Joint estimation of multiple related biological networks

Chris J. Oates, Jim Korkola, Joe W. Gray, Sach Mukherjee

Research output: Contribution to journalArticle

11 Scopus citations

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 de- signs 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.

Original languageEnglish (US)
Pages (from-to)1892-1919
Number of pages28
JournalAnnals of Applied Statistics
Volume8
Issue number3
DOIs
StatePublished - Sep 1 2014

Keywords

  • Bayesian network
  • Belief propagation
  • Hierarchical model
  • Information sharing

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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