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.
Highlights
In this paper, we develop a reciprocal graphical model (RGM) to infer gene regulatory relationships and gene networks
Without this assumption, RGMs that belong to the same Markov equivalence class cannot be differentiated
We focus on genes that are mapped to the RAS-MAPK pathway, which is critical for initiation of carcinogenesis in colon adenocarcinoma (COAD) (Colussi et al, 2013)
Summary
We develop a reciprocal graphical model (RGM) to infer gene regulatory relationships and gene networks. This includes in particular directed edges without time course or interventional data. Exploiting genomic data from multiple modalities/platforms, we are able to determine the directionality of certain regulatory relationships, which would be otherwise indistinguishable due to Markov equivalence. Such inference about directionality becomes possible because basic biology fixes the directionality for some edges, for example, between DNA methylation and gene expression of the same gene. The connection of RGMs with simultaneous equation models (SEMs) facilitates computation-efficient implementation of full posterior inference
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