Variational inference for microbiome survey data with application to global ocean data.
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| Main Authors: | , , , , |
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| Format: | Artículo científico |
| Language: | en |
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ISME communications
2025
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| _version_ | 1868266204028731393 |
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| author | Mishra, Aditya McNichol, Jesse Fuhrman, Jed Blei, David Müller, Christian L |
| author_facet | Mishra, Aditya McNichol, Jesse Fuhrman, Jed Blei, David Müller, Christian L Mishra, Aditya McNichol, Jesse Fuhrman, Jed Blei, David Müller, Christian L |
| collection | PubMed - marine biology |
| contents | Variational inference for microbiome survey data with application to global ocean data. Mishra, Aditya McNichol, Jesse Fuhrman, Jed Blei, David Müller, Christian L 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. |
| format | Artículo científico |
| id | pubmed_40352106 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | ISME communications |
| record_format | pubmed |
| spellingShingle | Variational inference for microbiome survey data with application to global ocean data. Mishra, Aditya McNichol, Jesse Fuhrman, Jed Blei, David Müller, Christian L Variational inference for microbiome survey data with application to global ocean data. Mishra, Aditya McNichol, Jesse Fuhrman, Jed Blei, David Müller, Christian L 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. |
| title | Variational inference for microbiome survey data with application to global ocean data. |
| url | https://pubmed.ncbi.nlm.nih.gov/40352106/ |