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        "author": "Tavi Nathanson",
        "author_email": "tavi@hammerlab.org",
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            "Development Status :: 3 - Alpha",
            "Environment :: Console",
            "Intended Audience :: Science/Research",
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            "Operating System :: OS Independent",
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        "description": "|PyPI| |Build Status| |Coverage Status|\n\nCohorts\n=======\n\nCohorts is a library for analyzing and plotting clinical data, mutations\nand neoepitopes in patient cohorts.\n\nIt calls out to external libraries like\n`topiary <https://github.com/hammerlab/topiary>`__ and caches the\nresults for easy manipulation.\n\nCohorts requires Python 3 (3.3+). We are no longer maintaining\ncompatability with Python 2. For context, see this `Python 3\nstatement <www.python3statement.org>`__.\n\nInstallation\n------------\n\nYou can install Cohorts using\n`pip <https://pip.pypa.io/en/latest/quickstart.html>`__:\n\n.. code:: bash\n\n    pip install cohorts\n\nFeatures\n--------\n\n-  Data management: construct a ``Cohort`` consisting of ``Patient``\\ s\n   with ``Sample``\\ s.\n-  Use ``varcode`` and ``topiary`` to generate and cache variant effects\n   and predicted neoantigens.\n-  Provenance: track the state of the world (package and data versions)\n   for a given analysis.\n-  Aggregation functions: built-in functions such as\n   ``missense_snv_count``, ``neoantigen_count``,\n   ``expressed_neoantigen_count``; or create your own functions.\n-  Plotting: survival curves via ``lifelines``, response/no response\n   plots (with Mann-Whitney and Fisher\u2019s Exact results), ROC curves.\n   Example: ``cohort.plot_survival(on=missense_snv_count, how=\"pfs\")``.\n-  Filtering: filter collections of variants/effects/neoantigens by, for\n   example, variant statistics.\n-  Pre-define data sets to work with. Example:\n   ``cohort.as_dataframe(join_with=[\"tcr\", \"pdl1\"])``.\n\nIn addition, several other libraries make use of ``cohorts``: \\*\n`pygdc <http://github.com/hammerlab/pygdc>`__ \\*\n`query_tcga <http://github.com/jburos/query_tcga>`__\n\nQuick Start\n-----------\n\nOne way to get started using Cohorts is to use it to analyze TCGA data.\n\nAs an example, we can create a cohort using\n`query_tcga <http://github.com/jburos/query_tcga>`__:\n\n.. code:: python\n\n    from query_tcga import cohort, config\n\n    # provide authentication token\n    config.load_config('config.ini')\n\n    # load patient data\n    blca_patients = cohort.prep_patients(project_name='TCGA-BLCA',\n                                         project_data_dir='data')\n\n    # create cohort\n    blca_cohort = cohort.prep_cohort(patients=blca_patients,\n                                     cache_dir='data-cache')\n\nThen, use ``plot_survival()`` to summarize a potential biomarker (e.g.\n``snv_count``) by survival:.\n\n.. code:: python\n\n    from cohorts.functions import snv_count\n    blca_cohort.plot_survival(snv_count, how='os', threshold='median')\n\nWhich should produce a summary of results including this plot:\n\n.. figure:: /docs/survival_plot_example.png\n   :alt: Survival plot example\n\n   Survival plot example\n\nWe could alternatively use ``plot_benefit()`` to summarize OS>12mo\ninstead of survival:\n\n.. code:: python\n\n    blca_cohort.plot_benefit(snv_count)\n\n.. figure:: /docs/benefit_plot_example.png\n   :alt: Benefit plot example\n\n   Benefit plot example\n\nSee the full example in the `quick-start\nnotebook <http://nbviewer.jupyter.org/github/hammerlab/tcga-blca/blob/master/Quick-start%20-%20using%20Cohorts%20with%20TCGA%20data.ipynb>`__\n\nBuilding from Scratch\n---------------------\n\n.. code:: python\n\n    patient_1 = Patient(\n        id=\"patient_1\",\n        os=70,\n        pfs=24,\n        deceased=True,\n        progressed=True,\n        benefit=False\n    )\n        \n    patient_2 = Patient(\n        id=\"patient_2\",\n        os=100,\n        pfs=50,\n        deceased=False,\n        progressed=True,\n        benefit=False\n    )\n\n    cohort = Cohort(\n        patients=[patient_1, patient_2],\n        cache_dir=\"/where/cohorts/results/get/saved\"\n    )\n\n    cohort.plot_survival(on=\"os\")\n\n.. code:: python\n\n    sample_1_tumor = Sample(\n        is_tumor=True,\n        bam_path_dna=\"/path/to/dna/bam\",\n        bam_path_rna=\"/path/to/rna/bam\"\n    )\n\n    patient_1 = Patient(\n        id=\"patient_1\",\n        ...\n        snv_vcf_paths=[\"/where/my/mutect/vcfs/live\",\n                       \"/where/my/strelka/vcfs/live\"]\n        indel_vcfs_paths=[...],\n        tumor_sample=sample_1_tumor,\n        ...\n    )\n\n    cohort = Cohort(\n        ...\n        patients=[patient_1]\n    )\n\n.. |PyPI| image:: https://img.shields.io/pypi/v/cohorts.svg?maxAge=21600\n   :target: \n.. |Build Status| image:: https://travis-ci.org/hammerlab/cohorts.svg?branch=master\n   :target: https://travis-ci.org/hammerlab/cohorts\n.. |Coverage Status| image:: https://coveralls.io/repos/hammerlab/cohorts/badge.svg?branch=master&service=github\n   :target: https://coveralls.io/github/hammerlab/cohorts?branch=master",
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