Empowering differential networks using Bayesian analysis

Jarod Smith; Mohammad Arashi; Andriëtte Bekker

doi:10.1371/journal.pone.0261193

Empowering differential networks using Bayesian analysis

PLoS One. 2022 Jan 25;17(1):e0261193. doi: 10.1371/journal.pone.0261193. eCollection 2022.

Authors

Jarod Smith¹, Mohammad Arashi^{1

2}, Andriëtte Bekker¹

Affiliations

¹ Department of Statistics, University of Pretoria, Pretoria, South Africa.
² Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.

Abstract

Differential networks (DN) are important tools for modeling the changes in conditional dependencies between multiple samples. A Bayesian approach for estimating DNs, from the classical viewpoint, is introduced with a computationally efficient threshold selection for graphical model determination. The algorithm separately estimates the precision matrices of the DN using the Bayesian adaptive graphical lasso procedure. Synthetic experiments illustrate that the Bayesian DN performs exceptionally well in numerical accuracy and graphical structure determination in comparison to state of the art methods. The proposed method is applied to South African COVID-19 data to investigate the change in DN structure between various phases of the pandemic.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
COVID-19 / epidemiology*
Data Interpretation, Statistical
Humans

Grants and funding

This work was based upon research supported in part by the National Research Foundation (NRF) of South Africa, SARChI Research Chair UID: 71199; Ref.: SRUG190308422768 grant No. 120839. The opinions expressed and conclusions arrived at are those of the authors and are not necessarily to be attributed to the NRF. The research of the corresponding author is supported by a grant from Ferdowsi University of Mashhad (N.2/55265).