Function for soft clustering based on fuzzy c-means.

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Description

This function is a wrapper function for cmeans of the e1071 package. It performs soft clustering of genes based on their expression values using the fuzzy c-means algorithm.

Usage

1
mfuzz(eset,centers,m,...)

Arguments

eset

object of the class “ExpressionSet”.

centers

number of clusters.

m

fuzzification parameter.

...

additional parameters for cmeans.

Details

This function is the core function for soft clustering. It groups genes based on the Euclidean distance and the c-means objective function which is a weighted square error function. Each gene is assigned a membership value between 0 and 1 for each cluster. Hence, genes can be assigned to different clusters in a gradual manner. This contrasts hard clustering where each gene can belongs to a single cluster.

Value

An object of class flcust (see cmeans) which is a list with components:

centers

the final cluster centers.

size

the number of data points in each cluster of the closest hard clustering.

cluster

a vector of integers containing the indices of the clusters where the data points are assigned to for the closest hard clustering, as obtained by assigning points to the (first) class with maximal membership.

iter

the number of iterations performed.

membership

a matrix with the membership values of the data points to the clusters.

withinerror

the value of the objective function.

call

the call used to create the object.

Note

Note that the clustering is based soley on the exprs matrix and no information is used from the phenoData. In particular, the ordering of samples (arrays) is the same as the ordering of the columns in the exprs matrix. Also, replicated arrays in the exprs matrix are treated as independent by the mfuzz function i.e. they should be averagered prior to clustering or placed into different distinct “ExpressionSet” objects.

Author(s)

Matthias E. Futschik (http://www.sysbiolab.eu)

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

See Also

cmeans

Examples

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if (interactive()){
data(yeast)
# Data pre-processing
yeastF <- filter.NA(yeast)
yeastF <- fill.NA(yeastF) # for illustration only; rather use knn method
yeastF <- standardise(yeastF)

# Soft clustering and visualisation
cl <- mfuzz(yeastF,c=20,m=1.25)
mfuzz.plot(yeastF,cl=cl,mfrow=c(2,2))

# Plotting center of cluster 1 
X11(); plot(cl[[1]][1,],type="l",ylab="Expression") 

# Getting the membership values for the first 10 genes in cluster 1
cl[[4]][1:10,1] 
}

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