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Genome Biol. 2003;4(11):R76. Epub 2003 Oct 24.

Application of independent component analysis to microarrays.

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  • 1Department of Computer Science, Stanford University, Stanford, CA94305-9010, USA.


We apply linear and nonlinear independent component analysis (ICA) to project microarray data into statistically independent components that correspond to putative biological processes, and to cluster genes according to over- or under-expression in each component. We test the statistical significance of enrichment of gene annotations within clusters. ICA outperforms other leading methods, such as principal component analysis, k-means clustering and the Plaid model, in constructing functionally coherent clusters on microarray datasets from Saccharomyces cerevisiae, Caenorhabditis elegans and human.

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