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Identifying microbial drivers in biological phenotypes with a Bayesian network regression model.

Samuel Ozminkowski1, Claudia Solís-Lemus2

  • 1Department of Statistics and Wisconsin Institute for Discovery University of Wisconsin-Madison Madison Wisconsin USA.

Ecology and Evolution
|May 22, 2024
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Summary
This summary is machine-generated.

Bayesian Network Regression models can identify key microbial drivers of biological traits. While effective for many microbiome datasets, performance varies, necessitating careful application guidance.

Keywords:
high‐dimensionalinfluential edgesinfluential nodesmicrobiomenetworkssparsity

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Area of Science:

  • Microbiome research
  • Statistical modeling
  • Bioinformatics

Background:

  • Bayesian Network Regression (BNR) models are used in brain research to link brain regions to traits.
  • Their application to microbiome research, to identify microbial drivers of phenotypes, is unexplored.
  • Microbial networks present challenges like high dimensionality and sparsity, differing from brain networks.

Purpose of the Study:

  • To investigate the suitability of BNR models for microbial datasets.
  • To assess if BNR models, focusing on interaction effects, can identify key microbial drivers of phenotypic variability.
  • To provide practical advice for applying BNR models in microbiome research.

Main Methods:

  • Evaluation of BNR models using synthetic and real microbial data across diverse biological scenarios.
  • Testing the model's ability to identify influential nodes and edges in microbial networks.
  • Development of an accessible Julia package for BNR model implementation.

Main Results:

  • BNR models successfully identified influential microbial nodes and edges driving phenotypic changes in most tested scenarios.
  • Specific scenarios were identified where the BNR model performed poorly, highlighting limitations.
  • The study provides practical guidance for domain scientists using BNR for microbiome data.

Conclusions:

  • BNR models offer a viable framework for microbiome researchers to uncover connections between microbes and phenotypes.
  • The developed Julia package facilitates the application of BNR models in microbiome studies.
  • Understanding model limitations is crucial for effective application in microbial ecology.