Metadata-Version: 2.1
Name: scArchest
Version: 0.0.0
Summary: Transfer learning with Architecture Surgery on Single-cell data
Home-page: https://github.com/theislab/scarchesp
Author: 
Author-email: 
License: MIT
Description: |PyPI|
        
        scArches - single-cell architecture surgery
        =========================================================================
        .. raw:: html
        
         <img src="https://user-images.githubusercontent.com/33202701/89729020-15f7c200-da32-11ea-989b-1b9a3283f642.png" width="900px" align="center">
        
        This is a Pytorch version of scArches  which can be found `here <https://github.com/theislab/scArches/>`_. scArches is a package to integrate newly produced single-cell datasets into integrated reference atlases. Our method can facilitate large collaborative projects with decentralise training and integration of multiple datasets by different groups. scArches is compatible with `scanpy <https://scanpy.readthedocs.io/en/stable/>`_, and hosts efficient implementations of all conditional generative models for single-cell data.
        
        What can you do with scArches?
        -------------------------------
        - Integrate many single-cell datasets and share the trained model and the data (if possible).
        - Download a pre-trained model for your atlas of interest, update it with new datasets and share with your collaborators.
        - Construct a customized reference by downloading a reference atlas, add a few  pre-trained adaptors (datasets) and project your own data in to this customized reference atlas.
        - Project and integrate query datasets on the top of a reference and use latent representation for downstream tasks, e.g.: diff testing, clustering.
        
        Usage and installation
        -------------------------------
        See `here <https://scarchesp.readthedocs.io/>`_ for documentation and tutorials.
        
        Support and contribute
        -------------------------------
        If you have a question or new architecture or a model that could be integrated into our pipeline, you can
        post an `issue <https://github.com/theislab/scarchesp/issues/new>`__ or reach us by `email <mailto:cottoneyejoe.server@gmail.com,mo.lotfollahi@gmail.com,mohsen.naghipourfar@gmail.com>`_. Our package supports tf/keras now but pytorch version will be added very soon.
        
        
        Reference
        -------------------------------
        If scArches is useful in your research, please consider citing this `preprint <https://www.biorxiv.org/content/10.1101/2020.07.16.205997v1/>`_.
        
        
        .. |PyPI| image:: https://img.shields.io/pypi/v/scarches.svg
           :target: https://pypi.org/project/scarchesp
        
        .. |PyPIDownloads| image:: https://pepy.tech/badge/scarches
           :target: https://pepy.tech/project/scarchesp
        
        .. |Docs| image:: https://readthedocs.org/projects/scarches/badge/?version=latest
           :target: https://scarchesp.readthedocs.io
        
        .. |travis| image:: https://travis-ci.com/theislab/scarches.svg?branch=master
            :target: https://travis-ci.com/theislab/scarchesp
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Environment :: Console
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Description-Content-Type: text/markdown
