Elucidating the altered transcriptional programs in breast cancer using independent component analysis.

ABSTRACT: The quantity of mRNA transcripts in a cell is determined by a complex interplay of cooperative and counteracting biological processes. Independent Component Analysis (ICA) is one of a few number of unsupervised algorithms that have been applied to microarray gene expression data in an attempt to understand phenotype differences in terms of changes in the activation/inhibition patterns of biological pathways. While the ICA model has been shown to outperform other linear representations of the data such as Principal Components Analysis (PCA), a validation using explicit pathway and regulatory element information has not yet been performed. We apply a range of popular ICA algorithms to six of the largest microarray cancer datasets and use pathway-knowledge and regulatory-element databases for

SUBMITTER: Teschendorff AE 

PROVIDER: S-EPMC1950343 | biostudies-literature | 2007 Aug

REPOSITORIES: biostudies-literature