Smith, JarodArashi, MohammadBekker, Andriette, 1958-2022-11-042022-11-042022-01-25Smith, J., Arashi, M. & Bekker, A. (2022) Empowering differential networks using Bayesian analysis. PLoS One 17(1): e0261193. https://doi.org/10.1371/journal.pone.0261193.1932-6203 (online)10.1371/journal.pone.0261193https://repository.up.ac.za/handle/2263/88138DATA AVAILABILITY STATEMENT : The data underlying the results presented in the study are available from https://archive.ics.uci.edu/ml/datasets/ spambase for the spambase dataset. The corresponding COVID-19 data are available from https://www.nicd.ac.za/diseases-a-z-index/diseaseindex-covid-19/surveillance-reports/ and https:// ourworldindata.org/coronavirus/country/southafrica.SUPPORTING INFORMATION : S1 File. Supplementary material. Contains a block Gibbs sampler, as well as, additional optimal threshold; adjacency heatmaps and graphical network figures for dimensions p = 30 and p = 100. https://doi.org/10.1371/journal.pone.0261193.s001Differential 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.en© 2022 Smith et al. This is an open access article distributed under the terms of the Creative Commons Attribution License.Differential networksBayesian analysisArticle