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.

scvi-tools is part of the scverse project (website, governance) and is fiscally sponsored by NumFOCUS. 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.

Analysis of single-cell omics data

scvi-tools is composed of models that perform many analysis tasks across single- or multi-omics:

• 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 probablistic 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 this 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,

conda install scvi-tools -c conda-forge

and for pip,

pip install scvi-tools

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 (linked above).

Reference

If you use scvi-tools 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 publicaton 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.