Bayesian Inference of Networks Across Multiple Sample Groups and Data Types

TL;DR

This paper introduces a Bayesian hierarchical framework for joint network inference across multiple sample groups and data types, accommodating heterogeneity and different variable counts, with applications in medical multi-omics data analysis.

Contribution

It presents a novel Bayesian model linking network structures across groups and data types without assuming influence directionality or network similarity, enabling flexible joint estimation.

Findings

Effective in simulation studies

Successfully applied to COPD multi-omics data

Handles varying variable and subject counts

Abstract

In this paper, we develop a graphical modeling framework for the inference of networks across multiple sample groups and data types. In medical studies, this setting arises whenever a set of subjects, which may be heterogeneous due to differing disease stage or subtype, is profiled across multiple platforms, such as metabolomics, proteomics, or transcriptomics data. Our proposed Bayesian hierarchical model first links the network structures within each platform using a Markov random field prior to relate edge selection across sample groups, and then links the network similarity parameters across platforms. This enables joint estimation in a flexible manner, as we make no assumptions on the directionality of influence across the data types or the extent of network similarity across the sample groups and platforms. In addition, our model formulation allows the number of variables and number of subjects to differ across the data types, and only requires that we have data for the same set of groups. We illustrate the proposed approach through both simulation studies and an application to gene expression levels and metabolite abundances on subjects with varying severity levels of Chronic Obstructive Pulmonary Disease (COPD).