cola Report for recount2:SRP053101

Date: 2019-12-26 00:44:25 CET, cola version: 1.3.2

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Summary

All available functions which can be applied to this res_list object:

res_list
#> A 'ConsensusPartitionList' object with 24 methods.
#>   On a matrix with 16620 rows and 87 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] 16620    87

Density distribution

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)

plot of chunk density-heatmap

Suggest the best k

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
CV:skmeans 2 1.000 0.958 0.983 **
ATC:skmeans 2 1.000 0.976 0.989 **
ATC:pam 2 1.000 0.957 0.983 **
CV:hclust 2 0.952 0.933 0.967 **
MAD:NMF 2 0.928 0.930 0.971 *
SD:NMF 2 0.927 0.926 0.969 *
ATC:NMF 3 0.828 0.888 0.947
CV:NMF 2 0.814 0.882 0.951
SD:skmeans 3 0.788 0.858 0.927
ATC:mclust 2 0.738 0.920 0.949
MAD:pam 2 0.700 0.862 0.931
ATC:kmeans 3 0.696 0.813 0.896
MAD:skmeans 2 0.668 0.740 0.900
ATC:hclust 3 0.553 0.839 0.897
SD:pam 2 0.469 0.863 0.905
MAD:mclust 5 0.469 0.533 0.692
MAD:hclust 3 0.467 0.784 0.877
MAD:kmeans 2 0.463 0.791 0.888
SD:kmeans 2 0.463 0.819 0.899
SD:hclust 3 0.447 0.765 0.854
CV:pam 3 0.357 0.576 0.714
CV:kmeans 4 0.326 0.561 0.686
CV:mclust 3 0.265 0.666 0.754
SD:mclust 3 0.140 0.246 0.622

**: 1-PAC > 0.95, *: 1-PAC > 0.9

CDF of consensus matrices

Cumulative distribution function curves of consensus matrix for all methods.

collect_plots(res_list, fun = plot_ecdf)

plot of chunk collect-plots

Consensus heatmap

Consensus heatmaps for all methods. (What is a consensus heatmap?)

collect_plots(res_list, k = 2, fun = consensus_heatmap, mc.cores = 4)

plot of chunk tab-collect-consensus-heatmap-1

collect_plots(res_list, k = 3, fun = consensus_heatmap, mc.cores = 4)

plot of chunk tab-collect-consensus-heatmap-2

collect_plots(res_list, k = 4, fun = consensus_heatmap, mc.cores = 4)

plot of chunk tab-collect-consensus-heatmap-3

collect_plots(res_list, k = 5, fun = consensus_heatmap, mc.cores = 4)

plot of chunk tab-collect-consensus-heatmap-4

collect_plots(res_list, k = 6, fun = consensus_heatmap, mc.cores = 4)

plot of chunk tab-collect-consensus-heatmap-5

Membership heatmap

Membership heatmaps for all methods. (What is a membership heatmap?)

collect_plots(res_list, k = 2, fun = membership_heatmap, mc.cores = 4)

plot of chunk tab-collect-membership-heatmap-1

collect_plots(res_list, k = 3, fun = membership_heatmap, mc.cores = 4)

plot of chunk tab-collect-membership-heatmap-2

collect_plots(res_list, k = 4, fun = membership_heatmap, mc.cores = 4)

plot of chunk tab-collect-membership-heatmap-3

collect_plots(res_list, k = 5, fun = membership_heatmap, mc.cores = 4)

plot of chunk tab-collect-membership-heatmap-4

collect_plots(res_list, k = 6, fun = membership_heatmap, mc.cores = 4)

plot of chunk tab-collect-membership-heatmap-5

Signature heatmap

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)

plot of chunk tab-collect-get-signatures-1

collect_plots(res_list, k = 3, fun = get_signatures, mc.cores = 4)

plot of chunk tab-collect-get-signatures-2

collect_plots(res_list, k = 4, fun = get_signatures, mc.cores = 4)

plot of chunk tab-collect-get-signatures-3

collect_plots(res_list, k = 5, fun = get_signatures, mc.cores = 4)

plot of chunk tab-collect-get-signatures-4

collect_plots(res_list, k = 6, fun = get_signatures, mc.cores = 4)

plot of chunk tab-collect-get-signatures-5

Statistics table

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 0.927           0.926       0.969          0.462 0.536   0.536
#> CV:NMF      2 0.814           0.882       0.951          0.460 0.524   0.524
#> MAD:NMF     2 0.928           0.930       0.971          0.459 0.536   0.536
#> ATC:NMF     2 0.815           0.852       0.942          0.499 0.497   0.497
#> SD:skmeans  2 0.668           0.814       0.921          0.502 0.500   0.500
#> CV:skmeans  2 1.000           0.958       0.983          0.496 0.500   0.500
#> MAD:skmeans 2 0.668           0.740       0.900          0.503 0.496   0.496
#> ATC:skmeans 2 1.000           0.976       0.989          0.506 0.494   0.494
#> SD:mclust   2 0.542           0.716       0.861          0.340 0.777   0.777
#> CV:mclust   2 0.824           0.937       0.950          0.278 0.743   0.743
#> MAD:mclust  2 0.493           0.725       0.828          0.365 0.518   0.518
#> ATC:mclust  2 0.738           0.920       0.949          0.431 0.530   0.530
#> SD:kmeans   2 0.463           0.819       0.899          0.448 0.524   0.524
#> CV:kmeans   2 0.700           0.849       0.927          0.399 0.586   0.586
#> MAD:kmeans  2 0.463           0.791       0.888          0.464 0.524   0.524
#> ATC:kmeans  2 0.475           0.779       0.898          0.494 0.502   0.502
#> SD:pam      2 0.469           0.863       0.905          0.468 0.500   0.500
#> CV:pam      2 0.368           0.748       0.825          0.362 0.743   0.743
#> MAD:pam     2 0.700           0.862       0.931          0.498 0.500   0.500
#> ATC:pam     2 1.000           0.957       0.983          0.504 0.496   0.496
#> SD:hclust   2 0.758           0.916       0.960          0.334 0.682   0.682
#> CV:hclust   2 0.952           0.933       0.967          0.279 0.682   0.682
#> MAD:hclust  2 0.817           0.941       0.971          0.323 0.682   0.682
#> ATC:hclust  2 0.466           0.776       0.884          0.487 0.513   0.513
get_stats(res_list, k = 3)
#>             k 1-PAC mean_silhouette concordance area_increased  Rand Jaccard
#> SD:NMF      3 0.685           0.772       0.905          0.427 0.693   0.477
#> CV:NMF      3 0.673           0.770       0.896          0.447 0.632   0.398
#> MAD:NMF     3 0.679           0.765       0.898          0.443 0.681   0.461
#> ATC:NMF     3 0.828           0.888       0.947          0.329 0.711   0.483
#> SD:skmeans  3 0.788           0.858       0.927          0.323 0.763   0.555
#> CV:skmeans  3 0.699           0.808       0.904          0.336 0.793   0.605
#> MAD:skmeans 3 0.598           0.806       0.893          0.321 0.725   0.499
#> ATC:skmeans 3 0.825           0.929       0.947          0.301 0.703   0.475
#> SD:mclust   3 0.140           0.246       0.622          0.565 0.621   0.530
#> CV:mclust   3 0.265           0.666       0.754          1.062 0.647   0.529
#> MAD:mclust  3 0.200           0.398       0.709          0.603 0.691   0.467
#> ATC:mclust  3 0.645           0.853       0.905          0.412 0.823   0.680
#> SD:kmeans   3 0.324           0.427       0.633          0.392 0.801   0.640
#> CV:kmeans   3 0.306           0.496       0.661          0.442 0.743   0.584
#> MAD:kmeans  3 0.316           0.414       0.642          0.362 0.706   0.497
#> ATC:kmeans  3 0.696           0.813       0.896          0.319 0.752   0.544
#> SD:pam      3 0.591           0.797       0.889          0.300 0.873   0.754
#> CV:pam      3 0.357           0.576       0.714          0.647 0.644   0.520
#> MAD:pam     3 0.627           0.819       0.906          0.236 0.878   0.760
#> ATC:pam     3 0.855           0.871       0.946          0.274 0.745   0.543
#> SD:hclust   3 0.447           0.765       0.854          0.789 0.750   0.633
#> CV:hclust   3 0.399           0.578       0.717          0.914 0.694   0.552
#> MAD:hclust  3 0.467           0.784       0.877          0.849 0.718   0.586
#> ATC:hclust  3 0.553           0.839       0.897          0.236 0.888   0.781
get_stats(res_list, k = 4)
#>             k 1-PAC mean_silhouette concordance area_increased  Rand Jaccard
#> SD:NMF      4 0.617           0.665       0.837          0.136 0.794   0.474
#> CV:NMF      4 0.649           0.743       0.842          0.128 0.813   0.514
#> MAD:NMF     4 0.652           0.707       0.853          0.131 0.746   0.387
#> ATC:NMF     4 0.802           0.854       0.923          0.128 0.868   0.629
#> SD:skmeans  4 0.654           0.698       0.839          0.121 0.902   0.712
#> CV:skmeans  4 0.600           0.537       0.740          0.120 0.919   0.769
#> MAD:skmeans 4 0.663           0.682       0.837          0.122 0.862   0.616
#> ATC:skmeans 4 0.874           0.804       0.902          0.132 0.888   0.683
#> SD:mclust   4 0.231           0.507       0.605          0.261 0.624   0.320
#> CV:mclust   4 0.346           0.636       0.759          0.207 0.858   0.656
#> MAD:mclust  4 0.278           0.447       0.672          0.094 0.683   0.336
#> ATC:mclust  4 0.524           0.499       0.712          0.131 0.698   0.390
#> SD:kmeans   4 0.391           0.486       0.641          0.143 0.720   0.394
#> CV:kmeans   4 0.326           0.561       0.686          0.181 0.827   0.623
#> MAD:kmeans  4 0.382           0.496       0.699          0.135 0.843   0.595
#> ATC:kmeans  4 0.577           0.573       0.722          0.119 0.889   0.686
#> SD:pam      4 0.708           0.676       0.867          0.179 0.819   0.580
#> CV:pam      4 0.421           0.560       0.697          0.182 0.749   0.440
#> MAD:pam     4 0.735           0.807       0.913          0.160 0.857   0.652
#> ATC:pam     4 0.740           0.828       0.907          0.102 0.721   0.411
#> SD:hclust   4 0.545           0.596       0.770          0.194 0.842   0.641
#> CV:hclust   4 0.416           0.562       0.713          0.182 0.827   0.602
#> MAD:hclust  4 0.573           0.683       0.779          0.185 0.867   0.684
#> ATC:hclust  4 0.602           0.806       0.841          0.124 0.929   0.824
get_stats(res_list, k = 5)
#>             k 1-PAC mean_silhouette concordance area_increased  Rand Jaccard
#> SD:NMF      5 0.650           0.657       0.813         0.0673 0.852   0.500
#> CV:NMF      5 0.656           0.625       0.768         0.0646 0.878   0.569
#> MAD:NMF     5 0.751           0.749       0.867         0.0702 0.865   0.530
#> ATC:NMF     5 0.732           0.689       0.798         0.0694 0.898   0.637
#> SD:skmeans  5 0.677           0.677       0.813         0.0640 0.927   0.724
#> CV:skmeans  5 0.625           0.526       0.697         0.0651 0.876   0.595
#> MAD:skmeans 5 0.688           0.663       0.821         0.0653 0.908   0.666
#> ATC:skmeans 5 0.833           0.832       0.898         0.0581 0.906   0.664
#> SD:mclust   5 0.329           0.287       0.555         0.0921 0.744   0.322
#> CV:mclust   5 0.482           0.584       0.726         0.0908 0.948   0.816
#> MAD:mclust  5 0.469           0.533       0.692         0.2044 0.789   0.417
#> ATC:mclust  5 0.612           0.783       0.817         0.0735 0.751   0.372
#> SD:kmeans   5 0.456           0.426       0.643         0.0739 0.923   0.719
#> CV:kmeans   5 0.395           0.453       0.634         0.0924 0.944   0.849
#> MAD:kmeans  5 0.510           0.515       0.688         0.0773 0.895   0.637
#> ATC:kmeans  5 0.600           0.561       0.719         0.0717 0.866   0.558
#> SD:pam      5 0.694           0.621       0.807         0.0787 0.917   0.715
#> CV:pam      5 0.479           0.518       0.737         0.0553 0.859   0.554
#> MAD:pam     5 0.765           0.742       0.866         0.0857 0.903   0.679
#> ATC:pam     5 0.862           0.837       0.930         0.1071 0.838   0.524
#> SD:hclust   5 0.710           0.671       0.825         0.0822 0.914   0.713
#> CV:hclust   5 0.541           0.633       0.727         0.1404 0.906   0.733
#> MAD:hclust  5 0.675           0.748       0.862         0.0968 0.886   0.641
#> ATC:hclust  5 0.800           0.818       0.891         0.0603 0.993   0.977
get_stats(res_list, k = 6)
#>             k 1-PAC mean_silhouette concordance area_increased  Rand Jaccard
#> SD:NMF      6 0.689           0.549       0.741         0.0441 0.899   0.564
#> CV:NMF      6 0.691           0.659       0.783         0.0412 0.911   0.604
#> MAD:NMF     6 0.707           0.556       0.756         0.0419 0.896   0.552
#> ATC:NMF     6 0.670           0.524       0.742         0.0399 0.915   0.630
#> SD:skmeans  6 0.688           0.554       0.747         0.0409 0.979   0.898
#> CV:skmeans  6 0.653           0.503       0.681         0.0418 0.936   0.715
#> MAD:skmeans 6 0.702           0.623       0.759         0.0396 1.000   1.000
#> ATC:skmeans 6 0.798           0.752       0.843         0.0390 0.947   0.760
#> SD:mclust   6 0.464           0.488       0.669         0.0756 0.796   0.338
#> CV:mclust   6 0.516           0.617       0.706         0.0629 0.918   0.669
#> MAD:mclust  6 0.520           0.391       0.596         0.0343 0.868   0.477
#> ATC:mclust  6 0.548           0.337       0.593         0.0920 0.769   0.301
#> SD:kmeans   6 0.563           0.443       0.640         0.0488 0.977   0.898
#> CV:kmeans   6 0.453           0.344       0.556         0.0535 0.874   0.635
#> MAD:kmeans  6 0.583           0.471       0.665         0.0459 0.928   0.688
#> ATC:kmeans  6 0.657           0.479       0.683         0.0453 0.933   0.716
#> SD:pam      6 0.728           0.648       0.797         0.0366 0.961   0.828
#> CV:pam      6 0.526           0.435       0.637         0.0566 0.830   0.421
#> MAD:pam     6 0.779           0.763       0.859         0.0381 0.971   0.870
#> ATC:pam     6 0.857           0.823       0.911         0.0589 0.903   0.593
#> SD:hclust   6 0.737           0.557       0.754         0.0429 0.878   0.549
#> CV:hclust   6 0.608           0.658       0.780         0.0553 0.944   0.811
#> MAD:hclust  6 0.744           0.694       0.787         0.0465 1.000   1.000
#> ATC:hclust  6 0.767           0.818       0.849         0.0687 0.850   0.560

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)

plot of chunk tab-collect-stats-from-consensus-partition-list-1

collect_stats(res_list, k = 3)

plot of chunk tab-collect-stats-from-consensus-partition-list-2

collect_stats(res_list, k = 4)

plot of chunk tab-collect-stats-from-consensus-partition-list-3

collect_stats(res_list, k = 5)

plot of chunk tab-collect-stats-from-consensus-partition-list-4

collect_stats(res_list, k = 6)

plot of chunk tab-collect-stats-from-consensus-partition-list-5

Partition from all methods

Collect partitions from all methods:

collect_classes(res_list, k = 2)

plot of chunk tab-collect-classes-from-consensus-partition-list-1

collect_classes(res_list, k = 3)

plot of chunk tab-collect-classes-from-consensus-partition-list-2

collect_classes(res_list, k = 4)

plot of chunk tab-collect-classes-from-consensus-partition-list-3

collect_classes(res_list, k = 5)

plot of chunk tab-collect-classes-from-consensus-partition-list-4

collect_classes(res_list, k = 6)

plot of chunk tab-collect-classes-from-consensus-partition-list-5

Top rows overlap

Overlap of top rows from different top-row methods:

top_rows_overlap(res_list, top_n = 1000, method = "euler")

plot of chunk tab-top-rows-overlap-by-euler-1

top_rows_overlap(res_list, top_n = 2000, method = "euler")

plot of chunk tab-top-rows-overlap-by-euler-2

top_rows_overlap(res_list, top_n = 3000, method = "euler")

plot of chunk tab-top-rows-overlap-by-euler-3

top_rows_overlap(res_list, top_n = 4000, method = "euler")

plot of chunk tab-top-rows-overlap-by-euler-4

top_rows_overlap(res_list, top_n = 5000, method = "euler")

plot of chunk tab-top-rows-overlap-by-euler-5

Also visualize the correspondance of rankings between different top-row methods:

top_rows_overlap(res_list, top_n = 1000, method = "correspondance")

plot of chunk tab-top-rows-overlap-by-correspondance-1

top_rows_overlap(res_list, top_n = 2000, method = "correspondance")

plot of chunk tab-top-rows-overlap-by-correspondance-2

top_rows_overlap(res_list, top_n = 3000, method = "correspondance")

plot of chunk tab-top-rows-overlap-by-correspondance-3

top_rows_overlap(res_list, top_n = 4000, method = "correspondance")

plot of chunk tab-top-rows-overlap-by-correspondance-4

top_rows_overlap(res_list, top_n = 5000, method = "correspondance")

plot of chunk tab-top-rows-overlap-by-correspondance-5

Heatmaps of the top rows:

top_rows_heatmap(res_list, top_n = 1000)

plot of chunk tab-top-rows-heatmap-1

top_rows_heatmap(res_list, top_n = 2000)

plot of chunk tab-top-rows-heatmap-2

top_rows_heatmap(res_list, top_n = 3000)

plot of chunk tab-top-rows-heatmap-3

top_rows_heatmap(res_list, top_n = 4000)

plot of chunk tab-top-rows-heatmap-4

top_rows_heatmap(res_list, top_n = 5000)

plot of chunk tab-top-rows-heatmap-5

Results for each method


SD:hclust

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 16620 rows and 87 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)

plot of chunk SD-hclust-collect-plots

The plots are:

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:

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)

plot of chunk SD-hclust-select-partition-number

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 0.758           0.916       0.960         0.3342 0.682   0.682
#> 3 3 0.447           0.765       0.854         0.7894 0.750   0.633
#> 4 4 0.545           0.596       0.770         0.1945 0.842   0.641
#> 5 5 0.710           0.671       0.825         0.0822 0.914   0.713
#> 6 6 0.737           0.557       0.754         0.0429 0.878   0.549

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2   0.574      0.887 0.136 0.864
#> SRR1785239     2   0.574      0.887 0.136 0.864
#> SRR1785240     1   0.000      0.961 1.000 0.000
#> SRR1785241     1   0.000      0.961 1.000 0.000
#> SRR1785242     1   0.000      0.961 1.000 0.000
#> SRR1785243     1   0.000      0.961 1.000 0.000
#> SRR1785244     1   0.000      0.961 1.000 0.000
#> SRR1785245     1   0.000      0.961 1.000 0.000
#> SRR1785246     1   0.000      0.961 1.000 0.000
#> SRR1785247     1   0.000      0.961 1.000 0.000
#> SRR1785248     2   0.529      0.899 0.120 0.880
#> SRR1785250     1   0.000      0.961 1.000 0.000
#> SRR1785251     1   0.000      0.961 1.000 0.000
#> SRR1785252     1   0.000      0.961 1.000 0.000
#> SRR1785253     1   0.000      0.961 1.000 0.000
#> SRR1785254     1   0.895      0.554 0.688 0.312
#> SRR1785255     1   0.895      0.554 0.688 0.312
#> SRR1785256     1   0.000      0.961 1.000 0.000
#> SRR1785257     1   0.000      0.961 1.000 0.000
#> SRR1785258     1   0.000      0.961 1.000 0.000
#> SRR1785259     1   0.000      0.961 1.000 0.000
#> SRR1785262     1   0.000      0.961 1.000 0.000
#> SRR1785263     1   0.000      0.961 1.000 0.000
#> SRR1785260     1   0.000      0.961 1.000 0.000
#> SRR1785261     1   0.000      0.961 1.000 0.000
#> SRR1785264     2   0.529      0.899 0.120 0.880
#> SRR1785265     2   0.529      0.899 0.120 0.880
#> SRR1785266     2   0.000      0.935 0.000 1.000
#> SRR1785267     2   0.000      0.935 0.000 1.000
#> SRR1785268     1   0.000      0.961 1.000 0.000
#> SRR1785269     1   0.000      0.961 1.000 0.000
#> SRR1785270     1   0.000      0.961 1.000 0.000
#> SRR1785271     1   0.000      0.961 1.000 0.000
#> SRR1785272     1   0.000      0.961 1.000 0.000
#> SRR1785273     1   0.000      0.961 1.000 0.000
#> SRR1785276     1   0.000      0.961 1.000 0.000
#> SRR1785277     1   0.000      0.961 1.000 0.000
#> SRR1785274     1   0.000      0.961 1.000 0.000
#> SRR1785275     1   0.000      0.961 1.000 0.000
#> SRR1785280     2   0.000      0.935 0.000 1.000
#> SRR1785281     2   0.000      0.935 0.000 1.000
#> SRR1785278     1   0.000      0.961 1.000 0.000
#> SRR1785279     1   0.000      0.961 1.000 0.000
#> SRR1785282     1   0.000      0.961 1.000 0.000
#> SRR1785283     1   0.000      0.961 1.000 0.000
#> SRR1785284     1   0.000      0.961 1.000 0.000
#> SRR1785285     1   0.000      0.961 1.000 0.000
#> SRR1785286     1   0.000      0.961 1.000 0.000
#> SRR1785287     1   0.000      0.961 1.000 0.000
#> SRR1785288     1   0.000      0.961 1.000 0.000
#> SRR1785289     1   0.000      0.961 1.000 0.000
#> SRR1785290     2   0.634      0.858 0.160 0.840
#> SRR1785291     2   0.634      0.858 0.160 0.840
#> SRR1785296     1   0.184      0.939 0.972 0.028
#> SRR1785297     1   0.184      0.939 0.972 0.028
#> SRR1785292     2   0.000      0.935 0.000 1.000
#> SRR1785293     2   0.000      0.935 0.000 1.000
#> SRR1785294     1   0.118      0.949 0.984 0.016
#> SRR1785295     1   0.118      0.949 0.984 0.016
#> SRR1785298     1   0.895      0.554 0.688 0.312
#> SRR1785299     1   0.895      0.554 0.688 0.312
#> SRR1785300     1   0.000      0.961 1.000 0.000
#> SRR1785301     1   0.000      0.961 1.000 0.000
#> SRR1785304     1   0.000      0.961 1.000 0.000
#> SRR1785305     1   0.000      0.961 1.000 0.000
#> SRR1785306     1   0.827      0.649 0.740 0.260
#> SRR1785307     1   0.827      0.649 0.740 0.260
#> SRR1785302     1   0.881      0.576 0.700 0.300
#> SRR1785303     1   0.881      0.576 0.700 0.300
#> SRR1785308     1   0.000      0.961 1.000 0.000
#> SRR1785309     1   0.000      0.961 1.000 0.000
#> SRR1785310     1   0.118      0.949 0.984 0.016
#> SRR1785311     1   0.118      0.949 0.984 0.016
#> SRR1785312     1   0.000      0.961 1.000 0.000
#> SRR1785313     1   0.000      0.961 1.000 0.000
#> SRR1785314     1   0.000      0.961 1.000 0.000
#> SRR1785315     1   0.000      0.961 1.000 0.000
#> SRR1785318     2   0.000      0.935 0.000 1.000
#> SRR1785319     2   0.000      0.935 0.000 1.000
#> SRR1785316     1   0.000      0.961 1.000 0.000
#> SRR1785317     1   0.000      0.961 1.000 0.000
#> SRR1785324     2   0.000      0.935 0.000 1.000
#> SRR1785325     2   0.000      0.935 0.000 1.000
#> SRR1785320     1   0.000      0.961 1.000 0.000
#> SRR1785321     1   0.000      0.961 1.000 0.000
#> SRR1785322     1   0.000      0.961 1.000 0.000
#> SRR1785323     1   0.000      0.961 1.000 0.000

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     2   0.447     0.8974 0.060 0.864 0.076
#> SRR1785239     2   0.447     0.8974 0.060 0.864 0.076
#> SRR1785240     1   0.296     0.7931 0.900 0.000 0.100
#> SRR1785241     1   0.296     0.7931 0.900 0.000 0.100
#> SRR1785242     3   0.207     0.9163 0.060 0.000 0.940
#> SRR1785243     3   0.207     0.9163 0.060 0.000 0.940
#> SRR1785244     1   0.304     0.7977 0.896 0.000 0.104
#> SRR1785245     1   0.304     0.7977 0.896 0.000 0.104
#> SRR1785246     3   0.196     0.9248 0.056 0.000 0.944
#> SRR1785247     3   0.196     0.9248 0.056 0.000 0.944
#> SRR1785248     2   0.409     0.9065 0.052 0.880 0.068
#> SRR1785250     3   0.304     0.9056 0.104 0.000 0.896
#> SRR1785251     3   0.304     0.9056 0.104 0.000 0.896
#> SRR1785252     3   0.207     0.9163 0.060 0.000 0.940
#> SRR1785253     3   0.207     0.9163 0.060 0.000 0.940
#> SRR1785254     1   0.683     0.4843 0.656 0.312 0.032
#> SRR1785255     1   0.683     0.4843 0.656 0.312 0.032
#> SRR1785256     1   0.319     0.7952 0.888 0.000 0.112
#> SRR1785257     1   0.319     0.7952 0.888 0.000 0.112
#> SRR1785258     1   0.629     0.2913 0.536 0.000 0.464
#> SRR1785259     1   0.629     0.2913 0.536 0.000 0.464
#> SRR1785262     3   0.271     0.9250 0.088 0.000 0.912
#> SRR1785263     3   0.271     0.9250 0.088 0.000 0.912
#> SRR1785260     1   0.116     0.7880 0.972 0.000 0.028
#> SRR1785261     1   0.116     0.7880 0.972 0.000 0.028
#> SRR1785264     2   0.409     0.9065 0.052 0.880 0.068
#> SRR1785265     2   0.409     0.9065 0.052 0.880 0.068
#> SRR1785266     2   0.000     0.9377 0.000 1.000 0.000
#> SRR1785267     2   0.000     0.9377 0.000 1.000 0.000
#> SRR1785268     1   0.630     0.2888 0.516 0.000 0.484
#> SRR1785269     1   0.630     0.2888 0.516 0.000 0.484
#> SRR1785270     1   0.288     0.7925 0.904 0.000 0.096
#> SRR1785271     1   0.288     0.7925 0.904 0.000 0.096
#> SRR1785272     1   0.630     0.0518 0.520 0.000 0.480
#> SRR1785273     1   0.630     0.0518 0.520 0.000 0.480
#> SRR1785276     3   0.362     0.8955 0.136 0.000 0.864
#> SRR1785277     3   0.362     0.8955 0.136 0.000 0.864
#> SRR1785274     3   0.334     0.9087 0.120 0.000 0.880
#> SRR1785275     3   0.334     0.9087 0.120 0.000 0.880
#> SRR1785280     2   0.000     0.9377 0.000 1.000 0.000
#> SRR1785281     2   0.000     0.9377 0.000 1.000 0.000
#> SRR1785278     1   0.382     0.7790 0.852 0.000 0.148
#> SRR1785279     1   0.382     0.7790 0.852 0.000 0.148
#> SRR1785282     1   0.319     0.7952 0.888 0.000 0.112
#> SRR1785283     1   0.319     0.7952 0.888 0.000 0.112
#> SRR1785284     1   0.296     0.7931 0.900 0.000 0.100
#> SRR1785285     1   0.296     0.7931 0.900 0.000 0.100
#> SRR1785286     1   0.254     0.7989 0.920 0.000 0.080
#> SRR1785287     1   0.254     0.7989 0.920 0.000 0.080
#> SRR1785288     1   0.304     0.7977 0.896 0.000 0.104
#> SRR1785289     1   0.304     0.7977 0.896 0.000 0.104
#> SRR1785290     2   0.478     0.8399 0.124 0.840 0.036
#> SRR1785291     2   0.478     0.8399 0.124 0.840 0.036
#> SRR1785296     1   0.158     0.7933 0.964 0.028 0.008
#> SRR1785297     1   0.158     0.7933 0.964 0.028 0.008
#> SRR1785292     2   0.000     0.9377 0.000 1.000 0.000
#> SRR1785293     2   0.000     0.9377 0.000 1.000 0.000
#> SRR1785294     1   0.134     0.7961 0.972 0.016 0.012
#> SRR1785295     1   0.134     0.7961 0.972 0.016 0.012
#> SRR1785298     1   0.683     0.4843 0.656 0.312 0.032
#> SRR1785299     1   0.683     0.4843 0.656 0.312 0.032
#> SRR1785300     1   0.319     0.7952 0.888 0.000 0.112
#> SRR1785301     1   0.319     0.7952 0.888 0.000 0.112
#> SRR1785304     1   0.116     0.7880 0.972 0.000 0.028
#> SRR1785305     1   0.116     0.7880 0.972 0.000 0.028
#> SRR1785306     1   0.642     0.6361 0.708 0.260 0.032
#> SRR1785307     1   0.642     0.6361 0.708 0.260 0.032
#> SRR1785302     1   0.674     0.5059 0.668 0.300 0.032
#> SRR1785303     1   0.674     0.5059 0.668 0.300 0.032
#> SRR1785308     3   0.254     0.9025 0.080 0.000 0.920
#> SRR1785309     3   0.254     0.9025 0.080 0.000 0.920
#> SRR1785310     1   0.134     0.7961 0.972 0.016 0.012
#> SRR1785311     1   0.134     0.7961 0.972 0.016 0.012
#> SRR1785312     1   0.630     0.2888 0.516 0.000 0.484
#> SRR1785313     1   0.630     0.2888 0.516 0.000 0.484
#> SRR1785314     1   0.288     0.7925 0.904 0.000 0.096
#> SRR1785315     1   0.288     0.7925 0.904 0.000 0.096
#> SRR1785318     2   0.000     0.9377 0.000 1.000 0.000
#> SRR1785319     2   0.000     0.9377 0.000 1.000 0.000
#> SRR1785316     1   0.319     0.7952 0.888 0.000 0.112
#> SRR1785317     1   0.319     0.7952 0.888 0.000 0.112
#> SRR1785324     2   0.000     0.9377 0.000 1.000 0.000
#> SRR1785325     2   0.000     0.9377 0.000 1.000 0.000
#> SRR1785320     3   0.362     0.8955 0.136 0.000 0.864
#> SRR1785321     3   0.362     0.8955 0.136 0.000 0.864
#> SRR1785322     1   0.382     0.7790 0.852 0.000 0.148
#> SRR1785323     1   0.382     0.7790 0.852 0.000 0.148

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     2  0.3891     0.9011 0.012 0.852 0.036 0.100
#> SRR1785239     2  0.3891     0.9011 0.012 0.852 0.036 0.100
#> SRR1785240     1  0.6887     0.3185 0.456 0.000 0.104 0.440
#> SRR1785241     1  0.6887     0.3185 0.456 0.000 0.104 0.440
#> SRR1785242     3  0.0804     0.8355 0.008 0.000 0.980 0.012
#> SRR1785243     3  0.0804     0.8355 0.008 0.000 0.980 0.012
#> SRR1785244     1  0.0592     0.4661 0.984 0.000 0.000 0.016
#> SRR1785245     1  0.0592     0.4661 0.984 0.000 0.000 0.016
#> SRR1785246     3  0.1940     0.8431 0.076 0.000 0.924 0.000
#> SRR1785247     3  0.1940     0.8431 0.076 0.000 0.924 0.000
#> SRR1785248     2  0.3342     0.9070 0.000 0.868 0.032 0.100
#> SRR1785250     3  0.3764     0.7426 0.216 0.000 0.784 0.000
#> SRR1785251     3  0.3764     0.7426 0.216 0.000 0.784 0.000
#> SRR1785252     3  0.0804     0.8355 0.008 0.000 0.980 0.012
#> SRR1785253     3  0.0804     0.8355 0.008 0.000 0.980 0.012
#> SRR1785254     1  0.8054     0.2220 0.424 0.300 0.008 0.268
#> SRR1785255     1  0.8054     0.2220 0.424 0.300 0.008 0.268
#> SRR1785256     1  0.0000     0.4749 1.000 0.000 0.000 0.000
#> SRR1785257     1  0.0000     0.4749 1.000 0.000 0.000 0.000
#> SRR1785258     3  0.6552    -0.0726 0.440 0.000 0.484 0.076
#> SRR1785259     3  0.6552    -0.0726 0.440 0.000 0.484 0.076
#> SRR1785262     3  0.1209     0.8423 0.032 0.000 0.964 0.004
#> SRR1785263     3  0.1209     0.8423 0.032 0.000 0.964 0.004
#> SRR1785260     4  0.4836     0.7710 0.320 0.000 0.008 0.672
#> SRR1785261     4  0.4836     0.7710 0.320 0.000 0.008 0.672
#> SRR1785264     2  0.3342     0.9070 0.000 0.868 0.032 0.100
#> SRR1785265     2  0.3342     0.9070 0.000 0.868 0.032 0.100
#> SRR1785266     2  0.0000     0.9386 0.000 1.000 0.000 0.000
#> SRR1785267     2  0.0000     0.9386 0.000 1.000 0.000 0.000
#> SRR1785268     1  0.4761     0.2534 0.628 0.000 0.372 0.000
#> SRR1785269     1  0.4761     0.2534 0.628 0.000 0.372 0.000
#> SRR1785270     1  0.6929     0.3160 0.452 0.000 0.108 0.440
#> SRR1785271     1  0.6929     0.3160 0.452 0.000 0.108 0.440
#> SRR1785272     1  0.4746     0.0213 0.632 0.000 0.368 0.000
#> SRR1785273     1  0.4746     0.0213 0.632 0.000 0.368 0.000
#> SRR1785276     3  0.3123     0.8230 0.156 0.000 0.844 0.000
#> SRR1785277     3  0.3123     0.8230 0.156 0.000 0.844 0.000
#> SRR1785274     3  0.2660     0.8192 0.036 0.000 0.908 0.056
#> SRR1785275     3  0.2660     0.8192 0.036 0.000 0.908 0.056
#> SRR1785280     2  0.0000     0.9386 0.000 1.000 0.000 0.000
#> SRR1785281     2  0.0000     0.9386 0.000 1.000 0.000 0.000
#> SRR1785278     1  0.1118     0.4724 0.964 0.000 0.036 0.000
#> SRR1785279     1  0.1118     0.4724 0.964 0.000 0.036 0.000
#> SRR1785282     1  0.0000     0.4749 1.000 0.000 0.000 0.000
#> SRR1785283     1  0.0000     0.4749 1.000 0.000 0.000 0.000
#> SRR1785284     1  0.6887     0.3185 0.456 0.000 0.104 0.440
#> SRR1785285     1  0.6887     0.3185 0.456 0.000 0.104 0.440
#> SRR1785286     4  0.5007     0.4440 0.172 0.000 0.068 0.760
#> SRR1785287     4  0.5007     0.4440 0.172 0.000 0.068 0.760
#> SRR1785288     1  0.0592     0.4661 0.984 0.000 0.000 0.016
#> SRR1785289     1  0.0592     0.4661 0.984 0.000 0.000 0.016
#> SRR1785290     2  0.4241     0.8535 0.036 0.828 0.012 0.124
#> SRR1785291     2  0.4241     0.8535 0.036 0.828 0.012 0.124
#> SRR1785296     4  0.5783     0.7828 0.448 0.016 0.008 0.528
#> SRR1785297     4  0.5783     0.7828 0.448 0.016 0.008 0.528
#> SRR1785292     2  0.0000     0.9386 0.000 1.000 0.000 0.000
#> SRR1785293     2  0.0000     0.9386 0.000 1.000 0.000 0.000
#> SRR1785294     4  0.5447     0.7828 0.460 0.004 0.008 0.528
#> SRR1785295     4  0.5447     0.7828 0.460 0.004 0.008 0.528
#> SRR1785298     1  0.8054     0.2220 0.424 0.300 0.008 0.268
#> SRR1785299     1  0.8054     0.2220 0.424 0.300 0.008 0.268
#> SRR1785300     1  0.0000     0.4749 1.000 0.000 0.000 0.000
#> SRR1785301     1  0.0000     0.4749 1.000 0.000 0.000 0.000
#> SRR1785304     4  0.4836     0.7710 0.320 0.000 0.008 0.672
#> SRR1785305     4  0.4836     0.7710 0.320 0.000 0.008 0.672
#> SRR1785306     1  0.8649     0.2576 0.384 0.248 0.036 0.332
#> SRR1785307     1  0.8649     0.2576 0.384 0.248 0.036 0.332
#> SRR1785302     1  0.8021     0.2313 0.436 0.288 0.008 0.268
#> SRR1785303     1  0.8021     0.2313 0.436 0.288 0.008 0.268
#> SRR1785308     3  0.3324     0.8038 0.136 0.000 0.852 0.012
#> SRR1785309     3  0.3324     0.8038 0.136 0.000 0.852 0.012
#> SRR1785310     4  0.5447     0.7828 0.460 0.004 0.008 0.528
#> SRR1785311     4  0.5447     0.7828 0.460 0.004 0.008 0.528
#> SRR1785312     1  0.4761     0.2534 0.628 0.000 0.372 0.000
#> SRR1785313     1  0.4761     0.2534 0.628 0.000 0.372 0.000
#> SRR1785314     1  0.6929     0.3160 0.452 0.000 0.108 0.440
#> SRR1785315     1  0.6929     0.3160 0.452 0.000 0.108 0.440
#> SRR1785318     2  0.0000     0.9386 0.000 1.000 0.000 0.000
#> SRR1785319     2  0.0000     0.9386 0.000 1.000 0.000 0.000
#> SRR1785316     1  0.0000     0.4749 1.000 0.000 0.000 0.000
#> SRR1785317     1  0.0000     0.4749 1.000 0.000 0.000 0.000
#> SRR1785324     2  0.0000     0.9386 0.000 1.000 0.000 0.000
#> SRR1785325     2  0.0000     0.9386 0.000 1.000 0.000 0.000
#> SRR1785320     3  0.3123     0.8230 0.156 0.000 0.844 0.000
#> SRR1785321     3  0.3123     0.8230 0.156 0.000 0.844 0.000
#> SRR1785322     1  0.1118     0.4724 0.964 0.000 0.036 0.000
#> SRR1785323     1  0.1118     0.4724 0.964 0.000 0.036 0.000

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     2  0.3987      0.890 0.012 0.840 0.036 0.060 0.052
#> SRR1785239     2  0.3987      0.890 0.012 0.840 0.036 0.060 0.052
#> SRR1785240     5  0.1403      0.839 0.024 0.000 0.024 0.000 0.952
#> SRR1785241     5  0.1403      0.839 0.024 0.000 0.024 0.000 0.952
#> SRR1785242     3  0.1043      0.841 0.000 0.000 0.960 0.000 0.040
#> SRR1785243     3  0.1043      0.841 0.000 0.000 0.960 0.000 0.040
#> SRR1785244     1  0.0510      0.640 0.984 0.000 0.000 0.016 0.000
#> SRR1785245     1  0.0510      0.640 0.984 0.000 0.000 0.016 0.000
#> SRR1785246     3  0.1168      0.849 0.032 0.000 0.960 0.000 0.008
#> SRR1785247     3  0.1168      0.849 0.032 0.000 0.960 0.000 0.008
#> SRR1785248     2  0.3515      0.897 0.000 0.856 0.032 0.060 0.052
#> SRR1785250     3  0.3123      0.740 0.184 0.000 0.812 0.000 0.004
#> SRR1785251     3  0.3123      0.740 0.184 0.000 0.812 0.000 0.004
#> SRR1785252     3  0.1043      0.841 0.000 0.000 0.960 0.000 0.040
#> SRR1785253     3  0.1043      0.841 0.000 0.000 0.960 0.000 0.040
#> SRR1785254     1  0.7996      0.171 0.388 0.292 0.000 0.224 0.096
#> SRR1785255     1  0.7996      0.171 0.388 0.292 0.000 0.224 0.096
#> SRR1785256     1  0.0000      0.648 1.000 0.000 0.000 0.000 0.000
#> SRR1785257     1  0.0000      0.648 1.000 0.000 0.000 0.000 0.000
#> SRR1785258     3  0.6757      0.102 0.400 0.000 0.464 0.060 0.076
#> SRR1785259     3  0.6757      0.102 0.400 0.000 0.464 0.060 0.076
#> SRR1785262     3  0.1568      0.848 0.020 0.000 0.944 0.000 0.036
#> SRR1785263     3  0.1568      0.848 0.020 0.000 0.944 0.000 0.036
#> SRR1785260     4  0.1557      0.621 0.052 0.000 0.000 0.940 0.008
#> SRR1785261     4  0.1557      0.621 0.052 0.000 0.000 0.940 0.008
#> SRR1785264     2  0.3515      0.897 0.000 0.856 0.032 0.060 0.052
#> SRR1785265     2  0.3515      0.897 0.000 0.856 0.032 0.060 0.052
#> SRR1785266     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785267     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785268     1  0.4331      0.237 0.596 0.000 0.400 0.000 0.004
#> SRR1785269     1  0.4331      0.237 0.596 0.000 0.400 0.000 0.004
#> SRR1785270     5  0.1399      0.839 0.020 0.000 0.028 0.000 0.952
#> SRR1785271     5  0.1399      0.839 0.020 0.000 0.028 0.000 0.952
#> SRR1785272     1  0.4088      0.188 0.632 0.000 0.368 0.000 0.000
#> SRR1785273     1  0.4088      0.188 0.632 0.000 0.368 0.000 0.000
#> SRR1785276     3  0.2389      0.832 0.116 0.000 0.880 0.000 0.004
#> SRR1785277     3  0.2389      0.832 0.116 0.000 0.880 0.000 0.004
#> SRR1785274     3  0.2917      0.829 0.024 0.000 0.888 0.040 0.048
#> SRR1785275     3  0.2917      0.829 0.024 0.000 0.888 0.040 0.048
#> SRR1785280     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785278     1  0.0963      0.641 0.964 0.000 0.036 0.000 0.000
#> SRR1785279     1  0.0963      0.641 0.964 0.000 0.036 0.000 0.000
#> SRR1785282     1  0.0000      0.648 1.000 0.000 0.000 0.000 0.000
#> SRR1785283     1  0.0000      0.648 1.000 0.000 0.000 0.000 0.000
#> SRR1785284     5  0.1403      0.839 0.024 0.000 0.024 0.000 0.952
#> SRR1785285     5  0.1403      0.839 0.024 0.000 0.024 0.000 0.952
#> SRR1785286     5  0.5315      0.226 0.016 0.000 0.024 0.432 0.528
#> SRR1785287     5  0.5315      0.226 0.016 0.000 0.024 0.432 0.528
#> SRR1785288     1  0.0510      0.640 0.984 0.000 0.000 0.016 0.000
#> SRR1785289     1  0.0510      0.640 0.984 0.000 0.000 0.016 0.000
#> SRR1785290     2  0.4142      0.850 0.008 0.816 0.012 0.072 0.092
#> SRR1785291     2  0.4142      0.850 0.008 0.816 0.012 0.072 0.092
#> SRR1785296     4  0.5728      0.724 0.388 0.012 0.000 0.540 0.060
#> SRR1785297     4  0.5728      0.724 0.388 0.012 0.000 0.540 0.060
#> SRR1785292     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785293     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785294     4  0.5401      0.723 0.404 0.000 0.000 0.536 0.060
#> SRR1785295     4  0.5401      0.723 0.404 0.000 0.000 0.536 0.060
#> SRR1785298     1  0.7996      0.171 0.388 0.292 0.000 0.224 0.096
#> SRR1785299     1  0.7996      0.171 0.388 0.292 0.000 0.224 0.096
#> SRR1785300     1  0.0000      0.648 1.000 0.000 0.000 0.000 0.000
#> SRR1785301     1  0.0000      0.648 1.000 0.000 0.000 0.000 0.000
#> SRR1785304     4  0.1557      0.621 0.052 0.000 0.000 0.940 0.008
#> SRR1785305     4  0.1557      0.621 0.052 0.000 0.000 0.940 0.008
#> SRR1785306     5  0.4626      0.595 0.020 0.236 0.008 0.012 0.724
#> SRR1785307     5  0.4626      0.595 0.020 0.236 0.008 0.012 0.724
#> SRR1785302     1  0.8040      0.173 0.392 0.280 0.000 0.224 0.104
#> SRR1785303     1  0.8040      0.173 0.392 0.280 0.000 0.224 0.104
#> SRR1785308     3  0.2864      0.799 0.136 0.000 0.852 0.000 0.012
#> SRR1785309     3  0.2864      0.799 0.136 0.000 0.852 0.000 0.012
#> SRR1785310     4  0.5401      0.723 0.404 0.000 0.000 0.536 0.060
#> SRR1785311     4  0.5401      0.723 0.404 0.000 0.000 0.536 0.060
#> SRR1785312     1  0.4331      0.237 0.596 0.000 0.400 0.000 0.004
#> SRR1785313     1  0.4331      0.237 0.596 0.000 0.400 0.000 0.004
#> SRR1785314     5  0.1399      0.839 0.020 0.000 0.028 0.000 0.952
#> SRR1785315     5  0.1399      0.839 0.020 0.000 0.028 0.000 0.952
#> SRR1785318     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785316     1  0.0000      0.648 1.000 0.000 0.000 0.000 0.000
#> SRR1785317     1  0.0000      0.648 1.000 0.000 0.000 0.000 0.000
#> SRR1785324     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785325     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785320     3  0.2389      0.832 0.116 0.000 0.880 0.000 0.004
#> SRR1785321     3  0.2389      0.832 0.116 0.000 0.880 0.000 0.004
#> SRR1785322     1  0.0963      0.641 0.964 0.000 0.036 0.000 0.000
#> SRR1785323     1  0.0963      0.641 0.964 0.000 0.036 0.000 0.000

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     2  0.6384     0.2387 0.012 0.424 0.024 0.136 0.000 0.404
#> SRR1785239     2  0.6384     0.2387 0.012 0.424 0.024 0.136 0.000 0.404
#> SRR1785240     5  0.0146     0.8382 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR1785241     5  0.0146     0.8382 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR1785242     3  0.1777     0.5826 0.000 0.000 0.928 0.004 0.024 0.044
#> SRR1785243     3  0.1777     0.5826 0.000 0.000 0.928 0.004 0.024 0.044
#> SRR1785244     1  0.3872     0.6929 0.604 0.000 0.000 0.004 0.000 0.392
#> SRR1785245     1  0.3872     0.6929 0.604 0.000 0.000 0.004 0.000 0.392
#> SRR1785246     3  0.4105     0.6575 0.008 0.000 0.640 0.344 0.004 0.004
#> SRR1785247     3  0.4105     0.6575 0.008 0.000 0.640 0.344 0.004 0.004
#> SRR1785248     2  0.6087     0.2441 0.000 0.424 0.020 0.148 0.000 0.408
#> SRR1785250     3  0.4994     0.6041 0.156 0.000 0.660 0.180 0.004 0.000
#> SRR1785251     3  0.4994     0.6041 0.156 0.000 0.660 0.180 0.004 0.000
#> SRR1785252     3  0.1777     0.5826 0.000 0.000 0.928 0.004 0.024 0.044
#> SRR1785253     3  0.1777     0.5826 0.000 0.000 0.928 0.004 0.024 0.044
#> SRR1785254     6  0.2113     0.7407 0.028 0.004 0.000 0.000 0.060 0.908
#> SRR1785255     6  0.2113     0.7407 0.028 0.004 0.000 0.000 0.060 0.908
#> SRR1785256     1  0.3672     0.7036 0.632 0.000 0.000 0.000 0.000 0.368
#> SRR1785257     1  0.3672     0.7036 0.632 0.000 0.000 0.000 0.000 0.368
#> SRR1785258     3  0.5447     0.2695 0.020 0.000 0.480 0.000 0.068 0.432
#> SRR1785259     3  0.5447     0.2695 0.020 0.000 0.480 0.000 0.068 0.432
#> SRR1785262     3  0.4689     0.6580 0.008 0.000 0.620 0.336 0.028 0.008
#> SRR1785263     3  0.4689     0.6580 0.008 0.000 0.620 0.336 0.028 0.008
#> SRR1785260     4  0.5649     1.0000 0.356 0.000 0.000 0.484 0.000 0.160
#> SRR1785261     4  0.5649     1.0000 0.356 0.000 0.000 0.484 0.000 0.160
#> SRR1785264     2  0.6087     0.2441 0.000 0.424 0.020 0.148 0.000 0.408
#> SRR1785265     2  0.6087     0.2441 0.000 0.424 0.020 0.148 0.000 0.408
#> SRR1785266     2  0.0000     0.7682 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785267     2  0.0000     0.7682 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785268     3  0.6433     0.1260 0.216 0.000 0.408 0.016 0.004 0.356
#> SRR1785269     3  0.6433     0.1260 0.216 0.000 0.408 0.016 0.004 0.356
#> SRR1785270     5  0.0146     0.8381 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1785271     5  0.0146     0.8381 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1785272     1  0.5363     0.3154 0.608 0.000 0.192 0.196 0.000 0.004
#> SRR1785273     1  0.5363     0.3154 0.608 0.000 0.192 0.196 0.000 0.004
#> SRR1785276     3  0.5096     0.6510 0.056 0.000 0.564 0.368 0.004 0.008
#> SRR1785277     3  0.5096     0.6510 0.056 0.000 0.564 0.368 0.004 0.008
#> SRR1785274     3  0.5630     0.6414 0.008 0.000 0.560 0.336 0.024 0.072
#> SRR1785275     3  0.5630     0.6414 0.008 0.000 0.560 0.336 0.024 0.072
#> SRR1785280     2  0.0000     0.7682 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000     0.7682 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785278     1  0.4370     0.6911 0.616 0.000 0.020 0.008 0.000 0.356
#> SRR1785279     1  0.4370     0.6911 0.616 0.000 0.020 0.008 0.000 0.356
#> SRR1785282     1  0.3672     0.7036 0.632 0.000 0.000 0.000 0.000 0.368
#> SRR1785283     1  0.3672     0.7036 0.632 0.000 0.000 0.000 0.000 0.368
#> SRR1785284     5  0.0146     0.8382 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR1785285     5  0.0146     0.8382 0.000 0.000 0.000 0.000 0.996 0.004
#> SRR1785286     5  0.5221     0.1268 0.372 0.000 0.000 0.020 0.552 0.056
#> SRR1785287     5  0.5221     0.1268 0.372 0.000 0.000 0.020 0.552 0.056
#> SRR1785288     1  0.3872     0.6929 0.604 0.000 0.000 0.004 0.000 0.392
#> SRR1785289     1  0.3872     0.6929 0.604 0.000 0.000 0.004 0.000 0.392
#> SRR1785290     6  0.6843    -0.2189 0.004 0.376 0.008 0.144 0.052 0.416
#> SRR1785291     6  0.6843    -0.2189 0.004 0.376 0.008 0.144 0.052 0.416
#> SRR1785296     1  0.4118    -0.1194 0.740 0.000 0.000 0.020 0.032 0.208
#> SRR1785297     1  0.4118    -0.1194 0.740 0.000 0.000 0.020 0.032 0.208
#> SRR1785292     2  0.0260     0.7665 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1785293     2  0.0260     0.7665 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1785294     1  0.4000    -0.0819 0.756 0.000 0.000 0.020 0.032 0.192
#> SRR1785295     1  0.4000    -0.0819 0.756 0.000 0.000 0.020 0.032 0.192
#> SRR1785298     6  0.2113     0.7407 0.028 0.004 0.000 0.000 0.060 0.908
#> SRR1785299     6  0.2113     0.7407 0.028 0.004 0.000 0.000 0.060 0.908
#> SRR1785300     1  0.3672     0.7036 0.632 0.000 0.000 0.000 0.000 0.368
#> SRR1785301     1  0.3672     0.7036 0.632 0.000 0.000 0.000 0.000 0.368
#> SRR1785304     4  0.5649     1.0000 0.356 0.000 0.000 0.484 0.000 0.160
#> SRR1785305     4  0.5649     1.0000 0.356 0.000 0.000 0.484 0.000 0.160
#> SRR1785306     5  0.4606     0.6063 0.000 0.000 0.008 0.112 0.712 0.168
#> SRR1785307     5  0.4606     0.6063 0.000 0.000 0.008 0.112 0.712 0.168
#> SRR1785302     6  0.2145     0.7341 0.028 0.000 0.000 0.000 0.072 0.900
#> SRR1785303     6  0.2145     0.7341 0.028 0.000 0.000 0.000 0.072 0.900
#> SRR1785308     3  0.3196     0.5435 0.136 0.000 0.824 0.004 0.000 0.036
#> SRR1785309     3  0.3196     0.5435 0.136 0.000 0.824 0.004 0.000 0.036
#> SRR1785310     1  0.4000    -0.0819 0.756 0.000 0.000 0.020 0.032 0.192
#> SRR1785311     1  0.4000    -0.0819 0.756 0.000 0.000 0.020 0.032 0.192
#> SRR1785312     3  0.6433     0.1260 0.216 0.000 0.408 0.016 0.004 0.356
#> SRR1785313     3  0.6433     0.1260 0.216 0.000 0.408 0.016 0.004 0.356
#> SRR1785314     5  0.0146     0.8381 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1785315     5  0.0146     0.8381 0.000 0.000 0.004 0.000 0.996 0.000
#> SRR1785318     2  0.0000     0.7682 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000     0.7682 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785316     1  0.4153     0.6969 0.636 0.000 0.000 0.024 0.000 0.340
#> SRR1785317     1  0.4153     0.6969 0.636 0.000 0.000 0.024 0.000 0.340
#> SRR1785324     2  0.0260     0.7665 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1785325     2  0.0260     0.7665 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1785320     3  0.5096     0.6510 0.056 0.000 0.564 0.368 0.004 0.008
#> SRR1785321     3  0.5096     0.6510 0.056 0.000 0.564 0.368 0.004 0.008
#> SRR1785322     1  0.4370     0.6911 0.616 0.000 0.020 0.008 0.000 0.356
#> SRR1785323     1  0.4370     0.6911 0.616 0.000 0.020 0.008 0.000 0.356

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-SD-hclust-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-SD-hclust-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-SD-hclust-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-SD-hclust-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-SD-hclust-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-SD-hclust-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-SD-hclust-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-SD-hclust-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-SD-hclust-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-SD-hclust-membership-heatmap-5

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)

plot of chunk tab-SD-hclust-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-SD-hclust-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-SD-hclust-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-SD-hclust-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-SD-hclust-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-SD-hclust-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-SD-hclust-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-SD-hclust-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-SD-hclust-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-SD-hclust-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk SD-hclust-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-SD-hclust-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-SD-hclust-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-SD-hclust-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-SD-hclust-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-SD-hclust-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk SD-hclust-collect-classes

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.


SD:kmeans

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#>   Subgroups are detected by 'kmeans' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 2.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk SD-kmeans-collect-plots

The plots are:

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:

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)

plot of chunk SD-kmeans-select-partition-number

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 0.463           0.819       0.899         0.4477 0.524   0.524
#> 3 3 0.324           0.427       0.633         0.3922 0.801   0.640
#> 4 4 0.391           0.486       0.641         0.1433 0.720   0.394
#> 5 5 0.456           0.426       0.643         0.0739 0.923   0.719
#> 6 6 0.563           0.443       0.640         0.0488 0.977   0.898

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2  0.9933      0.330 0.452 0.548
#> SRR1785239     2  0.9933      0.330 0.452 0.548
#> SRR1785240     1  0.0376      0.918 0.996 0.004
#> SRR1785241     1  0.0376      0.918 0.996 0.004
#> SRR1785242     1  0.9170      0.492 0.668 0.332
#> SRR1785243     1  0.9170      0.492 0.668 0.332
#> SRR1785244     1  0.0000      0.919 1.000 0.000
#> SRR1785245     1  0.0000      0.919 1.000 0.000
#> SRR1785246     1  0.2236      0.904 0.964 0.036
#> SRR1785247     1  0.2236      0.904 0.964 0.036
#> SRR1785248     2  0.0672      0.829 0.008 0.992
#> SRR1785250     1  0.2236      0.904 0.964 0.036
#> SRR1785251     1  0.2236      0.904 0.964 0.036
#> SRR1785252     1  0.9170      0.492 0.668 0.332
#> SRR1785253     1  0.9170      0.492 0.668 0.332
#> SRR1785254     2  0.7139      0.823 0.196 0.804
#> SRR1785255     2  0.7139      0.823 0.196 0.804
#> SRR1785256     1  0.0000      0.919 1.000 0.000
#> SRR1785257     1  0.0000      0.919 1.000 0.000
#> SRR1785258     1  0.0376      0.919 0.996 0.004
#> SRR1785259     1  0.0376      0.919 0.996 0.004
#> SRR1785262     1  0.0938      0.917 0.988 0.012
#> SRR1785263     1  0.0938      0.917 0.988 0.012
#> SRR1785260     1  0.5842      0.805 0.860 0.140
#> SRR1785261     1  0.5842      0.805 0.860 0.140
#> SRR1785264     2  0.2236      0.843 0.036 0.964
#> SRR1785265     2  0.2236      0.843 0.036 0.964
#> SRR1785266     2  0.1414      0.838 0.020 0.980
#> SRR1785267     2  0.1414      0.838 0.020 0.980
#> SRR1785268     1  0.0000      0.919 1.000 0.000
#> SRR1785269     1  0.0000      0.919 1.000 0.000
#> SRR1785270     2  0.7056      0.826 0.192 0.808
#> SRR1785271     2  0.7056      0.826 0.192 0.808
#> SRR1785272     1  0.2236      0.904 0.964 0.036
#> SRR1785273     1  0.2236      0.904 0.964 0.036
#> SRR1785276     1  0.0938      0.916 0.988 0.012
#> SRR1785277     1  0.0938      0.916 0.988 0.012
#> SRR1785274     1  0.4022      0.866 0.920 0.080
#> SRR1785275     1  0.4022      0.866 0.920 0.080
#> SRR1785280     2  0.1414      0.838 0.020 0.980
#> SRR1785281     2  0.1414      0.838 0.020 0.980
#> SRR1785278     1  0.0000      0.919 1.000 0.000
#> SRR1785279     1  0.0000      0.919 1.000 0.000
#> SRR1785282     1  0.0000      0.919 1.000 0.000
#> SRR1785283     1  0.0000      0.919 1.000 0.000
#> SRR1785284     1  0.6438      0.772 0.836 0.164
#> SRR1785285     1  0.6438      0.772 0.836 0.164
#> SRR1785286     1  0.6531      0.766 0.832 0.168
#> SRR1785287     1  0.6531      0.766 0.832 0.168
#> SRR1785288     1  0.0000      0.919 1.000 0.000
#> SRR1785289     1  0.0000      0.919 1.000 0.000
#> SRR1785290     2  0.3274      0.845 0.060 0.940
#> SRR1785291     2  0.3274      0.845 0.060 0.940
#> SRR1785296     2  0.9896      0.408 0.440 0.560
#> SRR1785297     2  0.9896      0.408 0.440 0.560
#> SRR1785292     2  0.2236      0.841 0.036 0.964
#> SRR1785293     2  0.2236      0.841 0.036 0.964
#> SRR1785294     1  0.6343      0.777 0.840 0.160
#> SRR1785295     1  0.6343      0.777 0.840 0.160
#> SRR1785298     2  0.9795      0.471 0.416 0.584
#> SRR1785299     2  0.9795      0.471 0.416 0.584
#> SRR1785300     1  0.0000      0.919 1.000 0.000
#> SRR1785301     1  0.0000      0.919 1.000 0.000
#> SRR1785304     2  0.5946      0.838 0.144 0.856
#> SRR1785305     2  0.5946      0.838 0.144 0.856
#> SRR1785306     2  0.6712      0.832 0.176 0.824
#> SRR1785307     2  0.6712      0.832 0.176 0.824
#> SRR1785302     2  0.7139      0.823 0.196 0.804
#> SRR1785303     2  0.7139      0.823 0.196 0.804
#> SRR1785308     1  0.2603      0.899 0.956 0.044
#> SRR1785309     1  0.2603      0.899 0.956 0.044
#> SRR1785310     1  0.6247      0.782 0.844 0.156
#> SRR1785311     1  0.6247      0.782 0.844 0.156
#> SRR1785312     1  0.0376      0.918 0.996 0.004
#> SRR1785313     1  0.0376      0.918 0.996 0.004
#> SRR1785314     2  0.7056      0.826 0.192 0.808
#> SRR1785315     2  0.7056      0.826 0.192 0.808
#> SRR1785318     2  0.1414      0.838 0.020 0.980
#> SRR1785319     2  0.1414      0.838 0.020 0.980
#> SRR1785316     1  0.0000      0.919 1.000 0.000
#> SRR1785317     1  0.0000      0.919 1.000 0.000
#> SRR1785324     2  0.2236      0.841 0.036 0.964
#> SRR1785325     2  0.2236      0.841 0.036 0.964
#> SRR1785320     1  0.0000      0.919 1.000 0.000
#> SRR1785321     1  0.0000      0.919 1.000 0.000
#> SRR1785322     1  0.0376      0.919 0.996 0.004
#> SRR1785323     1  0.0376      0.919 0.996 0.004

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     3  0.9991    0.22485 0.332 0.316 0.352
#> SRR1785239     3  0.9991    0.22485 0.332 0.316 0.352
#> SRR1785240     1  0.6825    0.24824 0.496 0.012 0.492
#> SRR1785241     1  0.6825    0.24824 0.496 0.012 0.492
#> SRR1785242     3  0.9109   -0.00542 0.364 0.148 0.488
#> SRR1785243     3  0.9109   -0.00542 0.364 0.148 0.488
#> SRR1785244     1  0.5497    0.50057 0.708 0.000 0.292
#> SRR1785245     1  0.5497    0.50057 0.708 0.000 0.292
#> SRR1785246     1  0.6497    0.46576 0.648 0.016 0.336
#> SRR1785247     1  0.6497    0.46576 0.648 0.016 0.336
#> SRR1785248     2  0.1860    0.73777 0.000 0.948 0.052
#> SRR1785250     1  0.6096    0.50538 0.704 0.016 0.280
#> SRR1785251     1  0.6096    0.50538 0.704 0.016 0.280
#> SRR1785252     3  0.9109   -0.00542 0.364 0.148 0.488
#> SRR1785253     3  0.9109   -0.00542 0.364 0.148 0.488
#> SRR1785254     3  0.8784    0.22897 0.116 0.388 0.496
#> SRR1785255     3  0.8784    0.22897 0.116 0.388 0.496
#> SRR1785256     1  0.2796    0.63283 0.908 0.000 0.092
#> SRR1785257     1  0.2796    0.63283 0.908 0.000 0.092
#> SRR1785258     1  0.5365    0.53434 0.744 0.004 0.252
#> SRR1785259     1  0.5365    0.53434 0.744 0.004 0.252
#> SRR1785262     1  0.6608    0.46237 0.628 0.016 0.356
#> SRR1785263     1  0.6608    0.46237 0.628 0.016 0.356
#> SRR1785260     1  0.7256    0.23682 0.532 0.028 0.440
#> SRR1785261     1  0.7256    0.23682 0.532 0.028 0.440
#> SRR1785264     2  0.4128    0.71662 0.012 0.856 0.132
#> SRR1785265     2  0.4128    0.71662 0.012 0.856 0.132
#> SRR1785266     2  0.0475    0.76400 0.004 0.992 0.004
#> SRR1785267     2  0.0475    0.76400 0.004 0.992 0.004
#> SRR1785268     1  0.0592    0.64635 0.988 0.000 0.012
#> SRR1785269     1  0.0592    0.64635 0.988 0.000 0.012
#> SRR1785270     3  0.8275    0.02260 0.076 0.452 0.472
#> SRR1785271     3  0.8275    0.02260 0.076 0.452 0.472
#> SRR1785272     1  0.5115    0.54647 0.768 0.004 0.228
#> SRR1785273     1  0.5115    0.54647 0.768 0.004 0.228
#> SRR1785276     1  0.5687    0.53205 0.756 0.020 0.224
#> SRR1785277     1  0.5687    0.53205 0.756 0.020 0.224
#> SRR1785274     1  0.7319    0.27370 0.548 0.032 0.420
#> SRR1785275     1  0.7319    0.27370 0.548 0.032 0.420
#> SRR1785280     2  0.0592    0.75988 0.000 0.988 0.012
#> SRR1785281     2  0.0592    0.75988 0.000 0.988 0.012
#> SRR1785278     1  0.1529    0.64489 0.960 0.000 0.040
#> SRR1785279     1  0.1529    0.64489 0.960 0.000 0.040
#> SRR1785282     1  0.1411    0.64446 0.964 0.000 0.036
#> SRR1785283     1  0.1411    0.64446 0.964 0.000 0.036
#> SRR1785284     3  0.6627    0.17327 0.336 0.020 0.644
#> SRR1785285     3  0.6627    0.17327 0.336 0.020 0.644
#> SRR1785286     3  0.6566    0.10752 0.348 0.016 0.636
#> SRR1785287     3  0.6566    0.10752 0.348 0.016 0.636
#> SRR1785288     1  0.5497    0.49598 0.708 0.000 0.292
#> SRR1785289     1  0.5497    0.49598 0.708 0.000 0.292
#> SRR1785290     2  0.5643    0.62307 0.020 0.760 0.220
#> SRR1785291     2  0.5643    0.62307 0.020 0.760 0.220
#> SRR1785296     3  0.9309    0.40628 0.216 0.264 0.520
#> SRR1785297     3  0.9309    0.40628 0.216 0.264 0.520
#> SRR1785292     2  0.2486    0.76334 0.008 0.932 0.060
#> SRR1785293     2  0.2486    0.76334 0.008 0.932 0.060
#> SRR1785294     1  0.7489    0.16411 0.496 0.036 0.468
#> SRR1785295     1  0.7489    0.16411 0.496 0.036 0.468
#> SRR1785298     3  0.9503    0.38720 0.208 0.316 0.476
#> SRR1785299     3  0.9503    0.38720 0.208 0.316 0.476
#> SRR1785300     1  0.5327    0.51409 0.728 0.000 0.272
#> SRR1785301     1  0.5327    0.51409 0.728 0.000 0.272
#> SRR1785304     2  0.7634    0.22231 0.044 0.524 0.432
#> SRR1785305     2  0.7634    0.22231 0.044 0.524 0.432
#> SRR1785306     3  0.7920   -0.05500 0.056 0.468 0.476
#> SRR1785307     3  0.7920   -0.05500 0.056 0.468 0.476
#> SRR1785302     3  0.8708    0.18667 0.108 0.404 0.488
#> SRR1785303     3  0.8708    0.18667 0.108 0.404 0.488
#> SRR1785308     1  0.6630    0.46892 0.672 0.028 0.300
#> SRR1785309     1  0.6630    0.46892 0.672 0.028 0.300
#> SRR1785310     1  0.7392    0.16768 0.500 0.032 0.468
#> SRR1785311     1  0.7392    0.16768 0.500 0.032 0.468
#> SRR1785312     1  0.1163    0.64594 0.972 0.000 0.028
#> SRR1785313     1  0.1163    0.64594 0.972 0.000 0.028
#> SRR1785314     2  0.7990    0.01340 0.060 0.488 0.452
#> SRR1785315     2  0.7990    0.01340 0.060 0.488 0.452
#> SRR1785318     2  0.0829    0.76719 0.004 0.984 0.012
#> SRR1785319     2  0.0829    0.76719 0.004 0.984 0.012
#> SRR1785316     1  0.4974    0.54562 0.764 0.000 0.236
#> SRR1785317     1  0.4974    0.54562 0.764 0.000 0.236
#> SRR1785324     2  0.2774    0.75970 0.008 0.920 0.072
#> SRR1785325     2  0.2774    0.75970 0.008 0.920 0.072
#> SRR1785320     1  0.1529    0.64651 0.960 0.000 0.040
#> SRR1785321     1  0.1529    0.64651 0.960 0.000 0.040
#> SRR1785322     1  0.2537    0.63690 0.920 0.000 0.080
#> SRR1785323     1  0.2537    0.63690 0.920 0.000 0.080

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     3  0.8205     0.4415 0.100 0.156 0.576 0.168
#> SRR1785239     3  0.8205     0.4415 0.100 0.156 0.576 0.168
#> SRR1785240     4  0.7752    -0.1406 0.184 0.008 0.348 0.460
#> SRR1785241     4  0.7752    -0.1406 0.184 0.008 0.348 0.460
#> SRR1785242     3  0.4389     0.6183 0.044 0.040 0.840 0.076
#> SRR1785243     3  0.4389     0.6183 0.044 0.040 0.840 0.076
#> SRR1785244     1  0.4485     0.5634 0.772 0.000 0.028 0.200
#> SRR1785245     1  0.4485     0.5634 0.772 0.000 0.028 0.200
#> SRR1785246     3  0.6412     0.4608 0.320 0.000 0.592 0.088
#> SRR1785247     3  0.6412     0.4608 0.320 0.000 0.592 0.088
#> SRR1785248     2  0.2859     0.7978 0.000 0.880 0.112 0.008
#> SRR1785250     3  0.5024     0.4013 0.360 0.000 0.632 0.008
#> SRR1785251     3  0.5024     0.4013 0.360 0.000 0.632 0.008
#> SRR1785252     3  0.4389     0.6183 0.044 0.040 0.840 0.076
#> SRR1785253     3  0.4389     0.6183 0.044 0.040 0.840 0.076
#> SRR1785254     4  0.7130     0.4865 0.072 0.256 0.052 0.620
#> SRR1785255     4  0.7130     0.4865 0.072 0.256 0.052 0.620
#> SRR1785256     1  0.3621     0.6570 0.860 0.000 0.072 0.068
#> SRR1785257     1  0.3621     0.6570 0.860 0.000 0.072 0.068
#> SRR1785258     1  0.6700    -0.1001 0.480 0.000 0.432 0.088
#> SRR1785259     1  0.6700    -0.1001 0.480 0.000 0.432 0.088
#> SRR1785262     3  0.7329     0.4317 0.296 0.000 0.516 0.188
#> SRR1785263     3  0.7329     0.4317 0.296 0.000 0.516 0.188
#> SRR1785260     4  0.7065     0.3082 0.424 0.024 0.064 0.488
#> SRR1785261     4  0.7065     0.3082 0.424 0.024 0.064 0.488
#> SRR1785264     2  0.6577     0.5957 0.004 0.648 0.172 0.176
#> SRR1785265     2  0.6577     0.5957 0.004 0.648 0.172 0.176
#> SRR1785266     2  0.1635     0.8295 0.000 0.948 0.044 0.008
#> SRR1785267     2  0.1635     0.8295 0.000 0.948 0.044 0.008
#> SRR1785268     1  0.3958     0.6278 0.824 0.000 0.144 0.032
#> SRR1785269     1  0.3958     0.6278 0.824 0.000 0.144 0.032
#> SRR1785270     4  0.7641     0.4152 0.048 0.204 0.148 0.600
#> SRR1785271     4  0.7641     0.4152 0.048 0.204 0.148 0.600
#> SRR1785272     1  0.5000    -0.1810 0.500 0.000 0.500 0.000
#> SRR1785273     3  0.5000     0.0964 0.500 0.000 0.500 0.000
#> SRR1785276     1  0.7365    -0.1232 0.440 0.000 0.400 0.160
#> SRR1785277     1  0.7365    -0.1232 0.440 0.000 0.400 0.160
#> SRR1785274     3  0.7673     0.4560 0.208 0.004 0.480 0.308
#> SRR1785275     3  0.7673     0.4560 0.208 0.004 0.480 0.308
#> SRR1785280     2  0.1661     0.8240 0.000 0.944 0.052 0.004
#> SRR1785281     2  0.1661     0.8240 0.000 0.944 0.052 0.004
#> SRR1785278     1  0.3037     0.6542 0.880 0.000 0.100 0.020
#> SRR1785279     1  0.3037     0.6542 0.880 0.000 0.100 0.020
#> SRR1785282     1  0.2198     0.6613 0.920 0.000 0.072 0.008
#> SRR1785283     1  0.2198     0.6613 0.920 0.000 0.072 0.008
#> SRR1785284     4  0.5962     0.4774 0.204 0.008 0.088 0.700
#> SRR1785285     4  0.5962     0.4774 0.204 0.008 0.088 0.700
#> SRR1785286     4  0.4862     0.5266 0.228 0.008 0.020 0.744
#> SRR1785287     4  0.4862     0.5266 0.228 0.008 0.020 0.744
#> SRR1785288     1  0.4706     0.5205 0.748 0.000 0.028 0.224
#> SRR1785289     1  0.4706     0.5205 0.748 0.000 0.028 0.224
#> SRR1785290     2  0.6680     0.3122 0.004 0.564 0.088 0.344
#> SRR1785291     2  0.6680     0.3122 0.004 0.564 0.088 0.344
#> SRR1785296     4  0.8180     0.5455 0.144 0.152 0.120 0.584
#> SRR1785297     4  0.8180     0.5455 0.144 0.152 0.120 0.584
#> SRR1785292     2  0.1722     0.8223 0.000 0.944 0.008 0.048
#> SRR1785293     2  0.1722     0.8223 0.000 0.944 0.008 0.048
#> SRR1785294     4  0.6623     0.3799 0.392 0.028 0.036 0.544
#> SRR1785295     4  0.6623     0.3799 0.392 0.028 0.036 0.544
#> SRR1785298     4  0.8376     0.5298 0.152 0.200 0.096 0.552
#> SRR1785299     4  0.8376     0.5298 0.152 0.200 0.096 0.552
#> SRR1785300     1  0.3764     0.5830 0.816 0.000 0.012 0.172
#> SRR1785301     1  0.3764     0.5830 0.816 0.000 0.012 0.172
#> SRR1785304     4  0.6860     0.2562 0.028 0.332 0.060 0.580
#> SRR1785305     4  0.6860     0.2562 0.028 0.332 0.060 0.580
#> SRR1785306     4  0.6419     0.4155 0.016 0.276 0.068 0.640
#> SRR1785307     4  0.6419     0.4155 0.016 0.276 0.068 0.640
#> SRR1785302     4  0.6780     0.4597 0.064 0.284 0.032 0.620
#> SRR1785303     4  0.6780     0.4597 0.064 0.284 0.032 0.620
#> SRR1785308     3  0.5106     0.4522 0.312 0.008 0.672 0.008
#> SRR1785309     3  0.5106     0.4522 0.312 0.008 0.672 0.008
#> SRR1785310     4  0.6656     0.3633 0.408 0.028 0.036 0.528
#> SRR1785311     4  0.6656     0.3633 0.408 0.028 0.036 0.528
#> SRR1785312     1  0.4452     0.6227 0.796 0.000 0.156 0.048
#> SRR1785313     1  0.4452     0.6227 0.796 0.000 0.156 0.048
#> SRR1785314     4  0.6432     0.4160 0.020 0.272 0.064 0.644
#> SRR1785315     4  0.6432     0.4160 0.020 0.272 0.064 0.644
#> SRR1785318     2  0.0937     0.8312 0.000 0.976 0.012 0.012
#> SRR1785319     2  0.0937     0.8312 0.000 0.976 0.012 0.012
#> SRR1785316     1  0.3160     0.6238 0.872 0.000 0.020 0.108
#> SRR1785317     1  0.3160     0.6238 0.872 0.000 0.020 0.108
#> SRR1785324     2  0.1807     0.8181 0.000 0.940 0.008 0.052
#> SRR1785325     2  0.1807     0.8181 0.000 0.940 0.008 0.052
#> SRR1785320     1  0.4462     0.6201 0.792 0.000 0.164 0.044
#> SRR1785321     1  0.4462     0.6201 0.792 0.000 0.164 0.044
#> SRR1785322     1  0.4485     0.5044 0.740 0.000 0.248 0.012
#> SRR1785323     1  0.4485     0.5044 0.740 0.000 0.248 0.012

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     3   0.794    0.36499 0.120 0.088 0.532 0.048 0.212
#> SRR1785239     3   0.794    0.36499 0.120 0.088 0.532 0.048 0.212
#> SRR1785240     5   0.774    0.27332 0.152 0.000 0.260 0.120 0.468
#> SRR1785241     5   0.774    0.27332 0.152 0.000 0.260 0.120 0.468
#> SRR1785242     3   0.374    0.58902 0.060 0.020 0.848 0.008 0.064
#> SRR1785243     3   0.374    0.58902 0.060 0.020 0.848 0.008 0.064
#> SRR1785244     1   0.685    0.35819 0.508 0.004 0.028 0.324 0.136
#> SRR1785245     1   0.685    0.35819 0.508 0.004 0.028 0.324 0.136
#> SRR1785246     3   0.664    0.42143 0.372 0.000 0.488 0.032 0.108
#> SRR1785247     3   0.664    0.42143 0.372 0.000 0.488 0.032 0.108
#> SRR1785248     2   0.451    0.68644 0.004 0.780 0.148 0.024 0.044
#> SRR1785250     3   0.556    0.35399 0.416 0.004 0.532 0.012 0.036
#> SRR1785251     3   0.556    0.35399 0.416 0.004 0.532 0.012 0.036
#> SRR1785252     3   0.374    0.59171 0.064 0.020 0.848 0.008 0.060
#> SRR1785253     3   0.374    0.59171 0.064 0.020 0.848 0.008 0.060
#> SRR1785254     5   0.755    0.16861 0.036 0.120 0.040 0.312 0.492
#> SRR1785255     5   0.755    0.16861 0.036 0.120 0.040 0.312 0.492
#> SRR1785256     1   0.390    0.60708 0.816 0.000 0.020 0.128 0.036
#> SRR1785257     1   0.390    0.60708 0.816 0.000 0.020 0.128 0.036
#> SRR1785258     1   0.661   -0.09954 0.504 0.000 0.360 0.036 0.100
#> SRR1785259     1   0.661   -0.09954 0.504 0.000 0.360 0.036 0.100
#> SRR1785262     3   0.790    0.37177 0.300 0.000 0.416 0.108 0.176
#> SRR1785263     3   0.790    0.37177 0.300 0.000 0.416 0.108 0.176
#> SRR1785260     4   0.415    0.52360 0.168 0.008 0.012 0.788 0.024
#> SRR1785261     4   0.415    0.52360 0.168 0.008 0.012 0.788 0.024
#> SRR1785264     2   0.835    0.30415 0.020 0.440 0.168 0.124 0.248
#> SRR1785265     2   0.835    0.30415 0.020 0.440 0.168 0.124 0.248
#> SRR1785266     2   0.153    0.76618 0.004 0.952 0.028 0.008 0.008
#> SRR1785267     2   0.153    0.76618 0.004 0.952 0.028 0.008 0.008
#> SRR1785268     1   0.245    0.59083 0.904 0.000 0.056 0.004 0.036
#> SRR1785269     1   0.245    0.59083 0.904 0.000 0.056 0.004 0.036
#> SRR1785270     5   0.639    0.46477 0.008 0.100 0.104 0.120 0.668
#> SRR1785271     5   0.639    0.46477 0.008 0.100 0.104 0.120 0.668
#> SRR1785272     1   0.601   -0.00983 0.556 0.004 0.360 0.024 0.056
#> SRR1785273     1   0.601   -0.00983 0.556 0.004 0.360 0.024 0.056
#> SRR1785276     1   0.684   -0.01488 0.512 0.000 0.232 0.020 0.236
#> SRR1785277     1   0.684   -0.01488 0.512 0.000 0.232 0.020 0.236
#> SRR1785274     5   0.769   -0.02493 0.196 0.000 0.356 0.068 0.380
#> SRR1785275     5   0.769   -0.02493 0.196 0.000 0.356 0.068 0.380
#> SRR1785280     2   0.149    0.76659 0.004 0.952 0.032 0.008 0.004
#> SRR1785281     2   0.149    0.76659 0.004 0.952 0.032 0.008 0.004
#> SRR1785278     1   0.225    0.61366 0.920 0.000 0.016 0.036 0.028
#> SRR1785279     1   0.225    0.61366 0.920 0.000 0.016 0.036 0.028
#> SRR1785282     1   0.252    0.62131 0.904 0.000 0.012 0.056 0.028
#> SRR1785283     1   0.252    0.62131 0.904 0.000 0.012 0.056 0.028
#> SRR1785284     5   0.688    0.21635 0.084 0.004 0.060 0.336 0.516
#> SRR1785285     5   0.688    0.21635 0.084 0.004 0.060 0.336 0.516
#> SRR1785286     4   0.587    0.30829 0.076 0.000 0.020 0.596 0.308
#> SRR1785287     4   0.587    0.30829 0.076 0.000 0.020 0.596 0.308
#> SRR1785288     1   0.688    0.29771 0.476 0.004 0.028 0.364 0.128
#> SRR1785289     1   0.688    0.29771 0.476 0.004 0.028 0.364 0.128
#> SRR1785290     2   0.826    0.06695 0.008 0.352 0.088 0.288 0.264
#> SRR1785291     2   0.826    0.06695 0.008 0.352 0.088 0.288 0.264
#> SRR1785296     4   0.667    0.45443 0.040 0.060 0.056 0.636 0.208
#> SRR1785297     4   0.667    0.45443 0.040 0.060 0.056 0.636 0.208
#> SRR1785292     2   0.282    0.75380 0.000 0.892 0.020 0.052 0.036
#> SRR1785293     2   0.282    0.75380 0.000 0.892 0.020 0.052 0.036
#> SRR1785294     4   0.419    0.56679 0.140 0.008 0.012 0.800 0.040
#> SRR1785295     4   0.419    0.56679 0.140 0.008 0.012 0.800 0.040
#> SRR1785298     4   0.796    0.30641 0.056 0.120 0.056 0.492 0.276
#> SRR1785299     4   0.796    0.30641 0.056 0.120 0.056 0.492 0.276
#> SRR1785300     1   0.484    0.44702 0.612 0.000 0.000 0.356 0.032
#> SRR1785301     1   0.484    0.44702 0.612 0.000 0.000 0.356 0.032
#> SRR1785304     4   0.572    0.41161 0.004 0.124 0.012 0.668 0.192
#> SRR1785305     4   0.572    0.41161 0.004 0.124 0.012 0.668 0.192
#> SRR1785306     5   0.688    0.35597 0.004 0.136 0.048 0.248 0.564
#> SRR1785307     5   0.688    0.35597 0.004 0.136 0.048 0.248 0.564
#> SRR1785302     4   0.717    0.19627 0.012 0.156 0.024 0.472 0.336
#> SRR1785303     4   0.717    0.19627 0.012 0.156 0.024 0.472 0.336
#> SRR1785308     3   0.535    0.49606 0.284 0.008 0.656 0.032 0.020
#> SRR1785309     3   0.535    0.49606 0.284 0.008 0.656 0.032 0.020
#> SRR1785310     4   0.431    0.55033 0.156 0.008 0.000 0.776 0.060
#> SRR1785311     4   0.431    0.55033 0.156 0.008 0.000 0.776 0.060
#> SRR1785312     1   0.275    0.58810 0.888 0.000 0.060 0.004 0.048
#> SRR1785313     1   0.275    0.58810 0.888 0.000 0.060 0.004 0.048
#> SRR1785314     5   0.607    0.35791 0.000 0.128 0.020 0.232 0.620
#> SRR1785315     5   0.607    0.35791 0.000 0.128 0.020 0.232 0.620
#> SRR1785318     2   0.102    0.76863 0.004 0.972 0.012 0.008 0.004
#> SRR1785319     2   0.102    0.76863 0.004 0.972 0.012 0.008 0.004
#> SRR1785316     1   0.555    0.56936 0.708 0.004 0.032 0.164 0.092
#> SRR1785317     1   0.555    0.56936 0.708 0.004 0.032 0.164 0.092
#> SRR1785324     2   0.286    0.74961 0.000 0.892 0.032 0.036 0.040
#> SRR1785325     2   0.286    0.74961 0.000 0.892 0.032 0.036 0.040
#> SRR1785320     1   0.314    0.59322 0.868 0.000 0.056 0.008 0.068
#> SRR1785321     1   0.314    0.59322 0.868 0.000 0.056 0.008 0.068
#> SRR1785322     1   0.448    0.51410 0.792 0.000 0.112 0.048 0.048
#> SRR1785323     1   0.448    0.51410 0.792 0.000 0.112 0.048 0.048

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5 p6
#> SRR1785238     3   0.865     0.2211 0.076 0.076 0.420 0.072 0.220 NA
#> SRR1785239     3   0.865     0.2211 0.076 0.076 0.420 0.072 0.220 NA
#> SRR1785240     5   0.751     0.2605 0.100 0.000 0.192 0.092 0.512 NA
#> SRR1785241     5   0.751     0.2605 0.100 0.000 0.192 0.092 0.512 NA
#> SRR1785242     3   0.200     0.5443 0.028 0.004 0.920 0.004 0.044 NA
#> SRR1785243     3   0.200     0.5443 0.028 0.004 0.920 0.004 0.044 NA
#> SRR1785244     1   0.721     0.3652 0.416 0.000 0.004 0.280 0.096 NA
#> SRR1785245     1   0.721     0.3652 0.416 0.000 0.004 0.280 0.096 NA
#> SRR1785246     3   0.767     0.3483 0.304 0.000 0.364 0.036 0.072 NA
#> SRR1785247     3   0.767     0.3483 0.304 0.000 0.364 0.036 0.072 NA
#> SRR1785248     2   0.497     0.7230 0.000 0.740 0.108 0.016 0.052 NA
#> SRR1785250     3   0.600     0.3330 0.312 0.000 0.504 0.016 0.000 NA
#> SRR1785251     3   0.600     0.3330 0.312 0.000 0.504 0.016 0.000 NA
#> SRR1785252     3   0.221     0.5446 0.028 0.004 0.912 0.004 0.048 NA
#> SRR1785253     3   0.221     0.5446 0.028 0.004 0.912 0.004 0.048 NA
#> SRR1785254     5   0.702     0.1867 0.016 0.080 0.032 0.296 0.512 NA
#> SRR1785255     5   0.702     0.1867 0.016 0.080 0.032 0.296 0.512 NA
#> SRR1785256     1   0.386     0.5890 0.808 0.000 0.020 0.116 0.012 NA
#> SRR1785257     1   0.386     0.5890 0.808 0.000 0.020 0.116 0.012 NA
#> SRR1785258     1   0.682     0.1202 0.540 0.000 0.256 0.048 0.060 NA
#> SRR1785259     1   0.682     0.1202 0.540 0.000 0.256 0.048 0.060 NA
#> SRR1785262     3   0.861     0.3294 0.248 0.000 0.324 0.156 0.132 NA
#> SRR1785263     3   0.861     0.3294 0.248 0.000 0.324 0.156 0.132 NA
#> SRR1785260     4   0.401     0.5277 0.092 0.004 0.008 0.808 0.024 NA
#> SRR1785261     4   0.401     0.5277 0.092 0.004 0.008 0.808 0.024 NA
#> SRR1785264     2   0.826     0.3336 0.012 0.444 0.132 0.096 0.196 NA
#> SRR1785265     2   0.826     0.3336 0.012 0.444 0.132 0.096 0.196 NA
#> SRR1785266     2   0.231     0.8280 0.000 0.904 0.020 0.004 0.012 NA
#> SRR1785267     2   0.231     0.8280 0.000 0.904 0.020 0.004 0.012 NA
#> SRR1785268     1   0.279     0.5679 0.876 0.000 0.032 0.008 0.008 NA
#> SRR1785269     1   0.279     0.5679 0.876 0.000 0.032 0.008 0.008 NA
#> SRR1785270     5   0.390     0.5414 0.000 0.064 0.024 0.036 0.824 NA
#> SRR1785271     5   0.390     0.5414 0.000 0.064 0.024 0.036 0.824 NA
#> SRR1785272     1   0.634    -0.0352 0.424 0.000 0.324 0.016 0.000 NA
#> SRR1785273     1   0.634    -0.0352 0.424 0.000 0.324 0.016 0.000 NA
#> SRR1785276     1   0.773     0.0735 0.420 0.000 0.136 0.040 0.132 NA
#> SRR1785277     1   0.773     0.0735 0.420 0.000 0.136 0.040 0.132 NA
#> SRR1785274     5   0.847    -0.1894 0.156 0.004 0.312 0.096 0.312 NA
#> SRR1785275     3   0.847     0.0592 0.156 0.004 0.312 0.096 0.312 NA
#> SRR1785280     2   0.149     0.8386 0.000 0.944 0.024 0.000 0.004 NA
#> SRR1785281     2   0.149     0.8386 0.000 0.944 0.024 0.000 0.004 NA
#> SRR1785278     1   0.267     0.5970 0.892 0.000 0.024 0.032 0.008 NA
#> SRR1785279     1   0.267     0.5970 0.892 0.000 0.024 0.032 0.008 NA
#> SRR1785282     1   0.327     0.6057 0.852 0.000 0.016 0.060 0.008 NA
#> SRR1785283     1   0.327     0.6057 0.852 0.000 0.016 0.060 0.008 NA
#> SRR1785284     5   0.641     0.3680 0.040 0.000 0.036 0.264 0.564 NA
#> SRR1785285     5   0.641     0.3680 0.040 0.000 0.036 0.264 0.564 NA
#> SRR1785286     4   0.510     0.2694 0.020 0.000 0.032 0.636 0.292 NA
#> SRR1785287     4   0.510     0.2694 0.020 0.000 0.032 0.636 0.292 NA
#> SRR1785288     1   0.715     0.2858 0.368 0.000 0.000 0.336 0.096 NA
#> SRR1785289     1   0.715     0.2858 0.368 0.000 0.000 0.336 0.096 NA
#> SRR1785290     4   0.826     0.1048 0.004 0.288 0.064 0.300 0.256 NA
#> SRR1785291     4   0.826     0.1048 0.004 0.288 0.064 0.300 0.256 NA
#> SRR1785296     4   0.566     0.4952 0.032 0.024 0.044 0.696 0.164 NA
#> SRR1785297     4   0.566     0.4952 0.032 0.024 0.044 0.696 0.164 NA
#> SRR1785292     2   0.248     0.8256 0.000 0.896 0.008 0.012 0.020 NA
#> SRR1785293     2   0.248     0.8256 0.000 0.896 0.008 0.012 0.020 NA
#> SRR1785294     4   0.259     0.5565 0.092 0.004 0.004 0.880 0.004 NA
#> SRR1785295     4   0.259     0.5565 0.092 0.004 0.004 0.880 0.004 NA
#> SRR1785298     4   0.707     0.3587 0.032 0.072 0.036 0.552 0.244 NA
#> SRR1785299     4   0.707     0.3587 0.032 0.072 0.036 0.552 0.244 NA
#> SRR1785300     1   0.493     0.4669 0.596 0.000 0.004 0.348 0.016 NA
#> SRR1785301     1   0.493     0.4669 0.596 0.000 0.004 0.348 0.016 NA
#> SRR1785304     4   0.575     0.4716 0.000 0.076 0.008 0.660 0.152 NA
#> SRR1785305     4   0.575     0.4716 0.000 0.076 0.008 0.660 0.152 NA
#> SRR1785306     5   0.554     0.4590 0.000 0.068 0.036 0.180 0.680 NA
#> SRR1785307     5   0.554     0.4590 0.000 0.068 0.036 0.180 0.680 NA
#> SRR1785302     4   0.709     0.2366 0.008 0.092 0.028 0.464 0.336 NA
#> SRR1785303     4   0.709     0.2366 0.008 0.092 0.028 0.464 0.336 NA
#> SRR1785308     3   0.475     0.4621 0.192 0.000 0.692 0.008 0.000 NA
#> SRR1785309     3   0.475     0.4621 0.192 0.000 0.692 0.008 0.000 NA
#> SRR1785310     4   0.266     0.5472 0.084 0.004 0.004 0.880 0.024 NA
#> SRR1785311     4   0.266     0.5472 0.084 0.004 0.004 0.880 0.024 NA
#> SRR1785312     1   0.345     0.5646 0.824 0.000 0.040 0.004 0.012 NA
#> SRR1785313     1   0.345     0.5646 0.824 0.000 0.040 0.004 0.012 NA
#> SRR1785314     5   0.502     0.5028 0.000 0.080 0.012 0.132 0.728 NA
#> SRR1785315     5   0.502     0.5028 0.000 0.080 0.012 0.132 0.728 NA
#> SRR1785318     2   0.107     0.8395 0.000 0.964 0.008 0.000 0.008 NA
#> SRR1785319     2   0.107     0.8395 0.000 0.964 0.008 0.000 0.008 NA
#> SRR1785316     1   0.555     0.5534 0.624 0.000 0.008 0.144 0.012 NA
#> SRR1785317     1   0.555     0.5534 0.624 0.000 0.008 0.144 0.012 NA
#> SRR1785324     2   0.223     0.8245 0.000 0.908 0.004 0.008 0.024 NA
#> SRR1785325     2   0.223     0.8245 0.000 0.908 0.004 0.008 0.024 NA
#> SRR1785320     1   0.425     0.5493 0.752 0.000 0.020 0.008 0.036 NA
#> SRR1785321     1   0.425     0.5493 0.752 0.000 0.020 0.008 0.036 NA
#> SRR1785322     1   0.475     0.5245 0.744 0.000 0.076 0.040 0.008 NA
#> SRR1785323     1   0.475     0.5245 0.744 0.000 0.076 0.040 0.008 NA

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-SD-kmeans-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-SD-kmeans-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-SD-kmeans-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-SD-kmeans-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-SD-kmeans-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-SD-kmeans-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-SD-kmeans-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-SD-kmeans-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-SD-kmeans-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-SD-kmeans-membership-heatmap-5

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)

plot of chunk tab-SD-kmeans-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-SD-kmeans-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-SD-kmeans-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-SD-kmeans-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-SD-kmeans-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-SD-kmeans-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-SD-kmeans-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-SD-kmeans-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-SD-kmeans-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-SD-kmeans-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk SD-kmeans-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-SD-kmeans-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-SD-kmeans-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-SD-kmeans-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-SD-kmeans-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-SD-kmeans-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk SD-kmeans-collect-classes

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.


SD:skmeans

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#>   Subgroups are detected by 'skmeans' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 3.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk SD-skmeans-collect-plots

The plots are:

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:

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)

plot of chunk SD-skmeans-select-partition-number

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 0.668           0.814       0.921         0.5023 0.500   0.500
#> 3 3 0.788           0.858       0.927         0.3231 0.763   0.555
#> 4 4 0.654           0.698       0.839         0.1213 0.902   0.712
#> 5 5 0.677           0.677       0.813         0.0640 0.927   0.724
#> 6 6 0.688           0.554       0.747         0.0409 0.979   0.898

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2   0.961      0.471 0.384 0.616
#> SRR1785239     2   0.961      0.471 0.384 0.616
#> SRR1785240     1   0.000      0.903 1.000 0.000
#> SRR1785241     1   0.000      0.903 1.000 0.000
#> SRR1785242     2   0.961      0.471 0.384 0.616
#> SRR1785243     2   0.961      0.471 0.384 0.616
#> SRR1785244     1   0.000      0.903 1.000 0.000
#> SRR1785245     1   0.000      0.903 1.000 0.000
#> SRR1785246     1   0.000      0.903 1.000 0.000
#> SRR1785247     1   0.000      0.903 1.000 0.000
#> SRR1785248     2   0.000      0.904 0.000 1.000
#> SRR1785250     1   0.000      0.903 1.000 0.000
#> SRR1785251     1   0.000      0.903 1.000 0.000
#> SRR1785252     2   0.961      0.471 0.384 0.616
#> SRR1785253     2   0.961      0.471 0.384 0.616
#> SRR1785254     2   0.000      0.904 0.000 1.000
#> SRR1785255     2   0.000      0.904 0.000 1.000
#> SRR1785256     1   0.000      0.903 1.000 0.000
#> SRR1785257     1   0.000      0.903 1.000 0.000
#> SRR1785258     1   0.000      0.903 1.000 0.000
#> SRR1785259     1   0.000      0.903 1.000 0.000
#> SRR1785262     1   0.000      0.903 1.000 0.000
#> SRR1785263     1   0.000      0.903 1.000 0.000
#> SRR1785260     1   0.961      0.476 0.616 0.384
#> SRR1785261     1   0.961      0.476 0.616 0.384
#> SRR1785264     2   0.000      0.904 0.000 1.000
#> SRR1785265     2   0.000      0.904 0.000 1.000
#> SRR1785266     2   0.000      0.904 0.000 1.000
#> SRR1785267     2   0.000      0.904 0.000 1.000
#> SRR1785268     1   0.000      0.903 1.000 0.000
#> SRR1785269     1   0.000      0.903 1.000 0.000
#> SRR1785270     2   0.000      0.904 0.000 1.000
#> SRR1785271     2   0.000      0.904 0.000 1.000
#> SRR1785272     1   0.000      0.903 1.000 0.000
#> SRR1785273     1   0.000      0.903 1.000 0.000
#> SRR1785276     1   0.000      0.903 1.000 0.000
#> SRR1785277     1   0.000      0.903 1.000 0.000
#> SRR1785274     2   0.961      0.471 0.384 0.616
#> SRR1785275     2   0.961      0.471 0.384 0.616
#> SRR1785280     2   0.000      0.904 0.000 1.000
#> SRR1785281     2   0.000      0.904 0.000 1.000
#> SRR1785278     1   0.000      0.903 1.000 0.000
#> SRR1785279     1   0.000      0.903 1.000 0.000
#> SRR1785282     1   0.000      0.903 1.000 0.000
#> SRR1785283     1   0.000      0.903 1.000 0.000
#> SRR1785284     1   0.961      0.476 0.616 0.384
#> SRR1785285     1   0.961      0.476 0.616 0.384
#> SRR1785286     1   0.961      0.476 0.616 0.384
#> SRR1785287     1   0.961      0.476 0.616 0.384
#> SRR1785288     1   0.000      0.903 1.000 0.000
#> SRR1785289     1   0.000      0.903 1.000 0.000
#> SRR1785290     2   0.000      0.904 0.000 1.000
#> SRR1785291     2   0.000      0.904 0.000 1.000
#> SRR1785296     2   0.000      0.904 0.000 1.000
#> SRR1785297     2   0.000      0.904 0.000 1.000
#> SRR1785292     2   0.000      0.904 0.000 1.000
#> SRR1785293     2   0.000      0.904 0.000 1.000
#> SRR1785294     1   0.961      0.476 0.616 0.384
#> SRR1785295     1   0.961      0.476 0.616 0.384
#> SRR1785298     2   0.000      0.904 0.000 1.000
#> SRR1785299     2   0.000      0.904 0.000 1.000
#> SRR1785300     1   0.000      0.903 1.000 0.000
#> SRR1785301     1   0.000      0.903 1.000 0.000
#> SRR1785304     2   0.000      0.904 0.000 1.000
#> SRR1785305     2   0.000      0.904 0.000 1.000
#> SRR1785306     2   0.000      0.904 0.000 1.000
#> SRR1785307     2   0.000      0.904 0.000 1.000
#> SRR1785302     2   0.000      0.904 0.000 1.000
#> SRR1785303     2   0.000      0.904 0.000 1.000
#> SRR1785308     1   0.000      0.903 1.000 0.000
#> SRR1785309     1   0.000      0.903 1.000 0.000
#> SRR1785310     1   0.961      0.476 0.616 0.384
#> SRR1785311     1   0.961      0.476 0.616 0.384
#> SRR1785312     1   0.000      0.903 1.000 0.000
#> SRR1785313     1   0.000      0.903 1.000 0.000
#> SRR1785314     2   0.000      0.904 0.000 1.000
#> SRR1785315     2   0.000      0.904 0.000 1.000
#> SRR1785318     2   0.000      0.904 0.000 1.000
#> SRR1785319     2   0.000      0.904 0.000 1.000
#> SRR1785316     1   0.000      0.903 1.000 0.000
#> SRR1785317     1   0.000      0.903 1.000 0.000
#> SRR1785324     2   0.000      0.904 0.000 1.000
#> SRR1785325     2   0.000      0.904 0.000 1.000
#> SRR1785320     1   0.000      0.903 1.000 0.000
#> SRR1785321     1   0.000      0.903 1.000 0.000
#> SRR1785322     1   0.000      0.903 1.000 0.000
#> SRR1785323     1   0.000      0.903 1.000 0.000

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     3  0.6079      0.405 0.000 0.388 0.612
#> SRR1785239     3  0.6079      0.405 0.000 0.388 0.612
#> SRR1785240     3  0.2066      0.843 0.060 0.000 0.940
#> SRR1785241     3  0.2066      0.843 0.060 0.000 0.940
#> SRR1785242     3  0.0000      0.880 0.000 0.000 1.000
#> SRR1785243     3  0.0000      0.880 0.000 0.000 1.000
#> SRR1785244     1  0.0000      0.855 1.000 0.000 0.000
#> SRR1785245     1  0.0000      0.855 1.000 0.000 0.000
#> SRR1785246     3  0.0000      0.880 0.000 0.000 1.000
#> SRR1785247     3  0.0000      0.880 0.000 0.000 1.000
#> SRR1785248     2  0.1163      0.968 0.000 0.972 0.028
#> SRR1785250     3  0.4399      0.747 0.188 0.000 0.812
#> SRR1785251     3  0.4399      0.747 0.188 0.000 0.812
#> SRR1785252     3  0.0000      0.880 0.000 0.000 1.000
#> SRR1785253     3  0.0000      0.880 0.000 0.000 1.000
#> SRR1785254     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785255     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785256     1  0.2537      0.841 0.920 0.000 0.080
#> SRR1785257     1  0.2537      0.841 0.920 0.000 0.080
#> SRR1785258     3  0.1031      0.873 0.024 0.000 0.976
#> SRR1785259     3  0.1031      0.873 0.024 0.000 0.976
#> SRR1785262     3  0.0000      0.880 0.000 0.000 1.000
#> SRR1785263     3  0.0000      0.880 0.000 0.000 1.000
#> SRR1785260     1  0.1411      0.847 0.964 0.036 0.000
#> SRR1785261     1  0.1411      0.847 0.964 0.036 0.000
#> SRR1785264     2  0.0424      0.987 0.000 0.992 0.008
#> SRR1785265     2  0.0424      0.987 0.000 0.992 0.008
#> SRR1785266     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785267     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785268     1  0.5058      0.734 0.756 0.000 0.244
#> SRR1785269     1  0.5058      0.734 0.756 0.000 0.244
#> SRR1785270     2  0.1525      0.965 0.004 0.964 0.032
#> SRR1785271     2  0.1525      0.965 0.004 0.964 0.032
#> SRR1785272     3  0.4555      0.732 0.200 0.000 0.800
#> SRR1785273     3  0.4555      0.732 0.200 0.000 0.800
#> SRR1785276     3  0.1411      0.868 0.036 0.000 0.964
#> SRR1785277     3  0.1411      0.868 0.036 0.000 0.964
#> SRR1785274     3  0.0000      0.880 0.000 0.000 1.000
#> SRR1785275     3  0.0000      0.880 0.000 0.000 1.000
#> SRR1785280     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785281     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785278     1  0.4002      0.805 0.840 0.000 0.160
#> SRR1785279     1  0.4002      0.805 0.840 0.000 0.160
#> SRR1785282     1  0.3619      0.819 0.864 0.000 0.136
#> SRR1785283     1  0.3619      0.819 0.864 0.000 0.136
#> SRR1785284     1  0.5356      0.685 0.784 0.020 0.196
#> SRR1785285     1  0.5356      0.685 0.784 0.020 0.196
#> SRR1785286     1  0.2414      0.839 0.940 0.040 0.020
#> SRR1785287     1  0.2414      0.839 0.940 0.040 0.020
#> SRR1785288     1  0.0000      0.855 1.000 0.000 0.000
#> SRR1785289     1  0.0000      0.855 1.000 0.000 0.000
#> SRR1785290     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785291     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785296     2  0.0747      0.982 0.016 0.984 0.000
#> SRR1785297     2  0.0747      0.982 0.016 0.984 0.000
#> SRR1785292     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785293     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785294     1  0.1411      0.847 0.964 0.036 0.000
#> SRR1785295     1  0.1411      0.847 0.964 0.036 0.000
#> SRR1785298     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785299     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785300     1  0.0000      0.855 1.000 0.000 0.000
#> SRR1785301     1  0.0000      0.855 1.000 0.000 0.000
#> SRR1785304     2  0.1411      0.966 0.036 0.964 0.000
#> SRR1785305     2  0.1411      0.966 0.036 0.964 0.000
#> SRR1785306     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785307     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785302     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785303     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785308     3  0.4452      0.743 0.192 0.000 0.808
#> SRR1785309     3  0.4452      0.743 0.192 0.000 0.808
#> SRR1785310     1  0.1411      0.847 0.964 0.036 0.000
#> SRR1785311     1  0.1411      0.847 0.964 0.036 0.000
#> SRR1785312     1  0.5138      0.724 0.748 0.000 0.252
#> SRR1785313     1  0.5138      0.724 0.748 0.000 0.252
#> SRR1785314     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785315     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785318     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785319     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785316     1  0.0000      0.855 1.000 0.000 0.000
#> SRR1785317     1  0.0000      0.855 1.000 0.000 0.000
#> SRR1785324     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785325     2  0.0000      0.992 0.000 1.000 0.000
#> SRR1785320     1  0.5016      0.740 0.760 0.000 0.240
#> SRR1785321     1  0.5016      0.740 0.760 0.000 0.240
#> SRR1785322     1  0.6280      0.247 0.540 0.000 0.460
#> SRR1785323     1  0.6280      0.247 0.540 0.000 0.460

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     3  0.4972      0.180 0.000 0.456 0.544 0.000
#> SRR1785239     3  0.4972      0.180 0.000 0.456 0.544 0.000
#> SRR1785240     3  0.5085      0.557 0.032 0.000 0.708 0.260
#> SRR1785241     3  0.5085      0.557 0.032 0.000 0.708 0.260
#> SRR1785242     3  0.0469      0.745 0.000 0.012 0.988 0.000
#> SRR1785243     3  0.0469      0.745 0.000 0.012 0.988 0.000
#> SRR1785244     1  0.3610      0.745 0.800 0.000 0.000 0.200
#> SRR1785245     1  0.3610      0.745 0.800 0.000 0.000 0.200
#> SRR1785246     3  0.1211      0.749 0.040 0.000 0.960 0.000
#> SRR1785247     3  0.1211      0.749 0.040 0.000 0.960 0.000
#> SRR1785248     2  0.0000      0.847 0.000 1.000 0.000 0.000
#> SRR1785250     3  0.4250      0.643 0.276 0.000 0.724 0.000
#> SRR1785251     3  0.4250      0.643 0.276 0.000 0.724 0.000
#> SRR1785252     3  0.0469      0.745 0.000 0.012 0.988 0.000
#> SRR1785253     3  0.0469      0.745 0.000 0.012 0.988 0.000
#> SRR1785254     2  0.3047      0.789 0.000 0.872 0.012 0.116
#> SRR1785255     2  0.3047      0.789 0.000 0.872 0.012 0.116
#> SRR1785256     1  0.1004      0.874 0.972 0.000 0.004 0.024
#> SRR1785257     1  0.1004      0.874 0.972 0.000 0.004 0.024
#> SRR1785258     3  0.3569      0.685 0.196 0.000 0.804 0.000
#> SRR1785259     3  0.3569      0.685 0.196 0.000 0.804 0.000
#> SRR1785262     3  0.1042      0.743 0.008 0.000 0.972 0.020
#> SRR1785263     3  0.1042      0.743 0.008 0.000 0.972 0.020
#> SRR1785260     4  0.4019      0.708 0.196 0.012 0.000 0.792
#> SRR1785261     4  0.4019      0.708 0.196 0.012 0.000 0.792
#> SRR1785264     2  0.0000      0.847 0.000 1.000 0.000 0.000
#> SRR1785265     2  0.0000      0.847 0.000 1.000 0.000 0.000
#> SRR1785266     2  0.0000      0.847 0.000 1.000 0.000 0.000
#> SRR1785267     2  0.0000      0.847 0.000 1.000 0.000 0.000
#> SRR1785268     1  0.1867      0.856 0.928 0.000 0.072 0.000
#> SRR1785269     1  0.1867      0.856 0.928 0.000 0.072 0.000
#> SRR1785270     2  0.5549      0.622 0.000 0.672 0.048 0.280
#> SRR1785271     2  0.5549      0.622 0.000 0.672 0.048 0.280
#> SRR1785272     3  0.4955      0.356 0.444 0.000 0.556 0.000
#> SRR1785273     3  0.4948      0.367 0.440 0.000 0.560 0.000
#> SRR1785276     3  0.4950      0.484 0.376 0.000 0.620 0.004
#> SRR1785277     3  0.4950      0.484 0.376 0.000 0.620 0.004
#> SRR1785274     3  0.3668      0.640 0.004 0.000 0.808 0.188
#> SRR1785275     3  0.3668      0.640 0.004 0.000 0.808 0.188
#> SRR1785280     2  0.0000      0.847 0.000 1.000 0.000 0.000
#> SRR1785281     2  0.0000      0.847 0.000 1.000 0.000 0.000
#> SRR1785278     1  0.0817      0.875 0.976 0.000 0.024 0.000
#> SRR1785279     1  0.0817      0.875 0.976 0.000 0.024 0.000
#> SRR1785282     1  0.0707      0.876 0.980 0.000 0.020 0.000
#> SRR1785283     1  0.0707      0.876 0.980 0.000 0.020 0.000
#> SRR1785284     4  0.4621      0.596 0.128 0.000 0.076 0.796
#> SRR1785285     4  0.4621      0.596 0.128 0.000 0.076 0.796
#> SRR1785286     4  0.0804      0.696 0.012 0.000 0.008 0.980
#> SRR1785287     4  0.0804      0.696 0.012 0.000 0.008 0.980
#> SRR1785288     1  0.3801      0.719 0.780 0.000 0.000 0.220
#> SRR1785289     1  0.3801      0.719 0.780 0.000 0.000 0.220
#> SRR1785290     2  0.0000      0.847 0.000 1.000 0.000 0.000
#> SRR1785291     2  0.0000      0.847 0.000 1.000 0.000 0.000
#> SRR1785296     4  0.4866      0.394 0.000 0.404 0.000 0.596
#> SRR1785297     4  0.4866      0.394 0.000 0.404 0.000 0.596
#> SRR1785292     2  0.0000      0.847 0.000 1.000 0.000 0.000
#> SRR1785293     2  0.0000      0.847 0.000 1.000 0.000 0.000
#> SRR1785294     4  0.4462      0.724 0.164 0.044 0.000 0.792
#> SRR1785295     4  0.4462      0.724 0.164 0.044 0.000 0.792
#> SRR1785298     2  0.4830      0.194 0.000 0.608 0.000 0.392
#> SRR1785299     2  0.4830      0.194 0.000 0.608 0.000 0.392
#> SRR1785300     1  0.3266      0.779 0.832 0.000 0.000 0.168
#> SRR1785301     1  0.3266      0.779 0.832 0.000 0.000 0.168
#> SRR1785304     4  0.5427      0.367 0.016 0.416 0.000 0.568
#> SRR1785305     4  0.5427      0.367 0.016 0.416 0.000 0.568
#> SRR1785306     2  0.4690      0.678 0.000 0.724 0.016 0.260
#> SRR1785307     2  0.4690      0.678 0.000 0.724 0.016 0.260
#> SRR1785302     2  0.4098      0.643 0.000 0.784 0.012 0.204
#> SRR1785303     2  0.4098      0.643 0.000 0.784 0.012 0.204
#> SRR1785308     3  0.4164      0.650 0.264 0.000 0.736 0.000
#> SRR1785309     3  0.4164      0.650 0.264 0.000 0.736 0.000
#> SRR1785310     4  0.4019      0.708 0.196 0.012 0.000 0.792
#> SRR1785311     4  0.4019      0.708 0.196 0.012 0.000 0.792
#> SRR1785312     1  0.1867      0.856 0.928 0.000 0.072 0.000
#> SRR1785313     1  0.1867      0.856 0.928 0.000 0.072 0.000
#> SRR1785314     2  0.4744      0.660 0.000 0.704 0.012 0.284
#> SRR1785315     2  0.4744      0.660 0.000 0.704 0.012 0.284
#> SRR1785318     2  0.0000      0.847 0.000 1.000 0.000 0.000
#> SRR1785319     2  0.0000      0.847 0.000 1.000 0.000 0.000
#> SRR1785316     1  0.1211      0.865 0.960 0.000 0.000 0.040
#> SRR1785317     1  0.1211      0.865 0.960 0.000 0.000 0.040
#> SRR1785324     2  0.0000      0.847 0.000 1.000 0.000 0.000
#> SRR1785325     2  0.0000      0.847 0.000 1.000 0.000 0.000
#> SRR1785320     1  0.1902      0.859 0.932 0.000 0.064 0.004
#> SRR1785321     1  0.1902      0.859 0.932 0.000 0.064 0.004
#> SRR1785322     1  0.3074      0.765 0.848 0.000 0.152 0.000
#> SRR1785323     1  0.3074      0.765 0.848 0.000 0.152 0.000

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     3  0.4930      0.309 0.000 0.388 0.580 0.000 0.032
#> SRR1785239     3  0.4930      0.309 0.000 0.388 0.580 0.000 0.032
#> SRR1785240     5  0.3838      0.561 0.000 0.000 0.280 0.004 0.716
#> SRR1785241     5  0.3838      0.561 0.000 0.000 0.280 0.004 0.716
#> SRR1785242     3  0.0290      0.666 0.000 0.000 0.992 0.000 0.008
#> SRR1785243     3  0.0290      0.666 0.000 0.000 0.992 0.000 0.008
#> SRR1785244     1  0.4708      0.734 0.712 0.000 0.000 0.220 0.068
#> SRR1785245     1  0.4708      0.734 0.712 0.000 0.000 0.220 0.068
#> SRR1785246     3  0.3102      0.670 0.056 0.000 0.860 0.000 0.084
#> SRR1785247     3  0.3102      0.670 0.056 0.000 0.860 0.000 0.084
#> SRR1785248     2  0.0771      0.827 0.000 0.976 0.020 0.000 0.004
#> SRR1785250     3  0.4138      0.660 0.276 0.000 0.708 0.000 0.016
#> SRR1785251     3  0.4138      0.660 0.276 0.000 0.708 0.000 0.016
#> SRR1785252     3  0.0290      0.666 0.000 0.000 0.992 0.000 0.008
#> SRR1785253     3  0.0290      0.666 0.000 0.000 0.992 0.000 0.008
#> SRR1785254     2  0.4549      0.198 0.000 0.528 0.000 0.008 0.464
#> SRR1785255     2  0.4549      0.198 0.000 0.528 0.000 0.008 0.464
#> SRR1785256     1  0.2878      0.833 0.880 0.000 0.004 0.068 0.048
#> SRR1785257     1  0.2878      0.833 0.880 0.000 0.004 0.068 0.048
#> SRR1785258     3  0.5098      0.496 0.300 0.000 0.644 0.004 0.052
#> SRR1785259     3  0.5098      0.496 0.300 0.000 0.644 0.004 0.052
#> SRR1785262     3  0.3892      0.626 0.024 0.000 0.820 0.036 0.120
#> SRR1785263     3  0.3892      0.626 0.024 0.000 0.820 0.036 0.120
#> SRR1785260     4  0.0451      0.803 0.008 0.000 0.000 0.988 0.004
#> SRR1785261     4  0.0451      0.803 0.008 0.000 0.000 0.988 0.004
#> SRR1785264     2  0.0807      0.833 0.000 0.976 0.012 0.000 0.012
#> SRR1785265     2  0.0807      0.833 0.000 0.976 0.012 0.000 0.012
#> SRR1785266     2  0.0162      0.838 0.000 0.996 0.000 0.000 0.004
#> SRR1785267     2  0.0162      0.838 0.000 0.996 0.000 0.000 0.004
#> SRR1785268     1  0.1310      0.841 0.956 0.000 0.020 0.000 0.024
#> SRR1785269     1  0.1310      0.841 0.956 0.000 0.020 0.000 0.024
#> SRR1785270     5  0.3797      0.662 0.000 0.232 0.008 0.004 0.756
#> SRR1785271     5  0.3797      0.662 0.000 0.232 0.008 0.004 0.756
#> SRR1785272     3  0.4367      0.555 0.372 0.000 0.620 0.000 0.008
#> SRR1785273     3  0.4367      0.555 0.372 0.000 0.620 0.000 0.008
#> SRR1785276     3  0.6416      0.431 0.356 0.000 0.464 0.000 0.180
#> SRR1785277     3  0.6416      0.431 0.356 0.000 0.464 0.000 0.180
#> SRR1785274     5  0.4397      0.363 0.004 0.000 0.432 0.000 0.564
#> SRR1785275     5  0.4397      0.363 0.004 0.000 0.432 0.000 0.564
#> SRR1785280     2  0.0162      0.838 0.000 0.996 0.000 0.000 0.004
#> SRR1785281     2  0.0162      0.838 0.000 0.996 0.000 0.000 0.004
#> SRR1785278     1  0.0451      0.848 0.988 0.000 0.004 0.000 0.008
#> SRR1785279     1  0.0451      0.848 0.988 0.000 0.004 0.000 0.008
#> SRR1785282     1  0.0566      0.850 0.984 0.000 0.000 0.012 0.004
#> SRR1785283     1  0.0566      0.850 0.984 0.000 0.000 0.012 0.004
#> SRR1785284     5  0.3634      0.581 0.012 0.004 0.032 0.116 0.836
#> SRR1785285     5  0.3634      0.581 0.012 0.004 0.032 0.116 0.836
#> SRR1785286     4  0.4268      0.443 0.008 0.000 0.000 0.648 0.344
#> SRR1785287     4  0.4268      0.443 0.008 0.000 0.000 0.648 0.344
#> SRR1785288     1  0.4983      0.675 0.664 0.000 0.000 0.272 0.064
#> SRR1785289     1  0.4983      0.675 0.664 0.000 0.000 0.272 0.064
#> SRR1785290     2  0.1041      0.827 0.000 0.964 0.000 0.004 0.032
#> SRR1785291     2  0.1041      0.827 0.000 0.964 0.000 0.004 0.032
#> SRR1785296     4  0.4524      0.669 0.000 0.208 0.004 0.736 0.052
#> SRR1785297     4  0.4524      0.669 0.000 0.208 0.004 0.736 0.052
#> SRR1785292     2  0.0324      0.838 0.000 0.992 0.000 0.004 0.004
#> SRR1785293     2  0.0324      0.838 0.000 0.992 0.000 0.004 0.004
#> SRR1785294     4  0.0162      0.806 0.000 0.004 0.000 0.996 0.000
#> SRR1785295     4  0.0162      0.806 0.000 0.004 0.000 0.996 0.000
#> SRR1785298     2  0.6145      0.362 0.000 0.532 0.000 0.312 0.156
#> SRR1785299     2  0.6145      0.362 0.000 0.532 0.000 0.312 0.156
#> SRR1785300     1  0.4058      0.752 0.740 0.000 0.000 0.236 0.024
#> SRR1785301     1  0.4058      0.752 0.740 0.000 0.000 0.236 0.024
#> SRR1785304     4  0.3795      0.709 0.000 0.192 0.000 0.780 0.028
#> SRR1785305     4  0.3795      0.709 0.000 0.192 0.000 0.780 0.028
#> SRR1785306     5  0.4302      0.522 0.000 0.344 0.004 0.004 0.648
#> SRR1785307     5  0.4302      0.522 0.000 0.344 0.004 0.004 0.648
#> SRR1785302     2  0.5831      0.533 0.000 0.608 0.000 0.220 0.172
#> SRR1785303     2  0.5831      0.533 0.000 0.608 0.000 0.220 0.172
#> SRR1785308     3  0.3300      0.688 0.204 0.000 0.792 0.000 0.004
#> SRR1785309     3  0.3300      0.688 0.204 0.000 0.792 0.000 0.004
#> SRR1785310     4  0.0451      0.804 0.004 0.000 0.000 0.988 0.008
#> SRR1785311     4  0.0451      0.804 0.004 0.000 0.000 0.988 0.008
#> SRR1785312     1  0.1310      0.841 0.956 0.000 0.020 0.000 0.024
#> SRR1785313     1  0.1310      0.841 0.956 0.000 0.020 0.000 0.024
#> SRR1785314     5  0.4638      0.569 0.000 0.324 0.000 0.028 0.648
#> SRR1785315     5  0.4638      0.569 0.000 0.324 0.000 0.028 0.648
#> SRR1785318     2  0.0000      0.838 0.000 1.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000      0.838 0.000 1.000 0.000 0.000 0.000
#> SRR1785316     1  0.2006      0.845 0.916 0.000 0.000 0.072 0.012
#> SRR1785317     1  0.2006      0.845 0.916 0.000 0.000 0.072 0.012
#> SRR1785324     2  0.0566      0.835 0.000 0.984 0.000 0.004 0.012
#> SRR1785325     2  0.0566      0.835 0.000 0.984 0.000 0.004 0.012
#> SRR1785320     1  0.1579      0.837 0.944 0.000 0.024 0.000 0.032
#> SRR1785321     1  0.1579      0.837 0.944 0.000 0.024 0.000 0.032
#> SRR1785322     1  0.2411      0.761 0.884 0.000 0.108 0.000 0.008
#> SRR1785323     1  0.2411      0.761 0.884 0.000 0.108 0.000 0.008

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     3  0.4937     0.2250 0.000 0.400 0.548 0.000 0.020 0.032
#> SRR1785239     3  0.4937     0.2250 0.000 0.400 0.548 0.000 0.020 0.032
#> SRR1785240     5  0.4937     0.5191 0.000 0.000 0.196 0.000 0.652 0.152
#> SRR1785241     5  0.4937     0.5191 0.000 0.000 0.196 0.000 0.652 0.152
#> SRR1785242     3  0.0405     0.5498 0.000 0.008 0.988 0.000 0.004 0.000
#> SRR1785243     3  0.0405     0.5498 0.000 0.008 0.988 0.000 0.004 0.000
#> SRR1785244     1  0.5734     0.5065 0.628 0.000 0.000 0.156 0.048 0.168
#> SRR1785245     1  0.5734     0.5065 0.628 0.000 0.000 0.156 0.048 0.168
#> SRR1785246     3  0.5235     0.3206 0.064 0.000 0.596 0.000 0.024 0.316
#> SRR1785247     3  0.5235     0.3206 0.064 0.000 0.596 0.000 0.024 0.316
#> SRR1785248     2  0.0858     0.8007 0.000 0.968 0.028 0.000 0.000 0.004
#> SRR1785250     3  0.4937     0.3454 0.196 0.000 0.652 0.000 0.000 0.152
#> SRR1785251     3  0.4937     0.3454 0.196 0.000 0.652 0.000 0.000 0.152
#> SRR1785252     3  0.0405     0.5498 0.000 0.008 0.988 0.000 0.004 0.000
#> SRR1785253     3  0.0405     0.5498 0.000 0.008 0.988 0.000 0.004 0.000
#> SRR1785254     2  0.6088     0.0780 0.000 0.368 0.000 0.000 0.356 0.276
#> SRR1785255     2  0.6088     0.0780 0.000 0.368 0.000 0.000 0.356 0.276
#> SRR1785256     1  0.3361     0.6144 0.832 0.000 0.004 0.040 0.012 0.112
#> SRR1785257     1  0.3361     0.6144 0.832 0.000 0.004 0.040 0.012 0.112
#> SRR1785258     3  0.6041     0.1831 0.208 0.000 0.544 0.000 0.024 0.224
#> SRR1785259     3  0.6041     0.1831 0.208 0.000 0.544 0.000 0.024 0.224
#> SRR1785262     3  0.6350     0.3517 0.028 0.000 0.592 0.052 0.108 0.220
#> SRR1785263     3  0.6350     0.3517 0.028 0.000 0.592 0.052 0.108 0.220
#> SRR1785260     4  0.0767     0.8710 0.004 0.000 0.000 0.976 0.008 0.012
#> SRR1785261     4  0.0767     0.8710 0.004 0.000 0.000 0.976 0.008 0.012
#> SRR1785264     2  0.1148     0.8021 0.000 0.960 0.020 0.000 0.004 0.016
#> SRR1785265     2  0.1148     0.8021 0.000 0.960 0.020 0.000 0.004 0.016
#> SRR1785266     2  0.0405     0.8087 0.000 0.988 0.008 0.000 0.000 0.004
#> SRR1785267     2  0.0405     0.8087 0.000 0.988 0.008 0.000 0.000 0.004
#> SRR1785268     1  0.3163     0.4722 0.764 0.000 0.004 0.000 0.000 0.232
#> SRR1785269     1  0.3163     0.4722 0.764 0.000 0.004 0.000 0.000 0.232
#> SRR1785270     5  0.2766     0.6487 0.000 0.124 0.004 0.000 0.852 0.020
#> SRR1785271     5  0.2766     0.6487 0.000 0.124 0.004 0.000 0.852 0.020
#> SRR1785272     3  0.5736     0.0303 0.320 0.000 0.492 0.000 0.000 0.188
#> SRR1785273     3  0.5736     0.0303 0.320 0.000 0.492 0.000 0.000 0.188
#> SRR1785276     6  0.6663     1.0000 0.244 0.008 0.132 0.000 0.084 0.532
#> SRR1785277     6  0.6663     1.0000 0.244 0.008 0.132 0.000 0.084 0.532
#> SRR1785274     5  0.5850     0.1952 0.000 0.000 0.384 0.000 0.424 0.192
#> SRR1785275     5  0.5850     0.1952 0.000 0.000 0.384 0.000 0.424 0.192
#> SRR1785280     2  0.0146     0.8104 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1785281     2  0.0146     0.8104 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1785278     1  0.1858     0.6007 0.904 0.000 0.004 0.000 0.000 0.092
#> SRR1785279     1  0.1858     0.6007 0.904 0.000 0.004 0.000 0.000 0.092
#> SRR1785282     1  0.0405     0.6351 0.988 0.000 0.004 0.008 0.000 0.000
#> SRR1785283     1  0.0405     0.6351 0.988 0.000 0.004 0.008 0.000 0.000
#> SRR1785284     5  0.2940     0.6056 0.004 0.000 0.000 0.036 0.848 0.112
#> SRR1785285     5  0.2940     0.6056 0.004 0.000 0.000 0.036 0.848 0.112
#> SRR1785286     5  0.5007     0.1723 0.012 0.000 0.000 0.440 0.504 0.044
#> SRR1785287     5  0.5007     0.1723 0.012 0.000 0.000 0.440 0.504 0.044
#> SRR1785288     1  0.5892     0.4774 0.600 0.000 0.000 0.200 0.044 0.156
#> SRR1785289     1  0.5892     0.4774 0.600 0.000 0.000 0.200 0.044 0.156
#> SRR1785290     2  0.1672     0.7930 0.000 0.932 0.000 0.004 0.016 0.048
#> SRR1785291     2  0.1672     0.7930 0.000 0.932 0.000 0.004 0.016 0.048
#> SRR1785296     4  0.3915     0.8007 0.000 0.108 0.008 0.804 0.020 0.060
#> SRR1785297     4  0.3915     0.8007 0.000 0.108 0.008 0.804 0.020 0.060
#> SRR1785292     2  0.0363     0.8102 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1785293     2  0.0363     0.8102 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1785294     4  0.0146     0.8760 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1785295     4  0.0146     0.8760 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1785298     2  0.6827     0.2644 0.000 0.428 0.000 0.268 0.056 0.248
#> SRR1785299     2  0.6827     0.2644 0.000 0.428 0.000 0.268 0.056 0.248
#> SRR1785300     1  0.4573     0.5651 0.720 0.000 0.000 0.180 0.016 0.084
#> SRR1785301     1  0.4573     0.5651 0.720 0.000 0.000 0.180 0.016 0.084
#> SRR1785304     4  0.3838     0.7735 0.000 0.156 0.000 0.784 0.020 0.040
#> SRR1785305     4  0.3838     0.7735 0.000 0.156 0.000 0.784 0.020 0.040
#> SRR1785306     5  0.3776     0.6125 0.000 0.196 0.000 0.000 0.756 0.048
#> SRR1785307     5  0.3776     0.6125 0.000 0.196 0.000 0.000 0.756 0.048
#> SRR1785302     2  0.7084     0.3924 0.000 0.460 0.000 0.132 0.176 0.232
#> SRR1785303     2  0.7084     0.3924 0.000 0.460 0.000 0.132 0.176 0.232
#> SRR1785308     3  0.3229     0.4871 0.140 0.000 0.816 0.000 0.000 0.044
#> SRR1785309     3  0.3229     0.4871 0.140 0.000 0.816 0.000 0.000 0.044
#> SRR1785310     4  0.0520     0.8740 0.000 0.000 0.000 0.984 0.008 0.008
#> SRR1785311     4  0.0520     0.8740 0.000 0.000 0.000 0.984 0.008 0.008
#> SRR1785312     1  0.3349     0.4609 0.748 0.000 0.008 0.000 0.000 0.244
#> SRR1785313     1  0.3349     0.4609 0.748 0.000 0.008 0.000 0.000 0.244
#> SRR1785314     5  0.3343     0.6334 0.000 0.176 0.000 0.004 0.796 0.024
#> SRR1785315     5  0.3343     0.6334 0.000 0.176 0.000 0.004 0.796 0.024
#> SRR1785318     2  0.0000     0.8108 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000     0.8108 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785316     1  0.3689     0.6119 0.808 0.000 0.008 0.068 0.004 0.112
#> SRR1785317     1  0.3689     0.6119 0.808 0.000 0.008 0.068 0.004 0.112
#> SRR1785324     2  0.0622     0.8087 0.000 0.980 0.000 0.000 0.008 0.012
#> SRR1785325     2  0.0622     0.8087 0.000 0.980 0.000 0.000 0.008 0.012
#> SRR1785320     1  0.3935     0.3933 0.688 0.000 0.016 0.000 0.004 0.292
#> SRR1785321     1  0.3935     0.3933 0.688 0.000 0.016 0.000 0.004 0.292
#> SRR1785322     1  0.4253     0.3441 0.732 0.000 0.108 0.000 0.000 0.160
#> SRR1785323     1  0.4253     0.3441 0.732 0.000 0.108 0.000 0.000 0.160

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-SD-skmeans-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-SD-skmeans-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-SD-skmeans-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-SD-skmeans-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-SD-skmeans-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-SD-skmeans-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-SD-skmeans-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-SD-skmeans-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-SD-skmeans-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-SD-skmeans-membership-heatmap-5

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)

plot of chunk tab-SD-skmeans-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-SD-skmeans-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-SD-skmeans-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-SD-skmeans-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-SD-skmeans-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-SD-skmeans-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-SD-skmeans-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-SD-skmeans-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-SD-skmeans-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-SD-skmeans-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk SD-skmeans-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-SD-skmeans-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-SD-skmeans-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-SD-skmeans-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-SD-skmeans-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-SD-skmeans-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk SD-skmeans-collect-classes

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.


SD:pam

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#>   Subgroups are detected by 'pam' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 2.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk SD-pam-collect-plots

The plots are:

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:

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)

plot of chunk SD-pam-select-partition-number

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 0.469           0.863       0.905         0.4678 0.500   0.500
#> 3 3 0.591           0.797       0.889         0.2996 0.873   0.754
#> 4 4 0.708           0.676       0.867         0.1785 0.819   0.580
#> 5 5 0.694           0.621       0.807         0.0787 0.917   0.715
#> 6 6 0.728           0.648       0.797         0.0366 0.961   0.828

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2  0.6623      0.891 0.172 0.828
#> SRR1785239     2  0.6623      0.891 0.172 0.828
#> SRR1785240     1  0.8267      0.561 0.740 0.260
#> SRR1785241     1  0.6973      0.712 0.812 0.188
#> SRR1785242     2  0.6712      0.892 0.176 0.824
#> SRR1785243     2  0.6712      0.892 0.176 0.824
#> SRR1785244     1  0.0000      0.939 1.000 0.000
#> SRR1785245     1  0.0000      0.939 1.000 0.000
#> SRR1785246     2  0.7139      0.890 0.196 0.804
#> SRR1785247     2  0.7139      0.890 0.196 0.804
#> SRR1785248     2  0.0000      0.822 0.000 1.000
#> SRR1785250     2  0.7602      0.868 0.220 0.780
#> SRR1785251     2  0.7815      0.854 0.232 0.768
#> SRR1785252     2  0.6623      0.891 0.172 0.828
#> SRR1785253     2  0.6623      0.891 0.172 0.828
#> SRR1785254     1  0.2778      0.923 0.952 0.048
#> SRR1785255     1  0.1633      0.933 0.976 0.024
#> SRR1785256     1  0.0000      0.939 1.000 0.000
#> SRR1785257     1  0.0000      0.939 1.000 0.000
#> SRR1785258     2  0.7139      0.890 0.196 0.804
#> SRR1785259     2  0.7139      0.890 0.196 0.804
#> SRR1785262     1  0.0000      0.939 1.000 0.000
#> SRR1785263     1  0.0000      0.939 1.000 0.000
#> SRR1785260     1  0.0000      0.939 1.000 0.000
#> SRR1785261     1  0.0000      0.939 1.000 0.000
#> SRR1785264     2  0.0376      0.825 0.004 0.996
#> SRR1785265     2  0.0376      0.825 0.004 0.996
#> SRR1785266     2  0.0000      0.822 0.000 1.000
#> SRR1785267     2  0.0000      0.822 0.000 1.000
#> SRR1785268     1  0.4690      0.848 0.900 0.100
#> SRR1785269     1  0.2778      0.905 0.952 0.048
#> SRR1785270     2  0.0376      0.825 0.004 0.996
#> SRR1785271     2  0.0376      0.825 0.004 0.996
#> SRR1785272     2  0.7219      0.887 0.200 0.800
#> SRR1785273     2  0.7219      0.887 0.200 0.800
#> SRR1785276     2  0.6623      0.891 0.172 0.828
#> SRR1785277     2  0.6623      0.891 0.172 0.828
#> SRR1785274     2  0.6973      0.891 0.188 0.812
#> SRR1785275     2  0.6973      0.891 0.188 0.812
#> SRR1785280     2  0.0000      0.822 0.000 1.000
#> SRR1785281     2  0.0000      0.822 0.000 1.000
#> SRR1785278     1  0.5408      0.820 0.876 0.124
#> SRR1785279     1  0.5629      0.809 0.868 0.132
#> SRR1785282     2  0.7219      0.887 0.200 0.800
#> SRR1785283     2  0.7219      0.887 0.200 0.800
#> SRR1785284     1  0.0376      0.939 0.996 0.004
#> SRR1785285     1  0.0376      0.939 0.996 0.004
#> SRR1785286     1  0.0000      0.939 1.000 0.000
#> SRR1785287     1  0.0000      0.939 1.000 0.000
#> SRR1785288     1  0.0000      0.939 1.000 0.000
#> SRR1785289     1  0.0000      0.939 1.000 0.000
#> SRR1785290     2  0.6623      0.891 0.172 0.828
#> SRR1785291     2  0.6623      0.891 0.172 0.828
#> SRR1785296     1  0.1633      0.933 0.976 0.024
#> SRR1785297     1  0.1633      0.933 0.976 0.024
#> SRR1785292     2  0.9732      0.158 0.404 0.596
#> SRR1785293     2  0.9686      0.185 0.396 0.604
#> SRR1785294     1  0.1414      0.935 0.980 0.020
#> SRR1785295     1  0.1184      0.936 0.984 0.016
#> SRR1785298     1  0.1633      0.933 0.976 0.024
#> SRR1785299     1  0.1633      0.933 0.976 0.024
#> SRR1785300     1  0.0000      0.939 1.000 0.000
#> SRR1785301     1  0.0000      0.939 1.000 0.000
#> SRR1785304     1  0.1633      0.933 0.976 0.024
#> SRR1785305     1  0.1633      0.933 0.976 0.024
#> SRR1785306     1  0.1843      0.931 0.972 0.028
#> SRR1785307     1  0.2778      0.920 0.952 0.048
#> SRR1785302     1  0.1633      0.933 0.976 0.024
#> SRR1785303     1  0.1633      0.933 0.976 0.024
#> SRR1785308     2  0.7139      0.890 0.196 0.804
#> SRR1785309     2  0.7139      0.890 0.196 0.804
#> SRR1785310     1  0.0376      0.939 0.996 0.004
#> SRR1785311     1  0.0376      0.939 0.996 0.004
#> SRR1785312     1  0.0376      0.938 0.996 0.004
#> SRR1785313     1  0.0938      0.934 0.988 0.012
#> SRR1785314     1  0.7376      0.758 0.792 0.208
#> SRR1785315     1  0.8386      0.683 0.732 0.268
#> SRR1785318     2  0.0000      0.822 0.000 1.000
#> SRR1785319     2  0.0000      0.822 0.000 1.000
#> SRR1785316     1  0.0000      0.939 1.000 0.000
#> SRR1785317     1  0.0000      0.939 1.000 0.000
#> SRR1785324     1  0.9833      0.400 0.576 0.424
#> SRR1785325     1  0.9522      0.517 0.628 0.372
#> SRR1785320     1  0.0376      0.938 0.996 0.004
#> SRR1785321     1  0.0376      0.938 0.996 0.004
#> SRR1785322     2  0.7139      0.890 0.196 0.804
#> SRR1785323     2  0.7139      0.890 0.196 0.804

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     3  0.4235      0.757 0.176 0.000 0.824
#> SRR1785239     3  0.4235      0.757 0.176 0.000 0.824
#> SRR1785240     3  0.6302     -0.281 0.480 0.000 0.520
#> SRR1785241     1  0.6252      0.470 0.556 0.000 0.444
#> SRR1785242     3  0.0592      0.832 0.012 0.000 0.988
#> SRR1785243     3  0.0592      0.832 0.012 0.000 0.988
#> SRR1785244     1  0.1529      0.885 0.960 0.000 0.040
#> SRR1785245     1  0.1529      0.885 0.960 0.000 0.040
#> SRR1785246     3  0.0000      0.829 0.000 0.000 1.000
#> SRR1785247     3  0.0000      0.829 0.000 0.000 1.000
#> SRR1785248     3  0.6124      0.684 0.036 0.220 0.744
#> SRR1785250     3  0.0747      0.827 0.016 0.000 0.984
#> SRR1785251     3  0.0892      0.825 0.020 0.000 0.980
#> SRR1785252     3  0.1411      0.829 0.036 0.000 0.964
#> SRR1785253     3  0.1411      0.829 0.036 0.000 0.964
#> SRR1785254     1  0.1031      0.875 0.976 0.000 0.024
#> SRR1785255     1  0.0000      0.883 1.000 0.000 0.000
#> SRR1785256     1  0.5178      0.783 0.744 0.000 0.256
#> SRR1785257     1  0.5178      0.783 0.744 0.000 0.256
#> SRR1785258     3  0.0000      0.829 0.000 0.000 1.000
#> SRR1785259     3  0.0000      0.829 0.000 0.000 1.000
#> SRR1785262     1  0.5178      0.783 0.744 0.000 0.256
#> SRR1785263     1  0.5178      0.783 0.744 0.000 0.256
#> SRR1785260     1  0.1411      0.885 0.964 0.000 0.036
#> SRR1785261     1  0.1411      0.885 0.964 0.000 0.036
#> SRR1785264     3  0.6348      0.706 0.060 0.188 0.752
#> SRR1785265     3  0.6388      0.708 0.064 0.184 0.752
#> SRR1785266     3  0.6274      0.314 0.000 0.456 0.544
#> SRR1785267     3  0.6154      0.428 0.000 0.408 0.592
#> SRR1785268     1  0.5926      0.656 0.644 0.000 0.356
#> SRR1785269     1  0.5591      0.732 0.696 0.000 0.304
#> SRR1785270     3  0.7866      0.334 0.060 0.388 0.552
#> SRR1785271     3  0.7491      0.490 0.056 0.324 0.620
#> SRR1785272     3  0.0892      0.831 0.020 0.000 0.980
#> SRR1785273     3  0.0892      0.831 0.020 0.000 0.980
#> SRR1785276     3  0.4235      0.757 0.176 0.000 0.824
#> SRR1785277     3  0.4235      0.757 0.176 0.000 0.824
#> SRR1785274     3  0.1411      0.829 0.036 0.000 0.964
#> SRR1785275     3  0.1411      0.829 0.036 0.000 0.964
#> SRR1785280     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785281     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785278     1  0.5560      0.707 0.700 0.000 0.300
#> SRR1785279     1  0.5621      0.694 0.692 0.000 0.308
#> SRR1785282     3  0.2537      0.806 0.080 0.000 0.920
#> SRR1785283     3  0.2537      0.806 0.080 0.000 0.920
#> SRR1785284     1  0.0000      0.883 1.000 0.000 0.000
#> SRR1785285     1  0.0000      0.883 1.000 0.000 0.000
#> SRR1785286     1  0.1411      0.885 0.964 0.000 0.036
#> SRR1785287     1  0.1411      0.885 0.964 0.000 0.036
#> SRR1785288     1  0.1411      0.885 0.964 0.000 0.036
#> SRR1785289     1  0.1411      0.885 0.964 0.000 0.036
#> SRR1785290     3  0.5178      0.697 0.256 0.000 0.744
#> SRR1785291     3  0.5178      0.697 0.256 0.000 0.744
#> SRR1785296     1  0.0000      0.883 1.000 0.000 0.000
#> SRR1785297     1  0.0000      0.883 1.000 0.000 0.000
#> SRR1785292     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785293     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785294     1  0.0000      0.883 1.000 0.000 0.000
#> SRR1785295     1  0.0000      0.883 1.000 0.000 0.000
#> SRR1785298     1  0.0000      0.883 1.000 0.000 0.000
#> SRR1785299     1  0.0000      0.883 1.000 0.000 0.000
#> SRR1785300     1  0.4235      0.827 0.824 0.000 0.176
#> SRR1785301     1  0.4235      0.827 0.824 0.000 0.176
#> SRR1785304     1  0.0000      0.883 1.000 0.000 0.000
#> SRR1785305     1  0.0000      0.883 1.000 0.000 0.000
#> SRR1785306     1  0.2537      0.849 0.920 0.000 0.080
#> SRR1785307     1  0.2796      0.843 0.908 0.000 0.092
#> SRR1785302     1  0.0000      0.883 1.000 0.000 0.000
#> SRR1785303     1  0.0000      0.883 1.000 0.000 0.000
#> SRR1785308     3  0.0592      0.832 0.012 0.000 0.988
#> SRR1785309     3  0.0592      0.832 0.012 0.000 0.988
#> SRR1785310     1  0.0000      0.883 1.000 0.000 0.000
#> SRR1785311     1  0.0000      0.883 1.000 0.000 0.000
#> SRR1785312     1  0.4504      0.821 0.804 0.000 0.196
#> SRR1785313     1  0.4452      0.826 0.808 0.000 0.192
#> SRR1785314     2  0.6400      0.718 0.208 0.740 0.052
#> SRR1785315     2  0.6318      0.744 0.172 0.760 0.068
#> SRR1785318     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785319     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785316     1  0.1411      0.885 0.964 0.000 0.036
#> SRR1785317     1  0.1529      0.885 0.960 0.000 0.040
#> SRR1785324     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785325     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785320     1  0.4702      0.813 0.788 0.000 0.212
#> SRR1785321     1  0.4750      0.811 0.784 0.000 0.216
#> SRR1785322     3  0.2165      0.816 0.064 0.000 0.936
#> SRR1785323     3  0.2066      0.818 0.060 0.000 0.940

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     3  0.0000     0.7869 0.000 0.000 1.000 0.000
#> SRR1785239     3  0.0000     0.7869 0.000 0.000 1.000 0.000
#> SRR1785240     1  0.7551     0.3161 0.448 0.000 0.196 0.356
#> SRR1785241     1  0.7276     0.2239 0.448 0.000 0.148 0.404
#> SRR1785242     3  0.0000     0.7869 0.000 0.000 1.000 0.000
#> SRR1785243     3  0.0000     0.7869 0.000 0.000 1.000 0.000
#> SRR1785244     4  0.2589     0.8019 0.116 0.000 0.000 0.884
#> SRR1785245     4  0.2589     0.8017 0.116 0.000 0.000 0.884
#> SRR1785246     1  0.4776     0.3208 0.624 0.000 0.376 0.000
#> SRR1785247     1  0.4679     0.3726 0.648 0.000 0.352 0.000
#> SRR1785248     3  0.1118     0.7789 0.000 0.036 0.964 0.000
#> SRR1785250     1  0.4134     0.4955 0.740 0.000 0.260 0.000
#> SRR1785251     1  0.3907     0.5296 0.768 0.000 0.232 0.000
#> SRR1785252     3  0.0000     0.7869 0.000 0.000 1.000 0.000
#> SRR1785253     3  0.0000     0.7869 0.000 0.000 1.000 0.000
#> SRR1785254     4  0.1004     0.8776 0.004 0.000 0.024 0.972
#> SRR1785255     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785256     4  0.5938    -0.0690 0.480 0.000 0.036 0.484
#> SRR1785257     4  0.5938    -0.0690 0.480 0.000 0.036 0.484
#> SRR1785258     1  0.4855     0.2336 0.600 0.000 0.400 0.000
#> SRR1785259     1  0.4866     0.2235 0.596 0.000 0.404 0.000
#> SRR1785262     4  0.5921     0.0223 0.448 0.000 0.036 0.516
#> SRR1785263     4  0.5921     0.0223 0.448 0.000 0.036 0.516
#> SRR1785260     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785261     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785264     3  0.1209     0.7794 0.000 0.032 0.964 0.004
#> SRR1785265     3  0.1209     0.7794 0.000 0.032 0.964 0.004
#> SRR1785266     3  0.4972     0.2968 0.000 0.456 0.544 0.000
#> SRR1785267     3  0.4877     0.4082 0.000 0.408 0.592 0.000
#> SRR1785268     1  0.0000     0.7158 1.000 0.000 0.000 0.000
#> SRR1785269     1  0.0000     0.7158 1.000 0.000 0.000 0.000
#> SRR1785270     3  0.4164     0.4665 0.000 0.264 0.736 0.000
#> SRR1785271     3  0.3726     0.5624 0.000 0.212 0.788 0.000
#> SRR1785272     3  0.4855     0.3551 0.400 0.000 0.600 0.000
#> SRR1785273     3  0.4877     0.3345 0.408 0.000 0.592 0.000
#> SRR1785276     3  0.0000     0.7869 0.000 0.000 1.000 0.000
#> SRR1785277     3  0.0000     0.7869 0.000 0.000 1.000 0.000
#> SRR1785274     3  0.4250     0.5430 0.276 0.000 0.724 0.000
#> SRR1785275     3  0.4382     0.5112 0.296 0.000 0.704 0.000
#> SRR1785280     2  0.0000     0.9305 0.000 1.000 0.000 0.000
#> SRR1785281     2  0.0000     0.9305 0.000 1.000 0.000 0.000
#> SRR1785278     1  0.0000     0.7158 1.000 0.000 0.000 0.000
#> SRR1785279     1  0.0469     0.7122 0.988 0.000 0.000 0.012
#> SRR1785282     1  0.0000     0.7158 1.000 0.000 0.000 0.000
#> SRR1785283     1  0.0000     0.7158 1.000 0.000 0.000 0.000
#> SRR1785284     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785285     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785286     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785287     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785288     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785289     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785290     3  0.1118     0.7720 0.000 0.000 0.964 0.036
#> SRR1785291     3  0.1118     0.7720 0.000 0.000 0.964 0.036
#> SRR1785296     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785297     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785292     2  0.0000     0.9305 0.000 1.000 0.000 0.000
#> SRR1785293     2  0.0000     0.9305 0.000 1.000 0.000 0.000
#> SRR1785294     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785295     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785298     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785299     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785300     1  0.5000    -0.0117 0.504 0.000 0.000 0.496
#> SRR1785301     1  0.5000    -0.0117 0.504 0.000 0.000 0.496
#> SRR1785304     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785305     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785306     4  0.2868     0.7869 0.000 0.000 0.136 0.864
#> SRR1785307     4  0.3311     0.7470 0.000 0.000 0.172 0.828
#> SRR1785302     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785303     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785308     3  0.3528     0.6716 0.192 0.000 0.808 0.000
#> SRR1785309     3  0.3486     0.6742 0.188 0.000 0.812 0.000
#> SRR1785310     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785311     4  0.0000     0.8967 0.000 0.000 0.000 1.000
#> SRR1785312     1  0.0469     0.7120 0.988 0.000 0.000 0.012
#> SRR1785313     1  0.2081     0.6638 0.916 0.000 0.000 0.084
#> SRR1785314     2  0.5851     0.6548 0.000 0.680 0.236 0.084
#> SRR1785315     2  0.5579     0.6525 0.000 0.688 0.252 0.060
#> SRR1785318     2  0.0000     0.9305 0.000 1.000 0.000 0.000
#> SRR1785319     2  0.0000     0.9305 0.000 1.000 0.000 0.000
#> SRR1785316     4  0.1557     0.8584 0.056 0.000 0.000 0.944
#> SRR1785317     4  0.1867     0.8454 0.072 0.000 0.000 0.928
#> SRR1785324     2  0.0000     0.9305 0.000 1.000 0.000 0.000
#> SRR1785325     2  0.0000     0.9305 0.000 1.000 0.000 0.000
#> SRR1785320     1  0.0000     0.7158 1.000 0.000 0.000 0.000
#> SRR1785321     1  0.0000     0.7158 1.000 0.000 0.000 0.000
#> SRR1785322     3  0.4804     0.3868 0.384 0.000 0.616 0.000
#> SRR1785323     3  0.4804     0.3868 0.384 0.000 0.616 0.000

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     3  0.0794     0.6740 0.028 0.000 0.972 0.000 0.000
#> SRR1785239     3  0.0794     0.6740 0.028 0.000 0.972 0.000 0.000
#> SRR1785240     5  0.5676     0.5056 0.108 0.000 0.104 0.076 0.712
#> SRR1785241     5  0.5689     0.5152 0.108 0.000 0.088 0.092 0.712
#> SRR1785242     3  0.3508     0.6148 0.000 0.000 0.748 0.000 0.252
#> SRR1785243     3  0.3508     0.6148 0.000 0.000 0.748 0.000 0.252
#> SRR1785244     4  0.2462     0.8099 0.112 0.000 0.000 0.880 0.008
#> SRR1785245     4  0.2462     0.8097 0.112 0.000 0.000 0.880 0.008
#> SRR1785246     1  0.6569     0.1236 0.464 0.000 0.232 0.000 0.304
#> SRR1785247     1  0.6551     0.1290 0.468 0.000 0.228 0.000 0.304
#> SRR1785248     3  0.2891     0.6474 0.000 0.000 0.824 0.000 0.176
#> SRR1785250     5  0.4836    -0.0678 0.356 0.000 0.032 0.000 0.612
#> SRR1785251     5  0.4949    -0.1294 0.396 0.000 0.032 0.000 0.572
#> SRR1785252     3  0.3707     0.6084 0.000 0.000 0.716 0.000 0.284
#> SRR1785253     3  0.3707     0.6084 0.000 0.000 0.716 0.000 0.284
#> SRR1785254     4  0.1267     0.9071 0.004 0.000 0.024 0.960 0.012
#> SRR1785255     4  0.0290     0.9312 0.000 0.000 0.000 0.992 0.008
#> SRR1785256     5  0.5268     0.5218 0.148 0.000 0.000 0.172 0.680
#> SRR1785257     5  0.5268     0.5218 0.148 0.000 0.000 0.172 0.680
#> SRR1785258     5  0.6476     0.2769 0.244 0.000 0.260 0.000 0.496
#> SRR1785259     5  0.6476     0.2759 0.244 0.000 0.260 0.000 0.496
#> SRR1785262     5  0.4948     0.5308 0.108 0.000 0.000 0.184 0.708
#> SRR1785263     5  0.4948     0.5308 0.108 0.000 0.000 0.184 0.708
#> SRR1785260     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785261     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785264     3  0.0880     0.6710 0.000 0.000 0.968 0.000 0.032
#> SRR1785265     3  0.0880     0.6710 0.000 0.000 0.968 0.000 0.032
#> SRR1785266     3  0.4305     0.1008 0.000 0.488 0.512 0.000 0.000
#> SRR1785267     3  0.4262     0.2337 0.000 0.440 0.560 0.000 0.000
#> SRR1785268     1  0.0290     0.6649 0.992 0.000 0.000 0.000 0.008
#> SRR1785269     1  0.0290     0.6649 0.992 0.000 0.000 0.000 0.008
#> SRR1785270     3  0.4873     0.4761 0.000 0.244 0.688 0.000 0.068
#> SRR1785271     3  0.4489     0.5485 0.000 0.192 0.740 0.000 0.068
#> SRR1785272     3  0.5938     0.0468 0.112 0.000 0.512 0.000 0.376
#> SRR1785273     3  0.5925    -0.0727 0.104 0.000 0.472 0.000 0.424
#> SRR1785276     3  0.2433     0.6600 0.056 0.000 0.908 0.024 0.012
#> SRR1785277     3  0.2758     0.6517 0.076 0.000 0.888 0.024 0.012
#> SRR1785274     5  0.4367     0.1981 0.004 0.000 0.416 0.000 0.580
#> SRR1785275     5  0.4299     0.2540 0.004 0.000 0.388 0.000 0.608
#> SRR1785280     2  0.0000     0.9111 0.000 1.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000     0.9111 0.000 1.000 0.000 0.000 0.000
#> SRR1785278     1  0.4015     0.3740 0.652 0.000 0.000 0.000 0.348
#> SRR1785279     1  0.4252     0.3768 0.652 0.000 0.000 0.008 0.340
#> SRR1785282     1  0.4045     0.3615 0.644 0.000 0.000 0.000 0.356
#> SRR1785283     1  0.4074     0.3479 0.636 0.000 0.000 0.000 0.364
#> SRR1785284     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785285     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785286     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785287     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785288     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785289     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785290     3  0.0880     0.6719 0.000 0.000 0.968 0.032 0.000
#> SRR1785291     3  0.0880     0.6719 0.000 0.000 0.968 0.032 0.000
#> SRR1785296     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785297     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785292     2  0.0000     0.9111 0.000 1.000 0.000 0.000 0.000
#> SRR1785293     2  0.0000     0.9111 0.000 1.000 0.000 0.000 0.000
#> SRR1785294     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785295     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785298     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785299     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785300     5  0.6534     0.3340 0.196 0.000 0.000 0.388 0.416
#> SRR1785301     5  0.6534     0.3340 0.196 0.000 0.000 0.388 0.416
#> SRR1785304     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785305     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785306     4  0.5658     0.3070 0.000 0.000 0.096 0.572 0.332
#> SRR1785307     4  0.5903     0.2543 0.000 0.000 0.120 0.548 0.332
#> SRR1785302     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785303     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785308     3  0.5499     0.4476 0.068 0.000 0.532 0.000 0.400
#> SRR1785309     3  0.5499     0.4476 0.068 0.000 0.532 0.000 0.400
#> SRR1785310     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785311     4  0.0000     0.9373 0.000 0.000 0.000 1.000 0.000
#> SRR1785312     1  0.0290     0.6649 0.992 0.000 0.000 0.000 0.008
#> SRR1785313     1  0.0290     0.6649 0.992 0.000 0.000 0.000 0.008
#> SRR1785314     2  0.5831     0.5341 0.000 0.632 0.268 0.064 0.036
#> SRR1785315     2  0.5735     0.5171 0.000 0.628 0.284 0.052 0.036
#> SRR1785318     2  0.0000     0.9111 0.000 1.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000     0.9111 0.000 1.000 0.000 0.000 0.000
#> SRR1785316     4  0.2471     0.8064 0.136 0.000 0.000 0.864 0.000
#> SRR1785317     4  0.2966     0.7864 0.136 0.000 0.000 0.848 0.016
#> SRR1785324     2  0.0000     0.9111 0.000 1.000 0.000 0.000 0.000
#> SRR1785325     2  0.0000     0.9111 0.000 1.000 0.000 0.000 0.000
#> SRR1785320     1  0.0000     0.6611 1.000 0.000 0.000 0.000 0.000
#> SRR1785321     1  0.0000     0.6611 1.000 0.000 0.000 0.000 0.000
#> SRR1785322     3  0.5523     0.1649 0.080 0.000 0.572 0.000 0.348
#> SRR1785323     3  0.5523     0.1649 0.080 0.000 0.572 0.000 0.348

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     3  0.0000      0.590 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785239     3  0.0000      0.590 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785240     6  0.2906      0.625 0.032 0.000 0.044 0.052 0.000 0.872
#> SRR1785241     6  0.2838      0.635 0.032 0.000 0.024 0.072 0.000 0.872
#> SRR1785242     3  0.5223      0.431 0.000 0.000 0.592 0.000 0.272 0.136
#> SRR1785243     3  0.5223      0.431 0.000 0.000 0.592 0.000 0.272 0.136
#> SRR1785244     4  0.3134      0.760 0.036 0.000 0.000 0.820 0.000 0.144
#> SRR1785245     4  0.3134      0.760 0.036 0.000 0.000 0.820 0.000 0.144
#> SRR1785246     1  0.5861      0.216 0.484 0.000 0.252 0.000 0.000 0.264
#> SRR1785247     1  0.5846      0.222 0.488 0.000 0.248 0.000 0.000 0.264
#> SRR1785248     3  0.3336      0.535 0.000 0.000 0.812 0.000 0.132 0.056
#> SRR1785250     6  0.6071     -0.239 0.336 0.000 0.000 0.000 0.272 0.392
#> SRR1785251     1  0.6082      0.160 0.368 0.000 0.000 0.000 0.272 0.360
#> SRR1785252     3  0.5434      0.426 0.000 0.000 0.564 0.000 0.272 0.164
#> SRR1785253     3  0.5434      0.426 0.000 0.000 0.564 0.000 0.272 0.164
#> SRR1785254     4  0.0891      0.935 0.000 0.000 0.024 0.968 0.000 0.008
#> SRR1785255     4  0.0146      0.957 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1785256     6  0.3092      0.636 0.060 0.000 0.000 0.104 0.000 0.836
#> SRR1785257     6  0.3092      0.636 0.060 0.000 0.000 0.104 0.000 0.836
#> SRR1785258     6  0.4834      0.473 0.120 0.000 0.224 0.000 0.000 0.656
#> SRR1785259     6  0.4819      0.473 0.116 0.000 0.228 0.000 0.000 0.656
#> SRR1785262     6  0.2771      0.641 0.032 0.000 0.000 0.116 0.000 0.852
#> SRR1785263     6  0.2771      0.641 0.032 0.000 0.000 0.116 0.000 0.852
#> SRR1785260     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785261     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785264     3  0.1478      0.585 0.000 0.032 0.944 0.020 0.004 0.000
#> SRR1785265     3  0.1760      0.580 0.000 0.020 0.928 0.048 0.004 0.000
#> SRR1785266     3  0.3847      0.178 0.000 0.456 0.544 0.000 0.000 0.000
#> SRR1785267     3  0.3774      0.293 0.000 0.408 0.592 0.000 0.000 0.000
#> SRR1785268     1  0.0146      0.668 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1785269     1  0.0146      0.668 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1785270     5  0.5582      0.712 0.000 0.156 0.172 0.000 0.636 0.036
#> SRR1785271     5  0.5529      0.702 0.000 0.124 0.204 0.000 0.636 0.036
#> SRR1785272     3  0.6344      0.174 0.088 0.000 0.516 0.000 0.092 0.304
#> SRR1785273     3  0.6362      0.097 0.080 0.000 0.484 0.000 0.092 0.344
#> SRR1785276     3  0.2581      0.562 0.128 0.000 0.856 0.000 0.000 0.016
#> SRR1785277     3  0.2744      0.557 0.144 0.000 0.840 0.000 0.000 0.016
#> SRR1785274     6  0.3804      0.150 0.000 0.000 0.424 0.000 0.000 0.576
#> SRR1785275     6  0.3765      0.195 0.000 0.000 0.404 0.000 0.000 0.596
#> SRR1785280     2  0.0000      1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000      1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785278     1  0.3620      0.393 0.648 0.000 0.000 0.000 0.000 0.352
#> SRR1785279     1  0.3833      0.396 0.648 0.000 0.000 0.008 0.000 0.344
#> SRR1785282     1  0.3659      0.373 0.636 0.000 0.000 0.000 0.000 0.364
#> SRR1785283     1  0.3684      0.359 0.628 0.000 0.000 0.000 0.000 0.372
#> SRR1785284     4  0.0547      0.945 0.000 0.000 0.000 0.980 0.000 0.020
#> SRR1785285     4  0.0547      0.945 0.000 0.000 0.000 0.980 0.000 0.020
#> SRR1785286     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785287     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785288     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785289     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785290     3  0.1910      0.552 0.000 0.000 0.892 0.108 0.000 0.000
#> SRR1785291     3  0.1910      0.552 0.000 0.000 0.892 0.108 0.000 0.000
#> SRR1785296     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785297     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785292     2  0.0000      1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785293     2  0.0000      1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785294     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785295     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785298     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785299     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785300     6  0.5089      0.445 0.108 0.000 0.000 0.300 0.000 0.592
#> SRR1785301     6  0.5089      0.445 0.108 0.000 0.000 0.300 0.000 0.592
#> SRR1785304     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785305     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785306     5  0.6427      0.542 0.000 0.000 0.048 0.172 0.500 0.280
#> SRR1785307     5  0.6502      0.554 0.000 0.000 0.060 0.160 0.500 0.280
#> SRR1785302     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785303     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785308     3  0.6830      0.351 0.056 0.000 0.372 0.000 0.364 0.208
#> SRR1785309     3  0.6830      0.351 0.056 0.000 0.372 0.000 0.364 0.208
#> SRR1785310     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785311     4  0.0000      0.960 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785312     1  0.0146      0.668 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1785313     1  0.0146      0.668 0.996 0.000 0.000 0.000 0.000 0.004
#> SRR1785314     5  0.4808      0.659 0.000 0.272 0.092 0.000 0.636 0.000
#> SRR1785315     5  0.4808      0.659 0.000 0.272 0.092 0.000 0.636 0.000
#> SRR1785318     2  0.0000      1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000      1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785316     4  0.3693      0.738 0.120 0.000 0.000 0.788 0.092 0.000
#> SRR1785317     4  0.3900      0.732 0.116 0.000 0.000 0.784 0.092 0.008
#> SRR1785324     2  0.0000      1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785325     2  0.0000      1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785320     1  0.0260      0.666 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1785321     1  0.0260      0.666 0.992 0.000 0.000 0.000 0.008 0.000
#> SRR1785322     3  0.4925      0.193 0.040 0.000 0.596 0.000 0.020 0.344
#> SRR1785323     3  0.4787      0.182 0.040 0.000 0.596 0.000 0.012 0.352

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-SD-pam-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-SD-pam-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-SD-pam-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-SD-pam-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-SD-pam-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-SD-pam-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-SD-pam-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-SD-pam-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-SD-pam-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-SD-pam-membership-heatmap-5

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)

plot of chunk tab-SD-pam-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-SD-pam-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-SD-pam-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-SD-pam-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-SD-pam-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-SD-pam-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-SD-pam-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-SD-pam-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-SD-pam-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-SD-pam-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk SD-pam-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-SD-pam-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-SD-pam-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-SD-pam-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-SD-pam-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-SD-pam-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk SD-pam-collect-classes

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.


SD:mclust

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 16620 rows and 87 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 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)

plot of chunk SD-mclust-collect-plots

The plots are:

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:

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)

plot of chunk SD-mclust-select-partition-number

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 0.542           0.716       0.861         0.3402 0.777   0.777
#> 3 3 0.140           0.246       0.622         0.5652 0.621   0.530
#> 4 4 0.231           0.507       0.605         0.2607 0.624   0.320
#> 5 5 0.329           0.287       0.555         0.0921 0.744   0.322
#> 6 6 0.464           0.488       0.669         0.0756 0.796   0.338

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     1  0.7815      0.675 0.768 0.232
#> SRR1785239     1  0.7883      0.673 0.764 0.236
#> SRR1785240     1  0.0000      0.818 1.000 0.000
#> SRR1785241     1  0.0000      0.818 1.000 0.000
#> SRR1785242     1  0.9775      0.476 0.588 0.412
#> SRR1785243     1  0.9775      0.476 0.588 0.412
#> SRR1785244     1  0.0000      0.818 1.000 0.000
#> SRR1785245     1  0.0000      0.818 1.000 0.000
#> SRR1785246     1  0.0000      0.818 1.000 0.000
#> SRR1785247     1  0.0000      0.818 1.000 0.000
#> SRR1785248     2  0.5946      0.790 0.144 0.856
#> SRR1785250     1  0.9775      0.476 0.588 0.412
#> SRR1785251     1  0.9775      0.476 0.588 0.412
#> SRR1785252     1  0.9815      0.467 0.580 0.420
#> SRR1785253     1  0.9815      0.467 0.580 0.420
#> SRR1785254     1  0.0000      0.818 1.000 0.000
#> SRR1785255     1  0.0000      0.818 1.000 0.000
#> SRR1785256     1  0.0000      0.818 1.000 0.000
#> SRR1785257     1  0.0000      0.818 1.000 0.000
#> SRR1785258     1  0.0000      0.818 1.000 0.000
#> SRR1785259     1  0.0000      0.818 1.000 0.000
#> SRR1785262     1  0.0000      0.818 1.000 0.000
#> SRR1785263     1  0.0000      0.818 1.000 0.000
#> SRR1785260     1  0.9963      0.418 0.536 0.464
#> SRR1785261     1  0.9963      0.418 0.536 0.464
#> SRR1785264     1  0.9795      0.470 0.584 0.416
#> SRR1785265     1  0.9815      0.465 0.580 0.420
#> SRR1785266     2  0.1414      0.887 0.020 0.980
#> SRR1785267     2  0.1414      0.887 0.020 0.980
#> SRR1785268     1  0.0000      0.818 1.000 0.000
#> SRR1785269     1  0.0000      0.818 1.000 0.000
#> SRR1785270     1  0.0000      0.818 1.000 0.000
#> SRR1785271     1  0.0000      0.818 1.000 0.000
#> SRR1785272     1  0.9710      0.486 0.600 0.400
#> SRR1785273     1  0.9710      0.486 0.600 0.400
#> SRR1785276     1  0.0000      0.818 1.000 0.000
#> SRR1785277     1  0.0000      0.818 1.000 0.000
#> SRR1785274     1  0.0000      0.818 1.000 0.000
#> SRR1785275     1  0.0000      0.818 1.000 0.000
#> SRR1785280     2  0.0376      0.887 0.004 0.996
#> SRR1785281     2  0.0376      0.887 0.004 0.996
#> SRR1785278     1  0.0000      0.818 1.000 0.000
#> SRR1785279     1  0.0000      0.818 1.000 0.000
#> SRR1785282     1  0.2778      0.795 0.952 0.048
#> SRR1785283     1  0.2778      0.795 0.952 0.048
#> SRR1785284     1  0.0000      0.818 1.000 0.000
#> SRR1785285     1  0.0000      0.818 1.000 0.000
#> SRR1785286     1  0.0000      0.818 1.000 0.000
#> SRR1785287     1  0.0000      0.818 1.000 0.000
#> SRR1785288     1  0.4022      0.783 0.920 0.080
#> SRR1785289     1  0.4022      0.783 0.920 0.080
#> SRR1785290     1  0.9833      0.463 0.576 0.424
#> SRR1785291     1  0.9850      0.460 0.572 0.428
#> SRR1785296     1  0.9983      0.400 0.524 0.476
#> SRR1785297     1  0.9983      0.400 0.524 0.476
#> SRR1785292     2  0.3274      0.866 0.060 0.940
#> SRR1785293     2  0.3274      0.866 0.060 0.940
#> SRR1785294     1  0.9881      0.452 0.564 0.436
#> SRR1785295     1  0.9866      0.456 0.568 0.432
#> SRR1785298     1  0.8207      0.668 0.744 0.256
#> SRR1785299     1  0.8144      0.672 0.748 0.252
#> SRR1785300     1  0.2423      0.804 0.960 0.040
#> SRR1785301     1  0.2043      0.807 0.968 0.032
#> SRR1785304     1  0.9815      0.467 0.580 0.420
#> SRR1785305     1  0.9815      0.467 0.580 0.420
#> SRR1785306     1  0.0000      0.818 1.000 0.000
#> SRR1785307     1  0.0000      0.818 1.000 0.000
#> SRR1785302     1  0.3879      0.785 0.924 0.076
#> SRR1785303     1  0.3879      0.785 0.924 0.076
#> SRR1785308     1  0.9710      0.486 0.600 0.400
#> SRR1785309     1  0.9710      0.486 0.600 0.400
#> SRR1785310     1  0.3733      0.786 0.928 0.072
#> SRR1785311     1  0.3733      0.786 0.928 0.072
#> SRR1785312     1  0.0000      0.818 1.000 0.000
#> SRR1785313     1  0.0000      0.818 1.000 0.000
#> SRR1785314     1  0.0000      0.818 1.000 0.000
#> SRR1785315     1  0.0000      0.818 1.000 0.000
#> SRR1785318     2  0.0672      0.889 0.008 0.992
#> SRR1785319     2  0.0672      0.889 0.008 0.992
#> SRR1785316     1  0.3879      0.785 0.924 0.076
#> SRR1785317     1  0.3733      0.787 0.928 0.072
#> SRR1785324     2  0.8713      0.574 0.292 0.708
#> SRR1785325     2  0.8713      0.574 0.292 0.708
#> SRR1785320     1  0.0000      0.818 1.000 0.000
#> SRR1785321     1  0.0000      0.818 1.000 0.000
#> SRR1785322     1  0.1414      0.809 0.980 0.020
#> SRR1785323     1  0.2603      0.797 0.956 0.044

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     1  0.9464    0.02758 0.500 0.252 0.248
#> SRR1785239     1  0.9463    0.03228 0.500 0.256 0.244
#> SRR1785240     1  0.5785    0.04399 0.668 0.000 0.332
#> SRR1785241     1  0.5733    0.06690 0.676 0.000 0.324
#> SRR1785242     3  0.9964    0.10146 0.296 0.336 0.368
#> SRR1785243     3  0.9964    0.10146 0.296 0.336 0.368
#> SRR1785244     1  0.5331    0.37066 0.792 0.024 0.184
#> SRR1785245     1  0.5331    0.37066 0.792 0.024 0.184
#> SRR1785246     1  0.4195    0.41535 0.852 0.012 0.136
#> SRR1785247     1  0.4033    0.41787 0.856 0.008 0.136
#> SRR1785248     2  0.6761    0.39322 0.252 0.700 0.048
#> SRR1785250     1  0.9050    0.00377 0.536 0.296 0.168
#> SRR1785251     1  0.9050    0.00377 0.536 0.296 0.168
#> SRR1785252     3  0.9964    0.10146 0.296 0.336 0.368
#> SRR1785253     3  0.9964    0.10146 0.296 0.336 0.368
#> SRR1785254     3  0.6305    0.50461 0.484 0.000 0.516
#> SRR1785255     3  0.6305    0.50461 0.484 0.000 0.516
#> SRR1785256     1  0.0424    0.47680 0.992 0.000 0.008
#> SRR1785257     1  0.0424    0.47680 0.992 0.000 0.008
#> SRR1785258     1  0.4121    0.33972 0.832 0.000 0.168
#> SRR1785259     1  0.4121    0.33972 0.832 0.000 0.168
#> SRR1785262     1  0.2063    0.46389 0.948 0.008 0.044
#> SRR1785263     1  0.2063    0.46389 0.948 0.008 0.044
#> SRR1785260     2  0.9556    0.08650 0.372 0.432 0.196
#> SRR1785261     2  0.9556    0.08650 0.372 0.432 0.196
#> SRR1785264     1  0.9457    0.03159 0.468 0.340 0.192
#> SRR1785265     1  0.9389    0.02894 0.468 0.352 0.180
#> SRR1785266     2  0.5094    0.46605 0.136 0.824 0.040
#> SRR1785267     2  0.5094    0.46605 0.136 0.824 0.040
#> SRR1785268     1  0.1267    0.47306 0.972 0.004 0.024
#> SRR1785269     1  0.1267    0.47306 0.972 0.004 0.024
#> SRR1785270     3  0.6307    0.50585 0.488 0.000 0.512
#> SRR1785271     3  0.6307    0.50585 0.488 0.000 0.512
#> SRR1785272     1  0.7982    0.13798 0.556 0.376 0.068
#> SRR1785273     1  0.8807    0.04446 0.504 0.376 0.120
#> SRR1785276     1  0.6451   -0.26065 0.608 0.008 0.384
#> SRR1785277     1  0.6451   -0.26065 0.608 0.008 0.384
#> SRR1785274     1  0.6154   -0.30922 0.592 0.000 0.408
#> SRR1785275     1  0.6154   -0.30922 0.592 0.000 0.408
#> SRR1785280     2  0.1919    0.49996 0.024 0.956 0.020
#> SRR1785281     2  0.1919    0.49996 0.024 0.956 0.020
#> SRR1785278     1  0.0475    0.47746 0.992 0.004 0.004
#> SRR1785279     1  0.0475    0.47746 0.992 0.004 0.004
#> SRR1785282     1  0.3295    0.47609 0.896 0.096 0.008
#> SRR1785283     1  0.3295    0.47609 0.896 0.096 0.008
#> SRR1785284     1  0.6267   -0.41292 0.548 0.000 0.452
#> SRR1785285     1  0.6267   -0.41292 0.548 0.000 0.452
#> SRR1785286     1  0.4291    0.34846 0.820 0.000 0.180
#> SRR1785287     1  0.4291    0.34846 0.820 0.000 0.180
#> SRR1785288     1  0.7909    0.36162 0.664 0.148 0.188
#> SRR1785289     1  0.7909    0.36162 0.664 0.148 0.188
#> SRR1785290     2  0.9311    0.05269 0.364 0.468 0.168
#> SRR1785291     2  0.9335    0.04781 0.376 0.456 0.168
#> SRR1785296     2  0.9305    0.02914 0.380 0.456 0.164
#> SRR1785297     2  0.9305    0.02914 0.380 0.456 0.164
#> SRR1785292     2  0.4196    0.46325 0.024 0.864 0.112
#> SRR1785293     2  0.4196    0.46325 0.024 0.864 0.112
#> SRR1785294     2  0.8581   -0.01426 0.444 0.460 0.096
#> SRR1785295     2  0.8405   -0.03659 0.456 0.460 0.084
#> SRR1785298     1  0.9346    0.08729 0.516 0.260 0.224
#> SRR1785299     1  0.9405    0.07570 0.508 0.260 0.232
#> SRR1785300     1  0.7388    0.39162 0.704 0.136 0.160
#> SRR1785301     1  0.7327    0.39216 0.708 0.132 0.160
#> SRR1785304     2  0.9909   -0.06931 0.364 0.368 0.268
#> SRR1785305     2  0.9909   -0.06931 0.364 0.368 0.268
#> SRR1785306     3  0.6302    0.51631 0.480 0.000 0.520
#> SRR1785307     3  0.6302    0.51631 0.480 0.000 0.520
#> SRR1785302     1  0.9112    0.06085 0.524 0.168 0.308
#> SRR1785303     1  0.9112    0.06085 0.524 0.168 0.308
#> SRR1785308     1  0.9371   -0.04661 0.452 0.376 0.172
#> SRR1785309     1  0.9371   -0.04661 0.452 0.376 0.172
#> SRR1785310     1  0.7680    0.36340 0.680 0.188 0.132
#> SRR1785311     1  0.7680    0.36340 0.680 0.188 0.132
#> SRR1785312     1  0.3851    0.39430 0.860 0.004 0.136
#> SRR1785313     1  0.3784    0.39467 0.864 0.004 0.132
#> SRR1785314     3  0.6302    0.51631 0.480 0.000 0.520
#> SRR1785315     3  0.6302    0.51631 0.480 0.000 0.520
#> SRR1785318     2  0.2313    0.49853 0.024 0.944 0.032
#> SRR1785319     2  0.2313    0.49853 0.024 0.944 0.032
#> SRR1785316     1  0.7393    0.39332 0.704 0.140 0.156
#> SRR1785317     1  0.7451    0.39154 0.700 0.144 0.156
#> SRR1785324     2  0.6843    0.32136 0.028 0.640 0.332
#> SRR1785325     2  0.6659    0.33502 0.028 0.668 0.304
#> SRR1785320     1  0.3784    0.39467 0.864 0.004 0.132
#> SRR1785321     1  0.3784    0.39467 0.864 0.004 0.132
#> SRR1785322     1  0.4371    0.46537 0.860 0.108 0.032
#> SRR1785323     1  0.4295    0.46714 0.864 0.104 0.032

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     4   0.549    0.51781 0.056 0.064 0.100 0.780
#> SRR1785239     4   0.542    0.51653 0.056 0.060 0.100 0.784
#> SRR1785240     3   0.570    0.66377 0.184 0.004 0.720 0.092
#> SRR1785241     3   0.573    0.66107 0.188 0.004 0.716 0.092
#> SRR1785242     4   0.619    0.40868 0.088 0.096 0.076 0.740
#> SRR1785243     4   0.619    0.40868 0.088 0.096 0.076 0.740
#> SRR1785244     1   0.712    0.57911 0.580 0.004 0.180 0.236
#> SRR1785245     1   0.711    0.57977 0.580 0.004 0.176 0.240
#> SRR1785246     1   0.746    0.40215 0.532 0.004 0.252 0.212
#> SRR1785247     1   0.746    0.40215 0.532 0.004 0.252 0.212
#> SRR1785248     2   0.648    0.62768 0.008 0.544 0.056 0.392
#> SRR1785250     4   0.622    0.29289 0.296 0.056 0.012 0.636
#> SRR1785251     4   0.622    0.29289 0.296 0.056 0.012 0.636
#> SRR1785252     4   0.619    0.40868 0.088 0.096 0.076 0.740
#> SRR1785253     4   0.619    0.40868 0.088 0.096 0.076 0.740
#> SRR1785254     3   0.627    0.59241 0.112 0.000 0.648 0.240
#> SRR1785255     3   0.627    0.59241 0.112 0.000 0.648 0.240
#> SRR1785256     1   0.331    0.65852 0.840 0.000 0.004 0.156
#> SRR1785257     1   0.331    0.65852 0.840 0.000 0.004 0.156
#> SRR1785258     1   0.705    0.42462 0.568 0.000 0.252 0.180
#> SRR1785259     1   0.708    0.41787 0.564 0.000 0.252 0.184
#> SRR1785262     1   0.724    0.46358 0.568 0.012 0.136 0.284
#> SRR1785263     1   0.724    0.46765 0.568 0.012 0.136 0.284
#> SRR1785260     1   0.853   -0.11769 0.412 0.360 0.044 0.184
#> SRR1785261     1   0.855   -0.12504 0.408 0.360 0.044 0.188
#> SRR1785264     4   0.560    0.45014 0.016 0.136 0.096 0.752
#> SRR1785265     4   0.552    0.43836 0.016 0.144 0.084 0.756
#> SRR1785266     2   0.585    0.82599 0.008 0.680 0.056 0.256
#> SRR1785267     2   0.582    0.82563 0.008 0.684 0.056 0.252
#> SRR1785268     1   0.297    0.65782 0.856 0.000 0.000 0.144
#> SRR1785269     1   0.297    0.65782 0.856 0.000 0.000 0.144
#> SRR1785270     3   0.354    0.70562 0.028 0.000 0.852 0.120
#> SRR1785271     3   0.354    0.70562 0.028 0.000 0.852 0.120
#> SRR1785272     4   0.627    0.23618 0.312 0.048 0.016 0.624
#> SRR1785273     4   0.627    0.23618 0.312 0.048 0.016 0.624
#> SRR1785276     3   0.794    0.18015 0.332 0.004 0.412 0.252
#> SRR1785277     3   0.793    0.18181 0.336 0.004 0.412 0.248
#> SRR1785274     3   0.705    0.54171 0.272 0.004 0.576 0.148
#> SRR1785275     3   0.705    0.53793 0.272 0.004 0.576 0.148
#> SRR1785280     2   0.384    0.83413 0.000 0.776 0.000 0.224
#> SRR1785281     2   0.384    0.83413 0.000 0.776 0.000 0.224
#> SRR1785278     1   0.385    0.65036 0.800 0.000 0.008 0.192
#> SRR1785279     1   0.389    0.64961 0.796 0.000 0.008 0.196
#> SRR1785282     1   0.534    0.57239 0.668 0.032 0.000 0.300
#> SRR1785283     1   0.534    0.57239 0.668 0.032 0.000 0.300
#> SRR1785284     3   0.471    0.71143 0.052 0.004 0.788 0.156
#> SRR1785285     3   0.471    0.71143 0.052 0.004 0.788 0.156
#> SRR1785286     3   0.719    0.39738 0.376 0.004 0.496 0.124
#> SRR1785287     3   0.723    0.36003 0.400 0.004 0.472 0.124
#> SRR1785288     1   0.572    0.46770 0.756 0.084 0.032 0.128
#> SRR1785289     1   0.572    0.46770 0.756 0.084 0.032 0.128
#> SRR1785290     4   0.551    0.35420 0.024 0.268 0.016 0.692
#> SRR1785291     4   0.553    0.35065 0.024 0.272 0.016 0.688
#> SRR1785296     4   0.682    0.44100 0.064 0.328 0.024 0.584
#> SRR1785297     4   0.682    0.44100 0.064 0.328 0.024 0.584
#> SRR1785292     2   0.565    0.80763 0.000 0.664 0.052 0.284
#> SRR1785293     2   0.565    0.80763 0.000 0.664 0.052 0.284
#> SRR1785294     4   0.743    0.43040 0.128 0.360 0.012 0.500
#> SRR1785295     4   0.754    0.43045 0.128 0.360 0.016 0.496
#> SRR1785298     4   0.679    0.51653 0.124 0.136 0.052 0.688
#> SRR1785299     4   0.679    0.51653 0.124 0.136 0.052 0.688
#> SRR1785300     1   0.576    0.54199 0.744 0.080 0.024 0.152
#> SRR1785301     1   0.571    0.54272 0.748 0.080 0.024 0.148
#> SRR1785304     4   0.603    0.39292 0.052 0.244 0.020 0.684
#> SRR1785305     4   0.603    0.39292 0.052 0.244 0.020 0.684
#> SRR1785306     3   0.316    0.69231 0.052 0.000 0.884 0.064
#> SRR1785307     3   0.316    0.69231 0.052 0.000 0.884 0.064
#> SRR1785302     4   0.765    0.28062 0.112 0.036 0.312 0.540
#> SRR1785303     4   0.765    0.28062 0.112 0.036 0.312 0.540
#> SRR1785308     4   0.619    0.26456 0.284 0.052 0.016 0.648
#> SRR1785309     4   0.619    0.26456 0.284 0.052 0.016 0.648
#> SRR1785310     4   0.807   -0.00747 0.388 0.120 0.044 0.448
#> SRR1785311     4   0.809    0.01938 0.376 0.124 0.044 0.456
#> SRR1785312     1   0.540    0.64532 0.756 0.008 0.092 0.144
#> SRR1785313     1   0.540    0.64532 0.756 0.008 0.092 0.144
#> SRR1785314     3   0.316    0.69231 0.052 0.000 0.884 0.064
#> SRR1785315     3   0.316    0.69231 0.052 0.000 0.884 0.064
#> SRR1785318     2   0.384    0.83495 0.000 0.776 0.000 0.224
#> SRR1785319     2   0.384    0.83495 0.000 0.776 0.000 0.224
#> SRR1785316     1   0.570    0.55111 0.744 0.096 0.016 0.144
#> SRR1785317     1   0.575    0.54836 0.740 0.096 0.016 0.148
#> SRR1785324     2   0.694    0.67926 0.000 0.552 0.136 0.312
#> SRR1785325     2   0.694    0.67926 0.000 0.552 0.136 0.312
#> SRR1785320     1   0.546    0.64499 0.752 0.008 0.096 0.144
#> SRR1785321     1   0.540    0.64532 0.756 0.008 0.092 0.144
#> SRR1785322     1   0.609    0.49692 0.596 0.020 0.024 0.360
#> SRR1785323     1   0.631    0.49478 0.596 0.032 0.024 0.348

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     4  0.8126     0.3003 0.364 0.144 0.120 0.364 0.008
#> SRR1785239     4  0.8125     0.3160 0.356 0.144 0.120 0.372 0.008
#> SRR1785240     5  0.6695     0.2389 0.264 0.000 0.000 0.308 0.428
#> SRR1785241     5  0.6710     0.2358 0.272 0.000 0.000 0.304 0.424
#> SRR1785242     1  0.6068    -0.1916 0.468 0.056 0.448 0.028 0.000
#> SRR1785243     1  0.6068    -0.1916 0.468 0.056 0.448 0.028 0.000
#> SRR1785244     1  0.6709     0.1813 0.484 0.000 0.024 0.136 0.356
#> SRR1785245     1  0.6709     0.1813 0.484 0.000 0.024 0.136 0.356
#> SRR1785246     1  0.2983     0.3263 0.864 0.000 0.096 0.000 0.040
#> SRR1785247     1  0.2813     0.3344 0.876 0.000 0.084 0.000 0.040
#> SRR1785248     2  0.6211     0.2745 0.156 0.600 0.016 0.228 0.000
#> SRR1785250     3  0.5690     0.2915 0.436 0.068 0.492 0.000 0.004
#> SRR1785251     3  0.5690     0.2915 0.436 0.068 0.492 0.000 0.004
#> SRR1785252     1  0.6068    -0.1916 0.468 0.056 0.448 0.028 0.000
#> SRR1785253     1  0.6068    -0.1916 0.468 0.056 0.448 0.028 0.000
#> SRR1785254     5  0.7171     0.1107 0.292 0.004 0.008 0.332 0.364
#> SRR1785255     5  0.7171     0.1107 0.292 0.004 0.008 0.332 0.364
#> SRR1785256     1  0.5760     0.2700 0.684 0.000 0.160 0.036 0.120
#> SRR1785257     1  0.5803     0.2687 0.680 0.000 0.160 0.036 0.124
#> SRR1785258     1  0.4124     0.3698 0.820 0.008 0.024 0.044 0.104
#> SRR1785259     1  0.3811     0.3598 0.836 0.008 0.024 0.028 0.104
#> SRR1785262     1  0.5509     0.3619 0.744 0.020 0.112 0.072 0.052
#> SRR1785263     1  0.5566     0.3622 0.740 0.020 0.112 0.076 0.052
#> SRR1785260     4  0.5673     0.1062 0.040 0.020 0.000 0.512 0.428
#> SRR1785261     4  0.5673     0.1062 0.040 0.020 0.000 0.512 0.428
#> SRR1785264     4  0.7580     0.4695 0.188 0.212 0.100 0.500 0.000
#> SRR1785265     4  0.7560     0.4698 0.192 0.204 0.100 0.504 0.000
#> SRR1785266     2  0.2483     0.8787 0.028 0.908 0.016 0.048 0.000
#> SRR1785267     2  0.2483     0.8787 0.028 0.908 0.016 0.048 0.000
#> SRR1785268     1  0.5604    -0.0730 0.468 0.000 0.460 0.000 0.072
#> SRR1785269     1  0.5604    -0.0730 0.468 0.000 0.460 0.000 0.072
#> SRR1785270     5  0.6060     0.2506 0.092 0.000 0.008 0.424 0.476
#> SRR1785271     5  0.6060     0.2506 0.092 0.000 0.008 0.424 0.476
#> SRR1785272     3  0.4268     0.3417 0.444 0.000 0.556 0.000 0.000
#> SRR1785273     3  0.4268     0.3417 0.444 0.000 0.556 0.000 0.000
#> SRR1785276     1  0.5342     0.3734 0.728 0.000 0.052 0.144 0.076
#> SRR1785277     1  0.5130     0.3780 0.748 0.000 0.052 0.124 0.076
#> SRR1785274     1  0.6622    -0.1038 0.504 0.000 0.008 0.284 0.204
#> SRR1785275     1  0.6590    -0.0970 0.508 0.000 0.008 0.288 0.196
#> SRR1785280     2  0.0510     0.9106 0.000 0.984 0.000 0.016 0.000
#> SRR1785281     2  0.0510     0.9106 0.000 0.984 0.000 0.016 0.000
#> SRR1785278     1  0.5539     0.3527 0.728 0.004 0.060 0.092 0.116
#> SRR1785279     1  0.5645     0.3534 0.720 0.004 0.064 0.092 0.120
#> SRR1785282     1  0.6942     0.3319 0.648 0.072 0.056 0.088 0.136
#> SRR1785283     1  0.6942     0.3319 0.648 0.072 0.056 0.088 0.136
#> SRR1785284     5  0.6302     0.2602 0.132 0.000 0.004 0.420 0.444
#> SRR1785285     5  0.6302     0.2602 0.132 0.000 0.004 0.420 0.444
#> SRR1785286     4  0.7042    -0.2522 0.300 0.000 0.008 0.364 0.328
#> SRR1785287     4  0.7048    -0.2490 0.308 0.000 0.008 0.360 0.324
#> SRR1785288     5  0.5844     0.1259 0.272 0.008 0.000 0.112 0.608
#> SRR1785289     5  0.5770     0.1262 0.272 0.004 0.000 0.116 0.608
#> SRR1785290     4  0.6314     0.4440 0.180 0.312 0.000 0.508 0.000
#> SRR1785291     4  0.6325     0.4384 0.180 0.316 0.000 0.504 0.000
#> SRR1785296     4  0.4933     0.4909 0.200 0.084 0.004 0.712 0.000
#> SRR1785297     4  0.4879     0.4907 0.200 0.080 0.004 0.716 0.000
#> SRR1785292     2  0.0798     0.9084 0.000 0.976 0.000 0.016 0.008
#> SRR1785293     2  0.0798     0.9084 0.000 0.976 0.000 0.016 0.008
#> SRR1785294     4  0.6112     0.4681 0.144 0.064 0.008 0.684 0.100
#> SRR1785295     4  0.6319     0.4634 0.144 0.072 0.008 0.668 0.108
#> SRR1785298     4  0.6847     0.4632 0.236 0.104 0.084 0.576 0.000
#> SRR1785299     4  0.6847     0.4632 0.236 0.104 0.084 0.576 0.000
#> SRR1785300     5  0.6173     0.0989 0.388 0.004 0.004 0.104 0.500
#> SRR1785301     5  0.6173     0.0989 0.388 0.004 0.004 0.104 0.500
#> SRR1785304     4  0.6268     0.4878 0.148 0.240 0.004 0.596 0.012
#> SRR1785305     4  0.6268     0.4878 0.148 0.240 0.004 0.596 0.012
#> SRR1785306     4  0.6427    -0.2704 0.152 0.000 0.004 0.452 0.392
#> SRR1785307     4  0.6427    -0.2704 0.152 0.000 0.004 0.452 0.392
#> SRR1785302     4  0.7131     0.4047 0.356 0.084 0.012 0.488 0.060
#> SRR1785303     4  0.7212     0.4075 0.352 0.084 0.016 0.488 0.060
#> SRR1785308     3  0.4235     0.3475 0.424 0.000 0.576 0.000 0.000
#> SRR1785309     3  0.4235     0.3475 0.424 0.000 0.576 0.000 0.000
#> SRR1785310     4  0.8444     0.3884 0.272 0.048 0.092 0.444 0.144
#> SRR1785311     4  0.8444     0.3884 0.272 0.048 0.092 0.444 0.144
#> SRR1785312     3  0.6095     0.1356 0.416 0.000 0.460 0.000 0.124
#> SRR1785313     3  0.6095     0.1356 0.416 0.000 0.460 0.000 0.124
#> SRR1785314     4  0.6424    -0.2706 0.152 0.000 0.004 0.456 0.388
#> SRR1785315     4  0.6424    -0.2706 0.152 0.000 0.004 0.456 0.388
#> SRR1785318     2  0.0162     0.9114 0.000 0.996 0.000 0.004 0.000
#> SRR1785319     2  0.0162     0.9114 0.000 0.996 0.000 0.004 0.000
#> SRR1785316     5  0.6262     0.0735 0.408 0.008 0.016 0.072 0.496
#> SRR1785317     5  0.6304     0.0783 0.404 0.008 0.016 0.076 0.496
#> SRR1785324     2  0.1697     0.8804 0.000 0.932 0.000 0.008 0.060
#> SRR1785325     2  0.1697     0.8804 0.000 0.932 0.000 0.008 0.060
#> SRR1785320     3  0.6095     0.1356 0.416 0.000 0.460 0.000 0.124
#> SRR1785321     3  0.6095     0.1356 0.416 0.000 0.460 0.000 0.124
#> SRR1785322     1  0.5908     0.1728 0.648 0.080 0.232 0.040 0.000
#> SRR1785323     1  0.5807     0.2322 0.680 0.080 0.196 0.040 0.004

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     4  0.7993      0.261 0.008 0.296 0.040 0.384 0.164 0.108
#> SRR1785239     4  0.7993      0.261 0.008 0.296 0.040 0.384 0.164 0.108
#> SRR1785240     5  0.5520      0.283 0.232 0.000 0.020 0.000 0.612 0.136
#> SRR1785241     5  0.5520      0.283 0.232 0.000 0.020 0.000 0.612 0.136
#> SRR1785242     6  0.3163      0.995 0.000 0.004 0.232 0.000 0.000 0.764
#> SRR1785243     6  0.3163      0.995 0.000 0.004 0.232 0.000 0.000 0.764
#> SRR1785244     5  0.6960      0.364 0.160 0.000 0.076 0.180 0.548 0.036
#> SRR1785245     5  0.7008      0.353 0.160 0.000 0.076 0.188 0.540 0.036
#> SRR1785246     1  0.8602      0.465 0.428 0.036 0.096 0.136 0.112 0.192
#> SRR1785247     1  0.8563      0.473 0.432 0.036 0.088 0.136 0.116 0.192
#> SRR1785248     2  0.4007      0.640 0.004 0.764 0.004 0.180 0.004 0.044
#> SRR1785250     3  0.3210      0.536 0.036 0.020 0.864 0.024 0.000 0.056
#> SRR1785251     3  0.3210      0.536 0.036 0.020 0.864 0.024 0.000 0.056
#> SRR1785252     6  0.3189      0.995 0.000 0.004 0.236 0.000 0.000 0.760
#> SRR1785253     6  0.3189      0.995 0.000 0.004 0.236 0.000 0.000 0.760
#> SRR1785254     5  0.4237      0.653 0.076 0.036 0.000 0.016 0.796 0.076
#> SRR1785255     5  0.4445      0.647 0.076 0.040 0.004 0.016 0.788 0.076
#> SRR1785256     1  0.6004      0.547 0.660 0.000 0.084 0.032 0.084 0.140
#> SRR1785257     1  0.6004      0.547 0.660 0.000 0.084 0.032 0.084 0.140
#> SRR1785258     1  0.5739      0.574 0.644 0.000 0.052 0.012 0.088 0.204
#> SRR1785259     1  0.5739      0.574 0.644 0.000 0.052 0.012 0.088 0.204
#> SRR1785262     1  0.8921      0.428 0.384 0.084 0.052 0.132 0.140 0.208
#> SRR1785263     1  0.8945      0.427 0.380 0.084 0.052 0.136 0.140 0.208
#> SRR1785260     4  0.3777      0.338 0.088 0.012 0.020 0.820 0.060 0.000
#> SRR1785261     4  0.3777      0.338 0.088 0.012 0.020 0.820 0.060 0.000
#> SRR1785264     2  0.7226     -0.174 0.000 0.412 0.040 0.340 0.164 0.044
#> SRR1785265     2  0.7226     -0.174 0.000 0.412 0.040 0.340 0.164 0.044
#> SRR1785266     2  0.2282      0.756 0.000 0.888 0.000 0.088 0.000 0.024
#> SRR1785267     2  0.2282      0.756 0.000 0.888 0.000 0.088 0.000 0.024
#> SRR1785268     1  0.3536      0.498 0.804 0.000 0.060 0.004 0.000 0.132
#> SRR1785269     1  0.3536      0.498 0.804 0.000 0.060 0.004 0.000 0.132
#> SRR1785270     5  0.1124      0.695 0.008 0.000 0.000 0.000 0.956 0.036
#> SRR1785271     5  0.1124      0.695 0.008 0.000 0.000 0.000 0.956 0.036
#> SRR1785272     3  0.1198      0.588 0.004 0.020 0.960 0.012 0.000 0.004
#> SRR1785273     3  0.1053      0.587 0.004 0.020 0.964 0.012 0.000 0.000
#> SRR1785276     1  0.5834      0.541 0.600 0.000 0.040 0.000 0.144 0.216
#> SRR1785277     1  0.5802      0.544 0.604 0.000 0.040 0.000 0.140 0.216
#> SRR1785274     1  0.6222      0.384 0.496 0.000 0.036 0.000 0.320 0.148
#> SRR1785275     1  0.6212      0.393 0.500 0.000 0.036 0.000 0.316 0.148
#> SRR1785280     2  0.0363      0.785 0.000 0.988 0.000 0.012 0.000 0.000
#> SRR1785281     2  0.0363      0.785 0.000 0.988 0.000 0.012 0.000 0.000
#> SRR1785278     1  0.7731      0.492 0.488 0.000 0.132 0.112 0.088 0.180
#> SRR1785279     1  0.7749      0.489 0.484 0.000 0.140 0.112 0.084 0.180
#> SRR1785282     3  0.8504      0.292 0.240 0.060 0.348 0.212 0.012 0.128
#> SRR1785283     3  0.8504      0.292 0.240 0.060 0.348 0.212 0.012 0.128
#> SRR1785284     5  0.1781      0.697 0.008 0.000 0.000 0.008 0.924 0.060
#> SRR1785285     5  0.1781      0.697 0.008 0.000 0.000 0.008 0.924 0.060
#> SRR1785286     5  0.2806      0.692 0.040 0.000 0.016 0.012 0.884 0.048
#> SRR1785287     5  0.2806      0.692 0.040 0.000 0.016 0.012 0.884 0.048
#> SRR1785288     4  0.7012      0.146 0.124 0.000 0.024 0.512 0.248 0.092
#> SRR1785289     4  0.7012      0.146 0.124 0.000 0.024 0.512 0.248 0.092
#> SRR1785290     4  0.6408      0.241 0.000 0.340 0.000 0.452 0.172 0.036
#> SRR1785291     4  0.6414      0.235 0.000 0.344 0.000 0.448 0.172 0.036
#> SRR1785296     4  0.6040      0.400 0.000 0.208 0.004 0.584 0.168 0.036
#> SRR1785297     4  0.6040      0.400 0.000 0.208 0.004 0.584 0.168 0.036
#> SRR1785292     2  0.1970      0.764 0.000 0.912 0.000 0.060 0.028 0.000
#> SRR1785293     2  0.1970      0.764 0.000 0.912 0.000 0.060 0.028 0.000
#> SRR1785294     4  0.5126      0.405 0.028 0.056 0.016 0.684 0.216 0.000
#> SRR1785295     4  0.5100      0.405 0.028 0.056 0.016 0.688 0.212 0.000
#> SRR1785298     4  0.7162      0.337 0.000 0.276 0.028 0.464 0.172 0.060
#> SRR1785299     4  0.7162      0.337 0.000 0.276 0.028 0.464 0.172 0.060
#> SRR1785300     4  0.6789      0.159 0.212 0.004 0.028 0.512 0.220 0.024
#> SRR1785301     4  0.6808      0.156 0.212 0.004 0.028 0.508 0.224 0.024
#> SRR1785304     4  0.6630      0.337 0.016 0.228 0.004 0.536 0.180 0.036
#> SRR1785305     4  0.6630      0.337 0.016 0.228 0.004 0.536 0.180 0.036
#> SRR1785306     5  0.1257      0.700 0.020 0.000 0.000 0.000 0.952 0.028
#> SRR1785307     5  0.1257      0.700 0.020 0.000 0.000 0.000 0.952 0.028
#> SRR1785302     5  0.7057      0.145 0.044 0.164 0.000 0.236 0.508 0.048
#> SRR1785303     5  0.7010      0.124 0.036 0.172 0.000 0.240 0.504 0.048
#> SRR1785308     3  0.0862      0.583 0.004 0.016 0.972 0.008 0.000 0.000
#> SRR1785309     3  0.0862      0.583 0.004 0.016 0.972 0.008 0.000 0.000
#> SRR1785310     5  0.7273      0.103 0.060 0.056 0.036 0.348 0.460 0.040
#> SRR1785311     5  0.7273      0.103 0.060 0.056 0.036 0.348 0.460 0.040
#> SRR1785312     1  0.2922      0.545 0.876 0.000 0.060 0.008 0.028 0.028
#> SRR1785313     1  0.2922      0.545 0.876 0.000 0.060 0.008 0.028 0.028
#> SRR1785314     5  0.1408      0.700 0.020 0.000 0.000 0.000 0.944 0.036
#> SRR1785315     5  0.1408      0.700 0.020 0.000 0.000 0.000 0.944 0.036
#> SRR1785318     2  0.0146      0.784 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1785319     2  0.0146      0.784 0.000 0.996 0.000 0.004 0.000 0.000
#> SRR1785316     4  0.7610      0.207 0.200 0.008 0.068 0.504 0.144 0.076
#> SRR1785317     4  0.7610      0.207 0.200 0.008 0.068 0.504 0.144 0.076
#> SRR1785324     2  0.2894      0.736 0.004 0.860 0.000 0.020 0.104 0.012
#> SRR1785325     2  0.2894      0.736 0.004 0.860 0.000 0.020 0.104 0.012
#> SRR1785320     1  0.2727      0.544 0.880 0.000 0.064 0.000 0.028 0.028
#> SRR1785321     1  0.2668      0.544 0.884 0.000 0.060 0.000 0.028 0.028
#> SRR1785322     3  0.7519      0.394 0.096 0.188 0.500 0.176 0.016 0.024
#> SRR1785323     3  0.7624      0.384 0.108 0.188 0.488 0.176 0.016 0.024

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-SD-mclust-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-SD-mclust-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-SD-mclust-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-SD-mclust-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-SD-mclust-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-SD-mclust-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-SD-mclust-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-SD-mclust-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-SD-mclust-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-SD-mclust-membership-heatmap-5

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)

plot of chunk tab-SD-mclust-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-SD-mclust-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-SD-mclust-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-SD-mclust-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-SD-mclust-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-SD-mclust-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-SD-mclust-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-SD-mclust-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-SD-mclust-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-SD-mclust-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk SD-mclust-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-SD-mclust-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-SD-mclust-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-SD-mclust-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-SD-mclust-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-SD-mclust-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk SD-mclust-collect-classes

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.


SD:NMF*

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#>   Subgroups are detected by 'NMF' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 2.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk SD-NMF-collect-plots

The plots are:

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:

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)

plot of chunk SD-NMF-select-partition-number

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 0.927           0.926       0.969         0.4617 0.536   0.536
#> 3 3 0.685           0.772       0.905         0.4266 0.693   0.477
#> 4 4 0.617           0.665       0.837         0.1356 0.794   0.474
#> 5 5 0.650           0.657       0.813         0.0673 0.852   0.500
#> 6 6 0.689           0.549       0.741         0.0441 0.899   0.564

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2   0.891      0.578 0.308 0.692
#> SRR1785239     2   0.876      0.600 0.296 0.704
#> SRR1785240     1   0.000      0.975 1.000 0.000
#> SRR1785241     1   0.000      0.975 1.000 0.000
#> SRR1785242     1   0.917      0.497 0.668 0.332
#> SRR1785243     1   0.900      0.533 0.684 0.316
#> SRR1785244     1   0.000      0.975 1.000 0.000
#> SRR1785245     1   0.000      0.975 1.000 0.000
#> SRR1785246     1   0.000      0.975 1.000 0.000
#> SRR1785247     1   0.000      0.975 1.000 0.000
#> SRR1785248     2   0.000      0.951 0.000 1.000
#> SRR1785250     1   0.000      0.975 1.000 0.000
#> SRR1785251     1   0.000      0.975 1.000 0.000
#> SRR1785252     1   0.388      0.901 0.924 0.076
#> SRR1785253     1   0.327      0.918 0.940 0.060
#> SRR1785254     2   0.000      0.951 0.000 1.000
#> SRR1785255     2   0.000      0.951 0.000 1.000
#> SRR1785256     1   0.000      0.975 1.000 0.000
#> SRR1785257     1   0.000      0.975 1.000 0.000
#> SRR1785258     1   0.000      0.975 1.000 0.000
#> SRR1785259     1   0.000      0.975 1.000 0.000
#> SRR1785262     1   0.000      0.975 1.000 0.000
#> SRR1785263     1   0.000      0.975 1.000 0.000
#> SRR1785260     1   0.000      0.975 1.000 0.000
#> SRR1785261     1   0.000      0.975 1.000 0.000
#> SRR1785264     2   0.000      0.951 0.000 1.000
#> SRR1785265     2   0.000      0.951 0.000 1.000
#> SRR1785266     2   0.000      0.951 0.000 1.000
#> SRR1785267     2   0.000      0.951 0.000 1.000
#> SRR1785268     1   0.000      0.975 1.000 0.000
#> SRR1785269     1   0.000      0.975 1.000 0.000
#> SRR1785270     2   0.000      0.951 0.000 1.000
#> SRR1785271     2   0.000      0.951 0.000 1.000
#> SRR1785272     1   0.000      0.975 1.000 0.000
#> SRR1785273     1   0.000      0.975 1.000 0.000
#> SRR1785276     1   0.000      0.975 1.000 0.000
#> SRR1785277     1   0.000      0.975 1.000 0.000
#> SRR1785274     1   0.000      0.975 1.000 0.000
#> SRR1785275     1   0.000      0.975 1.000 0.000
#> SRR1785280     2   0.000      0.951 0.000 1.000
#> SRR1785281     2   0.000      0.951 0.000 1.000
#> SRR1785278     1   0.000      0.975 1.000 0.000
#> SRR1785279     1   0.000      0.975 1.000 0.000
#> SRR1785282     1   0.000      0.975 1.000 0.000
#> SRR1785283     1   0.000      0.975 1.000 0.000
#> SRR1785284     1   0.000      0.975 1.000 0.000
#> SRR1785285     1   0.000      0.975 1.000 0.000
#> SRR1785286     1   0.000      0.975 1.000 0.000
#> SRR1785287     1   0.000      0.975 1.000 0.000
#> SRR1785288     1   0.000      0.975 1.000 0.000
#> SRR1785289     1   0.000      0.975 1.000 0.000
#> SRR1785290     2   0.000      0.951 0.000 1.000
#> SRR1785291     2   0.000      0.951 0.000 1.000
#> SRR1785296     1   0.814      0.652 0.748 0.252
#> SRR1785297     1   0.814      0.652 0.748 0.252
#> SRR1785292     2   0.000      0.951 0.000 1.000
#> SRR1785293     2   0.000      0.951 0.000 1.000
#> SRR1785294     1   0.000      0.975 1.000 0.000
#> SRR1785295     1   0.000      0.975 1.000 0.000
#> SRR1785298     2   0.958      0.421 0.380 0.620
#> SRR1785299     2   0.943      0.470 0.360 0.640
#> SRR1785300     1   0.000      0.975 1.000 0.000
#> SRR1785301     1   0.000      0.975 1.000 0.000
#> SRR1785304     2   0.000      0.951 0.000 1.000
#> SRR1785305     2   0.000      0.951 0.000 1.000
#> SRR1785306     2   0.000      0.951 0.000 1.000
#> SRR1785307     2   0.000      0.951 0.000 1.000
#> SRR1785302     2   0.224      0.923 0.036 0.964
#> SRR1785303     2   0.224      0.923 0.036 0.964
#> SRR1785308     1   0.000      0.975 1.000 0.000
#> SRR1785309     1   0.000      0.975 1.000 0.000
#> SRR1785310     1   0.000      0.975 1.000 0.000
#> SRR1785311     1   0.000      0.975 1.000 0.000
#> SRR1785312     1   0.000      0.975 1.000 0.000
#> SRR1785313     1   0.000      0.975 1.000 0.000
#> SRR1785314     2   0.000      0.951 0.000 1.000
#> SRR1785315     2   0.000      0.951 0.000 1.000
#> SRR1785318     2   0.000      0.951 0.000 1.000
#> SRR1785319     2   0.000      0.951 0.000 1.000
#> SRR1785316     1   0.000      0.975 1.000 0.000
#> SRR1785317     1   0.000      0.975 1.000 0.000
#> SRR1785324     2   0.000      0.951 0.000 1.000
#> SRR1785325     2   0.000      0.951 0.000 1.000
#> SRR1785320     1   0.000      0.975 1.000 0.000
#> SRR1785321     1   0.000      0.975 1.000 0.000
#> SRR1785322     1   0.000      0.975 1.000 0.000
#> SRR1785323     1   0.000      0.975 1.000 0.000

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     3  0.5905     0.3922 0.000 0.352 0.648
#> SRR1785239     3  0.6062     0.3141 0.000 0.384 0.616
#> SRR1785240     3  0.6126     0.4219 0.400 0.000 0.600
#> SRR1785241     3  0.6140     0.4121 0.404 0.000 0.596
#> SRR1785242     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785243     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785244     1  0.0000     0.9080 1.000 0.000 0.000
#> SRR1785245     1  0.0000     0.9080 1.000 0.000 0.000
#> SRR1785246     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785247     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785248     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785250     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785251     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785252     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785253     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785254     2  0.5327     0.6194 0.272 0.728 0.000
#> SRR1785255     2  0.5254     0.6321 0.264 0.736 0.000
#> SRR1785256     1  0.0747     0.8983 0.984 0.000 0.016
#> SRR1785257     1  0.0892     0.8951 0.980 0.000 0.020
#> SRR1785258     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785259     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785262     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785263     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785260     1  0.0000     0.9080 1.000 0.000 0.000
#> SRR1785261     1  0.0000     0.9080 1.000 0.000 0.000
#> SRR1785264     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785265     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785266     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785267     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785268     3  0.5882     0.5286 0.348 0.000 0.652
#> SRR1785269     3  0.5810     0.5486 0.336 0.000 0.664
#> SRR1785270     2  0.4575     0.7407 0.184 0.812 0.004
#> SRR1785271     2  0.4233     0.7663 0.160 0.836 0.004
#> SRR1785272     3  0.2537     0.8260 0.080 0.000 0.920
#> SRR1785273     3  0.2711     0.8226 0.088 0.000 0.912
#> SRR1785276     3  0.0747     0.8539 0.016 0.000 0.984
#> SRR1785277     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785274     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785275     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785280     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785281     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785278     1  0.0237     0.9065 0.996 0.000 0.004
#> SRR1785279     1  0.0237     0.9065 0.996 0.000 0.004
#> SRR1785282     1  0.0237     0.9058 0.996 0.000 0.004
#> SRR1785283     1  0.0237     0.9058 0.996 0.000 0.004
#> SRR1785284     1  0.0424     0.9043 0.992 0.000 0.008
#> SRR1785285     1  0.0237     0.9065 0.996 0.000 0.004
#> SRR1785286     1  0.0000     0.9080 1.000 0.000 0.000
#> SRR1785287     1  0.0000     0.9080 1.000 0.000 0.000
#> SRR1785288     1  0.0000     0.9080 1.000 0.000 0.000
#> SRR1785289     1  0.0000     0.9080 1.000 0.000 0.000
#> SRR1785290     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785291     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785296     1  0.5591     0.5293 0.696 0.304 0.000
#> SRR1785297     1  0.5560     0.5374 0.700 0.300 0.000
#> SRR1785292     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785293     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785294     1  0.0000     0.9080 1.000 0.000 0.000
#> SRR1785295     1  0.0000     0.9080 1.000 0.000 0.000
#> SRR1785298     1  0.6307    -0.0182 0.512 0.488 0.000
#> SRR1785299     2  0.6215     0.2677 0.428 0.572 0.000
#> SRR1785300     1  0.0000     0.9080 1.000 0.000 0.000
#> SRR1785301     1  0.0000     0.9080 1.000 0.000 0.000
#> SRR1785304     1  0.3340     0.8043 0.880 0.120 0.000
#> SRR1785305     1  0.3941     0.7677 0.844 0.156 0.000
#> SRR1785306     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785307     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785302     1  0.6295     0.0583 0.528 0.472 0.000
#> SRR1785303     1  0.6299     0.0431 0.524 0.476 0.000
#> SRR1785308     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785309     3  0.0000     0.8589 0.000 0.000 1.000
#> SRR1785310     1  0.0000     0.9080 1.000 0.000 0.000
#> SRR1785311     1  0.0000     0.9080 1.000 0.000 0.000
#> SRR1785312     3  0.4750     0.7084 0.216 0.000 0.784
#> SRR1785313     3  0.4002     0.7633 0.160 0.000 0.840
#> SRR1785314     2  0.6180     0.2970 0.416 0.584 0.000
#> SRR1785315     2  0.5988     0.4266 0.368 0.632 0.000
#> SRR1785318     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785319     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785316     1  0.0000     0.9080 1.000 0.000 0.000
#> SRR1785317     1  0.0000     0.9080 1.000 0.000 0.000
#> SRR1785324     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785325     2  0.0000     0.8931 0.000 1.000 0.000
#> SRR1785320     3  0.6045     0.4667 0.380 0.000 0.620
#> SRR1785321     3  0.6045     0.4667 0.380 0.000 0.620
#> SRR1785322     3  0.3752     0.7809 0.144 0.000 0.856
#> SRR1785323     3  0.3267     0.8039 0.116 0.000 0.884

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     3  0.4277      0.516 0.000 0.280 0.720 0.000
#> SRR1785239     3  0.4585      0.423 0.000 0.332 0.668 0.000
#> SRR1785240     1  0.0188      0.764 0.996 0.000 0.000 0.004
#> SRR1785241     1  0.0188      0.764 0.996 0.000 0.000 0.004
#> SRR1785242     3  0.3528      0.686 0.192 0.000 0.808 0.000
#> SRR1785243     3  0.3569      0.683 0.196 0.000 0.804 0.000
#> SRR1785244     4  0.5028      0.234 0.400 0.000 0.004 0.596
#> SRR1785245     4  0.5028      0.234 0.400 0.000 0.004 0.596
#> SRR1785246     1  0.4250      0.410 0.724 0.000 0.276 0.000
#> SRR1785247     1  0.4193      0.427 0.732 0.000 0.268 0.000
#> SRR1785248     2  0.0000      0.944 0.000 1.000 0.000 0.000
#> SRR1785250     3  0.0469      0.747 0.012 0.000 0.988 0.000
#> SRR1785251     3  0.0469      0.747 0.012 0.000 0.988 0.000
#> SRR1785252     3  0.2868      0.717 0.136 0.000 0.864 0.000
#> SRR1785253     3  0.2868      0.717 0.136 0.000 0.864 0.000
#> SRR1785254     1  0.6785      0.265 0.484 0.420 0.000 0.096
#> SRR1785255     1  0.6822      0.283 0.488 0.412 0.000 0.100
#> SRR1785256     4  0.5994      0.646 0.156 0.000 0.152 0.692
#> SRR1785257     4  0.5993      0.649 0.148 0.000 0.160 0.692
#> SRR1785258     3  0.5000      0.154 0.496 0.000 0.504 0.000
#> SRR1785259     3  0.4998      0.169 0.488 0.000 0.512 0.000
#> SRR1785262     3  0.5137      0.333 0.452 0.000 0.544 0.004
#> SRR1785263     3  0.5132      0.342 0.448 0.000 0.548 0.004
#> SRR1785260     4  0.0188      0.789 0.004 0.000 0.000 0.996
#> SRR1785261     4  0.0188      0.789 0.004 0.000 0.000 0.996
#> SRR1785264     2  0.0000      0.944 0.000 1.000 0.000 0.000
#> SRR1785265     2  0.0000      0.944 0.000 1.000 0.000 0.000
#> SRR1785266     2  0.0000      0.944 0.000 1.000 0.000 0.000
#> SRR1785267     2  0.0000      0.944 0.000 1.000 0.000 0.000
#> SRR1785268     3  0.5913      0.263 0.352 0.000 0.600 0.048
#> SRR1785269     3  0.5954      0.282 0.344 0.000 0.604 0.052
#> SRR1785270     1  0.0657      0.766 0.984 0.004 0.000 0.012
#> SRR1785271     1  0.0657      0.766 0.984 0.004 0.000 0.012
#> SRR1785272     3  0.0592      0.744 0.000 0.000 0.984 0.016
#> SRR1785273     3  0.0592      0.744 0.000 0.000 0.984 0.016
#> SRR1785276     1  0.1792      0.747 0.932 0.000 0.068 0.000
#> SRR1785277     1  0.1716      0.747 0.936 0.000 0.064 0.000
#> SRR1785274     1  0.0592      0.759 0.984 0.000 0.016 0.000
#> SRR1785275     1  0.0592      0.759 0.984 0.000 0.016 0.000
#> SRR1785280     2  0.0000      0.944 0.000 1.000 0.000 0.000
#> SRR1785281     2  0.0000      0.944 0.000 1.000 0.000 0.000
#> SRR1785278     4  0.7371      0.407 0.268 0.000 0.212 0.520
#> SRR1785279     4  0.7426      0.399 0.264 0.000 0.224 0.512
#> SRR1785282     4  0.4933      0.609 0.016 0.000 0.296 0.688
#> SRR1785283     4  0.4933      0.609 0.016 0.000 0.296 0.688
#> SRR1785284     1  0.2868      0.744 0.864 0.000 0.000 0.136
#> SRR1785285     1  0.2868      0.744 0.864 0.000 0.000 0.136
#> SRR1785286     1  0.4746      0.514 0.632 0.000 0.000 0.368
#> SRR1785287     1  0.4761      0.507 0.628 0.000 0.000 0.372
#> SRR1785288     4  0.0000      0.791 0.000 0.000 0.000 1.000
#> SRR1785289     4  0.0000      0.791 0.000 0.000 0.000 1.000
#> SRR1785290     2  0.0000      0.944 0.000 1.000 0.000 0.000
#> SRR1785291     2  0.0000      0.944 0.000 1.000 0.000 0.000
#> SRR1785296     4  0.4621      0.555 0.000 0.284 0.008 0.708
#> SRR1785297     4  0.4673      0.542 0.000 0.292 0.008 0.700
#> SRR1785292     2  0.0000      0.944 0.000 1.000 0.000 0.000
#> SRR1785293     2  0.0000      0.944 0.000 1.000 0.000 0.000
#> SRR1785294     4  0.1557      0.786 0.000 0.000 0.056 0.944
#> SRR1785295     4  0.1557      0.786 0.000 0.000 0.056 0.944
#> SRR1785298     4  0.4843      0.342 0.000 0.396 0.000 0.604
#> SRR1785299     4  0.4948      0.216 0.000 0.440 0.000 0.560
#> SRR1785300     4  0.0817      0.793 0.000 0.000 0.024 0.976
#> SRR1785301     4  0.0707      0.793 0.000 0.000 0.020 0.980
#> SRR1785304     4  0.1004      0.787 0.004 0.024 0.000 0.972
#> SRR1785305     4  0.1209      0.784 0.004 0.032 0.000 0.964
#> SRR1785306     1  0.3638      0.726 0.848 0.120 0.000 0.032
#> SRR1785307     1  0.3749      0.720 0.840 0.128 0.000 0.032
#> SRR1785302     2  0.4761      0.353 0.000 0.628 0.000 0.372
#> SRR1785303     2  0.4830      0.300 0.000 0.608 0.000 0.392
#> SRR1785308     3  0.0188      0.746 0.000 0.000 0.996 0.004
#> SRR1785309     3  0.0188      0.746 0.000 0.000 0.996 0.004
#> SRR1785310     4  0.0376      0.791 0.004 0.000 0.004 0.992
#> SRR1785311     4  0.0376      0.791 0.004 0.000 0.004 0.992
#> SRR1785312     1  0.2799      0.734 0.884 0.000 0.108 0.008
#> SRR1785313     1  0.2737      0.736 0.888 0.000 0.104 0.008
#> SRR1785314     1  0.5000      0.708 0.772 0.100 0.000 0.128
#> SRR1785315     1  0.4956      0.710 0.776 0.108 0.000 0.116
#> SRR1785318     2  0.0000      0.944 0.000 1.000 0.000 0.000
#> SRR1785319     2  0.0000      0.944 0.000 1.000 0.000 0.000
#> SRR1785316     4  0.2546      0.773 0.008 0.000 0.092 0.900
#> SRR1785317     4  0.2546      0.773 0.008 0.000 0.092 0.900
#> SRR1785324     2  0.0000      0.944 0.000 1.000 0.000 0.000
#> SRR1785325     2  0.0000      0.944 0.000 1.000 0.000 0.000
#> SRR1785320     1  0.5599      0.515 0.664 0.000 0.288 0.048
#> SRR1785321     1  0.5365      0.557 0.692 0.000 0.264 0.044
#> SRR1785322     3  0.1209      0.738 0.004 0.000 0.964 0.032
#> SRR1785323     3  0.1209      0.738 0.004 0.000 0.964 0.032

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     2  0.5182     0.2215 0.044 0.544 0.412 0.000 0.000
#> SRR1785239     2  0.5118     0.2265 0.040 0.548 0.412 0.000 0.000
#> SRR1785240     5  0.1281     0.7589 0.000 0.000 0.032 0.012 0.956
#> SRR1785241     5  0.1281     0.7589 0.000 0.000 0.032 0.012 0.956
#> SRR1785242     3  0.2411     0.6759 0.008 0.000 0.884 0.000 0.108
#> SRR1785243     3  0.2411     0.6759 0.008 0.000 0.884 0.000 0.108
#> SRR1785244     1  0.3912     0.7368 0.804 0.000 0.000 0.108 0.088
#> SRR1785245     1  0.3912     0.7373 0.804 0.000 0.000 0.108 0.088
#> SRR1785246     5  0.4734     0.3548 0.036 0.000 0.312 0.000 0.652
#> SRR1785247     5  0.4748     0.3794 0.040 0.000 0.300 0.000 0.660
#> SRR1785248     2  0.0404     0.8732 0.000 0.988 0.012 0.000 0.000
#> SRR1785250     3  0.2017     0.7076 0.080 0.000 0.912 0.000 0.008
#> SRR1785251     3  0.2017     0.7076 0.080 0.000 0.912 0.000 0.008
#> SRR1785252     3  0.2189     0.6957 0.012 0.000 0.904 0.000 0.084
#> SRR1785253     3  0.2189     0.6957 0.012 0.000 0.904 0.000 0.084
#> SRR1785254     5  0.8161     0.3117 0.140 0.284 0.004 0.160 0.412
#> SRR1785255     5  0.8229     0.2828 0.140 0.296 0.004 0.168 0.392
#> SRR1785256     1  0.7098     0.4717 0.512 0.000 0.272 0.168 0.048
#> SRR1785257     1  0.6961     0.4468 0.508 0.000 0.300 0.152 0.040
#> SRR1785258     3  0.6394     0.1815 0.344 0.000 0.476 0.000 0.180
#> SRR1785259     3  0.6463     0.2508 0.300 0.000 0.488 0.000 0.212
#> SRR1785262     3  0.6869     0.2032 0.004 0.000 0.412 0.308 0.276
#> SRR1785263     3  0.6891     0.1818 0.004 0.000 0.400 0.316 0.280
#> SRR1785260     4  0.0451     0.8604 0.008 0.000 0.004 0.988 0.000
#> SRR1785261     4  0.0451     0.8604 0.008 0.000 0.004 0.988 0.000
#> SRR1785264     2  0.1282     0.8564 0.004 0.952 0.044 0.000 0.000
#> SRR1785265     2  0.1282     0.8564 0.004 0.952 0.044 0.000 0.000
#> SRR1785266     2  0.0162     0.8745 0.000 0.996 0.004 0.000 0.000
#> SRR1785267     2  0.0162     0.8745 0.000 0.996 0.004 0.000 0.000
#> SRR1785268     1  0.1750     0.7257 0.936 0.000 0.036 0.000 0.028
#> SRR1785269     1  0.2074     0.7266 0.920 0.000 0.044 0.000 0.036
#> SRR1785270     5  0.1502     0.7645 0.056 0.004 0.000 0.000 0.940
#> SRR1785271     5  0.1408     0.7678 0.044 0.008 0.000 0.000 0.948
#> SRR1785272     3  0.3932     0.5813 0.328 0.000 0.672 0.000 0.000
#> SRR1785273     3  0.4066     0.5827 0.324 0.000 0.672 0.004 0.000
#> SRR1785276     5  0.3797     0.6238 0.232 0.004 0.008 0.000 0.756
#> SRR1785277     5  0.3611     0.6521 0.208 0.004 0.008 0.000 0.780
#> SRR1785274     5  0.0510     0.7616 0.000 0.000 0.016 0.000 0.984
#> SRR1785275     5  0.0566     0.7637 0.004 0.000 0.012 0.000 0.984
#> SRR1785280     2  0.0162     0.8745 0.000 0.996 0.004 0.000 0.000
#> SRR1785281     2  0.0162     0.8745 0.000 0.996 0.004 0.000 0.000
#> SRR1785278     1  0.1195     0.7429 0.960 0.000 0.000 0.012 0.028
#> SRR1785279     1  0.1300     0.7440 0.956 0.000 0.000 0.016 0.028
#> SRR1785282     1  0.3090     0.6880 0.860 0.000 0.088 0.052 0.000
#> SRR1785283     1  0.3159     0.6885 0.856 0.000 0.088 0.056 0.000
#> SRR1785284     5  0.3631     0.7402 0.072 0.000 0.000 0.104 0.824
#> SRR1785285     5  0.3682     0.7388 0.072 0.000 0.000 0.108 0.820
#> SRR1785286     4  0.3352     0.6921 0.004 0.000 0.004 0.800 0.192
#> SRR1785287     4  0.3317     0.6975 0.004 0.000 0.004 0.804 0.188
#> SRR1785288     1  0.4268     0.4979 0.648 0.000 0.000 0.344 0.008
#> SRR1785289     1  0.4283     0.4915 0.644 0.000 0.000 0.348 0.008
#> SRR1785290     2  0.0451     0.8737 0.000 0.988 0.008 0.004 0.000
#> SRR1785291     2  0.0451     0.8737 0.000 0.988 0.008 0.004 0.000
#> SRR1785296     4  0.3127     0.7750 0.004 0.128 0.020 0.848 0.000
#> SRR1785297     4  0.3394     0.7509 0.004 0.152 0.020 0.824 0.000
#> SRR1785292     2  0.0162     0.8742 0.000 0.996 0.004 0.000 0.000
#> SRR1785293     2  0.0162     0.8742 0.000 0.996 0.004 0.000 0.000
#> SRR1785294     4  0.1205     0.8487 0.040 0.000 0.004 0.956 0.000
#> SRR1785295     4  0.1282     0.8464 0.044 0.000 0.004 0.952 0.000
#> SRR1785298     2  0.4310     0.7070 0.032 0.772 0.012 0.180 0.004
#> SRR1785299     2  0.4199     0.7205 0.032 0.784 0.012 0.168 0.004
#> SRR1785300     4  0.3861     0.4840 0.284 0.000 0.004 0.712 0.000
#> SRR1785301     4  0.4009     0.4179 0.312 0.000 0.004 0.684 0.000
#> SRR1785304     4  0.0510     0.8599 0.000 0.016 0.000 0.984 0.000
#> SRR1785305     4  0.0510     0.8599 0.000 0.016 0.000 0.984 0.000
#> SRR1785306     5  0.3236     0.7513 0.008 0.028 0.004 0.100 0.860
#> SRR1785307     5  0.3210     0.7524 0.008 0.032 0.004 0.092 0.864
#> SRR1785302     2  0.6297     0.3745 0.276 0.556 0.008 0.160 0.000
#> SRR1785303     2  0.6361     0.3849 0.252 0.556 0.008 0.184 0.000
#> SRR1785308     3  0.2179     0.6972 0.100 0.000 0.896 0.004 0.000
#> SRR1785309     3  0.2179     0.6975 0.100 0.000 0.896 0.004 0.000
#> SRR1785310     4  0.0451     0.8590 0.008 0.000 0.000 0.988 0.004
#> SRR1785311     4  0.0290     0.8594 0.008 0.000 0.000 0.992 0.000
#> SRR1785312     1  0.4653     0.0830 0.516 0.000 0.012 0.000 0.472
#> SRR1785313     1  0.4561     0.0285 0.504 0.000 0.008 0.000 0.488
#> SRR1785314     5  0.5325     0.6964 0.056 0.136 0.000 0.076 0.732
#> SRR1785315     5  0.5301     0.6957 0.052 0.140 0.000 0.076 0.732
#> SRR1785318     2  0.0162     0.8741 0.000 0.996 0.000 0.000 0.004
#> SRR1785319     2  0.0162     0.8741 0.000 0.996 0.000 0.000 0.004
#> SRR1785316     1  0.2179     0.7396 0.896 0.000 0.004 0.100 0.000
#> SRR1785317     1  0.2179     0.7396 0.896 0.000 0.004 0.100 0.000
#> SRR1785324     2  0.0162     0.8741 0.000 0.996 0.000 0.000 0.004
#> SRR1785325     2  0.0162     0.8741 0.000 0.996 0.000 0.000 0.004
#> SRR1785320     1  0.3527     0.6982 0.804 0.000 0.024 0.000 0.172
#> SRR1785321     1  0.3527     0.6990 0.804 0.000 0.024 0.000 0.172
#> SRR1785322     3  0.4457     0.5157 0.368 0.000 0.620 0.012 0.000
#> SRR1785323     3  0.4392     0.5036 0.380 0.000 0.612 0.008 0.000

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     2  0.4366    0.62672 0.068 0.720 0.204 0.000 0.000 0.008
#> SRR1785239     2  0.4176    0.63662 0.064 0.732 0.200 0.000 0.000 0.004
#> SRR1785240     5  0.1036    0.78813 0.008 0.000 0.024 0.004 0.964 0.000
#> SRR1785241     5  0.1036    0.78813 0.008 0.000 0.024 0.004 0.964 0.000
#> SRR1785242     3  0.2728    0.66147 0.000 0.000 0.860 0.000 0.100 0.040
#> SRR1785243     3  0.3017    0.65805 0.000 0.000 0.840 0.000 0.108 0.052
#> SRR1785244     6  0.4847    0.40454 0.284 0.000 0.000 0.016 0.056 0.644
#> SRR1785245     6  0.4847    0.40454 0.284 0.000 0.000 0.016 0.056 0.644
#> SRR1785246     1  0.6544    0.01329 0.400 0.000 0.208 0.004 0.364 0.024
#> SRR1785247     1  0.6546    0.00279 0.396 0.000 0.208 0.004 0.368 0.024
#> SRR1785248     2  0.2436    0.79172 0.000 0.880 0.088 0.000 0.000 0.032
#> SRR1785250     3  0.4985    0.41787 0.284 0.000 0.648 0.032 0.016 0.020
#> SRR1785251     3  0.4966    0.42548 0.280 0.000 0.652 0.032 0.016 0.020
#> SRR1785252     3  0.3021    0.66334 0.044 0.000 0.860 0.000 0.076 0.020
#> SRR1785253     3  0.3021    0.66334 0.044 0.000 0.860 0.000 0.076 0.020
#> SRR1785254     6  0.6347   -0.00924 0.008 0.132 0.016 0.008 0.384 0.452
#> SRR1785255     6  0.6253   -0.01834 0.004 0.132 0.016 0.008 0.388 0.452
#> SRR1785256     6  0.7479    0.32194 0.248 0.000 0.232 0.048 0.048 0.424
#> SRR1785257     6  0.7468    0.31467 0.248 0.000 0.244 0.044 0.048 0.416
#> SRR1785258     3  0.6052    0.44963 0.052 0.000 0.552 0.000 0.112 0.284
#> SRR1785259     3  0.6006    0.46569 0.052 0.000 0.564 0.000 0.112 0.272
#> SRR1785262     3  0.6733    0.28986 0.016 0.000 0.408 0.244 0.316 0.016
#> SRR1785263     3  0.6688    0.29891 0.016 0.000 0.420 0.228 0.320 0.016
#> SRR1785260     4  0.0405    0.81197 0.000 0.000 0.000 0.988 0.004 0.008
#> SRR1785261     4  0.0260    0.81147 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1785264     2  0.5743    0.43884 0.008 0.556 0.224 0.000 0.000 0.212
#> SRR1785265     2  0.5741    0.43746 0.008 0.556 0.228 0.000 0.000 0.208
#> SRR1785266     2  0.0146    0.83002 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1785267     2  0.0146    0.83002 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1785268     1  0.2068    0.56663 0.904 0.000 0.008 0.000 0.008 0.080
#> SRR1785269     1  0.2169    0.56651 0.900 0.000 0.012 0.000 0.008 0.080
#> SRR1785270     5  0.2950    0.73770 0.148 0.000 0.000 0.000 0.828 0.024
#> SRR1785271     5  0.2945    0.73162 0.156 0.000 0.000 0.000 0.824 0.020
#> SRR1785272     1  0.5585    0.33941 0.556 0.000 0.332 0.028 0.000 0.084
#> SRR1785273     1  0.5637    0.33436 0.552 0.000 0.336 0.036 0.000 0.076
#> SRR1785276     1  0.3754    0.53041 0.788 0.004 0.028 0.000 0.164 0.016
#> SRR1785277     1  0.4210    0.51579 0.760 0.004 0.048 0.000 0.168 0.020
#> SRR1785274     5  0.2122    0.78336 0.028 0.000 0.024 0.000 0.916 0.032
#> SRR1785275     5  0.2195    0.78375 0.028 0.000 0.024 0.000 0.912 0.036
#> SRR1785280     2  0.0000    0.83059 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000    0.83059 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785278     1  0.4180    0.37636 0.632 0.000 0.008 0.012 0.000 0.348
#> SRR1785279     1  0.4299    0.36633 0.620 0.000 0.012 0.012 0.000 0.356
#> SRR1785282     1  0.5234    0.24271 0.520 0.000 0.052 0.020 0.000 0.408
#> SRR1785283     1  0.5228    0.24196 0.524 0.000 0.052 0.020 0.000 0.404
#> SRR1785284     5  0.4357    0.58481 0.016 0.000 0.000 0.020 0.660 0.304
#> SRR1785285     5  0.4374    0.57884 0.016 0.000 0.000 0.020 0.656 0.308
#> SRR1785286     4  0.4787    0.13844 0.000 0.000 0.000 0.516 0.432 0.052
#> SRR1785287     4  0.4731    0.15723 0.000 0.000 0.000 0.524 0.428 0.048
#> SRR1785288     6  0.4844    0.45343 0.200 0.000 0.000 0.112 0.008 0.680
#> SRR1785289     6  0.4934    0.45550 0.196 0.000 0.000 0.124 0.008 0.672
#> SRR1785290     2  0.2666    0.78725 0.000 0.872 0.028 0.008 0.000 0.092
#> SRR1785291     2  0.2222    0.79927 0.000 0.896 0.012 0.008 0.000 0.084
#> SRR1785296     4  0.2653    0.75571 0.004 0.080 0.008 0.880 0.000 0.028
#> SRR1785297     4  0.2808    0.74520 0.004 0.092 0.008 0.868 0.000 0.028
#> SRR1785292     2  0.0363    0.82977 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1785293     2  0.0363    0.82977 0.000 0.988 0.000 0.000 0.000 0.012
#> SRR1785294     4  0.0837    0.80339 0.020 0.000 0.004 0.972 0.000 0.004
#> SRR1785295     4  0.1003    0.79984 0.028 0.000 0.004 0.964 0.000 0.004
#> SRR1785298     2  0.5973    0.08637 0.004 0.464 0.024 0.056 0.020 0.432
#> SRR1785299     2  0.5872    0.13606 0.008 0.484 0.032 0.032 0.020 0.424
#> SRR1785300     4  0.4236    0.46139 0.036 0.000 0.000 0.656 0.000 0.308
#> SRR1785301     4  0.4363    0.42668 0.040 0.000 0.000 0.636 0.000 0.324
#> SRR1785304     4  0.1086    0.81199 0.000 0.012 0.000 0.964 0.012 0.012
#> SRR1785305     4  0.1086    0.81199 0.000 0.012 0.000 0.964 0.012 0.012
#> SRR1785306     5  0.3276    0.76308 0.000 0.020 0.028 0.032 0.860 0.060
#> SRR1785307     5  0.3223    0.76515 0.000 0.028 0.024 0.028 0.864 0.056
#> SRR1785302     6  0.4678    0.42515 0.012 0.216 0.008 0.016 0.032 0.716
#> SRR1785303     6  0.4677    0.41713 0.008 0.220 0.008 0.016 0.036 0.712
#> SRR1785308     3  0.4078    0.59207 0.068 0.000 0.748 0.004 0.000 0.180
#> SRR1785309     3  0.4078    0.59207 0.068 0.000 0.748 0.004 0.000 0.180
#> SRR1785310     4  0.1218    0.81069 0.004 0.000 0.000 0.956 0.012 0.028
#> SRR1785311     4  0.1053    0.81210 0.004 0.000 0.000 0.964 0.012 0.020
#> SRR1785312     1  0.2402    0.54951 0.856 0.000 0.000 0.000 0.140 0.004
#> SRR1785313     1  0.2442    0.54747 0.852 0.000 0.000 0.000 0.144 0.004
#> SRR1785314     5  0.5999    0.65399 0.084 0.108 0.000 0.016 0.648 0.144
#> SRR1785315     5  0.6038    0.65049 0.084 0.112 0.000 0.016 0.644 0.144
#> SRR1785318     2  0.0632    0.82811 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR1785319     2  0.0632    0.82811 0.000 0.976 0.000 0.000 0.000 0.024
#> SRR1785316     1  0.4524    0.25332 0.560 0.000 0.000 0.036 0.000 0.404
#> SRR1785317     1  0.4682    0.22203 0.540 0.000 0.004 0.036 0.000 0.420
#> SRR1785324     2  0.0000    0.83059 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785325     2  0.0000    0.83059 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785320     1  0.2595    0.56167 0.872 0.000 0.000 0.000 0.084 0.044
#> SRR1785321     1  0.2595    0.56167 0.872 0.000 0.000 0.000 0.084 0.044
#> SRR1785322     1  0.5697    0.39297 0.576 0.000 0.296 0.040 0.000 0.088
#> SRR1785323     1  0.5665    0.43119 0.596 0.000 0.268 0.040 0.000 0.096

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-SD-NMF-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-SD-NMF-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-SD-NMF-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-SD-NMF-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-SD-NMF-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-SD-NMF-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-SD-NMF-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-SD-NMF-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-SD-NMF-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-SD-NMF-membership-heatmap-5

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)

plot of chunk tab-SD-NMF-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-SD-NMF-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-SD-NMF-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-SD-NMF-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-SD-NMF-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-SD-NMF-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-SD-NMF-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-SD-NMF-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-SD-NMF-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-SD-NMF-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk SD-NMF-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-SD-NMF-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-SD-NMF-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-SD-NMF-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-SD-NMF-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-SD-NMF-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk SD-NMF-collect-classes

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.


CV:hclust**

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#>   Subgroups are detected by 'hclust' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 2.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk CV-hclust-collect-plots

The plots are:

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:

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)

plot of chunk CV-hclust-select-partition-number

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 0.952           0.933       0.967         0.2794 0.682   0.682
#> 3 3 0.399           0.578       0.717         0.9144 0.694   0.552
#> 4 4 0.416           0.562       0.713         0.1822 0.827   0.602
#> 5 5 0.541           0.633       0.727         0.1404 0.906   0.733
#> 6 6 0.608           0.658       0.780         0.0553 0.944   0.811

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2  0.9954      0.402 0.460 0.540
#> SRR1785239     2  0.9954      0.402 0.460 0.540
#> SRR1785240     1  0.0000      0.996 1.000 0.000
#> SRR1785241     1  0.0000      0.996 1.000 0.000
#> SRR1785242     1  0.0000      0.996 1.000 0.000
#> SRR1785243     1  0.0000      0.996 1.000 0.000
#> SRR1785244     1  0.0000      0.996 1.000 0.000
#> SRR1785245     1  0.0000      0.996 1.000 0.000
#> SRR1785246     1  0.0000      0.996 1.000 0.000
#> SRR1785247     1  0.0000      0.996 1.000 0.000
#> SRR1785248     2  0.2423      0.810 0.040 0.960
#> SRR1785250     1  0.0000      0.996 1.000 0.000
#> SRR1785251     1  0.0000      0.996 1.000 0.000
#> SRR1785252     1  0.0000      0.996 1.000 0.000
#> SRR1785253     1  0.0000      0.996 1.000 0.000
#> SRR1785254     1  0.0000      0.996 1.000 0.000
#> SRR1785255     1  0.0000      0.996 1.000 0.000
#> SRR1785256     1  0.0000      0.996 1.000 0.000
#> SRR1785257     1  0.0000      0.996 1.000 0.000
#> SRR1785258     1  0.0000      0.996 1.000 0.000
#> SRR1785259     1  0.0000      0.996 1.000 0.000
#> SRR1785262     1  0.0000      0.996 1.000 0.000
#> SRR1785263     1  0.0000      0.996 1.000 0.000
#> SRR1785260     1  0.0000      0.996 1.000 0.000
#> SRR1785261     1  0.0000      0.996 1.000 0.000
#> SRR1785264     2  0.9754      0.501 0.408 0.592
#> SRR1785265     2  0.9754      0.501 0.408 0.592
#> SRR1785266     2  0.0000      0.826 0.000 1.000
#> SRR1785267     2  0.0000      0.826 0.000 1.000
#> SRR1785268     1  0.0000      0.996 1.000 0.000
#> SRR1785269     1  0.0000      0.996 1.000 0.000
#> SRR1785270     1  0.1414      0.976 0.980 0.020
#> SRR1785271     1  0.1414      0.976 0.980 0.020
#> SRR1785272     1  0.0000      0.996 1.000 0.000
#> SRR1785273     1  0.0000      0.996 1.000 0.000
#> SRR1785276     1  0.0000      0.996 1.000 0.000
#> SRR1785277     1  0.0000      0.996 1.000 0.000
#> SRR1785274     1  0.0000      0.996 1.000 0.000
#> SRR1785275     1  0.0000      0.996 1.000 0.000
#> SRR1785280     2  0.0000      0.826 0.000 1.000
#> SRR1785281     2  0.0000      0.826 0.000 1.000
#> SRR1785278     1  0.0000      0.996 1.000 0.000
#> SRR1785279     1  0.0000      0.996 1.000 0.000
#> SRR1785282     1  0.0000      0.996 1.000 0.000
#> SRR1785283     1  0.0000      0.996 1.000 0.000
#> SRR1785284     1  0.0000      0.996 1.000 0.000
#> SRR1785285     1  0.0000      0.996 1.000 0.000
#> SRR1785286     1  0.0000      0.996 1.000 0.000
#> SRR1785287     1  0.0000      0.996 1.000 0.000
#> SRR1785288     1  0.0000      0.996 1.000 0.000
#> SRR1785289     1  0.0000      0.996 1.000 0.000
#> SRR1785290     2  0.9944      0.408 0.456 0.544
#> SRR1785291     2  0.9944      0.408 0.456 0.544
#> SRR1785296     1  0.0000      0.996 1.000 0.000
#> SRR1785297     1  0.0000      0.996 1.000 0.000
#> SRR1785292     2  0.0000      0.826 0.000 1.000
#> SRR1785293     2  0.0000      0.826 0.000 1.000
#> SRR1785294     1  0.0000      0.996 1.000 0.000
#> SRR1785295     1  0.0000      0.996 1.000 0.000
#> SRR1785298     1  0.0000      0.996 1.000 0.000
#> SRR1785299     1  0.0000      0.996 1.000 0.000
#> SRR1785300     1  0.0000      0.996 1.000 0.000
#> SRR1785301     1  0.0000      0.996 1.000 0.000
#> SRR1785304     1  0.3431      0.921 0.936 0.064
#> SRR1785305     1  0.3431      0.921 0.936 0.064
#> SRR1785306     1  0.0672      0.988 0.992 0.008
#> SRR1785307     1  0.0672      0.988 0.992 0.008
#> SRR1785302     1  0.0000      0.996 1.000 0.000
#> SRR1785303     1  0.0000      0.996 1.000 0.000
#> SRR1785308     1  0.0000      0.996 1.000 0.000
#> SRR1785309     1  0.0000      0.996 1.000 0.000
#> SRR1785310     1  0.0000      0.996 1.000 0.000
#> SRR1785311     1  0.0000      0.996 1.000 0.000
#> SRR1785312     1  0.0000      0.996 1.000 0.000
#> SRR1785313     1  0.0000      0.996 1.000 0.000
#> SRR1785314     1  0.1414      0.976 0.980 0.020
#> SRR1785315     1  0.1414      0.976 0.980 0.020
#> SRR1785318     2  0.0000      0.826 0.000 1.000
#> SRR1785319     2  0.0000      0.826 0.000 1.000
#> SRR1785316     1  0.0000      0.996 1.000 0.000
#> SRR1785317     1  0.0000      0.996 1.000 0.000
#> SRR1785324     2  0.0000      0.826 0.000 1.000
#> SRR1785325     2  0.0000      0.826 0.000 1.000
#> SRR1785320     1  0.0000      0.996 1.000 0.000
#> SRR1785321     1  0.0000      0.996 1.000 0.000
#> SRR1785322     1  0.0000      0.996 1.000 0.000
#> SRR1785323     1  0.0000      0.996 1.000 0.000

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     2  0.8066      0.532 0.404 0.528 0.068
#> SRR1785239     2  0.8066      0.532 0.404 0.528 0.068
#> SRR1785240     1  0.3686      0.558 0.860 0.000 0.140
#> SRR1785241     1  0.3686      0.558 0.860 0.000 0.140
#> SRR1785242     1  0.4931      0.402 0.768 0.000 0.232
#> SRR1785243     1  0.4931      0.402 0.768 0.000 0.232
#> SRR1785244     3  0.6308      0.851 0.492 0.000 0.508
#> SRR1785245     3  0.6308      0.851 0.492 0.000 0.508
#> SRR1785246     3  0.6305      0.860 0.484 0.000 0.516
#> SRR1785247     3  0.6305      0.860 0.484 0.000 0.516
#> SRR1785248     2  0.1711      0.825 0.032 0.960 0.008
#> SRR1785250     3  0.5988      0.819 0.368 0.000 0.632
#> SRR1785251     3  0.5988      0.819 0.368 0.000 0.632
#> SRR1785252     1  0.4974      0.395 0.764 0.000 0.236
#> SRR1785253     1  0.4974      0.395 0.764 0.000 0.236
#> SRR1785254     1  0.2711      0.603 0.912 0.000 0.088
#> SRR1785255     1  0.2711      0.603 0.912 0.000 0.088
#> SRR1785256     1  0.6308     -0.821 0.508 0.000 0.492
#> SRR1785257     1  0.6308     -0.821 0.508 0.000 0.492
#> SRR1785258     1  0.2878      0.598 0.904 0.000 0.096
#> SRR1785259     1  0.2878      0.598 0.904 0.000 0.096
#> SRR1785262     1  0.2796      0.585 0.908 0.000 0.092
#> SRR1785263     1  0.2796      0.585 0.908 0.000 0.092
#> SRR1785260     1  0.6280      0.363 0.540 0.000 0.460
#> SRR1785261     1  0.6280      0.363 0.540 0.000 0.460
#> SRR1785264     2  0.8037      0.608 0.352 0.572 0.076
#> SRR1785265     2  0.8037      0.608 0.352 0.572 0.076
#> SRR1785266     2  0.0424      0.835 0.000 0.992 0.008
#> SRR1785267     2  0.0424      0.835 0.000 0.992 0.008
#> SRR1785268     3  0.6215      0.903 0.428 0.000 0.572
#> SRR1785269     3  0.6215      0.903 0.428 0.000 0.572
#> SRR1785270     1  0.2448      0.604 0.924 0.000 0.076
#> SRR1785271     1  0.2448      0.604 0.924 0.000 0.076
#> SRR1785272     3  0.6244      0.906 0.440 0.000 0.560
#> SRR1785273     3  0.6244      0.906 0.440 0.000 0.560
#> SRR1785276     3  0.6305      0.860 0.484 0.000 0.516
#> SRR1785277     3  0.6305      0.860 0.484 0.000 0.516
#> SRR1785274     1  0.2796      0.585 0.908 0.000 0.092
#> SRR1785275     1  0.2796      0.585 0.908 0.000 0.092
#> SRR1785280     2  0.0000      0.836 0.000 1.000 0.000
#> SRR1785281     2  0.0000      0.836 0.000 1.000 0.000
#> SRR1785278     3  0.6225      0.905 0.432 0.000 0.568
#> SRR1785279     3  0.6225      0.905 0.432 0.000 0.568
#> SRR1785282     3  0.6308      0.851 0.492 0.000 0.508
#> SRR1785283     3  0.6308      0.851 0.492 0.000 0.508
#> SRR1785284     1  0.3686      0.558 0.860 0.000 0.140
#> SRR1785285     1  0.3686      0.558 0.860 0.000 0.140
#> SRR1785286     1  0.3038      0.608 0.896 0.000 0.104
#> SRR1785287     1  0.3038      0.608 0.896 0.000 0.104
#> SRR1785288     3  0.6308      0.851 0.492 0.000 0.508
#> SRR1785289     3  0.6308      0.851 0.492 0.000 0.508
#> SRR1785290     2  0.8573      0.564 0.372 0.524 0.104
#> SRR1785291     2  0.8573      0.564 0.372 0.524 0.104
#> SRR1785296     1  0.5291      0.541 0.732 0.000 0.268
#> SRR1785297     1  0.5291      0.541 0.732 0.000 0.268
#> SRR1785292     2  0.0000      0.836 0.000 1.000 0.000
#> SRR1785293     2  0.0000      0.836 0.000 1.000 0.000
#> SRR1785294     1  0.5760      0.484 0.672 0.000 0.328
#> SRR1785295     1  0.5760      0.484 0.672 0.000 0.328
#> SRR1785298     1  0.2711      0.603 0.912 0.000 0.088
#> SRR1785299     1  0.2711      0.603 0.912 0.000 0.088
#> SRR1785300     1  0.6308     -0.821 0.508 0.000 0.492
#> SRR1785301     1  0.6308     -0.821 0.508 0.000 0.492
#> SRR1785304     1  0.7459      0.221 0.584 0.044 0.372
#> SRR1785305     1  0.7459      0.221 0.584 0.044 0.372
#> SRR1785306     1  0.1860      0.615 0.948 0.000 0.052
#> SRR1785307     1  0.1860      0.615 0.948 0.000 0.052
#> SRR1785302     1  0.2959      0.604 0.900 0.000 0.100
#> SRR1785303     1  0.2959      0.604 0.900 0.000 0.100
#> SRR1785308     3  0.6045      0.833 0.380 0.000 0.620
#> SRR1785309     3  0.6045      0.833 0.380 0.000 0.620
#> SRR1785310     1  0.5785      0.478 0.668 0.000 0.332
#> SRR1785311     1  0.5785      0.478 0.668 0.000 0.332
#> SRR1785312     3  0.6302      0.875 0.480 0.000 0.520
#> SRR1785313     3  0.6302      0.875 0.480 0.000 0.520
#> SRR1785314     1  0.2448      0.604 0.924 0.000 0.076
#> SRR1785315     1  0.2448      0.604 0.924 0.000 0.076
#> SRR1785318     2  0.0000      0.836 0.000 1.000 0.000
#> SRR1785319     2  0.0000      0.836 0.000 1.000 0.000
#> SRR1785316     3  0.6244      0.906 0.440 0.000 0.560
#> SRR1785317     3  0.6244      0.906 0.440 0.000 0.560
#> SRR1785324     2  0.0000      0.836 0.000 1.000 0.000
#> SRR1785325     2  0.0000      0.836 0.000 1.000 0.000
#> SRR1785320     1  0.6307     -0.843 0.512 0.000 0.488
#> SRR1785321     1  0.6307     -0.843 0.512 0.000 0.488
#> SRR1785322     3  0.6225      0.905 0.432 0.000 0.568
#> SRR1785323     3  0.6225      0.905 0.432 0.000 0.568

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     2  0.7304      0.494 0.052 0.528 0.052 0.368
#> SRR1785239     2  0.7304      0.494 0.052 0.528 0.052 0.368
#> SRR1785240     4  0.5611      0.568 0.412 0.000 0.024 0.564
#> SRR1785241     4  0.5611      0.568 0.412 0.000 0.024 0.564
#> SRR1785242     4  0.5926      0.171 0.308 0.000 0.060 0.632
#> SRR1785243     4  0.5926      0.171 0.308 0.000 0.060 0.632
#> SRR1785244     1  0.1389      0.728 0.952 0.000 0.000 0.048
#> SRR1785245     1  0.1389      0.728 0.952 0.000 0.000 0.048
#> SRR1785246     3  0.6808      1.000 0.300 0.000 0.572 0.128
#> SRR1785247     3  0.6808      1.000 0.300 0.000 0.572 0.128
#> SRR1785248     2  0.1406      0.783 0.000 0.960 0.016 0.024
#> SRR1785250     1  0.4744      0.425 0.736 0.000 0.024 0.240
#> SRR1785251     1  0.4744      0.425 0.736 0.000 0.024 0.240
#> SRR1785252     4  0.5764      0.165 0.304 0.000 0.052 0.644
#> SRR1785253     4  0.5764      0.165 0.304 0.000 0.052 0.644
#> SRR1785254     4  0.5070      0.618 0.416 0.000 0.004 0.580
#> SRR1785255     4  0.5070      0.618 0.416 0.000 0.004 0.580
#> SRR1785256     1  0.1792      0.714 0.932 0.000 0.000 0.068
#> SRR1785257     1  0.1792      0.714 0.932 0.000 0.000 0.068
#> SRR1785258     4  0.4925      0.613 0.428 0.000 0.000 0.572
#> SRR1785259     4  0.4925      0.613 0.428 0.000 0.000 0.572
#> SRR1785262     4  0.6620      0.551 0.320 0.000 0.104 0.576
#> SRR1785263     4  0.6620      0.551 0.320 0.000 0.104 0.576
#> SRR1785260     4  0.7214      0.237 0.144 0.000 0.380 0.476
#> SRR1785261     4  0.7214      0.237 0.144 0.000 0.380 0.476
#> SRR1785264     2  0.6450      0.573 0.012 0.572 0.052 0.364
#> SRR1785265     2  0.6450      0.573 0.012 0.572 0.052 0.364
#> SRR1785266     2  0.0336      0.795 0.000 0.992 0.000 0.008
#> SRR1785267     2  0.0336      0.795 0.000 0.992 0.000 0.008
#> SRR1785268     1  0.0657      0.723 0.984 0.000 0.012 0.004
#> SRR1785269     1  0.0657      0.723 0.984 0.000 0.012 0.004
#> SRR1785270     4  0.4964      0.582 0.256 0.000 0.028 0.716
#> SRR1785271     4  0.4964      0.582 0.256 0.000 0.028 0.716
#> SRR1785272     1  0.0804      0.730 0.980 0.000 0.012 0.008
#> SRR1785273     1  0.0804      0.730 0.980 0.000 0.012 0.008
#> SRR1785276     3  0.6808      1.000 0.300 0.000 0.572 0.128
#> SRR1785277     3  0.6808      1.000 0.300 0.000 0.572 0.128
#> SRR1785274     4  0.6620      0.551 0.320 0.000 0.104 0.576
#> SRR1785275     4  0.6620      0.551 0.320 0.000 0.104 0.576
#> SRR1785280     2  0.0000      0.796 0.000 1.000 0.000 0.000
#> SRR1785281     2  0.0000      0.796 0.000 1.000 0.000 0.000
#> SRR1785278     1  0.0469      0.726 0.988 0.000 0.012 0.000
#> SRR1785279     1  0.0469      0.726 0.988 0.000 0.012 0.000
#> SRR1785282     1  0.1389      0.728 0.952 0.000 0.000 0.048
#> SRR1785283     1  0.1389      0.728 0.952 0.000 0.000 0.048
#> SRR1785284     4  0.5611      0.568 0.412 0.000 0.024 0.564
#> SRR1785285     4  0.5611      0.568 0.412 0.000 0.024 0.564
#> SRR1785286     4  0.5793      0.621 0.384 0.000 0.036 0.580
#> SRR1785287     4  0.5793      0.621 0.384 0.000 0.036 0.580
#> SRR1785288     1  0.1389      0.728 0.952 0.000 0.000 0.048
#> SRR1785289     1  0.1389      0.728 0.952 0.000 0.000 0.048
#> SRR1785290     2  0.7140      0.536 0.012 0.524 0.100 0.364
#> SRR1785291     2  0.7140      0.536 0.012 0.524 0.100 0.364
#> SRR1785296     4  0.7423      0.489 0.344 0.000 0.180 0.476
#> SRR1785297     4  0.7423      0.489 0.344 0.000 0.180 0.476
#> SRR1785292     2  0.0000      0.796 0.000 1.000 0.000 0.000
#> SRR1785293     2  0.0000      0.796 0.000 1.000 0.000 0.000
#> SRR1785294     1  0.7476     -0.408 0.412 0.000 0.176 0.412
#> SRR1785295     1  0.7476     -0.408 0.412 0.000 0.176 0.412
#> SRR1785298     4  0.5070      0.618 0.416 0.000 0.004 0.580
#> SRR1785299     4  0.5070      0.618 0.416 0.000 0.004 0.580
#> SRR1785300     1  0.1792      0.714 0.932 0.000 0.000 0.068
#> SRR1785301     1  0.1792      0.714 0.932 0.000 0.000 0.068
#> SRR1785304     4  0.6484      0.236 0.016 0.044 0.388 0.552
#> SRR1785305     4  0.6484      0.236 0.016 0.044 0.388 0.552
#> SRR1785306     4  0.5036      0.602 0.280 0.000 0.024 0.696
#> SRR1785307     4  0.5036      0.602 0.280 0.000 0.024 0.696
#> SRR1785302     4  0.5080      0.616 0.420 0.000 0.004 0.576
#> SRR1785303     4  0.5080      0.616 0.420 0.000 0.004 0.576
#> SRR1785308     1  0.4567      0.447 0.740 0.000 0.016 0.244
#> SRR1785309     1  0.4567      0.447 0.740 0.000 0.016 0.244
#> SRR1785310     1  0.7476     -0.399 0.416 0.000 0.176 0.408
#> SRR1785311     1  0.7476     -0.399 0.416 0.000 0.176 0.408
#> SRR1785312     1  0.2799      0.626 0.884 0.000 0.108 0.008
#> SRR1785313     1  0.2799      0.626 0.884 0.000 0.108 0.008
#> SRR1785314     4  0.4964      0.582 0.256 0.000 0.028 0.716
#> SRR1785315     4  0.4964      0.582 0.256 0.000 0.028 0.716
#> SRR1785318     2  0.0000      0.796 0.000 1.000 0.000 0.000
#> SRR1785319     2  0.0000      0.796 0.000 1.000 0.000 0.000
#> SRR1785316     1  0.0804      0.730 0.980 0.000 0.012 0.008
#> SRR1785317     1  0.0804      0.730 0.980 0.000 0.012 0.008
#> SRR1785324     2  0.0000      0.796 0.000 1.000 0.000 0.000
#> SRR1785325     2  0.0000      0.796 0.000 1.000 0.000 0.000
#> SRR1785320     1  0.5859      0.150 0.652 0.000 0.284 0.064
#> SRR1785321     1  0.5859      0.150 0.652 0.000 0.284 0.064
#> SRR1785322     1  0.0469      0.726 0.988 0.000 0.012 0.000
#> SRR1785323     1  0.0469      0.726 0.988 0.000 0.012 0.000

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     2  0.7177      0.450 0.032 0.464 0.008 0.348 0.148
#> SRR1785239     2  0.7177      0.450 0.032 0.464 0.008 0.348 0.148
#> SRR1785240     5  0.3599      0.545 0.160 0.000 0.008 0.020 0.812
#> SRR1785241     5  0.3599      0.545 0.160 0.000 0.008 0.020 0.812
#> SRR1785242     5  0.8038      0.145 0.160 0.000 0.292 0.140 0.408
#> SRR1785243     5  0.8038      0.145 0.160 0.000 0.292 0.140 0.408
#> SRR1785244     1  0.1764      0.835 0.928 0.000 0.000 0.008 0.064
#> SRR1785245     1  0.1764      0.835 0.928 0.000 0.000 0.008 0.064
#> SRR1785246     3  0.3752      1.000 0.000 0.000 0.708 0.000 0.292
#> SRR1785247     3  0.3752      1.000 0.000 0.000 0.708 0.000 0.292
#> SRR1785248     2  0.1310      0.768 0.000 0.956 0.000 0.024 0.020
#> SRR1785250     1  0.5648      0.467 0.628 0.000 0.288 0.060 0.024
#> SRR1785251     1  0.5648      0.467 0.628 0.000 0.288 0.060 0.024
#> SRR1785252     5  0.8091      0.131 0.156 0.000 0.292 0.152 0.400
#> SRR1785253     5  0.8091      0.131 0.156 0.000 0.292 0.152 0.400
#> SRR1785254     5  0.4584      0.577 0.312 0.000 0.000 0.028 0.660
#> SRR1785255     5  0.4584      0.577 0.312 0.000 0.000 0.028 0.660
#> SRR1785256     1  0.2136      0.817 0.904 0.000 0.000 0.008 0.088
#> SRR1785257     1  0.2136      0.817 0.904 0.000 0.000 0.008 0.088
#> SRR1785258     5  0.4558      0.573 0.324 0.000 0.000 0.024 0.652
#> SRR1785259     5  0.4558      0.573 0.324 0.000 0.000 0.024 0.652
#> SRR1785262     5  0.4986      0.424 0.064 0.000 0.076 0.096 0.764
#> SRR1785263     5  0.4986      0.424 0.064 0.000 0.076 0.096 0.764
#> SRR1785260     4  0.5349      0.828 0.116 0.000 0.004 0.676 0.204
#> SRR1785261     4  0.5349      0.828 0.116 0.000 0.004 0.676 0.204
#> SRR1785264     2  0.6314      0.499 0.000 0.500 0.008 0.364 0.128
#> SRR1785265     2  0.6314      0.499 0.000 0.500 0.008 0.364 0.128
#> SRR1785266     2  0.0609      0.779 0.000 0.980 0.000 0.020 0.000
#> SRR1785267     2  0.0609      0.779 0.000 0.980 0.000 0.020 0.000
#> SRR1785268     1  0.0671      0.839 0.980 0.000 0.000 0.016 0.004
#> SRR1785269     1  0.0671      0.839 0.980 0.000 0.000 0.016 0.004
#> SRR1785270     5  0.4208      0.450 0.052 0.000 0.012 0.148 0.788
#> SRR1785271     5  0.4208      0.450 0.052 0.000 0.012 0.148 0.788
#> SRR1785272     1  0.0510      0.845 0.984 0.000 0.000 0.000 0.016
#> SRR1785273     1  0.0510      0.845 0.984 0.000 0.000 0.000 0.016
#> SRR1785276     3  0.3752      1.000 0.000 0.000 0.708 0.000 0.292
#> SRR1785277     3  0.3752      1.000 0.000 0.000 0.708 0.000 0.292
#> SRR1785274     5  0.4986      0.423 0.064 0.000 0.076 0.096 0.764
#> SRR1785275     5  0.4986      0.423 0.064 0.000 0.076 0.096 0.764
#> SRR1785280     2  0.0000      0.782 0.000 1.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000      0.782 0.000 1.000 0.000 0.000 0.000
#> SRR1785278     1  0.0510      0.841 0.984 0.000 0.000 0.016 0.000
#> SRR1785279     1  0.0510      0.841 0.984 0.000 0.000 0.016 0.000
#> SRR1785282     1  0.1764      0.835 0.928 0.000 0.000 0.008 0.064
#> SRR1785283     1  0.1764      0.835 0.928 0.000 0.000 0.008 0.064
#> SRR1785284     5  0.3599      0.545 0.160 0.000 0.008 0.020 0.812
#> SRR1785285     5  0.3599      0.545 0.160 0.000 0.008 0.020 0.812
#> SRR1785286     5  0.4330      0.590 0.204 0.000 0.008 0.036 0.752
#> SRR1785287     5  0.4330      0.590 0.204 0.000 0.008 0.036 0.752
#> SRR1785288     1  0.1764      0.835 0.928 0.000 0.000 0.008 0.064
#> SRR1785289     1  0.1764      0.835 0.928 0.000 0.000 0.008 0.064
#> SRR1785290     2  0.6375      0.437 0.000 0.452 0.008 0.412 0.128
#> SRR1785291     2  0.6375      0.437 0.000 0.452 0.008 0.412 0.128
#> SRR1785296     5  0.6206      0.428 0.252 0.000 0.000 0.200 0.548
#> SRR1785297     5  0.6206      0.428 0.252 0.000 0.000 0.200 0.548
#> SRR1785292     2  0.0162      0.782 0.000 0.996 0.000 0.004 0.000
#> SRR1785293     2  0.0162      0.782 0.000 0.996 0.000 0.004 0.000
#> SRR1785294     5  0.6432      0.362 0.320 0.000 0.000 0.196 0.484
#> SRR1785295     5  0.6432      0.362 0.320 0.000 0.000 0.196 0.484
#> SRR1785298     5  0.4584      0.577 0.312 0.000 0.000 0.028 0.660
#> SRR1785299     5  0.4584      0.577 0.312 0.000 0.000 0.028 0.660
#> SRR1785300     1  0.2136      0.817 0.904 0.000 0.000 0.008 0.088
#> SRR1785301     1  0.2136      0.817 0.904 0.000 0.000 0.008 0.088
#> SRR1785304     4  0.3266      0.823 0.000 0.000 0.004 0.796 0.200
#> SRR1785305     4  0.3266      0.823 0.000 0.000 0.004 0.796 0.200
#> SRR1785306     5  0.3690      0.496 0.052 0.000 0.008 0.112 0.828
#> SRR1785307     5  0.3690      0.496 0.052 0.000 0.008 0.112 0.828
#> SRR1785302     5  0.4584      0.572 0.312 0.000 0.000 0.028 0.660
#> SRR1785303     5  0.4584      0.572 0.312 0.000 0.000 0.028 0.660
#> SRR1785308     1  0.5587      0.503 0.640 0.000 0.280 0.044 0.036
#> SRR1785309     1  0.5587      0.503 0.640 0.000 0.280 0.044 0.036
#> SRR1785310     5  0.6442      0.356 0.324 0.000 0.000 0.196 0.480
#> SRR1785311     5  0.6442      0.356 0.324 0.000 0.000 0.196 0.480
#> SRR1785312     1  0.2304      0.796 0.892 0.000 0.100 0.000 0.008
#> SRR1785313     1  0.2304      0.796 0.892 0.000 0.100 0.000 0.008
#> SRR1785314     5  0.4208      0.450 0.052 0.000 0.012 0.148 0.788
#> SRR1785315     5  0.4208      0.450 0.052 0.000 0.012 0.148 0.788
#> SRR1785318     2  0.0000      0.782 0.000 1.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000      0.782 0.000 1.000 0.000 0.000 0.000
#> SRR1785316     1  0.0510      0.845 0.984 0.000 0.000 0.000 0.016
#> SRR1785317     1  0.0510      0.845 0.984 0.000 0.000 0.000 0.016
#> SRR1785324     2  0.0000      0.782 0.000 1.000 0.000 0.000 0.000
#> SRR1785325     2  0.0000      0.782 0.000 1.000 0.000 0.000 0.000
#> SRR1785320     1  0.5524      0.468 0.600 0.000 0.320 0.004 0.076
#> SRR1785321     1  0.5524      0.468 0.600 0.000 0.320 0.004 0.076
#> SRR1785322     1  0.0510      0.841 0.984 0.000 0.000 0.016 0.000
#> SRR1785323     1  0.0510      0.841 0.984 0.000 0.000 0.016 0.000

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     3  0.6022      0.413 0.032 0.360 0.520 0.020 0.068 0.000
#> SRR1785239     3  0.6022      0.413 0.032 0.360 0.520 0.020 0.068 0.000
#> SRR1785240     5  0.3031      0.618 0.084 0.000 0.016 0.024 0.864 0.012
#> SRR1785241     5  0.3031      0.618 0.084 0.000 0.016 0.024 0.864 0.012
#> SRR1785242     3  0.5203      0.210 0.096 0.000 0.572 0.000 0.328 0.004
#> SRR1785243     3  0.5203      0.210 0.096 0.000 0.572 0.000 0.328 0.004
#> SRR1785244     1  0.2100      0.785 0.884 0.000 0.000 0.004 0.112 0.000
#> SRR1785245     1  0.2100      0.785 0.884 0.000 0.000 0.004 0.112 0.000
#> SRR1785246     6  0.0000      1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1785247     6  0.0000      1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1785248     2  0.1686      0.899 0.000 0.924 0.064 0.000 0.012 0.000
#> SRR1785250     1  0.4833      0.267 0.488 0.000 0.464 0.044 0.000 0.004
#> SRR1785251     1  0.4833      0.267 0.488 0.000 0.464 0.044 0.000 0.004
#> SRR1785252     3  0.5228      0.226 0.088 0.000 0.584 0.004 0.320 0.004
#> SRR1785253     3  0.5228      0.226 0.088 0.000 0.584 0.004 0.320 0.004
#> SRR1785254     5  0.3837      0.653 0.224 0.000 0.016 0.016 0.744 0.000
#> SRR1785255     5  0.3837      0.653 0.224 0.000 0.016 0.016 0.744 0.000
#> SRR1785256     1  0.2362      0.768 0.860 0.000 0.000 0.004 0.136 0.000
#> SRR1785257     1  0.2362      0.768 0.860 0.000 0.000 0.004 0.136 0.000
#> SRR1785258     5  0.4062      0.647 0.236 0.000 0.028 0.012 0.724 0.000
#> SRR1785259     5  0.4062      0.647 0.236 0.000 0.028 0.012 0.724 0.000
#> SRR1785262     5  0.5921      0.406 0.044 0.000 0.160 0.000 0.596 0.200
#> SRR1785263     5  0.5921      0.406 0.044 0.000 0.160 0.000 0.596 0.200
#> SRR1785260     4  0.3044      0.827 0.116 0.000 0.000 0.836 0.048 0.000
#> SRR1785261     4  0.3044      0.827 0.116 0.000 0.000 0.836 0.048 0.000
#> SRR1785264     3  0.5287      0.363 0.000 0.396 0.528 0.024 0.052 0.000
#> SRR1785265     3  0.5287      0.363 0.000 0.396 0.528 0.024 0.052 0.000
#> SRR1785266     2  0.0622      0.973 0.000 0.980 0.012 0.008 0.000 0.000
#> SRR1785267     2  0.0622      0.973 0.000 0.980 0.012 0.008 0.000 0.000
#> SRR1785268     1  0.2046      0.778 0.916 0.000 0.044 0.032 0.008 0.000
#> SRR1785269     1  0.2046      0.778 0.916 0.000 0.044 0.032 0.008 0.000
#> SRR1785270     5  0.4093      0.504 0.000 0.000 0.112 0.104 0.772 0.012
#> SRR1785271     5  0.4093      0.504 0.000 0.000 0.112 0.104 0.772 0.012
#> SRR1785272     1  0.1078      0.792 0.964 0.000 0.008 0.012 0.016 0.000
#> SRR1785273     1  0.1078      0.792 0.964 0.000 0.008 0.012 0.016 0.000
#> SRR1785276     6  0.0000      1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1785277     6  0.0000      1.000 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1785274     5  0.5921      0.402 0.044 0.000 0.160 0.000 0.596 0.200
#> SRR1785275     5  0.5921      0.402 0.044 0.000 0.160 0.000 0.596 0.200
#> SRR1785280     2  0.0000      0.984 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000      0.984 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785278     1  0.1649      0.779 0.932 0.000 0.036 0.032 0.000 0.000
#> SRR1785279     1  0.1649      0.779 0.932 0.000 0.036 0.032 0.000 0.000
#> SRR1785282     1  0.2100      0.785 0.884 0.000 0.000 0.004 0.112 0.000
#> SRR1785283     1  0.2100      0.785 0.884 0.000 0.000 0.004 0.112 0.000
#> SRR1785284     5  0.3031      0.618 0.084 0.000 0.016 0.024 0.864 0.012
#> SRR1785285     5  0.3031      0.618 0.084 0.000 0.016 0.024 0.864 0.012
#> SRR1785286     5  0.3032      0.665 0.108 0.000 0.004 0.020 0.852 0.016
#> SRR1785287     5  0.3032      0.665 0.108 0.000 0.004 0.020 0.852 0.016
#> SRR1785288     1  0.2100      0.785 0.884 0.000 0.000 0.004 0.112 0.000
#> SRR1785289     1  0.2100      0.785 0.884 0.000 0.000 0.004 0.112 0.000
#> SRR1785290     3  0.5881      0.403 0.000 0.348 0.524 0.080 0.048 0.000
#> SRR1785291     3  0.5881      0.403 0.000 0.348 0.524 0.080 0.048 0.000
#> SRR1785296     5  0.5922      0.546 0.188 0.000 0.020 0.212 0.576 0.004
#> SRR1785297     5  0.5922      0.546 0.188 0.000 0.020 0.212 0.576 0.004
#> SRR1785292     2  0.0260      0.981 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1785293     2  0.0260      0.981 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1785294     5  0.6122      0.486 0.260 0.000 0.012 0.212 0.512 0.004
#> SRR1785295     5  0.6122      0.486 0.260 0.000 0.012 0.212 0.512 0.004
#> SRR1785298     5  0.3837      0.653 0.224 0.000 0.016 0.016 0.744 0.000
#> SRR1785299     5  0.3837      0.653 0.224 0.000 0.016 0.016 0.744 0.000
#> SRR1785300     1  0.2362      0.768 0.860 0.000 0.000 0.004 0.136 0.000
#> SRR1785301     1  0.2362      0.768 0.860 0.000 0.000 0.004 0.136 0.000
#> SRR1785304     4  0.2696      0.822 0.000 0.000 0.116 0.856 0.028 0.000
#> SRR1785305     4  0.2696      0.822 0.000 0.000 0.116 0.856 0.028 0.000
#> SRR1785306     5  0.3366      0.546 0.000 0.000 0.100 0.052 0.832 0.016
#> SRR1785307     5  0.3366      0.546 0.000 0.000 0.100 0.052 0.832 0.016
#> SRR1785302     5  0.4124      0.647 0.224 0.000 0.036 0.012 0.728 0.000
#> SRR1785303     5  0.4124      0.647 0.224 0.000 0.036 0.012 0.728 0.000
#> SRR1785308     1  0.4517      0.352 0.560 0.000 0.412 0.012 0.016 0.000
#> SRR1785309     1  0.4517      0.352 0.560 0.000 0.412 0.012 0.016 0.000
#> SRR1785310     5  0.6137      0.481 0.264 0.000 0.012 0.212 0.508 0.004
#> SRR1785311     5  0.6137      0.481 0.264 0.000 0.012 0.212 0.508 0.004
#> SRR1785312     1  0.2938      0.757 0.860 0.000 0.020 0.004 0.016 0.100
#> SRR1785313     1  0.2938      0.757 0.860 0.000 0.020 0.004 0.016 0.100
#> SRR1785314     5  0.4093      0.504 0.000 0.000 0.112 0.104 0.772 0.012
#> SRR1785315     5  0.4093      0.504 0.000 0.000 0.112 0.104 0.772 0.012
#> SRR1785318     2  0.0000      0.984 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000      0.984 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785316     1  0.1078      0.792 0.964 0.000 0.008 0.012 0.016 0.000
#> SRR1785317     1  0.1078      0.792 0.964 0.000 0.008 0.012 0.016 0.000
#> SRR1785324     2  0.0000      0.984 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785325     2  0.0000      0.984 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785320     1  0.4856      0.445 0.572 0.000 0.000 0.000 0.068 0.360
#> SRR1785321     1  0.4856      0.445 0.572 0.000 0.000 0.000 0.068 0.360
#> SRR1785322     1  0.1649      0.779 0.932 0.000 0.036 0.032 0.000 0.000
#> SRR1785323     1  0.1649      0.779 0.932 0.000 0.036 0.032 0.000 0.000

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-CV-hclust-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-CV-hclust-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-CV-hclust-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-CV-hclust-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-CV-hclust-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-CV-hclust-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-CV-hclust-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-CV-hclust-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-CV-hclust-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-CV-hclust-membership-heatmap-5

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)

plot of chunk tab-CV-hclust-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-CV-hclust-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-CV-hclust-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-CV-hclust-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-CV-hclust-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-CV-hclust-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-CV-hclust-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-CV-hclust-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-CV-hclust-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-CV-hclust-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk CV-hclust-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-CV-hclust-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-CV-hclust-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-CV-hclust-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-CV-hclust-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-CV-hclust-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk CV-hclust-collect-classes

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.


CV:kmeans

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 16620 rows and 87 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 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)

plot of chunk CV-kmeans-collect-plots

The plots are:

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:

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)

plot of chunk CV-kmeans-select-partition-number

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 0.700           0.849       0.927         0.3994 0.586   0.586
#> 3 3 0.306           0.496       0.661         0.4420 0.743   0.584
#> 4 4 0.326           0.561       0.686         0.1805 0.827   0.623
#> 5 5 0.395           0.453       0.634         0.0924 0.944   0.849
#> 6 6 0.453           0.344       0.556         0.0535 0.874   0.635

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     1  1.0000     -0.167 0.504 0.496
#> SRR1785239     1  1.0000     -0.167 0.504 0.496
#> SRR1785240     1  0.0672      0.935 0.992 0.008
#> SRR1785241     1  0.0672      0.935 0.992 0.008
#> SRR1785242     1  0.7674      0.693 0.776 0.224
#> SRR1785243     1  0.7674      0.693 0.776 0.224
#> SRR1785244     1  0.0000      0.938 1.000 0.000
#> SRR1785245     1  0.0000      0.938 1.000 0.000
#> SRR1785246     1  0.1184      0.929 0.984 0.016
#> SRR1785247     1  0.1184      0.929 0.984 0.016
#> SRR1785248     2  0.1633      0.873 0.024 0.976
#> SRR1785250     1  0.0672      0.935 0.992 0.008
#> SRR1785251     1  0.0672      0.935 0.992 0.008
#> SRR1785252     1  0.7674      0.693 0.776 0.224
#> SRR1785253     1  0.7674      0.693 0.776 0.224
#> SRR1785254     2  0.9170      0.642 0.332 0.668
#> SRR1785255     2  0.9170      0.642 0.332 0.668
#> SRR1785256     1  0.0000      0.938 1.000 0.000
#> SRR1785257     1  0.0000      0.938 1.000 0.000
#> SRR1785258     1  0.0000      0.938 1.000 0.000
#> SRR1785259     1  0.0000      0.938 1.000 0.000
#> SRR1785262     1  0.0000      0.938 1.000 0.000
#> SRR1785263     1  0.0000      0.938 1.000 0.000
#> SRR1785260     1  0.0376      0.937 0.996 0.004
#> SRR1785261     1  0.0376      0.937 0.996 0.004
#> SRR1785264     2  0.1843      0.876 0.028 0.972
#> SRR1785265     2  0.1843      0.876 0.028 0.972
#> SRR1785266     2  0.1843      0.876 0.028 0.972
#> SRR1785267     2  0.1843      0.876 0.028 0.972
#> SRR1785268     1  0.0376      0.937 0.996 0.004
#> SRR1785269     1  0.0376      0.937 0.996 0.004
#> SRR1785270     2  0.8813      0.689 0.300 0.700
#> SRR1785271     2  0.8813      0.689 0.300 0.700
#> SRR1785272     1  0.0376      0.937 0.996 0.004
#> SRR1785273     1  0.0376      0.937 0.996 0.004
#> SRR1785276     1  0.2778      0.916 0.952 0.048
#> SRR1785277     1  0.2778      0.916 0.952 0.048
#> SRR1785274     1  0.0938      0.933 0.988 0.012
#> SRR1785275     1  0.0938      0.933 0.988 0.012
#> SRR1785280     2  0.1184      0.867 0.016 0.984
#> SRR1785281     2  0.1184      0.867 0.016 0.984
#> SRR1785278     1  0.0000      0.938 1.000 0.000
#> SRR1785279     1  0.0000      0.938 1.000 0.000
#> SRR1785282     1  0.0000      0.938 1.000 0.000
#> SRR1785283     1  0.0000      0.938 1.000 0.000
#> SRR1785284     1  0.0938      0.933 0.988 0.012
#> SRR1785285     1  0.0938      0.933 0.988 0.012
#> SRR1785286     1  0.0938      0.933 0.988 0.012
#> SRR1785287     1  0.0938      0.933 0.988 0.012
#> SRR1785288     1  0.0000      0.938 1.000 0.000
#> SRR1785289     1  0.0000      0.938 1.000 0.000
#> SRR1785290     2  0.3733      0.867 0.072 0.928
#> SRR1785291     2  0.3733      0.867 0.072 0.928
#> SRR1785296     1  0.7602      0.694 0.780 0.220
#> SRR1785297     1  0.7602      0.694 0.780 0.220
#> SRR1785292     2  0.1843      0.876 0.028 0.972
#> SRR1785293     2  0.1843      0.876 0.028 0.972
#> SRR1785294     1  0.0000      0.938 1.000 0.000
#> SRR1785295     1  0.0000      0.938 1.000 0.000
#> SRR1785298     1  0.2043      0.919 0.968 0.032
#> SRR1785299     1  0.2043      0.919 0.968 0.032
#> SRR1785300     1  0.0000      0.938 1.000 0.000
#> SRR1785301     1  0.0000      0.938 1.000 0.000
#> SRR1785304     2  0.3584      0.867 0.068 0.932
#> SRR1785305     2  0.3584      0.867 0.068 0.932
#> SRR1785306     2  0.9248      0.621 0.340 0.660
#> SRR1785307     2  0.9248      0.621 0.340 0.660
#> SRR1785302     1  0.7602      0.698 0.780 0.220
#> SRR1785303     1  0.7602      0.698 0.780 0.220
#> SRR1785308     1  0.0376      0.937 0.996 0.004
#> SRR1785309     1  0.0376      0.937 0.996 0.004
#> SRR1785310     1  0.0000      0.938 1.000 0.000
#> SRR1785311     1  0.0000      0.938 1.000 0.000
#> SRR1785312     1  0.1184      0.929 0.984 0.016
#> SRR1785313     1  0.1184      0.929 0.984 0.016
#> SRR1785314     2  0.8955      0.670 0.312 0.688
#> SRR1785315     2  0.8955      0.670 0.312 0.688
#> SRR1785318     2  0.1843      0.876 0.028 0.972
#> SRR1785319     2  0.1843      0.876 0.028 0.972
#> SRR1785316     1  0.0000      0.938 1.000 0.000
#> SRR1785317     1  0.0000      0.938 1.000 0.000
#> SRR1785324     2  0.1843      0.876 0.028 0.972
#> SRR1785325     2  0.1843      0.876 0.028 0.972
#> SRR1785320     1  0.1184      0.929 0.984 0.016
#> SRR1785321     1  0.1184      0.929 0.984 0.016
#> SRR1785322     1  0.0376      0.937 0.996 0.004
#> SRR1785323     1  0.0376      0.937 0.996 0.004

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     3  0.9509    0.50389 0.336 0.200 0.464
#> SRR1785239     3  0.9509    0.50389 0.336 0.200 0.464
#> SRR1785240     3  0.6683    0.09985 0.496 0.008 0.496
#> SRR1785241     1  0.6683   -0.15730 0.496 0.008 0.496
#> SRR1785242     3  0.7785    0.35894 0.420 0.052 0.528
#> SRR1785243     3  0.7785    0.35894 0.420 0.052 0.528
#> SRR1785244     1  0.1964    0.66592 0.944 0.000 0.056
#> SRR1785245     1  0.1964    0.66592 0.944 0.000 0.056
#> SRR1785246     3  0.6500    0.12418 0.464 0.004 0.532
#> SRR1785247     3  0.6500    0.12418 0.464 0.004 0.532
#> SRR1785248     2  0.1711    0.88760 0.008 0.960 0.032
#> SRR1785250     1  0.5443    0.53826 0.736 0.004 0.260
#> SRR1785251     1  0.5443    0.53826 0.736 0.004 0.260
#> SRR1785252     3  0.7767    0.34916 0.412 0.052 0.536
#> SRR1785253     3  0.7767    0.34916 0.412 0.052 0.536
#> SRR1785254     3  0.9489    0.53258 0.228 0.280 0.492
#> SRR1785255     3  0.9489    0.53258 0.228 0.280 0.492
#> SRR1785256     1  0.2448    0.66271 0.924 0.000 0.076
#> SRR1785257     1  0.2448    0.66271 0.924 0.000 0.076
#> SRR1785258     1  0.5098    0.46733 0.752 0.000 0.248
#> SRR1785259     1  0.5098    0.46733 0.752 0.000 0.248
#> SRR1785262     1  0.6267   -0.00514 0.548 0.000 0.452
#> SRR1785263     1  0.6267   -0.00514 0.548 0.000 0.452
#> SRR1785260     1  0.5201    0.54357 0.760 0.004 0.236
#> SRR1785261     1  0.5201    0.54357 0.760 0.004 0.236
#> SRR1785264     2  0.4059    0.84466 0.012 0.860 0.128
#> SRR1785265     2  0.4059    0.84466 0.012 0.860 0.128
#> SRR1785266     2  0.1453    0.88819 0.008 0.968 0.024
#> SRR1785267     2  0.1453    0.88819 0.008 0.968 0.024
#> SRR1785268     1  0.4233    0.60121 0.836 0.004 0.160
#> SRR1785269     1  0.4233    0.60121 0.836 0.004 0.160
#> SRR1785270     3  0.8610    0.38698 0.120 0.324 0.556
#> SRR1785271     3  0.8610    0.38698 0.120 0.324 0.556
#> SRR1785272     1  0.3482    0.62790 0.872 0.000 0.128
#> SRR1785273     1  0.3482    0.62790 0.872 0.000 0.128
#> SRR1785276     3  0.7102    0.24228 0.420 0.024 0.556
#> SRR1785277     3  0.7102    0.24228 0.420 0.024 0.556
#> SRR1785274     1  0.6664   -0.10871 0.528 0.008 0.464
#> SRR1785275     1  0.6664   -0.10871 0.528 0.008 0.464
#> SRR1785280     2  0.1163    0.88330 0.000 0.972 0.028
#> SRR1785281     2  0.1163    0.88330 0.000 0.972 0.028
#> SRR1785278     1  0.1964    0.66466 0.944 0.000 0.056
#> SRR1785279     1  0.1964    0.66466 0.944 0.000 0.056
#> SRR1785282     1  0.0592    0.66703 0.988 0.000 0.012
#> SRR1785283     1  0.0592    0.66703 0.988 0.000 0.012
#> SRR1785284     1  0.6672   -0.10293 0.520 0.008 0.472
#> SRR1785285     1  0.6672   -0.10293 0.520 0.008 0.472
#> SRR1785286     1  0.6669   -0.06855 0.524 0.008 0.468
#> SRR1785287     1  0.6669   -0.06855 0.524 0.008 0.468
#> SRR1785288     1  0.2261    0.66408 0.932 0.000 0.068
#> SRR1785289     1  0.2261    0.66408 0.932 0.000 0.068
#> SRR1785290     2  0.6756    0.69433 0.056 0.712 0.232
#> SRR1785291     2  0.6756    0.69433 0.056 0.712 0.232
#> SRR1785296     3  0.8273    0.31581 0.448 0.076 0.476
#> SRR1785297     3  0.8273    0.31581 0.448 0.076 0.476
#> SRR1785292     2  0.1015    0.89298 0.008 0.980 0.012
#> SRR1785293     2  0.1015    0.89298 0.008 0.980 0.012
#> SRR1785294     1  0.5070    0.55287 0.772 0.004 0.224
#> SRR1785295     1  0.5070    0.55287 0.772 0.004 0.224
#> SRR1785298     1  0.7814   -0.18334 0.512 0.052 0.436
#> SRR1785299     1  0.7814   -0.18334 0.512 0.052 0.436
#> SRR1785300     1  0.2711    0.65869 0.912 0.000 0.088
#> SRR1785301     1  0.2711    0.65869 0.912 0.000 0.088
#> SRR1785304     2  0.7416    0.61632 0.068 0.656 0.276
#> SRR1785305     2  0.7416    0.61632 0.068 0.656 0.276
#> SRR1785306     3  0.8168    0.45840 0.108 0.280 0.612
#> SRR1785307     3  0.8168    0.45840 0.108 0.280 0.612
#> SRR1785302     3  0.8566    0.38102 0.424 0.096 0.480
#> SRR1785303     3  0.8566    0.38102 0.424 0.096 0.480
#> SRR1785308     1  0.3752    0.61494 0.856 0.000 0.144
#> SRR1785309     1  0.3752    0.61494 0.856 0.000 0.144
#> SRR1785310     1  0.4784    0.57155 0.796 0.004 0.200
#> SRR1785311     1  0.4784    0.57155 0.796 0.004 0.200
#> SRR1785312     1  0.4682    0.56028 0.804 0.004 0.192
#> SRR1785313     1  0.4682    0.56028 0.804 0.004 0.192
#> SRR1785314     3  0.8573    0.37715 0.116 0.328 0.556
#> SRR1785315     3  0.8573    0.37715 0.116 0.328 0.556
#> SRR1785318     2  0.1015    0.89251 0.008 0.980 0.012
#> SRR1785319     2  0.1015    0.89251 0.008 0.980 0.012
#> SRR1785316     1  0.1411    0.66462 0.964 0.000 0.036
#> SRR1785317     1  0.1411    0.66462 0.964 0.000 0.036
#> SRR1785324     2  0.1170    0.89298 0.008 0.976 0.016
#> SRR1785325     2  0.1170    0.89298 0.008 0.976 0.016
#> SRR1785320     1  0.4047    0.59451 0.848 0.004 0.148
#> SRR1785321     1  0.4047    0.59451 0.848 0.004 0.148
#> SRR1785322     1  0.3038    0.65953 0.896 0.000 0.104
#> SRR1785323     1  0.3038    0.65953 0.896 0.000 0.104

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3 p4
#> SRR1785238     3  0.8949      0.531 0.200 0.100 0.476 NA
#> SRR1785239     3  0.8949      0.531 0.200 0.100 0.476 NA
#> SRR1785240     3  0.5962      0.539 0.260 0.000 0.660 NA
#> SRR1785241     3  0.5962      0.539 0.260 0.000 0.660 NA
#> SRR1785242     3  0.7941      0.534 0.192 0.040 0.556 NA
#> SRR1785243     3  0.7941      0.534 0.192 0.040 0.556 NA
#> SRR1785244     1  0.3245      0.684 0.880 0.000 0.056 NA
#> SRR1785245     1  0.3245      0.684 0.880 0.000 0.056 NA
#> SRR1785246     3  0.8041      0.303 0.256 0.008 0.424 NA
#> SRR1785247     3  0.8041      0.303 0.256 0.008 0.424 NA
#> SRR1785248     2  0.3239      0.795 0.000 0.880 0.052 NA
#> SRR1785250     1  0.6897      0.447 0.544 0.000 0.124 NA
#> SRR1785251     1  0.6897      0.447 0.544 0.000 0.124 NA
#> SRR1785252     3  0.8201      0.507 0.204 0.040 0.516 NA
#> SRR1785253     3  0.8201      0.507 0.204 0.040 0.516 NA
#> SRR1785254     3  0.8230      0.490 0.128 0.148 0.580 NA
#> SRR1785255     3  0.8230      0.490 0.128 0.148 0.580 NA
#> SRR1785256     1  0.3667      0.658 0.856 0.000 0.088 NA
#> SRR1785257     1  0.3667      0.658 0.856 0.000 0.088 NA
#> SRR1785258     1  0.6572      0.341 0.608 0.000 0.272 NA
#> SRR1785259     1  0.6572      0.341 0.608 0.000 0.272 NA
#> SRR1785262     3  0.6910      0.468 0.324 0.000 0.548 NA
#> SRR1785263     3  0.6910      0.468 0.324 0.000 0.548 NA
#> SRR1785260     1  0.6644      0.456 0.640 0.004 0.192 NA
#> SRR1785261     1  0.6644      0.456 0.640 0.004 0.192 NA
#> SRR1785264     2  0.6165      0.693 0.008 0.696 0.164 NA
#> SRR1785265     2  0.6165      0.693 0.008 0.696 0.164 NA
#> SRR1785266     2  0.0592      0.819 0.000 0.984 0.000 NA
#> SRR1785267     2  0.0592      0.819 0.000 0.984 0.000 NA
#> SRR1785268     1  0.6027      0.559 0.664 0.000 0.092 NA
#> SRR1785269     1  0.6027      0.559 0.664 0.000 0.092 NA
#> SRR1785270     3  0.6851      0.451 0.044 0.120 0.676 NA
#> SRR1785271     3  0.6851      0.451 0.044 0.120 0.676 NA
#> SRR1785272     1  0.4919      0.620 0.752 0.000 0.048 NA
#> SRR1785273     1  0.4919      0.620 0.752 0.000 0.048 NA
#> SRR1785276     3  0.8011      0.365 0.220 0.012 0.448 NA
#> SRR1785277     3  0.8011      0.365 0.220 0.012 0.448 NA
#> SRR1785274     3  0.5721      0.538 0.284 0.000 0.660 NA
#> SRR1785275     3  0.5721      0.538 0.284 0.000 0.660 NA
#> SRR1785280     2  0.0817      0.819 0.000 0.976 0.000 NA
#> SRR1785281     2  0.0817      0.819 0.000 0.976 0.000 NA
#> SRR1785278     1  0.3333      0.683 0.872 0.000 0.040 NA
#> SRR1785279     1  0.3333      0.683 0.872 0.000 0.040 NA
#> SRR1785282     1  0.1209      0.695 0.964 0.000 0.004 NA
#> SRR1785283     1  0.1209      0.695 0.964 0.000 0.004 NA
#> SRR1785284     3  0.6141      0.500 0.316 0.004 0.620 NA
#> SRR1785285     3  0.6141      0.500 0.316 0.004 0.620 NA
#> SRR1785286     3  0.6509      0.424 0.360 0.004 0.564 NA
#> SRR1785287     3  0.6509      0.424 0.360 0.004 0.564 NA
#> SRR1785288     1  0.3245      0.682 0.880 0.000 0.056 NA
#> SRR1785289     1  0.3245      0.682 0.880 0.000 0.056 NA
#> SRR1785290     2  0.7986      0.479 0.024 0.508 0.272 NA
#> SRR1785291     2  0.7986      0.479 0.024 0.508 0.272 NA
#> SRR1785296     3  0.8342      0.491 0.280 0.056 0.504 NA
#> SRR1785297     3  0.8342      0.491 0.280 0.056 0.504 NA
#> SRR1785292     2  0.1284      0.823 0.000 0.964 0.012 NA
#> SRR1785293     2  0.1284      0.823 0.000 0.964 0.012 NA
#> SRR1785294     1  0.6730      0.416 0.628 0.004 0.212 NA
#> SRR1785295     1  0.6730      0.416 0.628 0.004 0.212 NA
#> SRR1785298     3  0.7749      0.369 0.412 0.036 0.452 NA
#> SRR1785299     3  0.7749      0.369 0.412 0.036 0.452 NA
#> SRR1785300     1  0.4022      0.650 0.836 0.000 0.096 NA
#> SRR1785301     1  0.4022      0.650 0.836 0.000 0.096 NA
#> SRR1785304     2  0.8746      0.388 0.048 0.424 0.272 NA
#> SRR1785305     2  0.8746      0.388 0.048 0.424 0.272 NA
#> SRR1785306     3  0.6027      0.485 0.032 0.116 0.736 NA
#> SRR1785307     3  0.6027      0.485 0.032 0.116 0.736 NA
#> SRR1785302     3  0.8181      0.518 0.244 0.064 0.544 NA
#> SRR1785303     3  0.8181      0.518 0.244 0.064 0.544 NA
#> SRR1785308     1  0.5432      0.578 0.716 0.000 0.068 NA
#> SRR1785309     1  0.5432      0.578 0.716 0.000 0.068 NA
#> SRR1785310     1  0.5998      0.488 0.696 0.004 0.192 NA
#> SRR1785311     1  0.5998      0.488 0.696 0.004 0.192 NA
#> SRR1785312     1  0.5859      0.527 0.652 0.000 0.064 NA
#> SRR1785313     1  0.5859      0.527 0.652 0.000 0.064 NA
#> SRR1785314     3  0.6992      0.431 0.036 0.132 0.656 NA
#> SRR1785315     3  0.6992      0.431 0.036 0.132 0.656 NA
#> SRR1785318     2  0.0804      0.824 0.000 0.980 0.012 NA
#> SRR1785319     2  0.0804      0.824 0.000 0.980 0.012 NA
#> SRR1785316     1  0.2198      0.694 0.920 0.000 0.008 NA
#> SRR1785317     1  0.2198      0.694 0.920 0.000 0.008 NA
#> SRR1785324     2  0.0937      0.823 0.000 0.976 0.012 NA
#> SRR1785325     2  0.0937      0.823 0.000 0.976 0.012 NA
#> SRR1785320     1  0.5184      0.585 0.732 0.000 0.056 NA
#> SRR1785321     1  0.5184      0.585 0.732 0.000 0.056 NA
#> SRR1785322     1  0.5531      0.628 0.732 0.000 0.140 NA
#> SRR1785323     1  0.5531      0.628 0.732 0.000 0.140 NA

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3 p4    p5
#> SRR1785238     5   0.870     0.2669 0.144 0.064 0.188 NA 0.464
#> SRR1785239     5   0.870     0.2669 0.144 0.064 0.188 NA 0.464
#> SRR1785240     5   0.822     0.1917 0.140 0.004 0.220 NA 0.424
#> SRR1785241     5   0.822     0.1917 0.140 0.004 0.220 NA 0.424
#> SRR1785242     5   0.793     0.2560 0.120 0.020 0.232 NA 0.504
#> SRR1785243     5   0.793     0.2560 0.120 0.020 0.232 NA 0.504
#> SRR1785244     1   0.317     0.5991 0.876 0.000 0.044 NA 0.040
#> SRR1785245     1   0.317     0.5991 0.876 0.000 0.044 NA 0.040
#> SRR1785246     3   0.636     0.9084 0.132 0.008 0.644 NA 0.176
#> SRR1785247     3   0.636     0.9084 0.132 0.008 0.644 NA 0.176
#> SRR1785248     2   0.372     0.8079 0.000 0.844 0.044 NA 0.040
#> SRR1785250     1   0.814     0.2593 0.384 0.000 0.292 NA 0.128
#> SRR1785251     1   0.814     0.2593 0.384 0.000 0.292 NA 0.128
#> SRR1785252     5   0.787     0.2413 0.108 0.020 0.244 NA 0.504
#> SRR1785253     5   0.787     0.2413 0.108 0.020 0.244 NA 0.504
#> SRR1785254     5   0.618     0.3982 0.076 0.080 0.008 NA 0.676
#> SRR1785255     5   0.618     0.3982 0.076 0.080 0.008 NA 0.676
#> SRR1785256     1   0.327     0.6054 0.852 0.000 0.016 NA 0.112
#> SRR1785257     1   0.327     0.6054 0.852 0.000 0.016 NA 0.112
#> SRR1785258     1   0.753     0.1372 0.452 0.000 0.160 NA 0.312
#> SRR1785259     1   0.753     0.1372 0.452 0.000 0.160 NA 0.312
#> SRR1785262     5   0.765     0.2243 0.236 0.004 0.200 NA 0.484
#> SRR1785263     5   0.765     0.2243 0.236 0.004 0.200 NA 0.484
#> SRR1785260     1   0.630     0.3425 0.540 0.000 0.008 NA 0.308
#> SRR1785261     1   0.630     0.3425 0.540 0.000 0.008 NA 0.308
#> SRR1785264     2   0.646     0.6408 0.004 0.632 0.048 NA 0.172
#> SRR1785265     2   0.646     0.6408 0.004 0.632 0.048 NA 0.172
#> SRR1785266     2   0.137     0.8405 0.000 0.956 0.008 NA 0.008
#> SRR1785267     2   0.137     0.8405 0.000 0.956 0.008 NA 0.008
#> SRR1785268     1   0.719     0.2834 0.496 0.000 0.312 NA 0.076
#> SRR1785269     1   0.719     0.2834 0.496 0.000 0.312 NA 0.076
#> SRR1785270     5   0.710     0.2609 0.008 0.064 0.080 NA 0.448
#> SRR1785271     5   0.710     0.2609 0.008 0.064 0.080 NA 0.448
#> SRR1785272     1   0.560     0.5541 0.700 0.000 0.168 NA 0.044
#> SRR1785273     1   0.560     0.5541 0.700 0.000 0.168 NA 0.044
#> SRR1785276     3   0.657     0.9063 0.108 0.008 0.620 NA 0.212
#> SRR1785277     3   0.657     0.9063 0.108 0.008 0.620 NA 0.212
#> SRR1785274     5   0.797     0.1587 0.164 0.004 0.228 NA 0.468
#> SRR1785275     5   0.797     0.1587 0.164 0.004 0.228 NA 0.468
#> SRR1785280     2   0.112     0.8390 0.000 0.964 0.016 NA 0.000
#> SRR1785281     2   0.112     0.8390 0.000 0.964 0.016 NA 0.000
#> SRR1785278     1   0.510     0.5555 0.748 0.000 0.124 NA 0.044
#> SRR1785279     1   0.510     0.5555 0.748 0.000 0.124 NA 0.044
#> SRR1785282     1   0.191     0.6148 0.932 0.000 0.044 NA 0.008
#> SRR1785283     1   0.191     0.6148 0.932 0.000 0.044 NA 0.008
#> SRR1785284     5   0.812     0.2366 0.164 0.004 0.176 NA 0.452
#> SRR1785285     5   0.812     0.2366 0.164 0.004 0.176 NA 0.452
#> SRR1785286     5   0.734     0.3333 0.212 0.000 0.092 NA 0.532
#> SRR1785287     5   0.734     0.3333 0.212 0.000 0.092 NA 0.532
#> SRR1785288     1   0.309     0.6004 0.880 0.000 0.032 NA 0.044
#> SRR1785289     1   0.309     0.6004 0.880 0.000 0.032 NA 0.044
#> SRR1785290     2   0.732     0.3161 0.008 0.432 0.028 NA 0.352
#> SRR1785291     2   0.732     0.3161 0.008 0.432 0.028 NA 0.352
#> SRR1785296     5   0.548     0.4029 0.180 0.024 0.032 NA 0.720
#> SRR1785297     5   0.548     0.4029 0.180 0.024 0.032 NA 0.720
#> SRR1785292     2   0.168     0.8438 0.000 0.944 0.012 NA 0.012
#> SRR1785293     2   0.168     0.8438 0.000 0.944 0.012 NA 0.012
#> SRR1785294     1   0.688     0.2784 0.480 0.000 0.040 NA 0.356
#> SRR1785295     1   0.688     0.2784 0.480 0.000 0.040 NA 0.356
#> SRR1785298     5   0.558     0.3831 0.244 0.028 0.024 NA 0.676
#> SRR1785299     5   0.558     0.3831 0.244 0.028 0.024 NA 0.676
#> SRR1785300     1   0.357     0.5931 0.812 0.000 0.000 NA 0.152
#> SRR1785301     1   0.357     0.5931 0.812 0.000 0.000 NA 0.152
#> SRR1785304     5   0.807    -0.0576 0.068 0.280 0.008 NA 0.376
#> SRR1785305     5   0.807    -0.0576 0.068 0.280 0.008 NA 0.376
#> SRR1785306     5   0.599     0.3466 0.008 0.036 0.064 NA 0.644
#> SRR1785307     5   0.599     0.3466 0.008 0.036 0.064 NA 0.644
#> SRR1785302     5   0.607     0.4139 0.104 0.036 0.044 NA 0.708
#> SRR1785303     5   0.607     0.4139 0.104 0.036 0.044 NA 0.708
#> SRR1785308     1   0.583     0.5449 0.688 0.000 0.164 NA 0.064
#> SRR1785309     1   0.583     0.5449 0.688 0.000 0.164 NA 0.064
#> SRR1785310     1   0.561     0.3649 0.584 0.000 0.008 NA 0.340
#> SRR1785311     1   0.561     0.3649 0.584 0.000 0.008 NA 0.340
#> SRR1785312     1   0.605     0.1280 0.472 0.000 0.444 NA 0.024
#> SRR1785313     1   0.605     0.1280 0.472 0.000 0.444 NA 0.024
#> SRR1785314     5   0.689     0.2783 0.012 0.064 0.056 NA 0.488
#> SRR1785315     5   0.689     0.2783 0.012 0.064 0.056 NA 0.488
#> SRR1785318     2   0.141     0.8438 0.000 0.956 0.012 NA 0.012
#> SRR1785319     2   0.141     0.8438 0.000 0.956 0.012 NA 0.012
#> SRR1785316     1   0.293     0.6084 0.876 0.000 0.076 NA 0.004
#> SRR1785317     1   0.293     0.6084 0.876 0.000 0.076 NA 0.004
#> SRR1785324     2   0.130     0.8442 0.000 0.960 0.008 NA 0.012
#> SRR1785325     2   0.130     0.8442 0.000 0.960 0.008 NA 0.012
#> SRR1785320     1   0.492     0.4008 0.656 0.000 0.304 NA 0.012
#> SRR1785321     1   0.492     0.4008 0.656 0.000 0.304 NA 0.012
#> SRR1785322     1   0.670     0.5020 0.620 0.000 0.144 NA 0.108
#> SRR1785323     1   0.670     0.5020 0.620 0.000 0.144 NA 0.108

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     4   0.808    -0.0626 0.104 0.032 0.248 0.456 0.100 0.060
#> SRR1785239     4   0.808    -0.0626 0.104 0.032 0.248 0.456 0.100 0.060
#> SRR1785240     5   0.788     0.3621 0.104 0.000 0.052 0.288 0.392 0.164
#> SRR1785241     5   0.788     0.3621 0.104 0.000 0.052 0.288 0.392 0.164
#> SRR1785242     4   0.751    -0.3661 0.052 0.012 0.364 0.400 0.124 0.048
#> SRR1785243     4   0.751    -0.3661 0.052 0.012 0.364 0.400 0.124 0.048
#> SRR1785244     1   0.240     0.5333 0.908 0.000 0.012 0.028 0.032 0.020
#> SRR1785245     1   0.240     0.5333 0.908 0.000 0.012 0.028 0.032 0.020
#> SRR1785246     6   0.594     0.6347 0.076 0.000 0.036 0.084 0.136 0.668
#> SRR1785247     6   0.594     0.6347 0.076 0.000 0.036 0.084 0.136 0.668
#> SRR1785248     2   0.463     0.7727 0.000 0.764 0.096 0.092 0.024 0.024
#> SRR1785250     3   0.627     0.3585 0.188 0.000 0.604 0.064 0.016 0.128
#> SRR1785251     3   0.627     0.3585 0.188 0.000 0.604 0.064 0.016 0.128
#> SRR1785252     3   0.741     0.2502 0.048 0.012 0.400 0.380 0.108 0.052
#> SRR1785253     3   0.741     0.2502 0.048 0.012 0.400 0.380 0.108 0.052
#> SRR1785254     4   0.573     0.2136 0.072 0.008 0.052 0.692 0.144 0.032
#> SRR1785255     4   0.573     0.2136 0.072 0.008 0.052 0.692 0.144 0.032
#> SRR1785256     1   0.414     0.5334 0.776 0.000 0.028 0.152 0.008 0.036
#> SRR1785257     1   0.414     0.5334 0.776 0.000 0.028 0.152 0.008 0.036
#> SRR1785258     1   0.783    -0.0204 0.412 0.000 0.148 0.284 0.080 0.076
#> SRR1785259     1   0.783    -0.0204 0.412 0.000 0.148 0.284 0.080 0.076
#> SRR1785262     4   0.803     0.0990 0.152 0.000 0.148 0.452 0.152 0.096
#> SRR1785263     4   0.803     0.0990 0.152 0.000 0.148 0.452 0.152 0.096
#> SRR1785260     1   0.703     0.2376 0.460 0.000 0.052 0.340 0.060 0.088
#> SRR1785261     1   0.703     0.2376 0.460 0.000 0.052 0.340 0.060 0.088
#> SRR1785264     2   0.666     0.5124 0.000 0.548 0.132 0.240 0.052 0.028
#> SRR1785265     2   0.666     0.5124 0.000 0.548 0.132 0.240 0.052 0.028
#> SRR1785266     2   0.201     0.8709 0.000 0.920 0.036 0.000 0.012 0.032
#> SRR1785267     2   0.201     0.8709 0.000 0.920 0.036 0.000 0.012 0.032
#> SRR1785268     1   0.713    -0.1402 0.360 0.000 0.284 0.024 0.028 0.304
#> SRR1785269     1   0.713    -0.1402 0.360 0.000 0.284 0.024 0.028 0.304
#> SRR1785270     5   0.449     0.5380 0.000 0.032 0.004 0.220 0.716 0.028
#> SRR1785271     5   0.449     0.5380 0.000 0.032 0.004 0.220 0.716 0.028
#> SRR1785272     1   0.585     0.2657 0.540 0.000 0.340 0.044 0.004 0.072
#> SRR1785273     1   0.585     0.2657 0.540 0.000 0.340 0.044 0.004 0.072
#> SRR1785276     6   0.617     0.6273 0.080 0.004 0.028 0.080 0.164 0.644
#> SRR1785277     6   0.617     0.6273 0.080 0.004 0.028 0.080 0.164 0.644
#> SRR1785274     4   0.797    -0.2448 0.120 0.000 0.048 0.360 0.308 0.164
#> SRR1785275     4   0.797    -0.2448 0.120 0.000 0.048 0.360 0.308 0.164
#> SRR1785280     2   0.248     0.8663 0.000 0.892 0.036 0.000 0.012 0.060
#> SRR1785281     2   0.248     0.8663 0.000 0.892 0.036 0.000 0.012 0.060
#> SRR1785278     1   0.605     0.4002 0.644 0.000 0.140 0.052 0.028 0.136
#> SRR1785279     1   0.605     0.4002 0.644 0.000 0.140 0.052 0.028 0.136
#> SRR1785282     1   0.191     0.5201 0.920 0.000 0.056 0.008 0.000 0.016
#> SRR1785283     1   0.191     0.5201 0.920 0.000 0.056 0.008 0.000 0.016
#> SRR1785284     5   0.759     0.3552 0.152 0.000 0.020 0.284 0.408 0.136
#> SRR1785285     5   0.759     0.3552 0.152 0.000 0.020 0.284 0.408 0.136
#> SRR1785286     4   0.735    -0.0414 0.188 0.000 0.020 0.444 0.260 0.088
#> SRR1785287     4   0.735    -0.0414 0.188 0.000 0.020 0.444 0.260 0.088
#> SRR1785288     1   0.238     0.5346 0.908 0.000 0.012 0.032 0.032 0.016
#> SRR1785289     1   0.238     0.5346 0.908 0.000 0.012 0.032 0.032 0.016
#> SRR1785290     4   0.702    -0.0838 0.004 0.348 0.108 0.444 0.080 0.016
#> SRR1785291     4   0.702    -0.0838 0.004 0.348 0.108 0.444 0.080 0.016
#> SRR1785296     4   0.475     0.3278 0.148 0.000 0.072 0.740 0.020 0.020
#> SRR1785297     4   0.475     0.3278 0.148 0.000 0.072 0.740 0.020 0.020
#> SRR1785292     2   0.153     0.8719 0.000 0.948 0.020 0.012 0.012 0.008
#> SRR1785293     2   0.153     0.8719 0.000 0.948 0.020 0.012 0.012 0.008
#> SRR1785294     1   0.595     0.2089 0.448 0.000 0.060 0.444 0.016 0.032
#> SRR1785295     1   0.595     0.2089 0.448 0.000 0.060 0.444 0.016 0.032
#> SRR1785298     4   0.553     0.3013 0.224 0.000 0.016 0.652 0.068 0.040
#> SRR1785299     4   0.553     0.3013 0.224 0.000 0.016 0.652 0.068 0.040
#> SRR1785300     1   0.396     0.5304 0.768 0.000 0.016 0.180 0.004 0.032
#> SRR1785301     1   0.396     0.5304 0.768 0.000 0.016 0.180 0.004 0.032
#> SRR1785304     4   0.855     0.1785 0.068 0.168 0.092 0.460 0.124 0.088
#> SRR1785305     4   0.855     0.1785 0.068 0.168 0.092 0.460 0.124 0.088
#> SRR1785306     5   0.615     0.3959 0.000 0.008 0.092 0.412 0.452 0.036
#> SRR1785307     5   0.615     0.3959 0.000 0.008 0.092 0.412 0.452 0.036
#> SRR1785302     4   0.663     0.2081 0.108 0.000 0.092 0.608 0.144 0.048
#> SRR1785303     4   0.663     0.2081 0.108 0.000 0.092 0.608 0.144 0.048
#> SRR1785308     1   0.573     0.1294 0.496 0.000 0.396 0.084 0.008 0.016
#> SRR1785309     1   0.573     0.1294 0.496 0.000 0.396 0.084 0.008 0.016
#> SRR1785310     1   0.485     0.2801 0.528 0.000 0.020 0.428 0.000 0.024
#> SRR1785311     1   0.485     0.2801 0.528 0.000 0.020 0.428 0.000 0.024
#> SRR1785312     6   0.553     0.3314 0.388 0.000 0.088 0.000 0.016 0.508
#> SRR1785313     6   0.553     0.3314 0.388 0.000 0.088 0.000 0.016 0.508
#> SRR1785314     5   0.497     0.5158 0.000 0.032 0.024 0.236 0.684 0.024
#> SRR1785315     5   0.497     0.5158 0.000 0.032 0.024 0.236 0.684 0.024
#> SRR1785318     2   0.127     0.8733 0.000 0.956 0.016 0.000 0.016 0.012
#> SRR1785319     2   0.127     0.8733 0.000 0.956 0.016 0.000 0.016 0.012
#> SRR1785316     1   0.377     0.4885 0.804 0.000 0.124 0.016 0.004 0.052
#> SRR1785317     1   0.377     0.4885 0.804 0.000 0.124 0.016 0.004 0.052
#> SRR1785324     2   0.157     0.8718 0.000 0.944 0.008 0.008 0.008 0.032
#> SRR1785325     2   0.157     0.8718 0.000 0.944 0.008 0.008 0.008 0.032
#> SRR1785320     1   0.501     0.0152 0.576 0.000 0.044 0.004 0.012 0.364
#> SRR1785321     1   0.501     0.0152 0.576 0.000 0.044 0.004 0.012 0.364
#> SRR1785322     1   0.768     0.2948 0.460 0.000 0.236 0.120 0.056 0.128
#> SRR1785323     1   0.768     0.2948 0.460 0.000 0.236 0.120 0.056 0.128

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-CV-kmeans-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-CV-kmeans-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-CV-kmeans-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-CV-kmeans-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-CV-kmeans-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-CV-kmeans-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-CV-kmeans-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-CV-kmeans-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-CV-kmeans-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-CV-kmeans-membership-heatmap-5

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)

plot of chunk tab-CV-kmeans-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-CV-kmeans-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-CV-kmeans-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-CV-kmeans-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-CV-kmeans-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-CV-kmeans-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-CV-kmeans-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-CV-kmeans-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-CV-kmeans-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-CV-kmeans-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk CV-kmeans-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-CV-kmeans-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-CV-kmeans-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-CV-kmeans-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-CV-kmeans-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-CV-kmeans-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk CV-kmeans-collect-classes

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.


CV:skmeans**

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 16620 rows and 87 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)

plot of chunk CV-skmeans-collect-plots

The plots are:

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:

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)

plot of chunk CV-skmeans-select-partition-number

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           0.958       0.983         0.4961 0.500   0.500
#> 3 3 0.699           0.808       0.904         0.3365 0.793   0.605
#> 4 4 0.600           0.537       0.740         0.1202 0.919   0.769
#> 5 5 0.625           0.526       0.697         0.0651 0.876   0.595
#> 6 6 0.653           0.503       0.681         0.0418 0.936   0.715

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2   0.000      0.961 0.000 1.000
#> SRR1785239     2   0.000      0.961 0.000 1.000
#> SRR1785240     1   0.000      1.000 1.000 0.000
#> SRR1785241     1   0.000      1.000 1.000 0.000
#> SRR1785242     2   0.000      0.961 0.000 1.000
#> SRR1785243     2   0.000      0.961 0.000 1.000
#> SRR1785244     1   0.000      1.000 1.000 0.000
#> SRR1785245     1   0.000      1.000 1.000 0.000
#> SRR1785246     1   0.000      1.000 1.000 0.000
#> SRR1785247     1   0.000      1.000 1.000 0.000
#> SRR1785248     2   0.000      0.961 0.000 1.000
#> SRR1785250     1   0.000      1.000 1.000 0.000
#> SRR1785251     1   0.000      1.000 1.000 0.000
#> SRR1785252     2   0.000      0.961 0.000 1.000
#> SRR1785253     2   0.000      0.961 0.000 1.000
#> SRR1785254     2   0.000      0.961 0.000 1.000
#> SRR1785255     2   0.000      0.961 0.000 1.000
#> SRR1785256     1   0.000      1.000 1.000 0.000
#> SRR1785257     1   0.000      1.000 1.000 0.000
#> SRR1785258     1   0.000      1.000 1.000 0.000
#> SRR1785259     1   0.000      1.000 1.000 0.000
#> SRR1785262     1   0.000      1.000 1.000 0.000
#> SRR1785263     1   0.000      1.000 1.000 0.000
#> SRR1785260     1   0.000      1.000 1.000 0.000
#> SRR1785261     1   0.000      1.000 1.000 0.000
#> SRR1785264     2   0.000      0.961 0.000 1.000
#> SRR1785265     2   0.000      0.961 0.000 1.000
#> SRR1785266     2   0.000      0.961 0.000 1.000
#> SRR1785267     2   0.000      0.961 0.000 1.000
#> SRR1785268     1   0.000      1.000 1.000 0.000
#> SRR1785269     1   0.000      1.000 1.000 0.000
#> SRR1785270     2   0.000      0.961 0.000 1.000
#> SRR1785271     2   0.000      0.961 0.000 1.000
#> SRR1785272     1   0.000      1.000 1.000 0.000
#> SRR1785273     1   0.000      1.000 1.000 0.000
#> SRR1785276     2   0.995      0.204 0.460 0.540
#> SRR1785277     2   0.995      0.204 0.460 0.540
#> SRR1785274     1   0.000      1.000 1.000 0.000
#> SRR1785275     1   0.000      1.000 1.000 0.000
#> SRR1785280     2   0.000      0.961 0.000 1.000
#> SRR1785281     2   0.000      0.961 0.000 1.000
#> SRR1785278     1   0.000      1.000 1.000 0.000
#> SRR1785279     1   0.000      1.000 1.000 0.000
#> SRR1785282     1   0.000      1.000 1.000 0.000
#> SRR1785283     1   0.000      1.000 1.000 0.000
#> SRR1785284     1   0.000      1.000 1.000 0.000
#> SRR1785285     1   0.000      1.000 1.000 0.000
#> SRR1785286     1   0.000      1.000 1.000 0.000
#> SRR1785287     1   0.000      1.000 1.000 0.000
#> SRR1785288     1   0.000      1.000 1.000 0.000
#> SRR1785289     1   0.000      1.000 1.000 0.000
#> SRR1785290     2   0.000      0.961 0.000 1.000
#> SRR1785291     2   0.000      0.961 0.000 1.000
#> SRR1785296     2   0.000      0.961 0.000 1.000
#> SRR1785297     2   0.000      0.961 0.000 1.000
#> SRR1785292     2   0.000      0.961 0.000 1.000
#> SRR1785293     2   0.000      0.961 0.000 1.000
#> SRR1785294     1   0.000      1.000 1.000 0.000
#> SRR1785295     1   0.000      1.000 1.000 0.000
#> SRR1785298     2   0.850      0.633 0.276 0.724
#> SRR1785299     2   0.850      0.633 0.276 0.724
#> SRR1785300     1   0.000      1.000 1.000 0.000
#> SRR1785301     1   0.000      1.000 1.000 0.000
#> SRR1785304     2   0.000      0.961 0.000 1.000
#> SRR1785305     2   0.000      0.961 0.000 1.000
#> SRR1785306     2   0.000      0.961 0.000 1.000
#> SRR1785307     2   0.000      0.961 0.000 1.000
#> SRR1785302     2   0.000      0.961 0.000 1.000
#> SRR1785303     2   0.000      0.961 0.000 1.000
#> SRR1785308     1   0.000      1.000 1.000 0.000
#> SRR1785309     1   0.000      1.000 1.000 0.000
#> SRR1785310     1   0.000      1.000 1.000 0.000
#> SRR1785311     1   0.000      1.000 1.000 0.000
#> SRR1785312     1   0.000      1.000 1.000 0.000
#> SRR1785313     1   0.000      1.000 1.000 0.000
#> SRR1785314     2   0.000      0.961 0.000 1.000
#> SRR1785315     2   0.000      0.961 0.000 1.000
#> SRR1785318     2   0.000      0.961 0.000 1.000
#> SRR1785319     2   0.000      0.961 0.000 1.000
#> SRR1785316     1   0.000      1.000 1.000 0.000
#> SRR1785317     1   0.000      1.000 1.000 0.000
#> SRR1785324     2   0.000      0.961 0.000 1.000
#> SRR1785325     2   0.000      0.961 0.000 1.000
#> SRR1785320     1   0.000      1.000 1.000 0.000
#> SRR1785321     1   0.000      1.000 1.000 0.000
#> SRR1785322     1   0.000      1.000 1.000 0.000
#> SRR1785323     1   0.000      1.000 1.000 0.000

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     2  0.1289     0.8598 0.000 0.968 0.032
#> SRR1785239     2  0.1289     0.8598 0.000 0.968 0.032
#> SRR1785240     3  0.0892     0.8589 0.020 0.000 0.980
#> SRR1785241     3  0.0892     0.8589 0.020 0.000 0.980
#> SRR1785242     3  0.4555     0.7279 0.000 0.200 0.800
#> SRR1785243     3  0.4504     0.7325 0.000 0.196 0.804
#> SRR1785244     1  0.0237     0.9217 0.996 0.000 0.004
#> SRR1785245     1  0.0237     0.9217 0.996 0.000 0.004
#> SRR1785246     3  0.0892     0.8567 0.020 0.000 0.980
#> SRR1785247     3  0.0892     0.8567 0.020 0.000 0.980
#> SRR1785248     2  0.0000     0.8795 0.000 1.000 0.000
#> SRR1785250     1  0.4887     0.8200 0.772 0.000 0.228
#> SRR1785251     1  0.4887     0.8200 0.772 0.000 0.228
#> SRR1785252     3  0.3715     0.7978 0.004 0.128 0.868
#> SRR1785253     3  0.3715     0.7978 0.004 0.128 0.868
#> SRR1785254     2  0.0237     0.8780 0.000 0.996 0.004
#> SRR1785255     2  0.0237     0.8780 0.000 0.996 0.004
#> SRR1785256     1  0.0237     0.9217 0.996 0.000 0.004
#> SRR1785257     1  0.0237     0.9217 0.996 0.000 0.004
#> SRR1785258     1  0.4974     0.7848 0.764 0.000 0.236
#> SRR1785259     1  0.5016     0.7794 0.760 0.000 0.240
#> SRR1785262     3  0.1289     0.8552 0.032 0.000 0.968
#> SRR1785263     3  0.1289     0.8552 0.032 0.000 0.968
#> SRR1785260     1  0.1289     0.9151 0.968 0.000 0.032
#> SRR1785261     1  0.1289     0.9151 0.968 0.000 0.032
#> SRR1785264     2  0.0000     0.8795 0.000 1.000 0.000
#> SRR1785265     2  0.0000     0.8795 0.000 1.000 0.000
#> SRR1785266     2  0.0000     0.8795 0.000 1.000 0.000
#> SRR1785267     2  0.0000     0.8795 0.000 1.000 0.000
#> SRR1785268     1  0.4121     0.8615 0.832 0.000 0.168
#> SRR1785269     1  0.4121     0.8615 0.832 0.000 0.168
#> SRR1785270     2  0.6286     0.0528 0.000 0.536 0.464
#> SRR1785271     2  0.6286     0.0528 0.000 0.536 0.464
#> SRR1785272     1  0.2356     0.9054 0.928 0.000 0.072
#> SRR1785273     1  0.2356     0.9054 0.928 0.000 0.072
#> SRR1785276     3  0.0983     0.8586 0.016 0.004 0.980
#> SRR1785277     3  0.0983     0.8586 0.016 0.004 0.980
#> SRR1785274     3  0.0829     0.8582 0.012 0.004 0.984
#> SRR1785275     3  0.0829     0.8582 0.012 0.004 0.984
#> SRR1785280     2  0.0000     0.8795 0.000 1.000 0.000
#> SRR1785281     2  0.0000     0.8795 0.000 1.000 0.000
#> SRR1785278     1  0.2448     0.9087 0.924 0.000 0.076
#> SRR1785279     1  0.2448     0.9087 0.924 0.000 0.076
#> SRR1785282     1  0.0000     0.9217 1.000 0.000 0.000
#> SRR1785283     1  0.0000     0.9217 1.000 0.000 0.000
#> SRR1785284     3  0.2796     0.8318 0.092 0.000 0.908
#> SRR1785285     3  0.2796     0.8318 0.092 0.000 0.908
#> SRR1785286     3  0.4887     0.7313 0.228 0.000 0.772
#> SRR1785287     3  0.4887     0.7313 0.228 0.000 0.772
#> SRR1785288     1  0.0237     0.9217 0.996 0.000 0.004
#> SRR1785289     1  0.0237     0.9217 0.996 0.000 0.004
#> SRR1785290     2  0.0000     0.8795 0.000 1.000 0.000
#> SRR1785291     2  0.0000     0.8795 0.000 1.000 0.000
#> SRR1785296     2  0.6703     0.5819 0.052 0.712 0.236
#> SRR1785297     2  0.6796     0.5768 0.056 0.708 0.236
#> SRR1785292     2  0.0000     0.8795 0.000 1.000 0.000
#> SRR1785293     2  0.0000     0.8795 0.000 1.000 0.000
#> SRR1785294     1  0.1289     0.9151 0.968 0.000 0.032
#> SRR1785295     1  0.1289     0.9151 0.968 0.000 0.032
#> SRR1785298     2  0.9052     0.3386 0.216 0.556 0.228
#> SRR1785299     2  0.9086     0.3320 0.220 0.552 0.228
#> SRR1785300     1  0.0237     0.9217 0.996 0.000 0.004
#> SRR1785301     1  0.0237     0.9217 0.996 0.000 0.004
#> SRR1785304     2  0.0424     0.8761 0.000 0.992 0.008
#> SRR1785305     2  0.0424     0.8761 0.000 0.992 0.008
#> SRR1785306     3  0.6307     0.0335 0.000 0.488 0.512
#> SRR1785307     3  0.6305     0.0489 0.000 0.484 0.516
#> SRR1785302     2  0.1411     0.8593 0.000 0.964 0.036
#> SRR1785303     2  0.1411     0.8593 0.000 0.964 0.036
#> SRR1785308     1  0.2261     0.9054 0.932 0.000 0.068
#> SRR1785309     1  0.2261     0.9054 0.932 0.000 0.068
#> SRR1785310     1  0.1289     0.9151 0.968 0.000 0.032
#> SRR1785311     1  0.1289     0.9151 0.968 0.000 0.032
#> SRR1785312     1  0.3879     0.8747 0.848 0.000 0.152
#> SRR1785313     1  0.3879     0.8747 0.848 0.000 0.152
#> SRR1785314     2  0.5291     0.5755 0.000 0.732 0.268
#> SRR1785315     2  0.5291     0.5755 0.000 0.732 0.268
#> SRR1785318     2  0.0000     0.8795 0.000 1.000 0.000
#> SRR1785319     2  0.0000     0.8795 0.000 1.000 0.000
#> SRR1785316     1  0.0237     0.9216 0.996 0.000 0.004
#> SRR1785317     1  0.0237     0.9216 0.996 0.000 0.004
#> SRR1785324     2  0.0000     0.8795 0.000 1.000 0.000
#> SRR1785325     2  0.0000     0.8795 0.000 1.000 0.000
#> SRR1785320     1  0.3879     0.8763 0.848 0.000 0.152
#> SRR1785321     1  0.3879     0.8763 0.848 0.000 0.152
#> SRR1785322     1  0.3267     0.8942 0.884 0.000 0.116
#> SRR1785323     1  0.3267     0.8942 0.884 0.000 0.116

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     2  0.3542    0.71415 0.000 0.852 0.028 0.120
#> SRR1785239     2  0.3542    0.71415 0.000 0.852 0.028 0.120
#> SRR1785240     3  0.0927    0.62700 0.008 0.000 0.976 0.016
#> SRR1785241     3  0.0927    0.62700 0.008 0.000 0.976 0.016
#> SRR1785242     3  0.6616    0.40979 0.000 0.108 0.584 0.308
#> SRR1785243     3  0.6616    0.40979 0.000 0.108 0.584 0.308
#> SRR1785244     1  0.0188    0.67806 0.996 0.000 0.000 0.004
#> SRR1785245     1  0.0188    0.67806 0.996 0.000 0.000 0.004
#> SRR1785246     3  0.4372    0.54552 0.004 0.000 0.728 0.268
#> SRR1785247     3  0.4372    0.54552 0.004 0.000 0.728 0.268
#> SRR1785248     2  0.0188    0.85329 0.000 0.996 0.000 0.004
#> SRR1785250     4  0.6716    0.01364 0.320 0.000 0.112 0.568
#> SRR1785251     4  0.6685    0.00527 0.324 0.000 0.108 0.568
#> SRR1785252     3  0.6324    0.41715 0.000 0.076 0.584 0.340
#> SRR1785253     3  0.6324    0.41715 0.000 0.076 0.584 0.340
#> SRR1785254     2  0.1520    0.83200 0.000 0.956 0.020 0.024
#> SRR1785255     2  0.1624    0.83059 0.000 0.952 0.020 0.028
#> SRR1785256     1  0.0921    0.67219 0.972 0.000 0.000 0.028
#> SRR1785257     1  0.0921    0.67219 0.972 0.000 0.000 0.028
#> SRR1785258     4  0.7716    0.08136 0.380 0.000 0.224 0.396
#> SRR1785259     4  0.7745    0.09442 0.372 0.000 0.232 0.396
#> SRR1785262     3  0.4122    0.55652 0.004 0.000 0.760 0.236
#> SRR1785263     3  0.4122    0.55652 0.004 0.000 0.760 0.236
#> SRR1785260     1  0.4543    0.39318 0.676 0.000 0.000 0.324
#> SRR1785261     1  0.4543    0.39318 0.676 0.000 0.000 0.324
#> SRR1785264     2  0.0188    0.85329 0.000 0.996 0.000 0.004
#> SRR1785265     2  0.0188    0.85329 0.000 0.996 0.000 0.004
#> SRR1785266     2  0.0188    0.85329 0.000 0.996 0.000 0.004
#> SRR1785267     2  0.0188    0.85329 0.000 0.996 0.000 0.004
#> SRR1785268     1  0.6264    0.41234 0.560 0.000 0.064 0.376
#> SRR1785269     1  0.6264    0.41234 0.560 0.000 0.064 0.376
#> SRR1785270     2  0.5888    0.26060 0.000 0.540 0.424 0.036
#> SRR1785271     2  0.5888    0.26060 0.000 0.540 0.424 0.036
#> SRR1785272     1  0.5227    0.48871 0.704 0.000 0.040 0.256
#> SRR1785273     1  0.5227    0.48871 0.704 0.000 0.040 0.256
#> SRR1785276     3  0.5223    0.52556 0.008 0.016 0.684 0.292
#> SRR1785277     3  0.5247    0.52446 0.008 0.016 0.680 0.296
#> SRR1785274     3  0.1545    0.62760 0.008 0.000 0.952 0.040
#> SRR1785275     3  0.1545    0.62760 0.008 0.000 0.952 0.040
#> SRR1785280     2  0.0000    0.85411 0.000 1.000 0.000 0.000
#> SRR1785281     2  0.0000    0.85411 0.000 1.000 0.000 0.000
#> SRR1785278     1  0.4252    0.58958 0.744 0.000 0.004 0.252
#> SRR1785279     1  0.4252    0.58958 0.744 0.000 0.004 0.252
#> SRR1785282     1  0.1302    0.67671 0.956 0.000 0.000 0.044
#> SRR1785283     1  0.1302    0.67671 0.956 0.000 0.000 0.044
#> SRR1785284     3  0.4532    0.55182 0.052 0.000 0.792 0.156
#> SRR1785285     3  0.4532    0.55182 0.052 0.000 0.792 0.156
#> SRR1785286     3  0.6931    0.32794 0.184 0.000 0.588 0.228
#> SRR1785287     3  0.6931    0.32794 0.184 0.000 0.588 0.228
#> SRR1785288     1  0.0188    0.67806 0.996 0.000 0.000 0.004
#> SRR1785289     1  0.0188    0.67806 0.996 0.000 0.000 0.004
#> SRR1785290     2  0.0188    0.85253 0.000 0.996 0.000 0.004
#> SRR1785291     2  0.0188    0.85253 0.000 0.996 0.000 0.004
#> SRR1785296     4  0.7938    0.28701 0.020 0.332 0.172 0.476
#> SRR1785297     4  0.7986    0.29057 0.024 0.328 0.168 0.480
#> SRR1785292     2  0.0000    0.85411 0.000 1.000 0.000 0.000
#> SRR1785293     2  0.0000    0.85411 0.000 1.000 0.000 0.000
#> SRR1785294     1  0.4697    0.35926 0.644 0.000 0.000 0.356
#> SRR1785295     1  0.4697    0.35926 0.644 0.000 0.000 0.356
#> SRR1785298     4  0.9104    0.31308 0.304 0.228 0.076 0.392
#> SRR1785299     4  0.9039    0.31021 0.304 0.224 0.072 0.400
#> SRR1785300     1  0.1118    0.66891 0.964 0.000 0.000 0.036
#> SRR1785301     1  0.1211    0.66684 0.960 0.000 0.000 0.040
#> SRR1785304     2  0.3942    0.57867 0.000 0.764 0.000 0.236
#> SRR1785305     2  0.3942    0.57867 0.000 0.764 0.000 0.236
#> SRR1785306     3  0.6783    0.13097 0.000 0.388 0.512 0.100
#> SRR1785307     3  0.6775    0.14319 0.000 0.384 0.516 0.100
#> SRR1785302     4  0.7004    0.12841 0.004 0.428 0.100 0.468
#> SRR1785303     4  0.7004    0.12841 0.004 0.428 0.100 0.468
#> SRR1785308     1  0.5434    0.45756 0.696 0.000 0.052 0.252
#> SRR1785309     1  0.5434    0.45756 0.696 0.000 0.052 0.252
#> SRR1785310     1  0.4720    0.38676 0.672 0.000 0.004 0.324
#> SRR1785311     1  0.4720    0.38676 0.672 0.000 0.004 0.324
#> SRR1785312     1  0.6553    0.45289 0.584 0.000 0.100 0.316
#> SRR1785313     1  0.6553    0.45289 0.584 0.000 0.100 0.316
#> SRR1785314     2  0.6355    0.37293 0.000 0.576 0.348 0.076
#> SRR1785315     2  0.6340    0.38129 0.000 0.580 0.344 0.076
#> SRR1785318     2  0.0000    0.85411 0.000 1.000 0.000 0.000
#> SRR1785319     2  0.0000    0.85411 0.000 1.000 0.000 0.000
#> SRR1785316     1  0.1389    0.67662 0.952 0.000 0.000 0.048
#> SRR1785317     1  0.1389    0.67662 0.952 0.000 0.000 0.048
#> SRR1785324     2  0.0000    0.85411 0.000 1.000 0.000 0.000
#> SRR1785325     2  0.0000    0.85411 0.000 1.000 0.000 0.000
#> SRR1785320     1  0.6167    0.51813 0.648 0.000 0.096 0.256
#> SRR1785321     1  0.6167    0.51813 0.648 0.000 0.096 0.256
#> SRR1785322     1  0.4978    0.52865 0.664 0.000 0.012 0.324
#> SRR1785323     1  0.4978    0.52865 0.664 0.000 0.012 0.324

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     2  0.3538     0.7377 0.000 0.804 0.176 0.016 0.004
#> SRR1785239     2  0.3575     0.7333 0.000 0.800 0.180 0.016 0.004
#> SRR1785240     5  0.4283     0.4206 0.004 0.000 0.292 0.012 0.692
#> SRR1785241     5  0.4283     0.4206 0.004 0.000 0.292 0.012 0.692
#> SRR1785242     3  0.2899     0.5854 0.000 0.028 0.872 0.004 0.096
#> SRR1785243     3  0.2899     0.5854 0.000 0.028 0.872 0.004 0.096
#> SRR1785244     1  0.1357     0.5849 0.948 0.000 0.000 0.048 0.004
#> SRR1785245     1  0.1357     0.5849 0.948 0.000 0.000 0.048 0.004
#> SRR1785246     5  0.6493     0.2526 0.020 0.000 0.188 0.220 0.572
#> SRR1785247     5  0.6493     0.2526 0.020 0.000 0.188 0.220 0.572
#> SRR1785248     2  0.0290     0.9028 0.000 0.992 0.008 0.000 0.000
#> SRR1785250     3  0.6875     0.4291 0.176 0.000 0.576 0.188 0.060
#> SRR1785251     3  0.6845     0.4348 0.172 0.000 0.580 0.188 0.060
#> SRR1785252     3  0.2178     0.6017 0.000 0.024 0.920 0.008 0.048
#> SRR1785253     3  0.2178     0.6017 0.000 0.024 0.920 0.008 0.048
#> SRR1785254     2  0.4261     0.7636 0.000 0.804 0.024 0.076 0.096
#> SRR1785255     2  0.4319     0.7597 0.000 0.800 0.024 0.080 0.096
#> SRR1785256     1  0.2989     0.5048 0.852 0.000 0.008 0.132 0.008
#> SRR1785257     1  0.2989     0.5048 0.852 0.000 0.008 0.132 0.008
#> SRR1785258     3  0.6787     0.4122 0.288 0.000 0.548 0.104 0.060
#> SRR1785259     3  0.6737     0.4343 0.276 0.000 0.560 0.104 0.060
#> SRR1785262     3  0.5486     0.3215 0.004 0.000 0.640 0.096 0.260
#> SRR1785263     3  0.5463     0.3298 0.004 0.000 0.644 0.096 0.256
#> SRR1785260     4  0.4256     0.5251 0.436 0.000 0.000 0.564 0.000
#> SRR1785261     4  0.4256     0.5251 0.436 0.000 0.000 0.564 0.000
#> SRR1785264     2  0.0451     0.9021 0.000 0.988 0.008 0.004 0.000
#> SRR1785265     2  0.0451     0.9021 0.000 0.988 0.008 0.004 0.000
#> SRR1785266     2  0.0000     0.9050 0.000 1.000 0.000 0.000 0.000
#> SRR1785267     2  0.0000     0.9050 0.000 1.000 0.000 0.000 0.000
#> SRR1785268     1  0.7418     0.4112 0.508 0.000 0.112 0.260 0.120
#> SRR1785269     1  0.7418     0.4112 0.508 0.000 0.112 0.260 0.120
#> SRR1785270     5  0.5713     0.4510 0.000 0.316 0.024 0.056 0.604
#> SRR1785271     5  0.5713     0.4510 0.000 0.316 0.024 0.056 0.604
#> SRR1785272     1  0.5138     0.2510 0.592 0.000 0.368 0.032 0.008
#> SRR1785273     1  0.5138     0.2510 0.592 0.000 0.368 0.032 0.008
#> SRR1785276     5  0.6554     0.3161 0.028 0.016 0.112 0.236 0.608
#> SRR1785277     5  0.6554     0.3161 0.028 0.016 0.112 0.236 0.608
#> SRR1785274     5  0.4425     0.4016 0.000 0.000 0.296 0.024 0.680
#> SRR1785275     5  0.4404     0.4024 0.000 0.000 0.292 0.024 0.684
#> SRR1785280     2  0.0000     0.9050 0.000 1.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000     0.9050 0.000 1.000 0.000 0.000 0.000
#> SRR1785278     1  0.5783     0.5731 0.684 0.000 0.048 0.176 0.092
#> SRR1785279     1  0.5783     0.5731 0.684 0.000 0.048 0.176 0.092
#> SRR1785282     1  0.0000     0.6031 1.000 0.000 0.000 0.000 0.000
#> SRR1785283     1  0.0000     0.6031 1.000 0.000 0.000 0.000 0.000
#> SRR1785284     5  0.4572     0.4700 0.020 0.000 0.180 0.044 0.756
#> SRR1785285     5  0.4605     0.4709 0.020 0.000 0.176 0.048 0.756
#> SRR1785286     5  0.7401     0.3235 0.096 0.000 0.164 0.216 0.524
#> SRR1785287     5  0.7420     0.3196 0.100 0.000 0.164 0.212 0.524
#> SRR1785288     1  0.1357     0.5849 0.948 0.000 0.000 0.048 0.004
#> SRR1785289     1  0.1357     0.5849 0.948 0.000 0.000 0.048 0.004
#> SRR1785290     2  0.1059     0.8963 0.000 0.968 0.008 0.020 0.004
#> SRR1785291     2  0.1059     0.8963 0.000 0.968 0.008 0.020 0.004
#> SRR1785296     4  0.6667     0.2852 0.000 0.188 0.296 0.504 0.012
#> SRR1785297     4  0.6680     0.2813 0.000 0.188 0.300 0.500 0.012
#> SRR1785292     2  0.0162     0.9041 0.000 0.996 0.004 0.000 0.000
#> SRR1785293     2  0.0162     0.9041 0.000 0.996 0.004 0.000 0.000
#> SRR1785294     4  0.4622     0.5240 0.440 0.000 0.012 0.548 0.000
#> SRR1785295     4  0.4622     0.5240 0.440 0.000 0.012 0.548 0.000
#> SRR1785298     4  0.8955     0.4621 0.228 0.144 0.072 0.420 0.136
#> SRR1785299     4  0.9027     0.4582 0.228 0.160 0.072 0.408 0.132
#> SRR1785300     1  0.2732     0.4665 0.840 0.000 0.000 0.160 0.000
#> SRR1785301     1  0.2732     0.4665 0.840 0.000 0.000 0.160 0.000
#> SRR1785304     2  0.4350     0.3581 0.000 0.588 0.000 0.408 0.004
#> SRR1785305     2  0.4350     0.3581 0.000 0.588 0.000 0.408 0.004
#> SRR1785306     5  0.7547     0.4066 0.000 0.228 0.144 0.120 0.508
#> SRR1785307     5  0.7547     0.4066 0.000 0.228 0.144 0.120 0.508
#> SRR1785302     4  0.8331     0.0211 0.000 0.184 0.208 0.384 0.224
#> SRR1785303     4  0.8299     0.0161 0.000 0.172 0.208 0.388 0.232
#> SRR1785308     1  0.4610     0.1117 0.556 0.000 0.432 0.012 0.000
#> SRR1785309     1  0.4604     0.1225 0.560 0.000 0.428 0.012 0.000
#> SRR1785310     4  0.4420     0.5078 0.448 0.000 0.004 0.548 0.000
#> SRR1785311     4  0.4420     0.5078 0.448 0.000 0.004 0.548 0.000
#> SRR1785312     1  0.7106     0.4223 0.508 0.000 0.040 0.244 0.208
#> SRR1785313     1  0.7106     0.4223 0.508 0.000 0.040 0.244 0.208
#> SRR1785314     5  0.6133     0.4064 0.000 0.336 0.028 0.076 0.560
#> SRR1785315     5  0.6133     0.4064 0.000 0.336 0.028 0.076 0.560
#> SRR1785318     2  0.0000     0.9050 0.000 1.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000     0.9050 0.000 1.000 0.000 0.000 0.000
#> SRR1785316     1  0.0693     0.6044 0.980 0.000 0.008 0.012 0.000
#> SRR1785317     1  0.0693     0.6044 0.980 0.000 0.008 0.012 0.000
#> SRR1785324     2  0.0162     0.9041 0.000 0.996 0.004 0.000 0.000
#> SRR1785325     2  0.0162     0.9041 0.000 0.996 0.004 0.000 0.000
#> SRR1785320     1  0.6088     0.5049 0.612 0.000 0.012 0.208 0.168
#> SRR1785321     1  0.6088     0.5049 0.612 0.000 0.012 0.208 0.168
#> SRR1785322     1  0.7047     0.4822 0.552 0.000 0.104 0.248 0.096
#> SRR1785323     1  0.7047     0.4822 0.552 0.000 0.104 0.248 0.096

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     2  0.4548     0.6503 0.004 0.720 0.204 0.020 0.000 0.052
#> SRR1785239     2  0.4538     0.6551 0.004 0.724 0.200 0.024 0.000 0.048
#> SRR1785240     5  0.2113     0.5139 0.004 0.000 0.092 0.000 0.896 0.008
#> SRR1785241     5  0.2113     0.5139 0.004 0.000 0.092 0.000 0.896 0.008
#> SRR1785242     3  0.3883     0.4993 0.000 0.012 0.744 0.024 0.220 0.000
#> SRR1785243     3  0.3883     0.4993 0.000 0.012 0.744 0.024 0.220 0.000
#> SRR1785244     1  0.1932     0.5940 0.924 0.000 0.000 0.040 0.016 0.020
#> SRR1785245     1  0.1932     0.5940 0.924 0.000 0.000 0.040 0.016 0.020
#> SRR1785246     6  0.5336     0.4615 0.016 0.000 0.112 0.004 0.228 0.640
#> SRR1785247     6  0.5297     0.4661 0.016 0.000 0.108 0.004 0.228 0.644
#> SRR1785248     2  0.0862     0.8685 0.000 0.972 0.016 0.004 0.000 0.008
#> SRR1785250     3  0.5605     0.3349 0.116 0.000 0.652 0.064 0.000 0.168
#> SRR1785251     3  0.5605     0.3349 0.116 0.000 0.652 0.064 0.000 0.168
#> SRR1785252     3  0.3385     0.5173 0.000 0.004 0.796 0.028 0.172 0.000
#> SRR1785253     3  0.3385     0.5173 0.000 0.004 0.796 0.028 0.172 0.000
#> SRR1785254     2  0.6231     0.5681 0.000 0.640 0.032 0.104 0.088 0.136
#> SRR1785255     2  0.6186     0.5739 0.000 0.644 0.032 0.104 0.084 0.136
#> SRR1785256     1  0.4100     0.4278 0.756 0.000 0.012 0.192 0.028 0.012
#> SRR1785257     1  0.4100     0.4278 0.756 0.000 0.012 0.192 0.028 0.012
#> SRR1785258     3  0.7508     0.3778 0.208 0.000 0.488 0.052 0.096 0.156
#> SRR1785259     3  0.7484     0.3863 0.196 0.000 0.496 0.052 0.100 0.156
#> SRR1785262     3  0.6427     0.2273 0.012 0.000 0.428 0.116 0.408 0.036
#> SRR1785263     3  0.6429     0.2112 0.012 0.000 0.420 0.116 0.416 0.036
#> SRR1785260     4  0.3684     0.6509 0.332 0.000 0.000 0.664 0.000 0.004
#> SRR1785261     4  0.3684     0.6509 0.332 0.000 0.000 0.664 0.000 0.004
#> SRR1785264     2  0.0767     0.8703 0.000 0.976 0.012 0.004 0.000 0.008
#> SRR1785265     2  0.0767     0.8703 0.000 0.976 0.012 0.004 0.000 0.008
#> SRR1785266     2  0.0291     0.8730 0.000 0.992 0.004 0.004 0.000 0.000
#> SRR1785267     2  0.0291     0.8730 0.000 0.992 0.004 0.004 0.000 0.000
#> SRR1785268     6  0.6544     0.4004 0.324 0.000 0.164 0.052 0.000 0.460
#> SRR1785269     6  0.6551     0.4072 0.316 0.000 0.168 0.052 0.000 0.464
#> SRR1785270     5  0.7392     0.5082 0.000 0.224 0.040 0.136 0.492 0.108
#> SRR1785271     5  0.7392     0.5082 0.000 0.224 0.040 0.136 0.492 0.108
#> SRR1785272     1  0.5683     0.2474 0.496 0.000 0.392 0.024 0.000 0.088
#> SRR1785273     1  0.5683     0.2474 0.496 0.000 0.392 0.024 0.000 0.088
#> SRR1785276     6  0.4377     0.4897 0.016 0.000 0.020 0.016 0.228 0.720
#> SRR1785277     6  0.4377     0.4897 0.016 0.000 0.020 0.016 0.228 0.720
#> SRR1785274     5  0.4044     0.4699 0.000 0.000 0.128 0.020 0.780 0.072
#> SRR1785275     5  0.3990     0.4715 0.000 0.000 0.128 0.020 0.784 0.068
#> SRR1785280     2  0.0000     0.8748 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000     0.8748 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785278     1  0.5984     0.0377 0.556 0.000 0.084 0.052 0.004 0.304
#> SRR1785279     1  0.5984     0.0377 0.556 0.000 0.084 0.052 0.004 0.304
#> SRR1785282     1  0.1116     0.5949 0.960 0.000 0.004 0.008 0.000 0.028
#> SRR1785283     1  0.1036     0.5962 0.964 0.000 0.004 0.008 0.000 0.024
#> SRR1785284     5  0.1439     0.5419 0.016 0.000 0.008 0.012 0.952 0.012
#> SRR1785285     5  0.1533     0.5409 0.016 0.000 0.008 0.016 0.948 0.012
#> SRR1785286     5  0.5341     0.4036 0.084 0.000 0.036 0.176 0.688 0.016
#> SRR1785287     5  0.5371     0.4009 0.084 0.000 0.036 0.180 0.684 0.016
#> SRR1785288     1  0.1838     0.5937 0.928 0.000 0.000 0.040 0.012 0.020
#> SRR1785289     1  0.1838     0.5937 0.928 0.000 0.000 0.040 0.012 0.020
#> SRR1785290     2  0.1370     0.8574 0.000 0.948 0.004 0.036 0.000 0.012
#> SRR1785291     2  0.1370     0.8574 0.000 0.948 0.004 0.036 0.000 0.012
#> SRR1785296     4  0.6523     0.3516 0.000 0.144 0.212 0.568 0.056 0.020
#> SRR1785297     4  0.6352     0.3588 0.000 0.136 0.216 0.580 0.052 0.016
#> SRR1785292     2  0.0000     0.8748 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785293     2  0.0000     0.8748 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785294     4  0.3693     0.6642 0.280 0.000 0.008 0.708 0.000 0.004
#> SRR1785295     4  0.3693     0.6642 0.280 0.000 0.008 0.708 0.000 0.004
#> SRR1785298     4  0.9280     0.3399 0.176 0.092 0.072 0.344 0.140 0.176
#> SRR1785299     4  0.9294     0.3478 0.172 0.104 0.076 0.352 0.132 0.164
#> SRR1785300     1  0.3593     0.3896 0.756 0.000 0.004 0.224 0.004 0.012
#> SRR1785301     1  0.3593     0.3896 0.756 0.000 0.004 0.224 0.004 0.012
#> SRR1785304     2  0.4080     0.2653 0.000 0.536 0.000 0.456 0.000 0.008
#> SRR1785305     2  0.4080     0.2653 0.000 0.536 0.000 0.456 0.000 0.008
#> SRR1785306     5  0.8402     0.4016 0.000 0.132 0.148 0.176 0.396 0.148
#> SRR1785307     5  0.8383     0.4071 0.000 0.132 0.152 0.172 0.400 0.144
#> SRR1785302     3  0.8773     0.0708 0.008 0.096 0.276 0.252 0.128 0.240
#> SRR1785303     3  0.8751     0.0724 0.008 0.092 0.276 0.256 0.128 0.240
#> SRR1785308     1  0.5053     0.1666 0.496 0.000 0.448 0.020 0.000 0.036
#> SRR1785309     1  0.5053     0.1666 0.496 0.000 0.448 0.020 0.000 0.036
#> SRR1785310     4  0.4105     0.6434 0.344 0.000 0.004 0.640 0.004 0.008
#> SRR1785311     4  0.4105     0.6434 0.344 0.000 0.004 0.640 0.004 0.008
#> SRR1785312     6  0.4091     0.5709 0.292 0.000 0.012 0.008 0.004 0.684
#> SRR1785313     6  0.4091     0.5709 0.292 0.000 0.012 0.008 0.004 0.684
#> SRR1785314     5  0.7895     0.4842 0.000 0.224 0.060 0.156 0.436 0.124
#> SRR1785315     5  0.7895     0.4842 0.000 0.224 0.060 0.156 0.436 0.124
#> SRR1785318     2  0.0000     0.8748 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000     0.8748 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785316     1  0.2518     0.5550 0.880 0.000 0.012 0.016 0.000 0.092
#> SRR1785317     1  0.2467     0.5583 0.884 0.000 0.012 0.016 0.000 0.088
#> SRR1785324     2  0.0000     0.8748 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785325     2  0.0000     0.8748 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785320     6  0.4304     0.4070 0.448 0.000 0.000 0.008 0.008 0.536
#> SRR1785321     6  0.4296     0.4204 0.440 0.000 0.000 0.008 0.008 0.544
#> SRR1785322     1  0.7283    -0.0841 0.400 0.000 0.184 0.096 0.008 0.312
#> SRR1785323     1  0.7283    -0.0841 0.400 0.000 0.184 0.096 0.008 0.312

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-CV-skmeans-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-CV-skmeans-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-CV-skmeans-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-CV-skmeans-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-CV-skmeans-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-CV-skmeans-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-CV-skmeans-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-CV-skmeans-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-CV-skmeans-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-CV-skmeans-membership-heatmap-5

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)

plot of chunk tab-CV-skmeans-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-CV-skmeans-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-CV-skmeans-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-CV-skmeans-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-CV-skmeans-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-CV-skmeans-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-CV-skmeans-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-CV-skmeans-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-CV-skmeans-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-CV-skmeans-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk CV-skmeans-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-CV-skmeans-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-CV-skmeans-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-CV-skmeans-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-CV-skmeans-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-CV-skmeans-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk CV-skmeans-collect-classes

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.


CV:pam

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#>   Subgroups are detected by 'pam' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 3.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk CV-pam-collect-plots

The plots are:

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:

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)

plot of chunk CV-pam-select-partition-number

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 0.368           0.748       0.825         0.3616 0.743   0.743
#> 3 3 0.357           0.576       0.714         0.6469 0.644   0.520
#> 4 4 0.421           0.560       0.697         0.1819 0.749   0.440
#> 5 5 0.479           0.518       0.737         0.0553 0.859   0.554
#> 6 6 0.526           0.435       0.637         0.0566 0.830   0.421

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     1  0.7883      0.691 0.764 0.236
#> SRR1785239     1  0.7883      0.691 0.764 0.236
#> SRR1785240     1  0.6887      0.755 0.816 0.184
#> SRR1785241     1  0.6887      0.755 0.816 0.184
#> SRR1785242     1  0.8207      0.743 0.744 0.256
#> SRR1785243     1  0.7674      0.750 0.776 0.224
#> SRR1785244     1  0.0000      0.753 1.000 0.000
#> SRR1785245     1  0.0000      0.753 1.000 0.000
#> SRR1785246     1  0.7602      0.752 0.780 0.220
#> SRR1785247     1  0.7299      0.754 0.796 0.204
#> SRR1785248     2  0.0000      0.987 0.000 1.000
#> SRR1785250     1  0.0672      0.754 0.992 0.008
#> SRR1785251     1  0.0938      0.756 0.988 0.012
#> SRR1785252     1  0.9815      0.647 0.580 0.420
#> SRR1785253     1  0.9815      0.647 0.580 0.420
#> SRR1785254     1  0.7950      0.745 0.760 0.240
#> SRR1785255     1  0.8016      0.745 0.756 0.244
#> SRR1785256     1  0.0000      0.753 1.000 0.000
#> SRR1785257     1  0.0000      0.753 1.000 0.000
#> SRR1785258     1  0.1633      0.749 0.976 0.024
#> SRR1785259     1  0.1633      0.749 0.976 0.024
#> SRR1785262     1  0.6973      0.755 0.812 0.188
#> SRR1785263     1  0.6973      0.755 0.812 0.188
#> SRR1785260     1  0.7453      0.753 0.788 0.212
#> SRR1785261     1  0.8713      0.724 0.708 0.292
#> SRR1785264     2  0.4022      0.865 0.080 0.920
#> SRR1785265     2  0.0000      0.987 0.000 1.000
#> SRR1785266     2  0.0376      0.983 0.004 0.996
#> SRR1785267     2  0.0000      0.987 0.000 1.000
#> SRR1785268     1  0.0000      0.753 1.000 0.000
#> SRR1785269     1  0.0000      0.753 1.000 0.000
#> SRR1785270     1  0.9686      0.521 0.604 0.396
#> SRR1785271     1  0.9775      0.514 0.588 0.412
#> SRR1785272     1  0.1633      0.749 0.976 0.024
#> SRR1785273     1  0.2043      0.749 0.968 0.032
#> SRR1785276     1  0.9815      0.647 0.580 0.420
#> SRR1785277     1  0.9815      0.647 0.580 0.420
#> SRR1785274     1  0.7056      0.755 0.808 0.192
#> SRR1785275     1  0.7056      0.755 0.808 0.192
#> SRR1785280     2  0.0000      0.987 0.000 1.000
#> SRR1785281     2  0.0000      0.987 0.000 1.000
#> SRR1785278     1  0.0000      0.753 1.000 0.000
#> SRR1785279     1  0.0000      0.753 1.000 0.000
#> SRR1785282     1  0.1633      0.749 0.976 0.024
#> SRR1785283     1  0.1633      0.749 0.976 0.024
#> SRR1785284     1  0.9491      0.683 0.632 0.368
#> SRR1785285     1  0.9661      0.665 0.608 0.392
#> SRR1785286     1  0.9661      0.664 0.608 0.392
#> SRR1785287     1  0.9635      0.666 0.612 0.388
#> SRR1785288     1  0.0000      0.753 1.000 0.000
#> SRR1785289     1  0.0000      0.753 1.000 0.000
#> SRR1785290     1  0.9866      0.632 0.568 0.432
#> SRR1785291     1  0.9866      0.632 0.568 0.432
#> SRR1785296     1  0.9686      0.662 0.604 0.396
#> SRR1785297     1  0.9686      0.662 0.604 0.396
#> SRR1785292     2  0.0000      0.987 0.000 1.000
#> SRR1785293     2  0.0000      0.987 0.000 1.000
#> SRR1785294     1  0.9661      0.664 0.608 0.392
#> SRR1785295     1  0.9686      0.662 0.604 0.396
#> SRR1785298     1  0.9686      0.662 0.604 0.396
#> SRR1785299     1  0.9686      0.662 0.604 0.396
#> SRR1785300     1  0.0000      0.753 1.000 0.000
#> SRR1785301     1  0.0000      0.753 1.000 0.000
#> SRR1785304     1  0.9732      0.650 0.596 0.404
#> SRR1785305     1  0.9754      0.645 0.592 0.408
#> SRR1785306     1  0.9815      0.647 0.580 0.420
#> SRR1785307     1  0.9686      0.662 0.604 0.396
#> SRR1785302     1  0.9686      0.662 0.604 0.396
#> SRR1785303     1  0.9686      0.662 0.604 0.396
#> SRR1785308     1  0.1633      0.749 0.976 0.024
#> SRR1785309     1  0.1633      0.749 0.976 0.024
#> SRR1785310     1  0.9686      0.662 0.604 0.396
#> SRR1785311     1  0.9661      0.664 0.608 0.392
#> SRR1785312     1  0.0000      0.753 1.000 0.000
#> SRR1785313     1  0.0000      0.753 1.000 0.000
#> SRR1785314     1  0.9833      0.642 0.576 0.424
#> SRR1785315     1  0.9993      0.542 0.516 0.484
#> SRR1785318     2  0.0000      0.987 0.000 1.000
#> SRR1785319     2  0.0000      0.987 0.000 1.000
#> SRR1785316     1  0.0000      0.753 1.000 0.000
#> SRR1785317     1  0.0000      0.753 1.000 0.000
#> SRR1785324     2  0.0000      0.987 0.000 1.000
#> SRR1785325     2  0.1184      0.968 0.016 0.984
#> SRR1785320     1  0.0000      0.753 1.000 0.000
#> SRR1785321     1  0.0000      0.753 1.000 0.000
#> SRR1785322     1  0.2778      0.754 0.952 0.048
#> SRR1785323     1  0.1633      0.749 0.976 0.024

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     3   0.595     0.5633 0.360 0.000 0.640
#> SRR1785239     3   0.599     0.5561 0.368 0.000 0.632
#> SRR1785240     3   0.510     0.7149 0.248 0.000 0.752
#> SRR1785241     3   0.506     0.7134 0.244 0.000 0.756
#> SRR1785242     3   0.525     0.7237 0.264 0.000 0.736
#> SRR1785243     3   0.525     0.7232 0.264 0.000 0.736
#> SRR1785244     1   0.573     0.6603 0.676 0.000 0.324
#> SRR1785245     1   0.571     0.6620 0.680 0.000 0.320
#> SRR1785246     3   0.355     0.6800 0.132 0.000 0.868
#> SRR1785247     3   0.394     0.6768 0.156 0.000 0.844
#> SRR1785248     2   0.644     0.6166 0.276 0.696 0.028
#> SRR1785250     3   0.348     0.5833 0.128 0.000 0.872
#> SRR1785251     3   0.319     0.5998 0.112 0.000 0.888
#> SRR1785252     3   0.599     0.6802 0.368 0.000 0.632
#> SRR1785253     3   0.599     0.6802 0.368 0.000 0.632
#> SRR1785254     1   0.516     0.5751 0.832 0.072 0.096
#> SRR1785255     1   0.497     0.5723 0.840 0.060 0.100
#> SRR1785256     1   0.599     0.6469 0.632 0.000 0.368
#> SRR1785257     1   0.590     0.6549 0.648 0.000 0.352
#> SRR1785258     1   0.615     0.5732 0.592 0.000 0.408
#> SRR1785259     3   0.603    -0.0381 0.376 0.000 0.624
#> SRR1785262     3   0.510     0.7184 0.248 0.000 0.752
#> SRR1785263     3   0.518     0.7168 0.256 0.000 0.744
#> SRR1785260     1   0.450     0.5588 0.804 0.000 0.196
#> SRR1785261     1   0.412     0.5425 0.832 0.000 0.168
#> SRR1785264     2   0.890     0.3660 0.292 0.552 0.156
#> SRR1785265     2   0.667     0.6072 0.276 0.688 0.036
#> SRR1785266     2   0.000     0.9033 0.000 1.000 0.000
#> SRR1785267     2   0.000     0.9033 0.000 1.000 0.000
#> SRR1785268     3   0.604    -0.2273 0.380 0.000 0.620
#> SRR1785269     3   0.586    -0.1356 0.344 0.000 0.656
#> SRR1785270     3   0.619     0.7174 0.216 0.040 0.744
#> SRR1785271     3   0.611     0.7243 0.240 0.028 0.732
#> SRR1785272     3   0.484     0.4745 0.224 0.000 0.776
#> SRR1785273     3   0.465     0.4902 0.208 0.000 0.792
#> SRR1785276     3   0.571     0.6288 0.320 0.000 0.680
#> SRR1785277     3   0.571     0.6288 0.320 0.000 0.680
#> SRR1785274     3   0.518     0.7120 0.256 0.000 0.744
#> SRR1785275     3   0.543     0.6869 0.284 0.000 0.716
#> SRR1785280     2   0.000     0.9033 0.000 1.000 0.000
#> SRR1785281     2   0.000     0.9033 0.000 1.000 0.000
#> SRR1785278     1   0.562     0.6672 0.692 0.000 0.308
#> SRR1785279     1   0.568     0.6641 0.684 0.000 0.316
#> SRR1785282     1   0.565     0.6658 0.688 0.000 0.312
#> SRR1785283     1   0.562     0.6672 0.692 0.000 0.308
#> SRR1785284     3   0.603     0.6883 0.376 0.000 0.624
#> SRR1785285     3   0.627     0.6306 0.456 0.000 0.544
#> SRR1785286     1   0.400     0.4041 0.840 0.000 0.160
#> SRR1785287     1   0.280     0.4965 0.908 0.000 0.092
#> SRR1785288     1   0.536     0.6720 0.724 0.000 0.276
#> SRR1785289     1   0.536     0.6720 0.724 0.000 0.276
#> SRR1785290     1   0.346     0.5041 0.904 0.036 0.060
#> SRR1785291     1   0.347     0.5078 0.904 0.056 0.040
#> SRR1785296     3   0.630     0.6087 0.484 0.000 0.516
#> SRR1785297     3   0.631     0.6038 0.488 0.000 0.512
#> SRR1785292     2   0.000     0.9033 0.000 1.000 0.000
#> SRR1785293     2   0.000     0.9033 0.000 1.000 0.000
#> SRR1785294     1   0.296     0.4770 0.900 0.000 0.100
#> SRR1785295     1   0.296     0.4776 0.900 0.000 0.100
#> SRR1785298     1   0.619    -0.4729 0.580 0.000 0.420
#> SRR1785299     1   0.630    -0.5729 0.528 0.000 0.472
#> SRR1785300     1   0.571     0.6620 0.680 0.000 0.320
#> SRR1785301     1   0.571     0.6620 0.680 0.000 0.320
#> SRR1785304     1   0.337     0.5129 0.904 0.072 0.024
#> SRR1785305     1   0.350     0.5080 0.900 0.072 0.028
#> SRR1785306     3   0.617     0.6578 0.412 0.000 0.588
#> SRR1785307     3   0.617     0.6578 0.412 0.000 0.588
#> SRR1785302     1   0.288     0.4868 0.904 0.000 0.096
#> SRR1785303     1   0.271     0.4941 0.912 0.000 0.088
#> SRR1785308     1   0.540     0.6719 0.720 0.000 0.280
#> SRR1785309     1   0.543     0.6713 0.716 0.000 0.284
#> SRR1785310     1   0.271     0.4960 0.912 0.000 0.088
#> SRR1785311     1   0.236     0.5134 0.928 0.000 0.072
#> SRR1785312     1   0.613     0.5881 0.600 0.000 0.400
#> SRR1785313     1   0.615     0.5763 0.592 0.000 0.408
#> SRR1785314     1   0.953    -0.3749 0.484 0.228 0.288
#> SRR1785315     1   0.841    -0.0304 0.556 0.344 0.100
#> SRR1785318     2   0.000     0.9033 0.000 1.000 0.000
#> SRR1785319     2   0.000     0.9033 0.000 1.000 0.000
#> SRR1785316     1   0.536     0.6720 0.724 0.000 0.276
#> SRR1785317     1   0.543     0.6713 0.716 0.000 0.284
#> SRR1785324     2   0.000     0.9033 0.000 1.000 0.000
#> SRR1785325     2   0.000     0.9033 0.000 1.000 0.000
#> SRR1785320     1   0.576     0.6581 0.672 0.000 0.328
#> SRR1785321     1   0.610     0.6092 0.608 0.000 0.392
#> SRR1785322     1   0.533     0.6720 0.728 0.000 0.272
#> SRR1785323     1   0.559     0.6689 0.696 0.000 0.304

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     1   0.729     0.4223 0.532 0.000 0.200 0.268
#> SRR1785239     1   0.729     0.4223 0.532 0.000 0.200 0.268
#> SRR1785240     3   0.545     0.6481 0.244 0.000 0.700 0.056
#> SRR1785241     3   0.540     0.6434 0.248 0.000 0.700 0.052
#> SRR1785242     3   0.471     0.6608 0.024 0.000 0.740 0.236
#> SRR1785243     3   0.510     0.6827 0.076 0.000 0.756 0.168
#> SRR1785244     1   0.424     0.6896 0.800 0.000 0.032 0.168
#> SRR1785245     1   0.483     0.6736 0.768 0.000 0.056 0.176
#> SRR1785246     3   0.419     0.5677 0.228 0.000 0.764 0.008
#> SRR1785247     3   0.404     0.5499 0.248 0.000 0.752 0.000
#> SRR1785248     2   0.618     0.5289 0.000 0.608 0.072 0.320
#> SRR1785250     3   0.555     0.6628 0.200 0.000 0.716 0.084
#> SRR1785251     3   0.558     0.6673 0.184 0.000 0.720 0.096
#> SRR1785252     3   0.482     0.4964 0.000 0.000 0.612 0.388
#> SRR1785253     3   0.480     0.5003 0.000 0.000 0.616 0.384
#> SRR1785254     4   0.468     0.5969 0.184 0.000 0.044 0.772
#> SRR1785255     4   0.455     0.6044 0.180 0.000 0.040 0.780
#> SRR1785256     4   0.771    -0.0276 0.364 0.000 0.224 0.412
#> SRR1785257     4   0.765     0.0594 0.340 0.000 0.220 0.440
#> SRR1785258     1   0.737     0.3039 0.504 0.000 0.188 0.308
#> SRR1785259     1   0.603     0.4620 0.644 0.000 0.280 0.076
#> SRR1785262     3   0.600     0.6452 0.240 0.000 0.668 0.092
#> SRR1785263     3   0.610     0.6483 0.232 0.000 0.664 0.104
#> SRR1785260     4   0.515     0.5301 0.208 0.000 0.056 0.736
#> SRR1785261     4   0.420     0.5891 0.192 0.000 0.020 0.788
#> SRR1785264     2   0.741     0.2124 0.000 0.444 0.168 0.388
#> SRR1785265     2   0.542     0.5467 0.000 0.640 0.028 0.332
#> SRR1785266     2   0.000     0.8848 0.000 1.000 0.000 0.000
#> SRR1785267     2   0.000     0.8848 0.000 1.000 0.000 0.000
#> SRR1785268     1   0.344     0.5101 0.816 0.000 0.184 0.000
#> SRR1785269     1   0.353     0.5021 0.808 0.000 0.192 0.000
#> SRR1785270     3   0.607     0.6695 0.088 0.032 0.728 0.152
#> SRR1785271     3   0.572     0.6648 0.072 0.012 0.724 0.192
#> SRR1785272     1   0.514     0.6333 0.756 0.000 0.160 0.084
#> SRR1785273     1   0.611     0.5537 0.680 0.000 0.176 0.144
#> SRR1785276     3   0.614     0.5030 0.184 0.000 0.676 0.140
#> SRR1785277     3   0.610     0.5069 0.184 0.000 0.680 0.136
#> SRR1785274     3   0.620     0.6780 0.168 0.000 0.672 0.160
#> SRR1785275     3   0.640     0.6676 0.164 0.000 0.652 0.184
#> SRR1785280     2   0.000     0.8848 0.000 1.000 0.000 0.000
#> SRR1785281     2   0.000     0.8848 0.000 1.000 0.000 0.000
#> SRR1785278     1   0.393     0.6913 0.808 0.000 0.016 0.176
#> SRR1785279     1   0.399     0.6908 0.808 0.000 0.020 0.172
#> SRR1785282     1   0.433     0.6928 0.792 0.000 0.032 0.176
#> SRR1785283     1   0.389     0.6917 0.804 0.000 0.012 0.184
#> SRR1785284     4   0.649    -0.0439 0.072 0.000 0.436 0.492
#> SRR1785285     4   0.682     0.0664 0.100 0.000 0.412 0.488
#> SRR1785286     4   0.423     0.6528 0.132 0.000 0.052 0.816
#> SRR1785287     4   0.364     0.6223 0.172 0.000 0.008 0.820
#> SRR1785288     1   0.430     0.6344 0.716 0.000 0.000 0.284
#> SRR1785289     1   0.456     0.5832 0.672 0.000 0.000 0.328
#> SRR1785290     4   0.162     0.6377 0.020 0.000 0.028 0.952
#> SRR1785291     4   0.192     0.6400 0.024 0.004 0.028 0.944
#> SRR1785296     4   0.450     0.2049 0.000 0.000 0.316 0.684
#> SRR1785297     4   0.448     0.2154 0.000 0.000 0.312 0.688
#> SRR1785292     2   0.000     0.8848 0.000 1.000 0.000 0.000
#> SRR1785293     2   0.000     0.8848 0.000 1.000 0.000 0.000
#> SRR1785294     4   0.307     0.6334 0.152 0.000 0.000 0.848
#> SRR1785295     4   0.247     0.6547 0.108 0.000 0.000 0.892
#> SRR1785298     4   0.585     0.3589 0.068 0.000 0.272 0.660
#> SRR1785299     4   0.599     0.3050 0.068 0.000 0.296 0.636
#> SRR1785300     1   0.642     0.2533 0.508 0.000 0.068 0.424
#> SRR1785301     4   0.641     0.0126 0.416 0.000 0.068 0.516
#> SRR1785304     4   0.255     0.6530 0.092 0.008 0.000 0.900
#> SRR1785305     4   0.166     0.6503 0.052 0.004 0.000 0.944
#> SRR1785306     3   0.438     0.5828 0.000 0.000 0.704 0.296
#> SRR1785307     3   0.445     0.5783 0.000 0.000 0.692 0.308
#> SRR1785302     4   0.220     0.6197 0.024 0.000 0.048 0.928
#> SRR1785303     4   0.220     0.6197 0.024 0.000 0.048 0.928
#> SRR1785308     1   0.507     0.6833 0.748 0.000 0.060 0.192
#> SRR1785309     1   0.507     0.6833 0.748 0.000 0.060 0.192
#> SRR1785310     4   0.234     0.6569 0.100 0.000 0.000 0.900
#> SRR1785311     4   0.322     0.6237 0.164 0.000 0.000 0.836
#> SRR1785312     1   0.442     0.5115 0.784 0.000 0.184 0.032
#> SRR1785313     1   0.509     0.5059 0.752 0.000 0.180 0.068
#> SRR1785314     4   0.621     0.1181 0.004 0.056 0.348 0.592
#> SRR1785315     4   0.704     0.2423 0.004 0.228 0.176 0.592
#> SRR1785318     2   0.000     0.8848 0.000 1.000 0.000 0.000
#> SRR1785319     2   0.000     0.8848 0.000 1.000 0.000 0.000
#> SRR1785316     1   0.410     0.6566 0.744 0.000 0.000 0.256
#> SRR1785317     1   0.398     0.6697 0.760 0.000 0.000 0.240
#> SRR1785324     2   0.000     0.8848 0.000 1.000 0.000 0.000
#> SRR1785325     2   0.000     0.8848 0.000 1.000 0.000 0.000
#> SRR1785320     1   0.471     0.6327 0.792 0.000 0.120 0.088
#> SRR1785321     1   0.390     0.5432 0.816 0.000 0.164 0.020
#> SRR1785322     1   0.564     0.3721 0.548 0.000 0.024 0.428
#> SRR1785323     1   0.600     0.2111 0.508 0.000 0.040 0.452

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     1  0.6151     0.3927 0.532 0.000 0.364 0.084 0.020
#> SRR1785239     1  0.6186     0.3940 0.532 0.000 0.360 0.088 0.020
#> SRR1785240     3  0.4432     0.6181 0.008 0.000 0.772 0.144 0.076
#> SRR1785241     3  0.4473     0.6146 0.008 0.000 0.768 0.148 0.076
#> SRR1785242     3  0.1764     0.6242 0.000 0.000 0.928 0.064 0.008
#> SRR1785243     3  0.2136     0.6526 0.000 0.000 0.904 0.088 0.008
#> SRR1785244     1  0.6662     0.5305 0.532 0.000 0.080 0.328 0.060
#> SRR1785245     1  0.7054     0.4885 0.488 0.000 0.116 0.336 0.060
#> SRR1785246     3  0.5028     0.3193 0.444 0.000 0.524 0.000 0.032
#> SRR1785247     3  0.5044     0.2934 0.464 0.000 0.504 0.000 0.032
#> SRR1785248     2  0.6194     0.4848 0.000 0.620 0.220 0.132 0.028
#> SRR1785250     3  0.3688     0.6447 0.028 0.000 0.828 0.124 0.020
#> SRR1785251     3  0.3455     0.6499 0.024 0.000 0.844 0.112 0.020
#> SRR1785252     3  0.3151     0.5918 0.000 0.000 0.836 0.144 0.020
#> SRR1785253     3  0.3016     0.5872 0.000 0.000 0.848 0.132 0.020
#> SRR1785254     4  0.1741     0.6746 0.000 0.000 0.024 0.936 0.040
#> SRR1785255     4  0.1648     0.6761 0.000 0.000 0.020 0.940 0.040
#> SRR1785256     4  0.6187     0.3875 0.056 0.000 0.244 0.624 0.076
#> SRR1785257     4  0.5881     0.4242 0.040 0.000 0.236 0.648 0.076
#> SRR1785258     4  0.7598     0.0830 0.208 0.000 0.236 0.476 0.080
#> SRR1785259     1  0.7718     0.3578 0.372 0.000 0.348 0.216 0.064
#> SRR1785262     3  0.4648     0.6053 0.008 0.000 0.748 0.172 0.072
#> SRR1785263     3  0.4552     0.6149 0.008 0.000 0.756 0.168 0.068
#> SRR1785260     4  0.2519     0.6314 0.000 0.000 0.100 0.884 0.016
#> SRR1785261     4  0.1981     0.6589 0.000 0.000 0.064 0.920 0.016
#> SRR1785264     2  0.6920     0.2566 0.000 0.476 0.308 0.196 0.020
#> SRR1785265     2  0.5531     0.5489 0.000 0.692 0.156 0.132 0.020
#> SRR1785266     2  0.0000     0.8681 0.000 1.000 0.000 0.000 0.000
#> SRR1785267     2  0.0000     0.8681 0.000 1.000 0.000 0.000 0.000
#> SRR1785268     1  0.0609     0.4198 0.980 0.000 0.000 0.000 0.020
#> SRR1785269     1  0.0609     0.4198 0.980 0.000 0.000 0.000 0.020
#> SRR1785270     5  0.2199     0.8920 0.000 0.016 0.060 0.008 0.916
#> SRR1785271     5  0.2243     0.8959 0.000 0.016 0.056 0.012 0.916
#> SRR1785272     1  0.7168     0.5315 0.536 0.000 0.192 0.208 0.064
#> SRR1785273     1  0.7074     0.4989 0.528 0.000 0.276 0.128 0.068
#> SRR1785276     1  0.6237    -0.4170 0.456 0.000 0.448 0.064 0.032
#> SRR1785277     1  0.6285    -0.4165 0.456 0.000 0.444 0.068 0.032
#> SRR1785274     3  0.4011     0.6517 0.008 0.000 0.808 0.112 0.072
#> SRR1785275     3  0.4535     0.6163 0.008 0.000 0.760 0.160 0.072
#> SRR1785280     2  0.0000     0.8681 0.000 1.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000     0.8681 0.000 1.000 0.000 0.000 0.000
#> SRR1785278     1  0.6444     0.5330 0.540 0.000 0.060 0.340 0.060
#> SRR1785279     1  0.6577     0.5313 0.536 0.000 0.072 0.332 0.060
#> SRR1785282     1  0.6498     0.5336 0.536 0.000 0.064 0.340 0.060
#> SRR1785283     1  0.6400     0.5312 0.540 0.000 0.056 0.344 0.060
#> SRR1785284     3  0.4658     0.4075 0.000 0.000 0.576 0.408 0.016
#> SRR1785285     3  0.4747     0.2898 0.000 0.000 0.496 0.488 0.016
#> SRR1785286     4  0.2674     0.6359 0.000 0.000 0.120 0.868 0.012
#> SRR1785287     4  0.1211     0.6797 0.000 0.000 0.016 0.960 0.024
#> SRR1785288     1  0.5039     0.4259 0.512 0.000 0.032 0.456 0.000
#> SRR1785289     4  0.5032    -0.3292 0.448 0.000 0.032 0.520 0.000
#> SRR1785290     4  0.3574     0.5771 0.000 0.000 0.168 0.804 0.028
#> SRR1785291     4  0.3525     0.5823 0.000 0.004 0.156 0.816 0.024
#> SRR1785296     3  0.4658     0.1873 0.000 0.000 0.504 0.484 0.012
#> SRR1785297     3  0.4659     0.1767 0.000 0.000 0.500 0.488 0.012
#> SRR1785292     2  0.0000     0.8681 0.000 1.000 0.000 0.000 0.000
#> SRR1785293     2  0.0000     0.8681 0.000 1.000 0.000 0.000 0.000
#> SRR1785294     4  0.0771     0.6772 0.000 0.000 0.020 0.976 0.004
#> SRR1785295     4  0.1628     0.6652 0.000 0.000 0.056 0.936 0.008
#> SRR1785298     4  0.4717    -0.0362 0.000 0.000 0.396 0.584 0.020
#> SRR1785299     4  0.4767    -0.1123 0.000 0.000 0.420 0.560 0.020
#> SRR1785300     4  0.6543     0.3160 0.156 0.000 0.140 0.628 0.076
#> SRR1785301     4  0.5490     0.4869 0.064 0.000 0.140 0.720 0.076
#> SRR1785304     4  0.2866     0.6521 0.000 0.020 0.076 0.884 0.020
#> SRR1785305     4  0.3110     0.6253 0.000 0.004 0.112 0.856 0.028
#> SRR1785306     3  0.4696     0.5618 0.000 0.000 0.736 0.156 0.108
#> SRR1785307     3  0.5046     0.5359 0.000 0.000 0.704 0.140 0.156
#> SRR1785302     4  0.3976     0.5148 0.004 0.000 0.216 0.760 0.020
#> SRR1785303     4  0.3883     0.5165 0.004 0.000 0.216 0.764 0.016
#> SRR1785308     1  0.6221     0.5215 0.520 0.000 0.108 0.360 0.012
#> SRR1785309     1  0.6221     0.5215 0.520 0.000 0.108 0.360 0.012
#> SRR1785310     4  0.1942     0.6560 0.000 0.000 0.068 0.920 0.012
#> SRR1785311     4  0.0162     0.6791 0.000 0.000 0.004 0.996 0.000
#> SRR1785312     1  0.0771     0.4194 0.976 0.000 0.000 0.004 0.020
#> SRR1785313     1  0.0898     0.4179 0.972 0.000 0.000 0.008 0.020
#> SRR1785314     5  0.2470     0.8781 0.000 0.000 0.012 0.104 0.884
#> SRR1785315     5  0.2786     0.8899 0.000 0.020 0.012 0.084 0.884
#> SRR1785318     2  0.0000     0.8681 0.000 1.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000     0.8681 0.000 1.000 0.000 0.000 0.000
#> SRR1785316     1  0.5330     0.4706 0.532 0.000 0.036 0.424 0.008
#> SRR1785317     1  0.5727     0.4932 0.532 0.000 0.036 0.404 0.028
#> SRR1785324     2  0.0000     0.8681 0.000 1.000 0.000 0.000 0.000
#> SRR1785325     2  0.0000     0.8681 0.000 1.000 0.000 0.000 0.000
#> SRR1785320     1  0.2877     0.5413 0.848 0.000 0.004 0.144 0.004
#> SRR1785321     1  0.1444     0.4612 0.948 0.000 0.000 0.040 0.012
#> SRR1785322     4  0.6341     0.0808 0.296 0.000 0.056 0.580 0.068
#> SRR1785323     4  0.5865     0.2762 0.228 0.000 0.060 0.656 0.056

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     1  0.4731     0.3579 0.524 0.000 0.428 0.048 0.000 0.000
#> SRR1785239     1  0.4629     0.3611 0.524 0.000 0.436 0.040 0.000 0.000
#> SRR1785240     5  0.7450     0.4094 0.200 0.000 0.164 0.256 0.380 0.000
#> SRR1785241     5  0.7450     0.4094 0.200 0.000 0.164 0.256 0.380 0.000
#> SRR1785242     3  0.5297    -0.0226 0.048 0.000 0.624 0.052 0.276 0.000
#> SRR1785243     3  0.6062    -0.1055 0.100 0.000 0.560 0.064 0.276 0.000
#> SRR1785244     1  0.2773     0.6082 0.828 0.000 0.004 0.164 0.000 0.004
#> SRR1785245     1  0.2933     0.5877 0.796 0.000 0.000 0.200 0.000 0.004
#> SRR1785246     5  0.0713     0.3662 0.000 0.000 0.028 0.000 0.972 0.000
#> SRR1785247     5  0.0000     0.3540 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1785248     3  0.3394     0.4157 0.000 0.200 0.776 0.024 0.000 0.000
#> SRR1785250     5  0.7328     0.3881 0.304 0.000 0.212 0.080 0.392 0.012
#> SRR1785251     5  0.7349     0.3817 0.292 0.000 0.224 0.080 0.392 0.012
#> SRR1785252     3  0.2701     0.4315 0.028 0.000 0.864 0.004 0.104 0.000
#> SRR1785253     3  0.2558     0.4281 0.028 0.000 0.868 0.000 0.104 0.000
#> SRR1785254     4  0.4682     0.4978 0.112 0.000 0.192 0.692 0.004 0.000
#> SRR1785255     4  0.4682     0.4980 0.112 0.000 0.192 0.692 0.004 0.000
#> SRR1785256     4  0.5150     0.2000 0.324 0.000 0.000 0.580 0.092 0.004
#> SRR1785257     4  0.5079     0.2468 0.304 0.000 0.000 0.600 0.092 0.004
#> SRR1785258     1  0.5093     0.0537 0.476 0.000 0.040 0.468 0.012 0.004
#> SRR1785259     1  0.5856     0.3924 0.624 0.000 0.080 0.212 0.080 0.004
#> SRR1785262     5  0.7321     0.3560 0.304 0.000 0.136 0.156 0.400 0.004
#> SRR1785263     5  0.7323     0.3602 0.304 0.000 0.140 0.152 0.400 0.004
#> SRR1785260     4  0.2572     0.5435 0.136 0.000 0.012 0.852 0.000 0.000
#> SRR1785261     4  0.3370     0.5518 0.148 0.000 0.048 0.804 0.000 0.000
#> SRR1785264     3  0.5506     0.4237 0.000 0.236 0.632 0.080 0.052 0.000
#> SRR1785265     3  0.4594     0.2361 0.000 0.404 0.560 0.032 0.004 0.000
#> SRR1785266     2  0.0260     0.9927 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1785267     2  0.0260     0.9927 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1785268     5  0.3765    -0.0387 0.404 0.000 0.000 0.000 0.596 0.000
#> SRR1785269     5  0.3765    -0.0387 0.404 0.000 0.000 0.000 0.596 0.000
#> SRR1785270     6  0.0508     1.0000 0.000 0.000 0.012 0.000 0.004 0.984
#> SRR1785271     6  0.0508     1.0000 0.000 0.000 0.012 0.000 0.004 0.984
#> SRR1785272     1  0.3579     0.5638 0.816 0.000 0.120 0.048 0.004 0.012
#> SRR1785273     1  0.3926     0.5394 0.792 0.000 0.140 0.044 0.012 0.012
#> SRR1785276     5  0.1141     0.3369 0.000 0.000 0.052 0.000 0.948 0.000
#> SRR1785277     5  0.0713     0.3486 0.000 0.000 0.028 0.000 0.972 0.000
#> SRR1785274     5  0.7373     0.3778 0.244 0.000 0.188 0.164 0.404 0.000
#> SRR1785275     5  0.7323     0.3435 0.268 0.000 0.164 0.164 0.404 0.000
#> SRR1785280     2  0.0000     0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000     0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785278     1  0.2135     0.6207 0.872 0.000 0.000 0.128 0.000 0.000
#> SRR1785279     1  0.2048     0.6165 0.880 0.000 0.000 0.120 0.000 0.000
#> SRR1785282     1  0.3287     0.5950 0.768 0.000 0.012 0.220 0.000 0.000
#> SRR1785283     1  0.2969     0.5916 0.776 0.000 0.000 0.224 0.000 0.000
#> SRR1785284     3  0.7010     0.2675 0.140 0.000 0.376 0.372 0.112 0.000
#> SRR1785285     4  0.6732    -0.1944 0.148 0.000 0.336 0.440 0.076 0.000
#> SRR1785286     4  0.5711     0.3563 0.132 0.000 0.328 0.528 0.012 0.000
#> SRR1785287     4  0.4722     0.5117 0.116 0.000 0.192 0.688 0.004 0.000
#> SRR1785288     1  0.3531     0.5185 0.672 0.000 0.000 0.328 0.000 0.000
#> SRR1785289     1  0.3684     0.4674 0.628 0.000 0.000 0.372 0.000 0.000
#> SRR1785290     3  0.4004     0.2934 0.012 0.000 0.620 0.368 0.000 0.000
#> SRR1785291     3  0.4150     0.2640 0.016 0.000 0.592 0.392 0.000 0.000
#> SRR1785296     3  0.5422     0.4105 0.012 0.000 0.564 0.324 0.100 0.000
#> SRR1785297     3  0.5422     0.4105 0.012 0.000 0.564 0.324 0.100 0.000
#> SRR1785292     2  0.0000     0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785293     2  0.0000     0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785294     4  0.5273     0.4123 0.136 0.000 0.284 0.580 0.000 0.000
#> SRR1785295     4  0.5346     0.3274 0.128 0.000 0.324 0.548 0.000 0.000
#> SRR1785298     3  0.5790     0.2124 0.092 0.000 0.520 0.356 0.032 0.000
#> SRR1785299     3  0.5798     0.2153 0.092 0.000 0.516 0.360 0.032 0.000
#> SRR1785300     4  0.3915     0.0866 0.412 0.000 0.000 0.584 0.000 0.004
#> SRR1785301     4  0.3714     0.2926 0.340 0.000 0.000 0.656 0.000 0.004
#> SRR1785304     4  0.4900     0.3518 0.068 0.012 0.280 0.640 0.000 0.000
#> SRR1785305     4  0.4625     0.0911 0.036 0.004 0.388 0.572 0.000 0.000
#> SRR1785306     5  0.6575     0.1064 0.028 0.000 0.364 0.004 0.404 0.200
#> SRR1785307     5  0.6922     0.1627 0.028 0.000 0.304 0.016 0.404 0.248
#> SRR1785302     3  0.4685     0.2254 0.044 0.000 0.520 0.436 0.000 0.000
#> SRR1785303     3  0.4844     0.1762 0.056 0.000 0.504 0.440 0.000 0.000
#> SRR1785308     1  0.5710     0.4836 0.572 0.000 0.232 0.184 0.000 0.012
#> SRR1785309     1  0.5684     0.4838 0.576 0.000 0.232 0.180 0.000 0.012
#> SRR1785310     4  0.5353     0.2554 0.120 0.000 0.352 0.528 0.000 0.000
#> SRR1785311     4  0.5223     0.4289 0.136 0.000 0.272 0.592 0.000 0.000
#> SRR1785312     5  0.3765    -0.0387 0.404 0.000 0.000 0.000 0.596 0.000
#> SRR1785313     5  0.3765    -0.0387 0.404 0.000 0.000 0.000 0.596 0.000
#> SRR1785314     6  0.0508     1.0000 0.000 0.000 0.012 0.000 0.004 0.984
#> SRR1785315     6  0.0508     1.0000 0.000 0.000 0.012 0.000 0.004 0.984
#> SRR1785318     2  0.0000     0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000     0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785316     1  0.3746     0.5375 0.712 0.000 0.000 0.272 0.004 0.012
#> SRR1785317     1  0.3420     0.5714 0.748 0.000 0.000 0.240 0.000 0.012
#> SRR1785324     2  0.0000     0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785325     2  0.0000     0.9982 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785320     1  0.4756     0.3554 0.564 0.000 0.000 0.056 0.380 0.000
#> SRR1785321     1  0.4184     0.1578 0.504 0.000 0.000 0.012 0.484 0.000
#> SRR1785322     1  0.4914     0.1067 0.516 0.000 0.052 0.428 0.000 0.004
#> SRR1785323     4  0.4386    -0.0122 0.464 0.000 0.016 0.516 0.000 0.004

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-CV-pam-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-CV-pam-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-CV-pam-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-CV-pam-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-CV-pam-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-CV-pam-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-CV-pam-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-CV-pam-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-CV-pam-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-CV-pam-membership-heatmap-5

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)

plot of chunk tab-CV-pam-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-CV-pam-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-CV-pam-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-CV-pam-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-CV-pam-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-CV-pam-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-CV-pam-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-CV-pam-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-CV-pam-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-CV-pam-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk CV-pam-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-CV-pam-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-CV-pam-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-CV-pam-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-CV-pam-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-CV-pam-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk CV-pam-collect-classes

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.


CV:mclust

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#>   Subgroups are detected by 'mclust' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 3.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk CV-mclust-collect-plots

The plots are:

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:

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)

plot of chunk CV-mclust-select-partition-number

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 0.824           0.937       0.950         0.2777 0.743   0.743
#> 3 3 0.265           0.666       0.754         1.0618 0.647   0.529
#> 4 4 0.346           0.636       0.759         0.2075 0.858   0.656
#> 5 5 0.482           0.584       0.726         0.0908 0.948   0.816
#> 6 6 0.516           0.617       0.706         0.0629 0.918   0.669

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     1  0.4431      0.926 0.908 0.092
#> SRR1785239     1  0.4431      0.926 0.908 0.092
#> SRR1785240     1  0.2043      0.960 0.968 0.032
#> SRR1785241     1  0.2043      0.960 0.968 0.032
#> SRR1785242     1  0.1414      0.960 0.980 0.020
#> SRR1785243     1  0.1414      0.960 0.980 0.020
#> SRR1785244     1  0.1184      0.956 0.984 0.016
#> SRR1785245     1  0.1184      0.956 0.984 0.016
#> SRR1785246     1  0.5737      0.869 0.864 0.136
#> SRR1785247     1  0.5737      0.869 0.864 0.136
#> SRR1785248     2  0.2236      0.934 0.036 0.964
#> SRR1785250     1  0.2603      0.957 0.956 0.044
#> SRR1785251     1  0.2603      0.957 0.956 0.044
#> SRR1785252     1  0.2043      0.959 0.968 0.032
#> SRR1785253     1  0.2043      0.959 0.968 0.032
#> SRR1785254     1  0.2948      0.952 0.948 0.052
#> SRR1785255     1  0.2603      0.956 0.956 0.044
#> SRR1785256     1  0.1414      0.957 0.980 0.020
#> SRR1785257     1  0.1414      0.957 0.980 0.020
#> SRR1785258     1  0.1414      0.960 0.980 0.020
#> SRR1785259     1  0.1414      0.960 0.980 0.020
#> SRR1785262     1  0.1414      0.960 0.980 0.020
#> SRR1785263     1  0.1843      0.961 0.972 0.028
#> SRR1785260     1  0.1184      0.956 0.984 0.016
#> SRR1785261     1  0.1184      0.956 0.984 0.016
#> SRR1785264     2  0.9580      0.399 0.380 0.620
#> SRR1785265     2  0.8386      0.648 0.268 0.732
#> SRR1785266     2  0.1414      0.941 0.020 0.980
#> SRR1785267     2  0.1414      0.941 0.020 0.980
#> SRR1785268     1  0.2603      0.957 0.956 0.044
#> SRR1785269     1  0.2603      0.957 0.956 0.044
#> SRR1785270     1  0.2043      0.959 0.968 0.032
#> SRR1785271     1  0.2043      0.959 0.968 0.032
#> SRR1785272     1  0.2423      0.951 0.960 0.040
#> SRR1785273     1  0.2603      0.951 0.956 0.044
#> SRR1785276     1  0.5737      0.869 0.864 0.136
#> SRR1785277     1  0.5737      0.869 0.864 0.136
#> SRR1785274     1  0.2043      0.960 0.968 0.032
#> SRR1785275     1  0.1843      0.961 0.972 0.028
#> SRR1785280     2  0.1414      0.941 0.020 0.980
#> SRR1785281     2  0.1414      0.941 0.020 0.980
#> SRR1785278     1  0.0938      0.960 0.988 0.012
#> SRR1785279     1  0.1184      0.960 0.984 0.016
#> SRR1785282     1  0.2236      0.956 0.964 0.036
#> SRR1785283     1  0.2423      0.955 0.960 0.040
#> SRR1785284     1  0.1414      0.960 0.980 0.020
#> SRR1785285     1  0.1414      0.960 0.980 0.020
#> SRR1785286     1  0.1184      0.956 0.984 0.016
#> SRR1785287     1  0.1184      0.956 0.984 0.016
#> SRR1785288     1  0.1184      0.956 0.984 0.016
#> SRR1785289     1  0.1184      0.956 0.984 0.016
#> SRR1785290     1  0.3114      0.951 0.944 0.056
#> SRR1785291     1  0.3274      0.949 0.940 0.060
#> SRR1785296     1  0.1184      0.961 0.984 0.016
#> SRR1785297     1  0.1184      0.961 0.984 0.016
#> SRR1785292     2  0.1633      0.941 0.024 0.976
#> SRR1785293     2  0.1633      0.941 0.024 0.976
#> SRR1785294     1  0.1184      0.956 0.984 0.016
#> SRR1785295     1  0.1184      0.956 0.984 0.016
#> SRR1785298     1  0.1414      0.958 0.980 0.020
#> SRR1785299     1  0.1633      0.958 0.976 0.024
#> SRR1785300     1  0.1184      0.956 0.984 0.016
#> SRR1785301     1  0.1184      0.956 0.984 0.016
#> SRR1785304     1  0.3274      0.949 0.940 0.060
#> SRR1785305     1  0.3274      0.949 0.940 0.060
#> SRR1785306     1  0.2236      0.957 0.964 0.036
#> SRR1785307     1  0.2236      0.957 0.964 0.036
#> SRR1785302     1  0.2043      0.959 0.968 0.032
#> SRR1785303     1  0.2043      0.959 0.968 0.032
#> SRR1785308     1  0.2236      0.956 0.964 0.036
#> SRR1785309     1  0.2423      0.955 0.960 0.040
#> SRR1785310     1  0.1184      0.956 0.984 0.016
#> SRR1785311     1  0.1184      0.956 0.984 0.016
#> SRR1785312     1  0.5629      0.871 0.868 0.132
#> SRR1785313     1  0.5629      0.871 0.868 0.132
#> SRR1785314     1  0.2236      0.957 0.964 0.036
#> SRR1785315     1  0.2236      0.957 0.964 0.036
#> SRR1785318     2  0.1633      0.941 0.024 0.976
#> SRR1785319     2  0.1633      0.941 0.024 0.976
#> SRR1785316     1  0.2778      0.951 0.952 0.048
#> SRR1785317     1  0.2778      0.951 0.952 0.048
#> SRR1785324     2  0.1414      0.941 0.020 0.980
#> SRR1785325     2  0.1414      0.941 0.020 0.980
#> SRR1785320     1  0.5178      0.888 0.884 0.116
#> SRR1785321     1  0.5178      0.888 0.884 0.116
#> SRR1785322     1  0.2603      0.957 0.956 0.044
#> SRR1785323     1  0.2603      0.957 0.956 0.044

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     1  0.9145      0.659 0.532 0.184 0.284
#> SRR1785239     1  0.9137      0.664 0.536 0.188 0.276
#> SRR1785240     3  0.3267      0.835 0.116 0.000 0.884
#> SRR1785241     3  0.3267      0.835 0.116 0.000 0.884
#> SRR1785242     1  0.8513      0.675 0.568 0.116 0.316
#> SRR1785243     1  0.8548      0.675 0.568 0.120 0.312
#> SRR1785244     1  0.5404      0.391 0.740 0.004 0.256
#> SRR1785245     1  0.5244      0.440 0.756 0.004 0.240
#> SRR1785246     3  0.3896      0.794 0.060 0.052 0.888
#> SRR1785247     3  0.3896      0.794 0.060 0.052 0.888
#> SRR1785248     2  0.3406      0.757 0.028 0.904 0.068
#> SRR1785250     1  0.7851      0.706 0.644 0.100 0.256
#> SRR1785251     1  0.7851      0.706 0.644 0.100 0.256
#> SRR1785252     1  0.8872      0.658 0.536 0.140 0.324
#> SRR1785253     1  0.8872      0.658 0.536 0.140 0.324
#> SRR1785254     1  0.8454      0.496 0.480 0.088 0.432
#> SRR1785255     1  0.8334      0.489 0.480 0.080 0.440
#> SRR1785256     1  0.2496      0.702 0.928 0.004 0.068
#> SRR1785257     1  0.2590      0.704 0.924 0.004 0.072
#> SRR1785258     1  0.6738      0.613 0.624 0.020 0.356
#> SRR1785259     1  0.6879      0.619 0.616 0.024 0.360
#> SRR1785262     1  0.7015      0.599 0.584 0.024 0.392
#> SRR1785263     1  0.7130      0.516 0.544 0.024 0.432
#> SRR1785260     1  0.2681      0.693 0.932 0.040 0.028
#> SRR1785261     1  0.2681      0.693 0.932 0.040 0.028
#> SRR1785264     2  0.9325      0.110 0.252 0.520 0.228
#> SRR1785265     2  0.9084      0.200 0.232 0.552 0.216
#> SRR1785266     2  0.0592      0.773 0.000 0.988 0.012
#> SRR1785267     2  0.0592      0.773 0.000 0.988 0.012
#> SRR1785268     3  0.6975      0.385 0.356 0.028 0.616
#> SRR1785269     3  0.6934      0.414 0.348 0.028 0.624
#> SRR1785270     3  0.3649      0.827 0.068 0.036 0.896
#> SRR1785271     3  0.3669      0.825 0.064 0.040 0.896
#> SRR1785272     1  0.7485      0.712 0.680 0.096 0.224
#> SRR1785273     1  0.7525      0.710 0.676 0.096 0.228
#> SRR1785276     3  0.4094      0.783 0.028 0.100 0.872
#> SRR1785277     3  0.4094      0.783 0.028 0.100 0.872
#> SRR1785274     3  0.3116      0.835 0.108 0.000 0.892
#> SRR1785275     3  0.3116      0.836 0.108 0.000 0.892
#> SRR1785280     2  0.0747      0.771 0.000 0.984 0.016
#> SRR1785281     2  0.0747      0.771 0.000 0.984 0.016
#> SRR1785278     1  0.4452      0.711 0.808 0.000 0.192
#> SRR1785279     1  0.4291      0.706 0.820 0.000 0.180
#> SRR1785282     1  0.2173      0.692 0.944 0.008 0.048
#> SRR1785283     1  0.2599      0.695 0.932 0.016 0.052
#> SRR1785284     3  0.3482      0.834 0.128 0.000 0.872
#> SRR1785285     3  0.3551      0.833 0.132 0.000 0.868
#> SRR1785286     3  0.4682      0.801 0.192 0.004 0.804
#> SRR1785287     3  0.4682      0.801 0.192 0.004 0.804
#> SRR1785288     1  0.1647      0.674 0.960 0.004 0.036
#> SRR1785289     1  0.1525      0.676 0.964 0.004 0.032
#> SRR1785290     2  0.9873     -0.288 0.348 0.392 0.260
#> SRR1785291     2  0.9873     -0.288 0.348 0.392 0.260
#> SRR1785296     1  0.8378      0.696 0.596 0.120 0.284
#> SRR1785297     1  0.8263      0.705 0.612 0.120 0.268
#> SRR1785292     2  0.1267      0.787 0.024 0.972 0.004
#> SRR1785293     2  0.1267      0.787 0.024 0.972 0.004
#> SRR1785294     1  0.7412      0.735 0.696 0.112 0.192
#> SRR1785295     1  0.7478      0.735 0.692 0.116 0.192
#> SRR1785298     1  0.7065      0.721 0.664 0.048 0.288
#> SRR1785299     1  0.7095      0.720 0.660 0.048 0.292
#> SRR1785300     1  0.1129      0.677 0.976 0.004 0.020
#> SRR1785301     1  0.1129      0.677 0.976 0.004 0.020
#> SRR1785304     1  0.9934      0.352 0.376 0.344 0.280
#> SRR1785305     1  0.9934      0.352 0.376 0.344 0.280
#> SRR1785306     3  0.4217      0.813 0.100 0.032 0.868
#> SRR1785307     3  0.4217      0.813 0.100 0.032 0.868
#> SRR1785302     1  0.7741      0.677 0.608 0.068 0.324
#> SRR1785303     1  0.7741      0.677 0.608 0.068 0.324
#> SRR1785308     1  0.6882      0.723 0.732 0.096 0.172
#> SRR1785309     1  0.6935      0.721 0.728 0.096 0.176
#> SRR1785310     1  0.6059      0.740 0.764 0.048 0.188
#> SRR1785311     1  0.5526      0.739 0.792 0.036 0.172
#> SRR1785312     3  0.5573      0.745 0.160 0.044 0.796
#> SRR1785313     3  0.5514      0.747 0.156 0.044 0.800
#> SRR1785314     3  0.3434      0.826 0.064 0.032 0.904
#> SRR1785315     3  0.3434      0.826 0.064 0.032 0.904
#> SRR1785318     2  0.1399      0.786 0.028 0.968 0.004
#> SRR1785319     2  0.1399      0.786 0.028 0.968 0.004
#> SRR1785316     1  0.3207      0.668 0.904 0.012 0.084
#> SRR1785317     1  0.3207      0.668 0.904 0.012 0.084
#> SRR1785324     2  0.1877      0.784 0.032 0.956 0.012
#> SRR1785325     2  0.1751      0.785 0.028 0.960 0.012
#> SRR1785320     3  0.6414      0.634 0.248 0.036 0.716
#> SRR1785321     3  0.6295      0.657 0.236 0.036 0.728
#> SRR1785322     1  0.7003      0.716 0.692 0.060 0.248
#> SRR1785323     1  0.6875      0.716 0.700 0.056 0.244

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     3  0.7397     0.6123 0.152 0.016 0.568 0.264
#> SRR1785239     3  0.7278     0.6411 0.152 0.016 0.588 0.244
#> SRR1785240     1  0.1978     0.7626 0.928 0.000 0.004 0.068
#> SRR1785241     1  0.1978     0.7626 0.928 0.000 0.004 0.068
#> SRR1785242     3  0.6626     0.7066 0.260 0.004 0.620 0.116
#> SRR1785243     3  0.6601     0.7069 0.256 0.004 0.624 0.116
#> SRR1785244     4  0.6534     0.0235 0.424 0.004 0.064 0.508
#> SRR1785245     4  0.6534     0.0235 0.424 0.004 0.064 0.508
#> SRR1785246     1  0.3719     0.7180 0.848 0.008 0.124 0.020
#> SRR1785247     1  0.3719     0.7180 0.848 0.008 0.124 0.020
#> SRR1785248     2  0.5251     0.6472 0.020 0.768 0.160 0.052
#> SRR1785250     3  0.6242     0.6936 0.140 0.004 0.680 0.176
#> SRR1785251     3  0.6242     0.6936 0.140 0.004 0.680 0.176
#> SRR1785252     3  0.6066     0.6966 0.268 0.004 0.656 0.072
#> SRR1785253     3  0.6066     0.6966 0.268 0.004 0.656 0.072
#> SRR1785254     4  0.5832     0.6418 0.132 0.032 0.088 0.748
#> SRR1785255     4  0.5832     0.6418 0.132 0.032 0.088 0.748
#> SRR1785256     4  0.3300     0.6903 0.144 0.000 0.008 0.848
#> SRR1785257     4  0.3300     0.6928 0.144 0.000 0.008 0.848
#> SRR1785258     4  0.6022     0.5309 0.336 0.004 0.048 0.612
#> SRR1785259     4  0.6004     0.5406 0.332 0.004 0.048 0.616
#> SRR1785262     4  0.5152     0.6111 0.316 0.000 0.020 0.664
#> SRR1785263     4  0.5233     0.5933 0.332 0.000 0.020 0.648
#> SRR1785260     4  0.2485     0.6808 0.016 0.004 0.064 0.916
#> SRR1785261     4  0.2485     0.6808 0.016 0.004 0.064 0.916
#> SRR1785264     2  0.7527     0.5121 0.108 0.624 0.196 0.072
#> SRR1785265     2  0.7140     0.5502 0.080 0.652 0.196 0.072
#> SRR1785266     2  0.0469     0.7658 0.000 0.988 0.012 0.000
#> SRR1785267     2  0.0469     0.7658 0.000 0.988 0.012 0.000
#> SRR1785268     1  0.7024     0.4509 0.580 0.004 0.148 0.268
#> SRR1785269     1  0.7001     0.4594 0.584 0.004 0.148 0.264
#> SRR1785270     1  0.2409     0.7477 0.924 0.004 0.040 0.032
#> SRR1785271     1  0.2409     0.7477 0.924 0.004 0.040 0.032
#> SRR1785272     3  0.4789     0.7007 0.020 0.004 0.740 0.236
#> SRR1785273     3  0.4756     0.7006 0.020 0.004 0.744 0.232
#> SRR1785276     1  0.3489     0.7151 0.856 0.012 0.124 0.008
#> SRR1785277     1  0.3489     0.7151 0.856 0.012 0.124 0.008
#> SRR1785274     1  0.1902     0.7621 0.932 0.000 0.004 0.064
#> SRR1785275     1  0.1902     0.7621 0.932 0.000 0.004 0.064
#> SRR1785280     2  0.0469     0.7658 0.000 0.988 0.012 0.000
#> SRR1785281     2  0.0469     0.7658 0.000 0.988 0.012 0.000
#> SRR1785278     4  0.5072     0.6383 0.208 0.000 0.052 0.740
#> SRR1785279     4  0.5072     0.6377 0.208 0.000 0.052 0.740
#> SRR1785282     4  0.3424     0.6772 0.068 0.004 0.052 0.876
#> SRR1785283     4  0.3424     0.6772 0.068 0.004 0.052 0.876
#> SRR1785284     1  0.2125     0.7607 0.920 0.000 0.004 0.076
#> SRR1785285     1  0.2197     0.7593 0.916 0.000 0.004 0.080
#> SRR1785286     1  0.3612     0.7274 0.840 0.012 0.004 0.144
#> SRR1785287     1  0.3632     0.7236 0.832 0.008 0.004 0.156
#> SRR1785288     4  0.5218     0.6071 0.200 0.000 0.064 0.736
#> SRR1785289     4  0.5256     0.6091 0.204 0.000 0.064 0.732
#> SRR1785290     2  0.8421     0.3293 0.112 0.488 0.084 0.316
#> SRR1785291     2  0.8421     0.3293 0.112 0.488 0.084 0.316
#> SRR1785296     4  0.6064     0.6439 0.172 0.012 0.108 0.708
#> SRR1785297     4  0.6009     0.6447 0.172 0.012 0.104 0.712
#> SRR1785292     2  0.0188     0.7687 0.004 0.996 0.000 0.000
#> SRR1785293     2  0.0376     0.7697 0.004 0.992 0.000 0.004
#> SRR1785294     4  0.4188     0.6888 0.108 0.012 0.044 0.836
#> SRR1785295     4  0.4124     0.6908 0.096 0.016 0.044 0.844
#> SRR1785298     4  0.4657     0.6781 0.124 0.012 0.056 0.808
#> SRR1785299     4  0.5048     0.6685 0.128 0.016 0.068 0.788
#> SRR1785300     4  0.2413     0.6788 0.020 0.000 0.064 0.916
#> SRR1785301     4  0.2816     0.6830 0.036 0.000 0.064 0.900
#> SRR1785304     2  0.8395     0.3426 0.112 0.496 0.084 0.308
#> SRR1785305     2  0.8395     0.3426 0.112 0.496 0.084 0.308
#> SRR1785306     1  0.6101     0.3723 0.644 0.004 0.068 0.284
#> SRR1785307     1  0.6124     0.3624 0.640 0.004 0.068 0.288
#> SRR1785302     4  0.7133     0.5919 0.220 0.020 0.144 0.616
#> SRR1785303     4  0.7133     0.5919 0.220 0.020 0.144 0.616
#> SRR1785308     3  0.4699     0.6909 0.000 0.004 0.676 0.320
#> SRR1785309     3  0.4655     0.6930 0.000 0.004 0.684 0.312
#> SRR1785310     4  0.3748     0.6921 0.088 0.008 0.044 0.860
#> SRR1785311     4  0.3726     0.6970 0.092 0.008 0.040 0.860
#> SRR1785312     1  0.6296     0.6298 0.676 0.004 0.152 0.168
#> SRR1785313     1  0.6172     0.6408 0.688 0.004 0.152 0.156
#> SRR1785314     1  0.2164     0.7304 0.924 0.004 0.068 0.004
#> SRR1785315     1  0.2164     0.7304 0.924 0.004 0.068 0.004
#> SRR1785318     2  0.0336     0.7700 0.000 0.992 0.000 0.008
#> SRR1785319     2  0.0336     0.7700 0.000 0.992 0.000 0.008
#> SRR1785316     4  0.4837     0.6392 0.076 0.008 0.120 0.796
#> SRR1785317     4  0.4891     0.6373 0.076 0.008 0.124 0.792
#> SRR1785324     2  0.0524     0.7699 0.000 0.988 0.004 0.008
#> SRR1785325     2  0.0524     0.7699 0.000 0.988 0.004 0.008
#> SRR1785320     1  0.7055     0.4999 0.580 0.004 0.156 0.260
#> SRR1785321     1  0.6932     0.5260 0.600 0.004 0.156 0.240
#> SRR1785322     4  0.5949     0.6001 0.144 0.004 0.144 0.708
#> SRR1785323     4  0.5948     0.5983 0.140 0.004 0.148 0.708

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     3  0.7631    0.40695 0.036 0.084 0.556 0.212 0.112
#> SRR1785239     3  0.7659    0.42219 0.040 0.092 0.560 0.204 0.104
#> SRR1785240     5  0.4444    0.76148 0.104 0.000 0.088 0.020 0.788
#> SRR1785241     5  0.4389    0.76321 0.104 0.000 0.084 0.020 0.792
#> SRR1785242     3  0.5736    0.60132 0.028 0.064 0.732 0.060 0.116
#> SRR1785243     3  0.5815    0.60076 0.032 0.064 0.728 0.060 0.116
#> SRR1785244     4  0.3443    0.58493 0.076 0.000 0.012 0.852 0.060
#> SRR1785245     4  0.3266    0.58725 0.076 0.000 0.008 0.860 0.056
#> SRR1785246     5  0.6162    0.46867 0.360 0.000 0.112 0.008 0.520
#> SRR1785247     5  0.6123    0.47577 0.360 0.000 0.108 0.008 0.524
#> SRR1785248     2  0.4715    0.73747 0.016 0.764 0.164 0.044 0.012
#> SRR1785250     3  0.4963    0.51332 0.040 0.004 0.756 0.052 0.148
#> SRR1785251     3  0.4963    0.51332 0.040 0.004 0.756 0.052 0.148
#> SRR1785252     3  0.6137    0.58336 0.016 0.064 0.644 0.036 0.240
#> SRR1785253     3  0.6137    0.58336 0.016 0.064 0.644 0.036 0.240
#> SRR1785254     4  0.7674    0.32239 0.016 0.076 0.124 0.480 0.304
#> SRR1785255     4  0.7628    0.32434 0.016 0.076 0.116 0.480 0.312
#> SRR1785256     4  0.4826    0.59669 0.076 0.000 0.104 0.772 0.048
#> SRR1785257     4  0.4776    0.59789 0.076 0.000 0.100 0.776 0.048
#> SRR1785258     3  0.8128    0.10465 0.108 0.036 0.448 0.312 0.096
#> SRR1785259     3  0.8279    0.08807 0.108 0.036 0.428 0.316 0.112
#> SRR1785262     4  0.8548    0.00365 0.060 0.040 0.292 0.344 0.264
#> SRR1785263     4  0.8594   -0.01537 0.064 0.040 0.296 0.336 0.264
#> SRR1785260     4  0.1012    0.60231 0.020 0.000 0.012 0.968 0.000
#> SRR1785261     4  0.1012    0.60231 0.020 0.000 0.012 0.968 0.000
#> SRR1785264     2  0.6091    0.66905 0.016 0.676 0.188 0.048 0.072
#> SRR1785265     2  0.5976    0.67873 0.016 0.684 0.188 0.048 0.064
#> SRR1785266     2  0.1329    0.81108 0.004 0.956 0.032 0.000 0.008
#> SRR1785267     2  0.1329    0.81108 0.004 0.956 0.032 0.000 0.008
#> SRR1785268     1  0.6168    0.76506 0.640 0.000 0.124 0.040 0.196
#> SRR1785269     1  0.6168    0.76506 0.640 0.000 0.124 0.040 0.196
#> SRR1785270     5  0.0854    0.75950 0.004 0.000 0.012 0.008 0.976
#> SRR1785271     5  0.0854    0.75950 0.004 0.000 0.012 0.008 0.976
#> SRR1785272     3  0.4322    0.54725 0.076 0.000 0.788 0.124 0.012
#> SRR1785273     3  0.4275    0.54594 0.076 0.000 0.792 0.120 0.012
#> SRR1785276     5  0.4347    0.60613 0.356 0.004 0.004 0.000 0.636
#> SRR1785277     5  0.4347    0.60613 0.356 0.004 0.004 0.000 0.636
#> SRR1785274     5  0.4439    0.75879 0.108 0.000 0.084 0.020 0.788
#> SRR1785275     5  0.4494    0.75839 0.108 0.000 0.088 0.020 0.784
#> SRR1785280     2  0.0932    0.81377 0.004 0.972 0.020 0.000 0.004
#> SRR1785281     2  0.0932    0.81377 0.004 0.972 0.020 0.000 0.004
#> SRR1785278     4  0.6290    0.46547 0.096 0.000 0.252 0.608 0.044
#> SRR1785279     4  0.6290    0.46547 0.096 0.000 0.252 0.608 0.044
#> SRR1785282     4  0.4924    0.54827 0.112 0.000 0.136 0.740 0.012
#> SRR1785283     4  0.4965    0.54808 0.112 0.000 0.140 0.736 0.012
#> SRR1785284     5  0.3730    0.76535 0.032 0.000 0.100 0.032 0.836
#> SRR1785285     5  0.3853    0.76465 0.032 0.000 0.092 0.044 0.832
#> SRR1785286     5  0.4121    0.70158 0.024 0.000 0.024 0.164 0.788
#> SRR1785287     5  0.4071    0.70021 0.024 0.000 0.020 0.168 0.788
#> SRR1785288     4  0.2633    0.59498 0.068 0.000 0.012 0.896 0.024
#> SRR1785289     4  0.2521    0.59587 0.068 0.000 0.008 0.900 0.024
#> SRR1785290     2  0.7292    0.56034 0.028 0.584 0.144 0.184 0.060
#> SRR1785291     2  0.7295    0.55240 0.028 0.580 0.132 0.200 0.060
#> SRR1785296     4  0.8351    0.14009 0.036 0.064 0.336 0.376 0.188
#> SRR1785297     4  0.8344    0.15402 0.036 0.064 0.328 0.384 0.188
#> SRR1785292     2  0.0290    0.81628 0.000 0.992 0.000 0.008 0.000
#> SRR1785293     2  0.0290    0.81628 0.000 0.992 0.000 0.008 0.000
#> SRR1785294     4  0.4127    0.58689 0.024 0.012 0.072 0.828 0.064
#> SRR1785295     4  0.4127    0.58689 0.024 0.012 0.072 0.828 0.064
#> SRR1785298     4  0.7668    0.34992 0.036 0.052 0.156 0.532 0.224
#> SRR1785299     4  0.7684    0.34537 0.036 0.052 0.164 0.532 0.216
#> SRR1785300     4  0.0566    0.60588 0.012 0.000 0.000 0.984 0.004
#> SRR1785301     4  0.0566    0.60588 0.012 0.000 0.000 0.984 0.004
#> SRR1785304     2  0.7194    0.54943 0.024 0.584 0.108 0.216 0.068
#> SRR1785305     2  0.7157    0.54567 0.024 0.584 0.100 0.224 0.068
#> SRR1785306     5  0.3379    0.67096 0.016 0.000 0.008 0.148 0.828
#> SRR1785307     5  0.3564    0.66613 0.024 0.000 0.008 0.148 0.820
#> SRR1785302     4  0.7775    0.23813 0.024 0.044 0.156 0.396 0.380
#> SRR1785303     4  0.7729    0.24078 0.024 0.044 0.148 0.396 0.388
#> SRR1785308     3  0.4114    0.55445 0.044 0.000 0.776 0.176 0.004
#> SRR1785309     3  0.4114    0.55445 0.044 0.000 0.776 0.176 0.004
#> SRR1785310     4  0.3737    0.59456 0.020 0.008 0.060 0.848 0.064
#> SRR1785311     4  0.3737    0.59456 0.020 0.008 0.060 0.848 0.064
#> SRR1785312     1  0.4541    0.86284 0.788 0.000 0.084 0.032 0.096
#> SRR1785313     1  0.4541    0.86284 0.788 0.000 0.084 0.032 0.096
#> SRR1785314     5  0.0566    0.75487 0.004 0.000 0.012 0.000 0.984
#> SRR1785315     5  0.0566    0.75487 0.004 0.000 0.012 0.000 0.984
#> SRR1785318     2  0.0324    0.81617 0.000 0.992 0.004 0.004 0.000
#> SRR1785319     2  0.0324    0.81617 0.000 0.992 0.004 0.004 0.000
#> SRR1785316     4  0.5433    0.51555 0.160 0.000 0.148 0.684 0.008
#> SRR1785317     4  0.5470    0.51234 0.160 0.000 0.152 0.680 0.008
#> SRR1785324     2  0.0671    0.81362 0.016 0.980 0.000 0.000 0.004
#> SRR1785325     2  0.0833    0.81419 0.016 0.976 0.004 0.000 0.004
#> SRR1785320     1  0.4271    0.86866 0.808 0.000 0.096 0.040 0.056
#> SRR1785321     1  0.4271    0.86866 0.808 0.000 0.096 0.040 0.056
#> SRR1785322     4  0.6614    0.39640 0.040 0.000 0.236 0.580 0.144
#> SRR1785323     4  0.6626    0.39396 0.040 0.000 0.232 0.580 0.148

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     3  0.6864     0.3934 0.140 0.012 0.532 0.248 0.052 0.016
#> SRR1785239     3  0.6811     0.3996 0.140 0.012 0.536 0.248 0.048 0.016
#> SRR1785240     5  0.5378     0.6552 0.032 0.000 0.088 0.012 0.672 0.196
#> SRR1785241     5  0.5432     0.6494 0.032 0.000 0.088 0.012 0.664 0.204
#> SRR1785242     3  0.5283     0.6089 0.036 0.016 0.720 0.140 0.076 0.012
#> SRR1785243     3  0.5253     0.6081 0.040 0.016 0.720 0.140 0.076 0.008
#> SRR1785244     1  0.4159     0.6712 0.800 0.000 0.036 0.016 0.056 0.092
#> SRR1785245     1  0.4015     0.6683 0.808 0.000 0.028 0.016 0.056 0.092
#> SRR1785246     6  0.7014    -0.0508 0.004 0.000 0.148 0.092 0.344 0.412
#> SRR1785247     6  0.6975    -0.0701 0.004 0.000 0.140 0.092 0.352 0.412
#> SRR1785248     2  0.5795     0.5869 0.008 0.564 0.096 0.312 0.004 0.016
#> SRR1785250     3  0.3875     0.6257 0.028 0.008 0.816 0.020 0.112 0.016
#> SRR1785251     3  0.3875     0.6257 0.028 0.008 0.816 0.020 0.112 0.016
#> SRR1785252     3  0.5548     0.6068 0.020 0.012 0.664 0.104 0.192 0.008
#> SRR1785253     3  0.5463     0.6097 0.020 0.012 0.672 0.096 0.192 0.008
#> SRR1785254     4  0.6955     0.7258 0.216 0.024 0.080 0.528 0.152 0.000
#> SRR1785255     4  0.7082     0.7120 0.204 0.028 0.084 0.520 0.164 0.000
#> SRR1785256     1  0.4497     0.7120 0.768 0.000 0.116 0.048 0.008 0.060
#> SRR1785257     1  0.4507     0.7140 0.776 0.004 0.104 0.048 0.008 0.060
#> SRR1785258     3  0.6464     0.5275 0.108 0.000 0.612 0.088 0.032 0.160
#> SRR1785259     3  0.6550     0.5174 0.096 0.000 0.600 0.088 0.036 0.180
#> SRR1785262     3  0.7631     0.4694 0.120 0.000 0.504 0.092 0.112 0.172
#> SRR1785263     3  0.7734     0.4553 0.116 0.000 0.488 0.088 0.128 0.180
#> SRR1785260     1  0.0405     0.7027 0.988 0.000 0.004 0.008 0.000 0.000
#> SRR1785261     1  0.0260     0.7021 0.992 0.000 0.000 0.008 0.000 0.000
#> SRR1785264     2  0.6495     0.5137 0.016 0.488 0.104 0.356 0.020 0.016
#> SRR1785265     2  0.6421     0.5196 0.016 0.492 0.104 0.356 0.016 0.016
#> SRR1785266     2  0.2526     0.7316 0.000 0.876 0.024 0.096 0.000 0.004
#> SRR1785267     2  0.2526     0.7316 0.000 0.876 0.024 0.096 0.000 0.004
#> SRR1785268     6  0.4977     0.6241 0.008 0.000 0.120 0.036 0.112 0.724
#> SRR1785269     6  0.4801     0.6307 0.004 0.000 0.120 0.032 0.112 0.732
#> SRR1785270     5  0.1557     0.7385 0.004 0.004 0.008 0.036 0.944 0.004
#> SRR1785271     5  0.1557     0.7385 0.004 0.004 0.008 0.036 0.944 0.004
#> SRR1785272     3  0.3367     0.6335 0.116 0.000 0.832 0.004 0.020 0.028
#> SRR1785273     3  0.3367     0.6335 0.116 0.000 0.832 0.004 0.020 0.028
#> SRR1785276     5  0.5683     0.2938 0.000 0.000 0.024 0.088 0.496 0.392
#> SRR1785277     5  0.5683     0.2938 0.000 0.000 0.024 0.088 0.496 0.392
#> SRR1785274     5  0.5525     0.6581 0.028 0.000 0.088 0.024 0.668 0.192
#> SRR1785275     5  0.5569     0.6562 0.028 0.000 0.092 0.024 0.664 0.192
#> SRR1785280     2  0.2575     0.7304 0.000 0.872 0.024 0.100 0.000 0.004
#> SRR1785281     2  0.2575     0.7304 0.000 0.872 0.024 0.100 0.000 0.004
#> SRR1785278     1  0.6909     0.5418 0.524 0.000 0.260 0.096 0.032 0.088
#> SRR1785279     1  0.6864     0.5475 0.528 0.000 0.260 0.100 0.032 0.080
#> SRR1785282     1  0.5544     0.6779 0.656 0.000 0.204 0.048 0.008 0.084
#> SRR1785283     1  0.5544     0.6779 0.656 0.000 0.204 0.048 0.008 0.084
#> SRR1785284     5  0.3707     0.7341 0.040 0.000 0.108 0.016 0.820 0.016
#> SRR1785285     5  0.3717     0.7341 0.052 0.000 0.100 0.012 0.820 0.016
#> SRR1785286     5  0.3904     0.6797 0.152 0.004 0.020 0.024 0.792 0.008
#> SRR1785287     5  0.3856     0.6810 0.156 0.004 0.016 0.024 0.792 0.008
#> SRR1785288     1  0.2533     0.6944 0.884 0.000 0.000 0.004 0.056 0.056
#> SRR1785289     1  0.2533     0.6944 0.884 0.000 0.000 0.004 0.056 0.056
#> SRR1785290     2  0.7089     0.3246 0.092 0.392 0.116 0.384 0.016 0.000
#> SRR1785291     2  0.7089     0.3246 0.092 0.392 0.116 0.384 0.016 0.000
#> SRR1785296     4  0.6213     0.6916 0.208 0.004 0.176 0.568 0.044 0.000
#> SRR1785297     4  0.6182     0.7178 0.228 0.004 0.156 0.568 0.044 0.000
#> SRR1785292     2  0.0436     0.7378 0.004 0.988 0.000 0.004 0.000 0.004
#> SRR1785293     2  0.0436     0.7378 0.004 0.988 0.000 0.004 0.000 0.004
#> SRR1785294     1  0.3844     0.6090 0.808 0.000 0.084 0.084 0.020 0.004
#> SRR1785295     1  0.3843     0.6116 0.808 0.000 0.080 0.088 0.020 0.004
#> SRR1785298     4  0.5385     0.7374 0.324 0.000 0.036 0.588 0.048 0.004
#> SRR1785299     4  0.5385     0.7374 0.324 0.000 0.036 0.588 0.048 0.004
#> SRR1785300     1  0.0146     0.7037 0.996 0.000 0.004 0.000 0.000 0.000
#> SRR1785301     1  0.0260     0.7041 0.992 0.000 0.008 0.000 0.000 0.000
#> SRR1785304     2  0.7193     0.3177 0.164 0.476 0.112 0.236 0.012 0.000
#> SRR1785305     2  0.7193     0.3177 0.164 0.476 0.112 0.236 0.012 0.000
#> SRR1785306     5  0.2638     0.7062 0.068 0.000 0.012 0.020 0.888 0.012
#> SRR1785307     5  0.2638     0.7062 0.068 0.000 0.012 0.020 0.888 0.012
#> SRR1785302     4  0.6642     0.7494 0.208 0.004 0.060 0.556 0.160 0.012
#> SRR1785303     4  0.6675     0.7460 0.204 0.004 0.060 0.552 0.168 0.012
#> SRR1785308     3  0.2909     0.6126 0.156 0.000 0.828 0.004 0.000 0.012
#> SRR1785309     3  0.2909     0.6126 0.156 0.000 0.828 0.004 0.000 0.012
#> SRR1785310     1  0.3635     0.6281 0.824 0.000 0.072 0.080 0.020 0.004
#> SRR1785311     1  0.3632     0.6246 0.824 0.000 0.068 0.084 0.020 0.004
#> SRR1785312     6  0.2125     0.7277 0.004 0.000 0.068 0.004 0.016 0.908
#> SRR1785313     6  0.2125     0.7277 0.004 0.000 0.068 0.004 0.016 0.908
#> SRR1785314     5  0.0632     0.7367 0.000 0.000 0.000 0.024 0.976 0.000
#> SRR1785315     5  0.0632     0.7367 0.000 0.000 0.000 0.024 0.976 0.000
#> SRR1785318     2  0.0405     0.7376 0.008 0.988 0.000 0.004 0.000 0.000
#> SRR1785319     2  0.0405     0.7376 0.008 0.988 0.000 0.004 0.000 0.000
#> SRR1785316     1  0.5732     0.6631 0.636 0.000 0.200 0.040 0.008 0.116
#> SRR1785317     1  0.5824     0.6604 0.632 0.000 0.200 0.040 0.012 0.116
#> SRR1785324     2  0.0436     0.7374 0.004 0.988 0.000 0.004 0.004 0.000
#> SRR1785325     2  0.0436     0.7374 0.004 0.988 0.000 0.004 0.004 0.000
#> SRR1785320     6  0.1668     0.7264 0.004 0.000 0.060 0.000 0.008 0.928
#> SRR1785321     6  0.1668     0.7264 0.004 0.000 0.060 0.000 0.008 0.928
#> SRR1785322     1  0.5943     0.5120 0.576 0.004 0.280 0.024 0.108 0.008
#> SRR1785323     1  0.5750     0.5270 0.588 0.004 0.276 0.020 0.108 0.004

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-CV-mclust-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-CV-mclust-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-CV-mclust-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-CV-mclust-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-CV-mclust-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-CV-mclust-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-CV-mclust-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-CV-mclust-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-CV-mclust-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-CV-mclust-membership-heatmap-5

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)

plot of chunk tab-CV-mclust-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-CV-mclust-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-CV-mclust-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-CV-mclust-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-CV-mclust-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-CV-mclust-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-CV-mclust-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-CV-mclust-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-CV-mclust-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-CV-mclust-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk CV-mclust-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-CV-mclust-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-CV-mclust-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-CV-mclust-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-CV-mclust-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-CV-mclust-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk CV-mclust-collect-classes

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.


CV:NMF

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#>   Subgroups are detected by 'NMF' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 2.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk CV-NMF-collect-plots

The plots are:

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:

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)

plot of chunk CV-NMF-select-partition-number

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 0.814           0.882       0.951         0.4604 0.524   0.524
#> 3 3 0.673           0.770       0.896         0.4469 0.632   0.398
#> 4 4 0.649           0.743       0.842         0.1277 0.813   0.514
#> 5 5 0.656           0.625       0.768         0.0646 0.878   0.569
#> 6 6 0.691           0.659       0.783         0.0412 0.911   0.604

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2  0.6048     0.7977 0.148 0.852
#> SRR1785239     2  0.5842     0.8045 0.140 0.860
#> SRR1785240     1  0.0000     0.9775 1.000 0.000
#> SRR1785241     1  0.0000     0.9775 1.000 0.000
#> SRR1785242     2  0.0000     0.8947 0.000 1.000
#> SRR1785243     2  0.0000     0.8947 0.000 1.000
#> SRR1785244     1  0.0000     0.9775 1.000 0.000
#> SRR1785245     1  0.0000     0.9775 1.000 0.000
#> SRR1785246     1  0.0000     0.9775 1.000 0.000
#> SRR1785247     1  0.0000     0.9775 1.000 0.000
#> SRR1785248     2  0.0000     0.8947 0.000 1.000
#> SRR1785250     1  0.0000     0.9775 1.000 0.000
#> SRR1785251     1  0.0000     0.9775 1.000 0.000
#> SRR1785252     2  0.8267     0.6845 0.260 0.740
#> SRR1785253     2  0.9209     0.5668 0.336 0.664
#> SRR1785254     2  0.0000     0.8947 0.000 1.000
#> SRR1785255     2  0.0000     0.8947 0.000 1.000
#> SRR1785256     1  0.0000     0.9775 1.000 0.000
#> SRR1785257     1  0.0000     0.9775 1.000 0.000
#> SRR1785258     1  0.0000     0.9775 1.000 0.000
#> SRR1785259     1  0.0000     0.9775 1.000 0.000
#> SRR1785262     1  0.0000     0.9775 1.000 0.000
#> SRR1785263     1  0.0000     0.9775 1.000 0.000
#> SRR1785260     1  0.0000     0.9775 1.000 0.000
#> SRR1785261     1  0.0000     0.9775 1.000 0.000
#> SRR1785264     2  0.0000     0.8947 0.000 1.000
#> SRR1785265     2  0.0000     0.8947 0.000 1.000
#> SRR1785266     2  0.0000     0.8947 0.000 1.000
#> SRR1785267     2  0.0000     0.8947 0.000 1.000
#> SRR1785268     1  0.0000     0.9775 1.000 0.000
#> SRR1785269     1  0.0000     0.9775 1.000 0.000
#> SRR1785270     2  0.2043     0.8790 0.032 0.968
#> SRR1785271     2  0.0672     0.8911 0.008 0.992
#> SRR1785272     1  0.0000     0.9775 1.000 0.000
#> SRR1785273     1  0.0000     0.9775 1.000 0.000
#> SRR1785276     1  0.0000     0.9775 1.000 0.000
#> SRR1785277     1  0.0000     0.9775 1.000 0.000
#> SRR1785274     1  0.0000     0.9775 1.000 0.000
#> SRR1785275     1  0.0000     0.9775 1.000 0.000
#> SRR1785280     2  0.0000     0.8947 0.000 1.000
#> SRR1785281     2  0.0000     0.8947 0.000 1.000
#> SRR1785278     1  0.0000     0.9775 1.000 0.000
#> SRR1785279     1  0.0000     0.9775 1.000 0.000
#> SRR1785282     1  0.0000     0.9775 1.000 0.000
#> SRR1785283     1  0.0000     0.9775 1.000 0.000
#> SRR1785284     1  0.0000     0.9775 1.000 0.000
#> SRR1785285     1  0.0000     0.9775 1.000 0.000
#> SRR1785286     1  0.0000     0.9775 1.000 0.000
#> SRR1785287     1  0.0000     0.9775 1.000 0.000
#> SRR1785288     1  0.0000     0.9775 1.000 0.000
#> SRR1785289     1  0.0000     0.9775 1.000 0.000
#> SRR1785290     2  0.0000     0.8947 0.000 1.000
#> SRR1785291     2  0.0000     0.8947 0.000 1.000
#> SRR1785296     2  0.9954     0.2450 0.460 0.540
#> SRR1785297     2  0.9998     0.1441 0.492 0.508
#> SRR1785292     2  0.0000     0.8947 0.000 1.000
#> SRR1785293     2  0.0000     0.8947 0.000 1.000
#> SRR1785294     1  0.0000     0.9775 1.000 0.000
#> SRR1785295     1  0.0376     0.9735 0.996 0.004
#> SRR1785298     1  0.8861     0.4972 0.696 0.304
#> SRR1785299     2  1.0000     0.1308 0.500 0.500
#> SRR1785300     1  0.0000     0.9775 1.000 0.000
#> SRR1785301     1  0.0000     0.9775 1.000 0.000
#> SRR1785304     2  0.0000     0.8947 0.000 1.000
#> SRR1785305     2  0.0000     0.8947 0.000 1.000
#> SRR1785306     2  0.8861     0.6143 0.304 0.696
#> SRR1785307     2  0.7674     0.7220 0.224 0.776
#> SRR1785302     1  0.7815     0.6491 0.768 0.232
#> SRR1785303     1  0.2423     0.9339 0.960 0.040
#> SRR1785308     1  0.0000     0.9775 1.000 0.000
#> SRR1785309     1  0.0000     0.9775 1.000 0.000
#> SRR1785310     1  0.0000     0.9775 1.000 0.000
#> SRR1785311     1  0.0000     0.9775 1.000 0.000
#> SRR1785312     1  0.0000     0.9775 1.000 0.000
#> SRR1785313     1  0.0000     0.9775 1.000 0.000
#> SRR1785314     1  0.9963    -0.0357 0.536 0.464
#> SRR1785315     2  0.9170     0.5694 0.332 0.668
#> SRR1785318     2  0.0000     0.8947 0.000 1.000
#> SRR1785319     2  0.0000     0.8947 0.000 1.000
#> SRR1785316     1  0.0000     0.9775 1.000 0.000
#> SRR1785317     1  0.0000     0.9775 1.000 0.000
#> SRR1785324     2  0.0000     0.8947 0.000 1.000
#> SRR1785325     2  0.0000     0.8947 0.000 1.000
#> SRR1785320     1  0.0000     0.9775 1.000 0.000
#> SRR1785321     1  0.0000     0.9775 1.000 0.000
#> SRR1785322     1  0.0000     0.9775 1.000 0.000
#> SRR1785323     1  0.0000     0.9775 1.000 0.000

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     3  0.6299      0.140 0.000 0.476 0.524
#> SRR1785239     3  0.6280      0.190 0.000 0.460 0.540
#> SRR1785240     3  0.2066      0.851 0.060 0.000 0.940
#> SRR1785241     3  0.1964      0.853 0.056 0.000 0.944
#> SRR1785242     3  0.2590      0.823 0.004 0.072 0.924
#> SRR1785243     3  0.3715      0.775 0.004 0.128 0.868
#> SRR1785244     1  0.0000      0.898 1.000 0.000 0.000
#> SRR1785245     1  0.0000      0.898 1.000 0.000 0.000
#> SRR1785246     3  0.0000      0.856 0.000 0.000 1.000
#> SRR1785247     3  0.0000      0.856 0.000 0.000 1.000
#> SRR1785248     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785250     3  0.0747      0.858 0.016 0.000 0.984
#> SRR1785251     3  0.0747      0.858 0.016 0.000 0.984
#> SRR1785252     3  0.0237      0.856 0.004 0.000 0.996
#> SRR1785253     3  0.0237      0.856 0.004 0.000 0.996
#> SRR1785254     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785255     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785256     1  0.0000      0.898 1.000 0.000 0.000
#> SRR1785257     1  0.0000      0.898 1.000 0.000 0.000
#> SRR1785258     3  0.4555      0.747 0.200 0.000 0.800
#> SRR1785259     3  0.4452      0.755 0.192 0.000 0.808
#> SRR1785262     3  0.0892      0.858 0.020 0.000 0.980
#> SRR1785263     3  0.0592      0.858 0.012 0.000 0.988
#> SRR1785260     1  0.0000      0.898 1.000 0.000 0.000
#> SRR1785261     1  0.0000      0.898 1.000 0.000 0.000
#> SRR1785264     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785265     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785266     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785267     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785268     3  0.3686      0.795 0.140 0.000 0.860
#> SRR1785269     3  0.2878      0.831 0.096 0.000 0.904
#> SRR1785270     2  0.4700      0.740 0.008 0.812 0.180
#> SRR1785271     2  0.4099      0.779 0.008 0.852 0.140
#> SRR1785272     3  0.5905      0.537 0.352 0.000 0.648
#> SRR1785273     3  0.5882      0.544 0.348 0.000 0.652
#> SRR1785276     3  0.0000      0.856 0.000 0.000 1.000
#> SRR1785277     3  0.0000      0.856 0.000 0.000 1.000
#> SRR1785274     3  0.0892      0.858 0.020 0.000 0.980
#> SRR1785275     3  0.2959      0.824 0.100 0.000 0.900
#> SRR1785280     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785281     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785278     1  0.0237      0.897 0.996 0.000 0.004
#> SRR1785279     1  0.0237      0.897 0.996 0.000 0.004
#> SRR1785282     1  0.0237      0.897 0.996 0.000 0.004
#> SRR1785283     1  0.0424      0.896 0.992 0.000 0.008
#> SRR1785284     1  0.3340      0.802 0.880 0.000 0.120
#> SRR1785285     1  0.2066      0.859 0.940 0.000 0.060
#> SRR1785286     1  0.1031      0.885 0.976 0.000 0.024
#> SRR1785287     1  0.0592      0.892 0.988 0.000 0.012
#> SRR1785288     1  0.0237      0.897 0.996 0.000 0.004
#> SRR1785289     1  0.0237      0.897 0.996 0.000 0.004
#> SRR1785290     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785291     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785296     1  0.6280      0.210 0.540 0.460 0.000
#> SRR1785297     1  0.5678      0.558 0.684 0.316 0.000
#> SRR1785292     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785293     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785294     1  0.0000      0.898 1.000 0.000 0.000
#> SRR1785295     1  0.0237      0.897 0.996 0.004 0.000
#> SRR1785298     1  0.1753      0.873 0.952 0.048 0.000
#> SRR1785299     1  0.3412      0.809 0.876 0.124 0.000
#> SRR1785300     1  0.0000      0.898 1.000 0.000 0.000
#> SRR1785301     1  0.0000      0.898 1.000 0.000 0.000
#> SRR1785304     1  0.5810      0.512 0.664 0.336 0.000
#> SRR1785305     1  0.5810      0.512 0.664 0.336 0.000
#> SRR1785306     2  0.6468      0.283 0.004 0.552 0.444
#> SRR1785307     2  0.6451      0.304 0.004 0.560 0.436
#> SRR1785302     2  0.6386      0.209 0.412 0.584 0.004
#> SRR1785303     1  0.5502      0.656 0.744 0.248 0.008
#> SRR1785308     3  0.5926      0.529 0.356 0.000 0.644
#> SRR1785309     3  0.5882      0.544 0.348 0.000 0.652
#> SRR1785310     1  0.0000      0.898 1.000 0.000 0.000
#> SRR1785311     1  0.0000      0.898 1.000 0.000 0.000
#> SRR1785312     3  0.0747      0.858 0.016 0.000 0.984
#> SRR1785313     3  0.0892      0.858 0.020 0.000 0.980
#> SRR1785314     2  0.9687      0.335 0.268 0.460 0.272
#> SRR1785315     2  0.7437      0.646 0.108 0.692 0.200
#> SRR1785318     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785319     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785316     1  0.0237      0.897 0.996 0.000 0.004
#> SRR1785317     1  0.0237      0.897 0.996 0.000 0.004
#> SRR1785324     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785325     2  0.0000      0.887 0.000 1.000 0.000
#> SRR1785320     3  0.1860      0.849 0.052 0.000 0.948
#> SRR1785321     3  0.1529      0.854 0.040 0.000 0.960
#> SRR1785322     1  0.6225      0.218 0.568 0.000 0.432
#> SRR1785323     1  0.6111      0.327 0.604 0.000 0.396

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     3  0.2530      0.719 0.000 0.112 0.888 0.000
#> SRR1785239     3  0.2334      0.729 0.000 0.088 0.908 0.004
#> SRR1785240     1  0.0336      0.802 0.992 0.000 0.000 0.008
#> SRR1785241     1  0.0336      0.802 0.992 0.000 0.000 0.008
#> SRR1785242     3  0.5268      0.560 0.396 0.012 0.592 0.000
#> SRR1785243     3  0.6201      0.565 0.376 0.060 0.564 0.000
#> SRR1785244     4  0.2589      0.749 0.116 0.000 0.000 0.884
#> SRR1785245     4  0.1792      0.788 0.068 0.000 0.000 0.932
#> SRR1785246     1  0.3266      0.790 0.832 0.000 0.168 0.000
#> SRR1785247     1  0.3219      0.791 0.836 0.000 0.164 0.000
#> SRR1785248     2  0.0000      0.958 0.000 1.000 0.000 0.000
#> SRR1785250     3  0.2149      0.742 0.088 0.000 0.912 0.000
#> SRR1785251     3  0.2149      0.742 0.088 0.000 0.912 0.000
#> SRR1785252     3  0.4585      0.646 0.332 0.000 0.668 0.000
#> SRR1785253     3  0.4585      0.645 0.332 0.000 0.668 0.000
#> SRR1785254     2  0.3088      0.830 0.008 0.864 0.000 0.128
#> SRR1785255     2  0.3591      0.779 0.008 0.824 0.000 0.168
#> SRR1785256     4  0.2216      0.775 0.092 0.000 0.000 0.908
#> SRR1785257     4  0.2011      0.782 0.080 0.000 0.000 0.920
#> SRR1785258     3  0.4741      0.706 0.228 0.000 0.744 0.028
#> SRR1785259     3  0.4535      0.710 0.240 0.000 0.744 0.016
#> SRR1785262     3  0.5493      0.482 0.456 0.000 0.528 0.016
#> SRR1785263     3  0.5168      0.416 0.496 0.000 0.500 0.004
#> SRR1785260     4  0.0000      0.814 0.000 0.000 0.000 1.000
#> SRR1785261     4  0.0000      0.814 0.000 0.000 0.000 1.000
#> SRR1785264     2  0.0000      0.958 0.000 1.000 0.000 0.000
#> SRR1785265     2  0.0000      0.958 0.000 1.000 0.000 0.000
#> SRR1785266     2  0.0000      0.958 0.000 1.000 0.000 0.000
#> SRR1785267     2  0.0000      0.958 0.000 1.000 0.000 0.000
#> SRR1785268     1  0.5250      0.289 0.552 0.000 0.440 0.008
#> SRR1785269     1  0.5126      0.288 0.552 0.000 0.444 0.004
#> SRR1785270     1  0.0657      0.805 0.984 0.012 0.000 0.004
#> SRR1785271     1  0.0779      0.805 0.980 0.016 0.000 0.004
#> SRR1785272     3  0.2081      0.736 0.000 0.000 0.916 0.084
#> SRR1785273     3  0.2011      0.737 0.000 0.000 0.920 0.080
#> SRR1785276     1  0.3356      0.786 0.824 0.000 0.176 0.000
#> SRR1785277     1  0.3356      0.786 0.824 0.000 0.176 0.000
#> SRR1785274     1  0.0188      0.802 0.996 0.000 0.000 0.004
#> SRR1785275     1  0.0336      0.802 0.992 0.000 0.000 0.008
#> SRR1785280     2  0.0000      0.958 0.000 1.000 0.000 0.000
#> SRR1785281     2  0.0000      0.958 0.000 1.000 0.000 0.000
#> SRR1785278     4  0.5038      0.631 0.020 0.000 0.296 0.684
#> SRR1785279     4  0.4770      0.642 0.012 0.000 0.288 0.700
#> SRR1785282     4  0.4477      0.608 0.000 0.000 0.312 0.688
#> SRR1785283     4  0.4522      0.597 0.000 0.000 0.320 0.680
#> SRR1785284     1  0.3172      0.726 0.840 0.000 0.000 0.160
#> SRR1785285     1  0.3400      0.709 0.820 0.000 0.000 0.180
#> SRR1785286     1  0.4193      0.625 0.732 0.000 0.000 0.268
#> SRR1785287     1  0.4277      0.610 0.720 0.000 0.000 0.280
#> SRR1785288     4  0.0000      0.814 0.000 0.000 0.000 1.000
#> SRR1785289     4  0.0000      0.814 0.000 0.000 0.000 1.000
#> SRR1785290     2  0.0000      0.958 0.000 1.000 0.000 0.000
#> SRR1785291     2  0.0000      0.958 0.000 1.000 0.000 0.000
#> SRR1785296     4  0.5244      0.328 0.000 0.436 0.008 0.556
#> SRR1785297     4  0.4621      0.618 0.000 0.284 0.008 0.708
#> SRR1785292     2  0.0000      0.958 0.000 1.000 0.000 0.000
#> SRR1785293     2  0.0000      0.958 0.000 1.000 0.000 0.000
#> SRR1785294     4  0.1557      0.803 0.000 0.000 0.056 0.944
#> SRR1785295     4  0.2408      0.781 0.000 0.000 0.104 0.896
#> SRR1785298     4  0.3219      0.762 0.020 0.112 0.000 0.868
#> SRR1785299     4  0.3672      0.723 0.012 0.164 0.000 0.824
#> SRR1785300     4  0.0000      0.814 0.000 0.000 0.000 1.000
#> SRR1785301     4  0.0000      0.814 0.000 0.000 0.000 1.000
#> SRR1785304     4  0.2408      0.781 0.000 0.104 0.000 0.896
#> SRR1785305     4  0.2216      0.787 0.000 0.092 0.000 0.908
#> SRR1785306     1  0.1109      0.806 0.968 0.004 0.028 0.000
#> SRR1785307     1  0.1356      0.806 0.960 0.008 0.032 0.000
#> SRR1785302     2  0.7365      0.538 0.056 0.636 0.128 0.180
#> SRR1785303     4  0.8582      0.189 0.068 0.368 0.140 0.424
#> SRR1785308     3  0.2530      0.730 0.004 0.000 0.896 0.100
#> SRR1785309     3  0.2466      0.732 0.004 0.000 0.900 0.096
#> SRR1785310     4  0.0469      0.813 0.000 0.000 0.012 0.988
#> SRR1785311     4  0.0336      0.813 0.000 0.000 0.008 0.992
#> SRR1785312     1  0.4103      0.744 0.744 0.000 0.256 0.000
#> SRR1785313     1  0.4040      0.750 0.752 0.000 0.248 0.000
#> SRR1785314     1  0.3960      0.796 0.860 0.036 0.072 0.032
#> SRR1785315     1  0.4555      0.782 0.832 0.068 0.064 0.036
#> SRR1785318     2  0.0000      0.958 0.000 1.000 0.000 0.000
#> SRR1785319     2  0.0000      0.958 0.000 1.000 0.000 0.000
#> SRR1785316     4  0.4564      0.593 0.000 0.000 0.328 0.672
#> SRR1785317     4  0.4624      0.576 0.000 0.000 0.340 0.660
#> SRR1785324     2  0.0000      0.958 0.000 1.000 0.000 0.000
#> SRR1785325     2  0.0000      0.958 0.000 1.000 0.000 0.000
#> SRR1785320     1  0.3837      0.764 0.776 0.000 0.224 0.000
#> SRR1785321     1  0.3649      0.776 0.796 0.000 0.204 0.000
#> SRR1785322     3  0.5257      0.559 0.060 0.000 0.728 0.212
#> SRR1785323     3  0.5434      0.479 0.052 0.000 0.696 0.252

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     1  0.5945     0.4330 0.600 0.268 0.124 0.000 0.008
#> SRR1785239     1  0.6159     0.4251 0.600 0.224 0.164 0.000 0.012
#> SRR1785240     5  0.3596     0.6394 0.000 0.000 0.200 0.016 0.784
#> SRR1785241     5  0.3492     0.6524 0.000 0.000 0.188 0.016 0.796
#> SRR1785242     3  0.2806     0.6621 0.000 0.004 0.844 0.000 0.152
#> SRR1785243     3  0.2891     0.6481 0.000 0.000 0.824 0.000 0.176
#> SRR1785244     4  0.5330     0.1746 0.396 0.000 0.000 0.548 0.056
#> SRR1785245     4  0.4854     0.4367 0.308 0.000 0.000 0.648 0.044
#> SRR1785246     5  0.4054     0.5376 0.248 0.000 0.020 0.000 0.732
#> SRR1785247     5  0.4026     0.5428 0.244 0.000 0.020 0.000 0.736
#> SRR1785248     2  0.0162     0.9130 0.004 0.996 0.000 0.000 0.000
#> SRR1785250     3  0.3320     0.6145 0.164 0.000 0.820 0.004 0.012
#> SRR1785251     3  0.3154     0.6231 0.148 0.000 0.836 0.004 0.012
#> SRR1785252     3  0.2233     0.6830 0.004 0.000 0.892 0.000 0.104
#> SRR1785253     3  0.2233     0.6830 0.004 0.000 0.892 0.000 0.104
#> SRR1785254     2  0.4789     0.3569 0.004 0.608 0.000 0.368 0.020
#> SRR1785255     2  0.4949     0.3398 0.004 0.600 0.000 0.368 0.028
#> SRR1785256     4  0.3180     0.7345 0.000 0.000 0.076 0.856 0.068
#> SRR1785257     4  0.2989     0.7433 0.000 0.000 0.072 0.868 0.060
#> SRR1785258     3  0.5474     0.4848 0.260 0.000 0.656 0.020 0.064
#> SRR1785259     3  0.5619     0.4627 0.272 0.000 0.628 0.008 0.092
#> SRR1785262     3  0.5162     0.4344 0.000 0.000 0.628 0.064 0.308
#> SRR1785263     3  0.4714     0.4336 0.000 0.000 0.644 0.032 0.324
#> SRR1785260     4  0.0000     0.7998 0.000 0.000 0.000 1.000 0.000
#> SRR1785261     4  0.0000     0.7998 0.000 0.000 0.000 1.000 0.000
#> SRR1785264     2  0.0865     0.8977 0.004 0.972 0.024 0.000 0.000
#> SRR1785265     2  0.0771     0.9012 0.004 0.976 0.020 0.000 0.000
#> SRR1785266     2  0.0000     0.9142 0.000 1.000 0.000 0.000 0.000
#> SRR1785267     2  0.0000     0.9142 0.000 1.000 0.000 0.000 0.000
#> SRR1785268     1  0.3350     0.6073 0.844 0.000 0.112 0.004 0.040
#> SRR1785269     1  0.3322     0.6122 0.848 0.000 0.104 0.004 0.044
#> SRR1785270     5  0.2963     0.7138 0.016 0.004 0.092 0.012 0.876
#> SRR1785271     5  0.3161     0.7144 0.024 0.008 0.084 0.012 0.872
#> SRR1785272     1  0.5889     0.0218 0.480 0.000 0.444 0.060 0.016
#> SRR1785273     3  0.5893    -0.0720 0.456 0.000 0.468 0.060 0.016
#> SRR1785276     5  0.4555     0.3821 0.344 0.000 0.020 0.000 0.636
#> SRR1785277     5  0.4508     0.4068 0.332 0.000 0.020 0.000 0.648
#> SRR1785274     5  0.2624     0.7019 0.000 0.000 0.116 0.012 0.872
#> SRR1785275     5  0.2624     0.7019 0.000 0.000 0.116 0.012 0.872
#> SRR1785280     2  0.0000     0.9142 0.000 1.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000     0.9142 0.000 1.000 0.000 0.000 0.000
#> SRR1785278     1  0.2507     0.6462 0.908 0.000 0.028 0.044 0.020
#> SRR1785279     1  0.2575     0.6456 0.904 0.000 0.036 0.044 0.016
#> SRR1785282     1  0.4822     0.5545 0.704 0.000 0.076 0.220 0.000
#> SRR1785283     1  0.4986     0.5448 0.688 0.000 0.084 0.228 0.000
#> SRR1785284     5  0.4214     0.6666 0.004 0.000 0.088 0.120 0.788
#> SRR1785285     5  0.4295     0.6607 0.004 0.000 0.084 0.132 0.780
#> SRR1785286     5  0.5513     0.2216 0.000 0.000 0.068 0.408 0.524
#> SRR1785287     4  0.5459    -0.1175 0.000 0.000 0.060 0.472 0.468
#> SRR1785288     4  0.2813     0.7057 0.168 0.000 0.000 0.832 0.000
#> SRR1785289     4  0.2773     0.7096 0.164 0.000 0.000 0.836 0.000
#> SRR1785290     2  0.0162     0.9130 0.004 0.996 0.000 0.000 0.000
#> SRR1785291     2  0.0162     0.9130 0.004 0.996 0.000 0.000 0.000
#> SRR1785296     4  0.5348     0.5785 0.000 0.232 0.112 0.656 0.000
#> SRR1785297     4  0.4334     0.6964 0.000 0.156 0.080 0.764 0.000
#> SRR1785292     2  0.0162     0.9130 0.004 0.996 0.000 0.000 0.000
#> SRR1785293     2  0.0000     0.9142 0.000 1.000 0.000 0.000 0.000
#> SRR1785294     4  0.2370     0.7724 0.056 0.000 0.040 0.904 0.000
#> SRR1785295     4  0.2889     0.7487 0.084 0.000 0.044 0.872 0.000
#> SRR1785298     4  0.3919     0.6930 0.000 0.188 0.000 0.776 0.036
#> SRR1785299     4  0.3656     0.7142 0.000 0.168 0.000 0.800 0.032
#> SRR1785300     4  0.0162     0.7997 0.000 0.000 0.000 0.996 0.004
#> SRR1785301     4  0.0162     0.7997 0.000 0.000 0.000 0.996 0.004
#> SRR1785304     4  0.2068     0.7798 0.004 0.092 0.000 0.904 0.000
#> SRR1785305     4  0.1732     0.7867 0.000 0.080 0.000 0.920 0.000
#> SRR1785306     5  0.4820     0.6465 0.088 0.000 0.180 0.004 0.728
#> SRR1785307     5  0.4624     0.6592 0.096 0.000 0.164 0.000 0.740
#> SRR1785302     2  0.6706     0.2298 0.312 0.544 0.108 0.020 0.016
#> SRR1785303     1  0.7616     0.2370 0.448 0.352 0.124 0.056 0.020
#> SRR1785308     3  0.4537     0.5244 0.184 0.000 0.740 0.076 0.000
#> SRR1785309     3  0.4479     0.5259 0.184 0.000 0.744 0.072 0.000
#> SRR1785310     4  0.1582     0.7904 0.028 0.000 0.028 0.944 0.000
#> SRR1785311     4  0.1018     0.7963 0.016 0.000 0.016 0.968 0.000
#> SRR1785312     1  0.4016     0.5097 0.716 0.000 0.012 0.000 0.272
#> SRR1785313     1  0.4109     0.4973 0.700 0.000 0.012 0.000 0.288
#> SRR1785314     5  0.5309     0.6430 0.208 0.008 0.064 0.016 0.704
#> SRR1785315     5  0.5572     0.6440 0.200 0.020 0.060 0.020 0.700
#> SRR1785318     2  0.0000     0.9142 0.000 1.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000     0.9142 0.000 1.000 0.000 0.000 0.000
#> SRR1785316     1  0.5117     0.5253 0.672 0.000 0.088 0.240 0.000
#> SRR1785317     1  0.5303     0.5207 0.660 0.000 0.108 0.232 0.000
#> SRR1785324     2  0.0000     0.9142 0.000 1.000 0.000 0.000 0.000
#> SRR1785325     2  0.0000     0.9142 0.000 1.000 0.000 0.000 0.000
#> SRR1785320     1  0.4309     0.4793 0.676 0.000 0.016 0.000 0.308
#> SRR1785321     1  0.4384     0.4555 0.660 0.000 0.016 0.000 0.324
#> SRR1785322     1  0.3016     0.6267 0.868 0.000 0.100 0.016 0.016
#> SRR1785323     1  0.3113     0.6277 0.864 0.000 0.100 0.016 0.020

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     1  0.6214     0.3623 0.536 0.224 0.204 0.000 0.000 0.036
#> SRR1785239     1  0.6323     0.3636 0.544 0.168 0.240 0.004 0.000 0.044
#> SRR1785240     5  0.2030     0.7203 0.000 0.000 0.064 0.000 0.908 0.028
#> SRR1785241     5  0.1921     0.7284 0.000 0.000 0.052 0.000 0.916 0.032
#> SRR1785242     3  0.3368     0.6351 0.000 0.000 0.756 0.000 0.232 0.012
#> SRR1785243     3  0.3342     0.6376 0.000 0.000 0.760 0.000 0.228 0.012
#> SRR1785244     1  0.5562     0.4569 0.572 0.000 0.000 0.236 0.188 0.004
#> SRR1785245     1  0.5605     0.4416 0.560 0.000 0.000 0.256 0.180 0.004
#> SRR1785246     6  0.2053     0.8544 0.000 0.000 0.004 0.000 0.108 0.888
#> SRR1785247     6  0.2100     0.8513 0.000 0.000 0.004 0.000 0.112 0.884
#> SRR1785248     2  0.0810     0.9114 0.004 0.976 0.004 0.000 0.008 0.008
#> SRR1785250     3  0.4337     0.5522 0.164 0.000 0.752 0.000 0.040 0.044
#> SRR1785251     3  0.4171     0.5601 0.160 0.000 0.764 0.000 0.040 0.036
#> SRR1785252     3  0.2996     0.6647 0.008 0.000 0.832 0.000 0.144 0.016
#> SRR1785253     3  0.2982     0.6637 0.008 0.000 0.828 0.000 0.152 0.012
#> SRR1785254     2  0.6085     0.4223 0.016 0.572 0.008 0.284 0.100 0.020
#> SRR1785255     2  0.6328     0.3557 0.012 0.532 0.008 0.284 0.148 0.016
#> SRR1785256     4  0.4458     0.6756 0.028 0.000 0.040 0.732 0.196 0.004
#> SRR1785257     4  0.3608     0.7415 0.016 0.000 0.028 0.804 0.148 0.004
#> SRR1785258     3  0.5512     0.4484 0.092 0.000 0.536 0.000 0.356 0.016
#> SRR1785259     3  0.5480     0.4453 0.084 0.000 0.520 0.000 0.380 0.016
#> SRR1785262     3  0.5545     0.4568 0.000 0.000 0.548 0.112 0.328 0.012
#> SRR1785263     3  0.4738     0.4880 0.000 0.000 0.596 0.036 0.356 0.012
#> SRR1785260     4  0.0291     0.8453 0.004 0.000 0.000 0.992 0.000 0.004
#> SRR1785261     4  0.0291     0.8453 0.004 0.000 0.000 0.992 0.000 0.004
#> SRR1785264     2  0.3079     0.8512 0.024 0.868 0.068 0.000 0.020 0.020
#> SRR1785265     2  0.2332     0.8807 0.012 0.908 0.048 0.000 0.012 0.020
#> SRR1785266     2  0.0146     0.9151 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1785267     2  0.0260     0.9145 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1785268     1  0.5288     0.4350 0.640 0.000 0.060 0.000 0.048 0.252
#> SRR1785269     1  0.5309     0.4338 0.636 0.000 0.060 0.000 0.048 0.256
#> SRR1785270     5  0.2493     0.7474 0.036 0.004 0.000 0.000 0.884 0.076
#> SRR1785271     5  0.2493     0.7474 0.036 0.004 0.000 0.000 0.884 0.076
#> SRR1785272     3  0.5344     0.0606 0.400 0.000 0.520 0.024 0.000 0.056
#> SRR1785273     3  0.5266     0.1077 0.384 0.000 0.540 0.024 0.000 0.052
#> SRR1785276     6  0.2173     0.8825 0.028 0.000 0.004 0.000 0.064 0.904
#> SRR1785277     6  0.2069     0.8803 0.020 0.000 0.004 0.000 0.068 0.908
#> SRR1785274     5  0.1605     0.7475 0.004 0.000 0.016 0.000 0.936 0.044
#> SRR1785275     5  0.1726     0.7486 0.000 0.000 0.012 0.012 0.932 0.044
#> SRR1785280     2  0.0291     0.9149 0.000 0.992 0.004 0.000 0.000 0.004
#> SRR1785281     2  0.0291     0.9149 0.000 0.992 0.004 0.000 0.000 0.004
#> SRR1785278     1  0.2342     0.5820 0.888 0.000 0.000 0.004 0.020 0.088
#> SRR1785279     1  0.2537     0.5824 0.880 0.000 0.000 0.008 0.024 0.088
#> SRR1785282     1  0.4252     0.5612 0.776 0.000 0.108 0.076 0.000 0.040
#> SRR1785283     1  0.4353     0.5552 0.764 0.000 0.124 0.076 0.000 0.036
#> SRR1785284     5  0.2628     0.7444 0.012 0.000 0.004 0.056 0.888 0.040
#> SRR1785285     5  0.2618     0.7431 0.012 0.000 0.004 0.060 0.888 0.036
#> SRR1785286     5  0.3894     0.5096 0.000 0.000 0.004 0.324 0.664 0.008
#> SRR1785287     5  0.4062     0.4721 0.000 0.000 0.004 0.344 0.640 0.012
#> SRR1785288     1  0.4660     0.2550 0.540 0.000 0.000 0.416 0.044 0.000
#> SRR1785289     1  0.4716     0.2894 0.552 0.000 0.004 0.404 0.040 0.000
#> SRR1785290     2  0.2546     0.8801 0.016 0.904 0.008 0.040 0.012 0.020
#> SRR1785291     2  0.2546     0.8802 0.016 0.904 0.008 0.040 0.012 0.020
#> SRR1785296     4  0.4336     0.7268 0.000 0.116 0.092 0.768 0.016 0.008
#> SRR1785297     4  0.3462     0.7816 0.000 0.072 0.072 0.836 0.012 0.008
#> SRR1785292     2  0.0696     0.9125 0.004 0.980 0.004 0.000 0.004 0.008
#> SRR1785293     2  0.0551     0.9135 0.004 0.984 0.004 0.000 0.000 0.008
#> SRR1785294     4  0.1788     0.8213 0.028 0.000 0.040 0.928 0.000 0.004
#> SRR1785295     4  0.2451     0.7936 0.040 0.000 0.068 0.888 0.000 0.004
#> SRR1785298     4  0.7300     0.3996 0.088 0.184 0.004 0.524 0.160 0.040
#> SRR1785299     4  0.7384     0.3798 0.076 0.240 0.012 0.508 0.124 0.040
#> SRR1785300     4  0.0767     0.8425 0.012 0.000 0.000 0.976 0.004 0.008
#> SRR1785301     4  0.0881     0.8425 0.012 0.000 0.000 0.972 0.008 0.008
#> SRR1785304     4  0.1368     0.8416 0.008 0.012 0.004 0.956 0.004 0.016
#> SRR1785305     4  0.1368     0.8416 0.008 0.012 0.004 0.956 0.004 0.016
#> SRR1785306     5  0.6095     0.5605 0.132 0.004 0.080 0.004 0.628 0.152
#> SRR1785307     5  0.6180     0.5682 0.132 0.004 0.076 0.012 0.632 0.144
#> SRR1785302     1  0.7503     0.3154 0.492 0.168 0.060 0.004 0.212 0.064
#> SRR1785303     1  0.7187     0.3908 0.552 0.092 0.060 0.012 0.212 0.072
#> SRR1785308     3  0.3313     0.5467 0.140 0.000 0.820 0.024 0.000 0.016
#> SRR1785309     3  0.3318     0.5469 0.140 0.000 0.820 0.020 0.000 0.020
#> SRR1785310     4  0.0603     0.8428 0.016 0.000 0.004 0.980 0.000 0.000
#> SRR1785311     4  0.0520     0.8443 0.008 0.000 0.008 0.984 0.000 0.000
#> SRR1785312     6  0.2982     0.7933 0.164 0.000 0.004 0.000 0.012 0.820
#> SRR1785313     6  0.2544     0.8394 0.120 0.000 0.004 0.000 0.012 0.864
#> SRR1785314     5  0.5381     0.6027 0.200 0.012 0.028 0.000 0.668 0.092
#> SRR1785315     5  0.5682     0.5973 0.196 0.024 0.028 0.000 0.652 0.100
#> SRR1785318     2  0.0260     0.9145 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1785319     2  0.0260     0.9145 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1785316     1  0.5175     0.5311 0.700 0.000 0.136 0.100 0.000 0.064
#> SRR1785317     1  0.5181     0.5242 0.696 0.000 0.152 0.088 0.000 0.064
#> SRR1785324     2  0.0146     0.9151 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1785325     2  0.0146     0.9151 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1785320     6  0.2402     0.8494 0.140 0.000 0.000 0.000 0.004 0.856
#> SRR1785321     6  0.2278     0.8555 0.128 0.000 0.000 0.000 0.004 0.868
#> SRR1785322     1  0.5170     0.4916 0.680 0.000 0.064 0.000 0.060 0.196
#> SRR1785323     1  0.5087     0.4974 0.688 0.000 0.064 0.000 0.056 0.192

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-CV-NMF-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-CV-NMF-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-CV-NMF-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-CV-NMF-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-CV-NMF-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-CV-NMF-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-CV-NMF-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-CV-NMF-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-CV-NMF-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-CV-NMF-membership-heatmap-5

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)

plot of chunk tab-CV-NMF-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-CV-NMF-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-CV-NMF-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-CV-NMF-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-CV-NMF-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-CV-NMF-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-CV-NMF-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-CV-NMF-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-CV-NMF-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-CV-NMF-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk CV-NMF-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-CV-NMF-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-CV-NMF-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-CV-NMF-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-CV-NMF-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-CV-NMF-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk CV-NMF-collect-classes

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.


MAD:hclust

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#>   Subgroups are detected by 'hclust' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 3.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk MAD-hclust-collect-plots

The plots are:

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:

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)

plot of chunk MAD-hclust-select-partition-number

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 0.817           0.941       0.971         0.3231 0.682   0.682
#> 3 3 0.467           0.784       0.877         0.8493 0.718   0.586
#> 4 4 0.573           0.683       0.779         0.1850 0.867   0.684
#> 5 5 0.675           0.748       0.862         0.0968 0.886   0.641
#> 6 6 0.744           0.694       0.787         0.0465 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

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2  0.4562      0.902 0.096 0.904
#> SRR1785239     2  0.4562      0.902 0.096 0.904
#> SRR1785240     1  0.0000      0.976 1.000 0.000
#> SRR1785241     1  0.0000      0.976 1.000 0.000
#> SRR1785242     1  0.0376      0.973 0.996 0.004
#> SRR1785243     1  0.0376      0.973 0.996 0.004
#> SRR1785244     1  0.0000      0.976 1.000 0.000
#> SRR1785245     1  0.0000      0.976 1.000 0.000
#> SRR1785246     1  0.0000      0.976 1.000 0.000
#> SRR1785247     1  0.0000      0.976 1.000 0.000
#> SRR1785248     2  0.3733      0.917 0.072 0.928
#> SRR1785250     1  0.0000      0.976 1.000 0.000
#> SRR1785251     1  0.0000      0.976 1.000 0.000
#> SRR1785252     1  0.0376      0.973 0.996 0.004
#> SRR1785253     1  0.0376      0.973 0.996 0.004
#> SRR1785254     1  0.7376      0.743 0.792 0.208
#> SRR1785255     1  0.7376      0.743 0.792 0.208
#> SRR1785256     1  0.0000      0.976 1.000 0.000
#> SRR1785257     1  0.0000      0.976 1.000 0.000
#> SRR1785258     1  0.0376      0.973 0.996 0.004
#> SRR1785259     1  0.0376      0.973 0.996 0.004
#> SRR1785262     1  0.0000      0.976 1.000 0.000
#> SRR1785263     1  0.0000      0.976 1.000 0.000
#> SRR1785260     1  0.0000      0.976 1.000 0.000
#> SRR1785261     1  0.0000      0.976 1.000 0.000
#> SRR1785264     2  0.3879      0.915 0.076 0.924
#> SRR1785265     2  0.3879      0.915 0.076 0.924
#> SRR1785266     2  0.0376      0.937 0.004 0.996
#> SRR1785267     2  0.0376      0.937 0.004 0.996
#> SRR1785268     1  0.0000      0.976 1.000 0.000
#> SRR1785269     1  0.0000      0.976 1.000 0.000
#> SRR1785270     1  0.0000      0.976 1.000 0.000
#> SRR1785271     1  0.0000      0.976 1.000 0.000
#> SRR1785272     1  0.0000      0.976 1.000 0.000
#> SRR1785273     1  0.0000      0.976 1.000 0.000
#> SRR1785276     1  0.0000      0.976 1.000 0.000
#> SRR1785277     1  0.0000      0.976 1.000 0.000
#> SRR1785274     1  0.0376      0.973 0.996 0.004
#> SRR1785275     1  0.0376      0.973 0.996 0.004
#> SRR1785280     2  0.0000      0.937 0.000 1.000
#> SRR1785281     2  0.0000      0.937 0.000 1.000
#> SRR1785278     1  0.0000      0.976 1.000 0.000
#> SRR1785279     1  0.0000      0.976 1.000 0.000
#> SRR1785282     1  0.0000      0.976 1.000 0.000
#> SRR1785283     1  0.0000      0.976 1.000 0.000
#> SRR1785284     1  0.0000      0.976 1.000 0.000
#> SRR1785285     1  0.0000      0.976 1.000 0.000
#> SRR1785286     1  0.0000      0.976 1.000 0.000
#> SRR1785287     1  0.0000      0.976 1.000 0.000
#> SRR1785288     1  0.0000      0.976 1.000 0.000
#> SRR1785289     1  0.0000      0.976 1.000 0.000
#> SRR1785290     2  0.8386      0.677 0.268 0.732
#> SRR1785291     2  0.8386      0.677 0.268 0.732
#> SRR1785296     1  0.1843      0.953 0.972 0.028
#> SRR1785297     1  0.1843      0.953 0.972 0.028
#> SRR1785292     2  0.0000      0.937 0.000 1.000
#> SRR1785293     2  0.0000      0.937 0.000 1.000
#> SRR1785294     1  0.0000      0.976 1.000 0.000
#> SRR1785295     1  0.0000      0.976 1.000 0.000
#> SRR1785298     1  0.7376      0.743 0.792 0.208
#> SRR1785299     1  0.7376      0.743 0.792 0.208
#> SRR1785300     1  0.0000      0.976 1.000 0.000
#> SRR1785301     1  0.0000      0.976 1.000 0.000
#> SRR1785304     1  0.0000      0.976 1.000 0.000
#> SRR1785305     1  0.0000      0.976 1.000 0.000
#> SRR1785306     1  0.4939      0.873 0.892 0.108
#> SRR1785307     1  0.4939      0.873 0.892 0.108
#> SRR1785302     1  0.7376      0.743 0.792 0.208
#> SRR1785303     1  0.7376      0.743 0.792 0.208
#> SRR1785308     1  0.0000      0.976 1.000 0.000
#> SRR1785309     1  0.0000      0.976 1.000 0.000
#> SRR1785310     1  0.0000      0.976 1.000 0.000
#> SRR1785311     1  0.0000      0.976 1.000 0.000
#> SRR1785312     1  0.0000      0.976 1.000 0.000
#> SRR1785313     1  0.0000      0.976 1.000 0.000
#> SRR1785314     1  0.0000      0.976 1.000 0.000
#> SRR1785315     1  0.0000      0.976 1.000 0.000
#> SRR1785318     2  0.0000      0.937 0.000 1.000
#> SRR1785319     2  0.0000      0.937 0.000 1.000
#> SRR1785316     1  0.0000      0.976 1.000 0.000
#> SRR1785317     1  0.0000      0.976 1.000 0.000
#> SRR1785324     2  0.0000      0.937 0.000 1.000
#> SRR1785325     2  0.0000      0.937 0.000 1.000
#> SRR1785320     1  0.0000      0.976 1.000 0.000
#> SRR1785321     1  0.0000      0.976 1.000 0.000
#> SRR1785322     1  0.0000      0.976 1.000 0.000
#> SRR1785323     1  0.0000      0.976 1.000 0.000

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     2  0.3293      0.889 0.012 0.900 0.088
#> SRR1785239     2  0.3293      0.889 0.012 0.900 0.088
#> SRR1785240     1  0.5058      0.770 0.756 0.000 0.244
#> SRR1785241     1  0.5058      0.770 0.756 0.000 0.244
#> SRR1785242     3  0.0000      0.817 0.000 0.000 1.000
#> SRR1785243     3  0.0000      0.817 0.000 0.000 1.000
#> SRR1785244     1  0.0237      0.840 0.996 0.000 0.004
#> SRR1785245     1  0.0237      0.840 0.996 0.000 0.004
#> SRR1785246     3  0.0747      0.824 0.016 0.000 0.984
#> SRR1785247     3  0.0747      0.824 0.016 0.000 0.984
#> SRR1785248     2  0.2749      0.903 0.012 0.924 0.064
#> SRR1785250     3  0.1643      0.824 0.044 0.000 0.956
#> SRR1785251     3  0.1643      0.824 0.044 0.000 0.956
#> SRR1785252     3  0.0000      0.817 0.000 0.000 1.000
#> SRR1785253     3  0.0000      0.817 0.000 0.000 1.000
#> SRR1785254     1  0.7800      0.671 0.668 0.204 0.128
#> SRR1785255     1  0.7800      0.671 0.668 0.204 0.128
#> SRR1785256     1  0.0237      0.840 0.996 0.000 0.004
#> SRR1785257     1  0.0237      0.840 0.996 0.000 0.004
#> SRR1785258     3  0.6280     -0.166 0.460 0.000 0.540
#> SRR1785259     3  0.6280     -0.166 0.460 0.000 0.540
#> SRR1785262     3  0.1529      0.827 0.040 0.000 0.960
#> SRR1785263     3  0.1529      0.827 0.040 0.000 0.960
#> SRR1785260     1  0.0000      0.839 1.000 0.000 0.000
#> SRR1785261     1  0.0000      0.839 1.000 0.000 0.000
#> SRR1785264     2  0.2845      0.900 0.012 0.920 0.068
#> SRR1785265     2  0.2845      0.900 0.012 0.920 0.068
#> SRR1785266     2  0.0424      0.926 0.000 0.992 0.008
#> SRR1785267     2  0.0424      0.926 0.000 0.992 0.008
#> SRR1785268     1  0.5098      0.712 0.752 0.000 0.248
#> SRR1785269     1  0.5098      0.712 0.752 0.000 0.248
#> SRR1785270     1  0.5058      0.770 0.756 0.000 0.244
#> SRR1785271     1  0.5058      0.770 0.756 0.000 0.244
#> SRR1785272     3  0.6192      0.518 0.420 0.000 0.580
#> SRR1785273     3  0.6192      0.518 0.420 0.000 0.580
#> SRR1785276     3  0.3267      0.821 0.116 0.000 0.884
#> SRR1785277     3  0.3267      0.821 0.116 0.000 0.884
#> SRR1785274     3  0.2537      0.809 0.080 0.000 0.920
#> SRR1785275     3  0.2537      0.809 0.080 0.000 0.920
#> SRR1785280     2  0.0000      0.926 0.000 1.000 0.000
#> SRR1785281     2  0.0000      0.926 0.000 1.000 0.000
#> SRR1785278     1  0.0424      0.839 0.992 0.000 0.008
#> SRR1785279     1  0.0424      0.839 0.992 0.000 0.008
#> SRR1785282     1  0.0237      0.840 0.996 0.000 0.004
#> SRR1785283     1  0.0237      0.840 0.996 0.000 0.004
#> SRR1785284     1  0.5058      0.770 0.756 0.000 0.244
#> SRR1785285     1  0.5058      0.770 0.756 0.000 0.244
#> SRR1785286     1  0.4504      0.799 0.804 0.000 0.196
#> SRR1785287     1  0.4504      0.799 0.804 0.000 0.196
#> SRR1785288     1  0.0237      0.840 0.996 0.000 0.004
#> SRR1785289     1  0.0237      0.840 0.996 0.000 0.004
#> SRR1785290     2  0.7039      0.660 0.144 0.728 0.128
#> SRR1785291     2  0.7039      0.660 0.144 0.728 0.128
#> SRR1785296     1  0.3966      0.832 0.876 0.024 0.100
#> SRR1785297     1  0.3966      0.832 0.876 0.024 0.100
#> SRR1785292     2  0.0000      0.926 0.000 1.000 0.000
#> SRR1785293     2  0.0000      0.926 0.000 1.000 0.000
#> SRR1785294     1  0.2878      0.836 0.904 0.000 0.096
#> SRR1785295     1  0.2878      0.836 0.904 0.000 0.096
#> SRR1785298     1  0.7800      0.671 0.668 0.204 0.128
#> SRR1785299     1  0.7800      0.671 0.668 0.204 0.128
#> SRR1785300     1  0.0237      0.840 0.996 0.000 0.004
#> SRR1785301     1  0.0237      0.840 0.996 0.000 0.004
#> SRR1785304     1  0.0000      0.839 1.000 0.000 0.000
#> SRR1785305     1  0.0000      0.839 1.000 0.000 0.000
#> SRR1785306     1  0.7391      0.734 0.696 0.108 0.196
#> SRR1785307     1  0.7391      0.734 0.696 0.108 0.196
#> SRR1785302     1  0.7800      0.671 0.668 0.204 0.128
#> SRR1785303     1  0.7800      0.671 0.668 0.204 0.128
#> SRR1785308     3  0.4887      0.714 0.228 0.000 0.772
#> SRR1785309     3  0.4887      0.714 0.228 0.000 0.772
#> SRR1785310     1  0.2878      0.836 0.904 0.000 0.096
#> SRR1785311     1  0.2878      0.836 0.904 0.000 0.096
#> SRR1785312     1  0.5098      0.712 0.752 0.000 0.248
#> SRR1785313     1  0.5098      0.712 0.752 0.000 0.248
#> SRR1785314     1  0.5058      0.770 0.756 0.000 0.244
#> SRR1785315     1  0.5058      0.770 0.756 0.000 0.244
#> SRR1785318     2  0.0000      0.926 0.000 1.000 0.000
#> SRR1785319     2  0.0000      0.926 0.000 1.000 0.000
#> SRR1785316     1  0.0237      0.840 0.996 0.000 0.004
#> SRR1785317     1  0.0237      0.840 0.996 0.000 0.004
#> SRR1785324     2  0.0000      0.926 0.000 1.000 0.000
#> SRR1785325     2  0.0000      0.926 0.000 1.000 0.000
#> SRR1785320     3  0.3267      0.821 0.116 0.000 0.884
#> SRR1785321     3  0.3267      0.821 0.116 0.000 0.884
#> SRR1785322     1  0.0424      0.839 0.992 0.000 0.008
#> SRR1785323     1  0.0424      0.839 0.992 0.000 0.008

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     2  0.2894      0.896 0.072 0.900 0.020 0.008
#> SRR1785239     2  0.2894      0.896 0.072 0.900 0.020 0.008
#> SRR1785240     1  0.2943      0.544 0.892 0.000 0.076 0.032
#> SRR1785241     1  0.2943      0.544 0.892 0.000 0.076 0.032
#> SRR1785242     3  0.0672      0.856 0.008 0.000 0.984 0.008
#> SRR1785243     3  0.0672      0.856 0.008 0.000 0.984 0.008
#> SRR1785244     1  0.5055      0.573 0.624 0.000 0.008 0.368
#> SRR1785245     1  0.5055      0.573 0.624 0.000 0.008 0.368
#> SRR1785246     3  0.0469      0.858 0.012 0.000 0.988 0.000
#> SRR1785247     3  0.0469      0.858 0.012 0.000 0.988 0.000
#> SRR1785248     2  0.2124      0.906 0.068 0.924 0.000 0.008
#> SRR1785250     3  0.1118      0.855 0.036 0.000 0.964 0.000
#> SRR1785251     3  0.1118      0.855 0.036 0.000 0.964 0.000
#> SRR1785252     3  0.0672      0.856 0.008 0.000 0.984 0.008
#> SRR1785253     3  0.0672      0.856 0.008 0.000 0.984 0.008
#> SRR1785254     1  0.6662      0.445 0.652 0.204 0.012 0.132
#> SRR1785255     1  0.6662      0.445 0.652 0.204 0.012 0.132
#> SRR1785256     1  0.5093      0.584 0.640 0.000 0.012 0.348
#> SRR1785257     1  0.5093      0.584 0.640 0.000 0.012 0.348
#> SRR1785258     1  0.5693      0.236 0.504 0.000 0.472 0.024
#> SRR1785259     1  0.5693      0.236 0.504 0.000 0.472 0.024
#> SRR1785262     3  0.2281      0.823 0.096 0.000 0.904 0.000
#> SRR1785263     3  0.2281      0.823 0.096 0.000 0.904 0.000
#> SRR1785260     4  0.1118      0.808 0.036 0.000 0.000 0.964
#> SRR1785261     4  0.1118      0.808 0.036 0.000 0.000 0.964
#> SRR1785264     2  0.2198      0.904 0.072 0.920 0.000 0.008
#> SRR1785265     2  0.2198      0.904 0.072 0.920 0.000 0.008
#> SRR1785266     2  0.0376      0.927 0.004 0.992 0.000 0.004
#> SRR1785267     2  0.0376      0.927 0.004 0.992 0.000 0.004
#> SRR1785268     1  0.6934      0.516 0.580 0.000 0.256 0.164
#> SRR1785269     1  0.6934      0.516 0.580 0.000 0.256 0.164
#> SRR1785270     1  0.2943      0.544 0.892 0.000 0.076 0.032
#> SRR1785271     1  0.2943      0.544 0.892 0.000 0.076 0.032
#> SRR1785272     3  0.6752      0.459 0.132 0.000 0.588 0.280
#> SRR1785273     3  0.6752      0.459 0.132 0.000 0.588 0.280
#> SRR1785276     3  0.2983      0.841 0.068 0.000 0.892 0.040
#> SRR1785277     3  0.2983      0.841 0.068 0.000 0.892 0.040
#> SRR1785274     3  0.3196      0.782 0.136 0.000 0.856 0.008
#> SRR1785275     3  0.3196      0.782 0.136 0.000 0.856 0.008
#> SRR1785280     2  0.0000      0.928 0.000 1.000 0.000 0.000
#> SRR1785281     2  0.0000      0.928 0.000 1.000 0.000 0.000
#> SRR1785278     1  0.5203      0.582 0.636 0.000 0.016 0.348
#> SRR1785279     1  0.5203      0.582 0.636 0.000 0.016 0.348
#> SRR1785282     1  0.5093      0.584 0.640 0.000 0.012 0.348
#> SRR1785283     1  0.5093      0.584 0.640 0.000 0.012 0.348
#> SRR1785284     1  0.2943      0.544 0.892 0.000 0.076 0.032
#> SRR1785285     1  0.2943      0.544 0.892 0.000 0.076 0.032
#> SRR1785286     1  0.5619      0.190 0.676 0.000 0.056 0.268
#> SRR1785287     1  0.5619      0.190 0.676 0.000 0.056 0.268
#> SRR1785288     1  0.5055      0.573 0.624 0.000 0.008 0.368
#> SRR1785289     1  0.5055      0.573 0.624 0.000 0.008 0.368
#> SRR1785290     2  0.5331      0.662 0.224 0.728 0.012 0.036
#> SRR1785291     2  0.5331      0.662 0.224 0.728 0.012 0.036
#> SRR1785296     4  0.5062      0.830 0.212 0.024 0.016 0.748
#> SRR1785297     4  0.5062      0.830 0.212 0.024 0.016 0.748
#> SRR1785292     2  0.0000      0.928 0.000 1.000 0.000 0.000
#> SRR1785293     2  0.0000      0.928 0.000 1.000 0.000 0.000
#> SRR1785294     4  0.4364      0.849 0.220 0.000 0.016 0.764
#> SRR1785295     4  0.4364      0.849 0.220 0.000 0.016 0.764
#> SRR1785298     1  0.6662      0.445 0.652 0.204 0.012 0.132
#> SRR1785299     1  0.6662      0.445 0.652 0.204 0.012 0.132
#> SRR1785300     1  0.5093      0.584 0.640 0.000 0.012 0.348
#> SRR1785301     1  0.5093      0.584 0.640 0.000 0.012 0.348
#> SRR1785304     4  0.1118      0.808 0.036 0.000 0.000 0.964
#> SRR1785305     4  0.1118      0.808 0.036 0.000 0.000 0.964
#> SRR1785306     1  0.4347      0.517 0.832 0.108 0.036 0.024
#> SRR1785307     1  0.4347      0.517 0.832 0.108 0.036 0.024
#> SRR1785302     1  0.6662      0.445 0.652 0.204 0.012 0.132
#> SRR1785303     1  0.6662      0.445 0.652 0.204 0.012 0.132
#> SRR1785308     3  0.4931      0.719 0.092 0.000 0.776 0.132
#> SRR1785309     3  0.4931      0.719 0.092 0.000 0.776 0.132
#> SRR1785310     4  0.4364      0.849 0.220 0.000 0.016 0.764
#> SRR1785311     4  0.4364      0.849 0.220 0.000 0.016 0.764
#> SRR1785312     1  0.6934      0.516 0.580 0.000 0.256 0.164
#> SRR1785313     1  0.6934      0.516 0.580 0.000 0.256 0.164
#> SRR1785314     1  0.2943      0.544 0.892 0.000 0.076 0.032
#> SRR1785315     1  0.2943      0.544 0.892 0.000 0.076 0.032
#> SRR1785318     2  0.0000      0.928 0.000 1.000 0.000 0.000
#> SRR1785319     2  0.0000      0.928 0.000 1.000 0.000 0.000
#> SRR1785316     1  0.5093      0.584 0.640 0.000 0.012 0.348
#> SRR1785317     1  0.5093      0.584 0.640 0.000 0.012 0.348
#> SRR1785324     2  0.0000      0.928 0.000 1.000 0.000 0.000
#> SRR1785325     2  0.0000      0.928 0.000 1.000 0.000 0.000
#> SRR1785320     3  0.2983      0.841 0.068 0.000 0.892 0.040
#> SRR1785321     3  0.2983      0.841 0.068 0.000 0.892 0.040
#> SRR1785322     1  0.5203      0.582 0.636 0.000 0.016 0.348
#> SRR1785323     1  0.5203      0.582 0.636 0.000 0.016 0.348

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     2  0.2590    0.89694 0.000 0.900 0.028 0.012 0.060
#> SRR1785239     2  0.2590    0.89694 0.000 0.900 0.028 0.012 0.060
#> SRR1785240     5  0.0162    0.91209 0.004 0.000 0.000 0.000 0.996
#> SRR1785241     5  0.0162    0.91209 0.004 0.000 0.000 0.000 0.996
#> SRR1785242     3  0.0162    0.80718 0.000 0.000 0.996 0.004 0.000
#> SRR1785243     3  0.0162    0.80718 0.000 0.000 0.996 0.004 0.000
#> SRR1785244     1  0.1270    0.77368 0.948 0.000 0.000 0.052 0.000
#> SRR1785245     1  0.1270    0.77368 0.948 0.000 0.000 0.052 0.000
#> SRR1785246     3  0.0693    0.80943 0.008 0.000 0.980 0.000 0.012
#> SRR1785247     3  0.0693    0.80943 0.008 0.000 0.980 0.000 0.012
#> SRR1785248     2  0.2026    0.90674 0.000 0.924 0.008 0.012 0.056
#> SRR1785250     3  0.1197    0.81346 0.048 0.000 0.952 0.000 0.000
#> SRR1785251     3  0.1197    0.81346 0.048 0.000 0.952 0.000 0.000
#> SRR1785252     3  0.0162    0.80718 0.000 0.000 0.996 0.004 0.000
#> SRR1785253     3  0.0162    0.80718 0.000 0.000 0.996 0.004 0.000
#> SRR1785254     1  0.7806    0.35535 0.468 0.204 0.004 0.088 0.236
#> SRR1785255     1  0.7806    0.35535 0.468 0.204 0.004 0.088 0.236
#> SRR1785256     1  0.0000    0.79051 1.000 0.000 0.000 0.000 0.000
#> SRR1785257     1  0.0000    0.79051 1.000 0.000 0.000 0.000 0.000
#> SRR1785258     3  0.6160    0.00705 0.404 0.000 0.484 0.008 0.104
#> SRR1785259     3  0.6160    0.00705 0.404 0.000 0.484 0.008 0.104
#> SRR1785262     3  0.2439    0.76043 0.004 0.000 0.876 0.000 0.120
#> SRR1785263     3  0.2439    0.76043 0.004 0.000 0.876 0.000 0.120
#> SRR1785260     4  0.0404    0.74656 0.012 0.000 0.000 0.988 0.000
#> SRR1785261     4  0.0404    0.74656 0.012 0.000 0.000 0.988 0.000
#> SRR1785264     2  0.2095    0.90468 0.000 0.920 0.008 0.012 0.060
#> SRR1785265     2  0.2095    0.90468 0.000 0.920 0.008 0.012 0.060
#> SRR1785266     2  0.0324    0.92786 0.000 0.992 0.004 0.000 0.004
#> SRR1785267     2  0.0324    0.92786 0.000 0.992 0.004 0.000 0.004
#> SRR1785268     1  0.3452    0.58061 0.756 0.000 0.244 0.000 0.000
#> SRR1785269     1  0.3452    0.58061 0.756 0.000 0.244 0.000 0.000
#> SRR1785270     5  0.0162    0.91209 0.004 0.000 0.000 0.000 0.996
#> SRR1785271     5  0.0162    0.91209 0.004 0.000 0.000 0.000 0.996
#> SRR1785272     3  0.4235    0.47803 0.424 0.000 0.576 0.000 0.000
#> SRR1785273     3  0.4235    0.47803 0.424 0.000 0.576 0.000 0.000
#> SRR1785276     3  0.2439    0.80200 0.120 0.000 0.876 0.000 0.004
#> SRR1785277     3  0.2439    0.80200 0.120 0.000 0.876 0.000 0.004
#> SRR1785274     3  0.2972    0.75395 0.024 0.000 0.864 0.004 0.108
#> SRR1785275     3  0.2972    0.75395 0.024 0.000 0.864 0.004 0.108
#> SRR1785280     2  0.0000    0.92833 0.000 1.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000    0.92833 0.000 1.000 0.000 0.000 0.000
#> SRR1785278     1  0.0162    0.78905 0.996 0.000 0.000 0.000 0.004
#> SRR1785279     1  0.0162    0.78905 0.996 0.000 0.000 0.000 0.004
#> SRR1785282     1  0.0000    0.79051 1.000 0.000 0.000 0.000 0.000
#> SRR1785283     1  0.0000    0.79051 1.000 0.000 0.000 0.000 0.000
#> SRR1785284     5  0.0162    0.91209 0.004 0.000 0.000 0.000 0.996
#> SRR1785285     5  0.0162    0.91209 0.004 0.000 0.000 0.000 0.996
#> SRR1785286     5  0.4210    0.60333 0.036 0.000 0.000 0.224 0.740
#> SRR1785287     5  0.4210    0.60333 0.036 0.000 0.000 0.224 0.740
#> SRR1785288     1  0.1270    0.77368 0.948 0.000 0.000 0.052 0.000
#> SRR1785289     1  0.1270    0.77368 0.948 0.000 0.000 0.052 0.000
#> SRR1785290     2  0.4506    0.65131 0.012 0.728 0.004 0.020 0.236
#> SRR1785291     2  0.4506    0.65131 0.012 0.728 0.004 0.020 0.236
#> SRR1785296     4  0.5843    0.77737 0.100 0.024 0.004 0.664 0.208
#> SRR1785297     4  0.5843    0.77737 0.100 0.024 0.004 0.664 0.208
#> SRR1785292     2  0.0000    0.92833 0.000 1.000 0.000 0.000 0.000
#> SRR1785293     2  0.0000    0.92833 0.000 1.000 0.000 0.000 0.000
#> SRR1785294     4  0.5312    0.79097 0.124 0.000 0.000 0.668 0.208
#> SRR1785295     4  0.5312    0.79097 0.124 0.000 0.000 0.668 0.208
#> SRR1785298     1  0.7806    0.35535 0.468 0.204 0.004 0.088 0.236
#> SRR1785299     1  0.7806    0.35535 0.468 0.204 0.004 0.088 0.236
#> SRR1785300     1  0.0000    0.79051 1.000 0.000 0.000 0.000 0.000
#> SRR1785301     1  0.0000    0.79051 1.000 0.000 0.000 0.000 0.000
#> SRR1785304     4  0.0404    0.74656 0.012 0.000 0.000 0.988 0.000
#> SRR1785305     4  0.0404    0.74656 0.012 0.000 0.000 0.988 0.000
#> SRR1785306     5  0.2445    0.81444 0.004 0.108 0.000 0.004 0.884
#> SRR1785307     5  0.2445    0.81444 0.004 0.108 0.000 0.004 0.884
#> SRR1785302     1  0.7806    0.35535 0.468 0.204 0.004 0.088 0.236
#> SRR1785303     1  0.7806    0.35535 0.468 0.204 0.004 0.088 0.236
#> SRR1785308     3  0.3521    0.70632 0.232 0.000 0.764 0.004 0.000
#> SRR1785309     3  0.3521    0.70632 0.232 0.000 0.764 0.004 0.000
#> SRR1785310     4  0.5312    0.79097 0.124 0.000 0.000 0.668 0.208
#> SRR1785311     4  0.5312    0.79097 0.124 0.000 0.000 0.668 0.208
#> SRR1785312     1  0.3452    0.58061 0.756 0.000 0.244 0.000 0.000
#> SRR1785313     1  0.3452    0.58061 0.756 0.000 0.244 0.000 0.000
#> SRR1785314     5  0.0162    0.91209 0.004 0.000 0.000 0.000 0.996
#> SRR1785315     5  0.0162    0.91209 0.004 0.000 0.000 0.000 0.996
#> SRR1785318     2  0.0000    0.92833 0.000 1.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000    0.92833 0.000 1.000 0.000 0.000 0.000
#> SRR1785316     1  0.0000    0.79051 1.000 0.000 0.000 0.000 0.000
#> SRR1785317     1  0.0000    0.79051 1.000 0.000 0.000 0.000 0.000
#> SRR1785324     2  0.0000    0.92833 0.000 1.000 0.000 0.000 0.000
#> SRR1785325     2  0.0000    0.92833 0.000 1.000 0.000 0.000 0.000
#> SRR1785320     3  0.2439    0.80200 0.120 0.000 0.876 0.000 0.004
#> SRR1785321     3  0.2439    0.80200 0.120 0.000 0.876 0.000 0.004
#> SRR1785322     1  0.0162    0.78905 0.996 0.000 0.000 0.000 0.004
#> SRR1785323     1  0.0162    0.78905 0.996 0.000 0.000 0.000 0.004

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5 p6
#> SRR1785238     2  0.1341     0.7811 0.000 0.948 0.028 0.024 0.000 NA
#> SRR1785239     2  0.1341     0.7811 0.000 0.948 0.028 0.024 0.000 NA
#> SRR1785240     5  0.0000     0.9115 0.000 0.000 0.000 0.000 1.000 NA
#> SRR1785241     5  0.0000     0.9115 0.000 0.000 0.000 0.000 1.000 NA
#> SRR1785242     3  0.0000     0.6407 0.000 0.000 1.000 0.000 0.000 NA
#> SRR1785243     3  0.0000     0.6407 0.000 0.000 1.000 0.000 0.000 NA
#> SRR1785244     1  0.2145     0.7533 0.900 0.000 0.000 0.028 0.000 NA
#> SRR1785245     1  0.2145     0.7533 0.900 0.000 0.000 0.028 0.000 NA
#> SRR1785246     3  0.4158     0.6517 0.004 0.000 0.588 0.004 0.004 NA
#> SRR1785247     3  0.4158     0.6517 0.004 0.000 0.588 0.004 0.004 NA
#> SRR1785248     2  0.1167     0.7940 0.000 0.960 0.008 0.020 0.000 NA
#> SRR1785250     3  0.1865     0.6455 0.040 0.000 0.920 0.000 0.000 NA
#> SRR1785251     3  0.1865     0.6455 0.040 0.000 0.920 0.000 0.000 NA
#> SRR1785252     3  0.0000     0.6407 0.000 0.000 1.000 0.000 0.000 NA
#> SRR1785253     3  0.0000     0.6407 0.000 0.000 1.000 0.000 0.000 NA
#> SRR1785254     1  0.7728     0.3591 0.404 0.244 0.000 0.224 0.036 NA
#> SRR1785255     1  0.7728     0.3591 0.404 0.244 0.000 0.224 0.036 NA
#> SRR1785256     1  0.0000     0.7721 1.000 0.000 0.000 0.000 0.000 NA
#> SRR1785257     1  0.0000     0.7721 1.000 0.000 0.000 0.000 0.000 NA
#> SRR1785258     3  0.6847     0.0846 0.388 0.044 0.456 0.032 0.028 NA
#> SRR1785259     3  0.6847     0.0846 0.388 0.044 0.456 0.032 0.028 NA
#> SRR1785262     3  0.5493     0.6094 0.000 0.000 0.488 0.004 0.112 NA
#> SRR1785263     3  0.5493     0.6094 0.000 0.000 0.488 0.004 0.112 NA
#> SRR1785260     4  0.3023     0.7712 0.000 0.000 0.000 0.768 0.000 NA
#> SRR1785261     4  0.3023     0.7712 0.000 0.000 0.000 0.768 0.000 NA
#> SRR1785264     2  0.0891     0.7884 0.000 0.968 0.008 0.024 0.000 NA
#> SRR1785265     2  0.0891     0.7884 0.000 0.968 0.008 0.024 0.000 NA
#> SRR1785266     2  0.2320     0.8287 0.000 0.864 0.000 0.004 0.000 NA
#> SRR1785267     2  0.2320     0.8287 0.000 0.864 0.000 0.004 0.000 NA
#> SRR1785268     1  0.3630     0.5648 0.756 0.000 0.212 0.000 0.000 NA
#> SRR1785269     1  0.3630     0.5648 0.756 0.000 0.212 0.000 0.000 NA
#> SRR1785270     5  0.0000     0.9115 0.000 0.000 0.000 0.000 1.000 NA
#> SRR1785271     5  0.0000     0.9115 0.000 0.000 0.000 0.000 1.000 NA
#> SRR1785272     3  0.4355     0.3077 0.420 0.000 0.556 0.000 0.000 NA
#> SRR1785273     3  0.4355     0.3077 0.420 0.000 0.556 0.000 0.000 NA
#> SRR1785276     3  0.5372     0.6513 0.112 0.000 0.484 0.000 0.000 NA
#> SRR1785277     3  0.5372     0.6513 0.112 0.000 0.484 0.000 0.000 NA
#> SRR1785274     3  0.6106     0.6078 0.008 0.036 0.492 0.032 0.032 NA
#> SRR1785275     3  0.6106     0.6078 0.008 0.036 0.492 0.032 0.032 NA
#> SRR1785280     2  0.3221     0.8331 0.000 0.736 0.000 0.000 0.000 NA
#> SRR1785281     2  0.3221     0.8331 0.000 0.736 0.000 0.000 0.000 NA
#> SRR1785278     1  0.0146     0.7710 0.996 0.000 0.000 0.000 0.000 NA
#> SRR1785279     1  0.0146     0.7710 0.996 0.000 0.000 0.000 0.000 NA
#> SRR1785282     1  0.0000     0.7721 1.000 0.000 0.000 0.000 0.000 NA
#> SRR1785283     1  0.0000     0.7721 1.000 0.000 0.000 0.000 0.000 NA
#> SRR1785284     5  0.0000     0.9115 0.000 0.000 0.000 0.000 1.000 NA
#> SRR1785285     5  0.0000     0.9115 0.000 0.000 0.000 0.000 1.000 NA
#> SRR1785286     5  0.3695     0.6458 0.024 0.000 0.000 0.244 0.732 NA
#> SRR1785287     5  0.3695     0.6458 0.024 0.000 0.000 0.244 0.732 NA
#> SRR1785288     1  0.2145     0.7533 0.900 0.000 0.000 0.028 0.000 NA
#> SRR1785289     1  0.2145     0.7533 0.900 0.000 0.000 0.028 0.000 NA
#> SRR1785290     2  0.3568     0.5744 0.000 0.764 0.000 0.212 0.016 NA
#> SRR1785291     2  0.3568     0.5744 0.000 0.764 0.000 0.212 0.016 NA
#> SRR1785296     4  0.2736     0.8307 0.072 0.028 0.000 0.880 0.016 NA
#> SRR1785297     4  0.2736     0.8307 0.072 0.028 0.000 0.880 0.016 NA
#> SRR1785292     2  0.3221     0.8331 0.000 0.736 0.000 0.000 0.000 NA
#> SRR1785293     2  0.3221     0.8331 0.000 0.736 0.000 0.000 0.000 NA
#> SRR1785294     4  0.2356     0.8410 0.096 0.000 0.000 0.884 0.016 NA
#> SRR1785295     4  0.2356     0.8410 0.096 0.000 0.000 0.884 0.016 NA
#> SRR1785298     1  0.7728     0.3591 0.404 0.244 0.000 0.224 0.036 NA
#> SRR1785299     1  0.7728     0.3591 0.404 0.244 0.000 0.224 0.036 NA
#> SRR1785300     1  0.0000     0.7721 1.000 0.000 0.000 0.000 0.000 NA
#> SRR1785301     1  0.0000     0.7721 1.000 0.000 0.000 0.000 0.000 NA
#> SRR1785304     4  0.3023     0.7712 0.000 0.000 0.000 0.768 0.000 NA
#> SRR1785305     4  0.3023     0.7712 0.000 0.000 0.000 0.768 0.000 NA
#> SRR1785306     5  0.3249     0.7900 0.000 0.128 0.000 0.044 0.824 NA
#> SRR1785307     5  0.3249     0.7900 0.000 0.128 0.000 0.044 0.824 NA
#> SRR1785302     1  0.7728     0.3591 0.404 0.244 0.000 0.224 0.036 NA
#> SRR1785303     1  0.7728     0.3591 0.404 0.244 0.000 0.224 0.036 NA
#> SRR1785308     3  0.3217     0.5288 0.224 0.000 0.768 0.000 0.000 NA
#> SRR1785309     3  0.3217     0.5288 0.224 0.000 0.768 0.000 0.000 NA
#> SRR1785310     4  0.2356     0.8410 0.096 0.000 0.000 0.884 0.016 NA
#> SRR1785311     4  0.2356     0.8410 0.096 0.000 0.000 0.884 0.016 NA
#> SRR1785312     1  0.3630     0.5648 0.756 0.000 0.212 0.000 0.000 NA
#> SRR1785313     1  0.3630     0.5648 0.756 0.000 0.212 0.000 0.000 NA
#> SRR1785314     5  0.0000     0.9115 0.000 0.000 0.000 0.000 1.000 NA
#> SRR1785315     5  0.0000     0.9115 0.000 0.000 0.000 0.000 1.000 NA
#> SRR1785318     2  0.3221     0.8331 0.000 0.736 0.000 0.000 0.000 NA
#> SRR1785319     2  0.3221     0.8331 0.000 0.736 0.000 0.000 0.000 NA
#> SRR1785316     1  0.0363     0.7707 0.988 0.000 0.000 0.000 0.000 NA
#> SRR1785317     1  0.0363     0.7707 0.988 0.000 0.000 0.000 0.000 NA
#> SRR1785324     2  0.3221     0.8331 0.000 0.736 0.000 0.000 0.000 NA
#> SRR1785325     2  0.3221     0.8331 0.000 0.736 0.000 0.000 0.000 NA
#> SRR1785320     3  0.5372     0.6513 0.112 0.000 0.484 0.000 0.000 NA
#> SRR1785321     3  0.5372     0.6513 0.112 0.000 0.484 0.000 0.000 NA
#> SRR1785322     1  0.0146     0.7710 0.996 0.000 0.000 0.000 0.000 NA
#> SRR1785323     1  0.0146     0.7710 0.996 0.000 0.000 0.000 0.000 NA

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-MAD-hclust-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-MAD-hclust-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-MAD-hclust-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-MAD-hclust-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-MAD-hclust-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-MAD-hclust-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-MAD-hclust-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-MAD-hclust-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-MAD-hclust-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-MAD-hclust-membership-heatmap-5

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)

plot of chunk tab-MAD-hclust-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-MAD-hclust-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-MAD-hclust-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-MAD-hclust-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-MAD-hclust-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-MAD-hclust-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-MAD-hclust-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-MAD-hclust-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-MAD-hclust-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-MAD-hclust-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk MAD-hclust-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-MAD-hclust-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-MAD-hclust-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-MAD-hclust-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-MAD-hclust-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-MAD-hclust-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk MAD-hclust-collect-classes

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.


MAD:kmeans

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#>   Subgroups are detected by 'kmeans' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 2.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk MAD-kmeans-collect-plots

The plots are:

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:

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)

plot of chunk MAD-kmeans-select-partition-number

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 0.463           0.791       0.888         0.4642 0.524   0.524
#> 3 3 0.316           0.414       0.642         0.3616 0.706   0.497
#> 4 4 0.382           0.496       0.699         0.1351 0.843   0.595
#> 5 5 0.510           0.515       0.688         0.0773 0.895   0.637
#> 6 6 0.583           0.471       0.665         0.0459 0.928   0.688

suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:

suggest_best_k(res)
#> [1] 2

Following shows the table of the partitions (You need to click the show/hide code output link to see it). The membership matrix (columns with name p*) is inferred by clue::cl_consensus() function with the SE method. Basically the value in the membership matrix represents the probability to belong to a certain group. The finall class label for an item is determined with the group with highest probability it belongs to.

In get_classes() function, the entropy is calculated from the membership matrix and the silhouette score is calculated from the consensus matrix.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2  0.9909      0.195 0.444 0.556
#> SRR1785239     2  0.9909      0.195 0.444 0.556
#> SRR1785240     1  0.1633      0.876 0.976 0.024
#> SRR1785241     1  0.1633      0.876 0.976 0.024
#> SRR1785242     1  0.9552      0.449 0.624 0.376
#> SRR1785243     1  0.9552      0.449 0.624 0.376
#> SRR1785244     1  0.0376      0.882 0.996 0.004
#> SRR1785245     1  0.0376      0.882 0.996 0.004
#> SRR1785246     1  0.4431      0.851 0.908 0.092
#> SRR1785247     1  0.4431      0.851 0.908 0.092
#> SRR1785248     2  0.0672      0.846 0.008 0.992
#> SRR1785250     1  0.4431      0.851 0.908 0.092
#> SRR1785251     1  0.4431      0.851 0.908 0.092
#> SRR1785252     1  0.9552      0.449 0.624 0.376
#> SRR1785253     1  0.9552      0.449 0.624 0.376
#> SRR1785254     2  0.6531      0.840 0.168 0.832
#> SRR1785255     2  0.6531      0.840 0.168 0.832
#> SRR1785256     1  0.0000      0.883 1.000 0.000
#> SRR1785257     1  0.0000      0.883 1.000 0.000
#> SRR1785258     1  0.0000      0.883 1.000 0.000
#> SRR1785259     1  0.0000      0.883 1.000 0.000
#> SRR1785262     1  0.3584      0.863 0.932 0.068
#> SRR1785263     1  0.3584      0.863 0.932 0.068
#> SRR1785260     1  0.7139      0.737 0.804 0.196
#> SRR1785261     1  0.7139      0.737 0.804 0.196
#> SRR1785264     2  0.2423      0.858 0.040 0.960
#> SRR1785265     2  0.2423      0.858 0.040 0.960
#> SRR1785266     2  0.1184      0.852 0.016 0.984
#> SRR1785267     2  0.1184      0.852 0.016 0.984
#> SRR1785268     1  0.0000      0.883 1.000 0.000
#> SRR1785269     1  0.0000      0.883 1.000 0.000
#> SRR1785270     2  0.6438      0.842 0.164 0.836
#> SRR1785271     2  0.6438      0.842 0.164 0.836
#> SRR1785272     1  0.4431      0.851 0.908 0.092
#> SRR1785273     1  0.4431      0.851 0.908 0.092
#> SRR1785276     1  0.0672      0.882 0.992 0.008
#> SRR1785277     1  0.0672      0.882 0.992 0.008
#> SRR1785274     1  0.7139      0.729 0.804 0.196
#> SRR1785275     1  0.7139      0.729 0.804 0.196
#> SRR1785280     2  0.1184      0.852 0.016 0.984
#> SRR1785281     2  0.1184      0.852 0.016 0.984
#> SRR1785278     1  0.0000      0.883 1.000 0.000
#> SRR1785279     1  0.0000      0.883 1.000 0.000
#> SRR1785282     1  0.0000      0.883 1.000 0.000
#> SRR1785283     1  0.0000      0.883 1.000 0.000
#> SRR1785284     1  0.7602      0.685 0.780 0.220
#> SRR1785285     1  0.7602      0.685 0.780 0.220
#> SRR1785286     1  0.7745      0.681 0.772 0.228
#> SRR1785287     1  0.7745      0.681 0.772 0.228
#> SRR1785288     1  0.0376      0.882 0.996 0.004
#> SRR1785289     1  0.0376      0.882 0.996 0.004
#> SRR1785290     2  0.2423      0.858 0.040 0.960
#> SRR1785291     2  0.2423      0.858 0.040 0.960
#> SRR1785296     2  0.9580      0.426 0.380 0.620
#> SRR1785297     2  0.9580      0.426 0.380 0.620
#> SRR1785292     2  0.2778      0.860 0.048 0.952
#> SRR1785293     2  0.2778      0.860 0.048 0.952
#> SRR1785294     1  0.7950      0.685 0.760 0.240
#> SRR1785295     1  0.7950      0.685 0.760 0.240
#> SRR1785298     2  0.9129      0.554 0.328 0.672
#> SRR1785299     2  0.9129      0.554 0.328 0.672
#> SRR1785300     1  0.0376      0.882 0.996 0.004
#> SRR1785301     1  0.0376      0.882 0.996 0.004
#> SRR1785304     2  0.5294      0.857 0.120 0.880
#> SRR1785305     2  0.5294      0.857 0.120 0.880
#> SRR1785306     2  0.5294      0.857 0.120 0.880
#> SRR1785307     2  0.5294      0.857 0.120 0.880
#> SRR1785302     2  0.6343      0.844 0.160 0.840
#> SRR1785303     2  0.6343      0.844 0.160 0.840
#> SRR1785308     1  0.4431      0.851 0.908 0.092
#> SRR1785309     1  0.4431      0.851 0.908 0.092
#> SRR1785310     1  0.7528      0.695 0.784 0.216
#> SRR1785311     1  0.7528      0.695 0.784 0.216
#> SRR1785312     1  0.0000      0.883 1.000 0.000
#> SRR1785313     1  0.0000      0.883 1.000 0.000
#> SRR1785314     2  0.6247      0.846 0.156 0.844
#> SRR1785315     2  0.6247      0.846 0.156 0.844
#> SRR1785318     2  0.1184      0.852 0.016 0.984
#> SRR1785319     2  0.1184      0.852 0.016 0.984
#> SRR1785316     1  0.0376      0.882 0.996 0.004
#> SRR1785317     1  0.0376      0.882 0.996 0.004
#> SRR1785324     2  0.3274      0.859 0.060 0.940
#> SRR1785325     2  0.3274      0.859 0.060 0.940
#> SRR1785320     1  0.0000      0.883 1.000 0.000
#> SRR1785321     1  0.0000      0.883 1.000 0.000
#> SRR1785322     1  0.2948      0.870 0.948 0.052
#> SRR1785323     1  0.2948      0.870 0.948 0.052

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     1  0.9836     0.0873 0.420 0.268 0.312
#> SRR1785239     1  0.9836     0.0873 0.420 0.268 0.312
#> SRR1785240     1  0.6950    -0.0352 0.508 0.016 0.476
#> SRR1785241     1  0.6950    -0.0352 0.508 0.016 0.476
#> SRR1785242     1  0.9291    -0.0690 0.476 0.168 0.356
#> SRR1785243     1  0.9291    -0.0690 0.476 0.168 0.356
#> SRR1785244     3  0.5621     0.3430 0.308 0.000 0.692
#> SRR1785245     3  0.5621     0.3430 0.308 0.000 0.692
#> SRR1785246     3  0.6617     0.4800 0.388 0.012 0.600
#> SRR1785247     3  0.6617     0.4800 0.388 0.012 0.600
#> SRR1785248     2  0.1411     0.7683 0.036 0.964 0.000
#> SRR1785250     3  0.5958     0.5676 0.300 0.008 0.692
#> SRR1785251     3  0.5958     0.5676 0.300 0.008 0.692
#> SRR1785252     1  0.9311    -0.0849 0.468 0.168 0.364
#> SRR1785253     1  0.9311    -0.0849 0.468 0.168 0.364
#> SRR1785254     1  0.9154     0.1386 0.468 0.384 0.148
#> SRR1785255     1  0.9154     0.1386 0.468 0.384 0.148
#> SRR1785256     3  0.2066     0.6453 0.060 0.000 0.940
#> SRR1785257     3  0.2066     0.6453 0.060 0.000 0.940
#> SRR1785258     3  0.5138     0.5570 0.252 0.000 0.748
#> SRR1785259     3  0.5138     0.5570 0.252 0.000 0.748
#> SRR1785262     3  0.6869     0.4032 0.424 0.016 0.560
#> SRR1785263     3  0.6869     0.4032 0.424 0.016 0.560
#> SRR1785260     1  0.8141     0.1299 0.472 0.068 0.460
#> SRR1785261     1  0.8141     0.1299 0.472 0.068 0.460
#> SRR1785264     2  0.4249     0.7315 0.108 0.864 0.028
#> SRR1785265     2  0.4249     0.7315 0.108 0.864 0.028
#> SRR1785266     2  0.0237     0.7891 0.004 0.996 0.000
#> SRR1785267     2  0.0237     0.7891 0.004 0.996 0.000
#> SRR1785268     3  0.0424     0.6649 0.008 0.000 0.992
#> SRR1785269     3  0.0424     0.6649 0.008 0.000 0.992
#> SRR1785270     1  0.8972     0.0525 0.460 0.412 0.128
#> SRR1785271     1  0.8972     0.0525 0.460 0.412 0.128
#> SRR1785272     3  0.5517     0.5848 0.268 0.004 0.728
#> SRR1785273     3  0.5517     0.5848 0.268 0.004 0.728
#> SRR1785276     3  0.5366     0.5764 0.208 0.016 0.776
#> SRR1785277     3  0.5366     0.5764 0.208 0.016 0.776
#> SRR1785274     3  0.8065     0.1388 0.452 0.064 0.484
#> SRR1785275     3  0.8065     0.1388 0.452 0.064 0.484
#> SRR1785280     2  0.0237     0.7891 0.004 0.996 0.000
#> SRR1785281     2  0.0237     0.7891 0.004 0.996 0.000
#> SRR1785278     3  0.0892     0.6606 0.020 0.000 0.980
#> SRR1785279     3  0.0892     0.6606 0.020 0.000 0.980
#> SRR1785282     3  0.1529     0.6520 0.040 0.000 0.960
#> SRR1785283     3  0.1529     0.6520 0.040 0.000 0.960
#> SRR1785284     1  0.7181     0.3859 0.648 0.048 0.304
#> SRR1785285     1  0.7181     0.3859 0.648 0.048 0.304
#> SRR1785286     1  0.7112     0.4110 0.680 0.060 0.260
#> SRR1785287     1  0.7112     0.4110 0.680 0.060 0.260
#> SRR1785288     3  0.5706     0.3230 0.320 0.000 0.680
#> SRR1785289     3  0.5706     0.3230 0.320 0.000 0.680
#> SRR1785290     2  0.5122     0.6788 0.200 0.788 0.012
#> SRR1785291     2  0.5122     0.6788 0.200 0.788 0.012
#> SRR1785296     1  0.8590     0.2663 0.560 0.320 0.120
#> SRR1785297     1  0.8590     0.2663 0.560 0.320 0.120
#> SRR1785292     2  0.1964     0.7862 0.056 0.944 0.000
#> SRR1785293     2  0.1964     0.7862 0.056 0.944 0.000
#> SRR1785294     1  0.8419     0.2285 0.504 0.088 0.408
#> SRR1785295     1  0.8419     0.2285 0.504 0.088 0.408
#> SRR1785298     1  0.9004     0.2173 0.488 0.376 0.136
#> SRR1785299     1  0.9004     0.2173 0.488 0.376 0.136
#> SRR1785300     3  0.5431     0.3900 0.284 0.000 0.716
#> SRR1785301     3  0.5431     0.3900 0.284 0.000 0.716
#> SRR1785304     2  0.6994     0.3426 0.424 0.556 0.020
#> SRR1785305     2  0.6994     0.3426 0.424 0.556 0.020
#> SRR1785306     2  0.8063     0.1171 0.448 0.488 0.064
#> SRR1785307     2  0.8063     0.1171 0.448 0.488 0.064
#> SRR1785302     1  0.8686     0.0186 0.464 0.432 0.104
#> SRR1785303     1  0.8686     0.0186 0.464 0.432 0.104
#> SRR1785308     3  0.5896     0.5656 0.292 0.008 0.700
#> SRR1785309     3  0.5896     0.5656 0.292 0.008 0.700
#> SRR1785310     1  0.8093     0.2489 0.516 0.068 0.416
#> SRR1785311     1  0.8093     0.2489 0.516 0.068 0.416
#> SRR1785312     3  0.0592     0.6644 0.012 0.000 0.988
#> SRR1785313     3  0.0592     0.6644 0.012 0.000 0.988
#> SRR1785314     1  0.8277    -0.1001 0.468 0.456 0.076
#> SRR1785315     1  0.8277    -0.1001 0.468 0.456 0.076
#> SRR1785318     2  0.0237     0.7907 0.004 0.996 0.000
#> SRR1785319     2  0.0237     0.7907 0.004 0.996 0.000
#> SRR1785316     3  0.5098     0.4326 0.248 0.000 0.752
#> SRR1785317     3  0.5098     0.4326 0.248 0.000 0.752
#> SRR1785324     2  0.2448     0.7780 0.076 0.924 0.000
#> SRR1785325     2  0.2448     0.7780 0.076 0.924 0.000
#> SRR1785320     3  0.1289     0.6641 0.032 0.000 0.968
#> SRR1785321     3  0.1289     0.6641 0.032 0.000 0.968
#> SRR1785322     3  0.4235     0.6306 0.176 0.000 0.824
#> SRR1785323     3  0.4235     0.6306 0.176 0.000 0.824

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     3  0.7753    0.62921 0.168 0.156 0.608 0.068
#> SRR1785239     3  0.7753    0.62921 0.168 0.156 0.608 0.068
#> SRR1785240     4  0.7732    0.14871 0.212 0.004 0.320 0.464
#> SRR1785241     4  0.7732    0.14871 0.212 0.004 0.320 0.464
#> SRR1785242     3  0.6670    0.66852 0.168 0.100 0.688 0.044
#> SRR1785243     3  0.6670    0.66852 0.168 0.100 0.688 0.044
#> SRR1785244     1  0.6091    0.37687 0.596 0.000 0.060 0.344
#> SRR1785245     1  0.6091    0.37687 0.596 0.000 0.060 0.344
#> SRR1785246     3  0.5755    0.44508 0.444 0.000 0.528 0.028
#> SRR1785247     3  0.5755    0.44508 0.444 0.000 0.528 0.028
#> SRR1785248     2  0.2589    0.78695 0.000 0.884 0.116 0.000
#> SRR1785250     1  0.5427   -0.20959 0.544 0.004 0.444 0.008
#> SRR1785251     1  0.5427   -0.20959 0.544 0.004 0.444 0.008
#> SRR1785252     3  0.6630    0.66824 0.172 0.100 0.688 0.040
#> SRR1785253     3  0.6630    0.66824 0.172 0.100 0.688 0.040
#> SRR1785254     4  0.8008    0.55277 0.068 0.180 0.172 0.580
#> SRR1785255     4  0.8008    0.55277 0.068 0.180 0.172 0.580
#> SRR1785256     1  0.2563    0.60362 0.908 0.000 0.020 0.072
#> SRR1785257     1  0.2563    0.60362 0.908 0.000 0.020 0.072
#> SRR1785258     1  0.5511   -0.00479 0.620 0.000 0.352 0.028
#> SRR1785259     1  0.5511   -0.00479 0.620 0.000 0.352 0.028
#> SRR1785262     3  0.7125    0.49089 0.392 0.000 0.476 0.132
#> SRR1785263     3  0.7125    0.49089 0.392 0.000 0.476 0.132
#> SRR1785260     4  0.7855    0.43959 0.264 0.052 0.124 0.560
#> SRR1785261     4  0.7855    0.43959 0.264 0.052 0.124 0.560
#> SRR1785264     2  0.6276    0.60986 0.016 0.676 0.228 0.080
#> SRR1785265     2  0.6276    0.60986 0.016 0.676 0.228 0.080
#> SRR1785266     2  0.1022    0.82099 0.000 0.968 0.032 0.000
#> SRR1785267     2  0.1022    0.82099 0.000 0.968 0.032 0.000
#> SRR1785268     1  0.1661    0.60697 0.944 0.000 0.052 0.004
#> SRR1785269     1  0.1661    0.60697 0.944 0.000 0.052 0.004
#> SRR1785270     4  0.8152    0.50416 0.052 0.188 0.216 0.544
#> SRR1785271     4  0.8152    0.50416 0.052 0.188 0.216 0.544
#> SRR1785272     1  0.5152   -0.01766 0.608 0.004 0.384 0.004
#> SRR1785273     1  0.5152   -0.01766 0.608 0.004 0.384 0.004
#> SRR1785276     1  0.5549    0.18906 0.672 0.000 0.280 0.048
#> SRR1785277     1  0.5549    0.18906 0.672 0.000 0.280 0.048
#> SRR1785274     3  0.8185    0.42397 0.288 0.016 0.440 0.256
#> SRR1785275     3  0.8185    0.42397 0.288 0.016 0.440 0.256
#> SRR1785280     2  0.0921    0.82089 0.000 0.972 0.028 0.000
#> SRR1785281     2  0.0921    0.82089 0.000 0.972 0.028 0.000
#> SRR1785278     1  0.1004    0.61659 0.972 0.000 0.024 0.004
#> SRR1785279     1  0.1004    0.61659 0.972 0.000 0.024 0.004
#> SRR1785282     1  0.1406    0.61632 0.960 0.000 0.024 0.016
#> SRR1785283     1  0.1406    0.61632 0.960 0.000 0.024 0.016
#> SRR1785284     4  0.6039    0.56249 0.104 0.016 0.164 0.716
#> SRR1785285     4  0.6039    0.56249 0.104 0.016 0.164 0.716
#> SRR1785286     4  0.3802    0.60762 0.064 0.008 0.068 0.860
#> SRR1785287     4  0.3802    0.60762 0.064 0.008 0.068 0.860
#> SRR1785288     1  0.6176    0.33962 0.572 0.000 0.060 0.368
#> SRR1785289     1  0.6176    0.33962 0.572 0.000 0.060 0.368
#> SRR1785290     2  0.7196    0.25052 0.008 0.544 0.128 0.320
#> SRR1785291     2  0.7196    0.25052 0.008 0.544 0.128 0.320
#> SRR1785296     4  0.7866    0.53839 0.052 0.164 0.204 0.580
#> SRR1785297     4  0.7866    0.53839 0.052 0.164 0.204 0.580
#> SRR1785292     2  0.2450    0.79489 0.000 0.912 0.016 0.072
#> SRR1785293     2  0.2450    0.79489 0.000 0.912 0.016 0.072
#> SRR1785294     4  0.7151    0.54094 0.200 0.060 0.092 0.648
#> SRR1785295     4  0.7151    0.54094 0.200 0.060 0.092 0.648
#> SRR1785298     4  0.8015    0.53170 0.056 0.188 0.188 0.568
#> SRR1785299     4  0.8015    0.53170 0.056 0.188 0.188 0.568
#> SRR1785300     1  0.5222    0.48653 0.688 0.000 0.032 0.280
#> SRR1785301     1  0.5222    0.48653 0.688 0.000 0.032 0.280
#> SRR1785304     4  0.7010    0.37490 0.012 0.292 0.112 0.584
#> SRR1785305     4  0.7010    0.37490 0.012 0.292 0.112 0.584
#> SRR1785306     4  0.7452    0.49373 0.012 0.224 0.200 0.564
#> SRR1785307     4  0.7452    0.49373 0.012 0.224 0.200 0.564
#> SRR1785302     4  0.7092    0.52023 0.036 0.244 0.096 0.624
#> SRR1785303     4  0.7092    0.52023 0.036 0.244 0.096 0.624
#> SRR1785308     3  0.5798    0.35113 0.452 0.012 0.524 0.012
#> SRR1785309     3  0.5798    0.35113 0.452 0.012 0.524 0.012
#> SRR1785310     4  0.6643    0.53289 0.216 0.056 0.056 0.672
#> SRR1785311     4  0.6643    0.53289 0.216 0.056 0.056 0.672
#> SRR1785312     1  0.1661    0.61019 0.944 0.000 0.052 0.004
#> SRR1785313     1  0.1661    0.61019 0.944 0.000 0.052 0.004
#> SRR1785314     4  0.7340    0.49121 0.016 0.236 0.164 0.584
#> SRR1785315     4  0.7340    0.49121 0.016 0.236 0.164 0.584
#> SRR1785318     2  0.0895    0.82311 0.000 0.976 0.020 0.004
#> SRR1785319     2  0.0895    0.82311 0.000 0.976 0.020 0.004
#> SRR1785316     1  0.4508    0.54908 0.780 0.000 0.036 0.184
#> SRR1785317     1  0.4508    0.54908 0.780 0.000 0.036 0.184
#> SRR1785324     2  0.2635    0.78849 0.000 0.904 0.020 0.076
#> SRR1785325     2  0.2635    0.78849 0.000 0.904 0.020 0.076
#> SRR1785320     1  0.1970    0.61010 0.932 0.000 0.060 0.008
#> SRR1785321     1  0.1970    0.61010 0.932 0.000 0.060 0.008
#> SRR1785322     1  0.4018    0.39451 0.772 0.000 0.224 0.004
#> SRR1785323     1  0.4018    0.39451 0.772 0.000 0.224 0.004

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     3  0.6127     0.4925 0.040 0.076 0.712 0.072 0.100
#> SRR1785239     3  0.6127     0.4925 0.040 0.076 0.712 0.072 0.100
#> SRR1785240     5  0.7350     0.4637 0.100 0.004 0.232 0.124 0.540
#> SRR1785241     5  0.7350     0.4637 0.100 0.004 0.232 0.124 0.540
#> SRR1785242     3  0.3326     0.6057 0.028 0.040 0.876 0.012 0.044
#> SRR1785243     3  0.3326     0.6057 0.028 0.040 0.876 0.012 0.044
#> SRR1785244     1  0.6193     0.5250 0.592 0.000 0.016 0.256 0.136
#> SRR1785245     1  0.6193     0.5250 0.592 0.000 0.016 0.256 0.136
#> SRR1785246     3  0.6063     0.5499 0.324 0.000 0.576 0.032 0.068
#> SRR1785247     3  0.6063     0.5499 0.324 0.000 0.576 0.032 0.068
#> SRR1785248     2  0.4194     0.6912 0.000 0.780 0.172 0.020 0.028
#> SRR1785250     3  0.5806     0.5160 0.372 0.004 0.560 0.036 0.028
#> SRR1785251     3  0.5806     0.5160 0.372 0.004 0.560 0.036 0.028
#> SRR1785252     3  0.3251     0.6062 0.028 0.040 0.880 0.012 0.040
#> SRR1785253     3  0.3251     0.6062 0.028 0.040 0.880 0.012 0.040
#> SRR1785254     5  0.7198     0.3410 0.020 0.104 0.044 0.320 0.512
#> SRR1785255     5  0.7198     0.3410 0.020 0.104 0.044 0.320 0.512
#> SRR1785256     1  0.3610     0.6868 0.844 0.000 0.020 0.088 0.048
#> SRR1785257     1  0.3610     0.6868 0.844 0.000 0.020 0.088 0.048
#> SRR1785258     1  0.6445     0.0922 0.520 0.000 0.348 0.024 0.108
#> SRR1785259     1  0.6445     0.0922 0.520 0.000 0.348 0.024 0.108
#> SRR1785262     3  0.7280     0.5351 0.232 0.000 0.536 0.128 0.104
#> SRR1785263     3  0.7280     0.5351 0.232 0.000 0.536 0.128 0.104
#> SRR1785260     4  0.4782     0.5815 0.124 0.012 0.032 0.780 0.052
#> SRR1785261     4  0.4782     0.5815 0.124 0.012 0.032 0.780 0.052
#> SRR1785264     2  0.7657     0.4176 0.000 0.476 0.268 0.124 0.132
#> SRR1785265     2  0.7657     0.4176 0.000 0.476 0.268 0.124 0.132
#> SRR1785266     2  0.2026     0.7721 0.000 0.928 0.044 0.016 0.012
#> SRR1785267     2  0.2026     0.7721 0.000 0.928 0.044 0.016 0.012
#> SRR1785268     1  0.2791     0.6606 0.892 0.000 0.056 0.016 0.036
#> SRR1785269     1  0.2791     0.6606 0.892 0.000 0.056 0.016 0.036
#> SRR1785270     5  0.6364     0.5607 0.020 0.112 0.060 0.132 0.676
#> SRR1785271     5  0.6364     0.5607 0.020 0.112 0.060 0.132 0.676
#> SRR1785272     3  0.6190     0.3793 0.444 0.004 0.468 0.060 0.024
#> SRR1785273     3  0.6190     0.3793 0.444 0.004 0.468 0.060 0.024
#> SRR1785276     1  0.7040     0.2266 0.552 0.004 0.260 0.068 0.116
#> SRR1785277     1  0.7040     0.2266 0.552 0.004 0.260 0.068 0.116
#> SRR1785274     5  0.7462     0.1376 0.128 0.004 0.396 0.068 0.404
#> SRR1785275     5  0.7462     0.1376 0.128 0.004 0.396 0.068 0.404
#> SRR1785280     2  0.0968     0.7818 0.000 0.972 0.012 0.004 0.012
#> SRR1785281     2  0.0968     0.7818 0.000 0.972 0.012 0.004 0.012
#> SRR1785278     1  0.2515     0.6817 0.908 0.000 0.032 0.040 0.020
#> SRR1785279     1  0.2515     0.6817 0.908 0.000 0.032 0.040 0.020
#> SRR1785282     1  0.2899     0.6850 0.888 0.000 0.032 0.056 0.024
#> SRR1785283     1  0.2899     0.6850 0.888 0.000 0.032 0.056 0.024
#> SRR1785284     5  0.5925     0.3244 0.072 0.000 0.020 0.336 0.572
#> SRR1785285     5  0.5925     0.3244 0.072 0.000 0.020 0.336 0.572
#> SRR1785286     4  0.5444     0.1415 0.044 0.000 0.012 0.576 0.368
#> SRR1785287     4  0.5444     0.1415 0.044 0.000 0.012 0.576 0.368
#> SRR1785288     1  0.6267     0.4821 0.560 0.000 0.012 0.292 0.136
#> SRR1785289     1  0.6267     0.4821 0.560 0.000 0.012 0.292 0.136
#> SRR1785290     2  0.8012     0.0629 0.000 0.376 0.116 0.332 0.176
#> SRR1785291     2  0.8012     0.0629 0.000 0.376 0.116 0.332 0.176
#> SRR1785296     4  0.6385     0.5472 0.036 0.060 0.136 0.684 0.084
#> SRR1785297     4  0.6385     0.5472 0.036 0.060 0.136 0.684 0.084
#> SRR1785292     2  0.2067     0.7704 0.000 0.924 0.004 0.044 0.028
#> SRR1785293     2  0.2067     0.7704 0.000 0.924 0.004 0.044 0.028
#> SRR1785294     4  0.3685     0.6172 0.108 0.012 0.028 0.840 0.012
#> SRR1785295     4  0.3685     0.6172 0.108 0.012 0.028 0.840 0.012
#> SRR1785298     4  0.7238     0.4740 0.024 0.096 0.128 0.604 0.148
#> SRR1785299     4  0.7238     0.4740 0.024 0.096 0.128 0.604 0.148
#> SRR1785300     1  0.4969     0.6047 0.676 0.000 0.004 0.264 0.056
#> SRR1785301     1  0.4969     0.6047 0.676 0.000 0.004 0.264 0.056
#> SRR1785304     4  0.5645     0.4690 0.004 0.196 0.008 0.668 0.124
#> SRR1785305     4  0.5645     0.4690 0.004 0.196 0.008 0.668 0.124
#> SRR1785306     5  0.6687     0.5092 0.000 0.136 0.052 0.228 0.584
#> SRR1785307     5  0.6687     0.5092 0.000 0.136 0.052 0.228 0.584
#> SRR1785302     4  0.6928     0.1686 0.004 0.120 0.040 0.508 0.328
#> SRR1785303     4  0.6928     0.1686 0.004 0.120 0.040 0.508 0.328
#> SRR1785308     3  0.5002     0.5974 0.248 0.012 0.700 0.020 0.020
#> SRR1785309     3  0.5002     0.5974 0.248 0.012 0.700 0.020 0.020
#> SRR1785310     4  0.4072     0.6080 0.116 0.012 0.024 0.820 0.028
#> SRR1785311     4  0.4072     0.6080 0.116 0.012 0.024 0.820 0.028
#> SRR1785312     1  0.2842     0.6611 0.888 0.000 0.056 0.012 0.044
#> SRR1785313     1  0.2842     0.6611 0.888 0.000 0.056 0.012 0.044
#> SRR1785314     5  0.6292     0.4982 0.000 0.132 0.028 0.236 0.604
#> SRR1785315     5  0.6292     0.4982 0.000 0.132 0.028 0.236 0.604
#> SRR1785318     2  0.1095     0.7818 0.000 0.968 0.012 0.008 0.012
#> SRR1785319     2  0.1095     0.7818 0.000 0.968 0.012 0.008 0.012
#> SRR1785316     1  0.4980     0.6472 0.740 0.000 0.032 0.168 0.060
#> SRR1785317     1  0.4980     0.6472 0.740 0.000 0.032 0.168 0.060
#> SRR1785324     2  0.2313     0.7668 0.000 0.912 0.004 0.040 0.044
#> SRR1785325     2  0.2313     0.7668 0.000 0.912 0.004 0.040 0.044
#> SRR1785320     1  0.3584     0.6582 0.852 0.000 0.060 0.056 0.032
#> SRR1785321     1  0.3584     0.6582 0.852 0.000 0.060 0.056 0.032
#> SRR1785322     1  0.5527     0.3719 0.680 0.004 0.232 0.044 0.040
#> SRR1785323     1  0.5527     0.3719 0.680 0.004 0.232 0.044 0.040

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     3   0.708     0.5169 0.064 0.060 0.620 0.068 0.100 0.088
#> SRR1785239     3   0.708     0.5169 0.064 0.060 0.620 0.068 0.100 0.088
#> SRR1785240     5   0.654     0.4884 0.036 0.000 0.168 0.084 0.604 0.108
#> SRR1785241     5   0.654     0.4884 0.036 0.000 0.168 0.084 0.604 0.108
#> SRR1785242     3   0.259     0.6090 0.056 0.028 0.892 0.004 0.020 0.000
#> SRR1785243     3   0.259     0.6090 0.056 0.028 0.892 0.004 0.020 0.000
#> SRR1785244     6   0.656     0.9507 0.384 0.000 0.000 0.120 0.072 0.424
#> SRR1785245     6   0.656     0.9507 0.384 0.000 0.000 0.120 0.072 0.424
#> SRR1785246     3   0.748     0.4205 0.296 0.008 0.444 0.048 0.048 0.156
#> SRR1785247     3   0.748     0.4205 0.296 0.008 0.444 0.048 0.048 0.156
#> SRR1785248     2   0.538     0.6705 0.000 0.668 0.216 0.020 0.032 0.064
#> SRR1785250     3   0.636     0.2454 0.400 0.004 0.456 0.036 0.012 0.092
#> SRR1785251     3   0.636     0.2454 0.400 0.004 0.456 0.036 0.012 0.092
#> SRR1785252     3   0.273     0.6094 0.056 0.028 0.888 0.004 0.020 0.004
#> SRR1785253     3   0.273     0.6094 0.056 0.028 0.888 0.004 0.020 0.004
#> SRR1785254     5   0.670     0.3841 0.008 0.064 0.024 0.304 0.524 0.076
#> SRR1785255     5   0.670     0.3841 0.008 0.064 0.024 0.304 0.524 0.076
#> SRR1785256     1   0.468     0.3813 0.752 0.000 0.040 0.052 0.016 0.140
#> SRR1785257     1   0.468     0.3813 0.752 0.000 0.040 0.052 0.016 0.140
#> SRR1785258     1   0.659     0.1365 0.524 0.000 0.284 0.012 0.080 0.100
#> SRR1785259     1   0.659     0.1365 0.524 0.000 0.284 0.012 0.080 0.100
#> SRR1785262     3   0.831     0.4861 0.164 0.008 0.436 0.148 0.100 0.144
#> SRR1785263     3   0.831     0.4861 0.164 0.008 0.436 0.148 0.100 0.144
#> SRR1785260     4   0.460     0.5213 0.040 0.004 0.016 0.748 0.024 0.168
#> SRR1785261     4   0.460     0.5213 0.040 0.004 0.016 0.748 0.024 0.168
#> SRR1785264     2   0.823     0.3804 0.000 0.396 0.244 0.124 0.120 0.116
#> SRR1785265     2   0.823     0.3804 0.000 0.396 0.244 0.124 0.120 0.116
#> SRR1785266     2   0.333     0.7905 0.000 0.848 0.088 0.012 0.028 0.024
#> SRR1785267     2   0.333     0.7905 0.000 0.848 0.088 0.012 0.028 0.024
#> SRR1785268     1   0.184     0.5491 0.928 0.000 0.008 0.004 0.012 0.048
#> SRR1785269     1   0.184     0.5491 0.928 0.000 0.008 0.004 0.012 0.048
#> SRR1785270     5   0.437     0.6670 0.000 0.064 0.048 0.060 0.796 0.032
#> SRR1785271     5   0.437     0.6670 0.000 0.064 0.048 0.060 0.796 0.032
#> SRR1785272     1   0.667    -0.0225 0.480 0.004 0.312 0.040 0.008 0.156
#> SRR1785273     1   0.667    -0.0225 0.480 0.004 0.312 0.040 0.008 0.156
#> SRR1785276     1   0.761     0.2431 0.504 0.004 0.176 0.064 0.096 0.156
#> SRR1785277     1   0.761     0.2431 0.504 0.004 0.176 0.064 0.096 0.156
#> SRR1785274     3   0.827     0.1999 0.104 0.008 0.360 0.108 0.316 0.104
#> SRR1785275     3   0.827     0.1999 0.104 0.008 0.360 0.108 0.316 0.104
#> SRR1785280     2   0.145     0.8153 0.000 0.948 0.032 0.008 0.008 0.004
#> SRR1785281     2   0.145     0.8153 0.000 0.948 0.032 0.008 0.008 0.004
#> SRR1785278     1   0.216     0.5298 0.908 0.000 0.008 0.028 0.000 0.056
#> SRR1785279     1   0.216     0.5298 0.908 0.000 0.008 0.028 0.000 0.056
#> SRR1785282     1   0.262     0.4945 0.876 0.000 0.012 0.012 0.004 0.096
#> SRR1785283     1   0.262     0.4945 0.876 0.000 0.012 0.012 0.004 0.096
#> SRR1785284     5   0.537     0.5564 0.008 0.000 0.004 0.204 0.632 0.152
#> SRR1785285     5   0.537     0.5564 0.008 0.000 0.004 0.204 0.632 0.152
#> SRR1785286     4   0.547    -0.1039 0.004 0.000 0.008 0.456 0.452 0.080
#> SRR1785287     4   0.547    -0.1039 0.004 0.000 0.008 0.456 0.452 0.080
#> SRR1785288     6   0.663     0.9522 0.368 0.000 0.000 0.152 0.060 0.420
#> SRR1785289     6   0.663     0.9522 0.368 0.000 0.000 0.152 0.060 0.420
#> SRR1785290     4   0.807     0.1856 0.000 0.268 0.088 0.396 0.148 0.100
#> SRR1785291     4   0.807     0.1856 0.000 0.268 0.088 0.396 0.148 0.100
#> SRR1785296     4   0.442     0.5585 0.012 0.028 0.072 0.796 0.072 0.020
#> SRR1785297     4   0.442     0.5585 0.012 0.028 0.072 0.796 0.072 0.020
#> SRR1785292     2   0.307     0.7961 0.000 0.864 0.004 0.040 0.024 0.068
#> SRR1785293     2   0.307     0.7961 0.000 0.864 0.004 0.040 0.024 0.068
#> SRR1785294     4   0.385     0.5812 0.044 0.004 0.012 0.820 0.024 0.096
#> SRR1785295     4   0.385     0.5812 0.044 0.004 0.012 0.820 0.024 0.096
#> SRR1785298     4   0.637     0.4698 0.012 0.068 0.048 0.648 0.148 0.076
#> SRR1785299     4   0.637     0.4698 0.012 0.068 0.048 0.648 0.148 0.076
#> SRR1785300     1   0.589    -0.3003 0.596 0.000 0.016 0.176 0.012 0.200
#> SRR1785301     1   0.589    -0.3003 0.596 0.000 0.016 0.176 0.012 0.200
#> SRR1785304     4   0.552     0.5196 0.000 0.096 0.016 0.696 0.076 0.116
#> SRR1785305     4   0.552     0.5196 0.000 0.096 0.016 0.696 0.076 0.116
#> SRR1785306     5   0.504     0.6383 0.000 0.056 0.028 0.172 0.716 0.028
#> SRR1785307     5   0.504     0.6383 0.000 0.056 0.028 0.172 0.716 0.028
#> SRR1785302     4   0.606     0.2517 0.000 0.080 0.016 0.540 0.328 0.036
#> SRR1785303     4   0.606     0.2517 0.000 0.080 0.016 0.540 0.328 0.036
#> SRR1785308     3   0.565     0.4027 0.284 0.004 0.592 0.008 0.012 0.100
#> SRR1785309     3   0.565     0.4027 0.284 0.004 0.592 0.008 0.012 0.100
#> SRR1785310     4   0.421     0.5711 0.044 0.004 0.000 0.788 0.064 0.100
#> SRR1785311     4   0.421     0.5711 0.044 0.004 0.000 0.788 0.064 0.100
#> SRR1785312     1   0.234     0.5420 0.900 0.000 0.012 0.004 0.016 0.068
#> SRR1785313     1   0.234     0.5420 0.900 0.000 0.012 0.004 0.016 0.068
#> SRR1785314     5   0.462     0.6578 0.000 0.080 0.016 0.132 0.752 0.020
#> SRR1785315     5   0.462     0.6578 0.000 0.080 0.016 0.132 0.752 0.020
#> SRR1785318     2   0.143     0.8156 0.000 0.952 0.008 0.012 0.020 0.008
#> SRR1785319     2   0.143     0.8156 0.000 0.952 0.008 0.012 0.020 0.008
#> SRR1785316     1   0.510    -0.1061 0.640 0.000 0.008 0.060 0.016 0.276
#> SRR1785317     1   0.510    -0.1061 0.640 0.000 0.008 0.060 0.016 0.276
#> SRR1785324     2   0.311     0.7899 0.000 0.868 0.008 0.044 0.040 0.040
#> SRR1785325     2   0.311     0.7899 0.000 0.868 0.008 0.044 0.040 0.040
#> SRR1785320     1   0.373     0.4894 0.812 0.000 0.020 0.008 0.040 0.120
#> SRR1785321     1   0.373     0.4894 0.812 0.000 0.020 0.008 0.040 0.120
#> SRR1785322     1   0.529     0.4672 0.684 0.000 0.120 0.052 0.000 0.144
#> SRR1785323     1   0.529     0.4672 0.684 0.000 0.120 0.052 0.000 0.144

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-MAD-kmeans-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-MAD-kmeans-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-MAD-kmeans-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-MAD-kmeans-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-MAD-kmeans-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-MAD-kmeans-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-MAD-kmeans-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-MAD-kmeans-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-MAD-kmeans-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-MAD-kmeans-membership-heatmap-5

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)

plot of chunk tab-MAD-kmeans-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-MAD-kmeans-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-MAD-kmeans-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-MAD-kmeans-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-MAD-kmeans-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-MAD-kmeans-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-MAD-kmeans-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-MAD-kmeans-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-MAD-kmeans-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-MAD-kmeans-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk MAD-kmeans-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-MAD-kmeans-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-MAD-kmeans-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-MAD-kmeans-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-MAD-kmeans-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-MAD-kmeans-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk MAD-kmeans-collect-classes

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.


MAD:skmeans

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#>   Subgroups are detected by 'skmeans' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 2.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk MAD-skmeans-collect-plots

The plots are:

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:

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)

plot of chunk MAD-skmeans-select-partition-number

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 0.668           0.740       0.900         0.5028 0.496   0.496
#> 3 3 0.598           0.806       0.893         0.3212 0.725   0.499
#> 4 4 0.663           0.682       0.837         0.1219 0.862   0.616
#> 5 5 0.688           0.663       0.821         0.0653 0.908   0.666
#> 6 6 0.702           0.623       0.759         0.0396 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] 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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     1   0.999      0.185 0.516 0.484
#> SRR1785239     1   0.999      0.185 0.516 0.484
#> SRR1785240     1   0.000      0.892 1.000 0.000
#> SRR1785241     1   0.000      0.892 1.000 0.000
#> SRR1785242     1   0.999      0.185 0.516 0.484
#> SRR1785243     1   0.999      0.185 0.516 0.484
#> SRR1785244     1   0.000      0.892 1.000 0.000
#> SRR1785245     1   0.000      0.892 1.000 0.000
#> SRR1785246     1   0.000      0.892 1.000 0.000
#> SRR1785247     1   0.000      0.892 1.000 0.000
#> SRR1785248     2   0.000      0.853 0.000 1.000
#> SRR1785250     1   0.000      0.892 1.000 0.000
#> SRR1785251     1   0.000      0.892 1.000 0.000
#> SRR1785252     1   0.999      0.185 0.516 0.484
#> SRR1785253     1   0.999      0.185 0.516 0.484
#> SRR1785254     2   0.000      0.853 0.000 1.000
#> SRR1785255     2   0.000      0.853 0.000 1.000
#> SRR1785256     1   0.000      0.892 1.000 0.000
#> SRR1785257     1   0.000      0.892 1.000 0.000
#> SRR1785258     1   0.000      0.892 1.000 0.000
#> SRR1785259     1   0.000      0.892 1.000 0.000
#> SRR1785262     1   0.000      0.892 1.000 0.000
#> SRR1785263     1   0.000      0.892 1.000 0.000
#> SRR1785260     2   0.999      0.258 0.484 0.516
#> SRR1785261     2   0.999      0.258 0.484 0.516
#> SRR1785264     2   0.000      0.853 0.000 1.000
#> SRR1785265     2   0.000      0.853 0.000 1.000
#> SRR1785266     2   0.000      0.853 0.000 1.000
#> SRR1785267     2   0.000      0.853 0.000 1.000
#> SRR1785268     1   0.000      0.892 1.000 0.000
#> SRR1785269     1   0.000      0.892 1.000 0.000
#> SRR1785270     2   0.000      0.853 0.000 1.000
#> SRR1785271     2   0.000      0.853 0.000 1.000
#> SRR1785272     1   0.000      0.892 1.000 0.000
#> SRR1785273     1   0.000      0.892 1.000 0.000
#> SRR1785276     1   0.000      0.892 1.000 0.000
#> SRR1785277     1   0.000      0.892 1.000 0.000
#> SRR1785274     1   0.999      0.185 0.516 0.484
#> SRR1785275     1   0.999      0.185 0.516 0.484
#> SRR1785280     2   0.000      0.853 0.000 1.000
#> SRR1785281     2   0.000      0.853 0.000 1.000
#> SRR1785278     1   0.000      0.892 1.000 0.000
#> SRR1785279     1   0.000      0.892 1.000 0.000
#> SRR1785282     1   0.000      0.892 1.000 0.000
#> SRR1785283     1   0.000      0.892 1.000 0.000
#> SRR1785284     2   0.999      0.258 0.484 0.516
#> SRR1785285     2   0.999      0.258 0.484 0.516
#> SRR1785286     2   0.999      0.258 0.484 0.516
#> SRR1785287     2   0.999      0.258 0.484 0.516
#> SRR1785288     1   0.000      0.892 1.000 0.000
#> SRR1785289     1   0.000      0.892 1.000 0.000
#> SRR1785290     2   0.000      0.853 0.000 1.000
#> SRR1785291     2   0.000      0.853 0.000 1.000
#> SRR1785296     2   0.000      0.853 0.000 1.000
#> SRR1785297     2   0.000      0.853 0.000 1.000
#> SRR1785292     2   0.000      0.853 0.000 1.000
#> SRR1785293     2   0.000      0.853 0.000 1.000
#> SRR1785294     2   0.999      0.258 0.484 0.516
#> SRR1785295     2   0.999      0.258 0.484 0.516
#> SRR1785298     2   0.000      0.853 0.000 1.000
#> SRR1785299     2   0.000      0.853 0.000 1.000
#> SRR1785300     1   0.000      0.892 1.000 0.000
#> SRR1785301     1   0.000      0.892 1.000 0.000
#> SRR1785304     2   0.000      0.853 0.000 1.000
#> SRR1785305     2   0.000      0.853 0.000 1.000
#> SRR1785306     2   0.000      0.853 0.000 1.000
#> SRR1785307     2   0.000      0.853 0.000 1.000
#> SRR1785302     2   0.000      0.853 0.000 1.000
#> SRR1785303     2   0.000      0.853 0.000 1.000
#> SRR1785308     1   0.000      0.892 1.000 0.000
#> SRR1785309     1   0.000      0.892 1.000 0.000
#> SRR1785310     2   0.999      0.258 0.484 0.516
#> SRR1785311     2   0.999      0.258 0.484 0.516
#> SRR1785312     1   0.000      0.892 1.000 0.000
#> SRR1785313     1   0.000      0.892 1.000 0.000
#> SRR1785314     2   0.000      0.853 0.000 1.000
#> SRR1785315     2   0.000      0.853 0.000 1.000
#> SRR1785318     2   0.000      0.853 0.000 1.000
#> SRR1785319     2   0.000      0.853 0.000 1.000
#> SRR1785316     1   0.000      0.892 1.000 0.000
#> SRR1785317     1   0.000      0.892 1.000 0.000
#> SRR1785324     2   0.000      0.853 0.000 1.000
#> SRR1785325     2   0.000      0.853 0.000 1.000
#> SRR1785320     1   0.000      0.892 1.000 0.000
#> SRR1785321     1   0.000      0.892 1.000 0.000
#> SRR1785322     1   0.000      0.892 1.000 0.000
#> SRR1785323     1   0.000      0.892 1.000 0.000

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     3  0.5591      0.533 0.000 0.304 0.696
#> SRR1785239     3  0.5591      0.533 0.000 0.304 0.696
#> SRR1785240     3  0.5335      0.695 0.232 0.008 0.760
#> SRR1785241     3  0.5335      0.695 0.232 0.008 0.760
#> SRR1785242     3  0.0592      0.845 0.000 0.012 0.988
#> SRR1785243     3  0.0592      0.845 0.000 0.012 0.988
#> SRR1785244     1  0.0592      0.774 0.988 0.000 0.012
#> SRR1785245     1  0.0592      0.774 0.988 0.000 0.012
#> SRR1785246     3  0.0000      0.846 0.000 0.000 1.000
#> SRR1785247     3  0.0000      0.846 0.000 0.000 1.000
#> SRR1785248     2  0.3551      0.851 0.000 0.868 0.132
#> SRR1785250     3  0.2796      0.826 0.092 0.000 0.908
#> SRR1785251     3  0.2796      0.826 0.092 0.000 0.908
#> SRR1785252     3  0.0592      0.845 0.000 0.012 0.988
#> SRR1785253     3  0.0592      0.845 0.000 0.012 0.988
#> SRR1785254     2  0.3941      0.821 0.156 0.844 0.000
#> SRR1785255     2  0.3941      0.821 0.156 0.844 0.000
#> SRR1785256     1  0.4346      0.718 0.816 0.000 0.184
#> SRR1785257     1  0.4346      0.718 0.816 0.000 0.184
#> SRR1785258     3  0.4452      0.728 0.192 0.000 0.808
#> SRR1785259     3  0.4452      0.728 0.192 0.000 0.808
#> SRR1785262     3  0.0237      0.846 0.004 0.000 0.996
#> SRR1785263     3  0.0237      0.846 0.004 0.000 0.996
#> SRR1785260     1  0.4700      0.699 0.812 0.180 0.008
#> SRR1785261     1  0.4700      0.699 0.812 0.180 0.008
#> SRR1785264     2  0.3116      0.878 0.000 0.892 0.108
#> SRR1785265     2  0.3116      0.878 0.000 0.892 0.108
#> SRR1785266     2  0.0424      0.959 0.000 0.992 0.008
#> SRR1785267     2  0.0424      0.959 0.000 0.992 0.008
#> SRR1785268     1  0.5397      0.646 0.720 0.000 0.280
#> SRR1785269     1  0.5397      0.646 0.720 0.000 0.280
#> SRR1785270     2  0.3375      0.882 0.100 0.892 0.008
#> SRR1785271     2  0.3375      0.882 0.100 0.892 0.008
#> SRR1785272     3  0.2878      0.824 0.096 0.000 0.904
#> SRR1785273     3  0.2878      0.824 0.096 0.000 0.904
#> SRR1785276     3  0.4654      0.714 0.208 0.000 0.792
#> SRR1785277     3  0.4654      0.714 0.208 0.000 0.792
#> SRR1785274     3  0.0892      0.843 0.000 0.020 0.980
#> SRR1785275     3  0.0892      0.843 0.000 0.020 0.980
#> SRR1785280     2  0.0000      0.961 0.000 1.000 0.000
#> SRR1785281     2  0.0000      0.961 0.000 1.000 0.000
#> SRR1785278     1  0.5254      0.665 0.736 0.000 0.264
#> SRR1785279     1  0.5254      0.665 0.736 0.000 0.264
#> SRR1785282     1  0.5138      0.676 0.748 0.000 0.252
#> SRR1785283     1  0.5138      0.676 0.748 0.000 0.252
#> SRR1785284     1  0.4174      0.722 0.872 0.036 0.092
#> SRR1785285     1  0.4174      0.722 0.872 0.036 0.092
#> SRR1785286     1  0.6922      0.644 0.720 0.200 0.080
#> SRR1785287     1  0.6922      0.644 0.720 0.200 0.080
#> SRR1785288     1  0.0592      0.774 0.988 0.000 0.012
#> SRR1785289     1  0.0592      0.774 0.988 0.000 0.012
#> SRR1785290     2  0.0000      0.961 0.000 1.000 0.000
#> SRR1785291     2  0.0000      0.961 0.000 1.000 0.000
#> SRR1785296     2  0.1182      0.954 0.012 0.976 0.012
#> SRR1785297     2  0.1182      0.954 0.012 0.976 0.012
#> SRR1785292     2  0.0000      0.961 0.000 1.000 0.000
#> SRR1785293     2  0.0000      0.961 0.000 1.000 0.000
#> SRR1785294     1  0.5220      0.676 0.780 0.208 0.012
#> SRR1785295     1  0.5220      0.676 0.780 0.208 0.012
#> SRR1785298     2  0.0661      0.958 0.004 0.988 0.008
#> SRR1785299     2  0.0661      0.958 0.004 0.988 0.008
#> SRR1785300     1  0.0592      0.774 0.988 0.000 0.012
#> SRR1785301     1  0.0592      0.774 0.988 0.000 0.012
#> SRR1785304     2  0.1289      0.948 0.032 0.968 0.000
#> SRR1785305     2  0.1289      0.948 0.032 0.968 0.000
#> SRR1785306     2  0.0424      0.960 0.008 0.992 0.000
#> SRR1785307     2  0.0424      0.960 0.008 0.992 0.000
#> SRR1785302     2  0.0424      0.960 0.008 0.992 0.000
#> SRR1785303     2  0.0424      0.960 0.008 0.992 0.000
#> SRR1785308     3  0.2796      0.826 0.092 0.000 0.908
#> SRR1785309     3  0.2796      0.826 0.092 0.000 0.908
#> SRR1785310     1  0.4346      0.701 0.816 0.184 0.000
#> SRR1785311     1  0.4346      0.701 0.816 0.184 0.000
#> SRR1785312     1  0.5397      0.646 0.720 0.000 0.280
#> SRR1785313     1  0.5397      0.646 0.720 0.000 0.280
#> SRR1785314     2  0.0424      0.960 0.008 0.992 0.000
#> SRR1785315     2  0.0424      0.960 0.008 0.992 0.000
#> SRR1785318     2  0.0000      0.961 0.000 1.000 0.000
#> SRR1785319     2  0.0000      0.961 0.000 1.000 0.000
#> SRR1785316     1  0.0592      0.774 0.988 0.000 0.012
#> SRR1785317     1  0.0592      0.774 0.988 0.000 0.012
#> SRR1785324     2  0.0424      0.960 0.008 0.992 0.000
#> SRR1785325     2  0.0424      0.960 0.008 0.992 0.000
#> SRR1785320     1  0.5363      0.651 0.724 0.000 0.276
#> SRR1785321     1  0.5363      0.651 0.724 0.000 0.276
#> SRR1785322     3  0.5058      0.656 0.244 0.000 0.756
#> SRR1785323     3  0.5058      0.656 0.244 0.000 0.756

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     3  0.4500     0.4835 0.000 0.316 0.684 0.000
#> SRR1785239     3  0.4500     0.4835 0.000 0.316 0.684 0.000
#> SRR1785240     3  0.6261     0.5106 0.060 0.016 0.652 0.272
#> SRR1785241     3  0.6261     0.5106 0.060 0.016 0.652 0.272
#> SRR1785242     3  0.0592     0.7377 0.000 0.016 0.984 0.000
#> SRR1785243     3  0.0592     0.7377 0.000 0.016 0.984 0.000
#> SRR1785244     1  0.3649     0.7235 0.796 0.000 0.000 0.204
#> SRR1785245     1  0.3649     0.7235 0.796 0.000 0.000 0.204
#> SRR1785246     3  0.1854     0.7421 0.048 0.000 0.940 0.012
#> SRR1785247     3  0.1854     0.7421 0.048 0.000 0.940 0.012
#> SRR1785248     2  0.0817     0.8278 0.000 0.976 0.024 0.000
#> SRR1785250     3  0.4621     0.6048 0.284 0.000 0.708 0.008
#> SRR1785251     3  0.4621     0.6048 0.284 0.000 0.708 0.008
#> SRR1785252     3  0.0592     0.7377 0.000 0.016 0.984 0.000
#> SRR1785253     3  0.0592     0.7377 0.000 0.016 0.984 0.000
#> SRR1785254     2  0.4034     0.7398 0.004 0.804 0.012 0.180
#> SRR1785255     2  0.4034     0.7398 0.004 0.804 0.012 0.180
#> SRR1785256     1  0.0817     0.8484 0.976 0.000 0.000 0.024
#> SRR1785257     1  0.0817     0.8484 0.976 0.000 0.000 0.024
#> SRR1785258     3  0.4776     0.3503 0.376 0.000 0.624 0.000
#> SRR1785259     3  0.4776     0.3503 0.376 0.000 0.624 0.000
#> SRR1785262     3  0.1706     0.7362 0.016 0.000 0.948 0.036
#> SRR1785263     3  0.1706     0.7362 0.016 0.000 0.948 0.036
#> SRR1785260     4  0.4669     0.7807 0.100 0.092 0.004 0.804
#> SRR1785261     4  0.4669     0.7807 0.100 0.092 0.004 0.804
#> SRR1785264     2  0.0707     0.8301 0.000 0.980 0.020 0.000
#> SRR1785265     2  0.0707     0.8301 0.000 0.980 0.020 0.000
#> SRR1785266     2  0.0707     0.8301 0.000 0.980 0.020 0.000
#> SRR1785267     2  0.0707     0.8301 0.000 0.980 0.020 0.000
#> SRR1785268     1  0.0592     0.8495 0.984 0.000 0.016 0.000
#> SRR1785269     1  0.0592     0.8495 0.984 0.000 0.016 0.000
#> SRR1785270     2  0.4898     0.6749 0.000 0.716 0.024 0.260
#> SRR1785271     2  0.4898     0.6749 0.000 0.716 0.024 0.260
#> SRR1785272     3  0.5277     0.2640 0.460 0.000 0.532 0.008
#> SRR1785273     3  0.5277     0.2640 0.460 0.000 0.532 0.008
#> SRR1785276     1  0.4769     0.4363 0.684 0.000 0.308 0.008
#> SRR1785277     1  0.4769     0.4363 0.684 0.000 0.308 0.008
#> SRR1785274     3  0.3448     0.6588 0.004 0.000 0.828 0.168
#> SRR1785275     3  0.3448     0.6588 0.004 0.000 0.828 0.168
#> SRR1785280     2  0.0592     0.8309 0.000 0.984 0.016 0.000
#> SRR1785281     2  0.0592     0.8309 0.000 0.984 0.016 0.000
#> SRR1785278     1  0.0469     0.8504 0.988 0.000 0.012 0.000
#> SRR1785279     1  0.0469     0.8504 0.988 0.000 0.012 0.000
#> SRR1785282     1  0.0188     0.8506 0.996 0.000 0.004 0.000
#> SRR1785283     1  0.0188     0.8506 0.996 0.000 0.004 0.000
#> SRR1785284     4  0.4739     0.5874 0.160 0.024 0.024 0.792
#> SRR1785285     4  0.4739     0.5874 0.160 0.024 0.024 0.792
#> SRR1785286     4  0.0992     0.7026 0.004 0.008 0.012 0.976
#> SRR1785287     4  0.0992     0.7026 0.004 0.008 0.012 0.976
#> SRR1785288     1  0.3873     0.6920 0.772 0.000 0.000 0.228
#> SRR1785289     1  0.3873     0.6920 0.772 0.000 0.000 0.228
#> SRR1785290     2  0.0469     0.8304 0.000 0.988 0.012 0.000
#> SRR1785291     2  0.0469     0.8304 0.000 0.988 0.012 0.000
#> SRR1785296     4  0.5189     0.5469 0.000 0.372 0.012 0.616
#> SRR1785297     4  0.5189     0.5469 0.000 0.372 0.012 0.616
#> SRR1785292     2  0.0188     0.8280 0.000 0.996 0.000 0.004
#> SRR1785293     2  0.0188     0.8280 0.000 0.996 0.000 0.004
#> SRR1785294     4  0.4762     0.7765 0.080 0.120 0.004 0.796
#> SRR1785295     4  0.4762     0.7765 0.080 0.120 0.004 0.796
#> SRR1785298     2  0.5088    -0.0264 0.000 0.572 0.004 0.424
#> SRR1785299     2  0.5088    -0.0264 0.000 0.572 0.004 0.424
#> SRR1785300     1  0.3024     0.7829 0.852 0.000 0.000 0.148
#> SRR1785301     1  0.3024     0.7829 0.852 0.000 0.000 0.148
#> SRR1785304     4  0.5039     0.4873 0.004 0.404 0.000 0.592
#> SRR1785305     4  0.5039     0.4873 0.004 0.404 0.000 0.592
#> SRR1785306     2  0.4399     0.7113 0.000 0.760 0.016 0.224
#> SRR1785307     2  0.4399     0.7113 0.000 0.760 0.016 0.224
#> SRR1785302     2  0.3726     0.6215 0.000 0.788 0.000 0.212
#> SRR1785303     2  0.3726     0.6215 0.000 0.788 0.000 0.212
#> SRR1785308     3  0.4483     0.6040 0.284 0.004 0.712 0.000
#> SRR1785309     3  0.4483     0.6040 0.284 0.004 0.712 0.000
#> SRR1785310     4  0.4487     0.7813 0.100 0.092 0.000 0.808
#> SRR1785311     4  0.4487     0.7813 0.100 0.092 0.000 0.808
#> SRR1785312     1  0.0592     0.8495 0.984 0.000 0.016 0.000
#> SRR1785313     1  0.0592     0.8495 0.984 0.000 0.016 0.000
#> SRR1785314     2  0.4690     0.6824 0.000 0.724 0.016 0.260
#> SRR1785315     2  0.4690     0.6824 0.000 0.724 0.016 0.260
#> SRR1785318     2  0.0592     0.8309 0.000 0.984 0.016 0.000
#> SRR1785319     2  0.0592     0.8309 0.000 0.984 0.016 0.000
#> SRR1785316     1  0.1022     0.8455 0.968 0.000 0.000 0.032
#> SRR1785317     1  0.1022     0.8455 0.968 0.000 0.000 0.032
#> SRR1785324     2  0.0188     0.8280 0.000 0.996 0.000 0.004
#> SRR1785325     2  0.0188     0.8280 0.000 0.996 0.000 0.004
#> SRR1785320     1  0.0592     0.8495 0.984 0.000 0.016 0.000
#> SRR1785321     1  0.0592     0.8495 0.984 0.000 0.016 0.000
#> SRR1785322     1  0.4877     0.3559 0.664 0.000 0.328 0.008
#> SRR1785323     1  0.4877     0.3559 0.664 0.000 0.328 0.008

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     3  0.4470     0.3924 0.000 0.372 0.616 0.000 0.012
#> SRR1785239     3  0.4470     0.3924 0.000 0.372 0.616 0.000 0.012
#> SRR1785240     5  0.3734     0.6030 0.004 0.000 0.240 0.004 0.752
#> SRR1785241     5  0.3734     0.6030 0.004 0.000 0.240 0.004 0.752
#> SRR1785242     3  0.0693     0.7170 0.000 0.012 0.980 0.000 0.008
#> SRR1785243     3  0.0693     0.7170 0.000 0.012 0.980 0.000 0.008
#> SRR1785244     1  0.4433     0.7241 0.740 0.000 0.000 0.200 0.060
#> SRR1785245     1  0.4433     0.7241 0.740 0.000 0.000 0.200 0.060
#> SRR1785246     3  0.2196     0.7291 0.056 0.000 0.916 0.004 0.024
#> SRR1785247     3  0.2196     0.7291 0.056 0.000 0.916 0.004 0.024
#> SRR1785248     2  0.0865     0.8607 0.000 0.972 0.024 0.000 0.004
#> SRR1785250     3  0.4153     0.6741 0.240 0.000 0.736 0.004 0.020
#> SRR1785251     3  0.4153     0.6741 0.240 0.000 0.736 0.004 0.020
#> SRR1785252     3  0.0693     0.7170 0.000 0.012 0.980 0.000 0.008
#> SRR1785253     3  0.0693     0.7170 0.000 0.012 0.980 0.000 0.008
#> SRR1785254     5  0.4331     0.2924 0.000 0.400 0.000 0.004 0.596
#> SRR1785255     5  0.4331     0.2924 0.000 0.400 0.000 0.004 0.596
#> SRR1785256     1  0.2568     0.7993 0.900 0.000 0.012 0.064 0.024
#> SRR1785257     1  0.2568     0.7993 0.900 0.000 0.012 0.064 0.024
#> SRR1785258     3  0.4798     0.0985 0.440 0.000 0.540 0.000 0.020
#> SRR1785259     3  0.4798     0.0985 0.440 0.000 0.540 0.000 0.020
#> SRR1785262     3  0.2523     0.7137 0.028 0.000 0.908 0.024 0.040
#> SRR1785263     3  0.2523     0.7137 0.028 0.000 0.908 0.024 0.040
#> SRR1785260     4  0.0451     0.8562 0.004 0.000 0.000 0.988 0.008
#> SRR1785261     4  0.0451     0.8562 0.004 0.000 0.000 0.988 0.008
#> SRR1785264     2  0.1082     0.8577 0.000 0.964 0.028 0.000 0.008
#> SRR1785265     2  0.1082     0.8577 0.000 0.964 0.028 0.000 0.008
#> SRR1785266     2  0.0579     0.8729 0.000 0.984 0.008 0.000 0.008
#> SRR1785267     2  0.0579     0.8729 0.000 0.984 0.008 0.000 0.008
#> SRR1785268     1  0.0693     0.8009 0.980 0.000 0.008 0.000 0.012
#> SRR1785269     1  0.0693     0.8009 0.980 0.000 0.008 0.000 0.012
#> SRR1785270     5  0.3048     0.6978 0.000 0.176 0.000 0.004 0.820
#> SRR1785271     5  0.3048     0.6978 0.000 0.176 0.000 0.004 0.820
#> SRR1785272     3  0.4607     0.5150 0.368 0.000 0.616 0.004 0.012
#> SRR1785273     3  0.4607     0.5150 0.368 0.000 0.616 0.004 0.012
#> SRR1785276     1  0.5898     0.1226 0.548 0.004 0.348 0.000 0.100
#> SRR1785277     1  0.5898     0.1226 0.548 0.004 0.348 0.000 0.100
#> SRR1785274     5  0.4359     0.4014 0.004 0.000 0.412 0.000 0.584
#> SRR1785275     5  0.4359     0.4014 0.004 0.000 0.412 0.000 0.584
#> SRR1785280     2  0.0290     0.8753 0.000 0.992 0.000 0.000 0.008
#> SRR1785281     2  0.0290     0.8753 0.000 0.992 0.000 0.000 0.008
#> SRR1785278     1  0.0451     0.8048 0.988 0.000 0.000 0.008 0.004
#> SRR1785279     1  0.0451     0.8048 0.988 0.000 0.000 0.008 0.004
#> SRR1785282     1  0.0609     0.8069 0.980 0.000 0.000 0.020 0.000
#> SRR1785283     1  0.0609     0.8069 0.980 0.000 0.000 0.020 0.000
#> SRR1785284     5  0.2519     0.6341 0.016 0.000 0.000 0.100 0.884
#> SRR1785285     5  0.2519     0.6341 0.016 0.000 0.000 0.100 0.884
#> SRR1785286     5  0.4297     0.1786 0.000 0.000 0.000 0.472 0.528
#> SRR1785287     5  0.4297     0.1786 0.000 0.000 0.000 0.472 0.528
#> SRR1785288     1  0.4830     0.6655 0.684 0.000 0.000 0.256 0.060
#> SRR1785289     1  0.4830     0.6655 0.684 0.000 0.000 0.256 0.060
#> SRR1785290     2  0.1117     0.8681 0.000 0.964 0.000 0.016 0.020
#> SRR1785291     2  0.1117     0.8681 0.000 0.964 0.000 0.016 0.020
#> SRR1785296     4  0.4041     0.7632 0.000 0.176 0.004 0.780 0.040
#> SRR1785297     4  0.4041     0.7632 0.000 0.176 0.004 0.780 0.040
#> SRR1785292     2  0.0798     0.8749 0.000 0.976 0.000 0.008 0.016
#> SRR1785293     2  0.0798     0.8749 0.000 0.976 0.000 0.008 0.016
#> SRR1785294     4  0.0162     0.8585 0.004 0.000 0.000 0.996 0.000
#> SRR1785295     4  0.0162     0.8585 0.004 0.000 0.000 0.996 0.000
#> SRR1785298     2  0.5979     0.2496 0.000 0.520 0.000 0.360 0.120
#> SRR1785299     2  0.5979     0.2496 0.000 0.520 0.000 0.360 0.120
#> SRR1785300     1  0.3795     0.7470 0.780 0.000 0.000 0.192 0.028
#> SRR1785301     1  0.3795     0.7470 0.780 0.000 0.000 0.192 0.028
#> SRR1785304     4  0.3630     0.7523 0.000 0.204 0.000 0.780 0.016
#> SRR1785305     4  0.3630     0.7523 0.000 0.204 0.000 0.780 0.016
#> SRR1785306     5  0.3906     0.6172 0.000 0.292 0.000 0.004 0.704
#> SRR1785307     5  0.3906     0.6172 0.000 0.292 0.000 0.004 0.704
#> SRR1785302     2  0.5779     0.5356 0.000 0.616 0.000 0.212 0.172
#> SRR1785303     2  0.5779     0.5356 0.000 0.616 0.000 0.212 0.172
#> SRR1785308     3  0.3851     0.6929 0.212 0.004 0.768 0.000 0.016
#> SRR1785309     3  0.3851     0.6929 0.212 0.004 0.768 0.000 0.016
#> SRR1785310     4  0.0324     0.8582 0.004 0.000 0.000 0.992 0.004
#> SRR1785311     4  0.0324     0.8582 0.004 0.000 0.000 0.992 0.004
#> SRR1785312     1  0.0798     0.8014 0.976 0.000 0.008 0.000 0.016
#> SRR1785313     1  0.0798     0.8014 0.976 0.000 0.008 0.000 0.016
#> SRR1785314     5  0.3551     0.6791 0.000 0.220 0.000 0.008 0.772
#> SRR1785315     5  0.3551     0.6791 0.000 0.220 0.000 0.008 0.772
#> SRR1785318     2  0.0510     0.8749 0.000 0.984 0.000 0.000 0.016
#> SRR1785319     2  0.0510     0.8749 0.000 0.984 0.000 0.000 0.016
#> SRR1785316     1  0.2358     0.7962 0.888 0.000 0.000 0.104 0.008
#> SRR1785317     1  0.2358     0.7962 0.888 0.000 0.000 0.104 0.008
#> SRR1785324     2  0.0898     0.8739 0.000 0.972 0.000 0.008 0.020
#> SRR1785325     2  0.0898     0.8739 0.000 0.972 0.000 0.008 0.020
#> SRR1785320     1  0.0798     0.8022 0.976 0.000 0.008 0.000 0.016
#> SRR1785321     1  0.0798     0.8022 0.976 0.000 0.008 0.000 0.016
#> SRR1785322     1  0.4822     0.2231 0.632 0.000 0.340 0.012 0.016
#> SRR1785323     1  0.4822     0.2231 0.632 0.000 0.340 0.012 0.016

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5 p6
#> SRR1785238     3  0.4884      0.383 0.000 0.324 0.608 0.000 0.008 NA
#> SRR1785239     3  0.4884      0.383 0.000 0.324 0.608 0.000 0.008 NA
#> SRR1785240     5  0.4464      0.584 0.000 0.000 0.140 0.000 0.712 NA
#> SRR1785241     5  0.4464      0.584 0.000 0.000 0.140 0.000 0.712 NA
#> SRR1785242     3  0.0405      0.670 0.000 0.008 0.988 0.000 0.000 NA
#> SRR1785243     3  0.0405      0.670 0.000 0.008 0.988 0.000 0.000 NA
#> SRR1785244     1  0.5915      0.641 0.572 0.000 0.000 0.108 0.048 NA
#> SRR1785245     1  0.5915      0.641 0.572 0.000 0.000 0.108 0.048 NA
#> SRR1785246     3  0.4799      0.640 0.076 0.000 0.704 0.000 0.028 NA
#> SRR1785247     3  0.4799      0.640 0.076 0.000 0.704 0.000 0.028 NA
#> SRR1785248     2  0.1478      0.827 0.000 0.944 0.032 0.000 0.004 NA
#> SRR1785250     3  0.4772      0.617 0.208 0.000 0.668 0.000 0.000 NA
#> SRR1785251     3  0.4772      0.617 0.208 0.000 0.668 0.000 0.000 NA
#> SRR1785252     3  0.0405      0.670 0.000 0.008 0.988 0.000 0.000 NA
#> SRR1785253     3  0.0405      0.670 0.000 0.008 0.988 0.000 0.000 NA
#> SRR1785254     5  0.6039      0.209 0.000 0.300 0.000 0.000 0.420 NA
#> SRR1785255     5  0.6039      0.209 0.000 0.300 0.000 0.000 0.420 NA
#> SRR1785256     1  0.3716      0.682 0.732 0.000 0.000 0.012 0.008 NA
#> SRR1785257     1  0.3716      0.682 0.732 0.000 0.000 0.012 0.008 NA
#> SRR1785258     3  0.6460      0.172 0.332 0.000 0.416 0.000 0.024 NA
#> SRR1785259     3  0.6460      0.172 0.332 0.000 0.416 0.000 0.024 NA
#> SRR1785262     3  0.4989      0.600 0.016 0.000 0.700 0.028 0.052 NA
#> SRR1785263     3  0.4989      0.600 0.016 0.000 0.700 0.028 0.052 NA
#> SRR1785260     4  0.0717      0.909 0.000 0.000 0.000 0.976 0.008 NA
#> SRR1785261     4  0.0717      0.909 0.000 0.000 0.000 0.976 0.008 NA
#> SRR1785264     2  0.1793      0.823 0.000 0.928 0.032 0.000 0.004 NA
#> SRR1785265     2  0.1793      0.823 0.000 0.928 0.032 0.000 0.004 NA
#> SRR1785266     2  0.1167      0.833 0.000 0.960 0.020 0.000 0.008 NA
#> SRR1785267     2  0.1167      0.833 0.000 0.960 0.020 0.000 0.008 NA
#> SRR1785268     1  0.1556      0.700 0.920 0.000 0.000 0.000 0.000 NA
#> SRR1785269     1  0.1556      0.700 0.920 0.000 0.000 0.000 0.000 NA
#> SRR1785270     5  0.2308      0.692 0.000 0.108 0.004 0.000 0.880 NA
#> SRR1785271     5  0.2308      0.692 0.000 0.108 0.004 0.000 0.880 NA
#> SRR1785272     3  0.5381      0.509 0.296 0.000 0.560 0.000 0.000 NA
#> SRR1785273     3  0.5381      0.509 0.296 0.000 0.560 0.000 0.000 NA
#> SRR1785276     1  0.6574      0.167 0.448 0.004 0.148 0.000 0.048 NA
#> SRR1785277     1  0.6574      0.167 0.448 0.004 0.148 0.000 0.048 NA
#> SRR1785274     5  0.6248      0.163 0.016 0.000 0.388 0.000 0.400 NA
#> SRR1785275     5  0.6248      0.163 0.016 0.000 0.388 0.000 0.400 NA
#> SRR1785280     2  0.0291      0.842 0.000 0.992 0.004 0.000 0.004 NA
#> SRR1785281     2  0.0291      0.842 0.000 0.992 0.004 0.000 0.004 NA
#> SRR1785278     1  0.0865      0.713 0.964 0.000 0.000 0.000 0.000 NA
#> SRR1785279     1  0.0865      0.713 0.964 0.000 0.000 0.000 0.000 NA
#> SRR1785282     1  0.1267      0.721 0.940 0.000 0.000 0.000 0.000 NA
#> SRR1785283     1  0.1267      0.721 0.940 0.000 0.000 0.000 0.000 NA
#> SRR1785284     5  0.2622      0.659 0.000 0.000 0.004 0.024 0.868 NA
#> SRR1785285     5  0.2622      0.659 0.000 0.000 0.004 0.024 0.868 NA
#> SRR1785286     5  0.5308      0.338 0.000 0.000 0.004 0.352 0.544 NA
#> SRR1785287     5  0.5308      0.338 0.000 0.000 0.004 0.352 0.544 NA
#> SRR1785288     1  0.6250      0.605 0.532 0.000 0.000 0.164 0.044 NA
#> SRR1785289     1  0.6250      0.605 0.532 0.000 0.000 0.164 0.044 NA
#> SRR1785290     2  0.1503      0.830 0.000 0.944 0.000 0.016 0.008 NA
#> SRR1785291     2  0.1503      0.830 0.000 0.944 0.000 0.016 0.008 NA
#> SRR1785296     4  0.3756      0.827 0.000 0.080 0.012 0.824 0.024 NA
#> SRR1785297     4  0.3756      0.827 0.000 0.080 0.012 0.824 0.024 NA
#> SRR1785292     2  0.0520      0.841 0.000 0.984 0.000 0.000 0.008 NA
#> SRR1785293     2  0.0520      0.841 0.000 0.984 0.000 0.000 0.008 NA
#> SRR1785294     4  0.0000      0.911 0.000 0.000 0.000 1.000 0.000 NA
#> SRR1785295     4  0.0000      0.911 0.000 0.000 0.000 1.000 0.000 NA
#> SRR1785298     2  0.6869      0.233 0.000 0.428 0.000 0.288 0.064 NA
#> SRR1785299     2  0.6869      0.233 0.000 0.428 0.000 0.288 0.064 NA
#> SRR1785300     1  0.4954      0.664 0.676 0.000 0.000 0.100 0.016 NA
#> SRR1785301     1  0.4954      0.664 0.676 0.000 0.000 0.100 0.016 NA
#> SRR1785304     4  0.2325      0.862 0.000 0.100 0.000 0.884 0.008 NA
#> SRR1785305     4  0.2325      0.862 0.000 0.100 0.000 0.884 0.008 NA
#> SRR1785306     5  0.3839      0.630 0.000 0.212 0.000 0.004 0.748 NA
#> SRR1785307     5  0.3839      0.630 0.000 0.212 0.000 0.004 0.748 NA
#> SRR1785302     2  0.7205      0.241 0.000 0.432 0.000 0.132 0.192 NA
#> SRR1785303     2  0.7205      0.241 0.000 0.432 0.000 0.132 0.192 NA
#> SRR1785308     3  0.4079      0.646 0.172 0.000 0.744 0.000 0.000 NA
#> SRR1785309     3  0.4079      0.646 0.172 0.000 0.744 0.000 0.000 NA
#> SRR1785310     4  0.0806      0.907 0.000 0.000 0.000 0.972 0.008 NA
#> SRR1785311     4  0.0806      0.907 0.000 0.000 0.000 0.972 0.008 NA
#> SRR1785312     1  0.2092      0.703 0.876 0.000 0.000 0.000 0.000 NA
#> SRR1785313     1  0.2092      0.703 0.876 0.000 0.000 0.000 0.000 NA
#> SRR1785314     5  0.3370      0.676 0.000 0.140 0.000 0.004 0.812 NA
#> SRR1785315     5  0.3370      0.676 0.000 0.140 0.000 0.004 0.812 NA
#> SRR1785318     2  0.0260      0.842 0.000 0.992 0.000 0.000 0.008 NA
#> SRR1785319     2  0.0260      0.842 0.000 0.992 0.000 0.000 0.008 NA
#> SRR1785316     1  0.4257      0.708 0.728 0.000 0.000 0.060 0.008 NA
#> SRR1785317     1  0.4257      0.708 0.728 0.000 0.000 0.060 0.008 NA
#> SRR1785324     2  0.0909      0.836 0.000 0.968 0.000 0.000 0.020 NA
#> SRR1785325     2  0.0909      0.836 0.000 0.968 0.000 0.000 0.020 NA
#> SRR1785320     1  0.2595      0.691 0.836 0.000 0.000 0.000 0.004 NA
#> SRR1785321     1  0.2595      0.691 0.836 0.000 0.000 0.000 0.004 NA
#> SRR1785322     1  0.5178      0.108 0.580 0.000 0.304 0.000 0.000 NA
#> SRR1785323     1  0.5178      0.108 0.580 0.000 0.304 0.000 0.000 NA

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-MAD-skmeans-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-MAD-skmeans-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-MAD-skmeans-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-MAD-skmeans-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-MAD-skmeans-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-MAD-skmeans-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-MAD-skmeans-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-MAD-skmeans-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-MAD-skmeans-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-MAD-skmeans-membership-heatmap-5

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)

plot of chunk tab-MAD-skmeans-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-MAD-skmeans-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-MAD-skmeans-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-MAD-skmeans-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-MAD-skmeans-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-MAD-skmeans-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-MAD-skmeans-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-MAD-skmeans-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-MAD-skmeans-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-MAD-skmeans-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk MAD-skmeans-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-MAD-skmeans-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-MAD-skmeans-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-MAD-skmeans-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-MAD-skmeans-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-MAD-skmeans-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk MAD-skmeans-collect-classes

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.


MAD:pam

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#>   Subgroups are detected by 'pam' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 2.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk MAD-pam-collect-plots

The plots are:

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:

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)

plot of chunk MAD-pam-select-partition-number

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 0.700           0.862       0.931         0.4975 0.500   0.500
#> 3 3 0.627           0.819       0.906         0.2364 0.878   0.760
#> 4 4 0.735           0.807       0.913         0.1596 0.857   0.652
#> 5 5 0.765           0.742       0.866         0.0857 0.903   0.679
#> 6 6 0.779           0.763       0.859         0.0381 0.971   0.870

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2  0.2043     0.9400 0.032 0.968
#> SRR1785239     2  0.2043     0.9400 0.032 0.968
#> SRR1785240     1  0.7674     0.7184 0.776 0.224
#> SRR1785241     1  0.6887     0.7693 0.816 0.184
#> SRR1785242     2  0.2043     0.9400 0.032 0.968
#> SRR1785243     2  0.2043     0.9400 0.032 0.968
#> SRR1785244     1  0.0000     0.9173 1.000 0.000
#> SRR1785245     1  0.0000     0.9173 1.000 0.000
#> SRR1785246     2  0.2603     0.9383 0.044 0.956
#> SRR1785247     2  0.2603     0.9383 0.044 0.956
#> SRR1785248     2  0.0000     0.9300 0.000 1.000
#> SRR1785250     2  0.7219     0.7686 0.200 0.800
#> SRR1785251     2  0.7219     0.7686 0.200 0.800
#> SRR1785252     2  0.2043     0.9400 0.032 0.968
#> SRR1785253     2  0.2043     0.9400 0.032 0.968
#> SRR1785254     1  0.4022     0.8824 0.920 0.080
#> SRR1785255     1  0.1414     0.9134 0.980 0.020
#> SRR1785256     1  0.0000     0.9173 1.000 0.000
#> SRR1785257     1  0.0000     0.9173 1.000 0.000
#> SRR1785258     2  0.2603     0.9383 0.044 0.956
#> SRR1785259     2  0.2603     0.9383 0.044 0.956
#> SRR1785262     1  0.0000     0.9173 1.000 0.000
#> SRR1785263     1  0.0376     0.9165 0.996 0.004
#> SRR1785260     1  0.0000     0.9173 1.000 0.000
#> SRR1785261     1  0.0000     0.9173 1.000 0.000
#> SRR1785264     2  0.0000     0.9300 0.000 1.000
#> SRR1785265     2  0.0000     0.9300 0.000 1.000
#> SRR1785266     2  0.0000     0.9300 0.000 1.000
#> SRR1785267     2  0.0000     0.9300 0.000 1.000
#> SRR1785268     1  0.7453     0.7315 0.788 0.212
#> SRR1785269     1  0.8207     0.6647 0.744 0.256
#> SRR1785270     2  0.0000     0.9300 0.000 1.000
#> SRR1785271     2  0.0000     0.9300 0.000 1.000
#> SRR1785272     2  0.3114     0.9315 0.056 0.944
#> SRR1785273     2  0.3274     0.9286 0.060 0.940
#> SRR1785276     2  0.2043     0.9400 0.032 0.968
#> SRR1785277     2  0.2043     0.9400 0.032 0.968
#> SRR1785274     2  0.2603     0.9383 0.044 0.956
#> SRR1785275     2  0.2603     0.9383 0.044 0.956
#> SRR1785280     2  0.0000     0.9300 0.000 1.000
#> SRR1785281     2  0.0000     0.9300 0.000 1.000
#> SRR1785278     1  0.8207     0.6650 0.744 0.256
#> SRR1785279     1  0.7056     0.7579 0.808 0.192
#> SRR1785282     2  0.5059     0.8792 0.112 0.888
#> SRR1785283     2  0.6623     0.8078 0.172 0.828
#> SRR1785284     1  0.0000     0.9173 1.000 0.000
#> SRR1785285     1  0.0000     0.9173 1.000 0.000
#> SRR1785286     1  0.0000     0.9173 1.000 0.000
#> SRR1785287     1  0.0000     0.9173 1.000 0.000
#> SRR1785288     1  0.0000     0.9173 1.000 0.000
#> SRR1785289     1  0.0000     0.9173 1.000 0.000
#> SRR1785290     2  0.1633     0.9380 0.024 0.976
#> SRR1785291     2  0.1633     0.9380 0.024 0.976
#> SRR1785296     1  0.0938     0.9147 0.988 0.012
#> SRR1785297     1  0.0938     0.9147 0.988 0.012
#> SRR1785292     2  0.9944     0.0635 0.456 0.544
#> SRR1785293     2  0.9866     0.1533 0.432 0.568
#> SRR1785294     1  0.0938     0.9147 0.988 0.012
#> SRR1785295     1  0.0672     0.9158 0.992 0.008
#> SRR1785298     1  0.0938     0.9147 0.988 0.012
#> SRR1785299     1  0.0938     0.9147 0.988 0.012
#> SRR1785300     1  0.0000     0.9173 1.000 0.000
#> SRR1785301     1  0.0000     0.9173 1.000 0.000
#> SRR1785304     1  0.1414     0.9111 0.980 0.020
#> SRR1785305     1  0.1414     0.9111 0.980 0.020
#> SRR1785306     1  0.2603     0.9001 0.956 0.044
#> SRR1785307     1  0.5294     0.8400 0.880 0.120
#> SRR1785302     1  0.1414     0.9111 0.980 0.020
#> SRR1785303     1  0.1414     0.9111 0.980 0.020
#> SRR1785308     2  0.2778     0.9366 0.048 0.952
#> SRR1785309     2  0.2778     0.9366 0.048 0.952
#> SRR1785310     1  0.0000     0.9173 1.000 0.000
#> SRR1785311     1  0.0000     0.9173 1.000 0.000
#> SRR1785312     1  0.0376     0.9163 0.996 0.004
#> SRR1785313     1  0.0938     0.9130 0.988 0.012
#> SRR1785314     1  0.9087     0.5717 0.676 0.324
#> SRR1785315     1  0.9608     0.4433 0.616 0.384
#> SRR1785318     2  0.0000     0.9300 0.000 1.000
#> SRR1785319     2  0.0000     0.9300 0.000 1.000
#> SRR1785316     1  0.0000     0.9173 1.000 0.000
#> SRR1785317     1  0.0000     0.9173 1.000 0.000
#> SRR1785324     1  0.9552     0.4637 0.624 0.376
#> SRR1785325     1  0.9491     0.4823 0.632 0.368
#> SRR1785320     1  0.6801     0.7722 0.820 0.180
#> SRR1785321     1  0.6712     0.7768 0.824 0.176
#> SRR1785322     2  0.2603     0.9383 0.044 0.956
#> SRR1785323     2  0.2603     0.9383 0.044 0.956

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     3  0.4555      0.751 0.200 0.000 0.800
#> SRR1785239     3  0.4555      0.751 0.200 0.000 0.800
#> SRR1785240     1  0.6215      0.510 0.572 0.000 0.428
#> SRR1785241     1  0.6062      0.590 0.616 0.000 0.384
#> SRR1785242     3  0.0000      0.899 0.000 0.000 1.000
#> SRR1785243     3  0.0000      0.899 0.000 0.000 1.000
#> SRR1785244     1  0.0237      0.863 0.996 0.000 0.004
#> SRR1785245     1  0.0237      0.863 0.996 0.000 0.004
#> SRR1785246     3  0.0000      0.899 0.000 0.000 1.000
#> SRR1785247     3  0.0000      0.899 0.000 0.000 1.000
#> SRR1785248     3  0.4605      0.735 0.000 0.204 0.796
#> SRR1785250     3  0.0000      0.899 0.000 0.000 1.000
#> SRR1785251     3  0.0000      0.899 0.000 0.000 1.000
#> SRR1785252     3  0.0000      0.899 0.000 0.000 1.000
#> SRR1785253     3  0.0000      0.899 0.000 0.000 1.000
#> SRR1785254     1  0.2066      0.836 0.940 0.000 0.060
#> SRR1785255     1  0.0237      0.862 0.996 0.000 0.004
#> SRR1785256     1  0.4605      0.781 0.796 0.000 0.204
#> SRR1785257     1  0.4605      0.781 0.796 0.000 0.204
#> SRR1785258     3  0.0000      0.899 0.000 0.000 1.000
#> SRR1785259     3  0.0000      0.899 0.000 0.000 1.000
#> SRR1785262     1  0.4605      0.781 0.796 0.000 0.204
#> SRR1785263     1  0.4654      0.780 0.792 0.000 0.208
#> SRR1785260     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785261     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785264     3  0.5020      0.742 0.012 0.192 0.796
#> SRR1785265     3  0.5200      0.746 0.020 0.184 0.796
#> SRR1785266     3  0.4931      0.705 0.000 0.232 0.768
#> SRR1785267     3  0.4750      0.723 0.000 0.216 0.784
#> SRR1785268     1  0.6180      0.533 0.584 0.000 0.416
#> SRR1785269     1  0.6280      0.437 0.540 0.000 0.460
#> SRR1785270     2  0.5517      0.624 0.004 0.728 0.268
#> SRR1785271     2  0.5591      0.557 0.000 0.696 0.304
#> SRR1785272     3  0.0000      0.899 0.000 0.000 1.000
#> SRR1785273     3  0.0000      0.899 0.000 0.000 1.000
#> SRR1785276     3  0.4555      0.751 0.200 0.000 0.800
#> SRR1785277     3  0.4555      0.751 0.200 0.000 0.800
#> SRR1785274     3  0.0000      0.899 0.000 0.000 1.000
#> SRR1785275     3  0.0000      0.899 0.000 0.000 1.000
#> SRR1785280     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785281     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785278     1  0.6252      0.468 0.556 0.000 0.444
#> SRR1785279     1  0.6095      0.568 0.608 0.000 0.392
#> SRR1785282     3  0.0237      0.898 0.004 0.000 0.996
#> SRR1785283     3  0.0237      0.898 0.004 0.000 0.996
#> SRR1785284     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785285     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785286     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785287     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785288     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785289     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785290     3  0.4605      0.748 0.204 0.000 0.796
#> SRR1785291     3  0.4605      0.748 0.204 0.000 0.796
#> SRR1785296     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785297     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785292     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785293     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785294     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785295     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785298     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785299     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785300     1  0.4555      0.782 0.800 0.000 0.200
#> SRR1785301     1  0.4555      0.782 0.800 0.000 0.200
#> SRR1785304     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785305     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785306     1  0.3755      0.779 0.872 0.120 0.008
#> SRR1785307     1  0.4519      0.765 0.852 0.116 0.032
#> SRR1785302     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785303     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785308     3  0.0000      0.899 0.000 0.000 1.000
#> SRR1785309     3  0.0000      0.899 0.000 0.000 1.000
#> SRR1785310     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785311     1  0.0000      0.863 1.000 0.000 0.000
#> SRR1785312     1  0.4605      0.781 0.796 0.000 0.204
#> SRR1785313     1  0.4702      0.777 0.788 0.000 0.212
#> SRR1785314     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785315     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785318     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785319     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785316     1  0.0237      0.863 0.996 0.000 0.004
#> SRR1785317     1  0.0892      0.858 0.980 0.000 0.020
#> SRR1785324     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785325     2  0.0000      0.942 0.000 1.000 0.000
#> SRR1785320     1  0.6062      0.589 0.616 0.000 0.384
#> SRR1785321     1  0.6045      0.596 0.620 0.000 0.380
#> SRR1785322     3  0.0237      0.898 0.004 0.000 0.996
#> SRR1785323     3  0.0237      0.898 0.004 0.000 0.996

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     3  0.0000      0.867 0.000 0.000 1.000 0.000
#> SRR1785239     3  0.0000      0.867 0.000 0.000 1.000 0.000
#> SRR1785240     4  0.7064      0.420 0.208 0.000 0.220 0.572
#> SRR1785241     4  0.6785      0.490 0.208 0.000 0.184 0.608
#> SRR1785242     3  0.0000      0.867 0.000 0.000 1.000 0.000
#> SRR1785243     3  0.0000      0.867 0.000 0.000 1.000 0.000
#> SRR1785244     4  0.0707      0.911 0.020 0.000 0.000 0.980
#> SRR1785245     4  0.0592      0.913 0.016 0.000 0.000 0.984
#> SRR1785246     1  0.4830      0.346 0.608 0.000 0.392 0.000
#> SRR1785247     1  0.4776      0.391 0.624 0.000 0.376 0.000
#> SRR1785248     3  0.0000      0.867 0.000 0.000 1.000 0.000
#> SRR1785250     1  0.4304      0.586 0.716 0.000 0.284 0.000
#> SRR1785251     1  0.4250      0.599 0.724 0.000 0.276 0.000
#> SRR1785252     3  0.0000      0.867 0.000 0.000 1.000 0.000
#> SRR1785253     3  0.0000      0.867 0.000 0.000 1.000 0.000
#> SRR1785254     4  0.1474      0.883 0.000 0.000 0.052 0.948
#> SRR1785255     4  0.0188      0.916 0.000 0.000 0.004 0.996
#> SRR1785256     4  0.3726      0.751 0.212 0.000 0.000 0.788
#> SRR1785257     4  0.3801      0.742 0.220 0.000 0.000 0.780
#> SRR1785258     1  0.2345      0.791 0.900 0.000 0.100 0.000
#> SRR1785259     1  0.4103      0.590 0.744 0.000 0.256 0.000
#> SRR1785262     4  0.3688      0.754 0.208 0.000 0.000 0.792
#> SRR1785263     4  0.3870      0.751 0.208 0.000 0.004 0.788
#> SRR1785260     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785261     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785264     3  0.0000      0.867 0.000 0.000 1.000 0.000
#> SRR1785265     3  0.0000      0.867 0.000 0.000 1.000 0.000
#> SRR1785266     3  0.3528      0.739 0.000 0.192 0.808 0.000
#> SRR1785267     3  0.3311      0.760 0.000 0.172 0.828 0.000
#> SRR1785268     1  0.0000      0.858 1.000 0.000 0.000 0.000
#> SRR1785269     1  0.0000      0.858 1.000 0.000 0.000 0.000
#> SRR1785270     2  0.4989      0.310 0.000 0.528 0.472 0.000
#> SRR1785271     2  0.5000      0.247 0.000 0.504 0.496 0.000
#> SRR1785272     3  0.4855      0.347 0.400 0.000 0.600 0.000
#> SRR1785273     3  0.4761      0.423 0.372 0.000 0.628 0.000
#> SRR1785276     3  0.0000      0.867 0.000 0.000 1.000 0.000
#> SRR1785277     3  0.0000      0.867 0.000 0.000 1.000 0.000
#> SRR1785274     3  0.3528      0.747 0.192 0.000 0.808 0.000
#> SRR1785275     3  0.3610      0.738 0.200 0.000 0.800 0.000
#> SRR1785280     2  0.0000      0.902 0.000 1.000 0.000 0.000
#> SRR1785281     2  0.0000      0.902 0.000 1.000 0.000 0.000
#> SRR1785278     1  0.0000      0.858 1.000 0.000 0.000 0.000
#> SRR1785279     1  0.0000      0.858 1.000 0.000 0.000 0.000
#> SRR1785282     1  0.0000      0.858 1.000 0.000 0.000 0.000
#> SRR1785283     1  0.0000      0.858 1.000 0.000 0.000 0.000
#> SRR1785284     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785285     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785286     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785287     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785288     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785289     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785290     3  0.0000      0.867 0.000 0.000 1.000 0.000
#> SRR1785291     3  0.0188      0.865 0.000 0.000 0.996 0.004
#> SRR1785296     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785297     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785292     2  0.0000      0.902 0.000 1.000 0.000 0.000
#> SRR1785293     2  0.0000      0.902 0.000 1.000 0.000 0.000
#> SRR1785294     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785295     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785298     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785299     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785300     4  0.3688      0.754 0.208 0.000 0.000 0.792
#> SRR1785301     4  0.3688      0.754 0.208 0.000 0.000 0.792
#> SRR1785304     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785305     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785306     4  0.3731      0.798 0.000 0.120 0.036 0.844
#> SRR1785307     4  0.5423      0.693 0.000 0.116 0.144 0.740
#> SRR1785302     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785303     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785308     3  0.3219      0.774 0.164 0.000 0.836 0.000
#> SRR1785309     3  0.3356      0.764 0.176 0.000 0.824 0.000
#> SRR1785310     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785311     4  0.0000      0.918 0.000 0.000 0.000 1.000
#> SRR1785312     1  0.0000      0.858 1.000 0.000 0.000 0.000
#> SRR1785313     1  0.0000      0.858 1.000 0.000 0.000 0.000
#> SRR1785314     2  0.0921      0.887 0.000 0.972 0.028 0.000
#> SRR1785315     2  0.0469      0.897 0.000 0.988 0.012 0.000
#> SRR1785318     2  0.0000      0.902 0.000 1.000 0.000 0.000
#> SRR1785319     2  0.0000      0.902 0.000 1.000 0.000 0.000
#> SRR1785316     4  0.0336      0.916 0.008 0.000 0.000 0.992
#> SRR1785317     4  0.0817      0.909 0.024 0.000 0.000 0.976
#> SRR1785324     2  0.0000      0.902 0.000 1.000 0.000 0.000
#> SRR1785325     2  0.0000      0.902 0.000 1.000 0.000 0.000
#> SRR1785320     1  0.0000      0.858 1.000 0.000 0.000 0.000
#> SRR1785321     1  0.0000      0.858 1.000 0.000 0.000 0.000
#> SRR1785322     3  0.3649      0.735 0.204 0.000 0.796 0.000
#> SRR1785323     3  0.3649      0.735 0.204 0.000 0.796 0.000

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     3  0.0000     0.8373 0.000 0.000 1.000 0.000 0.000
#> SRR1785239     3  0.0000     0.8373 0.000 0.000 1.000 0.000 0.000
#> SRR1785240     5  0.1798     0.6090 0.004 0.000 0.064 0.004 0.928
#> SRR1785241     5  0.1857     0.6127 0.004 0.000 0.060 0.008 0.928
#> SRR1785242     3  0.1544     0.8230 0.000 0.000 0.932 0.000 0.068
#> SRR1785243     3  0.1544     0.8230 0.000 0.000 0.932 0.000 0.068
#> SRR1785244     4  0.4151     0.3884 0.004 0.000 0.000 0.652 0.344
#> SRR1785245     4  0.4084     0.4272 0.004 0.000 0.000 0.668 0.328
#> SRR1785246     1  0.6333     0.4396 0.496 0.000 0.328 0.000 0.176
#> SRR1785247     1  0.6284     0.4610 0.508 0.000 0.320 0.000 0.172
#> SRR1785248     3  0.1544     0.8230 0.000 0.000 0.932 0.000 0.068
#> SRR1785250     1  0.6269     0.5396 0.528 0.000 0.188 0.000 0.284
#> SRR1785251     1  0.6111     0.5706 0.560 0.000 0.180 0.000 0.260
#> SRR1785252     3  0.1671     0.8227 0.000 0.000 0.924 0.000 0.076
#> SRR1785253     3  0.1671     0.8227 0.000 0.000 0.924 0.000 0.076
#> SRR1785254     4  0.1341     0.8715 0.000 0.000 0.056 0.944 0.000
#> SRR1785255     4  0.0162     0.9192 0.000 0.000 0.004 0.996 0.000
#> SRR1785256     5  0.3282     0.6985 0.008 0.000 0.000 0.188 0.804
#> SRR1785257     5  0.3282     0.6985 0.008 0.000 0.000 0.188 0.804
#> SRR1785258     5  0.4138     0.4342 0.276 0.000 0.016 0.000 0.708
#> SRR1785259     5  0.4268     0.4426 0.268 0.000 0.024 0.000 0.708
#> SRR1785262     5  0.3196     0.6985 0.004 0.000 0.000 0.192 0.804
#> SRR1785263     5  0.3196     0.6985 0.004 0.000 0.000 0.192 0.804
#> SRR1785260     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785261     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785264     3  0.0000     0.8373 0.000 0.000 1.000 0.000 0.000
#> SRR1785265     3  0.0000     0.8373 0.000 0.000 1.000 0.000 0.000
#> SRR1785266     3  0.3039     0.7329 0.000 0.192 0.808 0.000 0.000
#> SRR1785267     3  0.2852     0.7502 0.000 0.172 0.828 0.000 0.000
#> SRR1785268     1  0.1671     0.7996 0.924 0.000 0.000 0.000 0.076
#> SRR1785269     1  0.1671     0.7996 0.924 0.000 0.000 0.000 0.076
#> SRR1785270     2  0.7366     0.1134 0.072 0.404 0.396 0.000 0.128
#> SRR1785271     3  0.7360    -0.1559 0.072 0.380 0.420 0.000 0.128
#> SRR1785272     3  0.5958     0.3868 0.200 0.000 0.592 0.000 0.208
#> SRR1785273     3  0.5725     0.4641 0.156 0.000 0.620 0.000 0.224
#> SRR1785276     3  0.0162     0.8372 0.004 0.000 0.996 0.000 0.000
#> SRR1785277     3  0.0290     0.8367 0.008 0.000 0.992 0.000 0.000
#> SRR1785274     5  0.4440    -0.1190 0.004 0.000 0.468 0.000 0.528
#> SRR1785275     5  0.4425    -0.0739 0.004 0.000 0.452 0.000 0.544
#> SRR1785280     2  0.0000     0.9265 0.000 1.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000     0.9265 0.000 1.000 0.000 0.000 0.000
#> SRR1785278     1  0.2648     0.7823 0.848 0.000 0.000 0.000 0.152
#> SRR1785279     1  0.2648     0.7823 0.848 0.000 0.000 0.000 0.152
#> SRR1785282     1  0.2966     0.7583 0.816 0.000 0.000 0.000 0.184
#> SRR1785283     1  0.2852     0.7691 0.828 0.000 0.000 0.000 0.172
#> SRR1785284     4  0.2329     0.8139 0.000 0.000 0.000 0.876 0.124
#> SRR1785285     4  0.2329     0.8139 0.000 0.000 0.000 0.876 0.124
#> SRR1785286     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785287     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785288     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785289     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785290     3  0.0000     0.8373 0.000 0.000 1.000 0.000 0.000
#> SRR1785291     3  0.0290     0.8353 0.000 0.000 0.992 0.008 0.000
#> SRR1785296     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785297     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785292     2  0.0000     0.9265 0.000 1.000 0.000 0.000 0.000
#> SRR1785293     2  0.0000     0.9265 0.000 1.000 0.000 0.000 0.000
#> SRR1785294     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785295     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785298     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785299     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785300     5  0.3884     0.6325 0.004 0.000 0.000 0.288 0.708
#> SRR1785301     5  0.3884     0.6325 0.004 0.000 0.000 0.288 0.708
#> SRR1785304     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785305     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785306     4  0.6414     0.5254 0.072 0.052 0.016 0.636 0.224
#> SRR1785307     4  0.7355     0.4321 0.072 0.052 0.072 0.580 0.224
#> SRR1785302     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785303     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785308     3  0.3491     0.7323 0.004 0.000 0.768 0.000 0.228
#> SRR1785309     3  0.3579     0.7203 0.004 0.000 0.756 0.000 0.240
#> SRR1785310     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785311     4  0.0000     0.9219 0.000 0.000 0.000 1.000 0.000
#> SRR1785312     1  0.1671     0.7996 0.924 0.000 0.000 0.000 0.076
#> SRR1785313     1  0.1671     0.7996 0.924 0.000 0.000 0.000 0.076
#> SRR1785314     2  0.2166     0.8801 0.072 0.912 0.012 0.000 0.004
#> SRR1785315     2  0.2054     0.8828 0.072 0.916 0.008 0.000 0.004
#> SRR1785318     2  0.0000     0.9265 0.000 1.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000     0.9265 0.000 1.000 0.000 0.000 0.000
#> SRR1785316     4  0.0671     0.9095 0.016 0.000 0.000 0.980 0.004
#> SRR1785317     4  0.1117     0.8956 0.020 0.000 0.000 0.964 0.016
#> SRR1785324     2  0.0000     0.9265 0.000 1.000 0.000 0.000 0.000
#> SRR1785325     2  0.0000     0.9265 0.000 1.000 0.000 0.000 0.000
#> SRR1785320     1  0.1965     0.7994 0.904 0.000 0.000 0.000 0.096
#> SRR1785321     1  0.1908     0.8002 0.908 0.000 0.000 0.000 0.092
#> SRR1785322     3  0.3455     0.6796 0.008 0.000 0.784 0.000 0.208
#> SRR1785323     3  0.3621     0.6900 0.020 0.000 0.788 0.000 0.192

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     3  0.0000      0.815 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785239     3  0.0000      0.815 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785240     6  0.0000      0.622 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1785241     6  0.0000      0.622 0.000 0.000 0.000 0.000 0.000 1.000
#> SRR1785242     3  0.2912      0.756 0.000 0.000 0.784 0.000 0.216 0.000
#> SRR1785243     3  0.2912      0.756 0.000 0.000 0.784 0.000 0.216 0.000
#> SRR1785244     4  0.4379      0.223 0.004 0.000 0.000 0.576 0.020 0.400
#> SRR1785245     4  0.4343      0.271 0.004 0.000 0.000 0.592 0.020 0.384
#> SRR1785246     1  0.5195      0.380 0.540 0.000 0.360 0.000 0.000 0.100
#> SRR1785247     1  0.5015      0.408 0.564 0.000 0.352 0.000 0.000 0.084
#> SRR1785248     3  0.2762      0.764 0.000 0.000 0.804 0.000 0.196 0.000
#> SRR1785250     1  0.6398      0.514 0.532 0.000 0.068 0.000 0.256 0.144
#> SRR1785251     1  0.6004      0.555 0.576 0.000 0.060 0.000 0.256 0.108
#> SRR1785252     3  0.3161      0.754 0.000 0.000 0.776 0.000 0.216 0.008
#> SRR1785253     3  0.3161      0.754 0.000 0.000 0.776 0.000 0.216 0.008
#> SRR1785254     4  0.1909      0.872 0.000 0.000 0.052 0.920 0.024 0.004
#> SRR1785255     4  0.0146      0.928 0.000 0.000 0.004 0.996 0.000 0.000
#> SRR1785256     6  0.2632      0.728 0.004 0.000 0.000 0.164 0.000 0.832
#> SRR1785257     6  0.2632      0.728 0.004 0.000 0.000 0.164 0.000 0.832
#> SRR1785258     6  0.3320      0.569 0.212 0.000 0.016 0.000 0.000 0.772
#> SRR1785259     6  0.3374      0.572 0.208 0.000 0.020 0.000 0.000 0.772
#> SRR1785262     6  0.2527      0.727 0.000 0.000 0.000 0.168 0.000 0.832
#> SRR1785263     6  0.2527      0.727 0.000 0.000 0.000 0.168 0.000 0.832
#> SRR1785260     4  0.0000      0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785261     4  0.0000      0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785264     3  0.0000      0.815 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785265     3  0.0000      0.815 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785266     3  0.2730      0.721 0.000 0.192 0.808 0.000 0.000 0.000
#> SRR1785267     3  0.2562      0.738 0.000 0.172 0.828 0.000 0.000 0.000
#> SRR1785268     1  0.0547      0.790 0.980 0.000 0.000 0.000 0.000 0.020
#> SRR1785269     1  0.0547      0.790 0.980 0.000 0.000 0.000 0.000 0.020
#> SRR1785270     5  0.4882      0.842 0.000 0.076 0.044 0.000 0.712 0.168
#> SRR1785271     5  0.4893      0.841 0.000 0.072 0.048 0.000 0.712 0.168
#> SRR1785272     3  0.6034      0.458 0.200 0.000 0.588 0.000 0.052 0.160
#> SRR1785273     3  0.5880      0.516 0.164 0.000 0.612 0.000 0.052 0.172
#> SRR1785276     3  0.0458      0.815 0.016 0.000 0.984 0.000 0.000 0.000
#> SRR1785277     3  0.0632      0.814 0.024 0.000 0.976 0.000 0.000 0.000
#> SRR1785274     6  0.3847     -0.197 0.000 0.000 0.456 0.000 0.000 0.544
#> SRR1785275     6  0.3817     -0.128 0.000 0.000 0.432 0.000 0.000 0.568
#> SRR1785280     2  0.0000      1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000      1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785278     1  0.2048      0.775 0.880 0.000 0.000 0.000 0.000 0.120
#> SRR1785279     1  0.2048      0.775 0.880 0.000 0.000 0.000 0.000 0.120
#> SRR1785282     1  0.2597      0.737 0.824 0.000 0.000 0.000 0.000 0.176
#> SRR1785283     1  0.2491      0.748 0.836 0.000 0.000 0.000 0.000 0.164
#> SRR1785284     4  0.2527      0.758 0.000 0.000 0.000 0.832 0.000 0.168
#> SRR1785285     4  0.2527      0.758 0.000 0.000 0.000 0.832 0.000 0.168
#> SRR1785286     4  0.0000      0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785287     4  0.0000      0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785288     4  0.0547      0.921 0.000 0.000 0.000 0.980 0.020 0.000
#> SRR1785289     4  0.0547      0.921 0.000 0.000 0.000 0.980 0.020 0.000
#> SRR1785290     3  0.0000      0.815 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785291     3  0.0260      0.814 0.000 0.000 0.992 0.008 0.000 0.000
#> SRR1785296     4  0.0000      0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785297     4  0.0000      0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785292     2  0.0000      1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785293     2  0.0000      1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785294     4  0.0000      0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785295     4  0.0000      0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785298     4  0.0000      0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785299     4  0.0000      0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785300     6  0.2996      0.688 0.000 0.000 0.000 0.228 0.000 0.772
#> SRR1785301     6  0.2996      0.688 0.000 0.000 0.000 0.228 0.000 0.772
#> SRR1785304     4  0.0000      0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785305     4  0.0000      0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785306     5  0.4738      0.818 0.000 0.028 0.004 0.044 0.696 0.228
#> SRR1785307     5  0.4826      0.822 0.000 0.024 0.016 0.036 0.696 0.228
#> SRR1785302     4  0.0000      0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785303     4  0.0000      0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785308     3  0.5594      0.652 0.020 0.000 0.588 0.000 0.268 0.124
#> SRR1785309     3  0.5726      0.634 0.020 0.000 0.572 0.000 0.268 0.140
#> SRR1785310     4  0.0000      0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785311     4  0.0000      0.931 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785312     1  0.0547      0.790 0.980 0.000 0.000 0.000 0.000 0.020
#> SRR1785313     1  0.0547      0.790 0.980 0.000 0.000 0.000 0.000 0.020
#> SRR1785314     5  0.3351      0.699 0.000 0.288 0.000 0.000 0.712 0.000
#> SRR1785315     5  0.3351      0.699 0.000 0.288 0.000 0.000 0.712 0.000
#> SRR1785318     2  0.0000      1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000      1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785316     4  0.2288      0.858 0.028 0.000 0.000 0.896 0.072 0.004
#> SRR1785317     4  0.2669      0.843 0.032 0.000 0.000 0.880 0.072 0.016
#> SRR1785324     2  0.0000      1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785325     2  0.0000      1.000 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785320     1  0.1267      0.795 0.940 0.000 0.000 0.000 0.000 0.060
#> SRR1785321     1  0.1267      0.795 0.940 0.000 0.000 0.000 0.000 0.060
#> SRR1785322     3  0.3825      0.692 0.016 0.000 0.776 0.000 0.036 0.172
#> SRR1785323     3  0.3819      0.708 0.028 0.000 0.788 0.000 0.032 0.152

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-MAD-pam-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-MAD-pam-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-MAD-pam-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-MAD-pam-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-MAD-pam-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-MAD-pam-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-MAD-pam-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-MAD-pam-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-MAD-pam-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-MAD-pam-membership-heatmap-5

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)

plot of chunk tab-MAD-pam-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-MAD-pam-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-MAD-pam-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-MAD-pam-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-MAD-pam-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-MAD-pam-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-MAD-pam-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-MAD-pam-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-MAD-pam-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-MAD-pam-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk MAD-pam-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-MAD-pam-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-MAD-pam-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-MAD-pam-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-MAD-pam-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-MAD-pam-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk MAD-pam-collect-classes

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.


MAD:mclust

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 16620 rows and 87 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 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)

plot of chunk MAD-mclust-collect-plots

The plots are:

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:

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)

plot of chunk MAD-mclust-select-partition-number

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 0.493           0.725       0.828         0.3645 0.518   0.518
#> 3 3 0.200           0.398       0.709         0.6033 0.691   0.467
#> 4 4 0.278           0.447       0.672         0.0940 0.683   0.336
#> 5 5 0.469           0.533       0.692         0.2044 0.789   0.417
#> 6 6 0.520           0.391       0.596         0.0343 0.868   0.477

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     1  0.9998    -0.5955 0.508 0.492
#> SRR1785239     2  0.9998     0.6170 0.492 0.508
#> SRR1785240     1  0.1414     0.9110 0.980 0.020
#> SRR1785241     1  0.1414     0.9110 0.980 0.020
#> SRR1785242     2  0.9983     0.6484 0.476 0.524
#> SRR1785243     2  0.9983     0.6484 0.476 0.524
#> SRR1785244     1  0.0000     0.9132 1.000 0.000
#> SRR1785245     1  0.0000     0.9132 1.000 0.000
#> SRR1785246     1  0.7219     0.5484 0.800 0.200
#> SRR1785247     1  0.7219     0.5484 0.800 0.200
#> SRR1785248     2  0.2603     0.5441 0.044 0.956
#> SRR1785250     2  0.9993     0.6454 0.484 0.516
#> SRR1785251     2  0.9993     0.6454 0.484 0.516
#> SRR1785252     2  0.9983     0.6484 0.476 0.524
#> SRR1785253     2  0.9983     0.6484 0.476 0.524
#> SRR1785254     1  0.0672     0.9134 0.992 0.008
#> SRR1785255     1  0.0672     0.9134 0.992 0.008
#> SRR1785256     1  0.0672     0.9099 0.992 0.008
#> SRR1785257     1  0.0672     0.9099 0.992 0.008
#> SRR1785258     1  0.0672     0.9134 0.992 0.008
#> SRR1785259     1  0.0672     0.9134 0.992 0.008
#> SRR1785262     1  0.7376     0.5448 0.792 0.208
#> SRR1785263     1  0.7376     0.5448 0.792 0.208
#> SRR1785260     2  0.9993     0.6454 0.484 0.516
#> SRR1785261     2  0.9993     0.6454 0.484 0.516
#> SRR1785264     2  0.9866     0.6431 0.432 0.568
#> SRR1785265     2  0.9866     0.6431 0.432 0.568
#> SRR1785266     2  0.2236     0.5441 0.036 0.964
#> SRR1785267     2  0.2236     0.5441 0.036 0.964
#> SRR1785268     1  0.0938     0.9068 0.988 0.012
#> SRR1785269     1  0.0938     0.9068 0.988 0.012
#> SRR1785270     1  0.1414     0.9110 0.980 0.020
#> SRR1785271     1  0.1414     0.9110 0.980 0.020
#> SRR1785272     2  0.9996     0.6413 0.488 0.512
#> SRR1785273     2  0.9996     0.6413 0.488 0.512
#> SRR1785276     1  0.0672     0.9134 0.992 0.008
#> SRR1785277     1  0.0672     0.9134 0.992 0.008
#> SRR1785274     1  0.1184     0.9126 0.984 0.016
#> SRR1785275     1  0.1184     0.9126 0.984 0.016
#> SRR1785280     2  0.1633     0.5459 0.024 0.976
#> SRR1785281     2  0.1633     0.5459 0.024 0.976
#> SRR1785278     1  0.0000     0.9132 1.000 0.000
#> SRR1785279     1  0.0000     0.9132 1.000 0.000
#> SRR1785282     1  0.0000     0.9132 1.000 0.000
#> SRR1785283     1  0.0000     0.9132 1.000 0.000
#> SRR1785284     1  0.1414     0.9110 0.980 0.020
#> SRR1785285     1  0.1414     0.9110 0.980 0.020
#> SRR1785286     1  0.1414     0.9110 0.980 0.020
#> SRR1785287     1  0.1414     0.9110 0.980 0.020
#> SRR1785288     1  0.0376     0.9130 0.996 0.004
#> SRR1785289     1  0.0376     0.9130 0.996 0.004
#> SRR1785290     2  0.9970     0.6505 0.468 0.532
#> SRR1785291     2  0.9970     0.6505 0.468 0.532
#> SRR1785296     2  0.9988     0.6421 0.480 0.520
#> SRR1785297     2  0.9983     0.6484 0.476 0.524
#> SRR1785292     2  0.1633     0.5459 0.024 0.976
#> SRR1785293     2  0.1633     0.5459 0.024 0.976
#> SRR1785294     2  0.9983     0.6484 0.476 0.524
#> SRR1785295     2  0.9983     0.6484 0.476 0.524
#> SRR1785298     1  0.9044     0.1306 0.680 0.320
#> SRR1785299     1  0.9044     0.1306 0.680 0.320
#> SRR1785300     1  0.0376     0.9130 0.996 0.004
#> SRR1785301     1  0.0376     0.9130 0.996 0.004
#> SRR1785304     2  0.9983     0.6484 0.476 0.524
#> SRR1785305     2  0.9983     0.6484 0.476 0.524
#> SRR1785306     1  0.1414     0.9110 0.980 0.020
#> SRR1785307     1  0.1414     0.9110 0.980 0.020
#> SRR1785302     1  0.0938     0.9118 0.988 0.012
#> SRR1785303     1  0.0938     0.9118 0.988 0.012
#> SRR1785308     2  0.9996     0.6413 0.488 0.512
#> SRR1785309     2  0.9996     0.6413 0.488 0.512
#> SRR1785310     1  0.0938     0.9118 0.988 0.012
#> SRR1785311     1  0.0938     0.9118 0.988 0.012
#> SRR1785312     1  0.0938     0.9068 0.988 0.012
#> SRR1785313     1  0.0938     0.9068 0.988 0.012
#> SRR1785314     1  0.1414     0.9110 0.980 0.020
#> SRR1785315     1  0.1414     0.9110 0.980 0.020
#> SRR1785318     2  0.1633     0.5459 0.024 0.976
#> SRR1785319     2  0.1633     0.5459 0.024 0.976
#> SRR1785316     1  0.0376     0.9130 0.996 0.004
#> SRR1785317     1  0.0376     0.9130 0.996 0.004
#> SRR1785324     2  0.9963     0.0376 0.464 0.536
#> SRR1785325     2  0.9963     0.0376 0.464 0.536
#> SRR1785320     1  0.0938     0.9068 0.988 0.012
#> SRR1785321     1  0.0938     0.9068 0.988 0.012
#> SRR1785322     1  0.6801     0.6067 0.820 0.180
#> SRR1785323     1  0.5294     0.7367 0.880 0.120

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     3  0.9371      0.165 0.188 0.324 0.488
#> SRR1785239     3  0.9371      0.165 0.188 0.324 0.488
#> SRR1785240     1  0.4605      0.518 0.796 0.000 0.204
#> SRR1785241     1  0.4605      0.518 0.796 0.000 0.204
#> SRR1785242     3  0.5875      0.365 0.056 0.160 0.784
#> SRR1785243     3  0.5875      0.365 0.056 0.160 0.784
#> SRR1785244     1  0.6355      0.506 0.696 0.024 0.280
#> SRR1785245     1  0.6355      0.506 0.696 0.024 0.280
#> SRR1785246     3  0.5850      0.412 0.188 0.040 0.772
#> SRR1785247     3  0.5850      0.412 0.188 0.040 0.772
#> SRR1785248     2  0.1163      0.723 0.028 0.972 0.000
#> SRR1785250     3  0.3181      0.478 0.024 0.064 0.912
#> SRR1785251     3  0.3181      0.478 0.024 0.064 0.912
#> SRR1785252     3  0.5875      0.365 0.056 0.160 0.784
#> SRR1785253     3  0.5875      0.365 0.056 0.160 0.784
#> SRR1785254     1  0.3234      0.591 0.908 0.020 0.072
#> SRR1785255     1  0.3502      0.588 0.896 0.020 0.084
#> SRR1785256     3  0.6307     -0.177 0.488 0.000 0.512
#> SRR1785257     3  0.6307     -0.177 0.488 0.000 0.512
#> SRR1785258     3  0.6307     -0.177 0.488 0.000 0.512
#> SRR1785259     3  0.6307     -0.177 0.488 0.000 0.512
#> SRR1785262     3  0.4702      0.378 0.212 0.000 0.788
#> SRR1785263     3  0.4702      0.378 0.212 0.000 0.788
#> SRR1785260     2  0.8716      0.500 0.172 0.588 0.240
#> SRR1785261     2  0.8716      0.500 0.172 0.588 0.240
#> SRR1785264     2  0.8390      0.380 0.100 0.560 0.340
#> SRR1785265     2  0.8352      0.401 0.100 0.568 0.332
#> SRR1785266     2  0.0892      0.722 0.020 0.980 0.000
#> SRR1785267     2  0.0892      0.722 0.020 0.980 0.000
#> SRR1785268     3  0.6308     -0.181 0.492 0.000 0.508
#> SRR1785269     3  0.6308     -0.181 0.492 0.000 0.508
#> SRR1785270     1  0.0747      0.587 0.984 0.000 0.016
#> SRR1785271     1  0.0747      0.587 0.984 0.000 0.016
#> SRR1785272     3  0.4280      0.467 0.020 0.124 0.856
#> SRR1785273     3  0.4540      0.474 0.028 0.124 0.848
#> SRR1785276     1  0.6669      0.206 0.524 0.008 0.468
#> SRR1785277     1  0.6672      0.200 0.520 0.008 0.472
#> SRR1785274     1  0.5497      0.433 0.708 0.000 0.292
#> SRR1785275     1  0.5497      0.433 0.708 0.000 0.292
#> SRR1785280     2  0.0592      0.719 0.012 0.988 0.000
#> SRR1785281     2  0.0592      0.719 0.012 0.988 0.000
#> SRR1785278     1  0.7396      0.152 0.488 0.032 0.480
#> SRR1785279     1  0.7396      0.152 0.488 0.032 0.480
#> SRR1785282     1  0.8507      0.196 0.484 0.092 0.424
#> SRR1785283     1  0.8507      0.196 0.484 0.092 0.424
#> SRR1785284     1  0.1031      0.590 0.976 0.000 0.024
#> SRR1785285     1  0.1163      0.591 0.972 0.000 0.028
#> SRR1785286     1  0.3116      0.587 0.892 0.000 0.108
#> SRR1785287     1  0.3192      0.588 0.888 0.000 0.112
#> SRR1785288     1  0.8657      0.443 0.592 0.164 0.244
#> SRR1785289     1  0.8657      0.443 0.592 0.164 0.244
#> SRR1785290     2  0.6462      0.671 0.116 0.764 0.120
#> SRR1785291     2  0.6462      0.671 0.116 0.764 0.120
#> SRR1785296     2  0.6902      0.641 0.116 0.736 0.148
#> SRR1785297     2  0.6902      0.641 0.116 0.736 0.148
#> SRR1785292     2  0.3784      0.660 0.132 0.864 0.004
#> SRR1785293     2  0.3784      0.660 0.132 0.864 0.004
#> SRR1785294     2  0.7545      0.608 0.136 0.692 0.172
#> SRR1785295     2  0.7545      0.608 0.136 0.692 0.172
#> SRR1785298     3  0.9880      0.171 0.260 0.356 0.384
#> SRR1785299     3  0.9880      0.171 0.260 0.356 0.384
#> SRR1785300     1  0.8301      0.456 0.592 0.108 0.300
#> SRR1785301     1  0.8301      0.456 0.592 0.108 0.300
#> SRR1785304     2  0.8103      0.624 0.248 0.632 0.120
#> SRR1785305     2  0.8103      0.624 0.248 0.632 0.120
#> SRR1785306     1  0.1860      0.553 0.948 0.000 0.052
#> SRR1785307     1  0.1860      0.553 0.948 0.000 0.052
#> SRR1785302     1  0.8392      0.366 0.624 0.176 0.200
#> SRR1785303     1  0.8478      0.350 0.616 0.180 0.204
#> SRR1785308     3  0.4280      0.467 0.020 0.124 0.856
#> SRR1785309     3  0.4280      0.467 0.020 0.124 0.856
#> SRR1785310     3  0.9696      0.170 0.388 0.216 0.396
#> SRR1785311     3  0.9696      0.170 0.388 0.216 0.396
#> SRR1785312     3  0.6308     -0.181 0.492 0.000 0.508
#> SRR1785313     3  0.6308     -0.181 0.492 0.000 0.508
#> SRR1785314     1  0.1529      0.562 0.960 0.000 0.040
#> SRR1785315     1  0.1529      0.562 0.960 0.000 0.040
#> SRR1785318     2  0.0592      0.719 0.012 0.988 0.000
#> SRR1785319     2  0.0592      0.719 0.012 0.988 0.000
#> SRR1785316     1  0.8607      0.444 0.592 0.152 0.256
#> SRR1785317     1  0.8637      0.441 0.588 0.152 0.260
#> SRR1785324     2  0.7909      0.554 0.148 0.664 0.188
#> SRR1785325     2  0.7909      0.554 0.148 0.664 0.188
#> SRR1785320     3  0.6308     -0.181 0.492 0.000 0.508
#> SRR1785321     3  0.6308     -0.181 0.492 0.000 0.508
#> SRR1785322     3  0.7525      0.411 0.208 0.108 0.684
#> SRR1785323     3  0.7457      0.409 0.208 0.104 0.688

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     3   0.262    0.51471 0.036 0.028 0.920 0.016
#> SRR1785239     3   0.271    0.51435 0.036 0.032 0.916 0.016
#> SRR1785240     4   0.657    0.49975 0.116 0.000 0.280 0.604
#> SRR1785241     4   0.657    0.49975 0.116 0.000 0.280 0.604
#> SRR1785242     3   0.199    0.51098 0.016 0.024 0.944 0.016
#> SRR1785243     3   0.199    0.51098 0.016 0.024 0.944 0.016
#> SRR1785244     1   0.714    0.51704 0.468 0.000 0.400 0.132
#> SRR1785245     1   0.717    0.52278 0.468 0.000 0.396 0.136
#> SRR1785246     3   0.669    0.21881 0.152 0.008 0.644 0.196
#> SRR1785247     3   0.669    0.21881 0.152 0.008 0.644 0.196
#> SRR1785248     2   0.530    0.74676 0.036 0.788 0.088 0.088
#> SRR1785250     3   0.240    0.47169 0.092 0.004 0.904 0.000
#> SRR1785251     3   0.247    0.46907 0.096 0.004 0.900 0.000
#> SRR1785252     3   0.199    0.51201 0.016 0.024 0.944 0.016
#> SRR1785253     3   0.199    0.51201 0.016 0.024 0.944 0.016
#> SRR1785254     4   0.599    0.67484 0.152 0.000 0.156 0.692
#> SRR1785255     4   0.612    0.66263 0.152 0.000 0.168 0.680
#> SRR1785256     1   0.538    0.57778 0.588 0.000 0.396 0.016
#> SRR1785257     1   0.536    0.58053 0.592 0.000 0.392 0.016
#> SRR1785258     3   0.769   -0.17748 0.268 0.000 0.456 0.276
#> SRR1785259     3   0.769   -0.17748 0.268 0.000 0.456 0.276
#> SRR1785262     3   0.396    0.42049 0.144 0.000 0.824 0.032
#> SRR1785263     3   0.396    0.42049 0.144 0.000 0.824 0.032
#> SRR1785260     2   0.954    0.01609 0.312 0.328 0.244 0.116
#> SRR1785261     2   0.954    0.00876 0.320 0.324 0.240 0.116
#> SRR1785264     3   0.554    0.42472 0.036 0.236 0.712 0.016
#> SRR1785265     3   0.555    0.41495 0.036 0.252 0.700 0.012
#> SRR1785266     2   0.499    0.76015 0.028 0.804 0.084 0.084
#> SRR1785267     2   0.499    0.76015 0.028 0.804 0.084 0.084
#> SRR1785268     1   0.524    0.59198 0.628 0.000 0.356 0.016
#> SRR1785269     1   0.524    0.59198 0.628 0.000 0.356 0.016
#> SRR1785270     4   0.314    0.74719 0.100 0.000 0.024 0.876
#> SRR1785271     4   0.314    0.74719 0.100 0.000 0.024 0.876
#> SRR1785272     3   0.233    0.46867 0.088 0.000 0.908 0.004
#> SRR1785273     3   0.215    0.46993 0.088 0.000 0.912 0.000
#> SRR1785276     3   0.788   -0.11022 0.220 0.008 0.460 0.312
#> SRR1785277     3   0.788   -0.11022 0.220 0.008 0.460 0.312
#> SRR1785274     4   0.717    0.43910 0.168 0.000 0.296 0.536
#> SRR1785275     4   0.717    0.43910 0.168 0.000 0.296 0.536
#> SRR1785280     2   0.131    0.79579 0.004 0.960 0.036 0.000
#> SRR1785281     2   0.131    0.79579 0.004 0.960 0.036 0.000
#> SRR1785278     1   0.570    0.46797 0.488 0.000 0.488 0.024
#> SRR1785279     1   0.570    0.46797 0.488 0.000 0.488 0.024
#> SRR1785282     3   0.551   -0.50899 0.484 0.000 0.500 0.016
#> SRR1785283     3   0.551   -0.50899 0.484 0.000 0.500 0.016
#> SRR1785284     4   0.505    0.74616 0.104 0.008 0.104 0.784
#> SRR1785285     4   0.505    0.74616 0.104 0.008 0.104 0.784
#> SRR1785286     1   0.784    0.20080 0.380 0.000 0.264 0.356
#> SRR1785287     1   0.784    0.20080 0.380 0.000 0.264 0.356
#> SRR1785288     1   0.628    0.50101 0.672 0.040 0.248 0.040
#> SRR1785289     1   0.620    0.50230 0.676 0.040 0.248 0.036
#> SRR1785290     3   0.651    0.22822 0.036 0.428 0.516 0.020
#> SRR1785291     3   0.651    0.22822 0.036 0.428 0.516 0.020
#> SRR1785296     3   0.778    0.31733 0.044 0.332 0.520 0.104
#> SRR1785297     3   0.793    0.30537 0.052 0.336 0.508 0.104
#> SRR1785292     2   0.195    0.79077 0.004 0.940 0.044 0.012
#> SRR1785293     2   0.195    0.79077 0.004 0.940 0.044 0.012
#> SRR1785294     3   0.851    0.28930 0.104 0.312 0.484 0.100
#> SRR1785295     3   0.851    0.28718 0.100 0.312 0.484 0.104
#> SRR1785298     3   0.475    0.49153 0.088 0.040 0.820 0.052
#> SRR1785299     3   0.475    0.49153 0.088 0.040 0.820 0.052
#> SRR1785300     1   0.619    0.56135 0.608 0.032 0.340 0.020
#> SRR1785301     1   0.619    0.56135 0.608 0.032 0.340 0.020
#> SRR1785304     3   0.855    0.22094 0.088 0.348 0.452 0.112
#> SRR1785305     3   0.855    0.22094 0.088 0.348 0.452 0.112
#> SRR1785306     4   0.325    0.74946 0.140 0.000 0.008 0.852
#> SRR1785307     4   0.325    0.74946 0.140 0.000 0.008 0.852
#> SRR1785302     3   0.794    0.07587 0.172 0.024 0.500 0.304
#> SRR1785303     3   0.791    0.09003 0.164 0.024 0.500 0.312
#> SRR1785308     3   0.215    0.46993 0.088 0.000 0.912 0.000
#> SRR1785309     3   0.215    0.46993 0.088 0.000 0.912 0.000
#> SRR1785310     3   0.560    0.44512 0.124 0.048 0.768 0.060
#> SRR1785311     3   0.560    0.44512 0.124 0.048 0.768 0.060
#> SRR1785312     1   0.549    0.53980 0.700 0.000 0.240 0.060
#> SRR1785313     1   0.549    0.53980 0.700 0.000 0.240 0.060
#> SRR1785314     4   0.358    0.74961 0.140 0.008 0.008 0.844
#> SRR1785315     4   0.358    0.74961 0.140 0.008 0.008 0.844
#> SRR1785318     2   0.131    0.79579 0.004 0.960 0.036 0.000
#> SRR1785319     2   0.131    0.79579 0.004 0.960 0.036 0.000
#> SRR1785316     1   0.619    0.56098 0.604 0.036 0.344 0.016
#> SRR1785317     1   0.619    0.56098 0.604 0.036 0.344 0.016
#> SRR1785324     2   0.511    0.58419 0.000 0.704 0.264 0.032
#> SRR1785325     2   0.511    0.58419 0.000 0.704 0.264 0.032
#> SRR1785320     1   0.549    0.53980 0.700 0.000 0.240 0.060
#> SRR1785321     1   0.549    0.53980 0.700 0.000 0.240 0.060
#> SRR1785322     3   0.419    0.37688 0.164 0.004 0.808 0.024
#> SRR1785323     3   0.428    0.36270 0.172 0.004 0.800 0.024

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     4  0.7253     0.2674 0.028 0.124 0.408 0.420 0.020
#> SRR1785239     4  0.7214     0.2743 0.024 0.128 0.408 0.420 0.020
#> SRR1785240     5  0.5323     0.4473 0.188 0.008 0.076 0.016 0.712
#> SRR1785241     5  0.5323     0.4473 0.188 0.008 0.076 0.016 0.712
#> SRR1785242     3  0.5147     0.6689 0.184 0.068 0.724 0.020 0.004
#> SRR1785243     3  0.5147     0.6689 0.184 0.068 0.724 0.020 0.004
#> SRR1785244     1  0.7403     0.2852 0.408 0.004 0.080 0.104 0.404
#> SRR1785245     1  0.7403     0.2852 0.408 0.004 0.080 0.104 0.404
#> SRR1785246     3  0.6017     0.4545 0.392 0.008 0.532 0.024 0.044
#> SRR1785247     3  0.6017     0.4545 0.392 0.008 0.532 0.024 0.044
#> SRR1785248     2  0.4305     0.4916 0.000 0.688 0.012 0.296 0.004
#> SRR1785250     3  0.4419     0.7072 0.212 0.004 0.740 0.044 0.000
#> SRR1785251     3  0.4419     0.7072 0.212 0.004 0.740 0.044 0.000
#> SRR1785252     3  0.5196     0.6651 0.184 0.028 0.724 0.060 0.004
#> SRR1785253     3  0.5196     0.6651 0.184 0.028 0.724 0.060 0.004
#> SRR1785254     5  0.3455     0.7077 0.076 0.016 0.024 0.020 0.864
#> SRR1785255     5  0.3717     0.6976 0.088 0.016 0.028 0.020 0.848
#> SRR1785256     1  0.0451     0.5378 0.988 0.000 0.008 0.004 0.000
#> SRR1785257     1  0.0451     0.5378 0.988 0.000 0.008 0.004 0.000
#> SRR1785258     1  0.6966     0.3354 0.524 0.008 0.192 0.020 0.256
#> SRR1785259     1  0.6966     0.3354 0.524 0.008 0.192 0.020 0.256
#> SRR1785262     3  0.5219     0.5239 0.400 0.000 0.560 0.008 0.032
#> SRR1785263     3  0.5219     0.5239 0.400 0.000 0.560 0.008 0.032
#> SRR1785260     4  0.3773     0.4998 0.056 0.024 0.052 0.852 0.016
#> SRR1785261     4  0.3905     0.4945 0.064 0.024 0.052 0.844 0.016
#> SRR1785264     4  0.6916     0.4562 0.000 0.264 0.296 0.432 0.008
#> SRR1785265     4  0.6921     0.4498 0.000 0.272 0.288 0.432 0.008
#> SRR1785266     2  0.3561     0.6514 0.000 0.740 0.000 0.260 0.000
#> SRR1785267     2  0.3561     0.6514 0.000 0.740 0.000 0.260 0.000
#> SRR1785268     1  0.1571     0.5150 0.936 0.000 0.060 0.004 0.000
#> SRR1785269     1  0.1571     0.5150 0.936 0.000 0.060 0.004 0.000
#> SRR1785270     5  0.0807     0.7060 0.000 0.000 0.012 0.012 0.976
#> SRR1785271     5  0.0807     0.7060 0.000 0.000 0.012 0.012 0.976
#> SRR1785272     3  0.2971     0.6913 0.156 0.000 0.836 0.008 0.000
#> SRR1785273     3  0.3252     0.6897 0.156 0.008 0.828 0.008 0.000
#> SRR1785276     1  0.5871     0.3772 0.676 0.020 0.200 0.016 0.088
#> SRR1785277     1  0.5819     0.3804 0.680 0.020 0.200 0.016 0.084
#> SRR1785274     1  0.6829    -0.0663 0.484 0.008 0.128 0.020 0.360
#> SRR1785275     1  0.6836    -0.0783 0.480 0.008 0.128 0.020 0.364
#> SRR1785280     2  0.0609     0.8470 0.000 0.980 0.000 0.020 0.000
#> SRR1785281     2  0.0609     0.8470 0.000 0.980 0.000 0.020 0.000
#> SRR1785278     1  0.4178     0.5335 0.808 0.020 0.084 0.088 0.000
#> SRR1785279     1  0.4229     0.5354 0.804 0.020 0.080 0.096 0.000
#> SRR1785282     1  0.5706     0.4801 0.636 0.004 0.220 0.140 0.000
#> SRR1785283     1  0.5706     0.4801 0.636 0.004 0.220 0.140 0.000
#> SRR1785284     5  0.1764     0.7078 0.000 0.000 0.064 0.008 0.928
#> SRR1785285     5  0.1764     0.7078 0.000 0.000 0.064 0.008 0.928
#> SRR1785286     5  0.6187     0.5311 0.320 0.000 0.068 0.040 0.572
#> SRR1785287     5  0.6187     0.5311 0.320 0.000 0.068 0.040 0.572
#> SRR1785288     1  0.7591     0.3851 0.376 0.004 0.036 0.336 0.248
#> SRR1785289     1  0.7591     0.3851 0.376 0.004 0.036 0.336 0.248
#> SRR1785290     4  0.5489     0.1138 0.000 0.460 0.044 0.488 0.008
#> SRR1785291     4  0.5491     0.0853 0.000 0.468 0.044 0.480 0.008
#> SRR1785296     4  0.4127     0.6009 0.004 0.104 0.076 0.808 0.008
#> SRR1785297     4  0.4127     0.6009 0.004 0.104 0.076 0.808 0.008
#> SRR1785292     2  0.2426     0.8318 0.000 0.900 0.000 0.064 0.036
#> SRR1785293     2  0.2426     0.8318 0.000 0.900 0.000 0.064 0.036
#> SRR1785294     4  0.3414     0.5975 0.004 0.056 0.056 0.864 0.020
#> SRR1785295     4  0.3274     0.5950 0.004 0.056 0.048 0.872 0.020
#> SRR1785298     4  0.6735     0.5542 0.004 0.100 0.292 0.556 0.048
#> SRR1785299     4  0.6735     0.5542 0.004 0.100 0.292 0.556 0.048
#> SRR1785300     1  0.6183     0.4670 0.584 0.004 0.064 0.312 0.036
#> SRR1785301     1  0.6183     0.4670 0.584 0.004 0.064 0.312 0.036
#> SRR1785304     4  0.4897     0.5387 0.000 0.164 0.040 0.748 0.048
#> SRR1785305     4  0.4897     0.5387 0.000 0.164 0.040 0.748 0.048
#> SRR1785306     5  0.3737     0.6819 0.224 0.008 0.004 0.000 0.764
#> SRR1785307     5  0.3737     0.6819 0.224 0.008 0.004 0.000 0.764
#> SRR1785302     4  0.8745     0.1274 0.200 0.052 0.080 0.364 0.304
#> SRR1785303     4  0.8745     0.1271 0.200 0.052 0.080 0.364 0.304
#> SRR1785308     3  0.2971     0.6913 0.156 0.000 0.836 0.008 0.000
#> SRR1785309     3  0.2971     0.6913 0.156 0.000 0.836 0.008 0.000
#> SRR1785310     4  0.7559     0.4986 0.064 0.052 0.276 0.532 0.076
#> SRR1785311     4  0.7559     0.4986 0.064 0.052 0.276 0.532 0.076
#> SRR1785312     1  0.4555     0.4485 0.720 0.000 0.056 0.000 0.224
#> SRR1785313     1  0.4555     0.4485 0.720 0.000 0.056 0.000 0.224
#> SRR1785314     5  0.3737     0.6819 0.224 0.000 0.004 0.008 0.764
#> SRR1785315     5  0.3737     0.6819 0.224 0.000 0.004 0.008 0.764
#> SRR1785318     2  0.0510     0.8466 0.000 0.984 0.000 0.016 0.000
#> SRR1785319     2  0.0510     0.8466 0.000 0.984 0.000 0.016 0.000
#> SRR1785316     1  0.5994     0.4694 0.588 0.000 0.060 0.316 0.036
#> SRR1785317     1  0.5994     0.4694 0.588 0.000 0.060 0.316 0.036
#> SRR1785324     2  0.2390     0.8055 0.000 0.896 0.000 0.020 0.084
#> SRR1785325     2  0.2570     0.8053 0.000 0.888 0.000 0.028 0.084
#> SRR1785320     1  0.4709     0.4497 0.716 0.000 0.056 0.004 0.224
#> SRR1785321     1  0.4828     0.4523 0.712 0.000 0.056 0.008 0.224
#> SRR1785322     3  0.5350     0.6607 0.168 0.008 0.720 0.084 0.020
#> SRR1785323     3  0.5455     0.6552 0.168 0.008 0.712 0.092 0.020

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     2   0.827   -0.10174 0.120 0.332 0.180 0.316 0.032 0.020
#> SRR1785239     2   0.827   -0.10174 0.120 0.332 0.180 0.316 0.032 0.020
#> SRR1785240     1   0.625   -0.07531 0.452 0.000 0.032 0.076 0.416 0.024
#> SRR1785241     1   0.625   -0.07531 0.452 0.000 0.032 0.076 0.416 0.024
#> SRR1785242     3   0.703    0.35934 0.252 0.040 0.472 0.004 0.020 0.212
#> SRR1785243     3   0.703    0.35934 0.252 0.040 0.472 0.004 0.020 0.212
#> SRR1785244     1   0.780    0.07884 0.336 0.000 0.008 0.212 0.252 0.192
#> SRR1785245     1   0.780    0.07884 0.336 0.000 0.008 0.212 0.252 0.192
#> SRR1785246     1   0.707    0.00491 0.484 0.004 0.244 0.044 0.024 0.200
#> SRR1785247     1   0.707    0.00491 0.484 0.004 0.244 0.044 0.024 0.200
#> SRR1785248     2   0.445    0.52135 0.024 0.740 0.004 0.180 0.000 0.052
#> SRR1785250     3   0.619    0.51621 0.120 0.028 0.548 0.016 0.000 0.288
#> SRR1785251     3   0.619    0.51621 0.120 0.028 0.548 0.016 0.000 0.288
#> SRR1785252     3   0.697    0.36090 0.252 0.036 0.476 0.004 0.020 0.212
#> SRR1785253     3   0.697    0.36090 0.252 0.036 0.476 0.004 0.020 0.212
#> SRR1785254     5   0.414    0.66946 0.208 0.016 0.000 0.024 0.744 0.008
#> SRR1785255     5   0.403    0.67235 0.208 0.016 0.000 0.024 0.748 0.004
#> SRR1785256     6   0.504    0.13000 0.444 0.000 0.008 0.036 0.008 0.504
#> SRR1785257     6   0.504    0.13000 0.444 0.000 0.008 0.036 0.008 0.504
#> SRR1785258     1   0.281    0.42767 0.888 0.000 0.028 0.036 0.028 0.020
#> SRR1785259     1   0.289    0.42749 0.884 0.000 0.028 0.036 0.028 0.024
#> SRR1785262     1   0.690    0.09628 0.532 0.048 0.208 0.184 0.020 0.008
#> SRR1785263     1   0.690    0.09628 0.532 0.048 0.208 0.184 0.020 0.008
#> SRR1785260     4   0.568    0.46843 0.012 0.044 0.008 0.676 0.160 0.100
#> SRR1785261     4   0.568    0.46843 0.012 0.044 0.008 0.676 0.160 0.100
#> SRR1785264     2   0.680    0.16912 0.016 0.508 0.112 0.308 0.024 0.032
#> SRR1785265     2   0.678    0.15677 0.016 0.504 0.108 0.316 0.024 0.032
#> SRR1785266     2   0.379    0.56501 0.016 0.800 0.004 0.132 0.000 0.048
#> SRR1785267     2   0.383    0.56324 0.016 0.796 0.004 0.136 0.000 0.048
#> SRR1785268     6   0.468    0.11117 0.448 0.000 0.020 0.008 0.004 0.520
#> SRR1785269     6   0.468    0.11117 0.448 0.000 0.020 0.008 0.004 0.520
#> SRR1785270     5   0.307    0.71000 0.084 0.000 0.000 0.036 0.856 0.024
#> SRR1785271     5   0.307    0.71000 0.084 0.000 0.000 0.036 0.856 0.024
#> SRR1785272     3   0.370    0.56144 0.016 0.000 0.744 0.008 0.000 0.232
#> SRR1785273     3   0.395    0.55850 0.016 0.008 0.736 0.008 0.000 0.232
#> SRR1785276     1   0.554    0.39939 0.716 0.024 0.136 0.036 0.052 0.036
#> SRR1785277     1   0.554    0.39939 0.716 0.024 0.136 0.036 0.052 0.036
#> SRR1785274     1   0.581    0.27549 0.596 0.000 0.036 0.052 0.288 0.028
#> SRR1785275     1   0.581    0.27549 0.596 0.000 0.036 0.052 0.288 0.028
#> SRR1785280     2   0.128    0.62326 0.000 0.944 0.004 0.052 0.000 0.000
#> SRR1785281     2   0.128    0.62326 0.000 0.944 0.004 0.052 0.000 0.000
#> SRR1785278     1   0.786   -0.28151 0.356 0.032 0.048 0.192 0.028 0.344
#> SRR1785279     1   0.782   -0.28291 0.356 0.032 0.044 0.196 0.028 0.344
#> SRR1785282     6   0.810    0.46417 0.196 0.024 0.152 0.260 0.008 0.360
#> SRR1785283     6   0.810    0.46417 0.196 0.024 0.152 0.260 0.008 0.360
#> SRR1785284     5   0.545    0.57671 0.256 0.000 0.008 0.072 0.632 0.032
#> SRR1785285     5   0.540    0.57942 0.256 0.000 0.008 0.068 0.636 0.032
#> SRR1785286     5   0.338    0.68835 0.016 0.020 0.008 0.084 0.852 0.020
#> SRR1785287     5   0.343    0.68686 0.016 0.020 0.008 0.088 0.848 0.020
#> SRR1785288     6   0.614    0.48903 0.092 0.000 0.000 0.424 0.052 0.432
#> SRR1785289     6   0.614    0.48903 0.092 0.000 0.000 0.424 0.052 0.432
#> SRR1785290     4   0.553    0.29504 0.016 0.348 0.008 0.572 0.032 0.024
#> SRR1785291     4   0.560    0.28862 0.016 0.348 0.008 0.568 0.032 0.028
#> SRR1785296     4   0.399    0.58856 0.004 0.164 0.012 0.780 0.032 0.008
#> SRR1785297     4   0.399    0.58856 0.004 0.164 0.012 0.780 0.032 0.008
#> SRR1785292     2   0.456    0.51817 0.000 0.720 0.000 0.080 0.184 0.016
#> SRR1785293     2   0.456    0.51817 0.000 0.720 0.000 0.080 0.184 0.016
#> SRR1785294     4   0.411    0.62491 0.008 0.100 0.008 0.804 0.052 0.028
#> SRR1785295     4   0.426    0.62351 0.008 0.100 0.008 0.796 0.052 0.036
#> SRR1785298     4   0.606    0.50516 0.024 0.200 0.116 0.620 0.040 0.000
#> SRR1785299     4   0.606    0.50516 0.024 0.200 0.116 0.620 0.040 0.000
#> SRR1785300     6   0.597    0.53925 0.072 0.000 0.004 0.416 0.044 0.464
#> SRR1785301     6   0.597    0.53925 0.072 0.000 0.004 0.416 0.044 0.464
#> SRR1785304     4   0.618    0.52239 0.012 0.148 0.004 0.564 0.252 0.020
#> SRR1785305     4   0.620    0.51748 0.012 0.152 0.004 0.560 0.252 0.020
#> SRR1785306     5   0.385    0.68194 0.084 0.020 0.096 0.000 0.800 0.000
#> SRR1785307     5   0.385    0.68194 0.084 0.020 0.096 0.000 0.800 0.000
#> SRR1785302     5   0.583   -0.12233 0.012 0.100 0.008 0.340 0.536 0.004
#> SRR1785303     5   0.583   -0.12233 0.012 0.100 0.008 0.340 0.536 0.004
#> SRR1785308     3   0.370    0.56144 0.016 0.000 0.744 0.008 0.000 0.232
#> SRR1785309     3   0.370    0.56144 0.016 0.000 0.744 0.008 0.000 0.232
#> SRR1785310     4   0.715    0.43958 0.028 0.064 0.048 0.472 0.344 0.044
#> SRR1785311     4   0.714    0.44351 0.028 0.064 0.048 0.476 0.340 0.044
#> SRR1785312     1   0.389    0.27247 0.664 0.000 0.008 0.000 0.004 0.324
#> SRR1785313     1   0.389    0.27247 0.664 0.000 0.008 0.000 0.004 0.324
#> SRR1785314     5   0.162    0.71642 0.016 0.020 0.008 0.012 0.944 0.000
#> SRR1785315     5   0.162    0.71642 0.016 0.020 0.008 0.012 0.944 0.000
#> SRR1785318     2   0.114    0.62347 0.000 0.948 0.000 0.052 0.000 0.000
#> SRR1785319     2   0.114    0.62347 0.000 0.948 0.000 0.052 0.000 0.000
#> SRR1785316     6   0.647    0.54008 0.072 0.012 0.016 0.392 0.040 0.468
#> SRR1785317     6   0.647    0.54008 0.072 0.012 0.016 0.392 0.040 0.468
#> SRR1785324     2   0.403    0.51755 0.008 0.744 0.000 0.016 0.216 0.016
#> SRR1785325     2   0.403    0.51755 0.008 0.744 0.000 0.016 0.216 0.016
#> SRR1785320     1   0.380    0.27243 0.664 0.000 0.004 0.000 0.004 0.328
#> SRR1785321     1   0.380    0.27296 0.664 0.000 0.004 0.000 0.004 0.328
#> SRR1785322     3   0.694    0.39509 0.048 0.040 0.532 0.132 0.004 0.244
#> SRR1785323     3   0.707    0.38242 0.048 0.040 0.524 0.136 0.008 0.244

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-MAD-mclust-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-MAD-mclust-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-MAD-mclust-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-MAD-mclust-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-MAD-mclust-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-MAD-mclust-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-MAD-mclust-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-MAD-mclust-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-MAD-mclust-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-MAD-mclust-membership-heatmap-5

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)

plot of chunk tab-MAD-mclust-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-MAD-mclust-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-MAD-mclust-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-MAD-mclust-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-MAD-mclust-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-MAD-mclust-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-MAD-mclust-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-MAD-mclust-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-MAD-mclust-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-MAD-mclust-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk MAD-mclust-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-MAD-mclust-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-MAD-mclust-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-MAD-mclust-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-MAD-mclust-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-MAD-mclust-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk MAD-mclust-collect-classes

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.


MAD:NMF*

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#>   Subgroups are detected by 'NMF' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 2.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk MAD-NMF-collect-plots

The plots are:

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:

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)

plot of chunk MAD-NMF-select-partition-number

The numeric values for all these statistics can be obtained by get_stats().

get_stats(res)
#>   k 1-PAC mean_silhouette concordance area_increased  Rand Jaccard
#> 2 2 0.928           0.930       0.971         0.4586 0.536   0.536
#> 3 3 0.679           0.765       0.898         0.4432 0.681   0.461
#> 4 4 0.652           0.707       0.853         0.1309 0.746   0.387
#> 5 5 0.751           0.749       0.867         0.0702 0.865   0.530
#> 6 6 0.707           0.556       0.756         0.0419 0.896   0.552

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2  0.9881      0.266 0.436 0.564
#> SRR1785239     2  0.9795      0.324 0.416 0.584
#> SRR1785240     1  0.0000      0.981 1.000 0.000
#> SRR1785241     1  0.0000      0.981 1.000 0.000
#> SRR1785242     1  0.6343      0.805 0.840 0.160
#> SRR1785243     1  0.6531      0.794 0.832 0.168
#> SRR1785244     1  0.0000      0.981 1.000 0.000
#> SRR1785245     1  0.0000      0.981 1.000 0.000
#> SRR1785246     1  0.0000      0.981 1.000 0.000
#> SRR1785247     1  0.0000      0.981 1.000 0.000
#> SRR1785248     2  0.0000      0.946 0.000 1.000
#> SRR1785250     1  0.0000      0.981 1.000 0.000
#> SRR1785251     1  0.0000      0.981 1.000 0.000
#> SRR1785252     1  0.1184      0.968 0.984 0.016
#> SRR1785253     1  0.1184      0.968 0.984 0.016
#> SRR1785254     2  0.0000      0.946 0.000 1.000
#> SRR1785255     2  0.0000      0.946 0.000 1.000
#> SRR1785256     1  0.0000      0.981 1.000 0.000
#> SRR1785257     1  0.0000      0.981 1.000 0.000
#> SRR1785258     1  0.0000      0.981 1.000 0.000
#> SRR1785259     1  0.0000      0.981 1.000 0.000
#> SRR1785262     1  0.0000      0.981 1.000 0.000
#> SRR1785263     1  0.0000      0.981 1.000 0.000
#> SRR1785260     1  0.0000      0.981 1.000 0.000
#> SRR1785261     1  0.0000      0.981 1.000 0.000
#> SRR1785264     2  0.0000      0.946 0.000 1.000
#> SRR1785265     2  0.0000      0.946 0.000 1.000
#> SRR1785266     2  0.0000      0.946 0.000 1.000
#> SRR1785267     2  0.0000      0.946 0.000 1.000
#> SRR1785268     1  0.0000      0.981 1.000 0.000
#> SRR1785269     1  0.0000      0.981 1.000 0.000
#> SRR1785270     2  0.0000      0.946 0.000 1.000
#> SRR1785271     2  0.0000      0.946 0.000 1.000
#> SRR1785272     1  0.0000      0.981 1.000 0.000
#> SRR1785273     1  0.0000      0.981 1.000 0.000
#> SRR1785276     1  0.0000      0.981 1.000 0.000
#> SRR1785277     1  0.0000      0.981 1.000 0.000
#> SRR1785274     1  0.0000      0.981 1.000 0.000
#> SRR1785275     1  0.0000      0.981 1.000 0.000
#> SRR1785280     2  0.0000      0.946 0.000 1.000
#> SRR1785281     2  0.0000      0.946 0.000 1.000
#> SRR1785278     1  0.0000      0.981 1.000 0.000
#> SRR1785279     1  0.0000      0.981 1.000 0.000
#> SRR1785282     1  0.0000      0.981 1.000 0.000
#> SRR1785283     1  0.0000      0.981 1.000 0.000
#> SRR1785284     1  0.2423      0.946 0.960 0.040
#> SRR1785285     1  0.2948      0.934 0.948 0.052
#> SRR1785286     1  0.0000      0.981 1.000 0.000
#> SRR1785287     1  0.0000      0.981 1.000 0.000
#> SRR1785288     1  0.0000      0.981 1.000 0.000
#> SRR1785289     1  0.0000      0.981 1.000 0.000
#> SRR1785290     2  0.0000      0.946 0.000 1.000
#> SRR1785291     2  0.0000      0.946 0.000 1.000
#> SRR1785296     1  0.8016      0.663 0.756 0.244
#> SRR1785297     1  0.8386      0.617 0.732 0.268
#> SRR1785292     2  0.0000      0.946 0.000 1.000
#> SRR1785293     2  0.0000      0.946 0.000 1.000
#> SRR1785294     1  0.0000      0.981 1.000 0.000
#> SRR1785295     1  0.0000      0.981 1.000 0.000
#> SRR1785298     2  0.9323      0.487 0.348 0.652
#> SRR1785299     2  0.9209      0.511 0.336 0.664
#> SRR1785300     1  0.0000      0.981 1.000 0.000
#> SRR1785301     1  0.0000      0.981 1.000 0.000
#> SRR1785304     2  0.0000      0.946 0.000 1.000
#> SRR1785305     2  0.0000      0.946 0.000 1.000
#> SRR1785306     2  0.0000      0.946 0.000 1.000
#> SRR1785307     2  0.0000      0.946 0.000 1.000
#> SRR1785302     2  0.0672      0.940 0.008 0.992
#> SRR1785303     2  0.0672      0.940 0.008 0.992
#> SRR1785308     1  0.0000      0.981 1.000 0.000
#> SRR1785309     1  0.0000      0.981 1.000 0.000
#> SRR1785310     1  0.0000      0.981 1.000 0.000
#> SRR1785311     1  0.0000      0.981 1.000 0.000
#> SRR1785312     1  0.0000      0.981 1.000 0.000
#> SRR1785313     1  0.0000      0.981 1.000 0.000
#> SRR1785314     2  0.0000      0.946 0.000 1.000
#> SRR1785315     2  0.0000      0.946 0.000 1.000
#> SRR1785318     2  0.0000      0.946 0.000 1.000
#> SRR1785319     2  0.0000      0.946 0.000 1.000
#> SRR1785316     1  0.0000      0.981 1.000 0.000
#> SRR1785317     1  0.0000      0.981 1.000 0.000
#> SRR1785324     2  0.0000      0.946 0.000 1.000
#> SRR1785325     2  0.0000      0.946 0.000 1.000
#> SRR1785320     1  0.0000      0.981 1.000 0.000
#> SRR1785321     1  0.0000      0.981 1.000 0.000
#> SRR1785322     1  0.0000      0.981 1.000 0.000
#> SRR1785323     1  0.0000      0.981 1.000 0.000

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     3  0.4235      0.677 0.000 0.176 0.824
#> SRR1785239     3  0.4887      0.596 0.000 0.228 0.772
#> SRR1785240     3  0.6299      0.244 0.476 0.000 0.524
#> SRR1785241     3  0.6295      0.255 0.472 0.000 0.528
#> SRR1785242     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785243     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785244     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785245     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785246     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785247     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785248     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785250     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785251     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785252     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785253     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785254     2  0.5621      0.582 0.308 0.692 0.000
#> SRR1785255     2  0.5497      0.609 0.292 0.708 0.000
#> SRR1785256     1  0.3038      0.826 0.896 0.000 0.104
#> SRR1785257     1  0.3116      0.821 0.892 0.000 0.108
#> SRR1785258     3  0.0237      0.860 0.004 0.000 0.996
#> SRR1785259     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785262     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785263     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785260     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785261     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785264     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785265     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785266     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785267     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785268     3  0.5968      0.495 0.364 0.000 0.636
#> SRR1785269     3  0.5882      0.519 0.348 0.000 0.652
#> SRR1785270     2  0.5327      0.641 0.272 0.728 0.000
#> SRR1785271     2  0.5216      0.657 0.260 0.740 0.000
#> SRR1785272     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785273     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785276     3  0.0424      0.858 0.008 0.000 0.992
#> SRR1785277     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785274     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785275     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785280     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785281     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785278     1  0.0592      0.912 0.988 0.000 0.012
#> SRR1785279     1  0.0747      0.909 0.984 0.000 0.016
#> SRR1785282     1  0.1529      0.889 0.960 0.000 0.040
#> SRR1785283     1  0.1529      0.889 0.960 0.000 0.040
#> SRR1785284     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785285     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785286     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785287     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785288     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785289     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785290     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785291     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785296     2  0.8872      0.472 0.296 0.552 0.152
#> SRR1785297     2  0.8962      0.452 0.304 0.540 0.156
#> SRR1785292     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785293     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785294     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785295     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785298     2  0.5216      0.645 0.260 0.740 0.000
#> SRR1785299     2  0.4931      0.699 0.212 0.784 0.004
#> SRR1785300     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785301     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785304     1  0.5835      0.408 0.660 0.340 0.000
#> SRR1785305     1  0.6168      0.205 0.588 0.412 0.000
#> SRR1785306     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785307     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785302     1  0.5968      0.339 0.636 0.364 0.000
#> SRR1785303     1  0.6062      0.280 0.616 0.384 0.000
#> SRR1785308     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785309     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785310     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785311     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785312     3  0.6204      0.378 0.424 0.000 0.576
#> SRR1785313     3  0.6126      0.430 0.400 0.000 0.600
#> SRR1785314     2  0.6280      0.228 0.460 0.540 0.000
#> SRR1785315     2  0.5810      0.544 0.336 0.664 0.000
#> SRR1785318     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785319     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785316     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785317     1  0.0000      0.919 1.000 0.000 0.000
#> SRR1785324     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785325     2  0.0000      0.860 0.000 1.000 0.000
#> SRR1785320     3  0.6140      0.422 0.404 0.000 0.596
#> SRR1785321     3  0.6154      0.415 0.408 0.000 0.592
#> SRR1785322     3  0.0000      0.862 0.000 0.000 1.000
#> SRR1785323     3  0.0000      0.862 0.000 0.000 1.000

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     3  0.3166     0.8110 0.016 0.116 0.868 0.000
#> SRR1785239     3  0.3443     0.7922 0.016 0.136 0.848 0.000
#> SRR1785240     1  0.0000     0.7861 1.000 0.000 0.000 0.000
#> SRR1785241     1  0.0000     0.7861 1.000 0.000 0.000 0.000
#> SRR1785242     3  0.1302     0.8664 0.044 0.000 0.956 0.000
#> SRR1785243     3  0.1302     0.8664 0.044 0.000 0.956 0.000
#> SRR1785244     1  0.3975     0.6232 0.760 0.000 0.000 0.240
#> SRR1785245     1  0.4072     0.6068 0.748 0.000 0.000 0.252
#> SRR1785246     3  0.2921     0.8169 0.140 0.000 0.860 0.000
#> SRR1785247     3  0.2973     0.8146 0.144 0.000 0.856 0.000
#> SRR1785248     2  0.0000     0.9483 0.000 1.000 0.000 0.000
#> SRR1785250     3  0.0000     0.8713 0.000 0.000 1.000 0.000
#> SRR1785251     3  0.0000     0.8713 0.000 0.000 1.000 0.000
#> SRR1785252     3  0.0921     0.8689 0.028 0.000 0.972 0.000
#> SRR1785253     3  0.0921     0.8689 0.028 0.000 0.972 0.000
#> SRR1785254     1  0.3545     0.7361 0.828 0.164 0.000 0.008
#> SRR1785255     1  0.3351     0.7455 0.844 0.148 0.000 0.008
#> SRR1785256     4  0.7431     0.1150 0.380 0.000 0.172 0.448
#> SRR1785257     4  0.7458     0.1016 0.380 0.000 0.176 0.444
#> SRR1785258     1  0.3649     0.6954 0.796 0.000 0.204 0.000
#> SRR1785259     1  0.3486     0.7120 0.812 0.000 0.188 0.000
#> SRR1785262     3  0.3831     0.7238 0.204 0.000 0.792 0.004
#> SRR1785263     3  0.3751     0.7323 0.196 0.000 0.800 0.004
#> SRR1785260     4  0.0779     0.7587 0.016 0.000 0.004 0.980
#> SRR1785261     4  0.0779     0.7587 0.016 0.000 0.004 0.980
#> SRR1785264     2  0.0000     0.9483 0.000 1.000 0.000 0.000
#> SRR1785265     2  0.0000     0.9483 0.000 1.000 0.000 0.000
#> SRR1785266     2  0.0000     0.9483 0.000 1.000 0.000 0.000
#> SRR1785267     2  0.0000     0.9483 0.000 1.000 0.000 0.000
#> SRR1785268     3  0.5663     0.0863 0.440 0.000 0.536 0.024
#> SRR1785269     3  0.5663     0.0863 0.440 0.000 0.536 0.024
#> SRR1785270     1  0.1302     0.7851 0.956 0.044 0.000 0.000
#> SRR1785271     1  0.1302     0.7851 0.956 0.044 0.000 0.000
#> SRR1785272     3  0.1022     0.8667 0.000 0.000 0.968 0.032
#> SRR1785273     3  0.1022     0.8667 0.000 0.000 0.968 0.032
#> SRR1785276     1  0.3649     0.7095 0.796 0.000 0.204 0.000
#> SRR1785277     1  0.3873     0.6877 0.772 0.000 0.228 0.000
#> SRR1785274     1  0.0817     0.7857 0.976 0.000 0.024 0.000
#> SRR1785275     1  0.0817     0.7857 0.976 0.000 0.024 0.000
#> SRR1785280     2  0.0000     0.9483 0.000 1.000 0.000 0.000
#> SRR1785281     2  0.0000     0.9483 0.000 1.000 0.000 0.000
#> SRR1785278     4  0.6683     0.1838 0.416 0.000 0.088 0.496
#> SRR1785279     4  0.6672     0.1958 0.408 0.000 0.088 0.504
#> SRR1785282     4  0.6388     0.5908 0.156 0.000 0.192 0.652
#> SRR1785283     4  0.6286     0.5984 0.140 0.000 0.200 0.660
#> SRR1785284     1  0.1867     0.7678 0.928 0.000 0.000 0.072
#> SRR1785285     1  0.1867     0.7678 0.928 0.000 0.000 0.072
#> SRR1785286     1  0.4585     0.4776 0.668 0.000 0.000 0.332
#> SRR1785287     1  0.4624     0.4611 0.660 0.000 0.000 0.340
#> SRR1785288     4  0.2011     0.7490 0.080 0.000 0.000 0.920
#> SRR1785289     4  0.2011     0.7490 0.080 0.000 0.000 0.920
#> SRR1785290     2  0.0000     0.9483 0.000 1.000 0.000 0.000
#> SRR1785291     2  0.0000     0.9483 0.000 1.000 0.000 0.000
#> SRR1785296     4  0.5966     0.4878 0.000 0.280 0.072 0.648
#> SRR1785297     4  0.6036     0.4677 0.000 0.292 0.072 0.636
#> SRR1785292     2  0.0000     0.9483 0.000 1.000 0.000 0.000
#> SRR1785293     2  0.0000     0.9483 0.000 1.000 0.000 0.000
#> SRR1785294     4  0.1557     0.7499 0.000 0.000 0.056 0.944
#> SRR1785295     4  0.1557     0.7499 0.000 0.000 0.056 0.944
#> SRR1785298     4  0.5947     0.3001 0.000 0.384 0.044 0.572
#> SRR1785299     4  0.6071     0.1090 0.000 0.452 0.044 0.504
#> SRR1785300     4  0.0895     0.7610 0.020 0.000 0.004 0.976
#> SRR1785301     4  0.1489     0.7578 0.044 0.000 0.004 0.952
#> SRR1785304     4  0.1824     0.7455 0.004 0.060 0.000 0.936
#> SRR1785305     4  0.1743     0.7473 0.004 0.056 0.000 0.940
#> SRR1785306     1  0.4790     0.3627 0.620 0.380 0.000 0.000
#> SRR1785307     1  0.4855     0.3113 0.600 0.400 0.000 0.000
#> SRR1785302     2  0.6356     0.3146 0.084 0.596 0.000 0.320
#> SRR1785303     2  0.5623     0.4617 0.048 0.660 0.000 0.292
#> SRR1785308     3  0.0817     0.8690 0.000 0.000 0.976 0.024
#> SRR1785309     3  0.0707     0.8700 0.000 0.000 0.980 0.020
#> SRR1785310     4  0.0336     0.7591 0.008 0.000 0.000 0.992
#> SRR1785311     4  0.0469     0.7586 0.012 0.000 0.000 0.988
#> SRR1785312     1  0.4194     0.6878 0.764 0.000 0.228 0.008
#> SRR1785313     1  0.4194     0.6880 0.764 0.000 0.228 0.008
#> SRR1785314     1  0.2222     0.7793 0.924 0.060 0.000 0.016
#> SRR1785315     1  0.2271     0.7762 0.916 0.076 0.000 0.008
#> SRR1785318     2  0.0000     0.9483 0.000 1.000 0.000 0.000
#> SRR1785319     2  0.0000     0.9483 0.000 1.000 0.000 0.000
#> SRR1785316     4  0.2773     0.7234 0.116 0.000 0.004 0.880
#> SRR1785317     4  0.2773     0.7234 0.116 0.000 0.004 0.880
#> SRR1785324     2  0.0000     0.9483 0.000 1.000 0.000 0.000
#> SRR1785325     2  0.0000     0.9483 0.000 1.000 0.000 0.000
#> SRR1785320     1  0.4661     0.6537 0.728 0.000 0.256 0.016
#> SRR1785321     1  0.4661     0.6537 0.728 0.000 0.256 0.016
#> SRR1785322     3  0.1118     0.8646 0.000 0.000 0.964 0.036
#> SRR1785323     3  0.1118     0.8646 0.000 0.000 0.964 0.036

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     3  0.4851      0.489 0.036 0.340 0.624 0.000 0.000
#> SRR1785239     3  0.5002      0.437 0.040 0.364 0.596 0.000 0.000
#> SRR1785240     5  0.0162      0.844 0.004 0.000 0.000 0.000 0.996
#> SRR1785241     5  0.0162      0.844 0.004 0.000 0.000 0.000 0.996
#> SRR1785242     3  0.1282      0.804 0.004 0.000 0.952 0.000 0.044
#> SRR1785243     3  0.1282      0.804 0.004 0.000 0.952 0.000 0.044
#> SRR1785244     1  0.2629      0.804 0.860 0.000 0.000 0.004 0.136
#> SRR1785245     1  0.2771      0.809 0.860 0.000 0.000 0.012 0.128
#> SRR1785246     3  0.2921      0.766 0.020 0.000 0.856 0.000 0.124
#> SRR1785247     3  0.2873      0.769 0.020 0.000 0.860 0.000 0.120
#> SRR1785248     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785250     3  0.0451      0.811 0.008 0.000 0.988 0.004 0.000
#> SRR1785251     3  0.0451      0.811 0.008 0.000 0.988 0.004 0.000
#> SRR1785252     3  0.0510      0.811 0.000 0.000 0.984 0.000 0.016
#> SRR1785253     3  0.0510      0.811 0.000 0.000 0.984 0.000 0.016
#> SRR1785254     5  0.5472      0.586 0.188 0.156 0.000 0.000 0.656
#> SRR1785255     5  0.5587      0.588 0.188 0.152 0.000 0.004 0.656
#> SRR1785256     1  0.5326      0.713 0.696 0.000 0.212 0.028 0.064
#> SRR1785257     1  0.5330      0.716 0.696 0.000 0.208 0.024 0.072
#> SRR1785258     5  0.6581      0.232 0.324 0.000 0.224 0.000 0.452
#> SRR1785259     5  0.6424      0.346 0.288 0.000 0.212 0.000 0.500
#> SRR1785262     3  0.6610      0.248 0.004 0.000 0.476 0.316 0.204
#> SRR1785263     3  0.6543      0.262 0.004 0.000 0.488 0.316 0.192
#> SRR1785260     4  0.0000      0.844 0.000 0.000 0.000 1.000 0.000
#> SRR1785261     4  0.0000      0.844 0.000 0.000 0.000 1.000 0.000
#> SRR1785264     2  0.0451      0.926 0.004 0.988 0.008 0.000 0.000
#> SRR1785265     2  0.0290      0.928 0.000 0.992 0.008 0.000 0.000
#> SRR1785266     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785267     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785268     1  0.1281      0.846 0.956 0.000 0.032 0.000 0.012
#> SRR1785269     1  0.1364      0.846 0.952 0.000 0.036 0.000 0.012
#> SRR1785270     5  0.0703      0.846 0.024 0.000 0.000 0.000 0.976
#> SRR1785271     5  0.0703      0.846 0.024 0.000 0.000 0.000 0.976
#> SRR1785272     3  0.3582      0.713 0.224 0.000 0.768 0.008 0.000
#> SRR1785273     3  0.3355      0.743 0.184 0.000 0.804 0.012 0.000
#> SRR1785276     5  0.4522      0.679 0.196 0.000 0.068 0.000 0.736
#> SRR1785277     5  0.4571      0.684 0.188 0.000 0.076 0.000 0.736
#> SRR1785274     5  0.0451      0.845 0.008 0.000 0.004 0.000 0.988
#> SRR1785275     5  0.0451      0.845 0.008 0.000 0.004 0.000 0.988
#> SRR1785280     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785278     1  0.0671      0.850 0.980 0.000 0.004 0.000 0.016
#> SRR1785279     1  0.0671      0.850 0.980 0.000 0.004 0.000 0.016
#> SRR1785282     1  0.2504      0.827 0.896 0.000 0.040 0.064 0.000
#> SRR1785283     1  0.2554      0.825 0.892 0.000 0.036 0.072 0.000
#> SRR1785284     5  0.1579      0.841 0.032 0.000 0.000 0.024 0.944
#> SRR1785285     5  0.1668      0.840 0.032 0.000 0.000 0.028 0.940
#> SRR1785286     4  0.4165      0.489 0.008 0.000 0.000 0.672 0.320
#> SRR1785287     4  0.4235      0.460 0.008 0.000 0.000 0.656 0.336
#> SRR1785288     1  0.3635      0.678 0.748 0.000 0.000 0.248 0.004
#> SRR1785289     1  0.3607      0.681 0.752 0.000 0.000 0.244 0.004
#> SRR1785290     2  0.0290      0.928 0.000 0.992 0.000 0.008 0.000
#> SRR1785291     2  0.0290      0.928 0.000 0.992 0.000 0.008 0.000
#> SRR1785296     4  0.2438      0.789 0.000 0.060 0.040 0.900 0.000
#> SRR1785297     4  0.2344      0.790 0.000 0.064 0.032 0.904 0.000
#> SRR1785292     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785293     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785294     4  0.0000      0.844 0.000 0.000 0.000 1.000 0.000
#> SRR1785295     4  0.0000      0.844 0.000 0.000 0.000 1.000 0.000
#> SRR1785298     2  0.2645      0.850 0.044 0.888 0.000 0.068 0.000
#> SRR1785299     2  0.2446      0.861 0.044 0.900 0.000 0.056 0.000
#> SRR1785300     4  0.4192      0.186 0.404 0.000 0.000 0.596 0.000
#> SRR1785301     4  0.4283      0.020 0.456 0.000 0.000 0.544 0.000
#> SRR1785304     4  0.0000      0.844 0.000 0.000 0.000 1.000 0.000
#> SRR1785305     4  0.0000      0.844 0.000 0.000 0.000 1.000 0.000
#> SRR1785306     5  0.1026      0.841 0.004 0.004 0.000 0.024 0.968
#> SRR1785307     5  0.1026      0.841 0.004 0.004 0.000 0.024 0.968
#> SRR1785302     2  0.5589      0.250 0.372 0.548 0.000 0.080 0.000
#> SRR1785303     2  0.5701      0.349 0.328 0.580 0.000 0.088 0.004
#> SRR1785308     3  0.1478      0.804 0.064 0.000 0.936 0.000 0.000
#> SRR1785309     3  0.1478      0.804 0.064 0.000 0.936 0.000 0.000
#> SRR1785310     4  0.0162      0.843 0.004 0.000 0.000 0.996 0.000
#> SRR1785311     4  0.0162      0.843 0.004 0.000 0.000 0.996 0.000
#> SRR1785312     1  0.4250      0.619 0.720 0.000 0.028 0.000 0.252
#> SRR1785313     1  0.4506      0.531 0.676 0.000 0.028 0.000 0.296
#> SRR1785314     5  0.2418      0.832 0.024 0.020 0.000 0.044 0.912
#> SRR1785315     5  0.2430      0.834 0.028 0.020 0.000 0.040 0.912
#> SRR1785318     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785316     1  0.2077      0.833 0.908 0.000 0.000 0.084 0.008
#> SRR1785317     1  0.2011      0.830 0.908 0.000 0.000 0.088 0.004
#> SRR1785324     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785325     2  0.0000      0.932 0.000 1.000 0.000 0.000 0.000
#> SRR1785320     1  0.2012      0.840 0.920 0.000 0.020 0.000 0.060
#> SRR1785321     1  0.2012      0.840 0.920 0.000 0.020 0.000 0.060
#> SRR1785322     3  0.3700      0.696 0.240 0.000 0.752 0.008 0.000
#> SRR1785323     3  0.3756      0.688 0.248 0.000 0.744 0.008 0.000

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     2  0.5491     0.2920 0.048 0.540 0.368 0.000 0.000 0.044
#> SRR1785239     2  0.5199     0.3235 0.040 0.560 0.368 0.000 0.000 0.032
#> SRR1785240     5  0.0976     0.7143 0.008 0.000 0.016 0.000 0.968 0.008
#> SRR1785241     5  0.0862     0.7148 0.008 0.000 0.016 0.000 0.972 0.004
#> SRR1785242     3  0.3013     0.6586 0.000 0.000 0.844 0.000 0.088 0.068
#> SRR1785243     3  0.2962     0.6595 0.000 0.000 0.848 0.000 0.084 0.068
#> SRR1785244     6  0.4466     0.3941 0.336 0.000 0.000 0.000 0.044 0.620
#> SRR1785245     6  0.4466     0.3941 0.336 0.000 0.000 0.000 0.044 0.620
#> SRR1785246     3  0.5876     0.4811 0.288 0.000 0.564 0.008 0.120 0.020
#> SRR1785247     3  0.5898     0.4650 0.304 0.000 0.552 0.008 0.116 0.020
#> SRR1785248     2  0.2058     0.8126 0.000 0.908 0.056 0.000 0.000 0.036
#> SRR1785250     3  0.3194     0.6414 0.132 0.000 0.828 0.032 0.000 0.008
#> SRR1785251     3  0.3153     0.6435 0.128 0.000 0.832 0.032 0.000 0.008
#> SRR1785252     3  0.1268     0.6760 0.008 0.000 0.952 0.000 0.036 0.004
#> SRR1785253     3  0.1268     0.6760 0.008 0.000 0.952 0.000 0.036 0.004
#> SRR1785254     5  0.5503     0.3995 0.008 0.104 0.004 0.000 0.560 0.324
#> SRR1785255     5  0.5538     0.4024 0.008 0.112 0.004 0.000 0.564 0.312
#> SRR1785256     6  0.7107     0.2777 0.368 0.000 0.116 0.008 0.116 0.392
#> SRR1785257     6  0.7103     0.2909 0.360 0.000 0.116 0.008 0.116 0.400
#> SRR1785258     5  0.7276    -0.0727 0.092 0.000 0.308 0.000 0.316 0.284
#> SRR1785259     5  0.7228    -0.0222 0.088 0.000 0.300 0.000 0.344 0.268
#> SRR1785262     3  0.6141     0.3341 0.004 0.000 0.484 0.292 0.212 0.008
#> SRR1785263     3  0.6104     0.3544 0.004 0.000 0.496 0.280 0.212 0.008
#> SRR1785260     4  0.0260     0.9580 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1785261     4  0.0260     0.9580 0.000 0.000 0.000 0.992 0.008 0.000
#> SRR1785264     2  0.4354     0.6584 0.008 0.684 0.040 0.000 0.000 0.268
#> SRR1785265     2  0.4289     0.6618 0.008 0.688 0.036 0.000 0.000 0.268
#> SRR1785266     2  0.0260     0.8323 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1785267     2  0.0260     0.8323 0.000 0.992 0.000 0.000 0.000 0.008
#> SRR1785268     1  0.1777     0.5964 0.932 0.000 0.032 0.000 0.012 0.024
#> SRR1785269     1  0.1857     0.5955 0.928 0.000 0.028 0.000 0.012 0.032
#> SRR1785270     5  0.2979     0.6687 0.188 0.004 0.000 0.000 0.804 0.004
#> SRR1785271     5  0.2913     0.6729 0.180 0.004 0.000 0.000 0.812 0.004
#> SRR1785272     3  0.5839     0.2169 0.400 0.000 0.484 0.048 0.000 0.068
#> SRR1785273     3  0.5876     0.2438 0.388 0.000 0.492 0.052 0.000 0.068
#> SRR1785276     1  0.4246     0.5257 0.796 0.020 0.068 0.000 0.084 0.032
#> SRR1785277     1  0.4321     0.5145 0.784 0.016 0.088 0.000 0.088 0.024
#> SRR1785274     5  0.2078     0.7017 0.032 0.000 0.040 0.000 0.916 0.012
#> SRR1785275     5  0.2271     0.7049 0.032 0.000 0.036 0.000 0.908 0.024
#> SRR1785280     2  0.0146     0.8325 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1785281     2  0.0146     0.8325 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1785278     1  0.3713     0.4095 0.704 0.000 0.004 0.000 0.008 0.284
#> SRR1785279     1  0.3733     0.3969 0.700 0.000 0.004 0.000 0.008 0.288
#> SRR1785282     1  0.4787    -0.1222 0.488 0.000 0.028 0.012 0.000 0.472
#> SRR1785283     1  0.4875    -0.1497 0.480 0.000 0.024 0.020 0.000 0.476
#> SRR1785284     5  0.3883     0.6747 0.040 0.000 0.000 0.028 0.788 0.144
#> SRR1785285     5  0.3920     0.6728 0.040 0.000 0.000 0.028 0.784 0.148
#> SRR1785286     5  0.4322     0.1888 0.000 0.000 0.000 0.452 0.528 0.020
#> SRR1785287     5  0.4310     0.2234 0.000 0.000 0.000 0.440 0.540 0.020
#> SRR1785288     6  0.5073     0.4684 0.268 0.000 0.000 0.080 0.016 0.636
#> SRR1785289     6  0.5073     0.4684 0.268 0.000 0.000 0.080 0.016 0.636
#> SRR1785290     2  0.3085     0.7756 0.004 0.828 0.012 0.008 0.000 0.148
#> SRR1785291     2  0.2948     0.7795 0.004 0.836 0.008 0.008 0.000 0.144
#> SRR1785296     4  0.2451     0.9109 0.008 0.036 0.024 0.904 0.000 0.028
#> SRR1785297     4  0.2522     0.9066 0.008 0.040 0.024 0.900 0.000 0.028
#> SRR1785292     2  0.0547     0.8311 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1785293     2  0.0547     0.8311 0.000 0.980 0.000 0.000 0.000 0.020
#> SRR1785294     4  0.0632     0.9482 0.024 0.000 0.000 0.976 0.000 0.000
#> SRR1785295     4  0.0632     0.9482 0.024 0.000 0.000 0.976 0.000 0.000
#> SRR1785298     2  0.5130     0.2955 0.020 0.540 0.004 0.036 0.000 0.400
#> SRR1785299     2  0.4912     0.3301 0.016 0.560 0.004 0.028 0.000 0.392
#> SRR1785300     6  0.5850     0.2112 0.096 0.000 0.000 0.436 0.028 0.440
#> SRR1785301     6  0.5876     0.2627 0.100 0.000 0.000 0.412 0.028 0.460
#> SRR1785304     4  0.1059     0.9523 0.000 0.004 0.000 0.964 0.016 0.016
#> SRR1785305     4  0.1059     0.9523 0.000 0.004 0.000 0.964 0.016 0.016
#> SRR1785306     5  0.1059     0.7178 0.000 0.016 0.000 0.016 0.964 0.004
#> SRR1785307     5  0.1059     0.7178 0.000 0.016 0.000 0.016 0.964 0.004
#> SRR1785302     6  0.5030     0.4235 0.056 0.172 0.000 0.020 0.036 0.716
#> SRR1785303     6  0.5057     0.4136 0.052 0.192 0.000 0.020 0.032 0.704
#> SRR1785308     3  0.3979     0.5507 0.020 0.000 0.708 0.008 0.000 0.264
#> SRR1785309     3  0.3957     0.5540 0.020 0.000 0.712 0.008 0.000 0.260
#> SRR1785310     4  0.0909     0.9546 0.000 0.000 0.000 0.968 0.012 0.020
#> SRR1785311     4  0.0909     0.9546 0.000 0.000 0.000 0.968 0.012 0.020
#> SRR1785312     1  0.2275     0.5774 0.888 0.000 0.008 0.000 0.096 0.008
#> SRR1785313     1  0.2355     0.5695 0.876 0.000 0.008 0.000 0.112 0.004
#> SRR1785314     5  0.4250     0.6913 0.120 0.036 0.000 0.032 0.788 0.024
#> SRR1785315     5  0.4250     0.6913 0.120 0.036 0.000 0.032 0.788 0.024
#> SRR1785318     2  0.0692     0.8285 0.004 0.976 0.000 0.000 0.000 0.020
#> SRR1785319     2  0.0692     0.8285 0.004 0.976 0.000 0.000 0.000 0.020
#> SRR1785316     1  0.4470     0.2168 0.604 0.000 0.000 0.040 0.000 0.356
#> SRR1785317     1  0.4470     0.2168 0.604 0.000 0.000 0.040 0.000 0.356
#> SRR1785324     2  0.0146     0.8325 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1785325     2  0.0146     0.8325 0.000 0.996 0.000 0.000 0.000 0.004
#> SRR1785320     1  0.2008     0.5940 0.920 0.000 0.004 0.004 0.040 0.032
#> SRR1785321     1  0.1921     0.5950 0.924 0.000 0.004 0.004 0.044 0.024
#> SRR1785322     1  0.6112    -0.0507 0.456 0.000 0.404 0.060 0.000 0.080
#> SRR1785323     1  0.5916    -0.0186 0.472 0.000 0.404 0.044 0.000 0.080

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-MAD-NMF-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-MAD-NMF-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-MAD-NMF-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-MAD-NMF-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-MAD-NMF-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-MAD-NMF-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-MAD-NMF-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-MAD-NMF-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-MAD-NMF-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-MAD-NMF-membership-heatmap-5

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)

plot of chunk tab-MAD-NMF-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-MAD-NMF-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-MAD-NMF-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-MAD-NMF-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-MAD-NMF-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-MAD-NMF-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-MAD-NMF-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-MAD-NMF-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-MAD-NMF-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-MAD-NMF-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk MAD-NMF-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-MAD-NMF-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-MAD-NMF-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-MAD-NMF-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-MAD-NMF-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-MAD-NMF-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk MAD-NMF-collect-classes

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.


ATC:hclust

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#>   Subgroups are detected by 'hclust' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 3.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk ATC-hclust-collect-plots

The plots are:

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:

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)

plot of chunk ATC-hclust-select-partition-number

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 0.466           0.776       0.884         0.4872 0.513   0.513
#> 3 3 0.553           0.839       0.897         0.2358 0.888   0.781
#> 4 4 0.602           0.806       0.841         0.1236 0.929   0.824
#> 5 5 0.800           0.818       0.891         0.0603 0.993   0.977
#> 6 6 0.767           0.818       0.849         0.0687 0.850   0.560

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2  0.3584      0.935 0.068 0.932
#> SRR1785239     2  0.3584      0.935 0.068 0.932
#> SRR1785240     1  0.6048      0.763 0.852 0.148
#> SRR1785241     1  0.6048      0.763 0.852 0.148
#> SRR1785242     1  0.9850      0.373 0.572 0.428
#> SRR1785243     1  0.9850      0.373 0.572 0.428
#> SRR1785244     1  0.0000      0.817 1.000 0.000
#> SRR1785245     1  0.0000      0.817 1.000 0.000
#> SRR1785246     2  0.2948      0.939 0.052 0.948
#> SRR1785247     2  0.2948      0.939 0.052 0.948
#> SRR1785248     2  0.0000      0.932 0.000 1.000
#> SRR1785250     1  0.9850      0.373 0.572 0.428
#> SRR1785251     1  0.9850      0.373 0.572 0.428
#> SRR1785252     1  0.9850      0.373 0.572 0.428
#> SRR1785253     1  0.9850      0.373 0.572 0.428
#> SRR1785254     1  0.0672      0.816 0.992 0.008
#> SRR1785255     1  0.0672      0.816 0.992 0.008
#> SRR1785256     1  0.0000      0.817 1.000 0.000
#> SRR1785257     1  0.0000      0.817 1.000 0.000
#> SRR1785258     1  0.0000      0.817 1.000 0.000
#> SRR1785259     1  0.0000      0.817 1.000 0.000
#> SRR1785262     2  0.2948      0.939 0.052 0.948
#> SRR1785263     2  0.2948      0.939 0.052 0.948
#> SRR1785260     2  0.0672      0.935 0.008 0.992
#> SRR1785261     2  0.0672      0.935 0.008 0.992
#> SRR1785264     2  0.3584      0.935 0.068 0.932
#> SRR1785265     2  0.3584      0.935 0.068 0.932
#> SRR1785266     2  0.0000      0.932 0.000 1.000
#> SRR1785267     2  0.0000      0.932 0.000 1.000
#> SRR1785268     1  0.0000      0.817 1.000 0.000
#> SRR1785269     1  0.0000      0.817 1.000 0.000
#> SRR1785270     1  0.5519      0.772 0.872 0.128
#> SRR1785271     1  0.5519      0.772 0.872 0.128
#> SRR1785272     1  0.9850      0.373 0.572 0.428
#> SRR1785273     1  0.9850      0.373 0.572 0.428
#> SRR1785276     1  0.2603      0.808 0.956 0.044
#> SRR1785277     1  0.2603      0.808 0.956 0.044
#> SRR1785274     2  0.6438      0.819 0.164 0.836
#> SRR1785275     2  0.6438      0.819 0.164 0.836
#> SRR1785280     2  0.0000      0.932 0.000 1.000
#> SRR1785281     2  0.0000      0.932 0.000 1.000
#> SRR1785278     1  0.0000      0.817 1.000 0.000
#> SRR1785279     1  0.0000      0.817 1.000 0.000
#> SRR1785282     1  0.0000      0.817 1.000 0.000
#> SRR1785283     1  0.0000      0.817 1.000 0.000
#> SRR1785284     1  0.5842      0.767 0.860 0.140
#> SRR1785285     1  0.5842      0.767 0.860 0.140
#> SRR1785286     1  0.9866      0.366 0.568 0.432
#> SRR1785287     1  0.9866      0.366 0.568 0.432
#> SRR1785288     1  0.0000      0.817 1.000 0.000
#> SRR1785289     1  0.0000      0.817 1.000 0.000
#> SRR1785290     2  0.2603      0.939 0.044 0.956
#> SRR1785291     2  0.2603      0.939 0.044 0.956
#> SRR1785296     2  0.2043      0.943 0.032 0.968
#> SRR1785297     2  0.2043      0.943 0.032 0.968
#> SRR1785292     2  0.5408      0.847 0.124 0.876
#> SRR1785293     2  0.5408      0.847 0.124 0.876
#> SRR1785294     2  0.2043      0.943 0.032 0.968
#> SRR1785295     2  0.2043      0.943 0.032 0.968
#> SRR1785298     2  0.2423      0.943 0.040 0.960
#> SRR1785299     2  0.2423      0.943 0.040 0.960
#> SRR1785300     1  0.0000      0.817 1.000 0.000
#> SRR1785301     1  0.0000      0.817 1.000 0.000
#> SRR1785304     2  0.3114      0.918 0.056 0.944
#> SRR1785305     2  0.3114      0.918 0.056 0.944
#> SRR1785306     1  0.6801      0.744 0.820 0.180
#> SRR1785307     1  0.6801      0.744 0.820 0.180
#> SRR1785302     1  0.7528      0.711 0.784 0.216
#> SRR1785303     1  0.7528      0.711 0.784 0.216
#> SRR1785308     1  0.9850      0.373 0.572 0.428
#> SRR1785309     1  0.9850      0.373 0.572 0.428
#> SRR1785310     1  0.9866      0.366 0.568 0.432
#> SRR1785311     1  0.9866      0.366 0.568 0.432
#> SRR1785312     1  0.0000      0.817 1.000 0.000
#> SRR1785313     1  0.0000      0.817 1.000 0.000
#> SRR1785314     1  0.7528      0.711 0.784 0.216
#> SRR1785315     1  0.7528      0.711 0.784 0.216
#> SRR1785318     2  0.0000      0.932 0.000 1.000
#> SRR1785319     2  0.0000      0.932 0.000 1.000
#> SRR1785316     1  0.0000      0.817 1.000 0.000
#> SRR1785317     1  0.0000      0.817 1.000 0.000
#> SRR1785324     2  0.5408      0.847 0.124 0.876
#> SRR1785325     2  0.5408      0.847 0.124 0.876
#> SRR1785320     1  0.0000      0.817 1.000 0.000
#> SRR1785321     1  0.0000      0.817 1.000 0.000
#> SRR1785322     2  0.4022      0.927 0.080 0.920
#> SRR1785323     2  0.4022      0.927 0.080 0.920

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     2  0.5008      0.852 0.016 0.804 0.180
#> SRR1785239     2  0.5008      0.852 0.016 0.804 0.180
#> SRR1785240     1  0.4228      0.826 0.844 0.148 0.008
#> SRR1785241     1  0.4228      0.826 0.844 0.148 0.008
#> SRR1785242     3  0.0000      0.997 0.000 0.000 1.000
#> SRR1785243     3  0.0000      0.997 0.000 0.000 1.000
#> SRR1785244     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785245     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785246     2  0.5733      0.732 0.000 0.676 0.324
#> SRR1785247     2  0.5733      0.732 0.000 0.676 0.324
#> SRR1785248     2  0.1163      0.842 0.000 0.972 0.028
#> SRR1785250     3  0.0000      0.997 0.000 0.000 1.000
#> SRR1785251     3  0.0000      0.997 0.000 0.000 1.000
#> SRR1785252     3  0.0000      0.997 0.000 0.000 1.000
#> SRR1785253     3  0.0000      0.997 0.000 0.000 1.000
#> SRR1785254     1  0.0424      0.884 0.992 0.008 0.000
#> SRR1785255     1  0.0424      0.884 0.992 0.008 0.000
#> SRR1785256     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785257     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785258     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785259     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785262     2  0.5254      0.792 0.000 0.736 0.264
#> SRR1785263     2  0.5254      0.792 0.000 0.736 0.264
#> SRR1785260     2  0.0592      0.848 0.000 0.988 0.012
#> SRR1785261     2  0.0592      0.848 0.000 0.988 0.012
#> SRR1785264     2  0.5008      0.852 0.016 0.804 0.180
#> SRR1785265     2  0.5008      0.852 0.016 0.804 0.180
#> SRR1785266     2  0.0892      0.843 0.000 0.980 0.020
#> SRR1785267     2  0.0892      0.843 0.000 0.980 0.020
#> SRR1785268     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785269     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785270     1  0.3482      0.841 0.872 0.128 0.000
#> SRR1785271     1  0.3482      0.841 0.872 0.128 0.000
#> SRR1785272     3  0.0592      0.987 0.000 0.012 0.988
#> SRR1785273     3  0.0592      0.987 0.000 0.012 0.988
#> SRR1785276     1  0.2152      0.868 0.948 0.036 0.016
#> SRR1785277     1  0.2152      0.868 0.948 0.036 0.016
#> SRR1785274     2  0.7245      0.770 0.120 0.712 0.168
#> SRR1785275     2  0.7245      0.770 0.120 0.712 0.168
#> SRR1785280     2  0.0892      0.843 0.000 0.980 0.020
#> SRR1785281     2  0.0892      0.843 0.000 0.980 0.020
#> SRR1785278     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785279     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785282     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785283     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785284     1  0.3816      0.830 0.852 0.148 0.000
#> SRR1785285     1  0.3816      0.830 0.852 0.148 0.000
#> SRR1785286     1  0.6608      0.368 0.560 0.432 0.008
#> SRR1785287     1  0.6608      0.368 0.560 0.432 0.008
#> SRR1785288     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785289     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785290     2  0.3482      0.859 0.000 0.872 0.128
#> SRR1785291     2  0.3482      0.859 0.000 0.872 0.128
#> SRR1785296     2  0.4121      0.857 0.000 0.832 0.168
#> SRR1785297     2  0.4121      0.857 0.000 0.832 0.168
#> SRR1785292     2  0.3267      0.774 0.116 0.884 0.000
#> SRR1785293     2  0.3267      0.774 0.116 0.884 0.000
#> SRR1785294     2  0.4121      0.857 0.000 0.832 0.168
#> SRR1785295     2  0.4121      0.857 0.000 0.832 0.168
#> SRR1785298     2  0.4473      0.858 0.008 0.828 0.164
#> SRR1785299     2  0.4473      0.858 0.008 0.828 0.164
#> SRR1785300     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785301     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785304     2  0.1753      0.828 0.048 0.952 0.000
#> SRR1785305     2  0.1753      0.828 0.048 0.952 0.000
#> SRR1785306     1  0.4399      0.803 0.812 0.188 0.000
#> SRR1785307     1  0.4399      0.803 0.812 0.188 0.000
#> SRR1785302     1  0.4842      0.766 0.776 0.224 0.000
#> SRR1785303     1  0.4842      0.766 0.776 0.224 0.000
#> SRR1785308     3  0.0000      0.997 0.000 0.000 1.000
#> SRR1785309     3  0.0000      0.997 0.000 0.000 1.000
#> SRR1785310     1  0.6608      0.368 0.560 0.432 0.008
#> SRR1785311     1  0.6608      0.368 0.560 0.432 0.008
#> SRR1785312     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785313     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785314     1  0.4842      0.766 0.776 0.224 0.000
#> SRR1785315     1  0.4842      0.766 0.776 0.224 0.000
#> SRR1785318     2  0.0892      0.843 0.000 0.980 0.020
#> SRR1785319     2  0.0892      0.843 0.000 0.980 0.020
#> SRR1785316     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785317     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785324     2  0.3267      0.774 0.116 0.884 0.000
#> SRR1785325     2  0.3267      0.774 0.116 0.884 0.000
#> SRR1785320     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785321     1  0.0000      0.885 1.000 0.000 0.000
#> SRR1785322     2  0.5348      0.849 0.028 0.796 0.176
#> SRR1785323     2  0.5348      0.849 0.028 0.796 0.176

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     4  0.1229      0.887 0.008 0.004 0.020 0.968
#> SRR1785239     4  0.1229      0.887 0.008 0.004 0.020 0.968
#> SRR1785240     1  0.5331      0.752 0.756 0.100 0.004 0.140
#> SRR1785241     1  0.5331      0.752 0.756 0.100 0.004 0.140
#> SRR1785242     3  0.3074      0.996 0.000 0.000 0.848 0.152
#> SRR1785243     3  0.3074      0.996 0.000 0.000 0.848 0.152
#> SRR1785244     1  0.0000      0.851 1.000 0.000 0.000 0.000
#> SRR1785245     1  0.0000      0.851 1.000 0.000 0.000 0.000
#> SRR1785246     4  0.3219      0.775 0.000 0.000 0.164 0.836
#> SRR1785247     4  0.3219      0.775 0.000 0.000 0.164 0.836
#> SRR1785248     2  0.2976      0.797 0.000 0.872 0.008 0.120
#> SRR1785250     3  0.3074      0.996 0.000 0.000 0.848 0.152
#> SRR1785251     3  0.3074      0.996 0.000 0.000 0.848 0.152
#> SRR1785252     3  0.3074      0.996 0.000 0.000 0.848 0.152
#> SRR1785253     3  0.3074      0.996 0.000 0.000 0.848 0.152
#> SRR1785254     1  0.0524      0.849 0.988 0.004 0.000 0.008
#> SRR1785255     1  0.0524      0.849 0.988 0.004 0.000 0.008
#> SRR1785256     1  0.0000      0.851 1.000 0.000 0.000 0.000
#> SRR1785257     1  0.0000      0.851 1.000 0.000 0.000 0.000
#> SRR1785258     1  0.0000      0.851 1.000 0.000 0.000 0.000
#> SRR1785259     1  0.0000      0.851 1.000 0.000 0.000 0.000
#> SRR1785262     4  0.2593      0.838 0.000 0.004 0.104 0.892
#> SRR1785263     4  0.2593      0.838 0.000 0.004 0.104 0.892
#> SRR1785260     4  0.3208      0.760 0.000 0.004 0.148 0.848
#> SRR1785261     4  0.3208      0.760 0.000 0.004 0.148 0.848
#> SRR1785264     4  0.1229      0.887 0.008 0.004 0.020 0.968
#> SRR1785265     4  0.1229      0.887 0.008 0.004 0.020 0.968
#> SRR1785266     2  0.2647      0.803 0.000 0.880 0.000 0.120
#> SRR1785267     2  0.2647      0.803 0.000 0.880 0.000 0.120
#> SRR1785268     1  0.0188      0.851 0.996 0.000 0.000 0.004
#> SRR1785269     1  0.0188      0.851 0.996 0.000 0.000 0.004
#> SRR1785270     1  0.5010      0.761 0.772 0.120 0.000 0.108
#> SRR1785271     1  0.5010      0.761 0.772 0.120 0.000 0.108
#> SRR1785272     3  0.3219      0.984 0.000 0.000 0.836 0.164
#> SRR1785273     3  0.3219      0.984 0.000 0.000 0.836 0.164
#> SRR1785276     1  0.1743      0.829 0.940 0.004 0.000 0.056
#> SRR1785277     1  0.1743      0.829 0.940 0.004 0.000 0.056
#> SRR1785274     4  0.3317      0.778 0.112 0.008 0.012 0.868
#> SRR1785275     4  0.3317      0.778 0.112 0.008 0.012 0.868
#> SRR1785280     2  0.2647      0.803 0.000 0.880 0.000 0.120
#> SRR1785281     2  0.2647      0.803 0.000 0.880 0.000 0.120
#> SRR1785278     1  0.0188      0.851 0.996 0.000 0.000 0.004
#> SRR1785279     1  0.0188      0.851 0.996 0.000 0.000 0.004
#> SRR1785282     1  0.0188      0.851 0.996 0.000 0.000 0.004
#> SRR1785283     1  0.0188      0.851 0.996 0.000 0.000 0.004
#> SRR1785284     1  0.5562      0.742 0.740 0.124 0.004 0.132
#> SRR1785285     1  0.5562      0.742 0.740 0.124 0.004 0.132
#> SRR1785286     1  0.8015      0.373 0.472 0.144 0.032 0.352
#> SRR1785287     1  0.8015      0.373 0.472 0.144 0.032 0.352
#> SRR1785288     1  0.0000      0.851 1.000 0.000 0.000 0.000
#> SRR1785289     1  0.0000      0.851 1.000 0.000 0.000 0.000
#> SRR1785290     4  0.3447      0.801 0.000 0.128 0.020 0.852
#> SRR1785291     4  0.3447      0.801 0.000 0.128 0.020 0.852
#> SRR1785296     4  0.0524      0.886 0.000 0.004 0.008 0.988
#> SRR1785297     4  0.0524      0.886 0.000 0.004 0.008 0.988
#> SRR1785292     2  0.6664      0.611 0.000 0.616 0.152 0.232
#> SRR1785293     2  0.6664      0.611 0.000 0.616 0.152 0.232
#> SRR1785294     4  0.0524      0.886 0.000 0.004 0.008 0.988
#> SRR1785295     4  0.0524      0.886 0.000 0.004 0.008 0.988
#> SRR1785298     4  0.0376      0.887 0.004 0.000 0.004 0.992
#> SRR1785299     4  0.0376      0.887 0.004 0.000 0.004 0.992
#> SRR1785300     1  0.0000      0.851 1.000 0.000 0.000 0.000
#> SRR1785301     1  0.0000      0.851 1.000 0.000 0.000 0.000
#> SRR1785304     4  0.4638      0.696 0.000 0.060 0.152 0.788
#> SRR1785305     4  0.4638      0.696 0.000 0.060 0.152 0.788
#> SRR1785306     1  0.6484      0.710 0.696 0.136 0.028 0.140
#> SRR1785307     1  0.6484      0.710 0.696 0.136 0.028 0.140
#> SRR1785302     1  0.6870      0.678 0.660 0.172 0.028 0.140
#> SRR1785303     1  0.6870      0.678 0.660 0.172 0.028 0.140
#> SRR1785308     3  0.3074      0.996 0.000 0.000 0.848 0.152
#> SRR1785309     3  0.3074      0.996 0.000 0.000 0.848 0.152
#> SRR1785310     1  0.8015      0.373 0.472 0.144 0.032 0.352
#> SRR1785311     1  0.8015      0.373 0.472 0.144 0.032 0.352
#> SRR1785312     1  0.0000      0.851 1.000 0.000 0.000 0.000
#> SRR1785313     1  0.0000      0.851 1.000 0.000 0.000 0.000
#> SRR1785314     1  0.6870      0.678 0.660 0.172 0.028 0.140
#> SRR1785315     1  0.6870      0.678 0.660 0.172 0.028 0.140
#> SRR1785318     2  0.2647      0.803 0.000 0.880 0.000 0.120
#> SRR1785319     2  0.2647      0.803 0.000 0.880 0.000 0.120
#> SRR1785316     1  0.0000      0.851 1.000 0.000 0.000 0.000
#> SRR1785317     1  0.0000      0.851 1.000 0.000 0.000 0.000
#> SRR1785324     2  0.6664      0.611 0.000 0.616 0.152 0.232
#> SRR1785325     2  0.6664      0.611 0.000 0.616 0.152 0.232
#> SRR1785320     1  0.0000      0.851 1.000 0.000 0.000 0.000
#> SRR1785321     1  0.0000      0.851 1.000 0.000 0.000 0.000
#> SRR1785322     4  0.1598      0.883 0.020 0.004 0.020 0.956
#> SRR1785323     4  0.1598      0.883 0.020 0.004 0.020 0.956

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     4  0.1012      0.892 0.000 0.000 0.020 0.968 0.012
#> SRR1785239     4  0.1012      0.892 0.000 0.000 0.020 0.968 0.012
#> SRR1785240     1  0.4219      0.697 0.716 0.000 0.000 0.024 0.260
#> SRR1785241     1  0.4219      0.697 0.716 0.000 0.000 0.024 0.260
#> SRR1785242     3  0.0510      0.986 0.000 0.000 0.984 0.016 0.000
#> SRR1785243     3  0.0510      0.986 0.000 0.000 0.984 0.016 0.000
#> SRR1785244     1  0.0162      0.828 0.996 0.004 0.000 0.000 0.000
#> SRR1785245     1  0.0162      0.828 0.996 0.004 0.000 0.000 0.000
#> SRR1785246     4  0.2773      0.817 0.000 0.000 0.164 0.836 0.000
#> SRR1785247     4  0.2773      0.817 0.000 0.000 0.164 0.836 0.000
#> SRR1785248     2  0.0451      0.989 0.000 0.988 0.008 0.004 0.000
#> SRR1785250     3  0.0510      0.986 0.000 0.000 0.984 0.016 0.000
#> SRR1785251     3  0.0510      0.986 0.000 0.000 0.984 0.016 0.000
#> SRR1785252     3  0.0510      0.986 0.000 0.000 0.984 0.016 0.000
#> SRR1785253     3  0.0510      0.986 0.000 0.000 0.984 0.016 0.000
#> SRR1785254     1  0.0671      0.827 0.980 0.004 0.000 0.000 0.016
#> SRR1785255     1  0.0671      0.827 0.980 0.004 0.000 0.000 0.016
#> SRR1785256     1  0.0162      0.828 0.996 0.000 0.000 0.004 0.000
#> SRR1785257     1  0.0162      0.828 0.996 0.000 0.000 0.004 0.000
#> SRR1785258     1  0.0162      0.828 0.996 0.004 0.000 0.000 0.000
#> SRR1785259     1  0.0162      0.828 0.996 0.004 0.000 0.000 0.000
#> SRR1785262     4  0.2392      0.858 0.000 0.004 0.104 0.888 0.004
#> SRR1785263     4  0.2392      0.858 0.000 0.004 0.104 0.888 0.004
#> SRR1785260     4  0.3169      0.804 0.000 0.004 0.016 0.840 0.140
#> SRR1785261     4  0.3169      0.804 0.000 0.004 0.016 0.840 0.140
#> SRR1785264     4  0.1012      0.892 0.000 0.000 0.020 0.968 0.012
#> SRR1785265     4  0.1012      0.892 0.000 0.000 0.020 0.968 0.012
#> SRR1785266     2  0.0162      0.998 0.000 0.996 0.000 0.004 0.000
#> SRR1785267     2  0.0162      0.998 0.000 0.996 0.000 0.004 0.000
#> SRR1785268     1  0.0451      0.828 0.988 0.000 0.000 0.008 0.004
#> SRR1785269     1  0.0451      0.828 0.988 0.000 0.000 0.008 0.004
#> SRR1785270     1  0.3715      0.704 0.736 0.000 0.000 0.004 0.260
#> SRR1785271     1  0.3715      0.704 0.736 0.000 0.000 0.004 0.260
#> SRR1785272     3  0.1478      0.942 0.000 0.000 0.936 0.064 0.000
#> SRR1785273     3  0.1478      0.942 0.000 0.000 0.936 0.064 0.000
#> SRR1785276     1  0.1809      0.801 0.928 0.000 0.000 0.060 0.012
#> SRR1785277     1  0.1809      0.801 0.928 0.000 0.000 0.060 0.012
#> SRR1785274     4  0.3907      0.733 0.100 0.000 0.012 0.820 0.068
#> SRR1785275     4  0.3907      0.733 0.100 0.000 0.012 0.820 0.068
#> SRR1785280     2  0.0162      0.998 0.000 0.996 0.000 0.004 0.000
#> SRR1785281     2  0.0162      0.998 0.000 0.996 0.000 0.004 0.000
#> SRR1785278     1  0.0451      0.828 0.988 0.000 0.000 0.008 0.004
#> SRR1785279     1  0.0451      0.828 0.988 0.000 0.000 0.008 0.004
#> SRR1785282     1  0.0451      0.828 0.988 0.000 0.000 0.008 0.004
#> SRR1785283     1  0.0451      0.828 0.988 0.000 0.000 0.008 0.004
#> SRR1785284     1  0.4130      0.678 0.696 0.000 0.000 0.012 0.292
#> SRR1785285     1  0.4130      0.678 0.696 0.000 0.000 0.012 0.292
#> SRR1785286     1  0.6620      0.253 0.436 0.000 0.000 0.228 0.336
#> SRR1785287     1  0.6620      0.253 0.436 0.000 0.000 0.228 0.336
#> SRR1785288     1  0.0162      0.828 0.996 0.004 0.000 0.000 0.000
#> SRR1785289     1  0.0162      0.828 0.996 0.004 0.000 0.000 0.000
#> SRR1785290     4  0.3632      0.788 0.000 0.176 0.020 0.800 0.004
#> SRR1785291     4  0.3632      0.788 0.000 0.176 0.020 0.800 0.004
#> SRR1785296     4  0.0740      0.890 0.000 0.004 0.008 0.980 0.008
#> SRR1785297     4  0.0740      0.890 0.000 0.004 0.008 0.980 0.008
#> SRR1785292     5  0.1306      1.000 0.000 0.008 0.016 0.016 0.960
#> SRR1785293     5  0.1306      1.000 0.000 0.008 0.016 0.016 0.960
#> SRR1785294     4  0.0740      0.890 0.000 0.004 0.008 0.980 0.008
#> SRR1785295     4  0.0740      0.890 0.000 0.004 0.008 0.980 0.008
#> SRR1785298     4  0.0324      0.891 0.000 0.000 0.004 0.992 0.004
#> SRR1785299     4  0.0324      0.891 0.000 0.000 0.004 0.992 0.004
#> SRR1785300     1  0.0162      0.828 0.996 0.004 0.000 0.000 0.000
#> SRR1785301     1  0.0162      0.828 0.996 0.004 0.000 0.000 0.000
#> SRR1785304     4  0.3883      0.726 0.000 0.004 0.016 0.764 0.216
#> SRR1785305     4  0.3883      0.726 0.000 0.004 0.016 0.764 0.216
#> SRR1785306     1  0.4418      0.636 0.652 0.000 0.000 0.016 0.332
#> SRR1785307     1  0.4418      0.636 0.652 0.000 0.000 0.016 0.332
#> SRR1785302     1  0.4551      0.594 0.616 0.000 0.000 0.016 0.368
#> SRR1785303     1  0.4551      0.594 0.616 0.000 0.000 0.016 0.368
#> SRR1785308     3  0.0510      0.986 0.000 0.000 0.984 0.016 0.000
#> SRR1785309     3  0.0510      0.986 0.000 0.000 0.984 0.016 0.000
#> SRR1785310     1  0.6620      0.253 0.436 0.000 0.000 0.228 0.336
#> SRR1785311     1  0.6620      0.253 0.436 0.000 0.000 0.228 0.336
#> SRR1785312     1  0.0162      0.828 0.996 0.004 0.000 0.000 0.000
#> SRR1785313     1  0.0162      0.828 0.996 0.004 0.000 0.000 0.000
#> SRR1785314     1  0.4551      0.594 0.616 0.000 0.000 0.016 0.368
#> SRR1785315     1  0.4551      0.594 0.616 0.000 0.000 0.016 0.368
#> SRR1785318     2  0.0162      0.998 0.000 0.996 0.000 0.004 0.000
#> SRR1785319     2  0.0162      0.998 0.000 0.996 0.000 0.004 0.000
#> SRR1785316     1  0.0162      0.828 0.996 0.004 0.000 0.000 0.000
#> SRR1785317     1  0.0162      0.828 0.996 0.004 0.000 0.000 0.000
#> SRR1785324     5  0.1306      1.000 0.000 0.008 0.016 0.016 0.960
#> SRR1785325     5  0.1306      1.000 0.000 0.008 0.016 0.016 0.960
#> SRR1785320     1  0.0162      0.828 0.996 0.004 0.000 0.000 0.000
#> SRR1785321     1  0.0162      0.828 0.996 0.004 0.000 0.000 0.000
#> SRR1785322     4  0.1299      0.888 0.008 0.000 0.020 0.960 0.012
#> SRR1785323     4  0.1299      0.888 0.008 0.000 0.020 0.960 0.012

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     4  0.1452      0.872 0.000 0.000 0.020 0.948 0.020 0.012
#> SRR1785239     4  0.1452      0.872 0.000 0.000 0.020 0.948 0.020 0.012
#> SRR1785240     5  0.4010      0.612 0.408 0.000 0.000 0.008 0.584 0.000
#> SRR1785241     5  0.4010      0.612 0.408 0.000 0.000 0.008 0.584 0.000
#> SRR1785242     3  0.0000      0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785243     3  0.0000      0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785244     1  0.0000      0.978 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785245     1  0.0000      0.978 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785246     4  0.2416      0.757 0.000 0.000 0.156 0.844 0.000 0.000
#> SRR1785247     4  0.2416      0.757 0.000 0.000 0.156 0.844 0.000 0.000
#> SRR1785248     2  0.0260      0.989 0.000 0.992 0.008 0.000 0.000 0.000
#> SRR1785250     3  0.0000      0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785251     3  0.0000      0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785252     3  0.0000      0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785253     3  0.0000      0.981 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785254     1  0.0713      0.956 0.972 0.000 0.000 0.000 0.028 0.000
#> SRR1785255     1  0.0713      0.956 0.972 0.000 0.000 0.000 0.028 0.000
#> SRR1785256     1  0.0363      0.974 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1785257     1  0.0363      0.974 0.988 0.000 0.000 0.000 0.012 0.000
#> SRR1785258     1  0.0000      0.978 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785259     1  0.0000      0.978 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785262     4  0.1908      0.819 0.000 0.000 0.096 0.900 0.000 0.004
#> SRR1785263     4  0.1908      0.819 0.000 0.000 0.096 0.900 0.000 0.004
#> SRR1785260     6  0.3830      0.910 0.000 0.000 0.000 0.376 0.004 0.620
#> SRR1785261     6  0.3830      0.910 0.000 0.000 0.000 0.376 0.004 0.620
#> SRR1785264     4  0.1452      0.872 0.000 0.000 0.020 0.948 0.020 0.012
#> SRR1785265     4  0.1452      0.872 0.000 0.000 0.020 0.948 0.020 0.012
#> SRR1785266     2  0.0000      0.998 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785267     2  0.0000      0.998 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785268     1  0.0547      0.970 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1785269     1  0.0547      0.970 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1785270     5  0.3817      0.584 0.432 0.000 0.000 0.000 0.568 0.000
#> SRR1785271     5  0.3817      0.584 0.432 0.000 0.000 0.000 0.568 0.000
#> SRR1785272     3  0.1327      0.922 0.000 0.000 0.936 0.064 0.000 0.000
#> SRR1785273     3  0.1327      0.922 0.000 0.000 0.936 0.064 0.000 0.000
#> SRR1785276     1  0.2136      0.879 0.904 0.000 0.000 0.048 0.048 0.000
#> SRR1785277     1  0.2136      0.879 0.904 0.000 0.000 0.048 0.048 0.000
#> SRR1785274     4  0.3940      0.631 0.068 0.000 0.012 0.796 0.116 0.008
#> SRR1785275     4  0.3940      0.631 0.068 0.000 0.012 0.796 0.116 0.008
#> SRR1785280     2  0.0000      0.998 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785281     2  0.0000      0.998 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785278     1  0.0547      0.970 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1785279     1  0.0547      0.970 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1785282     1  0.0547      0.970 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1785283     1  0.0547      0.970 0.980 0.000 0.000 0.000 0.020 0.000
#> SRR1785284     5  0.3727      0.639 0.388 0.000 0.000 0.000 0.612 0.000
#> SRR1785285     5  0.3727      0.639 0.388 0.000 0.000 0.000 0.612 0.000
#> SRR1785286     5  0.6262      0.557 0.192 0.000 0.000 0.088 0.580 0.140
#> SRR1785287     5  0.6262      0.557 0.192 0.000 0.000 0.088 0.580 0.140
#> SRR1785288     1  0.0000      0.978 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785289     1  0.0000      0.978 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785290     4  0.3470      0.675 0.000 0.176 0.020 0.792 0.000 0.012
#> SRR1785291     4  0.3470      0.675 0.000 0.176 0.020 0.792 0.000 0.012
#> SRR1785296     4  0.0146      0.861 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1785297     4  0.0146      0.861 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1785292     5  0.4093     -0.119 0.000 0.008 0.000 0.000 0.516 0.476
#> SRR1785293     5  0.4093     -0.119 0.000 0.008 0.000 0.000 0.516 0.476
#> SRR1785294     4  0.0146      0.861 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1785295     4  0.0146      0.861 0.000 0.000 0.000 0.996 0.000 0.004
#> SRR1785298     4  0.0622      0.866 0.000 0.000 0.000 0.980 0.012 0.008
#> SRR1785299     4  0.0622      0.866 0.000 0.000 0.000 0.980 0.012 0.008
#> SRR1785300     1  0.0000      0.978 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785301     1  0.0000      0.978 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785304     6  0.3753      0.918 0.000 0.004 0.000 0.292 0.008 0.696
#> SRR1785305     6  0.3753      0.918 0.000 0.004 0.000 0.292 0.008 0.696
#> SRR1785306     5  0.3592      0.676 0.344 0.000 0.000 0.000 0.656 0.000
#> SRR1785307     5  0.3592      0.676 0.344 0.000 0.000 0.000 0.656 0.000
#> SRR1785302     5  0.3446      0.692 0.308 0.000 0.000 0.000 0.692 0.000
#> SRR1785303     5  0.3446      0.692 0.308 0.000 0.000 0.000 0.692 0.000
#> SRR1785308     3  0.0146      0.979 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1785309     3  0.0146      0.979 0.000 0.000 0.996 0.000 0.000 0.004
#> SRR1785310     5  0.6262      0.557 0.192 0.000 0.000 0.088 0.580 0.140
#> SRR1785311     5  0.6262      0.557 0.192 0.000 0.000 0.088 0.580 0.140
#> SRR1785312     1  0.0000      0.978 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785313     1  0.0000      0.978 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785314     5  0.3446      0.692 0.308 0.000 0.000 0.000 0.692 0.000
#> SRR1785315     5  0.3446      0.692 0.308 0.000 0.000 0.000 0.692 0.000
#> SRR1785318     2  0.0000      0.998 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785319     2  0.0000      0.998 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785316     1  0.0000      0.978 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785317     1  0.0000      0.978 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785324     5  0.4093     -0.119 0.000 0.008 0.000 0.000 0.516 0.476
#> SRR1785325     5  0.4093     -0.119 0.000 0.008 0.000 0.000 0.516 0.476
#> SRR1785320     1  0.0000      0.978 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785321     1  0.0000      0.978 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785322     4  0.1592      0.867 0.000 0.000 0.020 0.940 0.032 0.008
#> SRR1785323     4  0.1592      0.867 0.000 0.000 0.020 0.940 0.032 0.008

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-ATC-hclust-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-ATC-hclust-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-ATC-hclust-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-ATC-hclust-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-ATC-hclust-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-ATC-hclust-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-ATC-hclust-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-ATC-hclust-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-ATC-hclust-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-ATC-hclust-membership-heatmap-5

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)

plot of chunk tab-ATC-hclust-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-ATC-hclust-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-ATC-hclust-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-ATC-hclust-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-ATC-hclust-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-ATC-hclust-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-ATC-hclust-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-ATC-hclust-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-ATC-hclust-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-ATC-hclust-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk ATC-hclust-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-ATC-hclust-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-ATC-hclust-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-ATC-hclust-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-ATC-hclust-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-ATC-hclust-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk ATC-hclust-collect-classes

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.


ATC:kmeans

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 16620 rows and 87 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)

plot of chunk ATC-kmeans-collect-plots

The plots are:

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:

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)

plot of chunk ATC-kmeans-select-partition-number

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 0.475           0.779       0.898         0.4943 0.502   0.502
#> 3 3 0.696           0.813       0.896         0.3195 0.752   0.544
#> 4 4 0.577           0.573       0.722         0.1187 0.889   0.686
#> 5 5 0.600           0.561       0.719         0.0717 0.866   0.558
#> 6 6 0.657           0.479       0.683         0.0453 0.933   0.716

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2  0.6438      0.787 0.164 0.836
#> SRR1785239     2  0.6438      0.787 0.164 0.836
#> SRR1785240     1  0.0000      0.931 1.000 0.000
#> SRR1785241     1  0.0000      0.931 1.000 0.000
#> SRR1785242     2  0.6973      0.771 0.188 0.812
#> SRR1785243     2  0.6973      0.771 0.188 0.812
#> SRR1785244     1  0.0000      0.931 1.000 0.000
#> SRR1785245     1  0.0000      0.931 1.000 0.000
#> SRR1785246     2  0.6973      0.771 0.188 0.812
#> SRR1785247     2  0.6973      0.771 0.188 0.812
#> SRR1785248     2  0.0000      0.828 0.000 1.000
#> SRR1785250     2  0.6973      0.771 0.188 0.812
#> SRR1785251     2  0.6973      0.771 0.188 0.812
#> SRR1785252     2  0.6973      0.771 0.188 0.812
#> SRR1785253     2  0.6973      0.771 0.188 0.812
#> SRR1785254     1  0.0000      0.931 1.000 0.000
#> SRR1785255     1  0.0000      0.931 1.000 0.000
#> SRR1785256     1  0.0000      0.931 1.000 0.000
#> SRR1785257     1  0.0000      0.931 1.000 0.000
#> SRR1785258     1  0.0000      0.931 1.000 0.000
#> SRR1785259     1  0.0000      0.931 1.000 0.000
#> SRR1785262     2  0.0000      0.828 0.000 1.000
#> SRR1785263     2  0.0000      0.828 0.000 1.000
#> SRR1785260     2  0.0672      0.831 0.008 0.992
#> SRR1785261     2  0.0672      0.831 0.008 0.992
#> SRR1785264     2  0.0672      0.831 0.008 0.992
#> SRR1785265     2  0.0672      0.831 0.008 0.992
#> SRR1785266     2  0.0672      0.831 0.008 0.992
#> SRR1785267     2  0.0672      0.831 0.008 0.992
#> SRR1785268     1  0.0000      0.931 1.000 0.000
#> SRR1785269     1  0.0000      0.931 1.000 0.000
#> SRR1785270     1  0.3114      0.887 0.944 0.056
#> SRR1785271     1  0.3114      0.887 0.944 0.056
#> SRR1785272     2  0.6973      0.771 0.188 0.812
#> SRR1785273     2  0.6973      0.771 0.188 0.812
#> SRR1785276     1  0.0000      0.931 1.000 0.000
#> SRR1785277     1  0.0000      0.931 1.000 0.000
#> SRR1785274     1  0.9795      0.014 0.584 0.416
#> SRR1785275     1  0.9795      0.014 0.584 0.416
#> SRR1785280     2  0.0672      0.831 0.008 0.992
#> SRR1785281     2  0.0672      0.831 0.008 0.992
#> SRR1785278     1  0.0000      0.931 1.000 0.000
#> SRR1785279     1  0.0000      0.931 1.000 0.000
#> SRR1785282     1  0.0000      0.931 1.000 0.000
#> SRR1785283     1  0.0000      0.931 1.000 0.000
#> SRR1785284     1  0.3114      0.887 0.944 0.056
#> SRR1785285     1  0.3114      0.887 0.944 0.056
#> SRR1785286     2  0.9732      0.336 0.404 0.596
#> SRR1785287     2  0.9732      0.336 0.404 0.596
#> SRR1785288     1  0.0000      0.931 1.000 0.000
#> SRR1785289     1  0.0000      0.931 1.000 0.000
#> SRR1785290     2  0.0672      0.831 0.008 0.992
#> SRR1785291     2  0.0672      0.831 0.008 0.992
#> SRR1785296     2  0.0672      0.831 0.008 0.992
#> SRR1785297     2  0.0672      0.831 0.008 0.992
#> SRR1785292     2  0.7950      0.636 0.240 0.760
#> SRR1785293     2  0.7950      0.636 0.240 0.760
#> SRR1785294     2  0.0672      0.831 0.008 0.992
#> SRR1785295     2  0.0672      0.831 0.008 0.992
#> SRR1785298     2  0.0672      0.831 0.008 0.992
#> SRR1785299     2  0.0672      0.831 0.008 0.992
#> SRR1785300     1  0.0000      0.931 1.000 0.000
#> SRR1785301     1  0.0000      0.931 1.000 0.000
#> SRR1785304     2  0.0672      0.831 0.008 0.992
#> SRR1785305     2  0.0672      0.831 0.008 0.992
#> SRR1785306     2  0.9732      0.336 0.404 0.596
#> SRR1785307     2  0.9732      0.336 0.404 0.596
#> SRR1785302     1  0.7815      0.687 0.768 0.232
#> SRR1785303     1  0.7815      0.687 0.768 0.232
#> SRR1785308     2  0.6973      0.771 0.188 0.812
#> SRR1785309     2  0.6973      0.771 0.188 0.812
#> SRR1785310     2  0.9732      0.336 0.404 0.596
#> SRR1785311     2  0.9732      0.336 0.404 0.596
#> SRR1785312     1  0.0000      0.931 1.000 0.000
#> SRR1785313     1  0.0000      0.931 1.000 0.000
#> SRR1785314     1  0.8207      0.652 0.744 0.256
#> SRR1785315     1  0.8207      0.652 0.744 0.256
#> SRR1785318     2  0.0672      0.831 0.008 0.992
#> SRR1785319     2  0.0672      0.831 0.008 0.992
#> SRR1785316     1  0.0000      0.931 1.000 0.000
#> SRR1785317     1  0.0000      0.931 1.000 0.000
#> SRR1785324     2  0.9732      0.336 0.404 0.596
#> SRR1785325     2  0.9732      0.336 0.404 0.596
#> SRR1785320     1  0.0000      0.931 1.000 0.000
#> SRR1785321     1  0.0000      0.931 1.000 0.000
#> SRR1785322     2  0.7299      0.765 0.204 0.796
#> SRR1785323     2  0.7299      0.765 0.204 0.796

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     3  0.5158      0.655 0.004 0.232 0.764
#> SRR1785239     3  0.5158      0.655 0.004 0.232 0.764
#> SRR1785240     1  0.0592      0.970 0.988 0.012 0.000
#> SRR1785241     1  0.0592      0.970 0.988 0.012 0.000
#> SRR1785242     3  0.0661      0.875 0.004 0.008 0.988
#> SRR1785243     3  0.0661      0.875 0.004 0.008 0.988
#> SRR1785244     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785245     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785246     3  0.0237      0.874 0.004 0.000 0.996
#> SRR1785247     3  0.0237      0.874 0.004 0.000 0.996
#> SRR1785248     3  0.0424      0.874 0.000 0.008 0.992
#> SRR1785250     3  0.0000      0.873 0.000 0.000 1.000
#> SRR1785251     3  0.0000      0.873 0.000 0.000 1.000
#> SRR1785252     3  0.0661      0.875 0.004 0.008 0.988
#> SRR1785253     3  0.0661      0.875 0.004 0.008 0.988
#> SRR1785254     1  0.0237      0.975 0.996 0.004 0.000
#> SRR1785255     1  0.0237      0.975 0.996 0.004 0.000
#> SRR1785256     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785257     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785258     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785259     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785262     3  0.2796      0.819 0.000 0.092 0.908
#> SRR1785263     3  0.2796      0.819 0.000 0.092 0.908
#> SRR1785260     2  0.6062      0.552 0.000 0.616 0.384
#> SRR1785261     2  0.6062      0.552 0.000 0.616 0.384
#> SRR1785264     2  0.4654      0.745 0.000 0.792 0.208
#> SRR1785265     2  0.4654      0.745 0.000 0.792 0.208
#> SRR1785266     2  0.4842      0.737 0.000 0.776 0.224
#> SRR1785267     2  0.4842      0.737 0.000 0.776 0.224
#> SRR1785268     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785269     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785270     1  0.4452      0.809 0.808 0.192 0.000
#> SRR1785271     1  0.4452      0.809 0.808 0.192 0.000
#> SRR1785272     3  0.0237      0.874 0.004 0.000 0.996
#> SRR1785273     3  0.0237      0.874 0.004 0.000 0.996
#> SRR1785276     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785277     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785274     3  0.8985      0.389 0.160 0.300 0.540
#> SRR1785275     3  0.8985      0.389 0.160 0.300 0.540
#> SRR1785280     2  0.4654      0.744 0.000 0.792 0.208
#> SRR1785281     2  0.4654      0.744 0.000 0.792 0.208
#> SRR1785278     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785279     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785282     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785283     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785284     1  0.3686      0.863 0.860 0.140 0.000
#> SRR1785285     1  0.3686      0.863 0.860 0.140 0.000
#> SRR1785286     2  0.1711      0.770 0.032 0.960 0.008
#> SRR1785287     2  0.1711      0.770 0.032 0.960 0.008
#> SRR1785288     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785289     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785290     2  0.5216      0.715 0.000 0.740 0.260
#> SRR1785291     2  0.5216      0.715 0.000 0.740 0.260
#> SRR1785296     2  0.6215      0.496 0.000 0.572 0.428
#> SRR1785297     2  0.6215      0.496 0.000 0.572 0.428
#> SRR1785292     2  0.0237      0.770 0.004 0.996 0.000
#> SRR1785293     2  0.0237      0.770 0.004 0.996 0.000
#> SRR1785294     2  0.6180      0.514 0.000 0.584 0.416
#> SRR1785295     2  0.6180      0.514 0.000 0.584 0.416
#> SRR1785298     2  0.6192      0.508 0.000 0.580 0.420
#> SRR1785299     2  0.6192      0.508 0.000 0.580 0.420
#> SRR1785300     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785301     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785304     2  0.0592      0.770 0.000 0.988 0.012
#> SRR1785305     2  0.0592      0.770 0.000 0.988 0.012
#> SRR1785306     2  0.1163      0.769 0.028 0.972 0.000
#> SRR1785307     2  0.1163      0.769 0.028 0.972 0.000
#> SRR1785302     2  0.4002      0.650 0.160 0.840 0.000
#> SRR1785303     2  0.4002      0.650 0.160 0.840 0.000
#> SRR1785308     3  0.0661      0.875 0.004 0.008 0.988
#> SRR1785309     3  0.0661      0.875 0.004 0.008 0.988
#> SRR1785310     2  0.1711      0.770 0.032 0.960 0.008
#> SRR1785311     2  0.1711      0.770 0.032 0.960 0.008
#> SRR1785312     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785313     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785314     2  0.1411      0.766 0.036 0.964 0.000
#> SRR1785315     2  0.1411      0.766 0.036 0.964 0.000
#> SRR1785318     2  0.4702      0.744 0.000 0.788 0.212
#> SRR1785319     2  0.4702      0.744 0.000 0.788 0.212
#> SRR1785316     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785317     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785324     2  0.0237      0.770 0.004 0.996 0.000
#> SRR1785325     2  0.0237      0.770 0.004 0.996 0.000
#> SRR1785320     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785321     1  0.0000      0.978 1.000 0.000 0.000
#> SRR1785322     3  0.6354      0.689 0.052 0.204 0.744
#> SRR1785323     3  0.6354      0.689 0.052 0.204 0.744

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     3  0.6201     0.4957 0.000 0.080 0.620 0.300
#> SRR1785239     3  0.6201     0.4957 0.000 0.080 0.620 0.300
#> SRR1785240     1  0.5730     0.6126 0.616 0.344 0.000 0.040
#> SRR1785241     1  0.5730     0.6126 0.616 0.344 0.000 0.040
#> SRR1785242     3  0.2335     0.7714 0.000 0.020 0.920 0.060
#> SRR1785243     3  0.2335     0.7714 0.000 0.020 0.920 0.060
#> SRR1785244     1  0.2924     0.8528 0.884 0.100 0.000 0.016
#> SRR1785245     1  0.2924     0.8528 0.884 0.100 0.000 0.016
#> SRR1785246     3  0.2759     0.7563 0.000 0.044 0.904 0.052
#> SRR1785247     3  0.2759     0.7563 0.000 0.044 0.904 0.052
#> SRR1785248     3  0.5374     0.5873 0.000 0.052 0.704 0.244
#> SRR1785250     3  0.0592     0.7795 0.000 0.000 0.984 0.016
#> SRR1785251     3  0.0592     0.7795 0.000 0.000 0.984 0.016
#> SRR1785252     3  0.2335     0.7714 0.000 0.020 0.920 0.060
#> SRR1785253     3  0.2335     0.7714 0.000 0.020 0.920 0.060
#> SRR1785254     1  0.3597     0.8282 0.836 0.148 0.000 0.016
#> SRR1785255     1  0.3597     0.8282 0.836 0.148 0.000 0.016
#> SRR1785256     1  0.2214     0.8694 0.928 0.044 0.000 0.028
#> SRR1785257     1  0.2214     0.8694 0.928 0.044 0.000 0.028
#> SRR1785258     1  0.0469     0.8753 0.988 0.012 0.000 0.000
#> SRR1785259     1  0.0469     0.8753 0.988 0.012 0.000 0.000
#> SRR1785262     3  0.5646     0.5206 0.000 0.048 0.656 0.296
#> SRR1785263     3  0.5646     0.5206 0.000 0.048 0.656 0.296
#> SRR1785260     4  0.5444     0.5490 0.000 0.048 0.264 0.688
#> SRR1785261     4  0.5444     0.5490 0.000 0.048 0.264 0.688
#> SRR1785264     4  0.6709     0.4444 0.000 0.212 0.172 0.616
#> SRR1785265     4  0.6709     0.4444 0.000 0.212 0.172 0.616
#> SRR1785266     4  0.6104     0.4712 0.000 0.232 0.104 0.664
#> SRR1785267     4  0.6104     0.4712 0.000 0.232 0.104 0.664
#> SRR1785268     1  0.2483     0.8659 0.916 0.052 0.000 0.032
#> SRR1785269     1  0.2483     0.8659 0.916 0.052 0.000 0.032
#> SRR1785270     2  0.5161    -0.0799 0.400 0.592 0.000 0.008
#> SRR1785271     2  0.5161    -0.0799 0.400 0.592 0.000 0.008
#> SRR1785272     3  0.0524     0.7792 0.000 0.008 0.988 0.004
#> SRR1785273     3  0.0524     0.7792 0.000 0.008 0.988 0.004
#> SRR1785276     1  0.4274     0.8197 0.828 0.120 0.012 0.040
#> SRR1785277     1  0.4274     0.8197 0.828 0.120 0.012 0.040
#> SRR1785274     2  0.9358     0.0670 0.096 0.372 0.268 0.264
#> SRR1785275     2  0.9358     0.0670 0.096 0.372 0.268 0.264
#> SRR1785280     4  0.6466     0.4280 0.000 0.288 0.104 0.608
#> SRR1785281     4  0.6466     0.4280 0.000 0.288 0.104 0.608
#> SRR1785278     1  0.2830     0.8607 0.900 0.060 0.000 0.040
#> SRR1785279     1  0.2830     0.8607 0.900 0.060 0.000 0.040
#> SRR1785282     1  0.2483     0.8659 0.916 0.052 0.000 0.032
#> SRR1785283     1  0.2483     0.8659 0.916 0.052 0.000 0.032
#> SRR1785284     1  0.5604     0.3712 0.504 0.476 0.000 0.020
#> SRR1785285     1  0.5604     0.3712 0.504 0.476 0.000 0.020
#> SRR1785286     2  0.5168     0.4118 0.004 0.504 0.000 0.492
#> SRR1785287     2  0.5168     0.4118 0.004 0.504 0.000 0.492
#> SRR1785288     1  0.2924     0.8528 0.884 0.100 0.000 0.016
#> SRR1785289     1  0.2924     0.8528 0.884 0.100 0.000 0.016
#> SRR1785290     4  0.4852     0.5532 0.000 0.072 0.152 0.776
#> SRR1785291     4  0.4852     0.5532 0.000 0.072 0.152 0.776
#> SRR1785296     4  0.4820     0.5434 0.000 0.012 0.296 0.692
#> SRR1785297     4  0.4820     0.5434 0.000 0.012 0.296 0.692
#> SRR1785292     2  0.5147     0.1626 0.004 0.536 0.000 0.460
#> SRR1785293     2  0.5147     0.1626 0.004 0.536 0.000 0.460
#> SRR1785294     4  0.5471     0.5513 0.000 0.048 0.268 0.684
#> SRR1785295     4  0.5471     0.5513 0.000 0.048 0.268 0.684
#> SRR1785298     4  0.6375     0.4859 0.000 0.088 0.312 0.600
#> SRR1785299     4  0.6375     0.4859 0.000 0.088 0.312 0.600
#> SRR1785300     1  0.2489     0.8717 0.912 0.068 0.000 0.020
#> SRR1785301     1  0.2489     0.8717 0.912 0.068 0.000 0.020
#> SRR1785304     4  0.4699    -0.0337 0.000 0.320 0.004 0.676
#> SRR1785305     4  0.4699    -0.0337 0.000 0.320 0.004 0.676
#> SRR1785306     2  0.4964     0.5041 0.004 0.616 0.000 0.380
#> SRR1785307     2  0.4964     0.5041 0.004 0.616 0.000 0.380
#> SRR1785302     2  0.6054     0.5100 0.056 0.592 0.000 0.352
#> SRR1785303     2  0.6054     0.5100 0.056 0.592 0.000 0.352
#> SRR1785308     3  0.1733     0.7782 0.000 0.024 0.948 0.028
#> SRR1785309     3  0.1733     0.7782 0.000 0.024 0.948 0.028
#> SRR1785310     4  0.5463    -0.3982 0.004 0.488 0.008 0.500
#> SRR1785311     4  0.5463    -0.3982 0.004 0.488 0.008 0.500
#> SRR1785312     1  0.0469     0.8753 0.988 0.012 0.000 0.000
#> SRR1785313     1  0.0469     0.8753 0.988 0.012 0.000 0.000
#> SRR1785314     2  0.5112     0.5062 0.008 0.608 0.000 0.384
#> SRR1785315     2  0.5112     0.5062 0.008 0.608 0.000 0.384
#> SRR1785318     4  0.6245     0.4422 0.000 0.268 0.096 0.636
#> SRR1785319     4  0.6245     0.4422 0.000 0.268 0.096 0.636
#> SRR1785316     1  0.2142     0.8647 0.928 0.056 0.000 0.016
#> SRR1785317     1  0.2142     0.8647 0.928 0.056 0.000 0.016
#> SRR1785324     2  0.4920     0.3334 0.004 0.628 0.000 0.368
#> SRR1785325     2  0.4920     0.3334 0.004 0.628 0.000 0.368
#> SRR1785320     1  0.0469     0.8753 0.988 0.012 0.000 0.000
#> SRR1785321     1  0.0469     0.8753 0.988 0.012 0.000 0.000
#> SRR1785322     3  0.8596     0.3445 0.064 0.192 0.488 0.256
#> SRR1785323     3  0.8596     0.3445 0.064 0.192 0.488 0.256

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     4  0.7295     0.3467 0.004 0.140 0.316 0.484 0.056
#> SRR1785239     4  0.7295     0.3467 0.004 0.140 0.316 0.484 0.056
#> SRR1785240     5  0.6865    -0.0622 0.352 0.016 0.000 0.184 0.448
#> SRR1785241     5  0.6865    -0.0622 0.352 0.016 0.000 0.184 0.448
#> SRR1785242     3  0.1357     0.8324 0.000 0.048 0.948 0.000 0.004
#> SRR1785243     3  0.1357     0.8324 0.000 0.048 0.948 0.000 0.004
#> SRR1785244     1  0.4158     0.7867 0.828 0.024 0.016 0.056 0.076
#> SRR1785245     1  0.4158     0.7867 0.828 0.024 0.016 0.056 0.076
#> SRR1785246     3  0.4591     0.4646 0.000 0.012 0.648 0.332 0.008
#> SRR1785247     3  0.4591     0.4646 0.000 0.012 0.648 0.332 0.008
#> SRR1785248     3  0.4589     0.1239 0.000 0.472 0.520 0.004 0.004
#> SRR1785250     3  0.1525     0.8270 0.000 0.004 0.948 0.036 0.012
#> SRR1785251     3  0.1525     0.8270 0.000 0.004 0.948 0.036 0.012
#> SRR1785252     3  0.1357     0.8324 0.000 0.048 0.948 0.000 0.004
#> SRR1785253     3  0.1357     0.8324 0.000 0.048 0.948 0.000 0.004
#> SRR1785254     1  0.5849     0.6544 0.684 0.028 0.016 0.076 0.196
#> SRR1785255     1  0.5849     0.6544 0.684 0.028 0.016 0.076 0.196
#> SRR1785256     1  0.4130     0.8058 0.788 0.008 0.004 0.164 0.036
#> SRR1785257     1  0.4130     0.8058 0.788 0.008 0.004 0.164 0.036
#> SRR1785258     1  0.0981     0.8368 0.972 0.012 0.000 0.008 0.008
#> SRR1785259     1  0.0981     0.8368 0.972 0.012 0.000 0.008 0.008
#> SRR1785262     4  0.6114     0.4525 0.000 0.140 0.272 0.580 0.008
#> SRR1785263     4  0.6114     0.4525 0.000 0.140 0.272 0.580 0.008
#> SRR1785260     4  0.6898     0.4521 0.000 0.260 0.128 0.552 0.060
#> SRR1785261     4  0.6898     0.4521 0.000 0.260 0.128 0.552 0.060
#> SRR1785264     4  0.7826     0.3044 0.000 0.284 0.124 0.444 0.148
#> SRR1785265     4  0.7826     0.3044 0.000 0.284 0.124 0.444 0.148
#> SRR1785266     2  0.1568     0.7967 0.000 0.944 0.036 0.020 0.000
#> SRR1785267     2  0.1568     0.7967 0.000 0.944 0.036 0.020 0.000
#> SRR1785268     1  0.4090     0.8058 0.792 0.008 0.004 0.160 0.036
#> SRR1785269     1  0.4090     0.8058 0.792 0.008 0.004 0.160 0.036
#> SRR1785270     5  0.4117     0.5412 0.108 0.008 0.008 0.064 0.812
#> SRR1785271     5  0.4117     0.5412 0.108 0.008 0.008 0.064 0.812
#> SRR1785272     3  0.2629     0.7947 0.000 0.004 0.880 0.104 0.012
#> SRR1785273     3  0.2629     0.7947 0.000 0.004 0.880 0.104 0.012
#> SRR1785276     1  0.5067     0.7442 0.716 0.016 0.004 0.208 0.056
#> SRR1785277     1  0.5067     0.7442 0.716 0.016 0.004 0.208 0.056
#> SRR1785274     4  0.7710     0.1673 0.036 0.048 0.120 0.468 0.328
#> SRR1785275     4  0.7710     0.1673 0.036 0.048 0.120 0.468 0.328
#> SRR1785280     2  0.2130     0.7902 0.000 0.924 0.044 0.016 0.016
#> SRR1785281     2  0.2130     0.7902 0.000 0.924 0.044 0.016 0.016
#> SRR1785278     1  0.4390     0.7922 0.764 0.008 0.004 0.184 0.040
#> SRR1785279     1  0.4390     0.7922 0.764 0.008 0.004 0.184 0.040
#> SRR1785282     1  0.3972     0.8089 0.800 0.008 0.004 0.156 0.032
#> SRR1785283     1  0.3972     0.8089 0.800 0.008 0.004 0.156 0.032
#> SRR1785284     5  0.6074     0.2893 0.280 0.016 0.008 0.088 0.608
#> SRR1785285     5  0.6074     0.2893 0.280 0.016 0.008 0.088 0.608
#> SRR1785286     5  0.4734     0.3046 0.000 0.036 0.000 0.312 0.652
#> SRR1785287     5  0.4734     0.3046 0.000 0.036 0.000 0.312 0.652
#> SRR1785288     1  0.3963     0.7891 0.836 0.016 0.016 0.052 0.080
#> SRR1785289     1  0.3963     0.7891 0.836 0.016 0.016 0.052 0.080
#> SRR1785290     2  0.6165     0.1138 0.000 0.552 0.092 0.336 0.020
#> SRR1785291     2  0.6165     0.1138 0.000 0.552 0.092 0.336 0.020
#> SRR1785296     4  0.6839     0.4387 0.000 0.276 0.140 0.540 0.044
#> SRR1785297     4  0.6839     0.4387 0.000 0.276 0.140 0.540 0.044
#> SRR1785292     5  0.5509     0.2156 0.000 0.464 0.000 0.064 0.472
#> SRR1785293     5  0.5509     0.2156 0.000 0.464 0.000 0.064 0.472
#> SRR1785294     4  0.6860     0.4513 0.000 0.264 0.128 0.552 0.056
#> SRR1785295     4  0.6860     0.4513 0.000 0.264 0.128 0.552 0.056
#> SRR1785298     4  0.6292     0.4840 0.000 0.212 0.140 0.616 0.032
#> SRR1785299     4  0.6292     0.4840 0.000 0.212 0.140 0.616 0.032
#> SRR1785300     1  0.3917     0.8301 0.840 0.016 0.016 0.068 0.060
#> SRR1785301     1  0.3917     0.8301 0.840 0.016 0.016 0.068 0.060
#> SRR1785304     4  0.6797    -0.0028 0.000 0.288 0.000 0.356 0.356
#> SRR1785305     4  0.6797    -0.0028 0.000 0.288 0.000 0.356 0.356
#> SRR1785306     5  0.3442     0.5854 0.000 0.104 0.000 0.060 0.836
#> SRR1785307     5  0.3442     0.5854 0.000 0.104 0.000 0.060 0.836
#> SRR1785302     5  0.3664     0.5783 0.004 0.064 0.000 0.104 0.828
#> SRR1785303     5  0.3664     0.5783 0.004 0.064 0.000 0.104 0.828
#> SRR1785308     3  0.1106     0.8367 0.000 0.024 0.964 0.012 0.000
#> SRR1785309     3  0.1106     0.8367 0.000 0.024 0.964 0.012 0.000
#> SRR1785310     4  0.5114     0.0666 0.000 0.036 0.000 0.492 0.472
#> SRR1785311     4  0.5114     0.0666 0.000 0.036 0.000 0.492 0.472
#> SRR1785312     1  0.0807     0.8378 0.976 0.012 0.000 0.012 0.000
#> SRR1785313     1  0.0807     0.8378 0.976 0.012 0.000 0.012 0.000
#> SRR1785314     5  0.3579     0.5845 0.000 0.100 0.000 0.072 0.828
#> SRR1785315     5  0.3579     0.5845 0.000 0.100 0.000 0.072 0.828
#> SRR1785318     2  0.1728     0.7978 0.000 0.940 0.036 0.004 0.020
#> SRR1785319     2  0.1728     0.7978 0.000 0.940 0.036 0.004 0.020
#> SRR1785316     1  0.2651     0.8202 0.908 0.012 0.016 0.028 0.036
#> SRR1785317     1  0.2651     0.8202 0.908 0.012 0.016 0.028 0.036
#> SRR1785324     5  0.5484     0.3517 0.000 0.392 0.000 0.068 0.540
#> SRR1785325     5  0.5484     0.3517 0.000 0.392 0.000 0.068 0.540
#> SRR1785320     1  0.0566     0.8373 0.984 0.012 0.000 0.004 0.000
#> SRR1785321     1  0.0566     0.8373 0.984 0.012 0.000 0.004 0.000
#> SRR1785322     4  0.6024     0.3456 0.028 0.016 0.220 0.660 0.076
#> SRR1785323     4  0.6024     0.3456 0.028 0.016 0.220 0.660 0.076

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     4   0.698    0.38861 0.000 0.048 0.144 0.492 0.036 0.280
#> SRR1785239     4   0.698    0.38861 0.000 0.048 0.144 0.492 0.036 0.280
#> SRR1785240     6   0.726    0.25007 0.204 0.044 0.004 0.024 0.308 0.416
#> SRR1785241     6   0.726    0.25007 0.204 0.044 0.004 0.024 0.308 0.416
#> SRR1785242     3   0.128    0.82309 0.000 0.012 0.956 0.024 0.004 0.004
#> SRR1785243     3   0.128    0.82309 0.000 0.012 0.956 0.024 0.004 0.004
#> SRR1785244     1   0.196    0.58480 0.928 0.016 0.012 0.000 0.012 0.032
#> SRR1785245     1   0.196    0.58480 0.928 0.016 0.012 0.000 0.012 0.032
#> SRR1785246     3   0.650    0.21905 0.000 0.020 0.416 0.340 0.004 0.220
#> SRR1785247     3   0.650    0.21905 0.000 0.020 0.416 0.340 0.004 0.220
#> SRR1785248     2   0.605    0.27359 0.000 0.476 0.392 0.096 0.008 0.028
#> SRR1785250     3   0.259    0.82336 0.000 0.020 0.892 0.056 0.004 0.028
#> SRR1785251     3   0.259    0.82336 0.000 0.020 0.892 0.056 0.004 0.028
#> SRR1785252     3   0.128    0.82309 0.000 0.012 0.956 0.024 0.004 0.004
#> SRR1785253     3   0.128    0.82309 0.000 0.012 0.956 0.024 0.004 0.004
#> SRR1785254     1   0.529    0.35717 0.704 0.052 0.012 0.000 0.140 0.092
#> SRR1785255     1   0.529    0.35717 0.704 0.052 0.012 0.000 0.140 0.092
#> SRR1785256     1   0.422    0.33829 0.520 0.004 0.000 0.000 0.008 0.468
#> SRR1785257     1   0.422    0.33829 0.520 0.004 0.000 0.000 0.008 0.468
#> SRR1785258     1   0.393    0.62654 0.756 0.032 0.008 0.000 0.004 0.200
#> SRR1785259     1   0.393    0.62654 0.756 0.032 0.008 0.000 0.004 0.200
#> SRR1785262     4   0.421    0.56690 0.000 0.048 0.048 0.784 0.004 0.116
#> SRR1785263     4   0.421    0.56690 0.000 0.048 0.048 0.784 0.004 0.116
#> SRR1785260     4   0.260    0.57019 0.000 0.084 0.004 0.876 0.036 0.000
#> SRR1785261     4   0.260    0.57019 0.000 0.084 0.004 0.876 0.036 0.000
#> SRR1785264     4   0.769    0.39229 0.000 0.128 0.044 0.448 0.140 0.240
#> SRR1785265     4   0.769    0.39229 0.000 0.128 0.044 0.448 0.140 0.240
#> SRR1785266     2   0.371    0.88070 0.000 0.816 0.016 0.120 0.028 0.020
#> SRR1785267     2   0.371    0.88070 0.000 0.816 0.016 0.120 0.028 0.020
#> SRR1785268     1   0.432    0.31754 0.504 0.008 0.000 0.000 0.008 0.480
#> SRR1785269     1   0.432    0.31754 0.504 0.008 0.000 0.000 0.008 0.480
#> SRR1785270     5   0.594    0.46526 0.144 0.056 0.008 0.004 0.644 0.144
#> SRR1785271     5   0.594    0.46526 0.144 0.056 0.008 0.004 0.644 0.144
#> SRR1785272     3   0.419    0.78219 0.000 0.016 0.776 0.100 0.004 0.104
#> SRR1785273     3   0.419    0.78219 0.000 0.016 0.776 0.100 0.004 0.104
#> SRR1785276     6   0.556    0.07917 0.368 0.020 0.000 0.032 0.032 0.548
#> SRR1785277     6   0.556    0.07917 0.368 0.020 0.000 0.032 0.032 0.548
#> SRR1785274     6   0.705    0.01195 0.000 0.028 0.052 0.244 0.188 0.488
#> SRR1785275     6   0.705    0.01195 0.000 0.028 0.052 0.244 0.188 0.488
#> SRR1785280     2   0.340    0.88335 0.000 0.836 0.020 0.100 0.040 0.004
#> SRR1785281     2   0.340    0.88335 0.000 0.836 0.020 0.100 0.040 0.004
#> SRR1785278     6   0.422   -0.30343 0.468 0.004 0.000 0.000 0.008 0.520
#> SRR1785279     6   0.422   -0.30343 0.468 0.004 0.000 0.000 0.008 0.520
#> SRR1785282     1   0.431    0.36306 0.524 0.008 0.000 0.000 0.008 0.460
#> SRR1785283     1   0.431    0.36306 0.524 0.008 0.000 0.000 0.008 0.460
#> SRR1785284     5   0.674    0.26351 0.248 0.052 0.004 0.004 0.500 0.192
#> SRR1785285     5   0.674    0.26351 0.248 0.052 0.004 0.004 0.500 0.192
#> SRR1785286     5   0.440    0.31828 0.000 0.004 0.000 0.352 0.616 0.028
#> SRR1785287     5   0.440    0.31828 0.000 0.004 0.000 0.352 0.616 0.028
#> SRR1785288     1   0.217    0.57989 0.916 0.016 0.008 0.000 0.016 0.044
#> SRR1785289     1   0.217    0.57989 0.916 0.016 0.008 0.000 0.016 0.044
#> SRR1785290     4   0.590    0.13089 0.000 0.368 0.016 0.520 0.028 0.068
#> SRR1785291     4   0.590    0.13089 0.000 0.368 0.016 0.520 0.028 0.068
#> SRR1785296     4   0.239    0.56880 0.000 0.092 0.004 0.884 0.020 0.000
#> SRR1785297     4   0.239    0.56880 0.000 0.092 0.004 0.884 0.020 0.000
#> SRR1785292     5   0.544    0.33200 0.000 0.348 0.008 0.032 0.568 0.044
#> SRR1785293     5   0.544    0.33200 0.000 0.348 0.008 0.032 0.568 0.044
#> SRR1785294     4   0.245    0.57150 0.000 0.084 0.004 0.884 0.028 0.000
#> SRR1785295     4   0.245    0.57150 0.000 0.084 0.004 0.884 0.028 0.000
#> SRR1785298     4   0.275    0.59900 0.000 0.004 0.004 0.868 0.028 0.096
#> SRR1785299     4   0.275    0.59900 0.000 0.004 0.004 0.868 0.028 0.096
#> SRR1785300     1   0.366    0.57988 0.780 0.028 0.000 0.000 0.012 0.180
#> SRR1785301     1   0.366    0.57988 0.780 0.028 0.000 0.000 0.012 0.180
#> SRR1785304     4   0.636    0.00859 0.000 0.120 0.008 0.444 0.392 0.036
#> SRR1785305     4   0.636    0.00859 0.000 0.120 0.008 0.444 0.392 0.036
#> SRR1785306     5   0.240    0.66248 0.000 0.020 0.000 0.044 0.900 0.036
#> SRR1785307     5   0.240    0.66248 0.000 0.020 0.000 0.044 0.900 0.036
#> SRR1785302     5   0.251    0.65574 0.000 0.016 0.000 0.052 0.892 0.040
#> SRR1785303     5   0.251    0.65574 0.000 0.016 0.000 0.052 0.892 0.040
#> SRR1785308     3   0.198    0.82590 0.000 0.004 0.924 0.032 0.008 0.032
#> SRR1785309     3   0.198    0.82590 0.000 0.004 0.924 0.032 0.008 0.032
#> SRR1785310     4   0.526    0.27560 0.000 0.008 0.000 0.572 0.328 0.092
#> SRR1785311     4   0.526    0.27560 0.000 0.008 0.000 0.572 0.328 0.092
#> SRR1785312     1   0.362    0.62207 0.760 0.024 0.000 0.000 0.004 0.212
#> SRR1785313     1   0.362    0.62207 0.760 0.024 0.000 0.000 0.004 0.212
#> SRR1785314     5   0.130    0.66216 0.000 0.012 0.000 0.040 0.948 0.000
#> SRR1785315     5   0.130    0.66216 0.000 0.012 0.000 0.040 0.948 0.000
#> SRR1785318     2   0.336    0.88412 0.000 0.832 0.012 0.112 0.040 0.004
#> SRR1785319     2   0.336    0.88412 0.000 0.832 0.012 0.112 0.040 0.004
#> SRR1785316     1   0.130    0.62310 0.948 0.004 0.004 0.000 0.000 0.044
#> SRR1785317     1   0.130    0.62310 0.948 0.004 0.004 0.000 0.000 0.044
#> SRR1785324     5   0.520    0.39546 0.000 0.316 0.008 0.024 0.608 0.044
#> SRR1785325     5   0.520    0.39546 0.000 0.316 0.008 0.024 0.608 0.044
#> SRR1785320     1   0.353    0.62742 0.772 0.024 0.000 0.000 0.004 0.200
#> SRR1785321     1   0.353    0.62742 0.772 0.024 0.000 0.000 0.004 0.200
#> SRR1785322     4   0.673    0.28298 0.000 0.020 0.104 0.424 0.056 0.396
#> SRR1785323     4   0.673    0.28298 0.000 0.020 0.104 0.424 0.056 0.396

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-ATC-kmeans-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-ATC-kmeans-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-ATC-kmeans-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-ATC-kmeans-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-ATC-kmeans-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-ATC-kmeans-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-ATC-kmeans-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-ATC-kmeans-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-ATC-kmeans-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-ATC-kmeans-membership-heatmap-5

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)

plot of chunk tab-ATC-kmeans-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-ATC-kmeans-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-ATC-kmeans-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-ATC-kmeans-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-ATC-kmeans-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-ATC-kmeans-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-ATC-kmeans-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-ATC-kmeans-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-ATC-kmeans-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-ATC-kmeans-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk ATC-kmeans-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-ATC-kmeans-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-ATC-kmeans-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-ATC-kmeans-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-ATC-kmeans-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-ATC-kmeans-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk ATC-kmeans-collect-classes

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.


ATC:skmeans**

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#>   Subgroups are detected by 'skmeans' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 2.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk ATC-skmeans-collect-plots

The plots are:

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:

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)

plot of chunk ATC-skmeans-select-partition-number

The numeric values for all these statistics can be obtained by get_stats().

get_stats(res)
#>   k 1-PAC mean_silhouette concordance area_increased  Rand Jaccard
#> 2 2 1.000           0.976       0.989         0.5055 0.494   0.494
#> 3 3 0.825           0.929       0.947         0.3010 0.703   0.475
#> 4 4 0.874           0.804       0.902         0.1324 0.888   0.683
#> 5 5 0.833           0.832       0.898         0.0581 0.906   0.664
#> 6 6 0.798           0.752       0.843         0.0390 0.947   0.760

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2   0.000      0.978 0.000 1.000
#> SRR1785239     2   0.000      0.978 0.000 1.000
#> SRR1785240     1   0.000      1.000 1.000 0.000
#> SRR1785241     1   0.000      1.000 1.000 0.000
#> SRR1785242     2   0.000      0.978 0.000 1.000
#> SRR1785243     2   0.000      0.978 0.000 1.000
#> SRR1785244     1   0.000      1.000 1.000 0.000
#> SRR1785245     1   0.000      1.000 1.000 0.000
#> SRR1785246     2   0.000      0.978 0.000 1.000
#> SRR1785247     2   0.000      0.978 0.000 1.000
#> SRR1785248     2   0.000      0.978 0.000 1.000
#> SRR1785250     2   0.000      0.978 0.000 1.000
#> SRR1785251     2   0.000      0.978 0.000 1.000
#> SRR1785252     2   0.000      0.978 0.000 1.000
#> SRR1785253     2   0.000      0.978 0.000 1.000
#> SRR1785254     1   0.000      1.000 1.000 0.000
#> SRR1785255     1   0.000      1.000 1.000 0.000
#> SRR1785256     1   0.000      1.000 1.000 0.000
#> SRR1785257     1   0.000      1.000 1.000 0.000
#> SRR1785258     1   0.000      1.000 1.000 0.000
#> SRR1785259     1   0.000      1.000 1.000 0.000
#> SRR1785262     2   0.000      0.978 0.000 1.000
#> SRR1785263     2   0.000      0.978 0.000 1.000
#> SRR1785260     2   0.000      0.978 0.000 1.000
#> SRR1785261     2   0.000      0.978 0.000 1.000
#> SRR1785264     2   0.000      0.978 0.000 1.000
#> SRR1785265     2   0.000      0.978 0.000 1.000
#> SRR1785266     2   0.000      0.978 0.000 1.000
#> SRR1785267     2   0.000      0.978 0.000 1.000
#> SRR1785268     1   0.000      1.000 1.000 0.000
#> SRR1785269     1   0.000      1.000 1.000 0.000
#> SRR1785270     1   0.000      1.000 1.000 0.000
#> SRR1785271     1   0.000      1.000 1.000 0.000
#> SRR1785272     2   0.000      0.978 0.000 1.000
#> SRR1785273     2   0.000      0.978 0.000 1.000
#> SRR1785276     1   0.000      1.000 1.000 0.000
#> SRR1785277     1   0.000      1.000 1.000 0.000
#> SRR1785274     2   0.904      0.546 0.320 0.680
#> SRR1785275     2   0.904      0.546 0.320 0.680
#> SRR1785280     2   0.000      0.978 0.000 1.000
#> SRR1785281     2   0.000      0.978 0.000 1.000
#> SRR1785278     1   0.000      1.000 1.000 0.000
#> SRR1785279     1   0.000      1.000 1.000 0.000
#> SRR1785282     1   0.000      1.000 1.000 0.000
#> SRR1785283     1   0.000      1.000 1.000 0.000
#> SRR1785284     1   0.000      1.000 1.000 0.000
#> SRR1785285     1   0.000      1.000 1.000 0.000
#> SRR1785286     1   0.000      1.000 1.000 0.000
#> SRR1785287     1   0.000      1.000 1.000 0.000
#> SRR1785288     1   0.000      1.000 1.000 0.000
#> SRR1785289     1   0.000      1.000 1.000 0.000
#> SRR1785290     2   0.000      0.978 0.000 1.000
#> SRR1785291     2   0.000      0.978 0.000 1.000
#> SRR1785296     2   0.000      0.978 0.000 1.000
#> SRR1785297     2   0.000      0.978 0.000 1.000
#> SRR1785292     2   0.595      0.831 0.144 0.856
#> SRR1785293     2   0.595      0.831 0.144 0.856
#> SRR1785294     2   0.000      0.978 0.000 1.000
#> SRR1785295     2   0.000      0.978 0.000 1.000
#> SRR1785298     2   0.000      0.978 0.000 1.000
#> SRR1785299     2   0.000      0.978 0.000 1.000
#> SRR1785300     1   0.000      1.000 1.000 0.000
#> SRR1785301     1   0.000      1.000 1.000 0.000
#> SRR1785304     2   0.000      0.978 0.000 1.000
#> SRR1785305     2   0.000      0.978 0.000 1.000
#> SRR1785306     1   0.000      1.000 1.000 0.000
#> SRR1785307     1   0.000      1.000 1.000 0.000
#> SRR1785302     1   0.000      1.000 1.000 0.000
#> SRR1785303     1   0.000      1.000 1.000 0.000
#> SRR1785308     2   0.000      0.978 0.000 1.000
#> SRR1785309     2   0.000      0.978 0.000 1.000
#> SRR1785310     1   0.000      1.000 1.000 0.000
#> SRR1785311     1   0.000      1.000 1.000 0.000
#> SRR1785312     1   0.000      1.000 1.000 0.000
#> SRR1785313     1   0.000      1.000 1.000 0.000
#> SRR1785314     1   0.000      1.000 1.000 0.000
#> SRR1785315     1   0.000      1.000 1.000 0.000
#> SRR1785318     2   0.000      0.978 0.000 1.000
#> SRR1785319     2   0.000      0.978 0.000 1.000
#> SRR1785316     1   0.000      1.000 1.000 0.000
#> SRR1785317     1   0.000      1.000 1.000 0.000
#> SRR1785324     1   0.000      1.000 1.000 0.000
#> SRR1785325     1   0.000      1.000 1.000 0.000
#> SRR1785320     1   0.000      1.000 1.000 0.000
#> SRR1785321     1   0.000      1.000 1.000 0.000
#> SRR1785322     2   0.000      0.978 0.000 1.000
#> SRR1785323     2   0.000      0.978 0.000 1.000

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785239     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785240     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785241     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785242     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785243     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785244     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785245     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785246     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785247     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785248     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785250     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785251     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785252     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785253     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785254     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785255     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785256     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785257     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785258     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785259     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785262     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785263     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785260     2  0.4654      0.866 0.000 0.792 0.208
#> SRR1785261     2  0.4654      0.866 0.000 0.792 0.208
#> SRR1785264     2  0.4750      0.860 0.000 0.784 0.216
#> SRR1785265     2  0.4750      0.860 0.000 0.784 0.216
#> SRR1785266     2  0.4654      0.866 0.000 0.792 0.208
#> SRR1785267     2  0.4654      0.866 0.000 0.792 0.208
#> SRR1785268     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785269     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785270     1  0.1163      0.974 0.972 0.028 0.000
#> SRR1785271     1  0.1163      0.974 0.972 0.028 0.000
#> SRR1785272     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785273     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785276     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785277     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785274     3  0.4555      0.726 0.200 0.000 0.800
#> SRR1785275     3  0.4555      0.726 0.200 0.000 0.800
#> SRR1785280     2  0.4654      0.866 0.000 0.792 0.208
#> SRR1785281     2  0.4654      0.866 0.000 0.792 0.208
#> SRR1785278     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785279     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785282     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785283     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785284     1  0.0424      0.992 0.992 0.008 0.000
#> SRR1785285     1  0.0424      0.992 0.992 0.008 0.000
#> SRR1785286     2  0.0000      0.859 0.000 1.000 0.000
#> SRR1785287     2  0.0000      0.859 0.000 1.000 0.000
#> SRR1785288     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785289     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785290     2  0.4654      0.866 0.000 0.792 0.208
#> SRR1785291     2  0.4654      0.866 0.000 0.792 0.208
#> SRR1785296     2  0.4654      0.866 0.000 0.792 0.208
#> SRR1785297     2  0.4654      0.866 0.000 0.792 0.208
#> SRR1785292     2  0.0000      0.859 0.000 1.000 0.000
#> SRR1785293     2  0.0000      0.859 0.000 1.000 0.000
#> SRR1785294     2  0.4654      0.866 0.000 0.792 0.208
#> SRR1785295     2  0.4654      0.866 0.000 0.792 0.208
#> SRR1785298     2  0.4974      0.840 0.000 0.764 0.236
#> SRR1785299     2  0.4974      0.840 0.000 0.764 0.236
#> SRR1785300     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785301     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785304     2  0.0000      0.859 0.000 1.000 0.000
#> SRR1785305     2  0.0000      0.859 0.000 1.000 0.000
#> SRR1785306     2  0.0000      0.859 0.000 1.000 0.000
#> SRR1785307     2  0.0000      0.859 0.000 1.000 0.000
#> SRR1785302     2  0.3879      0.727 0.152 0.848 0.000
#> SRR1785303     2  0.3879      0.727 0.152 0.848 0.000
#> SRR1785308     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785309     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785310     2  0.0000      0.859 0.000 1.000 0.000
#> SRR1785311     2  0.0000      0.859 0.000 1.000 0.000
#> SRR1785312     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785313     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785314     2  0.0000      0.859 0.000 1.000 0.000
#> SRR1785315     2  0.0000      0.859 0.000 1.000 0.000
#> SRR1785318     2  0.4654      0.866 0.000 0.792 0.208
#> SRR1785319     2  0.4654      0.866 0.000 0.792 0.208
#> SRR1785316     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785317     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785324     2  0.0000      0.859 0.000 1.000 0.000
#> SRR1785325     2  0.0000      0.859 0.000 1.000 0.000
#> SRR1785320     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785321     1  0.0000      0.998 1.000 0.000 0.000
#> SRR1785322     3  0.0000      0.972 0.000 0.000 1.000
#> SRR1785323     3  0.0000      0.972 0.000 0.000 1.000

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     3  0.0336      0.953 0.000 0.008 0.992 0.000
#> SRR1785239     3  0.0336      0.953 0.000 0.008 0.992 0.000
#> SRR1785240     1  0.0469      0.958 0.988 0.000 0.000 0.012
#> SRR1785241     1  0.0469      0.958 0.988 0.000 0.000 0.012
#> SRR1785242     3  0.0188      0.956 0.000 0.004 0.996 0.000
#> SRR1785243     3  0.0188      0.956 0.000 0.004 0.996 0.000
#> SRR1785244     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785245     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785246     3  0.0000      0.955 0.000 0.000 1.000 0.000
#> SRR1785247     3  0.0000      0.955 0.000 0.000 1.000 0.000
#> SRR1785248     2  0.4746      0.428 0.000 0.632 0.368 0.000
#> SRR1785250     3  0.0000      0.955 0.000 0.000 1.000 0.000
#> SRR1785251     3  0.0000      0.955 0.000 0.000 1.000 0.000
#> SRR1785252     3  0.0188      0.956 0.000 0.004 0.996 0.000
#> SRR1785253     3  0.0188      0.956 0.000 0.004 0.996 0.000
#> SRR1785254     1  0.0469      0.958 0.988 0.000 0.000 0.012
#> SRR1785255     1  0.0469      0.958 0.988 0.000 0.000 0.012
#> SRR1785256     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785257     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785258     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785259     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785262     3  0.6673      0.466 0.000 0.140 0.608 0.252
#> SRR1785263     3  0.6673      0.466 0.000 0.140 0.608 0.252
#> SRR1785260     2  0.4401      0.741 0.000 0.724 0.004 0.272
#> SRR1785261     2  0.4401      0.741 0.000 0.724 0.004 0.272
#> SRR1785264     2  0.2179      0.723 0.000 0.924 0.012 0.064
#> SRR1785265     2  0.2179      0.723 0.000 0.924 0.012 0.064
#> SRR1785266     2  0.0592      0.755 0.000 0.984 0.000 0.016
#> SRR1785267     2  0.0592      0.755 0.000 0.984 0.000 0.016
#> SRR1785268     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785269     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785270     4  0.4967      0.134 0.452 0.000 0.000 0.548
#> SRR1785271     4  0.4967      0.134 0.452 0.000 0.000 0.548
#> SRR1785272     3  0.0000      0.955 0.000 0.000 1.000 0.000
#> SRR1785273     3  0.0000      0.955 0.000 0.000 1.000 0.000
#> SRR1785276     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785277     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785274     3  0.0188      0.956 0.000 0.004 0.996 0.000
#> SRR1785275     3  0.0188      0.956 0.000 0.004 0.996 0.000
#> SRR1785280     2  0.1902      0.728 0.000 0.932 0.004 0.064
#> SRR1785281     2  0.1902      0.728 0.000 0.932 0.004 0.064
#> SRR1785278     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785279     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785282     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785283     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785284     1  0.4898      0.244 0.584 0.000 0.000 0.416
#> SRR1785285     1  0.4898      0.244 0.584 0.000 0.000 0.416
#> SRR1785286     4  0.1716      0.579 0.000 0.064 0.000 0.936
#> SRR1785287     4  0.1716      0.579 0.000 0.064 0.000 0.936
#> SRR1785288     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785289     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785290     2  0.0000      0.758 0.000 1.000 0.000 0.000
#> SRR1785291     2  0.0000      0.758 0.000 1.000 0.000 0.000
#> SRR1785296     2  0.4372      0.742 0.000 0.728 0.004 0.268
#> SRR1785297     2  0.4372      0.742 0.000 0.728 0.004 0.268
#> SRR1785292     4  0.4277      0.742 0.000 0.280 0.000 0.720
#> SRR1785293     4  0.4277      0.742 0.000 0.280 0.000 0.720
#> SRR1785294     2  0.4401      0.741 0.000 0.724 0.004 0.272
#> SRR1785295     2  0.4401      0.741 0.000 0.724 0.004 0.272
#> SRR1785298     2  0.4372      0.742 0.000 0.728 0.004 0.268
#> SRR1785299     2  0.4372      0.742 0.000 0.728 0.004 0.268
#> SRR1785300     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785301     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785304     4  0.4972      0.253 0.000 0.456 0.000 0.544
#> SRR1785305     4  0.4972      0.253 0.000 0.456 0.000 0.544
#> SRR1785306     4  0.4193      0.750 0.000 0.268 0.000 0.732
#> SRR1785307     4  0.4193      0.750 0.000 0.268 0.000 0.732
#> SRR1785302     4  0.4193      0.750 0.000 0.268 0.000 0.732
#> SRR1785303     4  0.4193      0.750 0.000 0.268 0.000 0.732
#> SRR1785308     3  0.0188      0.956 0.000 0.004 0.996 0.000
#> SRR1785309     3  0.0188      0.956 0.000 0.004 0.996 0.000
#> SRR1785310     4  0.2281      0.556 0.000 0.096 0.000 0.904
#> SRR1785311     4  0.2281      0.556 0.000 0.096 0.000 0.904
#> SRR1785312     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785313     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785314     4  0.4193      0.750 0.000 0.268 0.000 0.732
#> SRR1785315     4  0.4193      0.750 0.000 0.268 0.000 0.732
#> SRR1785318     2  0.1557      0.735 0.000 0.944 0.000 0.056
#> SRR1785319     2  0.1557      0.735 0.000 0.944 0.000 0.056
#> SRR1785316     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785317     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785324     4  0.4193      0.750 0.000 0.268 0.000 0.732
#> SRR1785325     4  0.4193      0.750 0.000 0.268 0.000 0.732
#> SRR1785320     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785321     1  0.0000      0.966 1.000 0.000 0.000 0.000
#> SRR1785322     3  0.0000      0.955 0.000 0.000 1.000 0.000
#> SRR1785323     3  0.0000      0.955 0.000 0.000 1.000 0.000

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     3  0.4249      0.299 0.000 0.432 0.568 0.000 0.000
#> SRR1785239     3  0.4249      0.299 0.000 0.432 0.568 0.000 0.000
#> SRR1785240     1  0.4382      0.748 0.760 0.004 0.000 0.060 0.176
#> SRR1785241     1  0.4382      0.748 0.760 0.004 0.000 0.060 0.176
#> SRR1785242     3  0.0510      0.925 0.000 0.016 0.984 0.000 0.000
#> SRR1785243     3  0.0510      0.925 0.000 0.016 0.984 0.000 0.000
#> SRR1785244     1  0.1560      0.924 0.948 0.004 0.000 0.020 0.028
#> SRR1785245     1  0.1560      0.924 0.948 0.004 0.000 0.020 0.028
#> SRR1785246     3  0.0162      0.924 0.000 0.004 0.996 0.000 0.000
#> SRR1785247     3  0.0162      0.924 0.000 0.004 0.996 0.000 0.000
#> SRR1785248     2  0.2179      0.834 0.000 0.888 0.112 0.000 0.000
#> SRR1785250     3  0.0000      0.924 0.000 0.000 1.000 0.000 0.000
#> SRR1785251     3  0.0000      0.924 0.000 0.000 1.000 0.000 0.000
#> SRR1785252     3  0.0510      0.925 0.000 0.016 0.984 0.000 0.000
#> SRR1785253     3  0.0510      0.925 0.000 0.016 0.984 0.000 0.000
#> SRR1785254     1  0.5089      0.532 0.636 0.004 0.000 0.048 0.312
#> SRR1785255     1  0.5089      0.532 0.636 0.004 0.000 0.048 0.312
#> SRR1785256     1  0.0162      0.944 0.996 0.000 0.000 0.004 0.000
#> SRR1785257     1  0.0162      0.944 0.996 0.000 0.000 0.004 0.000
#> SRR1785258     1  0.0000      0.945 1.000 0.000 0.000 0.000 0.000
#> SRR1785259     1  0.0000      0.945 1.000 0.000 0.000 0.000 0.000
#> SRR1785262     4  0.4958      0.361 0.000 0.032 0.400 0.568 0.000
#> SRR1785263     4  0.4958      0.361 0.000 0.032 0.400 0.568 0.000
#> SRR1785260     4  0.2648      0.768 0.000 0.152 0.000 0.848 0.000
#> SRR1785261     4  0.2648      0.768 0.000 0.152 0.000 0.848 0.000
#> SRR1785264     2  0.0451      0.965 0.000 0.988 0.000 0.004 0.008
#> SRR1785265     2  0.0451      0.965 0.000 0.988 0.000 0.004 0.008
#> SRR1785266     2  0.0771      0.970 0.000 0.976 0.000 0.020 0.004
#> SRR1785267     2  0.0771      0.970 0.000 0.976 0.000 0.020 0.004
#> SRR1785268     1  0.0162      0.944 0.996 0.000 0.000 0.004 0.000
#> SRR1785269     1  0.0162      0.944 0.996 0.000 0.000 0.004 0.000
#> SRR1785270     5  0.3018      0.778 0.068 0.004 0.000 0.056 0.872
#> SRR1785271     5  0.3018      0.778 0.068 0.004 0.000 0.056 0.872
#> SRR1785272     3  0.0000      0.924 0.000 0.000 1.000 0.000 0.000
#> SRR1785273     3  0.0000      0.924 0.000 0.000 1.000 0.000 0.000
#> SRR1785276     1  0.0000      0.945 1.000 0.000 0.000 0.000 0.000
#> SRR1785277     1  0.0000      0.945 1.000 0.000 0.000 0.000 0.000
#> SRR1785274     3  0.2797      0.882 0.008 0.016 0.896 0.060 0.020
#> SRR1785275     3  0.2797      0.882 0.008 0.016 0.896 0.060 0.020
#> SRR1785280     2  0.0290      0.966 0.000 0.992 0.000 0.000 0.008
#> SRR1785281     2  0.0290      0.966 0.000 0.992 0.000 0.000 0.008
#> SRR1785278     1  0.0162      0.944 0.996 0.000 0.000 0.004 0.000
#> SRR1785279     1  0.0162      0.944 0.996 0.000 0.000 0.004 0.000
#> SRR1785282     1  0.0162      0.944 0.996 0.000 0.000 0.004 0.000
#> SRR1785283     1  0.0162      0.944 0.996 0.000 0.000 0.004 0.000
#> SRR1785284     5  0.4585      0.635 0.216 0.004 0.000 0.052 0.728
#> SRR1785285     5  0.4585      0.635 0.216 0.004 0.000 0.052 0.728
#> SRR1785286     4  0.4126      0.442 0.000 0.000 0.000 0.620 0.380
#> SRR1785287     4  0.4126      0.442 0.000 0.000 0.000 0.620 0.380
#> SRR1785288     1  0.1646      0.922 0.944 0.004 0.000 0.020 0.032
#> SRR1785289     1  0.1646      0.922 0.944 0.004 0.000 0.020 0.032
#> SRR1785290     2  0.0794      0.965 0.000 0.972 0.000 0.028 0.000
#> SRR1785291     2  0.0794      0.965 0.000 0.972 0.000 0.028 0.000
#> SRR1785296     4  0.3109      0.749 0.000 0.200 0.000 0.800 0.000
#> SRR1785297     4  0.3109      0.749 0.000 0.200 0.000 0.800 0.000
#> SRR1785292     5  0.3695      0.777 0.000 0.164 0.000 0.036 0.800
#> SRR1785293     5  0.3695      0.777 0.000 0.164 0.000 0.036 0.800
#> SRR1785294     4  0.2690      0.768 0.000 0.156 0.000 0.844 0.000
#> SRR1785295     4  0.2690      0.768 0.000 0.156 0.000 0.844 0.000
#> SRR1785298     4  0.3143      0.746 0.000 0.204 0.000 0.796 0.000
#> SRR1785299     4  0.3143      0.746 0.000 0.204 0.000 0.796 0.000
#> SRR1785300     1  0.1082      0.934 0.964 0.000 0.000 0.008 0.028
#> SRR1785301     1  0.1082      0.934 0.964 0.000 0.000 0.008 0.028
#> SRR1785304     4  0.4201      0.676 0.000 0.044 0.000 0.752 0.204
#> SRR1785305     4  0.4201      0.676 0.000 0.044 0.000 0.752 0.204
#> SRR1785306     5  0.1364      0.844 0.000 0.012 0.000 0.036 0.952
#> SRR1785307     5  0.1364      0.844 0.000 0.012 0.000 0.036 0.952
#> SRR1785302     5  0.0703      0.846 0.000 0.024 0.000 0.000 0.976
#> SRR1785303     5  0.0703      0.846 0.000 0.024 0.000 0.000 0.976
#> SRR1785308     3  0.0510      0.925 0.000 0.016 0.984 0.000 0.000
#> SRR1785309     3  0.0510      0.925 0.000 0.016 0.984 0.000 0.000
#> SRR1785310     4  0.2605      0.726 0.000 0.000 0.000 0.852 0.148
#> SRR1785311     4  0.2605      0.726 0.000 0.000 0.000 0.852 0.148
#> SRR1785312     1  0.0000      0.945 1.000 0.000 0.000 0.000 0.000
#> SRR1785313     1  0.0000      0.945 1.000 0.000 0.000 0.000 0.000
#> SRR1785314     5  0.1485      0.846 0.000 0.020 0.000 0.032 0.948
#> SRR1785315     5  0.1485      0.846 0.000 0.020 0.000 0.032 0.948
#> SRR1785318     2  0.0771      0.970 0.000 0.976 0.000 0.020 0.004
#> SRR1785319     2  0.0771      0.970 0.000 0.976 0.000 0.020 0.004
#> SRR1785316     1  0.0162      0.944 0.996 0.000 0.000 0.004 0.000
#> SRR1785317     1  0.0162      0.944 0.996 0.000 0.000 0.004 0.000
#> SRR1785324     5  0.3309      0.803 0.000 0.128 0.000 0.036 0.836
#> SRR1785325     5  0.3309      0.803 0.000 0.128 0.000 0.036 0.836
#> SRR1785320     1  0.0000      0.945 1.000 0.000 0.000 0.000 0.000
#> SRR1785321     1  0.0000      0.945 1.000 0.000 0.000 0.000 0.000
#> SRR1785322     3  0.1357      0.906 0.000 0.004 0.948 0.048 0.000
#> SRR1785323     3  0.1357      0.906 0.000 0.004 0.948 0.048 0.000

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     3  0.4264     0.4124 0.000 0.352 0.620 0.000 0.000 0.028
#> SRR1785239     3  0.4264     0.4124 0.000 0.352 0.620 0.000 0.000 0.028
#> SRR1785240     6  0.4756     0.6277 0.304 0.000 0.000 0.004 0.064 0.628
#> SRR1785241     6  0.4756     0.6277 0.304 0.000 0.000 0.004 0.064 0.628
#> SRR1785242     3  0.0508     0.8629 0.000 0.004 0.984 0.000 0.000 0.012
#> SRR1785243     3  0.0508     0.8629 0.000 0.004 0.984 0.000 0.000 0.012
#> SRR1785244     1  0.3076     0.6833 0.760 0.000 0.000 0.000 0.000 0.240
#> SRR1785245     1  0.3076     0.6833 0.760 0.000 0.000 0.000 0.000 0.240
#> SRR1785246     3  0.1949     0.8439 0.000 0.004 0.904 0.004 0.000 0.088
#> SRR1785247     3  0.1949     0.8439 0.000 0.004 0.904 0.004 0.000 0.088
#> SRR1785248     2  0.1556     0.8973 0.000 0.920 0.080 0.000 0.000 0.000
#> SRR1785250     3  0.0692     0.8601 0.000 0.000 0.976 0.004 0.000 0.020
#> SRR1785251     3  0.0692     0.8601 0.000 0.000 0.976 0.004 0.000 0.020
#> SRR1785252     3  0.0508     0.8629 0.000 0.004 0.984 0.000 0.000 0.012
#> SRR1785253     3  0.0508     0.8629 0.000 0.004 0.984 0.000 0.000 0.012
#> SRR1785254     6  0.5310     0.6013 0.348 0.000 0.000 0.000 0.116 0.536
#> SRR1785255     6  0.5334     0.6084 0.344 0.000 0.000 0.000 0.120 0.536
#> SRR1785256     1  0.1387     0.8812 0.932 0.000 0.000 0.000 0.000 0.068
#> SRR1785257     1  0.1387     0.8812 0.932 0.000 0.000 0.000 0.000 0.068
#> SRR1785258     1  0.0000     0.8878 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785259     1  0.0000     0.8878 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785262     4  0.4373     0.4009 0.000 0.004 0.344 0.624 0.000 0.028
#> SRR1785263     4  0.4373     0.4009 0.000 0.004 0.344 0.624 0.000 0.028
#> SRR1785260     4  0.1327     0.7820 0.000 0.064 0.000 0.936 0.000 0.000
#> SRR1785261     4  0.1327     0.7820 0.000 0.064 0.000 0.936 0.000 0.000
#> SRR1785264     2  0.1261     0.9654 0.000 0.956 0.004 0.004 0.008 0.028
#> SRR1785265     2  0.1261     0.9654 0.000 0.956 0.004 0.004 0.008 0.028
#> SRR1785266     2  0.0260     0.9825 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1785267     2  0.0260     0.9825 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1785268     1  0.1007     0.8789 0.956 0.000 0.000 0.000 0.000 0.044
#> SRR1785269     1  0.1007     0.8789 0.956 0.000 0.000 0.000 0.000 0.044
#> SRR1785270     6  0.4256     0.4082 0.016 0.000 0.000 0.000 0.464 0.520
#> SRR1785271     6  0.4256     0.4082 0.016 0.000 0.000 0.000 0.464 0.520
#> SRR1785272     3  0.0692     0.8601 0.000 0.000 0.976 0.004 0.000 0.020
#> SRR1785273     3  0.0692     0.8601 0.000 0.000 0.976 0.004 0.000 0.020
#> SRR1785276     1  0.0000     0.8878 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785277     1  0.0000     0.8878 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785274     3  0.5477     0.5878 0.020 0.004 0.548 0.056 0.004 0.368
#> SRR1785275     3  0.5477     0.5878 0.020 0.004 0.548 0.056 0.004 0.368
#> SRR1785280     2  0.0146     0.9810 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1785281     2  0.0146     0.9810 0.000 0.996 0.000 0.000 0.004 0.000
#> SRR1785278     1  0.1267     0.8817 0.940 0.000 0.000 0.000 0.000 0.060
#> SRR1785279     1  0.1267     0.8817 0.940 0.000 0.000 0.000 0.000 0.060
#> SRR1785282     1  0.1007     0.8789 0.956 0.000 0.000 0.000 0.000 0.044
#> SRR1785283     1  0.1007     0.8789 0.956 0.000 0.000 0.000 0.000 0.044
#> SRR1785284     6  0.5293     0.6496 0.108 0.000 0.000 0.004 0.320 0.568
#> SRR1785285     6  0.5293     0.6496 0.108 0.000 0.000 0.004 0.320 0.568
#> SRR1785286     5  0.4650    -0.0214 0.000 0.000 0.000 0.472 0.488 0.040
#> SRR1785287     5  0.4650    -0.0214 0.000 0.000 0.000 0.472 0.488 0.040
#> SRR1785288     1  0.3175     0.6601 0.744 0.000 0.000 0.000 0.000 0.256
#> SRR1785289     1  0.3175     0.6601 0.744 0.000 0.000 0.000 0.000 0.256
#> SRR1785290     2  0.0260     0.9825 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1785291     2  0.0260     0.9825 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1785296     4  0.2278     0.7745 0.000 0.128 0.000 0.868 0.000 0.004
#> SRR1785297     4  0.2278     0.7745 0.000 0.128 0.000 0.868 0.000 0.004
#> SRR1785292     5  0.1958     0.7729 0.000 0.100 0.000 0.000 0.896 0.004
#> SRR1785293     5  0.1958     0.7729 0.000 0.100 0.000 0.000 0.896 0.004
#> SRR1785294     4  0.1387     0.7827 0.000 0.068 0.000 0.932 0.000 0.000
#> SRR1785295     4  0.1387     0.7827 0.000 0.068 0.000 0.932 0.000 0.000
#> SRR1785298     4  0.2278     0.7745 0.000 0.128 0.000 0.868 0.000 0.004
#> SRR1785299     4  0.2278     0.7745 0.000 0.128 0.000 0.868 0.000 0.004
#> SRR1785300     1  0.3101     0.7450 0.756 0.000 0.000 0.000 0.000 0.244
#> SRR1785301     1  0.3101     0.7450 0.756 0.000 0.000 0.000 0.000 0.244
#> SRR1785304     4  0.5180     0.0806 0.000 0.032 0.000 0.504 0.432 0.032
#> SRR1785305     4  0.5180     0.0806 0.000 0.032 0.000 0.504 0.432 0.032
#> SRR1785306     5  0.1152     0.7837 0.000 0.004 0.000 0.000 0.952 0.044
#> SRR1785307     5  0.1152     0.7837 0.000 0.004 0.000 0.000 0.952 0.044
#> SRR1785302     5  0.1411     0.7651 0.000 0.004 0.000 0.000 0.936 0.060
#> SRR1785303     5  0.1411     0.7651 0.000 0.004 0.000 0.000 0.936 0.060
#> SRR1785308     3  0.0508     0.8629 0.000 0.004 0.984 0.000 0.000 0.012
#> SRR1785309     3  0.0508     0.8629 0.000 0.004 0.984 0.000 0.000 0.012
#> SRR1785310     4  0.3139     0.6660 0.000 0.000 0.000 0.816 0.152 0.032
#> SRR1785311     4  0.3139     0.6660 0.000 0.000 0.000 0.816 0.152 0.032
#> SRR1785312     1  0.0000     0.8878 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785313     1  0.0000     0.8878 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785314     5  0.0777     0.7934 0.000 0.004 0.000 0.000 0.972 0.024
#> SRR1785315     5  0.0777     0.7934 0.000 0.004 0.000 0.000 0.972 0.024
#> SRR1785318     2  0.0260     0.9825 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1785319     2  0.0260     0.9825 0.000 0.992 0.000 0.000 0.008 0.000
#> SRR1785316     1  0.1863     0.8380 0.896 0.000 0.000 0.000 0.000 0.104
#> SRR1785317     1  0.1814     0.8410 0.900 0.000 0.000 0.000 0.000 0.100
#> SRR1785324     5  0.1471     0.7934 0.000 0.064 0.000 0.000 0.932 0.004
#> SRR1785325     5  0.1471     0.7934 0.000 0.064 0.000 0.000 0.932 0.004
#> SRR1785320     1  0.0000     0.8878 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785321     1  0.0000     0.8878 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785322     3  0.3912     0.7682 0.000 0.004 0.748 0.044 0.000 0.204
#> SRR1785323     3  0.3912     0.7682 0.000 0.004 0.748 0.044 0.000 0.204

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-ATC-skmeans-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-ATC-skmeans-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-ATC-skmeans-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-ATC-skmeans-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-ATC-skmeans-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-ATC-skmeans-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-ATC-skmeans-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-ATC-skmeans-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-ATC-skmeans-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-ATC-skmeans-membership-heatmap-5

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)

plot of chunk tab-ATC-skmeans-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-ATC-skmeans-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-ATC-skmeans-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-ATC-skmeans-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-ATC-skmeans-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-ATC-skmeans-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-ATC-skmeans-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-ATC-skmeans-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-ATC-skmeans-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-ATC-skmeans-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk ATC-skmeans-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-ATC-skmeans-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-ATC-skmeans-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-ATC-skmeans-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-ATC-skmeans-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-ATC-skmeans-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk ATC-skmeans-collect-classes

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.


ATC:pam**

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 16620 rows and 87 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 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)

plot of chunk ATC-pam-collect-plots

The plots are:

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:

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)

plot of chunk ATC-pam-select-partition-number

The numeric values for all these statistics can be obtained by get_stats().

get_stats(res)
#>   k 1-PAC mean_silhouette concordance area_increased  Rand Jaccard
#> 2 2 1.000           0.957       0.983         0.5037 0.496   0.496
#> 3 3 0.855           0.871       0.946         0.2739 0.745   0.543
#> 4 4 0.740           0.828       0.907         0.1025 0.721   0.411
#> 5 5 0.862           0.837       0.930         0.1071 0.838   0.524
#> 6 6 0.857           0.823       0.911         0.0589 0.903   0.593

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2  0.0000      0.987 0.000 1.000
#> SRR1785239     2  0.0000      0.987 0.000 1.000
#> SRR1785240     1  0.0000      0.975 1.000 0.000
#> SRR1785241     1  0.0000      0.975 1.000 0.000
#> SRR1785242     2  0.0000      0.987 0.000 1.000
#> SRR1785243     2  0.0000      0.987 0.000 1.000
#> SRR1785244     1  0.0000      0.975 1.000 0.000
#> SRR1785245     1  0.0000      0.975 1.000 0.000
#> SRR1785246     2  0.0000      0.987 0.000 1.000
#> SRR1785247     2  0.0000      0.987 0.000 1.000
#> SRR1785248     2  0.0000      0.987 0.000 1.000
#> SRR1785250     2  0.0000      0.987 0.000 1.000
#> SRR1785251     2  0.0000      0.987 0.000 1.000
#> SRR1785252     2  0.0000      0.987 0.000 1.000
#> SRR1785253     2  0.0000      0.987 0.000 1.000
#> SRR1785254     1  0.0000      0.975 1.000 0.000
#> SRR1785255     1  0.0000      0.975 1.000 0.000
#> SRR1785256     1  0.0000      0.975 1.000 0.000
#> SRR1785257     1  0.0000      0.975 1.000 0.000
#> SRR1785258     1  0.0000      0.975 1.000 0.000
#> SRR1785259     1  0.0000      0.975 1.000 0.000
#> SRR1785262     2  0.0000      0.987 0.000 1.000
#> SRR1785263     2  0.0000      0.987 0.000 1.000
#> SRR1785260     2  0.0000      0.987 0.000 1.000
#> SRR1785261     2  0.0000      0.987 0.000 1.000
#> SRR1785264     2  0.0376      0.985 0.004 0.996
#> SRR1785265     2  0.0376      0.985 0.004 0.996
#> SRR1785266     2  0.0000      0.987 0.000 1.000
#> SRR1785267     2  0.0000      0.987 0.000 1.000
#> SRR1785268     1  0.0000      0.975 1.000 0.000
#> SRR1785269     1  0.0000      0.975 1.000 0.000
#> SRR1785270     1  0.0000      0.975 1.000 0.000
#> SRR1785271     1  0.0000      0.975 1.000 0.000
#> SRR1785272     2  0.0000      0.987 0.000 1.000
#> SRR1785273     2  0.0000      0.987 0.000 1.000
#> SRR1785276     1  0.1414      0.959 0.980 0.020
#> SRR1785277     1  0.9460      0.425 0.636 0.364
#> SRR1785274     2  0.0376      0.985 0.004 0.996
#> SRR1785275     2  0.0376      0.985 0.004 0.996
#> SRR1785280     2  0.0000      0.987 0.000 1.000
#> SRR1785281     2  0.0000      0.987 0.000 1.000
#> SRR1785278     1  0.0000      0.975 1.000 0.000
#> SRR1785279     1  0.0000      0.975 1.000 0.000
#> SRR1785282     1  0.0000      0.975 1.000 0.000
#> SRR1785283     1  0.0000      0.975 1.000 0.000
#> SRR1785284     1  0.0000      0.975 1.000 0.000
#> SRR1785285     1  0.0000      0.975 1.000 0.000
#> SRR1785286     1  0.0000      0.975 1.000 0.000
#> SRR1785287     1  0.2043      0.949 0.968 0.032
#> SRR1785288     1  0.0000      0.975 1.000 0.000
#> SRR1785289     1  0.0000      0.975 1.000 0.000
#> SRR1785290     2  0.0000      0.987 0.000 1.000
#> SRR1785291     2  0.0000      0.987 0.000 1.000
#> SRR1785296     2  0.0000      0.987 0.000 1.000
#> SRR1785297     2  0.0000      0.987 0.000 1.000
#> SRR1785292     2  0.0000      0.987 0.000 1.000
#> SRR1785293     2  0.0000      0.987 0.000 1.000
#> SRR1785294     2  0.0000      0.987 0.000 1.000
#> SRR1785295     2  0.0000      0.987 0.000 1.000
#> SRR1785298     2  0.0000      0.987 0.000 1.000
#> SRR1785299     2  0.0000      0.987 0.000 1.000
#> SRR1785300     1  0.0000      0.975 1.000 0.000
#> SRR1785301     1  0.0000      0.975 1.000 0.000
#> SRR1785304     2  0.0000      0.987 0.000 1.000
#> SRR1785305     2  0.0000      0.987 0.000 1.000
#> SRR1785306     2  0.2778      0.942 0.048 0.952
#> SRR1785307     2  0.2236      0.954 0.036 0.964
#> SRR1785302     1  0.0000      0.975 1.000 0.000
#> SRR1785303     1  0.0000      0.975 1.000 0.000
#> SRR1785308     2  0.0376      0.985 0.004 0.996
#> SRR1785309     2  0.0000      0.987 0.000 1.000
#> SRR1785310     1  0.2603      0.938 0.956 0.044
#> SRR1785311     1  0.5629      0.841 0.868 0.132
#> SRR1785312     1  0.0000      0.975 1.000 0.000
#> SRR1785313     1  0.0000      0.975 1.000 0.000
#> SRR1785314     1  0.0000      0.975 1.000 0.000
#> SRR1785315     1  0.0000      0.975 1.000 0.000
#> SRR1785318     2  0.0000      0.987 0.000 1.000
#> SRR1785319     2  0.0000      0.987 0.000 1.000
#> SRR1785316     1  0.0000      0.975 1.000 0.000
#> SRR1785317     1  0.0000      0.975 1.000 0.000
#> SRR1785324     2  0.0376      0.985 0.004 0.996
#> SRR1785325     2  0.0376      0.985 0.004 0.996
#> SRR1785320     1  0.0000      0.975 1.000 0.000
#> SRR1785321     1  0.0000      0.975 1.000 0.000
#> SRR1785322     2  0.9866      0.205 0.432 0.568
#> SRR1785323     1  0.9491      0.425 0.632 0.368

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785239     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785240     1   0.524    0.73205 0.804 0.168 0.028
#> SRR1785241     1   0.524    0.73205 0.804 0.168 0.028
#> SRR1785242     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785243     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785244     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785245     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785246     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785247     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785248     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785250     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785251     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785252     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785253     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785254     1   0.334    0.82454 0.880 0.120 0.000
#> SRR1785255     1   0.586    0.44020 0.656 0.344 0.000
#> SRR1785256     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785257     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785258     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785259     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785262     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785263     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785260     3   0.465    0.73839 0.000 0.208 0.792
#> SRR1785261     3   0.465    0.73839 0.000 0.208 0.792
#> SRR1785264     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785265     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785266     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785267     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785268     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785269     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785270     2   0.153    0.92028 0.040 0.960 0.000
#> SRR1785271     2   0.153    0.92028 0.040 0.960 0.000
#> SRR1785272     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785273     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785276     1   0.631   -0.00442 0.512 0.000 0.488
#> SRR1785277     3   0.254    0.86716 0.080 0.000 0.920
#> SRR1785274     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785275     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785280     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785281     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785278     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785279     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785282     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785283     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785284     2   0.562    0.53005 0.308 0.692 0.000
#> SRR1785285     2   0.562    0.53005 0.308 0.692 0.000
#> SRR1785286     2   0.000    0.95322 0.000 1.000 0.000
#> SRR1785287     2   0.000    0.95322 0.000 1.000 0.000
#> SRR1785288     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785289     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785290     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785291     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785296     3   0.141    0.90900 0.000 0.036 0.964
#> SRR1785297     3   0.141    0.90900 0.000 0.036 0.964
#> SRR1785292     2   0.000    0.95322 0.000 1.000 0.000
#> SRR1785293     2   0.000    0.95322 0.000 1.000 0.000
#> SRR1785294     3   0.465    0.73839 0.000 0.208 0.792
#> SRR1785295     3   0.465    0.73839 0.000 0.208 0.792
#> SRR1785298     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785299     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785300     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785301     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785304     2   0.000    0.95322 0.000 1.000 0.000
#> SRR1785305     2   0.000    0.95322 0.000 1.000 0.000
#> SRR1785306     2   0.000    0.95322 0.000 1.000 0.000
#> SRR1785307     2   0.000    0.95322 0.000 1.000 0.000
#> SRR1785302     2   0.000    0.95322 0.000 1.000 0.000
#> SRR1785303     2   0.000    0.95322 0.000 1.000 0.000
#> SRR1785308     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785309     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785310     3   0.945    0.28507 0.304 0.208 0.488
#> SRR1785311     3   0.945    0.28507 0.304 0.208 0.488
#> SRR1785312     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785313     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785314     2   0.000    0.95322 0.000 1.000 0.000
#> SRR1785315     2   0.000    0.95322 0.000 1.000 0.000
#> SRR1785318     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785319     3   0.000    0.93230 0.000 0.000 1.000
#> SRR1785316     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785317     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785324     2   0.000    0.95322 0.000 1.000 0.000
#> SRR1785325     2   0.000    0.95322 0.000 1.000 0.000
#> SRR1785320     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785321     1   0.000    0.93955 1.000 0.000 0.000
#> SRR1785322     3   0.562    0.54685 0.308 0.000 0.692
#> SRR1785323     3   0.579    0.49789 0.332 0.000 0.668

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     1  0.6169      0.542 0.596 0.024 0.024 0.356
#> SRR1785239     1  0.6197      0.527 0.588 0.024 0.024 0.364
#> SRR1785240     1  0.4138      0.796 0.820 0.148 0.024 0.008
#> SRR1785241     1  0.4138      0.796 0.820 0.148 0.024 0.008
#> SRR1785242     3  0.0000      0.870 0.000 0.000 1.000 0.000
#> SRR1785243     3  0.0000      0.870 0.000 0.000 1.000 0.000
#> SRR1785244     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785245     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785246     4  0.1792      0.840 0.000 0.000 0.068 0.932
#> SRR1785247     4  0.0817      0.872 0.000 0.000 0.024 0.976
#> SRR1785248     3  0.0817      0.856 0.000 0.000 0.976 0.024
#> SRR1785250     3  0.0469      0.865 0.000 0.000 0.988 0.012
#> SRR1785251     3  0.0469      0.865 0.000 0.000 0.988 0.012
#> SRR1785252     3  0.0000      0.870 0.000 0.000 1.000 0.000
#> SRR1785253     3  0.0000      0.870 0.000 0.000 1.000 0.000
#> SRR1785254     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785255     1  0.0188      0.881 0.996 0.004 0.000 0.000
#> SRR1785256     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785257     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785258     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785259     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785262     4  0.0000      0.887 0.000 0.000 0.000 1.000
#> SRR1785263     4  0.0000      0.887 0.000 0.000 0.000 1.000
#> SRR1785260     4  0.3219      0.794 0.000 0.164 0.000 0.836
#> SRR1785261     4  0.3219      0.794 0.000 0.164 0.000 0.836
#> SRR1785264     1  0.6169      0.542 0.596 0.024 0.024 0.356
#> SRR1785265     1  0.6374      0.340 0.508 0.024 0.024 0.444
#> SRR1785266     4  0.0000      0.887 0.000 0.000 0.000 1.000
#> SRR1785267     4  0.0000      0.887 0.000 0.000 0.000 1.000
#> SRR1785268     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785269     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785270     2  0.1211      0.944 0.040 0.960 0.000 0.000
#> SRR1785271     2  0.1211      0.944 0.040 0.960 0.000 0.000
#> SRR1785272     3  0.4977      0.284 0.000 0.000 0.540 0.460
#> SRR1785273     3  0.5000      0.175 0.000 0.000 0.504 0.496
#> SRR1785276     1  0.4393      0.806 0.820 0.024 0.024 0.132
#> SRR1785277     1  0.4829      0.782 0.784 0.024 0.024 0.168
#> SRR1785274     1  0.4873      0.778 0.780 0.024 0.024 0.172
#> SRR1785275     1  0.4873      0.778 0.780 0.024 0.024 0.172
#> SRR1785280     3  0.3726      0.727 0.000 0.000 0.788 0.212
#> SRR1785281     3  0.3726      0.727 0.000 0.000 0.788 0.212
#> SRR1785278     1  0.0817      0.875 0.976 0.024 0.000 0.000
#> SRR1785279     1  0.0707      0.876 0.980 0.020 0.000 0.000
#> SRR1785282     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785283     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785284     1  0.3764      0.751 0.784 0.216 0.000 0.000
#> SRR1785285     1  0.3764      0.751 0.784 0.216 0.000 0.000
#> SRR1785286     2  0.0000      0.991 0.000 1.000 0.000 0.000
#> SRR1785287     2  0.0000      0.991 0.000 1.000 0.000 0.000
#> SRR1785288     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785289     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785290     4  0.0000      0.887 0.000 0.000 0.000 1.000
#> SRR1785291     4  0.0000      0.887 0.000 0.000 0.000 1.000
#> SRR1785296     4  0.1118      0.878 0.000 0.036 0.000 0.964
#> SRR1785297     4  0.1118      0.878 0.000 0.036 0.000 0.964
#> SRR1785292     2  0.0000      0.991 0.000 1.000 0.000 0.000
#> SRR1785293     2  0.0000      0.991 0.000 1.000 0.000 0.000
#> SRR1785294     4  0.3219      0.794 0.000 0.164 0.000 0.836
#> SRR1785295     4  0.3219      0.794 0.000 0.164 0.000 0.836
#> SRR1785298     4  0.1629      0.861 0.000 0.024 0.024 0.952
#> SRR1785299     4  0.0817      0.872 0.000 0.000 0.024 0.976
#> SRR1785300     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785301     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785304     4  0.4877      0.420 0.000 0.408 0.000 0.592
#> SRR1785305     4  0.4331      0.653 0.000 0.288 0.000 0.712
#> SRR1785306     2  0.0000      0.991 0.000 1.000 0.000 0.000
#> SRR1785307     2  0.0000      0.991 0.000 1.000 0.000 0.000
#> SRR1785302     2  0.0000      0.991 0.000 1.000 0.000 0.000
#> SRR1785303     2  0.0000      0.991 0.000 1.000 0.000 0.000
#> SRR1785308     3  0.0000      0.870 0.000 0.000 1.000 0.000
#> SRR1785309     3  0.0000      0.870 0.000 0.000 1.000 0.000
#> SRR1785310     1  0.6590      0.667 0.676 0.188 0.024 0.112
#> SRR1785311     1  0.7850      0.402 0.532 0.188 0.024 0.256
#> SRR1785312     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785313     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785314     2  0.0000      0.991 0.000 1.000 0.000 0.000
#> SRR1785315     2  0.0000      0.991 0.000 1.000 0.000 0.000
#> SRR1785318     4  0.0000      0.887 0.000 0.000 0.000 1.000
#> SRR1785319     4  0.0000      0.887 0.000 0.000 0.000 1.000
#> SRR1785316     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785317     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785324     2  0.0000      0.991 0.000 1.000 0.000 0.000
#> SRR1785325     2  0.0000      0.991 0.000 1.000 0.000 0.000
#> SRR1785320     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785321     1  0.0000      0.882 1.000 0.000 0.000 0.000
#> SRR1785322     1  0.4446      0.804 0.816 0.024 0.024 0.136
#> SRR1785323     1  0.4446      0.804 0.816 0.024 0.024 0.136

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     2  0.0162     0.8431 0.000 0.996 0.000 0.004 0.000
#> SRR1785239     2  0.0162     0.8431 0.000 0.996 0.000 0.004 0.000
#> SRR1785240     1  0.2806     0.7826 0.844 0.152 0.000 0.000 0.004
#> SRR1785241     1  0.2806     0.7826 0.844 0.152 0.000 0.000 0.004
#> SRR1785242     3  0.0000     0.9996 0.000 0.000 1.000 0.000 0.000
#> SRR1785243     3  0.0000     0.9996 0.000 0.000 1.000 0.000 0.000
#> SRR1785244     1  0.0000     0.9414 1.000 0.000 0.000 0.000 0.000
#> SRR1785245     1  0.0000     0.9414 1.000 0.000 0.000 0.000 0.000
#> SRR1785246     2  0.0162     0.8431 0.000 0.996 0.000 0.004 0.000
#> SRR1785247     2  0.0162     0.8431 0.000 0.996 0.000 0.004 0.000
#> SRR1785248     3  0.0162     0.9965 0.000 0.000 0.996 0.004 0.000
#> SRR1785250     3  0.0000     0.9996 0.000 0.000 1.000 0.000 0.000
#> SRR1785251     3  0.0000     0.9996 0.000 0.000 1.000 0.000 0.000
#> SRR1785252     3  0.0000     0.9996 0.000 0.000 1.000 0.000 0.000
#> SRR1785253     3  0.0000     0.9996 0.000 0.000 1.000 0.000 0.000
#> SRR1785254     1  0.2648     0.7820 0.848 0.000 0.000 0.000 0.152
#> SRR1785255     1  0.4161     0.2940 0.608 0.000 0.000 0.000 0.392
#> SRR1785256     1  0.0162     0.9418 0.996 0.004 0.000 0.000 0.000
#> SRR1785257     1  0.0162     0.9418 0.996 0.004 0.000 0.000 0.000
#> SRR1785258     1  0.0000     0.9414 1.000 0.000 0.000 0.000 0.000
#> SRR1785259     1  0.0000     0.9414 1.000 0.000 0.000 0.000 0.000
#> SRR1785262     4  0.0162     0.8944 0.000 0.004 0.000 0.996 0.000
#> SRR1785263     4  0.0162     0.8944 0.000 0.004 0.000 0.996 0.000
#> SRR1785260     4  0.0162     0.8947 0.000 0.000 0.000 0.996 0.004
#> SRR1785261     4  0.0162     0.8947 0.000 0.000 0.000 0.996 0.004
#> SRR1785264     2  0.0000     0.8436 0.000 1.000 0.000 0.000 0.000
#> SRR1785265     2  0.0000     0.8436 0.000 1.000 0.000 0.000 0.000
#> SRR1785266     4  0.1270     0.8750 0.000 0.052 0.000 0.948 0.000
#> SRR1785267     4  0.1270     0.8750 0.000 0.052 0.000 0.948 0.000
#> SRR1785268     1  0.0162     0.9418 0.996 0.004 0.000 0.000 0.000
#> SRR1785269     1  0.0162     0.9418 0.996 0.004 0.000 0.000 0.000
#> SRR1785270     5  0.0880     0.9151 0.032 0.000 0.000 0.000 0.968
#> SRR1785271     5  0.0794     0.9172 0.028 0.000 0.000 0.000 0.972
#> SRR1785272     2  0.0162     0.8428 0.000 0.996 0.004 0.000 0.000
#> SRR1785273     2  0.0162     0.8428 0.000 0.996 0.004 0.000 0.000
#> SRR1785276     1  0.4300     0.0328 0.524 0.476 0.000 0.000 0.000
#> SRR1785277     2  0.1671     0.8000 0.076 0.924 0.000 0.000 0.000
#> SRR1785274     2  0.0000     0.8436 0.000 1.000 0.000 0.000 0.000
#> SRR1785275     2  0.0000     0.8436 0.000 1.000 0.000 0.000 0.000
#> SRR1785280     2  0.1168     0.8300 0.000 0.960 0.000 0.008 0.032
#> SRR1785281     2  0.1168     0.8300 0.000 0.960 0.000 0.008 0.032
#> SRR1785278     1  0.0162     0.9418 0.996 0.004 0.000 0.000 0.000
#> SRR1785279     1  0.0162     0.9418 0.996 0.004 0.000 0.000 0.000
#> SRR1785282     1  0.0162     0.9418 0.996 0.004 0.000 0.000 0.000
#> SRR1785283     1  0.0162     0.9418 0.996 0.004 0.000 0.000 0.000
#> SRR1785284     5  0.4118     0.4898 0.336 0.004 0.000 0.000 0.660
#> SRR1785285     5  0.4029     0.5327 0.316 0.004 0.000 0.000 0.680
#> SRR1785286     5  0.0880     0.9113 0.000 0.000 0.000 0.032 0.968
#> SRR1785287     5  0.0880     0.9113 0.000 0.000 0.000 0.032 0.968
#> SRR1785288     1  0.0000     0.9414 1.000 0.000 0.000 0.000 0.000
#> SRR1785289     1  0.0000     0.9414 1.000 0.000 0.000 0.000 0.000
#> SRR1785290     4  0.4171     0.4033 0.000 0.396 0.000 0.604 0.000
#> SRR1785291     4  0.4192     0.3865 0.000 0.404 0.000 0.596 0.000
#> SRR1785296     4  0.0000     0.8944 0.000 0.000 0.000 1.000 0.000
#> SRR1785297     4  0.0000     0.8944 0.000 0.000 0.000 1.000 0.000
#> SRR1785292     5  0.0000     0.9250 0.000 0.000 0.000 0.000 1.000
#> SRR1785293     5  0.0000     0.9250 0.000 0.000 0.000 0.000 1.000
#> SRR1785294     4  0.0162     0.8947 0.000 0.000 0.000 0.996 0.004
#> SRR1785295     4  0.0162     0.8947 0.000 0.000 0.000 0.996 0.004
#> SRR1785298     2  0.4138     0.4102 0.000 0.616 0.000 0.384 0.000
#> SRR1785299     2  0.4278     0.2614 0.000 0.548 0.000 0.452 0.000
#> SRR1785300     1  0.0162     0.9418 0.996 0.004 0.000 0.000 0.000
#> SRR1785301     1  0.0162     0.9418 0.996 0.004 0.000 0.000 0.000
#> SRR1785304     4  0.3534     0.6302 0.000 0.000 0.000 0.744 0.256
#> SRR1785305     4  0.2280     0.8101 0.000 0.000 0.000 0.880 0.120
#> SRR1785306     5  0.0000     0.9250 0.000 0.000 0.000 0.000 1.000
#> SRR1785307     5  0.0000     0.9250 0.000 0.000 0.000 0.000 1.000
#> SRR1785302     5  0.0880     0.9151 0.032 0.000 0.000 0.000 0.968
#> SRR1785303     5  0.0880     0.9151 0.032 0.000 0.000 0.000 0.968
#> SRR1785308     3  0.0000     0.9996 0.000 0.000 1.000 0.000 0.000
#> SRR1785309     3  0.0000     0.9996 0.000 0.000 1.000 0.000 0.000
#> SRR1785310     2  0.6191     0.4995 0.168 0.564 0.000 0.264 0.004
#> SRR1785311     2  0.5819     0.4558 0.096 0.564 0.000 0.336 0.004
#> SRR1785312     1  0.0162     0.9418 0.996 0.004 0.000 0.000 0.000
#> SRR1785313     1  0.0162     0.9418 0.996 0.004 0.000 0.000 0.000
#> SRR1785314     5  0.0000     0.9250 0.000 0.000 0.000 0.000 1.000
#> SRR1785315     5  0.0000     0.9250 0.000 0.000 0.000 0.000 1.000
#> SRR1785318     4  0.1485     0.8805 0.000 0.020 0.000 0.948 0.032
#> SRR1785319     4  0.1485     0.8805 0.000 0.020 0.000 0.948 0.032
#> SRR1785316     1  0.0000     0.9414 1.000 0.000 0.000 0.000 0.000
#> SRR1785317     1  0.0000     0.9414 1.000 0.000 0.000 0.000 0.000
#> SRR1785324     5  0.0000     0.9250 0.000 0.000 0.000 0.000 1.000
#> SRR1785325     5  0.0000     0.9250 0.000 0.000 0.000 0.000 1.000
#> SRR1785320     1  0.0000     0.9414 1.000 0.000 0.000 0.000 0.000
#> SRR1785321     1  0.0000     0.9414 1.000 0.000 0.000 0.000 0.000
#> SRR1785322     2  0.3661     0.5932 0.276 0.724 0.000 0.000 0.000
#> SRR1785323     2  0.3774     0.5598 0.296 0.704 0.000 0.000 0.000

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     2  0.0000      0.881 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785239     2  0.0000      0.881 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785240     1  0.0937      0.847 0.960 0.040 0.000 0.000 0.000 0.000
#> SRR1785241     1  0.0937      0.847 0.960 0.040 0.000 0.000 0.000 0.000
#> SRR1785242     3  0.0000      0.996 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785243     3  0.0000      0.996 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785244     6  0.1814      0.854 0.100 0.000 0.000 0.000 0.000 0.900
#> SRR1785245     6  0.1814      0.854 0.100 0.000 0.000 0.000 0.000 0.900
#> SRR1785246     2  0.0000      0.881 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785247     2  0.0000      0.881 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785248     3  0.0865      0.968 0.000 0.000 0.964 0.000 0.000 0.036
#> SRR1785250     3  0.0000      0.996 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785251     3  0.0000      0.996 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785252     3  0.0000      0.996 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785253     3  0.0000      0.996 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785254     6  0.3819      0.642 0.372 0.000 0.000 0.000 0.004 0.624
#> SRR1785255     6  0.3911      0.643 0.368 0.000 0.000 0.000 0.008 0.624
#> SRR1785256     1  0.0000      0.860 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785257     1  0.0000      0.860 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785258     6  0.1814      0.854 0.100 0.000 0.000 0.000 0.000 0.900
#> SRR1785259     6  0.1814      0.854 0.100 0.000 0.000 0.000 0.000 0.900
#> SRR1785262     4  0.0000      0.885 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785263     4  0.0000      0.885 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785260     4  0.0000      0.885 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785261     4  0.0000      0.885 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785264     2  0.0000      0.881 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785265     2  0.0000      0.881 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785266     4  0.2070      0.857 0.000 0.008 0.000 0.892 0.000 0.100
#> SRR1785267     4  0.2070      0.857 0.000 0.008 0.000 0.892 0.000 0.100
#> SRR1785268     1  0.0000      0.860 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785269     1  0.0000      0.860 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785270     5  0.0260      0.992 0.008 0.000 0.000 0.000 0.992 0.000
#> SRR1785271     5  0.0260      0.992 0.008 0.000 0.000 0.000 0.992 0.000
#> SRR1785272     2  0.0000      0.881 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785273     2  0.0000      0.881 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785276     1  0.1327      0.834 0.936 0.064 0.000 0.000 0.000 0.000
#> SRR1785277     1  0.3797      0.302 0.580 0.420 0.000 0.000 0.000 0.000
#> SRR1785274     2  0.0000      0.881 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785275     2  0.0000      0.881 0.000 1.000 0.000 0.000 0.000 0.000
#> SRR1785280     2  0.2070      0.820 0.000 0.892 0.000 0.000 0.008 0.100
#> SRR1785281     2  0.2070      0.820 0.000 0.892 0.000 0.000 0.008 0.100
#> SRR1785278     1  0.0000      0.860 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785279     1  0.0000      0.860 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785282     1  0.0000      0.860 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785283     1  0.0000      0.860 1.000 0.000 0.000 0.000 0.000 0.000
#> SRR1785284     1  0.1007      0.839 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1785285     1  0.1007      0.839 0.956 0.000 0.000 0.000 0.044 0.000
#> SRR1785286     5  0.0260      0.991 0.000 0.000 0.000 0.008 0.992 0.000
#> SRR1785287     5  0.0260      0.991 0.000 0.000 0.000 0.008 0.992 0.000
#> SRR1785288     6  0.1814      0.854 0.100 0.000 0.000 0.000 0.000 0.900
#> SRR1785289     6  0.1814      0.854 0.100 0.000 0.000 0.000 0.000 0.900
#> SRR1785290     4  0.3747      0.368 0.000 0.396 0.000 0.604 0.000 0.000
#> SRR1785291     4  0.3774      0.342 0.000 0.408 0.000 0.592 0.000 0.000
#> SRR1785296     4  0.0000      0.885 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785297     4  0.0000      0.885 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785292     5  0.0000      0.996 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1785293     5  0.0000      0.996 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1785294     4  0.0000      0.885 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785295     4  0.0000      0.885 0.000 0.000 0.000 1.000 0.000 0.000
#> SRR1785298     2  0.3672      0.380 0.000 0.632 0.000 0.368 0.000 0.000
#> SRR1785299     2  0.3828      0.209 0.000 0.560 0.000 0.440 0.000 0.000
#> SRR1785300     1  0.0363      0.853 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1785301     1  0.0363      0.853 0.988 0.000 0.000 0.000 0.000 0.012
#> SRR1785304     4  0.3151      0.641 0.000 0.000 0.000 0.748 0.252 0.000
#> SRR1785305     4  0.2003      0.809 0.000 0.000 0.000 0.884 0.116 0.000
#> SRR1785306     5  0.0000      0.996 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1785307     5  0.0000      0.996 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1785302     5  0.0260      0.992 0.008 0.000 0.000 0.000 0.992 0.000
#> SRR1785303     5  0.0260      0.992 0.008 0.000 0.000 0.000 0.992 0.000
#> SRR1785308     3  0.0000      0.996 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785309     3  0.0000      0.996 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785310     1  0.3313      0.736 0.816 0.060 0.000 0.124 0.000 0.000
#> SRR1785311     1  0.5184      0.452 0.584 0.120 0.000 0.296 0.000 0.000
#> SRR1785312     6  0.3446      0.646 0.308 0.000 0.000 0.000 0.000 0.692
#> SRR1785313     1  0.3672      0.237 0.632 0.000 0.000 0.000 0.000 0.368
#> SRR1785314     5  0.0000      0.996 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1785315     5  0.0000      0.996 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1785318     4  0.2070      0.856 0.000 0.000 0.000 0.892 0.008 0.100
#> SRR1785319     4  0.2070      0.856 0.000 0.000 0.000 0.892 0.008 0.100
#> SRR1785316     6  0.3684      0.647 0.372 0.000 0.000 0.000 0.000 0.628
#> SRR1785317     6  0.3860      0.446 0.472 0.000 0.000 0.000 0.000 0.528
#> SRR1785324     5  0.0000      0.996 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1785325     5  0.0000      0.996 0.000 0.000 0.000 0.000 1.000 0.000
#> SRR1785320     6  0.1814      0.854 0.100 0.000 0.000 0.000 0.000 0.900
#> SRR1785321     6  0.1814      0.854 0.100 0.000 0.000 0.000 0.000 0.900
#> SRR1785322     2  0.3804      0.159 0.424 0.576 0.000 0.000 0.000 0.000
#> SRR1785323     1  0.3592      0.470 0.656 0.344 0.000 0.000 0.000 0.000

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-ATC-pam-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-ATC-pam-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-ATC-pam-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-ATC-pam-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-ATC-pam-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-ATC-pam-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-ATC-pam-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-ATC-pam-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-ATC-pam-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-ATC-pam-membership-heatmap-5

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)

plot of chunk tab-ATC-pam-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-ATC-pam-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-ATC-pam-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-ATC-pam-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-ATC-pam-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-ATC-pam-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-ATC-pam-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-ATC-pam-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-ATC-pam-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-ATC-pam-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk ATC-pam-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-ATC-pam-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-ATC-pam-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-ATC-pam-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-ATC-pam-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-ATC-pam-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk ATC-pam-collect-classes

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.


ATC:mclust

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#>   Subgroups are detected by 'mclust' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 2.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk ATC-mclust-collect-plots

The plots are:

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:

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)

plot of chunk ATC-mclust-select-partition-number

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 0.738           0.920       0.949         0.4311 0.530   0.530
#> 3 3 0.645           0.853       0.905         0.4125 0.823   0.680
#> 4 4 0.524           0.499       0.712         0.1309 0.698   0.390
#> 5 5 0.612           0.783       0.817         0.0735 0.751   0.372
#> 6 6 0.548           0.337       0.593         0.0920 0.769   0.301

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2   0.000      0.994 0.000 1.000
#> SRR1785239     2   0.000      0.994 0.000 1.000
#> SRR1785240     1   0.881      0.746 0.700 0.300
#> SRR1785241     1   0.881      0.746 0.700 0.300
#> SRR1785242     2   0.000      0.994 0.000 1.000
#> SRR1785243     2   0.000      0.994 0.000 1.000
#> SRR1785244     1   0.000      0.853 1.000 0.000
#> SRR1785245     1   0.000      0.853 1.000 0.000
#> SRR1785246     2   0.000      0.994 0.000 1.000
#> SRR1785247     2   0.000      0.994 0.000 1.000
#> SRR1785248     2   0.000      0.994 0.000 1.000
#> SRR1785250     2   0.000      0.994 0.000 1.000
#> SRR1785251     2   0.000      0.994 0.000 1.000
#> SRR1785252     2   0.000      0.994 0.000 1.000
#> SRR1785253     2   0.000      0.994 0.000 1.000
#> SRR1785254     1   0.881      0.746 0.700 0.300
#> SRR1785255     1   0.881      0.746 0.700 0.300
#> SRR1785256     1   0.000      0.853 1.000 0.000
#> SRR1785257     1   0.000      0.853 1.000 0.000
#> SRR1785258     1   0.000      0.853 1.000 0.000
#> SRR1785259     1   0.000      0.853 1.000 0.000
#> SRR1785262     2   0.000      0.994 0.000 1.000
#> SRR1785263     2   0.000      0.994 0.000 1.000
#> SRR1785260     2   0.000      0.994 0.000 1.000
#> SRR1785261     2   0.000      0.994 0.000 1.000
#> SRR1785264     2   0.000      0.994 0.000 1.000
#> SRR1785265     2   0.000      0.994 0.000 1.000
#> SRR1785266     2   0.000      0.994 0.000 1.000
#> SRR1785267     2   0.000      0.994 0.000 1.000
#> SRR1785268     1   0.000      0.853 1.000 0.000
#> SRR1785269     1   0.000      0.853 1.000 0.000
#> SRR1785270     1   0.881      0.746 0.700 0.300
#> SRR1785271     1   0.881      0.746 0.700 0.300
#> SRR1785272     2   0.000      0.994 0.000 1.000
#> SRR1785273     2   0.000      0.994 0.000 1.000
#> SRR1785276     1   0.881      0.746 0.700 0.300
#> SRR1785277     1   0.913      0.702 0.672 0.328
#> SRR1785274     2   0.000      0.994 0.000 1.000
#> SRR1785275     2   0.000      0.994 0.000 1.000
#> SRR1785280     2   0.000      0.994 0.000 1.000
#> SRR1785281     2   0.000      0.994 0.000 1.000
#> SRR1785278     1   0.881      0.746 0.700 0.300
#> SRR1785279     1   0.881      0.746 0.700 0.300
#> SRR1785282     1   0.871      0.751 0.708 0.292
#> SRR1785283     1   0.871      0.751 0.708 0.292
#> SRR1785284     1   0.881      0.746 0.700 0.300
#> SRR1785285     1   0.881      0.746 0.700 0.300
#> SRR1785286     2   0.000      0.994 0.000 1.000
#> SRR1785287     2   0.000      0.994 0.000 1.000
#> SRR1785288     1   0.000      0.853 1.000 0.000
#> SRR1785289     1   0.000      0.853 1.000 0.000
#> SRR1785290     2   0.000      0.994 0.000 1.000
#> SRR1785291     2   0.000      0.994 0.000 1.000
#> SRR1785296     2   0.000      0.994 0.000 1.000
#> SRR1785297     2   0.000      0.994 0.000 1.000
#> SRR1785292     2   0.000      0.994 0.000 1.000
#> SRR1785293     2   0.000      0.994 0.000 1.000
#> SRR1785294     2   0.000      0.994 0.000 1.000
#> SRR1785295     2   0.000      0.994 0.000 1.000
#> SRR1785298     2   0.000      0.994 0.000 1.000
#> SRR1785299     2   0.000      0.994 0.000 1.000
#> SRR1785300     1   0.000      0.853 1.000 0.000
#> SRR1785301     1   0.000      0.853 1.000 0.000
#> SRR1785304     2   0.000      0.994 0.000 1.000
#> SRR1785305     2   0.000      0.994 0.000 1.000
#> SRR1785306     2   0.000      0.994 0.000 1.000
#> SRR1785307     2   0.000      0.994 0.000 1.000
#> SRR1785302     2   0.662      0.744 0.172 0.828
#> SRR1785303     2   0.443      0.876 0.092 0.908
#> SRR1785308     2   0.000      0.994 0.000 1.000
#> SRR1785309     2   0.000      0.994 0.000 1.000
#> SRR1785310     2   0.000      0.994 0.000 1.000
#> SRR1785311     2   0.000      0.994 0.000 1.000
#> SRR1785312     1   0.000      0.853 1.000 0.000
#> SRR1785313     1   0.000      0.853 1.000 0.000
#> SRR1785314     2   0.000      0.994 0.000 1.000
#> SRR1785315     2   0.000      0.994 0.000 1.000
#> SRR1785318     2   0.000      0.994 0.000 1.000
#> SRR1785319     2   0.000      0.994 0.000 1.000
#> SRR1785316     1   0.000      0.853 1.000 0.000
#> SRR1785317     1   0.000      0.853 1.000 0.000
#> SRR1785324     2   0.000      0.994 0.000 1.000
#> SRR1785325     2   0.000      0.994 0.000 1.000
#> SRR1785320     1   0.000      0.853 1.000 0.000
#> SRR1785321     1   0.000      0.853 1.000 0.000
#> SRR1785322     2   0.000      0.994 0.000 1.000
#> SRR1785323     2   0.000      0.994 0.000 1.000

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     2  0.4399      0.867 0.000 0.812 0.188
#> SRR1785239     2  0.4399      0.867 0.000 0.812 0.188
#> SRR1785240     1  0.5334      0.827 0.820 0.060 0.120
#> SRR1785241     1  0.5334      0.827 0.820 0.060 0.120
#> SRR1785242     3  0.0000      0.861 0.000 0.000 1.000
#> SRR1785243     3  0.0000      0.861 0.000 0.000 1.000
#> SRR1785244     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785245     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785246     3  0.5138      0.817 0.000 0.252 0.748
#> SRR1785247     3  0.6008      0.661 0.000 0.372 0.628
#> SRR1785248     2  0.6095      0.617 0.000 0.608 0.392
#> SRR1785250     3  0.4452      0.847 0.000 0.192 0.808
#> SRR1785251     3  0.4452      0.847 0.000 0.192 0.808
#> SRR1785252     3  0.0000      0.861 0.000 0.000 1.000
#> SRR1785253     3  0.0000      0.861 0.000 0.000 1.000
#> SRR1785254     1  0.5241      0.824 0.820 0.048 0.132
#> SRR1785255     1  0.5241      0.824 0.820 0.048 0.132
#> SRR1785256     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785257     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785258     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785259     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785262     2  0.0000      0.850 0.000 1.000 0.000
#> SRR1785263     2  0.0000      0.850 0.000 1.000 0.000
#> SRR1785260     2  0.0000      0.850 0.000 1.000 0.000
#> SRR1785261     2  0.0000      0.850 0.000 1.000 0.000
#> SRR1785264     2  0.4399      0.867 0.000 0.812 0.188
#> SRR1785265     2  0.4399      0.867 0.000 0.812 0.188
#> SRR1785266     2  0.4452      0.866 0.000 0.808 0.192
#> SRR1785267     2  0.4452      0.866 0.000 0.808 0.192
#> SRR1785268     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785269     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785270     2  0.8857      0.375 0.344 0.524 0.132
#> SRR1785271     2  0.8101      0.603 0.228 0.640 0.132
#> SRR1785272     3  0.4452      0.847 0.000 0.192 0.808
#> SRR1785273     3  0.4452      0.847 0.000 0.192 0.808
#> SRR1785276     1  0.5241      0.824 0.820 0.048 0.132
#> SRR1785277     1  0.5241      0.824 0.820 0.048 0.132
#> SRR1785274     2  0.4128      0.872 0.012 0.856 0.132
#> SRR1785275     2  0.4128      0.872 0.012 0.856 0.132
#> SRR1785280     2  0.4452      0.866 0.000 0.808 0.192
#> SRR1785281     2  0.4452      0.866 0.000 0.808 0.192
#> SRR1785278     1  0.5307      0.827 0.820 0.056 0.124
#> SRR1785279     1  0.5307      0.827 0.820 0.056 0.124
#> SRR1785282     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785283     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785284     1  0.6854      0.718 0.740 0.136 0.124
#> SRR1785285     1  0.6389      0.761 0.768 0.108 0.124
#> SRR1785286     2  0.0237      0.850 0.004 0.996 0.000
#> SRR1785287     2  0.0237      0.850 0.004 0.996 0.000
#> SRR1785288     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785289     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785290     2  0.4452      0.866 0.000 0.808 0.192
#> SRR1785291     2  0.4452      0.866 0.000 0.808 0.192
#> SRR1785296     2  0.0000      0.850 0.000 1.000 0.000
#> SRR1785297     2  0.0000      0.850 0.000 1.000 0.000
#> SRR1785292     2  0.4755      0.867 0.008 0.808 0.184
#> SRR1785293     2  0.4755      0.867 0.008 0.808 0.184
#> SRR1785294     2  0.0000      0.850 0.000 1.000 0.000
#> SRR1785295     2  0.0000      0.850 0.000 1.000 0.000
#> SRR1785298     2  0.0000      0.850 0.000 1.000 0.000
#> SRR1785299     2  0.0000      0.850 0.000 1.000 0.000
#> SRR1785300     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785301     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785304     2  0.0000      0.850 0.000 1.000 0.000
#> SRR1785305     2  0.0000      0.850 0.000 1.000 0.000
#> SRR1785306     2  0.4128      0.872 0.012 0.856 0.132
#> SRR1785307     2  0.4128      0.872 0.012 0.856 0.132
#> SRR1785302     2  0.4418      0.868 0.020 0.848 0.132
#> SRR1785303     2  0.4128      0.872 0.012 0.856 0.132
#> SRR1785308     3  0.0237      0.861 0.000 0.004 0.996
#> SRR1785309     3  0.0237      0.861 0.000 0.004 0.996
#> SRR1785310     2  0.0237      0.850 0.004 0.996 0.000
#> SRR1785311     2  0.0237      0.850 0.004 0.996 0.000
#> SRR1785312     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785313     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785314     2  0.4128      0.872 0.012 0.856 0.132
#> SRR1785315     2  0.4128      0.872 0.012 0.856 0.132
#> SRR1785318     2  0.4452      0.866 0.000 0.808 0.192
#> SRR1785319     2  0.4452      0.866 0.000 0.808 0.192
#> SRR1785316     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785317     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785324     2  0.4861      0.867 0.012 0.808 0.180
#> SRR1785325     2  0.4861      0.867 0.012 0.808 0.180
#> SRR1785320     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785321     1  0.0000      0.920 1.000 0.000 0.000
#> SRR1785322     2  0.0237      0.848 0.004 0.996 0.000
#> SRR1785323     2  0.2066      0.806 0.060 0.940 0.000

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     2  0.6882      0.602 0.124 0.548 0.000 0.328
#> SRR1785239     2  0.6824      0.602 0.116 0.548 0.000 0.336
#> SRR1785240     2  0.7456      0.560 0.200 0.492 0.000 0.308
#> SRR1785241     2  0.7456      0.560 0.200 0.492 0.000 0.308
#> SRR1785242     3  0.4605      0.647 0.000 0.336 0.664 0.000
#> SRR1785243     3  0.4605      0.647 0.000 0.336 0.664 0.000
#> SRR1785244     1  0.0000      0.585 1.000 0.000 0.000 0.000
#> SRR1785245     1  0.0000      0.585 1.000 0.000 0.000 0.000
#> SRR1785246     4  0.4888      0.555 0.000 0.000 0.412 0.588
#> SRR1785247     4  0.4920      0.613 0.000 0.004 0.368 0.628
#> SRR1785248     2  0.2973      0.404 0.000 0.856 0.144 0.000
#> SRR1785250     3  0.4999     -0.519 0.000 0.000 0.508 0.492
#> SRR1785251     3  0.4999     -0.519 0.000 0.000 0.508 0.492
#> SRR1785252     3  0.4605      0.647 0.000 0.336 0.664 0.000
#> SRR1785253     3  0.4605      0.647 0.000 0.336 0.664 0.000
#> SRR1785254     1  0.5781     -0.450 0.492 0.480 0.028 0.000
#> SRR1785255     1  0.5781     -0.450 0.492 0.480 0.028 0.000
#> SRR1785256     1  0.4454      0.596 0.692 0.000 0.000 0.308
#> SRR1785257     1  0.4454      0.596 0.692 0.000 0.000 0.308
#> SRR1785258     1  0.4454      0.596 0.692 0.000 0.000 0.308
#> SRR1785259     1  0.4454      0.596 0.692 0.000 0.000 0.308
#> SRR1785262     4  0.5857      0.707 0.000 0.056 0.308 0.636
#> SRR1785263     4  0.5857      0.707 0.000 0.056 0.308 0.636
#> SRR1785260     4  0.5557      0.697 0.000 0.040 0.308 0.652
#> SRR1785261     4  0.5557      0.697 0.000 0.040 0.308 0.652
#> SRR1785264     2  0.5152      0.612 0.020 0.664 0.000 0.316
#> SRR1785265     2  0.4936      0.610 0.012 0.672 0.000 0.316
#> SRR1785266     2  0.4382      0.607 0.000 0.704 0.000 0.296
#> SRR1785267     2  0.4382      0.607 0.000 0.704 0.000 0.296
#> SRR1785268     1  0.4454      0.596 0.692 0.000 0.000 0.308
#> SRR1785269     1  0.4769      0.587 0.684 0.008 0.000 0.308
#> SRR1785270     1  0.5781     -0.450 0.492 0.480 0.028 0.000
#> SRR1785271     1  0.5781     -0.450 0.492 0.480 0.028 0.000
#> SRR1785272     4  0.4985      0.457 0.000 0.000 0.468 0.532
#> SRR1785273     4  0.4985      0.457 0.000 0.000 0.468 0.532
#> SRR1785276     2  0.7456      0.560 0.200 0.492 0.000 0.308
#> SRR1785277     2  0.7456      0.560 0.200 0.492 0.000 0.308
#> SRR1785274     2  0.6961      0.601 0.136 0.548 0.000 0.316
#> SRR1785275     2  0.6985      0.600 0.140 0.548 0.000 0.312
#> SRR1785280     2  0.0000      0.502 0.000 1.000 0.000 0.000
#> SRR1785281     2  0.0000      0.502 0.000 1.000 0.000 0.000
#> SRR1785278     2  0.7456      0.560 0.200 0.492 0.000 0.308
#> SRR1785279     2  0.7456      0.560 0.200 0.492 0.000 0.308
#> SRR1785282     2  0.7638      0.514 0.232 0.460 0.000 0.308
#> SRR1785283     2  0.7638      0.514 0.232 0.460 0.000 0.308
#> SRR1785284     2  0.4999      0.441 0.492 0.508 0.000 0.000
#> SRR1785285     2  0.4999      0.441 0.492 0.508 0.000 0.000
#> SRR1785286     4  0.7258      0.579 0.208 0.056 0.100 0.636
#> SRR1785287     4  0.7397      0.624 0.156 0.056 0.152 0.636
#> SRR1785288     1  0.0000      0.585 1.000 0.000 0.000 0.000
#> SRR1785289     1  0.0000      0.585 1.000 0.000 0.000 0.000
#> SRR1785290     2  0.4830      0.586 0.000 0.608 0.000 0.392
#> SRR1785291     2  0.4624      0.604 0.000 0.660 0.000 0.340
#> SRR1785296     4  0.1557      0.526 0.000 0.056 0.000 0.944
#> SRR1785297     4  0.1557      0.526 0.000 0.056 0.000 0.944
#> SRR1785292     2  0.4454      0.524 0.308 0.692 0.000 0.000
#> SRR1785293     2  0.4454      0.524 0.308 0.692 0.000 0.000
#> SRR1785294     4  0.5857      0.707 0.000 0.056 0.308 0.636
#> SRR1785295     4  0.5857      0.707 0.000 0.056 0.308 0.636
#> SRR1785298     4  0.1557      0.526 0.000 0.056 0.000 0.944
#> SRR1785299     4  0.1557      0.526 0.000 0.056 0.000 0.944
#> SRR1785300     1  0.0000      0.585 1.000 0.000 0.000 0.000
#> SRR1785301     1  0.0000      0.585 1.000 0.000 0.000 0.000
#> SRR1785304     4  0.5857      0.476 0.308 0.056 0.000 0.636
#> SRR1785305     4  0.5857      0.476 0.308 0.056 0.000 0.636
#> SRR1785306     2  0.5250      0.501 0.440 0.552 0.000 0.008
#> SRR1785307     2  0.5244      0.502 0.436 0.556 0.000 0.008
#> SRR1785302     2  0.4977      0.484 0.460 0.540 0.000 0.000
#> SRR1785303     2  0.4967      0.493 0.452 0.548 0.000 0.000
#> SRR1785308     3  0.0921      0.519 0.000 0.028 0.972 0.000
#> SRR1785309     3  0.0921      0.519 0.000 0.028 0.972 0.000
#> SRR1785310     4  0.6125      0.708 0.008 0.056 0.300 0.636
#> SRR1785311     4  0.6125      0.708 0.008 0.056 0.300 0.636
#> SRR1785312     1  0.4454      0.596 0.692 0.000 0.000 0.308
#> SRR1785313     1  0.4454      0.596 0.692 0.000 0.000 0.308
#> SRR1785314     2  0.5257      0.498 0.444 0.548 0.000 0.008
#> SRR1785315     2  0.5257      0.498 0.444 0.548 0.000 0.008
#> SRR1785318     2  0.0000      0.502 0.000 1.000 0.000 0.000
#> SRR1785319     2  0.0000      0.502 0.000 1.000 0.000 0.000
#> SRR1785316     1  0.0000      0.585 1.000 0.000 0.000 0.000
#> SRR1785317     1  0.0000      0.585 1.000 0.000 0.000 0.000
#> SRR1785324     2  0.4454      0.524 0.308 0.692 0.000 0.000
#> SRR1785325     2  0.4454      0.524 0.308 0.692 0.000 0.000
#> SRR1785320     1  0.4454      0.596 0.692 0.000 0.000 0.308
#> SRR1785321     1  0.4454      0.596 0.692 0.000 0.000 0.308
#> SRR1785322     4  0.6522      0.690 0.020 0.068 0.276 0.636
#> SRR1785323     4  0.3485      0.544 0.004 0.076 0.048 0.872

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     4  0.4567      0.714 0.008 0.032 0.016 0.760 0.184
#> SRR1785239     4  0.4567      0.714 0.008 0.032 0.016 0.760 0.184
#> SRR1785240     1  0.5038      0.790 0.780 0.032 0.068 0.032 0.088
#> SRR1785241     1  0.5342      0.768 0.756 0.032 0.068 0.032 0.112
#> SRR1785242     3  0.1608      0.776 0.000 0.072 0.928 0.000 0.000
#> SRR1785243     3  0.1608      0.776 0.000 0.072 0.928 0.000 0.000
#> SRR1785244     1  0.0960      0.868 0.972 0.008 0.004 0.000 0.016
#> SRR1785245     1  0.0960      0.868 0.972 0.008 0.004 0.000 0.016
#> SRR1785246     4  0.5810      0.253 0.000 0.000 0.212 0.612 0.176
#> SRR1785247     4  0.5578      0.336 0.000 0.000 0.180 0.644 0.176
#> SRR1785248     2  0.5200      0.912 0.000 0.720 0.084 0.172 0.024
#> SRR1785250     3  0.5362      0.764 0.008 0.004 0.700 0.116 0.172
#> SRR1785251     3  0.5362      0.764 0.008 0.004 0.700 0.116 0.172
#> SRR1785252     3  0.1732      0.773 0.000 0.080 0.920 0.000 0.000
#> SRR1785253     3  0.1732      0.773 0.000 0.080 0.920 0.000 0.000
#> SRR1785254     1  0.3815      0.833 0.832 0.000 0.060 0.020 0.088
#> SRR1785255     1  0.3815      0.833 0.832 0.000 0.060 0.020 0.088
#> SRR1785256     1  0.0162      0.870 0.996 0.004 0.000 0.000 0.000
#> SRR1785257     1  0.0162      0.870 0.996 0.004 0.000 0.000 0.000
#> SRR1785258     1  0.3400      0.791 0.808 0.180 0.004 0.004 0.004
#> SRR1785259     1  0.3400      0.791 0.808 0.180 0.004 0.004 0.004
#> SRR1785262     4  0.1732      0.747 0.000 0.000 0.080 0.920 0.000
#> SRR1785263     4  0.1732      0.747 0.000 0.000 0.080 0.920 0.000
#> SRR1785260     4  0.2165      0.768 0.004 0.056 0.016 0.920 0.004
#> SRR1785261     4  0.2165      0.768 0.004 0.056 0.016 0.920 0.004
#> SRR1785264     4  0.4472      0.711 0.008 0.036 0.008 0.760 0.188
#> SRR1785265     4  0.4472      0.711 0.008 0.036 0.008 0.760 0.188
#> SRR1785266     2  0.4328      0.851 0.000 0.724 0.008 0.248 0.020
#> SRR1785267     2  0.4328      0.851 0.000 0.724 0.008 0.248 0.020
#> SRR1785268     1  0.0566      0.869 0.984 0.004 0.000 0.012 0.000
#> SRR1785269     1  0.0566      0.869 0.984 0.004 0.000 0.012 0.000
#> SRR1785270     1  0.4597      0.636 0.672 0.000 0.004 0.024 0.300
#> SRR1785271     1  0.5690      0.199 0.492 0.000 0.004 0.068 0.436
#> SRR1785272     3  0.5994      0.714 0.008 0.000 0.616 0.200 0.176
#> SRR1785273     3  0.5994      0.714 0.008 0.000 0.616 0.200 0.176
#> SRR1785276     1  0.5638      0.744 0.732 0.032 0.060 0.040 0.136
#> SRR1785277     1  0.5760      0.730 0.720 0.032 0.060 0.040 0.148
#> SRR1785274     4  0.4287      0.720 0.008 0.032 0.008 0.776 0.176
#> SRR1785275     4  0.4287      0.720 0.008 0.032 0.008 0.776 0.176
#> SRR1785280     2  0.4159      0.915 0.000 0.780 0.036 0.172 0.012
#> SRR1785281     2  0.4159      0.915 0.000 0.780 0.036 0.172 0.012
#> SRR1785278     1  0.3209      0.849 0.876 0.024 0.008 0.024 0.068
#> SRR1785279     1  0.3209      0.849 0.876 0.024 0.008 0.024 0.068
#> SRR1785282     1  0.2364      0.860 0.908 0.008 0.000 0.020 0.064
#> SRR1785283     1  0.2364      0.860 0.908 0.008 0.000 0.020 0.064
#> SRR1785284     1  0.3911      0.834 0.840 0.012 0.068 0.020 0.060
#> SRR1785285     1  0.3911      0.834 0.840 0.012 0.068 0.020 0.060
#> SRR1785286     4  0.4137      0.602 0.012 0.000 0.008 0.732 0.248
#> SRR1785287     4  0.4082      0.615 0.012 0.000 0.008 0.740 0.240
#> SRR1785288     1  0.0324      0.869 0.992 0.004 0.000 0.000 0.004
#> SRR1785289     1  0.0324      0.869 0.992 0.004 0.000 0.000 0.004
#> SRR1785290     4  0.3575      0.737 0.008 0.124 0.008 0.836 0.024
#> SRR1785291     4  0.3713      0.730 0.008 0.136 0.008 0.824 0.024
#> SRR1785296     4  0.2255      0.766 0.004 0.060 0.008 0.916 0.012
#> SRR1785297     4  0.2255      0.766 0.004 0.060 0.008 0.916 0.012
#> SRR1785292     5  0.3929      0.897 0.000 0.036 0.004 0.172 0.788
#> SRR1785293     5  0.3929      0.897 0.000 0.036 0.004 0.172 0.788
#> SRR1785294     4  0.1845      0.768 0.000 0.056 0.016 0.928 0.000
#> SRR1785295     4  0.1845      0.768 0.000 0.056 0.016 0.928 0.000
#> SRR1785298     4  0.1248      0.783 0.008 0.004 0.008 0.964 0.016
#> SRR1785299     4  0.1248      0.783 0.008 0.004 0.008 0.964 0.016
#> SRR1785300     1  0.0000      0.870 1.000 0.000 0.000 0.000 0.000
#> SRR1785301     1  0.0000      0.870 1.000 0.000 0.000 0.000 0.000
#> SRR1785304     5  0.4639      0.720 0.008 0.012 0.000 0.344 0.636
#> SRR1785305     5  0.4735      0.660 0.008 0.012 0.000 0.372 0.608
#> SRR1785306     5  0.3875      0.900 0.008 0.012 0.008 0.180 0.792
#> SRR1785307     5  0.3938      0.901 0.008 0.016 0.008 0.176 0.792
#> SRR1785302     5  0.4858      0.848 0.056 0.012 0.008 0.180 0.744
#> SRR1785303     5  0.4432      0.882 0.032 0.012 0.008 0.180 0.768
#> SRR1785308     3  0.2378      0.792 0.000 0.000 0.904 0.048 0.048
#> SRR1785309     3  0.2378      0.792 0.000 0.000 0.904 0.048 0.048
#> SRR1785310     4  0.4080      0.713 0.012 0.016 0.012 0.792 0.168
#> SRR1785311     4  0.4080      0.713 0.012 0.016 0.012 0.792 0.168
#> SRR1785312     1  0.3400      0.791 0.808 0.180 0.004 0.004 0.004
#> SRR1785313     1  0.3400      0.791 0.808 0.180 0.004 0.004 0.004
#> SRR1785314     5  0.3289      0.906 0.000 0.004 0.008 0.172 0.816
#> SRR1785315     5  0.3289      0.906 0.000 0.004 0.008 0.172 0.816
#> SRR1785318     2  0.5090      0.912 0.000 0.728 0.076 0.172 0.024
#> SRR1785319     2  0.5090      0.912 0.000 0.728 0.076 0.172 0.024
#> SRR1785316     1  0.0162      0.869 0.996 0.004 0.000 0.000 0.000
#> SRR1785317     1  0.0162      0.869 0.996 0.004 0.000 0.000 0.000
#> SRR1785324     5  0.3612      0.899 0.000 0.028 0.000 0.172 0.800
#> SRR1785325     5  0.3612      0.899 0.000 0.028 0.000 0.172 0.800
#> SRR1785320     1  0.3400      0.791 0.808 0.180 0.004 0.004 0.004
#> SRR1785321     1  0.3400      0.791 0.808 0.180 0.004 0.004 0.004
#> SRR1785322     4  0.4080      0.739 0.012 0.000 0.076 0.808 0.104
#> SRR1785323     4  0.4020      0.740 0.012 0.000 0.072 0.812 0.104

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     1   0.732    0.12489 0.432 0.340 0.072 0.008 0.028 0.120
#> SRR1785239     1   0.732    0.12489 0.432 0.340 0.072 0.008 0.028 0.120
#> SRR1785240     1   0.659    0.38247 0.588 0.140 0.084 0.000 0.024 0.164
#> SRR1785241     1   0.658    0.37663 0.592 0.156 0.088 0.000 0.024 0.140
#> SRR1785242     3   0.000    0.81012 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785243     3   0.000    0.81012 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785244     6   0.225    0.70118 0.016 0.012 0.000 0.000 0.072 0.900
#> SRR1785245     6   0.225    0.70118 0.016 0.012 0.000 0.000 0.072 0.900
#> SRR1785246     4   0.232    0.68019 0.048 0.000 0.060 0.892 0.000 0.000
#> SRR1785247     4   0.232    0.68019 0.048 0.000 0.060 0.892 0.000 0.000
#> SRR1785248     3   0.550   -0.08359 0.084 0.396 0.508 0.004 0.004 0.004
#> SRR1785250     4   0.253    0.67059 0.024 0.000 0.096 0.876 0.004 0.000
#> SRR1785251     4   0.253    0.67059 0.024 0.000 0.096 0.876 0.004 0.000
#> SRR1785252     3   0.000    0.81012 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785253     3   0.000    0.81012 0.000 0.000 1.000 0.000 0.000 0.000
#> SRR1785254     5   0.715   -0.00928 0.048 0.372 0.060 0.004 0.428 0.088
#> SRR1785255     5   0.715   -0.00928 0.048 0.372 0.060 0.004 0.428 0.088
#> SRR1785256     1   0.350    0.19541 0.728 0.000 0.004 0.000 0.004 0.264
#> SRR1785257     1   0.348    0.20273 0.732 0.000 0.004 0.000 0.004 0.260
#> SRR1785258     6   0.026    0.75640 0.008 0.000 0.000 0.000 0.000 0.992
#> SRR1785259     6   0.026    0.75640 0.008 0.000 0.000 0.000 0.000 0.992
#> SRR1785262     4   0.271    0.65065 0.000 0.048 0.000 0.864 0.088 0.000
#> SRR1785263     4   0.276    0.64984 0.000 0.048 0.000 0.860 0.092 0.000
#> SRR1785260     5   0.545    0.11471 0.020 0.208 0.000 0.144 0.628 0.000
#> SRR1785261     5   0.545    0.11471 0.020 0.208 0.000 0.144 0.628 0.000
#> SRR1785264     1   0.730    0.21511 0.460 0.288 0.116 0.008 0.012 0.116
#> SRR1785265     1   0.730    0.21511 0.460 0.288 0.116 0.008 0.012 0.116
#> SRR1785266     2   0.411    0.49186 0.084 0.772 0.128 0.000 0.000 0.016
#> SRR1785267     2   0.411    0.49186 0.084 0.772 0.128 0.000 0.000 0.016
#> SRR1785268     1   0.333    0.26346 0.768 0.000 0.008 0.000 0.004 0.220
#> SRR1785269     1   0.328    0.29089 0.784 0.000 0.012 0.000 0.004 0.200
#> SRR1785270     5   0.670   -0.00471 0.028 0.364 0.060 0.000 0.464 0.084
#> SRR1785271     5   0.657    0.00166 0.024 0.344 0.060 0.000 0.492 0.080
#> SRR1785272     4   0.251    0.67817 0.056 0.000 0.064 0.880 0.000 0.000
#> SRR1785273     4   0.251    0.67817 0.056 0.000 0.064 0.880 0.000 0.000
#> SRR1785276     1   0.732    0.26085 0.520 0.236 0.068 0.020 0.032 0.124
#> SRR1785277     1   0.730    0.26439 0.524 0.232 0.068 0.020 0.032 0.124
#> SRR1785274     1   0.721    0.25802 0.500 0.256 0.096 0.008 0.020 0.120
#> SRR1785275     1   0.719    0.25938 0.504 0.252 0.096 0.008 0.020 0.120
#> SRR1785280     2   0.447    0.35331 0.252 0.692 0.036 0.000 0.020 0.000
#> SRR1785281     2   0.447    0.35331 0.252 0.692 0.036 0.000 0.020 0.000
#> SRR1785278     1   0.353    0.34603 0.812 0.004 0.028 0.016 0.000 0.140
#> SRR1785279     1   0.357    0.34322 0.808 0.004 0.028 0.016 0.000 0.144
#> SRR1785282     1   0.359    0.31373 0.784 0.016 0.000 0.000 0.020 0.180
#> SRR1785283     1   0.356    0.31793 0.788 0.016 0.000 0.000 0.020 0.176
#> SRR1785284     1   0.865   -0.02348 0.312 0.288 0.104 0.024 0.208 0.064
#> SRR1785285     1   0.865   -0.02348 0.312 0.288 0.104 0.024 0.208 0.064
#> SRR1785286     4   0.505    0.54557 0.064 0.016 0.000 0.612 0.308 0.000
#> SRR1785287     4   0.505    0.54557 0.064 0.016 0.000 0.612 0.308 0.000
#> SRR1785288     6   0.556    0.43471 0.432 0.012 0.008 0.000 0.072 0.476
#> SRR1785289     6   0.556    0.43471 0.432 0.012 0.008 0.000 0.072 0.476
#> SRR1785290     2   0.484    0.42104 0.012 0.744 0.144 0.064 0.028 0.008
#> SRR1785291     2   0.463    0.42982 0.012 0.756 0.148 0.052 0.024 0.008
#> SRR1785296     4   0.659    0.12291 0.024 0.284 0.000 0.360 0.332 0.000
#> SRR1785297     4   0.659    0.12291 0.024 0.284 0.000 0.360 0.332 0.000
#> SRR1785292     2   0.501    0.29647 0.028 0.496 0.012 0.000 0.456 0.008
#> SRR1785293     2   0.501    0.29647 0.028 0.496 0.012 0.000 0.456 0.008
#> SRR1785294     5   0.636   -0.07682 0.020 0.232 0.000 0.304 0.444 0.000
#> SRR1785295     5   0.637   -0.07280 0.020 0.236 0.000 0.300 0.444 0.000
#> SRR1785298     4   0.672    0.20652 0.084 0.312 0.020 0.500 0.084 0.000
#> SRR1785299     4   0.641    0.20690 0.056 0.312 0.020 0.528 0.084 0.000
#> SRR1785300     1   0.595   -0.36853 0.472 0.016 0.008 0.072 0.012 0.420
#> SRR1785301     1   0.596   -0.39008 0.460 0.016 0.008 0.072 0.012 0.432
#> SRR1785304     5   0.462   -0.28228 0.004 0.032 0.000 0.420 0.544 0.000
#> SRR1785305     5   0.449   -0.28559 0.000 0.032 0.000 0.424 0.544 0.000
#> SRR1785306     5   0.695   -0.18712 0.172 0.360 0.084 0.000 0.384 0.000
#> SRR1785307     5   0.695   -0.18428 0.172 0.356 0.084 0.000 0.388 0.000
#> SRR1785302     5   0.782    0.00356 0.312 0.228 0.096 0.032 0.332 0.000
#> SRR1785303     5   0.782    0.00356 0.312 0.228 0.096 0.032 0.332 0.000
#> SRR1785308     3   0.374    0.70706 0.072 0.000 0.800 0.012 0.000 0.116
#> SRR1785309     3   0.374    0.70706 0.072 0.000 0.800 0.012 0.000 0.116
#> SRR1785310     4   0.484    0.58358 0.064 0.016 0.000 0.660 0.260 0.000
#> SRR1785311     4   0.484    0.58358 0.064 0.016 0.000 0.660 0.260 0.000
#> SRR1785312     6   0.026    0.75640 0.008 0.000 0.000 0.000 0.000 0.992
#> SRR1785313     6   0.026    0.75640 0.008 0.000 0.000 0.000 0.000 0.992
#> SRR1785314     2   0.658    0.13592 0.096 0.428 0.080 0.000 0.392 0.004
#> SRR1785315     5   0.645   -0.24323 0.080 0.416 0.080 0.000 0.420 0.004
#> SRR1785318     2   0.343    0.44557 0.000 0.720 0.276 0.000 0.004 0.000
#> SRR1785319     2   0.343    0.44557 0.000 0.720 0.276 0.000 0.004 0.000
#> SRR1785316     6   0.407    0.42967 0.448 0.000 0.008 0.000 0.000 0.544
#> SRR1785317     6   0.397    0.42679 0.452 0.000 0.004 0.000 0.000 0.544
#> SRR1785324     2   0.521    0.30136 0.028 0.520 0.024 0.000 0.420 0.008
#> SRR1785325     2   0.514    0.30088 0.024 0.520 0.024 0.000 0.424 0.008
#> SRR1785320     6   0.026    0.75640 0.008 0.000 0.000 0.000 0.000 0.992
#> SRR1785321     6   0.026    0.75640 0.008 0.000 0.000 0.000 0.000 0.992
#> SRR1785322     4   0.413    0.61024 0.212 0.036 0.000 0.736 0.016 0.000
#> SRR1785323     4   0.423    0.60579 0.216 0.040 0.000 0.728 0.016 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)

plot of chunk tab-ATC-mclust-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-ATC-mclust-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-ATC-mclust-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-ATC-mclust-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-ATC-mclust-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-ATC-mclust-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-ATC-mclust-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-ATC-mclust-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-ATC-mclust-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-ATC-mclust-membership-heatmap-5

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)

plot of chunk tab-ATC-mclust-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-ATC-mclust-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-ATC-mclust-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-ATC-mclust-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-ATC-mclust-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-ATC-mclust-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-ATC-mclust-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-ATC-mclust-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-ATC-mclust-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-ATC-mclust-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk ATC-mclust-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-ATC-mclust-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-ATC-mclust-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-ATC-mclust-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-ATC-mclust-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-ATC-mclust-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk ATC-mclust-collect-classes

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.


ATC:NMF

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 16620 rows and 87 columns.
#>   Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#>   Subgroups are detected by 'NMF' method.
#>   Performed in total 1250 partitions by row resampling.
#>   Best k for subgroups seems to be 3.
#> 
#> Following methods can be applied to this 'ConsensusPartition' object:
#>  [1] "cola_report"             "collect_classes"         "collect_plots"          
#>  [4] "collect_stats"           "colnames"                "compare_signatures"     
#>  [7] "consensus_heatmap"       "dimension_reduction"     "functional_enrichment"  
#> [10] "get_anno_col"            "get_anno"                "get_classes"            
#> [13] "get_consensus"           "get_matrix"              "get_membership"         
#> [16] "get_param"               "get_signatures"          "get_stats"              
#> [19] "is_best_k"               "is_stable_k"             "membership_heatmap"     
#> [22] "ncol"                    "nrow"                    "plot_ecdf"              
#> [25] "rownames"                "select_partition_number" "show"                   
#> [28] "suggest_best_k"          "test_to_known_factors"

collect_plots() function collects all the plots made from res for all k (number of partitions) into one single page to provide an easy and fast comparison between different k.

collect_plots(res)

plot of chunk ATC-NMF-collect-plots

The plots are:

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:

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)

plot of chunk ATC-NMF-select-partition-number

The numeric values for all these statistics can be obtained by get_stats().

get_stats(res)
#>   k 1-PAC mean_silhouette concordance area_increased  Rand Jaccard
#> 2 2 0.815           0.852       0.942         0.4990 0.497   0.497
#> 3 3 0.828           0.888       0.947         0.3287 0.711   0.483
#> 4 4 0.802           0.854       0.923         0.1283 0.868   0.629
#> 5 5 0.732           0.689       0.798         0.0694 0.898   0.637
#> 6 6 0.670           0.524       0.742         0.0399 0.915   0.630

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.

show/hide code output

cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#>            class entropy silhouette    p1    p2
#> SRR1785238     2  0.0000     0.9475 0.000 1.000
#> SRR1785239     2  0.0000     0.9475 0.000 1.000
#> SRR1785240     1  0.1184     0.9159 0.984 0.016
#> SRR1785241     1  0.1184     0.9159 0.984 0.016
#> SRR1785242     2  0.0000     0.9475 0.000 1.000
#> SRR1785243     2  0.0000     0.9475 0.000 1.000
#> SRR1785244     1  0.0000     0.9252 1.000 0.000
#> SRR1785245     1  0.0000     0.9252 1.000 0.000
#> SRR1785246     2  0.0000     0.9475 0.000 1.000
#> SRR1785247     2  0.0000     0.9475 0.000 1.000
#> SRR1785248     2  0.0000     0.9475 0.000 1.000
#> SRR1785250     2  0.0000     0.9475 0.000 1.000
#> SRR1785251     2  0.0000     0.9475 0.000 1.000
#> SRR1785252     2  0.0000     0.9475 0.000 1.000
#> SRR1785253     2  0.0000     0.9475 0.000 1.000
#> SRR1785254     1  0.0000     0.9252 1.000 0.000
#> SRR1785255     1  0.0000     0.9252 1.000 0.000
#> SRR1785256     1  0.2236     0.9004 0.964 0.036
#> SRR1785257     1  0.2423     0.8970 0.960 0.040
#> SRR1785258     1  0.0376     0.9231 0.996 0.004
#> SRR1785259     1  0.0376     0.9231 0.996 0.004
#> SRR1785262     2  0.0000     0.9475 0.000 1.000
#> SRR1785263     2  0.0000     0.9475 0.000 1.000
#> SRR1785260     1  0.9944     0.1898 0.544 0.456
#> SRR1785261     1  0.9998     0.0621 0.508 0.492
#> SRR1785264     2  0.6148     0.8092 0.152 0.848
#> SRR1785265     2  0.4161     0.8843 0.084 0.916
#> SRR1785266     2  0.0000     0.9475 0.000 1.000
#> SRR1785267     2  0.0000     0.9475 0.000 1.000
#> SRR1785268     1  0.9998     0.0661 0.508 0.492
#> SRR1785269     2  0.9460     0.4004 0.364 0.636
#> SRR1785270     1  0.0000     0.9252 1.000 0.000
#> SRR1785271     1  0.0000     0.9252 1.000 0.000
#> SRR1785272     2  0.0000     0.9475 0.000 1.000
#> SRR1785273     2  0.0000     0.9475 0.000 1.000
#> SRR1785276     2  0.6438     0.7915 0.164 0.836
#> SRR1785277     2  0.2423     0.9212 0.040 0.960
#> SRR1785274     2  0.1633     0.9326 0.024 0.976
#> SRR1785275     2  0.1843     0.9299 0.028 0.972
#> SRR1785280     2  0.0000     0.9475 0.000 1.000
#> SRR1785281     2  0.0000     0.9475 0.000 1.000
#> SRR1785278     1  0.1184     0.9157 0.984 0.016
#> SRR1785279     1  0.1633     0.9100 0.976 0.024
#> SRR1785282     1  0.9850     0.2756 0.572 0.428
#> SRR1785283     1  0.9963     0.1666 0.536 0.464
#> SRR1785284     1  0.0000     0.9252 1.000 0.000
#> SRR1785285     1  0.0000     0.9252 1.000 0.000
#> SRR1785286     1  0.0000     0.9252 1.000 0.000
#> SRR1785287     1  0.0000     0.9252 1.000 0.000
#> SRR1785288     1  0.0000     0.9252 1.000 0.000
#> SRR1785289     1  0.0000     0.9252 1.000 0.000
#> SRR1785290     2  0.0000     0.9475 0.000 1.000
#> SRR1785291     2  0.0000     0.9475 0.000 1.000
#> SRR1785296     2  0.0000     0.9475 0.000 1.000
#> SRR1785297     2  0.0000     0.9475 0.000 1.000
#> SRR1785292     1  0.0000     0.9252 1.000 0.000
#> SRR1785293     1  0.0000     0.9252 1.000 0.000
#> SRR1785294     2  0.4939     0.8594 0.108 0.892
#> SRR1785295     2  0.3879     0.8914 0.076 0.924
#> SRR1785298     2  0.0000     0.9475 0.000 1.000
#> SRR1785299     2  0.0000     0.9475 0.000 1.000
#> SRR1785300     1  0.0000     0.9252 1.000 0.000
#> SRR1785301     1  0.0000     0.9252 1.000 0.000
#> SRR1785304     1  0.0000     0.9252 1.000 0.000
#> SRR1785305     1  0.0000     0.9252 1.000 0.000
#> SRR1785306     1  0.0000     0.9252 1.000 0.000
#> SRR1785307     1  0.0000     0.9252 1.000 0.000
#> SRR1785302     1  0.0000     0.9252 1.000 0.000
#> SRR1785303     1  0.0000     0.9252 1.000 0.000
#> SRR1785308     2  0.0000     0.9475 0.000 1.000
#> SRR1785309     2  0.0000     0.9475 0.000 1.000
#> SRR1785310     1  0.0000     0.9252 1.000 0.000
#> SRR1785311     1  0.0000     0.9252 1.000 0.000
#> SRR1785312     1  0.8499     0.6034 0.724 0.276
#> SRR1785313     1  0.9608     0.3881 0.616 0.384
#> SRR1785314     1  0.0000     0.9252 1.000 0.000
#> SRR1785315     1  0.0000     0.9252 1.000 0.000
#> SRR1785318     2  0.9661     0.3484 0.392 0.608
#> SRR1785319     2  0.9896     0.2079 0.440 0.560
#> SRR1785316     1  0.0000     0.9252 1.000 0.000
#> SRR1785317     1  0.0000     0.9252 1.000 0.000
#> SRR1785324     1  0.0000     0.9252 1.000 0.000
#> SRR1785325     1  0.0000     0.9252 1.000 0.000
#> SRR1785320     1  0.0000     0.9252 1.000 0.000
#> SRR1785321     1  0.0000     0.9252 1.000 0.000
#> SRR1785322     2  0.0000     0.9475 0.000 1.000
#> SRR1785323     2  0.0000     0.9475 0.000 1.000

show/hide code output

cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#>            class entropy silhouette    p1    p2    p3
#> SRR1785238     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785239     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785240     1  0.4399      0.792 0.812 0.000 0.188
#> SRR1785241     1  0.4504      0.785 0.804 0.000 0.196
#> SRR1785242     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785243     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785244     1  0.0000      0.895 1.000 0.000 0.000
#> SRR1785245     1  0.0000      0.895 1.000 0.000 0.000
#> SRR1785246     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785247     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785248     3  0.5948      0.450 0.000 0.360 0.640
#> SRR1785250     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785251     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785252     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785253     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785254     1  0.0237      0.893 0.996 0.004 0.000
#> SRR1785255     1  0.0892      0.886 0.980 0.020 0.000
#> SRR1785256     1  0.2711      0.868 0.912 0.000 0.088
#> SRR1785257     1  0.2878      0.864 0.904 0.000 0.096
#> SRR1785258     1  0.0000      0.895 1.000 0.000 0.000
#> SRR1785259     1  0.0000      0.895 1.000 0.000 0.000
#> SRR1785262     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785263     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785260     3  0.3551      0.809 0.132 0.000 0.868
#> SRR1785261     3  0.3551      0.809 0.132 0.000 0.868
#> SRR1785264     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785265     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785266     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785267     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785268     3  0.6307     -0.110 0.488 0.000 0.512
#> SRR1785269     3  0.5431      0.547 0.284 0.000 0.716
#> SRR1785270     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785271     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785272     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785273     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785276     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785277     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785274     3  0.0892      0.933 0.000 0.020 0.980
#> SRR1785275     3  0.0747      0.937 0.000 0.016 0.984
#> SRR1785280     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785281     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785278     1  0.3038      0.859 0.896 0.000 0.104
#> SRR1785279     1  0.2796      0.866 0.908 0.000 0.092
#> SRR1785282     1  0.5016      0.734 0.760 0.000 0.240
#> SRR1785283     1  0.5291      0.694 0.732 0.000 0.268
#> SRR1785284     1  0.0000      0.895 1.000 0.000 0.000
#> SRR1785285     1  0.0000      0.895 1.000 0.000 0.000
#> SRR1785286     1  0.5678      0.588 0.684 0.316 0.000
#> SRR1785287     1  0.5810      0.552 0.664 0.336 0.000
#> SRR1785288     1  0.0000      0.895 1.000 0.000 0.000
#> SRR1785289     1  0.0000      0.895 1.000 0.000 0.000
#> SRR1785290     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785291     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785296     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785297     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785292     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785293     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785294     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785295     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785298     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785299     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785300     1  0.0000      0.895 1.000 0.000 0.000
#> SRR1785301     1  0.0000      0.895 1.000 0.000 0.000
#> SRR1785304     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785305     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785306     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785307     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785302     2  0.3192      0.861 0.112 0.888 0.000
#> SRR1785303     2  0.2356      0.913 0.072 0.928 0.000
#> SRR1785308     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785309     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785310     1  0.4346      0.770 0.816 0.184 0.000
#> SRR1785311     1  0.4921      0.844 0.844 0.084 0.072
#> SRR1785312     1  0.5098      0.723 0.752 0.000 0.248
#> SRR1785313     1  0.6192      0.369 0.580 0.000 0.420
#> SRR1785314     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785315     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785318     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785319     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785316     1  0.0000      0.895 1.000 0.000 0.000
#> SRR1785317     1  0.0000      0.895 1.000 0.000 0.000
#> SRR1785324     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785325     2  0.0000      0.991 0.000 1.000 0.000
#> SRR1785320     1  0.0000      0.895 1.000 0.000 0.000
#> SRR1785321     1  0.0000      0.895 1.000 0.000 0.000
#> SRR1785322     3  0.0000      0.949 0.000 0.000 1.000
#> SRR1785323     3  0.0000      0.949 0.000 0.000 1.000

show/hide code output

cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#>            class entropy silhouette    p1    p2    p3    p4
#> SRR1785238     3  0.0000      0.925 0.000 0.000 1.000 0.000
#> SRR1785239     3  0.0000      0.925 0.000 0.000 1.000 0.000
#> SRR1785240     1  0.5022      0.717 0.736 0.000 0.044 0.220
#> SRR1785241     1  0.5312      0.655 0.692 0.000 0.040 0.268
#> SRR1785242     3  0.0188      0.925 0.000 0.000 0.996 0.004
#> SRR1785243     3  0.0188      0.925 0.000 0.000 0.996 0.004
#> SRR1785244     1  0.0469      0.869 0.988 0.000 0.000 0.012
#> SRR1785245     1  0.0592      0.868 0.984 0.000 0.000 0.016
#> SRR1785246     3  0.1118      0.912 0.000 0.000 0.964 0.036
#> SRR1785247     3  0.1118      0.912 0.000 0.000 0.964 0.036
#> SRR1785248     3  0.5759      0.611 0.000 0.232 0.688 0.080
#> SRR1785250     3  0.2469      0.849 0.000 0.000 0.892 0.108
#> SRR1785251     3  0.2469      0.849 0.000 0.000 0.892 0.108
#> SRR1785252     3  0.0000      0.925 0.000 0.000 1.000 0.000
#> SRR1785253     3  0.0000      0.925 0.000 0.000 1.000 0.000
#> SRR1785254     1  0.1975      0.857 0.936 0.048 0.000 0.016
#> SRR1785255     1  0.2799      0.825 0.884 0.108 0.000 0.008
#> SRR1785256     1  0.0592      0.871 0.984 0.000 0.016 0.000
#> SRR1785257     1  0.0469      0.871 0.988 0.000 0.012 0.000
#> SRR1785258     1  0.2704      0.835 0.876 0.000 0.124 0.000
#> SRR1785259     1  0.2868      0.828 0.864 0.000 0.136 0.000
#> SRR1785262     4  0.3688      0.785 0.000 0.000 0.208 0.792
#> SRR1785263     4  0.3610      0.795 0.000 0.000 0.200 0.800
#> SRR1785260     4  0.0188      0.922 0.000 0.000 0.004 0.996
#> SRR1785261     4  0.0188      0.922 0.000 0.000 0.004 0.996
#> SRR1785264     2  0.0188      0.951 0.000 0.996 0.004 0.000
#> SRR1785265     2  0.0921      0.934 0.000 0.972 0.028 0.000
#> SRR1785266     2  0.0000      0.953 0.000 1.000 0.000 0.000
#> SRR1785267     2  0.0000      0.953 0.000 1.000 0.000 0.000
#> SRR1785268     1  0.4843      0.490 0.604 0.000 0.396 0.000
#> SRR1785269     3  0.4992     -0.168 0.476 0.000 0.524 0.000
#> SRR1785270     2  0.0000      0.953 0.000 1.000 0.000 0.000
#> SRR1785271     2  0.0000      0.953 0.000 1.000 0.000 0.000
#> SRR1785272     3  0.0592      0.922 0.000 0.000 0.984 0.016
#> SRR1785273     3  0.0592      0.922 0.000 0.000 0.984 0.016
#> SRR1785276     3  0.0921      0.904 0.028 0.000 0.972 0.000
#> SRR1785277     3  0.0336      0.920 0.008 0.000 0.992 0.000
#> SRR1785274     3  0.0336      0.923 0.000 0.008 0.992 0.000
#> SRR1785275     3  0.0188      0.924 0.000 0.004 0.996 0.000
#> SRR1785280     2  0.0000      0.953 0.000 1.000 0.000 0.000
#> SRR1785281     2  0.0000      0.953 0.000 1.000 0.000 0.000
#> SRR1785278     1  0.0921      0.871 0.972 0.000 0.028 0.000
#> SRR1785279     1  0.1118      0.870 0.964 0.000 0.036 0.000
#> SRR1785282     1  0.4134      0.714 0.740 0.000 0.260 0.000
#> SRR1785283     1  0.4406      0.662 0.700 0.000 0.300 0.000
#> SRR1785284     1  0.3024      0.799 0.852 0.000 0.000 0.148
#> SRR1785285     1  0.3172      0.790 0.840 0.000 0.000 0.160
#> SRR1785286     4  0.0469      0.917 0.012 0.000 0.000 0.988
#> SRR1785287     4  0.0469      0.917 0.012 0.000 0.000 0.988
#> SRR1785288     1  0.1211      0.862 0.960 0.000 0.000 0.040
#> SRR1785289     1  0.1302      0.860 0.956 0.000 0.000 0.044
#> SRR1785290     2  0.3464      0.838 0.000 0.868 0.056 0.076
#> SRR1785291     2  0.3071      0.861 0.000 0.888 0.044 0.068
#> SRR1785296     4  0.1389      0.913 0.000 0.000 0.048 0.952
#> SRR1785297     4  0.1389      0.913 0.000 0.000 0.048 0.952
#> SRR1785292     2  0.0000      0.953 0.000 1.000 0.000 0.000
#> SRR1785293     2  0.0000      0.953 0.000 1.000 0.000 0.000
#> SRR1785294     4  0.0707      0.922 0.000 0.000 0.020 0.980
#> SRR1785295     4  0.0707      0.922 0.000 0.000 0.020 0.980
#> SRR1785298     4  0.3569      0.797 0.000 0.000 0.196 0.804
#> SRR1785299     4  0.3569      0.797 0.000 0.000 0.196 0.804
#> SRR1785300     1  0.0592      0.868 0.984 0.000 0.000 0.016
#> SRR1785301     1  0.0592      0.868 0.984 0.000 0.000 0.016
#> SRR1785304     4  0.1576      0.894 0.004 0.048 0.000 0.948
#> SRR1785305     4  0.1474      0.892 0.000 0.052 0.000 0.948
#> SRR1785306     2  0.1118      0.933 0.000 0.964 0.000 0.036
#> SRR1785307     2  0.1022      0.936 0.000 0.968 0.000 0.032
#> SRR1785302     2  0.4382      0.547 0.296 0.704 0.000 0.000
#> SRR1785303     2  0.3688      0.714 0.208 0.792 0.000 0.000
#> SRR1785308     3  0.0000      0.925 0.000 0.000 1.000 0.000
#> SRR1785309     3  0.0000      0.925 0.000 0.000 1.000 0.000
#> SRR1785310     4  0.0000      0.921 0.000 0.000 0.000 1.000
#> SRR1785311     4  0.0000      0.921 0.000 0.000 0.000 1.000
#> SRR1785312     1  0.4543      0.627 0.676 0.000 0.324 0.000
#> SRR1785313     1  0.4804      0.517 0.616 0.000 0.384 0.000
#> SRR1785314     2  0.0000      0.953 0.000 1.000 0.000 0.000
#> SRR1785315     2  0.0469      0.948 0.000 0.988 0.000 0.012
#> SRR1785318     2  0.0000      0.953 0.000 1.000 0.000 0.000
#> SRR1785319     2  0.0000      0.953 0.000 1.000 0.000 0.000
#> SRR1785316     1  0.0188      0.868 0.996 0.000 0.000 0.004
#> SRR1785317     1  0.0336      0.869 0.992 0.000 0.000 0.008
#> SRR1785324     2  0.0000      0.953 0.000 1.000 0.000 0.000
#> SRR1785325     2  0.0000      0.953 0.000 1.000 0.000 0.000
#> SRR1785320     1  0.2011      0.857 0.920 0.000 0.080 0.000
#> SRR1785321     1  0.2216      0.852 0.908 0.000 0.092 0.000
#> SRR1785322     3  0.2281      0.868 0.000 0.000 0.904 0.096
#> SRR1785323     3  0.2281      0.867 0.000 0.000 0.904 0.096

show/hide code output

cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#>            class entropy silhouette    p1    p2    p3    p4    p5
#> SRR1785238     3  0.5414      0.572 0.412 0.060 0.528 0.000 0.000
#> SRR1785239     3  0.5386      0.585 0.396 0.060 0.544 0.000 0.000
#> SRR1785240     5  0.5197      0.381 0.008 0.004 0.292 0.044 0.652
#> SRR1785241     5  0.5236      0.372 0.008 0.004 0.300 0.044 0.644
#> SRR1785242     3  0.1732      0.729 0.080 0.000 0.920 0.000 0.000
#> SRR1785243     3  0.1671      0.729 0.076 0.000 0.924 0.000 0.000
#> SRR1785244     5  0.1410      0.665 0.060 0.000 0.000 0.000 0.940
#> SRR1785245     5  0.1478      0.665 0.064 0.000 0.000 0.000 0.936
#> SRR1785246     3  0.4367      0.711 0.192 0.000 0.748 0.060 0.000
#> SRR1785247     3  0.4558      0.704 0.208 0.000 0.728 0.064 0.000
#> SRR1785248     3  0.2813      0.679 0.032 0.084 0.880 0.004 0.000
#> SRR1785250     3  0.2676      0.731 0.080 0.000 0.884 0.036 0.000
#> SRR1785251     3  0.2554      0.730 0.072 0.000 0.892 0.036 0.000
#> SRR1785252     3  0.2690      0.729 0.156 0.000 0.844 0.000 0.000
#> SRR1785253     3  0.2648      0.729 0.152 0.000 0.848 0.000 0.000
#> SRR1785254     5  0.3201      0.653 0.096 0.052 0.000 0.000 0.852
#> SRR1785255     5  0.3401      0.647 0.096 0.064 0.000 0.000 0.840
#> SRR1785256     5  0.3949      0.472 0.332 0.000 0.000 0.000 0.668
#> SRR1785257     5  0.3966      0.467 0.336 0.000 0.000 0.000 0.664
#> SRR1785258     1  0.3109      0.694 0.800 0.000 0.000 0.000 0.200
#> SRR1785259     1  0.3086      0.715 0.816 0.000 0.004 0.000 0.180
#> SRR1785262     3  0.3693      0.636 0.012 0.000 0.804 0.168 0.016
#> SRR1785263     3  0.3613      0.641 0.012 0.000 0.812 0.160 0.016
#> SRR1785260     4  0.0000      0.923 0.000 0.000 0.000 1.000 0.000
#> SRR1785261     4  0.0000      0.923 0.000 0.000 0.000 1.000 0.000
#> SRR1785264     2  0.1082      0.908 0.028 0.964 0.000 0.000 0.008
#> SRR1785265     2  0.1082      0.909 0.028 0.964 0.000 0.000 0.008
#> SRR1785266     2  0.0771      0.911 0.020 0.976 0.004 0.000 0.000
#> SRR1785267     2  0.0771      0.911 0.020 0.976 0.004 0.000 0.000
#> SRR1785268     1  0.1800      0.749 0.932 0.000 0.048 0.000 0.020
#> SRR1785269     1  0.1809      0.724 0.928 0.000 0.060 0.000 0.012
#> SRR1785270     5  0.4301      0.500 0.020 0.204 0.020 0.000 0.756
#> SRR1785271     5  0.4584      0.442 0.016 0.256 0.020 0.000 0.708
#> SRR1785272     3  0.4818      0.545 0.460 0.000 0.520 0.020 0.000
#> SRR1785273     3  0.4878      0.561 0.440 0.000 0.536 0.024 0.000
#> SRR1785276     3  0.6626      0.125 0.316 0.012 0.500 0.000 0.172
#> SRR1785277     3  0.5686      0.424 0.284 0.012 0.632 0.008 0.064
#> SRR1785274     3  0.5069      0.639 0.076 0.040 0.768 0.012 0.104
#> SRR1785275     3  0.5027      0.640 0.076 0.028 0.764 0.012 0.120
#> SRR1785280     2  0.1648      0.901 0.020 0.940 0.040 0.000 0.000
#> SRR1785281     2  0.1648      0.901 0.020 0.940 0.040 0.000 0.000
#> SRR1785278     5  0.4210      0.343 0.412 0.000 0.000 0.000 0.588
#> SRR1785279     5  0.4210      0.339 0.412 0.000 0.000 0.000 0.588
#> SRR1785282     1  0.1741      0.775 0.936 0.000 0.024 0.000 0.040
#> SRR1785283     1  0.1741      0.775 0.936 0.000 0.024 0.000 0.040
#> SRR1785284     5  0.0854      0.642 0.004 0.000 0.008 0.012 0.976
#> SRR1785285     5  0.0693      0.641 0.000 0.000 0.008 0.012 0.980
#> SRR1785286     4  0.2237      0.871 0.008 0.000 0.004 0.904 0.084
#> SRR1785287     4  0.1924      0.886 0.008 0.000 0.004 0.924 0.064
#> SRR1785288     5  0.2293      0.666 0.084 0.000 0.000 0.016 0.900
#> SRR1785289     5  0.2482      0.665 0.084 0.000 0.000 0.024 0.892
#> SRR1785290     2  0.2354      0.868 0.008 0.904 0.076 0.012 0.000
#> SRR1785291     2  0.1913      0.893 0.008 0.932 0.044 0.016 0.000
#> SRR1785296     4  0.0162      0.923 0.004 0.000 0.000 0.996 0.000
#> SRR1785297     4  0.0162      0.923 0.004 0.000 0.000 0.996 0.000
#> SRR1785292     2  0.0000      0.912 0.000 1.000 0.000 0.000 0.000
#> SRR1785293     2  0.0162      0.912 0.004 0.996 0.000 0.000 0.000
#> SRR1785294     4  0.0162      0.923 0.004 0.000 0.000 0.996 0.000
#> SRR1785295     4  0.0162      0.923 0.004 0.000 0.000 0.996 0.000
#> SRR1785298     4  0.0671      0.916 0.004 0.000 0.016 0.980 0.000
#> SRR1785299     4  0.0510      0.918 0.000 0.000 0.016 0.984 0.000
#> SRR1785300     5  0.6208      0.261 0.376 0.000 0.000 0.144 0.480
#> SRR1785301     5  0.6153      0.264 0.380 0.000 0.000 0.136 0.484
#> SRR1785304     4  0.1673      0.899 0.016 0.032 0.000 0.944 0.008
#> SRR1785305     4  0.1547      0.901 0.016 0.032 0.000 0.948 0.004
#> SRR1785306     2  0.4544      0.760 0.008 0.752 0.020 0.020 0.200
#> SRR1785307     2  0.4516      0.753 0.008 0.748 0.020 0.016 0.208
#> SRR1785302     2  0.5181      0.595 0.272 0.668 0.000 0.028 0.032
#> SRR1785303     2  0.5139      0.645 0.240 0.692 0.000 0.036 0.032
#> SRR1785308     3  0.4291      0.544 0.464 0.000 0.536 0.000 0.000
#> SRR1785309     3  0.4291      0.544 0.464 0.000 0.536 0.000 0.000
#> SRR1785310     4  0.0000      0.923 0.000 0.000 0.000 1.000 0.000
#> SRR1785311     4  0.0162      0.922 0.000 0.000 0.004 0.996 0.000
#> SRR1785312     1  0.4356      0.401 0.648 0.000 0.012 0.000 0.340
#> SRR1785313     1  0.4397      0.571 0.696 0.000 0.028 0.000 0.276
#> SRR1785314     2  0.2297      0.891 0.008 0.912 0.000 0.020 0.060
#> SRR1785315     2  0.2172      0.892 0.004 0.916 0.000 0.020 0.060
#> SRR1785318     2  0.0771      0.911 0.020 0.976 0.004 0.000 0.000
#> SRR1785319     2  0.0771      0.911 0.020 0.976 0.004 0.000 0.000
#> SRR1785316     5  0.2377      0.654 0.128 0.000 0.000 0.000 0.872
#> SRR1785317     5  0.2377      0.654 0.128 0.000 0.000 0.000 0.872
#> SRR1785324     2  0.0404      0.912 0.000 0.988 0.000 0.000 0.012
#> SRR1785325     2  0.0404      0.912 0.000 0.988 0.000 0.000 0.012
#> SRR1785320     5  0.4297      0.195 0.472 0.000 0.000 0.000 0.528
#> SRR1785321     5  0.4306      0.134 0.492 0.000 0.000 0.000 0.508
#> SRR1785322     4  0.5954      0.400 0.216 0.000 0.192 0.592 0.000
#> SRR1785323     4  0.5672      0.475 0.180 0.000 0.188 0.632 0.000

show/hide code output

cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#>            class entropy silhouette    p1    p2    p3    p4    p5    p6
#> SRR1785238     3  0.6076   4.49e-01 0.364 0.164 0.456 0.000 0.000 0.016
#> SRR1785239     3  0.6078   5.03e-01 0.292 0.192 0.500 0.000 0.000 0.016
#> SRR1785240     6  0.5404   5.80e-01 0.008 0.000 0.096 0.020 0.236 0.640
#> SRR1785241     6  0.5481   5.92e-01 0.008 0.000 0.108 0.020 0.228 0.636
#> SRR1785242     3  0.1745   6.63e-01 0.020 0.000 0.924 0.000 0.000 0.056
#> SRR1785243     3  0.1950   6.62e-01 0.024 0.000 0.912 0.000 0.000 0.064
#> SRR1785244     5  0.0458   5.73e-01 0.016 0.000 0.000 0.000 0.984 0.000
#> SRR1785245     5  0.0603   5.73e-01 0.016 0.000 0.000 0.000 0.980 0.004
#> SRR1785246     6  0.6485   4.08e-01 0.108 0.000 0.220 0.124 0.000 0.548
#> SRR1785247     6  0.6541   3.96e-01 0.112 0.000 0.224 0.124 0.000 0.540
#> SRR1785248     3  0.3168   6.07e-01 0.024 0.076 0.852 0.000 0.000 0.048
#> SRR1785250     3  0.2842   6.49e-01 0.012 0.000 0.868 0.076 0.000 0.044
#> SRR1785251     3  0.2774   6.50e-01 0.012 0.000 0.872 0.076 0.000 0.040
#> SRR1785252     3  0.2201   6.74e-01 0.052 0.000 0.900 0.000 0.000 0.048
#> SRR1785253     3  0.2328   6.72e-01 0.056 0.000 0.892 0.000 0.000 0.052
#> SRR1785254     5  0.4872   4.71e-01 0.048 0.128 0.000 0.000 0.724 0.100
#> SRR1785255     5  0.5265   4.27e-01 0.044 0.176 0.000 0.000 0.676 0.104
#> SRR1785256     5  0.3860  -1.15e-01 0.472 0.000 0.000 0.000 0.528 0.000
#> SRR1785257     5  0.3991  -1.20e-01 0.472 0.000 0.000 0.000 0.524 0.004
#> SRR1785258     1  0.3152   5.75e-01 0.824 0.004 0.004 0.000 0.148 0.020
#> SRR1785259     1  0.3160   5.86e-01 0.836 0.004 0.012 0.000 0.128 0.020
#> SRR1785262     3  0.6085   1.69e-01 0.008 0.000 0.476 0.244 0.000 0.272
#> SRR1785263     3  0.6122   1.14e-01 0.008 0.000 0.460 0.236 0.000 0.296
#> SRR1785260     4  0.0405   8.42e-01 0.004 0.000 0.000 0.988 0.000 0.008
#> SRR1785261     4  0.0260   8.42e-01 0.000 0.000 0.000 0.992 0.000 0.008
#> SRR1785264     2  0.1779   7.96e-01 0.064 0.920 0.000 0.000 0.000 0.016
#> SRR1785265     2  0.1946   7.93e-01 0.072 0.912 0.004 0.000 0.000 0.012
#> SRR1785266     2  0.1777   8.04e-01 0.024 0.932 0.012 0.000 0.000 0.032
#> SRR1785267     2  0.1777   8.04e-01 0.024 0.932 0.012 0.000 0.000 0.032
#> SRR1785268     1  0.2138   6.11e-01 0.908 0.000 0.036 0.000 0.052 0.004
#> SRR1785269     1  0.2007   6.09e-01 0.916 0.000 0.036 0.000 0.044 0.004
#> SRR1785270     5  0.5058   6.30e-02 0.004 0.068 0.000 0.000 0.536 0.392
#> SRR1785271     5  0.5368  -2.99e-02 0.004 0.096 0.000 0.000 0.492 0.408
#> SRR1785272     3  0.5388   6.12e-01 0.268 0.000 0.616 0.088 0.000 0.028
#> SRR1785273     3  0.5201   6.42e-01 0.228 0.000 0.656 0.084 0.000 0.032
#> SRR1785276     1  0.8002   3.18e-01 0.424 0.028 0.292 0.052 0.132 0.072
#> SRR1785277     1  0.7945   1.10e-01 0.376 0.032 0.368 0.060 0.072 0.092
#> SRR1785274     6  0.4171   6.56e-01 0.036 0.012 0.144 0.008 0.016 0.784
#> SRR1785275     6  0.4056   6.60e-01 0.028 0.008 0.140 0.008 0.024 0.792
#> SRR1785280     2  0.2815   7.81e-01 0.024 0.876 0.056 0.000 0.000 0.044
#> SRR1785281     2  0.2815   7.81e-01 0.024 0.876 0.056 0.000 0.000 0.044
#> SRR1785278     5  0.3868  -1.68e-01 0.496 0.000 0.000 0.000 0.504 0.000
#> SRR1785279     5  0.3868  -1.68e-01 0.496 0.000 0.000 0.000 0.504 0.000
#> SRR1785282     1  0.1888   6.10e-01 0.916 0.000 0.012 0.000 0.068 0.004
#> SRR1785283     1  0.1982   6.10e-01 0.912 0.000 0.016 0.000 0.068 0.004
#> SRR1785284     5  0.3189   4.11e-01 0.004 0.000 0.000 0.000 0.760 0.236
#> SRR1785285     5  0.3265   3.93e-01 0.004 0.000 0.000 0.000 0.748 0.248
#> SRR1785286     4  0.4532   1.50e-01 0.000 0.000 0.000 0.500 0.032 0.468
#> SRR1785287     4  0.4212   2.97e-01 0.000 0.000 0.000 0.560 0.016 0.424
#> SRR1785288     5  0.1148   5.71e-01 0.004 0.000 0.000 0.016 0.960 0.020
#> SRR1785289     5  0.1321   5.69e-01 0.004 0.000 0.000 0.024 0.952 0.020
#> SRR1785290     2  0.4618   4.65e-01 0.008 0.624 0.336 0.008 0.000 0.024
#> SRR1785291     2  0.4620   5.99e-01 0.008 0.684 0.264 0.016 0.004 0.024
#> SRR1785296     4  0.1624   8.25e-01 0.004 0.000 0.020 0.936 0.000 0.040
#> SRR1785297     4  0.1826   8.18e-01 0.004 0.000 0.020 0.924 0.000 0.052
#> SRR1785292     2  0.0405   8.11e-01 0.000 0.988 0.000 0.008 0.000 0.004
#> SRR1785293     2  0.0405   8.11e-01 0.000 0.988 0.000 0.008 0.000 0.004
#> SRR1785294     4  0.0777   8.39e-01 0.000 0.000 0.000 0.972 0.004 0.024
#> SRR1785295     4  0.0777   8.39e-01 0.000 0.000 0.000 0.972 0.004 0.024
#> SRR1785298     4  0.2796   7.89e-01 0.004 0.004 0.056 0.872 0.000 0.064
#> SRR1785299     4  0.2591   7.91e-01 0.004 0.000 0.052 0.880 0.000 0.064
#> SRR1785300     5  0.7219   1.33e-05 0.288 0.000 0.000 0.300 0.328 0.084
#> SRR1785301     5  0.7117   6.06e-03 0.284 0.000 0.000 0.300 0.344 0.072
#> SRR1785304     4  0.4014   7.39e-01 0.024 0.036 0.000 0.804 0.024 0.112
#> SRR1785305     4  0.4014   7.39e-01 0.024 0.036 0.000 0.804 0.024 0.112
#> SRR1785306     6  0.5088   5.08e-01 0.000 0.244 0.004 0.036 0.052 0.664
#> SRR1785307     6  0.5234   5.19e-01 0.000 0.232 0.004 0.036 0.068 0.660
#> SRR1785302     2  0.6764   5.27e-01 0.124 0.576 0.000 0.128 0.024 0.148
#> SRR1785303     2  0.6573   5.50e-01 0.100 0.596 0.000 0.128 0.024 0.152
#> SRR1785308     3  0.4219   5.94e-01 0.320 0.000 0.648 0.000 0.000 0.032
#> SRR1785309     3  0.4170   6.05e-01 0.308 0.000 0.660 0.000 0.000 0.032
#> SRR1785310     4  0.1406   8.40e-01 0.004 0.000 0.020 0.952 0.008 0.016
#> SRR1785311     4  0.1406   8.40e-01 0.004 0.000 0.020 0.952 0.008 0.016
#> SRR1785312     1  0.4274   3.97e-01 0.636 0.000 0.024 0.000 0.336 0.004
#> SRR1785313     1  0.4209   5.14e-01 0.716 0.000 0.044 0.000 0.232 0.008
#> SRR1785314     2  0.5781   5.20e-01 0.016 0.608 0.000 0.100 0.024 0.252
#> SRR1785315     2  0.5588   5.14e-01 0.008 0.608 0.000 0.100 0.020 0.264
#> SRR1785318     2  0.1321   8.08e-01 0.024 0.952 0.004 0.000 0.000 0.020
#> SRR1785319     2  0.1321   8.08e-01 0.024 0.952 0.004 0.000 0.000 0.020
#> SRR1785316     5  0.0603   5.74e-01 0.016 0.000 0.000 0.000 0.980 0.004
#> SRR1785317     5  0.0603   5.74e-01 0.016 0.000 0.000 0.000 0.980 0.004
#> SRR1785324     2  0.1082   8.03e-01 0.000 0.956 0.000 0.000 0.004 0.040
#> SRR1785325     2  0.1010   8.04e-01 0.000 0.960 0.000 0.000 0.004 0.036
#> SRR1785320     1  0.4175   1.33e-01 0.524 0.000 0.000 0.000 0.464 0.012
#> SRR1785321     1  0.4246   1.62e-01 0.532 0.000 0.000 0.000 0.452 0.016
#> SRR1785322     1  0.6323   1.03e-01 0.428 0.000 0.148 0.388 0.000 0.036
#> SRR1785323     1  0.6233   9.96e-02 0.428 0.000 0.152 0.392 0.000 0.028

Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.

consensus_heatmap(res, k = 2)

plot of chunk tab-ATC-NMF-consensus-heatmap-1

consensus_heatmap(res, k = 3)

plot of chunk tab-ATC-NMF-consensus-heatmap-2

consensus_heatmap(res, k = 4)

plot of chunk tab-ATC-NMF-consensus-heatmap-3

consensus_heatmap(res, k = 5)

plot of chunk tab-ATC-NMF-consensus-heatmap-4

consensus_heatmap(res, k = 6)

plot of chunk tab-ATC-NMF-consensus-heatmap-5

Heatmaps for the membership of samples in all partitions to see how consistent they are:

membership_heatmap(res, k = 2)

plot of chunk tab-ATC-NMF-membership-heatmap-1

membership_heatmap(res, k = 3)

plot of chunk tab-ATC-NMF-membership-heatmap-2

membership_heatmap(res, k = 4)

plot of chunk tab-ATC-NMF-membership-heatmap-3

membership_heatmap(res, k = 5)

plot of chunk tab-ATC-NMF-membership-heatmap-4

membership_heatmap(res, k = 6)

plot of chunk tab-ATC-NMF-membership-heatmap-5

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)

plot of chunk tab-ATC-NMF-get-signatures-1

get_signatures(res, k = 3)

plot of chunk tab-ATC-NMF-get-signatures-2

get_signatures(res, k = 4)

plot of chunk tab-ATC-NMF-get-signatures-3

get_signatures(res, k = 5)

plot of chunk tab-ATC-NMF-get-signatures-4

get_signatures(res, k = 6)

plot of chunk tab-ATC-NMF-get-signatures-5

Signature heatmaps where rows are not scaled:

get_signatures(res, k = 2, scale_rows = FALSE)

plot of chunk tab-ATC-NMF-get-signatures-no-scale-1

get_signatures(res, k = 3, scale_rows = FALSE)

plot of chunk tab-ATC-NMF-get-signatures-no-scale-2

get_signatures(res, k = 4, scale_rows = FALSE)

plot of chunk tab-ATC-NMF-get-signatures-no-scale-3

get_signatures(res, k = 5, scale_rows = FALSE)

plot of chunk tab-ATC-NMF-get-signatures-no-scale-4

get_signatures(res, k = 6, scale_rows = FALSE)

plot of chunk tab-ATC-NMF-get-signatures-no-scale-5

Compare the overlap of signatures from different k:

compare_signatures(res)

plot of chunk ATC-NMF-signature_compare

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:

  1. which_row: row indices corresponding to the input matrix.
  2. fdr: FDR for the differential test.
  3. mean_x: The mean value in group x.
  4. scaled_mean_x: The mean value in group x after rows are scaled.
  5. 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")

plot of chunk tab-ATC-NMF-dimension-reduction-1

dimension_reduction(res, k = 3, method = "UMAP")

plot of chunk tab-ATC-NMF-dimension-reduction-2

dimension_reduction(res, k = 4, method = "UMAP")

plot of chunk tab-ATC-NMF-dimension-reduction-3

dimension_reduction(res, k = 5, method = "UMAP")

plot of chunk tab-ATC-NMF-dimension-reduction-4

dimension_reduction(res, k = 6, method = "UMAP")

plot of chunk tab-ATC-NMF-dimension-reduction-5

Following heatmap shows how subgroups are split when increasing k:

collect_classes(res)

plot of chunk ATC-NMF-collect-classes

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.

Session info

sessionInfo()
#> R version 3.6.0 (2019-04-26)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#> 
#> Matrix products: default
#> BLAS:   /usr/lib64/libblas.so.3.4.2
#> LAPACK: /usr/lib64/liblapack.so.3.4.2
#> 
#> locale:
#>  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C               LC_TIME=en_GB.UTF-8       
#>  [4] LC_COLLATE=en_GB.UTF-8     LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
#>  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
#> [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
#> 
#> attached base packages:
#> [1] grid      stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] genefilter_1.66.0    ComplexHeatmap_2.3.1 markdown_1.1         knitr_1.26          
#> [5] GetoptLong_0.1.7     cola_1.3.2          
#> 
#> loaded via a namespace (and not attached):
#>  [1] circlize_0.4.8       shape_1.4.4          xfun_0.11            slam_0.1-46         
#>  [5] lattice_0.20-38      splines_3.6.0        colorspace_1.4-1     vctrs_0.2.0         
#>  [9] stats4_3.6.0         blob_1.2.0           XML_3.98-1.20        survival_2.44-1.1   
#> [13] rlang_0.4.2          pillar_1.4.2         DBI_1.0.0            BiocGenerics_0.30.0 
#> [17] bit64_0.9-7          RColorBrewer_1.1-2   matrixStats_0.55.0   stringr_1.4.0       
#> [21] GlobalOptions_0.1.1  evaluate_0.14        memoise_1.1.0        Biobase_2.44.0      
#> [25] IRanges_2.18.3       parallel_3.6.0       AnnotationDbi_1.46.1 highr_0.8           
#> [29] Rcpp_1.0.3           xtable_1.8-4         backports_1.1.5      S4Vectors_0.22.1    
#> [33] annotate_1.62.0      skmeans_0.2-11       bit_1.1-14           microbenchmark_1.4-7
#> [37] brew_1.0-6           impute_1.58.0        rjson_0.2.20         png_0.1-7           
#> [41] digest_0.6.23        stringi_1.4.3        polyclip_1.10-0      clue_0.3-57         
#> [45] tools_3.6.0          bitops_1.0-6         magrittr_1.5         eulerr_6.0.0        
#> [49] RCurl_1.95-4.12      RSQLite_2.1.4        tibble_2.1.3         cluster_2.1.0       
#> [53] crayon_1.3.4         pkgconfig_2.0.3      zeallot_0.1.0        Matrix_1.2-17       
#> [57] xml2_1.2.2           httr_1.4.1           R6_2.4.1             mclust_5.4.5        
#> [61] compiler_3.6.0