Genome Biology
Volume 4
Issue 5 |
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  SoftwareClustering gene-expression data with repeated measurementsKa Yee Yeung1  , Mario Medvedovic2 and Roger E Bumgarner1  1Department of Microbiology, University of Washington, Seattle, WA 98195, USA 2Center for Genome Information, Department of Environmental Health, University of Cincinnati Medical Center, 3223 Eden Ave. ML 56, Cincinnati, OH 45267-0056, USA  author email corresponding author email
Genome Biology 2003,
4:R34doi:10.1186/gb-2003-4-5-r34
Subject areas: Bioinformatics, Genome studies 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. | 
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