We present a new modelling approach for longitudinal count data that is motivated by the increasing availability of longitudinal RNA-sequencing experiments. The distribution of RNA-seq counts typically exhibits overdispersion, zero-inflation and heavy tails; moreover, in longitudinal designs repeated measurements from the same subject are typically (positively) correlated. We propose a generalized linear mixed model based on the Poisson-Tweedie distribution that can flexibly handle each of the aforementioned features of longitudinal RNA-seq counts. We develop a computational approach to accurately evaluate the likelihood of the proposed model and to perform maximum likelihood estimation. Our approach is implemented in the R package ptmixed, which can be freely downloaded from CRAN. We assess the performance of ptmixed on simulated data and we present an application to a dataset with longitudinal RNA-sequencing measurements from healthy and dystrophic mice. The applicability of the Poisson-Tweedie mixed-effects model is not restricted to longitudinal RNA-seq data, but it extends to any scenario where non-independent measurements of a discrete overdispersed response variable are available.

中文翻译：

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Poisson-Tweedie 混合效应模型：一种灵活的纵向 RNA-seq 数据分析方法

我们提出了一种新的纵向计数数据建模方法，其动机是纵向 RNA 测序实验的可用性不断提高。RNA-seq 计数的分布通常表现出过度分散、零膨胀和重尾；此外，在纵向设计中，来自同一对象的重复测量通常（正）相关。我们提出了一种基于 Poisson-Tweedie 分布的广义线性混合模型，该模型可以灵活地处理上述纵向 RNA-seq 计数的每个特征。我们开发了一种计算方法来准确评估所提出模型的可能性并执行最大似然估计。我们的方法是在 R 包 ptmixed 中实现的，它可以从 CRAN 免费下载。我们评估了 ptmixed 在模拟数据上的性能，并且我们将应用程序应用于具有来自健康和营养不良小鼠的纵向 RNA 测序测量的数据集。Poisson-Tweedie 混合效应模型的适用性不限于纵向 RNA-seq 数据，但它扩展到任何可用离散过度分散响应变量的非独立测量的场景。