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Activity Number: 626 - Bayesian Methods in Genetics and Genomics
Type: Contributed
Date/Time: Thursday, August 3, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Genomics and Genetics
Abstract #324691 View Presentation
Title: A Hierarchical Bayesian Non-Parametric Approach to Modeling Microbial Community Structure
Author(s): John D O'Brien* and Chris Quince
Companies: Bowdoin College and University of Warwick
Keywords: Bayesian ; mixture model ; Dirichlet process ; latent allocation ; metagenomic ; ecology
Abstract:

The explosion of metagenomic sequencing data has led to intense interest in the modeling of microbial community structure, which plays an important role in medicine, agriculture, and ecology. Mixture models have become one of the most common ways to analyze these data, with the underlying mixture components being identified with particular ecological niches. In this work, we expand this framework to a hierarchical Dirichlet process with an underlying latent allocation, allowing for more flexible mixture structure within individual samples. Important for interpretation, we demonstrate an explicit connection between these Bayesian non-parametric priors and certain important models in ecological theory. Our formulation allows for a Gibbs sampling strategy to efficiently fit these models to complex data sets and we provide two examples from the recent literature. We also present a thermodynamic integration approach to determining the optimal number of niches in a data set.


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

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