| Name | scvelo JSON |
| Version |
0.3.3
JSON |
| download |
| home_page | None |
| Summary | RNA velocity generalized through dynamical modeling |
| upload_time | 2024-12-09 07:51:43 |
| maintainer | None |
| docs_url | None |
| author | Volker Bergen, Philipp Weiler |
| requires_python | >=3.8 |
| license | BSD 3-Clause License Copyright (c) 2018, Theis Lab All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
rna
velocity
single cell
transcriptomics
stochastic
dynamical
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[![PyPi][badge-pypi]][link-pypi]
[![PyPIDownloads][badge-pypidownloads]][link-pypidownloads]
[![CI][badge-ci]][link-ci]
[badge-pypi]: https://img.shields.io/pypi/v/scvelo.svg
[link-pypi]: https://pypi.org/project/scvelo
[badge-pypidownloads]: https://pepy.tech/badge/scvelo
[link-pypidownloads]: https://pepy.tech/project/scvelo
[badge-ci]: https://img.shields.io/github/actions/workflow/status/theislab/scvelo/ci.yml?branch=main
[link-ci]: https://github.com/theislab/scvelo/actions/workflows/ci.yml
# scVelo - RNA velocity generalized through dynamical modeling
<img src="https://user-images.githubusercontent.com/31883718/67709134-a0989480-f9bd-11e9-8ae6-f6391f5d95a0.png" width="400px" align="left">
**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
<sup>[1](https://doi.org/10.1038/s41586-018-0414-6)</sup>. scVelo collects different
methods for inferring RNA velocity using an expectation-maximization framework
<sup>[2](https://doi.org/10.1038/s41587-020-0591-3)</sup>, deep generative modeling
<sup>[3](https://doi.org/10.1038/s41592-023-01994-w)</sup>,
or metabolically labeled transcripts<sup>[4](https://doi.org/10.1101/2023.07.19.549685)</sup>.
## 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).
Raw data
{
"_id": null,
"home_page": null,
"name": "scvelo",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": "Philipp Weiler <philipp.weiler@helmholtz-muenchen.de>",
"keywords": "RNA, velocity, single cell, transcriptomics, stochastic, dynamical",
"author": "Volker Bergen, Philipp Weiler",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/ae/31/bb5292e3b48bf021b6a9e75308a7e80d415e0e2721f642808bd902c64d13/scvelo-0.3.3.tar.gz",
"platform": null,
"description": "[![PyPi][badge-pypi]][link-pypi]\n[![PyPIDownloads][badge-pypidownloads]][link-pypidownloads]\n[![CI][badge-ci]][link-ci]\n\n[badge-pypi]: https://img.shields.io/pypi/v/scvelo.svg\n[link-pypi]: https://pypi.org/project/scvelo\n[badge-pypidownloads]: https://pepy.tech/badge/scvelo\n[link-pypidownloads]: https://pepy.tech/project/scvelo\n[badge-ci]: https://img.shields.io/github/actions/workflow/status/theislab/scvelo/ci.yml?branch=main\n[link-ci]: https://github.com/theislab/scvelo/actions/workflows/ci.yml\n\n# scVelo - RNA velocity generalized through dynamical modeling\n\n<img src=\"https://user-images.githubusercontent.com/31883718/67709134-a0989480-f9bd-11e9-8ae6-f6391f5d95a0.png\" width=\"400px\" align=\"left\">\n\n**scVelo** is a scalable toolkit for RNA velocity analysis in single cells; RNA velocity\nenables the recovery of directed dynamic information by leveraging splicing kinetics\n<sup>[1](https://doi.org/10.1038/s41586-018-0414-6)</sup>. scVelo collects different\nmethods for inferring RNA velocity using an expectation-maximization framework\n<sup>[2](https://doi.org/10.1038/s41587-020-0591-3)</sup>, deep generative modeling\n<sup>[3](https://doi.org/10.1038/s41592-023-01994-w)</sup>,\nor metabolically labeled transcripts<sup>[4](https://doi.org/10.1101/2023.07.19.549685)</sup>.\n\n## scVelo's key applications\n\n- estimate RNA velocity to study cellular dynamics.\n- identify putative driver genes and regimes of regulatory changes.\n- infer a latent time to reconstruct the temporal sequence of transcriptomic events.\n- estimate reaction rates of transcription, splicing and degradation.\n- use statistical tests, e.g., to detect different kinetics regimes.\n\n## Citing scVelo\n\nIf you include or rely on scVelo when publishing research, please adhere to the\nfollowing citation guide:\n\n### EM and steady-state model\n\nIf you use the _EM_ (_dynamical_) or _steady-state model_, cite\n\n```bibtex\n@article{Bergen2020,\n title = {Generalizing RNA velocity to transient cell states through dynamical modeling},\n volume = {38},\n ISSN = {1546-1696},\n url = {http://dx.doi.org/10.1038/s41587-020-0591-3},\n DOI = {10.1038/s41587-020-0591-3},\n number = {12},\n journal = {Nature Biotechnology},\n publisher = {Springer Science and Business Media LLC},\n author = {Bergen, Volker and Lange, Marius and Peidli, Stefan and Wolf, F. Alexander and Theis, Fabian J.},\n year = {2020},\n month = aug,\n pages = {1408\u20131414}\n}\n```\n\n### RNA velocity inference through metabolic labeling information\n\nIf you use the implemented method for estimating RNA velocity from metabolic labeling\ninformation, cite\n\n```bibtex\n@article{Weiler2024,\n author = {Weiler, Philipp and Lange, Marius and Klein, Michal and Pe'er, Dana and Theis, Fabian},\n publisher = {Springer Science and Business Media LLC},\n url = {http://dx.doi.org/10.1038/s41592-024-02303-9},\n doi = {10.1038/s41592-024-02303-9},\n issn = {1548-7105},\n journal = {Nature Methods},\n month = jun,\n number = {7},\n pages = {1196--1205},\n title = {CellRank 2: unified fate mapping in multiview single-cell data},\n volume = {21},\n year = {2024},\n}\n```\n\n## Support\n\nFound a bug or would like to see a feature implemented? Feel free to submit an\n[issue](https://github.com/theislab/scvelo/issues/new/choose).\nHave a question or would like to start a new discussion? Head over to\n[GitHub discussions](https://github.com/theislab/scvelo/discussions).\nYour help to improve scVelo is highly appreciated.\nFor further information visit [scvelo.org](https://scvelo.org).\n",
"bugtrack_url": null,
"license": "BSD 3-Clause License Copyright (c) 2018, Theis Lab All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ",
"summary": "RNA velocity generalized through dynamical modeling",
"version": "0.3.3",
"project_urls": {
"Documentation": "https://scvelo.readthedocs.io/",
"Home-page": "https://github.com/theislab/scvelo",
"Source": "https://github.com/theislab/scvelo"
},
"split_keywords": [
"rna",
" velocity",
" single cell",
" transcriptomics",
" stochastic",
" dynamical"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "5b9a97224ad312fca04b8dec60c1b743096344c3b2fff5f56a9a817576c5cf4d",
"md5": "09516a89da3a580424913b8154d36332",
"sha256": "cb9821a86d0c6b9cb79d9fd867480d7caece27530d1d30b49052a0fa9d94a187"
},
"downloads": -1,
"filename": "scvelo-0.3.3-py3-none-any.whl",
"has_sig": false,
"md5_digest": "09516a89da3a580424913b8154d36332",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 196841,
"upload_time": "2024-12-09T07:51:40",
"upload_time_iso_8601": "2024-12-09T07:51:40.783557Z",
"url": "https://files.pythonhosted.org/packages/5b/9a/97224ad312fca04b8dec60c1b743096344c3b2fff5f56a9a817576c5cf4d/scvelo-0.3.3-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "ae31bb5292e3b48bf021b6a9e75308a7e80d415e0e2721f642808bd902c64d13",
"md5": "ab731f6192a45c37620925a4aee44a5e",
"sha256": "9383a8712f81f0f2d2f1bddcfc81889ede06a0027de537b5b9af83ed821390c7"
},
"downloads": -1,
"filename": "scvelo-0.3.3.tar.gz",
"has_sig": false,
"md5_digest": "ab731f6192a45c37620925a4aee44a5e",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 27327093,
"upload_time": "2024-12-09T07:51:43",
"upload_time_iso_8601": "2024-12-09T07:51:43.486604Z",
"url": "https://files.pythonhosted.org/packages/ae/31/bb5292e3b48bf021b6a9e75308a7e80d415e0e2721f642808bd902c64d13/scvelo-0.3.3.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-12-09 07:51:43",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "theislab",
"github_project": "scvelo",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "scvelo"
}
