TFvelo: gene regulation inspired RNA velocity estimation

• Published in: Nature communications 2024-02, Vol.15 (1), p.1387-15, Article 1387
• Main Authors: Li, Jiachen, Pan, Xiaoyong, Yuan, Ye, Shen, Hong-Bin
• Format: Article
• Language: English
• Subjects: 38; 38/91; 631/114/1314; 631/114/2114; 631/114/2397; 631/114/2785; 631/61/514/1949; Cell fate; Datasets; Dynamic models; Dynamics; Gene regulation; Genes; Humanities and Social Sciences; Modelling; multidisciplinary; Ribonucleic acid; RNA; Science; Science (multidisciplinary); Synthetic data; Velocity
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-45661-w

RNA velocity is closely related with cell fate and is an important indicator for the prediction of cell states with elegant physical explanation derived from single-cell RNA-seq data. Most existing RNA velocity models aim to extract dynamics from the phase delay between unspliced and spliced mRNA for each individual gene. However, unspliced/spliced mRNA abundance may not provide sufficient signal for dynamic modeling, leading to poor fit in phase portraits. Motivated by the idea that RNA velocity could be driven by the transcriptional regulation, we propose TFvelo, which expands RNA velocity concept to various single-cell datasets without relying on splicing information, by introducing gene regulatory information. Our experiments on synthetic data and multiple scRNA-Seq datasets show that TFvelo can accurately fit genes dynamics on phase portraits, and effectively infer cell pseudo-time and trajectory from RNA abundance data. TFvelo opens a robust and accurate avenue for modeling RNA velocity for single cell data. Most RNA velocity models extract dynamics from the phase delay between unspliced and spliced mRNA for each gene. Here, authors propose TFvelo, broadening RNA velocity beyond splicing information to include gene regulation. TFvelo accurately models genes dynamics and infers cell pseudo-time from RNA abundance data.