Deep generative modeling for single-cell transcriptomics
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jules-samaran and jeff-regier updated simulation.loom files to match latest loom version (#216)
* updated simulation.loom files to match latest loom version

* loompy>=2.0 in setup requirements
Latest commit 0a2f143 Nov 11, 2018

README.rst

scVI

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Single-cell Variational Inference

Quick Start

  1. Install Python 3.6 or later. We typically use the Miniconda Python distribution.
  1. 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.
  1. Install scVI through conda (conda install scvi -c bioconda) or through pip (pip install scvi). Alternatively, you may download or clone this repository and run python setup.py install.
  2. Follow along with our Jupyter notebooks to quickly get familiar with scVI!
    1. data loading
    2. basic usage
    3. reproducing results from the paper

References

Romain Lopez, Jeffrey Regier, Michael Cole, Michael I. Jordan, Nir Yosef. "Deep generative modeling for single-cell transcriptomics" Nature Methods, in press (accepted Oct 26, 2018). Preprint available at https://www.biorxiv.org/content/early/2018/03/30/292037