Metadata-Version: 1.1
Name: scvi
Version: 0.2.2
Summary: Single-cell Variational Inference
Home-page: https://github.com/YosefLab/scVI
Author: Romain Lopez, Jeffrey Regier, Maxime Langevin, Edouard Mehlman, Yining Liu
Author-email: romain_lopez@berkeley.edu
License: MIT license
Description: ====
        scVI
        ====
        
        .. image:: https://travis-ci.org/YosefLab/scVI.svg?branch=master
            :target: https://travis-ci.org/YosefLab/scVI
        
        .. image:: https://codecov.io/gh/YosefLab/scVI/branch/master/graph/badge.svg
          :target: https://codecov.io/gh/YosefLab/scVI
        
        .. image:: https://readthedocs.org/projects/scvi/badge/?version=latest
                :target: https://scvi.readthedocs.io/en/latest/?badge=latest
                :alt: Documentation Status
        
        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.
        
        .. _Miniconda: https://conda.io/miniconda.html
        
        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.
        
        .. _PyTorch: http://pytorch.org
        
        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_.
        
        .. _setup.py: https://github.com/YosefLab/scVI/tree/master/setup.py
        
        
        4. Refer to `this Jupyter notebook`__ to see how to import datasets into scVI.
        
        .. __: https://github.com/YosefLab/scVI/tree/master/tests/notebooks/data_loading.ipynb
        
        5. Refer to `this Jupyter notebook`__ to see how to train the scVI model, impute missing data, detect differential expression, and more!
        
        .. __: https://github.com/YosefLab/scVI/tree/master/tests/notebooks/basic_tutorial.ipynb
        
        
        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
        
        
        =======
        History
        =======
        
        0.1.0 (2018-06-12)
        0.1.1 (2018-06-14)
        0.1.2 (2018-06-16)
        ------------------
        
        * First release on PyPI.
        
Keywords: scvi
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.6
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
