Heterogeneous Reciprocal Graphical Models

TL;DR

This paper introduces hierarchical reciprocal graphical models that infer gene networks from heterogeneous data, accommodating known groups or discovering unknown subpopulations, with applications to cancer genomics.

Contribution

It presents a novel hierarchical Bayesian framework for jointly estimating gene networks across multiple groups or clusters, incorporating correlation and sparsity priors.

Findings

Effective in simulation studies

Successfully applied to cancer genomic data

Improves network inference accuracy

Abstract

We develop novel hierarchical reciprocal graphical models to infer gene networks from heterogeneous data. In the case of data that can be naturally divided into known groups, we propose to connect graphs by introducing a hierarchical prior across group-specific graphs, including a correlation on edge strengths across graphs. Thresholding priors are applied to induce sparsity of the estimated networks. In the case of unknown groups, we cluster subjects into subpopulations and jointly estimate cluster-specific gene networks, again using similar hierarchical priors across clusters. We illustrate the proposed approach by simulation studies and two applications with multiplatform genomic data for multiple cancers.