clustering.kmeans: Kmeans and hierarchical clustering

In EMA: Easy Microarray Data Analysis

Description

Kmeans clustering to summarize the genes information and hierarchical clustering on the kmeans' groups

Usage

clustering.kmeans(data, N = 100, iter.max = 20,
title = "Kmeans - Hierarchical Clustering",
dist.s = "pearson", dist.g = "pearsonabs", method = "ward")

Arguments

data	Expression matrix, genes on rows and samples on columns
N	The number of a priori clusters for the kmeans
iter.max	The maximum number of iterations allowed for the kmeans clustering
title	The plot title
dist.s	The distance used for the sample clustering
dist.g	The distance used for the genes clustering
method	The linkage used for both clusterings

Details

The goal of this analysis is to first summarizes the genes information using the kmeans clustering. Then, a two-ways clustering is performed using the center of each kmean groups, and all the samples.

Value

A list with the kmeans object and the two hierarchical clusterings.

c.km	An object of class 'kmeans'.
c.sample	An object of class 'agnes'. The hierarchical clustering on samples
c.kcenters	An object of class 'agnes'. The hierarchical clustering on the kmeans centers

Author(s)

Nicolas Servant, Eleonore Gravier, Pierre Gestraud, Cecile Laurent, Caroline Paccard, Anne Biton, Jonas Mandel, Bernard Asselain, Emmanuel Barillot, Philippe Hupe

See Also

kmeans,agnes

Examples

data(marty)
##Example on 100 genes for 5 classes
clustering.kmeans(marty[1:100,], N=5)

EMA documentation built on March 26, 2020, 8:40 p.m.