Long Wenxin, Liu Tianyu, Xue Lingzhou, Zhao Hongyu
Department of Statistics, The Pennsylvania State University, University Park, 16802, PA, USA.
Department of Biostatistics, Yale University, New Haven, 06510, CT, USA.
Genome Biol. 2025 Aug 11;26(1):239. doi: 10.1186/s13059-025-03701-8.
RNA velocity has emerged as a powerful tool to interpret transcriptional dynamics and infer trajectory from snapshot datasets. However, current methods fail to utilize the spatial information inherent in spatial transcriptomics and lack scalability in multi-batch datasets. Here, we introduce spVelo, a scalable framework for RNA velocity inference of multi-batch spatial transcriptomics data. spVelo supports several downstream applications, including uncertainty quantification, complex trajectory pattern discovery, driver marker identification, gene regulatory network inference, and temporal cell-cell communication inference. spVelo has the potential to provide deeper insights into complex tissue organization and underscore biological mechanisms based on spatially resolved patterns.
RNA速度已成为解释转录动态并从快照数据集中推断轨迹的强大工具。然而，当前方法未能利用空间转录组学中固有的空间信息，并且在多批次数据集中缺乏可扩展性。在这里，我们介绍了spVelo，这是一个用于多批次空间转录组学数据的RNA速度推断的可扩展框架。spVelo支持多种下游应用，包括不确定性量化、复杂轨迹模式发现、驱动标记识别、基因调控网络推断和时间性细胞间通讯推断。spVelo有潜力基于空间分辨模式，为复杂的组织结构提供更深入的见解，并突出生物学机制。
