Independent component analysis: mining microarray data for fundamental human gene expression modules.

ABSTRACT: As public microarray repositories rapidly accumulate gene expression data, these resources contain increasingly valuable information about cellular processes in human biology. This presents a unique opportunity for intelligent data mining methods to extract information about the transcriptional modules underlying these biological processes. Modeling cellular gene expression as a combination of functional modules, we use independent component analysis (ICA) to derive 423 fundamental components of human biology from a 9395-array compendium of heterogeneous expression data. Annotation using the Gene Ontology (GO) suggests that while some of these components represent known biological modules, others may describe biology not well characterized by existing manually-curated ontologies. In order

SUBMITTER: Engreitz JM 

PROVIDER: S-EPMC2991480 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature