scverse / scvi-tools
Deep probabilistic analysis of single-cell and spatial omics data
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Crawler view[![Stars][gh-stars-badge]][gh-stars-link] [![PyPI][pypi-badge]][pypi-link] [![PyPIDownloads][pepy-badge]][pepy-link] [![CondaDownloads][conda-badge]][conda-link] [![Docs][docs-badge]][docs-link] [![Build][build-badge]][build-link] [![Coverage][coverage-badge]][coverage-link] [scvi-tools] (single-cell variational inference tools) is a package for probabilistic modeling and analysis of single-cell omics data, built on top of [PyTorch] and [AnnData]. Analysis of single-cell omics data scvi-tools is composed of models that perform many analysis tasks across single-cell, multi, and spatial omics data: • Dimensionality reduction • Data integration • Automated annotation • Factor analysis • Doublet detection • Spatial deconvolution • and more! In the [user guide], we provide an overview of each model. All model implementations have a high-level API that interacts with [Scanpy] and includes standard save/load functions, GPU acceleration, etc. Rapid development of novel probabilistic models scvi-tools contains the building blocks to develop and deploy novel probabilistic models. These building blocks are powered by popular probabilistic and machine learning frameworks such as [PyTorch Lightning] and [Pyro]. For an overview of how the scvi-tools package is structured, you may refer to the [codebase overview] page. We recommend checking out the [skeleton repository] as a starting point for developing and deploying new models with scvi-tools. Basic installation For conda, and for pip, Please be sure to install a version of [PyTorch] that is compatible with your GPU (if applicable). Resources • Tutorials, API reference, and installation guides are available in the [documentation]. • For discussion of usage, check out our [forum]. • Please use the [issues] to submit bug reports. • If you'd like to contribute, check out our [contributing guide]. • If you find a model useful for your research, please consider citing the corresponding publication. Reference If you use in your work, please cite > **A Python library for probabilistic analysis of single-cell omics data** > > Adam Gayoso, Romain Lopez, Galen Xing, Pierre Boyeau, Valeh Valiollah Pour Amiri, Justin Hong, > Katherine Wu, Michael Jayasuriya, Edouard Mehlman, Maxime Langevin, Yining Liu, Jules Samaran, > Gabriel Misrachi, Achille Nazaret, Oscar Clivio, Chenling Xu, Tal Ashuach, Mariano Gabitto, > Mohammad Lotfollahi, Valentine Svensson, Eduardo da Veiga Beltrame, Vitalii Kleshchevnikov, > Carlos Talavera-López, Lior Pachter, Fabian J. Theis, Aaron Streets, Michael I. Jordan, > Jeffrey Regier & Nir Yosef > > _Nature Biotechnology_ 2022 Feb 07. doi: 10.1038/s41587-021-01206-w. along with the publication describing the model used. You can cite the scverse publication as follows: > **The scverse project provides a computational ecosystem for single-cell omics data analysis** > > Isaac Virshup, Danila Bredikhin, Lukas Heumos, Giovanni Palla, Gregor Sturm, Adam Gayoso, > Ilia Kats, Mikaela Koutrouli, Scverse Community, Bonnie Berger, Dana Pe’er, Aviv Regev, > Sarah A. Teichmann, Francesca Finotello, F. Alexander Wolf, Nir Yosef, Oliver Stegle & > Fabian J. Theis > > _Nature Biotechnology_ 2023 Apr 10. doi: 10.1038/s41587-023-01733-8. scvi-tools is part of the scverse® project (website, governance) and is fiscally sponsored by NumFOCUS. If you like scverse® and want to support our mission, please consider making a tax-deductible donation to help the project pay for developer time, professional services, travel, workshops, and a variety of other needs. Copyright (c) 2026, Yosef Lab, Weizmann Institute of Science [anndata]: https://anndata.readthedocs.io/en/latest/ [build-badge]: https://github.com/scverse/scvi-tools/actions/workflows/build.yml/badge.svg [build-link]: https://github.com/scverse/scvi-tools/actions/workflows/build.yml/ [codebase overview]: https://docs.scvi-tools.org/en/stable/user_guide/background/codebase_overview.html [conda-badge]: https://img.shields.io/conda/dn/conda-forge/scvi-tools?logo=Anaconda [conda-link]: https://anaconda.org/conda-forge/scvi-tools [contributing guide]: https://docs.scvi-tools.org/en/stable/developer/code.html [coverage-badge]: https://codecov.io/gh/scverse/scvi-tools/branch/main/graph/badge.svg [coverage-link]: https://codecov.io/gh/scverse/scvi-tools [docs-badge]: https://readthedocs.org/projects/scvi/badge/?version=latest [docs-link]: https://scvi.readthedocs.io/en/stable/?badge=stable [documentation]: https://docs.scvi-tools.org/ [forum]: https://discourse.scvi-tools.org [gh-stars-badge]: https://img.shields.io/github/stars/scverse/scvi-tools?style=flat&logo=GitHub&color=blue [gh-stars-link]: https://github.com/scverse/scvi-tools/stargazers [issues]: https://github.com/scverse/scvi-tools/issues [pepy-badge]: https://static.pepy.tech/badge/scvi-tools [pepy-link]: https://pepy.tech/project/scvi-tools [pypi-badge]: https://img.shields.io/pypi/v/scvi-tools.svg [pypi-link]: https://pypi.org/project/scvi-tools [pyro]: https://pyro.ai/ [pytorch]: https://pytorch.org [pytorch lightning]: https://lightning.ai/docs/pytorch/stable/ [scanpy]: http://scanpy.readthedocs.io/ [scvi-tools]: https://scvi-tools.org/ [skeleton repository]: https://github.com/scverse/simple-scvi [user guide]: https://docs.scvi-tools.org/en/stable/user_guide/index.html