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Bayesian hierarchical negative binomial models for multivariable analyses with applications to human microbiome count

Amanda H Pendegraft1, Boyi Guo1, Nengjun Yi1

  • 1Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America.

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|August 23, 2019
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Summary
This summary is machine-generated.

This study introduces a new Bayesian model for analyzing human microbiome data. It effectively handles many host characteristics, improving our understanding of microbes in health and disease.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Statistical Genetics

Background:

  • Metagenomic data analysis is crucial for understanding the human microbiome's role in health and disease.
  • Current statistical methods limit the number of host characteristics and samples analyzed, potentially losing valuable information.
  • Next-generation sequencing enables detailed analysis of microbial communities through operational taxonomic units (OTUs).

Purpose of the Study:

  • To present a novel Bayesian hierarchical negative binomial model for analyzing human microbiome count data.
  • To address limitations of current statistical tools in handling multivariable host characteristics.
  • To provide an efficient technique for expanding analyses of complex microbiome datasets.

Main Methods:

  • Development of a Bayesian hierarchical negative binomial model.
  • Simulation studies to compare the proposed model against three competing negative binomial models.
  • Application of the model to subsets of data from the American Gut Project.

Main Results:

  • The Bayesian model often outperforms competing models in terms of type I error control.
  • The model maintains consistent statistical power in simulations.
  • The model successfully identified significant differences in OTUs between individuals with and without inflammatory bowel disease or irritable bowel syndrome.

Conclusions:

  • The Bayesian hierarchical negative binomial model is an efficient and powerful tool for analyzing human microbiome data with numerous covariates.
  • This approach enhances the ability to identify microbial signatures associated with specific health conditions.
  • The findings support the model's utility in advancing research on the human microbiome in health and disease.