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Identifying unmeasured heterogeneity in microbiome data via quantile thresholding (QuanT).

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    Summary

    We developed Quantile Thresholding (QuanT), a new method to identify hidden technical variation in microbiome data. QuanT effectively addresses unmeasured heterogeneity, improving the accuracy of downstream microbiome analyses.

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    Area of Science:

    • Microbiology
    • Bioinformatics
    • Computational Biology

    Background:

    • High-throughput microbiome data exhibit technical heterogeneity from varied experimental designs and processing.
    • Unmeasured factors introduce bias, leading to spurious conclusions if not addressed.
    • Existing methods for unmeasured heterogeneity are unsuitable for microbiome data's unique characteristics like sparsity and over-dispersion.

    Purpose of the Study:

    • To introduce Quantile Thresholding (QuanT), a novel non-parametric approach for identifying unmeasured heterogeneity specific to microbiome data.
    • To provide a robust method for mitigating latent technical variation in microbiome datasets.

    Main Methods:

    • Quantile Thresholding (QuanT) utilizes quantile regression across multiple quantile levels.
    • Abundance data is thresholded to uncover latent heterogeneity.
    • Thresholded binary residual matrices are generated to represent the identified heterogeneity.

    Main Results:

    • QuanT was validated on both synthetic and real microbiome datasets.
    • The method demonstrated superior performance in capturing and mitigating unmeasured heterogeneity.
    • Improved accuracy was observed in downstream analyses including prediction, differential abundance testing, and diversity evaluations.

    Conclusions:

    • Quantile Thresholding (QuanT) is an effective approach for addressing unmeasured heterogeneity in microbiome data.
    • The method enhances the reliability and accuracy of microbiome data analysis.
    • QuanT offers a valuable tool for large-scale multi-center microbiome studies and public dataset integration.