Mfuzz (version 2.32.0)

cselection: Repeated soft clustering for detection of empty clusters for estimation of optimised number of clusters

Description

This function performs repeated soft clustering for a range of cluster numbers c and reports the number of empty clusters detected.

Usage

cselection(eset,m,crange=seq(4,32,4),repeats=5,visu=TRUE,...)

Arguments

eset

object of class ExpressionSet.

m

value of fuzzy c-means parameter m.

crange

range of number of clusters c.

repeats

number of repeated clusterings.

visu

If visu=TRUE plot of number of empty clusters is produced.

...

additional arguments for underlying mfuzz.

Value

generated.

Details

A soft cluster is considered as empty, if none of the genes has a corresponding membership value larger than 0.5

References

M.E. Futschik and B. Charlisle, Noise robust clustering of gene expression time-course data, Journal of Bioinformatics and Computational Biology, 3 (4), 965-988, 2005

L. Kumar and M. Futschik, Mfuzz: a software package for soft clustering of microarray data, Bioinformation, 2(1) 5-7,2007

Examples

if (interactive()){
data(yeast)
# Data pre-processing
yeastF <- filter.NA(yeast)
yeastF <- fill.NA(yeastF)
yeastF <- standardise(yeastF)

#### parameter selection
# Empty clusters should not appear
cl <- mfuzz(yeastF,c=20,m=1.25)
mfuzz.plot(yeastF,cl=cl,mfrow=c(4,5))

# Note: The following calculation might take some time

 tmp  <- cselection(yeastF,m=1.25,crange=seq(5,40,5),repeats=5,visu=TRUE)
 # derivation of number of non-empty clusters (crosses) from diagnonal
 # line  indicate appearance of empty clusters 

# Empty clusters might appear 
cl <- mfuzz(yeastF,c=40,m=1.25)
mfuzz.plot(yeastF,cl=cl,mfrow=c(4,5)) 
 }