logan-blaine / scvi-tools

Deep probabilistic analysis of single-cell omics data

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- 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 used scvi-tools in your research, please consider citing

@article{Gayoso2022,
         author={Gayoso, Adam and Lopez, Romain and Xing, Galen and Boyeau, Pierre and Valiollah Pour Amiri, Valeh and Hong, Justin and Wu, Katherine and Jayasuriya, Michael and   Mehlman, Edouard and Langevin, Maxime and Liu, Yining and Samaran, Jules and Misrachi, Gabriel and Nazaret, Achille and Clivio, Oscar and Xu, Chenling and Ashuach, Tal and Gabitto, Mariano and Lotfollahi, Mohammad and Svensson, Valentine and da Veiga Beltrame, Eduardo and Kleshchevnikov, Vitalii and Talavera-L{\'o}pez, Carlos and Pachter, Lior and Theis, Fabian J. and Streets, Aaron and Jordan, Michael I. and Regier, Jeffrey and Yosef, Nir},
         title={A Python library for probabilistic analysis of single-cell omics data},
         journal={Nature Biotechnology},
         year={2022},
         month={Feb},
         day={07},
         issn={1546-1696},
         doi={10.1038/s41587-021-01206-w},
         url={https://doi.org/10.1038/s41587-021-01206-w}
}

along with the publicaton describing the model used.