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A two-part mixed-effects model for analyzing longitudinal microbiome compositional data.

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This study introduces a new statistical model for analyzing longitudinal microbiome data, which is often sparse and complex. The proposed method accurately identifies microbes associated with health outcomes in repeated measurements.

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Area of Science:

  • Microbiome research
  • Statistical modeling
  • Human health

Background:

  • Human microbial communities are linked to diseases like obesity and IBD.
  • High-throughput sequencing is used to study microbial composition and its health impacts.
  • Longitudinal microbiome data present challenges due to skewness, sparsity, and correlations.

Purpose of the Study:

  • To develop a statistical method for analyzing longitudinal microbiome data.
  • To identify microbes associated with clinical outcomes or environmental factors in longitudinal studies.
  • To address the unique characteristics of microbiome compositional data.

Main Methods:

  • Proposed a two-part zero-inflated Beta regression model with random effects (ZIBR).
  • The model incorporates a logistic component for microbe presence/absence and a Beta component for non-zero abundance.
  • Random effects are included to handle correlations in repeated measurements.

Main Results:

  • The ZIBR model demonstrated superior performance compared to existing methods in simulations and real data analysis.
  • Successfully identified microbes associated with clinical covariates in longitudinal microbiome data.
  • The model effectively accounts for the zero-inflated and correlated nature of the data.

Conclusions:

  • The ZIBR model is a robust tool for association analysis in longitudinal microbiome studies.
  • It aids in identifying relevant taxa from repeated measures, advancing microbiome research.
  • Provides a valuable method for understanding the link between microbes and human health over time.