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Updated: Jul 24, 2025

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

Yi-Juan Hu, Glen A Satten

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    Summary
    This summary is machine-generated.

    We introduce LDM-clr, a new method for analyzing microbiome data. This approach allows for compositional analysis of differential abundance, accommodating complex study designs and various covariates.

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    Area of Science:

    • Microbiome research
    • Statistical modeling
    • Bioinformatics

    Background:

    • Microbiome data analysis often requires specialized statistical methods to account for compositional effects.
    • Existing methods may not fully capture the complexity of microbiome data, especially when dealing with various covariates and study designs.

    Approach:

    • We present LDM-clr, an extension of the Linear Decomposition Model (LDM).
    • LDM-clr fits linear models to centered-log-ratio-transformed taxa count data.
    • The method is implemented within the existing LDM R package, ensuring compatibility and leveraging its features.

    Key Points:

    • LDM-clr enables compositional analysis of differential abundance at both taxon and community levels.
    • It supports a wide range of covariates and diverse study designs, including association and mediation analyses.
    • The approach handles the inherent compositional nature of microbiome count data effectively.

    Conclusions:

    • LDM-clr provides a robust framework for testing compositional hypotheses in microbiome studies.
    • This extends the capabilities of the LDM tool for advanced microbiome data analysis.
    • The method is readily available via an R package on GitHub.