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        "author": "Greene Lab",
        "author_email": "team@greenelab.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "PathCORE-T\n----------\nPython 3 implementation of methods described in\n`Chen et al.'s 2017 PathCORE-T paper <https://doi.org/10.1101/147645>`_.\n\nNote that this software was renamed from PathCORE to PathCORE-T in Oct 2017.\nThe T specifies that pathway co-occurrence relationships are identified using\nfeatures extracted from **transcriptomic** data. \nThe module itself is still named `pathcore` to maintain backwards\ncompatibility for users of the original PathCORE software package. \n\nThis code has been tested on Python 3.5.\nThe documentation for the modules in the package can be\n`accessed here <http://pathcore-demo.herokuapp.com/static/data/docs_pathcore/index.html>`_.\n\nInstallation\n----------------\nTo install the current PyPI version (recommended), run::\n\n    pip install PathCORE-T\n\nFor the latest GitHub version, run::\n\n    pip install git+https://github.com/greenelab/PathCORE-T.git#egg=PathCORE-T\n\nExamples\n---------\nWe recommend that users of the PathCORE-T software begin by reviewing the\nexamples in the `PathCORE-T-analysis <https://github.com/greenelab/PathCORE-T-analysis>`_\nrepository. The analysis repository contains shell scripts and wrapper\nanalysis scripts that demonstrate how to run the methods in this package\non features constructed from a broad compendium according to the \n`workflow we describe in our paper <https://github.com/greenelab/PathCORE-T-analysis#the-pathcore-analysis-workflow>`_.\n\nSpecifically, `this Jupyter notebook <https://github.com/greenelab/PathCORE-T-analysis/blob/master/jupyter-notebooks/Supplemental_PAO1_FastICA_example.ipynb>`_\nis a simple example of the workflow and a great place to start.\n\nPackage contents\n----------------\n\n=====================================\nfeature_pathway_overrepresentation.py\n=====================================\nThe methods in this module are used to identify the pathways\noverrepresented in features extracted from a transcriptomic dataset\nof genes-by-samples. Features must preserve the genes in the dataset\nand assign weights to these genes based on some distribution.\n[`feature_pathway_overrepresentation documentation. <http://pathcore-demo.herokuapp.com/static/data/docs_pathcore/source/pathcore.html#module-pathcore.feature_pathway_overrepresentation>`_]\n\n===========\nnetwork.py\n===========\nContains the data structure ``CoNetwork`` that stores information\nabout the pathway co-occurrence network. The output from\na pathway enrichment analysis in ``feature_pathway_overrepresentation.py``\nserves as input into the ``CoNetwork`` constructor.\n[`CoNetwork documentation. <http://pathcore-demo.herokuapp.com/static/data/docs_pathcore/source/pathcore.html#module-pathcore.network>`_]\n\n============================\nnetwork_permutation_test.py\n============================\nThe methods in this module are used to filter the constructed\nco-occurence network. We implement a permutation test that evaluates\nand removes edges (pathway-pathway relationships) in the network\nthat cannot be distinguished from a null model of random associations.\nThe null model is created by generating *N* permutations of the network.\n[`network_permutation_test documentation. <http://pathcore-demo.herokuapp.com/static/data/docs_pathcore/source/pathcore.html#module-pathcore.network_permutation_test>`_]\n\nAcknowledgements\n----------------\nThis work was supported by the Penn Institute for Bioinformatics",
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