cselection {Mfuzz} | R Documentation |
This function performs repeated soft clustering for a range of cluster numbers c and reports the number of empty clusters detected.
cselection(eset,m,crange=seq(4,32,4),repeats=5,visu=TRUE,...)
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 . |
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.
Matthias E. Futschik (http://itb.biologie.hu-berlin.de/~futschik)
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
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)) }