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{Weiler2024,
author = {Weiler, Philipp and Lange, Marius and Klein, Michal and Pe'er, Dana and Theis, Fabian},
publisher = {Springer Science and Business Media LLC},
url = {http://dx.doi.org/10.1038/s41592-024-02303-9},
doi = {10.1038/s41592-024-02303-9},
issn = {1548-7105},
journal = {Nature Methods},
month = jun,
number = {7},
pages = {1196--1205},
title = {CellRank 2: unified fate mapping in multiview single-cell data},
volume = {21},
year = {2024},
}
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.
Release files for scvelo 0.3.4
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| scvelo-0.3.4.tar.gz | 27.3 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| scvelo-0.3.4-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 27.5 MB
Release files / scvelo-0.3.4.tar.gz
| Download URL | scvelo-0.3.4.tar.gz |
|---|---|
| Size | 27.3 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / scvelo-0.3.4-py3-none-any.whl
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| Size | 191.2 kB |
| Tags | Python 3 |
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Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.7
|
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