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Identification of Key Factors Regulating Self-renewal and Differentiation in EML Hematopoietic Precursor Cells by RNA-sequencing Analysis
Published on: November 11, 2014
scITDG: a tool for identifying time-dependent genes in single-cell transcriptome sequencing data
Yandong Zheng1,2,3, Chengyu Liu1,2,3, Weiqi Zhang4,3,5
1State Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China.
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.
Supplementary Information:
The online version contains supplementary material available at 10.1007/s42995-025-00311-y.
