{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "from __future__ import absolute_import\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import cohorts\n",
    "from cohorts.functions import missense_snv_count, snv_count\n",
    "from query_tcga import cohort"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Initializing the cohort\n",
    "\n",
    "In this example, we will demonstrate how to use [query_tcga](http://github.com/jburos/query_tcga) to initialize a cohort object. We will then do some simple mock-analyses using this object.\n",
    "\n",
    "This proceeds in two steps:\n",
    "  1. `prep_patients()`: create a list of Patients (objects of class `cohort.Patient`)\n",
    "  2. `prep_cohort()`: create a Cohort from the list of patients (object class `cohorts.Cohort`)\n",
    "\n",
    "For example, to query the `BLCA` cohort:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dataframe_hash': -6111177348469020104, 'provenance_file_summary': None}\n"
     ]
    }
   ],
   "source": [
    "blca_patients = cohort.prep_patients(project_name='TCGA-BLCA',\n",
    "                                     project_data_dir='data',\n",
    "                                     data_dir='data/gdc')\n",
    "\n",
    "blca_cohort = cohort.prep_cohort(patients=blca_patients,\n",
    "                                 cache_dir='data-cache3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to proceed with our analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## Analysis of outcomes: Survival by Somatic Mutation Burden"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this cohort, we have VCF data so we can plot survival according to `snv_count`.\n",
    "\n",
    "`plot_survival` plots `OS` as the outcome, using the median value as the threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# no condition 71\n",
      "# with condition 70\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<lifelines.StatisticalResult: \n",
       "Results\n",
       "   df: 1\n",
       "   alpha: 0.95\n",
       "   t 0: -1\n",
       "   test: logrank\n",
       "   null distribution: chi squared\n",
       "\n",
       "   __ p-value ___|__ test statistic __|____ test result ____|__ is significant __\n",
       "         0.19330 |              1.692 |  Cannot Reject Null |       False       \n",
       ">"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IDQ2tyt+BiIg0SGGQxMfHY9KkSRg4cCAaNmxY4Y6eP3+OgwcPYufOnZVaYHV0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\nCCGERyRIhBBCeESCRAghhEf+Bov0KhMH+kftAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117afd190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blca_cohort.plot_survival(snv_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a number of functions pre-defined in `cohorts` which provide common summaries of genetic data. \n",
    "\n",
    "Some examples:\n",
    "\n",
    " - `snv_count` summarizes the number of somatic SNVs per patient\n",
    " - `missense_snv_count` summarizes the number of missense SNVs per patient\n",
    " - `neoantigen_count` counts number of predicted neoantigens, after running topiary on VCFs\n",
    " - `expressed_snv_count` same as `snv_count` but filtered to expressed variants\n",
    "\n",
    "Note that not every function will work for every cohort. If your cohort does not have mRNA data, for example, the `expressed_snv_count` function will produce an error. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of outcomes: Benefit by SNV count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we might want to summarize the association of SNV count with `benefit`, which is defined as OS > 1 year by default (see `?cohort.prep_patients`). \n",
    "\n",
    "To accomplish this, we can use the `plot_benefit` function for the cohort:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney test: U=2337.0, p-value=0.643264297939 (two-sided)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "MannWhitneyResults(U=2337.0, p_value=0.643264297939, sided_str='two-sided')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cs2cP3nrrrSa3Z2RkwMLCAitXrsSRI0dQXFyMHj16YPbs2QgICJD6VVRUICEhAV999RVE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\nhPALEpCEEEL4BQlIQggh/ML/AB5X8Tz5w126AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c26e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blca_cohort.plot_benefit(cohorts.functions.snv_count)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data exploration: Correlation of SNV count with Age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We might want to summarize correlation between `snv_count` and `age at diagnosis`. \n",
    "\n",
    "One peculiarity of the `plot_correlation` function is that it currently requires a function as input. Unlike `plot_survival` and `plot_benefit`, you cannot pass a variable name as a string.\n",
    "\n",
    "So, for now, we create a mini-function to return the desired variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def age_at_dx(row):\n",
    "    return row['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CorrelationResults(coeff=-0.0470557604077, p_value=0.579517637023, stat_func=pearsonr)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VKpo2b874dTRhQkQKSShbWMaYk4HUK2tdB1lrNxlj5gCXAlXASuAyaxPvhhhO\n/okOTns7L195JSf97nd79TqaMCEi+SyUgQW8BHzcd6wUeAh4wQurucBV3p93gOuA5caYidbaxpzW\nNkMTZs5k2zPPJN1ZuK0ft5vXhAkRKSShDCxr7S5gdeIxY8wioBP4ijFmGDALmGutvcs7/yxucE0H\nFuW2xpkpjsUoqapKWiw8NBbLeNcJTZgQkUISiTEsY8xE4NvAHGttLXACUAY8Gi9jra0DngGmBlLJ\nDB1TXU3JqFEMKimhZNQojqmu7ppE0Wgt21asYF11ddDVFBEJjUgEFnATYK2193mPD/P+3uAr9zYw\nPme12gtlY8dy/H33UfWJT1BcWcmbd9/NjtVJjUp292MihohIvgpll2AiY8whwOeBixMOx4AWa227\nr3ijdy4SknZtTzFXpLW2ljXz54dqY1oRkaCEPrBwg6oW+FXCsSKgp62Jg9kBsh/6mobevmdP0jqr\n+rVr3Zs7KrxEpABFoUvwLOARa21bwrF6oMQYM9hXttw7FwmlY8b0er5oUPLb01JTo/EtESlYoW5h\nGWPGAR8GZvpOrcdtZR0MvJVw/BAg9Ouw4hKnpRdXVeG0t9Pwxhs4wPBJkwDYsXJlyudqkbCIFJpQ\nBxbwMdyuv1W+488BLcDZwO0AxpgRwMlAqOd59zV1PfH80OHDGVpZSUdTE47j4LS2dpXTImERKTRh\nD6wjgO3elPUu1tomY8xiYIExxsFtcc0B6oAlua9m+vz7/9W+9BKVxx7bFVxJEzF8SkaNcsew0lgk\nHMY7CYuI7I2wB9Z+wM4ezs0GOnAXEA/D3ZrpwrDvcuHvymtvbGTbihVdG9f2equRykqOv+eetL5O\nlDbGVbiKSDpCHVjW2m/3cq4DN7Rm565Ge8+//19cPKh6Og+ZdQNGaWPcKIWriAQn1IGVj+ITLXa8\n8AIdu3Z1HY+HUeJEjJKqKhygdfv2jPcKjNLGuFEKVxEJjgIrx+L7/7U2NLCuurrbxrUDtT9glDbG\njVK4ikhwFFgByfbGtVHaGDdK4SoiwVFgFaCwTXKIUriKhNGOnTsZ5PS0+U92NTU3M6x+7/driMVi\nFBX1fv91BVbAWuvreWPhQna++iqde/ZASQmVRx/N4VdfnbUQ0SQHkfzS5ji0dQazK11xSQmNK1ey\nq4+w6U1TczPjp02joqKi13IZBZa3OPdbwGRgjLX2cGPMLNytk/w7p4tP06ZNvDxzJm0NDQyNxTim\nupr1d9/d3ILrAAAeyklEQVTN9sTdLHbvZsfKlayrrk4KkYFsFWmSg0h+GV1ZyejKyqCrkXVpB5Yx\n5mDg/4AxJG8+ez0wxxgzxVr7ysBXMX+8PHNm100bW2pqePnKK+nYsydlWX+IZNoq6i3gNMlBRKIo\nkxbWbbhh9Svgs8BwY8w+wKvAScAtwGkDXsM80tbQ0O1x0dChKcv6QyTTVlFvAadJDiISRZns1j4F\n2AV8HdgNYK3dA3wa9z5Uxw947fJIa309jm9QdGgsxvAjj0wuWFTEyBNP7BYi/p3d+2oV9RpwifVI\nc6C2tb6eNfPns2rGDNbMm0erL3xFRLItkxZWvKx/ZK0CKAWaBqRGeWrdokVJm9cWFRdzTHU1Q2Ox\npPVYh06fzoYlS3jl+99P6srLtFXUW7dffyZdaKJG/gjbLFGRdGUSWCuAM3C7BMsAjDFXANOBwcDf\nBrpyUeT/MDj04ovZsGQJ2597LqncsIMPpmzsWICkD/418+f3uDluJgHRW8D1Z9KFJmrkD/3yIVGV\nSWBdAXwU+CLvT7j4H9wWVy1w9cBWLZr8Hwb1a9d2TbRI1FOXXl+b46art7VN/Zl0oYka+UO/fEhU\npR1Y1toNxphJuDdTPBmoBN4DngV+bK3dmp0qRov/h98/0WJQSQmVxx1HR2srq2bM6HMGX0+vuzf6\nM+kiqIka6r4aePrlQ6Iqo3VY1tptwDVZqkte8H8YDI3FklpYVZ/4BECfM/h62hw3HX19yPdnZ4mg\ndqNQ99XA0yxRiapM1mH9by+nHdyZg/8GHrLWvrOX9Yos/4dBfBJF4ofDK9//ftJzEltPfW2Om458\n+pBX99XA01ZYElWZtLC+xvtjV4kzBROPOcANxpjTrLXP7n31oifVh4H/cTpdMnvzoZJPH/LqvhKR\nuEwC6zLc7sCRwDJgE3AgMA1oAO4HPo67iHge8KkBrGdeSdUl01M3Xn/GcPLpQ17dVyISl0lgHQTs\nD3zcWvtC/KAx5kTcLZsagc8AdcBxA1nJfJOq9eSfzh7vxutP914+fcir+0pE4jIJrOlAY2JYAVhr\nVxpjGoHvWGtvNMbUAVUDWcl8k6rV1FM3Xn+69/QhLyL5KJOtmYqAcmPM1xIPGmO+DpQDJcaYjwH7\n4baypAfxVlOjtWxbsYJ11dU9br3kP95aW6vtkUSkIGXSwnoQuBRYYoy5HtgCHIA7juUAvwdOxw22\nVQNcz7ySqtV09G23pezGS+zea62tpaWmhpaamsjP/hMRyVQmgTUTdwzrc8AHvT9xv8fdCeMWYBvw\ng4GpXn5KNSmip268xOOrZsxIWtMV5dl/IiKZymSni93AmcaYI4ETcTe93QqstNa+aYw5GrgVmGWt\nbclKbfNEJrMEE+XT7D8RkUxlsnB4OG4gHQ8M4/3xr0HGmBgQs9ZmtHNGocpklmCisM/+0zZKIpJN\nmQTM7cA3ejlfu5d1KWjpzAYM++y/fNphQ0TCJ5NZgp/FnVzxLeBpYCXuuqvfesdnDXjtCkimN2gM\no3zaYUNEwieTwKoEtltrfwo8BBxqrV2OewfiZtxJGdJPB553HkXFxVBURFFxMQeed17O67C3dxXO\nh9AVkfDKpEuwBqgyxozFbV2NNsZ8FHjXe51Ds1C/vOUf79m5Zk3XHYmd1lZev/56Tvrd73Jap73t\n0gv7GJtIvtqxcyeDHKfvglmyT0kJFPlvRp++pubmtMplEliP4+528TgwCfdeWM8AncBQ3J3aJU2p\nwiGR/z5aubC3XXphH2MTyVdtjkNbZ2cgX3t3czNVp55KRUXFXr1OLI0JWpkE1izcLZfarLWOMeZa\n4F7chcJtwJz+VLJQ+cOgaPBgnPb2rsedra08ddppDI3FOKa6mrKxY7NeJ02bF4mm0ZWVjK6sDORr\nNzQ1UVFRsdeBlY60x7CstQ3W2nOAL3uPl+C2tM4HPmyt/X12qpif/OM9I449lpJRoxhUXOwecBw6\nW1poqanh+W98IyfbME2YOZP9Jk+m3Bj2mzxZXXoiEioZr5uy1rYl/HstsHZAa5TAGDMFuAk3GLfh\n3sLkBmttp3d+Du52UVW442qXWZvi/vIhlGq8pzgWS1qPFee0trKuujrr3W3q0hORMAvtQl/vtiV/\nApbi3ofrWOBGoANYYIyZC1zl/XkHuA5YboyZaK1tDKbWvev1nlfe3YV3v/tuyudqiriIFLrQBhbw\nQ+DP1trp3uMVxpiRwGRjTDXumNpca+1dAMaYZ3GDazqwKIgK96WnWXiJx3ui8SQRKXShDCxjTBXu\nfoVnJh631s72zn8KKAMeTThXZ4x5BphKSAPL30ra/txzrJk3r1urqmjIEErHjaNj1y6GDh/OvmPH\najxJRApeKAML+Ij3d7Mx5g/Ap4EG4CfADcB47/wG3/PexhdyYeKfhdfZ0sK2FSsoGTUqqZzT3s6w\nD36w1/Ek7dsnIoUmrIE1Cne6/M+BXwP/A5wMXIu7q8YgoMVa2+57XiMQ2k/tCTNnUr92bdItQgCG\nxGK079lDR+P7Q299jVlp3z4RKTRhDayh3t9/ttZe7f37GWPMKNzQugV3/8JUglk9l4biWIziyspu\ngVV24IGUjRvHthUruo4ljlmlak1p3z4RKTRhDaxd3t9/8R3/K+7mu3VAiTFmsLW2I+F8OVCfg/r1\nm79bsGTUqPfHp3rY1ihVa0qLfEWk0IQ1sN7y/i72HY+3vFpxuwwPTigLcAgQ6nVYE2bOpKOtjbo1\naygCyse7w3GJa6ASp7mXjhnTbVJG85YtHH3bbdq3T0QKSlgD6w3cTXW/iDuGFfc5YAvuLU1+DJyN\ne58ujDEjcMe5Qj2QUxyLMbi4uGu8avvKld0WBftbVP5JGaX7769FviJScEIZWN5ehbOB+40xP8G9\nncmngQuBb1prdxljFuMuIHaA9bh7GdYBS4Kqd7r6Gn/yPx4Si1FxxBFqTYlIQQtlYAFYa39pjGkF\nZgNfAzYBM7w9DPGOd+AuIB6GuzXThWHd5SJRX+NP/vNlBx6o1pSIFLzQBhaAtfYB4IEeznXghtbs\nnFZqAPR13yjdV0pEpLtQB1a+6m38qdsUdm9TXBGRQqfAChktCBYRSS3t+2FJbmhBsIhIagqskPHf\n2FELgkVEXOoSDJm9nXAR5Ka42pBXRLJJgRUyve14kU4ABDkGpvE3EckmBVZItdbXs+rii7s2ym20\nltqXXqLy2GN7Da4gx8A0/iYi2aTACpHELrXW2tpuu7q3NzaybcUKOtvaOOqmm1K+RpCb4mpDXpFg\n7Ni5k0FOTzew2Dv7lJRAUVGP55uam7PydVNRYIVIYpdab+rWrOnxXDYXHfc1RqUFzyLBaHMc2joH\n/s5Ku5ubqTr1VCoqKnotF8vRWLUCK0TS7ULr7feobG6K29cYlTbkFQnG6MpKRldWDvjrNjQ1UVFR\n0Wdg5YoCKwTiLRf/bURKRo2ivbmZjl27ko4PnzQpl9XrojEqEQmSAisE/F2BQ8rLqTzuuK4utbW3\n3tp1/6zhkyYx8eqre3il7NIYlYgESYEVAv6WSun++yd1rR3dwwSLXNMYlYgESYEVAlFpuWiMSkSC\npMAKgVQtF+0aISKSTIEVAqlaLmvmz9euESIiCbT5bUhpRp6ISDIFVkhp13YRkWTqEgwpzcgTEUmm\nwAopzcgTEUmmwAoZzQ4UEUlNgRUyuqeUiEhqmnQRMpodKCKSmlpYWZaqiw/H6bHbz7/rRUlVFWvm\nz1cXoYgUPAVWlqXq4gOSjtWvXUtxZSWlY8Zw6MUXJ80O7GhtVRehiAgKrKxLp4uvpaaGlpqalIG0\nasaMPp8vIlIINIaVZakWABdXVfVYvtvO7VpA3G+t9fWsmT+fVTNmsGbePFobGoKukojsBbWwsizV\nAuA3br21x/L+QNIC4v7TjEuR/KLAyrJUC4Bbtm9PejykvJzS/fdPGUhaQNx/mnEpkl8UWAHwzwSs\nPO44hVIWROU+YyKSntAGljGmEtie4tRD1trzvDJzgEuBKmAlcJm1CZ9QIaVuvtzQ91kkv4Q2sIAj\nAQf4NLAr4fgOAGPMXOAq7887wHXAcmPMRGttY47rmhF18+WGvs9SKHbs3MkgxwFgn5KSruUze6up\nuXlAXmeghDmwJgHvWWuf8p8wxgwDZgFzrbV3eceexQ2u6cCiXFY0E9orUEQGWpvj0NbZye7mZqpO\nPZWKiooBe+1YiD6fwh5Ya3o493GgDHg0fsBaW2eMeQaYSogDSzPXRGSgja6sZHRlJQ1NTVRUVAxo\nYIVJ2ANrjzFmJXAM7njWj6y1twPjvTIbfM95Gzgzd1XMnGauiYj0TygDyxgzCJiIO3Y1C9gIfBb4\noTGmFGgDWqy17b6nNgLhab+moJlrIiL9E8rA8nwW2Gitfdt7/DdjTDnuJIubcSdkpNKZi8r1l2au\niYj0TygDy1rbCaxIcerPwAygCSgxxgy21nYknC8H6rNfw/7TzDURkf4JZWAZY8YAnwOWWWt3JJwq\n9f6uBYqAg4G3Es4fAoR+HVaQNEtRRKIqrJvflgD3AF/xHT8XN5CWAS3A2fETxpgRwMnA8hzVMZLi\nsxQbrWXbihWsq64OukoiImkJZQvLWvtvY8xvgAXGGAf4J3AecA5wlrV2tzFmccL59cAcoA5YElS9\no0CzFEUkqkIZWJ5v4O5ecTkwBje0pllrH/POzwY6cGcRDsPdmunCsO9yETTNUhSRqAptYFlrW4Br\nvT+pznfghtbsXNYr6jRLUUSiKrSBJdmhWYoiElVhnXQhIiKSRIElIiKRoMASEZFIUGCJiEgkKLBE\nRCQSFFgiIhIJCiwREYkEBZaIiESCAktERCJBgSUiIpGgwBIRkUhQYImISCRo81sRkYjbtXs3DSUl\nNDU3B12VrFJgiYhE3AdOOYX9vXvbxWKxgGuTPQosEZGIKy8vp6KiIuhqZJ3GsEREJBIUWCIiEgkK\nLBERiQQFloiIRIICS0REIkGBJSIikaDAEhGRSFBgiYhIJCiwREQkEhRYIiISCQosERGJBAWWiIhE\nggJLREQiQYElIiKREPrbixhjioHXgL9ba7+RcHwOcClQBawELrPW2mBqKSIi2RaFFtY8wCQeMMbM\nBWYDC4HzgQpguTGmPOe1ExGRnAh1YBljjgYuA2oSjg0DZgFzrbV3WWv/CJwGxIDpgVRURESyLrSB\nZYwZDCzBbUVtSTh1AlAGPBo/YK2tA54BpuayjiIikjuhDSzgGmAo8EPf8cO8vzf4jr8NjM92pURE\nJBihDCxjzIdxx6imW2vbfadjQEuK443eORERyUOhCyxjTBHwM+Bn1trVKYoUAU4PT+/MWsVERCRQ\nYZzW/l1gHHCGN45V5B0v8h7XAyXGmMHW2o6E55V750RECspbL79M48aNtDsOHznxxKCrkzWha2EB\nZwNjgTqgDWgFjgQu8v7dihtiB/uedwigdVgiUnCG7dxJ+Y4dtLzzDo7TUwdU9IUxsC4FPgocl/Dn\nTdxZgccBDwAtuMEGgDFmBHAysDzXlRURkdwIXZegtXa9/5gxphnYYa19xXu8GFhgjHGA9cAc3BbZ\nklzWVUREcid0gdUDh+SJFrOBDtwFxMNwt2a60FrbGEDdREQkByIRWNbaY3yPO3BDa3YwNRIRkVwL\n4xiWiIhIN5FoYeWT1vp61i1aRPOWLZSOGcOEmTMpjmm9s4hIXxRYObZu0SK2Pf00AI3WQlERk+bO\nDbhWIiLhpy7BHGvesqXXxyIikpoCK8dKx4xJfrz//gHVREQkWtQlmGMTZs6EoiJ3DGv//Zlw5ZVB\nV0lEJBIUWDlWHItpzEpEpB/UJSgiIpGgwBIRkUhQYImISCQosEREJBIUWCIiEgkKLBERiQQFloiI\nRIICS0REIkGBJSIikaDAEhGRSNDWTJI23ctLRIKkwJK06V5eIuFU29HBkI4OmgcPDroqWaXAkrTp\nXl4i4TTxM59h7NixQVcj6zSGJWnTvbxEJEhqYUnadC8vEQmSAkvSpnt5iUiQ1CUoIiKRoMASEZFI\nUGCJiEgkKLBERCQSFFgiIhIJCiwREYkEBZaIiESCAktERCIhtAuHjTFDgbnAV4AqYBXwPWvtKwll\n5gCXeudXApdZa20A1RURkSwLcwtrEfAd4GbgLGA38LQxZhyAMWYuMBtYCJwPVADLjTHlwVRXRESy\nKZSBZYyJAdOBudbae621TwJfBIYCFxpjhgGzvPN3WWv/CJwGxJ8nIiJ5JpSBBTQBxwP3JxxrBxyg\nBPg4UAY8Gj9pra0DngGm5qyWIiKSM6Ecw7LWdgCvARhjioCDgXlAJ7AU+LRXdIPvqW8DZ+amliIi\nkkthbWElug54C/gycKu1dj1u11+LtbbdV7bROyciInkmlC0sn2XA08BkYK4xpgRoxu0eTKWzj9cb\nDLB169YBq6CIyECbMmXKB4HNKX4xL1ihDyxr7T+8f/6fNxnje8A1QIkxZrDXfRhXDtT38ZJjAL78\n5S8PeF1FRAbQv3CHQ/7dV8EpU6YcbK3ts1zUhTKwjDEfAE4HfmetbUo49QrupItaID629VbC+UOA\nvtZhvQCcBPwH6OijrIhIkDancf7gNMrlhVAGFjAc+F/cbr+fJxw/DdgGPAK0AGcDtwMYY0YAJ+Mu\nNu6RtbYFeHbgqywiklted+G/g65HrhQ5Tk9DQcEyxjwInIq7OPht4Au4u1p83Vr7C2PMrcB3gWuB\n9cAcYDRwhLW2MZhai4hItoS1hQXwVdzW0jW4405vAOdaax/2zs/G7dKbBQzD3ZrpQoWViEh+Cm0L\nS0REJFEU1mGJiIiEuktwwBljLgG+D4wFXgVmWmufD7ZWvTPGVALbU5x6yFp7nlem113rjTHFwK3A\nBbhbWv0F+K619j8JZYbjbjj8OdxfZH6P+/3JWRerMeZMYKm1NuY7npPrM8aMBRbjrvnbgzvh51pr\nbVsurtUYcwzwoq+oA/yPtfYqr0xkrtUYMwi4ArgYOBB4B/iJtfauhDJ58d72da359t4GpWBaWMaY\ni4C7gV8A04CdwJ+NMQcFWrG+HYn7H/tTuHsoxv/8ANLetf4e3Nu0XAV8zXvNx7xtr+KWAZ/E/fC4\nHHeLq19l66L8jDGfAH6Z4nhOrs/7sPgrMA53V5UbgG8D/zMgF5h8TSmv1av3Ltx9NOPv8wnAjxPK\nROlarwduxP2Z+zzwALDIGPM9rx759N72eq3k33sbiEJqYc0DfmqtvRHAGLMcd83Wlbi/GYXVJOA9\na+1T/hP+Xeu9Y8/i/nY3HfcH5lDgQuACa+1DXpk1uNd+FvCIMWYy7pKA4621L3pl3sX98DjKWvtq\nti7O+wG7AvcHaxdQHND1fRl3Hd8H47/RGmP2AHcbYxZYa2uyea2eScA/rLUv9PD8QyJ0rYNwf7YW\nWmtv8Q4/bYzZD/ieMean5Ml729e14i69yZv3NkgF0cIyxnwIOIjk3d3bgccI/+7uk4A1PZxLZ9f6\nU3FbaI8llHkLWJtQ5lPAtvgPgedpoIHsf39OB67G/fC603cul9c3BXg5sfsFd73fUO/cQOjtWqH3\n9zpex6hcawy3K+ph33ELjMJ93/Llve31Wo0xpeTXexuYgggsYDzuf4a3fMffBg71NbnDZhJQZoxZ\naYxpNsZsSuhmGO/9nWrX+vi5w4Ct1trmPsokfW+stQ7ugsTxZNdq4GDvt2z/lNVcXt/4FGVqcT8M\nBup70Nu1AnwEONAY84oxpsUYs94Y89WE85G5VmttnbX2u9ba13ynzsTdlWGs9zjy720f17rJq3/e\nvLdBKpQuwfjAtn8CQSNuaJfhdtGEitfVMBG3brOAjcBngR96v7W10feu9TG6X3e8zNg0ymR193vf\nb4J+6ezKP1DXl/XvQW/XaowZgzvx4EO4aw/rgC8B9xtjOq21S/uoY6iuNRVjzMW4rabLyLP31s+7\n1inAdwrhvc2VQgmseAuqvzu8B+mzwEZr7dve4795g9JXATfT9zUVDVCZIAxU3aPwPdgJfAZ43Vr7\nnnfsKWPMAbgL6JcS4Ws1xnwZd9LT76y1PzHG/CCNOkTyehOu9UHvWvchj9/bXCqUwIrv4F4OJA46\nlgMd1trdua9S36y1ncCKFKf+DMzAvTNziel91/p677Gfv8zoHsqsy7zmA6ae3F1fOq+TNdbaPcDy\nFKf+DJxmjNmXiF6rMWYmcBvuWMpXEuqQd+9tqmvN5/c21wplDGs97m8eh/iOHwK8mfvqpMcYM8YY\nc4kxZqTvVKn3d+Ku9YkSd61fD4w27n3EeiuT9L3xxvU+mFAmCPH3LZvXt66XMpW43ShZ/x4YYw4z\nxnzTGDPUd6oUaPZ+qYrctRpjbsadJfdz4IsJXYB59972dK35+t4GoSACy7p3Kd6Eu7s7AN5/ns+S\n+jefsCjh/bUZic7F/c+3jPd3rQeSdq2PX9eTuC3pzyeUOQw43FdmjDHmuISvcSrub2VPDtC19Mdz\n5O76ngSOM8bsn1DmHKAV+NsAXU9vDgB+ApzhOz4t4etH6lqNMZfjjtlUW2u/4fUYxOXVe9vHtebd\nexuUgtlL0Bjz37irv2/BW1EPfAI4yob4xmfGmF/h/ie+FvgncB7wdeAsa+1jJo1d640xD+D2oX8f\nd8D3ZtxB2OO8WUYYY/6O+4N1Fe76oNuA5621Z+XoUuMLSWf5dn/IyfV5k1jewJ3gcp1X9lZgibX2\n8mxfqzfB5mncmVyzce/XNgP3ljqfiK+Fi8q1GmNG496A0HrX4feiV/fIv7dpXOsruKFzGHnw3gap\nUMawsNbe7Q1+Xo67ePNV4DNhDivPN3D/412Ou2v9P4Fp1tr4eo10dq3/GlCNG9aDcFfCXx7/IfB8\nHjfQ78H9zfcRYGZ2LqlX/t+gcnJ91tpmY8wU3PVRS3H7++/E/RDNlq76WWs7jTFn4X5IzQdGAi8D\nn7LJC7e/RjSu9TTcD9SP4Lam/EaRP+9tOtd6Jvnz3gamYFpYIiISbQUxhiUiItGnwBIRkUhQYImI\nSCQosEREJBIUWCIiEgkKLBERiQQFlkieCvltc0QyVjALh0UKhbcf3Q+A3bi3nxfJC2phieSf3wHX\nA/sEXRGRgaTAEsk/5fR8TySRyNLWTBIZxphPADfi7tlWinv78B9ba+/zznfiflAfjbv/4lTczUN/\nYq290esqew/3VgtnWWsf9Z5XDGzzjn/aWpvWDvXGmP1wNxb9LO5dq98EFlprf5NQJgbcgLtj9ge8\nOv/UWvvjhDL3A18F7rfWfsM7djLuZriOtXawd2wF8Emv7OHAdNxW1CPAt6y1uxLKOHg3LrXW6hdT\nyQv6jyyR4N0u4c+4t58owd3483DgHmPMF33FH8G9lcNQ3N2/5xtjTrfWtgC/xf0wPz+h/Om4YbUp\ng7DaF/d2DRcBlV59JgG/Msac55XZB3gWd0fyscAewACLjDF3JbycQ3otoni5G3A3jN0HNyi/jBvQ\n4AZvi/fvBtzb6ojkBQWWRMXxuDt6rwYqrLUjcXe+foz3b2gZ9y+gChgHbPWOfcb7++e4LY/PJ9ws\n70u4QfCrDOpzEe6tQLYDh1lrK4E7vNf5ulfmu8ARuDfaPMpaO9x7HsA3ffc1SndGXxFuUI0HRgB/\n8Y59BsBaex7wvFf2DmvtQRlck0ioKbAkKl7FvQnd8cDfjDHXAU8BZ1trf+Ere4+1ttlaW4PbwgHv\ntuHW2r/j3ntpGPBZr6X0Oa/MLzOozym44fR7a+2/vGNzgf2stad7jz/nlfmZtfZ17+svxQ1dSLhZ\nXwYc4BFr7b+9W8v/0Tue6rboInlFgSWR4IXCNGAt7o035+F2yf3bGHOqr/j2hH/vxm2BJP5f/4V3\n7DzcUNkXeMla+88MqlTp/b0joY67rbU7Esrs5/39L5LFH3+gl9fvbcmJ//pAP8tSAPSfXCLDWvsn\n3BbWYcAlwBO4Y0P+llF7wr9TjQ390js+Ffii9+9MWlfgdvMBdN2K3Bgzwhjz9YSuvve8vz/oe+7B\n3t/x7sr47dRLEspU9PK1+7o+zaSSvKTAkkgwxlxujNmFe1faHdba/wV+4p0ebowZ3MvTkz7ArbUb\ngRW4Ey2m4QbAb7o/rVcrcFtp5xhjjHfse8AS3Du9wvvjS5cYYz7iXceXgY955/+f9/dO7+9jjTFD\nvGu5MMP6JIoHWqyP74tIpCiwJCr+ADTjzsTbZozZhjsb0AF+7Y3n9CTVhIafJ/z7L9ba7SnK9OZ+\nwOKG3hvGmJ24u0s4uJNBAH4MrMPtPnzNGFPP+627xdbaV7xy8ZmJ44HNwL+BYzKsT6J/417zFUCd\nN7VeJPIUWBIJ3hjWycAy3DGcfXED43rg2wlFe+oi8x//PdDkHf95t2f0XZ9m3PVOv8AdxxoCvAT8\nf/F1WNbaXcAJuMG1ESjGDbDLrLVXJLzW48DVwH9wZzz+H3BmL9fT1/X9yKtLK/Au7tR3kcjTwmER\nEYkEbX4rksAYcyUws49iD1hrv5eL+ojI+xRYIsliJMz868GIXFRERJKpS1BERCJBky5ERCQSFFgi\nIhIJCiwREYkEBZaIiESCAktERCJBgSUiIpHw/wP0yDm8KYxXogAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1188862d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blca_cohort.plot_correlation(snv_count, age_at_dx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we can alternatively plot `snv_count` by benefit :"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## More flexibility: Prepare dataframe for analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can retrieve the cohort data as a dataframe, including calculated functions like `snv_count`, etc. \n",
    "\n",
    "This is to enable opportunities for more detailed data exploration and/or analysis using external libraries.\n",
    "\n",
    "Here it is possible to provide a list of functions so that multiple summary statistics can be computed in the same dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['snv_count']\n"
     ]
    }
   ],
   "source": [
    "extra_cols, df = blca_cohort.as_dataframe(on=[cohorts.functions.snv_count])\n",
    "print(extra_cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also note that **the function now returns a tuple** when a list is passed to the parameter `on`. \n",
    "    (By comparison, when no `on` parameter is given, `as_dataframe()` simply returns a dataframe.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other options to `prep_cohort`:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The idea behind `cohorts` is to encourage the majority of analyses to use the \"standard\" options for this particular cohort. \n",
    "\n",
    "Thus, the user can set various *default* options at a cohort-level at the time of creation, but then override these on an analysis-by-analysis basis as needed.\n",
    "\n",
    "Examples of options that utilize this framework include:\n",
    "   - `normalized_per_mb`: whether to normalize metrics like SNV count to coverage\n",
    "   - `filter_fn`: QC filters to apply to *all variants* prior to summary stats being calculated\n",
    "   - `plot_benefit_name`: Descriptor to use for \"benefit\" plots \n",
    "   - etc.\n",
    "   \n",
    "See `?cohorts.Cohort` for a more complete list of options.\n"
   ]
  }
 ],
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