Reciprocal Graphical Models for Integrative Gene Regulatory Network Analysis

TL;DR

This paper introduces a Gaussian reciprocal graphical model that integrates gene expression, copy number, and methylation data to infer gene regulatory networks with directional relationships, advancing systems biology analysis.

Contribution

The paper presents a novel reciprocal graphical model that enables inference of regulatory directionality by integrating multiple genomic data types, using Bayesian model selection for structure estimation.

Findings

Successful simulation validation of the model.

Application to ZODIAC gene interaction analysis.

Application to colon adenocarcinoma pathway analysis.

Abstract

Constructing gene regulatory networks is a fundamental task in systems biology. We introduce a Gaussian reciprocal graphical model for inference about gene regulatory relationships by integrating mRNA gene expression and DNA level information including copy number and methylation. Data integration allows for inference on the directionality of certain regulatory relationships, which would be otherwise indistinguishable due to Markov equivalence. Efficient inference is developed based on simultaneous equation models. Bayesian model selection techniques are adopted to estimate the graph structure. We illustrate our approach by simulations and two applications in ZODIAC pairwise gene interaction analysis and colon adenocarcinoma pathway analysis.