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Bayesian inference of networks across multiple sample groups and data types.

Authors
  • Shaddox, Elin1
  • Peterson, Christine B2
  • Stingo, Francesco C3
  • Hanania, Nicola A4
  • Cruickshank-Quinn, Charmion5
  • Kechris, Katerina6
  • Bowler, Russell7
  • Vannucci, Marina1
  • 1 Department of Statistics, Rice University, Houston, TX, USA.
  • 2 Department of Biostatistics, UT MD Anderson Cancer Center, Houston, TX, USA.
  • 3 Department of Statistics, Computer Science, Applications "G. Parenti", University of Florence, Florence, Italy. , (Italy)
  • 4 Department of Medicine-Pulmonary, Baylor College of Medicine, Houston, TX, USA.
  • 5 Department of Pharmaceutical Sciences, School of Pharmacy, University of Colorado, Denver, CO, USA.
  • 6 Department of Biostatistics and Informatics, Colorado SPH, University of Colorado, Denver, CO, USA.
  • 7 Department of Medicine, National Jewish Health, Denver, CO, USA.
Type
Published Article
Journal
Biostatistics (Oxford, England)
Publication Date
Jul 01, 2020
Volume
21
Issue
3
Pages
561–576
Identifiers
DOI: 10.1093/biostatistics/kxy078
PMID: 30590505
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

In this article, 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. Bayesian inference; Chronic obstructive pulmonary disease (COPD); Data integration; Gaussian graphical model; Markov random field prior; Spike and slab prior. © The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected]

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