This is a fork of the original SCVI-TOOLS repo made for the thesis "Hybrid Variational Autoencoder Clustering of Single-Cell RNA-seq Data"

Written by Sarah Narrowe Danielsson

The hybrid model created for the thesis can be found in scvi-tools/scvi/model/_hybridvi.py and scvi-tools/scvi/module/_hybridvae.py. The results of the thesis can be found in scvi-tools/output/ and scvi-tools/cross_valid_results/.

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 new models into scvi-tools.

Basic installation

For conda,

conda install scvi-tools -c bioconda -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

@article{Gayoso2021scvitools,
	author = {Gayoso, Adam and Lopez, Romain and Xing, Galen and Boyeau, Pierre 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 Lotfollahi, Mohammad and Svensson, Valentine and da Veiga Beltrame, Eduardo and Talavera-Lopez, Carlos and Pachter, Lior and Theis, Fabian J and Streets, Aaron and Jordan, Michael I and Regier, Jeffrey and Yosef, Nir},
	title = {scvi-tools: a library for deep probabilistic analysis of single-cell omics data},
	year = {2021},
	doi = {10.1101/2021.04.28.441833},
	publisher = {Cold Spring Harbor Laboratory},
	URL = {https://www.biorxiv.org/content/early/2021/04/29/2021.04.28.441833},
	eprint = {https://www.biorxiv.org/content/early/2021/04/29/2021.04.28.441833.full.pdf},
	journal = {bioRxiv}
}