benhur {clusterStab} | R Documentation |
This function estimates the number of clusters in e.g., microarray data using an iterative process proposed by Asa Ben-Hur.
## S4 method for signature 'ExpressionSet': benhur(object, freq, upper, seednum = NULL, linkmeth = "average", iterations = 100) ## S4 method for signature 'matrix': benhur(object, freq, upper, seednum = NULL, linkmeth = "average", iterations = 100)
object |
Either a matrix or ExpressionSet |
freq |
The proportion of samples to use. This should be somewhere between 0.6 - 0.8 for best results. |
upper |
The upper limit for number of clusters. |
seednum |
A value to pass to set.seed , which will allow
for exact reproducibility at a later date. |
linkmeth |
Linkage method to pass to hclust . Valid values
include "average", "centroid", "ward", "single", "mcquitty", or "median". |
iterations |
The number of iterations to use. The default of 100 is a reasonable number. |
This function may be used to estimate the number of true clusters that
exist in a set of microarray data. This estimate can be used to as
input for clusterComp
to estimate the stability of the clusters.
The primary output from this function is a set of histograms that show for each cluster size how often similar clusters are formed from subsets of the data. As the number of clusters increases, the pairwise similarity of cluster membership will decrease. The basic idea is to choose the histogram corresponding to the largest number of clusters in which the majority of the data in the histogram is concentrated at or near 1.
If overlay is set to TRUE
, an additional CDF plot will be
produced. This can be used in conjunction with the histograms to
determine at which cluster number the data are no longer concentrated
at or near 1.
The output from this function is an object of class benhur
. See
the benhur-class
man page for more information.
Originally written by Mark Smolkin <marksmolkin@hotmail.com> further modifications by James W. MacDonald <jmacdon@med.umich.edu>
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.
data(sample.ExpressionSet) tmp <- benhur(sample.ExpressionSet, 0.7, 5) hist(tmp) ecdf(tmp)