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

Application of independent component analysis to microarrays.

Author information

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

Abstract

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.

PMID:
14611662
PMCID:
PMC329130
DOI:
10.1186/gb-2003-4-11-r76
[Indexed for MEDLINE]
Free PMC Article

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