Modeling dynamic correlation in zero-inflated bivariate count data with applications to single-cell RNA sequencing data.

ABSTRACT: 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 ou

SUBMITTER: Yang Z 

PROVIDER: S-EPMC8477913 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature