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Bayesian nonparametric modelling of multiple graphs with an application to ethnic metabolic differences

Molinari, M; Cremaschi, A; De Iorio, M; Chaturvedi, N; Hughes, AD; Tillin, T; (2022) Bayesian nonparametric modelling of multiple graphs with an application to ethnic metabolic differences. Journal of the Royal Statistical Society. Series C: Applied Statistics 10.1111/rssc.12570. (In press). Green open access

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Abstract

We propose a novel approach to the estimation of multiple Gaussian graphical models (GGMs) to analyse patterns of association among a set of metabolites, under different conditions. Our motivating application is the SABRE (Southall And Brent REvisited) study, a triethnic cohort study conducted in the United Kingdom. Through joint modelling of pattern of association corresponding to different ethnic groups, we are able to identify potential ethnic differences in metabolite levels and associations, with the aim of gaining a better understanding of different risk of cardiometabolic disorders across ethnicities. We model the relationship between a set of metabolites and a set of covariates through a sparse seemingly unrelated regressions model and we use GGMs to represent the conditional dependence structure among metabolites. We specify a dependent generalised Dirichlet process prior on the edge inclusion probabilities to borrow strength across groups and we adopt the horseshoe prior to identify important biomarkers. Inference is performed via Markov chain Monte Carlo.

Type: Article
Title: Bayesian nonparametric modelling of multiple graphs with an application to ethnic metabolic differences
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/rssc.12570
Publisher version: https://doi.org/10.1111/rssc.12570
Language: English
Additional information: © 2022 The Authors. Journal of the Royal Statistical Society: Series C (Applied Statistics) published by John Wiley & Sons Ltd on behalf of Royal Statistical Society. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: biomarkers; Dirichlet process; Gaussian graphical models; MCMC; metabolomics
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science > Population Science and Experimental Medicine
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
URI: https://discovery.ucl.ac.uk/id/eprint/10149756
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