Bayesian joint modeling of multiple gene networks and diverse genomic data to identify target genes of a transcription factor

TL;DR

This paper presents a Bayesian joint modeling approach that integrates multiple gene networks and diverse genomic data to improve the identification of transcription factor target genes, leveraging biological priors and multiple MRFs.

Contribution

It introduces a novel mixture model incorporating multiple Markov random fields and allows for correlated genomic data, enhancing existing methods with a fully Bayesian framework.

Findings

Improved accuracy in identifying target genes in E. coli data

Demonstrated statistical efficiency gains over existing methods

Validated the model through simulation studies

Abstract

We consider integrative modeling of multiple gene networks and diverse genomic data, including protein-DNA binding, gene expression and DNA sequence data, to accurately identify the regulatory target genes of a transcription factor (TF). Rather than treating all the genes equally and independently a priori in existing joint modeling approaches, we incorporate the biological prior knowledge that neighboring genes on a gene network tend to be (or not to be) regulated together by a TF. A key contribution of our work is that, to maximize the use of all existing biological knowledge, we allow incorporation of multiple gene networks into joint modeling of genomic data by introducing a mixture model based on the use of multiple Markov random fields (MRFs). Another important contribution of our work is to allow different genomic data to be correlated and to examine the validity and effect of the independence assumption as adopted in existing methods. Due to a fully Bayesian approach, inference about model parameters can be carried out based on MCMC samples. Application to an E. coli data set, together with simulation studies, demonstrates the utility and statistical efficiency gains with the proposed joint model.