RNA velocity has opened up new ways of studying cellular differentiation in single-cell RNA-sequencing data. It describes the rate of gene expression change for an individual gene at a given time point based on the ratio of its spliced and unspliced messenger RNA (mRNA). However, errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. Here we present scVelo, a method that overcomes these limitations by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to systems with transient cell states, which are common in development and in response to perturbations. We apply scVelo to disentangling subpopulation kinetics in neurogenesis and pancreatic endocrinogenesis. We infer gene-specific rates of transcription, splicing and degradation, recover each cell’s position in the underlying differentiation processes and detect putative driver genes. scVelo will facilitate the study of lineage decisions and gene regulation.

RNA 速度开辟了在单细胞 RNA 测序数据中研究细胞分化的新方法。它描述了单个基因在给定时间点的基因表达变化率，基于其剪接和未剪接的信使 RNA (mRNA) 的比率。然而，如果违反了共同剪接率的中心假设和对具有稳态 mRNA 水平的完整剪接动力学的观察，则会出现速度估计错误。在这里，我们介绍了 scVelo，一种通过使用基于似然的动力学模型解决剪接动力学的完整转录动力学来克服这些限制的方法。这将 RNA 速度推广到具有瞬态细胞状态的系统，这在发育和对扰动的响应中很常见。我们将 scVelo 应用于解开神经发生和胰腺内分泌发生中的亚群动力学。我们推断特定基因的转录、剪接和降解率，恢复每个细胞在潜在分化过程中的位置，并检测推定的驱动基因。scVelo 将促进谱系决定和基因调控的研究。