scVelo - RNA velocity generalized through dynamical modeling

scVelo is a scalable toolkit for RNA velocity analysis in single cells; RNA velocity enables the recovery of directed dynamic information by leveraging splicing kinetics ¹. scVelo collects different methods for inferring RNA velocity using an expectation-maximization framework ², deep generative modeling ³, or metabolically labeled transcripts⁴.

scVelo's key applications

estimate RNA velocity to study cellular dynamics.
identify putative driver genes and regimes of regulatory changes.
infer a latent time to reconstruct the temporal sequence of transcriptomic events.
estimate reaction rates of transcription, splicing and degradation.
use statistical tests, e.g., to detect different kinetics regimes.

Citing scVelo

If you include or rely on scVelo when publishing research, please adhere to the following citation guide:

EM and steady-state model

If you use the EM (dynamical) or steady-state model, cite

@article{Bergen2020,
  title = {Generalizing RNA velocity to transient cell states through dynamical modeling},
  volume = {38},
  ISSN = {1546-1696},
  url = {http://dx.doi.org/10.1038/s41587-020-0591-3},
  DOI = {10.1038/s41587-020-0591-3},
  number = {12},
  journal = {Nature Biotechnology},
  publisher = {Springer Science and Business Media LLC},
  author = {Bergen, Volker and Lange, Marius and Peidli, Stefan and Wolf, F. Alexander and Theis, Fabian J.},
  year = {2020},
  month = aug,
  pages = {1408–1414}
}

RNA velocity inference through metabolic labeling information

If you use the implemented method for estimating RNA velocity from metabolic labeling information, cite

@article{Weiler2023,
  title = {Unified fate mapping in multiview single-cell data},
  url = {http://dx.doi.org/10.1101/2023.07.19.549685},
  DOI = {10.1101/2023.07.19.549685},
  publisher = {Cold Spring Harbor Laboratory},
  author = {Weiler, Philipp and Lange, Marius and Klein, Michal and Pe’er, Dana and Theis, Fabian J.},
  year = {2023},
  month = jul
}

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