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Mishra, A.* ; McNichol, J.* ; Fuhrman, J.* ; Blei, D.* ; Müller, C.L.

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

ISME Commun. 5:ycaf062 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Tara Ocean Expedition ; Association Learning ; Microbiome ; Probabilistic Model ; Variational Inference; Omics
ISSN (print) / ISBN 2730-6151
e-ISSN 2730-6151
Zeitschrift ISME Communications
Quellenangaben Band: 5, Heft: 1, Seiten: , Artikelnummer: ycaf062 Supplement: ,
Verlag Springer
Verlagsort Great Clarendon St, Oxford Ox2 6dp, England
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Förderungen Simons Collaboration on CBIOMES
NSF
Simons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES)
Simons Foundation
Office of Naval Research (ONR)
National Science Foundation (NSF)
Flatiron Institute, Simons Foundation
University of Georgia