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Compositional analysis of microbiome data using the linear decomposition model (LDM).

Yi-Juan Hu1, Glen A Satten2

  • 1Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, United States.

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|November 6, 2023
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Summary
This summary is machine-generated.

We introduce linear decomposition model-centered log ratio (LDM-clr), a new method for analyzing microbiome data. This approach allows for compositional analysis of differential abundance with various covariates and study designs.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Statistical Modeling

Background:

  • Microbiome data analysis often requires testing compositional hypotheses.
  • Existing methods may have limitations in handling complex study designs and covariates.

Purpose of the Study:

  • To introduce linear decomposition model-centered log ratio (LDM-clr) for microbiome data analysis.
  • To extend the linear decomposition model (LDM) approach for fitting linear models to centered-log-ratio-transformed taxa count data.
  • To enable compositional analysis of differential abundance at both taxon and community levels.

Main Methods:

  • LDM-clr extends the existing LDM program.
  • It fits linear models to centered-log-ratio-transformed taxa count data.
  • Supports a wide range of covariates and study designs for association or mediation analysis.

Main Results:

  • LDM-clr provides a robust framework for compositional microbiome data analysis.
  • The method facilitates differential abundance testing at multiple levels.
  • It integrates seamlessly with existing LDM functionalities.

Conclusions:

  • LDM-clr offers a powerful and flexible tool for microbiome research.
  • The R package LDM, including LDM-clr, is available on GitHub.
  • This advancement supports more comprehensive microbiome data interpretation.