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Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases

Molinari, Marco; Cremaschi, Andrea; De Iorio, Maria; Chaturvedi, Nishi; Hughes, Alun; Tillin, Therese; (2022) Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases. Journal of Applied Statistics 10.1080/02664763.2022.2116746. (In press). Green open access

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Abstract

We propose a novel approach to the estimation of multiple Graphical Models to analyse temporal patterns of association among a set of metabolites over different groups of patients. Our motivating application is the Southall And Brent REvisited (SABRE) study, a tri-ethnic cohort study conducted in the UK. We are interested in identifying potential ethnic differences in metabolite levels and associations as well as their evolution over time, with the aim of gaining a better understanding of different risk of cardio-metabolic disorders across ethnicities. Within a Bayesian framework, we employ a nodewise regression approach to infer the structure of the graphs, borrowing information across time as well as across ethnicities. The response variables of interest are metabolite levels measured at two time points and for two ethnic groups, Europeans and South-Asians. We use nodewise regression to estimate the high-dimensional precision matrices of the metabolites, imposing sparsity on the regression coefficients through the dynamic horseshoe prior, thus favouring sparser graphs. We provide the code to fit the proposed model using the software Stan, which performs posterior inference using Hamiltonian Monte Carlo sampling, as well as a detailed description of a block Gibbs sampling scheme.

Type: Article
Title: Bayesian dynamic network modelling: an application to metabolic associations in cardiovascular diseases
Open access status: An open access version is available from UCL Discovery
DOI: 10.1080/02664763.2022.2116746
Publisher version: https://doi.org/10.1080/02664763.2022.2116746
Language: English
Additional information: © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
Keywords: Dynamic shrinkage priors, Gibbs sampling, graphical models, metabolomics, nodewise regression
UCL classification: UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10155640
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