Clustering gene-expression data with repeated measurements
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
Genome Biology 2003, 4:R34 doi:10.1186/gb-2003-4-5-r34Published: 25 April 2003
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.