Judging the quality of gene expression-based clustering methods using gene annotation

Genome Res. 2002 Oct;12(10):1574-81. doi: 10.1101/gr.397002.


We compare several commonly used expression-based gene clustering algorithms using a figure of merit based on the mutual information between cluster membership and known gene attributes. By studying various publicly available expression data sets we conclude that enrichment of clusters for biological function is, in general, highest at rather low cluster numbers. As a measure of dissimilarity between the expression patterns of two genes, no method outperforms Euclidean distance for ratio-based measurements, or Pearson distance for non-ratio-based measurements at the optimal choice of cluster number. We show the self-organized-map approach to be best for both measurement types at higher numbers of clusters. Clusters of genes derived from single- and average-linkage hierarchical clustering tend to produce worse-than-random results.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cluster Analysis*
  • Computational Biology / methods*
  • Computational Biology / statistics & numerical data
  • Data Interpretation, Statistical
  • Gene Expression Profiling / methods
  • Gene Expression Profiling / statistics & numerical data*
  • Gene Expression Regulation, Fungal / genetics
  • Quality Control*
  • Saccharomyces cerevisiae / genetics