Metadata-Version: 2.1
Name: scprep
Version: 0.10.0
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/v0.10.0.tar.gz
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
Requires-Dist: numpy (>=1.10.0)
Requires-Dist: scipy (>=0.18.0)
Requires-Dist: scikit-learn (>=0.19.1)
Requires-Dist: future
Requires-Dist: pandas (<0.24,>=0.19.0)
Requires-Dist: decorator
Provides-Extra: doc
Requires-Dist: sphinx ; extra == 'doc'
Requires-Dist: sphinxcontrib-napoleon ; extra == 'doc'
Requires-Dist: autodocsumm ; extra == 'doc'
Provides-Extra: test
Requires-Dist: nose ; extra == 'test'
Requires-Dist: nose2 ; extra == 'test'
Requires-Dist: fcsparser ; extra == 'test'
Requires-Dist: tables ; extra == 'test'
Requires-Dist: h5py ; extra == 'test'
Requires-Dist: matplotlib ; extra == 'test'
Requires-Dist: coverage ; extra == 'test'
Requires-Dist: coveralls ; extra == 'test'

=============
scprep
=============

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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

Usage example
-------------

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)

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

