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A multiview model for relative and absolute microbial abundances

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  • Brian D. Williamson
  • James P. Hughes
  • Amy D. Willis

Abstract

The absolute abundance of bacterial taxa in human host‐associated environments plays a critical role in reproductive and gastrointestinal health. However, obtaining the absolute abundance of many bacterial species is typically prohibitively expensive. In contrast, relative abundance data for many species are comparatively cheap and easy to collect (e.g., with universal primers for the 16S rRNA gene). In this paper, we propose a method to jointly model relative abundance data for many taxa and absolute abundance data for a subset of taxa. Our method provides point and interval estimates for the absolute abundance of all taxa. Crucially, our proposal accounts for differences in the efficiency of taxon detection in the relative and absolute abundance data. We show that modeling taxon‐specific efficiencies substantially reduces the estimation error for absolute abundance, and controls the coverage of interval estimators. We demonstrate the performance of our proposed method via a simulation study, a study of the effect of HIV acquisition on microbial abundances, and a sensitivity study where we jackknife the taxa with observed absolute abundances.

Suggested Citation

  • Brian D. Williamson & James P. Hughes & Amy D. Willis, 2022. "A multiview model for relative and absolute microbial abundances," Biometrics, The International Biometric Society, vol. 78(3), pages 1181-1194, September.
  • Handle: RePEc:bla:biomet:v:78:y:2022:i:3:p:1181-1194
    DOI: 10.1111/biom.13503
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    References listed on IDEAS

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    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. Doris Vandeputte & Gunter Kathagen & Kevin D’hoe & Sara Vieira-Silva & Mireia Valles-Colomer & João Sabino & Jun Wang & Raul Y. Tito & Lindsey De Commer & Youssef Darzi & Séverine Vermeire & Gwen Falo, 2017. "Quantitative microbiome profiling links gut community variation to microbial load," Nature, Nature, vol. 551(7681), pages 507-511, November.
    3. 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.
    4. Ching Jian & Panu Luukkonen & Hannele Yki-Järvinen & Anne Salonen & Katri Korpela, 2020. "Quantitative PCR provides a simple and accessible method for quantitative microbiota profiling," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-10, January.
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