Bayesian Analysis

Reciprocal Graphical Models for Integrative Gene Regulatory Network Analysis

Yang Ni, Yuan Ji, and Peter Müller

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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 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.

Article information

Source
Bayesian Anal. (2017), 16 pages.

Dates
First available in Project Euclid: 1 December 2017

Permanent link to this document
https://projecteuclid.org/euclid.ba/1512097377

Digital Object Identifier
doi:10.1214/17-BA1087

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

Rights
Creative Commons Attribution 4.0 International License.

Citation

Ni, Yang; Ji, Yuan; Müller, Peter. Reciprocal Graphical Models for Integrative Gene Regulatory Network Analysis. Bayesian Anal., advance publication, 1 December 2017. doi:10.1214/17-BA1087. https://projecteuclid.org/euclid.ba/1512097377


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