https://github.com/theislab/scvelo
RNA Velocity generalized through dynamical modeling
https://github.com/theislab/scvelo
Last synced: 10 months ago
JSON representation
RNA Velocity generalized through dynamical modeling
- Host: GitHub
- URL: https://github.com/theislab/scvelo
- Owner: theislab
- License: bsd-3-clause
- Created: 2018-08-20T19:03:07.000Z (almost 8 years ago)
- Default Branch: main
- Last Pushed: 2025-02-24T18:17:51.000Z (over 1 year ago)
- Last Synced: 2025-09-08T14:01:14.466Z (11 months ago)
- Language: Python
- Homepage: https://scvelo.org
- Size: 46.5 MB
- Stars: 462
- Watchers: 12
- Forks: 109
- Open Issues: 72
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.rst
- License: LICENSE
Awesome Lists containing this project
- awesome-computational-biology - scVelo - cell transcriptomics, inferring the direction and speed of cell differentiation. (Preprocessing Tools / Clinical Trial)
- awesome-single-cell - scVelo - [Python] - scVelo is a scalable toolkit for RNA velocity analysis in single cells. It generalizes the concept of RNA velocity by relaxing previously made assumptions with a dynamical model. It allows to identify putative driver genes, infer a latent time, estimate reaction rates of transcription, splicing and degradation, and detect competing kinetics. (Software packages / Pseudotime and trajectory inference)
README
[![PyPi][badge-pypi]][link-pypi]
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[link-pypidownloads]: https://pepy.tech/project/scvelo
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# 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](https://doi.org/10.1038/s41586-018-0414-6). scVelo collects different
methods for inferring RNA velocity using an expectation-maximization framework
[2](https://doi.org/10.1038/s41587-020-0591-3), deep generative modeling
[3](https://doi.org/10.1038/s41592-023-01994-w),
or metabolically labeled transcripts[4](https://doi.org/10.1101/2023.07.19.549685).
## 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
```bibtex
@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
```bibtex
@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](https://github.com/theislab/scvelo/issues/new/choose).
Have a question or would like to start a new discussion? Head over to
[GitHub discussions](https://github.com/theislab/scvelo/discussions).
Your help to improve scVelo is highly appreciated.
For further information visit [scvelo.org](https://scvelo.org).