TIME-CoExpress: Temporal Trajectory Modeling of Dynamic Gene Co-expression Patterns Using Single-Cell Transcriptomics Data

Abstract

The rapid advancements of single-cell RNA sequencing (scRNAseq) technology provide high-resolution views of transcriptomic activity within a single cell. Most routine analyses of scRNAseq data focus on individual genes; however, the one-gene-at-a-time analysis is likely to miss meaningful genetic interactions. Gene co-expression analysis addresses this issue by identifying coordinated gene expression changes in response to cellular conditions, such as developmental or temporal trajectory. Identifying differential co-expression gene combinations along the cell temporal trajectory using scRNAseq data can provide deeper insight into the biological processes. Existing approaches for gene co-expression analysis assume a restrictive linear change of gene co-expression. In this paper, we propose a copula-based approach with proper data-driven smoothing functions to model non-linear gene co-expression changes along cellular temporal trajectories. Our proposed approach provides flexibility to incorporate characteristics such as over-dispersion and zero-inflation rate observed in scRNAseq data into the modeling framework. We conducted a series of simulation analyses to evaluate the performance of the proposed algorithm. We demonstrate the implementation of the proposed algorithm using a scRNAseq dataset and identify differential co-expression gene pairs along cell temporal trajectory in pituitary embryonic development comparing Nxn-/- mutated versus wild-type mice.