Application of independent component analysis to microarrays.
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.
Lee
Su-In
SI
Department of Computer Science, Stanford University, Stanford, CA94305-9010, USA.
Batzoglou
Serafim
S
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