Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada.
Department of Statistical and Actuarial Sciences, Department of Computer Science, University of Western Ontario, London, Canada.
Biometrics. 2023 Jun;79(2):1089-1102. doi: 10.1111/biom.13657. Epub 2022 Apr 20.
Zero-inflated count data arise frequently from genomics studies. Analysis of such data is often based on a mixture model which facilitates excess zeros in combination with a Poisson distribution, and various inference methods have been proposed under such a model. Those analysis procedures, however, are challenged by the presence of measurement error in count responses. In this article, we propose a new measurement error model to describe error-contaminated count data. We show that ignoring the measurement error effects in the analysis may generally lead to invalid inference results, and meanwhile, we identify situations where ignoring measurement error can still yield consistent estimators. Furthermore, we propose a Bayesian method to address the effects of measurement error under the zero-inflated Poisson model and discuss the identifiability issues. We develop a data-augmentation algorithm that is easy to implement. Simulation studies are conducted to evaluate the performance of the proposed method. We apply our method to analyze the data arising from a prostate adenocarcinoma genomic study.
零膨胀计数数据在基因组学研究中经常出现。此类数据的分析通常基于混合模型，该模型便于过度零与泊松分布相结合，并且已经提出了各种基于此类模型的推断方法。然而，这些分析程序受到计数响应中测量误差的存在的挑战。在本文中，我们提出了一种新的测量误差模型来描述存在误差污染的计数数据。我们表明，在分析中忽略测量误差的影响通常会导致无效的推断结果，同时，我们确定了在忽略测量误差的情况下仍能产生一致估计量的情况。此外，我们提出了一种贝叶斯方法来解决零膨胀泊松模型下测量误差的影响，并讨论了可识别性问题。我们开发了一种易于实现的数据增强算法。进行了模拟研究以评估所提出方法的性能。我们将我们的方法应用于分析前列腺腺癌基因组学研究中产生的数据。
