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Empowering differential networks using Bayesian analysis

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  • Jarod Smith
  • Mohammad Arashi
  • Andriëtte Bekker

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.

Suggested Citation

  • Jarod Smith & Mohammad Arashi & Andriëtte Bekker, 2022. "Empowering differential networks using Bayesian analysis," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-19, January.
    • Handle: RePEc:plo:pone00:0261193
    DOI: 10.1371/journal.pone.0261193
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    References listed on IDEAS

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    1. Carlos M. Carvalho & Nicholas G. Polson & James G. Scott, 2010. "The horseshoe estimator for sparse signals," Biometrika, Biometrika Trust, vol. 97(2), pages 465-480.
    2. Huili Yuan & Ruibin Xi & Chong Chen & Minghua Deng, 2017. "Differential network analysis via lasso penalized D-trace loss," Biometrika, Biometrika Trust, vol. 104(4), pages 755-770.
    3. Ming Yuan & Yi Lin, 2007. "Model selection and estimation in the Gaussian graphical model," Biometrika, Biometrika Trust, vol. 94(1), pages 19-35.
    4. Zhou Tang & Zhangsheng Yu & Cheng Wang, 2020. "A fast iterative algorithm for high-dimensional differential network," Computational Statistics, Springer, vol. 35(1), pages 95-109, March.
    5. Christine Peterson & Francesco C. Stingo & Marina Vannucci, 2015. "Bayesian Inference of Multiple Gaussian Graphical Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 159-174, March.
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