ORIGINAL RESEARCH article
Front. Genet.
Sec. Computational Genomics
KSRV: A Kernel PCA-Based Framework for Inferring Spatial RNA Velocity at Single-Cell Resolution
Provisionally accepted
- Wuhan Textile University, Wuhan, China
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Understanding the temporal dynamics of gene expression within spatial contexts is essential for deciphering cellular differentiation. RNA velocity, which estimates the future state of gene expression by distinguishing spliced from unspliced mRNA, offers a powerful tool for studying these dynamics. However, current spatial transcriptomics technologies face limitations in simultaneously capturing both spliced and unspliced transcripts at high resolution. To address this challenge, a novel computational framework called KSRV (Kernel PCA–based Spatial RNA Velocity) that integrates single-cell RNA-seq with spatial transcriptomics using Kernel Principal Component Analysis. It enables accurately inference of RNA velocity in spatially resolved tissue at single-cell resolution. KSRV was validated by using 10x Visium data and MERFISH datasets. The results demonstrate its both accuracy and robustness comparing with the existed method such as SIRV and spVelo. Furthermore, KSRV successfully revealed spatial differentiation trajectories in the mouse brain and during mouse organogenesis, highlighting its potential for advancing our understanding of spatially dynamic biological processes.
Keywords: RNA velocity, scRNA-seq data, Cell Differentiation, Kernel PCA, data integration
Received: 30 Aug 2025; Accepted: 28 Oct 2025.
Copyright: © 2025 He, Jiang, Qiu, Shi and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ben-gong Zhang, bgzhang@wtu.edu.cn
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