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Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data

Author

Listed:
  • Patricio S La Rosa
  • J Paul Brooks
  • Elena Deych
  • Edward L Boone
  • David J Edwards
  • Qin Wang
  • Erica Sodergren
  • George Weinstock
  • William D Shannon

Abstract

This paper presents new biostatistical methods for the analysis of microbiome data based on a fully parametric approach using all the data. The Dirichlet-multinomial distribution allows the analyst to calculate power and sample sizes for experimental design, perform tests of hypotheses (e.g., compare microbiomes across groups), and to estimate parameters describing microbiome properties. The use of a fully parametric model for these data has the benefit over alternative non-parametric approaches such as bootstrapping and permutation testing, in that this model is able to retain more information contained in the data. This paper details the statistical approaches for several tests of hypothesis and power/sample size calculations, and applies them for illustration to taxonomic abundance distribution and rank abundance distribution data using HMP Jumpstart data on 24 subjects for saliva, subgingival, and supragingival samples. Software for running these analyses is available.

Suggested Citation

  • Patricio S La Rosa & J Paul Brooks & Elena Deych & Edward L Boone & David J Edwards & Qin Wang & Erica Sodergren & George Weinstock & William D Shannon, 2012. "Hypothesis Testing and Power Calculations for Taxonomic-Based Human Microbiome Data," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-13, December.
    • Handle: RePEc:plo:pone00:0052078
    DOI: 10.1371/journal.pone.0052078
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    References listed on IDEAS

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    1. Peter J. Turnbaugh & Ruth E. Ley & Micah Hamady & Claire M. Fraser-Liggett & Rob Knight & Jeffrey I. Gordon, 2007. "The Human Microbiome Project," Nature, Nature, vol. 449(7164), pages 804-810, October.
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    1. Mahbaneh Eshaghzadeh Torbati & Makedonka Mitreva & Vanathi Gopalakrishnan, 2016. "Application of Taxonomic Modeling to Microbiota Data Mining for Detection of Helminth Infection in Global Populations," Data, MDPI, vol. 1(3), pages 1-14, December.
    2. Zhigang Li & Katherine Lee & Margaret R. Karagas & Juliette C. Madan & Anne G. Hoen & A. James O’Malley & Hongzhe Li, 2018. "Conditional Regression Based on a Multivariate Zero-Inflated Logistic-Normal Model for Microbiome Relative Abundance Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 587-608, December.
    3. Yongli Han & Courtney Baker & Emily Vogtmann & Xing Hua & Jianxin Shi & Danping Liu, 2021. "Modeling Longitudinal Microbiome Compositional Data: A Two-Part Linear Mixed Model with Shared Random Effects," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(2), pages 243-266, July.
    4. Zheng-Zheng Tang & Guanhua Chen, 2021. "Robust and Powerful Differential Composition Tests for Clustered Microbiome Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(2), pages 200-216, July.
    5. Yaru Song & Hongyu Zhao & Tao Wang, 2020. "An adaptive independence test for microbiome community data," Biometrics, The International Biometric Society, vol. 76(2), pages 414-426, June.
    6. Shaikh Mateen R. & Beyene Joseph, 2017. "Statistical models and computational algorithms for discovering relationships in microbiome data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(1), pages 1-12, March.

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