cluster_by_kmeans: Cluster similarity matrix by k-means clustering

In simplifyEnrichment: Simplify Functional Enrichment Results

Description

Cluster similarity matrix by k-means clustering

Usage

cluster_by_kmeans(mat, max_k = max(2, min(round(nrow(mat)/5), 100)), ...)

Arguments

mat	The similarity matrix.
max_k	maximal k for k-means clustering.
...	Other arguments passed to kmeans.

Details

The best number of k for k-means clustering is identified according to the "elbow" or "knee" method on the distribution of within-cluster sum of squares (WSS) at each k.

Value

A vector of cluster labels (in numeric).

Examples

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simplifyEnrichment documentation built on Nov. 8, 2020, 5:07 p.m.