PuSH - Publication Server of Helmholtz Zentrum München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
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)
Publ. Version/Full Text Research data 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.
Impact Factor
Scopus SNIP
Altmetric
6.100
0.000
Tags
Annotations
Special Publikation
Hide on homepage

Edit extra information
Edit own tags
Private
Edit own annotation
Private
Hide on publication lists
on hompage
Mark as special
publikation
Publication type Article: Journal article
Document type Scientific Article
Keywords Tara Ocean Expedition ; Association Learning ; Microbiome ; Probabilistic Model ; Variational Inference; Omics
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 2730-6151
e-ISSN 2730-6151
Journal ISME Communications
Quellenangaben Volume: 5, Issue: 1, Pages: , Article Number: ycaf062 Supplement: ,
Publisher Springer
Publishing Place Great Clarendon St, Oxford Ox2 6dp, England
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
Grants 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
DOI 10.1093/ismeco/ycaf062
WOS ID 001484463100001
Scopus ID 105004756904
PubMed ID 40352106
Erfassungsdatum 2025-05-13
[➜Report correction]