Bioinformatics Vol. 19 no. 8 2003
Pages 973-980
© 2003 Oxford University Press
Fuzzy C-means method for clustering microarray data
Institut de Génétique et de Biologie Moléculaire et Cellulaire, CNRS-IMSERM-ULP, BP 10142, 67404 Illkirch Cedex, France
Received on August 14, 2002
; revised on November 14, 2002
; accepted on January 3, 2003
Motivation: Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes.
Results: A major problem in applying the FCM method for clustering microarray data is the choice of the fuzziness parameter m. We show that the commonly used value m = 2 is not appropriate for some data sets, and that optimal values for m vary widely from one data set to another. We propose an empirical method, based on the distribution of distances between genes in a given data set, to determine an adequate value for m. By setting threshold levels for the membership values, genes which are tigthly associated to a given cluster can be selected. Using a yeast cell cycle data set as an example, we show that this selection increases the overall biological significance of the genes within the cluster.
Availability: Supplementary text and Matlab functions are available at http://www-igbmc.u-strasbg.fr/fcm/
Contact: doulaye{at}titus.u-strasbg.fr
* To whom correspondence should be addressed.
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