Clustering gene-expression data with repeated measurements.
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.
Department of Microbiology, University of Washington, Seattle, WA 98195, USA. kayee@u.washington.edu
Ka Yee
KY
Yeung
Mario
M
Medvedovic
Roger E
RE
Bumgarner
D000465:Algorithms; D016000:Cluster Analysis; D005690:Galactose / Q000494:pharmacology; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D015966:Gene Expression Regulation, Fungal / Q000187:drug effects; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012441:Saccharomyces cerevisiae / Q000187:drug effects / Q000235:genetics
D005690:Galactose