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Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data

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  • Lizhen Xu
  • Andrew D Paterson
  • Williams Turpin
  • Wei Xu

Abstract

Typical data in a microbiome study consist of the operational taxonomic unit (OTU) counts that have the characteristic of excess zeros, which are often ignored by investigators. In this paper, we compare the performance of different competing methods to model data with zero inflated features through extensive simulations and application to a microbiome study. These methods include standard parametric and non-parametric models, hurdle models, and zero inflated models. We examine varying degrees of zero inflation, with or without dispersion in the count component, as well as different magnitude and direction of the covariate effect on structural zeros and the count components. We focus on the assessment of type I error, power to detect the overall covariate effect, measures of model fit, and bias and effectiveness of parameter estimations. We also evaluate the abilities of model selection strategies using Akaike information criterion (AIC) or Vuong test to identify the correct model. The simulation studies show that hurdle and zero inflated models have well controlled type I errors, higher power, better goodness of fit measures, and are more accurate and efficient in the parameter estimation. Besides that, the hurdle models have similar goodness of fit and parameter estimation for the count component as their corresponding zero inflated models. However, the estimation and interpretation of the parameters for the zero components differs, and hurdle models are more stable when structural zeros are absent. We then discuss the model selection strategy for zero inflated data and implement it in a gut microbiome study of > 400 independent subjects.

Suggested Citation

  • Lizhen Xu & Andrew D Paterson & Williams Turpin & Wei Xu, 2015. "Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-30, July.
  • Handle: RePEc:plo:pone00:0129606
    DOI: 10.1371/journal.pone.0129606
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    Cited by:

    1. Mozhaeva, Irina, 2022. "Inequalities in utilization of institutional care among older people in Estonia," Health Policy, Elsevier, vol. 126(7), pages 704-714.
    2. Dongyang Yang & Wei Xu, 2023. "Estimation of Mediation Effect on Zero-Inflated Microbiome Mediators," Mathematics, MDPI, vol. 11(13), pages 1-16, June.
    3. Pratheepa Jeganathan & Susan P. Holmes, 2021. "A Statistical Perspective on the Challenges in Molecular Microbial Biology," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(2), pages 131-160, June.
    4. Tianchen Xu & Ryan T. Demmer & Gen Li, 2021. "Zero‐inflated Poisson factor model with application to microbiome read counts," Biometrics, The International Biometric Society, vol. 77(1), pages 91-101, March.
    5. Bo Chen & Wei Xu, 2020. "Generalized estimating equation modeling on correlated microbiome sequencing data with longitudinal measures," PLOS Computational Biology, Public Library of Science, vol. 16(9), pages 1-22, September.
    6. Costantino, Francesco & Di Gravio, Giulio & Patriarca, Riccardo & Petrella, Lea, 2018. "Spare parts management for irregular demand items," Omega, Elsevier, vol. 81(C), pages 57-66.
    7. Ying Jiang & Linghan Zhang & Junyi Zhang, 2019. "Energy consumption by rural migrant workers and urban residents with a hukou in China: quality-of-life-related factors and built environment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(3), pages 1431-1453, December.
    8. Cindy Xin Feng, 2021. "A comparison of zero-inflated and hurdle models for modeling zero-inflated count data," Journal of Statistical Distributions and Applications, Springer, vol. 8(1), pages 1-19, December.
    9. Mei Dong & Longhai Li & Man Chen & Anthony Kusalik & Wei Xu, 2020. "Predictive analysis methods for human microbiome data with application to Parkinson’s disease," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-18, August.

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