Mfuzz (version 2.32.0)

Dmin: Calculation of minimum centroid distance for a range of cluster numbers for estimation of optimised number of clusters

Description

This function performs repeated soft clustering for a range of cluster numbers c and reports the minimum centroid distance.

Usage

Dmin(eset,m,crange=seq(4,40,4),repeats=3,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 average minimum centroid distance is produced

Value

of cluster number is returned.

Details

The minimum centroid distance is defined as the minimum distance between two cluster centers produced by the c-means clusterings.

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 Schwaemmle and Jensen, Bioinformatics,Vol. 26 (22), 2841-2848, 2010

Examples

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

#### parameter selection
# For fuzzifier m, we could use mestimate
m1 <- mestimate(yeastF)
m1 # 1.15

# or the function partcoef (see example there)

# For selection of c, either cselection (see example there)
# or

 tmp  <- Dmin(eset,m=m1,crange=seq(4,40,4),repeats=3,visu=TRUE)# Note: This calculation might take some time

 # It seems that the decrease for c ~ 20 - 25 24 and thus 20 might be
 # a suitable number of clusters 
 }