Cluster analysis and display of genome-wide expression patterns

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ID: 274537
1998
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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.
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eisen1998proceedingscluster Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Michael B. Eisen;Paul T. Spellman;Patrick O. Brown;David Botstein;Michael B. Eisen;Paul T. Spellman;Patrick O. Brown;David Botstein;
Journal proceedings of the national academy of sciences
Year 1998
DOI
DOI not found
URL
https://www.pnas.org/content/95/25/14863.short
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