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    "info": {
        "author": "Bo Gao",
        "author_email": "kaye_gao@hotmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Bio-Informatics"
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        "description": "Mecan4CNA\n=========\n\nA copy number profile usually needs to be calibrated for the position of\nbaseline (normal copy numbers) due to sample impurity and measurement\nbias. It\u2019s crucial to normalize CN profiles when comparing them in\nanalysis, because usually each profile has a different signal scale.\n\nMecan4CNA (Minimum Error Calibration and Normalization for Copy Number\nAnalysis) uses an algebraic method to estimate the baseline and the\ndistance between DNA levels (referred to as level distance). It can be\nused for both single file analysis and multi-file normalization.\n\nKey features:\n\n-  Calibration of a segmentation file, so that the signal of normal is\n   aligned to 2 (0 in log2)\n-  Estimation of the distance between DNA levels\n-  Normalizing multiple files to a uniformed signal scale, so that 3\n   (0.585 in log2) and 1 (-1 in log2) actually correspond to one copy\n   gain and one copy lost\n-  Needs only a segmentation file (from any platform)\n-  Detailed results and plots for in-depth analysis\n-  Fast\n\nHow to install\n--------------\n\nThe easiest way is to install through pip:\n\n::\n\n    pip install mecan4cna\n    mecan4cna --help\n\nHow to use\n----------\n\nSee the\n`manual <https://github.com/baudisgroup/mecan4cna/blob/master/manual.md>`__\nfor details.\n\nQuick start\n~~~~~~~~~~~\n\n::\n\n    mecan4can -i [SEGMENT_FILE] -o [OUTPUT_PATH]\n\nDemo mode\n~~~~~~~~~\n\n::\n\n    mecan4can --demo\n\nThis will copy 5 example files to the current directory and run with\ndefault settings. It invokes the ``run_mecan_example.sh`` script, which\nwill also be copied over and can be used as a template for customized\nanalysis.\n\nGeneral Usage\n~~~~~~~~~~~~~\n\n::\n\n    Usage: mecan4cna [OPTIONS]\n\n    Options:\n      -i, --input_file FILENAME       The input file.\n      -o, --output_path TEXT          The path for output files.\n      -n, --normalize                 Calibrate and normalize the input file.\n      -p, --plot                      Whether to save the signal histogram.\n      -b, --bins_per_interval INTEGER RANGE\n                                      The number of bins in each copy number\n                                      interval.\n      -v, --intervals INTEGER RANGE   The number of copy number intervals.\n      --demo                          Copy example files and run a demo script in\n                                      the current directory.\n      -pt, --peak_thresh INTEGER RANGE\n                                      The minimum probes of a peak.\n      -st, --segment_thresh INTEGER RANGE\n                                      The minimum probes of a segment.\n      --model_steps INTEGER RANGE     The incremental step size in modeling.\n      --mpd_coef FLOAT                Minimum Peak Distance coefficient in peak\n                                      detection.\n      --max_level_distance FLOAT      The maximum value of level distance.\n      --min_level_distance FLOAT      The minimum value of level distance.\n      --min_model_score INTEGER RANGE\n                                      The minimum value of the model score.\n      --info_lost_ratio_thresh FLOAT  The threshold of information lost ratio.\n      --info_lost_range_low FLOAT     The low end of information lost range.\n      --info_lost_range_high FLOAT    The high end of information lost range.\n      --ld_scaler FLOAT               The scaler of level distance in\n                                      normalization.\n      --help                          Show this message and exit.\n\nRequired options are:\n\n-  ``-i FILENAME``\n-  ``-o OUTPUTPATH``\n\nInput file format\n~~~~~~~~~~~~~~~~~\n\nThe input should be a segmentation file:\n\n-  have at least **5** columns\uff1aid, chromosome, start, end, probes and\n   value (in exact order, names do not matter). Any additional columns\n   will be ignored.\n-  the first line of the file is assumed to be column names, and will be\n   ignored. Do not put empty lines at the beginning of the file.\n-  be **tab separated**, without quoted values\n\nAn example:\n\n::\n\n    id  chro    start   end num_probes  seg_mean\n    GSM378022   1   775852  143752373   9992    0.025\n    GSM378022   1   143782024   214220966   6381    0.1607\n    GSM378022   2   88585000    144628991   4256    0.0131\n    GSM378022   2   144635510   146290468   146 0.1432\n    GSM378022   3   48603   8994748 1469    0.0544\n\nOutput files\n~~~~~~~~~~~~\n\n4 files will be created in the output path. If mecan fails to detect\nanything (not enough aberrant segments or no valid models), only the\nhistogram will be created:\n\n-  base_level.txt : contains the estimated baseline and level distance.\n-  histogram.pdf : a visual illustration of signal distributions.\n-  models.tsv : a tab separated table that details all information of\n   all models.\n-  peaks.tsv : a tab separated table shows the determined signal peaks\n   and their relative DNA levels compared to the baseline.\n\nCalibration and normalization\n-----------------------------\n\nWith the ``-n`` flag, the input file will be normalized and saved as\n``normalized.tsv``.\n\nImport as a python library\n--------------------------\n\n.. code:: python\n\n    import mecan4can.algorithms as alg\n    import mecan4can.common as comm\n\n    with open('examples\\segment_example_1.tsv', 'r') as fin:\n        segments = comm.file2list(fin)\n    m = alg.mecan()\n    r = m.run(segments)\n\nCommon problems\n---------------\n\nError of matplotlib\n~~~~~~~~~~~~~~~~~~~\n\nIt seems there is a bug in the latest version (3.0.3) of matplotlib,\nwhich may cause problems in OSX. Mecan uses an older verison of\nmatplotlib (2.0.2) to avoid this problem. If you need to use the latest\nversion and run into runtime problems, please check the following links.\n\n-  `matplotlib\n   documentation <https://matplotlib.org/faq/osx_framework.html>`__\n-  `matplotlib github\n   discussion <https://github.com/matplotlib/matplotlib/issues/13414>`__",
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