cluster_terms: Cluster functional terms
In simplifyEnrichment: Simplify Functional Enrichment Results

Description Usage Arguments Details Value Examples

Cluster functional terms

1 2	cluster_terms(mat, method = "binary_cut", control = list(), catch_error = FALSE, verbose = TRUE)

`mat`	A similarity matrix.
`method`	Method for clustering the matrix.
`control`	A list of parameters passed to the corresponding clustering function.
`catch_error`	Internally used.
`verbose`	Whether to print messages.

The following methods are the default:

kmeans: see cluster_by_kmeans.
dynamicTreeCut: see cluster_by_dynamicTreeCut.
mclust: see cluster_by_mclust.
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 can be used here.

New clustering methods can be registered by register_clustering_methods.

Please note it is better to directly call cluster_terms for clustering while not the individual cluster_by_* functions because cluster_terms does additional cluster label adjustment.

A numeric vector of cluster labels (in numeric).

If catch_error is set to TRUE and if the clustering produces an error, the function returns a try-error object.