A dependency-aware deep generative model for inferring RNA velocity from spatial transcriptomics.

Authors

Sishuo Chen, Liyi Yu, Peng Jiang, Lihua Zhang, Tian Tian

Published in

Bioinformatics (Oxford, England). Volume 42. Issue Supplement_1. Jul 01, 2026.

Abstract

The development of spatial transcriptomics enables transcriptome-wide profiling of cells within their tissue context, offering new opportunities to study spatially organized cellular state transitions. RNA velocity provides a powerful framework for inferring transcriptional dynamics from snapshot data, but most existing methods were designed for dissociated single-cell data and ignore spatial dependency.
We present spaVelo, a dependency-aware deep generative model for RNA velocity inference from spatial transcriptomics data. spaVelo integrates spatial information into transcriptional kinetics using a spatial-aware variational autoencoder and a spatially modulated transcriptional scaling factor, enabling the modeling of spot-specific and heterogeneous dynamics. Across simulated and real datasets, spaVelo reconstructs biologically coherent velocity fields and developmental trajectories, outperforming existing methods, particularly in tissues with complex spatial organization.
The source code of spaVelo is available at: https://github.com/1062638515/spaVelo.

PMID:
42412830
Bibliographic data and abstract were imported from PubMed on 08 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.