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Latent environment allocation of microbial community data.

Koichi Higashi1, Shinya Suzuki2, Shin Kurosawa2

  • 1Genome Evolution Laboratory, National Institute of Genetics, Mishima, Japan.

Plos Computational Biology
|June 7, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new model to understand microbial communities in changing environments. It identifies environmental topics linking microbes and sample descriptions, aiding in analyzing community typicality and heterogeneity.

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Area of Science:

  • Microbiology
  • Environmental Science
  • Computational Biology

Background:

  • Microbial community structures are increasingly studied in relation to environmental labels.
  • Environmental conditions are dynamic, necessitating consideration of mixed states rather than discrete categories for community analysis.

Purpose of the Study:

  • To develop a method for analyzing microbial community structures in relation to complex, changing environments.
  • To identify latent environmental topics that link microbial co-occurrence patterns with textual descriptions of samples.

Main Methods:

  • Applied a hierarchical Bayesian model to a large dataset (>30,000 samples) of microbial communities and associated sample descriptions.
  • Extracted latent environmental topics representing co-occurring microbes and word sets.
  • Developed the Latent Environment Allocation (LEA) web application.

Main Results:

  • Identified latent environmental topics that capture the core elements of environmental mixtures.
  • Visualized topic-based samples to clarify connections between diverse environments.
  • Demonstrated LEA's utility in evaluating microbial community typicality and heterogeneity without predefined environmental categories.

Conclusions:

  • Latent environmental topics provide a novel way to understand microbial community formation in dynamic environments.
  • The LEA application facilitates the analysis of novel microbial samples and semantic searching within large microbiome datasets.