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Activity Number: 385 - Recent Advances in Bayesian Methods for the Analysis of Microbiome Count Data
Type: Invited
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #308084
Title: A Bayesian Nonparametric Analysis for Zero-Inflated Multivariate Count Data with Application to Microbiome Study
Author(s): Kurtis Shuler and Juhee Lee* and Irene Chen and Samul Verbanic
Companies: UCSC and UCSC and University of California Santa Barbara and University of California Santa Barbara
Keywords: Bayesian nonparametrics; Dependent Dirichlet process; High-throughput Sequencing; Microbiome; Multivariate Count; Zero Inflation

The rapid development of high-throughput sequencing technology in recent years is providing unprecedented opportunities to profile microbial communities from a variety of environments, but analysis of such multivariate taxon count data remains challenging. We develop a Bayesian nonparametric regression model with zero inflation to analyze complex multivariate count data from microbiome studies. The baseline counts of taxa in samples are carefully constructed to produce unbiased estimates of differential abundance. A Bayesian nonparametric approach flexibly models microbial associations with covariates such as environmental factors and clinical characteristics. Importantly, the approach provides straightforward community-level insights into how microbial communities are related to covariates, such as taxa richness and diversity. We show that our model outperforms popular alternatives through simulation stud- ies. We also apply the model to a skin microbiome dataset comparing the microbial communities present in chronic wounds versus in healthy skin.

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

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