United Network for Organ Sharing, Richmond, VA, United States of America.
Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, United States of America.
PLoS One. 2019 Jan 8;14(1):e0209923. doi: 10.1371/journal.pone.0209923. eCollection 2019.
Chromosomal aberrations, such as micronuclei (MN), have served as biomarkers of genotoxic exposure and cancer risk. Guidelines for the process of scoring MN have been presented by the HUman MicroNucleus (HUMN) project. However, these guidelines were developed for assay performance but do not address how to statistically analyze the data generated by the assay. This has led to the application of various statistical methods that may render different interpretations and conclusions. By combining MN with data from other high-throughput genomic technologies such as gene expression microarray data, we may elucidate molecular features involved in micronucleation. Traditional methods that can model discrete (synonymously, count) data, such as MN frequency, require that the number of explanatory variables (P) is less than the number of samples (N). Due to this limitation, penalized models have been developed to enable model fitting for such over-parameterized datasets. Because penalized models in the discrete response setting are lacking, particularly when the count outcome is over-dispersed, herein we present our penalized negative binomial regression model that can be fit when P > N. Using simulation studies we demonstrate the performance of our method in comparison to commonly used penalized Poisson models when the outcome is over-dispersed and applied it to MN frequency and gene expression data collected as part of the Norwegian Mother and Child Cohort Study. Our countgmifs R package is available for download from the Comprehensive R Archive Network and can be applied to datasets having a discrete outcome that is either Poisson or negative binomial distributed and a high-dimensional covariate space.
染色体畸变，如微核（MN），已被用作遗传毒性暴露和癌症风险的生物标志物。HUman MicroNucleus（HUMN）项目提出了评分 MN 的过程指南。然而，这些指南是为检测性能制定的，但并未解决如何统计分析检测产生的数据。这导致了各种统计方法的应用，这些方法可能会产生不同的解释和结论。通过将 MN 与其他高通量基因组技术的数据（如基因表达微阵列数据）相结合，我们可以阐明参与微核形成的分子特征。可以对离散（同义词，计数）数据（如 MN 频率）进行建模的传统方法要求解释变量的数量（P）小于样本的数量（N）。由于这个限制，已经开发了惩罚模型来实现这种超参数化数据集的模型拟合。由于离散响应设置中缺乏惩罚模型，特别是当计数结果是过离散时，在此我们提出了我们的惩罚负二项回归模型，当 P > N 时可以拟合该模型。通过模拟研究，我们展示了当结果过离散时，与常用的惩罚泊松模型相比，我们的方法的性能，以及将其应用于 MN 频率和基因表达数据的收集作为挪威母亲和儿童队列研究的一部分。我们的 countgmifs R 包可从 Comprehensive R Archive Network 下载，并可应用于具有泊松或负二项分布的离散结果和高维协变量空间的数据集。
