Metadata-Version: 2.1
Name: scvi
Version: 0.2.3
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
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
Requires-Dist: numpy (<1.15,>=1.0)
Requires-Dist: torch (>=0.4.1)
Requires-Dist: matplotlib (>=2.0)
Requires-Dist: scikit-learn (<0.20.0,>=0.18)
Requires-Dist: scipy (>=1.1)
Requires-Dist: h5py (>=2.8)
Requires-Dist: pandas (>=0.2)
Requires-Dist: loompy (>=2.0)
Requires-Dist: tqdm (>=4)
Requires-Dist: anndata (>=0.6)
Requires-Dist: xlrd (>=1.0)
Requires-Dist: jupyter (>=1.0.0)
Requires-Dist: nbconvert (>=5.4.0)
Requires-Dist: nbformat (>=4.4.0)
Requires-Dist: ipython (>=7)

====
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 download or clone this repository and run ``python setup.py install``.

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

   a. `data loading`__
   b. `basic usage`__ 
   c. `reproducing results from the paper`__ 

.. __: https://github.com/YosefLab/scVI/tree/master/tests/notebooks/data_loading.ipynb
.. __: https://github.com/YosefLab/scVI/tree/master/tests/notebooks/basic_tutorial.ipynb
.. __: https://github.com/YosefLab/scVI/blob/master/tests/notebooks/scVI_reproducibility.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.


