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Abstract:
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Analysis of high-dimensional microbiome data from designed experiments remains an open problem 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 microbial taxa. Other approaches model the proportions or counts of individual taxa as response variables in mixed models, but these methods are not designed to capture correlation patterns among microbial communities. In this paper, we propose a novel Bayesian mixed-effects model that exploits cross-taxa relationships within the microbiome, a model we call MIMIX (MIcrobiome MIXed model). MIMIX offers a global test for a treatment effect, local tests and estimation of treatment effects on individual taxa, and quantification of the relative contribution from heterogeneous sources to microbiome variability. We demonstrate the model on a 2x2 factorial experiment of the effects of nutrient supplements and herbivore exclusion on the foliar fungal microbiome of Andropogon gerardii, a perennial bunchgrass, as part of the global Nutrient Network research initiative.
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