December 2022 Phylogenetically informed Bayesian truncated copula graphical models for microbial association networks
Hee Cheol Chung, Irina Gaynanova, Yang Ni
Author Affiliations +
Ann. Appl. Stat. 16(4): 2437-2457 (December 2022). DOI: 10.1214/21-AOAS1598


Microorganisms play critical roles in host health. The advancement of high-throughput sequencing technology provides opportunities for a deeper understanding of microbial interactions. However, due to the technological limitations of 16S ribosomal RNA sequencing, microbiome data are zero-inflated, and a quantitative comparison of microbial abundances cannot be made across subjects. By leveraging a recent microbiome profiling technique that quantifies 16S ribosomal RNA microbial counts, we propose a novel Bayesian graphical model that incorporates microorganisms’ evolutionary history through a phylogenetic tree prior and explicitly accounts for zero inflation using the truncated Gaussian copula. Our simulation study reveals that the evolutionary information substantially improves the network estimation accuracy. We apply the proposed model to the quantitative gut microbiome data of 106 healthy subjects and identify three distinct microbial communities that are not found by existing microbial network estimation models. We further find that these communities are discriminated based on microorganisms’ ability to utilize oxygen as an energy source.

Funding Statement

This work has been partially supported by the Texas A&M Institute of Data Science (TAMIDS) and the Texas A&M Strategic Transformative Research Program.
Gaynanova’s research was partially supported by the National Science Foundation (NSF CAREER DMS-2044823).
Ni’s research was partially supported by the National Science Foundation (NSF Grants DMS-1918851, DMS-2112943).


The authors thank Grace Yoon for constructive discussions on QMP data analysis. Portions of this research were conducted with the advanced computing resources provided by Texas A&M High Performance Research Computing.


Download Citation

Hee Cheol Chung. Irina Gaynanova. Yang Ni. "Phylogenetically informed Bayesian truncated copula graphical models for microbial association networks." Ann. Appl. Stat. 16 (4) 2437 - 2457, December 2022.


Received: 1 May 2021; Revised: 1 December 2021; Published: December 2022
First available in Project Euclid: 26 September 2022

Digital Object Identifier: 10.1214/21-AOAS1598

Keywords: Gaussian copula , Markov random field , phylogenetic tree , Zero inflation

Rights: Copyright © 2022 Institute of Mathematical Statistics


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Vol.16 • No. 4 • December 2022
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