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Zero-inflated generalized Dirichlet multinomial regression model for microbiome compositional data analysis.

Zheng-Zheng Tang1, Guanhua Chen2

  • 1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA and Wisconsin Institute for Discovery, Madison, WI, USA.

Biostatistics (Oxford, England)
|June 26, 2018
PubMed
Summary
This summary is machine-generated.

We introduce a new statistical model for analyzing microbial data, improving the understanding of links between the microbiome and human health. This method accurately handles complex data, including many zeros, to reveal microbial composition patterns.

Keywords:
Compositional data analysisDifferential abundanceHierarchical modelMicrobiomeScore testZero-inflated model

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Area of Science:

  • Microbiome research
  • Statistical modeling
  • Bioinformatics

Background:

  • High-throughput sequencing is crucial for quantifying microbial taxa and linking them to human diseases.
  • Existing models struggle with excessive zero counts and complex correlation/dispersion patterns in microbial data.
  • Accurate modeling is vital for the statistical power of microbiome-disease association studies.

Purpose of the Study:

  • To develop a novel probability distribution and regression model for multivariate taxon counts.
  • To address limitations of existing models in handling zero-inflated and complex correlation structures.
  • To enable robust analysis of microbial abundance linked to covariates like disease status.

Main Methods:

  • Development of the zero-inflated generalized Dirichlet multinomial (ZIGDM) distribution.
  • Proposal of a ZIGDM regression model for microbial abundance data.
  • Implementation of a fast expectation-maximization algorithm for parameter estimation.

Main Results:

  • The ZIGDM distribution effectively models multivariate taxon counts with excessive zeros.
  • The ZIGDM regression model successfully links microbial abundances to covariates.
  • Developed statistical tests reveal differential mean and dispersion in microbial compositions.
  • Simulations and a gut microbiome dataset analysis demonstrate method advantages.

Conclusions:

  • The proposed ZIGDM distribution and regression model offer a powerful new tool for microbiome data analysis.
  • These methods overcome key limitations of existing statistical approaches.
  • The approach enhances the ability to detect associations between the microbiome and human health traits.