To help you get started, we've collected the most common ways that scvelo is being used within popular public projects.

theislab / scvelo / tests / test_basic.py View on Github

def test_einsum():
    from scvelo.tools.utils import prod_sum_obs, prod_sum_var, norm

    Ms, Mu = np.random.rand(5, 4), np.random.rand(5, 4)
    assert np.allclose(prod_sum_obs(Ms, Mu), np.sum(Ms * Mu, 0))
    assert np.allclose(prod_sum_var(Ms, Mu), np.sum(Ms * Mu, 1))
    assert np.allclose(norm(Ms), np.linalg.norm(Ms, axis=1))

View more ways to use scvelo

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}
}

veloVI

If you use veloVI (VI model), cite

@article{Gayoso2023,
  title = {Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells},
  ISSN = {1548-7105},
  url = {http://dx.doi.org/10.1038/s41592-023-01994-w},
  DOI = {10.1038/s41592-023-01994-w},
  journal = {Nature Methods},
  publisher = {Springer Science and Business Media LLC},
  author = {Gayoso, Adam and Weiler, Philipp and Lotfollahi, Mohammad and Klein, Dominik and Hong, Justin and Streets, Aaron and Theis, Fabian J. and Yosef, Nir},
  year = {2023},
  month = sep
}

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
}

Support

Found a bug or would like to see a feature implemented? Feel free to submit an issue. Have a question or would like to start a new discussion? Head over to GitHub discussions. Your help to improve scVelo is highly appreciated. For further information visit scvelo.org.