clusterVoting: Estimating number of clusters through internal exhaustive...

In BioSok/omada: Machine learning tools for automated transcriptome clustering analysis

Estimating number of clusters through internal exhaustive ensemble majority voting

Description

Estimating number of clusters through internal exhaustive ensemble majority voting

Usage

clusterVoting(data, min.k, max.k, algorithm)

Arguments

data	A dataframe, where columns are features and rows are data points
min.k	Minimum number of clusters for which we calculate stabilities
max.k	Maximum number of clusters for which we calculate stabilities
algorithm	The clustering algorithm to use for the multiple clustering runs to be measured

Value

An object of class "clusterVoting" containing a matrix with metric scores for every k and internal index, cluster memberships for every k, a dataframe with the k votes for every index, k vote frequencies and the frequency barplot of the k votes

Examples

clusterVoting(toy_genes, 4,14,"sc")

BioSok/omada documentation built on Aug. 21, 2023, 2:38 p.m.