Department of Statistics, University of South Carolina, Columbia, South Carolina, USA.
Biometrics. 2022 Jun;78(2):766-776. doi: 10.1111/biom.13457. Epub 2021 Mar 30.
Interactions between biological molecules in a cell are tightly coordinated and often highly dynamic. As a result of these varying signaling activities, changes in gene coexpression patterns could often be observed. The advancements in next-generation sequencing technologies bring new statistical challenges for studying these dynamic changes of gene coexpression. In recent years, methods have been developed to examine genomic information from individual cells. Single-cell RNA sequencing (scRNA-seq) data are count-based, and often exhibit characteristics such as overdispersion and zero inflation. To explore the dynamic dependence structure in scRNA-seq data and other zero-inflated count data, new approaches are needed. In this paper, we consider overdispersion and zero inflation in count outcomes and propose a ZEro-inflated negative binomial dynamic COrrelation model (ZENCO). The observed count data are modeled as a mixture of two components: success amplifications and dropout events in ZENCO. A latent variable is incorporated into ZENCO to model the covariate-dependent correlation structure. We conduct simulation studies to evaluate the performance of our proposed method and to compare it with existing approaches. We also illustrate the implementation of our proposed approach using scRNA-seq data from a study of minimal residual disease in melanoma.
细胞内生物分子之间的相互作用是紧密协调的，通常具有高度动态性。由于这些不同的信号活动，基因共表达模式的变化经常可以观察到。下一代测序技术的进步为研究这些基因共表达的动态变化带来了新的统计挑战。近年来，已经开发出了一些方法来研究单细胞的基因组信息。单细胞 RNA 测序 (scRNA-seq) 数据基于计数，通常表现出过度分散和零膨胀等特征。为了探索 scRNA-seq 数据和其他零膨胀计数数据中的动态依赖结构，需要新的方法。在本文中，我们考虑了计数结果中的过度分散和零膨胀，并提出了一个零膨胀负二项式动态相关模型 (ZENCO)。观察到的计数数据被建模为两个分量的混合：ZENCO 中的成功扩增和缺失事件。我们将一个潜在变量纳入 ZENCO 中，以模拟协变量相关的相关结构。我们进行了模拟研究来评估我们提出的方法的性能，并将其与现有方法进行比较。我们还使用黑色素瘤微小残留病研究中的 scRNA-seq 数据说明了我们提出的方法的实现。
