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Clustering gene-expression data with repeated measurements

Ka Yee Yeung1*, Mario Medvedovic2 and Roger E Bumgarner1

Author Affiliations

1 Department of Microbiology, University of Washington, Seattle, WA 98195, USA

2 Center for Genome Information, Department of Environmental Health, University of Cincinnati Medical Center, 3223 Eden Ave. ML 56, Cincinnati, OH 45267-0056, USA

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Genome Biology 2003, 4:R34  doi:10.1186/gb-2003-4-5-r34

Published: 25 April 2003

Abstract

Clustering is a common methodology for the analysis of array data, and many research laboratories are generating array data with repeated measurements. We evaluated several clustering algorithms that incorporate repeated measurements, and show that algorithms that take advantage of repeated measurements yield more accurate and more stable clusters. In particular, we show that the infinite mixture model-based approach with a built-in error model produces superior results.