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    "info": {
        "author": "Rani Powers (fork originally from gseapy by Zhuoqing Fang)",
        "author_email": "rani.powers@ucdenver.edu",
        "bugtrack_url": null,
        "classifiers": [
            "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 :: 2.7",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Software Development :: Libraries"
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        "description": "GSEA-InContext: Gene Set Enrichment Analysis In Context\n========\n\nGene Set Enrichment Analysis (GSEA) is routinely used to analyze and interpret coordinate changes in transcriptomics experiments. For an experiment where less than seven samples per condition are compared, GSEA employs a competitive null hypothesis to test significance. A gene set enrichment score is tested against a null distribution of enrichment scores generated from permuted gene sets, where genes are randomly selected from the input experiment. Looking across a variety of biological conditions, however, genes are not randomly distributed with many showing consistent patterns of up- or down-regulation. As a result, common patterns of positively and negatively enriched gene sets are observed across experiments. Placing a single experiment into the context of a relevant set of background experiments allows us to identify both the common and experiment-specific patterns of gene set enrichment. We developed the GSEA-InContext method to allow a user to account for gene expression patterns within a defined background set of experiments to identify statistically significantly enriched gene sets in their own experiment.\n\nSee below for examples on running the GSEA-InContext algorithm.\n\nThis repo is a fork of [GSEApy](https://github.com/BioNinja/GSEApy) (original documentation [here](http://gseapy.rtfd.io/). We have added a new tool ``GSEA_InContext`` which runs the GSEAPreranked algorithm but uses a background set of ranked lists to calculate an empirical null distribution for informing the permutation procedure. For examples using the original GSEApy library, [visit this page](http://gseapy.readthedocs.io/en/master/gseapy_example.html).\n\n\nAbout GSEA-InContext\n--------------------------------------------------------------------------------------------\n\nCurrently, there are no methods available for a user to easily compare their GSEA results to GSEA results obtained in other experiments to discern similar and/or distinct patterns affected across experiments. GSEA-InContext accounts for gene-specific variation estimated from an experimental background. Whereas GSEA identifies all signiificantly enriched gene sets in an experiment, this method allows the user to ask a complementary question; namely, which gene sets are uniquely enriched in a single experiment compared to many other, independent experiments.\n\nOur method applies the same approach as GSEA to calculate the nominal p-value. However, in contrast to GSEAPreranked, GSEA-InContext employs an alternative significance testing procedure to generate the null distribution, in which permuted gene sets are generated using the density of gene ranks estimated from a set of user-defined background experiments. We estimate a gene's probability density using a Gaussian kernel over the experiments in the background set.\n\nThe GSEA-InContext algorithm can be run using the ``incontext`` subcommand. Additional subcommands can be run as in the original GSEApy, including: ``gsea``, ``prerank``, ``ssgsea``, ``replot`` ``enrichr``. See the original `GSEApy <https://github.com/BioNinja/GSEApy>`_ repository.\n\nThe full ``GSEA`` is described in:\n[GSEA](http://www.broadinstitute.org/cancer/software/gsea/wiki/index.php/Main_Page) documentation. All file formats for GSEApy are identical to ``GSEA`` desktop version.\n\n\nData & Availability\n---------------------\n\nThe data, results and analysis described in [our preprint](https://www.biorxiv.org/content/early/2018/02/04/259440) are hosted in a Synapse project available [here](https://www.synapse.org/GSEA_InContext)(doi:10.7303/syn11804693).\n\n\nDependencies & Requirements\n--------------\n* Python 3.4+\n* Numpy >= 1.13.0\n* Pandas\n* Matplotlib\n* Beautifulsoup4\n* Requests (for enrichr API)\n\nYou may also need to install lxml and html5lib to parse xml files.\n\nRunning GSEApy and GSEA-InContext\n--------------------------------------------------------------------------------------------\n\nBefore you start:\n-----------------\n\nConvert all gene symbol names to uppercase. The ranked lists input to ``prerank`` or ``incontext`` can be supplied as file paths (.rnk) or a two-column Pandas DataFrame (columns ``gene_name`` and ``fold_change``). The background ranked lists input to ``incontext`` is supplied as a text file containing the list of .rnk files to use in permutation, or as a .csv file containing pre-permuted gene lists created with the ``make_background_dist()`` function.\n\n\nRun GSEAPY inside Python console:\n------------------\n\n| Running GSEAPreranked and GSEA-InContext in Python using file paths as input\n\n```\n\n    import gseapy\n\n    # Run GSEA Prerank\n    gseapy.prerank(rnk='gsea_data.rnk', gene_sets='gene_sets.gmt', outdir='out')\n\n    # Run GSEA-InContext\n    gseapy.incontext(rnk='gsea_data.rnk', gene_sets='gene_sets.gmt', backround_rnks = 'permuted_background.csv', outdir='out')\n```\n\nA full example can be seen in ``run_example.py``. The full analysis of Kegg and Hallmarks gene sets was run with ``run_all_442.py``.\n\n\nBug Reports\n------------------\n\nIf you would like to report any bugs when you running the ``incontext`` module, please create an issue on GitHub [here](https://github.com/CostelloLab/GSEA-InContext). For issues relating to other modules, you may wish to visit the[original GSEAPY repo](https://github.com/BioNinja/GSEApy).\n\n\n\n",
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        "keywords": "Gene Ontology,GO,Biology,Enrichment,Bioinformatics,Computational Biology",
        "license": "MIT",
        "maintainer": "",
        "maintainer_email": "",
        "name": "gsea-incontext",
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