| clustering.kmeans {EMA} | R Documentation |
Kmeans clustering to summarize the genes information and hierarchical clustering on the kmeans' groups
clustering.kmeans(data, N = 100, iter.max = 20, title = "Kmeans - Hierarchical Clustering", dist.s = "pearson", dist.g = "pearsonabs", method = "ward")
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 |
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.
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 |
EMA group
data(marty) ##Example on 100 genes for 5 classes clustering.kmeans(marty[1:100,], N=5)