| Affiliation: | (1) Department of Electrical Engineering, Stanford University, 94305-9010 Stanford, CA, USA;(2) Department of Computer Science, Stanford University, 94305-9010 Stanford, CA, 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. |
