scvi 0.2.0

Single-cell Variational Inference

scVI

Single-cell Variational Inference

• Free software: MIT license

• Documentation: https://scvi.readthedocs.io.

Quick Start

1. Install Python 3.6 or later. We typically use the Miniconda Python distribution.

2. 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.

3. Install scvi through conda (conda install scvi -c bioconda) or through pip (pip install scvi). Alternatively, you may clone this repository and manually install the dependencies listed in setup.py.

4. Refer to this Jupyter notebook to see how to import datasets into scVI.

5. Refer to this Jupyter notebook to see how to train the scVI model, impute missing data, detect differential expression, and more!

References

Romain Lopez, Jeffrey Regier, Michael B Cole, Michael Jordan, Nir Yosef. “Bayesian Inference for a Generative Model of Transcriptome Profiles from Single-cell RNA Sequencing.” In submission. Preprint available at https://www.biorxiv.org/content/early/2018/03/30/292037

History

0.1.0 (2018-06-12) 0.1.1 (2018-06-14) 0.1.2 (2018-06-16) ——————

• First release on PyPI.