Modeling dependent gene expression

TL;DR

This paper introduces a Bayesian method for analyzing gene expression dependencies, leveraging prior pathway knowledge to improve inference about gene interactions and differences across phenotypes.

Contribution

It presents a novel Bayesian framework that models gene dependencies and integrates prior pathway information for more accurate inference.

Findings

Effective in capturing gene dependence structures

Improves inference of expression differences across phenotypes

Demonstrated on ovarian cancer gene data

Abstract

In this paper we propose a Bayesian approach for inference about dependence of high throughput gene expression. Our goals are to use prior knowledge about pathways to anchor inference about dependence among genes; to account for this dependence while making inferences about differences in mean expression across phenotypes; and to explore differences in the dependence itself across phenotypes. Useful features of the proposed approach are a model-based parsimonious representation of expression as an ordinal outcome, a novel and flexible representation of prior information on the nature of dependencies, and the use of a coherent probability model over both the structure and strength of the dependencies of interest. We evaluate our approach through simulations and in the analysis of data on expression of genes in the Complement and Coagulation Cascade pathway in ovarian cancer.