clusterStab (version 1.44.0)

clusterComp: Estimate Microarray Cluster Stability

Description

This function estimates the stability of clustering solutions using microarray data. Currently only agglomerative hierarchical clustering is supported.

Usage

"clusterComp"(object, cl, seednum = NULL, B = 100,
sub.frac = 0.8, method = "ave", distmeth = "euclidean", adj.score = FALSE)
"clusterComp"(object, cl, seednum = NULL, B = 100,
sub.frac = 0.8, method = "ave", distmeth = "euclidean", adj.score = FALSE)

Arguments

object

Either a matrix or ExpressionSet

cl

The number of clusters. This may be estimated using benhur

seednum

A value to pass to set.seed, which will allow for exact reproducibility at a later date.

B

The number of permutations.

sub.frac

The proportion of genes to use in each subsample. This value should be in the range of 0.75 - 0.85 for best results

method

The linkage method to pass to hclust. Valid values include "average", "centroid", "ward", "single", "mcquitty", or "median".

distmeth

The distance method to use. Valid values include "euclidean" and "pearson", where pearson implies 1-pearson correlation.

adj.score

Boolean. Should the stability scores be adjusted for cluster size? Defaults to FALSE.

Value

The output from this function is an object of class clusterComp. See the clusterComp-class man page for more information.

Details

This function estimates the stability of a clustering solution by repeatedly subsampling the data and comparing the cluster membership of the subsamples to the original clusters.

References

A. Ben-Hur, A. Elisseeff and I. Guyon. A stability based method for discovering structure in clustered data. Pacific Symposium on Biocomputing, 2002. Smolkin, M. and Ghosh, D. (2003). Cluster stability scores for microarray data in cancer studies . BMC Bioinformatics 4, 36 - 42.

Examples

data(sample.ExpressionSet)
clusterComp(sample.ExpressionSet, 3)