Variance of gene expression identifies altered network constraints in neurological disease.

ABSTRACT: Gene expression analysis has become a ubiquitous tool for studying a wide range of human diseases. In a typical analysis we compare distinct phenotypic groups and attempt to identify genes that are, on average, significantly different between them. Here we describe an innovative approach to the analysis of gene expression data, one that identifies differences in expression variance between groups as an informative metric of the group phenotype. We find that genes with different expression variance profiles are not randomly distributed across cell signaling networks. Genes with low-expression variance, or higher constraint, are significantly more connected to other network members and tend to function as core members of signal transduction pathways. Genes with higher expression variance hav

SUBMITTER: Mar JC 

PROVIDER: S-EPMC3154954 | biostudies-literature | 2011 Aug

REPOSITORIES: biostudies-literature