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bioRxiv · 10.1101/2024.03.18.585474

Variational inference for microbiome survey data with application to global ocean data

Mishra, A. K.·McNichol, J.·Fuhrman, J.·Blei, D.·Muller, C. L.
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Abstract

Linking sequence-derived microbial taxa abundances to host (patho-)physiology or habitat characteristics in a reproducible and interpretable manner has remained a formidable challenge for the analysis of microbiome survey data. Here, we introduce a flexible probabilistic modeling framework, VI-MIDAS (Variational Inference for MIcrobiome survey DAta analysiS), that enables joint estimation of context-dependent drivers and broad patterns of associations of microbial taxon abundances from microbiome survey data. VI-MIDAS comprises mechanisms for direct coupling of taxon abundances with covariates and taxa-specific latent coupling which can incorporate spatio-temporal information and taxon-taxon interactions. We leverage mean-field variational inference for posterior VI-MIDAS model parameter estimation and illustrate model building and analysis using Tara Ocean Expedition survey data. Using VI-MIDAS latent embedding model and tools from network analysis, we show that marine microbial communities can be broadly categorized into five modules, including SAR11-, Nitrosopumilus-, and Alteromondales-dominated communities, each associated with specific environmental and spatiotemporal signatures. VI-MIDAS also finds evidence for largely positive taxon-taxon associations in SAR11 or Rhodospirillales clades, and negative associations with Alteromonadales and Flavobacteriales classes. Our results indicate that VI-MIDAS provides a powerful integrative statistical analysis framework for discovering broad patterns of associations between microbial taxa and context-specific covariate data from microbiome survey data.

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Mishra, A. K., McNichol, J., Fuhrman, J., Blei, D., Muller, C. L.. 2024-03-19. Variational inference for microbiome survey data with application to global ocean data. https://doi.org/10.1101/2024.03.18.585474

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