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Tools","Software packages"],"sub_categories":["Clinical Trial","Pseudotime and trajectory inference"],"readme":"[![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\u003cimg src=\"https://user-images.githubusercontent.com/31883718/67709134-a0989480-f9bd-11e9-8ae6-f6391f5d95a0.png\" width=\"400px\" align=\"left\"\u003e\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\u003csup\u003e[1](https://doi.org/10.1038/s41586-018-0414-6)\u003c/sup\u003e. scVelo collects different\nmethods for inferring RNA velocity using an expectation-maximization framework\n\u003csup\u003e[2](https://doi.org/10.1038/s41587-020-0591-3)\u003c/sup\u003e, deep generative modeling\n\u003csup\u003e[3](https://doi.org/10.1038/s41592-023-01994-w)\u003c/sup\u003e,\nor metabolically labeled transcripts\u003csup\u003e[4](https://doi.org/10.1101/2023.07.19.549685)\u003c/sup\u003e.\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 = {1408–1414}\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","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheislab%2Fscvelo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftheislab%2Fscvelo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheislab%2Fscvelo/lists"}