Single-cell Variational Inference
Project description
scVI
Single-cell Variational Inference
Free software: MIT license
Documentation: https://scvi.readthedocs.io.
Quick Start
Install Python 3.7. We typically use the Miniconda Python distribution and Linux.
Install PyTorch. If you have an Nvidia GPU, be sure to install a version of PyTorch that supports it – scVI runs much faster with a discrete GPU.
Install scVI through conda:
conda install scvi -c bioconda -c conda-forge
Alternatively, you may try pip (pip install scvi), or you may clone this repository and run python setup.py install.
If you wish to use multiple GPUs for hyperparameter tuning, install MongoDb.
Follow along with our Jupyter notebooks to quickly get familiar with scVI!
- Getting started:
- Analyzing several datasets:
References
Romain Lopez, Jeffrey Regier, Michael Cole, Michael I. Jordan, Nir Yosef. “Deep generative modeling for single-cell transcriptomics.” Nature Methods, 2018. [pdf]
Chenling Xu∗, Romain Lopez∗, Edouard Mehlman∗, Jeffrey Regier, Michael I. Jordan, Nir Yosef. “Harmonization and Annotation of Single-cell Transcriptomics data with Deep Generative Models.” Submitted, 2019. [pdf]
Romain Lopez∗, Achille Nazaret∗, Maxime Langevin*, Jules Samaran*, Jeffrey Regier*, Michael I. Jordan, Nir Yosef. “A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements.” ICML Workshop on Computational Biology, 2019. [pdf]
Adam Gayoso, Romain Lopez, Zoë Steier, Jeffrey Regier, Aaron Streets, Nir Yosef. “A joint model of RNA expression and surface protein abundance in single cells.” bioRxiv, 2019. [pdf]
Oscar Clivio, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, Nir Yosef. “Detecting zero-inflated genes in single-cell transcriptomics data.” bioRxiv, 2019. [pdf]
Pierre Boyeau, Romain Lopez, Jeffrey Regier, Adam Gayoso, Michael I. Jordan, Nir Yosef. “Deep generative models for detecting differential expression in single cells.” bioRxiv, 2019. [pdf]
History
0.1.0 (2018-06-12)
First release on PyPI.
0.5.0 (2019-10-17)
Unfortunately we did not save history for previous versions. New features include:
AutoZI & TotalVI
Tests for LDVAE notebook
Add how to load CITE-SEQ data on dataloading notebook
Made the intro tutorial more user friendly
Removed requirements.txt and rely only on setup.py
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