Modeling association in microbial communities with clique loglinear models

TL;DR

This paper introduces a statistical method using clique loglinear models and Bayesian model averaging to identify significant associations among microbial components in metagenomic data, aiding understanding of microbial interactions.

Contribution

It presents a novel approach combining clique loglinear models with Bayesian model averaging for analyzing microbial associations in metagenomic samples.

Findings

Successfully applied to Human Microbiome Project data

Identified significant microbial dependencies and potential syntrophy

Demonstrated effectiveness in complex microbiome datasets

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. KEYWORDS: contingency tables, graphical models, model selection, microbiome, next generation sequencing