A modelling framework for detecting and leveraging node-level information in Bayesian network inference

TL;DR

This paper introduces a Bayesian Gaussian graphical model that incorporates node-level information to improve network inference, with applications in gene networks and immune disease pathways.

Contribution

The authors develop a hierarchical Bayesian framework with spike-and-slab priors for node relevance, enabling joint inference of network structure and node importance.

Findings

Efficient variational algorithm scales to large networks.

Identifies biologically relevant hub genes.

Improves network detection accuracy.

Abstract

Bayesian graphical models are powerful tools to infer complex relationships in high dimension, yet are often fraught with computational and statistical challenges. If exploited in a principled way, the increasing information collected alongside the data of primary interest constitutes an opportunity to mitigate these difficulties by guiding the detection of dependence structures. For instance, gene network inference may be informed by the use of publicly available summary statistics on the regulation of genes by genetic variants. Here we present a novel Gaussian graphical modelling framework to identify and leverage information on the centrality of nodes in conditional independence graphs. Specifically, we consider a fully joint hierarchical model to simultaneously infer (i) sparse precision matrices and (ii) the relevance of node-level information for uncovering the sought-after network structure. We encode such information as candidate auxiliary variables using a spike-and-slab submodel on the propensity of nodes to be hubs, which allows hypothesis-free selection and interpretation of a sparse subset of relevant variables. As efficient exploration of large posterior spaces is needed for real-world applications, we develop a variational expectation conditional maximisation algorithm that scales inference to hundreds of samples, nodes and auxiliary variables. We illustrate and exploit the advantages of our approach in simulations and in a gene network study which identifies hub genes involved in biological pathways relevant to immune-mediated diseases.