cmp_make_clusters: Apply various clustering methods

In jokergoo/simplifyEnrichment: Simplify Functional Enrichment Results

Apply various clustering methods

Description

Apply various clustering methods

Usage

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

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

## End(Not run)

jokergoo/simplifyEnrichment documentation built on Nov. 3, 2023, 2:57 p.m.