Apply various clustering methods

cmp_make_clusters(mat, method = setdiff(all_clustering_methods(), "mclust"),
    verbose = TRUE)

Arguments

mat: The similarity matrix.
method: Which methods to compare. All available methods are in all_clustering_methods. A value of all takes all available methods. By default mclust is excluded because its long runtime.
verbose: Whether to print messages.

Details

The function compares following default clustering methods by default:

kmeans: see cluster_by_kmeans.
pam: see cluster_by_pam.
dynamicTreeCut: see cluster_by_dynamicTreeCut.
mclust: see cluster_by_mclust. By default it is not included.
apcluster: see cluster_by_apcluster.
hdbscan: see cluster_by_hdbscan.
fast_greedy: see cluster_by_igraph.
louvain: see cluster_by_igraph.
walktrap: see cluster_by_igraph.
MCL: see cluster_by_MCL.
binary_cut: see binary_cut.

Also the user-defined methods in all_clustering_methods are also compared.

Value

A list of cluster label vectors for different clustering methods.

Examples

# \dontrun{
mat = readRDS(system.file("extdata", "random_GO_BP_sim_mat.rds",
    package = "simplifyEnrichment"))
clt = cmp_make_clusters(mat)
#> Cluster 500 terms by 'binary_cut'...
#>  19 clusters, used 0.9992318 secs.
#> Cluster 500 terms by 'kmeans'...
#>  22 clusters, used 3.361614 secs.
#> Cluster 500 terms by 'pam'...
#>  5 clusters, used 19.078 secs.
#> Cluster 500 terms by 'dynamicTreeCut'...
#>  60 clusters, used 0.1692579 secs.
#> Cluster 500 terms by 'apcluster'...
#>  41 clusters, used 1.076988 secs.
#> Cluster 500 terms by 'hdbscan'...
#>  14 clusters, used 0.1735349 secs.
#> Cluster 500 terms by 'fast_greedy'...
#>  6 clusters, used 0.1356049 secs.
#> Cluster 500 terms by 'louvain'...
#>  6 clusters, used 0.09236598 secs.
#> Cluster 500 terms by 'walktrap'...
#>  6 clusters, used 0.2795451 secs.
#> Cluster 500 terms by 'MCL'...
#>  6 clusters, used 1.924442 secs.
# }