Metadata-Version: 1.1
Name: gsea_api
Version: 0.2.5
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
        ===================
        
        |Build Status| |MIT License| |DOI|
        
        Pandas API for Gene Set Enrichment Analysis in Python (GSEApy, cudaGSEA,
        GSEA)
        
        -  aims to provide a unified API for various GSEA implementations; uses
           pandas DataFrames and a hierarchy of Pythonic classes.
        -  file exports (exporting input for GSEA) use low-level numpy functions
           and are much faster than in pandas
        -  aims to allow researchers to easily compare different implementations
           of GSEA, and to integrate those in projects which require
           high-performance GSEA (e.g. massive screening for drug-repositioning)
        -  provides useful utilities for work with GMT files, or gene sets and
           pathways in general in Python
        
        Example usage
        ~~~~~~~~~~~~~
        
        .. code:: python
        
           from pandas import read_table
           from gsea_api.expression_set import ExpressionSet
           from gsea_api.gsea import GSEADesktop
           from gsea_api.molecular_signatures_db import GeneSets
        
           reactome_pathways = GeneSets.from_gmt('ReactomePathways.gmt')
        
           gsea = GSEADesktop()
        
           design = ['Disease', 'Disease', 'Disease', 'Control', 'Control', 'Control']
           matrix = read_table('expression_data.tsv', index_col='Gene')
        
           result = gsea.run(
               # note: contrast() is not necessary in this simple case
               ExpressionSet(matrix, design).contrast('Disease', 'Control'),
               reactome_pathways,
               metric='Signal2Noise',
               permutations=1000
           )
        
        Where ``expression_data.tsv`` is in the following format:
        
        ::
        
           Gene    Patient_1   Patient_2   Patient_3   Patient_4   Patient_5   Patient_6
           TACC2   0.2 0.1 0.4 0.6 0.7 2.1
           TP53    2.3 0.2 2.1 2.0 0.3 0.6
        
        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
        
        Use it with:
        
        .. code:: python
        
           from gsea_api.gsea import GSEApy
        
           gsea = GSEApy()
        
        Installing cudaGSEA
        ^^^^^^^^^^^^^^^^^^^
        
        Please clone this fork of cudaGSEA to thirdparty directory and compile
        the binary version (using the instructions from `this
        repository <https://github.com/krassowski/cudaGSEA>`__):
        
        ::
        
           git clone https://github.com/krassowski/cudaGSEA
        
        or use `the original version <https://github.com/gravitino/cudaGSEA>`__,
        which does not implement FDR calculations.
        
        Use it with:
        
        .. code:: python
        
           from gsea_api.gsea import cudaGSEA
        
           # CPU implementation can be used with use_cpu=True
           gsea = cudaGSEA(fdr='full', use_cpu=False)
        
        Citation
        ~~~~~~~~
        
        |DOI|
        
        Please also cite the authors of the wrapped tools that you use.
        
        References
        ~~~~~~~~~~
        
        The initial version of this code was written for a `Master thesis
        project <https://github.com/krassowski/drug-disease-profile-matching>`__
        at Imperial College London.
        
        .. |Build Status| image:: https://travis-ci.com/krassowski/gsea-api.svg?branch=master
           :target: https://travis-ci.com/krassowski/gsea-api
        .. |MIT License| image:: https://img.shields.io/badge/license-MIT-blue.svg?style=flat
           :target: http://choosealicense.com/licenses/mit/
        .. |DOI| image:: https://zenodo.org/badge/188071398.svg
           :target: https://zenodo.org/badge/latestdoi/188071398
        
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
