title: Application of independent component analysis to microarrays.
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.
authors: Department of Computer Science, Stanford University, Stanford, CA94305-9010, USA.
authors: Su-In
authors: SI
authors: Lee
authors: Serafim
authors: S
authors: Batzoglou
mesh_terms: D000465:Algorithms; D000818:Animals; D017173:Caenorhabditis elegans / Q000235:genetics; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D012441:Saccharomyces cerevisiae / Q000235:genetics; D013223:Statistics as Topic / Q000379:methods