 0.568 0.104
#> SRR1091404 1 0.4054 0.648 0.796 0.000 0.016 0.188
#> SRR1402109 4 0.4462 0.688 0.044 0.000 0.164 0.792
#> SRR1407336 4 0.2385 0.727 0.028 0.000 0.052 0.920
#> SRR1097417 3 0.3533 0.695 0.008 0.104 0.864 0.024
#> SRR1396227 1 0.5173 0.330 0.660 0.000 0.320 0.020
#> SRR1400775 2 0.2408 0.860 0.000 0.896 0.104 0.000
#> SRR1392861 4 0.4769 0.473 0.000 0.008 0.308 0.684
#> SRR1472929 1 0.6007 0.125 0.520 0.444 0.032 0.004
#> SRR1436740 4 0.6187 0.584 0.144 0.000 0.184 0.672
#> SRR1477057 2 0.5110 0.733 0.132 0.764 0.104 0.000
#> SRR1311980 3 0.2053 0.704 0.072 0.004 0.924 0.000
#> SRR1069400 4 0.3877 0.713 0.048 0.000 0.112 0.840
#> SRR1351016 1 0.5091 0.635 0.752 0.000 0.068 0.180
#> SRR1096291 4 0.4232 0.668 0.168 0.004 0.024 0.804
#> SRR1418145 2 0.2830 0.825 0.060 0.904 0.004 0.032
#> SRR1488111 2 0.2081 0.869 0.000 0.916 0.084 0.000
#> SRR1370495 1 0.6850 0.320 0.560 0.360 0.036 0.044
#> SRR1352639 1 0.2636 0.602 0.916 0.052 0.012 0.020
#> SRR1348911 3 0.3215 0.707 0.020 0.076 0.888 0.016
#> SRR1467386 1 0.5427 0.356 0.568 0.000 0.016 0.416
#> SRR1415956 1 0.4035 0.652 0.804 0.000 0.020 0.176
#> SRR1500495 1 0.6100 0.553 0.644 0.000 0.084 0.272
#> SRR1405099 1 0.3969 0.651 0.804 0.000 0.016 0.180
#> SRR1345585 3 0.6500 0.231 0.016 0.048 0.568 0.368
#> SRR1093196 4 0.4050 0.709 0.036 0.000 0.144 0.820
#> SRR1466006 2 0.0804 0.861 0.008 0.980 0.012 0.000
#> SRR1351557 2 0.1302 0.872 0.000 0.956 0.044 0.000
#> SRR1382687 3 0.7824 0.222 0.244 0.004 0.456 0.296
#> SRR1375549 1 0.5515 0.584 0.776 0.092 0.040 0.092
#> SRR1101765 4 0.5975 0.596 0.192 0.044 0.044 0.720
#> SRR1334461 1 0.6118 0.358 0.604 0.348 0.032 0.016
#> SRR1094073 2 0.2149 0.867 0.000 0.912 0.088 0.000
#> SRR1077549 4 0.5962 0.540 0.260 0.000 0.080 0.660
#> SRR1440332 4 0.3279 0.709 0.096 0.000 0.032 0.872
#> SRR1454177 4 0.4194 0.602 0.008 0.000 0.228 0.764
#> SRR1082447 1 0.3634 0.607 0.856 0.000 0.048 0.096
#> SRR1420043 4 0.3128 0.725 0.040 0.000 0.076 0.884
#> SRR1432500 4 0.3266 0.694 0.108 0.000 0.024 0.868
#> SRR1378045 3 0.3172 0.674 0.000 0.160 0.840 0.000
#> SRR1334200 2 0.6428 0.611 0.208 0.688 0.056 0.048
#> SRR1069539 4 0.6904 0.449 0.148 0.248 0.004 0.600
#> SRR1343031 4 0.3716 0.713 0.052 0.000 0.096 0.852
#> SRR1319690 4 0.5990 0.607 0.164 0.000 0.144 0.692
#> SRR1310604 2 0.1674 0.848 0.032 0.952 0.004 0.012
#> SRR1327747 4 0.2739 0.715 0.060 0.000 0.036 0.904
#> SRR1072456 2 0.1474 0.871 0.000 0.948 0.052 0.000
#> SRR1367896 3 0.2643 0.711 0.024 0.028 0.920 0.028
#> SRR1480107 1 0.4245 0.642 0.784 0.000 0.020 0.196
#> SRR1377756 1 0.7641 -0.107 0.416 0.000 0.208 0.376
#> SRR1435272 4 0.1305 0.729 0.004 0.000 0.036 0.960
#> SRR1089230 4 0.4260 0.652 0.180 0.004 0.020 0.796
#> SRR1389522 1 0.7728 0.232 0.424 0.000 0.236 0.340
#> SRR1080600 2 0.3702 0.795 0.100 0.860 0.012 0.028
#> SRR1086935 3 0.5564 0.165 0.000 0.020 0.544 0.436
#> SRR1344060 2 0.5997 0.128 0.436 0.528 0.032 0.004
#> SRR1467922 2 0.2149 0.867 0.000 0.912 0.088 0.000
#> SRR1090984 3 0.3389 0.678 0.104 0.004 0.868 0.024
#> SRR1456991 1 0.4464 0.639 0.768 0.000 0.024 0.208
#> SRR1085039 4 0.5943 0.203 0.360 0.000 0.048 0.592
#> SRR1069303 3 0.5080 0.308 0.420 0.000 0.576 0.004
#> SRR1091500 2 0.2216 0.866 0.000 0.908 0.092 0.000
#> SRR1075198 2 0.2365 0.832 0.064 0.920 0.004 0.012
#> SRR1086915 4 0.3990 0.657 0.176 0.004 0.012 0.808
#> SRR1499503 2 0.1211 0.872 0.000 0.960 0.040 0.000
#> SRR1094312 2 0.2216 0.866 0.000 0.908 0.092 0.000
#> SRR1352437 3 0.5596 0.466 0.332 0.000 0.632 0.036
#> SRR1436323 4 0.5859 0.635 0.156 0.000 0.140 0.704
#> SRR1073507 1 0.4095 0.635 0.804 0.000 0.024 0.172
#> SRR1401972 3 0.4905 0.425 0.364 0.000 0.632 0.004
#> SRR1415510 2 0.1302 0.872 0.000 0.956 0.044 0.000
#> SRR1327279 4 0.5118 0.620 0.176 0.000 0.072 0.752
#> SRR1086983 4 0.6238 0.496 0.296 0.000 0.084 0.620
#> SRR1105174 1 0.5339 0.493 0.688 0.000 0.040 0.272
#> SRR1468893 1 0.4055 0.574 0.832 0.000 0.060 0.108
#> SRR1362555 2 0.2781 0.823 0.072 0.904 0.008 0.016
#> SRR1074526 2 0.7313 0.580 0.064 0.640 0.192 0.104
#> SRR1326225 2 0.2345 0.863 0.000 0.900 0.100 0.000
#> SRR1401933 1 0.4906 0.532 0.776 0.000 0.084 0.140
#> SRR1324062 3 0.3547 0.696 0.064 0.000 0.864 0.072
#> SRR1102296 3 0.4936 0.423 0.372 0.000 0.624 0.004
#> SRR1085087 1 0.6442 0.601 0.676 0.016 0.108 0.200
#> SRR1079046 1 0.6440 0.276 0.568 0.372 0.044 0.016
#> SRR1328339 3 0.4533 0.600 0.232 0.004 0.752 0.012
#> SRR1079782 2 0.2040 0.841 0.048 0.936 0.004 0.012
#> SRR1092257 2 0.2281 0.864 0.000 0.904 0.096 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.0955 0.8229 0.000 0.968 0.004 0.000 0.028
#> SRR1429287 2 0.3098 0.7667 0.000 0.836 0.016 0.000 0.148
#> SRR1359238 4 0.2819 0.6431 0.060 0.000 0.004 0.884 0.052
#> SRR1309597 4 0.6992 0.3852 0.140 0.000 0.176 0.584 0.100
#> SRR1441398 1 0.6625 0.5372 0.596 0.000 0.052 0.212 0.140
#> SRR1084055 2 0.1485 0.8223 0.000 0.948 0.020 0.000 0.032
#> SRR1417566 3 0.3385 0.6628 0.016 0.084 0.856 0.000 0.044
#> SRR1351857 4 0.3730 0.6284 0.036 0.000 0.004 0.808 0.152
#> SRR1487485 3 0.7369 0.3111 0.004 0.288 0.420 0.264 0.024
#> SRR1335875 3 0.3796 0.6666 0.016 0.088 0.840 0.048 0.008
#> SRR1073947 1 0.4348 0.5190 0.768 0.000 0.180 0.032 0.020
#> SRR1443483 4 0.4112 0.5925 0.048 0.000 0.136 0.800 0.016
#> SRR1346794 5 0.7194 -0.1008 0.292 0.000 0.032 0.216 0.460
#> SRR1405245 1 0.7180 0.2743 0.500 0.000 0.204 0.044 0.252
#> SRR1409677 4 0.3754 0.6140 0.020 0.000 0.008 0.796 0.176
#> SRR1095549 4 0.6846 -0.0735 0.404 0.000 0.048 0.448 0.100
#> SRR1323788 1 0.7857 0.2071 0.432 0.000 0.148 0.124 0.296
#> SRR1314054 2 0.1341 0.8106 0.000 0.944 0.056 0.000 0.000
#> SRR1077944 1 0.2790 0.5390 0.892 0.000 0.028 0.020 0.060
#> SRR1480587 2 0.0963 0.8212 0.000 0.964 0.000 0.000 0.036
#> SRR1311205 1 0.4670 0.6083 0.748 0.000 0.032 0.188 0.032
#> SRR1076369 5 0.5456 0.3018 0.120 0.000 0.016 0.172 0.692
#> SRR1453549 4 0.5059 0.3899 0.008 0.008 0.316 0.644 0.024
#> SRR1345782 1 0.5140 0.5524 0.668 0.000 0.044 0.272 0.016
#> SRR1447850 2 0.2753 0.7296 0.000 0.856 0.136 0.000 0.008
#> SRR1391553 3 0.2583 0.6571 0.000 0.132 0.864 0.000 0.004
#> SRR1444156 2 0.0609 0.8252 0.000 0.980 0.020 0.000 0.000
#> SRR1471731 3 0.3981 0.6314 0.044 0.000 0.820 0.108 0.028
#> SRR1120987 2 0.8498 0.0779 0.052 0.436 0.072 0.248 0.192
#> SRR1477363 1 0.6476 0.4586 0.544 0.000 0.024 0.308 0.124
#> SRR1391961 5 0.6835 0.4464 0.292 0.276 0.004 0.000 0.428
#> SRR1373879 4 0.3602 0.6075 0.036 0.000 0.140 0.820 0.004
#> SRR1318732 3 0.7354 0.1090 0.260 0.008 0.408 0.016 0.308
#> SRR1091404 1 0.4480 0.6011 0.776 0.000 0.016 0.140 0.068
#> SRR1402109 4 0.3299 0.6178 0.040 0.000 0.108 0.848 0.004
#> SRR1407336 4 0.1597 0.6491 0.008 0.000 0.020 0.948 0.024
#> SRR1097417 3 0.5425 0.6228 0.008 0.140 0.732 0.080 0.040
#> SRR1396227 1 0.5264 0.2471 0.604 0.000 0.340 0.004 0.052
#> SRR1400775 2 0.0963 0.8218 0.000 0.964 0.036 0.000 0.000
#> SRR1392861 4 0.5583 0.4820 0.004 0.016 0.224 0.672 0.084
#> SRR1472929 5 0.6000 0.5251 0.268 0.160 0.000 0.000 0.572
#> SRR1436740 4 0.7624 0.4000 0.208 0.000 0.180 0.500 0.112
#> SRR1477057 2 0.6236 0.4841 0.120 0.656 0.068 0.000 0.156
#> SRR1311980 3 0.1893 0.6700 0.048 0.024 0.928 0.000 0.000
#> SRR1069400 4 0.2792 0.6271 0.040 0.000 0.072 0.884 0.004
#> SRR1351016 1 0.4503 0.5726 0.780 0.000 0.112 0.092 0.016
#> SRR1096291 4 0.5436 0.5271 0.084 0.004 0.008 0.672 0.232
#> SRR1418145 2 0.4691 0.5435 0.000 0.636 0.004 0.020 0.340
#> SRR1488111 2 0.1364 0.8261 0.000 0.952 0.036 0.000 0.012
#> SRR1370495 5 0.5903 0.5154 0.292 0.120 0.000 0.004 0.584
#> SRR1352639 1 0.5075 0.2133 0.620 0.012 0.020 0.004 0.344
#> SRR1348911 3 0.4177 0.6481 0.004 0.148 0.796 0.036 0.016
#> SRR1467386 1 0.5593 0.4713 0.628 0.000 0.032 0.296 0.044
#> SRR1415956 1 0.5236 0.5981 0.720 0.000 0.020 0.148 0.112
#> SRR1500495 1 0.6461 0.5127 0.584 0.000 0.044 0.268 0.104
#> SRR1405099 1 0.5170 0.5944 0.724 0.000 0.020 0.156 0.100
#> SRR1345585 3 0.6736 0.0834 0.024 0.048 0.464 0.424 0.040
#> SRR1093196 4 0.5306 0.5924 0.072 0.000 0.160 0.724 0.044
#> SRR1466006 2 0.3143 0.7204 0.000 0.796 0.000 0.000 0.204
#> SRR1351557 2 0.0865 0.8236 0.000 0.972 0.004 0.000 0.024
#> SRR1382687 3 0.7920 0.0713 0.288 0.000 0.348 0.072 0.292
#> SRR1375549 5 0.4474 0.4071 0.332 0.004 0.000 0.012 0.652
#> SRR1101765 5 0.4746 0.2199 0.032 0.000 0.008 0.276 0.684
#> SRR1334461 5 0.6011 0.4882 0.320 0.120 0.000 0.004 0.556
#> SRR1094073 2 0.0609 0.8252 0.000 0.980 0.020 0.000 0.000
#> SRR1077549 4 0.7127 0.2198 0.368 0.000 0.112 0.456 0.064
#> SRR1440332 4 0.3784 0.5861 0.140 0.000 0.024 0.816 0.020
#> SRR1454177 4 0.5232 0.5571 0.024 0.000 0.164 0.720 0.092
#> SRR1082447 1 0.4268 0.3841 0.708 0.000 0.016 0.004 0.272
#> SRR1420043 4 0.1695 0.6472 0.008 0.000 0.044 0.940 0.008
#> SRR1432500 4 0.3728 0.5688 0.164 0.000 0.008 0.804 0.024
#> SRR1378045 3 0.3933 0.6194 0.000 0.196 0.776 0.008 0.020
#> SRR1334200 5 0.3584 0.5331 0.020 0.148 0.000 0.012 0.820
#> SRR1069539 4 0.6427 0.2402 0.008 0.112 0.008 0.508 0.364
#> SRR1343031 4 0.2673 0.6281 0.044 0.000 0.060 0.892 0.004
#> SRR1319690 4 0.7307 0.1837 0.200 0.000 0.052 0.488 0.260
#> SRR1310604 2 0.3752 0.6287 0.000 0.708 0.000 0.000 0.292
#> SRR1327747 4 0.4940 0.5679 0.052 0.000 0.020 0.720 0.208
#> SRR1072456 2 0.1908 0.7994 0.000 0.908 0.000 0.000 0.092
#> SRR1367896 3 0.4357 0.6496 0.016 0.056 0.804 0.112 0.012
#> SRR1480107 1 0.3862 0.5999 0.816 0.000 0.032 0.132 0.020
#> SRR1377756 5 0.7797 -0.1437 0.368 0.000 0.168 0.092 0.372
#> SRR1435272 4 0.2992 0.6427 0.012 0.000 0.024 0.872 0.092
#> SRR1089230 4 0.6006 0.4272 0.096 0.000 0.012 0.564 0.328
#> SRR1389522 4 0.7428 -0.1921 0.384 0.000 0.144 0.404 0.068
#> SRR1080600 2 0.4440 0.3105 0.000 0.528 0.000 0.004 0.468
#> SRR1086935 3 0.8133 0.1824 0.024 0.096 0.440 0.304 0.136
#> SRR1344060 5 0.6083 0.5381 0.204 0.224 0.000 0.000 0.572
#> SRR1467922 2 0.0865 0.8229 0.000 0.972 0.024 0.000 0.004
#> SRR1090984 3 0.4602 0.6147 0.100 0.020 0.776 0.000 0.104
#> SRR1456991 1 0.4411 0.5985 0.780 0.000 0.036 0.152 0.032
#> SRR1085039 4 0.6017 -0.1202 0.436 0.000 0.020 0.480 0.064
#> SRR1069303 3 0.5334 0.3902 0.328 0.008 0.612 0.000 0.052
#> SRR1091500 2 0.1082 0.8246 0.000 0.964 0.028 0.000 0.008
#> SRR1075198 2 0.3895 0.5876 0.000 0.680 0.000 0.000 0.320
#> SRR1086915 4 0.5699 0.4774 0.092 0.000 0.008 0.612 0.288
#> SRR1499503 2 0.0794 0.8226 0.000 0.972 0.000 0.000 0.028
#> SRR1094312 2 0.0794 0.8246 0.000 0.972 0.028 0.000 0.000
#> SRR1352437 3 0.5398 0.4970 0.260 0.004 0.668 0.044 0.024
#> SRR1436323 4 0.6852 0.4437 0.212 0.000 0.140 0.580 0.068
#> SRR1073507 1 0.3636 0.5700 0.848 0.000 0.032 0.072 0.048
#> SRR1401972 3 0.5110 0.4783 0.272 0.012 0.668 0.000 0.048
#> SRR1415510 2 0.1012 0.8241 0.000 0.968 0.012 0.000 0.020
#> SRR1327279 4 0.5091 0.4815 0.208 0.000 0.056 0.712 0.024
#> SRR1086983 4 0.7840 0.2749 0.320 0.000 0.088 0.400 0.192
#> SRR1105174 1 0.6683 0.3979 0.532 0.000 0.020 0.180 0.268
#> SRR1468893 1 0.5769 0.1962 0.524 0.000 0.048 0.020 0.408
#> SRR1362555 2 0.4251 0.5114 0.000 0.624 0.000 0.004 0.372
#> SRR1074526 5 0.6387 0.3465 0.020 0.296 0.060 0.032 0.592
#> SRR1326225 2 0.0880 0.8239 0.000 0.968 0.032 0.000 0.000
#> SRR1401933 1 0.6237 0.1032 0.460 0.000 0.100 0.012 0.428
#> SRR1324062 3 0.3587 0.6619 0.048 0.020 0.864 0.044 0.024
#> SRR1102296 3 0.4670 0.4506 0.328 0.008 0.648 0.000 0.016
#> SRR1085087 1 0.6757 0.4719 0.608 0.004 0.200 0.112 0.076
#> SRR1079046 5 0.5375 0.5484 0.196 0.124 0.004 0.000 0.676
#> SRR1328339 3 0.4375 0.5956 0.200 0.008 0.756 0.004 0.032
#> SRR1079782 2 0.3814 0.6406 0.000 0.720 0.004 0.000 0.276
#> SRR1092257 2 0.1041 0.8248 0.000 0.964 0.032 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.0858 0.8302 0.000 0.968 0.000 0.000 0.028 0.004
#> SRR1429287 2 0.3121 0.6983 0.000 0.796 0.008 0.000 0.192 0.004
#> SRR1359238 4 0.4522 0.5181 0.140 0.000 0.012 0.760 0.036 0.052
#> SRR1309597 4 0.8160 0.2416 0.136 0.000 0.200 0.360 0.052 0.252
#> SRR1441398 1 0.6890 0.1890 0.456 0.000 0.016 0.120 0.072 0.336
#> SRR1084055 2 0.1219 0.8226 0.000 0.948 0.004 0.000 0.048 0.000
#> SRR1417566 3 0.3408 0.6411 0.000 0.048 0.800 0.000 0.000 0.152
#> SRR1351857 4 0.4398 0.4904 0.056 0.000 0.004 0.776 0.068 0.096
#> SRR1487485 3 0.7627 0.2452 0.012 0.276 0.452 0.140 0.032 0.088
#> SRR1335875 3 0.2543 0.6638 0.020 0.036 0.904 0.020 0.016 0.004
#> SRR1073947 1 0.3596 0.5128 0.828 0.000 0.104 0.012 0.032 0.024
#> SRR1443483 4 0.7539 0.4162 0.132 0.000 0.200 0.492 0.052 0.124
#> SRR1346794 6 0.7591 0.3364 0.204 0.000 0.020 0.136 0.204 0.436
#> SRR1405245 6 0.4755 0.5620 0.184 0.000 0.064 0.008 0.024 0.720
#> SRR1409677 4 0.4106 0.4930 0.028 0.000 0.004 0.788 0.116 0.064
#> SRR1095549 1 0.6991 0.2711 0.492 0.000 0.044 0.252 0.032 0.180
#> SRR1323788 6 0.3415 0.6127 0.120 0.000 0.024 0.024 0.004 0.828
#> SRR1314054 2 0.0547 0.8268 0.000 0.980 0.020 0.000 0.000 0.000
#> SRR1077944 1 0.3354 0.4703 0.792 0.000 0.000 0.008 0.016 0.184
#> SRR1480587 2 0.1531 0.8154 0.000 0.928 0.000 0.000 0.068 0.004
#> SRR1311205 1 0.4885 0.5204 0.744 0.000 0.012 0.108 0.052 0.084
#> SRR1076369 6 0.5819 0.1431 0.000 0.000 0.008 0.148 0.368 0.476
#> SRR1453549 4 0.5685 0.2815 0.032 0.000 0.380 0.528 0.016 0.044
#> SRR1345782 1 0.5288 0.4962 0.716 0.000 0.040 0.132 0.032 0.080
#> SRR1447850 2 0.2214 0.7626 0.000 0.892 0.092 0.000 0.004 0.012
#> SRR1391553 3 0.3406 0.6526 0.000 0.136 0.816 0.004 0.004 0.040
#> SRR1444156 2 0.0146 0.8320 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1471731 3 0.5390 0.5933 0.040 0.000 0.688 0.108 0.012 0.152
#> SRR1120987 4 0.7615 0.0703 0.028 0.352 0.028 0.412 0.104 0.076
#> SRR1477363 1 0.6525 0.1022 0.428 0.000 0.016 0.148 0.024 0.384
#> SRR1391961 5 0.4813 0.6365 0.144 0.156 0.008 0.000 0.692 0.000
#> SRR1373879 4 0.6999 0.4596 0.108 0.000 0.212 0.544 0.040 0.096
#> SRR1318732 6 0.3240 0.5972 0.028 0.008 0.144 0.000 0.000 0.820
#> SRR1091404 1 0.3481 0.5566 0.836 0.000 0.000 0.044 0.052 0.068
#> SRR1402109 4 0.6712 0.4725 0.096 0.000 0.168 0.592 0.044 0.100
#> SRR1407336 4 0.4397 0.5463 0.056 0.000 0.076 0.792 0.028 0.048
#> SRR1097417 3 0.4989 0.5952 0.012 0.088 0.760 0.028 0.072 0.040
#> SRR1396227 1 0.6140 0.0911 0.556 0.000 0.272 0.000 0.072 0.100
#> SRR1400775 2 0.0520 0.8318 0.000 0.984 0.008 0.000 0.008 0.000
#> SRR1392861 4 0.4144 0.4767 0.008 0.004 0.172 0.768 0.016 0.032
#> SRR1472929 5 0.3667 0.6859 0.124 0.040 0.004 0.000 0.812 0.020
#> SRR1436740 4 0.6764 0.2335 0.296 0.000 0.068 0.504 0.020 0.112
#> SRR1477057 2 0.6171 0.1051 0.120 0.516 0.048 0.000 0.316 0.000
#> SRR1311980 3 0.3075 0.6741 0.096 0.004 0.852 0.008 0.000 0.040
#> SRR1069400 4 0.6946 0.4677 0.116 0.000 0.148 0.576 0.052 0.108
#> SRR1351016 1 0.2689 0.5440 0.888 0.000 0.056 0.008 0.032 0.016
#> SRR1096291 4 0.5159 0.4195 0.008 0.000 0.016 0.676 0.104 0.196
#> SRR1418145 2 0.6179 0.0731 0.004 0.460 0.012 0.092 0.408 0.024
#> SRR1488111 2 0.1490 0.8248 0.000 0.948 0.016 0.008 0.024 0.004
#> SRR1370495 5 0.2806 0.6801 0.136 0.016 0.000 0.004 0.844 0.000
#> SRR1352639 1 0.6315 0.1622 0.472 0.004 0.012 0.008 0.340 0.164
#> SRR1348911 3 0.3288 0.6534 0.016 0.076 0.860 0.012 0.020 0.016
#> SRR1467386 1 0.3827 0.5175 0.776 0.000 0.000 0.164 0.008 0.052
#> SRR1415956 1 0.6723 0.3538 0.556 0.000 0.036 0.084 0.084 0.240
#> SRR1500495 1 0.6925 0.1930 0.452 0.000 0.024 0.156 0.044 0.324
#> SRR1405099 1 0.6058 0.3954 0.604 0.000 0.016 0.072 0.068 0.240
#> SRR1345585 3 0.7766 0.1591 0.056 0.040 0.476 0.224 0.032 0.172
#> SRR1093196 4 0.6290 0.4559 0.112 0.000 0.096 0.628 0.024 0.140
#> SRR1466006 2 0.3910 0.4985 0.000 0.660 0.004 0.000 0.328 0.008
#> SRR1351557 2 0.0692 0.8308 0.000 0.976 0.000 0.000 0.020 0.004
#> SRR1382687 6 0.3649 0.6083 0.032 0.000 0.108 0.044 0.000 0.816
#> SRR1375549 5 0.4373 0.5789 0.192 0.000 0.000 0.004 0.720 0.084
#> SRR1101765 5 0.6203 0.1743 0.008 0.004 0.008 0.292 0.508 0.180
#> SRR1334461 5 0.3109 0.6658 0.168 0.016 0.000 0.004 0.812 0.000
#> SRR1094073 2 0.0146 0.8320 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1077549 1 0.5265 0.1471 0.568 0.000 0.020 0.360 0.008 0.044
#> SRR1440332 4 0.6966 0.3572 0.252 0.000 0.076 0.528 0.044 0.100
#> SRR1454177 4 0.4145 0.5080 0.036 0.000 0.104 0.800 0.024 0.036
#> SRR1082447 1 0.4746 0.0135 0.508 0.000 0.000 0.000 0.048 0.444
#> SRR1420043 4 0.5420 0.5261 0.072 0.000 0.124 0.712 0.036 0.056
#> SRR1432500 4 0.5798 0.3339 0.296 0.000 0.036 0.592 0.036 0.040
#> SRR1378045 3 0.3746 0.6120 0.000 0.192 0.760 0.000 0.000 0.048
#> SRR1334200 5 0.3783 0.6056 0.000 0.048 0.004 0.012 0.796 0.140
#> SRR1069539 4 0.6131 0.2717 0.000 0.048 0.016 0.552 0.308 0.076
#> SRR1343031 4 0.6686 0.4641 0.148 0.000 0.132 0.596 0.044 0.080
#> SRR1319690 6 0.7561 0.1157 0.144 0.000 0.068 0.280 0.064 0.444
#> SRR1310604 2 0.4070 0.3197 0.000 0.568 0.000 0.004 0.424 0.004
#> SRR1327747 4 0.6157 0.3587 0.064 0.000 0.012 0.580 0.084 0.260
#> SRR1072456 2 0.2558 0.7530 0.000 0.840 0.000 0.000 0.156 0.004
#> SRR1367896 3 0.4262 0.5841 0.040 0.008 0.812 0.052 0.036 0.052
#> SRR1480107 1 0.2172 0.5601 0.912 0.000 0.000 0.020 0.044 0.024
#> SRR1377756 6 0.3756 0.6212 0.064 0.000 0.032 0.080 0.004 0.820
#> SRR1435272 4 0.2420 0.5253 0.008 0.000 0.016 0.904 0.028 0.044
#> SRR1089230 4 0.5526 0.3344 0.012 0.000 0.004 0.604 0.128 0.252
#> SRR1389522 1 0.8256 0.0448 0.360 0.000 0.220 0.224 0.064 0.132
#> SRR1080600 5 0.5298 0.3097 0.000 0.332 0.012 0.028 0.592 0.036
#> SRR1086935 4 0.7420 0.0552 0.008 0.068 0.320 0.436 0.032 0.136
#> SRR1344060 5 0.4515 0.6821 0.092 0.076 0.004 0.000 0.768 0.060
#> SRR1467922 2 0.0146 0.8320 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1090984 3 0.4563 0.4501 0.024 0.004 0.636 0.000 0.012 0.324
#> SRR1456991 1 0.2756 0.5648 0.880 0.000 0.012 0.044 0.060 0.004
#> SRR1085039 1 0.7040 0.2680 0.468 0.000 0.032 0.296 0.048 0.156
#> SRR1069303 3 0.6167 0.4235 0.332 0.000 0.520 0.004 0.084 0.060
#> SRR1091500 2 0.0146 0.8324 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1075198 2 0.4783 0.2192 0.000 0.528 0.012 0.016 0.436 0.008
#> SRR1086915 4 0.5339 0.3690 0.012 0.000 0.008 0.640 0.108 0.232
#> SRR1499503 2 0.0858 0.8302 0.000 0.968 0.000 0.000 0.028 0.004
#> SRR1094312 2 0.0146 0.8324 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1352437 3 0.5684 0.4674 0.344 0.000 0.560 0.036 0.020 0.040
#> SRR1436323 4 0.7428 0.1837 0.272 0.000 0.088 0.408 0.016 0.216
#> SRR1073507 1 0.2883 0.5412 0.864 0.000 0.000 0.032 0.016 0.088
#> SRR1401972 3 0.5875 0.4663 0.324 0.000 0.552 0.004 0.056 0.064
#> SRR1415510 2 0.1592 0.8209 0.000 0.940 0.020 0.000 0.032 0.008
#> SRR1327279 4 0.7075 0.1605 0.368 0.000 0.120 0.420 0.036 0.056
#> SRR1086983 4 0.6518 0.0743 0.180 0.000 0.008 0.428 0.024 0.360
#> SRR1105174 6 0.6154 0.1705 0.340 0.000 0.008 0.076 0.056 0.520
#> SRR1468893 6 0.4419 0.5875 0.140 0.000 0.004 0.016 0.088 0.752
#> SRR1362555 5 0.4591 0.0446 0.000 0.420 0.012 0.008 0.552 0.008
#> SRR1074526 5 0.7000 0.4227 0.016 0.268 0.040 0.068 0.536 0.072
#> SRR1326225 2 0.0260 0.8309 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1401933 6 0.4861 0.5799 0.120 0.000 0.020 0.032 0.084 0.744
#> SRR1324062 3 0.4981 0.6446 0.080 0.004 0.744 0.076 0.008 0.088
#> SRR1102296 3 0.5232 0.4263 0.384 0.000 0.548 0.004 0.024 0.040
#> SRR1085087 1 0.5042 0.4396 0.740 0.004 0.132 0.036 0.056 0.032
#> SRR1079046 5 0.3620 0.6727 0.072 0.032 0.000 0.000 0.824 0.072
#> SRR1328339 3 0.4377 0.6127 0.204 0.004 0.728 0.000 0.012 0.052
#> SRR1079782 2 0.4365 0.4591 0.000 0.640 0.012 0.008 0.332 0.008
#> SRR1092257 2 0.0862 0.8296 0.000 0.972 0.016 0.000 0.008 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", "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 17611 rows and 118 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 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 0.492 0.857 0.890 0.3808 0.644 0.644
#> 3 3 0.639 0.824 0.913 0.5927 0.720 0.582
#> 4 4 0.628 0.716 0.833 0.1730 0.848 0.645
#> 5 5 0.725 0.770 0.865 0.0981 0.879 0.614
#> 6 6 0.694 0.624 0.794 0.0277 0.951 0.783
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
#> SRR1396765 2 0.0376 0.913 0.004 0.996
#> SRR1429287 2 0.0376 0.913 0.004 0.996
#> SRR1359238 1 0.4815 0.876 0.896 0.104
#> SRR1309597 1 0.0000 0.851 1.000 0.000
#> SRR1441398 1 0.0000 0.851 1.000 0.000
#> SRR1084055 2 0.0000 0.915 0.000 1.000
#> SRR1417566 1 0.7219 0.887 0.800 0.200
#> SRR1351857 1 0.4431 0.874 0.908 0.092
#> SRR1487485 1 0.7219 0.887 0.800 0.200
#> SRR1335875 1 0.7219 0.887 0.800 0.200
#> SRR1073947 1 0.7219 0.887 0.800 0.200
#> SRR1443483 1 0.0000 0.851 1.000 0.000
#> SRR1346794 1 0.0000 0.851 1.000 0.000
#> SRR1405245 1 0.1414 0.857 0.980 0.020
#> SRR1409677 1 0.0000 0.851 1.000 0.000
#> SRR1095549 1 0.0000 0.851 1.000 0.000
#> SRR1323788 1 0.7219 0.887 0.800 0.200
#> SRR1314054 1 0.7299 0.885 0.796 0.204
#> SRR1077944 1 0.0000 0.851 1.000 0.000
#> SRR1480587 2 0.1414 0.908 0.020 0.980
#> SRR1311205 1 0.0000 0.851 1.000 0.000
#> SRR1076369 1 0.0000 0.851 1.000 0.000
#> SRR1453549 1 0.7219 0.887 0.800 0.200
#> SRR1345782 1 0.0672 0.853 0.992 0.008
#> SRR1447850 1 0.8081 0.845 0.752 0.248
#> SRR1391553 1 0.7299 0.885 0.796 0.204
#> SRR1444156 2 0.0000 0.915 0.000 1.000
#> SRR1471731 1 0.7219 0.887 0.800 0.200
#> SRR1120987 1 0.7219 0.887 0.800 0.200
#> SRR1477363 1 0.0000 0.851 1.000 0.000
#> SRR1391961 2 0.9710 0.052 0.400 0.600
#> SRR1373879 1 0.7219 0.887 0.800 0.200
#> SRR1318732 1 0.3274 0.867 0.940 0.060
#> SRR1091404 1 0.0000 0.851 1.000 0.000
#> SRR1402109 1 0.7219 0.887 0.800 0.200
#> SRR1407336 1 0.7219 0.887 0.800 0.200
#> SRR1097417 1 0.7950 0.853 0.760 0.240
#> SRR1396227 1 0.7299 0.885 0.796 0.204
#> SRR1400775 2 0.0000 0.915 0.000 1.000
#> SRR1392861 1 0.7219 0.887 0.800 0.200
#> SRR1472929 2 0.7219 0.794 0.200 0.800
#> SRR1436740 1 0.7219 0.887 0.800 0.200
#> SRR1477057 2 0.0000 0.915 0.000 1.000
#> SRR1311980 1 0.7299 0.885 0.796 0.204
#> SRR1069400 1 0.6887 0.886 0.816 0.184
#> SRR1351016 1 0.7219 0.887 0.800 0.200
#> SRR1096291 1 0.7219 0.887 0.800 0.200
#> SRR1418145 1 0.6973 0.887 0.812 0.188
#> SRR1488111 1 0.7219 0.887 0.800 0.200
#> SRR1370495 2 0.8763 0.731 0.296 0.704
#> SRR1352639 1 0.0000 0.851 1.000 0.000
#> SRR1348911 1 0.7219 0.887 0.800 0.200
#> SRR1467386 1 0.0000 0.851 1.000 0.000
#> SRR1415956 1 0.0000 0.851 1.000 0.000
#> SRR1500495 1 0.0000 0.851 1.000 0.000
#> SRR1405099 1 0.0000 0.851 1.000 0.000
#> SRR1345585 1 0.7219 0.887 0.800 0.200
#> SRR1093196 1 0.7219 0.887 0.800 0.200
#> SRR1466006 2 0.0000 0.915 0.000 1.000
#> SRR1351557 2 0.0000 0.915 0.000 1.000
#> SRR1382687 1 0.7219 0.887 0.800 0.200
#> SRR1375549 1 0.9881 -0.166 0.564 0.436
#> SRR1101765 1 0.0000 0.851 1.000 0.000
#> SRR1334461 2 0.7453 0.791 0.212 0.788
#> SRR1094073 2 0.0000 0.915 0.000 1.000
#> SRR1077549 1 0.7219 0.887 0.800 0.200
#> SRR1440332 1 0.0000 0.851 1.000 0.000
#> SRR1454177 1 0.7219 0.887 0.800 0.200
#> SRR1082447 1 0.4431 0.874 0.908 0.092
#> SRR1420043 1 0.7219 0.887 0.800 0.200
#> SRR1432500 1 0.0000 0.851 1.000 0.000
#> SRR1378045 1 0.7219 0.887 0.800 0.200
#> SRR1334200 1 0.7453 0.879 0.788 0.212
#> SRR1069539 1 0.7219 0.887 0.800 0.200
#> SRR1343031 1 0.5946 0.882 0.856 0.144
#> SRR1319690 1 0.0000 0.851 1.000 0.000
#> SRR1310604 2 0.2423 0.900 0.040 0.960
#> SRR1327747 1 0.0000 0.851 1.000 0.000
#> SRR1072456 2 0.0376 0.914 0.004 0.996
#> SRR1367896 1 0.7219 0.887 0.800 0.200
#> SRR1480107 1 0.0000 0.851 1.000 0.000
#> SRR1377756 1 0.7219 0.887 0.800 0.200
#> SRR1435272 1 0.7219 0.887 0.800 0.200
#> SRR1089230 1 0.0672 0.853 0.992 0.008
#> SRR1389522 1 0.0000 0.851 1.000 0.000
#> SRR1080600 2 0.7299 0.793 0.204 0.796
#> SRR1086935 1 0.7219 0.887 0.800 0.200
#> SRR1344060 2 0.0000 0.915 0.000 1.000
#> SRR1467922 2 0.0000 0.915 0.000 1.000
#> SRR1090984 1 0.7219 0.887 0.800 0.200
#> SRR1456991 1 0.0000 0.851 1.000 0.000
#> SRR1085039 1 0.0000 0.851 1.000 0.000
#> SRR1069303 1 0.7299 0.885 0.796 0.204
#> SRR1091500 2 0.0000 0.915 0.000 1.000
#> SRR1075198 2 0.5178 0.854 0.116 0.884
#> SRR1086915 1 0.5737 0.881 0.864 0.136
#> SRR1499503 2 0.0000 0.915 0.000 1.000
#> SRR1094312 2 0.0000 0.915 0.000 1.000
#> SRR1352437 1 0.7219 0.887 0.800 0.200
#> SRR1436323 1 0.7219 0.887 0.800 0.200
#> SRR1073507 1 0.0000 0.851 1.000 0.000
#> SRR1401972 1 0.7219 0.887 0.800 0.200
#> SRR1415510 2 0.1184 0.904 0.016 0.984
#> SRR1327279 1 0.0000 0.851 1.000 0.000
#> SRR1086983 1 0.7219 0.887 0.800 0.200
#> SRR1105174 1 0.0000 0.851 1.000 0.000
#> SRR1468893 1 0.0000 0.851 1.000 0.000
#> SRR1362555 2 0.7219 0.794 0.200 0.800
#> SRR1074526 1 0.7376 0.882 0.792 0.208
#> SRR1326225 2 0.0000 0.915 0.000 1.000
#> SRR1401933 1 0.0000 0.851 1.000 0.000
#> SRR1324062 1 0.7219 0.887 0.800 0.200
#> SRR1102296 1 0.7219 0.887 0.800 0.200
#> SRR1085087 1 0.0938 0.855 0.988 0.012
#> SRR1079046 1 0.2603 0.816 0.956 0.044
#> SRR1328339 1 0.7219 0.887 0.800 0.200
#> SRR1079782 2 0.7602 0.796 0.220 0.780
#> SRR1092257 1 0.7299 0.885 0.796 0.204
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.0237 0.9401 0.000 0.996 0.004
#> SRR1429287 2 0.3038 0.8894 0.000 0.896 0.104
#> SRR1359238 3 0.2878 0.8429 0.096 0.000 0.904
#> SRR1309597 1 0.0237 0.9010 0.996 0.000 0.004
#> SRR1441398 1 0.0000 0.9003 1.000 0.000 0.000
#> SRR1084055 2 0.0000 0.9412 0.000 1.000 0.000
#> SRR1417566 3 0.1289 0.8782 0.032 0.000 0.968
#> SRR1351857 3 0.4346 0.7877 0.184 0.000 0.816
#> SRR1487485 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1335875 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1073947 3 0.4750 0.6887 0.216 0.000 0.784
#> SRR1443483 1 0.2796 0.8501 0.908 0.000 0.092
#> SRR1346794 1 0.0237 0.9010 0.996 0.000 0.004
#> SRR1405245 1 0.1031 0.8933 0.976 0.000 0.024
#> SRR1409677 3 0.6295 0.2829 0.472 0.000 0.528
#> SRR1095549 3 0.4555 0.7486 0.200 0.000 0.800
#> SRR1323788 3 0.2959 0.8425 0.100 0.000 0.900
#> SRR1314054 3 0.3412 0.8257 0.000 0.124 0.876
#> SRR1077944 3 0.5431 0.6498 0.284 0.000 0.716
#> SRR1480587 2 0.0000 0.9412 0.000 1.000 0.000
#> SRR1311205 1 0.0237 0.9007 0.996 0.000 0.004
#> SRR1076369 1 0.4002 0.7669 0.840 0.000 0.160
#> SRR1453549 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1345782 3 0.6026 0.5314 0.376 0.000 0.624
#> SRR1447850 3 0.4235 0.7809 0.000 0.176 0.824
#> SRR1391553 3 0.0237 0.8866 0.000 0.004 0.996
#> SRR1444156 2 0.0000 0.9412 0.000 1.000 0.000
#> SRR1471731 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1120987 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1477363 1 0.0237 0.9010 0.996 0.000 0.004
#> SRR1391961 3 0.8976 0.0393 0.128 0.416 0.456
#> SRR1373879 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1318732 1 0.4925 0.8076 0.844 0.076 0.080
#> SRR1091404 1 0.2448 0.8522 0.924 0.000 0.076
#> SRR1402109 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1407336 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1097417 3 0.1529 0.8690 0.000 0.040 0.960
#> SRR1396227 3 0.2860 0.8550 0.084 0.004 0.912
#> SRR1400775 2 0.0000 0.9412 0.000 1.000 0.000
#> SRR1392861 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1472929 1 0.5178 0.6322 0.744 0.256 0.000
#> SRR1436740 3 0.0237 0.8867 0.004 0.000 0.996
#> SRR1477057 2 0.3851 0.8537 0.004 0.860 0.136
#> SRR1311980 3 0.0475 0.8859 0.004 0.004 0.992
#> SRR1069400 3 0.0747 0.8840 0.016 0.000 0.984
#> SRR1351016 3 0.0424 0.8866 0.008 0.000 0.992
#> SRR1096291 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1418145 3 0.3532 0.8367 0.108 0.008 0.884
#> SRR1488111 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1370495 1 0.1289 0.8854 0.968 0.032 0.000
#> SRR1352639 1 0.0237 0.9010 0.996 0.000 0.004
#> SRR1348911 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1467386 3 0.5465 0.6651 0.288 0.000 0.712
#> SRR1415956 1 0.0000 0.9003 1.000 0.000 0.000
#> SRR1500495 1 0.0237 0.9010 0.996 0.000 0.004
#> SRR1405099 1 0.0000 0.9003 1.000 0.000 0.000
#> SRR1345585 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1093196 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1466006 2 0.0000 0.9412 0.000 1.000 0.000
#> SRR1351557 2 0.0000 0.9412 0.000 1.000 0.000
#> SRR1382687 3 0.2796 0.8477 0.092 0.000 0.908
#> SRR1375549 1 0.0237 0.8997 0.996 0.004 0.000
#> SRR1101765 3 0.5621 0.6458 0.308 0.000 0.692
#> SRR1334461 1 0.2959 0.8298 0.900 0.100 0.000
#> SRR1094073 2 0.0000 0.9412 0.000 1.000 0.000
#> SRR1077549 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1440332 1 0.3941 0.8018 0.844 0.000 0.156
#> SRR1454177 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1082447 1 0.5363 0.6416 0.724 0.000 0.276
#> SRR1420043 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1432500 3 0.6008 0.5147 0.372 0.000 0.628
#> SRR1378045 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1334200 3 0.3539 0.8383 0.100 0.012 0.888
#> SRR1069539 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1343031 3 0.1964 0.8678 0.056 0.000 0.944
#> SRR1319690 1 0.3340 0.8308 0.880 0.000 0.120
#> SRR1310604 2 0.4094 0.8808 0.028 0.872 0.100
#> SRR1327747 3 0.6308 0.0837 0.492 0.000 0.508
#> SRR1072456 2 0.0000 0.9412 0.000 1.000 0.000
#> SRR1367896 3 0.1643 0.8687 0.044 0.000 0.956
#> SRR1480107 1 0.0000 0.9003 1.000 0.000 0.000
#> SRR1377756 3 0.2959 0.8425 0.100 0.000 0.900
#> SRR1435272 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1089230 3 0.4452 0.7575 0.192 0.000 0.808
#> SRR1389522 1 0.2448 0.8636 0.924 0.000 0.076
#> SRR1080600 2 0.3644 0.8352 0.124 0.872 0.004
#> SRR1086935 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1344060 2 0.1751 0.9314 0.012 0.960 0.028
#> SRR1467922 2 0.0000 0.9412 0.000 1.000 0.000
#> SRR1090984 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1456991 1 0.0000 0.9003 1.000 0.000 0.000
#> SRR1085039 3 0.5178 0.7048 0.256 0.000 0.744
#> SRR1069303 3 0.5378 0.7094 0.236 0.008 0.756
#> SRR1091500 2 0.0000 0.9412 0.000 1.000 0.000
#> SRR1075198 2 0.4087 0.8840 0.068 0.880 0.052
#> SRR1086915 3 0.2165 0.8635 0.064 0.000 0.936
#> SRR1499503 2 0.1964 0.9214 0.000 0.944 0.056
#> SRR1094312 2 0.0000 0.9412 0.000 1.000 0.000
#> SRR1352437 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1436323 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1073507 3 0.6095 0.5003 0.392 0.000 0.608
#> SRR1401972 3 0.0237 0.8867 0.004 0.000 0.996
#> SRR1415510 2 0.3686 0.8530 0.000 0.860 0.140
#> SRR1327279 3 0.5905 0.5761 0.352 0.000 0.648
#> SRR1086983 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1105174 1 0.0237 0.9010 0.996 0.000 0.004
#> SRR1468893 1 0.0000 0.9003 1.000 0.000 0.000
#> SRR1362555 1 0.5621 0.5264 0.692 0.308 0.000
#> SRR1074526 3 0.0892 0.8822 0.000 0.020 0.980
#> SRR1326225 2 0.2448 0.9090 0.000 0.924 0.076
#> SRR1401933 1 0.6045 0.2585 0.620 0.000 0.380
#> SRR1324062 3 0.0747 0.8840 0.016 0.000 0.984
#> SRR1102296 3 0.0000 0.8876 0.000 0.000 1.000
#> SRR1085087 3 0.5859 0.5944 0.344 0.000 0.656
#> SRR1079046 1 0.0475 0.8997 0.992 0.004 0.004
#> SRR1328339 3 0.0237 0.8867 0.004 0.000 0.996
#> SRR1079782 2 0.4423 0.8513 0.088 0.864 0.048
#> SRR1092257 3 0.3686 0.8157 0.000 0.140 0.860
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.0657 0.8860 0.012 0.984 0.000 0.004
#> SRR1429287 2 0.2737 0.8223 0.104 0.888 0.000 0.008
#> SRR1359238 4 0.0672 0.6269 0.008 0.000 0.008 0.984
#> SRR1309597 3 0.0000 0.8794 0.000 0.000 1.000 0.000
#> SRR1441398 3 0.0000 0.8794 0.000 0.000 1.000 0.000
#> SRR1084055 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> SRR1417566 4 0.4955 0.8008 0.344 0.000 0.008 0.648
#> SRR1351857 4 0.1042 0.6182 0.008 0.000 0.020 0.972
#> SRR1487485 4 0.4605 0.8049 0.336 0.000 0.000 0.664
#> SRR1335875 4 0.4605 0.8049 0.336 0.000 0.000 0.664
#> SRR1073947 1 0.1722 0.6494 0.944 0.000 0.008 0.048
#> SRR1443483 3 0.0804 0.8726 0.008 0.000 0.980 0.012
#> SRR1346794 3 0.0336 0.8778 0.008 0.000 0.992 0.000
#> SRR1405245 3 0.2227 0.8453 0.036 0.000 0.928 0.036
#> SRR1409677 4 0.3355 0.4754 0.004 0.000 0.160 0.836
#> SRR1095549 4 0.6497 0.6527 0.160 0.000 0.200 0.640
#> SRR1323788 4 0.5339 0.7945 0.356 0.000 0.020 0.624
#> SRR1314054 4 0.6346 0.7013 0.152 0.192 0.000 0.656
#> SRR1077944 1 0.4502 0.6351 0.748 0.000 0.236 0.016
#> SRR1480587 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> SRR1311205 3 0.0000 0.8794 0.000 0.000 1.000 0.000
#> SRR1076369 3 0.5295 0.3220 0.008 0.000 0.504 0.488
#> SRR1453549 4 0.4605 0.8049 0.336 0.000 0.000 0.664
#> SRR1345782 1 0.5279 0.6313 0.704 0.000 0.252 0.044
#> SRR1447850 4 0.6845 0.6410 0.168 0.236 0.000 0.596
#> SRR1391553 4 0.4761 0.8044 0.332 0.004 0.000 0.664
#> SRR1444156 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> SRR1471731 4 0.4605 0.8049 0.336 0.000 0.000 0.664
#> SRR1120987 4 0.0000 0.6373 0.000 0.000 0.000 1.000
#> SRR1477363 3 0.0000 0.8794 0.000 0.000 1.000 0.000
#> SRR1391961 1 0.2730 0.6582 0.896 0.088 0.016 0.000
#> SRR1373879 4 0.4605 0.8049 0.336 0.000 0.000 0.664
#> SRR1318732 3 0.4617 0.6822 0.032 0.000 0.764 0.204
#> SRR1091404 1 0.4804 0.4599 0.616 0.000 0.384 0.000
#> SRR1402109 4 0.4605 0.8049 0.336 0.000 0.000 0.664
#> SRR1407336 4 0.4643 0.8044 0.344 0.000 0.000 0.656
#> SRR1097417 4 0.5677 0.7838 0.332 0.040 0.000 0.628
#> SRR1396227 1 0.1674 0.6559 0.952 0.004 0.012 0.032
#> SRR1400775 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> SRR1392861 4 0.4605 0.8049 0.336 0.000 0.000 0.664
#> SRR1472929 3 0.3392 0.7771 0.020 0.124 0.856 0.000
#> SRR1436740 1 0.4585 0.5910 0.668 0.000 0.000 0.332
#> SRR1477057 1 0.4482 0.4822 0.728 0.264 0.000 0.008
#> SRR1311980 1 0.2944 0.5376 0.868 0.004 0.000 0.128
#> SRR1069400 4 0.5069 0.8024 0.320 0.000 0.016 0.664
#> SRR1351016 1 0.1489 0.6463 0.952 0.000 0.004 0.044
#> SRR1096291 4 0.0000 0.6373 0.000 0.000 0.000 1.000
#> SRR1418145 4 0.1229 0.6148 0.008 0.004 0.020 0.968
#> SRR1488111 4 0.4605 0.8049 0.336 0.000 0.000 0.664
#> SRR1370495 3 0.0707 0.8740 0.020 0.000 0.980 0.000
#> SRR1352639 3 0.1305 0.8640 0.004 0.000 0.960 0.036
#> SRR1348911 4 0.4605 0.8049 0.336 0.000 0.000 0.664
#> SRR1467386 1 0.6080 0.6164 0.664 0.000 0.236 0.100
#> SRR1415956 3 0.0000 0.8794 0.000 0.000 1.000 0.000
#> SRR1500495 3 0.0000 0.8794 0.000 0.000 1.000 0.000
#> SRR1405099 3 0.0000 0.8794 0.000 0.000 1.000 0.000
#> SRR1345585 4 0.4605 0.8049 0.336 0.000 0.000 0.664
#> SRR1093196 4 0.4643 0.8040 0.344 0.000 0.000 0.656
#> SRR1466006 2 0.0895 0.8837 0.004 0.976 0.000 0.020
#> SRR1351557 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> SRR1382687 4 0.4284 0.7305 0.200 0.000 0.020 0.780
#> SRR1375549 1 0.5538 0.5297 0.644 0.000 0.036 0.320
#> SRR1101765 4 0.1706 0.5953 0.016 0.000 0.036 0.948
#> SRR1334461 3 0.1042 0.8719 0.020 0.008 0.972 0.000
#> SRR1094073 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> SRR1077549 4 0.4999 0.6185 0.492 0.000 0.000 0.508
#> SRR1440332 3 0.2048 0.8237 0.008 0.000 0.928 0.064
#> SRR1454177 4 0.4477 0.8032 0.312 0.000 0.000 0.688
#> SRR1082447 1 0.5938 0.1864 0.488 0.000 0.476 0.036
#> SRR1420043 4 0.4605 0.8049 0.336 0.000 0.000 0.664
#> SRR1432500 4 0.6412 0.4663 0.080 0.000 0.348 0.572
#> SRR1378045 4 0.4605 0.8049 0.336 0.000 0.000 0.664
#> SRR1334200 4 0.1762 0.6086 0.016 0.012 0.020 0.952
#> SRR1069539 4 0.0000 0.6373 0.000 0.000 0.000 1.000
#> SRR1343031 4 0.5697 0.7828 0.280 0.000 0.056 0.664
#> SRR1319690 3 0.1209 0.8588 0.004 0.000 0.964 0.032
#> SRR1310604 2 0.4695 0.7732 0.140 0.804 0.028 0.028
#> SRR1327747 4 0.5244 0.0424 0.008 0.000 0.436 0.556
#> SRR1072456 2 0.0188 0.8899 0.004 0.996 0.000 0.000
#> SRR1367896 4 0.5755 0.7813 0.332 0.000 0.044 0.624
#> SRR1480107 1 0.4804 0.4599 0.616 0.000 0.384 0.000
#> SRR1377756 4 0.1411 0.6090 0.020 0.000 0.020 0.960
#> SRR1435272 4 0.4605 0.8049 0.336 0.000 0.000 0.664
#> SRR1089230 4 0.1042 0.6173 0.008 0.000 0.020 0.972
#> SRR1389522 3 0.0336 0.8770 0.000 0.000 0.992 0.008
#> SRR1080600 2 0.5173 0.6386 0.020 0.660 0.000 0.320
#> SRR1086935 4 0.4477 0.8033 0.312 0.000 0.000 0.688
#> SRR1344060 2 0.5565 0.6978 0.068 0.700 0.000 0.232
#> SRR1467922 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> SRR1090984 4 0.4605 0.8049 0.336 0.000 0.000 0.664
#> SRR1456991 1 0.4761 0.4620 0.628 0.000 0.372 0.000
#> SRR1085039 4 0.6555 0.6424 0.156 0.000 0.212 0.632
#> SRR1069303 1 0.1962 0.6631 0.944 0.008 0.024 0.024
#> SRR1091500 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> SRR1075198 2 0.3895 0.7785 0.012 0.804 0.000 0.184
#> SRR1086915 4 0.0188 0.6350 0.000 0.000 0.004 0.996
#> SRR1499503 2 0.1867 0.8536 0.072 0.928 0.000 0.000
#> SRR1094312 2 0.0000 0.8908 0.000 1.000 0.000 0.000
#> SRR1352437 4 0.4776 0.7775 0.376 0.000 0.000 0.624
#> SRR1436323 4 0.4776 0.7820 0.376 0.000 0.000 0.624
#> SRR1073507 1 0.6675 0.5491 0.616 0.000 0.228 0.156
#> SRR1401972 1 0.1389 0.6425 0.952 0.000 0.000 0.048
#> SRR1415510 2 0.4095 0.7033 0.192 0.792 0.000 0.016
#> SRR1327279 1 0.7834 0.0120 0.372 0.000 0.260 0.368
#> SRR1086983 4 0.0000 0.6373 0.000 0.000 0.000 1.000
#> SRR1105174 3 0.0000 0.8794 0.000 0.000 1.000 0.000
#> SRR1468893 3 0.5311 0.5451 0.024 0.000 0.648 0.328
#> SRR1362555 3 0.6192 0.3436 0.020 0.364 0.588 0.028
#> SRR1074526 4 0.4910 0.7908 0.276 0.020 0.000 0.704
#> SRR1326225 2 0.2345 0.8313 0.100 0.900 0.000 0.000
#> SRR1401933 1 0.5038 0.5220 0.652 0.000 0.012 0.336
#> SRR1324062 4 0.4781 0.8044 0.336 0.000 0.004 0.660
#> SRR1102296 4 0.4605 0.8049 0.336 0.000 0.000 0.664
#> SRR1085087 1 0.4900 0.6374 0.732 0.000 0.236 0.032
#> SRR1079046 3 0.3737 0.7796 0.020 0.004 0.840 0.136
#> SRR1328339 1 0.2760 0.5324 0.872 0.000 0.000 0.128
#> SRR1079782 2 0.5364 0.5819 0.008 0.616 0.008 0.368
#> SRR1092257 4 0.6275 0.6887 0.136 0.204 0.000 0.660
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.1498 0.9230 0.024 0.952 0.016 0.008 0.000
#> SRR1429287 2 0.3398 0.8917 0.044 0.864 0.044 0.048 0.000
#> SRR1359238 4 0.2775 0.8046 0.000 0.004 0.100 0.876 0.020
#> SRR1309597 5 0.0000 0.8956 0.000 0.000 0.000 0.000 1.000
#> SRR1441398 5 0.0000 0.8956 0.000 0.000 0.000 0.000 1.000
#> SRR1084055 2 0.0609 0.9253 0.020 0.980 0.000 0.000 0.000
#> SRR1417566 3 0.0451 0.8785 0.008 0.000 0.988 0.004 0.000
#> SRR1351857 4 0.2712 0.8073 0.000 0.000 0.088 0.880 0.032
#> SRR1487485 3 0.0000 0.8799 0.000 0.000 1.000 0.000 0.000
#> SRR1335875 3 0.0162 0.8800 0.000 0.000 0.996 0.000 0.004
#> SRR1073947 1 0.3274 0.7497 0.780 0.000 0.220 0.000 0.000
#> SRR1443483 5 0.0771 0.8904 0.000 0.000 0.004 0.020 0.976
#> SRR1346794 5 0.0290 0.8934 0.000 0.000 0.000 0.008 0.992
#> SRR1405245 5 0.3355 0.7960 0.132 0.000 0.000 0.036 0.832
#> SRR1409677 4 0.2416 0.7839 0.000 0.000 0.012 0.888 0.100
#> SRR1095549 3 0.3109 0.7035 0.000 0.000 0.800 0.000 0.200
#> SRR1323788 3 0.2989 0.7932 0.132 0.000 0.852 0.008 0.008
#> SRR1314054 3 0.3319 0.7595 0.020 0.160 0.820 0.000 0.000
#> SRR1077944 1 0.3840 0.7600 0.780 0.000 0.016 0.008 0.196
#> SRR1480587 2 0.0000 0.9283 0.000 1.000 0.000 0.000 0.000
#> SRR1311205 5 0.0000 0.8956 0.000 0.000 0.000 0.000 1.000
#> SRR1076369 4 0.1270 0.7936 0.000 0.000 0.000 0.948 0.052
#> SRR1453549 3 0.0000 0.8799 0.000 0.000 1.000 0.000 0.000
#> SRR1345782 1 0.4495 0.7575 0.736 0.000 0.064 0.000 0.200
#> SRR1447850 3 0.4985 0.6614 0.088 0.200 0.708 0.004 0.000
#> SRR1391553 3 0.0404 0.8786 0.012 0.000 0.988 0.000 0.000
#> SRR1444156 2 0.0290 0.9279 0.008 0.992 0.000 0.000 0.000
#> SRR1471731 3 0.0000 0.8799 0.000 0.000 1.000 0.000 0.000
#> SRR1120987 4 0.2020 0.8049 0.000 0.000 0.100 0.900 0.000
#> SRR1477363 5 0.0000 0.8956 0.000 0.000 0.000 0.000 1.000
#> SRR1391961 1 0.4196 0.7299 0.828 0.068 0.048 0.044 0.012
#> SRR1373879 3 0.0000 0.8799 0.000 0.000 1.000 0.000 0.000
#> SRR1318732 5 0.4760 0.7239 0.132 0.004 0.004 0.108 0.752
#> SRR1091404 1 0.3274 0.7465 0.780 0.000 0.000 0.000 0.220
#> SRR1402109 3 0.0000 0.8799 0.000 0.000 1.000 0.000 0.000
#> SRR1407336 3 0.0693 0.8774 0.008 0.000 0.980 0.012 0.000
#> SRR1097417 3 0.0963 0.8651 0.000 0.036 0.964 0.000 0.000
#> SRR1396227 1 0.3109 0.7550 0.800 0.000 0.200 0.000 0.000
#> SRR1400775 2 0.0609 0.9253 0.020 0.980 0.000 0.000 0.000
#> SRR1392861 3 0.0162 0.8797 0.000 0.000 0.996 0.004 0.000
#> SRR1472929 5 0.5000 0.7161 0.048 0.160 0.000 0.048 0.744
#> SRR1436740 4 0.5275 0.4922 0.276 0.000 0.084 0.640 0.000
#> SRR1477057 1 0.4130 0.7297 0.804 0.108 0.076 0.012 0.000
#> SRR1311980 1 0.4150 0.5103 0.612 0.000 0.388 0.000 0.000
#> SRR1069400 3 0.0671 0.8755 0.000 0.000 0.980 0.004 0.016
#> SRR1351016 1 0.3398 0.7523 0.780 0.000 0.216 0.000 0.004
#> SRR1096291 4 0.2020 0.8053 0.000 0.000 0.100 0.900 0.000
#> SRR1418145 4 0.2073 0.8033 0.016 0.004 0.044 0.928 0.008
#> SRR1488111 3 0.1095 0.8697 0.012 0.008 0.968 0.012 0.000
#> SRR1370495 5 0.2589 0.8508 0.048 0.008 0.000 0.044 0.900
#> SRR1352639 5 0.1928 0.8683 0.004 0.000 0.004 0.072 0.920
#> SRR1348911 3 0.0162 0.8794 0.004 0.000 0.996 0.000 0.000
#> SRR1467386 1 0.5166 0.7211 0.680 0.000 0.108 0.000 0.212
#> SRR1415956 5 0.0000 0.8956 0.000 0.000 0.000 0.000 1.000
#> SRR1500495 5 0.0000 0.8956 0.000 0.000 0.000 0.000 1.000
#> SRR1405099 5 0.0000 0.8956 0.000 0.000 0.000 0.000 1.000
#> SRR1345585 3 0.0162 0.8800 0.000 0.000 0.996 0.000 0.004
#> SRR1093196 3 0.0693 0.8774 0.008 0.000 0.980 0.012 0.000
#> SRR1466006 2 0.1106 0.9229 0.012 0.964 0.000 0.024 0.000
#> SRR1351557 2 0.0290 0.9278 0.008 0.992 0.000 0.000 0.000
#> SRR1382687 3 0.4471 0.7268 0.132 0.000 0.772 0.088 0.008
#> SRR1375549 4 0.4817 0.2043 0.404 0.000 0.000 0.572 0.024
#> SRR1101765 4 0.0771 0.7955 0.000 0.000 0.004 0.976 0.020
#> SRR1334461 5 0.2804 0.8475 0.048 0.016 0.000 0.044 0.892
#> SRR1094073 2 0.0290 0.9279 0.008 0.992 0.000 0.000 0.000
#> SRR1077549 3 0.3561 0.5557 0.260 0.000 0.740 0.000 0.000
#> SRR1440332 5 0.1557 0.8529 0.000 0.000 0.052 0.008 0.940
#> SRR1454177 3 0.3561 0.5943 0.000 0.000 0.740 0.260 0.000
#> SRR1082447 1 0.6141 0.3138 0.460 0.000 0.112 0.004 0.424
#> SRR1420043 3 0.0000 0.8799 0.000 0.000 1.000 0.000 0.000
#> SRR1432500 3 0.4135 0.5181 0.000 0.000 0.656 0.004 0.340
#> SRR1378045 3 0.0162 0.8797 0.000 0.004 0.996 0.000 0.000
#> SRR1334200 4 0.1883 0.7934 0.048 0.000 0.012 0.932 0.008
#> SRR1069539 4 0.2020 0.8049 0.000 0.000 0.100 0.900 0.000
#> SRR1343031 3 0.1430 0.8530 0.000 0.000 0.944 0.004 0.052
#> SRR1319690 5 0.0703 0.8839 0.000 0.000 0.024 0.000 0.976
#> SRR1310604 2 0.3964 0.8670 0.044 0.844 0.052 0.044 0.016
#> SRR1327747 4 0.5441 0.5400 0.000 0.000 0.096 0.624 0.280
#> SRR1072456 2 0.1469 0.9183 0.036 0.948 0.000 0.016 0.000
#> SRR1367896 3 0.1043 0.8636 0.000 0.000 0.960 0.000 0.040
#> SRR1480107 1 0.3398 0.7479 0.780 0.000 0.000 0.004 0.216
#> SRR1377756 4 0.4059 0.7705 0.132 0.000 0.060 0.800 0.008
#> SRR1435272 3 0.0162 0.8797 0.000 0.000 0.996 0.004 0.000
#> SRR1089230 4 0.1408 0.7978 0.000 0.000 0.008 0.948 0.044
#> SRR1389522 5 0.0404 0.8920 0.000 0.000 0.012 0.000 0.988
#> SRR1080600 4 0.4848 0.0216 0.024 0.420 0.000 0.556 0.000
#> SRR1086935 3 0.4088 0.3471 0.000 0.000 0.632 0.368 0.000
#> SRR1344060 2 0.5166 0.7013 0.100 0.692 0.004 0.204 0.000
#> SRR1467922 2 0.0290 0.9279 0.008 0.992 0.000 0.000 0.000
#> SRR1090984 3 0.0162 0.8800 0.000 0.000 0.996 0.000 0.004
#> SRR1456991 1 0.3274 0.7454 0.780 0.000 0.000 0.000 0.220
#> SRR1085039 3 0.3388 0.6986 0.000 0.000 0.792 0.008 0.200
#> SRR1069303 1 0.3210 0.7514 0.788 0.000 0.212 0.000 0.000
#> SRR1091500 2 0.0609 0.9253 0.020 0.980 0.000 0.000 0.000
#> SRR1075198 2 0.4065 0.7909 0.048 0.772 0.000 0.180 0.000
#> SRR1086915 4 0.2540 0.8090 0.000 0.000 0.088 0.888 0.024
#> SRR1499503 2 0.2234 0.9099 0.032 0.920 0.036 0.012 0.000
#> SRR1094312 2 0.0290 0.9279 0.008 0.992 0.000 0.000 0.000
#> SRR1352437 3 0.1410 0.8526 0.060 0.000 0.940 0.000 0.000
#> SRR1436323 3 0.1502 0.8484 0.056 0.000 0.940 0.004 0.000
#> SRR1073507 1 0.3690 0.7521 0.780 0.000 0.000 0.020 0.200
#> SRR1401972 1 0.3177 0.7530 0.792 0.000 0.208 0.000 0.000
#> SRR1415510 2 0.3498 0.8179 0.024 0.832 0.132 0.012 0.000
#> SRR1327279 3 0.6780 0.0297 0.284 0.000 0.472 0.008 0.236
#> SRR1086983 4 0.2329 0.7923 0.000 0.000 0.124 0.876 0.000
#> SRR1105174 5 0.0000 0.8956 0.000 0.000 0.000 0.000 1.000
#> SRR1468893 4 0.5816 0.4461 0.132 0.000 0.000 0.588 0.280
#> SRR1362555 5 0.6859 0.2189 0.048 0.356 0.000 0.108 0.488
#> SRR1074526 4 0.5061 0.2273 0.016 0.012 0.432 0.540 0.000
#> SRR1326225 2 0.2367 0.8873 0.020 0.904 0.072 0.004 0.000
#> SRR1401933 1 0.3143 0.5977 0.796 0.000 0.000 0.204 0.000
#> SRR1324062 3 0.0000 0.8799 0.000 0.000 1.000 0.000 0.000
#> SRR1102296 3 0.0162 0.8794 0.004 0.000 0.996 0.000 0.000
#> SRR1085087 1 0.3849 0.7688 0.800 0.000 0.020 0.016 0.164
#> SRR1079046 5 0.4488 0.6276 0.020 0.004 0.004 0.268 0.704
#> SRR1328339 1 0.4171 0.5062 0.604 0.000 0.396 0.000 0.000
#> SRR1079782 4 0.3019 0.7432 0.048 0.088 0.000 0.864 0.000
#> SRR1092257 3 0.6149 0.5246 0.020 0.160 0.620 0.200 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.3244 0.64145 0.000 0.732 0.000 0.000 0.268 0.000
#> SRR1429287 2 0.4618 0.62296 0.000 0.684 0.032 0.032 0.252 0.000
#> SRR1359238 4 0.2643 0.72123 0.000 0.000 0.128 0.856 0.008 0.008
#> SRR1309597 1 0.0146 0.88336 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1441398 1 0.0000 0.88531 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1084055 2 0.1444 0.70593 0.000 0.928 0.000 0.000 0.072 0.000
#> SRR1417566 3 0.1728 0.80646 0.000 0.000 0.924 0.004 0.064 0.008
#> SRR1351857 4 0.2752 0.73412 0.012 0.000 0.104 0.864 0.000 0.020
#> SRR1487485 3 0.0000 0.83828 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1335875 3 0.0146 0.83839 0.004 0.000 0.996 0.000 0.000 0.000
#> SRR1073947 6 0.4946 0.60460 0.100 0.000 0.284 0.000 0.000 0.616
#> SRR1443483 1 0.0922 0.86426 0.968 0.000 0.004 0.024 0.004 0.000
#> SRR1346794 1 0.0000 0.88531 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1405245 1 0.4792 0.56407 0.720 0.000 0.000 0.028 0.124 0.128
#> SRR1409677 4 0.2282 0.70599 0.088 0.000 0.024 0.888 0.000 0.000
#> SRR1095549 3 0.3296 0.66710 0.180 0.000 0.796 0.000 0.004 0.020
#> SRR1323788 3 0.4978 0.59454 0.000 0.000 0.700 0.028 0.144 0.128
#> SRR1314054 3 0.5020 0.30527 0.000 0.372 0.548 0.000 0.080 0.000
#> SRR1077944 6 0.5202 0.60780 0.188 0.000 0.196 0.000 0.000 0.616
#> SRR1480587 2 0.1910 0.72354 0.000 0.892 0.000 0.000 0.108 0.000
#> SRR1311205 1 0.0000 0.88531 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1076369 4 0.0790 0.71362 0.032 0.000 0.000 0.968 0.000 0.000
#> SRR1453549 3 0.0000 0.83828 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1345782 6 0.5461 0.59185 0.164 0.000 0.248 0.000 0.004 0.584
#> SRR1447850 2 0.7554 0.04815 0.000 0.384 0.156 0.004 0.224 0.232
#> SRR1391553 3 0.0632 0.83551 0.000 0.000 0.976 0.000 0.024 0.000
#> SRR1444156 2 0.0713 0.71061 0.000 0.972 0.000 0.000 0.028 0.000
#> SRR1471731 3 0.1015 0.83422 0.000 0.004 0.968 0.004 0.012 0.012
#> SRR1120987 4 0.1814 0.73809 0.000 0.000 0.100 0.900 0.000 0.000
#> SRR1477363 1 0.0000 0.88531 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1391961 5 0.4612 0.41668 0.000 0.052 0.004 0.000 0.636 0.308
#> SRR1373879 3 0.0000 0.83828 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1318732 1 0.5630 0.48785 0.656 0.000 0.004 0.052 0.160 0.128
#> SRR1091404 6 0.4596 0.51907 0.340 0.000 0.036 0.008 0.000 0.616
#> SRR1402109 3 0.0146 0.83789 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1407336 3 0.1442 0.82433 0.000 0.000 0.944 0.012 0.004 0.040
#> SRR1097417 3 0.1010 0.82461 0.000 0.036 0.960 0.000 0.004 0.000
#> SRR1396227 6 0.2234 0.57582 0.000 0.000 0.124 0.004 0.000 0.872
#> SRR1400775 2 0.1267 0.70046 0.000 0.940 0.000 0.000 0.060 0.000
#> SRR1392861 3 0.0146 0.83816 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1472929 5 0.4596 0.63853 0.204 0.096 0.000 0.004 0.696 0.000
#> SRR1436740 4 0.4167 0.34661 0.000 0.000 0.020 0.612 0.000 0.368
#> SRR1477057 5 0.7024 0.20475 0.000 0.268 0.056 0.004 0.396 0.276
#> SRR1311980 6 0.3727 0.36173 0.000 0.000 0.388 0.000 0.000 0.612
#> SRR1069400 3 0.0717 0.83540 0.016 0.000 0.976 0.000 0.008 0.000
#> SRR1351016 6 0.3659 0.52248 0.000 0.000 0.364 0.000 0.000 0.636
#> SRR1096291 4 0.2020 0.73869 0.000 0.000 0.096 0.896 0.008 0.000
#> SRR1418145 4 0.3189 0.71944 0.000 0.004 0.060 0.848 0.080 0.008
#> SRR1488111 3 0.1007 0.82549 0.000 0.000 0.956 0.000 0.044 0.000
#> SRR1370495 5 0.3695 0.54754 0.376 0.000 0.000 0.000 0.624 0.000
#> SRR1352639 1 0.2034 0.80415 0.912 0.000 0.004 0.060 0.024 0.000
#> SRR1348911 3 0.0146 0.83781 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1467386 6 0.5506 0.56913 0.180 0.000 0.264 0.000 0.000 0.556
#> SRR1415956 1 0.0000 0.88531 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1500495 1 0.0000 0.88531 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1405099 1 0.0000 0.88531 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1345585 3 0.0146 0.83839 0.004 0.000 0.996 0.000 0.000 0.000
#> SRR1093196 3 0.0951 0.83438 0.000 0.000 0.968 0.008 0.004 0.020
#> SRR1466006 2 0.2527 0.70140 0.000 0.832 0.000 0.000 0.168 0.000
#> SRR1351557 2 0.2135 0.72179 0.000 0.872 0.000 0.000 0.128 0.000
#> SRR1382687 3 0.5436 0.55264 0.000 0.000 0.664 0.048 0.160 0.128
#> SRR1375549 4 0.6157 -0.16975 0.012 0.000 0.000 0.400 0.396 0.192
#> SRR1101765 4 0.1003 0.71141 0.016 0.000 0.000 0.964 0.020 0.000
#> SRR1334461 5 0.3515 0.60300 0.324 0.000 0.000 0.000 0.676 0.000
#> SRR1094073 2 0.0937 0.71797 0.000 0.960 0.000 0.000 0.040 0.000
#> SRR1077549 3 0.3409 0.44435 0.000 0.000 0.700 0.000 0.000 0.300
#> SRR1440332 1 0.1364 0.82239 0.944 0.000 0.048 0.004 0.004 0.000
#> SRR1454177 3 0.3360 0.56177 0.000 0.000 0.732 0.264 0.000 0.004
#> SRR1082447 1 0.5297 -0.18870 0.496 0.000 0.088 0.004 0.000 0.412
#> SRR1420043 3 0.0000 0.83828 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1432500 3 0.3699 0.46141 0.336 0.000 0.660 0.004 0.000 0.000
#> SRR1378045 3 0.0146 0.83830 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR1334200 4 0.3296 0.65703 0.000 0.000 0.008 0.828 0.116 0.048
#> SRR1069539 4 0.1908 0.73891 0.000 0.000 0.096 0.900 0.004 0.000
#> SRR1343031 3 0.1349 0.81075 0.056 0.000 0.940 0.000 0.004 0.000
#> SRR1319690 1 0.0458 0.87163 0.984 0.000 0.016 0.000 0.000 0.000
#> SRR1310604 2 0.4174 0.54904 0.004 0.628 0.016 0.000 0.352 0.000
#> SRR1327747 4 0.5042 0.44462 0.288 0.000 0.108 0.604 0.000 0.000
#> SRR1072456 2 0.3482 0.60395 0.000 0.684 0.000 0.000 0.316 0.000
#> SRR1367896 3 0.1082 0.82383 0.040 0.000 0.956 0.000 0.004 0.000
#> SRR1480107 6 0.3795 0.47192 0.364 0.000 0.000 0.000 0.004 0.632
#> SRR1377756 4 0.5041 0.58945 0.000 0.000 0.024 0.688 0.160 0.128
#> SRR1435272 3 0.0146 0.83816 0.000 0.000 0.996 0.004 0.000 0.000
#> SRR1089230 4 0.0976 0.71902 0.016 0.000 0.008 0.968 0.000 0.008
#> SRR1389522 1 0.0146 0.88306 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1080600 4 0.5595 -0.12542 0.000 0.392 0.000 0.464 0.144 0.000
#> SRR1086935 3 0.4289 0.29731 0.000 0.000 0.612 0.360 0.028 0.000
#> SRR1344060 5 0.4301 0.46500 0.000 0.144 0.004 0.100 0.748 0.004
#> SRR1467922 2 0.1556 0.72230 0.000 0.920 0.000 0.000 0.080 0.000
#> SRR1090984 3 0.0858 0.83049 0.004 0.000 0.968 0.000 0.028 0.000
#> SRR1456991 6 0.3930 0.39233 0.420 0.000 0.000 0.000 0.004 0.576
#> SRR1085039 3 0.3183 0.65150 0.200 0.000 0.788 0.008 0.004 0.000
#> SRR1069303 6 0.2762 0.57259 0.000 0.000 0.196 0.000 0.000 0.804
#> SRR1091500 2 0.1411 0.69857 0.000 0.936 0.004 0.000 0.060 0.000
#> SRR1075198 2 0.5179 0.42262 0.000 0.516 0.000 0.092 0.392 0.000
#> SRR1086915 4 0.2121 0.73891 0.012 0.000 0.096 0.892 0.000 0.000
#> SRR1499503 2 0.4152 0.59964 0.000 0.664 0.032 0.000 0.304 0.000
#> SRR1094312 2 0.1075 0.70395 0.000 0.952 0.000 0.000 0.048 0.000
#> SRR1352437 3 0.3266 0.55792 0.000 0.000 0.728 0.000 0.000 0.272
#> SRR1436323 3 0.2191 0.76477 0.000 0.000 0.876 0.004 0.000 0.120
#> SRR1073507 6 0.5036 0.48354 0.140 0.000 0.000 0.228 0.000 0.632
#> SRR1401972 6 0.2871 0.57293 0.000 0.000 0.192 0.004 0.000 0.804
#> SRR1415510 2 0.5150 0.51048 0.000 0.624 0.188 0.000 0.188 0.000
#> SRR1327279 3 0.5975 0.13683 0.232 0.000 0.532 0.008 0.004 0.224
#> SRR1086983 4 0.2575 0.73521 0.000 0.000 0.100 0.872 0.024 0.004
#> SRR1105174 1 0.0000 0.88531 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1468893 4 0.6954 0.27899 0.236 0.000 0.000 0.480 0.156 0.128
#> SRR1362555 5 0.5058 0.61218 0.172 0.088 0.000 0.044 0.696 0.000
#> SRR1074526 4 0.4967 0.30440 0.000 0.016 0.392 0.552 0.040 0.000
#> SRR1326225 2 0.3544 0.68473 0.000 0.800 0.080 0.000 0.120 0.000
#> SRR1401933 6 0.5277 0.16046 0.000 0.000 0.000 0.364 0.108 0.528
#> SRR1324062 3 0.0000 0.83828 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1102296 3 0.0146 0.83826 0.000 0.000 0.996 0.000 0.004 0.000
#> SRR1085087 6 0.2958 0.54241 0.096 0.000 0.012 0.004 0.028 0.860
#> SRR1079046 5 0.6044 0.41990 0.348 0.000 0.004 0.216 0.432 0.000
#> SRR1328339 3 0.3833 -0.04084 0.000 0.000 0.556 0.000 0.000 0.444
#> SRR1079782 4 0.5351 0.39009 0.000 0.200 0.000 0.592 0.208 0.000
#> SRR1092257 2 0.6922 -0.00786 0.000 0.376 0.368 0.180 0.076 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 17611 rows and 118 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.512 0.861 0.895 0.4268 0.572 0.572
#> 3 3 0.359 0.669 0.807 0.4144 0.787 0.632
#> 4 4 0.434 0.651 0.745 0.1211 0.935 0.839
#> 5 5 0.572 0.734 0.791 0.0663 0.927 0.809
#> 6 6 0.619 0.606 0.767 0.0460 0.996 0.989
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
#> SRR1396765 2 0.0000 0.9630 0.000 1.000
#> SRR1429287 2 0.0000 0.9630 0.000 1.000
#> SRR1359238 1 0.0000 0.8413 1.000 0.000
#> SRR1309597 1 0.1414 0.8479 0.980 0.020
#> SRR1441398 1 0.2423 0.8506 0.960 0.040
#> SRR1084055 2 0.0000 0.9630 0.000 1.000
#> SRR1417566 1 0.8386 0.8248 0.732 0.268
#> SRR1351857 1 0.0000 0.8413 1.000 0.000
#> SRR1487485 1 0.8207 0.8334 0.744 0.256
#> SRR1335875 1 0.8386 0.8248 0.732 0.268
#> SRR1073947 1 0.8327 0.8280 0.736 0.264
#> SRR1443483 1 0.0672 0.8448 0.992 0.008
#> SRR1346794 1 0.0672 0.8448 0.992 0.008
#> SRR1405245 1 0.7376 0.8492 0.792 0.208
#> SRR1409677 1 0.1184 0.8472 0.984 0.016
#> SRR1095549 1 0.0000 0.8413 1.000 0.000
#> SRR1323788 1 0.7219 0.8490 0.800 0.200
#> SRR1314054 2 0.0000 0.9630 0.000 1.000
#> SRR1077944 1 0.0938 0.8467 0.988 0.012
#> SRR1480587 2 0.0000 0.9630 0.000 1.000
#> SRR1311205 1 0.0672 0.8448 0.992 0.008
#> SRR1076369 1 0.8144 0.8361 0.748 0.252
#> SRR1453549 1 0.7376 0.8485 0.792 0.208
#> SRR1345782 1 0.0672 0.8448 0.992 0.008
#> SRR1447850 2 0.0000 0.9630 0.000 1.000
#> SRR1391553 1 0.8386 0.8248 0.732 0.268
#> SRR1444156 2 0.0000 0.9630 0.000 1.000
#> SRR1471731 1 0.8016 0.8377 0.756 0.244
#> SRR1120987 1 0.8386 0.8248 0.732 0.268
#> SRR1477363 1 0.0000 0.8413 1.000 0.000
#> SRR1391961 2 0.0000 0.9630 0.000 1.000
#> SRR1373879 1 0.3114 0.8567 0.944 0.056
#> SRR1318732 1 0.8081 0.8376 0.752 0.248
#> SRR1091404 1 0.1843 0.8496 0.972 0.028
#> SRR1402109 1 0.0000 0.8413 1.000 0.000
#> SRR1407336 1 0.0000 0.8413 1.000 0.000
#> SRR1097417 2 0.9850 -0.0922 0.428 0.572
#> SRR1396227 1 0.8386 0.8248 0.732 0.268
#> SRR1400775 2 0.0000 0.9630 0.000 1.000
#> SRR1392861 1 0.8081 0.8376 0.752 0.248
#> SRR1472929 2 0.0000 0.9630 0.000 1.000
#> SRR1436740 1 0.8144 0.8357 0.748 0.252
#> SRR1477057 2 0.0000 0.9630 0.000 1.000
#> SRR1311980 1 0.8386 0.8248 0.732 0.268
#> SRR1069400 1 0.0938 0.8461 0.988 0.012
#> SRR1351016 1 0.2778 0.8506 0.952 0.048
#> SRR1096291 1 0.6531 0.8579 0.832 0.168
#> SRR1418145 1 0.9427 0.6960 0.640 0.360
#> SRR1488111 2 0.5946 0.7755 0.144 0.856
#> SRR1370495 2 0.0000 0.9630 0.000 1.000
#> SRR1352639 1 0.8207 0.8334 0.744 0.256
#> SRR1348911 1 0.8386 0.8248 0.732 0.268
#> SRR1467386 1 0.0672 0.8448 0.992 0.008
#> SRR1415956 1 0.3114 0.8501 0.944 0.056
#> SRR1500495 1 0.0672 0.8448 0.992 0.008
#> SRR1405099 1 0.2948 0.8505 0.948 0.052
#> SRR1345585 1 0.6247 0.8592 0.844 0.156
#> SRR1093196 1 0.0938 0.8461 0.988 0.012
#> SRR1466006 2 0.0000 0.9630 0.000 1.000
#> SRR1351557 2 0.0000 0.9630 0.000 1.000
#> SRR1382687 1 0.7376 0.8485 0.792 0.208
#> SRR1375549 2 0.8267 0.5408 0.260 0.740
#> SRR1101765 1 0.8016 0.8378 0.756 0.244
#> SRR1334461 2 0.0000 0.9630 0.000 1.000
#> SRR1094073 2 0.0000 0.9630 0.000 1.000
#> SRR1077549 1 0.7376 0.8502 0.792 0.208
#> SRR1440332 1 0.0000 0.8413 1.000 0.000
#> SRR1454177 1 0.8081 0.8376 0.752 0.248
#> SRR1082447 1 0.7376 0.8492 0.792 0.208
#> SRR1420043 1 0.2603 0.8553 0.956 0.044
#> SRR1432500 1 0.0000 0.8413 1.000 0.000
#> SRR1378045 1 0.8386 0.8248 0.732 0.268
#> SRR1334200 2 0.0376 0.9590 0.004 0.996
#> SRR1069539 1 0.8081 0.8376 0.752 0.248
#> SRR1343031 1 0.0000 0.8413 1.000 0.000
#> SRR1319690 1 0.0672 0.8448 0.992 0.008
#> SRR1310604 2 0.0000 0.9630 0.000 1.000
#> SRR1327747 1 0.0672 0.8448 0.992 0.008
#> SRR1072456 2 0.0000 0.9630 0.000 1.000
#> SRR1367896 1 0.8267 0.8308 0.740 0.260
#> SRR1480107 1 0.2603 0.8510 0.956 0.044
#> SRR1377756 1 0.7219 0.8490 0.800 0.200
#> SRR1435272 1 0.2778 0.8510 0.952 0.048
#> SRR1089230 1 0.7602 0.8469 0.780 0.220
#> SRR1389522 1 0.1633 0.8490 0.976 0.024
#> SRR1080600 2 0.0000 0.9630 0.000 1.000
#> SRR1086935 1 0.8386 0.8248 0.732 0.268
#> SRR1344060 2 0.0000 0.9630 0.000 1.000
#> SRR1467922 2 0.0000 0.9630 0.000 1.000
#> SRR1090984 1 0.8267 0.8308 0.740 0.260
#> SRR1456991 1 0.3114 0.8500 0.944 0.056
#> SRR1085039 1 0.0000 0.8413 1.000 0.000
#> SRR1069303 1 0.8813 0.7878 0.700 0.300
#> SRR1091500 2 0.0000 0.9630 0.000 1.000
#> SRR1075198 2 0.0000 0.9630 0.000 1.000
#> SRR1086915 1 0.5842 0.8581 0.860 0.140
#> SRR1499503 2 0.0000 0.9630 0.000 1.000
#> SRR1094312 2 0.0000 0.9630 0.000 1.000
#> SRR1352437 1 0.8386 0.8248 0.732 0.268
#> SRR1436323 1 0.0000 0.8413 1.000 0.000
#> SRR1073507 1 0.3114 0.8573 0.944 0.056
#> SRR1401972 1 0.8386 0.8248 0.732 0.268
#> SRR1415510 2 0.0000 0.9630 0.000 1.000
#> SRR1327279 1 0.0000 0.8413 1.000 0.000
#> SRR1086983 1 0.6623 0.8564 0.828 0.172
#> SRR1105174 1 0.3431 0.8579 0.936 0.064
#> SRR1468893 1 0.7376 0.8492 0.792 0.208
#> SRR1362555 2 0.0000 0.9630 0.000 1.000
#> SRR1074526 2 0.6712 0.7191 0.176 0.824
#> SRR1326225 2 0.0000 0.9630 0.000 1.000
#> SRR1401933 1 0.7376 0.8492 0.792 0.208
#> SRR1324062 1 0.8327 0.8279 0.736 0.264
#> SRR1102296 1 0.8386 0.8248 0.732 0.268
#> SRR1085087 1 0.7219 0.8520 0.800 0.200
#> SRR1079046 2 0.0000 0.9630 0.000 1.000
#> SRR1328339 1 0.8267 0.8308 0.740 0.260
#> SRR1079782 2 0.0000 0.9630 0.000 1.000
#> SRR1092257 2 0.0000 0.9630 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.0892 0.8838 0.020 0.980 0.000
#> SRR1429287 2 0.0892 0.8845 0.020 0.980 0.000
#> SRR1359238 3 0.1529 0.7727 0.040 0.000 0.960
#> SRR1309597 3 0.1529 0.7690 0.040 0.000 0.960
#> SRR1441398 3 0.4504 0.6594 0.196 0.000 0.804
#> SRR1084055 2 0.0892 0.8758 0.020 0.980 0.000
#> SRR1417566 1 0.8965 0.6915 0.564 0.240 0.196
#> SRR1351857 3 0.1529 0.7727 0.040 0.000 0.960
#> SRR1487485 3 0.9780 -0.3896 0.344 0.240 0.416
#> SRR1335875 1 0.6922 0.7432 0.720 0.080 0.200
#> SRR1073947 1 0.5576 0.6977 0.812 0.084 0.104
#> SRR1443483 3 0.0237 0.7766 0.004 0.000 0.996
#> SRR1346794 3 0.0892 0.7773 0.020 0.000 0.980
#> SRR1405245 3 0.6297 0.6408 0.060 0.184 0.756
#> SRR1409677 3 0.1989 0.7736 0.048 0.004 0.948
#> SRR1095549 3 0.0237 0.7777 0.004 0.000 0.996
#> SRR1323788 3 0.5277 0.6530 0.024 0.180 0.796
#> SRR1314054 2 0.0424 0.8840 0.008 0.992 0.000
#> SRR1077944 3 0.3267 0.7559 0.116 0.000 0.884
#> SRR1480587 2 0.0424 0.8850 0.008 0.992 0.000
#> SRR1311205 3 0.2711 0.7528 0.088 0.000 0.912
#> SRR1076369 3 0.7413 0.5260 0.092 0.224 0.684
#> SRR1453549 3 0.6062 0.6496 0.160 0.064 0.776
#> SRR1345782 3 0.2356 0.7601 0.072 0.000 0.928
#> SRR1447850 2 0.0592 0.8851 0.012 0.988 0.000
#> SRR1391553 1 0.6827 0.7436 0.728 0.080 0.192
#> SRR1444156 2 0.0747 0.8760 0.016 0.984 0.000
#> SRR1471731 3 0.7557 0.4564 0.264 0.080 0.656
#> SRR1120987 1 0.9631 0.5642 0.468 0.244 0.288
#> SRR1477363 3 0.0237 0.7777 0.004 0.000 0.996
#> SRR1391961 2 0.3941 0.8360 0.156 0.844 0.000
#> SRR1373879 3 0.0848 0.7788 0.008 0.008 0.984
#> SRR1318732 1 0.9715 0.4595 0.400 0.220 0.380
#> SRR1091404 3 0.6079 0.2579 0.388 0.000 0.612
#> SRR1402109 3 0.0237 0.7766 0.004 0.000 0.996
#> SRR1407336 3 0.0000 0.7772 0.000 0.000 1.000
#> SRR1097417 1 0.9046 0.5282 0.516 0.332 0.152
#> SRR1396227 1 0.4925 0.6994 0.844 0.080 0.076
#> SRR1400775 2 0.0592 0.8786 0.012 0.988 0.000
#> SRR1392861 3 0.7058 0.5983 0.212 0.080 0.708
#> SRR1472929 2 0.4062 0.8339 0.164 0.836 0.000
#> SRR1436740 3 0.7501 0.5790 0.212 0.104 0.684
#> SRR1477057 2 0.3573 0.8444 0.120 0.876 0.004
#> SRR1311980 1 0.6827 0.7436 0.728 0.080 0.192
#> SRR1069400 3 0.0424 0.7762 0.008 0.000 0.992
#> SRR1351016 3 0.6527 0.1998 0.404 0.008 0.588
#> SRR1096291 3 0.4217 0.7366 0.032 0.100 0.868
#> SRR1418145 1 0.9496 0.4730 0.440 0.372 0.188
#> SRR1488111 2 0.8684 -0.1532 0.392 0.500 0.108
#> SRR1370495 2 0.4399 0.8191 0.188 0.812 0.000
#> SRR1352639 1 0.9680 0.6032 0.456 0.244 0.300
#> SRR1348911 1 0.8303 0.7413 0.632 0.172 0.196
#> SRR1467386 3 0.3038 0.7593 0.104 0.000 0.896
#> SRR1415956 1 0.6309 0.0795 0.504 0.000 0.496
#> SRR1500495 3 0.1529 0.7719 0.040 0.000 0.960
#> SRR1405099 3 0.6280 0.0453 0.460 0.000 0.540
#> SRR1345585 3 0.6962 0.2196 0.316 0.036 0.648
#> SRR1093196 3 0.0237 0.7785 0.004 0.000 0.996
#> SRR1466006 2 0.0747 0.8847 0.016 0.984 0.000
#> SRR1351557 2 0.0000 0.8839 0.000 1.000 0.000
#> SRR1382687 3 0.5842 0.6366 0.036 0.196 0.768
#> SRR1375549 1 0.7674 -0.0622 0.480 0.476 0.044
#> SRR1101765 3 0.8005 0.5152 0.128 0.224 0.648
#> SRR1334461 2 0.4062 0.8339 0.164 0.836 0.000
#> SRR1094073 2 0.0237 0.8825 0.004 0.996 0.000
#> SRR1077549 3 0.6446 0.6430 0.212 0.052 0.736
#> SRR1440332 3 0.0237 0.7766 0.004 0.000 0.996
#> SRR1454177 3 0.7058 0.5983 0.212 0.080 0.708
#> SRR1082447 3 0.7382 0.6282 0.116 0.184 0.700
#> SRR1420043 3 0.0661 0.7786 0.008 0.004 0.988
#> SRR1432500 3 0.1529 0.7727 0.040 0.000 0.960
#> SRR1378045 1 0.8876 0.7203 0.576 0.204 0.220
#> SRR1334200 2 0.7703 0.4324 0.232 0.664 0.104
#> SRR1069539 3 0.9624 -0.1039 0.292 0.240 0.468
#> SRR1343031 3 0.0237 0.7766 0.004 0.000 0.996
#> SRR1319690 3 0.0592 0.7756 0.012 0.000 0.988
#> SRR1310604 2 0.5339 0.7607 0.096 0.824 0.080
#> SRR1327747 3 0.0237 0.7771 0.004 0.000 0.996
#> SRR1072456 2 0.0592 0.8853 0.012 0.988 0.000
#> SRR1367896 1 0.7101 0.7394 0.704 0.080 0.216
#> SRR1480107 3 0.5254 0.6178 0.264 0.000 0.736
#> SRR1377756 3 0.6203 0.6454 0.056 0.184 0.760
#> SRR1435272 3 0.2116 0.7749 0.040 0.012 0.948
#> SRR1089230 3 0.6229 0.6580 0.064 0.172 0.764
#> SRR1389522 3 0.6330 0.0267 0.396 0.004 0.600
#> SRR1080600 2 0.3340 0.8445 0.120 0.880 0.000
#> SRR1086935 3 0.9040 0.2528 0.204 0.240 0.556
#> SRR1344060 2 0.4002 0.8345 0.160 0.840 0.000
#> SRR1467922 2 0.0424 0.8850 0.008 0.992 0.000
#> SRR1090984 1 0.9351 0.6779 0.516 0.228 0.256
#> SRR1456991 1 0.6244 0.2567 0.560 0.000 0.440
#> SRR1085039 3 0.1529 0.7727 0.040 0.000 0.960
#> SRR1069303 1 0.4636 0.6550 0.852 0.104 0.044
#> SRR1091500 2 0.0747 0.8785 0.016 0.984 0.000
#> SRR1075198 2 0.3038 0.8564 0.104 0.896 0.000
#> SRR1086915 3 0.4642 0.7424 0.060 0.084 0.856
#> SRR1499503 2 0.0424 0.8850 0.008 0.992 0.000
#> SRR1094312 2 0.0747 0.8760 0.016 0.984 0.000
#> SRR1352437 1 0.6291 0.7388 0.768 0.080 0.152
#> SRR1436323 3 0.0661 0.7802 0.008 0.004 0.988
#> SRR1073507 3 0.3715 0.7531 0.128 0.004 0.868
#> SRR1401972 1 0.6372 0.7408 0.764 0.084 0.152
#> SRR1415510 2 0.2796 0.8563 0.092 0.908 0.000
#> SRR1327279 3 0.1163 0.7762 0.028 0.000 0.972
#> SRR1086983 3 0.4995 0.7378 0.068 0.092 0.840
#> SRR1105174 3 0.3083 0.7758 0.060 0.024 0.916
#> SRR1468893 3 0.7179 0.6338 0.104 0.184 0.712
#> SRR1362555 2 0.5243 0.7449 0.072 0.828 0.100
#> SRR1074526 2 0.8378 0.2087 0.284 0.596 0.120
#> SRR1326225 2 0.0747 0.8760 0.016 0.984 0.000
#> SRR1401933 3 0.7036 0.6366 0.096 0.184 0.720
#> SRR1324062 1 0.7876 0.5843 0.612 0.080 0.308
#> SRR1102296 1 0.6349 0.7389 0.764 0.080 0.156
#> SRR1085087 1 0.8134 0.6193 0.584 0.088 0.328
#> SRR1079046 2 0.4291 0.8236 0.180 0.820 0.000
#> SRR1328339 1 0.7222 0.7389 0.696 0.084 0.220
#> SRR1079782 2 0.3038 0.8564 0.104 0.896 0.000
#> SRR1092257 2 0.0892 0.8758 0.020 0.980 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.4511 0.7589 0.000 0.724 0.008 NA
#> SRR1429287 2 0.4621 0.7592 0.000 0.708 0.008 NA
#> SRR1359238 1 0.0937 0.7737 0.976 0.000 0.012 NA
#> SRR1309597 1 0.4440 0.7350 0.804 0.000 0.060 NA
#> SRR1441398 1 0.6989 0.5219 0.600 0.004 0.184 NA
#> SRR1084055 2 0.5125 0.7380 0.000 0.604 0.008 NA
#> SRR1417566 3 0.4965 0.7527 0.112 0.100 0.784 NA
#> SRR1351857 1 0.1584 0.7733 0.952 0.000 0.012 NA
#> SRR1487485 3 0.8359 0.5448 0.248 0.200 0.504 NA
#> SRR1335875 3 0.3488 0.7708 0.108 0.020 0.864 NA
#> SRR1073947 3 0.5911 0.3906 0.304 0.016 0.648 NA
#> SRR1443483 1 0.3198 0.7656 0.880 0.000 0.040 NA
#> SRR1346794 1 0.3945 0.7504 0.828 0.004 0.024 NA
#> SRR1405245 1 0.6603 0.6824 0.708 0.128 0.068 NA
#> SRR1409677 1 0.3109 0.7645 0.880 0.004 0.016 NA
#> SRR1095549 1 0.1824 0.7727 0.936 0.000 0.004 NA
#> SRR1323788 1 0.5943 0.6940 0.736 0.124 0.024 NA
#> SRR1314054 2 0.5326 0.7383 0.000 0.604 0.016 NA
#> SRR1077944 1 0.2660 0.7648 0.908 0.000 0.036 NA
#> SRR1480587 2 0.4955 0.7498 0.000 0.648 0.008 NA
#> SRR1311205 1 0.3617 0.7541 0.860 0.000 0.064 NA
#> SRR1076369 1 0.8311 0.4844 0.568 0.176 0.116 NA
#> SRR1453549 1 0.6099 0.6143 0.688 0.012 0.220 NA
#> SRR1345782 1 0.3156 0.7640 0.884 0.000 0.048 NA
#> SRR1447850 2 0.4908 0.7545 0.000 0.692 0.016 NA
#> SRR1391553 3 0.3853 0.7679 0.100 0.040 0.852 NA
#> SRR1444156 2 0.5125 0.7380 0.000 0.604 0.008 NA
#> SRR1471731 1 0.5838 0.3027 0.560 0.012 0.412 NA
#> SRR1120987 3 0.7948 0.6105 0.228 0.200 0.540 NA
#> SRR1477363 1 0.2179 0.7732 0.924 0.000 0.012 NA
#> SRR1391961 2 0.3934 0.6641 0.000 0.836 0.048 NA
#> SRR1373879 1 0.2473 0.7709 0.908 0.000 0.012 NA
#> SRR1318732 3 0.8868 0.5214 0.248 0.144 0.488 NA
#> SRR1091404 1 0.6786 0.4636 0.608 0.004 0.256 NA
#> SRR1402109 1 0.1584 0.7732 0.952 0.000 0.012 NA
#> SRR1407336 1 0.2255 0.7730 0.920 0.000 0.012 NA
#> SRR1097417 3 0.6636 0.5773 0.108 0.256 0.628 NA
#> SRR1396227 3 0.3215 0.7308 0.076 0.016 0.888 NA
#> SRR1400775 2 0.5125 0.7380 0.000 0.604 0.008 NA
#> SRR1392861 1 0.6000 0.5831 0.688 0.020 0.240 NA
#> SRR1472929 2 0.3523 0.6596 0.000 0.856 0.032 NA
#> SRR1436740 1 0.5849 0.5894 0.692 0.032 0.248 NA
#> SRR1477057 2 0.7211 0.7093 0.008 0.540 0.128 NA
#> SRR1311980 3 0.2465 0.7406 0.044 0.020 0.924 NA
#> SRR1069400 1 0.3907 0.7511 0.836 0.000 0.044 NA
#> SRR1351016 1 0.6704 0.3728 0.568 0.004 0.336 NA
#> SRR1096291 1 0.5734 0.7066 0.744 0.116 0.016 NA
#> SRR1418145 3 0.7530 0.3864 0.112 0.392 0.476 NA
#> SRR1488111 2 0.7138 0.0650 0.056 0.516 0.392 NA
#> SRR1370495 2 0.3674 0.6496 0.000 0.848 0.036 NA
#> SRR1352639 3 0.7913 0.6029 0.236 0.200 0.536 NA
#> SRR1348911 3 0.3976 0.7717 0.112 0.044 0.840 NA
#> SRR1467386 1 0.3194 0.7584 0.888 0.004 0.056 NA
#> SRR1415956 1 0.7636 0.3481 0.500 0.004 0.268 NA
#> SRR1500495 1 0.3181 0.7654 0.888 0.004 0.064 NA
#> SRR1405099 1 0.7654 0.3476 0.496 0.004 0.272 NA
#> SRR1345585 3 0.6993 0.2865 0.440 0.032 0.480 NA
#> SRR1093196 1 0.2542 0.7713 0.904 0.000 0.012 NA
#> SRR1466006 2 0.4661 0.7535 0.000 0.728 0.016 NA
#> SRR1351557 2 0.5055 0.7443 0.000 0.624 0.008 NA
#> SRR1382687 1 0.6047 0.6813 0.724 0.128 0.020 NA
#> SRR1375549 2 0.7930 -0.0821 0.100 0.496 0.352 NA
#> SRR1101765 1 0.8111 0.5266 0.588 0.164 0.108 NA
#> SRR1334461 2 0.3523 0.6596 0.000 0.856 0.032 NA
#> SRR1094073 2 0.5125 0.7380 0.000 0.604 0.008 NA
#> SRR1077549 1 0.4647 0.7045 0.796 0.012 0.156 NA
#> SRR1440332 1 0.1938 0.7728 0.936 0.000 0.012 NA
#> SRR1454177 1 0.6030 0.5849 0.684 0.020 0.244 NA
#> SRR1082447 1 0.6570 0.6808 0.712 0.124 0.080 NA
#> SRR1420043 1 0.1909 0.7762 0.940 0.004 0.008 NA
#> SRR1432500 1 0.1406 0.7720 0.960 0.000 0.016 NA
#> SRR1378045 3 0.5429 0.6518 0.048 0.184 0.748 NA
#> SRR1334200 2 0.6001 0.4235 0.096 0.728 0.152 NA
#> SRR1069539 1 0.9398 -0.3472 0.340 0.212 0.340 NA
#> SRR1343031 1 0.1302 0.7732 0.956 0.000 0.000 NA
#> SRR1319690 1 0.3428 0.7574 0.844 0.000 0.012 NA
#> SRR1310604 2 0.2218 0.6767 0.036 0.932 0.028 NA
#> SRR1327747 1 0.3105 0.7615 0.856 0.000 0.004 NA
#> SRR1072456 2 0.5088 0.7582 0.000 0.688 0.024 NA
#> SRR1367896 3 0.3166 0.7702 0.116 0.016 0.868 NA
#> SRR1480107 1 0.7059 0.4835 0.592 0.004 0.200 NA
#> SRR1377756 1 0.5510 0.6903 0.760 0.124 0.016 NA
#> SRR1435272 1 0.2352 0.7731 0.928 0.012 0.016 NA
#> SRR1089230 1 0.6212 0.6846 0.724 0.132 0.036 NA
#> SRR1389522 1 0.5564 0.4525 0.656 0.012 0.312 NA
#> SRR1080600 2 0.2125 0.6913 0.004 0.932 0.052 NA
#> SRR1086935 3 0.8125 0.3544 0.364 0.104 0.472 NA
#> SRR1344060 2 0.4060 0.6555 0.004 0.836 0.048 NA
#> SRR1467922 2 0.4831 0.7552 0.000 0.704 0.016 NA
#> SRR1090984 3 0.4362 0.7698 0.136 0.040 0.816 NA
#> SRR1456991 1 0.7508 0.3034 0.496 0.004 0.324 NA
#> SRR1085039 1 0.1059 0.7729 0.972 0.000 0.016 NA
#> SRR1069303 3 0.3069 0.6918 0.008 0.060 0.896 NA
#> SRR1091500 2 0.5125 0.7380 0.000 0.604 0.008 NA
#> SRR1075198 2 0.1022 0.7010 0.000 0.968 0.032 NA
#> SRR1086915 1 0.5715 0.7116 0.756 0.108 0.028 NA
#> SRR1499503 2 0.4511 0.7583 0.000 0.724 0.008 NA
#> SRR1094312 2 0.5125 0.7380 0.000 0.604 0.008 NA
#> SRR1352437 3 0.2718 0.7296 0.056 0.020 0.912 NA
#> SRR1436323 1 0.1661 0.7745 0.944 0.000 0.004 NA
#> SRR1073507 1 0.4544 0.7518 0.832 0.036 0.076 NA
#> SRR1401972 3 0.2718 0.7296 0.056 0.020 0.912 NA
#> SRR1415510 2 0.2214 0.7127 0.000 0.928 0.044 NA
#> SRR1327279 1 0.2060 0.7734 0.932 0.000 0.016 NA
#> SRR1086983 1 0.3911 0.7477 0.856 0.092 0.028 NA
#> SRR1105174 1 0.4478 0.7528 0.832 0.088 0.028 NA
#> SRR1468893 1 0.6685 0.6699 0.700 0.128 0.060 NA
#> SRR1362555 2 0.2797 0.6527 0.056 0.908 0.028 NA
#> SRR1074526 2 0.6797 0.2392 0.100 0.608 0.280 NA
#> SRR1326225 2 0.5125 0.7380 0.000 0.604 0.008 NA
#> SRR1401933 1 0.6023 0.6848 0.736 0.124 0.032 NA
#> SRR1324062 3 0.4768 0.7297 0.192 0.016 0.772 NA
#> SRR1102296 3 0.3166 0.7458 0.080 0.020 0.888 NA
#> SRR1085087 3 0.5565 0.6054 0.308 0.032 0.656 NA
#> SRR1079046 2 0.3822 0.6464 0.004 0.844 0.032 NA
#> SRR1328339 3 0.3607 0.7677 0.124 0.016 0.852 NA
#> SRR1079782 2 0.1109 0.7036 0.000 0.968 0.028 NA
#> SRR1092257 2 0.5125 0.7380 0.000 0.604 0.008 NA
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.4216 0.71127 0.000 0.780 0.000 NA 0.100
#> SRR1429287 2 0.3752 0.75049 0.000 0.812 0.000 NA 0.064
#> SRR1359238 1 0.1282 0.83218 0.952 0.000 0.000 NA 0.004
#> SRR1309597 1 0.4377 0.78434 0.760 0.000 0.048 NA 0.008
#> SRR1441398 1 0.3701 0.80749 0.836 0.000 0.044 NA 0.020
#> SRR1084055 2 0.0162 0.90398 0.000 0.996 0.000 NA 0.004
#> SRR1417566 3 0.1699 0.75207 0.036 0.004 0.944 NA 0.008
#> SRR1351857 1 0.1043 0.83208 0.960 0.000 0.000 NA 0.000
#> SRR1487485 3 0.6761 0.60008 0.068 0.044 0.652 NA 0.160
#> SRR1335875 3 0.1186 0.74830 0.020 0.000 0.964 NA 0.008
#> SRR1073947 1 0.6617 0.64803 0.616 0.000 0.176 NA 0.072
#> SRR1443483 1 0.3063 0.82265 0.864 0.000 0.036 NA 0.004
#> SRR1346794 1 0.3597 0.79884 0.800 0.000 0.012 NA 0.008
#> SRR1405245 1 0.4529 0.73203 0.732 0.000 0.040 NA 0.008
#> SRR1409677 1 0.4145 0.77329 0.736 0.004 0.004 NA 0.012
#> SRR1095549 1 0.0932 0.83085 0.972 0.000 0.004 NA 0.004
#> SRR1323788 1 0.2777 0.82050 0.864 0.000 0.016 NA 0.000
#> SRR1314054 2 0.0000 0.90446 0.000 1.000 0.000 NA 0.000
#> SRR1077944 1 0.2208 0.82709 0.908 0.000 0.020 NA 0.000
#> SRR1480587 2 0.1270 0.88049 0.000 0.948 0.000 NA 0.052
#> SRR1311205 1 0.2102 0.82752 0.916 0.000 0.012 NA 0.004
#> SRR1076369 1 0.7262 0.38410 0.456 0.052 0.056 NA 0.040
#> SRR1453549 1 0.5686 0.64228 0.656 0.000 0.208 NA 0.012
#> SRR1345782 1 0.1892 0.82464 0.916 0.000 0.004 NA 0.000
#> SRR1447850 2 0.0794 0.89449 0.000 0.972 0.000 NA 0.000
#> SRR1391553 3 0.1805 0.74845 0.020 0.008 0.944 NA 0.016
#> SRR1444156 2 0.0000 0.90446 0.000 1.000 0.000 NA 0.000
#> SRR1471731 1 0.5757 0.21951 0.496 0.000 0.416 NA 0.000
#> SRR1120987 3 0.4759 0.73367 0.064 0.036 0.792 NA 0.016
#> SRR1477363 1 0.1082 0.83120 0.964 0.000 0.000 NA 0.008
#> SRR1391961 5 0.3928 0.77118 0.000 0.176 0.008 NA 0.788
#> SRR1373879 1 0.2805 0.81713 0.872 0.000 0.012 NA 0.008
#> SRR1318732 3 0.6429 0.49376 0.108 0.004 0.480 NA 0.012
#> SRR1091404 1 0.4659 0.79120 0.744 0.000 0.084 NA 0.004
#> SRR1402109 1 0.1630 0.82972 0.944 0.000 0.016 NA 0.004
#> SRR1407336 1 0.2644 0.82102 0.888 0.000 0.012 NA 0.012
#> SRR1097417 3 0.5135 0.64110 0.044 0.072 0.752 NA 0.128
#> SRR1396227 3 0.5801 0.67068 0.096 0.000 0.700 NA 0.076
#> SRR1400775 2 0.0162 0.90398 0.000 0.996 0.000 NA 0.004
#> SRR1392861 1 0.6318 0.53967 0.576 0.000 0.260 NA 0.016
#> SRR1472929 5 0.4230 0.77391 0.000 0.192 0.008 NA 0.764
#> SRR1436740 1 0.5682 0.68916 0.664 0.000 0.168 NA 0.012
#> SRR1477057 2 0.4077 0.70867 0.000 0.800 0.128 NA 0.064
#> SRR1311980 3 0.3086 0.73311 0.020 0.000 0.876 NA 0.068
#> SRR1069400 1 0.4195 0.78699 0.768 0.000 0.036 NA 0.008
#> SRR1351016 1 0.4225 0.81070 0.784 0.000 0.076 NA 0.004
#> SRR1096291 1 0.4598 0.75597 0.716 0.000 0.008 NA 0.036
#> SRR1418145 3 0.8337 0.19569 0.040 0.076 0.448 NA 0.256
#> SRR1488111 3 0.8084 0.06962 0.008 0.188 0.464 NA 0.212
#> SRR1370495 5 0.3304 0.78135 0.000 0.168 0.016 NA 0.816
#> SRR1352639 3 0.6497 0.56415 0.284 0.008 0.588 NA 0.064
#> SRR1348911 3 0.1243 0.75160 0.028 0.008 0.960 NA 0.004
#> SRR1467386 1 0.2825 0.81417 0.860 0.000 0.016 NA 0.000
#> SRR1415956 1 0.4565 0.78856 0.788 0.000 0.068 NA 0.040
#> SRR1500495 1 0.2522 0.82681 0.896 0.000 0.024 NA 0.004
#> SRR1405099 1 0.4539 0.78707 0.788 0.000 0.060 NA 0.040
#> SRR1345585 3 0.6832 0.44788 0.300 0.000 0.472 NA 0.012
#> SRR1093196 1 0.3272 0.81255 0.848 0.000 0.016 NA 0.016
#> SRR1466006 2 0.4334 0.71348 0.000 0.764 0.000 NA 0.080
#> SRR1351557 2 0.0162 0.90426 0.000 0.996 0.000 NA 0.004
#> SRR1382687 1 0.4624 0.70416 0.676 0.000 0.016 NA 0.012
#> SRR1375549 5 0.8203 0.00125 0.208 0.052 0.280 NA 0.424
#> SRR1101765 1 0.7356 0.46985 0.512 0.060 0.056 NA 0.048
#> SRR1334461 5 0.3813 0.77263 0.000 0.164 0.008 NA 0.800
#> SRR1094073 2 0.0000 0.90446 0.000 1.000 0.000 NA 0.000
#> SRR1077549 1 0.2609 0.83028 0.896 0.000 0.048 NA 0.004
#> SRR1440332 1 0.1697 0.82800 0.932 0.000 0.008 NA 0.000
#> SRR1454177 1 0.5696 0.67536 0.668 0.000 0.176 NA 0.016
#> SRR1082447 1 0.3224 0.82063 0.824 0.000 0.016 NA 0.000
#> SRR1420043 1 0.1854 0.83313 0.936 0.000 0.020 NA 0.008
#> SRR1432500 1 0.1704 0.82805 0.928 0.000 0.004 NA 0.000
#> SRR1378045 3 0.2956 0.72506 0.008 0.064 0.888 NA 0.024
#> SRR1334200 5 0.7441 0.72784 0.032 0.188 0.048 NA 0.560
#> SRR1069539 3 0.9224 0.15900 0.168 0.048 0.300 NA 0.196
#> SRR1343031 1 0.0867 0.83006 0.976 0.000 0.008 NA 0.008
#> SRR1319690 1 0.3387 0.80679 0.828 0.000 0.012 NA 0.012
#> SRR1310604 5 0.5760 0.76474 0.000 0.240 0.024 NA 0.648
#> SRR1327747 1 0.2957 0.81467 0.860 0.000 0.008 NA 0.012
#> SRR1072456 2 0.2536 0.80443 0.000 0.868 0.000 NA 0.128
#> SRR1367896 3 0.2264 0.75479 0.060 0.000 0.912 NA 0.004
#> SRR1480107 1 0.4361 0.78857 0.788 0.000 0.040 NA 0.032
#> SRR1377756 1 0.3790 0.73413 0.724 0.000 0.004 NA 0.000
#> SRR1435272 1 0.3173 0.82442 0.856 0.000 0.016 NA 0.016
#> SRR1089230 1 0.4851 0.76014 0.704 0.004 0.008 NA 0.040
#> SRR1389522 1 0.5527 0.62214 0.656 0.000 0.232 NA 0.008
#> SRR1080600 5 0.6349 0.75161 0.004 0.240 0.024 NA 0.604
#> SRR1086935 3 0.4633 0.71396 0.140 0.008 0.776 NA 0.016
#> SRR1344060 5 0.4260 0.76663 0.000 0.164 0.008 NA 0.776
#> SRR1467922 2 0.0703 0.89672 0.000 0.976 0.000 NA 0.000
#> SRR1090984 3 0.2729 0.75348 0.084 0.000 0.884 NA 0.004
#> SRR1456991 1 0.5651 0.75466 0.692 0.000 0.104 NA 0.036
#> SRR1085039 1 0.1082 0.83150 0.964 0.000 0.008 NA 0.000
#> SRR1069303 3 0.3850 0.70643 0.008 0.008 0.828 NA 0.108
#> SRR1091500 2 0.0703 0.89087 0.000 0.976 0.000 NA 0.024
#> SRR1075198 5 0.6260 0.74749 0.000 0.244 0.024 NA 0.600
#> SRR1086915 1 0.4605 0.76210 0.708 0.000 0.004 NA 0.040
#> SRR1499503 2 0.4361 0.69359 0.000 0.768 0.000 NA 0.108
#> SRR1094312 2 0.0162 0.90398 0.000 0.996 0.000 NA 0.004
#> SRR1352437 3 0.3976 0.72334 0.024 0.000 0.824 NA 0.068
#> SRR1436323 1 0.2526 0.82283 0.896 0.000 0.012 NA 0.012
#> SRR1073507 1 0.3012 0.81393 0.852 0.000 0.024 NA 0.000
#> SRR1401972 3 0.3794 0.71716 0.016 0.000 0.832 NA 0.072
#> SRR1415510 5 0.6528 0.69532 0.000 0.292 0.028 NA 0.552
#> SRR1327279 1 0.0992 0.83035 0.968 0.000 0.008 NA 0.000
#> SRR1086983 1 0.1934 0.83421 0.928 0.000 0.016 NA 0.004
#> SRR1105174 1 0.1952 0.82597 0.912 0.000 0.000 NA 0.004
#> SRR1468893 1 0.4442 0.70087 0.676 0.000 0.016 NA 0.004
#> SRR1362555 5 0.4783 0.77464 0.008 0.232 0.024 NA 0.720
#> SRR1074526 5 0.8898 0.29310 0.032 0.196 0.300 NA 0.332
#> SRR1326225 2 0.0000 0.90446 0.000 1.000 0.000 NA 0.000
#> SRR1401933 1 0.4156 0.73362 0.700 0.000 0.004 NA 0.008
#> SRR1324062 3 0.4367 0.72583 0.128 0.000 0.784 NA 0.012
#> SRR1102296 3 0.3365 0.73356 0.028 0.000 0.864 NA 0.052
#> SRR1085087 3 0.4910 0.59504 0.276 0.000 0.672 NA 0.004
#> SRR1079046 5 0.3443 0.77879 0.000 0.164 0.008 NA 0.816
#> SRR1328339 3 0.2966 0.73116 0.136 0.000 0.848 NA 0.000
#> SRR1079782 5 0.6297 0.74537 0.000 0.244 0.024 NA 0.596
#> SRR1092257 2 0.0404 0.90195 0.000 0.988 0.000 NA 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.2491 0.8529 0.000 0.836 0.000 0.000 0.164 NA
#> SRR1429287 2 0.2883 0.7890 0.000 0.788 0.000 0.000 0.212 NA
#> SRR1359238 1 0.0820 0.7633 0.972 0.000 0.012 0.016 0.000 NA
#> SRR1309597 1 0.3399 0.6957 0.816 0.000 0.024 0.140 0.000 NA
#> SRR1441398 1 0.3850 0.7125 0.716 0.000 0.020 0.260 0.000 NA
#> SRR1084055 2 0.0260 0.9136 0.000 0.992 0.000 0.000 0.008 NA
#> SRR1417566 3 0.1338 0.5669 0.032 0.000 0.952 0.004 0.008 NA
#> SRR1351857 1 0.2019 0.7670 0.900 0.000 0.012 0.088 0.000 NA
#> SRR1487485 3 0.6833 0.0574 0.080 0.012 0.616 0.120 0.108 NA
#> SRR1335875 3 0.1616 0.5761 0.020 0.000 0.932 0.000 0.000 NA
#> SRR1073947 1 0.6171 0.5359 0.580 0.000 0.168 0.188 0.000 NA
#> SRR1443483 1 0.2294 0.7643 0.896 0.000 0.020 0.076 0.000 NA
#> SRR1346794 1 0.2215 0.7486 0.900 0.000 0.012 0.076 0.000 NA
#> SRR1405245 1 0.4182 0.6587 0.660 0.000 0.024 0.312 0.000 NA
#> SRR1409677 1 0.2999 0.7275 0.852 0.000 0.004 0.072 0.000 NA
#> SRR1095549 1 0.1765 0.7651 0.904 0.000 0.000 0.096 0.000 NA
#> SRR1323788 1 0.3844 0.6846 0.676 0.000 0.008 0.312 0.000 NA
#> SRR1314054 2 0.0547 0.9154 0.000 0.980 0.000 0.000 0.020 NA
#> SRR1077944 1 0.2631 0.7627 0.856 0.000 0.012 0.128 0.000 NA
#> SRR1480587 2 0.1556 0.8961 0.000 0.920 0.000 0.000 0.080 NA
#> SRR1311205 1 0.3253 0.7445 0.788 0.000 0.020 0.192 0.000 NA
#> SRR1076369 1 0.6482 0.1093 0.476 0.000 0.040 0.376 0.040 NA
#> SRR1453549 1 0.5504 0.4424 0.640 0.000 0.224 0.072 0.000 NA
#> SRR1345782 1 0.3240 0.7250 0.752 0.000 0.004 0.244 0.000 NA
#> SRR1447850 2 0.3664 0.8373 0.000 0.812 0.000 0.108 0.020 NA
#> SRR1391553 3 0.2245 0.5706 0.016 0.000 0.908 0.000 0.036 NA
#> SRR1444156 2 0.0547 0.9154 0.000 0.980 0.000 0.000 0.020 NA
#> SRR1471731 1 0.6480 -0.1419 0.444 0.000 0.372 0.068 0.000 NA
#> SRR1120987 3 0.4839 0.4549 0.032 0.000 0.744 0.100 0.016 NA
#> SRR1477363 1 0.1814 0.7669 0.900 0.000 0.000 0.100 0.000 NA
#> SRR1391961 5 0.3603 0.7278 0.000 0.056 0.000 0.012 0.808 NA
#> SRR1373879 1 0.1942 0.7412 0.916 0.000 0.012 0.064 0.000 NA
#> SRR1318732 3 0.6536 -0.3898 0.096 0.000 0.444 0.380 0.004 NA
#> SRR1091404 1 0.3699 0.7450 0.780 0.000 0.032 0.176 0.000 NA
#> SRR1402109 1 0.0862 0.7581 0.972 0.000 0.008 0.016 0.000 NA
#> SRR1407336 1 0.1578 0.7459 0.936 0.000 0.012 0.048 0.000 NA
#> SRR1097417 3 0.4136 0.3663 0.036 0.000 0.720 0.004 0.236 NA
#> SRR1396227 3 0.5728 0.4089 0.056 0.000 0.580 0.072 0.000 NA
#> SRR1400775 2 0.0146 0.9130 0.000 0.996 0.000 0.000 0.004 NA
#> SRR1392861 1 0.6816 0.2117 0.504 0.000 0.216 0.112 0.000 NA
#> SRR1472929 5 0.3227 0.7360 0.000 0.028 0.000 0.016 0.832 NA
#> SRR1436740 1 0.6049 0.4541 0.612 0.000 0.172 0.104 0.000 NA
#> SRR1477057 2 0.3691 0.7544 0.000 0.788 0.148 0.000 0.060 NA
#> SRR1311980 3 0.3483 0.5207 0.016 0.000 0.748 0.000 0.000 NA
#> SRR1069400 1 0.2257 0.7410 0.904 0.000 0.016 0.060 0.000 NA
#> SRR1351016 1 0.3516 0.7448 0.792 0.000 0.024 0.172 0.000 NA
#> SRR1096291 1 0.4473 0.6349 0.732 0.000 0.012 0.100 0.000 NA
#> SRR1418145 3 0.7446 -0.3526 0.036 0.008 0.440 0.140 0.312 NA
#> SRR1488111 3 0.6882 -0.0952 0.016 0.132 0.504 0.040 0.292 NA
#> SRR1370495 5 0.2325 0.7596 0.000 0.020 0.008 0.004 0.900 NA
#> SRR1352639 3 0.7626 -0.4118 0.100 0.000 0.452 0.200 0.040 NA
#> SRR1348911 3 0.1148 0.5735 0.020 0.000 0.960 0.004 0.000 NA
#> SRR1467386 1 0.3740 0.7200 0.728 0.000 0.012 0.252 0.000 NA
#> SRR1415956 1 0.3957 0.7108 0.712 0.000 0.020 0.260 0.000 NA
#> SRR1500495 1 0.3141 0.7469 0.788 0.000 0.012 0.200 0.000 NA
#> SRR1405099 1 0.4035 0.6892 0.680 0.000 0.020 0.296 0.000 NA
#> SRR1345585 3 0.6926 -0.3507 0.284 0.000 0.464 0.176 0.008 NA
#> SRR1093196 1 0.2015 0.7440 0.916 0.000 0.012 0.056 0.000 NA
#> SRR1466006 2 0.3583 0.8383 0.000 0.820 0.000 0.108 0.040 NA
#> SRR1351557 2 0.0865 0.9148 0.000 0.964 0.000 0.000 0.036 NA
#> SRR1382687 1 0.3756 0.6339 0.736 0.000 0.008 0.240 0.000 NA
#> SRR1375549 5 0.8075 -0.3612 0.044 0.000 0.292 0.136 0.344 NA
#> SRR1101765 1 0.7259 0.0460 0.468 0.000 0.048 0.300 0.084 NA
#> SRR1334461 5 0.2976 0.7354 0.000 0.020 0.000 0.012 0.844 NA
#> SRR1094073 2 0.0547 0.9154 0.000 0.980 0.000 0.000 0.020 NA
#> SRR1077549 1 0.3962 0.7167 0.764 0.000 0.116 0.120 0.000 NA
#> SRR1440332 1 0.2562 0.7528 0.828 0.000 0.000 0.172 0.000 NA
#> SRR1454177 1 0.6221 0.4141 0.592 0.000 0.176 0.104 0.000 NA
#> SRR1082447 1 0.3708 0.7227 0.752 0.000 0.020 0.220 0.000 NA
#> SRR1420043 1 0.2825 0.7617 0.844 0.000 0.008 0.136 0.000 NA
#> SRR1432500 1 0.3078 0.7480 0.796 0.000 0.012 0.192 0.000 NA
#> SRR1378045 3 0.2987 0.5429 0.028 0.024 0.876 0.004 0.060 NA
#> SRR1334200 5 0.4646 0.6216 0.012 0.012 0.044 0.172 0.744 NA
#> SRR1069539 4 0.8637 0.0000 0.152 0.000 0.264 0.284 0.200 NA
#> SRR1343031 1 0.1524 0.7649 0.932 0.000 0.008 0.060 0.000 NA
#> SRR1319690 1 0.2222 0.7601 0.896 0.000 0.008 0.084 0.000 NA
#> SRR1310604 5 0.1649 0.7697 0.000 0.036 0.032 0.000 0.932 NA
#> SRR1327747 1 0.1655 0.7585 0.932 0.000 0.008 0.052 0.000 NA
#> SRR1072456 2 0.2553 0.8523 0.000 0.848 0.000 0.000 0.144 NA
#> SRR1367896 3 0.2307 0.5570 0.044 0.000 0.908 0.016 0.004 NA
#> SRR1480107 1 0.4311 0.6811 0.668 0.000 0.024 0.296 0.000 NA
#> SRR1377756 1 0.3240 0.6490 0.752 0.000 0.000 0.244 0.000 NA
#> SRR1435272 1 0.2506 0.7548 0.880 0.000 0.000 0.068 0.000 NA
#> SRR1089230 1 0.4280 0.6541 0.736 0.000 0.004 0.092 0.000 NA
#> SRR1389522 1 0.6165 0.1673 0.556 0.000 0.176 0.224 0.000 NA
#> SRR1080600 5 0.2415 0.7634 0.000 0.036 0.032 0.024 0.904 NA
#> SRR1086935 3 0.5037 0.4326 0.036 0.000 0.728 0.112 0.016 NA
#> SRR1344060 5 0.4394 0.6778 0.000 0.020 0.000 0.120 0.752 NA
#> SRR1467922 2 0.3171 0.8581 0.000 0.844 0.000 0.104 0.024 NA
#> SRR1090984 3 0.3050 0.5216 0.048 0.000 0.864 0.040 0.000 NA
#> SRR1456991 1 0.4021 0.7188 0.748 0.000 0.024 0.204 0.000 NA
#> SRR1085039 1 0.1434 0.7670 0.940 0.000 0.012 0.048 0.000 NA
#> SRR1069303 3 0.4130 0.4948 0.000 0.000 0.696 0.000 0.044 NA
#> SRR1091500 2 0.0260 0.9120 0.000 0.992 0.000 0.000 0.008 NA
#> SRR1075198 5 0.1649 0.7697 0.000 0.036 0.032 0.000 0.932 NA
#> SRR1086915 1 0.4169 0.6558 0.744 0.000 0.004 0.080 0.000 NA
#> SRR1499503 2 0.2300 0.8716 0.000 0.856 0.000 0.000 0.144 NA
#> SRR1094312 2 0.0146 0.9130 0.000 0.996 0.000 0.000 0.004 NA
#> SRR1352437 3 0.4074 0.5014 0.012 0.000 0.704 0.020 0.000 NA
#> SRR1436323 1 0.1370 0.7522 0.948 0.000 0.012 0.036 0.000 NA
#> SRR1073507 1 0.3724 0.7127 0.716 0.000 0.012 0.268 0.000 NA
#> SRR1401972 3 0.4014 0.5011 0.012 0.000 0.704 0.016 0.000 NA
#> SRR1415510 5 0.2798 0.7147 0.000 0.112 0.036 0.000 0.852 NA
#> SRR1327279 1 0.2513 0.7606 0.852 0.000 0.008 0.140 0.000 NA
#> SRR1086983 1 0.3485 0.7532 0.784 0.000 0.028 0.184 0.000 NA
#> SRR1105174 1 0.3201 0.7452 0.780 0.000 0.012 0.208 0.000 NA
#> SRR1468893 1 0.4415 0.5905 0.556 0.000 0.020 0.420 0.000 NA
#> SRR1362555 5 0.1575 0.7697 0.000 0.032 0.032 0.000 0.936 NA
#> SRR1074526 5 0.6648 0.1173 0.012 0.040 0.284 0.116 0.532 NA
#> SRR1326225 2 0.0547 0.9154 0.000 0.980 0.000 0.000 0.020 NA
#> SRR1401933 1 0.3954 0.6513 0.684 0.000 0.016 0.296 0.000 NA
#> SRR1324062 3 0.5495 0.4386 0.132 0.000 0.644 0.036 0.000 NA
#> SRR1102296 3 0.3730 0.5214 0.008 0.000 0.740 0.016 0.000 NA
#> SRR1085087 3 0.5331 0.2009 0.200 0.000 0.660 0.040 0.000 NA
#> SRR1079046 5 0.2876 0.7537 0.000 0.020 0.004 0.020 0.868 NA
#> SRR1328339 3 0.2886 0.5270 0.060 0.000 0.872 0.028 0.000 NA
#> SRR1079782 5 0.1649 0.7697 0.000 0.036 0.032 0.000 0.932 NA
#> SRR1092257 2 0.0547 0.9154 0.000 0.980 0.000 0.000 0.020 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17611 rows and 118 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 0.739 0.885 0.949 0.4512 0.554 0.554
#> 3 3 0.669 0.816 0.909 0.3415 0.797 0.653
#> 4 4 0.488 0.448 0.705 0.1935 0.862 0.681
#> 5 5 0.534 0.556 0.734 0.0862 0.762 0.382
#> 6 6 0.601 0.505 0.711 0.0511 0.875 0.509
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
#> SRR1396765 2 0.0000 0.933 0.000 1.000
#> SRR1429287 2 0.0000 0.933 0.000 1.000
#> SRR1359238 1 0.0000 0.949 1.000 0.000
#> SRR1309597 1 0.0000 0.949 1.000 0.000
#> SRR1441398 1 0.0000 0.949 1.000 0.000
#> SRR1084055 2 0.0000 0.933 0.000 1.000
#> SRR1417566 2 0.2603 0.905 0.044 0.956
#> SRR1351857 1 0.0000 0.949 1.000 0.000
#> SRR1487485 2 0.0000 0.933 0.000 1.000
#> SRR1335875 2 0.9686 0.345 0.396 0.604
#> SRR1073947 1 0.0000 0.949 1.000 0.000
#> SRR1443483 1 0.0000 0.949 1.000 0.000
#> SRR1346794 1 0.0000 0.949 1.000 0.000
#> SRR1405245 1 0.0000 0.949 1.000 0.000
#> SRR1409677 1 0.0000 0.949 1.000 0.000
#> SRR1095549 1 0.0000 0.949 1.000 0.000
#> SRR1323788 1 0.0000 0.949 1.000 0.000
#> SRR1314054 2 0.0000 0.933 0.000 1.000
#> SRR1077944 1 0.0000 0.949 1.000 0.000
#> SRR1480587 2 0.0000 0.933 0.000 1.000
#> SRR1311205 1 0.0000 0.949 1.000 0.000
#> SRR1076369 1 0.0000 0.949 1.000 0.000
#> SRR1453549 1 0.6887 0.764 0.816 0.184
#> SRR1345782 1 0.0000 0.949 1.000 0.000
#> SRR1447850 2 0.0000 0.933 0.000 1.000
#> SRR1391553 2 0.0000 0.933 0.000 1.000
#> SRR1444156 2 0.0000 0.933 0.000 1.000
#> SRR1471731 1 0.0000 0.949 1.000 0.000
#> SRR1120987 2 0.5519 0.830 0.128 0.872
#> SRR1477363 1 0.0000 0.949 1.000 0.000
#> SRR1391961 1 0.9552 0.421 0.624 0.376
#> SRR1373879 1 0.0000 0.949 1.000 0.000
#> SRR1318732 1 0.1414 0.933 0.980 0.020
#> SRR1091404 1 0.0000 0.949 1.000 0.000
#> SRR1402109 1 0.0000 0.949 1.000 0.000
#> SRR1407336 1 0.0000 0.949 1.000 0.000
#> SRR1097417 2 0.0376 0.931 0.004 0.996
#> SRR1396227 1 0.0000 0.949 1.000 0.000
#> SRR1400775 2 0.0000 0.933 0.000 1.000
#> SRR1392861 1 0.9000 0.560 0.684 0.316
#> SRR1472929 1 0.4690 0.864 0.900 0.100
#> SRR1436740 1 0.0000 0.949 1.000 0.000
#> SRR1477057 2 0.0000 0.933 0.000 1.000
#> SRR1311980 2 0.9358 0.463 0.352 0.648
#> SRR1069400 1 0.0000 0.949 1.000 0.000
#> SRR1351016 1 0.0000 0.949 1.000 0.000
#> SRR1096291 1 0.0000 0.949 1.000 0.000
#> SRR1418145 2 0.7602 0.727 0.220 0.780
#> SRR1488111 2 0.0000 0.933 0.000 1.000
#> SRR1370495 1 0.0000 0.949 1.000 0.000
#> SRR1352639 1 0.0000 0.949 1.000 0.000
#> SRR1348911 2 0.7883 0.690 0.236 0.764
#> SRR1467386 1 0.0000 0.949 1.000 0.000
#> SRR1415956 1 0.0000 0.949 1.000 0.000
#> SRR1500495 1 0.0000 0.949 1.000 0.000
#> SRR1405099 1 0.0000 0.949 1.000 0.000
#> SRR1345585 1 0.6148 0.794 0.848 0.152
#> SRR1093196 1 0.0000 0.949 1.000 0.000
#> SRR1466006 2 0.0000 0.933 0.000 1.000
#> SRR1351557 2 0.0000 0.933 0.000 1.000
#> SRR1382687 1 0.0000 0.949 1.000 0.000
#> SRR1375549 1 0.0000 0.949 1.000 0.000
#> SRR1101765 1 0.0000 0.949 1.000 0.000
#> SRR1334461 1 0.0000 0.949 1.000 0.000
#> SRR1094073 2 0.0000 0.933 0.000 1.000
#> SRR1077549 1 0.0000 0.949 1.000 0.000
#> SRR1440332 1 0.0000 0.949 1.000 0.000
#> SRR1454177 1 0.0000 0.949 1.000 0.000
#> SRR1082447 1 0.0000 0.949 1.000 0.000
#> SRR1420043 1 0.0000 0.949 1.000 0.000
#> SRR1432500 1 0.0000 0.949 1.000 0.000
#> SRR1378045 2 0.0000 0.933 0.000 1.000
#> SRR1334200 1 0.4690 0.855 0.900 0.100
#> SRR1069539 1 0.9286 0.458 0.656 0.344
#> SRR1343031 1 0.0000 0.949 1.000 0.000
#> SRR1319690 1 0.0000 0.949 1.000 0.000
#> SRR1310604 2 0.0000 0.933 0.000 1.000
#> SRR1327747 1 0.0000 0.949 1.000 0.000
#> SRR1072456 2 0.0000 0.933 0.000 1.000
#> SRR1367896 1 0.8499 0.633 0.724 0.276
#> SRR1480107 1 0.0000 0.949 1.000 0.000
#> SRR1377756 1 0.0000 0.949 1.000 0.000
#> SRR1435272 1 0.0000 0.949 1.000 0.000
#> SRR1089230 1 0.0000 0.949 1.000 0.000
#> SRR1389522 1 0.0000 0.949 1.000 0.000
#> SRR1080600 2 0.7528 0.724 0.216 0.784
#> SRR1086935 2 0.0000 0.933 0.000 1.000
#> SRR1344060 2 0.9087 0.539 0.324 0.676
#> SRR1467922 2 0.0000 0.933 0.000 1.000
#> SRR1090984 1 0.7674 0.712 0.776 0.224
#> SRR1456991 1 0.0000 0.949 1.000 0.000
#> SRR1085039 1 0.0000 0.949 1.000 0.000
#> SRR1069303 1 0.8207 0.665 0.744 0.256
#> SRR1091500 2 0.0000 0.933 0.000 1.000
#> SRR1075198 2 0.4161 0.871 0.084 0.916
#> SRR1086915 1 0.0000 0.949 1.000 0.000
#> SRR1499503 2 0.0000 0.933 0.000 1.000
#> SRR1094312 2 0.0000 0.933 0.000 1.000
#> SRR1352437 1 0.9358 0.480 0.648 0.352
#> SRR1436323 1 0.0000 0.949 1.000 0.000
#> SRR1073507 1 0.0000 0.949 1.000 0.000
#> SRR1401972 1 0.8909 0.574 0.692 0.308
#> SRR1415510 2 0.0000 0.933 0.000 1.000
#> SRR1327279 1 0.0000 0.949 1.000 0.000
#> SRR1086983 1 0.0000 0.949 1.000 0.000
#> SRR1105174 1 0.0000 0.949 1.000 0.000
#> SRR1468893 1 0.0000 0.949 1.000 0.000
#> SRR1362555 2 0.8207 0.667 0.256 0.744
#> SRR1074526 2 0.4022 0.876 0.080 0.920
#> SRR1326225 2 0.0000 0.933 0.000 1.000
#> SRR1401933 1 0.0000 0.949 1.000 0.000
#> SRR1324062 1 0.8081 0.677 0.752 0.248
#> SRR1102296 1 0.9000 0.559 0.684 0.316
#> SRR1085087 1 0.0000 0.949 1.000 0.000
#> SRR1079046 1 0.3114 0.904 0.944 0.056
#> SRR1328339 1 0.0376 0.946 0.996 0.004
#> SRR1079782 2 0.0000 0.933 0.000 1.000
#> SRR1092257 2 0.0000 0.933 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.5706 0.6366 0.000 0.680 0.320
#> SRR1429287 2 0.5431 0.6868 0.000 0.716 0.284
#> SRR1359238 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1309597 1 0.5785 0.6082 0.668 0.332 0.000
#> SRR1441398 1 0.4555 0.7927 0.800 0.200 0.000
#> SRR1084055 2 0.4974 0.7470 0.000 0.764 0.236
#> SRR1417566 3 0.3340 0.7603 0.120 0.000 0.880
#> SRR1351857 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1487485 3 0.0592 0.8486 0.012 0.000 0.988
#> SRR1335875 3 0.5201 0.6237 0.236 0.004 0.760
#> SRR1073947 1 0.0892 0.9110 0.980 0.020 0.000
#> SRR1443483 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1346794 1 0.4121 0.8246 0.832 0.168 0.000
#> SRR1405245 1 0.1753 0.9037 0.952 0.048 0.000
#> SRR1409677 1 0.1753 0.8976 0.952 0.048 0.000
#> SRR1095549 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1323788 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1314054 3 0.0000 0.8501 0.000 0.000 1.000
#> SRR1077944 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1480587 2 0.2878 0.8384 0.000 0.904 0.096
#> SRR1311205 1 0.2878 0.8753 0.904 0.096 0.000
#> SRR1076369 2 0.0237 0.8629 0.004 0.996 0.000
#> SRR1453549 1 0.1529 0.8969 0.960 0.000 0.040
#> SRR1345782 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1447850 3 0.0000 0.8501 0.000 0.000 1.000
#> SRR1391553 3 0.0000 0.8501 0.000 0.000 1.000
#> SRR1444156 3 0.0237 0.8503 0.000 0.004 0.996
#> SRR1471731 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1120987 3 0.4725 0.7720 0.088 0.060 0.852
#> SRR1477363 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1391961 2 0.0000 0.8656 0.000 1.000 0.000
#> SRR1373879 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1318732 1 0.5174 0.8127 0.832 0.092 0.076
#> SRR1091404 1 0.6168 0.4427 0.588 0.412 0.000
#> SRR1402109 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1407336 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1097417 3 0.4887 0.6218 0.000 0.228 0.772
#> SRR1396227 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1400775 3 0.0592 0.8493 0.000 0.012 0.988
#> SRR1392861 3 0.6305 0.0936 0.484 0.000 0.516
#> SRR1472929 2 0.0000 0.8656 0.000 1.000 0.000
#> SRR1436740 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1477057 2 0.2165 0.8500 0.000 0.936 0.064
#> SRR1311980 3 0.6267 0.1837 0.452 0.000 0.548
#> SRR1069400 1 0.1163 0.9092 0.972 0.028 0.000
#> SRR1351016 1 0.0237 0.9151 0.996 0.004 0.000
#> SRR1096291 1 0.2796 0.8674 0.908 0.092 0.000
#> SRR1418145 2 0.4121 0.7966 0.000 0.832 0.168
#> SRR1488111 3 0.2356 0.8017 0.000 0.072 0.928
#> SRR1370495 2 0.0000 0.8656 0.000 1.000 0.000
#> SRR1352639 2 0.0237 0.8629 0.004 0.996 0.000
#> SRR1348911 3 0.2537 0.8003 0.080 0.000 0.920
#> SRR1467386 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1415956 1 0.3941 0.8333 0.844 0.156 0.000
#> SRR1500495 1 0.4121 0.8229 0.832 0.168 0.000
#> SRR1405099 1 0.4702 0.7809 0.788 0.212 0.000
#> SRR1345585 1 0.5016 0.6853 0.760 0.000 0.240
#> SRR1093196 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1466006 2 0.5098 0.7275 0.000 0.752 0.248
#> SRR1351557 3 0.5733 0.3355 0.000 0.324 0.676
#> SRR1382687 1 0.0237 0.9148 0.996 0.004 0.000
#> SRR1375549 2 0.0000 0.8656 0.000 1.000 0.000
#> SRR1101765 2 0.0237 0.8629 0.004 0.996 0.000
#> SRR1334461 2 0.0000 0.8656 0.000 1.000 0.000
#> SRR1094073 3 0.0424 0.8499 0.000 0.008 0.992
#> SRR1077549 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1440332 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1454177 1 0.0237 0.9145 0.996 0.000 0.004
#> SRR1082447 1 0.0237 0.9150 0.996 0.004 0.000
#> SRR1420043 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1432500 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1378045 3 0.0000 0.8501 0.000 0.000 1.000
#> SRR1334200 2 0.0000 0.8656 0.000 1.000 0.000
#> SRR1069539 2 0.7398 0.6654 0.120 0.700 0.180
#> SRR1343031 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1319690 1 0.4178 0.8200 0.828 0.172 0.000
#> SRR1310604 2 0.0000 0.8656 0.000 1.000 0.000
#> SRR1327747 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1072456 2 0.0237 0.8645 0.000 0.996 0.004
#> SRR1367896 1 0.4982 0.8139 0.828 0.036 0.136
#> SRR1480107 1 0.2261 0.8906 0.932 0.068 0.000
#> SRR1377756 1 0.1031 0.9085 0.976 0.024 0.000
#> SRR1435272 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1089230 1 0.2066 0.8901 0.940 0.060 0.000
#> SRR1389522 1 0.6095 0.4898 0.608 0.392 0.000
#> SRR1080600 2 0.1860 0.8544 0.000 0.948 0.052
#> SRR1086935 3 0.0892 0.8436 0.020 0.000 0.980
#> SRR1344060 2 0.0000 0.8656 0.000 1.000 0.000
#> SRR1467922 3 0.2066 0.8128 0.000 0.060 0.940
#> SRR1090984 1 0.4379 0.8637 0.868 0.072 0.060
#> SRR1456991 1 0.5835 0.5966 0.660 0.340 0.000
#> SRR1085039 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1069303 1 0.6644 0.7413 0.748 0.092 0.160
#> SRR1091500 3 0.0747 0.8481 0.000 0.016 0.984
#> SRR1075198 2 0.3686 0.8151 0.000 0.860 0.140
#> SRR1086915 1 0.1411 0.9033 0.964 0.036 0.000
#> SRR1499503 2 0.4605 0.7711 0.000 0.796 0.204
#> SRR1094312 3 0.0592 0.8493 0.000 0.012 0.988
#> SRR1352437 1 0.5591 0.5475 0.696 0.000 0.304
#> SRR1436323 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1073507 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1401972 1 0.4346 0.7561 0.816 0.000 0.184
#> SRR1415510 2 0.6062 0.5308 0.000 0.616 0.384
#> SRR1327279 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1086983 1 0.0000 0.9157 1.000 0.000 0.000
#> SRR1105174 1 0.3038 0.8725 0.896 0.104 0.000
#> SRR1468893 1 0.2165 0.8988 0.936 0.064 0.000
#> SRR1362555 2 0.0000 0.8656 0.000 1.000 0.000
#> SRR1074526 2 0.5882 0.4184 0.000 0.652 0.348
#> SRR1326225 3 0.0592 0.8488 0.000 0.012 0.988
#> SRR1401933 1 0.0424 0.9138 0.992 0.008 0.000
#> SRR1324062 1 0.3816 0.8019 0.852 0.000 0.148
#> SRR1102296 1 0.5726 0.7027 0.760 0.024 0.216
#> SRR1085087 1 0.0237 0.9151 0.996 0.004 0.000
#> SRR1079046 2 0.0000 0.8656 0.000 1.000 0.000
#> SRR1328339 1 0.3686 0.8454 0.860 0.140 0.000
#> SRR1079782 2 0.5785 0.6179 0.000 0.668 0.332
#> SRR1092257 3 0.0237 0.8503 0.000 0.004 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.3810 0.7070 0.008 0.804 0.188 0.000
#> SRR1429287 2 0.2021 0.7857 0.012 0.932 0.056 0.000
#> SRR1359238 4 0.3123 0.5394 0.156 0.000 0.000 0.844
#> SRR1309597 4 0.5851 0.1472 0.084 0.236 0.000 0.680
#> SRR1441398 4 0.5311 -0.0943 0.328 0.024 0.000 0.648
#> SRR1084055 2 0.4103 0.6255 0.000 0.744 0.256 0.000
#> SRR1417566 3 0.5110 0.4970 0.012 0.008 0.688 0.292
#> SRR1351857 4 0.4916 0.4347 0.424 0.000 0.000 0.576
#> SRR1487485 3 0.6421 0.4949 0.012 0.080 0.644 0.264
#> SRR1335875 3 0.6970 0.3336 0.168 0.000 0.576 0.256
#> SRR1073947 1 0.5461 0.4286 0.508 0.008 0.004 0.480
#> SRR1443483 4 0.4083 0.3928 0.068 0.100 0.000 0.832
#> SRR1346794 4 0.6810 0.3475 0.248 0.156 0.000 0.596
#> SRR1405245 4 0.5268 0.3993 0.396 0.012 0.000 0.592
#> SRR1409677 4 0.5273 0.4077 0.456 0.008 0.000 0.536
#> SRR1095549 4 0.1557 0.5292 0.056 0.000 0.000 0.944
#> SRR1323788 4 0.4713 0.4426 0.360 0.000 0.000 0.640
#> SRR1314054 3 0.0188 0.7265 0.004 0.000 0.996 0.000
#> SRR1077944 4 0.4008 0.5333 0.244 0.000 0.000 0.756
#> SRR1480587 2 0.2222 0.7796 0.016 0.924 0.060 0.000
#> SRR1311205 4 0.4730 -0.1714 0.364 0.000 0.000 0.636
#> SRR1076369 2 0.5339 0.4198 0.384 0.600 0.000 0.016
#> SRR1453549 4 0.2111 0.4945 0.044 0.000 0.024 0.932
#> SRR1345782 4 0.3024 0.3821 0.148 0.000 0.000 0.852
#> SRR1447850 3 0.0188 0.7265 0.004 0.000 0.996 0.000
#> SRR1391553 3 0.0188 0.7265 0.004 0.000 0.996 0.000
#> SRR1444156 3 0.0188 0.7265 0.004 0.000 0.996 0.000
#> SRR1471731 4 0.2411 0.5304 0.040 0.000 0.040 0.920
#> SRR1120987 3 0.7620 0.3164 0.324 0.004 0.480 0.192
#> SRR1477363 4 0.2589 0.4202 0.116 0.000 0.000 0.884
#> SRR1391961 2 0.5151 0.4285 0.464 0.532 0.000 0.004
#> SRR1373879 4 0.2281 0.4487 0.096 0.000 0.000 0.904
#> SRR1318732 1 0.7627 -0.2665 0.436 0.176 0.004 0.384
#> SRR1091404 1 0.6770 0.4581 0.496 0.096 0.000 0.408
#> SRR1402109 4 0.1211 0.4983 0.040 0.000 0.000 0.960
#> SRR1407336 4 0.2149 0.5352 0.088 0.000 0.000 0.912
#> SRR1097417 3 0.5645 0.6040 0.060 0.028 0.748 0.164
#> SRR1396227 4 0.4713 0.2182 0.360 0.000 0.000 0.640
#> SRR1400775 3 0.1902 0.7011 0.004 0.064 0.932 0.000
#> SRR1392861 4 0.6843 0.2615 0.112 0.000 0.356 0.532
#> SRR1472929 2 0.2704 0.7741 0.124 0.876 0.000 0.000
#> SRR1436740 4 0.3907 0.5277 0.232 0.000 0.000 0.768
#> SRR1477057 2 0.5558 0.5735 0.364 0.608 0.028 0.000
#> SRR1311980 3 0.5025 0.5743 0.032 0.000 0.716 0.252
#> SRR1069400 4 0.4072 0.3820 0.052 0.120 0.000 0.828
#> SRR1351016 4 0.4819 -0.0932 0.344 0.004 0.000 0.652
#> SRR1096291 4 0.4972 0.4130 0.456 0.000 0.000 0.544
#> SRR1418145 2 0.1520 0.7905 0.020 0.956 0.024 0.000
#> SRR1488111 3 0.5158 -0.1172 0.004 0.472 0.524 0.000
#> SRR1370495 2 0.4454 0.6428 0.308 0.692 0.000 0.000
#> SRR1352639 2 0.5838 0.4242 0.444 0.524 0.000 0.032
#> SRR1348911 3 0.6423 0.4544 0.092 0.008 0.644 0.256
#> SRR1467386 4 0.2973 0.5045 0.144 0.000 0.000 0.856
#> SRR1415956 4 0.5407 -0.4655 0.484 0.012 0.000 0.504
#> SRR1500495 4 0.5353 -0.3603 0.432 0.012 0.000 0.556
#> SRR1405099 1 0.5165 0.4432 0.512 0.004 0.000 0.484
#> SRR1345585 4 0.5474 0.2896 0.056 0.164 0.024 0.756
#> SRR1093196 4 0.3907 0.5285 0.232 0.000 0.000 0.768
#> SRR1466006 2 0.2032 0.7839 0.036 0.936 0.028 0.000
#> SRR1351557 2 0.5119 0.3092 0.004 0.556 0.440 0.000
#> SRR1382687 4 0.4977 0.4112 0.460 0.000 0.000 0.540
#> SRR1375549 2 0.3873 0.7297 0.228 0.772 0.000 0.000
#> SRR1101765 2 0.5329 0.3942 0.420 0.568 0.000 0.012
#> SRR1334461 2 0.4967 0.4576 0.452 0.548 0.000 0.000
#> SRR1094073 3 0.0376 0.7257 0.004 0.004 0.992 0.000
#> SRR1077549 4 0.3157 0.5364 0.144 0.000 0.004 0.852
#> SRR1440332 4 0.0336 0.5173 0.008 0.000 0.000 0.992
#> SRR1454177 4 0.3972 0.5333 0.204 0.000 0.008 0.788
#> SRR1082447 1 0.5290 -0.4295 0.516 0.008 0.000 0.476
#> SRR1420043 4 0.1211 0.5269 0.040 0.000 0.000 0.960
#> SRR1432500 4 0.3444 0.3818 0.184 0.000 0.000 0.816
#> SRR1378045 3 0.0188 0.7265 0.004 0.000 0.996 0.000
#> SRR1334200 2 0.2149 0.7696 0.088 0.912 0.000 0.000
#> SRR1069539 2 0.4442 0.7203 0.092 0.832 0.024 0.052
#> SRR1343031 4 0.1022 0.5030 0.032 0.000 0.000 0.968
#> SRR1319690 4 0.5198 0.4444 0.252 0.040 0.000 0.708
#> SRR1310604 2 0.1151 0.7909 0.024 0.968 0.008 0.000
#> SRR1327747 4 0.5599 0.4721 0.316 0.040 0.000 0.644
#> SRR1072456 2 0.2563 0.7908 0.072 0.908 0.020 0.000
#> SRR1367896 4 0.5916 0.2594 0.144 0.032 0.084 0.740
#> SRR1480107 4 0.5409 -0.4331 0.492 0.012 0.000 0.496
#> SRR1377756 4 0.4985 0.4043 0.468 0.000 0.000 0.532
#> SRR1435272 4 0.3975 0.5281 0.240 0.000 0.000 0.760
#> SRR1089230 4 0.5402 0.3915 0.472 0.012 0.000 0.516
#> SRR1389522 4 0.6790 -0.1832 0.296 0.128 0.000 0.576
#> SRR1080600 2 0.1677 0.7839 0.040 0.948 0.012 0.000
#> SRR1086935 3 0.7711 0.1894 0.340 0.000 0.428 0.232
#> SRR1344060 2 0.2216 0.7824 0.092 0.908 0.000 0.000
#> SRR1467922 3 0.0188 0.7265 0.004 0.000 0.996 0.000
#> SRR1090984 4 0.7620 0.1417 0.324 0.000 0.220 0.456
#> SRR1456991 1 0.5600 0.4496 0.512 0.020 0.000 0.468
#> SRR1085039 4 0.4220 0.2478 0.248 0.004 0.000 0.748
#> SRR1069303 1 0.7485 0.3612 0.560 0.012 0.212 0.216
#> SRR1091500 3 0.2714 0.6720 0.004 0.112 0.884 0.000
#> SRR1075198 2 0.0707 0.7872 0.000 0.980 0.020 0.000
#> SRR1086915 4 0.5155 0.4018 0.468 0.004 0.000 0.528
#> SRR1499503 2 0.1743 0.7822 0.004 0.940 0.056 0.000
#> SRR1094312 3 0.2401 0.6813 0.004 0.092 0.904 0.000
#> SRR1352437 3 0.5927 0.5003 0.076 0.000 0.660 0.264
#> SRR1436323 4 0.3024 0.5389 0.148 0.000 0.000 0.852
#> SRR1073507 4 0.4222 0.5197 0.272 0.000 0.000 0.728
#> SRR1401972 3 0.6742 0.4753 0.232 0.000 0.608 0.160
#> SRR1415510 2 0.2799 0.7523 0.008 0.884 0.108 0.000
#> SRR1327279 4 0.2589 0.4204 0.116 0.000 0.000 0.884
#> SRR1086983 4 0.4855 0.4486 0.400 0.000 0.000 0.600
#> SRR1105174 4 0.4978 0.4080 0.384 0.004 0.000 0.612
#> SRR1468893 4 0.5285 0.3933 0.468 0.008 0.000 0.524
#> SRR1362555 2 0.0895 0.7897 0.020 0.976 0.004 0.000
#> SRR1074526 1 0.6712 -0.2707 0.576 0.332 0.084 0.008
#> SRR1326225 3 0.3710 0.5700 0.004 0.192 0.804 0.000
#> SRR1401933 4 0.5281 0.4018 0.464 0.008 0.000 0.528
#> SRR1324062 3 0.6276 0.2960 0.064 0.000 0.556 0.380
#> SRR1102296 1 0.7729 0.4158 0.496 0.008 0.208 0.288
#> SRR1085087 1 0.5167 0.3478 0.508 0.004 0.000 0.488
#> SRR1079046 2 0.4790 0.6001 0.380 0.620 0.000 0.000
#> SRR1328339 1 0.4999 0.4348 0.508 0.000 0.000 0.492
#> SRR1079782 2 0.1637 0.7826 0.000 0.940 0.060 0.000
#> SRR1092257 3 0.0188 0.7265 0.004 0.000 0.996 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 5 0.1493 0.853812 0.024 0.028 0.000 0.000 0.948
#> SRR1429287 5 0.2696 0.838420 0.012 0.052 0.000 0.040 0.896
#> SRR1359238 4 0.6114 0.331586 0.128 0.000 0.400 0.472 0.000
#> SRR1309597 3 0.3334 0.691040 0.080 0.000 0.852 0.004 0.064
#> SRR1441398 3 0.2621 0.693666 0.112 0.000 0.876 0.004 0.008
#> SRR1084055 5 0.2409 0.833754 0.032 0.068 0.000 0.000 0.900
#> SRR1417566 3 0.6489 -0.000367 0.100 0.440 0.440 0.008 0.012
#> SRR1351857 4 0.3750 0.632580 0.060 0.000 0.116 0.820 0.004
#> SRR1487485 3 0.5748 0.609464 0.124 0.120 0.708 0.008 0.040
#> SRR1335875 3 0.5376 0.277917 0.056 0.424 0.520 0.000 0.000
#> SRR1073947 1 0.3196 0.606440 0.804 0.000 0.192 0.004 0.000
#> SRR1443483 3 0.2142 0.722439 0.048 0.000 0.920 0.004 0.028
#> SRR1346794 4 0.7451 0.196318 0.124 0.000 0.372 0.420 0.084
#> SRR1405245 3 0.4974 0.412243 0.040 0.000 0.640 0.316 0.004
#> SRR1409677 4 0.3585 0.628656 0.052 0.000 0.088 0.844 0.016
#> SRR1095549 3 0.3368 0.666605 0.024 0.000 0.820 0.156 0.000
#> SRR1323788 4 0.5048 -0.041408 0.032 0.000 0.476 0.492 0.000
#> SRR1314054 2 0.0162 0.755330 0.004 0.996 0.000 0.000 0.000
#> SRR1077944 4 0.6349 0.449721 0.232 0.000 0.244 0.524 0.000
#> SRR1480587 5 0.2484 0.833487 0.068 0.004 0.028 0.000 0.900
#> SRR1311205 3 0.2852 0.664056 0.172 0.000 0.828 0.000 0.000
#> SRR1076369 4 0.5883 0.059116 0.056 0.000 0.024 0.552 0.368
#> SRR1453549 3 0.4398 0.685649 0.068 0.052 0.804 0.076 0.000
#> SRR1345782 3 0.1725 0.725696 0.044 0.000 0.936 0.020 0.000
#> SRR1447850 2 0.0000 0.756122 0.000 1.000 0.000 0.000 0.000
#> SRR1391553 2 0.0000 0.756122 0.000 1.000 0.000 0.000 0.000
#> SRR1444156 2 0.1704 0.750921 0.068 0.928 0.000 0.000 0.004
#> SRR1471731 3 0.4254 0.673449 0.096 0.012 0.796 0.096 0.000
#> SRR1120987 4 0.6383 0.554400 0.168 0.104 0.032 0.664 0.032
#> SRR1477363 3 0.2719 0.718814 0.068 0.000 0.884 0.048 0.000
#> SRR1391961 1 0.4570 0.392119 0.632 0.000 0.020 0.000 0.348
#> SRR1373879 3 0.2077 0.730033 0.040 0.000 0.920 0.040 0.000
#> SRR1318732 4 0.6944 0.185597 0.056 0.000 0.328 0.504 0.112
#> SRR1091404 1 0.4369 0.599776 0.720 0.000 0.252 0.012 0.016
#> SRR1402109 3 0.3119 0.700213 0.072 0.000 0.860 0.068 0.000
#> SRR1407336 3 0.5759 0.139606 0.108 0.000 0.568 0.324 0.000
#> SRR1097417 3 0.5070 0.490422 0.016 0.316 0.640 0.000 0.028
#> SRR1396227 1 0.5816 0.382703 0.608 0.000 0.164 0.228 0.000
#> SRR1400775 2 0.2074 0.746339 0.000 0.896 0.000 0.000 0.104
#> SRR1392861 4 0.8000 0.423340 0.116 0.252 0.208 0.424 0.000
#> SRR1472929 5 0.2654 0.822452 0.064 0.000 0.048 0.000 0.888
#> SRR1436740 4 0.6218 0.572390 0.200 0.064 0.092 0.644 0.000
#> SRR1477057 1 0.5126 0.158478 0.536 0.024 0.000 0.008 0.432
#> SRR1311980 2 0.4443 -0.156905 0.000 0.524 0.472 0.004 0.000
#> SRR1069400 3 0.3175 0.723582 0.064 0.000 0.872 0.020 0.044
#> SRR1351016 1 0.4341 0.292686 0.592 0.000 0.404 0.004 0.000
#> SRR1096291 4 0.4752 0.625601 0.084 0.000 0.144 0.756 0.016
#> SRR1418145 5 0.2233 0.815588 0.004 0.000 0.000 0.104 0.892
#> SRR1488111 5 0.3318 0.753666 0.008 0.192 0.000 0.000 0.800
#> SRR1370495 5 0.4030 0.416105 0.352 0.000 0.000 0.000 0.648
#> SRR1352639 1 0.4796 0.101948 0.516 0.000 0.012 0.004 0.468
#> SRR1348911 3 0.4797 0.609027 0.104 0.172 0.724 0.000 0.000
#> SRR1467386 4 0.6664 0.287737 0.232 0.000 0.360 0.408 0.000
#> SRR1415956 3 0.3452 0.578239 0.244 0.000 0.756 0.000 0.000
#> SRR1500495 3 0.2179 0.692017 0.112 0.000 0.888 0.000 0.000
#> SRR1405099 1 0.4201 0.358471 0.592 0.000 0.408 0.000 0.000
#> SRR1345585 3 0.3893 0.690192 0.076 0.004 0.832 0.016 0.072
#> SRR1093196 4 0.6200 0.452038 0.160 0.000 0.320 0.520 0.000
#> SRR1466006 5 0.2142 0.836949 0.048 0.000 0.004 0.028 0.920
#> SRR1351557 5 0.4289 0.722286 0.064 0.176 0.000 0.000 0.760
#> SRR1382687 4 0.2597 0.612153 0.024 0.000 0.092 0.884 0.000
#> SRR1375549 5 0.6785 0.271745 0.228 0.000 0.012 0.268 0.492
#> SRR1101765 4 0.3621 0.446517 0.020 0.000 0.000 0.788 0.192
#> SRR1334461 1 0.4101 0.348831 0.628 0.000 0.000 0.000 0.372
#> SRR1094073 2 0.3517 0.727627 0.068 0.832 0.000 0.000 0.100
#> SRR1077549 4 0.6878 0.466718 0.220 0.016 0.280 0.484 0.000
#> SRR1440332 3 0.3647 0.662839 0.052 0.000 0.816 0.132 0.000
#> SRR1454177 4 0.7240 0.478847 0.136 0.076 0.276 0.512 0.000
#> SRR1082447 4 0.3387 0.548274 0.128 0.000 0.032 0.836 0.004
#> SRR1420043 3 0.4711 0.580181 0.116 0.000 0.736 0.148 0.000
#> SRR1432500 3 0.5038 0.551916 0.132 0.000 0.704 0.164 0.000
#> SRR1378045 2 0.1942 0.748281 0.068 0.920 0.012 0.000 0.000
#> SRR1334200 5 0.3759 0.691964 0.016 0.000 0.000 0.220 0.764
#> SRR1069539 5 0.3073 0.825014 0.052 0.000 0.008 0.068 0.872
#> SRR1343031 3 0.4022 0.654645 0.100 0.000 0.796 0.104 0.000
#> SRR1319690 3 0.3073 0.691801 0.024 0.000 0.856 0.116 0.004
#> SRR1310604 5 0.0963 0.848609 0.036 0.000 0.000 0.000 0.964
#> SRR1327747 4 0.5631 0.525510 0.072 0.000 0.268 0.640 0.020
#> SRR1072456 5 0.2172 0.845886 0.076 0.000 0.016 0.000 0.908
#> SRR1367896 3 0.2790 0.723133 0.028 0.060 0.892 0.000 0.020
#> SRR1480107 1 0.3970 0.604532 0.752 0.000 0.224 0.024 0.000
#> SRR1377756 4 0.1725 0.602458 0.020 0.000 0.044 0.936 0.000
#> SRR1435272 4 0.6886 0.443848 0.136 0.040 0.316 0.508 0.000
#> SRR1089230 4 0.0693 0.600048 0.000 0.000 0.008 0.980 0.012
#> SRR1389522 3 0.2139 0.712235 0.052 0.000 0.916 0.000 0.032
#> SRR1080600 5 0.2228 0.838787 0.048 0.000 0.000 0.040 0.912
#> SRR1086935 4 0.3710 0.552098 0.024 0.192 0.000 0.784 0.000
#> SRR1344060 5 0.2124 0.835453 0.056 0.000 0.000 0.028 0.916
#> SRR1467922 2 0.2110 0.751167 0.072 0.912 0.000 0.000 0.016
#> SRR1090984 3 0.8290 0.118190 0.144 0.272 0.364 0.220 0.000
#> SRR1456991 1 0.4161 0.401069 0.608 0.000 0.392 0.000 0.000
#> SRR1085039 3 0.4589 0.548257 0.248 0.000 0.704 0.048 0.000
#> SRR1069303 1 0.4744 0.540444 0.764 0.156 0.028 0.048 0.004
#> SRR1091500 2 0.2964 0.723674 0.024 0.856 0.000 0.000 0.120
#> SRR1075198 5 0.0671 0.853150 0.016 0.000 0.004 0.000 0.980
#> SRR1086915 4 0.1605 0.612223 0.040 0.000 0.012 0.944 0.004
#> SRR1499503 5 0.0579 0.854612 0.000 0.008 0.008 0.000 0.984
#> SRR1094312 2 0.3630 0.662134 0.016 0.780 0.000 0.000 0.204
#> SRR1352437 2 0.7135 0.215199 0.228 0.512 0.044 0.216 0.000
#> SRR1436323 4 0.6304 0.349849 0.156 0.000 0.384 0.460 0.000
#> SRR1073507 4 0.5714 0.488377 0.292 0.000 0.116 0.592 0.000
#> SRR1401972 1 0.6769 0.161927 0.444 0.308 0.004 0.244 0.000
#> SRR1415510 5 0.5081 0.716388 0.104 0.080 0.060 0.000 0.756
#> SRR1327279 3 0.3058 0.697799 0.044 0.000 0.860 0.096 0.000
#> SRR1086983 4 0.4454 0.621735 0.112 0.000 0.128 0.760 0.000
#> SRR1105174 4 0.6475 0.161492 0.212 0.000 0.304 0.484 0.000
#> SRR1468893 4 0.1901 0.582240 0.040 0.000 0.024 0.932 0.004
#> SRR1362555 5 0.0324 0.853351 0.004 0.000 0.004 0.000 0.992
#> SRR1074526 4 0.7903 0.084323 0.212 0.140 0.000 0.464 0.184
#> SRR1326225 2 0.4126 0.311804 0.000 0.620 0.000 0.000 0.380
#> SRR1401933 4 0.1822 0.591881 0.036 0.000 0.024 0.936 0.004
#> SRR1324062 2 0.7328 0.255041 0.120 0.528 0.240 0.112 0.000
#> SRR1102296 1 0.5083 0.599154 0.700 0.140 0.160 0.000 0.000
#> SRR1085087 1 0.4797 0.587317 0.736 0.000 0.176 0.080 0.008
#> SRR1079046 1 0.6202 0.380278 0.564 0.000 0.008 0.144 0.284
#> SRR1328339 3 0.4088 0.319491 0.368 0.000 0.632 0.000 0.000
#> SRR1079782 5 0.0693 0.854886 0.008 0.012 0.000 0.000 0.980
#> SRR1092257 2 0.2179 0.744640 0.004 0.896 0.000 0.000 0.100
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.0972 0.81190 0.000 0.964 0.008 0.000 0.028 0.000
#> SRR1429287 2 0.2835 0.79308 0.008 0.876 0.004 0.036 0.004 0.072
#> SRR1359238 4 0.2805 0.58756 0.012 0.000 0.160 0.828 0.000 0.000
#> SRR1309597 3 0.3098 0.63416 0.060 0.032 0.868 0.028 0.000 0.012
#> SRR1441398 3 0.2680 0.64532 0.108 0.000 0.860 0.000 0.000 0.032
#> SRR1084055 2 0.1774 0.80611 0.024 0.936 0.004 0.004 0.028 0.004
#> SRR1417566 3 0.5607 0.20711 0.004 0.000 0.524 0.004 0.348 0.120
#> SRR1351857 4 0.4332 0.41259 0.000 0.000 0.048 0.664 0.000 0.288
#> SRR1487485 3 0.5171 0.59801 0.028 0.008 0.728 0.076 0.136 0.024
#> SRR1335875 3 0.6767 0.39722 0.128 0.000 0.488 0.112 0.272 0.000
#> SRR1073947 1 0.2373 0.62899 0.888 0.004 0.084 0.024 0.000 0.000
#> SRR1443483 3 0.3145 0.62914 0.012 0.012 0.828 0.144 0.000 0.004
#> SRR1346794 6 0.6637 0.39804 0.164 0.036 0.320 0.012 0.000 0.468
#> SRR1405245 3 0.4426 0.13817 0.020 0.000 0.596 0.008 0.000 0.376
#> SRR1409677 4 0.4480 0.56514 0.004 0.040 0.056 0.756 0.000 0.144
#> SRR1095549 3 0.5209 0.57421 0.044 0.000 0.688 0.132 0.000 0.136
#> SRR1323788 6 0.4227 0.41733 0.004 0.000 0.344 0.020 0.000 0.632
#> SRR1314054 5 0.2278 0.74812 0.000 0.004 0.000 0.128 0.868 0.000
#> SRR1077944 1 0.6763 0.21026 0.436 0.000 0.052 0.236 0.000 0.276
#> SRR1480587 2 0.3366 0.74838 0.000 0.824 0.080 0.000 0.092 0.004
#> SRR1311205 3 0.4234 0.51369 0.324 0.000 0.644 0.032 0.000 0.000
#> SRR1076369 6 0.2251 0.62745 0.000 0.052 0.036 0.008 0.000 0.904
#> SRR1453549 4 0.4095 -0.03242 0.008 0.000 0.480 0.512 0.000 0.000
#> SRR1345782 3 0.3834 0.51923 0.024 0.000 0.708 0.268 0.000 0.000
#> SRR1447850 5 0.2092 0.74724 0.000 0.000 0.000 0.124 0.876 0.000
#> SRR1391553 5 0.2333 0.74908 0.000 0.000 0.004 0.120 0.872 0.004
#> SRR1444156 5 0.0405 0.73983 0.000 0.004 0.008 0.000 0.988 0.000
#> SRR1471731 3 0.6019 0.43824 0.056 0.004 0.656 0.144 0.024 0.116
#> SRR1120987 4 0.4342 0.44008 0.028 0.052 0.000 0.772 0.012 0.136
#> SRR1477363 3 0.3863 0.64096 0.164 0.000 0.776 0.048 0.000 0.012
#> SRR1391961 1 0.5552 0.17412 0.552 0.360 0.028 0.012 0.000 0.048
#> SRR1373879 3 0.3728 0.40243 0.004 0.000 0.652 0.344 0.000 0.000
#> SRR1318732 6 0.4316 0.33766 0.004 0.008 0.432 0.004 0.000 0.552
#> SRR1091404 1 0.2309 0.62343 0.888 0.000 0.084 0.000 0.000 0.028
#> SRR1402109 3 0.3854 0.11042 0.000 0.000 0.536 0.464 0.000 0.000
#> SRR1407336 4 0.3320 0.55305 0.016 0.000 0.212 0.772 0.000 0.000
#> SRR1097417 3 0.6578 0.44566 0.024 0.024 0.512 0.328 0.096 0.016
#> SRR1396227 1 0.5187 0.45949 0.672 0.000 0.036 0.072 0.004 0.216
#> SRR1400775 5 0.4204 0.73255 0.040 0.124 0.000 0.060 0.776 0.000
#> SRR1392861 4 0.1245 0.59696 0.000 0.000 0.016 0.952 0.032 0.000
#> SRR1472929 2 0.3341 0.74831 0.116 0.816 0.068 0.000 0.000 0.000
#> SRR1436740 4 0.5421 0.35455 0.132 0.000 0.008 0.640 0.012 0.208
#> SRR1477057 2 0.4999 0.22494 0.456 0.496 0.000 0.024 0.020 0.004
#> SRR1311980 5 0.5517 0.24741 0.000 0.000 0.352 0.124 0.520 0.004
#> SRR1069400 3 0.4455 0.37868 0.016 0.016 0.616 0.352 0.000 0.000
#> SRR1351016 1 0.2702 0.62972 0.868 0.000 0.092 0.036 0.000 0.004
#> SRR1096291 4 0.3248 0.50971 0.004 0.004 0.000 0.768 0.000 0.224
#> SRR1418145 2 0.1565 0.80825 0.004 0.940 0.000 0.028 0.000 0.028
#> SRR1488111 2 0.2678 0.76263 0.004 0.860 0.000 0.116 0.020 0.000
#> SRR1370495 2 0.3309 0.62165 0.280 0.720 0.000 0.000 0.000 0.000
#> SRR1352639 2 0.4471 0.17186 0.472 0.500 0.028 0.000 0.000 0.000
#> SRR1348911 3 0.4681 0.47885 0.032 0.000 0.620 0.016 0.332 0.000
#> SRR1467386 1 0.6740 0.12065 0.416 0.000 0.096 0.372 0.000 0.116
#> SRR1415956 3 0.3898 0.52763 0.296 0.000 0.684 0.000 0.000 0.020
#> SRR1500495 3 0.3053 0.64221 0.168 0.000 0.812 0.020 0.000 0.000
#> SRR1405099 1 0.3620 0.31744 0.648 0.000 0.352 0.000 0.000 0.000
#> SRR1345585 3 0.2922 0.63173 0.008 0.028 0.876 0.028 0.000 0.060
#> SRR1093196 4 0.4575 0.59104 0.064 0.000 0.100 0.756 0.000 0.080
#> SRR1466006 2 0.2065 0.79882 0.004 0.912 0.052 0.000 0.000 0.032
#> SRR1351557 2 0.3271 0.67303 0.000 0.760 0.008 0.000 0.232 0.000
#> SRR1382687 6 0.3277 0.64140 0.000 0.000 0.084 0.092 0.000 0.824
#> SRR1375549 6 0.5624 0.10102 0.388 0.084 0.004 0.016 0.000 0.508
#> SRR1101765 6 0.3414 0.57146 0.008 0.140 0.000 0.040 0.000 0.812
#> SRR1334461 1 0.4275 0.29256 0.644 0.328 0.020 0.008 0.000 0.000
#> SRR1094073 5 0.3634 0.49326 0.000 0.296 0.008 0.000 0.696 0.000
#> SRR1077549 4 0.3220 0.57506 0.128 0.000 0.028 0.832 0.004 0.008
#> SRR1440332 4 0.3998 -0.03697 0.004 0.000 0.492 0.504 0.000 0.000
#> SRR1454177 4 0.0870 0.60749 0.004 0.000 0.012 0.972 0.000 0.012
#> SRR1082447 6 0.3056 0.55787 0.160 0.000 0.008 0.012 0.000 0.820
#> SRR1420043 4 0.3409 0.45537 0.000 0.000 0.300 0.700 0.000 0.000
#> SRR1432500 4 0.3853 0.45090 0.016 0.000 0.304 0.680 0.000 0.000
#> SRR1378045 5 0.2945 0.63131 0.000 0.000 0.156 0.000 0.824 0.020
#> SRR1334200 2 0.4435 0.35763 0.004 0.576 0.004 0.016 0.000 0.400
#> SRR1069539 2 0.3264 0.68137 0.008 0.796 0.000 0.184 0.000 0.012
#> SRR1343031 4 0.4084 0.22284 0.012 0.000 0.400 0.588 0.000 0.000
#> SRR1319690 3 0.2419 0.65200 0.016 0.000 0.896 0.060 0.000 0.028
#> SRR1310604 2 0.1003 0.81068 0.028 0.964 0.004 0.004 0.000 0.000
#> SRR1327747 6 0.5975 0.47394 0.016 0.020 0.308 0.104 0.000 0.552
#> SRR1072456 2 0.2375 0.79866 0.020 0.896 0.016 0.000 0.068 0.000
#> SRR1367896 3 0.3499 0.59927 0.012 0.004 0.780 0.196 0.008 0.000
#> SRR1480107 1 0.2032 0.62235 0.920 0.000 0.036 0.024 0.000 0.020
#> SRR1377756 6 0.2373 0.64215 0.004 0.000 0.024 0.084 0.000 0.888
#> SRR1435272 4 0.2320 0.60254 0.000 0.000 0.132 0.864 0.000 0.004
#> SRR1089230 6 0.3081 0.56251 0.000 0.004 0.000 0.220 0.000 0.776
#> SRR1389522 3 0.3392 0.63413 0.040 0.012 0.820 0.128 0.000 0.000
#> SRR1080600 2 0.1562 0.80763 0.004 0.940 0.032 0.000 0.000 0.024
#> SRR1086935 6 0.5767 0.34631 0.000 0.000 0.004 0.300 0.180 0.516
#> SRR1344060 2 0.2747 0.78148 0.028 0.868 0.004 0.004 0.000 0.096
#> SRR1467922 5 0.0862 0.73754 0.000 0.008 0.016 0.000 0.972 0.004
#> SRR1090984 6 0.7255 0.29388 0.116 0.000 0.256 0.000 0.224 0.404
#> SRR1456991 1 0.3398 0.44636 0.740 0.008 0.252 0.000 0.000 0.000
#> SRR1085039 3 0.6274 0.27471 0.156 0.000 0.488 0.320 0.000 0.036
#> SRR1069303 1 0.4629 0.57567 0.760 0.000 0.012 0.120 0.064 0.044
#> SRR1091500 5 0.5410 0.69252 0.020 0.160 0.004 0.116 0.684 0.016
#> SRR1075198 2 0.0603 0.81165 0.004 0.980 0.016 0.000 0.000 0.000
#> SRR1086915 6 0.3872 0.28898 0.000 0.004 0.000 0.392 0.000 0.604
#> SRR1499503 2 0.0436 0.81241 0.004 0.988 0.004 0.000 0.004 0.000
#> SRR1094312 5 0.4155 0.51491 0.012 0.316 0.000 0.012 0.660 0.000
#> SRR1352437 4 0.6398 -0.02245 0.280 0.000 0.004 0.436 0.268 0.012
#> SRR1436323 4 0.7581 -0.00376 0.188 0.000 0.216 0.348 0.000 0.248
#> SRR1073507 1 0.6695 0.10942 0.372 0.000 0.040 0.364 0.000 0.224
#> SRR1401972 1 0.7225 0.34778 0.464 0.000 0.004 0.184 0.148 0.200
#> SRR1415510 2 0.4213 0.67392 0.004 0.744 0.092 0.000 0.160 0.000
#> SRR1327279 4 0.4141 0.17117 0.012 0.000 0.432 0.556 0.000 0.000
#> SRR1086983 4 0.4524 0.33876 0.000 0.000 0.048 0.616 0.000 0.336
#> SRR1105174 6 0.4783 0.47211 0.204 0.000 0.128 0.000 0.000 0.668
#> SRR1468893 6 0.1838 0.64013 0.020 0.000 0.012 0.040 0.000 0.928
#> SRR1362555 2 0.0291 0.81133 0.004 0.992 0.004 0.000 0.000 0.000
#> SRR1074526 6 0.7326 0.36745 0.088 0.136 0.004 0.152 0.072 0.548
#> SRR1326225 2 0.4775 0.42161 0.000 0.632 0.000 0.084 0.284 0.000
#> SRR1401933 6 0.1909 0.64142 0.024 0.000 0.004 0.052 0.000 0.920
#> SRR1324062 4 0.5626 -0.03363 0.048 0.000 0.032 0.544 0.364 0.012
#> SRR1102296 1 0.2806 0.62530 0.872 0.000 0.060 0.012 0.056 0.000
#> SRR1085087 1 0.4164 0.56698 0.756 0.004 0.032 0.188 0.004 0.016
#> SRR1079046 1 0.6580 0.24348 0.488 0.184 0.012 0.016 0.008 0.292
#> SRR1328339 3 0.4789 0.39588 0.364 0.000 0.584 0.000 0.044 0.008
#> SRR1079782 2 0.0363 0.81147 0.000 0.988 0.012 0.000 0.000 0.000
#> SRR1092257 5 0.4996 0.65960 0.004 0.216 0.000 0.128 0.652 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 17611 rows and 118 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 0.190 0.676 0.777 0.3586 0.524 0.524
#> 3 3 0.212 0.588 0.706 0.5408 0.767 0.614
#> 4 4 0.264 0.442 0.640 0.1822 0.873 0.725
#> 5 5 0.355 0.581 0.689 0.0894 0.864 0.643
#> 6 6 0.426 0.580 0.698 0.0364 0.989 0.961
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
#> SRR1396765 2 0.0376 0.8066 0.004 0.996
#> SRR1429287 2 0.2043 0.8055 0.032 0.968
#> SRR1359238 2 0.9795 -0.2519 0.416 0.584
#> SRR1309597 2 0.4939 0.7790 0.108 0.892
#> SRR1441398 1 0.9580 0.7654 0.620 0.380
#> SRR1084055 2 0.0376 0.8066 0.004 0.996
#> SRR1417566 2 0.8443 0.4623 0.272 0.728
#> SRR1351857 2 0.9358 -0.0449 0.352 0.648
#> SRR1487485 2 0.4815 0.7814 0.104 0.896
#> SRR1335875 2 0.5519 0.7693 0.128 0.872
#> SRR1073947 1 0.9635 0.7781 0.612 0.388
#> SRR1443483 2 0.4939 0.7790 0.108 0.892
#> SRR1346794 2 0.9754 -0.0405 0.408 0.592
#> SRR1405245 1 0.9815 0.7422 0.580 0.420
#> SRR1409677 2 0.3274 0.8012 0.060 0.940
#> SRR1095549 2 0.9944 -0.3958 0.456 0.544
#> SRR1323788 2 0.9988 -0.4786 0.480 0.520
#> SRR1314054 2 0.0376 0.8058 0.004 0.996
#> SRR1077944 1 0.9608 0.7805 0.616 0.384
#> SRR1480587 2 0.0938 0.8097 0.012 0.988
#> SRR1311205 1 0.9710 0.7743 0.600 0.400
#> SRR1076369 1 0.9044 0.6997 0.680 0.320
#> SRR1453549 2 0.3274 0.8115 0.060 0.940
#> SRR1345782 1 0.9393 0.7938 0.644 0.356
#> SRR1447850 2 0.0938 0.8074 0.012 0.988
#> SRR1391553 2 0.5294 0.7734 0.120 0.880
#> SRR1444156 2 0.0000 0.8048 0.000 1.000
#> SRR1471731 2 0.5178 0.7796 0.116 0.884
#> SRR1120987 2 0.3114 0.8053 0.056 0.944
#> SRR1477363 1 0.9393 0.7956 0.644 0.356
#> SRR1391961 1 0.3733 0.5704 0.928 0.072
#> SRR1373879 2 0.5059 0.7792 0.112 0.888
#> SRR1318732 2 0.7815 0.5921 0.232 0.768
#> SRR1091404 1 0.9044 0.7914 0.680 0.320
#> SRR1402109 2 0.5059 0.7792 0.112 0.888
#> SRR1407336 2 0.5629 0.7640 0.132 0.868
#> SRR1097417 2 0.5629 0.7665 0.132 0.868
#> SRR1396227 1 0.9977 0.6609 0.528 0.472
#> SRR1400775 2 0.0376 0.8066 0.004 0.996
#> SRR1392861 2 0.2948 0.8029 0.052 0.948
#> SRR1472929 1 0.0672 0.5044 0.992 0.008
#> SRR1436740 2 0.2948 0.8029 0.052 0.948
#> SRR1477057 2 0.9732 -0.2877 0.404 0.596
#> SRR1311980 2 0.5629 0.7644 0.132 0.868
#> SRR1069400 2 0.5519 0.7690 0.128 0.872
#> SRR1351016 1 0.9710 0.7743 0.600 0.400
#> SRR1096291 2 0.3274 0.8094 0.060 0.940
#> SRR1418145 2 0.3733 0.8003 0.072 0.928
#> SRR1488111 2 0.3114 0.8053 0.056 0.944
#> SRR1370495 1 0.9993 0.6302 0.516 0.484
#> SRR1352639 2 0.6343 0.7431 0.160 0.840
#> SRR1348911 2 0.5294 0.7758 0.120 0.880
#> SRR1467386 1 0.9909 0.7193 0.556 0.444
#> SRR1415956 1 0.8555 0.7682 0.720 0.280
#> SRR1500495 1 0.9580 0.7654 0.620 0.380
#> SRR1405099 1 0.8555 0.7682 0.720 0.280
#> SRR1345585 2 0.5946 0.7473 0.144 0.856
#> SRR1093196 2 0.4939 0.7840 0.108 0.892
#> SRR1466006 2 0.4161 0.7704 0.084 0.916
#> SRR1351557 2 0.0376 0.8066 0.004 0.996
#> SRR1382687 1 0.9686 0.7769 0.604 0.396
#> SRR1375549 1 0.9686 0.7314 0.604 0.396
#> SRR1101765 1 0.9087 0.6964 0.676 0.324
#> SRR1334461 1 0.0000 0.5058 1.000 0.000
#> SRR1094073 2 0.0376 0.8066 0.004 0.996
#> SRR1077549 1 0.9866 0.7391 0.568 0.432
#> SRR1440332 1 0.9998 0.5967 0.508 0.492
#> SRR1454177 2 0.2948 0.8029 0.052 0.948
#> SRR1082447 1 0.9129 0.7944 0.672 0.328
#> SRR1420043 2 0.3274 0.8115 0.060 0.940
#> SRR1432500 1 0.9996 0.6328 0.512 0.488
#> SRR1378045 2 0.0000 0.8048 0.000 1.000
#> SRR1334200 1 0.7528 0.5728 0.784 0.216
#> SRR1069539 2 0.3274 0.8094 0.060 0.940
#> SRR1343031 2 0.5629 0.7664 0.132 0.868
#> SRR1319690 1 0.9963 0.5920 0.536 0.464
#> SRR1310604 2 0.0376 0.8066 0.004 0.996
#> SRR1327747 2 0.9775 -0.0942 0.412 0.588
#> SRR1072456 2 0.0376 0.8066 0.004 0.996
#> SRR1367896 2 0.5178 0.7747 0.116 0.884
#> SRR1480107 1 0.9044 0.7914 0.680 0.320
#> SRR1377756 1 0.9427 0.7953 0.640 0.360
#> SRR1435272 2 0.4939 0.7524 0.108 0.892
#> SRR1089230 2 0.3114 0.8018 0.056 0.944
#> SRR1389522 2 0.5737 0.7617 0.136 0.864
#> SRR1080600 2 0.4161 0.7704 0.084 0.916
#> SRR1086935 2 0.0672 0.8063 0.008 0.992
#> SRR1344060 1 0.7376 0.5764 0.792 0.208
#> SRR1467922 2 0.0000 0.8048 0.000 1.000
#> SRR1090984 2 0.8909 0.3608 0.308 0.692
#> SRR1456991 1 0.9129 0.7936 0.672 0.328
#> SRR1085039 1 0.9044 0.7915 0.680 0.320
#> SRR1069303 1 0.9686 0.7726 0.604 0.396
#> SRR1091500 2 0.1633 0.8086 0.024 0.976
#> SRR1075198 2 0.3431 0.8001 0.064 0.936
#> SRR1086915 2 0.7056 0.6065 0.192 0.808
#> SRR1499503 2 0.0376 0.8066 0.004 0.996
#> SRR1094312 2 0.0376 0.8066 0.004 0.996
#> SRR1352437 1 0.9686 0.7726 0.604 0.396
#> SRR1436323 2 0.5178 0.7796 0.116 0.884
#> SRR1073507 1 0.9866 0.7391 0.568 0.432
#> SRR1401972 1 0.9686 0.7726 0.604 0.396
#> SRR1415510 2 0.0376 0.8066 0.004 0.996
#> SRR1327279 1 0.9754 0.7632 0.592 0.408
#> SRR1086983 2 0.9358 -0.0449 0.352 0.648
#> SRR1105174 1 0.8555 0.7682 0.720 0.280
#> SRR1468893 1 0.9286 0.7956 0.656 0.344
#> SRR1362555 2 0.3431 0.8001 0.064 0.936
#> SRR1074526 1 0.5629 0.5876 0.868 0.132
#> SRR1326225 2 0.0376 0.8066 0.004 0.996
#> SRR1401933 1 0.9933 0.6981 0.548 0.452
#> SRR1324062 1 0.9795 0.7541 0.584 0.416
#> SRR1102296 2 0.9795 -0.2144 0.416 0.584
#> SRR1085087 1 0.9661 0.7755 0.608 0.392
#> SRR1079046 1 0.9710 0.7036 0.600 0.400
#> SRR1328339 2 0.8144 0.5168 0.252 0.748
#> SRR1079782 2 0.3431 0.8001 0.064 0.936
#> SRR1092257 2 0.3114 0.8053 0.056 0.944
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 3 0.2584 0.6314 0.008 0.064 0.928
#> SRR1429287 3 0.4821 0.6641 0.064 0.088 0.848
#> SRR1359238 1 0.6109 0.6147 0.760 0.048 0.192
#> SRR1309597 3 0.7400 0.5989 0.264 0.072 0.664
#> SRR1441398 1 0.5722 0.7113 0.800 0.132 0.068
#> SRR1084055 3 0.2584 0.6314 0.008 0.064 0.928
#> SRR1417566 1 0.8033 -0.0220 0.512 0.064 0.424
#> SRR1351857 1 0.6882 0.5818 0.732 0.096 0.172
#> SRR1487485 3 0.7184 0.6118 0.240 0.072 0.688
#> SRR1335875 3 0.7828 0.5371 0.340 0.068 0.592
#> SRR1073947 1 0.0237 0.7400 0.996 0.004 0.000
#> SRR1443483 3 0.7400 0.5989 0.264 0.072 0.664
#> SRR1346794 1 0.8949 0.3556 0.532 0.148 0.320
#> SRR1405245 1 0.5085 0.7262 0.836 0.092 0.072
#> SRR1409677 3 0.8880 0.3272 0.416 0.120 0.464
#> SRR1095549 1 0.5988 0.6641 0.776 0.056 0.168
#> SRR1323788 1 0.6264 0.6746 0.764 0.068 0.168
#> SRR1314054 3 0.4665 0.6436 0.048 0.100 0.852
#> SRR1077944 1 0.4628 0.7414 0.856 0.088 0.056
#> SRR1480587 3 0.2845 0.6330 0.012 0.068 0.920
#> SRR1311205 1 0.2050 0.7499 0.952 0.020 0.028
#> SRR1076369 1 0.8637 0.2014 0.564 0.308 0.128
#> SRR1453549 3 0.8586 0.4474 0.376 0.104 0.520
#> SRR1345782 1 0.3722 0.7364 0.888 0.088 0.024
#> SRR1447850 3 0.5304 0.6528 0.068 0.108 0.824
#> SRR1391553 3 0.7981 0.5281 0.340 0.076 0.584
#> SRR1444156 3 0.2796 0.6128 0.000 0.092 0.908
#> SRR1471731 3 0.8180 0.4530 0.392 0.076 0.532
#> SRR1120987 3 0.5618 0.6675 0.156 0.048 0.796
#> SRR1477363 1 0.2651 0.7380 0.928 0.060 0.012
#> SRR1391961 2 0.6684 0.8501 0.292 0.676 0.032
#> SRR1373879 3 0.8157 0.4711 0.384 0.076 0.540
#> SRR1318732 3 0.8463 0.1925 0.444 0.088 0.468
#> SRR1091404 1 0.2356 0.7135 0.928 0.072 0.000
#> SRR1402109 3 0.8157 0.4711 0.384 0.076 0.540
#> SRR1407336 3 0.8227 0.4643 0.384 0.080 0.536
#> SRR1097417 3 0.7639 0.6042 0.256 0.088 0.656
#> SRR1396227 1 0.4232 0.7330 0.872 0.044 0.084
#> SRR1400775 3 0.4137 0.6352 0.032 0.096 0.872
#> SRR1392861 3 0.8876 0.3328 0.412 0.120 0.468
#> SRR1472929 2 0.5109 0.8406 0.212 0.780 0.008
#> SRR1436740 3 0.8876 0.3328 0.412 0.120 0.468
#> SRR1477057 1 0.7283 0.0608 0.512 0.028 0.460
#> SRR1311980 3 0.7996 0.4799 0.380 0.068 0.552
#> SRR1069400 3 0.8037 0.5159 0.352 0.076 0.572
#> SRR1351016 1 0.2050 0.7499 0.952 0.020 0.028
#> SRR1096291 3 0.6703 0.6235 0.268 0.040 0.692
#> SRR1418145 3 0.6796 0.6329 0.236 0.056 0.708
#> SRR1488111 3 0.5618 0.6675 0.156 0.048 0.796
#> SRR1370495 1 0.6400 0.6104 0.740 0.052 0.208
#> SRR1352639 3 0.6964 0.6011 0.264 0.052 0.684
#> SRR1348911 3 0.7283 0.6067 0.260 0.068 0.672
#> SRR1467386 1 0.3780 0.7440 0.892 0.044 0.064
#> SRR1415956 1 0.3267 0.6806 0.884 0.116 0.000
#> SRR1500495 1 0.5722 0.7113 0.800 0.132 0.068
#> SRR1405099 1 0.3267 0.6806 0.884 0.116 0.000
#> SRR1345585 3 0.7903 0.4942 0.356 0.068 0.576
#> SRR1093196 3 0.8143 0.5054 0.360 0.080 0.560
#> SRR1466006 3 0.3532 0.6225 0.008 0.108 0.884
#> SRR1351557 3 0.3846 0.6196 0.016 0.108 0.876
#> SRR1382687 1 0.4379 0.7341 0.868 0.060 0.072
#> SRR1375549 1 0.6728 0.6550 0.748 0.128 0.124
#> SRR1101765 1 0.8668 0.1903 0.564 0.304 0.132
#> SRR1334461 2 0.5138 0.8427 0.252 0.748 0.000
#> SRR1094073 3 0.3846 0.6196 0.016 0.108 0.876
#> SRR1077549 1 0.1832 0.7459 0.956 0.008 0.036
#> SRR1440332 1 0.4217 0.7258 0.868 0.032 0.100
#> SRR1454177 3 0.8876 0.3328 0.412 0.120 0.468
#> SRR1082447 1 0.2774 0.7204 0.920 0.072 0.008
#> SRR1420043 3 0.8586 0.4474 0.376 0.104 0.520
#> SRR1432500 1 0.3587 0.7325 0.892 0.020 0.088
#> SRR1378045 3 0.4015 0.6326 0.028 0.096 0.876
#> SRR1334200 2 0.8685 0.8001 0.212 0.596 0.192
#> SRR1069539 3 0.6703 0.6235 0.268 0.040 0.692
#> SRR1343031 3 0.8144 0.4801 0.380 0.076 0.544
#> SRR1319690 1 0.7256 0.6627 0.712 0.124 0.164
#> SRR1310604 3 0.2584 0.6314 0.008 0.064 0.928
#> SRR1327747 1 0.8863 0.3846 0.544 0.144 0.312
#> SRR1072456 3 0.2584 0.6314 0.008 0.064 0.928
#> SRR1367896 3 0.7446 0.6011 0.260 0.076 0.664
#> SRR1480107 1 0.2261 0.7158 0.932 0.068 0.000
#> SRR1377756 1 0.2550 0.7403 0.932 0.056 0.012
#> SRR1435272 1 0.8814 -0.1541 0.480 0.116 0.404
#> SRR1089230 3 0.8932 0.3021 0.420 0.124 0.456
#> SRR1389522 3 0.7666 0.5811 0.288 0.076 0.636
#> SRR1080600 3 0.3532 0.6225 0.008 0.108 0.884
#> SRR1086935 3 0.8630 0.4902 0.328 0.120 0.552
#> SRR1344060 2 0.8614 0.8205 0.228 0.600 0.172
#> SRR1467922 3 0.2796 0.6128 0.000 0.092 0.908
#> SRR1090984 1 0.8215 0.1441 0.540 0.080 0.380
#> SRR1456991 1 0.2261 0.7189 0.932 0.068 0.000
#> SRR1085039 1 0.2356 0.7129 0.928 0.072 0.000
#> SRR1069303 1 0.0661 0.7411 0.988 0.008 0.004
#> SRR1091500 3 0.5237 0.6273 0.056 0.120 0.824
#> SRR1075198 3 0.5538 0.6644 0.132 0.060 0.808
#> SRR1086915 1 0.8491 0.1986 0.572 0.116 0.312
#> SRR1499503 3 0.2584 0.6314 0.008 0.064 0.928
#> SRR1094312 3 0.4137 0.6352 0.032 0.096 0.872
#> SRR1352437 1 0.0661 0.7411 0.988 0.008 0.004
#> SRR1436323 3 0.8211 0.4290 0.404 0.076 0.520
#> SRR1073507 1 0.1832 0.7459 0.956 0.008 0.036
#> SRR1401972 1 0.0661 0.7411 0.988 0.008 0.004
#> SRR1415510 3 0.2749 0.6334 0.012 0.064 0.924
#> SRR1327279 1 0.1170 0.7454 0.976 0.008 0.016
#> SRR1086983 1 0.6882 0.5818 0.732 0.096 0.172
#> SRR1105174 1 0.3192 0.6786 0.888 0.112 0.000
#> SRR1468893 1 0.2496 0.7314 0.928 0.068 0.004
#> SRR1362555 3 0.5538 0.6644 0.132 0.060 0.808
#> SRR1074526 2 0.7666 0.8497 0.288 0.636 0.076
#> SRR1326225 3 0.2584 0.6314 0.008 0.064 0.928
#> SRR1401933 1 0.4914 0.7322 0.844 0.068 0.088
#> SRR1324062 1 0.1905 0.7509 0.956 0.016 0.028
#> SRR1102296 1 0.6781 0.5209 0.704 0.052 0.244
#> SRR1085087 1 0.0592 0.7418 0.988 0.012 0.000
#> SRR1079046 1 0.7221 0.5575 0.716 0.148 0.136
#> SRR1328339 1 0.7993 -0.1809 0.484 0.060 0.456
#> SRR1079782 3 0.5538 0.6644 0.132 0.060 0.808
#> SRR1092257 3 0.5558 0.6678 0.152 0.048 0.800
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.467 0.39338 0.000 0.700 0.292 0.008
#> SRR1429287 2 0.439 0.32959 0.024 0.816 0.140 0.020
#> SRR1359238 1 0.624 0.53833 0.668 0.168 0.164 0.000
#> SRR1309597 3 0.705 0.74656 0.124 0.392 0.484 0.000
#> SRR1441398 1 0.617 0.65082 0.712 0.068 0.184 0.036
#> SRR1084055 2 0.467 0.39338 0.000 0.700 0.292 0.008
#> SRR1417566 1 0.787 -0.34068 0.388 0.328 0.284 0.000
#> SRR1351857 1 0.725 0.51029 0.620 0.196 0.156 0.028
#> SRR1487485 3 0.675 0.70367 0.092 0.440 0.468 0.000
#> SRR1335875 3 0.747 0.72303 0.180 0.368 0.452 0.000
#> SRR1073947 1 0.204 0.76022 0.936 0.012 0.048 0.004
#> SRR1443483 3 0.701 0.74410 0.120 0.392 0.488 0.000
#> SRR1346794 1 0.865 0.17900 0.460 0.280 0.204 0.056
#> SRR1405245 1 0.532 0.68083 0.768 0.072 0.144 0.016
#> SRR1409677 2 0.841 -0.00362 0.276 0.440 0.256 0.028
#> SRR1095549 1 0.613 0.60004 0.692 0.148 0.156 0.004
#> SRR1323788 1 0.604 0.59768 0.708 0.148 0.136 0.008
#> SRR1314054 2 0.523 0.40577 0.012 0.684 0.292 0.012
#> SRR1077944 1 0.426 0.75308 0.848 0.060 0.052 0.040
#> SRR1480587 2 0.475 0.38689 0.000 0.688 0.304 0.008
#> SRR1311205 1 0.258 0.75877 0.912 0.036 0.052 0.000
#> SRR1076369 1 0.827 0.37925 0.544 0.112 0.096 0.248
#> SRR1453549 2 0.815 -0.29200 0.216 0.416 0.352 0.016
#> SRR1345782 1 0.340 0.75816 0.888 0.028 0.044 0.040
#> SRR1447850 2 0.557 0.40178 0.028 0.692 0.264 0.016
#> SRR1391553 3 0.740 0.73339 0.168 0.376 0.456 0.000
#> SRR1444156 2 0.574 0.31878 0.000 0.540 0.432 0.028
#> SRR1471731 2 0.777 -0.60552 0.240 0.384 0.376 0.000
#> SRR1120987 2 0.446 0.29360 0.100 0.824 0.064 0.012
#> SRR1477363 1 0.263 0.75986 0.920 0.024 0.036 0.020
#> SRR1391961 4 0.404 0.86256 0.116 0.012 0.032 0.840
#> SRR1373879 3 0.773 0.68067 0.232 0.356 0.412 0.000
#> SRR1318732 2 0.792 -0.47093 0.324 0.344 0.332 0.000
#> SRR1091404 1 0.204 0.75471 0.936 0.012 0.004 0.048
#> SRR1402109 3 0.773 0.68067 0.232 0.356 0.412 0.000
#> SRR1407336 3 0.777 0.65283 0.240 0.376 0.384 0.000
#> SRR1097417 3 0.773 0.71143 0.108 0.384 0.476 0.032
#> SRR1396227 1 0.466 0.73017 0.804 0.108 0.084 0.004
#> SRR1400775 2 0.487 0.41194 0.004 0.720 0.260 0.016
#> SRR1392861 2 0.843 0.03617 0.256 0.432 0.284 0.028
#> SRR1472929 4 0.194 0.83386 0.012 0.000 0.052 0.936
#> SRR1436740 2 0.843 0.03617 0.256 0.432 0.284 0.028
#> SRR1477057 1 0.685 0.15726 0.472 0.448 0.068 0.012
#> SRR1311980 3 0.765 0.70807 0.220 0.336 0.444 0.000
#> SRR1069400 3 0.764 0.72815 0.208 0.376 0.416 0.000
#> SRR1351016 1 0.266 0.75882 0.908 0.036 0.056 0.000
#> SRR1096291 2 0.636 -0.03402 0.160 0.656 0.184 0.000
#> SRR1418145 2 0.514 0.21082 0.180 0.764 0.036 0.020
#> SRR1488111 2 0.446 0.29360 0.100 0.824 0.064 0.012
#> SRR1370495 1 0.600 0.64095 0.700 0.224 0.044 0.032
#> SRR1352639 2 0.484 0.20277 0.224 0.748 0.012 0.016
#> SRR1348911 3 0.703 0.68353 0.120 0.408 0.472 0.000
#> SRR1467386 1 0.427 0.74337 0.828 0.068 0.100 0.004
#> SRR1415956 1 0.316 0.72311 0.884 0.000 0.052 0.064
#> SRR1500495 1 0.617 0.65082 0.712 0.068 0.184 0.036
#> SRR1405099 1 0.316 0.72311 0.884 0.000 0.052 0.064
#> SRR1345585 2 0.769 -0.65008 0.220 0.416 0.364 0.000
#> SRR1093196 2 0.767 -0.63811 0.216 0.416 0.368 0.000
#> SRR1466006 2 0.553 0.23325 0.000 0.704 0.228 0.068
#> SRR1351557 2 0.469 0.40916 0.000 0.724 0.260 0.016
#> SRR1382687 1 0.462 0.72406 0.812 0.048 0.124 0.016
#> SRR1375549 1 0.604 0.69947 0.736 0.148 0.048 0.068
#> SRR1101765 1 0.822 0.37385 0.548 0.112 0.092 0.248
#> SRR1334461 4 0.164 0.85317 0.060 0.000 0.000 0.940
#> SRR1094073 2 0.469 0.40916 0.000 0.724 0.260 0.016
#> SRR1077549 1 0.258 0.75714 0.912 0.036 0.052 0.000
#> SRR1440332 1 0.521 0.67166 0.756 0.104 0.140 0.000
#> SRR1454177 2 0.843 0.03617 0.256 0.432 0.284 0.028
#> SRR1082447 1 0.214 0.75706 0.936 0.012 0.012 0.040
#> SRR1420043 2 0.815 -0.29200 0.216 0.416 0.352 0.016
#> SRR1432500 1 0.461 0.71556 0.808 0.100 0.088 0.004
#> SRR1378045 3 0.577 -0.29287 0.000 0.464 0.508 0.028
#> SRR1334200 4 0.689 0.80719 0.064 0.144 0.108 0.684
#> SRR1069539 2 0.636 -0.03402 0.160 0.656 0.184 0.000
#> SRR1343031 3 0.774 0.70305 0.232 0.360 0.408 0.000
#> SRR1319690 1 0.732 0.55454 0.628 0.144 0.188 0.040
#> SRR1310604 2 0.467 0.39338 0.000 0.700 0.292 0.008
#> SRR1327747 1 0.851 0.21957 0.476 0.264 0.212 0.048
#> SRR1072456 2 0.467 0.39338 0.000 0.700 0.292 0.008
#> SRR1367896 3 0.722 0.73471 0.112 0.392 0.488 0.008
#> SRR1480107 1 0.182 0.75411 0.944 0.008 0.004 0.044
#> SRR1377756 1 0.263 0.76233 0.920 0.024 0.036 0.020
#> SRR1435272 2 0.856 0.02467 0.324 0.376 0.272 0.028
#> SRR1089230 2 0.847 0.03106 0.256 0.420 0.296 0.028
#> SRR1389522 3 0.747 0.74598 0.140 0.376 0.476 0.008
#> SRR1080600 2 0.553 0.23325 0.000 0.704 0.228 0.068
#> SRR1086935 2 0.806 0.05028 0.168 0.480 0.324 0.028
#> SRR1344060 4 0.686 0.83021 0.080 0.120 0.108 0.692
#> SRR1467922 2 0.574 0.31878 0.000 0.540 0.432 0.028
#> SRR1090984 1 0.779 -0.23870 0.424 0.280 0.296 0.000
#> SRR1456991 1 0.188 0.75575 0.944 0.008 0.008 0.040
#> SRR1085039 1 0.186 0.75236 0.944 0.004 0.012 0.040
#> SRR1069303 1 0.216 0.75713 0.928 0.008 0.060 0.004
#> SRR1091500 2 0.565 0.40778 0.020 0.700 0.248 0.032
#> SRR1075198 2 0.342 0.31378 0.088 0.876 0.016 0.020
#> SRR1086915 1 0.820 0.17819 0.460 0.324 0.188 0.028
#> SRR1499503 2 0.467 0.39338 0.000 0.700 0.292 0.008
#> SRR1094312 2 0.487 0.41194 0.004 0.720 0.260 0.016
#> SRR1352437 1 0.216 0.75713 0.928 0.008 0.060 0.004
#> SRR1436323 2 0.780 -0.59220 0.248 0.376 0.376 0.000
#> SRR1073507 1 0.258 0.75714 0.912 0.036 0.052 0.000
#> SRR1401972 1 0.216 0.75713 0.928 0.008 0.060 0.004
#> SRR1415510 2 0.485 0.39103 0.004 0.696 0.292 0.008
#> SRR1327279 1 0.260 0.75763 0.908 0.024 0.068 0.000
#> SRR1086983 1 0.722 0.51414 0.624 0.192 0.156 0.028
#> SRR1105174 1 0.300 0.72255 0.896 0.004 0.036 0.064
#> SRR1468893 1 0.219 0.75364 0.932 0.004 0.044 0.020
#> SRR1362555 2 0.341 0.31305 0.088 0.876 0.012 0.024
#> SRR1074526 4 0.589 0.85053 0.128 0.020 0.116 0.736
#> SRR1326225 2 0.467 0.39338 0.000 0.700 0.292 0.008
#> SRR1401933 1 0.444 0.73650 0.816 0.112 0.068 0.004
#> SRR1324062 1 0.294 0.76061 0.900 0.044 0.052 0.004
#> SRR1102296 1 0.712 0.38161 0.584 0.200 0.212 0.004
#> SRR1085087 1 0.228 0.75978 0.928 0.020 0.048 0.004
#> SRR1079046 1 0.660 0.62299 0.712 0.108 0.080 0.100
#> SRR1328339 2 0.792 -0.46361 0.340 0.348 0.312 0.000
#> SRR1079782 2 0.341 0.31305 0.088 0.876 0.012 0.024
#> SRR1092257 2 0.439 0.29794 0.100 0.828 0.060 0.012
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.239 0.6902 0.000 0.880 0.116 0.004 0.000
#> SRR1429287 2 0.745 0.4459 0.008 0.456 0.228 0.276 0.032
#> SRR1359238 1 0.658 0.3895 0.548 0.028 0.288 0.136 0.000
#> SRR1309597 3 0.204 0.6728 0.036 0.028 0.928 0.008 0.000
#> SRR1441398 1 0.458 0.5819 0.696 0.000 0.272 0.012 0.020
#> SRR1084055 2 0.239 0.6902 0.000 0.880 0.116 0.004 0.000
#> SRR1417566 3 0.622 0.4174 0.336 0.056 0.564 0.040 0.004
#> SRR1351857 1 0.657 0.0431 0.524 0.060 0.068 0.348 0.000
#> SRR1487485 3 0.322 0.6608 0.028 0.076 0.868 0.028 0.000
#> SRR1335875 3 0.476 0.6766 0.104 0.076 0.776 0.044 0.000
#> SRR1073947 1 0.271 0.7093 0.876 0.000 0.024 0.100 0.000
#> SRR1443483 3 0.195 0.6703 0.032 0.028 0.932 0.008 0.000
#> SRR1346794 1 0.733 0.0743 0.472 0.028 0.364 0.092 0.044
#> SRR1405245 1 0.384 0.6241 0.736 0.000 0.256 0.004 0.004
#> SRR1409677 4 0.745 0.7925 0.160 0.108 0.208 0.524 0.000
#> SRR1095549 1 0.571 0.5148 0.632 0.008 0.248 0.112 0.000
#> SRR1323788 1 0.477 0.5530 0.680 0.008 0.280 0.032 0.000
#> SRR1314054 2 0.449 0.6265 0.004 0.760 0.080 0.156 0.000
#> SRR1077944 1 0.378 0.7121 0.828 0.008 0.120 0.036 0.008
#> SRR1480587 2 0.274 0.6847 0.000 0.860 0.132 0.004 0.004
#> SRR1311205 1 0.362 0.7065 0.824 0.000 0.108 0.068 0.000
#> SRR1076369 1 0.674 0.3616 0.568 0.004 0.040 0.128 0.260
#> SRR1453549 3 0.721 0.0291 0.104 0.084 0.484 0.328 0.000
#> SRR1345782 1 0.286 0.7226 0.872 0.000 0.104 0.016 0.008
#> SRR1447850 2 0.563 0.5512 0.012 0.652 0.104 0.232 0.000
#> SRR1391553 3 0.465 0.6873 0.096 0.076 0.784 0.044 0.000
#> SRR1444156 2 0.406 0.5193 0.000 0.800 0.112 0.084 0.004
#> SRR1471731 3 0.597 0.5894 0.108 0.040 0.660 0.192 0.000
#> SRR1120987 2 0.773 0.3871 0.068 0.420 0.272 0.240 0.000
#> SRR1477363 1 0.207 0.7262 0.920 0.000 0.060 0.016 0.004
#> SRR1391961 5 0.536 0.8048 0.072 0.016 0.008 0.200 0.704
#> SRR1373879 3 0.487 0.6614 0.100 0.028 0.760 0.112 0.000
#> SRR1318732 3 0.595 0.5191 0.276 0.056 0.628 0.032 0.008
#> SRR1091404 1 0.188 0.7228 0.936 0.000 0.032 0.020 0.012
#> SRR1402109 3 0.487 0.6614 0.100 0.028 0.760 0.112 0.000
#> SRR1407336 3 0.535 0.6492 0.112 0.036 0.724 0.128 0.000
#> SRR1097417 3 0.256 0.6482 0.024 0.032 0.912 0.008 0.024
#> SRR1396227 1 0.477 0.6310 0.748 0.020 0.060 0.172 0.000
#> SRR1400775 2 0.309 0.6533 0.000 0.856 0.040 0.104 0.000
#> SRR1392861 4 0.699 0.8622 0.144 0.108 0.160 0.588 0.000
#> SRR1472929 5 0.492 0.7782 0.004 0.004 0.072 0.200 0.720
#> SRR1436740 4 0.699 0.8622 0.144 0.108 0.160 0.588 0.000
#> SRR1477057 1 0.726 0.0846 0.456 0.344 0.060 0.140 0.000
#> SRR1311980 3 0.517 0.6780 0.116 0.060 0.748 0.076 0.000
#> SRR1069400 3 0.366 0.6865 0.096 0.012 0.836 0.056 0.000
#> SRR1351016 1 0.363 0.7066 0.824 0.000 0.104 0.072 0.000
#> SRR1096291 3 0.797 -0.1108 0.084 0.276 0.376 0.264 0.000
#> SRR1418145 2 0.842 0.1999 0.120 0.356 0.292 0.224 0.008
#> SRR1488111 2 0.773 0.3871 0.068 0.420 0.272 0.240 0.000
#> SRR1370495 1 0.657 0.5320 0.656 0.080 0.100 0.148 0.016
#> SRR1352639 2 0.852 0.2684 0.200 0.380 0.268 0.140 0.012
#> SRR1348911 3 0.350 0.6465 0.056 0.092 0.844 0.008 0.000
#> SRR1467386 1 0.433 0.6582 0.776 0.012 0.052 0.160 0.000
#> SRR1415956 1 0.240 0.7012 0.912 0.000 0.040 0.012 0.036
#> SRR1500495 1 0.458 0.5819 0.696 0.000 0.272 0.012 0.020
#> SRR1405099 1 0.240 0.7012 0.912 0.000 0.040 0.012 0.036
#> SRR1345585 3 0.523 0.6562 0.132 0.060 0.740 0.068 0.000
#> SRR1093196 3 0.577 0.6179 0.100 0.052 0.692 0.156 0.000
#> SRR1466006 2 0.709 0.5394 0.000 0.532 0.276 0.092 0.100
#> SRR1351557 2 0.287 0.6729 0.000 0.880 0.072 0.044 0.004
#> SRR1382687 1 0.388 0.6868 0.800 0.004 0.164 0.024 0.008
#> SRR1375549 1 0.511 0.6280 0.764 0.020 0.060 0.124 0.032
#> SRR1101765 1 0.663 0.3565 0.572 0.004 0.032 0.128 0.264
#> SRR1334461 5 0.389 0.7894 0.008 0.004 0.008 0.208 0.772
#> SRR1094073 2 0.287 0.6729 0.000 0.880 0.072 0.044 0.004
#> SRR1077549 1 0.365 0.6935 0.828 0.008 0.044 0.120 0.000
#> SRR1440332 1 0.568 0.5752 0.644 0.008 0.228 0.120 0.000
#> SRR1454177 4 0.699 0.8622 0.144 0.108 0.160 0.588 0.000
#> SRR1082447 1 0.157 0.7265 0.948 0.000 0.032 0.012 0.008
#> SRR1420043 3 0.721 0.0291 0.104 0.084 0.484 0.328 0.000
#> SRR1432500 1 0.528 0.5921 0.704 0.016 0.096 0.184 0.000
#> SRR1378045 2 0.519 0.3859 0.000 0.672 0.244 0.080 0.004
#> SRR1334200 5 0.544 0.7492 0.048 0.116 0.044 0.040 0.752
#> SRR1069539 3 0.797 -0.1108 0.084 0.276 0.376 0.264 0.000
#> SRR1343031 3 0.418 0.6767 0.112 0.012 0.800 0.076 0.000
#> SRR1319690 1 0.545 0.4739 0.628 0.012 0.316 0.024 0.020
#> SRR1310604 2 0.239 0.6902 0.000 0.880 0.116 0.004 0.000
#> SRR1327747 1 0.738 0.1010 0.468 0.036 0.368 0.084 0.044
#> SRR1072456 2 0.239 0.6902 0.000 0.880 0.116 0.004 0.000
#> SRR1367896 3 0.220 0.6655 0.032 0.032 0.924 0.008 0.004
#> SRR1480107 1 0.187 0.7227 0.936 0.000 0.036 0.016 0.012
#> SRR1377756 1 0.207 0.7270 0.920 0.000 0.060 0.016 0.004
#> SRR1435272 4 0.727 0.8014 0.216 0.092 0.152 0.540 0.000
#> SRR1089230 4 0.680 0.8522 0.148 0.100 0.144 0.608 0.000
#> SRR1389522 3 0.263 0.6762 0.052 0.024 0.904 0.016 0.004
#> SRR1080600 2 0.709 0.5394 0.000 0.532 0.276 0.092 0.100
#> SRR1086935 4 0.670 0.7233 0.068 0.148 0.180 0.604 0.000
#> SRR1344060 5 0.550 0.7685 0.068 0.100 0.040 0.040 0.752
#> SRR1467922 2 0.406 0.5193 0.000 0.800 0.112 0.084 0.004
#> SRR1090984 3 0.569 0.3517 0.372 0.032 0.568 0.020 0.008
#> SRR1456991 1 0.184 0.7235 0.936 0.000 0.040 0.016 0.008
#> SRR1085039 1 0.139 0.7234 0.956 0.000 0.024 0.012 0.008
#> SRR1069303 1 0.263 0.6969 0.860 0.000 0.004 0.136 0.000
#> SRR1091500 2 0.405 0.5991 0.008 0.788 0.016 0.176 0.012
#> SRR1075198 2 0.780 0.4589 0.064 0.460 0.276 0.188 0.012
#> SRR1086915 4 0.727 0.5160 0.360 0.088 0.100 0.452 0.000
#> SRR1499503 2 0.239 0.6902 0.000 0.880 0.116 0.004 0.000
#> SRR1094312 2 0.309 0.6533 0.000 0.856 0.040 0.104 0.000
#> SRR1352437 1 0.263 0.6969 0.860 0.000 0.004 0.136 0.000
#> SRR1436323 3 0.607 0.5767 0.112 0.040 0.648 0.200 0.000
#> SRR1073507 1 0.365 0.6935 0.828 0.008 0.044 0.120 0.000
#> SRR1401972 1 0.263 0.6969 0.860 0.000 0.004 0.136 0.000
#> SRR1415510 2 0.296 0.6834 0.000 0.848 0.140 0.004 0.008
#> SRR1327279 1 0.370 0.7032 0.820 0.000 0.096 0.084 0.000
#> SRR1086983 1 0.653 0.0455 0.524 0.060 0.064 0.352 0.000
#> SRR1105174 1 0.187 0.6977 0.936 0.000 0.012 0.016 0.036
#> SRR1468893 1 0.234 0.7188 0.908 0.000 0.012 0.068 0.012
#> SRR1362555 2 0.785 0.4513 0.064 0.452 0.276 0.196 0.012
#> SRR1074526 5 0.425 0.7827 0.112 0.016 0.004 0.064 0.804
#> SRR1326225 2 0.239 0.6902 0.000 0.880 0.116 0.004 0.000
#> SRR1401933 1 0.482 0.6484 0.764 0.024 0.068 0.140 0.004
#> SRR1324062 1 0.382 0.7108 0.824 0.008 0.084 0.084 0.000
#> SRR1102296 1 0.640 0.2690 0.524 0.056 0.364 0.056 0.000
#> SRR1085087 1 0.302 0.7082 0.864 0.000 0.048 0.088 0.000
#> SRR1079046 1 0.540 0.5840 0.748 0.052 0.016 0.116 0.068
#> SRR1328339 3 0.584 0.5145 0.268 0.060 0.632 0.040 0.000
#> SRR1079782 2 0.785 0.4513 0.064 0.452 0.276 0.196 0.012
#> SRR1092257 2 0.772 0.3951 0.068 0.424 0.268 0.240 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.1858 0.6534 0.000 0.904 0.092 0.004 0.000 0.000
#> SRR1429287 2 0.7427 0.4603 0.000 0.400 0.188 0.220 0.000 0.192
#> SRR1359238 1 0.6117 0.4236 0.524 0.012 0.272 0.184 0.000 0.008
#> SRR1309597 3 0.0993 0.7014 0.024 0.012 0.964 0.000 0.000 0.000
#> SRR1441398 1 0.4397 0.5521 0.672 0.000 0.284 0.032 0.000 0.012
#> SRR1084055 2 0.1858 0.6534 0.000 0.904 0.092 0.004 0.000 0.000
#> SRR1417566 3 0.6007 0.4392 0.312 0.036 0.548 0.096 0.000 0.008
#> SRR1351857 1 0.5096 0.1078 0.496 0.024 0.020 0.452 0.000 0.008
#> SRR1487485 3 0.2978 0.6910 0.012 0.056 0.860 0.072 0.000 0.000
#> SRR1335875 3 0.4203 0.7061 0.092 0.056 0.796 0.044 0.000 0.012
#> SRR1073947 1 0.2581 0.7082 0.860 0.000 0.020 0.120 0.000 0.000
#> SRR1443483 3 0.1053 0.6978 0.020 0.012 0.964 0.000 0.004 0.000
#> SRR1346794 1 0.6808 0.0735 0.464 0.020 0.324 0.148 0.000 0.044
#> SRR1405245 1 0.3812 0.5979 0.712 0.000 0.268 0.016 0.000 0.004
#> SRR1409677 4 0.5149 0.6960 0.136 0.048 0.096 0.712 0.000 0.008
#> SRR1095549 1 0.5575 0.5344 0.600 0.008 0.224 0.164 0.000 0.004
#> SRR1323788 1 0.4786 0.5414 0.660 0.008 0.256 0.076 0.000 0.000
#> SRR1314054 2 0.5405 0.5719 0.000 0.684 0.056 0.140 0.004 0.116
#> SRR1077944 1 0.2979 0.7123 0.840 0.000 0.116 0.044 0.000 0.000
#> SRR1480587 2 0.2261 0.6473 0.000 0.884 0.104 0.004 0.000 0.008
#> SRR1311205 1 0.3468 0.7075 0.816 0.000 0.088 0.092 0.000 0.004
#> SRR1076369 1 0.5989 0.3451 0.564 0.004 0.032 0.080 0.012 0.308
#> SRR1453549 3 0.6102 0.1540 0.100 0.028 0.452 0.412 0.000 0.008
#> SRR1345782 1 0.2263 0.7224 0.884 0.000 0.100 0.016 0.000 0.000
#> SRR1447850 2 0.6095 0.5300 0.000 0.580 0.064 0.228 0.000 0.128
#> SRR1391553 3 0.4251 0.7170 0.084 0.056 0.792 0.060 0.000 0.008
#> SRR1444156 2 0.4090 0.4547 0.000 0.792 0.048 0.092 0.000 0.068
#> SRR1471731 3 0.5317 0.6133 0.088 0.024 0.620 0.268 0.000 0.000
#> SRR1120987 2 0.8235 0.4225 0.060 0.376 0.216 0.184 0.000 0.164
#> SRR1477363 1 0.2118 0.7248 0.916 0.004 0.048 0.020 0.000 0.012
#> SRR1391961 5 0.3098 0.7536 0.040 0.000 0.000 0.004 0.836 0.120
#> SRR1373879 3 0.4435 0.6980 0.076 0.012 0.748 0.156 0.000 0.008
#> SRR1318732 3 0.5785 0.5505 0.256 0.036 0.604 0.096 0.000 0.008
#> SRR1091404 1 0.1321 0.7221 0.952 0.000 0.024 0.020 0.004 0.000
#> SRR1402109 3 0.4435 0.6980 0.076 0.012 0.748 0.156 0.000 0.008
#> SRR1407336 3 0.4761 0.6811 0.088 0.012 0.704 0.192 0.004 0.000
#> SRR1097417 3 0.2074 0.6747 0.016 0.016 0.924 0.000 0.028 0.016
#> SRR1396227 1 0.4423 0.6391 0.720 0.020 0.012 0.224 0.000 0.024
#> SRR1400775 2 0.4008 0.6148 0.000 0.796 0.040 0.068 0.000 0.096
#> SRR1392861 4 0.3829 0.7462 0.112 0.048 0.036 0.804 0.000 0.000
#> SRR1472929 5 0.1528 0.7974 0.000 0.000 0.048 0.000 0.936 0.016
#> SRR1436740 4 0.3829 0.7462 0.112 0.048 0.036 0.804 0.000 0.000
#> SRR1477057 1 0.7074 0.1337 0.448 0.320 0.016 0.128 0.000 0.088
#> SRR1311980 3 0.4609 0.7112 0.100 0.040 0.764 0.084 0.000 0.012
#> SRR1069400 3 0.3565 0.7169 0.076 0.000 0.820 0.092 0.004 0.008
#> SRR1351016 1 0.3466 0.7076 0.816 0.000 0.084 0.096 0.000 0.004
#> SRR1096291 4 0.8286 0.1295 0.080 0.248 0.292 0.308 0.008 0.064
#> SRR1418145 2 0.8635 0.2795 0.100 0.328 0.240 0.196 0.004 0.132
#> SRR1488111 2 0.8235 0.4225 0.060 0.376 0.216 0.184 0.000 0.164
#> SRR1370495 1 0.6369 0.5409 0.636 0.072 0.040 0.148 0.004 0.100
#> SRR1352639 2 0.8648 0.2970 0.192 0.344 0.216 0.132 0.004 0.112
#> SRR1348911 3 0.2842 0.6749 0.044 0.076 0.868 0.012 0.000 0.000
#> SRR1467386 1 0.4044 0.6533 0.740 0.004 0.040 0.212 0.000 0.004
#> SRR1415956 1 0.2244 0.7010 0.912 0.000 0.036 0.032 0.004 0.016
#> SRR1500495 1 0.4397 0.5521 0.672 0.000 0.284 0.032 0.000 0.012
#> SRR1405099 1 0.2244 0.7010 0.912 0.000 0.036 0.032 0.004 0.016
#> SRR1345585 3 0.5060 0.6847 0.112 0.036 0.708 0.140 0.000 0.004
#> SRR1093196 3 0.5178 0.6439 0.084 0.028 0.652 0.236 0.000 0.000
#> SRR1466006 2 0.6693 0.4463 0.000 0.516 0.232 0.060 0.008 0.184
#> SRR1351557 2 0.3066 0.6331 0.000 0.860 0.056 0.024 0.000 0.060
#> SRR1382687 1 0.3892 0.6875 0.792 0.008 0.144 0.040 0.000 0.016
#> SRR1375549 1 0.4571 0.6279 0.760 0.024 0.016 0.124 0.000 0.076
#> SRR1101765 1 0.5868 0.3398 0.568 0.004 0.024 0.080 0.012 0.312
#> SRR1334461 5 0.0547 0.8306 0.000 0.000 0.000 0.000 0.980 0.020
#> SRR1094073 2 0.3066 0.6331 0.000 0.860 0.056 0.024 0.000 0.060
#> SRR1077549 1 0.3388 0.6887 0.804 0.004 0.036 0.156 0.000 0.000
#> SRR1440332 1 0.5207 0.5821 0.628 0.000 0.212 0.156 0.000 0.004
#> SRR1454177 4 0.3829 0.7462 0.112 0.048 0.036 0.804 0.000 0.000
#> SRR1082447 1 0.1092 0.7253 0.960 0.000 0.020 0.020 0.000 0.000
#> SRR1420043 3 0.6102 0.1540 0.100 0.028 0.452 0.412 0.000 0.008
#> SRR1432500 1 0.4738 0.5998 0.684 0.004 0.072 0.232 0.000 0.008
#> SRR1378045 2 0.5504 0.3028 0.000 0.656 0.188 0.092 0.000 0.064
#> SRR1334200 6 0.4959 0.8005 0.008 0.076 0.028 0.012 0.140 0.736
#> SRR1069539 4 0.8286 0.1295 0.080 0.248 0.292 0.308 0.008 0.064
#> SRR1343031 3 0.3997 0.7087 0.092 0.000 0.784 0.112 0.004 0.008
#> SRR1319690 1 0.5257 0.4556 0.612 0.008 0.296 0.072 0.000 0.012
#> SRR1310604 2 0.1858 0.6534 0.000 0.904 0.092 0.004 0.000 0.000
#> SRR1327747 1 0.6669 0.1059 0.460 0.012 0.332 0.156 0.000 0.040
#> SRR1072456 2 0.1858 0.6534 0.000 0.904 0.092 0.004 0.000 0.000
#> SRR1367896 3 0.1369 0.6901 0.016 0.016 0.952 0.000 0.016 0.000
#> SRR1480107 1 0.1313 0.7219 0.952 0.000 0.028 0.016 0.004 0.000
#> SRR1377756 1 0.2183 0.7266 0.912 0.004 0.052 0.020 0.000 0.012
#> SRR1435272 4 0.4646 0.7031 0.188 0.040 0.036 0.728 0.000 0.008
#> SRR1089230 4 0.4056 0.7341 0.116 0.044 0.028 0.800 0.004 0.008
#> SRR1389522 3 0.1914 0.7052 0.040 0.012 0.928 0.008 0.012 0.000
#> SRR1080600 2 0.6693 0.4463 0.000 0.516 0.232 0.060 0.008 0.184
#> SRR1086935 4 0.3715 0.6383 0.036 0.084 0.044 0.828 0.004 0.004
#> SRR1344060 6 0.4855 0.8220 0.016 0.064 0.028 0.004 0.148 0.740
#> SRR1467922 2 0.4090 0.4547 0.000 0.792 0.048 0.092 0.000 0.068
#> SRR1090984 3 0.5600 0.3590 0.352 0.020 0.548 0.072 0.000 0.008
#> SRR1456991 1 0.1245 0.7226 0.952 0.000 0.032 0.016 0.000 0.000
#> SRR1085039 1 0.0909 0.7221 0.968 0.000 0.012 0.020 0.000 0.000
#> SRR1069303 1 0.2491 0.6956 0.836 0.000 0.000 0.164 0.000 0.000
#> SRR1091500 2 0.4564 0.5592 0.000 0.720 0.008 0.132 0.000 0.140
#> SRR1075198 2 0.8107 0.4670 0.056 0.416 0.224 0.152 0.004 0.148
#> SRR1086915 4 0.5335 0.4211 0.332 0.040 0.032 0.588 0.000 0.008
#> SRR1499503 2 0.1858 0.6534 0.000 0.904 0.092 0.004 0.000 0.000
#> SRR1094312 2 0.4008 0.6148 0.000 0.796 0.040 0.068 0.000 0.096
#> SRR1352437 1 0.2491 0.6956 0.836 0.000 0.000 0.164 0.000 0.000
#> SRR1436323 3 0.5396 0.6011 0.092 0.024 0.608 0.276 0.000 0.000
#> SRR1073507 1 0.3388 0.6887 0.804 0.004 0.036 0.156 0.000 0.000
#> SRR1401972 1 0.2491 0.6956 0.836 0.000 0.000 0.164 0.000 0.000
#> SRR1415510 2 0.2488 0.6411 0.000 0.864 0.124 0.008 0.000 0.004
#> SRR1327279 1 0.3554 0.7014 0.808 0.000 0.080 0.108 0.000 0.004
#> SRR1086983 1 0.4871 0.1101 0.496 0.024 0.020 0.460 0.000 0.000
#> SRR1105174 1 0.1722 0.6970 0.936 0.000 0.008 0.036 0.004 0.016
#> SRR1468893 1 0.2290 0.7177 0.892 0.000 0.004 0.084 0.000 0.020
#> SRR1362555 2 0.8152 0.4615 0.056 0.408 0.224 0.156 0.004 0.152
#> SRR1074526 6 0.4023 0.6555 0.052 0.000 0.000 0.008 0.188 0.752
#> SRR1326225 2 0.1858 0.6534 0.000 0.904 0.092 0.004 0.000 0.000
#> SRR1401933 1 0.4391 0.6512 0.740 0.020 0.016 0.196 0.000 0.028
#> SRR1324062 1 0.3759 0.7085 0.808 0.004 0.076 0.100 0.000 0.012
#> SRR1102296 1 0.5932 0.2298 0.512 0.056 0.368 0.060 0.000 0.004
#> SRR1085087 1 0.3005 0.7069 0.848 0.000 0.036 0.108 0.000 0.008
#> SRR1079046 1 0.5026 0.5908 0.728 0.036 0.008 0.100 0.004 0.124
#> SRR1328339 3 0.5496 0.5541 0.252 0.052 0.624 0.072 0.000 0.000
#> SRR1079782 2 0.8152 0.4615 0.056 0.408 0.224 0.156 0.004 0.152
#> SRR1092257 2 0.8219 0.4289 0.060 0.380 0.216 0.180 0.000 0.164
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 17611 rows and 118 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 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 0.658 0.823 0.929 0.4400 0.579 0.579
#> 3 3 0.460 0.651 0.810 0.4444 0.714 0.529
#> 4 4 0.549 0.595 0.753 0.1443 0.868 0.648
#> 5 5 0.639 0.653 0.786 0.0647 0.932 0.766
#> 6 6 0.662 0.551 0.704 0.0494 0.896 0.605
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
#> SRR1396765 2 0.0000 0.9225 0.000 1.000
#> SRR1429287 2 0.0000 0.9225 0.000 1.000
#> SRR1359238 1 0.0000 0.9153 1.000 0.000
#> SRR1309597 1 0.9580 0.4109 0.620 0.380
#> SRR1441398 1 0.0000 0.9153 1.000 0.000
#> SRR1084055 2 0.0000 0.9225 0.000 1.000
#> SRR1417566 1 0.8386 0.6320 0.732 0.268
#> SRR1351857 1 0.0000 0.9153 1.000 0.000
#> SRR1487485 2 0.9686 0.3152 0.396 0.604
#> SRR1335875 1 0.7674 0.6949 0.776 0.224
#> SRR1073947 1 0.0000 0.9153 1.000 0.000
#> SRR1443483 1 0.9580 0.4109 0.620 0.380
#> SRR1346794 1 0.0000 0.9153 1.000 0.000
#> SRR1405245 1 0.0000 0.9153 1.000 0.000
#> SRR1409677 1 0.0000 0.9153 1.000 0.000
#> SRR1095549 1 0.0000 0.9153 1.000 0.000
#> SRR1323788 1 0.0000 0.9153 1.000 0.000
#> SRR1314054 2 0.0000 0.9225 0.000 1.000
#> SRR1077944 1 0.0000 0.9153 1.000 0.000
#> SRR1480587 2 0.0000 0.9225 0.000 1.000
#> SRR1311205 1 0.0000 0.9153 1.000 0.000
#> SRR1076369 1 0.0376 0.9125 0.996 0.004
#> SRR1453549 1 0.0000 0.9153 1.000 0.000
#> SRR1345782 1 0.0000 0.9153 1.000 0.000
#> SRR1447850 2 0.0000 0.9225 0.000 1.000
#> SRR1391553 2 0.9686 0.3152 0.396 0.604
#> SRR1444156 2 0.0000 0.9225 0.000 1.000
#> SRR1471731 1 0.8499 0.6192 0.724 0.276
#> SRR1120987 1 0.0000 0.9153 1.000 0.000
#> SRR1477363 1 0.0000 0.9153 1.000 0.000
#> SRR1391961 1 0.9710 0.2965 0.600 0.400
#> SRR1373879 1 0.0000 0.9153 1.000 0.000
#> SRR1318732 1 0.9393 0.4672 0.644 0.356
#> SRR1091404 1 0.0000 0.9153 1.000 0.000
#> SRR1402109 1 0.0000 0.9153 1.000 0.000
#> SRR1407336 1 0.7299 0.7220 0.796 0.204
#> SRR1097417 2 0.8016 0.6450 0.244 0.756
#> SRR1396227 1 0.0000 0.9153 1.000 0.000
#> SRR1400775 2 0.0000 0.9225 0.000 1.000
#> SRR1392861 1 0.0000 0.9153 1.000 0.000
#> SRR1472929 2 0.0938 0.9137 0.012 0.988
#> SRR1436740 1 0.0000 0.9153 1.000 0.000
#> SRR1477057 2 0.0000 0.9225 0.000 1.000
#> SRR1311980 1 0.7745 0.6895 0.772 0.228
#> SRR1069400 1 0.9358 0.4761 0.648 0.352
#> SRR1351016 1 0.0000 0.9153 1.000 0.000
#> SRR1096291 1 0.1414 0.9006 0.980 0.020
#> SRR1418145 1 0.0000 0.9153 1.000 0.000
#> SRR1488111 2 0.7745 0.6694 0.228 0.772
#> SRR1370495 1 0.0000 0.9153 1.000 0.000
#> SRR1352639 1 0.0672 0.9092 0.992 0.008
#> SRR1348911 1 0.9608 0.4013 0.616 0.384
#> SRR1467386 1 0.0000 0.9153 1.000 0.000
#> SRR1415956 1 0.0000 0.9153 1.000 0.000
#> SRR1500495 1 0.0000 0.9153 1.000 0.000
#> SRR1405099 1 0.0000 0.9153 1.000 0.000
#> SRR1345585 2 0.9686 0.3152 0.396 0.604
#> SRR1093196 1 0.8499 0.6192 0.724 0.276
#> SRR1466006 2 0.0000 0.9225 0.000 1.000
#> SRR1351557 2 0.0000 0.9225 0.000 1.000
#> SRR1382687 1 0.0000 0.9153 1.000 0.000
#> SRR1375549 1 0.0000 0.9153 1.000 0.000
#> SRR1101765 1 0.0000 0.9153 1.000 0.000
#> SRR1334461 1 0.9710 0.2965 0.600 0.400
#> SRR1094073 2 0.0000 0.9225 0.000 1.000
#> SRR1077549 1 0.0000 0.9153 1.000 0.000
#> SRR1440332 1 0.0000 0.9153 1.000 0.000
#> SRR1454177 1 0.0000 0.9153 1.000 0.000
#> SRR1082447 1 0.0000 0.9153 1.000 0.000
#> SRR1420043 1 0.0000 0.9153 1.000 0.000
#> SRR1432500 1 0.0000 0.9153 1.000 0.000
#> SRR1378045 2 0.0000 0.9225 0.000 1.000
#> SRR1334200 2 0.0938 0.9137 0.012 0.988
#> SRR1069539 2 0.9963 0.0919 0.464 0.536
#> SRR1343031 1 0.0000 0.9153 1.000 0.000
#> SRR1319690 1 0.0000 0.9153 1.000 0.000
#> SRR1310604 2 0.0000 0.9225 0.000 1.000
#> SRR1327747 1 0.0000 0.9153 1.000 0.000
#> SRR1072456 2 0.0000 0.9225 0.000 1.000
#> SRR1367896 1 0.9608 0.4013 0.616 0.384
#> SRR1480107 1 0.0000 0.9153 1.000 0.000
#> SRR1377756 1 0.0000 0.9153 1.000 0.000
#> SRR1435272 1 0.0000 0.9153 1.000 0.000
#> SRR1089230 1 0.0000 0.9153 1.000 0.000
#> SRR1389522 1 0.5519 0.8047 0.872 0.128
#> SRR1080600 2 0.0000 0.9225 0.000 1.000
#> SRR1086935 1 0.8713 0.5917 0.708 0.292
#> SRR1344060 2 0.5408 0.8080 0.124 0.876
#> SRR1467922 2 0.0000 0.9225 0.000 1.000
#> SRR1090984 1 0.0000 0.9153 1.000 0.000
#> SRR1456991 1 0.0000 0.9153 1.000 0.000
#> SRR1085039 1 0.0000 0.9153 1.000 0.000
#> SRR1069303 1 0.0000 0.9153 1.000 0.000
#> SRR1091500 2 0.0000 0.9225 0.000 1.000
#> SRR1075198 2 0.0000 0.9225 0.000 1.000
#> SRR1086915 1 0.0000 0.9153 1.000 0.000
#> SRR1499503 2 0.0000 0.9225 0.000 1.000
#> SRR1094312 2 0.0000 0.9225 0.000 1.000
#> SRR1352437 1 0.0000 0.9153 1.000 0.000
#> SRR1436323 1 0.0000 0.9153 1.000 0.000
#> SRR1073507 1 0.0000 0.9153 1.000 0.000
#> SRR1401972 1 0.0000 0.9153 1.000 0.000
#> SRR1415510 2 0.0000 0.9225 0.000 1.000
#> SRR1327279 1 0.0000 0.9153 1.000 0.000
#> SRR1086983 1 0.0000 0.9153 1.000 0.000
#> SRR1105174 1 0.0000 0.9153 1.000 0.000
#> SRR1468893 1 0.0000 0.9153 1.000 0.000
#> SRR1362555 2 0.0000 0.9225 0.000 1.000
#> SRR1074526 1 0.9977 0.0809 0.528 0.472
#> SRR1326225 2 0.0000 0.9225 0.000 1.000
#> SRR1401933 1 0.0000 0.9153 1.000 0.000
#> SRR1324062 1 0.0000 0.9153 1.000 0.000
#> SRR1102296 1 0.0000 0.9153 1.000 0.000
#> SRR1085087 1 0.0000 0.9153 1.000 0.000
#> SRR1079046 1 0.9491 0.3767 0.632 0.368
#> SRR1328339 1 0.8327 0.6393 0.736 0.264
#> SRR1079782 2 0.0000 0.9225 0.000 1.000
#> SRR1092257 2 0.0000 0.9225 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.0237 0.9317 0.000 0.996 0.004
#> SRR1429287 2 0.2165 0.9144 0.000 0.936 0.064
#> SRR1359238 3 0.6308 0.2360 0.492 0.000 0.508
#> SRR1309597 3 0.7085 0.6487 0.188 0.096 0.716
#> SRR1441398 1 0.4887 0.5248 0.772 0.000 0.228
#> SRR1084055 2 0.1643 0.9243 0.000 0.956 0.044
#> SRR1417566 3 0.7759 0.2165 0.476 0.048 0.476
#> SRR1351857 1 0.5678 0.4275 0.684 0.000 0.316
#> SRR1487485 3 0.6295 0.6100 0.036 0.236 0.728
#> SRR1335875 3 0.7065 0.6361 0.288 0.048 0.664
#> SRR1073947 1 0.2448 0.7278 0.924 0.000 0.076
#> SRR1443483 3 0.6605 0.6728 0.152 0.096 0.752
#> SRR1346794 1 0.5216 0.4826 0.740 0.000 0.260
#> SRR1405245 1 0.5098 0.4910 0.752 0.000 0.248
#> SRR1409677 3 0.6468 0.3466 0.444 0.004 0.552
#> SRR1095549 1 0.5327 0.4512 0.728 0.000 0.272
#> SRR1323788 1 0.4291 0.5946 0.820 0.000 0.180
#> SRR1314054 2 0.0424 0.9306 0.000 0.992 0.008
#> SRR1077944 1 0.0000 0.7418 1.000 0.000 0.000
#> SRR1480587 2 0.0424 0.9313 0.000 0.992 0.008
#> SRR1311205 1 0.2796 0.6896 0.908 0.000 0.092
#> SRR1076369 1 0.6460 0.0981 0.556 0.004 0.440
#> SRR1453549 3 0.4291 0.6997 0.180 0.000 0.820
#> SRR1345782 1 0.1529 0.7277 0.960 0.000 0.040
#> SRR1447850 2 0.1643 0.9137 0.000 0.956 0.044
#> SRR1391553 3 0.5977 0.5884 0.020 0.252 0.728
#> SRR1444156 2 0.0592 0.9304 0.000 0.988 0.012
#> SRR1471731 3 0.5222 0.7089 0.144 0.040 0.816
#> SRR1120987 1 0.6617 0.1470 0.556 0.008 0.436
#> SRR1477363 1 0.0000 0.7418 1.000 0.000 0.000
#> SRR1391961 1 0.5688 0.6015 0.788 0.044 0.168
#> SRR1373879 3 0.4452 0.7012 0.192 0.000 0.808
#> SRR1318732 3 0.8559 0.3772 0.388 0.100 0.512
#> SRR1091404 1 0.0592 0.7400 0.988 0.000 0.012
#> SRR1402109 3 0.4452 0.6980 0.192 0.000 0.808
#> SRR1407336 3 0.4390 0.7079 0.148 0.012 0.840
#> SRR1097417 3 0.6254 0.6211 0.116 0.108 0.776
#> SRR1396227 1 0.2165 0.7315 0.936 0.000 0.064
#> SRR1400775 2 0.0000 0.9317 0.000 1.000 0.000
#> SRR1392861 3 0.4834 0.6876 0.204 0.004 0.792
#> SRR1472929 2 0.7564 0.6342 0.068 0.636 0.296
#> SRR1436740 3 0.6451 0.3663 0.436 0.004 0.560
#> SRR1477057 2 0.2066 0.9157 0.000 0.940 0.060
#> SRR1311980 3 0.5508 0.7045 0.188 0.028 0.784
#> SRR1069400 3 0.6151 0.6858 0.160 0.068 0.772
#> SRR1351016 1 0.2356 0.7294 0.928 0.000 0.072
#> SRR1096291 3 0.5905 0.5124 0.352 0.000 0.648
#> SRR1418145 1 0.6617 0.1470 0.556 0.008 0.436
#> SRR1488111 3 0.7030 0.2937 0.024 0.396 0.580
#> SRR1370495 1 0.2955 0.7070 0.912 0.008 0.080
#> SRR1352639 1 0.2056 0.7369 0.952 0.024 0.024
#> SRR1348911 3 0.7227 0.6538 0.200 0.096 0.704
#> SRR1467386 1 0.4002 0.6720 0.840 0.000 0.160
#> SRR1415956 1 0.2796 0.6922 0.908 0.000 0.092
#> SRR1500495 1 0.4887 0.5248 0.772 0.000 0.228
#> SRR1405099 1 0.0237 0.7410 0.996 0.000 0.004
#> SRR1345585 3 0.6977 0.6196 0.076 0.212 0.712
#> SRR1093196 3 0.5173 0.7089 0.148 0.036 0.816
#> SRR1466006 2 0.0424 0.9319 0.000 0.992 0.008
#> SRR1351557 2 0.0237 0.9312 0.000 0.996 0.004
#> SRR1382687 1 0.4504 0.6382 0.804 0.000 0.196
#> SRR1375549 1 0.2096 0.7282 0.944 0.004 0.052
#> SRR1101765 1 0.2682 0.7213 0.920 0.004 0.076
#> SRR1334461 1 0.5688 0.6015 0.788 0.044 0.168
#> SRR1094073 2 0.0424 0.9312 0.000 0.992 0.008
#> SRR1077549 3 0.6305 0.2467 0.484 0.000 0.516
#> SRR1440332 3 0.6008 0.4997 0.372 0.000 0.628
#> SRR1454177 3 0.6314 0.4586 0.392 0.004 0.604
#> SRR1082447 1 0.0000 0.7418 1.000 0.000 0.000
#> SRR1420043 3 0.4399 0.6966 0.188 0.000 0.812
#> SRR1432500 1 0.5529 0.4632 0.704 0.000 0.296
#> SRR1378045 2 0.6019 0.5576 0.012 0.700 0.288
#> SRR1334200 2 0.6652 0.7688 0.084 0.744 0.172
#> SRR1069539 3 0.6854 0.6063 0.068 0.216 0.716
#> SRR1343031 3 0.4504 0.7007 0.196 0.000 0.804
#> SRR1319690 1 0.6280 -0.1391 0.540 0.000 0.460
#> SRR1310604 2 0.2066 0.9183 0.000 0.940 0.060
#> SRR1327747 3 0.5733 0.6159 0.324 0.000 0.676
#> SRR1072456 2 0.1289 0.9262 0.000 0.968 0.032
#> SRR1367896 3 0.6605 0.6718 0.152 0.096 0.752
#> SRR1480107 1 0.0000 0.7418 1.000 0.000 0.000
#> SRR1377756 1 0.2796 0.7185 0.908 0.000 0.092
#> SRR1435272 3 0.6359 0.4359 0.404 0.004 0.592
#> SRR1089230 3 0.6274 0.3196 0.456 0.000 0.544
#> SRR1389522 3 0.6337 0.6410 0.264 0.028 0.708
#> SRR1080600 2 0.1753 0.9239 0.000 0.952 0.048
#> SRR1086935 3 0.8374 0.5706 0.144 0.240 0.616
#> SRR1344060 2 0.9149 0.3997 0.316 0.516 0.168
#> SRR1467922 2 0.0592 0.9304 0.000 0.988 0.012
#> SRR1090984 1 0.5254 0.4641 0.736 0.000 0.264
#> SRR1456991 1 0.0000 0.7418 1.000 0.000 0.000
#> SRR1085039 1 0.0000 0.7418 1.000 0.000 0.000
#> SRR1069303 1 0.3192 0.7089 0.888 0.000 0.112
#> SRR1091500 2 0.1643 0.9207 0.000 0.956 0.044
#> SRR1075198 2 0.1163 0.9307 0.000 0.972 0.028
#> SRR1086915 1 0.5859 0.3785 0.656 0.000 0.344
#> SRR1499503 2 0.0424 0.9313 0.000 0.992 0.008
#> SRR1094312 2 0.0000 0.9317 0.000 1.000 0.000
#> SRR1352437 1 0.5158 0.5854 0.764 0.004 0.232
#> SRR1436323 3 0.4399 0.6969 0.188 0.000 0.812
#> SRR1073507 1 0.4002 0.6720 0.840 0.000 0.160
#> SRR1401972 1 0.3192 0.7089 0.888 0.000 0.112
#> SRR1415510 2 0.0592 0.9313 0.000 0.988 0.012
#> SRR1327279 1 0.5650 0.4127 0.688 0.000 0.312
#> SRR1086983 1 0.5810 0.3901 0.664 0.000 0.336
#> SRR1105174 1 0.0000 0.7418 1.000 0.000 0.000
#> SRR1468893 1 0.1163 0.7398 0.972 0.000 0.028
#> SRR1362555 2 0.2866 0.9062 0.008 0.916 0.076
#> SRR1074526 1 0.6138 0.5819 0.768 0.060 0.172
#> SRR1326225 2 0.0424 0.9313 0.000 0.992 0.008
#> SRR1401933 1 0.3816 0.6880 0.852 0.000 0.148
#> SRR1324062 1 0.5690 0.4867 0.708 0.004 0.288
#> SRR1102296 1 0.0237 0.7410 0.996 0.000 0.004
#> SRR1085087 1 0.5070 0.5961 0.772 0.004 0.224
#> SRR1079046 1 0.4418 0.6499 0.848 0.020 0.132
#> SRR1328339 1 0.6823 0.3660 0.668 0.036 0.296
#> SRR1079782 2 0.1411 0.9246 0.000 0.964 0.036
#> SRR1092257 2 0.2356 0.9088 0.000 0.928 0.072
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.0188 0.9240 0.000 0.996 0.004 0.000
#> SRR1429287 2 0.2909 0.8919 0.000 0.888 0.020 0.092
#> SRR1359238 4 0.5358 0.7550 0.252 0.000 0.048 0.700
#> SRR1309597 3 0.4610 0.7783 0.084 0.016 0.820 0.080
#> SRR1441398 1 0.4454 0.4189 0.692 0.000 0.308 0.000
#> SRR1084055 2 0.1297 0.9170 0.000 0.964 0.016 0.020
#> SRR1417566 3 0.4855 0.5794 0.268 0.000 0.712 0.020
#> SRR1351857 4 0.4621 0.7167 0.284 0.000 0.008 0.708
#> SRR1487485 3 0.4830 0.7676 0.016 0.068 0.804 0.112
#> SRR1335875 3 0.4088 0.7489 0.140 0.000 0.820 0.040
#> SRR1073947 1 0.4642 0.4130 0.740 0.000 0.020 0.240
#> SRR1443483 3 0.4641 0.7822 0.064 0.016 0.816 0.104
#> SRR1346794 1 0.5311 0.3634 0.648 0.000 0.328 0.024
#> SRR1405245 1 0.4817 0.2783 0.612 0.000 0.388 0.000
#> SRR1409677 4 0.5664 0.7941 0.156 0.000 0.124 0.720
#> SRR1095549 1 0.6356 0.3160 0.604 0.000 0.308 0.088
#> SRR1323788 1 0.4690 0.4690 0.712 0.000 0.276 0.012
#> SRR1314054 2 0.0779 0.9215 0.000 0.980 0.004 0.016
#> SRR1077944 1 0.1297 0.6387 0.964 0.000 0.020 0.016
#> SRR1480587 2 0.0188 0.9240 0.000 0.996 0.004 0.000
#> SRR1311205 1 0.3108 0.6223 0.872 0.000 0.112 0.016
#> SRR1076369 1 0.6813 0.1682 0.516 0.000 0.380 0.104
#> SRR1453549 3 0.4767 0.7169 0.020 0.000 0.724 0.256
#> SRR1345782 1 0.2522 0.6327 0.908 0.000 0.076 0.016
#> SRR1447850 2 0.1722 0.9098 0.000 0.944 0.008 0.048
#> SRR1391553 3 0.4553 0.7528 0.012 0.092 0.820 0.076
#> SRR1444156 2 0.0188 0.9233 0.000 0.996 0.004 0.000
#> SRR1471731 3 0.5070 0.4936 0.004 0.000 0.580 0.416
#> SRR1120987 4 0.4644 0.7796 0.164 0.004 0.044 0.788
#> SRR1477363 1 0.1520 0.6370 0.956 0.000 0.020 0.024
#> SRR1391961 1 0.7471 0.4309 0.568 0.016 0.176 0.240
#> SRR1373879 3 0.4379 0.7679 0.036 0.000 0.792 0.172
#> SRR1318732 3 0.5478 0.6512 0.228 0.016 0.720 0.036
#> SRR1091404 1 0.0657 0.6409 0.984 0.000 0.012 0.004
#> SRR1402109 3 0.5050 0.7025 0.028 0.000 0.704 0.268
#> SRR1407336 3 0.4936 0.5683 0.004 0.000 0.624 0.372
#> SRR1097417 3 0.4867 0.7150 0.064 0.020 0.804 0.112
#> SRR1396227 1 0.3485 0.5826 0.856 0.000 0.028 0.116
#> SRR1400775 2 0.0188 0.9240 0.000 0.996 0.004 0.000
#> SRR1392861 4 0.5279 0.6971 0.072 0.000 0.192 0.736
#> SRR1472929 2 0.9833 0.1666 0.172 0.304 0.292 0.232
#> SRR1436740 4 0.5339 0.8020 0.156 0.000 0.100 0.744
#> SRR1477057 2 0.3084 0.8980 0.012 0.896 0.028 0.064
#> SRR1311980 3 0.4424 0.7679 0.088 0.000 0.812 0.100
#> SRR1069400 3 0.4498 0.7786 0.044 0.008 0.808 0.140
#> SRR1351016 1 0.3803 0.5562 0.836 0.000 0.032 0.132
#> SRR1096291 4 0.5119 0.7731 0.112 0.000 0.124 0.764
#> SRR1418145 4 0.4598 0.7783 0.160 0.004 0.044 0.792
#> SRR1488111 4 0.6099 0.5265 0.008 0.172 0.120 0.700
#> SRR1370495 1 0.4219 0.5967 0.832 0.004 0.076 0.088
#> SRR1352639 1 0.4825 0.5678 0.792 0.020 0.036 0.152
#> SRR1348911 3 0.4443 0.7612 0.120 0.012 0.820 0.048
#> SRR1467386 1 0.5165 -0.2257 0.512 0.000 0.004 0.484
#> SRR1415956 1 0.2334 0.6280 0.908 0.000 0.088 0.004
#> SRR1500495 1 0.4406 0.4334 0.700 0.000 0.300 0.000
#> SRR1405099 1 0.0592 0.6405 0.984 0.000 0.016 0.000
#> SRR1345585 3 0.4694 0.7769 0.044 0.048 0.824 0.084
#> SRR1093196 3 0.4950 0.5624 0.004 0.000 0.620 0.376
#> SRR1466006 2 0.0000 0.9239 0.000 1.000 0.000 0.000
#> SRR1351557 2 0.0000 0.9239 0.000 1.000 0.000 0.000
#> SRR1382687 1 0.5510 -0.2356 0.504 0.000 0.016 0.480
#> SRR1375549 1 0.2408 0.6293 0.920 0.000 0.036 0.044
#> SRR1101765 1 0.4735 0.5688 0.784 0.000 0.068 0.148
#> SRR1334461 1 0.7402 0.4338 0.576 0.016 0.168 0.240
#> SRR1094073 2 0.0188 0.9233 0.000 0.996 0.004 0.000
#> SRR1077549 4 0.5404 0.7601 0.248 0.000 0.052 0.700
#> SRR1440332 4 0.7396 0.4424 0.216 0.000 0.268 0.516
#> SRR1454177 4 0.5432 0.7769 0.124 0.000 0.136 0.740
#> SRR1082447 1 0.0937 0.6396 0.976 0.000 0.012 0.012
#> SRR1420043 3 0.5050 0.4927 0.004 0.000 0.588 0.408
#> SRR1432500 4 0.5137 0.3702 0.452 0.000 0.004 0.544
#> SRR1378045 3 0.4964 0.3747 0.004 0.380 0.616 0.000
#> SRR1334200 2 0.9668 0.2725 0.204 0.368 0.164 0.264
#> SRR1069539 4 0.5895 0.4287 0.024 0.032 0.268 0.676
#> SRR1343031 3 0.5309 0.7134 0.044 0.000 0.700 0.256
#> SRR1319690 3 0.4781 0.4793 0.336 0.000 0.660 0.004
#> SRR1310604 2 0.2699 0.9006 0.000 0.904 0.028 0.068
#> SRR1327747 3 0.7088 0.6166 0.204 0.000 0.568 0.228
#> SRR1072456 2 0.1174 0.9176 0.000 0.968 0.012 0.020
#> SRR1367896 3 0.4422 0.7810 0.044 0.016 0.824 0.116
#> SRR1480107 1 0.0895 0.6373 0.976 0.000 0.004 0.020
#> SRR1377756 1 0.5057 0.2392 0.648 0.000 0.012 0.340
#> SRR1435272 4 0.5428 0.7920 0.140 0.000 0.120 0.740
#> SRR1089230 4 0.5265 0.8022 0.160 0.000 0.092 0.748
#> SRR1389522 3 0.4359 0.7763 0.100 0.000 0.816 0.084
#> SRR1080600 2 0.2965 0.8939 0.000 0.892 0.036 0.072
#> SRR1086935 4 0.5998 0.7228 0.064 0.052 0.144 0.740
#> SRR1344060 1 0.9823 -0.0914 0.304 0.284 0.164 0.248
#> SRR1467922 2 0.0188 0.9233 0.000 0.996 0.004 0.000
#> SRR1090984 1 0.5151 0.0944 0.532 0.000 0.464 0.004
#> SRR1456991 1 0.1174 0.6396 0.968 0.000 0.012 0.020
#> SRR1085039 1 0.2197 0.6108 0.916 0.000 0.004 0.080
#> SRR1069303 1 0.5323 0.1917 0.628 0.000 0.020 0.352
#> SRR1091500 2 0.0895 0.9215 0.000 0.976 0.020 0.004
#> SRR1075198 2 0.2124 0.9062 0.000 0.924 0.008 0.068
#> SRR1086915 4 0.4360 0.7477 0.248 0.000 0.008 0.744
#> SRR1499503 2 0.0336 0.9235 0.000 0.992 0.008 0.000
#> SRR1094312 2 0.0188 0.9240 0.000 0.996 0.004 0.000
#> SRR1352437 1 0.5688 -0.1768 0.512 0.000 0.024 0.464
#> SRR1436323 3 0.5126 0.4321 0.004 0.000 0.552 0.444
#> SRR1073507 1 0.5165 -0.2265 0.512 0.000 0.004 0.484
#> SRR1401972 1 0.5323 0.1917 0.628 0.000 0.020 0.352
#> SRR1415510 2 0.0336 0.9235 0.000 0.992 0.008 0.000
#> SRR1327279 1 0.6559 -0.3701 0.468 0.000 0.076 0.456
#> SRR1086983 4 0.4647 0.7100 0.288 0.000 0.008 0.704
#> SRR1105174 1 0.0707 0.6405 0.980 0.000 0.020 0.000
#> SRR1468893 1 0.2222 0.6197 0.924 0.000 0.016 0.060
#> SRR1362555 2 0.3851 0.8754 0.004 0.852 0.056 0.088
#> SRR1074526 1 0.7678 0.4196 0.556 0.024 0.172 0.248
#> SRR1326225 2 0.0188 0.9233 0.000 0.996 0.004 0.000
#> SRR1401933 1 0.5856 -0.2079 0.504 0.000 0.032 0.464
#> SRR1324062 4 0.6074 0.3133 0.456 0.000 0.044 0.500
#> SRR1102296 1 0.1733 0.6362 0.948 0.000 0.024 0.028
#> SRR1085087 1 0.5503 -0.1893 0.516 0.000 0.016 0.468
#> SRR1079046 1 0.4901 0.5653 0.780 0.000 0.108 0.112
#> SRR1328339 1 0.5165 0.0115 0.512 0.000 0.484 0.004
#> SRR1079782 2 0.2480 0.8977 0.000 0.904 0.008 0.088
#> SRR1092257 2 0.3224 0.8747 0.000 0.864 0.016 0.120
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.0566 0.8890 0.000 0.984 0.000 0.012 0.004
#> SRR1429287 2 0.5377 0.7471 0.000 0.712 0.024 0.140 0.124
#> SRR1359238 4 0.4054 0.6515 0.236 0.000 0.008 0.744 0.012
#> SRR1309597 3 0.2104 0.7527 0.044 0.000 0.924 0.024 0.008
#> SRR1441398 1 0.3649 0.6039 0.808 0.000 0.152 0.000 0.040
#> SRR1084055 2 0.1205 0.8870 0.000 0.956 0.000 0.004 0.040
#> SRR1417566 3 0.5170 0.5682 0.232 0.000 0.688 0.012 0.068
#> SRR1351857 4 0.3242 0.7422 0.172 0.000 0.000 0.816 0.012
#> SRR1487485 3 0.1960 0.7503 0.000 0.020 0.928 0.048 0.004
#> SRR1335875 3 0.1740 0.7401 0.056 0.000 0.932 0.000 0.012
#> SRR1073947 1 0.5094 0.5487 0.704 0.000 0.020 0.220 0.056
#> SRR1443483 3 0.2116 0.7543 0.028 0.000 0.924 0.040 0.008
#> SRR1346794 1 0.5898 0.3924 0.624 0.000 0.264 0.024 0.088
#> SRR1405245 1 0.3885 0.5838 0.784 0.000 0.176 0.000 0.040
#> SRR1409677 4 0.2438 0.8179 0.040 0.000 0.060 0.900 0.000
#> SRR1095549 1 0.6425 0.3544 0.592 0.000 0.268 0.080 0.060
#> SRR1323788 1 0.4190 0.6122 0.792 0.000 0.140 0.012 0.056
#> SRR1314054 2 0.1116 0.8875 0.000 0.964 0.004 0.004 0.028
#> SRR1077944 1 0.1461 0.6994 0.952 0.000 0.004 0.016 0.028
#> SRR1480587 2 0.1503 0.8864 0.000 0.952 0.008 0.020 0.020
#> SRR1311205 1 0.1195 0.6952 0.960 0.000 0.028 0.000 0.012
#> SRR1076369 1 0.7039 0.1116 0.464 0.000 0.308 0.024 0.204
#> SRR1453549 3 0.3476 0.7341 0.020 0.000 0.816 0.160 0.004
#> SRR1345782 1 0.1074 0.6979 0.968 0.000 0.012 0.004 0.016
#> SRR1447850 2 0.3730 0.8051 0.000 0.828 0.012 0.112 0.048
#> SRR1391553 3 0.2162 0.7395 0.020 0.020 0.928 0.004 0.028
#> SRR1444156 2 0.0324 0.8879 0.000 0.992 0.004 0.000 0.004
#> SRR1471731 3 0.4822 0.5476 0.012 0.000 0.632 0.340 0.016
#> SRR1120987 4 0.2507 0.7918 0.028 0.000 0.020 0.908 0.044
#> SRR1477363 1 0.1012 0.7003 0.968 0.000 0.000 0.012 0.020
#> SRR1391961 5 0.3280 0.8334 0.160 0.004 0.012 0.000 0.824
#> SRR1373879 3 0.2349 0.7514 0.012 0.000 0.900 0.084 0.004
#> SRR1318732 3 0.5331 0.5957 0.228 0.008 0.692 0.016 0.056
#> SRR1091404 1 0.1484 0.6941 0.944 0.000 0.000 0.008 0.048
#> SRR1402109 3 0.3578 0.7042 0.008 0.000 0.784 0.204 0.004
#> SRR1407336 3 0.3928 0.6121 0.000 0.000 0.700 0.296 0.004
#> SRR1097417 3 0.3504 0.6337 0.016 0.000 0.816 0.008 0.160
#> SRR1396227 1 0.4532 0.6777 0.792 0.000 0.040 0.080 0.088
#> SRR1400775 2 0.0566 0.8884 0.000 0.984 0.004 0.000 0.012
#> SRR1392861 4 0.2069 0.8024 0.012 0.000 0.076 0.912 0.000
#> SRR1472929 5 0.4178 0.8005 0.024 0.140 0.016 0.016 0.804
#> SRR1436740 4 0.1990 0.8250 0.040 0.000 0.028 0.928 0.004
#> SRR1477057 2 0.5139 0.7776 0.008 0.748 0.036 0.060 0.148
#> SRR1311980 3 0.2217 0.7427 0.044 0.000 0.920 0.012 0.024
#> SRR1069400 3 0.2086 0.7543 0.020 0.000 0.924 0.048 0.008
#> SRR1351016 1 0.3907 0.6693 0.828 0.000 0.032 0.096 0.044
#> SRR1096291 4 0.2747 0.7819 0.020 0.000 0.036 0.896 0.048
#> SRR1418145 4 0.3135 0.7467 0.020 0.000 0.024 0.868 0.088
#> SRR1488111 4 0.4717 0.6441 0.000 0.048 0.072 0.780 0.100
#> SRR1370495 1 0.5149 0.5262 0.692 0.000 0.016 0.060 0.232
#> SRR1352639 1 0.4874 0.6043 0.768 0.012 0.016 0.084 0.120
#> SRR1348911 3 0.1682 0.7426 0.044 0.000 0.940 0.004 0.012
#> SRR1467386 1 0.4731 0.1437 0.528 0.000 0.000 0.456 0.016
#> SRR1415956 1 0.2321 0.6794 0.912 0.000 0.024 0.008 0.056
#> SRR1500495 1 0.3141 0.6402 0.852 0.000 0.108 0.000 0.040
#> SRR1405099 1 0.1043 0.6934 0.960 0.000 0.000 0.000 0.040
#> SRR1345585 3 0.1918 0.7509 0.012 0.016 0.940 0.020 0.012
#> SRR1093196 3 0.3816 0.6052 0.000 0.000 0.696 0.304 0.000
#> SRR1466006 2 0.1772 0.8838 0.000 0.940 0.008 0.020 0.032
#> SRR1351557 2 0.0566 0.8915 0.000 0.984 0.000 0.012 0.004
#> SRR1382687 1 0.4544 0.6232 0.740 0.000 0.012 0.208 0.040
#> SRR1375549 1 0.3646 0.6674 0.828 0.000 0.008 0.044 0.120
#> SRR1101765 1 0.6502 0.2838 0.536 0.000 0.008 0.216 0.240
#> SRR1334461 5 0.3205 0.8258 0.176 0.004 0.004 0.000 0.816
#> SRR1094073 2 0.0324 0.8879 0.000 0.992 0.004 0.000 0.004
#> SRR1077549 4 0.3947 0.6546 0.236 0.000 0.008 0.748 0.008
#> SRR1440332 1 0.6923 0.1256 0.448 0.000 0.200 0.336 0.016
#> SRR1454177 4 0.1981 0.8204 0.028 0.000 0.048 0.924 0.000
#> SRR1082447 1 0.2390 0.6884 0.896 0.000 0.000 0.020 0.084
#> SRR1420043 3 0.4122 0.6043 0.004 0.000 0.688 0.304 0.004
#> SRR1432500 4 0.4802 -0.0161 0.480 0.000 0.004 0.504 0.012
#> SRR1378045 3 0.4269 0.4706 0.000 0.300 0.684 0.000 0.016
#> SRR1334200 5 0.3151 0.8336 0.024 0.092 0.004 0.012 0.868
#> SRR1069539 4 0.4651 0.5875 0.000 0.004 0.156 0.748 0.092
#> SRR1343031 3 0.3751 0.6977 0.012 0.000 0.772 0.212 0.004
#> SRR1319690 3 0.5455 0.2938 0.416 0.000 0.528 0.004 0.052
#> SRR1310604 2 0.4202 0.8218 0.000 0.804 0.024 0.056 0.116
#> SRR1327747 3 0.7106 0.5057 0.260 0.000 0.520 0.168 0.052
#> SRR1072456 2 0.1686 0.8848 0.000 0.944 0.008 0.020 0.028
#> SRR1367896 3 0.1989 0.7517 0.020 0.000 0.932 0.032 0.016
#> SRR1480107 1 0.1331 0.6942 0.952 0.000 0.000 0.008 0.040
#> SRR1377756 1 0.3573 0.6795 0.812 0.000 0.000 0.152 0.036
#> SRR1435272 4 0.1750 0.8231 0.028 0.000 0.036 0.936 0.000
#> SRR1089230 4 0.1557 0.8200 0.052 0.000 0.000 0.940 0.008
#> SRR1389522 3 0.2178 0.7518 0.048 0.000 0.920 0.024 0.008
#> SRR1080600 2 0.4662 0.7929 0.000 0.764 0.024 0.060 0.152
#> SRR1086935 4 0.2150 0.8171 0.016 0.020 0.032 0.928 0.004
#> SRR1344060 5 0.3038 0.8449 0.032 0.080 0.004 0.008 0.876
#> SRR1467922 2 0.0324 0.8879 0.000 0.992 0.004 0.000 0.004
#> SRR1090984 3 0.5901 0.0598 0.456 0.000 0.460 0.008 0.076
#> SRR1456991 1 0.0865 0.6974 0.972 0.000 0.004 0.000 0.024
#> SRR1085039 1 0.1965 0.6951 0.924 0.000 0.000 0.052 0.024
#> SRR1069303 1 0.6371 0.3668 0.544 0.000 0.036 0.336 0.084
#> SRR1091500 2 0.0671 0.8880 0.000 0.980 0.004 0.000 0.016
#> SRR1075198 2 0.4779 0.8001 0.000 0.764 0.024 0.092 0.120
#> SRR1086915 4 0.1671 0.8179 0.076 0.000 0.000 0.924 0.000
#> SRR1499503 2 0.0968 0.8873 0.000 0.972 0.004 0.012 0.012
#> SRR1094312 2 0.0566 0.8884 0.000 0.984 0.004 0.000 0.012
#> SRR1352437 1 0.6495 0.1593 0.464 0.000 0.040 0.420 0.076
#> SRR1436323 3 0.5012 0.4960 0.016 0.000 0.600 0.368 0.016
#> SRR1073507 1 0.4747 0.0380 0.500 0.000 0.000 0.484 0.016
#> SRR1401972 1 0.6371 0.3668 0.544 0.000 0.036 0.336 0.084
#> SRR1415510 2 0.1721 0.8846 0.000 0.944 0.016 0.020 0.020
#> SRR1327279 1 0.5995 0.0974 0.512 0.000 0.072 0.400 0.016
#> SRR1086983 4 0.3355 0.7294 0.184 0.000 0.000 0.804 0.012
#> SRR1105174 1 0.1557 0.6929 0.940 0.000 0.000 0.008 0.052
#> SRR1468893 1 0.2291 0.6993 0.908 0.000 0.000 0.036 0.056
#> SRR1362555 2 0.5392 0.7681 0.008 0.724 0.024 0.092 0.152
#> SRR1074526 5 0.3465 0.8389 0.116 0.004 0.012 0.024 0.844
#> SRR1326225 2 0.0324 0.8879 0.000 0.992 0.004 0.000 0.004
#> SRR1401933 1 0.6195 0.4126 0.552 0.000 0.028 0.340 0.080
#> SRR1324062 1 0.6147 0.2410 0.524 0.000 0.048 0.384 0.044
#> SRR1102296 1 0.2792 0.6854 0.884 0.000 0.040 0.004 0.072
#> SRR1085087 1 0.5949 0.1309 0.492 0.000 0.028 0.432 0.048
#> SRR1079046 1 0.4922 0.4109 0.636 0.000 0.008 0.028 0.328
#> SRR1328339 3 0.5790 0.1856 0.424 0.000 0.500 0.008 0.068
#> SRR1079782 2 0.5400 0.7448 0.000 0.708 0.024 0.156 0.112
#> SRR1092257 2 0.5663 0.6660 0.000 0.664 0.020 0.216 0.100
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.0405 0.7754 0.000 0.988 0.000 0.008 0.004 0.000
#> SRR1429287 2 0.6939 0.5218 0.004 0.492 0.000 0.220 0.096 0.188
#> SRR1359238 4 0.5588 0.5723 0.404 0.000 0.060 0.500 0.000 0.036
#> SRR1309597 3 0.1375 0.8106 0.004 0.000 0.952 0.008 0.008 0.028
#> SRR1441398 6 0.5175 0.3698 0.308 0.000 0.100 0.000 0.004 0.588
#> SRR1084055 2 0.1148 0.7766 0.000 0.960 0.000 0.004 0.020 0.016
#> SRR1417566 6 0.5841 0.2763 0.044 0.000 0.384 0.028 0.028 0.516
#> SRR1351857 4 0.4062 0.7271 0.344 0.000 0.004 0.640 0.000 0.012
#> SRR1487485 3 0.1708 0.8128 0.000 0.000 0.932 0.024 0.004 0.040
#> SRR1335875 3 0.3370 0.7726 0.032 0.000 0.852 0.020 0.024 0.072
#> SRR1073947 1 0.1338 0.5322 0.952 0.000 0.004 0.008 0.004 0.032
#> SRR1443483 3 0.0912 0.8111 0.004 0.000 0.972 0.008 0.012 0.004
#> SRR1346794 6 0.4743 0.5220 0.088 0.000 0.144 0.020 0.012 0.736
#> SRR1405245 6 0.5486 0.3435 0.316 0.000 0.132 0.000 0.004 0.548
#> SRR1409677 4 0.4028 0.7901 0.192 0.000 0.044 0.752 0.000 0.012
#> SRR1095549 6 0.6308 0.4360 0.208 0.000 0.184 0.040 0.008 0.560
#> SRR1323788 6 0.4779 0.4332 0.264 0.000 0.072 0.008 0.000 0.656
#> SRR1314054 2 0.1173 0.7745 0.000 0.960 0.000 0.016 0.016 0.008
#> SRR1077944 6 0.4107 0.0700 0.452 0.000 0.000 0.004 0.004 0.540
#> SRR1480587 2 0.2157 0.7696 0.000 0.916 0.008 0.008 0.028 0.040
#> SRR1311205 1 0.4487 0.3219 0.608 0.000 0.024 0.004 0.004 0.360
#> SRR1076369 6 0.5212 0.5137 0.024 0.000 0.196 0.020 0.072 0.688
#> SRR1453549 3 0.3830 0.7876 0.008 0.000 0.796 0.120 0.004 0.072
#> SRR1345782 1 0.4622 0.3272 0.608 0.000 0.036 0.000 0.008 0.348
#> SRR1447850 2 0.3692 0.7020 0.000 0.816 0.008 0.116 0.020 0.040
#> SRR1391553 3 0.3966 0.7540 0.028 0.004 0.812 0.024 0.024 0.108
#> SRR1444156 2 0.0862 0.7697 0.000 0.972 0.004 0.008 0.000 0.016
#> SRR1471731 3 0.6448 0.4486 0.080 0.000 0.500 0.324 0.004 0.092
#> SRR1120987 4 0.3820 0.7145 0.128 0.000 0.000 0.796 0.020 0.056
#> SRR1477363 1 0.3986 0.1570 0.532 0.000 0.000 0.000 0.004 0.464
#> SRR1391961 5 0.2685 0.8952 0.080 0.000 0.004 0.000 0.872 0.044
#> SRR1373879 3 0.1152 0.8142 0.000 0.000 0.952 0.044 0.004 0.000
#> SRR1318732 6 0.4447 0.2723 0.000 0.000 0.420 0.012 0.012 0.556
#> SRR1091404 1 0.4279 0.1406 0.548 0.000 0.000 0.004 0.012 0.436
#> SRR1402109 3 0.2872 0.7695 0.012 0.000 0.832 0.152 0.004 0.000
#> SRR1407336 3 0.4127 0.6359 0.012 0.000 0.692 0.280 0.004 0.012
#> SRR1097417 3 0.2822 0.7228 0.004 0.000 0.852 0.016 0.124 0.004
#> SRR1396227 1 0.4578 0.1531 0.548 0.000 0.004 0.008 0.016 0.424
#> SRR1400775 2 0.0881 0.7748 0.000 0.972 0.000 0.012 0.008 0.008
#> SRR1392861 4 0.3755 0.7945 0.192 0.000 0.028 0.768 0.000 0.012
#> SRR1472929 5 0.2186 0.8858 0.000 0.056 0.012 0.000 0.908 0.024
#> SRR1436740 4 0.3642 0.7988 0.236 0.000 0.008 0.744 0.000 0.012
#> SRR1477057 2 0.7448 0.5213 0.052 0.508 0.008 0.152 0.068 0.212
#> SRR1311980 3 0.3398 0.7744 0.040 0.000 0.852 0.020 0.024 0.064
#> SRR1069400 3 0.1015 0.8114 0.004 0.000 0.968 0.012 0.012 0.004
#> SRR1351016 1 0.3166 0.4842 0.800 0.000 0.008 0.008 0.000 0.184
#> SRR1096291 4 0.3783 0.6694 0.060 0.000 0.032 0.832 0.028 0.048
#> SRR1418145 4 0.4525 0.5521 0.060 0.000 0.000 0.748 0.048 0.144
#> SRR1488111 4 0.5507 0.4376 0.016 0.028 0.024 0.696 0.064 0.172
#> SRR1370495 6 0.7253 -0.0765 0.304 0.000 0.000 0.184 0.124 0.388
#> SRR1352639 1 0.6852 0.2216 0.472 0.000 0.000 0.196 0.084 0.248
#> SRR1348911 3 0.2608 0.7900 0.012 0.000 0.896 0.020 0.028 0.044
#> SRR1467386 1 0.2714 0.4859 0.848 0.000 0.004 0.136 0.000 0.012
#> SRR1415956 6 0.3847 0.2169 0.348 0.000 0.000 0.000 0.008 0.644
#> SRR1500495 6 0.5177 0.2617 0.364 0.000 0.084 0.000 0.004 0.548
#> SRR1405099 1 0.4184 0.1188 0.504 0.000 0.000 0.000 0.012 0.484
#> SRR1345585 3 0.2306 0.7908 0.000 0.000 0.888 0.016 0.004 0.092
#> SRR1093196 3 0.4620 0.5952 0.012 0.000 0.636 0.320 0.004 0.028
#> SRR1466006 2 0.2442 0.7655 0.000 0.900 0.008 0.008 0.036 0.048
#> SRR1351557 2 0.0891 0.7785 0.000 0.968 0.000 0.008 0.000 0.024
#> SRR1382687 1 0.4867 0.1783 0.536 0.000 0.004 0.040 0.004 0.416
#> SRR1375549 6 0.3841 0.3339 0.244 0.000 0.000 0.000 0.032 0.724
#> SRR1101765 6 0.5644 0.3575 0.128 0.000 0.000 0.120 0.092 0.660
#> SRR1334461 5 0.2758 0.9025 0.080 0.012 0.000 0.000 0.872 0.036
#> SRR1094073 2 0.0622 0.7717 0.000 0.980 0.000 0.008 0.000 0.012
#> SRR1077549 4 0.4908 0.5281 0.468 0.000 0.036 0.484 0.000 0.012
#> SRR1440332 1 0.7393 0.1146 0.440 0.000 0.228 0.180 0.008 0.144
#> SRR1454177 4 0.3748 0.7989 0.212 0.000 0.020 0.756 0.000 0.012
#> SRR1082447 6 0.4310 0.1752 0.404 0.000 0.000 0.004 0.016 0.576
#> SRR1420043 3 0.4190 0.6024 0.012 0.000 0.668 0.304 0.000 0.016
#> SRR1432500 1 0.3477 0.4714 0.804 0.000 0.016 0.160 0.004 0.016
#> SRR1378045 2 0.6164 -0.0837 0.000 0.460 0.416 0.032 0.024 0.068
#> SRR1334200 5 0.1956 0.9048 0.008 0.040 0.004 0.008 0.928 0.012
#> SRR1069539 4 0.4406 0.5603 0.008 0.000 0.088 0.780 0.052 0.072
#> SRR1343031 3 0.3252 0.7781 0.032 0.000 0.832 0.124 0.008 0.004
#> SRR1319690 6 0.4524 0.5070 0.036 0.000 0.312 0.004 0.004 0.644
#> SRR1310604 2 0.6736 0.5681 0.000 0.556 0.012 0.176 0.104 0.152
#> SRR1327747 6 0.5593 0.3824 0.008 0.000 0.272 0.136 0.004 0.580
#> SRR1072456 2 0.2233 0.7683 0.000 0.912 0.008 0.008 0.032 0.040
#> SRR1367896 3 0.0665 0.8103 0.000 0.000 0.980 0.008 0.008 0.004
#> SRR1480107 1 0.3802 0.3752 0.676 0.000 0.000 0.000 0.012 0.312
#> SRR1377756 1 0.4563 0.1379 0.504 0.000 0.000 0.020 0.008 0.468
#> SRR1435272 4 0.3381 0.8006 0.212 0.000 0.008 0.772 0.000 0.008
#> SRR1089230 4 0.3921 0.7956 0.224 0.000 0.004 0.736 0.000 0.036
#> SRR1389522 3 0.1026 0.8100 0.004 0.000 0.968 0.008 0.012 0.008
#> SRR1080600 2 0.6783 0.5622 0.000 0.552 0.012 0.172 0.112 0.152
#> SRR1086935 4 0.3963 0.7901 0.208 0.000 0.016 0.748 0.000 0.028
#> SRR1344060 5 0.1749 0.9070 0.016 0.044 0.004 0.004 0.932 0.000
#> SRR1467922 2 0.0862 0.7697 0.000 0.972 0.004 0.008 0.000 0.016
#> SRR1090984 6 0.5774 0.5011 0.092 0.000 0.268 0.016 0.024 0.600
#> SRR1456991 1 0.3955 0.3530 0.648 0.000 0.004 0.000 0.008 0.340
#> SRR1085039 1 0.3152 0.4794 0.792 0.000 0.000 0.008 0.004 0.196
#> SRR1069303 1 0.3421 0.4983 0.840 0.000 0.008 0.052 0.016 0.084
#> SRR1091500 2 0.1180 0.7730 0.000 0.960 0.000 0.012 0.012 0.016
#> SRR1075198 2 0.6813 0.5455 0.000 0.524 0.004 0.192 0.108 0.172
#> SRR1086915 4 0.3584 0.7961 0.244 0.000 0.004 0.740 0.000 0.012
#> SRR1499503 2 0.1086 0.7715 0.000 0.964 0.000 0.012 0.012 0.012
#> SRR1094312 2 0.0779 0.7752 0.000 0.976 0.000 0.008 0.008 0.008
#> SRR1352437 1 0.3921 0.4544 0.800 0.000 0.008 0.100 0.012 0.080
#> SRR1436323 3 0.7085 0.2352 0.100 0.000 0.396 0.352 0.004 0.148
#> SRR1073507 1 0.2848 0.4495 0.828 0.000 0.004 0.160 0.000 0.008
#> SRR1401972 1 0.3421 0.4983 0.840 0.000 0.008 0.052 0.016 0.084
#> SRR1415510 2 0.2373 0.7687 0.000 0.908 0.016 0.012 0.024 0.040
#> SRR1327279 1 0.4570 0.4668 0.744 0.000 0.104 0.128 0.004 0.020
#> SRR1086983 4 0.3965 0.6974 0.376 0.000 0.004 0.616 0.004 0.000
#> SRR1105174 1 0.4303 0.1055 0.524 0.000 0.000 0.004 0.012 0.460
#> SRR1468893 6 0.4264 -0.1230 0.484 0.000 0.000 0.000 0.016 0.500
#> SRR1362555 2 0.7198 0.5054 0.004 0.480 0.004 0.192 0.120 0.200
#> SRR1074526 5 0.2999 0.8921 0.060 0.004 0.004 0.004 0.864 0.064
#> SRR1326225 2 0.0717 0.7707 0.000 0.976 0.000 0.008 0.000 0.016
#> SRR1401933 6 0.6489 0.1192 0.264 0.000 0.024 0.200 0.012 0.500
#> SRR1324062 1 0.3925 0.4984 0.800 0.000 0.008 0.096 0.012 0.084
#> SRR1102296 1 0.4837 0.3749 0.652 0.000 0.012 0.016 0.032 0.288
#> SRR1085087 1 0.3770 0.4360 0.800 0.000 0.004 0.136 0.016 0.044
#> SRR1079046 6 0.4823 0.3253 0.216 0.000 0.000 0.000 0.124 0.660
#> SRR1328339 6 0.5899 0.4785 0.084 0.000 0.324 0.016 0.024 0.552
#> SRR1079782 2 0.6916 0.5188 0.004 0.488 0.000 0.232 0.088 0.188
#> SRR1092257 2 0.6990 0.4369 0.008 0.440 0.000 0.312 0.072 0.168
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 17611 rows and 118 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.787 0.898 0.957 0.4959 0.503 0.503
#> 3 3 0.586 0.659 0.845 0.3404 0.770 0.574
#> 4 4 0.742 0.811 0.903 0.1292 0.829 0.552
#> 5 5 0.701 0.642 0.799 0.0602 0.887 0.595
#> 6 6 0.702 0.627 0.777 0.0398 0.926 0.678
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
#> SRR1396765 2 0.0000 0.942 0.000 1.000
#> SRR1429287 2 0.0000 0.942 0.000 1.000
#> SRR1359238 1 0.0000 0.962 1.000 0.000
#> SRR1309597 2 0.4431 0.871 0.092 0.908
#> SRR1441398 1 0.0000 0.962 1.000 0.000
#> SRR1084055 2 0.0000 0.942 0.000 1.000
#> SRR1417566 2 0.0000 0.942 0.000 1.000
#> SRR1351857 1 0.0000 0.962 1.000 0.000
#> SRR1487485 2 0.0000 0.942 0.000 1.000
#> SRR1335875 2 0.0000 0.942 0.000 1.000
#> SRR1073947 1 0.0000 0.962 1.000 0.000
#> SRR1443483 2 0.4431 0.871 0.092 0.908
#> SRR1346794 1 0.0000 0.962 1.000 0.000
#> SRR1405245 1 0.0000 0.962 1.000 0.000
#> SRR1409677 1 0.0000 0.962 1.000 0.000
#> SRR1095549 1 0.0000 0.962 1.000 0.000
#> SRR1323788 1 0.0000 0.962 1.000 0.000
#> SRR1314054 2 0.0000 0.942 0.000 1.000
#> SRR1077944 1 0.0000 0.962 1.000 0.000
#> SRR1480587 2 0.0000 0.942 0.000 1.000
#> SRR1311205 1 0.0000 0.962 1.000 0.000
#> SRR1076369 1 0.9754 0.228 0.592 0.408
#> SRR1453549 1 0.2948 0.916 0.948 0.052
#> SRR1345782 1 0.0000 0.962 1.000 0.000
#> SRR1447850 2 0.0000 0.942 0.000 1.000
#> SRR1391553 2 0.0000 0.942 0.000 1.000
#> SRR1444156 2 0.0000 0.942 0.000 1.000
#> SRR1471731 2 0.9710 0.390 0.400 0.600
#> SRR1120987 1 0.0000 0.962 1.000 0.000
#> SRR1477363 1 0.0000 0.962 1.000 0.000
#> SRR1391961 1 0.9710 0.340 0.600 0.400
#> SRR1373879 1 0.6247 0.794 0.844 0.156
#> SRR1318732 2 0.1184 0.931 0.016 0.984
#> SRR1091404 1 0.0000 0.962 1.000 0.000
#> SRR1402109 1 0.1184 0.949 0.984 0.016
#> SRR1407336 2 0.9710 0.390 0.400 0.600
#> SRR1097417 2 0.0000 0.942 0.000 1.000
#> SRR1396227 1 0.0000 0.962 1.000 0.000
#> SRR1400775 2 0.0000 0.942 0.000 1.000
#> SRR1392861 1 0.6438 0.783 0.836 0.164
#> SRR1472929 2 0.0000 0.942 0.000 1.000
#> SRR1436740 1 0.0000 0.962 1.000 0.000
#> SRR1477057 2 0.0000 0.942 0.000 1.000
#> SRR1311980 2 0.5178 0.847 0.116 0.884
#> SRR1069400 2 0.7219 0.748 0.200 0.800
#> SRR1351016 1 0.0000 0.962 1.000 0.000
#> SRR1096291 2 0.9710 0.390 0.400 0.600
#> SRR1418145 1 0.0000 0.962 1.000 0.000
#> SRR1488111 2 0.0000 0.942 0.000 1.000
#> SRR1370495 1 0.2423 0.928 0.960 0.040
#> SRR1352639 1 0.7219 0.740 0.800 0.200
#> SRR1348911 2 0.0000 0.942 0.000 1.000
#> SRR1467386 1 0.0000 0.962 1.000 0.000
#> SRR1415956 1 0.0000 0.962 1.000 0.000
#> SRR1500495 1 0.0000 0.962 1.000 0.000
#> SRR1405099 1 0.0000 0.962 1.000 0.000
#> SRR1345585 2 0.0000 0.942 0.000 1.000
#> SRR1093196 2 0.9710 0.390 0.400 0.600
#> SRR1466006 2 0.0000 0.942 0.000 1.000
#> SRR1351557 2 0.0000 0.942 0.000 1.000
#> SRR1382687 1 0.0000 0.962 1.000 0.000
#> SRR1375549 1 0.0000 0.962 1.000 0.000
#> SRR1101765 1 0.0000 0.962 1.000 0.000
#> SRR1334461 1 0.9710 0.340 0.600 0.400
#> SRR1094073 2 0.0000 0.942 0.000 1.000
#> SRR1077549 1 0.0000 0.962 1.000 0.000
#> SRR1440332 1 0.0000 0.962 1.000 0.000
#> SRR1454177 1 0.0000 0.962 1.000 0.000
#> SRR1082447 1 0.0000 0.962 1.000 0.000
#> SRR1420043 1 0.0000 0.962 1.000 0.000
#> SRR1432500 1 0.0000 0.962 1.000 0.000
#> SRR1378045 2 0.0000 0.942 0.000 1.000
#> SRR1334200 2 0.0000 0.942 0.000 1.000
#> SRR1069539 2 0.0000 0.942 0.000 1.000
#> SRR1343031 1 0.0000 0.962 1.000 0.000
#> SRR1319690 1 0.0000 0.962 1.000 0.000
#> SRR1310604 2 0.0000 0.942 0.000 1.000
#> SRR1327747 1 0.0938 0.953 0.988 0.012
#> SRR1072456 2 0.0000 0.942 0.000 1.000
#> SRR1367896 2 0.0000 0.942 0.000 1.000
#> SRR1480107 1 0.0000 0.962 1.000 0.000
#> SRR1377756 1 0.0000 0.962 1.000 0.000
#> SRR1435272 1 0.0000 0.962 1.000 0.000
#> SRR1089230 1 0.0000 0.962 1.000 0.000
#> SRR1389522 2 0.4431 0.871 0.092 0.908
#> SRR1080600 2 0.0000 0.942 0.000 1.000
#> SRR1086935 2 0.9710 0.390 0.400 0.600
#> SRR1344060 2 0.1184 0.930 0.016 0.984
#> SRR1467922 2 0.0000 0.942 0.000 1.000
#> SRR1090984 1 0.0000 0.962 1.000 0.000
#> SRR1456991 1 0.0000 0.962 1.000 0.000
#> SRR1085039 1 0.0000 0.962 1.000 0.000
#> SRR1069303 1 0.0000 0.962 1.000 0.000
#> SRR1091500 2 0.0000 0.942 0.000 1.000
#> SRR1075198 2 0.0000 0.942 0.000 1.000
#> SRR1086915 1 0.0000 0.962 1.000 0.000
#> SRR1499503 2 0.0000 0.942 0.000 1.000
#> SRR1094312 2 0.0000 0.942 0.000 1.000
#> SRR1352437 1 0.0000 0.962 1.000 0.000
#> SRR1436323 1 0.2043 0.936 0.968 0.032
#> SRR1073507 1 0.0000 0.962 1.000 0.000
#> SRR1401972 1 0.0000 0.962 1.000 0.000
#> SRR1415510 2 0.0000 0.942 0.000 1.000
#> SRR1327279 1 0.0000 0.962 1.000 0.000
#> SRR1086983 1 0.0000 0.962 1.000 0.000
#> SRR1105174 1 0.0000 0.962 1.000 0.000
#> SRR1468893 1 0.0000 0.962 1.000 0.000
#> SRR1362555 2 0.0000 0.942 0.000 1.000
#> SRR1074526 2 0.6247 0.790 0.156 0.844
#> SRR1326225 2 0.0000 0.942 0.000 1.000
#> SRR1401933 1 0.0000 0.962 1.000 0.000
#> SRR1324062 1 0.0000 0.962 1.000 0.000
#> SRR1102296 1 0.4939 0.857 0.892 0.108
#> SRR1085087 1 0.0000 0.962 1.000 0.000
#> SRR1079046 1 0.8327 0.637 0.736 0.264
#> SRR1328339 2 0.0000 0.942 0.000 1.000
#> SRR1079782 2 0.0000 0.942 0.000 1.000
#> SRR1092257 2 0.0000 0.942 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1429287 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1359238 1 0.5785 0.60085 0.668 0.000 0.332
#> SRR1309597 3 0.4891 0.70279 0.124 0.040 0.836
#> SRR1441398 1 0.6291 -0.16001 0.532 0.000 0.468
#> SRR1084055 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1417566 3 0.7523 0.58852 0.260 0.080 0.660
#> SRR1351857 1 0.5560 0.63192 0.700 0.000 0.300
#> SRR1487485 3 0.2711 0.72697 0.000 0.088 0.912
#> SRR1335875 3 0.6803 0.59247 0.040 0.280 0.680
#> SRR1073947 1 0.3412 0.72939 0.876 0.000 0.124
#> SRR1443483 3 0.2492 0.73347 0.016 0.048 0.936
#> SRR1346794 1 0.6295 -0.17179 0.528 0.000 0.472
#> SRR1405245 1 0.6307 -0.21380 0.512 0.000 0.488
#> SRR1409677 1 0.6302 0.37515 0.520 0.000 0.480
#> SRR1095549 1 0.6267 -0.09245 0.548 0.000 0.452
#> SRR1323788 1 0.6168 -0.00440 0.588 0.000 0.412
#> SRR1314054 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1077944 1 0.0000 0.73199 1.000 0.000 0.000
#> SRR1480587 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1311205 1 0.2796 0.66699 0.908 0.000 0.092
#> SRR1076369 3 0.6509 0.25995 0.472 0.004 0.524
#> SRR1453549 3 0.0000 0.72950 0.000 0.000 1.000
#> SRR1345782 1 0.1163 0.71798 0.972 0.000 0.028
#> SRR1447850 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1391553 3 0.5785 0.53499 0.000 0.332 0.668
#> SRR1444156 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1471731 3 0.0000 0.72950 0.000 0.000 1.000
#> SRR1120987 1 0.9405 0.40506 0.484 0.192 0.324
#> SRR1477363 1 0.0000 0.73199 1.000 0.000 0.000
#> SRR1391961 1 0.5621 0.52129 0.692 0.308 0.000
#> SRR1373879 3 0.0000 0.72950 0.000 0.000 1.000
#> SRR1318732 3 0.7610 0.61923 0.216 0.108 0.676
#> SRR1091404 1 0.0000 0.73199 1.000 0.000 0.000
#> SRR1402109 3 0.0000 0.72950 0.000 0.000 1.000
#> SRR1407336 3 0.0000 0.72950 0.000 0.000 1.000
#> SRR1097417 3 0.5760 0.54065 0.000 0.328 0.672
#> SRR1396227 1 0.0237 0.73254 0.996 0.000 0.004
#> SRR1400775 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1392861 3 0.1289 0.70378 0.032 0.000 0.968
#> SRR1472929 2 0.0892 0.94542 0.000 0.980 0.020
#> SRR1436740 1 0.6299 0.38270 0.524 0.000 0.476
#> SRR1477057 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1311980 3 0.1315 0.72713 0.020 0.008 0.972
#> SRR1069400 3 0.0237 0.73020 0.000 0.004 0.996
#> SRR1351016 1 0.3340 0.73013 0.880 0.000 0.120
#> SRR1096291 2 0.7905 0.34243 0.072 0.588 0.340
#> SRR1418145 1 0.9335 0.41609 0.492 0.184 0.324
#> SRR1488111 2 0.2625 0.87733 0.000 0.916 0.084
#> SRR1370495 1 0.4291 0.63408 0.820 0.180 0.000
#> SRR1352639 1 0.6111 0.36345 0.604 0.396 0.000
#> SRR1348911 3 0.5988 0.57247 0.008 0.304 0.688
#> SRR1467386 1 0.4178 0.71394 0.828 0.000 0.172
#> SRR1415956 1 0.2356 0.68605 0.928 0.000 0.072
#> SRR1500495 1 0.6286 -0.14955 0.536 0.000 0.464
#> SRR1405099 1 0.0000 0.73199 1.000 0.000 0.000
#> SRR1345585 3 0.5618 0.62196 0.008 0.260 0.732
#> SRR1093196 3 0.0000 0.72950 0.000 0.000 1.000
#> SRR1466006 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1351557 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1382687 1 0.2711 0.73403 0.912 0.000 0.088
#> SRR1375549 1 0.0000 0.73199 1.000 0.000 0.000
#> SRR1101765 1 0.0000 0.73199 1.000 0.000 0.000
#> SRR1334461 1 0.5216 0.56881 0.740 0.260 0.000
#> SRR1094073 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1077549 1 0.5882 0.58350 0.652 0.000 0.348
#> SRR1440332 3 0.6305 -0.33114 0.484 0.000 0.516
#> SRR1454177 3 0.6305 -0.33348 0.484 0.000 0.516
#> SRR1082447 1 0.0000 0.73199 1.000 0.000 0.000
#> SRR1420043 3 0.0000 0.72950 0.000 0.000 1.000
#> SRR1432500 1 0.5327 0.65286 0.728 0.000 0.272
#> SRR1378045 3 0.5810 0.52886 0.000 0.336 0.664
#> SRR1334200 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1069539 2 0.5178 0.62282 0.000 0.744 0.256
#> SRR1343031 3 0.0000 0.72950 0.000 0.000 1.000
#> SRR1319690 3 0.5760 0.53035 0.328 0.000 0.672
#> SRR1310604 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1327747 3 0.4178 0.67625 0.172 0.000 0.828
#> SRR1072456 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1367896 3 0.2165 0.73119 0.000 0.064 0.936
#> SRR1480107 1 0.0000 0.73199 1.000 0.000 0.000
#> SRR1377756 1 0.1411 0.73474 0.964 0.000 0.036
#> SRR1435272 1 0.6309 0.34261 0.504 0.000 0.496
#> SRR1089230 1 0.5926 0.57419 0.644 0.000 0.356
#> SRR1389522 3 0.4514 0.68691 0.156 0.012 0.832
#> SRR1080600 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1086935 3 0.7895 0.00788 0.056 0.436 0.508
#> SRR1344060 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1467922 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1090984 3 0.6309 0.21631 0.496 0.000 0.504
#> SRR1456991 1 0.0000 0.73199 1.000 0.000 0.000
#> SRR1085039 1 0.0000 0.73199 1.000 0.000 0.000
#> SRR1069303 1 0.3412 0.72939 0.876 0.000 0.124
#> SRR1091500 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1075198 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1086915 1 0.5678 0.61657 0.684 0.000 0.316
#> SRR1499503 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1094312 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1352437 1 0.4235 0.71233 0.824 0.000 0.176
#> SRR1436323 3 0.0000 0.72950 0.000 0.000 1.000
#> SRR1073507 1 0.4291 0.71065 0.820 0.000 0.180
#> SRR1401972 1 0.3412 0.72939 0.876 0.000 0.124
#> SRR1415510 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1327279 1 0.5560 0.63159 0.700 0.000 0.300
#> SRR1086983 1 0.5560 0.63192 0.700 0.000 0.300
#> SRR1105174 1 0.0000 0.73199 1.000 0.000 0.000
#> SRR1468893 1 0.0000 0.73199 1.000 0.000 0.000
#> SRR1362555 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1074526 2 0.2878 0.85364 0.096 0.904 0.000
#> SRR1326225 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1401933 1 0.3551 0.72759 0.868 0.000 0.132
#> SRR1324062 1 0.4750 0.69138 0.784 0.000 0.216
#> SRR1102296 1 0.0000 0.73199 1.000 0.000 0.000
#> SRR1085087 1 0.4291 0.71065 0.820 0.000 0.180
#> SRR1079046 1 0.2625 0.68766 0.916 0.084 0.000
#> SRR1328339 3 0.7283 0.28632 0.460 0.028 0.512
#> SRR1079782 2 0.0000 0.96640 0.000 1.000 0.000
#> SRR1092257 2 0.0000 0.96640 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1429287 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1359238 4 0.0000 0.846 0.000 0.000 0.000 1.000
#> SRR1309597 3 0.0188 0.882 0.004 0.000 0.996 0.000
#> SRR1441398 1 0.2266 0.835 0.912 0.000 0.084 0.004
#> SRR1084055 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1417566 3 0.4008 0.664 0.244 0.000 0.756 0.000
#> SRR1351857 4 0.0000 0.846 0.000 0.000 0.000 1.000
#> SRR1487485 3 0.0000 0.882 0.000 0.000 1.000 0.000
#> SRR1335875 3 0.0336 0.882 0.008 0.000 0.992 0.000
#> SRR1073947 1 0.4866 0.108 0.596 0.000 0.000 0.404
#> SRR1443483 3 0.0000 0.882 0.000 0.000 1.000 0.000
#> SRR1346794 1 0.3852 0.738 0.808 0.000 0.180 0.012
#> SRR1405245 1 0.2944 0.808 0.868 0.000 0.128 0.004
#> SRR1409677 4 0.0707 0.837 0.000 0.000 0.020 0.980
#> SRR1095549 1 0.5188 0.611 0.716 0.000 0.240 0.044
#> SRR1323788 1 0.2714 0.817 0.884 0.000 0.112 0.004
#> SRR1314054 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1077944 1 0.0336 0.865 0.992 0.000 0.000 0.008
#> SRR1480587 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1311205 1 0.0188 0.865 0.996 0.000 0.000 0.004
#> SRR1076369 1 0.5105 0.571 0.696 0.000 0.276 0.028
#> SRR1453549 3 0.1716 0.864 0.000 0.000 0.936 0.064
#> SRR1345782 1 0.0336 0.865 0.992 0.000 0.000 0.008
#> SRR1447850 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1391553 3 0.0376 0.881 0.004 0.004 0.992 0.000
#> SRR1444156 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1471731 3 0.4220 0.711 0.004 0.000 0.748 0.248
#> SRR1120987 4 0.0188 0.845 0.000 0.004 0.000 0.996
#> SRR1477363 1 0.0469 0.864 0.988 0.000 0.000 0.012
#> SRR1391961 1 0.3751 0.701 0.800 0.196 0.000 0.004
#> SRR1373879 3 0.1302 0.870 0.000 0.000 0.956 0.044
#> SRR1318732 3 0.3649 0.719 0.204 0.000 0.796 0.000
#> SRR1091404 1 0.0336 0.865 0.992 0.000 0.000 0.008
#> SRR1402109 3 0.2530 0.841 0.000 0.000 0.888 0.112
#> SRR1407336 3 0.3444 0.790 0.000 0.000 0.816 0.184
#> SRR1097417 3 0.0000 0.882 0.000 0.000 1.000 0.000
#> SRR1396227 1 0.1211 0.848 0.960 0.000 0.000 0.040
#> SRR1400775 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1392861 4 0.0707 0.836 0.000 0.000 0.020 0.980
#> SRR1472929 2 0.2494 0.888 0.036 0.916 0.048 0.000
#> SRR1436740 4 0.0000 0.846 0.000 0.000 0.000 1.000
#> SRR1477057 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1311980 3 0.0336 0.882 0.008 0.000 0.992 0.000
#> SRR1069400 3 0.0000 0.882 0.000 0.000 1.000 0.000
#> SRR1351016 1 0.3649 0.647 0.796 0.000 0.000 0.204
#> SRR1096291 4 0.0524 0.841 0.000 0.004 0.008 0.988
#> SRR1418145 4 0.0188 0.845 0.000 0.004 0.000 0.996
#> SRR1488111 2 0.1389 0.918 0.000 0.952 0.000 0.048
#> SRR1370495 1 0.3768 0.710 0.808 0.184 0.000 0.008
#> SRR1352639 2 0.6764 0.104 0.404 0.500 0.000 0.096
#> SRR1348911 3 0.0336 0.882 0.008 0.000 0.992 0.000
#> SRR1467386 4 0.3610 0.784 0.200 0.000 0.000 0.800
#> SRR1415956 1 0.0000 0.865 1.000 0.000 0.000 0.000
#> SRR1500495 1 0.1824 0.847 0.936 0.000 0.060 0.004
#> SRR1405099 1 0.0336 0.865 0.992 0.000 0.000 0.008
#> SRR1345585 3 0.0188 0.882 0.004 0.000 0.996 0.000
#> SRR1093196 3 0.3444 0.790 0.000 0.000 0.816 0.184
#> SRR1466006 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1351557 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1382687 4 0.4843 0.542 0.396 0.000 0.000 0.604
#> SRR1375549 1 0.0592 0.863 0.984 0.000 0.000 0.016
#> SRR1101765 1 0.3569 0.726 0.804 0.000 0.000 0.196
#> SRR1334461 1 0.3626 0.713 0.812 0.184 0.000 0.004
#> SRR1094073 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1077549 4 0.0188 0.845 0.000 0.000 0.004 0.996
#> SRR1440332 4 0.5330 0.727 0.120 0.000 0.132 0.748
#> SRR1454177 4 0.0336 0.843 0.000 0.000 0.008 0.992
#> SRR1082447 1 0.0336 0.865 0.992 0.000 0.000 0.008
#> SRR1420043 3 0.3649 0.768 0.000 0.000 0.796 0.204
#> SRR1432500 4 0.3311 0.798 0.172 0.000 0.000 0.828
#> SRR1378045 3 0.2654 0.812 0.004 0.108 0.888 0.000
#> SRR1334200 2 0.0336 0.950 0.008 0.992 0.000 0.000
#> SRR1069539 2 0.6536 0.427 0.000 0.580 0.096 0.324
#> SRR1343031 3 0.2704 0.833 0.000 0.000 0.876 0.124
#> SRR1319690 3 0.4907 0.265 0.420 0.000 0.580 0.000
#> SRR1310604 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1327747 3 0.5964 0.670 0.228 0.000 0.676 0.096
#> SRR1072456 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1367896 3 0.0000 0.882 0.000 0.000 1.000 0.000
#> SRR1480107 1 0.0336 0.865 0.992 0.000 0.000 0.008
#> SRR1377756 4 0.4916 0.469 0.424 0.000 0.000 0.576
#> SRR1435272 4 0.0188 0.845 0.000 0.000 0.004 0.996
#> SRR1089230 4 0.0188 0.845 0.004 0.000 0.000 0.996
#> SRR1389522 3 0.0000 0.882 0.000 0.000 1.000 0.000
#> SRR1080600 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1086935 4 0.0469 0.840 0.000 0.012 0.000 0.988
#> SRR1344060 2 0.0707 0.941 0.020 0.980 0.000 0.000
#> SRR1467922 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1090984 1 0.4008 0.673 0.756 0.000 0.244 0.000
#> SRR1456991 1 0.0336 0.865 0.992 0.000 0.000 0.008
#> SRR1085039 1 0.3172 0.726 0.840 0.000 0.000 0.160
#> SRR1069303 4 0.4643 0.624 0.344 0.000 0.000 0.656
#> SRR1091500 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1075198 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1086915 4 0.0000 0.846 0.000 0.000 0.000 1.000
#> SRR1499503 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1094312 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1352437 4 0.3649 0.780 0.204 0.000 0.000 0.796
#> SRR1436323 3 0.4920 0.518 0.004 0.000 0.628 0.368
#> SRR1073507 4 0.3610 0.782 0.200 0.000 0.000 0.800
#> SRR1401972 4 0.4643 0.624 0.344 0.000 0.000 0.656
#> SRR1415510 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1327279 4 0.5410 0.758 0.192 0.000 0.080 0.728
#> SRR1086983 4 0.0000 0.846 0.000 0.000 0.000 1.000
#> SRR1105174 1 0.0469 0.864 0.988 0.000 0.000 0.012
#> SRR1468893 1 0.1211 0.851 0.960 0.000 0.000 0.040
#> SRR1362555 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1074526 2 0.4546 0.611 0.256 0.732 0.000 0.012
#> SRR1326225 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1401933 4 0.4661 0.560 0.348 0.000 0.000 0.652
#> SRR1324062 4 0.4406 0.687 0.300 0.000 0.000 0.700
#> SRR1102296 1 0.0336 0.865 0.992 0.000 0.000 0.008
#> SRR1085087 4 0.3801 0.768 0.220 0.000 0.000 0.780
#> SRR1079046 1 0.0336 0.865 0.992 0.000 0.000 0.008
#> SRR1328339 1 0.4164 0.638 0.736 0.000 0.264 0.000
#> SRR1079782 2 0.0000 0.956 0.000 1.000 0.000 0.000
#> SRR1092257 2 0.0188 0.953 0.000 0.996 0.000 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.0162 0.9734 0.000 0.996 0.000 0.000 0.004
#> SRR1429287 2 0.0865 0.9625 0.000 0.972 0.000 0.004 0.024
#> SRR1359238 4 0.1764 0.7689 0.064 0.000 0.008 0.928 0.000
#> SRR1309597 3 0.0609 0.7854 0.000 0.000 0.980 0.000 0.020
#> SRR1441398 1 0.4719 0.5359 0.696 0.000 0.056 0.000 0.248
#> SRR1084055 2 0.0000 0.9737 0.000 1.000 0.000 0.000 0.000
#> SRR1417566 3 0.5908 0.3564 0.108 0.000 0.512 0.000 0.380
#> SRR1351857 4 0.0955 0.7835 0.028 0.000 0.004 0.968 0.000
#> SRR1487485 3 0.0963 0.7851 0.000 0.000 0.964 0.000 0.036
#> SRR1335875 3 0.2922 0.7555 0.056 0.000 0.872 0.000 0.072
#> SRR1073947 1 0.3283 0.6044 0.832 0.000 0.000 0.140 0.028
#> SRR1443483 3 0.0290 0.7849 0.000 0.000 0.992 0.000 0.008
#> SRR1346794 5 0.4902 0.2892 0.304 0.000 0.048 0.000 0.648
#> SRR1405245 1 0.4850 0.5345 0.700 0.000 0.076 0.000 0.224
#> SRR1409677 4 0.0880 0.7751 0.000 0.000 0.032 0.968 0.000
#> SRR1095549 5 0.6138 0.2149 0.376 0.000 0.068 0.028 0.528
#> SRR1323788 1 0.4848 0.4859 0.644 0.000 0.032 0.004 0.320
#> SRR1314054 2 0.0000 0.9737 0.000 1.000 0.000 0.000 0.000
#> SRR1077944 1 0.2690 0.6395 0.844 0.000 0.000 0.000 0.156
#> SRR1480587 2 0.0162 0.9734 0.000 0.996 0.000 0.000 0.004
#> SRR1311205 1 0.1386 0.6657 0.952 0.000 0.016 0.000 0.032
#> SRR1076369 5 0.3304 0.5204 0.128 0.000 0.028 0.004 0.840
#> SRR1453549 3 0.2632 0.7841 0.000 0.000 0.888 0.072 0.040
#> SRR1345782 1 0.1907 0.6616 0.928 0.000 0.028 0.000 0.044
#> SRR1447850 2 0.1121 0.9367 0.000 0.956 0.000 0.044 0.000
#> SRR1391553 3 0.3857 0.7295 0.000 0.084 0.808 0.000 0.108
#> SRR1444156 2 0.0000 0.9737 0.000 1.000 0.000 0.000 0.000
#> SRR1471731 3 0.5314 0.6154 0.004 0.000 0.632 0.296 0.068
#> SRR1120987 4 0.0671 0.7857 0.004 0.000 0.000 0.980 0.016
#> SRR1477363 1 0.2377 0.6468 0.872 0.000 0.000 0.000 0.128
#> SRR1391961 5 0.5808 0.5223 0.232 0.160 0.000 0.000 0.608
#> SRR1373879 3 0.1251 0.7832 0.000 0.000 0.956 0.036 0.008
#> SRR1318732 3 0.5952 0.3903 0.128 0.000 0.548 0.000 0.324
#> SRR1091404 1 0.4182 0.1089 0.600 0.000 0.000 0.000 0.400
#> SRR1402109 3 0.2929 0.7477 0.000 0.000 0.840 0.152 0.008
#> SRR1407336 3 0.3967 0.6625 0.000 0.000 0.724 0.264 0.012
#> SRR1097417 3 0.2329 0.7398 0.000 0.000 0.876 0.000 0.124
#> SRR1396227 1 0.4649 0.5744 0.720 0.000 0.000 0.068 0.212
#> SRR1400775 2 0.0000 0.9737 0.000 1.000 0.000 0.000 0.000
#> SRR1392861 4 0.0404 0.7857 0.000 0.000 0.012 0.988 0.000
#> SRR1472929 5 0.5312 0.3550 0.016 0.388 0.028 0.000 0.568
#> SRR1436740 4 0.0000 0.7884 0.000 0.000 0.000 1.000 0.000
#> SRR1477057 2 0.0000 0.9737 0.000 1.000 0.000 0.000 0.000
#> SRR1311980 3 0.2172 0.7746 0.016 0.000 0.908 0.000 0.076
#> SRR1069400 3 0.0609 0.7833 0.000 0.000 0.980 0.000 0.020
#> SRR1351016 1 0.2616 0.6357 0.888 0.000 0.000 0.076 0.036
#> SRR1096291 4 0.2149 0.7477 0.000 0.000 0.036 0.916 0.048
#> SRR1418145 4 0.0771 0.7841 0.004 0.000 0.000 0.976 0.020
#> SRR1488111 2 0.3141 0.7862 0.000 0.832 0.000 0.152 0.016
#> SRR1370495 5 0.5391 0.4884 0.300 0.084 0.000 0.000 0.616
#> SRR1352639 1 0.5792 0.3852 0.680 0.188 0.000 0.052 0.080
#> SRR1348911 3 0.1357 0.7815 0.004 0.000 0.948 0.000 0.048
#> SRR1467386 4 0.4278 0.2538 0.452 0.000 0.000 0.548 0.000
#> SRR1415956 1 0.3534 0.5718 0.744 0.000 0.000 0.000 0.256
#> SRR1500495 1 0.4453 0.5617 0.724 0.000 0.048 0.000 0.228
#> SRR1405099 1 0.2377 0.6408 0.872 0.000 0.000 0.000 0.128
#> SRR1345585 3 0.1608 0.7793 0.000 0.000 0.928 0.000 0.072
#> SRR1093196 3 0.4026 0.6850 0.000 0.000 0.736 0.244 0.020
#> SRR1466006 2 0.0290 0.9719 0.000 0.992 0.000 0.000 0.008
#> SRR1351557 2 0.0162 0.9734 0.000 0.996 0.000 0.000 0.004
#> SRR1382687 1 0.5659 0.5712 0.632 0.000 0.000 0.164 0.204
#> SRR1375549 5 0.3636 0.4587 0.272 0.000 0.000 0.000 0.728
#> SRR1101765 5 0.4177 0.5186 0.164 0.000 0.000 0.064 0.772
#> SRR1334461 5 0.5699 0.5078 0.264 0.128 0.000 0.000 0.608
#> SRR1094073 2 0.0000 0.9737 0.000 1.000 0.000 0.000 0.000
#> SRR1077549 4 0.2997 0.7077 0.148 0.000 0.012 0.840 0.000
#> SRR1440332 1 0.6443 0.2852 0.552 0.000 0.152 0.280 0.016
#> SRR1454177 4 0.0000 0.7884 0.000 0.000 0.000 1.000 0.000
#> SRR1082447 1 0.4297 0.0406 0.528 0.000 0.000 0.000 0.472
#> SRR1420043 3 0.3857 0.6022 0.000 0.000 0.688 0.312 0.000
#> SRR1432500 4 0.4451 0.1308 0.492 0.000 0.004 0.504 0.000
#> SRR1378045 3 0.4698 0.6402 0.000 0.172 0.732 0.000 0.096
#> SRR1334200 5 0.4359 0.3070 0.004 0.412 0.000 0.000 0.584
#> SRR1069539 4 0.6303 0.4078 0.000 0.204 0.092 0.636 0.068
#> SRR1343031 3 0.3154 0.7434 0.004 0.000 0.836 0.148 0.012
#> SRR1319690 3 0.6581 0.1768 0.224 0.000 0.452 0.000 0.324
#> SRR1310604 2 0.0880 0.9603 0.000 0.968 0.000 0.000 0.032
#> SRR1327747 3 0.7853 0.3079 0.132 0.000 0.428 0.136 0.304
#> SRR1072456 2 0.0162 0.9734 0.000 0.996 0.000 0.000 0.004
#> SRR1367896 3 0.0510 0.7834 0.000 0.000 0.984 0.000 0.016
#> SRR1480107 1 0.0510 0.6603 0.984 0.000 0.000 0.000 0.016
#> SRR1377756 1 0.5775 0.5584 0.608 0.000 0.000 0.244 0.148
#> SRR1435272 4 0.0000 0.7884 0.000 0.000 0.000 1.000 0.000
#> SRR1089230 4 0.0290 0.7877 0.000 0.000 0.000 0.992 0.008
#> SRR1389522 3 0.0609 0.7833 0.000 0.000 0.980 0.000 0.020
#> SRR1080600 2 0.1965 0.9033 0.000 0.904 0.000 0.000 0.096
#> SRR1086935 4 0.0566 0.7866 0.004 0.000 0.000 0.984 0.012
#> SRR1344060 5 0.4940 0.3535 0.032 0.392 0.000 0.000 0.576
#> SRR1467922 2 0.0000 0.9737 0.000 1.000 0.000 0.000 0.000
#> SRR1090984 5 0.5827 0.2499 0.260 0.000 0.144 0.000 0.596
#> SRR1456991 1 0.0703 0.6607 0.976 0.000 0.000 0.000 0.024
#> SRR1085039 1 0.3164 0.6388 0.852 0.000 0.000 0.104 0.044
#> SRR1069303 1 0.4987 0.2818 0.616 0.000 0.000 0.340 0.044
#> SRR1091500 2 0.0000 0.9737 0.000 1.000 0.000 0.000 0.000
#> SRR1075198 2 0.1121 0.9528 0.000 0.956 0.000 0.000 0.044
#> SRR1086915 4 0.0162 0.7884 0.004 0.000 0.000 0.996 0.000
#> SRR1499503 2 0.0162 0.9734 0.000 0.996 0.000 0.000 0.004
#> SRR1094312 2 0.0000 0.9737 0.000 1.000 0.000 0.000 0.000
#> SRR1352437 4 0.5165 0.2070 0.448 0.000 0.000 0.512 0.040
#> SRR1436323 3 0.5990 0.4937 0.008 0.000 0.540 0.356 0.096
#> SRR1073507 4 0.4242 0.3103 0.428 0.000 0.000 0.572 0.000
#> SRR1401972 1 0.4987 0.2818 0.616 0.000 0.000 0.340 0.044
#> SRR1415510 2 0.0162 0.9734 0.000 0.996 0.000 0.000 0.004
#> SRR1327279 1 0.5865 0.2017 0.568 0.000 0.104 0.324 0.004
#> SRR1086983 4 0.1571 0.7712 0.060 0.000 0.000 0.936 0.004
#> SRR1105174 1 0.3003 0.6127 0.812 0.000 0.000 0.000 0.188
#> SRR1468893 1 0.3438 0.6313 0.808 0.000 0.000 0.020 0.172
#> SRR1362555 2 0.1732 0.9207 0.000 0.920 0.000 0.000 0.080
#> SRR1074526 5 0.4800 0.5502 0.052 0.272 0.000 0.000 0.676
#> SRR1326225 2 0.0000 0.9737 0.000 1.000 0.000 0.000 0.000
#> SRR1401933 4 0.7068 -0.0421 0.256 0.000 0.012 0.372 0.360
#> SRR1324062 1 0.4898 0.2297 0.592 0.000 0.000 0.376 0.032
#> SRR1102296 1 0.1894 0.6478 0.920 0.008 0.000 0.000 0.072
#> SRR1085087 4 0.4743 0.1759 0.472 0.000 0.000 0.512 0.016
#> SRR1079046 5 0.3508 0.4913 0.252 0.000 0.000 0.000 0.748
#> SRR1328339 5 0.6411 0.1471 0.312 0.000 0.196 0.000 0.492
#> SRR1079782 2 0.1281 0.9553 0.000 0.956 0.000 0.012 0.032
#> SRR1092257 2 0.1211 0.9517 0.000 0.960 0.000 0.024 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.0000 0.9266 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1429287 2 0.3375 0.8429 0.000 0.828 0.000 0.008 0.088 0.076
#> SRR1359238 4 0.3755 0.7371 0.112 0.000 0.052 0.812 0.016 0.008
#> SRR1309597 3 0.1075 0.7558 0.000 0.000 0.952 0.000 0.000 0.048
#> SRR1441398 1 0.5406 0.2625 0.556 0.000 0.040 0.000 0.048 0.356
#> SRR1084055 2 0.0458 0.9222 0.000 0.984 0.000 0.000 0.016 0.000
#> SRR1417566 6 0.3759 0.5566 0.020 0.024 0.132 0.000 0.016 0.808
#> SRR1351857 4 0.2267 0.7988 0.064 0.000 0.008 0.904 0.020 0.004
#> SRR1487485 3 0.2520 0.7175 0.000 0.000 0.844 0.004 0.000 0.152
#> SRR1335875 3 0.4768 0.4703 0.044 0.008 0.628 0.000 0.004 0.316
#> SRR1073947 1 0.2188 0.5963 0.912 0.000 0.000 0.032 0.020 0.036
#> SRR1443483 3 0.0146 0.7626 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1346794 6 0.5058 0.4622 0.124 0.000 0.016 0.000 0.188 0.672
#> SRR1405245 1 0.5223 0.3139 0.588 0.000 0.040 0.000 0.040 0.332
#> SRR1409677 4 0.1364 0.8190 0.004 0.000 0.048 0.944 0.000 0.004
#> SRR1095549 6 0.7702 0.3429 0.244 0.000 0.144 0.044 0.124 0.444
#> SRR1323788 6 0.5677 -0.0151 0.416 0.000 0.024 0.004 0.072 0.484
#> SRR1314054 2 0.0458 0.9229 0.000 0.984 0.000 0.000 0.016 0.000
#> SRR1077944 1 0.3746 0.5315 0.760 0.000 0.000 0.000 0.048 0.192
#> SRR1480587 2 0.0000 0.9266 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1311205 1 0.1983 0.5904 0.916 0.000 0.012 0.000 0.012 0.060
#> SRR1076369 5 0.4695 0.5066 0.032 0.000 0.024 0.000 0.648 0.296
#> SRR1453549 3 0.4314 0.7109 0.004 0.000 0.736 0.104 0.000 0.156
#> SRR1345782 1 0.2743 0.5869 0.880 0.000 0.032 0.000 0.028 0.060
#> SRR1447850 2 0.1498 0.9016 0.000 0.940 0.000 0.032 0.028 0.000
#> SRR1391553 6 0.5934 -0.0607 0.000 0.184 0.376 0.000 0.004 0.436
#> SRR1444156 2 0.0000 0.9266 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1471731 3 0.6105 0.2079 0.000 0.000 0.380 0.308 0.000 0.312
#> SRR1120987 4 0.2532 0.7928 0.008 0.000 0.000 0.884 0.032 0.076
#> SRR1477363 1 0.3663 0.5259 0.776 0.000 0.004 0.000 0.040 0.180
#> SRR1391961 5 0.3031 0.7841 0.048 0.072 0.000 0.000 0.860 0.020
#> SRR1373879 3 0.1007 0.7621 0.000 0.000 0.956 0.044 0.000 0.000
#> SRR1318732 6 0.4663 0.5434 0.040 0.004 0.196 0.000 0.040 0.720
#> SRR1091404 1 0.5333 0.3696 0.576 0.000 0.004 0.000 0.300 0.120
#> SRR1402109 3 0.1957 0.7388 0.000 0.000 0.888 0.112 0.000 0.000
#> SRR1407336 3 0.3529 0.6577 0.000 0.000 0.764 0.208 0.000 0.028
#> SRR1097417 3 0.2618 0.7127 0.000 0.000 0.872 0.000 0.076 0.052
#> SRR1396227 1 0.6337 0.2374 0.444 0.000 0.000 0.096 0.068 0.392
#> SRR1400775 2 0.0146 0.9257 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1392861 4 0.0363 0.8362 0.000 0.000 0.012 0.988 0.000 0.000
#> SRR1472929 5 0.3372 0.7344 0.008 0.176 0.020 0.000 0.796 0.000
#> SRR1436740 4 0.0260 0.8373 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1477057 2 0.1225 0.9121 0.000 0.952 0.000 0.000 0.036 0.012
#> SRR1311980 3 0.4134 0.5203 0.028 0.000 0.656 0.000 0.000 0.316
#> SRR1069400 3 0.0260 0.7639 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR1351016 1 0.2137 0.5975 0.912 0.000 0.000 0.028 0.012 0.048
#> SRR1096291 4 0.4339 0.7168 0.000 0.000 0.076 0.776 0.068 0.080
#> SRR1418145 4 0.3368 0.7496 0.004 0.000 0.000 0.824 0.084 0.088
#> SRR1488111 2 0.5765 0.5915 0.000 0.624 0.000 0.212 0.076 0.088
#> SRR1370495 5 0.2731 0.7339 0.068 0.012 0.000 0.000 0.876 0.044
#> SRR1352639 1 0.5948 0.4540 0.656 0.076 0.004 0.012 0.148 0.104
#> SRR1348911 3 0.3722 0.5848 0.008 0.004 0.724 0.000 0.004 0.260
#> SRR1467386 1 0.4788 0.3165 0.564 0.000 0.000 0.392 0.024 0.020
#> SRR1415956 1 0.4988 0.2877 0.552 0.000 0.004 0.000 0.064 0.380
#> SRR1500495 1 0.5149 0.3265 0.596 0.000 0.036 0.000 0.040 0.328
#> SRR1405099 1 0.3905 0.5022 0.744 0.000 0.004 0.000 0.040 0.212
#> SRR1345585 3 0.3266 0.6033 0.000 0.000 0.728 0.000 0.000 0.272
#> SRR1093196 3 0.4633 0.6084 0.000 0.000 0.676 0.224 0.000 0.100
#> SRR1466006 2 0.0458 0.9236 0.000 0.984 0.000 0.000 0.016 0.000
#> SRR1351557 2 0.0000 0.9266 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1382687 1 0.6086 0.3809 0.488 0.000 0.000 0.164 0.020 0.328
#> SRR1375549 5 0.3950 0.6059 0.040 0.000 0.000 0.000 0.720 0.240
#> SRR1101765 5 0.4035 0.6321 0.032 0.000 0.000 0.016 0.744 0.208
#> SRR1334461 5 0.2830 0.7766 0.068 0.064 0.000 0.000 0.864 0.004
#> SRR1094073 2 0.0000 0.9266 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077549 4 0.4832 0.4948 0.268 0.000 0.056 0.660 0.008 0.008
#> SRR1440332 1 0.5581 0.4214 0.628 0.000 0.188 0.160 0.004 0.020
#> SRR1454177 4 0.0000 0.8381 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1082447 1 0.6044 0.1497 0.416 0.000 0.000 0.000 0.308 0.276
#> SRR1420043 3 0.3420 0.6422 0.000 0.000 0.748 0.240 0.000 0.012
#> SRR1432500 1 0.3811 0.5377 0.732 0.000 0.004 0.244 0.016 0.004
#> SRR1378045 6 0.6163 0.1166 0.000 0.304 0.276 0.000 0.004 0.416
#> SRR1334200 5 0.2558 0.7486 0.000 0.156 0.000 0.000 0.840 0.004
#> SRR1069539 4 0.6415 0.5526 0.000 0.048 0.160 0.624 0.084 0.084
#> SRR1343031 3 0.2163 0.7390 0.008 0.000 0.892 0.096 0.000 0.004
#> SRR1319690 6 0.6178 0.5298 0.156 0.000 0.212 0.000 0.060 0.572
#> SRR1310604 2 0.1657 0.9002 0.000 0.928 0.000 0.000 0.056 0.016
#> SRR1327747 6 0.6786 0.3621 0.040 0.000 0.296 0.100 0.052 0.512
#> SRR1072456 2 0.0458 0.9222 0.000 0.984 0.000 0.000 0.016 0.000
#> SRR1367896 3 0.0146 0.7629 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1480107 1 0.1461 0.5980 0.940 0.000 0.000 0.000 0.044 0.016
#> SRR1377756 1 0.6124 0.4801 0.564 0.000 0.000 0.176 0.044 0.216
#> SRR1435272 4 0.0000 0.8381 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1089230 4 0.0405 0.8368 0.000 0.000 0.000 0.988 0.004 0.008
#> SRR1389522 3 0.0146 0.7626 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1080600 2 0.3695 0.7798 0.000 0.776 0.000 0.000 0.164 0.060
#> SRR1086935 4 0.0405 0.8368 0.000 0.000 0.000 0.988 0.004 0.008
#> SRR1344060 5 0.2520 0.7543 0.000 0.152 0.000 0.000 0.844 0.004
#> SRR1467922 2 0.0000 0.9266 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1090984 6 0.4492 0.5754 0.076 0.000 0.088 0.000 0.072 0.764
#> SRR1456991 1 0.1716 0.5952 0.932 0.000 0.004 0.000 0.036 0.028
#> SRR1085039 1 0.3181 0.5999 0.852 0.000 0.000 0.028 0.076 0.044
#> SRR1069303 1 0.6279 0.4384 0.532 0.000 0.000 0.260 0.048 0.160
#> SRR1091500 2 0.0260 0.9255 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1075198 2 0.3175 0.8414 0.000 0.832 0.000 0.000 0.088 0.080
#> SRR1086915 4 0.0146 0.8377 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1499503 2 0.0000 0.9266 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1094312 2 0.0000 0.9266 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1352437 1 0.6412 0.3047 0.472 0.000 0.000 0.332 0.048 0.148
#> SRR1436323 4 0.6315 -0.2098 0.000 0.000 0.288 0.368 0.008 0.336
#> SRR1073507 1 0.4688 0.3168 0.572 0.000 0.000 0.388 0.028 0.012
#> SRR1401972 1 0.6305 0.4360 0.528 0.000 0.000 0.260 0.048 0.164
#> SRR1415510 2 0.0291 0.9247 0.000 0.992 0.000 0.000 0.004 0.004
#> SRR1327279 1 0.4254 0.5459 0.772 0.000 0.104 0.104 0.008 0.012
#> SRR1086983 4 0.2402 0.7782 0.084 0.000 0.000 0.888 0.008 0.020
#> SRR1105174 1 0.4556 0.5010 0.704 0.000 0.004 0.000 0.100 0.192
#> SRR1468893 1 0.5594 0.4863 0.612 0.000 0.000 0.060 0.068 0.260
#> SRR1362555 2 0.4215 0.7271 0.000 0.724 0.000 0.000 0.196 0.080
#> SRR1074526 5 0.2554 0.7814 0.000 0.076 0.000 0.000 0.876 0.048
#> SRR1326225 2 0.0000 0.9266 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1401933 6 0.5847 0.2026 0.060 0.000 0.000 0.344 0.064 0.532
#> SRR1324062 1 0.5776 0.4432 0.544 0.000 0.000 0.284 0.012 0.160
#> SRR1102296 1 0.4203 0.5044 0.704 0.004 0.004 0.004 0.024 0.260
#> SRR1085087 1 0.5630 0.2791 0.516 0.000 0.000 0.380 0.032 0.072
#> SRR1079046 5 0.2358 0.7375 0.016 0.000 0.000 0.000 0.876 0.108
#> SRR1328339 6 0.5120 0.5430 0.116 0.004 0.116 0.000 0.052 0.712
#> SRR1079782 2 0.3859 0.8215 0.000 0.804 0.000 0.028 0.088 0.080
#> SRR1092257 2 0.4109 0.8102 0.000 0.792 0.000 0.060 0.060 0.088
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 17611 rows and 118 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 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 0.852 0.897 0.956 0.3910 0.618 0.618
#> 3 3 0.441 0.647 0.832 0.6391 0.684 0.511
#> 4 4 0.595 0.673 0.817 0.1506 0.812 0.535
#> 5 5 0.583 0.498 0.734 0.0651 0.948 0.810
#> 6 6 0.662 0.483 0.739 0.0364 0.917 0.676
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1396765 2 0.0000 0.9284 0.000 1.000
#> SRR1429287 2 0.0000 0.9284 0.000 1.000
#> SRR1359238 1 0.0938 0.9588 0.988 0.012
#> SRR1309597 1 0.0938 0.9588 0.988 0.012
#> SRR1441398 1 0.0000 0.9590 1.000 0.000
#> SRR1084055 2 0.0938 0.9220 0.012 0.988
#> SRR1417566 1 0.0938 0.9588 0.988 0.012
#> SRR1351857 1 0.0938 0.9588 0.988 0.012
#> SRR1487485 1 0.9635 0.3519 0.612 0.388
#> SRR1335875 1 0.0938 0.9588 0.988 0.012
#> SRR1073947 1 0.0000 0.9590 1.000 0.000
#> SRR1443483 1 0.0938 0.9588 0.988 0.012
#> SRR1346794 1 0.0938 0.9588 0.988 0.012
#> SRR1405245 1 0.0000 0.9590 1.000 0.000
#> SRR1409677 1 0.0938 0.9588 0.988 0.012
#> SRR1095549 1 0.0938 0.9588 0.988 0.012
#> SRR1323788 1 0.0938 0.9588 0.988 0.012
#> SRR1314054 2 0.0376 0.9270 0.004 0.996
#> SRR1077944 1 0.0000 0.9590 1.000 0.000
#> SRR1480587 2 0.0000 0.9284 0.000 1.000
#> SRR1311205 1 0.0000 0.9590 1.000 0.000
#> SRR1076369 1 0.0000 0.9590 1.000 0.000
#> SRR1453549 1 0.0938 0.9588 0.988 0.012
#> SRR1345782 1 0.0000 0.9590 1.000 0.000
#> SRR1447850 2 0.1414 0.9185 0.020 0.980
#> SRR1391553 2 0.6973 0.7693 0.188 0.812
#> SRR1444156 2 0.0000 0.9284 0.000 1.000
#> SRR1471731 1 0.0938 0.9588 0.988 0.012
#> SRR1120987 1 0.9358 0.4252 0.648 0.352
#> SRR1477363 1 0.0000 0.9590 1.000 0.000
#> SRR1391961 1 0.2778 0.9222 0.952 0.048
#> SRR1373879 1 0.0938 0.9588 0.988 0.012
#> SRR1318732 1 0.3114 0.9238 0.944 0.056
#> SRR1091404 1 0.0000 0.9590 1.000 0.000
#> SRR1402109 1 0.0938 0.9588 0.988 0.012
#> SRR1407336 1 0.0938 0.9588 0.988 0.012
#> SRR1097417 1 0.9970 0.0916 0.532 0.468
#> SRR1396227 1 0.0000 0.9590 1.000 0.000
#> SRR1400775 2 0.0000 0.9284 0.000 1.000
#> SRR1392861 1 0.0938 0.9588 0.988 0.012
#> SRR1472929 1 0.7602 0.7040 0.780 0.220
#> SRR1436740 1 0.0938 0.9588 0.988 0.012
#> SRR1477057 2 0.8386 0.6694 0.268 0.732
#> SRR1311980 1 0.0000 0.9590 1.000 0.000
#> SRR1069400 1 0.0938 0.9588 0.988 0.012
#> SRR1351016 1 0.0000 0.9590 1.000 0.000
#> SRR1096291 1 0.0938 0.9588 0.988 0.012
#> SRR1418145 1 0.0938 0.9588 0.988 0.012
#> SRR1488111 2 0.9608 0.4107 0.384 0.616
#> SRR1370495 1 0.0000 0.9590 1.000 0.000
#> SRR1352639 1 0.0376 0.9573 0.996 0.004
#> SRR1348911 1 0.0000 0.9590 1.000 0.000
#> SRR1467386 1 0.0938 0.9588 0.988 0.012
#> SRR1415956 1 0.0000 0.9590 1.000 0.000
#> SRR1500495 1 0.0000 0.9590 1.000 0.000
#> SRR1405099 1 0.0000 0.9590 1.000 0.000
#> SRR1345585 1 0.4298 0.8892 0.912 0.088
#> SRR1093196 1 0.0938 0.9588 0.988 0.012
#> SRR1466006 2 0.0000 0.9284 0.000 1.000
#> SRR1351557 2 0.0000 0.9284 0.000 1.000
#> SRR1382687 1 0.0938 0.9588 0.988 0.012
#> SRR1375549 1 0.0000 0.9590 1.000 0.000
#> SRR1101765 1 0.0938 0.9588 0.988 0.012
#> SRR1334461 1 0.2423 0.9292 0.960 0.040
#> SRR1094073 2 0.0000 0.9284 0.000 1.000
#> SRR1077549 1 0.0000 0.9590 1.000 0.000
#> SRR1440332 1 0.0000 0.9590 1.000 0.000
#> SRR1454177 1 0.0938 0.9588 0.988 0.012
#> SRR1082447 1 0.0000 0.9590 1.000 0.000
#> SRR1420043 1 0.0938 0.9588 0.988 0.012
#> SRR1432500 1 0.0000 0.9590 1.000 0.000
#> SRR1378045 2 0.1184 0.9208 0.016 0.984
#> SRR1334200 2 0.9998 0.0241 0.492 0.508
#> SRR1069539 1 0.1184 0.9567 0.984 0.016
#> SRR1343031 1 0.0000 0.9590 1.000 0.000
#> SRR1319690 1 0.0938 0.9588 0.988 0.012
#> SRR1310604 2 0.0000 0.9284 0.000 1.000
#> SRR1327747 1 0.0938 0.9588 0.988 0.012
#> SRR1072456 2 0.0938 0.9220 0.012 0.988
#> SRR1367896 1 0.0376 0.9574 0.996 0.004
#> SRR1480107 1 0.0000 0.9590 1.000 0.000
#> SRR1377756 1 0.0938 0.9588 0.988 0.012
#> SRR1435272 1 0.0938 0.9588 0.988 0.012
#> SRR1089230 1 0.0938 0.9588 0.988 0.012
#> SRR1389522 1 0.0000 0.9590 1.000 0.000
#> SRR1080600 2 0.0000 0.9284 0.000 1.000
#> SRR1086935 2 0.8555 0.6344 0.280 0.720
#> SRR1344060 1 0.9044 0.5100 0.680 0.320
#> SRR1467922 2 0.0000 0.9284 0.000 1.000
#> SRR1090984 1 0.0376 0.9591 0.996 0.004
#> SRR1456991 1 0.0000 0.9590 1.000 0.000
#> SRR1085039 1 0.0000 0.9590 1.000 0.000
#> SRR1069303 1 0.0000 0.9590 1.000 0.000
#> SRR1091500 2 0.0938 0.9220 0.012 0.988
#> SRR1075198 2 0.0000 0.9284 0.000 1.000
#> SRR1086915 1 0.0938 0.9588 0.988 0.012
#> SRR1499503 2 0.0000 0.9284 0.000 1.000
#> SRR1094312 2 0.0000 0.9284 0.000 1.000
#> SRR1352437 1 0.8763 0.5377 0.704 0.296
#> SRR1436323 1 0.0938 0.9588 0.988 0.012
#> SRR1073507 1 0.0000 0.9590 1.000 0.000
#> SRR1401972 1 0.0000 0.9590 1.000 0.000
#> SRR1415510 2 0.0376 0.9270 0.004 0.996
#> SRR1327279 1 0.0000 0.9590 1.000 0.000
#> SRR1086983 1 0.0938 0.9588 0.988 0.012
#> SRR1105174 1 0.0000 0.9590 1.000 0.000
#> SRR1468893 1 0.0000 0.9590 1.000 0.000
#> SRR1362555 1 0.8499 0.6053 0.724 0.276
#> SRR1074526 2 0.8016 0.7084 0.244 0.756
#> SRR1326225 2 0.0000 0.9284 0.000 1.000
#> SRR1401933 1 0.0672 0.9590 0.992 0.008
#> SRR1324062 1 0.0000 0.9590 1.000 0.000
#> SRR1102296 1 0.4562 0.8672 0.904 0.096
#> SRR1085087 1 0.0000 0.9590 1.000 0.000
#> SRR1079046 1 0.6712 0.7690 0.824 0.176
#> SRR1328339 1 0.0000 0.9590 1.000 0.000
#> SRR1079782 2 0.0000 0.9284 0.000 1.000
#> SRR1092257 2 0.2603 0.9010 0.044 0.956
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.0000 0.8777 0.000 1.000 0.000
#> SRR1429287 2 0.4178 0.7509 0.000 0.828 0.172
#> SRR1359238 1 0.6062 0.5077 0.616 0.000 0.384
#> SRR1309597 3 0.6225 0.4557 0.432 0.000 0.568
#> SRR1441398 1 0.0000 0.7612 1.000 0.000 0.000
#> SRR1084055 2 0.0000 0.8777 0.000 1.000 0.000
#> SRR1417566 3 0.6180 0.3933 0.416 0.000 0.584
#> SRR1351857 3 0.4555 0.5906 0.200 0.000 0.800
#> SRR1487485 3 0.4351 0.7486 0.168 0.004 0.828
#> SRR1335875 1 0.5327 0.5593 0.728 0.000 0.272
#> SRR1073947 1 0.0000 0.7612 1.000 0.000 0.000
#> SRR1443483 3 0.5988 0.5613 0.368 0.000 0.632
#> SRR1346794 3 0.6180 0.5005 0.416 0.000 0.584
#> SRR1405245 1 0.5254 0.5044 0.736 0.000 0.264
#> SRR1409677 3 0.4178 0.5739 0.172 0.000 0.828
#> SRR1095549 1 0.5810 0.1872 0.664 0.000 0.336
#> SRR1323788 1 0.6235 0.2012 0.564 0.000 0.436
#> SRR1314054 2 0.1289 0.8651 0.000 0.968 0.032
#> SRR1077944 1 0.0000 0.7612 1.000 0.000 0.000
#> SRR1480587 2 0.0000 0.8777 0.000 1.000 0.000
#> SRR1311205 1 0.0000 0.7612 1.000 0.000 0.000
#> SRR1076369 1 0.5968 0.0845 0.636 0.000 0.364
#> SRR1453549 1 0.6111 0.3217 0.604 0.000 0.396
#> SRR1345782 1 0.0000 0.7612 1.000 0.000 0.000
#> SRR1447850 2 0.3340 0.8016 0.000 0.880 0.120
#> SRR1391553 2 0.8163 0.4864 0.124 0.628 0.248
#> SRR1444156 2 0.0000 0.8777 0.000 1.000 0.000
#> SRR1471731 3 0.4235 0.7472 0.176 0.000 0.824
#> SRR1120987 1 0.6745 0.4475 0.560 0.012 0.428
#> SRR1477363 1 0.0000 0.7612 1.000 0.000 0.000
#> SRR1391961 1 0.1399 0.7547 0.968 0.028 0.004
#> SRR1373879 3 0.4178 0.7480 0.172 0.000 0.828
#> SRR1318732 3 0.4291 0.7477 0.180 0.000 0.820
#> SRR1091404 1 0.0000 0.7612 1.000 0.000 0.000
#> SRR1402109 3 0.4452 0.7425 0.192 0.000 0.808
#> SRR1407336 3 0.4178 0.7480 0.172 0.000 0.828
#> SRR1097417 3 0.5947 0.7302 0.172 0.052 0.776
#> SRR1396227 1 0.0000 0.7612 1.000 0.000 0.000
#> SRR1400775 2 0.0000 0.8777 0.000 1.000 0.000
#> SRR1392861 3 0.0000 0.7238 0.000 0.000 1.000
#> SRR1472929 1 0.6225 0.1528 0.568 0.432 0.000
#> SRR1436740 3 0.0000 0.7238 0.000 0.000 1.000
#> SRR1477057 2 0.7572 0.5923 0.184 0.688 0.128
#> SRR1311980 1 0.5058 0.5972 0.756 0.000 0.244
#> SRR1069400 3 0.4605 0.7398 0.204 0.000 0.796
#> SRR1351016 1 0.0424 0.7602 0.992 0.000 0.008
#> SRR1096291 3 0.4235 0.6216 0.176 0.000 0.824
#> SRR1418145 1 0.6235 0.3743 0.564 0.000 0.436
#> SRR1488111 3 0.9458 0.1773 0.184 0.368 0.448
#> SRR1370495 1 0.0000 0.7612 1.000 0.000 0.000
#> SRR1352639 1 0.4452 0.5916 0.808 0.000 0.192
#> SRR1348911 1 0.5517 0.5616 0.728 0.004 0.268
#> SRR1467386 1 0.3941 0.6893 0.844 0.000 0.156
#> SRR1415956 1 0.0000 0.7612 1.000 0.000 0.000
#> SRR1500495 1 0.0237 0.7609 0.996 0.000 0.004
#> SRR1405099 1 0.0000 0.7612 1.000 0.000 0.000
#> SRR1345585 3 0.4291 0.7465 0.180 0.000 0.820
#> SRR1093196 3 0.4178 0.7480 0.172 0.000 0.828
#> SRR1466006 2 0.0000 0.8777 0.000 1.000 0.000
#> SRR1351557 2 0.0000 0.8777 0.000 1.000 0.000
#> SRR1382687 1 0.5178 0.5830 0.744 0.000 0.256
#> SRR1375549 1 0.4178 0.6735 0.828 0.000 0.172
#> SRR1101765 3 0.6299 -0.1789 0.476 0.000 0.524
#> SRR1334461 1 0.1031 0.7555 0.976 0.024 0.000
#> SRR1094073 2 0.0000 0.8777 0.000 1.000 0.000
#> SRR1077549 1 0.6154 0.2191 0.592 0.000 0.408
#> SRR1440332 1 0.1643 0.7509 0.956 0.000 0.044
#> SRR1454177 3 0.0000 0.7238 0.000 0.000 1.000
#> SRR1082447 1 0.2878 0.7278 0.904 0.000 0.096
#> SRR1420043 3 0.0424 0.7269 0.008 0.000 0.992
#> SRR1432500 1 0.4750 0.6668 0.784 0.000 0.216
#> SRR1378045 2 0.2625 0.8343 0.000 0.916 0.084
#> SRR1334200 3 0.9591 0.3538 0.296 0.232 0.472
#> SRR1069539 3 0.0000 0.7238 0.000 0.000 1.000
#> SRR1343031 3 0.4931 0.7176 0.232 0.000 0.768
#> SRR1319690 1 0.6302 0.0082 0.520 0.000 0.480
#> SRR1310604 2 0.3482 0.7964 0.000 0.872 0.128
#> SRR1327747 3 0.3038 0.6853 0.104 0.000 0.896
#> SRR1072456 2 0.0000 0.8777 0.000 1.000 0.000
#> SRR1367896 3 0.4605 0.7366 0.204 0.000 0.796
#> SRR1480107 1 0.0000 0.7612 1.000 0.000 0.000
#> SRR1377756 1 0.6180 0.4181 0.584 0.000 0.416
#> SRR1435272 3 0.0000 0.7238 0.000 0.000 1.000
#> SRR1089230 3 0.0000 0.7238 0.000 0.000 1.000
#> SRR1389522 3 0.6267 0.4273 0.452 0.000 0.548
#> SRR1080600 2 0.6244 0.2475 0.000 0.560 0.440
#> SRR1086935 3 0.0237 0.7228 0.000 0.004 0.996
#> SRR1344060 2 0.6126 0.3653 0.400 0.600 0.000
#> SRR1467922 2 0.0000 0.8777 0.000 1.000 0.000
#> SRR1090984 1 0.4842 0.6156 0.776 0.000 0.224
#> SRR1456991 1 0.0000 0.7612 1.000 0.000 0.000
#> SRR1085039 1 0.4291 0.6719 0.820 0.000 0.180
#> SRR1069303 1 0.3192 0.7302 0.888 0.000 0.112
#> SRR1091500 2 0.0000 0.8777 0.000 1.000 0.000
#> SRR1075198 2 0.5678 0.5136 0.000 0.684 0.316
#> SRR1086915 3 0.5397 0.3399 0.280 0.000 0.720
#> SRR1499503 2 0.0000 0.8777 0.000 1.000 0.000
#> SRR1094312 2 0.0000 0.8777 0.000 1.000 0.000
#> SRR1352437 1 0.8374 0.5043 0.616 0.144 0.240
#> SRR1436323 3 0.4178 0.7480 0.172 0.000 0.828
#> SRR1073507 1 0.4235 0.6736 0.824 0.000 0.176
#> SRR1401972 1 0.1860 0.7499 0.948 0.000 0.052
#> SRR1415510 2 0.2165 0.8483 0.000 0.936 0.064
#> SRR1327279 1 0.1643 0.7509 0.956 0.000 0.044
#> SRR1086983 1 0.6252 0.4317 0.556 0.000 0.444
#> SRR1105174 1 0.4178 0.6735 0.828 0.000 0.172
#> SRR1468893 1 0.0000 0.7612 1.000 0.000 0.000
#> SRR1362555 2 0.5968 0.4422 0.364 0.636 0.000
#> SRR1074526 1 0.8896 0.4467 0.564 0.172 0.264
#> SRR1326225 2 0.0000 0.8777 0.000 1.000 0.000
#> SRR1401933 1 0.3412 0.7193 0.876 0.000 0.124
#> SRR1324062 1 0.4796 0.6261 0.780 0.000 0.220
#> SRR1102296 1 0.3141 0.7399 0.912 0.068 0.020
#> SRR1085087 1 0.4346 0.6736 0.816 0.000 0.184
#> SRR1079046 1 0.7165 0.6033 0.716 0.112 0.172
#> SRR1328339 1 0.0000 0.7612 1.000 0.000 0.000
#> SRR1079782 2 0.0747 0.8736 0.000 0.984 0.016
#> SRR1092257 2 0.2496 0.8425 0.004 0.928 0.068
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.0000 0.8464 0.000 1.000 0.000 0.000
#> SRR1429287 4 0.4994 0.1022 0.000 0.480 0.000 0.520
#> SRR1359238 4 0.5280 0.7067 0.124 0.000 0.124 0.752
#> SRR1309597 3 0.6134 0.5575 0.236 0.000 0.660 0.104
#> SRR1441398 1 0.2345 0.8008 0.900 0.000 0.000 0.100
#> SRR1084055 2 0.0000 0.8464 0.000 1.000 0.000 0.000
#> SRR1417566 3 0.4998 -0.1529 0.488 0.000 0.512 0.000
#> SRR1351857 4 0.2345 0.7410 0.000 0.000 0.100 0.900
#> SRR1487485 3 0.0000 0.7972 0.000 0.000 1.000 0.000
#> SRR1335875 1 0.3610 0.7476 0.800 0.000 0.200 0.000
#> SRR1073947 1 0.0000 0.8263 1.000 0.000 0.000 0.000
#> SRR1443483 3 0.2928 0.7389 0.052 0.000 0.896 0.052
#> SRR1346794 4 0.7327 0.2330 0.176 0.000 0.320 0.504
#> SRR1405245 1 0.5786 0.5511 0.640 0.000 0.308 0.052
#> SRR1409677 4 0.2760 0.7379 0.000 0.000 0.128 0.872
#> SRR1095549 1 0.4781 0.3662 0.660 0.000 0.336 0.004
#> SRR1323788 1 0.4998 0.2463 0.512 0.000 0.488 0.000
#> SRR1314054 2 0.1389 0.8223 0.000 0.952 0.048 0.000
#> SRR1077944 1 0.1389 0.8213 0.952 0.000 0.000 0.048
#> SRR1480587 2 0.0000 0.8464 0.000 1.000 0.000 0.000
#> SRR1311205 1 0.0336 0.8272 0.992 0.000 0.000 0.008
#> SRR1076369 3 0.7145 0.4527 0.252 0.000 0.556 0.192
#> SRR1453549 1 0.4406 0.6525 0.700 0.000 0.300 0.000
#> SRR1345782 1 0.0000 0.8263 1.000 0.000 0.000 0.000
#> SRR1447850 2 0.1940 0.8041 0.000 0.924 0.076 0.000
#> SRR1391553 2 0.6650 0.5009 0.176 0.624 0.200 0.000
#> SRR1444156 2 0.0000 0.8464 0.000 1.000 0.000 0.000
#> SRR1471731 3 0.0188 0.7967 0.004 0.000 0.996 0.000
#> SRR1120987 4 0.2345 0.7256 0.100 0.000 0.000 0.900
#> SRR1477363 1 0.2345 0.8008 0.900 0.000 0.000 0.100
#> SRR1391961 1 0.0188 0.8267 0.996 0.004 0.000 0.000
#> SRR1373879 3 0.0000 0.7972 0.000 0.000 1.000 0.000
#> SRR1318732 3 0.3307 0.7466 0.028 0.000 0.868 0.104
#> SRR1091404 1 0.1389 0.8213 0.952 0.000 0.000 0.048
#> SRR1402109 3 0.0000 0.7972 0.000 0.000 1.000 0.000
#> SRR1407336 3 0.1557 0.7640 0.000 0.000 0.944 0.056
#> SRR1097417 3 0.1792 0.7589 0.000 0.068 0.932 0.000
#> SRR1396227 1 0.0000 0.8263 1.000 0.000 0.000 0.000
#> SRR1400775 2 0.0000 0.8464 0.000 1.000 0.000 0.000
#> SRR1392861 3 0.0000 0.7972 0.000 0.000 1.000 0.000
#> SRR1472929 1 0.6476 0.4840 0.616 0.272 0.000 0.112
#> SRR1436740 4 0.3311 0.7263 0.000 0.000 0.172 0.828
#> SRR1477057 2 0.6808 0.3678 0.320 0.560 0.120 0.000
#> SRR1311980 1 0.3528 0.7551 0.808 0.000 0.192 0.000
#> SRR1069400 3 0.0336 0.7953 0.000 0.000 0.992 0.008
#> SRR1351016 1 0.0592 0.8275 0.984 0.000 0.016 0.000
#> SRR1096291 4 0.6351 0.3884 0.080 0.000 0.332 0.588
#> SRR1418145 4 0.2610 0.7429 0.012 0.000 0.088 0.900
#> SRR1488111 2 0.8383 -0.0656 0.336 0.340 0.308 0.016
#> SRR1370495 1 0.1637 0.8225 0.940 0.000 0.000 0.060
#> SRR1352639 3 0.8037 0.1021 0.312 0.004 0.384 0.300
#> SRR1348911 1 0.3610 0.7476 0.800 0.000 0.200 0.000
#> SRR1467386 1 0.3052 0.7906 0.860 0.000 0.136 0.004
#> SRR1415956 1 0.2345 0.8008 0.900 0.000 0.000 0.100
#> SRR1500495 1 0.2530 0.8003 0.896 0.000 0.004 0.100
#> SRR1405099 1 0.2345 0.8008 0.900 0.000 0.000 0.100
#> SRR1345585 3 0.1489 0.7791 0.004 0.000 0.952 0.044
#> SRR1093196 3 0.0188 0.7962 0.000 0.000 0.996 0.004
#> SRR1466006 2 0.0000 0.8464 0.000 1.000 0.000 0.000
#> SRR1351557 2 0.0000 0.8464 0.000 1.000 0.000 0.000
#> SRR1382687 1 0.3402 0.7753 0.832 0.000 0.164 0.004
#> SRR1375549 4 0.2704 0.7051 0.124 0.000 0.000 0.876
#> SRR1101765 4 0.2345 0.7080 0.100 0.000 0.000 0.900
#> SRR1334461 1 0.2081 0.8088 0.916 0.000 0.000 0.084
#> SRR1094073 2 0.0000 0.8464 0.000 1.000 0.000 0.000
#> SRR1077549 1 0.5105 0.3082 0.564 0.000 0.432 0.004
#> SRR1440332 1 0.2844 0.8109 0.900 0.000 0.048 0.052
#> SRR1454177 4 0.4992 0.3161 0.000 0.000 0.476 0.524
#> SRR1082447 1 0.1716 0.8211 0.936 0.000 0.000 0.064
#> SRR1420043 3 0.0000 0.7972 0.000 0.000 1.000 0.000
#> SRR1432500 4 0.4072 0.7199 0.120 0.000 0.052 0.828
#> SRR1378045 2 0.1474 0.8209 0.000 0.948 0.052 0.000
#> SRR1334200 1 0.9969 -0.1068 0.288 0.244 0.224 0.244
#> SRR1069539 4 0.4972 0.2635 0.000 0.000 0.456 0.544
#> SRR1343031 3 0.0592 0.7933 0.016 0.000 0.984 0.000
#> SRR1319690 3 0.5990 0.3306 0.336 0.000 0.608 0.056
#> SRR1310604 2 0.4538 0.6236 0.000 0.760 0.216 0.024
#> SRR1327747 4 0.4790 0.3180 0.000 0.000 0.380 0.620
#> SRR1072456 2 0.0000 0.8464 0.000 1.000 0.000 0.000
#> SRR1367896 3 0.0000 0.7972 0.000 0.000 1.000 0.000
#> SRR1480107 1 0.1389 0.8213 0.952 0.000 0.000 0.048
#> SRR1377756 4 0.2799 0.7437 0.008 0.000 0.108 0.884
#> SRR1435272 4 0.3311 0.7263 0.000 0.000 0.172 0.828
#> SRR1089230 4 0.2345 0.7410 0.000 0.000 0.100 0.900
#> SRR1389522 3 0.5417 0.5804 0.240 0.000 0.704 0.056
#> SRR1080600 3 0.6443 0.1954 0.000 0.400 0.528 0.072
#> SRR1086935 4 0.4382 0.6096 0.000 0.000 0.296 0.704
#> SRR1344060 2 0.6538 0.4773 0.292 0.600 0.000 0.108
#> SRR1467922 2 0.0000 0.8464 0.000 1.000 0.000 0.000
#> SRR1090984 1 0.3311 0.7711 0.828 0.000 0.172 0.000
#> SRR1456991 1 0.1302 0.8222 0.956 0.000 0.000 0.044
#> SRR1085039 1 0.2704 0.7947 0.876 0.000 0.000 0.124
#> SRR1069303 1 0.3032 0.7823 0.868 0.000 0.008 0.124
#> SRR1091500 2 0.0000 0.8464 0.000 1.000 0.000 0.000
#> SRR1075198 2 0.6180 0.4231 0.000 0.624 0.296 0.080
#> SRR1086915 4 0.2345 0.7410 0.000 0.000 0.100 0.900
#> SRR1499503 2 0.0000 0.8464 0.000 1.000 0.000 0.000
#> SRR1094312 2 0.0000 0.8464 0.000 1.000 0.000 0.000
#> SRR1352437 1 0.3757 0.7768 0.828 0.020 0.152 0.000
#> SRR1436323 3 0.0188 0.7960 0.000 0.000 0.996 0.004
#> SRR1073507 4 0.4999 0.0982 0.492 0.000 0.000 0.508
#> SRR1401972 1 0.2216 0.8102 0.908 0.000 0.092 0.000
#> SRR1415510 2 0.0188 0.8447 0.000 0.996 0.004 0.000
#> SRR1327279 1 0.2844 0.8109 0.900 0.000 0.048 0.052
#> SRR1086983 4 0.3778 0.7260 0.100 0.000 0.052 0.848
#> SRR1105174 4 0.2760 0.7033 0.128 0.000 0.000 0.872
#> SRR1468893 4 0.4193 0.5507 0.268 0.000 0.000 0.732
#> SRR1362555 2 0.5875 0.5941 0.092 0.684 0.000 0.224
#> SRR1074526 1 0.7312 0.5958 0.660 0.112 0.124 0.104
#> SRR1326225 2 0.0000 0.8464 0.000 1.000 0.000 0.000
#> SRR1401933 1 0.3160 0.7975 0.872 0.000 0.020 0.108
#> SRR1324062 1 0.3172 0.7798 0.840 0.000 0.160 0.000
#> SRR1102296 1 0.2530 0.8065 0.896 0.004 0.100 0.000
#> SRR1085087 1 0.3688 0.7217 0.792 0.000 0.000 0.208
#> SRR1079046 4 0.2704 0.7051 0.124 0.000 0.000 0.876
#> SRR1328339 1 0.1389 0.8213 0.952 0.000 0.000 0.048
#> SRR1079782 2 0.4304 0.5959 0.000 0.716 0.000 0.284
#> SRR1092257 2 0.4454 0.5553 0.000 0.692 0.000 0.308
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.2953 0.7155 0.000 0.844 0.000 0.012 0.144
#> SRR1429287 4 0.6241 0.1219 0.000 0.324 0.000 0.512 0.164
#> SRR1359238 4 0.6637 0.5481 0.116 0.000 0.124 0.628 0.132
#> SRR1309597 3 0.5375 0.0755 0.076 0.000 0.604 0.000 0.320
#> SRR1441398 1 0.4045 0.4718 0.644 0.000 0.000 0.000 0.356
#> SRR1084055 2 0.1043 0.7584 0.000 0.960 0.000 0.000 0.040
#> SRR1417566 1 0.6786 -0.1584 0.384 0.000 0.292 0.000 0.324
#> SRR1351857 4 0.2806 0.6904 0.004 0.000 0.152 0.844 0.000
#> SRR1487485 3 0.3895 0.3726 0.000 0.000 0.680 0.000 0.320
#> SRR1335875 1 0.5878 0.2728 0.556 0.000 0.120 0.000 0.324
#> SRR1073947 1 0.0000 0.7315 1.000 0.000 0.000 0.000 0.000
#> SRR1443483 3 0.2166 0.4533 0.072 0.000 0.912 0.004 0.012
#> SRR1346794 5 0.6340 0.2475 0.196 0.000 0.128 0.048 0.628
#> SRR1405245 1 0.6507 0.0685 0.472 0.000 0.212 0.000 0.316
#> SRR1409677 4 0.2891 0.6851 0.000 0.000 0.176 0.824 0.000
#> SRR1095549 1 0.5517 0.1224 0.520 0.000 0.420 0.004 0.056
#> SRR1323788 5 0.6489 0.0969 0.192 0.000 0.360 0.000 0.448
#> SRR1314054 2 0.0880 0.7513 0.000 0.968 0.032 0.000 0.000
#> SRR1077944 1 0.1608 0.7216 0.928 0.000 0.000 0.000 0.072
#> SRR1480587 2 0.0000 0.7643 0.000 1.000 0.000 0.000 0.000
#> SRR1311205 1 0.0324 0.7325 0.992 0.000 0.000 0.004 0.004
#> SRR1076369 3 0.7066 0.0408 0.200 0.000 0.548 0.060 0.192
#> SRR1453549 1 0.6748 -0.1012 0.404 0.000 0.276 0.000 0.320
#> SRR1345782 1 0.0162 0.7317 0.996 0.000 0.000 0.000 0.004
#> SRR1447850 2 0.1648 0.7455 0.000 0.940 0.040 0.020 0.000
#> SRR1391553 2 0.7824 -0.1585 0.136 0.420 0.124 0.000 0.320
#> SRR1444156 2 0.0000 0.7643 0.000 1.000 0.000 0.000 0.000
#> SRR1471731 3 0.4251 0.3274 0.004 0.000 0.624 0.000 0.372
#> SRR1120987 4 0.2719 0.6523 0.144 0.000 0.004 0.852 0.000
#> SRR1477363 1 0.4045 0.4718 0.644 0.000 0.000 0.000 0.356
#> SRR1391961 1 0.2899 0.7127 0.872 0.000 0.008 0.020 0.100
#> SRR1373879 3 0.3305 0.4439 0.000 0.000 0.776 0.000 0.224
#> SRR1318732 5 0.4183 0.1786 0.008 0.000 0.324 0.000 0.668
#> SRR1091404 1 0.1544 0.7215 0.932 0.000 0.000 0.000 0.068
#> SRR1402109 3 0.0703 0.5226 0.000 0.000 0.976 0.000 0.024
#> SRR1407336 3 0.0290 0.5216 0.000 0.000 0.992 0.008 0.000
#> SRR1097417 3 0.4252 0.3014 0.000 0.072 0.780 0.004 0.144
#> SRR1396227 1 0.0162 0.7319 0.996 0.000 0.000 0.000 0.004
#> SRR1400775 2 0.0000 0.7643 0.000 1.000 0.000 0.000 0.000
#> SRR1392861 3 0.4165 0.3663 0.000 0.000 0.672 0.008 0.320
#> SRR1472929 5 0.8696 0.1503 0.136 0.056 0.232 0.132 0.444
#> SRR1436740 4 0.2891 0.6851 0.000 0.000 0.176 0.824 0.000
#> SRR1477057 2 0.5587 0.3945 0.256 0.644 0.088 0.012 0.000
#> SRR1311980 1 0.2723 0.7011 0.864 0.000 0.124 0.000 0.012
#> SRR1069400 3 0.0162 0.5233 0.004 0.000 0.996 0.000 0.000
#> SRR1351016 1 0.0162 0.7319 0.996 0.000 0.004 0.000 0.000
#> SRR1096291 4 0.4484 0.5292 0.044 0.000 0.192 0.752 0.012
#> SRR1418145 4 0.0566 0.6481 0.000 0.000 0.012 0.984 0.004
#> SRR1488111 5 0.8762 0.2083 0.084 0.108 0.176 0.168 0.464
#> SRR1370495 1 0.4010 0.6373 0.792 0.000 0.000 0.072 0.136
#> SRR1352639 4 0.8083 -0.1582 0.292 0.000 0.116 0.388 0.204
#> SRR1348911 1 0.4479 0.6282 0.744 0.000 0.184 0.000 0.072
#> SRR1467386 1 0.4352 0.6330 0.772 0.000 0.076 0.004 0.148
#> SRR1415956 1 0.4045 0.4718 0.644 0.000 0.000 0.000 0.356
#> SRR1500495 1 0.4045 0.4718 0.644 0.000 0.000 0.000 0.356
#> SRR1405099 1 0.4045 0.4718 0.644 0.000 0.000 0.000 0.356
#> SRR1345585 3 0.4517 0.2187 0.008 0.000 0.556 0.000 0.436
#> SRR1093196 3 0.3521 0.4413 0.000 0.000 0.764 0.004 0.232
#> SRR1466006 2 0.4835 0.6625 0.000 0.724 0.000 0.120 0.156
#> SRR1351557 2 0.1106 0.7589 0.000 0.964 0.000 0.024 0.012
#> SRR1382687 1 0.5195 0.5116 0.676 0.000 0.108 0.000 0.216
#> SRR1375549 4 0.4101 0.6298 0.048 0.000 0.000 0.768 0.184
#> SRR1101765 4 0.3238 0.6492 0.028 0.000 0.000 0.836 0.136
#> SRR1334461 1 0.4603 0.5507 0.668 0.000 0.000 0.032 0.300
#> SRR1094073 2 0.0000 0.7643 0.000 1.000 0.000 0.000 0.000
#> SRR1077549 1 0.3521 0.6500 0.764 0.000 0.232 0.004 0.000
#> SRR1440332 1 0.2286 0.7155 0.888 0.000 0.108 0.004 0.000
#> SRR1454177 4 0.4310 0.4610 0.000 0.000 0.392 0.604 0.004
#> SRR1082447 1 0.2989 0.7176 0.868 0.000 0.000 0.060 0.072
#> SRR1420043 3 0.3895 0.3726 0.000 0.000 0.680 0.000 0.320
#> SRR1432500 4 0.3093 0.6414 0.168 0.000 0.008 0.824 0.000
#> SRR1378045 2 0.1725 0.7340 0.000 0.936 0.020 0.000 0.044
#> SRR1334200 5 0.8425 0.1283 0.032 0.116 0.224 0.172 0.456
#> SRR1069539 4 0.5047 0.1284 0.000 0.000 0.472 0.496 0.032
#> SRR1343031 3 0.0703 0.5119 0.024 0.000 0.976 0.000 0.000
#> SRR1319690 5 0.4270 0.1596 0.004 0.000 0.336 0.004 0.656
#> SRR1310604 2 0.8308 0.1869 0.000 0.368 0.248 0.148 0.236
#> SRR1327747 5 0.6728 0.0652 0.004 0.000 0.268 0.264 0.464
#> SRR1072456 2 0.2953 0.7155 0.000 0.844 0.000 0.012 0.144
#> SRR1367896 3 0.0000 0.5233 0.000 0.000 1.000 0.000 0.000
#> SRR1480107 1 0.1544 0.7215 0.932 0.000 0.000 0.000 0.068
#> SRR1377756 4 0.3047 0.6896 0.004 0.000 0.160 0.832 0.004
#> SRR1435272 4 0.2891 0.6851 0.000 0.000 0.176 0.824 0.000
#> SRR1089230 4 0.2806 0.6899 0.000 0.000 0.152 0.844 0.004
#> SRR1389522 3 0.5027 0.2283 0.112 0.000 0.700 0.000 0.188
#> SRR1080600 3 0.8055 -0.0686 0.000 0.156 0.436 0.176 0.232
#> SRR1086935 4 0.5935 0.3561 0.000 0.000 0.268 0.580 0.152
#> SRR1344060 2 0.8076 0.3575 0.188 0.456 0.008 0.116 0.232
#> SRR1467922 2 0.0000 0.7643 0.000 1.000 0.000 0.000 0.000
#> SRR1090984 1 0.4312 0.6361 0.772 0.000 0.104 0.000 0.124
#> SRR1456991 1 0.1478 0.7228 0.936 0.000 0.000 0.000 0.064
#> SRR1085039 1 0.5003 0.6867 0.764 0.000 0.080 0.084 0.072
#> SRR1069303 1 0.2629 0.6897 0.860 0.000 0.000 0.136 0.004
#> SRR1091500 2 0.0000 0.7643 0.000 1.000 0.000 0.000 0.000
#> SRR1075198 2 0.8219 0.2763 0.000 0.408 0.188 0.176 0.228
#> SRR1086915 4 0.2690 0.6894 0.000 0.000 0.156 0.844 0.000
#> SRR1499503 2 0.2953 0.7155 0.000 0.844 0.000 0.012 0.144
#> SRR1094312 2 0.0000 0.7643 0.000 1.000 0.000 0.000 0.000
#> SRR1352437 1 0.3971 0.6594 0.800 0.000 0.100 0.000 0.100
#> SRR1436323 3 0.4251 0.3286 0.000 0.000 0.624 0.004 0.372
#> SRR1073507 4 0.4307 0.0322 0.496 0.000 0.000 0.504 0.000
#> SRR1401972 1 0.1041 0.7300 0.964 0.000 0.032 0.000 0.004
#> SRR1415510 2 0.4658 0.2385 0.000 0.504 0.000 0.012 0.484
#> SRR1327279 1 0.2424 0.7051 0.868 0.000 0.132 0.000 0.000
#> SRR1086983 4 0.2964 0.6477 0.152 0.000 0.004 0.840 0.004
#> SRR1105174 4 0.4977 0.4661 0.040 0.000 0.000 0.604 0.356
#> SRR1468893 4 0.5862 0.4363 0.220 0.000 0.000 0.604 0.176
#> SRR1362555 2 0.8302 0.2944 0.164 0.384 0.000 0.224 0.228
#> SRR1074526 1 0.7764 0.4018 0.536 0.044 0.208 0.056 0.156
#> SRR1326225 2 0.0000 0.7643 0.000 1.000 0.000 0.000 0.000
#> SRR1401933 1 0.4564 0.6495 0.772 0.000 0.040 0.036 0.152
#> SRR1324062 1 0.2439 0.7086 0.876 0.000 0.120 0.000 0.004
#> SRR1102296 1 0.1915 0.7266 0.928 0.040 0.032 0.000 0.000
#> SRR1085087 1 0.3336 0.6134 0.772 0.000 0.000 0.228 0.000
#> SRR1079046 4 0.3687 0.6329 0.028 0.000 0.000 0.792 0.180
#> SRR1328339 1 0.1544 0.7215 0.932 0.000 0.000 0.000 0.068
#> SRR1079782 2 0.6235 0.4542 0.000 0.500 0.000 0.344 0.156
#> SRR1092257 2 0.4637 0.5589 0.000 0.672 0.000 0.292 0.036
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.3190 0.69771 0.000 0.772 0.000 0.000 0.008 0.220
#> SRR1429287 4 0.6238 0.12344 0.000 0.272 0.000 0.452 0.012 0.264
#> SRR1359238 4 0.4437 0.67593 0.068 0.000 0.044 0.768 0.004 0.116
#> SRR1309597 3 0.7625 0.02974 0.076 0.000 0.420 0.048 0.284 0.172
#> SRR1441398 1 0.6492 0.17907 0.472 0.000 0.000 0.048 0.312 0.168
#> SRR1084055 2 0.0993 0.83957 0.000 0.964 0.000 0.000 0.012 0.024
#> SRR1417566 6 0.6159 -0.08455 0.252 0.000 0.348 0.004 0.000 0.396
#> SRR1351857 4 0.1075 0.79829 0.000 0.000 0.048 0.952 0.000 0.000
#> SRR1487485 3 0.3828 0.33832 0.000 0.000 0.560 0.000 0.000 0.440
#> SRR1335875 1 0.4751 0.11685 0.500 0.000 0.048 0.000 0.000 0.452
#> SRR1073947 1 0.0000 0.66602 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1443483 3 0.1168 0.52507 0.028 0.000 0.956 0.000 0.016 0.000
#> SRR1346794 6 0.6948 0.06914 0.268 0.000 0.000 0.056 0.308 0.368
#> SRR1405245 1 0.7153 0.11231 0.448 0.000 0.032 0.044 0.284 0.192
#> SRR1409677 4 0.2454 0.74773 0.000 0.000 0.160 0.840 0.000 0.000
#> SRR1095549 3 0.4712 0.06959 0.452 0.000 0.512 0.000 0.024 0.012
#> SRR1323788 6 0.5994 0.09857 0.156 0.000 0.220 0.008 0.028 0.588
#> SRR1314054 2 0.0363 0.84378 0.000 0.988 0.012 0.000 0.000 0.000
#> SRR1077944 1 0.1075 0.66118 0.952 0.000 0.000 0.000 0.048 0.000
#> SRR1480587 2 0.0458 0.84621 0.000 0.984 0.000 0.000 0.000 0.016
#> SRR1311205 1 0.0146 0.66637 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1076369 3 0.5079 0.38117 0.088 0.000 0.720 0.052 0.132 0.008
#> SRR1453549 6 0.5932 0.09745 0.336 0.000 0.224 0.000 0.000 0.440
#> SRR1345782 1 0.0146 0.66625 0.996 0.000 0.000 0.000 0.004 0.000
#> SRR1447850 2 0.1377 0.83115 0.000 0.952 0.024 0.004 0.004 0.016
#> SRR1391553 6 0.6415 0.20317 0.132 0.372 0.052 0.000 0.000 0.444
#> SRR1444156 2 0.0146 0.84795 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1471731 3 0.4218 0.36444 0.004 0.000 0.584 0.012 0.000 0.400
#> SRR1120987 4 0.1267 0.77787 0.060 0.000 0.000 0.940 0.000 0.000
#> SRR1477363 1 0.6404 0.18310 0.472 0.000 0.000 0.040 0.320 0.168
#> SRR1391961 5 0.3867 0.49249 0.328 0.000 0.000 0.000 0.660 0.012
#> SRR1373879 3 0.3515 0.42364 0.000 0.000 0.676 0.000 0.000 0.324
#> SRR1318732 6 0.5504 0.17884 0.004 0.000 0.048 0.056 0.280 0.612
#> SRR1091404 1 0.1075 0.66118 0.952 0.000 0.000 0.000 0.048 0.000
#> SRR1402109 3 0.1141 0.53947 0.000 0.000 0.948 0.000 0.000 0.052
#> SRR1407336 3 0.0000 0.54502 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1097417 3 0.3992 0.36921 0.004 0.064 0.756 0.000 0.000 0.176
#> SRR1396227 1 0.0363 0.66596 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1400775 2 0.0000 0.84833 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1392861 3 0.3817 0.34549 0.000 0.000 0.568 0.000 0.000 0.432
#> SRR1472929 5 0.7571 0.47521 0.120 0.032 0.132 0.000 0.420 0.296
#> SRR1436740 4 0.1610 0.79127 0.000 0.000 0.084 0.916 0.000 0.000
#> SRR1477057 2 0.4283 0.37232 0.288 0.672 0.004 0.000 0.000 0.036
#> SRR1311980 1 0.2954 0.62020 0.844 0.000 0.048 0.000 0.000 0.108
#> SRR1069400 3 0.0000 0.54502 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1351016 1 0.0000 0.66602 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1096291 4 0.4868 0.39373 0.016 0.000 0.332 0.608 0.000 0.044
#> SRR1418145 4 0.1204 0.77991 0.000 0.000 0.000 0.944 0.000 0.056
#> SRR1488111 6 0.4390 0.18082 0.088 0.048 0.084 0.004 0.000 0.776
#> SRR1370495 1 0.4062 0.43133 0.744 0.000 0.000 0.060 0.004 0.192
#> SRR1352639 6 0.6767 -0.15739 0.368 0.000 0.044 0.172 0.008 0.408
#> SRR1348911 1 0.4746 0.48231 0.668 0.000 0.116 0.000 0.000 0.216
#> SRR1467386 1 0.3309 0.50886 0.720 0.000 0.000 0.000 0.000 0.280
#> SRR1415956 1 0.6404 0.18310 0.472 0.000 0.000 0.040 0.320 0.168
#> SRR1500495 1 0.6515 0.17274 0.468 0.000 0.000 0.048 0.312 0.172
#> SRR1405099 1 0.6404 0.18310 0.472 0.000 0.000 0.040 0.320 0.168
#> SRR1345585 3 0.5947 0.27981 0.004 0.000 0.480 0.040 0.076 0.400
#> SRR1093196 3 0.3288 0.45150 0.000 0.000 0.724 0.000 0.000 0.276
#> SRR1466006 2 0.3746 0.66522 0.000 0.712 0.000 0.004 0.012 0.272
#> SRR1351557 2 0.0865 0.83682 0.000 0.964 0.000 0.000 0.000 0.036
#> SRR1382687 1 0.4466 0.35443 0.612 0.000 0.032 0.004 0.000 0.352
#> SRR1375549 4 0.1152 0.77638 0.000 0.000 0.000 0.952 0.044 0.004
#> SRR1101765 4 0.1285 0.77827 0.000 0.000 0.004 0.944 0.052 0.000
#> SRR1334461 5 0.4012 0.59564 0.076 0.000 0.000 0.000 0.748 0.176
#> SRR1094073 2 0.0000 0.84833 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077549 1 0.4475 0.49939 0.692 0.000 0.220 0.000 0.000 0.088
#> SRR1440332 1 0.1765 0.63930 0.904 0.000 0.096 0.000 0.000 0.000
#> SRR1454177 4 0.3774 0.55235 0.000 0.000 0.328 0.664 0.000 0.008
#> SRR1082447 1 0.3185 0.61830 0.832 0.000 0.000 0.116 0.048 0.004
#> SRR1420043 3 0.3828 0.33832 0.000 0.000 0.560 0.000 0.000 0.440
#> SRR1432500 4 0.1349 0.78104 0.056 0.000 0.004 0.940 0.000 0.000
#> SRR1378045 2 0.2095 0.76944 0.000 0.904 0.016 0.000 0.004 0.076
#> SRR1334200 5 0.6823 0.39981 0.000 0.064 0.156 0.004 0.388 0.388
#> SRR1069539 3 0.4886 0.33271 0.000 0.000 0.652 0.244 0.004 0.100
#> SRR1343031 3 0.0000 0.54502 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1319690 6 0.5467 0.17541 0.004 0.000 0.052 0.048 0.284 0.612
#> SRR1310604 6 0.6539 -0.15485 0.000 0.292 0.336 0.000 0.020 0.352
#> SRR1327747 6 0.6322 -0.12342 0.000 0.000 0.260 0.316 0.012 0.412
#> SRR1072456 2 0.3190 0.69771 0.000 0.772 0.000 0.000 0.008 0.220
#> SRR1367896 3 0.0000 0.54502 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1480107 1 0.0865 0.66139 0.964 0.000 0.000 0.000 0.036 0.000
#> SRR1377756 4 0.1204 0.79733 0.000 0.000 0.056 0.944 0.000 0.000
#> SRR1435272 4 0.1267 0.79685 0.000 0.000 0.060 0.940 0.000 0.000
#> SRR1089230 4 0.1075 0.79816 0.000 0.000 0.048 0.952 0.000 0.000
#> SRR1389522 3 0.6355 0.23465 0.044 0.000 0.592 0.040 0.228 0.096
#> SRR1080600 3 0.5608 -0.05202 0.000 0.100 0.500 0.004 0.008 0.388
#> SRR1086935 4 0.4633 0.50988 0.000 0.000 0.100 0.676 0.000 0.224
#> SRR1344060 5 0.4734 0.54536 0.000 0.120 0.000 0.000 0.672 0.208
#> SRR1467922 2 0.0146 0.84795 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1090984 1 0.4115 0.49140 0.696 0.000 0.032 0.004 0.000 0.268
#> SRR1456991 1 0.0865 0.66139 0.964 0.000 0.000 0.000 0.036 0.000
#> SRR1085039 1 0.5119 0.51838 0.692 0.000 0.088 0.172 0.048 0.000
#> SRR1069303 1 0.2912 0.58337 0.816 0.000 0.000 0.172 0.012 0.000
#> SRR1091500 2 0.0000 0.84833 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1075198 6 0.6334 -0.20142 0.000 0.368 0.232 0.004 0.008 0.388
#> SRR1086915 4 0.1075 0.79829 0.000 0.000 0.048 0.952 0.000 0.000
#> SRR1499503 2 0.3230 0.70259 0.000 0.776 0.000 0.000 0.012 0.212
#> SRR1094312 2 0.0000 0.84833 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1352437 1 0.3876 0.53044 0.728 0.000 0.016 0.000 0.012 0.244
#> SRR1436323 3 0.4517 0.31461 0.000 0.000 0.524 0.032 0.000 0.444
#> SRR1073507 1 0.3868 0.05061 0.508 0.000 0.000 0.492 0.000 0.000
#> SRR1401972 1 0.0508 0.66596 0.984 0.000 0.004 0.000 0.012 0.000
#> SRR1415510 6 0.4218 0.00609 0.000 0.400 0.000 0.004 0.012 0.584
#> SRR1327279 1 0.2003 0.62763 0.884 0.000 0.116 0.000 0.000 0.000
#> SRR1086983 4 0.1204 0.77959 0.056 0.000 0.000 0.944 0.000 0.000
#> SRR1105174 4 0.6119 0.24325 0.020 0.000 0.000 0.480 0.332 0.168
#> SRR1468893 4 0.3337 0.49058 0.260 0.000 0.000 0.736 0.004 0.000
#> SRR1362555 6 0.7584 -0.23977 0.244 0.244 0.000 0.012 0.116 0.384
#> SRR1074526 5 0.4211 0.51533 0.276 0.004 0.020 0.004 0.692 0.004
#> SRR1326225 2 0.0146 0.84795 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1401933 1 0.5166 0.48794 0.672 0.000 0.064 0.052 0.000 0.212
#> SRR1324062 1 0.2258 0.64771 0.896 0.000 0.044 0.000 0.000 0.060
#> SRR1102296 1 0.1471 0.64671 0.932 0.064 0.004 0.000 0.000 0.000
#> SRR1085087 1 0.3690 0.45950 0.700 0.000 0.000 0.288 0.012 0.000
#> SRR1079046 4 0.2915 0.67835 0.000 0.000 0.000 0.808 0.184 0.008
#> SRR1328339 1 0.1010 0.66129 0.960 0.000 0.000 0.000 0.036 0.004
#> SRR1079782 2 0.5686 0.31203 0.000 0.456 0.000 0.160 0.000 0.384
#> SRR1092257 2 0.4734 0.56377 0.000 0.680 0.000 0.152 0.000 0.168
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 17611 rows and 118 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 0.978 0.990 0.4017 0.594 0.594
#> 3 3 0.576 0.809 0.874 0.6055 0.740 0.562
#> 4 4 0.484 0.519 0.711 0.0824 0.864 0.642
#> 5 5 0.523 0.420 0.649 0.0657 0.826 0.499
#> 6 6 0.565 0.417 0.660 0.0483 0.832 0.451
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
#> SRR1396765 2 0.000 0.970 0.000 1.000
#> SRR1429287 2 0.000 0.970 0.000 1.000
#> SRR1359238 1 0.000 0.997 1.000 0.000
#> SRR1309597 1 0.000 0.997 1.000 0.000
#> SRR1441398 1 0.000 0.997 1.000 0.000
#> SRR1084055 2 0.000 0.970 0.000 1.000
#> SRR1417566 1 0.000 0.997 1.000 0.000
#> SRR1351857 1 0.000 0.997 1.000 0.000
#> SRR1487485 1 0.000 0.997 1.000 0.000
#> SRR1335875 1 0.000 0.997 1.000 0.000
#> SRR1073947 1 0.000 0.997 1.000 0.000
#> SRR1443483 1 0.000 0.997 1.000 0.000
#> SRR1346794 1 0.000 0.997 1.000 0.000
#> SRR1405245 1 0.000 0.997 1.000 0.000
#> SRR1409677 1 0.000 0.997 1.000 0.000
#> SRR1095549 1 0.000 0.997 1.000 0.000
#> SRR1323788 1 0.000 0.997 1.000 0.000
#> SRR1314054 2 0.000 0.970 0.000 1.000
#> SRR1077944 1 0.000 0.997 1.000 0.000
#> SRR1480587 2 0.000 0.970 0.000 1.000
#> SRR1311205 1 0.000 0.997 1.000 0.000
#> SRR1076369 1 0.000 0.997 1.000 0.000
#> SRR1453549 1 0.000 0.997 1.000 0.000
#> SRR1345782 1 0.000 0.997 1.000 0.000
#> SRR1447850 2 0.000 0.970 0.000 1.000
#> SRR1391553 1 0.000 0.997 1.000 0.000
#> SRR1444156 2 0.000 0.970 0.000 1.000
#> SRR1471731 1 0.000 0.997 1.000 0.000
#> SRR1120987 1 0.000 0.997 1.000 0.000
#> SRR1477363 1 0.000 0.997 1.000 0.000
#> SRR1391961 2 0.518 0.875 0.116 0.884
#> SRR1373879 1 0.000 0.997 1.000 0.000
#> SRR1318732 1 0.000 0.997 1.000 0.000
#> SRR1091404 1 0.000 0.997 1.000 0.000
#> SRR1402109 1 0.000 0.997 1.000 0.000
#> SRR1407336 1 0.000 0.997 1.000 0.000
#> SRR1097417 1 0.808 0.657 0.752 0.248
#> SRR1396227 1 0.000 0.997 1.000 0.000
#> SRR1400775 2 0.000 0.970 0.000 1.000
#> SRR1392861 1 0.000 0.997 1.000 0.000
#> SRR1472929 2 0.000 0.970 0.000 1.000
#> SRR1436740 1 0.000 0.997 1.000 0.000
#> SRR1477057 2 0.000 0.970 0.000 1.000
#> SRR1311980 1 0.000 0.997 1.000 0.000
#> SRR1069400 1 0.000 0.997 1.000 0.000
#> SRR1351016 1 0.000 0.997 1.000 0.000
#> SRR1096291 1 0.000 0.997 1.000 0.000
#> SRR1418145 1 0.000 0.997 1.000 0.000
#> SRR1488111 1 0.000 0.997 1.000 0.000
#> SRR1370495 1 0.000 0.997 1.000 0.000
#> SRR1352639 1 0.000 0.997 1.000 0.000
#> SRR1348911 1 0.000 0.997 1.000 0.000
#> SRR1467386 1 0.000 0.997 1.000 0.000
#> SRR1415956 1 0.000 0.997 1.000 0.000
#> SRR1500495 1 0.000 0.997 1.000 0.000
#> SRR1405099 1 0.000 0.997 1.000 0.000
#> SRR1345585 1 0.000 0.997 1.000 0.000
#> SRR1093196 1 0.000 0.997 1.000 0.000
#> SRR1466006 2 0.000 0.970 0.000 1.000
#> SRR1351557 2 0.000 0.970 0.000 1.000
#> SRR1382687 1 0.000 0.997 1.000 0.000
#> SRR1375549 1 0.000 0.997 1.000 0.000
#> SRR1101765 1 0.184 0.968 0.972 0.028
#> SRR1334461 2 0.388 0.912 0.076 0.924
#> SRR1094073 2 0.000 0.970 0.000 1.000
#> SRR1077549 1 0.000 0.997 1.000 0.000
#> SRR1440332 1 0.000 0.997 1.000 0.000
#> SRR1454177 1 0.000 0.997 1.000 0.000
#> SRR1082447 1 0.000 0.997 1.000 0.000
#> SRR1420043 1 0.000 0.997 1.000 0.000
#> SRR1432500 1 0.000 0.997 1.000 0.000
#> SRR1378045 2 0.946 0.447 0.364 0.636
#> SRR1334200 2 0.000 0.970 0.000 1.000
#> SRR1069539 1 0.000 0.997 1.000 0.000
#> SRR1343031 1 0.000 0.997 1.000 0.000
#> SRR1319690 1 0.000 0.997 1.000 0.000
#> SRR1310604 2 0.000 0.970 0.000 1.000
#> SRR1327747 1 0.000 0.997 1.000 0.000
#> SRR1072456 2 0.000 0.970 0.000 1.000
#> SRR1367896 1 0.000 0.997 1.000 0.000
#> SRR1480107 1 0.000 0.997 1.000 0.000
#> SRR1377756 1 0.000 0.997 1.000 0.000
#> SRR1435272 1 0.000 0.997 1.000 0.000
#> SRR1089230 1 0.000 0.997 1.000 0.000
#> SRR1389522 1 0.000 0.997 1.000 0.000
#> SRR1080600 2 0.000 0.970 0.000 1.000
#> SRR1086935 1 0.000 0.997 1.000 0.000
#> SRR1344060 2 0.000 0.970 0.000 1.000
#> SRR1467922 2 0.000 0.970 0.000 1.000
#> SRR1090984 1 0.000 0.997 1.000 0.000
#> SRR1456991 1 0.000 0.997 1.000 0.000
#> SRR1085039 1 0.000 0.997 1.000 0.000
#> SRR1069303 1 0.000 0.997 1.000 0.000
#> SRR1091500 2 0.000 0.970 0.000 1.000
#> SRR1075198 2 0.000 0.970 0.000 1.000
#> SRR1086915 1 0.000 0.997 1.000 0.000
#> SRR1499503 2 0.000 0.970 0.000 1.000
#> SRR1094312 2 0.000 0.970 0.000 1.000
#> SRR1352437 1 0.000 0.997 1.000 0.000
#> SRR1436323 1 0.000 0.997 1.000 0.000
#> SRR1073507 1 0.000 0.997 1.000 0.000
#> SRR1401972 1 0.000 0.997 1.000 0.000
#> SRR1415510 2 0.000 0.970 0.000 1.000
#> SRR1327279 1 0.000 0.997 1.000 0.000
#> SRR1086983 1 0.000 0.997 1.000 0.000
#> SRR1105174 1 0.000 0.997 1.000 0.000
#> SRR1468893 1 0.000 0.997 1.000 0.000
#> SRR1362555 2 0.000 0.970 0.000 1.000
#> SRR1074526 2 0.529 0.871 0.120 0.880
#> SRR1326225 2 0.000 0.970 0.000 1.000
#> SRR1401933 1 0.000 0.997 1.000 0.000
#> SRR1324062 1 0.000 0.997 1.000 0.000
#> SRR1102296 1 0.000 0.997 1.000 0.000
#> SRR1085087 1 0.000 0.997 1.000 0.000
#> SRR1079046 2 0.529 0.871 0.120 0.880
#> SRR1328339 1 0.000 0.997 1.000 0.000
#> SRR1079782 2 0.595 0.839 0.144 0.856
#> SRR1092257 2 0.000 0.970 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1429287 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1359238 1 0.6126 0.506 0.600 0.000 0.400
#> SRR1309597 3 0.0592 0.835 0.012 0.000 0.988
#> SRR1441398 3 0.3619 0.814 0.136 0.000 0.864
#> SRR1084055 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1417566 3 0.6309 0.310 0.500 0.000 0.500
#> SRR1351857 1 0.4399 0.842 0.812 0.000 0.188
#> SRR1487485 3 0.0747 0.834 0.016 0.000 0.984
#> SRR1335875 3 0.2448 0.834 0.076 0.000 0.924
#> SRR1073947 1 0.4062 0.840 0.836 0.000 0.164
#> SRR1443483 3 0.0747 0.834 0.016 0.000 0.984
#> SRR1346794 3 0.5016 0.719 0.240 0.000 0.760
#> SRR1405245 3 0.2537 0.834 0.080 0.000 0.920
#> SRR1409677 1 0.5178 0.794 0.744 0.000 0.256
#> SRR1095549 3 0.4842 0.744 0.224 0.000 0.776
#> SRR1323788 3 0.3941 0.794 0.156 0.000 0.844
#> SRR1314054 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1077944 1 0.5363 0.740 0.724 0.000 0.276
#> SRR1480587 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1311205 3 0.4842 0.739 0.224 0.000 0.776
#> SRR1076369 3 0.5810 0.643 0.336 0.000 0.664
#> SRR1453549 3 0.3412 0.817 0.124 0.000 0.876
#> SRR1345782 3 0.4291 0.782 0.180 0.000 0.820
#> SRR1447850 2 0.1774 0.931 0.016 0.960 0.024
#> SRR1391553 3 0.6018 0.611 0.308 0.008 0.684
#> SRR1444156 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1471731 3 0.2261 0.837 0.068 0.000 0.932
#> SRR1120987 1 0.1525 0.797 0.964 0.004 0.032
#> SRR1477363 1 0.4702 0.816 0.788 0.000 0.212
#> SRR1391961 2 0.3918 0.844 0.120 0.868 0.012
#> SRR1373879 3 0.0747 0.834 0.016 0.000 0.984
#> SRR1318732 3 0.1411 0.830 0.036 0.000 0.964
#> SRR1091404 1 0.1289 0.797 0.968 0.000 0.032
#> SRR1402109 3 0.1289 0.838 0.032 0.000 0.968
#> SRR1407336 3 0.1031 0.837 0.024 0.000 0.976
#> SRR1097417 3 0.8556 0.459 0.164 0.232 0.604
#> SRR1396227 1 0.3116 0.837 0.892 0.000 0.108
#> SRR1400775 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1392861 1 0.5291 0.788 0.732 0.000 0.268
#> SRR1472929 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1436740 1 0.4346 0.841 0.816 0.000 0.184
#> SRR1477057 2 0.0592 0.957 0.012 0.988 0.000
#> SRR1311980 3 0.3752 0.805 0.144 0.000 0.856
#> SRR1069400 3 0.0747 0.834 0.016 0.000 0.984
#> SRR1351016 1 0.5497 0.720 0.708 0.000 0.292
#> SRR1096291 1 0.3340 0.759 0.880 0.000 0.120
#> SRR1418145 1 0.1860 0.797 0.948 0.000 0.052
#> SRR1488111 1 0.5207 0.691 0.824 0.052 0.124
#> SRR1370495 1 0.5180 0.654 0.812 0.156 0.032
#> SRR1352639 1 0.1643 0.796 0.956 0.000 0.044
#> SRR1348911 3 0.1529 0.830 0.040 0.000 0.960
#> SRR1467386 1 0.4887 0.815 0.772 0.000 0.228
#> SRR1415956 3 0.5497 0.634 0.292 0.000 0.708
#> SRR1500495 3 0.3551 0.816 0.132 0.000 0.868
#> SRR1405099 1 0.5016 0.787 0.760 0.000 0.240
#> SRR1345585 3 0.0747 0.834 0.016 0.000 0.984
#> SRR1093196 3 0.0747 0.834 0.016 0.000 0.984
#> SRR1466006 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1351557 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1382687 1 0.5926 0.620 0.644 0.000 0.356
#> SRR1375549 1 0.0892 0.792 0.980 0.000 0.020
#> SRR1101765 1 0.5348 0.630 0.796 0.176 0.028
#> SRR1334461 2 0.3120 0.889 0.080 0.908 0.012
#> SRR1094073 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1077549 1 0.5058 0.798 0.756 0.000 0.244
#> SRR1440332 3 0.3941 0.799 0.156 0.000 0.844
#> SRR1454177 1 0.4399 0.840 0.812 0.000 0.188
#> SRR1082447 1 0.3192 0.834 0.888 0.000 0.112
#> SRR1420043 3 0.1163 0.837 0.028 0.000 0.972
#> SRR1432500 1 0.4702 0.831 0.788 0.000 0.212
#> SRR1378045 2 0.8955 0.238 0.144 0.524 0.332
#> SRR1334200 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1069539 1 0.7011 0.590 0.720 0.092 0.188
#> SRR1343031 3 0.1643 0.839 0.044 0.000 0.956
#> SRR1319690 3 0.1289 0.830 0.032 0.000 0.968
#> SRR1310604 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1327747 3 0.2261 0.839 0.068 0.000 0.932
#> SRR1072456 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1367896 3 0.0747 0.834 0.016 0.000 0.984
#> SRR1480107 1 0.4178 0.837 0.828 0.000 0.172
#> SRR1377756 1 0.4291 0.842 0.820 0.000 0.180
#> SRR1435272 1 0.4750 0.828 0.784 0.000 0.216
#> SRR1089230 1 0.4346 0.841 0.816 0.000 0.184
#> SRR1389522 3 0.1163 0.831 0.028 0.000 0.972
#> SRR1080600 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1086935 1 0.3583 0.771 0.900 0.056 0.044
#> SRR1344060 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1467922 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1090984 3 0.6180 0.473 0.416 0.000 0.584
#> SRR1456991 3 0.6267 0.163 0.452 0.000 0.548
#> SRR1085039 1 0.4235 0.837 0.824 0.000 0.176
#> SRR1069303 1 0.1031 0.801 0.976 0.000 0.024
#> SRR1091500 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1075198 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1086915 1 0.4291 0.842 0.820 0.000 0.180
#> SRR1499503 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1094312 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1352437 1 0.1289 0.801 0.968 0.000 0.032
#> SRR1436323 3 0.3879 0.800 0.152 0.000 0.848
#> SRR1073507 1 0.4178 0.843 0.828 0.000 0.172
#> SRR1401972 1 0.1031 0.802 0.976 0.000 0.024
#> SRR1415510 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1327279 1 0.5948 0.612 0.640 0.000 0.360
#> SRR1086983 1 0.4346 0.841 0.816 0.000 0.184
#> SRR1105174 1 0.4452 0.831 0.808 0.000 0.192
#> SRR1468893 1 0.4178 0.837 0.828 0.000 0.172
#> SRR1362555 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1074526 2 0.4861 0.764 0.180 0.808 0.012
#> SRR1326225 2 0.0000 0.963 0.000 1.000 0.000
#> SRR1401933 1 0.4399 0.842 0.812 0.000 0.188
#> SRR1324062 1 0.4291 0.842 0.820 0.000 0.180
#> SRR1102296 1 0.0892 0.792 0.980 0.000 0.020
#> SRR1085087 1 0.1163 0.802 0.972 0.000 0.028
#> SRR1079046 2 0.4805 0.770 0.176 0.812 0.012
#> SRR1328339 3 0.8119 0.419 0.432 0.068 0.500
#> SRR1079782 2 0.0592 0.955 0.012 0.988 0.000
#> SRR1092257 2 0.0000 0.963 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.1716 0.8926 0.064 0.936 0.000 0.000
#> SRR1429287 2 0.0817 0.8903 0.024 0.976 0.000 0.000
#> SRR1359238 4 0.3249 0.6302 0.008 0.000 0.140 0.852
#> SRR1309597 3 0.2868 0.7100 0.000 0.000 0.864 0.136
#> SRR1441398 3 0.5812 0.5953 0.048 0.000 0.624 0.328
#> SRR1084055 2 0.1716 0.8926 0.064 0.936 0.000 0.000
#> SRR1417566 3 0.6350 0.2063 0.252 0.000 0.636 0.112
#> SRR1351857 4 0.0707 0.6870 0.000 0.000 0.020 0.980
#> SRR1487485 3 0.2589 0.7048 0.000 0.000 0.884 0.116
#> SRR1335875 3 0.4807 0.6253 0.024 0.000 0.728 0.248
#> SRR1073947 4 0.0927 0.6849 0.016 0.000 0.008 0.976
#> SRR1443483 3 0.2868 0.7100 0.000 0.000 0.864 0.136
#> SRR1346794 4 0.6395 -0.2873 0.064 0.000 0.464 0.472
#> SRR1405245 3 0.5712 0.6306 0.048 0.000 0.644 0.308
#> SRR1409677 4 0.2799 0.6517 0.008 0.000 0.108 0.884
#> SRR1095549 4 0.6031 -0.2014 0.044 0.000 0.420 0.536
#> SRR1323788 3 0.5775 0.5052 0.032 0.000 0.560 0.408
#> SRR1314054 2 0.0592 0.8928 0.016 0.984 0.000 0.000
#> SRR1077944 4 0.2830 0.6732 0.060 0.000 0.040 0.900
#> SRR1480587 2 0.0336 0.8954 0.008 0.992 0.000 0.000
#> SRR1311205 4 0.6229 -0.1846 0.056 0.000 0.416 0.528
#> SRR1076369 3 0.7792 -0.0343 0.256 0.000 0.412 0.332
#> SRR1453549 3 0.5217 0.5840 0.012 0.000 0.608 0.380
#> SRR1345782 4 0.6275 -0.3090 0.056 0.000 0.460 0.484
#> SRR1447850 2 0.3400 0.7889 0.180 0.820 0.000 0.000
#> SRR1391553 3 0.4839 0.4063 0.184 0.000 0.764 0.052
#> SRR1444156 2 0.1716 0.8926 0.064 0.936 0.000 0.000
#> SRR1471731 3 0.4456 0.6890 0.004 0.000 0.716 0.280
#> SRR1120987 4 0.7768 -0.3471 0.384 0.004 0.200 0.412
#> SRR1477363 4 0.2335 0.6754 0.060 0.000 0.020 0.920
#> SRR1391961 1 0.5288 -0.3825 0.520 0.472 0.000 0.008
#> SRR1373879 3 0.3908 0.7124 0.004 0.000 0.784 0.212
#> SRR1318732 3 0.3402 0.6974 0.004 0.000 0.832 0.164
#> SRR1091404 4 0.7220 -0.2360 0.384 0.000 0.144 0.472
#> SRR1402109 3 0.4539 0.6823 0.008 0.000 0.720 0.272
#> SRR1407336 3 0.4088 0.7093 0.004 0.000 0.764 0.232
#> SRR1097417 3 0.8531 -0.1421 0.264 0.156 0.504 0.076
#> SRR1396227 4 0.3521 0.5850 0.084 0.000 0.052 0.864
#> SRR1400775 2 0.2011 0.8912 0.080 0.920 0.000 0.000
#> SRR1392861 4 0.5323 0.1390 0.020 0.000 0.352 0.628
#> SRR1472929 2 0.3975 0.7144 0.240 0.760 0.000 0.000
#> SRR1436740 4 0.2882 0.6606 0.024 0.000 0.084 0.892
#> SRR1477057 2 0.3893 0.7766 0.196 0.796 0.000 0.008
#> SRR1311980 3 0.4546 0.6960 0.012 0.000 0.732 0.256
#> SRR1069400 3 0.2868 0.7100 0.000 0.000 0.864 0.136
#> SRR1351016 4 0.2408 0.6856 0.036 0.000 0.044 0.920
#> SRR1096291 4 0.7924 -0.4156 0.336 0.000 0.328 0.336
#> SRR1418145 4 0.7830 -0.3809 0.356 0.000 0.260 0.384
#> SRR1488111 1 0.7908 0.3482 0.360 0.000 0.304 0.336
#> SRR1370495 1 0.7968 0.3856 0.424 0.008 0.224 0.344
#> SRR1352639 1 0.7800 0.3503 0.380 0.000 0.248 0.372
#> SRR1348911 3 0.4990 0.6280 0.060 0.000 0.756 0.184
#> SRR1467386 4 0.2329 0.6839 0.012 0.000 0.072 0.916
#> SRR1415956 4 0.6058 0.0719 0.060 0.000 0.336 0.604
#> SRR1500495 3 0.6147 0.5177 0.056 0.000 0.564 0.380
#> SRR1405099 4 0.2489 0.6740 0.068 0.000 0.020 0.912
#> SRR1345585 3 0.2216 0.6875 0.000 0.000 0.908 0.092
#> SRR1093196 3 0.3982 0.7096 0.004 0.000 0.776 0.220
#> SRR1466006 2 0.1716 0.8926 0.064 0.936 0.000 0.000
#> SRR1351557 2 0.1716 0.8926 0.064 0.936 0.000 0.000
#> SRR1382687 4 0.3681 0.5877 0.008 0.000 0.176 0.816
#> SRR1375549 1 0.7314 0.2720 0.428 0.000 0.152 0.420
#> SRR1101765 1 0.7508 0.2508 0.428 0.008 0.140 0.424
#> SRR1334461 1 0.5288 -0.3825 0.520 0.472 0.000 0.008
#> SRR1094073 2 0.1716 0.8926 0.064 0.936 0.000 0.000
#> SRR1077549 4 0.2610 0.6691 0.012 0.000 0.088 0.900
#> SRR1440332 3 0.5657 0.4520 0.024 0.000 0.540 0.436
#> SRR1454177 4 0.3853 0.5877 0.020 0.000 0.160 0.820
#> SRR1082447 4 0.3497 0.6336 0.104 0.000 0.036 0.860
#> SRR1420043 3 0.4769 0.6549 0.008 0.000 0.684 0.308
#> SRR1432500 4 0.1890 0.6891 0.008 0.000 0.056 0.936
#> SRR1378045 3 0.8129 -0.0929 0.268 0.352 0.372 0.008
#> SRR1334200 2 0.0817 0.8909 0.024 0.976 0.000 0.000
#> SRR1069539 1 0.8121 0.3707 0.372 0.008 0.356 0.264
#> SRR1343031 3 0.4483 0.6732 0.004 0.000 0.712 0.284
#> SRR1319690 3 0.4420 0.7036 0.012 0.000 0.748 0.240
#> SRR1310604 2 0.0188 0.8935 0.004 0.996 0.000 0.000
#> SRR1327747 3 0.4844 0.6729 0.012 0.000 0.688 0.300
#> SRR1072456 2 0.0188 0.8949 0.004 0.996 0.000 0.000
#> SRR1367896 3 0.2760 0.7082 0.000 0.000 0.872 0.128
#> SRR1480107 4 0.2179 0.6733 0.064 0.000 0.012 0.924
#> SRR1377756 4 0.0779 0.6872 0.004 0.000 0.016 0.980
#> SRR1435272 4 0.3790 0.5865 0.016 0.000 0.164 0.820
#> SRR1089230 4 0.1356 0.6811 0.008 0.000 0.032 0.960
#> SRR1389522 3 0.3311 0.7121 0.000 0.000 0.828 0.172
#> SRR1080600 2 0.0000 0.8943 0.000 1.000 0.000 0.000
#> SRR1086935 4 0.8171 -0.3001 0.368 0.024 0.184 0.424
#> SRR1344060 2 0.4977 0.4398 0.460 0.540 0.000 0.000
#> SRR1467922 2 0.1716 0.8926 0.064 0.936 0.000 0.000
#> SRR1090984 4 0.7269 0.1745 0.156 0.000 0.356 0.488
#> SRR1456991 4 0.4318 0.5988 0.068 0.000 0.116 0.816
#> SRR1085039 4 0.1970 0.6738 0.060 0.000 0.008 0.932
#> SRR1069303 4 0.6506 0.1291 0.240 0.000 0.132 0.628
#> SRR1091500 2 0.3569 0.8331 0.196 0.804 0.000 0.000
#> SRR1075198 2 0.1637 0.8725 0.060 0.940 0.000 0.000
#> SRR1086915 4 0.0804 0.6840 0.008 0.000 0.012 0.980
#> SRR1499503 2 0.0469 0.8957 0.012 0.988 0.000 0.000
#> SRR1094312 2 0.1716 0.8926 0.064 0.936 0.000 0.000
#> SRR1352437 4 0.6578 0.1139 0.244 0.000 0.136 0.620
#> SRR1436323 3 0.5055 0.5981 0.008 0.000 0.624 0.368
#> SRR1073507 4 0.0804 0.6826 0.008 0.000 0.012 0.980
#> SRR1401972 4 0.6630 0.0861 0.252 0.000 0.136 0.612
#> SRR1415510 2 0.0000 0.8943 0.000 1.000 0.000 0.000
#> SRR1327279 4 0.2924 0.6594 0.016 0.000 0.100 0.884
#> SRR1086983 4 0.1388 0.6836 0.012 0.000 0.028 0.960
#> SRR1105174 4 0.2413 0.6743 0.064 0.000 0.020 0.916
#> SRR1468893 4 0.1722 0.6767 0.048 0.000 0.008 0.944
#> SRR1362555 2 0.1629 0.8792 0.024 0.952 0.000 0.024
#> SRR1074526 2 0.5296 0.3348 0.496 0.496 0.000 0.008
#> SRR1326225 2 0.1716 0.8926 0.064 0.936 0.000 0.000
#> SRR1401933 4 0.1109 0.6833 0.004 0.000 0.028 0.968
#> SRR1324062 4 0.1059 0.6857 0.012 0.000 0.016 0.972
#> SRR1102296 1 0.7463 0.3689 0.456 0.000 0.180 0.364
#> SRR1085087 4 0.6147 0.2289 0.200 0.000 0.128 0.672
#> SRR1079046 1 0.5500 -0.3728 0.520 0.464 0.000 0.016
#> SRR1328339 3 0.7762 -0.2467 0.356 0.004 0.436 0.204
#> SRR1079782 2 0.4704 0.6737 0.204 0.764 0.004 0.028
#> SRR1092257 2 0.2859 0.8494 0.112 0.880 0.000 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.0000 0.85774 0.000 1.000 0.000 0.000 0.000
#> SRR1429287 2 0.3547 0.82129 0.000 0.836 0.004 0.060 0.100
#> SRR1359238 1 0.5791 -0.05084 0.472 0.000 0.076 0.448 0.004
#> SRR1309597 3 0.0898 0.69485 0.000 0.000 0.972 0.020 0.008
#> SRR1441398 4 0.6278 0.36224 0.180 0.000 0.264 0.552 0.004
#> SRR1084055 2 0.0290 0.85554 0.000 0.992 0.000 0.000 0.008
#> SRR1417566 3 0.6508 0.48877 0.316 0.004 0.512 0.164 0.004
#> SRR1351857 1 0.5091 0.20117 0.624 0.000 0.044 0.328 0.004
#> SRR1487485 3 0.0833 0.69399 0.004 0.000 0.976 0.016 0.004
#> SRR1335875 3 0.5772 0.52936 0.104 0.004 0.592 0.300 0.000
#> SRR1073947 1 0.4015 0.21613 0.652 0.000 0.000 0.348 0.000
#> SRR1443483 3 0.1153 0.69721 0.004 0.000 0.964 0.024 0.008
#> SRR1346794 4 0.6227 0.31609 0.184 0.000 0.280 0.536 0.000
#> SRR1405245 4 0.6265 0.34949 0.160 0.000 0.296 0.540 0.004
#> SRR1409677 4 0.6666 0.01380 0.356 0.000 0.200 0.440 0.004
#> SRR1095549 4 0.6521 0.33000 0.308 0.000 0.192 0.496 0.004
#> SRR1323788 4 0.6717 0.32720 0.324 0.000 0.200 0.468 0.008
#> SRR1314054 2 0.3359 0.82826 0.000 0.848 0.004 0.052 0.096
#> SRR1077944 4 0.4630 0.09616 0.396 0.000 0.016 0.588 0.000
#> SRR1480587 2 0.1341 0.85701 0.000 0.944 0.000 0.000 0.056
#> SRR1311205 4 0.5940 0.32149 0.292 0.000 0.140 0.568 0.000
#> SRR1076369 1 0.6979 -0.06738 0.408 0.000 0.288 0.296 0.008
#> SRR1453549 3 0.4927 0.62307 0.052 0.000 0.692 0.248 0.008
#> SRR1345782 4 0.5974 0.33039 0.284 0.000 0.148 0.568 0.000
#> SRR1447850 2 0.5374 0.69196 0.000 0.696 0.012 0.164 0.128
#> SRR1391553 3 0.4329 0.57791 0.236 0.008 0.736 0.012 0.008
#> SRR1444156 2 0.0000 0.85774 0.000 1.000 0.000 0.000 0.000
#> SRR1471731 3 0.5077 0.62898 0.084 0.000 0.728 0.168 0.020
#> SRR1120987 1 0.4770 0.18955 0.716 0.000 0.024 0.232 0.028
#> SRR1477363 4 0.4655 -0.03856 0.476 0.000 0.012 0.512 0.000
#> SRR1391961 5 0.1981 0.87459 0.000 0.048 0.000 0.028 0.924
#> SRR1373879 3 0.3690 0.68824 0.020 0.000 0.780 0.200 0.000
#> SRR1318732 3 0.2339 0.69630 0.028 0.000 0.912 0.052 0.008
#> SRR1091404 1 0.4577 0.22471 0.716 0.000 0.028 0.244 0.012
#> SRR1402109 3 0.5083 0.62344 0.084 0.000 0.692 0.220 0.004
#> SRR1407336 3 0.3901 0.68714 0.024 0.000 0.776 0.196 0.004
#> SRR1097417 3 0.7289 0.39756 0.296 0.144 0.508 0.024 0.028
#> SRR1396227 1 0.4183 0.19950 0.668 0.000 0.008 0.324 0.000
#> SRR1400775 2 0.0693 0.85951 0.000 0.980 0.000 0.008 0.012
#> SRR1392861 4 0.7091 -0.03389 0.224 0.000 0.292 0.460 0.024
#> SRR1472929 5 0.4403 0.26510 0.000 0.436 0.000 0.004 0.560
#> SRR1436740 4 0.6942 0.11825 0.260 0.000 0.216 0.500 0.024
#> SRR1477057 2 0.5586 0.57395 0.016 0.640 0.004 0.060 0.280
#> SRR1311980 3 0.3613 0.70484 0.016 0.000 0.812 0.160 0.012
#> SRR1069400 3 0.2411 0.70754 0.000 0.000 0.884 0.108 0.008
#> SRR1351016 1 0.4659 0.01221 0.496 0.000 0.012 0.492 0.000
#> SRR1096291 1 0.6354 -0.03834 0.528 0.000 0.316 0.148 0.008
#> SRR1418145 1 0.5345 0.19740 0.696 0.000 0.096 0.192 0.016
#> SRR1488111 1 0.7375 0.00698 0.476 0.008 0.192 0.288 0.036
#> SRR1370495 1 0.5185 0.25567 0.756 0.012 0.108 0.092 0.032
#> SRR1352639 1 0.5076 0.24294 0.744 0.000 0.140 0.080 0.036
#> SRR1348911 3 0.5805 0.58991 0.160 0.000 0.640 0.192 0.008
#> SRR1467386 1 0.5102 0.14794 0.580 0.000 0.044 0.376 0.000
#> SRR1415956 4 0.5409 0.25423 0.316 0.000 0.080 0.604 0.000
#> SRR1500495 4 0.6325 0.36552 0.212 0.000 0.232 0.552 0.004
#> SRR1405099 4 0.4562 -0.06057 0.492 0.000 0.008 0.500 0.000
#> SRR1345585 3 0.1836 0.67539 0.040 0.000 0.936 0.016 0.008
#> SRR1093196 3 0.3648 0.68275 0.024 0.000 0.812 0.156 0.008
#> SRR1466006 2 0.0000 0.85774 0.000 1.000 0.000 0.000 0.000
#> SRR1351557 2 0.0324 0.85914 0.000 0.992 0.000 0.004 0.004
#> SRR1382687 1 0.6105 -0.09730 0.464 0.000 0.108 0.424 0.004
#> SRR1375549 1 0.4032 0.26447 0.792 0.000 0.020 0.164 0.024
#> SRR1101765 1 0.4495 0.25821 0.772 0.012 0.020 0.172 0.024
#> SRR1334461 5 0.1981 0.87459 0.000 0.048 0.000 0.028 0.924
#> SRR1094073 2 0.0000 0.85774 0.000 1.000 0.000 0.000 0.000
#> SRR1077549 1 0.5701 0.05568 0.568 0.000 0.100 0.332 0.000
#> SRR1440332 4 0.6630 0.32678 0.316 0.000 0.208 0.472 0.004
#> SRR1454177 4 0.7056 0.11023 0.260 0.000 0.240 0.476 0.024
#> SRR1082447 1 0.4818 0.04128 0.520 0.000 0.020 0.460 0.000
#> SRR1420043 3 0.5094 0.62412 0.084 0.000 0.712 0.192 0.012
#> SRR1432500 1 0.5113 0.19110 0.604 0.000 0.040 0.352 0.004
#> SRR1378045 2 0.8068 -0.05713 0.224 0.364 0.336 0.012 0.064
#> SRR1334200 2 0.3435 0.80594 0.000 0.820 0.004 0.020 0.156
#> SRR1069539 3 0.7046 0.33954 0.416 0.032 0.440 0.092 0.020
#> SRR1343031 3 0.5719 0.45976 0.120 0.000 0.596 0.284 0.000
#> SRR1319690 3 0.5290 0.38951 0.044 0.000 0.560 0.392 0.004
#> SRR1310604 2 0.2112 0.84942 0.000 0.908 0.004 0.004 0.084
#> SRR1327747 3 0.5615 0.39643 0.064 0.000 0.568 0.360 0.008
#> SRR1072456 2 0.1571 0.85549 0.000 0.936 0.004 0.000 0.060
#> SRR1367896 3 0.1357 0.70494 0.000 0.000 0.948 0.048 0.004
#> SRR1480107 1 0.4306 0.01988 0.508 0.000 0.000 0.492 0.000
#> SRR1377756 1 0.5281 0.12624 0.564 0.000 0.044 0.388 0.004
#> SRR1435272 4 0.6972 0.12521 0.248 0.000 0.232 0.496 0.024
#> SRR1089230 4 0.6145 -0.04706 0.440 0.000 0.112 0.444 0.004
#> SRR1389522 3 0.4088 0.53315 0.000 0.000 0.688 0.304 0.008
#> SRR1080600 2 0.1638 0.85461 0.000 0.932 0.004 0.000 0.064
#> SRR1086935 1 0.6173 0.11153 0.572 0.040 0.020 0.340 0.028
#> SRR1344060 5 0.2233 0.84562 0.000 0.104 0.004 0.000 0.892
#> SRR1467922 2 0.0000 0.85774 0.000 1.000 0.000 0.000 0.000
#> SRR1090984 4 0.6534 0.15427 0.388 0.000 0.196 0.416 0.000
#> SRR1456991 4 0.4653 -0.02786 0.472 0.000 0.012 0.516 0.000
#> SRR1085039 1 0.4331 0.16516 0.596 0.000 0.004 0.400 0.000
#> SRR1069303 1 0.1764 0.30702 0.928 0.000 0.008 0.064 0.000
#> SRR1091500 2 0.3264 0.73105 0.000 0.820 0.000 0.016 0.164
#> SRR1075198 2 0.3037 0.83549 0.000 0.864 0.004 0.032 0.100
#> SRR1086915 1 0.5363 0.17371 0.572 0.000 0.052 0.372 0.004
#> SRR1499503 2 0.0963 0.85958 0.000 0.964 0.000 0.000 0.036
#> SRR1094312 2 0.0162 0.85810 0.000 0.996 0.000 0.004 0.000
#> SRR1352437 1 0.2707 0.26385 0.860 0.000 0.008 0.132 0.000
#> SRR1436323 3 0.5648 0.54604 0.164 0.000 0.660 0.168 0.008
#> SRR1073507 1 0.4714 0.21327 0.644 0.000 0.032 0.324 0.000
#> SRR1401972 1 0.0898 0.30807 0.972 0.000 0.008 0.020 0.000
#> SRR1415510 2 0.1638 0.85461 0.000 0.932 0.004 0.000 0.064
#> SRR1327279 1 0.5597 -0.00245 0.488 0.000 0.060 0.448 0.004
#> SRR1086983 1 0.4697 0.21499 0.660 0.000 0.036 0.304 0.000
#> SRR1105174 4 0.4562 -0.06561 0.496 0.000 0.008 0.496 0.000
#> SRR1468893 1 0.4150 0.18633 0.612 0.000 0.000 0.388 0.000
#> SRR1362555 2 0.5030 0.74368 0.064 0.756 0.004 0.040 0.136
#> SRR1074526 5 0.3108 0.86492 0.028 0.064 0.004 0.024 0.880
#> SRR1326225 2 0.0000 0.85774 0.000 1.000 0.000 0.000 0.000
#> SRR1401933 1 0.5396 0.07950 0.532 0.000 0.048 0.416 0.004
#> SRR1324062 1 0.5176 0.11877 0.572 0.000 0.048 0.380 0.000
#> SRR1102296 1 0.4205 0.27939 0.804 0.000 0.020 0.068 0.108
#> SRR1085087 1 0.2358 0.30078 0.888 0.000 0.008 0.104 0.000
#> SRR1079046 5 0.2333 0.86505 0.016 0.040 0.000 0.028 0.916
#> SRR1328339 1 0.7999 -0.27521 0.408 0.016 0.344 0.152 0.080
#> SRR1079782 2 0.6658 0.59443 0.116 0.636 0.004 0.136 0.108
#> SRR1092257 2 0.5426 0.67453 0.000 0.676 0.004 0.160 0.160
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.0508 0.83093 0.000 0.984 0.000 0.004 0.012 0.000
#> SRR1429287 2 0.5117 0.72402 0.000 0.692 0.028 0.208 0.048 0.024
#> SRR1359238 1 0.4426 0.34655 0.684 0.000 0.004 0.036 0.008 0.268
#> SRR1309597 3 0.1364 0.55405 0.020 0.000 0.952 0.012 0.000 0.016
#> SRR1441398 6 0.5332 0.10772 0.392 0.000 0.064 0.012 0.004 0.528
#> SRR1084055 2 0.0937 0.81779 0.000 0.960 0.000 0.000 0.040 0.000
#> SRR1417566 6 0.4974 0.09204 0.084 0.004 0.248 0.008 0.000 0.656
#> SRR1351857 1 0.2165 0.55643 0.912 0.000 0.004 0.024 0.008 0.052
#> SRR1487485 3 0.1377 0.55003 0.016 0.004 0.952 0.004 0.000 0.024
#> SRR1335875 6 0.6166 -0.12476 0.184 0.004 0.400 0.008 0.000 0.404
#> SRR1073947 1 0.1349 0.54858 0.940 0.000 0.000 0.004 0.000 0.056
#> SRR1443483 3 0.1251 0.55134 0.024 0.000 0.956 0.012 0.000 0.008
#> SRR1346794 6 0.5016 0.13464 0.392 0.000 0.076 0.000 0.000 0.532
#> SRR1405245 6 0.5342 0.10839 0.396 0.000 0.076 0.012 0.000 0.516
#> SRR1409677 1 0.5242 0.29377 0.668 0.000 0.084 0.204 0.000 0.044
#> SRR1095549 1 0.4097 -0.01525 0.504 0.000 0.008 0.000 0.000 0.488
#> SRR1323788 1 0.5251 0.01554 0.500 0.000 0.032 0.028 0.004 0.436
#> SRR1314054 2 0.5169 0.71053 0.000 0.684 0.028 0.216 0.048 0.024
#> SRR1077944 1 0.4126 0.01315 0.512 0.000 0.004 0.004 0.000 0.480
#> SRR1480587 2 0.1049 0.83059 0.000 0.960 0.000 0.008 0.032 0.000
#> SRR1311205 6 0.4253 0.03353 0.460 0.000 0.016 0.000 0.000 0.524
#> SRR1076369 6 0.4423 0.35034 0.136 0.000 0.112 0.012 0.000 0.740
#> SRR1453549 6 0.7410 -0.00145 0.296 0.000 0.268 0.116 0.000 0.320
#> SRR1345782 6 0.4482 0.04539 0.448 0.000 0.012 0.012 0.000 0.528
#> SRR1447850 2 0.6552 0.53529 0.000 0.528 0.032 0.304 0.060 0.076
#> SRR1391553 3 0.5313 0.48301 0.036 0.016 0.668 0.056 0.000 0.224
#> SRR1444156 2 0.0000 0.83277 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1471731 4 0.6844 -0.10476 0.096 0.000 0.368 0.404 0.000 0.132
#> SRR1120987 6 0.5855 -0.13984 0.396 0.000 0.000 0.192 0.000 0.412
#> SRR1477363 1 0.3383 0.39115 0.728 0.000 0.000 0.004 0.000 0.268
#> SRR1391961 5 0.1257 0.84563 0.000 0.020 0.000 0.000 0.952 0.028
#> SRR1373879 3 0.7155 0.35841 0.120 0.000 0.420 0.176 0.000 0.284
#> SRR1318732 3 0.2074 0.56302 0.036 0.000 0.912 0.004 0.000 0.048
#> SRR1091404 6 0.4788 0.08289 0.396 0.000 0.016 0.028 0.000 0.560
#> SRR1402109 3 0.7134 0.27655 0.096 0.000 0.416 0.244 0.000 0.244
#> SRR1407336 3 0.7145 0.33587 0.132 0.000 0.448 0.188 0.000 0.232
#> SRR1097417 3 0.6382 0.32302 0.012 0.152 0.488 0.016 0.004 0.328
#> SRR1396227 1 0.2544 0.52345 0.864 0.000 0.012 0.004 0.000 0.120
#> SRR1400775 2 0.1572 0.82972 0.000 0.936 0.000 0.028 0.036 0.000
#> SRR1392861 4 0.4273 0.81842 0.260 0.000 0.032 0.696 0.000 0.012
#> SRR1472929 5 0.4453 0.27119 0.000 0.424 0.000 0.012 0.552 0.012
#> SRR1436740 4 0.4383 0.80166 0.276 0.000 0.024 0.680 0.000 0.020
#> SRR1477057 2 0.5905 0.66255 0.000 0.624 0.028 0.212 0.112 0.024
#> SRR1311980 3 0.6227 0.40399 0.084 0.000 0.536 0.088 0.000 0.292
#> SRR1069400 3 0.3624 0.55220 0.060 0.000 0.784 0.000 0.000 0.156
#> SRR1351016 1 0.3565 0.32631 0.692 0.000 0.000 0.004 0.000 0.304
#> SRR1096291 6 0.5141 0.14731 0.080 0.000 0.196 0.044 0.000 0.680
#> SRR1418145 1 0.6089 -0.05760 0.436 0.000 0.028 0.128 0.000 0.408
#> SRR1488111 6 0.6158 0.08878 0.040 0.016 0.100 0.248 0.004 0.592
#> SRR1370495 6 0.5777 0.25639 0.260 0.000 0.060 0.072 0.004 0.604
#> SRR1352639 6 0.6129 0.24181 0.284 0.008 0.084 0.032 0.016 0.576
#> SRR1348911 3 0.5106 0.38958 0.044 0.004 0.568 0.016 0.000 0.368
#> SRR1467386 1 0.1921 0.55549 0.916 0.000 0.000 0.052 0.000 0.032
#> SRR1415956 6 0.3991 -0.00573 0.472 0.000 0.000 0.004 0.000 0.524
#> SRR1500495 6 0.5003 0.08811 0.420 0.000 0.036 0.012 0.004 0.528
#> SRR1405099 1 0.3628 0.39063 0.720 0.000 0.000 0.004 0.008 0.268
#> SRR1345585 3 0.1196 0.55269 0.008 0.000 0.952 0.000 0.000 0.040
#> SRR1093196 3 0.6603 -0.02835 0.068 0.000 0.404 0.396 0.000 0.132
#> SRR1466006 2 0.0260 0.83230 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1351557 2 0.0603 0.83503 0.000 0.980 0.000 0.016 0.004 0.000
#> SRR1382687 1 0.4018 0.45341 0.768 0.000 0.012 0.036 0.008 0.176
#> SRR1375549 6 0.4917 0.18864 0.320 0.000 0.020 0.036 0.004 0.620
#> SRR1101765 6 0.5467 0.20177 0.316 0.028 0.020 0.040 0.000 0.596
#> SRR1334461 5 0.1257 0.84563 0.000 0.020 0.000 0.000 0.952 0.028
#> SRR1094073 2 0.0000 0.83277 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1077549 1 0.3944 0.47024 0.768 0.000 0.008 0.060 0.000 0.164
#> SRR1440332 1 0.5885 0.05544 0.512 0.000 0.064 0.048 0.004 0.372
#> SRR1454177 4 0.4152 0.81947 0.264 0.000 0.024 0.700 0.000 0.012
#> SRR1082447 1 0.3788 0.40667 0.704 0.000 0.012 0.004 0.000 0.280
#> SRR1420043 3 0.6704 -0.05373 0.076 0.000 0.396 0.392 0.000 0.136
#> SRR1432500 1 0.2445 0.56004 0.896 0.000 0.004 0.060 0.008 0.032
#> SRR1378045 3 0.7807 0.02488 0.032 0.324 0.388 0.032 0.044 0.180
#> SRR1334200 2 0.4595 0.71099 0.000 0.696 0.012 0.068 0.224 0.000
#> SRR1069539 6 0.5739 -0.00796 0.024 0.024 0.256 0.080 0.000 0.616
#> SRR1343031 1 0.7253 -0.15071 0.352 0.000 0.228 0.100 0.000 0.320
#> SRR1319690 6 0.6205 0.18007 0.328 0.000 0.200 0.016 0.000 0.456
#> SRR1310604 2 0.3354 0.79671 0.000 0.824 0.020 0.028 0.128 0.000
#> SRR1327747 6 0.6118 0.17975 0.340 0.000 0.240 0.004 0.000 0.416
#> SRR1072456 2 0.1391 0.82881 0.000 0.944 0.000 0.016 0.040 0.000
#> SRR1367896 3 0.3207 0.57242 0.052 0.004 0.840 0.004 0.000 0.100
#> SRR1480107 1 0.3463 0.44760 0.748 0.000 0.000 0.004 0.008 0.240
#> SRR1377756 1 0.1036 0.56342 0.964 0.000 0.000 0.008 0.004 0.024
#> SRR1435272 4 0.4203 0.81863 0.260 0.000 0.028 0.700 0.000 0.012
#> SRR1089230 1 0.4663 0.24431 0.680 0.000 0.012 0.244 0.000 0.064
#> SRR1389522 3 0.5304 0.33274 0.200 0.000 0.600 0.000 0.000 0.200
#> SRR1080600 2 0.1801 0.82577 0.000 0.924 0.004 0.016 0.056 0.000
#> SRR1086935 6 0.6250 -0.35024 0.236 0.004 0.004 0.364 0.000 0.392
#> SRR1344060 5 0.1958 0.80022 0.000 0.100 0.000 0.004 0.896 0.000
#> SRR1467922 2 0.0000 0.83277 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1090984 6 0.4421 0.27265 0.256 0.000 0.056 0.004 0.000 0.684
#> SRR1456991 1 0.3807 0.22846 0.628 0.000 0.000 0.004 0.000 0.368
#> SRR1085039 1 0.2234 0.53840 0.872 0.000 0.000 0.004 0.000 0.124
#> SRR1069303 1 0.3699 0.25679 0.660 0.000 0.000 0.004 0.000 0.336
#> SRR1091500 2 0.5165 0.45260 0.000 0.600 0.000 0.072 0.312 0.016
#> SRR1075198 2 0.4858 0.75963 0.000 0.724 0.028 0.156 0.084 0.008
#> SRR1086915 1 0.2052 0.54839 0.912 0.000 0.000 0.028 0.004 0.056
#> SRR1499503 2 0.0891 0.83097 0.000 0.968 0.000 0.008 0.024 0.000
#> SRR1094312 2 0.0291 0.83421 0.000 0.992 0.000 0.004 0.004 0.000
#> SRR1352437 1 0.4131 0.16984 0.600 0.000 0.000 0.016 0.000 0.384
#> SRR1436323 3 0.7210 0.03555 0.160 0.000 0.420 0.276 0.000 0.144
#> SRR1073507 1 0.1367 0.55386 0.944 0.000 0.000 0.044 0.000 0.012
#> SRR1401972 1 0.3872 0.16653 0.604 0.000 0.000 0.004 0.000 0.392
#> SRR1415510 2 0.2257 0.82154 0.000 0.904 0.020 0.016 0.060 0.000
#> SRR1327279 1 0.3920 0.47922 0.772 0.000 0.008 0.036 0.008 0.176
#> SRR1086983 1 0.1845 0.54395 0.920 0.000 0.000 0.052 0.000 0.028
#> SRR1105174 1 0.3827 0.37793 0.680 0.000 0.000 0.004 0.008 0.308
#> SRR1468893 1 0.1152 0.55909 0.952 0.000 0.000 0.004 0.000 0.044
#> SRR1362555 2 0.5014 0.71269 0.008 0.684 0.012 0.096 0.200 0.000
#> SRR1074526 5 0.2681 0.80529 0.048 0.020 0.000 0.004 0.888 0.040
#> SRR1326225 2 0.0000 0.83277 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1401933 1 0.3756 0.44265 0.736 0.000 0.000 0.016 0.008 0.240
#> SRR1324062 1 0.3394 0.48752 0.804 0.000 0.000 0.052 0.000 0.144
#> SRR1102296 6 0.6255 0.17291 0.336 0.000 0.020 0.056 0.064 0.524
#> SRR1085087 1 0.3707 0.28963 0.680 0.000 0.000 0.008 0.000 0.312
#> SRR1079046 5 0.1772 0.84343 0.000 0.020 0.008 0.008 0.936 0.028
#> SRR1328339 6 0.5755 0.13790 0.060 0.008 0.200 0.016 0.056 0.660
#> SRR1079782 2 0.6576 0.56732 0.000 0.564 0.032 0.208 0.040 0.156
#> SRR1092257 2 0.5488 0.68803 0.000 0.652 0.028 0.232 0.064 0.024
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 17611 rows and 118 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 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 0.895 0.928 0.971 0.4112 0.594 0.594
#> 3 3 0.472 0.656 0.818 0.5852 0.682 0.497
#> 4 4 0.576 0.707 0.827 0.1415 0.825 0.549
#> 5 5 0.600 0.529 0.767 0.0574 0.819 0.428
#> 6 6 0.579 0.427 0.692 0.0423 0.882 0.536
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR1396765 2 0.0000 0.9546 0.000 1.000
#> SRR1429287 2 0.0000 0.9546 0.000 1.000
#> SRR1359238 1 0.0000 0.9739 1.000 0.000
#> SRR1309597 1 0.0000 0.9739 1.000 0.000
#> SRR1441398 1 0.0000 0.9739 1.000 0.000
#> SRR1084055 2 0.0000 0.9546 0.000 1.000
#> SRR1417566 1 0.3584 0.9102 0.932 0.068
#> SRR1351857 1 0.0000 0.9739 1.000 0.000
#> SRR1487485 2 0.5629 0.8329 0.132 0.868
#> SRR1335875 1 0.0376 0.9707 0.996 0.004
#> SRR1073947 1 0.0000 0.9739 1.000 0.000
#> SRR1443483 1 0.0000 0.9739 1.000 0.000
#> SRR1346794 1 0.0000 0.9739 1.000 0.000
#> SRR1405245 1 0.0000 0.9739 1.000 0.000
#> SRR1409677 1 0.0000 0.9739 1.000 0.000
#> SRR1095549 1 0.0000 0.9739 1.000 0.000
#> SRR1323788 1 0.0000 0.9739 1.000 0.000
#> SRR1314054 2 0.0000 0.9546 0.000 1.000
#> SRR1077944 1 0.0000 0.9739 1.000 0.000
#> SRR1480587 2 0.0000 0.9546 0.000 1.000
#> SRR1311205 1 0.0000 0.9739 1.000 0.000
#> SRR1076369 1 0.0000 0.9739 1.000 0.000
#> SRR1453549 1 0.0000 0.9739 1.000 0.000
#> SRR1345782 1 0.0000 0.9739 1.000 0.000
#> SRR1447850 2 0.0000 0.9546 0.000 1.000
#> SRR1391553 2 0.0000 0.9546 0.000 1.000
#> SRR1444156 2 0.0000 0.9546 0.000 1.000
#> SRR1471731 1 0.5946 0.8185 0.856 0.144
#> SRR1120987 1 0.1843 0.9498 0.972 0.028
#> SRR1477363 1 0.0000 0.9739 1.000 0.000
#> SRR1391961 1 0.5946 0.8190 0.856 0.144
#> SRR1373879 1 0.0000 0.9739 1.000 0.000
#> SRR1318732 1 0.7056 0.7515 0.808 0.192
#> SRR1091404 1 0.0000 0.9739 1.000 0.000
#> SRR1402109 1 0.0000 0.9739 1.000 0.000
#> SRR1407336 1 0.0000 0.9739 1.000 0.000
#> SRR1097417 2 0.0376 0.9523 0.004 0.996
#> SRR1396227 1 0.0000 0.9739 1.000 0.000
#> SRR1400775 2 0.0000 0.9546 0.000 1.000
#> SRR1392861 1 0.0000 0.9739 1.000 0.000
#> SRR1472929 2 0.1414 0.9408 0.020 0.980
#> SRR1436740 1 0.0000 0.9739 1.000 0.000
#> SRR1477057 2 0.0672 0.9497 0.008 0.992
#> SRR1311980 1 0.0672 0.9675 0.992 0.008
#> SRR1069400 1 0.0000 0.9739 1.000 0.000
#> SRR1351016 1 0.0000 0.9739 1.000 0.000
#> SRR1096291 1 0.0000 0.9739 1.000 0.000
#> SRR1418145 1 0.0000 0.9739 1.000 0.000
#> SRR1488111 2 0.7950 0.6907 0.240 0.760
#> SRR1370495 1 0.0000 0.9739 1.000 0.000
#> SRR1352639 1 0.0000 0.9739 1.000 0.000
#> SRR1348911 1 0.2043 0.9464 0.968 0.032
#> SRR1467386 1 0.0000 0.9739 1.000 0.000
#> SRR1415956 1 0.0000 0.9739 1.000 0.000
#> SRR1500495 1 0.0000 0.9739 1.000 0.000
#> SRR1405099 1 0.0000 0.9739 1.000 0.000
#> SRR1345585 2 0.8555 0.6215 0.280 0.720
#> SRR1093196 1 0.0672 0.9673 0.992 0.008
#> SRR1466006 2 0.0000 0.9546 0.000 1.000
#> SRR1351557 2 0.0000 0.9546 0.000 1.000
#> SRR1382687 1 0.0000 0.9739 1.000 0.000
#> SRR1375549 1 0.0000 0.9739 1.000 0.000
#> SRR1101765 1 0.0000 0.9739 1.000 0.000
#> SRR1334461 1 0.0000 0.9739 1.000 0.000
#> SRR1094073 2 0.0000 0.9546 0.000 1.000
#> SRR1077549 1 0.0000 0.9739 1.000 0.000
#> SRR1440332 1 0.0000 0.9739 1.000 0.000
#> SRR1454177 1 0.0000 0.9739 1.000 0.000
#> SRR1082447 1 0.0000 0.9739 1.000 0.000
#> SRR1420043 1 0.0000 0.9739 1.000 0.000
#> SRR1432500 1 0.0000 0.9739 1.000 0.000
#> SRR1378045 2 0.0000 0.9546 0.000 1.000
#> SRR1334200 1 0.8081 0.6614 0.752 0.248
#> SRR1069539 1 0.9993 0.0188 0.516 0.484
#> SRR1343031 1 0.0000 0.9739 1.000 0.000
#> SRR1319690 1 0.0000 0.9739 1.000 0.000
#> SRR1310604 2 0.0000 0.9546 0.000 1.000
#> SRR1327747 1 0.0000 0.9739 1.000 0.000
#> SRR1072456 2 0.0000 0.9546 0.000 1.000
#> SRR1367896 1 0.8861 0.5535 0.696 0.304
#> SRR1480107 1 0.0000 0.9739 1.000 0.000
#> SRR1377756 1 0.0000 0.9739 1.000 0.000
#> SRR1435272 1 0.0000 0.9739 1.000 0.000
#> SRR1089230 1 0.0000 0.9739 1.000 0.000
#> SRR1389522 1 0.0000 0.9739 1.000 0.000
#> SRR1080600 2 0.0000 0.9546 0.000 1.000
#> SRR1086935 2 0.9963 0.1577 0.464 0.536
#> SRR1344060 1 0.9732 0.3008 0.596 0.404
#> SRR1467922 2 0.0000 0.9546 0.000 1.000
#> SRR1090984 1 0.0000 0.9739 1.000 0.000
#> SRR1456991 1 0.0000 0.9739 1.000 0.000
#> SRR1085039 1 0.0000 0.9739 1.000 0.000
#> SRR1069303 1 0.0000 0.9739 1.000 0.000
#> SRR1091500 2 0.0000 0.9546 0.000 1.000
#> SRR1075198 2 0.0000 0.9546 0.000 1.000
#> SRR1086915 1 0.0000 0.9739 1.000 0.000
#> SRR1499503 2 0.0000 0.9546 0.000 1.000
#> SRR1094312 2 0.0000 0.9546 0.000 1.000
#> SRR1352437 1 0.0000 0.9739 1.000 0.000
#> SRR1436323 1 0.0000 0.9739 1.000 0.000
#> SRR1073507 1 0.0000 0.9739 1.000 0.000
#> SRR1401972 1 0.0000 0.9739 1.000 0.000
#> SRR1415510 2 0.0000 0.9546 0.000 1.000
#> SRR1327279 1 0.0000 0.9739 1.000 0.000
#> SRR1086983 1 0.0000 0.9739 1.000 0.000
#> SRR1105174 1 0.0000 0.9739 1.000 0.000
#> SRR1468893 1 0.0000 0.9739 1.000 0.000
#> SRR1362555 2 0.7883 0.6963 0.236 0.764
#> SRR1074526 1 0.0000 0.9739 1.000 0.000
#> SRR1326225 2 0.0000 0.9546 0.000 1.000
#> SRR1401933 1 0.0000 0.9739 1.000 0.000
#> SRR1324062 1 0.0000 0.9739 1.000 0.000
#> SRR1102296 1 0.0000 0.9739 1.000 0.000
#> SRR1085087 1 0.0000 0.9739 1.000 0.000
#> SRR1079046 1 0.0000 0.9739 1.000 0.000
#> SRR1328339 1 0.0000 0.9739 1.000 0.000
#> SRR1079782 2 0.0000 0.9546 0.000 1.000
#> SRR1092257 2 0.0938 0.9470 0.012 0.988
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.0237 0.9261 0.004 0.996 0.000
#> SRR1429287 2 0.0237 0.9255 0.000 0.996 0.004
#> SRR1359238 3 0.4399 0.6861 0.188 0.000 0.812
#> SRR1309597 3 0.5138 0.5344 0.252 0.000 0.748
#> SRR1441398 3 0.6095 0.2895 0.392 0.000 0.608
#> SRR1084055 2 0.0000 0.9263 0.000 1.000 0.000
#> SRR1417566 2 0.8345 0.4292 0.116 0.596 0.288
#> SRR1351857 3 0.5363 0.6077 0.276 0.000 0.724
#> SRR1487485 3 0.5327 0.5139 0.000 0.272 0.728
#> SRR1335875 3 0.7232 0.5185 0.116 0.172 0.712
#> SRR1073947 1 0.2537 0.7369 0.920 0.000 0.080
#> SRR1443483 3 0.2682 0.6732 0.076 0.004 0.920
#> SRR1346794 3 0.6095 0.2911 0.392 0.000 0.608
#> SRR1405245 3 0.5968 0.3510 0.364 0.000 0.636
#> SRR1409677 3 0.4555 0.6765 0.200 0.000 0.800
#> SRR1095549 3 0.5465 0.4800 0.288 0.000 0.712
#> SRR1323788 3 0.4291 0.6187 0.180 0.000 0.820
#> SRR1314054 2 0.0237 0.9255 0.000 0.996 0.004
#> SRR1077944 1 0.4931 0.5922 0.768 0.000 0.232
#> SRR1480587 2 0.0237 0.9261 0.004 0.996 0.000
#> SRR1311205 3 0.5905 0.3846 0.352 0.000 0.648
#> SRR1076369 1 0.5988 0.4095 0.632 0.000 0.368
#> SRR1453549 3 0.2165 0.7005 0.064 0.000 0.936
#> SRR1345782 3 0.6126 0.2713 0.400 0.000 0.600
#> SRR1447850 2 0.0424 0.9242 0.000 0.992 0.008
#> SRR1391553 2 0.2356 0.8782 0.000 0.928 0.072
#> SRR1444156 2 0.0000 0.9263 0.000 1.000 0.000
#> SRR1471731 3 0.2261 0.6821 0.000 0.068 0.932
#> SRR1120987 3 0.6483 0.2420 0.452 0.004 0.544
#> SRR1477363 1 0.5216 0.4939 0.740 0.000 0.260
#> SRR1391961 1 0.2400 0.7548 0.932 0.004 0.064
#> SRR1373879 3 0.0424 0.6939 0.008 0.000 0.992
#> SRR1318732 3 0.7189 0.4253 0.292 0.052 0.656
#> SRR1091404 1 0.1289 0.7647 0.968 0.000 0.032
#> SRR1402109 3 0.1031 0.6903 0.024 0.000 0.976
#> SRR1407336 3 0.0237 0.6928 0.004 0.000 0.996
#> SRR1097417 2 0.7323 0.6153 0.104 0.700 0.196
#> SRR1396227 1 0.5058 0.5420 0.756 0.000 0.244
#> SRR1400775 2 0.0000 0.9263 0.000 1.000 0.000
#> SRR1392861 3 0.4399 0.6819 0.188 0.000 0.812
#> SRR1472929 1 0.6007 0.6308 0.764 0.044 0.192
#> SRR1436740 3 0.4555 0.6765 0.200 0.000 0.800
#> SRR1477057 2 0.0424 0.9236 0.008 0.992 0.000
#> SRR1311980 3 0.1860 0.6828 0.052 0.000 0.948
#> SRR1069400 3 0.3116 0.6591 0.108 0.000 0.892
#> SRR1351016 1 0.6225 0.0162 0.568 0.000 0.432
#> SRR1096291 3 0.4504 0.6786 0.196 0.000 0.804
#> SRR1418145 3 0.6180 0.3399 0.416 0.000 0.584
#> SRR1488111 2 0.4586 0.8092 0.048 0.856 0.096
#> SRR1370495 1 0.2537 0.7460 0.920 0.000 0.080
#> SRR1352639 1 0.1753 0.7549 0.952 0.000 0.048
#> SRR1348911 3 0.7569 0.4578 0.088 0.248 0.664
#> SRR1467386 3 0.5016 0.6646 0.240 0.000 0.760
#> SRR1415956 1 0.4346 0.6701 0.816 0.000 0.184
#> SRR1500495 3 0.6168 0.2416 0.412 0.000 0.588
#> SRR1405099 1 0.1753 0.7614 0.952 0.000 0.048
#> SRR1345585 3 0.5536 0.5366 0.012 0.236 0.752
#> SRR1093196 3 0.0661 0.6940 0.004 0.008 0.988
#> SRR1466006 2 0.0000 0.9263 0.000 1.000 0.000
#> SRR1351557 2 0.0000 0.9263 0.000 1.000 0.000
#> SRR1382687 3 0.4796 0.6781 0.220 0.000 0.780
#> SRR1375549 1 0.1529 0.7518 0.960 0.000 0.040
#> SRR1101765 1 0.2165 0.7384 0.936 0.000 0.064
#> SRR1334461 1 0.1753 0.7611 0.952 0.000 0.048
#> SRR1094073 2 0.0000 0.9263 0.000 1.000 0.000
#> SRR1077549 3 0.4452 0.6806 0.192 0.000 0.808
#> SRR1440332 3 0.2356 0.6828 0.072 0.000 0.928
#> SRR1454177 3 0.4452 0.6806 0.192 0.000 0.808
#> SRR1082447 1 0.0892 0.7639 0.980 0.000 0.020
#> SRR1420043 3 0.1031 0.6971 0.024 0.000 0.976
#> SRR1432500 3 0.4842 0.6736 0.224 0.000 0.776
#> SRR1378045 2 0.3340 0.8312 0.000 0.880 0.120
#> SRR1334200 1 0.6067 0.5904 0.736 0.236 0.028
#> SRR1069539 2 0.6460 0.2070 0.004 0.556 0.440
#> SRR1343031 3 0.2261 0.6769 0.068 0.000 0.932
#> SRR1319690 3 0.5678 0.4368 0.316 0.000 0.684
#> SRR1310604 2 0.0892 0.9183 0.020 0.980 0.000
#> SRR1327747 3 0.3752 0.6417 0.144 0.000 0.856
#> SRR1072456 2 0.0424 0.9247 0.008 0.992 0.000
#> SRR1367896 3 0.5764 0.6033 0.076 0.124 0.800
#> SRR1480107 1 0.1163 0.7669 0.972 0.000 0.028
#> SRR1377756 3 0.5465 0.6225 0.288 0.000 0.712
#> SRR1435272 3 0.4452 0.6806 0.192 0.000 0.808
#> SRR1089230 3 0.4654 0.6721 0.208 0.000 0.792
#> SRR1389522 3 0.6008 0.3384 0.372 0.000 0.628
#> SRR1080600 2 0.0237 0.9261 0.004 0.996 0.000
#> SRR1086935 3 0.6807 0.6327 0.172 0.092 0.736
#> SRR1344060 1 0.6562 0.5448 0.700 0.264 0.036
#> SRR1467922 2 0.0000 0.9263 0.000 1.000 0.000
#> SRR1090984 1 0.5327 0.5913 0.728 0.000 0.272
#> SRR1456991 1 0.4121 0.6801 0.832 0.000 0.168
#> SRR1085039 1 0.3038 0.7217 0.896 0.000 0.104
#> SRR1069303 1 0.5254 0.5010 0.736 0.000 0.264
#> SRR1091500 2 0.3983 0.7990 0.144 0.852 0.004
#> SRR1075198 2 0.0237 0.9261 0.004 0.996 0.000
#> SRR1086915 3 0.4842 0.6601 0.224 0.000 0.776
#> SRR1499503 2 0.0237 0.9261 0.004 0.996 0.000
#> SRR1094312 2 0.0000 0.9263 0.000 1.000 0.000
#> SRR1352437 3 0.6252 0.2664 0.444 0.000 0.556
#> SRR1436323 3 0.0892 0.6971 0.020 0.000 0.980
#> SRR1073507 3 0.5926 0.5151 0.356 0.000 0.644
#> SRR1401972 1 0.5621 0.4132 0.692 0.000 0.308
#> SRR1415510 2 0.0892 0.9182 0.020 0.980 0.000
#> SRR1327279 3 0.5098 0.6735 0.248 0.000 0.752
#> SRR1086983 3 0.4750 0.6664 0.216 0.000 0.784
#> SRR1105174 1 0.0592 0.7663 0.988 0.000 0.012
#> SRR1468893 1 0.3816 0.6799 0.852 0.000 0.148
#> SRR1362555 1 0.6274 0.1023 0.544 0.456 0.000
#> SRR1074526 1 0.0237 0.7647 0.996 0.000 0.004
#> SRR1326225 2 0.0000 0.9263 0.000 1.000 0.000
#> SRR1401933 3 0.4842 0.6653 0.224 0.000 0.776
#> SRR1324062 3 0.4702 0.6694 0.212 0.000 0.788
#> SRR1102296 1 0.0592 0.7645 0.988 0.000 0.012
#> SRR1085087 1 0.6267 0.0199 0.548 0.000 0.452
#> SRR1079046 1 0.0237 0.7647 0.996 0.000 0.004
#> SRR1328339 1 0.4555 0.6439 0.800 0.000 0.200
#> SRR1079782 2 0.0892 0.9169 0.000 0.980 0.020
#> SRR1092257 2 0.5473 0.7455 0.140 0.808 0.052
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.0592 0.8759 0.000 0.984 0.016 0.000
#> SRR1429287 2 0.0779 0.8780 0.004 0.980 0.000 0.016
#> SRR1359238 4 0.2973 0.7582 0.000 0.000 0.144 0.856
#> SRR1309597 3 0.3432 0.7363 0.008 0.004 0.848 0.140
#> SRR1441398 3 0.3764 0.7439 0.172 0.000 0.816 0.012
#> SRR1084055 2 0.0779 0.8772 0.004 0.980 0.016 0.000
#> SRR1417566 3 0.7409 0.2567 0.144 0.404 0.448 0.004
#> SRR1351857 4 0.1488 0.8174 0.032 0.000 0.012 0.956
#> SRR1487485 3 0.4964 0.6526 0.000 0.032 0.724 0.244
#> SRR1335875 3 0.3854 0.6864 0.012 0.008 0.828 0.152
#> SRR1073947 1 0.1042 0.8249 0.972 0.000 0.008 0.020
#> SRR1443483 3 0.3351 0.7298 0.000 0.008 0.844 0.148
#> SRR1346794 3 0.5123 0.7046 0.232 0.000 0.724 0.044
#> SRR1405245 3 0.3708 0.7546 0.148 0.000 0.832 0.020
#> SRR1409677 4 0.0188 0.8177 0.000 0.000 0.004 0.996
#> SRR1095549 3 0.6353 0.6910 0.208 0.000 0.652 0.140
#> SRR1323788 4 0.7279 -0.0597 0.148 0.000 0.408 0.444
#> SRR1314054 2 0.3495 0.8654 0.000 0.844 0.140 0.016
#> SRR1077944 1 0.6071 0.5712 0.684 0.000 0.172 0.144
#> SRR1480587 2 0.2216 0.8548 0.000 0.908 0.092 0.000
#> SRR1311205 3 0.5766 0.7174 0.192 0.000 0.704 0.104
#> SRR1076369 3 0.4149 0.7325 0.168 0.028 0.804 0.000
#> SRR1453549 4 0.3074 0.7436 0.000 0.000 0.152 0.848
#> SRR1345782 3 0.4245 0.7323 0.196 0.000 0.784 0.020
#> SRR1447850 2 0.4841 0.8327 0.000 0.780 0.140 0.080
#> SRR1391553 2 0.4746 0.6111 0.000 0.632 0.368 0.000
#> SRR1444156 2 0.2921 0.8676 0.000 0.860 0.140 0.000
#> SRR1471731 4 0.1940 0.7959 0.000 0.000 0.076 0.924
#> SRR1120987 4 0.2412 0.7767 0.008 0.084 0.000 0.908
#> SRR1477363 1 0.6566 0.5248 0.624 0.000 0.140 0.236
#> SRR1391961 1 0.1209 0.8145 0.964 0.032 0.004 0.000
#> SRR1373879 4 0.4985 0.0366 0.000 0.000 0.468 0.532
#> SRR1318732 3 0.3428 0.7530 0.144 0.000 0.844 0.012
#> SRR1091404 1 0.0592 0.8211 0.984 0.000 0.016 0.000
#> SRR1402109 3 0.4992 0.1659 0.000 0.000 0.524 0.476
#> SRR1407336 4 0.4522 0.4511 0.000 0.000 0.320 0.680
#> SRR1097417 3 0.2198 0.6684 0.008 0.072 0.920 0.000
#> SRR1396227 1 0.5388 0.1320 0.532 0.000 0.012 0.456
#> SRR1400775 2 0.2921 0.8676 0.000 0.860 0.140 0.000
#> SRR1392861 4 0.0000 0.8172 0.000 0.000 0.000 1.000
#> SRR1472929 3 0.7220 0.3308 0.260 0.196 0.544 0.000
#> SRR1436740 4 0.0188 0.8169 0.004 0.000 0.000 0.996
#> SRR1477057 2 0.3046 0.8786 0.004 0.884 0.096 0.016
#> SRR1311980 3 0.5097 0.1741 0.000 0.004 0.568 0.428
#> SRR1069400 3 0.3123 0.7267 0.000 0.000 0.844 0.156
#> SRR1351016 1 0.4389 0.7373 0.812 0.000 0.116 0.072
#> SRR1096291 4 0.0469 0.8178 0.000 0.000 0.012 0.988
#> SRR1418145 4 0.2610 0.7750 0.012 0.088 0.000 0.900
#> SRR1488111 2 0.5060 0.3634 0.004 0.584 0.000 0.412
#> SRR1370495 1 0.3495 0.7419 0.844 0.140 0.016 0.000
#> SRR1352639 1 0.5530 0.7387 0.760 0.144 0.024 0.072
#> SRR1348911 3 0.0927 0.7324 0.000 0.008 0.976 0.016
#> SRR1467386 4 0.4050 0.7217 0.168 0.000 0.024 0.808
#> SRR1415956 1 0.3726 0.6308 0.788 0.000 0.212 0.000
#> SRR1500495 3 0.3852 0.7402 0.180 0.000 0.808 0.012
#> SRR1405099 1 0.1510 0.8203 0.956 0.000 0.028 0.016
#> SRR1345585 3 0.4185 0.7498 0.036 0.012 0.832 0.120
#> SRR1093196 4 0.3266 0.7192 0.000 0.000 0.168 0.832
#> SRR1466006 2 0.0817 0.8757 0.000 0.976 0.024 0.000
#> SRR1351557 2 0.1118 0.8832 0.000 0.964 0.036 0.000
#> SRR1382687 4 0.4586 0.7083 0.136 0.000 0.068 0.796
#> SRR1375549 1 0.1398 0.8226 0.956 0.004 0.000 0.040
#> SRR1101765 1 0.0188 0.8231 0.996 0.004 0.000 0.000
#> SRR1334461 1 0.2654 0.7725 0.888 0.108 0.004 0.000
#> SRR1094073 2 0.2647 0.8746 0.000 0.880 0.120 0.000
#> SRR1077549 4 0.0469 0.8170 0.000 0.000 0.012 0.988
#> SRR1440332 4 0.5172 0.2633 0.008 0.000 0.404 0.588
#> SRR1454177 4 0.0000 0.8172 0.000 0.000 0.000 1.000
#> SRR1082447 1 0.0895 0.8248 0.976 0.000 0.004 0.020
#> SRR1420043 4 0.3266 0.7258 0.000 0.000 0.168 0.832
#> SRR1432500 4 0.2466 0.7922 0.004 0.000 0.096 0.900
#> SRR1378045 3 0.3649 0.5038 0.000 0.204 0.796 0.000
#> SRR1334200 1 0.3946 0.7212 0.812 0.168 0.020 0.000
#> SRR1069539 4 0.7228 0.2643 0.004 0.332 0.140 0.524
#> SRR1343031 3 0.4564 0.5360 0.000 0.000 0.672 0.328
#> SRR1319690 3 0.3999 0.7590 0.140 0.000 0.824 0.036
#> SRR1310604 2 0.3142 0.8176 0.008 0.860 0.132 0.000
#> SRR1327747 3 0.4951 0.6915 0.044 0.000 0.744 0.212
#> SRR1072456 2 0.2266 0.8587 0.004 0.912 0.084 0.000
#> SRR1367896 3 0.3208 0.7300 0.000 0.004 0.848 0.148
#> SRR1480107 1 0.0376 0.8236 0.992 0.000 0.004 0.004
#> SRR1377756 4 0.3494 0.7326 0.172 0.000 0.004 0.824
#> SRR1435272 4 0.0000 0.8172 0.000 0.000 0.000 1.000
#> SRR1089230 4 0.0336 0.8173 0.008 0.000 0.000 0.992
#> SRR1389522 3 0.4150 0.7575 0.076 0.056 0.848 0.020
#> SRR1080600 2 0.3528 0.7596 0.000 0.808 0.192 0.000
#> SRR1086935 4 0.1398 0.8032 0.000 0.040 0.004 0.956
#> SRR1344060 1 0.4175 0.6969 0.784 0.200 0.016 0.000
#> SRR1467922 2 0.3024 0.8672 0.000 0.852 0.148 0.000
#> SRR1090984 3 0.5447 0.2901 0.460 0.008 0.528 0.004
#> SRR1456991 1 0.3873 0.6069 0.772 0.000 0.228 0.000
#> SRR1085039 1 0.1820 0.8200 0.944 0.000 0.020 0.036
#> SRR1069303 1 0.4040 0.6701 0.752 0.000 0.000 0.248
#> SRR1091500 2 0.4746 0.8492 0.064 0.792 0.140 0.004
#> SRR1075198 2 0.0707 0.8755 0.000 0.980 0.020 0.000
#> SRR1086915 4 0.0469 0.8174 0.012 0.000 0.000 0.988
#> SRR1499503 2 0.2216 0.8531 0.000 0.908 0.092 0.000
#> SRR1094312 2 0.2011 0.8820 0.000 0.920 0.080 0.000
#> SRR1352437 4 0.4821 0.6961 0.160 0.008 0.048 0.784
#> SRR1436323 4 0.1716 0.8021 0.000 0.000 0.064 0.936
#> SRR1073507 4 0.3801 0.6854 0.220 0.000 0.000 0.780
#> SRR1401972 1 0.4482 0.6416 0.728 0.008 0.000 0.264
#> SRR1415510 2 0.2589 0.8653 0.000 0.884 0.116 0.000
#> SRR1327279 4 0.4937 0.6963 0.064 0.000 0.172 0.764
#> SRR1086983 4 0.0707 0.8165 0.020 0.000 0.000 0.980
#> SRR1105174 1 0.1209 0.8172 0.964 0.000 0.032 0.004
#> SRR1468893 1 0.3088 0.7721 0.864 0.000 0.008 0.128
#> SRR1362555 1 0.5339 0.4487 0.624 0.356 0.020 0.000
#> SRR1074526 1 0.0188 0.8231 0.996 0.004 0.000 0.000
#> SRR1326225 2 0.2973 0.8677 0.000 0.856 0.144 0.000
#> SRR1401933 4 0.2081 0.7963 0.084 0.000 0.000 0.916
#> SRR1324062 4 0.0524 0.8188 0.008 0.000 0.004 0.988
#> SRR1102296 1 0.0592 0.8251 0.984 0.000 0.000 0.016
#> SRR1085087 4 0.5125 0.3055 0.388 0.008 0.000 0.604
#> SRR1079046 1 0.0188 0.8231 0.996 0.004 0.000 0.000
#> SRR1328339 3 0.5172 0.4251 0.404 0.008 0.588 0.000
#> SRR1079782 2 0.1584 0.8715 0.000 0.952 0.012 0.036
#> SRR1092257 2 0.4346 0.8396 0.004 0.824 0.076 0.096
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 5 0.3999 0.31705 0.000 0.344 0.000 0.000 0.656
#> SRR1429287 5 0.4636 0.35485 0.000 0.308 0.004 0.024 0.664
#> SRR1359238 4 0.1792 0.80427 0.000 0.000 0.084 0.916 0.000
#> SRR1309597 3 0.0486 0.67190 0.004 0.000 0.988 0.004 0.004
#> SRR1441398 3 0.4196 0.49208 0.356 0.000 0.640 0.004 0.000
#> SRR1084055 5 0.2864 0.52607 0.000 0.136 0.012 0.000 0.852
#> SRR1417566 3 0.6478 0.50250 0.152 0.300 0.536 0.004 0.008
#> SRR1351857 4 0.3607 0.66203 0.004 0.000 0.000 0.752 0.244
#> SRR1487485 3 0.1845 0.67168 0.000 0.016 0.928 0.056 0.000
#> SRR1335875 3 0.5793 0.45950 0.352 0.068 0.568 0.008 0.004
#> SRR1073947 1 0.0703 0.71949 0.976 0.000 0.000 0.000 0.024
#> SRR1443483 3 0.0865 0.66077 0.000 0.000 0.972 0.004 0.024
#> SRR1346794 3 0.5064 0.35003 0.416 0.000 0.552 0.004 0.028
#> SRR1405245 3 0.4196 0.49093 0.356 0.000 0.640 0.004 0.000
#> SRR1409677 4 0.0703 0.82869 0.000 0.000 0.000 0.976 0.024
#> SRR1095549 3 0.6308 0.42965 0.284 0.000 0.588 0.044 0.084
#> SRR1323788 3 0.5925 0.39081 0.384 0.000 0.528 0.076 0.012
#> SRR1314054 2 0.1341 0.75557 0.000 0.944 0.000 0.000 0.056
#> SRR1077944 1 0.2127 0.67474 0.892 0.000 0.108 0.000 0.000
#> SRR1480587 2 0.5597 0.06937 0.000 0.488 0.072 0.000 0.440
#> SRR1311205 3 0.4397 0.35465 0.432 0.000 0.564 0.004 0.000
#> SRR1076369 5 0.5882 0.43054 0.148 0.000 0.264 0.000 0.588
#> SRR1453549 3 0.5403 0.20424 0.056 0.000 0.488 0.456 0.000
#> SRR1345782 3 0.3752 0.56463 0.292 0.000 0.708 0.000 0.000
#> SRR1447850 2 0.0963 0.73868 0.000 0.964 0.000 0.036 0.000
#> SRR1391553 2 0.1408 0.71245 0.008 0.948 0.044 0.000 0.000
#> SRR1444156 2 0.0000 0.74757 0.000 1.000 0.000 0.000 0.000
#> SRR1471731 4 0.2886 0.75190 0.008 0.000 0.148 0.844 0.000
#> SRR1120987 4 0.2011 0.80029 0.004 0.000 0.000 0.908 0.088
#> SRR1477363 1 0.3274 0.55291 0.780 0.000 0.220 0.000 0.000
#> SRR1391961 1 0.4283 0.17284 0.544 0.000 0.000 0.000 0.456
#> SRR1373879 3 0.4621 0.29119 0.008 0.000 0.576 0.412 0.004
#> SRR1318732 3 0.2393 0.67514 0.080 0.000 0.900 0.004 0.016
#> SRR1091404 1 0.3957 0.50623 0.712 0.000 0.008 0.000 0.280
#> SRR1402109 3 0.4545 0.18157 0.004 0.000 0.560 0.432 0.004
#> SRR1407336 4 0.2519 0.79548 0.000 0.000 0.100 0.884 0.016
#> SRR1097417 3 0.3636 0.48710 0.000 0.000 0.728 0.000 0.272
#> SRR1396227 1 0.1153 0.71556 0.964 0.000 0.024 0.004 0.008
#> SRR1400775 2 0.0794 0.75763 0.000 0.972 0.000 0.000 0.028
#> SRR1392861 4 0.0162 0.82900 0.000 0.000 0.004 0.996 0.000
#> SRR1472929 5 0.3918 0.52326 0.008 0.008 0.232 0.000 0.752
#> SRR1436740 4 0.0324 0.82933 0.004 0.000 0.000 0.992 0.004
#> SRR1477057 2 0.5013 0.57313 0.080 0.680 0.000 0.000 0.240
#> SRR1311980 3 0.6689 0.54413 0.264 0.116 0.568 0.052 0.000
#> SRR1069400 3 0.1704 0.63391 0.000 0.000 0.928 0.004 0.068
#> SRR1351016 1 0.3231 0.58508 0.800 0.000 0.196 0.000 0.004
#> SRR1096291 4 0.3366 0.70195 0.000 0.000 0.004 0.784 0.212
#> SRR1418145 4 0.3816 0.54533 0.000 0.000 0.000 0.696 0.304
#> SRR1488111 4 0.5754 0.33132 0.000 0.260 0.000 0.604 0.136
#> SRR1370495 5 0.2929 0.53278 0.180 0.000 0.000 0.000 0.820
#> SRR1352639 5 0.4504 0.26436 0.428 0.000 0.000 0.008 0.564
#> SRR1348911 3 0.4237 0.64662 0.152 0.076 0.772 0.000 0.000
#> SRR1467386 1 0.4740 0.08322 0.516 0.000 0.016 0.468 0.000
#> SRR1415956 1 0.3160 0.59127 0.808 0.000 0.188 0.000 0.004
#> SRR1500495 3 0.4182 0.42205 0.400 0.000 0.600 0.000 0.000
#> SRR1405099 1 0.1270 0.70531 0.948 0.000 0.052 0.000 0.000
#> SRR1345585 3 0.0609 0.66556 0.000 0.000 0.980 0.000 0.020
#> SRR1093196 4 0.2471 0.76647 0.000 0.000 0.136 0.864 0.000
#> SRR1466006 5 0.5320 0.22255 0.000 0.368 0.060 0.000 0.572
#> SRR1351557 2 0.4101 0.41226 0.000 0.628 0.000 0.000 0.372
#> SRR1382687 1 0.6019 0.04711 0.528 0.000 0.368 0.096 0.008
#> SRR1375549 1 0.2411 0.69908 0.884 0.000 0.000 0.008 0.108
#> SRR1101765 5 0.5558 0.11067 0.360 0.000 0.000 0.080 0.560
#> SRR1334461 5 0.4307 -0.10549 0.496 0.000 0.000 0.000 0.504
#> SRR1094073 2 0.3730 0.57189 0.000 0.712 0.000 0.000 0.288
#> SRR1077549 4 0.0671 0.82847 0.004 0.000 0.016 0.980 0.000
#> SRR1440332 3 0.5071 0.26999 0.036 0.000 0.540 0.424 0.000
#> SRR1454177 4 0.0000 0.82957 0.000 0.000 0.000 1.000 0.000
#> SRR1082447 1 0.1043 0.71738 0.960 0.000 0.000 0.000 0.040
#> SRR1420043 4 0.2561 0.74788 0.000 0.000 0.144 0.856 0.000
#> SRR1432500 4 0.1205 0.82497 0.004 0.000 0.040 0.956 0.000
#> SRR1378045 3 0.4446 0.11741 0.000 0.476 0.520 0.000 0.004
#> SRR1334200 5 0.1205 0.55438 0.040 0.000 0.000 0.004 0.956
#> SRR1069539 5 0.4067 0.45500 0.000 0.004 0.020 0.228 0.748
#> SRR1343031 4 0.5114 -0.00557 0.000 0.000 0.476 0.488 0.036
#> SRR1319690 3 0.2731 0.67069 0.104 0.000 0.876 0.016 0.004
#> SRR1310604 5 0.2233 0.56013 0.000 0.016 0.080 0.000 0.904
#> SRR1327747 3 0.3142 0.65620 0.004 0.000 0.856 0.108 0.032
#> SRR1072456 5 0.6264 0.20894 0.000 0.344 0.160 0.000 0.496
#> SRR1367896 3 0.1365 0.65724 0.004 0.000 0.952 0.004 0.040
#> SRR1480107 1 0.1041 0.71847 0.964 0.000 0.004 0.000 0.032
#> SRR1377756 1 0.4401 0.53205 0.684 0.000 0.004 0.296 0.016
#> SRR1435272 4 0.0000 0.82957 0.000 0.000 0.000 1.000 0.000
#> SRR1089230 4 0.1557 0.82096 0.008 0.000 0.000 0.940 0.052
#> SRR1389522 3 0.1168 0.66425 0.008 0.000 0.960 0.000 0.032
#> SRR1080600 5 0.3696 0.53337 0.000 0.016 0.212 0.000 0.772
#> SRR1086935 4 0.0671 0.82935 0.004 0.000 0.000 0.980 0.016
#> SRR1344060 5 0.2069 0.54792 0.076 0.012 0.000 0.000 0.912
#> SRR1467922 2 0.0000 0.74757 0.000 1.000 0.000 0.000 0.000
#> SRR1090984 1 0.4684 -0.09910 0.536 0.004 0.452 0.000 0.008
#> SRR1456991 1 0.3333 0.56147 0.788 0.000 0.208 0.000 0.004
#> SRR1085039 1 0.3284 0.66643 0.828 0.000 0.024 0.000 0.148
#> SRR1069303 1 0.1117 0.71997 0.964 0.000 0.000 0.016 0.020
#> SRR1091500 2 0.1197 0.73583 0.000 0.952 0.000 0.000 0.048
#> SRR1075198 5 0.5120 0.42558 0.000 0.252 0.056 0.012 0.680
#> SRR1086915 4 0.1124 0.82663 0.004 0.000 0.000 0.960 0.036
#> SRR1499503 5 0.6148 0.29960 0.000 0.304 0.160 0.000 0.536
#> SRR1094312 2 0.2966 0.69079 0.000 0.816 0.000 0.000 0.184
#> SRR1352437 1 0.3589 0.67183 0.824 0.040 0.004 0.132 0.000
#> SRR1436323 4 0.2295 0.80446 0.004 0.000 0.088 0.900 0.008
#> SRR1073507 4 0.4151 0.40496 0.344 0.000 0.004 0.652 0.000
#> SRR1401972 1 0.0671 0.72102 0.980 0.000 0.000 0.016 0.004
#> SRR1415510 5 0.6512 0.15886 0.000 0.348 0.200 0.000 0.452
#> SRR1327279 4 0.6357 0.07641 0.128 0.000 0.340 0.520 0.012
#> SRR1086983 4 0.1638 0.80660 0.064 0.000 0.004 0.932 0.000
#> SRR1105174 1 0.2278 0.70898 0.908 0.000 0.060 0.000 0.032
#> SRR1468893 1 0.0727 0.71933 0.980 0.000 0.012 0.004 0.004
#> SRR1362555 5 0.3888 0.52570 0.176 0.032 0.000 0.004 0.788
#> SRR1074526 1 0.4304 0.10980 0.516 0.000 0.000 0.000 0.484
#> SRR1326225 2 0.1792 0.75024 0.000 0.916 0.000 0.000 0.084
#> SRR1401933 1 0.4945 0.24404 0.536 0.000 0.004 0.440 0.020
#> SRR1324062 1 0.4904 0.17144 0.504 0.000 0.024 0.472 0.000
#> SRR1102296 1 0.0854 0.71816 0.976 0.008 0.012 0.000 0.004
#> SRR1085087 1 0.3538 0.65027 0.804 0.000 0.004 0.176 0.016
#> SRR1079046 1 0.1851 0.70106 0.912 0.000 0.000 0.000 0.088
#> SRR1328339 1 0.4430 -0.10046 0.540 0.004 0.456 0.000 0.000
#> SRR1079782 5 0.5495 0.10231 0.000 0.408 0.008 0.048 0.536
#> SRR1092257 2 0.5523 0.38604 0.008 0.584 0.000 0.060 0.348
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.5155 0.4125 0.000 0.596 0.000 0.000 0.124 0.280
#> SRR1429287 2 0.2095 0.6202 0.008 0.920 0.000 0.040 0.012 0.020
#> SRR1359238 4 0.2070 0.6716 0.000 0.000 0.092 0.896 0.012 0.000
#> SRR1309597 3 0.0653 0.6090 0.004 0.012 0.980 0.000 0.004 0.000
#> SRR1441398 3 0.4325 0.2356 0.456 0.000 0.524 0.000 0.020 0.000
#> SRR1084055 2 0.5635 0.2766 0.000 0.492 0.008 0.000 0.380 0.120
#> SRR1417566 1 0.5146 0.1111 0.576 0.020 0.360 0.000 0.036 0.008
#> SRR1351857 4 0.4384 0.4775 0.036 0.012 0.000 0.684 0.268 0.000
#> SRR1487485 3 0.3338 0.5929 0.016 0.012 0.860 0.068 0.028 0.016
#> SRR1335875 3 0.6069 0.3975 0.312 0.000 0.536 0.000 0.064 0.088
#> SRR1073947 5 0.4747 0.1678 0.308 0.004 0.052 0.000 0.632 0.004
#> SRR1443483 3 0.1237 0.6055 0.000 0.020 0.956 0.004 0.020 0.000
#> SRR1346794 1 0.5707 0.3482 0.648 0.148 0.148 0.004 0.052 0.000
#> SRR1405245 3 0.4212 0.3001 0.424 0.000 0.560 0.000 0.016 0.000
#> SRR1409677 4 0.1226 0.6656 0.004 0.040 0.000 0.952 0.004 0.000
#> SRR1095549 1 0.7100 0.0495 0.440 0.012 0.276 0.064 0.208 0.000
#> SRR1323788 1 0.4479 0.2783 0.672 0.000 0.280 0.024 0.024 0.000
#> SRR1314054 6 0.1918 0.7594 0.000 0.088 0.000 0.008 0.000 0.904
#> SRR1077944 1 0.3893 0.5414 0.768 0.000 0.140 0.000 0.092 0.000
#> SRR1480587 2 0.5801 0.4844 0.000 0.580 0.260 0.000 0.032 0.128
#> SRR1311205 3 0.5316 0.1876 0.416 0.000 0.480 0.000 0.104 0.000
#> SRR1076369 2 0.7184 0.1016 0.348 0.364 0.200 0.004 0.084 0.000
#> SRR1453549 4 0.4695 0.1869 0.008 0.000 0.476 0.492 0.020 0.004
#> SRR1345782 3 0.5495 0.4961 0.156 0.000 0.604 0.012 0.228 0.000
#> SRR1447850 6 0.1152 0.7505 0.000 0.004 0.000 0.044 0.000 0.952
#> SRR1391553 6 0.2002 0.7140 0.012 0.004 0.076 0.000 0.000 0.908
#> SRR1444156 6 0.0146 0.7559 0.000 0.004 0.000 0.000 0.000 0.996
#> SRR1471731 4 0.4887 0.5890 0.092 0.020 0.136 0.732 0.020 0.000
#> SRR1120987 4 0.4118 0.4342 0.000 0.312 0.000 0.660 0.028 0.000
#> SRR1477363 1 0.4990 0.4182 0.644 0.000 0.204 0.000 0.152 0.000
#> SRR1391961 5 0.2747 0.5693 0.096 0.044 0.000 0.000 0.860 0.000
#> SRR1373879 4 0.4403 0.2238 0.000 0.000 0.468 0.508 0.024 0.000
#> SRR1318732 3 0.5644 0.3026 0.344 0.048 0.548 0.000 0.060 0.000
#> SRR1091404 5 0.3872 0.2035 0.392 0.004 0.000 0.000 0.604 0.000
#> SRR1402109 4 0.4253 0.2569 0.000 0.000 0.460 0.524 0.016 0.000
#> SRR1407336 4 0.2623 0.6557 0.000 0.000 0.132 0.852 0.016 0.000
#> SRR1097417 3 0.4752 0.2403 0.000 0.024 0.516 0.004 0.448 0.008
#> SRR1396227 1 0.1701 0.5488 0.920 0.000 0.008 0.000 0.072 0.000
#> SRR1400775 6 0.2020 0.7549 0.000 0.096 0.000 0.000 0.008 0.896
#> SRR1392861 4 0.0405 0.6742 0.000 0.000 0.008 0.988 0.004 0.000
#> SRR1472929 5 0.5226 -0.2454 0.000 0.448 0.092 0.000 0.460 0.000
#> SRR1436740 4 0.0291 0.6712 0.000 0.004 0.000 0.992 0.004 0.000
#> SRR1477057 6 0.6845 0.2965 0.084 0.236 0.000 0.000 0.204 0.476
#> SRR1311980 3 0.6364 0.4785 0.232 0.000 0.584 0.092 0.020 0.072
#> SRR1069400 3 0.3133 0.5665 0.000 0.040 0.856 0.032 0.072 0.000
#> SRR1351016 1 0.5897 0.2520 0.456 0.000 0.180 0.004 0.360 0.000
#> SRR1096291 4 0.4049 0.3915 0.000 0.332 0.000 0.648 0.020 0.000
#> SRR1418145 2 0.4270 0.3687 0.024 0.660 0.000 0.308 0.008 0.000
#> SRR1488111 4 0.6135 0.0324 0.008 0.396 0.000 0.472 0.044 0.080
#> SRR1370495 2 0.3103 0.5818 0.064 0.836 0.000 0.000 0.100 0.000
#> SRR1352639 2 0.5739 0.1499 0.284 0.528 0.000 0.000 0.184 0.004
#> SRR1348911 3 0.6019 0.3842 0.044 0.000 0.552 0.012 0.076 0.316
#> SRR1467386 4 0.5066 0.3702 0.308 0.000 0.012 0.608 0.072 0.000
#> SRR1415956 1 0.3542 0.5178 0.788 0.000 0.160 0.000 0.052 0.000
#> SRR1500495 3 0.4256 0.2180 0.464 0.000 0.520 0.000 0.016 0.000
#> SRR1405099 1 0.3938 0.4919 0.728 0.000 0.044 0.000 0.228 0.000
#> SRR1345585 3 0.2954 0.5656 0.028 0.060 0.868 0.000 0.044 0.000
#> SRR1093196 4 0.1327 0.6801 0.000 0.000 0.064 0.936 0.000 0.000
#> SRR1466006 2 0.4268 0.5930 0.000 0.748 0.180 0.000 0.040 0.032
#> SRR1351557 2 0.3986 0.2782 0.004 0.608 0.000 0.000 0.004 0.384
#> SRR1382687 1 0.3739 0.4613 0.776 0.000 0.176 0.040 0.008 0.000
#> SRR1375549 1 0.4851 0.2454 0.636 0.292 0.000 0.012 0.060 0.000
#> SRR1101765 1 0.6699 -0.0268 0.428 0.360 0.000 0.080 0.132 0.000
#> SRR1334461 5 0.2888 0.5691 0.092 0.056 0.000 0.000 0.852 0.000
#> SRR1094073 6 0.3531 0.4362 0.000 0.328 0.000 0.000 0.000 0.672
#> SRR1077549 4 0.2250 0.6698 0.000 0.000 0.092 0.888 0.020 0.000
#> SRR1440332 4 0.4653 0.1889 0.012 0.000 0.476 0.492 0.020 0.000
#> SRR1454177 4 0.0405 0.6747 0.000 0.000 0.008 0.988 0.004 0.000
#> SRR1082447 1 0.2234 0.5152 0.872 0.000 0.004 0.000 0.124 0.000
#> SRR1420043 4 0.3104 0.6211 0.000 0.000 0.184 0.800 0.016 0.000
#> SRR1432500 4 0.3867 0.6382 0.004 0.004 0.124 0.788 0.080 0.000
#> SRR1378045 6 0.5382 -0.0129 0.032 0.012 0.464 0.000 0.024 0.468
#> SRR1334200 2 0.4059 0.5190 0.088 0.760 0.000 0.004 0.148 0.000
#> SRR1069539 2 0.3592 0.5896 0.000 0.812 0.020 0.124 0.044 0.000
#> SRR1343031 4 0.5110 0.3118 0.000 0.004 0.396 0.528 0.072 0.000
#> SRR1319690 3 0.4063 0.4898 0.280 0.008 0.692 0.000 0.020 0.000
#> SRR1310604 2 0.5011 0.4425 0.000 0.616 0.112 0.000 0.272 0.000
#> SRR1327747 3 0.7200 0.1561 0.288 0.160 0.460 0.036 0.056 0.000
#> SRR1072456 2 0.6114 0.4843 0.000 0.576 0.204 0.000 0.052 0.168
#> SRR1367896 3 0.2784 0.5884 0.000 0.020 0.868 0.020 0.092 0.000
#> SRR1480107 1 0.3975 0.2924 0.600 0.000 0.008 0.000 0.392 0.000
#> SRR1377756 1 0.3259 0.5064 0.844 0.032 0.004 0.100 0.020 0.000
#> SRR1435272 4 0.0291 0.6730 0.000 0.000 0.004 0.992 0.004 0.000
#> SRR1089230 4 0.6495 0.2544 0.260 0.268 0.000 0.444 0.028 0.000
#> SRR1389522 3 0.2518 0.5990 0.004 0.012 0.884 0.012 0.088 0.000
#> SRR1080600 2 0.2307 0.6233 0.004 0.896 0.068 0.000 0.032 0.000
#> SRR1086935 4 0.5689 0.4658 0.196 0.176 0.000 0.608 0.012 0.008
#> SRR1344060 5 0.4325 -0.1525 0.020 0.456 0.000 0.000 0.524 0.000
#> SRR1467922 6 0.0692 0.7633 0.000 0.020 0.000 0.000 0.004 0.976
#> SRR1090984 1 0.4115 0.1514 0.624 0.000 0.360 0.000 0.004 0.012
#> SRR1456991 1 0.5667 0.2514 0.472 0.000 0.160 0.000 0.368 0.000
#> SRR1085039 5 0.4428 0.3985 0.244 0.000 0.072 0.000 0.684 0.000
#> SRR1069303 1 0.3371 0.4917 0.780 0.000 0.000 0.016 0.200 0.004
#> SRR1091500 6 0.1257 0.7578 0.000 0.028 0.000 0.000 0.020 0.952
#> SRR1075198 2 0.0665 0.6268 0.000 0.980 0.008 0.008 0.000 0.004
#> SRR1086915 4 0.5305 0.4172 0.120 0.268 0.000 0.604 0.008 0.000
#> SRR1499503 2 0.6347 0.4191 0.000 0.544 0.144 0.000 0.068 0.244
#> SRR1094312 6 0.3387 0.6891 0.000 0.164 0.000 0.000 0.040 0.796
#> SRR1352437 1 0.7170 0.1250 0.424 0.000 0.000 0.136 0.280 0.160
#> SRR1436323 4 0.5697 0.3739 0.320 0.012 0.088 0.564 0.016 0.000
#> SRR1073507 4 0.3837 0.5994 0.068 0.000 0.008 0.784 0.140 0.000
#> SRR1401972 1 0.3073 0.5132 0.816 0.000 0.000 0.016 0.164 0.004
#> SRR1415510 2 0.5643 0.4701 0.016 0.568 0.336 0.000 0.044 0.036
#> SRR1327279 4 0.6370 0.2465 0.008 0.004 0.256 0.404 0.328 0.000
#> SRR1086983 4 0.1845 0.6613 0.072 0.008 0.000 0.916 0.004 0.000
#> SRR1105174 1 0.2997 0.5577 0.844 0.000 0.060 0.000 0.096 0.000
#> SRR1468893 1 0.1493 0.5399 0.936 0.004 0.000 0.004 0.056 0.000
#> SRR1362555 2 0.2201 0.6195 0.024 0.912 0.012 0.000 0.048 0.004
#> SRR1074526 5 0.4597 0.4377 0.276 0.072 0.000 0.000 0.652 0.000
#> SRR1326225 6 0.2597 0.6965 0.000 0.176 0.000 0.000 0.000 0.824
#> SRR1401933 1 0.5098 0.3953 0.700 0.112 0.004 0.152 0.032 0.000
#> SRR1324062 1 0.5573 0.1768 0.476 0.004 0.020 0.444 0.048 0.008
#> SRR1102296 1 0.4302 0.3744 0.628 0.000 0.004 0.000 0.344 0.024
#> SRR1085087 1 0.6506 0.1968 0.436 0.024 0.000 0.208 0.328 0.004
#> SRR1079046 1 0.3707 0.4261 0.784 0.136 0.000 0.000 0.080 0.000
#> SRR1328339 1 0.4591 -0.1574 0.500 0.000 0.464 0.000 0.036 0.000
#> SRR1079782 2 0.3462 0.6175 0.008 0.848 0.004 0.060 0.024 0.056
#> SRR1092257 2 0.6335 0.3591 0.028 0.572 0.000 0.048 0.092 0.260
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 17611 rows and 118 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.350 0.833 0.884 0.4138 0.566 0.566
#> 3 3 0.424 0.711 0.828 0.4862 0.780 0.616
#> 4 4 0.490 0.477 0.702 0.1617 0.856 0.630
#> 5 5 0.496 0.400 0.621 0.0515 0.921 0.747
#> 6 6 0.518 0.482 0.595 0.0445 0.869 0.554
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
#> SRR1396765 2 0.0000 0.884 0.000 1.000
#> SRR1429287 1 0.6801 0.872 0.820 0.180
#> SRR1359238 1 0.5629 0.891 0.868 0.132
#> SRR1309597 1 0.0000 0.850 1.000 0.000
#> SRR1441398 1 0.0000 0.850 1.000 0.000
#> SRR1084055 1 0.9209 0.647 0.664 0.336
#> SRR1417566 1 0.5519 0.891 0.872 0.128
#> SRR1351857 2 0.0000 0.884 0.000 1.000
#> SRR1487485 1 0.7219 0.862 0.800 0.200
#> SRR1335875 1 0.5842 0.891 0.860 0.140
#> SRR1073947 1 0.6247 0.887 0.844 0.156
#> SRR1443483 1 0.5178 0.890 0.884 0.116
#> SRR1346794 1 0.5629 0.891 0.868 0.132
#> SRR1405245 1 0.0000 0.850 1.000 0.000
#> SRR1409677 2 0.3114 0.868 0.056 0.944
#> SRR1095549 2 0.7674 0.721 0.224 0.776
#> SRR1323788 1 0.7815 0.826 0.768 0.232
#> SRR1314054 2 0.0000 0.884 0.000 1.000
#> SRR1077944 1 0.5842 0.891 0.860 0.140
#> SRR1480587 1 0.0000 0.850 1.000 0.000
#> SRR1311205 1 0.0000 0.850 1.000 0.000
#> SRR1076369 1 0.5842 0.889 0.860 0.140
#> SRR1453549 1 0.5629 0.891 0.868 0.132
#> SRR1345782 1 0.7815 0.826 0.768 0.232
#> SRR1447850 1 0.8443 0.787 0.728 0.272
#> SRR1391553 1 0.6531 0.879 0.832 0.168
#> SRR1444156 2 0.0000 0.884 0.000 1.000
#> SRR1471731 1 0.6531 0.879 0.832 0.168
#> SRR1120987 2 0.0672 0.884 0.008 0.992
#> SRR1477363 1 0.5737 0.890 0.864 0.136
#> SRR1391961 1 0.0000 0.850 1.000 0.000
#> SRR1373879 2 0.7883 0.702 0.236 0.764
#> SRR1318732 1 0.6531 0.879 0.832 0.168
#> SRR1091404 2 0.9552 0.358 0.376 0.624
#> SRR1402109 1 0.8555 0.755 0.720 0.280
#> SRR1407336 2 0.8081 0.682 0.248 0.752
#> SRR1097417 1 0.2236 0.859 0.964 0.036
#> SRR1396227 1 0.5737 0.891 0.864 0.136
#> SRR1400775 1 0.9491 0.620 0.632 0.368
#> SRR1392861 2 0.0672 0.884 0.008 0.992
#> SRR1472929 1 0.0000 0.850 1.000 0.000
#> SRR1436740 2 0.0000 0.884 0.000 1.000
#> SRR1477057 1 0.6048 0.888 0.852 0.148
#> SRR1311980 1 0.0000 0.850 1.000 0.000
#> SRR1069400 1 0.5946 0.888 0.856 0.144
#> SRR1351016 1 0.0672 0.853 0.992 0.008
#> SRR1096291 2 0.0000 0.884 0.000 1.000
#> SRR1418145 1 0.9491 0.632 0.632 0.368
#> SRR1488111 1 0.6048 0.888 0.852 0.148
#> SRR1370495 1 0.0000 0.850 1.000 0.000
#> SRR1352639 1 0.8386 0.785 0.732 0.268
#> SRR1348911 1 0.0000 0.850 1.000 0.000
#> SRR1467386 2 0.5519 0.827 0.128 0.872
#> SRR1415956 1 0.0000 0.850 1.000 0.000
#> SRR1500495 1 0.0000 0.850 1.000 0.000
#> SRR1405099 1 0.0000 0.850 1.000 0.000
#> SRR1345585 1 0.6531 0.879 0.832 0.168
#> SRR1093196 1 0.7453 0.850 0.788 0.212
#> SRR1466006 1 0.0000 0.850 1.000 0.000
#> SRR1351557 1 0.6973 0.867 0.812 0.188
#> SRR1382687 1 0.5737 0.890 0.864 0.136
#> SRR1375549 1 0.6048 0.888 0.852 0.148
#> SRR1101765 2 0.0000 0.884 0.000 1.000
#> SRR1334461 1 0.0000 0.850 1.000 0.000
#> SRR1094073 2 0.0000 0.884 0.000 1.000
#> SRR1077549 2 0.7674 0.721 0.224 0.776
#> SRR1440332 1 0.7528 0.846 0.784 0.216
#> SRR1454177 2 0.0000 0.884 0.000 1.000
#> SRR1082447 2 0.8909 0.557 0.308 0.692
#> SRR1420043 1 0.5629 0.891 0.868 0.132
#> SRR1432500 1 0.5946 0.889 0.856 0.144
#> SRR1378045 1 0.9209 0.682 0.664 0.336
#> SRR1334200 1 0.0000 0.850 1.000 0.000
#> SRR1069539 2 0.0000 0.884 0.000 1.000
#> SRR1343031 1 0.8555 0.755 0.720 0.280
#> SRR1319690 1 0.5629 0.891 0.868 0.132
#> SRR1310604 1 0.6712 0.871 0.824 0.176
#> SRR1327747 1 0.5519 0.891 0.872 0.128
#> SRR1072456 1 0.0000 0.850 1.000 0.000
#> SRR1367896 1 0.0000 0.850 1.000 0.000
#> SRR1480107 1 0.0672 0.853 0.992 0.008
#> SRR1377756 1 0.5629 0.891 0.868 0.132
#> SRR1435272 2 0.0000 0.884 0.000 1.000
#> SRR1089230 2 0.0000 0.884 0.000 1.000
#> SRR1389522 1 0.5178 0.890 0.884 0.116
#> SRR1080600 1 0.6801 0.868 0.820 0.180
#> SRR1086935 2 0.0000 0.884 0.000 1.000
#> SRR1344060 1 0.0000 0.850 1.000 0.000
#> SRR1467922 2 0.0000 0.884 0.000 1.000
#> SRR1090984 1 0.0000 0.850 1.000 0.000
#> SRR1456991 1 0.0672 0.853 0.992 0.008
#> SRR1085039 2 0.8909 0.557 0.308 0.692
#> SRR1069303 1 0.5519 0.891 0.872 0.128
#> SRR1091500 2 0.0000 0.884 0.000 1.000
#> SRR1075198 1 0.7139 0.861 0.804 0.196
#> SRR1086915 2 0.0672 0.884 0.008 0.992
#> SRR1499503 2 0.9522 0.251 0.372 0.628
#> SRR1094312 1 0.9491 0.620 0.632 0.368
#> SRR1352437 2 0.4022 0.858 0.080 0.920
#> SRR1436323 1 0.6048 0.888 0.852 0.148
#> SRR1073507 2 0.5519 0.827 0.128 0.872
#> SRR1401972 1 0.5519 0.891 0.872 0.128
#> SRR1415510 1 0.6048 0.888 0.852 0.148
#> SRR1327279 2 0.7674 0.721 0.224 0.776
#> SRR1086983 2 0.5519 0.827 0.128 0.872
#> SRR1105174 2 0.5737 0.820 0.136 0.864
#> SRR1468893 1 0.0376 0.852 0.996 0.004
#> SRR1362555 1 0.0000 0.850 1.000 0.000
#> SRR1074526 2 0.0000 0.884 0.000 1.000
#> SRR1326225 2 0.0938 0.882 0.012 0.988
#> SRR1401933 1 0.6343 0.884 0.840 0.160
#> SRR1324062 1 0.5842 0.891 0.860 0.140
#> SRR1102296 1 0.9635 0.555 0.612 0.388
#> SRR1085087 2 0.4022 0.858 0.080 0.920
#> SRR1079046 1 0.6801 0.872 0.820 0.180
#> SRR1328339 1 0.5519 0.891 0.872 0.128
#> SRR1079782 1 0.7056 0.864 0.808 0.192
#> SRR1092257 2 0.1414 0.881 0.020 0.980
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 3 0.0237 0.823 0.004 0.000 0.996
#> SRR1429287 1 0.0592 0.809 0.988 0.012 0.000
#> SRR1359238 1 0.3192 0.801 0.888 0.112 0.000
#> SRR1309597 2 0.0892 0.763 0.020 0.980 0.000
#> SRR1441398 2 0.5216 0.728 0.260 0.740 0.000
#> SRR1084055 1 0.9474 0.320 0.496 0.272 0.232
#> SRR1417566 1 0.4172 0.789 0.840 0.156 0.004
#> SRR1351857 3 0.0592 0.825 0.012 0.000 0.988
#> SRR1487485 1 0.2434 0.812 0.940 0.036 0.024
#> SRR1335875 1 0.2878 0.808 0.904 0.096 0.000
#> SRR1073947 1 0.3607 0.807 0.880 0.112 0.008
#> SRR1443483 1 0.6286 0.271 0.536 0.464 0.000
#> SRR1346794 1 0.3340 0.799 0.880 0.120 0.000
#> SRR1405245 2 0.5216 0.728 0.260 0.740 0.000
#> SRR1409677 3 0.2448 0.809 0.076 0.000 0.924
#> SRR1095549 3 0.7067 0.539 0.376 0.028 0.596
#> SRR1323788 1 0.4966 0.790 0.840 0.100 0.060
#> SRR1314054 3 0.0237 0.823 0.004 0.000 0.996
#> SRR1077944 1 0.3644 0.795 0.872 0.124 0.004
#> SRR1480587 2 0.0892 0.763 0.020 0.980 0.000
#> SRR1311205 2 0.5810 0.673 0.336 0.664 0.000
#> SRR1076369 1 0.6505 0.200 0.528 0.468 0.004
#> SRR1453549 1 0.3192 0.801 0.888 0.112 0.000
#> SRR1345782 1 0.4966 0.790 0.840 0.100 0.060
#> SRR1447850 1 0.3349 0.760 0.888 0.004 0.108
#> SRR1391553 1 0.2066 0.810 0.940 0.060 0.000
#> SRR1444156 3 0.0237 0.823 0.004 0.000 0.996
#> SRR1471731 1 0.2066 0.810 0.940 0.060 0.000
#> SRR1120987 3 0.1163 0.825 0.028 0.000 0.972
#> SRR1477363 1 0.3116 0.802 0.892 0.108 0.000
#> SRR1391961 2 0.1860 0.769 0.052 0.948 0.000
#> SRR1373879 3 0.7114 0.515 0.388 0.028 0.584
#> SRR1318732 1 0.2066 0.810 0.940 0.060 0.000
#> SRR1091404 1 0.7268 -0.133 0.524 0.028 0.448
#> SRR1402109 1 0.6100 0.719 0.784 0.096 0.120
#> SRR1407336 3 0.7156 0.489 0.400 0.028 0.572
#> SRR1097417 2 0.4784 0.636 0.200 0.796 0.004
#> SRR1396227 1 0.2796 0.810 0.908 0.092 0.000
#> SRR1400775 1 0.5366 0.666 0.776 0.016 0.208
#> SRR1392861 3 0.2165 0.815 0.064 0.000 0.936
#> SRR1472929 2 0.0747 0.760 0.016 0.984 0.000
#> SRR1436740 3 0.0592 0.824 0.012 0.000 0.988
#> SRR1477057 1 0.1860 0.815 0.948 0.052 0.000
#> SRR1311980 2 0.5216 0.728 0.260 0.740 0.000
#> SRR1069400 1 0.6057 0.499 0.656 0.340 0.004
#> SRR1351016 2 0.6235 0.497 0.436 0.564 0.000
#> SRR1096291 3 0.0592 0.825 0.012 0.000 0.988
#> SRR1418145 1 0.4861 0.681 0.800 0.008 0.192
#> SRR1488111 1 0.1860 0.815 0.948 0.052 0.000
#> SRR1370495 2 0.0892 0.763 0.020 0.980 0.000
#> SRR1352639 1 0.4709 0.776 0.852 0.056 0.092
#> SRR1348911 2 0.5706 0.680 0.320 0.680 0.000
#> SRR1467386 3 0.5623 0.680 0.280 0.004 0.716
#> SRR1415956 2 0.5529 0.710 0.296 0.704 0.000
#> SRR1500495 2 0.5810 0.673 0.336 0.664 0.000
#> SRR1405099 2 0.5529 0.710 0.296 0.704 0.000
#> SRR1345585 1 0.2066 0.810 0.940 0.060 0.000
#> SRR1093196 1 0.2031 0.807 0.952 0.016 0.032
#> SRR1466006 2 0.2625 0.762 0.084 0.916 0.000
#> SRR1351557 1 0.1170 0.809 0.976 0.016 0.008
#> SRR1382687 1 0.3116 0.802 0.892 0.108 0.000
#> SRR1375549 1 0.1860 0.815 0.948 0.052 0.000
#> SRR1101765 3 0.1289 0.824 0.032 0.000 0.968
#> SRR1334461 2 0.1860 0.769 0.052 0.948 0.000
#> SRR1094073 3 0.0237 0.823 0.004 0.000 0.996
#> SRR1077549 3 0.7067 0.539 0.376 0.028 0.596
#> SRR1440332 1 0.4253 0.805 0.872 0.080 0.048
#> SRR1454177 3 0.0237 0.823 0.004 0.000 0.996
#> SRR1082447 3 0.7278 0.336 0.456 0.028 0.516
#> SRR1420043 1 0.3619 0.787 0.864 0.136 0.000
#> SRR1432500 1 0.3112 0.807 0.900 0.096 0.004
#> SRR1378045 1 0.5236 0.712 0.804 0.028 0.168
#> SRR1334200 2 0.1643 0.766 0.044 0.956 0.000
#> SRR1069539 3 0.0592 0.825 0.012 0.000 0.988
#> SRR1343031 1 0.6100 0.719 0.784 0.096 0.120
#> SRR1319690 1 0.3267 0.802 0.884 0.116 0.000
#> SRR1310604 1 0.6881 0.500 0.648 0.320 0.032
#> SRR1327747 1 0.3482 0.796 0.872 0.128 0.000
#> SRR1072456 2 0.2625 0.762 0.084 0.916 0.000
#> SRR1367896 2 0.2537 0.752 0.080 0.920 0.000
#> SRR1480107 2 0.6235 0.497 0.436 0.564 0.000
#> SRR1377756 1 0.3619 0.787 0.864 0.136 0.000
#> SRR1435272 3 0.0237 0.823 0.004 0.000 0.996
#> SRR1089230 3 0.0592 0.825 0.012 0.000 0.988
#> SRR1389522 1 0.6168 0.406 0.588 0.412 0.000
#> SRR1080600 1 0.6988 0.496 0.644 0.320 0.036
#> SRR1086935 3 0.0237 0.823 0.004 0.000 0.996
#> SRR1344060 2 0.1643 0.766 0.044 0.956 0.000
#> SRR1467922 3 0.0237 0.823 0.004 0.000 0.996
#> SRR1090984 2 0.5968 0.611 0.364 0.636 0.000
#> SRR1456991 2 0.6204 0.521 0.424 0.576 0.000
#> SRR1085039 3 0.7278 0.336 0.456 0.028 0.516
#> SRR1069303 1 0.3551 0.790 0.868 0.132 0.000
#> SRR1091500 3 0.0237 0.823 0.004 0.000 0.996
#> SRR1075198 1 0.1491 0.809 0.968 0.016 0.016
#> SRR1086915 3 0.1289 0.825 0.032 0.000 0.968
#> SRR1499503 3 0.6505 0.108 0.468 0.004 0.528
#> SRR1094312 1 0.5366 0.666 0.776 0.016 0.208
#> SRR1352437 3 0.4351 0.769 0.168 0.004 0.828
#> SRR1436323 1 0.2537 0.813 0.920 0.080 0.000
#> SRR1073507 3 0.5623 0.680 0.280 0.004 0.716
#> SRR1401972 1 0.3551 0.790 0.868 0.132 0.000
#> SRR1415510 1 0.5929 0.528 0.676 0.320 0.004
#> SRR1327279 3 0.7067 0.539 0.376 0.028 0.596
#> SRR1086983 3 0.5623 0.680 0.280 0.004 0.716
#> SRR1105174 3 0.5722 0.667 0.292 0.004 0.704
#> SRR1468893 2 0.5785 0.677 0.332 0.668 0.000
#> SRR1362555 2 0.0892 0.763 0.020 0.980 0.000
#> SRR1074526 3 0.0237 0.819 0.000 0.004 0.996
#> SRR1326225 3 0.1399 0.820 0.028 0.004 0.968
#> SRR1401933 1 0.1411 0.814 0.964 0.036 0.000
#> SRR1324062 1 0.2959 0.807 0.900 0.100 0.000
#> SRR1102296 1 0.5455 0.655 0.776 0.020 0.204
#> SRR1085087 3 0.4409 0.767 0.172 0.004 0.824
#> SRR1079046 1 0.0592 0.809 0.988 0.012 0.000
#> SRR1328339 1 0.4233 0.784 0.836 0.160 0.004
#> SRR1079782 1 0.0848 0.807 0.984 0.008 0.008
#> SRR1092257 3 0.1643 0.823 0.044 0.000 0.956
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 4 0.0779 0.7819 0.000 0.016 0.004 0.980
#> SRR1429287 2 0.4999 -0.1114 0.492 0.508 0.000 0.000
#> SRR1359238 1 0.1256 0.6162 0.964 0.028 0.008 0.000
#> SRR1309597 3 0.0336 0.6661 0.008 0.000 0.992 0.000
#> SRR1441398 3 0.5898 0.5849 0.316 0.056 0.628 0.000
#> SRR1084055 2 0.9094 0.3095 0.096 0.444 0.244 0.216
#> SRR1417566 1 0.3970 0.5721 0.836 0.124 0.036 0.004
#> SRR1351857 4 0.0779 0.7862 0.004 0.016 0.000 0.980
#> SRR1487485 1 0.5789 0.3010 0.600 0.368 0.008 0.024
#> SRR1335875 1 0.2867 0.6048 0.884 0.104 0.012 0.000
#> SRR1073947 1 0.2926 0.5956 0.888 0.096 0.012 0.004
#> SRR1443483 3 0.7840 -0.1650 0.268 0.340 0.392 0.000
#> SRR1346794 1 0.1629 0.6169 0.952 0.024 0.024 0.000
#> SRR1405245 3 0.5898 0.5849 0.316 0.056 0.628 0.000
#> SRR1409677 4 0.2271 0.7685 0.008 0.076 0.000 0.916
#> SRR1095549 4 0.7086 0.4858 0.160 0.292 0.000 0.548
#> SRR1323788 1 0.5443 0.3976 0.720 0.232 0.024 0.024
#> SRR1314054 4 0.0779 0.7819 0.000 0.016 0.004 0.980
#> SRR1077944 1 0.2007 0.6176 0.940 0.036 0.020 0.004
#> SRR1480587 3 0.0336 0.6661 0.008 0.000 0.992 0.000
#> SRR1311205 3 0.6471 0.4739 0.416 0.072 0.512 0.000
#> SRR1076369 2 0.7561 0.1939 0.192 0.424 0.384 0.000
#> SRR1453549 1 0.1042 0.6162 0.972 0.020 0.008 0.000
#> SRR1345782 1 0.5443 0.3976 0.720 0.232 0.024 0.024
#> SRR1447850 2 0.6845 0.0669 0.448 0.452 0.000 0.100
#> SRR1391553 1 0.5420 0.3696 0.624 0.352 0.024 0.000
#> SRR1444156 4 0.1209 0.7776 0.000 0.032 0.004 0.964
#> SRR1471731 1 0.5420 0.3696 0.624 0.352 0.024 0.000
#> SRR1120987 4 0.1114 0.7864 0.008 0.016 0.004 0.972
#> SRR1477363 1 0.0804 0.6173 0.980 0.012 0.008 0.000
#> SRR1391961 3 0.3082 0.6500 0.032 0.084 0.884 0.000
#> SRR1373879 4 0.7188 0.4629 0.172 0.292 0.000 0.536
#> SRR1318732 1 0.5420 0.3696 0.624 0.352 0.024 0.000
#> SRR1091404 4 0.7844 0.1038 0.308 0.288 0.000 0.404
#> SRR1402109 2 0.7802 0.2403 0.384 0.480 0.052 0.084
#> SRR1407336 4 0.7238 0.4409 0.172 0.304 0.000 0.524
#> SRR1097417 3 0.5198 0.4805 0.040 0.252 0.708 0.000
#> SRR1396227 1 0.2928 0.6024 0.880 0.108 0.012 0.000
#> SRR1400775 2 0.7416 0.3056 0.312 0.496 0.000 0.192
#> SRR1392861 4 0.2300 0.7756 0.028 0.048 0.000 0.924
#> SRR1472929 3 0.0188 0.6641 0.004 0.000 0.996 0.000
#> SRR1436740 4 0.0779 0.7847 0.004 0.016 0.000 0.980
#> SRR1477057 1 0.4697 0.3266 0.644 0.356 0.000 0.000
#> SRR1311980 3 0.5898 0.5849 0.316 0.056 0.628 0.000
#> SRR1069400 2 0.7796 0.3877 0.288 0.424 0.288 0.000
#> SRR1351016 1 0.6792 -0.2592 0.476 0.096 0.428 0.000
#> SRR1096291 4 0.0779 0.7862 0.004 0.016 0.000 0.980
#> SRR1418145 1 0.7510 -0.1463 0.436 0.380 0.000 0.184
#> SRR1488111 1 0.4697 0.3266 0.644 0.356 0.000 0.000
#> SRR1370495 3 0.0336 0.6661 0.008 0.000 0.992 0.000
#> SRR1352639 1 0.5986 0.2040 0.620 0.320 0.000 0.060
#> SRR1348911 3 0.6206 0.4850 0.404 0.056 0.540 0.000
#> SRR1467386 4 0.6167 0.6255 0.124 0.208 0.000 0.668
#> SRR1415956 3 0.6228 0.5373 0.364 0.064 0.572 0.000
#> SRR1500495 3 0.6471 0.4739 0.416 0.072 0.512 0.000
#> SRR1405099 3 0.6228 0.5373 0.364 0.064 0.572 0.000
#> SRR1345585 1 0.5420 0.3696 0.624 0.352 0.024 0.000
#> SRR1093196 1 0.5833 0.0780 0.532 0.440 0.004 0.024
#> SRR1466006 3 0.3427 0.6334 0.028 0.112 0.860 0.000
#> SRR1351557 2 0.5296 -0.1445 0.496 0.496 0.008 0.000
#> SRR1382687 1 0.0804 0.6173 0.980 0.012 0.008 0.000
#> SRR1375549 1 0.4679 0.3334 0.648 0.352 0.000 0.000
#> SRR1101765 4 0.1042 0.7871 0.008 0.020 0.000 0.972
#> SRR1334461 3 0.3082 0.6500 0.032 0.084 0.884 0.000
#> SRR1094073 4 0.1209 0.7776 0.000 0.032 0.004 0.964
#> SRR1077549 4 0.7086 0.4858 0.160 0.292 0.000 0.548
#> SRR1440332 1 0.4671 0.4330 0.752 0.220 0.000 0.028
#> SRR1454177 4 0.0921 0.7805 0.000 0.028 0.000 0.972
#> SRR1082447 4 0.7643 0.3021 0.256 0.276 0.000 0.468
#> SRR1420043 1 0.1151 0.6119 0.968 0.008 0.024 0.000
#> SRR1432500 1 0.1585 0.6157 0.952 0.040 0.004 0.004
#> SRR1378045 2 0.7273 0.2543 0.400 0.452 0.000 0.148
#> SRR1334200 3 0.1677 0.6609 0.012 0.040 0.948 0.000
#> SRR1069539 4 0.0779 0.7862 0.004 0.016 0.000 0.980
#> SRR1343031 2 0.7802 0.2403 0.384 0.480 0.052 0.084
#> SRR1319690 1 0.2101 0.6110 0.928 0.060 0.012 0.000
#> SRR1310604 2 0.7541 0.4052 0.148 0.552 0.280 0.020
#> SRR1327747 1 0.2222 0.6064 0.924 0.060 0.016 0.000
#> SRR1072456 3 0.3427 0.6334 0.028 0.112 0.860 0.000
#> SRR1367896 3 0.3554 0.6184 0.020 0.136 0.844 0.000
#> SRR1480107 1 0.6792 -0.2592 0.476 0.096 0.428 0.000
#> SRR1377756 1 0.1151 0.6119 0.968 0.008 0.024 0.000
#> SRR1435272 4 0.0921 0.7805 0.000 0.028 0.000 0.972
#> SRR1089230 4 0.0895 0.7855 0.004 0.020 0.000 0.976
#> SRR1389522 3 0.7905 -0.2706 0.312 0.320 0.368 0.000
#> SRR1080600 2 0.7251 0.3766 0.112 0.584 0.280 0.024
#> SRR1086935 4 0.1209 0.7776 0.000 0.032 0.004 0.964
#> SRR1344060 3 0.1677 0.6609 0.012 0.040 0.948 0.000
#> SRR1467922 4 0.0779 0.7819 0.000 0.016 0.004 0.980
#> SRR1090984 3 0.6327 0.4015 0.444 0.060 0.496 0.000
#> SRR1456991 1 0.6884 -0.2859 0.464 0.104 0.432 0.000
#> SRR1085039 4 0.7643 0.3021 0.256 0.276 0.000 0.468
#> SRR1069303 1 0.1411 0.6087 0.960 0.020 0.020 0.000
#> SRR1091500 4 0.1209 0.7776 0.000 0.032 0.004 0.964
#> SRR1075198 1 0.5451 0.1687 0.524 0.464 0.008 0.004
#> SRR1086915 4 0.1059 0.7857 0.012 0.016 0.000 0.972
#> SRR1499503 4 0.7005 0.1437 0.104 0.392 0.004 0.500
#> SRR1094312 2 0.7416 0.3056 0.312 0.496 0.000 0.192
#> SRR1352437 4 0.4469 0.7196 0.080 0.112 0.000 0.808
#> SRR1436323 1 0.4307 0.5315 0.784 0.192 0.024 0.000
#> SRR1073507 4 0.6167 0.6255 0.124 0.208 0.000 0.668
#> SRR1401972 1 0.1411 0.6087 0.960 0.020 0.020 0.000
#> SRR1415510 2 0.7851 0.3477 0.324 0.396 0.280 0.000
#> SRR1327279 4 0.7086 0.4858 0.160 0.292 0.000 0.548
#> SRR1086983 4 0.6167 0.6255 0.124 0.208 0.000 0.668
#> SRR1105174 4 0.6265 0.6148 0.124 0.220 0.000 0.656
#> SRR1468893 3 0.6024 0.4693 0.416 0.044 0.540 0.000
#> SRR1362555 3 0.0336 0.6661 0.008 0.000 0.992 0.000
#> SRR1074526 4 0.1824 0.7795 0.000 0.060 0.004 0.936
#> SRR1326225 4 0.1771 0.7790 0.012 0.036 0.004 0.948
#> SRR1401933 1 0.4713 0.3176 0.640 0.360 0.000 0.000
#> SRR1324062 1 0.2676 0.6063 0.896 0.092 0.012 0.000
#> SRR1102296 1 0.7260 -0.0485 0.532 0.280 0.000 0.188
#> SRR1085087 4 0.4535 0.7175 0.084 0.112 0.000 0.804
#> SRR1079046 2 0.4999 -0.1114 0.492 0.508 0.000 0.000
#> SRR1328339 1 0.3241 0.5764 0.884 0.072 0.040 0.004
#> SRR1079782 1 0.5000 0.0669 0.504 0.496 0.000 0.000
#> SRR1092257 4 0.1920 0.7828 0.024 0.028 0.004 0.944
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 4 0.3093 0.7213 0.000 0.008 NA 0.824 0.000
#> SRR1429287 2 0.4457 0.2224 0.368 0.620 NA 0.000 0.000
#> SRR1359238 1 0.1205 0.5572 0.956 0.040 NA 0.000 0.000
#> SRR1309597 5 0.0000 0.5740 0.000 0.000 NA 0.000 1.000
#> SRR1441398 5 0.6544 0.2982 0.308 0.004 NA 0.000 0.492
#> SRR1084055 2 0.8754 0.1436 0.048 0.424 NA 0.168 0.236
#> SRR1417566 1 0.3828 0.5029 0.828 0.100 NA 0.004 0.008
#> SRR1351857 4 0.1560 0.7423 0.004 0.028 NA 0.948 0.000
#> SRR1487485 1 0.5416 -0.0501 0.488 0.468 NA 0.028 0.000
#> SRR1335875 1 0.2727 0.5307 0.868 0.116 NA 0.000 0.000
#> SRR1073947 1 0.2608 0.5304 0.888 0.088 NA 0.004 0.000
#> SRR1443483 5 0.8440 -0.0470 0.252 0.204 NA 0.000 0.348
#> SRR1346794 1 0.1356 0.5594 0.956 0.028 NA 0.000 0.004
#> SRR1405245 5 0.6544 0.2982 0.308 0.004 NA 0.000 0.492
#> SRR1409677 4 0.2228 0.7395 0.008 0.068 NA 0.912 0.000
#> SRR1095549 4 0.7058 0.5032 0.152 0.240 NA 0.544 0.000
#> SRR1323788 1 0.5208 0.3177 0.720 0.176 NA 0.028 0.000
#> SRR1314054 4 0.3093 0.7213 0.000 0.008 NA 0.824 0.000
#> SRR1077944 1 0.1708 0.5580 0.944 0.032 NA 0.004 0.004
#> SRR1480587 5 0.0000 0.5740 0.000 0.000 NA 0.000 1.000
#> SRR1311205 1 0.6912 -0.1725 0.408 0.008 NA 0.000 0.348
#> SRR1076369 2 0.8474 0.0879 0.168 0.308 NA 0.000 0.272
#> SRR1453549 1 0.0955 0.5586 0.968 0.028 NA 0.000 0.000
#> SRR1345782 1 0.5208 0.3177 0.720 0.176 NA 0.028 0.000
#> SRR1447850 2 0.6008 0.3270 0.316 0.572 NA 0.100 0.000
#> SRR1391553 1 0.5296 0.0456 0.508 0.456 NA 0.004 0.008
#> SRR1444156 4 0.4168 0.6977 0.000 0.044 NA 0.756 0.000
#> SRR1471731 1 0.5296 0.0456 0.508 0.456 NA 0.004 0.008
#> SRR1120987 4 0.1498 0.7487 0.008 0.016 NA 0.952 0.000
#> SRR1477363 1 0.0510 0.5591 0.984 0.016 NA 0.000 0.000
#> SRR1391961 5 0.5119 0.4817 0.028 0.008 NA 0.000 0.576
#> SRR1373879 4 0.7140 0.4855 0.160 0.244 NA 0.532 0.000
#> SRR1318732 1 0.5296 0.0456 0.508 0.456 NA 0.004 0.008
#> SRR1091404 4 0.7672 0.1651 0.292 0.252 NA 0.400 0.000
#> SRR1402109 1 0.8349 -0.2444 0.364 0.364 NA 0.084 0.032
#> SRR1407336 4 0.7239 0.4633 0.164 0.248 NA 0.520 0.000
#> SRR1097417 5 0.6725 0.3568 0.016 0.160 NA 0.000 0.468
#> SRR1396227 1 0.2920 0.5181 0.852 0.132 NA 0.000 0.000
#> SRR1400775 2 0.7030 0.4000 0.240 0.528 NA 0.188 0.000
#> SRR1392861 4 0.2855 0.7434 0.028 0.040 NA 0.892 0.000
#> SRR1472929 5 0.0162 0.5741 0.000 0.000 NA 0.000 0.996
#> SRR1436740 4 0.1412 0.7437 0.004 0.008 NA 0.952 0.000
#> SRR1477057 1 0.4610 0.0971 0.556 0.432 NA 0.000 0.000
#> SRR1311980 5 0.6544 0.2982 0.308 0.004 NA 0.000 0.492
#> SRR1069400 2 0.8354 0.2364 0.264 0.332 NA 0.000 0.264
#> SRR1351016 1 0.7368 0.0843 0.468 0.048 NA 0.000 0.264
#> SRR1096291 4 0.1560 0.7423 0.004 0.028 NA 0.948 0.000
#> SRR1418145 2 0.6584 0.2930 0.344 0.464 NA 0.188 0.000
#> SRR1488111 1 0.4610 0.0971 0.556 0.432 NA 0.000 0.000
#> SRR1370495 5 0.0000 0.5740 0.000 0.000 NA 0.000 1.000
#> SRR1352639 1 0.6227 0.1225 0.588 0.296 NA 0.064 0.000
#> SRR1348911 1 0.6950 -0.1690 0.396 0.008 NA 0.000 0.348
#> SRR1467386 4 0.5970 0.6203 0.120 0.176 NA 0.664 0.000
#> SRR1415956 5 0.6861 0.2218 0.356 0.008 NA 0.000 0.416
#> SRR1500495 1 0.6912 -0.1725 0.408 0.008 NA 0.000 0.348
#> SRR1405099 5 0.6861 0.2218 0.356 0.008 NA 0.000 0.416
#> SRR1345585 1 0.5296 0.0456 0.508 0.456 NA 0.004 0.008
#> SRR1093196 2 0.5720 0.1912 0.400 0.536 NA 0.028 0.000
#> SRR1466006 5 0.5470 0.4663 0.020 0.032 NA 0.000 0.560
#> SRR1351557 2 0.4703 0.2502 0.352 0.628 NA 0.004 0.004
#> SRR1382687 1 0.0510 0.5591 0.984 0.016 NA 0.000 0.000
#> SRR1375549 1 0.4597 0.1128 0.564 0.424 NA 0.000 0.000
#> SRR1101765 4 0.0992 0.7485 0.008 0.024 NA 0.968 0.000
#> SRR1334461 5 0.5119 0.4817 0.028 0.008 NA 0.000 0.576
#> SRR1094073 4 0.4168 0.6977 0.000 0.044 NA 0.756 0.000
#> SRR1077549 4 0.7058 0.5032 0.152 0.240 NA 0.544 0.000
#> SRR1440332 1 0.4862 0.3569 0.724 0.212 NA 0.028 0.000
#> SRR1454177 4 0.2124 0.7308 0.000 0.028 NA 0.916 0.000
#> SRR1082447 4 0.7473 0.3379 0.244 0.236 NA 0.464 0.000
#> SRR1420043 1 0.0833 0.5590 0.976 0.004 NA 0.000 0.004
#> SRR1432500 1 0.1282 0.5528 0.952 0.044 NA 0.004 0.000
#> SRR1378045 2 0.7579 0.2946 0.328 0.440 NA 0.144 0.000
#> SRR1334200 5 0.3552 0.5535 0.012 0.012 NA 0.000 0.812
#> SRR1069539 4 0.1560 0.7423 0.004 0.028 NA 0.948 0.000
#> SRR1343031 2 0.8349 0.1830 0.364 0.364 NA 0.084 0.032
#> SRR1319690 1 0.1809 0.5496 0.928 0.060 NA 0.000 0.000
#> SRR1310604 2 0.7783 0.2294 0.096 0.500 NA 0.020 0.268
#> SRR1327747 1 0.1942 0.5443 0.920 0.068 NA 0.000 0.000
#> SRR1072456 5 0.5470 0.4663 0.020 0.032 NA 0.000 0.560
#> SRR1367896 5 0.5556 0.4648 0.012 0.048 NA 0.000 0.552
#> SRR1480107 1 0.7368 0.0843 0.468 0.048 NA 0.000 0.264
#> SRR1377756 1 0.0833 0.5590 0.976 0.004 NA 0.000 0.004
#> SRR1435272 4 0.2124 0.7308 0.000 0.028 NA 0.916 0.000
#> SRR1089230 4 0.1646 0.7412 0.004 0.032 NA 0.944 0.000
#> SRR1389522 5 0.8375 -0.1305 0.288 0.220 NA 0.000 0.336
#> SRR1080600 2 0.7474 0.1967 0.060 0.528 NA 0.024 0.268
#> SRR1086935 4 0.2824 0.7192 0.000 0.032 NA 0.872 0.000
#> SRR1344060 5 0.3592 0.5529 0.012 0.012 NA 0.000 0.808
#> SRR1467922 4 0.3456 0.7128 0.000 0.016 NA 0.800 0.000
#> SRR1090984 1 0.6912 -0.0861 0.436 0.012 NA 0.000 0.336
#> SRR1456991 1 0.7421 0.0625 0.456 0.048 NA 0.000 0.264
#> SRR1085039 4 0.7473 0.3379 0.244 0.236 NA 0.464 0.000
#> SRR1069303 1 0.1403 0.5561 0.952 0.024 NA 0.000 0.000
#> SRR1091500 4 0.4134 0.7002 0.000 0.044 NA 0.760 0.000
#> SRR1075198 2 0.4908 0.1836 0.380 0.596 NA 0.008 0.004
#> SRR1086915 4 0.1012 0.7458 0.012 0.020 NA 0.968 0.000
#> SRR1499503 2 0.7534 -0.0043 0.052 0.420 NA 0.312 0.000
#> SRR1094312 2 0.7030 0.4000 0.240 0.528 NA 0.188 0.000
#> SRR1352437 4 0.4138 0.7012 0.080 0.104 NA 0.804 0.000
#> SRR1436323 1 0.4338 0.3662 0.712 0.264 NA 0.000 0.008
#> SRR1073507 4 0.5970 0.6203 0.120 0.176 NA 0.664 0.000
#> SRR1401972 1 0.1403 0.5561 0.952 0.024 NA 0.000 0.000
#> SRR1415510 2 0.8144 0.3378 0.284 0.352 NA 0.000 0.260
#> SRR1327279 4 0.7058 0.5032 0.152 0.240 NA 0.544 0.000
#> SRR1086983 4 0.5970 0.6203 0.120 0.176 NA 0.664 0.000
#> SRR1105174 4 0.6103 0.6113 0.120 0.184 NA 0.652 0.000
#> SRR1468893 5 0.6938 0.1489 0.396 0.020 NA 0.000 0.408
#> SRR1362555 5 0.0290 0.5740 0.000 0.000 NA 0.000 0.992
#> SRR1074526 4 0.5010 0.6787 0.000 0.076 NA 0.676 0.000
#> SRR1326225 4 0.4382 0.6979 0.004 0.060 NA 0.760 0.000
#> SRR1401933 1 0.4538 0.0424 0.540 0.452 NA 0.000 0.000
#> SRR1324062 1 0.2573 0.5336 0.880 0.104 NA 0.000 0.000
#> SRR1102296 1 0.7032 -0.1198 0.492 0.288 NA 0.188 0.000
#> SRR1085087 4 0.4194 0.6996 0.084 0.104 NA 0.800 0.000
#> SRR1079046 2 0.4457 0.2224 0.368 0.620 NA 0.000 0.000
#> SRR1328339 1 0.3012 0.5246 0.880 0.040 NA 0.004 0.008
#> SRR1079782 2 0.4362 0.2439 0.360 0.632 NA 0.004 0.000
#> SRR1092257 4 0.2444 0.7484 0.024 0.028 NA 0.912 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.509 0.595032 0.000 0.568 0.004 0.348 0.000 0.080
#> SRR1429287 3 0.709 0.041224 0.348 0.008 0.424 0.096 0.004 0.120
#> SRR1359238 1 0.153 0.631310 0.944 0.004 0.032 0.016 0.000 0.004
#> SRR1309597 5 0.419 0.640037 0.000 0.008 0.000 0.004 0.548 0.440
#> SRR1441398 6 0.451 0.771320 0.300 0.008 0.000 0.004 0.032 0.656
#> SRR1084055 3 0.637 0.166990 0.008 0.140 0.620 0.080 0.140 0.012
#> SRR1417566 1 0.408 0.610204 0.812 0.004 0.056 0.076 0.012 0.040
#> SRR1351857 2 0.413 0.582875 0.004 0.600 0.004 0.388 0.000 0.004
#> SRR1487485 1 0.731 0.199766 0.476 0.020 0.228 0.164 0.000 0.112
#> SRR1335875 1 0.331 0.641345 0.852 0.004 0.044 0.060 0.000 0.040
#> SRR1073947 1 0.293 0.626544 0.856 0.000 0.020 0.104 0.000 0.020
#> SRR1443483 3 0.761 0.192201 0.208 0.004 0.428 0.020 0.240 0.100
#> SRR1346794 1 0.202 0.632174 0.928 0.008 0.028 0.020 0.004 0.012
#> SRR1405245 6 0.451 0.771320 0.300 0.008 0.000 0.004 0.032 0.656
#> SRR1409677 4 0.327 0.340419 0.000 0.248 0.000 0.748 0.000 0.004
#> SRR1095549 4 0.352 0.645707 0.112 0.004 0.064 0.816 0.000 0.004
#> SRR1323788 1 0.477 0.461034 0.692 0.004 0.064 0.224 0.000 0.016
#> SRR1314054 2 0.508 0.601322 0.000 0.572 0.004 0.344 0.000 0.080
#> SRR1077944 1 0.216 0.632540 0.916 0.008 0.012 0.052 0.004 0.008
#> SRR1480587 5 0.433 0.640536 0.000 0.008 0.004 0.004 0.544 0.440
#> SRR1311205 6 0.443 0.844456 0.400 0.004 0.004 0.000 0.016 0.576
#> SRR1076369 3 0.570 0.174443 0.104 0.004 0.544 0.016 0.332 0.000
#> SRR1453549 1 0.120 0.629557 0.960 0.004 0.016 0.016 0.000 0.004
#> SRR1345782 1 0.477 0.461034 0.692 0.004 0.064 0.224 0.000 0.016
#> SRR1447850 3 0.799 0.154887 0.292 0.060 0.396 0.144 0.004 0.104
#> SRR1391553 1 0.730 0.280498 0.504 0.008 0.188 0.160 0.012 0.128
#> SRR1444156 2 0.343 0.680191 0.000 0.824 0.008 0.084 0.000 0.084
#> SRR1471731 1 0.730 0.280498 0.504 0.008 0.188 0.160 0.012 0.128
#> SRR1120987 4 0.387 0.018143 0.004 0.392 0.000 0.604 0.000 0.000
#> SRR1477363 1 0.112 0.630855 0.960 0.004 0.008 0.028 0.000 0.000
#> SRR1391961 5 0.143 0.680085 0.020 0.000 0.008 0.000 0.948 0.024
#> SRR1373879 4 0.367 0.641053 0.120 0.004 0.068 0.804 0.000 0.004
#> SRR1318732 1 0.730 0.280498 0.504 0.008 0.188 0.160 0.012 0.128
#> SRR1091404 4 0.485 0.426398 0.252 0.004 0.072 0.664 0.000 0.008
#> SRR1402109 3 0.747 0.215042 0.316 0.004 0.328 0.284 0.036 0.032
#> SRR1407336 4 0.372 0.632930 0.128 0.000 0.076 0.792 0.000 0.004
#> SRR1097417 5 0.459 0.400455 0.004 0.008 0.312 0.008 0.648 0.020
#> SRR1396227 1 0.350 0.630709 0.836 0.000 0.056 0.056 0.000 0.052
#> SRR1400775 3 0.789 0.304130 0.208 0.100 0.400 0.244 0.000 0.048
#> SRR1392861 4 0.397 0.228880 0.016 0.300 0.000 0.680 0.000 0.004
#> SRR1472929 5 0.419 0.644610 0.000 0.008 0.000 0.004 0.552 0.436
#> SRR1436740 4 0.396 -0.162445 0.000 0.440 0.000 0.556 0.000 0.004
#> SRR1477057 1 0.620 0.267233 0.552 0.008 0.300 0.052 0.004 0.084
#> SRR1311980 6 0.451 0.771320 0.300 0.008 0.000 0.004 0.032 0.656
#> SRR1069400 3 0.599 0.352111 0.216 0.004 0.564 0.020 0.196 0.000
#> SRR1351016 6 0.512 0.734406 0.460 0.004 0.008 0.032 0.008 0.488
#> SRR1096291 2 0.413 0.582875 0.004 0.600 0.004 0.388 0.000 0.004
#> SRR1418145 1 0.813 -0.172632 0.332 0.116 0.324 0.160 0.000 0.068
#> SRR1488111 1 0.620 0.267233 0.552 0.008 0.300 0.052 0.004 0.084
#> SRR1370495 5 0.433 0.640536 0.000 0.008 0.004 0.004 0.544 0.440
#> SRR1352639 1 0.607 0.274878 0.548 0.004 0.164 0.260 0.000 0.024
#> SRR1348911 6 0.548 0.803536 0.396 0.004 0.004 0.000 0.096 0.500
#> SRR1467386 4 0.226 0.646378 0.080 0.028 0.000 0.892 0.000 0.000
#> SRR1415956 6 0.374 0.826025 0.348 0.000 0.000 0.000 0.004 0.648
#> SRR1500495 6 0.443 0.844456 0.400 0.004 0.004 0.000 0.016 0.576
#> SRR1405099 6 0.374 0.826025 0.348 0.000 0.000 0.000 0.004 0.648
#> SRR1345585 1 0.730 0.280498 0.504 0.008 0.188 0.160 0.012 0.128
#> SRR1093196 3 0.723 0.099323 0.344 0.012 0.400 0.144 0.000 0.100
#> SRR1466006 5 0.137 0.670360 0.004 0.000 0.040 0.004 0.948 0.004
#> SRR1351557 3 0.763 0.032083 0.336 0.008 0.356 0.176 0.008 0.116
#> SRR1382687 1 0.112 0.630855 0.960 0.004 0.008 0.028 0.000 0.000
#> SRR1375549 1 0.618 0.281582 0.560 0.008 0.292 0.052 0.004 0.084
#> SRR1101765 4 0.444 -0.088006 0.004 0.392 0.008 0.584 0.000 0.012
#> SRR1334461 5 0.143 0.680085 0.020 0.000 0.008 0.000 0.948 0.024
#> SRR1094073 2 0.343 0.680191 0.000 0.824 0.008 0.084 0.000 0.084
#> SRR1077549 4 0.352 0.645707 0.112 0.004 0.064 0.816 0.000 0.004
#> SRR1440332 1 0.488 0.458010 0.688 0.004 0.160 0.144 0.000 0.004
#> SRR1454177 2 0.345 0.670310 0.000 0.716 0.000 0.280 0.000 0.004
#> SRR1082447 4 0.420 0.549513 0.204 0.000 0.056 0.732 0.000 0.008
#> SRR1420043 1 0.147 0.614505 0.952 0.008 0.008 0.020 0.004 0.008
#> SRR1432500 1 0.177 0.642934 0.924 0.004 0.012 0.060 0.000 0.000
#> SRR1378045 3 0.758 0.311048 0.272 0.088 0.424 0.188 0.004 0.024
#> SRR1334200 5 0.391 0.708262 0.004 0.000 0.016 0.004 0.716 0.260
#> SRR1069539 2 0.413 0.582875 0.004 0.600 0.004 0.388 0.000 0.004
#> SRR1343031 3 0.747 0.215042 0.316 0.004 0.328 0.284 0.036 0.032
#> SRR1319690 1 0.189 0.631468 0.928 0.004 0.044 0.016 0.004 0.004
#> SRR1310604 3 0.444 0.272855 0.040 0.008 0.744 0.028 0.180 0.000
#> SRR1327747 1 0.201 0.620846 0.924 0.008 0.036 0.024 0.000 0.008
#> SRR1072456 5 0.137 0.670360 0.004 0.000 0.040 0.004 0.948 0.004
#> SRR1367896 5 0.452 0.535835 0.004 0.000 0.200 0.000 0.704 0.092
#> SRR1480107 6 0.512 0.734406 0.460 0.004 0.008 0.032 0.008 0.488
#> SRR1377756 1 0.147 0.614505 0.952 0.008 0.008 0.020 0.004 0.008
#> SRR1435272 2 0.345 0.670310 0.000 0.716 0.000 0.280 0.000 0.004
#> SRR1089230 2 0.409 0.601645 0.004 0.616 0.004 0.372 0.000 0.004
#> SRR1389522 3 0.755 0.265941 0.248 0.004 0.440 0.024 0.196 0.088
#> SRR1080600 3 0.400 0.230935 0.008 0.016 0.768 0.028 0.180 0.000
#> SRR1086935 2 0.266 0.693886 0.000 0.816 0.000 0.184 0.000 0.000
#> SRR1344060 5 0.388 0.709023 0.004 0.000 0.016 0.004 0.720 0.256
#> SRR1467922 2 0.470 0.625483 0.000 0.684 0.008 0.224 0.000 0.084
#> SRR1090984 6 0.543 0.785124 0.440 0.004 0.012 0.000 0.068 0.476
#> SRR1456991 6 0.537 0.744591 0.448 0.004 0.012 0.032 0.016 0.488
#> SRR1085039 4 0.420 0.549513 0.204 0.000 0.056 0.732 0.000 0.008
#> SRR1069303 1 0.179 0.608038 0.932 0.000 0.016 0.020 0.000 0.032
#> SRR1091500 2 0.337 0.683502 0.000 0.824 0.004 0.088 0.000 0.084
#> SRR1075198 1 0.757 -0.041150 0.360 0.004 0.328 0.184 0.008 0.116
#> SRR1086915 4 0.368 0.226881 0.004 0.300 0.000 0.692 0.000 0.004
#> SRR1499503 3 0.688 -0.000832 0.012 0.320 0.468 0.104 0.000 0.096
#> SRR1094312 3 0.789 0.304130 0.208 0.100 0.400 0.244 0.000 0.048
#> SRR1352437 4 0.397 0.516478 0.060 0.180 0.000 0.756 0.000 0.004
#> SRR1436323 1 0.535 0.509259 0.708 0.008 0.132 0.072 0.004 0.076
#> SRR1073507 4 0.226 0.646378 0.080 0.028 0.000 0.892 0.000 0.000
#> SRR1401972 1 0.179 0.608038 0.932 0.000 0.016 0.020 0.000 0.032
#> SRR1415510 3 0.705 0.366047 0.264 0.016 0.488 0.036 0.180 0.016
#> SRR1327279 4 0.352 0.645707 0.112 0.004 0.064 0.816 0.000 0.004
#> SRR1086983 4 0.226 0.646378 0.080 0.028 0.000 0.892 0.000 0.000
#> SRR1105174 4 0.201 0.647770 0.080 0.016 0.000 0.904 0.000 0.000
#> SRR1468893 6 0.400 0.822789 0.388 0.000 0.004 0.000 0.004 0.604
#> SRR1362555 5 0.432 0.647377 0.000 0.008 0.004 0.004 0.552 0.432
#> SRR1074526 2 0.558 0.558321 0.000 0.632 0.032 0.172 0.000 0.164
#> SRR1326225 2 0.568 0.572681 0.004 0.608 0.036 0.256 0.000 0.096
#> SRR1401933 1 0.619 0.236609 0.532 0.008 0.320 0.060 0.000 0.080
#> SRR1324062 1 0.303 0.641842 0.864 0.000 0.036 0.060 0.000 0.040
#> SRR1102296 1 0.696 0.060803 0.440 0.040 0.144 0.344 0.000 0.032
#> SRR1085087 4 0.396 0.524881 0.064 0.172 0.000 0.760 0.000 0.004
#> SRR1079046 3 0.709 0.041224 0.348 0.008 0.424 0.096 0.004 0.120
#> SRR1328339 1 0.329 0.577489 0.864 0.004 0.048 0.028 0.016 0.040
#> SRR1079782 3 0.740 0.026795 0.340 0.008 0.364 0.172 0.000 0.116
#> SRR1092257 4 0.440 0.001731 0.020 0.412 0.004 0.564 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 17611 rows and 118 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.971 0.988 0.4718 0.533 0.533
#> 3 3 0.605 0.802 0.884 0.3357 0.746 0.560
#> 4 4 0.585 0.597 0.789 0.1566 0.862 0.644
#> 5 5 0.595 0.558 0.738 0.0736 0.887 0.623
#> 6 6 0.647 0.476 0.667 0.0470 0.930 0.703
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
#> SRR1396765 2 0.0000 0.995 0.000 1.000
#> SRR1429287 1 0.0000 0.984 1.000 0.000
#> SRR1359238 1 0.0000 0.984 1.000 0.000
#> SRR1309597 1 0.0000 0.984 1.000 0.000
#> SRR1441398 1 0.0000 0.984 1.000 0.000
#> SRR1084055 2 0.0000 0.995 0.000 1.000
#> SRR1417566 1 0.0000 0.984 1.000 0.000
#> SRR1351857 2 0.0000 0.995 0.000 1.000
#> SRR1487485 1 0.0000 0.984 1.000 0.000
#> SRR1335875 1 0.0000 0.984 1.000 0.000
#> SRR1073947 1 0.0000 0.984 1.000 0.000
#> SRR1443483 1 0.0000 0.984 1.000 0.000
#> SRR1346794 1 0.0000 0.984 1.000 0.000
#> SRR1405245 1 0.0000 0.984 1.000 0.000
#> SRR1409677 2 0.0000 0.995 0.000 1.000
#> SRR1095549 2 0.0000 0.995 0.000 1.000
#> SRR1323788 1 0.0000 0.984 1.000 0.000
#> SRR1314054 2 0.0000 0.995 0.000 1.000
#> SRR1077944 1 0.0000 0.984 1.000 0.000
#> SRR1480587 1 0.0000 0.984 1.000 0.000
#> SRR1311205 1 0.0000 0.984 1.000 0.000
#> SRR1076369 1 0.0000 0.984 1.000 0.000
#> SRR1453549 1 0.0376 0.981 0.996 0.004
#> SRR1345782 1 0.0000 0.984 1.000 0.000
#> SRR1447850 2 0.0000 0.995 0.000 1.000
#> SRR1391553 1 0.0000 0.984 1.000 0.000
#> SRR1444156 2 0.0000 0.995 0.000 1.000
#> SRR1471731 1 0.0000 0.984 1.000 0.000
#> SRR1120987 2 0.0000 0.995 0.000 1.000
#> SRR1477363 1 0.0000 0.984 1.000 0.000
#> SRR1391961 1 0.0000 0.984 1.000 0.000
#> SRR1373879 2 0.0000 0.995 0.000 1.000
#> SRR1318732 1 0.0000 0.984 1.000 0.000
#> SRR1091404 1 0.9866 0.262 0.568 0.432
#> SRR1402109 1 0.0376 0.981 0.996 0.004
#> SRR1407336 2 0.0000 0.995 0.000 1.000
#> SRR1097417 1 0.0000 0.984 1.000 0.000
#> SRR1396227 1 0.0000 0.984 1.000 0.000
#> SRR1400775 2 0.0000 0.995 0.000 1.000
#> SRR1392861 2 0.0000 0.995 0.000 1.000
#> SRR1472929 1 0.0000 0.984 1.000 0.000
#> SRR1436740 2 0.0000 0.995 0.000 1.000
#> SRR1477057 1 0.0000 0.984 1.000 0.000
#> SRR1311980 1 0.0000 0.984 1.000 0.000
#> SRR1069400 1 0.0000 0.984 1.000 0.000
#> SRR1351016 1 0.0000 0.984 1.000 0.000
#> SRR1096291 2 0.0000 0.995 0.000 1.000
#> SRR1418145 2 0.0672 0.987 0.008 0.992
#> SRR1488111 1 0.0000 0.984 1.000 0.000
#> SRR1370495 1 0.0000 0.984 1.000 0.000
#> SRR1352639 1 0.0376 0.981 0.996 0.004
#> SRR1348911 1 0.0000 0.984 1.000 0.000
#> SRR1467386 2 0.0000 0.995 0.000 1.000
#> SRR1415956 1 0.0000 0.984 1.000 0.000
#> SRR1500495 1 0.0000 0.984 1.000 0.000
#> SRR1405099 1 0.0000 0.984 1.000 0.000
#> SRR1345585 1 0.0000 0.984 1.000 0.000
#> SRR1093196 1 0.9710 0.352 0.600 0.400
#> SRR1466006 1 0.0000 0.984 1.000 0.000
#> SRR1351557 1 0.0000 0.984 1.000 0.000
#> SRR1382687 1 0.0000 0.984 1.000 0.000
#> SRR1375549 1 0.0000 0.984 1.000 0.000
#> SRR1101765 2 0.0000 0.995 0.000 1.000
#> SRR1334461 1 0.0000 0.984 1.000 0.000
#> SRR1094073 2 0.0000 0.995 0.000 1.000
#> SRR1077549 2 0.0000 0.995 0.000 1.000
#> SRR1440332 1 0.0376 0.981 0.996 0.004
#> SRR1454177 2 0.0000 0.995 0.000 1.000
#> SRR1082447 2 0.0000 0.995 0.000 1.000
#> SRR1420043 1 0.0000 0.984 1.000 0.000
#> SRR1432500 1 0.0376 0.981 0.996 0.004
#> SRR1378045 2 0.7139 0.753 0.196 0.804
#> SRR1334200 1 0.0000 0.984 1.000 0.000
#> SRR1069539 2 0.0000 0.995 0.000 1.000
#> SRR1343031 1 0.0376 0.981 0.996 0.004
#> SRR1319690 1 0.0000 0.984 1.000 0.000
#> SRR1310604 1 0.4161 0.902 0.916 0.084
#> SRR1327747 1 0.0000 0.984 1.000 0.000
#> SRR1072456 1 0.0000 0.984 1.000 0.000
#> SRR1367896 1 0.0000 0.984 1.000 0.000
#> SRR1480107 1 0.0000 0.984 1.000 0.000
#> SRR1377756 1 0.0000 0.984 1.000 0.000
#> SRR1435272 2 0.0000 0.995 0.000 1.000
#> SRR1089230 2 0.0000 0.995 0.000 1.000
#> SRR1389522 1 0.0000 0.984 1.000 0.000
#> SRR1080600 1 0.3274 0.928 0.940 0.060
#> SRR1086935 2 0.0000 0.995 0.000 1.000
#> SRR1344060 1 0.0000 0.984 1.000 0.000
#> SRR1467922 2 0.0000 0.995 0.000 1.000
#> SRR1090984 1 0.0000 0.984 1.000 0.000
#> SRR1456991 1 0.0000 0.984 1.000 0.000
#> SRR1085039 2 0.0000 0.995 0.000 1.000
#> SRR1069303 1 0.0000 0.984 1.000 0.000
#> SRR1091500 2 0.0000 0.995 0.000 1.000
#> SRR1075198 1 0.0376 0.981 0.996 0.004
#> SRR1086915 2 0.0000 0.995 0.000 1.000
#> SRR1499503 2 0.0000 0.995 0.000 1.000
#> SRR1094312 2 0.0000 0.995 0.000 1.000
#> SRR1352437 2 0.0000 0.995 0.000 1.000
#> SRR1436323 1 0.0000 0.984 1.000 0.000
#> SRR1073507 2 0.0000 0.995 0.000 1.000
#> SRR1401972 1 0.0376 0.981 0.996 0.004
#> SRR1415510 1 0.0000 0.984 1.000 0.000
#> SRR1327279 2 0.0000 0.995 0.000 1.000
#> SRR1086983 2 0.0000 0.995 0.000 1.000
#> SRR1105174 2 0.0000 0.995 0.000 1.000
#> SRR1468893 1 0.0000 0.984 1.000 0.000
#> SRR1362555 1 0.0000 0.984 1.000 0.000
#> SRR1074526 2 0.0000 0.995 0.000 1.000
#> SRR1326225 2 0.0000 0.995 0.000 1.000
#> SRR1401933 1 0.0000 0.984 1.000 0.000
#> SRR1324062 1 0.0000 0.984 1.000 0.000
#> SRR1102296 2 0.0000 0.995 0.000 1.000
#> SRR1085087 2 0.0000 0.995 0.000 1.000
#> SRR1079046 1 0.0000 0.984 1.000 0.000
#> SRR1328339 1 0.0000 0.984 1.000 0.000
#> SRR1079782 1 0.6623 0.793 0.828 0.172
#> SRR1092257 2 0.0000 0.995 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 3 0.2066 0.890 0.000 0.060 0.940
#> SRR1429287 1 0.2796 0.842 0.908 0.092 0.000
#> SRR1359238 1 0.0000 0.861 1.000 0.000 0.000
#> SRR1309597 2 0.2261 0.880 0.068 0.932 0.000
#> SRR1441398 2 0.4346 0.862 0.184 0.816 0.000
#> SRR1084055 3 0.5635 0.777 0.036 0.180 0.784
#> SRR1417566 1 0.2537 0.846 0.920 0.080 0.000
#> SRR1351857 3 0.0000 0.905 0.000 0.000 1.000
#> SRR1487485 1 0.2878 0.843 0.904 0.096 0.000
#> SRR1335875 1 0.1411 0.859 0.964 0.036 0.000
#> SRR1073947 1 0.2339 0.844 0.940 0.012 0.048
#> SRR1443483 1 0.5497 0.643 0.708 0.292 0.000
#> SRR1346794 1 0.0892 0.861 0.980 0.020 0.000
#> SRR1405245 2 0.4346 0.862 0.184 0.816 0.000
#> SRR1409677 3 0.1411 0.902 0.036 0.000 0.964
#> SRR1095549 3 0.2448 0.889 0.076 0.000 0.924
#> SRR1323788 1 0.0661 0.861 0.988 0.008 0.004
#> SRR1314054 3 0.2066 0.890 0.000 0.060 0.940
#> SRR1077944 1 0.0592 0.861 0.988 0.012 0.000
#> SRR1480587 2 0.2261 0.880 0.068 0.932 0.000
#> SRR1311205 2 0.4346 0.862 0.184 0.816 0.000
#> SRR1076369 1 0.4555 0.771 0.800 0.200 0.000
#> SRR1453549 1 0.0592 0.858 0.988 0.000 0.012
#> SRR1345782 1 0.1411 0.859 0.964 0.036 0.000
#> SRR1447850 1 0.7213 0.567 0.668 0.060 0.272
#> SRR1391553 1 0.2711 0.841 0.912 0.088 0.000
#> SRR1444156 3 0.2066 0.890 0.000 0.060 0.940
#> SRR1471731 1 0.6192 0.118 0.580 0.420 0.000
#> SRR1120987 3 0.0000 0.905 0.000 0.000 1.000
#> SRR1477363 1 0.0892 0.861 0.980 0.020 0.000
#> SRR1391961 2 0.6274 0.115 0.456 0.544 0.000
#> SRR1373879 3 0.2448 0.889 0.076 0.000 0.924
#> SRR1318732 1 0.5216 0.599 0.740 0.260 0.000
#> SRR1091404 1 0.3590 0.821 0.896 0.028 0.076
#> SRR1402109 1 0.3237 0.835 0.912 0.032 0.056
#> SRR1407336 3 0.6659 0.171 0.460 0.008 0.532
#> SRR1097417 2 0.6267 0.124 0.452 0.548 0.000
#> SRR1396227 1 0.0983 0.861 0.980 0.016 0.004
#> SRR1400775 1 0.7199 0.579 0.676 0.064 0.260
#> SRR1392861 3 0.1964 0.898 0.056 0.000 0.944
#> SRR1472929 2 0.2165 0.879 0.064 0.936 0.000
#> SRR1436740 3 0.1411 0.902 0.036 0.000 0.964
#> SRR1477057 1 0.2625 0.843 0.916 0.084 0.000
#> SRR1311980 2 0.4346 0.862 0.184 0.816 0.000
#> SRR1069400 1 0.4178 0.787 0.828 0.172 0.000
#> SRR1351016 1 0.5706 0.455 0.680 0.320 0.000
#> SRR1096291 3 0.0000 0.905 0.000 0.000 1.000
#> SRR1418145 1 0.2959 0.812 0.900 0.000 0.100
#> SRR1488111 1 0.2625 0.843 0.916 0.084 0.000
#> SRR1370495 2 0.2261 0.880 0.068 0.932 0.000
#> SRR1352639 1 0.2280 0.841 0.940 0.008 0.052
#> SRR1348911 2 0.2165 0.879 0.064 0.936 0.000
#> SRR1467386 3 0.2261 0.893 0.068 0.000 0.932
#> SRR1415956 2 0.4399 0.859 0.188 0.812 0.000
#> SRR1500495 2 0.4399 0.859 0.188 0.812 0.000
#> SRR1405099 2 0.4399 0.859 0.188 0.812 0.000
#> SRR1345585 1 0.3267 0.836 0.884 0.116 0.000
#> SRR1093196 1 0.2680 0.832 0.924 0.008 0.068
#> SRR1466006 2 0.2356 0.871 0.072 0.928 0.000
#> SRR1351557 1 0.2448 0.851 0.924 0.076 0.000
#> SRR1382687 1 0.0892 0.861 0.980 0.020 0.000
#> SRR1375549 1 0.2625 0.843 0.916 0.084 0.000
#> SRR1101765 3 0.0747 0.903 0.000 0.016 0.984
#> SRR1334461 2 0.2165 0.879 0.064 0.936 0.000
#> SRR1094073 3 0.2066 0.890 0.000 0.060 0.940
#> SRR1077549 3 0.2261 0.893 0.068 0.000 0.932
#> SRR1440332 1 0.2280 0.841 0.940 0.008 0.052
#> SRR1454177 3 0.0000 0.905 0.000 0.000 1.000
#> SRR1082447 3 0.6274 0.191 0.456 0.000 0.544
#> SRR1420043 1 0.1411 0.859 0.964 0.036 0.000
#> SRR1432500 1 0.2651 0.837 0.928 0.012 0.060
#> SRR1378045 1 0.6318 0.691 0.760 0.068 0.172
#> SRR1334200 2 0.2165 0.879 0.064 0.936 0.000
#> SRR1069539 3 0.1031 0.901 0.000 0.024 0.976
#> SRR1343031 1 0.3237 0.835 0.912 0.032 0.056
#> SRR1319690 1 0.2711 0.841 0.912 0.088 0.000
#> SRR1310604 1 0.4121 0.788 0.832 0.168 0.000
#> SRR1327747 1 0.2711 0.841 0.912 0.088 0.000
#> SRR1072456 2 0.2356 0.870 0.072 0.928 0.000
#> SRR1367896 2 0.2448 0.867 0.076 0.924 0.000
#> SRR1480107 1 0.2711 0.841 0.912 0.088 0.000
#> SRR1377756 1 0.0892 0.861 0.980 0.020 0.000
#> SRR1435272 3 0.0000 0.905 0.000 0.000 1.000
#> SRR1089230 3 0.0000 0.905 0.000 0.000 1.000
#> SRR1389522 1 0.6225 0.304 0.568 0.432 0.000
#> SRR1080600 1 0.5016 0.755 0.760 0.240 0.000
#> SRR1086935 3 0.1529 0.897 0.000 0.040 0.960
#> SRR1344060 2 0.2165 0.879 0.064 0.936 0.000
#> SRR1467922 3 0.2066 0.890 0.000 0.060 0.940
#> SRR1090984 2 0.4178 0.865 0.172 0.828 0.000
#> SRR1456991 2 0.4399 0.859 0.188 0.812 0.000
#> SRR1085039 3 0.6079 0.399 0.388 0.000 0.612
#> SRR1069303 1 0.1411 0.859 0.964 0.036 0.000
#> SRR1091500 3 0.2066 0.890 0.000 0.060 0.940
#> SRR1075198 1 0.2625 0.848 0.916 0.084 0.000
#> SRR1086915 3 0.1860 0.899 0.052 0.000 0.948
#> SRR1499503 3 0.3649 0.880 0.036 0.068 0.896
#> SRR1094312 1 0.7363 0.540 0.656 0.064 0.280
#> SRR1352437 3 0.1753 0.899 0.048 0.000 0.952
#> SRR1436323 1 0.2711 0.841 0.912 0.088 0.000
#> SRR1073507 3 0.1860 0.898 0.052 0.000 0.948
#> SRR1401972 1 0.2804 0.838 0.924 0.016 0.060
#> SRR1415510 1 0.4235 0.784 0.824 0.176 0.000
#> SRR1327279 3 0.5138 0.685 0.252 0.000 0.748
#> SRR1086983 3 0.1529 0.901 0.040 0.000 0.960
#> SRR1105174 3 0.2261 0.893 0.068 0.000 0.932
#> SRR1468893 2 0.5733 0.666 0.324 0.676 0.000
#> SRR1362555 2 0.2711 0.878 0.088 0.912 0.000
#> SRR1074526 3 0.2066 0.890 0.000 0.060 0.940
#> SRR1326225 3 0.2066 0.890 0.000 0.060 0.940
#> SRR1401933 1 0.0747 0.861 0.984 0.016 0.000
#> SRR1324062 1 0.1411 0.859 0.964 0.036 0.000
#> SRR1102296 1 0.5465 0.586 0.712 0.000 0.288
#> SRR1085087 3 0.2261 0.893 0.068 0.000 0.932
#> SRR1079046 1 0.2066 0.854 0.940 0.060 0.000
#> SRR1328339 1 0.2959 0.843 0.900 0.100 0.000
#> SRR1079782 1 0.2096 0.841 0.944 0.004 0.052
#> SRR1092257 3 0.0747 0.903 0.000 0.016 0.984
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 4 0.3219 0.749881 0.000 0.000 0.164 0.836
#> SRR1429287 1 0.3497 0.687183 0.860 0.036 0.104 0.000
#> SRR1359238 1 0.1902 0.732350 0.932 0.004 0.064 0.000
#> SRR1309597 2 0.0592 0.879369 0.016 0.984 0.000 0.000
#> SRR1441398 2 0.1824 0.875143 0.060 0.936 0.004 0.000
#> SRR1084055 3 0.4282 0.409419 0.008 0.024 0.808 0.160
#> SRR1417566 1 0.1452 0.732840 0.956 0.008 0.036 0.000
#> SRR1351857 4 0.0592 0.807689 0.000 0.000 0.016 0.984
#> SRR1487485 1 0.5510 0.241230 0.600 0.024 0.376 0.000
#> SRR1335875 1 0.2124 0.730685 0.924 0.008 0.068 0.000
#> SRR1073947 1 0.3681 0.667375 0.816 0.008 0.176 0.000
#> SRR1443483 3 0.7391 0.141980 0.396 0.164 0.440 0.000
#> SRR1346794 1 0.0524 0.737002 0.988 0.004 0.008 0.000
#> SRR1405245 2 0.1978 0.873358 0.068 0.928 0.004 0.000
#> SRR1409677 4 0.2342 0.782589 0.008 0.000 0.080 0.912
#> SRR1095549 3 0.5151 -0.045771 0.004 0.000 0.532 0.464
#> SRR1323788 1 0.3831 0.628951 0.792 0.004 0.204 0.000
#> SRR1314054 4 0.3219 0.749881 0.000 0.000 0.164 0.836
#> SRR1077944 1 0.2675 0.715746 0.892 0.008 0.100 0.000
#> SRR1480587 2 0.0592 0.879369 0.016 0.984 0.000 0.000
#> SRR1311205 2 0.2125 0.870440 0.076 0.920 0.004 0.000
#> SRR1076369 1 0.6443 0.032161 0.528 0.072 0.400 0.000
#> SRR1453549 1 0.1940 0.728923 0.924 0.000 0.076 0.000
#> SRR1345782 1 0.4741 0.445516 0.668 0.004 0.328 0.000
#> SRR1447850 1 0.6178 -0.000987 0.480 0.004 0.476 0.040
#> SRR1391553 1 0.1042 0.734593 0.972 0.020 0.008 0.000
#> SRR1444156 4 0.3219 0.749881 0.000 0.000 0.164 0.836
#> SRR1471731 1 0.2271 0.711418 0.916 0.076 0.008 0.000
#> SRR1120987 4 0.0188 0.809992 0.000 0.000 0.004 0.996
#> SRR1477363 1 0.2675 0.718213 0.892 0.008 0.100 0.000
#> SRR1391961 3 0.7869 0.223629 0.312 0.296 0.392 0.000
#> SRR1373879 3 0.5392 -0.039957 0.012 0.000 0.528 0.460
#> SRR1318732 1 0.1545 0.730092 0.952 0.040 0.008 0.000
#> SRR1091404 3 0.5827 0.204096 0.396 0.000 0.568 0.036
#> SRR1402109 3 0.5062 0.411550 0.284 0.000 0.692 0.024
#> SRR1407336 3 0.5346 0.494180 0.076 0.000 0.732 0.192
#> SRR1097417 3 0.7854 0.225326 0.304 0.296 0.400 0.000
#> SRR1396227 1 0.2799 0.714132 0.884 0.008 0.108 0.000
#> SRR1400775 3 0.5460 0.397407 0.276 0.004 0.684 0.036
#> SRR1392861 4 0.4452 0.634578 0.008 0.000 0.260 0.732
#> SRR1472929 2 0.0657 0.873273 0.004 0.984 0.012 0.000
#> SRR1436740 4 0.2124 0.786659 0.008 0.000 0.068 0.924
#> SRR1477057 1 0.2882 0.707761 0.892 0.024 0.084 0.000
#> SRR1311980 2 0.1978 0.873358 0.068 0.928 0.004 0.000
#> SRR1069400 1 0.6337 -0.043999 0.476 0.060 0.464 0.000
#> SRR1351016 1 0.2944 0.680066 0.868 0.128 0.004 0.000
#> SRR1096291 4 0.0188 0.809539 0.000 0.000 0.004 0.996
#> SRR1418145 1 0.6057 0.271573 0.588 0.004 0.364 0.044
#> SRR1488111 1 0.2742 0.712569 0.900 0.024 0.076 0.000
#> SRR1370495 2 0.0592 0.879369 0.016 0.984 0.000 0.000
#> SRR1352639 1 0.5165 0.060030 0.512 0.004 0.484 0.000
#> SRR1348911 2 0.0657 0.878463 0.012 0.984 0.004 0.000
#> SRR1467386 4 0.5090 0.545824 0.016 0.000 0.324 0.660
#> SRR1415956 2 0.2125 0.870440 0.076 0.920 0.004 0.000
#> SRR1500495 2 0.2125 0.870440 0.076 0.920 0.004 0.000
#> SRR1405099 2 0.2125 0.870440 0.076 0.920 0.004 0.000
#> SRR1345585 1 0.4932 0.481825 0.728 0.032 0.240 0.000
#> SRR1093196 3 0.5976 0.160425 0.452 0.008 0.516 0.024
#> SRR1466006 2 0.5875 0.583160 0.092 0.684 0.224 0.000
#> SRR1351557 1 0.3842 0.675659 0.836 0.036 0.128 0.000
#> SRR1382687 1 0.2048 0.733730 0.928 0.008 0.064 0.000
#> SRR1375549 1 0.2742 0.712569 0.900 0.024 0.076 0.000
#> SRR1101765 4 0.0000 0.809769 0.000 0.000 0.000 1.000
#> SRR1334461 2 0.0657 0.873273 0.004 0.984 0.012 0.000
#> SRR1094073 4 0.3219 0.749881 0.000 0.000 0.164 0.836
#> SRR1077549 4 0.5075 0.520214 0.012 0.000 0.344 0.644
#> SRR1440332 1 0.5151 0.120497 0.532 0.004 0.464 0.000
#> SRR1454177 4 0.0000 0.809769 0.000 0.000 0.000 1.000
#> SRR1082447 3 0.6714 0.264481 0.100 0.000 0.540 0.360
#> SRR1420043 1 0.2480 0.719306 0.904 0.008 0.088 0.000
#> SRR1432500 1 0.5427 0.383761 0.640 0.004 0.336 0.020
#> SRR1378045 3 0.4567 0.489417 0.236 0.004 0.748 0.012
#> SRR1334200 2 0.0657 0.877227 0.012 0.984 0.004 0.000
#> SRR1069539 4 0.0921 0.805817 0.000 0.000 0.028 0.972
#> SRR1343031 3 0.5105 0.421111 0.276 0.000 0.696 0.028
#> SRR1319690 1 0.1297 0.736220 0.964 0.020 0.016 0.000
#> SRR1310604 3 0.5883 0.357993 0.300 0.060 0.640 0.000
#> SRR1327747 1 0.1411 0.732714 0.960 0.020 0.020 0.000
#> SRR1072456 2 0.4464 0.687306 0.024 0.768 0.208 0.000
#> SRR1367896 2 0.3908 0.700928 0.004 0.784 0.212 0.000
#> SRR1480107 1 0.3398 0.706679 0.872 0.068 0.060 0.000
#> SRR1377756 1 0.2546 0.718303 0.900 0.008 0.092 0.000
#> SRR1435272 4 0.0336 0.809303 0.000 0.000 0.008 0.992
#> SRR1089230 4 0.0000 0.809769 0.000 0.000 0.000 1.000
#> SRR1389522 3 0.7463 0.160705 0.384 0.176 0.440 0.000
#> SRR1080600 3 0.5062 0.463564 0.184 0.064 0.752 0.000
#> SRR1086935 4 0.0817 0.805670 0.000 0.000 0.024 0.976
#> SRR1344060 2 0.0804 0.871110 0.008 0.980 0.012 0.000
#> SRR1467922 4 0.3583 0.733645 0.000 0.004 0.180 0.816
#> SRR1090984 2 0.4220 0.675830 0.248 0.748 0.004 0.000
#> SRR1456991 2 0.4632 0.595287 0.308 0.688 0.004 0.000
#> SRR1085039 3 0.6677 0.255610 0.096 0.000 0.540 0.364
#> SRR1069303 1 0.2675 0.717502 0.892 0.008 0.100 0.000
#> SRR1091500 4 0.3219 0.749881 0.000 0.000 0.164 0.836
#> SRR1075198 1 0.5792 0.212092 0.552 0.032 0.416 0.000
#> SRR1086915 4 0.4690 0.628112 0.016 0.000 0.260 0.724
#> SRR1499503 3 0.4034 0.395306 0.012 0.004 0.804 0.180
#> SRR1094312 3 0.5284 0.437306 0.240 0.004 0.716 0.040
#> SRR1352437 4 0.4606 0.626993 0.012 0.000 0.264 0.724
#> SRR1436323 1 0.1284 0.733320 0.964 0.024 0.012 0.000
#> SRR1073507 4 0.5090 0.545824 0.016 0.000 0.324 0.660
#> SRR1401972 1 0.4897 0.425556 0.668 0.004 0.324 0.004
#> SRR1415510 1 0.5148 0.574372 0.736 0.056 0.208 0.000
#> SRR1327279 3 0.6500 0.226444 0.080 0.000 0.544 0.376
#> SRR1086983 4 0.2662 0.774246 0.016 0.000 0.084 0.900
#> SRR1105174 4 0.5130 0.534958 0.016 0.000 0.332 0.652
#> SRR1468893 2 0.4372 0.636210 0.268 0.728 0.004 0.000
#> SRR1362555 2 0.0592 0.879369 0.016 0.984 0.000 0.000
#> SRR1074526 4 0.3311 0.747883 0.000 0.000 0.172 0.828
#> SRR1326225 4 0.3494 0.740600 0.000 0.004 0.172 0.824
#> SRR1401933 1 0.1722 0.732849 0.944 0.008 0.048 0.000
#> SRR1324062 1 0.2737 0.716130 0.888 0.008 0.104 0.000
#> SRR1102296 3 0.6458 0.187808 0.408 0.000 0.520 0.072
#> SRR1085087 4 0.5110 0.539094 0.016 0.000 0.328 0.656
#> SRR1079046 1 0.2775 0.709603 0.896 0.020 0.084 0.000
#> SRR1328339 1 0.5231 0.274154 0.604 0.012 0.384 0.000
#> SRR1079782 1 0.4946 0.430883 0.680 0.008 0.308 0.004
#> SRR1092257 4 0.0188 0.809992 0.000 0.000 0.004 0.996
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.0324 0.7005 0.000 0.992 0.004 0.004 0.000
#> SRR1429287 1 0.5329 0.5893 0.692 0.000 0.212 0.076 0.020
#> SRR1359238 1 0.2439 0.7267 0.876 0.000 0.120 0.004 0.000
#> SRR1309597 5 0.1197 0.8509 0.000 0.000 0.048 0.000 0.952
#> SRR1441398 5 0.1041 0.8533 0.032 0.000 0.000 0.004 0.964
#> SRR1084055 3 0.6200 0.4469 0.000 0.256 0.548 0.196 0.000
#> SRR1417566 1 0.3652 0.6487 0.784 0.000 0.200 0.012 0.004
#> SRR1351857 2 0.4088 0.6117 0.000 0.632 0.000 0.368 0.000
#> SRR1487485 3 0.5363 0.1705 0.372 0.000 0.572 0.052 0.004
#> SRR1335875 1 0.1267 0.7384 0.960 0.000 0.004 0.024 0.012
#> SRR1073947 1 0.5392 0.4780 0.668 0.000 0.056 0.252 0.024
#> SRR1443483 3 0.5679 0.5282 0.188 0.000 0.672 0.120 0.020
#> SRR1346794 1 0.1365 0.7382 0.952 0.000 0.040 0.004 0.004
#> SRR1405245 5 0.1205 0.8527 0.040 0.000 0.000 0.004 0.956
#> SRR1409677 4 0.4227 0.0327 0.000 0.420 0.000 0.580 0.000
#> SRR1095549 4 0.3859 0.6216 0.004 0.084 0.096 0.816 0.000
#> SRR1323788 1 0.4988 0.5345 0.716 0.000 0.084 0.192 0.008
#> SRR1314054 2 0.0324 0.7005 0.000 0.992 0.004 0.004 0.000
#> SRR1077944 1 0.1399 0.7332 0.952 0.000 0.020 0.028 0.000
#> SRR1480587 5 0.1197 0.8509 0.000 0.000 0.048 0.000 0.952
#> SRR1311205 5 0.1764 0.8441 0.064 0.000 0.000 0.008 0.928
#> SRR1076369 3 0.4788 0.5433 0.144 0.000 0.760 0.068 0.028
#> SRR1453549 1 0.1701 0.7385 0.936 0.000 0.048 0.016 0.000
#> SRR1345782 1 0.6413 0.2472 0.552 0.000 0.232 0.208 0.008
#> SRR1447850 3 0.8591 0.2489 0.248 0.268 0.268 0.216 0.000
#> SRR1391553 1 0.2228 0.7319 0.908 0.000 0.076 0.012 0.004
#> SRR1444156 2 0.0404 0.6983 0.000 0.988 0.012 0.000 0.000
#> SRR1471731 1 0.3584 0.7088 0.836 0.000 0.112 0.012 0.040
#> SRR1120987 2 0.3966 0.6742 0.000 0.664 0.000 0.336 0.000
#> SRR1477363 1 0.0404 0.7380 0.988 0.000 0.000 0.012 0.000
#> SRR1391961 3 0.5360 0.5221 0.088 0.000 0.736 0.068 0.108
#> SRR1373879 4 0.4059 0.6154 0.012 0.068 0.112 0.808 0.000
#> SRR1318732 1 0.4097 0.6367 0.756 0.000 0.216 0.008 0.020
#> SRR1091404 4 0.6649 -0.1311 0.268 0.000 0.284 0.448 0.000
#> SRR1402109 3 0.6644 0.2956 0.192 0.000 0.432 0.372 0.004
#> SRR1407336 4 0.5347 -0.0631 0.040 0.008 0.396 0.556 0.000
#> SRR1097417 3 0.4832 0.5307 0.060 0.000 0.772 0.060 0.108
#> SRR1396227 1 0.1836 0.7304 0.936 0.000 0.016 0.040 0.008
#> SRR1400775 3 0.8352 0.3670 0.136 0.272 0.320 0.272 0.000
#> SRR1392861 4 0.3612 0.5087 0.000 0.268 0.000 0.732 0.000
#> SRR1472929 5 0.1732 0.8394 0.000 0.000 0.080 0.000 0.920
#> SRR1436740 4 0.4448 -0.2300 0.000 0.480 0.004 0.516 0.000
#> SRR1477057 1 0.4272 0.6737 0.780 0.000 0.152 0.060 0.008
#> SRR1311980 5 0.1205 0.8527 0.040 0.000 0.000 0.004 0.956
#> SRR1069400 3 0.5523 0.5224 0.200 0.000 0.668 0.124 0.008
#> SRR1351016 1 0.3474 0.6701 0.832 0.000 0.028 0.008 0.132
#> SRR1096291 2 0.3966 0.6742 0.000 0.664 0.000 0.336 0.000
#> SRR1418145 1 0.6767 0.1542 0.428 0.008 0.196 0.368 0.000
#> SRR1488111 1 0.3871 0.6870 0.808 0.000 0.132 0.056 0.004
#> SRR1370495 5 0.1197 0.8509 0.000 0.000 0.048 0.000 0.952
#> SRR1352639 1 0.6529 0.0685 0.436 0.000 0.168 0.392 0.004
#> SRR1348911 5 0.2583 0.8141 0.000 0.000 0.132 0.004 0.864
#> SRR1467386 4 0.3562 0.6050 0.016 0.196 0.000 0.788 0.000
#> SRR1415956 5 0.1764 0.8441 0.064 0.000 0.000 0.008 0.928
#> SRR1500495 5 0.1764 0.8441 0.064 0.000 0.000 0.008 0.928
#> SRR1405099 5 0.1764 0.8441 0.064 0.000 0.000 0.008 0.928
#> SRR1345585 1 0.5233 0.0672 0.488 0.000 0.476 0.008 0.028
#> SRR1093196 3 0.6569 0.2930 0.304 0.000 0.464 0.232 0.000
#> SRR1466006 3 0.4151 0.1558 0.004 0.000 0.652 0.000 0.344
#> SRR1351557 1 0.6000 0.4606 0.596 0.000 0.292 0.092 0.020
#> SRR1382687 1 0.0579 0.7390 0.984 0.000 0.008 0.008 0.000
#> SRR1375549 1 0.3543 0.6991 0.832 0.000 0.124 0.036 0.008
#> SRR1101765 2 0.3949 0.6788 0.000 0.668 0.000 0.332 0.000
#> SRR1334461 5 0.2763 0.8025 0.000 0.000 0.148 0.004 0.848
#> SRR1094073 2 0.0404 0.6983 0.000 0.988 0.012 0.000 0.000
#> SRR1077549 4 0.3773 0.6225 0.004 0.164 0.032 0.800 0.000
#> SRR1440332 1 0.6252 0.1895 0.508 0.000 0.164 0.328 0.000
#> SRR1454177 2 0.4084 0.6808 0.000 0.668 0.004 0.328 0.000
#> SRR1082447 4 0.4539 0.5804 0.068 0.036 0.108 0.788 0.000
#> SRR1420043 1 0.1012 0.7372 0.968 0.000 0.020 0.012 0.000
#> SRR1432500 1 0.4297 0.4987 0.692 0.000 0.020 0.288 0.000
#> SRR1378045 3 0.7892 0.4870 0.144 0.168 0.464 0.224 0.000
#> SRR1334200 5 0.2286 0.8311 0.000 0.000 0.108 0.004 0.888
#> SRR1069539 2 0.3752 0.6930 0.000 0.708 0.000 0.292 0.000
#> SRR1343031 3 0.6585 0.2940 0.180 0.000 0.440 0.376 0.004
#> SRR1319690 1 0.1644 0.7390 0.940 0.000 0.048 0.008 0.004
#> SRR1310604 3 0.4723 0.5627 0.064 0.000 0.768 0.136 0.032
#> SRR1327747 1 0.2463 0.7259 0.888 0.000 0.100 0.004 0.008
#> SRR1072456 3 0.4321 0.0495 0.004 0.000 0.600 0.000 0.396
#> SRR1367896 3 0.4968 -0.1274 0.000 0.000 0.516 0.028 0.456
#> SRR1480107 1 0.3542 0.6725 0.840 0.000 0.028 0.020 0.112
#> SRR1377756 1 0.0693 0.7369 0.980 0.000 0.008 0.012 0.000
#> SRR1435272 2 0.4101 0.6772 0.000 0.664 0.004 0.332 0.000
#> SRR1089230 2 0.4084 0.6808 0.000 0.668 0.004 0.328 0.000
#> SRR1389522 3 0.5509 0.5463 0.164 0.000 0.700 0.108 0.028
#> SRR1080600 3 0.4790 0.5618 0.032 0.024 0.780 0.132 0.032
#> SRR1086935 2 0.3838 0.6951 0.000 0.716 0.004 0.280 0.000
#> SRR1344060 5 0.3366 0.7428 0.000 0.000 0.212 0.004 0.784
#> SRR1467922 2 0.1568 0.6468 0.000 0.944 0.036 0.020 0.000
#> SRR1090984 5 0.5407 0.6317 0.180 0.000 0.128 0.008 0.684
#> SRR1456991 5 0.5262 0.3264 0.388 0.000 0.036 0.008 0.568
#> SRR1085039 4 0.4462 0.5985 0.060 0.044 0.100 0.796 0.000
#> SRR1069303 1 0.2420 0.7206 0.912 0.000 0.016 0.036 0.036
#> SRR1091500 2 0.0404 0.6983 0.000 0.988 0.012 0.000 0.000
#> SRR1075198 3 0.6882 0.0787 0.356 0.000 0.460 0.160 0.024
#> SRR1086915 4 0.3586 0.5165 0.000 0.264 0.000 0.736 0.000
#> SRR1499503 3 0.6630 0.3615 0.000 0.376 0.404 0.220 0.000
#> SRR1094312 3 0.8252 0.3640 0.116 0.292 0.320 0.272 0.000
#> SRR1352437 4 0.3521 0.5636 0.004 0.232 0.000 0.764 0.000
#> SRR1436323 1 0.2284 0.7271 0.896 0.000 0.096 0.004 0.004
#> SRR1073507 4 0.3496 0.6024 0.012 0.200 0.000 0.788 0.000
#> SRR1401972 1 0.3828 0.6321 0.788 0.000 0.020 0.184 0.008
#> SRR1415510 1 0.6376 0.1342 0.448 0.000 0.444 0.076 0.032
#> SRR1327279 4 0.4460 0.5844 0.048 0.044 0.116 0.792 0.000
#> SRR1086983 4 0.4074 0.2546 0.000 0.364 0.000 0.636 0.000
#> SRR1105174 4 0.3511 0.6142 0.012 0.184 0.004 0.800 0.000
#> SRR1468893 5 0.3756 0.6239 0.248 0.000 0.000 0.008 0.744
#> SRR1362555 5 0.1341 0.8502 0.000 0.000 0.056 0.000 0.944
#> SRR1074526 2 0.0290 0.6979 0.000 0.992 0.008 0.000 0.000
#> SRR1326225 2 0.0671 0.6881 0.000 0.980 0.016 0.004 0.000
#> SRR1401933 1 0.3384 0.7058 0.848 0.000 0.088 0.060 0.004
#> SRR1324062 1 0.2417 0.7244 0.912 0.000 0.016 0.040 0.032
#> SRR1102296 4 0.5847 0.1316 0.312 0.008 0.096 0.584 0.000
#> SRR1085087 4 0.3586 0.6082 0.020 0.188 0.000 0.792 0.000
#> SRR1079046 1 0.4059 0.6768 0.800 0.000 0.132 0.060 0.008
#> SRR1328339 1 0.5673 0.1486 0.544 0.000 0.388 0.056 0.012
#> SRR1079782 1 0.6170 0.4224 0.576 0.000 0.232 0.188 0.004
#> SRR1092257 2 0.3949 0.6787 0.000 0.668 0.000 0.332 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.0520 0.66932 0.000 0.984 0.008 0.008 0.000 0.000
#> SRR1429287 6 0.4724 0.10073 0.468 0.004 0.020 0.004 0.004 0.500
#> SRR1359238 1 0.3485 0.54271 0.784 0.000 0.028 0.004 0.000 0.184
#> SRR1309597 5 0.2866 0.77600 0.000 0.004 0.052 0.000 0.860 0.084
#> SRR1441398 5 0.0146 0.77749 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1084055 3 0.7403 0.05615 0.000 0.268 0.364 0.128 0.000 0.240
#> SRR1417566 1 0.5386 0.36361 0.588 0.000 0.264 0.000 0.004 0.144
#> SRR1351857 2 0.4897 0.54759 0.000 0.556 0.004 0.384 0.000 0.056
#> SRR1487485 3 0.6425 -0.16387 0.244 0.000 0.408 0.012 0.004 0.332
#> SRR1335875 1 0.1841 0.62888 0.920 0.000 0.000 0.008 0.008 0.064
#> SRR1073947 1 0.5027 0.42097 0.672 0.000 0.064 0.236 0.008 0.020
#> SRR1443483 3 0.3610 0.46255 0.092 0.000 0.820 0.072 0.004 0.012
#> SRR1346794 1 0.2837 0.60952 0.856 0.000 0.056 0.000 0.000 0.088
#> SRR1405245 5 0.0146 0.77749 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1409677 4 0.3996 0.47780 0.000 0.180 0.012 0.760 0.000 0.048
#> SRR1095549 4 0.3705 0.64512 0.000 0.008 0.180 0.776 0.000 0.036
#> SRR1323788 1 0.5496 0.33688 0.592 0.000 0.240 0.160 0.000 0.008
#> SRR1314054 2 0.0260 0.67026 0.000 0.992 0.000 0.008 0.000 0.000
#> SRR1077944 1 0.2314 0.63523 0.900 0.000 0.056 0.036 0.000 0.008
#> SRR1480587 5 0.3096 0.77553 0.004 0.004 0.048 0.000 0.848 0.096
#> SRR1311205 5 0.1442 0.76437 0.040 0.000 0.004 0.000 0.944 0.012
#> SRR1076369 3 0.3271 0.46044 0.028 0.000 0.848 0.032 0.004 0.088
#> SRR1453549 1 0.2265 0.62157 0.900 0.000 0.024 0.008 0.000 0.068
#> SRR1345782 1 0.5962 0.16167 0.500 0.000 0.328 0.156 0.000 0.016
#> SRR1447850 6 0.6686 0.46248 0.144 0.224 0.020 0.064 0.000 0.548
#> SRR1391553 1 0.4214 0.53591 0.736 0.000 0.076 0.000 0.004 0.184
#> SRR1444156 2 0.0951 0.66861 0.000 0.968 0.004 0.008 0.000 0.020
#> SRR1471731 1 0.5375 0.46049 0.648 0.000 0.112 0.000 0.032 0.208
#> SRR1120987 2 0.5080 0.57635 0.000 0.552 0.012 0.380 0.000 0.056
#> SRR1477363 1 0.1148 0.63586 0.960 0.000 0.000 0.020 0.004 0.016
#> SRR1391961 3 0.4094 0.44339 0.012 0.004 0.752 0.004 0.028 0.200
#> SRR1373879 4 0.3090 0.67558 0.000 0.004 0.140 0.828 0.000 0.028
#> SRR1318732 1 0.5513 0.39761 0.596 0.000 0.188 0.000 0.008 0.208
#> SRR1091404 4 0.7242 0.05862 0.196 0.000 0.260 0.416 0.000 0.128
#> SRR1402109 3 0.6535 0.29645 0.164 0.000 0.512 0.256 0.000 0.068
#> SRR1407336 4 0.5479 0.16607 0.012 0.000 0.380 0.516 0.000 0.092
#> SRR1097417 3 0.3937 0.44627 0.008 0.004 0.764 0.004 0.028 0.192
#> SRR1396227 1 0.2128 0.62614 0.908 0.000 0.000 0.032 0.004 0.056
#> SRR1400775 6 0.7196 0.36758 0.056 0.244 0.076 0.112 0.000 0.512
#> SRR1392861 4 0.3088 0.59821 0.000 0.120 0.000 0.832 0.000 0.048
#> SRR1472929 5 0.4125 0.73327 0.000 0.004 0.100 0.000 0.756 0.140
#> SRR1436740 4 0.4724 0.20253 0.000 0.276 0.012 0.656 0.000 0.056
#> SRR1477057 1 0.4421 0.09255 0.552 0.000 0.020 0.004 0.000 0.424
#> SRR1311980 5 0.0146 0.77749 0.004 0.000 0.000 0.000 0.996 0.000
#> SRR1069400 3 0.4676 0.42258 0.104 0.000 0.748 0.072 0.000 0.076
#> SRR1351016 1 0.3980 0.58201 0.784 0.000 0.056 0.000 0.136 0.024
#> SRR1096291 2 0.4840 0.58583 0.000 0.580 0.004 0.360 0.000 0.056
#> SRR1418145 6 0.5742 0.46932 0.292 0.000 0.004 0.180 0.000 0.524
#> SRR1488111 1 0.4223 0.24766 0.612 0.000 0.016 0.004 0.000 0.368
#> SRR1370495 5 0.3096 0.77553 0.004 0.004 0.048 0.000 0.848 0.096
#> SRR1352639 1 0.7072 0.00451 0.412 0.000 0.200 0.296 0.000 0.092
#> SRR1348911 5 0.5317 0.62747 0.000 0.004 0.236 0.004 0.620 0.136
#> SRR1467386 4 0.1285 0.69423 0.004 0.052 0.000 0.944 0.000 0.000
#> SRR1415956 5 0.1410 0.76216 0.044 0.000 0.004 0.000 0.944 0.008
#> SRR1500495 5 0.1410 0.76216 0.044 0.000 0.004 0.000 0.944 0.008
#> SRR1405099 5 0.1410 0.76216 0.044 0.000 0.004 0.000 0.944 0.008
#> SRR1345585 3 0.6243 -0.08286 0.376 0.000 0.408 0.008 0.004 0.204
#> SRR1093196 6 0.7295 0.17743 0.184 0.000 0.284 0.136 0.000 0.396
#> SRR1466006 3 0.5991 0.19713 0.004 0.008 0.512 0.004 0.148 0.324
#> SRR1351557 6 0.3864 0.43199 0.344 0.000 0.000 0.004 0.004 0.648
#> SRR1382687 1 0.1138 0.63641 0.960 0.000 0.000 0.012 0.004 0.024
#> SRR1375549 1 0.4213 0.30029 0.636 0.000 0.020 0.004 0.000 0.340
#> SRR1101765 2 0.4934 0.58960 0.000 0.568 0.016 0.376 0.000 0.040
#> SRR1334461 5 0.5909 0.53851 0.000 0.004 0.264 0.004 0.524 0.204
#> SRR1094073 2 0.0862 0.66907 0.000 0.972 0.004 0.008 0.000 0.016
#> SRR1077549 4 0.2492 0.70287 0.000 0.036 0.068 0.888 0.000 0.008
#> SRR1440332 1 0.6648 0.12131 0.472 0.000 0.232 0.244 0.000 0.052
#> SRR1454177 2 0.4879 0.60452 0.000 0.584 0.008 0.356 0.000 0.052
#> SRR1082447 4 0.3765 0.66711 0.040 0.000 0.132 0.800 0.000 0.028
#> SRR1420043 1 0.1945 0.63708 0.920 0.000 0.056 0.016 0.004 0.004
#> SRR1432500 1 0.3828 0.46099 0.724 0.000 0.008 0.252 0.000 0.016
#> SRR1378045 3 0.8216 0.03662 0.100 0.176 0.376 0.092 0.000 0.256
#> SRR1334200 5 0.5717 0.61008 0.004 0.004 0.168 0.004 0.580 0.240
#> SRR1069539 2 0.4505 0.63333 0.000 0.668 0.004 0.272 0.000 0.056
#> SRR1343031 3 0.6565 0.29038 0.164 0.000 0.504 0.264 0.000 0.068
#> SRR1319690 1 0.2532 0.62934 0.884 0.000 0.060 0.000 0.004 0.052
#> SRR1310604 3 0.5698 -0.01756 0.036 0.004 0.456 0.056 0.000 0.448
#> SRR1327747 1 0.4275 0.52590 0.728 0.000 0.076 0.000 0.004 0.192
#> SRR1072456 3 0.5720 0.14799 0.000 0.004 0.560 0.004 0.180 0.252
#> SRR1367896 3 0.5337 0.19128 0.000 0.004 0.628 0.004 0.188 0.176
#> SRR1480107 1 0.3816 0.59186 0.808 0.000 0.056 0.008 0.112 0.016
#> SRR1377756 1 0.1053 0.64067 0.964 0.000 0.012 0.020 0.004 0.000
#> SRR1435272 2 0.4929 0.58651 0.000 0.564 0.008 0.376 0.000 0.052
#> SRR1089230 2 0.4879 0.60443 0.000 0.584 0.008 0.356 0.000 0.052
#> SRR1389522 3 0.2853 0.47379 0.068 0.000 0.868 0.056 0.004 0.004
#> SRR1080600 6 0.5405 -0.15709 0.016 0.004 0.456 0.048 0.004 0.472
#> SRR1086935 2 0.4513 0.63952 0.000 0.676 0.008 0.264 0.000 0.052
#> SRR1344060 5 0.6188 0.41023 0.000 0.004 0.328 0.004 0.436 0.228
#> SRR1467922 2 0.1801 0.63014 0.000 0.924 0.016 0.004 0.000 0.056
#> SRR1090984 5 0.7211 0.31623 0.204 0.000 0.164 0.000 0.448 0.184
#> SRR1456991 1 0.5359 0.13109 0.484 0.000 0.060 0.000 0.436 0.020
#> SRR1085039 4 0.3573 0.67906 0.036 0.000 0.120 0.816 0.000 0.028
#> SRR1069303 1 0.2701 0.62021 0.884 0.000 0.000 0.028 0.044 0.044
#> SRR1091500 2 0.1167 0.66747 0.000 0.960 0.012 0.008 0.000 0.020
#> SRR1075198 6 0.5866 0.47549 0.180 0.004 0.168 0.028 0.004 0.616
#> SRR1086915 4 0.3049 0.61550 0.000 0.104 0.004 0.844 0.000 0.048
#> SRR1499503 2 0.7379 -0.29622 0.000 0.364 0.232 0.124 0.000 0.280
#> SRR1094312 6 0.7145 0.33391 0.040 0.276 0.080 0.112 0.000 0.492
#> SRR1352437 4 0.2854 0.63420 0.004 0.088 0.000 0.860 0.000 0.048
#> SRR1436323 1 0.4313 0.52937 0.728 0.000 0.084 0.000 0.004 0.184
#> SRR1073507 4 0.1285 0.69423 0.004 0.052 0.000 0.944 0.000 0.000
#> SRR1401972 1 0.3254 0.56813 0.820 0.000 0.000 0.124 0.000 0.056
#> SRR1415510 6 0.5433 0.49614 0.260 0.004 0.120 0.004 0.004 0.608
#> SRR1327279 4 0.3488 0.65005 0.012 0.000 0.160 0.800 0.000 0.028
#> SRR1086983 4 0.3490 0.51000 0.000 0.176 0.000 0.784 0.000 0.040
#> SRR1105174 4 0.1862 0.70090 0.004 0.044 0.016 0.928 0.000 0.008
#> SRR1468893 5 0.3301 0.58159 0.216 0.000 0.004 0.000 0.772 0.008
#> SRR1362555 5 0.3218 0.77273 0.004 0.004 0.044 0.000 0.836 0.112
#> SRR1074526 2 0.0862 0.66785 0.000 0.972 0.004 0.008 0.000 0.016
#> SRR1326225 2 0.1511 0.64339 0.000 0.940 0.012 0.004 0.000 0.044
#> SRR1401933 1 0.3547 0.34296 0.668 0.000 0.000 0.000 0.000 0.332
#> SRR1324062 1 0.2755 0.61964 0.880 0.000 0.000 0.028 0.036 0.056
#> SRR1102296 4 0.6074 0.33300 0.264 0.000 0.092 0.568 0.000 0.076
#> SRR1085087 4 0.1477 0.69328 0.004 0.048 0.000 0.940 0.000 0.008
#> SRR1079046 1 0.4387 0.15467 0.572 0.000 0.020 0.004 0.000 0.404
#> SRR1328339 3 0.4622 -0.09148 0.484 0.000 0.488 0.004 0.008 0.016
#> SRR1079782 6 0.4362 0.39141 0.388 0.000 0.000 0.028 0.000 0.584
#> SRR1092257 2 0.5080 0.57635 0.000 0.552 0.012 0.380 0.000 0.056
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
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 17611 rows and 118 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 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 0.986 0.994 0.5019 0.499 0.499
#> 3 3 0.743 0.899 0.915 0.3000 0.794 0.606
#> 4 4 0.579 0.572 0.784 0.1195 0.874 0.659
#> 5 5 0.630 0.495 0.745 0.0634 0.845 0.533
#> 6 6 0.703 0.571 0.765 0.0458 0.888 0.576
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
#> SRR1396765 2 0.0000 0.999 0.000 1.000
#> SRR1429287 1 0.0000 0.990 1.000 0.000
#> SRR1359238 1 0.0000 0.990 1.000 0.000
#> SRR1309597 1 0.0000 0.990 1.000 0.000
#> SRR1441398 1 0.0000 0.990 1.000 0.000
#> SRR1084055 2 0.0000 0.999 0.000 1.000
#> SRR1417566 1 0.0000 0.990 1.000 0.000
#> SRR1351857 2 0.0000 0.999 0.000 1.000
#> SRR1487485 1 0.0000 0.990 1.000 0.000
#> SRR1335875 1 0.0000 0.990 1.000 0.000
#> SRR1073947 1 0.0000 0.990 1.000 0.000
#> SRR1443483 1 0.0000 0.990 1.000 0.000
#> SRR1346794 1 0.0000 0.990 1.000 0.000
#> SRR1405245 1 0.0000 0.990 1.000 0.000
#> SRR1409677 2 0.0000 0.999 0.000 1.000
#> SRR1095549 2 0.0000 0.999 0.000 1.000
#> SRR1323788 1 0.0000 0.990 1.000 0.000
#> SRR1314054 2 0.0000 0.999 0.000 1.000
#> SRR1077944 1 0.0000 0.990 1.000 0.000
#> SRR1480587 1 0.0000 0.990 1.000 0.000
#> SRR1311205 1 0.0000 0.990 1.000 0.000
#> SRR1076369 1 0.0000 0.990 1.000 0.000
#> SRR1453549 1 0.8267 0.650 0.740 0.260
#> SRR1345782 1 0.0000 0.990 1.000 0.000
#> SRR1447850 2 0.0000 0.999 0.000 1.000
#> SRR1391553 1 0.0000 0.990 1.000 0.000
#> SRR1444156 2 0.0000 0.999 0.000 1.000
#> SRR1471731 1 0.0000 0.990 1.000 0.000
#> SRR1120987 2 0.0000 0.999 0.000 1.000
#> SRR1477363 1 0.0000 0.990 1.000 0.000
#> SRR1391961 1 0.0000 0.990 1.000 0.000
#> SRR1373879 2 0.0000 0.999 0.000 1.000
#> SRR1318732 1 0.0000 0.990 1.000 0.000
#> SRR1091404 2 0.0000 0.999 0.000 1.000
#> SRR1402109 2 0.0000 0.999 0.000 1.000
#> SRR1407336 2 0.0000 0.999 0.000 1.000
#> SRR1097417 1 0.0000 0.990 1.000 0.000
#> SRR1396227 1 0.0000 0.990 1.000 0.000
#> SRR1400775 2 0.0000 0.999 0.000 1.000
#> SRR1392861 2 0.0000 0.999 0.000 1.000
#> SRR1472929 1 0.0000 0.990 1.000 0.000
#> SRR1436740 2 0.0000 0.999 0.000 1.000
#> SRR1477057 1 0.0000 0.990 1.000 0.000
#> SRR1311980 1 0.0000 0.990 1.000 0.000
#> SRR1069400 1 0.0376 0.986 0.996 0.004
#> SRR1351016 1 0.0000 0.990 1.000 0.000
#> SRR1096291 2 0.0000 0.999 0.000 1.000
#> SRR1418145 2 0.0000 0.999 0.000 1.000
#> SRR1488111 1 0.0000 0.990 1.000 0.000
#> SRR1370495 1 0.0000 0.990 1.000 0.000
#> SRR1352639 2 0.0000 0.999 0.000 1.000
#> SRR1348911 1 0.0000 0.990 1.000 0.000
#> SRR1467386 2 0.0000 0.999 0.000 1.000
#> SRR1415956 1 0.0000 0.990 1.000 0.000
#> SRR1500495 1 0.0000 0.990 1.000 0.000
#> SRR1405099 1 0.0000 0.990 1.000 0.000
#> SRR1345585 1 0.0000 0.990 1.000 0.000
#> SRR1093196 2 0.0000 0.999 0.000 1.000
#> SRR1466006 1 0.0000 0.990 1.000 0.000
#> SRR1351557 1 0.0000 0.990 1.000 0.000
#> SRR1382687 1 0.0000 0.990 1.000 0.000
#> SRR1375549 1 0.0000 0.990 1.000 0.000
#> SRR1101765 2 0.0000 0.999 0.000 1.000
#> SRR1334461 1 0.0000 0.990 1.000 0.000
#> SRR1094073 2 0.0000 0.999 0.000 1.000
#> SRR1077549 2 0.0000 0.999 0.000 1.000
#> SRR1440332 2 0.2043 0.966 0.032 0.968
#> SRR1454177 2 0.0000 0.999 0.000 1.000
#> SRR1082447 2 0.0000 0.999 0.000 1.000
#> SRR1420043 1 0.0000 0.990 1.000 0.000
#> SRR1432500 2 0.0000 0.999 0.000 1.000
#> SRR1378045 2 0.0000 0.999 0.000 1.000
#> SRR1334200 1 0.0000 0.990 1.000 0.000
#> SRR1069539 2 0.0000 0.999 0.000 1.000
#> SRR1343031 2 0.0000 0.999 0.000 1.000
#> SRR1319690 1 0.0000 0.990 1.000 0.000
#> SRR1310604 2 0.0000 0.999 0.000 1.000
#> SRR1327747 1 0.0000 0.990 1.000 0.000
#> SRR1072456 1 0.0000 0.990 1.000 0.000
#> SRR1367896 1 0.0000 0.990 1.000 0.000
#> SRR1480107 1 0.0000 0.990 1.000 0.000
#> SRR1377756 1 0.0000 0.990 1.000 0.000
#> SRR1435272 2 0.0000 0.999 0.000 1.000
#> SRR1089230 2 0.0000 0.999 0.000 1.000
#> SRR1389522 1 0.0000 0.990 1.000 0.000
#> SRR1080600 2 0.0000 0.999 0.000 1.000
#> SRR1086935 2 0.0000 0.999 0.000 1.000
#> SRR1344060 1 0.0000 0.990 1.000 0.000
#> SRR1467922 2 0.0000 0.999 0.000 1.000
#> SRR1090984 1 0.0000 0.990 1.000 0.000
#> SRR1456991 1 0.0000 0.990 1.000 0.000
#> SRR1085039 2 0.0000 0.999 0.000 1.000
#> SRR1069303 1 0.0000 0.990 1.000 0.000
#> SRR1091500 2 0.0000 0.999 0.000 1.000
#> SRR1075198 1 0.9635 0.374 0.612 0.388
#> SRR1086915 2 0.0000 0.999 0.000 1.000
#> SRR1499503 2 0.0000 0.999 0.000 1.000
#> SRR1094312 2 0.0000 0.999 0.000 1.000
#> SRR1352437 2 0.0000 0.999 0.000 1.000
#> SRR1436323 1 0.0000 0.990 1.000 0.000
#> SRR1073507 2 0.0000 0.999 0.000 1.000
#> SRR1401972 2 0.0000 0.999 0.000 1.000
#> SRR1415510 1 0.0000 0.990 1.000 0.000
#> SRR1327279 2 0.0000 0.999 0.000 1.000
#> SRR1086983 2 0.0000 0.999 0.000 1.000
#> SRR1105174 2 0.0000 0.999 0.000 1.000
#> SRR1468893 1 0.0000 0.990 1.000 0.000
#> SRR1362555 1 0.0000 0.990 1.000 0.000
#> SRR1074526 2 0.0000 0.999 0.000 1.000
#> SRR1326225 2 0.0000 0.999 0.000 1.000
#> SRR1401933 1 0.0000 0.990 1.000 0.000
#> SRR1324062 1 0.0000 0.990 1.000 0.000
#> SRR1102296 2 0.0000 0.999 0.000 1.000
#> SRR1085087 2 0.0000 0.999 0.000 1.000
#> SRR1079046 1 0.0000 0.990 1.000 0.000
#> SRR1328339 1 0.0000 0.990 1.000 0.000
#> SRR1079782 2 0.0000 0.999 0.000 1.000
#> SRR1092257 2 0.0000 0.999 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 3 0.0237 0.979 0.000 0.004 0.996
#> SRR1429287 2 0.5216 0.790 0.260 0.740 0.000
#> SRR1359238 1 0.0424 0.880 0.992 0.008 0.000
#> SRR1309597 2 0.3482 0.881 0.128 0.872 0.000
#> SRR1441398 1 0.4178 0.861 0.828 0.172 0.000
#> SRR1084055 3 0.5431 0.625 0.000 0.284 0.716
#> SRR1417566 2 0.3482 0.881 0.128 0.872 0.000
#> SRR1351857 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1487485 2 0.0237 0.894 0.004 0.996 0.000
#> SRR1335875 1 0.0592 0.883 0.988 0.012 0.000
#> SRR1073947 1 0.1267 0.884 0.972 0.024 0.004
#> SRR1443483 2 0.0237 0.894 0.004 0.996 0.000
#> SRR1346794 1 0.3038 0.892 0.896 0.104 0.000
#> SRR1405245 1 0.4178 0.861 0.828 0.172 0.000
#> SRR1409677 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1095549 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1323788 1 0.3267 0.891 0.884 0.116 0.000
#> SRR1314054 3 0.0237 0.979 0.000 0.004 0.996
#> SRR1077944 1 0.3116 0.892 0.892 0.108 0.000
#> SRR1480587 2 0.3482 0.881 0.128 0.872 0.000
#> SRR1311205 1 0.4178 0.861 0.828 0.172 0.000
#> SRR1076369 2 0.0237 0.894 0.004 0.996 0.000
#> SRR1453549 1 0.2261 0.823 0.932 0.000 0.068
#> SRR1345782 1 0.3879 0.872 0.848 0.152 0.000
#> SRR1447850 3 0.0983 0.969 0.016 0.004 0.980
#> SRR1391553 1 0.3619 0.884 0.864 0.136 0.000
#> SRR1444156 3 0.0237 0.979 0.000 0.004 0.996
#> SRR1471731 1 0.4291 0.852 0.820 0.180 0.000
#> SRR1120987 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1477363 1 0.0592 0.883 0.988 0.012 0.000
#> SRR1391961 2 0.0237 0.894 0.004 0.996 0.000
#> SRR1373879 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1318732 2 0.3752 0.868 0.144 0.856 0.000
#> SRR1091404 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1402109 3 0.4178 0.821 0.000 0.172 0.828
#> SRR1407336 3 0.0424 0.976 0.000 0.008 0.992
#> SRR1097417 2 0.0000 0.892 0.000 1.000 0.000
#> SRR1396227 1 0.0592 0.883 0.988 0.012 0.000
#> SRR1400775 3 0.0237 0.979 0.000 0.004 0.996
#> SRR1392861 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1472929 2 0.3482 0.881 0.128 0.872 0.000
#> SRR1436740 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1477057 1 0.2356 0.867 0.928 0.072 0.000
#> SRR1311980 1 0.4178 0.861 0.828 0.172 0.000
#> SRR1069400 2 0.0000 0.892 0.000 1.000 0.000
#> SRR1351016 1 0.3340 0.890 0.880 0.120 0.000
#> SRR1096291 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1418145 3 0.0237 0.978 0.004 0.000 0.996
#> SRR1488111 1 0.0592 0.881 0.988 0.012 0.000
#> SRR1370495 2 0.3879 0.860 0.152 0.848 0.000
#> SRR1352639 3 0.0661 0.973 0.008 0.004 0.988
#> SRR1348911 2 0.3482 0.881 0.128 0.872 0.000
#> SRR1467386 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1415956 1 0.3686 0.883 0.860 0.140 0.000
#> SRR1500495 1 0.3686 0.883 0.860 0.140 0.000
#> SRR1405099 1 0.3686 0.883 0.860 0.140 0.000
#> SRR1345585 2 0.0424 0.895 0.008 0.992 0.000
#> SRR1093196 3 0.0592 0.975 0.000 0.012 0.988
#> SRR1466006 2 0.0237 0.894 0.004 0.996 0.000
#> SRR1351557 2 0.5178 0.794 0.256 0.744 0.000
#> SRR1382687 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1375549 1 0.0592 0.881 0.988 0.012 0.000
#> SRR1101765 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1334461 2 0.3482 0.881 0.128 0.872 0.000
#> SRR1094073 3 0.0237 0.979 0.000 0.004 0.996
#> SRR1077549 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1440332 1 0.6169 0.462 0.636 0.004 0.360
#> SRR1454177 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1082447 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1420043 1 0.3038 0.892 0.896 0.104 0.000
#> SRR1432500 1 0.3752 0.747 0.856 0.000 0.144
#> SRR1378045 3 0.3482 0.871 0.000 0.128 0.872
#> SRR1334200 2 0.3482 0.881 0.128 0.872 0.000
#> SRR1069539 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1343031 3 0.4172 0.836 0.004 0.156 0.840
#> SRR1319690 1 0.3340 0.890 0.880 0.120 0.000
#> SRR1310604 2 0.4390 0.755 0.012 0.840 0.148
#> SRR1327747 1 0.6215 0.335 0.572 0.428 0.000
#> SRR1072456 2 0.0424 0.895 0.008 0.992 0.000
#> SRR1367896 2 0.0237 0.894 0.004 0.996 0.000
#> SRR1480107 1 0.3267 0.890 0.884 0.116 0.000
#> SRR1377756 1 0.0747 0.884 0.984 0.016 0.000
#> SRR1435272 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1089230 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1389522 2 0.0237 0.894 0.004 0.996 0.000
#> SRR1080600 2 0.4059 0.772 0.012 0.860 0.128
#> SRR1086935 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1344060 2 0.3482 0.881 0.128 0.872 0.000
#> SRR1467922 3 0.0237 0.979 0.000 0.004 0.996
#> SRR1090984 2 0.3941 0.856 0.156 0.844 0.000
#> SRR1456991 1 0.3686 0.883 0.860 0.140 0.000
#> SRR1085039 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1069303 1 0.0747 0.884 0.984 0.016 0.000
#> SRR1091500 3 0.0237 0.979 0.000 0.004 0.996
#> SRR1075198 2 0.4136 0.780 0.020 0.864 0.116
#> SRR1086915 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1499503 3 0.0892 0.969 0.000 0.020 0.980
#> SRR1094312 3 0.0237 0.979 0.000 0.004 0.996
#> SRR1352437 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1436323 1 0.4121 0.863 0.832 0.168 0.000
#> SRR1073507 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1401972 1 0.3551 0.760 0.868 0.000 0.132
#> SRR1415510 2 0.1411 0.890 0.036 0.964 0.000
#> SRR1327279 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1086983 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1105174 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1468893 1 0.3192 0.892 0.888 0.112 0.000
#> SRR1362555 2 0.3879 0.860 0.152 0.848 0.000
#> SRR1074526 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1326225 3 0.0237 0.979 0.000 0.004 0.996
#> SRR1401933 1 0.0000 0.877 1.000 0.000 0.000
#> SRR1324062 1 0.0747 0.884 0.984 0.016 0.000
#> SRR1102296 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1085087 3 0.0000 0.980 0.000 0.000 1.000
#> SRR1079046 1 0.0592 0.881 0.988 0.012 0.000
#> SRR1328339 2 0.2261 0.892 0.068 0.932 0.000
#> SRR1079782 3 0.3644 0.866 0.124 0.004 0.872
#> SRR1092257 3 0.0000 0.980 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 4 0.4713 0.0910 0.000 0.360 0.000 0.640
#> SRR1429287 3 0.7416 0.1942 0.312 0.192 0.496 0.000
#> SRR1359238 1 0.4614 0.7079 0.792 0.064 0.144 0.000
#> SRR1309597 3 0.1022 0.7411 0.032 0.000 0.968 0.000
#> SRR1441398 3 0.4916 -0.1353 0.424 0.000 0.576 0.000
#> SRR1084055 2 0.5231 0.5893 0.000 0.676 0.028 0.296
#> SRR1417566 3 0.0707 0.7461 0.020 0.000 0.980 0.000
#> SRR1351857 4 0.2345 0.7502 0.000 0.100 0.000 0.900
#> SRR1487485 3 0.5028 0.4004 0.004 0.400 0.596 0.000
#> SRR1335875 1 0.1940 0.7519 0.924 0.000 0.076 0.000
#> SRR1073947 1 0.4105 0.6730 0.840 0.100 0.052 0.008
#> SRR1443483 3 0.4477 0.5751 0.000 0.312 0.688 0.000
#> SRR1346794 1 0.3074 0.7391 0.848 0.000 0.152 0.000
#> SRR1405245 1 0.5000 0.3451 0.500 0.000 0.500 0.000
#> SRR1409677 4 0.0000 0.7551 0.000 0.000 0.000 1.000
#> SRR1095549 4 0.2814 0.7354 0.000 0.132 0.000 0.868
#> SRR1323788 1 0.5752 0.6325 0.732 0.132 0.128 0.008
#> SRR1314054 4 0.4713 0.0910 0.000 0.360 0.000 0.640
#> SRR1077944 1 0.2773 0.7387 0.880 0.004 0.116 0.000
#> SRR1480587 3 0.1118 0.7389 0.036 0.000 0.964 0.000
#> SRR1311205 3 0.4925 -0.1500 0.428 0.000 0.572 0.000
#> SRR1076369 3 0.3649 0.6810 0.000 0.204 0.796 0.000
#> SRR1453549 1 0.1284 0.7200 0.964 0.012 0.000 0.024
#> SRR1345782 3 0.7068 0.3270 0.296 0.156 0.548 0.000
#> SRR1447850 2 0.5699 0.5455 0.032 0.588 0.000 0.380
#> SRR1391553 1 0.4925 0.5363 0.572 0.000 0.428 0.000
#> SRR1444156 4 0.4713 0.0910 0.000 0.360 0.000 0.640
#> SRR1471731 3 0.5155 -0.3428 0.468 0.004 0.528 0.000
#> SRR1120987 4 0.0000 0.7551 0.000 0.000 0.000 1.000
#> SRR1477363 1 0.0336 0.7311 0.992 0.000 0.008 0.000
#> SRR1391961 3 0.3448 0.7132 0.004 0.168 0.828 0.000
#> SRR1373879 4 0.2530 0.7462 0.000 0.112 0.000 0.888
#> SRR1318732 3 0.2281 0.6854 0.096 0.000 0.904 0.000
#> SRR1091404 4 0.2814 0.7343 0.000 0.132 0.000 0.868
#> SRR1402109 4 0.6974 0.2444 0.008 0.412 0.088 0.492
#> SRR1407336 4 0.4072 0.6155 0.000 0.252 0.000 0.748
#> SRR1097417 3 0.3726 0.6772 0.000 0.212 0.788 0.000
#> SRR1396227 1 0.1211 0.7430 0.960 0.000 0.040 0.000
#> SRR1400775 2 0.4989 0.4433 0.000 0.528 0.000 0.472
#> SRR1392861 4 0.0000 0.7551 0.000 0.000 0.000 1.000
#> SRR1472929 3 0.1510 0.7523 0.016 0.028 0.956 0.000
#> SRR1436740 4 0.0000 0.7551 0.000 0.000 0.000 1.000
#> SRR1477057 1 0.5432 0.6729 0.716 0.068 0.216 0.000
#> SRR1311980 1 0.5000 0.3451 0.500 0.000 0.500 0.000
#> SRR1069400 3 0.4477 0.5751 0.000 0.312 0.688 0.000
#> SRR1351016 1 0.4522 0.6604 0.680 0.000 0.320 0.000
#> SRR1096291 4 0.0000 0.7551 0.000 0.000 0.000 1.000
#> SRR1418145 4 0.3883 0.5867 0.012 0.144 0.012 0.832
#> SRR1488111 1 0.5172 0.6906 0.744 0.068 0.188 0.000
#> SRR1370495 3 0.1637 0.7221 0.060 0.000 0.940 0.000
#> SRR1352639 4 0.6565 0.4737 0.148 0.224 0.000 0.628
#> SRR1348911 3 0.1510 0.7523 0.016 0.028 0.956 0.000
#> SRR1467386 4 0.2345 0.7498 0.000 0.100 0.000 0.900
#> SRR1415956 1 0.4855 0.5498 0.600 0.000 0.400 0.000
#> SRR1500495 1 0.4898 0.5282 0.584 0.000 0.416 0.000
#> SRR1405099 1 0.4605 0.6418 0.664 0.000 0.336 0.000
#> SRR1345585 3 0.2329 0.7496 0.012 0.072 0.916 0.000
#> SRR1093196 2 0.4936 0.5111 0.000 0.624 0.004 0.372
#> SRR1466006 3 0.2179 0.7509 0.012 0.064 0.924 0.000
#> SRR1351557 2 0.6603 0.3156 0.104 0.580 0.316 0.000
#> SRR1382687 1 0.0000 0.7279 1.000 0.000 0.000 0.000
#> SRR1375549 1 0.5172 0.6906 0.744 0.068 0.188 0.000
#> SRR1101765 4 0.0000 0.7551 0.000 0.000 0.000 1.000
#> SRR1334461 3 0.1510 0.7523 0.016 0.028 0.956 0.000
#> SRR1094073 4 0.4713 0.0910 0.000 0.360 0.000 0.640
#> SRR1077549 4 0.2589 0.7444 0.000 0.116 0.000 0.884
#> SRR1440332 4 0.7660 0.3292 0.244 0.212 0.012 0.532
#> SRR1454177 4 0.0000 0.7551 0.000 0.000 0.000 1.000
#> SRR1082447 4 0.2530 0.7462 0.000 0.112 0.000 0.888
#> SRR1420043 1 0.2589 0.7397 0.884 0.000 0.116 0.000
#> SRR1432500 1 0.6269 0.2683 0.632 0.096 0.000 0.272
#> SRR1378045 2 0.3933 0.5864 0.000 0.792 0.008 0.200
#> SRR1334200 3 0.0592 0.7475 0.016 0.000 0.984 0.000
#> SRR1069539 4 0.0000 0.7551 0.000 0.000 0.000 1.000
#> SRR1343031 4 0.6871 0.2584 0.008 0.412 0.080 0.500
#> SRR1319690 1 0.4250 0.6993 0.724 0.000 0.276 0.000
#> SRR1310604 2 0.6603 0.3178 0.000 0.580 0.316 0.104
#> SRR1327747 3 0.5110 0.1161 0.352 0.012 0.636 0.000
#> SRR1072456 3 0.3108 0.7449 0.016 0.112 0.872 0.000
#> SRR1367896 3 0.3688 0.6808 0.000 0.208 0.792 0.000
#> SRR1480107 1 0.4103 0.7043 0.744 0.000 0.256 0.000
#> SRR1377756 1 0.1022 0.7387 0.968 0.000 0.032 0.000
#> SRR1435272 4 0.0000 0.7551 0.000 0.000 0.000 1.000
#> SRR1089230 4 0.0000 0.7551 0.000 0.000 0.000 1.000
#> SRR1389522 3 0.3873 0.6688 0.000 0.228 0.772 0.000
#> SRR1080600 2 0.4608 0.2264 0.000 0.692 0.304 0.004
#> SRR1086935 4 0.0000 0.7551 0.000 0.000 0.000 1.000
#> SRR1344060 3 0.1510 0.7523 0.016 0.028 0.956 0.000
#> SRR1467922 2 0.4994 0.4257 0.000 0.520 0.000 0.480
#> SRR1090984 3 0.1867 0.7113 0.072 0.000 0.928 0.000
#> SRR1456991 1 0.4994 0.3854 0.520 0.000 0.480 0.000
#> SRR1085039 4 0.2530 0.7462 0.000 0.112 0.000 0.888
#> SRR1069303 1 0.1792 0.7499 0.932 0.000 0.068 0.000
#> SRR1091500 4 0.4713 0.0910 0.000 0.360 0.000 0.640
#> SRR1075198 2 0.4673 0.3051 0.008 0.700 0.292 0.000
#> SRR1086915 4 0.0000 0.7551 0.000 0.000 0.000 1.000
#> SRR1499503 2 0.4898 0.5155 0.000 0.584 0.000 0.416
#> SRR1094312 2 0.4989 0.4433 0.000 0.528 0.000 0.472
#> SRR1352437 4 0.0188 0.7551 0.000 0.004 0.000 0.996
#> SRR1436323 1 0.5329 0.5438 0.568 0.012 0.420 0.000
#> SRR1073507 4 0.2345 0.7498 0.000 0.100 0.000 0.900
#> SRR1401972 1 0.2053 0.6839 0.924 0.004 0.000 0.072
#> SRR1415510 3 0.5987 0.0899 0.040 0.440 0.520 0.000
#> SRR1327279 4 0.3219 0.7110 0.000 0.164 0.000 0.836
#> SRR1086983 4 0.2345 0.7498 0.000 0.100 0.000 0.900
#> SRR1105174 4 0.2469 0.7474 0.000 0.108 0.000 0.892
#> SRR1468893 1 0.4040 0.7162 0.752 0.000 0.248 0.000
#> SRR1362555 3 0.1557 0.7252 0.056 0.000 0.944 0.000
#> SRR1074526 4 0.1389 0.7330 0.000 0.048 0.000 0.952
#> SRR1326225 4 0.4998 -0.3968 0.000 0.488 0.000 0.512
#> SRR1401933 1 0.3239 0.7165 0.880 0.068 0.052 0.000
#> SRR1324062 1 0.1716 0.7490 0.936 0.000 0.064 0.000
#> SRR1102296 4 0.3801 0.4798 0.000 0.220 0.000 0.780
#> SRR1085087 4 0.2345 0.7498 0.000 0.100 0.000 0.900
#> SRR1079046 1 0.5007 0.6966 0.760 0.068 0.172 0.000
#> SRR1328339 3 0.1970 0.7502 0.008 0.060 0.932 0.000
#> SRR1079782 2 0.7413 0.5693 0.120 0.576 0.028 0.276
#> SRR1092257 4 0.4304 0.3224 0.000 0.284 0.000 0.716
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 4 0.4304 0.2041 0.000 0.484 0.000 0.516 0.000
#> SRR1429287 3 0.6591 0.1253 0.108 0.304 0.548 0.000 0.040
#> SRR1359238 3 0.7444 -0.0173 0.344 0.224 0.392 0.000 0.040
#> SRR1309597 3 0.2074 0.5082 0.000 0.000 0.896 0.000 0.104
#> SRR1441398 3 0.4327 0.4022 0.360 0.000 0.632 0.000 0.008
#> SRR1084055 2 0.5786 0.5435 0.000 0.632 0.004 0.196 0.168
#> SRR1417566 3 0.3183 0.4820 0.016 0.000 0.828 0.000 0.156
#> SRR1351857 4 0.0771 0.8256 0.000 0.004 0.000 0.976 0.020
#> SRR1487485 3 0.6693 -0.1735 0.000 0.364 0.396 0.000 0.240
#> SRR1335875 1 0.3339 0.6572 0.836 0.040 0.124 0.000 0.000
#> SRR1073947 1 0.3462 0.6902 0.860 0.012 0.028 0.016 0.084
#> SRR1443483 5 0.1608 0.6373 0.000 0.000 0.072 0.000 0.928
#> SRR1346794 1 0.3631 0.6185 0.788 0.008 0.196 0.000 0.008
#> SRR1405245 3 0.4341 0.3993 0.364 0.000 0.628 0.000 0.008
#> SRR1409677 4 0.0609 0.8280 0.000 0.020 0.000 0.980 0.000
#> SRR1095549 4 0.2798 0.7430 0.008 0.000 0.000 0.852 0.140
#> SRR1323788 1 0.4870 0.5179 0.680 0.000 0.028 0.016 0.276
#> SRR1314054 4 0.4304 0.2041 0.000 0.484 0.000 0.516 0.000
#> SRR1077944 1 0.1981 0.7001 0.920 0.000 0.064 0.000 0.016
#> SRR1480587 3 0.1965 0.5117 0.000 0.000 0.904 0.000 0.096
#> SRR1311205 3 0.4341 0.3993 0.364 0.000 0.628 0.000 0.008
#> SRR1076369 5 0.4327 0.5236 0.000 0.008 0.360 0.000 0.632
#> SRR1453549 1 0.4821 0.5826 0.776 0.064 0.120 0.008 0.032
#> SRR1345782 5 0.5281 0.1910 0.348 0.000 0.044 0.008 0.600
#> SRR1447850 2 0.1430 0.5696 0.000 0.944 0.000 0.052 0.004
#> SRR1391553 3 0.4443 0.4223 0.300 0.012 0.680 0.000 0.008
#> SRR1444156 4 0.4304 0.2041 0.000 0.484 0.000 0.516 0.000
#> SRR1471731 3 0.3073 0.5217 0.116 0.024 0.856 0.000 0.004
#> SRR1120987 4 0.0794 0.8259 0.000 0.028 0.000 0.972 0.000
#> SRR1477363 1 0.0404 0.7106 0.988 0.000 0.012 0.000 0.000
#> SRR1391961 5 0.4331 0.4519 0.000 0.004 0.400 0.000 0.596
#> SRR1373879 4 0.1121 0.8189 0.000 0.000 0.000 0.956 0.044
#> SRR1318732 3 0.2653 0.5202 0.024 0.000 0.880 0.000 0.096
#> SRR1091404 4 0.1124 0.8228 0.000 0.004 0.000 0.960 0.036
#> SRR1402109 5 0.2561 0.4864 0.000 0.000 0.000 0.144 0.856
#> SRR1407336 4 0.4682 0.4370 0.000 0.024 0.000 0.620 0.356
#> SRR1097417 5 0.4327 0.5236 0.000 0.008 0.360 0.000 0.632
#> SRR1396227 1 0.2427 0.7078 0.912 0.028 0.048 0.004 0.008
#> SRR1400775 2 0.3305 0.5351 0.000 0.776 0.000 0.224 0.000
#> SRR1392861 4 0.0510 0.8287 0.000 0.016 0.000 0.984 0.000
#> SRR1472929 3 0.3684 0.3410 0.000 0.000 0.720 0.000 0.280
#> SRR1436740 4 0.0703 0.8272 0.000 0.024 0.000 0.976 0.000
#> SRR1477057 3 0.6793 0.2565 0.172 0.228 0.560 0.000 0.040
#> SRR1311980 3 0.4327 0.4037 0.360 0.000 0.632 0.000 0.008
#> SRR1069400 5 0.1671 0.6367 0.000 0.000 0.076 0.000 0.924
#> SRR1351016 3 0.4434 0.2372 0.460 0.000 0.536 0.000 0.004
#> SRR1096291 4 0.0671 0.8288 0.000 0.016 0.000 0.980 0.004
#> SRR1418145 4 0.4890 0.6330 0.016 0.092 0.104 0.772 0.016
#> SRR1488111 3 0.7318 0.1102 0.268 0.232 0.460 0.000 0.040
#> SRR1370495 3 0.2020 0.5110 0.000 0.000 0.900 0.000 0.100
#> SRR1352639 1 0.7435 0.1435 0.360 0.032 0.000 0.264 0.344
#> SRR1348911 3 0.3814 0.3523 0.004 0.000 0.720 0.000 0.276
#> SRR1467386 4 0.0992 0.8215 0.008 0.000 0.000 0.968 0.024
#> SRR1415956 3 0.4367 0.3211 0.416 0.000 0.580 0.000 0.004
#> SRR1500495 3 0.4331 0.3486 0.400 0.000 0.596 0.000 0.004
#> SRR1405099 3 0.4434 0.2303 0.460 0.000 0.536 0.000 0.004
#> SRR1345585 3 0.3333 0.3965 0.000 0.004 0.788 0.000 0.208
#> SRR1093196 2 0.7162 0.4066 0.000 0.484 0.040 0.188 0.288
#> SRR1466006 3 0.3829 0.3953 0.000 0.028 0.776 0.000 0.196
#> SRR1351557 2 0.5881 0.3404 0.048 0.564 0.356 0.000 0.032
#> SRR1382687 1 0.1970 0.6840 0.924 0.012 0.060 0.000 0.004
#> SRR1375549 3 0.7301 0.1131 0.268 0.228 0.464 0.000 0.040
#> SRR1101765 4 0.0794 0.8258 0.000 0.028 0.000 0.972 0.000
#> SRR1334461 3 0.3816 0.3101 0.000 0.000 0.696 0.000 0.304
#> SRR1094073 4 0.4304 0.2041 0.000 0.484 0.000 0.516 0.000
#> SRR1077549 4 0.1484 0.8127 0.008 0.000 0.000 0.944 0.048
#> SRR1440332 1 0.6682 0.1261 0.396 0.000 0.000 0.236 0.368
#> SRR1454177 4 0.0510 0.8287 0.000 0.016 0.000 0.984 0.000
#> SRR1082447 4 0.1082 0.8208 0.008 0.000 0.000 0.964 0.028
#> SRR1420043 1 0.1952 0.6881 0.912 0.000 0.084 0.000 0.004
#> SRR1432500 1 0.4556 0.4483 0.680 0.000 0.004 0.292 0.024
#> SRR1378045 2 0.4546 0.4548 0.000 0.668 0.000 0.028 0.304
#> SRR1334200 3 0.2074 0.5064 0.000 0.000 0.896 0.000 0.104
#> SRR1069539 4 0.0865 0.8275 0.000 0.024 0.000 0.972 0.004
#> SRR1343031 5 0.2561 0.4864 0.000 0.000 0.000 0.144 0.856
#> SRR1319690 3 0.4889 0.1871 0.476 0.004 0.504 0.000 0.016
#> SRR1310604 2 0.7691 0.2904 0.000 0.448 0.200 0.080 0.272
#> SRR1327747 3 0.3932 0.4703 0.116 0.032 0.820 0.000 0.032
#> SRR1072456 3 0.4331 0.0673 0.000 0.004 0.596 0.000 0.400
#> SRR1367896 5 0.4101 0.4957 0.000 0.000 0.372 0.000 0.628
#> SRR1480107 1 0.4410 -0.0186 0.556 0.000 0.440 0.000 0.004
#> SRR1377756 1 0.0671 0.7085 0.980 0.000 0.016 0.000 0.004
#> SRR1435272 4 0.0510 0.8287 0.000 0.016 0.000 0.984 0.000
#> SRR1089230 4 0.0510 0.8287 0.000 0.016 0.000 0.984 0.000
#> SRR1389522 5 0.2690 0.6550 0.000 0.000 0.156 0.000 0.844
#> SRR1080600 2 0.6374 0.2599 0.000 0.504 0.196 0.000 0.300
#> SRR1086935 4 0.0794 0.8259 0.000 0.028 0.000 0.972 0.000
#> SRR1344060 3 0.3752 0.3225 0.000 0.000 0.708 0.000 0.292
#> SRR1467922 2 0.3816 0.4072 0.000 0.696 0.000 0.304 0.000
#> SRR1090984 3 0.2179 0.5136 0.004 0.000 0.896 0.000 0.100
#> SRR1456991 3 0.4436 0.3540 0.396 0.000 0.596 0.000 0.008
#> SRR1085039 4 0.1082 0.8208 0.008 0.000 0.000 0.964 0.028
#> SRR1069303 1 0.2881 0.6906 0.876 0.024 0.092 0.000 0.008
#> SRR1091500 4 0.4304 0.2041 0.000 0.484 0.000 0.516 0.000
#> SRR1075198 2 0.6715 0.3676 0.028 0.500 0.340 0.000 0.132
#> SRR1086915 4 0.0510 0.8287 0.000 0.016 0.000 0.984 0.000
#> SRR1499503 2 0.4465 0.5593 0.000 0.736 0.000 0.204 0.060
#> SRR1094312 2 0.3336 0.5306 0.000 0.772 0.000 0.228 0.000
#> SRR1352437 4 0.1059 0.8278 0.008 0.020 0.000 0.968 0.004
#> SRR1436323 3 0.4447 0.4689 0.172 0.028 0.768 0.000 0.032
#> SRR1073507 4 0.0992 0.8215 0.008 0.000 0.000 0.968 0.024
#> SRR1401972 1 0.2704 0.6991 0.896 0.028 0.004 0.064 0.008
#> SRR1415510 2 0.6388 0.3200 0.048 0.524 0.364 0.000 0.064
#> SRR1327279 4 0.3835 0.6011 0.008 0.000 0.000 0.732 0.260
#> SRR1086983 4 0.0898 0.8222 0.008 0.000 0.000 0.972 0.020
#> SRR1105174 4 0.0992 0.8215 0.008 0.000 0.000 0.968 0.024
#> SRR1468893 1 0.4201 0.0834 0.592 0.000 0.408 0.000 0.000
#> SRR1362555 3 0.1965 0.5117 0.000 0.000 0.904 0.000 0.096
#> SRR1074526 4 0.4015 0.4734 0.000 0.348 0.000 0.652 0.000
#> SRR1326225 2 0.3983 0.3237 0.000 0.660 0.000 0.340 0.000
#> SRR1401933 1 0.6761 0.3738 0.568 0.208 0.184 0.000 0.040
#> SRR1324062 1 0.2881 0.6906 0.876 0.024 0.092 0.000 0.008
#> SRR1102296 4 0.4844 0.3430 0.012 0.416 0.000 0.564 0.008
#> SRR1085087 4 0.0898 0.8222 0.008 0.000 0.000 0.972 0.020
#> SRR1079046 3 0.7400 0.0428 0.304 0.228 0.428 0.000 0.040
#> SRR1328339 3 0.4798 -0.0839 0.020 0.000 0.540 0.000 0.440
#> SRR1079782 2 0.6588 0.4180 0.072 0.624 0.232 0.044 0.028
#> SRR1092257 4 0.3003 0.6916 0.000 0.188 0.000 0.812 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.3695 0.5370 0.000 0.624 0.000 0.376 0.000 0.000
#> SRR1429287 6 0.3043 0.7209 0.008 0.020 0.000 0.000 0.140 0.832
#> SRR1359238 6 0.2449 0.6510 0.080 0.000 0.012 0.000 0.020 0.888
#> SRR1309597 5 0.0858 0.6893 0.004 0.000 0.000 0.000 0.968 0.028
#> SRR1441398 5 0.3499 0.5483 0.320 0.000 0.000 0.000 0.680 0.000
#> SRR1084055 2 0.3698 0.5523 0.004 0.812 0.124 0.044 0.012 0.004
#> SRR1417566 5 0.1636 0.6929 0.024 0.000 0.004 0.000 0.936 0.036
#> SRR1351857 4 0.1297 0.8948 0.000 0.012 0.040 0.948 0.000 0.000
#> SRR1487485 5 0.7098 0.0725 0.008 0.288 0.192 0.000 0.436 0.076
#> SRR1335875 1 0.3686 0.6395 0.828 0.028 0.008 0.000 0.072 0.064
#> SRR1073947 1 0.2615 0.6571 0.896 0.024 0.052 0.008 0.016 0.004
#> SRR1443483 3 0.1327 0.5909 0.000 0.000 0.936 0.000 0.064 0.000
#> SRR1346794 1 0.5655 0.3725 0.520 0.004 0.012 0.000 0.100 0.364
#> SRR1405245 5 0.3515 0.5444 0.324 0.000 0.000 0.000 0.676 0.000
#> SRR1409677 4 0.0865 0.9018 0.000 0.036 0.000 0.964 0.000 0.000
#> SRR1095549 4 0.3601 0.5523 0.004 0.000 0.312 0.684 0.000 0.000
#> SRR1323788 1 0.5329 0.2959 0.560 0.004 0.348 0.000 0.008 0.080
#> SRR1314054 2 0.3695 0.5370 0.000 0.624 0.000 0.376 0.000 0.000
#> SRR1077944 1 0.4450 0.5994 0.688 0.004 0.016 0.000 0.028 0.264
#> SRR1480587 5 0.0865 0.6884 0.000 0.000 0.000 0.000 0.964 0.036
#> SRR1311205 5 0.3515 0.5444 0.324 0.000 0.000 0.000 0.676 0.000
#> SRR1076369 3 0.5013 0.1733 0.008 0.012 0.488 0.000 0.464 0.028
#> SRR1453549 6 0.4012 0.3909 0.276 0.004 0.012 0.008 0.000 0.700
#> SRR1345782 3 0.5165 0.3554 0.256 0.004 0.656 0.004 0.032 0.048
#> SRR1447850 2 0.1584 0.5575 0.000 0.928 0.000 0.008 0.000 0.064
#> SRR1391553 5 0.5123 0.5135 0.188 0.000 0.000 0.000 0.628 0.184
#> SRR1444156 2 0.3647 0.5520 0.000 0.640 0.000 0.360 0.000 0.000
#> SRR1471731 5 0.4619 0.5078 0.088 0.000 0.000 0.000 0.668 0.244
#> SRR1120987 4 0.0865 0.9018 0.000 0.036 0.000 0.964 0.000 0.000
#> SRR1477363 1 0.2805 0.6526 0.828 0.000 0.012 0.000 0.000 0.160
#> SRR1391961 5 0.4617 -0.1731 0.008 0.004 0.464 0.000 0.508 0.016
#> SRR1373879 4 0.2002 0.8737 0.004 0.012 0.076 0.908 0.000 0.000
#> SRR1318732 5 0.2088 0.6894 0.028 0.000 0.000 0.000 0.904 0.068
#> SRR1091404 4 0.1913 0.8856 0.004 0.012 0.060 0.920 0.000 0.004
#> SRR1402109 3 0.1196 0.5804 0.008 0.000 0.952 0.040 0.000 0.000
#> SRR1407336 3 0.4374 -0.1014 0.004 0.016 0.532 0.448 0.000 0.000
#> SRR1097417 3 0.5012 0.1833 0.008 0.012 0.492 0.000 0.460 0.028
#> SRR1396227 1 0.2820 0.6628 0.884 0.032 0.008 0.000 0.032 0.044
#> SRR1400775 2 0.1010 0.6084 0.000 0.960 0.000 0.036 0.000 0.004
#> SRR1392861 4 0.0865 0.9018 0.000 0.036 0.000 0.964 0.000 0.000
#> SRR1472929 5 0.1663 0.6480 0.000 0.000 0.088 0.000 0.912 0.000
#> SRR1436740 4 0.0865 0.9018 0.000 0.036 0.000 0.964 0.000 0.000
#> SRR1477057 6 0.3643 0.6883 0.024 0.008 0.000 0.000 0.200 0.768
#> SRR1311980 5 0.3499 0.5473 0.320 0.000 0.000 0.000 0.680 0.000
#> SRR1069400 3 0.1615 0.5899 0.000 0.004 0.928 0.000 0.064 0.004
#> SRR1351016 5 0.3961 0.3389 0.440 0.000 0.000 0.000 0.556 0.004
#> SRR1096291 4 0.1320 0.9017 0.000 0.036 0.016 0.948 0.000 0.000
#> SRR1418145 4 0.4428 0.5056 0.004 0.052 0.000 0.676 0.000 0.268
#> SRR1488111 6 0.2691 0.7173 0.032 0.008 0.000 0.000 0.088 0.872
#> SRR1370495 5 0.1010 0.6894 0.004 0.000 0.000 0.000 0.960 0.036
#> SRR1352639 3 0.5751 0.0688 0.412 0.016 0.480 0.084 0.000 0.008
#> SRR1348911 5 0.1745 0.6705 0.020 0.000 0.056 0.000 0.924 0.000
#> SRR1467386 4 0.0603 0.8958 0.004 0.000 0.016 0.980 0.000 0.000
#> SRR1415956 5 0.3899 0.4174 0.404 0.000 0.000 0.000 0.592 0.004
#> SRR1500495 5 0.3819 0.4738 0.372 0.000 0.000 0.000 0.624 0.004
#> SRR1405099 5 0.3982 0.2963 0.460 0.000 0.000 0.000 0.536 0.004
#> SRR1345585 5 0.3818 0.5939 0.004 0.000 0.084 0.000 0.784 0.128
#> SRR1093196 2 0.6380 0.0182 0.000 0.444 0.396 0.096 0.004 0.060
#> SRR1466006 5 0.4428 0.5206 0.008 0.012 0.116 0.000 0.756 0.108
#> SRR1351557 6 0.4910 0.5935 0.008 0.304 0.000 0.000 0.068 0.620
#> SRR1382687 1 0.3729 0.5445 0.692 0.000 0.012 0.000 0.000 0.296
#> SRR1375549 6 0.2680 0.7163 0.032 0.000 0.000 0.000 0.108 0.860
#> SRR1101765 4 0.1141 0.8912 0.000 0.052 0.000 0.948 0.000 0.000
#> SRR1334461 5 0.1863 0.6386 0.000 0.000 0.104 0.000 0.896 0.000
#> SRR1094073 2 0.3647 0.5520 0.000 0.640 0.000 0.360 0.000 0.000
#> SRR1077549 4 0.1753 0.8610 0.004 0.000 0.084 0.912 0.000 0.000
#> SRR1440332 3 0.6177 0.1738 0.316 0.000 0.532 0.080 0.004 0.068
#> SRR1454177 4 0.0865 0.9018 0.000 0.036 0.000 0.964 0.000 0.000
#> SRR1082447 4 0.1285 0.8840 0.004 0.000 0.052 0.944 0.000 0.000
#> SRR1420043 1 0.4521 0.5919 0.680 0.004 0.012 0.000 0.036 0.268
#> SRR1432500 1 0.6242 0.3222 0.512 0.004 0.020 0.256 0.000 0.208
#> SRR1378045 2 0.3076 0.4967 0.000 0.760 0.240 0.000 0.000 0.000
#> SRR1334200 5 0.1863 0.6700 0.004 0.000 0.016 0.000 0.920 0.060
#> SRR1069539 4 0.1480 0.9001 0.000 0.040 0.020 0.940 0.000 0.000
#> SRR1343031 3 0.1196 0.5804 0.008 0.000 0.952 0.040 0.000 0.000
#> SRR1319690 5 0.6388 -0.0547 0.332 0.000 0.012 0.000 0.372 0.284
#> SRR1310604 2 0.7493 -0.0123 0.008 0.448 0.156 0.004 0.208 0.176
#> SRR1327747 6 0.4467 0.5619 0.044 0.004 0.012 0.000 0.236 0.704
#> SRR1072456 5 0.3742 0.5061 0.008 0.004 0.176 0.000 0.780 0.032
#> SRR1367896 3 0.4534 0.1784 0.008 0.004 0.516 0.000 0.460 0.012
#> SRR1480107 1 0.4067 -0.0980 0.548 0.000 0.000 0.000 0.444 0.008
#> SRR1377756 1 0.3341 0.6298 0.776 0.004 0.012 0.000 0.000 0.208
#> SRR1435272 4 0.0865 0.9018 0.000 0.036 0.000 0.964 0.000 0.000
#> SRR1089230 4 0.0865 0.9018 0.000 0.036 0.000 0.964 0.000 0.000
#> SRR1389522 3 0.2558 0.5687 0.000 0.004 0.840 0.000 0.156 0.000
#> SRR1080600 2 0.6815 0.1384 0.008 0.524 0.160 0.000 0.216 0.092
#> SRR1086935 4 0.0937 0.8994 0.000 0.040 0.000 0.960 0.000 0.000
#> SRR1344060 5 0.2051 0.6378 0.004 0.000 0.096 0.000 0.896 0.004
#> SRR1467922 2 0.2300 0.6536 0.000 0.856 0.000 0.144 0.000 0.000
#> SRR1090984 5 0.1196 0.6905 0.008 0.000 0.000 0.000 0.952 0.040
#> SRR1456991 5 0.3819 0.4738 0.372 0.000 0.000 0.000 0.624 0.004
#> SRR1085039 4 0.1285 0.8840 0.004 0.000 0.052 0.944 0.000 0.000
#> SRR1069303 1 0.2956 0.6624 0.876 0.028 0.008 0.000 0.044 0.044
#> SRR1091500 2 0.3659 0.5463 0.000 0.636 0.000 0.364 0.000 0.000
#> SRR1075198 6 0.5791 0.5137 0.004 0.352 0.032 0.000 0.080 0.532
#> SRR1086915 4 0.0865 0.9018 0.000 0.036 0.000 0.964 0.000 0.000
#> SRR1499503 2 0.1461 0.6141 0.000 0.940 0.016 0.044 0.000 0.000
#> SRR1094312 2 0.1010 0.6084 0.000 0.960 0.000 0.036 0.000 0.004
#> SRR1352437 4 0.0632 0.9010 0.000 0.024 0.000 0.976 0.000 0.000
#> SRR1436323 6 0.4666 0.2797 0.048 0.000 0.000 0.000 0.388 0.564
#> SRR1073507 4 0.0935 0.8917 0.004 0.000 0.032 0.964 0.000 0.000
#> SRR1401972 1 0.2893 0.6522 0.880 0.032 0.008 0.036 0.000 0.044
#> SRR1415510 6 0.4663 0.6772 0.012 0.160 0.008 0.000 0.092 0.728
#> SRR1327279 4 0.4086 0.2032 0.008 0.000 0.464 0.528 0.000 0.000
#> SRR1086983 4 0.0000 0.8992 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1105174 4 0.1285 0.8840 0.004 0.000 0.052 0.944 0.000 0.000
#> SRR1468893 1 0.4136 -0.0486 0.560 0.000 0.000 0.000 0.428 0.012
#> SRR1362555 5 0.1285 0.6878 0.004 0.000 0.000 0.000 0.944 0.052
#> SRR1074526 2 0.4218 0.4017 0.000 0.556 0.016 0.428 0.000 0.000
#> SRR1326225 2 0.2912 0.6565 0.000 0.784 0.000 0.216 0.000 0.000
#> SRR1401933 6 0.4403 0.5573 0.280 0.020 0.000 0.000 0.024 0.676
#> SRR1324062 1 0.2956 0.6624 0.876 0.028 0.008 0.000 0.044 0.044
#> SRR1102296 2 0.5630 0.4596 0.096 0.524 0.008 0.364 0.000 0.008
#> SRR1085087 4 0.0748 0.8950 0.004 0.004 0.016 0.976 0.000 0.000
#> SRR1079046 6 0.3710 0.6920 0.120 0.016 0.000 0.000 0.060 0.804
#> SRR1328339 5 0.3683 0.5439 0.044 0.000 0.192 0.000 0.764 0.000
#> SRR1079782 6 0.5782 0.6263 0.108 0.236 0.000 0.020 0.020 0.616
#> SRR1092257 4 0.2260 0.7852 0.000 0.140 0.000 0.860 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 17611 rows and 118 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.564 0.773 0.894 0.4522 0.560 0.560
#> 3 3 0.900 0.904 0.962 0.4266 0.728 0.544
#> 4 4 0.801 0.851 0.922 0.1564 0.857 0.623
#> 5 5 0.732 0.747 0.869 0.0541 0.946 0.794
#> 6 6 0.763 0.716 0.825 0.0342 0.956 0.805
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
#> SRR1396765 1 0.0000 0.8422 1.000 0.000
#> SRR1429287 1 0.9710 0.5232 0.600 0.400
#> SRR1359238 1 0.9286 0.5960 0.656 0.344
#> SRR1309597 2 0.0000 0.9350 0.000 1.000
#> SRR1441398 2 0.0000 0.9350 0.000 1.000
#> SRR1084055 1 0.0000 0.8422 1.000 0.000
#> SRR1417566 2 0.9896 -0.0564 0.440 0.560
#> SRR1351857 1 0.0000 0.8422 1.000 0.000
#> SRR1487485 1 0.6712 0.7560 0.824 0.176
#> SRR1335875 1 0.9710 0.5232 0.600 0.400
#> SRR1073947 2 0.6438 0.7335 0.164 0.836
#> SRR1443483 2 0.0938 0.9237 0.012 0.988
#> SRR1346794 1 0.9710 0.5232 0.600 0.400
#> SRR1405245 2 0.0000 0.9350 0.000 1.000
#> SRR1409677 1 0.0000 0.8422 1.000 0.000
#> SRR1095549 1 0.0000 0.8422 1.000 0.000
#> SRR1323788 1 0.9710 0.5232 0.600 0.400
#> SRR1314054 1 0.0000 0.8422 1.000 0.000
#> SRR1077944 1 0.9710 0.5232 0.600 0.400
#> SRR1480587 2 0.0000 0.9350 0.000 1.000
#> SRR1311205 2 0.0000 0.9350 0.000 1.000
#> SRR1076369 2 0.0000 0.9350 0.000 1.000
#> SRR1453549 1 0.6712 0.7560 0.824 0.176
#> SRR1345782 2 0.9775 0.0676 0.412 0.588
#> SRR1447850 1 0.0000 0.8422 1.000 0.000
#> SRR1391553 1 0.9833 0.4716 0.576 0.424
#> SRR1444156 1 0.0000 0.8422 1.000 0.000
#> SRR1471731 2 0.0000 0.9350 0.000 1.000
#> SRR1120987 1 0.0000 0.8422 1.000 0.000
#> SRR1477363 1 0.9710 0.5232 0.600 0.400
#> SRR1391961 2 0.4298 0.8392 0.088 0.912
#> SRR1373879 1 0.0000 0.8422 1.000 0.000
#> SRR1318732 2 0.0000 0.9350 0.000 1.000
#> SRR1091404 1 0.1843 0.8335 0.972 0.028
#> SRR1402109 1 0.6712 0.7560 0.824 0.176
#> SRR1407336 1 0.0000 0.8422 1.000 0.000
#> SRR1097417 2 0.8813 0.5107 0.300 0.700
#> SRR1396227 1 0.9710 0.5232 0.600 0.400
#> SRR1400775 1 0.0000 0.8422 1.000 0.000
#> SRR1392861 1 0.0000 0.8422 1.000 0.000
#> SRR1472929 2 0.0000 0.9350 0.000 1.000
#> SRR1436740 1 0.0000 0.8422 1.000 0.000
#> SRR1477057 1 0.9710 0.5232 0.600 0.400
#> SRR1311980 2 0.0000 0.9350 0.000 1.000
#> SRR1069400 1 0.7299 0.7353 0.796 0.204
#> SRR1351016 2 0.0000 0.9350 0.000 1.000
#> SRR1096291 1 0.0000 0.8422 1.000 0.000
#> SRR1418145 1 0.0000 0.8422 1.000 0.000
#> SRR1488111 1 0.9710 0.5232 0.600 0.400
#> SRR1370495 2 0.0000 0.9350 0.000 1.000
#> SRR1352639 1 0.2236 0.8310 0.964 0.036
#> SRR1348911 2 0.0000 0.9350 0.000 1.000
#> SRR1467386 1 0.0000 0.8422 1.000 0.000
#> SRR1415956 2 0.0000 0.9350 0.000 1.000
#> SRR1500495 2 0.0000 0.9350 0.000 1.000
#> SRR1405099 2 0.0000 0.9350 0.000 1.000
#> SRR1345585 2 0.9970 -0.1801 0.468 0.532
#> SRR1093196 1 0.1843 0.8335 0.972 0.028
#> SRR1466006 2 0.0000 0.9350 0.000 1.000
#> SRR1351557 1 0.9710 0.5232 0.600 0.400
#> SRR1382687 1 0.9710 0.5232 0.600 0.400
#> SRR1375549 1 0.9710 0.5232 0.600 0.400
#> SRR1101765 1 0.0000 0.8422 1.000 0.000
#> SRR1334461 2 0.0000 0.9350 0.000 1.000
#> SRR1094073 1 0.0000 0.8422 1.000 0.000
#> SRR1077549 1 0.0000 0.8422 1.000 0.000
#> SRR1440332 1 0.6712 0.7560 0.824 0.176
#> SRR1454177 1 0.0000 0.8422 1.000 0.000
#> SRR1082447 1 0.0000 0.8422 1.000 0.000
#> SRR1420043 1 0.9710 0.5232 0.600 0.400
#> SRR1432500 1 0.2236 0.8305 0.964 0.036
#> SRR1378045 1 0.5059 0.7936 0.888 0.112
#> SRR1334200 2 0.0000 0.9350 0.000 1.000
#> SRR1069539 1 0.0000 0.8422 1.000 0.000
#> SRR1343031 1 0.6801 0.7537 0.820 0.180
#> SRR1319690 1 0.9710 0.5232 0.600 0.400
#> SRR1310604 1 0.1414 0.8362 0.980 0.020
#> SRR1327747 1 0.9710 0.5232 0.600 0.400
#> SRR1072456 2 0.0000 0.9350 0.000 1.000
#> SRR1367896 2 0.0000 0.9350 0.000 1.000
#> SRR1480107 2 0.0000 0.9350 0.000 1.000
#> SRR1377756 1 0.9710 0.5232 0.600 0.400
#> SRR1435272 1 0.0000 0.8422 1.000 0.000
#> SRR1089230 1 0.0000 0.8422 1.000 0.000
#> SRR1389522 2 0.1414 0.9155 0.020 0.980
#> SRR1080600 1 0.0672 0.8399 0.992 0.008
#> SRR1086935 1 0.0000 0.8422 1.000 0.000
#> SRR1344060 2 0.0000 0.9350 0.000 1.000
#> SRR1467922 1 0.0000 0.8422 1.000 0.000
#> SRR1090984 2 0.0000 0.9350 0.000 1.000
#> SRR1456991 2 0.0000 0.9350 0.000 1.000
#> SRR1085039 1 0.0000 0.8422 1.000 0.000
#> SRR1069303 2 0.0000 0.9350 0.000 1.000
#> SRR1091500 1 0.0000 0.8422 1.000 0.000
#> SRR1075198 1 0.9000 0.6277 0.684 0.316
#> SRR1086915 1 0.0000 0.8422 1.000 0.000
#> SRR1499503 1 0.0000 0.8422 1.000 0.000
#> SRR1094312 1 0.0000 0.8422 1.000 0.000
#> SRR1352437 1 0.0000 0.8422 1.000 0.000
#> SRR1436323 1 0.9710 0.5232 0.600 0.400
#> SRR1073507 1 0.0000 0.8422 1.000 0.000
#> SRR1401972 1 0.7674 0.7186 0.776 0.224
#> SRR1415510 1 0.9491 0.5664 0.632 0.368
#> SRR1327279 1 0.0000 0.8422 1.000 0.000
#> SRR1086983 1 0.0000 0.8422 1.000 0.000
#> SRR1105174 1 0.0000 0.8422 1.000 0.000
#> SRR1468893 2 0.0000 0.9350 0.000 1.000
#> SRR1362555 2 0.0000 0.9350 0.000 1.000
#> SRR1074526 1 0.0000 0.8422 1.000 0.000
#> SRR1326225 1 0.0000 0.8422 1.000 0.000
#> SRR1401933 1 0.9710 0.5232 0.600 0.400
#> SRR1324062 2 0.0000 0.9350 0.000 1.000
#> SRR1102296 1 0.0000 0.8422 1.000 0.000
#> SRR1085087 1 0.0000 0.8422 1.000 0.000
#> SRR1079046 1 0.9710 0.5232 0.600 0.400
#> SRR1328339 2 0.0000 0.9350 0.000 1.000
#> SRR1079782 1 0.0000 0.8422 1.000 0.000
#> SRR1092257 1 0.0000 0.8422 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1429287 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1359238 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1309597 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1441398 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1084055 1 0.5905 0.4701 0.648 0.000 0.352
#> SRR1417566 1 0.5926 0.4791 0.644 0.356 0.000
#> SRR1351857 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1487485 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1335875 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1073947 1 0.6252 0.2439 0.556 0.444 0.000
#> SRR1443483 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1346794 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1405245 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1409677 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1095549 1 0.0237 0.9325 0.996 0.000 0.004
#> SRR1323788 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1314054 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1077944 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1480587 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1311205 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1076369 2 0.0592 0.9684 0.012 0.988 0.000
#> SRR1453549 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1345782 1 0.4399 0.7546 0.812 0.188 0.000
#> SRR1447850 1 0.0237 0.9324 0.996 0.000 0.004
#> SRR1391553 1 0.6045 0.4056 0.620 0.380 0.000
#> SRR1444156 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1471731 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1120987 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1477363 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1391961 2 0.6307 -0.0503 0.488 0.512 0.000
#> SRR1373879 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1318732 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1091404 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1402109 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1407336 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1097417 1 0.6154 0.3454 0.592 0.408 0.000
#> SRR1396227 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1400775 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1392861 3 0.5882 0.4699 0.348 0.000 0.652
#> SRR1472929 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1436740 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1477057 1 0.0237 0.9324 0.996 0.004 0.000
#> SRR1311980 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1069400 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1351016 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1096291 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1418145 1 0.5216 0.6469 0.740 0.000 0.260
#> SRR1488111 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1370495 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1352639 1 0.0237 0.9325 0.996 0.004 0.000
#> SRR1348911 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1467386 1 0.1964 0.8908 0.944 0.000 0.056
#> SRR1415956 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1500495 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1405099 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1345585 1 0.6286 0.1648 0.536 0.464 0.000
#> SRR1093196 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1466006 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1351557 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1382687 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1375549 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1101765 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1334461 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1094073 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1077549 3 0.2165 0.9154 0.064 0.000 0.936
#> SRR1440332 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1454177 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1082447 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1420043 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1432500 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1378045 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1334200 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1069539 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1343031 1 0.0237 0.9325 0.996 0.004 0.000
#> SRR1319690 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1310604 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1327747 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1072456 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1367896 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1480107 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1377756 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1435272 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1089230 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1389522 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1080600 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1086935 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1344060 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1467922 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1090984 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1456991 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1085039 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1069303 2 0.0237 0.9769 0.004 0.996 0.000
#> SRR1091500 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1075198 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1086915 3 0.0424 0.9695 0.008 0.000 0.992
#> SRR1499503 1 0.0237 0.9325 0.996 0.000 0.004
#> SRR1094312 1 0.0237 0.9324 0.996 0.000 0.004
#> SRR1352437 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1436323 1 0.4062 0.7788 0.836 0.164 0.000
#> SRR1073507 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1401972 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1415510 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1327279 1 0.3340 0.8299 0.880 0.000 0.120
#> SRR1086983 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1105174 3 0.3686 0.8275 0.140 0.000 0.860
#> SRR1468893 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1362555 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1074526 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1326225 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1401933 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1324062 2 0.0000 0.9809 0.000 1.000 0.000
#> SRR1102296 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1085087 3 0.0000 0.9766 0.000 0.000 1.000
#> SRR1079046 1 0.0000 0.9348 1.000 0.000 0.000
#> SRR1328339 2 0.0237 0.9770 0.004 0.996 0.000
#> SRR1079782 1 0.4750 0.7132 0.784 0.000 0.216
#> SRR1092257 3 0.0000 0.9766 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 4 0.0000 0.9557 0.000 0.000 0.000 1.000
#> SRR1429287 2 0.0817 0.8859 0.000 0.976 0.024 0.000
#> SRR1359238 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1309597 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1441398 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1084055 3 0.0707 0.8593 0.000 0.020 0.980 0.000
#> SRR1417566 2 0.3649 0.7197 0.204 0.796 0.000 0.000
#> SRR1351857 4 0.0188 0.9542 0.000 0.000 0.004 0.996
#> SRR1487485 2 0.1716 0.8758 0.000 0.936 0.064 0.000
#> SRR1335875 2 0.0779 0.8980 0.004 0.980 0.016 0.000
#> SRR1073947 3 0.7146 0.5401 0.228 0.212 0.560 0.000
#> SRR1443483 3 0.3688 0.7212 0.208 0.000 0.792 0.000
#> SRR1346794 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1405245 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1409677 4 0.1389 0.9324 0.000 0.000 0.048 0.952
#> SRR1095549 3 0.0817 0.8816 0.000 0.024 0.976 0.000
#> SRR1323788 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1314054 4 0.0000 0.9557 0.000 0.000 0.000 1.000
#> SRR1077944 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1480587 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1311205 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1076369 3 0.4094 0.8425 0.056 0.116 0.828 0.000
#> SRR1453549 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1345782 3 0.3610 0.7938 0.000 0.200 0.800 0.000
#> SRR1447850 2 0.4234 0.7909 0.000 0.816 0.132 0.052
#> SRR1391553 2 0.4304 0.6179 0.284 0.716 0.000 0.000
#> SRR1444156 4 0.0188 0.9546 0.000 0.004 0.000 0.996
#> SRR1471731 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1120987 4 0.0000 0.9557 0.000 0.000 0.000 1.000
#> SRR1477363 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1391961 3 0.3610 0.7938 0.000 0.200 0.800 0.000
#> SRR1373879 3 0.0707 0.8740 0.000 0.020 0.980 0.000
#> SRR1318732 1 0.0188 0.9353 0.996 0.004 0.000 0.000
#> SRR1091404 2 0.2345 0.8562 0.000 0.900 0.100 0.000
#> SRR1402109 3 0.0817 0.8816 0.000 0.024 0.976 0.000
#> SRR1407336 3 0.0817 0.8816 0.000 0.024 0.976 0.000
#> SRR1097417 3 0.2704 0.8545 0.000 0.124 0.876 0.000
#> SRR1396227 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1400775 2 0.3801 0.7481 0.000 0.780 0.220 0.000
#> SRR1392861 4 0.6946 0.4578 0.000 0.252 0.168 0.580
#> SRR1472929 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1436740 4 0.0000 0.9557 0.000 0.000 0.000 1.000
#> SRR1477057 2 0.0817 0.8859 0.000 0.976 0.024 0.000
#> SRR1311980 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1069400 3 0.2647 0.8557 0.000 0.120 0.880 0.000
#> SRR1351016 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1096291 4 0.0000 0.9557 0.000 0.000 0.000 1.000
#> SRR1418145 2 0.4567 0.6084 0.000 0.716 0.008 0.276
#> SRR1488111 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1370495 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1352639 3 0.2868 0.8468 0.000 0.136 0.864 0.000
#> SRR1348911 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1467386 2 0.5102 0.7406 0.000 0.764 0.100 0.136
#> SRR1415956 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1500495 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1405099 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1345585 2 0.5155 0.1895 0.468 0.528 0.004 0.000
#> SRR1093196 3 0.3123 0.8073 0.000 0.156 0.844 0.000
#> SRR1466006 1 0.0376 0.9321 0.992 0.004 0.004 0.000
#> SRR1351557 2 0.1022 0.8841 0.000 0.968 0.032 0.000
#> SRR1382687 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1375549 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1101765 4 0.0000 0.9557 0.000 0.000 0.000 1.000
#> SRR1334461 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1094073 4 0.0188 0.9546 0.000 0.004 0.000 0.996
#> SRR1077549 3 0.1151 0.8797 0.000 0.024 0.968 0.008
#> SRR1440332 2 0.3074 0.8036 0.000 0.848 0.152 0.000
#> SRR1454177 4 0.0000 0.9557 0.000 0.000 0.000 1.000
#> SRR1082447 3 0.1867 0.8679 0.000 0.072 0.928 0.000
#> SRR1420043 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1432500 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1378045 3 0.4933 0.1518 0.000 0.432 0.568 0.000
#> SRR1334200 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1069539 4 0.0000 0.9557 0.000 0.000 0.000 1.000
#> SRR1343031 3 0.1022 0.8819 0.000 0.032 0.968 0.000
#> SRR1319690 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1310604 2 0.3649 0.7540 0.000 0.796 0.204 0.000
#> SRR1327747 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1072456 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1367896 1 0.4898 0.2275 0.584 0.000 0.416 0.000
#> SRR1480107 1 0.3400 0.7548 0.820 0.180 0.000 0.000
#> SRR1377756 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1435272 4 0.0000 0.9557 0.000 0.000 0.000 1.000
#> SRR1089230 4 0.0000 0.9557 0.000 0.000 0.000 1.000
#> SRR1389522 3 0.3972 0.7266 0.204 0.008 0.788 0.000
#> SRR1080600 3 0.2469 0.8576 0.000 0.108 0.892 0.000
#> SRR1086935 4 0.0000 0.9557 0.000 0.000 0.000 1.000
#> SRR1344060 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1467922 4 0.2635 0.9048 0.000 0.020 0.076 0.904
#> SRR1090984 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1456991 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1085039 3 0.0817 0.8816 0.000 0.024 0.976 0.000
#> SRR1069303 1 0.3569 0.7357 0.804 0.196 0.000 0.000
#> SRR1091500 4 0.0376 0.9529 0.000 0.004 0.004 0.992
#> SRR1075198 2 0.2469 0.8516 0.000 0.892 0.108 0.000
#> SRR1086915 4 0.1211 0.9372 0.000 0.000 0.040 0.960
#> SRR1499503 3 0.1557 0.8579 0.000 0.056 0.944 0.000
#> SRR1094312 2 0.3801 0.7481 0.000 0.780 0.220 0.000
#> SRR1352437 4 0.1557 0.9270 0.000 0.000 0.056 0.944
#> SRR1436323 2 0.1059 0.8935 0.016 0.972 0.012 0.000
#> SRR1073507 4 0.2149 0.9021 0.000 0.000 0.088 0.912
#> SRR1401972 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1415510 2 0.0817 0.8859 0.000 0.976 0.024 0.000
#> SRR1327279 3 0.0817 0.8816 0.000 0.024 0.976 0.000
#> SRR1086983 4 0.0000 0.9557 0.000 0.000 0.000 1.000
#> SRR1105174 3 0.1151 0.8797 0.000 0.024 0.968 0.008
#> SRR1468893 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1362555 1 0.0000 0.9384 1.000 0.000 0.000 0.000
#> SRR1074526 4 0.3688 0.7210 0.000 0.000 0.208 0.792
#> SRR1326225 4 0.0937 0.9443 0.000 0.012 0.012 0.976
#> SRR1401933 2 0.0707 0.8992 0.000 0.980 0.020 0.000
#> SRR1324062 1 0.3569 0.7357 0.804 0.196 0.000 0.000
#> SRR1102296 2 0.3172 0.8075 0.000 0.840 0.160 0.000
#> SRR1085087 4 0.2216 0.8992 0.000 0.000 0.092 0.908
#> SRR1079046 2 0.0817 0.8859 0.000 0.976 0.024 0.000
#> SRR1328339 1 0.7535 0.0313 0.464 0.200 0.336 0.000
#> SRR1079782 2 0.4692 0.6843 0.000 0.756 0.032 0.212
#> SRR1092257 4 0.0000 0.9557 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 4 0.4219 0.0169 0.000 0.416 0.000 0.584 0.000
#> SRR1429287 1 0.0566 0.8660 0.984 0.000 0.012 0.000 0.004
#> SRR1359238 1 0.0510 0.8650 0.984 0.000 0.016 0.000 0.000
#> SRR1309597 5 0.2516 0.8590 0.000 0.140 0.000 0.000 0.860
#> SRR1441398 5 0.2516 0.8590 0.000 0.140 0.000 0.000 0.860
#> SRR1084055 2 0.2690 0.6690 0.000 0.844 0.156 0.000 0.000
#> SRR1417566 1 0.2813 0.7286 0.832 0.000 0.000 0.000 0.168
#> SRR1351857 4 0.0162 0.8469 0.000 0.000 0.004 0.996 0.000
#> SRR1487485 1 0.3477 0.7968 0.832 0.112 0.056 0.000 0.000
#> SRR1335875 1 0.0000 0.8694 1.000 0.000 0.000 0.000 0.000
#> SRR1073947 3 0.8106 0.2875 0.284 0.000 0.376 0.108 0.232
#> SRR1443483 3 0.1341 0.8266 0.000 0.000 0.944 0.000 0.056
#> SRR1346794 1 0.0000 0.8694 1.000 0.000 0.000 0.000 0.000
#> SRR1405245 5 0.2516 0.8590 0.000 0.140 0.000 0.000 0.860
#> SRR1409677 4 0.0794 0.8345 0.000 0.000 0.028 0.972 0.000
#> SRR1095549 3 0.0566 0.8333 0.004 0.000 0.984 0.012 0.000
#> SRR1323788 1 0.0000 0.8694 1.000 0.000 0.000 0.000 0.000
#> SRR1314054 4 0.4294 -0.1743 0.000 0.468 0.000 0.532 0.000
#> SRR1077944 1 0.0000 0.8694 1.000 0.000 0.000 0.000 0.000
#> SRR1480587 5 0.2516 0.8590 0.000 0.140 0.000 0.000 0.860
#> SRR1311205 5 0.0000 0.8928 0.000 0.000 0.000 0.000 1.000
#> SRR1076369 3 0.2516 0.7820 0.000 0.000 0.860 0.000 0.140
#> SRR1453549 1 0.0000 0.8694 1.000 0.000 0.000 0.000 0.000
#> SRR1345782 3 0.2690 0.7498 0.156 0.000 0.844 0.000 0.000
#> SRR1447850 2 0.3646 0.6695 0.040 0.848 0.072 0.040 0.000
#> SRR1391553 1 0.3857 0.6091 0.688 0.000 0.000 0.000 0.312
#> SRR1444156 2 0.4171 0.4853 0.000 0.604 0.000 0.396 0.000
#> SRR1471731 5 0.0000 0.8928 0.000 0.000 0.000 0.000 1.000
#> SRR1120987 4 0.0000 0.8480 0.000 0.000 0.000 1.000 0.000
#> SRR1477363 1 0.0000 0.8694 1.000 0.000 0.000 0.000 0.000
#> SRR1391961 3 0.2516 0.7820 0.000 0.000 0.860 0.000 0.140
#> SRR1373879 3 0.3595 0.7646 0.004 0.048 0.828 0.120 0.000
#> SRR1318732 5 0.0162 0.8913 0.004 0.000 0.000 0.000 0.996
#> SRR1091404 1 0.1478 0.8457 0.936 0.000 0.064 0.000 0.000
#> SRR1402109 3 0.0162 0.8323 0.004 0.000 0.996 0.000 0.000
#> SRR1407336 3 0.0162 0.8323 0.004 0.000 0.996 0.000 0.000
#> SRR1097417 3 0.2516 0.7820 0.000 0.000 0.860 0.000 0.140
#> SRR1396227 1 0.0000 0.8694 1.000 0.000 0.000 0.000 0.000
#> SRR1400775 1 0.5841 0.5490 0.596 0.256 0.148 0.000 0.000
#> SRR1392861 4 0.5500 0.4062 0.212 0.000 0.140 0.648 0.000
#> SRR1472929 5 0.2516 0.8590 0.000 0.140 0.000 0.000 0.860
#> SRR1436740 4 0.0000 0.8480 0.000 0.000 0.000 1.000 0.000
#> SRR1477057 1 0.3106 0.7760 0.840 0.020 0.000 0.000 0.140
#> SRR1311980 5 0.2516 0.8590 0.000 0.140 0.000 0.000 0.860
#> SRR1069400 3 0.1043 0.8254 0.040 0.000 0.960 0.000 0.000
#> SRR1351016 5 0.0000 0.8928 0.000 0.000 0.000 0.000 1.000
#> SRR1096291 4 0.0404 0.8450 0.000 0.012 0.000 0.988 0.000
#> SRR1418145 1 0.3437 0.7632 0.832 0.048 0.000 0.120 0.000
#> SRR1488111 1 0.0000 0.8694 1.000 0.000 0.000 0.000 0.000
#> SRR1370495 5 0.0000 0.8928 0.000 0.000 0.000 0.000 1.000
#> SRR1352639 3 0.1908 0.8067 0.092 0.000 0.908 0.000 0.000
#> SRR1348911 5 0.0000 0.8928 0.000 0.000 0.000 0.000 1.000
#> SRR1467386 4 0.4935 0.3124 0.344 0.000 0.040 0.616 0.000
#> SRR1415956 5 0.2516 0.8590 0.000 0.140 0.000 0.000 0.860
#> SRR1500495 5 0.2516 0.8590 0.000 0.140 0.000 0.000 0.860
#> SRR1405099 5 0.2516 0.8590 0.000 0.140 0.000 0.000 0.860
#> SRR1345585 1 0.4297 0.2504 0.528 0.000 0.000 0.000 0.472
#> SRR1093196 3 0.3016 0.7343 0.132 0.020 0.848 0.000 0.000
#> SRR1466006 5 0.0162 0.8915 0.000 0.000 0.004 0.000 0.996
#> SRR1351557 1 0.3534 0.7156 0.744 0.256 0.000 0.000 0.000
#> SRR1382687 1 0.0000 0.8694 1.000 0.000 0.000 0.000 0.000
#> SRR1375549 1 0.0324 0.8672 0.992 0.000 0.004 0.000 0.004
#> SRR1101765 4 0.0000 0.8480 0.000 0.000 0.000 1.000 0.000
#> SRR1334461 5 0.0000 0.8928 0.000 0.000 0.000 0.000 1.000
#> SRR1094073 2 0.4171 0.4853 0.000 0.604 0.000 0.396 0.000
#> SRR1077549 3 0.2439 0.7920 0.004 0.000 0.876 0.120 0.000
#> SRR1440332 1 0.3508 0.6674 0.748 0.000 0.252 0.000 0.000
#> SRR1454177 4 0.0404 0.8450 0.000 0.012 0.000 0.988 0.000
#> SRR1082447 3 0.3437 0.7762 0.048 0.000 0.832 0.120 0.000
#> SRR1420043 1 0.0000 0.8694 1.000 0.000 0.000 0.000 0.000
#> SRR1432500 1 0.0000 0.8694 1.000 0.000 0.000 0.000 0.000
#> SRR1378045 2 0.5915 0.2700 0.108 0.508 0.384 0.000 0.000
#> SRR1334200 5 0.0000 0.8928 0.000 0.000 0.000 0.000 1.000
#> SRR1069539 4 0.2127 0.7498 0.000 0.108 0.000 0.892 0.000
#> SRR1343031 3 0.0162 0.8323 0.004 0.000 0.996 0.000 0.000
#> SRR1319690 1 0.0000 0.8694 1.000 0.000 0.000 0.000 0.000
#> SRR1310604 1 0.5087 0.6722 0.700 0.148 0.152 0.000 0.000
#> SRR1327747 1 0.0000 0.8694 1.000 0.000 0.000 0.000 0.000
#> SRR1072456 5 0.0162 0.8915 0.000 0.000 0.004 0.000 0.996
#> SRR1367896 5 0.4227 0.1420 0.000 0.000 0.420 0.000 0.580
#> SRR1480107 5 0.1732 0.8469 0.080 0.000 0.000 0.000 0.920
#> SRR1377756 1 0.0000 0.8694 1.000 0.000 0.000 0.000 0.000
#> SRR1435272 4 0.0404 0.8450 0.000 0.012 0.000 0.988 0.000
#> SRR1089230 4 0.0404 0.8450 0.000 0.012 0.000 0.988 0.000
#> SRR1389522 3 0.2886 0.7773 0.008 0.000 0.844 0.000 0.148
#> SRR1080600 2 0.4503 0.5220 0.040 0.704 0.256 0.000 0.000
#> SRR1086935 4 0.2280 0.7338 0.000 0.120 0.000 0.880 0.000
#> SRR1344060 5 0.0000 0.8928 0.000 0.000 0.000 0.000 1.000
#> SRR1467922 2 0.3412 0.6489 0.000 0.820 0.028 0.152 0.000
#> SRR1090984 5 0.0000 0.8928 0.000 0.000 0.000 0.000 1.000
#> SRR1456991 5 0.0000 0.8928 0.000 0.000 0.000 0.000 1.000
#> SRR1085039 3 0.2439 0.7920 0.004 0.000 0.876 0.120 0.000
#> SRR1069303 5 0.3774 0.6186 0.296 0.000 0.000 0.000 0.704
#> SRR1091500 2 0.4161 0.4908 0.000 0.608 0.000 0.392 0.000
#> SRR1075198 1 0.5447 0.6092 0.640 0.248 0.112 0.000 0.000
#> SRR1086915 4 0.0771 0.8398 0.004 0.000 0.020 0.976 0.000
#> SRR1499503 2 0.2648 0.6694 0.000 0.848 0.152 0.000 0.000
#> SRR1094312 2 0.2763 0.6701 0.004 0.848 0.148 0.000 0.000
#> SRR1352437 4 0.0794 0.8345 0.000 0.000 0.028 0.972 0.000
#> SRR1436323 1 0.2516 0.7803 0.860 0.000 0.000 0.000 0.140
#> SRR1073507 4 0.1043 0.8236 0.000 0.000 0.040 0.960 0.000
#> SRR1401972 1 0.0000 0.8694 1.000 0.000 0.000 0.000 0.000
#> SRR1415510 1 0.2519 0.8233 0.884 0.100 0.016 0.000 0.000
#> SRR1327279 3 0.0566 0.8333 0.004 0.000 0.984 0.012 0.000
#> SRR1086983 4 0.0000 0.8480 0.000 0.000 0.000 1.000 0.000
#> SRR1105174 3 0.3430 0.6908 0.004 0.000 0.776 0.220 0.000
#> SRR1468893 5 0.0000 0.8928 0.000 0.000 0.000 0.000 1.000
#> SRR1362555 5 0.0510 0.8907 0.000 0.016 0.000 0.000 0.984
#> SRR1074526 2 0.4171 0.4853 0.000 0.604 0.000 0.396 0.000
#> SRR1326225 2 0.4150 0.4955 0.000 0.612 0.000 0.388 0.000
#> SRR1401933 1 0.0000 0.8694 1.000 0.000 0.000 0.000 0.000
#> SRR1324062 5 0.3796 0.6134 0.300 0.000 0.000 0.000 0.700
#> SRR1102296 1 0.7198 0.4480 0.532 0.256 0.092 0.120 0.000
#> SRR1085087 4 0.2077 0.7872 0.000 0.040 0.040 0.920 0.000
#> SRR1079046 1 0.2806 0.7975 0.844 0.152 0.000 0.000 0.004
#> SRR1328339 5 0.4682 0.3196 0.024 0.000 0.356 0.000 0.620
#> SRR1079782 1 0.5449 0.5975 0.636 0.256 0.000 0.108 0.000
#> SRR1092257 4 0.0000 0.8480 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
#> SRR1396765 4 0.3727 0.5397 0.000 0.388 0.000 0.612 0.000 0.000
#> SRR1429287 1 0.0146 0.8459 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1359238 1 0.0865 0.8334 0.964 0.000 0.036 0.000 0.000 0.000
#> SRR1309597 5 0.2793 0.8215 0.000 0.000 0.000 0.000 0.800 0.200
#> SRR1441398 5 0.2793 0.8215 0.000 0.000 0.000 0.000 0.800 0.200
#> SRR1084055 2 0.5173 0.6355 0.000 0.616 0.160 0.000 0.000 0.224
#> SRR1417566 1 0.2454 0.7111 0.840 0.000 0.000 0.000 0.160 0.000
#> SRR1351857 6 0.4199 0.8306 0.000 0.016 0.000 0.416 0.000 0.568
#> SRR1487485 1 0.4627 0.6631 0.696 0.004 0.196 0.000 0.000 0.104
#> SRR1335875 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1073947 6 0.5751 0.3945 0.280 0.000 0.128 0.024 0.000 0.568
#> SRR1443483 3 0.2454 0.7725 0.000 0.000 0.840 0.000 0.160 0.000
#> SRR1346794 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1405245 5 0.2793 0.8215 0.000 0.000 0.000 0.000 0.800 0.200
#> SRR1409677 4 0.0000 0.6547 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1095549 3 0.2527 0.7086 0.000 0.000 0.832 0.168 0.000 0.000
#> SRR1323788 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1314054 2 0.3515 0.1142 0.000 0.676 0.000 0.324 0.000 0.000
#> SRR1077944 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1480587 5 0.2793 0.8228 0.000 0.000 0.000 0.000 0.800 0.200
#> SRR1311205 5 0.0000 0.8721 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1076369 3 0.2697 0.7526 0.000 0.000 0.812 0.000 0.188 0.000
#> SRR1453549 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1345782 3 0.2454 0.7392 0.160 0.000 0.840 0.000 0.000 0.000
#> SRR1447850 2 0.5195 0.6336 0.000 0.612 0.160 0.000 0.000 0.228
#> SRR1391553 1 0.3563 0.5645 0.664 0.000 0.000 0.000 0.336 0.000
#> SRR1444156 2 0.0937 0.6823 0.000 0.960 0.000 0.040 0.000 0.000
#> SRR1471731 5 0.0000 0.8721 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1120987 4 0.0458 0.6652 0.000 0.016 0.000 0.984 0.000 0.000
#> SRR1477363 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1391961 3 0.2838 0.7508 0.000 0.000 0.808 0.000 0.188 0.004
#> SRR1373879 6 0.4951 0.8390 0.000 0.040 0.016 0.384 0.000 0.560
#> SRR1318732 5 0.0000 0.8721 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1091404 1 0.1007 0.8309 0.956 0.000 0.044 0.000 0.000 0.000
#> SRR1402109 3 0.0000 0.7588 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1407336 3 0.0000 0.7588 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1097417 3 0.2442 0.7767 0.000 0.000 0.852 0.000 0.144 0.004
#> SRR1396227 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1400775 1 0.6020 0.5158 0.572 0.040 0.160 0.000 0.000 0.228
#> SRR1392861 4 0.3126 0.3091 0.248 0.000 0.000 0.752 0.000 0.000
#> SRR1472929 5 0.2823 0.8205 0.000 0.000 0.000 0.000 0.796 0.204
#> SRR1436740 4 0.0000 0.6547 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1477057 1 0.3555 0.7082 0.776 0.000 0.000 0.000 0.184 0.040
#> SRR1311980 5 0.2793 0.8215 0.000 0.000 0.000 0.000 0.800 0.200
#> SRR1069400 3 0.2092 0.7604 0.124 0.000 0.876 0.000 0.000 0.000
#> SRR1351016 5 0.0000 0.8721 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1096291 4 0.3578 0.5949 0.000 0.340 0.000 0.660 0.000 0.000
#> SRR1418145 1 0.5891 0.4408 0.576 0.040 0.124 0.260 0.000 0.000
#> SRR1488111 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1370495 5 0.0000 0.8721 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1352639 3 0.1367 0.7646 0.044 0.000 0.944 0.000 0.000 0.012
#> SRR1348911 5 0.0000 0.8721 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1467386 6 0.3944 0.8450 0.004 0.000 0.000 0.428 0.000 0.568
#> SRR1415956 5 0.2793 0.8215 0.000 0.000 0.000 0.000 0.800 0.200
#> SRR1500495 5 0.2697 0.8252 0.000 0.000 0.000 0.000 0.812 0.188
#> SRR1405099 5 0.2793 0.8215 0.000 0.000 0.000 0.000 0.800 0.200
#> SRR1345585 1 0.3866 0.2342 0.516 0.000 0.000 0.000 0.484 0.000
#> SRR1093196 3 0.2404 0.6778 0.112 0.016 0.872 0.000 0.000 0.000
#> SRR1466006 5 0.0146 0.8712 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR1351557 1 0.5733 0.5535 0.608 0.040 0.124 0.000 0.000 0.228
#> SRR1382687 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1375549 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1101765 4 0.0000 0.6547 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1334461 5 0.0146 0.8712 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR1094073 2 0.0937 0.6823 0.000 0.960 0.000 0.040 0.000 0.000
#> SRR1077549 6 0.4658 0.8475 0.000 0.000 0.048 0.384 0.000 0.568
#> SRR1440332 1 0.2793 0.6944 0.800 0.000 0.200 0.000 0.000 0.000
#> SRR1454177 4 0.4636 0.5579 0.000 0.160 0.000 0.692 0.000 0.148
#> SRR1082447 6 0.4735 0.8488 0.000 0.004 0.044 0.384 0.000 0.568
#> SRR1420043 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1432500 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1378045 3 0.7347 -0.3249 0.116 0.304 0.352 0.000 0.000 0.228
#> SRR1334200 5 0.0000 0.8721 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1069539 4 0.3717 0.5455 0.000 0.384 0.000 0.616 0.000 0.000
#> SRR1343031 3 0.2135 0.7593 0.128 0.000 0.872 0.000 0.000 0.000
#> SRR1319690 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1310604 1 0.5077 0.6563 0.696 0.040 0.160 0.000 0.000 0.104
#> SRR1327747 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1072456 5 0.0146 0.8712 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR1367896 5 0.3930 0.0938 0.000 0.000 0.420 0.000 0.576 0.004
#> SRR1480107 5 0.1075 0.8534 0.048 0.000 0.000 0.000 0.952 0.000
#> SRR1377756 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1435272 4 0.2454 0.7097 0.000 0.160 0.000 0.840 0.000 0.000
#> SRR1089230 4 0.2527 0.7105 0.000 0.168 0.000 0.832 0.000 0.000
#> SRR1389522 3 0.2558 0.7749 0.004 0.000 0.840 0.000 0.156 0.000
#> SRR1080600 2 0.5870 0.4714 0.000 0.476 0.292 0.000 0.000 0.232
#> SRR1086935 4 0.3717 0.5455 0.000 0.384 0.000 0.616 0.000 0.000
#> SRR1344060 5 0.0146 0.8712 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR1467922 2 0.1297 0.6823 0.000 0.948 0.000 0.040 0.000 0.012
#> SRR1090984 5 0.0000 0.8721 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1456991 5 0.0000 0.8721 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1085039 6 0.4658 0.8475 0.000 0.000 0.048 0.384 0.000 0.568
#> SRR1069303 5 0.3428 0.6062 0.304 0.000 0.000 0.000 0.696 0.000
#> SRR1091500 2 0.0937 0.6823 0.000 0.960 0.000 0.040 0.000 0.000
#> SRR1075198 1 0.5999 0.5216 0.576 0.040 0.160 0.000 0.000 0.224
#> SRR1086915 4 0.0000 0.6547 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1499503 2 0.5173 0.6355 0.000 0.616 0.160 0.000 0.000 0.224
#> SRR1094312 2 0.5195 0.6336 0.000 0.612 0.160 0.000 0.000 0.228
#> SRR1352437 6 0.3817 0.8437 0.000 0.000 0.000 0.432 0.000 0.568
#> SRR1436323 1 0.2697 0.7189 0.812 0.000 0.000 0.000 0.188 0.000
#> SRR1073507 6 0.3817 0.8437 0.000 0.000 0.000 0.432 0.000 0.568
#> SRR1401972 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1415510 1 0.2848 0.7840 0.856 0.004 0.036 0.000 0.000 0.104
#> SRR1327279 3 0.2454 0.7164 0.000 0.000 0.840 0.160 0.000 0.000
#> SRR1086983 6 0.3817 0.8437 0.000 0.000 0.000 0.432 0.000 0.568
#> SRR1105174 6 0.4610 0.8495 0.000 0.000 0.044 0.388 0.000 0.568
#> SRR1468893 5 0.0000 0.8721 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1362555 5 0.0632 0.8692 0.000 0.000 0.000 0.000 0.976 0.024
#> SRR1074526 2 0.0937 0.6823 0.000 0.960 0.000 0.040 0.000 0.000
#> SRR1326225 2 0.0937 0.6823 0.000 0.960 0.000 0.040 0.000 0.000
#> SRR1401933 1 0.0000 0.8473 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1324062 5 0.3428 0.6062 0.304 0.000 0.000 0.000 0.696 0.000
#> SRR1102296 6 0.3968 0.3782 0.004 0.040 0.124 0.036 0.000 0.796
#> SRR1085087 6 0.4395 0.8457 0.000 0.028 0.000 0.404 0.000 0.568
#> SRR1079046 1 0.3282 0.7477 0.808 0.012 0.016 0.000 0.000 0.164
#> SRR1328339 5 0.3835 0.4009 0.012 0.000 0.320 0.000 0.668 0.000
#> SRR1079782 1 0.7941 0.1826 0.392 0.040 0.124 0.216 0.000 0.228
#> SRR1092257 4 0.3796 0.5975 0.000 0.060 0.000 0.764 0.000 0.176
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 17611 rows and 118 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.209 0.736 0.800 0.4400 0.498 0.498
#> 3 3 0.251 0.341 0.657 0.3576 0.692 0.496
#> 4 4 0.288 0.454 0.638 0.1344 0.705 0.405
#> 5 5 0.482 0.401 0.726 0.0779 0.795 0.462
#> 6 6 0.535 0.427 0.682 0.0600 0.919 0.721
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
#> SRR1396765 2 0.1414 0.827 0.020 0.980
#> SRR1429287 2 0.5294 0.865 0.120 0.880
#> SRR1359238 1 0.9129 0.672 0.672 0.328
#> SRR1309597 2 0.5408 0.865 0.124 0.876
#> SRR1441398 1 0.8861 0.703 0.696 0.304
#> SRR1084055 2 0.0938 0.819 0.012 0.988
#> SRR1417566 2 1.0000 -0.152 0.500 0.500
#> SRR1351857 2 0.1414 0.824 0.020 0.980
#> SRR1487485 2 0.5408 0.865 0.124 0.876
#> SRR1335875 1 0.1414 0.749 0.980 0.020
#> SRR1073947 1 0.6148 0.787 0.848 0.152
#> SRR1443483 2 0.5294 0.864 0.120 0.880
#> SRR1346794 1 0.1414 0.748 0.980 0.020
#> SRR1405245 1 0.8499 0.728 0.724 0.276
#> SRR1409677 1 0.9087 0.761 0.676 0.324
#> SRR1095549 2 0.1414 0.824 0.020 0.980
#> SRR1323788 1 0.7056 0.782 0.808 0.192
#> SRR1314054 2 0.1414 0.827 0.020 0.980
#> SRR1077944 1 0.0938 0.746 0.988 0.012
#> SRR1480587 2 0.5408 0.865 0.124 0.876
#> SRR1311205 1 0.7674 0.771 0.776 0.224
#> SRR1076369 2 0.5408 0.865 0.124 0.876
#> SRR1453549 1 0.5737 0.786 0.864 0.136
#> SRR1345782 1 0.7453 0.776 0.788 0.212
#> SRR1447850 2 0.5059 0.808 0.112 0.888
#> SRR1391553 1 0.5737 0.786 0.864 0.136
#> SRR1444156 2 0.1414 0.827 0.020 0.980
#> SRR1471731 1 0.9170 0.668 0.668 0.332
#> SRR1120987 1 0.9866 0.654 0.568 0.432
#> SRR1477363 1 0.0672 0.741 0.992 0.008
#> SRR1391961 2 0.5178 0.863 0.116 0.884
#> SRR1373879 1 0.9933 0.615 0.548 0.452
#> SRR1318732 2 0.9850 0.180 0.428 0.572
#> SRR1091404 2 0.9909 0.103 0.444 0.556
#> SRR1402109 2 0.9815 0.228 0.420 0.580
#> SRR1407336 2 0.1843 0.828 0.028 0.972
#> SRR1097417 2 0.5059 0.861 0.112 0.888
#> SRR1396227 1 0.0376 0.742 0.996 0.004
#> SRR1400775 2 0.3733 0.853 0.072 0.928
#> SRR1392861 1 0.9522 0.723 0.628 0.372
#> SRR1472929 2 0.5294 0.864 0.120 0.880
#> SRR1436740 1 0.9044 0.764 0.680 0.320
#> SRR1477057 2 0.9087 0.526 0.324 0.676
#> SRR1311980 1 0.9044 0.684 0.680 0.320
#> SRR1069400 2 0.5519 0.864 0.128 0.872
#> SRR1351016 1 0.0376 0.744 0.996 0.004
#> SRR1096291 2 0.1633 0.825 0.024 0.976
#> SRR1418145 1 0.9286 0.707 0.656 0.344
#> SRR1488111 1 0.9209 0.668 0.664 0.336
#> SRR1370495 2 0.5408 0.865 0.124 0.876
#> SRR1352639 1 0.5629 0.787 0.868 0.132
#> SRR1348911 2 0.5519 0.864 0.128 0.872
#> SRR1467386 1 0.8555 0.770 0.720 0.280
#> SRR1415956 1 0.2603 0.760 0.956 0.044
#> SRR1500495 1 0.0376 0.744 0.996 0.004
#> SRR1405099 1 0.2423 0.759 0.960 0.040
#> SRR1345585 2 0.5408 0.865 0.124 0.876
#> SRR1093196 1 0.9983 0.262 0.524 0.476
#> SRR1466006 2 0.5059 0.863 0.112 0.888
#> SRR1351557 2 0.5294 0.865 0.120 0.880
#> SRR1382687 1 0.0672 0.741 0.992 0.008
#> SRR1375549 1 0.9129 0.678 0.672 0.328
#> SRR1101765 2 0.8499 0.372 0.276 0.724
#> SRR1334461 2 0.5519 0.864 0.128 0.872
#> SRR1094073 2 0.1414 0.827 0.020 0.980
#> SRR1077549 1 0.9170 0.756 0.668 0.332
#> SRR1440332 1 0.6973 0.783 0.812 0.188
#> SRR1454177 1 0.9754 0.683 0.592 0.408
#> SRR1082447 1 0.9000 0.764 0.684 0.316
#> SRR1420043 1 0.0000 0.741 1.000 0.000
#> SRR1432500 1 0.0376 0.744 0.996 0.004
#> SRR1378045 2 0.5519 0.865 0.128 0.872
#> SRR1334200 2 0.5408 0.865 0.124 0.876
#> SRR1069539 2 0.1414 0.824 0.020 0.980
#> SRR1343031 2 0.5519 0.864 0.128 0.872
#> SRR1319690 1 0.7745 0.754 0.772 0.228
#> SRR1310604 2 0.5294 0.865 0.120 0.880
#> SRR1327747 1 0.9170 0.668 0.668 0.332
#> SRR1072456 2 0.5059 0.863 0.112 0.888
#> SRR1367896 2 0.5059 0.861 0.112 0.888
#> SRR1480107 1 0.0672 0.743 0.992 0.008
#> SRR1377756 1 0.0672 0.741 0.992 0.008
#> SRR1435272 1 0.9393 0.736 0.644 0.356
#> SRR1089230 1 0.9954 0.592 0.540 0.460
#> SRR1389522 2 0.5294 0.864 0.120 0.880
#> SRR1080600 2 0.5059 0.863 0.112 0.888
#> SRR1086935 2 0.9954 -0.410 0.460 0.540
#> SRR1344060 2 0.5519 0.864 0.128 0.872
#> SRR1467922 2 0.1414 0.827 0.020 0.980
#> SRR1090984 2 0.9608 0.354 0.384 0.616
#> SRR1456991 1 0.5294 0.784 0.880 0.120
#> SRR1085039 1 0.8713 0.769 0.708 0.292
#> SRR1069303 1 0.0000 0.741 1.000 0.000
#> SRR1091500 2 0.1414 0.827 0.020 0.980
#> SRR1075198 2 0.5408 0.865 0.124 0.876
#> SRR1086915 1 0.9044 0.763 0.680 0.320
#> SRR1499503 2 0.0938 0.824 0.012 0.988
#> SRR1094312 2 0.2603 0.840 0.044 0.956
#> SRR1352437 1 0.8555 0.771 0.720 0.280
#> SRR1436323 1 0.8955 0.695 0.688 0.312
#> SRR1073507 1 0.8555 0.770 0.720 0.280
#> SRR1401972 1 0.0376 0.742 0.996 0.004
#> SRR1415510 2 0.5408 0.865 0.124 0.876
#> SRR1327279 1 0.9286 0.750 0.656 0.344
#> SRR1086983 1 0.8608 0.770 0.716 0.284
#> SRR1105174 1 0.8661 0.770 0.712 0.288
#> SRR1468893 1 0.0938 0.744 0.988 0.012
#> SRR1362555 2 0.5408 0.865 0.124 0.876
#> SRR1074526 2 0.0938 0.819 0.012 0.988
#> SRR1326225 2 0.1184 0.826 0.016 0.984
#> SRR1401933 1 0.7815 0.768 0.768 0.232
#> SRR1324062 1 0.0000 0.741 1.000 0.000
#> SRR1102296 1 0.7815 0.784 0.768 0.232
#> SRR1085087 1 0.8499 0.771 0.724 0.276
#> SRR1079046 1 0.9460 0.614 0.636 0.364
#> SRR1328339 2 0.5842 0.858 0.140 0.860
#> SRR1079782 1 0.9209 0.668 0.664 0.336
#> SRR1092257 1 0.9998 0.532 0.508 0.492
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.7163 0.2550 0.332 0.628 0.040
#> SRR1429287 2 0.2066 0.5270 0.000 0.940 0.060
#> SRR1359238 1 0.6280 0.4823 0.540 0.460 0.000
#> SRR1309597 2 0.6553 0.2660 0.020 0.656 0.324
#> SRR1441398 1 0.8763 0.4335 0.552 0.136 0.312
#> SRR1084055 3 0.8636 0.4129 0.104 0.396 0.500
#> SRR1417566 1 0.6308 0.4365 0.508 0.492 0.000
#> SRR1351857 3 0.9180 0.5346 0.376 0.152 0.472
#> SRR1487485 2 0.0592 0.5246 0.000 0.988 0.012
#> SRR1335875 1 0.8109 0.5804 0.568 0.352 0.080
#> SRR1073947 1 0.9914 0.2162 0.392 0.328 0.280
#> SRR1443483 2 0.6678 -0.2727 0.008 0.512 0.480
#> SRR1346794 1 0.7085 0.5862 0.612 0.356 0.032
#> SRR1405245 1 0.8452 0.4466 0.556 0.104 0.340
#> SRR1409677 1 0.0829 0.4706 0.984 0.012 0.004
#> SRR1095549 3 0.9280 0.5416 0.388 0.160 0.452
#> SRR1323788 1 0.9596 0.3602 0.452 0.336 0.212
#> SRR1314054 2 0.7163 0.2550 0.332 0.628 0.040
#> SRR1077944 1 0.6627 0.5920 0.644 0.336 0.020
#> SRR1480587 2 0.5706 0.2777 0.000 0.680 0.320
#> SRR1311205 1 0.8554 0.4506 0.560 0.116 0.324
#> SRR1076369 2 0.6307 -0.2915 0.000 0.512 0.488
#> SRR1453549 1 0.5968 0.5777 0.636 0.364 0.000
#> SRR1345782 2 0.9941 -0.2044 0.292 0.384 0.324
#> SRR1447850 2 0.3141 0.5268 0.020 0.912 0.068
#> SRR1391553 2 0.5634 0.4294 0.144 0.800 0.056
#> SRR1444156 2 0.7251 0.2583 0.348 0.612 0.040
#> SRR1471731 2 0.3722 0.5195 0.088 0.888 0.024
#> SRR1120987 1 0.4591 0.3810 0.848 0.120 0.032
#> SRR1477363 1 0.7828 0.5894 0.592 0.340 0.068
#> SRR1391961 2 0.6680 -0.2801 0.008 0.508 0.484
#> SRR1373879 1 0.9731 -0.2435 0.444 0.248 0.308
#> SRR1318732 2 0.4465 0.4190 0.176 0.820 0.004
#> SRR1091404 2 0.9514 -0.2279 0.328 0.468 0.204
#> SRR1402109 2 0.8501 -0.2018 0.092 0.488 0.420
#> SRR1407336 2 0.8783 -0.2328 0.112 0.468 0.420
#> SRR1097417 3 0.6307 0.2470 0.000 0.488 0.512
#> SRR1396227 1 0.7705 0.5916 0.604 0.332 0.064
#> SRR1400775 2 0.2096 0.5299 0.052 0.944 0.004
#> SRR1392861 1 0.1399 0.4692 0.968 0.028 0.004
#> SRR1472929 3 0.4654 0.5522 0.000 0.208 0.792
#> SRR1436740 1 0.0592 0.4740 0.988 0.012 0.000
#> SRR1477057 2 0.2947 0.5308 0.020 0.920 0.060
#> SRR1311980 1 0.8771 0.4338 0.556 0.140 0.304
#> SRR1069400 2 0.6816 -0.2594 0.012 0.516 0.472
#> SRR1351016 1 0.8230 0.5803 0.564 0.348 0.088
#> SRR1096291 1 0.9077 -0.4005 0.508 0.152 0.340
#> SRR1418145 2 0.3619 0.5045 0.136 0.864 0.000
#> SRR1488111 2 0.3888 0.5259 0.048 0.888 0.064
#> SRR1370495 2 0.6396 0.2862 0.016 0.664 0.320
#> SRR1352639 1 0.5810 0.5892 0.664 0.336 0.000
#> SRR1348911 1 0.9391 0.3533 0.496 0.200 0.304
#> SRR1467386 1 0.1163 0.4934 0.972 0.028 0.000
#> SRR1415956 1 0.7533 0.4521 0.564 0.044 0.392
#> SRR1500495 1 0.7533 0.4521 0.564 0.044 0.392
#> SRR1405099 1 0.7533 0.4521 0.564 0.044 0.392
#> SRR1345585 2 0.0829 0.5259 0.004 0.984 0.012
#> SRR1093196 2 0.6286 -0.3516 0.464 0.536 0.000
#> SRR1466006 2 0.5785 0.2764 0.000 0.668 0.332
#> SRR1351557 2 0.2301 0.5283 0.004 0.936 0.060
#> SRR1382687 1 0.7448 0.5926 0.616 0.332 0.052
#> SRR1375549 2 0.3889 0.5206 0.084 0.884 0.032
#> SRR1101765 1 0.6730 0.1109 0.680 0.284 0.036
#> SRR1334461 3 0.5678 0.5451 0.032 0.192 0.776
#> SRR1094073 2 0.7208 0.2572 0.340 0.620 0.040
#> SRR1077549 1 0.6570 -0.1316 0.668 0.024 0.308
#> SRR1440332 1 0.5835 0.5883 0.660 0.340 0.000
#> SRR1454177 1 0.2918 0.4365 0.924 0.044 0.032
#> SRR1082447 1 0.9987 0.0956 0.348 0.344 0.308
#> SRR1420043 1 0.8194 0.5853 0.572 0.340 0.088
#> SRR1432500 1 0.5785 0.5898 0.668 0.332 0.000
#> SRR1378045 2 0.1289 0.5293 0.032 0.968 0.000
#> SRR1334200 2 0.5678 0.2810 0.000 0.684 0.316
#> SRR1069539 3 0.9189 0.5472 0.336 0.164 0.500
#> SRR1343031 2 0.8203 -0.2201 0.072 0.484 0.444
#> SRR1319690 1 0.6267 0.4928 0.548 0.452 0.000
#> SRR1310604 2 0.1129 0.5227 0.004 0.976 0.020
#> SRR1327747 2 0.6309 -0.4351 0.496 0.504 0.000
#> SRR1072456 2 0.5178 0.3555 0.000 0.744 0.256
#> SRR1367896 3 0.4654 0.5522 0.000 0.208 0.792
#> SRR1480107 1 0.9189 0.5475 0.500 0.336 0.164
#> SRR1377756 1 0.8034 0.5883 0.584 0.336 0.080
#> SRR1435272 1 0.1399 0.4491 0.968 0.004 0.028
#> SRR1089230 1 0.4799 0.3637 0.836 0.132 0.032
#> SRR1389522 2 0.6816 -0.2594 0.012 0.516 0.472
#> SRR1080600 2 0.1163 0.5212 0.000 0.972 0.028
#> SRR1086935 2 0.7236 0.2507 0.392 0.576 0.032
#> SRR1344060 3 0.5024 0.5526 0.004 0.220 0.776
#> SRR1467922 2 0.6955 0.2576 0.332 0.636 0.032
#> SRR1090984 2 0.8059 -0.3462 0.444 0.492 0.064
#> SRR1456991 1 0.8280 0.5797 0.564 0.344 0.092
#> SRR1085039 1 0.9974 0.1114 0.368 0.324 0.308
#> SRR1069303 1 0.8194 0.5853 0.572 0.340 0.088
#> SRR1091500 2 0.7251 0.2583 0.348 0.612 0.040
#> SRR1075198 2 0.0475 0.5276 0.004 0.992 0.004
#> SRR1086915 1 0.1289 0.4851 0.968 0.032 0.000
#> SRR1499503 2 0.6855 0.2640 0.316 0.652 0.032
#> SRR1094312 2 0.2269 0.5185 0.040 0.944 0.016
#> SRR1352437 1 0.0592 0.4807 0.988 0.012 0.000
#> SRR1436323 2 0.3573 0.5012 0.120 0.876 0.004
#> SRR1073507 1 0.5986 -0.0375 0.704 0.012 0.284
#> SRR1401972 1 0.7262 0.5928 0.624 0.332 0.044
#> SRR1415510 2 0.0475 0.5282 0.004 0.992 0.004
#> SRR1327279 3 0.9967 -0.0205 0.296 0.340 0.364
#> SRR1086983 1 0.1950 0.4412 0.952 0.008 0.040
#> SRR1105174 1 0.6570 -0.0911 0.668 0.024 0.308
#> SRR1468893 1 0.8698 0.5258 0.564 0.136 0.300
#> SRR1362555 2 0.7084 0.3011 0.044 0.652 0.304
#> SRR1074526 3 0.9215 0.5457 0.332 0.168 0.500
#> SRR1326225 2 0.7163 0.2550 0.332 0.628 0.040
#> SRR1401933 2 0.3618 0.5110 0.104 0.884 0.012
#> SRR1324062 1 0.8194 0.5853 0.572 0.340 0.088
#> SRR1102296 1 0.5706 0.5903 0.680 0.320 0.000
#> SRR1085087 1 0.0892 0.4873 0.980 0.020 0.000
#> SRR1079046 2 0.3983 0.5247 0.048 0.884 0.068
#> SRR1328339 1 0.6521 0.4291 0.504 0.492 0.004
#> SRR1079782 2 0.4281 0.5233 0.072 0.872 0.056
#> SRR1092257 2 0.7129 0.2546 0.392 0.580 0.028
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 4 0.4175 0.69726 0.000 0.200 0.016 0.784
#> SRR1429287 2 0.0376 0.61676 0.004 0.992 0.000 0.004
#> SRR1359238 1 0.5113 0.62955 0.684 0.292 0.024 0.000
#> SRR1309597 1 0.6299 -0.42481 0.496 0.456 0.040 0.008
#> SRR1441398 1 0.3074 0.35925 0.848 0.000 0.152 0.000
#> SRR1084055 3 0.5397 0.34567 0.000 0.068 0.720 0.212
#> SRR1417566 1 0.5830 0.56600 0.620 0.332 0.048 0.000
#> SRR1351857 3 0.5632 0.36670 0.012 0.008 0.560 0.420
#> SRR1487485 2 0.5007 0.54265 0.208 0.752 0.028 0.012
#> SRR1335875 1 0.4560 0.63850 0.700 0.296 0.004 0.000
#> SRR1073947 3 0.9328 0.42222 0.176 0.288 0.408 0.128
#> SRR1443483 3 0.3873 0.50279 0.000 0.228 0.772 0.000
#> SRR1346794 1 0.4560 0.63850 0.700 0.296 0.004 0.000
#> SRR1405245 1 0.0188 0.51381 0.996 0.000 0.004 0.000
#> SRR1409677 1 0.7122 0.30583 0.560 0.000 0.192 0.248
#> SRR1095549 3 0.4993 0.48085 0.020 0.008 0.728 0.244
#> SRR1323788 3 0.9285 0.43999 0.164 0.288 0.416 0.132
#> SRR1314054 4 0.4175 0.69726 0.000 0.200 0.016 0.784
#> SRR1077944 1 0.4770 0.64013 0.700 0.288 0.012 0.000
#> SRR1480587 2 0.5402 0.24671 0.288 0.680 0.024 0.008
#> SRR1311205 1 0.0188 0.51395 0.996 0.004 0.000 0.000
#> SRR1076369 3 0.7911 0.12084 0.208 0.308 0.472 0.012
#> SRR1453549 1 0.4770 0.64013 0.700 0.288 0.012 0.000
#> SRR1345782 3 0.9268 0.44394 0.160 0.292 0.416 0.132
#> SRR1447850 2 0.3216 0.49431 0.004 0.864 0.124 0.008
#> SRR1391553 2 0.4713 0.27753 0.360 0.640 0.000 0.000
#> SRR1444156 4 0.4175 0.69726 0.000 0.200 0.016 0.784
#> SRR1471731 2 0.4158 0.54799 0.224 0.768 0.008 0.000
#> SRR1120987 1 0.5696 -0.01543 0.492 0.000 0.024 0.484
#> SRR1477363 1 0.4560 0.63850 0.700 0.296 0.004 0.000
#> SRR1391961 3 0.5349 0.48934 0.008 0.212 0.732 0.048
#> SRR1373879 3 0.6373 0.51262 0.092 0.012 0.664 0.232
#> SRR1318732 2 0.5352 0.12428 0.388 0.596 0.016 0.000
#> SRR1091404 3 0.9442 0.23425 0.260 0.248 0.380 0.112
#> SRR1402109 3 0.8105 0.51094 0.052 0.300 0.516 0.132
#> SRR1407336 3 0.6145 0.56514 0.020 0.128 0.716 0.136
#> SRR1097417 3 0.5998 0.46424 0.000 0.212 0.680 0.108
#> SRR1396227 1 0.4770 0.64013 0.700 0.288 0.012 0.000
#> SRR1400775 2 0.5436 0.46301 0.008 0.756 0.128 0.108
#> SRR1392861 1 0.7210 0.27465 0.540 0.000 0.276 0.184
#> SRR1472929 1 0.6806 -0.21915 0.516 0.012 0.404 0.068
#> SRR1436740 1 0.6295 0.27263 0.580 0.000 0.072 0.348
#> SRR1477057 2 0.0376 0.61676 0.004 0.992 0.000 0.004
#> SRR1311980 1 0.3402 0.28251 0.832 0.164 0.004 0.000
#> SRR1069400 3 0.4454 0.46402 0.000 0.308 0.692 0.000
#> SRR1351016 1 0.4535 0.63853 0.704 0.292 0.004 0.000
#> SRR1096291 3 0.6805 0.39415 0.076 0.008 0.512 0.404
#> SRR1418145 2 0.7555 0.38970 0.288 0.552 0.136 0.024
#> SRR1488111 2 0.2654 0.63270 0.108 0.888 0.000 0.004
#> SRR1370495 2 0.5427 0.29327 0.416 0.568 0.016 0.000
#> SRR1352639 1 0.5815 0.61484 0.652 0.288 0.060 0.000
#> SRR1348911 1 0.4685 0.39490 0.784 0.060 0.156 0.000
#> SRR1467386 3 0.7179 0.48169 0.208 0.004 0.576 0.212
#> SRR1415956 1 0.0336 0.51297 0.992 0.000 0.008 0.000
#> SRR1500495 1 0.0188 0.51381 0.996 0.000 0.004 0.000
#> SRR1405099 1 0.1302 0.49846 0.956 0.000 0.044 0.000
#> SRR1345585 2 0.5251 0.47777 0.252 0.712 0.028 0.008
#> SRR1093196 1 0.6522 0.54428 0.600 0.328 0.052 0.020
#> SRR1466006 2 0.6949 0.37923 0.064 0.680 0.116 0.140
#> SRR1351557 2 0.0376 0.61676 0.004 0.992 0.000 0.004
#> SRR1382687 1 0.4770 0.64013 0.700 0.288 0.012 0.000
#> SRR1375549 2 0.3074 0.59854 0.152 0.848 0.000 0.000
#> SRR1101765 4 0.6897 0.37364 0.332 0.124 0.000 0.544
#> SRR1334461 3 0.5269 0.33862 0.364 0.016 0.620 0.000
#> SRR1094073 4 0.4175 0.69726 0.000 0.200 0.016 0.784
#> SRR1077549 3 0.6784 0.49870 0.156 0.000 0.600 0.244
#> SRR1440332 1 0.6920 0.55926 0.588 0.288 0.116 0.008
#> SRR1454177 4 0.7176 -0.10282 0.196 0.000 0.252 0.552
#> SRR1082447 3 0.8210 0.55195 0.164 0.120 0.580 0.136
#> SRR1420043 1 0.4382 0.63793 0.704 0.296 0.000 0.000
#> SRR1432500 1 0.4963 0.64058 0.696 0.284 0.020 0.000
#> SRR1378045 2 0.8080 0.48334 0.152 0.584 0.176 0.088
#> SRR1334200 2 0.6005 0.29467 0.356 0.600 0.036 0.008
#> SRR1069539 3 0.5236 0.33479 0.000 0.008 0.560 0.432
#> SRR1343031 3 0.7476 0.51296 0.020 0.300 0.548 0.132
#> SRR1319690 1 0.4697 0.63731 0.696 0.296 0.008 0.000
#> SRR1310604 2 0.6994 0.51449 0.124 0.648 0.032 0.196
#> SRR1327747 1 0.4980 0.62215 0.680 0.304 0.016 0.000
#> SRR1072456 2 0.9030 0.43397 0.184 0.484 0.132 0.200
#> SRR1367896 3 0.5623 0.36841 0.196 0.012 0.728 0.064
#> SRR1480107 1 0.9181 0.20646 0.412 0.296 0.192 0.100
#> SRR1377756 1 0.4673 0.63988 0.700 0.292 0.008 0.000
#> SRR1435272 1 0.7273 -0.03163 0.452 0.000 0.148 0.400
#> SRR1089230 4 0.7538 -0.00994 0.260 0.000 0.248 0.492
#> SRR1389522 3 0.3873 0.50279 0.000 0.228 0.772 0.000
#> SRR1080600 2 0.6426 0.38209 0.000 0.620 0.108 0.272
#> SRR1086935 4 0.7147 0.49454 0.128 0.308 0.008 0.556
#> SRR1344060 3 0.6363 0.22676 0.400 0.048 0.544 0.008
#> SRR1467922 4 0.5510 0.39043 0.000 0.480 0.016 0.504
#> SRR1090984 1 0.5517 0.46473 0.568 0.412 0.020 0.000
#> SRR1456991 1 0.7553 0.47000 0.536 0.296 0.152 0.016
#> SRR1085039 3 0.8019 0.55049 0.164 0.104 0.596 0.136
#> SRR1069303 1 0.4382 0.63793 0.704 0.296 0.000 0.000
#> SRR1091500 4 0.3837 0.68660 0.000 0.224 0.000 0.776
#> SRR1075198 2 0.3824 0.62073 0.048 0.868 0.028 0.056
#> SRR1086915 1 0.6996 0.34425 0.580 0.000 0.192 0.228
#> SRR1499503 4 0.7084 0.46592 0.000 0.284 0.164 0.552
#> SRR1094312 2 0.5771 0.39918 0.000 0.712 0.144 0.144
#> SRR1352437 1 0.6885 0.37403 0.596 0.000 0.196 0.208
#> SRR1436323 2 0.5273 -0.08538 0.456 0.536 0.008 0.000
#> SRR1073507 3 0.6860 0.49723 0.164 0.000 0.592 0.244
#> SRR1401972 1 0.4770 0.64013 0.700 0.288 0.012 0.000
#> SRR1415510 2 0.2846 0.61059 0.012 0.908 0.028 0.052
#> SRR1327279 3 0.7910 0.55204 0.156 0.092 0.604 0.148
#> SRR1086983 3 0.6922 0.49256 0.168 0.000 0.584 0.248
#> SRR1105174 3 0.6860 0.49723 0.164 0.000 0.592 0.244
#> SRR1468893 1 0.2011 0.56330 0.920 0.080 0.000 0.000
#> SRR1362555 2 0.5536 0.31286 0.384 0.592 0.024 0.000
#> SRR1074526 3 0.5070 0.19480 0.000 0.004 0.580 0.416
#> SRR1326225 4 0.4059 0.69593 0.000 0.200 0.012 0.788
#> SRR1401933 2 0.3710 0.57830 0.192 0.804 0.000 0.004
#> SRR1324062 1 0.4535 0.63975 0.704 0.292 0.004 0.000
#> SRR1102296 1 0.6125 0.58479 0.692 0.144 0.160 0.004
#> SRR1085087 1 0.6303 0.45229 0.660 0.004 0.228 0.108
#> SRR1079046 2 0.2466 0.63382 0.096 0.900 0.000 0.004
#> SRR1328339 1 0.7016 0.53356 0.560 0.308 0.128 0.004
#> SRR1079782 2 0.2922 0.63232 0.104 0.884 0.008 0.004
#> SRR1092257 4 0.7149 0.48978 0.108 0.336 0.012 0.544
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.0703 0.561785 0.000 0.976 0.000 0.000 0.024
#> SRR1429287 5 0.4362 0.639743 0.360 0.004 0.000 0.004 0.632
#> SRR1359238 1 0.1363 0.601370 0.956 0.004 0.032 0.004 0.004
#> SRR1309597 5 0.7251 0.188339 0.240 0.128 0.084 0.004 0.544
#> SRR1441398 1 0.4538 0.342631 0.620 0.000 0.016 0.000 0.364
#> SRR1084055 3 0.4657 0.531822 0.000 0.108 0.740 0.152 0.000
#> SRR1417566 1 0.0727 0.608491 0.980 0.004 0.012 0.004 0.000
#> SRR1351857 4 0.2377 0.610786 0.000 0.128 0.000 0.872 0.000
#> SRR1487485 1 0.7514 -0.148494 0.536 0.176 0.088 0.008 0.192
#> SRR1335875 1 0.0162 0.610859 0.996 0.000 0.004 0.000 0.000
#> SRR1073947 4 0.4510 0.297098 0.432 0.000 0.008 0.560 0.000
#> SRR1443483 3 0.3209 0.625634 0.008 0.000 0.812 0.180 0.000
#> SRR1346794 1 0.0000 0.611407 1.000 0.000 0.000 0.000 0.000
#> SRR1405245 1 0.4538 0.342631 0.620 0.000 0.016 0.000 0.364
#> SRR1409677 1 0.7603 -0.130175 0.368 0.228 0.040 0.360 0.004
#> SRR1095549 4 0.0162 0.692698 0.004 0.000 0.000 0.996 0.000
#> SRR1323788 4 0.4088 0.383921 0.368 0.000 0.000 0.632 0.000
#> SRR1314054 2 0.1818 0.559209 0.000 0.932 0.044 0.000 0.024
#> SRR1077944 1 0.0579 0.611054 0.984 0.000 0.008 0.008 0.000
#> SRR1480587 5 0.3257 0.207070 0.004 0.124 0.028 0.000 0.844
#> SRR1311205 1 0.4525 0.343839 0.624 0.000 0.016 0.000 0.360
#> SRR1076369 3 0.4260 0.523495 0.256 0.004 0.720 0.020 0.000
#> SRR1453549 1 0.0932 0.607045 0.972 0.000 0.020 0.004 0.004
#> SRR1345782 4 0.4101 0.379208 0.372 0.000 0.000 0.628 0.000
#> SRR1447850 5 0.4869 0.634140 0.340 0.004 0.008 0.016 0.632
#> SRR1391553 1 0.3333 0.339593 0.788 0.000 0.004 0.000 0.208
#> SRR1444156 2 0.1965 0.557952 0.000 0.924 0.052 0.000 0.024
#> SRR1471731 1 0.4644 -0.149551 0.604 0.000 0.012 0.004 0.380
#> SRR1120987 2 0.7093 0.294000 0.312 0.452 0.016 0.216 0.004
#> SRR1477363 1 0.0000 0.611407 1.000 0.000 0.000 0.000 0.000
#> SRR1391961 3 0.3684 0.630748 0.024 0.004 0.800 0.172 0.000
#> SRR1373879 4 0.0794 0.702099 0.028 0.000 0.000 0.972 0.000
#> SRR1318732 1 0.3732 0.382017 0.796 0.004 0.016 0.004 0.180
#> SRR1091404 1 0.5001 0.196982 0.620 0.004 0.036 0.340 0.000
#> SRR1402109 4 0.4211 0.381594 0.360 0.000 0.004 0.636 0.000
#> SRR1407336 4 0.0324 0.692412 0.004 0.000 0.004 0.992 0.000
#> SRR1097417 3 0.3002 0.601472 0.008 0.048 0.876 0.068 0.000
#> SRR1396227 1 0.0000 0.611407 1.000 0.000 0.000 0.000 0.000
#> SRR1400775 2 0.7860 -0.367172 0.344 0.348 0.040 0.012 0.256
#> SRR1392861 4 0.5320 0.211239 0.384 0.040 0.008 0.568 0.000
#> SRR1472929 3 0.6374 0.369644 0.132 0.004 0.500 0.004 0.360
#> SRR1436740 1 0.7239 -0.108416 0.388 0.236 0.024 0.352 0.000
#> SRR1477057 5 0.4362 0.639743 0.360 0.004 0.000 0.004 0.632
#> SRR1311980 1 0.4538 0.342631 0.620 0.000 0.016 0.000 0.364
#> SRR1069400 3 0.6194 0.340336 0.156 0.000 0.516 0.328 0.000
#> SRR1351016 1 0.0671 0.608926 0.980 0.000 0.016 0.000 0.004
#> SRR1096291 4 0.1410 0.666321 0.000 0.060 0.000 0.940 0.000
#> SRR1418145 1 0.7240 -0.102471 0.536 0.148 0.036 0.020 0.260
#> SRR1488111 5 0.4211 0.639243 0.360 0.000 0.000 0.004 0.636
#> SRR1370495 5 0.4944 0.256122 0.156 0.116 0.004 0.000 0.724
#> SRR1352639 1 0.2193 0.560320 0.900 0.000 0.008 0.092 0.000
#> SRR1348911 1 0.4843 0.363383 0.648 0.004 0.024 0.004 0.320
#> SRR1467386 4 0.3209 0.574222 0.180 0.000 0.008 0.812 0.000
#> SRR1415956 1 0.4538 0.342631 0.620 0.000 0.016 0.000 0.364
#> SRR1500495 1 0.4538 0.342631 0.620 0.000 0.016 0.000 0.364
#> SRR1405099 1 0.4538 0.342631 0.620 0.000 0.016 0.000 0.364
#> SRR1345585 1 0.6773 0.001219 0.612 0.140 0.072 0.004 0.172
#> SRR1093196 1 0.2601 0.575385 0.908 0.036 0.036 0.008 0.012
#> SRR1466006 3 0.6689 0.172861 0.004 0.200 0.416 0.000 0.380
#> SRR1351557 5 0.4464 0.638701 0.356 0.004 0.000 0.008 0.632
#> SRR1382687 1 0.0000 0.611407 1.000 0.000 0.000 0.000 0.000
#> SRR1375549 5 0.4438 0.606668 0.384 0.000 0.004 0.004 0.608
#> SRR1101765 2 0.6112 0.367338 0.300 0.572 0.012 0.116 0.000
#> SRR1334461 3 0.6427 0.439400 0.160 0.004 0.556 0.008 0.272
#> SRR1094073 2 0.1965 0.557952 0.000 0.924 0.052 0.000 0.024
#> SRR1077549 4 0.0404 0.697201 0.012 0.000 0.000 0.988 0.000
#> SRR1440332 1 0.3210 0.446155 0.788 0.000 0.000 0.212 0.000
#> SRR1454177 2 0.7065 0.201933 0.096 0.476 0.060 0.364 0.004
#> SRR1082447 4 0.0671 0.699902 0.016 0.000 0.004 0.980 0.000
#> SRR1420043 1 0.0510 0.610050 0.984 0.000 0.016 0.000 0.000
#> SRR1432500 1 0.0162 0.611421 0.996 0.000 0.004 0.000 0.000
#> SRR1378045 1 0.7972 -0.126106 0.492 0.188 0.012 0.192 0.116
#> SRR1334200 5 0.6922 0.214472 0.180 0.144 0.076 0.004 0.596
#> SRR1069539 4 0.5193 0.250787 0.000 0.364 0.052 0.584 0.000
#> SRR1343031 4 0.4060 0.383536 0.360 0.000 0.000 0.640 0.000
#> SRR1319690 1 0.0771 0.607175 0.976 0.000 0.020 0.000 0.004
#> SRR1310604 1 0.6788 -0.138733 0.496 0.360 0.084 0.000 0.060
#> SRR1327747 1 0.1116 0.603345 0.964 0.004 0.028 0.004 0.000
#> SRR1072456 3 0.7797 0.214521 0.172 0.268 0.452 0.000 0.108
#> SRR1367896 3 0.3209 0.622886 0.000 0.000 0.812 0.180 0.008
#> SRR1480107 1 0.4290 0.265559 0.680 0.000 0.016 0.304 0.000
#> SRR1377756 1 0.0510 0.610050 0.984 0.000 0.016 0.000 0.000
#> SRR1435272 2 0.7621 0.133456 0.320 0.328 0.032 0.316 0.004
#> SRR1089230 2 0.7377 0.363113 0.200 0.516 0.060 0.220 0.004
#> SRR1389522 3 0.3550 0.626220 0.020 0.000 0.796 0.184 0.000
#> SRR1080600 2 0.6391 -0.054755 0.020 0.472 0.408 0.000 0.100
#> SRR1086935 2 0.4644 0.484849 0.004 0.720 0.052 0.000 0.224
#> SRR1344060 3 0.4734 0.497732 0.268 0.004 0.692 0.004 0.032
#> SRR1467922 2 0.1671 0.545082 0.000 0.924 0.000 0.000 0.076
#> SRR1090984 1 0.1121 0.606826 0.968 0.004 0.016 0.004 0.008
#> SRR1456991 1 0.1372 0.602643 0.956 0.000 0.016 0.024 0.004
#> SRR1085039 4 0.1341 0.697464 0.056 0.000 0.000 0.944 0.000
#> SRR1069303 1 0.0510 0.610050 0.984 0.000 0.016 0.000 0.000
#> SRR1091500 2 0.0703 0.561785 0.000 0.976 0.000 0.000 0.024
#> SRR1075198 1 0.7700 -0.284672 0.480 0.180 0.072 0.008 0.260
#> SRR1086915 1 0.7326 -0.006253 0.424 0.176 0.036 0.360 0.004
#> SRR1499503 2 0.4313 0.469280 0.000 0.800 0.076 0.100 0.024
#> SRR1094312 2 0.7732 -0.332473 0.336 0.380 0.024 0.020 0.240
#> SRR1352437 1 0.6492 -0.000174 0.452 0.120 0.016 0.412 0.000
#> SRR1436323 1 0.3078 0.467928 0.848 0.000 0.016 0.004 0.132
#> SRR1073507 4 0.1410 0.694924 0.060 0.000 0.000 0.940 0.000
#> SRR1401972 1 0.0162 0.611421 0.996 0.000 0.004 0.000 0.000
#> SRR1415510 1 0.7848 -0.399493 0.408 0.236 0.064 0.004 0.288
#> SRR1327279 4 0.0404 0.697201 0.012 0.000 0.000 0.988 0.000
#> SRR1086983 4 0.1197 0.701261 0.048 0.000 0.000 0.952 0.000
#> SRR1105174 4 0.1197 0.701261 0.048 0.000 0.000 0.952 0.000
#> SRR1468893 1 0.3011 0.533234 0.844 0.000 0.016 0.000 0.140
#> SRR1362555 5 0.5104 0.282436 0.224 0.036 0.028 0.004 0.708
#> SRR1074526 2 0.6468 -0.005217 0.000 0.452 0.360 0.188 0.000
#> SRR1326225 2 0.0703 0.561785 0.000 0.976 0.000 0.000 0.024
#> SRR1401933 1 0.4900 -0.380632 0.512 0.000 0.024 0.000 0.464
#> SRR1324062 1 0.0510 0.610050 0.984 0.000 0.016 0.000 0.000
#> SRR1102296 1 0.2612 0.553332 0.868 0.000 0.008 0.124 0.000
#> SRR1085087 1 0.4718 0.118443 0.540 0.000 0.016 0.444 0.000
#> SRR1079046 5 0.4211 0.639243 0.360 0.000 0.000 0.004 0.636
#> SRR1328339 1 0.2813 0.501223 0.832 0.000 0.000 0.168 0.000
#> SRR1079782 5 0.4347 0.640024 0.356 0.000 0.004 0.004 0.636
#> SRR1092257 2 0.6453 0.409296 0.004 0.540 0.012 0.132 0.312
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.0363 0.70164 0.000 0.988 0.012 0.000 0.000 0.000
#> SRR1429287 6 0.2969 0.63186 0.224 0.000 0.000 0.000 0.000 0.776
#> SRR1359238 1 0.1346 0.61386 0.952 0.000 0.016 0.008 0.024 0.000
#> SRR1309597 5 0.5663 0.31837 0.168 0.000 0.012 0.000 0.576 0.244
#> SRR1441398 5 0.3867 -0.05135 0.488 0.000 0.000 0.000 0.512 0.000
#> SRR1084055 3 0.4895 0.53721 0.000 0.040 0.716 0.040 0.016 0.188
#> SRR1417566 1 0.2527 0.54962 0.868 0.000 0.000 0.000 0.108 0.024
#> SRR1351857 4 0.3393 0.57400 0.000 0.192 0.004 0.784 0.000 0.020
#> SRR1487485 1 0.7097 -0.39011 0.408 0.008 0.060 0.000 0.232 0.292
#> SRR1335875 1 0.0632 0.61311 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR1073947 4 0.3899 0.41860 0.404 0.000 0.000 0.592 0.004 0.000
#> SRR1443483 3 0.2744 0.69696 0.000 0.000 0.840 0.144 0.016 0.000
#> SRR1346794 1 0.0508 0.61794 0.984 0.000 0.000 0.012 0.004 0.000
#> SRR1405245 1 0.3860 0.00283 0.528 0.000 0.000 0.000 0.472 0.000
#> SRR1409677 1 0.8194 -0.13399 0.312 0.096 0.016 0.236 0.308 0.032
#> SRR1095549 4 0.0000 0.73052 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1323788 4 0.3351 0.53340 0.288 0.000 0.000 0.712 0.000 0.000
#> SRR1314054 2 0.0458 0.70069 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR1077944 1 0.0508 0.61734 0.984 0.000 0.000 0.012 0.004 0.000
#> SRR1480587 5 0.4325 0.09398 0.008 0.000 0.008 0.000 0.504 0.480
#> SRR1311205 1 0.3804 0.07500 0.576 0.000 0.000 0.000 0.424 0.000
#> SRR1076369 3 0.5072 0.60960 0.152 0.000 0.700 0.044 0.104 0.000
#> SRR1453549 1 0.0508 0.61753 0.984 0.000 0.004 0.012 0.000 0.000
#> SRR1345782 4 0.3198 0.55553 0.260 0.000 0.000 0.740 0.000 0.000
#> SRR1447850 6 0.2664 0.60446 0.184 0.000 0.000 0.000 0.000 0.816
#> SRR1391553 1 0.3970 0.22760 0.692 0.000 0.000 0.000 0.028 0.280
#> SRR1444156 2 0.0458 0.70069 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR1471731 6 0.5400 0.45959 0.376 0.000 0.000 0.000 0.120 0.504
#> SRR1120987 2 0.8100 0.36850 0.204 0.372 0.028 0.072 0.292 0.032
#> SRR1477363 1 0.0405 0.61782 0.988 0.000 0.000 0.008 0.004 0.000
#> SRR1391961 3 0.3300 0.70064 0.012 0.000 0.816 0.148 0.024 0.000
#> SRR1373879 4 0.0622 0.73687 0.012 0.000 0.000 0.980 0.008 0.000
#> SRR1318732 1 0.4653 0.30159 0.684 0.000 0.000 0.000 0.120 0.196
#> SRR1091404 1 0.4708 -0.02264 0.528 0.000 0.020 0.436 0.016 0.000
#> SRR1402109 4 0.3748 0.56456 0.224 0.000 0.016 0.748 0.012 0.000
#> SRR1407336 4 0.0363 0.72224 0.000 0.000 0.000 0.988 0.012 0.000
#> SRR1097417 3 0.1124 0.66645 0.000 0.000 0.956 0.036 0.008 0.000
#> SRR1396227 1 0.0363 0.61766 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1400775 6 0.6981 0.45957 0.220 0.260 0.028 0.000 0.032 0.460
#> SRR1392861 4 0.7063 0.03905 0.300 0.016 0.000 0.356 0.296 0.032
#> SRR1472929 3 0.4801 0.38812 0.036 0.000 0.520 0.000 0.436 0.008
#> SRR1436740 1 0.8194 -0.13399 0.312 0.096 0.016 0.236 0.308 0.032
#> SRR1477057 6 0.3126 0.63710 0.248 0.000 0.000 0.000 0.000 0.752
#> SRR1311980 5 0.4757 -0.00216 0.468 0.000 0.000 0.000 0.484 0.048
#> SRR1069400 3 0.5989 0.31945 0.172 0.000 0.484 0.332 0.012 0.000
#> SRR1351016 1 0.0865 0.60924 0.964 0.000 0.000 0.000 0.036 0.000
#> SRR1096291 4 0.1732 0.70476 0.000 0.072 0.004 0.920 0.000 0.004
#> SRR1418145 1 0.5852 -0.19276 0.540 0.044 0.020 0.016 0.016 0.364
#> SRR1488111 6 0.3175 0.63515 0.256 0.000 0.000 0.000 0.000 0.744
#> SRR1370495 5 0.5575 0.22996 0.140 0.000 0.000 0.000 0.460 0.400
#> SRR1352639 1 0.1219 0.60687 0.948 0.000 0.000 0.048 0.004 0.000
#> SRR1348911 1 0.5840 0.03838 0.520 0.000 0.156 0.000 0.312 0.012
#> SRR1467386 4 0.3107 0.68904 0.136 0.000 0.000 0.832 0.016 0.016
#> SRR1415956 1 0.3843 0.04676 0.548 0.000 0.000 0.000 0.452 0.000
#> SRR1500495 1 0.3843 0.04676 0.548 0.000 0.000 0.000 0.452 0.000
#> SRR1405099 1 0.3843 0.04676 0.548 0.000 0.000 0.000 0.452 0.000
#> SRR1345585 1 0.6721 -0.26967 0.464 0.000 0.036 0.008 0.236 0.256
#> SRR1093196 1 0.3738 0.53167 0.824 0.012 0.024 0.000 0.088 0.052
#> SRR1466006 6 0.6185 0.15108 0.000 0.028 0.348 0.000 0.152 0.472
#> SRR1351557 6 0.2793 0.61821 0.200 0.000 0.000 0.000 0.000 0.800
#> SRR1382687 1 0.0363 0.61766 0.988 0.000 0.000 0.012 0.000 0.000
#> SRR1375549 6 0.3446 0.60367 0.308 0.000 0.000 0.000 0.000 0.692
#> SRR1101765 2 0.7451 0.49023 0.172 0.504 0.028 0.048 0.216 0.032
#> SRR1334461 3 0.4270 0.62600 0.072 0.000 0.736 0.000 0.184 0.008
#> SRR1094073 2 0.0458 0.70069 0.000 0.984 0.016 0.000 0.000 0.000
#> SRR1077549 4 0.0146 0.73306 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR1440332 1 0.2402 0.55113 0.856 0.000 0.000 0.140 0.004 0.000
#> SRR1454177 2 0.8092 0.28458 0.084 0.336 0.016 0.228 0.304 0.032
#> SRR1082447 4 0.0632 0.73911 0.024 0.000 0.000 0.976 0.000 0.000
#> SRR1420043 1 0.0000 0.61694 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1432500 1 0.0820 0.61473 0.972 0.000 0.000 0.012 0.016 0.000
#> SRR1378045 1 0.8348 -0.37924 0.352 0.044 0.016 0.196 0.116 0.276
#> SRR1334200 5 0.5166 0.19464 0.060 0.000 0.012 0.000 0.528 0.400
#> SRR1069539 4 0.3672 0.44285 0.000 0.304 0.008 0.688 0.000 0.000
#> SRR1343031 4 0.3136 0.58273 0.228 0.000 0.004 0.768 0.000 0.000
#> SRR1319690 1 0.1007 0.60813 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1310604 6 0.8751 0.20288 0.200 0.232 0.108 0.000 0.184 0.276
#> SRR1327747 1 0.2651 0.54586 0.860 0.000 0.000 0.000 0.112 0.028
#> SRR1072456 3 0.7563 0.10782 0.144 0.040 0.484 0.000 0.160 0.172
#> SRR1367896 3 0.2886 0.69739 0.000 0.000 0.836 0.144 0.016 0.004
#> SRR1480107 1 0.3168 0.48842 0.804 0.000 0.000 0.172 0.024 0.000
#> SRR1377756 1 0.0000 0.61694 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1435272 5 0.8055 -0.36098 0.284 0.288 0.012 0.080 0.304 0.032
#> SRR1089230 2 0.7834 0.47866 0.112 0.432 0.016 0.128 0.280 0.032
#> SRR1389522 3 0.3018 0.69184 0.004 0.000 0.816 0.168 0.012 0.000
#> SRR1080600 6 0.7909 -0.06629 0.024 0.304 0.192 0.000 0.148 0.332
#> SRR1086935 2 0.3407 0.66236 0.000 0.800 0.016 0.000 0.168 0.016
#> SRR1344060 3 0.5193 0.51783 0.172 0.000 0.644 0.000 0.176 0.008
#> SRR1467922 2 0.0363 0.70164 0.000 0.988 0.012 0.000 0.000 0.000
#> SRR1090984 1 0.4183 0.44753 0.752 0.000 0.004 0.000 0.128 0.116
#> SRR1456991 1 0.2300 0.54682 0.856 0.000 0.000 0.000 0.144 0.000
#> SRR1085039 4 0.2112 0.72654 0.088 0.000 0.000 0.896 0.016 0.000
#> SRR1069303 1 0.0632 0.61311 0.976 0.000 0.000 0.000 0.024 0.000
#> SRR1091500 2 0.0363 0.70164 0.000 0.988 0.012 0.000 0.000 0.000
#> SRR1075198 6 0.6836 0.45150 0.288 0.008 0.040 0.000 0.228 0.436
#> SRR1086915 1 0.8191 -0.12667 0.312 0.084 0.020 0.248 0.304 0.032
#> SRR1499503 2 0.6644 0.31639 0.000 0.544 0.140 0.004 0.104 0.208
#> SRR1094312 6 0.6325 0.29921 0.132 0.384 0.028 0.000 0.008 0.448
#> SRR1352437 1 0.7799 -0.06855 0.332 0.052 0.012 0.264 0.308 0.032
#> SRR1436323 1 0.4594 0.26564 0.676 0.000 0.000 0.000 0.092 0.232
#> SRR1073507 4 0.2554 0.72358 0.088 0.000 0.000 0.880 0.012 0.020
#> SRR1401972 1 0.0508 0.61754 0.984 0.000 0.000 0.012 0.004 0.000
#> SRR1415510 6 0.6804 0.45030 0.260 0.008 0.040 0.000 0.240 0.452
#> SRR1327279 4 0.0000 0.73052 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1086983 4 0.2790 0.71944 0.088 0.000 0.000 0.868 0.012 0.032
#> SRR1105174 4 0.2019 0.72760 0.088 0.000 0.000 0.900 0.012 0.000
#> SRR1468893 1 0.3464 0.30674 0.688 0.000 0.000 0.000 0.312 0.000
#> SRR1362555 5 0.5082 0.19881 0.080 0.000 0.000 0.000 0.512 0.408
#> SRR1074526 2 0.6029 0.03469 0.000 0.412 0.332 0.256 0.000 0.000
#> SRR1326225 2 0.0363 0.70164 0.000 0.988 0.012 0.000 0.000 0.000
#> SRR1401933 6 0.4516 0.48144 0.420 0.000 0.000 0.008 0.020 0.552
#> SRR1324062 1 0.0458 0.61475 0.984 0.000 0.000 0.000 0.016 0.000
#> SRR1102296 1 0.3626 0.44483 0.776 0.000 0.008 0.188 0.028 0.000
#> SRR1085087 1 0.7103 -0.04079 0.364 0.012 0.012 0.304 0.288 0.020
#> SRR1079046 6 0.3151 0.63633 0.252 0.000 0.000 0.000 0.000 0.748
#> SRR1328339 1 0.2950 0.53189 0.828 0.000 0.000 0.148 0.024 0.000
#> SRR1079782 6 0.3126 0.63710 0.248 0.000 0.000 0.000 0.000 0.752
#> SRR1092257 2 0.5351 0.63426 0.000 0.712 0.028 0.052 0.132 0.076
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 17611 rows and 118 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.488 0.818 0.898 0.4020 0.618 0.618
#> 3 3 0.374 0.708 0.816 0.4873 0.733 0.590
#> 4 4 0.445 0.486 0.725 0.2041 0.728 0.438
#> 5 5 0.482 0.435 0.645 0.0853 0.722 0.285
#> 6 6 0.574 0.542 0.720 0.0491 0.833 0.407
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
#> SRR1396765 2 0.7674 0.771 0.224 0.776
#> SRR1429287 1 0.0000 0.884 1.000 0.000
#> SRR1359238 1 0.0672 0.883 0.992 0.008
#> SRR1309597 1 0.0000 0.884 1.000 0.000
#> SRR1441398 1 0.1184 0.882 0.984 0.016
#> SRR1084055 1 0.8327 0.493 0.736 0.264
#> SRR1417566 1 0.0000 0.884 1.000 0.000
#> SRR1351857 2 0.2043 0.841 0.032 0.968
#> SRR1487485 1 0.0000 0.884 1.000 0.000
#> SRR1335875 1 0.7299 0.812 0.796 0.204
#> SRR1073947 1 0.7745 0.793 0.772 0.228
#> SRR1443483 1 0.0000 0.884 1.000 0.000
#> SRR1346794 1 0.6623 0.829 0.828 0.172
#> SRR1405245 1 0.2948 0.874 0.948 0.052
#> SRR1409677 2 0.0000 0.846 0.000 1.000
#> SRR1095549 2 0.9358 0.389 0.352 0.648
#> SRR1323788 1 0.7602 0.800 0.780 0.220
#> SRR1314054 2 0.7674 0.771 0.224 0.776
#> SRR1077944 1 0.7602 0.800 0.780 0.220
#> SRR1480587 1 0.0000 0.884 1.000 0.000
#> SRR1311205 1 0.3584 0.870 0.932 0.068
#> SRR1076369 1 0.0000 0.884 1.000 0.000
#> SRR1453549 1 0.7453 0.806 0.788 0.212
#> SRR1345782 1 0.7376 0.809 0.792 0.208
#> SRR1447850 1 0.0000 0.884 1.000 0.000
#> SRR1391553 1 0.3584 0.870 0.932 0.068
#> SRR1444156 2 0.7602 0.773 0.220 0.780
#> SRR1471731 1 0.0000 0.884 1.000 0.000
#> SRR1120987 2 0.0000 0.846 0.000 1.000
#> SRR1477363 1 0.7602 0.800 0.780 0.220
#> SRR1391961 1 0.0000 0.884 1.000 0.000
#> SRR1373879 1 0.5519 0.848 0.872 0.128
#> SRR1318732 1 0.0000 0.884 1.000 0.000
#> SRR1091404 1 0.8016 0.635 0.756 0.244
#> SRR1402109 1 0.0376 0.884 0.996 0.004
#> SRR1407336 1 0.0376 0.884 0.996 0.004
#> SRR1097417 1 0.0000 0.884 1.000 0.000
#> SRR1396227 1 0.7602 0.800 0.780 0.220
#> SRR1400775 1 0.0000 0.884 1.000 0.000
#> SRR1392861 2 0.2423 0.829 0.040 0.960
#> SRR1472929 1 0.0000 0.884 1.000 0.000
#> SRR1436740 2 0.0376 0.845 0.004 0.996
#> SRR1477057 1 0.0000 0.884 1.000 0.000
#> SRR1311980 1 0.0000 0.884 1.000 0.000
#> SRR1069400 1 0.0000 0.884 1.000 0.000
#> SRR1351016 1 0.7299 0.812 0.796 0.204
#> SRR1096291 2 0.6887 0.790 0.184 0.816
#> SRR1418145 2 0.8499 0.603 0.276 0.724
#> SRR1488111 1 0.0938 0.883 0.988 0.012
#> SRR1370495 1 0.0000 0.884 1.000 0.000
#> SRR1352639 1 0.7139 0.816 0.804 0.196
#> SRR1348911 1 0.0000 0.884 1.000 0.000
#> SRR1467386 2 0.4022 0.802 0.080 0.920
#> SRR1415956 1 0.7299 0.812 0.796 0.204
#> SRR1500495 1 0.7219 0.814 0.800 0.200
#> SRR1405099 1 0.7299 0.812 0.796 0.204
#> SRR1345585 1 0.0000 0.884 1.000 0.000
#> SRR1093196 1 0.0000 0.884 1.000 0.000
#> SRR1466006 1 0.0000 0.884 1.000 0.000
#> SRR1351557 1 0.0000 0.884 1.000 0.000
#> SRR1382687 1 0.7745 0.793 0.772 0.228
#> SRR1375549 1 0.1414 0.882 0.980 0.020
#> SRR1101765 2 0.7219 0.782 0.200 0.800
#> SRR1334461 1 0.0000 0.884 1.000 0.000
#> SRR1094073 2 0.7602 0.773 0.220 0.780
#> SRR1077549 2 0.8955 0.448 0.312 0.688
#> SRR1440332 1 0.7453 0.806 0.788 0.212
#> SRR1454177 2 0.0000 0.846 0.000 1.000
#> SRR1082447 1 0.9170 0.657 0.668 0.332
#> SRR1420043 1 0.7602 0.800 0.780 0.220
#> SRR1432500 1 0.8144 0.769 0.748 0.252
#> SRR1378045 1 0.0000 0.884 1.000 0.000
#> SRR1334200 1 0.0000 0.884 1.000 0.000
#> SRR1069539 2 0.7528 0.775 0.216 0.784
#> SRR1343031 1 0.4815 0.857 0.896 0.104
#> SRR1319690 1 0.0938 0.883 0.988 0.012
#> SRR1310604 1 0.8661 0.427 0.712 0.288
#> SRR1327747 1 0.0000 0.884 1.000 0.000
#> SRR1072456 1 0.0000 0.884 1.000 0.000
#> SRR1367896 1 0.0000 0.884 1.000 0.000
#> SRR1480107 1 0.7376 0.809 0.792 0.208
#> SRR1377756 1 0.7745 0.793 0.772 0.228
#> SRR1435272 2 0.0000 0.846 0.000 1.000
#> SRR1089230 2 0.0000 0.846 0.000 1.000
#> SRR1389522 1 0.0000 0.884 1.000 0.000
#> SRR1080600 1 0.3733 0.824 0.928 0.072
#> SRR1086935 2 0.0000 0.846 0.000 1.000
#> SRR1344060 1 0.0000 0.884 1.000 0.000
#> SRR1467922 1 0.0376 0.882 0.996 0.004
#> SRR1090984 1 0.0000 0.884 1.000 0.000
#> SRR1456991 1 0.6973 0.821 0.812 0.188
#> SRR1085039 2 0.9427 0.315 0.360 0.640
#> SRR1069303 1 0.7602 0.800 0.780 0.220
#> SRR1091500 2 0.7674 0.771 0.224 0.776
#> SRR1075198 1 0.0000 0.884 1.000 0.000
#> SRR1086915 2 0.0000 0.846 0.000 1.000
#> SRR1499503 1 0.0376 0.882 0.996 0.004
#> SRR1094312 1 0.0000 0.884 1.000 0.000
#> SRR1352437 2 0.0376 0.845 0.004 0.996
#> SRR1436323 1 0.0376 0.883 0.996 0.004
#> SRR1073507 2 0.0376 0.845 0.004 0.996
#> SRR1401972 1 0.7745 0.793 0.772 0.228
#> SRR1415510 1 0.0000 0.884 1.000 0.000
#> SRR1327279 1 0.7602 0.800 0.780 0.220
#> SRR1086983 2 0.0000 0.846 0.000 1.000
#> SRR1105174 2 0.0000 0.846 0.000 1.000
#> SRR1468893 1 0.7602 0.800 0.780 0.220
#> SRR1362555 1 0.0000 0.884 1.000 0.000
#> SRR1074526 2 0.7815 0.765 0.232 0.768
#> SRR1326225 2 0.9775 0.533 0.412 0.588
#> SRR1401933 1 0.7453 0.806 0.788 0.212
#> SRR1324062 1 0.7602 0.800 0.780 0.220
#> SRR1102296 1 0.7602 0.800 0.780 0.220
#> SRR1085087 1 0.8955 0.689 0.688 0.312
#> SRR1079046 1 0.6247 0.836 0.844 0.156
#> SRR1328339 1 0.0376 0.884 0.996 0.004
#> SRR1079782 1 0.2948 0.874 0.948 0.052
#> SRR1092257 2 0.0000 0.846 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR1396765 2 0.1860 0.757 0.000 0.948 0.052
#> SRR1429287 1 0.6617 0.419 0.556 0.436 0.008
#> SRR1359238 1 0.6208 0.780 0.768 0.164 0.068
#> SRR1309597 1 0.4654 0.752 0.792 0.208 0.000
#> SRR1441398 1 0.0848 0.781 0.984 0.008 0.008
#> SRR1084055 2 0.2448 0.792 0.076 0.924 0.000
#> SRR1417566 1 0.1753 0.772 0.952 0.048 0.000
#> SRR1351857 3 0.7504 0.599 0.200 0.112 0.688
#> SRR1487485 1 0.5529 0.665 0.704 0.296 0.000
#> SRR1335875 1 0.5167 0.783 0.792 0.016 0.192
#> SRR1073947 1 0.4931 0.745 0.768 0.000 0.232
#> SRR1443483 1 0.1964 0.760 0.944 0.056 0.000
#> SRR1346794 1 0.4235 0.786 0.824 0.000 0.176
#> SRR1405245 1 0.2879 0.802 0.924 0.024 0.052
#> SRR1409677 3 0.1031 0.820 0.024 0.000 0.976
#> SRR1095549 2 0.9431 0.352 0.292 0.496 0.212
#> SRR1323788 1 0.1753 0.783 0.952 0.000 0.048
#> SRR1314054 2 0.2703 0.761 0.016 0.928 0.056
#> SRR1077944 1 0.4452 0.770 0.808 0.000 0.192
#> SRR1480587 1 0.5760 0.650 0.672 0.328 0.000
#> SRR1311205 1 0.0475 0.780 0.992 0.004 0.004
#> SRR1076369 2 0.6307 0.348 0.488 0.512 0.000
#> SRR1453549 1 0.4796 0.768 0.780 0.000 0.220
#> SRR1345782 1 0.1163 0.781 0.972 0.000 0.028
#> SRR1447850 1 0.6129 0.696 0.700 0.284 0.016
#> SRR1391553 1 0.5737 0.800 0.804 0.104 0.092
#> SRR1444156 2 0.2711 0.734 0.000 0.912 0.088
#> SRR1471731 1 0.4605 0.769 0.796 0.204 0.000
#> SRR1120987 3 0.1529 0.801 0.000 0.040 0.960
#> SRR1477363 1 0.4887 0.761 0.772 0.000 0.228
#> SRR1391961 2 0.4974 0.726 0.236 0.764 0.000
#> SRR1373879 1 0.3038 0.735 0.896 0.000 0.104
#> SRR1318732 1 0.3038 0.779 0.896 0.104 0.000
#> SRR1091404 3 0.9100 0.417 0.248 0.204 0.548
#> SRR1402109 1 0.0892 0.775 0.980 0.020 0.000
#> SRR1407336 1 0.6295 -0.315 0.528 0.472 0.000
#> SRR1097417 2 0.4399 0.741 0.188 0.812 0.000
#> SRR1396227 1 0.4796 0.764 0.780 0.000 0.220
#> SRR1400775 2 0.6102 0.418 0.320 0.672 0.008
#> SRR1392861 3 0.4750 0.745 0.216 0.000 0.784
#> SRR1472929 1 0.4750 0.662 0.784 0.216 0.000
#> SRR1436740 3 0.1860 0.819 0.052 0.000 0.948
#> SRR1477057 1 0.5461 0.758 0.768 0.216 0.016
#> SRR1311980 1 0.4682 0.775 0.804 0.192 0.004
#> SRR1069400 2 0.6267 0.482 0.452 0.548 0.000
#> SRR1351016 1 0.4555 0.777 0.800 0.000 0.200
#> SRR1096291 3 0.8763 0.448 0.196 0.216 0.588
#> SRR1418145 3 0.6031 0.752 0.116 0.096 0.788
#> SRR1488111 1 0.5618 0.784 0.796 0.156 0.048
#> SRR1370495 1 0.5420 0.743 0.752 0.240 0.008
#> SRR1352639 1 0.2066 0.792 0.940 0.000 0.060
#> SRR1348911 1 0.2261 0.757 0.932 0.068 0.000
#> SRR1467386 3 0.3192 0.798 0.112 0.000 0.888
#> SRR1415956 1 0.4399 0.781 0.812 0.000 0.188
#> SRR1500495 1 0.4346 0.784 0.816 0.000 0.184
#> SRR1405099 1 0.4654 0.772 0.792 0.000 0.208
#> SRR1345585 1 0.4504 0.684 0.804 0.196 0.000
#> SRR1093196 1 0.4291 0.763 0.820 0.180 0.000
#> SRR1466006 2 0.1964 0.790 0.056 0.944 0.000
#> SRR1351557 2 0.5502 0.593 0.248 0.744 0.008
#> SRR1382687 1 0.5291 0.721 0.732 0.000 0.268
#> SRR1375549 1 0.5659 0.785 0.796 0.152 0.052
#> SRR1101765 3 0.5327 0.565 0.000 0.272 0.728
#> SRR1334461 1 0.3192 0.739 0.888 0.112 0.000
#> SRR1094073 2 0.5343 0.727 0.132 0.816 0.052
#> SRR1077549 3 0.5431 0.731 0.284 0.000 0.716
#> SRR1440332 1 0.2261 0.787 0.932 0.000 0.068
#> SRR1454177 3 0.0424 0.817 0.008 0.000 0.992
#> SRR1082447 3 0.6180 0.590 0.416 0.000 0.584
#> SRR1420043 1 0.4796 0.762 0.780 0.000 0.220
#> SRR1432500 3 0.5363 0.591 0.276 0.000 0.724
#> SRR1378045 1 0.4346 0.632 0.816 0.184 0.000
#> SRR1334200 1 0.6260 0.369 0.552 0.448 0.000
#> SRR1069539 2 0.8008 0.571 0.192 0.656 0.152
#> SRR1343031 1 0.0424 0.778 0.992 0.008 0.000
#> SRR1319690 1 0.5618 0.786 0.796 0.156 0.048
#> SRR1310604 2 0.1860 0.797 0.052 0.948 0.000
#> SRR1327747 1 0.3941 0.776 0.844 0.156 0.000
#> SRR1072456 2 0.3551 0.781 0.132 0.868 0.000
#> SRR1367896 1 0.5882 0.199 0.652 0.348 0.000
#> SRR1480107 1 0.4452 0.774 0.808 0.000 0.192
#> SRR1377756 1 0.5397 0.700 0.720 0.000 0.280
#> SRR1435272 3 0.0424 0.817 0.008 0.000 0.992
#> SRR1089230 3 0.3134 0.797 0.052 0.032 0.916
#> SRR1389522 1 0.3619 0.687 0.864 0.136 0.000
#> SRR1080600 2 0.1860 0.797 0.052 0.948 0.000
#> SRR1086935 3 0.1529 0.801 0.000 0.040 0.960
#> SRR1344060 1 0.6280 -0.066 0.540 0.460 0.000
#> SRR1467922 2 0.1163 0.790 0.028 0.972 0.000
#> SRR1090984 1 0.4504 0.767 0.804 0.196 0.000
#> SRR1456991 1 0.4121 0.789 0.832 0.000 0.168
#> SRR1085039 3 0.4974 0.767 0.236 0.000 0.764
#> SRR1069303 1 0.4931 0.758 0.768 0.000 0.232
#> SRR1091500 2 0.2537 0.740 0.000 0.920 0.080
#> SRR1075198 2 0.4452 0.718 0.192 0.808 0.000
#> SRR1086915 3 0.0237 0.816 0.004 0.000 0.996
#> SRR1499503 2 0.2165 0.797 0.064 0.936 0.000
#> SRR1094312 2 0.5070 0.638 0.224 0.772 0.004
#> SRR1352437 3 0.2711 0.810 0.088 0.000 0.912
#> SRR1436323 1 0.5072 0.770 0.792 0.196 0.012
#> SRR1073507 3 0.2448 0.820 0.076 0.000 0.924
#> SRR1401972 1 0.5968 0.559 0.636 0.000 0.364
#> SRR1415510 2 0.1643 0.792 0.044 0.956 0.000
#> SRR1327279 1 0.2066 0.776 0.940 0.000 0.060
#> SRR1086983 3 0.1289 0.818 0.032 0.000 0.968
#> SRR1105174 3 0.2711 0.821 0.088 0.000 0.912
#> SRR1468893 1 0.4887 0.765 0.772 0.000 0.228
#> SRR1362555 1 0.4931 0.753 0.768 0.232 0.000
#> SRR1074526 2 0.5689 0.693 0.184 0.780 0.036
#> SRR1326225 2 0.0424 0.785 0.008 0.992 0.000
#> SRR1401933 1 0.5743 0.785 0.784 0.044 0.172
#> SRR1324062 1 0.4796 0.764 0.780 0.000 0.220
#> SRR1102296 1 0.4796 0.764 0.780 0.000 0.220
#> SRR1085087 3 0.4605 0.717 0.204 0.000 0.796
#> SRR1079046 1 0.6144 0.790 0.780 0.132 0.088
#> SRR1328339 1 0.1163 0.772 0.972 0.028 0.000
#> SRR1079782 1 0.5875 0.783 0.784 0.160 0.056
#> SRR1092257 3 0.3116 0.756 0.000 0.108 0.892
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR1396765 2 0.2831 0.6122 0.000 0.876 0.004 0.120
#> SRR1429287 3 0.4857 0.4333 0.000 0.284 0.700 0.016
#> SRR1359238 3 0.6494 0.5151 0.040 0.044 0.648 0.268
#> SRR1309597 3 0.2053 0.6909 0.072 0.004 0.924 0.000
#> SRR1441398 1 0.4855 0.3626 0.600 0.000 0.400 0.000
#> SRR1084055 2 0.4250 0.5521 0.276 0.724 0.000 0.000
#> SRR1417566 3 0.4877 0.0850 0.408 0.000 0.592 0.000
#> SRR1351857 2 0.7870 0.1942 0.300 0.392 0.000 0.308
#> SRR1487485 3 0.2032 0.6961 0.036 0.028 0.936 0.000
#> SRR1335875 3 0.4231 0.6548 0.096 0.000 0.824 0.080
#> SRR1073947 1 0.7088 0.4393 0.568 0.000 0.228 0.204
#> SRR1443483 1 0.5314 0.5774 0.748 0.108 0.144 0.000
#> SRR1346794 3 0.3636 0.6171 0.172 0.000 0.820 0.008
#> SRR1405245 3 0.5060 0.1525 0.412 0.000 0.584 0.004
#> SRR1409677 4 0.0336 0.7709 0.008 0.000 0.000 0.992
#> SRR1095549 1 0.3402 0.4433 0.832 0.164 0.000 0.004
#> SRR1323788 1 0.5074 0.5353 0.724 0.000 0.236 0.040
#> SRR1314054 2 0.2704 0.6091 0.000 0.876 0.000 0.124
#> SRR1077944 1 0.6084 0.4963 0.656 0.000 0.252 0.092
#> SRR1480587 3 0.1576 0.6911 0.004 0.048 0.948 0.000
#> SRR1311205 1 0.4679 0.4364 0.648 0.000 0.352 0.000
#> SRR1076369 1 0.5471 0.2445 0.684 0.268 0.048 0.000
#> SRR1453549 3 0.5594 0.5811 0.112 0.000 0.724 0.164
#> SRR1345782 1 0.2676 0.6130 0.896 0.000 0.092 0.012
#> SRR1447850 3 0.4910 0.4405 0.000 0.276 0.704 0.020
#> SRR1391553 3 0.1209 0.6976 0.032 0.000 0.964 0.004
#> SRR1444156 2 0.3787 0.6078 0.000 0.840 0.036 0.124
#> SRR1471731 3 0.1109 0.6987 0.028 0.004 0.968 0.000
#> SRR1120987 4 0.1059 0.7529 0.000 0.016 0.012 0.972
#> SRR1477363 3 0.6407 0.4848 0.204 0.000 0.648 0.148
#> SRR1391961 1 0.4996 -0.1721 0.516 0.484 0.000 0.000
#> SRR1373879 1 0.1697 0.5653 0.952 0.016 0.004 0.028
#> SRR1318732 3 0.2216 0.6733 0.092 0.000 0.908 0.000
#> SRR1091404 1 0.6995 -0.0335 0.496 0.384 0.000 0.120
#> SRR1402109 1 0.4257 0.6099 0.812 0.048 0.140 0.000
#> SRR1407336 1 0.4050 0.4415 0.808 0.168 0.024 0.000
#> SRR1097417 2 0.5016 0.3996 0.396 0.600 0.004 0.000
#> SRR1396227 3 0.6273 0.4298 0.264 0.000 0.636 0.100
#> SRR1400775 3 0.4560 0.4099 0.000 0.296 0.700 0.004
#> SRR1392861 4 0.4675 0.6635 0.244 0.000 0.020 0.736
#> SRR1472929 1 0.6725 0.4203 0.548 0.104 0.348 0.000
#> SRR1436740 4 0.2222 0.7800 0.060 0.000 0.016 0.924
#> SRR1477057 3 0.3706 0.6568 0.000 0.112 0.848 0.040
#> SRR1311980 3 0.2334 0.6777 0.088 0.000 0.908 0.004
#> SRR1069400 1 0.4590 0.4046 0.772 0.192 0.036 0.000
#> SRR1351016 3 0.6407 0.3034 0.332 0.000 0.584 0.084
#> SRR1096291 1 0.8046 -0.2208 0.376 0.324 0.004 0.296
#> SRR1418145 4 0.7279 0.2577 0.004 0.144 0.336 0.516
#> SRR1488111 3 0.3144 0.6809 0.000 0.072 0.884 0.044
#> SRR1370495 3 0.1822 0.6993 0.008 0.044 0.944 0.004
#> SRR1352639 1 0.4999 0.4521 0.660 0.000 0.328 0.012
#> SRR1348911 1 0.6055 0.4221 0.576 0.052 0.372 0.000
#> SRR1467386 4 0.3208 0.7612 0.148 0.000 0.004 0.848
#> SRR1415956 1 0.5399 0.1447 0.520 0.000 0.468 0.012
#> SRR1500495 1 0.5488 0.1898 0.532 0.000 0.452 0.016
#> SRR1405099 1 0.6555 0.1099 0.480 0.000 0.444 0.076
#> SRR1345585 3 0.4079 0.5869 0.180 0.020 0.800 0.000
#> SRR1093196 3 0.1635 0.6952 0.044 0.008 0.948 0.000
#> SRR1466006 2 0.5212 0.2888 0.008 0.572 0.420 0.000
#> SRR1351557 3 0.4456 0.4286 0.000 0.280 0.716 0.004
#> SRR1382687 3 0.6883 0.4316 0.156 0.000 0.584 0.260
#> SRR1375549 3 0.3383 0.6892 0.000 0.052 0.872 0.076
#> SRR1101765 4 0.4744 0.4547 0.000 0.284 0.012 0.704
#> SRR1334461 1 0.5250 0.3208 0.660 0.316 0.024 0.000
#> SRR1094073 2 0.4723 0.6237 0.036 0.816 0.040 0.108
#> SRR1077549 1 0.4175 0.4592 0.776 0.012 0.000 0.212
#> SRR1440332 1 0.5219 0.5292 0.712 0.000 0.244 0.044
#> SRR1454177 4 0.0592 0.7720 0.016 0.000 0.000 0.984
#> SRR1082447 1 0.4100 0.5067 0.816 0.036 0.000 0.148
#> SRR1420043 3 0.6277 -0.0642 0.468 0.000 0.476 0.056
#> SRR1432500 4 0.4500 0.7005 0.192 0.000 0.032 0.776
#> SRR1378045 3 0.6419 -0.1301 0.420 0.068 0.512 0.000
#> SRR1334200 3 0.2988 0.6564 0.012 0.112 0.876 0.000
#> SRR1069539 2 0.5848 0.4196 0.376 0.584 0.000 0.040
#> SRR1343031 1 0.2101 0.5400 0.928 0.060 0.012 0.000
#> SRR1319690 3 0.6005 0.6142 0.144 0.036 0.736 0.084
#> SRR1310604 2 0.4914 0.6105 0.208 0.748 0.044 0.000
#> SRR1327747 3 0.1637 0.6927 0.060 0.000 0.940 0.000
#> SRR1072456 2 0.7344 0.4901 0.268 0.524 0.208 0.000
#> SRR1367896 1 0.4225 0.4102 0.792 0.184 0.024 0.000
#> SRR1480107 1 0.6084 0.5037 0.660 0.000 0.244 0.096
#> SRR1377756 3 0.7706 0.1806 0.268 0.000 0.452 0.280
#> SRR1435272 4 0.0895 0.7709 0.020 0.004 0.000 0.976
#> SRR1089230 4 0.3048 0.6950 0.016 0.108 0.000 0.876
#> SRR1389522 1 0.3142 0.4761 0.860 0.132 0.008 0.000
#> SRR1080600 2 0.3877 0.6582 0.048 0.840 0.112 0.000
#> SRR1086935 4 0.1637 0.7390 0.000 0.060 0.000 0.940
#> SRR1344060 1 0.6412 0.1091 0.592 0.320 0.088 0.000
#> SRR1467922 2 0.4866 0.3265 0.000 0.596 0.404 0.000
#> SRR1090984 3 0.2399 0.6997 0.048 0.032 0.920 0.000
#> SRR1456991 1 0.6004 0.4817 0.648 0.000 0.276 0.076
#> SRR1085039 1 0.5678 -0.0333 0.532 0.012 0.008 0.448
#> SRR1069303 1 0.7661 0.1074 0.412 0.000 0.376 0.212
#> SRR1091500 2 0.4633 0.5683 0.000 0.780 0.048 0.172
#> SRR1075198 3 0.5161 0.3906 0.024 0.300 0.676 0.000
#> SRR1086915 4 0.0000 0.7669 0.000 0.000 0.000 1.000
#> SRR1499503 2 0.4467 0.6318 0.172 0.788 0.040 0.000
#> SRR1094312 3 0.4632 0.3920 0.000 0.308 0.688 0.004
#> SRR1352437 4 0.2976 0.7746 0.120 0.000 0.008 0.872
#> SRR1436323 3 0.1209 0.6976 0.032 0.000 0.964 0.004
#> SRR1073507 4 0.3831 0.7248 0.204 0.000 0.004 0.792
#> SRR1401972 4 0.7140 0.3437 0.204 0.000 0.236 0.560
#> SRR1415510 2 0.4998 0.1105 0.000 0.512 0.488 0.000
#> SRR1327279 1 0.1510 0.5847 0.956 0.000 0.028 0.016
#> SRR1086983 4 0.2868 0.7725 0.136 0.000 0.000 0.864
#> SRR1105174 4 0.4917 0.5502 0.328 0.004 0.004 0.664
#> SRR1468893 3 0.6785 0.4388 0.208 0.000 0.608 0.184
#> SRR1362555 3 0.0672 0.6993 0.008 0.008 0.984 0.000
#> SRR1074526 2 0.4382 0.5306 0.296 0.704 0.000 0.000
#> SRR1326225 2 0.2281 0.6516 0.000 0.904 0.096 0.000
#> SRR1401933 3 0.1675 0.7011 0.004 0.004 0.948 0.044
#> SRR1324062 3 0.6037 0.3867 0.304 0.000 0.628 0.068
#> SRR1102296 3 0.6953 0.0132 0.412 0.000 0.476 0.112
#> SRR1085087 4 0.4418 0.7108 0.184 0.000 0.032 0.784
#> SRR1079046 3 0.5940 0.5417 0.000 0.188 0.692 0.120
#> SRR1328339 1 0.5010 0.5285 0.700 0.024 0.276 0.000
#> SRR1079782 3 0.4175 0.5278 0.000 0.212 0.776 0.012
#> SRR1092257 4 0.5359 0.4167 0.000 0.288 0.036 0.676
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR1396765 2 0.5716 0.23957 0.000 0.616 0.000 0.144 0.240
#> SRR1429287 2 0.5496 0.28273 0.340 0.592 0.008 0.000 0.060
#> SRR1359238 1 0.7603 0.39561 0.488 0.184 0.024 0.268 0.036
#> SRR1309597 3 0.6082 0.34438 0.148 0.312 0.540 0.000 0.000
#> SRR1441398 3 0.4297 0.57751 0.124 0.064 0.796 0.004 0.012
#> SRR1084055 5 0.2206 0.69341 0.004 0.068 0.016 0.000 0.912
#> SRR1417566 3 0.4497 0.52024 0.060 0.208 0.732 0.000 0.000
#> SRR1351857 5 0.6170 0.33146 0.000 0.016 0.104 0.324 0.556
#> SRR1487485 3 0.5717 0.38138 0.104 0.324 0.572 0.000 0.000
#> SRR1335875 1 0.3844 0.61179 0.792 0.176 0.024 0.008 0.000
#> SRR1073947 1 0.5885 0.39215 0.676 0.000 0.132 0.152 0.040
#> SRR1443483 3 0.1818 0.57861 0.000 0.024 0.932 0.000 0.044
#> SRR1346794 3 0.6324 0.35312 0.164 0.304 0.528 0.004 0.000
#> SRR1405245 3 0.6240 0.14691 0.360 0.152 0.488 0.000 0.000
#> SRR1409677 4 0.2773 0.68827 0.164 0.000 0.000 0.836 0.000
#> SRR1095549 3 0.5748 0.28501 0.020 0.000 0.648 0.096 0.236
#> SRR1323788 3 0.4280 0.54276 0.120 0.000 0.800 0.048 0.032
#> SRR1314054 2 0.5974 0.24372 0.000 0.604 0.004 0.168 0.224
#> SRR1077944 3 0.7315 -0.15276 0.232 0.012 0.408 0.336 0.012
#> SRR1480587 2 0.6740 0.04739 0.304 0.412 0.284 0.000 0.000
#> SRR1311205 3 0.3216 0.59067 0.096 0.044 0.856 0.004 0.000
#> SRR1076369 3 0.5595 0.27377 0.000 0.084 0.560 0.000 0.356
#> SRR1453549 1 0.7666 0.42527 0.492 0.232 0.148 0.128 0.000
#> SRR1345782 3 0.4630 0.50255 0.072 0.000 0.776 0.028 0.124
#> SRR1447850 2 0.4763 0.32227 0.336 0.632 0.000 0.000 0.032
#> SRR1391553 3 0.6262 0.31654 0.164 0.332 0.504 0.000 0.000
#> SRR1444156 2 0.5999 0.26123 0.008 0.612 0.000 0.160 0.220
#> SRR1471731 3 0.6252 0.31323 0.164 0.328 0.508 0.000 0.000
#> SRR1120987 4 0.1579 0.67354 0.024 0.032 0.000 0.944 0.000
#> SRR1477363 1 0.4490 0.66389 0.792 0.108 0.040 0.060 0.000
#> SRR1391961 5 0.2172 0.71376 0.076 0.000 0.016 0.000 0.908
#> SRR1373879 3 0.7639 -0.10263 0.108 0.000 0.396 0.120 0.376
#> SRR1318732 3 0.5355 0.43571 0.084 0.292 0.624 0.000 0.000
#> SRR1091404 5 0.4018 0.68760 0.136 0.012 0.012 0.028 0.812
#> SRR1402109 3 0.2540 0.55189 0.024 0.000 0.888 0.000 0.088
#> SRR1407336 3 0.2388 0.56337 0.000 0.028 0.900 0.000 0.072
#> SRR1097417 5 0.2792 0.70615 0.004 0.040 0.072 0.000 0.884
#> SRR1396227 1 0.3980 0.66575 0.816 0.080 0.092 0.012 0.000
#> SRR1400775 2 0.4555 0.52547 0.200 0.732 0.000 0.000 0.068
#> SRR1392861 4 0.3933 0.61950 0.020 0.008 0.196 0.776 0.000
#> SRR1472929 3 0.6272 0.51335 0.052 0.160 0.644 0.000 0.144
#> SRR1436740 4 0.3129 0.68511 0.156 0.008 0.004 0.832 0.000
#> SRR1477057 1 0.5176 0.42940 0.636 0.312 0.012 0.000 0.040
#> SRR1311980 3 0.6783 0.11414 0.328 0.288 0.384 0.000 0.000
#> SRR1069400 3 0.3375 0.55015 0.000 0.056 0.840 0.000 0.104
#> SRR1351016 1 0.3142 0.67674 0.868 0.056 0.068 0.008 0.000
#> SRR1096291 4 0.6002 0.47040 0.000 0.152 0.144 0.664 0.040
#> SRR1418145 4 0.6220 -0.06492 0.140 0.428 0.000 0.432 0.000
#> SRR1488111 1 0.5832 0.35850 0.568 0.356 0.056 0.016 0.004
#> SRR1370495 1 0.5370 0.43702 0.640 0.296 0.040 0.000 0.024
#> SRR1352639 3 0.3477 0.56300 0.152 0.012 0.824 0.008 0.004
#> SRR1348911 3 0.3790 0.60180 0.084 0.068 0.832 0.000 0.016
#> SRR1467386 4 0.4702 0.62345 0.256 0.000 0.036 0.700 0.008
#> SRR1415956 1 0.4842 0.55975 0.716 0.024 0.232 0.004 0.024
#> SRR1500495 1 0.5064 0.54703 0.684 0.048 0.256 0.008 0.004
#> SRR1405099 1 0.2822 0.64891 0.888 0.000 0.064 0.012 0.036
#> SRR1345585 3 0.4640 0.48787 0.048 0.256 0.696 0.000 0.000
#> SRR1093196 3 0.5187 0.45796 0.084 0.260 0.656 0.000 0.000
#> SRR1466006 2 0.6162 0.49566 0.096 0.672 0.112 0.000 0.120
#> SRR1351557 2 0.3484 0.51509 0.144 0.824 0.028 0.004 0.000
#> SRR1382687 1 0.6550 0.57889 0.600 0.124 0.052 0.224 0.000
#> SRR1375549 1 0.5325 0.57433 0.712 0.208 0.028 0.032 0.020
#> SRR1101765 5 0.5799 0.55860 0.048 0.184 0.000 0.088 0.680
#> SRR1334461 5 0.3670 0.65929 0.180 0.004 0.020 0.000 0.796
#> SRR1094073 2 0.6589 0.27764 0.008 0.604 0.028 0.184 0.176
#> SRR1077549 3 0.7152 -0.22080 0.076 0.000 0.416 0.412 0.096
#> SRR1440332 3 0.4394 0.52258 0.084 0.000 0.788 0.112 0.016
#> SRR1454177 4 0.1410 0.69731 0.060 0.000 0.000 0.940 0.000
#> SRR1082447 5 0.8004 0.19259 0.212 0.000 0.192 0.148 0.448
#> SRR1420043 3 0.7882 0.13496 0.244 0.112 0.448 0.196 0.000
#> SRR1432500 4 0.5006 0.37564 0.408 0.000 0.020 0.564 0.008
#> SRR1378045 3 0.3488 0.55151 0.024 0.168 0.808 0.000 0.000
#> SRR1334200 2 0.7446 0.24514 0.256 0.496 0.168 0.000 0.080
#> SRR1069539 4 0.7823 -0.03807 0.000 0.184 0.096 0.428 0.292
#> SRR1343031 3 0.3811 0.50027 0.036 0.000 0.808 0.008 0.148
#> SRR1319690 1 0.4439 0.62343 0.804 0.096 0.020 0.012 0.068
#> SRR1310604 5 0.4927 0.50696 0.004 0.276 0.040 0.004 0.676
#> SRR1327747 3 0.6380 0.31098 0.144 0.336 0.512 0.008 0.000
#> SRR1072456 3 0.7384 0.05385 0.036 0.220 0.376 0.000 0.368
#> SRR1367896 3 0.4040 0.41532 0.000 0.016 0.724 0.000 0.260
#> SRR1480107 1 0.5380 0.46696 0.688 0.000 0.224 0.052 0.036
#> SRR1377756 1 0.6819 0.41084 0.548 0.052 0.124 0.276 0.000
#> SRR1435272 4 0.0955 0.68807 0.028 0.004 0.000 0.968 0.000
#> SRR1089230 4 0.2012 0.63483 0.000 0.060 0.000 0.920 0.020
#> SRR1389522 3 0.3544 0.46739 0.008 0.004 0.788 0.000 0.200
#> SRR1080600 2 0.4521 0.39112 0.000 0.748 0.088 0.000 0.164
#> SRR1086935 4 0.2471 0.58733 0.000 0.136 0.000 0.864 0.000
#> SRR1344060 5 0.3497 0.70246 0.024 0.040 0.084 0.000 0.852
#> SRR1467922 2 0.3892 0.48612 0.024 0.844 0.036 0.020 0.076
#> SRR1090984 1 0.6641 0.31677 0.540 0.304 0.120 0.000 0.036
#> SRR1456991 1 0.5052 0.52942 0.732 0.004 0.188 0.040 0.036
#> SRR1085039 4 0.8446 0.15145 0.284 0.000 0.156 0.296 0.264
#> SRR1069303 1 0.3232 0.59594 0.864 0.000 0.016 0.084 0.036
#> SRR1091500 5 0.6019 0.20452 0.096 0.368 0.000 0.008 0.528
#> SRR1075198 2 0.4608 0.34407 0.036 0.700 0.260 0.004 0.000
#> SRR1086915 4 0.1774 0.69323 0.052 0.016 0.000 0.932 0.000
#> SRR1499503 2 0.5619 0.00977 0.000 0.516 0.064 0.004 0.416
#> SRR1094312 2 0.5754 0.46882 0.260 0.604 0.000 0.000 0.136
#> SRR1352437 4 0.4473 0.53586 0.324 0.000 0.020 0.656 0.000
#> SRR1436323 3 0.6752 0.10901 0.264 0.352 0.384 0.000 0.000
#> SRR1073507 4 0.5809 0.58805 0.240 0.000 0.092 0.644 0.024
#> SRR1401972 1 0.4230 0.48499 0.764 0.000 0.016 0.196 0.024
#> SRR1415510 2 0.3056 0.53296 0.040 0.884 0.052 0.004 0.020
#> SRR1327279 3 0.6165 0.36987 0.100 0.000 0.648 0.056 0.196
#> SRR1086983 4 0.3033 0.69616 0.084 0.000 0.052 0.864 0.000
#> SRR1105174 4 0.6384 0.55800 0.216 0.000 0.156 0.600 0.028
#> SRR1468893 1 0.2418 0.67245 0.912 0.024 0.020 0.044 0.000
#> SRR1362555 2 0.6783 0.00208 0.316 0.388 0.296 0.000 0.000
#> SRR1074526 5 0.1282 0.70538 0.000 0.044 0.004 0.000 0.952
#> SRR1326225 2 0.4487 0.22947 0.004 0.660 0.008 0.004 0.324
#> SRR1401933 1 0.5780 0.43332 0.616 0.284 0.084 0.016 0.000
#> SRR1324062 1 0.3297 0.67617 0.860 0.048 0.080 0.012 0.000
#> SRR1102296 1 0.4257 0.63235 0.800 0.012 0.140 0.020 0.028
#> SRR1085087 1 0.5517 -0.11303 0.536 0.000 0.032 0.412 0.020
#> SRR1079046 1 0.4615 0.59837 0.784 0.100 0.000 0.036 0.080
#> SRR1328339 3 0.3009 0.58831 0.080 0.016 0.876 0.000 0.028
#> SRR1079782 2 0.5503 0.27923 0.328 0.596 0.072 0.004 0.000
#> SRR1092257 2 0.5942 0.15857 0.056 0.524 0.000 0.396 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR1396765 2 0.4196 0.6729 0.000 0.784 0.000 0.076 0.092 0.048
#> SRR1429287 6 0.2781 0.6624 0.044 0.036 0.000 0.000 0.040 0.880
#> SRR1359238 6 0.3284 0.5902 0.004 0.004 0.000 0.172 0.016 0.804
#> SRR1309597 6 0.3410 0.6167 0.000 0.008 0.216 0.000 0.008 0.768
#> SRR1441398 3 0.4407 0.5284 0.264 0.000 0.680 0.004 0.000 0.052
#> SRR1084055 5 0.2713 0.6387 0.004 0.040 0.036 0.000 0.888 0.032
#> SRR1417566 3 0.4194 0.5419 0.032 0.048 0.764 0.000 0.000 0.156
#> SRR1351857 5 0.6017 0.4862 0.012 0.048 0.116 0.212 0.612 0.000
#> SRR1487485 6 0.5213 0.2757 0.044 0.024 0.420 0.000 0.000 0.512
#> SRR1335875 1 0.2886 0.7204 0.876 0.060 0.028 0.004 0.000 0.032
#> SRR1073947 1 0.3664 0.7388 0.824 0.000 0.052 0.072 0.052 0.000
#> SRR1443483 3 0.1176 0.6264 0.000 0.024 0.956 0.000 0.000 0.020
#> SRR1346794 6 0.5023 0.4397 0.036 0.016 0.344 0.008 0.000 0.596
#> SRR1405245 3 0.6323 0.1901 0.296 0.004 0.444 0.004 0.004 0.248
#> SRR1409677 4 0.1644 0.7717 0.076 0.000 0.000 0.920 0.000 0.004
#> SRR1095549 3 0.2870 0.6007 0.032 0.004 0.860 0.004 0.100 0.000
#> SRR1323788 3 0.4418 0.6067 0.168 0.000 0.752 0.032 0.008 0.040
#> SRR1314054 2 0.2968 0.6941 0.000 0.852 0.004 0.092 0.052 0.000
#> SRR1077944 4 0.6181 0.6255 0.088 0.004 0.152 0.628 0.008 0.120
#> SRR1480587 6 0.4752 0.6556 0.100 0.116 0.048 0.000 0.000 0.736
#> SRR1311205 3 0.3570 0.5845 0.228 0.004 0.752 0.000 0.000 0.016
#> SRR1076369 5 0.6104 0.1920 0.000 0.016 0.408 0.000 0.412 0.164
#> SRR1453549 6 0.3349 0.6401 0.016 0.004 0.016 0.132 0.004 0.828
#> SRR1345782 3 0.3649 0.6303 0.140 0.000 0.808 0.012 0.012 0.028
#> SRR1447850 6 0.6024 0.1002 0.248 0.348 0.000 0.000 0.000 0.404
#> SRR1391553 6 0.5906 0.3847 0.076 0.044 0.344 0.004 0.000 0.532
#> SRR1444156 2 0.2474 0.7032 0.000 0.880 0.000 0.080 0.040 0.000
#> SRR1471731 6 0.6243 0.5050 0.104 0.084 0.256 0.000 0.000 0.556
#> SRR1120987 4 0.0665 0.7628 0.004 0.008 0.000 0.980 0.000 0.008
#> SRR1477363 1 0.6070 0.3935 0.516 0.008 0.016 0.104 0.008 0.348
#> SRR1391961 5 0.1036 0.6303 0.024 0.008 0.004 0.000 0.964 0.000
#> SRR1373879 3 0.6305 0.3867 0.236 0.000 0.532 0.020 0.200 0.012
#> SRR1318732 3 0.5730 0.0490 0.048 0.064 0.528 0.000 0.000 0.360
#> SRR1091404 5 0.3116 0.6198 0.012 0.004 0.000 0.016 0.836 0.132
#> SRR1402109 3 0.1901 0.6538 0.076 0.000 0.912 0.000 0.008 0.004
#> SRR1407336 3 0.2078 0.6077 0.000 0.012 0.916 0.000 0.032 0.040
#> SRR1097417 5 0.2475 0.6481 0.000 0.012 0.060 0.000 0.892 0.036
#> SRR1396227 1 0.2375 0.7465 0.896 0.036 0.060 0.008 0.000 0.000
#> SRR1400775 2 0.3311 0.6508 0.204 0.780 0.004 0.000 0.000 0.012
#> SRR1392861 4 0.3421 0.7110 0.004 0.004 0.160 0.804 0.000 0.028
#> SRR1472929 3 0.6019 0.1020 0.004 0.016 0.496 0.000 0.140 0.344
#> SRR1436740 4 0.2355 0.7626 0.112 0.004 0.000 0.876 0.000 0.008
#> SRR1477057 6 0.5381 0.4041 0.328 0.052 0.000 0.000 0.040 0.580
#> SRR1311980 6 0.6324 0.3919 0.164 0.044 0.280 0.000 0.000 0.512
#> SRR1069400 3 0.4599 0.4701 0.004 0.012 0.732 0.000 0.140 0.112
#> SRR1351016 1 0.5215 0.7025 0.708 0.004 0.072 0.040 0.012 0.164
#> SRR1096291 4 0.5011 0.5679 0.000 0.140 0.168 0.680 0.008 0.004
#> SRR1418145 4 0.5913 0.5555 0.140 0.124 0.000 0.632 0.000 0.104
#> SRR1488111 6 0.2401 0.6750 0.060 0.016 0.000 0.020 0.004 0.900
#> SRR1370495 6 0.5050 0.5941 0.216 0.072 0.000 0.000 0.036 0.676
#> SRR1352639 3 0.4905 0.2550 0.420 0.052 0.524 0.000 0.000 0.004
#> SRR1348911 3 0.4307 0.6020 0.200 0.008 0.740 0.000 0.020 0.032
#> SRR1467386 4 0.3575 0.7346 0.136 0.004 0.024 0.816 0.012 0.008
#> SRR1415956 1 0.3655 0.6218 0.756 0.004 0.220 0.000 0.004 0.016
#> SRR1500495 1 0.4622 0.5730 0.692 0.000 0.244 0.012 0.008 0.044
#> SRR1405099 1 0.2152 0.7683 0.920 0.000 0.028 0.016 0.024 0.012
#> SRR1345585 3 0.4650 -0.1699 0.000 0.040 0.488 0.000 0.000 0.472
#> SRR1093196 3 0.6497 -0.2283 0.088 0.092 0.424 0.000 0.000 0.396
#> SRR1466006 6 0.5331 0.6269 0.028 0.096 0.112 0.000 0.048 0.716
#> SRR1351557 2 0.4787 0.4126 0.108 0.656 0.000 0.000 0.000 0.236
#> SRR1382687 4 0.6214 0.4481 0.148 0.008 0.020 0.536 0.004 0.284
#> SRR1375549 6 0.3220 0.6442 0.084 0.004 0.000 0.028 0.032 0.852
#> SRR1101765 5 0.5662 0.4348 0.004 0.016 0.000 0.236 0.600 0.144
#> SRR1334461 5 0.2190 0.6242 0.044 0.000 0.008 0.000 0.908 0.040
#> SRR1094073 2 0.2637 0.6988 0.000 0.876 0.012 0.088 0.024 0.000
#> SRR1077549 3 0.5684 0.0330 0.140 0.000 0.476 0.380 0.000 0.004
#> SRR1440332 3 0.4972 0.5946 0.108 0.000 0.720 0.128 0.004 0.040
#> SRR1454177 4 0.0937 0.7725 0.040 0.000 0.000 0.960 0.000 0.000
#> SRR1082447 1 0.6090 0.1130 0.404 0.000 0.224 0.004 0.368 0.000
#> SRR1420043 6 0.5700 0.3308 0.016 0.004 0.088 0.308 0.008 0.576
#> SRR1432500 4 0.4753 0.7263 0.104 0.004 0.032 0.756 0.012 0.092
#> SRR1378045 3 0.3657 0.5884 0.044 0.060 0.824 0.000 0.000 0.072
#> SRR1334200 6 0.1401 0.6731 0.004 0.000 0.020 0.000 0.028 0.948
#> SRR1069539 2 0.7378 -0.1124 0.000 0.324 0.112 0.256 0.308 0.000
#> SRR1343031 3 0.2586 0.6455 0.100 0.000 0.868 0.000 0.032 0.000
#> SRR1319690 6 0.2718 0.6527 0.076 0.004 0.000 0.020 0.020 0.880
#> SRR1310604 5 0.5404 0.4995 0.000 0.048 0.060 0.000 0.616 0.276
#> SRR1327747 6 0.2933 0.6619 0.000 0.004 0.124 0.016 0.008 0.848
#> SRR1072456 5 0.7540 0.0752 0.024 0.064 0.300 0.000 0.316 0.296
#> SRR1367896 3 0.3498 0.5525 0.020 0.020 0.812 0.000 0.144 0.004
#> SRR1480107 1 0.4438 0.6803 0.768 0.000 0.140 0.040 0.032 0.020
#> SRR1377756 4 0.6539 0.5281 0.152 0.004 0.060 0.560 0.008 0.216
#> SRR1435272 4 0.0146 0.7610 0.000 0.000 0.000 0.996 0.004 0.000
#> SRR1089230 4 0.2006 0.7041 0.000 0.104 0.000 0.892 0.004 0.000
#> SRR1389522 3 0.2854 0.6122 0.036 0.008 0.868 0.000 0.084 0.004
#> SRR1080600 6 0.7707 -0.0055 0.008 0.244 0.152 0.000 0.244 0.352
#> SRR1086935 4 0.2219 0.6818 0.000 0.136 0.000 0.864 0.000 0.000
#> SRR1344060 5 0.2265 0.6426 0.004 0.008 0.084 0.000 0.896 0.008
#> SRR1467922 2 0.2189 0.6978 0.032 0.912 0.008 0.000 0.004 0.044
#> SRR1090984 6 0.1251 0.6776 0.024 0.000 0.008 0.000 0.012 0.956
#> SRR1456991 1 0.4898 0.6906 0.744 0.000 0.132 0.040 0.028 0.056
#> SRR1085039 5 0.6786 0.0692 0.304 0.000 0.088 0.148 0.460 0.000
#> SRR1069303 1 0.1409 0.7612 0.948 0.012 0.000 0.032 0.008 0.000
#> SRR1091500 2 0.5241 0.5347 0.104 0.600 0.000 0.000 0.288 0.008
#> SRR1075198 6 0.6242 0.5027 0.044 0.144 0.260 0.004 0.000 0.548
#> SRR1086915 4 0.0909 0.7690 0.012 0.000 0.000 0.968 0.000 0.020
#> SRR1499503 5 0.7088 0.2592 0.004 0.292 0.112 0.000 0.444 0.148
#> SRR1094312 2 0.4289 0.6219 0.228 0.720 0.004 0.000 0.012 0.036
#> SRR1352437 1 0.3330 0.5589 0.716 0.000 0.000 0.284 0.000 0.000
#> SRR1436323 6 0.2244 0.6818 0.004 0.004 0.100 0.004 0.000 0.888
#> SRR1073507 4 0.4813 0.4649 0.324 0.000 0.048 0.616 0.012 0.000
#> SRR1401972 1 0.1973 0.7603 0.916 0.012 0.000 0.064 0.004 0.004
#> SRR1415510 6 0.2382 0.6731 0.000 0.020 0.072 0.004 0.008 0.896
#> SRR1327279 3 0.4072 0.4693 0.292 0.000 0.684 0.004 0.016 0.004
#> SRR1086983 4 0.1720 0.7745 0.040 0.000 0.032 0.928 0.000 0.000
#> SRR1105174 4 0.5440 0.3989 0.316 0.000 0.096 0.572 0.016 0.000
#> SRR1468893 1 0.3743 0.7219 0.804 0.004 0.008 0.072 0.000 0.112
#> SRR1362555 6 0.4700 0.6636 0.112 0.084 0.060 0.000 0.000 0.744
#> SRR1074526 5 0.1480 0.6326 0.000 0.040 0.020 0.000 0.940 0.000
#> SRR1326225 2 0.4341 0.6213 0.004 0.736 0.000 0.000 0.124 0.136
#> SRR1401933 6 0.5032 0.5373 0.288 0.084 0.000 0.008 0.000 0.620
#> SRR1324062 1 0.2258 0.7375 0.896 0.044 0.060 0.000 0.000 0.000
#> SRR1102296 1 0.2591 0.7345 0.880 0.052 0.064 0.004 0.000 0.000
#> SRR1085087 1 0.2846 0.7206 0.840 0.000 0.016 0.140 0.004 0.000
#> SRR1079046 1 0.5788 0.4564 0.620 0.020 0.000 0.032 0.084 0.244
#> SRR1328339 3 0.3430 0.5921 0.208 0.000 0.772 0.000 0.004 0.016
#> SRR1079782 6 0.5879 0.4795 0.272 0.176 0.008 0.004 0.000 0.540
#> SRR1092257 2 0.3493 0.6900 0.136 0.800 0.000 0.064 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.
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