TitleValidating clustering for gene expression data.
Publication TypeJournal Article
Year of Publication2001
AuthorsYeung KY, Haynor DR, Ruzzo WL
JournalBioinformatics (Oxford, England)
Date or Month Published2001 Apr
KeywordsAlgorithms, Animals, Barrett Esophagus, Central Nervous System, Databases, Factual, Female, Gene Expression, Humans, Ovary, Rats, Saccharomyces cerevisiae, Software Validation
AbstractMOTIVATION: Many clustering algorithms have been proposed for the analysis of gene expression data, but little guidance is available to help choose among them. We provide a systematic framework for assessing the results of clustering algorithms. Clustering algorithms attempt to partition the genes into groups exhibiting similar patterns of variation in expression level. Our methodology is to apply a clustering algorithm to the data from all but one experimental condition. The remaining condition is used to assess the predictive power of the resulting clusters-meaningful clusters should exhibit less variation in the remaining condition than clusters formed by chance. RESULTS: We successfully applied our methodology to compare six clustering algorithms on four gene expression data sets. We found our quantitative measures of cluster quality to be positively correlated with external standards of cluster quality.
Alternate JournalBioinformatics
Citation Key1893
PubMed ID11301299