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A Bayesian zero-inflated Dirichlet-multinomial regression model for multivariate compositional count data.

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This summary is machine-generated.

We developed a Bayesian zero-inflated Dirichlet-multinomial model to accurately analyze omics count data with excess zeros. This method improves inference for complex biological datasets, including microbiome research.

Keywords:
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Area of Science:

  • Statistical Modeling
  • Computational Biology
  • Omics Data Analysis

Background:

  • The Dirichlet-multinomial (DM) distribution is widely used for multivariate count data in omics research.
  • Existing DM models struggle with excess zeros, a common issue in high-throughput sequencing data, potentially biasing results.
  • Accurate modeling of compositional data with overdispersion and excess zeros is crucial for reliable omics inference.

Purpose of the Study:

  • To introduce a novel Bayesian zero-inflated Dirichlet-multinomial (ZI-DM) model for multivariate compositional count data.
  • To extend the ZI-DM model to regression settings for variable selection in high-dimensional covariate spaces.
  • To provide a scalable and interpretable statistical framework for analyzing omics data with excess zeros.

Main Methods:

  • Development of a Bayesian zero-inflated Dirichlet-multinomial model.
  • Integration of sparsity-inducing priors for variable selection in high-dimensional regression.
  • Implementation of scalable modeling decisions to maintain interpretability.
  • Validation through extensive simulations and a human gut microbiome dataset application.

Main Results:

  • The proposed Bayesian ZI-DM model effectively handles excess zeros in multivariate count data.
  • The regression extension enables robust variable selection in high-dimensional omics data.
  • The method demonstrates superior performance compared to existing approaches in simulations and a real-world microbiome study.
  • An accompanying R package facilitates practical application of the developed methodology.

Conclusions:

  • The Bayesian ZI-DM model offers a significant advancement for analyzing omics count data with excess zeros.
  • The approach provides a flexible, scalable, and interpretable tool for statistical inference in omics research.
  • This methodology enhances the ability to draw reliable conclusions from complex biological datasets.