Metadata-Version: 2.1
Name: pypairs
Version: 3.2.3
Summary: A python scRNA-Seq classifier
Home-page: https://github.com/rfechtner/pypairs
Author: Ron Fechtner
Author-email: ronfechtner@gmail.com
License: BSD
Description: | |PyPI| |bioconda| |Build Status| |Docs|  
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        PyPairs - A python scRNA-Seq classifier
        =======================================
        
        This is a python-reimplementation of the Pairs algorithm as described by A. Scialdone et. al. (2015).
        Original Paper available under: https://doi.org/10.1016/j.ymeth.2015.06.021
        
        A supervised maschine learning algorithm aiming to classify single cells based on their transcriptomic signal.
        Initially created to predict cell cycle phase from scRNA-Seq data, this algorithm can be used for various applications.
        
        Build to be fully compatible with `Scanpy <https://scanpy.readthedocs.io/en/stable/>`_. For more details see the
        `full documentation <https://pypairs.readthedocs.io/en/latest/>`_.
        
        Getting Started
        ---------------
        
        Note: Version 3 still under development.
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        
        Installation
        ~~~~~~~~~~~~
        
        This package is hosted at PyPi ( https://pypi.org/project/pypairs/ ) and can be installed on any system running
        Python3 via pip with::
        
            pip install pypairs
        
        Alternatively, pypairs can be installed using `Conda <https://conda.io/docs/>`_ (most easily obtained via the
        `Miniconda Python distribution <https://conda.io/miniconda.html>`_::
        
            conda install -c bioconda pypairs
        
        Minimal Example
        ~~~~~~~~~~~~~~~
        
        Assuming you have two scRNA count files (csv, columns = samples, rows = genes) and one annotation file (csv, no header,
        two rows: "gene, class") a minimal example would look like this
        
        .. code:: python
        
            from pypairs import pairs, datasets
        
            # Load samples from the oscope scRNA-Seq dataset with known cell cycle
            training_data = datasets.leng15(mode='sorted')
        
            # Run sandbag() to identify marker pairs
            marker_pairs = pairs.sandbag(training_data, fraction=0.6)
        
            # Load samples from the oscope scRNA-Seq dataset without known cell cycle
            testing_data = datasets.leng15(mode='unsorted')
        
            # Run cyclone() score and predict cell cycle classes
            result = pairs.cyclone(testing_data, marker_pairs)
        
            # Further downstream analysis
            print(result)
        
        Core Dependencies
        -----------------
        
        - `Numpy <http://www.numpy.org/>`_
        - `Numba <https://numba.pydata.org/>`_
        - `Pandas <https://pandas.pydata.org/>`_
        - `AnnData <https://github.com/theislab/anndata>`_
        
        Authors
        -------
        
        * **Antonio Scialdone** - *original algorithm*
        * **Ron Fechtner** - *implementation and extension in Python*
        
        License
        -------
        
        This project is licensed under the BSD 3-Clause License - see the `LICENSE <LICENSE>`_ file for details
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Framework :: Jupyter
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Visualization
Requires-Python: >=3.5
Provides-Extra: plotting
Provides-Extra: scanpy
