Single-cell Variational Inference
Project description
scVI
Single-cell Variational Inference
Free software: MIT license
Documentation: https://scvi.readthedocs.io.
Quick Start
If you intend to use parallel implementation of our hyperparameter tuning feature, install MongoDb.
Install Python 3.7. We typically use the Miniconda Python distribution.
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.
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]
History
0.1.0 (2018-06-12) 0.1.1 (2018-06-14) 0.1.2 (2018-06-16) ——————
First release on PyPI.
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