Clustering gene-expression data with repeated measurements

Genome Biol. 2003;4(5):R34. doi: 10.1186/gb-2003-4-5-r34. Epub 2003 Apr 25.


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.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Algorithms*
  • Cluster Analysis*
  • Galactose / pharmacology
  • Gene Expression Profiling / statistics & numerical data*
  • Gene Expression Regulation, Fungal / drug effects
  • Oligonucleotide Array Sequence Analysis / methods
  • Saccharomyces cerevisiae / drug effects
  • Saccharomyces cerevisiae / genetics


  • Galactose