maggishaggy/scVI

Deep generative modeling for single-cell transcriptomics

scVI

Single-cell Variational Inference

• Free software: MIT license
• Documentation: https://scvi.readthedocs.io.

Quick Start

1. Install Python 3.7. 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 -c conda-forge

   Alternatively, you may try pip (pip install scvi), or you may clone this repository and run python setup.py install.

4. Follow along with our Jupyter notebooks to quickly get familiar with scVI!

   1. Getting started:

      • data loading
      • basic usage

   2. Analyzing several datasets:

      • harmonization
      • annotation

   3. Advanced topics:

      • interaction with scanpy
      • linear decoder for gene interpretation
      • reproducing results from the scVI paper

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]