Open Access
December 2018 Reciprocal Graphical Models for Integrative Gene Regulatory Network Analysis
Yang Ni, Yuan Ji, Peter Müller
Bayesian Anal. 13(4): 1095-1110 (December 2018). DOI: 10.1214/17-BA1087


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 messenger ribonucleic acid (mRNA) gene expression and deoxyribonucleic acid (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 application in colon adenocarcinoma pathway analysis.


Download Citation

Yang Ni. Yuan Ji. Peter Müller. "Reciprocal Graphical Models for Integrative Gene Regulatory Network Analysis." Bayesian Anal. 13 (4) 1095 - 1110, December 2018.


Published: December 2018
First available in Project Euclid: 1 December 2017

zbMATH: 06989977
MathSciNet: MR3855364
Digital Object Identifier: 10.1214/17-BA1087

Keywords: directed cycles , feedback loop , Markov equivalence , multimodal genomic data , simultaneous equation models

Vol.13 • No. 4 • December 2018
Back to Top