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June 2019 Modeling association in microbial communities with clique loglinear models
Adrian Dobra, Camilo Valdes, Dragana Ajdic, Bertrand Clarke, Jennifer Clarke
Ann. Appl. Stat. 13(2): 931-957 (June 2019). DOI: 10.1214/18-AOAS1229

Abstract

There is a growing awareness of the important roles that microbial communities play in complex biological processes. Modern investigation of these often uses next generation sequencing of metagenomic samples to determine community composition. We propose a statistical technique based on clique loglinear models and Bayes model averaging to identify microbial components in a metagenomic sample at various taxonomic levels that have significant associations. We describe the model class, a stochastic search technique for model selection, and the calculation of estimates of posterior probabilities of interest. We demonstrate our approach using data from the Human Microbiome Project and from a study of the skin microbiome in chronic wound healing. Our technique also identifies significant dependencies among microbial components as evidence of possible microbial syntrophy.

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Adrian Dobra. Camilo Valdes. Dragana Ajdic. Bertrand Clarke. Jennifer Clarke. "Modeling association in microbial communities with clique loglinear models." Ann. Appl. Stat. 13 (2) 931 - 957, June 2019. https://doi.org/10.1214/18-AOAS1229

Information

Received: 1 January 2018; Revised: 1 November 2018; Published: June 2019
First available in Project Euclid: 17 June 2019

zbMATH: 1423.62138
MathSciNet: MR3963558
Digital Object Identifier: 10.1214/18-AOAS1229

Rights: Copyright © 2019 Institute of Mathematical Statistics

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Vol.13 • No. 2 • June 2019
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