Metadata-Version: 2.1
Name: scArches
Version: 0.3.0
Summary: Transfer learning with Architecture Surgery on Single-cell data
Home-page: https://github.com/theislab/scarches
Author: Marco Wagenstetter, Mohsen Naghipourfar, Mohammad Lotfollahi, Sergei Rybakov
Author-email: marco.wagenstetter@tum.de, naghipourfar@ce.sharif.edu, mohammad.lotfollahi@helmholtz-muenchen.de, sergei.rybakov@helmholtz-muenchen.de
License: MIT
Description: |PyPI| |PyPIDownloads| |Docs| |travis|
        
        scArches (PyTorch) - 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?
        -------------------------------
        - Construct single or multi-modal (CITE-seq) reference atlases and share the trained model and the data (if possible).
        - Download a pre-trained model for your atlas of interest, update it wih new datasets and share with your collaborators.
        - Project and integrate query datasets on the top of a reference and use latent representation for downstream tasks, e.g.:diff testing, clustering, classification
        
        
        What are different models?
        ---------------
        scArches is itself and algorithm to map to project query on the top of reference datasets and is applicable
        to different models. Here we provide a short explanation and hints when to use which model. Our models are divided into
        three categories:
        
        
        What are different models?
        ---------------
        scArches is itself and algorithm to map to project query on the top of reference datasets and is applicable
        to different models. Here we provide a short explanation and hints when to use which model. Our models are divided into
        three categories:
        
        Unsupervised
         This class of algortihms need no `cell type` labels, meaning that you can creat a reference and project a query without having access to cell type labeles.
         We implemented two algorithms:
        
         - **scVI**  (`Lopez et al.,2018 <https://www.nature.com/articles/s41592-018-0229-2>`_.): Requires access to raw counts values for data integration and assumes
         count distribution on the data (NB, ZINB, Poission).
        
         - **trVAE** (`Lotfollahi et al.,2019 <https://arxiv.org/abs/1910.01791>`_.): It supports both normalized log tranformed or count data as input and applies additional MMD loss to have better mearging in the latent space.
        
        Supervised and Semi-supervised
         This class of algorithmes assume the user has access to `cell type` labels when creating the reference data and usaully perfomr better integration
         compared to. unsupervised methods. However, the query data still can be unlabaled. In addition to integration , you can classify your query cells using
         these methods.
        
         - **scANVI** (`Xu et al.,2019 <https://www.biorxiv.org/content/10.1101/532895v1>`_.): It neeeds cell type labels for reference data. Your query data can be either   unlabeled or labeled. In case of unlabeled query data you can use this method to also classify your query cells using reference labels.
        
        Multi-modal
         These algorithms can be used to contstruct multi-modal references atlas and map query data from either modalities on the top of the reference.
        
         - **totalVI** (`Gayoso al.,2019 <https://www.biorxiv.org/content/10.1101/532895v1>`_.): This model can be used to build multi-modal  CITE-seq reference atalses.
           Query datasets can be either from sc-RNAseq or CITE-seq. In addition to integrating query with reference one can use this model to impute the Proteins
           in the query datasets.
        
        Usage and installation
        -------------------------------
        See `here <https://scarches.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/scarches/issues/new>`__ or reach us by `email <mailto:cottoneyejoe.server@gmail.com,mo.lotfollahi@gmail.com,mohsen.naghipourfar@gmail.com>`_.
        
        
        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/scarches
        
        .. |PyPIDownloads| image:: https://pepy.tech/badge/scarches
           :target: https://pepy.tech/project/scarches
        
        .. |Docs| image:: https://readthedocs.org/projects/scarches/badge/?version=latest
           :target: https://scarches.readthedocs.io
        
        .. |travis| image:: https://travis-ci.com/theislab/scarches.svg?branch=master
            :target: https://travis-ci.com/theislab/scarches
        
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
