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Analysis of compositions of microbiomes with bias correction

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  • Huang Lin

    (University of Pittsburgh)

  • Shyamal Das Peddada

    (University of Pittsburgh)

Abstract

Differential abundance (DA) analysis of microbiome data continues to be a challenging problem due to the complexity of the data. In this article we define the notion of “sampling fraction” and demonstrate a major hurdle in performing DA analysis of microbiome data is the bias introduced by differences in the sampling fractions across samples. We introduce a methodology called Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC), which estimates the unknown sampling fractions and corrects the bias induced by their differences among samples. The absolute abundance data are modeled using a linear regression framework. This formulation makes a fundamental advancement in the field because, unlike the existing methods, it (a) provides statistically valid test with appropriate p-values, (b) provides confidence intervals for differential abundance of each taxon, (c) controls the False Discovery Rate (FDR), (d) maintains adequate power, and (e) is computationally simple to implement.

Suggested Citation

  • Huang Lin & Shyamal Das Peddada, 2020. "Analysis of compositions of microbiomes with bias correction," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17041-7
    DOI: 10.1038/s41467-020-17041-7
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    1. Laura Farnan & Anastasia Ivanova & Shyamal D Peddada, 2014. "Linear Mixed Effects Models under Inequality Constraints with Applications," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-8, January.
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    16. Shulei Wang, 2023. "Robust differential abundance test in compositional data," Biometrika, Biometrika Trust, vol. 110(1), pages 169-185.
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