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        "author": "Matthew The, Lukas K\u00e4ll, KTH",
        "author_email": "matthew.the@scilifelab.se",
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        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
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        "description": "Triqler: TRansparent Identification-Quantification-Linked Error Rates\n=====================================================================\n\nRequirements\n------------\n\nPython 2 or 3 installation\n\nPackages needed:\n\n- numpy 1.12+\n- scipy 0.17+\n\nInstallation via ``pip``\n************************\n\n::\n\n  pip install triqler\n\nInstallation from source\n************************\n\n::\n\n  git clone https://github.com/statisticalbiotechnology/triqler.git\n  cd triqler\n  pip install .\n\nUsage\n-----\n\n::\n\n  usage: python -m triqler [-h] [--out_file OUT] [--fold_change_eval F]\n                     [--decoy_pattern P] [--min_samples N] [--num_threads N]\n                     [--ttest]\n                     IN_FILE\n\n  positional arguments:\n    IN_FILE               List of PSMs with abundances (not log transformed!)\n                          and search engine score. See README for a detailed\n                          description of the columns.\n\n  optional arguments:\n    -h, --help            show this help message and exit\n    --out_file OUT        Path to output file (writing in TSV format). N.B. if\n                          more than 2 treatment groups are present, suffixes\n                          will be added before the file extension. (default:\n                          proteins.tsv)\n    --fold_change_eval F  log2 fold change evaluation threshold. (default: 1.0)\n    --decoy_pattern P     Prefix for decoy proteins. (default: decoy_)\n    --min_samples N       Minimum number of samples a peptide needed to be\n                          quantified in. (default: 2)\n    --num_threads N       Number of threads, by default this is equal to the\n                          number of CPU cores available on the device. (default:\n                          auto detect)\n    --ttest               Use t-test for evaluating differential expression\n                          instead of posterior probabilities. (default: False)\n\nExample\n-------\n\nA sample file ``iPRG2016.tsv`` is provided in the ``example`` folder. You can\nrun Triqler on this file by running the following command:\n\n::\n\n  python -m triqler --fold_change_eval 0.8 example/iPRG2016.tsv\n\nInterface\n---------\n\nThe simplest input format is a tab-separated file consisting of a header line \nfollowed by one PSM per line in the following format:\n\n::\n\n  run <tab> condition <tab> charge <tab> searchScore <tab> intensity <tab> peptide     <tab> proteins\n  r1  <tab> 1         <tab> 2      <tab> 1.345       <tab> 21359.123 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB \n  r2  <tab> 1         <tab> 2      <tab> 1.945       <tab> 24837.398 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB \n  r3  <tab> 2         <tab> 2      <tab> 1.684       <tab> 25498.869 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB\n  ...\n  r1  <tab> 1         <tab> 3      <tab> 0.452       <tab> 13642.232 <tab> A.NTPEPTIDE.- <tab> decoy_proteinA\n\n\nAlternatively, if you have match-between-run probabilities, a slightly more\ncomplicated input format can be used as input:\n\n::\n\n  run <tab> condition <tab> charge <tab> searchScore <tab> spectrumId <tab> linkPEP <tab> featureClusterId <tab> intensity <tab> peptide     <tab> proteins\n  r1  <tab> 1         <tab> 2      <tab> 1.345       <tab> 3          <tab> 0.0     <tab> 1                <tab> 21359.123 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB \n  r2  <tab> 1         <tab> 2      <tab> 1.345       <tab> 3          <tab> 0.021   <tab> 1                <tab> 24837.398 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB \n  r3  <tab> 2         <tab> 2      <tab> 1.684       <tab> 4          <tab> 0.0     <tab> 1                <tab> 25498.869 <tab> A.PEPTIDE.A <tab> proteinA <tab> proteinB\n  ...\n  r1  <tab> 1         <tab> 3      <tab> 0.452       <tab> 6568       <tab> 0.15    <tab> 9845             <tab> 13642.232 <tab> A.NTPEPTIDE.- <tab> decoy_proteinA\n\nSome remarks:\n\n- For Triqler to work, it also needs decoy PSMs, preferably resulting from a \n  search engine search with a reversed protein sequence database concatenated\n  to the target database.\n- The intensities should **not** be log transformed, Triqler will do this \n  transformation for you.\n- The search engine scores should be such that higher scores indicate a higher\n  confidence in the PSM.\n- We recommend usage of well calibrated search engine scores, e.g. the\n  SVM scores from Percolator.\n- Multiple proteins can be specified at the end of the line, separated by tabs. \n  However, it should be noted that Triqler currently discards shared peptides.\n\nThe output format is a tab-separated file consisting of a header line followed\nby one protein per line in the following format:\n\n::\n\n  q_value <tab> posterior_error_prob <tab> protein <tab> num_peptides <tab> protein_id_PEP <tab> log2_fold_change <tab> diff_exp_prob_<FC> <tab> <condition1>:<run1> <tab> <condition1>:<run2> <tab> ... <tab> <conditionM>:<runN> <tab> peptides\n\nSome remarks:\n\n- The reported protein expressions are the expected value of the protein's\n  expression in the run. 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