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Bioinformatics Advance Access originally published online on February 12, 2004
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A.Famili, G.Liu and Z.Liu, National Research Council of Canada © Canadian Crown Copyright 2004

Evaluation and optimization of clustering in gene expression data analysis

A. Fazel Famili *, Ganming Liu and Ziying Liu

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

Received on September 22, 2003; revised on December 11, 2003; accepted on December 17, 2003
Advance Access Publication February 12, 2004

Motivation: A measurement of cluster quality is needed to choose potential clusters of genes that contain biologically relevant patterns of gene expression. This is strongly desirable when a 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 performed efficiently. 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 datasets. 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.

Contact: fazel.famili{at}nrc-cnrc.gc.ca

* To whom correspondence should be addressed.


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