Metadata-Version: 1.1
Name: gsea_api
Version: 0.1.1
Summary: Pandas API for Gene Set Enrichment Analysis in Python (GSEApy, cudaGSEA, GSEA)
Home-page: https://github.com/krassowski/gsea-api
Author: Michal Krassowski
Author-email: krassowski.michal+pypi@gmail.com
License: MIT
Description: GSEA API for Pandas
        ===================
        
        Pandas API for Gene Set Enrichment Analysis in Python (GSEApy, cudaGSEA,
        GSEA)
        
        -  This Python wrapper around various GSEA implementations aims to
           provide a unified programming interface, built using the pandas
           DataFrames and a hierarchy of Pythonic classes.
        -  The file exports (providing input for GSEA) were written with
           performance in mind, using lower level numpy functions where
           necessary, thus are much faster than usual pandas-based exports.
        -  This project aims to allow scientists in the Python community to
           easily compare different implementations of GSEA, and to integrate
           those in projects which require high performance GSEA interface.
        -  The project is in work-in-progress state and scheduled to have a
           major refactor and a more complete documentation.
        
        Example usage
        ~~~~~~~~~~~~~
        
        .. code:: python
        
            from pandas import read_csv
            from gsea_api.expression_set import ExpressionSet
            from gsea_api.gsea import GSEADesktop
            from gsea_api.molecular_signatures_db import GeneMatrixTransposed
        
            reactome_pathways = GeneMatrixTransposed.from_gmt('ReactomePathways.gmt')
        
            gsea = GSEADesktop()
        
            design = ['Disease', 'Disease', 'Disease', 'Control', 'Control', 'Control']
            matrix = read_csv('expression_data.csv')
        
            result = gsea.run(
                # note: contrast() is not necessary in this simple case
                ExpressionSet(matrix, design).contrast('Disease', 'Control'),
                reactome_pathways,
                metric='Signal2Noise',
                permutations=1000
            )
        
        Installation
        ~~~~~~~~~~~~
        
        To install the API use:
        
        ::
        
            pip3 install gsea_api
        
        Installing GSEA from Broad Institute
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Login/register on `the official GSEA
        website <http://software.broadinstitute.org/gsea/login.jsp>`__ and
        download the ``gsea_3.0.jar`` file (or a newer version).
        
        Please place the downloaded file in the thirdparty directory.
        
        Installing GSEApy
        ^^^^^^^^^^^^^^^^^
        
        To use gsea.py please install it with:
        
        ::
        
            pip3 install gseapy
        
        and link its binary to the ``thirdparty`` directory
        
        ::
        
            ln -s virtual_environment_path/bin/gseapy thirdparty/gseapy
        
        Installing cudaGSEA
        ^^^^^^^^^^^^^^^^^^^
        
        Please clone this fork of cudaGSEA to thirdparty directory and compile
        the binary version:
        
        ::
        
            git clone https://github.com/krassowski/cudaGSEA
        
        or use `the original version <https://github.com/gravitino/cudaGSEA>`__,
        which does not implement FDR calculations.
        
        Citation
        ~~~~~~~~
        
        Please cite the authors of the wrapped tools that you use.
        
        References
        ~~~~~~~~~~
        
        The initial version of this code was written for my `Master thesis
        project <https://github.com/krassowski/drug-disease-profile-matching>`__
        at Imperial College London.
        
Keywords: gsea,gene,set,enrichment,cuda,pandas,api,GSEApy,cudaGSEA
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Topic :: Utilities
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
