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Zero‐inflated Poisson factor model with application to microbiome read counts

Author

Listed:
  • Tianchen Xu
  • Ryan T. Demmer
  • Gen Li

Abstract

Dimension reduction of high‐dimensional microbiome data facilitates subsequent analysis such as regression and clustering. Most existing reduction methods cannot fully accommodate the special features of the data such as count‐valued and excessive zero reads. We propose a zero‐inflated Poisson factor analysis model in this paper. The model assumes that microbiome read counts follow zero‐inflated Poisson distributions with library size as offset and Poisson rates negatively related to the inflated zero occurrences. The latent parameters of the model form a low‐rank matrix consisting of interpretable loadings and low‐dimensional scores that can be used for further analyses. We develop an efficient and robust expectation‐maximization algorithm for parameter estimation. We demonstrate the efficacy of the proposed method using comprehensive simulation studies. The application to the Oral Infections, Glucose Intolerance, and Insulin Resistance Study provides valuable insights into the relation between subgingival microbiome and periodontal disease.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:biomet:v:77:y:2021:i:1:p:91-101
    DOI: 10.1111/biom.13272
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    References listed on IDEAS

    as
    1. 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.
    2. Gen Li & Jianhua Z. Huang & Haipeng Shen, 2018. "Exponential Family Functional data analysis via a low‐rank model," Biometrics, The International Biometric Society, vol. 74(4), pages 1301-1310, December.
    3. Paul J McMurdie & Susan Holmes, 2014. "Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-12, April.
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