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

## TL;DR

The paper introduces VI-MIDAS, a statistical framework for analyzing microbiome data, revealing patterns in marine microbial communities and their environmental associations.

## Contribution

The novel contribution is the development of VI-MIDAS, a probabilistic modeling framework for joint estimation of microbial taxon associations and environmental covariates.

## Key findings

- Marine microbial communities can be grouped into five modules with distinct environmental signatures.
- Positive taxon–taxon associations are found in SAR11 and Rhodospirillales clades.
- Negative associations are observed with Alteromonadales and Flavobacteriales classes.

## 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.