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Deep generative modeling for single-cell transcriptomics

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scVI

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Single-cell Variational Inference

Quick Start

  1. Install Python 3.7. We typically use the Miniconda Python distribution and Linux.
  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 -c conda-forge

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

  2. If you wish to use multiple GPUs for hyperparameter tuning, install MongoDb.

  1. Follow along with our Jupyter notebooks to quickly get familiar with scVI!
    1. Getting started:
    2. Analyzing several datasets:
    3. Advanced topics:

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]

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