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Related Experiment Video

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Characterizing Microbiome Dynamics &#8211; Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
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A Bayesian framework for identifying consistent patterns of microbial abundance between body sites.

Richard Meier1, Jeffrey A Thompson1, Mei Chung2

  • 1Department of Biostatistics and Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA.

Statistical Applications in Genetics and Molecular Biology
|November 9, 2019
PubMed
Summary

This study introduces a Bayesian framework to detect consistent microbial patterns between the mouth and gut. This method could enable non-invasive disease assessment by analyzing oral microbiome samples for gut health insights.

Keywords:
Bayesianassociationconsistent patternmicrobial abundancemicrobiomezero-inflated beta regression

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Area of Science:

  • Microbiome research
  • Computational biology
  • Statistical modeling

Background:

  • The gut and oral microbiomes are linked to gut diseases, including cancer.
  • Consistent microbial patterns between body sites could allow non-invasive disease detection.
  • Existing methods lack general applicability for testing these cross-site associations.

Purpose of the Study:

  • To develop a Bayesian framework for identifying microbes with consistent patterns across body sites.
  • To enable non-invasive disease assessment through oral microbiome profiling.
  • To provide a statistically robust method for microbiome association studies.

Main Methods:

  • A Bayesian regression model is employed for each operational taxonomic unit (OTU).
  • Markov-Chain Monte Carlo (MCMC) methods estimate abundance and calculate correlation statistics.
  • Posterior distributions are used for formal hypothesis testing of microbial pattern consistency.

Main Results:

  • Simulations confirm the framework's viability and identify performance-influencing factors.
  • The method successfully identified OTUs with consistent gut-mouth patterns in pancreatic cancer patients.
  • The approach is well-powered for moderate sample sizes and association strengths.

Conclusions:

  • The developed Bayesian framework effectively identifies cross-body-site microbial associations.
  • This method offers a promising avenue for non-invasive disease diagnosis using oral microbiome data.
  • The framework is flexible and extendable to various research contexts and Bayesian analysis platforms.