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Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
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Related Experiment Video

Updated: Oct 6, 2025

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
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Adaptive and powerful microbiome multivariate association analysis via feature selection.

Kalins Banerjee, Jun Chen, Xiang Zhan

    NAR Genomics and Bioinformatics
    |January 20, 2022
    PubMed
    Summary
    This summary is machine-generated.

    The Adaptive Microbiome Association Test (AMAT) improves detecting links between microbial groups and health outcomes. This powerful tool enhances association analysis for high-dimensional microbiome data, offering robust and accurate results.

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    Area of Science:

    • Microbiome Research
    • Statistical Genetics
    • Computational Biology

    Background:

    • The human microbiome's role in health and disease is increasingly recognized.
    • Microbiome data is high-dimensional, posing challenges for statistical association analysis.
    • Existing methods for group-based microbiome association analysis can lack power due to noise accumulation.

    Purpose of the Study:

    • To introduce a novel, powerful tool for multivariate microbiome association analysis.
    • To address the challenge of detecting association signals in high-dimensional microbiome data.
    • To develop a method robust against noise accumulation while maintaining statistical power.

    Main Methods:

    • Developed the Adaptive Microbiome Association Test (AMAT).
    • AMAT integrates feature selection for high-dimensional inference and adaptive statistical testing.
    • Employs distance correlation learning to mitigate noise and a generalized linear model framework for data-adaptive testing.

    Main Results:

    • AMAT demonstrates high robustness and superior power compared to several existing methods in simulation studies.
    • The method effectively preserves the correct type I error rate.
    • Real data applications confirm AMAT's effectiveness in microbiome association analysis.

    Conclusions:

    • AMAT is a powerful and robust tool for multivariate microbiome association analysis.
    • It offers an effective solution for analyzing high-dimensional microbiome data, improving detection of microbial-health associations.
    • An R implementation of AMAT is publicly available for research use.