Metadata-Version: 2.1
Name: scprep
Version: 1.1.0
Summary: scprep
Home-page: https://github.com/KrishnaswamyLab/scprep
Author: Scott Gigante, Daniel Burkhardt and Jay Stanley, Yale University
Author-email: krishnaswamylab@gmail.com
License: GNU General Public License Version 3
Download-URL: https://github.com/KrishnaswamyLab/scprep/archive/v1.1.0.tar.gz
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        `scprep` provides an all-in-one framework for loading, preprocessing, and plotting matrices in Python, with a focus on single-cell genomics.
        
        The philosophy of `scprep`:
        
        * Data shouldn't be hidden in a complex and bespoke class object. `scprep` works with `numpy` arrays, `pandas` data frames, and `scipy` sparse matrices, all of which are popular data formats in Python and accepted as input to most common algorithms.
        * Your analysis pipeline shouldn't have to change based on data format. Changing from a `numpy` array to a `pandas` data frame introduces endless technical differences (e.g. in indexing matrices). `scprep` provides data-agnostic methods that work the same way on all formats.
        * Simple analysis should mean simple code. `scprep` takes care of annoying edge cases and sets nice defaults so you don't have to.
        * Using a framework shouldn't be limiting. Because nothing is hidden from you, you have access to the power of `numpy`, `scipy`, `pandas` and `matplotlib` just as you would if you used them directly.
        
        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 :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.6
Provides-Extra: test
Provides-Extra: doc
Provides-Extra: optional
