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        "author": "Brendan Hosking",
        "author_email": "brendan.hosking@csiro.au",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: Other/Proprietary License",
            "Operating System :: POSIX",
            "Programming Language :: Python :: 3 :: Only",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "### bitepi\n\nbitepi is a python wrapper around the BitEpi project found at [https://github.com/aehrc/BitEpi](https://github.com/aehrc/BitEpi). It provides a pandas\ninterface for identification of epistasis interactions.\nIt exposes a single **Epistasis** class, through which the analysis can be\nperformed by calling **compute_epistasis**.\n\nInput is two arrays, which can be lists, numpy arrays or pandas dataframes.\n\nsample_array contains mappings of sample names to case (1) or control (0).\nNote that the header is ignored for numpy arrays and pandas dataframes, and\nshould not be present in python lists.\n\nsample | case/control\n--- | ---\nS1 | 0\nS2 | 1\nS3 | 1\nS4 | 0\nS5 | 0\n\ngenotype_array contains the genotypes of each sample at each SNP, with a 0, 1\nand 2 representing 0|0, 0|1 and 1|1 respectively. Headers are used to match\nsamples to the sample_array, but the first column's header is ignored.\n\nSNP | S1 | S2 | S3 | S4 | S5\n---|---|---|---|---|---\nsnpA | 0 | 0 | 2 | 1 | 0\nsnpB | 2 | 1 | 2 | 1 | 2\nsnpC | 0 | 1 | 1 | 2 | 1\nsnpD | 0 | 2 | 2 | 2 | 1\nsnpE | 1 | 1 | 1 | 0 | 1\n\nThe sets of samples do not need to match exactly, unless **Epistasis** is\ncalled with *strict_intersect=True*. If the sample sets do not match, analysis is done on the intersect.\n\nThe output will be a dictionary, with metric codes e.g. \"IG.1\" as the keys and\npandas dataframes as the values.\n\n```python\nimport bitepi\nsample_array = [\n    ['S1', 0],\n    ['S2', 1],\n    ['S3', 1],\n    ['S4', 0],\n    ['S5', 0],\n]\ngenotype_array = [\n    ['SNP', 'S1', 'S2', 'S3', 'S4', 'S5'],\n    ['snpA', 0, 0, 2, 1, 2],\n    ['snpB', 2, 1, 2, 1, 2],\n    ['snpC', 0, 0, 1, 1, 1],\n    ['snpD', 2, 2, 1, 2, 1],\n    ['snpE', 0, 1, 1, 1, 2],\n]\nepistasis = bitepi.Epistasis(\n    genotype_array=genotype_array,\n    sample_array=sample_array,\n)\ninteractions = epistasis.compute_epistasis(\n    sort=True,\n    best_ig=True,\n)['best_ig']\n\nprint(interactions)\n```\n\nThis should return:\n\n```\n    SNP     SNP_P    PAIR_P  TRIPLET_P  QUADLET_P    SNP_IG   PAIR_IG  TRIPLET_IG  QUADLET_IG  PAIR TRIPLET_1 TRIPLET_2 QUADLET_1 QUADLET_2 QUADLET_3\n0  snpA  1.707692  2.000000   2.109091        0.0  1.187692  0.266667    0.109091         0.0  snpE      snpB      snpE      snpA      snpA      snpA\n1  snpB  1.642424  1.909091   2.109091        0.0  1.122424  0.266667    0.200000         0.0  snpC      snpC      snpE      snpA      snpA      snpA\n2  snpC  1.641026  1.909091   2.109091        0.0  1.121026  0.266667    0.200000         0.0  snpB      snpD      snpE      snpA      snpA      snpA\n3  snpD  1.642424  1.909091   2.109091        0.0  1.122424  0.175758    0.200000         0.0  snpE      snpC      snpE      snpA      snpA      snpA\n4  snpE  1.733333  2.000000   2.109091        0.0  1.213333  0.266667    0.200000         0.0  snpA      snpC      snpD      snpA      snpA      snpA\n\n```\n\nFor higher order interactions (p3, ig3, p4 and ig4) Epistasis may take several\nminutes to run, depending on the number of SNPs. If more information is\nrequired when running, the logging level can be increased to `logging.INFO`\nor `logging.DEBUG`. `logging.DEBUG` will provide the greatest detail, including\nlogging from within the binary.\n\n```python\nimport logging\nlogging.root.setLevel(logging.DEBUG)\n```",
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        "keywords": "BIONINFORMATICS,EPISTASIS",
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