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A Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data.

Juhee Lee1, Marilou Sison-Mangus2

  • 1Department of Applied Mathematics and Statistics, University of California, Santa Cruz, Santa Cruz, CA, United States.

Frontiers in Microbiology
|April 11, 2018
PubMed
Summary

We developed a Bayesian model to analyze ocean microbial communities, revealing how physical and biological factors influence their succession. This method improves the estimation of environmental impacts on microbial abundance without prior data normalization.

Keywords:
16S ribosomal RNA sequencingLaplace priorcount datametagenomicsmicrobiomenegative binomial modelprocess convolutionregularizing prior

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Area of Science:

  • Microbiology
  • Environmental Science
  • Bioinformatics

Background:

  • Microbial community succession is shaped by complex physical and biological interactions.
  • Understanding these dynamics is crucial for marine ecosystem health.
  • Ocean microbiome data, particularly 16S ribosomal RNA (rRNA) sequencing, provides insights into microbial community structure.

Purpose of the Study:

  • To develop a novel Bayesian semiparametric regression model for analyzing microbial community succession.
  • To investigate the influence of environmental factors (e.g., algal blooms, domoic acid) on microbial abundance and succession.
  • To improve the estimation of covariate effects on microbial operational taxonomic units (OTUs).

Main Methods:

  • A Bayesian semiparametric regression model incorporating a Laplace prior for sparse estimation of covariate effects.
  • A nonparametric prior was used to enhance data utilization across OTUs, samples, and time points.
  • Simultaneous normalization and covariate effect estimation for joint OTU analysis, avoiding pre-normalization steps.

Main Results:

  • The proposed model effectively estimates baseline microbial abundances and quantifies covariate effects.
  • The method demonstrated improved inference compared to existing approaches in simulation studies and real data analysis.
  • Successful application to 16S rRNA sequencing data from Monterey Bay ocean microbiome samples.

Conclusions:

  • The developed Bayesian model offers a robust framework for analyzing microbial succession dynamics.
  • This approach enhances the understanding of environmental drivers impacting marine microbial communities.
  • The method provides a more accurate and integrated way to analyze microbiome data, accounting for normalization and covariate effects simultaneously.