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        "author": "Zhuoqing Fang",
        "author_email": "fzq518@gmail.com",
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            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Software Development :: Libraries"
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        "description": "GSEAPY\n========\n\nGSEAPY: Gene Set Enrichment Analysis in Python.\n------------------------------------------------\n\n.. image:: https://badge.fury.io/py/gseapy.svg\n    :target: https://badge.fury.io/py/gseapy\n\n.. image:: https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat-square\n    :target: http://bioconda.github.io\n\n.. image:: https://travis-ci.org/zqfang/GSEApy.svg?branch=master\n    :target: https://travis-ci.org/zqfang/GSEApy\n\n.. image:: http://readthedocs.org/projects/gseapy/badge/?version=master\n    :target: http://gseapy.readthedocs.io/en/master/?badge=master\n    :alt: Documentation Status\n\n.. image:: https://img.shields.io/badge/license-MIT-blue.svg\n    :target:  https://img.shields.io/badge/license-MIT-blue.svg\n\n.. image:: https://img.shields.io/pypi/pyversions/gseapy.svg   \n    :alt: PyPI - Python Version\n\n\n\nFor examples of using GSEApy please click here: `Example <http://gseapy.readthedocs.io/en/master/gseapy_example.html>`_\n\n**Release notes** : https://github.com/zqfang/GSEApy/releases\n\n**FAQ**: `wiki <https://github.com/zqfang/GSEApy/wiki/FAQ>`_\n\n\nGSEAPY is a python wrapper for **GSEA** and **Enrichr**.\n--------------------------------------------------------------------------------------------\n\nGSEAPY can be used for **RNA-seq, ChIP-seq, Microarry** data. It can be used for convenient GO enrichment and to produce **publication quality figures** in python.\n\n\nGSEAPY has six sub-commands available: ``gsea``, ``prerank``, ``ssgsea``, ``replot`` ``enrichr``, ``biomart``.\n\n\n:gsea:    The ``gsea`` module produces `GSEA  <http://www.broadinstitute.org/cancer/software/gsea/wiki/index.php/Main_Page>`_ results.  The input requries a txt file(FPKM, Expected Counts, TPM, et.al), a cls file, and gene_sets file in gmt format.\n:prerank: The ``prerank`` module produces **Prerank tool** results.  The input expects a pre-ranked gene list dataset with correlation values, provided in .rnk format, and gene_sets file in gmt format.  ``prerank`` module is an API to `GSEA` pre-rank tools.\n:ssgsea: The ``ssgsea`` module performs **single sample GSEA(ssGSEA)** analysis.  The input expects a pd.Series (indexed by gene name), or a pd.DataFrame (include ``GCT`` file) with expression values and a ``GMT`` file. For multiple sample input, ssGSEA reconigzes gct format, too. ssGSEA enrichment score for the gene set is described by `D. Barbie et al 2009 <http://www.nature.com/nature/journal/v462/n7269/abs/nature08460.html>`_.\n:replot: The ``replot`` module reproduce GSEA desktop version results.  The only input for GSEApy is the location to ``GSEA`` Desktop output results.\n:enrichr: The ``enrichr`` module enable you perform gene set enrichment analysis using ``Enrichr`` API. Enrichr is open source and freely available online at: http://amp.pharm.mssm.edu/Enrichr . It runs very fast.\n:biomart: The ``biomart`` module helps you convert gene ids using BioMart API.\n\n\nPlease use 'gseapy COMMAND -h' to see the detail description for each option of each module.\n\n\nThe full ``GSEA`` is far too extensive to describe here; see\n`GSEA  <http://www.broadinstitute.org/cancer/software/gsea/wiki/index.php/Main_Page>`_ documentation for more information. All files' formats for GSEApy are identical to ``GSEA`` desktop version.\n\n\n**If you use gseapy in your research, you should cite the original ``GSEA`` and ``Enrichr`` paper.**\n\nWhy GSEAPY\n-----------------------------------------------------\n\nI would like to use Pandas to explore my data, but I did not find a convenient tool to\ndo gene set enrichment analysis in python. So, here are my reasons:\n\n* **Ability to run inside python interactive console without having to switch to R!!!**\n* User friendly for both wet and dry lab users.\n* Produce or reproduce publishable figures.\n* Perform batch jobs easy.\n* Easy to use in bash shell or your data analysis workflow, e.g. snakemake.\n\n\nGSEAPY ``Prerank`` module output\n-----------------------------------------------\nUsing the same data from ``GSEA``, GSEAPY reproduce the example above.\n\nUsing ``Prerank`` or ``replot`` module will reproduce the same figure for GSEA Java desktop outputs\n\n.. figure:: docs/gseapy_OCT4_KD.png\n\n\n\n\n\n   Generated by GSEAPY\n\n   **GSEAPY figures are supported by all matplotlib figure formats.**\n\n   You can modify ``GSEA`` plots easily in .pdf files. Please Enjoy.\n\n\nInstallation\n------------\n\n| Install gseapy package from bioconda or pypi.\n\n\n.. code:: shell\n\n   # if you have conda\n   $ conda install -c conda-forge -c bioconda gseapy\n\n   # or use pip to install the latest release\n   $ pip install gseapy\n\n| You may instead want to use the development version from Github, by running\n\n.. code:: shell\n\n   $ pip install git+git://github.com/zqfang/gseapy.git#egg=gseapy\n\nDependency\n--------------\n* Python 3.5+\n\nMandatory\n~~~~~~~~~\n\n* Numpy >= 1.13.0\n* Scipy\n* Pandas\n* Matplotlib\n* Beautifulsoup4\n* Requests (for Enrichr API)\n* Bioservices (for BioMart API)\n\nYou may also need to install **lxml, html5lib**, if you could not parse xml files.\n\n\n\nRun GSEAPY\n-----------------\n\nBefore you start:\n~~~~~~~~~~~~~~~~~~~~~~\n\nUnless you know exactly how GSEA works, you should **convert all gene symbol names to uppercase first.**\n\n\nFor command line usage:\n~~~~~~~~~~~~~~~~~~~~~~~\n\n.. code:: bash\n\n\n  # An example to reproduce figures using replot module.\n  $ gseapy replot -i ./Gsea.reports -o test\n\n\n  # An example to run GSEA using gseapy gsea module\n  $ gseapy gsea -d exptable.txt -c test.cls -g gene_sets.gmt -o test\n\n  # An example to run Prerank using gseapy prerank module\n  $ gseapy prerank -r gsea_data.rnk -g gene_sets.gmt -o test\n\n  # An example to run ssGSEA using gseapy ssgsea module\n  $ gseapy ssgsea -d expression.txt -g gene_sets.gmt -o test\n\n  # An example to use enrichr api\n  # see details of -g below, -d  is optional\n  $ gseapy enrichr -i gene_list.txt -g KEGG_2016 -d pathway_enrichment -o test\n\n\n\nRun gseapy inside python console:\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n1. Prepare expression.txt, gene_sets.gmt and test.cls required by GSEA, you could do this\n\n.. code:: python\n\n    import gseapy\n\n    # run GSEA.\n    gseapy.gsea(data='expression.txt', gene_sets='gene_sets.gmt', cls='test.cls', outdir='test')\n\n    # run prerank\n    gseapy.prerank(rnk='gsea_data.rnk', gene_sets='gene_sets.gmt', outdir='test')\n\n    # run ssGSEA\n    gseapy.ssgsea(data=\"expression.txt\", gene_sets= \"gene_sets.gmt\", outdir='test')\n\n\n    # An example to reproduce figures using replot module.\n    gseapy.replot(indir='./Gsea.reports', outdir='test')\n\n\n2. If you prefer to use Dataframe, dict, list in interactive python console, you could do this.\n\nsee detail here: `Example <http://gseapy.readthedocs.io/en/master/gseapy_example.html>`_\n\n.. code:: python\n\n\n    # assign dataframe, and use enrichr library data set 'KEGG_2016'\n    expression_dataframe = pd.DataFrame()\n\n    sample_name = ['A','A','A','B','B','B'] # always only two group,any names you like\n\n    # assign gene_sets parameter with enrichr library name or gmt file on your local computer.\n    gseapy.gsea(data=expression_dataframe, gene_sets='KEGG_2016', cls= sample_names, outdir='test')\n\n    # using prerank tool\n    gene_ranked_dataframe = pd.DataFrame()\n    gseapy.prerank(rnk=gene_ranked_dataframe, gene_sets='KEGG_2016', outdir='test')\n\n    # using ssGSEA\n    gseapy.ssgsea(data=ssGSEA_dataframe, gene_sets='KEGG_2016', outdir='test')\n\n\n3. For ``enrichr`` , you could assign a list, pd.Series, pd.DataFrame object, or a txt file (should be one gene name per row.)\n\n.. code:: python\n\n    # assign a list object to enrichr\n    gl = ['SCARA3', 'LOC100044683', 'CMBL', 'CLIC6', 'IL13RA1', 'TACSTD2', 'DKKL1', 'CSF1',\n         'SYNPO2L', 'TINAGL1', 'PTX3', 'BGN', 'HERC1', 'EFNA1', 'CIB2', 'PMP22', 'TMEM173']\n\n    gseapy.enrichr(gene_list=gl, description='pathway', gene_sets='KEGG_2016', outdir='test')\n\n    # or a txt file path.\n    gseapy.enrichr(gene_list='gene_list.txt', description='pathway', gene_sets='KEGG_2016',\n                   outdir='test', cutoff=0.05, format='png' )\n\n\nGSEAPY supported gene set libaries :\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nTo see the full list of gseapy supported gene set libraries, please click here: `Library <http://amp.pharm.mssm.edu/Enrichr/#stats>`_\n\nOr use ``get_library_name`` function inside python console.\n\n.. code:: python\n\n    #see full list of latest enrichr library names, which will pass to -g parameter:\n    names = gseapy.get_library_name()\n\n    # show top 20 entries.\n    print(names[:20])\n\n\n   ['Genome_Browser_PWMs',\n   'TRANSFAC_and_JASPAR_PWMs',\n   'ChEA_2013',\n   'Drug_Perturbations_from_GEO_2014',\n   'ENCODE_TF_ChIP-seq_2014',\n   'BioCarta_2013',\n   'Reactome_2013',\n   'WikiPathways_2013',\n   'Disease_Signatures_from_GEO_up_2014',\n   'KEGG_2016',\n   'TF-LOF_Expression_from_GEO',\n   'TargetScan_microRNA',\n   'PPI_Hub_Proteins',\n   'GO_Molecular_Function_2015',\n   'GeneSigDB',\n   'Chromosome_Location',\n   'Human_Gene_Atlas',\n   'Mouse_Gene_Atlas',\n   'GO_Cellular_Component_2015',\n   'GO_Biological_Process_2015',\n   'Human_Phenotype_Ontology',]\n\n\n\n\nBug Report\n~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nIf you would like to report any bugs when use gseapy, don't hesitate to create an issue on github here.\n\n\nTo get help of GSEApy\n------------------------------------\n\n1. See the wiki page: https://github.com/zqfang/GSEApy/wiki/FAQ\n\n2. Visit the document site at http://gseapy.rtfd.io/",
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