This function performs repeated soft clustering for a range of cluster numbers c and reports the number of empty clusters detected.
1  cselection(eset,m,crange=seq(4,32,4),repeats=5,visu=TRUE,...)

eset 
object of class ExpressionSet. 
m 
value of fuzzy cmeans parameter 
crange 
range of number of clusters 
repeats 
number of repeated clusterings. 
visu 
If 
... 
additional arguments for underlying 
A soft cluster is considered as empty, if none of the genes has a corresponding membership value larger than 0.5
A matrix with the number of empty clusters detected is generated.
The cselection
function may help to determine an accurate cluster number. However, it should be used with care, as the determination remains difficult especially for short time series and overlapping clusters. A better way is likely to perform clustering with a range of cluster numbers and subsequently assess their biological relevance e.g. by GO analyses.
Matthias E. Futschik (http://www.cbme.ualg.pt/mfutschik_cbme.html)
M.E. Futschik and B. Charlisle, Noise robust clustering of gene expression timecourse data, Journal of Bioinformatics and Computational Biology, 3 (4), 965988, 2005
L. Kumar and M. Futschik, Mfuzz: a software package for soft clustering of microarray data, Bioinformation, 2(1) 57,2007
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22  if (interactive()){
data(yeast)
# Data preprocessing
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 nonempty 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))
}

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