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Robust differential abundance test in compositional data

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  • Shulei Wang

Abstract

SummaryDifferential abundance tests for compositional data are essential and fundamental in various biomedical applications, such as single-cell, bulk RNA-seq and microbiome data analysis. However, because of the compositional constraint and the prevalence of zero counts in the data, differential abundance analysis on compositional data remains a complicated and unsolved statistical problem. This article proposes a new differential abundance test, the robust differential abundance test, to address these challenges. Compared with existing methods, the robust differential abundance test is simple and computationally efficient, is robust to prevalent zero counts in compositional datasets, can take the data’s compositional nature into account, and has a theoretical guarantee of controlling false discoveries in a general setting. Furthermore, in the presence of observed covariates, the robust differential abundance test can work with covariate-balancing techniques to remove potential confounding effects and draw reliable conclusions. The proposed test is applied to several numerical examples, and its merits are demonstrated using both simulated and real datasets.

Suggested Citation

  • Shulei Wang, 2023. "Robust differential abundance test in compositional data," Biometrika, Biometrika Trust, vol. 110(1), pages 169-185.
    • Handle: RePEc:oup:biomet:v:110:y:2023:i:1:p:169-185.
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    File URL: http://hdl.handle.net/10.1093/biomet/asac029
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    1. Ruoqi Yu & Paul R. Rosenbaum, 2019. "Directional penalties for optimal matching in observational studies," Biometrics, The International Biometric Society, vol. 75(4), pages 1380-1390, December.
    2. Yuanpei Cao & Anru Zhang & Hongzhe Li, 2020. "Multisample estimation of bacterial composition matrices in metagenomics data," Biometrika, Biometrika Trust, vol. 107(1), pages 75-92.
    3. Huang Lin & Shyamal Das Peddada, 2020. "Analysis of compositions of microbiomes with bias correction," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    4. Kosuke Imai & Marc Ratkovic, 2014. "Covariate balancing propensity score," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 243-263, January.
    5. Kim-Anh Lê Cao & Mary-Ellen Costello & Vanessa Anne Lakis & François Bartolo & Xin-Yi Chua & Rémi Brazeilles & Pascale Rondeau, 2016. "MixMC: A Multivariate Statistical Framework to Gain Insight into Microbial Communities," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-21, August.
    6. Efron, Bradley, 2004. "Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 96-104, January.
    7. James T. Morton & Clarisse Marotz & Alex Washburne & Justin Silverman & Livia S. Zaramela & Anna Edlund & Karsten Zengler & Rob Knight, 2019. "Establishing microbial composition measurement standards with reference frames," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    8. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
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