scITDG: a tool for identifying time-dependent genes in single-cell transcriptome sequencing data

Abstract

Our study introduces scITDG, a tool designed for the analysis of time-dependent gene expression in single-cell transcriptomic sequencing data, effectively filling a gap in current analytical resources. A key advantage of scITDG is its ability to identify dynamic gene expression patterns across multiple time points at single-cell resolution, which is pivotal for deciphering complex biological processes such as aging and tissue regeneration. The tool is compatible with widely used single-cell analysis platforms such as Seurat and Scanpy. By integrating natural cubic splines regression with bootstrapping resampling, scITDG enhances the functionality of these platforms and broadens their applicability. In this study, based on scITDG, we revealed intricate gene expression modules in mice aging and axolotl limb regeneration, providing valuable insights into cellular function and response mechanisms. The versatility of scITDG makes it applicable to a wide range of biological contexts, including development, circadian rhythms, disease progression, and therapeutic responses.

Keywords

Aging / Regeneration / scITDG R package / Single-cell sequencing / Time-dependent genes

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