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Bioinformatics Advance Access published online on February 12, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth124
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received September 22, 2003
Revised December 11, 2003
Accepted December 17, 2003

Article

Evaluation and optimization of clustering in gene expression data analysis

A. Fazel Famili 1*, Ganming Liu 1, Ziying Liu 1

1 Institute for Information Technology, National Research Council of Canada, Ottawa, ON. K1A 0R6 Canada

* To whom correspondence should be addressed. E-mail: fazel.famili{at}nrc-cnrc.gc.ca.


  Abstract

Motivation: A measurement of cluster quality is needed to choose potential clusters of genes that contain biologically relevant patterns of gene expressions. This is strongly desirable when large number of gene expression profiles have to be analyzed and proper clusters of genes need to be identified for further analysis, such as the search for meaningful patterns, identification of gene functions or gene response analysis.

Results: We propose a new cluster quality method, called stability, by which unsupervised learning of gene expression data can be efficiently performed. The method takes into account a cluster's stability on partition. We evaluate this method and demonstrate its performance using four independent, real gene expression and three simulated data sets. We demonstrate that our method outperforms other techniques listed in the literature. The method has applications in evaluating clustering validity as well as identifying stable clusters.

Availability: Please contact the first author.


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