MIMIX: a Bayesian Mixed-Effects Model for Microbiome Data from Designed Experiments

TL;DR

MIMIX is a Bayesian mixed-effects model designed to analyze high-dimensional microbiome data from designed experiments, capturing complex correlations and enabling detailed inference on treatment effects at the taxa level.

Contribution

The paper introduces MIMIX, a novel Bayesian mixed-effects model that accounts for cross-taxa correlations and heterogeneity in microbiome data, improving inference in experimental settings.

Findings

MIMIX successfully detects treatment effects on individual microbial taxa.

The model captures complex correlation patterns among microbes.

Application to real data demonstrates its practical utility.

Abstract

Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these data. For example, analysis of high-dimensional microbiome data from designed experiments remains an open area in microbiome research. Contemporary analyses work on metrics that summarize collective properties of the microbiome, but such reductions preclude inference on the fine-scale effects of environmental stimuli on individual microbial taxa. Other approaches model the proportions or counts of individual taxa as response variables in mixed models, but these methods fail to account for complex correlation patterns among microbial communities. In this paper, we propose a novel Bayesian mixed-effects model that exploits cross-taxa correlations within the microbiome, a model we call MIMIX (MIcrobiome MIXed model). MIMIX offers global tests for treatment effects, local tests and estimation of treatment effects on individual taxa, quantification of the relative contribution from heterogeneous sources to microbiome variability, and identification of latent ecological subcommunities in the microbiome. MIMIX is tailored to large microbiome experiments using a combination of Bayesian factor analysis to efficiently represent dependence between taxa and Bayesian variable selection methods to achieve sparsity. We demonstrate the model using a simulation experiment and on a 2x2 factorial experiment of the effects of nutrient supplement and herbivore exclusion on the foliar fungal microbiome of $\textit{Andropogon gerardii}$, a perennial bunchgrass, as part of the global Nutrient Network research initiative.