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Zero-inflated Poisson models with measurement error in the response.

Qihuang Zhang1, Grace Y Yi1,2

  • 1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada.

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|March 9, 2022
PubMed
Summary

Genomics studies often yield zero-inflated count data. This research introduces a measurement error model and Bayesian approach for accurate analysis of error-contaminated zero-inflated Poisson data, crucial for reliable genomic insights.

Keywords:
Bayesian statisticsdata augmentationmeasurement errormisclassificationmodel identifiabilityzero-inflated Poisson model

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Area of Science:

  • Genomics
  • Biostatistics
  • Statistical Modeling

Background:

  • Genomic studies frequently generate zero-inflated count data, often analyzed using zero-inflated Poisson (ZIP) mixture models.
  • Existing ZIP models are challenged by measurement error in count responses, potentially leading to invalid inferences.
  • Measurement error in genomic count data requires specialized modeling to ensure accurate analysis.

Purpose of the Study:

  • To propose a novel measurement error model for error-contaminated count data in genomic studies.
  • To investigate the impact of ignoring measurement error on ZIP model inference.
  • To develop a robust Bayesian method for analyzing ZIP models with measurement error.

Main Methods:

  • Development of a new measurement error model for count data.
  • Theoretical analysis to identify conditions for consistent estimators when measurement error is ignored.
  • Application of a Bayesian approach with a data-augmentation algorithm for ZIP models with measurement error.
  • Identifiability issues within the Bayesian framework were addressed.

Main Results:

  • Ignoring measurement error can lead to invalid inference in ZIP models.
  • Specific conditions were identified where ignoring measurement error yields consistent estimators.
  • The proposed Bayesian method effectively addresses measurement error effects.
  • Simulation studies demonstrated the performance of the developed data-augmentation algorithm.

Conclusions:

  • Accurate analysis of zero-inflated count data in genomics requires accounting for measurement error.
  • The proposed Bayesian measurement error model provides a valid and implementable approach for genomic data analysis.
  • The method was successfully applied to prostate adenocarcinoma genomic data, highlighting its practical utility.