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Activity Number: 245 - Bayesian Models for Clustering and Latent Allocation
Type: Contributed
Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #323018
Title: Bayesian Modeling of Co-Occurrence Microbial Interaction Networks
Author(s): Tejasv Bedi* and Michael Neugent and Xiaowei Zhan and Nicole De Nisco and Qiwei Li
Companies: University of Texas at Dallas and University of Texas at Dallas and The University of Texas Southwestern Medical Center and University of Texas at Dallas and The University of Texas at Dallas
Keywords: Stochastic Block Model; Network Analysis; Bayesian nonparametric; Dirichlet process; Microbiome data analysis

The human body consists of microbiomes associated with the development and prevention of several diseases. These microbial organisms form several complex interactions that are informative to the scientific community for explaining disease progression and prevention. The organization of the microbiome is assumed to be a singular assortative network, where interactions between taxa can readily be clustered into distinct communities. Here, we propose a weighted stochastic infinite block model (WSIBM) to study the community structures of co-occurrence microbial interaction networks. In particular, the network is fully connected where the edge between two taxa is defined as the pairwise Spearman correlation on their transformed relative abundance. Under the Bayesian nonparametric framework, we use WSIBM to cluster each taxon into different communities while estimating the number of communities. The posterior summary of taxa’s membership is obtained based on the posterior probability matrix, which could naturally solve the label switching problem. We finally fit the model on a microbiome dataset collected from postmenopausal patients who suffered from recurrent UTI.

Authors who are presenting talks have a * after their name.

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