Date: 2019-12-25 23:42:33 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 14550 rows and 135 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] 14550 135
The density distribution for each sample is visualized as in one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_list)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| SD:mclust | 5 | 1.000 | 1.000 | 1.000 | ** | 2,4 |
| MAD:mclust | 2 | 1.000 | 0.997 | 0.998 | ** | |
| ATC:hclust | 2 | 1.000 | 0.999 | 0.998 | ** | |
| ATC:NMF | 2 | 1.000 | 1.000 | 1.000 | ** | |
| SD:NMF | 4 | 0.997 | 0.955 | 0.971 | ** | |
| SD:pam | 6 | 0.979 | 0.962 | 0.971 | ** | |
| MAD:NMF | 5 | 0.946 | 0.888 | 0.941 | * | 2,3 |
| MAD:pam | 6 | 0.931 | 0.920 | 0.952 | * | 4 |
| CV:NMF | 6 | 0.928 | 0.926 | 0.927 | * | 2 |
| MAD:hclust | 5 | 0.916 | 0.970 | 0.982 | * | |
| ATC:pam | 3 | 0.909 | 0.960 | 0.978 | * | 2 |
| CV:mclust | 2 | 0.888 | 0.957 | 0.980 | ||
| ATC:mclust | 6 | 0.856 | 0.865 | 0.923 | ||
| MAD:skmeans | 4 | 0.817 | 0.887 | 0.919 | ||
| SD:skmeans | 2 | 0.789 | 0.852 | 0.941 | ||
| CV:pam | 2 | 0.705 | 0.978 | 0.965 | ||
| ATC:skmeans | 3 | 0.705 | 0.915 | 0.943 | ||
| ATC:kmeans | 3 | 0.692 | 0.909 | 0.931 | ||
| CV:skmeans | 2 | 0.691 | 0.916 | 0.959 | ||
| SD:hclust | 3 | 0.524 | 0.779 | 0.880 | ||
| CV:hclust | 3 | 0.503 | 0.873 | 0.823 | ||
| CV:kmeans | 2 | 0.369 | 0.754 | 0.850 | ||
| MAD:kmeans | 5 | 0.351 | 0.683 | 0.660 | ||
| SD:kmeans | 3 | 0.269 | 0.625 | 0.743 |
**: 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 0.867 0.946 0.974 0.438 0.580 0.580
#> CV:NMF 2 1.000 0.984 0.992 0.426 0.580 0.580
#> MAD:NMF 2 0.916 0.916 0.947 0.446 0.538 0.538
#> ATC:NMF 2 1.000 1.000 1.000 0.295 0.705 0.705
#> SD:skmeans 2 0.789 0.852 0.941 0.486 0.511 0.511
#> CV:skmeans 2 0.691 0.916 0.959 0.471 0.538 0.538
#> MAD:skmeans 2 0.705 0.871 0.936 0.481 0.511 0.511
#> ATC:skmeans 2 0.580 0.759 0.881 0.479 0.511 0.511
#> SD:mclust 2 1.000 1.000 1.000 0.503 0.498 0.498
#> CV:mclust 2 0.888 0.957 0.980 0.500 0.498 0.498
#> MAD:mclust 2 1.000 0.997 0.998 0.502 0.498 0.498
#> ATC:mclust 2 0.636 0.881 0.914 0.456 0.498 0.498
#> SD:kmeans 2 0.307 0.491 0.819 0.316 0.789 0.789
#> CV:kmeans 2 0.369 0.754 0.850 0.331 0.636 0.636
#> MAD:kmeans 2 0.253 0.543 0.805 0.346 0.705 0.705
#> ATC:kmeans 2 0.440 0.830 0.861 0.304 0.705 0.705
#> SD:pam 2 0.636 0.768 0.897 0.197 0.888 0.888
#> CV:pam 2 0.705 0.978 0.965 0.316 0.636 0.636
#> MAD:pam 2 0.868 0.918 0.960 0.419 0.580 0.580
#> ATC:pam 2 1.000 1.000 1.000 0.295 0.705 0.705
#> SD:hclust 2 0.412 0.749 0.895 0.224 0.888 0.888
#> CV:hclust 2 0.888 0.966 0.976 0.172 0.789 0.789
#> MAD:hclust 2 0.636 0.955 0.972 0.238 0.789 0.789
#> ATC:hclust 2 1.000 0.999 0.998 0.295 0.705 0.705
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.790 0.880 0.928 0.3050 0.818 0.693
#> CV:NMF 3 0.744 0.866 0.915 0.3292 0.818 0.693
#> MAD:NMF 3 0.991 0.964 0.971 0.2872 0.734 0.567
#> ATC:NMF 3 0.839 0.931 0.964 0.7347 0.769 0.673
#> SD:skmeans 3 0.776 0.896 0.920 0.2633 0.831 0.683
#> CV:skmeans 3 0.581 0.605 0.810 0.3118 0.825 0.683
#> MAD:skmeans 3 0.636 0.763 0.849 0.3286 0.831 0.683
#> ATC:skmeans 3 0.705 0.915 0.943 0.3378 0.639 0.413
#> SD:mclust 3 0.782 0.810 0.918 0.0989 0.726 0.560
#> CV:mclust 3 0.635 0.707 0.824 0.0985 0.803 0.648
#> MAD:mclust 3 0.587 0.767 0.824 0.1511 0.915 0.829
#> ATC:mclust 3 0.649 0.670 0.821 0.2191 0.803 0.648
#> SD:kmeans 3 0.269 0.625 0.743 0.6095 0.603 0.517
#> CV:kmeans 3 0.224 0.659 0.758 0.4879 1.000 1.000
#> MAD:kmeans 3 0.162 0.536 0.678 0.5550 0.713 0.608
#> ATC:kmeans 3 0.692 0.909 0.931 0.5538 0.832 0.762
#> SD:pam 3 0.718 0.830 0.935 1.1164 0.727 0.692
#> CV:pam 3 0.664 0.922 0.956 0.2319 0.979 0.967
#> MAD:pam 3 0.725 0.894 0.955 -0.0259 0.866 0.799
#> ATC:pam 3 0.909 0.960 0.978 0.4708 0.832 0.762
#> SD:hclust 3 0.524 0.779 0.880 0.8928 0.727 0.692
#> CV:hclust 3 0.503 0.873 0.823 0.9626 0.748 0.681
#> MAD:hclust 3 0.748 0.933 0.969 0.9797 0.748 0.681
#> ATC:hclust 3 0.755 0.855 0.942 0.6571 0.832 0.762
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.997 0.955 0.971 0.1241 0.852 0.685
#> CV:NMF 4 0.737 0.788 0.878 0.2169 0.858 0.666
#> MAD:NMF 4 0.725 0.813 0.886 0.1748 0.864 0.702
#> ATC:NMF 4 0.736 0.926 0.932 0.0876 0.986 0.970
#> SD:skmeans 4 0.672 0.870 0.875 0.1929 0.875 0.676
#> CV:skmeans 4 0.733 0.777 0.851 0.1850 0.797 0.526
#> MAD:skmeans 4 0.817 0.887 0.919 0.1577 0.875 0.676
#> ATC:skmeans 4 0.751 0.850 0.911 0.1601 0.897 0.710
#> SD:mclust 4 1.000 1.000 1.000 0.1274 0.789 0.604
#> CV:mclust 4 0.824 0.876 0.905 0.1212 0.852 0.689
#> MAD:mclust 4 0.542 0.571 0.704 0.1663 0.908 0.785
#> ATC:mclust 4 0.845 0.912 0.933 0.0597 0.943 0.873
#> SD:kmeans 4 0.272 0.620 0.676 0.2188 0.941 0.875
#> CV:kmeans 4 0.304 0.554 0.668 0.2277 0.789 0.683
#> MAD:kmeans 4 0.276 0.507 0.624 0.1924 0.743 0.508
#> ATC:kmeans 4 0.535 0.761 0.799 0.2931 1.000 1.000
#> SD:pam 4 0.678 0.825 0.916 0.1823 0.916 0.863
#> CV:pam 4 0.790 0.855 0.928 0.2820 0.916 0.863
#> MAD:pam 4 0.955 0.910 0.948 0.2332 0.888 0.821
#> ATC:pam 4 1.000 1.000 1.000 0.0828 0.993 0.987
#> SD:hclust 4 0.622 0.878 0.918 0.0897 0.916 0.863
#> CV:hclust 4 0.622 0.892 0.935 0.3506 0.993 0.987
#> MAD:hclust 4 0.769 0.930 0.946 0.0284 0.993 0.987
#> ATC:hclust 4 0.832 0.928 0.956 0.1515 0.839 0.708
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.735 0.657 0.811 0.1373 0.860 0.643
#> CV:NMF 5 0.892 0.932 0.920 0.0769 0.916 0.727
#> MAD:NMF 5 0.946 0.888 0.941 0.1124 0.850 0.597
#> ATC:NMF 5 0.590 0.711 0.834 0.2147 0.754 0.517
#> SD:skmeans 5 0.743 0.814 0.766 0.0690 0.972 0.891
#> CV:skmeans 5 0.721 0.768 0.807 0.0612 0.950 0.814
#> MAD:skmeans 5 0.777 0.791 0.798 0.0595 1.000 1.000
#> ATC:skmeans 5 0.751 0.782 0.848 0.0531 0.972 0.888
#> SD:mclust 5 1.000 1.000 1.000 0.0454 0.972 0.925
#> CV:mclust 5 0.866 0.949 0.964 0.0482 0.972 0.925
#> MAD:mclust 5 0.674 0.637 0.748 0.0711 0.797 0.483
#> ATC:mclust 5 0.769 0.884 0.936 0.0542 0.993 0.983
#> SD:kmeans 5 0.382 0.571 0.625 0.1320 0.763 0.478
#> CV:kmeans 5 0.395 0.627 0.652 0.1595 0.750 0.488
#> MAD:kmeans 5 0.351 0.683 0.660 0.1114 0.896 0.682
#> ATC:kmeans 5 0.514 0.512 0.632 0.1536 0.812 0.649
#> SD:pam 5 0.811 0.820 0.905 0.1824 0.818 0.670
#> CV:pam 5 0.843 0.969 0.948 0.1463 0.860 0.737
#> MAD:pam 5 0.756 0.887 0.920 0.1698 0.923 0.855
#> ATC:pam 5 1.000 0.993 0.994 0.0380 0.986 0.973
#> SD:hclust 5 0.825 0.967 0.954 0.2680 0.860 0.737
#> CV:hclust 5 0.755 0.783 0.889 0.2280 0.923 0.855
#> MAD:hclust 5 0.916 0.970 0.982 0.2302 0.860 0.737
#> ATC:hclust 5 0.811 0.889 0.918 0.0259 0.986 0.965
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.817 0.890 0.869 0.0506 0.916 0.714
#> CV:NMF 6 0.928 0.926 0.927 0.0538 0.979 0.914
#> MAD:NMF 6 0.822 0.844 0.871 0.0507 0.993 0.971
#> ATC:NMF 6 0.716 0.819 0.867 0.1240 0.792 0.441
#> SD:skmeans 6 0.778 0.614 0.722 0.0420 0.966 0.851
#> CV:skmeans 6 0.771 0.731 0.757 0.0430 0.986 0.937
#> MAD:skmeans 6 0.764 0.754 0.753 0.0435 0.911 0.656
#> ATC:skmeans 6 0.786 0.736 0.803 0.0385 0.973 0.878
#> SD:mclust 6 0.807 0.779 0.838 0.1148 0.979 0.939
#> CV:mclust 6 0.732 0.851 0.858 0.1010 0.972 0.919
#> MAD:mclust 6 0.699 0.722 0.834 0.0398 0.830 0.471
#> ATC:mclust 6 0.856 0.865 0.923 0.0682 0.972 0.930
#> SD:kmeans 6 0.480 0.634 0.646 0.0733 0.902 0.666
#> CV:kmeans 6 0.416 0.587 0.624 0.0520 0.923 0.738
#> MAD:kmeans 6 0.526 0.560 0.625 0.0781 0.944 0.773
#> ATC:kmeans 6 0.536 0.595 0.668 0.0809 0.778 0.442
#> SD:pam 6 0.979 0.962 0.971 0.0719 0.972 0.929
#> CV:pam 6 0.724 0.820 0.875 0.1359 0.986 0.964
#> MAD:pam 6 0.931 0.920 0.952 0.2544 0.805 0.571
#> ATC:pam 6 0.683 0.771 0.844 0.2735 1.000 1.000
#> SD:hclust 6 0.972 0.970 0.976 0.0654 0.986 0.964
#> CV:hclust 6 0.637 0.704 0.792 0.2531 0.805 0.571
#> MAD:hclust 6 1.000 0.998 0.998 0.0467 0.986 0.964
#> ATC:hclust 6 0.909 0.937 0.960 0.0568 0.986 0.964
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 14550 rows and 135 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 0.412 0.749 0.895 0.2239 0.888 0.888
#> 3 3 0.524 0.779 0.880 0.8928 0.727 0.692
#> 4 4 0.622 0.878 0.918 0.0897 0.916 0.863
#> 5 5 0.825 0.967 0.954 0.2680 0.860 0.737
#> 6 6 0.972 0.970 0.976 0.0654 0.986 0.964
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
#> SRR934216 1 0.991 -0.263 0.556 0.444
#> SRR934217 1 0.991 -0.263 0.556 0.444
#> SRR934218 1 0.991 -0.263 0.556 0.444
#> SRR934219 1 0.991 -0.263 0.556 0.444
#> SRR934220 1 0.991 -0.263 0.556 0.444
#> SRR934221 1 0.991 -0.263 0.556 0.444
#> SRR934222 1 0.991 -0.263 0.556 0.444
#> SRR934223 1 0.991 -0.263 0.556 0.444
#> SRR934224 1 0.000 0.863 1.000 0.000
#> SRR934225 1 0.000 0.863 1.000 0.000
#> SRR934226 1 0.000 0.863 1.000 0.000
#> SRR934227 1 0.000 0.863 1.000 0.000
#> SRR934228 1 0.000 0.863 1.000 0.000
#> SRR934229 1 0.000 0.863 1.000 0.000
#> SRR934230 1 0.000 0.863 1.000 0.000
#> SRR934231 1 0.000 0.863 1.000 0.000
#> SRR934232 1 0.844 0.622 0.728 0.272
#> SRR934233 1 0.844 0.622 0.728 0.272
#> SRR934234 1 0.844 0.622 0.728 0.272
#> SRR934235 1 0.844 0.622 0.728 0.272
#> SRR934236 1 0.844 0.622 0.728 0.272
#> SRR934237 1 0.844 0.622 0.728 0.272
#> SRR934238 1 0.844 0.622 0.728 0.272
#> SRR934239 1 0.844 0.622 0.728 0.272
#> SRR934240 1 0.844 0.622 0.728 0.272
#> SRR934241 1 0.844 0.622 0.728 0.272
#> SRR934242 1 0.844 0.622 0.728 0.272
#> SRR934243 1 0.844 0.622 0.728 0.272
#> SRR934244 1 0.844 0.622 0.728 0.272
#> SRR934245 1 0.844 0.622 0.728 0.272
#> SRR934246 1 0.844 0.622 0.728 0.272
#> SRR934247 1 0.844 0.622 0.728 0.272
#> SRR934248 1 0.518 0.754 0.884 0.116
#> SRR934249 1 0.518 0.754 0.884 0.116
#> SRR934250 1 0.518 0.754 0.884 0.116
#> SRR934251 1 0.518 0.754 0.884 0.116
#> SRR934252 1 0.518 0.754 0.884 0.116
#> SRR934253 1 0.518 0.754 0.884 0.116
#> SRR934254 1 0.518 0.754 0.884 0.116
#> SRR934255 1 0.518 0.754 0.884 0.116
#> SRR934256 1 0.844 0.622 0.728 0.272
#> SRR934257 1 0.844 0.622 0.728 0.272
#> SRR934258 1 0.844 0.622 0.728 0.272
#> SRR934259 1 0.844 0.622 0.728 0.272
#> SRR934260 1 0.844 0.622 0.728 0.272
#> SRR934261 1 0.844 0.622 0.728 0.272
#> SRR934262 1 0.844 0.622 0.728 0.272
#> SRR934263 1 0.844 0.622 0.728 0.272
#> SRR934264 1 0.518 0.754 0.884 0.116
#> SRR934265 1 0.518 0.754 0.884 0.116
#> SRR934266 1 0.518 0.754 0.884 0.116
#> SRR934267 1 0.518 0.754 0.884 0.116
#> SRR934268 1 0.518 0.754 0.884 0.116
#> SRR934269 1 0.518 0.754 0.884 0.116
#> SRR934270 1 0.518 0.754 0.884 0.116
#> SRR934271 1 0.518 0.754 0.884 0.116
#> SRR934272 1 0.000 0.863 1.000 0.000
#> SRR934273 1 0.000 0.863 1.000 0.000
#> SRR934274 1 0.000 0.863 1.000 0.000
#> SRR934275 1 0.000 0.863 1.000 0.000
#> SRR934276 1 0.000 0.863 1.000 0.000
#> SRR934277 1 0.000 0.863 1.000 0.000
#> SRR934278 1 0.000 0.863 1.000 0.000
#> SRR934279 1 0.000 0.863 1.000 0.000
#> SRR934280 1 0.000 0.863 1.000 0.000
#> SRR934281 1 0.000 0.863 1.000 0.000
#> SRR934282 1 0.000 0.863 1.000 0.000
#> SRR934283 1 0.000 0.863 1.000 0.000
#> SRR934284 1 0.000 0.863 1.000 0.000
#> SRR934285 1 0.000 0.863 1.000 0.000
#> SRR934286 1 0.000 0.863 1.000 0.000
#> SRR934287 1 0.000 0.863 1.000 0.000
#> SRR934288 1 0.000 0.863 1.000 0.000
#> SRR934289 1 0.000 0.863 1.000 0.000
#> SRR934290 1 0.000 0.863 1.000 0.000
#> SRR934291 1 0.000 0.863 1.000 0.000
#> SRR934292 1 0.000 0.863 1.000 0.000
#> SRR934293 1 0.000 0.863 1.000 0.000
#> SRR934294 1 0.000 0.863 1.000 0.000
#> SRR934295 1 0.000 0.863 1.000 0.000
#> SRR934296 1 0.000 0.863 1.000 0.000
#> SRR934297 1 0.000 0.863 1.000 0.000
#> SRR934298 1 0.000 0.863 1.000 0.000
#> SRR934299 1 0.000 0.863 1.000 0.000
#> SRR934300 1 0.000 0.863 1.000 0.000
#> SRR934301 1 0.000 0.863 1.000 0.000
#> SRR934302 1 0.000 0.863 1.000 0.000
#> SRR934303 1 0.000 0.863 1.000 0.000
#> SRR934304 2 0.844 1.000 0.272 0.728
#> SRR934305 2 0.844 1.000 0.272 0.728
#> SRR934306 2 0.844 1.000 0.272 0.728
#> SRR934307 2 0.844 1.000 0.272 0.728
#> SRR934308 2 0.844 1.000 0.272 0.728
#> SRR934309 2 0.844 1.000 0.272 0.728
#> SRR934310 2 0.844 1.000 0.272 0.728
#> SRR934311 2 0.844 1.000 0.272 0.728
#> SRR934312 1 0.000 0.863 1.000 0.000
#> SRR934313 1 0.000 0.863 1.000 0.000
#> SRR934314 1 0.000 0.863 1.000 0.000
#> SRR934315 1 0.000 0.863 1.000 0.000
#> SRR934316 1 0.000 0.863 1.000 0.000
#> SRR934317 1 0.000 0.863 1.000 0.000
#> SRR934318 1 0.000 0.863 1.000 0.000
#> SRR934319 1 0.000 0.863 1.000 0.000
#> SRR934320 1 0.000 0.863 1.000 0.000
#> SRR934321 1 0.000 0.863 1.000 0.000
#> SRR934322 1 0.000 0.863 1.000 0.000
#> SRR934323 1 0.000 0.863 1.000 0.000
#> SRR934324 1 0.000 0.863 1.000 0.000
#> SRR934325 1 0.000 0.863 1.000 0.000
#> SRR934326 1 0.000 0.863 1.000 0.000
#> SRR934327 1 0.000 0.863 1.000 0.000
#> SRR934328 1 0.000 0.863 1.000 0.000
#> SRR934329 1 0.000 0.863 1.000 0.000
#> SRR934330 1 0.000 0.863 1.000 0.000
#> SRR934331 1 0.000 0.863 1.000 0.000
#> SRR934332 1 0.000 0.863 1.000 0.000
#> SRR934333 1 0.000 0.863 1.000 0.000
#> SRR934334 1 0.000 0.863 1.000 0.000
#> SRR934335 1 0.000 0.863 1.000 0.000
#> SRR934344 1 0.000 0.863 1.000 0.000
#> SRR934345 1 0.000 0.863 1.000 0.000
#> SRR934346 1 0.000 0.863 1.000 0.000
#> SRR934347 1 0.000 0.863 1.000 0.000
#> SRR934348 1 0.000 0.863 1.000 0.000
#> SRR934349 1 0.000 0.863 1.000 0.000
#> SRR934350 1 0.000 0.863 1.000 0.000
#> SRR934351 1 0.000 0.863 1.000 0.000
#> SRR934336 1 0.000 0.863 1.000 0.000
#> SRR934337 1 0.000 0.863 1.000 0.000
#> SRR934338 1 0.000 0.863 1.000 0.000
#> SRR934339 1 0.000 0.863 1.000 0.000
#> SRR934340 1 0.000 0.863 1.000 0.000
#> SRR934341 1 0.000 0.863 1.000 0.000
#> SRR934342 1 0.000 0.863 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 1 0.999 -0.0703 0.356 0.316 0.328
#> SRR934217 1 0.999 -0.0703 0.356 0.316 0.328
#> SRR934218 1 0.999 -0.0703 0.356 0.316 0.328
#> SRR934219 1 0.999 -0.0703 0.356 0.316 0.328
#> SRR934220 1 0.999 -0.0703 0.356 0.316 0.328
#> SRR934221 1 0.999 -0.0703 0.356 0.316 0.328
#> SRR934222 1 0.999 -0.0703 0.356 0.316 0.328
#> SRR934223 1 0.999 -0.0703 0.356 0.316 0.328
#> SRR934224 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934225 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934226 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934227 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934228 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934229 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934230 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934231 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934232 2 0.334 0.7108 0.120 0.880 0.000
#> SRR934233 2 0.334 0.7108 0.120 0.880 0.000
#> SRR934234 2 0.334 0.7108 0.120 0.880 0.000
#> SRR934235 2 0.334 0.7108 0.120 0.880 0.000
#> SRR934236 2 0.334 0.7108 0.120 0.880 0.000
#> SRR934237 2 0.334 0.7108 0.120 0.880 0.000
#> SRR934238 2 0.334 0.7108 0.120 0.880 0.000
#> SRR934239 2 0.334 0.7108 0.120 0.880 0.000
#> SRR934240 2 0.568 0.8752 0.316 0.684 0.000
#> SRR934241 2 0.568 0.8752 0.316 0.684 0.000
#> SRR934242 2 0.568 0.8752 0.316 0.684 0.000
#> SRR934243 2 0.568 0.8752 0.316 0.684 0.000
#> SRR934244 2 0.568 0.8752 0.316 0.684 0.000
#> SRR934245 2 0.568 0.8752 0.316 0.684 0.000
#> SRR934246 2 0.568 0.8752 0.316 0.684 0.000
#> SRR934247 2 0.568 0.8752 0.316 0.684 0.000
#> SRR934248 1 0.568 0.5851 0.684 0.316 0.000
#> SRR934249 1 0.568 0.5851 0.684 0.316 0.000
#> SRR934250 1 0.568 0.5851 0.684 0.316 0.000
#> SRR934251 1 0.568 0.5851 0.684 0.316 0.000
#> SRR934252 1 0.568 0.5851 0.684 0.316 0.000
#> SRR934253 1 0.568 0.5851 0.684 0.316 0.000
#> SRR934254 1 0.568 0.5851 0.684 0.316 0.000
#> SRR934255 1 0.568 0.5851 0.684 0.316 0.000
#> SRR934256 2 0.568 0.8752 0.316 0.684 0.000
#> SRR934257 2 0.568 0.8752 0.316 0.684 0.000
#> SRR934258 2 0.568 0.8752 0.316 0.684 0.000
#> SRR934259 2 0.568 0.8752 0.316 0.684 0.000
#> SRR934260 2 0.568 0.8752 0.316 0.684 0.000
#> SRR934261 2 0.568 0.8752 0.316 0.684 0.000
#> SRR934262 2 0.568 0.8752 0.316 0.684 0.000
#> SRR934263 2 0.568 0.8752 0.316 0.684 0.000
#> SRR934264 1 0.568 0.5851 0.684 0.316 0.000
#> SRR934265 1 0.568 0.5851 0.684 0.316 0.000
#> SRR934266 1 0.568 0.5851 0.684 0.316 0.000
#> SRR934267 1 0.568 0.5851 0.684 0.316 0.000
#> SRR934268 1 0.568 0.5851 0.684 0.316 0.000
#> SRR934269 1 0.568 0.5851 0.684 0.316 0.000
#> SRR934270 1 0.568 0.5851 0.684 0.316 0.000
#> SRR934271 1 0.568 0.5851 0.684 0.316 0.000
#> SRR934272 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934273 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934274 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934275 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934276 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934277 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934278 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934279 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934280 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934281 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934282 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934283 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934284 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934285 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934286 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934287 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934288 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934289 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934290 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934291 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934292 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934293 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934294 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934295 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934296 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934297 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934298 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934299 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934300 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934301 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934302 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934303 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934304 3 0.000 1.0000 0.000 0.000 1.000
#> SRR934305 3 0.000 1.0000 0.000 0.000 1.000
#> SRR934306 3 0.000 1.0000 0.000 0.000 1.000
#> SRR934307 3 0.000 1.0000 0.000 0.000 1.000
#> SRR934308 3 0.000 1.0000 0.000 0.000 1.000
#> SRR934309 3 0.000 1.0000 0.000 0.000 1.000
#> SRR934310 3 0.000 1.0000 0.000 0.000 1.000
#> SRR934311 3 0.000 1.0000 0.000 0.000 1.000
#> SRR934312 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934313 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934314 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934315 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934316 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934317 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934318 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934319 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934320 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934321 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934322 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934323 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934324 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934325 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934326 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934327 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934328 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934329 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934330 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934331 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934332 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934333 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934334 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934335 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934344 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934345 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934346 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934347 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934348 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934349 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934350 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934351 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934336 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934337 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934338 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934339 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934340 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934341 1 0.000 0.8699 1.000 0.000 0.000
#> SRR934342 1 0.000 0.8699 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 4 0.000 1.000 0.000 0.000 0 1
#> SRR934217 4 0.000 1.000 0.000 0.000 0 1
#> SRR934218 4 0.000 1.000 0.000 0.000 0 1
#> SRR934219 4 0.000 1.000 0.000 0.000 0 1
#> SRR934220 4 0.000 1.000 0.000 0.000 0 1
#> SRR934221 4 0.000 1.000 0.000 0.000 0 1
#> SRR934222 4 0.000 1.000 0.000 0.000 0 1
#> SRR934223 4 0.000 1.000 0.000 0.000 0 1
#> SRR934224 1 0.000 0.928 1.000 0.000 0 0
#> SRR934225 1 0.000 0.928 1.000 0.000 0 0
#> SRR934226 1 0.000 0.928 1.000 0.000 0 0
#> SRR934227 1 0.000 0.928 1.000 0.000 0 0
#> SRR934228 1 0.000 0.928 1.000 0.000 0 0
#> SRR934229 1 0.000 0.928 1.000 0.000 0 0
#> SRR934230 1 0.000 0.928 1.000 0.000 0 0
#> SRR934231 1 0.000 0.928 1.000 0.000 0 0
#> SRR934232 2 0.265 0.719 0.120 0.880 0 0
#> SRR934233 2 0.265 0.719 0.120 0.880 0 0
#> SRR934234 2 0.265 0.719 0.120 0.880 0 0
#> SRR934235 2 0.265 0.719 0.120 0.880 0 0
#> SRR934236 2 0.265 0.719 0.120 0.880 0 0
#> SRR934237 2 0.265 0.719 0.120 0.880 0 0
#> SRR934238 2 0.265 0.719 0.120 0.880 0 0
#> SRR934239 2 0.265 0.719 0.120 0.880 0 0
#> SRR934240 2 0.450 0.878 0.316 0.684 0 0
#> SRR934241 2 0.450 0.878 0.316 0.684 0 0
#> SRR934242 2 0.450 0.878 0.316 0.684 0 0
#> SRR934243 2 0.450 0.878 0.316 0.684 0 0
#> SRR934244 2 0.450 0.878 0.316 0.684 0 0
#> SRR934245 2 0.450 0.878 0.316 0.684 0 0
#> SRR934246 2 0.450 0.878 0.316 0.684 0 0
#> SRR934247 2 0.450 0.878 0.316 0.684 0 0
#> SRR934248 1 0.450 0.593 0.684 0.316 0 0
#> SRR934249 1 0.450 0.593 0.684 0.316 0 0
#> SRR934250 1 0.450 0.593 0.684 0.316 0 0
#> SRR934251 1 0.450 0.593 0.684 0.316 0 0
#> SRR934252 1 0.450 0.593 0.684 0.316 0 0
#> SRR934253 1 0.450 0.593 0.684 0.316 0 0
#> SRR934254 1 0.450 0.593 0.684 0.316 0 0
#> SRR934255 1 0.450 0.593 0.684 0.316 0 0
#> SRR934256 2 0.450 0.878 0.316 0.684 0 0
#> SRR934257 2 0.450 0.878 0.316 0.684 0 0
#> SRR934258 2 0.450 0.878 0.316 0.684 0 0
#> SRR934259 2 0.450 0.878 0.316 0.684 0 0
#> SRR934260 2 0.450 0.878 0.316 0.684 0 0
#> SRR934261 2 0.450 0.878 0.316 0.684 0 0
#> SRR934262 2 0.450 0.878 0.316 0.684 0 0
#> SRR934263 2 0.450 0.878 0.316 0.684 0 0
#> SRR934264 1 0.450 0.593 0.684 0.316 0 0
#> SRR934265 1 0.450 0.593 0.684 0.316 0 0
#> SRR934266 1 0.450 0.593 0.684 0.316 0 0
#> SRR934267 1 0.450 0.593 0.684 0.316 0 0
#> SRR934268 1 0.450 0.593 0.684 0.316 0 0
#> SRR934269 1 0.450 0.593 0.684 0.316 0 0
#> SRR934270 1 0.450 0.593 0.684 0.316 0 0
#> SRR934271 1 0.450 0.593 0.684 0.316 0 0
#> SRR934272 1 0.000 0.928 1.000 0.000 0 0
#> SRR934273 1 0.000 0.928 1.000 0.000 0 0
#> SRR934274 1 0.000 0.928 1.000 0.000 0 0
#> SRR934275 1 0.000 0.928 1.000 0.000 0 0
#> SRR934276 1 0.000 0.928 1.000 0.000 0 0
#> SRR934277 1 0.000 0.928 1.000 0.000 0 0
#> SRR934278 1 0.000 0.928 1.000 0.000 0 0
#> SRR934279 1 0.000 0.928 1.000 0.000 0 0
#> SRR934280 1 0.000 0.928 1.000 0.000 0 0
#> SRR934281 1 0.000 0.928 1.000 0.000 0 0
#> SRR934282 1 0.000 0.928 1.000 0.000 0 0
#> SRR934283 1 0.000 0.928 1.000 0.000 0 0
#> SRR934284 1 0.000 0.928 1.000 0.000 0 0
#> SRR934285 1 0.000 0.928 1.000 0.000 0 0
#> SRR934286 1 0.000 0.928 1.000 0.000 0 0
#> SRR934287 1 0.000 0.928 1.000 0.000 0 0
#> SRR934288 1 0.000 0.928 1.000 0.000 0 0
#> SRR934289 1 0.000 0.928 1.000 0.000 0 0
#> SRR934290 1 0.000 0.928 1.000 0.000 0 0
#> SRR934291 1 0.000 0.928 1.000 0.000 0 0
#> SRR934292 1 0.000 0.928 1.000 0.000 0 0
#> SRR934293 1 0.000 0.928 1.000 0.000 0 0
#> SRR934294 1 0.000 0.928 1.000 0.000 0 0
#> SRR934295 1 0.000 0.928 1.000 0.000 0 0
#> SRR934296 1 0.000 0.928 1.000 0.000 0 0
#> SRR934297 1 0.000 0.928 1.000 0.000 0 0
#> SRR934298 1 0.000 0.928 1.000 0.000 0 0
#> SRR934299 1 0.000 0.928 1.000 0.000 0 0
#> SRR934300 1 0.000 0.928 1.000 0.000 0 0
#> SRR934301 1 0.000 0.928 1.000 0.000 0 0
#> SRR934302 1 0.000 0.928 1.000 0.000 0 0
#> SRR934303 1 0.000 0.928 1.000 0.000 0 0
#> SRR934304 3 0.000 1.000 0.000 0.000 1 0
#> SRR934305 3 0.000 1.000 0.000 0.000 1 0
#> SRR934306 3 0.000 1.000 0.000 0.000 1 0
#> SRR934307 3 0.000 1.000 0.000 0.000 1 0
#> SRR934308 3 0.000 1.000 0.000 0.000 1 0
#> SRR934309 3 0.000 1.000 0.000 0.000 1 0
#> SRR934310 3 0.000 1.000 0.000 0.000 1 0
#> SRR934311 3 0.000 1.000 0.000 0.000 1 0
#> SRR934312 1 0.000 0.928 1.000 0.000 0 0
#> SRR934313 1 0.000 0.928 1.000 0.000 0 0
#> SRR934314 1 0.000 0.928 1.000 0.000 0 0
#> SRR934315 1 0.000 0.928 1.000 0.000 0 0
#> SRR934316 1 0.000 0.928 1.000 0.000 0 0
#> SRR934317 1 0.000 0.928 1.000 0.000 0 0
#> SRR934318 1 0.000 0.928 1.000 0.000 0 0
#> SRR934319 1 0.000 0.928 1.000 0.000 0 0
#> SRR934320 1 0.000 0.928 1.000 0.000 0 0
#> SRR934321 1 0.000 0.928 1.000 0.000 0 0
#> SRR934322 1 0.000 0.928 1.000 0.000 0 0
#> SRR934323 1 0.000 0.928 1.000 0.000 0 0
#> SRR934324 1 0.000 0.928 1.000 0.000 0 0
#> SRR934325 1 0.000 0.928 1.000 0.000 0 0
#> SRR934326 1 0.000 0.928 1.000 0.000 0 0
#> SRR934327 1 0.000 0.928 1.000 0.000 0 0
#> SRR934328 1 0.000 0.928 1.000 0.000 0 0
#> SRR934329 1 0.000 0.928 1.000 0.000 0 0
#> SRR934330 1 0.000 0.928 1.000 0.000 0 0
#> SRR934331 1 0.000 0.928 1.000 0.000 0 0
#> SRR934332 1 0.000 0.928 1.000 0.000 0 0
#> SRR934333 1 0.000 0.928 1.000 0.000 0 0
#> SRR934334 1 0.000 0.928 1.000 0.000 0 0
#> SRR934335 1 0.000 0.928 1.000 0.000 0 0
#> SRR934344 1 0.000 0.928 1.000 0.000 0 0
#> SRR934345 1 0.000 0.928 1.000 0.000 0 0
#> SRR934346 1 0.000 0.928 1.000 0.000 0 0
#> SRR934347 1 0.000 0.928 1.000 0.000 0 0
#> SRR934348 1 0.000 0.928 1.000 0.000 0 0
#> SRR934349 1 0.000 0.928 1.000 0.000 0 0
#> SRR934350 1 0.000 0.928 1.000 0.000 0 0
#> SRR934351 1 0.000 0.928 1.000 0.000 0 0
#> SRR934336 1 0.000 0.928 1.000 0.000 0 0
#> SRR934337 1 0.000 0.928 1.000 0.000 0 0
#> SRR934338 1 0.000 0.928 1.000 0.000 0 0
#> SRR934339 1 0.000 0.928 1.000 0.000 0 0
#> SRR934340 1 0.000 0.928 1.000 0.000 0 0
#> SRR934341 1 0.000 0.928 1.000 0.000 0 0
#> SRR934342 1 0.000 0.928 1.000 0.000 0 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0.000 1.000 0.000 0.000 1 0.000 0
#> SRR934217 3 0.000 1.000 0.000 0.000 1 0.000 0
#> SRR934218 3 0.000 1.000 0.000 0.000 1 0.000 0
#> SRR934219 3 0.000 1.000 0.000 0.000 1 0.000 0
#> SRR934220 3 0.000 1.000 0.000 0.000 1 0.000 0
#> SRR934221 3 0.000 1.000 0.000 0.000 1 0.000 0
#> SRR934222 3 0.000 1.000 0.000 0.000 1 0.000 0
#> SRR934223 3 0.000 1.000 0.000 0.000 1 0.000 0
#> SRR934224 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934225 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934226 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934227 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934228 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934229 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934230 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934231 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934232 2 0.307 0.791 0.000 0.804 0 0.196 0
#> SRR934233 2 0.307 0.791 0.000 0.804 0 0.196 0
#> SRR934234 2 0.307 0.791 0.000 0.804 0 0.196 0
#> SRR934235 2 0.307 0.791 0.000 0.804 0 0.196 0
#> SRR934236 2 0.307 0.791 0.000 0.804 0 0.196 0
#> SRR934237 2 0.307 0.791 0.000 0.804 0 0.196 0
#> SRR934238 2 0.307 0.791 0.000 0.804 0 0.196 0
#> SRR934239 2 0.307 0.791 0.000 0.804 0 0.196 0
#> SRR934240 2 0.000 0.851 0.000 1.000 0 0.000 0
#> SRR934241 2 0.000 0.851 0.000 1.000 0 0.000 0
#> SRR934242 2 0.000 0.851 0.000 1.000 0 0.000 0
#> SRR934243 2 0.000 0.851 0.000 1.000 0 0.000 0
#> SRR934244 2 0.000 0.851 0.000 1.000 0 0.000 0
#> SRR934245 2 0.000 0.851 0.000 1.000 0 0.000 0
#> SRR934246 2 0.000 0.851 0.000 1.000 0 0.000 0
#> SRR934247 2 0.000 0.851 0.000 1.000 0 0.000 0
#> SRR934248 4 0.304 1.000 0.192 0.000 0 0.808 0
#> SRR934249 4 0.304 1.000 0.192 0.000 0 0.808 0
#> SRR934250 4 0.304 1.000 0.192 0.000 0 0.808 0
#> SRR934251 4 0.304 1.000 0.192 0.000 0 0.808 0
#> SRR934252 4 0.304 1.000 0.192 0.000 0 0.808 0
#> SRR934253 4 0.304 1.000 0.192 0.000 0 0.808 0
#> SRR934254 4 0.304 1.000 0.192 0.000 0 0.808 0
#> SRR934255 4 0.304 1.000 0.192 0.000 0 0.808 0
#> SRR934256 2 0.320 0.808 0.004 0.804 0 0.192 0
#> SRR934257 2 0.320 0.808 0.004 0.804 0 0.192 0
#> SRR934258 2 0.320 0.808 0.004 0.804 0 0.192 0
#> SRR934259 2 0.320 0.808 0.004 0.804 0 0.192 0
#> SRR934260 2 0.320 0.808 0.004 0.804 0 0.192 0
#> SRR934261 2 0.320 0.808 0.004 0.804 0 0.192 0
#> SRR934262 2 0.320 0.808 0.004 0.804 0 0.192 0
#> SRR934263 2 0.320 0.808 0.004 0.804 0 0.192 0
#> SRR934264 4 0.304 1.000 0.192 0.000 0 0.808 0
#> SRR934265 4 0.304 1.000 0.192 0.000 0 0.808 0
#> SRR934266 4 0.304 1.000 0.192 0.000 0 0.808 0
#> SRR934267 4 0.304 1.000 0.192 0.000 0 0.808 0
#> SRR934268 4 0.304 1.000 0.192 0.000 0 0.808 0
#> SRR934269 4 0.304 1.000 0.192 0.000 0 0.808 0
#> SRR934270 4 0.304 1.000 0.192 0.000 0 0.808 0
#> SRR934271 4 0.304 1.000 0.192 0.000 0 0.808 0
#> SRR934272 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934273 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934274 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934275 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934276 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934277 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934278 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934279 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934280 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934281 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934282 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934283 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934284 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934285 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934286 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934287 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934288 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934289 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934290 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934291 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934292 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934293 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934294 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934295 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934296 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934297 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934298 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934299 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934300 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934301 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934302 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934303 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934304 5 0.000 1.000 0.000 0.000 0 0.000 1
#> SRR934305 5 0.000 1.000 0.000 0.000 0 0.000 1
#> SRR934306 5 0.000 1.000 0.000 0.000 0 0.000 1
#> SRR934307 5 0.000 1.000 0.000 0.000 0 0.000 1
#> SRR934308 5 0.000 1.000 0.000 0.000 0 0.000 1
#> SRR934309 5 0.000 1.000 0.000 0.000 0 0.000 1
#> SRR934310 5 0.000 1.000 0.000 0.000 0 0.000 1
#> SRR934311 5 0.000 1.000 0.000 0.000 0 0.000 1
#> SRR934312 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934313 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934314 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934315 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934316 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934317 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934318 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934319 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934320 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934321 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934322 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934323 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934324 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934325 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934326 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934327 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934328 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934329 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934330 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934331 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934332 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934333 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934334 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934335 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934344 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934345 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934346 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934347 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934348 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934349 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934350 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934351 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934336 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934337 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934338 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934339 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934340 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934341 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934342 1 0.000 1.000 1.000 0.000 0 0.000 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.0000 1.000 0 0.000 1 0.000 0 0.000
#> SRR934217 3 0.0000 1.000 0 0.000 1 0.000 0 0.000
#> SRR934218 3 0.0000 1.000 0 0.000 1 0.000 0 0.000
#> SRR934219 3 0.0000 1.000 0 0.000 1 0.000 0 0.000
#> SRR934220 3 0.0000 1.000 0 0.000 1 0.000 0 0.000
#> SRR934221 3 0.0000 1.000 0 0.000 1 0.000 0 0.000
#> SRR934222 3 0.0000 1.000 0 0.000 1 0.000 0 0.000
#> SRR934223 3 0.0000 1.000 0 0.000 1 0.000 0 0.000
#> SRR934224 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934225 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934226 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934227 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934228 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934229 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934230 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934231 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934232 2 0.0146 0.866 0 0.996 0 0.004 0 0.000
#> SRR934233 2 0.0146 0.866 0 0.996 0 0.004 0 0.000
#> SRR934234 2 0.0146 0.866 0 0.996 0 0.004 0 0.000
#> SRR934235 2 0.0146 0.866 0 0.996 0 0.004 0 0.000
#> SRR934236 2 0.0146 0.866 0 0.996 0 0.004 0 0.000
#> SRR934237 2 0.0146 0.866 0 0.996 0 0.004 0 0.000
#> SRR934238 2 0.0146 0.866 0 0.996 0 0.004 0 0.000
#> SRR934239 2 0.0146 0.866 0 0.996 0 0.004 0 0.000
#> SRR934240 2 0.2730 0.851 0 0.808 0 0.000 0 0.192
#> SRR934241 2 0.2730 0.851 0 0.808 0 0.000 0 0.192
#> SRR934242 2 0.2730 0.851 0 0.808 0 0.000 0 0.192
#> SRR934243 2 0.2730 0.851 0 0.808 0 0.000 0 0.192
#> SRR934244 2 0.2730 0.851 0 0.808 0 0.000 0 0.192
#> SRR934245 2 0.2730 0.851 0 0.808 0 0.000 0 0.192
#> SRR934246 2 0.2730 0.851 0 0.808 0 0.000 0 0.192
#> SRR934247 2 0.2730 0.851 0 0.808 0 0.000 0 0.192
#> SRR934248 4 0.0000 0.891 0 0.000 0 1.000 0 0.000
#> SRR934249 4 0.0000 0.891 0 0.000 0 1.000 0 0.000
#> SRR934250 4 0.0000 0.891 0 0.000 0 1.000 0 0.000
#> SRR934251 4 0.0000 0.891 0 0.000 0 1.000 0 0.000
#> SRR934252 4 0.0000 0.891 0 0.000 0 1.000 0 0.000
#> SRR934253 4 0.0000 0.891 0 0.000 0 1.000 0 0.000
#> SRR934254 4 0.0000 0.891 0 0.000 0 1.000 0 0.000
#> SRR934255 4 0.0000 0.891 0 0.000 0 1.000 0 0.000
#> SRR934256 6 0.0000 1.000 0 0.000 0 0.000 0 1.000
#> SRR934257 6 0.0000 1.000 0 0.000 0 0.000 0 1.000
#> SRR934258 6 0.0000 1.000 0 0.000 0 0.000 0 1.000
#> SRR934259 6 0.0000 1.000 0 0.000 0 0.000 0 1.000
#> SRR934260 6 0.0000 1.000 0 0.000 0 0.000 0 1.000
#> SRR934261 6 0.0000 1.000 0 0.000 0 0.000 0 1.000
#> SRR934262 6 0.0000 1.000 0 0.000 0 0.000 0 1.000
#> SRR934263 6 0.0000 1.000 0 0.000 0 0.000 0 1.000
#> SRR934264 4 0.2730 0.892 0 0.192 0 0.808 0 0.000
#> SRR934265 4 0.2730 0.892 0 0.192 0 0.808 0 0.000
#> SRR934266 4 0.2730 0.892 0 0.192 0 0.808 0 0.000
#> SRR934267 4 0.2730 0.892 0 0.192 0 0.808 0 0.000
#> SRR934268 4 0.2730 0.892 0 0.192 0 0.808 0 0.000
#> SRR934269 4 0.2730 0.892 0 0.192 0 0.808 0 0.000
#> SRR934270 4 0.2730 0.892 0 0.192 0 0.808 0 0.000
#> SRR934271 4 0.2730 0.892 0 0.192 0 0.808 0 0.000
#> SRR934272 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934273 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934274 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934275 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934276 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934277 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934278 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934279 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934280 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934281 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934282 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934283 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934284 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934285 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934286 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934287 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934288 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934289 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934290 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934291 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934292 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934293 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934294 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934295 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934296 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934297 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934298 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934299 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934300 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934301 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934302 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934303 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934304 5 0.0000 1.000 0 0.000 0 0.000 1 0.000
#> SRR934305 5 0.0000 1.000 0 0.000 0 0.000 1 0.000
#> SRR934306 5 0.0000 1.000 0 0.000 0 0.000 1 0.000
#> SRR934307 5 0.0000 1.000 0 0.000 0 0.000 1 0.000
#> SRR934308 5 0.0000 1.000 0 0.000 0 0.000 1 0.000
#> SRR934309 5 0.0000 1.000 0 0.000 0 0.000 1 0.000
#> SRR934310 5 0.0000 1.000 0 0.000 0 0.000 1 0.000
#> SRR934311 5 0.0000 1.000 0 0.000 0 0.000 1 0.000
#> SRR934312 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934313 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934314 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934315 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934316 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934317 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934318 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934319 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934320 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934321 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934322 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934323 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934324 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934325 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934326 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934327 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934328 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934329 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934330 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934331 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934332 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934333 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934334 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934335 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934344 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934345 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934346 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934347 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934348 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934349 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934350 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934351 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934336 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934337 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934338 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934339 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934340 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934341 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
#> SRR934342 1 0.0000 1.000 1 0.000 0 0.000 0 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "kmeans"]
# you can also extract it by
# res = res_list["SD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 14550 rows and 135 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.307 0.491 0.819 0.3164 0.789 0.789
#> 3 3 0.269 0.625 0.743 0.6095 0.603 0.517
#> 4 4 0.272 0.620 0.676 0.2188 0.941 0.875
#> 5 5 0.382 0.571 0.625 0.1320 0.763 0.478
#> 6 6 0.480 0.634 0.646 0.0733 0.902 0.666
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
#> SRR934216 1 0.9754 0.0179 0.592 0.408
#> SRR934217 1 0.9754 0.0179 0.592 0.408
#> SRR934218 1 0.9754 0.0179 0.592 0.408
#> SRR934219 1 0.9754 0.0179 0.592 0.408
#> SRR934220 1 0.9754 0.0179 0.592 0.408
#> SRR934221 1 0.9754 0.0179 0.592 0.408
#> SRR934222 1 0.9754 0.0179 0.592 0.408
#> SRR934223 1 0.9754 0.0179 0.592 0.408
#> SRR934224 1 0.2043 0.7349 0.968 0.032
#> SRR934225 1 0.2043 0.7349 0.968 0.032
#> SRR934226 1 0.2043 0.7349 0.968 0.032
#> SRR934227 1 0.2043 0.7349 0.968 0.032
#> SRR934228 1 0.2043 0.7349 0.968 0.032
#> SRR934229 1 0.2043 0.7349 0.968 0.032
#> SRR934230 1 0.2043 0.7349 0.968 0.032
#> SRR934231 1 0.2043 0.7349 0.968 0.032
#> SRR934232 2 0.9977 0.4592 0.472 0.528
#> SRR934233 2 0.9977 0.4592 0.472 0.528
#> SRR934234 2 0.9977 0.4592 0.472 0.528
#> SRR934235 2 0.9977 0.4592 0.472 0.528
#> SRR934236 2 0.9977 0.4592 0.472 0.528
#> SRR934237 2 0.9977 0.4592 0.472 0.528
#> SRR934238 2 0.9977 0.4592 0.472 0.528
#> SRR934239 2 0.9977 0.4592 0.472 0.528
#> SRR934240 1 0.9896 -0.1325 0.560 0.440
#> SRR934241 1 0.9896 -0.1325 0.560 0.440
#> SRR934242 1 0.9896 -0.1325 0.560 0.440
#> SRR934243 1 0.9896 -0.1325 0.560 0.440
#> SRR934244 1 0.9896 -0.1325 0.560 0.440
#> SRR934245 1 0.9896 -0.1325 0.560 0.440
#> SRR934246 1 0.9896 -0.1325 0.560 0.440
#> SRR934247 1 0.9896 -0.1325 0.560 0.440
#> SRR934248 1 0.9866 -0.1135 0.568 0.432
#> SRR934249 1 0.9866 -0.1135 0.568 0.432
#> SRR934250 1 0.9866 -0.1135 0.568 0.432
#> SRR934251 1 0.9866 -0.1135 0.568 0.432
#> SRR934252 1 0.9866 -0.1135 0.568 0.432
#> SRR934253 1 0.9866 -0.1135 0.568 0.432
#> SRR934254 1 0.9866 -0.1135 0.568 0.432
#> SRR934255 1 0.9866 -0.1135 0.568 0.432
#> SRR934256 1 0.9044 0.2851 0.680 0.320
#> SRR934257 1 0.9044 0.2851 0.680 0.320
#> SRR934258 1 0.9044 0.2851 0.680 0.320
#> SRR934259 1 0.9044 0.2851 0.680 0.320
#> SRR934260 1 0.9044 0.2851 0.680 0.320
#> SRR934261 1 0.9044 0.2851 0.680 0.320
#> SRR934262 1 0.9044 0.2851 0.680 0.320
#> SRR934263 1 0.9044 0.2851 0.680 0.320
#> SRR934264 1 0.9754 -0.0463 0.592 0.408
#> SRR934265 1 0.9754 -0.0463 0.592 0.408
#> SRR934266 1 0.9754 -0.0463 0.592 0.408
#> SRR934267 1 0.9754 -0.0463 0.592 0.408
#> SRR934268 1 0.9754 -0.0463 0.592 0.408
#> SRR934269 1 0.9754 -0.0463 0.592 0.408
#> SRR934270 1 0.9754 -0.0463 0.592 0.408
#> SRR934271 1 0.9754 -0.0463 0.592 0.408
#> SRR934272 1 0.1184 0.7377 0.984 0.016
#> SRR934273 1 0.1184 0.7377 0.984 0.016
#> SRR934274 1 0.1184 0.7377 0.984 0.016
#> SRR934275 1 0.1184 0.7377 0.984 0.016
#> SRR934276 1 0.1184 0.7377 0.984 0.016
#> SRR934277 1 0.1184 0.7377 0.984 0.016
#> SRR934278 1 0.1184 0.7377 0.984 0.016
#> SRR934279 1 0.1184 0.7377 0.984 0.016
#> SRR934280 1 0.1633 0.7372 0.976 0.024
#> SRR934281 1 0.1633 0.7372 0.976 0.024
#> SRR934282 1 0.1633 0.7372 0.976 0.024
#> SRR934283 1 0.1633 0.7372 0.976 0.024
#> SRR934284 1 0.1633 0.7372 0.976 0.024
#> SRR934285 1 0.1633 0.7372 0.976 0.024
#> SRR934286 1 0.1633 0.7372 0.976 0.024
#> SRR934287 1 0.1633 0.7372 0.976 0.024
#> SRR934288 1 0.2236 0.7291 0.964 0.036
#> SRR934289 1 0.2236 0.7291 0.964 0.036
#> SRR934290 1 0.2236 0.7291 0.964 0.036
#> SRR934291 1 0.2236 0.7291 0.964 0.036
#> SRR934292 1 0.2236 0.7291 0.964 0.036
#> SRR934293 1 0.2236 0.7291 0.964 0.036
#> SRR934294 1 0.2236 0.7291 0.964 0.036
#> SRR934295 1 0.2236 0.7291 0.964 0.036
#> SRR934296 1 0.5294 0.6466 0.880 0.120
#> SRR934297 1 0.5294 0.6466 0.880 0.120
#> SRR934298 1 0.5294 0.6466 0.880 0.120
#> SRR934299 1 0.5294 0.6466 0.880 0.120
#> SRR934300 1 0.5294 0.6466 0.880 0.120
#> SRR934301 1 0.5294 0.6466 0.880 0.120
#> SRR934302 1 0.5294 0.6466 0.880 0.120
#> SRR934303 1 0.5294 0.6466 0.880 0.120
#> SRR934304 2 0.8207 0.6320 0.256 0.744
#> SRR934305 2 0.8207 0.6320 0.256 0.744
#> SRR934306 2 0.8207 0.6320 0.256 0.744
#> SRR934307 2 0.8207 0.6320 0.256 0.744
#> SRR934308 2 0.8207 0.6320 0.256 0.744
#> SRR934309 2 0.8207 0.6320 0.256 0.744
#> SRR934310 2 0.8207 0.6320 0.256 0.744
#> SRR934311 2 0.8207 0.6320 0.256 0.744
#> SRR934312 1 0.0672 0.7396 0.992 0.008
#> SRR934313 1 0.0672 0.7396 0.992 0.008
#> SRR934314 1 0.0672 0.7396 0.992 0.008
#> SRR934315 1 0.0672 0.7396 0.992 0.008
#> SRR934316 1 0.0672 0.7396 0.992 0.008
#> SRR934317 1 0.0672 0.7396 0.992 0.008
#> SRR934318 1 0.0672 0.7396 0.992 0.008
#> SRR934319 1 0.0672 0.7396 0.992 0.008
#> SRR934320 1 0.1633 0.7346 0.976 0.024
#> SRR934321 1 0.1633 0.7346 0.976 0.024
#> SRR934322 1 0.1633 0.7346 0.976 0.024
#> SRR934323 1 0.1633 0.7346 0.976 0.024
#> SRR934324 1 0.1633 0.7346 0.976 0.024
#> SRR934325 1 0.1633 0.7346 0.976 0.024
#> SRR934326 1 0.1633 0.7346 0.976 0.024
#> SRR934327 1 0.1633 0.7346 0.976 0.024
#> SRR934328 1 0.1633 0.7389 0.976 0.024
#> SRR934329 1 0.1633 0.7389 0.976 0.024
#> SRR934330 1 0.1633 0.7389 0.976 0.024
#> SRR934331 1 0.1633 0.7389 0.976 0.024
#> SRR934332 1 0.1633 0.7389 0.976 0.024
#> SRR934333 1 0.1633 0.7389 0.976 0.024
#> SRR934334 1 0.1633 0.7389 0.976 0.024
#> SRR934335 1 0.1633 0.7389 0.976 0.024
#> SRR934344 1 0.1633 0.7389 0.976 0.024
#> SRR934345 1 0.1633 0.7389 0.976 0.024
#> SRR934346 1 0.1633 0.7389 0.976 0.024
#> SRR934347 1 0.1633 0.7389 0.976 0.024
#> SRR934348 1 0.1633 0.7389 0.976 0.024
#> SRR934349 1 0.1633 0.7389 0.976 0.024
#> SRR934350 1 0.1633 0.7389 0.976 0.024
#> SRR934351 1 0.1633 0.7389 0.976 0.024
#> SRR934336 1 0.1414 0.7375 0.980 0.020
#> SRR934337 1 0.1414 0.7375 0.980 0.020
#> SRR934338 1 0.1414 0.7375 0.980 0.020
#> SRR934339 1 0.1414 0.7375 0.980 0.020
#> SRR934340 1 0.1414 0.7375 0.980 0.020
#> SRR934341 1 0.1414 0.7375 0.980 0.020
#> SRR934342 1 0.1414 0.7375 0.980 0.020
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 3 0.8967 0.443 0.380 0.132 0.488
#> SRR934217 3 0.8967 0.443 0.380 0.132 0.488
#> SRR934218 3 0.8967 0.443 0.380 0.132 0.488
#> SRR934219 3 0.8967 0.443 0.380 0.132 0.488
#> SRR934220 3 0.8967 0.443 0.380 0.132 0.488
#> SRR934221 3 0.8967 0.443 0.380 0.132 0.488
#> SRR934222 3 0.8967 0.443 0.380 0.132 0.488
#> SRR934223 3 0.8967 0.443 0.380 0.132 0.488
#> SRR934224 1 0.3888 0.770 0.888 0.064 0.048
#> SRR934225 1 0.3888 0.770 0.888 0.064 0.048
#> SRR934226 1 0.3888 0.770 0.888 0.064 0.048
#> SRR934227 1 0.3888 0.770 0.888 0.064 0.048
#> SRR934228 1 0.3888 0.770 0.888 0.064 0.048
#> SRR934229 1 0.3888 0.770 0.888 0.064 0.048
#> SRR934230 1 0.3888 0.770 0.888 0.064 0.048
#> SRR934231 1 0.3888 0.770 0.888 0.064 0.048
#> SRR934232 2 0.8216 0.604 0.188 0.640 0.172
#> SRR934233 2 0.8216 0.604 0.188 0.640 0.172
#> SRR934234 2 0.8216 0.604 0.188 0.640 0.172
#> SRR934235 2 0.8216 0.604 0.188 0.640 0.172
#> SRR934236 2 0.8216 0.604 0.188 0.640 0.172
#> SRR934237 2 0.8216 0.604 0.188 0.640 0.172
#> SRR934238 2 0.8216 0.604 0.188 0.640 0.172
#> SRR934239 2 0.8216 0.604 0.188 0.640 0.172
#> SRR934240 2 0.7053 0.721 0.244 0.692 0.064
#> SRR934241 2 0.7053 0.721 0.244 0.692 0.064
#> SRR934242 2 0.7053 0.721 0.244 0.692 0.064
#> SRR934243 2 0.7053 0.721 0.244 0.692 0.064
#> SRR934244 2 0.7053 0.721 0.244 0.692 0.064
#> SRR934245 2 0.7053 0.721 0.244 0.692 0.064
#> SRR934246 2 0.7053 0.721 0.244 0.692 0.064
#> SRR934247 2 0.7053 0.721 0.244 0.692 0.064
#> SRR934248 3 0.9871 0.406 0.308 0.280 0.412
#> SRR934249 3 0.9871 0.406 0.308 0.280 0.412
#> SRR934250 3 0.9871 0.406 0.308 0.280 0.412
#> SRR934251 3 0.9871 0.406 0.308 0.280 0.412
#> SRR934252 3 0.9871 0.406 0.308 0.280 0.412
#> SRR934253 3 0.9871 0.406 0.308 0.280 0.412
#> SRR934254 3 0.9871 0.406 0.308 0.280 0.412
#> SRR934255 3 0.9871 0.406 0.308 0.280 0.412
#> SRR934256 2 0.7236 0.606 0.392 0.576 0.032
#> SRR934257 2 0.7236 0.606 0.392 0.576 0.032
#> SRR934258 2 0.7236 0.606 0.392 0.576 0.032
#> SRR934259 2 0.7236 0.606 0.392 0.576 0.032
#> SRR934260 2 0.7236 0.606 0.392 0.576 0.032
#> SRR934261 2 0.7236 0.606 0.392 0.576 0.032
#> SRR934262 2 0.7236 0.606 0.392 0.576 0.032
#> SRR934263 2 0.7236 0.606 0.392 0.576 0.032
#> SRR934264 3 0.9776 0.374 0.384 0.232 0.384
#> SRR934265 3 0.9776 0.374 0.384 0.232 0.384
#> SRR934266 1 0.9776 -0.549 0.384 0.232 0.384
#> SRR934267 3 0.9776 0.374 0.384 0.232 0.384
#> SRR934268 1 0.9776 -0.549 0.384 0.232 0.384
#> SRR934269 1 0.9776 -0.549 0.384 0.232 0.384
#> SRR934270 1 0.9776 -0.549 0.384 0.232 0.384
#> SRR934271 1 0.9776 -0.549 0.384 0.232 0.384
#> SRR934272 1 0.2434 0.799 0.940 0.036 0.024
#> SRR934273 1 0.2434 0.799 0.940 0.036 0.024
#> SRR934274 1 0.2434 0.799 0.940 0.036 0.024
#> SRR934275 1 0.2434 0.799 0.940 0.036 0.024
#> SRR934276 1 0.2434 0.799 0.940 0.036 0.024
#> SRR934277 1 0.2434 0.799 0.940 0.036 0.024
#> SRR934278 1 0.2434 0.799 0.940 0.036 0.024
#> SRR934279 1 0.2434 0.799 0.940 0.036 0.024
#> SRR934280 1 0.1905 0.806 0.956 0.028 0.016
#> SRR934281 1 0.1905 0.806 0.956 0.028 0.016
#> SRR934282 1 0.1905 0.806 0.956 0.028 0.016
#> SRR934283 1 0.1905 0.806 0.956 0.028 0.016
#> SRR934284 1 0.1905 0.806 0.956 0.028 0.016
#> SRR934285 1 0.1905 0.806 0.956 0.028 0.016
#> SRR934286 1 0.1905 0.806 0.956 0.028 0.016
#> SRR934287 1 0.1905 0.806 0.956 0.028 0.016
#> SRR934288 1 0.5497 0.760 0.812 0.124 0.064
#> SRR934289 1 0.5497 0.760 0.812 0.124 0.064
#> SRR934290 1 0.5497 0.760 0.812 0.124 0.064
#> SRR934291 1 0.5497 0.760 0.812 0.124 0.064
#> SRR934292 1 0.5497 0.760 0.812 0.124 0.064
#> SRR934293 1 0.5497 0.760 0.812 0.124 0.064
#> SRR934294 1 0.5497 0.760 0.812 0.124 0.064
#> SRR934295 1 0.5497 0.760 0.812 0.124 0.064
#> SRR934296 1 0.6168 0.608 0.740 0.224 0.036
#> SRR934297 1 0.6168 0.608 0.740 0.224 0.036
#> SRR934298 1 0.6168 0.608 0.740 0.224 0.036
#> SRR934299 1 0.6168 0.608 0.740 0.224 0.036
#> SRR934300 1 0.6168 0.608 0.740 0.224 0.036
#> SRR934301 1 0.6168 0.608 0.740 0.224 0.036
#> SRR934302 1 0.6168 0.608 0.740 0.224 0.036
#> SRR934303 1 0.6168 0.608 0.740 0.224 0.036
#> SRR934304 3 0.5911 0.405 0.060 0.156 0.784
#> SRR934305 3 0.5911 0.405 0.060 0.156 0.784
#> SRR934306 3 0.5911 0.405 0.060 0.156 0.784
#> SRR934307 3 0.5911 0.405 0.060 0.156 0.784
#> SRR934308 3 0.5911 0.405 0.060 0.156 0.784
#> SRR934309 3 0.5970 0.405 0.060 0.160 0.780
#> SRR934310 3 0.5911 0.405 0.060 0.156 0.784
#> SRR934311 3 0.5911 0.405 0.060 0.156 0.784
#> SRR934312 1 0.0592 0.810 0.988 0.012 0.000
#> SRR934313 1 0.0592 0.810 0.988 0.012 0.000
#> SRR934314 1 0.0592 0.810 0.988 0.012 0.000
#> SRR934315 1 0.0592 0.810 0.988 0.012 0.000
#> SRR934316 1 0.0592 0.810 0.988 0.012 0.000
#> SRR934317 1 0.0592 0.810 0.988 0.012 0.000
#> SRR934318 1 0.0592 0.810 0.988 0.012 0.000
#> SRR934319 1 0.0592 0.810 0.988 0.012 0.000
#> SRR934320 1 0.2564 0.803 0.936 0.036 0.028
#> SRR934321 1 0.2564 0.803 0.936 0.036 0.028
#> SRR934322 1 0.2564 0.803 0.936 0.036 0.028
#> SRR934323 1 0.2564 0.803 0.936 0.036 0.028
#> SRR934324 1 0.2564 0.803 0.936 0.036 0.028
#> SRR934325 1 0.2564 0.803 0.936 0.036 0.028
#> SRR934326 1 0.2564 0.803 0.936 0.036 0.028
#> SRR934327 1 0.2564 0.803 0.936 0.036 0.028
#> SRR934328 1 0.5554 0.756 0.812 0.112 0.076
#> SRR934329 1 0.5554 0.756 0.812 0.112 0.076
#> SRR934330 1 0.5554 0.756 0.812 0.112 0.076
#> SRR934331 1 0.5554 0.756 0.812 0.112 0.076
#> SRR934332 1 0.5554 0.756 0.812 0.112 0.076
#> SRR934333 1 0.5554 0.756 0.812 0.112 0.076
#> SRR934334 1 0.5554 0.756 0.812 0.112 0.076
#> SRR934335 1 0.5554 0.756 0.812 0.112 0.076
#> SRR934344 1 0.5449 0.757 0.816 0.116 0.068
#> SRR934345 1 0.5449 0.757 0.816 0.116 0.068
#> SRR934346 1 0.5449 0.757 0.816 0.116 0.068
#> SRR934347 1 0.5449 0.757 0.816 0.116 0.068
#> SRR934348 1 0.5449 0.757 0.816 0.116 0.068
#> SRR934349 1 0.5449 0.757 0.816 0.116 0.068
#> SRR934350 1 0.5449 0.757 0.816 0.116 0.068
#> SRR934351 1 0.5449 0.757 0.816 0.116 0.068
#> SRR934336 1 0.2187 0.804 0.948 0.024 0.028
#> SRR934337 1 0.2187 0.804 0.948 0.024 0.028
#> SRR934338 1 0.2187 0.804 0.948 0.024 0.028
#> SRR934339 1 0.2187 0.804 0.948 0.024 0.028
#> SRR934340 1 0.2187 0.804 0.948 0.024 0.028
#> SRR934341 1 0.2187 0.804 0.948 0.024 0.028
#> SRR934342 1 0.2187 0.804 0.948 0.024 0.028
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 3 0.889 0.432 0.308 0.048 0.364 NA
#> SRR934217 3 0.889 0.432 0.308 0.048 0.364 NA
#> SRR934218 3 0.889 0.432 0.308 0.048 0.364 NA
#> SRR934219 3 0.889 0.432 0.308 0.048 0.364 NA
#> SRR934220 3 0.894 0.431 0.308 0.052 0.364 NA
#> SRR934221 3 0.889 0.432 0.308 0.048 0.364 NA
#> SRR934222 3 0.889 0.432 0.308 0.048 0.364 NA
#> SRR934223 3 0.889 0.432 0.308 0.048 0.364 NA
#> SRR934224 1 0.413 0.659 0.828 0.016 0.020 NA
#> SRR934225 1 0.413 0.659 0.828 0.016 0.020 NA
#> SRR934226 1 0.413 0.659 0.828 0.016 0.020 NA
#> SRR934227 1 0.413 0.659 0.828 0.016 0.020 NA
#> SRR934228 1 0.413 0.659 0.828 0.016 0.020 NA
#> SRR934229 1 0.413 0.659 0.828 0.016 0.020 NA
#> SRR934230 1 0.413 0.659 0.828 0.016 0.020 NA
#> SRR934231 1 0.413 0.659 0.828 0.016 0.020 NA
#> SRR934232 2 0.563 0.575 0.060 0.744 0.172 NA
#> SRR934233 2 0.563 0.575 0.060 0.744 0.172 NA
#> SRR934234 2 0.563 0.575 0.060 0.744 0.172 NA
#> SRR934235 2 0.563 0.575 0.060 0.744 0.172 NA
#> SRR934236 2 0.563 0.575 0.060 0.744 0.172 NA
#> SRR934237 2 0.563 0.575 0.060 0.744 0.172 NA
#> SRR934238 2 0.563 0.575 0.060 0.744 0.172 NA
#> SRR934239 2 0.563 0.575 0.060 0.744 0.172 NA
#> SRR934240 2 0.385 0.719 0.124 0.844 0.012 NA
#> SRR934241 2 0.385 0.719 0.124 0.844 0.012 NA
#> SRR934242 2 0.385 0.719 0.124 0.844 0.012 NA
#> SRR934243 2 0.385 0.719 0.124 0.844 0.012 NA
#> SRR934244 2 0.385 0.719 0.124 0.844 0.012 NA
#> SRR934245 2 0.385 0.719 0.124 0.844 0.012 NA
#> SRR934246 2 0.385 0.719 0.124 0.844 0.012 NA
#> SRR934247 2 0.385 0.719 0.124 0.844 0.012 NA
#> SRR934248 3 0.887 0.483 0.184 0.248 0.476 NA
#> SRR934249 3 0.887 0.483 0.184 0.248 0.476 NA
#> SRR934250 3 0.892 0.483 0.184 0.248 0.472 NA
#> SRR934251 3 0.892 0.483 0.184 0.248 0.472 NA
#> SRR934252 3 0.887 0.483 0.184 0.248 0.476 NA
#> SRR934253 3 0.892 0.483 0.184 0.248 0.472 NA
#> SRR934254 3 0.892 0.483 0.184 0.248 0.472 NA
#> SRR934255 3 0.887 0.483 0.184 0.248 0.476 NA
#> SRR934256 2 0.741 0.628 0.172 0.576 0.016 NA
#> SRR934257 2 0.741 0.628 0.172 0.576 0.016 NA
#> SRR934258 2 0.741 0.628 0.172 0.576 0.016 NA
#> SRR934259 2 0.741 0.628 0.172 0.576 0.016 NA
#> SRR934260 2 0.741 0.628 0.172 0.576 0.016 NA
#> SRR934261 2 0.741 0.628 0.172 0.576 0.016 NA
#> SRR934262 2 0.741 0.628 0.172 0.576 0.016 NA
#> SRR934263 2 0.741 0.628 0.172 0.576 0.016 NA
#> SRR934264 3 0.846 0.514 0.284 0.228 0.452 NA
#> SRR934265 3 0.846 0.514 0.284 0.228 0.452 NA
#> SRR934266 3 0.846 0.514 0.284 0.228 0.452 NA
#> SRR934267 3 0.846 0.514 0.284 0.228 0.452 NA
#> SRR934268 3 0.846 0.514 0.284 0.228 0.452 NA
#> SRR934269 3 0.846 0.514 0.284 0.228 0.452 NA
#> SRR934270 3 0.846 0.514 0.284 0.228 0.452 NA
#> SRR934271 3 0.846 0.514 0.284 0.228 0.452 NA
#> SRR934272 1 0.216 0.737 0.932 0.016 0.004 NA
#> SRR934273 1 0.216 0.737 0.932 0.016 0.004 NA
#> SRR934274 1 0.216 0.737 0.932 0.016 0.004 NA
#> SRR934275 1 0.216 0.737 0.932 0.016 0.004 NA
#> SRR934276 1 0.216 0.737 0.932 0.016 0.004 NA
#> SRR934277 1 0.216 0.737 0.932 0.016 0.004 NA
#> SRR934278 1 0.216 0.737 0.932 0.016 0.004 NA
#> SRR934279 1 0.216 0.737 0.932 0.016 0.004 NA
#> SRR934280 1 0.196 0.738 0.944 0.024 0.008 NA
#> SRR934281 1 0.196 0.738 0.944 0.024 0.008 NA
#> SRR934282 1 0.196 0.738 0.944 0.024 0.008 NA
#> SRR934283 1 0.196 0.738 0.944 0.024 0.008 NA
#> SRR934284 1 0.196 0.738 0.944 0.024 0.008 NA
#> SRR934285 1 0.196 0.738 0.944 0.024 0.008 NA
#> SRR934286 1 0.196 0.738 0.944 0.024 0.008 NA
#> SRR934287 1 0.196 0.738 0.944 0.024 0.008 NA
#> SRR934288 1 0.553 0.645 0.592 0.016 0.004 NA
#> SRR934289 1 0.553 0.645 0.592 0.016 0.004 NA
#> SRR934290 1 0.553 0.645 0.592 0.016 0.004 NA
#> SRR934291 1 0.553 0.645 0.592 0.016 0.004 NA
#> SRR934292 1 0.553 0.645 0.592 0.016 0.004 NA
#> SRR934293 1 0.553 0.645 0.592 0.016 0.004 NA
#> SRR934294 1 0.553 0.645 0.592 0.016 0.004 NA
#> SRR934295 1 0.553 0.645 0.592 0.016 0.004 NA
#> SRR934296 1 0.753 0.563 0.588 0.172 0.028 NA
#> SRR934297 1 0.753 0.563 0.588 0.172 0.028 NA
#> SRR934298 1 0.753 0.563 0.588 0.172 0.028 NA
#> SRR934299 1 0.753 0.563 0.588 0.172 0.028 NA
#> SRR934300 1 0.753 0.563 0.588 0.172 0.028 NA
#> SRR934301 1 0.753 0.563 0.588 0.172 0.028 NA
#> SRR934302 1 0.753 0.563 0.588 0.172 0.028 NA
#> SRR934303 1 0.753 0.563 0.588 0.172 0.028 NA
#> SRR934304 3 0.593 0.374 0.020 0.108 0.732 NA
#> SRR934305 3 0.593 0.374 0.020 0.108 0.732 NA
#> SRR934306 3 0.593 0.374 0.020 0.108 0.732 NA
#> SRR934307 3 0.593 0.374 0.020 0.108 0.732 NA
#> SRR934308 3 0.603 0.374 0.020 0.112 0.724 NA
#> SRR934309 3 0.599 0.374 0.020 0.112 0.728 NA
#> SRR934310 3 0.593 0.374 0.020 0.108 0.732 NA
#> SRR934311 3 0.593 0.374 0.020 0.108 0.732 NA
#> SRR934312 1 0.184 0.746 0.948 0.016 0.008 NA
#> SRR934313 1 0.184 0.746 0.948 0.016 0.008 NA
#> SRR934314 1 0.184 0.746 0.948 0.016 0.008 NA
#> SRR934315 1 0.184 0.746 0.948 0.016 0.008 NA
#> SRR934316 1 0.184 0.746 0.948 0.016 0.008 NA
#> SRR934317 1 0.184 0.746 0.948 0.016 0.008 NA
#> SRR934318 1 0.184 0.746 0.948 0.016 0.008 NA
#> SRR934319 1 0.184 0.746 0.948 0.016 0.008 NA
#> SRR934320 1 0.406 0.728 0.828 0.020 0.012 NA
#> SRR934321 1 0.406 0.728 0.828 0.020 0.012 NA
#> SRR934322 1 0.406 0.728 0.828 0.020 0.012 NA
#> SRR934323 1 0.406 0.728 0.828 0.020 0.012 NA
#> SRR934324 1 0.406 0.728 0.828 0.020 0.012 NA
#> SRR934325 1 0.406 0.728 0.828 0.020 0.012 NA
#> SRR934326 1 0.406 0.728 0.828 0.020 0.012 NA
#> SRR934327 1 0.406 0.728 0.828 0.020 0.012 NA
#> SRR934328 1 0.538 0.637 0.568 0.004 0.008 NA
#> SRR934329 1 0.538 0.637 0.568 0.004 0.008 NA
#> SRR934330 1 0.538 0.637 0.568 0.004 0.008 NA
#> SRR934331 1 0.538 0.637 0.568 0.004 0.008 NA
#> SRR934332 1 0.538 0.637 0.568 0.004 0.008 NA
#> SRR934333 1 0.538 0.637 0.568 0.004 0.008 NA
#> SRR934334 1 0.538 0.637 0.568 0.004 0.008 NA
#> SRR934335 1 0.538 0.637 0.568 0.004 0.008 NA
#> SRR934344 1 0.523 0.644 0.564 0.000 0.008 NA
#> SRR934345 1 0.523 0.644 0.564 0.000 0.008 NA
#> SRR934346 1 0.523 0.644 0.564 0.000 0.008 NA
#> SRR934347 1 0.523 0.644 0.564 0.000 0.008 NA
#> SRR934348 1 0.523 0.644 0.564 0.000 0.008 NA
#> SRR934349 1 0.523 0.644 0.564 0.000 0.008 NA
#> SRR934350 1 0.523 0.644 0.564 0.000 0.008 NA
#> SRR934351 1 0.523 0.644 0.564 0.000 0.008 NA
#> SRR934336 1 0.188 0.736 0.944 0.008 0.008 NA
#> SRR934337 1 0.188 0.736 0.944 0.008 0.008 NA
#> SRR934338 1 0.188 0.736 0.944 0.008 0.008 NA
#> SRR934339 1 0.188 0.736 0.944 0.008 0.008 NA
#> SRR934340 1 0.188 0.736 0.944 0.008 0.008 NA
#> SRR934341 1 0.188 0.736 0.944 0.008 0.008 NA
#> SRR934342 1 0.188 0.736 0.944 0.008 0.008 NA
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0.8614 0.602 0.188 0.256 0.400 0.024 0.132
#> SRR934217 3 0.8614 0.602 0.188 0.256 0.400 0.024 0.132
#> SRR934218 3 0.8614 0.602 0.188 0.256 0.400 0.024 0.132
#> SRR934219 3 0.8614 0.602 0.188 0.256 0.400 0.024 0.132
#> SRR934220 3 0.8614 0.602 0.188 0.256 0.400 0.024 0.132
#> SRR934221 3 0.8614 0.602 0.188 0.256 0.400 0.024 0.132
#> SRR934222 3 0.8614 0.602 0.188 0.256 0.400 0.024 0.132
#> SRR934223 3 0.8614 0.602 0.188 0.256 0.400 0.024 0.132
#> SRR934224 1 0.5924 0.661 0.652 0.080 0.044 0.000 0.224
#> SRR934225 1 0.5924 0.661 0.652 0.080 0.044 0.000 0.224
#> SRR934226 1 0.5924 0.661 0.652 0.080 0.044 0.000 0.224
#> SRR934227 1 0.5924 0.661 0.652 0.080 0.044 0.000 0.224
#> SRR934228 1 0.5924 0.661 0.652 0.080 0.044 0.000 0.224
#> SRR934229 1 0.5924 0.661 0.652 0.080 0.044 0.000 0.224
#> SRR934230 1 0.5924 0.661 0.652 0.080 0.044 0.000 0.224
#> SRR934231 1 0.5924 0.661 0.652 0.080 0.044 0.000 0.224
#> SRR934232 4 0.0968 0.341 0.012 0.000 0.004 0.972 0.012
#> SRR934233 4 0.0968 0.341 0.012 0.000 0.004 0.972 0.012
#> SRR934234 4 0.0968 0.341 0.012 0.000 0.004 0.972 0.012
#> SRR934235 4 0.0968 0.341 0.012 0.000 0.004 0.972 0.012
#> SRR934236 4 0.0968 0.341 0.012 0.000 0.004 0.972 0.012
#> SRR934237 4 0.0968 0.341 0.012 0.000 0.004 0.972 0.012
#> SRR934238 4 0.0968 0.341 0.012 0.000 0.004 0.972 0.012
#> SRR934239 4 0.0968 0.341 0.012 0.000 0.004 0.972 0.012
#> SRR934240 4 0.5962 -0.278 0.064 0.244 0.000 0.640 0.052
#> SRR934241 4 0.5962 -0.278 0.064 0.244 0.000 0.640 0.052
#> SRR934242 4 0.5962 -0.278 0.064 0.244 0.000 0.640 0.052
#> SRR934243 4 0.5962 -0.278 0.064 0.244 0.000 0.640 0.052
#> SRR934244 4 0.5962 -0.278 0.064 0.244 0.000 0.640 0.052
#> SRR934245 4 0.5962 -0.278 0.064 0.244 0.000 0.640 0.052
#> SRR934246 4 0.5962 -0.278 0.064 0.244 0.000 0.640 0.052
#> SRR934247 4 0.5962 -0.278 0.064 0.244 0.000 0.640 0.052
#> SRR934248 4 0.9101 0.403 0.188 0.120 0.192 0.412 0.088
#> SRR934249 4 0.9101 0.403 0.188 0.120 0.192 0.412 0.088
#> SRR934250 4 0.9089 0.403 0.192 0.116 0.192 0.412 0.088
#> SRR934251 4 0.9089 0.403 0.192 0.116 0.192 0.412 0.088
#> SRR934252 4 0.9089 0.403 0.192 0.116 0.192 0.412 0.088
#> SRR934253 4 0.9089 0.403 0.192 0.116 0.192 0.412 0.088
#> SRR934254 4 0.9089 0.403 0.192 0.116 0.192 0.412 0.088
#> SRR934255 4 0.9101 0.403 0.188 0.120 0.192 0.412 0.088
#> SRR934256 2 0.7711 1.000 0.088 0.448 0.008 0.328 0.128
#> SRR934257 2 0.7711 1.000 0.088 0.448 0.008 0.328 0.128
#> SRR934258 2 0.7711 1.000 0.088 0.448 0.008 0.328 0.128
#> SRR934259 2 0.7711 1.000 0.088 0.448 0.008 0.328 0.128
#> SRR934260 2 0.7711 1.000 0.088 0.448 0.008 0.328 0.128
#> SRR934261 2 0.7751 0.997 0.092 0.444 0.008 0.328 0.128
#> SRR934262 2 0.7711 1.000 0.088 0.448 0.008 0.328 0.128
#> SRR934263 2 0.7711 1.000 0.088 0.448 0.008 0.328 0.128
#> SRR934264 4 0.8809 0.374 0.248 0.084 0.200 0.404 0.064
#> SRR934265 4 0.8809 0.374 0.248 0.084 0.200 0.404 0.064
#> SRR934266 4 0.8809 0.374 0.248 0.084 0.200 0.404 0.064
#> SRR934267 4 0.8809 0.374 0.248 0.084 0.200 0.404 0.064
#> SRR934268 4 0.8809 0.374 0.248 0.084 0.200 0.404 0.064
#> SRR934269 4 0.8809 0.374 0.248 0.084 0.200 0.404 0.064
#> SRR934270 4 0.8809 0.374 0.248 0.084 0.200 0.404 0.064
#> SRR934271 4 0.8809 0.374 0.248 0.084 0.200 0.404 0.064
#> SRR934272 1 0.6277 0.686 0.528 0.048 0.044 0.004 0.376
#> SRR934273 1 0.6277 0.686 0.528 0.048 0.044 0.004 0.376
#> SRR934274 1 0.6277 0.686 0.528 0.048 0.044 0.004 0.376
#> SRR934275 1 0.6277 0.686 0.528 0.048 0.044 0.004 0.376
#> SRR934276 1 0.6277 0.686 0.528 0.048 0.044 0.004 0.376
#> SRR934277 1 0.6277 0.686 0.528 0.048 0.044 0.004 0.376
#> SRR934278 1 0.6277 0.686 0.528 0.048 0.044 0.004 0.376
#> SRR934279 1 0.6277 0.686 0.528 0.048 0.044 0.004 0.376
#> SRR934280 1 0.5321 0.738 0.624 0.036 0.008 0.008 0.324
#> SRR934281 1 0.5321 0.738 0.624 0.036 0.008 0.008 0.324
#> SRR934282 1 0.5321 0.738 0.624 0.036 0.008 0.008 0.324
#> SRR934283 1 0.5321 0.738 0.624 0.036 0.008 0.008 0.324
#> SRR934284 1 0.5321 0.738 0.624 0.036 0.008 0.008 0.324
#> SRR934285 1 0.5321 0.738 0.624 0.036 0.008 0.008 0.324
#> SRR934286 1 0.5321 0.738 0.624 0.036 0.008 0.008 0.324
#> SRR934287 1 0.5321 0.738 0.624 0.036 0.008 0.008 0.324
#> SRR934288 5 0.2513 0.746 0.040 0.048 0.008 0.000 0.904
#> SRR934289 5 0.2513 0.746 0.040 0.048 0.008 0.000 0.904
#> SRR934290 5 0.2513 0.746 0.040 0.048 0.008 0.000 0.904
#> SRR934291 5 0.2513 0.746 0.040 0.048 0.008 0.000 0.904
#> SRR934292 5 0.2513 0.746 0.040 0.048 0.008 0.000 0.904
#> SRR934293 5 0.2513 0.746 0.040 0.048 0.008 0.000 0.904
#> SRR934294 5 0.2513 0.746 0.040 0.048 0.008 0.000 0.904
#> SRR934295 5 0.2513 0.746 0.040 0.048 0.008 0.000 0.904
#> SRR934296 5 0.7799 0.416 0.192 0.100 0.036 0.124 0.548
#> SRR934297 5 0.7799 0.416 0.192 0.100 0.036 0.124 0.548
#> SRR934298 5 0.7799 0.416 0.192 0.100 0.036 0.124 0.548
#> SRR934299 5 0.7799 0.416 0.192 0.100 0.036 0.124 0.548
#> SRR934300 5 0.7799 0.416 0.192 0.100 0.036 0.124 0.548
#> SRR934301 5 0.7799 0.416 0.192 0.100 0.036 0.124 0.548
#> SRR934302 5 0.7799 0.416 0.192 0.100 0.036 0.124 0.548
#> SRR934303 5 0.7799 0.416 0.192 0.100 0.036 0.124 0.548
#> SRR934304 3 0.2971 0.545 0.008 0.000 0.836 0.156 0.000
#> SRR934305 3 0.3129 0.545 0.008 0.004 0.832 0.156 0.000
#> SRR934306 3 0.3081 0.545 0.012 0.000 0.832 0.156 0.000
#> SRR934307 3 0.3081 0.545 0.012 0.000 0.832 0.156 0.000
#> SRR934308 3 0.3013 0.544 0.008 0.000 0.832 0.160 0.000
#> SRR934309 3 0.3573 0.542 0.016 0.012 0.816 0.156 0.000
#> SRR934310 3 0.3081 0.545 0.012 0.000 0.832 0.156 0.000
#> SRR934311 3 0.2971 0.545 0.008 0.000 0.836 0.156 0.000
#> SRR934312 1 0.6093 0.670 0.500 0.044 0.032 0.004 0.420
#> SRR934313 1 0.6093 0.670 0.500 0.044 0.032 0.004 0.420
#> SRR934314 1 0.6093 0.670 0.500 0.044 0.032 0.004 0.420
#> SRR934315 1 0.6093 0.670 0.500 0.044 0.032 0.004 0.420
#> SRR934316 1 0.6093 0.670 0.500 0.044 0.032 0.004 0.420
#> SRR934317 1 0.6093 0.670 0.500 0.044 0.032 0.004 0.420
#> SRR934318 1 0.6088 0.675 0.504 0.044 0.032 0.004 0.416
#> SRR934319 1 0.6093 0.670 0.500 0.044 0.032 0.004 0.420
#> SRR934320 1 0.6180 0.631 0.580 0.068 0.012 0.020 0.320
#> SRR934321 1 0.6180 0.631 0.580 0.068 0.012 0.020 0.320
#> SRR934322 1 0.6180 0.631 0.580 0.068 0.012 0.020 0.320
#> SRR934323 1 0.6180 0.631 0.580 0.068 0.012 0.020 0.320
#> SRR934324 1 0.6180 0.631 0.580 0.068 0.012 0.020 0.320
#> SRR934325 1 0.6180 0.631 0.580 0.068 0.012 0.020 0.320
#> SRR934326 1 0.6180 0.631 0.580 0.068 0.012 0.020 0.320
#> SRR934327 1 0.6180 0.631 0.580 0.068 0.012 0.020 0.320
#> SRR934328 5 0.1179 0.755 0.016 0.016 0.004 0.000 0.964
#> SRR934329 5 0.1179 0.755 0.016 0.016 0.004 0.000 0.964
#> SRR934330 5 0.1179 0.755 0.016 0.016 0.004 0.000 0.964
#> SRR934331 5 0.1179 0.755 0.016 0.016 0.004 0.000 0.964
#> SRR934332 5 0.1179 0.755 0.016 0.016 0.004 0.000 0.964
#> SRR934333 5 0.1179 0.755 0.016 0.016 0.004 0.000 0.964
#> SRR934334 5 0.1179 0.755 0.016 0.016 0.004 0.000 0.964
#> SRR934335 5 0.1179 0.755 0.016 0.016 0.004 0.000 0.964
#> SRR934344 5 0.3203 0.694 0.124 0.008 0.020 0.000 0.848
#> SRR934345 5 0.3203 0.694 0.124 0.008 0.020 0.000 0.848
#> SRR934346 5 0.3203 0.694 0.124 0.008 0.020 0.000 0.848
#> SRR934347 5 0.3203 0.694 0.124 0.008 0.020 0.000 0.848
#> SRR934348 5 0.3203 0.694 0.124 0.008 0.020 0.000 0.848
#> SRR934349 5 0.3203 0.694 0.124 0.008 0.020 0.000 0.848
#> SRR934350 5 0.3203 0.694 0.124 0.008 0.020 0.000 0.848
#> SRR934351 5 0.3203 0.694 0.124 0.008 0.020 0.000 0.848
#> SRR934336 1 0.3870 0.745 0.732 0.000 0.004 0.004 0.260
#> SRR934337 1 0.3870 0.745 0.732 0.000 0.004 0.004 0.260
#> SRR934338 1 0.3870 0.745 0.732 0.000 0.004 0.004 0.260
#> SRR934339 1 0.3870 0.745 0.732 0.000 0.004 0.004 0.260
#> SRR934340 1 0.3870 0.745 0.732 0.000 0.004 0.004 0.260
#> SRR934341 1 0.3870 0.745 0.732 0.000 0.004 0.004 0.260
#> SRR934342 1 0.3870 0.745 0.732 0.000 0.004 0.004 0.260
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.782 0.9990 0.060 0.000 0.448 0.120 0.224 0.148
#> SRR934217 3 0.786 0.9970 0.064 0.000 0.444 0.120 0.224 0.148
#> SRR934218 3 0.782 0.9990 0.060 0.000 0.448 0.120 0.224 0.148
#> SRR934219 3 0.782 0.9990 0.060 0.000 0.448 0.120 0.224 0.148
#> SRR934220 3 0.786 0.9970 0.064 0.000 0.444 0.120 0.224 0.148
#> SRR934221 3 0.782 0.9990 0.060 0.000 0.448 0.120 0.224 0.148
#> SRR934222 3 0.782 0.9990 0.060 0.000 0.448 0.120 0.224 0.148
#> SRR934223 3 0.782 0.9990 0.060 0.000 0.448 0.120 0.224 0.148
#> SRR934224 6 0.527 0.4840 0.032 0.008 0.312 0.032 0.004 0.612
#> SRR934225 6 0.527 0.4840 0.032 0.008 0.312 0.032 0.004 0.612
#> SRR934226 6 0.527 0.4840 0.032 0.008 0.312 0.032 0.004 0.612
#> SRR934227 6 0.527 0.4840 0.032 0.008 0.312 0.032 0.004 0.612
#> SRR934228 6 0.527 0.4840 0.032 0.008 0.312 0.032 0.004 0.612
#> SRR934229 6 0.527 0.4840 0.032 0.008 0.312 0.032 0.004 0.612
#> SRR934230 6 0.527 0.4840 0.032 0.008 0.312 0.032 0.004 0.612
#> SRR934231 6 0.527 0.4840 0.032 0.008 0.312 0.032 0.004 0.612
#> SRR934232 4 0.785 0.1566 0.020 0.300 0.132 0.412 0.112 0.024
#> SRR934233 4 0.785 0.1566 0.020 0.300 0.132 0.412 0.112 0.024
#> SRR934234 4 0.785 0.1566 0.020 0.300 0.132 0.412 0.112 0.024
#> SRR934235 4 0.785 0.1566 0.020 0.300 0.132 0.412 0.112 0.024
#> SRR934236 4 0.785 0.1566 0.020 0.300 0.132 0.412 0.112 0.024
#> SRR934237 4 0.785 0.1566 0.020 0.300 0.132 0.412 0.112 0.024
#> SRR934238 4 0.785 0.1566 0.020 0.300 0.132 0.412 0.112 0.024
#> SRR934239 4 0.785 0.1566 0.020 0.300 0.132 0.412 0.112 0.024
#> SRR934240 2 0.768 0.6296 0.040 0.528 0.152 0.164 0.072 0.044
#> SRR934241 2 0.773 0.6280 0.048 0.528 0.144 0.164 0.072 0.044
#> SRR934242 2 0.768 0.6296 0.040 0.528 0.152 0.164 0.072 0.044
#> SRR934243 2 0.768 0.6296 0.040 0.528 0.152 0.164 0.072 0.044
#> SRR934244 2 0.768 0.6296 0.040 0.528 0.152 0.164 0.072 0.044
#> SRR934245 2 0.771 0.6289 0.044 0.528 0.148 0.164 0.072 0.044
#> SRR934246 2 0.768 0.6296 0.040 0.528 0.152 0.164 0.072 0.044
#> SRR934247 2 0.768 0.6296 0.040 0.528 0.152 0.164 0.072 0.044
#> SRR934248 4 0.349 0.5459 0.036 0.008 0.012 0.848 0.020 0.076
#> SRR934249 4 0.349 0.5459 0.036 0.008 0.012 0.848 0.020 0.076
#> SRR934250 4 0.349 0.5459 0.036 0.008 0.012 0.848 0.020 0.076
#> SRR934251 4 0.349 0.5459 0.036 0.008 0.012 0.848 0.020 0.076
#> SRR934252 4 0.349 0.5459 0.036 0.008 0.012 0.848 0.020 0.076
#> SRR934253 4 0.349 0.5459 0.036 0.008 0.012 0.848 0.020 0.076
#> SRR934254 4 0.349 0.5459 0.036 0.008 0.012 0.848 0.020 0.076
#> SRR934255 4 0.349 0.5459 0.036 0.008 0.012 0.848 0.020 0.076
#> SRR934256 2 0.252 0.6966 0.044 0.884 0.000 0.000 0.004 0.068
#> SRR934257 2 0.252 0.6966 0.044 0.884 0.000 0.000 0.004 0.068
#> SRR934258 2 0.263 0.6964 0.044 0.880 0.000 0.000 0.008 0.068
#> SRR934259 2 0.252 0.6966 0.044 0.884 0.000 0.000 0.004 0.068
#> SRR934260 2 0.273 0.6959 0.044 0.876 0.000 0.000 0.012 0.068
#> SRR934261 2 0.258 0.6962 0.048 0.880 0.000 0.000 0.004 0.068
#> SRR934262 2 0.263 0.6964 0.044 0.880 0.000 0.000 0.008 0.068
#> SRR934263 2 0.252 0.6966 0.044 0.884 0.000 0.000 0.004 0.068
#> SRR934264 4 0.571 0.5576 0.032 0.028 0.040 0.708 0.064 0.128
#> SRR934265 4 0.571 0.5576 0.032 0.028 0.040 0.708 0.064 0.128
#> SRR934266 4 0.571 0.5576 0.032 0.028 0.040 0.708 0.064 0.128
#> SRR934267 4 0.571 0.5576 0.032 0.028 0.040 0.708 0.064 0.128
#> SRR934268 4 0.571 0.5576 0.032 0.028 0.040 0.708 0.064 0.128
#> SRR934269 4 0.571 0.5576 0.032 0.028 0.040 0.708 0.064 0.128
#> SRR934270 4 0.571 0.5576 0.032 0.028 0.040 0.708 0.064 0.128
#> SRR934271 4 0.571 0.5576 0.032 0.028 0.040 0.708 0.064 0.128
#> SRR934272 6 0.456 0.6459 0.088 0.000 0.116 0.020 0.016 0.760
#> SRR934273 6 0.456 0.6459 0.088 0.000 0.116 0.020 0.016 0.760
#> SRR934274 6 0.456 0.6459 0.088 0.000 0.116 0.020 0.016 0.760
#> SRR934275 6 0.456 0.6459 0.088 0.000 0.116 0.020 0.016 0.760
#> SRR934276 6 0.456 0.6459 0.088 0.000 0.116 0.020 0.016 0.760
#> SRR934277 6 0.456 0.6459 0.088 0.000 0.116 0.020 0.016 0.760
#> SRR934278 6 0.456 0.6459 0.088 0.000 0.116 0.020 0.016 0.760
#> SRR934279 6 0.456 0.6459 0.088 0.000 0.116 0.020 0.016 0.760
#> SRR934280 6 0.291 0.6662 0.048 0.028 0.024 0.008 0.008 0.884
#> SRR934281 6 0.291 0.6662 0.048 0.028 0.024 0.008 0.008 0.884
#> SRR934282 6 0.291 0.6662 0.048 0.028 0.024 0.008 0.008 0.884
#> SRR934283 6 0.291 0.6662 0.048 0.028 0.024 0.008 0.008 0.884
#> SRR934284 6 0.291 0.6662 0.048 0.028 0.024 0.008 0.008 0.884
#> SRR934285 6 0.291 0.6662 0.048 0.028 0.024 0.008 0.008 0.884
#> SRR934286 6 0.291 0.6662 0.048 0.028 0.024 0.008 0.008 0.884
#> SRR934287 6 0.291 0.6662 0.048 0.028 0.024 0.008 0.008 0.884
#> SRR934288 1 0.575 0.8142 0.660 0.024 0.048 0.004 0.064 0.200
#> SRR934289 1 0.575 0.8142 0.660 0.024 0.048 0.004 0.064 0.200
#> SRR934290 1 0.575 0.8142 0.660 0.024 0.048 0.004 0.064 0.200
#> SRR934291 1 0.575 0.8142 0.660 0.024 0.048 0.004 0.064 0.200
#> SRR934292 1 0.575 0.8142 0.660 0.024 0.048 0.004 0.064 0.200
#> SRR934293 1 0.575 0.8142 0.660 0.024 0.048 0.004 0.064 0.200
#> SRR934294 1 0.575 0.8142 0.660 0.024 0.048 0.004 0.064 0.200
#> SRR934295 1 0.575 0.8142 0.660 0.024 0.048 0.004 0.064 0.200
#> SRR934296 6 0.837 -0.0109 0.332 0.108 0.108 0.020 0.084 0.348
#> SRR934297 6 0.837 -0.0109 0.332 0.108 0.108 0.020 0.084 0.348
#> SRR934298 6 0.837 -0.0109 0.332 0.108 0.108 0.020 0.084 0.348
#> SRR934299 6 0.837 -0.0109 0.332 0.108 0.108 0.020 0.084 0.348
#> SRR934300 6 0.837 -0.0109 0.332 0.108 0.108 0.020 0.084 0.348
#> SRR934301 6 0.837 -0.0109 0.332 0.108 0.108 0.020 0.084 0.348
#> SRR934302 6 0.837 -0.0109 0.332 0.108 0.108 0.020 0.084 0.348
#> SRR934303 6 0.837 -0.0109 0.332 0.108 0.108 0.020 0.084 0.348
#> SRR934304 5 0.370 0.9910 0.004 0.008 0.004 0.192 0.776 0.016
#> SRR934305 5 0.400 0.9878 0.004 0.012 0.012 0.192 0.764 0.016
#> SRR934306 5 0.356 0.9914 0.004 0.008 0.000 0.192 0.780 0.016
#> SRR934307 5 0.367 0.9912 0.008 0.008 0.000 0.192 0.776 0.016
#> SRR934308 5 0.401 0.9862 0.012 0.008 0.008 0.192 0.764 0.016
#> SRR934309 5 0.441 0.9750 0.012 0.016 0.016 0.196 0.744 0.016
#> SRR934310 5 0.367 0.9912 0.008 0.008 0.000 0.192 0.776 0.016
#> SRR934311 5 0.356 0.9914 0.004 0.008 0.000 0.192 0.780 0.016
#> SRR934312 6 0.451 0.6360 0.132 0.008 0.080 0.008 0.012 0.760
#> SRR934313 6 0.451 0.6360 0.132 0.008 0.080 0.008 0.012 0.760
#> SRR934314 6 0.451 0.6360 0.132 0.008 0.080 0.008 0.012 0.760
#> SRR934315 6 0.451 0.6360 0.132 0.008 0.080 0.008 0.012 0.760
#> SRR934316 6 0.451 0.6360 0.132 0.008 0.080 0.008 0.012 0.760
#> SRR934317 6 0.451 0.6360 0.132 0.008 0.080 0.008 0.012 0.760
#> SRR934318 6 0.451 0.6360 0.132 0.008 0.080 0.008 0.012 0.760
#> SRR934319 6 0.451 0.6360 0.132 0.008 0.080 0.008 0.012 0.760
#> SRR934320 6 0.604 0.5686 0.088 0.084 0.088 0.024 0.028 0.688
#> SRR934321 6 0.604 0.5686 0.088 0.084 0.088 0.024 0.028 0.688
#> SRR934322 6 0.604 0.5686 0.088 0.084 0.088 0.024 0.028 0.688
#> SRR934323 6 0.604 0.5686 0.088 0.084 0.088 0.024 0.028 0.688
#> SRR934324 6 0.604 0.5686 0.088 0.084 0.088 0.024 0.028 0.688
#> SRR934325 6 0.604 0.5686 0.088 0.084 0.088 0.024 0.028 0.688
#> SRR934326 6 0.604 0.5686 0.088 0.084 0.088 0.024 0.028 0.688
#> SRR934327 6 0.604 0.5686 0.088 0.084 0.088 0.024 0.028 0.688
#> SRR934328 1 0.345 0.8791 0.812 0.000 0.004 0.016 0.020 0.148
#> SRR934329 1 0.345 0.8791 0.812 0.000 0.004 0.016 0.020 0.148
#> SRR934330 1 0.345 0.8791 0.812 0.000 0.004 0.016 0.020 0.148
#> SRR934331 1 0.345 0.8791 0.812 0.000 0.004 0.016 0.020 0.148
#> SRR934332 1 0.345 0.8791 0.812 0.000 0.004 0.016 0.020 0.148
#> SRR934333 1 0.345 0.8791 0.812 0.000 0.004 0.016 0.020 0.148
#> SRR934334 1 0.345 0.8791 0.812 0.000 0.004 0.016 0.020 0.148
#> SRR934335 1 0.345 0.8791 0.812 0.000 0.004 0.016 0.020 0.148
#> SRR934344 1 0.394 0.8689 0.776 0.004 0.024 0.008 0.012 0.176
#> SRR934345 1 0.394 0.8689 0.776 0.004 0.024 0.008 0.012 0.176
#> SRR934346 1 0.394 0.8689 0.776 0.004 0.024 0.008 0.012 0.176
#> SRR934347 1 0.394 0.8689 0.776 0.004 0.024 0.008 0.012 0.176
#> SRR934348 1 0.394 0.8689 0.776 0.004 0.024 0.008 0.012 0.176
#> SRR934349 1 0.394 0.8689 0.776 0.004 0.024 0.008 0.012 0.176
#> SRR934350 1 0.394 0.8689 0.776 0.004 0.024 0.008 0.012 0.176
#> SRR934351 1 0.394 0.8689 0.776 0.004 0.024 0.008 0.012 0.176
#> SRR934336 6 0.305 0.6502 0.016 0.008 0.068 0.028 0.008 0.872
#> SRR934337 6 0.305 0.6502 0.016 0.008 0.068 0.028 0.008 0.872
#> SRR934338 6 0.305 0.6502 0.016 0.008 0.068 0.028 0.008 0.872
#> SRR934339 6 0.305 0.6502 0.016 0.008 0.068 0.028 0.008 0.872
#> SRR934340 6 0.305 0.6502 0.016 0.008 0.068 0.028 0.008 0.872
#> SRR934341 6 0.305 0.6502 0.016 0.008 0.068 0.028 0.008 0.872
#> SRR934342 6 0.305 0.6502 0.016 0.008 0.068 0.028 0.008 0.872
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 14550 rows and 135 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 0.789 0.852 0.941 0.486 0.511 0.511
#> 3 3 0.776 0.896 0.920 0.263 0.831 0.683
#> 4 4 0.672 0.870 0.875 0.193 0.875 0.676
#> 5 5 0.743 0.814 0.766 0.069 0.972 0.891
#> 6 6 0.778 0.614 0.722 0.042 0.966 0.851
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR934216 1 0.983 0.289 0.576 0.424
#> SRR934217 1 0.983 0.289 0.576 0.424
#> SRR934218 1 0.983 0.289 0.576 0.424
#> SRR934219 1 0.983 0.289 0.576 0.424
#> SRR934220 1 0.983 0.289 0.576 0.424
#> SRR934221 1 0.983 0.289 0.576 0.424
#> SRR934222 1 0.983 0.289 0.576 0.424
#> SRR934223 1 0.983 0.289 0.576 0.424
#> SRR934224 1 0.000 0.951 1.000 0.000
#> SRR934225 1 0.000 0.951 1.000 0.000
#> SRR934226 1 0.000 0.951 1.000 0.000
#> SRR934227 1 0.000 0.951 1.000 0.000
#> SRR934228 1 0.000 0.951 1.000 0.000
#> SRR934229 1 0.000 0.951 1.000 0.000
#> SRR934230 1 0.000 0.951 1.000 0.000
#> SRR934231 1 0.000 0.951 1.000 0.000
#> SRR934232 2 0.000 0.910 0.000 1.000
#> SRR934233 2 0.000 0.910 0.000 1.000
#> SRR934234 2 0.000 0.910 0.000 1.000
#> SRR934235 2 0.000 0.910 0.000 1.000
#> SRR934236 2 0.000 0.910 0.000 1.000
#> SRR934237 2 0.000 0.910 0.000 1.000
#> SRR934238 2 0.000 0.910 0.000 1.000
#> SRR934239 2 0.000 0.910 0.000 1.000
#> SRR934240 2 0.000 0.910 0.000 1.000
#> SRR934241 2 0.000 0.910 0.000 1.000
#> SRR934242 2 0.000 0.910 0.000 1.000
#> SRR934243 2 0.000 0.910 0.000 1.000
#> SRR934244 2 0.000 0.910 0.000 1.000
#> SRR934245 2 0.000 0.910 0.000 1.000
#> SRR934246 2 0.000 0.910 0.000 1.000
#> SRR934247 2 0.000 0.910 0.000 1.000
#> SRR934248 2 0.000 0.910 0.000 1.000
#> SRR934249 2 0.000 0.910 0.000 1.000
#> SRR934250 2 0.000 0.910 0.000 1.000
#> SRR934251 2 0.000 0.910 0.000 1.000
#> SRR934252 2 0.000 0.910 0.000 1.000
#> SRR934253 2 0.000 0.910 0.000 1.000
#> SRR934254 2 0.000 0.910 0.000 1.000
#> SRR934255 2 0.000 0.910 0.000 1.000
#> SRR934256 2 0.997 0.253 0.468 0.532
#> SRR934257 2 0.997 0.253 0.468 0.532
#> SRR934258 2 0.997 0.253 0.468 0.532
#> SRR934259 2 0.997 0.253 0.468 0.532
#> SRR934260 2 0.997 0.253 0.468 0.532
#> SRR934261 2 0.997 0.253 0.468 0.532
#> SRR934262 2 0.997 0.253 0.468 0.532
#> SRR934263 2 0.997 0.253 0.468 0.532
#> SRR934264 2 0.000 0.910 0.000 1.000
#> SRR934265 2 0.000 0.910 0.000 1.000
#> SRR934266 2 0.000 0.910 0.000 1.000
#> SRR934267 2 0.000 0.910 0.000 1.000
#> SRR934268 2 0.000 0.910 0.000 1.000
#> SRR934269 2 0.000 0.910 0.000 1.000
#> SRR934270 2 0.000 0.910 0.000 1.000
#> SRR934271 2 0.000 0.910 0.000 1.000
#> SRR934272 1 0.000 0.951 1.000 0.000
#> SRR934273 1 0.000 0.951 1.000 0.000
#> SRR934274 1 0.000 0.951 1.000 0.000
#> SRR934275 1 0.000 0.951 1.000 0.000
#> SRR934276 1 0.000 0.951 1.000 0.000
#> SRR934277 1 0.000 0.951 1.000 0.000
#> SRR934278 1 0.000 0.951 1.000 0.000
#> SRR934279 1 0.000 0.951 1.000 0.000
#> SRR934280 1 0.000 0.951 1.000 0.000
#> SRR934281 1 0.000 0.951 1.000 0.000
#> SRR934282 1 0.000 0.951 1.000 0.000
#> SRR934283 1 0.000 0.951 1.000 0.000
#> SRR934284 1 0.000 0.951 1.000 0.000
#> SRR934285 1 0.000 0.951 1.000 0.000
#> SRR934286 1 0.000 0.951 1.000 0.000
#> SRR934287 1 0.000 0.951 1.000 0.000
#> SRR934288 1 0.000 0.951 1.000 0.000
#> SRR934289 1 0.000 0.951 1.000 0.000
#> SRR934290 1 0.000 0.951 1.000 0.000
#> SRR934291 1 0.000 0.951 1.000 0.000
#> SRR934292 1 0.000 0.951 1.000 0.000
#> SRR934293 1 0.000 0.951 1.000 0.000
#> SRR934294 1 0.000 0.951 1.000 0.000
#> SRR934295 1 0.000 0.951 1.000 0.000
#> SRR934296 2 0.456 0.849 0.096 0.904
#> SRR934297 2 0.456 0.849 0.096 0.904
#> SRR934298 2 0.456 0.849 0.096 0.904
#> SRR934299 2 0.456 0.849 0.096 0.904
#> SRR934300 2 0.456 0.849 0.096 0.904
#> SRR934301 2 0.456 0.849 0.096 0.904
#> SRR934302 2 0.456 0.849 0.096 0.904
#> SRR934303 2 0.456 0.849 0.096 0.904
#> SRR934304 2 0.000 0.910 0.000 1.000
#> SRR934305 2 0.000 0.910 0.000 1.000
#> SRR934306 2 0.000 0.910 0.000 1.000
#> SRR934307 2 0.000 0.910 0.000 1.000
#> SRR934308 2 0.000 0.910 0.000 1.000
#> SRR934309 2 0.000 0.910 0.000 1.000
#> SRR934310 2 0.000 0.910 0.000 1.000
#> SRR934311 2 0.000 0.910 0.000 1.000
#> SRR934312 1 0.000 0.951 1.000 0.000
#> SRR934313 1 0.000 0.951 1.000 0.000
#> SRR934314 1 0.000 0.951 1.000 0.000
#> SRR934315 1 0.000 0.951 1.000 0.000
#> SRR934316 1 0.000 0.951 1.000 0.000
#> SRR934317 1 0.000 0.951 1.000 0.000
#> SRR934318 1 0.000 0.951 1.000 0.000
#> SRR934319 1 0.000 0.951 1.000 0.000
#> SRR934320 1 0.000 0.951 1.000 0.000
#> SRR934321 1 0.000 0.951 1.000 0.000
#> SRR934322 1 0.000 0.951 1.000 0.000
#> SRR934323 1 0.000 0.951 1.000 0.000
#> SRR934324 1 0.000 0.951 1.000 0.000
#> SRR934325 1 0.000 0.951 1.000 0.000
#> SRR934326 1 0.000 0.951 1.000 0.000
#> SRR934327 1 0.000 0.951 1.000 0.000
#> SRR934328 1 0.000 0.951 1.000 0.000
#> SRR934329 1 0.000 0.951 1.000 0.000
#> SRR934330 1 0.000 0.951 1.000 0.000
#> SRR934331 1 0.000 0.951 1.000 0.000
#> SRR934332 1 0.000 0.951 1.000 0.000
#> SRR934333 1 0.000 0.951 1.000 0.000
#> SRR934334 1 0.000 0.951 1.000 0.000
#> SRR934335 1 0.000 0.951 1.000 0.000
#> SRR934344 1 0.000 0.951 1.000 0.000
#> SRR934345 1 0.000 0.951 1.000 0.000
#> SRR934346 1 0.000 0.951 1.000 0.000
#> SRR934347 1 0.000 0.951 1.000 0.000
#> SRR934348 1 0.000 0.951 1.000 0.000
#> SRR934349 1 0.000 0.951 1.000 0.000
#> SRR934350 1 0.000 0.951 1.000 0.000
#> SRR934351 1 0.000 0.951 1.000 0.000
#> SRR934336 1 0.000 0.951 1.000 0.000
#> SRR934337 1 0.000 0.951 1.000 0.000
#> SRR934338 1 0.000 0.951 1.000 0.000
#> SRR934339 1 0.000 0.951 1.000 0.000
#> SRR934340 1 0.000 0.951 1.000 0.000
#> SRR934341 1 0.000 0.951 1.000 0.000
#> SRR934342 1 0.000 0.951 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 3 0.5741 0.856 0.036 0.188 0.776
#> SRR934217 3 0.5741 0.856 0.036 0.188 0.776
#> SRR934218 3 0.5741 0.856 0.036 0.188 0.776
#> SRR934219 3 0.5741 0.856 0.036 0.188 0.776
#> SRR934220 3 0.5741 0.856 0.036 0.188 0.776
#> SRR934221 3 0.5741 0.856 0.036 0.188 0.776
#> SRR934222 3 0.5741 0.856 0.036 0.188 0.776
#> SRR934223 3 0.5741 0.856 0.036 0.188 0.776
#> SRR934224 1 0.0983 0.960 0.980 0.004 0.016
#> SRR934225 1 0.0983 0.960 0.980 0.004 0.016
#> SRR934226 1 0.0983 0.960 0.980 0.004 0.016
#> SRR934227 1 0.0983 0.960 0.980 0.004 0.016
#> SRR934228 1 0.0983 0.960 0.980 0.004 0.016
#> SRR934229 1 0.0983 0.960 0.980 0.004 0.016
#> SRR934230 1 0.0983 0.960 0.980 0.004 0.016
#> SRR934231 1 0.0983 0.960 0.980 0.004 0.016
#> SRR934232 2 0.5363 0.797 0.000 0.724 0.276
#> SRR934233 2 0.5363 0.797 0.000 0.724 0.276
#> SRR934234 2 0.5363 0.797 0.000 0.724 0.276
#> SRR934235 2 0.5363 0.797 0.000 0.724 0.276
#> SRR934236 2 0.5363 0.797 0.000 0.724 0.276
#> SRR934237 2 0.5363 0.797 0.000 0.724 0.276
#> SRR934238 2 0.5363 0.797 0.000 0.724 0.276
#> SRR934239 2 0.5363 0.797 0.000 0.724 0.276
#> SRR934240 2 0.4702 0.829 0.000 0.788 0.212
#> SRR934241 2 0.4702 0.829 0.000 0.788 0.212
#> SRR934242 2 0.4702 0.829 0.000 0.788 0.212
#> SRR934243 2 0.4702 0.829 0.000 0.788 0.212
#> SRR934244 2 0.4702 0.829 0.000 0.788 0.212
#> SRR934245 2 0.4702 0.829 0.000 0.788 0.212
#> SRR934246 2 0.4702 0.829 0.000 0.788 0.212
#> SRR934247 2 0.4702 0.829 0.000 0.788 0.212
#> SRR934248 3 0.0237 0.879 0.000 0.004 0.996
#> SRR934249 3 0.0237 0.879 0.000 0.004 0.996
#> SRR934250 3 0.0237 0.879 0.000 0.004 0.996
#> SRR934251 3 0.0237 0.879 0.000 0.004 0.996
#> SRR934252 3 0.0237 0.879 0.000 0.004 0.996
#> SRR934253 3 0.0237 0.879 0.000 0.004 0.996
#> SRR934254 3 0.0237 0.879 0.000 0.004 0.996
#> SRR934255 3 0.0237 0.879 0.000 0.004 0.996
#> SRR934256 2 0.4682 0.753 0.192 0.804 0.004
#> SRR934257 2 0.4682 0.753 0.192 0.804 0.004
#> SRR934258 2 0.4682 0.753 0.192 0.804 0.004
#> SRR934259 2 0.4682 0.753 0.192 0.804 0.004
#> SRR934260 2 0.4682 0.753 0.192 0.804 0.004
#> SRR934261 2 0.4682 0.753 0.192 0.804 0.004
#> SRR934262 2 0.4682 0.753 0.192 0.804 0.004
#> SRR934263 2 0.4682 0.753 0.192 0.804 0.004
#> SRR934264 3 0.0000 0.879 0.000 0.000 1.000
#> SRR934265 3 0.0000 0.879 0.000 0.000 1.000
#> SRR934266 3 0.0000 0.879 0.000 0.000 1.000
#> SRR934267 3 0.0000 0.879 0.000 0.000 1.000
#> SRR934268 3 0.0000 0.879 0.000 0.000 1.000
#> SRR934269 3 0.0000 0.879 0.000 0.000 1.000
#> SRR934270 3 0.0000 0.879 0.000 0.000 1.000
#> SRR934271 3 0.0000 0.879 0.000 0.000 1.000
#> SRR934272 1 0.0661 0.963 0.988 0.004 0.008
#> SRR934273 1 0.0661 0.963 0.988 0.004 0.008
#> SRR934274 1 0.0661 0.963 0.988 0.004 0.008
#> SRR934275 1 0.0661 0.963 0.988 0.004 0.008
#> SRR934276 1 0.0661 0.963 0.988 0.004 0.008
#> SRR934277 1 0.0661 0.963 0.988 0.004 0.008
#> SRR934278 1 0.0661 0.963 0.988 0.004 0.008
#> SRR934279 1 0.0661 0.963 0.988 0.004 0.008
#> SRR934280 1 0.0237 0.964 0.996 0.000 0.004
#> SRR934281 1 0.0237 0.964 0.996 0.000 0.004
#> SRR934282 1 0.0237 0.964 0.996 0.000 0.004
#> SRR934283 1 0.0237 0.964 0.996 0.000 0.004
#> SRR934284 1 0.0237 0.964 0.996 0.000 0.004
#> SRR934285 1 0.0237 0.964 0.996 0.000 0.004
#> SRR934286 1 0.0237 0.964 0.996 0.000 0.004
#> SRR934287 1 0.0237 0.964 0.996 0.000 0.004
#> SRR934288 1 0.2796 0.931 0.908 0.092 0.000
#> SRR934289 1 0.2796 0.931 0.908 0.092 0.000
#> SRR934290 1 0.2796 0.931 0.908 0.092 0.000
#> SRR934291 1 0.2796 0.931 0.908 0.092 0.000
#> SRR934292 1 0.2796 0.931 0.908 0.092 0.000
#> SRR934293 1 0.2796 0.931 0.908 0.092 0.000
#> SRR934294 1 0.2796 0.931 0.908 0.092 0.000
#> SRR934295 1 0.2796 0.931 0.908 0.092 0.000
#> SRR934296 2 0.0592 0.789 0.000 0.988 0.012
#> SRR934297 2 0.0592 0.789 0.000 0.988 0.012
#> SRR934298 2 0.0592 0.789 0.000 0.988 0.012
#> SRR934299 2 0.0592 0.789 0.000 0.988 0.012
#> SRR934300 2 0.0592 0.789 0.000 0.988 0.012
#> SRR934301 2 0.0592 0.789 0.000 0.988 0.012
#> SRR934302 2 0.0592 0.789 0.000 0.988 0.012
#> SRR934303 2 0.0592 0.789 0.000 0.988 0.012
#> SRR934304 3 0.4346 0.874 0.000 0.184 0.816
#> SRR934305 3 0.4346 0.874 0.000 0.184 0.816
#> SRR934306 3 0.4346 0.874 0.000 0.184 0.816
#> SRR934307 3 0.4346 0.874 0.000 0.184 0.816
#> SRR934308 3 0.4346 0.874 0.000 0.184 0.816
#> SRR934309 3 0.4346 0.874 0.000 0.184 0.816
#> SRR934310 3 0.4346 0.874 0.000 0.184 0.816
#> SRR934311 3 0.4346 0.874 0.000 0.184 0.816
#> SRR934312 1 0.0237 0.964 0.996 0.000 0.004
#> SRR934313 1 0.0237 0.964 0.996 0.000 0.004
#> SRR934314 1 0.0237 0.964 0.996 0.000 0.004
#> SRR934315 1 0.0237 0.964 0.996 0.000 0.004
#> SRR934316 1 0.0237 0.964 0.996 0.000 0.004
#> SRR934317 1 0.0237 0.964 0.996 0.000 0.004
#> SRR934318 1 0.0237 0.964 0.996 0.000 0.004
#> SRR934319 1 0.0237 0.964 0.996 0.000 0.004
#> SRR934320 1 0.1529 0.955 0.960 0.040 0.000
#> SRR934321 1 0.1529 0.955 0.960 0.040 0.000
#> SRR934322 1 0.1529 0.955 0.960 0.040 0.000
#> SRR934323 1 0.1529 0.955 0.960 0.040 0.000
#> SRR934324 1 0.1529 0.955 0.960 0.040 0.000
#> SRR934325 1 0.1529 0.955 0.960 0.040 0.000
#> SRR934326 1 0.1529 0.955 0.960 0.040 0.000
#> SRR934327 1 0.1529 0.955 0.960 0.040 0.000
#> SRR934328 1 0.2711 0.934 0.912 0.088 0.000
#> SRR934329 1 0.2711 0.934 0.912 0.088 0.000
#> SRR934330 1 0.2711 0.934 0.912 0.088 0.000
#> SRR934331 1 0.2711 0.934 0.912 0.088 0.000
#> SRR934332 1 0.2711 0.934 0.912 0.088 0.000
#> SRR934333 1 0.2711 0.934 0.912 0.088 0.000
#> SRR934334 1 0.2711 0.934 0.912 0.088 0.000
#> SRR934335 1 0.2711 0.934 0.912 0.088 0.000
#> SRR934344 1 0.1964 0.951 0.944 0.056 0.000
#> SRR934345 1 0.1964 0.951 0.944 0.056 0.000
#> SRR934346 1 0.1964 0.951 0.944 0.056 0.000
#> SRR934347 1 0.1964 0.951 0.944 0.056 0.000
#> SRR934348 1 0.1964 0.951 0.944 0.056 0.000
#> SRR934349 1 0.1964 0.951 0.944 0.056 0.000
#> SRR934350 1 0.1964 0.951 0.944 0.056 0.000
#> SRR934351 1 0.1964 0.951 0.944 0.056 0.000
#> SRR934336 1 0.0661 0.963 0.988 0.004 0.008
#> SRR934337 1 0.0661 0.963 0.988 0.004 0.008
#> SRR934338 1 0.0661 0.963 0.988 0.004 0.008
#> SRR934339 1 0.0661 0.963 0.988 0.004 0.008
#> SRR934340 1 0.0661 0.963 0.988 0.004 0.008
#> SRR934341 1 0.0661 0.963 0.988 0.004 0.008
#> SRR934342 1 0.0661 0.963 0.988 0.004 0.008
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 3 0.5900 0.830 0.008 0.168 0.716 0.108
#> SRR934217 3 0.5900 0.830 0.008 0.168 0.716 0.108
#> SRR934218 3 0.5900 0.830 0.008 0.168 0.716 0.108
#> SRR934219 3 0.5900 0.830 0.008 0.168 0.716 0.108
#> SRR934220 3 0.5900 0.830 0.008 0.168 0.716 0.108
#> SRR934221 3 0.5900 0.830 0.008 0.168 0.716 0.108
#> SRR934222 3 0.5900 0.830 0.008 0.168 0.716 0.108
#> SRR934223 3 0.5900 0.830 0.008 0.168 0.716 0.108
#> SRR934224 1 0.1302 0.930 0.956 0.000 0.000 0.044
#> SRR934225 1 0.1302 0.930 0.956 0.000 0.000 0.044
#> SRR934226 1 0.1302 0.930 0.956 0.000 0.000 0.044
#> SRR934227 1 0.1302 0.930 0.956 0.000 0.000 0.044
#> SRR934228 1 0.1302 0.930 0.956 0.000 0.000 0.044
#> SRR934229 1 0.1302 0.930 0.956 0.000 0.000 0.044
#> SRR934230 1 0.1302 0.930 0.956 0.000 0.000 0.044
#> SRR934231 1 0.1302 0.930 0.956 0.000 0.000 0.044
#> SRR934232 2 0.4283 0.782 0.000 0.740 0.256 0.004
#> SRR934233 2 0.4283 0.782 0.000 0.740 0.256 0.004
#> SRR934234 2 0.4283 0.782 0.000 0.740 0.256 0.004
#> SRR934235 2 0.4283 0.782 0.000 0.740 0.256 0.004
#> SRR934236 2 0.4283 0.782 0.000 0.740 0.256 0.004
#> SRR934237 2 0.4283 0.782 0.000 0.740 0.256 0.004
#> SRR934238 2 0.4283 0.782 0.000 0.740 0.256 0.004
#> SRR934239 2 0.4283 0.782 0.000 0.740 0.256 0.004
#> SRR934240 2 0.3356 0.815 0.000 0.824 0.176 0.000
#> SRR934241 2 0.3356 0.815 0.000 0.824 0.176 0.000
#> SRR934242 2 0.3356 0.815 0.000 0.824 0.176 0.000
#> SRR934243 2 0.3356 0.815 0.000 0.824 0.176 0.000
#> SRR934244 2 0.3356 0.815 0.000 0.824 0.176 0.000
#> SRR934245 2 0.3356 0.815 0.000 0.824 0.176 0.000
#> SRR934246 2 0.3356 0.815 0.000 0.824 0.176 0.000
#> SRR934247 2 0.3356 0.815 0.000 0.824 0.176 0.000
#> SRR934248 3 0.0524 0.839 0.000 0.008 0.988 0.004
#> SRR934249 3 0.0524 0.839 0.000 0.008 0.988 0.004
#> SRR934250 3 0.0524 0.839 0.000 0.008 0.988 0.004
#> SRR934251 3 0.0524 0.839 0.000 0.008 0.988 0.004
#> SRR934252 3 0.0524 0.839 0.000 0.008 0.988 0.004
#> SRR934253 3 0.0524 0.839 0.000 0.008 0.988 0.004
#> SRR934254 3 0.0524 0.839 0.000 0.008 0.988 0.004
#> SRR934255 3 0.0524 0.839 0.000 0.008 0.988 0.004
#> SRR934256 2 0.4688 0.764 0.080 0.792 0.000 0.128
#> SRR934257 2 0.4688 0.764 0.080 0.792 0.000 0.128
#> SRR934258 2 0.4688 0.764 0.080 0.792 0.000 0.128
#> SRR934259 2 0.4688 0.764 0.080 0.792 0.000 0.128
#> SRR934260 2 0.4688 0.764 0.080 0.792 0.000 0.128
#> SRR934261 2 0.4688 0.764 0.080 0.792 0.000 0.128
#> SRR934262 2 0.4688 0.764 0.080 0.792 0.000 0.128
#> SRR934263 2 0.4688 0.764 0.080 0.792 0.000 0.128
#> SRR934264 3 0.0336 0.841 0.000 0.008 0.992 0.000
#> SRR934265 3 0.0336 0.841 0.000 0.008 0.992 0.000
#> SRR934266 3 0.0336 0.841 0.000 0.008 0.992 0.000
#> SRR934267 3 0.0336 0.841 0.000 0.008 0.992 0.000
#> SRR934268 3 0.0336 0.841 0.000 0.008 0.992 0.000
#> SRR934269 3 0.0336 0.841 0.000 0.008 0.992 0.000
#> SRR934270 3 0.0336 0.841 0.000 0.008 0.992 0.000
#> SRR934271 3 0.0336 0.841 0.000 0.008 0.992 0.000
#> SRR934272 1 0.1557 0.933 0.944 0.000 0.000 0.056
#> SRR934273 1 0.1557 0.933 0.944 0.000 0.000 0.056
#> SRR934274 1 0.1557 0.933 0.944 0.000 0.000 0.056
#> SRR934275 1 0.1557 0.933 0.944 0.000 0.000 0.056
#> SRR934276 1 0.1557 0.933 0.944 0.000 0.000 0.056
#> SRR934277 1 0.1557 0.933 0.944 0.000 0.000 0.056
#> SRR934278 1 0.1557 0.933 0.944 0.000 0.000 0.056
#> SRR934279 1 0.1557 0.933 0.944 0.000 0.000 0.056
#> SRR934280 1 0.0817 0.932 0.976 0.000 0.000 0.024
#> SRR934281 1 0.0817 0.932 0.976 0.000 0.000 0.024
#> SRR934282 1 0.0817 0.932 0.976 0.000 0.000 0.024
#> SRR934283 1 0.0817 0.932 0.976 0.000 0.000 0.024
#> SRR934284 1 0.0817 0.932 0.976 0.000 0.000 0.024
#> SRR934285 1 0.0817 0.932 0.976 0.000 0.000 0.024
#> SRR934286 1 0.0817 0.932 0.976 0.000 0.000 0.024
#> SRR934287 1 0.0817 0.932 0.976 0.000 0.000 0.024
#> SRR934288 4 0.2675 0.984 0.100 0.008 0.000 0.892
#> SRR934289 4 0.2675 0.984 0.100 0.008 0.000 0.892
#> SRR934290 4 0.2675 0.984 0.100 0.008 0.000 0.892
#> SRR934291 4 0.2675 0.984 0.100 0.008 0.000 0.892
#> SRR934292 4 0.2675 0.984 0.100 0.008 0.000 0.892
#> SRR934293 4 0.2675 0.984 0.100 0.008 0.000 0.892
#> SRR934294 4 0.2675 0.984 0.100 0.008 0.000 0.892
#> SRR934295 4 0.2675 0.984 0.100 0.008 0.000 0.892
#> SRR934296 2 0.3688 0.691 0.000 0.792 0.000 0.208
#> SRR934297 2 0.3688 0.691 0.000 0.792 0.000 0.208
#> SRR934298 2 0.3688 0.691 0.000 0.792 0.000 0.208
#> SRR934299 2 0.3688 0.691 0.000 0.792 0.000 0.208
#> SRR934300 2 0.3688 0.691 0.000 0.792 0.000 0.208
#> SRR934301 2 0.3688 0.691 0.000 0.792 0.000 0.208
#> SRR934302 2 0.3688 0.691 0.000 0.792 0.000 0.208
#> SRR934303 2 0.3688 0.691 0.000 0.792 0.000 0.208
#> SRR934304 3 0.5143 0.841 0.000 0.172 0.752 0.076
#> SRR934305 3 0.5143 0.841 0.000 0.172 0.752 0.076
#> SRR934306 3 0.5143 0.841 0.000 0.172 0.752 0.076
#> SRR934307 3 0.5143 0.841 0.000 0.172 0.752 0.076
#> SRR934308 3 0.5143 0.841 0.000 0.172 0.752 0.076
#> SRR934309 3 0.5143 0.841 0.000 0.172 0.752 0.076
#> SRR934310 3 0.5143 0.841 0.000 0.172 0.752 0.076
#> SRR934311 3 0.5143 0.841 0.000 0.172 0.752 0.076
#> SRR934312 1 0.2011 0.916 0.920 0.000 0.000 0.080
#> SRR934313 1 0.2011 0.916 0.920 0.000 0.000 0.080
#> SRR934314 1 0.2011 0.916 0.920 0.000 0.000 0.080
#> SRR934315 1 0.2011 0.916 0.920 0.000 0.000 0.080
#> SRR934316 1 0.2011 0.916 0.920 0.000 0.000 0.080
#> SRR934317 1 0.2011 0.916 0.920 0.000 0.000 0.080
#> SRR934318 1 0.2011 0.916 0.920 0.000 0.000 0.080
#> SRR934319 1 0.2011 0.916 0.920 0.000 0.000 0.080
#> SRR934320 1 0.3545 0.810 0.828 0.008 0.000 0.164
#> SRR934321 1 0.3545 0.810 0.828 0.008 0.000 0.164
#> SRR934322 1 0.3545 0.810 0.828 0.008 0.000 0.164
#> SRR934323 1 0.3545 0.810 0.828 0.008 0.000 0.164
#> SRR934324 1 0.3545 0.810 0.828 0.008 0.000 0.164
#> SRR934325 1 0.3545 0.810 0.828 0.008 0.000 0.164
#> SRR934326 1 0.3545 0.810 0.828 0.008 0.000 0.164
#> SRR934327 1 0.3545 0.810 0.828 0.008 0.000 0.164
#> SRR934328 4 0.2611 0.984 0.096 0.008 0.000 0.896
#> SRR934329 4 0.2611 0.984 0.096 0.008 0.000 0.896
#> SRR934330 4 0.2611 0.984 0.096 0.008 0.000 0.896
#> SRR934331 4 0.2611 0.984 0.096 0.008 0.000 0.896
#> SRR934332 4 0.2611 0.984 0.096 0.008 0.000 0.896
#> SRR934333 4 0.2611 0.984 0.096 0.008 0.000 0.896
#> SRR934334 4 0.2611 0.984 0.096 0.008 0.000 0.896
#> SRR934335 4 0.2611 0.984 0.096 0.008 0.000 0.896
#> SRR934344 4 0.2760 0.975 0.128 0.000 0.000 0.872
#> SRR934345 4 0.2760 0.975 0.128 0.000 0.000 0.872
#> SRR934346 4 0.2760 0.975 0.128 0.000 0.000 0.872
#> SRR934347 4 0.2760 0.975 0.128 0.000 0.000 0.872
#> SRR934348 4 0.2760 0.975 0.128 0.000 0.000 0.872
#> SRR934349 4 0.2760 0.975 0.128 0.000 0.000 0.872
#> SRR934350 4 0.2760 0.975 0.128 0.000 0.000 0.872
#> SRR934351 4 0.2760 0.975 0.128 0.000 0.000 0.872
#> SRR934336 1 0.0921 0.933 0.972 0.000 0.000 0.028
#> SRR934337 1 0.0921 0.933 0.972 0.000 0.000 0.028
#> SRR934338 1 0.0921 0.933 0.972 0.000 0.000 0.028
#> SRR934339 1 0.0921 0.933 0.972 0.000 0.000 0.028
#> SRR934340 1 0.0921 0.933 0.972 0.000 0.000 0.028
#> SRR934341 1 0.0921 0.933 0.972 0.000 0.000 0.028
#> SRR934342 1 0.0921 0.933 0.972 0.000 0.000 0.028
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 5 0.1095 0.887 0.008 0.000 0.012 0.012 0.968
#> SRR934217 5 0.1095 0.887 0.008 0.000 0.012 0.012 0.968
#> SRR934218 5 0.1095 0.887 0.008 0.000 0.012 0.012 0.968
#> SRR934219 5 0.1095 0.887 0.008 0.000 0.012 0.012 0.968
#> SRR934220 5 0.1095 0.887 0.008 0.000 0.012 0.012 0.968
#> SRR934221 5 0.1095 0.887 0.008 0.000 0.012 0.012 0.968
#> SRR934222 5 0.1095 0.887 0.008 0.000 0.012 0.012 0.968
#> SRR934223 5 0.1095 0.887 0.008 0.000 0.012 0.012 0.968
#> SRR934224 1 0.3128 0.806 0.880 0.008 0.016 0.032 0.064
#> SRR934225 1 0.3128 0.806 0.880 0.008 0.016 0.032 0.064
#> SRR934226 1 0.3128 0.806 0.880 0.008 0.016 0.032 0.064
#> SRR934227 1 0.3128 0.806 0.880 0.008 0.016 0.032 0.064
#> SRR934228 1 0.3128 0.806 0.880 0.008 0.016 0.032 0.064
#> SRR934229 1 0.3128 0.806 0.880 0.008 0.016 0.032 0.064
#> SRR934230 1 0.3128 0.806 0.880 0.008 0.016 0.032 0.064
#> SRR934231 1 0.3128 0.806 0.880 0.008 0.016 0.032 0.064
#> SRR934232 2 0.4425 0.507 0.000 0.544 0.000 0.452 0.004
#> SRR934233 2 0.4425 0.507 0.000 0.544 0.000 0.452 0.004
#> SRR934234 2 0.4425 0.507 0.000 0.544 0.000 0.452 0.004
#> SRR934235 2 0.4425 0.507 0.000 0.544 0.000 0.452 0.004
#> SRR934236 2 0.4425 0.507 0.000 0.544 0.000 0.452 0.004
#> SRR934237 2 0.4425 0.507 0.000 0.544 0.000 0.452 0.004
#> SRR934238 2 0.4425 0.507 0.000 0.544 0.000 0.452 0.004
#> SRR934239 2 0.4425 0.507 0.000 0.544 0.000 0.452 0.004
#> SRR934240 2 0.2929 0.721 0.000 0.820 0.000 0.180 0.000
#> SRR934241 2 0.2929 0.721 0.000 0.820 0.000 0.180 0.000
#> SRR934242 2 0.2929 0.721 0.000 0.820 0.000 0.180 0.000
#> SRR934243 2 0.2929 0.721 0.000 0.820 0.000 0.180 0.000
#> SRR934244 2 0.2929 0.721 0.000 0.820 0.000 0.180 0.000
#> SRR934245 2 0.2929 0.721 0.000 0.820 0.000 0.180 0.000
#> SRR934246 2 0.2929 0.721 0.000 0.820 0.000 0.180 0.000
#> SRR934247 2 0.2929 0.721 0.000 0.820 0.000 0.180 0.000
#> SRR934248 4 0.4415 0.964 0.000 0.008 0.000 0.604 0.388
#> SRR934249 4 0.4415 0.964 0.000 0.008 0.000 0.604 0.388
#> SRR934250 4 0.4415 0.964 0.000 0.008 0.000 0.604 0.388
#> SRR934251 4 0.4415 0.964 0.000 0.008 0.000 0.604 0.388
#> SRR934252 4 0.4415 0.964 0.000 0.008 0.000 0.604 0.388
#> SRR934253 4 0.4415 0.964 0.000 0.008 0.000 0.604 0.388
#> SRR934254 4 0.4415 0.964 0.000 0.008 0.000 0.604 0.388
#> SRR934255 4 0.4415 0.964 0.000 0.008 0.000 0.604 0.388
#> SRR934256 2 0.2949 0.714 0.028 0.880 0.076 0.016 0.000
#> SRR934257 2 0.2949 0.714 0.028 0.880 0.076 0.016 0.000
#> SRR934258 2 0.2949 0.714 0.028 0.880 0.076 0.016 0.000
#> SRR934259 2 0.2949 0.714 0.028 0.880 0.076 0.016 0.000
#> SRR934260 2 0.2949 0.714 0.028 0.880 0.076 0.016 0.000
#> SRR934261 2 0.2949 0.714 0.028 0.880 0.076 0.016 0.000
#> SRR934262 2 0.2949 0.714 0.028 0.880 0.076 0.016 0.000
#> SRR934263 2 0.2949 0.714 0.028 0.880 0.076 0.016 0.000
#> SRR934264 4 0.4597 0.963 0.000 0.012 0.000 0.564 0.424
#> SRR934265 4 0.4597 0.963 0.000 0.012 0.000 0.564 0.424
#> SRR934266 4 0.4597 0.963 0.000 0.012 0.000 0.564 0.424
#> SRR934267 4 0.4597 0.963 0.000 0.012 0.000 0.564 0.424
#> SRR934268 4 0.4597 0.963 0.000 0.012 0.000 0.564 0.424
#> SRR934269 4 0.4597 0.963 0.000 0.012 0.000 0.564 0.424
#> SRR934270 4 0.4597 0.963 0.000 0.012 0.000 0.564 0.424
#> SRR934271 4 0.4597 0.963 0.000 0.012 0.000 0.564 0.424
#> SRR934272 1 0.4544 0.810 0.764 0.008 0.028 0.180 0.020
#> SRR934273 1 0.4544 0.810 0.764 0.008 0.028 0.180 0.020
#> SRR934274 1 0.4544 0.810 0.764 0.008 0.028 0.180 0.020
#> SRR934275 1 0.4544 0.810 0.764 0.008 0.028 0.180 0.020
#> SRR934276 1 0.4544 0.810 0.764 0.008 0.028 0.180 0.020
#> SRR934277 1 0.4544 0.810 0.764 0.008 0.028 0.180 0.020
#> SRR934278 1 0.4544 0.810 0.764 0.008 0.028 0.180 0.020
#> SRR934279 1 0.4544 0.810 0.764 0.008 0.028 0.180 0.020
#> SRR934280 1 0.3606 0.823 0.816 0.024 0.008 0.152 0.000
#> SRR934281 1 0.3606 0.823 0.816 0.024 0.008 0.152 0.000
#> SRR934282 1 0.3606 0.823 0.816 0.024 0.008 0.152 0.000
#> SRR934283 1 0.3606 0.823 0.816 0.024 0.008 0.152 0.000
#> SRR934284 1 0.3606 0.823 0.816 0.024 0.008 0.152 0.000
#> SRR934285 1 0.3606 0.823 0.816 0.024 0.008 0.152 0.000
#> SRR934286 1 0.3606 0.823 0.816 0.024 0.008 0.152 0.000
#> SRR934287 1 0.3606 0.823 0.816 0.024 0.008 0.152 0.000
#> SRR934288 3 0.1716 0.972 0.024 0.016 0.944 0.016 0.000
#> SRR934289 3 0.1716 0.972 0.024 0.016 0.944 0.016 0.000
#> SRR934290 3 0.1716 0.972 0.024 0.016 0.944 0.016 0.000
#> SRR934291 3 0.1716 0.972 0.024 0.016 0.944 0.016 0.000
#> SRR934292 3 0.1716 0.972 0.024 0.016 0.944 0.016 0.000
#> SRR934293 3 0.1716 0.972 0.024 0.016 0.944 0.016 0.000
#> SRR934294 3 0.1716 0.972 0.024 0.016 0.944 0.016 0.000
#> SRR934295 3 0.1716 0.972 0.024 0.016 0.944 0.016 0.000
#> SRR934296 2 0.6323 0.554 0.000 0.624 0.092 0.060 0.224
#> SRR934297 2 0.6323 0.554 0.000 0.624 0.092 0.060 0.224
#> SRR934298 2 0.6323 0.554 0.000 0.624 0.092 0.060 0.224
#> SRR934299 2 0.6323 0.554 0.000 0.624 0.092 0.060 0.224
#> SRR934300 2 0.6323 0.554 0.000 0.624 0.092 0.060 0.224
#> SRR934301 2 0.6323 0.554 0.000 0.624 0.092 0.060 0.224
#> SRR934302 2 0.6323 0.554 0.000 0.624 0.092 0.060 0.224
#> SRR934303 2 0.6323 0.554 0.000 0.624 0.092 0.060 0.224
#> SRR934304 5 0.2616 0.880 0.000 0.036 0.000 0.076 0.888
#> SRR934305 5 0.2616 0.880 0.000 0.036 0.000 0.076 0.888
#> SRR934306 5 0.2616 0.880 0.000 0.036 0.000 0.076 0.888
#> SRR934307 5 0.2616 0.880 0.000 0.036 0.000 0.076 0.888
#> SRR934308 5 0.2616 0.880 0.000 0.036 0.000 0.076 0.888
#> SRR934309 5 0.2616 0.880 0.000 0.036 0.000 0.076 0.888
#> SRR934310 5 0.2616 0.880 0.000 0.036 0.000 0.076 0.888
#> SRR934311 5 0.2616 0.880 0.000 0.036 0.000 0.076 0.888
#> SRR934312 1 0.5868 0.782 0.664 0.020 0.084 0.220 0.012
#> SRR934313 1 0.5868 0.782 0.664 0.020 0.084 0.220 0.012
#> SRR934314 1 0.5868 0.782 0.664 0.020 0.084 0.220 0.012
#> SRR934315 1 0.5868 0.782 0.664 0.020 0.084 0.220 0.012
#> SRR934316 1 0.5868 0.782 0.664 0.020 0.084 0.220 0.012
#> SRR934317 1 0.5868 0.782 0.664 0.020 0.084 0.220 0.012
#> SRR934318 1 0.5868 0.782 0.664 0.020 0.084 0.220 0.012
#> SRR934319 1 0.5868 0.782 0.664 0.020 0.084 0.220 0.012
#> SRR934320 1 0.6233 0.679 0.688 0.124 0.096 0.076 0.016
#> SRR934321 1 0.6233 0.679 0.688 0.124 0.096 0.076 0.016
#> SRR934322 1 0.6233 0.679 0.688 0.124 0.096 0.076 0.016
#> SRR934323 1 0.6233 0.679 0.688 0.124 0.096 0.076 0.016
#> SRR934324 1 0.6233 0.679 0.688 0.124 0.096 0.076 0.016
#> SRR934325 1 0.6233 0.679 0.688 0.124 0.096 0.076 0.016
#> SRR934326 1 0.6233 0.679 0.688 0.124 0.096 0.076 0.016
#> SRR934327 1 0.6233 0.679 0.688 0.124 0.096 0.076 0.016
#> SRR934328 3 0.0404 0.977 0.012 0.000 0.988 0.000 0.000
#> SRR934329 3 0.0404 0.977 0.012 0.000 0.988 0.000 0.000
#> SRR934330 3 0.0404 0.977 0.012 0.000 0.988 0.000 0.000
#> SRR934331 3 0.0404 0.977 0.012 0.000 0.988 0.000 0.000
#> SRR934332 3 0.0404 0.977 0.012 0.000 0.988 0.000 0.000
#> SRR934333 3 0.0404 0.977 0.012 0.000 0.988 0.000 0.000
#> SRR934334 3 0.0404 0.977 0.012 0.000 0.988 0.000 0.000
#> SRR934335 3 0.0404 0.977 0.012 0.000 0.988 0.000 0.000
#> SRR934344 3 0.1329 0.973 0.032 0.004 0.956 0.008 0.000
#> SRR934345 3 0.1329 0.973 0.032 0.004 0.956 0.008 0.000
#> SRR934346 3 0.1329 0.973 0.032 0.004 0.956 0.008 0.000
#> SRR934347 3 0.1329 0.973 0.032 0.004 0.956 0.008 0.000
#> SRR934348 3 0.1329 0.973 0.032 0.004 0.956 0.008 0.000
#> SRR934349 3 0.1329 0.973 0.032 0.004 0.956 0.008 0.000
#> SRR934350 3 0.1329 0.973 0.032 0.004 0.956 0.008 0.000
#> SRR934351 3 0.1329 0.973 0.032 0.004 0.956 0.008 0.000
#> SRR934336 1 0.1679 0.818 0.948 0.020 0.012 0.004 0.016
#> SRR934337 1 0.1679 0.818 0.948 0.020 0.012 0.004 0.016
#> SRR934338 1 0.1679 0.818 0.948 0.020 0.012 0.004 0.016
#> SRR934339 1 0.1679 0.818 0.948 0.020 0.012 0.004 0.016
#> SRR934340 1 0.1679 0.818 0.948 0.020 0.012 0.004 0.016
#> SRR934341 1 0.1679 0.818 0.948 0.020 0.012 0.004 0.016
#> SRR934342 1 0.1679 0.818 0.948 0.020 0.012 0.004 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 5 0.229 0.902 0.004 0.000 0.072 0.008 0.900 0.016
#> SRR934217 5 0.229 0.902 0.004 0.000 0.072 0.008 0.900 0.016
#> SRR934218 5 0.229 0.902 0.004 0.000 0.072 0.008 0.900 0.016
#> SRR934219 5 0.229 0.902 0.004 0.000 0.072 0.008 0.900 0.016
#> SRR934220 5 0.229 0.902 0.004 0.000 0.072 0.008 0.900 0.016
#> SRR934221 5 0.229 0.902 0.004 0.000 0.072 0.008 0.900 0.016
#> SRR934222 5 0.229 0.902 0.004 0.000 0.072 0.008 0.900 0.016
#> SRR934223 5 0.229 0.902 0.004 0.000 0.072 0.008 0.900 0.016
#> SRR934224 6 0.178 0.438 0.008 0.000 0.024 0.008 0.024 0.936
#> SRR934225 6 0.178 0.438 0.008 0.000 0.024 0.008 0.024 0.936
#> SRR934226 6 0.178 0.438 0.008 0.000 0.024 0.008 0.024 0.936
#> SRR934227 6 0.178 0.438 0.008 0.000 0.024 0.008 0.024 0.936
#> SRR934228 6 0.178 0.438 0.008 0.000 0.024 0.008 0.024 0.936
#> SRR934229 6 0.178 0.438 0.008 0.000 0.024 0.008 0.024 0.936
#> SRR934230 6 0.178 0.438 0.008 0.000 0.024 0.008 0.024 0.936
#> SRR934231 6 0.178 0.438 0.008 0.000 0.024 0.008 0.024 0.936
#> SRR934232 2 0.385 0.446 0.000 0.612 0.004 0.384 0.000 0.000
#> SRR934233 2 0.385 0.446 0.000 0.612 0.004 0.384 0.000 0.000
#> SRR934234 2 0.385 0.446 0.000 0.612 0.004 0.384 0.000 0.000
#> SRR934235 2 0.385 0.446 0.000 0.612 0.004 0.384 0.000 0.000
#> SRR934236 2 0.385 0.446 0.000 0.612 0.004 0.384 0.000 0.000
#> SRR934237 2 0.385 0.446 0.000 0.612 0.004 0.384 0.000 0.000
#> SRR934238 2 0.385 0.446 0.000 0.612 0.004 0.384 0.000 0.000
#> SRR934239 2 0.385 0.446 0.000 0.612 0.004 0.384 0.000 0.000
#> SRR934240 2 0.200 0.656 0.000 0.884 0.000 0.116 0.000 0.000
#> SRR934241 2 0.200 0.656 0.000 0.884 0.000 0.116 0.000 0.000
#> SRR934242 2 0.200 0.656 0.000 0.884 0.000 0.116 0.000 0.000
#> SRR934243 2 0.200 0.656 0.000 0.884 0.000 0.116 0.000 0.000
#> SRR934244 2 0.200 0.656 0.000 0.884 0.000 0.116 0.000 0.000
#> SRR934245 2 0.200 0.656 0.000 0.884 0.000 0.116 0.000 0.000
#> SRR934246 2 0.200 0.656 0.000 0.884 0.000 0.116 0.000 0.000
#> SRR934247 2 0.200 0.656 0.000 0.884 0.000 0.116 0.000 0.000
#> SRR934248 4 0.292 0.969 0.000 0.000 0.008 0.808 0.184 0.000
#> SRR934249 4 0.292 0.969 0.000 0.000 0.008 0.808 0.184 0.000
#> SRR934250 4 0.292 0.969 0.000 0.000 0.008 0.808 0.184 0.000
#> SRR934251 4 0.292 0.969 0.000 0.000 0.008 0.808 0.184 0.000
#> SRR934252 4 0.292 0.969 0.000 0.000 0.008 0.808 0.184 0.000
#> SRR934253 4 0.292 0.969 0.000 0.000 0.008 0.808 0.184 0.000
#> SRR934254 4 0.292 0.969 0.000 0.000 0.008 0.808 0.184 0.000
#> SRR934255 4 0.292 0.969 0.000 0.000 0.008 0.808 0.184 0.000
#> SRR934256 2 0.388 0.647 0.028 0.728 0.240 0.004 0.000 0.000
#> SRR934257 2 0.388 0.647 0.028 0.728 0.240 0.004 0.000 0.000
#> SRR934258 2 0.388 0.647 0.028 0.728 0.240 0.004 0.000 0.000
#> SRR934259 2 0.388 0.647 0.028 0.728 0.240 0.004 0.000 0.000
#> SRR934260 2 0.388 0.647 0.028 0.728 0.240 0.004 0.000 0.000
#> SRR934261 2 0.388 0.647 0.028 0.728 0.240 0.004 0.000 0.000
#> SRR934262 2 0.388 0.647 0.028 0.728 0.240 0.004 0.000 0.000
#> SRR934263 2 0.388 0.647 0.028 0.728 0.240 0.004 0.000 0.000
#> SRR934264 4 0.326 0.968 0.000 0.000 0.012 0.772 0.216 0.000
#> SRR934265 4 0.326 0.968 0.000 0.000 0.012 0.772 0.216 0.000
#> SRR934266 4 0.326 0.968 0.000 0.000 0.012 0.772 0.216 0.000
#> SRR934267 4 0.326 0.968 0.000 0.000 0.012 0.772 0.216 0.000
#> SRR934268 4 0.326 0.968 0.000 0.000 0.012 0.772 0.216 0.000
#> SRR934269 4 0.326 0.968 0.000 0.000 0.012 0.772 0.216 0.000
#> SRR934270 4 0.326 0.968 0.000 0.000 0.012 0.772 0.216 0.000
#> SRR934271 4 0.326 0.968 0.000 0.000 0.012 0.772 0.216 0.000
#> SRR934272 6 0.429 -0.204 0.012 0.000 0.292 0.012 0.008 0.676
#> SRR934273 6 0.429 -0.204 0.012 0.000 0.292 0.012 0.008 0.676
#> SRR934274 6 0.429 -0.204 0.012 0.000 0.292 0.012 0.008 0.676
#> SRR934275 6 0.429 -0.204 0.012 0.000 0.292 0.012 0.008 0.676
#> SRR934276 6 0.429 -0.204 0.012 0.000 0.292 0.012 0.008 0.676
#> SRR934277 6 0.429 -0.204 0.012 0.000 0.292 0.012 0.008 0.676
#> SRR934278 6 0.429 -0.204 0.012 0.000 0.292 0.012 0.008 0.676
#> SRR934279 6 0.429 -0.204 0.012 0.000 0.292 0.012 0.008 0.676
#> SRR934280 6 0.420 -0.370 0.000 0.004 0.448 0.008 0.000 0.540
#> SRR934281 6 0.420 -0.370 0.000 0.004 0.448 0.008 0.000 0.540
#> SRR934282 6 0.420 -0.370 0.000 0.004 0.448 0.008 0.000 0.540
#> SRR934283 6 0.420 -0.370 0.000 0.004 0.448 0.008 0.000 0.540
#> SRR934284 6 0.420 -0.370 0.000 0.004 0.448 0.008 0.000 0.540
#> SRR934285 6 0.420 -0.370 0.000 0.004 0.448 0.008 0.000 0.540
#> SRR934286 6 0.420 -0.370 0.000 0.004 0.448 0.008 0.000 0.540
#> SRR934287 6 0.420 -0.370 0.000 0.004 0.448 0.008 0.000 0.540
#> SRR934288 1 0.224 0.944 0.916 0.016 0.032 0.028 0.004 0.004
#> SRR934289 1 0.224 0.944 0.916 0.016 0.032 0.028 0.004 0.004
#> SRR934290 1 0.224 0.944 0.916 0.016 0.032 0.028 0.004 0.004
#> SRR934291 1 0.224 0.944 0.916 0.016 0.032 0.028 0.004 0.004
#> SRR934292 1 0.224 0.944 0.916 0.016 0.032 0.028 0.004 0.004
#> SRR934293 1 0.224 0.944 0.916 0.016 0.032 0.028 0.004 0.004
#> SRR934294 1 0.224 0.944 0.916 0.016 0.032 0.028 0.004 0.004
#> SRR934295 1 0.224 0.944 0.916 0.016 0.032 0.028 0.004 0.004
#> SRR934296 2 0.776 0.441 0.056 0.412 0.252 0.072 0.208 0.000
#> SRR934297 2 0.776 0.441 0.056 0.412 0.252 0.072 0.208 0.000
#> SRR934298 2 0.776 0.441 0.056 0.412 0.252 0.072 0.208 0.000
#> SRR934299 2 0.776 0.441 0.056 0.412 0.252 0.072 0.208 0.000
#> SRR934300 2 0.776 0.441 0.056 0.412 0.252 0.072 0.208 0.000
#> SRR934301 2 0.776 0.441 0.056 0.412 0.252 0.072 0.208 0.000
#> SRR934302 2 0.776 0.441 0.056 0.412 0.252 0.072 0.208 0.000
#> SRR934303 2 0.776 0.441 0.056 0.412 0.252 0.072 0.208 0.000
#> SRR934304 5 0.175 0.899 0.000 0.020 0.000 0.056 0.924 0.000
#> SRR934305 5 0.175 0.899 0.000 0.020 0.000 0.056 0.924 0.000
#> SRR934306 5 0.175 0.899 0.000 0.020 0.000 0.056 0.924 0.000
#> SRR934307 5 0.175 0.899 0.000 0.020 0.000 0.056 0.924 0.000
#> SRR934308 5 0.175 0.899 0.000 0.020 0.000 0.056 0.924 0.000
#> SRR934309 5 0.175 0.899 0.000 0.020 0.000 0.056 0.924 0.000
#> SRR934310 5 0.175 0.899 0.000 0.020 0.000 0.056 0.924 0.000
#> SRR934311 5 0.175 0.899 0.000 0.020 0.000 0.056 0.924 0.000
#> SRR934312 3 0.502 1.000 0.052 0.000 0.504 0.008 0.000 0.436
#> SRR934313 3 0.502 1.000 0.052 0.000 0.504 0.008 0.000 0.436
#> SRR934314 3 0.502 1.000 0.052 0.000 0.504 0.008 0.000 0.436
#> SRR934315 3 0.502 1.000 0.052 0.000 0.504 0.008 0.000 0.436
#> SRR934316 3 0.502 1.000 0.052 0.000 0.504 0.008 0.000 0.436
#> SRR934317 3 0.502 1.000 0.052 0.000 0.504 0.008 0.000 0.436
#> SRR934318 3 0.502 1.000 0.052 0.000 0.504 0.008 0.000 0.436
#> SRR934319 3 0.502 1.000 0.052 0.000 0.504 0.008 0.000 0.436
#> SRR934320 6 0.654 0.322 0.060 0.068 0.276 0.044 0.000 0.552
#> SRR934321 6 0.654 0.322 0.060 0.068 0.276 0.044 0.000 0.552
#> SRR934322 6 0.654 0.322 0.060 0.068 0.276 0.044 0.000 0.552
#> SRR934323 6 0.654 0.322 0.060 0.068 0.276 0.044 0.000 0.552
#> SRR934324 6 0.654 0.322 0.060 0.068 0.276 0.044 0.000 0.552
#> SRR934325 6 0.654 0.322 0.060 0.068 0.276 0.044 0.000 0.552
#> SRR934326 6 0.654 0.322 0.060 0.068 0.276 0.044 0.000 0.552
#> SRR934327 6 0.654 0.322 0.060 0.068 0.276 0.044 0.000 0.552
#> SRR934328 1 0.000 0.964 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934329 1 0.000 0.964 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934330 1 0.000 0.964 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934331 1 0.000 0.964 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934332 1 0.000 0.964 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934333 1 0.000 0.964 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934334 1 0.000 0.964 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934335 1 0.000 0.964 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934344 1 0.106 0.959 0.964 0.004 0.016 0.000 0.000 0.016
#> SRR934345 1 0.106 0.959 0.964 0.004 0.016 0.000 0.000 0.016
#> SRR934346 1 0.106 0.959 0.964 0.004 0.016 0.000 0.000 0.016
#> SRR934347 1 0.106 0.959 0.964 0.004 0.016 0.000 0.000 0.016
#> SRR934348 1 0.106 0.959 0.964 0.004 0.016 0.000 0.000 0.016
#> SRR934349 1 0.106 0.959 0.964 0.004 0.016 0.000 0.000 0.016
#> SRR934350 1 0.106 0.959 0.964 0.004 0.016 0.000 0.000 0.016
#> SRR934351 1 0.106 0.959 0.964 0.004 0.016 0.000 0.000 0.016
#> SRR934336 6 0.252 0.424 0.000 0.008 0.104 0.004 0.008 0.876
#> SRR934337 6 0.252 0.424 0.000 0.008 0.104 0.004 0.008 0.876
#> SRR934338 6 0.252 0.424 0.000 0.008 0.104 0.004 0.008 0.876
#> SRR934339 6 0.252 0.424 0.000 0.008 0.104 0.004 0.008 0.876
#> SRR934340 6 0.252 0.424 0.000 0.008 0.104 0.004 0.008 0.876
#> SRR934341 6 0.252 0.424 0.000 0.008 0.104 0.004 0.008 0.876
#> SRR934342 6 0.252 0.424 0.000 0.008 0.104 0.004 0.008 0.876
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 14550 rows and 135 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.636 0.768 0.897 0.1968 0.888 0.888
#> 3 3 0.718 0.830 0.935 1.1164 0.727 0.692
#> 4 4 0.678 0.825 0.916 0.1823 0.916 0.863
#> 5 5 0.811 0.820 0.905 0.1824 0.818 0.670
#> 6 6 0.979 0.962 0.971 0.0719 0.972 0.929
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
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
#> SRR934216 1 0.9686 0.859 0.604 0.396
#> SRR934217 1 0.9686 0.859 0.604 0.396
#> SRR934218 1 0.9686 0.859 0.604 0.396
#> SRR934219 1 0.9686 0.859 0.604 0.396
#> SRR934220 1 0.9686 0.859 0.604 0.396
#> SRR934221 1 0.9686 0.859 0.604 0.396
#> SRR934222 1 0.9686 0.859 0.604 0.396
#> SRR934223 1 0.9686 0.859 0.604 0.396
#> SRR934224 1 0.9686 0.859 0.604 0.396
#> SRR934225 1 0.9686 0.859 0.604 0.396
#> SRR934226 1 0.9686 0.859 0.604 0.396
#> SRR934227 1 0.9686 0.859 0.604 0.396
#> SRR934228 1 0.9686 0.859 0.604 0.396
#> SRR934229 1 0.9686 0.859 0.604 0.396
#> SRR934230 1 0.9686 0.859 0.604 0.396
#> SRR934231 1 0.9686 0.859 0.604 0.396
#> SRR934232 1 0.2423 0.302 0.960 0.040
#> SRR934233 1 0.2423 0.302 0.960 0.040
#> SRR934234 1 0.2423 0.302 0.960 0.040
#> SRR934235 1 0.2423 0.302 0.960 0.040
#> SRR934236 1 0.2423 0.302 0.960 0.040
#> SRR934237 1 0.2423 0.302 0.960 0.040
#> SRR934238 1 0.2423 0.302 0.960 0.040
#> SRR934239 1 0.2423 0.302 0.960 0.040
#> SRR934240 1 0.1843 0.319 0.972 0.028
#> SRR934241 1 0.1843 0.319 0.972 0.028
#> SRR934242 1 0.1843 0.319 0.972 0.028
#> SRR934243 1 0.1843 0.319 0.972 0.028
#> SRR934244 1 0.1843 0.319 0.972 0.028
#> SRR934245 1 0.1843 0.319 0.972 0.028
#> SRR934246 1 0.1843 0.319 0.972 0.028
#> SRR934247 1 0.1843 0.319 0.972 0.028
#> SRR934248 1 0.9881 0.822 0.564 0.436
#> SRR934249 1 0.9881 0.822 0.564 0.436
#> SRR934250 1 0.9881 0.822 0.564 0.436
#> SRR934251 1 0.9881 0.822 0.564 0.436
#> SRR934252 1 0.9881 0.822 0.564 0.436
#> SRR934253 1 0.9881 0.822 0.564 0.436
#> SRR934254 1 0.9881 0.822 0.564 0.436
#> SRR934255 1 0.9881 0.822 0.564 0.436
#> SRR934256 1 0.0376 0.345 0.996 0.004
#> SRR934257 1 0.0376 0.345 0.996 0.004
#> SRR934258 1 0.0376 0.345 0.996 0.004
#> SRR934259 1 0.0376 0.345 0.996 0.004
#> SRR934260 1 0.0376 0.345 0.996 0.004
#> SRR934261 1 0.0376 0.345 0.996 0.004
#> SRR934262 1 0.0376 0.345 0.996 0.004
#> SRR934263 1 0.0376 0.345 0.996 0.004
#> SRR934264 1 0.9866 0.825 0.568 0.432
#> SRR934265 1 0.9866 0.825 0.568 0.432
#> SRR934266 1 0.9866 0.825 0.568 0.432
#> SRR934267 1 0.9866 0.825 0.568 0.432
#> SRR934268 1 0.9866 0.825 0.568 0.432
#> SRR934269 1 0.9866 0.825 0.568 0.432
#> SRR934270 1 0.9866 0.825 0.568 0.432
#> SRR934271 1 0.9866 0.825 0.568 0.432
#> SRR934272 1 0.9686 0.859 0.604 0.396
#> SRR934273 1 0.9686 0.859 0.604 0.396
#> SRR934274 1 0.9686 0.859 0.604 0.396
#> SRR934275 1 0.9686 0.859 0.604 0.396
#> SRR934276 1 0.9686 0.859 0.604 0.396
#> SRR934277 1 0.9686 0.859 0.604 0.396
#> SRR934278 1 0.9686 0.859 0.604 0.396
#> SRR934279 1 0.9686 0.859 0.604 0.396
#> SRR934280 1 0.9686 0.859 0.604 0.396
#> SRR934281 1 0.9686 0.859 0.604 0.396
#> SRR934282 1 0.9686 0.859 0.604 0.396
#> SRR934283 1 0.9686 0.859 0.604 0.396
#> SRR934284 1 0.9686 0.859 0.604 0.396
#> SRR934285 1 0.9686 0.859 0.604 0.396
#> SRR934286 1 0.9686 0.859 0.604 0.396
#> SRR934287 1 0.9686 0.859 0.604 0.396
#> SRR934288 1 0.9686 0.859 0.604 0.396
#> SRR934289 1 0.9686 0.859 0.604 0.396
#> SRR934290 1 0.9686 0.859 0.604 0.396
#> SRR934291 1 0.9686 0.859 0.604 0.396
#> SRR934292 1 0.9686 0.859 0.604 0.396
#> SRR934293 1 0.9686 0.859 0.604 0.396
#> SRR934294 1 0.9686 0.859 0.604 0.396
#> SRR934295 1 0.9686 0.859 0.604 0.396
#> SRR934296 1 0.9686 0.859 0.604 0.396
#> SRR934297 1 0.9686 0.859 0.604 0.396
#> SRR934298 1 0.9686 0.859 0.604 0.396
#> SRR934299 1 0.9686 0.859 0.604 0.396
#> SRR934300 1 0.9686 0.859 0.604 0.396
#> SRR934301 1 0.9686 0.859 0.604 0.396
#> SRR934302 1 0.9686 0.859 0.604 0.396
#> SRR934303 1 0.9686 0.859 0.604 0.396
#> SRR934304 2 0.0000 1.000 0.000 1.000
#> SRR934305 2 0.0000 1.000 0.000 1.000
#> SRR934306 2 0.0000 1.000 0.000 1.000
#> SRR934307 2 0.0000 1.000 0.000 1.000
#> SRR934308 2 0.0000 1.000 0.000 1.000
#> SRR934309 2 0.0000 1.000 0.000 1.000
#> SRR934310 2 0.0000 1.000 0.000 1.000
#> SRR934311 2 0.0000 1.000 0.000 1.000
#> SRR934312 1 0.9686 0.859 0.604 0.396
#> SRR934313 1 0.9686 0.859 0.604 0.396
#> SRR934314 1 0.9686 0.859 0.604 0.396
#> SRR934315 1 0.9686 0.859 0.604 0.396
#> SRR934316 1 0.9686 0.859 0.604 0.396
#> SRR934317 1 0.9686 0.859 0.604 0.396
#> SRR934318 1 0.9686 0.859 0.604 0.396
#> SRR934319 1 0.9686 0.859 0.604 0.396
#> SRR934320 1 0.9686 0.859 0.604 0.396
#> SRR934321 1 0.9686 0.859 0.604 0.396
#> SRR934322 1 0.9686 0.859 0.604 0.396
#> SRR934323 1 0.9686 0.859 0.604 0.396
#> SRR934324 1 0.9686 0.859 0.604 0.396
#> SRR934325 1 0.9686 0.859 0.604 0.396
#> SRR934326 1 0.9686 0.859 0.604 0.396
#> SRR934327 1 0.9686 0.859 0.604 0.396
#> SRR934328 1 0.9686 0.859 0.604 0.396
#> SRR934329 1 0.9686 0.859 0.604 0.396
#> SRR934330 1 0.9686 0.859 0.604 0.396
#> SRR934331 1 0.9686 0.859 0.604 0.396
#> SRR934332 1 0.9686 0.859 0.604 0.396
#> SRR934333 1 0.9686 0.859 0.604 0.396
#> SRR934334 1 0.9686 0.859 0.604 0.396
#> SRR934335 1 0.9686 0.859 0.604 0.396
#> SRR934344 1 0.9686 0.859 0.604 0.396
#> SRR934345 1 0.9686 0.859 0.604 0.396
#> SRR934346 1 0.9686 0.859 0.604 0.396
#> SRR934347 1 0.9686 0.859 0.604 0.396
#> SRR934348 1 0.9686 0.859 0.604 0.396
#> SRR934349 1 0.9686 0.859 0.604 0.396
#> SRR934350 1 0.9686 0.859 0.604 0.396
#> SRR934351 1 0.9686 0.859 0.604 0.396
#> SRR934336 1 0.9686 0.859 0.604 0.396
#> SRR934337 1 0.9686 0.859 0.604 0.396
#> SRR934338 1 0.9686 0.859 0.604 0.396
#> SRR934339 1 0.9686 0.859 0.604 0.396
#> SRR934340 1 0.9686 0.859 0.604 0.396
#> SRR934341 1 0.9686 0.859 0.604 0.396
#> SRR934342 1 0.9686 0.859 0.604 0.396
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 1 0.000 0.926 1.000 0.000 0
#> SRR934217 1 0.000 0.926 1.000 0.000 0
#> SRR934218 1 0.000 0.926 1.000 0.000 0
#> SRR934219 1 0.000 0.926 1.000 0.000 0
#> SRR934220 1 0.000 0.926 1.000 0.000 0
#> SRR934221 1 0.000 0.926 1.000 0.000 0
#> SRR934222 1 0.000 0.926 1.000 0.000 0
#> SRR934223 1 0.000 0.926 1.000 0.000 0
#> SRR934224 1 0.000 0.926 1.000 0.000 0
#> SRR934225 1 0.000 0.926 1.000 0.000 0
#> SRR934226 1 0.000 0.926 1.000 0.000 0
#> SRR934227 1 0.000 0.926 1.000 0.000 0
#> SRR934228 1 0.000 0.926 1.000 0.000 0
#> SRR934229 1 0.000 0.926 1.000 0.000 0
#> SRR934230 1 0.000 0.926 1.000 0.000 0
#> SRR934231 1 0.000 0.926 1.000 0.000 0
#> SRR934232 2 0.000 0.789 0.000 1.000 0
#> SRR934233 2 0.000 0.789 0.000 1.000 0
#> SRR934234 2 0.000 0.789 0.000 1.000 0
#> SRR934235 2 0.000 0.789 0.000 1.000 0
#> SRR934236 2 0.000 0.789 0.000 1.000 0
#> SRR934237 2 0.000 0.789 0.000 1.000 0
#> SRR934238 2 0.000 0.789 0.000 1.000 0
#> SRR934239 2 0.000 0.789 0.000 1.000 0
#> SRR934240 2 0.000 0.789 0.000 1.000 0
#> SRR934241 2 0.000 0.789 0.000 1.000 0
#> SRR934242 2 0.000 0.789 0.000 1.000 0
#> SRR934243 2 0.000 0.789 0.000 1.000 0
#> SRR934244 2 0.000 0.789 0.000 1.000 0
#> SRR934245 2 0.000 0.789 0.000 1.000 0
#> SRR934246 2 0.000 0.789 0.000 1.000 0
#> SRR934247 2 0.000 0.789 0.000 1.000 0
#> SRR934248 1 0.613 0.402 0.600 0.400 0
#> SRR934249 1 0.613 0.402 0.600 0.400 0
#> SRR934250 1 0.613 0.402 0.600 0.400 0
#> SRR934251 1 0.613 0.402 0.600 0.400 0
#> SRR934252 1 0.613 0.402 0.600 0.400 0
#> SRR934253 1 0.613 0.402 0.600 0.400 0
#> SRR934254 1 0.613 0.402 0.600 0.400 0
#> SRR934255 1 0.613 0.402 0.600 0.400 0
#> SRR934256 2 0.497 0.615 0.236 0.764 0
#> SRR934257 2 0.540 0.584 0.280 0.720 0
#> SRR934258 2 0.576 0.523 0.328 0.672 0
#> SRR934259 2 0.556 0.562 0.300 0.700 0
#> SRR934260 2 0.543 0.580 0.284 0.716 0
#> SRR934261 2 0.568 0.541 0.316 0.684 0
#> SRR934262 2 0.540 0.584 0.280 0.720 0
#> SRR934263 2 0.601 0.455 0.372 0.628 0
#> SRR934264 1 0.613 0.402 0.600 0.400 0
#> SRR934265 1 0.613 0.402 0.600 0.400 0
#> SRR934266 1 0.613 0.402 0.600 0.400 0
#> SRR934267 1 0.613 0.402 0.600 0.400 0
#> SRR934268 1 0.613 0.402 0.600 0.400 0
#> SRR934269 1 0.613 0.402 0.600 0.400 0
#> SRR934270 1 0.613 0.402 0.600 0.400 0
#> SRR934271 1 0.613 0.402 0.600 0.400 0
#> SRR934272 1 0.000 0.926 1.000 0.000 0
#> SRR934273 1 0.000 0.926 1.000 0.000 0
#> SRR934274 1 0.000 0.926 1.000 0.000 0
#> SRR934275 1 0.000 0.926 1.000 0.000 0
#> SRR934276 1 0.000 0.926 1.000 0.000 0
#> SRR934277 1 0.000 0.926 1.000 0.000 0
#> SRR934278 1 0.000 0.926 1.000 0.000 0
#> SRR934279 1 0.000 0.926 1.000 0.000 0
#> SRR934280 1 0.000 0.926 1.000 0.000 0
#> SRR934281 1 0.000 0.926 1.000 0.000 0
#> SRR934282 1 0.000 0.926 1.000 0.000 0
#> SRR934283 1 0.000 0.926 1.000 0.000 0
#> SRR934284 1 0.000 0.926 1.000 0.000 0
#> SRR934285 1 0.000 0.926 1.000 0.000 0
#> SRR934286 1 0.000 0.926 1.000 0.000 0
#> SRR934287 1 0.000 0.926 1.000 0.000 0
#> SRR934288 1 0.000 0.926 1.000 0.000 0
#> SRR934289 1 0.000 0.926 1.000 0.000 0
#> SRR934290 1 0.000 0.926 1.000 0.000 0
#> SRR934291 1 0.000 0.926 1.000 0.000 0
#> SRR934292 1 0.000 0.926 1.000 0.000 0
#> SRR934293 1 0.000 0.926 1.000 0.000 0
#> SRR934294 1 0.000 0.926 1.000 0.000 0
#> SRR934295 1 0.000 0.926 1.000 0.000 0
#> SRR934296 1 0.000 0.926 1.000 0.000 0
#> SRR934297 1 0.000 0.926 1.000 0.000 0
#> SRR934298 1 0.000 0.926 1.000 0.000 0
#> SRR934299 1 0.000 0.926 1.000 0.000 0
#> SRR934300 1 0.000 0.926 1.000 0.000 0
#> SRR934301 1 0.000 0.926 1.000 0.000 0
#> SRR934302 1 0.000 0.926 1.000 0.000 0
#> SRR934303 1 0.000 0.926 1.000 0.000 0
#> SRR934304 3 0.000 1.000 0.000 0.000 1
#> SRR934305 3 0.000 1.000 0.000 0.000 1
#> SRR934306 3 0.000 1.000 0.000 0.000 1
#> SRR934307 3 0.000 1.000 0.000 0.000 1
#> SRR934308 3 0.000 1.000 0.000 0.000 1
#> SRR934309 3 0.000 1.000 0.000 0.000 1
#> SRR934310 3 0.000 1.000 0.000 0.000 1
#> SRR934311 3 0.000 1.000 0.000 0.000 1
#> SRR934312 1 0.000 0.926 1.000 0.000 0
#> SRR934313 1 0.000 0.926 1.000 0.000 0
#> SRR934314 1 0.000 0.926 1.000 0.000 0
#> SRR934315 1 0.000 0.926 1.000 0.000 0
#> SRR934316 1 0.000 0.926 1.000 0.000 0
#> SRR934317 1 0.000 0.926 1.000 0.000 0
#> SRR934318 1 0.000 0.926 1.000 0.000 0
#> SRR934319 1 0.000 0.926 1.000 0.000 0
#> SRR934320 1 0.000 0.926 1.000 0.000 0
#> SRR934321 1 0.000 0.926 1.000 0.000 0
#> SRR934322 1 0.000 0.926 1.000 0.000 0
#> SRR934323 1 0.000 0.926 1.000 0.000 0
#> SRR934324 1 0.000 0.926 1.000 0.000 0
#> SRR934325 1 0.000 0.926 1.000 0.000 0
#> SRR934326 1 0.000 0.926 1.000 0.000 0
#> SRR934327 1 0.000 0.926 1.000 0.000 0
#> SRR934328 1 0.000 0.926 1.000 0.000 0
#> SRR934329 1 0.000 0.926 1.000 0.000 0
#> SRR934330 1 0.000 0.926 1.000 0.000 0
#> SRR934331 1 0.000 0.926 1.000 0.000 0
#> SRR934332 1 0.000 0.926 1.000 0.000 0
#> SRR934333 1 0.000 0.926 1.000 0.000 0
#> SRR934334 1 0.000 0.926 1.000 0.000 0
#> SRR934335 1 0.000 0.926 1.000 0.000 0
#> SRR934344 1 0.000 0.926 1.000 0.000 0
#> SRR934345 1 0.000 0.926 1.000 0.000 0
#> SRR934346 1 0.000 0.926 1.000 0.000 0
#> SRR934347 1 0.000 0.926 1.000 0.000 0
#> SRR934348 1 0.000 0.926 1.000 0.000 0
#> SRR934349 1 0.000 0.926 1.000 0.000 0
#> SRR934350 1 0.000 0.926 1.000 0.000 0
#> SRR934351 1 0.000 0.926 1.000 0.000 0
#> SRR934336 1 0.000 0.926 1.000 0.000 0
#> SRR934337 1 0.000 0.926 1.000 0.000 0
#> SRR934338 1 0.000 0.926 1.000 0.000 0
#> SRR934339 1 0.000 0.926 1.000 0.000 0
#> SRR934340 1 0.000 0.926 1.000 0.000 0
#> SRR934341 1 0.000 0.926 1.000 0.000 0
#> SRR934342 1 0.000 0.926 1.000 0.000 0
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 4 0.365 1.000 0.204 0.000 0 0.796
#> SRR934217 4 0.365 1.000 0.204 0.000 0 0.796
#> SRR934218 4 0.365 1.000 0.204 0.000 0 0.796
#> SRR934219 4 0.365 1.000 0.204 0.000 0 0.796
#> SRR934220 4 0.365 1.000 0.204 0.000 0 0.796
#> SRR934221 4 0.365 1.000 0.204 0.000 0 0.796
#> SRR934222 4 0.365 1.000 0.204 0.000 0 0.796
#> SRR934223 4 0.365 1.000 0.204 0.000 0 0.796
#> SRR934224 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934225 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934226 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934227 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934228 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934229 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934230 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934231 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934232 2 0.000 0.800 0.000 1.000 0 0.000
#> SRR934233 2 0.000 0.800 0.000 1.000 0 0.000
#> SRR934234 2 0.000 0.800 0.000 1.000 0 0.000
#> SRR934235 2 0.000 0.800 0.000 1.000 0 0.000
#> SRR934236 2 0.000 0.800 0.000 1.000 0 0.000
#> SRR934237 2 0.000 0.800 0.000 1.000 0 0.000
#> SRR934238 2 0.000 0.800 0.000 1.000 0 0.000
#> SRR934239 2 0.000 0.800 0.000 1.000 0 0.000
#> SRR934240 2 0.000 0.800 0.000 1.000 0 0.000
#> SRR934241 2 0.000 0.800 0.000 1.000 0 0.000
#> SRR934242 2 0.000 0.800 0.000 1.000 0 0.000
#> SRR934243 2 0.000 0.800 0.000 1.000 0 0.000
#> SRR934244 2 0.000 0.800 0.000 1.000 0 0.000
#> SRR934245 2 0.000 0.800 0.000 1.000 0 0.000
#> SRR934246 2 0.000 0.800 0.000 1.000 0 0.000
#> SRR934247 2 0.000 0.800 0.000 1.000 0 0.000
#> SRR934248 1 0.503 0.414 0.596 0.400 0 0.004
#> SRR934249 1 0.503 0.414 0.596 0.400 0 0.004
#> SRR934250 1 0.503 0.414 0.596 0.400 0 0.004
#> SRR934251 1 0.503 0.414 0.596 0.400 0 0.004
#> SRR934252 1 0.503 0.414 0.596 0.400 0 0.004
#> SRR934253 1 0.503 0.414 0.596 0.400 0 0.004
#> SRR934254 1 0.503 0.414 0.596 0.400 0 0.004
#> SRR934255 1 0.503 0.414 0.596 0.400 0 0.004
#> SRR934256 2 0.689 0.532 0.204 0.596 0 0.200
#> SRR934257 2 0.689 0.532 0.204 0.596 0 0.200
#> SRR934258 2 0.689 0.532 0.204 0.596 0 0.200
#> SRR934259 2 0.689 0.532 0.204 0.596 0 0.200
#> SRR934260 2 0.689 0.532 0.204 0.596 0 0.200
#> SRR934261 2 0.689 0.532 0.204 0.596 0 0.200
#> SRR934262 2 0.689 0.532 0.204 0.596 0 0.200
#> SRR934263 2 0.689 0.532 0.204 0.596 0 0.200
#> SRR934264 1 0.485 0.420 0.600 0.400 0 0.000
#> SRR934265 1 0.485 0.420 0.600 0.400 0 0.000
#> SRR934266 1 0.485 0.420 0.600 0.400 0 0.000
#> SRR934267 1 0.485 0.420 0.600 0.400 0 0.000
#> SRR934268 1 0.485 0.420 0.600 0.400 0 0.000
#> SRR934269 1 0.485 0.420 0.600 0.400 0 0.000
#> SRR934270 1 0.485 0.420 0.600 0.400 0 0.000
#> SRR934271 1 0.485 0.420 0.600 0.400 0 0.000
#> SRR934272 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934273 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934274 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934275 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934276 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934277 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934278 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934279 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934280 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934281 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934282 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934283 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934284 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934285 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934286 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934287 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934288 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934289 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934290 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934291 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934292 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934293 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934294 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934295 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934296 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934297 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934298 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934299 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934300 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934301 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934302 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934303 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934304 3 0.000 1.000 0.000 0.000 1 0.000
#> SRR934305 3 0.000 1.000 0.000 0.000 1 0.000
#> SRR934306 3 0.000 1.000 0.000 0.000 1 0.000
#> SRR934307 3 0.000 1.000 0.000 0.000 1 0.000
#> SRR934308 3 0.000 1.000 0.000 0.000 1 0.000
#> SRR934309 3 0.000 1.000 0.000 0.000 1 0.000
#> SRR934310 3 0.000 1.000 0.000 0.000 1 0.000
#> SRR934311 3 0.000 1.000 0.000 0.000 1 0.000
#> SRR934312 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934313 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934314 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934315 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934316 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934317 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934318 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934319 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934320 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934321 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934322 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934323 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934324 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934325 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934326 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934327 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934328 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934329 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934330 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934331 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934332 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934333 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934334 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934335 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934344 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934345 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934346 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934347 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934348 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934349 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934350 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934351 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934336 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934337 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934338 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934339 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934340 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934341 1 0.000 0.906 1.000 0.000 0 0.000
#> SRR934342 1 0.000 0.906 1.000 0.000 0 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0.000 1.000 0.000 0.000 1 0.000 0
#> SRR934217 3 0.000 1.000 0.000 0.000 1 0.000 0
#> SRR934218 3 0.000 1.000 0.000 0.000 1 0.000 0
#> SRR934219 3 0.000 1.000 0.000 0.000 1 0.000 0
#> SRR934220 3 0.000 1.000 0.000 0.000 1 0.000 0
#> SRR934221 3 0.000 1.000 0.000 0.000 1 0.000 0
#> SRR934222 3 0.000 1.000 0.000 0.000 1 0.000 0
#> SRR934223 3 0.000 1.000 0.000 0.000 1 0.000 0
#> SRR934224 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934225 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934226 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934227 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934228 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934229 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934230 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934231 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934232 2 0.000 0.607 0.000 1.000 0 0.000 0
#> SRR934233 2 0.000 0.607 0.000 1.000 0 0.000 0
#> SRR934234 2 0.000 0.607 0.000 1.000 0 0.000 0
#> SRR934235 2 0.000 0.607 0.000 1.000 0 0.000 0
#> SRR934236 2 0.000 0.607 0.000 1.000 0 0.000 0
#> SRR934237 2 0.000 0.607 0.000 1.000 0 0.000 0
#> SRR934238 2 0.000 0.607 0.000 1.000 0 0.000 0
#> SRR934239 2 0.000 0.607 0.000 1.000 0 0.000 0
#> SRR934240 2 0.000 0.607 0.000 1.000 0 0.000 0
#> SRR934241 2 0.000 0.607 0.000 1.000 0 0.000 0
#> SRR934242 2 0.000 0.607 0.000 1.000 0 0.000 0
#> SRR934243 2 0.000 0.607 0.000 1.000 0 0.000 0
#> SRR934244 2 0.000 0.607 0.000 1.000 0 0.000 0
#> SRR934245 2 0.000 0.607 0.000 1.000 0 0.000 0
#> SRR934246 2 0.000 0.607 0.000 1.000 0 0.000 0
#> SRR934247 2 0.000 0.607 0.000 1.000 0 0.000 0
#> SRR934248 4 0.430 0.111 0.000 0.476 0 0.524 0
#> SRR934249 4 0.430 0.111 0.000 0.476 0 0.524 0
#> SRR934250 4 0.430 0.111 0.000 0.476 0 0.524 0
#> SRR934251 4 0.430 0.111 0.000 0.476 0 0.524 0
#> SRR934252 4 0.430 0.111 0.000 0.476 0 0.524 0
#> SRR934253 4 0.430 0.111 0.000 0.476 0 0.524 0
#> SRR934254 4 0.430 0.111 0.000 0.476 0 0.524 0
#> SRR934255 4 0.430 0.111 0.000 0.476 0 0.524 0
#> SRR934256 4 0.628 0.390 0.156 0.368 0 0.476 0
#> SRR934257 4 0.628 0.390 0.156 0.368 0 0.476 0
#> SRR934258 4 0.628 0.390 0.156 0.368 0 0.476 0
#> SRR934259 4 0.628 0.390 0.156 0.368 0 0.476 0
#> SRR934260 4 0.628 0.390 0.156 0.368 0 0.476 0
#> SRR934261 4 0.628 0.390 0.156 0.368 0 0.476 0
#> SRR934262 4 0.628 0.390 0.156 0.368 0 0.476 0
#> SRR934263 4 0.628 0.390 0.156 0.368 0 0.476 0
#> SRR934264 2 0.667 0.254 0.232 0.400 0 0.368 0
#> SRR934265 2 0.667 0.254 0.232 0.400 0 0.368 0
#> SRR934266 2 0.667 0.254 0.232 0.400 0 0.368 0
#> SRR934267 2 0.667 0.254 0.232 0.400 0 0.368 0
#> SRR934268 2 0.667 0.254 0.232 0.400 0 0.368 0
#> SRR934269 2 0.667 0.254 0.232 0.400 0 0.368 0
#> SRR934270 2 0.667 0.254 0.232 0.400 0 0.368 0
#> SRR934271 2 0.667 0.254 0.232 0.400 0 0.368 0
#> SRR934272 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934273 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934274 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934275 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934276 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934277 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934278 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934279 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934280 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934281 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934282 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934283 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934284 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934285 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934286 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934287 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934288 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934289 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934290 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934291 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934292 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934293 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934294 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934295 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934296 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934297 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934298 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934299 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934300 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934301 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934302 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934303 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934304 5 0.000 1.000 0.000 0.000 0 0.000 1
#> SRR934305 5 0.000 1.000 0.000 0.000 0 0.000 1
#> SRR934306 5 0.000 1.000 0.000 0.000 0 0.000 1
#> SRR934307 5 0.000 1.000 0.000 0.000 0 0.000 1
#> SRR934308 5 0.000 1.000 0.000 0.000 0 0.000 1
#> SRR934309 5 0.000 1.000 0.000 0.000 0 0.000 1
#> SRR934310 5 0.000 1.000 0.000 0.000 0 0.000 1
#> SRR934311 5 0.000 1.000 0.000 0.000 0 0.000 1
#> SRR934312 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934313 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934314 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934315 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934316 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934317 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934318 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934319 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934320 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934321 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934322 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934323 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934324 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934325 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934326 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934327 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934328 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934329 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934330 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934331 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934332 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934333 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934334 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934335 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934344 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934345 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934346 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934347 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934348 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934349 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934350 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934351 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934336 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934337 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934338 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934339 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934340 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934341 1 0.000 1.000 1.000 0.000 0 0.000 0
#> SRR934342 1 0.000 1.000 1.000 0.000 0 0.000 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.0000 1.000 0.000 0.000 1 0.000 0 0
#> SRR934217 3 0.0000 1.000 0.000 0.000 1 0.000 0 0
#> SRR934218 3 0.0000 1.000 0.000 0.000 1 0.000 0 0
#> SRR934219 3 0.0000 1.000 0.000 0.000 1 0.000 0 0
#> SRR934220 3 0.0000 1.000 0.000 0.000 1 0.000 0 0
#> SRR934221 3 0.0000 1.000 0.000 0.000 1 0.000 0 0
#> SRR934222 3 0.0000 1.000 0.000 0.000 1 0.000 0 0
#> SRR934223 3 0.0000 1.000 0.000 0.000 1 0.000 0 0
#> SRR934224 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934225 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934226 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934227 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934228 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934229 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934230 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934231 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934232 2 0.0146 0.997 0.000 0.996 0 0.004 0 0
#> SRR934233 2 0.0146 0.997 0.000 0.996 0 0.004 0 0
#> SRR934234 2 0.0146 0.997 0.000 0.996 0 0.004 0 0
#> SRR934235 2 0.0146 0.997 0.000 0.996 0 0.004 0 0
#> SRR934236 2 0.0146 0.997 0.000 0.996 0 0.004 0 0
#> SRR934237 2 0.0146 0.997 0.000 0.996 0 0.004 0 0
#> SRR934238 2 0.0146 0.997 0.000 0.996 0 0.004 0 0
#> SRR934239 2 0.0146 0.997 0.000 0.996 0 0.004 0 0
#> SRR934240 2 0.0000 0.997 0.000 1.000 0 0.000 0 0
#> SRR934241 2 0.0000 0.997 0.000 1.000 0 0.000 0 0
#> SRR934242 2 0.0000 0.997 0.000 1.000 0 0.000 0 0
#> SRR934243 2 0.0000 0.997 0.000 1.000 0 0.000 0 0
#> SRR934244 2 0.0000 0.997 0.000 1.000 0 0.000 0 0
#> SRR934245 2 0.0000 0.997 0.000 1.000 0 0.000 0 0
#> SRR934246 2 0.0000 0.997 0.000 1.000 0 0.000 0 0
#> SRR934247 2 0.0000 0.997 0.000 1.000 0 0.000 0 0
#> SRR934248 4 0.0547 0.756 0.000 0.020 0 0.980 0 0
#> SRR934249 4 0.0547 0.756 0.000 0.020 0 0.980 0 0
#> SRR934250 4 0.0547 0.756 0.000 0.020 0 0.980 0 0
#> SRR934251 4 0.0547 0.756 0.000 0.020 0 0.980 0 0
#> SRR934252 4 0.0547 0.756 0.000 0.020 0 0.980 0 0
#> SRR934253 4 0.0547 0.756 0.000 0.020 0 0.980 0 0
#> SRR934254 4 0.0547 0.756 0.000 0.020 0 0.980 0 0
#> SRR934255 4 0.0547 0.756 0.000 0.020 0 0.980 0 0
#> SRR934256 6 0.0000 1.000 0.000 0.000 0 0.000 0 1
#> SRR934257 6 0.0000 1.000 0.000 0.000 0 0.000 0 1
#> SRR934258 6 0.0000 1.000 0.000 0.000 0 0.000 0 1
#> SRR934259 6 0.0000 1.000 0.000 0.000 0 0.000 0 1
#> SRR934260 6 0.0000 1.000 0.000 0.000 0 0.000 0 1
#> SRR934261 6 0.0000 1.000 0.000 0.000 0 0.000 0 1
#> SRR934262 6 0.0000 1.000 0.000 0.000 0 0.000 0 1
#> SRR934263 6 0.0000 1.000 0.000 0.000 0 0.000 0 1
#> SRR934264 4 0.3695 0.694 0.000 0.376 0 0.624 0 0
#> SRR934265 4 0.3695 0.694 0.000 0.376 0 0.624 0 0
#> SRR934266 4 0.3695 0.694 0.000 0.376 0 0.624 0 0
#> SRR934267 4 0.3695 0.694 0.000 0.376 0 0.624 0 0
#> SRR934268 4 0.3695 0.694 0.000 0.376 0 0.624 0 0
#> SRR934269 4 0.3695 0.694 0.000 0.376 0 0.624 0 0
#> SRR934270 4 0.3695 0.694 0.000 0.376 0 0.624 0 0
#> SRR934271 4 0.3695 0.694 0.000 0.376 0 0.624 0 0
#> SRR934272 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934273 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934274 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934275 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934276 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934277 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934278 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934279 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934280 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934281 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934282 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934283 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934284 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934285 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934286 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934287 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934288 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934289 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934290 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934291 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934292 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934293 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934294 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934295 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934296 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934297 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934298 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934299 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934300 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934301 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934302 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934303 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934304 5 0.0000 1.000 0.000 0.000 0 0.000 1 0
#> SRR934305 5 0.0000 1.000 0.000 0.000 0 0.000 1 0
#> SRR934306 5 0.0000 1.000 0.000 0.000 0 0.000 1 0
#> SRR934307 5 0.0000 1.000 0.000 0.000 0 0.000 1 0
#> SRR934308 5 0.0000 1.000 0.000 0.000 0 0.000 1 0
#> SRR934309 5 0.0000 1.000 0.000 0.000 0 0.000 1 0
#> SRR934310 5 0.0000 1.000 0.000 0.000 0 0.000 1 0
#> SRR934311 5 0.0000 1.000 0.000 0.000 0 0.000 1 0
#> SRR934312 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934313 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934314 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934315 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934316 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934317 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934318 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934319 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934320 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934321 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934322 1 0.0458 0.989 0.984 0.000 0 0.016 0 0
#> SRR934323 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934324 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934325 1 0.0458 0.989 0.984 0.000 0 0.016 0 0
#> SRR934326 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934327 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934328 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934329 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934330 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934331 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934332 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934333 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934334 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934335 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934344 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934345 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934346 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934347 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934348 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934349 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934350 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934351 1 0.0547 0.988 0.980 0.000 0 0.020 0 0
#> SRR934336 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934337 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934338 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934339 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934340 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934341 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
#> SRR934342 1 0.0000 0.992 1.000 0.000 0 0.000 0 0
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 14550 rows and 135 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5029 0.498 0.498
#> 3 3 0.782 0.810 0.918 0.0989 0.726 0.560
#> 4 4 1.000 1.000 1.000 0.1274 0.789 0.604
#> 5 5 1.000 1.000 1.000 0.0454 0.972 0.925
#> 6 6 0.807 0.779 0.838 0.1148 0.979 0.939
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
#> attr(,"optional")
#> [1] 2 4
There is also optional best \(k\) = 2 4 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR934216 2 0 1 0 1
#> SRR934217 2 0 1 0 1
#> SRR934218 2 0 1 0 1
#> SRR934219 2 0 1 0 1
#> SRR934220 2 0 1 0 1
#> SRR934221 2 0 1 0 1
#> SRR934222 2 0 1 0 1
#> SRR934223 2 0 1 0 1
#> SRR934224 1 0 1 1 0
#> SRR934225 1 0 1 1 0
#> SRR934226 1 0 1 1 0
#> SRR934227 1 0 1 1 0
#> SRR934228 1 0 1 1 0
#> SRR934229 1 0 1 1 0
#> SRR934230 1 0 1 1 0
#> SRR934231 1 0 1 1 0
#> SRR934232 2 0 1 0 1
#> SRR934233 2 0 1 0 1
#> SRR934234 2 0 1 0 1
#> SRR934235 2 0 1 0 1
#> SRR934236 2 0 1 0 1
#> SRR934237 2 0 1 0 1
#> SRR934238 2 0 1 0 1
#> SRR934239 2 0 1 0 1
#> SRR934240 2 0 1 0 1
#> SRR934241 2 0 1 0 1
#> SRR934242 2 0 1 0 1
#> SRR934243 2 0 1 0 1
#> SRR934244 2 0 1 0 1
#> SRR934245 2 0 1 0 1
#> SRR934246 2 0 1 0 1
#> SRR934247 2 0 1 0 1
#> SRR934248 2 0 1 0 1
#> SRR934249 2 0 1 0 1
#> SRR934250 2 0 1 0 1
#> SRR934251 2 0 1 0 1
#> SRR934252 2 0 1 0 1
#> SRR934253 2 0 1 0 1
#> SRR934254 2 0 1 0 1
#> SRR934255 2 0 1 0 1
#> SRR934256 2 0 1 0 1
#> SRR934257 2 0 1 0 1
#> SRR934258 2 0 1 0 1
#> SRR934259 2 0 1 0 1
#> SRR934260 2 0 1 0 1
#> SRR934261 2 0 1 0 1
#> SRR934262 2 0 1 0 1
#> SRR934263 2 0 1 0 1
#> SRR934264 2 0 1 0 1
#> SRR934265 2 0 1 0 1
#> SRR934266 2 0 1 0 1
#> SRR934267 2 0 1 0 1
#> SRR934268 2 0 1 0 1
#> SRR934269 2 0 1 0 1
#> SRR934270 2 0 1 0 1
#> SRR934271 2 0 1 0 1
#> SRR934272 1 0 1 1 0
#> SRR934273 1 0 1 1 0
#> SRR934274 1 0 1 1 0
#> SRR934275 1 0 1 1 0
#> SRR934276 1 0 1 1 0
#> SRR934277 1 0 1 1 0
#> SRR934278 1 0 1 1 0
#> SRR934279 1 0 1 1 0
#> SRR934280 1 0 1 1 0
#> SRR934281 1 0 1 1 0
#> SRR934282 1 0 1 1 0
#> SRR934283 1 0 1 1 0
#> SRR934284 1 0 1 1 0
#> SRR934285 1 0 1 1 0
#> SRR934286 1 0 1 1 0
#> SRR934287 1 0 1 1 0
#> SRR934288 1 0 1 1 0
#> SRR934289 1 0 1 1 0
#> SRR934290 1 0 1 1 0
#> SRR934291 1 0 1 1 0
#> SRR934292 1 0 1 1 0
#> SRR934293 1 0 1 1 0
#> SRR934294 1 0 1 1 0
#> SRR934295 1 0 1 1 0
#> SRR934296 2 0 1 0 1
#> SRR934297 2 0 1 0 1
#> SRR934298 2 0 1 0 1
#> SRR934299 2 0 1 0 1
#> SRR934300 2 0 1 0 1
#> SRR934301 2 0 1 0 1
#> SRR934302 2 0 1 0 1
#> SRR934303 2 0 1 0 1
#> SRR934304 2 0 1 0 1
#> SRR934305 2 0 1 0 1
#> SRR934306 2 0 1 0 1
#> SRR934307 2 0 1 0 1
#> SRR934308 2 0 1 0 1
#> SRR934309 2 0 1 0 1
#> SRR934310 2 0 1 0 1
#> SRR934311 2 0 1 0 1
#> SRR934312 1 0 1 1 0
#> SRR934313 1 0 1 1 0
#> SRR934314 1 0 1 1 0
#> SRR934315 1 0 1 1 0
#> SRR934316 1 0 1 1 0
#> SRR934317 1 0 1 1 0
#> SRR934318 1 0 1 1 0
#> SRR934319 1 0 1 1 0
#> SRR934320 1 0 1 1 0
#> SRR934321 1 0 1 1 0
#> SRR934322 1 0 1 1 0
#> SRR934323 1 0 1 1 0
#> SRR934324 1 0 1 1 0
#> SRR934325 1 0 1 1 0
#> SRR934326 1 0 1 1 0
#> SRR934327 1 0 1 1 0
#> SRR934328 1 0 1 1 0
#> SRR934329 1 0 1 1 0
#> SRR934330 1 0 1 1 0
#> SRR934331 1 0 1 1 0
#> SRR934332 1 0 1 1 0
#> SRR934333 1 0 1 1 0
#> SRR934334 1 0 1 1 0
#> SRR934335 1 0 1 1 0
#> SRR934344 1 0 1 1 0
#> SRR934345 1 0 1 1 0
#> SRR934346 1 0 1 1 0
#> SRR934347 1 0 1 1 0
#> SRR934348 1 0 1 1 0
#> SRR934349 1 0 1 1 0
#> SRR934350 1 0 1 1 0
#> SRR934351 1 0 1 1 0
#> SRR934336 1 0 1 1 0
#> SRR934337 1 0 1 1 0
#> SRR934338 1 0 1 1 0
#> SRR934339 1 0 1 1 0
#> SRR934340 1 0 1 1 0
#> SRR934341 1 0 1 1 0
#> SRR934342 1 0 1 1 0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 3 0.0892 0.373 0.020 0.000 0.980
#> SRR934217 3 0.0892 0.373 0.020 0.000 0.980
#> SRR934218 3 0.0892 0.373 0.020 0.000 0.980
#> SRR934219 3 0.0892 0.373 0.020 0.000 0.980
#> SRR934220 3 0.0892 0.373 0.020 0.000 0.980
#> SRR934221 3 0.0892 0.373 0.020 0.000 0.980
#> SRR934222 3 0.0892 0.373 0.020 0.000 0.980
#> SRR934223 3 0.0892 0.373 0.020 0.000 0.980
#> SRR934224 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934225 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934226 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934227 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934228 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934229 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934230 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934231 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934232 2 0.6235 0.996 0.000 0.564 0.436
#> SRR934233 2 0.6235 0.996 0.000 0.564 0.436
#> SRR934234 2 0.6235 0.996 0.000 0.564 0.436
#> SRR934235 2 0.6235 0.996 0.000 0.564 0.436
#> SRR934236 2 0.6235 0.996 0.000 0.564 0.436
#> SRR934237 2 0.6235 0.996 0.000 0.564 0.436
#> SRR934238 2 0.6235 0.996 0.000 0.564 0.436
#> SRR934239 2 0.6235 0.996 0.000 0.564 0.436
#> SRR934240 2 0.6235 0.996 0.000 0.564 0.436
#> SRR934241 2 0.6235 0.996 0.000 0.564 0.436
#> SRR934242 2 0.6235 0.996 0.000 0.564 0.436
#> SRR934243 2 0.6235 0.996 0.000 0.564 0.436
#> SRR934244 2 0.6235 0.996 0.000 0.564 0.436
#> SRR934245 2 0.6235 0.996 0.000 0.564 0.436
#> SRR934246 2 0.6235 0.996 0.000 0.564 0.436
#> SRR934247 2 0.6235 0.996 0.000 0.564 0.436
#> SRR934248 3 0.6215 0.813 0.000 0.428 0.572
#> SRR934249 3 0.6215 0.813 0.000 0.428 0.572
#> SRR934250 3 0.6215 0.813 0.000 0.428 0.572
#> SRR934251 3 0.6215 0.813 0.000 0.428 0.572
#> SRR934252 3 0.6215 0.813 0.000 0.428 0.572
#> SRR934253 3 0.6215 0.813 0.000 0.428 0.572
#> SRR934254 3 0.6215 0.813 0.000 0.428 0.572
#> SRR934255 3 0.6215 0.813 0.000 0.428 0.572
#> SRR934256 1 0.6235 0.337 0.564 0.000 0.436
#> SRR934257 1 0.6235 0.337 0.564 0.000 0.436
#> SRR934258 1 0.6235 0.337 0.564 0.000 0.436
#> SRR934259 1 0.6235 0.337 0.564 0.000 0.436
#> SRR934260 1 0.6235 0.337 0.564 0.000 0.436
#> SRR934261 1 0.6235 0.337 0.564 0.000 0.436
#> SRR934262 1 0.6235 0.337 0.564 0.000 0.436
#> SRR934263 1 0.6235 0.337 0.564 0.000 0.436
#> SRR934264 3 0.6215 0.813 0.000 0.428 0.572
#> SRR934265 3 0.6215 0.813 0.000 0.428 0.572
#> SRR934266 3 0.6215 0.813 0.000 0.428 0.572
#> SRR934267 3 0.6215 0.813 0.000 0.428 0.572
#> SRR934268 3 0.6215 0.813 0.000 0.428 0.572
#> SRR934269 3 0.6215 0.813 0.000 0.428 0.572
#> SRR934270 3 0.6215 0.813 0.000 0.428 0.572
#> SRR934271 3 0.6215 0.813 0.000 0.428 0.572
#> SRR934272 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934273 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934274 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934275 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934276 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934277 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934278 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934279 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934280 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934281 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934282 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934283 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934284 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934285 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934286 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934287 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934288 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934289 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934290 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934291 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934292 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934293 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934294 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934295 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934296 1 0.6274 0.304 0.544 0.000 0.456
#> SRR934297 1 0.6274 0.304 0.544 0.000 0.456
#> SRR934298 1 0.6274 0.304 0.544 0.000 0.456
#> SRR934299 1 0.6274 0.304 0.544 0.000 0.456
#> SRR934300 1 0.6274 0.304 0.544 0.000 0.456
#> SRR934301 1 0.6274 0.304 0.544 0.000 0.456
#> SRR934302 1 0.6274 0.304 0.544 0.000 0.456
#> SRR934303 1 0.6274 0.304 0.544 0.000 0.456
#> SRR934304 2 0.6215 0.992 0.000 0.572 0.428
#> SRR934305 2 0.6215 0.992 0.000 0.572 0.428
#> SRR934306 2 0.6215 0.992 0.000 0.572 0.428
#> SRR934307 2 0.6215 0.992 0.000 0.572 0.428
#> SRR934308 2 0.6215 0.992 0.000 0.572 0.428
#> SRR934309 2 0.6215 0.992 0.000 0.572 0.428
#> SRR934310 2 0.6215 0.992 0.000 0.572 0.428
#> SRR934311 2 0.6215 0.992 0.000 0.572 0.428
#> SRR934312 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934313 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934314 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934315 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934316 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934317 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934318 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934319 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934320 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934321 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934322 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934323 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934324 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934325 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934326 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934327 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934328 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934329 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934330 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934331 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934332 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934333 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934334 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934335 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934344 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934345 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934346 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934347 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934348 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934349 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934350 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934351 1 0.0000 0.906 1.000 0.000 0.000
#> SRR934336 1 0.0237 0.903 0.996 0.000 0.004
#> SRR934337 1 0.0237 0.903 0.996 0.000 0.004
#> SRR934338 1 0.0237 0.903 0.996 0.000 0.004
#> SRR934339 1 0.0237 0.903 0.996 0.000 0.004
#> SRR934340 1 0.0237 0.903 0.996 0.000 0.004
#> SRR934341 1 0.0237 0.903 0.996 0.000 0.004
#> SRR934342 1 0.0237 0.903 0.996 0.000 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 2 0 1 0 1 0 0
#> SRR934217 2 0 1 0 1 0 0
#> SRR934218 2 0 1 0 1 0 0
#> SRR934219 2 0 1 0 1 0 0
#> SRR934220 2 0 1 0 1 0 0
#> SRR934221 2 0 1 0 1 0 0
#> SRR934222 2 0 1 0 1 0 0
#> SRR934223 2 0 1 0 1 0 0
#> SRR934224 1 0 1 1 0 0 0
#> SRR934225 1 0 1 1 0 0 0
#> SRR934226 1 0 1 1 0 0 0
#> SRR934227 1 0 1 1 0 0 0
#> SRR934228 1 0 1 1 0 0 0
#> SRR934229 1 0 1 1 0 0 0
#> SRR934230 1 0 1 1 0 0 0
#> SRR934231 1 0 1 1 0 0 0
#> SRR934232 2 0 1 0 1 0 0
#> SRR934233 2 0 1 0 1 0 0
#> SRR934234 2 0 1 0 1 0 0
#> SRR934235 2 0 1 0 1 0 0
#> SRR934236 2 0 1 0 1 0 0
#> SRR934237 2 0 1 0 1 0 0
#> SRR934238 2 0 1 0 1 0 0
#> SRR934239 2 0 1 0 1 0 0
#> SRR934240 2 0 1 0 1 0 0
#> SRR934241 2 0 1 0 1 0 0
#> SRR934242 2 0 1 0 1 0 0
#> SRR934243 2 0 1 0 1 0 0
#> SRR934244 2 0 1 0 1 0 0
#> SRR934245 2 0 1 0 1 0 0
#> SRR934246 2 0 1 0 1 0 0
#> SRR934247 2 0 1 0 1 0 0
#> SRR934248 4 0 1 0 0 0 1
#> SRR934249 4 0 1 0 0 0 1
#> SRR934250 4 0 1 0 0 0 1
#> SRR934251 4 0 1 0 0 0 1
#> SRR934252 4 0 1 0 0 0 1
#> SRR934253 4 0 1 0 0 0 1
#> SRR934254 4 0 1 0 0 0 1
#> SRR934255 4 0 1 0 0 0 1
#> SRR934256 2 0 1 0 1 0 0
#> SRR934257 2 0 1 0 1 0 0
#> SRR934258 2 0 1 0 1 0 0
#> SRR934259 2 0 1 0 1 0 0
#> SRR934260 2 0 1 0 1 0 0
#> SRR934261 2 0 1 0 1 0 0
#> SRR934262 2 0 1 0 1 0 0
#> SRR934263 2 0 1 0 1 0 0
#> SRR934264 4 0 1 0 0 0 1
#> SRR934265 4 0 1 0 0 0 1
#> SRR934266 4 0 1 0 0 0 1
#> SRR934267 4 0 1 0 0 0 1
#> SRR934268 4 0 1 0 0 0 1
#> SRR934269 4 0 1 0 0 0 1
#> SRR934270 4 0 1 0 0 0 1
#> SRR934271 4 0 1 0 0 0 1
#> SRR934272 1 0 1 1 0 0 0
#> SRR934273 1 0 1 1 0 0 0
#> SRR934274 1 0 1 1 0 0 0
#> SRR934275 1 0 1 1 0 0 0
#> SRR934276 1 0 1 1 0 0 0
#> SRR934277 1 0 1 1 0 0 0
#> SRR934278 1 0 1 1 0 0 0
#> SRR934279 1 0 1 1 0 0 0
#> SRR934280 1 0 1 1 0 0 0
#> SRR934281 1 0 1 1 0 0 0
#> SRR934282 1 0 1 1 0 0 0
#> SRR934283 1 0 1 1 0 0 0
#> SRR934284 1 0 1 1 0 0 0
#> SRR934285 1 0 1 1 0 0 0
#> SRR934286 1 0 1 1 0 0 0
#> SRR934287 1 0 1 1 0 0 0
#> SRR934288 1 0 1 1 0 0 0
#> SRR934289 1 0 1 1 0 0 0
#> SRR934290 1 0 1 1 0 0 0
#> SRR934291 1 0 1 1 0 0 0
#> SRR934292 1 0 1 1 0 0 0
#> SRR934293 1 0 1 1 0 0 0
#> SRR934294 1 0 1 1 0 0 0
#> SRR934295 1 0 1 1 0 0 0
#> SRR934296 2 0 1 0 1 0 0
#> SRR934297 2 0 1 0 1 0 0
#> SRR934298 2 0 1 0 1 0 0
#> SRR934299 2 0 1 0 1 0 0
#> SRR934300 2 0 1 0 1 0 0
#> SRR934301 2 0 1 0 1 0 0
#> SRR934302 2 0 1 0 1 0 0
#> SRR934303 2 0 1 0 1 0 0
#> SRR934304 3 0 1 0 0 1 0
#> SRR934305 3 0 1 0 0 1 0
#> SRR934306 3 0 1 0 0 1 0
#> SRR934307 3 0 1 0 0 1 0
#> SRR934308 3 0 1 0 0 1 0
#> SRR934309 3 0 1 0 0 1 0
#> SRR934310 3 0 1 0 0 1 0
#> SRR934311 3 0 1 0 0 1 0
#> SRR934312 1 0 1 1 0 0 0
#> SRR934313 1 0 1 1 0 0 0
#> SRR934314 1 0 1 1 0 0 0
#> SRR934315 1 0 1 1 0 0 0
#> SRR934316 1 0 1 1 0 0 0
#> SRR934317 1 0 1 1 0 0 0
#> SRR934318 1 0 1 1 0 0 0
#> SRR934319 1 0 1 1 0 0 0
#> SRR934320 1 0 1 1 0 0 0
#> SRR934321 1 0 1 1 0 0 0
#> SRR934322 1 0 1 1 0 0 0
#> SRR934323 1 0 1 1 0 0 0
#> SRR934324 1 0 1 1 0 0 0
#> SRR934325 1 0 1 1 0 0 0
#> SRR934326 1 0 1 1 0 0 0
#> SRR934327 1 0 1 1 0 0 0
#> SRR934328 1 0 1 1 0 0 0
#> SRR934329 1 0 1 1 0 0 0
#> SRR934330 1 0 1 1 0 0 0
#> SRR934331 1 0 1 1 0 0 0
#> SRR934332 1 0 1 1 0 0 0
#> SRR934333 1 0 1 1 0 0 0
#> SRR934334 1 0 1 1 0 0 0
#> SRR934335 1 0 1 1 0 0 0
#> SRR934344 1 0 1 1 0 0 0
#> SRR934345 1 0 1 1 0 0 0
#> SRR934346 1 0 1 1 0 0 0
#> SRR934347 1 0 1 1 0 0 0
#> SRR934348 1 0 1 1 0 0 0
#> SRR934349 1 0 1 1 0 0 0
#> SRR934350 1 0 1 1 0 0 0
#> SRR934351 1 0 1 1 0 0 0
#> SRR934336 1 0 1 1 0 0 0
#> SRR934337 1 0 1 1 0 0 0
#> SRR934338 1 0 1 1 0 0 0
#> SRR934339 1 0 1 1 0 0 0
#> SRR934340 1 0 1 1 0 0 0
#> SRR934341 1 0 1 1 0 0 0
#> SRR934342 1 0 1 1 0 0 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0 1 0 0 1 0 0
#> SRR934217 3 0 1 0 0 1 0 0
#> SRR934218 3 0 1 0 0 1 0 0
#> SRR934219 3 0 1 0 0 1 0 0
#> SRR934220 3 0 1 0 0 1 0 0
#> SRR934221 3 0 1 0 0 1 0 0
#> SRR934222 3 0 1 0 0 1 0 0
#> SRR934223 3 0 1 0 0 1 0 0
#> SRR934224 1 0 1 1 0 0 0 0
#> SRR934225 1 0 1 1 0 0 0 0
#> SRR934226 1 0 1 1 0 0 0 0
#> SRR934227 1 0 1 1 0 0 0 0
#> SRR934228 1 0 1 1 0 0 0 0
#> SRR934229 1 0 1 1 0 0 0 0
#> SRR934230 1 0 1 1 0 0 0 0
#> SRR934231 1 0 1 1 0 0 0 0
#> SRR934232 2 0 1 0 1 0 0 0
#> SRR934233 2 0 1 0 1 0 0 0
#> SRR934234 2 0 1 0 1 0 0 0
#> SRR934235 2 0 1 0 1 0 0 0
#> SRR934236 2 0 1 0 1 0 0 0
#> SRR934237 2 0 1 0 1 0 0 0
#> SRR934238 2 0 1 0 1 0 0 0
#> SRR934239 2 0 1 0 1 0 0 0
#> SRR934240 2 0 1 0 1 0 0 0
#> SRR934241 2 0 1 0 1 0 0 0
#> SRR934242 2 0 1 0 1 0 0 0
#> SRR934243 2 0 1 0 1 0 0 0
#> SRR934244 2 0 1 0 1 0 0 0
#> SRR934245 2 0 1 0 1 0 0 0
#> SRR934246 2 0 1 0 1 0 0 0
#> SRR934247 2 0 1 0 1 0 0 0
#> SRR934248 4 0 1 0 0 0 1 0
#> SRR934249 4 0 1 0 0 0 1 0
#> SRR934250 4 0 1 0 0 0 1 0
#> SRR934251 4 0 1 0 0 0 1 0
#> SRR934252 4 0 1 0 0 0 1 0
#> SRR934253 4 0 1 0 0 0 1 0
#> SRR934254 4 0 1 0 0 0 1 0
#> SRR934255 4 0 1 0 0 0 1 0
#> SRR934256 2 0 1 0 1 0 0 0
#> SRR934257 2 0 1 0 1 0 0 0
#> SRR934258 2 0 1 0 1 0 0 0
#> SRR934259 2 0 1 0 1 0 0 0
#> SRR934260 2 0 1 0 1 0 0 0
#> SRR934261 2 0 1 0 1 0 0 0
#> SRR934262 2 0 1 0 1 0 0 0
#> SRR934263 2 0 1 0 1 0 0 0
#> SRR934264 4 0 1 0 0 0 1 0
#> SRR934265 4 0 1 0 0 0 1 0
#> SRR934266 4 0 1 0 0 0 1 0
#> SRR934267 4 0 1 0 0 0 1 0
#> SRR934268 4 0 1 0 0 0 1 0
#> SRR934269 4 0 1 0 0 0 1 0
#> SRR934270 4 0 1 0 0 0 1 0
#> SRR934271 4 0 1 0 0 0 1 0
#> SRR934272 1 0 1 1 0 0 0 0
#> SRR934273 1 0 1 1 0 0 0 0
#> SRR934274 1 0 1 1 0 0 0 0
#> SRR934275 1 0 1 1 0 0 0 0
#> SRR934276 1 0 1 1 0 0 0 0
#> SRR934277 1 0 1 1 0 0 0 0
#> SRR934278 1 0 1 1 0 0 0 0
#> SRR934279 1 0 1 1 0 0 0 0
#> SRR934280 1 0 1 1 0 0 0 0
#> SRR934281 1 0 1 1 0 0 0 0
#> SRR934282 1 0 1 1 0 0 0 0
#> SRR934283 1 0 1 1 0 0 0 0
#> SRR934284 1 0 1 1 0 0 0 0
#> SRR934285 1 0 1 1 0 0 0 0
#> SRR934286 1 0 1 1 0 0 0 0
#> SRR934287 1 0 1 1 0 0 0 0
#> SRR934288 1 0 1 1 0 0 0 0
#> SRR934289 1 0 1 1 0 0 0 0
#> SRR934290 1 0 1 1 0 0 0 0
#> SRR934291 1 0 1 1 0 0 0 0
#> SRR934292 1 0 1 1 0 0 0 0
#> SRR934293 1 0 1 1 0 0 0 0
#> SRR934294 1 0 1 1 0 0 0 0
#> SRR934295 1 0 1 1 0 0 0 0
#> SRR934296 2 0 1 0 1 0 0 0
#> SRR934297 2 0 1 0 1 0 0 0
#> SRR934298 2 0 1 0 1 0 0 0
#> SRR934299 2 0 1 0 1 0 0 0
#> SRR934300 2 0 1 0 1 0 0 0
#> SRR934301 2 0 1 0 1 0 0 0
#> SRR934302 2 0 1 0 1 0 0 0
#> SRR934303 2 0 1 0 1 0 0 0
#> SRR934304 5 0 1 0 0 0 0 1
#> SRR934305 5 0 1 0 0 0 0 1
#> SRR934306 5 0 1 0 0 0 0 1
#> SRR934307 5 0 1 0 0 0 0 1
#> SRR934308 5 0 1 0 0 0 0 1
#> SRR934309 5 0 1 0 0 0 0 1
#> SRR934310 5 0 1 0 0 0 0 1
#> SRR934311 5 0 1 0 0 0 0 1
#> SRR934312 1 0 1 1 0 0 0 0
#> SRR934313 1 0 1 1 0 0 0 0
#> SRR934314 1 0 1 1 0 0 0 0
#> SRR934315 1 0 1 1 0 0 0 0
#> SRR934316 1 0 1 1 0 0 0 0
#> SRR934317 1 0 1 1 0 0 0 0
#> SRR934318 1 0 1 1 0 0 0 0
#> SRR934319 1 0 1 1 0 0 0 0
#> SRR934320 1 0 1 1 0 0 0 0
#> SRR934321 1 0 1 1 0 0 0 0
#> SRR934322 1 0 1 1 0 0 0 0
#> SRR934323 1 0 1 1 0 0 0 0
#> SRR934324 1 0 1 1 0 0 0 0
#> SRR934325 1 0 1 1 0 0 0 0
#> SRR934326 1 0 1 1 0 0 0 0
#> SRR934327 1 0 1 1 0 0 0 0
#> SRR934328 1 0 1 1 0 0 0 0
#> SRR934329 1 0 1 1 0 0 0 0
#> SRR934330 1 0 1 1 0 0 0 0
#> SRR934331 1 0 1 1 0 0 0 0
#> SRR934332 1 0 1 1 0 0 0 0
#> SRR934333 1 0 1 1 0 0 0 0
#> SRR934334 1 0 1 1 0 0 0 0
#> SRR934335 1 0 1 1 0 0 0 0
#> SRR934344 1 0 1 1 0 0 0 0
#> SRR934345 1 0 1 1 0 0 0 0
#> SRR934346 1 0 1 1 0 0 0 0
#> SRR934347 1 0 1 1 0 0 0 0
#> SRR934348 1 0 1 1 0 0 0 0
#> SRR934349 1 0 1 1 0 0 0 0
#> SRR934350 1 0 1 1 0 0 0 0
#> SRR934351 1 0 1 1 0 0 0 0
#> SRR934336 1 0 1 1 0 0 0 0
#> SRR934337 1 0 1 1 0 0 0 0
#> SRR934338 1 0 1 1 0 0 0 0
#> SRR934339 1 0 1 1 0 0 0 0
#> SRR934340 1 0 1 1 0 0 0 0
#> SRR934341 1 0 1 1 0 0 0 0
#> SRR934342 1 0 1 1 0 0 0 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.000 1.000 0.000 0.000 1 0 0 0.000
#> SRR934217 3 0.000 1.000 0.000 0.000 1 0 0 0.000
#> SRR934218 3 0.000 1.000 0.000 0.000 1 0 0 0.000
#> SRR934219 3 0.000 1.000 0.000 0.000 1 0 0 0.000
#> SRR934220 3 0.000 1.000 0.000 0.000 1 0 0 0.000
#> SRR934221 3 0.000 1.000 0.000 0.000 1 0 0 0.000
#> SRR934222 3 0.000 1.000 0.000 0.000 1 0 0 0.000
#> SRR934223 3 0.000 1.000 0.000 0.000 1 0 0 0.000
#> SRR934224 1 0.386 0.666 0.524 0.000 0 0 0 0.476
#> SRR934225 1 0.386 0.666 0.524 0.000 0 0 0 0.476
#> SRR934226 1 0.386 0.666 0.524 0.000 0 0 0 0.476
#> SRR934227 1 0.386 0.666 0.524 0.000 0 0 0 0.476
#> SRR934228 1 0.386 0.666 0.524 0.000 0 0 0 0.476
#> SRR934229 1 0.386 0.666 0.524 0.000 0 0 0 0.476
#> SRR934230 1 0.386 0.666 0.524 0.000 0 0 0 0.476
#> SRR934231 1 0.386 0.666 0.524 0.000 0 0 0 0.476
#> SRR934232 2 0.000 0.754 0.000 1.000 0 0 0 0.000
#> SRR934233 2 0.000 0.754 0.000 1.000 0 0 0 0.000
#> SRR934234 2 0.000 0.754 0.000 1.000 0 0 0 0.000
#> SRR934235 2 0.000 0.754 0.000 1.000 0 0 0 0.000
#> SRR934236 2 0.000 0.754 0.000 1.000 0 0 0 0.000
#> SRR934237 2 0.000 0.754 0.000 1.000 0 0 0 0.000
#> SRR934238 2 0.000 0.754 0.000 1.000 0 0 0 0.000
#> SRR934239 2 0.000 0.754 0.000 1.000 0 0 0 0.000
#> SRR934240 2 0.000 0.754 0.000 1.000 0 0 0 0.000
#> SRR934241 2 0.000 0.754 0.000 1.000 0 0 0 0.000
#> SRR934242 2 0.000 0.754 0.000 1.000 0 0 0 0.000
#> SRR934243 2 0.000 0.754 0.000 1.000 0 0 0 0.000
#> SRR934244 2 0.000 0.754 0.000 1.000 0 0 0 0.000
#> SRR934245 2 0.000 0.754 0.000 1.000 0 0 0 0.000
#> SRR934246 2 0.000 0.754 0.000 1.000 0 0 0 0.000
#> SRR934247 2 0.000 0.754 0.000 1.000 0 0 0 0.000
#> SRR934248 4 0.000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934249 4 0.000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934250 4 0.000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934251 4 0.000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934252 4 0.000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934253 4 0.000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934254 4 0.000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934255 4 0.000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934256 6 0.386 1.000 0.000 0.476 0 0 0 0.524
#> SRR934257 6 0.386 1.000 0.000 0.476 0 0 0 0.524
#> SRR934258 6 0.386 1.000 0.000 0.476 0 0 0 0.524
#> SRR934259 6 0.386 1.000 0.000 0.476 0 0 0 0.524
#> SRR934260 6 0.386 1.000 0.000 0.476 0 0 0 0.524
#> SRR934261 6 0.386 1.000 0.000 0.476 0 0 0 0.524
#> SRR934262 6 0.386 1.000 0.000 0.476 0 0 0 0.524
#> SRR934263 6 0.386 1.000 0.000 0.476 0 0 0 0.524
#> SRR934264 4 0.000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934265 4 0.000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934266 4 0.000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934267 4 0.000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934268 4 0.000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934269 4 0.000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934270 4 0.000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934271 4 0.000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934272 1 0.358 0.734 0.660 0.000 0 0 0 0.340
#> SRR934273 1 0.358 0.734 0.660 0.000 0 0 0 0.340
#> SRR934274 1 0.358 0.734 0.660 0.000 0 0 0 0.340
#> SRR934275 1 0.358 0.734 0.660 0.000 0 0 0 0.340
#> SRR934276 1 0.358 0.734 0.660 0.000 0 0 0 0.340
#> SRR934277 1 0.358 0.734 0.660 0.000 0 0 0 0.340
#> SRR934278 1 0.358 0.734 0.660 0.000 0 0 0 0.340
#> SRR934279 1 0.358 0.734 0.660 0.000 0 0 0 0.340
#> SRR934280 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934281 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934282 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934283 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934284 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934285 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934286 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934287 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934288 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934289 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934290 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934291 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934292 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934293 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934294 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934295 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934296 2 0.352 -0.113 0.000 0.676 0 0 0 0.324
#> SRR934297 2 0.352 -0.113 0.000 0.676 0 0 0 0.324
#> SRR934298 2 0.352 -0.113 0.000 0.676 0 0 0 0.324
#> SRR934299 2 0.352 -0.113 0.000 0.676 0 0 0 0.324
#> SRR934300 2 0.352 -0.113 0.000 0.676 0 0 0 0.324
#> SRR934301 2 0.352 -0.113 0.000 0.676 0 0 0 0.324
#> SRR934302 2 0.352 -0.113 0.000 0.676 0 0 0 0.324
#> SRR934303 2 0.352 -0.113 0.000 0.676 0 0 0 0.324
#> SRR934304 5 0.000 1.000 0.000 0.000 0 0 1 0.000
#> SRR934305 5 0.000 1.000 0.000 0.000 0 0 1 0.000
#> SRR934306 5 0.000 1.000 0.000 0.000 0 0 1 0.000
#> SRR934307 5 0.000 1.000 0.000 0.000 0 0 1 0.000
#> SRR934308 5 0.000 1.000 0.000 0.000 0 0 1 0.000
#> SRR934309 5 0.000 1.000 0.000 0.000 0 0 1 0.000
#> SRR934310 5 0.000 1.000 0.000 0.000 0 0 1 0.000
#> SRR934311 5 0.000 1.000 0.000 0.000 0 0 1 0.000
#> SRR934312 1 0.026 0.811 0.992 0.000 0 0 0 0.008
#> SRR934313 1 0.026 0.811 0.992 0.000 0 0 0 0.008
#> SRR934314 1 0.026 0.811 0.992 0.000 0 0 0 0.008
#> SRR934315 1 0.026 0.811 0.992 0.000 0 0 0 0.008
#> SRR934316 1 0.026 0.811 0.992 0.000 0 0 0 0.008
#> SRR934317 1 0.026 0.811 0.992 0.000 0 0 0 0.008
#> SRR934318 1 0.026 0.811 0.992 0.000 0 0 0 0.008
#> SRR934319 1 0.026 0.811 0.992 0.000 0 0 0 0.008
#> SRR934320 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934321 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934322 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934323 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934324 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934325 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934326 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934327 1 0.000 0.810 1.000 0.000 0 0 0 0.000
#> SRR934328 1 0.026 0.811 0.992 0.000 0 0 0 0.008
#> SRR934329 1 0.026 0.811 0.992 0.000 0 0 0 0.008
#> SRR934330 1 0.026 0.811 0.992 0.000 0 0 0 0.008
#> SRR934331 1 0.026 0.811 0.992 0.000 0 0 0 0.008
#> SRR934332 1 0.026 0.811 0.992 0.000 0 0 0 0.008
#> SRR934333 1 0.026 0.811 0.992 0.000 0 0 0 0.008
#> SRR934334 1 0.026 0.811 0.992 0.000 0 0 0 0.008
#> SRR934335 1 0.026 0.811 0.992 0.000 0 0 0 0.008
#> SRR934344 1 0.372 0.715 0.616 0.000 0 0 0 0.384
#> SRR934345 1 0.372 0.715 0.616 0.000 0 0 0 0.384
#> SRR934346 1 0.372 0.715 0.616 0.000 0 0 0 0.384
#> SRR934347 1 0.372 0.715 0.616 0.000 0 0 0 0.384
#> SRR934348 1 0.372 0.715 0.616 0.000 0 0 0 0.384
#> SRR934349 1 0.372 0.715 0.616 0.000 0 0 0 0.384
#> SRR934350 1 0.372 0.715 0.616 0.000 0 0 0 0.384
#> SRR934351 1 0.372 0.715 0.616 0.000 0 0 0 0.384
#> SRR934336 1 0.386 0.666 0.524 0.000 0 0 0 0.476
#> SRR934337 1 0.386 0.666 0.524 0.000 0 0 0 0.476
#> SRR934338 1 0.386 0.666 0.524 0.000 0 0 0 0.476
#> SRR934339 1 0.386 0.666 0.524 0.000 0 0 0 0.476
#> SRR934340 1 0.386 0.666 0.524 0.000 0 0 0 0.476
#> SRR934341 1 0.386 0.666 0.524 0.000 0 0 0 0.476
#> SRR934342 1 0.386 0.666 0.524 0.000 0 0 0 0.476
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 14550 rows and 135 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 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 0.867 0.946 0.974 0.4381 0.580 0.580
#> 3 3 0.790 0.880 0.928 0.3050 0.818 0.693
#> 4 4 0.997 0.955 0.971 0.1241 0.852 0.685
#> 5 5 0.735 0.657 0.811 0.1373 0.860 0.643
#> 6 6 0.817 0.890 0.869 0.0506 0.916 0.714
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
#> SRR934216 1 0.7815 0.724 0.768 0.232
#> SRR934217 1 0.7950 0.712 0.760 0.240
#> SRR934218 1 0.7950 0.712 0.760 0.240
#> SRR934219 1 0.7453 0.752 0.788 0.212
#> SRR934220 1 0.7299 0.762 0.796 0.204
#> SRR934221 1 0.7528 0.746 0.784 0.216
#> SRR934222 1 0.7815 0.724 0.768 0.232
#> SRR934223 1 0.7745 0.729 0.772 0.228
#> SRR934224 1 0.0000 0.962 1.000 0.000
#> SRR934225 1 0.0000 0.962 1.000 0.000
#> SRR934226 1 0.0000 0.962 1.000 0.000
#> SRR934227 1 0.0000 0.962 1.000 0.000
#> SRR934228 1 0.0000 0.962 1.000 0.000
#> SRR934229 1 0.0000 0.962 1.000 0.000
#> SRR934230 1 0.0000 0.962 1.000 0.000
#> SRR934231 1 0.0000 0.962 1.000 0.000
#> SRR934232 2 0.0000 0.997 0.000 1.000
#> SRR934233 2 0.0000 0.997 0.000 1.000
#> SRR934234 2 0.0000 0.997 0.000 1.000
#> SRR934235 2 0.0000 0.997 0.000 1.000
#> SRR934236 2 0.0000 0.997 0.000 1.000
#> SRR934237 2 0.0000 0.997 0.000 1.000
#> SRR934238 2 0.0000 0.997 0.000 1.000
#> SRR934239 2 0.0000 0.997 0.000 1.000
#> SRR934240 2 0.1414 0.981 0.020 0.980
#> SRR934241 2 0.0672 0.992 0.008 0.992
#> SRR934242 2 0.0672 0.992 0.008 0.992
#> SRR934243 2 0.1414 0.981 0.020 0.980
#> SRR934244 2 0.0938 0.988 0.012 0.988
#> SRR934245 2 0.0672 0.992 0.008 0.992
#> SRR934246 2 0.0938 0.988 0.012 0.988
#> SRR934247 2 0.0672 0.992 0.008 0.992
#> SRR934248 2 0.0000 0.997 0.000 1.000
#> SRR934249 2 0.0000 0.997 0.000 1.000
#> SRR934250 2 0.0000 0.997 0.000 1.000
#> SRR934251 2 0.0000 0.997 0.000 1.000
#> SRR934252 2 0.0000 0.997 0.000 1.000
#> SRR934253 2 0.0000 0.997 0.000 1.000
#> SRR934254 2 0.0000 0.997 0.000 1.000
#> SRR934255 2 0.0000 0.997 0.000 1.000
#> SRR934256 1 0.0000 0.962 1.000 0.000
#> SRR934257 1 0.0000 0.962 1.000 0.000
#> SRR934258 1 0.0000 0.962 1.000 0.000
#> SRR934259 1 0.0000 0.962 1.000 0.000
#> SRR934260 1 0.0000 0.962 1.000 0.000
#> SRR934261 1 0.0000 0.962 1.000 0.000
#> SRR934262 1 0.0000 0.962 1.000 0.000
#> SRR934263 1 0.0000 0.962 1.000 0.000
#> SRR934264 2 0.0000 0.997 0.000 1.000
#> SRR934265 2 0.0000 0.997 0.000 1.000
#> SRR934266 2 0.0000 0.997 0.000 1.000
#> SRR934267 2 0.0000 0.997 0.000 1.000
#> SRR934268 2 0.0000 0.997 0.000 1.000
#> SRR934269 2 0.0000 0.997 0.000 1.000
#> SRR934270 2 0.0000 0.997 0.000 1.000
#> SRR934271 2 0.0000 0.997 0.000 1.000
#> SRR934272 1 0.0000 0.962 1.000 0.000
#> SRR934273 1 0.0000 0.962 1.000 0.000
#> SRR934274 1 0.0000 0.962 1.000 0.000
#> SRR934275 1 0.0000 0.962 1.000 0.000
#> SRR934276 1 0.0000 0.962 1.000 0.000
#> SRR934277 1 0.0000 0.962 1.000 0.000
#> SRR934278 1 0.0000 0.962 1.000 0.000
#> SRR934279 1 0.0000 0.962 1.000 0.000
#> SRR934280 1 0.0000 0.962 1.000 0.000
#> SRR934281 1 0.0000 0.962 1.000 0.000
#> SRR934282 1 0.0000 0.962 1.000 0.000
#> SRR934283 1 0.0000 0.962 1.000 0.000
#> SRR934284 1 0.0000 0.962 1.000 0.000
#> SRR934285 1 0.0000 0.962 1.000 0.000
#> SRR934286 1 0.0000 0.962 1.000 0.000
#> SRR934287 1 0.0000 0.962 1.000 0.000
#> SRR934288 1 0.0000 0.962 1.000 0.000
#> SRR934289 1 0.0000 0.962 1.000 0.000
#> SRR934290 1 0.0000 0.962 1.000 0.000
#> SRR934291 1 0.0000 0.962 1.000 0.000
#> SRR934292 1 0.0000 0.962 1.000 0.000
#> SRR934293 1 0.0000 0.962 1.000 0.000
#> SRR934294 1 0.0000 0.962 1.000 0.000
#> SRR934295 1 0.0000 0.962 1.000 0.000
#> SRR934296 1 0.7299 0.762 0.796 0.204
#> SRR934297 1 0.7299 0.762 0.796 0.204
#> SRR934298 1 0.7299 0.762 0.796 0.204
#> SRR934299 1 0.7299 0.762 0.796 0.204
#> SRR934300 1 0.7299 0.762 0.796 0.204
#> SRR934301 1 0.7299 0.762 0.796 0.204
#> SRR934302 1 0.7299 0.762 0.796 0.204
#> SRR934303 1 0.7299 0.762 0.796 0.204
#> SRR934304 2 0.0000 0.997 0.000 1.000
#> SRR934305 2 0.0000 0.997 0.000 1.000
#> SRR934306 2 0.0000 0.997 0.000 1.000
#> SRR934307 2 0.0000 0.997 0.000 1.000
#> SRR934308 2 0.0000 0.997 0.000 1.000
#> SRR934309 2 0.0000 0.997 0.000 1.000
#> SRR934310 2 0.0000 0.997 0.000 1.000
#> SRR934311 2 0.0000 0.997 0.000 1.000
#> SRR934312 1 0.0000 0.962 1.000 0.000
#> SRR934313 1 0.0000 0.962 1.000 0.000
#> SRR934314 1 0.0000 0.962 1.000 0.000
#> SRR934315 1 0.0000 0.962 1.000 0.000
#> SRR934316 1 0.0000 0.962 1.000 0.000
#> SRR934317 1 0.0000 0.962 1.000 0.000
#> SRR934318 1 0.0000 0.962 1.000 0.000
#> SRR934319 1 0.0000 0.962 1.000 0.000
#> SRR934320 1 0.0000 0.962 1.000 0.000
#> SRR934321 1 0.0000 0.962 1.000 0.000
#> SRR934322 1 0.0000 0.962 1.000 0.000
#> SRR934323 1 0.0000 0.962 1.000 0.000
#> SRR934324 1 0.0000 0.962 1.000 0.000
#> SRR934325 1 0.0000 0.962 1.000 0.000
#> SRR934326 1 0.0000 0.962 1.000 0.000
#> SRR934327 1 0.0000 0.962 1.000 0.000
#> SRR934328 1 0.0000 0.962 1.000 0.000
#> SRR934329 1 0.0000 0.962 1.000 0.000
#> SRR934330 1 0.0000 0.962 1.000 0.000
#> SRR934331 1 0.0000 0.962 1.000 0.000
#> SRR934332 1 0.0000 0.962 1.000 0.000
#> SRR934333 1 0.0000 0.962 1.000 0.000
#> SRR934334 1 0.0000 0.962 1.000 0.000
#> SRR934335 1 0.0000 0.962 1.000 0.000
#> SRR934344 1 0.0000 0.962 1.000 0.000
#> SRR934345 1 0.0000 0.962 1.000 0.000
#> SRR934346 1 0.0000 0.962 1.000 0.000
#> SRR934347 1 0.0000 0.962 1.000 0.000
#> SRR934348 1 0.0000 0.962 1.000 0.000
#> SRR934349 1 0.0000 0.962 1.000 0.000
#> SRR934350 1 0.0000 0.962 1.000 0.000
#> SRR934351 1 0.0000 0.962 1.000 0.000
#> SRR934336 1 0.0000 0.962 1.000 0.000
#> SRR934337 1 0.0000 0.962 1.000 0.000
#> SRR934338 1 0.0000 0.962 1.000 0.000
#> SRR934339 1 0.0000 0.962 1.000 0.000
#> SRR934340 1 0.0000 0.962 1.000 0.000
#> SRR934341 1 0.0000 0.962 1.000 0.000
#> SRR934342 1 0.0000 0.962 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 1 0.5058 0.693 0.756 0.000 0.244
#> SRR934217 1 0.5058 0.693 0.756 0.000 0.244
#> SRR934218 1 0.5058 0.693 0.756 0.000 0.244
#> SRR934219 1 0.5058 0.693 0.756 0.000 0.244
#> SRR934220 1 0.5058 0.693 0.756 0.000 0.244
#> SRR934221 1 0.5058 0.693 0.756 0.000 0.244
#> SRR934222 1 0.5058 0.693 0.756 0.000 0.244
#> SRR934223 1 0.5058 0.693 0.756 0.000 0.244
#> SRR934224 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934225 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934226 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934227 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934228 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934229 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934230 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934231 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934232 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934233 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934234 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934235 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934236 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934237 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934238 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934239 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934240 2 0.0237 0.695 0.000 0.996 0.004
#> SRR934241 2 0.0237 0.695 0.000 0.996 0.004
#> SRR934242 2 0.0237 0.695 0.000 0.996 0.004
#> SRR934243 2 0.0000 0.696 0.000 1.000 0.000
#> SRR934244 2 0.0237 0.695 0.000 0.996 0.004
#> SRR934245 2 0.0000 0.696 0.000 1.000 0.000
#> SRR934246 2 0.0000 0.696 0.000 1.000 0.000
#> SRR934247 2 0.0237 0.695 0.000 0.996 0.004
#> SRR934248 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934249 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934250 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934251 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934252 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934253 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934254 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934255 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934256 2 0.5058 0.713 0.244 0.756 0.000
#> SRR934257 2 0.5058 0.713 0.244 0.756 0.000
#> SRR934258 2 0.5058 0.713 0.244 0.756 0.000
#> SRR934259 2 0.5058 0.713 0.244 0.756 0.000
#> SRR934260 2 0.5058 0.713 0.244 0.756 0.000
#> SRR934261 2 0.5058 0.713 0.244 0.756 0.000
#> SRR934262 2 0.5058 0.713 0.244 0.756 0.000
#> SRR934263 2 0.5058 0.713 0.244 0.756 0.000
#> SRR934264 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934265 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934266 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934267 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934268 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934269 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934270 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934271 3 0.5058 0.919 0.000 0.244 0.756
#> SRR934272 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934273 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934274 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934275 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934276 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934277 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934278 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934279 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934280 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934281 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934282 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934283 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934284 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934285 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934286 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934287 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934288 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934289 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934290 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934291 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934292 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934293 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934294 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934295 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934296 2 0.6881 0.650 0.020 0.592 0.388
#> SRR934297 2 0.6783 0.642 0.016 0.588 0.396
#> SRR934298 2 0.7104 0.671 0.032 0.608 0.360
#> SRR934299 2 0.7032 0.667 0.028 0.604 0.368
#> SRR934300 2 0.6753 0.650 0.016 0.596 0.388
#> SRR934301 2 0.6553 0.623 0.008 0.580 0.412
#> SRR934302 2 0.7032 0.667 0.028 0.604 0.368
#> SRR934303 2 0.6832 0.661 0.020 0.604 0.376
#> SRR934304 3 0.0000 0.739 0.000 0.000 1.000
#> SRR934305 3 0.0000 0.739 0.000 0.000 1.000
#> SRR934306 3 0.0000 0.739 0.000 0.000 1.000
#> SRR934307 3 0.0000 0.739 0.000 0.000 1.000
#> SRR934308 3 0.0000 0.739 0.000 0.000 1.000
#> SRR934309 3 0.0000 0.739 0.000 0.000 1.000
#> SRR934310 3 0.0000 0.739 0.000 0.000 1.000
#> SRR934311 3 0.0000 0.739 0.000 0.000 1.000
#> SRR934312 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934313 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934314 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934315 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934316 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934317 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934318 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934319 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934320 1 0.0237 0.967 0.996 0.004 0.000
#> SRR934321 1 0.0747 0.957 0.984 0.016 0.000
#> SRR934322 1 0.0592 0.960 0.988 0.012 0.000
#> SRR934323 1 0.0592 0.960 0.988 0.012 0.000
#> SRR934324 1 0.0592 0.960 0.988 0.012 0.000
#> SRR934325 1 0.0747 0.957 0.984 0.016 0.000
#> SRR934326 1 0.0747 0.957 0.984 0.016 0.000
#> SRR934327 1 0.0592 0.960 0.988 0.012 0.000
#> SRR934328 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934329 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934330 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934331 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934332 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934333 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934334 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934335 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934344 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934345 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934346 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934347 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934348 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934349 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934350 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934351 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934336 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934337 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934338 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934339 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934340 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934341 1 0.0000 0.970 1.000 0.000 0.000
#> SRR934342 1 0.0000 0.970 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 3 0.0817 0.863 0.024 0.000 0.976 0.000
#> SRR934217 3 0.0921 0.860 0.028 0.000 0.972 0.000
#> SRR934218 3 0.0817 0.863 0.024 0.000 0.976 0.000
#> SRR934219 3 0.1022 0.856 0.032 0.000 0.968 0.000
#> SRR934220 3 0.1118 0.851 0.036 0.000 0.964 0.000
#> SRR934221 3 0.0817 0.863 0.024 0.000 0.976 0.000
#> SRR934222 3 0.0921 0.860 0.028 0.000 0.972 0.000
#> SRR934223 3 0.0817 0.863 0.024 0.000 0.976 0.000
#> SRR934224 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934225 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934226 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934227 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934228 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934229 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934230 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934231 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934232 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934233 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934234 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934235 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934236 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934237 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934238 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934239 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934240 2 0.0592 0.986 0.000 0.984 0.000 0.016
#> SRR934241 2 0.0592 0.986 0.000 0.984 0.000 0.016
#> SRR934242 2 0.0592 0.986 0.000 0.984 0.000 0.016
#> SRR934243 2 0.0592 0.986 0.000 0.984 0.000 0.016
#> SRR934244 2 0.0592 0.986 0.000 0.984 0.000 0.016
#> SRR934245 2 0.0592 0.986 0.000 0.984 0.000 0.016
#> SRR934246 2 0.0592 0.986 0.000 0.984 0.000 0.016
#> SRR934247 2 0.0592 0.986 0.000 0.984 0.000 0.016
#> SRR934248 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934249 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934250 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934251 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934252 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934253 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934254 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934255 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934256 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR934257 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR934258 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR934259 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR934260 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR934261 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR934262 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR934263 4 0.0000 1.000 0.000 0.000 0.000 1.000
#> SRR934264 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934265 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934266 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934267 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934268 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934269 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934270 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934271 2 0.0000 0.995 0.000 1.000 0.000 0.000
#> SRR934272 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934273 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934274 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934275 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934276 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934277 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934278 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934279 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934280 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934281 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934282 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934283 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934284 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934285 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934286 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934287 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934288 1 0.0592 0.984 0.984 0.000 0.016 0.000
#> SRR934289 1 0.0592 0.984 0.984 0.000 0.016 0.000
#> SRR934290 1 0.0592 0.984 0.984 0.000 0.016 0.000
#> SRR934291 1 0.0592 0.984 0.984 0.000 0.016 0.000
#> SRR934292 1 0.0592 0.984 0.984 0.000 0.016 0.000
#> SRR934293 1 0.0592 0.984 0.984 0.000 0.016 0.000
#> SRR934294 1 0.0592 0.984 0.984 0.000 0.016 0.000
#> SRR934295 1 0.0592 0.984 0.984 0.000 0.016 0.000
#> SRR934296 3 0.4431 0.678 0.000 0.000 0.696 0.304
#> SRR934297 3 0.4250 0.706 0.000 0.000 0.724 0.276
#> SRR934298 3 0.4624 0.624 0.000 0.000 0.660 0.340
#> SRR934299 3 0.4382 0.688 0.000 0.000 0.704 0.296
#> SRR934300 3 0.4382 0.688 0.000 0.000 0.704 0.296
#> SRR934301 3 0.4431 0.678 0.000 0.000 0.696 0.304
#> SRR934302 3 0.4331 0.696 0.000 0.000 0.712 0.288
#> SRR934303 3 0.4277 0.703 0.000 0.000 0.720 0.280
#> SRR934304 3 0.0592 0.864 0.000 0.016 0.984 0.000
#> SRR934305 3 0.0592 0.864 0.000 0.016 0.984 0.000
#> SRR934306 3 0.0592 0.864 0.000 0.016 0.984 0.000
#> SRR934307 3 0.0592 0.864 0.000 0.016 0.984 0.000
#> SRR934308 3 0.0592 0.864 0.000 0.016 0.984 0.000
#> SRR934309 3 0.0592 0.864 0.000 0.016 0.984 0.000
#> SRR934310 3 0.0592 0.864 0.000 0.016 0.984 0.000
#> SRR934311 3 0.0592 0.864 0.000 0.016 0.984 0.000
#> SRR934312 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934313 1 0.0336 0.987 0.992 0.000 0.000 0.008
#> SRR934314 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934315 1 0.0336 0.987 0.992 0.000 0.000 0.008
#> SRR934316 1 0.0188 0.988 0.996 0.000 0.000 0.004
#> SRR934317 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934318 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934319 1 0.0336 0.987 0.992 0.000 0.000 0.008
#> SRR934320 1 0.0336 0.986 0.992 0.000 0.000 0.008
#> SRR934321 1 0.0592 0.982 0.984 0.000 0.000 0.016
#> SRR934322 1 0.0336 0.986 0.992 0.000 0.000 0.008
#> SRR934323 1 0.1118 0.969 0.964 0.000 0.000 0.036
#> SRR934324 1 0.0592 0.982 0.984 0.000 0.000 0.016
#> SRR934325 1 0.1557 0.950 0.944 0.000 0.000 0.056
#> SRR934326 1 0.0469 0.984 0.988 0.000 0.000 0.012
#> SRR934327 1 0.0336 0.986 0.992 0.000 0.000 0.008
#> SRR934328 1 0.1798 0.959 0.944 0.000 0.016 0.040
#> SRR934329 1 0.1888 0.955 0.940 0.000 0.016 0.044
#> SRR934330 1 0.1297 0.974 0.964 0.000 0.016 0.020
#> SRR934331 1 0.1610 0.965 0.952 0.000 0.016 0.032
#> SRR934332 1 0.1510 0.968 0.956 0.000 0.016 0.028
#> SRR934333 1 0.1610 0.965 0.952 0.000 0.016 0.032
#> SRR934334 1 0.1510 0.969 0.956 0.000 0.016 0.028
#> SRR934335 1 0.1975 0.952 0.936 0.000 0.016 0.048
#> SRR934344 1 0.0592 0.984 0.984 0.000 0.016 0.000
#> SRR934345 1 0.0592 0.984 0.984 0.000 0.016 0.000
#> SRR934346 1 0.0592 0.984 0.984 0.000 0.016 0.000
#> SRR934347 1 0.0592 0.984 0.984 0.000 0.016 0.000
#> SRR934348 1 0.0592 0.984 0.984 0.000 0.016 0.000
#> SRR934349 1 0.0592 0.984 0.984 0.000 0.016 0.000
#> SRR934350 1 0.0592 0.984 0.984 0.000 0.016 0.000
#> SRR934351 1 0.0592 0.984 0.984 0.000 0.016 0.000
#> SRR934336 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934337 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934338 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934339 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934340 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934341 1 0.0000 0.989 1.000 0.000 0.000 0.000
#> SRR934342 1 0.0000 0.989 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0.4100 0.9839 0.044 0.000 0.764 0.000 0.192
#> SRR934217 3 0.4136 0.9878 0.048 0.000 0.764 0.000 0.188
#> SRR934218 3 0.4100 0.9839 0.044 0.000 0.764 0.000 0.192
#> SRR934219 3 0.4269 0.9853 0.056 0.000 0.756 0.000 0.188
#> SRR934220 3 0.4269 0.9853 0.056 0.000 0.756 0.000 0.188
#> SRR934221 3 0.4238 0.9889 0.052 0.000 0.756 0.000 0.192
#> SRR934222 3 0.4203 0.9893 0.052 0.000 0.760 0.000 0.188
#> SRR934223 3 0.4238 0.9889 0.052 0.000 0.756 0.000 0.192
#> SRR934224 1 0.0510 0.8690 0.984 0.000 0.016 0.000 0.000
#> SRR934225 1 0.0510 0.8690 0.984 0.000 0.016 0.000 0.000
#> SRR934226 1 0.0510 0.8690 0.984 0.000 0.016 0.000 0.000
#> SRR934227 1 0.0510 0.8690 0.984 0.000 0.016 0.000 0.000
#> SRR934228 1 0.0510 0.8690 0.984 0.000 0.016 0.000 0.000
#> SRR934229 1 0.0510 0.8690 0.984 0.000 0.016 0.000 0.000
#> SRR934230 1 0.0510 0.8690 0.984 0.000 0.016 0.000 0.000
#> SRR934231 1 0.0510 0.8690 0.984 0.000 0.016 0.000 0.000
#> SRR934232 4 0.0703 0.9256 0.000 0.000 0.024 0.976 0.000
#> SRR934233 4 0.0703 0.9256 0.000 0.000 0.024 0.976 0.000
#> SRR934234 4 0.0609 0.9254 0.000 0.000 0.020 0.980 0.000
#> SRR934235 4 0.0703 0.9256 0.000 0.000 0.024 0.976 0.000
#> SRR934236 4 0.0794 0.9253 0.000 0.000 0.028 0.972 0.000
#> SRR934237 4 0.0703 0.9258 0.000 0.000 0.024 0.976 0.000
#> SRR934238 4 0.0794 0.9253 0.000 0.000 0.028 0.972 0.000
#> SRR934239 4 0.0794 0.9253 0.000 0.000 0.028 0.972 0.000
#> SRR934240 4 0.3413 0.8719 0.000 0.000 0.124 0.832 0.044
#> SRR934241 4 0.3413 0.8719 0.000 0.000 0.124 0.832 0.044
#> SRR934242 4 0.3413 0.8719 0.000 0.000 0.124 0.832 0.044
#> SRR934243 4 0.3413 0.8719 0.000 0.000 0.124 0.832 0.044
#> SRR934244 4 0.3413 0.8719 0.000 0.000 0.124 0.832 0.044
#> SRR934245 4 0.3413 0.8719 0.000 0.000 0.124 0.832 0.044
#> SRR934246 4 0.3413 0.8719 0.000 0.000 0.124 0.832 0.044
#> SRR934247 4 0.3413 0.8719 0.000 0.000 0.124 0.832 0.044
#> SRR934248 4 0.1270 0.9174 0.000 0.000 0.052 0.948 0.000
#> SRR934249 4 0.1270 0.9174 0.000 0.000 0.052 0.948 0.000
#> SRR934250 4 0.1270 0.9174 0.000 0.000 0.052 0.948 0.000
#> SRR934251 4 0.1270 0.9174 0.000 0.000 0.052 0.948 0.000
#> SRR934252 4 0.1270 0.9174 0.000 0.000 0.052 0.948 0.000
#> SRR934253 4 0.1270 0.9174 0.000 0.000 0.052 0.948 0.000
#> SRR934254 4 0.1270 0.9174 0.000 0.000 0.052 0.948 0.000
#> SRR934255 4 0.1270 0.9174 0.000 0.000 0.052 0.948 0.000
#> SRR934256 2 0.0000 1.0000 0.000 1.000 0.000 0.000 0.000
#> SRR934257 2 0.0000 1.0000 0.000 1.000 0.000 0.000 0.000
#> SRR934258 2 0.0000 1.0000 0.000 1.000 0.000 0.000 0.000
#> SRR934259 2 0.0000 1.0000 0.000 1.000 0.000 0.000 0.000
#> SRR934260 2 0.0000 1.0000 0.000 1.000 0.000 0.000 0.000
#> SRR934261 2 0.0000 1.0000 0.000 1.000 0.000 0.000 0.000
#> SRR934262 2 0.0000 1.0000 0.000 1.000 0.000 0.000 0.000
#> SRR934263 2 0.0000 1.0000 0.000 1.000 0.000 0.000 0.000
#> SRR934264 4 0.1341 0.9056 0.056 0.000 0.000 0.944 0.000
#> SRR934265 4 0.1197 0.9107 0.048 0.000 0.000 0.952 0.000
#> SRR934266 4 0.1270 0.9079 0.052 0.000 0.000 0.948 0.000
#> SRR934267 4 0.1197 0.9107 0.048 0.000 0.000 0.952 0.000
#> SRR934268 4 0.1484 0.9100 0.048 0.000 0.008 0.944 0.000
#> SRR934269 4 0.1608 0.8894 0.072 0.000 0.000 0.928 0.000
#> SRR934270 4 0.1341 0.9056 0.056 0.000 0.000 0.944 0.000
#> SRR934271 4 0.1357 0.9105 0.048 0.000 0.004 0.948 0.000
#> SRR934272 1 0.0510 0.8708 0.984 0.000 0.000 0.000 0.016
#> SRR934273 1 0.0290 0.8728 0.992 0.000 0.000 0.000 0.008
#> SRR934274 1 0.0510 0.8708 0.984 0.000 0.000 0.000 0.016
#> SRR934275 1 0.0510 0.8708 0.984 0.000 0.000 0.000 0.016
#> SRR934276 1 0.0510 0.8708 0.984 0.000 0.000 0.000 0.016
#> SRR934277 1 0.0671 0.8709 0.980 0.000 0.004 0.000 0.016
#> SRR934278 1 0.0290 0.8728 0.992 0.000 0.000 0.000 0.008
#> SRR934279 1 0.0510 0.8708 0.984 0.000 0.000 0.000 0.016
#> SRR934280 1 0.0693 0.8731 0.980 0.000 0.008 0.000 0.012
#> SRR934281 1 0.0898 0.8713 0.972 0.000 0.008 0.000 0.020
#> SRR934282 1 0.0898 0.8713 0.972 0.000 0.008 0.000 0.020
#> SRR934283 1 0.0693 0.8731 0.980 0.000 0.008 0.000 0.012
#> SRR934284 1 0.0898 0.8713 0.972 0.000 0.008 0.000 0.020
#> SRR934285 1 0.0898 0.8713 0.972 0.000 0.008 0.000 0.020
#> SRR934286 1 0.0693 0.8731 0.980 0.000 0.008 0.000 0.012
#> SRR934287 1 0.0693 0.8731 0.980 0.000 0.008 0.000 0.012
#> SRR934288 1 0.5692 -0.0436 0.472 0.000 0.080 0.000 0.448
#> SRR934289 1 0.5836 -0.0533 0.468 0.004 0.080 0.000 0.448
#> SRR934290 1 0.6127 -0.0803 0.456 0.016 0.080 0.000 0.448
#> SRR934291 1 0.5692 -0.0436 0.472 0.000 0.080 0.000 0.448
#> SRR934292 1 0.5689 -0.0308 0.480 0.000 0.080 0.000 0.440
#> SRR934293 1 0.5836 -0.0533 0.468 0.004 0.080 0.000 0.448
#> SRR934294 1 0.5692 -0.0436 0.472 0.000 0.080 0.000 0.448
#> SRR934295 1 0.5692 -0.0436 0.472 0.000 0.080 0.000 0.448
#> SRR934296 5 0.1990 0.1893 0.004 0.040 0.028 0.000 0.928
#> SRR934297 5 0.2234 0.1890 0.012 0.036 0.032 0.000 0.920
#> SRR934298 5 0.2152 0.1857 0.004 0.044 0.032 0.000 0.920
#> SRR934299 5 0.2152 0.1834 0.004 0.044 0.032 0.000 0.920
#> SRR934300 5 0.2075 0.1861 0.004 0.040 0.032 0.000 0.924
#> SRR934301 5 0.2067 0.1881 0.004 0.044 0.028 0.000 0.924
#> SRR934302 5 0.2067 0.1881 0.004 0.044 0.028 0.000 0.924
#> SRR934303 5 0.2152 0.1834 0.004 0.044 0.032 0.000 0.920
#> SRR934304 5 0.4294 -0.3695 0.000 0.000 0.468 0.000 0.532
#> SRR934305 5 0.4294 -0.3695 0.000 0.000 0.468 0.000 0.532
#> SRR934306 5 0.4294 -0.3695 0.000 0.000 0.468 0.000 0.532
#> SRR934307 5 0.4294 -0.3695 0.000 0.000 0.468 0.000 0.532
#> SRR934308 5 0.4294 -0.3695 0.000 0.000 0.468 0.000 0.532
#> SRR934309 5 0.4294 -0.3695 0.000 0.000 0.468 0.000 0.532
#> SRR934310 5 0.4294 -0.3695 0.000 0.000 0.468 0.000 0.532
#> SRR934311 5 0.4294 -0.3695 0.000 0.000 0.468 0.000 0.532
#> SRR934312 1 0.2507 0.8372 0.908 0.020 0.044 0.000 0.028
#> SRR934313 1 0.2857 0.8180 0.888 0.064 0.020 0.000 0.028
#> SRR934314 1 0.2228 0.8433 0.920 0.040 0.012 0.000 0.028
#> SRR934315 1 0.2734 0.8162 0.888 0.076 0.008 0.000 0.028
#> SRR934316 1 0.2882 0.8191 0.888 0.060 0.024 0.000 0.028
#> SRR934317 1 0.2555 0.8314 0.904 0.052 0.016 0.000 0.028
#> SRR934318 1 0.2765 0.8261 0.896 0.044 0.036 0.000 0.024
#> SRR934319 1 0.2824 0.8174 0.888 0.068 0.016 0.000 0.028
#> SRR934320 1 0.0566 0.8711 0.984 0.012 0.004 0.000 0.000
#> SRR934321 1 0.0955 0.8633 0.968 0.028 0.004 0.000 0.000
#> SRR934322 1 0.0771 0.8681 0.976 0.020 0.004 0.000 0.000
#> SRR934323 1 0.1124 0.8601 0.960 0.036 0.004 0.000 0.000
#> SRR934324 1 0.0771 0.8679 0.976 0.020 0.004 0.000 0.000
#> SRR934325 1 0.0771 0.8702 0.976 0.020 0.004 0.000 0.000
#> SRR934326 1 0.0955 0.8633 0.968 0.028 0.004 0.000 0.000
#> SRR934327 1 0.1041 0.8623 0.964 0.032 0.004 0.000 0.000
#> SRR934328 5 0.8081 0.3400 0.144 0.196 0.232 0.000 0.428
#> SRR934329 5 0.8064 0.3349 0.136 0.204 0.232 0.000 0.428
#> SRR934330 5 0.8099 0.3459 0.156 0.184 0.232 0.000 0.428
#> SRR934331 5 0.8094 0.3447 0.152 0.188 0.232 0.000 0.428
#> SRR934332 5 0.8099 0.3459 0.156 0.184 0.232 0.000 0.428
#> SRR934333 5 0.8088 0.3427 0.148 0.192 0.232 0.000 0.428
#> SRR934334 5 0.8062 0.3424 0.144 0.192 0.232 0.000 0.432
#> SRR934335 5 0.8073 0.3371 0.140 0.200 0.232 0.000 0.428
#> SRR934344 5 0.6551 0.2840 0.384 0.000 0.200 0.000 0.416
#> SRR934345 5 0.6566 0.2921 0.380 0.000 0.204 0.000 0.416
#> SRR934346 5 0.6551 0.2840 0.384 0.000 0.200 0.000 0.416
#> SRR934347 5 0.6596 0.3071 0.372 0.000 0.212 0.000 0.416
#> SRR934348 5 0.6596 0.3071 0.372 0.000 0.212 0.000 0.416
#> SRR934349 5 0.6596 0.3071 0.372 0.000 0.212 0.000 0.416
#> SRR934350 5 0.6517 0.2655 0.392 0.000 0.192 0.000 0.416
#> SRR934351 5 0.6596 0.3071 0.372 0.000 0.212 0.000 0.416
#> SRR934336 1 0.0000 0.8730 1.000 0.000 0.000 0.000 0.000
#> SRR934337 1 0.0000 0.8730 1.000 0.000 0.000 0.000 0.000
#> SRR934338 1 0.0000 0.8730 1.000 0.000 0.000 0.000 0.000
#> SRR934339 1 0.0000 0.8730 1.000 0.000 0.000 0.000 0.000
#> SRR934340 1 0.0000 0.8730 1.000 0.000 0.000 0.000 0.000
#> SRR934341 1 0.0000 0.8730 1.000 0.000 0.000 0.000 0.000
#> SRR934342 1 0.0000 0.8730 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.4821 0.990 0.088 0.000 0.696 0.000 0.196 0.020
#> SRR934217 3 0.4869 0.992 0.088 0.000 0.696 0.000 0.192 0.024
#> SRR934218 3 0.4886 0.992 0.092 0.000 0.696 0.000 0.188 0.024
#> SRR934219 3 0.4930 0.987 0.092 0.000 0.696 0.000 0.184 0.028
#> SRR934220 3 0.4914 0.988 0.088 0.000 0.696 0.000 0.188 0.028
#> SRR934221 3 0.4886 0.992 0.092 0.000 0.696 0.000 0.188 0.024
#> SRR934222 3 0.4821 0.990 0.088 0.000 0.696 0.000 0.196 0.020
#> SRR934223 3 0.4821 0.990 0.088 0.000 0.696 0.000 0.196 0.020
#> SRR934224 6 0.1387 0.946 0.000 0.000 0.068 0.000 0.000 0.932
#> SRR934225 6 0.1387 0.946 0.000 0.000 0.068 0.000 0.000 0.932
#> SRR934226 6 0.1387 0.946 0.000 0.000 0.068 0.000 0.000 0.932
#> SRR934227 6 0.1387 0.946 0.000 0.000 0.068 0.000 0.000 0.932
#> SRR934228 6 0.1387 0.946 0.000 0.000 0.068 0.000 0.000 0.932
#> SRR934229 6 0.1387 0.946 0.000 0.000 0.068 0.000 0.000 0.932
#> SRR934230 6 0.1387 0.946 0.000 0.000 0.068 0.000 0.000 0.932
#> SRR934231 6 0.1387 0.946 0.000 0.000 0.068 0.000 0.000 0.932
#> SRR934232 4 0.3123 0.843 0.088 0.000 0.076 0.836 0.000 0.000
#> SRR934233 4 0.3068 0.843 0.088 0.000 0.072 0.840 0.000 0.000
#> SRR934234 4 0.3068 0.844 0.088 0.000 0.072 0.840 0.000 0.000
#> SRR934235 4 0.3013 0.844 0.088 0.000 0.068 0.844 0.000 0.000
#> SRR934236 4 0.3327 0.838 0.092 0.000 0.088 0.820 0.000 0.000
#> SRR934237 4 0.3123 0.843 0.088 0.000 0.076 0.836 0.000 0.000
#> SRR934238 4 0.3175 0.842 0.088 0.000 0.080 0.832 0.000 0.000
#> SRR934239 4 0.3227 0.841 0.088 0.000 0.084 0.828 0.000 0.000
#> SRR934240 4 0.5012 0.729 0.100 0.000 0.300 0.600 0.000 0.000
#> SRR934241 4 0.5012 0.729 0.100 0.000 0.300 0.600 0.000 0.000
#> SRR934242 4 0.5012 0.729 0.100 0.000 0.300 0.600 0.000 0.000
#> SRR934243 4 0.5012 0.729 0.100 0.000 0.300 0.600 0.000 0.000
#> SRR934244 4 0.5012 0.729 0.100 0.000 0.300 0.600 0.000 0.000
#> SRR934245 4 0.5012 0.729 0.100 0.000 0.300 0.600 0.000 0.000
#> SRR934246 4 0.5012 0.729 0.100 0.000 0.300 0.600 0.000 0.000
#> SRR934247 4 0.5012 0.729 0.100 0.000 0.300 0.600 0.000 0.000
#> SRR934248 4 0.1075 0.841 0.048 0.000 0.000 0.952 0.000 0.000
#> SRR934249 4 0.1075 0.841 0.048 0.000 0.000 0.952 0.000 0.000
#> SRR934250 4 0.1075 0.841 0.048 0.000 0.000 0.952 0.000 0.000
#> SRR934251 4 0.1075 0.841 0.048 0.000 0.000 0.952 0.000 0.000
#> SRR934252 4 0.1075 0.841 0.048 0.000 0.000 0.952 0.000 0.000
#> SRR934253 4 0.1075 0.841 0.048 0.000 0.000 0.952 0.000 0.000
#> SRR934254 4 0.1075 0.841 0.048 0.000 0.000 0.952 0.000 0.000
#> SRR934255 4 0.1075 0.841 0.048 0.000 0.000 0.952 0.000 0.000
#> SRR934256 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934257 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934258 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934259 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934260 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934261 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934262 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934263 2 0.0000 1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR934264 4 0.0260 0.852 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR934265 4 0.0260 0.852 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR934266 4 0.0405 0.851 0.004 0.000 0.008 0.988 0.000 0.000
#> SRR934267 4 0.0405 0.851 0.004 0.000 0.008 0.988 0.000 0.000
#> SRR934268 4 0.0520 0.851 0.008 0.000 0.008 0.984 0.000 0.000
#> SRR934269 4 0.0260 0.852 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR934270 4 0.0260 0.852 0.000 0.000 0.008 0.992 0.000 0.000
#> SRR934271 4 0.0520 0.851 0.008 0.000 0.008 0.984 0.000 0.000
#> SRR934272 6 0.0000 0.973 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934273 6 0.0000 0.973 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934274 6 0.0000 0.973 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934275 6 0.0000 0.973 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934276 6 0.0000 0.973 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934277 6 0.0000 0.973 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934278 6 0.0000 0.973 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934279 6 0.0000 0.973 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934280 6 0.0000 0.973 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934281 6 0.0000 0.973 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934282 6 0.0000 0.973 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934283 6 0.0000 0.973 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934284 6 0.0000 0.973 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934285 6 0.0000 0.973 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934286 6 0.0000 0.973 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934287 6 0.0000 0.973 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR934288 1 0.2933 0.893 0.796 0.000 0.004 0.000 0.000 0.200
#> SRR934289 1 0.3043 0.893 0.792 0.000 0.008 0.000 0.000 0.200
#> SRR934290 1 0.3043 0.893 0.792 0.000 0.008 0.000 0.000 0.200
#> SRR934291 1 0.3043 0.893 0.792 0.000 0.008 0.000 0.000 0.200
#> SRR934292 1 0.2964 0.892 0.792 0.000 0.004 0.000 0.000 0.204
#> SRR934293 1 0.3043 0.893 0.792 0.000 0.008 0.000 0.000 0.200
#> SRR934294 1 0.3043 0.893 0.792 0.000 0.008 0.000 0.000 0.200
#> SRR934295 1 0.3043 0.893 0.792 0.000 0.008 0.000 0.000 0.200
#> SRR934296 5 0.3582 0.771 0.252 0.000 0.016 0.000 0.732 0.000
#> SRR934297 5 0.3674 0.755 0.268 0.000 0.016 0.000 0.716 0.000
#> SRR934298 5 0.3606 0.767 0.256 0.000 0.016 0.000 0.728 0.000
#> SRR934299 5 0.3404 0.789 0.224 0.000 0.016 0.000 0.760 0.000
#> SRR934300 5 0.3875 0.739 0.280 0.000 0.016 0.000 0.700 0.004
#> SRR934301 5 0.3404 0.789 0.224 0.000 0.016 0.000 0.760 0.000
#> SRR934302 5 0.3348 0.791 0.216 0.000 0.016 0.000 0.768 0.000
#> SRR934303 5 0.3320 0.792 0.212 0.000 0.016 0.000 0.772 0.000
#> SRR934304 5 0.0000 0.776 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934305 5 0.0000 0.776 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934306 5 0.0000 0.776 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934307 5 0.0000 0.776 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934308 5 0.0000 0.776 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934309 5 0.0000 0.776 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934310 5 0.0000 0.776 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934311 5 0.0000 0.776 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934312 6 0.1074 0.956 0.000 0.012 0.028 0.000 0.000 0.960
#> SRR934313 6 0.1367 0.943 0.000 0.044 0.012 0.000 0.000 0.944
#> SRR934314 6 0.1138 0.956 0.004 0.024 0.012 0.000 0.000 0.960
#> SRR934315 6 0.1333 0.943 0.000 0.048 0.008 0.000 0.000 0.944
#> SRR934316 6 0.1644 0.935 0.000 0.040 0.028 0.000 0.000 0.932
#> SRR934317 6 0.1434 0.941 0.000 0.048 0.012 0.000 0.000 0.940
#> SRR934318 6 0.1334 0.947 0.000 0.032 0.020 0.000 0.000 0.948
#> SRR934319 6 0.1563 0.933 0.000 0.056 0.012 0.000 0.000 0.932
#> SRR934320 6 0.0603 0.972 0.004 0.000 0.016 0.000 0.000 0.980
#> SRR934321 6 0.0603 0.972 0.004 0.000 0.016 0.000 0.000 0.980
#> SRR934322 6 0.0603 0.972 0.004 0.000 0.016 0.000 0.000 0.980
#> SRR934323 6 0.0951 0.969 0.004 0.008 0.020 0.000 0.000 0.968
#> SRR934324 6 0.0837 0.970 0.004 0.004 0.020 0.000 0.000 0.972
#> SRR934325 6 0.1059 0.966 0.004 0.016 0.016 0.000 0.000 0.964
#> SRR934326 6 0.0603 0.972 0.004 0.000 0.016 0.000 0.000 0.980
#> SRR934327 6 0.0951 0.969 0.004 0.008 0.020 0.000 0.000 0.968
#> SRR934328 1 0.2723 0.775 0.856 0.120 0.004 0.000 0.000 0.020
#> SRR934329 1 0.2624 0.774 0.856 0.124 0.000 0.000 0.000 0.020
#> SRR934330 1 0.2723 0.775 0.856 0.120 0.004 0.000 0.000 0.020
#> SRR934331 1 0.2723 0.775 0.856 0.120 0.004 0.000 0.000 0.020
#> SRR934332 1 0.2667 0.774 0.852 0.128 0.000 0.000 0.000 0.020
#> SRR934333 1 0.2760 0.779 0.856 0.116 0.004 0.000 0.000 0.024
#> SRR934334 1 0.2723 0.775 0.856 0.120 0.004 0.000 0.000 0.020
#> SRR934335 1 0.2907 0.765 0.828 0.152 0.000 0.000 0.000 0.020
#> SRR934344 1 0.2823 0.893 0.796 0.000 0.000 0.000 0.000 0.204
#> SRR934345 1 0.2823 0.893 0.796 0.000 0.000 0.000 0.000 0.204
#> SRR934346 1 0.2823 0.893 0.796 0.000 0.000 0.000 0.000 0.204
#> SRR934347 1 0.2823 0.893 0.796 0.000 0.000 0.000 0.000 0.204
#> SRR934348 1 0.2823 0.893 0.796 0.000 0.000 0.000 0.000 0.204
#> SRR934349 1 0.2793 0.893 0.800 0.000 0.000 0.000 0.000 0.200
#> SRR934350 1 0.2823 0.893 0.796 0.000 0.000 0.000 0.000 0.204
#> SRR934351 1 0.2823 0.893 0.796 0.000 0.000 0.000 0.000 0.204
#> SRR934336 6 0.0458 0.972 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR934337 6 0.0458 0.972 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR934338 6 0.0458 0.972 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR934339 6 0.0363 0.973 0.000 0.000 0.012 0.000 0.000 0.988
#> SRR934340 6 0.0363 0.973 0.000 0.000 0.012 0.000 0.000 0.988
#> SRR934341 6 0.0363 0.973 0.000 0.000 0.012 0.000 0.000 0.988
#> SRR934342 6 0.0458 0.972 0.000 0.000 0.016 0.000 0.000 0.984
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 14550 rows and 135 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 0.888 0.966 0.976 0.172 0.789 0.789
#> 3 3 0.503 0.873 0.823 0.963 0.748 0.681
#> 4 4 0.622 0.892 0.935 0.351 0.993 0.987
#> 5 5 0.755 0.783 0.889 0.228 0.923 0.855
#> 6 6 0.637 0.704 0.792 0.253 0.805 0.571
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
#> SRR934216 2 0.971 0.644 0.4 0.6
#> SRR934217 2 0.971 0.644 0.4 0.6
#> SRR934218 2 0.971 0.644 0.4 0.6
#> SRR934219 2 0.971 0.644 0.4 0.6
#> SRR934220 2 0.971 0.644 0.4 0.6
#> SRR934221 2 0.971 0.644 0.4 0.6
#> SRR934222 2 0.971 0.644 0.4 0.6
#> SRR934223 2 0.971 0.644 0.4 0.6
#> SRR934224 1 0.000 1.000 1.0 0.0
#> SRR934225 1 0.000 1.000 1.0 0.0
#> SRR934226 1 0.000 1.000 1.0 0.0
#> SRR934227 1 0.000 1.000 1.0 0.0
#> SRR934228 1 0.000 1.000 1.0 0.0
#> SRR934229 1 0.000 1.000 1.0 0.0
#> SRR934230 1 0.000 1.000 1.0 0.0
#> SRR934231 1 0.000 1.000 1.0 0.0
#> SRR934232 1 0.000 1.000 1.0 0.0
#> SRR934233 1 0.000 1.000 1.0 0.0
#> SRR934234 1 0.000 1.000 1.0 0.0
#> SRR934235 1 0.000 1.000 1.0 0.0
#> SRR934236 1 0.000 1.000 1.0 0.0
#> SRR934237 1 0.000 1.000 1.0 0.0
#> SRR934238 1 0.000 1.000 1.0 0.0
#> SRR934239 1 0.000 1.000 1.0 0.0
#> SRR934240 1 0.000 1.000 1.0 0.0
#> SRR934241 1 0.000 1.000 1.0 0.0
#> SRR934242 1 0.000 1.000 1.0 0.0
#> SRR934243 1 0.000 1.000 1.0 0.0
#> SRR934244 1 0.000 1.000 1.0 0.0
#> SRR934245 1 0.000 1.000 1.0 0.0
#> SRR934246 1 0.000 1.000 1.0 0.0
#> SRR934247 1 0.000 1.000 1.0 0.0
#> SRR934248 1 0.000 1.000 1.0 0.0
#> SRR934249 1 0.000 1.000 1.0 0.0
#> SRR934250 1 0.000 1.000 1.0 0.0
#> SRR934251 1 0.000 1.000 1.0 0.0
#> SRR934252 1 0.000 1.000 1.0 0.0
#> SRR934253 1 0.000 1.000 1.0 0.0
#> SRR934254 1 0.000 1.000 1.0 0.0
#> SRR934255 1 0.000 1.000 1.0 0.0
#> SRR934256 1 0.000 1.000 1.0 0.0
#> SRR934257 1 0.000 1.000 1.0 0.0
#> SRR934258 1 0.000 1.000 1.0 0.0
#> SRR934259 1 0.000 1.000 1.0 0.0
#> SRR934260 1 0.000 1.000 1.0 0.0
#> SRR934261 1 0.000 1.000 1.0 0.0
#> SRR934262 1 0.000 1.000 1.0 0.0
#> SRR934263 1 0.000 1.000 1.0 0.0
#> SRR934264 1 0.000 1.000 1.0 0.0
#> SRR934265 1 0.000 1.000 1.0 0.0
#> SRR934266 1 0.000 1.000 1.0 0.0
#> SRR934267 1 0.000 1.000 1.0 0.0
#> SRR934268 1 0.000 1.000 1.0 0.0
#> SRR934269 1 0.000 1.000 1.0 0.0
#> SRR934270 1 0.000 1.000 1.0 0.0
#> SRR934271 1 0.000 1.000 1.0 0.0
#> SRR934272 1 0.000 1.000 1.0 0.0
#> SRR934273 1 0.000 1.000 1.0 0.0
#> SRR934274 1 0.000 1.000 1.0 0.0
#> SRR934275 1 0.000 1.000 1.0 0.0
#> SRR934276 1 0.000 1.000 1.0 0.0
#> SRR934277 1 0.000 1.000 1.0 0.0
#> SRR934278 1 0.000 1.000 1.0 0.0
#> SRR934279 1 0.000 1.000 1.0 0.0
#> SRR934280 1 0.000 1.000 1.0 0.0
#> SRR934281 1 0.000 1.000 1.0 0.0
#> SRR934282 1 0.000 1.000 1.0 0.0
#> SRR934283 1 0.000 1.000 1.0 0.0
#> SRR934284 1 0.000 1.000 1.0 0.0
#> SRR934285 1 0.000 1.000 1.0 0.0
#> SRR934286 1 0.000 1.000 1.0 0.0
#> SRR934287 1 0.000 1.000 1.0 0.0
#> SRR934288 1 0.000 1.000 1.0 0.0
#> SRR934289 1 0.000 1.000 1.0 0.0
#> SRR934290 1 0.000 1.000 1.0 0.0
#> SRR934291 1 0.000 1.000 1.0 0.0
#> SRR934292 1 0.000 1.000 1.0 0.0
#> SRR934293 1 0.000 1.000 1.0 0.0
#> SRR934294 1 0.000 1.000 1.0 0.0
#> SRR934295 1 0.000 1.000 1.0 0.0
#> SRR934296 1 0.000 1.000 1.0 0.0
#> SRR934297 1 0.000 1.000 1.0 0.0
#> SRR934298 1 0.000 1.000 1.0 0.0
#> SRR934299 1 0.000 1.000 1.0 0.0
#> SRR934300 1 0.000 1.000 1.0 0.0
#> SRR934301 1 0.000 1.000 1.0 0.0
#> SRR934302 1 0.000 1.000 1.0 0.0
#> SRR934303 1 0.000 1.000 1.0 0.0
#> SRR934304 2 0.000 0.780 0.0 1.0
#> SRR934305 2 0.000 0.780 0.0 1.0
#> SRR934306 2 0.000 0.780 0.0 1.0
#> SRR934307 2 0.000 0.780 0.0 1.0
#> SRR934308 2 0.000 0.780 0.0 1.0
#> SRR934309 2 0.000 0.780 0.0 1.0
#> SRR934310 2 0.000 0.780 0.0 1.0
#> SRR934311 2 0.000 0.780 0.0 1.0
#> SRR934312 1 0.000 1.000 1.0 0.0
#> SRR934313 1 0.000 1.000 1.0 0.0
#> SRR934314 1 0.000 1.000 1.0 0.0
#> SRR934315 1 0.000 1.000 1.0 0.0
#> SRR934316 1 0.000 1.000 1.0 0.0
#> SRR934317 1 0.000 1.000 1.0 0.0
#> SRR934318 1 0.000 1.000 1.0 0.0
#> SRR934319 1 0.000 1.000 1.0 0.0
#> SRR934320 1 0.000 1.000 1.0 0.0
#> SRR934321 1 0.000 1.000 1.0 0.0
#> SRR934322 1 0.000 1.000 1.0 0.0
#> SRR934323 1 0.000 1.000 1.0 0.0
#> SRR934324 1 0.000 1.000 1.0 0.0
#> SRR934325 1 0.000 1.000 1.0 0.0
#> SRR934326 1 0.000 1.000 1.0 0.0
#> SRR934327 1 0.000 1.000 1.0 0.0
#> SRR934328 1 0.000 1.000 1.0 0.0
#> SRR934329 1 0.000 1.000 1.0 0.0
#> SRR934330 1 0.000 1.000 1.0 0.0
#> SRR934331 1 0.000 1.000 1.0 0.0
#> SRR934332 1 0.000 1.000 1.0 0.0
#> SRR934333 1 0.000 1.000 1.0 0.0
#> SRR934334 1 0.000 1.000 1.0 0.0
#> SRR934335 1 0.000 1.000 1.0 0.0
#> SRR934344 1 0.000 1.000 1.0 0.0
#> SRR934345 1 0.000 1.000 1.0 0.0
#> SRR934346 1 0.000 1.000 1.0 0.0
#> SRR934347 1 0.000 1.000 1.0 0.0
#> SRR934348 1 0.000 1.000 1.0 0.0
#> SRR934349 1 0.000 1.000 1.0 0.0
#> SRR934350 1 0.000 1.000 1.0 0.0
#> SRR934351 1 0.000 1.000 1.0 0.0
#> SRR934336 1 0.000 1.000 1.0 0.0
#> SRR934337 1 0.000 1.000 1.0 0.0
#> SRR934338 1 0.000 1.000 1.0 0.0
#> SRR934339 1 0.000 1.000 1.0 0.0
#> SRR934340 1 0.000 1.000 1.0 0.0
#> SRR934341 1 0.000 1.000 1.0 0.0
#> SRR934342 1 0.000 1.000 1.0 0.0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 3 0.613 0.504 0.400 0.000 0.600
#> SRR934217 3 0.613 0.504 0.400 0.000 0.600
#> SRR934218 3 0.613 0.504 0.400 0.000 0.600
#> SRR934219 3 0.613 0.504 0.400 0.000 0.600
#> SRR934220 3 0.613 0.504 0.400 0.000 0.600
#> SRR934221 3 0.613 0.504 0.400 0.000 0.600
#> SRR934222 3 0.613 0.504 0.400 0.000 0.600
#> SRR934223 3 0.613 0.504 0.400 0.000 0.600
#> SRR934224 1 0.000 0.959 1.000 0.000 0.000
#> SRR934225 1 0.000 0.959 1.000 0.000 0.000
#> SRR934226 1 0.000 0.959 1.000 0.000 0.000
#> SRR934227 1 0.000 0.959 1.000 0.000 0.000
#> SRR934228 1 0.000 0.959 1.000 0.000 0.000
#> SRR934229 1 0.000 0.959 1.000 0.000 0.000
#> SRR934230 1 0.000 0.959 1.000 0.000 0.000
#> SRR934231 1 0.000 0.959 1.000 0.000 0.000
#> SRR934232 2 0.630 0.806 0.472 0.528 0.000
#> SRR934233 2 0.630 0.806 0.472 0.528 0.000
#> SRR934234 2 0.630 0.806 0.472 0.528 0.000
#> SRR934235 2 0.630 0.806 0.472 0.528 0.000
#> SRR934236 2 0.630 0.806 0.472 0.528 0.000
#> SRR934237 2 0.630 0.806 0.472 0.528 0.000
#> SRR934238 2 0.630 0.806 0.472 0.528 0.000
#> SRR934239 2 0.630 0.806 0.472 0.528 0.000
#> SRR934240 1 0.362 0.751 0.864 0.136 0.000
#> SRR934241 1 0.362 0.751 0.864 0.136 0.000
#> SRR934242 1 0.362 0.751 0.864 0.136 0.000
#> SRR934243 1 0.362 0.751 0.864 0.136 0.000
#> SRR934244 1 0.362 0.751 0.864 0.136 0.000
#> SRR934245 1 0.362 0.751 0.864 0.136 0.000
#> SRR934246 1 0.362 0.751 0.864 0.136 0.000
#> SRR934247 1 0.362 0.751 0.864 0.136 0.000
#> SRR934248 2 0.629 0.896 0.464 0.536 0.000
#> SRR934249 2 0.629 0.896 0.464 0.536 0.000
#> SRR934250 2 0.629 0.896 0.464 0.536 0.000
#> SRR934251 2 0.629 0.896 0.464 0.536 0.000
#> SRR934252 2 0.629 0.896 0.464 0.536 0.000
#> SRR934253 2 0.629 0.896 0.464 0.536 0.000
#> SRR934254 2 0.629 0.896 0.464 0.536 0.000
#> SRR934255 2 0.629 0.896 0.464 0.536 0.000
#> SRR934256 1 0.327 0.784 0.884 0.116 0.000
#> SRR934257 1 0.327 0.784 0.884 0.116 0.000
#> SRR934258 1 0.327 0.784 0.884 0.116 0.000
#> SRR934259 1 0.327 0.784 0.884 0.116 0.000
#> SRR934260 1 0.327 0.784 0.884 0.116 0.000
#> SRR934261 1 0.327 0.784 0.884 0.116 0.000
#> SRR934262 1 0.327 0.784 0.884 0.116 0.000
#> SRR934263 1 0.327 0.784 0.884 0.116 0.000
#> SRR934264 2 0.630 0.897 0.484 0.516 0.000
#> SRR934265 2 0.630 0.897 0.484 0.516 0.000
#> SRR934266 2 0.630 0.897 0.484 0.516 0.000
#> SRR934267 2 0.630 0.897 0.484 0.516 0.000
#> SRR934268 2 0.630 0.897 0.484 0.516 0.000
#> SRR934269 2 0.630 0.897 0.484 0.516 0.000
#> SRR934270 2 0.630 0.897 0.484 0.516 0.000
#> SRR934271 2 0.630 0.897 0.484 0.516 0.000
#> SRR934272 1 0.000 0.959 1.000 0.000 0.000
#> SRR934273 1 0.000 0.959 1.000 0.000 0.000
#> SRR934274 1 0.000 0.959 1.000 0.000 0.000
#> SRR934275 1 0.000 0.959 1.000 0.000 0.000
#> SRR934276 1 0.000 0.959 1.000 0.000 0.000
#> SRR934277 1 0.000 0.959 1.000 0.000 0.000
#> SRR934278 1 0.000 0.959 1.000 0.000 0.000
#> SRR934279 1 0.000 0.959 1.000 0.000 0.000
#> SRR934280 1 0.000 0.959 1.000 0.000 0.000
#> SRR934281 1 0.000 0.959 1.000 0.000 0.000
#> SRR934282 1 0.000 0.959 1.000 0.000 0.000
#> SRR934283 1 0.000 0.959 1.000 0.000 0.000
#> SRR934284 1 0.000 0.959 1.000 0.000 0.000
#> SRR934285 1 0.000 0.959 1.000 0.000 0.000
#> SRR934286 1 0.000 0.959 1.000 0.000 0.000
#> SRR934287 1 0.000 0.959 1.000 0.000 0.000
#> SRR934288 1 0.000 0.959 1.000 0.000 0.000
#> SRR934289 1 0.000 0.959 1.000 0.000 0.000
#> SRR934290 1 0.000 0.959 1.000 0.000 0.000
#> SRR934291 1 0.000 0.959 1.000 0.000 0.000
#> SRR934292 1 0.000 0.959 1.000 0.000 0.000
#> SRR934293 1 0.000 0.959 1.000 0.000 0.000
#> SRR934294 1 0.000 0.959 1.000 0.000 0.000
#> SRR934295 1 0.000 0.959 1.000 0.000 0.000
#> SRR934296 1 0.000 0.959 1.000 0.000 0.000
#> SRR934297 1 0.000 0.959 1.000 0.000 0.000
#> SRR934298 1 0.000 0.959 1.000 0.000 0.000
#> SRR934299 1 0.000 0.959 1.000 0.000 0.000
#> SRR934300 1 0.000 0.959 1.000 0.000 0.000
#> SRR934301 1 0.000 0.959 1.000 0.000 0.000
#> SRR934302 1 0.000 0.959 1.000 0.000 0.000
#> SRR934303 1 0.000 0.959 1.000 0.000 0.000
#> SRR934304 3 0.588 0.626 0.000 0.348 0.652
#> SRR934305 3 0.588 0.626 0.000 0.348 0.652
#> SRR934306 3 0.588 0.626 0.000 0.348 0.652
#> SRR934307 3 0.588 0.626 0.000 0.348 0.652
#> SRR934308 3 0.588 0.626 0.000 0.348 0.652
#> SRR934309 3 0.588 0.626 0.000 0.348 0.652
#> SRR934310 3 0.588 0.626 0.000 0.348 0.652
#> SRR934311 3 0.588 0.626 0.000 0.348 0.652
#> SRR934312 1 0.000 0.959 1.000 0.000 0.000
#> SRR934313 1 0.000 0.959 1.000 0.000 0.000
#> SRR934314 1 0.000 0.959 1.000 0.000 0.000
#> SRR934315 1 0.000 0.959 1.000 0.000 0.000
#> SRR934316 1 0.000 0.959 1.000 0.000 0.000
#> SRR934317 1 0.000 0.959 1.000 0.000 0.000
#> SRR934318 1 0.000 0.959 1.000 0.000 0.000
#> SRR934319 1 0.000 0.959 1.000 0.000 0.000
#> SRR934320 1 0.000 0.959 1.000 0.000 0.000
#> SRR934321 1 0.000 0.959 1.000 0.000 0.000
#> SRR934322 1 0.000 0.959 1.000 0.000 0.000
#> SRR934323 1 0.000 0.959 1.000 0.000 0.000
#> SRR934324 1 0.000 0.959 1.000 0.000 0.000
#> SRR934325 1 0.000 0.959 1.000 0.000 0.000
#> SRR934326 1 0.000 0.959 1.000 0.000 0.000
#> SRR934327 1 0.000 0.959 1.000 0.000 0.000
#> SRR934328 1 0.000 0.959 1.000 0.000 0.000
#> SRR934329 1 0.000 0.959 1.000 0.000 0.000
#> SRR934330 1 0.000 0.959 1.000 0.000 0.000
#> SRR934331 1 0.000 0.959 1.000 0.000 0.000
#> SRR934332 1 0.000 0.959 1.000 0.000 0.000
#> SRR934333 1 0.000 0.959 1.000 0.000 0.000
#> SRR934334 1 0.000 0.959 1.000 0.000 0.000
#> SRR934335 1 0.000 0.959 1.000 0.000 0.000
#> SRR934344 1 0.000 0.959 1.000 0.000 0.000
#> SRR934345 1 0.000 0.959 1.000 0.000 0.000
#> SRR934346 1 0.000 0.959 1.000 0.000 0.000
#> SRR934347 1 0.000 0.959 1.000 0.000 0.000
#> SRR934348 1 0.000 0.959 1.000 0.000 0.000
#> SRR934349 1 0.000 0.959 1.000 0.000 0.000
#> SRR934350 1 0.000 0.959 1.000 0.000 0.000
#> SRR934351 1 0.000 0.959 1.000 0.000 0.000
#> SRR934336 1 0.000 0.959 1.000 0.000 0.000
#> SRR934337 1 0.000 0.959 1.000 0.000 0.000
#> SRR934338 1 0.000 0.959 1.000 0.000 0.000
#> SRR934339 1 0.000 0.959 1.000 0.000 0.000
#> SRR934340 1 0.000 0.959 1.000 0.000 0.000
#> SRR934341 1 0.000 0.959 1.000 0.000 0.000
#> SRR934342 1 0.000 0.959 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 3 0.000 1.000 0.000 0.000 1 0
#> SRR934217 3 0.000 1.000 0.000 0.000 1 0
#> SRR934218 3 0.000 1.000 0.000 0.000 1 0
#> SRR934219 3 0.000 1.000 0.000 0.000 1 0
#> SRR934220 3 0.000 1.000 0.000 0.000 1 0
#> SRR934221 3 0.000 1.000 0.000 0.000 1 0
#> SRR934222 3 0.000 1.000 0.000 0.000 1 0
#> SRR934223 3 0.000 1.000 0.000 0.000 1 0
#> SRR934224 1 0.000 0.949 1.000 0.000 0 0
#> SRR934225 1 0.000 0.949 1.000 0.000 0 0
#> SRR934226 1 0.000 0.949 1.000 0.000 0 0
#> SRR934227 1 0.000 0.949 1.000 0.000 0 0
#> SRR934228 1 0.000 0.949 1.000 0.000 0 0
#> SRR934229 1 0.000 0.949 1.000 0.000 0 0
#> SRR934230 1 0.000 0.949 1.000 0.000 0 0
#> SRR934231 1 0.000 0.949 1.000 0.000 0 0
#> SRR934232 2 0.391 0.661 0.232 0.768 0 0
#> SRR934233 2 0.391 0.661 0.232 0.768 0 0
#> SRR934234 2 0.391 0.661 0.232 0.768 0 0
#> SRR934235 2 0.391 0.661 0.232 0.768 0 0
#> SRR934236 2 0.391 0.661 0.232 0.768 0 0
#> SRR934237 2 0.391 0.661 0.232 0.768 0 0
#> SRR934238 2 0.391 0.661 0.232 0.768 0 0
#> SRR934239 2 0.391 0.661 0.232 0.768 0 0
#> SRR934240 1 0.416 0.648 0.736 0.264 0 0
#> SRR934241 1 0.416 0.648 0.736 0.264 0 0
#> SRR934242 1 0.416 0.648 0.736 0.264 0 0
#> SRR934243 1 0.416 0.648 0.736 0.264 0 0
#> SRR934244 1 0.416 0.648 0.736 0.264 0 0
#> SRR934245 1 0.416 0.648 0.736 0.264 0 0
#> SRR934246 1 0.416 0.648 0.736 0.264 0 0
#> SRR934247 1 0.416 0.648 0.736 0.264 0 0
#> SRR934248 2 0.357 0.814 0.196 0.804 0 0
#> SRR934249 2 0.357 0.814 0.196 0.804 0 0
#> SRR934250 2 0.357 0.814 0.196 0.804 0 0
#> SRR934251 2 0.357 0.814 0.196 0.804 0 0
#> SRR934252 2 0.357 0.814 0.196 0.804 0 0
#> SRR934253 2 0.357 0.814 0.196 0.804 0 0
#> SRR934254 2 0.357 0.814 0.196 0.804 0 0
#> SRR934255 2 0.357 0.814 0.196 0.804 0 0
#> SRR934256 1 0.357 0.738 0.804 0.196 0 0
#> SRR934257 1 0.357 0.738 0.804 0.196 0 0
#> SRR934258 1 0.357 0.738 0.804 0.196 0 0
#> SRR934259 1 0.357 0.738 0.804 0.196 0 0
#> SRR934260 1 0.357 0.738 0.804 0.196 0 0
#> SRR934261 1 0.357 0.738 0.804 0.196 0 0
#> SRR934262 1 0.357 0.738 0.804 0.196 0 0
#> SRR934263 1 0.357 0.738 0.804 0.196 0 0
#> SRR934264 2 0.376 0.823 0.216 0.784 0 0
#> SRR934265 2 0.376 0.823 0.216 0.784 0 0
#> SRR934266 2 0.376 0.823 0.216 0.784 0 0
#> SRR934267 2 0.376 0.823 0.216 0.784 0 0
#> SRR934268 2 0.376 0.823 0.216 0.784 0 0
#> SRR934269 2 0.376 0.823 0.216 0.784 0 0
#> SRR934270 2 0.376 0.823 0.216 0.784 0 0
#> SRR934271 2 0.376 0.823 0.216 0.784 0 0
#> SRR934272 1 0.000 0.949 1.000 0.000 0 0
#> SRR934273 1 0.000 0.949 1.000 0.000 0 0
#> SRR934274 1 0.000 0.949 1.000 0.000 0 0
#> SRR934275 1 0.000 0.949 1.000 0.000 0 0
#> SRR934276 1 0.000 0.949 1.000 0.000 0 0
#> SRR934277 1 0.000 0.949 1.000 0.000 0 0
#> SRR934278 1 0.000 0.949 1.000 0.000 0 0
#> SRR934279 1 0.000 0.949 1.000 0.000 0 0
#> SRR934280 1 0.000 0.949 1.000 0.000 0 0
#> SRR934281 1 0.000 0.949 1.000 0.000 0 0
#> SRR934282 1 0.000 0.949 1.000 0.000 0 0
#> SRR934283 1 0.000 0.949 1.000 0.000 0 0
#> SRR934284 1 0.000 0.949 1.000 0.000 0 0
#> SRR934285 1 0.000 0.949 1.000 0.000 0 0
#> SRR934286 1 0.000 0.949 1.000 0.000 0 0
#> SRR934287 1 0.000 0.949 1.000 0.000 0 0
#> SRR934288 1 0.000 0.949 1.000 0.000 0 0
#> SRR934289 1 0.000 0.949 1.000 0.000 0 0
#> SRR934290 1 0.000 0.949 1.000 0.000 0 0
#> SRR934291 1 0.000 0.949 1.000 0.000 0 0
#> SRR934292 1 0.000 0.949 1.000 0.000 0 0
#> SRR934293 1 0.000 0.949 1.000 0.000 0 0
#> SRR934294 1 0.000 0.949 1.000 0.000 0 0
#> SRR934295 1 0.000 0.949 1.000 0.000 0 0
#> SRR934296 1 0.000 0.949 1.000 0.000 0 0
#> SRR934297 1 0.000 0.949 1.000 0.000 0 0
#> SRR934298 1 0.000 0.949 1.000 0.000 0 0
#> SRR934299 1 0.000 0.949 1.000 0.000 0 0
#> SRR934300 1 0.000 0.949 1.000 0.000 0 0
#> SRR934301 1 0.000 0.949 1.000 0.000 0 0
#> SRR934302 1 0.000 0.949 1.000 0.000 0 0
#> SRR934303 1 0.000 0.949 1.000 0.000 0 0
#> SRR934304 4 0.000 1.000 0.000 0.000 0 1
#> SRR934305 4 0.000 1.000 0.000 0.000 0 1
#> SRR934306 4 0.000 1.000 0.000 0.000 0 1
#> SRR934307 4 0.000 1.000 0.000 0.000 0 1
#> SRR934308 4 0.000 1.000 0.000 0.000 0 1
#> SRR934309 4 0.000 1.000 0.000 0.000 0 1
#> SRR934310 4 0.000 1.000 0.000 0.000 0 1
#> SRR934311 4 0.000 1.000 0.000 0.000 0 1
#> SRR934312 1 0.000 0.949 1.000 0.000 0 0
#> SRR934313 1 0.000 0.949 1.000 0.000 0 0
#> SRR934314 1 0.000 0.949 1.000 0.000 0 0
#> SRR934315 1 0.000 0.949 1.000 0.000 0 0
#> SRR934316 1 0.000 0.949 1.000 0.000 0 0
#> SRR934317 1 0.000 0.949 1.000 0.000 0 0
#> SRR934318 1 0.000 0.949 1.000 0.000 0 0
#> SRR934319 1 0.000 0.949 1.000 0.000 0 0
#> SRR934320 1 0.000 0.949 1.000 0.000 0 0
#> SRR934321 1 0.000 0.949 1.000 0.000 0 0
#> SRR934322 1 0.000 0.949 1.000 0.000 0 0
#> SRR934323 1 0.000 0.949 1.000 0.000 0 0
#> SRR934324 1 0.000 0.949 1.000 0.000 0 0
#> SRR934325 1 0.000 0.949 1.000 0.000 0 0
#> SRR934326 1 0.000 0.949 1.000 0.000 0 0
#> SRR934327 1 0.000 0.949 1.000 0.000 0 0
#> SRR934328 1 0.000 0.949 1.000 0.000 0 0
#> SRR934329 1 0.000 0.949 1.000 0.000 0 0
#> SRR934330 1 0.000 0.949 1.000 0.000 0 0
#> SRR934331 1 0.000 0.949 1.000 0.000 0 0
#> SRR934332 1 0.000 0.949 1.000 0.000 0 0
#> SRR934333 1 0.000 0.949 1.000 0.000 0 0
#> SRR934334 1 0.000 0.949 1.000 0.000 0 0
#> SRR934335 1 0.000 0.949 1.000 0.000 0 0
#> SRR934344 1 0.000 0.949 1.000 0.000 0 0
#> SRR934345 1 0.000 0.949 1.000 0.000 0 0
#> SRR934346 1 0.000 0.949 1.000 0.000 0 0
#> SRR934347 1 0.000 0.949 1.000 0.000 0 0
#> SRR934348 1 0.000 0.949 1.000 0.000 0 0
#> SRR934349 1 0.000 0.949 1.000 0.000 0 0
#> SRR934350 1 0.000 0.949 1.000 0.000 0 0
#> SRR934351 1 0.000 0.949 1.000 0.000 0 0
#> SRR934336 1 0.000 0.949 1.000 0.000 0 0
#> SRR934337 1 0.000 0.949 1.000 0.000 0 0
#> SRR934338 1 0.000 0.949 1.000 0.000 0 0
#> SRR934339 1 0.000 0.949 1.000 0.000 0 0
#> SRR934340 1 0.000 0.949 1.000 0.000 0 0
#> SRR934341 1 0.000 0.949 1.000 0.000 0 0
#> SRR934342 1 0.000 0.949 1.000 0.000 0 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR934217 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR934218 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR934219 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR934220 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR934221 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR934222 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR934223 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> SRR934224 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934225 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934226 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934227 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934228 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934229 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934230 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934231 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934232 4 0.4283 0.495 0.000 0.456 0 0.544 0.000
#> SRR934233 4 0.4283 0.495 0.000 0.456 0 0.544 0.000
#> SRR934234 4 0.4283 0.495 0.000 0.456 0 0.544 0.000
#> SRR934235 4 0.4283 0.495 0.000 0.456 0 0.544 0.000
#> SRR934236 4 0.4283 0.495 0.000 0.456 0 0.544 0.000
#> SRR934237 4 0.4283 0.495 0.000 0.456 0 0.544 0.000
#> SRR934238 4 0.4283 0.495 0.000 0.456 0 0.544 0.000
#> SRR934239 4 0.4283 0.495 0.000 0.456 0 0.544 0.000
#> SRR934240 2 0.4990 1.000 0.360 0.600 0 0.040 0.000
#> SRR934241 2 0.4990 1.000 0.360 0.600 0 0.040 0.000
#> SRR934242 2 0.4990 1.000 0.360 0.600 0 0.040 0.000
#> SRR934243 2 0.4990 1.000 0.360 0.600 0 0.040 0.000
#> SRR934244 2 0.4990 1.000 0.360 0.600 0 0.040 0.000
#> SRR934245 2 0.4990 1.000 0.360 0.600 0 0.040 0.000
#> SRR934246 2 0.4990 1.000 0.360 0.600 0 0.040 0.000
#> SRR934247 2 0.4990 1.000 0.360 0.600 0 0.040 0.000
#> SRR934248 4 0.4161 0.549 0.000 0.000 0 0.608 0.392
#> SRR934249 4 0.4161 0.549 0.000 0.000 0 0.608 0.392
#> SRR934250 4 0.4161 0.549 0.000 0.000 0 0.608 0.392
#> SRR934251 4 0.4161 0.549 0.000 0.000 0 0.608 0.392
#> SRR934252 4 0.4161 0.549 0.000 0.000 0 0.608 0.392
#> SRR934253 4 0.4161 0.549 0.000 0.000 0 0.608 0.392
#> SRR934254 4 0.4161 0.549 0.000 0.000 0 0.608 0.392
#> SRR934255 4 0.4161 0.549 0.000 0.000 0 0.608 0.392
#> SRR934256 1 0.4182 -0.268 0.600 0.400 0 0.000 0.000
#> SRR934257 1 0.4182 -0.268 0.600 0.400 0 0.000 0.000
#> SRR934258 1 0.4182 -0.268 0.600 0.400 0 0.000 0.000
#> SRR934259 1 0.4182 -0.268 0.600 0.400 0 0.000 0.000
#> SRR934260 1 0.4182 -0.268 0.600 0.400 0 0.000 0.000
#> SRR934261 1 0.4182 -0.268 0.600 0.400 0 0.000 0.000
#> SRR934262 1 0.4182 -0.268 0.600 0.400 0 0.000 0.000
#> SRR934263 1 0.4182 -0.268 0.600 0.400 0 0.000 0.000
#> SRR934264 4 0.0000 0.653 0.000 0.000 0 1.000 0.000
#> SRR934265 4 0.0000 0.653 0.000 0.000 0 1.000 0.000
#> SRR934266 4 0.0000 0.653 0.000 0.000 0 1.000 0.000
#> SRR934267 4 0.0000 0.653 0.000 0.000 0 1.000 0.000
#> SRR934268 4 0.0000 0.653 0.000 0.000 0 1.000 0.000
#> SRR934269 4 0.0000 0.653 0.000 0.000 0 1.000 0.000
#> SRR934270 4 0.0000 0.653 0.000 0.000 0 1.000 0.000
#> SRR934271 4 0.0000 0.653 0.000 0.000 0 1.000 0.000
#> SRR934272 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934273 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934274 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934275 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934276 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934277 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934278 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934279 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934280 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934281 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934282 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934283 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934284 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934285 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934286 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934287 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934288 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934289 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934290 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934291 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934292 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934293 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934294 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934295 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934296 1 0.2605 0.713 0.852 0.148 0 0.000 0.000
#> SRR934297 1 0.2605 0.713 0.852 0.148 0 0.000 0.000
#> SRR934298 1 0.2605 0.713 0.852 0.148 0 0.000 0.000
#> SRR934299 1 0.2605 0.713 0.852 0.148 0 0.000 0.000
#> SRR934300 1 0.2605 0.713 0.852 0.148 0 0.000 0.000
#> SRR934301 1 0.2605 0.713 0.852 0.148 0 0.000 0.000
#> SRR934302 1 0.2605 0.713 0.852 0.148 0 0.000 0.000
#> SRR934303 1 0.2605 0.713 0.852 0.148 0 0.000 0.000
#> SRR934304 5 0.4161 1.000 0.000 0.392 0 0.000 0.608
#> SRR934305 5 0.4161 1.000 0.000 0.392 0 0.000 0.608
#> SRR934306 5 0.4161 1.000 0.000 0.392 0 0.000 0.608
#> SRR934307 5 0.4161 1.000 0.000 0.392 0 0.000 0.608
#> SRR934308 5 0.4161 1.000 0.000 0.392 0 0.000 0.608
#> SRR934309 5 0.4161 1.000 0.000 0.392 0 0.000 0.608
#> SRR934310 5 0.4161 1.000 0.000 0.392 0 0.000 0.608
#> SRR934311 5 0.4161 1.000 0.000 0.392 0 0.000 0.608
#> SRR934312 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934313 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934314 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934315 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934316 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934317 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934318 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934319 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934320 1 0.0510 0.900 0.984 0.016 0 0.000 0.000
#> SRR934321 1 0.0510 0.900 0.984 0.016 0 0.000 0.000
#> SRR934322 1 0.0510 0.900 0.984 0.016 0 0.000 0.000
#> SRR934323 1 0.0510 0.900 0.984 0.016 0 0.000 0.000
#> SRR934324 1 0.0510 0.900 0.984 0.016 0 0.000 0.000
#> SRR934325 1 0.0510 0.900 0.984 0.016 0 0.000 0.000
#> SRR934326 1 0.0510 0.900 0.984 0.016 0 0.000 0.000
#> SRR934327 1 0.0510 0.900 0.984 0.016 0 0.000 0.000
#> SRR934328 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934329 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934330 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934331 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934332 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934333 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934334 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934335 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934344 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934345 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934346 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934347 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934348 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934349 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934350 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934351 1 0.0404 0.908 0.988 0.012 0 0.000 0.000
#> SRR934336 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934337 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934338 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934339 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934340 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934341 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
#> SRR934342 1 0.0000 0.911 1.000 0.000 0 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934217 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934218 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934219 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934220 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934221 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934222 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934223 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934224 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934225 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934226 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934227 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934228 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934229 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934230 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934231 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934232 4 0.385 0.332 0.000 0.456 0 0.544 0 0.000
#> SRR934233 4 0.385 0.332 0.000 0.456 0 0.544 0 0.000
#> SRR934234 4 0.385 0.332 0.000 0.456 0 0.544 0 0.000
#> SRR934235 4 0.385 0.332 0.000 0.456 0 0.544 0 0.000
#> SRR934236 4 0.385 0.332 0.000 0.456 0 0.544 0 0.000
#> SRR934237 4 0.385 0.332 0.000 0.456 0 0.544 0 0.000
#> SRR934238 4 0.385 0.332 0.000 0.456 0 0.544 0 0.000
#> SRR934239 4 0.385 0.332 0.000 0.456 0 0.544 0 0.000
#> SRR934240 2 0.584 1.000 0.168 0.604 0 0.040 0 0.188
#> SRR934241 2 0.584 1.000 0.168 0.604 0 0.040 0 0.188
#> SRR934242 2 0.584 1.000 0.168 0.604 0 0.040 0 0.188
#> SRR934243 2 0.584 1.000 0.168 0.604 0 0.040 0 0.188
#> SRR934244 2 0.584 1.000 0.168 0.604 0 0.040 0 0.188
#> SRR934245 2 0.584 1.000 0.168 0.604 0 0.040 0 0.188
#> SRR934246 2 0.584 1.000 0.168 0.604 0 0.040 0 0.188
#> SRR934247 2 0.584 1.000 0.168 0.604 0 0.040 0 0.188
#> SRR934248 4 0.375 0.550 0.000 0.396 0 0.604 0 0.000
#> SRR934249 4 0.375 0.550 0.000 0.396 0 0.604 0 0.000
#> SRR934250 4 0.375 0.550 0.000 0.396 0 0.604 0 0.000
#> SRR934251 4 0.375 0.550 0.000 0.396 0 0.604 0 0.000
#> SRR934252 4 0.375 0.550 0.000 0.396 0 0.604 0 0.000
#> SRR934253 4 0.375 0.550 0.000 0.396 0 0.604 0 0.000
#> SRR934254 4 0.375 0.550 0.000 0.396 0 0.604 0 0.000
#> SRR934255 4 0.375 0.550 0.000 0.396 0 0.604 0 0.000
#> SRR934256 6 0.611 -0.288 0.328 0.304 0 0.000 0 0.368
#> SRR934257 6 0.611 -0.288 0.328 0.304 0 0.000 0 0.368
#> SRR934258 6 0.611 -0.288 0.328 0.304 0 0.000 0 0.368
#> SRR934259 6 0.611 -0.288 0.328 0.304 0 0.000 0 0.368
#> SRR934260 6 0.611 -0.288 0.328 0.304 0 0.000 0 0.368
#> SRR934261 6 0.611 -0.288 0.328 0.304 0 0.000 0 0.368
#> SRR934262 6 0.611 -0.288 0.328 0.304 0 0.000 0 0.368
#> SRR934263 6 0.611 -0.288 0.328 0.304 0 0.000 0 0.368
#> SRR934264 4 0.000 0.656 0.000 0.000 0 1.000 0 0.000
#> SRR934265 4 0.000 0.656 0.000 0.000 0 1.000 0 0.000
#> SRR934266 4 0.000 0.656 0.000 0.000 0 1.000 0 0.000
#> SRR934267 4 0.000 0.656 0.000 0.000 0 1.000 0 0.000
#> SRR934268 4 0.000 0.656 0.000 0.000 0 1.000 0 0.000
#> SRR934269 4 0.000 0.656 0.000 0.000 0 1.000 0 0.000
#> SRR934270 4 0.000 0.656 0.000 0.000 0 1.000 0 0.000
#> SRR934271 4 0.000 0.656 0.000 0.000 0 1.000 0 0.000
#> SRR934272 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934273 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934274 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934275 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934276 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934277 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934278 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934279 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934280 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934281 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934282 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934283 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934284 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934285 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934286 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934287 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934288 1 0.376 0.800 0.600 0.000 0 0.000 0 0.400
#> SRR934289 1 0.376 0.800 0.600 0.000 0 0.000 0 0.400
#> SRR934290 1 0.376 0.800 0.600 0.000 0 0.000 0 0.400
#> SRR934291 1 0.376 0.800 0.600 0.000 0 0.000 0 0.400
#> SRR934292 1 0.376 0.800 0.600 0.000 0 0.000 0 0.400
#> SRR934293 1 0.376 0.800 0.600 0.000 0 0.000 0 0.400
#> SRR934294 1 0.376 0.800 0.600 0.000 0 0.000 0 0.400
#> SRR934295 1 0.376 0.800 0.600 0.000 0 0.000 0 0.400
#> SRR934296 1 0.509 0.230 0.628 0.152 0 0.000 0 0.220
#> SRR934297 1 0.509 0.230 0.628 0.152 0 0.000 0 0.220
#> SRR934298 1 0.509 0.230 0.628 0.152 0 0.000 0 0.220
#> SRR934299 1 0.509 0.230 0.628 0.152 0 0.000 0 0.220
#> SRR934300 1 0.509 0.230 0.628 0.152 0 0.000 0 0.220
#> SRR934301 1 0.509 0.230 0.628 0.152 0 0.000 0 0.220
#> SRR934302 1 0.509 0.230 0.628 0.152 0 0.000 0 0.220
#> SRR934303 1 0.509 0.230 0.628 0.152 0 0.000 0 0.220
#> SRR934304 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934305 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934306 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934307 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934308 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934309 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934310 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934311 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934312 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934313 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934314 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934315 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934316 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934317 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934318 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934319 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934320 6 0.132 0.809 0.036 0.016 0 0.000 0 0.948
#> SRR934321 6 0.132 0.809 0.036 0.016 0 0.000 0 0.948
#> SRR934322 6 0.132 0.809 0.036 0.016 0 0.000 0 0.948
#> SRR934323 6 0.132 0.809 0.036 0.016 0 0.000 0 0.948
#> SRR934324 6 0.132 0.809 0.036 0.016 0 0.000 0 0.948
#> SRR934325 6 0.132 0.809 0.036 0.016 0 0.000 0 0.948
#> SRR934326 6 0.132 0.809 0.036 0.016 0 0.000 0 0.948
#> SRR934327 6 0.132 0.809 0.036 0.016 0 0.000 0 0.948
#> SRR934328 1 0.366 0.810 0.636 0.000 0 0.000 0 0.364
#> SRR934329 1 0.366 0.810 0.636 0.000 0 0.000 0 0.364
#> SRR934330 1 0.366 0.810 0.636 0.000 0 0.000 0 0.364
#> SRR934331 1 0.366 0.810 0.636 0.000 0 0.000 0 0.364
#> SRR934332 1 0.366 0.810 0.636 0.000 0 0.000 0 0.364
#> SRR934333 1 0.366 0.810 0.636 0.000 0 0.000 0 0.364
#> SRR934334 1 0.366 0.810 0.636 0.000 0 0.000 0 0.364
#> SRR934335 1 0.366 0.810 0.636 0.000 0 0.000 0 0.364
#> SRR934344 1 0.371 0.813 0.620 0.000 0 0.000 0 0.380
#> SRR934345 1 0.371 0.813 0.620 0.000 0 0.000 0 0.380
#> SRR934346 1 0.371 0.813 0.620 0.000 0 0.000 0 0.380
#> SRR934347 1 0.371 0.813 0.620 0.000 0 0.000 0 0.380
#> SRR934348 1 0.371 0.813 0.620 0.000 0 0.000 0 0.380
#> SRR934349 1 0.371 0.813 0.620 0.000 0 0.000 0 0.380
#> SRR934350 1 0.371 0.813 0.620 0.000 0 0.000 0 0.380
#> SRR934351 1 0.371 0.813 0.620 0.000 0 0.000 0 0.380
#> SRR934336 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934337 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934338 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934339 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934340 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934341 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
#> SRR934342 6 0.000 0.855 0.000 0.000 0 0.000 0 1.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "kmeans"]
# you can also extract it by
# res = res_list["CV:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 14550 rows and 135 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 0.369 0.754 0.850 0.331 0.636 0.636
#> 3 3 0.224 0.659 0.758 0.488 1.000 1.000
#> 4 4 0.304 0.554 0.668 0.228 0.789 0.683
#> 5 5 0.395 0.627 0.652 0.159 0.750 0.488
#> 6 6 0.416 0.587 0.624 0.052 0.923 0.738
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR934216 1 0.9754 0.00991 0.592 0.408
#> SRR934217 1 0.9754 0.00991 0.592 0.408
#> SRR934218 1 0.9754 0.00991 0.592 0.408
#> SRR934219 1 0.9754 0.00991 0.592 0.408
#> SRR934220 1 0.9754 0.00991 0.592 0.408
#> SRR934221 1 0.9754 0.00991 0.592 0.408
#> SRR934222 1 0.9754 0.00991 0.592 0.408
#> SRR934223 1 0.9754 0.00991 0.592 0.408
#> SRR934224 1 0.1414 0.86459 0.980 0.020
#> SRR934225 1 0.1414 0.86459 0.980 0.020
#> SRR934226 1 0.1414 0.86459 0.980 0.020
#> SRR934227 1 0.1414 0.86459 0.980 0.020
#> SRR934228 1 0.1414 0.86459 0.980 0.020
#> SRR934229 1 0.1414 0.86459 0.980 0.020
#> SRR934230 1 0.1414 0.86459 0.980 0.020
#> SRR934231 1 0.1414 0.86459 0.980 0.020
#> SRR934232 2 0.9393 0.76416 0.356 0.644
#> SRR934233 2 0.9393 0.76416 0.356 0.644
#> SRR934234 2 0.9393 0.76416 0.356 0.644
#> SRR934235 2 0.9393 0.76416 0.356 0.644
#> SRR934236 2 0.9393 0.76416 0.356 0.644
#> SRR934237 2 0.9393 0.76416 0.356 0.644
#> SRR934238 2 0.9393 0.76416 0.356 0.644
#> SRR934239 2 0.9393 0.76416 0.356 0.644
#> SRR934240 1 0.9170 0.35234 0.668 0.332
#> SRR934241 1 0.9170 0.35234 0.668 0.332
#> SRR934242 1 0.9170 0.35234 0.668 0.332
#> SRR934243 1 0.9170 0.35234 0.668 0.332
#> SRR934244 1 0.9170 0.35234 0.668 0.332
#> SRR934245 1 0.9170 0.35234 0.668 0.332
#> SRR934246 1 0.9170 0.35234 0.668 0.332
#> SRR934247 1 0.9170 0.35234 0.668 0.332
#> SRR934248 2 0.9522 0.82635 0.372 0.628
#> SRR934249 2 0.9522 0.82635 0.372 0.628
#> SRR934250 2 0.9522 0.82635 0.372 0.628
#> SRR934251 2 0.9522 0.82635 0.372 0.628
#> SRR934252 2 0.9522 0.82635 0.372 0.628
#> SRR934253 2 0.9522 0.82635 0.372 0.628
#> SRR934254 2 0.9522 0.82635 0.372 0.628
#> SRR934255 2 0.9522 0.82635 0.372 0.628
#> SRR934256 1 0.6712 0.71707 0.824 0.176
#> SRR934257 1 0.6712 0.71707 0.824 0.176
#> SRR934258 1 0.6712 0.71707 0.824 0.176
#> SRR934259 1 0.6712 0.71707 0.824 0.176
#> SRR934260 1 0.6712 0.71707 0.824 0.176
#> SRR934261 1 0.6712 0.71707 0.824 0.176
#> SRR934262 1 0.6712 0.71707 0.824 0.176
#> SRR934263 1 0.6712 0.71707 0.824 0.176
#> SRR934264 2 0.9795 0.80548 0.416 0.584
#> SRR934265 2 0.9795 0.80548 0.416 0.584
#> SRR934266 2 0.9795 0.80548 0.416 0.584
#> SRR934267 2 0.9795 0.80548 0.416 0.584
#> SRR934268 2 0.9795 0.80548 0.416 0.584
#> SRR934269 2 0.9795 0.80548 0.416 0.584
#> SRR934270 2 0.9795 0.80548 0.416 0.584
#> SRR934271 2 0.9795 0.80548 0.416 0.584
#> SRR934272 1 0.1184 0.86542 0.984 0.016
#> SRR934273 1 0.1184 0.86542 0.984 0.016
#> SRR934274 1 0.1184 0.86542 0.984 0.016
#> SRR934275 1 0.1184 0.86542 0.984 0.016
#> SRR934276 1 0.1184 0.86542 0.984 0.016
#> SRR934277 1 0.1184 0.86542 0.984 0.016
#> SRR934278 1 0.1184 0.86542 0.984 0.016
#> SRR934279 1 0.1184 0.86542 0.984 0.016
#> SRR934280 1 0.1184 0.86660 0.984 0.016
#> SRR934281 1 0.1184 0.86660 0.984 0.016
#> SRR934282 1 0.1184 0.86660 0.984 0.016
#> SRR934283 1 0.1184 0.86660 0.984 0.016
#> SRR934284 1 0.1184 0.86660 0.984 0.016
#> SRR934285 1 0.1184 0.86660 0.984 0.016
#> SRR934286 1 0.1184 0.86660 0.984 0.016
#> SRR934287 1 0.1184 0.86660 0.984 0.016
#> SRR934288 1 0.2236 0.86387 0.964 0.036
#> SRR934289 1 0.2236 0.86387 0.964 0.036
#> SRR934290 1 0.2236 0.86387 0.964 0.036
#> SRR934291 1 0.2236 0.86387 0.964 0.036
#> SRR934292 1 0.2236 0.86387 0.964 0.036
#> SRR934293 1 0.2236 0.86387 0.964 0.036
#> SRR934294 1 0.2236 0.86387 0.964 0.036
#> SRR934295 1 0.2236 0.86387 0.964 0.036
#> SRR934296 1 0.2948 0.84840 0.948 0.052
#> SRR934297 1 0.2948 0.84840 0.948 0.052
#> SRR934298 1 0.2948 0.84840 0.948 0.052
#> SRR934299 1 0.2948 0.84840 0.948 0.052
#> SRR934300 1 0.2948 0.84840 0.948 0.052
#> SRR934301 1 0.2948 0.84840 0.948 0.052
#> SRR934302 1 0.2948 0.84840 0.948 0.052
#> SRR934303 1 0.2948 0.84840 0.948 0.052
#> SRR934304 2 0.7299 0.72266 0.204 0.796
#> SRR934305 2 0.7299 0.72266 0.204 0.796
#> SRR934306 2 0.7299 0.72266 0.204 0.796
#> SRR934307 2 0.7299 0.72266 0.204 0.796
#> SRR934308 2 0.7299 0.72266 0.204 0.796
#> SRR934309 2 0.7299 0.72266 0.204 0.796
#> SRR934310 2 0.7299 0.72266 0.204 0.796
#> SRR934311 2 0.7299 0.72266 0.204 0.796
#> SRR934312 1 0.0938 0.86583 0.988 0.012
#> SRR934313 1 0.0938 0.86583 0.988 0.012
#> SRR934314 1 0.0938 0.86583 0.988 0.012
#> SRR934315 1 0.0938 0.86583 0.988 0.012
#> SRR934316 1 0.0938 0.86583 0.988 0.012
#> SRR934317 1 0.0938 0.86583 0.988 0.012
#> SRR934318 1 0.0938 0.86583 0.988 0.012
#> SRR934319 1 0.0938 0.86583 0.988 0.012
#> SRR934320 1 0.2236 0.86039 0.964 0.036
#> SRR934321 1 0.2236 0.86039 0.964 0.036
#> SRR934322 1 0.2236 0.86039 0.964 0.036
#> SRR934323 1 0.2236 0.86039 0.964 0.036
#> SRR934324 1 0.2236 0.86039 0.964 0.036
#> SRR934325 1 0.2236 0.86039 0.964 0.036
#> SRR934326 1 0.2236 0.86039 0.964 0.036
#> SRR934327 1 0.2236 0.86039 0.964 0.036
#> SRR934328 1 0.2236 0.86387 0.964 0.036
#> SRR934329 1 0.2236 0.86387 0.964 0.036
#> SRR934330 1 0.2236 0.86387 0.964 0.036
#> SRR934331 1 0.2236 0.86387 0.964 0.036
#> SRR934332 1 0.2236 0.86387 0.964 0.036
#> SRR934333 1 0.2236 0.86387 0.964 0.036
#> SRR934334 1 0.2236 0.86387 0.964 0.036
#> SRR934335 1 0.2236 0.86387 0.964 0.036
#> SRR934344 1 0.2236 0.86387 0.964 0.036
#> SRR934345 1 0.2236 0.86387 0.964 0.036
#> SRR934346 1 0.2236 0.86387 0.964 0.036
#> SRR934347 1 0.2236 0.86387 0.964 0.036
#> SRR934348 1 0.2236 0.86387 0.964 0.036
#> SRR934349 1 0.2236 0.86387 0.964 0.036
#> SRR934350 1 0.2236 0.86387 0.964 0.036
#> SRR934351 1 0.2236 0.86387 0.964 0.036
#> SRR934336 1 0.0672 0.86638 0.992 0.008
#> SRR934337 1 0.0672 0.86638 0.992 0.008
#> SRR934338 1 0.0672 0.86638 0.992 0.008
#> SRR934339 1 0.0672 0.86638 0.992 0.008
#> SRR934340 1 0.0672 0.86638 0.992 0.008
#> SRR934341 1 0.0672 0.86638 0.992 0.008
#> SRR934342 1 0.0672 0.86638 0.992 0.008
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 1 0.9392 0.04174 0.436 NA 0.172
#> SRR934217 1 0.9392 0.04174 0.436 NA 0.172
#> SRR934218 1 0.9392 0.04174 0.436 NA 0.172
#> SRR934219 1 0.9392 0.04174 0.436 NA 0.172
#> SRR934220 1 0.9392 0.04174 0.436 NA 0.172
#> SRR934221 1 0.9392 0.04174 0.436 NA 0.172
#> SRR934222 1 0.9392 0.04174 0.436 NA 0.172
#> SRR934223 1 0.9392 0.04174 0.436 NA 0.172
#> SRR934224 1 0.2176 0.76823 0.948 NA 0.020
#> SRR934225 1 0.2176 0.76823 0.948 NA 0.020
#> SRR934226 1 0.2176 0.76823 0.948 NA 0.020
#> SRR934227 1 0.2176 0.76823 0.948 NA 0.020
#> SRR934228 1 0.2176 0.76823 0.948 NA 0.020
#> SRR934229 1 0.2176 0.76823 0.948 NA 0.020
#> SRR934230 1 0.2176 0.76823 0.948 NA 0.020
#> SRR934231 1 0.2176 0.76823 0.948 NA 0.020
#> SRR934232 3 0.6393 0.77614 0.148 NA 0.764
#> SRR934233 3 0.6393 0.77614 0.148 NA 0.764
#> SRR934234 3 0.6393 0.77614 0.148 NA 0.764
#> SRR934235 3 0.6393 0.77614 0.148 NA 0.764
#> SRR934236 3 0.6393 0.77614 0.148 NA 0.764
#> SRR934237 3 0.6393 0.77614 0.148 NA 0.764
#> SRR934238 3 0.6393 0.77614 0.148 NA 0.764
#> SRR934239 3 0.6393 0.77614 0.148 NA 0.764
#> SRR934240 1 0.9642 0.00365 0.416 NA 0.376
#> SRR934241 1 0.9642 0.00365 0.416 NA 0.376
#> SRR934242 1 0.9642 0.00365 0.416 NA 0.376
#> SRR934243 1 0.9642 0.00365 0.416 NA 0.376
#> SRR934244 1 0.9642 0.00365 0.416 NA 0.376
#> SRR934245 1 0.9642 0.00365 0.416 NA 0.376
#> SRR934246 1 0.9642 0.00365 0.416 NA 0.376
#> SRR934247 1 0.9642 0.00365 0.416 NA 0.376
#> SRR934248 3 0.6001 0.80222 0.176 NA 0.772
#> SRR934249 3 0.6001 0.80222 0.176 NA 0.772
#> SRR934250 3 0.6001 0.80222 0.176 NA 0.772
#> SRR934251 3 0.6001 0.80222 0.176 NA 0.772
#> SRR934252 3 0.6001 0.80222 0.176 NA 0.772
#> SRR934253 3 0.6001 0.80222 0.176 NA 0.772
#> SRR934254 3 0.6001 0.80222 0.176 NA 0.772
#> SRR934255 3 0.6001 0.80222 0.176 NA 0.772
#> SRR934256 1 0.8343 0.54472 0.612 NA 0.132
#> SRR934257 1 0.8343 0.54472 0.612 NA 0.132
#> SRR934258 1 0.8343 0.54472 0.612 NA 0.132
#> SRR934259 1 0.8343 0.54472 0.612 NA 0.132
#> SRR934260 1 0.8343 0.54472 0.612 NA 0.132
#> SRR934261 1 0.8343 0.54472 0.612 NA 0.132
#> SRR934262 1 0.8343 0.54472 0.612 NA 0.132
#> SRR934263 1 0.8343 0.54472 0.612 NA 0.132
#> SRR934264 3 0.5763 0.79809 0.244 NA 0.740
#> SRR934265 3 0.5763 0.79809 0.244 NA 0.740
#> SRR934266 3 0.5763 0.79809 0.244 NA 0.740
#> SRR934267 3 0.5763 0.79809 0.244 NA 0.740
#> SRR934268 3 0.5763 0.79809 0.244 NA 0.740
#> SRR934269 3 0.5763 0.79809 0.244 NA 0.740
#> SRR934270 3 0.5763 0.79809 0.244 NA 0.740
#> SRR934271 3 0.5763 0.79809 0.244 NA 0.740
#> SRR934272 1 0.1636 0.77052 0.964 NA 0.016
#> SRR934273 1 0.1636 0.77052 0.964 NA 0.016
#> SRR934274 1 0.1636 0.77052 0.964 NA 0.016
#> SRR934275 1 0.1636 0.77052 0.964 NA 0.016
#> SRR934276 1 0.1636 0.77052 0.964 NA 0.016
#> SRR934277 1 0.1636 0.77052 0.964 NA 0.016
#> SRR934278 1 0.1636 0.77052 0.964 NA 0.016
#> SRR934279 1 0.1636 0.77052 0.964 NA 0.016
#> SRR934280 1 0.1482 0.77533 0.968 NA 0.012
#> SRR934281 1 0.1482 0.77533 0.968 NA 0.012
#> SRR934282 1 0.1482 0.77533 0.968 NA 0.012
#> SRR934283 1 0.1482 0.77533 0.968 NA 0.012
#> SRR934284 1 0.1482 0.77533 0.968 NA 0.012
#> SRR934285 1 0.1482 0.77533 0.968 NA 0.012
#> SRR934286 1 0.1482 0.77533 0.968 NA 0.012
#> SRR934287 1 0.1482 0.77533 0.968 NA 0.012
#> SRR934288 1 0.5514 0.74280 0.800 NA 0.044
#> SRR934289 1 0.5514 0.74280 0.800 NA 0.044
#> SRR934290 1 0.5514 0.74280 0.800 NA 0.044
#> SRR934291 1 0.5514 0.74280 0.800 NA 0.044
#> SRR934292 1 0.5514 0.74280 0.800 NA 0.044
#> SRR934293 1 0.5514 0.74280 0.800 NA 0.044
#> SRR934294 1 0.5514 0.74280 0.800 NA 0.044
#> SRR934295 1 0.5514 0.74280 0.800 NA 0.044
#> SRR934296 1 0.4737 0.75839 0.852 NA 0.064
#> SRR934297 1 0.4737 0.75839 0.852 NA 0.064
#> SRR934298 1 0.4737 0.75839 0.852 NA 0.064
#> SRR934299 1 0.4737 0.75839 0.852 NA 0.064
#> SRR934300 1 0.4737 0.75839 0.852 NA 0.064
#> SRR934301 1 0.4737 0.75839 0.852 NA 0.064
#> SRR934302 1 0.4737 0.75839 0.852 NA 0.064
#> SRR934303 1 0.4737 0.75839 0.852 NA 0.064
#> SRR934304 3 0.8578 0.67914 0.100 NA 0.504
#> SRR934305 3 0.8578 0.67914 0.100 NA 0.504
#> SRR934306 3 0.8569 0.67913 0.100 NA 0.508
#> SRR934307 3 0.8569 0.67913 0.100 NA 0.508
#> SRR934308 3 0.8578 0.67914 0.100 NA 0.504
#> SRR934309 3 0.8569 0.67913 0.100 NA 0.508
#> SRR934310 3 0.8569 0.67913 0.100 NA 0.508
#> SRR934311 3 0.8569 0.67913 0.100 NA 0.508
#> SRR934312 1 0.0592 0.77609 0.988 NA 0.000
#> SRR934313 1 0.0592 0.77609 0.988 NA 0.000
#> SRR934314 1 0.0592 0.77609 0.988 NA 0.000
#> SRR934315 1 0.0592 0.77609 0.988 NA 0.000
#> SRR934316 1 0.0592 0.77609 0.988 NA 0.000
#> SRR934317 1 0.0592 0.77609 0.988 NA 0.000
#> SRR934318 1 0.0592 0.77609 0.988 NA 0.000
#> SRR934319 1 0.0592 0.77609 0.988 NA 0.000
#> SRR934320 1 0.3713 0.75722 0.892 NA 0.032
#> SRR934321 1 0.3713 0.75722 0.892 NA 0.032
#> SRR934322 1 0.3713 0.75722 0.892 NA 0.032
#> SRR934323 1 0.3713 0.75722 0.892 NA 0.032
#> SRR934324 1 0.3713 0.75722 0.892 NA 0.032
#> SRR934325 1 0.3713 0.75722 0.892 NA 0.032
#> SRR934326 1 0.3713 0.75722 0.892 NA 0.032
#> SRR934327 1 0.3713 0.75722 0.892 NA 0.032
#> SRR934328 1 0.6203 0.72264 0.760 NA 0.056
#> SRR934329 1 0.6203 0.72264 0.760 NA 0.056
#> SRR934330 1 0.6203 0.72264 0.760 NA 0.056
#> SRR934331 1 0.6203 0.72264 0.760 NA 0.056
#> SRR934332 1 0.6203 0.72264 0.760 NA 0.056
#> SRR934333 1 0.6203 0.72264 0.760 NA 0.056
#> SRR934334 1 0.6203 0.72264 0.760 NA 0.056
#> SRR934335 1 0.6203 0.72264 0.760 NA 0.056
#> SRR934344 1 0.6098 0.72705 0.768 NA 0.056
#> SRR934345 1 0.6098 0.72705 0.768 NA 0.056
#> SRR934346 1 0.6098 0.72705 0.768 NA 0.056
#> SRR934347 1 0.6098 0.72705 0.768 NA 0.056
#> SRR934348 1 0.6098 0.72705 0.768 NA 0.056
#> SRR934349 1 0.6098 0.72705 0.768 NA 0.056
#> SRR934350 1 0.6098 0.72705 0.768 NA 0.056
#> SRR934351 1 0.6098 0.72705 0.768 NA 0.056
#> SRR934336 1 0.1919 0.77045 0.956 NA 0.020
#> SRR934337 1 0.1919 0.77045 0.956 NA 0.020
#> SRR934338 1 0.1919 0.77045 0.956 NA 0.020
#> SRR934339 1 0.1919 0.77045 0.956 NA 0.020
#> SRR934340 1 0.1919 0.77045 0.956 NA 0.020
#> SRR934341 1 0.1919 0.77045 0.956 NA 0.020
#> SRR934342 1 0.1919 0.77045 0.956 NA 0.020
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 3 0.908 0.531 0.272 0.064 0.368 NA
#> SRR934217 3 0.908 0.531 0.272 0.064 0.364 NA
#> SRR934218 3 0.908 0.531 0.272 0.064 0.368 NA
#> SRR934219 3 0.908 0.531 0.272 0.064 0.368 NA
#> SRR934220 3 0.908 0.531 0.272 0.064 0.364 NA
#> SRR934221 3 0.908 0.531 0.272 0.064 0.368 NA
#> SRR934222 3 0.908 0.531 0.272 0.064 0.368 NA
#> SRR934223 3 0.908 0.531 0.272 0.064 0.368 NA
#> SRR934224 1 0.629 0.634 0.480 0.016 0.028 NA
#> SRR934225 1 0.629 0.634 0.480 0.016 0.028 NA
#> SRR934226 1 0.629 0.634 0.480 0.016 0.028 NA
#> SRR934227 1 0.629 0.634 0.480 0.016 0.028 NA
#> SRR934228 1 0.629 0.634 0.480 0.016 0.028 NA
#> SRR934229 1 0.629 0.634 0.480 0.016 0.028 NA
#> SRR934230 1 0.629 0.634 0.480 0.016 0.028 NA
#> SRR934231 1 0.629 0.634 0.480 0.016 0.028 NA
#> SRR934232 2 0.326 0.603 0.048 0.888 0.008 NA
#> SRR934233 2 0.326 0.603 0.048 0.888 0.008 NA
#> SRR934234 2 0.326 0.603 0.048 0.888 0.008 NA
#> SRR934235 2 0.326 0.603 0.048 0.888 0.008 NA
#> SRR934236 2 0.326 0.603 0.048 0.888 0.008 NA
#> SRR934237 2 0.326 0.603 0.048 0.888 0.008 NA
#> SRR934238 2 0.326 0.603 0.048 0.888 0.008 NA
#> SRR934239 2 0.326 0.603 0.048 0.888 0.008 NA
#> SRR934240 2 0.860 0.349 0.256 0.496 0.072 NA
#> SRR934241 2 0.860 0.349 0.256 0.496 0.072 NA
#> SRR934242 2 0.860 0.349 0.256 0.496 0.072 NA
#> SRR934243 2 0.860 0.349 0.256 0.496 0.072 NA
#> SRR934244 2 0.860 0.349 0.256 0.496 0.072 NA
#> SRR934245 2 0.860 0.349 0.256 0.496 0.072 NA
#> SRR934246 2 0.860 0.349 0.256 0.496 0.072 NA
#> SRR934247 2 0.860 0.349 0.256 0.496 0.072 NA
#> SRR934248 2 0.729 0.563 0.092 0.660 0.136 NA
#> SRR934249 2 0.729 0.563 0.092 0.660 0.136 NA
#> SRR934250 2 0.730 0.562 0.092 0.660 0.124 NA
#> SRR934251 2 0.729 0.563 0.092 0.660 0.136 NA
#> SRR934252 2 0.729 0.563 0.092 0.660 0.132 NA
#> SRR934253 2 0.730 0.562 0.092 0.660 0.128 NA
#> SRR934254 2 0.729 0.563 0.092 0.660 0.136 NA
#> SRR934255 2 0.729 0.563 0.092 0.660 0.136 NA
#> SRR934256 1 0.911 0.320 0.440 0.156 0.120 NA
#> SRR934257 1 0.911 0.320 0.440 0.156 0.120 NA
#> SRR934258 1 0.911 0.320 0.440 0.156 0.120 NA
#> SRR934259 1 0.911 0.320 0.440 0.156 0.120 NA
#> SRR934260 1 0.911 0.320 0.440 0.156 0.120 NA
#> SRR934261 1 0.911 0.320 0.440 0.156 0.120 NA
#> SRR934262 1 0.911 0.320 0.440 0.156 0.120 NA
#> SRR934263 1 0.911 0.320 0.440 0.156 0.120 NA
#> SRR934264 2 0.640 0.590 0.068 0.724 0.108 NA
#> SRR934265 2 0.640 0.590 0.068 0.724 0.108 NA
#> SRR934266 2 0.640 0.590 0.068 0.724 0.108 NA
#> SRR934267 2 0.640 0.590 0.068 0.724 0.108 NA
#> SRR934268 2 0.640 0.590 0.068 0.724 0.108 NA
#> SRR934269 2 0.640 0.590 0.068 0.724 0.108 NA
#> SRR934270 2 0.640 0.590 0.068 0.724 0.108 NA
#> SRR934271 2 0.640 0.590 0.068 0.724 0.108 NA
#> SRR934272 1 0.606 0.663 0.536 0.012 0.024 NA
#> SRR934273 1 0.606 0.663 0.536 0.012 0.024 NA
#> SRR934274 1 0.606 0.663 0.536 0.012 0.024 NA
#> SRR934275 1 0.606 0.663 0.536 0.012 0.024 NA
#> SRR934276 1 0.606 0.663 0.536 0.012 0.024 NA
#> SRR934277 1 0.606 0.663 0.536 0.012 0.024 NA
#> SRR934278 1 0.606 0.663 0.536 0.012 0.024 NA
#> SRR934279 1 0.606 0.663 0.536 0.012 0.024 NA
#> SRR934280 1 0.565 0.669 0.532 0.004 0.016 NA
#> SRR934281 1 0.565 0.669 0.532 0.004 0.016 NA
#> SRR934282 1 0.565 0.669 0.532 0.004 0.016 NA
#> SRR934283 1 0.565 0.669 0.532 0.004 0.016 NA
#> SRR934284 1 0.565 0.669 0.532 0.004 0.016 NA
#> SRR934285 1 0.565 0.669 0.532 0.004 0.016 NA
#> SRR934286 1 0.565 0.669 0.532 0.004 0.016 NA
#> SRR934287 1 0.565 0.669 0.532 0.004 0.016 NA
#> SRR934288 1 0.137 0.569 0.964 0.004 0.016 NA
#> SRR934289 1 0.137 0.569 0.964 0.004 0.016 NA
#> SRR934290 1 0.137 0.569 0.964 0.004 0.016 NA
#> SRR934291 1 0.137 0.569 0.964 0.004 0.016 NA
#> SRR934292 1 0.137 0.569 0.964 0.004 0.016 NA
#> SRR934293 1 0.137 0.569 0.964 0.004 0.016 NA
#> SRR934294 1 0.137 0.569 0.964 0.004 0.016 NA
#> SRR934295 1 0.137 0.569 0.964 0.004 0.016 NA
#> SRR934296 1 0.653 0.538 0.696 0.064 0.060 NA
#> SRR934297 1 0.653 0.538 0.696 0.064 0.060 NA
#> SRR934298 1 0.653 0.538 0.696 0.064 0.060 NA
#> SRR934299 1 0.653 0.538 0.696 0.064 0.060 NA
#> SRR934300 1 0.653 0.538 0.696 0.064 0.060 NA
#> SRR934301 1 0.653 0.538 0.696 0.064 0.060 NA
#> SRR934302 1 0.653 0.538 0.696 0.064 0.060 NA
#> SRR934303 1 0.653 0.538 0.696 0.064 0.060 NA
#> SRR934304 3 0.664 0.350 0.036 0.344 0.584 NA
#> SRR934305 3 0.664 0.350 0.036 0.344 0.584 NA
#> SRR934306 3 0.664 0.350 0.036 0.344 0.584 NA
#> SRR934307 3 0.664 0.350 0.036 0.344 0.584 NA
#> SRR934308 3 0.685 0.341 0.036 0.360 0.560 NA
#> SRR934309 3 0.664 0.350 0.036 0.344 0.584 NA
#> SRR934310 3 0.664 0.350 0.036 0.344 0.584 NA
#> SRR934311 3 0.664 0.350 0.036 0.344 0.584 NA
#> SRR934312 1 0.583 0.674 0.556 0.008 0.020 NA
#> SRR934313 1 0.583 0.674 0.556 0.008 0.020 NA
#> SRR934314 1 0.583 0.674 0.556 0.008 0.020 NA
#> SRR934315 1 0.583 0.674 0.556 0.008 0.020 NA
#> SRR934316 1 0.583 0.674 0.556 0.008 0.020 NA
#> SRR934317 1 0.583 0.674 0.556 0.008 0.020 NA
#> SRR934318 1 0.583 0.674 0.556 0.008 0.020 NA
#> SRR934319 1 0.583 0.674 0.556 0.008 0.020 NA
#> SRR934320 1 0.618 0.661 0.564 0.016 0.028 NA
#> SRR934321 1 0.618 0.661 0.564 0.016 0.028 NA
#> SRR934322 1 0.618 0.661 0.564 0.016 0.028 NA
#> SRR934323 1 0.618 0.661 0.564 0.016 0.028 NA
#> SRR934324 1 0.618 0.661 0.564 0.016 0.028 NA
#> SRR934325 1 0.618 0.661 0.564 0.016 0.028 NA
#> SRR934326 1 0.618 0.661 0.564 0.016 0.028 NA
#> SRR934327 1 0.618 0.661 0.564 0.016 0.028 NA
#> SRR934328 1 0.297 0.504 0.900 0.008 0.032 NA
#> SRR934329 1 0.297 0.504 0.900 0.008 0.032 NA
#> SRR934330 1 0.297 0.504 0.900 0.008 0.032 NA
#> SRR934331 1 0.297 0.504 0.900 0.008 0.032 NA
#> SRR934332 1 0.297 0.504 0.900 0.008 0.032 NA
#> SRR934333 1 0.297 0.504 0.900 0.008 0.032 NA
#> SRR934334 1 0.297 0.504 0.900 0.008 0.032 NA
#> SRR934335 1 0.297 0.504 0.900 0.008 0.032 NA
#> SRR934344 1 0.112 0.555 0.972 0.004 0.012 NA
#> SRR934345 1 0.112 0.555 0.972 0.004 0.012 NA
#> SRR934346 1 0.112 0.555 0.972 0.004 0.012 NA
#> SRR934347 1 0.112 0.555 0.972 0.004 0.012 NA
#> SRR934348 1 0.112 0.555 0.972 0.004 0.012 NA
#> SRR934349 1 0.112 0.555 0.972 0.004 0.012 NA
#> SRR934350 1 0.112 0.555 0.972 0.004 0.012 NA
#> SRR934351 1 0.112 0.555 0.972 0.004 0.012 NA
#> SRR934336 1 0.589 0.668 0.528 0.012 0.016 NA
#> SRR934337 1 0.589 0.668 0.528 0.012 0.016 NA
#> SRR934338 1 0.589 0.668 0.528 0.012 0.016 NA
#> SRR934339 1 0.589 0.668 0.528 0.012 0.016 NA
#> SRR934340 1 0.589 0.668 0.528 0.012 0.016 NA
#> SRR934341 1 0.589 0.668 0.528 0.012 0.016 NA
#> SRR934342 1 0.589 0.668 0.528 0.012 0.016 NA
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0.898 0.436 0.196 0.196 0.348 0.028 0.232
#> SRR934217 3 0.901 0.434 0.204 0.196 0.340 0.028 0.232
#> SRR934218 3 0.898 0.436 0.196 0.196 0.348 0.028 0.232
#> SRR934219 3 0.898 0.436 0.196 0.196 0.348 0.028 0.232
#> SRR934220 3 0.901 0.434 0.204 0.196 0.340 0.028 0.232
#> SRR934221 3 0.898 0.436 0.196 0.196 0.348 0.028 0.232
#> SRR934222 3 0.898 0.436 0.196 0.196 0.348 0.028 0.232
#> SRR934223 3 0.898 0.436 0.196 0.196 0.348 0.028 0.232
#> SRR934224 1 0.494 0.782 0.724 0.196 0.064 0.000 0.016
#> SRR934225 1 0.494 0.782 0.724 0.196 0.064 0.000 0.016
#> SRR934226 1 0.494 0.782 0.724 0.196 0.064 0.000 0.016
#> SRR934227 1 0.494 0.782 0.724 0.196 0.064 0.000 0.016
#> SRR934228 1 0.494 0.782 0.724 0.196 0.064 0.000 0.016
#> SRR934229 1 0.494 0.782 0.724 0.196 0.064 0.000 0.016
#> SRR934230 1 0.494 0.782 0.724 0.196 0.064 0.000 0.016
#> SRR934231 1 0.494 0.782 0.724 0.196 0.064 0.000 0.016
#> SRR934232 4 0.121 0.460 0.024 0.016 0.000 0.960 0.000
#> SRR934233 4 0.121 0.460 0.024 0.016 0.000 0.960 0.000
#> SRR934234 4 0.121 0.460 0.024 0.016 0.000 0.960 0.000
#> SRR934235 4 0.121 0.460 0.024 0.016 0.000 0.960 0.000
#> SRR934236 4 0.121 0.460 0.024 0.016 0.000 0.960 0.000
#> SRR934237 4 0.121 0.460 0.024 0.016 0.000 0.960 0.000
#> SRR934238 4 0.121 0.460 0.024 0.016 0.000 0.960 0.000
#> SRR934239 4 0.121 0.460 0.024 0.016 0.000 0.960 0.000
#> SRR934240 4 0.781 0.240 0.180 0.088 0.168 0.536 0.028
#> SRR934241 4 0.781 0.240 0.180 0.088 0.168 0.536 0.028
#> SRR934242 4 0.781 0.240 0.180 0.088 0.168 0.536 0.028
#> SRR934243 4 0.781 0.240 0.180 0.088 0.168 0.536 0.028
#> SRR934244 4 0.781 0.240 0.180 0.088 0.168 0.536 0.028
#> SRR934245 4 0.781 0.240 0.180 0.088 0.168 0.536 0.028
#> SRR934246 4 0.781 0.240 0.180 0.088 0.168 0.536 0.028
#> SRR934247 4 0.781 0.240 0.180 0.088 0.168 0.536 0.028
#> SRR934248 4 0.768 0.373 0.088 0.024 0.108 0.516 0.264
#> SRR934249 4 0.771 0.373 0.088 0.024 0.112 0.516 0.260
#> SRR934250 4 0.768 0.373 0.088 0.024 0.108 0.516 0.264
#> SRR934251 4 0.768 0.373 0.088 0.024 0.108 0.516 0.264
#> SRR934252 4 0.768 0.373 0.088 0.024 0.108 0.516 0.264
#> SRR934253 4 0.768 0.373 0.088 0.024 0.108 0.516 0.264
#> SRR934254 4 0.768 0.373 0.088 0.024 0.108 0.516 0.264
#> SRR934255 4 0.768 0.373 0.088 0.024 0.108 0.516 0.264
#> SRR934256 3 0.860 0.419 0.236 0.264 0.340 0.152 0.008
#> SRR934257 3 0.860 0.419 0.236 0.264 0.340 0.152 0.008
#> SRR934258 3 0.860 0.419 0.236 0.264 0.340 0.152 0.008
#> SRR934259 3 0.860 0.419 0.236 0.264 0.340 0.152 0.008
#> SRR934260 3 0.861 0.418 0.240 0.264 0.336 0.152 0.008
#> SRR934261 3 0.860 0.419 0.236 0.264 0.340 0.152 0.008
#> SRR934262 3 0.860 0.419 0.236 0.264 0.340 0.152 0.008
#> SRR934263 3 0.860 0.419 0.236 0.264 0.340 0.152 0.008
#> SRR934264 4 0.663 0.414 0.084 0.012 0.072 0.628 0.204
#> SRR934265 4 0.663 0.414 0.084 0.012 0.072 0.628 0.204
#> SRR934266 4 0.663 0.414 0.084 0.012 0.072 0.628 0.204
#> SRR934267 4 0.663 0.414 0.084 0.012 0.072 0.628 0.204
#> SRR934268 4 0.663 0.414 0.084 0.012 0.072 0.628 0.204
#> SRR934269 4 0.663 0.414 0.084 0.012 0.072 0.628 0.204
#> SRR934270 4 0.663 0.414 0.084 0.012 0.072 0.628 0.204
#> SRR934271 4 0.663 0.414 0.084 0.012 0.072 0.628 0.204
#> SRR934272 1 0.473 0.789 0.712 0.240 0.032 0.000 0.016
#> SRR934273 1 0.473 0.789 0.712 0.240 0.032 0.000 0.016
#> SRR934274 1 0.473 0.789 0.712 0.240 0.032 0.000 0.016
#> SRR934275 1 0.473 0.789 0.712 0.240 0.032 0.000 0.016
#> SRR934276 1 0.473 0.789 0.712 0.240 0.032 0.000 0.016
#> SRR934277 1 0.473 0.789 0.712 0.240 0.032 0.000 0.016
#> SRR934278 1 0.473 0.789 0.712 0.240 0.032 0.000 0.016
#> SRR934279 1 0.473 0.789 0.712 0.240 0.032 0.000 0.016
#> SRR934280 1 0.556 0.778 0.680 0.224 0.064 0.004 0.028
#> SRR934281 1 0.556 0.778 0.680 0.224 0.064 0.004 0.028
#> SRR934282 1 0.556 0.778 0.680 0.224 0.064 0.004 0.028
#> SRR934283 1 0.556 0.778 0.680 0.224 0.064 0.004 0.028
#> SRR934284 1 0.556 0.778 0.680 0.224 0.064 0.004 0.028
#> SRR934285 1 0.556 0.778 0.680 0.224 0.064 0.004 0.028
#> SRR934286 1 0.556 0.778 0.680 0.224 0.064 0.004 0.028
#> SRR934287 1 0.556 0.778 0.680 0.224 0.064 0.004 0.028
#> SRR934288 2 0.454 0.743 0.084 0.800 0.060 0.004 0.052
#> SRR934289 2 0.454 0.743 0.084 0.800 0.060 0.004 0.052
#> SRR934290 2 0.454 0.743 0.084 0.800 0.060 0.004 0.052
#> SRR934291 2 0.454 0.743 0.084 0.800 0.060 0.004 0.052
#> SRR934292 2 0.454 0.743 0.084 0.800 0.060 0.004 0.052
#> SRR934293 2 0.454 0.743 0.084 0.800 0.060 0.004 0.052
#> SRR934294 2 0.454 0.743 0.084 0.800 0.060 0.004 0.052
#> SRR934295 2 0.454 0.743 0.084 0.800 0.060 0.004 0.052
#> SRR934296 2 0.721 0.501 0.220 0.580 0.120 0.044 0.036
#> SRR934297 2 0.721 0.501 0.220 0.580 0.120 0.044 0.036
#> SRR934298 2 0.721 0.501 0.220 0.580 0.120 0.044 0.036
#> SRR934299 2 0.721 0.501 0.220 0.580 0.120 0.044 0.036
#> SRR934300 2 0.721 0.501 0.220 0.580 0.120 0.044 0.036
#> SRR934301 2 0.721 0.501 0.220 0.580 0.120 0.044 0.036
#> SRR934302 2 0.721 0.501 0.220 0.580 0.120 0.044 0.036
#> SRR934303 2 0.721 0.501 0.220 0.580 0.120 0.044 0.036
#> SRR934304 5 0.441 0.996 0.012 0.020 0.000 0.244 0.724
#> SRR934305 5 0.441 0.996 0.012 0.020 0.000 0.244 0.724
#> SRR934306 5 0.479 0.993 0.012 0.020 0.012 0.244 0.712
#> SRR934307 5 0.456 0.996 0.012 0.020 0.004 0.244 0.720
#> SRR934308 5 0.468 0.995 0.012 0.020 0.008 0.244 0.716
#> SRR934309 5 0.468 0.993 0.012 0.020 0.008 0.244 0.716
#> SRR934310 5 0.441 0.996 0.012 0.020 0.000 0.244 0.724
#> SRR934311 5 0.456 0.996 0.012 0.020 0.004 0.244 0.720
#> SRR934312 1 0.579 0.766 0.644 0.260 0.064 0.004 0.028
#> SRR934313 1 0.579 0.766 0.644 0.260 0.064 0.004 0.028
#> SRR934314 1 0.579 0.766 0.644 0.260 0.064 0.004 0.028
#> SRR934315 1 0.579 0.766 0.644 0.260 0.064 0.004 0.028
#> SRR934316 1 0.579 0.766 0.644 0.260 0.064 0.004 0.028
#> SRR934317 1 0.579 0.766 0.644 0.260 0.064 0.004 0.028
#> SRR934318 1 0.579 0.766 0.644 0.260 0.064 0.004 0.028
#> SRR934319 1 0.579 0.766 0.644 0.260 0.064 0.004 0.028
#> SRR934320 1 0.638 0.624 0.576 0.256 0.148 0.020 0.000
#> SRR934321 1 0.638 0.624 0.576 0.256 0.148 0.020 0.000
#> SRR934322 1 0.638 0.624 0.576 0.256 0.148 0.020 0.000
#> SRR934323 1 0.638 0.624 0.576 0.256 0.148 0.020 0.000
#> SRR934324 1 0.638 0.624 0.576 0.256 0.148 0.020 0.000
#> SRR934325 1 0.638 0.624 0.576 0.256 0.148 0.020 0.000
#> SRR934326 1 0.638 0.624 0.576 0.256 0.148 0.020 0.000
#> SRR934327 1 0.638 0.624 0.576 0.256 0.148 0.020 0.000
#> SRR934328 2 0.096 0.773 0.016 0.972 0.008 0.000 0.004
#> SRR934329 2 0.096 0.773 0.016 0.972 0.008 0.000 0.004
#> SRR934330 2 0.096 0.773 0.016 0.972 0.008 0.000 0.004
#> SRR934331 2 0.096 0.773 0.016 0.972 0.008 0.000 0.004
#> SRR934332 2 0.096 0.773 0.016 0.972 0.008 0.000 0.004
#> SRR934333 2 0.096 0.773 0.016 0.972 0.008 0.000 0.004
#> SRR934334 2 0.096 0.773 0.016 0.972 0.008 0.000 0.004
#> SRR934335 2 0.096 0.773 0.016 0.972 0.008 0.000 0.004
#> SRR934344 2 0.104 0.779 0.040 0.960 0.000 0.000 0.000
#> SRR934345 2 0.104 0.779 0.040 0.960 0.000 0.000 0.000
#> SRR934346 2 0.104 0.779 0.040 0.960 0.000 0.000 0.000
#> SRR934347 2 0.104 0.779 0.040 0.960 0.000 0.000 0.000
#> SRR934348 2 0.104 0.779 0.040 0.960 0.000 0.000 0.000
#> SRR934349 2 0.104 0.779 0.040 0.960 0.000 0.000 0.000
#> SRR934350 2 0.104 0.779 0.040 0.960 0.000 0.000 0.000
#> SRR934351 2 0.104 0.779 0.040 0.960 0.000 0.000 0.000
#> SRR934336 1 0.480 0.806 0.724 0.220 0.040 0.008 0.008
#> SRR934337 1 0.480 0.806 0.724 0.220 0.040 0.008 0.008
#> SRR934338 1 0.480 0.806 0.724 0.220 0.040 0.008 0.008
#> SRR934339 1 0.480 0.806 0.724 0.220 0.040 0.008 0.008
#> SRR934340 1 0.480 0.806 0.724 0.220 0.040 0.008 0.008
#> SRR934341 1 0.480 0.806 0.724 0.220 0.040 0.008 0.008
#> SRR934342 1 0.480 0.806 0.724 0.220 0.040 0.008 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.5243 0.99836 0.124 NA 0.660 0.024 0.000 0.192
#> SRR934217 3 0.5379 0.99712 0.124 NA 0.656 0.024 0.000 0.192
#> SRR934218 3 0.5379 0.99712 0.124 NA 0.656 0.024 0.000 0.192
#> SRR934219 3 0.5243 0.99836 0.124 NA 0.660 0.024 0.000 0.192
#> SRR934220 3 0.5516 0.99423 0.124 NA 0.652 0.024 0.004 0.192
#> SRR934221 3 0.5243 0.99836 0.124 NA 0.660 0.024 0.000 0.192
#> SRR934222 3 0.5243 0.99836 0.124 NA 0.660 0.024 0.000 0.192
#> SRR934223 3 0.5243 0.99836 0.124 NA 0.660 0.024 0.000 0.192
#> SRR934224 6 0.5244 0.61590 0.068 NA 0.072 0.008 0.020 0.736
#> SRR934225 6 0.5244 0.61590 0.068 NA 0.072 0.008 0.020 0.736
#> SRR934226 6 0.5244 0.61590 0.068 NA 0.072 0.008 0.020 0.736
#> SRR934227 6 0.5244 0.61590 0.068 NA 0.072 0.008 0.020 0.736
#> SRR934228 6 0.5244 0.61590 0.068 NA 0.072 0.008 0.020 0.736
#> SRR934229 6 0.5244 0.61590 0.068 NA 0.072 0.008 0.020 0.736
#> SRR934230 6 0.5244 0.61590 0.068 NA 0.072 0.008 0.020 0.736
#> SRR934231 6 0.5244 0.61590 0.068 NA 0.072 0.008 0.020 0.736
#> SRR934232 4 0.0858 0.57134 0.004 NA 0.000 0.968 0.000 0.028
#> SRR934233 4 0.0858 0.57134 0.004 NA 0.000 0.968 0.000 0.028
#> SRR934234 4 0.0858 0.57134 0.004 NA 0.000 0.968 0.000 0.028
#> SRR934235 4 0.0858 0.57134 0.004 NA 0.000 0.968 0.000 0.028
#> SRR934236 4 0.0858 0.57134 0.004 NA 0.000 0.968 0.000 0.028
#> SRR934237 4 0.0858 0.57134 0.004 NA 0.000 0.968 0.000 0.028
#> SRR934238 4 0.0858 0.57134 0.004 NA 0.000 0.968 0.000 0.028
#> SRR934239 4 0.0858 0.57134 0.004 NA 0.000 0.968 0.000 0.028
#> SRR934240 4 0.7933 0.22022 0.072 NA 0.020 0.500 0.164 0.140
#> SRR934241 4 0.7978 0.21973 0.072 NA 0.024 0.500 0.160 0.140
#> SRR934242 4 0.7933 0.22022 0.072 NA 0.020 0.500 0.164 0.140
#> SRR934243 4 0.7933 0.22022 0.072 NA 0.020 0.500 0.164 0.140
#> SRR934244 4 0.7933 0.22022 0.072 NA 0.020 0.500 0.164 0.140
#> SRR934245 4 0.7978 0.21973 0.072 NA 0.024 0.500 0.160 0.140
#> SRR934246 4 0.7933 0.22022 0.072 NA 0.020 0.500 0.164 0.140
#> SRR934247 4 0.7933 0.22022 0.072 NA 0.020 0.500 0.164 0.140
#> SRR934248 4 0.5792 0.54299 0.012 NA 0.044 0.484 0.004 0.032
#> SRR934249 4 0.5841 0.54309 0.012 NA 0.048 0.484 0.004 0.032
#> SRR934250 4 0.5841 0.54319 0.012 NA 0.048 0.484 0.004 0.032
#> SRR934251 4 0.5792 0.54299 0.012 NA 0.044 0.484 0.004 0.032
#> SRR934252 4 0.5937 0.54311 0.012 NA 0.048 0.484 0.008 0.032
#> SRR934253 4 0.5887 0.54308 0.012 NA 0.044 0.484 0.008 0.032
#> SRR934254 4 0.5841 0.54309 0.012 NA 0.048 0.484 0.004 0.032
#> SRR934255 4 0.5841 0.54309 0.012 NA 0.048 0.484 0.004 0.032
#> SRR934256 5 0.9121 0.42082 0.192 NA 0.024 0.136 0.308 0.208
#> SRR934257 5 0.9121 0.42082 0.192 NA 0.024 0.136 0.308 0.208
#> SRR934258 5 0.9121 0.42082 0.192 NA 0.024 0.136 0.308 0.208
#> SRR934259 5 0.9121 0.42082 0.192 NA 0.024 0.136 0.308 0.208
#> SRR934260 5 0.9140 0.41961 0.192 NA 0.024 0.136 0.304 0.208
#> SRR934261 5 0.9121 0.42082 0.192 NA 0.024 0.136 0.308 0.208
#> SRR934262 5 0.9121 0.42082 0.192 NA 0.024 0.136 0.308 0.208
#> SRR934263 5 0.9121 0.42082 0.192 NA 0.024 0.136 0.308 0.208
#> SRR934264 4 0.5902 0.59563 0.016 NA 0.052 0.636 0.008 0.064
#> SRR934265 4 0.5902 0.59563 0.016 NA 0.052 0.636 0.008 0.064
#> SRR934266 4 0.5902 0.59563 0.016 NA 0.052 0.636 0.008 0.064
#> SRR934267 4 0.5902 0.59563 0.016 NA 0.052 0.636 0.008 0.064
#> SRR934268 4 0.5902 0.59563 0.016 NA 0.052 0.636 0.008 0.064
#> SRR934269 4 0.5902 0.59563 0.016 NA 0.052 0.636 0.008 0.064
#> SRR934270 4 0.5902 0.59563 0.016 NA 0.052 0.636 0.008 0.064
#> SRR934271 4 0.5902 0.59563 0.016 NA 0.052 0.636 0.008 0.064
#> SRR934272 6 0.3559 0.67562 0.068 NA 0.068 0.004 0.004 0.836
#> SRR934273 6 0.3559 0.67562 0.068 NA 0.068 0.004 0.004 0.836
#> SRR934274 6 0.3559 0.67562 0.068 NA 0.068 0.004 0.004 0.836
#> SRR934275 6 0.3559 0.67562 0.068 NA 0.068 0.004 0.004 0.836
#> SRR934276 6 0.3559 0.67562 0.068 NA 0.068 0.004 0.004 0.836
#> SRR934277 6 0.3559 0.67562 0.068 NA 0.068 0.004 0.004 0.836
#> SRR934278 6 0.3559 0.67562 0.068 NA 0.068 0.004 0.004 0.836
#> SRR934279 6 0.3559 0.67562 0.068 NA 0.068 0.004 0.004 0.836
#> SRR934280 6 0.5367 0.62541 0.036 NA 0.060 0.008 0.064 0.732
#> SRR934281 6 0.5367 0.62541 0.036 NA 0.060 0.008 0.064 0.732
#> SRR934282 6 0.5367 0.62541 0.036 NA 0.060 0.008 0.064 0.732
#> SRR934283 6 0.5367 0.62541 0.036 NA 0.060 0.008 0.064 0.732
#> SRR934284 6 0.5367 0.62541 0.036 NA 0.060 0.008 0.064 0.732
#> SRR934285 6 0.5367 0.62541 0.036 NA 0.060 0.008 0.064 0.732
#> SRR934286 6 0.5367 0.62541 0.036 NA 0.060 0.008 0.064 0.732
#> SRR934287 6 0.5367 0.62541 0.036 NA 0.060 0.008 0.064 0.732
#> SRR934288 1 0.5831 0.76034 0.584 NA 0.040 0.000 0.012 0.292
#> SRR934289 1 0.5831 0.76034 0.584 NA 0.040 0.000 0.012 0.292
#> SRR934290 1 0.5831 0.76034 0.584 NA 0.040 0.000 0.012 0.292
#> SRR934291 1 0.5831 0.76034 0.584 NA 0.040 0.000 0.012 0.292
#> SRR934292 1 0.5831 0.76034 0.584 NA 0.040 0.000 0.012 0.292
#> SRR934293 1 0.5831 0.76034 0.584 NA 0.040 0.000 0.012 0.292
#> SRR934294 1 0.5831 0.76034 0.584 NA 0.040 0.000 0.012 0.292
#> SRR934295 1 0.5831 0.76034 0.584 NA 0.040 0.000 0.012 0.292
#> SRR934296 6 0.7617 0.00638 0.340 NA 0.052 0.032 0.072 0.436
#> SRR934297 6 0.7617 0.00638 0.340 NA 0.052 0.032 0.072 0.436
#> SRR934298 6 0.7617 0.00638 0.340 NA 0.052 0.032 0.072 0.436
#> SRR934299 6 0.7617 0.00638 0.340 NA 0.052 0.032 0.072 0.436
#> SRR934300 6 0.7617 0.00638 0.340 NA 0.052 0.032 0.072 0.436
#> SRR934301 6 0.7617 0.00638 0.340 NA 0.052 0.032 0.072 0.436
#> SRR934302 6 0.7617 0.00638 0.340 NA 0.052 0.032 0.072 0.436
#> SRR934303 6 0.7617 0.00638 0.340 NA 0.052 0.032 0.072 0.436
#> SRR934304 5 0.6724 0.35504 0.008 NA 0.168 0.160 0.552 0.000
#> SRR934305 5 0.6724 0.35504 0.008 NA 0.168 0.160 0.552 0.000
#> SRR934306 5 0.6657 0.35475 0.004 NA 0.168 0.160 0.552 0.000
#> SRR934307 5 0.6649 0.35486 0.004 NA 0.172 0.160 0.552 0.000
#> SRR934308 5 0.6957 0.34828 0.012 NA 0.180 0.160 0.528 0.000
#> SRR934309 5 0.6709 0.35283 0.004 NA 0.176 0.160 0.544 0.000
#> SRR934310 5 0.6622 0.35520 0.004 NA 0.168 0.160 0.556 0.000
#> SRR934311 5 0.6622 0.35520 0.004 NA 0.168 0.160 0.556 0.000
#> SRR934312 6 0.4632 0.64846 0.092 NA 0.084 0.000 0.032 0.768
#> SRR934313 6 0.4632 0.64846 0.092 NA 0.084 0.000 0.032 0.768
#> SRR934314 6 0.4632 0.64846 0.092 NA 0.084 0.000 0.032 0.768
#> SRR934315 6 0.4632 0.64846 0.092 NA 0.084 0.000 0.032 0.768
#> SRR934316 6 0.4632 0.64846 0.092 NA 0.084 0.000 0.032 0.768
#> SRR934317 6 0.4632 0.64846 0.092 NA 0.084 0.000 0.032 0.768
#> SRR934318 6 0.4632 0.64846 0.092 NA 0.084 0.000 0.032 0.768
#> SRR934319 6 0.4632 0.64846 0.092 NA 0.084 0.000 0.032 0.768
#> SRR934320 6 0.5212 0.61657 0.088 NA 0.016 0.028 0.068 0.748
#> SRR934321 6 0.5212 0.61657 0.088 NA 0.016 0.028 0.068 0.748
#> SRR934322 6 0.5212 0.61657 0.088 NA 0.016 0.028 0.068 0.748
#> SRR934323 6 0.5212 0.61657 0.088 NA 0.016 0.028 0.068 0.748
#> SRR934324 6 0.5212 0.61657 0.088 NA 0.016 0.028 0.068 0.748
#> SRR934325 6 0.5212 0.61657 0.088 NA 0.016 0.028 0.068 0.748
#> SRR934326 6 0.5212 0.61657 0.088 NA 0.016 0.028 0.068 0.748
#> SRR934327 6 0.5212 0.61657 0.088 NA 0.016 0.028 0.068 0.748
#> SRR934328 1 0.5114 0.81458 0.704 NA 0.024 0.004 0.012 0.180
#> SRR934329 1 0.5114 0.81458 0.704 NA 0.024 0.004 0.012 0.180
#> SRR934330 1 0.5114 0.81458 0.704 NA 0.024 0.004 0.012 0.180
#> SRR934331 1 0.5114 0.81458 0.704 NA 0.024 0.004 0.012 0.180
#> SRR934332 1 0.5114 0.81458 0.704 NA 0.024 0.004 0.012 0.180
#> SRR934333 1 0.5114 0.81458 0.704 NA 0.024 0.004 0.012 0.180
#> SRR934334 1 0.5114 0.81458 0.704 NA 0.024 0.004 0.012 0.180
#> SRR934335 1 0.5114 0.81458 0.704 NA 0.024 0.004 0.012 0.180
#> SRR934344 1 0.3581 0.84634 0.764 NA 0.004 0.004 0.008 0.216
#> SRR934345 1 0.3581 0.84634 0.764 NA 0.004 0.004 0.008 0.216
#> SRR934346 1 0.3581 0.84634 0.764 NA 0.004 0.004 0.008 0.216
#> SRR934347 1 0.3581 0.84634 0.764 NA 0.004 0.004 0.008 0.216
#> SRR934348 1 0.3581 0.84634 0.764 NA 0.004 0.004 0.008 0.216
#> SRR934349 1 0.3581 0.84634 0.764 NA 0.004 0.004 0.008 0.216
#> SRR934350 1 0.3581 0.84634 0.764 NA 0.004 0.004 0.008 0.216
#> SRR934351 1 0.3581 0.84634 0.764 NA 0.004 0.004 0.008 0.216
#> SRR934336 6 0.2963 0.68743 0.032 NA 0.044 0.004 0.008 0.880
#> SRR934337 6 0.2963 0.68743 0.032 NA 0.044 0.004 0.008 0.880
#> SRR934338 6 0.2963 0.68743 0.032 NA 0.044 0.004 0.008 0.880
#> SRR934339 6 0.2963 0.68743 0.032 NA 0.044 0.004 0.008 0.880
#> SRR934340 6 0.2963 0.68743 0.032 NA 0.044 0.004 0.008 0.880
#> SRR934341 6 0.2963 0.68743 0.032 NA 0.044 0.004 0.008 0.880
#> SRR934342 6 0.2963 0.68743 0.032 NA 0.044 0.004 0.008 0.880
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 14550 rows and 135 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 0.691 0.916 0.959 0.4708 0.538 0.538
#> 3 3 0.581 0.605 0.810 0.3118 0.825 0.683
#> 4 4 0.733 0.777 0.851 0.1850 0.797 0.526
#> 5 5 0.721 0.768 0.807 0.0612 0.950 0.814
#> 6 6 0.771 0.731 0.757 0.0430 0.986 0.937
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
#> SRR934216 2 0.697 0.793 0.188 0.812
#> SRR934217 2 0.697 0.793 0.188 0.812
#> SRR934218 2 0.697 0.793 0.188 0.812
#> SRR934219 2 0.697 0.793 0.188 0.812
#> SRR934220 2 0.697 0.793 0.188 0.812
#> SRR934221 2 0.697 0.793 0.188 0.812
#> SRR934222 2 0.697 0.793 0.188 0.812
#> SRR934223 2 0.697 0.793 0.188 0.812
#> SRR934224 1 0.000 0.951 1.000 0.000
#> SRR934225 1 0.000 0.951 1.000 0.000
#> SRR934226 1 0.000 0.951 1.000 0.000
#> SRR934227 1 0.000 0.951 1.000 0.000
#> SRR934228 1 0.000 0.951 1.000 0.000
#> SRR934229 1 0.000 0.951 1.000 0.000
#> SRR934230 1 0.000 0.951 1.000 0.000
#> SRR934231 1 0.000 0.951 1.000 0.000
#> SRR934232 2 0.000 0.965 0.000 1.000
#> SRR934233 2 0.000 0.965 0.000 1.000
#> SRR934234 2 0.000 0.965 0.000 1.000
#> SRR934235 2 0.000 0.965 0.000 1.000
#> SRR934236 2 0.000 0.965 0.000 1.000
#> SRR934237 2 0.000 0.965 0.000 1.000
#> SRR934238 2 0.000 0.965 0.000 1.000
#> SRR934239 2 0.000 0.965 0.000 1.000
#> SRR934240 2 0.000 0.965 0.000 1.000
#> SRR934241 2 0.000 0.965 0.000 1.000
#> SRR934242 2 0.000 0.965 0.000 1.000
#> SRR934243 2 0.000 0.965 0.000 1.000
#> SRR934244 2 0.000 0.965 0.000 1.000
#> SRR934245 2 0.000 0.965 0.000 1.000
#> SRR934246 2 0.000 0.965 0.000 1.000
#> SRR934247 2 0.000 0.965 0.000 1.000
#> SRR934248 2 0.000 0.965 0.000 1.000
#> SRR934249 2 0.000 0.965 0.000 1.000
#> SRR934250 2 0.000 0.965 0.000 1.000
#> SRR934251 2 0.000 0.965 0.000 1.000
#> SRR934252 2 0.000 0.965 0.000 1.000
#> SRR934253 2 0.000 0.965 0.000 1.000
#> SRR934254 2 0.000 0.965 0.000 1.000
#> SRR934255 2 0.000 0.965 0.000 1.000
#> SRR934256 1 0.662 0.802 0.828 0.172
#> SRR934257 1 0.662 0.802 0.828 0.172
#> SRR934258 1 0.662 0.802 0.828 0.172
#> SRR934259 1 0.662 0.802 0.828 0.172
#> SRR934260 1 0.662 0.802 0.828 0.172
#> SRR934261 1 0.662 0.802 0.828 0.172
#> SRR934262 1 0.662 0.802 0.828 0.172
#> SRR934263 1 0.662 0.802 0.828 0.172
#> SRR934264 2 0.000 0.965 0.000 1.000
#> SRR934265 2 0.000 0.965 0.000 1.000
#> SRR934266 2 0.000 0.965 0.000 1.000
#> SRR934267 2 0.000 0.965 0.000 1.000
#> SRR934268 2 0.000 0.965 0.000 1.000
#> SRR934269 2 0.000 0.965 0.000 1.000
#> SRR934270 2 0.000 0.965 0.000 1.000
#> SRR934271 2 0.000 0.965 0.000 1.000
#> SRR934272 1 0.000 0.951 1.000 0.000
#> SRR934273 1 0.000 0.951 1.000 0.000
#> SRR934274 1 0.000 0.951 1.000 0.000
#> SRR934275 1 0.000 0.951 1.000 0.000
#> SRR934276 1 0.000 0.951 1.000 0.000
#> SRR934277 1 0.000 0.951 1.000 0.000
#> SRR934278 1 0.000 0.951 1.000 0.000
#> SRR934279 1 0.000 0.951 1.000 0.000
#> SRR934280 1 0.000 0.951 1.000 0.000
#> SRR934281 1 0.000 0.951 1.000 0.000
#> SRR934282 1 0.000 0.951 1.000 0.000
#> SRR934283 1 0.000 0.951 1.000 0.000
#> SRR934284 1 0.000 0.951 1.000 0.000
#> SRR934285 1 0.000 0.951 1.000 0.000
#> SRR934286 1 0.000 0.951 1.000 0.000
#> SRR934287 1 0.000 0.951 1.000 0.000
#> SRR934288 1 0.000 0.951 1.000 0.000
#> SRR934289 1 0.000 0.951 1.000 0.000
#> SRR934290 1 0.000 0.951 1.000 0.000
#> SRR934291 1 0.000 0.951 1.000 0.000
#> SRR934292 1 0.000 0.951 1.000 0.000
#> SRR934293 1 0.000 0.951 1.000 0.000
#> SRR934294 1 0.000 0.951 1.000 0.000
#> SRR934295 1 0.000 0.951 1.000 0.000
#> SRR934296 1 0.913 0.589 0.672 0.328
#> SRR934297 1 0.913 0.589 0.672 0.328
#> SRR934298 1 0.913 0.589 0.672 0.328
#> SRR934299 1 0.913 0.589 0.672 0.328
#> SRR934300 1 0.913 0.589 0.672 0.328
#> SRR934301 1 0.913 0.589 0.672 0.328
#> SRR934302 1 0.913 0.589 0.672 0.328
#> SRR934303 1 0.913 0.589 0.672 0.328
#> SRR934304 2 0.000 0.965 0.000 1.000
#> SRR934305 2 0.000 0.965 0.000 1.000
#> SRR934306 2 0.000 0.965 0.000 1.000
#> SRR934307 2 0.000 0.965 0.000 1.000
#> SRR934308 2 0.000 0.965 0.000 1.000
#> SRR934309 2 0.000 0.965 0.000 1.000
#> SRR934310 2 0.000 0.965 0.000 1.000
#> SRR934311 2 0.000 0.965 0.000 1.000
#> SRR934312 1 0.000 0.951 1.000 0.000
#> SRR934313 1 0.000 0.951 1.000 0.000
#> SRR934314 1 0.000 0.951 1.000 0.000
#> SRR934315 1 0.000 0.951 1.000 0.000
#> SRR934316 1 0.000 0.951 1.000 0.000
#> SRR934317 1 0.000 0.951 1.000 0.000
#> SRR934318 1 0.000 0.951 1.000 0.000
#> SRR934319 1 0.000 0.951 1.000 0.000
#> SRR934320 1 0.000 0.951 1.000 0.000
#> SRR934321 1 0.000 0.951 1.000 0.000
#> SRR934322 1 0.000 0.951 1.000 0.000
#> SRR934323 1 0.000 0.951 1.000 0.000
#> SRR934324 1 0.000 0.951 1.000 0.000
#> SRR934325 1 0.000 0.951 1.000 0.000
#> SRR934326 1 0.000 0.951 1.000 0.000
#> SRR934327 1 0.000 0.951 1.000 0.000
#> SRR934328 1 0.000 0.951 1.000 0.000
#> SRR934329 1 0.000 0.951 1.000 0.000
#> SRR934330 1 0.000 0.951 1.000 0.000
#> SRR934331 1 0.000 0.951 1.000 0.000
#> SRR934332 1 0.000 0.951 1.000 0.000
#> SRR934333 1 0.000 0.951 1.000 0.000
#> SRR934334 1 0.000 0.951 1.000 0.000
#> SRR934335 1 0.000 0.951 1.000 0.000
#> SRR934344 1 0.000 0.951 1.000 0.000
#> SRR934345 1 0.000 0.951 1.000 0.000
#> SRR934346 1 0.000 0.951 1.000 0.000
#> SRR934347 1 0.000 0.951 1.000 0.000
#> SRR934348 1 0.000 0.951 1.000 0.000
#> SRR934349 1 0.000 0.951 1.000 0.000
#> SRR934350 1 0.000 0.951 1.000 0.000
#> SRR934351 1 0.000 0.951 1.000 0.000
#> SRR934336 1 0.000 0.951 1.000 0.000
#> SRR934337 1 0.000 0.951 1.000 0.000
#> SRR934338 1 0.000 0.951 1.000 0.000
#> SRR934339 1 0.000 0.951 1.000 0.000
#> SRR934340 1 0.000 0.951 1.000 0.000
#> SRR934341 1 0.000 0.951 1.000 0.000
#> SRR934342 1 0.000 0.951 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 2 0.9399 0.1950 0.188 0.480 0.332
#> SRR934217 2 0.9399 0.1950 0.188 0.480 0.332
#> SRR934218 2 0.9399 0.1950 0.188 0.480 0.332
#> SRR934219 2 0.9399 0.1950 0.188 0.480 0.332
#> SRR934220 2 0.9399 0.1950 0.188 0.480 0.332
#> SRR934221 2 0.9399 0.1950 0.188 0.480 0.332
#> SRR934222 2 0.9399 0.1950 0.188 0.480 0.332
#> SRR934223 2 0.9399 0.1950 0.188 0.480 0.332
#> SRR934224 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934225 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934226 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934227 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934228 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934229 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934230 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934231 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934232 3 0.4750 0.7063 0.000 0.216 0.784
#> SRR934233 3 0.4750 0.7063 0.000 0.216 0.784
#> SRR934234 3 0.4750 0.7063 0.000 0.216 0.784
#> SRR934235 3 0.4750 0.7063 0.000 0.216 0.784
#> SRR934236 3 0.4750 0.7063 0.000 0.216 0.784
#> SRR934237 3 0.4750 0.7063 0.000 0.216 0.784
#> SRR934238 3 0.4750 0.7063 0.000 0.216 0.784
#> SRR934239 3 0.4750 0.7063 0.000 0.216 0.784
#> SRR934240 3 0.5621 0.6322 0.000 0.308 0.692
#> SRR934241 3 0.5621 0.6322 0.000 0.308 0.692
#> SRR934242 3 0.5621 0.6322 0.000 0.308 0.692
#> SRR934243 3 0.5621 0.6322 0.000 0.308 0.692
#> SRR934244 3 0.5621 0.6322 0.000 0.308 0.692
#> SRR934245 3 0.5621 0.6322 0.000 0.308 0.692
#> SRR934246 3 0.5621 0.6322 0.000 0.308 0.692
#> SRR934247 3 0.5621 0.6322 0.000 0.308 0.692
#> SRR934248 3 0.0000 0.7472 0.000 0.000 1.000
#> SRR934249 3 0.0000 0.7472 0.000 0.000 1.000
#> SRR934250 3 0.0000 0.7472 0.000 0.000 1.000
#> SRR934251 3 0.0000 0.7472 0.000 0.000 1.000
#> SRR934252 3 0.0000 0.7472 0.000 0.000 1.000
#> SRR934253 3 0.0000 0.7472 0.000 0.000 1.000
#> SRR934254 3 0.0000 0.7472 0.000 0.000 1.000
#> SRR934255 3 0.0000 0.7472 0.000 0.000 1.000
#> SRR934256 2 0.6669 -0.0985 0.468 0.524 0.008
#> SRR934257 2 0.6669 -0.0985 0.468 0.524 0.008
#> SRR934258 2 0.6669 -0.0985 0.468 0.524 0.008
#> SRR934259 2 0.6669 -0.0985 0.468 0.524 0.008
#> SRR934260 2 0.6669 -0.0985 0.468 0.524 0.008
#> SRR934261 2 0.6669 -0.0985 0.468 0.524 0.008
#> SRR934262 2 0.6669 -0.0985 0.468 0.524 0.008
#> SRR934263 2 0.6669 -0.0985 0.468 0.524 0.008
#> SRR934264 3 0.0000 0.7472 0.000 0.000 1.000
#> SRR934265 3 0.0000 0.7472 0.000 0.000 1.000
#> SRR934266 3 0.0000 0.7472 0.000 0.000 1.000
#> SRR934267 3 0.0000 0.7472 0.000 0.000 1.000
#> SRR934268 3 0.0000 0.7472 0.000 0.000 1.000
#> SRR934269 3 0.0000 0.7472 0.000 0.000 1.000
#> SRR934270 3 0.0000 0.7472 0.000 0.000 1.000
#> SRR934271 3 0.0000 0.7472 0.000 0.000 1.000
#> SRR934272 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934273 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934274 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934275 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934276 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934277 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934278 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934279 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934280 1 0.0237 0.8093 0.996 0.004 0.000
#> SRR934281 1 0.0237 0.8093 0.996 0.004 0.000
#> SRR934282 1 0.0237 0.8093 0.996 0.004 0.000
#> SRR934283 1 0.0237 0.8093 0.996 0.004 0.000
#> SRR934284 1 0.0237 0.8093 0.996 0.004 0.000
#> SRR934285 1 0.0237 0.8093 0.996 0.004 0.000
#> SRR934286 1 0.0237 0.8093 0.996 0.004 0.000
#> SRR934287 1 0.0237 0.8093 0.996 0.004 0.000
#> SRR934288 1 0.5905 0.6112 0.648 0.352 0.000
#> SRR934289 1 0.5905 0.6112 0.648 0.352 0.000
#> SRR934290 1 0.5905 0.6112 0.648 0.352 0.000
#> SRR934291 1 0.5905 0.6112 0.648 0.352 0.000
#> SRR934292 1 0.5905 0.6112 0.648 0.352 0.000
#> SRR934293 1 0.5905 0.6112 0.648 0.352 0.000
#> SRR934294 1 0.5905 0.6112 0.648 0.352 0.000
#> SRR934295 1 0.5905 0.6112 0.648 0.352 0.000
#> SRR934296 2 0.0237 0.4391 0.000 0.996 0.004
#> SRR934297 2 0.0237 0.4391 0.000 0.996 0.004
#> SRR934298 2 0.0237 0.4391 0.000 0.996 0.004
#> SRR934299 2 0.0237 0.4391 0.000 0.996 0.004
#> SRR934300 2 0.0237 0.4391 0.000 0.996 0.004
#> SRR934301 2 0.0237 0.4391 0.000 0.996 0.004
#> SRR934302 2 0.0237 0.4391 0.000 0.996 0.004
#> SRR934303 2 0.0237 0.4391 0.000 0.996 0.004
#> SRR934304 3 0.6235 0.3158 0.000 0.436 0.564
#> SRR934305 3 0.6235 0.3158 0.000 0.436 0.564
#> SRR934306 3 0.6235 0.3158 0.000 0.436 0.564
#> SRR934307 3 0.6235 0.3158 0.000 0.436 0.564
#> SRR934308 3 0.6235 0.3158 0.000 0.436 0.564
#> SRR934309 3 0.6235 0.3158 0.000 0.436 0.564
#> SRR934310 3 0.6235 0.3158 0.000 0.436 0.564
#> SRR934311 3 0.6235 0.3158 0.000 0.436 0.564
#> SRR934312 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934313 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934314 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934315 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934316 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934317 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934318 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934319 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934320 1 0.3551 0.7617 0.868 0.132 0.000
#> SRR934321 1 0.3551 0.7617 0.868 0.132 0.000
#> SRR934322 1 0.3551 0.7617 0.868 0.132 0.000
#> SRR934323 1 0.3551 0.7617 0.868 0.132 0.000
#> SRR934324 1 0.3551 0.7617 0.868 0.132 0.000
#> SRR934325 1 0.3551 0.7617 0.868 0.132 0.000
#> SRR934326 1 0.3551 0.7617 0.868 0.132 0.000
#> SRR934327 1 0.3551 0.7617 0.868 0.132 0.000
#> SRR934328 1 0.5988 0.5911 0.632 0.368 0.000
#> SRR934329 1 0.5988 0.5911 0.632 0.368 0.000
#> SRR934330 1 0.5988 0.5911 0.632 0.368 0.000
#> SRR934331 1 0.5988 0.5911 0.632 0.368 0.000
#> SRR934332 1 0.5988 0.5911 0.632 0.368 0.000
#> SRR934333 1 0.5988 0.5911 0.632 0.368 0.000
#> SRR934334 1 0.5988 0.5911 0.632 0.368 0.000
#> SRR934335 1 0.5988 0.5911 0.632 0.368 0.000
#> SRR934344 1 0.5905 0.6112 0.648 0.352 0.000
#> SRR934345 1 0.5905 0.6112 0.648 0.352 0.000
#> SRR934346 1 0.5905 0.6112 0.648 0.352 0.000
#> SRR934347 1 0.5905 0.6112 0.648 0.352 0.000
#> SRR934348 1 0.5905 0.6112 0.648 0.352 0.000
#> SRR934349 1 0.5905 0.6112 0.648 0.352 0.000
#> SRR934350 1 0.5905 0.6112 0.648 0.352 0.000
#> SRR934351 1 0.5905 0.6112 0.648 0.352 0.000
#> SRR934336 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934337 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934338 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934339 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934340 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934341 1 0.0000 0.8095 1.000 0.000 0.000
#> SRR934342 1 0.0000 0.8095 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 3 0.7386 0.726 0.060 0.276 0.592 0.072
#> SRR934217 3 0.7386 0.726 0.060 0.276 0.592 0.072
#> SRR934218 3 0.7386 0.726 0.060 0.276 0.592 0.072
#> SRR934219 3 0.7386 0.726 0.060 0.276 0.592 0.072
#> SRR934220 3 0.7386 0.726 0.060 0.276 0.592 0.072
#> SRR934221 3 0.7386 0.726 0.060 0.276 0.592 0.072
#> SRR934222 3 0.7386 0.726 0.060 0.276 0.592 0.072
#> SRR934223 3 0.7386 0.726 0.060 0.276 0.592 0.072
#> SRR934224 1 0.1042 0.934 0.972 0.000 0.020 0.008
#> SRR934225 1 0.1042 0.934 0.972 0.000 0.020 0.008
#> SRR934226 1 0.1042 0.934 0.972 0.000 0.020 0.008
#> SRR934227 1 0.1042 0.934 0.972 0.000 0.020 0.008
#> SRR934228 1 0.1042 0.934 0.972 0.000 0.020 0.008
#> SRR934229 1 0.1042 0.934 0.972 0.000 0.020 0.008
#> SRR934230 1 0.1042 0.934 0.972 0.000 0.020 0.008
#> SRR934231 1 0.1042 0.934 0.972 0.000 0.020 0.008
#> SRR934232 2 0.4509 0.763 0.000 0.708 0.288 0.004
#> SRR934233 2 0.4509 0.763 0.000 0.708 0.288 0.004
#> SRR934234 2 0.4509 0.763 0.000 0.708 0.288 0.004
#> SRR934235 2 0.4509 0.763 0.000 0.708 0.288 0.004
#> SRR934236 2 0.4509 0.763 0.000 0.708 0.288 0.004
#> SRR934237 2 0.4509 0.763 0.000 0.708 0.288 0.004
#> SRR934238 2 0.4509 0.763 0.000 0.708 0.288 0.004
#> SRR934239 2 0.4509 0.763 0.000 0.708 0.288 0.004
#> SRR934240 2 0.5269 0.716 0.000 0.620 0.364 0.016
#> SRR934241 2 0.5269 0.716 0.000 0.620 0.364 0.016
#> SRR934242 2 0.5269 0.716 0.000 0.620 0.364 0.016
#> SRR934243 2 0.5269 0.716 0.000 0.620 0.364 0.016
#> SRR934244 2 0.5269 0.716 0.000 0.620 0.364 0.016
#> SRR934245 2 0.5269 0.716 0.000 0.620 0.364 0.016
#> SRR934246 2 0.5269 0.716 0.000 0.620 0.364 0.016
#> SRR934247 2 0.5269 0.716 0.000 0.620 0.364 0.016
#> SRR934248 2 0.0000 0.751 0.000 1.000 0.000 0.000
#> SRR934249 2 0.0000 0.751 0.000 1.000 0.000 0.000
#> SRR934250 2 0.0000 0.751 0.000 1.000 0.000 0.000
#> SRR934251 2 0.0000 0.751 0.000 1.000 0.000 0.000
#> SRR934252 2 0.0000 0.751 0.000 1.000 0.000 0.000
#> SRR934253 2 0.0000 0.751 0.000 1.000 0.000 0.000
#> SRR934254 2 0.0000 0.751 0.000 1.000 0.000 0.000
#> SRR934255 2 0.0000 0.751 0.000 1.000 0.000 0.000
#> SRR934256 4 0.8082 0.429 0.228 0.012 0.336 0.424
#> SRR934257 4 0.8082 0.429 0.228 0.012 0.336 0.424
#> SRR934258 4 0.8082 0.429 0.228 0.012 0.336 0.424
#> SRR934259 4 0.8082 0.429 0.228 0.012 0.336 0.424
#> SRR934260 4 0.8082 0.429 0.228 0.012 0.336 0.424
#> SRR934261 4 0.8082 0.429 0.228 0.012 0.336 0.424
#> SRR934262 4 0.8082 0.429 0.228 0.012 0.336 0.424
#> SRR934263 4 0.8082 0.429 0.228 0.012 0.336 0.424
#> SRR934264 2 0.0000 0.751 0.000 1.000 0.000 0.000
#> SRR934265 2 0.0000 0.751 0.000 1.000 0.000 0.000
#> SRR934266 2 0.0000 0.751 0.000 1.000 0.000 0.000
#> SRR934267 2 0.0000 0.751 0.000 1.000 0.000 0.000
#> SRR934268 2 0.0000 0.751 0.000 1.000 0.000 0.000
#> SRR934269 2 0.0000 0.751 0.000 1.000 0.000 0.000
#> SRR934270 2 0.0000 0.751 0.000 1.000 0.000 0.000
#> SRR934271 2 0.0000 0.751 0.000 1.000 0.000 0.000
#> SRR934272 1 0.2222 0.923 0.924 0.000 0.016 0.060
#> SRR934273 1 0.2222 0.923 0.924 0.000 0.016 0.060
#> SRR934274 1 0.2222 0.923 0.924 0.000 0.016 0.060
#> SRR934275 1 0.2222 0.923 0.924 0.000 0.016 0.060
#> SRR934276 1 0.2222 0.923 0.924 0.000 0.016 0.060
#> SRR934277 1 0.2222 0.923 0.924 0.000 0.016 0.060
#> SRR934278 1 0.2222 0.923 0.924 0.000 0.016 0.060
#> SRR934279 1 0.2222 0.923 0.924 0.000 0.016 0.060
#> SRR934280 1 0.0469 0.930 0.988 0.000 0.000 0.012
#> SRR934281 1 0.0469 0.930 0.988 0.000 0.000 0.012
#> SRR934282 1 0.0469 0.930 0.988 0.000 0.000 0.012
#> SRR934283 1 0.0469 0.930 0.988 0.000 0.000 0.012
#> SRR934284 1 0.0469 0.930 0.988 0.000 0.000 0.012
#> SRR934285 1 0.0469 0.930 0.988 0.000 0.000 0.012
#> SRR934286 1 0.0469 0.930 0.988 0.000 0.000 0.012
#> SRR934287 1 0.0469 0.930 0.988 0.000 0.000 0.012
#> SRR934288 4 0.1637 0.823 0.060 0.000 0.000 0.940
#> SRR934289 4 0.1637 0.823 0.060 0.000 0.000 0.940
#> SRR934290 4 0.1637 0.823 0.060 0.000 0.000 0.940
#> SRR934291 4 0.1637 0.823 0.060 0.000 0.000 0.940
#> SRR934292 4 0.1637 0.823 0.060 0.000 0.000 0.940
#> SRR934293 4 0.1637 0.823 0.060 0.000 0.000 0.940
#> SRR934294 4 0.1637 0.823 0.060 0.000 0.000 0.940
#> SRR934295 4 0.1637 0.823 0.060 0.000 0.000 0.940
#> SRR934296 3 0.4222 0.489 0.000 0.000 0.728 0.272
#> SRR934297 3 0.4222 0.489 0.000 0.000 0.728 0.272
#> SRR934298 3 0.4222 0.489 0.000 0.000 0.728 0.272
#> SRR934299 3 0.4222 0.489 0.000 0.000 0.728 0.272
#> SRR934300 3 0.4222 0.489 0.000 0.000 0.728 0.272
#> SRR934301 3 0.4222 0.489 0.000 0.000 0.728 0.272
#> SRR934302 3 0.4222 0.489 0.000 0.000 0.728 0.272
#> SRR934303 3 0.4222 0.489 0.000 0.000 0.728 0.272
#> SRR934304 3 0.5085 0.702 0.000 0.376 0.616 0.008
#> SRR934305 3 0.5085 0.702 0.000 0.376 0.616 0.008
#> SRR934306 3 0.5085 0.702 0.000 0.376 0.616 0.008
#> SRR934307 3 0.5085 0.702 0.000 0.376 0.616 0.008
#> SRR934308 3 0.5085 0.702 0.000 0.376 0.616 0.008
#> SRR934309 3 0.5085 0.702 0.000 0.376 0.616 0.008
#> SRR934310 3 0.5085 0.702 0.000 0.376 0.616 0.008
#> SRR934311 3 0.5085 0.702 0.000 0.376 0.616 0.008
#> SRR934312 1 0.2101 0.924 0.928 0.000 0.012 0.060
#> SRR934313 1 0.2101 0.924 0.928 0.000 0.012 0.060
#> SRR934314 1 0.2101 0.924 0.928 0.000 0.012 0.060
#> SRR934315 1 0.2101 0.924 0.928 0.000 0.012 0.060
#> SRR934316 1 0.2101 0.924 0.928 0.000 0.012 0.060
#> SRR934317 1 0.2101 0.924 0.928 0.000 0.012 0.060
#> SRR934318 1 0.2101 0.924 0.928 0.000 0.012 0.060
#> SRR934319 1 0.2101 0.924 0.928 0.000 0.012 0.060
#> SRR934320 1 0.4259 0.791 0.816 0.000 0.056 0.128
#> SRR934321 1 0.4259 0.791 0.816 0.000 0.056 0.128
#> SRR934322 1 0.4259 0.791 0.816 0.000 0.056 0.128
#> SRR934323 1 0.4259 0.791 0.816 0.000 0.056 0.128
#> SRR934324 1 0.4259 0.791 0.816 0.000 0.056 0.128
#> SRR934325 1 0.4259 0.791 0.816 0.000 0.056 0.128
#> SRR934326 1 0.4259 0.791 0.816 0.000 0.056 0.128
#> SRR934327 1 0.4259 0.791 0.816 0.000 0.056 0.128
#> SRR934328 4 0.0921 0.818 0.028 0.000 0.000 0.972
#> SRR934329 4 0.0921 0.818 0.028 0.000 0.000 0.972
#> SRR934330 4 0.0921 0.818 0.028 0.000 0.000 0.972
#> SRR934331 4 0.0921 0.818 0.028 0.000 0.000 0.972
#> SRR934332 4 0.0921 0.818 0.028 0.000 0.000 0.972
#> SRR934333 4 0.0921 0.818 0.028 0.000 0.000 0.972
#> SRR934334 4 0.0921 0.818 0.028 0.000 0.000 0.972
#> SRR934335 4 0.0921 0.818 0.028 0.000 0.000 0.972
#> SRR934344 4 0.1302 0.825 0.044 0.000 0.000 0.956
#> SRR934345 4 0.1302 0.825 0.044 0.000 0.000 0.956
#> SRR934346 4 0.1302 0.825 0.044 0.000 0.000 0.956
#> SRR934347 4 0.1302 0.825 0.044 0.000 0.000 0.956
#> SRR934348 4 0.1302 0.825 0.044 0.000 0.000 0.956
#> SRR934349 4 0.1302 0.825 0.044 0.000 0.000 0.956
#> SRR934350 4 0.1302 0.825 0.044 0.000 0.000 0.956
#> SRR934351 4 0.1302 0.825 0.044 0.000 0.000 0.956
#> SRR934336 1 0.0804 0.934 0.980 0.000 0.008 0.012
#> SRR934337 1 0.0804 0.934 0.980 0.000 0.008 0.012
#> SRR934338 1 0.0804 0.934 0.980 0.000 0.008 0.012
#> SRR934339 1 0.0804 0.934 0.980 0.000 0.008 0.012
#> SRR934340 1 0.0804 0.934 0.980 0.000 0.008 0.012
#> SRR934341 1 0.0804 0.934 0.980 0.000 0.008 0.012
#> SRR934342 1 0.0804 0.934 0.980 0.000 0.008 0.012
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0.3729 0.687 0.056 0.000 0.844 0.064 0.036
#> SRR934217 3 0.3729 0.687 0.056 0.000 0.844 0.064 0.036
#> SRR934218 3 0.3729 0.687 0.056 0.000 0.844 0.064 0.036
#> SRR934219 3 0.3729 0.687 0.056 0.000 0.844 0.064 0.036
#> SRR934220 3 0.3729 0.687 0.056 0.000 0.844 0.064 0.036
#> SRR934221 3 0.3729 0.687 0.056 0.000 0.844 0.064 0.036
#> SRR934222 3 0.3729 0.687 0.056 0.000 0.844 0.064 0.036
#> SRR934223 3 0.3729 0.687 0.056 0.000 0.844 0.064 0.036
#> SRR934224 1 0.2459 0.845 0.904 0.040 0.052 0.000 0.004
#> SRR934225 1 0.2459 0.845 0.904 0.040 0.052 0.000 0.004
#> SRR934226 1 0.2459 0.845 0.904 0.040 0.052 0.000 0.004
#> SRR934227 1 0.2459 0.845 0.904 0.040 0.052 0.000 0.004
#> SRR934228 1 0.2459 0.845 0.904 0.040 0.052 0.000 0.004
#> SRR934229 1 0.2459 0.845 0.904 0.040 0.052 0.000 0.004
#> SRR934230 1 0.2459 0.845 0.904 0.040 0.052 0.000 0.004
#> SRR934231 1 0.2459 0.845 0.904 0.040 0.052 0.000 0.004
#> SRR934232 4 0.3642 0.678 0.000 0.232 0.008 0.760 0.000
#> SRR934233 4 0.3642 0.678 0.000 0.232 0.008 0.760 0.000
#> SRR934234 4 0.3642 0.678 0.000 0.232 0.008 0.760 0.000
#> SRR934235 4 0.3642 0.678 0.000 0.232 0.008 0.760 0.000
#> SRR934236 4 0.3642 0.678 0.000 0.232 0.008 0.760 0.000
#> SRR934237 4 0.3642 0.678 0.000 0.232 0.008 0.760 0.000
#> SRR934238 4 0.3642 0.678 0.000 0.232 0.008 0.760 0.000
#> SRR934239 4 0.3642 0.678 0.000 0.232 0.008 0.760 0.000
#> SRR934240 2 0.4582 0.379 0.000 0.572 0.012 0.416 0.000
#> SRR934241 2 0.4582 0.379 0.000 0.572 0.012 0.416 0.000
#> SRR934242 2 0.4582 0.379 0.000 0.572 0.012 0.416 0.000
#> SRR934243 2 0.4582 0.379 0.000 0.572 0.012 0.416 0.000
#> SRR934244 2 0.4582 0.379 0.000 0.572 0.012 0.416 0.000
#> SRR934245 2 0.4582 0.379 0.000 0.572 0.012 0.416 0.000
#> SRR934246 2 0.4582 0.379 0.000 0.572 0.012 0.416 0.000
#> SRR934247 2 0.4582 0.379 0.000 0.572 0.012 0.416 0.000
#> SRR934248 4 0.0992 0.868 0.000 0.008 0.024 0.968 0.000
#> SRR934249 4 0.0992 0.868 0.000 0.008 0.024 0.968 0.000
#> SRR934250 4 0.0992 0.868 0.000 0.008 0.024 0.968 0.000
#> SRR934251 4 0.0992 0.868 0.000 0.008 0.024 0.968 0.000
#> SRR934252 4 0.0992 0.868 0.000 0.008 0.024 0.968 0.000
#> SRR934253 4 0.0992 0.868 0.000 0.008 0.024 0.968 0.000
#> SRR934254 4 0.0992 0.868 0.000 0.008 0.024 0.968 0.000
#> SRR934255 4 0.0992 0.868 0.000 0.008 0.024 0.968 0.000
#> SRR934256 2 0.5490 0.590 0.116 0.636 0.000 0.000 0.248
#> SRR934257 2 0.5490 0.590 0.116 0.636 0.000 0.000 0.248
#> SRR934258 2 0.5490 0.590 0.116 0.636 0.000 0.000 0.248
#> SRR934259 2 0.5490 0.590 0.116 0.636 0.000 0.000 0.248
#> SRR934260 2 0.5490 0.590 0.116 0.636 0.000 0.000 0.248
#> SRR934261 2 0.5490 0.590 0.116 0.636 0.000 0.000 0.248
#> SRR934262 2 0.5490 0.590 0.116 0.636 0.000 0.000 0.248
#> SRR934263 2 0.5490 0.590 0.116 0.636 0.000 0.000 0.248
#> SRR934264 4 0.0703 0.870 0.000 0.000 0.024 0.976 0.000
#> SRR934265 4 0.0703 0.870 0.000 0.000 0.024 0.976 0.000
#> SRR934266 4 0.0703 0.870 0.000 0.000 0.024 0.976 0.000
#> SRR934267 4 0.0703 0.870 0.000 0.000 0.024 0.976 0.000
#> SRR934268 4 0.0703 0.870 0.000 0.000 0.024 0.976 0.000
#> SRR934269 4 0.0703 0.870 0.000 0.000 0.024 0.976 0.000
#> SRR934270 4 0.0703 0.870 0.000 0.000 0.024 0.976 0.000
#> SRR934271 4 0.0703 0.870 0.000 0.000 0.024 0.976 0.000
#> SRR934272 1 0.3247 0.821 0.868 0.028 0.072 0.000 0.032
#> SRR934273 1 0.3247 0.821 0.868 0.028 0.072 0.000 0.032
#> SRR934274 1 0.3247 0.821 0.868 0.028 0.072 0.000 0.032
#> SRR934275 1 0.3247 0.821 0.868 0.028 0.072 0.000 0.032
#> SRR934276 1 0.3247 0.821 0.868 0.028 0.072 0.000 0.032
#> SRR934277 1 0.3247 0.821 0.868 0.028 0.072 0.000 0.032
#> SRR934278 1 0.3247 0.821 0.868 0.028 0.072 0.000 0.032
#> SRR934279 1 0.3247 0.821 0.868 0.028 0.072 0.000 0.032
#> SRR934280 1 0.2588 0.832 0.884 0.100 0.008 0.000 0.008
#> SRR934281 1 0.2588 0.832 0.884 0.100 0.008 0.000 0.008
#> SRR934282 1 0.2588 0.832 0.884 0.100 0.008 0.000 0.008
#> SRR934283 1 0.2588 0.832 0.884 0.100 0.008 0.000 0.008
#> SRR934284 1 0.2588 0.832 0.884 0.100 0.008 0.000 0.008
#> SRR934285 1 0.2588 0.832 0.884 0.100 0.008 0.000 0.008
#> SRR934286 1 0.2588 0.832 0.884 0.100 0.008 0.000 0.008
#> SRR934287 1 0.2588 0.832 0.884 0.100 0.008 0.000 0.008
#> SRR934288 5 0.0671 0.983 0.016 0.004 0.000 0.000 0.980
#> SRR934289 5 0.0671 0.983 0.016 0.004 0.000 0.000 0.980
#> SRR934290 5 0.0671 0.983 0.016 0.004 0.000 0.000 0.980
#> SRR934291 5 0.0671 0.983 0.016 0.004 0.000 0.000 0.980
#> SRR934292 5 0.0671 0.983 0.016 0.004 0.000 0.000 0.980
#> SRR934293 5 0.0671 0.983 0.016 0.004 0.000 0.000 0.980
#> SRR934294 5 0.0671 0.983 0.016 0.004 0.000 0.000 0.980
#> SRR934295 5 0.0671 0.983 0.016 0.004 0.000 0.000 0.980
#> SRR934296 3 0.6145 0.525 0.000 0.440 0.444 0.004 0.112
#> SRR934297 3 0.6145 0.525 0.000 0.440 0.444 0.004 0.112
#> SRR934298 3 0.6145 0.525 0.000 0.440 0.444 0.004 0.112
#> SRR934299 3 0.6145 0.525 0.000 0.440 0.444 0.004 0.112
#> SRR934300 3 0.6145 0.525 0.000 0.440 0.444 0.004 0.112
#> SRR934301 3 0.6145 0.525 0.000 0.440 0.444 0.004 0.112
#> SRR934302 3 0.6145 0.525 0.000 0.440 0.444 0.004 0.112
#> SRR934303 3 0.6145 0.525 0.000 0.440 0.444 0.004 0.112
#> SRR934304 3 0.4679 0.726 0.000 0.124 0.740 0.136 0.000
#> SRR934305 3 0.4679 0.726 0.000 0.124 0.740 0.136 0.000
#> SRR934306 3 0.4679 0.726 0.000 0.124 0.740 0.136 0.000
#> SRR934307 3 0.4679 0.726 0.000 0.124 0.740 0.136 0.000
#> SRR934308 3 0.4679 0.726 0.000 0.124 0.740 0.136 0.000
#> SRR934309 3 0.4679 0.726 0.000 0.124 0.740 0.136 0.000
#> SRR934310 3 0.4679 0.726 0.000 0.124 0.740 0.136 0.000
#> SRR934311 3 0.4679 0.726 0.000 0.124 0.740 0.136 0.000
#> SRR934312 1 0.3808 0.820 0.840 0.060 0.060 0.000 0.040
#> SRR934313 1 0.3808 0.820 0.840 0.060 0.060 0.000 0.040
#> SRR934314 1 0.3808 0.820 0.840 0.060 0.060 0.000 0.040
#> SRR934315 1 0.3808 0.820 0.840 0.060 0.060 0.000 0.040
#> SRR934316 1 0.3808 0.820 0.840 0.060 0.060 0.000 0.040
#> SRR934317 1 0.3808 0.820 0.840 0.060 0.060 0.000 0.040
#> SRR934318 1 0.3808 0.820 0.840 0.060 0.060 0.000 0.040
#> SRR934319 1 0.3808 0.820 0.840 0.060 0.060 0.000 0.040
#> SRR934320 1 0.5968 0.642 0.628 0.260 0.040 0.000 0.072
#> SRR934321 1 0.5968 0.642 0.628 0.260 0.040 0.000 0.072
#> SRR934322 1 0.5968 0.642 0.628 0.260 0.040 0.000 0.072
#> SRR934323 1 0.5968 0.642 0.628 0.260 0.040 0.000 0.072
#> SRR934324 1 0.5968 0.642 0.628 0.260 0.040 0.000 0.072
#> SRR934325 1 0.5968 0.642 0.628 0.260 0.040 0.000 0.072
#> SRR934326 1 0.5968 0.642 0.628 0.260 0.040 0.000 0.072
#> SRR934327 1 0.5968 0.642 0.628 0.260 0.040 0.000 0.072
#> SRR934328 5 0.0932 0.974 0.004 0.020 0.004 0.000 0.972
#> SRR934329 5 0.0932 0.974 0.004 0.020 0.004 0.000 0.972
#> SRR934330 5 0.0932 0.974 0.004 0.020 0.004 0.000 0.972
#> SRR934331 5 0.0932 0.974 0.004 0.020 0.004 0.000 0.972
#> SRR934332 5 0.0932 0.974 0.004 0.020 0.004 0.000 0.972
#> SRR934333 5 0.0932 0.974 0.004 0.020 0.004 0.000 0.972
#> SRR934334 5 0.0932 0.974 0.004 0.020 0.004 0.000 0.972
#> SRR934335 5 0.0932 0.974 0.004 0.020 0.004 0.000 0.972
#> SRR934344 5 0.0404 0.984 0.012 0.000 0.000 0.000 0.988
#> SRR934345 5 0.0404 0.984 0.012 0.000 0.000 0.000 0.988
#> SRR934346 5 0.0404 0.984 0.012 0.000 0.000 0.000 0.988
#> SRR934347 5 0.0404 0.984 0.012 0.000 0.000 0.000 0.988
#> SRR934348 5 0.0404 0.984 0.012 0.000 0.000 0.000 0.988
#> SRR934349 5 0.0404 0.984 0.012 0.000 0.000 0.000 0.988
#> SRR934350 5 0.0404 0.984 0.012 0.000 0.000 0.000 0.988
#> SRR934351 5 0.0404 0.984 0.012 0.000 0.000 0.000 0.988
#> SRR934336 1 0.2910 0.841 0.884 0.060 0.044 0.000 0.012
#> SRR934337 1 0.2910 0.841 0.884 0.060 0.044 0.000 0.012
#> SRR934338 1 0.2910 0.841 0.884 0.060 0.044 0.000 0.012
#> SRR934339 1 0.2910 0.841 0.884 0.060 0.044 0.000 0.012
#> SRR934340 1 0.2910 0.841 0.884 0.060 0.044 0.000 0.012
#> SRR934341 1 0.2910 0.841 0.884 0.060 0.044 0.000 0.012
#> SRR934342 1 0.2910 0.841 0.884 0.060 0.044 0.000 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.5224 1.000 0.012 0.008 0.528 0.036 0.412 0.004
#> SRR934217 3 0.5224 1.000 0.012 0.008 0.528 0.036 0.412 0.004
#> SRR934218 3 0.5224 1.000 0.012 0.008 0.528 0.036 0.412 0.004
#> SRR934219 3 0.5224 1.000 0.012 0.008 0.528 0.036 0.412 0.004
#> SRR934220 3 0.5224 1.000 0.012 0.008 0.528 0.036 0.412 0.004
#> SRR934221 3 0.5224 1.000 0.012 0.008 0.528 0.036 0.412 0.004
#> SRR934222 3 0.5224 1.000 0.012 0.008 0.528 0.036 0.412 0.004
#> SRR934223 3 0.5224 1.000 0.012 0.008 0.528 0.036 0.412 0.004
#> SRR934224 6 0.2001 0.746 0.004 0.004 0.092 0.000 0.000 0.900
#> SRR934225 6 0.2001 0.746 0.004 0.004 0.092 0.000 0.000 0.900
#> SRR934226 6 0.2001 0.746 0.004 0.004 0.092 0.000 0.000 0.900
#> SRR934227 6 0.2001 0.746 0.004 0.004 0.092 0.000 0.000 0.900
#> SRR934228 6 0.2001 0.746 0.004 0.004 0.092 0.000 0.000 0.900
#> SRR934229 6 0.2001 0.746 0.004 0.004 0.092 0.000 0.000 0.900
#> SRR934230 6 0.2001 0.746 0.004 0.004 0.092 0.000 0.000 0.900
#> SRR934231 6 0.2001 0.746 0.004 0.004 0.092 0.000 0.000 0.900
#> SRR934232 4 0.3923 0.575 0.000 0.372 0.008 0.620 0.000 0.000
#> SRR934233 4 0.3923 0.575 0.000 0.372 0.008 0.620 0.000 0.000
#> SRR934234 4 0.3923 0.575 0.000 0.372 0.008 0.620 0.000 0.000
#> SRR934235 4 0.3923 0.575 0.000 0.372 0.008 0.620 0.000 0.000
#> SRR934236 4 0.3923 0.575 0.000 0.372 0.008 0.620 0.000 0.000
#> SRR934237 4 0.3923 0.575 0.000 0.372 0.008 0.620 0.000 0.000
#> SRR934238 4 0.3923 0.575 0.000 0.372 0.008 0.620 0.000 0.000
#> SRR934239 4 0.3923 0.575 0.000 0.372 0.008 0.620 0.000 0.000
#> SRR934240 2 0.3136 0.523 0.000 0.768 0.000 0.228 0.004 0.000
#> SRR934241 2 0.3136 0.523 0.000 0.768 0.000 0.228 0.004 0.000
#> SRR934242 2 0.3136 0.523 0.000 0.768 0.000 0.228 0.004 0.000
#> SRR934243 2 0.3136 0.523 0.000 0.768 0.000 0.228 0.004 0.000
#> SRR934244 2 0.3136 0.523 0.000 0.768 0.000 0.228 0.004 0.000
#> SRR934245 2 0.3136 0.523 0.000 0.768 0.000 0.228 0.004 0.000
#> SRR934246 2 0.3136 0.523 0.000 0.768 0.000 0.228 0.004 0.000
#> SRR934247 2 0.3136 0.523 0.000 0.768 0.000 0.228 0.004 0.000
#> SRR934248 4 0.0935 0.804 0.000 0.004 0.032 0.964 0.000 0.000
#> SRR934249 4 0.0935 0.804 0.000 0.004 0.032 0.964 0.000 0.000
#> SRR934250 4 0.0935 0.804 0.000 0.004 0.032 0.964 0.000 0.000
#> SRR934251 4 0.0935 0.804 0.000 0.004 0.032 0.964 0.000 0.000
#> SRR934252 4 0.0935 0.804 0.000 0.004 0.032 0.964 0.000 0.000
#> SRR934253 4 0.0935 0.804 0.000 0.004 0.032 0.964 0.000 0.000
#> SRR934254 4 0.0935 0.804 0.000 0.004 0.032 0.964 0.000 0.000
#> SRR934255 4 0.0935 0.804 0.000 0.004 0.032 0.964 0.000 0.000
#> SRR934256 2 0.6648 0.638 0.156 0.608 0.080 0.000 0.088 0.068
#> SRR934257 2 0.6648 0.638 0.156 0.608 0.080 0.000 0.088 0.068
#> SRR934258 2 0.6648 0.638 0.156 0.608 0.080 0.000 0.088 0.068
#> SRR934259 2 0.6648 0.638 0.156 0.608 0.080 0.000 0.088 0.068
#> SRR934260 2 0.6648 0.638 0.156 0.608 0.080 0.000 0.088 0.068
#> SRR934261 2 0.6648 0.638 0.156 0.608 0.080 0.000 0.088 0.068
#> SRR934262 2 0.6648 0.638 0.156 0.608 0.080 0.000 0.088 0.068
#> SRR934263 2 0.6648 0.638 0.156 0.608 0.080 0.000 0.088 0.068
#> SRR934264 4 0.1124 0.816 0.000 0.036 0.000 0.956 0.008 0.000
#> SRR934265 4 0.1124 0.816 0.000 0.036 0.000 0.956 0.008 0.000
#> SRR934266 4 0.1124 0.816 0.000 0.036 0.000 0.956 0.008 0.000
#> SRR934267 4 0.1124 0.816 0.000 0.036 0.000 0.956 0.008 0.000
#> SRR934268 4 0.1124 0.816 0.000 0.036 0.000 0.956 0.008 0.000
#> SRR934269 4 0.1124 0.816 0.000 0.036 0.000 0.956 0.008 0.000
#> SRR934270 4 0.1124 0.816 0.000 0.036 0.000 0.956 0.008 0.000
#> SRR934271 4 0.1124 0.816 0.000 0.036 0.000 0.956 0.008 0.000
#> SRR934272 6 0.3446 0.711 0.000 0.000 0.308 0.000 0.000 0.692
#> SRR934273 6 0.3446 0.711 0.000 0.000 0.308 0.000 0.000 0.692
#> SRR934274 6 0.3446 0.711 0.000 0.000 0.308 0.000 0.000 0.692
#> SRR934275 6 0.3446 0.711 0.000 0.000 0.308 0.000 0.000 0.692
#> SRR934276 6 0.3446 0.711 0.000 0.000 0.308 0.000 0.000 0.692
#> SRR934277 6 0.3446 0.711 0.000 0.000 0.308 0.000 0.000 0.692
#> SRR934278 6 0.3446 0.711 0.000 0.000 0.308 0.000 0.000 0.692
#> SRR934279 6 0.3446 0.711 0.000 0.000 0.308 0.000 0.000 0.692
#> SRR934280 6 0.4438 0.724 0.000 0.080 0.208 0.000 0.004 0.708
#> SRR934281 6 0.4438 0.724 0.000 0.080 0.208 0.000 0.004 0.708
#> SRR934282 6 0.4438 0.724 0.000 0.080 0.208 0.000 0.004 0.708
#> SRR934283 6 0.4438 0.724 0.000 0.080 0.208 0.000 0.004 0.708
#> SRR934284 6 0.4438 0.724 0.000 0.080 0.208 0.000 0.004 0.708
#> SRR934285 6 0.4438 0.724 0.000 0.080 0.208 0.000 0.004 0.708
#> SRR934286 6 0.4438 0.724 0.000 0.080 0.208 0.000 0.004 0.708
#> SRR934287 6 0.4438 0.724 0.000 0.080 0.208 0.000 0.004 0.708
#> SRR934288 1 0.1528 0.961 0.944 0.012 0.028 0.000 0.016 0.000
#> SRR934289 1 0.1528 0.961 0.944 0.012 0.028 0.000 0.016 0.000
#> SRR934290 1 0.1528 0.961 0.944 0.012 0.028 0.000 0.016 0.000
#> SRR934291 1 0.1528 0.961 0.944 0.012 0.028 0.000 0.016 0.000
#> SRR934292 1 0.1528 0.961 0.944 0.012 0.028 0.000 0.016 0.000
#> SRR934293 1 0.1528 0.961 0.944 0.012 0.028 0.000 0.016 0.000
#> SRR934294 1 0.1528 0.961 0.944 0.012 0.028 0.000 0.016 0.000
#> SRR934295 1 0.1528 0.961 0.944 0.012 0.028 0.000 0.016 0.000
#> SRR934296 5 0.4108 0.637 0.072 0.164 0.000 0.008 0.756 0.000
#> SRR934297 5 0.4108 0.637 0.072 0.164 0.000 0.008 0.756 0.000
#> SRR934298 5 0.4108 0.637 0.072 0.164 0.000 0.008 0.756 0.000
#> SRR934299 5 0.4108 0.637 0.072 0.164 0.000 0.008 0.756 0.000
#> SRR934300 5 0.4108 0.637 0.072 0.164 0.000 0.008 0.756 0.000
#> SRR934301 5 0.4108 0.637 0.072 0.164 0.000 0.008 0.756 0.000
#> SRR934302 5 0.4108 0.637 0.072 0.164 0.000 0.008 0.756 0.000
#> SRR934303 5 0.4108 0.637 0.072 0.164 0.000 0.008 0.756 0.000
#> SRR934304 5 0.3806 0.419 0.000 0.000 0.136 0.088 0.776 0.000
#> SRR934305 5 0.3806 0.419 0.000 0.000 0.136 0.088 0.776 0.000
#> SRR934306 5 0.3806 0.419 0.000 0.000 0.136 0.088 0.776 0.000
#> SRR934307 5 0.3806 0.419 0.000 0.000 0.136 0.088 0.776 0.000
#> SRR934308 5 0.3806 0.419 0.000 0.000 0.136 0.088 0.776 0.000
#> SRR934309 5 0.3806 0.419 0.000 0.000 0.136 0.088 0.776 0.000
#> SRR934310 5 0.3806 0.419 0.000 0.000 0.136 0.088 0.776 0.000
#> SRR934311 5 0.3806 0.419 0.000 0.000 0.136 0.088 0.776 0.000
#> SRR934312 6 0.4840 0.692 0.012 0.032 0.368 0.000 0.004 0.584
#> SRR934313 6 0.4840 0.692 0.012 0.032 0.368 0.000 0.004 0.584
#> SRR934314 6 0.4840 0.692 0.012 0.032 0.368 0.000 0.004 0.584
#> SRR934315 6 0.4840 0.692 0.012 0.032 0.368 0.000 0.004 0.584
#> SRR934316 6 0.4840 0.692 0.012 0.032 0.368 0.000 0.004 0.584
#> SRR934317 6 0.4840 0.692 0.012 0.032 0.368 0.000 0.004 0.584
#> SRR934318 6 0.4840 0.692 0.012 0.032 0.368 0.000 0.004 0.584
#> SRR934319 6 0.4840 0.692 0.012 0.032 0.368 0.000 0.004 0.584
#> SRR934320 6 0.6424 0.498 0.032 0.208 0.136 0.000 0.040 0.584
#> SRR934321 6 0.6424 0.498 0.032 0.208 0.136 0.000 0.040 0.584
#> SRR934322 6 0.6424 0.498 0.032 0.208 0.136 0.000 0.040 0.584
#> SRR934323 6 0.6424 0.498 0.032 0.208 0.136 0.000 0.040 0.584
#> SRR934324 6 0.6424 0.498 0.032 0.208 0.136 0.000 0.040 0.584
#> SRR934325 6 0.6424 0.498 0.032 0.208 0.136 0.000 0.040 0.584
#> SRR934326 6 0.6424 0.498 0.032 0.208 0.136 0.000 0.040 0.584
#> SRR934327 6 0.6424 0.498 0.032 0.208 0.136 0.000 0.040 0.584
#> SRR934328 1 0.0146 0.976 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR934329 1 0.0146 0.976 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR934330 1 0.0146 0.976 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR934331 1 0.0146 0.976 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR934332 1 0.0146 0.976 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR934333 1 0.0146 0.976 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR934334 1 0.0146 0.976 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR934335 1 0.0146 0.976 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR934344 1 0.0551 0.974 0.984 0.004 0.008 0.000 0.000 0.004
#> SRR934345 1 0.0551 0.974 0.984 0.004 0.008 0.000 0.000 0.004
#> SRR934346 1 0.0551 0.974 0.984 0.004 0.008 0.000 0.000 0.004
#> SRR934347 1 0.0551 0.974 0.984 0.004 0.008 0.000 0.000 0.004
#> SRR934348 1 0.0551 0.974 0.984 0.004 0.008 0.000 0.000 0.004
#> SRR934349 1 0.0551 0.974 0.984 0.004 0.008 0.000 0.000 0.004
#> SRR934350 1 0.0551 0.974 0.984 0.004 0.008 0.000 0.000 0.004
#> SRR934351 1 0.0551 0.974 0.984 0.004 0.008 0.000 0.000 0.004
#> SRR934336 6 0.1390 0.742 0.004 0.032 0.016 0.000 0.000 0.948
#> SRR934337 6 0.1390 0.742 0.004 0.032 0.016 0.000 0.000 0.948
#> SRR934338 6 0.1390 0.742 0.004 0.032 0.016 0.000 0.000 0.948
#> SRR934339 6 0.1390 0.742 0.004 0.032 0.016 0.000 0.000 0.948
#> SRR934340 6 0.1390 0.742 0.004 0.032 0.016 0.000 0.000 0.948
#> SRR934341 6 0.1390 0.742 0.004 0.032 0.016 0.000 0.000 0.948
#> SRR934342 6 0.1390 0.742 0.004 0.032 0.016 0.000 0.000 0.948
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 14550 rows and 135 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.705 0.978 0.965 0.316 0.636 0.636
#> 3 3 0.664 0.922 0.956 0.232 0.979 0.967
#> 4 4 0.790 0.855 0.928 0.282 0.916 0.863
#> 5 5 0.843 0.969 0.948 0.146 0.860 0.737
#> 6 6 0.724 0.820 0.875 0.136 0.986 0.964
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
#> SRR934216 1 0.0000 0.999 1.000 0.000
#> SRR934217 1 0.0000 0.999 1.000 0.000
#> SRR934218 1 0.0000 0.999 1.000 0.000
#> SRR934219 1 0.0000 0.999 1.000 0.000
#> SRR934220 1 0.0000 0.999 1.000 0.000
#> SRR934221 1 0.0000 0.999 1.000 0.000
#> SRR934222 1 0.0000 0.999 1.000 0.000
#> SRR934223 1 0.0000 0.999 1.000 0.000
#> SRR934224 1 0.0000 0.999 1.000 0.000
#> SRR934225 1 0.0000 0.999 1.000 0.000
#> SRR934226 1 0.0000 0.999 1.000 0.000
#> SRR934227 1 0.0000 0.999 1.000 0.000
#> SRR934228 1 0.0000 0.999 1.000 0.000
#> SRR934229 1 0.0000 0.999 1.000 0.000
#> SRR934230 1 0.0000 0.999 1.000 0.000
#> SRR934231 1 0.0000 0.999 1.000 0.000
#> SRR934232 2 0.7139 0.937 0.196 0.804
#> SRR934233 2 0.7139 0.937 0.196 0.804
#> SRR934234 2 0.7139 0.937 0.196 0.804
#> SRR934235 2 0.7139 0.937 0.196 0.804
#> SRR934236 2 0.7139 0.937 0.196 0.804
#> SRR934237 2 0.7139 0.937 0.196 0.804
#> SRR934238 2 0.7139 0.937 0.196 0.804
#> SRR934239 2 0.7139 0.937 0.196 0.804
#> SRR934240 1 0.0376 0.996 0.996 0.004
#> SRR934241 1 0.0376 0.996 0.996 0.004
#> SRR934242 1 0.0376 0.996 0.996 0.004
#> SRR934243 1 0.0376 0.996 0.996 0.004
#> SRR934244 1 0.0376 0.996 0.996 0.004
#> SRR934245 1 0.0376 0.996 0.996 0.004
#> SRR934246 1 0.0376 0.996 0.996 0.004
#> SRR934247 1 0.0376 0.996 0.996 0.004
#> SRR934248 2 0.7139 0.937 0.196 0.804
#> SRR934249 2 0.7139 0.937 0.196 0.804
#> SRR934250 2 0.7139 0.937 0.196 0.804
#> SRR934251 2 0.7139 0.937 0.196 0.804
#> SRR934252 2 0.7139 0.937 0.196 0.804
#> SRR934253 2 0.7139 0.937 0.196 0.804
#> SRR934254 2 0.7139 0.937 0.196 0.804
#> SRR934255 2 0.7139 0.937 0.196 0.804
#> SRR934256 1 0.0376 0.996 0.996 0.004
#> SRR934257 1 0.0376 0.996 0.996 0.004
#> SRR934258 1 0.0376 0.996 0.996 0.004
#> SRR934259 1 0.0376 0.996 0.996 0.004
#> SRR934260 1 0.0376 0.996 0.996 0.004
#> SRR934261 1 0.0376 0.996 0.996 0.004
#> SRR934262 1 0.0376 0.996 0.996 0.004
#> SRR934263 1 0.0376 0.996 0.996 0.004
#> SRR934264 2 0.7139 0.937 0.196 0.804
#> SRR934265 2 0.7139 0.937 0.196 0.804
#> SRR934266 2 0.7139 0.937 0.196 0.804
#> SRR934267 2 0.7139 0.937 0.196 0.804
#> SRR934268 2 0.7139 0.937 0.196 0.804
#> SRR934269 2 0.7139 0.937 0.196 0.804
#> SRR934270 2 0.7139 0.937 0.196 0.804
#> SRR934271 2 0.7139 0.937 0.196 0.804
#> SRR934272 1 0.0000 0.999 1.000 0.000
#> SRR934273 1 0.0000 0.999 1.000 0.000
#> SRR934274 1 0.0000 0.999 1.000 0.000
#> SRR934275 1 0.0000 0.999 1.000 0.000
#> SRR934276 1 0.0000 0.999 1.000 0.000
#> SRR934277 1 0.0000 0.999 1.000 0.000
#> SRR934278 1 0.0000 0.999 1.000 0.000
#> SRR934279 1 0.0000 0.999 1.000 0.000
#> SRR934280 1 0.0000 0.999 1.000 0.000
#> SRR934281 1 0.0000 0.999 1.000 0.000
#> SRR934282 1 0.0000 0.999 1.000 0.000
#> SRR934283 1 0.0000 0.999 1.000 0.000
#> SRR934284 1 0.0000 0.999 1.000 0.000
#> SRR934285 1 0.0000 0.999 1.000 0.000
#> SRR934286 1 0.0000 0.999 1.000 0.000
#> SRR934287 1 0.0000 0.999 1.000 0.000
#> SRR934288 1 0.0000 0.999 1.000 0.000
#> SRR934289 1 0.0000 0.999 1.000 0.000
#> SRR934290 1 0.0000 0.999 1.000 0.000
#> SRR934291 1 0.0000 0.999 1.000 0.000
#> SRR934292 1 0.0000 0.999 1.000 0.000
#> SRR934293 1 0.0000 0.999 1.000 0.000
#> SRR934294 1 0.0000 0.999 1.000 0.000
#> SRR934295 1 0.0000 0.999 1.000 0.000
#> SRR934296 1 0.0000 0.999 1.000 0.000
#> SRR934297 1 0.0000 0.999 1.000 0.000
#> SRR934298 1 0.0000 0.999 1.000 0.000
#> SRR934299 1 0.0000 0.999 1.000 0.000
#> SRR934300 1 0.0000 0.999 1.000 0.000
#> SRR934301 1 0.0000 0.999 1.000 0.000
#> SRR934302 1 0.0000 0.999 1.000 0.000
#> SRR934303 1 0.0000 0.999 1.000 0.000
#> SRR934304 2 0.0000 0.839 0.000 1.000
#> SRR934305 2 0.0000 0.839 0.000 1.000
#> SRR934306 2 0.0000 0.839 0.000 1.000
#> SRR934307 2 0.0000 0.839 0.000 1.000
#> SRR934308 2 0.0000 0.839 0.000 1.000
#> SRR934309 2 0.0000 0.839 0.000 1.000
#> SRR934310 2 0.0000 0.839 0.000 1.000
#> SRR934311 2 0.0000 0.839 0.000 1.000
#> SRR934312 1 0.0000 0.999 1.000 0.000
#> SRR934313 1 0.0000 0.999 1.000 0.000
#> SRR934314 1 0.0000 0.999 1.000 0.000
#> SRR934315 1 0.0000 0.999 1.000 0.000
#> SRR934316 1 0.0000 0.999 1.000 0.000
#> SRR934317 1 0.0000 0.999 1.000 0.000
#> SRR934318 1 0.0000 0.999 1.000 0.000
#> SRR934319 1 0.0000 0.999 1.000 0.000
#> SRR934320 1 0.0000 0.999 1.000 0.000
#> SRR934321 1 0.0000 0.999 1.000 0.000
#> SRR934322 1 0.0000 0.999 1.000 0.000
#> SRR934323 1 0.0000 0.999 1.000 0.000
#> SRR934324 1 0.0000 0.999 1.000 0.000
#> SRR934325 1 0.0000 0.999 1.000 0.000
#> SRR934326 1 0.0000 0.999 1.000 0.000
#> SRR934327 1 0.0000 0.999 1.000 0.000
#> SRR934328 1 0.0000 0.999 1.000 0.000
#> SRR934329 1 0.0000 0.999 1.000 0.000
#> SRR934330 1 0.0000 0.999 1.000 0.000
#> SRR934331 1 0.0000 0.999 1.000 0.000
#> SRR934332 1 0.0000 0.999 1.000 0.000
#> SRR934333 1 0.0000 0.999 1.000 0.000
#> SRR934334 1 0.0000 0.999 1.000 0.000
#> SRR934335 1 0.0000 0.999 1.000 0.000
#> SRR934344 1 0.0000 0.999 1.000 0.000
#> SRR934345 1 0.0000 0.999 1.000 0.000
#> SRR934346 1 0.0000 0.999 1.000 0.000
#> SRR934347 1 0.0000 0.999 1.000 0.000
#> SRR934348 1 0.0000 0.999 1.000 0.000
#> SRR934349 1 0.0000 0.999 1.000 0.000
#> SRR934350 1 0.0000 0.999 1.000 0.000
#> SRR934351 1 0.0000 0.999 1.000 0.000
#> SRR934336 1 0.0000 0.999 1.000 0.000
#> SRR934337 1 0.0000 0.999 1.000 0.000
#> SRR934338 1 0.0000 0.999 1.000 0.000
#> SRR934339 1 0.0000 0.999 1.000 0.000
#> SRR934340 1 0.0000 0.999 1.000 0.000
#> SRR934341 1 0.0000 0.999 1.000 0.000
#> SRR934342 1 0.0000 0.999 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 1 0.000 0.964 1.000 0.000 0
#> SRR934217 1 0.000 0.964 1.000 0.000 0
#> SRR934218 1 0.000 0.964 1.000 0.000 0
#> SRR934219 1 0.000 0.964 1.000 0.000 0
#> SRR934220 1 0.000 0.964 1.000 0.000 0
#> SRR934221 1 0.000 0.964 1.000 0.000 0
#> SRR934222 1 0.000 0.964 1.000 0.000 0
#> SRR934223 1 0.000 0.964 1.000 0.000 0
#> SRR934224 1 0.000 0.964 1.000 0.000 0
#> SRR934225 1 0.000 0.964 1.000 0.000 0
#> SRR934226 1 0.000 0.964 1.000 0.000 0
#> SRR934227 1 0.000 0.964 1.000 0.000 0
#> SRR934228 1 0.000 0.964 1.000 0.000 0
#> SRR934229 1 0.000 0.964 1.000 0.000 0
#> SRR934230 1 0.000 0.964 1.000 0.000 0
#> SRR934231 1 0.000 0.964 1.000 0.000 0
#> SRR934232 2 0.000 0.743 0.000 1.000 0
#> SRR934233 2 0.000 0.743 0.000 1.000 0
#> SRR934234 2 0.000 0.743 0.000 1.000 0
#> SRR934235 2 0.000 0.743 0.000 1.000 0
#> SRR934236 2 0.000 0.743 0.000 1.000 0
#> SRR934237 2 0.000 0.743 0.000 1.000 0
#> SRR934238 2 0.000 0.743 0.000 1.000 0
#> SRR934239 2 0.000 0.743 0.000 1.000 0
#> SRR934240 1 0.455 0.781 0.800 0.200 0
#> SRR934241 1 0.455 0.781 0.800 0.200 0
#> SRR934242 1 0.455 0.781 0.800 0.200 0
#> SRR934243 1 0.455 0.781 0.800 0.200 0
#> SRR934244 1 0.455 0.781 0.800 0.200 0
#> SRR934245 1 0.455 0.781 0.800 0.200 0
#> SRR934246 1 0.455 0.781 0.800 0.200 0
#> SRR934247 1 0.455 0.781 0.800 0.200 0
#> SRR934248 2 0.418 0.881 0.172 0.828 0
#> SRR934249 2 0.418 0.881 0.172 0.828 0
#> SRR934250 2 0.418 0.881 0.172 0.828 0
#> SRR934251 2 0.418 0.881 0.172 0.828 0
#> SRR934252 2 0.418 0.881 0.172 0.828 0
#> SRR934253 2 0.418 0.881 0.172 0.828 0
#> SRR934254 2 0.418 0.881 0.172 0.828 0
#> SRR934255 2 0.418 0.881 0.172 0.828 0
#> SRR934256 1 0.455 0.781 0.800 0.200 0
#> SRR934257 1 0.455 0.781 0.800 0.200 0
#> SRR934258 1 0.455 0.781 0.800 0.200 0
#> SRR934259 1 0.455 0.781 0.800 0.200 0
#> SRR934260 1 0.455 0.781 0.800 0.200 0
#> SRR934261 1 0.455 0.781 0.800 0.200 0
#> SRR934262 1 0.455 0.781 0.800 0.200 0
#> SRR934263 1 0.455 0.781 0.800 0.200 0
#> SRR934264 2 0.418 0.881 0.172 0.828 0
#> SRR934265 2 0.418 0.881 0.172 0.828 0
#> SRR934266 2 0.418 0.881 0.172 0.828 0
#> SRR934267 2 0.418 0.881 0.172 0.828 0
#> SRR934268 2 0.418 0.881 0.172 0.828 0
#> SRR934269 2 0.418 0.881 0.172 0.828 0
#> SRR934270 2 0.418 0.881 0.172 0.828 0
#> SRR934271 2 0.418 0.881 0.172 0.828 0
#> SRR934272 1 0.000 0.964 1.000 0.000 0
#> SRR934273 1 0.000 0.964 1.000 0.000 0
#> SRR934274 1 0.000 0.964 1.000 0.000 0
#> SRR934275 1 0.000 0.964 1.000 0.000 0
#> SRR934276 1 0.000 0.964 1.000 0.000 0
#> SRR934277 1 0.000 0.964 1.000 0.000 0
#> SRR934278 1 0.000 0.964 1.000 0.000 0
#> SRR934279 1 0.000 0.964 1.000 0.000 0
#> SRR934280 1 0.000 0.964 1.000 0.000 0
#> SRR934281 1 0.000 0.964 1.000 0.000 0
#> SRR934282 1 0.000 0.964 1.000 0.000 0
#> SRR934283 1 0.000 0.964 1.000 0.000 0
#> SRR934284 1 0.000 0.964 1.000 0.000 0
#> SRR934285 1 0.000 0.964 1.000 0.000 0
#> SRR934286 1 0.000 0.964 1.000 0.000 0
#> SRR934287 1 0.000 0.964 1.000 0.000 0
#> SRR934288 1 0.000 0.964 1.000 0.000 0
#> SRR934289 1 0.000 0.964 1.000 0.000 0
#> SRR934290 1 0.000 0.964 1.000 0.000 0
#> SRR934291 1 0.000 0.964 1.000 0.000 0
#> SRR934292 1 0.000 0.964 1.000 0.000 0
#> SRR934293 1 0.000 0.964 1.000 0.000 0
#> SRR934294 1 0.000 0.964 1.000 0.000 0
#> SRR934295 1 0.000 0.964 1.000 0.000 0
#> SRR934296 1 0.000 0.964 1.000 0.000 0
#> SRR934297 1 0.000 0.964 1.000 0.000 0
#> SRR934298 1 0.000 0.964 1.000 0.000 0
#> SRR934299 1 0.000 0.964 1.000 0.000 0
#> SRR934300 1 0.000 0.964 1.000 0.000 0
#> SRR934301 1 0.000 0.964 1.000 0.000 0
#> SRR934302 1 0.000 0.964 1.000 0.000 0
#> SRR934303 1 0.000 0.964 1.000 0.000 0
#> SRR934304 3 0.000 1.000 0.000 0.000 1
#> SRR934305 3 0.000 1.000 0.000 0.000 1
#> SRR934306 3 0.000 1.000 0.000 0.000 1
#> SRR934307 3 0.000 1.000 0.000 0.000 1
#> SRR934308 3 0.000 1.000 0.000 0.000 1
#> SRR934309 3 0.000 1.000 0.000 0.000 1
#> SRR934310 3 0.000 1.000 0.000 0.000 1
#> SRR934311 3 0.000 1.000 0.000 0.000 1
#> SRR934312 1 0.000 0.964 1.000 0.000 0
#> SRR934313 1 0.000 0.964 1.000 0.000 0
#> SRR934314 1 0.000 0.964 1.000 0.000 0
#> SRR934315 1 0.000 0.964 1.000 0.000 0
#> SRR934316 1 0.000 0.964 1.000 0.000 0
#> SRR934317 1 0.000 0.964 1.000 0.000 0
#> SRR934318 1 0.000 0.964 1.000 0.000 0
#> SRR934319 1 0.000 0.964 1.000 0.000 0
#> SRR934320 1 0.000 0.964 1.000 0.000 0
#> SRR934321 1 0.000 0.964 1.000 0.000 0
#> SRR934322 1 0.000 0.964 1.000 0.000 0
#> SRR934323 1 0.000 0.964 1.000 0.000 0
#> SRR934324 1 0.000 0.964 1.000 0.000 0
#> SRR934325 1 0.000 0.964 1.000 0.000 0
#> SRR934326 1 0.000 0.964 1.000 0.000 0
#> SRR934327 1 0.000 0.964 1.000 0.000 0
#> SRR934328 1 0.000 0.964 1.000 0.000 0
#> SRR934329 1 0.000 0.964 1.000 0.000 0
#> SRR934330 1 0.000 0.964 1.000 0.000 0
#> SRR934331 1 0.000 0.964 1.000 0.000 0
#> SRR934332 1 0.000 0.964 1.000 0.000 0
#> SRR934333 1 0.000 0.964 1.000 0.000 0
#> SRR934334 1 0.000 0.964 1.000 0.000 0
#> SRR934335 1 0.000 0.964 1.000 0.000 0
#> SRR934344 1 0.000 0.964 1.000 0.000 0
#> SRR934345 1 0.000 0.964 1.000 0.000 0
#> SRR934346 1 0.000 0.964 1.000 0.000 0
#> SRR934347 1 0.000 0.964 1.000 0.000 0
#> SRR934348 1 0.000 0.964 1.000 0.000 0
#> SRR934349 1 0.000 0.964 1.000 0.000 0
#> SRR934350 1 0.000 0.964 1.000 0.000 0
#> SRR934351 1 0.000 0.964 1.000 0.000 0
#> SRR934336 1 0.000 0.964 1.000 0.000 0
#> SRR934337 1 0.000 0.964 1.000 0.000 0
#> SRR934338 1 0.000 0.964 1.000 0.000 0
#> SRR934339 1 0.000 0.964 1.000 0.000 0
#> SRR934340 1 0.000 0.964 1.000 0.000 0
#> SRR934341 1 0.000 0.964 1.000 0.000 0
#> SRR934342 1 0.000 0.964 1.000 0.000 0
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 4 0.0000 1.000 0.000 0.000 0 1.00
#> SRR934217 4 0.0000 1.000 0.000 0.000 0 1.00
#> SRR934218 4 0.0000 1.000 0.000 0.000 0 1.00
#> SRR934219 4 0.0000 1.000 0.000 0.000 0 1.00
#> SRR934220 4 0.0000 1.000 0.000 0.000 0 1.00
#> SRR934221 4 0.0000 1.000 0.000 0.000 0 1.00
#> SRR934222 4 0.0000 1.000 0.000 0.000 0 1.00
#> SRR934223 4 0.0000 1.000 0.000 0.000 0 1.00
#> SRR934224 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934225 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934226 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934227 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934228 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934229 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934230 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934231 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934232 2 0.0000 0.995 0.000 1.000 0 0.00
#> SRR934233 2 0.0000 0.995 0.000 1.000 0 0.00
#> SRR934234 2 0.0000 0.995 0.000 1.000 0 0.00
#> SRR934235 2 0.0000 0.995 0.000 1.000 0 0.00
#> SRR934236 2 0.0000 0.995 0.000 1.000 0 0.00
#> SRR934237 2 0.0000 0.995 0.000 1.000 0 0.00
#> SRR934238 2 0.0000 0.995 0.000 1.000 0 0.00
#> SRR934239 2 0.0000 0.995 0.000 1.000 0 0.00
#> SRR934240 1 0.0921 0.340 0.972 0.028 0 0.00
#> SRR934241 1 0.0921 0.340 0.972 0.028 0 0.00
#> SRR934242 1 0.0921 0.340 0.972 0.028 0 0.00
#> SRR934243 1 0.0921 0.340 0.972 0.028 0 0.00
#> SRR934244 1 0.0921 0.340 0.972 0.028 0 0.00
#> SRR934245 1 0.0921 0.340 0.972 0.028 0 0.00
#> SRR934246 1 0.0921 0.340 0.972 0.028 0 0.00
#> SRR934247 1 0.0921 0.340 0.972 0.028 0 0.00
#> SRR934248 2 0.0188 0.997 0.004 0.996 0 0.00
#> SRR934249 2 0.0188 0.997 0.004 0.996 0 0.00
#> SRR934250 2 0.0188 0.997 0.004 0.996 0 0.00
#> SRR934251 2 0.0188 0.997 0.004 0.996 0 0.00
#> SRR934252 2 0.0188 0.997 0.004 0.996 0 0.00
#> SRR934253 2 0.0188 0.997 0.004 0.996 0 0.00
#> SRR934254 2 0.0188 0.997 0.004 0.996 0 0.00
#> SRR934255 2 0.0188 0.997 0.004 0.996 0 0.00
#> SRR934256 1 0.0921 0.340 0.972 0.028 0 0.00
#> SRR934257 1 0.0921 0.340 0.972 0.028 0 0.00
#> SRR934258 1 0.0921 0.340 0.972 0.028 0 0.00
#> SRR934259 1 0.0921 0.340 0.972 0.028 0 0.00
#> SRR934260 1 0.0921 0.340 0.972 0.028 0 0.00
#> SRR934261 1 0.0921 0.340 0.972 0.028 0 0.00
#> SRR934262 1 0.0921 0.340 0.972 0.028 0 0.00
#> SRR934263 1 0.0921 0.340 0.972 0.028 0 0.00
#> SRR934264 2 0.0188 0.997 0.004 0.996 0 0.00
#> SRR934265 2 0.0188 0.997 0.004 0.996 0 0.00
#> SRR934266 2 0.0188 0.997 0.004 0.996 0 0.00
#> SRR934267 2 0.0188 0.997 0.004 0.996 0 0.00
#> SRR934268 2 0.0188 0.997 0.004 0.996 0 0.00
#> SRR934269 2 0.0188 0.997 0.004 0.996 0 0.00
#> SRR934270 2 0.0188 0.997 0.004 0.996 0 0.00
#> SRR934271 2 0.0188 0.997 0.004 0.996 0 0.00
#> SRR934272 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934273 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934274 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934275 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934276 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934277 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934278 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934279 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934280 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934281 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934282 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934283 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934284 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934285 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934286 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934287 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934288 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934289 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934290 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934291 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934292 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934293 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934294 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934295 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934296 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934297 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934298 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934299 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934300 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934301 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934302 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934303 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934304 3 0.0000 1.000 0.000 0.000 1 0.00
#> SRR934305 3 0.0000 1.000 0.000 0.000 1 0.00
#> SRR934306 3 0.0000 1.000 0.000 0.000 1 0.00
#> SRR934307 3 0.0000 1.000 0.000 0.000 1 0.00
#> SRR934308 3 0.0000 1.000 0.000 0.000 1 0.00
#> SRR934309 3 0.0000 1.000 0.000 0.000 1 0.00
#> SRR934310 3 0.0000 1.000 0.000 0.000 1 0.00
#> SRR934311 3 0.0000 1.000 0.000 0.000 1 0.00
#> SRR934312 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934313 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934314 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934315 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934316 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934317 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934318 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934319 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934320 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934321 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934322 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934323 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934324 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934325 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934326 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934327 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934328 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934329 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934330 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934331 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934332 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934333 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934334 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934335 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934344 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934345 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934346 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934347 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934348 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934349 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934350 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934351 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934336 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934337 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934338 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934339 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934340 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934341 1 0.4790 0.888 0.620 0.000 0 0.38
#> SRR934342 1 0.4790 0.888 0.620 0.000 0 0.38
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934217 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934218 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934219 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934220 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934221 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934222 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934223 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934224 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934225 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934226 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934227 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934228 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934229 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934230 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934231 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934232 4 0.0000 0.913 0.000 0.000 0 1.000 0
#> SRR934233 4 0.0000 0.913 0.000 0.000 0 1.000 0
#> SRR934234 4 0.0000 0.913 0.000 0.000 0 1.000 0
#> SRR934235 4 0.0000 0.913 0.000 0.000 0 1.000 0
#> SRR934236 4 0.0000 0.913 0.000 0.000 0 1.000 0
#> SRR934237 4 0.0000 0.913 0.000 0.000 0 1.000 0
#> SRR934238 4 0.0000 0.913 0.000 0.000 0 1.000 0
#> SRR934239 4 0.0000 0.913 0.000 0.000 0 1.000 0
#> SRR934240 2 0.3876 0.925 0.316 0.684 0 0.000 0
#> SRR934241 2 0.3816 0.933 0.304 0.696 0 0.000 0
#> SRR934242 2 0.4150 0.847 0.388 0.612 0 0.000 0
#> SRR934243 2 0.3707 0.941 0.284 0.716 0 0.000 0
#> SRR934244 2 0.4101 0.870 0.372 0.628 0 0.000 0
#> SRR934245 2 0.3636 0.944 0.272 0.728 0 0.000 0
#> SRR934246 2 0.3966 0.907 0.336 0.664 0 0.000 0
#> SRR934247 2 0.4114 0.865 0.376 0.624 0 0.000 0
#> SRR934248 4 0.3636 0.813 0.000 0.272 0 0.728 0
#> SRR934249 4 0.3636 0.813 0.000 0.272 0 0.728 0
#> SRR934250 4 0.3636 0.813 0.000 0.272 0 0.728 0
#> SRR934251 4 0.3636 0.813 0.000 0.272 0 0.728 0
#> SRR934252 4 0.3636 0.813 0.000 0.272 0 0.728 0
#> SRR934253 4 0.3636 0.813 0.000 0.272 0 0.728 0
#> SRR934254 4 0.3636 0.813 0.000 0.272 0 0.728 0
#> SRR934255 4 0.3636 0.813 0.000 0.272 0 0.728 0
#> SRR934256 2 0.3636 0.944 0.272 0.728 0 0.000 0
#> SRR934257 2 0.3636 0.944 0.272 0.728 0 0.000 0
#> SRR934258 2 0.3636 0.944 0.272 0.728 0 0.000 0
#> SRR934259 2 0.3636 0.944 0.272 0.728 0 0.000 0
#> SRR934260 2 0.3636 0.944 0.272 0.728 0 0.000 0
#> SRR934261 2 0.3636 0.944 0.272 0.728 0 0.000 0
#> SRR934262 2 0.3636 0.944 0.272 0.728 0 0.000 0
#> SRR934263 2 0.3636 0.944 0.272 0.728 0 0.000 0
#> SRR934264 4 0.0000 0.913 0.000 0.000 0 1.000 0
#> SRR934265 4 0.0000 0.913 0.000 0.000 0 1.000 0
#> SRR934266 4 0.0000 0.913 0.000 0.000 0 1.000 0
#> SRR934267 4 0.0000 0.913 0.000 0.000 0 1.000 0
#> SRR934268 4 0.0000 0.913 0.000 0.000 0 1.000 0
#> SRR934269 4 0.0000 0.913 0.000 0.000 0 1.000 0
#> SRR934270 4 0.0000 0.913 0.000 0.000 0 1.000 0
#> SRR934271 4 0.0000 0.913 0.000 0.000 0 1.000 0
#> SRR934272 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934273 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934274 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934275 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934276 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934277 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934278 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934279 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934280 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934281 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934282 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934283 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934284 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934285 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934286 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934287 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934288 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934289 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934290 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934291 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934292 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934293 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934294 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934295 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934296 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934297 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934298 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934299 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934300 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934301 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934302 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934303 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934304 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934305 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934306 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934307 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934308 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934309 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934310 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934311 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934312 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934313 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934314 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934315 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934316 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934317 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934318 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934319 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934320 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934321 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934322 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934323 1 0.0162 0.994 0.996 0.004 0 0.000 0
#> SRR934324 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934325 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934326 1 0.0162 0.994 0.996 0.004 0 0.000 0
#> SRR934327 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934328 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934329 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934330 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934331 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934332 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934333 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934334 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934335 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934344 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934345 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934346 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934347 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934348 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934349 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934350 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934351 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934336 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934337 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934338 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934339 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934340 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934341 1 0.0000 1.000 1.000 0.000 0 0.000 0
#> SRR934342 1 0.0000 1.000 1.000 0.000 0 0.000 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934217 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934218 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934219 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934220 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934221 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934222 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934223 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934224 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934225 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934226 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934227 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934228 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934229 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934230 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934231 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934232 4 0.2883 0.811 0.000 0.000 0 0.788 0 0.212
#> SRR934233 4 0.2883 0.811 0.000 0.000 0 0.788 0 0.212
#> SRR934234 4 0.2883 0.811 0.000 0.000 0 0.788 0 0.212
#> SRR934235 4 0.2883 0.811 0.000 0.000 0 0.788 0 0.212
#> SRR934236 4 0.2883 0.811 0.000 0.000 0 0.788 0 0.212
#> SRR934237 4 0.2883 0.811 0.000 0.000 0 0.788 0 0.212
#> SRR934238 4 0.2883 0.811 0.000 0.000 0 0.788 0 0.212
#> SRR934239 4 0.2883 0.811 0.000 0.000 0 0.788 0 0.212
#> SRR934240 2 0.5731 0.737 0.276 0.512 0 0.000 0 0.212
#> SRR934241 2 0.5639 0.746 0.252 0.536 0 0.000 0 0.212
#> SRR934242 2 0.5931 0.642 0.388 0.400 0 0.000 0 0.212
#> SRR934243 2 0.5529 0.747 0.228 0.560 0 0.000 0 0.212
#> SRR934244 2 0.5922 0.668 0.368 0.420 0 0.000 0 0.212
#> SRR934245 2 0.5374 0.738 0.200 0.588 0 0.000 0 0.212
#> SRR934246 2 0.5854 0.709 0.320 0.468 0 0.000 0 0.212
#> SRR934247 2 0.5922 0.667 0.368 0.420 0 0.000 0 0.212
#> SRR934248 6 0.3797 1.000 0.000 0.000 0 0.420 0 0.580
#> SRR934249 6 0.3797 1.000 0.000 0.000 0 0.420 0 0.580
#> SRR934250 6 0.3797 1.000 0.000 0.000 0 0.420 0 0.580
#> SRR934251 6 0.3797 1.000 0.000 0.000 0 0.420 0 0.580
#> SRR934252 6 0.3797 1.000 0.000 0.000 0 0.420 0 0.580
#> SRR934253 6 0.3797 1.000 0.000 0.000 0 0.420 0 0.580
#> SRR934254 6 0.3797 1.000 0.000 0.000 0 0.420 0 0.580
#> SRR934255 6 0.3797 1.000 0.000 0.000 0 0.420 0 0.580
#> SRR934256 2 0.1556 0.651 0.080 0.920 0 0.000 0 0.000
#> SRR934257 2 0.1556 0.652 0.080 0.920 0 0.000 0 0.000
#> SRR934258 2 0.2048 0.711 0.120 0.880 0 0.000 0 0.000
#> SRR934259 2 0.2562 0.745 0.172 0.828 0 0.000 0 0.000
#> SRR934260 2 0.2416 0.743 0.156 0.844 0 0.000 0 0.000
#> SRR934261 2 0.2454 0.745 0.160 0.840 0 0.000 0 0.000
#> SRR934262 2 0.2300 0.735 0.144 0.856 0 0.000 0 0.000
#> SRR934263 2 0.2562 0.745 0.172 0.828 0 0.000 0 0.000
#> SRR934264 4 0.0000 0.775 0.000 0.000 0 1.000 0 0.000
#> SRR934265 4 0.0000 0.775 0.000 0.000 0 1.000 0 0.000
#> SRR934266 4 0.0000 0.775 0.000 0.000 0 1.000 0 0.000
#> SRR934267 4 0.0000 0.775 0.000 0.000 0 1.000 0 0.000
#> SRR934268 4 0.0000 0.775 0.000 0.000 0 1.000 0 0.000
#> SRR934269 4 0.0000 0.775 0.000 0.000 0 1.000 0 0.000
#> SRR934270 4 0.0000 0.775 0.000 0.000 0 1.000 0 0.000
#> SRR934271 4 0.0000 0.775 0.000 0.000 0 1.000 0 0.000
#> SRR934272 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934273 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934274 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934275 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934276 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934277 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934278 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934279 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934280 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934281 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934282 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934283 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934284 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934285 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934286 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934287 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934288 1 0.4318 0.685 0.728 0.140 0 0.000 0 0.132
#> SRR934289 1 0.4474 0.670 0.708 0.172 0 0.000 0 0.120
#> SRR934290 1 0.5093 0.606 0.632 0.176 0 0.000 0 0.192
#> SRR934291 1 0.5062 0.609 0.636 0.168 0 0.000 0 0.196
#> SRR934292 1 0.2778 0.752 0.824 0.168 0 0.000 0 0.008
#> SRR934293 1 0.2597 0.752 0.824 0.176 0 0.000 0 0.000
#> SRR934294 1 0.3284 0.737 0.800 0.168 0 0.000 0 0.032
#> SRR934295 1 0.4030 0.700 0.748 0.172 0 0.000 0 0.080
#> SRR934296 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934297 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934298 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934299 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934300 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934301 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934302 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934303 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934304 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934305 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934306 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934307 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934308 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934309 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934310 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934311 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934312 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934313 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934314 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934315 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934316 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934317 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934318 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934319 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934320 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934321 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934322 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934323 1 0.0458 0.852 0.984 0.016 0 0.000 0 0.000
#> SRR934324 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934325 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934326 1 0.0146 0.862 0.996 0.004 0 0.000 0 0.000
#> SRR934327 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934328 1 0.5167 0.595 0.620 0.172 0 0.000 0 0.208
#> SRR934329 1 0.5167 0.595 0.620 0.172 0 0.000 0 0.208
#> SRR934330 1 0.5167 0.595 0.620 0.172 0 0.000 0 0.208
#> SRR934331 1 0.5167 0.595 0.620 0.172 0 0.000 0 0.208
#> SRR934332 1 0.5223 0.585 0.612 0.180 0 0.000 0 0.208
#> SRR934333 1 0.5223 0.585 0.612 0.180 0 0.000 0 0.208
#> SRR934334 1 0.5195 0.590 0.616 0.176 0 0.000 0 0.208
#> SRR934335 1 0.5195 0.590 0.616 0.176 0 0.000 0 0.208
#> SRR934344 1 0.5167 0.595 0.620 0.172 0 0.000 0 0.208
#> SRR934345 1 0.5167 0.595 0.620 0.172 0 0.000 0 0.208
#> SRR934346 1 0.5167 0.595 0.620 0.172 0 0.000 0 0.208
#> SRR934347 1 0.5167 0.595 0.620 0.172 0 0.000 0 0.208
#> SRR934348 1 0.5195 0.590 0.616 0.176 0 0.000 0 0.208
#> SRR934349 1 0.5167 0.595 0.620 0.172 0 0.000 0 0.208
#> SRR934350 1 0.5167 0.595 0.620 0.172 0 0.000 0 0.208
#> SRR934351 1 0.5167 0.595 0.620 0.172 0 0.000 0 0.208
#> SRR934336 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934337 1 0.0260 0.861 0.992 0.008 0 0.000 0 0.000
#> SRR934338 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934339 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934340 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934341 1 0.0000 0.866 1.000 0.000 0 0.000 0 0.000
#> SRR934342 1 0.0000 0.866 1.000 0.000 0 0.000 0 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 14550 rows and 135 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 0.888 0.957 0.980 0.4997 0.498 0.498
#> 3 3 0.635 0.707 0.824 0.0985 0.803 0.648
#> 4 4 0.824 0.876 0.905 0.1212 0.852 0.689
#> 5 5 0.866 0.949 0.964 0.0482 0.972 0.925
#> 6 6 0.732 0.851 0.858 0.1010 0.972 0.919
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
#> SRR934216 2 0.000 0.963 0.000 1.000
#> SRR934217 2 0.000 0.963 0.000 1.000
#> SRR934218 2 0.000 0.963 0.000 1.000
#> SRR934219 2 0.000 0.963 0.000 1.000
#> SRR934220 2 0.000 0.963 0.000 1.000
#> SRR934221 2 0.000 0.963 0.000 1.000
#> SRR934222 2 0.000 0.963 0.000 1.000
#> SRR934223 2 0.000 0.963 0.000 1.000
#> SRR934224 1 0.000 0.993 1.000 0.000
#> SRR934225 1 0.000 0.993 1.000 0.000
#> SRR934226 1 0.000 0.993 1.000 0.000
#> SRR934227 1 0.000 0.993 1.000 0.000
#> SRR934228 1 0.000 0.993 1.000 0.000
#> SRR934229 1 0.000 0.993 1.000 0.000
#> SRR934230 1 0.000 0.993 1.000 0.000
#> SRR934231 1 0.000 0.993 1.000 0.000
#> SRR934232 2 0.000 0.963 0.000 1.000
#> SRR934233 2 0.000 0.963 0.000 1.000
#> SRR934234 2 0.000 0.963 0.000 1.000
#> SRR934235 2 0.000 0.963 0.000 1.000
#> SRR934236 2 0.000 0.963 0.000 1.000
#> SRR934237 2 0.000 0.963 0.000 1.000
#> SRR934238 2 0.000 0.963 0.000 1.000
#> SRR934239 2 0.000 0.963 0.000 1.000
#> SRR934240 2 0.000 0.963 0.000 1.000
#> SRR934241 2 0.000 0.963 0.000 1.000
#> SRR934242 2 0.000 0.963 0.000 1.000
#> SRR934243 2 0.000 0.963 0.000 1.000
#> SRR934244 2 0.000 0.963 0.000 1.000
#> SRR934245 2 0.000 0.963 0.000 1.000
#> SRR934246 2 0.000 0.963 0.000 1.000
#> SRR934247 2 0.000 0.963 0.000 1.000
#> SRR934248 2 0.000 0.963 0.000 1.000
#> SRR934249 2 0.000 0.963 0.000 1.000
#> SRR934250 2 0.000 0.963 0.000 1.000
#> SRR934251 2 0.000 0.963 0.000 1.000
#> SRR934252 2 0.000 0.963 0.000 1.000
#> SRR934253 2 0.000 0.963 0.000 1.000
#> SRR934254 2 0.000 0.963 0.000 1.000
#> SRR934255 2 0.000 0.963 0.000 1.000
#> SRR934256 2 0.861 0.648 0.284 0.716
#> SRR934257 2 0.861 0.648 0.284 0.716
#> SRR934258 2 0.861 0.648 0.284 0.716
#> SRR934259 2 0.861 0.648 0.284 0.716
#> SRR934260 2 0.861 0.648 0.284 0.716
#> SRR934261 2 0.861 0.648 0.284 0.716
#> SRR934262 2 0.861 0.648 0.284 0.716
#> SRR934263 2 0.861 0.648 0.284 0.716
#> SRR934264 2 0.000 0.963 0.000 1.000
#> SRR934265 2 0.000 0.963 0.000 1.000
#> SRR934266 2 0.000 0.963 0.000 1.000
#> SRR934267 2 0.000 0.963 0.000 1.000
#> SRR934268 2 0.000 0.963 0.000 1.000
#> SRR934269 2 0.000 0.963 0.000 1.000
#> SRR934270 2 0.000 0.963 0.000 1.000
#> SRR934271 2 0.000 0.963 0.000 1.000
#> SRR934272 1 0.000 0.993 1.000 0.000
#> SRR934273 1 0.000 0.993 1.000 0.000
#> SRR934274 1 0.000 0.993 1.000 0.000
#> SRR934275 1 0.000 0.993 1.000 0.000
#> SRR934276 1 0.000 0.993 1.000 0.000
#> SRR934277 1 0.000 0.993 1.000 0.000
#> SRR934278 1 0.000 0.993 1.000 0.000
#> SRR934279 1 0.000 0.993 1.000 0.000
#> SRR934280 1 0.000 0.993 1.000 0.000
#> SRR934281 1 0.000 0.993 1.000 0.000
#> SRR934282 1 0.000 0.993 1.000 0.000
#> SRR934283 1 0.000 0.993 1.000 0.000
#> SRR934284 1 0.000 0.993 1.000 0.000
#> SRR934285 1 0.000 0.993 1.000 0.000
#> SRR934286 1 0.000 0.993 1.000 0.000
#> SRR934287 1 0.000 0.993 1.000 0.000
#> SRR934288 1 0.000 0.993 1.000 0.000
#> SRR934289 1 0.000 0.993 1.000 0.000
#> SRR934290 1 0.000 0.993 1.000 0.000
#> SRR934291 1 0.000 0.993 1.000 0.000
#> SRR934292 1 0.000 0.993 1.000 0.000
#> SRR934293 1 0.000 0.993 1.000 0.000
#> SRR934294 1 0.000 0.993 1.000 0.000
#> SRR934295 1 0.000 0.993 1.000 0.000
#> SRR934296 2 0.000 0.963 0.000 1.000
#> SRR934297 2 0.000 0.963 0.000 1.000
#> SRR934298 2 0.000 0.963 0.000 1.000
#> SRR934299 2 0.000 0.963 0.000 1.000
#> SRR934300 2 0.000 0.963 0.000 1.000
#> SRR934301 2 0.000 0.963 0.000 1.000
#> SRR934302 2 0.000 0.963 0.000 1.000
#> SRR934303 2 0.000 0.963 0.000 1.000
#> SRR934304 2 0.000 0.963 0.000 1.000
#> SRR934305 2 0.000 0.963 0.000 1.000
#> SRR934306 2 0.000 0.963 0.000 1.000
#> SRR934307 2 0.000 0.963 0.000 1.000
#> SRR934308 2 0.000 0.963 0.000 1.000
#> SRR934309 2 0.000 0.963 0.000 1.000
#> SRR934310 2 0.000 0.963 0.000 1.000
#> SRR934311 2 0.000 0.963 0.000 1.000
#> SRR934312 1 0.000 0.993 1.000 0.000
#> SRR934313 1 0.000 0.993 1.000 0.000
#> SRR934314 1 0.000 0.993 1.000 0.000
#> SRR934315 1 0.000 0.993 1.000 0.000
#> SRR934316 1 0.000 0.993 1.000 0.000
#> SRR934317 1 0.000 0.993 1.000 0.000
#> SRR934318 1 0.000 0.993 1.000 0.000
#> SRR934319 1 0.000 0.993 1.000 0.000
#> SRR934320 1 0.000 0.993 1.000 0.000
#> SRR934321 1 0.000 0.993 1.000 0.000
#> SRR934322 1 0.000 0.993 1.000 0.000
#> SRR934323 1 0.000 0.993 1.000 0.000
#> SRR934324 1 0.000 0.993 1.000 0.000
#> SRR934325 1 0.000 0.993 1.000 0.000
#> SRR934326 1 0.000 0.993 1.000 0.000
#> SRR934327 1 0.000 0.993 1.000 0.000
#> SRR934328 1 0.000 0.993 1.000 0.000
#> SRR934329 1 0.000 0.993 1.000 0.000
#> SRR934330 1 0.000 0.993 1.000 0.000
#> SRR934331 1 0.000 0.993 1.000 0.000
#> SRR934332 1 0.000 0.993 1.000 0.000
#> SRR934333 1 0.000 0.993 1.000 0.000
#> SRR934334 1 0.000 0.993 1.000 0.000
#> SRR934335 1 0.000 0.993 1.000 0.000
#> SRR934344 1 0.000 0.993 1.000 0.000
#> SRR934345 1 0.000 0.993 1.000 0.000
#> SRR934346 1 0.000 0.993 1.000 0.000
#> SRR934347 1 0.000 0.993 1.000 0.000
#> SRR934348 1 0.000 0.993 1.000 0.000
#> SRR934349 1 0.000 0.993 1.000 0.000
#> SRR934350 1 0.000 0.993 1.000 0.000
#> SRR934351 1 0.000 0.993 1.000 0.000
#> SRR934336 1 0.358 0.931 0.932 0.068
#> SRR934337 1 0.373 0.927 0.928 0.072
#> SRR934338 1 0.358 0.931 0.932 0.068
#> SRR934339 1 0.358 0.931 0.932 0.068
#> SRR934340 1 0.358 0.931 0.932 0.068
#> SRR934341 1 0.358 0.931 0.932 0.068
#> SRR934342 1 0.358 0.931 0.932 0.068
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 2 0.9517 0.338 0.280 0.488 0.232
#> SRR934217 2 0.9517 0.338 0.280 0.488 0.232
#> SRR934218 2 0.9517 0.338 0.280 0.488 0.232
#> SRR934219 2 0.9517 0.338 0.280 0.488 0.232
#> SRR934220 2 0.9517 0.338 0.280 0.488 0.232
#> SRR934221 2 0.9517 0.338 0.280 0.488 0.232
#> SRR934222 2 0.9517 0.338 0.280 0.488 0.232
#> SRR934223 2 0.9517 0.338 0.280 0.488 0.232
#> SRR934224 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934225 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934226 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934227 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934228 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934229 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934230 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934231 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934232 3 0.6520 0.162 0.004 0.488 0.508
#> SRR934233 3 0.6520 0.162 0.004 0.488 0.508
#> SRR934234 3 0.6520 0.162 0.004 0.488 0.508
#> SRR934235 3 0.6520 0.162 0.004 0.488 0.508
#> SRR934236 3 0.6520 0.162 0.004 0.488 0.508
#> SRR934237 3 0.6520 0.162 0.004 0.488 0.508
#> SRR934238 3 0.6520 0.162 0.004 0.488 0.508
#> SRR934239 3 0.6520 0.162 0.004 0.488 0.508
#> SRR934240 2 0.5928 0.293 0.008 0.696 0.296
#> SRR934241 2 0.5928 0.293 0.008 0.696 0.296
#> SRR934242 2 0.5928 0.293 0.008 0.696 0.296
#> SRR934243 2 0.5928 0.293 0.008 0.696 0.296
#> SRR934244 2 0.5928 0.293 0.008 0.696 0.296
#> SRR934245 2 0.5928 0.293 0.008 0.696 0.296
#> SRR934246 2 0.5928 0.293 0.008 0.696 0.296
#> SRR934247 2 0.5928 0.293 0.008 0.696 0.296
#> SRR934248 3 0.0000 0.727 0.000 0.000 1.000
#> SRR934249 3 0.0000 0.727 0.000 0.000 1.000
#> SRR934250 3 0.0000 0.727 0.000 0.000 1.000
#> SRR934251 3 0.0000 0.727 0.000 0.000 1.000
#> SRR934252 3 0.0000 0.727 0.000 0.000 1.000
#> SRR934253 3 0.0000 0.727 0.000 0.000 1.000
#> SRR934254 3 0.0000 0.727 0.000 0.000 1.000
#> SRR934255 3 0.0000 0.727 0.000 0.000 1.000
#> SRR934256 1 0.6668 0.503 0.696 0.264 0.040
#> SRR934257 1 0.6668 0.503 0.696 0.264 0.040
#> SRR934258 1 0.6668 0.503 0.696 0.264 0.040
#> SRR934259 1 0.6668 0.503 0.696 0.264 0.040
#> SRR934260 1 0.6668 0.503 0.696 0.264 0.040
#> SRR934261 1 0.6668 0.503 0.696 0.264 0.040
#> SRR934262 1 0.6668 0.503 0.696 0.264 0.040
#> SRR934263 1 0.6668 0.503 0.696 0.264 0.040
#> SRR934264 3 0.0592 0.729 0.000 0.012 0.988
#> SRR934265 3 0.0592 0.729 0.000 0.012 0.988
#> SRR934266 3 0.0592 0.729 0.000 0.012 0.988
#> SRR934267 3 0.0592 0.729 0.000 0.012 0.988
#> SRR934268 3 0.0592 0.729 0.000 0.012 0.988
#> SRR934269 3 0.0592 0.729 0.000 0.012 0.988
#> SRR934270 3 0.0592 0.729 0.000 0.012 0.988
#> SRR934271 3 0.0592 0.729 0.000 0.012 0.988
#> SRR934272 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934273 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934274 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934275 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934276 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934277 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934278 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934279 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934280 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934281 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934282 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934283 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934284 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934285 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934286 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934287 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934288 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934289 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934290 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934291 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934292 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934293 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934294 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934295 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934296 2 0.9212 0.366 0.180 0.516 0.304
#> SRR934297 2 0.9212 0.366 0.180 0.516 0.304
#> SRR934298 2 0.9212 0.366 0.180 0.516 0.304
#> SRR934299 2 0.9212 0.366 0.180 0.516 0.304
#> SRR934300 2 0.9212 0.366 0.180 0.516 0.304
#> SRR934301 2 0.9212 0.366 0.180 0.516 0.304
#> SRR934302 2 0.9212 0.366 0.180 0.516 0.304
#> SRR934303 2 0.9231 0.362 0.180 0.512 0.308
#> SRR934304 2 0.5733 0.328 0.000 0.676 0.324
#> SRR934305 2 0.5733 0.328 0.000 0.676 0.324
#> SRR934306 2 0.5733 0.328 0.000 0.676 0.324
#> SRR934307 2 0.5733 0.328 0.000 0.676 0.324
#> SRR934308 2 0.5733 0.328 0.000 0.676 0.324
#> SRR934309 2 0.5733 0.328 0.000 0.676 0.324
#> SRR934310 2 0.5733 0.328 0.000 0.676 0.324
#> SRR934311 2 0.5733 0.328 0.000 0.676 0.324
#> SRR934312 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934313 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934314 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934315 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934316 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934317 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934318 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934319 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934320 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934321 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934322 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934323 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934324 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934325 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934326 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934327 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934328 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934329 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934330 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934331 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934332 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934333 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934334 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934335 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934344 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934345 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934346 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934347 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934348 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934349 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934350 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934351 1 0.0000 0.958 1.000 0.000 0.000
#> SRR934336 1 0.1031 0.937 0.976 0.000 0.024
#> SRR934337 1 0.1031 0.937 0.976 0.000 0.024
#> SRR934338 1 0.1031 0.937 0.976 0.000 0.024
#> SRR934339 1 0.1031 0.937 0.976 0.000 0.024
#> SRR934340 1 0.1031 0.937 0.976 0.000 0.024
#> SRR934341 1 0.1031 0.937 0.976 0.000 0.024
#> SRR934342 1 0.1031 0.937 0.976 0.000 0.024
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 3 0.0524 1.000 0.000 0.004 0.988 0.008
#> SRR934217 3 0.0524 1.000 0.000 0.004 0.988 0.008
#> SRR934218 3 0.0524 1.000 0.000 0.004 0.988 0.008
#> SRR934219 3 0.0524 1.000 0.000 0.004 0.988 0.008
#> SRR934220 3 0.0524 1.000 0.000 0.004 0.988 0.008
#> SRR934221 3 0.0524 1.000 0.000 0.004 0.988 0.008
#> SRR934222 3 0.0524 1.000 0.000 0.004 0.988 0.008
#> SRR934223 3 0.0524 1.000 0.000 0.004 0.988 0.008
#> SRR934224 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934225 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934226 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934227 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934228 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934229 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934230 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934231 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934232 2 0.2149 0.684 0.000 0.912 0.000 0.088
#> SRR934233 2 0.2149 0.684 0.000 0.912 0.000 0.088
#> SRR934234 2 0.2149 0.684 0.000 0.912 0.000 0.088
#> SRR934235 2 0.2149 0.684 0.000 0.912 0.000 0.088
#> SRR934236 2 0.2149 0.684 0.000 0.912 0.000 0.088
#> SRR934237 2 0.2149 0.684 0.000 0.912 0.000 0.088
#> SRR934238 2 0.2149 0.684 0.000 0.912 0.000 0.088
#> SRR934239 2 0.2149 0.684 0.000 0.912 0.000 0.088
#> SRR934240 2 0.0000 0.705 0.000 1.000 0.000 0.000
#> SRR934241 2 0.0000 0.705 0.000 1.000 0.000 0.000
#> SRR934242 2 0.0000 0.705 0.000 1.000 0.000 0.000
#> SRR934243 2 0.0000 0.705 0.000 1.000 0.000 0.000
#> SRR934244 2 0.0000 0.705 0.000 1.000 0.000 0.000
#> SRR934245 2 0.0000 0.705 0.000 1.000 0.000 0.000
#> SRR934246 2 0.0000 0.705 0.000 1.000 0.000 0.000
#> SRR934247 2 0.0000 0.705 0.000 1.000 0.000 0.000
#> SRR934248 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934249 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934250 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934251 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934252 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934253 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934254 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934255 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934256 2 0.4643 0.493 0.344 0.656 0.000 0.000
#> SRR934257 2 0.4643 0.493 0.344 0.656 0.000 0.000
#> SRR934258 2 0.4643 0.493 0.344 0.656 0.000 0.000
#> SRR934259 2 0.4643 0.493 0.344 0.656 0.000 0.000
#> SRR934260 2 0.4643 0.493 0.344 0.656 0.000 0.000
#> SRR934261 2 0.4643 0.493 0.344 0.656 0.000 0.000
#> SRR934262 2 0.4643 0.493 0.344 0.656 0.000 0.000
#> SRR934263 2 0.4643 0.493 0.344 0.656 0.000 0.000
#> SRR934264 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934265 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934266 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934267 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934268 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934269 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934270 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934271 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934272 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934273 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934274 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934275 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934276 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934277 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934278 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934279 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934280 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934281 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934282 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934283 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934284 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934285 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934286 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934287 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934288 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934289 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934290 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934291 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934292 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934293 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934294 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934295 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934296 2 0.5395 0.571 0.000 0.736 0.092 0.172
#> SRR934297 2 0.5395 0.571 0.000 0.736 0.092 0.172
#> SRR934298 2 0.5395 0.571 0.000 0.736 0.092 0.172
#> SRR934299 2 0.5376 0.569 0.000 0.736 0.088 0.176
#> SRR934300 2 0.5395 0.571 0.000 0.736 0.092 0.172
#> SRR934301 2 0.5395 0.571 0.000 0.736 0.092 0.172
#> SRR934302 2 0.5395 0.571 0.000 0.736 0.092 0.172
#> SRR934303 2 0.5376 0.569 0.000 0.736 0.088 0.176
#> SRR934304 2 0.6860 0.483 0.000 0.592 0.244 0.164
#> SRR934305 2 0.6860 0.483 0.000 0.592 0.244 0.164
#> SRR934306 2 0.6860 0.483 0.000 0.592 0.244 0.164
#> SRR934307 2 0.6860 0.483 0.000 0.592 0.244 0.164
#> SRR934308 2 0.6860 0.483 0.000 0.592 0.244 0.164
#> SRR934309 2 0.6860 0.483 0.000 0.592 0.244 0.164
#> SRR934310 2 0.6860 0.483 0.000 0.592 0.244 0.164
#> SRR934311 2 0.6860 0.483 0.000 0.592 0.244 0.164
#> SRR934312 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934313 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934314 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934315 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934316 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934317 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934318 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934319 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934320 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934321 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934322 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934323 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934324 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934325 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934326 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934327 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934328 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934329 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934330 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934331 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934332 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934333 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934334 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934335 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> SRR934344 1 0.0188 0.996 0.996 0.000 0.004 0.000
#> SRR934345 1 0.0188 0.996 0.996 0.000 0.004 0.000
#> SRR934346 1 0.0188 0.996 0.996 0.000 0.004 0.000
#> SRR934347 1 0.0188 0.996 0.996 0.000 0.004 0.000
#> SRR934348 1 0.0188 0.996 0.996 0.000 0.004 0.000
#> SRR934349 1 0.0188 0.996 0.996 0.000 0.004 0.000
#> SRR934350 1 0.0188 0.996 0.996 0.000 0.004 0.000
#> SRR934351 1 0.0188 0.996 0.996 0.000 0.004 0.000
#> SRR934336 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934337 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934338 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934339 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934340 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934341 1 0.0188 0.997 0.996 0.000 0.004 0.000
#> SRR934342 1 0.0188 0.997 0.996 0.000 0.004 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR934217 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR934218 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR934219 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR934220 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR934221 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR934222 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR934223 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> SRR934224 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934225 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934226 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934227 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934228 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934229 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934230 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934231 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934232 2 0.2852 0.826 0.000 0.828 0.000 0.172 0.000
#> SRR934233 2 0.2852 0.826 0.000 0.828 0.000 0.172 0.000
#> SRR934234 2 0.2852 0.826 0.000 0.828 0.000 0.172 0.000
#> SRR934235 2 0.2852 0.826 0.000 0.828 0.000 0.172 0.000
#> SRR934236 2 0.2852 0.826 0.000 0.828 0.000 0.172 0.000
#> SRR934237 2 0.2852 0.826 0.000 0.828 0.000 0.172 0.000
#> SRR934238 2 0.2852 0.826 0.000 0.828 0.000 0.172 0.000
#> SRR934239 2 0.2852 0.826 0.000 0.828 0.000 0.172 0.000
#> SRR934240 2 0.2011 0.850 0.000 0.908 0.000 0.088 0.004
#> SRR934241 2 0.2011 0.850 0.000 0.908 0.000 0.088 0.004
#> SRR934242 2 0.2011 0.850 0.000 0.908 0.000 0.088 0.004
#> SRR934243 2 0.2011 0.850 0.000 0.908 0.000 0.088 0.004
#> SRR934244 2 0.2011 0.850 0.000 0.908 0.000 0.088 0.004
#> SRR934245 2 0.2011 0.850 0.000 0.908 0.000 0.088 0.004
#> SRR934246 2 0.2011 0.850 0.000 0.908 0.000 0.088 0.004
#> SRR934247 2 0.2011 0.850 0.000 0.908 0.000 0.088 0.004
#> SRR934248 4 0.0000 0.978 0.000 0.000 0.000 1.000 0.000
#> SRR934249 4 0.0000 0.978 0.000 0.000 0.000 1.000 0.000
#> SRR934250 4 0.0000 0.978 0.000 0.000 0.000 1.000 0.000
#> SRR934251 4 0.0000 0.978 0.000 0.000 0.000 1.000 0.000
#> SRR934252 4 0.0000 0.978 0.000 0.000 0.000 1.000 0.000
#> SRR934253 4 0.0000 0.978 0.000 0.000 0.000 1.000 0.000
#> SRR934254 4 0.0000 0.978 0.000 0.000 0.000 1.000 0.000
#> SRR934255 4 0.0000 0.978 0.000 0.000 0.000 1.000 0.000
#> SRR934256 2 0.2813 0.751 0.168 0.832 0.000 0.000 0.000
#> SRR934257 2 0.2813 0.751 0.168 0.832 0.000 0.000 0.000
#> SRR934258 2 0.2813 0.751 0.168 0.832 0.000 0.000 0.000
#> SRR934259 2 0.2813 0.751 0.168 0.832 0.000 0.000 0.000
#> SRR934260 2 0.2813 0.751 0.168 0.832 0.000 0.000 0.000
#> SRR934261 2 0.2813 0.751 0.168 0.832 0.000 0.000 0.000
#> SRR934262 2 0.2813 0.751 0.168 0.832 0.000 0.000 0.000
#> SRR934263 2 0.2813 0.751 0.168 0.832 0.000 0.000 0.000
#> SRR934264 4 0.0880 0.978 0.000 0.032 0.000 0.968 0.000
#> SRR934265 4 0.0880 0.978 0.000 0.032 0.000 0.968 0.000
#> SRR934266 4 0.0880 0.978 0.000 0.032 0.000 0.968 0.000
#> SRR934267 4 0.0880 0.978 0.000 0.032 0.000 0.968 0.000
#> SRR934268 4 0.0880 0.978 0.000 0.032 0.000 0.968 0.000
#> SRR934269 4 0.0880 0.978 0.000 0.032 0.000 0.968 0.000
#> SRR934270 4 0.0880 0.978 0.000 0.032 0.000 0.968 0.000
#> SRR934271 4 0.0880 0.978 0.000 0.032 0.000 0.968 0.000
#> SRR934272 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934273 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934274 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934275 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934276 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934277 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934278 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934279 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934280 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934281 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934282 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934283 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934284 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934285 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934286 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934287 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934288 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934289 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934290 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934291 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934292 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934293 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934294 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934295 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934296 2 0.2286 0.803 0.000 0.888 0.004 0.108 0.000
#> SRR934297 2 0.2286 0.803 0.000 0.888 0.004 0.108 0.000
#> SRR934298 2 0.2286 0.803 0.000 0.888 0.004 0.108 0.000
#> SRR934299 2 0.2286 0.803 0.000 0.888 0.004 0.108 0.000
#> SRR934300 2 0.2286 0.803 0.000 0.888 0.004 0.108 0.000
#> SRR934301 2 0.2286 0.803 0.000 0.888 0.004 0.108 0.000
#> SRR934302 2 0.2286 0.803 0.000 0.888 0.004 0.108 0.000
#> SRR934303 2 0.2286 0.803 0.000 0.888 0.004 0.108 0.000
#> SRR934304 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR934305 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR934306 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR934307 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR934308 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR934309 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR934310 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR934311 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> SRR934312 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934313 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934314 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934315 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934316 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934317 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934318 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934319 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934320 1 0.0404 0.989 0.988 0.012 0.000 0.000 0.000
#> SRR934321 1 0.0404 0.989 0.988 0.012 0.000 0.000 0.000
#> SRR934322 1 0.0404 0.989 0.988 0.012 0.000 0.000 0.000
#> SRR934323 1 0.0404 0.989 0.988 0.012 0.000 0.000 0.000
#> SRR934324 1 0.0404 0.989 0.988 0.012 0.000 0.000 0.000
#> SRR934325 1 0.0404 0.989 0.988 0.012 0.000 0.000 0.000
#> SRR934326 1 0.0404 0.989 0.988 0.012 0.000 0.000 0.000
#> SRR934327 1 0.0404 0.989 0.988 0.012 0.000 0.000 0.000
#> SRR934328 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934329 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934330 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934331 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934332 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934333 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934334 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934335 1 0.0162 0.995 0.996 0.004 0.000 0.000 0.000
#> SRR934344 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934345 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934346 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934347 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934348 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934349 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934350 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934351 1 0.0000 0.996 1.000 0.000 0.000 0.000 0.000
#> SRR934336 1 0.0404 0.988 0.988 0.012 0.000 0.000 0.000
#> SRR934337 1 0.0404 0.988 0.988 0.012 0.000 0.000 0.000
#> SRR934338 1 0.0404 0.988 0.988 0.012 0.000 0.000 0.000
#> SRR934339 1 0.0404 0.988 0.988 0.012 0.000 0.000 0.000
#> SRR934340 1 0.0404 0.988 0.988 0.012 0.000 0.000 0.000
#> SRR934341 1 0.0404 0.988 0.988 0.012 0.000 0.000 0.000
#> SRR934342 1 0.0404 0.988 0.988 0.012 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934217 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934218 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934219 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934220 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934221 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934222 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934223 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> SRR934224 1 0.3482 0.753 0.684 0.000 0 0.000 0 0.316
#> SRR934225 1 0.3482 0.753 0.684 0.000 0 0.000 0 0.316
#> SRR934226 1 0.3482 0.753 0.684 0.000 0 0.000 0 0.316
#> SRR934227 1 0.3499 0.753 0.680 0.000 0 0.000 0 0.320
#> SRR934228 1 0.3482 0.753 0.684 0.000 0 0.000 0 0.316
#> SRR934229 1 0.3482 0.753 0.684 0.000 0 0.000 0 0.316
#> SRR934230 1 0.3482 0.756 0.684 0.000 0 0.000 0 0.316
#> SRR934231 1 0.3499 0.753 0.680 0.000 0 0.000 0 0.320
#> SRR934232 2 0.0000 0.927 0.000 1.000 0 0.000 0 0.000
#> SRR934233 2 0.0000 0.927 0.000 1.000 0 0.000 0 0.000
#> SRR934234 2 0.0000 0.927 0.000 1.000 0 0.000 0 0.000
#> SRR934235 2 0.0000 0.927 0.000 1.000 0 0.000 0 0.000
#> SRR934236 2 0.0000 0.927 0.000 1.000 0 0.000 0 0.000
#> SRR934237 2 0.0000 0.927 0.000 1.000 0 0.000 0 0.000
#> SRR934238 2 0.0000 0.927 0.000 1.000 0 0.000 0 0.000
#> SRR934239 2 0.0000 0.927 0.000 1.000 0 0.000 0 0.000
#> SRR934240 2 0.1663 0.924 0.000 0.912 0 0.000 0 0.088
#> SRR934241 2 0.1663 0.924 0.000 0.912 0 0.000 0 0.088
#> SRR934242 2 0.1663 0.924 0.000 0.912 0 0.000 0 0.088
#> SRR934243 2 0.1663 0.924 0.000 0.912 0 0.000 0 0.088
#> SRR934244 2 0.1663 0.924 0.000 0.912 0 0.000 0 0.088
#> SRR934245 2 0.1663 0.924 0.000 0.912 0 0.000 0 0.088
#> SRR934246 2 0.1663 0.924 0.000 0.912 0 0.000 0 0.088
#> SRR934247 2 0.1663 0.924 0.000 0.912 0 0.000 0 0.088
#> SRR934248 4 0.0000 0.881 0.000 0.000 0 1.000 0 0.000
#> SRR934249 4 0.0000 0.881 0.000 0.000 0 1.000 0 0.000
#> SRR934250 4 0.0000 0.881 0.000 0.000 0 1.000 0 0.000
#> SRR934251 4 0.0000 0.881 0.000 0.000 0 1.000 0 0.000
#> SRR934252 4 0.0000 0.881 0.000 0.000 0 1.000 0 0.000
#> SRR934253 4 0.0000 0.881 0.000 0.000 0 1.000 0 0.000
#> SRR934254 4 0.0000 0.881 0.000 0.000 0 1.000 0 0.000
#> SRR934255 4 0.0000 0.881 0.000 0.000 0 1.000 0 0.000
#> SRR934256 6 0.5446 0.798 0.144 0.316 0 0.000 0 0.540
#> SRR934257 6 0.5446 0.798 0.144 0.316 0 0.000 0 0.540
#> SRR934258 6 0.5446 0.798 0.144 0.316 0 0.000 0 0.540
#> SRR934259 6 0.5446 0.798 0.144 0.316 0 0.000 0 0.540
#> SRR934260 6 0.5446 0.798 0.144 0.316 0 0.000 0 0.540
#> SRR934261 6 0.5446 0.798 0.144 0.316 0 0.000 0 0.540
#> SRR934262 6 0.5446 0.798 0.144 0.316 0 0.000 0 0.540
#> SRR934263 6 0.5446 0.798 0.144 0.316 0 0.000 0 0.540
#> SRR934264 4 0.2793 0.882 0.000 0.200 0 0.800 0 0.000
#> SRR934265 4 0.2793 0.882 0.000 0.200 0 0.800 0 0.000
#> SRR934266 4 0.2793 0.882 0.000 0.200 0 0.800 0 0.000
#> SRR934267 4 0.2793 0.882 0.000 0.200 0 0.800 0 0.000
#> SRR934268 4 0.2793 0.882 0.000 0.200 0 0.800 0 0.000
#> SRR934269 4 0.2793 0.882 0.000 0.200 0 0.800 0 0.000
#> SRR934270 4 0.2793 0.882 0.000 0.200 0 0.800 0 0.000
#> SRR934271 4 0.2793 0.882 0.000 0.200 0 0.800 0 0.000
#> SRR934272 1 0.3499 0.753 0.680 0.000 0 0.000 0 0.320
#> SRR934273 1 0.3499 0.753 0.680 0.000 0 0.000 0 0.320
#> SRR934274 1 0.3499 0.753 0.680 0.000 0 0.000 0 0.320
#> SRR934275 1 0.3499 0.753 0.680 0.000 0 0.000 0 0.320
#> SRR934276 1 0.3499 0.753 0.680 0.000 0 0.000 0 0.320
#> SRR934277 1 0.3499 0.753 0.680 0.000 0 0.000 0 0.320
#> SRR934278 1 0.3499 0.753 0.680 0.000 0 0.000 0 0.320
#> SRR934279 1 0.3499 0.753 0.680 0.000 0 0.000 0 0.320
#> SRR934280 1 0.0146 0.857 0.996 0.000 0 0.000 0 0.004
#> SRR934281 1 0.0146 0.857 0.996 0.000 0 0.000 0 0.004
#> SRR934282 1 0.0146 0.857 0.996 0.000 0 0.000 0 0.004
#> SRR934283 1 0.0146 0.857 0.996 0.000 0 0.000 0 0.004
#> SRR934284 1 0.0146 0.857 0.996 0.000 0 0.000 0 0.004
#> SRR934285 1 0.0146 0.857 0.996 0.000 0 0.000 0 0.004
#> SRR934286 1 0.0146 0.857 0.996 0.000 0 0.000 0 0.004
#> SRR934287 1 0.0146 0.857 0.996 0.000 0 0.000 0 0.004
#> SRR934288 1 0.0146 0.857 0.996 0.000 0 0.000 0 0.004
#> SRR934289 1 0.0146 0.857 0.996 0.000 0 0.000 0 0.004
#> SRR934290 1 0.0146 0.857 0.996 0.000 0 0.000 0 0.004
#> SRR934291 1 0.0146 0.857 0.996 0.000 0 0.000 0 0.004
#> SRR934292 1 0.0146 0.857 0.996 0.000 0 0.000 0 0.004
#> SRR934293 1 0.0146 0.857 0.996 0.000 0 0.000 0 0.004
#> SRR934294 1 0.0146 0.857 0.996 0.000 0 0.000 0 0.004
#> SRR934295 1 0.0146 0.857 0.996 0.000 0 0.000 0 0.004
#> SRR934296 6 0.4219 0.773 0.000 0.320 0 0.032 0 0.648
#> SRR934297 6 0.4219 0.773 0.000 0.320 0 0.032 0 0.648
#> SRR934298 6 0.4219 0.773 0.000 0.320 0 0.032 0 0.648
#> SRR934299 6 0.4219 0.773 0.000 0.320 0 0.032 0 0.648
#> SRR934300 6 0.4219 0.773 0.000 0.320 0 0.032 0 0.648
#> SRR934301 6 0.4219 0.773 0.000 0.320 0 0.032 0 0.648
#> SRR934302 6 0.4219 0.773 0.000 0.320 0 0.032 0 0.648
#> SRR934303 6 0.4219 0.773 0.000 0.320 0 0.032 0 0.648
#> SRR934304 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934305 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934306 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934307 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934308 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934309 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934310 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934311 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> SRR934312 1 0.1610 0.849 0.916 0.000 0 0.000 0 0.084
#> SRR934313 1 0.1610 0.849 0.916 0.000 0 0.000 0 0.084
#> SRR934314 1 0.1663 0.848 0.912 0.000 0 0.000 0 0.088
#> SRR934315 1 0.1610 0.849 0.916 0.000 0 0.000 0 0.084
#> SRR934316 1 0.1663 0.848 0.912 0.000 0 0.000 0 0.088
#> SRR934317 1 0.1663 0.848 0.912 0.000 0 0.000 0 0.088
#> SRR934318 1 0.1327 0.853 0.936 0.000 0 0.000 0 0.064
#> SRR934319 1 0.1663 0.848 0.912 0.000 0 0.000 0 0.088
#> SRR934320 1 0.2178 0.763 0.868 0.000 0 0.000 0 0.132
#> SRR934321 1 0.2260 0.756 0.860 0.000 0 0.000 0 0.140
#> SRR934322 1 0.2260 0.756 0.860 0.000 0 0.000 0 0.140
#> SRR934323 1 0.2260 0.756 0.860 0.000 0 0.000 0 0.140
#> SRR934324 1 0.2260 0.756 0.860 0.000 0 0.000 0 0.140
#> SRR934325 1 0.2178 0.763 0.868 0.000 0 0.000 0 0.132
#> SRR934326 1 0.2260 0.756 0.860 0.000 0 0.000 0 0.140
#> SRR934327 1 0.2260 0.756 0.860 0.000 0 0.000 0 0.140
#> SRR934328 1 0.0547 0.857 0.980 0.000 0 0.000 0 0.020
#> SRR934329 1 0.0547 0.857 0.980 0.000 0 0.000 0 0.020
#> SRR934330 1 0.0458 0.858 0.984 0.000 0 0.000 0 0.016
#> SRR934331 1 0.0547 0.857 0.980 0.000 0 0.000 0 0.020
#> SRR934332 1 0.0458 0.858 0.984 0.000 0 0.000 0 0.016
#> SRR934333 1 0.0547 0.857 0.980 0.000 0 0.000 0 0.020
#> SRR934334 1 0.0547 0.857 0.980 0.000 0 0.000 0 0.020
#> SRR934335 1 0.0547 0.857 0.980 0.000 0 0.000 0 0.020
#> SRR934344 1 0.0363 0.856 0.988 0.000 0 0.000 0 0.012
#> SRR934345 1 0.0363 0.856 0.988 0.000 0 0.000 0 0.012
#> SRR934346 1 0.0363 0.856 0.988 0.000 0 0.000 0 0.012
#> SRR934347 1 0.0363 0.856 0.988 0.000 0 0.000 0 0.012
#> SRR934348 1 0.0363 0.856 0.988 0.000 0 0.000 0 0.012
#> SRR934349 1 0.0363 0.856 0.988 0.000 0 0.000 0 0.012
#> SRR934350 1 0.0363 0.856 0.988 0.000 0 0.000 0 0.012
#> SRR934351 1 0.0363 0.856 0.988 0.000 0 0.000 0 0.012
#> SRR934336 1 0.3647 0.730 0.640 0.000 0 0.000 0 0.360
#> SRR934337 1 0.3647 0.730 0.640 0.000 0 0.000 0 0.360
#> SRR934338 1 0.3647 0.730 0.640 0.000 0 0.000 0 0.360
#> SRR934339 1 0.3647 0.730 0.640 0.000 0 0.000 0 0.360
#> SRR934340 1 0.3647 0.730 0.640 0.000 0 0.000 0 0.360
#> SRR934341 1 0.3647 0.730 0.640 0.000 0 0.000 0 0.360
#> SRR934342 1 0.3647 0.730 0.640 0.000 0 0.000 0 0.360
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 14550 rows and 135 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.984 0.992 0.4265 0.580 0.580
#> 3 3 0.744 0.866 0.915 0.3292 0.818 0.693
#> 4 4 0.737 0.788 0.878 0.2169 0.858 0.666
#> 5 5 0.892 0.932 0.920 0.0769 0.916 0.727
#> 6 6 0.928 0.926 0.927 0.0538 0.979 0.914
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR934216 1 0.000 0.988 1.000 0.000
#> SRR934217 1 0.000 0.988 1.000 0.000
#> SRR934218 1 0.000 0.988 1.000 0.000
#> SRR934219 1 0.000 0.988 1.000 0.000
#> SRR934220 1 0.000 0.988 1.000 0.000
#> SRR934221 1 0.000 0.988 1.000 0.000
#> SRR934222 1 0.000 0.988 1.000 0.000
#> SRR934223 1 0.000 0.988 1.000 0.000
#> SRR934224 1 0.000 0.988 1.000 0.000
#> SRR934225 1 0.000 0.988 1.000 0.000
#> SRR934226 1 0.000 0.988 1.000 0.000
#> SRR934227 1 0.000 0.988 1.000 0.000
#> SRR934228 1 0.000 0.988 1.000 0.000
#> SRR934229 1 0.000 0.988 1.000 0.000
#> SRR934230 1 0.000 0.988 1.000 0.000
#> SRR934231 1 0.000 0.988 1.000 0.000
#> SRR934232 2 0.000 1.000 0.000 1.000
#> SRR934233 2 0.000 1.000 0.000 1.000
#> SRR934234 2 0.000 1.000 0.000 1.000
#> SRR934235 2 0.000 1.000 0.000 1.000
#> SRR934236 2 0.000 1.000 0.000 1.000
#> SRR934237 2 0.000 1.000 0.000 1.000
#> SRR934238 2 0.000 1.000 0.000 1.000
#> SRR934239 2 0.000 1.000 0.000 1.000
#> SRR934240 2 0.000 1.000 0.000 1.000
#> SRR934241 2 0.000 1.000 0.000 1.000
#> SRR934242 2 0.000 1.000 0.000 1.000
#> SRR934243 2 0.000 1.000 0.000 1.000
#> SRR934244 2 0.000 1.000 0.000 1.000
#> SRR934245 2 0.000 1.000 0.000 1.000
#> SRR934246 2 0.000 1.000 0.000 1.000
#> SRR934247 2 0.000 1.000 0.000 1.000
#> SRR934248 2 0.000 1.000 0.000 1.000
#> SRR934249 2 0.000 1.000 0.000 1.000
#> SRR934250 2 0.000 1.000 0.000 1.000
#> SRR934251 2 0.000 1.000 0.000 1.000
#> SRR934252 2 0.000 1.000 0.000 1.000
#> SRR934253 2 0.000 1.000 0.000 1.000
#> SRR934254 2 0.000 1.000 0.000 1.000
#> SRR934255 2 0.000 1.000 0.000 1.000
#> SRR934256 1 0.000 0.988 1.000 0.000
#> SRR934257 1 0.000 0.988 1.000 0.000
#> SRR934258 1 0.000 0.988 1.000 0.000
#> SRR934259 1 0.000 0.988 1.000 0.000
#> SRR934260 1 0.000 0.988 1.000 0.000
#> SRR934261 1 0.000 0.988 1.000 0.000
#> SRR934262 1 0.000 0.988 1.000 0.000
#> SRR934263 1 0.000 0.988 1.000 0.000
#> SRR934264 2 0.000 1.000 0.000 1.000
#> SRR934265 2 0.000 1.000 0.000 1.000
#> SRR934266 2 0.000 1.000 0.000 1.000
#> SRR934267 2 0.000 1.000 0.000 1.000
#> SRR934268 2 0.000 1.000 0.000 1.000
#> SRR934269 2 0.000 1.000 0.000 1.000
#> SRR934270 2 0.000 1.000 0.000 1.000
#> SRR934271 2 0.000 1.000 0.000 1.000
#> SRR934272 1 0.000 0.988 1.000 0.000
#> SRR934273 1 0.000 0.988 1.000 0.000
#> SRR934274 1 0.000 0.988 1.000 0.000
#> SRR934275 1 0.000 0.988 1.000 0.000
#> SRR934276 1 0.000 0.988 1.000 0.000
#> SRR934277 1 0.000 0.988 1.000 0.000
#> SRR934278 1 0.000 0.988 1.000 0.000
#> SRR934279 1 0.000 0.988 1.000 0.000
#> SRR934280 1 0.000 0.988 1.000 0.000
#> SRR934281 1 0.000 0.988 1.000 0.000
#> SRR934282 1 0.000 0.988 1.000 0.000
#> SRR934283 1 0.000 0.988 1.000 0.000
#> SRR934284 1 0.000 0.988 1.000 0.000
#> SRR934285 1 0.000 0.988 1.000 0.000
#> SRR934286 1 0.000 0.988 1.000 0.000
#> SRR934287 1 0.000 0.988 1.000 0.000
#> SRR934288 1 0.000 0.988 1.000 0.000
#> SRR934289 1 0.000 0.988 1.000 0.000
#> SRR934290 1 0.000 0.988 1.000 0.000
#> SRR934291 1 0.000 0.988 1.000 0.000
#> SRR934292 1 0.000 0.988 1.000 0.000
#> SRR934293 1 0.000 0.988 1.000 0.000
#> SRR934294 1 0.000 0.988 1.000 0.000
#> SRR934295 1 0.000 0.988 1.000 0.000
#> SRR934296 1 0.529 0.872 0.880 0.120
#> SRR934297 1 0.625 0.828 0.844 0.156
#> SRR934298 1 0.529 0.872 0.880 0.120
#> SRR934299 1 0.653 0.812 0.832 0.168
#> SRR934300 1 0.584 0.848 0.860 0.140
#> SRR934301 1 0.697 0.785 0.812 0.188
#> SRR934302 1 0.416 0.910 0.916 0.084
#> SRR934303 1 0.506 0.881 0.888 0.112
#> SRR934304 2 0.000 1.000 0.000 1.000
#> SRR934305 2 0.000 1.000 0.000 1.000
#> SRR934306 2 0.000 1.000 0.000 1.000
#> SRR934307 2 0.000 1.000 0.000 1.000
#> SRR934308 2 0.000 1.000 0.000 1.000
#> SRR934309 2 0.000 1.000 0.000 1.000
#> SRR934310 2 0.000 1.000 0.000 1.000
#> SRR934311 2 0.000 1.000 0.000 1.000
#> SRR934312 1 0.000 0.988 1.000 0.000
#> SRR934313 1 0.000 0.988 1.000 0.000
#> SRR934314 1 0.000 0.988 1.000 0.000
#> SRR934315 1 0.000 0.988 1.000 0.000
#> SRR934316 1 0.000 0.988 1.000 0.000
#> SRR934317 1 0.000 0.988 1.000 0.000
#> SRR934318 1 0.000 0.988 1.000 0.000
#> SRR934319 1 0.000 0.988 1.000 0.000
#> SRR934320 1 0.000 0.988 1.000 0.000
#> SRR934321 1 0.000 0.988 1.000 0.000
#> SRR934322 1 0.000 0.988 1.000 0.000
#> SRR934323 1 0.000 0.988 1.000 0.000
#> SRR934324 1 0.000 0.988 1.000 0.000
#> SRR934325 1 0.000 0.988 1.000 0.000
#> SRR934326 1 0.000 0.988 1.000 0.000
#> SRR934327 1 0.000 0.988 1.000 0.000
#> SRR934328 1 0.000 0.988 1.000 0.000
#> SRR934329 1 0.000 0.988 1.000 0.000
#> SRR934330 1 0.000 0.988 1.000 0.000
#> SRR934331 1 0.000 0.988 1.000 0.000
#> SRR934332 1 0.000 0.988 1.000 0.000
#> SRR934333 1 0.000 0.988 1.000 0.000
#> SRR934334 1 0.000 0.988 1.000 0.000
#> SRR934335 1 0.000 0.988 1.000 0.000
#> SRR934344 1 0.000 0.988 1.000 0.000
#> SRR934345 1 0.000 0.988 1.000 0.000
#> SRR934346 1 0.000 0.988 1.000 0.000
#> SRR934347 1 0.000 0.988 1.000 0.000
#> SRR934348 1 0.000 0.988 1.000 0.000
#> SRR934349 1 0.000 0.988 1.000 0.000
#> SRR934350 1 0.000 0.988 1.000 0.000
#> SRR934351 1 0.000 0.988 1.000 0.000
#> SRR934336 1 0.000 0.988 1.000 0.000
#> SRR934337 1 0.000 0.988 1.000 0.000
#> SRR934338 1 0.000 0.988 1.000 0.000
#> SRR934339 1 0.000 0.988 1.000 0.000
#> SRR934340 1 0.000 0.988 1.000 0.000
#> SRR934341 1 0.000 0.988 1.000 0.000
#> SRR934342 1 0.000 0.988 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 3 0.4178 0.710 0.172 0.000 0.828
#> SRR934217 3 0.3412 0.717 0.124 0.000 0.876
#> SRR934218 3 0.3941 0.718 0.156 0.000 0.844
#> SRR934219 3 0.3941 0.718 0.156 0.000 0.844
#> SRR934220 3 0.3941 0.718 0.156 0.000 0.844
#> SRR934221 3 0.3482 0.718 0.128 0.000 0.872
#> SRR934222 3 0.3816 0.719 0.148 0.000 0.852
#> SRR934223 3 0.3551 0.718 0.132 0.000 0.868
#> SRR934224 1 0.2711 0.914 0.912 0.000 0.088
#> SRR934225 1 0.2711 0.914 0.912 0.000 0.088
#> SRR934226 1 0.2959 0.904 0.900 0.000 0.100
#> SRR934227 1 0.2711 0.914 0.912 0.000 0.088
#> SRR934228 1 0.2625 0.916 0.916 0.000 0.084
#> SRR934229 1 0.2796 0.910 0.908 0.000 0.092
#> SRR934230 1 0.2625 0.916 0.916 0.000 0.084
#> SRR934231 1 0.2625 0.916 0.916 0.000 0.084
#> SRR934232 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934233 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934234 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934235 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934236 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934237 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934238 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934239 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934240 2 0.4563 0.844 0.036 0.852 0.112
#> SRR934241 2 0.3532 0.877 0.008 0.884 0.108
#> SRR934242 2 0.3454 0.880 0.008 0.888 0.104
#> SRR934243 2 0.4892 0.827 0.048 0.840 0.112
#> SRR934244 2 0.4324 0.854 0.028 0.860 0.112
#> SRR934245 2 0.4446 0.849 0.032 0.856 0.112
#> SRR934246 2 0.4446 0.849 0.032 0.856 0.112
#> SRR934247 2 0.3846 0.870 0.016 0.876 0.108
#> SRR934248 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934249 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934250 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934251 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934252 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934253 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934254 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934255 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934256 1 0.3192 0.848 0.888 0.000 0.112
#> SRR934257 1 0.3192 0.848 0.888 0.000 0.112
#> SRR934258 1 0.3192 0.848 0.888 0.000 0.112
#> SRR934259 1 0.3192 0.848 0.888 0.000 0.112
#> SRR934260 1 0.3192 0.848 0.888 0.000 0.112
#> SRR934261 1 0.3192 0.848 0.888 0.000 0.112
#> SRR934262 1 0.3192 0.848 0.888 0.000 0.112
#> SRR934263 1 0.3192 0.848 0.888 0.000 0.112
#> SRR934264 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934265 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934266 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934267 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934268 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934269 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934270 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934271 2 0.0000 0.957 0.000 1.000 0.000
#> SRR934272 1 0.2625 0.916 0.916 0.000 0.084
#> SRR934273 1 0.2625 0.916 0.916 0.000 0.084
#> SRR934274 1 0.2625 0.916 0.916 0.000 0.084
#> SRR934275 1 0.2625 0.916 0.916 0.000 0.084
#> SRR934276 1 0.2625 0.916 0.916 0.000 0.084
#> SRR934277 1 0.2537 0.918 0.920 0.000 0.080
#> SRR934278 1 0.2711 0.914 0.912 0.000 0.088
#> SRR934279 1 0.2625 0.916 0.916 0.000 0.084
#> SRR934280 1 0.1411 0.930 0.964 0.000 0.036
#> SRR934281 1 0.1411 0.930 0.964 0.000 0.036
#> SRR934282 1 0.1411 0.930 0.964 0.000 0.036
#> SRR934283 1 0.1411 0.930 0.964 0.000 0.036
#> SRR934284 1 0.1411 0.930 0.964 0.000 0.036
#> SRR934285 1 0.1411 0.930 0.964 0.000 0.036
#> SRR934286 1 0.1411 0.930 0.964 0.000 0.036
#> SRR934287 1 0.1289 0.931 0.968 0.000 0.032
#> SRR934288 1 0.0424 0.929 0.992 0.000 0.008
#> SRR934289 1 0.0592 0.927 0.988 0.000 0.012
#> SRR934290 1 0.1529 0.911 0.960 0.000 0.040
#> SRR934291 1 0.0592 0.927 0.988 0.000 0.012
#> SRR934292 1 0.0747 0.925 0.984 0.000 0.016
#> SRR934293 1 0.0592 0.927 0.988 0.000 0.012
#> SRR934294 1 0.0747 0.925 0.984 0.000 0.016
#> SRR934295 1 0.0592 0.927 0.988 0.000 0.012
#> SRR934296 3 0.6095 0.519 0.392 0.000 0.608
#> SRR934297 3 0.5905 0.586 0.352 0.000 0.648
#> SRR934298 3 0.6126 0.502 0.400 0.000 0.600
#> SRR934299 3 0.5988 0.563 0.368 0.000 0.632
#> SRR934300 3 0.6126 0.501 0.400 0.000 0.600
#> SRR934301 3 0.5650 0.630 0.312 0.000 0.688
#> SRR934302 3 0.5905 0.586 0.352 0.000 0.648
#> SRR934303 3 0.6026 0.549 0.376 0.000 0.624
#> SRR934304 3 0.4887 0.610 0.000 0.228 0.772
#> SRR934305 3 0.4887 0.610 0.000 0.228 0.772
#> SRR934306 3 0.4887 0.610 0.000 0.228 0.772
#> SRR934307 3 0.4887 0.610 0.000 0.228 0.772
#> SRR934308 3 0.4887 0.610 0.000 0.228 0.772
#> SRR934309 3 0.4887 0.610 0.000 0.228 0.772
#> SRR934310 3 0.4887 0.610 0.000 0.228 0.772
#> SRR934311 3 0.4887 0.610 0.000 0.228 0.772
#> SRR934312 1 0.2356 0.922 0.928 0.000 0.072
#> SRR934313 1 0.2356 0.922 0.928 0.000 0.072
#> SRR934314 1 0.2356 0.922 0.928 0.000 0.072
#> SRR934315 1 0.2356 0.922 0.928 0.000 0.072
#> SRR934316 1 0.2356 0.922 0.928 0.000 0.072
#> SRR934317 1 0.2356 0.922 0.928 0.000 0.072
#> SRR934318 1 0.2356 0.922 0.928 0.000 0.072
#> SRR934319 1 0.2356 0.922 0.928 0.000 0.072
#> SRR934320 1 0.0424 0.929 0.992 0.000 0.008
#> SRR934321 1 0.0000 0.930 1.000 0.000 0.000
#> SRR934322 1 0.0424 0.929 0.992 0.000 0.008
#> SRR934323 1 0.0424 0.929 0.992 0.000 0.008
#> SRR934324 1 0.0424 0.929 0.992 0.000 0.008
#> SRR934325 1 0.0424 0.929 0.992 0.000 0.008
#> SRR934326 1 0.0237 0.929 0.996 0.000 0.004
#> SRR934327 1 0.0424 0.929 0.992 0.000 0.008
#> SRR934328 1 0.2625 0.876 0.916 0.000 0.084
#> SRR934329 1 0.2711 0.873 0.912 0.000 0.088
#> SRR934330 1 0.2711 0.873 0.912 0.000 0.088
#> SRR934331 1 0.3038 0.857 0.896 0.000 0.104
#> SRR934332 1 0.2356 0.886 0.928 0.000 0.072
#> SRR934333 1 0.1031 0.921 0.976 0.000 0.024
#> SRR934334 1 0.2711 0.873 0.912 0.000 0.088
#> SRR934335 1 0.2796 0.869 0.908 0.000 0.092
#> SRR934344 1 0.0424 0.929 0.992 0.000 0.008
#> SRR934345 1 0.0424 0.929 0.992 0.000 0.008
#> SRR934346 1 0.0000 0.930 1.000 0.000 0.000
#> SRR934347 1 0.0237 0.929 0.996 0.000 0.004
#> SRR934348 1 0.0237 0.929 0.996 0.000 0.004
#> SRR934349 1 0.0424 0.929 0.992 0.000 0.008
#> SRR934350 1 0.0424 0.929 0.992 0.000 0.008
#> SRR934351 1 0.0000 0.930 1.000 0.000 0.000
#> SRR934336 1 0.1964 0.927 0.944 0.000 0.056
#> SRR934337 1 0.2356 0.922 0.928 0.000 0.072
#> SRR934338 1 0.2261 0.924 0.932 0.000 0.068
#> SRR934339 1 0.2261 0.924 0.932 0.000 0.068
#> SRR934340 1 0.1860 0.928 0.948 0.000 0.052
#> SRR934341 1 0.2261 0.924 0.932 0.000 0.068
#> SRR934342 1 0.2066 0.926 0.940 0.000 0.060
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 3 0.6756 0.758 0.188 0.000 0.612 0.200
#> SRR934217 3 0.6756 0.758 0.188 0.000 0.612 0.200
#> SRR934218 3 0.6756 0.758 0.188 0.000 0.612 0.200
#> SRR934219 3 0.6756 0.758 0.188 0.000 0.612 0.200
#> SRR934220 3 0.6756 0.758 0.188 0.000 0.612 0.200
#> SRR934221 3 0.6756 0.758 0.188 0.000 0.612 0.200
#> SRR934222 3 0.6756 0.758 0.188 0.000 0.612 0.200
#> SRR934223 3 0.6756 0.758 0.188 0.000 0.612 0.200
#> SRR934224 1 0.0469 0.816 0.988 0.000 0.000 0.012
#> SRR934225 1 0.0469 0.816 0.988 0.000 0.000 0.012
#> SRR934226 1 0.0469 0.816 0.988 0.000 0.000 0.012
#> SRR934227 1 0.0469 0.816 0.988 0.000 0.000 0.012
#> SRR934228 1 0.0469 0.816 0.988 0.000 0.000 0.012
#> SRR934229 1 0.0469 0.816 0.988 0.000 0.000 0.012
#> SRR934230 1 0.0469 0.816 0.988 0.000 0.000 0.012
#> SRR934231 1 0.0469 0.816 0.988 0.000 0.000 0.012
#> SRR934232 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934233 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934234 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934235 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934236 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934237 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934238 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934239 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934240 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934241 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934242 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934243 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934244 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934245 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934246 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934247 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934248 2 0.0469 0.992 0.000 0.988 0.000 0.012
#> SRR934249 2 0.0469 0.992 0.000 0.988 0.000 0.012
#> SRR934250 2 0.0469 0.992 0.000 0.988 0.000 0.012
#> SRR934251 2 0.0469 0.992 0.000 0.988 0.000 0.012
#> SRR934252 2 0.0469 0.992 0.000 0.988 0.000 0.012
#> SRR934253 2 0.0469 0.992 0.000 0.988 0.000 0.012
#> SRR934254 2 0.0469 0.992 0.000 0.988 0.000 0.012
#> SRR934255 2 0.0469 0.992 0.000 0.988 0.000 0.012
#> SRR934256 4 0.3726 0.865 0.212 0.000 0.000 0.788
#> SRR934257 4 0.3726 0.865 0.212 0.000 0.000 0.788
#> SRR934258 4 0.3726 0.865 0.212 0.000 0.000 0.788
#> SRR934259 4 0.3726 0.865 0.212 0.000 0.000 0.788
#> SRR934260 4 0.3726 0.865 0.212 0.000 0.000 0.788
#> SRR934261 4 0.3726 0.865 0.212 0.000 0.000 0.788
#> SRR934262 4 0.3726 0.865 0.212 0.000 0.000 0.788
#> SRR934263 4 0.3726 0.865 0.212 0.000 0.000 0.788
#> SRR934264 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934265 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934266 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934267 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934268 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934269 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934270 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934271 2 0.0000 0.997 0.000 1.000 0.000 0.000
#> SRR934272 1 0.0469 0.816 0.988 0.000 0.000 0.012
#> SRR934273 1 0.0469 0.816 0.988 0.000 0.000 0.012
#> SRR934274 1 0.0469 0.816 0.988 0.000 0.000 0.012
#> SRR934275 1 0.0469 0.816 0.988 0.000 0.000 0.012
#> SRR934276 1 0.0469 0.816 0.988 0.000 0.000 0.012
#> SRR934277 1 0.0469 0.816 0.988 0.000 0.000 0.012
#> SRR934278 1 0.0469 0.816 0.988 0.000 0.000 0.012
#> SRR934279 1 0.0469 0.816 0.988 0.000 0.000 0.012
#> SRR934280 1 0.2149 0.804 0.912 0.000 0.000 0.088
#> SRR934281 1 0.2149 0.804 0.912 0.000 0.000 0.088
#> SRR934282 1 0.2408 0.792 0.896 0.000 0.000 0.104
#> SRR934283 1 0.2149 0.804 0.912 0.000 0.000 0.088
#> SRR934284 1 0.2149 0.804 0.912 0.000 0.000 0.088
#> SRR934285 1 0.2530 0.785 0.888 0.000 0.000 0.112
#> SRR934286 1 0.2149 0.804 0.912 0.000 0.000 0.088
#> SRR934287 1 0.2469 0.789 0.892 0.000 0.000 0.108
#> SRR934288 1 0.4406 0.431 0.700 0.000 0.000 0.300
#> SRR934289 1 0.4941 -0.157 0.564 0.000 0.000 0.436
#> SRR934290 4 0.4866 0.636 0.404 0.000 0.000 0.596
#> SRR934291 1 0.4967 -0.230 0.548 0.000 0.000 0.452
#> SRR934292 1 0.4981 -0.281 0.536 0.000 0.000 0.464
#> SRR934293 1 0.4730 0.201 0.636 0.000 0.000 0.364
#> SRR934294 1 0.4981 -0.282 0.536 0.000 0.000 0.464
#> SRR934295 1 0.4454 0.407 0.692 0.000 0.000 0.308
#> SRR934296 3 0.0921 0.875 0.000 0.000 0.972 0.028
#> SRR934297 3 0.0817 0.876 0.000 0.000 0.976 0.024
#> SRR934298 3 0.1716 0.855 0.000 0.000 0.936 0.064
#> SRR934299 3 0.1302 0.868 0.000 0.000 0.956 0.044
#> SRR934300 3 0.1022 0.873 0.000 0.000 0.968 0.032
#> SRR934301 3 0.0921 0.875 0.000 0.000 0.972 0.028
#> SRR934302 3 0.1022 0.873 0.000 0.000 0.968 0.032
#> SRR934303 3 0.0817 0.876 0.000 0.000 0.976 0.024
#> SRR934304 3 0.0000 0.880 0.000 0.000 1.000 0.000
#> SRR934305 3 0.0000 0.880 0.000 0.000 1.000 0.000
#> SRR934306 3 0.0000 0.880 0.000 0.000 1.000 0.000
#> SRR934307 3 0.0000 0.880 0.000 0.000 1.000 0.000
#> SRR934308 3 0.0000 0.880 0.000 0.000 1.000 0.000
#> SRR934309 3 0.0000 0.880 0.000 0.000 1.000 0.000
#> SRR934310 3 0.0000 0.880 0.000 0.000 1.000 0.000
#> SRR934311 3 0.0000 0.880 0.000 0.000 1.000 0.000
#> SRR934312 1 0.1022 0.824 0.968 0.000 0.000 0.032
#> SRR934313 1 0.1022 0.824 0.968 0.000 0.000 0.032
#> SRR934314 1 0.0921 0.824 0.972 0.000 0.000 0.028
#> SRR934315 1 0.1211 0.823 0.960 0.000 0.000 0.040
#> SRR934316 1 0.1389 0.821 0.952 0.000 0.000 0.048
#> SRR934317 1 0.1302 0.822 0.956 0.000 0.000 0.044
#> SRR934318 1 0.1474 0.820 0.948 0.000 0.000 0.052
#> SRR934319 1 0.1474 0.820 0.948 0.000 0.000 0.052
#> SRR934320 1 0.3219 0.727 0.836 0.000 0.000 0.164
#> SRR934321 1 0.2921 0.756 0.860 0.000 0.000 0.140
#> SRR934322 1 0.3311 0.714 0.828 0.000 0.000 0.172
#> SRR934323 1 0.3219 0.726 0.836 0.000 0.000 0.164
#> SRR934324 1 0.3123 0.737 0.844 0.000 0.000 0.156
#> SRR934325 1 0.3219 0.726 0.836 0.000 0.000 0.164
#> SRR934326 1 0.3219 0.726 0.836 0.000 0.000 0.164
#> SRR934327 1 0.3123 0.737 0.844 0.000 0.000 0.156
#> SRR934328 4 0.3726 0.865 0.212 0.000 0.000 0.788
#> SRR934329 4 0.3726 0.865 0.212 0.000 0.000 0.788
#> SRR934330 4 0.3726 0.865 0.212 0.000 0.000 0.788
#> SRR934331 4 0.3726 0.865 0.212 0.000 0.000 0.788
#> SRR934332 4 0.3801 0.860 0.220 0.000 0.000 0.780
#> SRR934333 4 0.3801 0.860 0.220 0.000 0.000 0.780
#> SRR934334 4 0.3726 0.865 0.212 0.000 0.000 0.788
#> SRR934335 4 0.3726 0.865 0.212 0.000 0.000 0.788
#> SRR934344 1 0.4999 -0.398 0.508 0.000 0.000 0.492
#> SRR934345 4 0.4972 0.533 0.456 0.000 0.000 0.544
#> SRR934346 1 0.4996 -0.366 0.516 0.000 0.000 0.484
#> SRR934347 4 0.4996 0.451 0.484 0.000 0.000 0.516
#> SRR934348 4 0.4972 0.533 0.456 0.000 0.000 0.544
#> SRR934349 4 0.5000 0.408 0.496 0.000 0.000 0.504
#> SRR934350 4 0.4967 0.543 0.452 0.000 0.000 0.548
#> SRR934351 4 0.4992 0.477 0.476 0.000 0.000 0.524
#> SRR934336 1 0.0707 0.824 0.980 0.000 0.000 0.020
#> SRR934337 1 0.0336 0.823 0.992 0.000 0.000 0.008
#> SRR934338 1 0.0188 0.820 0.996 0.000 0.000 0.004
#> SRR934339 1 0.0188 0.822 0.996 0.000 0.000 0.004
#> SRR934340 1 0.0592 0.824 0.984 0.000 0.000 0.016
#> SRR934341 1 0.0336 0.821 0.992 0.000 0.000 0.008
#> SRR934342 1 0.0336 0.823 0.992 0.000 0.000 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0.3972 0.996 0.032 0.008 0.788 0.000 0.172
#> SRR934217 3 0.3972 0.996 0.032 0.008 0.788 0.000 0.172
#> SRR934218 3 0.3928 0.990 0.028 0.008 0.788 0.000 0.176
#> SRR934219 3 0.3934 0.994 0.032 0.008 0.792 0.000 0.168
#> SRR934220 3 0.3934 0.994 0.032 0.008 0.792 0.000 0.168
#> SRR934221 3 0.3972 0.996 0.032 0.008 0.788 0.000 0.172
#> SRR934222 3 0.3934 0.994 0.032 0.008 0.792 0.000 0.168
#> SRR934223 3 0.3972 0.996 0.032 0.008 0.788 0.000 0.172
#> SRR934224 1 0.0566 0.975 0.984 0.004 0.012 0.000 0.000
#> SRR934225 1 0.0566 0.975 0.984 0.004 0.012 0.000 0.000
#> SRR934226 1 0.0566 0.975 0.984 0.004 0.012 0.000 0.000
#> SRR934227 1 0.0566 0.975 0.984 0.004 0.012 0.000 0.000
#> SRR934228 1 0.0566 0.975 0.984 0.004 0.012 0.000 0.000
#> SRR934229 1 0.0671 0.973 0.980 0.004 0.016 0.000 0.000
#> SRR934230 1 0.0566 0.975 0.984 0.004 0.012 0.000 0.000
#> SRR934231 1 0.0566 0.975 0.984 0.004 0.012 0.000 0.000
#> SRR934232 4 0.0000 0.972 0.000 0.000 0.000 1.000 0.000
#> SRR934233 4 0.0000 0.972 0.000 0.000 0.000 1.000 0.000
#> SRR934234 4 0.0000 0.972 0.000 0.000 0.000 1.000 0.000
#> SRR934235 4 0.0000 0.972 0.000 0.000 0.000 1.000 0.000
#> SRR934236 4 0.0000 0.972 0.000 0.000 0.000 1.000 0.000
#> SRR934237 4 0.0000 0.972 0.000 0.000 0.000 1.000 0.000
#> SRR934238 4 0.0000 0.972 0.000 0.000 0.000 1.000 0.000
#> SRR934239 4 0.0000 0.972 0.000 0.000 0.000 1.000 0.000
#> SRR934240 4 0.0510 0.968 0.000 0.000 0.016 0.984 0.000
#> SRR934241 4 0.0510 0.968 0.000 0.000 0.016 0.984 0.000
#> SRR934242 4 0.0510 0.968 0.000 0.000 0.016 0.984 0.000
#> SRR934243 4 0.0510 0.968 0.000 0.000 0.016 0.984 0.000
#> SRR934244 4 0.0510 0.968 0.000 0.000 0.016 0.984 0.000
#> SRR934245 4 0.0510 0.968 0.000 0.000 0.016 0.984 0.000
#> SRR934246 4 0.0510 0.968 0.000 0.000 0.016 0.984 0.000
#> SRR934247 4 0.0510 0.968 0.000 0.000 0.016 0.984 0.000
#> SRR934248 4 0.2426 0.930 0.000 0.064 0.036 0.900 0.000
#> SRR934249 4 0.2426 0.930 0.000 0.064 0.036 0.900 0.000
#> SRR934250 4 0.2426 0.930 0.000 0.064 0.036 0.900 0.000
#> SRR934251 4 0.2426 0.930 0.000 0.064 0.036 0.900 0.000
#> SRR934252 4 0.2426 0.930 0.000 0.064 0.036 0.900 0.000
#> SRR934253 4 0.2426 0.930 0.000 0.064 0.036 0.900 0.000
#> SRR934254 4 0.2426 0.930 0.000 0.064 0.036 0.900 0.000
#> SRR934255 4 0.2426 0.930 0.000 0.064 0.036 0.900 0.000
#> SRR934256 2 0.4179 0.795 0.072 0.776 0.152 0.000 0.000
#> SRR934257 2 0.4179 0.795 0.072 0.776 0.152 0.000 0.000
#> SRR934258 2 0.4237 0.795 0.076 0.772 0.152 0.000 0.000
#> SRR934259 2 0.4237 0.795 0.076 0.772 0.152 0.000 0.000
#> SRR934260 2 0.4179 0.795 0.072 0.776 0.152 0.000 0.000
#> SRR934261 2 0.4237 0.795 0.076 0.772 0.152 0.000 0.000
#> SRR934262 2 0.4237 0.795 0.076 0.772 0.152 0.000 0.000
#> SRR934263 2 0.4237 0.795 0.076 0.772 0.152 0.000 0.000
#> SRR934264 4 0.0290 0.971 0.000 0.000 0.008 0.992 0.000
#> SRR934265 4 0.0290 0.971 0.000 0.000 0.008 0.992 0.000
#> SRR934266 4 0.0290 0.971 0.000 0.000 0.008 0.992 0.000
#> SRR934267 4 0.0290 0.971 0.000 0.000 0.008 0.992 0.000
#> SRR934268 4 0.0290 0.971 0.000 0.000 0.008 0.992 0.000
#> SRR934269 4 0.0290 0.971 0.000 0.000 0.008 0.992 0.000
#> SRR934270 4 0.0290 0.971 0.000 0.000 0.008 0.992 0.000
#> SRR934271 4 0.0290 0.971 0.000 0.000 0.008 0.992 0.000
#> SRR934272 1 0.0162 0.978 0.996 0.004 0.000 0.000 0.000
#> SRR934273 1 0.0162 0.978 0.996 0.004 0.000 0.000 0.000
#> SRR934274 1 0.0162 0.978 0.996 0.004 0.000 0.000 0.000
#> SRR934275 1 0.0162 0.978 0.996 0.004 0.000 0.000 0.000
#> SRR934276 1 0.0162 0.978 0.996 0.004 0.000 0.000 0.000
#> SRR934277 1 0.0162 0.978 0.996 0.004 0.000 0.000 0.000
#> SRR934278 1 0.0162 0.978 0.996 0.004 0.000 0.000 0.000
#> SRR934279 1 0.0162 0.978 0.996 0.004 0.000 0.000 0.000
#> SRR934280 1 0.0865 0.977 0.972 0.024 0.004 0.000 0.000
#> SRR934281 1 0.0865 0.977 0.972 0.024 0.004 0.000 0.000
#> SRR934282 1 0.0771 0.977 0.976 0.020 0.004 0.000 0.000
#> SRR934283 1 0.0771 0.977 0.976 0.020 0.004 0.000 0.000
#> SRR934284 1 0.0865 0.977 0.972 0.024 0.004 0.000 0.000
#> SRR934285 1 0.0955 0.975 0.968 0.028 0.004 0.000 0.000
#> SRR934286 1 0.0771 0.977 0.976 0.020 0.004 0.000 0.000
#> SRR934287 1 0.0865 0.977 0.972 0.024 0.004 0.000 0.000
#> SRR934288 2 0.4232 0.680 0.312 0.676 0.012 0.000 0.000
#> SRR934289 2 0.3885 0.737 0.268 0.724 0.008 0.000 0.000
#> SRR934290 2 0.2971 0.834 0.156 0.836 0.008 0.000 0.000
#> SRR934291 2 0.3521 0.776 0.232 0.764 0.004 0.000 0.000
#> SRR934292 2 0.3750 0.775 0.232 0.756 0.012 0.000 0.000
#> SRR934293 2 0.4046 0.702 0.296 0.696 0.008 0.000 0.000
#> SRR934294 2 0.3967 0.744 0.264 0.724 0.012 0.000 0.000
#> SRR934295 2 0.4557 0.498 0.404 0.584 0.012 0.000 0.000
#> SRR934296 5 0.0880 0.965 0.000 0.032 0.000 0.000 0.968
#> SRR934297 5 0.1197 0.945 0.000 0.048 0.000 0.000 0.952
#> SRR934298 5 0.0703 0.969 0.000 0.024 0.000 0.000 0.976
#> SRR934299 5 0.0963 0.961 0.000 0.036 0.000 0.000 0.964
#> SRR934300 5 0.0880 0.965 0.000 0.032 0.000 0.000 0.968
#> SRR934301 5 0.0880 0.965 0.000 0.032 0.000 0.000 0.968
#> SRR934302 5 0.0963 0.961 0.000 0.036 0.000 0.000 0.964
#> SRR934303 5 0.0290 0.973 0.000 0.008 0.000 0.000 0.992
#> SRR934304 5 0.0000 0.973 0.000 0.000 0.000 0.000 1.000
#> SRR934305 5 0.0000 0.973 0.000 0.000 0.000 0.000 1.000
#> SRR934306 5 0.0000 0.973 0.000 0.000 0.000 0.000 1.000
#> SRR934307 5 0.0000 0.973 0.000 0.000 0.000 0.000 1.000
#> SRR934308 5 0.0000 0.973 0.000 0.000 0.000 0.000 1.000
#> SRR934309 5 0.0000 0.973 0.000 0.000 0.000 0.000 1.000
#> SRR934310 5 0.0000 0.973 0.000 0.000 0.000 0.000 1.000
#> SRR934311 5 0.0000 0.973 0.000 0.000 0.000 0.000 1.000
#> SRR934312 1 0.0609 0.978 0.980 0.020 0.000 0.000 0.000
#> SRR934313 1 0.0510 0.980 0.984 0.016 0.000 0.000 0.000
#> SRR934314 1 0.0609 0.978 0.980 0.020 0.000 0.000 0.000
#> SRR934315 1 0.0703 0.978 0.976 0.024 0.000 0.000 0.000
#> SRR934316 1 0.0404 0.980 0.988 0.012 0.000 0.000 0.000
#> SRR934317 1 0.0609 0.978 0.980 0.020 0.000 0.000 0.000
#> SRR934318 1 0.0794 0.977 0.972 0.028 0.000 0.000 0.000
#> SRR934319 1 0.0404 0.980 0.988 0.012 0.000 0.000 0.000
#> SRR934320 1 0.1818 0.950 0.932 0.044 0.024 0.000 0.000
#> SRR934321 1 0.1300 0.969 0.956 0.028 0.016 0.000 0.000
#> SRR934322 1 0.1818 0.950 0.932 0.044 0.024 0.000 0.000
#> SRR934323 1 0.1626 0.957 0.940 0.044 0.016 0.000 0.000
#> SRR934324 1 0.1741 0.954 0.936 0.040 0.024 0.000 0.000
#> SRR934325 1 0.1399 0.967 0.952 0.028 0.020 0.000 0.000
#> SRR934326 1 0.1364 0.966 0.952 0.036 0.012 0.000 0.000
#> SRR934327 1 0.1281 0.969 0.956 0.032 0.012 0.000 0.000
#> SRR934328 2 0.2708 0.855 0.072 0.884 0.044 0.000 0.000
#> SRR934329 2 0.2782 0.855 0.072 0.880 0.048 0.000 0.000
#> SRR934330 2 0.2632 0.856 0.072 0.888 0.040 0.000 0.000
#> SRR934331 2 0.2473 0.856 0.072 0.896 0.032 0.000 0.000
#> SRR934332 2 0.2853 0.854 0.072 0.876 0.052 0.000 0.000
#> SRR934333 2 0.3181 0.848 0.072 0.856 0.072 0.000 0.000
#> SRR934334 2 0.2708 0.855 0.072 0.884 0.044 0.000 0.000
#> SRR934335 2 0.2708 0.855 0.072 0.884 0.044 0.000 0.000
#> SRR934344 2 0.3090 0.859 0.104 0.856 0.040 0.000 0.000
#> SRR934345 2 0.2616 0.860 0.100 0.880 0.020 0.000 0.000
#> SRR934346 2 0.3409 0.854 0.112 0.836 0.052 0.000 0.000
#> SRR934347 2 0.3184 0.858 0.100 0.852 0.048 0.000 0.000
#> SRR934348 2 0.2962 0.858 0.084 0.868 0.048 0.000 0.000
#> SRR934349 2 0.3454 0.854 0.100 0.836 0.064 0.000 0.000
#> SRR934350 2 0.2628 0.861 0.088 0.884 0.028 0.000 0.000
#> SRR934351 2 0.3255 0.857 0.100 0.848 0.052 0.000 0.000
#> SRR934336 1 0.0771 0.980 0.976 0.020 0.004 0.000 0.000
#> SRR934337 1 0.0693 0.980 0.980 0.012 0.008 0.000 0.000
#> SRR934338 1 0.0798 0.979 0.976 0.016 0.008 0.000 0.000
#> SRR934339 1 0.0671 0.979 0.980 0.016 0.004 0.000 0.000
#> SRR934340 1 0.0771 0.980 0.976 0.020 0.004 0.000 0.000
#> SRR934341 1 0.1195 0.969 0.960 0.028 0.012 0.000 0.000
#> SRR934342 1 0.0798 0.979 0.976 0.016 0.008 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.0603 1.000 0.000 0.000 0.980 0.000 0.016 0.004
#> SRR934217 3 0.0603 1.000 0.000 0.000 0.980 0.000 0.016 0.004
#> SRR934218 3 0.0603 1.000 0.000 0.000 0.980 0.000 0.016 0.004
#> SRR934219 3 0.0603 1.000 0.000 0.000 0.980 0.000 0.016 0.004
#> SRR934220 3 0.0603 1.000 0.000 0.000 0.980 0.000 0.016 0.004
#> SRR934221 3 0.0603 1.000 0.000 0.000 0.980 0.000 0.016 0.004
#> SRR934222 3 0.0603 1.000 0.000 0.000 0.980 0.000 0.016 0.004
#> SRR934223 3 0.0603 1.000 0.000 0.000 0.980 0.000 0.016 0.004
#> SRR934224 6 0.0767 0.969 0.008 0.012 0.004 0.000 0.000 0.976
#> SRR934225 6 0.0881 0.968 0.008 0.012 0.008 0.000 0.000 0.972
#> SRR934226 6 0.0881 0.968 0.008 0.012 0.008 0.000 0.000 0.972
#> SRR934227 6 0.0767 0.969 0.008 0.012 0.004 0.000 0.000 0.976
#> SRR934228 6 0.0767 0.969 0.008 0.012 0.004 0.000 0.000 0.976
#> SRR934229 6 0.0881 0.968 0.008 0.012 0.008 0.000 0.000 0.972
#> SRR934230 6 0.0767 0.969 0.008 0.012 0.004 0.000 0.000 0.976
#> SRR934231 6 0.0767 0.969 0.008 0.012 0.004 0.000 0.000 0.976
#> SRR934232 4 0.1151 0.852 0.000 0.032 0.012 0.956 0.000 0.000
#> SRR934233 4 0.1151 0.852 0.000 0.032 0.012 0.956 0.000 0.000
#> SRR934234 4 0.1151 0.852 0.000 0.032 0.012 0.956 0.000 0.000
#> SRR934235 4 0.1151 0.852 0.000 0.032 0.012 0.956 0.000 0.000
#> SRR934236 4 0.1151 0.852 0.000 0.032 0.012 0.956 0.000 0.000
#> SRR934237 4 0.1151 0.852 0.000 0.032 0.012 0.956 0.000 0.000
#> SRR934238 4 0.1151 0.852 0.000 0.032 0.012 0.956 0.000 0.000
#> SRR934239 4 0.1151 0.852 0.000 0.032 0.012 0.956 0.000 0.000
#> SRR934240 4 0.4420 0.640 0.000 0.300 0.020 0.660 0.020 0.000
#> SRR934241 4 0.3645 0.766 0.000 0.176 0.020 0.784 0.020 0.000
#> SRR934242 4 0.3506 0.777 0.000 0.160 0.020 0.800 0.020 0.000
#> SRR934243 4 0.4588 0.577 0.000 0.344 0.020 0.616 0.020 0.000
#> SRR934244 4 0.4588 0.577 0.000 0.344 0.020 0.616 0.020 0.000
#> SRR934245 4 0.3804 0.750 0.000 0.196 0.020 0.764 0.020 0.000
#> SRR934246 4 0.4402 0.645 0.000 0.296 0.020 0.664 0.020 0.000
#> SRR934247 4 0.4055 0.716 0.000 0.232 0.020 0.728 0.020 0.000
#> SRR934248 4 0.3215 0.747 0.004 0.240 0.000 0.756 0.000 0.000
#> SRR934249 4 0.3215 0.747 0.004 0.240 0.000 0.756 0.000 0.000
#> SRR934250 4 0.3215 0.747 0.004 0.240 0.000 0.756 0.000 0.000
#> SRR934251 4 0.3215 0.747 0.004 0.240 0.000 0.756 0.000 0.000
#> SRR934252 4 0.3215 0.747 0.004 0.240 0.000 0.756 0.000 0.000
#> SRR934253 4 0.3215 0.747 0.004 0.240 0.000 0.756 0.000 0.000
#> SRR934254 4 0.3215 0.747 0.004 0.240 0.000 0.756 0.000 0.000
#> SRR934255 4 0.3215 0.747 0.004 0.240 0.000 0.756 0.000 0.000
#> SRR934256 2 0.3816 0.995 0.296 0.688 0.000 0.000 0.000 0.016
#> SRR934257 2 0.3835 0.994 0.300 0.684 0.000 0.000 0.000 0.016
#> SRR934258 2 0.3835 0.994 0.300 0.684 0.000 0.000 0.000 0.016
#> SRR934259 2 0.3898 0.992 0.296 0.684 0.000 0.000 0.000 0.020
#> SRR934260 2 0.3835 0.994 0.300 0.684 0.000 0.000 0.000 0.016
#> SRR934261 2 0.3816 0.995 0.296 0.688 0.000 0.000 0.000 0.016
#> SRR934262 2 0.3816 0.995 0.296 0.688 0.000 0.000 0.000 0.016
#> SRR934263 2 0.3879 0.990 0.292 0.688 0.000 0.000 0.000 0.020
#> SRR934264 4 0.0000 0.853 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934265 4 0.0000 0.853 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934266 4 0.0000 0.853 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934267 4 0.0000 0.853 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934268 4 0.0000 0.853 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934269 4 0.0000 0.853 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934270 4 0.0000 0.853 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934271 4 0.0000 0.853 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934272 6 0.0291 0.970 0.000 0.004 0.004 0.000 0.000 0.992
#> SRR934273 6 0.0291 0.970 0.000 0.004 0.004 0.000 0.000 0.992
#> SRR934274 6 0.0291 0.970 0.000 0.004 0.004 0.000 0.000 0.992
#> SRR934275 6 0.0291 0.970 0.000 0.004 0.004 0.000 0.000 0.992
#> SRR934276 6 0.0291 0.970 0.000 0.004 0.004 0.000 0.000 0.992
#> SRR934277 6 0.0291 0.970 0.000 0.004 0.004 0.000 0.000 0.992
#> SRR934278 6 0.0291 0.970 0.000 0.004 0.004 0.000 0.000 0.992
#> SRR934279 6 0.0291 0.970 0.000 0.004 0.004 0.000 0.000 0.992
#> SRR934280 6 0.0692 0.969 0.000 0.020 0.000 0.000 0.004 0.976
#> SRR934281 6 0.0777 0.968 0.000 0.024 0.000 0.000 0.004 0.972
#> SRR934282 6 0.0692 0.969 0.000 0.020 0.000 0.000 0.004 0.976
#> SRR934283 6 0.0632 0.969 0.000 0.024 0.000 0.000 0.000 0.976
#> SRR934284 6 0.0458 0.971 0.000 0.016 0.000 0.000 0.000 0.984
#> SRR934285 6 0.1010 0.965 0.000 0.036 0.000 0.000 0.004 0.960
#> SRR934286 6 0.0547 0.969 0.000 0.020 0.000 0.000 0.000 0.980
#> SRR934287 6 0.0777 0.968 0.000 0.024 0.000 0.000 0.004 0.972
#> SRR934288 1 0.1080 0.941 0.960 0.004 0.004 0.000 0.000 0.032
#> SRR934289 1 0.0858 0.951 0.968 0.000 0.004 0.000 0.000 0.028
#> SRR934290 1 0.0603 0.964 0.980 0.004 0.000 0.000 0.000 0.016
#> SRR934291 1 0.1003 0.951 0.964 0.004 0.004 0.000 0.000 0.028
#> SRR934292 1 0.0858 0.953 0.968 0.004 0.000 0.000 0.000 0.028
#> SRR934293 1 0.0937 0.936 0.960 0.000 0.000 0.000 0.000 0.040
#> SRR934294 1 0.0865 0.943 0.964 0.000 0.000 0.000 0.000 0.036
#> SRR934295 1 0.1615 0.877 0.928 0.004 0.004 0.000 0.000 0.064
#> SRR934296 5 0.0291 0.983 0.004 0.000 0.004 0.000 0.992 0.000
#> SRR934297 5 0.0547 0.973 0.020 0.000 0.000 0.000 0.980 0.000
#> SRR934298 5 0.0260 0.982 0.008 0.000 0.000 0.000 0.992 0.000
#> SRR934299 5 0.0291 0.979 0.004 0.000 0.004 0.000 0.992 0.000
#> SRR934300 5 0.0363 0.981 0.012 0.000 0.000 0.000 0.988 0.000
#> SRR934301 5 0.0520 0.982 0.008 0.000 0.008 0.000 0.984 0.000
#> SRR934302 5 0.0405 0.977 0.008 0.000 0.004 0.000 0.988 0.000
#> SRR934303 5 0.0146 0.983 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR934304 5 0.0547 0.985 0.000 0.000 0.020 0.000 0.980 0.000
#> SRR934305 5 0.0547 0.985 0.000 0.000 0.020 0.000 0.980 0.000
#> SRR934306 5 0.0547 0.985 0.000 0.000 0.020 0.000 0.980 0.000
#> SRR934307 5 0.0547 0.985 0.000 0.000 0.020 0.000 0.980 0.000
#> SRR934308 5 0.0547 0.985 0.000 0.000 0.020 0.000 0.980 0.000
#> SRR934309 5 0.0547 0.985 0.000 0.000 0.020 0.000 0.980 0.000
#> SRR934310 5 0.0547 0.985 0.000 0.000 0.020 0.000 0.980 0.000
#> SRR934311 5 0.0547 0.985 0.000 0.000 0.020 0.000 0.980 0.000
#> SRR934312 6 0.0858 0.968 0.000 0.028 0.004 0.000 0.000 0.968
#> SRR934313 6 0.1010 0.966 0.000 0.036 0.004 0.000 0.000 0.960
#> SRR934314 6 0.0777 0.969 0.000 0.024 0.004 0.000 0.000 0.972
#> SRR934315 6 0.1010 0.966 0.000 0.036 0.004 0.000 0.000 0.960
#> SRR934316 6 0.1226 0.963 0.000 0.040 0.004 0.000 0.004 0.952
#> SRR934317 6 0.1003 0.967 0.000 0.028 0.004 0.000 0.004 0.964
#> SRR934318 6 0.1010 0.966 0.000 0.036 0.004 0.000 0.000 0.960
#> SRR934319 6 0.0692 0.969 0.000 0.020 0.004 0.000 0.000 0.976
#> SRR934320 6 0.1918 0.930 0.008 0.088 0.000 0.000 0.000 0.904
#> SRR934321 6 0.1584 0.946 0.008 0.064 0.000 0.000 0.000 0.928
#> SRR934322 6 0.2118 0.917 0.008 0.104 0.000 0.000 0.000 0.888
#> SRR934323 6 0.2170 0.916 0.012 0.100 0.000 0.000 0.000 0.888
#> SRR934324 6 0.2070 0.924 0.012 0.092 0.000 0.000 0.000 0.896
#> SRR934325 6 0.1970 0.927 0.008 0.092 0.000 0.000 0.000 0.900
#> SRR934326 6 0.1866 0.933 0.008 0.084 0.000 0.000 0.000 0.908
#> SRR934327 6 0.1866 0.933 0.008 0.084 0.000 0.000 0.000 0.908
#> SRR934328 1 0.0508 0.967 0.984 0.012 0.000 0.000 0.000 0.004
#> SRR934329 1 0.0508 0.967 0.984 0.012 0.000 0.000 0.000 0.004
#> SRR934330 1 0.0508 0.967 0.984 0.012 0.000 0.000 0.000 0.004
#> SRR934331 1 0.0508 0.967 0.984 0.012 0.000 0.000 0.000 0.004
#> SRR934332 1 0.0508 0.967 0.984 0.012 0.000 0.000 0.000 0.004
#> SRR934333 1 0.0508 0.967 0.984 0.012 0.000 0.000 0.000 0.004
#> SRR934334 1 0.0508 0.967 0.984 0.012 0.000 0.000 0.000 0.004
#> SRR934335 1 0.0508 0.967 0.984 0.012 0.000 0.000 0.000 0.004
#> SRR934344 1 0.0146 0.971 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR934345 1 0.0146 0.971 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR934346 1 0.0146 0.971 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR934347 1 0.0146 0.971 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR934348 1 0.0146 0.971 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR934349 1 0.0146 0.971 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR934350 1 0.0146 0.971 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR934351 1 0.0146 0.971 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR934336 6 0.0622 0.969 0.008 0.012 0.000 0.000 0.000 0.980
#> SRR934337 6 0.0622 0.969 0.008 0.012 0.000 0.000 0.000 0.980
#> SRR934338 6 0.0622 0.969 0.008 0.012 0.000 0.000 0.000 0.980
#> SRR934339 6 0.0622 0.969 0.008 0.012 0.000 0.000 0.000 0.980
#> SRR934340 6 0.0717 0.969 0.008 0.016 0.000 0.000 0.000 0.976
#> SRR934341 6 0.0725 0.968 0.012 0.012 0.000 0.000 0.000 0.976
#> SRR934342 6 0.0622 0.969 0.008 0.012 0.000 0.000 0.000 0.980
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 14550 rows and 135 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 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 0.636 0.955 0.972 0.2383 0.789 0.789
#> 3 3 0.748 0.933 0.969 0.9797 0.748 0.681
#> 4 4 0.769 0.930 0.946 0.0284 0.993 0.987
#> 5 5 0.916 0.970 0.982 0.2302 0.860 0.737
#> 6 6 1.000 0.998 0.998 0.0467 0.986 0.964
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
#> SRR934216 2 0.118 0.991 0.016 0.984
#> SRR934217 2 0.118 0.991 0.016 0.984
#> SRR934218 2 0.118 0.991 0.016 0.984
#> SRR934219 2 0.118 0.991 0.016 0.984
#> SRR934220 2 0.118 0.991 0.016 0.984
#> SRR934221 2 0.118 0.991 0.016 0.984
#> SRR934222 2 0.118 0.991 0.016 0.984
#> SRR934223 2 0.118 0.991 0.016 0.984
#> SRR934224 1 0.000 0.967 1.000 0.000
#> SRR934225 1 0.000 0.967 1.000 0.000
#> SRR934226 1 0.000 0.967 1.000 0.000
#> SRR934227 1 0.000 0.967 1.000 0.000
#> SRR934228 1 0.000 0.967 1.000 0.000
#> SRR934229 1 0.000 0.967 1.000 0.000
#> SRR934230 1 0.000 0.967 1.000 0.000
#> SRR934231 1 0.000 0.967 1.000 0.000
#> SRR934232 1 0.506 0.905 0.888 0.112
#> SRR934233 1 0.506 0.905 0.888 0.112
#> SRR934234 1 0.506 0.905 0.888 0.112
#> SRR934235 1 0.506 0.905 0.888 0.112
#> SRR934236 1 0.506 0.905 0.888 0.112
#> SRR934237 1 0.506 0.905 0.888 0.112
#> SRR934238 1 0.506 0.905 0.888 0.112
#> SRR934239 1 0.506 0.905 0.888 0.112
#> SRR934240 1 0.506 0.905 0.888 0.112
#> SRR934241 1 0.506 0.905 0.888 0.112
#> SRR934242 1 0.506 0.905 0.888 0.112
#> SRR934243 1 0.506 0.905 0.888 0.112
#> SRR934244 1 0.506 0.905 0.888 0.112
#> SRR934245 1 0.506 0.905 0.888 0.112
#> SRR934246 1 0.506 0.905 0.888 0.112
#> SRR934247 1 0.506 0.905 0.888 0.112
#> SRR934248 1 0.518 0.902 0.884 0.116
#> SRR934249 1 0.518 0.902 0.884 0.116
#> SRR934250 1 0.518 0.902 0.884 0.116
#> SRR934251 1 0.518 0.902 0.884 0.116
#> SRR934252 1 0.518 0.902 0.884 0.116
#> SRR934253 1 0.518 0.902 0.884 0.116
#> SRR934254 1 0.518 0.902 0.884 0.116
#> SRR934255 1 0.518 0.902 0.884 0.116
#> SRR934256 1 0.000 0.967 1.000 0.000
#> SRR934257 1 0.000 0.967 1.000 0.000
#> SRR934258 1 0.000 0.967 1.000 0.000
#> SRR934259 1 0.000 0.967 1.000 0.000
#> SRR934260 1 0.000 0.967 1.000 0.000
#> SRR934261 1 0.000 0.967 1.000 0.000
#> SRR934262 1 0.000 0.967 1.000 0.000
#> SRR934263 1 0.000 0.967 1.000 0.000
#> SRR934264 1 0.518 0.902 0.884 0.116
#> SRR934265 1 0.518 0.902 0.884 0.116
#> SRR934266 1 0.518 0.902 0.884 0.116
#> SRR934267 1 0.518 0.902 0.884 0.116
#> SRR934268 1 0.518 0.902 0.884 0.116
#> SRR934269 1 0.518 0.902 0.884 0.116
#> SRR934270 1 0.518 0.902 0.884 0.116
#> SRR934271 1 0.518 0.902 0.884 0.116
#> SRR934272 1 0.000 0.967 1.000 0.000
#> SRR934273 1 0.000 0.967 1.000 0.000
#> SRR934274 1 0.000 0.967 1.000 0.000
#> SRR934275 1 0.000 0.967 1.000 0.000
#> SRR934276 1 0.000 0.967 1.000 0.000
#> SRR934277 1 0.000 0.967 1.000 0.000
#> SRR934278 1 0.000 0.967 1.000 0.000
#> SRR934279 1 0.000 0.967 1.000 0.000
#> SRR934280 1 0.000 0.967 1.000 0.000
#> SRR934281 1 0.000 0.967 1.000 0.000
#> SRR934282 1 0.000 0.967 1.000 0.000
#> SRR934283 1 0.000 0.967 1.000 0.000
#> SRR934284 1 0.000 0.967 1.000 0.000
#> SRR934285 1 0.000 0.967 1.000 0.000
#> SRR934286 1 0.000 0.967 1.000 0.000
#> SRR934287 1 0.000 0.967 1.000 0.000
#> SRR934288 1 0.000 0.967 1.000 0.000
#> SRR934289 1 0.000 0.967 1.000 0.000
#> SRR934290 1 0.000 0.967 1.000 0.000
#> SRR934291 1 0.000 0.967 1.000 0.000
#> SRR934292 1 0.000 0.967 1.000 0.000
#> SRR934293 1 0.000 0.967 1.000 0.000
#> SRR934294 1 0.000 0.967 1.000 0.000
#> SRR934295 1 0.000 0.967 1.000 0.000
#> SRR934296 1 0.000 0.967 1.000 0.000
#> SRR934297 1 0.000 0.967 1.000 0.000
#> SRR934298 1 0.000 0.967 1.000 0.000
#> SRR934299 1 0.000 0.967 1.000 0.000
#> SRR934300 1 0.000 0.967 1.000 0.000
#> SRR934301 1 0.000 0.967 1.000 0.000
#> SRR934302 1 0.000 0.967 1.000 0.000
#> SRR934303 1 0.000 0.967 1.000 0.000
#> SRR934304 2 0.000 0.991 0.000 1.000
#> SRR934305 2 0.000 0.991 0.000 1.000
#> SRR934306 2 0.000 0.991 0.000 1.000
#> SRR934307 2 0.000 0.991 0.000 1.000
#> SRR934308 2 0.000 0.991 0.000 1.000
#> SRR934309 2 0.000 0.991 0.000 1.000
#> SRR934310 2 0.000 0.991 0.000 1.000
#> SRR934311 2 0.000 0.991 0.000 1.000
#> SRR934312 1 0.000 0.967 1.000 0.000
#> SRR934313 1 0.000 0.967 1.000 0.000
#> SRR934314 1 0.000 0.967 1.000 0.000
#> SRR934315 1 0.000 0.967 1.000 0.000
#> SRR934316 1 0.000 0.967 1.000 0.000
#> SRR934317 1 0.000 0.967 1.000 0.000
#> SRR934318 1 0.000 0.967 1.000 0.000
#> SRR934319 1 0.000 0.967 1.000 0.000
#> SRR934320 1 0.000 0.967 1.000 0.000
#> SRR934321 1 0.000 0.967 1.000 0.000
#> SRR934322 1 0.000 0.967 1.000 0.000
#> SRR934323 1 0.000 0.967 1.000 0.000
#> SRR934324 1 0.000 0.967 1.000 0.000
#> SRR934325 1 0.000 0.967 1.000 0.000
#> SRR934326 1 0.000 0.967 1.000 0.000
#> SRR934327 1 0.000 0.967 1.000 0.000
#> SRR934328 1 0.000 0.967 1.000 0.000
#> SRR934329 1 0.000 0.967 1.000 0.000
#> SRR934330 1 0.000 0.967 1.000 0.000
#> SRR934331 1 0.000 0.967 1.000 0.000
#> SRR934332 1 0.000 0.967 1.000 0.000
#> SRR934333 1 0.000 0.967 1.000 0.000
#> SRR934334 1 0.000 0.967 1.000 0.000
#> SRR934335 1 0.000 0.967 1.000 0.000
#> SRR934344 1 0.000 0.967 1.000 0.000
#> SRR934345 1 0.000 0.967 1.000 0.000
#> SRR934346 1 0.000 0.967 1.000 0.000
#> SRR934347 1 0.000 0.967 1.000 0.000
#> SRR934348 1 0.000 0.967 1.000 0.000
#> SRR934349 1 0.000 0.967 1.000 0.000
#> SRR934350 1 0.000 0.967 1.000 0.000
#> SRR934351 1 0.000 0.967 1.000 0.000
#> SRR934336 1 0.000 0.967 1.000 0.000
#> SRR934337 1 0.000 0.967 1.000 0.000
#> SRR934338 1 0.000 0.967 1.000 0.000
#> SRR934339 1 0.000 0.967 1.000 0.000
#> SRR934340 1 0.000 0.967 1.000 0.000
#> SRR934341 1 0.000 0.967 1.000 0.000
#> SRR934342 1 0.000 0.967 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 3 0.0747 0.985 0.016 0.000 0.984
#> SRR934217 3 0.0747 0.985 0.016 0.000 0.984
#> SRR934218 3 0.0747 0.985 0.016 0.000 0.984
#> SRR934219 3 0.0747 0.985 0.016 0.000 0.984
#> SRR934220 3 0.0747 0.985 0.016 0.000 0.984
#> SRR934221 3 0.0747 0.985 0.016 0.000 0.984
#> SRR934222 3 0.0747 0.985 0.016 0.000 0.984
#> SRR934223 3 0.0747 0.985 0.016 0.000 0.984
#> SRR934224 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934225 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934226 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934227 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934228 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934229 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934230 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934231 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934232 2 0.0000 0.896 0.000 1.000 0.000
#> SRR934233 2 0.0000 0.896 0.000 1.000 0.000
#> SRR934234 2 0.0000 0.896 0.000 1.000 0.000
#> SRR934235 2 0.0000 0.896 0.000 1.000 0.000
#> SRR934236 2 0.0000 0.896 0.000 1.000 0.000
#> SRR934237 2 0.0000 0.896 0.000 1.000 0.000
#> SRR934238 2 0.0000 0.896 0.000 1.000 0.000
#> SRR934239 2 0.0000 0.896 0.000 1.000 0.000
#> SRR934240 2 0.0000 0.896 0.000 1.000 0.000
#> SRR934241 2 0.0000 0.896 0.000 1.000 0.000
#> SRR934242 2 0.0000 0.896 0.000 1.000 0.000
#> SRR934243 2 0.0000 0.896 0.000 1.000 0.000
#> SRR934244 2 0.0000 0.896 0.000 1.000 0.000
#> SRR934245 2 0.0000 0.896 0.000 1.000 0.000
#> SRR934246 2 0.0000 0.896 0.000 1.000 0.000
#> SRR934247 2 0.0000 0.896 0.000 1.000 0.000
#> SRR934248 1 0.4351 0.820 0.828 0.168 0.004
#> SRR934249 1 0.4351 0.820 0.828 0.168 0.004
#> SRR934250 1 0.4351 0.820 0.828 0.168 0.004
#> SRR934251 1 0.4351 0.820 0.828 0.168 0.004
#> SRR934252 1 0.4351 0.820 0.828 0.168 0.004
#> SRR934253 1 0.4351 0.820 0.828 0.168 0.004
#> SRR934254 1 0.4351 0.820 0.828 0.168 0.004
#> SRR934255 1 0.4351 0.820 0.828 0.168 0.004
#> SRR934256 2 0.4121 0.790 0.168 0.832 0.000
#> SRR934257 2 0.4121 0.790 0.168 0.832 0.000
#> SRR934258 2 0.4121 0.790 0.168 0.832 0.000
#> SRR934259 2 0.4121 0.790 0.168 0.832 0.000
#> SRR934260 2 0.4121 0.790 0.168 0.832 0.000
#> SRR934261 2 0.4121 0.790 0.168 0.832 0.000
#> SRR934262 2 0.4121 0.790 0.168 0.832 0.000
#> SRR934263 2 0.4121 0.790 0.168 0.832 0.000
#> SRR934264 1 0.4351 0.820 0.828 0.168 0.004
#> SRR934265 1 0.4351 0.820 0.828 0.168 0.004
#> SRR934266 1 0.4351 0.820 0.828 0.168 0.004
#> SRR934267 1 0.4351 0.820 0.828 0.168 0.004
#> SRR934268 1 0.4351 0.820 0.828 0.168 0.004
#> SRR934269 1 0.4351 0.820 0.828 0.168 0.004
#> SRR934270 1 0.4351 0.820 0.828 0.168 0.004
#> SRR934271 1 0.4351 0.820 0.828 0.168 0.004
#> SRR934272 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934273 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934274 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934275 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934276 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934277 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934278 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934279 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934280 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934281 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934282 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934283 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934284 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934285 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934286 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934287 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934288 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934289 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934290 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934291 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934292 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934293 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934294 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934295 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934296 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934297 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934298 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934299 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934300 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934301 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934302 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934303 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934304 3 0.0000 0.985 0.000 0.000 1.000
#> SRR934305 3 0.0000 0.985 0.000 0.000 1.000
#> SRR934306 3 0.0000 0.985 0.000 0.000 1.000
#> SRR934307 3 0.0000 0.985 0.000 0.000 1.000
#> SRR934308 3 0.0000 0.985 0.000 0.000 1.000
#> SRR934309 3 0.0000 0.985 0.000 0.000 1.000
#> SRR934310 3 0.0000 0.985 0.000 0.000 1.000
#> SRR934311 3 0.0000 0.985 0.000 0.000 1.000
#> SRR934312 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934313 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934314 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934315 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934316 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934317 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934318 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934319 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934320 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934321 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934322 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934323 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934324 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934325 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934326 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934327 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934328 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934329 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934330 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934331 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934332 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934333 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934334 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934335 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934344 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934345 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934346 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934347 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934348 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934349 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934350 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934351 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934336 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934337 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934338 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934339 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934340 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934341 1 0.0000 0.968 1.000 0.000 0.000
#> SRR934342 1 0.0000 0.968 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 4 0.328 1.000 0.016 0.000 0.124 0.86
#> SRR934217 4 0.328 1.000 0.016 0.000 0.124 0.86
#> SRR934218 4 0.328 1.000 0.016 0.000 0.124 0.86
#> SRR934219 4 0.328 1.000 0.016 0.000 0.124 0.86
#> SRR934220 4 0.328 1.000 0.016 0.000 0.124 0.86
#> SRR934221 4 0.328 1.000 0.016 0.000 0.124 0.86
#> SRR934222 4 0.328 1.000 0.016 0.000 0.124 0.86
#> SRR934223 4 0.328 1.000 0.016 0.000 0.124 0.86
#> SRR934224 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934225 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934226 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934227 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934228 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934229 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934230 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934231 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934232 2 0.000 0.890 0.000 1.000 0.000 0.00
#> SRR934233 2 0.000 0.890 0.000 1.000 0.000 0.00
#> SRR934234 2 0.000 0.890 0.000 1.000 0.000 0.00
#> SRR934235 2 0.000 0.890 0.000 1.000 0.000 0.00
#> SRR934236 2 0.000 0.890 0.000 1.000 0.000 0.00
#> SRR934237 2 0.000 0.890 0.000 1.000 0.000 0.00
#> SRR934238 2 0.000 0.890 0.000 1.000 0.000 0.00
#> SRR934239 2 0.000 0.890 0.000 1.000 0.000 0.00
#> SRR934240 2 0.000 0.890 0.000 1.000 0.000 0.00
#> SRR934241 2 0.000 0.890 0.000 1.000 0.000 0.00
#> SRR934242 2 0.000 0.890 0.000 1.000 0.000 0.00
#> SRR934243 2 0.000 0.890 0.000 1.000 0.000 0.00
#> SRR934244 2 0.000 0.890 0.000 1.000 0.000 0.00
#> SRR934245 2 0.000 0.890 0.000 1.000 0.000 0.00
#> SRR934246 2 0.000 0.890 0.000 1.000 0.000 0.00
#> SRR934247 2 0.000 0.890 0.000 1.000 0.000 0.00
#> SRR934248 1 0.449 0.801 0.800 0.060 0.000 0.14
#> SRR934249 1 0.449 0.801 0.800 0.060 0.000 0.14
#> SRR934250 1 0.449 0.801 0.800 0.060 0.000 0.14
#> SRR934251 1 0.449 0.801 0.800 0.060 0.000 0.14
#> SRR934252 1 0.449 0.801 0.800 0.060 0.000 0.14
#> SRR934253 1 0.449 0.801 0.800 0.060 0.000 0.14
#> SRR934254 1 0.449 0.801 0.800 0.060 0.000 0.14
#> SRR934255 1 0.449 0.801 0.800 0.060 0.000 0.14
#> SRR934256 2 0.327 0.778 0.168 0.832 0.000 0.00
#> SRR934257 2 0.327 0.778 0.168 0.832 0.000 0.00
#> SRR934258 2 0.327 0.778 0.168 0.832 0.000 0.00
#> SRR934259 2 0.327 0.778 0.168 0.832 0.000 0.00
#> SRR934260 2 0.327 0.778 0.168 0.832 0.000 0.00
#> SRR934261 2 0.327 0.778 0.168 0.832 0.000 0.00
#> SRR934262 2 0.327 0.778 0.168 0.832 0.000 0.00
#> SRR934263 2 0.327 0.778 0.168 0.832 0.000 0.00
#> SRR934264 1 0.449 0.801 0.800 0.060 0.000 0.14
#> SRR934265 1 0.449 0.801 0.800 0.060 0.000 0.14
#> SRR934266 1 0.449 0.801 0.800 0.060 0.000 0.14
#> SRR934267 1 0.449 0.801 0.800 0.060 0.000 0.14
#> SRR934268 1 0.449 0.801 0.800 0.060 0.000 0.14
#> SRR934269 1 0.449 0.801 0.800 0.060 0.000 0.14
#> SRR934270 1 0.449 0.801 0.800 0.060 0.000 0.14
#> SRR934271 1 0.449 0.801 0.800 0.060 0.000 0.14
#> SRR934272 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934273 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934274 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934275 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934276 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934277 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934278 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934279 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934280 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934281 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934282 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934283 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934284 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934285 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934286 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934287 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934288 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934289 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934290 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934291 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934292 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934293 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934294 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934295 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934296 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934297 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934298 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934299 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934300 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934301 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934302 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934303 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934304 3 0.000 1.000 0.000 0.000 1.000 0.00
#> SRR934305 3 0.000 1.000 0.000 0.000 1.000 0.00
#> SRR934306 3 0.000 1.000 0.000 0.000 1.000 0.00
#> SRR934307 3 0.000 1.000 0.000 0.000 1.000 0.00
#> SRR934308 3 0.000 1.000 0.000 0.000 1.000 0.00
#> SRR934309 3 0.000 1.000 0.000 0.000 1.000 0.00
#> SRR934310 3 0.000 1.000 0.000 0.000 1.000 0.00
#> SRR934311 3 0.000 1.000 0.000 0.000 1.000 0.00
#> SRR934312 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934313 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934314 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934315 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934316 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934317 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934318 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934319 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934320 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934321 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934322 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934323 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934324 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934325 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934326 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934327 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934328 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934329 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934330 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934331 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934332 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934333 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934334 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934335 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934344 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934345 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934346 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934347 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934348 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934349 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934350 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934351 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934336 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934337 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934338 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934339 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934340 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934341 1 0.000 0.965 1.000 0.000 0.000 0.00
#> SRR934342 1 0.000 0.965 1.000 0.000 0.000 0.00
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934217 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934218 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934219 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934220 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934221 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934222 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934223 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934224 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934225 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934226 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934227 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934228 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934229 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934230 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934231 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934232 2 0.0000 0.884 0.000 1.000 0 0.000 0
#> SRR934233 2 0.0000 0.884 0.000 1.000 0 0.000 0
#> SRR934234 2 0.0000 0.884 0.000 1.000 0 0.000 0
#> SRR934235 2 0.0000 0.884 0.000 1.000 0 0.000 0
#> SRR934236 2 0.0000 0.884 0.000 1.000 0 0.000 0
#> SRR934237 2 0.0000 0.884 0.000 1.000 0 0.000 0
#> SRR934238 2 0.0000 0.884 0.000 1.000 0 0.000 0
#> SRR934239 2 0.0000 0.884 0.000 1.000 0 0.000 0
#> SRR934240 2 0.0000 0.884 0.000 1.000 0 0.000 0
#> SRR934241 2 0.0000 0.884 0.000 1.000 0 0.000 0
#> SRR934242 2 0.0000 0.884 0.000 1.000 0 0.000 0
#> SRR934243 2 0.0000 0.884 0.000 1.000 0 0.000 0
#> SRR934244 2 0.0000 0.884 0.000 1.000 0 0.000 0
#> SRR934245 2 0.0000 0.884 0.000 1.000 0 0.000 0
#> SRR934246 2 0.0000 0.884 0.000 1.000 0 0.000 0
#> SRR934247 2 0.0000 0.884 0.000 1.000 0 0.000 0
#> SRR934248 4 0.0963 1.000 0.036 0.000 0 0.964 0
#> SRR934249 4 0.0963 1.000 0.036 0.000 0 0.964 0
#> SRR934250 4 0.0963 1.000 0.036 0.000 0 0.964 0
#> SRR934251 4 0.0963 1.000 0.036 0.000 0 0.964 0
#> SRR934252 4 0.0963 1.000 0.036 0.000 0 0.964 0
#> SRR934253 4 0.0963 1.000 0.036 0.000 0 0.964 0
#> SRR934254 4 0.0963 1.000 0.036 0.000 0 0.964 0
#> SRR934255 4 0.0963 1.000 0.036 0.000 0 0.964 0
#> SRR934256 2 0.3734 0.765 0.168 0.796 0 0.036 0
#> SRR934257 2 0.3734 0.765 0.168 0.796 0 0.036 0
#> SRR934258 2 0.3734 0.765 0.168 0.796 0 0.036 0
#> SRR934259 2 0.3734 0.765 0.168 0.796 0 0.036 0
#> SRR934260 2 0.3734 0.765 0.168 0.796 0 0.036 0
#> SRR934261 2 0.3734 0.765 0.168 0.796 0 0.036 0
#> SRR934262 2 0.3734 0.765 0.168 0.796 0 0.036 0
#> SRR934263 2 0.3734 0.765 0.168 0.796 0 0.036 0
#> SRR934264 4 0.0963 1.000 0.036 0.000 0 0.964 0
#> SRR934265 4 0.0963 1.000 0.036 0.000 0 0.964 0
#> SRR934266 4 0.0963 1.000 0.036 0.000 0 0.964 0
#> SRR934267 4 0.0963 1.000 0.036 0.000 0 0.964 0
#> SRR934268 4 0.0963 1.000 0.036 0.000 0 0.964 0
#> SRR934269 4 0.0963 1.000 0.036 0.000 0 0.964 0
#> SRR934270 4 0.0963 1.000 0.036 0.000 0 0.964 0
#> SRR934271 4 0.0963 1.000 0.036 0.000 0 0.964 0
#> SRR934272 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934273 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934274 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934275 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934276 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934277 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934278 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934279 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934280 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934281 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934282 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934283 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934284 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934285 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934286 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934287 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934288 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934289 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934290 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934291 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934292 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934293 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934294 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934295 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934296 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934297 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934298 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934299 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934300 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934301 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934302 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934303 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934304 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934305 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934306 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934307 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934308 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934309 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934310 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934311 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934312 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934313 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934314 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934315 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934316 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934317 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934318 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934319 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934320 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934321 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934322 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934323 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934324 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934325 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934326 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934327 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934328 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934329 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934330 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934331 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934332 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934333 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934334 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934335 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934344 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934345 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934346 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934347 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934348 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934349 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934350 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934351 1 0.0290 0.995 0.992 0.000 0 0.008 0
#> SRR934336 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934337 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934338 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934339 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934340 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934341 1 0.0000 0.997 1.000 0.000 0 0.000 0
#> SRR934342 1 0.0000 0.997 1.000 0.000 0 0.000 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.000 1.000 0.000 0 1 0.000 0 0
#> SRR934217 3 0.000 1.000 0.000 0 1 0.000 0 0
#> SRR934218 3 0.000 1.000 0.000 0 1 0.000 0 0
#> SRR934219 3 0.000 1.000 0.000 0 1 0.000 0 0
#> SRR934220 3 0.000 1.000 0.000 0 1 0.000 0 0
#> SRR934221 3 0.000 1.000 0.000 0 1 0.000 0 0
#> SRR934222 3 0.000 1.000 0.000 0 1 0.000 0 0
#> SRR934223 3 0.000 1.000 0.000 0 1 0.000 0 0
#> SRR934224 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934225 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934226 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934227 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934228 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934229 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934230 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934231 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934232 2 0.000 1.000 0.000 1 0 0.000 0 0
#> SRR934233 2 0.000 1.000 0.000 1 0 0.000 0 0
#> SRR934234 2 0.000 1.000 0.000 1 0 0.000 0 0
#> SRR934235 2 0.000 1.000 0.000 1 0 0.000 0 0
#> SRR934236 2 0.000 1.000 0.000 1 0 0.000 0 0
#> SRR934237 2 0.000 1.000 0.000 1 0 0.000 0 0
#> SRR934238 2 0.000 1.000 0.000 1 0 0.000 0 0
#> SRR934239 2 0.000 1.000 0.000 1 0 0.000 0 0
#> SRR934240 2 0.000 1.000 0.000 1 0 0.000 0 0
#> SRR934241 2 0.000 1.000 0.000 1 0 0.000 0 0
#> SRR934242 2 0.000 1.000 0.000 1 0 0.000 0 0
#> SRR934243 2 0.000 1.000 0.000 1 0 0.000 0 0
#> SRR934244 2 0.000 1.000 0.000 1 0 0.000 0 0
#> SRR934245 2 0.000 1.000 0.000 1 0 0.000 0 0
#> SRR934246 2 0.000 1.000 0.000 1 0 0.000 0 0
#> SRR934247 2 0.000 1.000 0.000 1 0 0.000 0 0
#> SRR934248 4 0.000 1.000 0.000 0 0 1.000 0 0
#> SRR934249 4 0.000 1.000 0.000 0 0 1.000 0 0
#> SRR934250 4 0.000 1.000 0.000 0 0 1.000 0 0
#> SRR934251 4 0.000 1.000 0.000 0 0 1.000 0 0
#> SRR934252 4 0.000 1.000 0.000 0 0 1.000 0 0
#> SRR934253 4 0.000 1.000 0.000 0 0 1.000 0 0
#> SRR934254 4 0.000 1.000 0.000 0 0 1.000 0 0
#> SRR934255 4 0.000 1.000 0.000 0 0 1.000 0 0
#> SRR934256 6 0.000 1.000 0.000 0 0 0.000 0 1
#> SRR934257 6 0.000 1.000 0.000 0 0 0.000 0 1
#> SRR934258 6 0.000 1.000 0.000 0 0 0.000 0 1
#> SRR934259 6 0.000 1.000 0.000 0 0 0.000 0 1
#> SRR934260 6 0.000 1.000 0.000 0 0 0.000 0 1
#> SRR934261 6 0.000 1.000 0.000 0 0 0.000 0 1
#> SRR934262 6 0.000 1.000 0.000 0 0 0.000 0 1
#> SRR934263 6 0.000 1.000 0.000 0 0 0.000 0 1
#> SRR934264 4 0.000 1.000 0.000 0 0 1.000 0 0
#> SRR934265 4 0.000 1.000 0.000 0 0 1.000 0 0
#> SRR934266 4 0.000 1.000 0.000 0 0 1.000 0 0
#> SRR934267 4 0.000 1.000 0.000 0 0 1.000 0 0
#> SRR934268 4 0.000 1.000 0.000 0 0 1.000 0 0
#> SRR934269 4 0.000 1.000 0.000 0 0 1.000 0 0
#> SRR934270 4 0.000 1.000 0.000 0 0 1.000 0 0
#> SRR934271 4 0.000 1.000 0.000 0 0 1.000 0 0
#> SRR934272 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934273 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934274 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934275 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934276 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934277 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934278 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934279 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934280 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934281 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934282 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934283 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934284 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934285 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934286 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934287 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934288 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934289 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934290 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934291 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934292 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934293 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934294 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934295 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934296 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934297 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934298 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934299 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934300 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934301 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934302 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934303 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934304 5 0.000 1.000 0.000 0 0 0.000 1 0
#> SRR934305 5 0.000 1.000 0.000 0 0 0.000 1 0
#> SRR934306 5 0.000 1.000 0.000 0 0 0.000 1 0
#> SRR934307 5 0.000 1.000 0.000 0 0 0.000 1 0
#> SRR934308 5 0.000 1.000 0.000 0 0 0.000 1 0
#> SRR934309 5 0.000 1.000 0.000 0 0 0.000 1 0
#> SRR934310 5 0.000 1.000 0.000 0 0 0.000 1 0
#> SRR934311 5 0.000 1.000 0.000 0 0 0.000 1 0
#> SRR934312 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934313 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934314 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934315 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934316 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934317 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934318 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934319 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934320 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934321 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934322 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934323 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934324 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934325 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934326 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934327 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934328 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934329 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934330 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934331 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934332 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934333 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934334 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934335 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934344 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934345 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934346 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934347 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934348 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934349 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934350 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934351 1 0.026 0.995 0.992 0 0 0.008 0 0
#> SRR934336 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934337 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934338 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934339 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934340 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934341 1 0.000 0.997 1.000 0 0 0.000 0 0
#> SRR934342 1 0.000 0.997 1.000 0 0 0.000 0 0
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 14550 rows and 135 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 0.253 0.543 0.805 0.3463 0.705 0.705
#> 3 3 0.162 0.536 0.678 0.5550 0.713 0.608
#> 4 4 0.276 0.507 0.624 0.1924 0.743 0.508
#> 5 5 0.351 0.683 0.660 0.1114 0.896 0.682
#> 6 6 0.526 0.560 0.625 0.0781 0.944 0.773
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
#> SRR934216 1 0.929 0.2961 0.656 0.344
#> SRR934217 1 0.929 0.2961 0.656 0.344
#> SRR934218 1 0.929 0.2961 0.656 0.344
#> SRR934219 1 0.929 0.2961 0.656 0.344
#> SRR934220 1 0.929 0.2961 0.656 0.344
#> SRR934221 1 0.929 0.2961 0.656 0.344
#> SRR934222 1 0.929 0.2961 0.656 0.344
#> SRR934223 1 0.929 0.2961 0.656 0.344
#> SRR934224 1 0.343 0.7215 0.936 0.064
#> SRR934225 1 0.343 0.7215 0.936 0.064
#> SRR934226 1 0.343 0.7215 0.936 0.064
#> SRR934227 1 0.343 0.7215 0.936 0.064
#> SRR934228 1 0.343 0.7215 0.936 0.064
#> SRR934229 1 0.343 0.7215 0.936 0.064
#> SRR934230 1 0.343 0.7215 0.936 0.064
#> SRR934231 1 0.343 0.7215 0.936 0.064
#> SRR934232 2 0.955 0.7079 0.376 0.624
#> SRR934233 2 0.955 0.7079 0.376 0.624
#> SRR934234 2 0.955 0.7079 0.376 0.624
#> SRR934235 2 0.955 0.7079 0.376 0.624
#> SRR934236 2 0.955 0.7079 0.376 0.624
#> SRR934237 2 0.955 0.7079 0.376 0.624
#> SRR934238 2 0.955 0.7079 0.376 0.624
#> SRR934239 2 0.955 0.7079 0.376 0.624
#> SRR934240 2 0.978 0.6728 0.412 0.588
#> SRR934241 2 0.978 0.6728 0.412 0.588
#> SRR934242 2 0.978 0.6728 0.412 0.588
#> SRR934243 2 0.978 0.6728 0.412 0.588
#> SRR934244 2 0.978 0.6728 0.412 0.588
#> SRR934245 2 0.978 0.6728 0.412 0.588
#> SRR934246 2 0.978 0.6728 0.412 0.588
#> SRR934247 2 0.978 0.6728 0.412 0.588
#> SRR934248 1 0.983 -0.0478 0.576 0.424
#> SRR934249 1 0.983 -0.0478 0.576 0.424
#> SRR934250 1 0.983 -0.0478 0.576 0.424
#> SRR934251 1 0.983 -0.0478 0.576 0.424
#> SRR934252 1 0.983 -0.0478 0.576 0.424
#> SRR934253 1 0.983 -0.0478 0.576 0.424
#> SRR934254 1 0.983 -0.0478 0.576 0.424
#> SRR934255 1 0.983 -0.0478 0.576 0.424
#> SRR934256 1 0.994 -0.2535 0.544 0.456
#> SRR934257 1 0.994 -0.2535 0.544 0.456
#> SRR934258 1 0.994 -0.2535 0.544 0.456
#> SRR934259 1 0.994 -0.2535 0.544 0.456
#> SRR934260 1 0.994 -0.2535 0.544 0.456
#> SRR934261 1 0.994 -0.2535 0.544 0.456
#> SRR934262 1 0.994 -0.2535 0.544 0.456
#> SRR934263 1 0.994 -0.2535 0.544 0.456
#> SRR934264 1 0.936 0.1773 0.648 0.352
#> SRR934265 1 0.936 0.1773 0.648 0.352
#> SRR934266 1 0.936 0.1773 0.648 0.352
#> SRR934267 1 0.936 0.1773 0.648 0.352
#> SRR934268 1 0.936 0.1773 0.648 0.352
#> SRR934269 1 0.936 0.1773 0.648 0.352
#> SRR934270 1 0.936 0.1773 0.648 0.352
#> SRR934271 1 0.936 0.1773 0.648 0.352
#> SRR934272 1 0.118 0.7389 0.984 0.016
#> SRR934273 1 0.118 0.7389 0.984 0.016
#> SRR934274 1 0.118 0.7389 0.984 0.016
#> SRR934275 1 0.118 0.7389 0.984 0.016
#> SRR934276 1 0.118 0.7389 0.984 0.016
#> SRR934277 1 0.118 0.7389 0.984 0.016
#> SRR934278 1 0.118 0.7389 0.984 0.016
#> SRR934279 1 0.118 0.7389 0.984 0.016
#> SRR934280 1 0.204 0.7368 0.968 0.032
#> SRR934281 1 0.204 0.7368 0.968 0.032
#> SRR934282 1 0.204 0.7368 0.968 0.032
#> SRR934283 1 0.204 0.7368 0.968 0.032
#> SRR934284 1 0.204 0.7368 0.968 0.032
#> SRR934285 1 0.204 0.7368 0.968 0.032
#> SRR934286 1 0.204 0.7368 0.968 0.032
#> SRR934287 1 0.204 0.7368 0.968 0.032
#> SRR934288 1 0.402 0.7286 0.920 0.080
#> SRR934289 1 0.402 0.7286 0.920 0.080
#> SRR934290 1 0.402 0.7286 0.920 0.080
#> SRR934291 1 0.402 0.7286 0.920 0.080
#> SRR934292 1 0.402 0.7286 0.920 0.080
#> SRR934293 1 0.402 0.7286 0.920 0.080
#> SRR934294 1 0.402 0.7286 0.920 0.080
#> SRR934295 1 0.402 0.7286 0.920 0.080
#> SRR934296 1 0.662 0.6070 0.828 0.172
#> SRR934297 1 0.662 0.6070 0.828 0.172
#> SRR934298 1 0.662 0.6070 0.828 0.172
#> SRR934299 1 0.662 0.6070 0.828 0.172
#> SRR934300 1 0.662 0.6070 0.828 0.172
#> SRR934301 1 0.662 0.6070 0.828 0.172
#> SRR934302 1 0.662 0.6070 0.828 0.172
#> SRR934303 1 0.662 0.6070 0.828 0.172
#> SRR934304 2 0.904 0.5005 0.320 0.680
#> SRR934305 2 0.904 0.5005 0.320 0.680
#> SRR934306 2 0.904 0.5005 0.320 0.680
#> SRR934307 2 0.904 0.5005 0.320 0.680
#> SRR934308 2 0.904 0.5005 0.320 0.680
#> SRR934309 2 0.904 0.5005 0.320 0.680
#> SRR934310 2 0.904 0.5005 0.320 0.680
#> SRR934311 2 0.904 0.5005 0.320 0.680
#> SRR934312 1 0.000 0.7420 1.000 0.000
#> SRR934313 1 0.000 0.7420 1.000 0.000
#> SRR934314 1 0.000 0.7420 1.000 0.000
#> SRR934315 1 0.000 0.7420 1.000 0.000
#> SRR934316 1 0.000 0.7420 1.000 0.000
#> SRR934317 1 0.000 0.7420 1.000 0.000
#> SRR934318 1 0.000 0.7420 1.000 0.000
#> SRR934319 1 0.000 0.7420 1.000 0.000
#> SRR934320 1 0.343 0.7286 0.936 0.064
#> SRR934321 1 0.343 0.7286 0.936 0.064
#> SRR934322 1 0.343 0.7286 0.936 0.064
#> SRR934323 1 0.343 0.7286 0.936 0.064
#> SRR934324 1 0.343 0.7286 0.936 0.064
#> SRR934325 1 0.343 0.7286 0.936 0.064
#> SRR934326 1 0.343 0.7286 0.936 0.064
#> SRR934327 1 0.343 0.7286 0.936 0.064
#> SRR934328 1 0.388 0.7321 0.924 0.076
#> SRR934329 1 0.388 0.7321 0.924 0.076
#> SRR934330 1 0.388 0.7321 0.924 0.076
#> SRR934331 1 0.388 0.7321 0.924 0.076
#> SRR934332 1 0.388 0.7321 0.924 0.076
#> SRR934333 1 0.388 0.7321 0.924 0.076
#> SRR934334 1 0.388 0.7321 0.924 0.076
#> SRR934335 1 0.388 0.7321 0.924 0.076
#> SRR934344 1 0.373 0.7337 0.928 0.072
#> SRR934345 1 0.373 0.7337 0.928 0.072
#> SRR934346 1 0.373 0.7337 0.928 0.072
#> SRR934347 1 0.373 0.7337 0.928 0.072
#> SRR934348 1 0.373 0.7337 0.928 0.072
#> SRR934349 1 0.373 0.7337 0.928 0.072
#> SRR934350 1 0.373 0.7337 0.928 0.072
#> SRR934351 1 0.373 0.7337 0.928 0.072
#> SRR934336 1 0.184 0.7382 0.972 0.028
#> SRR934337 1 0.184 0.7382 0.972 0.028
#> SRR934338 1 0.184 0.7382 0.972 0.028
#> SRR934339 1 0.184 0.7382 0.972 0.028
#> SRR934340 1 0.184 0.7382 0.972 0.028
#> SRR934341 1 0.184 0.7382 0.972 0.028
#> SRR934342 1 0.184 0.7382 0.972 0.028
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 3 0.704 0.502 0.400 0.024 0.576
#> SRR934217 3 0.704 0.502 0.400 0.024 0.576
#> SRR934218 3 0.704 0.502 0.400 0.024 0.576
#> SRR934219 3 0.704 0.502 0.400 0.024 0.576
#> SRR934220 3 0.704 0.502 0.400 0.024 0.576
#> SRR934221 3 0.704 0.502 0.400 0.024 0.576
#> SRR934222 3 0.704 0.502 0.400 0.024 0.576
#> SRR934223 3 0.704 0.502 0.400 0.024 0.576
#> SRR934224 1 0.417 0.630 0.872 0.036 0.092
#> SRR934225 1 0.417 0.630 0.872 0.036 0.092
#> SRR934226 1 0.417 0.630 0.872 0.036 0.092
#> SRR934227 1 0.417 0.630 0.872 0.036 0.092
#> SRR934228 1 0.417 0.630 0.872 0.036 0.092
#> SRR934229 1 0.417 0.630 0.872 0.036 0.092
#> SRR934230 1 0.417 0.630 0.872 0.036 0.092
#> SRR934231 1 0.417 0.630 0.872 0.036 0.092
#> SRR934232 2 0.823 0.628 0.156 0.636 0.208
#> SRR934233 2 0.823 0.628 0.156 0.636 0.208
#> SRR934234 2 0.823 0.628 0.156 0.636 0.208
#> SRR934235 2 0.823 0.628 0.156 0.636 0.208
#> SRR934236 2 0.823 0.628 0.156 0.636 0.208
#> SRR934237 2 0.823 0.628 0.156 0.636 0.208
#> SRR934238 2 0.823 0.628 0.156 0.636 0.208
#> SRR934239 2 0.823 0.628 0.156 0.636 0.208
#> SRR934240 2 0.744 0.726 0.220 0.684 0.096
#> SRR934241 2 0.744 0.726 0.220 0.684 0.096
#> SRR934242 2 0.744 0.726 0.220 0.684 0.096
#> SRR934243 2 0.744 0.726 0.220 0.684 0.096
#> SRR934244 2 0.744 0.726 0.220 0.684 0.096
#> SRR934245 2 0.744 0.726 0.220 0.684 0.096
#> SRR934246 2 0.744 0.726 0.220 0.684 0.096
#> SRR934247 2 0.744 0.726 0.220 0.684 0.096
#> SRR934248 3 0.990 0.477 0.292 0.304 0.404
#> SRR934249 3 0.990 0.477 0.292 0.304 0.404
#> SRR934250 3 0.990 0.477 0.292 0.304 0.404
#> SRR934251 3 0.990 0.477 0.292 0.304 0.404
#> SRR934252 3 0.990 0.477 0.292 0.304 0.404
#> SRR934253 3 0.990 0.477 0.292 0.304 0.404
#> SRR934254 3 0.990 0.477 0.292 0.304 0.404
#> SRR934255 3 0.990 0.477 0.292 0.304 0.404
#> SRR934256 2 0.754 0.598 0.332 0.612 0.056
#> SRR934257 2 0.754 0.598 0.332 0.612 0.056
#> SRR934258 2 0.754 0.598 0.332 0.612 0.056
#> SRR934259 2 0.754 0.598 0.332 0.612 0.056
#> SRR934260 2 0.754 0.598 0.332 0.612 0.056
#> SRR934261 2 0.754 0.598 0.332 0.612 0.056
#> SRR934262 2 0.754 0.598 0.332 0.612 0.056
#> SRR934263 2 0.754 0.598 0.332 0.612 0.056
#> SRR934264 1 0.946 -0.494 0.412 0.180 0.408
#> SRR934265 1 0.946 -0.494 0.412 0.180 0.408
#> SRR934266 1 0.946 -0.494 0.412 0.180 0.408
#> SRR934267 1 0.946 -0.494 0.412 0.180 0.408
#> SRR934268 1 0.946 -0.494 0.412 0.180 0.408
#> SRR934269 1 0.946 -0.494 0.412 0.180 0.408
#> SRR934270 1 0.946 -0.494 0.412 0.180 0.408
#> SRR934271 1 0.946 -0.494 0.412 0.180 0.408
#> SRR934272 1 0.140 0.672 0.968 0.004 0.028
#> SRR934273 1 0.140 0.672 0.968 0.004 0.028
#> SRR934274 1 0.140 0.672 0.968 0.004 0.028
#> SRR934275 1 0.140 0.672 0.968 0.004 0.028
#> SRR934276 1 0.140 0.672 0.968 0.004 0.028
#> SRR934277 1 0.140 0.672 0.968 0.004 0.028
#> SRR934278 1 0.140 0.672 0.968 0.004 0.028
#> SRR934279 1 0.140 0.672 0.968 0.004 0.028
#> SRR934280 1 0.212 0.674 0.948 0.040 0.012
#> SRR934281 1 0.212 0.674 0.948 0.040 0.012
#> SRR934282 1 0.212 0.674 0.948 0.040 0.012
#> SRR934283 1 0.212 0.674 0.948 0.040 0.012
#> SRR934284 1 0.212 0.674 0.948 0.040 0.012
#> SRR934285 1 0.212 0.674 0.948 0.040 0.012
#> SRR934286 1 0.212 0.674 0.948 0.040 0.012
#> SRR934287 1 0.212 0.674 0.948 0.040 0.012
#> SRR934288 1 0.836 0.553 0.616 0.244 0.140
#> SRR934289 1 0.836 0.553 0.616 0.244 0.140
#> SRR934290 1 0.836 0.553 0.616 0.244 0.140
#> SRR934291 1 0.836 0.553 0.616 0.244 0.140
#> SRR934292 1 0.836 0.553 0.616 0.244 0.140
#> SRR934293 1 0.836 0.553 0.616 0.244 0.140
#> SRR934294 1 0.836 0.553 0.616 0.244 0.140
#> SRR934295 1 0.836 0.553 0.616 0.244 0.140
#> SRR934296 1 0.820 0.485 0.616 0.268 0.116
#> SRR934297 1 0.820 0.485 0.616 0.268 0.116
#> SRR934298 1 0.820 0.485 0.616 0.268 0.116
#> SRR934299 1 0.820 0.485 0.616 0.268 0.116
#> SRR934300 1 0.820 0.485 0.616 0.268 0.116
#> SRR934301 1 0.820 0.485 0.616 0.268 0.116
#> SRR934302 1 0.820 0.485 0.616 0.268 0.116
#> SRR934303 1 0.820 0.485 0.616 0.268 0.116
#> SRR934304 3 0.776 0.553 0.144 0.180 0.676
#> SRR934305 3 0.776 0.553 0.144 0.180 0.676
#> SRR934306 3 0.776 0.553 0.144 0.180 0.676
#> SRR934307 3 0.776 0.553 0.144 0.180 0.676
#> SRR934308 3 0.776 0.553 0.144 0.180 0.676
#> SRR934309 3 0.776 0.553 0.144 0.180 0.676
#> SRR934310 3 0.776 0.553 0.144 0.180 0.676
#> SRR934311 3 0.776 0.553 0.144 0.180 0.676
#> SRR934312 1 0.101 0.681 0.980 0.012 0.008
#> SRR934313 1 0.101 0.681 0.980 0.012 0.008
#> SRR934314 1 0.101 0.681 0.980 0.012 0.008
#> SRR934315 1 0.101 0.681 0.980 0.012 0.008
#> SRR934316 1 0.101 0.681 0.980 0.012 0.008
#> SRR934317 1 0.101 0.681 0.980 0.012 0.008
#> SRR934318 1 0.101 0.681 0.980 0.012 0.008
#> SRR934319 1 0.101 0.681 0.980 0.012 0.008
#> SRR934320 1 0.464 0.666 0.852 0.104 0.044
#> SRR934321 1 0.464 0.666 0.852 0.104 0.044
#> SRR934322 1 0.464 0.666 0.852 0.104 0.044
#> SRR934323 1 0.464 0.666 0.852 0.104 0.044
#> SRR934324 1 0.464 0.666 0.852 0.104 0.044
#> SRR934325 1 0.464 0.666 0.852 0.104 0.044
#> SRR934326 1 0.464 0.666 0.852 0.104 0.044
#> SRR934327 1 0.464 0.666 0.852 0.104 0.044
#> SRR934328 1 0.846 0.547 0.608 0.244 0.148
#> SRR934329 1 0.846 0.547 0.608 0.244 0.148
#> SRR934330 1 0.846 0.547 0.608 0.244 0.148
#> SRR934331 1 0.846 0.547 0.608 0.244 0.148
#> SRR934332 1 0.846 0.547 0.608 0.244 0.148
#> SRR934333 1 0.846 0.547 0.608 0.244 0.148
#> SRR934334 1 0.846 0.547 0.608 0.244 0.148
#> SRR934335 1 0.846 0.547 0.608 0.244 0.148
#> SRR934344 1 0.786 0.574 0.664 0.204 0.132
#> SRR934345 1 0.786 0.574 0.664 0.204 0.132
#> SRR934346 1 0.786 0.574 0.664 0.204 0.132
#> SRR934347 1 0.786 0.574 0.664 0.204 0.132
#> SRR934348 1 0.786 0.574 0.664 0.204 0.132
#> SRR934349 1 0.786 0.574 0.664 0.204 0.132
#> SRR934350 1 0.786 0.574 0.664 0.204 0.132
#> SRR934351 1 0.786 0.574 0.664 0.204 0.132
#> SRR934336 1 0.288 0.656 0.924 0.024 0.052
#> SRR934337 1 0.288 0.656 0.924 0.024 0.052
#> SRR934338 1 0.288 0.656 0.924 0.024 0.052
#> SRR934339 1 0.288 0.656 0.924 0.024 0.052
#> SRR934340 1 0.288 0.656 0.924 0.024 0.052
#> SRR934341 1 0.288 0.656 0.924 0.024 0.052
#> SRR934342 1 0.288 0.656 0.924 0.024 0.052
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 1 0.860 -0.160 0.400 0.064 0.388 0.148
#> SRR934217 1 0.860 -0.160 0.400 0.064 0.388 0.148
#> SRR934218 1 0.865 -0.160 0.396 0.068 0.388 0.148
#> SRR934219 1 0.860 -0.160 0.400 0.064 0.388 0.148
#> SRR934220 1 0.860 -0.160 0.400 0.064 0.388 0.148
#> SRR934221 1 0.865 -0.160 0.396 0.068 0.388 0.148
#> SRR934222 1 0.860 -0.160 0.400 0.064 0.388 0.148
#> SRR934223 1 0.860 -0.160 0.400 0.064 0.388 0.148
#> SRR934224 1 0.536 0.515 0.676 0.000 0.036 0.288
#> SRR934225 1 0.536 0.515 0.676 0.000 0.036 0.288
#> SRR934226 1 0.536 0.515 0.676 0.000 0.036 0.288
#> SRR934227 1 0.536 0.515 0.676 0.000 0.036 0.288
#> SRR934228 1 0.536 0.515 0.676 0.000 0.036 0.288
#> SRR934229 1 0.536 0.515 0.676 0.000 0.036 0.288
#> SRR934230 1 0.536 0.515 0.676 0.000 0.036 0.288
#> SRR934231 1 0.536 0.515 0.676 0.000 0.036 0.288
#> SRR934232 2 0.567 0.559 0.104 0.764 0.096 0.036
#> SRR934233 2 0.567 0.559 0.104 0.764 0.096 0.036
#> SRR934234 2 0.567 0.559 0.104 0.764 0.096 0.036
#> SRR934235 2 0.567 0.559 0.104 0.764 0.096 0.036
#> SRR934236 2 0.567 0.559 0.104 0.764 0.096 0.036
#> SRR934237 2 0.567 0.559 0.104 0.764 0.096 0.036
#> SRR934238 2 0.567 0.559 0.104 0.764 0.096 0.036
#> SRR934239 2 0.567 0.559 0.104 0.764 0.096 0.036
#> SRR934240 2 0.322 0.713 0.036 0.888 0.008 0.068
#> SRR934241 2 0.322 0.713 0.036 0.888 0.008 0.068
#> SRR934242 2 0.322 0.713 0.036 0.888 0.008 0.068
#> SRR934243 2 0.322 0.713 0.036 0.888 0.008 0.068
#> SRR934244 2 0.322 0.713 0.036 0.888 0.008 0.068
#> SRR934245 2 0.322 0.713 0.036 0.888 0.008 0.068
#> SRR934246 2 0.322 0.713 0.036 0.888 0.008 0.068
#> SRR934247 2 0.322 0.713 0.036 0.888 0.008 0.068
#> SRR934248 3 0.990 0.627 0.236 0.272 0.300 0.192
#> SRR934249 3 0.990 0.627 0.236 0.272 0.300 0.192
#> SRR934250 3 0.990 0.627 0.236 0.272 0.300 0.192
#> SRR934251 3 0.990 0.627 0.236 0.272 0.300 0.192
#> SRR934252 3 0.990 0.627 0.236 0.272 0.300 0.192
#> SRR934253 3 0.990 0.627 0.236 0.272 0.300 0.192
#> SRR934254 3 0.990 0.627 0.236 0.272 0.300 0.192
#> SRR934255 3 0.990 0.627 0.236 0.272 0.300 0.192
#> SRR934256 2 0.793 0.626 0.156 0.580 0.060 0.204
#> SRR934257 2 0.793 0.626 0.156 0.580 0.060 0.204
#> SRR934258 2 0.793 0.626 0.156 0.580 0.060 0.204
#> SRR934259 2 0.793 0.626 0.156 0.580 0.060 0.204
#> SRR934260 2 0.793 0.626 0.156 0.580 0.060 0.204
#> SRR934261 2 0.795 0.626 0.152 0.580 0.064 0.204
#> SRR934262 2 0.793 0.626 0.156 0.580 0.060 0.204
#> SRR934263 2 0.793 0.626 0.156 0.580 0.060 0.204
#> SRR934264 1 0.958 -0.419 0.356 0.220 0.292 0.132
#> SRR934265 1 0.958 -0.419 0.356 0.220 0.292 0.132
#> SRR934266 1 0.958 -0.419 0.356 0.220 0.292 0.132
#> SRR934267 1 0.958 -0.419 0.356 0.220 0.292 0.132
#> SRR934268 1 0.958 -0.419 0.356 0.220 0.292 0.132
#> SRR934269 1 0.958 -0.419 0.356 0.220 0.292 0.132
#> SRR934270 1 0.958 -0.419 0.356 0.220 0.292 0.132
#> SRR934271 1 0.958 -0.419 0.356 0.220 0.292 0.132
#> SRR934272 1 0.585 0.543 0.580 0.024 0.008 0.388
#> SRR934273 1 0.585 0.543 0.580 0.024 0.008 0.388
#> SRR934274 1 0.585 0.543 0.580 0.024 0.008 0.388
#> SRR934275 1 0.585 0.543 0.580 0.024 0.008 0.388
#> SRR934276 1 0.585 0.543 0.580 0.024 0.008 0.388
#> SRR934277 1 0.585 0.543 0.580 0.024 0.008 0.388
#> SRR934278 1 0.585 0.543 0.580 0.024 0.008 0.388
#> SRR934279 1 0.585 0.543 0.580 0.024 0.008 0.388
#> SRR934280 1 0.706 0.509 0.496 0.044 0.040 0.420
#> SRR934281 1 0.706 0.509 0.496 0.044 0.040 0.420
#> SRR934282 1 0.706 0.509 0.496 0.044 0.040 0.420
#> SRR934283 1 0.706 0.509 0.496 0.044 0.040 0.420
#> SRR934284 1 0.706 0.509 0.496 0.044 0.040 0.420
#> SRR934285 1 0.706 0.509 0.496 0.044 0.040 0.420
#> SRR934286 1 0.706 0.509 0.496 0.044 0.040 0.420
#> SRR934287 1 0.706 0.509 0.496 0.044 0.040 0.420
#> SRR934288 4 0.199 0.813 0.024 0.012 0.020 0.944
#> SRR934289 4 0.199 0.813 0.024 0.012 0.020 0.944
#> SRR934290 4 0.199 0.813 0.024 0.012 0.020 0.944
#> SRR934291 4 0.199 0.813 0.024 0.012 0.020 0.944
#> SRR934292 4 0.199 0.813 0.024 0.012 0.020 0.944
#> SRR934293 4 0.199 0.813 0.024 0.012 0.020 0.944
#> SRR934294 4 0.199 0.813 0.024 0.012 0.020 0.944
#> SRR934295 4 0.199 0.813 0.024 0.012 0.020 0.944
#> SRR934296 4 0.656 0.616 0.120 0.156 0.032 0.692
#> SRR934297 4 0.656 0.616 0.120 0.156 0.032 0.692
#> SRR934298 4 0.656 0.616 0.120 0.156 0.032 0.692
#> SRR934299 4 0.656 0.616 0.120 0.156 0.032 0.692
#> SRR934300 4 0.656 0.616 0.120 0.156 0.032 0.692
#> SRR934301 4 0.656 0.616 0.120 0.156 0.032 0.692
#> SRR934302 4 0.656 0.616 0.120 0.156 0.032 0.692
#> SRR934303 4 0.656 0.616 0.120 0.156 0.032 0.692
#> SRR934304 3 0.613 0.620 0.044 0.192 0.712 0.052
#> SRR934305 3 0.613 0.620 0.044 0.192 0.712 0.052
#> SRR934306 3 0.613 0.620 0.044 0.192 0.712 0.052
#> SRR934307 3 0.613 0.620 0.044 0.192 0.712 0.052
#> SRR934308 3 0.613 0.620 0.044 0.192 0.712 0.052
#> SRR934309 3 0.617 0.619 0.044 0.196 0.708 0.052
#> SRR934310 3 0.613 0.620 0.044 0.192 0.712 0.052
#> SRR934311 3 0.613 0.620 0.044 0.192 0.712 0.052
#> SRR934312 1 0.635 0.509 0.512 0.032 0.016 0.440
#> SRR934313 1 0.635 0.509 0.512 0.032 0.016 0.440
#> SRR934314 1 0.635 0.509 0.512 0.032 0.016 0.440
#> SRR934315 1 0.635 0.509 0.512 0.032 0.016 0.440
#> SRR934316 1 0.635 0.509 0.512 0.032 0.016 0.440
#> SRR934317 1 0.635 0.509 0.512 0.032 0.016 0.440
#> SRR934318 1 0.635 0.509 0.512 0.032 0.016 0.440
#> SRR934319 1 0.635 0.509 0.512 0.032 0.016 0.440
#> SRR934320 1 0.743 0.425 0.496 0.056 0.052 0.396
#> SRR934321 1 0.743 0.425 0.496 0.056 0.052 0.396
#> SRR934322 1 0.743 0.425 0.496 0.056 0.052 0.396
#> SRR934323 1 0.743 0.425 0.496 0.056 0.052 0.396
#> SRR934324 1 0.743 0.425 0.496 0.056 0.052 0.396
#> SRR934325 1 0.743 0.425 0.496 0.056 0.052 0.396
#> SRR934326 1 0.743 0.425 0.496 0.056 0.052 0.396
#> SRR934327 1 0.743 0.425 0.496 0.056 0.052 0.396
#> SRR934328 4 0.151 0.815 0.020 0.008 0.012 0.960
#> SRR934329 4 0.151 0.815 0.020 0.008 0.012 0.960
#> SRR934330 4 0.151 0.815 0.020 0.008 0.012 0.960
#> SRR934331 4 0.151 0.815 0.020 0.008 0.012 0.960
#> SRR934332 4 0.151 0.815 0.020 0.008 0.012 0.960
#> SRR934333 4 0.151 0.815 0.020 0.008 0.012 0.960
#> SRR934334 4 0.151 0.815 0.020 0.008 0.012 0.960
#> SRR934335 4 0.151 0.815 0.020 0.008 0.012 0.960
#> SRR934344 4 0.346 0.754 0.124 0.004 0.016 0.856
#> SRR934345 4 0.346 0.754 0.124 0.004 0.016 0.856
#> SRR934346 4 0.346 0.754 0.124 0.004 0.016 0.856
#> SRR934347 4 0.346 0.754 0.124 0.004 0.016 0.856
#> SRR934348 4 0.346 0.754 0.124 0.004 0.016 0.856
#> SRR934349 4 0.346 0.754 0.124 0.004 0.016 0.856
#> SRR934350 4 0.346 0.754 0.124 0.004 0.016 0.856
#> SRR934351 4 0.346 0.754 0.124 0.004 0.016 0.856
#> SRR934336 1 0.584 0.557 0.608 0.008 0.028 0.356
#> SRR934337 1 0.584 0.557 0.608 0.008 0.028 0.356
#> SRR934338 1 0.584 0.557 0.608 0.008 0.028 0.356
#> SRR934339 1 0.584 0.557 0.608 0.008 0.028 0.356
#> SRR934340 1 0.584 0.557 0.608 0.008 0.028 0.356
#> SRR934341 1 0.584 0.557 0.608 0.008 0.028 0.356
#> SRR934342 1 0.584 0.557 0.608 0.008 0.028 0.356
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0.788 1.000 0.184 0.008 0.384 0.356 0.068
#> SRR934217 3 0.788 1.000 0.184 0.008 0.384 0.356 0.068
#> SRR934218 3 0.788 1.000 0.184 0.008 0.384 0.356 0.068
#> SRR934219 3 0.788 1.000 0.184 0.008 0.384 0.356 0.068
#> SRR934220 3 0.792 0.997 0.184 0.008 0.380 0.356 0.072
#> SRR934221 3 0.788 1.000 0.184 0.008 0.384 0.356 0.068
#> SRR934222 3 0.788 1.000 0.184 0.008 0.384 0.356 0.068
#> SRR934223 3 0.788 1.000 0.184 0.008 0.384 0.356 0.068
#> SRR934224 1 0.497 0.620 0.676 0.004 0.264 0.000 0.056
#> SRR934225 1 0.497 0.620 0.676 0.004 0.264 0.000 0.056
#> SRR934226 1 0.497 0.620 0.676 0.004 0.264 0.000 0.056
#> SRR934227 1 0.497 0.620 0.676 0.004 0.264 0.000 0.056
#> SRR934228 1 0.497 0.620 0.676 0.004 0.264 0.000 0.056
#> SRR934229 1 0.497 0.620 0.676 0.004 0.264 0.000 0.056
#> SRR934230 1 0.497 0.620 0.676 0.004 0.264 0.000 0.056
#> SRR934231 1 0.497 0.620 0.676 0.004 0.264 0.000 0.056
#> SRR934232 2 0.605 0.540 0.004 0.676 0.120 0.152 0.048
#> SRR934233 2 0.605 0.540 0.004 0.676 0.120 0.152 0.048
#> SRR934234 2 0.605 0.540 0.004 0.676 0.120 0.152 0.048
#> SRR934235 2 0.605 0.540 0.004 0.676 0.120 0.152 0.048
#> SRR934236 2 0.605 0.540 0.004 0.676 0.120 0.152 0.048
#> SRR934237 2 0.605 0.540 0.004 0.676 0.120 0.152 0.048
#> SRR934238 2 0.605 0.540 0.004 0.676 0.120 0.152 0.048
#> SRR934239 2 0.605 0.540 0.004 0.676 0.120 0.152 0.048
#> SRR934240 2 0.251 0.700 0.028 0.908 0.000 0.044 0.020
#> SRR934241 2 0.251 0.700 0.028 0.908 0.000 0.044 0.020
#> SRR934242 2 0.251 0.700 0.028 0.908 0.000 0.044 0.020
#> SRR934243 2 0.251 0.700 0.028 0.908 0.000 0.044 0.020
#> SRR934244 2 0.251 0.700 0.028 0.908 0.000 0.044 0.020
#> SRR934245 2 0.251 0.700 0.028 0.908 0.000 0.044 0.020
#> SRR934246 2 0.251 0.700 0.028 0.908 0.000 0.044 0.020
#> SRR934247 2 0.251 0.700 0.028 0.908 0.000 0.044 0.020
#> SRR934248 4 0.968 0.564 0.120 0.184 0.176 0.320 0.200
#> SRR934249 4 0.968 0.564 0.120 0.184 0.176 0.320 0.200
#> SRR934250 4 0.968 0.564 0.120 0.184 0.180 0.320 0.196
#> SRR934251 4 0.968 0.564 0.120 0.184 0.180 0.320 0.196
#> SRR934252 4 0.968 0.564 0.120 0.184 0.176 0.320 0.200
#> SRR934253 4 0.968 0.564 0.120 0.184 0.180 0.320 0.196
#> SRR934254 4 0.968 0.564 0.120 0.184 0.176 0.320 0.200
#> SRR934255 4 0.968 0.564 0.120 0.184 0.176 0.320 0.200
#> SRR934256 2 0.724 0.610 0.108 0.596 0.148 0.016 0.132
#> SRR934257 2 0.724 0.610 0.108 0.596 0.148 0.016 0.132
#> SRR934258 2 0.724 0.610 0.108 0.596 0.148 0.016 0.132
#> SRR934259 2 0.724 0.610 0.108 0.596 0.148 0.016 0.132
#> SRR934260 2 0.724 0.610 0.108 0.596 0.148 0.016 0.132
#> SRR934261 2 0.724 0.610 0.108 0.596 0.148 0.016 0.132
#> SRR934262 2 0.724 0.610 0.108 0.596 0.148 0.016 0.132
#> SRR934263 2 0.724 0.610 0.108 0.596 0.148 0.016 0.132
#> SRR934264 4 0.940 0.501 0.272 0.128 0.204 0.312 0.084
#> SRR934265 4 0.940 0.501 0.272 0.128 0.204 0.312 0.084
#> SRR934266 4 0.940 0.501 0.272 0.128 0.204 0.312 0.084
#> SRR934267 4 0.940 0.501 0.272 0.128 0.204 0.312 0.084
#> SRR934268 4 0.940 0.501 0.272 0.128 0.204 0.312 0.084
#> SRR934269 4 0.940 0.501 0.272 0.128 0.204 0.312 0.084
#> SRR934270 4 0.940 0.501 0.272 0.128 0.204 0.312 0.084
#> SRR934271 4 0.940 0.501 0.272 0.128 0.204 0.312 0.084
#> SRR934272 1 0.409 0.739 0.804 0.004 0.072 0.004 0.116
#> SRR934273 1 0.409 0.739 0.804 0.004 0.072 0.004 0.116
#> SRR934274 1 0.409 0.739 0.804 0.004 0.072 0.004 0.116
#> SRR934275 1 0.409 0.739 0.804 0.004 0.072 0.004 0.116
#> SRR934276 1 0.409 0.739 0.804 0.004 0.072 0.004 0.116
#> SRR934277 1 0.409 0.739 0.804 0.004 0.072 0.004 0.116
#> SRR934278 1 0.409 0.739 0.804 0.004 0.072 0.004 0.116
#> SRR934279 1 0.409 0.739 0.804 0.004 0.072 0.004 0.116
#> SRR934280 1 0.321 0.767 0.876 0.032 0.060 0.004 0.028
#> SRR934281 1 0.321 0.767 0.876 0.032 0.060 0.004 0.028
#> SRR934282 1 0.321 0.767 0.876 0.032 0.060 0.004 0.028
#> SRR934283 1 0.321 0.767 0.876 0.032 0.060 0.004 0.028
#> SRR934284 1 0.321 0.767 0.876 0.032 0.060 0.004 0.028
#> SRR934285 1 0.321 0.767 0.876 0.032 0.060 0.004 0.028
#> SRR934286 1 0.321 0.767 0.876 0.032 0.060 0.004 0.028
#> SRR934287 1 0.321 0.767 0.876 0.032 0.060 0.004 0.028
#> SRR934288 5 0.526 0.827 0.232 0.012 0.064 0.004 0.688
#> SRR934289 5 0.526 0.827 0.232 0.012 0.064 0.004 0.688
#> SRR934290 5 0.526 0.827 0.232 0.012 0.064 0.004 0.688
#> SRR934291 5 0.526 0.827 0.232 0.012 0.064 0.004 0.688
#> SRR934292 5 0.526 0.827 0.232 0.012 0.064 0.004 0.688
#> SRR934293 5 0.526 0.827 0.232 0.012 0.064 0.004 0.688
#> SRR934294 5 0.526 0.827 0.232 0.012 0.064 0.004 0.688
#> SRR934295 5 0.526 0.827 0.232 0.012 0.064 0.004 0.688
#> SRR934296 5 0.844 0.633 0.272 0.104 0.128 0.048 0.448
#> SRR934297 5 0.844 0.633 0.272 0.104 0.128 0.048 0.448
#> SRR934298 5 0.844 0.633 0.272 0.104 0.128 0.048 0.448
#> SRR934299 5 0.844 0.633 0.272 0.104 0.128 0.048 0.448
#> SRR934300 5 0.844 0.633 0.272 0.104 0.128 0.048 0.448
#> SRR934301 5 0.844 0.633 0.272 0.104 0.128 0.048 0.448
#> SRR934302 5 0.844 0.633 0.272 0.104 0.128 0.048 0.448
#> SRR934303 5 0.844 0.633 0.272 0.104 0.128 0.048 0.448
#> SRR934304 4 0.249 0.250 0.024 0.048 0.000 0.908 0.020
#> SRR934305 4 0.249 0.250 0.024 0.048 0.000 0.908 0.020
#> SRR934306 4 0.239 0.250 0.024 0.048 0.000 0.912 0.016
#> SRR934307 4 0.239 0.250 0.024 0.048 0.000 0.912 0.016
#> SRR934308 4 0.274 0.248 0.024 0.048 0.004 0.900 0.024
#> SRR934309 4 0.239 0.250 0.024 0.048 0.000 0.912 0.016
#> SRR934310 4 0.249 0.250 0.024 0.048 0.000 0.908 0.020
#> SRR934311 4 0.239 0.250 0.024 0.048 0.000 0.912 0.016
#> SRR934312 1 0.432 0.737 0.808 0.016 0.060 0.012 0.104
#> SRR934313 1 0.432 0.737 0.808 0.016 0.060 0.012 0.104
#> SRR934314 1 0.432 0.737 0.808 0.016 0.060 0.012 0.104
#> SRR934315 1 0.432 0.737 0.808 0.016 0.060 0.012 0.104
#> SRR934316 1 0.432 0.737 0.808 0.016 0.060 0.012 0.104
#> SRR934317 1 0.432 0.737 0.808 0.016 0.060 0.012 0.104
#> SRR934318 1 0.432 0.737 0.808 0.016 0.060 0.012 0.104
#> SRR934319 1 0.432 0.737 0.808 0.016 0.060 0.012 0.104
#> SRR934320 1 0.508 0.699 0.752 0.028 0.108 0.004 0.108
#> SRR934321 1 0.508 0.699 0.752 0.028 0.108 0.004 0.108
#> SRR934322 1 0.508 0.699 0.752 0.028 0.108 0.004 0.108
#> SRR934323 1 0.508 0.699 0.752 0.028 0.108 0.004 0.108
#> SRR934324 1 0.508 0.699 0.752 0.028 0.108 0.004 0.108
#> SRR934325 1 0.508 0.699 0.752 0.028 0.108 0.004 0.108
#> SRR934326 1 0.508 0.699 0.752 0.028 0.108 0.004 0.108
#> SRR934327 1 0.508 0.699 0.752 0.028 0.108 0.004 0.108
#> SRR934328 5 0.400 0.834 0.208 0.004 0.008 0.012 0.768
#> SRR934329 5 0.400 0.834 0.208 0.004 0.008 0.012 0.768
#> SRR934330 5 0.400 0.834 0.208 0.004 0.008 0.012 0.768
#> SRR934331 5 0.400 0.834 0.208 0.004 0.008 0.012 0.768
#> SRR934332 5 0.400 0.834 0.208 0.004 0.008 0.012 0.768
#> SRR934333 5 0.400 0.834 0.208 0.004 0.008 0.012 0.768
#> SRR934334 5 0.400 0.834 0.208 0.004 0.008 0.012 0.768
#> SRR934335 5 0.400 0.834 0.208 0.004 0.008 0.012 0.768
#> SRR934344 5 0.428 0.813 0.224 0.000 0.040 0.000 0.736
#> SRR934345 5 0.428 0.813 0.224 0.000 0.040 0.000 0.736
#> SRR934346 5 0.428 0.813 0.224 0.000 0.040 0.000 0.736
#> SRR934347 5 0.428 0.813 0.224 0.000 0.040 0.000 0.736
#> SRR934348 5 0.428 0.813 0.224 0.000 0.040 0.000 0.736
#> SRR934349 5 0.428 0.813 0.224 0.000 0.040 0.000 0.736
#> SRR934350 5 0.428 0.813 0.224 0.000 0.040 0.000 0.736
#> SRR934351 5 0.428 0.813 0.224 0.000 0.040 0.000 0.736
#> SRR934336 1 0.224 0.783 0.920 0.004 0.036 0.004 0.036
#> SRR934337 1 0.224 0.783 0.920 0.004 0.036 0.004 0.036
#> SRR934338 1 0.224 0.783 0.920 0.004 0.036 0.004 0.036
#> SRR934339 1 0.224 0.783 0.920 0.004 0.036 0.004 0.036
#> SRR934340 1 0.224 0.783 0.920 0.004 0.036 0.004 0.036
#> SRR934341 1 0.224 0.783 0.920 0.004 0.036 0.004 0.036
#> SRR934342 1 0.224 0.783 0.920 0.004 0.036 0.004 0.036
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.7554 0.3506 0.064 0.004 0.448 0.112 0.300 0.072
#> SRR934217 3 0.7554 0.3506 0.064 0.004 0.448 0.112 0.300 0.072
#> SRR934218 3 0.7554 0.3506 0.064 0.004 0.448 0.112 0.300 0.072
#> SRR934219 3 0.7554 0.3506 0.064 0.004 0.448 0.112 0.300 0.072
#> SRR934220 3 0.7585 0.3489 0.064 0.004 0.444 0.116 0.300 0.072
#> SRR934221 3 0.7554 0.3506 0.064 0.004 0.448 0.112 0.300 0.072
#> SRR934222 3 0.7554 0.3506 0.064 0.004 0.448 0.112 0.300 0.072
#> SRR934223 3 0.7554 0.3506 0.064 0.004 0.448 0.112 0.300 0.072
#> SRR934224 3 0.4385 0.1688 0.024 0.000 0.532 0.000 0.000 0.444
#> SRR934225 3 0.4385 0.1688 0.024 0.000 0.532 0.000 0.000 0.444
#> SRR934226 3 0.4385 0.1688 0.024 0.000 0.532 0.000 0.000 0.444
#> SRR934227 3 0.4385 0.1688 0.024 0.000 0.532 0.000 0.000 0.444
#> SRR934228 3 0.4385 0.1688 0.024 0.000 0.532 0.000 0.000 0.444
#> SRR934229 3 0.4385 0.1688 0.024 0.000 0.532 0.000 0.000 0.444
#> SRR934230 3 0.4385 0.1688 0.024 0.000 0.532 0.000 0.000 0.444
#> SRR934231 3 0.4385 0.1688 0.024 0.000 0.532 0.000 0.000 0.444
#> SRR934232 2 0.4999 0.4500 0.008 0.676 0.008 0.212 0.096 0.000
#> SRR934233 2 0.4999 0.4500 0.008 0.676 0.008 0.212 0.096 0.000
#> SRR934234 2 0.4999 0.4500 0.008 0.676 0.008 0.212 0.096 0.000
#> SRR934235 2 0.4999 0.4500 0.008 0.676 0.008 0.212 0.096 0.000
#> SRR934236 2 0.4999 0.4500 0.008 0.676 0.008 0.212 0.096 0.000
#> SRR934237 2 0.4999 0.4500 0.008 0.676 0.008 0.212 0.096 0.000
#> SRR934238 2 0.4999 0.4500 0.008 0.676 0.008 0.212 0.096 0.000
#> SRR934239 2 0.4999 0.4500 0.008 0.676 0.008 0.212 0.096 0.000
#> SRR934240 2 0.0976 0.6476 0.016 0.968 0.000 0.000 0.008 0.008
#> SRR934241 2 0.0976 0.6476 0.016 0.968 0.000 0.000 0.008 0.008
#> SRR934242 2 0.0976 0.6476 0.016 0.968 0.000 0.000 0.008 0.008
#> SRR934243 2 0.0976 0.6476 0.016 0.968 0.000 0.000 0.008 0.008
#> SRR934244 2 0.0976 0.6476 0.016 0.968 0.000 0.000 0.008 0.008
#> SRR934245 2 0.0976 0.6476 0.016 0.968 0.000 0.000 0.008 0.008
#> SRR934246 2 0.0976 0.6476 0.016 0.968 0.000 0.000 0.008 0.008
#> SRR934247 2 0.0976 0.6476 0.016 0.968 0.000 0.000 0.008 0.008
#> SRR934248 4 0.8744 0.9974 0.080 0.172 0.056 0.344 0.284 0.064
#> SRR934249 4 0.8784 0.9974 0.080 0.172 0.060 0.340 0.284 0.064
#> SRR934250 4 0.8744 0.9974 0.080 0.172 0.056 0.344 0.284 0.064
#> SRR934251 4 0.8784 0.9974 0.080 0.172 0.060 0.340 0.284 0.064
#> SRR934252 4 0.8784 0.9974 0.080 0.172 0.060 0.340 0.284 0.064
#> SRR934253 4 0.8784 0.9974 0.080 0.172 0.060 0.340 0.284 0.064
#> SRR934254 4 0.8744 0.9974 0.080 0.172 0.056 0.344 0.284 0.064
#> SRR934255 4 0.8744 0.9974 0.080 0.172 0.056 0.344 0.284 0.064
#> SRR934256 2 0.7388 0.5452 0.092 0.500 0.064 0.272 0.016 0.056
#> SRR934257 2 0.7395 0.5452 0.088 0.500 0.068 0.272 0.016 0.056
#> SRR934258 2 0.7479 0.5451 0.092 0.500 0.068 0.264 0.020 0.056
#> SRR934259 2 0.7395 0.5452 0.088 0.500 0.068 0.272 0.016 0.056
#> SRR934260 2 0.7567 0.5447 0.084 0.500 0.072 0.260 0.028 0.056
#> SRR934261 2 0.7591 0.5439 0.092 0.500 0.076 0.252 0.024 0.056
#> SRR934262 2 0.7448 0.5451 0.092 0.500 0.064 0.268 0.020 0.056
#> SRR934263 2 0.7388 0.5452 0.092 0.500 0.064 0.272 0.016 0.056
#> SRR934264 5 0.9169 0.0372 0.036 0.084 0.192 0.228 0.288 0.172
#> SRR934265 5 0.9169 0.0372 0.036 0.084 0.192 0.228 0.288 0.172
#> SRR934266 5 0.9169 0.0372 0.036 0.084 0.192 0.228 0.288 0.172
#> SRR934267 5 0.9169 0.0372 0.036 0.084 0.192 0.228 0.288 0.172
#> SRR934268 5 0.9169 0.0372 0.036 0.084 0.192 0.228 0.288 0.172
#> SRR934269 5 0.9169 0.0372 0.036 0.084 0.192 0.228 0.288 0.172
#> SRR934270 5 0.9169 0.0372 0.036 0.084 0.192 0.228 0.288 0.172
#> SRR934271 5 0.9169 0.0372 0.036 0.084 0.192 0.228 0.288 0.172
#> SRR934272 6 0.6348 0.5556 0.140 0.004 0.184 0.048 0.020 0.604
#> SRR934273 6 0.6348 0.5556 0.140 0.004 0.184 0.048 0.020 0.604
#> SRR934274 6 0.6348 0.5556 0.140 0.004 0.184 0.048 0.020 0.604
#> SRR934275 6 0.6348 0.5556 0.140 0.004 0.184 0.048 0.020 0.604
#> SRR934276 6 0.6348 0.5556 0.140 0.004 0.184 0.048 0.020 0.604
#> SRR934277 6 0.6348 0.5556 0.140 0.004 0.184 0.048 0.020 0.604
#> SRR934278 6 0.6348 0.5556 0.140 0.004 0.184 0.048 0.020 0.604
#> SRR934279 6 0.6348 0.5556 0.140 0.004 0.184 0.048 0.020 0.604
#> SRR934280 6 0.2367 0.6518 0.064 0.000 0.012 0.020 0.004 0.900
#> SRR934281 6 0.2367 0.6518 0.064 0.000 0.012 0.020 0.004 0.900
#> SRR934282 6 0.2367 0.6518 0.064 0.000 0.012 0.020 0.004 0.900
#> SRR934283 6 0.2367 0.6518 0.064 0.000 0.012 0.020 0.004 0.900
#> SRR934284 6 0.2367 0.6518 0.064 0.000 0.012 0.020 0.004 0.900
#> SRR934285 6 0.2367 0.6518 0.064 0.000 0.012 0.020 0.004 0.900
#> SRR934286 6 0.2367 0.6518 0.064 0.000 0.012 0.020 0.004 0.900
#> SRR934287 6 0.2367 0.6518 0.064 0.000 0.012 0.020 0.004 0.900
#> SRR934288 1 0.4790 0.7902 0.748 0.008 0.032 0.112 0.004 0.096
#> SRR934289 1 0.4790 0.7902 0.748 0.008 0.032 0.112 0.004 0.096
#> SRR934290 1 0.4790 0.7902 0.748 0.008 0.032 0.112 0.004 0.096
#> SRR934291 1 0.4790 0.7902 0.748 0.008 0.032 0.112 0.004 0.096
#> SRR934292 1 0.4790 0.7902 0.748 0.008 0.032 0.112 0.004 0.096
#> SRR934293 1 0.4790 0.7902 0.748 0.008 0.032 0.112 0.004 0.096
#> SRR934294 1 0.4790 0.7902 0.748 0.008 0.032 0.112 0.004 0.096
#> SRR934295 1 0.4790 0.7902 0.748 0.008 0.032 0.112 0.004 0.096
#> SRR934296 1 0.8340 0.6281 0.484 0.124 0.068 0.116 0.060 0.148
#> SRR934297 1 0.8340 0.6281 0.484 0.124 0.068 0.116 0.060 0.148
#> SRR934298 1 0.8351 0.6281 0.484 0.124 0.072 0.112 0.060 0.148
#> SRR934299 1 0.8340 0.6281 0.484 0.124 0.068 0.116 0.060 0.148
#> SRR934300 1 0.8340 0.6281 0.484 0.124 0.068 0.116 0.060 0.148
#> SRR934301 1 0.8340 0.6281 0.484 0.124 0.068 0.116 0.060 0.148
#> SRR934302 1 0.8340 0.6281 0.484 0.124 0.068 0.116 0.060 0.148
#> SRR934303 1 0.8340 0.6281 0.484 0.124 0.068 0.116 0.060 0.148
#> SRR934304 5 0.2076 0.4296 0.016 0.060 0.000 0.000 0.912 0.012
#> SRR934305 5 0.2307 0.4281 0.020 0.060 0.000 0.004 0.904 0.012
#> SRR934306 5 0.2127 0.4297 0.012 0.060 0.000 0.004 0.912 0.012
#> SRR934307 5 0.2127 0.4297 0.012 0.060 0.000 0.004 0.912 0.012
#> SRR934308 5 0.2164 0.4280 0.020 0.060 0.000 0.000 0.908 0.012
#> SRR934309 5 0.2688 0.4254 0.016 0.060 0.008 0.012 0.892 0.012
#> SRR934310 5 0.2127 0.4297 0.012 0.060 0.000 0.004 0.912 0.012
#> SRR934311 5 0.2076 0.4296 0.016 0.060 0.000 0.000 0.912 0.012
#> SRR934312 6 0.4904 0.6279 0.152 0.004 0.048 0.048 0.012 0.736
#> SRR934313 6 0.4904 0.6279 0.152 0.004 0.048 0.048 0.012 0.736
#> SRR934314 6 0.4904 0.6279 0.152 0.004 0.048 0.048 0.012 0.736
#> SRR934315 6 0.4904 0.6279 0.152 0.004 0.048 0.048 0.012 0.736
#> SRR934316 6 0.4904 0.6279 0.152 0.004 0.048 0.048 0.012 0.736
#> SRR934317 6 0.4904 0.6279 0.152 0.004 0.048 0.048 0.012 0.736
#> SRR934318 6 0.4904 0.6279 0.152 0.004 0.048 0.048 0.012 0.736
#> SRR934319 6 0.4904 0.6279 0.152 0.004 0.048 0.048 0.012 0.736
#> SRR934320 6 0.5735 0.5030 0.084 0.012 0.148 0.068 0.008 0.680
#> SRR934321 6 0.5735 0.5030 0.084 0.012 0.148 0.068 0.008 0.680
#> SRR934322 6 0.5735 0.5030 0.084 0.012 0.148 0.068 0.008 0.680
#> SRR934323 6 0.5735 0.5030 0.084 0.012 0.148 0.068 0.008 0.680
#> SRR934324 6 0.5735 0.5030 0.084 0.012 0.148 0.068 0.008 0.680
#> SRR934325 6 0.5735 0.5030 0.084 0.012 0.148 0.068 0.008 0.680
#> SRR934326 6 0.5735 0.5030 0.084 0.012 0.148 0.068 0.008 0.680
#> SRR934327 6 0.5735 0.5030 0.084 0.012 0.148 0.068 0.008 0.680
#> SRR934328 1 0.2380 0.8037 0.892 0.004 0.004 0.020 0.000 0.080
#> SRR934329 1 0.2380 0.8037 0.892 0.004 0.004 0.020 0.000 0.080
#> SRR934330 1 0.2380 0.8037 0.892 0.004 0.004 0.020 0.000 0.080
#> SRR934331 1 0.2380 0.8037 0.892 0.004 0.004 0.020 0.000 0.080
#> SRR934332 1 0.2380 0.8037 0.892 0.004 0.004 0.020 0.000 0.080
#> SRR934333 1 0.2380 0.8037 0.892 0.004 0.004 0.020 0.000 0.080
#> SRR934334 1 0.2380 0.8037 0.892 0.004 0.004 0.020 0.000 0.080
#> SRR934335 1 0.2380 0.8037 0.892 0.004 0.004 0.020 0.000 0.080
#> SRR934344 1 0.3095 0.7731 0.844 0.000 0.028 0.016 0.000 0.112
#> SRR934345 1 0.3095 0.7731 0.844 0.000 0.028 0.016 0.000 0.112
#> SRR934346 1 0.3095 0.7731 0.844 0.000 0.028 0.016 0.000 0.112
#> SRR934347 1 0.3095 0.7731 0.844 0.000 0.028 0.016 0.000 0.112
#> SRR934348 1 0.3095 0.7731 0.844 0.000 0.028 0.016 0.000 0.112
#> SRR934349 1 0.3095 0.7731 0.844 0.000 0.028 0.016 0.000 0.112
#> SRR934350 1 0.3095 0.7731 0.844 0.000 0.028 0.016 0.000 0.112
#> SRR934351 1 0.3095 0.7731 0.844 0.000 0.028 0.016 0.000 0.112
#> SRR934336 6 0.3856 0.5599 0.016 0.000 0.172 0.024 0.008 0.780
#> SRR934337 6 0.3856 0.5599 0.016 0.000 0.172 0.024 0.008 0.780
#> SRR934338 6 0.3856 0.5599 0.016 0.000 0.172 0.024 0.008 0.780
#> SRR934339 6 0.3856 0.5599 0.016 0.000 0.172 0.024 0.008 0.780
#> SRR934340 6 0.3856 0.5599 0.016 0.000 0.172 0.024 0.008 0.780
#> SRR934341 6 0.3856 0.5599 0.016 0.000 0.172 0.024 0.008 0.780
#> SRR934342 6 0.3856 0.5599 0.016 0.000 0.172 0.024 0.008 0.780
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 14550 rows and 135 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.705 0.871 0.936 0.4813 0.511 0.511
#> 3 3 0.636 0.763 0.849 0.3286 0.831 0.683
#> 4 4 0.817 0.887 0.919 0.1577 0.875 0.676
#> 5 5 0.777 0.791 0.798 0.0595 1.000 1.000
#> 6 6 0.764 0.754 0.753 0.0435 0.911 0.656
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
#> SRR934216 1 0.9248 0.508 0.660 0.340
#> SRR934217 1 0.9248 0.508 0.660 0.340
#> SRR934218 1 0.9248 0.508 0.660 0.340
#> SRR934219 1 0.9248 0.508 0.660 0.340
#> SRR934220 1 0.9248 0.508 0.660 0.340
#> SRR934221 1 0.9248 0.508 0.660 0.340
#> SRR934222 1 0.9248 0.508 0.660 0.340
#> SRR934223 1 0.9248 0.508 0.660 0.340
#> SRR934224 1 0.0938 0.949 0.988 0.012
#> SRR934225 1 0.0938 0.949 0.988 0.012
#> SRR934226 1 0.0938 0.949 0.988 0.012
#> SRR934227 1 0.0938 0.949 0.988 0.012
#> SRR934228 1 0.0938 0.949 0.988 0.012
#> SRR934229 1 0.0938 0.949 0.988 0.012
#> SRR934230 1 0.0938 0.949 0.988 0.012
#> SRR934231 1 0.0938 0.949 0.988 0.012
#> SRR934232 2 0.0672 0.890 0.008 0.992
#> SRR934233 2 0.0672 0.890 0.008 0.992
#> SRR934234 2 0.0672 0.890 0.008 0.992
#> SRR934235 2 0.0672 0.890 0.008 0.992
#> SRR934236 2 0.0672 0.890 0.008 0.992
#> SRR934237 2 0.0672 0.890 0.008 0.992
#> SRR934238 2 0.0672 0.890 0.008 0.992
#> SRR934239 2 0.0672 0.890 0.008 0.992
#> SRR934240 2 0.0938 0.890 0.012 0.988
#> SRR934241 2 0.0938 0.890 0.012 0.988
#> SRR934242 2 0.0938 0.890 0.012 0.988
#> SRR934243 2 0.0938 0.890 0.012 0.988
#> SRR934244 2 0.0938 0.890 0.012 0.988
#> SRR934245 2 0.0938 0.890 0.012 0.988
#> SRR934246 2 0.0938 0.890 0.012 0.988
#> SRR934247 2 0.0938 0.890 0.012 0.988
#> SRR934248 2 0.0376 0.889 0.004 0.996
#> SRR934249 2 0.0376 0.889 0.004 0.996
#> SRR934250 2 0.0376 0.889 0.004 0.996
#> SRR934251 2 0.0376 0.889 0.004 0.996
#> SRR934252 2 0.0376 0.889 0.004 0.996
#> SRR934253 2 0.0376 0.889 0.004 0.996
#> SRR934254 2 0.0376 0.889 0.004 0.996
#> SRR934255 2 0.0376 0.889 0.004 0.996
#> SRR934256 2 0.9323 0.602 0.348 0.652
#> SRR934257 2 0.9323 0.602 0.348 0.652
#> SRR934258 2 0.9323 0.602 0.348 0.652
#> SRR934259 2 0.9323 0.602 0.348 0.652
#> SRR934260 2 0.9323 0.602 0.348 0.652
#> SRR934261 2 0.9323 0.602 0.348 0.652
#> SRR934262 2 0.9323 0.602 0.348 0.652
#> SRR934263 2 0.9323 0.602 0.348 0.652
#> SRR934264 2 0.2236 0.878 0.036 0.964
#> SRR934265 2 0.2236 0.878 0.036 0.964
#> SRR934266 2 0.2236 0.878 0.036 0.964
#> SRR934267 2 0.2236 0.878 0.036 0.964
#> SRR934268 2 0.2236 0.878 0.036 0.964
#> SRR934269 2 0.2236 0.878 0.036 0.964
#> SRR934270 2 0.2236 0.878 0.036 0.964
#> SRR934271 2 0.2236 0.878 0.036 0.964
#> SRR934272 1 0.0672 0.951 0.992 0.008
#> SRR934273 1 0.0672 0.951 0.992 0.008
#> SRR934274 1 0.0672 0.951 0.992 0.008
#> SRR934275 1 0.0672 0.951 0.992 0.008
#> SRR934276 1 0.0672 0.951 0.992 0.008
#> SRR934277 1 0.0672 0.951 0.992 0.008
#> SRR934278 1 0.0672 0.951 0.992 0.008
#> SRR934279 1 0.0672 0.951 0.992 0.008
#> SRR934280 1 0.0000 0.954 1.000 0.000
#> SRR934281 1 0.0000 0.954 1.000 0.000
#> SRR934282 1 0.0000 0.954 1.000 0.000
#> SRR934283 1 0.0000 0.954 1.000 0.000
#> SRR934284 1 0.0000 0.954 1.000 0.000
#> SRR934285 1 0.0000 0.954 1.000 0.000
#> SRR934286 1 0.0000 0.954 1.000 0.000
#> SRR934287 1 0.0000 0.954 1.000 0.000
#> SRR934288 1 0.0672 0.952 0.992 0.008
#> SRR934289 1 0.0672 0.952 0.992 0.008
#> SRR934290 1 0.0672 0.952 0.992 0.008
#> SRR934291 1 0.0672 0.952 0.992 0.008
#> SRR934292 1 0.0672 0.952 0.992 0.008
#> SRR934293 1 0.0672 0.952 0.992 0.008
#> SRR934294 1 0.0672 0.952 0.992 0.008
#> SRR934295 1 0.0672 0.952 0.992 0.008
#> SRR934296 2 0.8555 0.696 0.280 0.720
#> SRR934297 2 0.8555 0.696 0.280 0.720
#> SRR934298 2 0.8555 0.696 0.280 0.720
#> SRR934299 2 0.8555 0.696 0.280 0.720
#> SRR934300 2 0.8555 0.696 0.280 0.720
#> SRR934301 2 0.8555 0.696 0.280 0.720
#> SRR934302 2 0.8555 0.696 0.280 0.720
#> SRR934303 2 0.8555 0.696 0.280 0.720
#> SRR934304 2 0.0376 0.889 0.004 0.996
#> SRR934305 2 0.0376 0.889 0.004 0.996
#> SRR934306 2 0.0376 0.889 0.004 0.996
#> SRR934307 2 0.0376 0.889 0.004 0.996
#> SRR934308 2 0.0376 0.889 0.004 0.996
#> SRR934309 2 0.0376 0.889 0.004 0.996
#> SRR934310 2 0.0376 0.889 0.004 0.996
#> SRR934311 2 0.0376 0.889 0.004 0.996
#> SRR934312 1 0.0000 0.954 1.000 0.000
#> SRR934313 1 0.0000 0.954 1.000 0.000
#> SRR934314 1 0.0000 0.954 1.000 0.000
#> SRR934315 1 0.0000 0.954 1.000 0.000
#> SRR934316 1 0.0000 0.954 1.000 0.000
#> SRR934317 1 0.0000 0.954 1.000 0.000
#> SRR934318 1 0.0000 0.954 1.000 0.000
#> SRR934319 1 0.0000 0.954 1.000 0.000
#> SRR934320 1 0.0376 0.953 0.996 0.004
#> SRR934321 1 0.0376 0.953 0.996 0.004
#> SRR934322 1 0.0376 0.953 0.996 0.004
#> SRR934323 1 0.0376 0.953 0.996 0.004
#> SRR934324 1 0.0376 0.953 0.996 0.004
#> SRR934325 1 0.0376 0.953 0.996 0.004
#> SRR934326 1 0.0376 0.953 0.996 0.004
#> SRR934327 1 0.0376 0.953 0.996 0.004
#> SRR934328 1 0.0672 0.952 0.992 0.008
#> SRR934329 1 0.0672 0.952 0.992 0.008
#> SRR934330 1 0.0672 0.952 0.992 0.008
#> SRR934331 1 0.0672 0.952 0.992 0.008
#> SRR934332 1 0.0672 0.952 0.992 0.008
#> SRR934333 1 0.0672 0.952 0.992 0.008
#> SRR934334 1 0.0672 0.952 0.992 0.008
#> SRR934335 1 0.0672 0.952 0.992 0.008
#> SRR934344 1 0.0376 0.953 0.996 0.004
#> SRR934345 1 0.0376 0.953 0.996 0.004
#> SRR934346 1 0.0376 0.953 0.996 0.004
#> SRR934347 1 0.0376 0.953 0.996 0.004
#> SRR934348 1 0.0376 0.953 0.996 0.004
#> SRR934349 1 0.0376 0.953 0.996 0.004
#> SRR934350 1 0.0376 0.953 0.996 0.004
#> SRR934351 1 0.0376 0.953 0.996 0.004
#> SRR934336 1 0.0672 0.951 0.992 0.008
#> SRR934337 1 0.0672 0.951 0.992 0.008
#> SRR934338 1 0.0672 0.951 0.992 0.008
#> SRR934339 1 0.0672 0.951 0.992 0.008
#> SRR934340 1 0.0672 0.951 0.992 0.008
#> SRR934341 1 0.0672 0.951 0.992 0.008
#> SRR934342 1 0.0672 0.951 0.992 0.008
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 3 0.6224 0.701 0.240 0.032 0.728
#> SRR934217 3 0.6224 0.701 0.240 0.032 0.728
#> SRR934218 3 0.6224 0.701 0.240 0.032 0.728
#> SRR934219 3 0.6224 0.701 0.240 0.032 0.728
#> SRR934220 3 0.6224 0.701 0.240 0.032 0.728
#> SRR934221 3 0.6224 0.701 0.240 0.032 0.728
#> SRR934222 3 0.6224 0.701 0.240 0.032 0.728
#> SRR934223 3 0.6224 0.701 0.240 0.032 0.728
#> SRR934224 1 0.1163 0.807 0.972 0.000 0.028
#> SRR934225 1 0.1163 0.807 0.972 0.000 0.028
#> SRR934226 1 0.1163 0.807 0.972 0.000 0.028
#> SRR934227 1 0.1163 0.807 0.972 0.000 0.028
#> SRR934228 1 0.1163 0.807 0.972 0.000 0.028
#> SRR934229 1 0.1163 0.807 0.972 0.000 0.028
#> SRR934230 1 0.1163 0.807 0.972 0.000 0.028
#> SRR934231 1 0.1163 0.807 0.972 0.000 0.028
#> SRR934232 2 0.6079 0.657 0.000 0.612 0.388
#> SRR934233 2 0.6079 0.657 0.000 0.612 0.388
#> SRR934234 2 0.6079 0.657 0.000 0.612 0.388
#> SRR934235 2 0.6079 0.657 0.000 0.612 0.388
#> SRR934236 2 0.6079 0.657 0.000 0.612 0.388
#> SRR934237 2 0.6079 0.657 0.000 0.612 0.388
#> SRR934238 2 0.6079 0.657 0.000 0.612 0.388
#> SRR934239 2 0.6079 0.657 0.000 0.612 0.388
#> SRR934240 2 0.5859 0.702 0.000 0.656 0.344
#> SRR934241 2 0.5859 0.702 0.000 0.656 0.344
#> SRR934242 2 0.5859 0.702 0.000 0.656 0.344
#> SRR934243 2 0.5859 0.702 0.000 0.656 0.344
#> SRR934244 2 0.5859 0.702 0.000 0.656 0.344
#> SRR934245 2 0.5859 0.702 0.000 0.656 0.344
#> SRR934246 2 0.5859 0.702 0.000 0.656 0.344
#> SRR934247 2 0.5859 0.702 0.000 0.656 0.344
#> SRR934248 3 0.0237 0.887 0.000 0.004 0.996
#> SRR934249 3 0.0237 0.887 0.000 0.004 0.996
#> SRR934250 3 0.0237 0.887 0.000 0.004 0.996
#> SRR934251 3 0.0237 0.887 0.000 0.004 0.996
#> SRR934252 3 0.0237 0.887 0.000 0.004 0.996
#> SRR934253 3 0.0237 0.887 0.000 0.004 0.996
#> SRR934254 3 0.0237 0.887 0.000 0.004 0.996
#> SRR934255 3 0.0237 0.887 0.000 0.004 0.996
#> SRR934256 2 0.2187 0.763 0.024 0.948 0.028
#> SRR934257 2 0.2187 0.763 0.024 0.948 0.028
#> SRR934258 2 0.2187 0.763 0.024 0.948 0.028
#> SRR934259 2 0.2187 0.763 0.024 0.948 0.028
#> SRR934260 2 0.2187 0.763 0.024 0.948 0.028
#> SRR934261 2 0.2187 0.763 0.024 0.948 0.028
#> SRR934262 2 0.2187 0.763 0.024 0.948 0.028
#> SRR934263 2 0.2187 0.763 0.024 0.948 0.028
#> SRR934264 3 0.0747 0.886 0.016 0.000 0.984
#> SRR934265 3 0.0747 0.886 0.016 0.000 0.984
#> SRR934266 3 0.0747 0.886 0.016 0.000 0.984
#> SRR934267 3 0.0747 0.886 0.016 0.000 0.984
#> SRR934268 3 0.0747 0.886 0.016 0.000 0.984
#> SRR934269 3 0.0747 0.886 0.016 0.000 0.984
#> SRR934270 3 0.0747 0.886 0.016 0.000 0.984
#> SRR934271 3 0.0747 0.886 0.016 0.000 0.984
#> SRR934272 1 0.0000 0.823 1.000 0.000 0.000
#> SRR934273 1 0.0000 0.823 1.000 0.000 0.000
#> SRR934274 1 0.0000 0.823 1.000 0.000 0.000
#> SRR934275 1 0.0000 0.823 1.000 0.000 0.000
#> SRR934276 1 0.0000 0.823 1.000 0.000 0.000
#> SRR934277 1 0.0000 0.823 1.000 0.000 0.000
#> SRR934278 1 0.0000 0.823 1.000 0.000 0.000
#> SRR934279 1 0.0000 0.823 1.000 0.000 0.000
#> SRR934280 1 0.0424 0.823 0.992 0.008 0.000
#> SRR934281 1 0.0424 0.823 0.992 0.008 0.000
#> SRR934282 1 0.0424 0.823 0.992 0.008 0.000
#> SRR934283 1 0.0424 0.823 0.992 0.008 0.000
#> SRR934284 1 0.0424 0.823 0.992 0.008 0.000
#> SRR934285 1 0.0424 0.823 0.992 0.008 0.000
#> SRR934286 1 0.0424 0.823 0.992 0.008 0.000
#> SRR934287 1 0.0424 0.823 0.992 0.008 0.000
#> SRR934288 1 0.6476 0.597 0.548 0.448 0.004
#> SRR934289 1 0.6476 0.597 0.548 0.448 0.004
#> SRR934290 1 0.6476 0.597 0.548 0.448 0.004
#> SRR934291 1 0.6476 0.597 0.548 0.448 0.004
#> SRR934292 1 0.6476 0.597 0.548 0.448 0.004
#> SRR934293 1 0.6476 0.597 0.548 0.448 0.004
#> SRR934294 1 0.6476 0.597 0.548 0.448 0.004
#> SRR934295 1 0.6476 0.597 0.548 0.448 0.004
#> SRR934296 2 0.0983 0.754 0.004 0.980 0.016
#> SRR934297 2 0.0983 0.754 0.004 0.980 0.016
#> SRR934298 2 0.0983 0.754 0.004 0.980 0.016
#> SRR934299 2 0.0983 0.754 0.004 0.980 0.016
#> SRR934300 2 0.0983 0.754 0.004 0.980 0.016
#> SRR934301 2 0.0983 0.754 0.004 0.980 0.016
#> SRR934302 2 0.0983 0.754 0.004 0.980 0.016
#> SRR934303 2 0.0983 0.754 0.004 0.980 0.016
#> SRR934304 3 0.0237 0.887 0.000 0.004 0.996
#> SRR934305 3 0.0237 0.887 0.000 0.004 0.996
#> SRR934306 3 0.0237 0.887 0.000 0.004 0.996
#> SRR934307 3 0.0237 0.887 0.000 0.004 0.996
#> SRR934308 3 0.0237 0.887 0.000 0.004 0.996
#> SRR934309 3 0.0237 0.887 0.000 0.004 0.996
#> SRR934310 3 0.0237 0.887 0.000 0.004 0.996
#> SRR934311 3 0.0237 0.887 0.000 0.004 0.996
#> SRR934312 1 0.0237 0.823 0.996 0.004 0.000
#> SRR934313 1 0.0237 0.823 0.996 0.004 0.000
#> SRR934314 1 0.0237 0.823 0.996 0.004 0.000
#> SRR934315 1 0.0237 0.823 0.996 0.004 0.000
#> SRR934316 1 0.0237 0.823 0.996 0.004 0.000
#> SRR934317 1 0.0237 0.823 0.996 0.004 0.000
#> SRR934318 1 0.0237 0.823 0.996 0.004 0.000
#> SRR934319 1 0.0237 0.823 0.996 0.004 0.000
#> SRR934320 1 0.3816 0.788 0.852 0.148 0.000
#> SRR934321 1 0.3816 0.788 0.852 0.148 0.000
#> SRR934322 1 0.3816 0.788 0.852 0.148 0.000
#> SRR934323 1 0.3816 0.788 0.852 0.148 0.000
#> SRR934324 1 0.3816 0.788 0.852 0.148 0.000
#> SRR934325 1 0.3816 0.788 0.852 0.148 0.000
#> SRR934326 1 0.3816 0.788 0.852 0.148 0.000
#> SRR934327 1 0.3816 0.788 0.852 0.148 0.000
#> SRR934328 1 0.7029 0.594 0.540 0.440 0.020
#> SRR934329 1 0.7029 0.594 0.540 0.440 0.020
#> SRR934330 1 0.7029 0.594 0.540 0.440 0.020
#> SRR934331 1 0.7029 0.594 0.540 0.440 0.020
#> SRR934332 1 0.7029 0.594 0.540 0.440 0.020
#> SRR934333 1 0.7029 0.594 0.540 0.440 0.020
#> SRR934334 1 0.7029 0.594 0.540 0.440 0.020
#> SRR934335 1 0.7029 0.594 0.540 0.440 0.020
#> SRR934344 1 0.6434 0.661 0.612 0.380 0.008
#> SRR934345 1 0.6434 0.661 0.612 0.380 0.008
#> SRR934346 1 0.6434 0.661 0.612 0.380 0.008
#> SRR934347 1 0.6434 0.661 0.612 0.380 0.008
#> SRR934348 1 0.6434 0.661 0.612 0.380 0.008
#> SRR934349 1 0.6434 0.661 0.612 0.380 0.008
#> SRR934350 1 0.6434 0.661 0.612 0.380 0.008
#> SRR934351 1 0.6434 0.661 0.612 0.380 0.008
#> SRR934336 1 0.0000 0.823 1.000 0.000 0.000
#> SRR934337 1 0.0000 0.823 1.000 0.000 0.000
#> SRR934338 1 0.0000 0.823 1.000 0.000 0.000
#> SRR934339 1 0.0000 0.823 1.000 0.000 0.000
#> SRR934340 1 0.0000 0.823 1.000 0.000 0.000
#> SRR934341 1 0.0000 0.823 1.000 0.000 0.000
#> SRR934342 1 0.0000 0.823 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 3 0.2797 0.864 0.068 0.000 0.900 0.032
#> SRR934217 3 0.2797 0.864 0.068 0.000 0.900 0.032
#> SRR934218 3 0.2797 0.864 0.068 0.000 0.900 0.032
#> SRR934219 3 0.2797 0.864 0.068 0.000 0.900 0.032
#> SRR934220 3 0.2797 0.864 0.068 0.000 0.900 0.032
#> SRR934221 3 0.2797 0.864 0.068 0.000 0.900 0.032
#> SRR934222 3 0.2797 0.864 0.068 0.000 0.900 0.032
#> SRR934223 3 0.2797 0.864 0.068 0.000 0.900 0.032
#> SRR934224 1 0.0376 0.964 0.992 0.000 0.004 0.004
#> SRR934225 1 0.0376 0.964 0.992 0.000 0.004 0.004
#> SRR934226 1 0.0376 0.964 0.992 0.000 0.004 0.004
#> SRR934227 1 0.0376 0.964 0.992 0.000 0.004 0.004
#> SRR934228 1 0.0376 0.964 0.992 0.000 0.004 0.004
#> SRR934229 1 0.0376 0.964 0.992 0.000 0.004 0.004
#> SRR934230 1 0.0376 0.964 0.992 0.000 0.004 0.004
#> SRR934231 1 0.0376 0.964 0.992 0.000 0.004 0.004
#> SRR934232 2 0.2281 0.780 0.000 0.904 0.096 0.000
#> SRR934233 2 0.2281 0.780 0.000 0.904 0.096 0.000
#> SRR934234 2 0.2281 0.780 0.000 0.904 0.096 0.000
#> SRR934235 2 0.2281 0.780 0.000 0.904 0.096 0.000
#> SRR934236 2 0.2281 0.780 0.000 0.904 0.096 0.000
#> SRR934237 2 0.2281 0.780 0.000 0.904 0.096 0.000
#> SRR934238 2 0.2281 0.780 0.000 0.904 0.096 0.000
#> SRR934239 2 0.2281 0.780 0.000 0.904 0.096 0.000
#> SRR934240 2 0.1211 0.803 0.000 0.960 0.040 0.000
#> SRR934241 2 0.1211 0.803 0.000 0.960 0.040 0.000
#> SRR934242 2 0.1211 0.803 0.000 0.960 0.040 0.000
#> SRR934243 2 0.1211 0.803 0.000 0.960 0.040 0.000
#> SRR934244 2 0.1211 0.803 0.000 0.960 0.040 0.000
#> SRR934245 2 0.1211 0.803 0.000 0.960 0.040 0.000
#> SRR934246 2 0.1211 0.803 0.000 0.960 0.040 0.000
#> SRR934247 2 0.1211 0.803 0.000 0.960 0.040 0.000
#> SRR934248 3 0.2760 0.904 0.000 0.128 0.872 0.000
#> SRR934249 3 0.2760 0.904 0.000 0.128 0.872 0.000
#> SRR934250 3 0.2760 0.904 0.000 0.128 0.872 0.000
#> SRR934251 3 0.2760 0.904 0.000 0.128 0.872 0.000
#> SRR934252 3 0.2760 0.904 0.000 0.128 0.872 0.000
#> SRR934253 3 0.2760 0.904 0.000 0.128 0.872 0.000
#> SRR934254 3 0.2760 0.904 0.000 0.128 0.872 0.000
#> SRR934255 3 0.2760 0.904 0.000 0.128 0.872 0.000
#> SRR934256 2 0.3196 0.770 0.008 0.856 0.000 0.136
#> SRR934257 2 0.3196 0.770 0.008 0.856 0.000 0.136
#> SRR934258 2 0.3196 0.770 0.008 0.856 0.000 0.136
#> SRR934259 2 0.3196 0.770 0.008 0.856 0.000 0.136
#> SRR934260 2 0.3196 0.770 0.008 0.856 0.000 0.136
#> SRR934261 2 0.3196 0.770 0.008 0.856 0.000 0.136
#> SRR934262 2 0.3196 0.770 0.008 0.856 0.000 0.136
#> SRR934263 2 0.3196 0.770 0.008 0.856 0.000 0.136
#> SRR934264 3 0.2345 0.916 0.000 0.100 0.900 0.000
#> SRR934265 3 0.2345 0.916 0.000 0.100 0.900 0.000
#> SRR934266 3 0.2345 0.916 0.000 0.100 0.900 0.000
#> SRR934267 3 0.2345 0.916 0.000 0.100 0.900 0.000
#> SRR934268 3 0.2345 0.916 0.000 0.100 0.900 0.000
#> SRR934269 3 0.2345 0.916 0.000 0.100 0.900 0.000
#> SRR934270 3 0.2345 0.916 0.000 0.100 0.900 0.000
#> SRR934271 3 0.2345 0.916 0.000 0.100 0.900 0.000
#> SRR934272 1 0.0469 0.965 0.988 0.000 0.000 0.012
#> SRR934273 1 0.0469 0.965 0.988 0.000 0.000 0.012
#> SRR934274 1 0.0469 0.965 0.988 0.000 0.000 0.012
#> SRR934275 1 0.0469 0.965 0.988 0.000 0.000 0.012
#> SRR934276 1 0.0469 0.965 0.988 0.000 0.000 0.012
#> SRR934277 1 0.0469 0.965 0.988 0.000 0.000 0.012
#> SRR934278 1 0.0469 0.965 0.988 0.000 0.000 0.012
#> SRR934279 1 0.0469 0.965 0.988 0.000 0.000 0.012
#> SRR934280 1 0.1174 0.965 0.968 0.020 0.000 0.012
#> SRR934281 1 0.1174 0.965 0.968 0.020 0.000 0.012
#> SRR934282 1 0.1174 0.965 0.968 0.020 0.000 0.012
#> SRR934283 1 0.1174 0.965 0.968 0.020 0.000 0.012
#> SRR934284 1 0.1174 0.965 0.968 0.020 0.000 0.012
#> SRR934285 1 0.1174 0.965 0.968 0.020 0.000 0.012
#> SRR934286 1 0.1174 0.965 0.968 0.020 0.000 0.012
#> SRR934287 1 0.1174 0.965 0.968 0.020 0.000 0.012
#> SRR934288 4 0.0376 0.990 0.004 0.004 0.000 0.992
#> SRR934289 4 0.0376 0.990 0.004 0.004 0.000 0.992
#> SRR934290 4 0.0376 0.990 0.004 0.004 0.000 0.992
#> SRR934291 4 0.0376 0.990 0.004 0.004 0.000 0.992
#> SRR934292 4 0.0376 0.990 0.004 0.004 0.000 0.992
#> SRR934293 4 0.0376 0.990 0.004 0.004 0.000 0.992
#> SRR934294 4 0.0376 0.990 0.004 0.004 0.000 0.992
#> SRR934295 4 0.0376 0.990 0.004 0.004 0.000 0.992
#> SRR934296 2 0.6007 0.456 0.000 0.548 0.044 0.408
#> SRR934297 2 0.6007 0.456 0.000 0.548 0.044 0.408
#> SRR934298 2 0.6007 0.456 0.000 0.548 0.044 0.408
#> SRR934299 2 0.6007 0.456 0.000 0.548 0.044 0.408
#> SRR934300 2 0.6007 0.456 0.000 0.548 0.044 0.408
#> SRR934301 2 0.6007 0.456 0.000 0.548 0.044 0.408
#> SRR934302 2 0.6007 0.456 0.000 0.548 0.044 0.408
#> SRR934303 2 0.6007 0.456 0.000 0.548 0.044 0.408
#> SRR934304 3 0.0336 0.912 0.000 0.008 0.992 0.000
#> SRR934305 3 0.0336 0.912 0.000 0.008 0.992 0.000
#> SRR934306 3 0.0336 0.912 0.000 0.008 0.992 0.000
#> SRR934307 3 0.0336 0.912 0.000 0.008 0.992 0.000
#> SRR934308 3 0.0336 0.912 0.000 0.008 0.992 0.000
#> SRR934309 3 0.0336 0.912 0.000 0.008 0.992 0.000
#> SRR934310 3 0.0336 0.912 0.000 0.008 0.992 0.000
#> SRR934311 3 0.0336 0.912 0.000 0.008 0.992 0.000
#> SRR934312 1 0.1042 0.965 0.972 0.008 0.000 0.020
#> SRR934313 1 0.1042 0.965 0.972 0.008 0.000 0.020
#> SRR934314 1 0.1042 0.965 0.972 0.008 0.000 0.020
#> SRR934315 1 0.1042 0.965 0.972 0.008 0.000 0.020
#> SRR934316 1 0.1042 0.965 0.972 0.008 0.000 0.020
#> SRR934317 1 0.1042 0.965 0.972 0.008 0.000 0.020
#> SRR934318 1 0.1042 0.965 0.972 0.008 0.000 0.020
#> SRR934319 1 0.1042 0.965 0.972 0.008 0.000 0.020
#> SRR934320 1 0.3947 0.884 0.848 0.072 0.004 0.076
#> SRR934321 1 0.3947 0.884 0.848 0.072 0.004 0.076
#> SRR934322 1 0.3947 0.884 0.848 0.072 0.004 0.076
#> SRR934323 1 0.3947 0.884 0.848 0.072 0.004 0.076
#> SRR934324 1 0.3947 0.884 0.848 0.072 0.004 0.076
#> SRR934325 1 0.3947 0.884 0.848 0.072 0.004 0.076
#> SRR934326 1 0.3947 0.884 0.848 0.072 0.004 0.076
#> SRR934327 1 0.3947 0.884 0.848 0.072 0.004 0.076
#> SRR934328 4 0.0000 0.992 0.000 0.000 0.000 1.000
#> SRR934329 4 0.0000 0.992 0.000 0.000 0.000 1.000
#> SRR934330 4 0.0000 0.992 0.000 0.000 0.000 1.000
#> SRR934331 4 0.0000 0.992 0.000 0.000 0.000 1.000
#> SRR934332 4 0.0000 0.992 0.000 0.000 0.000 1.000
#> SRR934333 4 0.0000 0.992 0.000 0.000 0.000 1.000
#> SRR934334 4 0.0000 0.992 0.000 0.000 0.000 1.000
#> SRR934335 4 0.0000 0.992 0.000 0.000 0.000 1.000
#> SRR934344 4 0.0524 0.988 0.008 0.000 0.004 0.988
#> SRR934345 4 0.0524 0.988 0.008 0.000 0.004 0.988
#> SRR934346 4 0.0524 0.988 0.008 0.000 0.004 0.988
#> SRR934347 4 0.0524 0.988 0.008 0.000 0.004 0.988
#> SRR934348 4 0.0524 0.988 0.008 0.000 0.004 0.988
#> SRR934349 4 0.0524 0.988 0.008 0.000 0.004 0.988
#> SRR934350 4 0.0524 0.988 0.008 0.000 0.004 0.988
#> SRR934351 4 0.0524 0.988 0.008 0.000 0.004 0.988
#> SRR934336 1 0.0859 0.965 0.980 0.008 0.004 0.008
#> SRR934337 1 0.0859 0.965 0.980 0.008 0.004 0.008
#> SRR934338 1 0.0859 0.965 0.980 0.008 0.004 0.008
#> SRR934339 1 0.0859 0.965 0.980 0.008 0.004 0.008
#> SRR934340 1 0.0859 0.965 0.980 0.008 0.004 0.008
#> SRR934341 1 0.0859 0.965 0.980 0.008 0.004 0.008
#> SRR934342 1 0.0859 0.965 0.980 0.008 0.004 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 4 0.5150 0.760 0.032 0.000 NA 0.588 0.008
#> SRR934217 4 0.5150 0.760 0.032 0.000 NA 0.588 0.008
#> SRR934218 4 0.5150 0.760 0.032 0.000 NA 0.588 0.008
#> SRR934219 4 0.5150 0.760 0.032 0.000 NA 0.588 0.008
#> SRR934220 4 0.5150 0.760 0.032 0.000 NA 0.588 0.008
#> SRR934221 4 0.5150 0.760 0.032 0.000 NA 0.588 0.008
#> SRR934222 4 0.5150 0.760 0.032 0.000 NA 0.588 0.008
#> SRR934223 4 0.5150 0.760 0.032 0.000 NA 0.588 0.008
#> SRR934224 1 0.1970 0.804 0.924 0.004 NA 0.012 0.000
#> SRR934225 1 0.1970 0.804 0.924 0.004 NA 0.012 0.000
#> SRR934226 1 0.1970 0.804 0.924 0.004 NA 0.012 0.000
#> SRR934227 1 0.1970 0.804 0.924 0.004 NA 0.012 0.000
#> SRR934228 1 0.1970 0.804 0.924 0.004 NA 0.012 0.000
#> SRR934229 1 0.1970 0.804 0.924 0.004 NA 0.012 0.000
#> SRR934230 1 0.1970 0.804 0.924 0.004 NA 0.012 0.000
#> SRR934231 1 0.1970 0.804 0.924 0.004 NA 0.012 0.000
#> SRR934232 2 0.4024 0.677 0.000 0.752 NA 0.220 0.000
#> SRR934233 2 0.4024 0.677 0.000 0.752 NA 0.220 0.000
#> SRR934234 2 0.4024 0.677 0.000 0.752 NA 0.220 0.000
#> SRR934235 2 0.4024 0.677 0.000 0.752 NA 0.220 0.000
#> SRR934236 2 0.4024 0.677 0.000 0.752 NA 0.220 0.000
#> SRR934237 2 0.4024 0.677 0.000 0.752 NA 0.220 0.000
#> SRR934238 2 0.4024 0.677 0.000 0.752 NA 0.220 0.000
#> SRR934239 2 0.4024 0.677 0.000 0.752 NA 0.220 0.000
#> SRR934240 2 0.1671 0.762 0.000 0.924 NA 0.076 0.000
#> SRR934241 2 0.1671 0.762 0.000 0.924 NA 0.076 0.000
#> SRR934242 2 0.1671 0.762 0.000 0.924 NA 0.076 0.000
#> SRR934243 2 0.1671 0.762 0.000 0.924 NA 0.076 0.000
#> SRR934244 2 0.1671 0.762 0.000 0.924 NA 0.076 0.000
#> SRR934245 2 0.1671 0.762 0.000 0.924 NA 0.076 0.000
#> SRR934246 2 0.1671 0.762 0.000 0.924 NA 0.076 0.000
#> SRR934247 2 0.1671 0.762 0.000 0.924 NA 0.076 0.000
#> SRR934248 4 0.1997 0.758 0.000 0.040 NA 0.924 0.000
#> SRR934249 4 0.1997 0.758 0.000 0.040 NA 0.924 0.000
#> SRR934250 4 0.1997 0.758 0.000 0.040 NA 0.924 0.000
#> SRR934251 4 0.1997 0.758 0.000 0.040 NA 0.924 0.000
#> SRR934252 4 0.1997 0.758 0.000 0.040 NA 0.924 0.000
#> SRR934253 4 0.1997 0.758 0.000 0.040 NA 0.924 0.000
#> SRR934254 4 0.1997 0.758 0.000 0.040 NA 0.924 0.000
#> SRR934255 4 0.1997 0.758 0.000 0.040 NA 0.924 0.000
#> SRR934256 2 0.3456 0.753 0.012 0.844 NA 0.000 0.036
#> SRR934257 2 0.3456 0.753 0.012 0.844 NA 0.000 0.036
#> SRR934258 2 0.3456 0.753 0.012 0.844 NA 0.000 0.036
#> SRR934259 2 0.3456 0.753 0.012 0.844 NA 0.000 0.036
#> SRR934260 2 0.3456 0.753 0.012 0.844 NA 0.000 0.036
#> SRR934261 2 0.3456 0.753 0.012 0.844 NA 0.000 0.036
#> SRR934262 2 0.3456 0.753 0.012 0.844 NA 0.000 0.036
#> SRR934263 2 0.3456 0.753 0.012 0.844 NA 0.000 0.036
#> SRR934264 4 0.0912 0.785 0.000 0.016 NA 0.972 0.000
#> SRR934265 4 0.0912 0.785 0.000 0.016 NA 0.972 0.000
#> SRR934266 4 0.0912 0.785 0.000 0.016 NA 0.972 0.000
#> SRR934267 4 0.0912 0.785 0.000 0.016 NA 0.972 0.000
#> SRR934268 4 0.0912 0.785 0.000 0.016 NA 0.972 0.000
#> SRR934269 4 0.0912 0.785 0.000 0.016 NA 0.972 0.000
#> SRR934270 4 0.0912 0.785 0.000 0.016 NA 0.972 0.000
#> SRR934271 4 0.0912 0.785 0.000 0.016 NA 0.972 0.000
#> SRR934272 1 0.4318 0.802 0.688 0.000 NA 0.008 0.008
#> SRR934273 1 0.4318 0.802 0.688 0.000 NA 0.008 0.008
#> SRR934274 1 0.4318 0.802 0.688 0.000 NA 0.008 0.008
#> SRR934275 1 0.4318 0.802 0.688 0.000 NA 0.008 0.008
#> SRR934276 1 0.4318 0.802 0.688 0.000 NA 0.008 0.008
#> SRR934277 1 0.4318 0.802 0.688 0.000 NA 0.008 0.008
#> SRR934278 1 0.4318 0.802 0.688 0.000 NA 0.008 0.008
#> SRR934279 1 0.4318 0.802 0.688 0.000 NA 0.008 0.008
#> SRR934280 1 0.3835 0.818 0.744 0.012 NA 0.000 0.000
#> SRR934281 1 0.3835 0.818 0.744 0.012 NA 0.000 0.000
#> SRR934282 1 0.3835 0.818 0.744 0.012 NA 0.000 0.000
#> SRR934283 1 0.3835 0.818 0.744 0.012 NA 0.000 0.000
#> SRR934284 1 0.3835 0.818 0.744 0.012 NA 0.000 0.000
#> SRR934285 1 0.3835 0.818 0.744 0.012 NA 0.000 0.000
#> SRR934286 1 0.3835 0.818 0.744 0.012 NA 0.000 0.000
#> SRR934287 1 0.3835 0.818 0.744 0.012 NA 0.000 0.000
#> SRR934288 5 0.1043 0.971 0.000 0.000 NA 0.000 0.960
#> SRR934289 5 0.1043 0.971 0.000 0.000 NA 0.000 0.960
#> SRR934290 5 0.1043 0.971 0.000 0.000 NA 0.000 0.960
#> SRR934291 5 0.1043 0.971 0.000 0.000 NA 0.000 0.960
#> SRR934292 5 0.1043 0.971 0.000 0.000 NA 0.000 0.960
#> SRR934293 5 0.1043 0.971 0.000 0.000 NA 0.000 0.960
#> SRR934294 5 0.1043 0.971 0.000 0.000 NA 0.000 0.960
#> SRR934295 5 0.1043 0.971 0.000 0.000 NA 0.000 0.960
#> SRR934296 2 0.6403 0.516 0.000 0.512 NA 0.000 0.256
#> SRR934297 2 0.6403 0.516 0.000 0.512 NA 0.000 0.256
#> SRR934298 2 0.6403 0.516 0.000 0.512 NA 0.000 0.256
#> SRR934299 2 0.6403 0.516 0.000 0.512 NA 0.000 0.256
#> SRR934300 2 0.6403 0.516 0.000 0.512 NA 0.000 0.256
#> SRR934301 2 0.6403 0.516 0.000 0.512 NA 0.000 0.256
#> SRR934302 2 0.6403 0.516 0.000 0.512 NA 0.000 0.256
#> SRR934303 2 0.6403 0.516 0.000 0.512 NA 0.000 0.256
#> SRR934304 4 0.4235 0.782 0.000 0.008 NA 0.656 0.000
#> SRR934305 4 0.4235 0.782 0.000 0.008 NA 0.656 0.000
#> SRR934306 4 0.4235 0.782 0.000 0.008 NA 0.656 0.000
#> SRR934307 4 0.4235 0.782 0.000 0.008 NA 0.656 0.000
#> SRR934308 4 0.4235 0.782 0.000 0.008 NA 0.656 0.000
#> SRR934309 4 0.4235 0.782 0.000 0.008 NA 0.656 0.000
#> SRR934310 4 0.4235 0.782 0.000 0.008 NA 0.656 0.000
#> SRR934311 4 0.4235 0.782 0.000 0.008 NA 0.656 0.000
#> SRR934312 1 0.4647 0.791 0.628 0.004 NA 0.000 0.016
#> SRR934313 1 0.4647 0.791 0.628 0.004 NA 0.000 0.016
#> SRR934314 1 0.4647 0.791 0.628 0.004 NA 0.000 0.016
#> SRR934315 1 0.4647 0.791 0.628 0.004 NA 0.000 0.016
#> SRR934316 1 0.4647 0.791 0.628 0.004 NA 0.000 0.016
#> SRR934317 1 0.4647 0.791 0.628 0.004 NA 0.000 0.016
#> SRR934318 1 0.4647 0.791 0.628 0.004 NA 0.000 0.016
#> SRR934319 1 0.4647 0.791 0.628 0.004 NA 0.000 0.016
#> SRR934320 1 0.5066 0.694 0.748 0.072 NA 0.000 0.044
#> SRR934321 1 0.5066 0.694 0.748 0.072 NA 0.000 0.044
#> SRR934322 1 0.5066 0.694 0.748 0.072 NA 0.000 0.044
#> SRR934323 1 0.5066 0.694 0.748 0.072 NA 0.000 0.044
#> SRR934324 1 0.5066 0.694 0.748 0.072 NA 0.000 0.044
#> SRR934325 1 0.5066 0.694 0.748 0.072 NA 0.000 0.044
#> SRR934326 1 0.5066 0.694 0.748 0.072 NA 0.000 0.044
#> SRR934327 1 0.5066 0.694 0.748 0.072 NA 0.000 0.044
#> SRR934328 5 0.0000 0.982 0.000 0.000 NA 0.000 1.000
#> SRR934329 5 0.0000 0.982 0.000 0.000 NA 0.000 1.000
#> SRR934330 5 0.0000 0.982 0.000 0.000 NA 0.000 1.000
#> SRR934331 5 0.0000 0.982 0.000 0.000 NA 0.000 1.000
#> SRR934332 5 0.0000 0.982 0.000 0.000 NA 0.000 1.000
#> SRR934333 5 0.0000 0.982 0.000 0.000 NA 0.000 1.000
#> SRR934334 5 0.0000 0.982 0.000 0.000 NA 0.000 1.000
#> SRR934335 5 0.0000 0.982 0.000 0.000 NA 0.000 1.000
#> SRR934344 5 0.0451 0.980 0.008 0.000 NA 0.000 0.988
#> SRR934345 5 0.0451 0.980 0.008 0.000 NA 0.000 0.988
#> SRR934346 5 0.0451 0.980 0.008 0.000 NA 0.000 0.988
#> SRR934347 5 0.0451 0.980 0.008 0.000 NA 0.000 0.988
#> SRR934348 5 0.0451 0.980 0.008 0.000 NA 0.000 0.988
#> SRR934349 5 0.0451 0.980 0.008 0.000 NA 0.000 0.988
#> SRR934350 5 0.0451 0.980 0.008 0.000 NA 0.000 0.988
#> SRR934351 5 0.0451 0.980 0.008 0.000 NA 0.000 0.988
#> SRR934336 1 0.0833 0.812 0.976 0.004 NA 0.004 0.000
#> SRR934337 1 0.0833 0.812 0.976 0.004 NA 0.004 0.000
#> SRR934338 1 0.0833 0.812 0.976 0.004 NA 0.004 0.000
#> SRR934339 1 0.0833 0.812 0.976 0.004 NA 0.004 0.000
#> SRR934340 1 0.0833 0.812 0.976 0.004 NA 0.004 0.000
#> SRR934341 1 0.0833 0.812 0.976 0.004 NA 0.004 0.000
#> SRR934342 1 0.0833 0.812 0.976 0.004 NA 0.004 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.2724 0.839 0.000 0.000 0.864 0.000 0.084 0.052
#> SRR934217 3 0.2724 0.839 0.000 0.000 0.864 0.000 0.084 0.052
#> SRR934218 3 0.2724 0.839 0.000 0.000 0.864 0.000 0.084 0.052
#> SRR934219 3 0.2724 0.839 0.000 0.000 0.864 0.000 0.084 0.052
#> SRR934220 3 0.2724 0.839 0.000 0.000 0.864 0.000 0.084 0.052
#> SRR934221 3 0.2724 0.839 0.000 0.000 0.864 0.000 0.084 0.052
#> SRR934222 3 0.2724 0.839 0.000 0.000 0.864 0.000 0.084 0.052
#> SRR934223 3 0.2724 0.839 0.000 0.000 0.864 0.000 0.084 0.052
#> SRR934224 5 0.2171 0.620 0.000 0.000 0.040 0.016 0.912 0.032
#> SRR934225 5 0.2171 0.620 0.000 0.000 0.040 0.016 0.912 0.032
#> SRR934226 5 0.2171 0.620 0.000 0.000 0.040 0.016 0.912 0.032
#> SRR934227 5 0.2171 0.620 0.000 0.000 0.040 0.016 0.912 0.032
#> SRR934228 5 0.2171 0.620 0.000 0.000 0.040 0.016 0.912 0.032
#> SRR934229 5 0.2171 0.620 0.000 0.000 0.040 0.016 0.912 0.032
#> SRR934230 5 0.2171 0.620 0.000 0.000 0.040 0.016 0.912 0.032
#> SRR934231 5 0.2171 0.620 0.000 0.000 0.040 0.016 0.912 0.032
#> SRR934232 2 0.5397 0.592 0.000 0.516 0.008 0.384 0.000 0.092
#> SRR934233 2 0.5397 0.592 0.000 0.516 0.008 0.384 0.000 0.092
#> SRR934234 2 0.5397 0.592 0.000 0.516 0.008 0.384 0.000 0.092
#> SRR934235 2 0.5397 0.592 0.000 0.516 0.008 0.384 0.000 0.092
#> SRR934236 2 0.5397 0.592 0.000 0.516 0.008 0.384 0.000 0.092
#> SRR934237 2 0.5397 0.592 0.000 0.516 0.008 0.384 0.000 0.092
#> SRR934238 2 0.5397 0.592 0.000 0.516 0.008 0.384 0.000 0.092
#> SRR934239 2 0.5397 0.592 0.000 0.516 0.008 0.384 0.000 0.092
#> SRR934240 2 0.4687 0.685 0.000 0.672 0.004 0.240 0.000 0.084
#> SRR934241 2 0.4687 0.685 0.000 0.672 0.004 0.240 0.000 0.084
#> SRR934242 2 0.4687 0.685 0.000 0.672 0.004 0.240 0.000 0.084
#> SRR934243 2 0.4687 0.685 0.000 0.672 0.004 0.240 0.000 0.084
#> SRR934244 2 0.4687 0.685 0.000 0.672 0.004 0.240 0.000 0.084
#> SRR934245 2 0.4687 0.685 0.000 0.672 0.004 0.240 0.000 0.084
#> SRR934246 2 0.4687 0.685 0.000 0.672 0.004 0.240 0.000 0.084
#> SRR934247 2 0.4687 0.685 0.000 0.672 0.004 0.240 0.000 0.084
#> SRR934248 4 0.3592 0.948 0.000 0.000 0.344 0.656 0.000 0.000
#> SRR934249 4 0.3592 0.948 0.000 0.000 0.344 0.656 0.000 0.000
#> SRR934250 4 0.3592 0.948 0.000 0.000 0.344 0.656 0.000 0.000
#> SRR934251 4 0.3592 0.948 0.000 0.000 0.344 0.656 0.000 0.000
#> SRR934252 4 0.3592 0.948 0.000 0.000 0.344 0.656 0.000 0.000
#> SRR934253 4 0.3592 0.948 0.000 0.000 0.344 0.656 0.000 0.000
#> SRR934254 4 0.3592 0.948 0.000 0.000 0.344 0.656 0.000 0.000
#> SRR934255 4 0.3592 0.948 0.000 0.000 0.344 0.656 0.000 0.000
#> SRR934256 2 0.1699 0.664 0.008 0.936 0.000 0.004 0.012 0.040
#> SRR934257 2 0.1699 0.664 0.008 0.936 0.000 0.004 0.012 0.040
#> SRR934258 2 0.1699 0.664 0.008 0.936 0.000 0.004 0.012 0.040
#> SRR934259 2 0.1699 0.664 0.008 0.936 0.000 0.004 0.012 0.040
#> SRR934260 2 0.1699 0.664 0.008 0.936 0.000 0.004 0.012 0.040
#> SRR934261 2 0.1699 0.664 0.008 0.936 0.000 0.004 0.012 0.040
#> SRR934262 2 0.1699 0.664 0.008 0.936 0.000 0.004 0.012 0.040
#> SRR934263 2 0.1699 0.664 0.008 0.936 0.000 0.004 0.012 0.040
#> SRR934264 4 0.3765 0.946 0.000 0.000 0.404 0.596 0.000 0.000
#> SRR934265 4 0.3765 0.946 0.000 0.000 0.404 0.596 0.000 0.000
#> SRR934266 4 0.3765 0.946 0.000 0.000 0.404 0.596 0.000 0.000
#> SRR934267 4 0.3765 0.946 0.000 0.000 0.404 0.596 0.000 0.000
#> SRR934268 4 0.3765 0.946 0.000 0.000 0.404 0.596 0.000 0.000
#> SRR934269 4 0.3765 0.946 0.000 0.000 0.404 0.596 0.000 0.000
#> SRR934270 4 0.3765 0.946 0.000 0.000 0.404 0.596 0.000 0.000
#> SRR934271 4 0.3765 0.946 0.000 0.000 0.404 0.596 0.000 0.000
#> SRR934272 6 0.4780 0.690 0.004 0.000 0.012 0.020 0.468 0.496
#> SRR934273 6 0.4780 0.690 0.004 0.000 0.012 0.020 0.468 0.496
#> SRR934274 6 0.4780 0.690 0.004 0.000 0.012 0.020 0.468 0.496
#> SRR934275 6 0.4780 0.690 0.004 0.000 0.012 0.020 0.468 0.496
#> SRR934276 6 0.4780 0.690 0.004 0.000 0.012 0.020 0.468 0.496
#> SRR934277 6 0.4780 0.690 0.004 0.000 0.012 0.020 0.468 0.496
#> SRR934278 6 0.4780 0.690 0.004 0.000 0.012 0.020 0.468 0.496
#> SRR934279 6 0.4780 0.690 0.004 0.000 0.012 0.020 0.468 0.496
#> SRR934280 6 0.4706 0.638 0.004 0.012 0.000 0.016 0.468 0.500
#> SRR934281 6 0.4706 0.638 0.004 0.012 0.000 0.016 0.468 0.500
#> SRR934282 6 0.4706 0.638 0.004 0.012 0.000 0.016 0.468 0.500
#> SRR934283 6 0.4706 0.638 0.004 0.012 0.000 0.016 0.468 0.500
#> SRR934284 6 0.4706 0.638 0.004 0.012 0.000 0.016 0.468 0.500
#> SRR934285 6 0.4706 0.638 0.004 0.012 0.000 0.016 0.468 0.500
#> SRR934286 6 0.4706 0.638 0.004 0.012 0.000 0.016 0.468 0.500
#> SRR934287 6 0.4706 0.638 0.004 0.012 0.000 0.016 0.468 0.500
#> SRR934288 1 0.1536 0.950 0.940 0.004 0.000 0.040 0.000 0.016
#> SRR934289 1 0.1536 0.950 0.940 0.004 0.000 0.040 0.000 0.016
#> SRR934290 1 0.1536 0.950 0.940 0.004 0.000 0.040 0.000 0.016
#> SRR934291 1 0.1536 0.950 0.940 0.004 0.000 0.040 0.000 0.016
#> SRR934292 1 0.1536 0.950 0.940 0.004 0.000 0.040 0.000 0.016
#> SRR934293 1 0.1536 0.950 0.940 0.004 0.000 0.040 0.000 0.016
#> SRR934294 1 0.1536 0.950 0.940 0.004 0.000 0.040 0.000 0.016
#> SRR934295 1 0.1536 0.950 0.940 0.004 0.000 0.040 0.000 0.016
#> SRR934296 2 0.7356 0.497 0.152 0.536 0.076 0.136 0.000 0.100
#> SRR934297 2 0.7356 0.497 0.152 0.536 0.076 0.136 0.000 0.100
#> SRR934298 2 0.7356 0.497 0.152 0.536 0.076 0.136 0.000 0.100
#> SRR934299 2 0.7356 0.497 0.152 0.536 0.076 0.136 0.000 0.100
#> SRR934300 2 0.7356 0.497 0.152 0.536 0.076 0.136 0.000 0.100
#> SRR934301 2 0.7356 0.497 0.152 0.536 0.076 0.136 0.000 0.100
#> SRR934302 2 0.7356 0.497 0.152 0.536 0.076 0.136 0.000 0.100
#> SRR934303 2 0.7356 0.497 0.152 0.536 0.076 0.136 0.000 0.100
#> SRR934304 3 0.1444 0.815 0.000 0.000 0.928 0.072 0.000 0.000
#> SRR934305 3 0.1444 0.815 0.000 0.000 0.928 0.072 0.000 0.000
#> SRR934306 3 0.1444 0.815 0.000 0.000 0.928 0.072 0.000 0.000
#> SRR934307 3 0.1444 0.815 0.000 0.000 0.928 0.072 0.000 0.000
#> SRR934308 3 0.1444 0.815 0.000 0.000 0.928 0.072 0.000 0.000
#> SRR934309 3 0.1444 0.815 0.000 0.000 0.928 0.072 0.000 0.000
#> SRR934310 3 0.1444 0.815 0.000 0.000 0.928 0.072 0.000 0.000
#> SRR934311 3 0.1444 0.815 0.000 0.000 0.928 0.072 0.000 0.000
#> SRR934312 6 0.3809 0.763 0.008 0.000 0.000 0.004 0.304 0.684
#> SRR934313 6 0.3809 0.763 0.008 0.000 0.000 0.004 0.304 0.684
#> SRR934314 6 0.3809 0.763 0.008 0.000 0.000 0.004 0.304 0.684
#> SRR934315 6 0.3809 0.763 0.008 0.000 0.000 0.004 0.304 0.684
#> SRR934316 6 0.3809 0.763 0.008 0.000 0.000 0.004 0.304 0.684
#> SRR934317 6 0.3809 0.763 0.008 0.000 0.000 0.004 0.304 0.684
#> SRR934318 6 0.3809 0.763 0.008 0.000 0.000 0.004 0.304 0.684
#> SRR934319 6 0.3809 0.763 0.008 0.000 0.000 0.004 0.304 0.684
#> SRR934320 5 0.5822 0.614 0.024 0.136 0.000 0.016 0.624 0.200
#> SRR934321 5 0.5822 0.614 0.024 0.136 0.000 0.016 0.624 0.200
#> SRR934322 5 0.5822 0.614 0.024 0.136 0.000 0.016 0.624 0.200
#> SRR934323 5 0.5822 0.614 0.024 0.136 0.000 0.016 0.624 0.200
#> SRR934324 5 0.5822 0.614 0.024 0.136 0.000 0.016 0.624 0.200
#> SRR934325 5 0.5822 0.614 0.024 0.136 0.000 0.016 0.624 0.200
#> SRR934326 5 0.5822 0.614 0.024 0.136 0.000 0.016 0.624 0.200
#> SRR934327 5 0.5822 0.614 0.024 0.136 0.000 0.016 0.624 0.200
#> SRR934328 1 0.0146 0.965 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR934329 1 0.0146 0.965 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR934330 1 0.0146 0.965 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR934331 1 0.0146 0.965 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR934332 1 0.0146 0.965 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR934333 1 0.0146 0.965 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR934334 1 0.0146 0.965 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR934335 1 0.0146 0.965 0.996 0.000 0.000 0.004 0.000 0.000
#> SRR934344 1 0.1138 0.958 0.960 0.000 0.000 0.004 0.024 0.012
#> SRR934345 1 0.1138 0.958 0.960 0.000 0.000 0.004 0.024 0.012
#> SRR934346 1 0.1138 0.958 0.960 0.000 0.000 0.004 0.024 0.012
#> SRR934347 1 0.1138 0.958 0.960 0.000 0.000 0.004 0.024 0.012
#> SRR934348 1 0.1138 0.958 0.960 0.000 0.000 0.004 0.024 0.012
#> SRR934349 1 0.1138 0.958 0.960 0.000 0.000 0.004 0.024 0.012
#> SRR934350 1 0.1138 0.958 0.960 0.000 0.000 0.004 0.024 0.012
#> SRR934351 1 0.1138 0.958 0.960 0.000 0.000 0.004 0.024 0.012
#> SRR934336 5 0.2257 0.617 0.000 0.000 0.000 0.008 0.876 0.116
#> SRR934337 5 0.2257 0.617 0.000 0.000 0.000 0.008 0.876 0.116
#> SRR934338 5 0.2257 0.617 0.000 0.000 0.000 0.008 0.876 0.116
#> SRR934339 5 0.2257 0.617 0.000 0.000 0.000 0.008 0.876 0.116
#> SRR934340 5 0.2257 0.617 0.000 0.000 0.000 0.008 0.876 0.116
#> SRR934341 5 0.2257 0.617 0.000 0.000 0.000 0.008 0.876 0.116
#> SRR934342 5 0.2257 0.617 0.000 0.000 0.000 0.008 0.876 0.116
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 14550 rows and 135 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.868 0.918 0.960 0.4193 0.580 0.580
#> 3 3 0.725 0.894 0.955 -0.0259 0.866 0.799
#> 4 4 0.955 0.910 0.948 0.2332 0.888 0.821
#> 5 5 0.756 0.887 0.920 0.1698 0.923 0.855
#> 6 6 0.931 0.920 0.952 0.2544 0.805 0.571
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 4
There is also optional best \(k\) = 4 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR934216 1 0.000 0.968 1.000 0.000
#> SRR934217 1 0.000 0.968 1.000 0.000
#> SRR934218 1 0.000 0.968 1.000 0.000
#> SRR934219 1 0.000 0.968 1.000 0.000
#> SRR934220 1 0.000 0.968 1.000 0.000
#> SRR934221 1 0.000 0.968 1.000 0.000
#> SRR934222 1 0.000 0.968 1.000 0.000
#> SRR934223 1 0.000 0.968 1.000 0.000
#> SRR934224 1 0.000 0.968 1.000 0.000
#> SRR934225 1 0.000 0.968 1.000 0.000
#> SRR934226 1 0.000 0.968 1.000 0.000
#> SRR934227 1 0.000 0.968 1.000 0.000
#> SRR934228 1 0.000 0.968 1.000 0.000
#> SRR934229 1 0.000 0.968 1.000 0.000
#> SRR934230 1 0.000 0.968 1.000 0.000
#> SRR934231 1 0.000 0.968 1.000 0.000
#> SRR934232 2 0.000 0.927 0.000 1.000
#> SRR934233 2 0.000 0.927 0.000 1.000
#> SRR934234 2 0.000 0.927 0.000 1.000
#> SRR934235 2 0.000 0.927 0.000 1.000
#> SRR934236 2 0.000 0.927 0.000 1.000
#> SRR934237 2 0.000 0.927 0.000 1.000
#> SRR934238 2 0.000 0.927 0.000 1.000
#> SRR934239 2 0.000 0.927 0.000 1.000
#> SRR934240 2 0.000 0.927 0.000 1.000
#> SRR934241 2 0.000 0.927 0.000 1.000
#> SRR934242 2 0.000 0.927 0.000 1.000
#> SRR934243 2 0.000 0.927 0.000 1.000
#> SRR934244 2 0.000 0.927 0.000 1.000
#> SRR934245 2 0.000 0.927 0.000 1.000
#> SRR934246 2 0.000 0.927 0.000 1.000
#> SRR934247 2 0.000 0.927 0.000 1.000
#> SRR934248 2 0.343 0.929 0.064 0.936
#> SRR934249 2 0.343 0.929 0.064 0.936
#> SRR934250 2 0.343 0.929 0.064 0.936
#> SRR934251 2 0.343 0.929 0.064 0.936
#> SRR934252 2 0.343 0.929 0.064 0.936
#> SRR934253 2 0.343 0.929 0.064 0.936
#> SRR934254 2 0.343 0.929 0.064 0.936
#> SRR934255 2 0.343 0.929 0.064 0.936
#> SRR934256 2 0.714 0.799 0.196 0.804
#> SRR934257 2 0.714 0.799 0.196 0.804
#> SRR934258 2 0.722 0.794 0.200 0.800
#> SRR934259 2 0.722 0.794 0.200 0.800
#> SRR934260 2 0.697 0.807 0.188 0.812
#> SRR934261 2 0.714 0.799 0.196 0.804
#> SRR934262 2 0.722 0.794 0.200 0.800
#> SRR934263 2 0.722 0.794 0.200 0.800
#> SRR934264 1 0.939 0.450 0.644 0.356
#> SRR934265 1 0.876 0.575 0.704 0.296
#> SRR934266 1 0.966 0.358 0.608 0.392
#> SRR934267 1 0.943 0.440 0.640 0.360
#> SRR934268 1 0.876 0.574 0.704 0.296
#> SRR934269 1 0.961 0.380 0.616 0.384
#> SRR934270 1 0.909 0.520 0.676 0.324
#> SRR934271 1 0.961 0.380 0.616 0.384
#> SRR934272 1 0.000 0.968 1.000 0.000
#> SRR934273 1 0.000 0.968 1.000 0.000
#> SRR934274 1 0.000 0.968 1.000 0.000
#> SRR934275 1 0.000 0.968 1.000 0.000
#> SRR934276 1 0.000 0.968 1.000 0.000
#> SRR934277 1 0.000 0.968 1.000 0.000
#> SRR934278 1 0.000 0.968 1.000 0.000
#> SRR934279 1 0.000 0.968 1.000 0.000
#> SRR934280 1 0.000 0.968 1.000 0.000
#> SRR934281 1 0.000 0.968 1.000 0.000
#> SRR934282 1 0.000 0.968 1.000 0.000
#> SRR934283 1 0.000 0.968 1.000 0.000
#> SRR934284 1 0.000 0.968 1.000 0.000
#> SRR934285 1 0.000 0.968 1.000 0.000
#> SRR934286 1 0.000 0.968 1.000 0.000
#> SRR934287 1 0.000 0.968 1.000 0.000
#> SRR934288 1 0.000 0.968 1.000 0.000
#> SRR934289 1 0.000 0.968 1.000 0.000
#> SRR934290 1 0.000 0.968 1.000 0.000
#> SRR934291 1 0.000 0.968 1.000 0.000
#> SRR934292 1 0.000 0.968 1.000 0.000
#> SRR934293 1 0.000 0.968 1.000 0.000
#> SRR934294 1 0.000 0.968 1.000 0.000
#> SRR934295 1 0.000 0.968 1.000 0.000
#> SRR934296 1 0.000 0.968 1.000 0.000
#> SRR934297 1 0.000 0.968 1.000 0.000
#> SRR934298 1 0.000 0.968 1.000 0.000
#> SRR934299 1 0.000 0.968 1.000 0.000
#> SRR934300 1 0.000 0.968 1.000 0.000
#> SRR934301 1 0.000 0.968 1.000 0.000
#> SRR934302 1 0.000 0.968 1.000 0.000
#> SRR934303 1 0.000 0.968 1.000 0.000
#> SRR934304 2 0.343 0.929 0.064 0.936
#> SRR934305 2 0.343 0.929 0.064 0.936
#> SRR934306 2 0.343 0.929 0.064 0.936
#> SRR934307 2 0.343 0.929 0.064 0.936
#> SRR934308 2 0.343 0.929 0.064 0.936
#> SRR934309 2 0.343 0.929 0.064 0.936
#> SRR934310 2 0.343 0.929 0.064 0.936
#> SRR934311 2 0.343 0.929 0.064 0.936
#> SRR934312 1 0.000 0.968 1.000 0.000
#> SRR934313 1 0.000 0.968 1.000 0.000
#> SRR934314 1 0.000 0.968 1.000 0.000
#> SRR934315 1 0.000 0.968 1.000 0.000
#> SRR934316 1 0.000 0.968 1.000 0.000
#> SRR934317 1 0.000 0.968 1.000 0.000
#> SRR934318 1 0.000 0.968 1.000 0.000
#> SRR934319 1 0.000 0.968 1.000 0.000
#> SRR934320 1 0.000 0.968 1.000 0.000
#> SRR934321 1 0.000 0.968 1.000 0.000
#> SRR934322 1 0.000 0.968 1.000 0.000
#> SRR934323 1 0.000 0.968 1.000 0.000
#> SRR934324 1 0.000 0.968 1.000 0.000
#> SRR934325 1 0.000 0.968 1.000 0.000
#> SRR934326 1 0.000 0.968 1.000 0.000
#> SRR934327 1 0.000 0.968 1.000 0.000
#> SRR934328 1 0.000 0.968 1.000 0.000
#> SRR934329 1 0.000 0.968 1.000 0.000
#> SRR934330 1 0.000 0.968 1.000 0.000
#> SRR934331 1 0.000 0.968 1.000 0.000
#> SRR934332 1 0.000 0.968 1.000 0.000
#> SRR934333 1 0.000 0.968 1.000 0.000
#> SRR934334 1 0.000 0.968 1.000 0.000
#> SRR934335 1 0.000 0.968 1.000 0.000
#> SRR934344 1 0.000 0.968 1.000 0.000
#> SRR934345 1 0.000 0.968 1.000 0.000
#> SRR934346 1 0.000 0.968 1.000 0.000
#> SRR934347 1 0.000 0.968 1.000 0.000
#> SRR934348 1 0.000 0.968 1.000 0.000
#> SRR934349 1 0.000 0.968 1.000 0.000
#> SRR934350 1 0.000 0.968 1.000 0.000
#> SRR934351 1 0.000 0.968 1.000 0.000
#> SRR934336 1 0.000 0.968 1.000 0.000
#> SRR934337 1 0.000 0.968 1.000 0.000
#> SRR934338 1 0.000 0.968 1.000 0.000
#> SRR934339 1 0.000 0.968 1.000 0.000
#> SRR934340 1 0.000 0.968 1.000 0.000
#> SRR934341 1 0.000 0.968 1.000 0.000
#> SRR934342 1 0.000 0.968 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 1 0.000 0.951 1.000 0.000 0
#> SRR934217 1 0.000 0.951 1.000 0.000 0
#> SRR934218 1 0.000 0.951 1.000 0.000 0
#> SRR934219 1 0.000 0.951 1.000 0.000 0
#> SRR934220 1 0.000 0.951 1.000 0.000 0
#> SRR934221 1 0.000 0.951 1.000 0.000 0
#> SRR934222 1 0.000 0.951 1.000 0.000 0
#> SRR934223 1 0.000 0.951 1.000 0.000 0
#> SRR934224 1 0.000 0.951 1.000 0.000 0
#> SRR934225 1 0.000 0.951 1.000 0.000 0
#> SRR934226 1 0.000 0.951 1.000 0.000 0
#> SRR934227 1 0.000 0.951 1.000 0.000 0
#> SRR934228 1 0.000 0.951 1.000 0.000 0
#> SRR934229 1 0.000 0.951 1.000 0.000 0
#> SRR934230 1 0.000 0.951 1.000 0.000 0
#> SRR934231 1 0.000 0.951 1.000 0.000 0
#> SRR934232 2 0.000 0.860 0.000 1.000 0
#> SRR934233 2 0.000 0.860 0.000 1.000 0
#> SRR934234 2 0.000 0.860 0.000 1.000 0
#> SRR934235 2 0.000 0.860 0.000 1.000 0
#> SRR934236 2 0.000 0.860 0.000 1.000 0
#> SRR934237 2 0.000 0.860 0.000 1.000 0
#> SRR934238 2 0.000 0.860 0.000 1.000 0
#> SRR934239 2 0.000 0.860 0.000 1.000 0
#> SRR934240 2 0.000 0.860 0.000 1.000 0
#> SRR934241 2 0.000 0.860 0.000 1.000 0
#> SRR934242 2 0.000 0.860 0.000 1.000 0
#> SRR934243 2 0.000 0.860 0.000 1.000 0
#> SRR934244 2 0.000 0.860 0.000 1.000 0
#> SRR934245 2 0.000 0.860 0.000 1.000 0
#> SRR934246 2 0.000 0.860 0.000 1.000 0
#> SRR934247 2 0.000 0.860 0.000 1.000 0
#> SRR934248 1 0.546 0.648 0.712 0.288 0
#> SRR934249 1 0.546 0.648 0.712 0.288 0
#> SRR934250 1 0.546 0.648 0.712 0.288 0
#> SRR934251 1 0.546 0.648 0.712 0.288 0
#> SRR934252 1 0.546 0.648 0.712 0.288 0
#> SRR934253 1 0.546 0.648 0.712 0.288 0
#> SRR934254 1 0.546 0.648 0.712 0.288 0
#> SRR934255 1 0.546 0.648 0.712 0.288 0
#> SRR934256 2 0.450 0.716 0.196 0.804 0
#> SRR934257 2 0.450 0.716 0.196 0.804 0
#> SRR934258 2 0.455 0.711 0.200 0.800 0
#> SRR934259 2 0.455 0.711 0.200 0.800 0
#> SRR934260 2 0.440 0.723 0.188 0.812 0
#> SRR934261 2 0.450 0.716 0.196 0.804 0
#> SRR934262 2 0.455 0.711 0.200 0.800 0
#> SRR934263 2 0.455 0.711 0.200 0.800 0
#> SRR934264 1 0.546 0.648 0.712 0.288 0
#> SRR934265 1 0.533 0.670 0.728 0.272 0
#> SRR934266 1 0.546 0.648 0.712 0.288 0
#> SRR934267 1 0.540 0.659 0.720 0.280 0
#> SRR934268 1 0.533 0.670 0.728 0.272 0
#> SRR934269 1 0.546 0.648 0.712 0.288 0
#> SRR934270 1 0.533 0.670 0.728 0.272 0
#> SRR934271 1 0.546 0.648 0.712 0.288 0
#> SRR934272 1 0.000 0.951 1.000 0.000 0
#> SRR934273 1 0.000 0.951 1.000 0.000 0
#> SRR934274 1 0.000 0.951 1.000 0.000 0
#> SRR934275 1 0.000 0.951 1.000 0.000 0
#> SRR934276 1 0.000 0.951 1.000 0.000 0
#> SRR934277 1 0.000 0.951 1.000 0.000 0
#> SRR934278 1 0.000 0.951 1.000 0.000 0
#> SRR934279 1 0.000 0.951 1.000 0.000 0
#> SRR934280 1 0.000 0.951 1.000 0.000 0
#> SRR934281 1 0.000 0.951 1.000 0.000 0
#> SRR934282 1 0.000 0.951 1.000 0.000 0
#> SRR934283 1 0.000 0.951 1.000 0.000 0
#> SRR934284 1 0.000 0.951 1.000 0.000 0
#> SRR934285 1 0.000 0.951 1.000 0.000 0
#> SRR934286 1 0.000 0.951 1.000 0.000 0
#> SRR934287 1 0.000 0.951 1.000 0.000 0
#> SRR934288 1 0.000 0.951 1.000 0.000 0
#> SRR934289 1 0.000 0.951 1.000 0.000 0
#> SRR934290 1 0.000 0.951 1.000 0.000 0
#> SRR934291 1 0.000 0.951 1.000 0.000 0
#> SRR934292 1 0.000 0.951 1.000 0.000 0
#> SRR934293 1 0.000 0.951 1.000 0.000 0
#> SRR934294 1 0.000 0.951 1.000 0.000 0
#> SRR934295 1 0.000 0.951 1.000 0.000 0
#> SRR934296 1 0.000 0.951 1.000 0.000 0
#> SRR934297 1 0.000 0.951 1.000 0.000 0
#> SRR934298 1 0.000 0.951 1.000 0.000 0
#> SRR934299 1 0.000 0.951 1.000 0.000 0
#> SRR934300 1 0.000 0.951 1.000 0.000 0
#> SRR934301 1 0.000 0.951 1.000 0.000 0
#> SRR934302 1 0.000 0.951 1.000 0.000 0
#> SRR934303 1 0.000 0.951 1.000 0.000 0
#> SRR934304 3 0.000 1.000 0.000 0.000 1
#> SRR934305 3 0.000 1.000 0.000 0.000 1
#> SRR934306 3 0.000 1.000 0.000 0.000 1
#> SRR934307 3 0.000 1.000 0.000 0.000 1
#> SRR934308 3 0.000 1.000 0.000 0.000 1
#> SRR934309 3 0.000 1.000 0.000 0.000 1
#> SRR934310 3 0.000 1.000 0.000 0.000 1
#> SRR934311 3 0.000 1.000 0.000 0.000 1
#> SRR934312 1 0.000 0.951 1.000 0.000 0
#> SRR934313 1 0.000 0.951 1.000 0.000 0
#> SRR934314 1 0.000 0.951 1.000 0.000 0
#> SRR934315 1 0.000 0.951 1.000 0.000 0
#> SRR934316 1 0.000 0.951 1.000 0.000 0
#> SRR934317 1 0.000 0.951 1.000 0.000 0
#> SRR934318 1 0.000 0.951 1.000 0.000 0
#> SRR934319 1 0.000 0.951 1.000 0.000 0
#> SRR934320 1 0.000 0.951 1.000 0.000 0
#> SRR934321 1 0.000 0.951 1.000 0.000 0
#> SRR934322 1 0.000 0.951 1.000 0.000 0
#> SRR934323 1 0.000 0.951 1.000 0.000 0
#> SRR934324 1 0.000 0.951 1.000 0.000 0
#> SRR934325 1 0.000 0.951 1.000 0.000 0
#> SRR934326 1 0.000 0.951 1.000 0.000 0
#> SRR934327 1 0.000 0.951 1.000 0.000 0
#> SRR934328 1 0.000 0.951 1.000 0.000 0
#> SRR934329 1 0.000 0.951 1.000 0.000 0
#> SRR934330 1 0.000 0.951 1.000 0.000 0
#> SRR934331 1 0.000 0.951 1.000 0.000 0
#> SRR934332 1 0.000 0.951 1.000 0.000 0
#> SRR934333 1 0.000 0.951 1.000 0.000 0
#> SRR934334 1 0.000 0.951 1.000 0.000 0
#> SRR934335 1 0.000 0.951 1.000 0.000 0
#> SRR934344 1 0.000 0.951 1.000 0.000 0
#> SRR934345 1 0.000 0.951 1.000 0.000 0
#> SRR934346 1 0.000 0.951 1.000 0.000 0
#> SRR934347 1 0.000 0.951 1.000 0.000 0
#> SRR934348 1 0.000 0.951 1.000 0.000 0
#> SRR934349 1 0.000 0.951 1.000 0.000 0
#> SRR934350 1 0.000 0.951 1.000 0.000 0
#> SRR934351 1 0.000 0.951 1.000 0.000 0
#> SRR934336 1 0.000 0.951 1.000 0.000 0
#> SRR934337 1 0.000 0.951 1.000 0.000 0
#> SRR934338 1 0.000 0.951 1.000 0.000 0
#> SRR934339 1 0.000 0.951 1.000 0.000 0
#> SRR934340 1 0.000 0.951 1.000 0.000 0
#> SRR934341 1 0.000 0.951 1.000 0.000 0
#> SRR934342 1 0.000 0.951 1.000 0.000 0
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 1 0.1716 0.893 0.936 0.000 0 0.064
#> SRR934217 1 0.1716 0.893 0.936 0.000 0 0.064
#> SRR934218 1 0.1716 0.893 0.936 0.000 0 0.064
#> SRR934219 1 0.1716 0.893 0.936 0.000 0 0.064
#> SRR934220 1 0.1716 0.893 0.936 0.000 0 0.064
#> SRR934221 1 0.1716 0.893 0.936 0.000 0 0.064
#> SRR934222 1 0.1716 0.893 0.936 0.000 0 0.064
#> SRR934223 1 0.1716 0.893 0.936 0.000 0 0.064
#> SRR934224 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934225 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934226 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934227 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934228 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934229 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934230 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934231 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934232 2 0.0000 0.955 0.000 1.000 0 0.000
#> SRR934233 2 0.0000 0.955 0.000 1.000 0 0.000
#> SRR934234 2 0.0000 0.955 0.000 1.000 0 0.000
#> SRR934235 2 0.0000 0.955 0.000 1.000 0 0.000
#> SRR934236 2 0.0000 0.955 0.000 1.000 0 0.000
#> SRR934237 2 0.0000 0.955 0.000 1.000 0 0.000
#> SRR934238 2 0.0000 0.955 0.000 1.000 0 0.000
#> SRR934239 2 0.0000 0.955 0.000 1.000 0 0.000
#> SRR934240 2 0.0000 0.955 0.000 1.000 0 0.000
#> SRR934241 2 0.0000 0.955 0.000 1.000 0 0.000
#> SRR934242 2 0.0000 0.955 0.000 1.000 0 0.000
#> SRR934243 2 0.0000 0.955 0.000 1.000 0 0.000
#> SRR934244 2 0.0000 0.955 0.000 1.000 0 0.000
#> SRR934245 2 0.0000 0.955 0.000 1.000 0 0.000
#> SRR934246 2 0.0000 0.955 0.000 1.000 0 0.000
#> SRR934247 2 0.0000 0.955 0.000 1.000 0 0.000
#> SRR934248 2 0.1716 0.911 0.064 0.936 0 0.000
#> SRR934249 2 0.1716 0.911 0.064 0.936 0 0.000
#> SRR934250 2 0.1716 0.911 0.064 0.936 0 0.000
#> SRR934251 2 0.1716 0.911 0.064 0.936 0 0.000
#> SRR934252 2 0.1716 0.911 0.064 0.936 0 0.000
#> SRR934253 2 0.1716 0.911 0.064 0.936 0 0.000
#> SRR934254 2 0.1716 0.911 0.064 0.936 0 0.000
#> SRR934255 2 0.1716 0.911 0.064 0.936 0 0.000
#> SRR934256 4 0.1716 1.000 0.000 0.064 0 0.936
#> SRR934257 4 0.1716 1.000 0.000 0.064 0 0.936
#> SRR934258 4 0.1716 1.000 0.000 0.064 0 0.936
#> SRR934259 4 0.1716 1.000 0.000 0.064 0 0.936
#> SRR934260 4 0.1716 1.000 0.000 0.064 0 0.936
#> SRR934261 4 0.1716 1.000 0.000 0.064 0 0.936
#> SRR934262 4 0.1716 1.000 0.000 0.064 0 0.936
#> SRR934263 4 0.1716 1.000 0.000 0.064 0 0.936
#> SRR934264 1 0.4730 0.487 0.636 0.364 0 0.000
#> SRR934265 1 0.4500 0.585 0.684 0.316 0 0.000
#> SRR934266 1 0.4804 0.441 0.616 0.384 0 0.000
#> SRR934267 1 0.4713 0.496 0.640 0.360 0 0.000
#> SRR934268 1 0.4406 0.614 0.700 0.300 0 0.000
#> SRR934269 1 0.4804 0.441 0.616 0.384 0 0.000
#> SRR934270 1 0.4522 0.578 0.680 0.320 0 0.000
#> SRR934271 1 0.4804 0.441 0.616 0.384 0 0.000
#> SRR934272 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934273 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934274 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934275 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934276 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934277 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934278 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934279 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934280 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934281 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934282 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934283 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934284 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934285 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934286 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934287 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934288 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934289 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934290 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934291 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934292 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934293 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934294 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934295 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934296 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934297 1 0.0188 0.932 0.996 0.000 0 0.004
#> SRR934298 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934299 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934300 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934301 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934302 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934303 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934304 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR934305 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR934306 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR934307 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR934308 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR934309 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR934310 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR934311 3 0.0000 1.000 0.000 0.000 1 0.000
#> SRR934312 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934313 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934314 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934315 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934316 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934317 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934318 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934319 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934320 1 0.2011 0.915 0.920 0.000 0 0.080
#> SRR934321 1 0.1940 0.916 0.924 0.000 0 0.076
#> SRR934322 1 0.2011 0.915 0.920 0.000 0 0.080
#> SRR934323 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934324 1 0.2011 0.915 0.920 0.000 0 0.080
#> SRR934325 1 0.2011 0.915 0.920 0.000 0 0.080
#> SRR934326 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934327 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934328 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934329 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934330 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934331 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934332 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934333 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934334 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934335 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934344 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934345 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934346 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934347 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934348 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934349 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934350 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934351 1 0.2081 0.913 0.916 0.000 0 0.084
#> SRR934336 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934337 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934338 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934339 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934340 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934341 1 0.0000 0.932 1.000 0.000 0 0.000
#> SRR934342 1 0.0000 0.932 1.000 0.000 0 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0.3177 1.000 0.208 0.000 0.792 0 0
#> SRR934217 3 0.3177 1.000 0.208 0.000 0.792 0 0
#> SRR934218 3 0.3177 1.000 0.208 0.000 0.792 0 0
#> SRR934219 3 0.3177 1.000 0.208 0.000 0.792 0 0
#> SRR934220 3 0.3177 1.000 0.208 0.000 0.792 0 0
#> SRR934221 3 0.3177 1.000 0.208 0.000 0.792 0 0
#> SRR934222 3 0.3177 1.000 0.208 0.000 0.792 0 0
#> SRR934223 3 0.3177 1.000 0.208 0.000 0.792 0 0
#> SRR934224 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934225 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934226 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934227 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934228 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934229 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934230 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934231 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934232 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934233 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934234 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934235 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934236 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934237 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934238 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934239 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934240 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934241 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934242 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934243 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934244 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934245 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934246 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934247 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934248 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934249 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934250 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934251 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934252 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934253 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934254 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934255 2 0.0000 1.000 0.000 1.000 0.000 0 0
#> SRR934256 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR934257 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR934258 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR934259 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR934260 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR934261 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR934262 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR934263 4 0.0000 1.000 0.000 0.000 0.000 1 0
#> SRR934264 1 0.4074 0.523 0.636 0.364 0.000 0 0
#> SRR934265 1 0.3876 0.598 0.684 0.316 0.000 0 0
#> SRR934266 1 0.4138 0.488 0.616 0.384 0.000 0 0
#> SRR934267 1 0.4060 0.530 0.640 0.360 0.000 0 0
#> SRR934268 1 0.3796 0.623 0.700 0.300 0.000 0 0
#> SRR934269 1 0.4138 0.488 0.616 0.384 0.000 0 0
#> SRR934270 1 0.3913 0.589 0.676 0.324 0.000 0 0
#> SRR934271 1 0.4138 0.488 0.616 0.384 0.000 0 0
#> SRR934272 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934273 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934274 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934275 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934276 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934277 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934278 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934279 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934280 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934281 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934282 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934283 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934284 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934285 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934286 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934287 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934288 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934289 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934290 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934291 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934292 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934293 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934294 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934295 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934296 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934297 1 0.0162 0.871 0.996 0.000 0.004 0 0
#> SRR934298 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934299 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934300 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934301 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934302 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934303 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934304 5 0.0000 1.000 0.000 0.000 0.000 0 1
#> SRR934305 5 0.0000 1.000 0.000 0.000 0.000 0 1
#> SRR934306 5 0.0000 1.000 0.000 0.000 0.000 0 1
#> SRR934307 5 0.0000 1.000 0.000 0.000 0.000 0 1
#> SRR934308 5 0.0000 1.000 0.000 0.000 0.000 0 1
#> SRR934309 5 0.0000 1.000 0.000 0.000 0.000 0 1
#> SRR934310 5 0.0000 1.000 0.000 0.000 0.000 0 1
#> SRR934311 5 0.0000 1.000 0.000 0.000 0.000 0 1
#> SRR934312 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934313 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934314 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934315 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934316 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934317 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934318 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934319 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934320 1 0.2813 0.839 0.832 0.000 0.168 0 0
#> SRR934321 1 0.2773 0.840 0.836 0.000 0.164 0 0
#> SRR934322 1 0.2852 0.838 0.828 0.000 0.172 0 0
#> SRR934323 1 0.2966 0.833 0.816 0.000 0.184 0 0
#> SRR934324 1 0.2773 0.840 0.836 0.000 0.164 0 0
#> SRR934325 1 0.2690 0.842 0.844 0.000 0.156 0 0
#> SRR934326 1 0.2891 0.836 0.824 0.000 0.176 0 0
#> SRR934327 1 0.2929 0.835 0.820 0.000 0.180 0 0
#> SRR934328 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934329 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934330 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934331 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934332 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934333 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934334 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934335 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934344 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934345 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934346 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934347 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934348 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934349 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934350 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934351 1 0.3177 0.825 0.792 0.000 0.208 0 0
#> SRR934336 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934337 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934338 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934339 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934340 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934341 1 0.0000 0.871 1.000 0.000 0.000 0 0
#> SRR934342 1 0.0000 0.871 1.000 0.000 0.000 0 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.0000 1.000 0.000 0.000 1 0 0 0.000
#> SRR934217 3 0.0000 1.000 0.000 0.000 1 0 0 0.000
#> SRR934218 3 0.0000 1.000 0.000 0.000 1 0 0 0.000
#> SRR934219 3 0.0000 1.000 0.000 0.000 1 0 0 0.000
#> SRR934220 3 0.0000 1.000 0.000 0.000 1 0 0 0.000
#> SRR934221 3 0.0000 1.000 0.000 0.000 1 0 0 0.000
#> SRR934222 3 0.0000 1.000 0.000 0.000 1 0 0 0.000
#> SRR934223 3 0.0000 1.000 0.000 0.000 1 0 0 0.000
#> SRR934224 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934225 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934226 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934227 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934228 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934229 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934230 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934231 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934232 2 0.0000 0.981 0.000 1.000 0 0 0 0.000
#> SRR934233 2 0.0000 0.981 0.000 1.000 0 0 0 0.000
#> SRR934234 2 0.0000 0.981 0.000 1.000 0 0 0 0.000
#> SRR934235 2 0.0000 0.981 0.000 1.000 0 0 0 0.000
#> SRR934236 2 0.0000 0.981 0.000 1.000 0 0 0 0.000
#> SRR934237 2 0.0000 0.981 0.000 1.000 0 0 0 0.000
#> SRR934238 2 0.0000 0.981 0.000 1.000 0 0 0 0.000
#> SRR934239 2 0.0000 0.981 0.000 1.000 0 0 0 0.000
#> SRR934240 2 0.0000 0.981 0.000 1.000 0 0 0 0.000
#> SRR934241 2 0.0000 0.981 0.000 1.000 0 0 0 0.000
#> SRR934242 2 0.0000 0.981 0.000 1.000 0 0 0 0.000
#> SRR934243 2 0.0000 0.981 0.000 1.000 0 0 0 0.000
#> SRR934244 2 0.0000 0.981 0.000 1.000 0 0 0 0.000
#> SRR934245 2 0.0000 0.981 0.000 1.000 0 0 0 0.000
#> SRR934246 2 0.0000 0.981 0.000 1.000 0 0 0 0.000
#> SRR934247 2 0.0000 0.981 0.000 1.000 0 0 0 0.000
#> SRR934248 2 0.1204 0.961 0.056 0.944 0 0 0 0.000
#> SRR934249 2 0.1204 0.961 0.056 0.944 0 0 0 0.000
#> SRR934250 2 0.1204 0.961 0.056 0.944 0 0 0 0.000
#> SRR934251 2 0.1204 0.961 0.056 0.944 0 0 0 0.000
#> SRR934252 2 0.1204 0.961 0.056 0.944 0 0 0 0.000
#> SRR934253 2 0.1204 0.961 0.056 0.944 0 0 0 0.000
#> SRR934254 2 0.1204 0.961 0.056 0.944 0 0 0 0.000
#> SRR934255 2 0.1204 0.961 0.056 0.944 0 0 0 0.000
#> SRR934256 4 0.0000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934257 4 0.0000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934258 4 0.0000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934259 4 0.0000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934260 4 0.0000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934261 4 0.0000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934262 4 0.0000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934263 4 0.0000 1.000 0.000 0.000 0 1 0 0.000
#> SRR934264 6 0.4672 0.428 0.056 0.348 0 0 0 0.596
#> SRR934265 6 0.4515 0.515 0.056 0.304 0 0 0 0.640
#> SRR934266 6 0.4717 0.391 0.056 0.364 0 0 0 0.580
#> SRR934267 6 0.4660 0.436 0.056 0.344 0 0 0 0.600
#> SRR934268 6 0.4481 0.529 0.056 0.296 0 0 0 0.648
#> SRR934269 6 0.4727 0.382 0.056 0.368 0 0 0 0.576
#> SRR934270 6 0.4563 0.492 0.056 0.316 0 0 0 0.628
#> SRR934271 6 0.4717 0.391 0.056 0.364 0 0 0 0.580
#> SRR934272 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934273 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934274 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934275 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934276 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934277 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934278 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934279 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934280 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934281 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934282 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934283 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934284 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934285 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934286 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934287 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934288 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934289 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934290 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934291 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934292 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934293 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934294 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934295 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934296 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934297 6 0.0146 0.930 0.004 0.000 0 0 0 0.996
#> SRR934298 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934299 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934300 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934301 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934302 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934303 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934304 5 0.0000 1.000 0.000 0.000 0 0 1 0.000
#> SRR934305 5 0.0000 1.000 0.000 0.000 0 0 1 0.000
#> SRR934306 5 0.0000 1.000 0.000 0.000 0 0 1 0.000
#> SRR934307 5 0.0000 1.000 0.000 0.000 0 0 1 0.000
#> SRR934308 5 0.0000 1.000 0.000 0.000 0 0 1 0.000
#> SRR934309 5 0.0000 1.000 0.000 0.000 0 0 1 0.000
#> SRR934310 5 0.0000 1.000 0.000 0.000 0 0 1 0.000
#> SRR934311 5 0.0000 1.000 0.000 0.000 0 0 1 0.000
#> SRR934312 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934313 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934314 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934315 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934316 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934317 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934318 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934319 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934320 1 0.2664 0.834 0.816 0.000 0 0 0 0.184
#> SRR934321 1 0.3101 0.751 0.756 0.000 0 0 0 0.244
#> SRR934322 1 0.2941 0.786 0.780 0.000 0 0 0 0.220
#> SRR934323 1 0.1714 0.923 0.908 0.000 0 0 0 0.092
#> SRR934324 1 0.3446 0.648 0.692 0.000 0 0 0 0.308
#> SRR934325 1 0.2416 0.866 0.844 0.000 0 0 0 0.156
#> SRR934326 1 0.2562 0.846 0.828 0.000 0 0 0 0.172
#> SRR934327 1 0.2340 0.872 0.852 0.000 0 0 0 0.148
#> SRR934328 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934329 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934330 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934331 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934332 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934333 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934334 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934335 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934344 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934345 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934346 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934347 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934348 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934349 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934350 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934351 1 0.1204 0.951 0.944 0.000 0 0 0 0.056
#> SRR934336 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934337 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934338 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934339 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934340 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934341 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
#> SRR934342 6 0.0000 0.934 0.000 0.000 0 0 0 1.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "mclust"]
# you can also extract it by
# res = res_list["MAD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 14550 rows and 135 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 0.997 0.998 0.5022 0.498 0.498
#> 3 3 0.587 0.767 0.824 0.1511 0.915 0.829
#> 4 4 0.542 0.571 0.704 0.1663 0.908 0.785
#> 5 5 0.674 0.637 0.748 0.0711 0.797 0.483
#> 6 6 0.699 0.722 0.834 0.0398 0.830 0.471
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
#> SRR934216 2 0.0376 0.997 0.004 0.996
#> SRR934217 2 0.0376 0.997 0.004 0.996
#> SRR934218 2 0.0376 0.997 0.004 0.996
#> SRR934219 2 0.0376 0.997 0.004 0.996
#> SRR934220 2 0.0376 0.997 0.004 0.996
#> SRR934221 2 0.0376 0.997 0.004 0.996
#> SRR934222 2 0.0376 0.997 0.004 0.996
#> SRR934223 2 0.0376 0.997 0.004 0.996
#> SRR934224 1 0.0000 0.997 1.000 0.000
#> SRR934225 1 0.0000 0.997 1.000 0.000
#> SRR934226 1 0.0000 0.997 1.000 0.000
#> SRR934227 1 0.0000 0.997 1.000 0.000
#> SRR934228 1 0.0000 0.997 1.000 0.000
#> SRR934229 1 0.0000 0.997 1.000 0.000
#> SRR934230 1 0.0000 0.997 1.000 0.000
#> SRR934231 1 0.0000 0.997 1.000 0.000
#> SRR934232 2 0.0000 0.998 0.000 1.000
#> SRR934233 2 0.0000 0.998 0.000 1.000
#> SRR934234 2 0.0000 0.998 0.000 1.000
#> SRR934235 2 0.0000 0.998 0.000 1.000
#> SRR934236 2 0.0000 0.998 0.000 1.000
#> SRR934237 2 0.0000 0.998 0.000 1.000
#> SRR934238 2 0.0000 0.998 0.000 1.000
#> SRR934239 2 0.0000 0.998 0.000 1.000
#> SRR934240 2 0.0000 0.998 0.000 1.000
#> SRR934241 2 0.0000 0.998 0.000 1.000
#> SRR934242 2 0.0000 0.998 0.000 1.000
#> SRR934243 2 0.0000 0.998 0.000 1.000
#> SRR934244 2 0.0000 0.998 0.000 1.000
#> SRR934245 2 0.0000 0.998 0.000 1.000
#> SRR934246 2 0.0000 0.998 0.000 1.000
#> SRR934247 2 0.0000 0.998 0.000 1.000
#> SRR934248 2 0.0000 0.998 0.000 1.000
#> SRR934249 2 0.0000 0.998 0.000 1.000
#> SRR934250 2 0.0000 0.998 0.000 1.000
#> SRR934251 2 0.0000 0.998 0.000 1.000
#> SRR934252 2 0.0000 0.998 0.000 1.000
#> SRR934253 2 0.0000 0.998 0.000 1.000
#> SRR934254 2 0.0000 0.998 0.000 1.000
#> SRR934255 2 0.0000 0.998 0.000 1.000
#> SRR934256 2 0.0376 0.997 0.004 0.996
#> SRR934257 2 0.0376 0.997 0.004 0.996
#> SRR934258 2 0.0376 0.997 0.004 0.996
#> SRR934259 2 0.0376 0.997 0.004 0.996
#> SRR934260 2 0.0376 0.997 0.004 0.996
#> SRR934261 2 0.0376 0.997 0.004 0.996
#> SRR934262 2 0.0376 0.997 0.004 0.996
#> SRR934263 2 0.0376 0.997 0.004 0.996
#> SRR934264 2 0.0000 0.998 0.000 1.000
#> SRR934265 2 0.0000 0.998 0.000 1.000
#> SRR934266 2 0.0000 0.998 0.000 1.000
#> SRR934267 2 0.0000 0.998 0.000 1.000
#> SRR934268 2 0.0000 0.998 0.000 1.000
#> SRR934269 2 0.0000 0.998 0.000 1.000
#> SRR934270 2 0.0000 0.998 0.000 1.000
#> SRR934271 2 0.0000 0.998 0.000 1.000
#> SRR934272 1 0.0000 0.997 1.000 0.000
#> SRR934273 1 0.0000 0.997 1.000 0.000
#> SRR934274 1 0.0000 0.997 1.000 0.000
#> SRR934275 1 0.0000 0.997 1.000 0.000
#> SRR934276 1 0.0000 0.997 1.000 0.000
#> SRR934277 1 0.0000 0.997 1.000 0.000
#> SRR934278 1 0.0000 0.997 1.000 0.000
#> SRR934279 1 0.0000 0.997 1.000 0.000
#> SRR934280 1 0.0000 0.997 1.000 0.000
#> SRR934281 1 0.0000 0.997 1.000 0.000
#> SRR934282 1 0.0000 0.997 1.000 0.000
#> SRR934283 1 0.0000 0.997 1.000 0.000
#> SRR934284 1 0.0000 0.997 1.000 0.000
#> SRR934285 1 0.0000 0.997 1.000 0.000
#> SRR934286 1 0.0000 0.997 1.000 0.000
#> SRR934287 1 0.0000 0.997 1.000 0.000
#> SRR934288 1 0.0672 0.995 0.992 0.008
#> SRR934289 1 0.0672 0.995 0.992 0.008
#> SRR934290 1 0.0672 0.995 0.992 0.008
#> SRR934291 1 0.0672 0.995 0.992 0.008
#> SRR934292 1 0.0672 0.995 0.992 0.008
#> SRR934293 1 0.0672 0.995 0.992 0.008
#> SRR934294 1 0.0672 0.995 0.992 0.008
#> SRR934295 1 0.0672 0.995 0.992 0.008
#> SRR934296 2 0.0376 0.997 0.004 0.996
#> SRR934297 2 0.0376 0.997 0.004 0.996
#> SRR934298 2 0.0376 0.997 0.004 0.996
#> SRR934299 2 0.0376 0.997 0.004 0.996
#> SRR934300 2 0.0376 0.997 0.004 0.996
#> SRR934301 2 0.0376 0.997 0.004 0.996
#> SRR934302 2 0.0376 0.997 0.004 0.996
#> SRR934303 2 0.0376 0.997 0.004 0.996
#> SRR934304 2 0.0000 0.998 0.000 1.000
#> SRR934305 2 0.0000 0.998 0.000 1.000
#> SRR934306 2 0.0000 0.998 0.000 1.000
#> SRR934307 2 0.0000 0.998 0.000 1.000
#> SRR934308 2 0.0000 0.998 0.000 1.000
#> SRR934309 2 0.0000 0.998 0.000 1.000
#> SRR934310 2 0.0000 0.998 0.000 1.000
#> SRR934311 2 0.0000 0.998 0.000 1.000
#> SRR934312 1 0.0000 0.997 1.000 0.000
#> SRR934313 1 0.0000 0.997 1.000 0.000
#> SRR934314 1 0.0000 0.997 1.000 0.000
#> SRR934315 1 0.0000 0.997 1.000 0.000
#> SRR934316 1 0.0000 0.997 1.000 0.000
#> SRR934317 1 0.0000 0.997 1.000 0.000
#> SRR934318 1 0.0000 0.997 1.000 0.000
#> SRR934319 1 0.0000 0.997 1.000 0.000
#> SRR934320 1 0.0000 0.997 1.000 0.000
#> SRR934321 1 0.0000 0.997 1.000 0.000
#> SRR934322 1 0.0000 0.997 1.000 0.000
#> SRR934323 1 0.0000 0.997 1.000 0.000
#> SRR934324 1 0.0000 0.997 1.000 0.000
#> SRR934325 1 0.0000 0.997 1.000 0.000
#> SRR934326 1 0.0000 0.997 1.000 0.000
#> SRR934327 1 0.0000 0.997 1.000 0.000
#> SRR934328 1 0.0672 0.995 0.992 0.008
#> SRR934329 1 0.0672 0.995 0.992 0.008
#> SRR934330 1 0.0672 0.995 0.992 0.008
#> SRR934331 1 0.0672 0.995 0.992 0.008
#> SRR934332 1 0.0672 0.995 0.992 0.008
#> SRR934333 1 0.0672 0.995 0.992 0.008
#> SRR934334 1 0.0672 0.995 0.992 0.008
#> SRR934335 1 0.0672 0.995 0.992 0.008
#> SRR934344 1 0.0672 0.995 0.992 0.008
#> SRR934345 1 0.0672 0.995 0.992 0.008
#> SRR934346 1 0.0672 0.995 0.992 0.008
#> SRR934347 1 0.0672 0.995 0.992 0.008
#> SRR934348 1 0.0672 0.995 0.992 0.008
#> SRR934349 1 0.0672 0.995 0.992 0.008
#> SRR934350 1 0.0672 0.995 0.992 0.008
#> SRR934351 1 0.0672 0.995 0.992 0.008
#> SRR934336 1 0.0000 0.997 1.000 0.000
#> SRR934337 1 0.0000 0.997 1.000 0.000
#> SRR934338 1 0.0000 0.997 1.000 0.000
#> SRR934339 1 0.0000 0.997 1.000 0.000
#> SRR934340 1 0.0000 0.997 1.000 0.000
#> SRR934341 1 0.0000 0.997 1.000 0.000
#> SRR934342 1 0.0000 0.997 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 2 0.5619 0.502 0.012 0.744 0.244
#> SRR934217 2 0.5619 0.502 0.012 0.744 0.244
#> SRR934218 2 0.5619 0.502 0.012 0.744 0.244
#> SRR934219 2 0.5619 0.502 0.012 0.744 0.244
#> SRR934220 2 0.5619 0.502 0.012 0.744 0.244
#> SRR934221 2 0.5619 0.502 0.012 0.744 0.244
#> SRR934222 2 0.5619 0.502 0.012 0.744 0.244
#> SRR934223 2 0.5619 0.502 0.012 0.744 0.244
#> SRR934224 1 0.1337 0.932 0.972 0.012 0.016
#> SRR934225 1 0.1337 0.932 0.972 0.012 0.016
#> SRR934226 1 0.1337 0.932 0.972 0.012 0.016
#> SRR934227 1 0.1337 0.932 0.972 0.012 0.016
#> SRR934228 1 0.1337 0.932 0.972 0.012 0.016
#> SRR934229 1 0.1337 0.932 0.972 0.012 0.016
#> SRR934230 1 0.1337 0.932 0.972 0.012 0.016
#> SRR934231 1 0.1337 0.932 0.972 0.012 0.016
#> SRR934232 2 0.6126 0.444 0.000 0.600 0.400
#> SRR934233 2 0.6126 0.444 0.000 0.600 0.400
#> SRR934234 2 0.6126 0.444 0.000 0.600 0.400
#> SRR934235 2 0.6126 0.444 0.000 0.600 0.400
#> SRR934236 2 0.6126 0.444 0.000 0.600 0.400
#> SRR934237 2 0.6126 0.444 0.000 0.600 0.400
#> SRR934238 2 0.6126 0.444 0.000 0.600 0.400
#> SRR934239 2 0.6126 0.444 0.000 0.600 0.400
#> SRR934240 2 0.6345 0.445 0.004 0.596 0.400
#> SRR934241 2 0.6345 0.445 0.004 0.596 0.400
#> SRR934242 2 0.6345 0.445 0.004 0.596 0.400
#> SRR934243 2 0.6345 0.445 0.004 0.596 0.400
#> SRR934244 2 0.6345 0.445 0.004 0.596 0.400
#> SRR934245 2 0.6345 0.445 0.004 0.596 0.400
#> SRR934246 2 0.6345 0.445 0.004 0.596 0.400
#> SRR934247 2 0.6345 0.445 0.004 0.596 0.400
#> SRR934248 3 0.0592 1.000 0.000 0.012 0.988
#> SRR934249 3 0.0592 1.000 0.000 0.012 0.988
#> SRR934250 3 0.0592 1.000 0.000 0.012 0.988
#> SRR934251 3 0.0592 1.000 0.000 0.012 0.988
#> SRR934252 3 0.0592 1.000 0.000 0.012 0.988
#> SRR934253 3 0.0592 1.000 0.000 0.012 0.988
#> SRR934254 3 0.0592 1.000 0.000 0.012 0.988
#> SRR934255 3 0.0592 1.000 0.000 0.012 0.988
#> SRR934256 2 0.8686 0.465 0.432 0.464 0.104
#> SRR934257 2 0.8686 0.465 0.432 0.464 0.104
#> SRR934258 2 0.8686 0.465 0.432 0.464 0.104
#> SRR934259 2 0.8686 0.465 0.432 0.464 0.104
#> SRR934260 2 0.8686 0.465 0.432 0.464 0.104
#> SRR934261 2 0.8686 0.465 0.432 0.464 0.104
#> SRR934262 2 0.8686 0.465 0.432 0.464 0.104
#> SRR934263 2 0.8686 0.465 0.432 0.464 0.104
#> SRR934264 3 0.0592 1.000 0.000 0.012 0.988
#> SRR934265 3 0.0592 1.000 0.000 0.012 0.988
#> SRR934266 3 0.0592 1.000 0.000 0.012 0.988
#> SRR934267 3 0.0592 1.000 0.000 0.012 0.988
#> SRR934268 3 0.0592 1.000 0.000 0.012 0.988
#> SRR934269 3 0.0592 1.000 0.000 0.012 0.988
#> SRR934270 3 0.0592 1.000 0.000 0.012 0.988
#> SRR934271 3 0.0592 1.000 0.000 0.012 0.988
#> SRR934272 1 0.1482 0.930 0.968 0.012 0.020
#> SRR934273 1 0.1482 0.930 0.968 0.012 0.020
#> SRR934274 1 0.1482 0.930 0.968 0.012 0.020
#> SRR934275 1 0.1482 0.930 0.968 0.012 0.020
#> SRR934276 1 0.1482 0.930 0.968 0.012 0.020
#> SRR934277 1 0.1482 0.930 0.968 0.012 0.020
#> SRR934278 1 0.1482 0.930 0.968 0.012 0.020
#> SRR934279 1 0.1482 0.930 0.968 0.012 0.020
#> SRR934280 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934281 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934282 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934283 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934284 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934285 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934286 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934287 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934288 1 0.4033 0.881 0.856 0.136 0.008
#> SRR934289 1 0.4195 0.880 0.852 0.136 0.012
#> SRR934290 1 0.4128 0.882 0.856 0.132 0.012
#> SRR934291 1 0.4033 0.881 0.856 0.136 0.008
#> SRR934292 1 0.4128 0.882 0.856 0.132 0.012
#> SRR934293 1 0.3965 0.884 0.860 0.132 0.008
#> SRR934294 1 0.4033 0.881 0.856 0.136 0.008
#> SRR934295 1 0.3965 0.884 0.860 0.132 0.008
#> SRR934296 2 0.6126 0.452 0.352 0.644 0.004
#> SRR934297 2 0.6126 0.452 0.352 0.644 0.004
#> SRR934298 2 0.6126 0.452 0.352 0.644 0.004
#> SRR934299 2 0.6126 0.452 0.352 0.644 0.004
#> SRR934300 2 0.6126 0.452 0.352 0.644 0.004
#> SRR934301 2 0.6126 0.452 0.352 0.644 0.004
#> SRR934302 2 0.6126 0.452 0.352 0.644 0.004
#> SRR934303 2 0.6126 0.452 0.352 0.644 0.004
#> SRR934304 2 0.4291 0.530 0.000 0.820 0.180
#> SRR934305 2 0.4291 0.530 0.000 0.820 0.180
#> SRR934306 2 0.4291 0.530 0.000 0.820 0.180
#> SRR934307 2 0.4291 0.530 0.000 0.820 0.180
#> SRR934308 2 0.4291 0.530 0.000 0.820 0.180
#> SRR934309 2 0.4291 0.530 0.000 0.820 0.180
#> SRR934310 2 0.4291 0.530 0.000 0.820 0.180
#> SRR934311 2 0.4291 0.530 0.000 0.820 0.180
#> SRR934312 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934313 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934314 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934315 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934316 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934317 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934318 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934319 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934320 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934321 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934322 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934323 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934324 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934325 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934326 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934327 1 0.0000 0.936 1.000 0.000 0.000
#> SRR934328 1 0.4164 0.875 0.848 0.144 0.008
#> SRR934329 1 0.4164 0.875 0.848 0.144 0.008
#> SRR934330 1 0.4164 0.875 0.848 0.144 0.008
#> SRR934331 1 0.4164 0.875 0.848 0.144 0.008
#> SRR934332 1 0.4099 0.877 0.852 0.140 0.008
#> SRR934333 1 0.4164 0.875 0.848 0.144 0.008
#> SRR934334 1 0.4164 0.875 0.848 0.144 0.008
#> SRR934335 1 0.4164 0.875 0.848 0.144 0.008
#> SRR934344 1 0.4931 0.868 0.828 0.140 0.032
#> SRR934345 1 0.4931 0.868 0.828 0.140 0.032
#> SRR934346 1 0.4931 0.868 0.828 0.140 0.032
#> SRR934347 1 0.4931 0.868 0.828 0.140 0.032
#> SRR934348 1 0.4931 0.868 0.828 0.140 0.032
#> SRR934349 1 0.4931 0.868 0.828 0.140 0.032
#> SRR934350 1 0.4931 0.868 0.828 0.140 0.032
#> SRR934351 1 0.4931 0.868 0.828 0.140 0.032
#> SRR934336 1 0.1182 0.932 0.976 0.012 0.012
#> SRR934337 1 0.1182 0.932 0.976 0.012 0.012
#> SRR934338 1 0.1182 0.932 0.976 0.012 0.012
#> SRR934339 1 0.1182 0.932 0.976 0.012 0.012
#> SRR934340 1 0.1182 0.932 0.976 0.012 0.012
#> SRR934341 1 0.1182 0.932 0.976 0.012 0.012
#> SRR934342 1 0.1182 0.932 0.976 0.012 0.012
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 3 0.6649 0.4554 0.080 0.048 0.684 0.188
#> SRR934217 3 0.6649 0.4554 0.080 0.048 0.684 0.188
#> SRR934218 3 0.6649 0.4554 0.080 0.048 0.684 0.188
#> SRR934219 3 0.6649 0.4554 0.080 0.048 0.684 0.188
#> SRR934220 3 0.6649 0.4554 0.080 0.048 0.684 0.188
#> SRR934221 3 0.6649 0.4554 0.080 0.048 0.684 0.188
#> SRR934222 3 0.6649 0.4554 0.080 0.048 0.684 0.188
#> SRR934223 3 0.6649 0.4554 0.080 0.048 0.684 0.188
#> SRR934224 1 0.4576 0.6526 0.728 0.012 0.000 0.260
#> SRR934225 1 0.4576 0.6526 0.728 0.012 0.000 0.260
#> SRR934226 1 0.4576 0.6526 0.728 0.012 0.000 0.260
#> SRR934227 1 0.4576 0.6526 0.728 0.012 0.000 0.260
#> SRR934228 1 0.4576 0.6526 0.728 0.012 0.000 0.260
#> SRR934229 1 0.4576 0.6526 0.728 0.012 0.000 0.260
#> SRR934230 1 0.4576 0.6526 0.728 0.012 0.000 0.260
#> SRR934231 1 0.4576 0.6526 0.728 0.012 0.000 0.260
#> SRR934232 2 0.7849 0.1005 0.000 0.400 0.316 0.284
#> SRR934233 2 0.7849 0.1005 0.000 0.400 0.316 0.284
#> SRR934234 2 0.7849 0.1005 0.000 0.400 0.316 0.284
#> SRR934235 2 0.7849 0.1005 0.000 0.400 0.316 0.284
#> SRR934236 2 0.7849 0.1005 0.000 0.400 0.316 0.284
#> SRR934237 2 0.7849 0.1005 0.000 0.400 0.316 0.284
#> SRR934238 2 0.7849 0.1005 0.000 0.400 0.316 0.284
#> SRR934239 2 0.7849 0.1005 0.000 0.400 0.316 0.284
#> SRR934240 3 0.7875 0.0761 0.000 0.328 0.384 0.288
#> SRR934241 3 0.7875 0.0761 0.000 0.328 0.384 0.288
#> SRR934242 3 0.7875 0.0761 0.000 0.328 0.384 0.288
#> SRR934243 3 0.7875 0.0761 0.000 0.328 0.384 0.288
#> SRR934244 3 0.7875 0.0761 0.000 0.328 0.384 0.288
#> SRR934245 3 0.7875 0.0761 0.000 0.328 0.384 0.288
#> SRR934246 3 0.7875 0.0761 0.000 0.328 0.384 0.288
#> SRR934247 3 0.7875 0.0761 0.000 0.328 0.384 0.288
#> SRR934248 2 0.0000 0.7386 0.000 1.000 0.000 0.000
#> SRR934249 2 0.0000 0.7386 0.000 1.000 0.000 0.000
#> SRR934250 2 0.0000 0.7386 0.000 1.000 0.000 0.000
#> SRR934251 2 0.0000 0.7386 0.000 1.000 0.000 0.000
#> SRR934252 2 0.0000 0.7386 0.000 1.000 0.000 0.000
#> SRR934253 2 0.0000 0.7386 0.000 1.000 0.000 0.000
#> SRR934254 2 0.0000 0.7386 0.000 1.000 0.000 0.000
#> SRR934255 2 0.0000 0.7386 0.000 1.000 0.000 0.000
#> SRR934256 4 0.7830 0.5450 0.260 0.000 0.356 0.384
#> SRR934257 4 0.7830 0.5450 0.260 0.000 0.356 0.384
#> SRR934258 4 0.7830 0.5450 0.260 0.000 0.356 0.384
#> SRR934259 4 0.7830 0.5450 0.260 0.000 0.356 0.384
#> SRR934260 4 0.7830 0.5450 0.260 0.000 0.356 0.384
#> SRR934261 4 0.7830 0.5450 0.260 0.000 0.356 0.384
#> SRR934262 4 0.7830 0.5450 0.260 0.000 0.356 0.384
#> SRR934263 4 0.7830 0.5450 0.260 0.000 0.356 0.384
#> SRR934264 2 0.0000 0.7386 0.000 1.000 0.000 0.000
#> SRR934265 2 0.0000 0.7386 0.000 1.000 0.000 0.000
#> SRR934266 2 0.0000 0.7386 0.000 1.000 0.000 0.000
#> SRR934267 2 0.0000 0.7386 0.000 1.000 0.000 0.000
#> SRR934268 2 0.0000 0.7386 0.000 1.000 0.000 0.000
#> SRR934269 2 0.0000 0.7386 0.000 1.000 0.000 0.000
#> SRR934270 2 0.0000 0.7386 0.000 1.000 0.000 0.000
#> SRR934271 2 0.0000 0.7386 0.000 1.000 0.000 0.000
#> SRR934272 1 0.4158 0.6678 0.768 0.008 0.000 0.224
#> SRR934273 1 0.4053 0.6661 0.768 0.004 0.000 0.228
#> SRR934274 1 0.4053 0.6661 0.768 0.004 0.000 0.228
#> SRR934275 1 0.4053 0.6661 0.768 0.004 0.000 0.228
#> SRR934276 1 0.4053 0.6661 0.768 0.004 0.000 0.228
#> SRR934277 1 0.4122 0.6645 0.760 0.004 0.000 0.236
#> SRR934278 1 0.4284 0.6684 0.764 0.012 0.000 0.224
#> SRR934279 1 0.4053 0.6661 0.768 0.004 0.000 0.228
#> SRR934280 1 0.0592 0.7303 0.984 0.000 0.000 0.016
#> SRR934281 1 0.0592 0.7303 0.984 0.000 0.000 0.016
#> SRR934282 1 0.0469 0.7299 0.988 0.000 0.000 0.012
#> SRR934283 1 0.0469 0.7299 0.988 0.000 0.000 0.012
#> SRR934284 1 0.0469 0.7299 0.988 0.000 0.000 0.012
#> SRR934285 1 0.0469 0.7299 0.988 0.000 0.000 0.012
#> SRR934286 1 0.0469 0.7299 0.988 0.000 0.000 0.012
#> SRR934287 1 0.0469 0.7299 0.988 0.000 0.000 0.012
#> SRR934288 1 0.4452 0.6125 0.732 0.008 0.000 0.260
#> SRR934289 1 0.4539 0.6074 0.720 0.008 0.000 0.272
#> SRR934290 1 0.4673 0.6039 0.700 0.008 0.000 0.292
#> SRR934291 1 0.4539 0.6113 0.720 0.008 0.000 0.272
#> SRR934292 1 0.4482 0.6090 0.728 0.008 0.000 0.264
#> SRR934293 1 0.4422 0.6139 0.736 0.008 0.000 0.256
#> SRR934294 1 0.4482 0.6132 0.728 0.008 0.000 0.264
#> SRR934295 1 0.4452 0.6105 0.732 0.008 0.000 0.260
#> SRR934296 4 0.7719 0.6102 0.268 0.000 0.284 0.448
#> SRR934297 4 0.7719 0.6102 0.268 0.000 0.284 0.448
#> SRR934298 4 0.7719 0.6102 0.268 0.000 0.284 0.448
#> SRR934299 4 0.7719 0.6102 0.268 0.000 0.284 0.448
#> SRR934300 4 0.7719 0.6102 0.268 0.000 0.284 0.448
#> SRR934301 4 0.7719 0.6102 0.268 0.000 0.284 0.448
#> SRR934302 4 0.7719 0.6102 0.268 0.000 0.284 0.448
#> SRR934303 4 0.7719 0.6102 0.268 0.000 0.284 0.448
#> SRR934304 3 0.0000 0.5322 0.000 0.000 1.000 0.000
#> SRR934305 3 0.0000 0.5322 0.000 0.000 1.000 0.000
#> SRR934306 3 0.0000 0.5322 0.000 0.000 1.000 0.000
#> SRR934307 3 0.0000 0.5322 0.000 0.000 1.000 0.000
#> SRR934308 3 0.0000 0.5322 0.000 0.000 1.000 0.000
#> SRR934309 3 0.0000 0.5322 0.000 0.000 1.000 0.000
#> SRR934310 3 0.0000 0.5322 0.000 0.000 1.000 0.000
#> SRR934311 3 0.0000 0.5322 0.000 0.000 1.000 0.000
#> SRR934312 1 0.2053 0.7246 0.924 0.004 0.000 0.072
#> SRR934313 1 0.1792 0.7230 0.932 0.000 0.000 0.068
#> SRR934314 1 0.2271 0.7261 0.916 0.008 0.000 0.076
#> SRR934315 1 0.2053 0.7246 0.924 0.004 0.000 0.072
#> SRR934316 1 0.2271 0.7261 0.916 0.008 0.000 0.076
#> SRR934317 1 0.2271 0.7261 0.916 0.008 0.000 0.076
#> SRR934318 1 0.2271 0.7261 0.916 0.008 0.000 0.076
#> SRR934319 1 0.2198 0.7260 0.920 0.008 0.000 0.072
#> SRR934320 1 0.0895 0.7305 0.976 0.000 0.004 0.020
#> SRR934321 1 0.0524 0.7316 0.988 0.000 0.004 0.008
#> SRR934322 1 0.0524 0.7316 0.988 0.000 0.004 0.008
#> SRR934323 1 0.0524 0.7316 0.988 0.000 0.004 0.008
#> SRR934324 1 0.0524 0.7316 0.988 0.000 0.004 0.008
#> SRR934325 1 0.0657 0.7310 0.984 0.000 0.004 0.012
#> SRR934326 1 0.0657 0.7310 0.984 0.000 0.004 0.012
#> SRR934327 1 0.0657 0.7314 0.984 0.000 0.004 0.012
#> SRR934328 1 0.4748 0.5706 0.716 0.000 0.016 0.268
#> SRR934329 1 0.4511 0.5814 0.724 0.000 0.008 0.268
#> SRR934330 1 0.4511 0.5814 0.724 0.000 0.008 0.268
#> SRR934331 1 0.4372 0.5861 0.728 0.000 0.004 0.268
#> SRR934332 1 0.4193 0.5902 0.732 0.000 0.000 0.268
#> SRR934333 1 0.4372 0.5861 0.728 0.000 0.004 0.268
#> SRR934334 1 0.4635 0.5764 0.720 0.000 0.012 0.268
#> SRR934335 1 0.4635 0.5764 0.720 0.000 0.012 0.268
#> SRR934344 1 0.5057 0.5699 0.648 0.012 0.000 0.340
#> SRR934345 1 0.5057 0.5699 0.648 0.012 0.000 0.340
#> SRR934346 1 0.5057 0.5699 0.648 0.012 0.000 0.340
#> SRR934347 1 0.5057 0.5699 0.648 0.012 0.000 0.340
#> SRR934348 1 0.5057 0.5699 0.648 0.012 0.000 0.340
#> SRR934349 1 0.5057 0.5699 0.648 0.012 0.000 0.340
#> SRR934350 1 0.5057 0.5699 0.648 0.012 0.000 0.340
#> SRR934351 1 0.5057 0.5699 0.648 0.012 0.000 0.340
#> SRR934336 1 0.4262 0.6587 0.756 0.008 0.000 0.236
#> SRR934337 1 0.4262 0.6587 0.756 0.008 0.000 0.236
#> SRR934338 1 0.4262 0.6587 0.756 0.008 0.000 0.236
#> SRR934339 1 0.4262 0.6587 0.756 0.008 0.000 0.236
#> SRR934340 1 0.4123 0.6699 0.772 0.008 0.000 0.220
#> SRR934341 1 0.4262 0.6587 0.756 0.008 0.000 0.236
#> SRR934342 1 0.4262 0.6587 0.756 0.008 0.000 0.236
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 5 0.0794 0.791 0.028 0.000 0.000 0.000 0.972
#> SRR934217 5 0.0794 0.791 0.028 0.000 0.000 0.000 0.972
#> SRR934218 5 0.0794 0.791 0.028 0.000 0.000 0.000 0.972
#> SRR934219 5 0.0794 0.791 0.028 0.000 0.000 0.000 0.972
#> SRR934220 5 0.0794 0.791 0.028 0.000 0.000 0.000 0.972
#> SRR934221 5 0.0794 0.791 0.028 0.000 0.000 0.000 0.972
#> SRR934222 5 0.0794 0.791 0.028 0.000 0.000 0.000 0.972
#> SRR934223 5 0.0794 0.791 0.028 0.000 0.000 0.000 0.972
#> SRR934224 1 0.0510 0.610 0.984 0.000 0.000 0.000 0.016
#> SRR934225 1 0.0510 0.610 0.984 0.000 0.000 0.000 0.016
#> SRR934226 1 0.0510 0.610 0.984 0.000 0.000 0.000 0.016
#> SRR934227 1 0.0510 0.610 0.984 0.000 0.000 0.000 0.016
#> SRR934228 1 0.0510 0.610 0.984 0.000 0.000 0.000 0.016
#> SRR934229 1 0.0510 0.610 0.984 0.000 0.000 0.000 0.016
#> SRR934230 1 0.0510 0.610 0.984 0.000 0.000 0.000 0.016
#> SRR934231 1 0.0510 0.610 0.984 0.000 0.000 0.000 0.016
#> SRR934232 2 0.0771 0.774 0.004 0.976 0.000 0.020 0.000
#> SRR934233 2 0.0771 0.774 0.004 0.976 0.000 0.020 0.000
#> SRR934234 2 0.0771 0.774 0.004 0.976 0.000 0.020 0.000
#> SRR934235 2 0.0771 0.774 0.004 0.976 0.000 0.020 0.000
#> SRR934236 2 0.0771 0.774 0.004 0.976 0.000 0.020 0.000
#> SRR934237 2 0.0771 0.774 0.004 0.976 0.000 0.020 0.000
#> SRR934238 2 0.0771 0.774 0.004 0.976 0.000 0.020 0.000
#> SRR934239 2 0.0771 0.774 0.004 0.976 0.000 0.020 0.000
#> SRR934240 2 0.0404 0.774 0.000 0.988 0.000 0.012 0.000
#> SRR934241 2 0.0404 0.774 0.000 0.988 0.000 0.012 0.000
#> SRR934242 2 0.0404 0.774 0.000 0.988 0.000 0.012 0.000
#> SRR934243 2 0.0404 0.774 0.000 0.988 0.000 0.012 0.000
#> SRR934244 2 0.0404 0.774 0.000 0.988 0.000 0.012 0.000
#> SRR934245 2 0.0404 0.774 0.000 0.988 0.000 0.012 0.000
#> SRR934246 2 0.0404 0.774 0.000 0.988 0.000 0.012 0.000
#> SRR934247 2 0.0404 0.774 0.000 0.988 0.000 0.012 0.000
#> SRR934248 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR934249 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR934250 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR934251 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR934252 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR934253 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR934254 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR934255 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR934256 2 0.6842 0.265 0.028 0.456 0.140 0.000 0.376
#> SRR934257 2 0.6842 0.265 0.028 0.456 0.140 0.000 0.376
#> SRR934258 2 0.6842 0.265 0.028 0.456 0.140 0.000 0.376
#> SRR934259 2 0.6842 0.265 0.028 0.456 0.140 0.000 0.376
#> SRR934260 2 0.6842 0.265 0.028 0.456 0.140 0.000 0.376
#> SRR934261 2 0.6842 0.265 0.028 0.456 0.140 0.000 0.376
#> SRR934262 2 0.6842 0.265 0.028 0.456 0.140 0.000 0.376
#> SRR934263 2 0.6842 0.265 0.028 0.456 0.140 0.000 0.376
#> SRR934264 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR934265 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR934266 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR934267 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR934268 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR934269 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR934270 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR934271 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000
#> SRR934272 1 0.0404 0.613 0.988 0.000 0.000 0.000 0.012
#> SRR934273 1 0.0404 0.613 0.988 0.000 0.000 0.000 0.012
#> SRR934274 1 0.0404 0.613 0.988 0.000 0.000 0.000 0.012
#> SRR934275 1 0.0404 0.613 0.988 0.000 0.000 0.000 0.012
#> SRR934276 1 0.0404 0.613 0.988 0.000 0.000 0.000 0.012
#> SRR934277 1 0.0404 0.613 0.988 0.000 0.000 0.000 0.012
#> SRR934278 1 0.0404 0.613 0.988 0.000 0.000 0.000 0.012
#> SRR934279 1 0.0404 0.613 0.988 0.000 0.000 0.000 0.012
#> SRR934280 1 0.4915 0.340 0.556 0.004 0.420 0.000 0.020
#> SRR934281 1 0.4915 0.340 0.556 0.004 0.420 0.000 0.020
#> SRR934282 1 0.4915 0.340 0.556 0.004 0.420 0.000 0.020
#> SRR934283 1 0.4915 0.340 0.556 0.004 0.420 0.000 0.020
#> SRR934284 1 0.4915 0.340 0.556 0.004 0.420 0.000 0.020
#> SRR934285 1 0.4915 0.340 0.556 0.004 0.420 0.000 0.020
#> SRR934286 1 0.4915 0.340 0.556 0.004 0.420 0.000 0.020
#> SRR934287 1 0.4915 0.340 0.556 0.004 0.420 0.000 0.020
#> SRR934288 3 0.3913 0.703 0.324 0.000 0.676 0.000 0.000
#> SRR934289 3 0.3913 0.703 0.324 0.000 0.676 0.000 0.000
#> SRR934290 3 0.3932 0.694 0.328 0.000 0.672 0.000 0.000
#> SRR934291 3 0.3913 0.703 0.324 0.000 0.676 0.000 0.000
#> SRR934292 3 0.3913 0.703 0.324 0.000 0.676 0.000 0.000
#> SRR934293 3 0.3913 0.703 0.324 0.000 0.676 0.000 0.000
#> SRR934294 3 0.3913 0.703 0.324 0.000 0.676 0.000 0.000
#> SRR934295 3 0.3913 0.703 0.324 0.000 0.676 0.000 0.000
#> SRR934296 3 0.6099 0.465 0.136 0.352 0.512 0.000 0.000
#> SRR934297 3 0.6099 0.465 0.136 0.352 0.512 0.000 0.000
#> SRR934298 3 0.6099 0.465 0.136 0.352 0.512 0.000 0.000
#> SRR934299 3 0.6099 0.465 0.136 0.352 0.512 0.000 0.000
#> SRR934300 3 0.6099 0.465 0.136 0.352 0.512 0.000 0.000
#> SRR934301 3 0.6099 0.465 0.136 0.352 0.512 0.000 0.000
#> SRR934302 3 0.6099 0.465 0.136 0.352 0.512 0.000 0.000
#> SRR934303 3 0.6099 0.465 0.136 0.352 0.512 0.000 0.000
#> SRR934304 5 0.5510 0.743 0.000 0.208 0.144 0.000 0.648
#> SRR934305 5 0.5510 0.743 0.000 0.208 0.144 0.000 0.648
#> SRR934306 5 0.5510 0.743 0.000 0.208 0.144 0.000 0.648
#> SRR934307 5 0.5510 0.743 0.000 0.208 0.144 0.000 0.648
#> SRR934308 5 0.5510 0.743 0.000 0.208 0.144 0.000 0.648
#> SRR934309 5 0.5510 0.743 0.000 0.208 0.144 0.000 0.648
#> SRR934310 5 0.5510 0.743 0.000 0.208 0.144 0.000 0.648
#> SRR934311 5 0.5510 0.743 0.000 0.208 0.144 0.000 0.648
#> SRR934312 1 0.5028 0.336 0.552 0.008 0.420 0.000 0.020
#> SRR934313 1 0.5028 0.336 0.552 0.008 0.420 0.000 0.020
#> SRR934314 1 0.5028 0.336 0.552 0.008 0.420 0.000 0.020
#> SRR934315 1 0.5028 0.336 0.552 0.008 0.420 0.000 0.020
#> SRR934316 1 0.5028 0.336 0.552 0.008 0.420 0.000 0.020
#> SRR934317 1 0.5028 0.336 0.552 0.008 0.420 0.000 0.020
#> SRR934318 1 0.5028 0.336 0.552 0.008 0.420 0.000 0.020
#> SRR934319 1 0.5028 0.336 0.552 0.008 0.420 0.000 0.020
#> SRR934320 1 0.4922 0.330 0.552 0.004 0.424 0.000 0.020
#> SRR934321 1 0.4922 0.330 0.552 0.004 0.424 0.000 0.020
#> SRR934322 1 0.4922 0.330 0.552 0.004 0.424 0.000 0.020
#> SRR934323 1 0.4922 0.330 0.552 0.004 0.424 0.000 0.020
#> SRR934324 1 0.4915 0.340 0.556 0.004 0.420 0.000 0.020
#> SRR934325 1 0.4922 0.330 0.552 0.004 0.424 0.000 0.020
#> SRR934326 1 0.4922 0.330 0.552 0.004 0.424 0.000 0.020
#> SRR934327 1 0.4922 0.330 0.552 0.004 0.424 0.000 0.020
#> SRR934328 3 0.3861 0.738 0.284 0.004 0.712 0.000 0.000
#> SRR934329 3 0.3861 0.738 0.284 0.004 0.712 0.000 0.000
#> SRR934330 3 0.3861 0.738 0.284 0.004 0.712 0.000 0.000
#> SRR934331 3 0.3861 0.738 0.284 0.004 0.712 0.000 0.000
#> SRR934332 3 0.3906 0.731 0.292 0.004 0.704 0.000 0.000
#> SRR934333 3 0.3861 0.738 0.284 0.004 0.712 0.000 0.000
#> SRR934334 3 0.3861 0.738 0.284 0.004 0.712 0.000 0.000
#> SRR934335 3 0.3861 0.738 0.284 0.004 0.712 0.000 0.000
#> SRR934344 3 0.3752 0.740 0.292 0.000 0.708 0.000 0.000
#> SRR934345 3 0.3752 0.740 0.292 0.000 0.708 0.000 0.000
#> SRR934346 3 0.3752 0.740 0.292 0.000 0.708 0.000 0.000
#> SRR934347 3 0.3752 0.740 0.292 0.000 0.708 0.000 0.000
#> SRR934348 3 0.3752 0.740 0.292 0.000 0.708 0.000 0.000
#> SRR934349 3 0.3752 0.740 0.292 0.000 0.708 0.000 0.000
#> SRR934350 3 0.3752 0.740 0.292 0.000 0.708 0.000 0.000
#> SRR934351 3 0.3752 0.740 0.292 0.000 0.708 0.000 0.000
#> SRR934336 1 0.0162 0.613 0.996 0.000 0.000 0.000 0.004
#> SRR934337 1 0.0162 0.613 0.996 0.000 0.000 0.000 0.004
#> SRR934338 1 0.0162 0.613 0.996 0.000 0.000 0.000 0.004
#> SRR934339 1 0.0162 0.613 0.996 0.000 0.000 0.000 0.004
#> SRR934340 1 0.1877 0.583 0.924 0.000 0.064 0.000 0.012
#> SRR934341 1 0.0162 0.613 0.996 0.000 0.000 0.000 0.004
#> SRR934342 1 0.0162 0.613 0.996 0.000 0.000 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.0000 1.0000 0.000 0.00 1.000 0 0.000 0.000
#> SRR934217 3 0.0000 1.0000 0.000 0.00 1.000 0 0.000 0.000
#> SRR934218 3 0.0000 1.0000 0.000 0.00 1.000 0 0.000 0.000
#> SRR934219 3 0.0000 1.0000 0.000 0.00 1.000 0 0.000 0.000
#> SRR934220 3 0.0000 1.0000 0.000 0.00 1.000 0 0.000 0.000
#> SRR934221 3 0.0000 1.0000 0.000 0.00 1.000 0 0.000 0.000
#> SRR934222 3 0.0000 1.0000 0.000 0.00 1.000 0 0.000 0.000
#> SRR934223 3 0.0000 1.0000 0.000 0.00 1.000 0 0.000 0.000
#> SRR934224 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934225 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934226 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934227 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934228 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934229 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934230 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934231 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934232 2 0.0000 0.9299 0.000 1.00 0.000 0 0.000 0.000
#> SRR934233 2 0.0000 0.9299 0.000 1.00 0.000 0 0.000 0.000
#> SRR934234 2 0.0000 0.9299 0.000 1.00 0.000 0 0.000 0.000
#> SRR934235 2 0.0000 0.9299 0.000 1.00 0.000 0 0.000 0.000
#> SRR934236 2 0.0000 0.9299 0.000 1.00 0.000 0 0.000 0.000
#> SRR934237 2 0.0000 0.9299 0.000 1.00 0.000 0 0.000 0.000
#> SRR934238 2 0.0000 0.9299 0.000 1.00 0.000 0 0.000 0.000
#> SRR934239 2 0.0000 0.9299 0.000 1.00 0.000 0 0.000 0.000
#> SRR934240 2 0.0000 0.9299 0.000 1.00 0.000 0 0.000 0.000
#> SRR934241 2 0.0000 0.9299 0.000 1.00 0.000 0 0.000 0.000
#> SRR934242 2 0.0000 0.9299 0.000 1.00 0.000 0 0.000 0.000
#> SRR934243 2 0.0000 0.9299 0.000 1.00 0.000 0 0.000 0.000
#> SRR934244 2 0.0000 0.9299 0.000 1.00 0.000 0 0.000 0.000
#> SRR934245 2 0.0000 0.9299 0.000 1.00 0.000 0 0.000 0.000
#> SRR934246 2 0.0000 0.9299 0.000 1.00 0.000 0 0.000 0.000
#> SRR934247 2 0.0000 0.9299 0.000 1.00 0.000 0 0.000 0.000
#> SRR934248 4 0.0000 1.0000 0.000 0.00 0.000 1 0.000 0.000
#> SRR934249 4 0.0000 1.0000 0.000 0.00 0.000 1 0.000 0.000
#> SRR934250 4 0.0000 1.0000 0.000 0.00 0.000 1 0.000 0.000
#> SRR934251 4 0.0000 1.0000 0.000 0.00 0.000 1 0.000 0.000
#> SRR934252 4 0.0000 1.0000 0.000 0.00 0.000 1 0.000 0.000
#> SRR934253 4 0.0000 1.0000 0.000 0.00 0.000 1 0.000 0.000
#> SRR934254 4 0.0000 1.0000 0.000 0.00 0.000 1 0.000 0.000
#> SRR934255 4 0.0000 1.0000 0.000 0.00 0.000 1 0.000 0.000
#> SRR934256 2 0.3869 0.1544 0.000 0.50 0.000 0 0.500 0.000
#> SRR934257 2 0.3869 0.1544 0.000 0.50 0.000 0 0.500 0.000
#> SRR934258 5 0.3869 -0.2584 0.000 0.50 0.000 0 0.500 0.000
#> SRR934259 5 0.3869 -0.2584 0.000 0.50 0.000 0 0.500 0.000
#> SRR934260 5 0.3869 -0.2584 0.000 0.50 0.000 0 0.500 0.000
#> SRR934261 5 0.3869 -0.2584 0.000 0.50 0.000 0 0.500 0.000
#> SRR934262 5 0.3869 -0.2584 0.000 0.50 0.000 0 0.500 0.000
#> SRR934263 5 0.3869 -0.2584 0.000 0.50 0.000 0 0.500 0.000
#> SRR934264 4 0.0000 1.0000 0.000 0.00 0.000 1 0.000 0.000
#> SRR934265 4 0.0000 1.0000 0.000 0.00 0.000 1 0.000 0.000
#> SRR934266 4 0.0000 1.0000 0.000 0.00 0.000 1 0.000 0.000
#> SRR934267 4 0.0000 1.0000 0.000 0.00 0.000 1 0.000 0.000
#> SRR934268 4 0.0000 1.0000 0.000 0.00 0.000 1 0.000 0.000
#> SRR934269 4 0.0000 1.0000 0.000 0.00 0.000 1 0.000 0.000
#> SRR934270 4 0.0000 1.0000 0.000 0.00 0.000 1 0.000 0.000
#> SRR934271 4 0.0000 1.0000 0.000 0.00 0.000 1 0.000 0.000
#> SRR934272 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934273 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934274 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934275 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934276 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934277 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934278 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934279 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934280 1 0.2941 0.7116 0.780 0.00 0.000 0 0.000 0.220
#> SRR934281 1 0.2941 0.7116 0.780 0.00 0.000 0 0.000 0.220
#> SRR934282 1 0.2941 0.7116 0.780 0.00 0.000 0 0.000 0.220
#> SRR934283 1 0.2941 0.7116 0.780 0.00 0.000 0 0.000 0.220
#> SRR934284 1 0.2941 0.7116 0.780 0.00 0.000 0 0.000 0.220
#> SRR934285 1 0.2941 0.7116 0.780 0.00 0.000 0 0.000 0.220
#> SRR934286 1 0.2941 0.7116 0.780 0.00 0.000 0 0.000 0.220
#> SRR934287 1 0.2941 0.7116 0.780 0.00 0.000 0 0.000 0.220
#> SRR934288 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934289 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934290 1 0.0146 0.7676 0.996 0.00 0.000 0 0.000 0.004
#> SRR934291 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934292 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934293 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934294 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934295 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934296 1 0.4083 0.0688 0.532 0.46 0.000 0 0.008 0.000
#> SRR934297 1 0.4083 0.0688 0.532 0.46 0.000 0 0.008 0.000
#> SRR934298 1 0.4083 0.0688 0.532 0.46 0.000 0 0.008 0.000
#> SRR934299 1 0.4083 0.0688 0.532 0.46 0.000 0 0.008 0.000
#> SRR934300 1 0.4083 0.0688 0.532 0.46 0.000 0 0.008 0.000
#> SRR934301 1 0.4083 0.0688 0.532 0.46 0.000 0 0.008 0.000
#> SRR934302 1 0.4083 0.0688 0.532 0.46 0.000 0 0.008 0.000
#> SRR934303 1 0.4083 0.0688 0.532 0.46 0.000 0 0.008 0.000
#> SRR934304 5 0.5544 0.1451 0.000 0.00 0.356 0 0.500 0.144
#> SRR934305 5 0.5544 0.1451 0.000 0.00 0.356 0 0.500 0.144
#> SRR934306 5 0.5544 0.1451 0.000 0.00 0.356 0 0.500 0.144
#> SRR934307 5 0.5544 0.1451 0.000 0.00 0.356 0 0.500 0.144
#> SRR934308 5 0.5544 0.1451 0.000 0.00 0.356 0 0.500 0.144
#> SRR934309 5 0.5544 0.1451 0.000 0.00 0.356 0 0.500 0.144
#> SRR934310 5 0.5544 0.1451 0.000 0.00 0.356 0 0.500 0.144
#> SRR934311 5 0.5544 0.1451 0.000 0.00 0.356 0 0.500 0.144
#> SRR934312 1 0.3050 0.6949 0.764 0.00 0.000 0 0.000 0.236
#> SRR934313 1 0.3050 0.6949 0.764 0.00 0.000 0 0.000 0.236
#> SRR934314 1 0.3050 0.6949 0.764 0.00 0.000 0 0.000 0.236
#> SRR934315 1 0.3050 0.6949 0.764 0.00 0.000 0 0.000 0.236
#> SRR934316 1 0.3050 0.6949 0.764 0.00 0.000 0 0.000 0.236
#> SRR934317 1 0.3050 0.6949 0.764 0.00 0.000 0 0.000 0.236
#> SRR934318 1 0.3050 0.6949 0.764 0.00 0.000 0 0.000 0.236
#> SRR934319 1 0.3023 0.6987 0.768 0.00 0.000 0 0.000 0.232
#> SRR934320 1 0.2941 0.7116 0.780 0.00 0.000 0 0.000 0.220
#> SRR934321 1 0.2941 0.7116 0.780 0.00 0.000 0 0.000 0.220
#> SRR934322 1 0.2941 0.7116 0.780 0.00 0.000 0 0.000 0.220
#> SRR934323 1 0.2941 0.7116 0.780 0.00 0.000 0 0.000 0.220
#> SRR934324 1 0.2941 0.7116 0.780 0.00 0.000 0 0.000 0.220
#> SRR934325 1 0.2941 0.7116 0.780 0.00 0.000 0 0.000 0.220
#> SRR934326 1 0.2941 0.7116 0.780 0.00 0.000 0 0.000 0.220
#> SRR934327 1 0.2941 0.7116 0.780 0.00 0.000 0 0.000 0.220
#> SRR934328 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934329 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934330 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934331 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934332 1 0.0260 0.7669 0.992 0.00 0.000 0 0.000 0.008
#> SRR934333 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934334 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934335 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934344 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934345 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934346 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934347 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934348 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934349 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934350 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934351 1 0.0000 0.7682 1.000 0.00 0.000 0 0.000 0.000
#> SRR934336 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934337 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934338 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934339 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934340 6 0.2969 0.8662 0.224 0.00 0.000 0 0.000 0.776
#> SRR934341 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
#> SRR934342 6 0.2300 0.9946 0.144 0.00 0.000 0 0.000 0.856
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 14550 rows and 135 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 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 0.916 0.916 0.947 0.4457 0.538 0.538
#> 3 3 0.991 0.964 0.971 0.2872 0.734 0.567
#> 4 4 0.725 0.813 0.886 0.1748 0.864 0.702
#> 5 5 0.946 0.888 0.941 0.1124 0.850 0.597
#> 6 6 0.822 0.844 0.871 0.0507 0.993 0.971
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR934216 2 0.9608 0.527 0.384 0.616
#> SRR934217 2 0.9460 0.565 0.364 0.636
#> SRR934218 2 0.9248 0.606 0.340 0.660
#> SRR934219 2 0.9795 0.457 0.416 0.584
#> SRR934220 2 0.9850 0.426 0.428 0.572
#> SRR934221 2 0.9732 0.485 0.404 0.596
#> SRR934222 2 0.9608 0.527 0.384 0.616
#> SRR934223 2 0.9833 0.437 0.424 0.576
#> SRR934224 1 0.1184 0.968 0.984 0.016
#> SRR934225 1 0.1184 0.968 0.984 0.016
#> SRR934226 1 0.1184 0.968 0.984 0.016
#> SRR934227 1 0.1184 0.968 0.984 0.016
#> SRR934228 1 0.1184 0.968 0.984 0.016
#> SRR934229 1 0.1184 0.968 0.984 0.016
#> SRR934230 1 0.1184 0.968 0.984 0.016
#> SRR934231 1 0.1184 0.968 0.984 0.016
#> SRR934232 2 0.0938 0.888 0.012 0.988
#> SRR934233 2 0.0938 0.888 0.012 0.988
#> SRR934234 2 0.0938 0.888 0.012 0.988
#> SRR934235 2 0.0938 0.888 0.012 0.988
#> SRR934236 2 0.0938 0.888 0.012 0.988
#> SRR934237 2 0.0938 0.888 0.012 0.988
#> SRR934238 2 0.0938 0.888 0.012 0.988
#> SRR934239 2 0.0938 0.888 0.012 0.988
#> SRR934240 2 0.4562 0.857 0.096 0.904
#> SRR934241 2 0.3879 0.869 0.076 0.924
#> SRR934242 2 0.4298 0.862 0.088 0.912
#> SRR934243 2 0.4562 0.857 0.096 0.904
#> SRR934244 2 0.5059 0.843 0.112 0.888
#> SRR934245 2 0.4161 0.865 0.084 0.916
#> SRR934246 2 0.4431 0.860 0.092 0.908
#> SRR934247 2 0.4161 0.865 0.084 0.916
#> SRR934248 2 0.2603 0.904 0.044 0.956
#> SRR934249 2 0.2603 0.904 0.044 0.956
#> SRR934250 2 0.2603 0.904 0.044 0.956
#> SRR934251 2 0.2603 0.904 0.044 0.956
#> SRR934252 2 0.2603 0.904 0.044 0.956
#> SRR934253 2 0.2603 0.904 0.044 0.956
#> SRR934254 2 0.2603 0.904 0.044 0.956
#> SRR934255 2 0.2603 0.904 0.044 0.956
#> SRR934256 1 0.3274 0.952 0.940 0.060
#> SRR934257 1 0.3274 0.952 0.940 0.060
#> SRR934258 1 0.3274 0.952 0.940 0.060
#> SRR934259 1 0.3274 0.952 0.940 0.060
#> SRR934260 1 0.3274 0.952 0.940 0.060
#> SRR934261 1 0.3274 0.952 0.940 0.060
#> SRR934262 1 0.3274 0.952 0.940 0.060
#> SRR934263 1 0.3274 0.952 0.940 0.060
#> SRR934264 2 0.3274 0.900 0.060 0.940
#> SRR934265 2 0.3274 0.900 0.060 0.940
#> SRR934266 2 0.3274 0.900 0.060 0.940
#> SRR934267 2 0.3274 0.900 0.060 0.940
#> SRR934268 2 0.3274 0.900 0.060 0.940
#> SRR934269 2 0.3274 0.900 0.060 0.940
#> SRR934270 2 0.3274 0.900 0.060 0.940
#> SRR934271 2 0.3274 0.900 0.060 0.940
#> SRR934272 1 0.0938 0.970 0.988 0.012
#> SRR934273 1 0.0938 0.970 0.988 0.012
#> SRR934274 1 0.0938 0.970 0.988 0.012
#> SRR934275 1 0.0938 0.970 0.988 0.012
#> SRR934276 1 0.0938 0.970 0.988 0.012
#> SRR934277 1 0.0938 0.970 0.988 0.012
#> SRR934278 1 0.0938 0.970 0.988 0.012
#> SRR934279 1 0.0938 0.970 0.988 0.012
#> SRR934280 1 0.2043 0.967 0.968 0.032
#> SRR934281 1 0.2043 0.967 0.968 0.032
#> SRR934282 1 0.2043 0.967 0.968 0.032
#> SRR934283 1 0.2043 0.967 0.968 0.032
#> SRR934284 1 0.2043 0.967 0.968 0.032
#> SRR934285 1 0.2043 0.967 0.968 0.032
#> SRR934286 1 0.2043 0.967 0.968 0.032
#> SRR934287 1 0.2043 0.967 0.968 0.032
#> SRR934288 1 0.2236 0.965 0.964 0.036
#> SRR934289 1 0.2236 0.965 0.964 0.036
#> SRR934290 1 0.2236 0.965 0.964 0.036
#> SRR934291 1 0.2236 0.965 0.964 0.036
#> SRR934292 1 0.2236 0.965 0.964 0.036
#> SRR934293 1 0.2236 0.965 0.964 0.036
#> SRR934294 1 0.2236 0.965 0.964 0.036
#> SRR934295 1 0.2236 0.965 0.964 0.036
#> SRR934296 1 0.3274 0.952 0.940 0.060
#> SRR934297 1 0.3114 0.955 0.944 0.056
#> SRR934298 1 0.3274 0.952 0.940 0.060
#> SRR934299 1 0.3274 0.952 0.940 0.060
#> SRR934300 1 0.3114 0.955 0.944 0.056
#> SRR934301 1 0.3274 0.952 0.940 0.060
#> SRR934302 1 0.3274 0.952 0.940 0.060
#> SRR934303 1 0.3274 0.952 0.940 0.060
#> SRR934304 2 0.2603 0.904 0.044 0.956
#> SRR934305 2 0.2603 0.904 0.044 0.956
#> SRR934306 2 0.2603 0.904 0.044 0.956
#> SRR934307 2 0.2603 0.904 0.044 0.956
#> SRR934308 2 0.2603 0.904 0.044 0.956
#> SRR934309 2 0.2603 0.904 0.044 0.956
#> SRR934310 2 0.2603 0.904 0.044 0.956
#> SRR934311 2 0.2603 0.904 0.044 0.956
#> SRR934312 1 0.0376 0.971 0.996 0.004
#> SRR934313 1 0.0376 0.971 0.996 0.004
#> SRR934314 1 0.0672 0.971 0.992 0.008
#> SRR934315 1 0.0000 0.971 1.000 0.000
#> SRR934316 1 0.0376 0.971 0.996 0.004
#> SRR934317 1 0.0672 0.971 0.992 0.008
#> SRR934318 1 0.0376 0.971 0.996 0.004
#> SRR934319 1 0.0000 0.971 1.000 0.000
#> SRR934320 1 0.2043 0.967 0.968 0.032
#> SRR934321 1 0.2043 0.967 0.968 0.032
#> SRR934322 1 0.2043 0.967 0.968 0.032
#> SRR934323 1 0.2043 0.967 0.968 0.032
#> SRR934324 1 0.2043 0.967 0.968 0.032
#> SRR934325 1 0.2043 0.967 0.968 0.032
#> SRR934326 1 0.2043 0.967 0.968 0.032
#> SRR934327 1 0.2043 0.967 0.968 0.032
#> SRR934328 1 0.0672 0.971 0.992 0.008
#> SRR934329 1 0.0672 0.971 0.992 0.008
#> SRR934330 1 0.0672 0.971 0.992 0.008
#> SRR934331 1 0.0672 0.971 0.992 0.008
#> SRR934332 1 0.0938 0.970 0.988 0.012
#> SRR934333 1 0.0672 0.971 0.992 0.008
#> SRR934334 1 0.0672 0.971 0.992 0.008
#> SRR934335 1 0.0672 0.971 0.992 0.008
#> SRR934344 1 0.0938 0.970 0.988 0.012
#> SRR934345 1 0.0938 0.970 0.988 0.012
#> SRR934346 1 0.0938 0.970 0.988 0.012
#> SRR934347 1 0.0938 0.970 0.988 0.012
#> SRR934348 1 0.0938 0.970 0.988 0.012
#> SRR934349 1 0.0938 0.970 0.988 0.012
#> SRR934350 1 0.0938 0.970 0.988 0.012
#> SRR934351 1 0.0938 0.970 0.988 0.012
#> SRR934336 1 0.0938 0.970 0.988 0.012
#> SRR934337 1 0.0938 0.970 0.988 0.012
#> SRR934338 1 0.0938 0.970 0.988 0.012
#> SRR934339 1 0.0938 0.970 0.988 0.012
#> SRR934340 1 0.0938 0.970 0.988 0.012
#> SRR934341 1 0.0938 0.970 0.988 0.012
#> SRR934342 1 0.0938 0.970 0.988 0.012
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 1 0.1643 0.954 0.956 0.044 0.000
#> SRR934217 1 0.1643 0.954 0.956 0.044 0.000
#> SRR934218 1 0.1643 0.954 0.956 0.044 0.000
#> SRR934219 1 0.1643 0.954 0.956 0.044 0.000
#> SRR934220 1 0.1643 0.954 0.956 0.044 0.000
#> SRR934221 1 0.1643 0.954 0.956 0.044 0.000
#> SRR934222 1 0.1643 0.954 0.956 0.044 0.000
#> SRR934223 1 0.1643 0.954 0.956 0.044 0.000
#> SRR934224 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934225 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934226 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934227 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934228 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934229 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934230 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934231 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934232 3 0.0237 0.986 0.000 0.004 0.996
#> SRR934233 3 0.0237 0.986 0.000 0.004 0.996
#> SRR934234 3 0.0237 0.986 0.000 0.004 0.996
#> SRR934235 3 0.0237 0.986 0.000 0.004 0.996
#> SRR934236 3 0.0237 0.986 0.000 0.004 0.996
#> SRR934237 3 0.0237 0.986 0.000 0.004 0.996
#> SRR934238 3 0.0237 0.986 0.000 0.004 0.996
#> SRR934239 3 0.0237 0.986 0.000 0.004 0.996
#> SRR934240 2 0.4555 0.818 0.000 0.800 0.200
#> SRR934241 2 0.4750 0.800 0.000 0.784 0.216
#> SRR934242 2 0.4399 0.828 0.000 0.812 0.188
#> SRR934243 2 0.4346 0.831 0.000 0.816 0.184
#> SRR934244 2 0.4555 0.818 0.000 0.800 0.200
#> SRR934245 2 0.4654 0.810 0.000 0.792 0.208
#> SRR934246 2 0.4399 0.828 0.000 0.812 0.188
#> SRR934247 2 0.4555 0.818 0.000 0.800 0.200
#> SRR934248 3 0.0000 0.987 0.000 0.000 1.000
#> SRR934249 3 0.0000 0.987 0.000 0.000 1.000
#> SRR934250 3 0.0000 0.987 0.000 0.000 1.000
#> SRR934251 3 0.0000 0.987 0.000 0.000 1.000
#> SRR934252 3 0.0000 0.987 0.000 0.000 1.000
#> SRR934253 3 0.0000 0.987 0.000 0.000 1.000
#> SRR934254 3 0.0000 0.987 0.000 0.000 1.000
#> SRR934255 3 0.0000 0.987 0.000 0.000 1.000
#> SRR934256 2 0.1643 0.926 0.044 0.956 0.000
#> SRR934257 2 0.1643 0.926 0.044 0.956 0.000
#> SRR934258 2 0.1643 0.926 0.044 0.956 0.000
#> SRR934259 2 0.1643 0.926 0.044 0.956 0.000
#> SRR934260 2 0.1643 0.926 0.044 0.956 0.000
#> SRR934261 2 0.1643 0.926 0.044 0.956 0.000
#> SRR934262 2 0.1643 0.926 0.044 0.956 0.000
#> SRR934263 2 0.1643 0.926 0.044 0.956 0.000
#> SRR934264 3 0.0000 0.987 0.000 0.000 1.000
#> SRR934265 3 0.0000 0.987 0.000 0.000 1.000
#> SRR934266 3 0.0000 0.987 0.000 0.000 1.000
#> SRR934267 3 0.0000 0.987 0.000 0.000 1.000
#> SRR934268 3 0.0000 0.987 0.000 0.000 1.000
#> SRR934269 3 0.0000 0.987 0.000 0.000 1.000
#> SRR934270 3 0.0000 0.987 0.000 0.000 1.000
#> SRR934271 3 0.0000 0.987 0.000 0.000 1.000
#> SRR934272 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934273 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934274 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934275 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934276 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934277 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934278 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934279 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934280 1 0.0747 0.983 0.984 0.016 0.000
#> SRR934281 1 0.0747 0.983 0.984 0.016 0.000
#> SRR934282 1 0.0747 0.983 0.984 0.016 0.000
#> SRR934283 1 0.0747 0.983 0.984 0.016 0.000
#> SRR934284 1 0.1163 0.978 0.972 0.028 0.000
#> SRR934285 1 0.0747 0.983 0.984 0.016 0.000
#> SRR934286 1 0.0892 0.982 0.980 0.020 0.000
#> SRR934287 1 0.0892 0.982 0.980 0.020 0.000
#> SRR934288 1 0.0747 0.983 0.984 0.016 0.000
#> SRR934289 1 0.0747 0.983 0.984 0.016 0.000
#> SRR934290 1 0.2448 0.937 0.924 0.076 0.000
#> SRR934291 1 0.0747 0.983 0.984 0.016 0.000
#> SRR934292 1 0.0747 0.983 0.984 0.016 0.000
#> SRR934293 1 0.1031 0.981 0.976 0.024 0.000
#> SRR934294 1 0.0747 0.983 0.984 0.016 0.000
#> SRR934295 1 0.0747 0.983 0.984 0.016 0.000
#> SRR934296 2 0.1643 0.926 0.044 0.956 0.000
#> SRR934297 2 0.1643 0.926 0.044 0.956 0.000
#> SRR934298 2 0.1643 0.926 0.044 0.956 0.000
#> SRR934299 2 0.1643 0.926 0.044 0.956 0.000
#> SRR934300 2 0.1643 0.926 0.044 0.956 0.000
#> SRR934301 2 0.1643 0.926 0.044 0.956 0.000
#> SRR934302 2 0.1643 0.926 0.044 0.956 0.000
#> SRR934303 2 0.1643 0.926 0.044 0.956 0.000
#> SRR934304 3 0.1643 0.964 0.000 0.044 0.956
#> SRR934305 3 0.1643 0.964 0.000 0.044 0.956
#> SRR934306 3 0.1643 0.964 0.000 0.044 0.956
#> SRR934307 3 0.1643 0.964 0.000 0.044 0.956
#> SRR934308 3 0.1643 0.964 0.000 0.044 0.956
#> SRR934309 3 0.1643 0.964 0.000 0.044 0.956
#> SRR934310 3 0.1643 0.964 0.000 0.044 0.956
#> SRR934311 3 0.1643 0.964 0.000 0.044 0.956
#> SRR934312 1 0.0237 0.985 0.996 0.004 0.000
#> SRR934313 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934314 1 0.0237 0.985 0.996 0.004 0.000
#> SRR934315 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934316 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934317 1 0.0237 0.985 0.996 0.004 0.000
#> SRR934318 1 0.0237 0.985 0.996 0.004 0.000
#> SRR934319 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934320 1 0.1163 0.978 0.972 0.028 0.000
#> SRR934321 1 0.1163 0.978 0.972 0.028 0.000
#> SRR934322 1 0.1031 0.980 0.976 0.024 0.000
#> SRR934323 1 0.1529 0.970 0.960 0.040 0.000
#> SRR934324 1 0.1289 0.975 0.968 0.032 0.000
#> SRR934325 1 0.1411 0.973 0.964 0.036 0.000
#> SRR934326 1 0.1411 0.973 0.964 0.036 0.000
#> SRR934327 1 0.1289 0.975 0.968 0.032 0.000
#> SRR934328 1 0.0892 0.981 0.980 0.020 0.000
#> SRR934329 1 0.0424 0.985 0.992 0.008 0.000
#> SRR934330 1 0.1411 0.970 0.964 0.036 0.000
#> SRR934331 1 0.1643 0.964 0.956 0.044 0.000
#> SRR934332 1 0.1289 0.974 0.968 0.032 0.000
#> SRR934333 1 0.1411 0.971 0.964 0.036 0.000
#> SRR934334 1 0.0892 0.981 0.980 0.020 0.000
#> SRR934335 1 0.1411 0.972 0.964 0.036 0.000
#> SRR934344 1 0.0237 0.985 0.996 0.004 0.000
#> SRR934345 1 0.0424 0.985 0.992 0.008 0.000
#> SRR934346 1 0.0424 0.985 0.992 0.008 0.000
#> SRR934347 1 0.0237 0.985 0.996 0.004 0.000
#> SRR934348 1 0.0424 0.985 0.992 0.008 0.000
#> SRR934349 1 0.0237 0.985 0.996 0.004 0.000
#> SRR934350 1 0.0424 0.985 0.992 0.008 0.000
#> SRR934351 1 0.0237 0.985 0.996 0.004 0.000
#> SRR934336 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934337 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934338 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934339 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934340 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934341 1 0.0000 0.985 1.000 0.000 0.000
#> SRR934342 1 0.0000 0.985 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 3 0.3335 0.847 0.128 0.000 0.856 0.016
#> SRR934217 3 0.3335 0.847 0.128 0.000 0.856 0.016
#> SRR934218 3 0.3335 0.847 0.128 0.000 0.856 0.016
#> SRR934219 3 0.3335 0.847 0.128 0.000 0.856 0.016
#> SRR934220 3 0.3335 0.847 0.128 0.000 0.856 0.016
#> SRR934221 3 0.3335 0.847 0.128 0.000 0.856 0.016
#> SRR934222 3 0.3335 0.847 0.128 0.000 0.856 0.016
#> SRR934223 3 0.3335 0.847 0.128 0.000 0.856 0.016
#> SRR934224 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934225 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934226 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934227 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934228 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934229 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934230 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934231 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934232 2 0.0000 0.920 0.000 1.000 0.000 0.000
#> SRR934233 2 0.0000 0.920 0.000 1.000 0.000 0.000
#> SRR934234 2 0.0000 0.920 0.000 1.000 0.000 0.000
#> SRR934235 2 0.0000 0.920 0.000 1.000 0.000 0.000
#> SRR934236 2 0.0000 0.920 0.000 1.000 0.000 0.000
#> SRR934237 2 0.0000 0.920 0.000 1.000 0.000 0.000
#> SRR934238 2 0.0000 0.920 0.000 1.000 0.000 0.000
#> SRR934239 2 0.0000 0.920 0.000 1.000 0.000 0.000
#> SRR934240 2 0.3763 0.849 0.000 0.832 0.144 0.024
#> SRR934241 2 0.3763 0.849 0.000 0.832 0.144 0.024
#> SRR934242 2 0.3763 0.849 0.000 0.832 0.144 0.024
#> SRR934243 2 0.3763 0.849 0.000 0.832 0.144 0.024
#> SRR934244 2 0.3763 0.849 0.000 0.832 0.144 0.024
#> SRR934245 2 0.3763 0.849 0.000 0.832 0.144 0.024
#> SRR934246 2 0.3763 0.849 0.000 0.832 0.144 0.024
#> SRR934247 2 0.3763 0.849 0.000 0.832 0.144 0.024
#> SRR934248 2 0.0000 0.920 0.000 1.000 0.000 0.000
#> SRR934249 2 0.0000 0.920 0.000 1.000 0.000 0.000
#> SRR934250 2 0.0000 0.920 0.000 1.000 0.000 0.000
#> SRR934251 2 0.0000 0.920 0.000 1.000 0.000 0.000
#> SRR934252 2 0.0000 0.920 0.000 1.000 0.000 0.000
#> SRR934253 2 0.0000 0.920 0.000 1.000 0.000 0.000
#> SRR934254 2 0.0000 0.920 0.000 1.000 0.000 0.000
#> SRR934255 2 0.0000 0.920 0.000 1.000 0.000 0.000
#> SRR934256 4 0.2973 0.773 0.000 0.000 0.144 0.856
#> SRR934257 4 0.2973 0.773 0.000 0.000 0.144 0.856
#> SRR934258 4 0.2973 0.773 0.000 0.000 0.144 0.856
#> SRR934259 4 0.2973 0.773 0.000 0.000 0.144 0.856
#> SRR934260 4 0.2973 0.773 0.000 0.000 0.144 0.856
#> SRR934261 4 0.2973 0.773 0.000 0.000 0.144 0.856
#> SRR934262 4 0.2973 0.773 0.000 0.000 0.144 0.856
#> SRR934263 4 0.2973 0.773 0.000 0.000 0.144 0.856
#> SRR934264 2 0.2149 0.877 0.088 0.912 0.000 0.000
#> SRR934265 2 0.2345 0.863 0.100 0.900 0.000 0.000
#> SRR934266 2 0.2011 0.884 0.080 0.920 0.000 0.000
#> SRR934267 2 0.1867 0.889 0.072 0.928 0.000 0.000
#> SRR934268 2 0.2216 0.874 0.092 0.908 0.000 0.000
#> SRR934269 2 0.2281 0.868 0.096 0.904 0.000 0.000
#> SRR934270 2 0.2081 0.881 0.084 0.916 0.000 0.000
#> SRR934271 2 0.1940 0.887 0.076 0.924 0.000 0.000
#> SRR934272 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934273 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934274 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934275 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934276 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934277 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934278 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934279 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934280 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934281 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934282 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934283 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934284 1 0.0336 0.883 0.992 0.000 0.000 0.008
#> SRR934285 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934286 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934287 1 0.0188 0.883 0.996 0.000 0.000 0.004
#> SRR934288 1 0.5311 0.624 0.648 0.000 0.024 0.328
#> SRR934289 1 0.5349 0.612 0.640 0.000 0.024 0.336
#> SRR934290 4 0.5691 -0.324 0.468 0.000 0.024 0.508
#> SRR934291 1 0.5250 0.637 0.660 0.000 0.024 0.316
#> SRR934292 1 0.5536 0.541 0.592 0.000 0.024 0.384
#> SRR934293 1 0.5611 0.496 0.564 0.000 0.024 0.412
#> SRR934294 1 0.5496 0.564 0.604 0.000 0.024 0.372
#> SRR934295 1 0.5010 0.678 0.700 0.000 0.024 0.276
#> SRR934296 4 0.3569 0.729 0.000 0.000 0.196 0.804
#> SRR934297 4 0.3486 0.737 0.000 0.000 0.188 0.812
#> SRR934298 4 0.3400 0.739 0.000 0.000 0.180 0.820
#> SRR934299 4 0.3486 0.737 0.000 0.000 0.188 0.812
#> SRR934300 4 0.3486 0.737 0.000 0.000 0.188 0.812
#> SRR934301 4 0.3528 0.733 0.000 0.000 0.192 0.808
#> SRR934302 4 0.3486 0.737 0.000 0.000 0.188 0.812
#> SRR934303 4 0.3486 0.737 0.000 0.000 0.188 0.812
#> SRR934304 3 0.3311 0.849 0.000 0.172 0.828 0.000
#> SRR934305 3 0.3311 0.849 0.000 0.172 0.828 0.000
#> SRR934306 3 0.3311 0.849 0.000 0.172 0.828 0.000
#> SRR934307 3 0.3311 0.849 0.000 0.172 0.828 0.000
#> SRR934308 3 0.3311 0.849 0.000 0.172 0.828 0.000
#> SRR934309 3 0.3311 0.849 0.000 0.172 0.828 0.000
#> SRR934310 3 0.3311 0.849 0.000 0.172 0.828 0.000
#> SRR934311 3 0.3311 0.849 0.000 0.172 0.828 0.000
#> SRR934312 1 0.0188 0.883 0.996 0.000 0.004 0.000
#> SRR934313 1 0.0188 0.883 0.996 0.000 0.004 0.000
#> SRR934314 1 0.0376 0.883 0.992 0.000 0.004 0.004
#> SRR934315 1 0.0376 0.883 0.992 0.000 0.004 0.004
#> SRR934316 1 0.0188 0.883 0.996 0.000 0.004 0.000
#> SRR934317 1 0.0188 0.883 0.996 0.000 0.004 0.000
#> SRR934318 1 0.0188 0.883 0.996 0.000 0.004 0.000
#> SRR934319 1 0.0188 0.883 0.996 0.000 0.004 0.000
#> SRR934320 1 0.0592 0.879 0.984 0.000 0.000 0.016
#> SRR934321 1 0.0336 0.882 0.992 0.000 0.000 0.008
#> SRR934322 1 0.0336 0.882 0.992 0.000 0.000 0.008
#> SRR934323 1 0.0817 0.875 0.976 0.000 0.000 0.024
#> SRR934324 1 0.0336 0.882 0.992 0.000 0.000 0.008
#> SRR934325 1 0.0592 0.879 0.984 0.000 0.000 0.016
#> SRR934326 1 0.0707 0.877 0.980 0.000 0.000 0.020
#> SRR934327 1 0.0336 0.882 0.992 0.000 0.000 0.008
#> SRR934328 1 0.5691 0.506 0.564 0.000 0.028 0.408
#> SRR934329 1 0.5708 0.491 0.556 0.000 0.028 0.416
#> SRR934330 1 0.5750 0.443 0.532 0.000 0.028 0.440
#> SRR934331 1 0.5731 0.468 0.544 0.000 0.028 0.428
#> SRR934332 1 0.5671 0.520 0.572 0.000 0.028 0.400
#> SRR934333 1 0.5638 0.540 0.584 0.000 0.028 0.388
#> SRR934334 1 0.5724 0.476 0.548 0.000 0.028 0.424
#> SRR934335 1 0.5750 0.443 0.532 0.000 0.028 0.440
#> SRR934344 1 0.3913 0.789 0.824 0.000 0.028 0.148
#> SRR934345 1 0.3913 0.789 0.824 0.000 0.028 0.148
#> SRR934346 1 0.3913 0.789 0.824 0.000 0.028 0.148
#> SRR934347 1 0.3913 0.789 0.824 0.000 0.028 0.148
#> SRR934348 1 0.3913 0.789 0.824 0.000 0.028 0.148
#> SRR934349 1 0.3913 0.789 0.824 0.000 0.028 0.148
#> SRR934350 1 0.3913 0.789 0.824 0.000 0.028 0.148
#> SRR934351 1 0.3913 0.789 0.824 0.000 0.028 0.148
#> SRR934336 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934337 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934338 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934339 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934340 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934341 1 0.0000 0.884 1.000 0.000 0.000 0.000
#> SRR934342 1 0.0000 0.884 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 5 0.1041 0.974 0.004 0.000 0.032 0.000 0.964
#> SRR934217 5 0.0880 0.973 0.000 0.000 0.032 0.000 0.968
#> SRR934218 5 0.1041 0.974 0.004 0.000 0.032 0.000 0.964
#> SRR934219 5 0.1041 0.974 0.004 0.000 0.032 0.000 0.964
#> SRR934220 5 0.1202 0.972 0.004 0.004 0.032 0.000 0.960
#> SRR934221 5 0.1041 0.974 0.004 0.000 0.032 0.000 0.964
#> SRR934222 5 0.1041 0.974 0.004 0.000 0.032 0.000 0.964
#> SRR934223 5 0.1041 0.974 0.004 0.000 0.032 0.000 0.964
#> SRR934224 1 0.0290 0.984 0.992 0.000 0.008 0.000 0.000
#> SRR934225 1 0.0290 0.984 0.992 0.000 0.008 0.000 0.000
#> SRR934226 1 0.0290 0.984 0.992 0.000 0.008 0.000 0.000
#> SRR934227 1 0.0290 0.984 0.992 0.000 0.008 0.000 0.000
#> SRR934228 1 0.0290 0.984 0.992 0.000 0.008 0.000 0.000
#> SRR934229 1 0.0290 0.984 0.992 0.000 0.008 0.000 0.000
#> SRR934230 1 0.0290 0.984 0.992 0.000 0.008 0.000 0.000
#> SRR934231 1 0.0290 0.984 0.992 0.000 0.008 0.000 0.000
#> SRR934232 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> SRR934233 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> SRR934234 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> SRR934235 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> SRR934236 4 0.0162 0.826 0.000 0.004 0.000 0.996 0.000
#> SRR934237 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> SRR934238 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> SRR934239 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> SRR934240 4 0.1251 0.813 0.000 0.036 0.008 0.956 0.000
#> SRR934241 4 0.1251 0.813 0.000 0.036 0.008 0.956 0.000
#> SRR934242 4 0.1251 0.813 0.000 0.036 0.008 0.956 0.000
#> SRR934243 4 0.1251 0.813 0.000 0.036 0.008 0.956 0.000
#> SRR934244 4 0.1251 0.813 0.000 0.036 0.008 0.956 0.000
#> SRR934245 4 0.1251 0.813 0.000 0.036 0.008 0.956 0.000
#> SRR934246 4 0.1251 0.813 0.000 0.036 0.008 0.956 0.000
#> SRR934247 4 0.1251 0.813 0.000 0.036 0.008 0.956 0.000
#> SRR934248 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> SRR934249 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> SRR934250 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> SRR934251 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> SRR934252 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> SRR934253 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> SRR934254 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> SRR934255 4 0.0000 0.827 0.000 0.000 0.000 1.000 0.000
#> SRR934256 2 0.1121 1.000 0.000 0.956 0.044 0.000 0.000
#> SRR934257 2 0.1121 1.000 0.000 0.956 0.044 0.000 0.000
#> SRR934258 2 0.1121 1.000 0.000 0.956 0.044 0.000 0.000
#> SRR934259 2 0.1121 1.000 0.000 0.956 0.044 0.000 0.000
#> SRR934260 2 0.1121 1.000 0.000 0.956 0.044 0.000 0.000
#> SRR934261 2 0.1121 1.000 0.000 0.956 0.044 0.000 0.000
#> SRR934262 2 0.1121 1.000 0.000 0.956 0.044 0.000 0.000
#> SRR934263 2 0.1121 1.000 0.000 0.956 0.044 0.000 0.000
#> SRR934264 4 0.4249 0.390 0.432 0.000 0.000 0.568 0.000
#> SRR934265 4 0.4201 0.447 0.408 0.000 0.000 0.592 0.000
#> SRR934266 4 0.4192 0.454 0.404 0.000 0.000 0.596 0.000
#> SRR934267 4 0.4235 0.411 0.424 0.000 0.000 0.576 0.000
#> SRR934268 4 0.4210 0.438 0.412 0.000 0.000 0.588 0.000
#> SRR934269 4 0.4201 0.447 0.408 0.000 0.000 0.592 0.000
#> SRR934270 4 0.4219 0.429 0.416 0.000 0.000 0.584 0.000
#> SRR934271 4 0.4182 0.461 0.400 0.000 0.000 0.600 0.000
#> SRR934272 1 0.0162 0.985 0.996 0.000 0.004 0.000 0.000
#> SRR934273 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR934274 1 0.0162 0.985 0.996 0.000 0.004 0.000 0.000
#> SRR934275 1 0.0162 0.985 0.996 0.000 0.004 0.000 0.000
#> SRR934276 1 0.0162 0.985 0.996 0.000 0.004 0.000 0.000
#> SRR934277 1 0.0162 0.985 0.996 0.000 0.004 0.000 0.000
#> SRR934278 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR934279 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR934280 1 0.0794 0.980 0.972 0.028 0.000 0.000 0.000
#> SRR934281 1 0.0794 0.980 0.972 0.028 0.000 0.000 0.000
#> SRR934282 1 0.0609 0.983 0.980 0.020 0.000 0.000 0.000
#> SRR934283 1 0.0794 0.980 0.972 0.028 0.000 0.000 0.000
#> SRR934284 1 0.0963 0.977 0.964 0.036 0.000 0.000 0.000
#> SRR934285 1 0.0794 0.980 0.972 0.028 0.000 0.000 0.000
#> SRR934286 1 0.0794 0.980 0.972 0.028 0.000 0.000 0.000
#> SRR934287 1 0.0880 0.978 0.968 0.032 0.000 0.000 0.000
#> SRR934288 3 0.0404 0.897 0.012 0.000 0.988 0.000 0.000
#> SRR934289 3 0.0671 0.897 0.016 0.004 0.980 0.000 0.000
#> SRR934290 3 0.0451 0.897 0.008 0.004 0.988 0.000 0.000
#> SRR934291 3 0.0404 0.897 0.012 0.000 0.988 0.000 0.000
#> SRR934292 3 0.0566 0.897 0.012 0.004 0.984 0.000 0.000
#> SRR934293 3 0.0566 0.897 0.012 0.004 0.984 0.000 0.000
#> SRR934294 3 0.0566 0.897 0.012 0.004 0.984 0.000 0.000
#> SRR934295 3 0.0566 0.897 0.012 0.004 0.984 0.000 0.000
#> SRR934296 3 0.5013 0.683 0.000 0.084 0.684 0.000 0.232
#> SRR934297 3 0.5077 0.699 0.004 0.088 0.696 0.000 0.212
#> SRR934298 3 0.4944 0.701 0.000 0.092 0.700 0.000 0.208
#> SRR934299 3 0.5032 0.689 0.000 0.092 0.688 0.000 0.220
#> SRR934300 3 0.5077 0.699 0.004 0.088 0.696 0.000 0.212
#> SRR934301 3 0.5191 0.658 0.000 0.088 0.660 0.000 0.252
#> SRR934302 3 0.5037 0.683 0.000 0.088 0.684 0.000 0.228
#> SRR934303 3 0.5167 0.659 0.000 0.088 0.664 0.000 0.248
#> SRR934304 5 0.0000 0.975 0.000 0.000 0.000 0.000 1.000
#> SRR934305 5 0.0000 0.975 0.000 0.000 0.000 0.000 1.000
#> SRR934306 5 0.0000 0.975 0.000 0.000 0.000 0.000 1.000
#> SRR934307 5 0.0000 0.975 0.000 0.000 0.000 0.000 1.000
#> SRR934308 5 0.0000 0.975 0.000 0.000 0.000 0.000 1.000
#> SRR934309 5 0.0000 0.975 0.000 0.000 0.000 0.000 1.000
#> SRR934310 5 0.0000 0.975 0.000 0.000 0.000 0.000 1.000
#> SRR934311 5 0.0000 0.975 0.000 0.000 0.000 0.000 1.000
#> SRR934312 1 0.0290 0.986 0.992 0.008 0.000 0.000 0.000
#> SRR934313 1 0.0579 0.985 0.984 0.008 0.008 0.000 0.000
#> SRR934314 1 0.0290 0.986 0.992 0.008 0.000 0.000 0.000
#> SRR934315 1 0.0290 0.986 0.992 0.008 0.000 0.000 0.000
#> SRR934316 1 0.0290 0.986 0.992 0.008 0.000 0.000 0.000
#> SRR934317 1 0.0290 0.986 0.992 0.008 0.000 0.000 0.000
#> SRR934318 1 0.0404 0.985 0.988 0.012 0.000 0.000 0.000
#> SRR934319 1 0.0290 0.986 0.992 0.008 0.000 0.000 0.000
#> SRR934320 1 0.1043 0.974 0.960 0.040 0.000 0.000 0.000
#> SRR934321 1 0.1043 0.974 0.960 0.040 0.000 0.000 0.000
#> SRR934322 1 0.1043 0.974 0.960 0.040 0.000 0.000 0.000
#> SRR934323 1 0.1121 0.971 0.956 0.044 0.000 0.000 0.000
#> SRR934324 1 0.1043 0.974 0.960 0.040 0.000 0.000 0.000
#> SRR934325 1 0.1121 0.971 0.956 0.044 0.000 0.000 0.000
#> SRR934326 1 0.1121 0.971 0.956 0.044 0.000 0.000 0.000
#> SRR934327 1 0.1043 0.974 0.960 0.040 0.000 0.000 0.000
#> SRR934328 3 0.0162 0.895 0.000 0.004 0.996 0.000 0.000
#> SRR934329 3 0.0162 0.895 0.000 0.004 0.996 0.000 0.000
#> SRR934330 3 0.0162 0.895 0.000 0.004 0.996 0.000 0.000
#> SRR934331 3 0.0162 0.895 0.000 0.004 0.996 0.000 0.000
#> SRR934332 3 0.0162 0.895 0.000 0.004 0.996 0.000 0.000
#> SRR934333 3 0.0162 0.895 0.000 0.004 0.996 0.000 0.000
#> SRR934334 3 0.0162 0.895 0.000 0.004 0.996 0.000 0.000
#> SRR934335 3 0.0162 0.895 0.000 0.004 0.996 0.000 0.000
#> SRR934344 3 0.1041 0.890 0.032 0.004 0.964 0.000 0.000
#> SRR934345 3 0.1041 0.890 0.032 0.004 0.964 0.000 0.000
#> SRR934346 3 0.1041 0.890 0.032 0.004 0.964 0.000 0.000
#> SRR934347 3 0.1041 0.890 0.032 0.004 0.964 0.000 0.000
#> SRR934348 3 0.1041 0.890 0.032 0.004 0.964 0.000 0.000
#> SRR934349 3 0.1041 0.890 0.032 0.004 0.964 0.000 0.000
#> SRR934350 3 0.1041 0.890 0.032 0.004 0.964 0.000 0.000
#> SRR934351 3 0.1041 0.890 0.032 0.004 0.964 0.000 0.000
#> SRR934336 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR934337 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR934338 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR934339 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR934340 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR934341 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
#> SRR934342 1 0.0000 0.986 1.000 0.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.0717 0.997 0.008 0.000 0.976 0.000 0.000 0.016
#> SRR934217 3 0.0717 0.997 0.008 0.000 0.976 0.000 0.000 0.016
#> SRR934218 3 0.0717 0.997 0.008 0.000 0.976 0.000 0.000 0.016
#> SRR934219 3 0.0806 0.992 0.008 0.000 0.972 0.000 0.000 0.020
#> SRR934220 3 0.0717 0.997 0.008 0.000 0.976 0.000 0.000 0.016
#> SRR934221 3 0.0806 0.992 0.008 0.000 0.972 0.000 0.000 0.020
#> SRR934222 3 0.0717 0.997 0.008 0.000 0.976 0.000 0.000 0.016
#> SRR934223 3 0.0717 0.997 0.008 0.000 0.976 0.000 0.000 0.016
#> SRR934224 6 0.4390 0.745 0.000 0.000 0.232 0.004 0.064 0.700
#> SRR934225 6 0.4439 0.736 0.000 0.000 0.240 0.004 0.064 0.692
#> SRR934226 6 0.4415 0.741 0.000 0.000 0.236 0.004 0.064 0.696
#> SRR934227 6 0.4314 0.758 0.000 0.000 0.220 0.004 0.064 0.712
#> SRR934228 6 0.4439 0.736 0.000 0.000 0.240 0.004 0.064 0.692
#> SRR934229 6 0.4365 0.750 0.000 0.000 0.228 0.004 0.064 0.704
#> SRR934230 6 0.4340 0.754 0.000 0.000 0.224 0.004 0.064 0.708
#> SRR934231 6 0.4365 0.750 0.000 0.000 0.228 0.004 0.064 0.704
#> SRR934232 4 0.1523 0.832 0.000 0.008 0.008 0.940 0.044 0.000
#> SRR934233 4 0.1268 0.833 0.000 0.008 0.004 0.952 0.036 0.000
#> SRR934234 4 0.1590 0.831 0.000 0.008 0.008 0.936 0.048 0.000
#> SRR934235 4 0.1307 0.833 0.000 0.008 0.008 0.952 0.032 0.000
#> SRR934236 4 0.1655 0.830 0.000 0.008 0.008 0.932 0.052 0.000
#> SRR934237 4 0.1590 0.831 0.000 0.008 0.008 0.936 0.048 0.000
#> SRR934238 4 0.1590 0.831 0.000 0.008 0.008 0.936 0.048 0.000
#> SRR934239 4 0.1382 0.833 0.000 0.008 0.008 0.948 0.036 0.000
#> SRR934240 4 0.4031 0.696 0.000 0.008 0.008 0.652 0.332 0.000
#> SRR934241 4 0.4031 0.696 0.000 0.008 0.008 0.652 0.332 0.000
#> SRR934242 4 0.4031 0.696 0.000 0.008 0.008 0.652 0.332 0.000
#> SRR934243 4 0.4031 0.696 0.000 0.008 0.008 0.652 0.332 0.000
#> SRR934244 4 0.4031 0.696 0.000 0.008 0.008 0.652 0.332 0.000
#> SRR934245 4 0.4031 0.696 0.000 0.008 0.008 0.652 0.332 0.000
#> SRR934246 4 0.4031 0.696 0.000 0.008 0.008 0.652 0.332 0.000
#> SRR934247 4 0.4031 0.696 0.000 0.008 0.008 0.652 0.332 0.000
#> SRR934248 4 0.0146 0.831 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR934249 4 0.0146 0.831 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR934250 4 0.0000 0.832 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934251 4 0.0000 0.832 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934252 4 0.0146 0.831 0.004 0.000 0.000 0.996 0.000 0.000
#> SRR934253 4 0.0000 0.832 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934254 4 0.0000 0.832 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934255 4 0.0000 0.832 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR934256 2 0.0260 1.000 0.008 0.992 0.000 0.000 0.000 0.000
#> SRR934257 2 0.0260 1.000 0.008 0.992 0.000 0.000 0.000 0.000
#> SRR934258 2 0.0260 1.000 0.008 0.992 0.000 0.000 0.000 0.000
#> SRR934259 2 0.0260 1.000 0.008 0.992 0.000 0.000 0.000 0.000
#> SRR934260 2 0.0260 1.000 0.008 0.992 0.000 0.000 0.000 0.000
#> SRR934261 2 0.0260 1.000 0.008 0.992 0.000 0.000 0.000 0.000
#> SRR934262 2 0.0260 1.000 0.008 0.992 0.000 0.000 0.000 0.000
#> SRR934263 2 0.0260 1.000 0.008 0.992 0.000 0.000 0.000 0.000
#> SRR934264 4 0.3834 0.694 0.000 0.000 0.172 0.772 0.048 0.008
#> SRR934265 4 0.3834 0.694 0.000 0.000 0.172 0.772 0.048 0.008
#> SRR934266 4 0.3765 0.703 0.000 0.000 0.164 0.780 0.048 0.008
#> SRR934267 4 0.3834 0.694 0.000 0.000 0.172 0.772 0.048 0.008
#> SRR934268 4 0.3730 0.706 0.000 0.000 0.160 0.784 0.048 0.008
#> SRR934269 4 0.3765 0.702 0.000 0.000 0.164 0.780 0.048 0.008
#> SRR934270 4 0.3800 0.699 0.000 0.000 0.168 0.776 0.048 0.008
#> SRR934271 4 0.3765 0.702 0.000 0.000 0.164 0.780 0.048 0.008
#> SRR934272 6 0.0547 0.875 0.000 0.000 0.020 0.000 0.000 0.980
#> SRR934273 6 0.0458 0.875 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR934274 6 0.0458 0.875 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR934275 6 0.0458 0.875 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR934276 6 0.0458 0.875 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR934277 6 0.0458 0.875 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR934278 6 0.0458 0.875 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR934279 6 0.0458 0.875 0.000 0.000 0.016 0.000 0.000 0.984
#> SRR934280 6 0.0692 0.874 0.000 0.000 0.004 0.000 0.020 0.976
#> SRR934281 6 0.0692 0.874 0.000 0.000 0.004 0.000 0.020 0.976
#> SRR934282 6 0.0547 0.875 0.000 0.000 0.000 0.000 0.020 0.980
#> SRR934283 6 0.0692 0.874 0.000 0.000 0.004 0.000 0.020 0.976
#> SRR934284 6 0.0692 0.874 0.000 0.000 0.004 0.000 0.020 0.976
#> SRR934285 6 0.0692 0.874 0.000 0.000 0.004 0.000 0.020 0.976
#> SRR934286 6 0.0692 0.874 0.000 0.000 0.004 0.000 0.020 0.976
#> SRR934287 6 0.0692 0.874 0.000 0.004 0.000 0.000 0.020 0.976
#> SRR934288 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934289 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934290 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934291 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934292 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934293 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934294 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934295 1 0.0000 0.896 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR934296 1 0.5352 0.561 0.620 0.000 0.056 0.000 0.276 0.048
#> SRR934297 1 0.4833 0.630 0.676 0.000 0.044 0.000 0.244 0.036
#> SRR934298 1 0.5078 0.614 0.660 0.004 0.040 0.000 0.252 0.044
#> SRR934299 1 0.5376 0.520 0.592 0.004 0.044 0.000 0.320 0.040
#> SRR934300 1 0.4922 0.611 0.660 0.000 0.040 0.000 0.260 0.040
#> SRR934301 1 0.5602 0.475 0.572 0.000 0.068 0.000 0.316 0.044
#> SRR934302 1 0.5286 0.551 0.616 0.000 0.056 0.000 0.288 0.040
#> SRR934303 1 0.5312 0.527 0.600 0.000 0.052 0.000 0.308 0.040
#> SRR934304 5 0.3765 1.000 0.000 0.000 0.404 0.000 0.596 0.000
#> SRR934305 5 0.3765 1.000 0.000 0.000 0.404 0.000 0.596 0.000
#> SRR934306 5 0.3765 1.000 0.000 0.000 0.404 0.000 0.596 0.000
#> SRR934307 5 0.3765 1.000 0.000 0.000 0.404 0.000 0.596 0.000
#> SRR934308 5 0.3765 1.000 0.000 0.000 0.404 0.000 0.596 0.000
#> SRR934309 5 0.3765 1.000 0.000 0.000 0.404 0.000 0.596 0.000
#> SRR934310 5 0.3765 1.000 0.000 0.000 0.404 0.000 0.596 0.000
#> SRR934311 5 0.3765 1.000 0.000 0.000 0.404 0.000 0.596 0.000
#> SRR934312 6 0.1401 0.865 0.000 0.004 0.028 0.000 0.020 0.948
#> SRR934313 6 0.1549 0.859 0.000 0.000 0.044 0.000 0.020 0.936
#> SRR934314 6 0.1237 0.868 0.000 0.004 0.020 0.000 0.020 0.956
#> SRR934315 6 0.1401 0.865 0.000 0.004 0.028 0.000 0.020 0.948
#> SRR934316 6 0.1401 0.865 0.000 0.004 0.028 0.000 0.020 0.948
#> SRR934317 6 0.1401 0.865 0.000 0.004 0.028 0.000 0.020 0.948
#> SRR934318 6 0.1401 0.865 0.000 0.004 0.028 0.000 0.020 0.948
#> SRR934319 6 0.1401 0.865 0.000 0.004 0.028 0.000 0.020 0.948
#> SRR934320 6 0.4418 0.825 0.000 0.076 0.084 0.004 0.060 0.776
#> SRR934321 6 0.4360 0.826 0.000 0.084 0.076 0.004 0.056 0.780
#> SRR934322 6 0.4151 0.835 0.000 0.068 0.076 0.004 0.056 0.796
#> SRR934323 6 0.4971 0.785 0.000 0.136 0.080 0.004 0.056 0.724
#> SRR934324 6 0.3985 0.841 0.000 0.060 0.072 0.004 0.056 0.808
#> SRR934325 6 0.4264 0.831 0.000 0.072 0.076 0.004 0.060 0.788
#> SRR934326 6 0.4418 0.824 0.000 0.084 0.076 0.004 0.060 0.776
#> SRR934327 6 0.4662 0.813 0.000 0.096 0.084 0.004 0.060 0.756
#> SRR934328 1 0.0146 0.896 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR934329 1 0.0146 0.896 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR934330 1 0.0146 0.896 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR934331 1 0.0146 0.896 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR934332 1 0.0146 0.896 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR934333 1 0.0146 0.896 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR934334 1 0.0146 0.896 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR934335 1 0.0146 0.896 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR934344 1 0.0405 0.895 0.988 0.000 0.008 0.000 0.004 0.000
#> SRR934345 1 0.0405 0.895 0.988 0.000 0.008 0.000 0.004 0.000
#> SRR934346 1 0.0405 0.895 0.988 0.000 0.008 0.000 0.004 0.000
#> SRR934347 1 0.0405 0.895 0.988 0.000 0.008 0.000 0.004 0.000
#> SRR934348 1 0.0405 0.895 0.988 0.000 0.008 0.000 0.004 0.000
#> SRR934349 1 0.0405 0.895 0.988 0.000 0.008 0.000 0.004 0.000
#> SRR934350 1 0.0405 0.895 0.988 0.000 0.008 0.000 0.004 0.000
#> SRR934351 1 0.0405 0.895 0.988 0.000 0.008 0.000 0.004 0.000
#> SRR934336 6 0.2786 0.856 0.000 0.000 0.084 0.000 0.056 0.860
#> SRR934337 6 0.2786 0.856 0.000 0.000 0.084 0.000 0.056 0.860
#> SRR934338 6 0.2837 0.855 0.000 0.000 0.088 0.000 0.056 0.856
#> SRR934339 6 0.2680 0.859 0.000 0.000 0.076 0.000 0.056 0.868
#> SRR934340 6 0.2680 0.859 0.000 0.000 0.076 0.000 0.056 0.868
#> SRR934341 6 0.2733 0.857 0.000 0.000 0.080 0.000 0.056 0.864
#> SRR934342 6 0.2786 0.856 0.000 0.000 0.084 0.000 0.056 0.860
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 14550 rows and 135 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.999 0.998 0.2954 0.705 0.705
#> 3 3 0.755 0.855 0.942 0.6571 0.832 0.762
#> 4 4 0.832 0.928 0.956 0.1515 0.839 0.708
#> 5 5 0.811 0.889 0.918 0.0259 0.986 0.965
#> 6 6 0.909 0.937 0.960 0.0568 0.986 0.964
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
#> SRR934216 1 0.0376 0.997 0.996 0.004
#> SRR934217 1 0.0376 0.997 0.996 0.004
#> SRR934218 1 0.0376 0.997 0.996 0.004
#> SRR934219 1 0.0376 0.997 0.996 0.004
#> SRR934220 1 0.0376 0.997 0.996 0.004
#> SRR934221 1 0.0376 0.997 0.996 0.004
#> SRR934222 1 0.0376 0.997 0.996 0.004
#> SRR934223 1 0.0376 0.997 0.996 0.004
#> SRR934224 1 0.0376 0.997 0.996 0.004
#> SRR934225 1 0.0376 0.997 0.996 0.004
#> SRR934226 1 0.0376 0.997 0.996 0.004
#> SRR934227 1 0.0376 0.997 0.996 0.004
#> SRR934228 1 0.0376 0.997 0.996 0.004
#> SRR934229 1 0.0376 0.997 0.996 0.004
#> SRR934230 1 0.0376 0.997 0.996 0.004
#> SRR934231 1 0.0376 0.997 0.996 0.004
#> SRR934232 2 0.0376 1.000 0.004 0.996
#> SRR934233 2 0.0376 1.000 0.004 0.996
#> SRR934234 2 0.0376 1.000 0.004 0.996
#> SRR934235 2 0.0376 1.000 0.004 0.996
#> SRR934236 2 0.0376 1.000 0.004 0.996
#> SRR934237 2 0.0376 1.000 0.004 0.996
#> SRR934238 2 0.0376 1.000 0.004 0.996
#> SRR934239 2 0.0376 1.000 0.004 0.996
#> SRR934240 2 0.0376 1.000 0.004 0.996
#> SRR934241 2 0.0376 1.000 0.004 0.996
#> SRR934242 2 0.0376 1.000 0.004 0.996
#> SRR934243 2 0.0376 1.000 0.004 0.996
#> SRR934244 2 0.0376 1.000 0.004 0.996
#> SRR934245 2 0.0376 1.000 0.004 0.996
#> SRR934246 2 0.0376 1.000 0.004 0.996
#> SRR934247 2 0.0376 1.000 0.004 0.996
#> SRR934248 1 0.0000 0.999 1.000 0.000
#> SRR934249 1 0.0000 0.999 1.000 0.000
#> SRR934250 1 0.0000 0.999 1.000 0.000
#> SRR934251 1 0.0000 0.999 1.000 0.000
#> SRR934252 1 0.0000 0.999 1.000 0.000
#> SRR934253 1 0.0000 0.999 1.000 0.000
#> SRR934254 1 0.0000 0.999 1.000 0.000
#> SRR934255 1 0.0000 0.999 1.000 0.000
#> SRR934256 2 0.0376 1.000 0.004 0.996
#> SRR934257 2 0.0376 1.000 0.004 0.996
#> SRR934258 2 0.0376 1.000 0.004 0.996
#> SRR934259 2 0.0376 1.000 0.004 0.996
#> SRR934260 2 0.0376 1.000 0.004 0.996
#> SRR934261 2 0.0376 1.000 0.004 0.996
#> SRR934262 2 0.0376 1.000 0.004 0.996
#> SRR934263 2 0.0376 1.000 0.004 0.996
#> SRR934264 1 0.0376 0.997 0.996 0.004
#> SRR934265 1 0.0376 0.997 0.996 0.004
#> SRR934266 1 0.0376 0.997 0.996 0.004
#> SRR934267 1 0.0376 0.997 0.996 0.004
#> SRR934268 1 0.0376 0.997 0.996 0.004
#> SRR934269 1 0.0376 0.997 0.996 0.004
#> SRR934270 1 0.0376 0.997 0.996 0.004
#> SRR934271 1 0.0376 0.997 0.996 0.004
#> SRR934272 1 0.0000 0.999 1.000 0.000
#> SRR934273 1 0.0000 0.999 1.000 0.000
#> SRR934274 1 0.0000 0.999 1.000 0.000
#> SRR934275 1 0.0000 0.999 1.000 0.000
#> SRR934276 1 0.0000 0.999 1.000 0.000
#> SRR934277 1 0.0000 0.999 1.000 0.000
#> SRR934278 1 0.0000 0.999 1.000 0.000
#> SRR934279 1 0.0000 0.999 1.000 0.000
#> SRR934280 1 0.0000 0.999 1.000 0.000
#> SRR934281 1 0.0000 0.999 1.000 0.000
#> SRR934282 1 0.0000 0.999 1.000 0.000
#> SRR934283 1 0.0000 0.999 1.000 0.000
#> SRR934284 1 0.0000 0.999 1.000 0.000
#> SRR934285 1 0.0000 0.999 1.000 0.000
#> SRR934286 1 0.0000 0.999 1.000 0.000
#> SRR934287 1 0.0000 0.999 1.000 0.000
#> SRR934288 1 0.0000 0.999 1.000 0.000
#> SRR934289 1 0.0000 0.999 1.000 0.000
#> SRR934290 1 0.0000 0.999 1.000 0.000
#> SRR934291 1 0.0000 0.999 1.000 0.000
#> SRR934292 1 0.0000 0.999 1.000 0.000
#> SRR934293 1 0.0000 0.999 1.000 0.000
#> SRR934294 1 0.0000 0.999 1.000 0.000
#> SRR934295 1 0.0000 0.999 1.000 0.000
#> SRR934296 1 0.0000 0.999 1.000 0.000
#> SRR934297 1 0.0000 0.999 1.000 0.000
#> SRR934298 1 0.0000 0.999 1.000 0.000
#> SRR934299 1 0.0000 0.999 1.000 0.000
#> SRR934300 1 0.0000 0.999 1.000 0.000
#> SRR934301 1 0.0000 0.999 1.000 0.000
#> SRR934302 1 0.0000 0.999 1.000 0.000
#> SRR934303 1 0.0000 0.999 1.000 0.000
#> SRR934304 1 0.0376 0.997 0.996 0.004
#> SRR934305 1 0.0376 0.997 0.996 0.004
#> SRR934306 1 0.0376 0.997 0.996 0.004
#> SRR934307 1 0.0376 0.997 0.996 0.004
#> SRR934308 1 0.0376 0.997 0.996 0.004
#> SRR934309 1 0.0376 0.997 0.996 0.004
#> SRR934310 1 0.0376 0.997 0.996 0.004
#> SRR934311 1 0.0376 0.997 0.996 0.004
#> SRR934312 1 0.0000 0.999 1.000 0.000
#> SRR934313 1 0.0000 0.999 1.000 0.000
#> SRR934314 1 0.0000 0.999 1.000 0.000
#> SRR934315 1 0.0000 0.999 1.000 0.000
#> SRR934316 1 0.0000 0.999 1.000 0.000
#> SRR934317 1 0.0000 0.999 1.000 0.000
#> SRR934318 1 0.0000 0.999 1.000 0.000
#> SRR934319 1 0.0000 0.999 1.000 0.000
#> SRR934320 1 0.0000 0.999 1.000 0.000
#> SRR934321 1 0.0000 0.999 1.000 0.000
#> SRR934322 1 0.0000 0.999 1.000 0.000
#> SRR934323 1 0.0000 0.999 1.000 0.000
#> SRR934324 1 0.0000 0.999 1.000 0.000
#> SRR934325 1 0.0000 0.999 1.000 0.000
#> SRR934326 1 0.0000 0.999 1.000 0.000
#> SRR934327 1 0.0000 0.999 1.000 0.000
#> SRR934328 1 0.0000 0.999 1.000 0.000
#> SRR934329 1 0.0000 0.999 1.000 0.000
#> SRR934330 1 0.0000 0.999 1.000 0.000
#> SRR934331 1 0.0000 0.999 1.000 0.000
#> SRR934332 1 0.0000 0.999 1.000 0.000
#> SRR934333 1 0.0000 0.999 1.000 0.000
#> SRR934334 1 0.0000 0.999 1.000 0.000
#> SRR934335 1 0.0000 0.999 1.000 0.000
#> SRR934344 1 0.0000 0.999 1.000 0.000
#> SRR934345 1 0.0000 0.999 1.000 0.000
#> SRR934346 1 0.0000 0.999 1.000 0.000
#> SRR934347 1 0.0000 0.999 1.000 0.000
#> SRR934348 1 0.0000 0.999 1.000 0.000
#> SRR934349 1 0.0000 0.999 1.000 0.000
#> SRR934350 1 0.0000 0.999 1.000 0.000
#> SRR934351 1 0.0000 0.999 1.000 0.000
#> SRR934336 1 0.0000 0.999 1.000 0.000
#> SRR934337 1 0.0000 0.999 1.000 0.000
#> SRR934338 1 0.0000 0.999 1.000 0.000
#> SRR934339 1 0.0000 0.999 1.000 0.000
#> SRR934340 1 0.0000 0.999 1.000 0.000
#> SRR934341 1 0.0000 0.999 1.000 0.000
#> SRR934342 1 0.0000 0.999 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 3 0.470 0.825 0.212 0 0.788
#> SRR934217 3 0.470 0.825 0.212 0 0.788
#> SRR934218 3 0.470 0.825 0.212 0 0.788
#> SRR934219 3 0.470 0.825 0.212 0 0.788
#> SRR934220 3 0.470 0.825 0.212 0 0.788
#> SRR934221 3 0.470 0.825 0.212 0 0.788
#> SRR934222 3 0.470 0.825 0.212 0 0.788
#> SRR934223 3 0.470 0.825 0.212 0 0.788
#> SRR934224 1 0.606 0.354 0.616 0 0.384
#> SRR934225 1 0.606 0.354 0.616 0 0.384
#> SRR934226 1 0.606 0.354 0.616 0 0.384
#> SRR934227 1 0.606 0.354 0.616 0 0.384
#> SRR934228 1 0.606 0.354 0.616 0 0.384
#> SRR934229 1 0.606 0.354 0.616 0 0.384
#> SRR934230 1 0.606 0.354 0.616 0 0.384
#> SRR934231 1 0.606 0.354 0.616 0 0.384
#> SRR934232 2 0.000 1.000 0.000 1 0.000
#> SRR934233 2 0.000 1.000 0.000 1 0.000
#> SRR934234 2 0.000 1.000 0.000 1 0.000
#> SRR934235 2 0.000 1.000 0.000 1 0.000
#> SRR934236 2 0.000 1.000 0.000 1 0.000
#> SRR934237 2 0.000 1.000 0.000 1 0.000
#> SRR934238 2 0.000 1.000 0.000 1 0.000
#> SRR934239 2 0.000 1.000 0.000 1 0.000
#> SRR934240 2 0.000 1.000 0.000 1 0.000
#> SRR934241 2 0.000 1.000 0.000 1 0.000
#> SRR934242 2 0.000 1.000 0.000 1 0.000
#> SRR934243 2 0.000 1.000 0.000 1 0.000
#> SRR934244 2 0.000 1.000 0.000 1 0.000
#> SRR934245 2 0.000 1.000 0.000 1 0.000
#> SRR934246 2 0.000 1.000 0.000 1 0.000
#> SRR934247 2 0.000 1.000 0.000 1 0.000
#> SRR934248 1 0.000 0.920 1.000 0 0.000
#> SRR934249 1 0.000 0.920 1.000 0 0.000
#> SRR934250 1 0.000 0.920 1.000 0 0.000
#> SRR934251 1 0.000 0.920 1.000 0 0.000
#> SRR934252 1 0.000 0.920 1.000 0 0.000
#> SRR934253 1 0.000 0.920 1.000 0 0.000
#> SRR934254 1 0.000 0.920 1.000 0 0.000
#> SRR934255 1 0.000 0.920 1.000 0 0.000
#> SRR934256 2 0.000 1.000 0.000 1 0.000
#> SRR934257 2 0.000 1.000 0.000 1 0.000
#> SRR934258 2 0.000 1.000 0.000 1 0.000
#> SRR934259 2 0.000 1.000 0.000 1 0.000
#> SRR934260 2 0.000 1.000 0.000 1 0.000
#> SRR934261 2 0.000 1.000 0.000 1 0.000
#> SRR934262 2 0.000 1.000 0.000 1 0.000
#> SRR934263 2 0.000 1.000 0.000 1 0.000
#> SRR934264 1 0.606 0.354 0.616 0 0.384
#> SRR934265 1 0.606 0.354 0.616 0 0.384
#> SRR934266 1 0.606 0.354 0.616 0 0.384
#> SRR934267 1 0.606 0.354 0.616 0 0.384
#> SRR934268 1 0.606 0.354 0.616 0 0.384
#> SRR934269 1 0.606 0.354 0.616 0 0.384
#> SRR934270 1 0.606 0.354 0.616 0 0.384
#> SRR934271 1 0.606 0.354 0.616 0 0.384
#> SRR934272 1 0.000 0.920 1.000 0 0.000
#> SRR934273 1 0.000 0.920 1.000 0 0.000
#> SRR934274 1 0.000 0.920 1.000 0 0.000
#> SRR934275 1 0.000 0.920 1.000 0 0.000
#> SRR934276 1 0.000 0.920 1.000 0 0.000
#> SRR934277 1 0.000 0.920 1.000 0 0.000
#> SRR934278 1 0.000 0.920 1.000 0 0.000
#> SRR934279 1 0.000 0.920 1.000 0 0.000
#> SRR934280 1 0.000 0.920 1.000 0 0.000
#> SRR934281 1 0.000 0.920 1.000 0 0.000
#> SRR934282 1 0.000 0.920 1.000 0 0.000
#> SRR934283 1 0.000 0.920 1.000 0 0.000
#> SRR934284 1 0.000 0.920 1.000 0 0.000
#> SRR934285 1 0.000 0.920 1.000 0 0.000
#> SRR934286 1 0.000 0.920 1.000 0 0.000
#> SRR934287 1 0.000 0.920 1.000 0 0.000
#> SRR934288 1 0.000 0.920 1.000 0 0.000
#> SRR934289 1 0.000 0.920 1.000 0 0.000
#> SRR934290 1 0.000 0.920 1.000 0 0.000
#> SRR934291 1 0.000 0.920 1.000 0 0.000
#> SRR934292 1 0.000 0.920 1.000 0 0.000
#> SRR934293 1 0.000 0.920 1.000 0 0.000
#> SRR934294 1 0.000 0.920 1.000 0 0.000
#> SRR934295 1 0.000 0.920 1.000 0 0.000
#> SRR934296 1 0.000 0.920 1.000 0 0.000
#> SRR934297 1 0.000 0.920 1.000 0 0.000
#> SRR934298 1 0.000 0.920 1.000 0 0.000
#> SRR934299 1 0.000 0.920 1.000 0 0.000
#> SRR934300 1 0.000 0.920 1.000 0 0.000
#> SRR934301 1 0.000 0.920 1.000 0 0.000
#> SRR934302 1 0.000 0.920 1.000 0 0.000
#> SRR934303 1 0.000 0.920 1.000 0 0.000
#> SRR934304 3 0.000 0.810 0.000 0 1.000
#> SRR934305 3 0.000 0.810 0.000 0 1.000
#> SRR934306 3 0.000 0.810 0.000 0 1.000
#> SRR934307 3 0.000 0.810 0.000 0 1.000
#> SRR934308 3 0.000 0.810 0.000 0 1.000
#> SRR934309 3 0.000 0.810 0.000 0 1.000
#> SRR934310 3 0.000 0.810 0.000 0 1.000
#> SRR934311 3 0.000 0.810 0.000 0 1.000
#> SRR934312 1 0.000 0.920 1.000 0 0.000
#> SRR934313 1 0.000 0.920 1.000 0 0.000
#> SRR934314 1 0.000 0.920 1.000 0 0.000
#> SRR934315 1 0.000 0.920 1.000 0 0.000
#> SRR934316 1 0.000 0.920 1.000 0 0.000
#> SRR934317 1 0.000 0.920 1.000 0 0.000
#> SRR934318 1 0.000 0.920 1.000 0 0.000
#> SRR934319 1 0.000 0.920 1.000 0 0.000
#> SRR934320 1 0.000 0.920 1.000 0 0.000
#> SRR934321 1 0.000 0.920 1.000 0 0.000
#> SRR934322 1 0.000 0.920 1.000 0 0.000
#> SRR934323 1 0.000 0.920 1.000 0 0.000
#> SRR934324 1 0.000 0.920 1.000 0 0.000
#> SRR934325 1 0.000 0.920 1.000 0 0.000
#> SRR934326 1 0.000 0.920 1.000 0 0.000
#> SRR934327 1 0.000 0.920 1.000 0 0.000
#> SRR934328 1 0.000 0.920 1.000 0 0.000
#> SRR934329 1 0.000 0.920 1.000 0 0.000
#> SRR934330 1 0.000 0.920 1.000 0 0.000
#> SRR934331 1 0.000 0.920 1.000 0 0.000
#> SRR934332 1 0.000 0.920 1.000 0 0.000
#> SRR934333 1 0.000 0.920 1.000 0 0.000
#> SRR934334 1 0.000 0.920 1.000 0 0.000
#> SRR934335 1 0.000 0.920 1.000 0 0.000
#> SRR934344 1 0.000 0.920 1.000 0 0.000
#> SRR934345 1 0.000 0.920 1.000 0 0.000
#> SRR934346 1 0.000 0.920 1.000 0 0.000
#> SRR934347 1 0.000 0.920 1.000 0 0.000
#> SRR934348 1 0.000 0.920 1.000 0 0.000
#> SRR934349 1 0.000 0.920 1.000 0 0.000
#> SRR934350 1 0.000 0.920 1.000 0 0.000
#> SRR934351 1 0.000 0.920 1.000 0 0.000
#> SRR934336 1 0.000 0.920 1.000 0 0.000
#> SRR934337 1 0.000 0.920 1.000 0 0.000
#> SRR934338 1 0.000 0.920 1.000 0 0.000
#> SRR934339 1 0.000 0.920 1.000 0 0.000
#> SRR934340 1 0.000 0.920 1.000 0 0.000
#> SRR934341 1 0.000 0.920 1.000 0 0.000
#> SRR934342 1 0.000 0.920 1.000 0 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 4 0.361 0.401 0.000 0 0.2 0.800
#> SRR934217 4 0.361 0.401 0.000 0 0.2 0.800
#> SRR934218 4 0.361 0.401 0.000 0 0.2 0.800
#> SRR934219 4 0.361 0.401 0.000 0 0.2 0.800
#> SRR934220 4 0.361 0.401 0.000 0 0.2 0.800
#> SRR934221 4 0.361 0.401 0.000 0 0.2 0.800
#> SRR934222 4 0.361 0.401 0.000 0 0.2 0.800
#> SRR934223 4 0.361 0.401 0.000 0 0.2 0.800
#> SRR934224 4 0.365 0.718 0.204 0 0.0 0.796
#> SRR934225 4 0.365 0.718 0.204 0 0.0 0.796
#> SRR934226 4 0.365 0.718 0.204 0 0.0 0.796
#> SRR934227 4 0.365 0.718 0.204 0 0.0 0.796
#> SRR934228 4 0.365 0.718 0.204 0 0.0 0.796
#> SRR934229 4 0.365 0.718 0.204 0 0.0 0.796
#> SRR934230 4 0.365 0.718 0.204 0 0.0 0.796
#> SRR934231 4 0.365 0.718 0.204 0 0.0 0.796
#> SRR934232 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934233 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934234 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934235 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934236 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934237 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934238 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934239 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934240 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934241 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934242 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934243 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934244 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934245 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934246 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934247 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934248 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934249 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934250 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934251 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934252 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934253 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934254 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934255 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934256 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934257 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934258 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934259 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934260 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934261 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934262 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934263 2 0.000 1.000 0.000 1 0.0 0.000
#> SRR934264 4 0.464 0.674 0.344 0 0.0 0.656
#> SRR934265 4 0.464 0.674 0.344 0 0.0 0.656
#> SRR934266 4 0.464 0.674 0.344 0 0.0 0.656
#> SRR934267 4 0.464 0.674 0.344 0 0.0 0.656
#> SRR934268 4 0.464 0.674 0.344 0 0.0 0.656
#> SRR934269 4 0.464 0.674 0.344 0 0.0 0.656
#> SRR934270 4 0.464 0.674 0.344 0 0.0 0.656
#> SRR934271 4 0.464 0.674 0.344 0 0.0 0.656
#> SRR934272 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934273 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934274 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934275 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934276 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934277 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934278 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934279 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934280 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934281 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934282 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934283 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934284 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934285 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934286 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934287 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934288 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934289 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934290 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934291 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934292 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934293 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934294 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934295 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934296 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934297 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934298 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934299 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934300 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934301 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934302 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934303 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934304 3 0.000 1.000 0.000 0 1.0 0.000
#> SRR934305 3 0.000 1.000 0.000 0 1.0 0.000
#> SRR934306 3 0.000 1.000 0.000 0 1.0 0.000
#> SRR934307 3 0.000 1.000 0.000 0 1.0 0.000
#> SRR934308 3 0.000 1.000 0.000 0 1.0 0.000
#> SRR934309 3 0.000 1.000 0.000 0 1.0 0.000
#> SRR934310 3 0.000 1.000 0.000 0 1.0 0.000
#> SRR934311 3 0.000 1.000 0.000 0 1.0 0.000
#> SRR934312 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934313 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934314 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934315 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934316 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934317 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934318 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934319 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934320 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934321 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934322 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934323 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934324 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934325 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934326 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934327 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934328 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934329 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934330 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934331 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934332 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934333 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934334 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934335 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934344 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934345 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934346 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934347 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934348 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934349 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934350 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934351 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934336 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934337 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934338 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934339 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934340 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934341 1 0.000 1.000 1.000 0 0.0 0.000
#> SRR934342 1 0.000 1.000 1.000 0 0.0 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 4 0.603 0.186 0.00 0.224 0.000 0.58 0.196
#> SRR934217 4 0.603 0.186 0.00 0.224 0.000 0.58 0.196
#> SRR934218 4 0.603 0.186 0.00 0.224 0.000 0.58 0.196
#> SRR934219 4 0.603 0.186 0.00 0.224 0.000 0.58 0.196
#> SRR934220 4 0.603 0.186 0.00 0.224 0.000 0.58 0.196
#> SRR934221 4 0.603 0.186 0.00 0.224 0.000 0.58 0.196
#> SRR934222 4 0.603 0.186 0.00 0.224 0.000 0.58 0.196
#> SRR934223 4 0.603 0.186 0.00 0.224 0.000 0.58 0.196
#> SRR934224 4 0.311 0.656 0.20 0.000 0.000 0.80 0.000
#> SRR934225 4 0.311 0.656 0.20 0.000 0.000 0.80 0.000
#> SRR934226 4 0.311 0.656 0.20 0.000 0.000 0.80 0.000
#> SRR934227 4 0.311 0.656 0.20 0.000 0.000 0.80 0.000
#> SRR934228 4 0.311 0.656 0.20 0.000 0.000 0.80 0.000
#> SRR934229 4 0.311 0.656 0.20 0.000 0.000 0.80 0.000
#> SRR934230 4 0.311 0.656 0.20 0.000 0.000 0.80 0.000
#> SRR934231 4 0.311 0.656 0.20 0.000 0.000 0.80 0.000
#> SRR934232 2 0.331 0.844 0.00 0.776 0.224 0.00 0.000
#> SRR934233 2 0.331 0.844 0.00 0.776 0.224 0.00 0.000
#> SRR934234 2 0.331 0.844 0.00 0.776 0.224 0.00 0.000
#> SRR934235 2 0.331 0.844 0.00 0.776 0.224 0.00 0.000
#> SRR934236 2 0.331 0.844 0.00 0.776 0.224 0.00 0.000
#> SRR934237 2 0.331 0.844 0.00 0.776 0.224 0.00 0.000
#> SRR934238 2 0.331 0.844 0.00 0.776 0.224 0.00 0.000
#> SRR934239 2 0.331 0.844 0.00 0.776 0.224 0.00 0.000
#> SRR934240 2 0.423 0.820 0.00 0.580 0.420 0.00 0.000
#> SRR934241 2 0.423 0.820 0.00 0.580 0.420 0.00 0.000
#> SRR934242 2 0.423 0.820 0.00 0.580 0.420 0.00 0.000
#> SRR934243 2 0.423 0.820 0.00 0.580 0.420 0.00 0.000
#> SRR934244 2 0.423 0.820 0.00 0.580 0.420 0.00 0.000
#> SRR934245 2 0.423 0.820 0.00 0.580 0.420 0.00 0.000
#> SRR934246 2 0.423 0.820 0.00 0.580 0.420 0.00 0.000
#> SRR934247 2 0.423 0.820 0.00 0.580 0.420 0.00 0.000
#> SRR934248 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934249 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934250 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934251 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934252 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934253 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934254 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934255 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934256 3 0.000 1.000 0.00 0.000 1.000 0.00 0.000
#> SRR934257 3 0.000 1.000 0.00 0.000 1.000 0.00 0.000
#> SRR934258 3 0.000 1.000 0.00 0.000 1.000 0.00 0.000
#> SRR934259 3 0.000 1.000 0.00 0.000 1.000 0.00 0.000
#> SRR934260 3 0.000 1.000 0.00 0.000 1.000 0.00 0.000
#> SRR934261 3 0.000 1.000 0.00 0.000 1.000 0.00 0.000
#> SRR934262 3 0.000 1.000 0.00 0.000 1.000 0.00 0.000
#> SRR934263 3 0.000 1.000 0.00 0.000 1.000 0.00 0.000
#> SRR934264 4 0.398 0.629 0.34 0.000 0.000 0.66 0.000
#> SRR934265 4 0.398 0.629 0.34 0.000 0.000 0.66 0.000
#> SRR934266 4 0.398 0.629 0.34 0.000 0.000 0.66 0.000
#> SRR934267 4 0.398 0.629 0.34 0.000 0.000 0.66 0.000
#> SRR934268 4 0.398 0.629 0.34 0.000 0.000 0.66 0.000
#> SRR934269 4 0.398 0.629 0.34 0.000 0.000 0.66 0.000
#> SRR934270 4 0.398 0.629 0.34 0.000 0.000 0.66 0.000
#> SRR934271 4 0.398 0.629 0.34 0.000 0.000 0.66 0.000
#> SRR934272 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934273 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934274 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934275 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934276 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934277 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934278 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934279 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934280 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934281 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934282 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934283 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934284 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934285 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934286 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934287 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934288 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934289 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934290 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934291 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934292 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934293 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934294 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934295 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934296 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934297 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934298 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934299 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934300 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934301 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934302 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934303 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934304 5 0.000 1.000 0.00 0.000 0.000 0.00 1.000
#> SRR934305 5 0.000 1.000 0.00 0.000 0.000 0.00 1.000
#> SRR934306 5 0.000 1.000 0.00 0.000 0.000 0.00 1.000
#> SRR934307 5 0.000 1.000 0.00 0.000 0.000 0.00 1.000
#> SRR934308 5 0.000 1.000 0.00 0.000 0.000 0.00 1.000
#> SRR934309 5 0.000 1.000 0.00 0.000 0.000 0.00 1.000
#> SRR934310 5 0.000 1.000 0.00 0.000 0.000 0.00 1.000
#> SRR934311 5 0.000 1.000 0.00 0.000 0.000 0.00 1.000
#> SRR934312 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934313 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934314 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934315 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934316 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934317 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934318 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934319 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934320 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934321 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934322 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934323 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934324 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934325 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934326 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934327 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934328 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934329 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934330 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934331 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934332 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934333 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934334 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934335 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934344 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934345 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934346 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934347 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934348 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934349 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934350 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934351 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934336 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934337 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934338 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934339 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934340 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934341 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
#> SRR934342 1 0.000 1.000 1.00 0.000 0.000 0.00 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.0000 1.000 0.000 0.0 1 0.0 0 0.000
#> SRR934217 3 0.0000 1.000 0.000 0.0 1 0.0 0 0.000
#> SRR934218 3 0.0000 1.000 0.000 0.0 1 0.0 0 0.000
#> SRR934219 3 0.0000 1.000 0.000 0.0 1 0.0 0 0.000
#> SRR934220 3 0.0000 1.000 0.000 0.0 1 0.0 0 0.000
#> SRR934221 3 0.0000 1.000 0.000 0.0 1 0.0 0 0.000
#> SRR934222 3 0.0000 1.000 0.000 0.0 1 0.0 0 0.000
#> SRR934223 3 0.0000 1.000 0.000 0.0 1 0.0 0 0.000
#> SRR934224 6 0.0000 0.683 0.000 0.0 0 0.0 0 1.000
#> SRR934225 6 0.0000 0.683 0.000 0.0 0 0.0 0 1.000
#> SRR934226 6 0.0000 0.683 0.000 0.0 0 0.0 0 1.000
#> SRR934227 6 0.0000 0.683 0.000 0.0 0 0.0 0 1.000
#> SRR934228 6 0.0000 0.683 0.000 0.0 0 0.0 0 1.000
#> SRR934229 6 0.0000 0.683 0.000 0.0 0 0.0 0 1.000
#> SRR934230 6 0.0000 0.683 0.000 0.0 0 0.0 0 1.000
#> SRR934231 6 0.0000 0.683 0.000 0.0 0 0.0 0 1.000
#> SRR934232 2 0.0000 0.880 0.000 1.0 0 0.0 0 0.000
#> SRR934233 2 0.0000 0.880 0.000 1.0 0 0.0 0 0.000
#> SRR934234 2 0.0000 0.880 0.000 1.0 0 0.0 0 0.000
#> SRR934235 2 0.0000 0.880 0.000 1.0 0 0.0 0 0.000
#> SRR934236 2 0.0000 0.880 0.000 1.0 0 0.0 0 0.000
#> SRR934237 2 0.0000 0.880 0.000 1.0 0 0.0 0 0.000
#> SRR934238 2 0.0000 0.880 0.000 1.0 0 0.0 0 0.000
#> SRR934239 2 0.0000 0.880 0.000 1.0 0 0.0 0 0.000
#> SRR934240 2 0.2793 0.867 0.000 0.8 0 0.2 0 0.000
#> SRR934241 2 0.2793 0.867 0.000 0.8 0 0.2 0 0.000
#> SRR934242 2 0.2793 0.867 0.000 0.8 0 0.2 0 0.000
#> SRR934243 2 0.2793 0.867 0.000 0.8 0 0.2 0 0.000
#> SRR934244 2 0.2793 0.867 0.000 0.8 0 0.2 0 0.000
#> SRR934245 2 0.2793 0.867 0.000 0.8 0 0.2 0 0.000
#> SRR934246 2 0.2793 0.867 0.000 0.8 0 0.2 0 0.000
#> SRR934247 2 0.2793 0.867 0.000 0.8 0 0.2 0 0.000
#> SRR934248 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934249 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934250 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934251 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934252 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934253 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934254 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934255 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934256 4 0.0000 1.000 0.000 0.0 0 1.0 0 0.000
#> SRR934257 4 0.0000 1.000 0.000 0.0 0 1.0 0 0.000
#> SRR934258 4 0.0000 1.000 0.000 0.0 0 1.0 0 0.000
#> SRR934259 4 0.0000 1.000 0.000 0.0 0 1.0 0 0.000
#> SRR934260 4 0.0000 1.000 0.000 0.0 0 1.0 0 0.000
#> SRR934261 4 0.0000 1.000 0.000 0.0 0 1.0 0 0.000
#> SRR934262 4 0.0000 1.000 0.000 0.0 0 1.0 0 0.000
#> SRR934263 4 0.0000 1.000 0.000 0.0 0 1.0 0 0.000
#> SRR934264 6 0.3409 0.723 0.300 0.0 0 0.0 0 0.700
#> SRR934265 6 0.3409 0.723 0.300 0.0 0 0.0 0 0.700
#> SRR934266 6 0.3409 0.723 0.300 0.0 0 0.0 0 0.700
#> SRR934267 6 0.3409 0.723 0.300 0.0 0 0.0 0 0.700
#> SRR934268 6 0.3409 0.723 0.300 0.0 0 0.0 0 0.700
#> SRR934269 6 0.3409 0.723 0.300 0.0 0 0.0 0 0.700
#> SRR934270 6 0.3409 0.723 0.300 0.0 0 0.0 0 0.700
#> SRR934271 6 0.3409 0.723 0.300 0.0 0 0.0 0 0.700
#> SRR934272 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934273 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934274 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934275 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934276 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934277 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934278 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934279 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934280 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934281 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934282 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934283 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934284 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934285 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934286 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934287 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934288 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934289 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934290 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934291 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934292 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934293 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934294 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934295 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934296 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934297 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934298 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934299 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934300 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934301 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934302 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934303 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934304 5 0.0000 1.000 0.000 0.0 0 0.0 1 0.000
#> SRR934305 5 0.0000 1.000 0.000 0.0 0 0.0 1 0.000
#> SRR934306 5 0.0000 1.000 0.000 0.0 0 0.0 1 0.000
#> SRR934307 5 0.0000 1.000 0.000 0.0 0 0.0 1 0.000
#> SRR934308 5 0.0000 1.000 0.000 0.0 0 0.0 1 0.000
#> SRR934309 5 0.0000 1.000 0.000 0.0 0 0.0 1 0.000
#> SRR934310 5 0.0000 1.000 0.000 0.0 0 0.0 1 0.000
#> SRR934311 5 0.0000 1.000 0.000 0.0 0 0.0 1 0.000
#> SRR934312 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934313 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934314 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934315 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934316 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934317 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934318 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934319 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934320 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934321 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934322 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934323 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934324 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934325 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934326 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934327 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934328 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934329 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934330 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934331 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934332 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934333 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934334 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934335 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934344 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934345 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934346 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934347 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934348 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934349 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934350 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934351 1 0.0000 0.978 1.000 0.0 0 0.0 0 0.000
#> SRR934336 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934337 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934338 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934339 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934340 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934341 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
#> SRR934342 1 0.0865 0.977 0.964 0.0 0 0.0 0 0.036
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 14550 rows and 135 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.440 0.830 0.861 0.3036 0.705 0.705
#> 3 3 0.692 0.909 0.931 0.5538 0.832 0.762
#> 4 4 0.535 0.761 0.799 0.2931 1.000 1.000
#> 5 5 0.514 0.512 0.632 0.1536 0.812 0.649
#> 6 6 0.536 0.595 0.668 0.0809 0.778 0.442
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
#> SRR934216 1 0.0672 0.562 0.992 0.008
#> SRR934217 1 0.0672 0.562 0.992 0.008
#> SRR934218 1 0.0672 0.562 0.992 0.008
#> SRR934219 1 0.0672 0.562 0.992 0.008
#> SRR934220 1 0.0672 0.562 0.992 0.008
#> SRR934221 1 0.0672 0.562 0.992 0.008
#> SRR934222 1 0.0672 0.562 0.992 0.008
#> SRR934223 1 0.0672 0.562 0.992 0.008
#> SRR934224 1 0.6973 0.761 0.812 0.188
#> SRR934225 1 0.6973 0.761 0.812 0.188
#> SRR934226 1 0.6973 0.761 0.812 0.188
#> SRR934227 1 0.6973 0.761 0.812 0.188
#> SRR934228 1 0.6973 0.761 0.812 0.188
#> SRR934229 1 0.6973 0.761 0.812 0.188
#> SRR934230 1 0.6973 0.761 0.812 0.188
#> SRR934231 1 0.6973 0.761 0.812 0.188
#> SRR934232 2 0.0672 0.997 0.008 0.992
#> SRR934233 2 0.0672 0.997 0.008 0.992
#> SRR934234 2 0.0672 0.997 0.008 0.992
#> SRR934235 2 0.0672 0.997 0.008 0.992
#> SRR934236 2 0.0672 0.997 0.008 0.992
#> SRR934237 2 0.0672 0.997 0.008 0.992
#> SRR934238 2 0.0672 0.997 0.008 0.992
#> SRR934239 2 0.0672 0.997 0.008 0.992
#> SRR934240 2 0.0672 0.997 0.008 0.992
#> SRR934241 2 0.0672 0.997 0.008 0.992
#> SRR934242 2 0.0672 0.997 0.008 0.992
#> SRR934243 2 0.0672 0.997 0.008 0.992
#> SRR934244 2 0.0672 0.997 0.008 0.992
#> SRR934245 2 0.0672 0.997 0.008 0.992
#> SRR934246 2 0.0672 0.997 0.008 0.992
#> SRR934247 2 0.0672 0.997 0.008 0.992
#> SRR934248 1 0.9710 0.828 0.600 0.400
#> SRR934249 1 0.9710 0.828 0.600 0.400
#> SRR934250 1 0.9710 0.828 0.600 0.400
#> SRR934251 1 0.9710 0.828 0.600 0.400
#> SRR934252 1 0.9710 0.828 0.600 0.400
#> SRR934253 1 0.9710 0.828 0.600 0.400
#> SRR934254 1 0.9710 0.828 0.600 0.400
#> SRR934255 1 0.9710 0.828 0.600 0.400
#> SRR934256 2 0.0672 0.994 0.008 0.992
#> SRR934257 2 0.0672 0.994 0.008 0.992
#> SRR934258 2 0.0672 0.994 0.008 0.992
#> SRR934259 2 0.0672 0.994 0.008 0.992
#> SRR934260 2 0.0672 0.994 0.008 0.992
#> SRR934261 2 0.0672 0.994 0.008 0.992
#> SRR934262 2 0.0672 0.994 0.008 0.992
#> SRR934263 2 0.0672 0.994 0.008 0.992
#> SRR934264 1 0.6887 0.758 0.816 0.184
#> SRR934265 1 0.6887 0.758 0.816 0.184
#> SRR934266 1 0.6887 0.758 0.816 0.184
#> SRR934267 1 0.6887 0.758 0.816 0.184
#> SRR934268 1 0.6887 0.758 0.816 0.184
#> SRR934269 1 0.6887 0.758 0.816 0.184
#> SRR934270 1 0.6887 0.758 0.816 0.184
#> SRR934271 1 0.6887 0.758 0.816 0.184
#> SRR934272 1 0.9170 0.860 0.668 0.332
#> SRR934273 1 0.9170 0.860 0.668 0.332
#> SRR934274 1 0.9170 0.860 0.668 0.332
#> SRR934275 1 0.9170 0.860 0.668 0.332
#> SRR934276 1 0.9170 0.860 0.668 0.332
#> SRR934277 1 0.9170 0.860 0.668 0.332
#> SRR934278 1 0.9170 0.860 0.668 0.332
#> SRR934279 1 0.9170 0.860 0.668 0.332
#> SRR934280 1 0.9427 0.860 0.640 0.360
#> SRR934281 1 0.9427 0.860 0.640 0.360
#> SRR934282 1 0.9427 0.860 0.640 0.360
#> SRR934283 1 0.9427 0.860 0.640 0.360
#> SRR934284 1 0.9427 0.860 0.640 0.360
#> SRR934285 1 0.9427 0.860 0.640 0.360
#> SRR934286 1 0.9427 0.860 0.640 0.360
#> SRR934287 1 0.9427 0.860 0.640 0.360
#> SRR934288 1 0.9427 0.860 0.640 0.360
#> SRR934289 1 0.9427 0.860 0.640 0.360
#> SRR934290 1 0.9427 0.860 0.640 0.360
#> SRR934291 1 0.9427 0.860 0.640 0.360
#> SRR934292 1 0.9427 0.860 0.640 0.360
#> SRR934293 1 0.9427 0.860 0.640 0.360
#> SRR934294 1 0.9427 0.860 0.640 0.360
#> SRR934295 1 0.9427 0.860 0.640 0.360
#> SRR934296 1 0.9635 0.841 0.612 0.388
#> SRR934297 1 0.9635 0.841 0.612 0.388
#> SRR934298 1 0.9635 0.841 0.612 0.388
#> SRR934299 1 0.9635 0.841 0.612 0.388
#> SRR934300 1 0.9635 0.841 0.612 0.388
#> SRR934301 1 0.9635 0.841 0.612 0.388
#> SRR934302 1 0.9635 0.841 0.612 0.388
#> SRR934303 1 0.9635 0.841 0.612 0.388
#> SRR934304 1 0.3584 0.498 0.932 0.068
#> SRR934305 1 0.3584 0.498 0.932 0.068
#> SRR934306 1 0.3584 0.498 0.932 0.068
#> SRR934307 1 0.3584 0.498 0.932 0.068
#> SRR934308 1 0.3584 0.498 0.932 0.068
#> SRR934309 1 0.3584 0.498 0.932 0.068
#> SRR934310 1 0.3584 0.498 0.932 0.068
#> SRR934311 1 0.3584 0.498 0.932 0.068
#> SRR934312 1 0.9427 0.860 0.640 0.360
#> SRR934313 1 0.9427 0.860 0.640 0.360
#> SRR934314 1 0.9427 0.860 0.640 0.360
#> SRR934315 1 0.9427 0.860 0.640 0.360
#> SRR934316 1 0.9427 0.860 0.640 0.360
#> SRR934317 1 0.9427 0.860 0.640 0.360
#> SRR934318 1 0.9427 0.860 0.640 0.360
#> SRR934319 1 0.9427 0.860 0.640 0.360
#> SRR934320 1 0.9460 0.860 0.636 0.364
#> SRR934321 1 0.9460 0.860 0.636 0.364
#> SRR934322 1 0.9460 0.860 0.636 0.364
#> SRR934323 1 0.9460 0.860 0.636 0.364
#> SRR934324 1 0.9460 0.860 0.636 0.364
#> SRR934325 1 0.9460 0.860 0.636 0.364
#> SRR934326 1 0.9460 0.860 0.636 0.364
#> SRR934327 1 0.9460 0.860 0.636 0.364
#> SRR934328 1 0.9427 0.860 0.640 0.360
#> SRR934329 1 0.9427 0.860 0.640 0.360
#> SRR934330 1 0.9427 0.860 0.640 0.360
#> SRR934331 1 0.9427 0.860 0.640 0.360
#> SRR934332 1 0.9427 0.860 0.640 0.360
#> SRR934333 1 0.9427 0.860 0.640 0.360
#> SRR934334 1 0.9427 0.860 0.640 0.360
#> SRR934335 1 0.9427 0.860 0.640 0.360
#> SRR934344 1 0.9170 0.860 0.668 0.332
#> SRR934345 1 0.9170 0.860 0.668 0.332
#> SRR934346 1 0.9170 0.860 0.668 0.332
#> SRR934347 1 0.9170 0.860 0.668 0.332
#> SRR934348 1 0.9170 0.860 0.668 0.332
#> SRR934349 1 0.9170 0.860 0.668 0.332
#> SRR934350 1 0.9170 0.860 0.668 0.332
#> SRR934351 1 0.9170 0.860 0.668 0.332
#> SRR934336 1 0.9170 0.860 0.668 0.332
#> SRR934337 1 0.9170 0.860 0.668 0.332
#> SRR934338 1 0.9170 0.860 0.668 0.332
#> SRR934339 1 0.9170 0.860 0.668 0.332
#> SRR934340 1 0.9170 0.860 0.668 0.332
#> SRR934341 1 0.9170 0.860 0.668 0.332
#> SRR934342 1 0.9170 0.860 0.668 0.332
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 3 0.5267 0.923 0.140 0.044 0.816
#> SRR934217 3 0.5267 0.923 0.140 0.044 0.816
#> SRR934218 3 0.5267 0.923 0.140 0.044 0.816
#> SRR934219 3 0.5267 0.923 0.140 0.044 0.816
#> SRR934220 3 0.5267 0.923 0.140 0.044 0.816
#> SRR934221 3 0.5267 0.923 0.140 0.044 0.816
#> SRR934222 3 0.5267 0.923 0.140 0.044 0.816
#> SRR934223 3 0.5267 0.923 0.140 0.044 0.816
#> SRR934224 1 0.5318 0.730 0.780 0.016 0.204
#> SRR934225 1 0.5318 0.730 0.780 0.016 0.204
#> SRR934226 1 0.5318 0.730 0.780 0.016 0.204
#> SRR934227 1 0.5318 0.730 0.780 0.016 0.204
#> SRR934228 1 0.5318 0.730 0.780 0.016 0.204
#> SRR934229 1 0.5318 0.730 0.780 0.016 0.204
#> SRR934230 1 0.5318 0.730 0.780 0.016 0.204
#> SRR934231 1 0.5318 0.730 0.780 0.016 0.204
#> SRR934232 2 0.3692 0.965 0.056 0.896 0.048
#> SRR934233 2 0.3692 0.965 0.056 0.896 0.048
#> SRR934234 2 0.3692 0.965 0.056 0.896 0.048
#> SRR934235 2 0.3692 0.965 0.056 0.896 0.048
#> SRR934236 2 0.3692 0.965 0.056 0.896 0.048
#> SRR934237 2 0.3692 0.965 0.056 0.896 0.048
#> SRR934238 2 0.3692 0.965 0.056 0.896 0.048
#> SRR934239 2 0.3692 0.965 0.056 0.896 0.048
#> SRR934240 2 0.2066 0.977 0.060 0.940 0.000
#> SRR934241 2 0.2066 0.977 0.060 0.940 0.000
#> SRR934242 2 0.2066 0.977 0.060 0.940 0.000
#> SRR934243 2 0.2066 0.977 0.060 0.940 0.000
#> SRR934244 2 0.2066 0.977 0.060 0.940 0.000
#> SRR934245 2 0.2066 0.977 0.060 0.940 0.000
#> SRR934246 2 0.2066 0.977 0.060 0.940 0.000
#> SRR934247 2 0.2066 0.977 0.060 0.940 0.000
#> SRR934248 1 0.3193 0.873 0.896 0.004 0.100
#> SRR934249 1 0.3193 0.873 0.896 0.004 0.100
#> SRR934250 1 0.3193 0.873 0.896 0.004 0.100
#> SRR934251 1 0.3193 0.873 0.896 0.004 0.100
#> SRR934252 1 0.3193 0.873 0.896 0.004 0.100
#> SRR934253 1 0.3193 0.873 0.896 0.004 0.100
#> SRR934254 1 0.3193 0.873 0.896 0.004 0.100
#> SRR934255 1 0.3193 0.873 0.896 0.004 0.100
#> SRR934256 2 0.3213 0.973 0.060 0.912 0.028
#> SRR934257 2 0.3213 0.973 0.060 0.912 0.028
#> SRR934258 2 0.3213 0.973 0.060 0.912 0.028
#> SRR934259 2 0.3213 0.973 0.060 0.912 0.028
#> SRR934260 2 0.3213 0.973 0.060 0.912 0.028
#> SRR934261 2 0.3213 0.973 0.060 0.912 0.028
#> SRR934262 2 0.3213 0.973 0.060 0.912 0.028
#> SRR934263 2 0.3213 0.973 0.060 0.912 0.028
#> SRR934264 1 0.5443 0.669 0.736 0.004 0.260
#> SRR934265 1 0.5443 0.669 0.736 0.004 0.260
#> SRR934266 1 0.5443 0.669 0.736 0.004 0.260
#> SRR934267 1 0.5443 0.669 0.736 0.004 0.260
#> SRR934268 1 0.5443 0.669 0.736 0.004 0.260
#> SRR934269 1 0.5443 0.669 0.736 0.004 0.260
#> SRR934270 1 0.5443 0.669 0.736 0.004 0.260
#> SRR934271 1 0.5443 0.669 0.736 0.004 0.260
#> SRR934272 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934273 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934274 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934275 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934276 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934277 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934278 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934279 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934280 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934281 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934282 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934283 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934284 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934285 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934286 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934287 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934288 1 0.0424 0.937 0.992 0.000 0.008
#> SRR934289 1 0.0424 0.937 0.992 0.000 0.008
#> SRR934290 1 0.0424 0.937 0.992 0.000 0.008
#> SRR934291 1 0.0424 0.937 0.992 0.000 0.008
#> SRR934292 1 0.0424 0.937 0.992 0.000 0.008
#> SRR934293 1 0.0424 0.937 0.992 0.000 0.008
#> SRR934294 1 0.0424 0.937 0.992 0.000 0.008
#> SRR934295 1 0.0424 0.937 0.992 0.000 0.008
#> SRR934296 1 0.1031 0.928 0.976 0.000 0.024
#> SRR934297 1 0.1031 0.928 0.976 0.000 0.024
#> SRR934298 1 0.1031 0.928 0.976 0.000 0.024
#> SRR934299 1 0.1031 0.928 0.976 0.000 0.024
#> SRR934300 1 0.1031 0.928 0.976 0.000 0.024
#> SRR934301 1 0.1031 0.928 0.976 0.000 0.024
#> SRR934302 1 0.1031 0.928 0.976 0.000 0.024
#> SRR934303 1 0.1031 0.928 0.976 0.000 0.024
#> SRR934304 3 0.3045 0.922 0.064 0.020 0.916
#> SRR934305 3 0.3045 0.922 0.064 0.020 0.916
#> SRR934306 3 0.3045 0.922 0.064 0.020 0.916
#> SRR934307 3 0.3045 0.922 0.064 0.020 0.916
#> SRR934308 3 0.3045 0.922 0.064 0.020 0.916
#> SRR934309 3 0.3045 0.922 0.064 0.020 0.916
#> SRR934310 3 0.3045 0.922 0.064 0.020 0.916
#> SRR934311 3 0.3045 0.922 0.064 0.020 0.916
#> SRR934312 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934313 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934314 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934315 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934316 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934317 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934318 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934319 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934320 1 0.0237 0.938 0.996 0.000 0.004
#> SRR934321 1 0.0237 0.938 0.996 0.000 0.004
#> SRR934322 1 0.0237 0.938 0.996 0.000 0.004
#> SRR934323 1 0.0237 0.938 0.996 0.000 0.004
#> SRR934324 1 0.0237 0.938 0.996 0.000 0.004
#> SRR934325 1 0.0237 0.938 0.996 0.000 0.004
#> SRR934326 1 0.0237 0.938 0.996 0.000 0.004
#> SRR934327 1 0.0237 0.938 0.996 0.000 0.004
#> SRR934328 1 0.0592 0.936 0.988 0.000 0.012
#> SRR934329 1 0.0592 0.936 0.988 0.000 0.012
#> SRR934330 1 0.0592 0.936 0.988 0.000 0.012
#> SRR934331 1 0.0592 0.936 0.988 0.000 0.012
#> SRR934332 1 0.0592 0.936 0.988 0.000 0.012
#> SRR934333 1 0.0592 0.936 0.988 0.000 0.012
#> SRR934334 1 0.0592 0.936 0.988 0.000 0.012
#> SRR934335 1 0.0592 0.936 0.988 0.000 0.012
#> SRR934344 1 0.0237 0.938 0.996 0.000 0.004
#> SRR934345 1 0.0237 0.938 0.996 0.000 0.004
#> SRR934346 1 0.0237 0.938 0.996 0.000 0.004
#> SRR934347 1 0.0237 0.938 0.996 0.000 0.004
#> SRR934348 1 0.0237 0.938 0.996 0.000 0.004
#> SRR934349 1 0.0237 0.938 0.996 0.000 0.004
#> SRR934350 1 0.0237 0.938 0.996 0.000 0.004
#> SRR934351 1 0.0237 0.938 0.996 0.000 0.004
#> SRR934336 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934337 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934338 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934339 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934340 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934341 1 0.0000 0.939 1.000 0.000 0.000
#> SRR934342 1 0.0000 0.939 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 3 0.1305 0.894 0.036 0.000 0.960 NA
#> SRR934217 3 0.1305 0.894 0.036 0.000 0.960 NA
#> SRR934218 3 0.1305 0.894 0.036 0.000 0.960 NA
#> SRR934219 3 0.1305 0.894 0.036 0.000 0.960 NA
#> SRR934220 3 0.1305 0.894 0.036 0.000 0.960 NA
#> SRR934221 3 0.1305 0.894 0.036 0.000 0.960 NA
#> SRR934222 3 0.1305 0.894 0.036 0.000 0.960 NA
#> SRR934223 3 0.1305 0.894 0.036 0.000 0.960 NA
#> SRR934224 1 0.7416 0.435 0.516 0.000 0.240 NA
#> SRR934225 1 0.7416 0.435 0.516 0.000 0.240 NA
#> SRR934226 1 0.7416 0.435 0.516 0.000 0.240 NA
#> SRR934227 1 0.7416 0.435 0.516 0.000 0.240 NA
#> SRR934228 1 0.7416 0.435 0.516 0.000 0.240 NA
#> SRR934229 1 0.7416 0.435 0.516 0.000 0.240 NA
#> SRR934230 1 0.7416 0.435 0.516 0.000 0.240 NA
#> SRR934231 1 0.7416 0.435 0.516 0.000 0.240 NA
#> SRR934232 2 0.3326 0.887 0.008 0.856 0.004 NA
#> SRR934233 2 0.3326 0.887 0.008 0.856 0.004 NA
#> SRR934234 2 0.3326 0.887 0.008 0.856 0.004 NA
#> SRR934235 2 0.3326 0.887 0.008 0.856 0.004 NA
#> SRR934236 2 0.3326 0.887 0.008 0.856 0.004 NA
#> SRR934237 2 0.3326 0.887 0.008 0.856 0.004 NA
#> SRR934238 2 0.3326 0.887 0.008 0.856 0.004 NA
#> SRR934239 2 0.3326 0.887 0.008 0.856 0.004 NA
#> SRR934240 2 0.0524 0.923 0.004 0.988 0.000 NA
#> SRR934241 2 0.0524 0.923 0.004 0.988 0.000 NA
#> SRR934242 2 0.0524 0.923 0.004 0.988 0.000 NA
#> SRR934243 2 0.0524 0.923 0.004 0.988 0.000 NA
#> SRR934244 2 0.0524 0.923 0.004 0.988 0.000 NA
#> SRR934245 2 0.0524 0.923 0.004 0.988 0.000 NA
#> SRR934246 2 0.0524 0.923 0.004 0.988 0.000 NA
#> SRR934247 2 0.0524 0.923 0.004 0.988 0.000 NA
#> SRR934248 1 0.6510 0.650 0.628 0.024 0.056 NA
#> SRR934249 1 0.6510 0.650 0.628 0.024 0.056 NA
#> SRR934250 1 0.6510 0.650 0.628 0.024 0.056 NA
#> SRR934251 1 0.6510 0.650 0.628 0.024 0.056 NA
#> SRR934252 1 0.6510 0.650 0.628 0.024 0.056 NA
#> SRR934253 1 0.6510 0.650 0.628 0.024 0.056 NA
#> SRR934254 1 0.6510 0.650 0.628 0.024 0.056 NA
#> SRR934255 1 0.6510 0.650 0.628 0.024 0.056 NA
#> SRR934256 2 0.2530 0.905 0.004 0.896 0.000 NA
#> SRR934257 2 0.2530 0.905 0.004 0.896 0.000 NA
#> SRR934258 2 0.2530 0.905 0.004 0.896 0.000 NA
#> SRR934259 2 0.2530 0.905 0.004 0.896 0.000 NA
#> SRR934260 2 0.2530 0.905 0.004 0.896 0.000 NA
#> SRR934261 2 0.2530 0.905 0.004 0.896 0.000 NA
#> SRR934262 2 0.2530 0.905 0.004 0.896 0.000 NA
#> SRR934263 2 0.2530 0.905 0.004 0.896 0.000 NA
#> SRR934264 1 0.7654 0.408 0.496 0.004 0.228 NA
#> SRR934265 1 0.7654 0.408 0.496 0.004 0.228 NA
#> SRR934266 1 0.7654 0.408 0.496 0.004 0.228 NA
#> SRR934267 1 0.7654 0.408 0.496 0.004 0.228 NA
#> SRR934268 1 0.7654 0.408 0.496 0.004 0.228 NA
#> SRR934269 1 0.7654 0.408 0.496 0.004 0.228 NA
#> SRR934270 1 0.7654 0.408 0.496 0.004 0.228 NA
#> SRR934271 1 0.7654 0.408 0.496 0.004 0.228 NA
#> SRR934272 1 0.2868 0.775 0.864 0.000 0.000 NA
#> SRR934273 1 0.2868 0.775 0.864 0.000 0.000 NA
#> SRR934274 1 0.2868 0.775 0.864 0.000 0.000 NA
#> SRR934275 1 0.2868 0.775 0.864 0.000 0.000 NA
#> SRR934276 1 0.2868 0.775 0.864 0.000 0.000 NA
#> SRR934277 1 0.2868 0.775 0.864 0.000 0.000 NA
#> SRR934278 1 0.2868 0.775 0.864 0.000 0.000 NA
#> SRR934279 1 0.2868 0.775 0.864 0.000 0.000 NA
#> SRR934280 1 0.1302 0.790 0.956 0.000 0.000 NA
#> SRR934281 1 0.1302 0.790 0.956 0.000 0.000 NA
#> SRR934282 1 0.1302 0.790 0.956 0.000 0.000 NA
#> SRR934283 1 0.1302 0.790 0.956 0.000 0.000 NA
#> SRR934284 1 0.1302 0.790 0.956 0.000 0.000 NA
#> SRR934285 1 0.1302 0.790 0.956 0.000 0.000 NA
#> SRR934286 1 0.1302 0.790 0.956 0.000 0.000 NA
#> SRR934287 1 0.1302 0.790 0.956 0.000 0.000 NA
#> SRR934288 1 0.4040 0.738 0.752 0.000 0.000 NA
#> SRR934289 1 0.4040 0.738 0.752 0.000 0.000 NA
#> SRR934290 1 0.4040 0.738 0.752 0.000 0.000 NA
#> SRR934291 1 0.4040 0.738 0.752 0.000 0.000 NA
#> SRR934292 1 0.4040 0.738 0.752 0.000 0.000 NA
#> SRR934293 1 0.4040 0.738 0.752 0.000 0.000 NA
#> SRR934294 1 0.4040 0.738 0.752 0.000 0.000 NA
#> SRR934295 1 0.4040 0.738 0.752 0.000 0.000 NA
#> SRR934296 1 0.4313 0.724 0.736 0.000 0.004 NA
#> SRR934297 1 0.4313 0.724 0.736 0.000 0.004 NA
#> SRR934298 1 0.4313 0.724 0.736 0.000 0.004 NA
#> SRR934299 1 0.4313 0.724 0.736 0.000 0.004 NA
#> SRR934300 1 0.4313 0.724 0.736 0.000 0.004 NA
#> SRR934301 1 0.4313 0.724 0.736 0.000 0.004 NA
#> SRR934302 1 0.4313 0.724 0.736 0.000 0.004 NA
#> SRR934303 1 0.4313 0.724 0.736 0.000 0.004 NA
#> SRR934304 3 0.3933 0.892 0.000 0.008 0.792 NA
#> SRR934305 3 0.3933 0.892 0.000 0.008 0.792 NA
#> SRR934306 3 0.3933 0.892 0.000 0.008 0.792 NA
#> SRR934307 3 0.3933 0.892 0.000 0.008 0.792 NA
#> SRR934308 3 0.3933 0.892 0.000 0.008 0.792 NA
#> SRR934309 3 0.3933 0.892 0.000 0.008 0.792 NA
#> SRR934310 3 0.3933 0.892 0.000 0.008 0.792 NA
#> SRR934311 3 0.3933 0.892 0.000 0.008 0.792 NA
#> SRR934312 1 0.0188 0.794 0.996 0.000 0.000 NA
#> SRR934313 1 0.0188 0.794 0.996 0.000 0.000 NA
#> SRR934314 1 0.0188 0.794 0.996 0.000 0.000 NA
#> SRR934315 1 0.0188 0.794 0.996 0.000 0.000 NA
#> SRR934316 1 0.0188 0.794 0.996 0.000 0.000 NA
#> SRR934317 1 0.0188 0.794 0.996 0.000 0.000 NA
#> SRR934318 1 0.0188 0.794 0.996 0.000 0.000 NA
#> SRR934319 1 0.0188 0.794 0.996 0.000 0.000 NA
#> SRR934320 1 0.1716 0.792 0.936 0.000 0.000 NA
#> SRR934321 1 0.1716 0.792 0.936 0.000 0.000 NA
#> SRR934322 1 0.1716 0.792 0.936 0.000 0.000 NA
#> SRR934323 1 0.1716 0.792 0.936 0.000 0.000 NA
#> SRR934324 1 0.1716 0.792 0.936 0.000 0.000 NA
#> SRR934325 1 0.1716 0.792 0.936 0.000 0.000 NA
#> SRR934326 1 0.1716 0.792 0.936 0.000 0.000 NA
#> SRR934327 1 0.1716 0.792 0.936 0.000 0.000 NA
#> SRR934328 1 0.4072 0.762 0.748 0.000 0.000 NA
#> SRR934329 1 0.4072 0.762 0.748 0.000 0.000 NA
#> SRR934330 1 0.4072 0.762 0.748 0.000 0.000 NA
#> SRR934331 1 0.4072 0.762 0.748 0.000 0.000 NA
#> SRR934332 1 0.4072 0.762 0.748 0.000 0.000 NA
#> SRR934333 1 0.4072 0.762 0.748 0.000 0.000 NA
#> SRR934334 1 0.4072 0.762 0.748 0.000 0.000 NA
#> SRR934335 1 0.4072 0.762 0.748 0.000 0.000 NA
#> SRR934344 1 0.3688 0.783 0.792 0.000 0.000 NA
#> SRR934345 1 0.3688 0.783 0.792 0.000 0.000 NA
#> SRR934346 1 0.3688 0.783 0.792 0.000 0.000 NA
#> SRR934347 1 0.3688 0.783 0.792 0.000 0.000 NA
#> SRR934348 1 0.3688 0.783 0.792 0.000 0.000 NA
#> SRR934349 1 0.3688 0.783 0.792 0.000 0.000 NA
#> SRR934350 1 0.3688 0.783 0.792 0.000 0.000 NA
#> SRR934351 1 0.3688 0.783 0.792 0.000 0.000 NA
#> SRR934336 1 0.2345 0.782 0.900 0.000 0.000 NA
#> SRR934337 1 0.2345 0.782 0.900 0.000 0.000 NA
#> SRR934338 1 0.2345 0.782 0.900 0.000 0.000 NA
#> SRR934339 1 0.2345 0.782 0.900 0.000 0.000 NA
#> SRR934340 1 0.2345 0.782 0.900 0.000 0.000 NA
#> SRR934341 1 0.2345 0.782 0.900 0.000 0.000 NA
#> SRR934342 1 0.2345 0.782 0.900 0.000 0.000 NA
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0.0880 0.842 0.032 0.000 0.968 NA 0.000
#> SRR934217 3 0.0880 0.842 0.032 0.000 0.968 NA 0.000
#> SRR934218 3 0.1041 0.841 0.032 0.000 0.964 NA 0.004
#> SRR934219 3 0.0880 0.842 0.032 0.000 0.968 NA 0.000
#> SRR934220 3 0.0880 0.842 0.032 0.000 0.968 NA 0.000
#> SRR934221 3 0.0880 0.842 0.032 0.000 0.968 NA 0.000
#> SRR934222 3 0.0880 0.842 0.032 0.000 0.968 NA 0.000
#> SRR934223 3 0.0880 0.842 0.032 0.000 0.968 NA 0.000
#> SRR934224 1 0.6860 0.382 0.552 0.000 0.220 NA 0.040
#> SRR934225 1 0.6860 0.382 0.552 0.000 0.220 NA 0.040
#> SRR934226 1 0.6860 0.382 0.552 0.000 0.220 NA 0.040
#> SRR934227 1 0.6860 0.382 0.552 0.000 0.220 NA 0.040
#> SRR934228 1 0.6860 0.382 0.552 0.000 0.220 NA 0.040
#> SRR934229 1 0.6860 0.382 0.552 0.000 0.220 NA 0.040
#> SRR934230 1 0.6860 0.382 0.552 0.000 0.220 NA 0.040
#> SRR934231 1 0.6860 0.382 0.552 0.000 0.220 NA 0.040
#> SRR934232 2 0.4123 0.843 0.000 0.796 0.004 NA 0.092
#> SRR934233 2 0.4123 0.843 0.000 0.796 0.004 NA 0.092
#> SRR934234 2 0.4123 0.843 0.000 0.796 0.004 NA 0.092
#> SRR934235 2 0.4123 0.843 0.000 0.796 0.004 NA 0.092
#> SRR934236 2 0.4123 0.843 0.000 0.796 0.004 NA 0.092
#> SRR934237 2 0.4123 0.843 0.000 0.796 0.004 NA 0.092
#> SRR934238 2 0.4123 0.843 0.000 0.796 0.004 NA 0.092
#> SRR934239 2 0.4123 0.843 0.000 0.796 0.004 NA 0.092
#> SRR934240 2 0.0000 0.893 0.000 1.000 0.000 NA 0.000
#> SRR934241 2 0.0000 0.893 0.000 1.000 0.000 NA 0.000
#> SRR934242 2 0.0000 0.893 0.000 1.000 0.000 NA 0.000
#> SRR934243 2 0.0000 0.893 0.000 1.000 0.000 NA 0.000
#> SRR934244 2 0.0000 0.893 0.000 1.000 0.000 NA 0.000
#> SRR934245 2 0.0000 0.893 0.000 1.000 0.000 NA 0.000
#> SRR934246 2 0.0000 0.893 0.000 1.000 0.000 NA 0.000
#> SRR934247 2 0.0000 0.893 0.000 1.000 0.000 NA 0.000
#> SRR934248 1 0.8380 0.204 0.420 0.032 0.100 NA 0.148
#> SRR934249 1 0.8380 0.204 0.420 0.032 0.100 NA 0.148
#> SRR934250 1 0.8380 0.204 0.420 0.032 0.100 NA 0.148
#> SRR934251 1 0.8380 0.204 0.420 0.032 0.100 NA 0.148
#> SRR934252 1 0.8380 0.204 0.420 0.032 0.100 NA 0.148
#> SRR934253 1 0.8380 0.204 0.420 0.032 0.100 NA 0.148
#> SRR934254 1 0.8380 0.204 0.420 0.032 0.100 NA 0.148
#> SRR934255 1 0.8380 0.204 0.420 0.032 0.100 NA 0.148
#> SRR934256 2 0.3176 0.866 0.000 0.856 0.000 NA 0.080
#> SRR934257 2 0.3176 0.866 0.000 0.856 0.000 NA 0.080
#> SRR934258 2 0.3176 0.866 0.000 0.856 0.000 NA 0.080
#> SRR934259 2 0.3176 0.866 0.000 0.856 0.000 NA 0.080
#> SRR934260 2 0.3176 0.866 0.000 0.856 0.000 NA 0.080
#> SRR934261 2 0.3176 0.866 0.000 0.856 0.000 NA 0.080
#> SRR934262 2 0.3176 0.866 0.000 0.856 0.000 NA 0.080
#> SRR934263 2 0.3176 0.866 0.000 0.856 0.000 NA 0.080
#> SRR934264 1 0.7005 0.273 0.456 0.000 0.260 NA 0.016
#> SRR934265 1 0.7005 0.273 0.456 0.000 0.260 NA 0.016
#> SRR934266 1 0.7005 0.273 0.456 0.000 0.260 NA 0.016
#> SRR934267 1 0.7005 0.273 0.456 0.000 0.260 NA 0.016
#> SRR934268 1 0.7005 0.273 0.456 0.000 0.260 NA 0.016
#> SRR934269 1 0.7005 0.273 0.456 0.000 0.260 NA 0.016
#> SRR934270 1 0.7005 0.273 0.456 0.000 0.260 NA 0.016
#> SRR934271 1 0.7005 0.273 0.456 0.000 0.260 NA 0.016
#> SRR934272 1 0.1549 0.436 0.944 0.000 0.000 NA 0.016
#> SRR934273 1 0.1549 0.436 0.944 0.000 0.000 NA 0.016
#> SRR934274 1 0.1549 0.436 0.944 0.000 0.000 NA 0.016
#> SRR934275 1 0.1549 0.436 0.944 0.000 0.000 NA 0.016
#> SRR934276 1 0.1549 0.436 0.944 0.000 0.000 NA 0.016
#> SRR934277 1 0.1549 0.436 0.944 0.000 0.000 NA 0.016
#> SRR934278 1 0.1549 0.436 0.944 0.000 0.000 NA 0.016
#> SRR934279 1 0.1549 0.436 0.944 0.000 0.000 NA 0.016
#> SRR934280 1 0.4272 0.269 0.752 0.000 0.000 NA 0.196
#> SRR934281 1 0.4272 0.269 0.752 0.000 0.000 NA 0.196
#> SRR934282 1 0.4272 0.269 0.752 0.000 0.000 NA 0.196
#> SRR934283 1 0.4272 0.269 0.752 0.000 0.000 NA 0.196
#> SRR934284 1 0.4272 0.269 0.752 0.000 0.000 NA 0.196
#> SRR934285 1 0.4272 0.269 0.752 0.000 0.000 NA 0.196
#> SRR934286 1 0.4272 0.269 0.752 0.000 0.000 NA 0.196
#> SRR934287 1 0.4272 0.269 0.752 0.000 0.000 NA 0.196
#> SRR934288 5 0.4182 0.808 0.352 0.000 0.000 NA 0.644
#> SRR934289 5 0.4182 0.808 0.352 0.000 0.000 NA 0.644
#> SRR934290 5 0.4182 0.808 0.352 0.000 0.000 NA 0.644
#> SRR934291 5 0.4182 0.808 0.352 0.000 0.000 NA 0.644
#> SRR934292 5 0.4182 0.808 0.352 0.000 0.000 NA 0.644
#> SRR934293 5 0.4182 0.808 0.352 0.000 0.000 NA 0.644
#> SRR934294 5 0.4182 0.808 0.352 0.000 0.000 NA 0.644
#> SRR934295 5 0.4182 0.808 0.352 0.000 0.000 NA 0.644
#> SRR934296 5 0.4836 0.756 0.356 0.000 0.000 NA 0.612
#> SRR934297 5 0.4836 0.756 0.356 0.000 0.000 NA 0.612
#> SRR934298 5 0.4836 0.756 0.356 0.000 0.000 NA 0.612
#> SRR934299 5 0.4836 0.756 0.356 0.000 0.000 NA 0.612
#> SRR934300 5 0.4836 0.756 0.356 0.000 0.000 NA 0.612
#> SRR934301 5 0.4836 0.756 0.356 0.000 0.000 NA 0.612
#> SRR934302 5 0.4836 0.756 0.356 0.000 0.000 NA 0.612
#> SRR934303 5 0.4836 0.756 0.356 0.000 0.000 NA 0.612
#> SRR934304 3 0.4906 0.838 0.000 0.008 0.664 NA 0.036
#> SRR934305 3 0.4906 0.838 0.000 0.008 0.664 NA 0.036
#> SRR934306 3 0.4906 0.838 0.000 0.008 0.664 NA 0.036
#> SRR934307 3 0.4906 0.838 0.000 0.008 0.664 NA 0.036
#> SRR934308 3 0.5005 0.837 0.000 0.008 0.664 NA 0.044
#> SRR934309 3 0.4906 0.838 0.000 0.008 0.664 NA 0.036
#> SRR934310 3 0.4906 0.838 0.000 0.008 0.664 NA 0.036
#> SRR934311 3 0.4906 0.838 0.000 0.008 0.664 NA 0.036
#> SRR934312 1 0.3513 0.287 0.800 0.000 0.000 NA 0.180
#> SRR934313 1 0.3513 0.287 0.800 0.000 0.000 NA 0.180
#> SRR934314 1 0.3513 0.287 0.800 0.000 0.000 NA 0.180
#> SRR934315 1 0.3513 0.287 0.800 0.000 0.000 NA 0.180
#> SRR934316 1 0.3513 0.287 0.800 0.000 0.000 NA 0.180
#> SRR934317 1 0.3513 0.287 0.800 0.000 0.000 NA 0.180
#> SRR934318 1 0.3513 0.287 0.800 0.000 0.000 NA 0.180
#> SRR934319 1 0.3513 0.287 0.800 0.000 0.000 NA 0.180
#> SRR934320 1 0.5137 0.199 0.676 0.000 0.000 NA 0.228
#> SRR934321 1 0.5137 0.199 0.676 0.000 0.000 NA 0.228
#> SRR934322 1 0.5137 0.199 0.676 0.000 0.000 NA 0.228
#> SRR934323 1 0.5137 0.199 0.676 0.000 0.000 NA 0.228
#> SRR934324 1 0.5137 0.199 0.676 0.000 0.000 NA 0.228
#> SRR934325 1 0.5137 0.199 0.676 0.000 0.000 NA 0.228
#> SRR934326 1 0.5137 0.199 0.676 0.000 0.000 NA 0.228
#> SRR934327 1 0.5137 0.199 0.676 0.000 0.000 NA 0.228
#> SRR934328 5 0.5733 0.689 0.440 0.000 0.000 NA 0.476
#> SRR934329 5 0.5733 0.689 0.440 0.000 0.000 NA 0.476
#> SRR934330 5 0.5733 0.689 0.440 0.000 0.000 NA 0.476
#> SRR934331 5 0.5733 0.689 0.440 0.000 0.000 NA 0.476
#> SRR934332 5 0.5733 0.689 0.440 0.000 0.000 NA 0.476
#> SRR934333 5 0.5733 0.689 0.440 0.000 0.000 NA 0.476
#> SRR934334 5 0.5733 0.689 0.440 0.000 0.000 NA 0.476
#> SRR934335 5 0.5733 0.689 0.440 0.000 0.000 NA 0.476
#> SRR934344 1 0.5538 -0.327 0.596 0.000 0.000 NA 0.312
#> SRR934345 1 0.5538 -0.327 0.596 0.000 0.000 NA 0.312
#> SRR934346 1 0.5538 -0.327 0.596 0.000 0.000 NA 0.312
#> SRR934347 1 0.5538 -0.327 0.596 0.000 0.000 NA 0.312
#> SRR934348 1 0.5538 -0.327 0.596 0.000 0.000 NA 0.312
#> SRR934349 1 0.5538 -0.327 0.596 0.000 0.000 NA 0.312
#> SRR934350 1 0.5538 -0.327 0.596 0.000 0.000 NA 0.312
#> SRR934351 1 0.5538 -0.327 0.596 0.000 0.000 NA 0.312
#> SRR934336 1 0.0579 0.434 0.984 0.000 0.000 NA 0.008
#> SRR934337 1 0.0579 0.434 0.984 0.000 0.000 NA 0.008
#> SRR934338 1 0.0579 0.434 0.984 0.000 0.000 NA 0.008
#> SRR934339 1 0.0579 0.434 0.984 0.000 0.000 NA 0.008
#> SRR934340 1 0.0579 0.434 0.984 0.000 0.000 NA 0.008
#> SRR934341 1 0.0579 0.434 0.984 0.000 0.000 NA 0.008
#> SRR934342 1 0.0579 0.434 0.984 0.000 0.000 NA 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.6115 0.766 0.024 0.000 0.564 0.252 NA 0.012
#> SRR934217 3 0.6115 0.766 0.024 0.000 0.564 0.252 NA 0.012
#> SRR934218 3 0.6115 0.766 0.024 0.000 0.564 0.252 NA 0.012
#> SRR934219 3 0.6115 0.766 0.024 0.000 0.564 0.252 NA 0.012
#> SRR934220 3 0.6154 0.765 0.028 0.000 0.564 0.252 NA 0.012
#> SRR934221 3 0.6115 0.766 0.024 0.000 0.564 0.252 NA 0.012
#> SRR934222 3 0.6115 0.766 0.024 0.000 0.564 0.252 NA 0.012
#> SRR934223 3 0.6115 0.766 0.024 0.000 0.564 0.252 NA 0.012
#> SRR934224 4 0.5270 0.644 0.032 0.000 0.016 0.668 NA 0.228
#> SRR934225 4 0.5270 0.644 0.032 0.000 0.016 0.668 NA 0.228
#> SRR934226 4 0.5270 0.644 0.032 0.000 0.016 0.668 NA 0.228
#> SRR934227 4 0.5270 0.644 0.032 0.000 0.016 0.668 NA 0.228
#> SRR934228 4 0.5270 0.644 0.032 0.000 0.016 0.668 NA 0.228
#> SRR934229 4 0.5270 0.644 0.032 0.000 0.016 0.668 NA 0.228
#> SRR934230 4 0.5270 0.644 0.032 0.000 0.016 0.668 NA 0.228
#> SRR934231 4 0.5270 0.644 0.032 0.000 0.016 0.668 NA 0.228
#> SRR934232 2 0.4293 0.790 0.020 0.736 0.000 0.028 NA 0.008
#> SRR934233 2 0.4336 0.790 0.020 0.736 0.000 0.032 NA 0.008
#> SRR934234 2 0.4336 0.790 0.020 0.736 0.000 0.032 NA 0.008
#> SRR934235 2 0.4293 0.790 0.020 0.736 0.000 0.028 NA 0.008
#> SRR934236 2 0.4293 0.790 0.020 0.736 0.000 0.028 NA 0.008
#> SRR934237 2 0.4336 0.790 0.020 0.736 0.000 0.032 NA 0.008
#> SRR934238 2 0.4293 0.790 0.020 0.736 0.000 0.028 NA 0.008
#> SRR934239 2 0.4293 0.790 0.020 0.736 0.000 0.028 NA 0.008
#> SRR934240 2 0.0146 0.848 0.000 0.996 0.000 0.000 NA 0.000
#> SRR934241 2 0.0146 0.848 0.000 0.996 0.000 0.000 NA 0.000
#> SRR934242 2 0.0146 0.848 0.000 0.996 0.000 0.000 NA 0.000
#> SRR934243 2 0.0146 0.848 0.000 0.996 0.000 0.000 NA 0.000
#> SRR934244 2 0.0146 0.848 0.000 0.996 0.000 0.000 NA 0.000
#> SRR934245 2 0.0146 0.848 0.000 0.996 0.000 0.000 NA 0.000
#> SRR934246 2 0.0146 0.848 0.000 0.996 0.000 0.000 NA 0.000
#> SRR934247 2 0.0146 0.848 0.000 0.996 0.000 0.000 NA 0.000
#> SRR934248 4 0.8513 0.502 0.192 0.036 0.036 0.372 NA 0.240
#> SRR934249 4 0.8513 0.502 0.192 0.036 0.036 0.372 NA 0.240
#> SRR934250 4 0.8513 0.502 0.192 0.036 0.036 0.372 NA 0.240
#> SRR934251 4 0.8513 0.502 0.192 0.036 0.036 0.372 NA 0.240
#> SRR934252 4 0.8513 0.502 0.192 0.036 0.036 0.372 NA 0.240
#> SRR934253 4 0.8513 0.502 0.192 0.036 0.036 0.372 NA 0.240
#> SRR934254 4 0.8513 0.502 0.192 0.036 0.036 0.372 NA 0.240
#> SRR934255 4 0.8513 0.502 0.192 0.036 0.036 0.372 NA 0.240
#> SRR934256 2 0.3919 0.806 0.024 0.772 0.000 0.032 NA 0.000
#> SRR934257 2 0.3955 0.806 0.024 0.772 0.000 0.036 NA 0.000
#> SRR934258 2 0.3955 0.806 0.024 0.772 0.000 0.036 NA 0.000
#> SRR934259 2 0.3919 0.806 0.024 0.772 0.000 0.032 NA 0.000
#> SRR934260 2 0.4029 0.806 0.028 0.772 0.000 0.040 NA 0.000
#> SRR934261 2 0.3988 0.806 0.024 0.772 0.000 0.040 NA 0.000
#> SRR934262 2 0.3919 0.806 0.024 0.772 0.000 0.032 NA 0.000
#> SRR934263 2 0.3919 0.806 0.024 0.772 0.000 0.032 NA 0.000
#> SRR934264 4 0.4924 0.686 0.024 0.000 0.052 0.668 NA 0.252
#> SRR934265 4 0.4924 0.686 0.024 0.000 0.052 0.668 NA 0.252
#> SRR934266 4 0.4924 0.686 0.024 0.000 0.052 0.668 NA 0.252
#> SRR934267 4 0.4924 0.686 0.024 0.000 0.052 0.668 NA 0.252
#> SRR934268 4 0.4924 0.686 0.024 0.000 0.052 0.668 NA 0.252
#> SRR934269 4 0.4924 0.686 0.024 0.000 0.052 0.668 NA 0.252
#> SRR934270 4 0.4924 0.686 0.024 0.000 0.052 0.668 NA 0.252
#> SRR934271 4 0.4924 0.686 0.024 0.000 0.052 0.668 NA 0.252
#> SRR934272 6 0.5074 0.350 0.104 0.000 0.000 0.252 NA 0.636
#> SRR934273 6 0.5074 0.350 0.104 0.000 0.000 0.252 NA 0.636
#> SRR934274 6 0.5074 0.350 0.104 0.000 0.000 0.252 NA 0.636
#> SRR934275 6 0.5074 0.350 0.104 0.000 0.000 0.252 NA 0.636
#> SRR934276 6 0.5074 0.350 0.104 0.000 0.000 0.252 NA 0.636
#> SRR934277 6 0.5074 0.350 0.104 0.000 0.000 0.252 NA 0.636
#> SRR934278 6 0.5074 0.350 0.104 0.000 0.000 0.252 NA 0.636
#> SRR934279 6 0.5074 0.350 0.104 0.000 0.000 0.252 NA 0.636
#> SRR934280 6 0.3017 0.555 0.052 0.000 0.000 0.004 NA 0.848
#> SRR934281 6 0.3017 0.555 0.052 0.000 0.000 0.004 NA 0.848
#> SRR934282 6 0.3017 0.555 0.052 0.000 0.000 0.004 NA 0.848
#> SRR934283 6 0.3017 0.555 0.052 0.000 0.000 0.004 NA 0.848
#> SRR934284 6 0.3017 0.555 0.052 0.000 0.000 0.004 NA 0.848
#> SRR934285 6 0.3017 0.555 0.052 0.000 0.000 0.004 NA 0.848
#> SRR934286 6 0.3017 0.555 0.052 0.000 0.000 0.004 NA 0.848
#> SRR934287 6 0.3017 0.555 0.052 0.000 0.000 0.004 NA 0.848
#> SRR934288 1 0.5094 0.611 0.596 0.000 0.000 0.004 NA 0.308
#> SRR934289 1 0.5094 0.611 0.596 0.000 0.000 0.004 NA 0.308
#> SRR934290 1 0.5094 0.611 0.596 0.000 0.000 0.004 NA 0.308
#> SRR934291 1 0.5094 0.611 0.596 0.000 0.000 0.004 NA 0.308
#> SRR934292 1 0.5094 0.611 0.596 0.000 0.000 0.004 NA 0.308
#> SRR934293 1 0.5094 0.611 0.596 0.000 0.000 0.004 NA 0.308
#> SRR934294 1 0.5094 0.611 0.596 0.000 0.000 0.004 NA 0.308
#> SRR934295 1 0.5094 0.611 0.596 0.000 0.000 0.004 NA 0.308
#> SRR934296 6 0.5823 -0.135 0.372 0.000 0.000 0.000 NA 0.440
#> SRR934297 6 0.5823 -0.135 0.372 0.000 0.000 0.000 NA 0.440
#> SRR934298 6 0.5823 -0.135 0.372 0.000 0.000 0.000 NA 0.440
#> SRR934299 6 0.5823 -0.135 0.372 0.000 0.000 0.000 NA 0.440
#> SRR934300 6 0.5823 -0.135 0.372 0.000 0.000 0.000 NA 0.440
#> SRR934301 6 0.5823 -0.135 0.372 0.000 0.000 0.000 NA 0.440
#> SRR934302 6 0.5823 -0.135 0.372 0.000 0.000 0.000 NA 0.440
#> SRR934303 6 0.5823 -0.135 0.372 0.000 0.000 0.000 NA 0.440
#> SRR934304 3 0.0520 0.788 0.000 0.008 0.984 0.008 NA 0.000
#> SRR934305 3 0.0665 0.788 0.004 0.008 0.980 0.008 NA 0.000
#> SRR934306 3 0.0810 0.788 0.004 0.008 0.976 0.008 NA 0.000
#> SRR934307 3 0.0520 0.788 0.000 0.008 0.984 0.008 NA 0.000
#> SRR934308 3 0.1038 0.787 0.008 0.008 0.968 0.008 NA 0.000
#> SRR934309 3 0.0665 0.788 0.004 0.008 0.980 0.008 NA 0.000
#> SRR934310 3 0.0520 0.788 0.000 0.008 0.984 0.008 NA 0.000
#> SRR934311 3 0.0520 0.788 0.000 0.008 0.984 0.008 NA 0.000
#> SRR934312 6 0.1268 0.586 0.036 0.000 0.000 0.008 NA 0.952
#> SRR934313 6 0.1268 0.586 0.036 0.000 0.000 0.008 NA 0.952
#> SRR934314 6 0.1268 0.586 0.036 0.000 0.000 0.008 NA 0.952
#> SRR934315 6 0.1268 0.586 0.036 0.000 0.000 0.008 NA 0.952
#> SRR934316 6 0.1268 0.586 0.036 0.000 0.000 0.008 NA 0.952
#> SRR934317 6 0.1268 0.586 0.036 0.000 0.000 0.008 NA 0.952
#> SRR934318 6 0.1268 0.586 0.036 0.000 0.000 0.008 NA 0.952
#> SRR934319 6 0.1268 0.586 0.036 0.000 0.000 0.008 NA 0.952
#> SRR934320 6 0.4547 0.518 0.076 0.000 0.000 0.048 NA 0.752
#> SRR934321 6 0.4547 0.518 0.076 0.000 0.000 0.048 NA 0.752
#> SRR934322 6 0.4547 0.518 0.076 0.000 0.000 0.048 NA 0.752
#> SRR934323 6 0.4547 0.518 0.076 0.000 0.000 0.048 NA 0.752
#> SRR934324 6 0.4547 0.518 0.076 0.000 0.000 0.048 NA 0.752
#> SRR934325 6 0.4547 0.518 0.076 0.000 0.000 0.048 NA 0.752
#> SRR934326 6 0.4547 0.518 0.076 0.000 0.000 0.048 NA 0.752
#> SRR934327 6 0.4547 0.518 0.076 0.000 0.000 0.048 NA 0.752
#> SRR934328 1 0.4178 0.727 0.700 0.000 0.000 0.032 NA 0.260
#> SRR934329 1 0.4178 0.727 0.700 0.000 0.000 0.032 NA 0.260
#> SRR934330 1 0.4178 0.727 0.700 0.000 0.000 0.032 NA 0.260
#> SRR934331 1 0.4178 0.727 0.700 0.000 0.000 0.032 NA 0.260
#> SRR934332 1 0.4178 0.727 0.700 0.000 0.000 0.032 NA 0.260
#> SRR934333 1 0.4178 0.727 0.700 0.000 0.000 0.032 NA 0.260
#> SRR934334 1 0.4178 0.727 0.700 0.000 0.000 0.032 NA 0.260
#> SRR934335 1 0.4178 0.727 0.700 0.000 0.000 0.032 NA 0.260
#> SRR934344 1 0.5649 0.599 0.568 0.000 0.000 0.132 NA 0.284
#> SRR934345 1 0.5649 0.599 0.568 0.000 0.000 0.132 NA 0.284
#> SRR934346 1 0.5649 0.599 0.568 0.000 0.000 0.132 NA 0.284
#> SRR934347 1 0.5649 0.599 0.568 0.000 0.000 0.132 NA 0.284
#> SRR934348 1 0.5649 0.599 0.568 0.000 0.000 0.132 NA 0.284
#> SRR934349 1 0.5649 0.599 0.568 0.000 0.000 0.132 NA 0.284
#> SRR934350 1 0.5649 0.599 0.568 0.000 0.000 0.132 NA 0.284
#> SRR934351 1 0.5649 0.599 0.568 0.000 0.000 0.132 NA 0.284
#> SRR934336 6 0.4413 0.453 0.056 0.000 0.000 0.208 NA 0.720
#> SRR934337 6 0.4413 0.453 0.056 0.000 0.000 0.208 NA 0.720
#> SRR934338 6 0.4413 0.453 0.056 0.000 0.000 0.208 NA 0.720
#> SRR934339 6 0.4413 0.453 0.056 0.000 0.000 0.208 NA 0.720
#> SRR934340 6 0.4413 0.453 0.056 0.000 0.000 0.208 NA 0.720
#> SRR934341 6 0.4413 0.453 0.056 0.000 0.000 0.208 NA 0.720
#> SRR934342 6 0.4413 0.453 0.056 0.000 0.000 0.208 NA 0.720
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 14550 rows and 135 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.580 0.759 0.881 0.4786 0.511 0.511
#> 3 3 0.705 0.915 0.943 0.3378 0.639 0.413
#> 4 4 0.751 0.850 0.911 0.1601 0.897 0.710
#> 5 5 0.751 0.782 0.848 0.0531 0.972 0.888
#> 6 6 0.786 0.736 0.803 0.0385 0.973 0.878
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
#> SRR934216 1 0.9710 0.889 0.600 0.400
#> SRR934217 1 0.9710 0.889 0.600 0.400
#> SRR934218 1 0.9710 0.889 0.600 0.400
#> SRR934219 1 0.9710 0.889 0.600 0.400
#> SRR934220 1 0.9710 0.889 0.600 0.400
#> SRR934221 1 0.9710 0.889 0.600 0.400
#> SRR934222 1 0.9710 0.889 0.600 0.400
#> SRR934223 1 0.9710 0.889 0.600 0.400
#> SRR934224 1 0.9710 0.889 0.600 0.400
#> SRR934225 1 0.9710 0.889 0.600 0.400
#> SRR934226 1 0.9710 0.889 0.600 0.400
#> SRR934227 1 0.9710 0.889 0.600 0.400
#> SRR934228 1 0.9710 0.889 0.600 0.400
#> SRR934229 1 0.9710 0.889 0.600 0.400
#> SRR934230 1 0.9710 0.889 0.600 0.400
#> SRR934231 1 0.9710 0.889 0.600 0.400
#> SRR934232 2 0.9710 0.779 0.400 0.600
#> SRR934233 2 0.9710 0.779 0.400 0.600
#> SRR934234 2 0.9710 0.779 0.400 0.600
#> SRR934235 2 0.9710 0.779 0.400 0.600
#> SRR934236 2 0.9710 0.779 0.400 0.600
#> SRR934237 2 0.9710 0.779 0.400 0.600
#> SRR934238 2 0.9710 0.779 0.400 0.600
#> SRR934239 2 0.9710 0.779 0.400 0.600
#> SRR934240 2 0.9710 0.779 0.400 0.600
#> SRR934241 2 0.9710 0.779 0.400 0.600
#> SRR934242 2 0.9710 0.779 0.400 0.600
#> SRR934243 2 0.9710 0.779 0.400 0.600
#> SRR934244 2 0.9710 0.779 0.400 0.600
#> SRR934245 2 0.9710 0.779 0.400 0.600
#> SRR934246 2 0.9710 0.779 0.400 0.600
#> SRR934247 2 0.9710 0.779 0.400 0.600
#> SRR934248 1 0.0376 0.441 0.996 0.004
#> SRR934249 1 0.0376 0.441 0.996 0.004
#> SRR934250 1 0.0376 0.441 0.996 0.004
#> SRR934251 1 0.0376 0.441 0.996 0.004
#> SRR934252 1 0.0376 0.441 0.996 0.004
#> SRR934253 1 0.0376 0.441 0.996 0.004
#> SRR934254 1 0.0376 0.441 0.996 0.004
#> SRR934255 1 0.0376 0.441 0.996 0.004
#> SRR934256 2 0.9710 0.779 0.400 0.600
#> SRR934257 2 0.9710 0.779 0.400 0.600
#> SRR934258 2 0.9710 0.779 0.400 0.600
#> SRR934259 2 0.9710 0.779 0.400 0.600
#> SRR934260 2 0.9710 0.779 0.400 0.600
#> SRR934261 2 0.9710 0.779 0.400 0.600
#> SRR934262 2 0.9710 0.779 0.400 0.600
#> SRR934263 2 0.9710 0.779 0.400 0.600
#> SRR934264 1 0.9710 0.889 0.600 0.400
#> SRR934265 1 0.9710 0.889 0.600 0.400
#> SRR934266 1 0.9710 0.889 0.600 0.400
#> SRR934267 1 0.9710 0.889 0.600 0.400
#> SRR934268 1 0.9710 0.889 0.600 0.400
#> SRR934269 1 0.9710 0.889 0.600 0.400
#> SRR934270 1 0.9710 0.889 0.600 0.400
#> SRR934271 1 0.9710 0.889 0.600 0.400
#> SRR934272 1 0.9710 0.889 0.600 0.400
#> SRR934273 1 0.9710 0.889 0.600 0.400
#> SRR934274 1 0.9710 0.889 0.600 0.400
#> SRR934275 1 0.9710 0.889 0.600 0.400
#> SRR934276 1 0.9710 0.889 0.600 0.400
#> SRR934277 1 0.9710 0.889 0.600 0.400
#> SRR934278 1 0.9710 0.889 0.600 0.400
#> SRR934279 1 0.9710 0.889 0.600 0.400
#> SRR934280 2 0.0376 0.602 0.004 0.996
#> SRR934281 2 0.0376 0.602 0.004 0.996
#> SRR934282 2 0.0376 0.602 0.004 0.996
#> SRR934283 2 0.0376 0.602 0.004 0.996
#> SRR934284 2 0.0376 0.602 0.004 0.996
#> SRR934285 2 0.0376 0.602 0.004 0.996
#> SRR934286 2 0.0376 0.602 0.004 0.996
#> SRR934287 2 0.0376 0.602 0.004 0.996
#> SRR934288 2 0.0000 0.605 0.000 1.000
#> SRR934289 2 0.0000 0.605 0.000 1.000
#> SRR934290 2 0.0000 0.605 0.000 1.000
#> SRR934291 2 0.0000 0.605 0.000 1.000
#> SRR934292 2 0.0000 0.605 0.000 1.000
#> SRR934293 2 0.0000 0.605 0.000 1.000
#> SRR934294 2 0.0000 0.605 0.000 1.000
#> SRR934295 2 0.0000 0.605 0.000 1.000
#> SRR934296 2 0.9710 0.779 0.400 0.600
#> SRR934297 2 0.9710 0.779 0.400 0.600
#> SRR934298 2 0.9710 0.779 0.400 0.600
#> SRR934299 2 0.9710 0.779 0.400 0.600
#> SRR934300 2 0.9710 0.779 0.400 0.600
#> SRR934301 2 0.9710 0.779 0.400 0.600
#> SRR934302 2 0.9710 0.779 0.400 0.600
#> SRR934303 2 0.9710 0.779 0.400 0.600
#> SRR934304 1 0.0000 0.447 1.000 0.000
#> SRR934305 1 0.0000 0.447 1.000 0.000
#> SRR934306 1 0.0000 0.447 1.000 0.000
#> SRR934307 1 0.0000 0.447 1.000 0.000
#> SRR934308 1 0.0000 0.447 1.000 0.000
#> SRR934309 1 0.0000 0.447 1.000 0.000
#> SRR934310 1 0.0000 0.447 1.000 0.000
#> SRR934311 1 0.0000 0.447 1.000 0.000
#> SRR934312 1 0.9710 0.889 0.600 0.400
#> SRR934313 1 0.9710 0.889 0.600 0.400
#> SRR934314 1 0.9710 0.889 0.600 0.400
#> SRR934315 1 0.9710 0.889 0.600 0.400
#> SRR934316 1 0.9710 0.889 0.600 0.400
#> SRR934317 1 0.9710 0.889 0.600 0.400
#> SRR934318 1 0.9710 0.889 0.600 0.400
#> SRR934319 1 0.9710 0.889 0.600 0.400
#> SRR934320 2 0.0376 0.602 0.004 0.996
#> SRR934321 2 0.0376 0.602 0.004 0.996
#> SRR934322 2 0.0376 0.602 0.004 0.996
#> SRR934323 2 0.0376 0.602 0.004 0.996
#> SRR934324 2 0.0376 0.602 0.004 0.996
#> SRR934325 2 0.0376 0.602 0.004 0.996
#> SRR934326 2 0.0376 0.602 0.004 0.996
#> SRR934327 2 0.0376 0.602 0.004 0.996
#> SRR934328 1 0.9710 0.889 0.600 0.400
#> SRR934329 1 0.9710 0.889 0.600 0.400
#> SRR934330 1 0.9710 0.889 0.600 0.400
#> SRR934331 1 0.9710 0.889 0.600 0.400
#> SRR934332 1 0.9710 0.889 0.600 0.400
#> SRR934333 1 0.9710 0.889 0.600 0.400
#> SRR934334 1 0.9710 0.889 0.600 0.400
#> SRR934335 1 0.9710 0.889 0.600 0.400
#> SRR934344 1 0.9710 0.889 0.600 0.400
#> SRR934345 1 0.9710 0.889 0.600 0.400
#> SRR934346 1 0.9710 0.889 0.600 0.400
#> SRR934347 1 0.9710 0.889 0.600 0.400
#> SRR934348 1 0.9710 0.889 0.600 0.400
#> SRR934349 1 0.9710 0.889 0.600 0.400
#> SRR934350 1 0.9710 0.889 0.600 0.400
#> SRR934351 1 0.9710 0.889 0.600 0.400
#> SRR934336 1 0.9710 0.889 0.600 0.400
#> SRR934337 1 0.9710 0.889 0.600 0.400
#> SRR934338 1 0.9710 0.889 0.600 0.400
#> SRR934339 1 0.9710 0.889 0.600 0.400
#> SRR934340 1 0.9710 0.889 0.600 0.400
#> SRR934341 1 0.9710 0.889 0.600 0.400
#> SRR934342 1 0.9710 0.889 0.600 0.400
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934217 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934218 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934219 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934220 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934221 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934222 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934223 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934224 3 0.4750 0.729 0.216 0.000 0.784
#> SRR934225 3 0.4750 0.729 0.216 0.000 0.784
#> SRR934226 3 0.4750 0.729 0.216 0.000 0.784
#> SRR934227 3 0.4750 0.729 0.216 0.000 0.784
#> SRR934228 3 0.4750 0.729 0.216 0.000 0.784
#> SRR934229 3 0.4750 0.729 0.216 0.000 0.784
#> SRR934230 3 0.4750 0.729 0.216 0.000 0.784
#> SRR934231 3 0.4750 0.729 0.216 0.000 0.784
#> SRR934232 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934233 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934234 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934235 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934236 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934237 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934238 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934239 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934240 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934241 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934242 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934243 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934244 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934245 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934246 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934247 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934248 3 0.2356 0.880 0.000 0.072 0.928
#> SRR934249 3 0.2356 0.880 0.000 0.072 0.928
#> SRR934250 3 0.2356 0.880 0.000 0.072 0.928
#> SRR934251 3 0.2356 0.880 0.000 0.072 0.928
#> SRR934252 3 0.2356 0.880 0.000 0.072 0.928
#> SRR934253 3 0.2356 0.880 0.000 0.072 0.928
#> SRR934254 3 0.2356 0.880 0.000 0.072 0.928
#> SRR934255 3 0.2356 0.880 0.000 0.072 0.928
#> SRR934256 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934257 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934258 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934259 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934260 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934261 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934262 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934263 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934264 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934265 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934266 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934267 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934268 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934269 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934270 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934271 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934272 1 0.3482 0.901 0.872 0.000 0.128
#> SRR934273 1 0.3482 0.901 0.872 0.000 0.128
#> SRR934274 1 0.3482 0.901 0.872 0.000 0.128
#> SRR934275 1 0.3482 0.901 0.872 0.000 0.128
#> SRR934276 1 0.3482 0.901 0.872 0.000 0.128
#> SRR934277 1 0.3482 0.901 0.872 0.000 0.128
#> SRR934278 1 0.3482 0.901 0.872 0.000 0.128
#> SRR934279 1 0.3482 0.901 0.872 0.000 0.128
#> SRR934280 1 0.0237 0.911 0.996 0.004 0.000
#> SRR934281 1 0.0237 0.911 0.996 0.004 0.000
#> SRR934282 1 0.0237 0.911 0.996 0.004 0.000
#> SRR934283 1 0.0237 0.911 0.996 0.004 0.000
#> SRR934284 1 0.0237 0.911 0.996 0.004 0.000
#> SRR934285 1 0.0237 0.911 0.996 0.004 0.000
#> SRR934286 1 0.0237 0.911 0.996 0.004 0.000
#> SRR934287 1 0.0237 0.911 0.996 0.004 0.000
#> SRR934288 1 0.4121 0.829 0.832 0.168 0.000
#> SRR934289 1 0.4121 0.829 0.832 0.168 0.000
#> SRR934290 1 0.4121 0.829 0.832 0.168 0.000
#> SRR934291 1 0.4121 0.829 0.832 0.168 0.000
#> SRR934292 1 0.4121 0.829 0.832 0.168 0.000
#> SRR934293 1 0.4121 0.829 0.832 0.168 0.000
#> SRR934294 1 0.4121 0.829 0.832 0.168 0.000
#> SRR934295 1 0.4121 0.829 0.832 0.168 0.000
#> SRR934296 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934297 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934298 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934299 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934300 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934301 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934302 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934303 2 0.0000 1.000 0.000 1.000 0.000
#> SRR934304 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934305 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934306 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934307 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934308 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934309 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934310 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934311 3 0.0000 0.928 0.000 0.000 1.000
#> SRR934312 1 0.0000 0.911 1.000 0.000 0.000
#> SRR934313 1 0.0000 0.911 1.000 0.000 0.000
#> SRR934314 1 0.0000 0.911 1.000 0.000 0.000
#> SRR934315 1 0.0000 0.911 1.000 0.000 0.000
#> SRR934316 1 0.0000 0.911 1.000 0.000 0.000
#> SRR934317 1 0.0000 0.911 1.000 0.000 0.000
#> SRR934318 1 0.0000 0.911 1.000 0.000 0.000
#> SRR934319 1 0.0000 0.911 1.000 0.000 0.000
#> SRR934320 1 0.2165 0.893 0.936 0.064 0.000
#> SRR934321 1 0.2165 0.893 0.936 0.064 0.000
#> SRR934322 1 0.2165 0.893 0.936 0.064 0.000
#> SRR934323 1 0.2165 0.893 0.936 0.064 0.000
#> SRR934324 1 0.2165 0.893 0.936 0.064 0.000
#> SRR934325 1 0.2165 0.893 0.936 0.064 0.000
#> SRR934326 1 0.2165 0.893 0.936 0.064 0.000
#> SRR934327 1 0.2165 0.893 0.936 0.064 0.000
#> SRR934328 1 0.3340 0.906 0.880 0.000 0.120
#> SRR934329 1 0.3340 0.906 0.880 0.000 0.120
#> SRR934330 1 0.3340 0.906 0.880 0.000 0.120
#> SRR934331 1 0.3340 0.906 0.880 0.000 0.120
#> SRR934332 1 0.3340 0.906 0.880 0.000 0.120
#> SRR934333 1 0.3340 0.906 0.880 0.000 0.120
#> SRR934334 1 0.3340 0.906 0.880 0.000 0.120
#> SRR934335 1 0.3340 0.906 0.880 0.000 0.120
#> SRR934344 1 0.3412 0.904 0.876 0.000 0.124
#> SRR934345 1 0.3412 0.904 0.876 0.000 0.124
#> SRR934346 1 0.3412 0.904 0.876 0.000 0.124
#> SRR934347 1 0.3412 0.904 0.876 0.000 0.124
#> SRR934348 1 0.3412 0.904 0.876 0.000 0.124
#> SRR934349 1 0.3412 0.904 0.876 0.000 0.124
#> SRR934350 1 0.3412 0.904 0.876 0.000 0.124
#> SRR934351 1 0.3412 0.904 0.876 0.000 0.124
#> SRR934336 1 0.2356 0.914 0.928 0.000 0.072
#> SRR934337 1 0.2356 0.914 0.928 0.000 0.072
#> SRR934338 1 0.2356 0.914 0.928 0.000 0.072
#> SRR934339 1 0.2356 0.914 0.928 0.000 0.072
#> SRR934340 1 0.2356 0.914 0.928 0.000 0.072
#> SRR934341 1 0.2356 0.914 0.928 0.000 0.072
#> SRR934342 1 0.2356 0.914 0.928 0.000 0.072
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 3 0.0592 0.850 0.000 0.000 0.984 0.016
#> SRR934217 3 0.0592 0.850 0.000 0.000 0.984 0.016
#> SRR934218 3 0.0592 0.850 0.000 0.000 0.984 0.016
#> SRR934219 3 0.0592 0.850 0.000 0.000 0.984 0.016
#> SRR934220 3 0.0592 0.850 0.000 0.000 0.984 0.016
#> SRR934221 3 0.0592 0.850 0.000 0.000 0.984 0.016
#> SRR934222 3 0.0592 0.850 0.000 0.000 0.984 0.016
#> SRR934223 3 0.0592 0.850 0.000 0.000 0.984 0.016
#> SRR934224 3 0.6070 0.278 0.404 0.000 0.548 0.048
#> SRR934225 3 0.6070 0.278 0.404 0.000 0.548 0.048
#> SRR934226 3 0.6070 0.278 0.404 0.000 0.548 0.048
#> SRR934227 3 0.6070 0.278 0.404 0.000 0.548 0.048
#> SRR934228 3 0.6070 0.278 0.404 0.000 0.548 0.048
#> SRR934229 3 0.6070 0.278 0.404 0.000 0.548 0.048
#> SRR934230 3 0.6070 0.278 0.404 0.000 0.548 0.048
#> SRR934231 3 0.6070 0.278 0.404 0.000 0.548 0.048
#> SRR934232 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934233 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934234 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934235 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934236 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934237 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934238 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934239 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934240 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934241 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934242 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934243 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934244 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934245 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934246 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934247 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934248 3 0.4046 0.766 0.000 0.124 0.828 0.048
#> SRR934249 3 0.4046 0.766 0.000 0.124 0.828 0.048
#> SRR934250 3 0.4046 0.766 0.000 0.124 0.828 0.048
#> SRR934251 3 0.4046 0.766 0.000 0.124 0.828 0.048
#> SRR934252 3 0.4046 0.766 0.000 0.124 0.828 0.048
#> SRR934253 3 0.4046 0.766 0.000 0.124 0.828 0.048
#> SRR934254 3 0.4046 0.766 0.000 0.124 0.828 0.048
#> SRR934255 3 0.4046 0.766 0.000 0.124 0.828 0.048
#> SRR934256 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934257 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934258 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934259 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934260 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934261 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934262 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934263 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> SRR934264 3 0.0707 0.851 0.000 0.000 0.980 0.020
#> SRR934265 3 0.0707 0.851 0.000 0.000 0.980 0.020
#> SRR934266 3 0.0707 0.851 0.000 0.000 0.980 0.020
#> SRR934267 3 0.0707 0.851 0.000 0.000 0.980 0.020
#> SRR934268 3 0.0707 0.851 0.000 0.000 0.980 0.020
#> SRR934269 3 0.0707 0.851 0.000 0.000 0.980 0.020
#> SRR934270 3 0.0707 0.851 0.000 0.000 0.980 0.020
#> SRR934271 3 0.0707 0.851 0.000 0.000 0.980 0.020
#> SRR934272 1 0.4843 0.804 0.784 0.000 0.104 0.112
#> SRR934273 1 0.4843 0.804 0.784 0.000 0.104 0.112
#> SRR934274 1 0.4843 0.804 0.784 0.000 0.104 0.112
#> SRR934275 1 0.4843 0.804 0.784 0.000 0.104 0.112
#> SRR934276 1 0.4843 0.804 0.784 0.000 0.104 0.112
#> SRR934277 1 0.4843 0.804 0.784 0.000 0.104 0.112
#> SRR934278 1 0.4843 0.804 0.784 0.000 0.104 0.112
#> SRR934279 1 0.4843 0.804 0.784 0.000 0.104 0.112
#> SRR934280 1 0.0469 0.877 0.988 0.000 0.000 0.012
#> SRR934281 1 0.0469 0.877 0.988 0.000 0.000 0.012
#> SRR934282 1 0.0469 0.877 0.988 0.000 0.000 0.012
#> SRR934283 1 0.0469 0.877 0.988 0.000 0.000 0.012
#> SRR934284 1 0.0469 0.877 0.988 0.000 0.000 0.012
#> SRR934285 1 0.0469 0.877 0.988 0.000 0.000 0.012
#> SRR934286 1 0.0469 0.877 0.988 0.000 0.000 0.012
#> SRR934287 1 0.0469 0.877 0.988 0.000 0.000 0.012
#> SRR934288 4 0.3229 0.917 0.072 0.048 0.000 0.880
#> SRR934289 4 0.3229 0.917 0.072 0.048 0.000 0.880
#> SRR934290 4 0.3229 0.917 0.072 0.048 0.000 0.880
#> SRR934291 4 0.3229 0.917 0.072 0.048 0.000 0.880
#> SRR934292 4 0.3229 0.917 0.072 0.048 0.000 0.880
#> SRR934293 4 0.3229 0.917 0.072 0.048 0.000 0.880
#> SRR934294 4 0.3229 0.917 0.072 0.048 0.000 0.880
#> SRR934295 4 0.3229 0.917 0.072 0.048 0.000 0.880
#> SRR934296 2 0.1867 0.938 0.000 0.928 0.000 0.072
#> SRR934297 2 0.1867 0.938 0.000 0.928 0.000 0.072
#> SRR934298 2 0.1867 0.938 0.000 0.928 0.000 0.072
#> SRR934299 2 0.1867 0.938 0.000 0.928 0.000 0.072
#> SRR934300 2 0.1867 0.938 0.000 0.928 0.000 0.072
#> SRR934301 2 0.1867 0.938 0.000 0.928 0.000 0.072
#> SRR934302 2 0.1867 0.938 0.000 0.928 0.000 0.072
#> SRR934303 2 0.1867 0.938 0.000 0.928 0.000 0.072
#> SRR934304 3 0.0336 0.848 0.000 0.000 0.992 0.008
#> SRR934305 3 0.0336 0.848 0.000 0.000 0.992 0.008
#> SRR934306 3 0.0336 0.848 0.000 0.000 0.992 0.008
#> SRR934307 3 0.0336 0.848 0.000 0.000 0.992 0.008
#> SRR934308 3 0.0336 0.848 0.000 0.000 0.992 0.008
#> SRR934309 3 0.0336 0.848 0.000 0.000 0.992 0.008
#> SRR934310 3 0.0336 0.848 0.000 0.000 0.992 0.008
#> SRR934311 3 0.0336 0.848 0.000 0.000 0.992 0.008
#> SRR934312 1 0.0817 0.879 0.976 0.000 0.000 0.024
#> SRR934313 1 0.0817 0.879 0.976 0.000 0.000 0.024
#> SRR934314 1 0.0817 0.879 0.976 0.000 0.000 0.024
#> SRR934315 1 0.0817 0.879 0.976 0.000 0.000 0.024
#> SRR934316 1 0.0817 0.879 0.976 0.000 0.000 0.024
#> SRR934317 1 0.0817 0.879 0.976 0.000 0.000 0.024
#> SRR934318 1 0.0817 0.879 0.976 0.000 0.000 0.024
#> SRR934319 1 0.0817 0.879 0.976 0.000 0.000 0.024
#> SRR934320 1 0.4379 0.754 0.792 0.036 0.000 0.172
#> SRR934321 1 0.4379 0.754 0.792 0.036 0.000 0.172
#> SRR934322 1 0.4379 0.754 0.792 0.036 0.000 0.172
#> SRR934323 1 0.4379 0.754 0.792 0.036 0.000 0.172
#> SRR934324 1 0.4379 0.754 0.792 0.036 0.000 0.172
#> SRR934325 1 0.4379 0.754 0.792 0.036 0.000 0.172
#> SRR934326 1 0.4379 0.754 0.792 0.036 0.000 0.172
#> SRR934327 1 0.4379 0.754 0.792 0.036 0.000 0.172
#> SRR934328 4 0.1059 0.952 0.016 0.000 0.012 0.972
#> SRR934329 4 0.1059 0.952 0.016 0.000 0.012 0.972
#> SRR934330 4 0.1059 0.952 0.016 0.000 0.012 0.972
#> SRR934331 4 0.1059 0.952 0.016 0.000 0.012 0.972
#> SRR934332 4 0.1059 0.952 0.016 0.000 0.012 0.972
#> SRR934333 4 0.1059 0.952 0.016 0.000 0.012 0.972
#> SRR934334 4 0.1059 0.952 0.016 0.000 0.012 0.972
#> SRR934335 4 0.1059 0.952 0.016 0.000 0.012 0.972
#> SRR934344 4 0.1833 0.945 0.032 0.000 0.024 0.944
#> SRR934345 4 0.1833 0.945 0.032 0.000 0.024 0.944
#> SRR934346 4 0.1833 0.945 0.032 0.000 0.024 0.944
#> SRR934347 4 0.1833 0.945 0.032 0.000 0.024 0.944
#> SRR934348 4 0.1833 0.945 0.032 0.000 0.024 0.944
#> SRR934349 4 0.1833 0.945 0.032 0.000 0.024 0.944
#> SRR934350 4 0.1833 0.945 0.032 0.000 0.024 0.944
#> SRR934351 4 0.1833 0.945 0.032 0.000 0.024 0.944
#> SRR934336 1 0.3164 0.858 0.884 0.000 0.052 0.064
#> SRR934337 1 0.3164 0.858 0.884 0.000 0.052 0.064
#> SRR934338 1 0.3164 0.858 0.884 0.000 0.052 0.064
#> SRR934339 1 0.3164 0.858 0.884 0.000 0.052 0.064
#> SRR934340 1 0.3164 0.858 0.884 0.000 0.052 0.064
#> SRR934341 1 0.3164 0.858 0.884 0.000 0.052 0.064
#> SRR934342 1 0.3164 0.858 0.884 0.000 0.052 0.064
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 5 0.0000 0.654 0.000 0.000 0.000 0.000 1.000
#> SRR934217 5 0.0000 0.654 0.000 0.000 0.000 0.000 1.000
#> SRR934218 5 0.0000 0.654 0.000 0.000 0.000 0.000 1.000
#> SRR934219 5 0.0000 0.654 0.000 0.000 0.000 0.000 1.000
#> SRR934220 5 0.0000 0.654 0.000 0.000 0.000 0.000 1.000
#> SRR934221 5 0.0000 0.654 0.000 0.000 0.000 0.000 1.000
#> SRR934222 5 0.0000 0.654 0.000 0.000 0.000 0.000 1.000
#> SRR934223 5 0.0000 0.654 0.000 0.000 0.000 0.000 1.000
#> SRR934224 5 0.6159 0.464 0.200 0.000 0.016 0.172 0.612
#> SRR934225 5 0.6159 0.464 0.200 0.000 0.016 0.172 0.612
#> SRR934226 5 0.6159 0.464 0.200 0.000 0.016 0.172 0.612
#> SRR934227 5 0.6159 0.464 0.200 0.000 0.016 0.172 0.612
#> SRR934228 5 0.6159 0.464 0.200 0.000 0.016 0.172 0.612
#> SRR934229 5 0.6159 0.464 0.200 0.000 0.016 0.172 0.612
#> SRR934230 5 0.6159 0.464 0.200 0.000 0.016 0.172 0.612
#> SRR934231 5 0.6159 0.464 0.200 0.000 0.016 0.172 0.612
#> SRR934232 2 0.0290 0.967 0.000 0.992 0.000 0.008 0.000
#> SRR934233 2 0.0290 0.967 0.000 0.992 0.000 0.008 0.000
#> SRR934234 2 0.0290 0.967 0.000 0.992 0.000 0.008 0.000
#> SRR934235 2 0.0290 0.967 0.000 0.992 0.000 0.008 0.000
#> SRR934236 2 0.0290 0.967 0.000 0.992 0.000 0.008 0.000
#> SRR934237 2 0.0290 0.967 0.000 0.992 0.000 0.008 0.000
#> SRR934238 2 0.0290 0.967 0.000 0.992 0.000 0.008 0.000
#> SRR934239 2 0.0290 0.967 0.000 0.992 0.000 0.008 0.000
#> SRR934240 2 0.0000 0.969 0.000 1.000 0.000 0.000 0.000
#> SRR934241 2 0.0000 0.969 0.000 1.000 0.000 0.000 0.000
#> SRR934242 2 0.0000 0.969 0.000 1.000 0.000 0.000 0.000
#> SRR934243 2 0.0000 0.969 0.000 1.000 0.000 0.000 0.000
#> SRR934244 2 0.0000 0.969 0.000 1.000 0.000 0.000 0.000
#> SRR934245 2 0.0000 0.969 0.000 1.000 0.000 0.000 0.000
#> SRR934246 2 0.0000 0.969 0.000 1.000 0.000 0.000 0.000
#> SRR934247 2 0.0000 0.969 0.000 1.000 0.000 0.000 0.000
#> SRR934248 4 0.4718 1.000 0.000 0.028 0.000 0.628 0.344
#> SRR934249 4 0.4718 1.000 0.000 0.028 0.000 0.628 0.344
#> SRR934250 4 0.4718 1.000 0.000 0.028 0.000 0.628 0.344
#> SRR934251 4 0.4718 1.000 0.000 0.028 0.000 0.628 0.344
#> SRR934252 4 0.4718 1.000 0.000 0.028 0.000 0.628 0.344
#> SRR934253 4 0.4718 1.000 0.000 0.028 0.000 0.628 0.344
#> SRR934254 4 0.4718 1.000 0.000 0.028 0.000 0.628 0.344
#> SRR934255 4 0.4718 1.000 0.000 0.028 0.000 0.628 0.344
#> SRR934256 2 0.0000 0.969 0.000 1.000 0.000 0.000 0.000
#> SRR934257 2 0.0000 0.969 0.000 1.000 0.000 0.000 0.000
#> SRR934258 2 0.0000 0.969 0.000 1.000 0.000 0.000 0.000
#> SRR934259 2 0.0000 0.969 0.000 1.000 0.000 0.000 0.000
#> SRR934260 2 0.0000 0.969 0.000 1.000 0.000 0.000 0.000
#> SRR934261 2 0.0000 0.969 0.000 1.000 0.000 0.000 0.000
#> SRR934262 2 0.0000 0.969 0.000 1.000 0.000 0.000 0.000
#> SRR934263 2 0.0000 0.969 0.000 1.000 0.000 0.000 0.000
#> SRR934264 5 0.2124 0.628 0.004 0.000 0.000 0.096 0.900
#> SRR934265 5 0.2124 0.628 0.004 0.000 0.000 0.096 0.900
#> SRR934266 5 0.2124 0.628 0.004 0.000 0.000 0.096 0.900
#> SRR934267 5 0.2124 0.628 0.004 0.000 0.000 0.096 0.900
#> SRR934268 5 0.2124 0.628 0.004 0.000 0.000 0.096 0.900
#> SRR934269 5 0.2124 0.628 0.004 0.000 0.000 0.096 0.900
#> SRR934270 5 0.2124 0.628 0.004 0.000 0.000 0.096 0.900
#> SRR934271 5 0.2124 0.628 0.004 0.000 0.000 0.096 0.900
#> SRR934272 1 0.6667 0.655 0.612 0.000 0.072 0.160 0.156
#> SRR934273 1 0.6667 0.655 0.612 0.000 0.072 0.160 0.156
#> SRR934274 1 0.6667 0.655 0.612 0.000 0.072 0.160 0.156
#> SRR934275 1 0.6667 0.655 0.612 0.000 0.072 0.160 0.156
#> SRR934276 1 0.6667 0.655 0.612 0.000 0.072 0.160 0.156
#> SRR934277 1 0.6667 0.655 0.612 0.000 0.072 0.160 0.156
#> SRR934278 1 0.6667 0.655 0.612 0.000 0.072 0.160 0.156
#> SRR934279 1 0.6667 0.655 0.612 0.000 0.072 0.160 0.156
#> SRR934280 1 0.1195 0.775 0.960 0.000 0.012 0.028 0.000
#> SRR934281 1 0.1195 0.775 0.960 0.000 0.012 0.028 0.000
#> SRR934282 1 0.1195 0.775 0.960 0.000 0.012 0.028 0.000
#> SRR934283 1 0.1195 0.775 0.960 0.000 0.012 0.028 0.000
#> SRR934284 1 0.1195 0.775 0.960 0.000 0.012 0.028 0.000
#> SRR934285 1 0.1195 0.775 0.960 0.000 0.012 0.028 0.000
#> SRR934286 1 0.1195 0.775 0.960 0.000 0.012 0.028 0.000
#> SRR934287 1 0.1195 0.775 0.960 0.000 0.012 0.028 0.000
#> SRR934288 3 0.2761 0.912 0.028 0.028 0.896 0.048 0.000
#> SRR934289 3 0.2761 0.912 0.028 0.028 0.896 0.048 0.000
#> SRR934290 3 0.2761 0.912 0.028 0.028 0.896 0.048 0.000
#> SRR934291 3 0.2761 0.912 0.028 0.028 0.896 0.048 0.000
#> SRR934292 3 0.2761 0.912 0.028 0.028 0.896 0.048 0.000
#> SRR934293 3 0.2761 0.912 0.028 0.028 0.896 0.048 0.000
#> SRR934294 3 0.2761 0.912 0.028 0.028 0.896 0.048 0.000
#> SRR934295 3 0.2761 0.912 0.028 0.028 0.896 0.048 0.000
#> SRR934296 2 0.2813 0.910 0.004 0.880 0.032 0.084 0.000
#> SRR934297 2 0.2813 0.910 0.004 0.880 0.032 0.084 0.000
#> SRR934298 2 0.2813 0.910 0.004 0.880 0.032 0.084 0.000
#> SRR934299 2 0.2813 0.910 0.004 0.880 0.032 0.084 0.000
#> SRR934300 2 0.2813 0.910 0.004 0.880 0.032 0.084 0.000
#> SRR934301 2 0.2813 0.910 0.004 0.880 0.032 0.084 0.000
#> SRR934302 2 0.2813 0.910 0.004 0.880 0.032 0.084 0.000
#> SRR934303 2 0.2813 0.910 0.004 0.880 0.032 0.084 0.000
#> SRR934304 5 0.3242 0.379 0.000 0.000 0.000 0.216 0.784
#> SRR934305 5 0.3242 0.379 0.000 0.000 0.000 0.216 0.784
#> SRR934306 5 0.3242 0.379 0.000 0.000 0.000 0.216 0.784
#> SRR934307 5 0.3242 0.379 0.000 0.000 0.000 0.216 0.784
#> SRR934308 5 0.3242 0.379 0.000 0.000 0.000 0.216 0.784
#> SRR934309 5 0.3242 0.379 0.000 0.000 0.000 0.216 0.784
#> SRR934310 5 0.3242 0.379 0.000 0.000 0.000 0.216 0.784
#> SRR934311 5 0.3242 0.379 0.000 0.000 0.000 0.216 0.784
#> SRR934312 1 0.1106 0.782 0.964 0.000 0.024 0.012 0.000
#> SRR934313 1 0.1106 0.782 0.964 0.000 0.024 0.012 0.000
#> SRR934314 1 0.1106 0.782 0.964 0.000 0.024 0.012 0.000
#> SRR934315 1 0.1106 0.782 0.964 0.000 0.024 0.012 0.000
#> SRR934316 1 0.1106 0.782 0.964 0.000 0.024 0.012 0.000
#> SRR934317 1 0.1106 0.782 0.964 0.000 0.024 0.012 0.000
#> SRR934318 1 0.1106 0.782 0.964 0.000 0.024 0.012 0.000
#> SRR934319 1 0.1106 0.782 0.964 0.000 0.024 0.012 0.000
#> SRR934320 1 0.5884 0.648 0.668 0.024 0.120 0.184 0.004
#> SRR934321 1 0.5884 0.648 0.668 0.024 0.120 0.184 0.004
#> SRR934322 1 0.5884 0.648 0.668 0.024 0.120 0.184 0.004
#> SRR934323 1 0.5884 0.648 0.668 0.024 0.120 0.184 0.004
#> SRR934324 1 0.5884 0.648 0.668 0.024 0.120 0.184 0.004
#> SRR934325 1 0.5884 0.648 0.668 0.024 0.120 0.184 0.004
#> SRR934326 1 0.5884 0.648 0.668 0.024 0.120 0.184 0.004
#> SRR934327 1 0.5884 0.648 0.668 0.024 0.120 0.184 0.004
#> SRR934328 3 0.0162 0.941 0.000 0.000 0.996 0.000 0.004
#> SRR934329 3 0.0162 0.941 0.000 0.000 0.996 0.000 0.004
#> SRR934330 3 0.0162 0.941 0.000 0.000 0.996 0.000 0.004
#> SRR934331 3 0.0162 0.941 0.000 0.000 0.996 0.000 0.004
#> SRR934332 3 0.0162 0.941 0.000 0.000 0.996 0.000 0.004
#> SRR934333 3 0.0162 0.941 0.000 0.000 0.996 0.000 0.004
#> SRR934334 3 0.0162 0.941 0.000 0.000 0.996 0.000 0.004
#> SRR934335 3 0.0162 0.941 0.000 0.000 0.996 0.000 0.004
#> SRR934344 3 0.1739 0.926 0.004 0.000 0.940 0.024 0.032
#> SRR934345 3 0.1739 0.926 0.004 0.000 0.940 0.024 0.032
#> SRR934346 3 0.1739 0.926 0.004 0.000 0.940 0.024 0.032
#> SRR934347 3 0.1739 0.926 0.004 0.000 0.940 0.024 0.032
#> SRR934348 3 0.1739 0.926 0.004 0.000 0.940 0.024 0.032
#> SRR934349 3 0.1739 0.926 0.004 0.000 0.940 0.024 0.032
#> SRR934350 3 0.1739 0.926 0.004 0.000 0.940 0.024 0.032
#> SRR934351 3 0.1739 0.926 0.004 0.000 0.940 0.024 0.032
#> SRR934336 1 0.5885 0.702 0.676 0.000 0.040 0.152 0.132
#> SRR934337 1 0.5885 0.702 0.676 0.000 0.040 0.152 0.132
#> SRR934338 1 0.5885 0.702 0.676 0.000 0.040 0.152 0.132
#> SRR934339 1 0.5885 0.702 0.676 0.000 0.040 0.152 0.132
#> SRR934340 1 0.5885 0.702 0.676 0.000 0.040 0.152 0.132
#> SRR934341 1 0.5885 0.702 0.676 0.000 0.040 0.152 0.132
#> SRR934342 1 0.5885 0.702 0.676 0.000 0.040 0.152 0.132
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 5 0.0146 0.689 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR934217 5 0.0146 0.689 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR934218 5 0.0146 0.689 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR934219 5 0.0146 0.689 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR934220 5 0.0146 0.689 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR934221 5 0.0146 0.689 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR934222 5 0.0146 0.689 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR934223 5 0.0146 0.689 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR934224 5 0.4980 0.528 0.000 0.000 0.052 0.012 0.576 0.360
#> SRR934225 5 0.4980 0.528 0.000 0.000 0.052 0.012 0.576 0.360
#> SRR934226 5 0.4980 0.528 0.000 0.000 0.052 0.012 0.576 0.360
#> SRR934227 5 0.4980 0.528 0.000 0.000 0.052 0.012 0.576 0.360
#> SRR934228 5 0.4980 0.528 0.000 0.000 0.052 0.012 0.576 0.360
#> SRR934229 5 0.4980 0.528 0.000 0.000 0.052 0.012 0.576 0.360
#> SRR934230 5 0.4980 0.528 0.000 0.000 0.052 0.012 0.576 0.360
#> SRR934231 5 0.4980 0.528 0.000 0.000 0.052 0.012 0.576 0.360
#> SRR934232 2 0.0632 0.905 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR934233 2 0.0632 0.905 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR934234 2 0.0632 0.905 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR934235 2 0.0632 0.905 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR934236 2 0.0632 0.905 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR934237 2 0.0632 0.905 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR934238 2 0.0632 0.905 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR934239 2 0.0632 0.905 0.000 0.976 0.000 0.024 0.000 0.000
#> SRR934240 2 0.0547 0.906 0.000 0.980 0.000 0.020 0.000 0.000
#> SRR934241 2 0.0547 0.906 0.000 0.980 0.000 0.020 0.000 0.000
#> SRR934242 2 0.0547 0.906 0.000 0.980 0.000 0.020 0.000 0.000
#> SRR934243 2 0.0547 0.906 0.000 0.980 0.000 0.020 0.000 0.000
#> SRR934244 2 0.0547 0.906 0.000 0.980 0.000 0.020 0.000 0.000
#> SRR934245 2 0.0547 0.906 0.000 0.980 0.000 0.020 0.000 0.000
#> SRR934246 2 0.0547 0.906 0.000 0.980 0.000 0.020 0.000 0.000
#> SRR934247 2 0.0547 0.906 0.000 0.980 0.000 0.020 0.000 0.000
#> SRR934248 4 0.2771 1.000 0.000 0.032 0.000 0.852 0.116 0.000
#> SRR934249 4 0.2771 1.000 0.000 0.032 0.000 0.852 0.116 0.000
#> SRR934250 4 0.2771 1.000 0.000 0.032 0.000 0.852 0.116 0.000
#> SRR934251 4 0.2771 1.000 0.000 0.032 0.000 0.852 0.116 0.000
#> SRR934252 4 0.2771 1.000 0.000 0.032 0.000 0.852 0.116 0.000
#> SRR934253 4 0.2771 1.000 0.000 0.032 0.000 0.852 0.116 0.000
#> SRR934254 4 0.2771 1.000 0.000 0.032 0.000 0.852 0.116 0.000
#> SRR934255 4 0.2771 1.000 0.000 0.032 0.000 0.852 0.116 0.000
#> SRR934256 2 0.0692 0.901 0.000 0.976 0.020 0.004 0.000 0.000
#> SRR934257 2 0.0692 0.901 0.000 0.976 0.020 0.004 0.000 0.000
#> SRR934258 2 0.0692 0.901 0.000 0.976 0.020 0.004 0.000 0.000
#> SRR934259 2 0.0692 0.901 0.000 0.976 0.020 0.004 0.000 0.000
#> SRR934260 2 0.0692 0.901 0.000 0.976 0.020 0.004 0.000 0.000
#> SRR934261 2 0.0692 0.901 0.000 0.976 0.020 0.004 0.000 0.000
#> SRR934262 2 0.0692 0.901 0.000 0.976 0.020 0.004 0.000 0.000
#> SRR934263 2 0.0692 0.901 0.000 0.976 0.020 0.004 0.000 0.000
#> SRR934264 5 0.4012 0.631 0.000 0.000 0.008 0.172 0.760 0.060
#> SRR934265 5 0.4012 0.631 0.000 0.000 0.008 0.172 0.760 0.060
#> SRR934266 5 0.4012 0.631 0.000 0.000 0.008 0.172 0.760 0.060
#> SRR934267 5 0.4012 0.631 0.000 0.000 0.008 0.172 0.760 0.060
#> SRR934268 5 0.4012 0.631 0.000 0.000 0.008 0.172 0.760 0.060
#> SRR934269 5 0.4012 0.631 0.000 0.000 0.008 0.172 0.760 0.060
#> SRR934270 5 0.4012 0.631 0.000 0.000 0.008 0.172 0.760 0.060
#> SRR934271 5 0.4012 0.631 0.000 0.000 0.008 0.172 0.760 0.060
#> SRR934272 6 0.2958 0.579 0.028 0.000 0.004 0.012 0.096 0.860
#> SRR934273 6 0.2958 0.579 0.028 0.000 0.004 0.012 0.096 0.860
#> SRR934274 6 0.2958 0.579 0.028 0.000 0.004 0.012 0.096 0.860
#> SRR934275 6 0.2958 0.579 0.028 0.000 0.004 0.012 0.096 0.860
#> SRR934276 6 0.2958 0.579 0.028 0.000 0.004 0.012 0.096 0.860
#> SRR934277 6 0.2958 0.579 0.028 0.000 0.004 0.012 0.096 0.860
#> SRR934278 6 0.2958 0.579 0.028 0.000 0.004 0.012 0.096 0.860
#> SRR934279 6 0.2958 0.579 0.028 0.000 0.004 0.012 0.096 0.860
#> SRR934280 6 0.3890 0.389 0.004 0.000 0.400 0.000 0.000 0.596
#> SRR934281 6 0.3890 0.389 0.004 0.000 0.400 0.000 0.000 0.596
#> SRR934282 6 0.3890 0.389 0.004 0.000 0.400 0.000 0.000 0.596
#> SRR934283 6 0.3890 0.389 0.004 0.000 0.400 0.000 0.000 0.596
#> SRR934284 6 0.3890 0.389 0.004 0.000 0.400 0.000 0.000 0.596
#> SRR934285 6 0.3890 0.389 0.004 0.000 0.400 0.000 0.000 0.596
#> SRR934286 6 0.3890 0.389 0.004 0.000 0.400 0.000 0.000 0.596
#> SRR934287 6 0.3890 0.389 0.004 0.000 0.400 0.000 0.000 0.596
#> SRR934288 1 0.3875 0.833 0.796 0.012 0.132 0.052 0.000 0.008
#> SRR934289 1 0.3875 0.833 0.796 0.012 0.132 0.052 0.000 0.008
#> SRR934290 1 0.3875 0.833 0.796 0.012 0.132 0.052 0.000 0.008
#> SRR934291 1 0.3875 0.833 0.796 0.012 0.132 0.052 0.000 0.008
#> SRR934292 1 0.3875 0.833 0.796 0.012 0.132 0.052 0.000 0.008
#> SRR934293 1 0.3875 0.833 0.796 0.012 0.132 0.052 0.000 0.008
#> SRR934294 1 0.3875 0.833 0.796 0.012 0.132 0.052 0.000 0.008
#> SRR934295 1 0.3875 0.833 0.796 0.012 0.132 0.052 0.000 0.008
#> SRR934296 2 0.5107 0.734 0.036 0.688 0.172 0.104 0.000 0.000
#> SRR934297 2 0.5107 0.734 0.036 0.688 0.172 0.104 0.000 0.000
#> SRR934298 2 0.5107 0.734 0.036 0.688 0.172 0.104 0.000 0.000
#> SRR934299 2 0.5107 0.734 0.036 0.688 0.172 0.104 0.000 0.000
#> SRR934300 2 0.5107 0.734 0.036 0.688 0.172 0.104 0.000 0.000
#> SRR934301 2 0.5107 0.734 0.036 0.688 0.172 0.104 0.000 0.000
#> SRR934302 2 0.5107 0.734 0.036 0.688 0.172 0.104 0.000 0.000
#> SRR934303 2 0.5107 0.734 0.036 0.688 0.172 0.104 0.000 0.000
#> SRR934304 5 0.3715 0.521 0.000 0.000 0.048 0.188 0.764 0.000
#> SRR934305 5 0.3715 0.521 0.000 0.000 0.048 0.188 0.764 0.000
#> SRR934306 5 0.3715 0.521 0.000 0.000 0.048 0.188 0.764 0.000
#> SRR934307 5 0.3715 0.521 0.000 0.000 0.048 0.188 0.764 0.000
#> SRR934308 5 0.3715 0.521 0.000 0.000 0.048 0.188 0.764 0.000
#> SRR934309 5 0.3715 0.521 0.000 0.000 0.048 0.188 0.764 0.000
#> SRR934310 5 0.3715 0.521 0.000 0.000 0.048 0.188 0.764 0.000
#> SRR934311 5 0.3715 0.521 0.000 0.000 0.048 0.188 0.764 0.000
#> SRR934312 6 0.3601 0.497 0.000 0.000 0.312 0.004 0.000 0.684
#> SRR934313 6 0.3601 0.497 0.000 0.000 0.312 0.004 0.000 0.684
#> SRR934314 6 0.3601 0.497 0.000 0.000 0.312 0.004 0.000 0.684
#> SRR934315 6 0.3601 0.497 0.000 0.000 0.312 0.004 0.000 0.684
#> SRR934316 6 0.3601 0.497 0.000 0.000 0.312 0.004 0.000 0.684
#> SRR934317 6 0.3601 0.497 0.000 0.000 0.312 0.004 0.000 0.684
#> SRR934318 6 0.3601 0.497 0.000 0.000 0.312 0.004 0.000 0.684
#> SRR934319 6 0.3601 0.497 0.000 0.000 0.312 0.004 0.000 0.684
#> SRR934320 3 0.5069 1.000 0.064 0.028 0.692 0.012 0.000 0.204
#> SRR934321 3 0.5069 1.000 0.064 0.028 0.692 0.012 0.000 0.204
#> SRR934322 3 0.5069 1.000 0.064 0.028 0.692 0.012 0.000 0.204
#> SRR934323 3 0.5069 1.000 0.064 0.028 0.692 0.012 0.000 0.204
#> SRR934324 3 0.5069 1.000 0.064 0.028 0.692 0.012 0.000 0.204
#> SRR934325 3 0.5069 1.000 0.064 0.028 0.692 0.012 0.000 0.204
#> SRR934326 3 0.5069 1.000 0.064 0.028 0.692 0.012 0.000 0.204
#> SRR934327 3 0.5069 1.000 0.064 0.028 0.692 0.012 0.000 0.204
#> SRR934328 1 0.0363 0.903 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR934329 1 0.0363 0.903 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR934330 1 0.0363 0.903 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR934331 1 0.0363 0.903 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR934332 1 0.0363 0.903 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR934333 1 0.0363 0.903 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR934334 1 0.0363 0.903 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR934335 1 0.0363 0.903 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR934344 1 0.1757 0.891 0.928 0.000 0.012 0.000 0.008 0.052
#> SRR934345 1 0.1757 0.891 0.928 0.000 0.012 0.000 0.008 0.052
#> SRR934346 1 0.1757 0.891 0.928 0.000 0.012 0.000 0.008 0.052
#> SRR934347 1 0.1757 0.891 0.928 0.000 0.012 0.000 0.008 0.052
#> SRR934348 1 0.1757 0.891 0.928 0.000 0.012 0.000 0.008 0.052
#> SRR934349 1 0.1757 0.891 0.928 0.000 0.012 0.000 0.008 0.052
#> SRR934350 1 0.1757 0.891 0.928 0.000 0.012 0.000 0.008 0.052
#> SRR934351 1 0.1757 0.891 0.928 0.000 0.012 0.000 0.008 0.052
#> SRR934336 6 0.3308 0.594 0.012 0.000 0.088 0.000 0.064 0.836
#> SRR934337 6 0.3308 0.594 0.012 0.000 0.088 0.000 0.064 0.836
#> SRR934338 6 0.3308 0.594 0.012 0.000 0.088 0.000 0.064 0.836
#> SRR934339 6 0.3308 0.594 0.012 0.000 0.088 0.000 0.064 0.836
#> SRR934340 6 0.3308 0.594 0.012 0.000 0.088 0.000 0.064 0.836
#> SRR934341 6 0.3308 0.594 0.012 0.000 0.088 0.000 0.064 0.836
#> SRR934342 6 0.3308 0.594 0.012 0.000 0.088 0.000 0.064 0.836
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 14550 rows and 135 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 1.000 1.000 1.000 0.2952 0.705 0.705
#> 3 3 0.909 0.960 0.978 0.4708 0.832 0.762
#> 4 4 1.000 1.000 1.000 0.0828 0.993 0.987
#> 5 5 1.000 0.993 0.994 0.0380 0.986 0.973
#> 6 6 0.683 0.771 0.844 0.2735 1.000 1.000
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> SRR934216 1 0 1 1 0
#> SRR934217 1 0 1 1 0
#> SRR934218 1 0 1 1 0
#> SRR934219 1 0 1 1 0
#> SRR934220 1 0 1 1 0
#> SRR934221 1 0 1 1 0
#> SRR934222 1 0 1 1 0
#> SRR934223 1 0 1 1 0
#> SRR934224 1 0 1 1 0
#> SRR934225 1 0 1 1 0
#> SRR934226 1 0 1 1 0
#> SRR934227 1 0 1 1 0
#> SRR934228 1 0 1 1 0
#> SRR934229 1 0 1 1 0
#> SRR934230 1 0 1 1 0
#> SRR934231 1 0 1 1 0
#> SRR934232 2 0 1 0 1
#> SRR934233 2 0 1 0 1
#> SRR934234 2 0 1 0 1
#> SRR934235 2 0 1 0 1
#> SRR934236 2 0 1 0 1
#> SRR934237 2 0 1 0 1
#> SRR934238 2 0 1 0 1
#> SRR934239 2 0 1 0 1
#> SRR934240 2 0 1 0 1
#> SRR934241 2 0 1 0 1
#> SRR934242 2 0 1 0 1
#> SRR934243 2 0 1 0 1
#> SRR934244 2 0 1 0 1
#> SRR934245 2 0 1 0 1
#> SRR934246 2 0 1 0 1
#> SRR934247 2 0 1 0 1
#> SRR934248 1 0 1 1 0
#> SRR934249 1 0 1 1 0
#> SRR934250 1 0 1 1 0
#> SRR934251 1 0 1 1 0
#> SRR934252 1 0 1 1 0
#> SRR934253 1 0 1 1 0
#> SRR934254 1 0 1 1 0
#> SRR934255 1 0 1 1 0
#> SRR934256 2 0 1 0 1
#> SRR934257 2 0 1 0 1
#> SRR934258 2 0 1 0 1
#> SRR934259 2 0 1 0 1
#> SRR934260 2 0 1 0 1
#> SRR934261 2 0 1 0 1
#> SRR934262 2 0 1 0 1
#> SRR934263 2 0 1 0 1
#> SRR934264 1 0 1 1 0
#> SRR934265 1 0 1 1 0
#> SRR934266 1 0 1 1 0
#> SRR934267 1 0 1 1 0
#> SRR934268 1 0 1 1 0
#> SRR934269 1 0 1 1 0
#> SRR934270 1 0 1 1 0
#> SRR934271 1 0 1 1 0
#> SRR934272 1 0 1 1 0
#> SRR934273 1 0 1 1 0
#> SRR934274 1 0 1 1 0
#> SRR934275 1 0 1 1 0
#> SRR934276 1 0 1 1 0
#> SRR934277 1 0 1 1 0
#> SRR934278 1 0 1 1 0
#> SRR934279 1 0 1 1 0
#> SRR934280 1 0 1 1 0
#> SRR934281 1 0 1 1 0
#> SRR934282 1 0 1 1 0
#> SRR934283 1 0 1 1 0
#> SRR934284 1 0 1 1 0
#> SRR934285 1 0 1 1 0
#> SRR934286 1 0 1 1 0
#> SRR934287 1 0 1 1 0
#> SRR934288 1 0 1 1 0
#> SRR934289 1 0 1 1 0
#> SRR934290 1 0 1 1 0
#> SRR934291 1 0 1 1 0
#> SRR934292 1 0 1 1 0
#> SRR934293 1 0 1 1 0
#> SRR934294 1 0 1 1 0
#> SRR934295 1 0 1 1 0
#> SRR934296 1 0 1 1 0
#> SRR934297 1 0 1 1 0
#> SRR934298 1 0 1 1 0
#> SRR934299 1 0 1 1 0
#> SRR934300 1 0 1 1 0
#> SRR934301 1 0 1 1 0
#> SRR934302 1 0 1 1 0
#> SRR934303 1 0 1 1 0
#> SRR934304 1 0 1 1 0
#> SRR934305 1 0 1 1 0
#> SRR934306 1 0 1 1 0
#> SRR934307 1 0 1 1 0
#> SRR934308 1 0 1 1 0
#> SRR934309 1 0 1 1 0
#> SRR934310 1 0 1 1 0
#> SRR934311 1 0 1 1 0
#> SRR934312 1 0 1 1 0
#> SRR934313 1 0 1 1 0
#> SRR934314 1 0 1 1 0
#> SRR934315 1 0 1 1 0
#> SRR934316 1 0 1 1 0
#> SRR934317 1 0 1 1 0
#> SRR934318 1 0 1 1 0
#> SRR934319 1 0 1 1 0
#> SRR934320 1 0 1 1 0
#> SRR934321 1 0 1 1 0
#> SRR934322 1 0 1 1 0
#> SRR934323 1 0 1 1 0
#> SRR934324 1 0 1 1 0
#> SRR934325 1 0 1 1 0
#> SRR934326 1 0 1 1 0
#> SRR934327 1 0 1 1 0
#> SRR934328 1 0 1 1 0
#> SRR934329 1 0 1 1 0
#> SRR934330 1 0 1 1 0
#> SRR934331 1 0 1 1 0
#> SRR934332 1 0 1 1 0
#> SRR934333 1 0 1 1 0
#> SRR934334 1 0 1 1 0
#> SRR934335 1 0 1 1 0
#> SRR934344 1 0 1 1 0
#> SRR934345 1 0 1 1 0
#> SRR934346 1 0 1 1 0
#> SRR934347 1 0 1 1 0
#> SRR934348 1 0 1 1 0
#> SRR934349 1 0 1 1 0
#> SRR934350 1 0 1 1 0
#> SRR934351 1 0 1 1 0
#> SRR934336 1 0 1 1 0
#> SRR934337 1 0 1 1 0
#> SRR934338 1 0 1 1 0
#> SRR934339 1 0 1 1 0
#> SRR934340 1 0 1 1 0
#> SRR934341 1 0 1 1 0
#> SRR934342 1 0 1 1 0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 3 0.603 0.679 0.376 0 0.624
#> SRR934217 3 0.603 0.679 0.376 0 0.624
#> SRR934218 3 0.603 0.679 0.376 0 0.624
#> SRR934219 3 0.603 0.679 0.376 0 0.624
#> SRR934220 3 0.603 0.679 0.376 0 0.624
#> SRR934221 3 0.603 0.679 0.376 0 0.624
#> SRR934222 3 0.603 0.679 0.376 0 0.624
#> SRR934223 3 0.603 0.679 0.376 0 0.624
#> SRR934224 1 0.000 1.000 1.000 0 0.000
#> SRR934225 1 0.000 1.000 1.000 0 0.000
#> SRR934226 1 0.000 1.000 1.000 0 0.000
#> SRR934227 1 0.000 1.000 1.000 0 0.000
#> SRR934228 1 0.000 1.000 1.000 0 0.000
#> SRR934229 1 0.000 1.000 1.000 0 0.000
#> SRR934230 1 0.000 1.000 1.000 0 0.000
#> SRR934231 1 0.000 1.000 1.000 0 0.000
#> SRR934232 2 0.000 1.000 0.000 1 0.000
#> SRR934233 2 0.000 1.000 0.000 1 0.000
#> SRR934234 2 0.000 1.000 0.000 1 0.000
#> SRR934235 2 0.000 1.000 0.000 1 0.000
#> SRR934236 2 0.000 1.000 0.000 1 0.000
#> SRR934237 2 0.000 1.000 0.000 1 0.000
#> SRR934238 2 0.000 1.000 0.000 1 0.000
#> SRR934239 2 0.000 1.000 0.000 1 0.000
#> SRR934240 2 0.000 1.000 0.000 1 0.000
#> SRR934241 2 0.000 1.000 0.000 1 0.000
#> SRR934242 2 0.000 1.000 0.000 1 0.000
#> SRR934243 2 0.000 1.000 0.000 1 0.000
#> SRR934244 2 0.000 1.000 0.000 1 0.000
#> SRR934245 2 0.000 1.000 0.000 1 0.000
#> SRR934246 2 0.000 1.000 0.000 1 0.000
#> SRR934247 2 0.000 1.000 0.000 1 0.000
#> SRR934248 1 0.000 1.000 1.000 0 0.000
#> SRR934249 1 0.000 1.000 1.000 0 0.000
#> SRR934250 1 0.000 1.000 1.000 0 0.000
#> SRR934251 1 0.000 1.000 1.000 0 0.000
#> SRR934252 1 0.000 1.000 1.000 0 0.000
#> SRR934253 1 0.000 1.000 1.000 0 0.000
#> SRR934254 1 0.000 1.000 1.000 0 0.000
#> SRR934255 1 0.000 1.000 1.000 0 0.000
#> SRR934256 2 0.000 1.000 0.000 1 0.000
#> SRR934257 2 0.000 1.000 0.000 1 0.000
#> SRR934258 2 0.000 1.000 0.000 1 0.000
#> SRR934259 2 0.000 1.000 0.000 1 0.000
#> SRR934260 2 0.000 1.000 0.000 1 0.000
#> SRR934261 2 0.000 1.000 0.000 1 0.000
#> SRR934262 2 0.000 1.000 0.000 1 0.000
#> SRR934263 2 0.000 1.000 0.000 1 0.000
#> SRR934264 1 0.000 1.000 1.000 0 0.000
#> SRR934265 1 0.000 1.000 1.000 0 0.000
#> SRR934266 1 0.000 1.000 1.000 0 0.000
#> SRR934267 1 0.000 1.000 1.000 0 0.000
#> SRR934268 1 0.000 1.000 1.000 0 0.000
#> SRR934269 1 0.000 1.000 1.000 0 0.000
#> SRR934270 1 0.000 1.000 1.000 0 0.000
#> SRR934271 1 0.000 1.000 1.000 0 0.000
#> SRR934272 1 0.000 1.000 1.000 0 0.000
#> SRR934273 1 0.000 1.000 1.000 0 0.000
#> SRR934274 1 0.000 1.000 1.000 0 0.000
#> SRR934275 1 0.000 1.000 1.000 0 0.000
#> SRR934276 1 0.000 1.000 1.000 0 0.000
#> SRR934277 1 0.000 1.000 1.000 0 0.000
#> SRR934278 1 0.000 1.000 1.000 0 0.000
#> SRR934279 1 0.000 1.000 1.000 0 0.000
#> SRR934280 1 0.000 1.000 1.000 0 0.000
#> SRR934281 1 0.000 1.000 1.000 0 0.000
#> SRR934282 1 0.000 1.000 1.000 0 0.000
#> SRR934283 1 0.000 1.000 1.000 0 0.000
#> SRR934284 1 0.000 1.000 1.000 0 0.000
#> SRR934285 1 0.000 1.000 1.000 0 0.000
#> SRR934286 1 0.000 1.000 1.000 0 0.000
#> SRR934287 1 0.000 1.000 1.000 0 0.000
#> SRR934288 1 0.000 1.000 1.000 0 0.000
#> SRR934289 1 0.000 1.000 1.000 0 0.000
#> SRR934290 1 0.000 1.000 1.000 0 0.000
#> SRR934291 1 0.000 1.000 1.000 0 0.000
#> SRR934292 1 0.000 1.000 1.000 0 0.000
#> SRR934293 1 0.000 1.000 1.000 0 0.000
#> SRR934294 1 0.000 1.000 1.000 0 0.000
#> SRR934295 1 0.000 1.000 1.000 0 0.000
#> SRR934296 1 0.000 1.000 1.000 0 0.000
#> SRR934297 1 0.000 1.000 1.000 0 0.000
#> SRR934298 1 0.000 1.000 1.000 0 0.000
#> SRR934299 1 0.000 1.000 1.000 0 0.000
#> SRR934300 1 0.000 1.000 1.000 0 0.000
#> SRR934301 1 0.000 1.000 1.000 0 0.000
#> SRR934302 1 0.000 1.000 1.000 0 0.000
#> SRR934303 1 0.000 1.000 1.000 0 0.000
#> SRR934304 3 0.000 0.643 0.000 0 1.000
#> SRR934305 3 0.000 0.643 0.000 0 1.000
#> SRR934306 3 0.000 0.643 0.000 0 1.000
#> SRR934307 3 0.000 0.643 0.000 0 1.000
#> SRR934308 3 0.000 0.643 0.000 0 1.000
#> SRR934309 3 0.000 0.643 0.000 0 1.000
#> SRR934310 3 0.000 0.643 0.000 0 1.000
#> SRR934311 3 0.000 0.643 0.000 0 1.000
#> SRR934312 1 0.000 1.000 1.000 0 0.000
#> SRR934313 1 0.000 1.000 1.000 0 0.000
#> SRR934314 1 0.000 1.000 1.000 0 0.000
#> SRR934315 1 0.000 1.000 1.000 0 0.000
#> SRR934316 1 0.000 1.000 1.000 0 0.000
#> SRR934317 1 0.000 1.000 1.000 0 0.000
#> SRR934318 1 0.000 1.000 1.000 0 0.000
#> SRR934319 1 0.000 1.000 1.000 0 0.000
#> SRR934320 1 0.000 1.000 1.000 0 0.000
#> SRR934321 1 0.000 1.000 1.000 0 0.000
#> SRR934322 1 0.000 1.000 1.000 0 0.000
#> SRR934323 1 0.000 1.000 1.000 0 0.000
#> SRR934324 1 0.000 1.000 1.000 0 0.000
#> SRR934325 1 0.000 1.000 1.000 0 0.000
#> SRR934326 1 0.000 1.000 1.000 0 0.000
#> SRR934327 1 0.000 1.000 1.000 0 0.000
#> SRR934328 1 0.000 1.000 1.000 0 0.000
#> SRR934329 1 0.000 1.000 1.000 0 0.000
#> SRR934330 1 0.000 1.000 1.000 0 0.000
#> SRR934331 1 0.000 1.000 1.000 0 0.000
#> SRR934332 1 0.000 1.000 1.000 0 0.000
#> SRR934333 1 0.000 1.000 1.000 0 0.000
#> SRR934334 1 0.000 1.000 1.000 0 0.000
#> SRR934335 1 0.000 1.000 1.000 0 0.000
#> SRR934344 1 0.000 1.000 1.000 0 0.000
#> SRR934345 1 0.000 1.000 1.000 0 0.000
#> SRR934346 1 0.000 1.000 1.000 0 0.000
#> SRR934347 1 0.000 1.000 1.000 0 0.000
#> SRR934348 1 0.000 1.000 1.000 0 0.000
#> SRR934349 1 0.000 1.000 1.000 0 0.000
#> SRR934350 1 0.000 1.000 1.000 0 0.000
#> SRR934351 1 0.000 1.000 1.000 0 0.000
#> SRR934336 1 0.000 1.000 1.000 0 0.000
#> SRR934337 1 0.000 1.000 1.000 0 0.000
#> SRR934338 1 0.000 1.000 1.000 0 0.000
#> SRR934339 1 0.000 1.000 1.000 0 0.000
#> SRR934340 1 0.000 1.000 1.000 0 0.000
#> SRR934341 1 0.000 1.000 1.000 0 0.000
#> SRR934342 1 0.000 1.000 1.000 0 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 3 0 1 0 0 1 0
#> SRR934217 3 0 1 0 0 1 0
#> SRR934218 3 0 1 0 0 1 0
#> SRR934219 3 0 1 0 0 1 0
#> SRR934220 3 0 1 0 0 1 0
#> SRR934221 3 0 1 0 0 1 0
#> SRR934222 3 0 1 0 0 1 0
#> SRR934223 3 0 1 0 0 1 0
#> SRR934224 1 0 1 1 0 0 0
#> SRR934225 1 0 1 1 0 0 0
#> SRR934226 1 0 1 1 0 0 0
#> SRR934227 1 0 1 1 0 0 0
#> SRR934228 1 0 1 1 0 0 0
#> SRR934229 1 0 1 1 0 0 0
#> SRR934230 1 0 1 1 0 0 0
#> SRR934231 1 0 1 1 0 0 0
#> SRR934232 2 0 1 0 1 0 0
#> SRR934233 2 0 1 0 1 0 0
#> SRR934234 2 0 1 0 1 0 0
#> SRR934235 2 0 1 0 1 0 0
#> SRR934236 2 0 1 0 1 0 0
#> SRR934237 2 0 1 0 1 0 0
#> SRR934238 2 0 1 0 1 0 0
#> SRR934239 2 0 1 0 1 0 0
#> SRR934240 2 0 1 0 1 0 0
#> SRR934241 2 0 1 0 1 0 0
#> SRR934242 2 0 1 0 1 0 0
#> SRR934243 2 0 1 0 1 0 0
#> SRR934244 2 0 1 0 1 0 0
#> SRR934245 2 0 1 0 1 0 0
#> SRR934246 2 0 1 0 1 0 0
#> SRR934247 2 0 1 0 1 0 0
#> SRR934248 1 0 1 1 0 0 0
#> SRR934249 1 0 1 1 0 0 0
#> SRR934250 1 0 1 1 0 0 0
#> SRR934251 1 0 1 1 0 0 0
#> SRR934252 1 0 1 1 0 0 0
#> SRR934253 1 0 1 1 0 0 0
#> SRR934254 1 0 1 1 0 0 0
#> SRR934255 1 0 1 1 0 0 0
#> SRR934256 2 0 1 0 1 0 0
#> SRR934257 2 0 1 0 1 0 0
#> SRR934258 2 0 1 0 1 0 0
#> SRR934259 2 0 1 0 1 0 0
#> SRR934260 2 0 1 0 1 0 0
#> SRR934261 2 0 1 0 1 0 0
#> SRR934262 2 0 1 0 1 0 0
#> SRR934263 2 0 1 0 1 0 0
#> SRR934264 1 0 1 1 0 0 0
#> SRR934265 1 0 1 1 0 0 0
#> SRR934266 1 0 1 1 0 0 0
#> SRR934267 1 0 1 1 0 0 0
#> SRR934268 1 0 1 1 0 0 0
#> SRR934269 1 0 1 1 0 0 0
#> SRR934270 1 0 1 1 0 0 0
#> SRR934271 1 0 1 1 0 0 0
#> SRR934272 1 0 1 1 0 0 0
#> SRR934273 1 0 1 1 0 0 0
#> SRR934274 1 0 1 1 0 0 0
#> SRR934275 1 0 1 1 0 0 0
#> SRR934276 1 0 1 1 0 0 0
#> SRR934277 1 0 1 1 0 0 0
#> SRR934278 1 0 1 1 0 0 0
#> SRR934279 1 0 1 1 0 0 0
#> SRR934280 1 0 1 1 0 0 0
#> SRR934281 1 0 1 1 0 0 0
#> SRR934282 1 0 1 1 0 0 0
#> SRR934283 1 0 1 1 0 0 0
#> SRR934284 1 0 1 1 0 0 0
#> SRR934285 1 0 1 1 0 0 0
#> SRR934286 1 0 1 1 0 0 0
#> SRR934287 1 0 1 1 0 0 0
#> SRR934288 1 0 1 1 0 0 0
#> SRR934289 1 0 1 1 0 0 0
#> SRR934290 1 0 1 1 0 0 0
#> SRR934291 1 0 1 1 0 0 0
#> SRR934292 1 0 1 1 0 0 0
#> SRR934293 1 0 1 1 0 0 0
#> SRR934294 1 0 1 1 0 0 0
#> SRR934295 1 0 1 1 0 0 0
#> SRR934296 1 0 1 1 0 0 0
#> SRR934297 1 0 1 1 0 0 0
#> SRR934298 1 0 1 1 0 0 0
#> SRR934299 1 0 1 1 0 0 0
#> SRR934300 1 0 1 1 0 0 0
#> SRR934301 1 0 1 1 0 0 0
#> SRR934302 1 0 1 1 0 0 0
#> SRR934303 1 0 1 1 0 0 0
#> SRR934304 4 0 1 0 0 0 1
#> SRR934305 4 0 1 0 0 0 1
#> SRR934306 4 0 1 0 0 0 1
#> SRR934307 4 0 1 0 0 0 1
#> SRR934308 4 0 1 0 0 0 1
#> SRR934309 4 0 1 0 0 0 1
#> SRR934310 4 0 1 0 0 0 1
#> SRR934311 4 0 1 0 0 0 1
#> SRR934312 1 0 1 1 0 0 0
#> SRR934313 1 0 1 1 0 0 0
#> SRR934314 1 0 1 1 0 0 0
#> SRR934315 1 0 1 1 0 0 0
#> SRR934316 1 0 1 1 0 0 0
#> SRR934317 1 0 1 1 0 0 0
#> SRR934318 1 0 1 1 0 0 0
#> SRR934319 1 0 1 1 0 0 0
#> SRR934320 1 0 1 1 0 0 0
#> SRR934321 1 0 1 1 0 0 0
#> SRR934322 1 0 1 1 0 0 0
#> SRR934323 1 0 1 1 0 0 0
#> SRR934324 1 0 1 1 0 0 0
#> SRR934325 1 0 1 1 0 0 0
#> SRR934326 1 0 1 1 0 0 0
#> SRR934327 1 0 1 1 0 0 0
#> SRR934328 1 0 1 1 0 0 0
#> SRR934329 1 0 1 1 0 0 0
#> SRR934330 1 0 1 1 0 0 0
#> SRR934331 1 0 1 1 0 0 0
#> SRR934332 1 0 1 1 0 0 0
#> SRR934333 1 0 1 1 0 0 0
#> SRR934334 1 0 1 1 0 0 0
#> SRR934335 1 0 1 1 0 0 0
#> SRR934344 1 0 1 1 0 0 0
#> SRR934345 1 0 1 1 0 0 0
#> SRR934346 1 0 1 1 0 0 0
#> SRR934347 1 0 1 1 0 0 0
#> SRR934348 1 0 1 1 0 0 0
#> SRR934349 1 0 1 1 0 0 0
#> SRR934350 1 0 1 1 0 0 0
#> SRR934351 1 0 1 1 0 0 0
#> SRR934336 1 0 1 1 0 0 0
#> SRR934337 1 0 1 1 0 0 0
#> SRR934338 1 0 1 1 0 0 0
#> SRR934339 1 0 1 1 0 0 0
#> SRR934340 1 0 1 1 0 0 0
#> SRR934341 1 0 1 1 0 0 0
#> SRR934342 1 0 1 1 0 0 0
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934217 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934218 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934219 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934220 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934221 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934222 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934223 3 0.0000 1.000 0.000 0.000 1 0.000 0
#> SRR934224 1 0.0290 0.992 0.992 0.000 0 0.008 0
#> SRR934225 1 0.0290 0.992 0.992 0.000 0 0.008 0
#> SRR934226 1 0.0290 0.992 0.992 0.000 0 0.008 0
#> SRR934227 1 0.0290 0.992 0.992 0.000 0 0.008 0
#> SRR934228 1 0.0290 0.992 0.992 0.000 0 0.008 0
#> SRR934229 1 0.0290 0.992 0.992 0.000 0 0.008 0
#> SRR934230 1 0.0290 0.992 0.992 0.000 0 0.008 0
#> SRR934231 1 0.0290 0.992 0.992 0.000 0 0.008 0
#> SRR934232 2 0.0000 0.991 0.000 1.000 0 0.000 0
#> SRR934233 2 0.0000 0.991 0.000 1.000 0 0.000 0
#> SRR934234 2 0.0000 0.991 0.000 1.000 0 0.000 0
#> SRR934235 2 0.0000 0.991 0.000 1.000 0 0.000 0
#> SRR934236 2 0.0000 0.991 0.000 1.000 0 0.000 0
#> SRR934237 2 0.0000 0.991 0.000 1.000 0 0.000 0
#> SRR934238 2 0.0000 0.991 0.000 1.000 0 0.000 0
#> SRR934239 2 0.0000 0.991 0.000 1.000 0 0.000 0
#> SRR934240 2 0.0404 0.991 0.000 0.988 0 0.012 0
#> SRR934241 2 0.0404 0.991 0.000 0.988 0 0.012 0
#> SRR934242 2 0.0404 0.991 0.000 0.988 0 0.012 0
#> SRR934243 2 0.0404 0.991 0.000 0.988 0 0.012 0
#> SRR934244 2 0.0404 0.991 0.000 0.988 0 0.012 0
#> SRR934245 2 0.0404 0.991 0.000 0.988 0 0.012 0
#> SRR934246 2 0.0404 0.991 0.000 0.988 0 0.012 0
#> SRR934247 2 0.0404 0.991 0.000 0.988 0 0.012 0
#> SRR934248 1 0.0162 0.993 0.996 0.000 0 0.004 0
#> SRR934249 1 0.0162 0.993 0.996 0.000 0 0.004 0
#> SRR934250 1 0.0162 0.993 0.996 0.000 0 0.004 0
#> SRR934251 1 0.0162 0.993 0.996 0.000 0 0.004 0
#> SRR934252 1 0.0162 0.993 0.996 0.000 0 0.004 0
#> SRR934253 1 0.0162 0.993 0.996 0.000 0 0.004 0
#> SRR934254 1 0.0162 0.993 0.996 0.000 0 0.004 0
#> SRR934255 1 0.0162 0.993 0.996 0.000 0 0.004 0
#> SRR934256 4 0.0404 1.000 0.000 0.012 0 0.988 0
#> SRR934257 4 0.0404 1.000 0.000 0.012 0 0.988 0
#> SRR934258 4 0.0404 1.000 0.000 0.012 0 0.988 0
#> SRR934259 4 0.0404 1.000 0.000 0.012 0 0.988 0
#> SRR934260 4 0.0404 1.000 0.000 0.012 0 0.988 0
#> SRR934261 4 0.0404 1.000 0.000 0.012 0 0.988 0
#> SRR934262 4 0.0404 1.000 0.000 0.012 0 0.988 0
#> SRR934263 4 0.0404 1.000 0.000 0.012 0 0.988 0
#> SRR934264 1 0.0290 0.992 0.992 0.000 0 0.008 0
#> SRR934265 1 0.0290 0.992 0.992 0.000 0 0.008 0
#> SRR934266 1 0.0290 0.992 0.992 0.000 0 0.008 0
#> SRR934267 1 0.0290 0.992 0.992 0.000 0 0.008 0
#> SRR934268 1 0.0290 0.992 0.992 0.000 0 0.008 0
#> SRR934269 1 0.0290 0.992 0.992 0.000 0 0.008 0
#> SRR934270 1 0.0290 0.992 0.992 0.000 0 0.008 0
#> SRR934271 1 0.0290 0.992 0.992 0.000 0 0.008 0
#> SRR934272 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934273 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934274 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934275 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934276 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934277 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934278 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934279 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934280 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934281 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934282 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934283 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934284 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934285 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934286 1 0.0162 0.993 0.996 0.004 0 0.000 0
#> SRR934287 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934288 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934289 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934290 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934291 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934292 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934293 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934294 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934295 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934296 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934297 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934298 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934299 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934300 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934301 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934302 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934303 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934304 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934305 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934306 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934307 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934308 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934309 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934310 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934311 5 0.0000 1.000 0.000 0.000 0 0.000 1
#> SRR934312 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934313 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934314 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934315 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934316 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934317 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934318 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934319 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934320 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934321 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934322 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934323 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934324 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934325 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934326 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934327 1 0.0566 0.988 0.984 0.012 0 0.004 0
#> SRR934328 1 0.0162 0.993 0.996 0.000 0 0.004 0
#> SRR934329 1 0.0162 0.993 0.996 0.000 0 0.004 0
#> SRR934330 1 0.0162 0.993 0.996 0.000 0 0.004 0
#> SRR934331 1 0.0162 0.993 0.996 0.000 0 0.004 0
#> SRR934332 1 0.0162 0.993 0.996 0.000 0 0.004 0
#> SRR934333 1 0.0162 0.993 0.996 0.000 0 0.004 0
#> SRR934334 1 0.0162 0.993 0.996 0.000 0 0.004 0
#> SRR934335 1 0.0162 0.993 0.996 0.000 0 0.004 0
#> SRR934344 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934345 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934346 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934347 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934348 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934349 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934350 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934351 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934336 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934337 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934338 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934339 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934340 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934341 1 0.0000 0.994 1.000 0.000 0 0.000 0
#> SRR934342 1 0.0000 0.994 1.000 0.000 0 0.000 0
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.0000 1.000 0.000 0.000 1 NA 0 0
#> SRR934217 3 0.0000 1.000 0.000 0.000 1 NA 0 0
#> SRR934218 3 0.0000 1.000 0.000 0.000 1 NA 0 0
#> SRR934219 3 0.0000 1.000 0.000 0.000 1 NA 0 0
#> SRR934220 3 0.0000 1.000 0.000 0.000 1 NA 0 0
#> SRR934221 3 0.0000 1.000 0.000 0.000 1 NA 0 0
#> SRR934222 3 0.0000 1.000 0.000 0.000 1 NA 0 0
#> SRR934223 3 0.0000 1.000 0.000 0.000 1 NA 0 0
#> SRR934224 1 0.1387 0.769 0.932 0.000 0 NA 0 0
#> SRR934225 1 0.1387 0.769 0.932 0.000 0 NA 0 0
#> SRR934226 1 0.1387 0.769 0.932 0.000 0 NA 0 0
#> SRR934227 1 0.1387 0.769 0.932 0.000 0 NA 0 0
#> SRR934228 1 0.1387 0.769 0.932 0.000 0 NA 0 0
#> SRR934229 1 0.1387 0.769 0.932 0.000 0 NA 0 0
#> SRR934230 1 0.1387 0.769 0.932 0.000 0 NA 0 0
#> SRR934231 1 0.1387 0.769 0.932 0.000 0 NA 0 0
#> SRR934232 2 0.0260 0.893 0.000 0.992 0 NA 0 0
#> SRR934233 2 0.0260 0.893 0.000 0.992 0 NA 0 0
#> SRR934234 2 0.0260 0.893 0.000 0.992 0 NA 0 0
#> SRR934235 2 0.0260 0.893 0.000 0.992 0 NA 0 0
#> SRR934236 2 0.0260 0.893 0.000 0.992 0 NA 0 0
#> SRR934237 2 0.0260 0.893 0.000 0.992 0 NA 0 0
#> SRR934238 2 0.0260 0.893 0.000 0.992 0 NA 0 0
#> SRR934239 2 0.0260 0.893 0.000 0.992 0 NA 0 0
#> SRR934240 2 0.2941 0.893 0.000 0.780 0 NA 0 0
#> SRR934241 2 0.2941 0.893 0.000 0.780 0 NA 0 0
#> SRR934242 2 0.2941 0.893 0.000 0.780 0 NA 0 0
#> SRR934243 2 0.2941 0.893 0.000 0.780 0 NA 0 0
#> SRR934244 2 0.2941 0.893 0.000 0.780 0 NA 0 0
#> SRR934245 2 0.2941 0.893 0.000 0.780 0 NA 0 0
#> SRR934246 2 0.2941 0.893 0.000 0.780 0 NA 0 0
#> SRR934247 2 0.2941 0.893 0.000 0.780 0 NA 0 0
#> SRR934248 1 0.3847 0.388 0.544 0.000 0 NA 0 0
#> SRR934249 1 0.3847 0.388 0.544 0.000 0 NA 0 0
#> SRR934250 1 0.3847 0.388 0.544 0.000 0 NA 0 0
#> SRR934251 1 0.3847 0.388 0.544 0.000 0 NA 0 0
#> SRR934252 1 0.3847 0.388 0.544 0.000 0 NA 0 0
#> SRR934253 1 0.3847 0.388 0.544 0.000 0 NA 0 0
#> SRR934254 1 0.3847 0.388 0.544 0.000 0 NA 0 0
#> SRR934255 1 0.3847 0.388 0.544 0.000 0 NA 0 0
#> SRR934256 6 0.0000 1.000 0.000 0.000 0 NA 0 1
#> SRR934257 6 0.0000 1.000 0.000 0.000 0 NA 0 1
#> SRR934258 6 0.0000 1.000 0.000 0.000 0 NA 0 1
#> SRR934259 6 0.0000 1.000 0.000 0.000 0 NA 0 1
#> SRR934260 6 0.0000 1.000 0.000 0.000 0 NA 0 1
#> SRR934261 6 0.0000 1.000 0.000 0.000 0 NA 0 1
#> SRR934262 6 0.0000 1.000 0.000 0.000 0 NA 0 1
#> SRR934263 6 0.0000 1.000 0.000 0.000 0 NA 0 1
#> SRR934264 1 0.3851 0.383 0.540 0.000 0 NA 0 0
#> SRR934265 1 0.3851 0.383 0.540 0.000 0 NA 0 0
#> SRR934266 1 0.3851 0.383 0.540 0.000 0 NA 0 0
#> SRR934267 1 0.3851 0.383 0.540 0.000 0 NA 0 0
#> SRR934268 1 0.3851 0.383 0.540 0.000 0 NA 0 0
#> SRR934269 1 0.3851 0.383 0.540 0.000 0 NA 0 0
#> SRR934270 1 0.3851 0.383 0.540 0.000 0 NA 0 0
#> SRR934271 1 0.3851 0.383 0.540 0.000 0 NA 0 0
#> SRR934272 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934273 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934274 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934275 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934276 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934277 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934278 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934279 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934280 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934281 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934282 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934283 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934284 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934285 1 0.0146 0.797 0.996 0.000 0 NA 0 0
#> SRR934286 1 0.0146 0.797 0.996 0.000 0 NA 0 0
#> SRR934287 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934288 1 0.3499 0.686 0.680 0.000 0 NA 0 0
#> SRR934289 1 0.3499 0.686 0.680 0.000 0 NA 0 0
#> SRR934290 1 0.3499 0.686 0.680 0.000 0 NA 0 0
#> SRR934291 1 0.3499 0.686 0.680 0.000 0 NA 0 0
#> SRR934292 1 0.3499 0.686 0.680 0.000 0 NA 0 0
#> SRR934293 1 0.3499 0.686 0.680 0.000 0 NA 0 0
#> SRR934294 1 0.3499 0.686 0.680 0.000 0 NA 0 0
#> SRR934295 1 0.3499 0.686 0.680 0.000 0 NA 0 0
#> SRR934296 1 0.3371 0.701 0.708 0.000 0 NA 0 0
#> SRR934297 1 0.3371 0.701 0.708 0.000 0 NA 0 0
#> SRR934298 1 0.3371 0.701 0.708 0.000 0 NA 0 0
#> SRR934299 1 0.3371 0.701 0.708 0.000 0 NA 0 0
#> SRR934300 1 0.3371 0.701 0.708 0.000 0 NA 0 0
#> SRR934301 1 0.3371 0.701 0.708 0.000 0 NA 0 0
#> SRR934302 1 0.3371 0.701 0.708 0.000 0 NA 0 0
#> SRR934303 1 0.3371 0.701 0.708 0.000 0 NA 0 0
#> SRR934304 5 0.0000 1.000 0.000 0.000 0 NA 1 0
#> SRR934305 5 0.0000 1.000 0.000 0.000 0 NA 1 0
#> SRR934306 5 0.0000 1.000 0.000 0.000 0 NA 1 0
#> SRR934307 5 0.0000 1.000 0.000 0.000 0 NA 1 0
#> SRR934308 5 0.0000 1.000 0.000 0.000 0 NA 1 0
#> SRR934309 5 0.0000 1.000 0.000 0.000 0 NA 1 0
#> SRR934310 5 0.0000 1.000 0.000 0.000 0 NA 1 0
#> SRR934311 5 0.0000 1.000 0.000 0.000 0 NA 1 0
#> SRR934312 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934313 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934314 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934315 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934316 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934317 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934318 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934319 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934320 1 0.3151 0.725 0.748 0.000 0 NA 0 0
#> SRR934321 1 0.3175 0.724 0.744 0.000 0 NA 0 0
#> SRR934322 1 0.3101 0.728 0.756 0.000 0 NA 0 0
#> SRR934323 1 0.3076 0.730 0.760 0.000 0 NA 0 0
#> SRR934324 1 0.3126 0.727 0.752 0.000 0 NA 0 0
#> SRR934325 1 0.3050 0.732 0.764 0.000 0 NA 0 0
#> SRR934326 1 0.3175 0.723 0.744 0.000 0 NA 0 0
#> SRR934327 1 0.3198 0.721 0.740 0.000 0 NA 0 0
#> SRR934328 1 0.3464 0.693 0.688 0.000 0 NA 0 0
#> SRR934329 1 0.3464 0.693 0.688 0.000 0 NA 0 0
#> SRR934330 1 0.3464 0.693 0.688 0.000 0 NA 0 0
#> SRR934331 1 0.3464 0.693 0.688 0.000 0 NA 0 0
#> SRR934332 1 0.3464 0.693 0.688 0.000 0 NA 0 0
#> SRR934333 1 0.3464 0.693 0.688 0.000 0 NA 0 0
#> SRR934334 1 0.3464 0.693 0.688 0.000 0 NA 0 0
#> SRR934335 1 0.3464 0.693 0.688 0.000 0 NA 0 0
#> SRR934344 1 0.0713 0.793 0.972 0.000 0 NA 0 0
#> SRR934345 1 0.0713 0.793 0.972 0.000 0 NA 0 0
#> SRR934346 1 0.0713 0.793 0.972 0.000 0 NA 0 0
#> SRR934347 1 0.0713 0.793 0.972 0.000 0 NA 0 0
#> SRR934348 1 0.0713 0.793 0.972 0.000 0 NA 0 0
#> SRR934349 1 0.0713 0.793 0.972 0.000 0 NA 0 0
#> SRR934350 1 0.0713 0.793 0.972 0.000 0 NA 0 0
#> SRR934351 1 0.0713 0.793 0.972 0.000 0 NA 0 0
#> SRR934336 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934337 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934338 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934339 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934340 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934341 1 0.0000 0.797 1.000 0.000 0 NA 0 0
#> SRR934342 1 0.0000 0.797 1.000 0.000 0 NA 0 0
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 14550 rows and 135 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.636 0.881 0.914 0.4560 0.498 0.498
#> 3 3 0.649 0.670 0.821 0.2191 0.803 0.648
#> 4 4 0.845 0.912 0.933 0.0597 0.943 0.873
#> 5 5 0.769 0.884 0.936 0.0542 0.993 0.983
#> 6 6 0.856 0.865 0.923 0.0682 0.972 0.930
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
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
#> SRR934216 2 0.0000 0.798 0.000 1.000
#> SRR934217 2 0.0000 0.798 0.000 1.000
#> SRR934218 2 0.0000 0.798 0.000 1.000
#> SRR934219 2 0.0000 0.798 0.000 1.000
#> SRR934220 2 0.0000 0.798 0.000 1.000
#> SRR934221 2 0.0000 0.798 0.000 1.000
#> SRR934222 2 0.0000 0.798 0.000 1.000
#> SRR934223 2 0.0000 0.798 0.000 1.000
#> SRR934224 1 0.2778 0.946 0.952 0.048
#> SRR934225 1 0.2778 0.946 0.952 0.048
#> SRR934226 1 0.2603 0.951 0.956 0.044
#> SRR934227 1 0.2778 0.946 0.952 0.048
#> SRR934228 1 0.2948 0.941 0.948 0.052
#> SRR934229 1 0.2778 0.946 0.952 0.048
#> SRR934230 1 0.1414 0.976 0.980 0.020
#> SRR934231 1 0.2603 0.951 0.956 0.044
#> SRR934232 2 0.0672 0.799 0.008 0.992
#> SRR934233 2 0.1184 0.798 0.016 0.984
#> SRR934234 2 0.0376 0.799 0.004 0.996
#> SRR934235 2 0.0376 0.799 0.004 0.996
#> SRR934236 2 0.0376 0.799 0.004 0.996
#> SRR934237 2 0.0376 0.799 0.004 0.996
#> SRR934238 2 0.1633 0.797 0.024 0.976
#> SRR934239 2 0.0672 0.799 0.008 0.992
#> SRR934240 2 0.0000 0.798 0.000 1.000
#> SRR934241 2 0.0000 0.798 0.000 1.000
#> SRR934242 2 0.0000 0.798 0.000 1.000
#> SRR934243 2 0.0000 0.798 0.000 1.000
#> SRR934244 2 0.0000 0.798 0.000 1.000
#> SRR934245 2 0.0000 0.798 0.000 1.000
#> SRR934246 2 0.0000 0.798 0.000 1.000
#> SRR934247 2 0.0000 0.798 0.000 1.000
#> SRR934248 2 0.9323 0.730 0.348 0.652
#> SRR934249 2 0.9323 0.730 0.348 0.652
#> SRR934250 2 0.9323 0.730 0.348 0.652
#> SRR934251 2 0.9323 0.730 0.348 0.652
#> SRR934252 2 0.9323 0.730 0.348 0.652
#> SRR934253 2 0.9323 0.730 0.348 0.652
#> SRR934254 2 0.9323 0.730 0.348 0.652
#> SRR934255 2 0.9323 0.730 0.348 0.652
#> SRR934256 2 0.9323 0.730 0.348 0.652
#> SRR934257 2 0.9323 0.730 0.348 0.652
#> SRR934258 2 0.9323 0.730 0.348 0.652
#> SRR934259 2 0.9323 0.730 0.348 0.652
#> SRR934260 2 0.9323 0.730 0.348 0.652
#> SRR934261 2 0.9323 0.730 0.348 0.652
#> SRR934262 2 0.9323 0.730 0.348 0.652
#> SRR934263 2 0.9323 0.730 0.348 0.652
#> SRR934264 2 0.9323 0.730 0.348 0.652
#> SRR934265 2 0.9323 0.730 0.348 0.652
#> SRR934266 2 0.9323 0.730 0.348 0.652
#> SRR934267 2 0.9323 0.730 0.348 0.652
#> SRR934268 2 0.9323 0.730 0.348 0.652
#> SRR934269 2 0.9323 0.730 0.348 0.652
#> SRR934270 2 0.9323 0.730 0.348 0.652
#> SRR934271 2 0.9323 0.730 0.348 0.652
#> SRR934272 1 0.0376 0.990 0.996 0.004
#> SRR934273 1 0.0376 0.990 0.996 0.004
#> SRR934274 1 0.0376 0.990 0.996 0.004
#> SRR934275 1 0.0376 0.990 0.996 0.004
#> SRR934276 1 0.0376 0.990 0.996 0.004
#> SRR934277 1 0.0376 0.990 0.996 0.004
#> SRR934278 1 0.0376 0.990 0.996 0.004
#> SRR934279 1 0.0376 0.990 0.996 0.004
#> SRR934280 1 0.0000 0.993 1.000 0.000
#> SRR934281 1 0.0000 0.993 1.000 0.000
#> SRR934282 1 0.0000 0.993 1.000 0.000
#> SRR934283 1 0.0000 0.993 1.000 0.000
#> SRR934284 1 0.0000 0.993 1.000 0.000
#> SRR934285 1 0.0000 0.993 1.000 0.000
#> SRR934286 1 0.0000 0.993 1.000 0.000
#> SRR934287 1 0.0000 0.993 1.000 0.000
#> SRR934288 1 0.0000 0.993 1.000 0.000
#> SRR934289 1 0.0000 0.993 1.000 0.000
#> SRR934290 1 0.0000 0.993 1.000 0.000
#> SRR934291 1 0.0000 0.993 1.000 0.000
#> SRR934292 1 0.0000 0.993 1.000 0.000
#> SRR934293 1 0.0000 0.993 1.000 0.000
#> SRR934294 1 0.0000 0.993 1.000 0.000
#> SRR934295 1 0.0000 0.993 1.000 0.000
#> SRR934296 2 0.9393 0.720 0.356 0.644
#> SRR934297 2 0.9393 0.720 0.356 0.644
#> SRR934298 2 0.9393 0.720 0.356 0.644
#> SRR934299 2 0.9393 0.720 0.356 0.644
#> SRR934300 2 0.9393 0.720 0.356 0.644
#> SRR934301 2 0.9393 0.720 0.356 0.644
#> SRR934302 2 0.9393 0.720 0.356 0.644
#> SRR934303 2 0.9393 0.720 0.356 0.644
#> SRR934304 2 0.0000 0.798 0.000 1.000
#> SRR934305 2 0.0000 0.798 0.000 1.000
#> SRR934306 2 0.0000 0.798 0.000 1.000
#> SRR934307 2 0.0000 0.798 0.000 1.000
#> SRR934308 2 0.0000 0.798 0.000 1.000
#> SRR934309 2 0.0000 0.798 0.000 1.000
#> SRR934310 2 0.0000 0.798 0.000 1.000
#> SRR934311 2 0.0000 0.798 0.000 1.000
#> SRR934312 1 0.0000 0.993 1.000 0.000
#> SRR934313 1 0.0000 0.993 1.000 0.000
#> SRR934314 1 0.0000 0.993 1.000 0.000
#> SRR934315 1 0.0000 0.993 1.000 0.000
#> SRR934316 1 0.0000 0.993 1.000 0.000
#> SRR934317 1 0.0000 0.993 1.000 0.000
#> SRR934318 1 0.0000 0.993 1.000 0.000
#> SRR934319 1 0.0000 0.993 1.000 0.000
#> SRR934320 1 0.0000 0.993 1.000 0.000
#> SRR934321 1 0.0000 0.993 1.000 0.000
#> SRR934322 1 0.0000 0.993 1.000 0.000
#> SRR934323 1 0.0000 0.993 1.000 0.000
#> SRR934324 1 0.0000 0.993 1.000 0.000
#> SRR934325 1 0.0000 0.993 1.000 0.000
#> SRR934326 1 0.0000 0.993 1.000 0.000
#> SRR934327 1 0.0000 0.993 1.000 0.000
#> SRR934328 1 0.0000 0.993 1.000 0.000
#> SRR934329 1 0.0000 0.993 1.000 0.000
#> SRR934330 1 0.0000 0.993 1.000 0.000
#> SRR934331 1 0.0000 0.993 1.000 0.000
#> SRR934332 1 0.0000 0.993 1.000 0.000
#> SRR934333 1 0.0000 0.993 1.000 0.000
#> SRR934334 1 0.0000 0.993 1.000 0.000
#> SRR934335 1 0.0000 0.993 1.000 0.000
#> SRR934344 1 0.0000 0.993 1.000 0.000
#> SRR934345 1 0.0000 0.993 1.000 0.000
#> SRR934346 1 0.0000 0.993 1.000 0.000
#> SRR934347 1 0.0000 0.993 1.000 0.000
#> SRR934348 1 0.0000 0.993 1.000 0.000
#> SRR934349 1 0.0000 0.993 1.000 0.000
#> SRR934350 1 0.0000 0.993 1.000 0.000
#> SRR934351 1 0.0000 0.993 1.000 0.000
#> SRR934336 1 0.0000 0.993 1.000 0.000
#> SRR934337 1 0.0000 0.993 1.000 0.000
#> SRR934338 1 0.0000 0.993 1.000 0.000
#> SRR934339 1 0.0000 0.993 1.000 0.000
#> SRR934340 1 0.0000 0.993 1.000 0.000
#> SRR934341 1 0.0000 0.993 1.000 0.000
#> SRR934342 1 0.0000 0.993 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 3 0.0000 0.6416 0.000 0.000 1.000
#> SRR934217 3 0.0000 0.6416 0.000 0.000 1.000
#> SRR934218 3 0.0000 0.6416 0.000 0.000 1.000
#> SRR934219 3 0.0000 0.6416 0.000 0.000 1.000
#> SRR934220 3 0.0000 0.6416 0.000 0.000 1.000
#> SRR934221 3 0.0000 0.6416 0.000 0.000 1.000
#> SRR934222 3 0.0000 0.6416 0.000 0.000 1.000
#> SRR934223 3 0.0000 0.6416 0.000 0.000 1.000
#> SRR934224 1 0.5580 0.6532 0.736 0.008 0.256
#> SRR934225 1 0.5580 0.6532 0.736 0.008 0.256
#> SRR934226 1 0.5580 0.6532 0.736 0.008 0.256
#> SRR934227 1 0.5580 0.6532 0.736 0.008 0.256
#> SRR934228 1 0.5580 0.6532 0.736 0.008 0.256
#> SRR934229 1 0.5580 0.6532 0.736 0.008 0.256
#> SRR934230 1 0.5580 0.6532 0.736 0.008 0.256
#> SRR934231 1 0.5580 0.6532 0.736 0.008 0.256
#> SRR934232 2 0.6513 0.4070 0.004 0.520 0.476
#> SRR934233 2 0.6505 0.4075 0.004 0.528 0.468
#> SRR934234 2 0.6505 0.4082 0.004 0.528 0.468
#> SRR934235 2 0.6505 0.4082 0.004 0.528 0.468
#> SRR934236 2 0.6513 0.4070 0.004 0.520 0.476
#> SRR934237 2 0.6518 0.4034 0.004 0.512 0.484
#> SRR934238 2 0.6500 0.4069 0.004 0.532 0.464
#> SRR934239 2 0.6505 0.4082 0.004 0.528 0.468
#> SRR934240 2 0.6309 0.3969 0.000 0.504 0.496
#> SRR934241 2 0.6309 0.3969 0.000 0.504 0.496
#> SRR934242 2 0.6309 0.3969 0.000 0.504 0.496
#> SRR934243 2 0.6309 0.3969 0.000 0.504 0.496
#> SRR934244 2 0.6309 0.3969 0.000 0.504 0.496
#> SRR934245 2 0.6309 0.3969 0.000 0.504 0.496
#> SRR934246 2 0.6309 0.3969 0.000 0.504 0.496
#> SRR934247 2 0.6309 0.3969 0.000 0.504 0.496
#> SRR934248 2 0.7181 0.3644 0.024 0.508 0.468
#> SRR934249 2 0.7181 0.3644 0.024 0.508 0.468
#> SRR934250 2 0.7181 0.3644 0.024 0.508 0.468
#> SRR934251 2 0.7181 0.3644 0.024 0.508 0.468
#> SRR934252 2 0.7181 0.3644 0.024 0.508 0.468
#> SRR934253 2 0.7181 0.3644 0.024 0.508 0.468
#> SRR934254 2 0.7181 0.3644 0.024 0.508 0.468
#> SRR934255 2 0.7181 0.3644 0.024 0.508 0.468
#> SRR934256 2 0.6936 -0.0156 0.016 0.524 0.460
#> SRR934257 2 0.6936 -0.0156 0.016 0.524 0.460
#> SRR934258 2 0.6936 -0.0156 0.016 0.524 0.460
#> SRR934259 2 0.6936 -0.0156 0.016 0.524 0.460
#> SRR934260 2 0.6936 -0.0156 0.016 0.524 0.460
#> SRR934261 2 0.6936 -0.0156 0.016 0.524 0.460
#> SRR934262 2 0.6936 -0.0156 0.016 0.524 0.460
#> SRR934263 2 0.6936 -0.0156 0.016 0.524 0.460
#> SRR934264 3 0.8957 -0.0400 0.128 0.400 0.472
#> SRR934265 3 0.8957 -0.0400 0.128 0.400 0.472
#> SRR934266 3 0.8957 -0.0400 0.128 0.400 0.472
#> SRR934267 3 0.8957 -0.0400 0.128 0.400 0.472
#> SRR934268 3 0.8957 -0.0400 0.128 0.400 0.472
#> SRR934269 3 0.8957 -0.0400 0.128 0.400 0.472
#> SRR934270 3 0.8957 -0.0400 0.128 0.400 0.472
#> SRR934271 3 0.8957 -0.0400 0.128 0.400 0.472
#> SRR934272 1 0.1411 0.9277 0.964 0.000 0.036
#> SRR934273 1 0.1411 0.9277 0.964 0.000 0.036
#> SRR934274 1 0.1411 0.9277 0.964 0.000 0.036
#> SRR934275 1 0.1411 0.9277 0.964 0.000 0.036
#> SRR934276 1 0.1411 0.9277 0.964 0.000 0.036
#> SRR934277 1 0.1411 0.9277 0.964 0.000 0.036
#> SRR934278 1 0.1411 0.9277 0.964 0.000 0.036
#> SRR934279 1 0.1411 0.9277 0.964 0.000 0.036
#> SRR934280 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934281 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934282 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934283 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934284 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934285 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934286 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934287 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934288 1 0.0424 0.9433 0.992 0.008 0.000
#> SRR934289 1 0.0424 0.9433 0.992 0.008 0.000
#> SRR934290 1 0.0424 0.9433 0.992 0.008 0.000
#> SRR934291 1 0.0424 0.9433 0.992 0.008 0.000
#> SRR934292 1 0.0424 0.9433 0.992 0.008 0.000
#> SRR934293 1 0.0424 0.9433 0.992 0.008 0.000
#> SRR934294 1 0.0424 0.9433 0.992 0.008 0.000
#> SRR934295 1 0.0424 0.9433 0.992 0.008 0.000
#> SRR934296 1 0.3983 0.8231 0.852 0.004 0.144
#> SRR934297 1 0.3983 0.8231 0.852 0.004 0.144
#> SRR934298 1 0.3983 0.8231 0.852 0.004 0.144
#> SRR934299 1 0.3983 0.8231 0.852 0.004 0.144
#> SRR934300 1 0.3983 0.8231 0.852 0.004 0.144
#> SRR934301 1 0.3983 0.8231 0.852 0.004 0.144
#> SRR934302 1 0.3983 0.8231 0.852 0.004 0.144
#> SRR934303 1 0.3983 0.8231 0.852 0.004 0.144
#> SRR934304 3 0.0424 0.6406 0.000 0.008 0.992
#> SRR934305 3 0.0424 0.6406 0.000 0.008 0.992
#> SRR934306 3 0.0424 0.6406 0.000 0.008 0.992
#> SRR934307 3 0.0424 0.6406 0.000 0.008 0.992
#> SRR934308 3 0.0424 0.6406 0.000 0.008 0.992
#> SRR934309 3 0.0424 0.6406 0.000 0.008 0.992
#> SRR934310 3 0.0424 0.6406 0.000 0.008 0.992
#> SRR934311 3 0.0424 0.6406 0.000 0.008 0.992
#> SRR934312 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934313 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934314 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934315 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934316 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934317 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934318 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934319 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934320 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934321 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934322 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934323 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934324 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934325 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934326 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934327 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934328 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934329 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934330 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934331 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934332 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934333 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934334 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934335 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934344 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934345 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934346 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934347 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934348 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934349 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934350 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934351 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934336 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934337 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934338 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934339 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934340 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934341 1 0.0000 0.9464 1.000 0.000 0.000
#> SRR934342 1 0.0424 0.9433 0.992 0.008 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 3 0.4164 0.780 0.000 0.264 0.736 0.000
#> SRR934217 3 0.4164 0.780 0.000 0.264 0.736 0.000
#> SRR934218 3 0.4164 0.780 0.000 0.264 0.736 0.000
#> SRR934219 3 0.4164 0.780 0.000 0.264 0.736 0.000
#> SRR934220 3 0.4164 0.780 0.000 0.264 0.736 0.000
#> SRR934221 3 0.4164 0.780 0.000 0.264 0.736 0.000
#> SRR934222 3 0.4164 0.780 0.000 0.264 0.736 0.000
#> SRR934223 3 0.4164 0.780 0.000 0.264 0.736 0.000
#> SRR934224 1 0.0859 0.951 0.980 0.004 0.008 0.008
#> SRR934225 1 0.0859 0.951 0.980 0.004 0.008 0.008
#> SRR934226 1 0.0859 0.951 0.980 0.004 0.008 0.008
#> SRR934227 1 0.0859 0.951 0.980 0.004 0.008 0.008
#> SRR934228 1 0.0859 0.951 0.980 0.004 0.008 0.008
#> SRR934229 1 0.0859 0.951 0.980 0.004 0.008 0.008
#> SRR934230 1 0.0859 0.951 0.980 0.004 0.008 0.008
#> SRR934231 1 0.0859 0.951 0.980 0.004 0.008 0.008
#> SRR934232 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> SRR934233 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> SRR934234 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> SRR934235 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> SRR934236 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> SRR934237 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> SRR934238 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> SRR934239 2 0.0000 0.946 0.000 1.000 0.000 0.000
#> SRR934240 2 0.0336 0.945 0.000 0.992 0.000 0.008
#> SRR934241 2 0.0336 0.945 0.000 0.992 0.000 0.008
#> SRR934242 2 0.0336 0.945 0.000 0.992 0.000 0.008
#> SRR934243 2 0.0336 0.945 0.000 0.992 0.000 0.008
#> SRR934244 2 0.0336 0.945 0.000 0.992 0.000 0.008
#> SRR934245 2 0.0336 0.945 0.000 0.992 0.000 0.008
#> SRR934246 2 0.0336 0.945 0.000 0.992 0.000 0.008
#> SRR934247 2 0.0336 0.945 0.000 0.992 0.000 0.008
#> SRR934248 2 0.0921 0.942 0.000 0.972 0.028 0.000
#> SRR934249 2 0.0921 0.942 0.000 0.972 0.028 0.000
#> SRR934250 2 0.0921 0.942 0.000 0.972 0.028 0.000
#> SRR934251 2 0.0921 0.942 0.000 0.972 0.028 0.000
#> SRR934252 2 0.0921 0.942 0.000 0.972 0.028 0.000
#> SRR934253 2 0.0921 0.942 0.000 0.972 0.028 0.000
#> SRR934254 2 0.0921 0.942 0.000 0.972 0.028 0.000
#> SRR934255 2 0.0921 0.942 0.000 0.972 0.028 0.000
#> SRR934256 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR934257 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR934258 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR934259 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR934260 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR934261 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR934262 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR934263 4 0.0336 1.000 0.000 0.000 0.008 0.992
#> SRR934264 2 0.3295 0.872 0.072 0.884 0.036 0.008
#> SRR934265 2 0.3295 0.872 0.072 0.884 0.036 0.008
#> SRR934266 2 0.3295 0.872 0.072 0.884 0.036 0.008
#> SRR934267 2 0.3295 0.872 0.072 0.884 0.036 0.008
#> SRR934268 2 0.3295 0.872 0.072 0.884 0.036 0.008
#> SRR934269 2 0.3295 0.872 0.072 0.884 0.036 0.008
#> SRR934270 2 0.3295 0.872 0.072 0.884 0.036 0.008
#> SRR934271 2 0.3295 0.872 0.072 0.884 0.036 0.008
#> SRR934272 1 0.1486 0.937 0.960 0.024 0.008 0.008
#> SRR934273 1 0.1486 0.937 0.960 0.024 0.008 0.008
#> SRR934274 1 0.1486 0.937 0.960 0.024 0.008 0.008
#> SRR934275 1 0.1486 0.937 0.960 0.024 0.008 0.008
#> SRR934276 1 0.1486 0.937 0.960 0.024 0.008 0.008
#> SRR934277 1 0.1486 0.937 0.960 0.024 0.008 0.008
#> SRR934278 1 0.1486 0.937 0.960 0.024 0.008 0.008
#> SRR934279 1 0.1486 0.937 0.960 0.024 0.008 0.008
#> SRR934280 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934281 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934282 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934283 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934284 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934285 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934286 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934287 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934288 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934289 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934290 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934291 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934292 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934293 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934294 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934295 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934296 1 0.4482 0.641 0.728 0.264 0.008 0.000
#> SRR934297 1 0.4482 0.641 0.728 0.264 0.008 0.000
#> SRR934298 1 0.4482 0.641 0.728 0.264 0.008 0.000
#> SRR934299 1 0.4482 0.641 0.728 0.264 0.008 0.000
#> SRR934300 1 0.4482 0.641 0.728 0.264 0.008 0.000
#> SRR934301 1 0.4482 0.641 0.728 0.264 0.008 0.000
#> SRR934302 1 0.4482 0.641 0.728 0.264 0.008 0.000
#> SRR934303 1 0.4482 0.641 0.728 0.264 0.008 0.000
#> SRR934304 3 0.0000 0.764 0.000 0.000 1.000 0.000
#> SRR934305 3 0.0000 0.764 0.000 0.000 1.000 0.000
#> SRR934306 3 0.0000 0.764 0.000 0.000 1.000 0.000
#> SRR934307 3 0.0000 0.764 0.000 0.000 1.000 0.000
#> SRR934308 3 0.0000 0.764 0.000 0.000 1.000 0.000
#> SRR934309 3 0.0000 0.764 0.000 0.000 1.000 0.000
#> SRR934310 3 0.0000 0.764 0.000 0.000 1.000 0.000
#> SRR934311 3 0.0000 0.764 0.000 0.000 1.000 0.000
#> SRR934312 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934313 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934314 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934315 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934316 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934317 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934318 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934319 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934320 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934321 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934322 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934323 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934324 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934325 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934326 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934327 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934328 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934329 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934330 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934331 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934332 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934333 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934334 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934335 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934344 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934345 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934346 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934347 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934348 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934349 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934350 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934351 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934336 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934337 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934338 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934339 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934340 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934341 1 0.0000 0.961 1.000 0.000 0.000 0.000
#> SRR934342 1 0.0000 0.961 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 3 0.104 1.000 0.000 0 0.960 0.000 0.04
#> SRR934217 3 0.104 1.000 0.000 0 0.960 0.000 0.04
#> SRR934218 3 0.104 1.000 0.000 0 0.960 0.000 0.04
#> SRR934219 3 0.104 1.000 0.000 0 0.960 0.000 0.04
#> SRR934220 3 0.104 1.000 0.000 0 0.960 0.000 0.04
#> SRR934221 3 0.104 1.000 0.000 0 0.960 0.000 0.04
#> SRR934222 3 0.104 1.000 0.000 0 0.960 0.000 0.04
#> SRR934223 3 0.104 1.000 0.000 0 0.960 0.000 0.04
#> SRR934224 1 0.340 0.747 0.764 0 0.236 0.000 0.00
#> SRR934225 1 0.340 0.747 0.764 0 0.236 0.000 0.00
#> SRR934226 1 0.340 0.747 0.764 0 0.236 0.000 0.00
#> SRR934227 1 0.340 0.747 0.764 0 0.236 0.000 0.00
#> SRR934228 1 0.340 0.747 0.764 0 0.236 0.000 0.00
#> SRR934229 1 0.340 0.747 0.764 0 0.236 0.000 0.00
#> SRR934230 1 0.340 0.747 0.764 0 0.236 0.000 0.00
#> SRR934231 1 0.340 0.747 0.764 0 0.236 0.000 0.00
#> SRR934232 4 0.000 0.857 0.000 0 0.000 1.000 0.00
#> SRR934233 4 0.000 0.857 0.000 0 0.000 1.000 0.00
#> SRR934234 4 0.000 0.857 0.000 0 0.000 1.000 0.00
#> SRR934235 4 0.000 0.857 0.000 0 0.000 1.000 0.00
#> SRR934236 4 0.000 0.857 0.000 0 0.000 1.000 0.00
#> SRR934237 4 0.000 0.857 0.000 0 0.000 1.000 0.00
#> SRR934238 4 0.000 0.857 0.000 0 0.000 1.000 0.00
#> SRR934239 4 0.000 0.857 0.000 0 0.000 1.000 0.00
#> SRR934240 4 0.000 0.857 0.000 0 0.000 1.000 0.00
#> SRR934241 4 0.000 0.857 0.000 0 0.000 1.000 0.00
#> SRR934242 4 0.000 0.857 0.000 0 0.000 1.000 0.00
#> SRR934243 4 0.000 0.857 0.000 0 0.000 1.000 0.00
#> SRR934244 4 0.000 0.857 0.000 0 0.000 1.000 0.00
#> SRR934245 4 0.000 0.857 0.000 0 0.000 1.000 0.00
#> SRR934246 4 0.000 0.857 0.000 0 0.000 1.000 0.00
#> SRR934247 4 0.000 0.857 0.000 0 0.000 1.000 0.00
#> SRR934248 4 0.154 0.846 0.000 0 0.068 0.932 0.00
#> SRR934249 4 0.154 0.846 0.000 0 0.068 0.932 0.00
#> SRR934250 4 0.154 0.846 0.000 0 0.068 0.932 0.00
#> SRR934251 4 0.154 0.846 0.000 0 0.068 0.932 0.00
#> SRR934252 4 0.154 0.846 0.000 0 0.068 0.932 0.00
#> SRR934253 4 0.154 0.846 0.000 0 0.068 0.932 0.00
#> SRR934254 4 0.154 0.846 0.000 0 0.068 0.932 0.00
#> SRR934255 4 0.154 0.846 0.000 0 0.068 0.932 0.00
#> SRR934256 2 0.000 1.000 0.000 1 0.000 0.000 0.00
#> SRR934257 2 0.000 1.000 0.000 1 0.000 0.000 0.00
#> SRR934258 2 0.000 1.000 0.000 1 0.000 0.000 0.00
#> SRR934259 2 0.000 1.000 0.000 1 0.000 0.000 0.00
#> SRR934260 2 0.000 1.000 0.000 1 0.000 0.000 0.00
#> SRR934261 2 0.000 1.000 0.000 1 0.000 0.000 0.00
#> SRR934262 2 0.000 1.000 0.000 1 0.000 0.000 0.00
#> SRR934263 2 0.000 1.000 0.000 1 0.000 0.000 0.00
#> SRR934264 4 0.536 0.510 0.068 0 0.344 0.588 0.00
#> SRR934265 4 0.536 0.510 0.068 0 0.344 0.588 0.00
#> SRR934266 4 0.536 0.510 0.068 0 0.344 0.588 0.00
#> SRR934267 4 0.536 0.510 0.068 0 0.344 0.588 0.00
#> SRR934268 4 0.536 0.510 0.068 0 0.344 0.588 0.00
#> SRR934269 4 0.536 0.510 0.068 0 0.344 0.588 0.00
#> SRR934270 4 0.536 0.510 0.068 0 0.344 0.588 0.00
#> SRR934271 4 0.536 0.510 0.068 0 0.344 0.588 0.00
#> SRR934272 1 0.321 0.778 0.788 0 0.212 0.000 0.00
#> SRR934273 1 0.321 0.778 0.788 0 0.212 0.000 0.00
#> SRR934274 1 0.321 0.778 0.788 0 0.212 0.000 0.00
#> SRR934275 1 0.321 0.778 0.788 0 0.212 0.000 0.00
#> SRR934276 1 0.321 0.778 0.788 0 0.212 0.000 0.00
#> SRR934277 1 0.321 0.778 0.788 0 0.212 0.000 0.00
#> SRR934278 1 0.321 0.778 0.788 0 0.212 0.000 0.00
#> SRR934279 1 0.321 0.778 0.788 0 0.212 0.000 0.00
#> SRR934280 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934281 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934282 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934283 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934284 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934285 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934286 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934287 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934288 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934289 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934290 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934291 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934292 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934293 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934294 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934295 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934296 1 0.251 0.867 0.892 0 0.080 0.028 0.00
#> SRR934297 1 0.251 0.867 0.892 0 0.080 0.028 0.00
#> SRR934298 1 0.251 0.867 0.892 0 0.080 0.028 0.00
#> SRR934299 1 0.251 0.867 0.892 0 0.080 0.028 0.00
#> SRR934300 1 0.251 0.867 0.892 0 0.080 0.028 0.00
#> SRR934301 1 0.251 0.867 0.892 0 0.080 0.028 0.00
#> SRR934302 1 0.251 0.867 0.892 0 0.080 0.028 0.00
#> SRR934303 1 0.251 0.867 0.892 0 0.080 0.028 0.00
#> SRR934304 5 0.000 1.000 0.000 0 0.000 0.000 1.00
#> SRR934305 5 0.000 1.000 0.000 0 0.000 0.000 1.00
#> SRR934306 5 0.000 1.000 0.000 0 0.000 0.000 1.00
#> SRR934307 5 0.000 1.000 0.000 0 0.000 0.000 1.00
#> SRR934308 5 0.000 1.000 0.000 0 0.000 0.000 1.00
#> SRR934309 5 0.000 1.000 0.000 0 0.000 0.000 1.00
#> SRR934310 5 0.000 1.000 0.000 0 0.000 0.000 1.00
#> SRR934311 5 0.000 1.000 0.000 0 0.000 0.000 1.00
#> SRR934312 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934313 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934314 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934315 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934316 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934317 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934318 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934319 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934320 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934321 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934322 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934323 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934324 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934325 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934326 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934327 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934328 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934329 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934330 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934331 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934332 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934333 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934334 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934335 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934344 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934345 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934346 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934347 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934348 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934349 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934350 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934351 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934336 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934337 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934338 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934339 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934340 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934341 1 0.000 0.939 1.000 0 0.000 0.000 0.00
#> SRR934342 1 0.000 0.939 1.000 0 0.000 0.000 0.00
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 3 0.0000 1.000 0.000 0.000 1 0.000 0 0
#> SRR934217 3 0.0000 1.000 0.000 0.000 1 0.000 0 0
#> SRR934218 3 0.0000 1.000 0.000 0.000 1 0.000 0 0
#> SRR934219 3 0.0000 1.000 0.000 0.000 1 0.000 0 0
#> SRR934220 3 0.0000 1.000 0.000 0.000 1 0.000 0 0
#> SRR934221 3 0.0000 1.000 0.000 0.000 1 0.000 0 0
#> SRR934222 3 0.0000 1.000 0.000 0.000 1 0.000 0 0
#> SRR934223 3 0.0000 1.000 0.000 0.000 1 0.000 0 0
#> SRR934224 1 0.3446 0.658 0.692 0.000 0 0.308 0 0
#> SRR934225 1 0.3446 0.658 0.692 0.000 0 0.308 0 0
#> SRR934226 1 0.3446 0.658 0.692 0.000 0 0.308 0 0
#> SRR934227 1 0.3446 0.658 0.692 0.000 0 0.308 0 0
#> SRR934228 1 0.3446 0.658 0.692 0.000 0 0.308 0 0
#> SRR934229 1 0.3446 0.658 0.692 0.000 0 0.308 0 0
#> SRR934230 1 0.3446 0.658 0.692 0.000 0 0.308 0 0
#> SRR934231 1 0.3446 0.658 0.692 0.000 0 0.308 0 0
#> SRR934232 2 0.0000 1.000 0.000 1.000 0 0.000 0 0
#> SRR934233 2 0.0000 1.000 0.000 1.000 0 0.000 0 0
#> SRR934234 2 0.0000 1.000 0.000 1.000 0 0.000 0 0
#> SRR934235 2 0.0000 1.000 0.000 1.000 0 0.000 0 0
#> SRR934236 2 0.0000 1.000 0.000 1.000 0 0.000 0 0
#> SRR934237 2 0.0000 1.000 0.000 1.000 0 0.000 0 0
#> SRR934238 2 0.0000 1.000 0.000 1.000 0 0.000 0 0
#> SRR934239 2 0.0000 1.000 0.000 1.000 0 0.000 0 0
#> SRR934240 2 0.0000 1.000 0.000 1.000 0 0.000 0 0
#> SRR934241 2 0.0000 1.000 0.000 1.000 0 0.000 0 0
#> SRR934242 2 0.0000 1.000 0.000 1.000 0 0.000 0 0
#> SRR934243 2 0.0000 1.000 0.000 1.000 0 0.000 0 0
#> SRR934244 2 0.0000 1.000 0.000 1.000 0 0.000 0 0
#> SRR934245 2 0.0000 1.000 0.000 1.000 0 0.000 0 0
#> SRR934246 2 0.0000 1.000 0.000 1.000 0 0.000 0 0
#> SRR934247 2 0.0000 1.000 0.000 1.000 0 0.000 0 0
#> SRR934248 4 0.3607 0.667 0.000 0.348 0 0.652 0 0
#> SRR934249 4 0.3607 0.667 0.000 0.348 0 0.652 0 0
#> SRR934250 4 0.3607 0.667 0.000 0.348 0 0.652 0 0
#> SRR934251 4 0.3607 0.667 0.000 0.348 0 0.652 0 0
#> SRR934252 4 0.3607 0.667 0.000 0.348 0 0.652 0 0
#> SRR934253 4 0.3607 0.667 0.000 0.348 0 0.652 0 0
#> SRR934254 4 0.3607 0.667 0.000 0.348 0 0.652 0 0
#> SRR934255 4 0.3607 0.667 0.000 0.348 0 0.652 0 0
#> SRR934256 6 0.0000 1.000 0.000 0.000 0 0.000 0 1
#> SRR934257 6 0.0000 1.000 0.000 0.000 0 0.000 0 1
#> SRR934258 6 0.0000 1.000 0.000 0.000 0 0.000 0 1
#> SRR934259 6 0.0000 1.000 0.000 0.000 0 0.000 0 1
#> SRR934260 6 0.0000 1.000 0.000 0.000 0 0.000 0 1
#> SRR934261 6 0.0000 1.000 0.000 0.000 0 0.000 0 1
#> SRR934262 6 0.0000 1.000 0.000 0.000 0 0.000 0 1
#> SRR934263 6 0.0000 1.000 0.000 0.000 0 0.000 0 1
#> SRR934264 4 0.0547 0.736 0.020 0.000 0 0.980 0 0
#> SRR934265 4 0.0547 0.736 0.020 0.000 0 0.980 0 0
#> SRR934266 4 0.0547 0.736 0.020 0.000 0 0.980 0 0
#> SRR934267 4 0.0547 0.736 0.020 0.000 0 0.980 0 0
#> SRR934268 4 0.0547 0.736 0.020 0.000 0 0.980 0 0
#> SRR934269 4 0.0547 0.736 0.020 0.000 0 0.980 0 0
#> SRR934270 4 0.0547 0.736 0.020 0.000 0 0.980 0 0
#> SRR934271 4 0.0547 0.736 0.020 0.000 0 0.980 0 0
#> SRR934272 1 0.3789 0.464 0.584 0.000 0 0.416 0 0
#> SRR934273 1 0.3789 0.464 0.584 0.000 0 0.416 0 0
#> SRR934274 1 0.3789 0.464 0.584 0.000 0 0.416 0 0
#> SRR934275 1 0.3789 0.464 0.584 0.000 0 0.416 0 0
#> SRR934276 1 0.3782 0.472 0.588 0.000 0 0.412 0 0
#> SRR934277 1 0.3789 0.464 0.584 0.000 0 0.416 0 0
#> SRR934278 1 0.3774 0.479 0.592 0.000 0 0.408 0 0
#> SRR934279 1 0.3789 0.464 0.584 0.000 0 0.416 0 0
#> SRR934280 1 0.0547 0.900 0.980 0.000 0 0.020 0 0
#> SRR934281 1 0.0547 0.900 0.980 0.000 0 0.020 0 0
#> SRR934282 1 0.0547 0.900 0.980 0.000 0 0.020 0 0
#> SRR934283 1 0.0547 0.900 0.980 0.000 0 0.020 0 0
#> SRR934284 1 0.0547 0.900 0.980 0.000 0 0.020 0 0
#> SRR934285 1 0.0363 0.902 0.988 0.000 0 0.012 0 0
#> SRR934286 1 0.0547 0.900 0.980 0.000 0 0.020 0 0
#> SRR934287 1 0.0547 0.900 0.980 0.000 0 0.020 0 0
#> SRR934288 1 0.0000 0.904 1.000 0.000 0 0.000 0 0
#> SRR934289 1 0.0000 0.904 1.000 0.000 0 0.000 0 0
#> SRR934290 1 0.0000 0.904 1.000 0.000 0 0.000 0 0
#> SRR934291 1 0.0000 0.904 1.000 0.000 0 0.000 0 0
#> SRR934292 1 0.0000 0.904 1.000 0.000 0 0.000 0 0
#> SRR934293 1 0.0000 0.904 1.000 0.000 0 0.000 0 0
#> SRR934294 1 0.0000 0.904 1.000 0.000 0 0.000 0 0
#> SRR934295 1 0.0000 0.904 1.000 0.000 0 0.000 0 0
#> SRR934296 1 0.1863 0.864 0.896 0.000 0 0.104 0 0
#> SRR934297 1 0.1863 0.864 0.896 0.000 0 0.104 0 0
#> SRR934298 1 0.1863 0.864 0.896 0.000 0 0.104 0 0
#> SRR934299 1 0.1863 0.864 0.896 0.000 0 0.104 0 0
#> SRR934300 1 0.1863 0.864 0.896 0.000 0 0.104 0 0
#> SRR934301 1 0.1863 0.864 0.896 0.000 0 0.104 0 0
#> SRR934302 1 0.1863 0.864 0.896 0.000 0 0.104 0 0
#> SRR934303 1 0.1863 0.864 0.896 0.000 0 0.104 0 0
#> SRR934304 5 0.0000 1.000 0.000 0.000 0 0.000 1 0
#> SRR934305 5 0.0000 1.000 0.000 0.000 0 0.000 1 0
#> SRR934306 5 0.0000 1.000 0.000 0.000 0 0.000 1 0
#> SRR934307 5 0.0000 1.000 0.000 0.000 0 0.000 1 0
#> SRR934308 5 0.0000 1.000 0.000 0.000 0 0.000 1 0
#> SRR934309 5 0.0000 1.000 0.000 0.000 0 0.000 1 0
#> SRR934310 5 0.0000 1.000 0.000 0.000 0 0.000 1 0
#> SRR934311 5 0.0000 1.000 0.000 0.000 0 0.000 1 0
#> SRR934312 1 0.0547 0.903 0.980 0.000 0 0.020 0 0
#> SRR934313 1 0.0547 0.903 0.980 0.000 0 0.020 0 0
#> SRR934314 1 0.0547 0.903 0.980 0.000 0 0.020 0 0
#> SRR934315 1 0.0547 0.903 0.980 0.000 0 0.020 0 0
#> SRR934316 1 0.0547 0.903 0.980 0.000 0 0.020 0 0
#> SRR934317 1 0.0547 0.903 0.980 0.000 0 0.020 0 0
#> SRR934318 1 0.0547 0.903 0.980 0.000 0 0.020 0 0
#> SRR934319 1 0.0547 0.903 0.980 0.000 0 0.020 0 0
#> SRR934320 1 0.0547 0.900 0.980 0.000 0 0.020 0 0
#> SRR934321 1 0.0547 0.900 0.980 0.000 0 0.020 0 0
#> SRR934322 1 0.0547 0.900 0.980 0.000 0 0.020 0 0
#> SRR934323 1 0.0547 0.900 0.980 0.000 0 0.020 0 0
#> SRR934324 1 0.0547 0.900 0.980 0.000 0 0.020 0 0
#> SRR934325 1 0.0547 0.900 0.980 0.000 0 0.020 0 0
#> SRR934326 1 0.0547 0.900 0.980 0.000 0 0.020 0 0
#> SRR934327 1 0.0547 0.900 0.980 0.000 0 0.020 0 0
#> SRR934328 1 0.0547 0.903 0.980 0.000 0 0.020 0 0
#> SRR934329 1 0.0547 0.903 0.980 0.000 0 0.020 0 0
#> SRR934330 1 0.0547 0.903 0.980 0.000 0 0.020 0 0
#> SRR934331 1 0.0547 0.903 0.980 0.000 0 0.020 0 0
#> SRR934332 1 0.0547 0.903 0.980 0.000 0 0.020 0 0
#> SRR934333 1 0.0547 0.903 0.980 0.000 0 0.020 0 0
#> SRR934334 1 0.0547 0.903 0.980 0.000 0 0.020 0 0
#> SRR934335 1 0.0547 0.903 0.980 0.000 0 0.020 0 0
#> SRR934344 1 0.0260 0.904 0.992 0.000 0 0.008 0 0
#> SRR934345 1 0.0260 0.904 0.992 0.000 0 0.008 0 0
#> SRR934346 1 0.0260 0.904 0.992 0.000 0 0.008 0 0
#> SRR934347 1 0.0146 0.905 0.996 0.000 0 0.004 0 0
#> SRR934348 1 0.0260 0.904 0.992 0.000 0 0.008 0 0
#> SRR934349 1 0.0363 0.904 0.988 0.000 0 0.012 0 0
#> SRR934350 1 0.0363 0.904 0.988 0.000 0 0.012 0 0
#> SRR934351 1 0.0260 0.904 0.992 0.000 0 0.008 0 0
#> SRR934336 1 0.0000 0.904 1.000 0.000 0 0.000 0 0
#> SRR934337 1 0.0000 0.904 1.000 0.000 0 0.000 0 0
#> SRR934338 1 0.0000 0.904 1.000 0.000 0 0.000 0 0
#> SRR934339 1 0.0000 0.904 1.000 0.000 0 0.000 0 0
#> SRR934340 1 0.0000 0.904 1.000 0.000 0 0.000 0 0
#> SRR934341 1 0.0000 0.904 1.000 0.000 0 0.000 0 0
#> SRR934342 1 0.0000 0.904 1.000 0.000 0 0.000 0 0
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 14550 rows and 135 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.2952 0.705 0.705
#> 3 3 0.839 0.931 0.964 0.7347 0.769 0.673
#> 4 4 0.736 0.926 0.932 0.0876 0.986 0.970
#> 5 5 0.590 0.711 0.834 0.2147 0.754 0.517
#> 6 6 0.716 0.819 0.867 0.1240 0.792 0.441
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
#> SRR934216 1 0 1 1 0
#> SRR934217 1 0 1 1 0
#> SRR934218 1 0 1 1 0
#> SRR934219 1 0 1 1 0
#> SRR934220 1 0 1 1 0
#> SRR934221 1 0 1 1 0
#> SRR934222 1 0 1 1 0
#> SRR934223 1 0 1 1 0
#> SRR934224 1 0 1 1 0
#> SRR934225 1 0 1 1 0
#> SRR934226 1 0 1 1 0
#> SRR934227 1 0 1 1 0
#> SRR934228 1 0 1 1 0
#> SRR934229 1 0 1 1 0
#> SRR934230 1 0 1 1 0
#> SRR934231 1 0 1 1 0
#> SRR934232 2 0 1 0 1
#> SRR934233 2 0 1 0 1
#> SRR934234 2 0 1 0 1
#> SRR934235 2 0 1 0 1
#> SRR934236 2 0 1 0 1
#> SRR934237 2 0 1 0 1
#> SRR934238 2 0 1 0 1
#> SRR934239 2 0 1 0 1
#> SRR934240 2 0 1 0 1
#> SRR934241 2 0 1 0 1
#> SRR934242 2 0 1 0 1
#> SRR934243 2 0 1 0 1
#> SRR934244 2 0 1 0 1
#> SRR934245 2 0 1 0 1
#> SRR934246 2 0 1 0 1
#> SRR934247 2 0 1 0 1
#> SRR934248 1 0 1 1 0
#> SRR934249 1 0 1 1 0
#> SRR934250 1 0 1 1 0
#> SRR934251 1 0 1 1 0
#> SRR934252 1 0 1 1 0
#> SRR934253 1 0 1 1 0
#> SRR934254 1 0 1 1 0
#> SRR934255 1 0 1 1 0
#> SRR934256 2 0 1 0 1
#> SRR934257 2 0 1 0 1
#> SRR934258 2 0 1 0 1
#> SRR934259 2 0 1 0 1
#> SRR934260 2 0 1 0 1
#> SRR934261 2 0 1 0 1
#> SRR934262 2 0 1 0 1
#> SRR934263 2 0 1 0 1
#> SRR934264 1 0 1 1 0
#> SRR934265 1 0 1 1 0
#> SRR934266 1 0 1 1 0
#> SRR934267 1 0 1 1 0
#> SRR934268 1 0 1 1 0
#> SRR934269 1 0 1 1 0
#> SRR934270 1 0 1 1 0
#> SRR934271 1 0 1 1 0
#> SRR934272 1 0 1 1 0
#> SRR934273 1 0 1 1 0
#> SRR934274 1 0 1 1 0
#> SRR934275 1 0 1 1 0
#> SRR934276 1 0 1 1 0
#> SRR934277 1 0 1 1 0
#> SRR934278 1 0 1 1 0
#> SRR934279 1 0 1 1 0
#> SRR934280 1 0 1 1 0
#> SRR934281 1 0 1 1 0
#> SRR934282 1 0 1 1 0
#> SRR934283 1 0 1 1 0
#> SRR934284 1 0 1 1 0
#> SRR934285 1 0 1 1 0
#> SRR934286 1 0 1 1 0
#> SRR934287 1 0 1 1 0
#> SRR934288 1 0 1 1 0
#> SRR934289 1 0 1 1 0
#> SRR934290 1 0 1 1 0
#> SRR934291 1 0 1 1 0
#> SRR934292 1 0 1 1 0
#> SRR934293 1 0 1 1 0
#> SRR934294 1 0 1 1 0
#> SRR934295 1 0 1 1 0
#> SRR934296 1 0 1 1 0
#> SRR934297 1 0 1 1 0
#> SRR934298 1 0 1 1 0
#> SRR934299 1 0 1 1 0
#> SRR934300 1 0 1 1 0
#> SRR934301 1 0 1 1 0
#> SRR934302 1 0 1 1 0
#> SRR934303 1 0 1 1 0
#> SRR934304 1 0 1 1 0
#> SRR934305 1 0 1 1 0
#> SRR934306 1 0 1 1 0
#> SRR934307 1 0 1 1 0
#> SRR934308 1 0 1 1 0
#> SRR934309 1 0 1 1 0
#> SRR934310 1 0 1 1 0
#> SRR934311 1 0 1 1 0
#> SRR934312 1 0 1 1 0
#> SRR934313 1 0 1 1 0
#> SRR934314 1 0 1 1 0
#> SRR934315 1 0 1 1 0
#> SRR934316 1 0 1 1 0
#> SRR934317 1 0 1 1 0
#> SRR934318 1 0 1 1 0
#> SRR934319 1 0 1 1 0
#> SRR934320 1 0 1 1 0
#> SRR934321 1 0 1 1 0
#> SRR934322 1 0 1 1 0
#> SRR934323 1 0 1 1 0
#> SRR934324 1 0 1 1 0
#> SRR934325 1 0 1 1 0
#> SRR934326 1 0 1 1 0
#> SRR934327 1 0 1 1 0
#> SRR934328 1 0 1 1 0
#> SRR934329 1 0 1 1 0
#> SRR934330 1 0 1 1 0
#> SRR934331 1 0 1 1 0
#> SRR934332 1 0 1 1 0
#> SRR934333 1 0 1 1 0
#> SRR934334 1 0 1 1 0
#> SRR934335 1 0 1 1 0
#> SRR934344 1 0 1 1 0
#> SRR934345 1 0 1 1 0
#> SRR934346 1 0 1 1 0
#> SRR934347 1 0 1 1 0
#> SRR934348 1 0 1 1 0
#> SRR934349 1 0 1 1 0
#> SRR934350 1 0 1 1 0
#> SRR934351 1 0 1 1 0
#> SRR934336 1 0 1 1 0
#> SRR934337 1 0 1 1 0
#> SRR934338 1 0 1 1 0
#> SRR934339 1 0 1 1 0
#> SRR934340 1 0 1 1 0
#> SRR934341 1 0 1 1 0
#> SRR934342 1 0 1 1 0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> SRR934216 3 0.4504 0.781 0.196 0.000 0.804
#> SRR934217 3 0.4291 0.791 0.180 0.000 0.820
#> SRR934218 3 0.5529 0.675 0.296 0.000 0.704
#> SRR934219 3 0.4887 0.755 0.228 0.000 0.772
#> SRR934220 3 0.5560 0.670 0.300 0.000 0.700
#> SRR934221 3 0.4796 0.763 0.220 0.000 0.780
#> SRR934222 3 0.4235 0.793 0.176 0.000 0.824
#> SRR934223 3 0.4750 0.766 0.216 0.000 0.784
#> SRR934224 1 0.0237 0.982 0.996 0.000 0.004
#> SRR934225 1 0.0237 0.982 0.996 0.000 0.004
#> SRR934226 1 0.0237 0.982 0.996 0.000 0.004
#> SRR934227 1 0.0237 0.982 0.996 0.000 0.004
#> SRR934228 1 0.0237 0.982 0.996 0.000 0.004
#> SRR934229 1 0.0237 0.982 0.996 0.000 0.004
#> SRR934230 1 0.0237 0.982 0.996 0.000 0.004
#> SRR934231 1 0.0237 0.982 0.996 0.000 0.004
#> SRR934232 2 0.3116 0.907 0.000 0.892 0.108
#> SRR934233 2 0.3619 0.885 0.000 0.864 0.136
#> SRR934234 2 0.3192 0.904 0.000 0.888 0.112
#> SRR934235 2 0.3752 0.877 0.000 0.856 0.144
#> SRR934236 2 0.2711 0.919 0.000 0.912 0.088
#> SRR934237 2 0.1964 0.935 0.000 0.944 0.056
#> SRR934238 2 0.3482 0.892 0.000 0.872 0.128
#> SRR934239 2 0.4346 0.833 0.000 0.816 0.184
#> SRR934240 2 0.0000 0.959 0.000 1.000 0.000
#> SRR934241 2 0.0000 0.959 0.000 1.000 0.000
#> SRR934242 2 0.0000 0.959 0.000 1.000 0.000
#> SRR934243 2 0.0000 0.959 0.000 1.000 0.000
#> SRR934244 2 0.0000 0.959 0.000 1.000 0.000
#> SRR934245 2 0.0000 0.959 0.000 1.000 0.000
#> SRR934246 2 0.0000 0.959 0.000 1.000 0.000
#> SRR934247 2 0.0000 0.959 0.000 1.000 0.000
#> SRR934248 3 0.1860 0.818 0.052 0.000 0.948
#> SRR934249 3 0.1643 0.816 0.044 0.000 0.956
#> SRR934250 3 0.4121 0.778 0.168 0.000 0.832
#> SRR934251 3 0.3267 0.813 0.116 0.000 0.884
#> SRR934252 3 0.4654 0.740 0.208 0.000 0.792
#> SRR934253 3 0.3412 0.810 0.124 0.000 0.876
#> SRR934254 3 0.2537 0.819 0.080 0.000 0.920
#> SRR934255 3 0.3340 0.811 0.120 0.000 0.880
#> SRR934256 2 0.0000 0.959 0.000 1.000 0.000
#> SRR934257 2 0.0000 0.959 0.000 1.000 0.000
#> SRR934258 2 0.0000 0.959 0.000 1.000 0.000
#> SRR934259 2 0.0000 0.959 0.000 1.000 0.000
#> SRR934260 2 0.0000 0.959 0.000 1.000 0.000
#> SRR934261 2 0.0000 0.959 0.000 1.000 0.000
#> SRR934262 2 0.0000 0.959 0.000 1.000 0.000
#> SRR934263 2 0.0000 0.959 0.000 1.000 0.000
#> SRR934264 1 0.0892 0.968 0.980 0.000 0.020
#> SRR934265 1 0.1031 0.965 0.976 0.000 0.024
#> SRR934266 1 0.1031 0.965 0.976 0.000 0.024
#> SRR934267 1 0.1411 0.953 0.964 0.000 0.036
#> SRR934268 1 0.1031 0.965 0.976 0.000 0.024
#> SRR934269 1 0.0892 0.968 0.980 0.000 0.020
#> SRR934270 1 0.2165 0.924 0.936 0.000 0.064
#> SRR934271 1 0.1163 0.961 0.972 0.000 0.028
#> SRR934272 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934273 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934274 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934275 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934276 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934277 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934278 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934279 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934280 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934281 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934282 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934283 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934284 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934285 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934286 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934287 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934288 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934289 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934290 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934291 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934292 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934293 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934294 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934295 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934296 1 0.2711 0.896 0.912 0.088 0.000
#> SRR934297 1 0.3412 0.853 0.876 0.124 0.000
#> SRR934298 1 0.1964 0.932 0.944 0.056 0.000
#> SRR934299 1 0.3619 0.836 0.864 0.136 0.000
#> SRR934300 1 0.2066 0.927 0.940 0.060 0.000
#> SRR934301 1 0.3340 0.858 0.880 0.120 0.000
#> SRR934302 1 0.4121 0.792 0.832 0.168 0.000
#> SRR934303 1 0.4121 0.793 0.832 0.168 0.000
#> SRR934304 3 0.0000 0.800 0.000 0.000 1.000
#> SRR934305 3 0.0000 0.800 0.000 0.000 1.000
#> SRR934306 3 0.0000 0.800 0.000 0.000 1.000
#> SRR934307 3 0.0000 0.800 0.000 0.000 1.000
#> SRR934308 3 0.0000 0.800 0.000 0.000 1.000
#> SRR934309 3 0.0000 0.800 0.000 0.000 1.000
#> SRR934310 3 0.0000 0.800 0.000 0.000 1.000
#> SRR934311 3 0.0000 0.800 0.000 0.000 1.000
#> SRR934312 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934313 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934314 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934315 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934316 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934317 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934318 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934319 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934320 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934321 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934322 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934323 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934324 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934325 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934326 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934327 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934328 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934329 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934330 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934331 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934332 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934333 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934334 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934335 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934344 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934345 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934346 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934347 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934348 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934349 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934350 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934351 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934336 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934337 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934338 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934339 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934340 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934341 1 0.0000 0.984 1.000 0.000 0.000
#> SRR934342 1 0.0000 0.984 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> SRR934216 3 0.2593 0.851 0.104 0.000 0.892 0.004
#> SRR934217 3 0.2530 0.854 0.100 0.000 0.896 0.004
#> SRR934218 3 0.2530 0.854 0.100 0.000 0.896 0.004
#> SRR934219 3 0.2530 0.854 0.100 0.000 0.896 0.004
#> SRR934220 3 0.2593 0.851 0.104 0.000 0.892 0.004
#> SRR934221 3 0.2593 0.851 0.104 0.000 0.892 0.004
#> SRR934222 3 0.2593 0.851 0.104 0.000 0.892 0.004
#> SRR934223 3 0.2530 0.854 0.100 0.000 0.896 0.004
#> SRR934224 1 0.1398 0.933 0.956 0.000 0.040 0.004
#> SRR934225 1 0.1398 0.933 0.956 0.000 0.040 0.004
#> SRR934226 1 0.1398 0.933 0.956 0.000 0.040 0.004
#> SRR934227 1 0.1398 0.933 0.956 0.000 0.040 0.004
#> SRR934228 1 0.1398 0.933 0.956 0.000 0.040 0.004
#> SRR934229 1 0.1398 0.933 0.956 0.000 0.040 0.004
#> SRR934230 1 0.1398 0.933 0.956 0.000 0.040 0.004
#> SRR934231 1 0.1398 0.933 0.956 0.000 0.040 0.004
#> SRR934232 2 0.0469 0.989 0.000 0.988 0.012 0.000
#> SRR934233 2 0.0336 0.992 0.000 0.992 0.008 0.000
#> SRR934234 2 0.0469 0.989 0.000 0.988 0.012 0.000
#> SRR934235 2 0.0336 0.992 0.000 0.992 0.008 0.000
#> SRR934236 2 0.0336 0.992 0.000 0.992 0.008 0.000
#> SRR934237 2 0.0000 0.994 0.000 1.000 0.000 0.000
#> SRR934238 2 0.0188 0.993 0.000 0.996 0.004 0.000
#> SRR934239 2 0.0469 0.989 0.000 0.988 0.012 0.000
#> SRR934240 2 0.0000 0.994 0.000 1.000 0.000 0.000
#> SRR934241 2 0.0000 0.994 0.000 1.000 0.000 0.000
#> SRR934242 2 0.0000 0.994 0.000 1.000 0.000 0.000
#> SRR934243 2 0.0000 0.994 0.000 1.000 0.000 0.000
#> SRR934244 2 0.0000 0.994 0.000 1.000 0.000 0.000
#> SRR934245 2 0.0000 0.994 0.000 1.000 0.000 0.000
#> SRR934246 2 0.0000 0.994 0.000 1.000 0.000 0.000
#> SRR934247 2 0.0000 0.994 0.000 1.000 0.000 0.000
#> SRR934248 3 0.4944 0.819 0.072 0.160 0.768 0.000
#> SRR934249 3 0.5143 0.807 0.076 0.172 0.752 0.000
#> SRR934250 3 0.4804 0.824 0.072 0.148 0.780 0.000
#> SRR934251 3 0.4944 0.817 0.072 0.160 0.768 0.000
#> SRR934252 3 0.5594 0.770 0.112 0.164 0.724 0.000
#> SRR934253 3 0.5011 0.815 0.076 0.160 0.764 0.000
#> SRR934254 3 0.4898 0.821 0.072 0.156 0.772 0.000
#> SRR934255 3 0.4829 0.821 0.068 0.156 0.776 0.000
#> SRR934256 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934257 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934258 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934259 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934260 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934261 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934262 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934263 4 0.3024 1.000 0.000 0.148 0.000 0.852
#> SRR934264 1 0.1585 0.931 0.952 0.004 0.040 0.004
#> SRR934265 1 0.1585 0.931 0.952 0.004 0.040 0.004
#> SRR934266 1 0.1585 0.931 0.952 0.004 0.040 0.004
#> SRR934267 1 0.1585 0.931 0.952 0.004 0.040 0.004
#> SRR934268 1 0.1585 0.931 0.952 0.004 0.040 0.004
#> SRR934269 1 0.1585 0.931 0.952 0.004 0.040 0.004
#> SRR934270 1 0.1585 0.931 0.952 0.004 0.040 0.004
#> SRR934271 1 0.1585 0.931 0.952 0.004 0.040 0.004
#> SRR934272 1 0.0336 0.943 0.992 0.000 0.008 0.000
#> SRR934273 1 0.0336 0.943 0.992 0.000 0.008 0.000
#> SRR934274 1 0.0524 0.943 0.988 0.000 0.008 0.004
#> SRR934275 1 0.0336 0.943 0.992 0.000 0.008 0.000
#> SRR934276 1 0.0672 0.943 0.984 0.000 0.008 0.008
#> SRR934277 1 0.0672 0.944 0.984 0.000 0.008 0.008
#> SRR934278 1 0.0336 0.943 0.992 0.000 0.008 0.000
#> SRR934279 1 0.0336 0.943 0.992 0.000 0.008 0.000
#> SRR934280 1 0.0524 0.942 0.988 0.000 0.008 0.004
#> SRR934281 1 0.0336 0.943 0.992 0.000 0.008 0.000
#> SRR934282 1 0.0657 0.944 0.984 0.000 0.004 0.012
#> SRR934283 1 0.0376 0.943 0.992 0.000 0.004 0.004
#> SRR934284 1 0.0376 0.943 0.992 0.000 0.004 0.004
#> SRR934285 1 0.0188 0.943 0.996 0.000 0.004 0.000
#> SRR934286 1 0.0657 0.942 0.984 0.000 0.012 0.004
#> SRR934287 1 0.0657 0.942 0.984 0.000 0.012 0.004
#> SRR934288 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934289 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934290 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934291 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934292 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934293 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934294 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934295 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934296 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934297 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934298 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934299 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934300 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934301 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934302 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934303 1 0.2408 0.925 0.896 0.000 0.000 0.104
#> SRR934304 3 0.1211 0.827 0.000 0.000 0.960 0.040
#> SRR934305 3 0.1211 0.827 0.000 0.000 0.960 0.040
#> SRR934306 3 0.1211 0.827 0.000 0.000 0.960 0.040
#> SRR934307 3 0.1211 0.827 0.000 0.000 0.960 0.040
#> SRR934308 3 0.1211 0.827 0.000 0.000 0.960 0.040
#> SRR934309 3 0.1211 0.827 0.000 0.000 0.960 0.040
#> SRR934310 3 0.1211 0.827 0.000 0.000 0.960 0.040
#> SRR934311 3 0.1211 0.827 0.000 0.000 0.960 0.040
#> SRR934312 1 0.1474 0.938 0.948 0.000 0.000 0.052
#> SRR934313 1 0.1302 0.940 0.956 0.000 0.000 0.044
#> SRR934314 1 0.1474 0.940 0.948 0.000 0.000 0.052
#> SRR934315 1 0.1302 0.940 0.956 0.000 0.000 0.044
#> SRR934316 1 0.1867 0.934 0.928 0.000 0.000 0.072
#> SRR934317 1 0.1867 0.934 0.928 0.000 0.000 0.072
#> SRR934318 1 0.0524 0.944 0.988 0.000 0.008 0.004
#> SRR934319 1 0.1716 0.936 0.936 0.000 0.000 0.064
#> SRR934320 1 0.1256 0.937 0.964 0.000 0.028 0.008
#> SRR934321 1 0.1042 0.939 0.972 0.000 0.020 0.008
#> SRR934322 1 0.1042 0.939 0.972 0.000 0.020 0.008
#> SRR934323 1 0.1151 0.938 0.968 0.000 0.024 0.008
#> SRR934324 1 0.1042 0.939 0.972 0.000 0.020 0.008
#> SRR934325 1 0.1042 0.939 0.972 0.000 0.020 0.008
#> SRR934326 1 0.1256 0.937 0.964 0.000 0.028 0.008
#> SRR934327 1 0.1042 0.939 0.972 0.000 0.020 0.008
#> SRR934328 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934329 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934330 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934331 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934332 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934333 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934334 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934335 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934344 1 0.2216 0.930 0.908 0.000 0.000 0.092
#> SRR934345 1 0.2216 0.930 0.908 0.000 0.000 0.092
#> SRR934346 1 0.2281 0.928 0.904 0.000 0.000 0.096
#> SRR934347 1 0.2216 0.930 0.908 0.000 0.000 0.092
#> SRR934348 1 0.2216 0.930 0.908 0.000 0.000 0.092
#> SRR934349 1 0.2216 0.930 0.908 0.000 0.000 0.092
#> SRR934350 1 0.2216 0.930 0.908 0.000 0.000 0.092
#> SRR934351 1 0.2216 0.930 0.908 0.000 0.000 0.092
#> SRR934336 1 0.1305 0.935 0.960 0.000 0.036 0.004
#> SRR934337 1 0.1398 0.933 0.956 0.000 0.040 0.004
#> SRR934338 1 0.1305 0.935 0.960 0.000 0.036 0.004
#> SRR934339 1 0.1305 0.935 0.960 0.000 0.036 0.004
#> SRR934340 1 0.1109 0.937 0.968 0.000 0.028 0.004
#> SRR934341 1 0.1398 0.933 0.956 0.000 0.040 0.004
#> SRR934342 1 0.1398 0.933 0.956 0.000 0.040 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> SRR934216 4 0.4101 0.50100 0.004 0.000 0.000 0.664 0.332
#> SRR934217 4 0.4135 0.48869 0.004 0.000 0.000 0.656 0.340
#> SRR934218 4 0.4118 0.49605 0.004 0.000 0.000 0.660 0.336
#> SRR934219 4 0.4135 0.48869 0.004 0.000 0.000 0.656 0.340
#> SRR934220 4 0.4101 0.50100 0.004 0.000 0.000 0.664 0.332
#> SRR934221 4 0.4101 0.50100 0.004 0.000 0.000 0.664 0.332
#> SRR934222 4 0.4101 0.50100 0.004 0.000 0.000 0.664 0.332
#> SRR934223 4 0.4135 0.48869 0.004 0.000 0.000 0.656 0.340
#> SRR934224 4 0.3366 0.70285 0.212 0.000 0.004 0.784 0.000
#> SRR934225 4 0.3366 0.70285 0.212 0.000 0.004 0.784 0.000
#> SRR934226 4 0.3398 0.70073 0.216 0.000 0.004 0.780 0.000
#> SRR934227 4 0.3366 0.70285 0.212 0.000 0.004 0.784 0.000
#> SRR934228 4 0.3366 0.70285 0.212 0.000 0.004 0.784 0.000
#> SRR934229 4 0.3366 0.70285 0.212 0.000 0.004 0.784 0.000
#> SRR934230 4 0.3333 0.70469 0.208 0.000 0.004 0.788 0.000
#> SRR934231 4 0.3398 0.70073 0.216 0.000 0.004 0.780 0.000
#> SRR934232 2 0.3366 0.83912 0.000 0.768 0.000 0.232 0.000
#> SRR934233 2 0.3424 0.83413 0.000 0.760 0.000 0.240 0.000
#> SRR934234 2 0.3395 0.83719 0.000 0.764 0.000 0.236 0.000
#> SRR934235 2 0.3366 0.83911 0.000 0.768 0.000 0.232 0.000
#> SRR934236 2 0.3395 0.83719 0.000 0.764 0.000 0.236 0.000
#> SRR934237 2 0.3452 0.82967 0.000 0.756 0.000 0.244 0.000
#> SRR934238 2 0.3366 0.83870 0.000 0.768 0.000 0.232 0.000
#> SRR934239 2 0.3305 0.83961 0.000 0.776 0.000 0.224 0.000
#> SRR934240 2 0.0404 0.83693 0.000 0.988 0.012 0.000 0.000
#> SRR934241 2 0.0404 0.83693 0.000 0.988 0.012 0.000 0.000
#> SRR934242 2 0.0404 0.83693 0.000 0.988 0.012 0.000 0.000
#> SRR934243 2 0.0404 0.83693 0.000 0.988 0.012 0.000 0.000
#> SRR934244 2 0.0404 0.83693 0.000 0.988 0.012 0.000 0.000
#> SRR934245 2 0.0404 0.83693 0.000 0.988 0.012 0.000 0.000
#> SRR934246 2 0.0404 0.83693 0.000 0.988 0.012 0.000 0.000
#> SRR934247 2 0.0404 0.83693 0.000 0.988 0.012 0.000 0.000
#> SRR934248 4 0.3142 0.60306 0.000 0.108 0.004 0.856 0.032
#> SRR934249 4 0.3256 0.55342 0.000 0.148 0.004 0.832 0.016
#> SRR934250 4 0.2520 0.61764 0.000 0.096 0.004 0.888 0.012
#> SRR934251 4 0.3111 0.56208 0.000 0.144 0.004 0.840 0.012
#> SRR934252 4 0.2575 0.61704 0.000 0.100 0.004 0.884 0.012
#> SRR934253 4 0.2775 0.61058 0.000 0.100 0.004 0.876 0.020
#> SRR934254 4 0.3340 0.54014 0.000 0.156 0.004 0.824 0.016
#> SRR934255 4 0.3124 0.56980 0.000 0.136 0.004 0.844 0.016
#> SRR934256 3 0.0290 1.00000 0.000 0.008 0.992 0.000 0.000
#> SRR934257 3 0.0290 1.00000 0.000 0.008 0.992 0.000 0.000
#> SRR934258 3 0.0290 1.00000 0.000 0.008 0.992 0.000 0.000
#> SRR934259 3 0.0290 1.00000 0.000 0.008 0.992 0.000 0.000
#> SRR934260 3 0.0290 1.00000 0.000 0.008 0.992 0.000 0.000
#> SRR934261 3 0.0290 1.00000 0.000 0.008 0.992 0.000 0.000
#> SRR934262 3 0.0290 1.00000 0.000 0.008 0.992 0.000 0.000
#> SRR934263 3 0.0290 1.00000 0.000 0.008 0.992 0.000 0.000
#> SRR934264 4 0.0865 0.70867 0.024 0.000 0.000 0.972 0.004
#> SRR934265 4 0.0703 0.70847 0.024 0.000 0.000 0.976 0.000
#> SRR934266 4 0.0865 0.70867 0.024 0.000 0.000 0.972 0.004
#> SRR934267 4 0.0771 0.70635 0.020 0.000 0.000 0.976 0.004
#> SRR934268 4 0.0865 0.70867 0.024 0.000 0.000 0.972 0.004
#> SRR934269 4 0.0865 0.70867 0.024 0.000 0.000 0.972 0.004
#> SRR934270 4 0.0771 0.70635 0.020 0.000 0.000 0.976 0.004
#> SRR934271 4 0.0865 0.70867 0.024 0.000 0.000 0.972 0.004
#> SRR934272 1 0.4201 0.32619 0.592 0.000 0.000 0.408 0.000
#> SRR934273 1 0.4114 0.41524 0.624 0.000 0.000 0.376 0.000
#> SRR934274 1 0.4088 0.43552 0.632 0.000 0.000 0.368 0.000
#> SRR934275 1 0.4201 0.32533 0.592 0.000 0.000 0.408 0.000
#> SRR934276 1 0.3983 0.50134 0.660 0.000 0.000 0.340 0.000
#> SRR934277 1 0.3857 0.55767 0.688 0.000 0.000 0.312 0.000
#> SRR934278 4 0.4307 0.00136 0.500 0.000 0.000 0.500 0.000
#> SRR934279 1 0.4210 0.32382 0.588 0.000 0.000 0.412 0.000
#> SRR934280 1 0.3039 0.73985 0.808 0.000 0.000 0.192 0.000
#> SRR934281 1 0.2966 0.74730 0.816 0.000 0.000 0.184 0.000
#> SRR934282 1 0.3039 0.73798 0.808 0.000 0.000 0.192 0.000
#> SRR934283 1 0.2929 0.74838 0.820 0.000 0.000 0.180 0.000
#> SRR934284 1 0.2813 0.75745 0.832 0.000 0.000 0.168 0.000
#> SRR934285 1 0.2732 0.76279 0.840 0.000 0.000 0.160 0.000
#> SRR934286 1 0.3508 0.66923 0.748 0.000 0.000 0.252 0.000
#> SRR934287 1 0.3305 0.70501 0.776 0.000 0.000 0.224 0.000
#> SRR934288 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934289 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934290 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934291 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934292 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934293 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934294 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934295 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934296 1 0.2280 0.73418 0.880 0.120 0.000 0.000 0.000
#> SRR934297 1 0.2127 0.74594 0.892 0.108 0.000 0.000 0.000
#> SRR934298 1 0.1908 0.75991 0.908 0.092 0.000 0.000 0.000
#> SRR934299 1 0.1965 0.75634 0.904 0.096 0.000 0.000 0.000
#> SRR934300 1 0.2179 0.75563 0.896 0.100 0.000 0.004 0.000
#> SRR934301 1 0.2329 0.73170 0.876 0.124 0.000 0.000 0.000
#> SRR934302 1 0.2124 0.75852 0.900 0.096 0.000 0.004 0.000
#> SRR934303 1 0.2230 0.73857 0.884 0.116 0.000 0.000 0.000
#> SRR934304 5 0.0162 1.00000 0.000 0.000 0.000 0.004 0.996
#> SRR934305 5 0.0162 1.00000 0.000 0.000 0.000 0.004 0.996
#> SRR934306 5 0.0162 1.00000 0.000 0.000 0.000 0.004 0.996
#> SRR934307 5 0.0162 1.00000 0.000 0.000 0.000 0.004 0.996
#> SRR934308 5 0.0162 1.00000 0.000 0.000 0.000 0.004 0.996
#> SRR934309 5 0.0162 1.00000 0.000 0.000 0.000 0.004 0.996
#> SRR934310 5 0.0162 1.00000 0.000 0.000 0.000 0.004 0.996
#> SRR934311 5 0.0162 1.00000 0.000 0.000 0.000 0.004 0.996
#> SRR934312 1 0.2516 0.77407 0.860 0.000 0.000 0.140 0.000
#> SRR934313 1 0.2891 0.74985 0.824 0.000 0.000 0.176 0.000
#> SRR934314 1 0.2690 0.76471 0.844 0.000 0.000 0.156 0.000
#> SRR934315 1 0.2773 0.76054 0.836 0.000 0.000 0.164 0.000
#> SRR934316 1 0.2179 0.78704 0.888 0.000 0.000 0.112 0.000
#> SRR934317 1 0.1965 0.79339 0.904 0.000 0.000 0.096 0.000
#> SRR934318 1 0.3480 0.66742 0.752 0.000 0.000 0.248 0.000
#> SRR934319 1 0.2471 0.77602 0.864 0.000 0.000 0.136 0.000
#> SRR934320 4 0.4390 0.26095 0.428 0.000 0.004 0.568 0.000
#> SRR934321 4 0.4452 -0.01425 0.496 0.000 0.004 0.500 0.000
#> SRR934322 1 0.4450 0.02837 0.508 0.000 0.004 0.488 0.000
#> SRR934323 4 0.4410 0.22378 0.440 0.000 0.004 0.556 0.000
#> SRR934324 1 0.4415 0.18173 0.552 0.000 0.004 0.444 0.000
#> SRR934325 1 0.4196 0.47721 0.640 0.000 0.004 0.356 0.000
#> SRR934326 4 0.4555 0.47978 0.344 0.000 0.020 0.636 0.000
#> SRR934327 1 0.4446 0.06109 0.520 0.000 0.004 0.476 0.000
#> SRR934328 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934329 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934330 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934331 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934332 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934333 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934334 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934335 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934344 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934345 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934346 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934347 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934348 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934349 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934350 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934351 1 0.0000 0.81970 1.000 0.000 0.000 0.000 0.000
#> SRR934336 4 0.3612 0.64863 0.268 0.000 0.000 0.732 0.000
#> SRR934337 4 0.3534 0.66232 0.256 0.000 0.000 0.744 0.000
#> SRR934338 4 0.3661 0.63939 0.276 0.000 0.000 0.724 0.000
#> SRR934339 4 0.3561 0.65930 0.260 0.000 0.000 0.740 0.000
#> SRR934340 4 0.3752 0.61533 0.292 0.000 0.000 0.708 0.000
#> SRR934341 4 0.3586 0.65417 0.264 0.000 0.000 0.736 0.000
#> SRR934342 4 0.3561 0.65930 0.260 0.000 0.000 0.740 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> SRR934216 4 0.6276 0.681 0.068 0.000 0.004 0.584 0.172 0.172
#> SRR934217 4 0.6251 0.675 0.064 0.000 0.004 0.584 0.184 0.164
#> SRR934218 4 0.6276 0.683 0.068 0.000 0.004 0.584 0.168 0.176
#> SRR934219 4 0.6251 0.675 0.064 0.000 0.004 0.584 0.184 0.164
#> SRR934220 4 0.6274 0.682 0.068 0.000 0.004 0.584 0.164 0.180
#> SRR934221 4 0.6276 0.681 0.068 0.000 0.004 0.584 0.172 0.172
#> SRR934222 4 0.6251 0.677 0.064 0.000 0.004 0.584 0.184 0.164
#> SRR934223 4 0.6274 0.676 0.068 0.000 0.004 0.584 0.180 0.164
#> SRR934224 6 0.4399 0.239 0.028 0.004 0.000 0.352 0.000 0.616
#> SRR934225 6 0.4399 0.230 0.028 0.004 0.000 0.352 0.000 0.616
#> SRR934226 6 0.4292 0.264 0.032 0.000 0.000 0.340 0.000 0.628
#> SRR934227 6 0.4399 0.239 0.028 0.004 0.000 0.352 0.000 0.616
#> SRR934228 6 0.4234 0.309 0.032 0.000 0.000 0.324 0.000 0.644
#> SRR934229 6 0.4302 0.310 0.028 0.004 0.000 0.324 0.000 0.644
#> SRR934230 6 0.4411 0.226 0.028 0.004 0.000 0.356 0.000 0.612
#> SRR934231 6 0.4373 0.255 0.028 0.004 0.000 0.344 0.000 0.624
#> SRR934232 2 0.4249 0.789 0.032 0.640 0.000 0.328 0.000 0.000
#> SRR934233 2 0.4264 0.786 0.032 0.636 0.000 0.332 0.000 0.000
#> SRR934234 2 0.4249 0.789 0.032 0.640 0.000 0.328 0.000 0.000
#> SRR934235 2 0.4249 0.789 0.032 0.640 0.000 0.328 0.000 0.000
#> SRR934236 2 0.4249 0.789 0.032 0.640 0.000 0.328 0.000 0.000
#> SRR934237 2 0.4249 0.789 0.032 0.640 0.000 0.328 0.000 0.000
#> SRR934238 2 0.4264 0.786 0.032 0.636 0.000 0.332 0.000 0.000
#> SRR934239 2 0.4264 0.786 0.032 0.636 0.000 0.332 0.000 0.000
#> SRR934240 2 0.0547 0.781 0.000 0.980 0.020 0.000 0.000 0.000
#> SRR934241 2 0.0547 0.781 0.000 0.980 0.020 0.000 0.000 0.000
#> SRR934242 2 0.0547 0.781 0.000 0.980 0.020 0.000 0.000 0.000
#> SRR934243 2 0.0547 0.781 0.000 0.980 0.020 0.000 0.000 0.000
#> SRR934244 2 0.0547 0.781 0.000 0.980 0.020 0.000 0.000 0.000
#> SRR934245 2 0.0547 0.781 0.000 0.980 0.020 0.000 0.000 0.000
#> SRR934246 2 0.0547 0.781 0.000 0.980 0.020 0.000 0.000 0.000
#> SRR934247 2 0.0547 0.781 0.000 0.980 0.020 0.000 0.000 0.000
#> SRR934248 4 0.2496 0.671 0.008 0.032 0.000 0.900 0.016 0.044
#> SRR934249 4 0.2263 0.661 0.008 0.032 0.000 0.912 0.012 0.036
#> SRR934250 4 0.2179 0.672 0.008 0.024 0.000 0.916 0.012 0.040
#> SRR934251 4 0.2233 0.667 0.008 0.032 0.000 0.912 0.008 0.040
#> SRR934252 4 0.2157 0.671 0.008 0.028 0.000 0.916 0.008 0.040
#> SRR934253 4 0.2263 0.661 0.008 0.032 0.000 0.912 0.012 0.036
#> SRR934254 4 0.2233 0.667 0.008 0.032 0.000 0.912 0.008 0.040
#> SRR934255 4 0.2334 0.666 0.008 0.032 0.000 0.908 0.012 0.040
#> SRR934256 3 0.0146 1.000 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR934257 3 0.0146 1.000 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR934258 3 0.0146 1.000 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR934259 3 0.0146 1.000 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR934260 3 0.0146 1.000 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR934261 3 0.0146 1.000 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR934262 3 0.0146 1.000 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR934263 3 0.0146 1.000 0.000 0.004 0.996 0.000 0.000 0.000
#> SRR934264 4 0.3426 0.736 0.004 0.000 0.000 0.720 0.000 0.276
#> SRR934265 4 0.3405 0.740 0.004 0.000 0.000 0.724 0.000 0.272
#> SRR934266 4 0.3405 0.741 0.004 0.000 0.000 0.724 0.000 0.272
#> SRR934267 4 0.3314 0.749 0.004 0.000 0.000 0.740 0.000 0.256
#> SRR934268 4 0.3383 0.744 0.004 0.000 0.000 0.728 0.000 0.268
#> SRR934269 4 0.3405 0.741 0.004 0.000 0.000 0.724 0.000 0.272
#> SRR934270 4 0.3360 0.744 0.004 0.000 0.000 0.732 0.000 0.264
#> SRR934271 4 0.3448 0.731 0.004 0.000 0.000 0.716 0.000 0.280
#> SRR934272 6 0.0935 0.863 0.032 0.000 0.000 0.004 0.000 0.964
#> SRR934273 6 0.1152 0.862 0.044 0.000 0.000 0.004 0.000 0.952
#> SRR934274 6 0.1152 0.862 0.044 0.000 0.000 0.004 0.000 0.952
#> SRR934275 6 0.1082 0.863 0.040 0.000 0.000 0.004 0.000 0.956
#> SRR934276 6 0.1082 0.862 0.040 0.000 0.000 0.004 0.000 0.956
#> SRR934277 6 0.1152 0.862 0.044 0.000 0.000 0.004 0.000 0.952
#> SRR934278 6 0.0935 0.863 0.032 0.000 0.000 0.004 0.000 0.964
#> SRR934279 6 0.1010 0.863 0.036 0.000 0.000 0.004 0.000 0.960
#> SRR934280 6 0.0458 0.862 0.016 0.000 0.000 0.000 0.000 0.984
#> SRR934281 6 0.0547 0.863 0.020 0.000 0.000 0.000 0.000 0.980
#> SRR934282 6 0.0547 0.863 0.020 0.000 0.000 0.000 0.000 0.980
#> SRR934283 6 0.0547 0.863 0.020 0.000 0.000 0.000 0.000 0.980
#> SRR934284 6 0.0632 0.863 0.024 0.000 0.000 0.000 0.000 0.976
#> SRR934285 6 0.0632 0.863 0.024 0.000 0.000 0.000 0.000 0.976
#> SRR934286 6 0.0260 0.858 0.008 0.000 0.000 0.000 0.000 0.992
#> SRR934287 6 0.0547 0.863 0.020 0.000 0.000 0.000 0.000 0.980
#> SRR934288 1 0.1556 0.957 0.920 0.000 0.000 0.000 0.000 0.080
#> SRR934289 1 0.1556 0.957 0.920 0.000 0.000 0.000 0.000 0.080
#> SRR934290 1 0.1610 0.954 0.916 0.000 0.000 0.000 0.000 0.084
#> SRR934291 1 0.1556 0.957 0.920 0.000 0.000 0.000 0.000 0.080
#> SRR934292 1 0.1556 0.957 0.920 0.000 0.000 0.000 0.000 0.080
#> SRR934293 1 0.1556 0.957 0.920 0.000 0.000 0.000 0.000 0.080
#> SRR934294 1 0.1556 0.957 0.920 0.000 0.000 0.000 0.000 0.080
#> SRR934295 1 0.1556 0.957 0.920 0.000 0.000 0.000 0.000 0.080
#> SRR934296 1 0.3009 0.920 0.844 0.040 0.000 0.000 0.004 0.112
#> SRR934297 1 0.3095 0.920 0.844 0.044 0.000 0.000 0.008 0.104
#> SRR934298 1 0.2793 0.926 0.856 0.028 0.000 0.000 0.004 0.112
#> SRR934299 1 0.3009 0.920 0.844 0.040 0.000 0.000 0.004 0.112
#> SRR934300 1 0.2699 0.931 0.864 0.020 0.000 0.000 0.008 0.108
#> SRR934301 1 0.3009 0.920 0.844 0.040 0.000 0.000 0.004 0.112
#> SRR934302 1 0.3009 0.920 0.844 0.040 0.000 0.000 0.004 0.112
#> SRR934303 1 0.3283 0.915 0.836 0.044 0.000 0.004 0.008 0.108
#> SRR934304 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934305 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934306 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934307 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934308 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934309 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934310 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934311 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR934312 6 0.1714 0.841 0.092 0.000 0.000 0.000 0.000 0.908
#> SRR934313 6 0.1714 0.841 0.092 0.000 0.000 0.000 0.000 0.908
#> SRR934314 6 0.1556 0.848 0.080 0.000 0.000 0.000 0.000 0.920
#> SRR934315 6 0.1610 0.846 0.084 0.000 0.000 0.000 0.000 0.916
#> SRR934316 6 0.1765 0.837 0.096 0.000 0.000 0.000 0.000 0.904
#> SRR934317 6 0.2219 0.800 0.136 0.000 0.000 0.000 0.000 0.864
#> SRR934318 6 0.1327 0.856 0.064 0.000 0.000 0.000 0.000 0.936
#> SRR934319 6 0.1714 0.840 0.092 0.000 0.000 0.000 0.000 0.908
#> SRR934320 6 0.2366 0.838 0.056 0.000 0.020 0.024 0.000 0.900
#> SRR934321 6 0.2488 0.823 0.076 0.000 0.020 0.016 0.000 0.888
#> SRR934322 6 0.2222 0.829 0.084 0.000 0.008 0.012 0.000 0.896
#> SRR934323 6 0.1983 0.841 0.060 0.000 0.012 0.012 0.000 0.916
#> SRR934324 6 0.2275 0.823 0.096 0.000 0.008 0.008 0.000 0.888
#> SRR934325 6 0.2361 0.812 0.104 0.000 0.004 0.012 0.000 0.880
#> SRR934326 6 0.2386 0.835 0.052 0.000 0.024 0.024 0.000 0.900
#> SRR934327 6 0.2729 0.813 0.088 0.000 0.024 0.016 0.000 0.872
#> SRR934328 1 0.1863 0.935 0.920 0.000 0.000 0.036 0.000 0.044
#> SRR934329 1 0.1863 0.935 0.920 0.000 0.000 0.036 0.000 0.044
#> SRR934330 1 0.1780 0.941 0.924 0.000 0.000 0.028 0.000 0.048
#> SRR934331 1 0.1863 0.935 0.920 0.000 0.000 0.036 0.000 0.044
#> SRR934332 1 0.1789 0.937 0.924 0.000 0.000 0.032 0.000 0.044
#> SRR934333 1 0.1780 0.940 0.924 0.000 0.000 0.028 0.000 0.048
#> SRR934334 1 0.1863 0.935 0.920 0.000 0.000 0.036 0.000 0.044
#> SRR934335 1 0.1863 0.935 0.920 0.000 0.000 0.036 0.000 0.044
#> SRR934344 1 0.1327 0.957 0.936 0.000 0.000 0.000 0.000 0.064
#> SRR934345 1 0.1327 0.957 0.936 0.000 0.000 0.000 0.000 0.064
#> SRR934346 1 0.1327 0.957 0.936 0.000 0.000 0.000 0.000 0.064
#> SRR934347 1 0.1387 0.957 0.932 0.000 0.000 0.000 0.000 0.068
#> SRR934348 1 0.1387 0.957 0.932 0.000 0.000 0.000 0.000 0.068
#> SRR934349 1 0.1327 0.957 0.936 0.000 0.000 0.000 0.000 0.064
#> SRR934350 1 0.1327 0.957 0.936 0.000 0.000 0.000 0.000 0.064
#> SRR934351 1 0.1387 0.957 0.932 0.000 0.000 0.000 0.000 0.068
#> SRR934336 6 0.0508 0.853 0.004 0.000 0.000 0.012 0.000 0.984
#> SRR934337 6 0.0692 0.850 0.004 0.000 0.000 0.020 0.000 0.976
#> SRR934338 6 0.0405 0.855 0.004 0.000 0.000 0.008 0.000 0.988
#> SRR934339 6 0.0603 0.851 0.004 0.000 0.000 0.016 0.000 0.980
#> SRR934340 6 0.0405 0.855 0.004 0.000 0.000 0.008 0.000 0.988
#> SRR934341 6 0.0405 0.855 0.004 0.000 0.000 0.008 0.000 0.988
#> SRR934342 6 0.0777 0.847 0.004 0.000 0.000 0.024 0.000 0.972
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):
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