Related Experiment Video
Updated: Jul 11, 2025
04:57
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
Published on: May 16, 2022
15.9K
Compositional analysis of microbiome data using the linear decomposition model (LDM)
1Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, United States.
Bioinformatics (Oxford, England)
|November 6, 2023
Summary
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.
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.
