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Dynamic dependence structures among genes are often observed when the co-expression patterns among genes change under various cellular conditions. The advancements in next-generation sequencing technologies bring new statistical challenges for studying the dynamic change of gene co-expression. In recent years, methods have been developed to examine the sequence 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 talk, we account for over-dispersion 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 drop out events in ZENCO. An augmented latent variable is incorporated into ZENCO in order to model the covariate-dependent correlation structure. We conduct simulation studies to compare the performance in identifying dynamic correlations using ZENCO and existing methods.
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