Abstract
Differential networks allow us to better understand the changes in cellular processes that are exhibited in conditions of interest, identifying variations in gene regulation or protein interaction between, for example, cases and controls, or in response to external stimuli. Here we present a novel methodology for the inference of differential gene regulatory networks from gene expression microarray data. Specifically we apply a Bayesian model selection approach to compare models of conserved and varying network structure, and use Gaussian graphical models to represent the network structures. We apply a variational inference approach to the learning of Gaussian graphical models of gene regulatory networks, that enables us to perform Bayesian model selection that is significantly more computationally efficient than Markov Chain Monte Carlo approaches. Our method is demonstrated to be more robust than independent analysis of data from multiple conditions when applied to synthetic network data, generating fewer false positive predictions of differential edges. We demonstrate the utility of our approach on real world gene expression microarray data by applying it to existing data from amyotrophic lateral sclerosis cases with and without mutations in C9orf72, and controls, where we are able to identify differential network interactions for further investigation.
Highlights
There has been much work in the literature on the inference of networks from gene expression data, utilising a variety of approaches including tests for correlation[1], graphical models[2,3], and mutual information based approaches[4]
We propose a fast and applicable method to perform Bayesian model selection on gene expression microarray data collected between multiple conditions, and demonstrate an application of this method to publicly available gene expression microarray data from Amyotrophic Lateral Sclerosis (ALS) cases and controls, investigating the impact of C9orf[72] GGGGCC repeat expansions in the underlying regulatory network by comparing patients with and without these mutations
We demonstrate that our Bayesian model selection approach is able to identify changes in network structures between conditions
Summary
There has been much work in the literature on the inference of networks from gene expression data, utilising a variety of approaches including tests for correlation[1], graphical models[2,3], and mutual information based approaches[4]. The aim of differential network inference approaches is similar to that of tests for differential expression, but rather than detecting changes in the expression of a single gene, the aim is to infer changes in the regulatory network structure itself between multiple conditions. This allows for more specific mechanistic insights into the molecular processes underlying changes between, for example, cases and controls in a disease study[6,8]. We propose a fast and applicable method to perform Bayesian model selection on gene expression microarray data collected between multiple conditions, and demonstrate an application of this method to publicly available gene expression microarray data from Amyotrophic Lateral Sclerosis (ALS) cases and controls, investigating the impact of C9orf[72] GGGGCC repeat expansions in the underlying regulatory network by comparing patients with and without these mutations
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