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Bayesian Modeling on Microbiome Data Analysis: Application to Subgingival Microbiome Study

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Abstract

The study of microbiome data has been widely used to investigate associations between the abundance of microbial taxa and human diseases. Identifying and understanding these relationships precisely gives the microbiome a key role in human health, disease status, and the development of new diagnostics and targeted therapeutics. Due to its unique features such as compositional data, excessive zero counts, overdispersion, and complexed structure between taxa, undertaking effective microbiome data analytics presents numerous obstacles. To quantify covariate-taxa effects on the subgingival microbiome study, we proposed a refined Bayesian zero-inflated negative binomial (ZINB) regression model with random subject effects. This proposed approach not only accommodates inflated zero counts and overdispersion similar to the existing ZINB model developed by Jiang et al. (Biostatistics 22(3):522–540, 2021), but also accounts for subject-level heterogeneity through the inclusion of random subject effects. In addition, an efficient Markov chain Monte Carlo (MCMC) sampling algorithm was developed for Bayesian computation. Overall effects of pre-selected group variables on predicted taxa abundance were estimated and tested under the proposed model. We conduct simulation studies and demonstrate that the proposed model outperforms the competing models in achieving a better power with controlling the type I error. The usefulness of the proposed model is applied to a real subgingival microbiome study.

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Code Availability

All code is written by a statistical software R 4.2.2 and the code can be available from the first author (yeongjin.gwon@unmc.edu).

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Acknowledgements

We would like to thank the Editor, the Associate Editor, and the two anonymous reviewers for their very helpful and constructive comments along with suggestions. This has led to a substantial improved version of the article. This research was partially supported by a Veterans Affairs Merit Award [Grant Number CX000896] and the National Institute of General Medical Sciences (NIGMS) Grant #5U54GM115458. Dr. Payne’s research is supported by the F. Gene and Rosemary Dixon Endowed Chair in Dentistry.

Author information

Authors and Affiliations

  1. Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, 984375 Nebraska Medical Center, Omaha, NE, 68198-4375, USA

    Yeongjin Gwon & Fang Yu

  2. Department of Surgical Specialties, College of Dentistry, University of Nebraska Medical Center, Lincoln, NE, 68583, USA

    Jeffrey B. Payne

  3. Division of Rheumatology, Department of Internal Medicine, University of Nebraska Medical Center, 986270 Nebraska Medical Center, Omaha, NE, 68198-6270, USA

    Jeffrey B. Payne & Ted R. Mikuls

  4. Veterans Affairs Nebraska-Western Iowa Health Care System, 4101 Woolworth Ave, Omaha, NE, 68105, USA

    Ted R. Mikuls

  5. Daugherty Water For Food Global Institute, University of Nebraska, Lincoln, NE, 68583, USA

    Yeongjin Gwon

Authors
  1. Yeongjin Gwon
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  2. Fang Yu
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  4. Ted R. Mikuls
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Corresponding authors

Correspondence to Yeongjin Gwon or Fang Yu.

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Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (pdf 930 KB)

Supplementary Information Online Supplementary Materials include six sections: (i) S1 (Summary of Taxa Information); (ii) S2 (Proof of Proposition 1); (iii) S3 (Derivation of Full Conditional Distributions); (iv) S4 (Additional Simulation Study based on Zero-Inflated Poisson Setting); (v) S5 (Additional Figures from Analysis of Subgingival Microbiome Study); and (vi) S6 (Trace and Autocorrelation Plots for the Selected Taxa from Analysis of Subgingival Microbiome Study). These will be available at the journal online website.

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Gwon, Y., Yu, F., Payne, J.B. et al. Bayesian Modeling on Microbiome Data Analysis: Application to Subgingival Microbiome Study. Stat Biosci (2023). https://doi.org/10.1007/s12561-023-09397-3

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  • DOI: https://doi.org/10.1007/s12561-023-09397-3

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