Quantifying differential gene connectivity between disease states for objective identification of disease-relevant genes.

ABSTRACT:

Background

Network modeling of whole transcriptome expression data enables characterization of complex epistatic (gene-gene) interactions that underlie cellular functions. Though numerous methods have been proposed and successfully implemented to develop these networks, there are no formal methods for comparing differences in network connectivity patterns as a function of phenotypic trait.

Results

Here we describe a novel approach for quantifying the differences in gene-gene connectivity patterns across disease states based on Graphical Gaussian Models (GGMs). We compare the posterior probabilities of connectivity for each gene pair across two disease states, expressed as a posterior odds-ratio (postOR) for each pair, which can be used to identify network components most rel

SUBMITTER: Chu JH 

PROVIDER: S-EPMC3128864 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature