Gene Sets Net Correlations Analysis (GSNCA): a multivariate differential coexpression test for gene sets.

ABSTRACT:

Motivation

To date, gene set analysis approaches primarily focus on identifying differentially expressed gene sets (pathways). Methods for identifying differentially coexpressed pathways also exist but are mostly based on aggregated pairwise correlations or other pairwise measures of coexpression. Instead, we propose Gene Sets Net Correlations Analysis (GSNCA), a multivariate differential coexpression test that accounts for the complete correlation structure between genes.

Results

In GSNCA, weight factors are assigned to genes in proportion to the genes' cross-correlations (intergene correlations). The problem of finding the weight vectors is formulated as an eigenvector problem with a unique solution. GSNCA tests the null hypothesis that for a gene set there is no differenc

SUBMITTER: Rahmatallah Y 

PROVIDER: S-EPMC4023302 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature