Metadata-Version: 2.1
Name: scvi
Version: 0.4.1
Summary: Single-cell Variational Inference
Home-page: https://github.com/YosefLab/scVI
Author: Romain Lopez, Jeffrey Regier, Maxime Langevin, Edouard Mehlman, Yining Liu, Achille Nazaret, Gabriel Misrachi, Oscar Clivio, Pierre Boyeau, Adam Gayoso
Author-email: romain_lopez@berkeley.edu
License: MIT license
Description: ====
        scVI
        ====
        
        |PyPI| |bioconda| |Docs| |Build Status| |Coverage| |Code Style|
        
        .. |PyPI| image:: https://img.shields.io/pypi/v/scVI.svg
            :target: https://pypi.org/project/scvi
        .. |bioconda| image:: https://img.shields.io/badge/bioconda-blue.svg
            :target: http://bioconda.github.io/recipes/scvi/README.html
        .. |Docs| image:: https://readthedocs.org/projects/scvi/badge/?version=latest
            :target: https://scvi.readthedocs.io/en/latest/?badge=latest
            :alt: Documentation Status
        .. |Build Status| image:: https://travis-ci.org/YosefLab/scVI.svg?branch=master
            :target: https://travis-ci.org/YosefLab/scVI
        .. |Coverage| image:: https://codecov.io/gh/YosefLab/scVI/branch/master/graph/badge.svg
            :target: https://codecov.io/gh/YosefLab/scVI
        .. |Code Style| image:: https://img.shields.io/badge/code%20style-black-000000.svg
            :target: https://github.com/python/black
        
        
        Single-cell Variational Inference
        
        * Free software: MIT license
        * Documentation: https://scvi.readthedocs.io.
        
        
        Quick Start
        -----------
        
        0. If you intend to use parallel implementation of our hyperparameter tuning feature, install MongoDb_.
        
        .. _MongoDb: https://docs.mongodb.com/manual/installation/
        
        1. Install Python 3.7. 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 -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!
        
           a. Getting started:
               * `data loading`__
               * `basic usage`__
           b. Analyzing several datasets:
               * `harmonization`__
               * `annotation`__
           c. Advanced topics:
               * `interaction with scanpy`__
               * `linear decoder for gene interpretation`__
               * `reproducing results from the scVI paper`__
               * `imputation of unobserved gene expression (gimVI)`__
               * `hyperparameter tuning for scVI with our autotune module`__
        
        
        .. __: https://github.com/YosefLab/scVI/blob/master/tests/notebooks/data_loading.ipynb
        .. __: https://github.com/YosefLab/scVI/blob/master/tests/notebooks/basic_tutorial.ipynb
        .. __: https://github.com/YosefLab/scVI/blob/master/tests/notebooks/harmonization.ipynb
        .. __: https://github.com/YosefLab/scVI/blob/master/tests/notebooks/annotation.ipynb
        .. __: https://github.com/YosefLab/scVI/blob/master/tests/notebooks/scanpy_pbmc3k.ipynb
        .. __: https://github.com/YosefLab/scVI/blob/master/tests/notebooks/linear_decoder.ipynb
        .. __: https://github.com/YosefLab/scVI/blob/master/tests/notebooks/scVI_reproducibility.ipynb
        .. __: https://github.com/YosefLab/scVI/blob/master/tests/notebooks/gimvi_tutorial.ipynb
        .. __: https://github.com/YosefLab/scVI/blob/master/tests/notebooks/autotune_advanced_notebook.ipynb
        
        References
        ----------
        
        Romain Lopez, Jeffrey Regier, Michael Cole, Michael I. Jordan, Nir Yosef.
        **"Deep generative modeling for single-cell transcriptomics."**
        Nature Methods, 2018. `[pdf]`__
        
        .. __: https://rdcu.be/bdHYQ
        
        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]`__
        
        .. __: https://www.biorxiv.org/content/biorxiv/early/2019/01/29/532895.full.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]`__
        
        .. __: https://arxiv.org/pdf/1905.02269.pdf
        
        
        
        =======
        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 :: 3.7
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Provides-Extra: test
