cmp_make_clusters: Apply various clustering methods
In simplifyEnrichment: Simplify Functional Enrichment Results

Description Usage Arguments Details Value Examples

Apply various clustering methods

1 2	cmp_make_clusters(mat, method = setdiff(all_clustering_methods(), "mclust"), verbose = TRUE)

`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.

The function compares following default clustering methods by default:

kmeans: see cluster_by_kmeans.
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.
leading_eigen: 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.

A list of cluster label vectors for different clustering methods.

## Not run: 
mat = readRDS(system.file("extdata", "random_GO_BP_sim_mat.rds",
    package = "simplifyEnrichment"))
clt = cmp_make_clusters(mat)

## End(Not run)