Cluster analysis and display of genome-wide expression patterns

Michael B. Eisen, Paul T. Spellman, Patrick O. Brown, David Botstein

Research output: Contribution to journalArticlepeer-review

12657 Scopus citations

Abstract

A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.

Original languageEnglish (US)
Pages (from-to)14863-14868
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Volume95
Issue number25
DOIs
StatePublished - Dec 8 1998

ASJC Scopus subject areas

  • General

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