Abstract
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.
| Original language | English (US) |
|---|---|
| Article number | 239 |
| Journal | Genome biology |
| Volume | 26 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2025 |
All Science Journal Classification (ASJC) codes
- Ecology, Evolution, Behavior and Systematics
- Genetics
- Cell Biology
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