Joint estimation of multiple related biological networks

TL;DR

This paper introduces a hierarchical Bayesian method for joint estimation of multiple related biological networks, especially in time-course data with interventions, demonstrating improved inference efficiency and accuracy through simulations and real data applications.

Contribution

It presents a novel hierarchical Bayesian framework for joint network estimation that leverages network similarities, along with an efficient algorithm and theoretical bounds on performance gains.

Findings

Joint estimation improves accuracy over separate methods.

The proposed algorithm is computationally efficient and exact.

Empirical results validate the method on simulated and real proteomic data.

Abstract

Graphical models are widely used to make inferences concerning interplay in multivariate systems. In many applications, data are collected from multiple related but nonidentical units whose underlying networks may differ but are likely to share features. Here we present a hierarchical Bayesian formulation for joint estimation of multiple networks in this nonidentically distributed setting. The approach is general: given a suitable class of graphical models, it uses an exchangeability assumption on networks to provide a corresponding joint formulation. Motivated by emerging experimental designs in molecular biology, we focus on time-course data with interventions, using dynamic Bayesian networks as the graphical models. We introduce a computationally efficient, deterministic algorithm for exact joint inference in this setting. We provide an upper bound on the gains that joint estimation offers relative to separate estimation for each network and empirical results that support and extend the theory, including an extensive simulation study and an application to proteomic data from human cancer cell lines. Finally, we describe approximations that are still more computationally efficient than the exact algorithm and that also demonstrate good empirical performance.