{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing external mutations\n",
    "\n",
    "For instructions on installation and basic exploration steps, see [Getting started](getting_started.ipynb).\n",
    "\n",
    "It is possible to post-process lists of mutations with `isomut2py` that were otherwise generated with an external variant caller tool. Here we demonstrate how external VCF files can be loaded and further analysed.\n",
    "\n",
    "As `isomut2py` expects `pandas` DataFrames as inputs to handle mutation lists, we will be using the `pandas` package for the loading of VCF files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Compiling C scripts...\n",
      "Done.\n"
     ]
    }
   ],
   "source": [
    "import isomut2py as im2\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "exampleDataDir = '../Documents/isomut2py_example_data/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing VCF files using pyvcf\n",
    "\n",
    "VCF files can be easily processed in python with the `pyvcf` package. This can be installed with:\n",
    "\n",
    "```\n",
    "pip install pyvcf\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import vcf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To convert VCF files to `pandas` DataFrames with columns that can be parsed by `isomut2py`, we first need to make sure to find the values of `sample_name`, `chr`, `pos`, `type`, `score`, `ref`, `mut`, `cov`, `mut_freq`, `cleanliness` and `ploidy`. In order to perform downstream analysis of mutations, fields `cov`, `mut_freq` and `cleanliness` can be left empty, but nonetheless have to be defined in the dataframe.\n",
    "\n",
    "Here we are importing VCF files generated by the tool MuTect2 (of GATK). If another tool was used for variant calling, make sure to modify parser function below. The example VCF files are located at `[exampleDataDir]/isomut2py_example_dataset/ExternalMutations/mutect2`. (For instructions on how to download the example datafiles, see [Getting started](getting_started.ipynb).)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def parse_VCF_record(record):\n",
    "    if record.is_deletion:\n",
    "        muttype = 'DEL'\n",
    "    elif record.is_indel:\n",
    "        muttype = 'INS'\n",
    "    elif record.is_snp:\n",
    "        muttype = 'SNV'\n",
    "    \n",
    "    return (record.CHROM, record.POS, muttype, \n",
    "            record.INFO['TLOD'][0], \n",
    "            record.REF, record.ALT[0], \n",
    "            int(record.samples[0].data.DP),\n",
    "            int(record.samples[0].data.AD[1])/int(record.samples[0].data.DP),\n",
    "            int(record.samples[1].data.AD[1])/int(record.samples[1].data.DP))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sample_dataframes = []\n",
    "for i in range(1,7):\n",
    "    vcf_reader = vcf.Reader(filename = exampleDataDir + 'isomut2py_example_dataset/ExternalMutations/mutect2/'+str(i)+'_somatic_m.vcf.gz')\n",
    "    d = []\n",
    "    for record in vcf_reader:\n",
    "        d.append(parse_VCF_record(record))\n",
    "    df = pd.DataFrame(d, columns=['chr', 'pos', 'type', 'score', 'ref', 'mut', 'cov', 'mut_freq', 'cleanliness'])\n",
    "    df['sample_name'] = 'sample_'+str(i)\n",
    "    df['ploidy'] = 2\n",
    "    sample_dataframes.append(df[['sample_name','chr', 'pos', 'type', 'score', 'ref', \n",
    "                                'mut', 'cov', 'mut_freq', 'cleanliness', 'ploidy']])\n",
    "    \n",
    "mutations_dataframe = pd.concat(sample_dataframes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have all the mutations found in 6 samples in a single dataframe. This can be further analysed with the functions described in [Further analysis, visualization](postprocessing.ipynb). For example the number of mutations found in each sample can be plotted with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: list of control samples not defined.\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x1296 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = im2.plot.plot_mutation_counts(mutations_dataframe=mutations_dataframe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing lists of mutations in arbitrary files\n",
    "\n",
    "If your lists of mutations are stored in some kind of text files as tables, the easiest way to import them is to use the `pandas` python package. (As `isomut2py` heavily relies on `pandas`, it should already be installed on your computer.)\n",
    "\n",
    "Here we merely include an example for such an import, make sure to modify the code and customize it to your table. (This data was generated with MuTect, that only detects SNPs, thus we don't expect to see any indels.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "exampleDataDir = '/nagyvinyok/adat83/sotejedlik/orsi/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_dataframes = []\n",
    "for i in range(1,7):\n",
    "    for sample_type in ['N', 'T']:\n",
    "        df_sample = pd.read_csv(exampleDataDir + 'isomut2py_example_dataset/ExternalMutations/mutect/SV0'+\n",
    "                                str(i)+sample_type+'_chr1.vcf', sep='\\t', comment='#')\n",
    "        \n",
    "        df_sample = df_sample[df_sample['judgement'] != 'REJECT']\n",
    "        df = pd.DataFrame()\n",
    "        df['chr'] = df_sample['contig']\n",
    "        df['pos'] = df_sample['position']\n",
    "        df['type'] = 'SNV'\n",
    "        df['score'] = df_sample['t_lod_fstar']\n",
    "        df['ref'] = df_sample['ref_allele']\n",
    "        df['mut'] = df_sample['alt_allele']\n",
    "        df['cov'] = df_sample['t_ref_count'] + df_sample['t_alt_count']\n",
    "        df['mut_freq'] = df_sample['t_alt_count']/df['cov']\n",
    "        df['cleanliness'] = df_sample['n_ref_count']/(df_sample['n_ref_count'] + df_sample['n_alt_count'])\n",
    "        df['ploidy'] = 2\n",
    "        df['sample_name'] = 'SV0' + str(i) + sample_type\n",
    "        df = df[['sample_name',\n",
    "         'chr',\n",
    "         'pos',\n",
    "         'type',\n",
    "         'score',\n",
    "         'ref',\n",
    "         'mut',\n",
    "         'cov',\n",
    "         'mut_freq',\n",
    "         'cleanliness',\n",
    "         'ploidy']]\n",
    "        sample_dataframes.append(df)\n",
    "    \n",
    "mutations_dataframe = pd.concat(sample_dataframes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: list of control samples not defined.\n"
     ]
    },
    {
     "data": {
      "image/png": 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HHnkkBx10ULPHmzdvXjnKXGcrX1qZucPmtshYc7P+4+w9be906dOlBapZfytf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\nIiIiIiIiKkn/ACzuw4I4m8KOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1008x1296 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f = im2.plot.plot_mutation_counts(mutations_dataframe=mutations_dataframe)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}