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Updated: May 14, 2025

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

Aditya Mishra1, Jesse McNichol2,3, Jed Fuhrman3

  • 1Department of Statistics, University of Georgia, Athens, GA, 30606, United States.

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|May 12, 2025
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Summary
This summary is machine-generated.

We developed VI-MIDAS, a new statistical framework for analyzing microbiome data. This tool links microbial taxa to environmental factors, revealing distinct marine microbial community structures and interactions.

Keywords:
Tara ocean expeditionassociation learningmicrobiomeprobabilistic modelvariational inference

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Statistical Modeling

Background:

  • Analyzing microbiome survey data to link microbial taxa abundances with host physiology or habitat characteristics is challenging.
  • Reproducible and interpretable methods are needed for microbiome data analysis.

Purpose of the Study:

  • Introduce VI-MIDAS (variational inference for microbiome survey data analysis), a flexible probabilistic modeling framework.
  • Enable joint estimation of context-dependent drivers and broad patterns of microbial taxon abundance associations.
  • Facilitate the integration of spatio-temporal information and taxon-taxon interactions.

Main Methods:

  • Developed a probabilistic modeling framework, VI-MIDAS, with mechanisms for direct coupling of taxon abundances with covariates and taxa-specific latent coupling.
  • Utilized mean-field variational inference for posterior model parameter estimation.
  • Applied the framework to Tara Ocean Expedition survey data, incorporating network analysis tools.

Main Results:

  • Identified five major marine microbial community modules, including SAR11-, Nitrosopumilus-, and Alteromondales-dominated communities.
  • Associated these modules with specific environmental and spatiotemporal signatures.
  • Revealed largely positive taxon-taxon associations within SAR11/Rhodospirillales clades and negative associations with Alteromonadales/Flavobacteriales classes.

Conclusions:

  • VI-MIDAS offers a powerful integrative statistical framework for microbiome data analysis.
  • The framework successfully discovers broad patterns of associations between microbial taxa and covariate data.
  • Demonstrated the utility of VI-MIDAS in characterizing marine microbial community structure and interactions.