spVelo: RNA velocity inference for multi-batch spatial transcriptomics data
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.
Authors
- Tianyu Liu (ORCID: https://orcid.org/0000-0002-9412-6573)
- Hongyu Zhao (ORCID: https://orcid.org/0000-0003-1195-9607)
- Wenxin Long
- Lingzhou Xue (ORCID: https://orcid.org/0000-0002-8252-0637)
Institutions
- Pennsylvania State University (US)
- Yale University (US)
Publication Details
- Journal
- Genome biology
- Published
- 2025-08-11
- DOI
- https://doi.org/10.1186/s13059-025-03701-8
- Citations
- 3
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 2.42
Funders
- National Institutes of Health