Evaluation and Optimization of Clustering in Gene Expression Data Analysis

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TypeArticle
Proceedings titleOxford University Press
ConferenceJournal of Bioinformatics, 2003
Subjectclustering; cluster quality; gene expression; microarray data analysis
AbstractMotivation: 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.<br /><br />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.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number46534
NPARC number8913653
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Record identifier7b1a57ef-d70d-46db-8bf6-578f42951f41
Record created2009-04-22
Record modified2016-05-09
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