Metadata-Version: 2.1
Name: scprep
Version: 0.11.1
Summary: scprep
Home-page: https://github.com/KrishnaswamyLab/scprep
Author: Jay Stanley, Scott Gigante, and Daniel Burkhardt, Krishnaswamy Lab, Yale University
Author-email: krishnaswamylab@gmail.com
License: GNU General Public License Version 2
Download-URL: https://github.com/KrishnaswamyLab/scprep/archive/v0.11.1.tar.gz
Description: =============
        scprep
        =============
        
        .. image:: https://img.shields.io/pypi/v/scprep.svg
            :target: https://pypi.org/project/scprep/
            :alt: Latest PyPi version
        .. image:: https://anaconda.org/bioconda/scprep/badges/version.svg
            :target: https://anaconda.org/bioconda/scprep/
            :alt: Latest Conda version
        .. image:: https://api.travis-ci.com/KrishnaswamyLab/scprep.svg?branch=master
            :target: https://travis-ci.com/KrishnaswamyLab/scprep
            :alt: Travis CI Build
        .. image:: https://img.shields.io/readthedocs/scprep.svg
            :target: https://scprep.readthedocs.io/
            :alt: Read the Docs
        .. image:: https://coveralls.io/repos/github/KrishnaswamyLab/scprep/badge.svg?branch=master
            :target: https://coveralls.io/github/KrishnaswamyLab/scprep?branch=master
            :alt: Coverage Status
        .. image:: https://img.shields.io/twitter/follow/KrishnaswamyLab.svg?style=social&label=Follow
            :target: https://twitter.com/KrishnaswamyLab
            :alt: Twitter
        .. image:: https://img.shields.io/github/stars/KrishnaswamyLab/scprep.svg?style=social&label=Stars
            :target: https://github.com/KrishnaswamyLab/scprep/
            :alt: GitHub stars
        
        
        Tools for loading and preprocessing biological matrices in Python.
        
        Installation
        ------------
        
        preprocessing is available on `pip`. Install by running the following in a terminal::
        
            pip install --user scprep
        
        Alternatively, scprep 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 scprep
        
        Quick Start
        -----------
        
        You can use `scprep` with your single cell data as follows::
        
            import scprep
            # Load data
            data_path = "~/mydata/my_10X_data"
            data = scprep.io.load_10X(data_path)
            # Remove empty columns and rows
            data = scprep.filter.remove_empty_cells(data)
            data = scprep.filter.remove_empty_genes(data)
            # Filter by library size to remove background
            scprep.plot.plot_library_size(data, cutoff=500)
            data = scprep.filter.filter_library_size(data, cutoff=500)
            # Filter by mitochondrial expression to remove dead cells
            mt_genes = scprep.select.get_gene_set(data, starts_with="MT")
            scprep.plot.plot_gene_set_expression(data, genes=mt_genes, percentile=90)
            data = scprep.filter.filter_gene_set_expression(data, genes=mt_genes, 
                                                            percentile=90)
            # Library size normalize
            data = scprep.normalize.library_size_normalize(data)
            # Square root transform
            data = scprep.transform.sqrt(data)
        
        Examples
        --------
        
        * `Scatter plots <https://scprep.readthedocs.io/en/stable/examples/scatter.html>`_
        * `Jitter plots <https://scprep.readthedocs.io/en/stable/examples/jitter.html>`_
        
        Help
        ----
        
        If you have any questions or require assistance using scprep, please read the documentation at https://scprep.readthedocs.io/ or contact us at https://krishnaswamylab.org/get-help
Keywords: big-data,computational-biology
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: Topic :: Scientific/Engineering :: Bio-Informatics
Provides-Extra: test
Provides-Extra: doc
