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MicroBVS: Dirichlet-tree multinomial regression models with Bayesian variable selection - an R package.

Matthew D Koslovsky1, Marina Vannucci2

  • 1Department of Statistics, Rice University, Houston, TX, USA. mkoslovsky12@gmail.com.

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Summary
This summary is machine-generated.

Researchers developed MicroBVS, an R package for analyzing microbial abundance data. This tool identifies factors influencing the microbiome by incorporating evolutionary relationships and model uncertainty for better insights.

Keywords:
Bayesian analysisCompositional dataDirichlet-tree multinomial regressionMicrobiomeVariable selection

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Area of Science:

  • Microbiome research
  • Computational biology
  • Statistical genetics

Background:

  • Understanding the human microbiome's relationship with factors like diet is crucial for developing health interventions.
  • Existing analytical tools often overlook evolutionary relationships among microbes and potential interactions between influencing factors.
  • Model selection uncertainty is a common limitation in current microbiome analysis methods.

Purpose of the Study:

  • To introduce MicroBVS, a novel R package designed for analyzing microbial taxa abundance data.
  • To provide a tool that addresses limitations in existing methods by accounting for phylogenetic structure and covariate relationships.
  • To facilitate the identification of covariates associated with microbial community composition.

Main Methods:

  • Development of an R package, MicroBVS, implementing Dirichlet-tree multinomial models.
  • Integration of Bayesian variable selection techniques within the models.
  • Accommodation of phylogenetic structure in abundance data and flexible parameterization of prior probabilities for covariates.

Main Results:

  • MicroBVS enables the identification of covariates linked to microbial taxa abundance.
  • The package's Bayesian model effectively incorporates phylogenetic relationships.
  • It allows for various parameterizations of prior inclusion probabilities for covariates.

Conclusions:

  • The MicroBVS software is a valuable tool for human microbiome research.
  • Its applicability extends to diverse research areas involving compositional data analysis.
  • The package can generate insights into the relationships between covariates and compositional data, even with complex structures.