{
    "info": {
        "author": "Mike Dacre",
        "author_email": "mike.dacre@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Environment :: Console",
            "Intended Audience :: End Users/Desktop",
            "Intended Audience :: System Administrators",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: MacOS :: MacOS X",
            "Operating System :: POSIX",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Utilities"
        ],
        "description": "###############\nEnrich p-values\n###############\n\nA simple script to compare p-values between a test and comparison dataset at a\nvariety of p-value cutoffs. By plotting the enrichment score at a variety of\ncutoffs, it is possible to pick the optimal cutoff for your data.\n\nVersion: 1.0-beta2\n\n.. image:: https://github.com/TheFraserLab/enrich_pvalues/raw/master/enrich_score.gif\n\n.. contents:: **Contents**\n\nAlgorithm\n=========\n\nFor each p-value in the interval between ``max_pval`` (default: 0.05) and\n``min_pval`` (default: 1e-15), we test at intervals of 1 and 5 for each order of\nmagnitute, e.g. 0.05, 0.01, 0.005, 0.001, 5e-4, 1e-4, 5e-5, 1e-6, ... 1e-15.\n\nTo test, we simply take all identities with a p-value less than the cutoff and\ncompare them to all identities in the comparison set with p-values below the\n``comp_set_pvalue``. We simply ask what percentage or the test set are in the\ncomparison set. We then do exactly the same with the entire set of identities in\nthe comparison set that have a p-value greater than 0.98.\n\nThe identities are generally going to be gene or SNP names, but they can be\nanything (e.g. coordinates) as long as they overlap in the test and comparison\ndata.\n\nInstallation\n============\n\nInstall via PyPI:\n\n.. code:: shell\n\n    pip install enrich_pvalues\n\nOr install from github:\n\n.. code:: shell\n\n    pip install https://github.com/TheFraserLab/enrich_pvalues/tarball/master\n\nIt should work with python 2 or 3, but python 3 is recommended.\n\nRequirements\n------------\n\nIn ``requirements.txt``, we use numpy, pandas, matplotlib, seaborn, tabulate,\nand tqdm.\n\nUsage\n=====\n\nFirst, dump a configuration file to describe your data:\n\n.. code:: shell\n\n   enrich_pvalues dump-config enrich_atac.json\n\nThis will also print a help table describing each option. You need to describe\nyour comparison data and your test data, and pick your p-value thresholds.\n\nNext, split your comparison dataset into two tables: significant, and\nnot-significant:\n\n.. code:: shell\n\n   enrich_pvalues split -c enrich_atac.json --prefix atac /path/to/comp_data.txt.gz\n\nNow, run the enrichment using those two tables and your test data:\n\n.. code:: shell\n\n   enrich_pvalues run -c enrich_atac.json -o atac_scores.xls -p atac /path/to/test_data.txt\n\nNote, the second to last argument is the prefix from the second step.\n\nFinally, plot the data. This can also be done by passing e.g. ``--plot myplot.png``\nto the run step.\n\n.. code:: shell\n\n   enrich_pvalues plot --prefix caQTL atac_scores.xls atac_plot.pdf\n\nNote: the scores can be excel format, pickled format, or text format, depending\non the suffix. Also, the prefix in this plot step is different, it is used to\ntitle the plot only, and so can be whatever you want.\n",
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        "keywords": "statistics p-values biology molecular-biology console",
        "license": "MIT",
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        "name": "enrich_pvalues",
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        "summary": "Compare one dataset to another at a variety of p-value cutoffs",
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