# KSRV: a Kernel PCA-Based framework for inferring spatial RNA velocity at single-cell resolution

## TL;DR

KSRV is a new computational method that improves the analysis of gene expression dynamics in tissues by combining single-cell RNA data with spatial transcriptomics.

## Contribution

KSRV introduces a novel framework using Kernel PCA to infer spatial RNA velocity at single-cell resolution.

## Key findings

- KSRV accurately infers RNA velocity in spatially resolved tissues compared to existing methods.
- Validation on 10x Visium and MERFISH datasets showed KSRV's robustness and accuracy.
- KSRV revealed spatial differentiation trajectories in mouse brain and organogenesis.

## Abstract

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.