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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<h1 class=\"alert alert-info\">UPMC HNSCC Cohort <small> <i class=\"icon-globe\"></i> Validation of Primary Findings in UPMC Cohort</small></h1>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "####Import Data and Packages  \n",
      "For full list of data and packages imported see the [Imports](../Analysis_Notebooks/Imports.ipynb) notebook."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import NotebookImport\n",
      "from Imports import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 162
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Read in UPMC Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "meta = pd.read_csv('../Extra_Data/UPMC_cohort/meta.csv', index_col=0)\n",
      "meta.index = pd.MultiIndex.from_tuples([('-'.join(i.split('-')[:-1]), i.split('-')[-1]) \n",
      "                                        for i in meta.index])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 163
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clin = pd.read_csv('../Extra_Data/UPMC_cohort/pitt_broad_data.csv', index_col=0)\n",
      "clin.Tumor_type = clin.Tumor_type.map(str.strip)\n",
      "surv = pd.concat([clin.os_5yr, clin.os_5yr_mons*30.5], keys=['event','days'], axis=1).stack()\n",
      "clin2 = pd.read_csv('../Extra_Data/UPMC_cohort/pitt_broad_data_update.csv', index_col=0)\n",
      "surv2 = pd.concat([clin2.os_5yr, clin2.os_5yr_mons*30.5], keys=['event','days'], axis=1).stack()\n",
      "surv = surv2.combine_first(surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 164
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Explore Clinical Features of UPMC Cohort"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "keepers = ti(clin.HPV == 'Negative')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 165
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cc = clin.ix[true_index(meta.Final_74_exome_analysis[:,'Tumor']>0)]\n",
      "cc = cc.ix[:,cc.apply(pd.value_counts).count().isin([2,3,4])]\n",
      "cc = cc.dropna(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 166
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cox_screen(cc.T, surv)[7:].head(10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"4\" halign=\"left\">hazard</th>\n",
        "      <th colspan=\"4\" halign=\"left\">LR</th>\n",
        "      <th colspan=\"2\" halign=\"left\">concordance</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th>exp(-coef)</th>\n",
        "      <th>exp(coef)</th>\n",
        "      <th>lower .95</th>\n",
        "      <th>upper .95</th>\n",
        "      <th>df</th>\n",
        "      <th>p</th>\n",
        "      <th>stat</th>\n",
        "      <th>q</th>\n",
        "      <th>se</th>\n",
        "      <th>stat</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Neoadj_tx</th>\n",
        "      <td> 0.01</td>\n",
        "      <td> 115.91</td>\n",
        "      <td> 10.25</td>\n",
        "      <td> 1311.28</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4.32e-04</td>\n",
        "      <td> 12.39</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Chemo </th>\n",
        "      <td> 0.04</td>\n",
        "      <td>  27.59</td>\n",
        "      <td>  5.33</td>\n",
        "      <td>  142.93</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1.54e-03</td>\n",
        "      <td> 12.95</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Adj_chemo</th>\n",
        "      <td> 0.04</td>\n",
        "      <td>  27.59</td>\n",
        "      <td>  5.33</td>\n",
        "      <td>  142.93</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1.54e-03</td>\n",
        "      <td> 12.95</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Recurrence</th>\n",
        "      <td> 0.45</td>\n",
        "      <td>   2.21</td>\n",
        "      <td>  1.18</td>\n",
        "      <td>    4.12</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1.41e-02</td>\n",
        "      <td>  6.02</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PNI</th>\n",
        "      <td> 0.42</td>\n",
        "      <td>   2.36</td>\n",
        "      <td>  0.86</td>\n",
        "      <td>    6.50</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1.49e-02</td>\n",
        "      <td>  8.41</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DistMets</th>\n",
        "      <td> 0.35</td>\n",
        "      <td>   2.82</td>\n",
        "      <td>  1.33</td>\n",
        "      <td>    6.01</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1.44e-02</td>\n",
        "      <td>  5.98</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.03</td>\n",
        "      <td> 0.55</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>EPS</th>\n",
        "      <td> 1.45</td>\n",
        "      <td>   0.69</td>\n",
        "      <td>  0.30</td>\n",
        "      <td>    1.58</td>\n",
        "      <td> 2</td>\n",
        "      <td> 2.14e-02</td>\n",
        "      <td>  7.69</td>\n",
        "      <td> 0.05</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Dif</th>\n",
        "      <td> 0.90</td>\n",
        "      <td>   1.11</td>\n",
        "      <td>  0.58</td>\n",
        "      <td>    2.13</td>\n",
        "      <td> 3</td>\n",
        "      <td> 6.69e-02</td>\n",
        "      <td>  7.16</td>\n",
        "      <td> 0.14</td>\n",
        "      <td> 0.04</td>\n",
        "      <td> 0.54</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Ethnicity</th>\n",
        "      <td> 6.71</td>\n",
        "      <td>   0.15</td>\n",
        "      <td>  0.02</td>\n",
        "      <td>    1.16</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1.54e-01</td>\n",
        "      <td>  2.04</td>\n",
        "      <td> 0.28</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.51</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Local_recurr</th>\n",
        "      <td> 0.55</td>\n",
        "      <td>   1.83</td>\n",
        "      <td>  0.84</td>\n",
        "      <td>    3.98</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1.53e-01</td>\n",
        "      <td>  2.04</td>\n",
        "      <td> 0.28</td>\n",
        "      <td> 0.03</td>\n",
        "      <td> 0.53</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 167,
       "text": [
        "                  hazard                                   LR                         concordance      \n",
        "              exp(-coef)  exp(coef)  lower .95  upper .95  df         p   stat     q           se  stat\n",
        "Neoadj_tx           0.01     115.91      10.25    1311.28   1  4.32e-04  12.39  0.00         0.00  0.53\n",
        "Chemo               0.04      27.59       5.33     142.93   2  1.54e-03  12.95  0.00         0.04  0.62\n",
        "Adj_chemo           0.04      27.59       5.33     142.93   2  1.54e-03  12.95  0.00         0.04  0.62\n",
        "Recurrence          0.45       2.21       1.18       4.12   1  1.41e-02   6.02  0.04         0.04  0.57\n",
        "PNI                 0.42       2.36       0.86       6.50   2  1.49e-02   8.41  0.04         0.04  0.62\n",
        "DistMets            0.35       2.82       1.33       6.01   1  1.44e-02   5.98  0.04         0.03  0.55\n",
        "EPS                 1.45       0.69       0.30       1.58   2  2.14e-02   7.69  0.05         0.04  0.62\n",
        "Dif                 0.90       1.11       0.58       2.13   3  6.69e-02   7.16  0.14         0.04  0.54\n",
        "Ethnicity           6.71       0.15       0.02       1.16   1  1.54e-01   2.04  0.28         0.01  0.51\n",
        "Local_recurr        0.55       1.83       0.84       3.98   1  1.53e-01   2.04  0.28         0.03  0.53\n",
        "\n",
        "[10 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 167
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = clin.Primary_site.ix[keepers]\n",
      "f = f.replace('Hypopharynx','Other').replace('Sinonasal','Other')\n",
      "get_surv_fit_lr(surv, f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 4.02</td>\n",
        "      <td> 0.259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Oral cavity</th>\n",
        "      <td> 36</td>\n",
        "      <td> 22</td>\n",
        "      <td>  2.99</td>\n",
        "      <td>   1.3</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.411</td>\n",
        "      <td> 0.277</td>\n",
        "      <td>  0.61</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Larynx</th>\n",
        "      <td> 15</td>\n",
        "      <td>  6</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   1.5</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.574</td>\n",
        "      <td> 0.366</td>\n",
        "      <td> 0.901</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Other</th>\n",
        "      <td>  8</td>\n",
        "      <td>  4</td>\n",
        "      <td>   3.7</td>\n",
        "      <td>   1.8</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   0.5</td>\n",
        "      <td>  0.25</td>\n",
        "      <td>     1</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Oropharynx</th>\n",
        "      <td>  4</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0.844</td>\n",
        "      <td> 0.535</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 168,
       "text": [
        "                 Stats          Median Survival              5y Survival               Log-Rank       \n",
        "            # Patients # Events          Median  Lower Upper        Surv  Lower  Upper     chi2      p\n",
        "                                                                                           4.02  0.259\n",
        "Oral cavity         36       22            2.99    1.3   NaN       0.411  0.277   0.61                \n",
        "Larynx              15        6             NaN    1.5   NaN       0.574  0.366  0.901                \n",
        "Other                8        4             3.7    1.8   NaN         0.5   0.25      1                \n",
        "Oropharynx           4        3           0.844  0.535   NaN         NaN    NaN    NaN                \n",
        "\n",
        "[5 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 168
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = clin.Nodal_stage.ix[keepers].fillna('.')\n",
      "f = f.replace('N2A','N2').replace('N2B','N2').replace('N2C','N2')\n",
      "get_surv_fit_lr(surv, f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 22.1</td>\n",
        "      <td> 0.000192</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>N2</th>\n",
        "      <td> 28</td>\n",
        "      <td> 20</td>\n",
        "      <td>  1.15</td>\n",
        "      <td> 0.978</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.298</td>\n",
        "      <td> 0.167</td>\n",
        "      <td> 0.532</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>N0</th>\n",
        "      <td> 19</td>\n",
        "      <td>  6</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  3.98</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.672</td>\n",
        "      <td> 0.487</td>\n",
        "      <td> 0.928</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>N1</th>\n",
        "      <td>  8</td>\n",
        "      <td>  4</td>\n",
        "      <td>   3.7</td>\n",
        "      <td>  2.11</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   0.5</td>\n",
        "      <td>  0.25</td>\n",
        "      <td>     1</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>.</th>\n",
        "      <td>  5</td>\n",
        "      <td>  2</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  1.55</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   0.6</td>\n",
        "      <td> 0.293</td>\n",
        "      <td>     1</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>N3</th>\n",
        "      <td>  3</td>\n",
        "      <td>  3</td>\n",
        "      <td> 0.894</td>\n",
        "      <td> 0.535</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>6 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 169,
       "text": [
        "        Stats          Median Survival              5y Survival               Log-Rank          \n",
        "   # Patients # Events          Median  Lower Upper        Surv  Lower  Upper     chi2         p\n",
        "                                                                                  22.1  0.000192\n",
        "N2         28       20            1.15  0.978   NaN       0.298  0.167  0.532                   \n",
        "N0         19        6             NaN   3.98   NaN       0.672  0.487  0.928                   \n",
        "N1          8        4             3.7   2.11   NaN         0.5   0.25      1                   \n",
        ".           5        2             NaN   1.55   NaN         0.6  0.293      1                   \n",
        "N3          3        3           0.894  0.535   NaN         NaN    NaN    NaN                   \n",
        "\n",
        "[6 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 169
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(f.isin(['N2','N3']), surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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iVOYhNDQU+/btAwCUlpYiNjZWMyYGPdylS5ewefNmREZGih2KWbly5QpKSkrw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\nehsMJzk5WfO3XC7HjRs34O7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       "text": [
        "<matplotlib.figure.Figure at 0x9445610>"
       ]
      }
     ],
     "prompt_number": 170
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = clin.clinical_stage.ix[keepers]\n",
      "f = f.replace('IVa','IV').replace('IVb','IV').replace('.', 'Unstaged').fillna('Unstaged')\n",
      "get_surv_fit_lr(surv, f)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>    </td>\n",
        "      <td>     </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 4.3</td>\n",
        "      <td> 0.231</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>IV</th>\n",
        "      <td> 43</td>\n",
        "      <td> 27</td>\n",
        "      <td> 1.6</td>\n",
        "      <td>  1.1</td>\n",
        "      <td> NaN</td>\n",
        "      <td>  0.38</td>\n",
        "      <td> 0.258</td>\n",
        "      <td>  0.56</td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>III</th>\n",
        "      <td> 12</td>\n",
        "      <td>  5</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 2.11</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.583</td>\n",
        "      <td> 0.362</td>\n",
        "      <td> 0.941</td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Unstaged</th>\n",
        "      <td>  5</td>\n",
        "      <td>  2</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 1.55</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   0.6</td>\n",
        "      <td> 0.293</td>\n",
        "      <td>     1</td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>II</th>\n",
        "      <td>  3</td>\n",
        "      <td>  1</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 3.98</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.667</td>\n",
        "      <td>   0.3</td>\n",
        "      <td>     1</td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 171,
       "text": [
        "              Stats          Median Survival             5y Survival               Log-Rank       \n",
        "         # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2      p\n",
        "                                                                                        4.3  0.231\n",
        "IV               43       27             1.6   1.1   NaN        0.38  0.258   0.56                \n",
        "III              12        5             NaN  2.11   NaN       0.583  0.362  0.941                \n",
        "Unstaged          5        2             NaN  1.55   NaN         0.6  0.293      1                \n",
        "II                3        1             NaN  3.98   NaN       0.667    0.3      1                \n",
        "\n",
        "[5 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 171
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = clin.Alcohol_amt.fillna(0).ix[keepers].replace('.', 0).astype(float) > 13\n",
      "f2 = clin.Alcohol_amt.fillna(0).ix[keepers].replace('.', 0).astype(float) < 7\n",
      "get_surv_fit_lr(surv, 1.*f - 1.*f2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>       </td>\n",
        "      <td>      </td>\n",
        "      <td> 3.25</td>\n",
        "      <td> 0.197</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>-1</th>\n",
        "      <td> 36</td>\n",
        "      <td> 19</td>\n",
        "      <td> 3.98</td>\n",
        "      <td>   1.8</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.497</td>\n",
        "      <td>  0.357</td>\n",
        "      <td> 0.692</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 18</td>\n",
        "      <td>  9</td>\n",
        "      <td> 3.65</td>\n",
        "      <td>  1.29</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.463</td>\n",
        "      <td>  0.276</td>\n",
        "      <td> 0.778</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  9</td>\n",
        "      <td>  7</td>\n",
        "      <td>  1.1</td>\n",
        "      <td> 0.953</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.222</td>\n",
        "      <td> 0.0655</td>\n",
        "      <td> 0.754</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 172,
       "text": [
        "        Stats          Median Survival              5y Survival                Log-Rank       \n",
        "   # Patients # Events          Median  Lower Upper        Surv   Lower  Upper     chi2      p\n",
        "                                                                                   3.25  0.197\n",
        "-1         36       19            3.98    1.8   NaN       0.497   0.357  0.692                \n",
        "0          18        9            3.65   1.29   NaN       0.463   0.276  0.778                \n",
        "1           9        7             1.1  0.953   NaN       0.222  0.0655  0.754                \n",
        "\n",
        "[4 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 172
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, clin.Tob_history.ix[keepers])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>    </td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 0.0913</td>\n",
        "      <td> 0.763</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yes</th>\n",
        "      <td> 55</td>\n",
        "      <td> 31</td>\n",
        "      <td> 3.65</td>\n",
        "      <td> 1.55</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.44</td>\n",
        "      <td> 0.324</td>\n",
        "      <td> 0.596</td>\n",
        "      <td>       </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>no</th>\n",
        "      <td>  8</td>\n",
        "      <td>  4</td>\n",
        "      <td> 1.43</td>\n",
        "      <td>  1.3</td>\n",
        "      <td> NaN</td>\n",
        "      <td>  0.5</td>\n",
        "      <td>  0.25</td>\n",
        "      <td>     1</td>\n",
        "      <td>       </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 173,
       "text": [
        "         Stats          Median Survival             5y Survival               Log-Rank       \n",
        "    # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2      p\n",
        "                                                                                0.0913  0.763\n",
        "yes         55       31            3.65  1.55   NaN        0.44  0.324  0.596                \n",
        "no           8        4            1.43   1.3   NaN         0.5   0.25      1                \n",
        "\n",
        "[3 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 173
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, clin.PNI.ix[keepers])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 6.78</td>\n",
        "      <td> 0.0338</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yes</th>\n",
        "      <td> 31</td>\n",
        "      <td> 22</td>\n",
        "      <td> 1.55</td>\n",
        "      <td>    1</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.323</td>\n",
        "      <td> 0.194</td>\n",
        "      <td> 0.537</td>\n",
        "      <td>     </td>\n",
        "      <td>       </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>no</th>\n",
        "      <td> 26</td>\n",
        "      <td>  9</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 3.98</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.636</td>\n",
        "      <td> 0.471</td>\n",
        "      <td> 0.858</td>\n",
        "      <td>     </td>\n",
        "      <td>       </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>unknown</th>\n",
        "      <td>  6</td>\n",
        "      <td>  4</td>\n",
        "      <td> 3.25</td>\n",
        "      <td>  1.1</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>     </td>\n",
        "      <td>       </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 174,
       "text": [
        "             Stats          Median Survival             5y Survival               Log-Rank        \n",
        "        # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2       p\n",
        "                                                                                      6.78  0.0338\n",
        "yes             31       22            1.55     1   NaN       0.323  0.194  0.537                 \n",
        "no              26        9             NaN  3.98   NaN       0.636  0.471  0.858                 \n",
        "unknown          6        4            3.25   1.1   NaN         NaN    NaN    NaN                 \n",
        "\n",
        "[4 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 174
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(clin['Chemo '], surv, figsize=(5,4), title='Chemo')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5uTA0NOTex+PxVBs8IUQjKZNudeogjgYhJycHIpFIZp1IJEJWVpbMPra2ttyy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\nbpffRywWo7i4uNa//9WJZfjw4TKP2go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dcHJyUlsMGzduxPPnz/HFF1+oLQaiXtSSJkSB\nTz/9FFu2bFFrDHv37uVGgpLGiZI0IQoMGzYMDx48QE5OjlrOf+rUKfTp04erT5PGicodhBCiwagl\nTQghGoySNCGEaDBK0oQQosEoSRNCiAajJE0IIRqMkjQhhGgwStKEEKLBKEkTQogGoyRNCCEajJI0\nIYRoMErShBCiwShJE0KIBqMkTQghGoySNCGEaDBK0oQQosEoSRNCiAb7f7biiAgJ+9I9AAAAAElF\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc8eaf10>"
       ]
      }
     ],
     "prompt_number": 175
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, clin.Date_Dx.map(lambda s: s[-2:]).ix[keepers].astype(int)>10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>    </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 0.0558</td>\n",
        "      <td> 0.813</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 62</td>\n",
        "      <td> 34</td>\n",
        "      <td> 3.17</td>\n",
        "      <td> 1.5</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.457</td>\n",
        "      <td> 0.347</td>\n",
        "      <td> 0.602</td>\n",
        "      <td>       </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td> 3.98</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>       </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 176,
       "text": [
        "       Stats          Median Survival             5y Survival               Log-Rank       \n",
        "  # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2      p\n",
        "                                                                              0.0558  0.813\n",
        "0         62       34            3.17   1.5   NaN       0.457  0.347  0.602                \n",
        "1          1        1            3.98   NaN   NaN         NaN    NaN    NaN                \n",
        "\n",
        "[3 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 176
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(clin.EPS, surv, figsize=(5,4), title='EPS')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Ad0lo8eLFcHJyQmFhITsZ7b59+9CpUye2SmT+/Pm4e/du9X8gDmnb6HhUDlj9qlKLTf3Y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\nw4g0ABhGpAHAMCINAIYRaQAw7AN51Ul4APXAjAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xca5d250>"
       ]
      }
     ],
     "prompt_number": 177
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(clin.HPV, surv, figsize=(5,4), title='HPV')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QSqUQCoWttt+203xoJ5rS15q0noqKCowdOxampqZIT09vcwn6yy+/RGRkJACA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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9117210>"
       ]
      }
     ],
     "prompt_number": 178
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(to_quants(clin.Age_Dx, q=.33, labels=True), surv, figsize=(5,4), title='Age')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Fq6UrurZ8C4PtBgMA3K16YIDtAKnnT+wYgDbGbeu954fdZsJY31iu/CgKVXcowLfffotJ\nkyahffv2AICioiJUVVVBKBTWec2ZM2cwc+ZM3L//aq2/jIwMdO7cGXl5edxitD///DNiY2Px+++/\nAwBKSkpw9+5d9OjRo9b9tLW6Q9oaiPWpb31EeTR2TUVCqlF1hxa5ffs2evToAQ8PD6xZswb5+fn1\nBmgAuHHjBtzc3LhtW1tb8Pl8bgFaALh48SJatmyJ3r17w8zMDBMmTECbNm2U9hzqUFdDYl2U0cBI\niCajwSwKsHfvXpSVlSEhIQG///47+vXrBzs7OwQEBCA4OFjqNSKRqFYgFwqFKCgo4LZzcnJw4sQJ\nHDlyBN26dcOiRYswZcoUnDx5Uuo9w8PDuZ+9vb3h7e3d5GdTNpPZs2TqHy2tf7WiNdTwSA2MpDES\nEhKQkJDQ5PtQdYeC5ebm4siRI1i+fDkePXoEsVh695yNGzfizJkziImJ4fZZW1sjISEBXbq8am2e\nPn06eDwetm3bBgDIz8+HpaUlCgsLYWwsWU+mrdUdslJWkJ4ydSNKWhjKfL5RC12cXDREKXkh2oWq\nO7TIxYsXsWTJEvTp0wcODg44cOAAFi1ahGfPntV5jbu7O27evMltp6enQywWw9nZmdtnb2+P8vJy\nbru0tBT6+vowMjJSzoNosMZUiTRGwLU/YFgmfQIdaaiBkagaVXcowODBg+Hn54eFCxfC19cXAhkC\nSp8+fSAWi7FlyxZMmDABYWFhGD16NNdoCACTJ0+Gp6cnEhMT0aNHD4SHh8PPzw86Om/e31aThaEQ\nrYlUeJ30u0nH8G7SMYl90vpry9sHm5CmoiCtADk5OTA0lP1fZgDQ09PD/v378cEHH2DBggXo378/\ndu3ahejoaERERCA5ORkdO3bEli1bMG3aNGRnZ2PQoEGIjo5W0lNoNlnrruWlijpvQuRBddJNYGJi\nApFIBENDQ6kz3qlyqtLmXietbA2NfKSueqSp5P2OUpBugrS0NNjb2yMtLQ0ApP4CHBwcVJIXCtJN\n01BJetwMxa0+0lxR75f6UcOhGtjb2wMAfHx8sHXrVhQXF8PBwUHiRbRDQw2TjWlcfFPR8HrloCCt\nAOvXr8fz588xbNgwdO3aFUuXLsXDh/Rh1SYNDappbC+QNxX1flE8qu5QIMYYLl++jD/++AO//vor\nTExMcPXqVZWkTdUdqtXY2fsq8/LwrL832iTdUma21Ibq7BtG1R0a4K+//sKff/6JS5cuIT8/Hy4u\nLurOEiFEy1FJWgGCgoJw9OhRbta68ePHY9iwYWjRooXK8kAladVSd5e9piw5pgw1+5FTI2JtVJJW\no7KyMmzatAlPnz7FL7/8glGjRqk0QBPVU9YISFk1ZckxZag5RaysjYhP8sUor6hCrqgUxSUVysqe\nVqMgrQA5OTkYMWIEBeY3SGNn71MGTZoRUNpc3rI0Is7/5TKyC0uwPv4BzvxV9zQKbzIacagAenp6\nOHToEEaOHKnurBAVaewISEU2HKq7qkWayX0duKoNGkKvWBSkFaCqqgpjx46Fh4cHTE1Nuf08Hg+H\nDx9WY84IIdqOGg4VYPv27VL383g8TJs2TSV5oIZDzVZVVITCZd/BbNm3Tb6XIkrSymx4VERJujk2\nPNKw8DccBek3R2OXHKuLopciqzbo2xMKGdTS3Obupt4dasTn86W+3sR5n4nyKarRsmagf7H4a5Td\nuFnH2bJryoLAr6PRi69QnbQC3Lt3j/uZMYb09HSsWrUKo0aNUmOuSHPV1Glb66ouqXjwF6pEhXLf\nt9rrjYgNGbc2Eeumvo0tJx/Cs2NLDH+rbaOrSwpelmN9/H2Eje0GANh0/C+McGsLR+vaq3xvPpmM\ngV1awbl1/WuQ1uX0vWyISiowsrsNvL45hsVjusHnLeWuO0pBWgFqTqTk6OiIrl27wtXVFbNmacZg\nA0Kaq/LKKlx4mMNt3/g7H/06Wkk993b6C7jbW8idVma+GM8LS/6XLsPTAunL4ykSVXcoyZUrV1Ba\nWlrvOVevXoWbmxuMjY3h5+eHvLy8Os89fvw4VZ+QZqe9tTH0dXloY8aHkK/f8AVvICpJK0DNSf+r\nqqrAGENkZN0jwiorK+Hv74+wsDBMnDgRISEhCAkJwY4dO2qdW1RUhKCgIKXknbzZalZ9lJ49q9L0\nFwCo+AR493+9TeBM/3nWRL07mqi8vByZmZncdnp6Ok6dOoUhQ4agb9++dV535swZzJw5E/fvv2pd\nz8jIQOfOnZGXlyexziEAzJs3D0KhEGvXrq1z9XHq3UFkpajeIYpW3dtEGwfDyDLzH/XuUINbt27B\nzs4OH3/8MRwcHHDkyBH4+vri5s2bGDt2LOLj6/6w3bhxA25ubty2ra0t+Hw+kpOTJc47ffo07t69\ni9mzZyvtOcibRROGtEtT/YdDET1DmhOq7miCjz/+GEFBQVi8eDEA4N///jfWr1+P6dOn49ixY4iI\niMCwYdL/wopEIgiFki3MQqEQBQUF3PbLly8xf/58/PbbbzLlJzw8nPvZ29sb3t7ejXsg8kaoq3dI\nTsAkGM+fC0MvL5Xmp2aVy4yBTvjp1EOt74KXkJCAhISEpt+IEbmZmJiwgoICxhhjT548YTo6OuzF\nixeMMcbKy8uZiYlJnddu2LCBTZw4UWKflZUVu3PnDrcdGhrKli5dyhhjLCUlhRkaGtZ5P/pVkqZ6\nPvH/mDgxUeXpZrS15V7yeF5YwnxXnuS2Z/50gd14nCf13PnbL7GLD3PkSocxxnb9mcrWHrnHGGOs\n9+KjLDrxkczXyvsdpeqOJtDX14ee3qt/Ri5cuIAuXbpwc3cUFRXVW//k7u6Omzf/GTiQnp4OsVgM\nZ2dnbl9iYiKWLl0KPp+Pzp07o7S0FEZGRrhx44aSnoi8ycxWLkeLHj3UnQ1SAwXpJnjnnXcQGRkJ\nkUiEXbt2YeDAgdyxqKgoeHh41Hltnz59IBaLsWXLFrx48QJhYWEYPXq0RKPh5cuXIRaLIRaL8eDB\nAxgYGODly5dwd3dX6nORN5OevT101FxXndXRBaIfNzfqGiFfH8sm/vOdCB3hgvZSBrIAwPyhndCp\njXwDWQBgUJdW8Pd4VT0zvFtr9HexlvteMpOr/E0YY4ylpqYyDw8PxuPxmIuLC0tPT2eMMebm5sYs\nLCzYtWvX6r3+8uXLrFu3bszIyIgNHz6c5ebmsl9++YU5OTlJTYvP59d5L/pVEm2V6dxJosoj07mT\nurPEqRSJWFVlpeS+ly9ZVVkZe3nhIisrLpb5XvJ+R6kLngIUFxdD8FoJ5ODBg+jbty8sLS1Vlgfq\ngke0lejHzRCtiZToFijL4r6q8ORtD1j/8Qd02/4z9Dvn/akwDgxE7tRpEATNgdlXi2S6F3XBUyNB\njX8RR40apdIATYg2M5k9Symz8TUXFKQJIUSDUXVHM0HVHUTbaeKyYLKSpXqGqjsIIVpNE0dBagIK\n0oQQjaCpw9XVjao7mgmq7iBE8ah3ByGEkHpRkCaEkDroWlkDupJhUsfcHDwDAwAAz9BA6Xmg6o5m\ngqo7CNFsVN1BCCHNEAVpQgjRYBSkCSFEg1GQJoQQDUZBmhBCNBgFaUII0WAUpNXo6tWrcHNzg7Gx\nMfz8/JCXl1frnB07dsDZ2RlCoRBjxozBkydP1JBTQoi6UJBWk8rKSvj7+yM4OBhZWVmwtrZGSEiI\nxDk3b95ESEgI9u3bhydPnsDGxgazZ89WU44JIepAg1nU5MyZM5g5cybu33812XlGRgY6d+6MvLw8\nbp3DlStX4uHDh9i8+dWabykpKejevTsKCgpq3Y8GsxCi2eT9juopIS9EBjdu3ICbmxu3bWtrCz6f\nj+TkZHTp0gUAEBAQAB6Px51z69YttGrVSuV5JYSoDwVpNRGJRBAKJVctFgqFEqVke3t77ufo6GiE\nhIQgKiqqznuGh4dzP3t7e8Pb21th+SWENE5CQgISEhKafB+q7lCTjRs34syZM4iJieH2WVtbIyEh\ngStJA6+qQaZOnYr09HRERUVh6NChUu9H1R2EaDaau0PLuLu74+bNm9x2eno6xGIxnJ2duX15eXnw\n8vLC22+/jTt37tQZoAkhzRcFaTXp06cPxGIxtmzZghcvXiAsLAyjR4/mGg0BICoqCn379sXKlSvR\nokULNeaWEKIuFKTVRE9PD/v378eGDRtgY2OD7OxsrF+/HtHR0Vxp+sqVK4iJiYG+vj73omBNyJuF\n6qSbCaqTJkSzUZ00IYQ0QxSkCSFEg1GQJoQQDUZBmhBCNBgFaUII0WAUpAkhRINRkCaEEA1GQZoQ\nQjQYBWlCCNFgFKQJIUSDUZAmhBANRkGaEEI0GAVpQgjRYBSkCSFEg1GQJoQQDUZBmqiFIhboVCXK\nr/JpW55VlV8K0mp09epVuLm5wdjYGH5+fsjLy6t1Tnx8PJycnGBiYoL3338fpaWlasip4tEXUrm0\nLb+A9uWZgnQzV1lZCX9/fwQHByMrKwvW1tYICQmROCcvLw+TJ0/GunXrkJaWhmfPnmHp0qVqyjEh\nRB0oSKvJuXPnwOfzMWPGDAiFQkREROD3339HeXk5d05cXBx69eoFPz8/WFhYICwsDDExMWrMNSFE\n1WiNQzXZsGEDzp49KxF0ra2tkZCQgC5dugAAFi5cCH19fSxfvhwAUFxcDBMTExQVFcHIyEjifjwe\nT3WZJ4TIRZ5wq6eEfBAZiEQiCIVCiX1CoRAFBQUS59jb23PbAoEAurq6KCgoqBWk6W8tIc0TVXeo\niVAoRGFhocS+wsJCmJqa1nlOcXExKisrJc4hhDRvFKTVxN3dHTdv3uS209PTIRaL4ezsXOc5165d\nQ4cOHWqVogkhzRcFaTXp06cPxGIxtmzZghcvXiAsLAyjR4+Gvr4+d46vry8uX76MuLg45ObmIiIi\nAgEBAWrMNSFE1ShIq4menh7279+PDRs2wMbGBtnZ2Vi/fj2io6O50rSFhQV27tyJkJAQ2Nvbw9ra\nGmFhYRL3kaWvtSby8/PDxYsX1Z2NBsXHx6Nbt24QCAQYNGgQHjx4oO4sNSg2NhaOjo4wMTGBj48P\nUlNT1Z0lmdy5cwcGBgZ49uyZurNSrwEDBoDP53OvGTNmKDdBRrRWRUUFs7OzY1u2bGEFBQUsMDCQ\nvffee+rOVr0qKipYbGws09fXZxcvXlR3dur19OlTJhQK2fHjx1lJSQlbtmwZ69atm7qzVa/s7Gwu\nz8XFxSw0NJQNGTJE3dlqUEVFBevbty/T0dFh2dnZ6s5Ovdq1a8cqKytVlh4FaS2WmJjIOnXqxG2n\np6czY2NjVlZWpsZc1c/V1ZXp6ekxHR0djQ/Se/fuZT4+Ptx2eXk509HRYfn5+WrMVf3279/Phg4d\nym3fuXOHWVpaqjFHslm5ciX7/PPPGY/H0+gg/fLlS+bk5KTSNKm6Q4vduHEDbm5u3LatrS34fD6S\nk5PVmKv6JSUloby8HHZ2durOSoO8vb0RFRXFbSclJcHAwKBW10lN4u/vj6NHjwIAysvLER0dDU9P\nTzXnqn5//fUXdu7cifDwcHVnpUEpKSkoKytDz549YWlpiQkTJii9eoaCtBaTpa81kV+rVq249oHD\nhw/D19cXixcvho6OZn9teDweDh8+DD6fjzVr1mDq1KnqzlKdqqqqMGPGDGzYsAEGBgbqzk6D8vPz\n4ezsjOjoaKSkpMDY2BjTpk1Tapo0mEWLydLXmjTNixcvMGvWLCQmJiIyMhKTJ09Wd5Zk4uvri7Ky\nMvz2229477330L9/f7Rq1Urd2aolKioKnTp1Qv/+/bkBWUyDB2a98847OHHiBLe9atUqWFtbo7i4\nGAKBQClpanaRgNRLlr7WRH5lZWXw8fGBoaEhkpOTtSJA//jjj1i/fj0AQEdHB+PHj4eFhQWysrLU\nnDPpTp8+jR07doDP53P9/x0cHBAbG6vmnEkXFxeHM2fOcNsVFRXQ1dVV6n8BFKS1mCx9rYn8YmJi\nYGhoiOjoaJiYmKg7OzKxs7PD6tWrkZSUhNLSUmzduhW6urrcfDCaZt++fSgpKYFYLIZYLAYApKWl\nYfTo0WrOmXTPnj1DUFAQUlNTIRKJ8Pnnn8Pf3x96esqrlKAgrcXq6mtNFOPKlSs4d+4c9PX1uVeL\nFi2Qnp5X9J3ZAAAgAElEQVSu7qzVacSIEQgKCsKwYcNgZWWFnTt34tChQ1pR3wto/kRh06dPx6hR\no+Dh4YG2bduiuLgY33//vVLTpFnwCCFEg1FJmhBCNBgFaUII0WAUpAkhRINRkCaEEA1GQZoQJQoO\nDkb//v0l9onFYtjZ2eGHH35QU66INqEgTYgSffvtt0hOTsb+/fu5fatXr0br1q0xe/ZsNeaMaAsK\n0oQokVAoxKpVq/DZZ5+hvLwcT548wZo1axAVFYWRI0fCyMgITk5OuHDhAnfNJ598glatWkEgEGDs\n2LEoKioCAAQGBuKLL75At27dsGbNGly5cgXdu3cHn89H9+7dcevWLXU9JlEiCtKEKNl7770HW1tb\nrF+/Hl999RUmTZqEL7/8Eu7u7sjJycG6deswfvx4VFRUID4+HqdPn8a9e/eQkZGBrKwsbN++nbvX\n7t278Z///AcLFy7EnDlzsHjxYhQVFWHatGkIDQ1V30MSpaEJlghRgaioKHh5ecHAwADXrl2Dq6sr\n4uPjoaOjAz8/Pzg7OyMxMRHu7u749ddfYWpqivT0dAgEAm61HR6PhylTpsDFxQUAUFRUhOvXr8Pd\n3R3/+te/EBgYqMYnJMpCJWlCVMDV1RVDhgzB7NmzkZ6ejoKCAggEAm4JpvPnzyMjIwMlJSV47733\n0KlTJ8yfPx/5+fkS93l9hsM9e/bg5s2bcHNzg6urK44cOaLqxyIqQCVpQlREIBDAyMgILVu2RMuW\nLZGdnc0dS05OhpWVFebPn4+BAwciIiICAOqcq1gsFiMrKwuxsbGorKzEb7/9hilTpmDs2LEwNDRU\nyfMQ1aCSNCEq1r59e9jY2GDjxo0oLS3FuXPn0K9fPxQUFKC0tBRisRjl5eU4duwYDh48iJKSEgCS\n8yzzeDxMnjwZJ0+eBI/Hg0AggLm5OQXoZoiCNCFq8Ouvv2Lfvn0wNzfHlClTsGnTJtjb22PRokWI\ni4uDubk5tm3bhjVr1mDjxo24ffs2eDweN0ucoaEhtm/fjrlz58LY2BifffYZ9uzZo+anIspAs+AR\nQogGo5I0IYRoMArShBCiwShIE0KIBqMgTQghGoyCNCGEaDAK0oQQosEoSBNCiAajIE0IIRqMgjQh\nhGgwCtKEEKLBaBa8ZqJ6TgdCFEGZs0W8yZ9Ved5XKkk3I4wxlb6+/vprlaeprnTfpGdtjp9Vdf8e\nm/K+anSQdnJygr6+PvT19aGjo8P93KJFC6Snpzfp3iKRCAEBARAIBLCzs0NkZCR37Pz58+jSpQsE\nAgE8PT1x7do1AEBqaio8PT1hZmaGBQsWcOenpaUhLCyswTQ//fRTnD9/HgCwY8cOODs7QygUYsyY\nMXjy5Em9+Tpy5AhWrFjRpGcm5E33+PFjdWeh0TQ6SD98+BDl5eUoLy8HAGRmZqK8vBxlZWVo165d\nk+69ePFiVFZWIiMjA4cPH8bKlStx7tw5lJWVYezYsVi0aBFycnIQEBCAgIAAAEBYWBhmzpyJrKws\n3L9/HwkJCQCAtWvXNri+XGpqKq5duwZPT0/cvHkTISEh2LdvH548eQIbGxtu5ei68jVixAj89ttv\n3FJKhGiz6mlXVf365ZdfGjzH1Ny04QdQIa2tk169ejVWrlyJ8vJyzJs3D0uXLgUAODg4YN68ediw\nYQOKi4sRERGBuXPn1rr+5MmT+OGHH2Bubg5zc3MMGDAA169fB5/Ph6mpKaZMmQIAmDVrFkJDQ1FU\nVARdXV20aNGCu4euri7u3bsHa2trmJub15vf9evXY/LkyQCA+Ph4jB8/Hm5ubgCA0NBQ9OjRo958\n9e3bF+PHj0dUVJRMpXZV8Pb2btL18f89im2/bUMlrxK6TBcf+H+AYYOHKz1deagjTXWmq2yjDviq\nJd2cpFy07GpZ7zkHxx5WUW5ko9El6brs2rULW7ZswbVr15CcnIxjx45h3bp13PG4uDjcvXsX586d\nwzfffMNVV7zuxo0b8PT0BPBq6aKzZ8+id+/e6N69O+7duwcAqKqqwvbt29G1a1cYGxsjIiIC33//\nPezs7NCtWzd4eXlhw4YNCAkJaTDP+/fvx6BBgwAAAQEB+Oqrr7hjt2/fRqtWrQAA169fl5ovAPD1\n9UVMTIw8b5lSNCWAxP/3KL7dsQxin2KUDSmB2KcY3+5Yhvj/HlVquvKiIN08NBSgNZFWlqS3b9+O\nr776Cra2tgCAJUuW4LPPPkNISAh4PB5CQ0NhbGyMTp06YdKkSThw4ABXUq1W3cLs6emJixcvwtPT\nE507dwYA6Ojo4M6dO3Bzc0NVVRVWr14NALCzs8Off/7J3ePy5cvo0qULrly5gqCgIPD5fERHR6NL\nly4SaVUvPOrg4AAAsLe3545FR0cjJCQEUVFRXNp15atLly549OgRCgoKJBYk1UbbftsGszEmEvvM\nxpgg+MdguIg6AgAmuUzGJJcpta7dc38X9tzfXWs/na/Y85ubnKRcAMoL1AkJCUr5o6qVJenMzEy0\nb9+e227fvj0yMjK47epgCACWlpb11uOeP38emZmZMDAwwBdffMHtd3V1RUVFBU6ePInFixdLLY1v\n3rwZM2fOxIwZMxAbG4uNGzdKrVp58uQJWrZsKbEvIyMDgwYNQkREBGJiYriqkPryxePxYGlpiczM\nTKnPEh4ezr2q68s1VSWvUup+ns6b2z1LXe5euocHe5O5V3OVm6Tc9hxlfee0siRta2uLlJQU9OvX\nDwCQkpIi0ZD45MkTvPXWWwBeNdhVl0SrlZWVYfDgwThy5AiMjY3Rpk0bBAQEIC4uDgcPHsSff/6J\n5cuXA3j17+Zbb72FtLQ0idL4yZMn8c4776CwsBAWFhZwcnKCk5OT1F4nPB6PKyEDQF5eHry8vDBh\nwgQcPXqUq+euL1/Vqqqq6nxfwsPDZX0L1U6X6Urdz6poNTdV69KrMzoJnbntv2KaX6AuTCtEWvzf\neHopu+GTAXTv3r3RaXh4eDT6GllozRqHOjo6ePr0KaytrbFr1y4sXboUJ06cgIGBAUaOHIlJkyYh\nJCQEDg4OaN++Pfbt24eUlBSMGDECly5dgqOjo8T9evfujSFDhuDLL7/kenFMmTIFgwYNQr9+/RAX\nFwcPDw8cO3YM06ZNw507d9C6dWvu+g8++ABbt24FAHTs2BFxcXH4+++/sWTJEpw5c0YirfT0dLi7\nuyM399W/WxEREbh//z527dpV6znryldwcDCqqqpgZGSEp0+fwszMTOI6Ho+nsj6uilBdJ/16lceL\nA4VYNHWRTI2HRHmU/Vni8XgqbzisKKnA3Z/vwX6YXYPnJi78U+p/zg35z3/+g2XLltV5XN73VWtK\n0q+PUpoyZQrS09Ph5uaGiooKBAUFcY13PB4PLi4ucHJyAp/Px6ZNm2oFaOBVP+U5c+bA2toaVlZW\nmDp1KubPnw8ej4fIyEhMnToV2dnZ6NKlC/bt2ycRoGNjYzFq1CguT5s2bcKQIUNgYmKCvXv31kqr\nXbt2EAgEyMjIgK2tLa5cuYK4uDj8+uuvEs9XVlZWZ74A4P79+3B0dKwVoLVRdSD++cDPqEAF9KCH\neVODKUATpdAz1IOBuSHKX1bIVCctT0k6NjZWnqw1SGtK0rJydHRETEwMevXqpe6sSPj444/h7OzM\n9YeWR1RUFLKzs/HNN9/UOqZtJWmiuZpjSRoAHuxNhmVXC5m64Mnz/NXtQXWR933VyoZDbbRgwQLs\n2LGjSffYu3cvgoKCFJQjQt4ssgToplBWd0kK0ipia2uLgQMH4vTp03Jdf/LkSQwaNAht2rRRcM4I\neTMou4+0soJ0s6vueFNRdQdRFFVUd2gyoZkQBfkFCr8vVXcQQrSGumaiO3XqVIPnKCNANwWVpJsJ\nKkkTRVFFSfpN/Kw2+y54hGirjIwMpKamNngej8dDr169JCbxIgRMi6WmpjIej8cWLVoksf/nn39m\nw4cPV3l+Tp06xVxcXOo8vnHjRrZ7927GGGNHjx5lXbt2ZUZGRmzgwIHs/v37jDHGkpOTmb6+PjM0\nNOReCQkJ7M6dOywoKKjOe2v5r7JZG+73LjNv68jaOLvX++IbC9nBgwfVnV2lf5YAKP0lNDWTmvap\nU6eU+mz1kfd91fqStI6ODtavX49p06bB2dm54QvURCQS4eeff8bly5eRnZ2NiRMnYv/+/fDy8kJk\nZCQmTJiAW7du4dGjR5gyZQp+/vnnWvdIS0vD/fv34eLiooYnIPIqK6+AlddUmHfyrPc83d+XorJS\n+pwmzU3vxQ3PdtgUF79pPoOitL7hUF9fH3PmzOFG5dVUWVmJTz75BGZmZmjdujU2btzIHXNwcMDa\ntWvRunVrXLp0Cd7e3liyZAk6d+4MS0tLbNmyBQsWLIBQKESfPn3w/PlzAMC1a9fg4eEBIyMjbgh6\nQ7Zt2wZfX1/weDwkJCRww78NDAzwySef4M6dO8jPz0dKSgo6dOgg9R6BgYFYtWqVHO+S5jhy/CT8\npwfj3cB/wX96MI4cP6nuLBEtUvj4VpOul7WbnCZNUKb1QRoAvv76a9y7d09qsFy+fDkuXLiA1NRU\nXLhwAZGRkdzwTR6Ph6NHj+L+/fvcCMW9e/fi+PHj+OmnnzB79mwIhUJkZ2fD2tqaC5Dz589HSEgI\nRCIRvv32W8yZM6fBPO7bt4+bT9rb25ubmhQAkpKSYGhoCKFQiJSUFBw5cgQ2Njawt7fHmjVruPOG\nDh2K2NjYeidZ0mRHjp9E+MZdyLIbiWf2fsiyG4nwjbsoUBOZFaY1LUjLSpOCtNZXdwCAQCDAunXr\nEBISAl9fyeGm27dvx7Zt27iVTj755BNs3boVo0ePBvBqVZTquTB4PB7mzp0LW1tb6Om9ems+/fRT\n8Pl8eHt748qVKwCAdevWoWfPnhCJRNDX129wSavKykpcunQJ7u7uAIBWrVpxk/wfPnwYM2bMQFhY\nGHR1X80M98477+Dw4cN4/PgxxowZA1tbWwQEBMDU1BSWlpa4desWdy9tsmX3AfDcJ0rs47lPxIyI\nn9Du7Ksl0mZ4d8CMgU61rv3p1EP8lPCo1n5tOZ8oRnlRPgrTkmQ6t+ZEZ8CrRTVkmZejesk+TdAs\ngjQAjB07Flu3bsWSJUskJt1vaO7pmpMVVQfPasbGxgBe1X2z/3WfSUhIwIQJE2BtbY2OHTs2mLfc\n3FxUVlZKpPXixQvMmjULiYmJiIyM5OaTfr06w83NDfPmzcN//vMfbp1FKysrZGVlSQ3Sr88b4O3t\nrXGrelQw6YMYeDrSpy0lqpGQkKBRJcf6vMx+BPHzxzKd++WXX9baJ+uCGW+//XZjs6Y0zSZIA8DG\njRvh7u4uMYlR9dzTNjY2AGrPPd1Yjx49wrJly/Do0SNYWFggNTUVu3fXv6pFzfmky8rK4OPjg86d\nOyM5ORkmJv9M1/ndd99h3rx5EAqFAICKigruDwWg3fNJ6/Gk9xFlVW9GY5mmqvkHfcmSJerLTANM\nO7wN2wHvNXjexW+GSy1Jy0qTvkvNKkg7ODjg008/xZIlSzB48GAAwLRp0/DVV1/h999/R2FhIdas\nWSNRz1sTa6CzeUlJCSorKyEWi/H8+XNuUdjS0tI6r7G0fDVnQFFREYyNjRETEwNDQ0NER0fXOvfo\n0aPIzc1FREQEUlJS8P333+OHH37gjj979gxt27atN4+aaubksQjfuEuiyqPqegx+CpuBET6D6r12\nxkCnRlUjaNr5hMhL6xsOa84D8Mknn0hUb3z++efo2bMnHBwc4OHhgeDgYK4+uqH71fyZx+PB1dUV\n06dPh4uLCzw9PTF27Fh4eHhg2rRp3Dk16ejooHfv3rh+/ToA4MqVKzh37hz09fW5V4sWLZCeno4d\nO3bg+vXrsLS0xPDhw7FgwQKMGDECwKsgn5OTw606o21G+AxC+PwpsE2Pg3VaHGzT47Ak+L0GAzQh\n1YT2Tfvsy1qto0lVhTQsXEU2btyIzMxMfPfdd3LfIy4uDjExMVJL4G/qUFttMHiYH/4qt4aJXdd6\nz8s5G40ta76ptxChCqoYFq6KftLSnkFZi8XKgoaFa7gPPvgAnp6eiIiI4HqONNb27dulNoYQzTbY\nuz/yfvsduHO/3vPsW5rU2UeeKIYmlZBlRSVpFdq6dSv09PQwbdq0Rl/78OFDLFu2DNu2bZN6nErS\nRFGaw1SlQlMzFLzIV3o6jSHv+0pBupmgIE0UpTnPgqeN1R1a33BICCHNGZWkmwkqSRNF0bbqDnMT\nE+QVFir0nspADYeEAMjJycEvv/wi05ehV69e6N+/vwpyRWrKaGursHvZZmU0fJIWU0p1x+PHj6Gj\noyPRD7hbt244e/Zsg9du376d6xcMvOpj/OzZM2VkE3/99Rd69eoFIyMjvPXWWzhx4gR3bPPmzWjb\nti1MTU0xbtw45OTkAADi4+Ph6OiINm3aYNeuXdz5e/fuRVxcXL3pMcbg5+eHly9forKyEp9++ila\ntWoFa2trfPTRR9w0lcXFxfjwww/RsmVLODo6Yu/evQBe9fm+cOGCot+GZuXPP//EhsWL8fDbZfW+\nToSHY3W45o6sI8qhLcPfX6e0OmkDAwOUl5ejvLwcL1++xLx58zBp0qRG3aO6NKSsf70CAgIwbtw4\n5ObmYvHixZgwYQLEYjFu3ryJL7/8EocOHUJGRgb09fXxxRdfAADmzZuHgwcP4vLlywgLC0NpaSkq\nKipw7Ngx+Pn51Zve7t278fbbb8PIyAhRUVE4efIk7t27h5s3b+LixYtYt24dAODjjz9GQUEB0tLS\ncODAAcyZMwfp6ekIDg7GZ599ppT3ojlxEQgQZlT/a2ILQzBQ9ZCmO1/PSF5F0fTArZKGQ319fbz/\n/vvIzMyESCQCABw7dgxOTk4wMjLCuHHjUFhYiIsXL2LGjBk4duwYRo4cyY2sa9euHZ4/f47c3FyM\nHDkSRkZGcHFxQWJiIoBXb3KvXr0wd+5cCIVCeHl54cSJE3B1dYWZmZnEtKDV8vLykJycjE8++QR8\nPh/jx49HixYt8OjRI5w8eRLjxo1Djx49YGJigqlTp3KjBfX09NCiRQuJ0YXbtm3Dhx9+2OD7EBkZ\nyXW/O3r0KEJDQ2FhYYE2bdpgxowZOH36NMRiMXbt2oUff/wRAoEA7u7uOHPmDExNTWFjYwNjY+Mm\nzUmgCAmHD+PTUaPwxbBh+HTUKCQcPqzW/JDm63yZYoO0tJ4dmh6kVVInXVpais2bN6N3794wMTHB\n48ePMXHiRMTGxqJPnz4ICQnBtGnTcODAAWzduhV79+7FoUOHALyq7sjMzISVlRV8fHzg6uqKAwcO\n4NSpUxgzZgzu3r0LALh69SomTpyIJ0+eYNCgQZg+fToSExORnZ2N/v37Y+rUqRITGVlYWEAkEnGV\n+dXpOTk5wdXVlTvv5cuX2LNnDzw9X62qERUVhREjRqCsrAyrV68GYwxXr17FrFmz6n0PHj58iJyc\nHG7I+vLly2Fvb88dv3XrFlq1aoWbN2/CxsYGX331Ffbu3YvWrVtjxYoV6NatGwDA19cXMTEx8PLy\nauqvRS4Jhw8j9rPP8XHeP31QV3/2OQDAu8Y0sYQ01bESMR7IMG3ohAkT5Lr/sGHD5LpOlZQWpEtL\nS8Hn8wG8mpu1qqqKm4Zz165dGDduHAYMGAAAWLlyJaysrJCbmyu1aoMxhszMTJw/fx6HDh2Cvr4+\nhg4dCh8fH+zevRs9e/aEubk5Pv74YwBAjx494OXlBUdHRzg6OsLQ0BBPnz6VCNLAq9bW4uJimJqa\noqqqCsHBwTA0NOSOf//995g/fz74fD7i4+MBAIMHD8ajR//MIxwZGYmgoCBs2LABkZGRcHd3R3R0\ndK20zp8/LzG9aPV/CWKxGOHh4dixYwfOnTuH5ORkPHjwAOPHj0dWVhbOnTuHcePG4dKlS+jYsSO6\nd++OLVu2yPdLUYDD338vEaAB4OO8fKwK+D84C19NAWkS+hGEC0NrXVu4JhKiyH/X2q/I83O/XQYm\nLgYsDGR+JqK52uvqwfd/caQucSViTJw4sd5zqt25c0eiENahQweJqYs1kdKCtIGBAcRiMbd99epV\n+Pr6olevXsjMzISjoyN3TCgUwtLSEllZWXXeLyMjA61bt4aBwT9fvuo3uGfPnrCyspI4//Ugqaur\nW2e9tkAgQEVFBW7cuAE/Pz8MGDAA/v7+AICgoCB8+OGH2LRpE8aMGYMnT55IDOkuLCxESkoKfH19\nER0djaSkJERFRWH16tW1pnt88uQJWrZsKbHvzJkzCAwMhIODAy5cuABnZ2cujfDwcOjq6mLw4MEY\nMGAAjh07ho4dO8LKygqZmZlSn0UV80nrlpVJ36+CUWREOTR5PmknfX2M5BvVf1J+nswlaSsrq1rf\niz/++EPO3KmGyrrg9ezZE15eXrh16xZsbW0lSqOFhYXIzc2FjY0Nrl69KvV6W1tbPH36FKWlpVyg\nfvToEfr06SNXfi5duoRNmzZh+/btAAB3d3cMGDAAjx8/xjfffAMXFxdMnDgRLVq0wJw5c7Bw4UKI\nRCKYm5tz91i3bh0++ugjXLt2DYMHD4ZAIMDIkSOlNu7p6OhIzCl9/vx5jB49Ghs2bMCUKVO4/fb2\n9mCMoaKigluppbS0lJuoXN3zSVe2aCF9P/XR1lraNJ90U2nj3B0qaTisqqrC5cuXkZiYiH79+mHy\n5MnYv38/zpw5g9LSUnzxxRfw9fWFhYUFdHV1UVxc/E8GdXQgEolgY2MDT09PfPnllygvL8eJEydw\n/PhxTJ48Wa7eH+3bt8f+/fvxxx9/oLS0FOfPn8eJEycwePBgWFlZ4bvvvsPjx48hFouxYsUKuLu7\nSwTo58+f48WLF+jQoQO6du2KkydPoqCgADExMXBzc6uVXuvWrSWW2frqq6+wYsUKiQANAI6Ojujb\nty/CwsJQVFSEY8eO4dKlS9z82OqeT9o3KAirLcwl9q2yMMOYmL2wyUyHTWa61KoIABAuDOXOef2l\nyPMtf94KXo2qJqK9PFsov9pK0wO30oJ0aWkp10fayMgIgYGBWLduHdzd3eHg4IC9e/ciMDAQ5ubm\nyMzM5Eq0vXv3RnJyMsaPHw8AXC+P3Nxc7NmzB/fu3YNQKMTcuXPx22+/cctd1RzFVNe80NVatmyJ\nPXv24IsvvoC5uTlmz56NqKgouLm5YebMmRg0aBA8PDxgY2ODK1euYP/+/RLX//vf/8bChQsBAJ07\nd8a4cePQvn17XLp0CZ9++mmt9Dw9PXHjxg1u+8qVK5g7d65EX3IfHx8Ar7rq3bhxAy1btkRwcDD2\n7NnDBebbt2+jX79+jfpdKJK3ry9Gr1iO9T27Y2PXLljfszvGrFhBjYZEKTwNFBukpVXraHqQpmHh\nKvT2229j165d6NSpk9z3mDhxIubMmcOtPF5NHcPC1TVZTX3pxsbG4rMpUzBBt/6avAfl5RD36Y2D\nJ2VbqVwTn1VZmvOw8LVr12LBggUKTV9WNCxcC3z22WfYsmULVq9eLdf1OTk5SEtLqxWg1UUTA9fb\nb7+NUTNn4mU9dfcA0A6AVyPyronPqs3UVTZ88eKFWtJtCgrSKjRhwgRER0dDJBLV6qIniw0bNmD5\n8uVKyFnzYWNjg1X/rt1tjxBtRUFaxQ4ePCj3tc251Z0QVXj8+LG6s9BoVCfdTKhitQvy5tCmOmlt\nQiuzEEJIM0MrsxBCiAajIE0IIRqMgjQhhGgwCtJa7urVq3Bzc4OxsTH8/Pwkhp6rgp+fHy5evKiS\ntOLj49GtWzcIBAIMGjQIDx48UHqasbGxcHR0hImJCXx8fJCamqr0NF93584dGBgYKG11otcNGDAA\nfD6fe82YMUPue8nyuYyPj4eTkxNMTEzw/vvvo1RBE/zLkra9vb3Es0ZERCgkbaD+74Rcz8yI1qqo\nqGB2dnZsy5YtrKCggAUGBrL33ntPZWnHxsYyfX19dvHiRaWn9/TpUyYUCtnx48dZSUkJW7ZsGevW\nrZtS08zOzubSLC4uZqGhoWzIkCFKTfN1FRUVrG/fvkxHR4dlZ2crPb127dqxysrKJt9Hls9lbm4u\ns7CwYIcOHWK5ubls6NCh7KuvvlJJ2mKxmDk6OjY5LWlp1/edkPeZKUhrscTERNapUyduOz09nRkb\nG7OysjKlp+3q6sr09PSYjo6OSoL03r17mY+PD7ddXl7OdHR0WH5+vtLS3L9/Pxs6dCi3fefOHWZp\naam09GpauXIl+/zzzxmPx1N6kH758iVzcnJSyL1k+VxGR0ez4cOHc9tnzpxhzs7OKkn77t27bPDg\nwU1Oq6aGvhPyPjNVd2ixGzduSMy4Z2trCz6fj+TkZKWnnZSUhPLyctjZ2Sk9LeDVJDivL4OWlJQE\nAwMDCIVCpaXp7++Po0ePAni1cEV0dDS3Qo+y/fXXX9i5c6dKpp8FgJSUFJSVlaFnz56wtLTEhAkT\n5K5ikeVzWfOc7t274+HDh3j58qX8DyFj2ikpKcjKykLnzp1hbW2NmTNnSsy8Ka+GvhPyPjMFaS0m\nEolqBSmhUIiCggI15Uh5WrVqBWdnZwDA4cOH4evri8WLF0vM0a0MPB4Phw8fBp/Px5o1azB16lSl\npge8mtp3xowZ2LBhg8QiF8qUn58PZ2dnREdHIyUlBcbGxtx6nI0ly+ey5tQIAoEAurq6Tf7sypJ2\ncXExOnXqhPj4eNy+fRsZGRncqk7KJO8z07BwLSYUClFYY/avwsJCboGA5ubFixeYNWsWEhMTERkZ\nicmTJ6skXV9fX5SVleG3337De++9h/79+3NT5CpDVFQUOnXqhP79+3Mj1JiSx5y98847OHHiBLe9\natUqWFtbo7i4GAKBoFH3kuVzWfOc4uJiVFZWNvmzK0vaEydOlFhuKyIiAsOHD8f333/fpLQbmzdZ\nn5lK0lrM3d0dN2/e5LbT09MhFou5EmdzUlZWBh8fHxgaGiI5OVklAfrHH3/E+vXrAbxafGL8+PGw\nsJUKA4MAAA6tSURBVLCod5k3RTh9+jR27NgBPp8PI6NXS0c5ODggNjZWaWnGxcVJrEJfvTKQPCV5\nWT6XNc+5du0aOnTowD2vvGRJe9euXbh9+za3XVFR0eg/RIrIm8zPrMB6c6Ji5eXlzM7Ojm3evJnl\n5+ezadOmsSlTpqg0Dw4ODippOIyOjmbvvPOO0tN53eHDh1m7du3Y7du3WUlJCfvpp5+Yra0tKykp\nUWk+VNFwuG3bNubq6spSUlJYYWEhmzZtGps4caJc95Llc/l6T4ecnBzm4+PDFi1a1OTnkCXtxYsX\ns4EDB7KnT5+y58+fs6FDh7JPP/20yWlXq+s7Ie8zU5DWcpcvX2bdunVjRkZGbPjw4Sw3N1el6asq\nSP/rX/9iOjo6TE9Pj3vp6+uzv//+W6npLlu2jLVt25aZmJgwb29vduPGDaWmJ42quuB9/vnnzNLS\nkhkbG7Px48c36bMk7XP5yy+/SPQgOXz4MOvQoQMTCARsypQpCvvj11DaZWVl7MMPP2SmpqbMzMyM\nzZw5k4nFYoWkzZjkd0IRz0wTLBFCiAajOmlCCNFgFKQJIUSDUZAmhBANRkGaEEI0GAXpN9Djx4+h\no6ODMWPG1Do2btw46Ojo4O+//27UPcPDwxEUFAQA0NfXx/PnzxWSVwDYtGkTOnfuDD6fD0dHR3zz\nzTcKHdwxY8YMuVdwDwwMxIoVK6QeY4zBz88PRUVFsLGxwdatWyWOHzp0CGZmZsjOzpYr7YYcOXKk\nzrxpAmmz7snzXt28eRODBg2CqakpzM3NMW7cOKSlpSksn6dPn0a3bt3kujYhIQGdO3duWgYU1u+E\naI3U1FSmr6/PrKysWFFREbe/uLiYWVlZsRYtWrC0tLRG3TM8PJzNmTNH0VllcXFxzMnJiSUlJbHK\nykp2/fp11rFjR7Z27VqFpyWPwMBAtmLFCqnHdu7cyRYvXswYezVBVJs2bbj3u7y8nHXq1ImtX79e\nqfnr1auXyrtlyqquWfca816VlpYyW1tbtm3bNlZaWsqePXvG5syZw9zd3ZWef1mcOnWKubi4NOke\nVJJ+Q+nq6mLAgAGIi4vj9h05cgQDBgyQmA/j8uXLeOutt8Dn8zFgwACuhFxQUIAxY8ZAIBDAy8sL\nWVlZ3AKjOjo63OQ8n3zyCVq1agWBQICxY8eiqKgIwKsJk5YuXYquXbvCxMQEoaGhUvOZkJCAUaNG\nwdXVFTo6OnB3d8eKFSu44bU1S7Kvbzs4OGDt2rVo3bo1EhMToaurK1ES69OnD3bt2sVdExERgREj\nRnDH79+/D1NTU5SUlCAyMpKbrGfw4MESow5ZHaX6yMhIbv6LgIAAdOnSBcuXLwcAfP/99zAyMsL8\n+fNx5MgRODk5gc/nw9/fn5tw5/Hjxxg4cCCMjY3Rtm1bbNy4kbu3jo4OfvjhB5ibm+PZs2dYuXIl\nWrduDWNjYwQGBqK8vBwAMH78eImJqTSFWCyGgYGB1LlX6nuvakpOTkZxcTGmT5+OFi1awMrKCqtX\nr4a1tTVKSkpqlWRf3w4PD8fs2bPRv39/BAcHY/DgwRKfpR9++AE+Pj44ffo0OnfujPT0dOjo6NT6\nDO3evRvXrl2Dh4cHjIyM0L59e+zbt09h7xWVpN9AqampzNDQkO3Zs0diVNmUKVPYnj17mKGhIUtL\nS2OFhYXMysqKxcbGsuLiYrZ48WLm7+/PGGMsKCiITZw4kYnFYnblyhVmYmLCgoKCGGP/jJA7evQo\n8/DwYLm5uSwvL4/16tWLbdiwgTHG2IABA5iLiwtLT09nKSkpzMzMjJ0+fbpWXvfu3cusrKzY2rVr\n2eXLl2tNw1qzJPv6toODAxs2bBg3nWmvXr3Y9u3bGWOMZWVlMYFAwIqKirhr7t69y/h8PjfAYPny\n5ez9999nDx48YPb29iw9PZ0VFxez0aNHs4ULF3LpLV++vFa+k5OTmZ2dncS++/fvMzMzM3br1i1m\nZWXFzp8/z1JTU5mlpSX7888/WWFhIfvggw9YaGgoY4yxSZMmsWXLlrHy8nJ29uxZpqurywoLC7n3\nePr06ay4uJjdvn2btW/fnuXk5LDc3Fzm5eXFdu7cyRhjLCkpibm6ujbwiVC9pKQkZmdnx3r06MEs\nLCzY+PHjJQbsSHuvpCkqKmKWlpZs9uzZ7MiRI7X+a6hZkn19++uvv2ZCoZBduHCBMcbYpk2bmLe3\nN3fu8OHD2datW1lCQgJ3jbTPkEgkYp6enmzHjh2soqKC7d69m5vSlkrSpEn8/Pzw3//+F6WlpSgr\nK8Px48fh5+fHHT906BD69OmDd999F0ZGRli0aBGOHTuG0tJSHDx4EF9++SUMDQ3Rs2dPvPvuu7Xu\n7+7ujl9//RWmpqYoKCiAQCDgVsng8XiYO3cubG1t4ejoiO7du0utBw8ICMC///1vHDt2DEOHDoWZ\nmRn8/f0lzmX11E+HhobCzMwMwKtS5ZEjRwAABw8exIgRIyTmbOjcuTMcHR1x6tQpAK9WZQkICIC1\ntTXi4+PRtm1b5OfnSzxHXc6fPw93d3eJfZ06dcKcOXPg5eWFUaNGoU+fPtizZw8mT56M/2/v3kKi\n6vo4jn/V5AkPqejkgRJTwlOGUZMaRSmFYIQ6KpUW6k0RhaQEGTokInURBoJCpggVRRGa5DEtC1I0\nCyvCDmKIhWgqRo464/m9EIdGRyveepyH/h/wQtfMnrXXrP2btdce196xYwe2trao1Wru3r0LQHp6\nOqdPn0an02Fubs7s7Cxfv37Vby89PR0rKys0Gg1jY2M0NTVhaWlJbW0tMTExAPj5+fHx40eTWxnx\nR6vuGWsrY6ytrXn69ClTU1OkpKSgUCgIDAz86ZHs/v37CQoKAiA6OpqWlhZGRkbQaDQ0NTWhUqkM\n+pexPmRjY0NeXh7x8fGMjIxgaWn5W++QJCH9F7O1tSU4OJja2loePnxIUFCQwVKKnz59oqamRn9h\nx87OjomJCXp7exkcHEShUOgf6+HhsWj7Op2OI0eO4O3tzalTpwwCBjBYSe6ff/5hZmbGaD0TEhKo\nqqpiaGiIxsZGpqamSE5ONvrYhYE9H9Awd1G0vr6emZkZysvLOXTo0KLnzx+EX758oaOjg/DwcGZn\nZ0lLS8PLy4vk5OSfuqja29uLk5PTor9nZmYyPDyMWq0G5tr4ypUr+jb29fVlYGCA6elpXr9+TUBA\nALt27eLq1atYWFgY3beQkBDUajVZWVkoFAri4uLo6+sD5j4MHR0d6enp+WGd/03zq+75+/tjZ2fH\npUuXePDggcG6zgvbaim+vr4UFxfT0dFBb28viYmJHD161Oitzhb2j+9XoHNxcWH79u3U19frp/6+\n7z+wdB968uQJnp6e7Nu3j/Ly8l9uj+VISP/lVCoVpaWllJWVoVKpDMoUCgUqlQqtVqv/aW5uZv36\n9bi7uxtcQe/t7TV47uzsLBkZGYSGhtLZ2UllZSWbN2/+5foplUrKysr0v2/ZsoWcnBxevXoFzIXQ\n9PS0vny5APX09MTDw4O6ujqeP39ucNYwf/DOh3RFRQVRUVGsWrWK3NxcHB0d6erqoq6ujrCwsB/W\n29zc3Oh86/zIfX7lM4VCQWpqqr59NRoNzc3NaLVajh07Rm1tLS9fvqSwsHDJ13r37h179uyhra2N\nnp4e7O3tyczM1Jcv9eG3kn5m1b2FbWVMfn6+wbeU1q5dS2pqKn5+frS3ty/qH58/f162XvPv//37\n9zl48OCicmN9qLOzkwsXLtDW1kZra+tvvV8iSEj/9SIjI6murqa6uprIyEiDsoiICBoaGmhsbGRi\nYoLCwkISEhKwsLAgPj6enJwcvn37xrNnzwyCdN74+DharZbJyUnq6uqoqKhAp9PpyxeOaoxNW0RE\nRKBWq2lra2N6epq+vj5yc3MJDQ0F5u680dDQwNjYGJWVlbS0tCy7v7GxsZw5c4bw8HBWr169qDwg\nIAAzMzPy8vL0o6Tx8XF0Oh2Tk5O0trZy7do1/X4sNdXi4uLyU6e8KpWKW7du8ebNG3Q6HVlZWaSn\npzM7O8vExASjo6MMDw9z7tw5ZmZm0Gq1i7bx4sULkpKSGBwcxMrKCgsLC1xdXYG5gB4aGsLNze2H\ndfk39ff3c+LECbq6utBoNKSnp6NSqVi16teWuN+7dy+PHj2iuLiYsbExxsfHuXPnDt3d3WzdupV1\n69bR3d1Ne3s7PT095Ofn6y9wGxMTE0NNTQ2PHz82+hVVWNyHdDod09PTaLVaBgYG9CP/33VjXQnp\nv9R8R3V0dCQwMBB/f38cHBwMylxcXLh+/TrHjx/H1taW4uJiSktLAcjIyMDJyQk3NzcSExMN5hPN\nzMwwMzMjIyODqqoqHBwcKCkpITc3l/z8fP1avgsPFmMHz/nz51GpVERHR2NtbY1SqWTNmjXcuHED\ngJMnTzI6OoqjoyMFBQUkJCQsu9+xsbG8fft20Sjp+9eOjY2lv79fP2JOSUmhq6sLOzs71Go1ly9f\nprq6mqqqKv2+LhQSEqIf7S/0/eMDAwPJzs4mKioKe3t7mpubKSkpwdbWlosXL7J79278/PzYuHEj\nKpWKpKSkRds7fPgwSqUSX19fnJ2dGRkZ0Y+k379/z4YNGxadtq+05ORkDhw4gFKpxM3NjdHRUaOL\n7i8XqAA+Pj7cu3ePoqIiFAoFzs7OlJSUUFNTg6urK15eXqSlpREcHMzOnTsN+oex987NzQ13d3eC\ng4OxsbExWo+FfWjTpk0kJyfj4+NDSEgI0dHRKJVKEhMTl+wfv0JWwRPiD9m2bRs3b97E29t7xepQ\nUFDAly9fyM7OXrE6iP+PjKSF+EPOnj1LUVHRitbh9u3b+v8EFf9NEtJC/CFxcXF8+PABjUazIq/f\n0NBAWFiYfn5a/DfJdIcQQpgwGUkLIYQJk5AWQggTJiEthBAmTEJaCCFMmIS0EEKYMAlpIYQwYRLS\nQghhwiSkhRDChElICyGECZOQFkIIEyYhLYQQJkxCWgghTJiEtBBCmDAJaSGEMGES0kIIYcIkpIUQ\nwoT9Dx/5HU0jQylVAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd24add0>"
       ]
      }
     ],
     "prompt_number": 179
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Process Mutation Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "maf = pd.read_csv('../Extra_Data/UPMC_cohort/maf.csv')\n",
      "maf = maf.dropna(how='all', axis=[0,1])\n",
      "maf['Tumor_Sample_Barcode'] = maf['Tumor_Sample_Barcode'].str.replace('-Tumor','').copy()\n",
      "maf = maf.set_index(['Hugo_Symbol','Tumor_Sample_Barcode'])\n",
      "maf = maf[maf.Validation_Status != 'Wildtype']\n",
      "non_silent = maf[maf.Variant_Classification != 'Synonymous']\n",
      "non_silent['counter'] = 1\n",
      "hit_matrix = non_silent.counter.groupby(level=[0,1]).sum().unstack()\n",
      "hit_matrix.columns = map(lambda s: s.replace('-Tumor',''), hit_matrix.columns)\n",
      "hit_matrix = hit_matrix.fillna(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 180
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The TCGA has a much higher mutation rate as compared to this data.  This could be due to differences in sample prep, sequencing depth, or bioinformatic processing.  The two cohorts are fairly well matched clinically, so we expect that this difference can be contributed to technical factors as compared to biological ones."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = subplots(1,3, figsize=(8,3), sharey=True)\n",
      "c = pd.concat([(mut.df > 0).sum(), (hit_matrix > 0).sum(0)], keys=['TCGA','UPMC'], axis=1).stack()\n",
      "violin_plot_series(c.clip_upper(500), ax=axs[0])\n",
      "\n",
      "hh = hit_matrix.ix[:, true_index(clin.HPV == 'Negative')].sum()\n",
      "yy = mut.df.ix[:, true_index(clinical.hpv.dropna() == False)].sum()\n",
      "c = pd.concat([yy,hh], keys=['TCGA','UPMC'], axis=1).stack()\n",
      "violin_plot_series(c.clip_upper(500), ax=axs[1])\n",
      "\n",
      "hh = hit_matrix.ix[:, true_index(clin.HPV == 'Positive')].sum()\n",
      "yy = mut.df.ix[:, true_index(clinical.hpv.dropna())].sum()\n",
      "c = pd.concat([yy,hh], keys=['TCGA','UPMC'], axis=1).stack()\n",
      "violin_plot_series(c.clip_upper(500), ax=axs[2])\n",
      "\n",
      "for ax in axs:\n",
      "    prettify_ax(ax)\n",
      "    ax.set_ylabel('')\n",
      "    ax.set_xlabel('Study')\n",
      "axs[0].set_ylabel('# of Mutations')\n",
      "fig.tight_layout()\n",
      "fig.savefig('./figdir/mutation_rate_comparison.pdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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mT56MHTt2FPwgMJQaa1/iOglklTA0igtf28+IUQrSkZVuNhAEvsdT0zdQmGWp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\nBf8fTOlPBQqlEXgAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9117710>"
       ]
      }
     ],
     "prompt_number": 181
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hh = hit_matrix.ix[:, true_index(clin.HPV == 'Negative')].sum()\n",
      "yy = mut.df.ix[:, true_index(clinical.hpv.dropna() == False)].sum()\n",
      "c = pd.concat([yy,hh], keys=['TCGA','UPMC'], axis=1).stack()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 282
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats.mannwhitneyu(hh.dropna(), yy.dropna())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 293,
       "text": [
        "(5909.0, 0.0002041546634473598)"
       ]
      }
     ],
     "prompt_number": 293
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The median mutation rate of TCGA HPV- samples is 104 verses 74 in the UPMC cohort."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "yy = mut.df.ix[:, true_index(clinical.hpv.dropna() == False)].sum()\n",
      "yy.median()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 182,
       "text": [
        "104.0"
       ]
      }
     ],
     "prompt_number": 182
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hh = hit_matrix.ix[:, true_index(clin.HPV == 'Negative')].sum()\n",
      "hh.median()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 183,
       "text": [
        "73.0"
       ]
      }
     ],
     "prompt_number": 183
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "TP53 Mutation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p53_mut = hit_matrix.ix['TP53'].ix[true_index(clin.HPV=='Negative')] > 0\n",
      "survival_and_stats(p53_mut, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ODLq78+fPY926dYiOjpY6FKty6dIllJaW4qGHHkLTpk0xatSoRtHUwSRvoIKC\nghpfrhqNBnl5eRJFRHLXvHlzsWNoYmIiQkJCMHfu3EZXUmisFAoFEhMToVKpsHTpUjz33HNSh9To\nVVZWIjIyEitWrKjRJ4nuLjc3F/7+/khISMClS5fg6uqKiRMnSh2WbUyGYwqGTKhDZGq3bt3ClClT\nsH//fixbtgzjxo2TOiSrEhISgtLSUmzevBkTJkzAo48+iubNm0sdVqMVFxeHTp066VXVs8reMI88\n8gh++OEHcXvx4sXw9vaGVquFWq2WLC4WCQxkyIQ6RKZUWlqKwYMHw9nZGampqUzw9bB69WrExsYC\nAJRKJSIiIuDp6YmMjAyJI2vc9u3bh7Vr10KlUokTgfn5+WHr1q0SR9b47dixAwcOHBC3y8vLYWdn\nJ3mNCJO8gQyZUIfIlDZs2ABnZ2ckJCTAzc1N6nCsSuvWrbFkyRKkpKSgpKQEn332Gezs7NC1a1ep\nQ2vUvv32WxQXF4u9wwEgLS0NYWFhEkfW+GVlZWHatGm4fPkyCgoKMGvWLISHh8PeXtoKcyZ5A9U1\noQ6RuRw7dgwHDx6Eg4ODeHN0dER6errUoTV6Tz75JKZNm4YhQ4bAy8sL69atw/bt2yUvVVkbDnc1\n3OTJkzFs2DD06tULrVq1glarxapVq6QOyzZWoSMiIrJFLMkTERHJFJM8ERGRTDHJExERyRSTPBER\nkUwxyRNRvUyfPh2PPvqo3j6dTofWrVvjk08+kSgqIqoNkzwR1cu7776L1NRUbNq0Sdy3ZMkStGjR\nAlOnTpUwMiKqjkmeiOpFo9Fg8eLFeOutt1BWVoZr165h6dKliIuLw1NPPQUXFxd06NABv/76q/iY\nf/3rX2jevDnUajVGjBiBwsJCAMCkSZPw9ttvo3v37li6dCmOHTuGHj16QKVSoUePHvj999+lukwi\nWWCSJ6J6mzBhAnx9fREbG4s5c+bgmWeewb///W8EBgbi5s2b+OijjxAREYHy8nLs2rUL+/btw59/\n/okrV64gIyMDX3zxhXiur776Chs3bsTrr7+OF198EXPnzkVhYSEmTpyImTNnSneRRDLABWqIyChx\ncXHo378/nJyccOLECXTr1g27du2CUqlEaGgo/P39sX//fgQGBuK///0v3N3dkZ6eDrVajZycHAC3\nZ1QbP348OnfuDAAoLCzEb7/9hsDAQLz66quYNGmShFdIZP1Ykicio3Tr1g2DBg3C1KlTkZ6ejry8\nPKjVaqj/9wWSAAAgAElEQVRUKqhUKhw6dAhXrlxBcXExJkyYgE6dOuGVV15Bbm6u3nmqruT49ddf\n49SpUwgICEC3bt2QlJRk6csikhWW5InIaGq1Gi4uLmjWrBmaNWuGzMxM8b7U1FR4eXnhlVdewWOP\nPYaYmBgAqHONbZ1Oh4yMDGzduhUVFRXYvHkzxo8fjxEjRsDZ2dki10MkNyzJE1GDtWvXDj4+Pli5\nciVKSkpw8OBBPPzww8jLy0NJSQl0Oh3Kysqwe/dubNu2DcXFxQD01ypXKBQYN24c9u7dC4VCAbVa\njSZNmjDBEzUAkzwRmcR///tffPvtt2jSpAnGjx+Pjz/+GG3atMHs2bOxY8cONGnSBJ9//jmWLl2K\nlStX4vTp01AoFOJKZ87Ozvjiiy/w0ksvwdXVFW+99Ra+/vpria+KyLpxFToiIiKZYkmeiIhIppjk\niYiIZIpJnoiISKaY5ImIiGSKSZ6IiEimmOSJiIhkikmeiIhIppjkiYiIZIpJnoiISKa4QI0R7kzD\nSURkCHNOLMrvI9thzPuIJXkjCYIgq9u8efMkj4HXZdvXJcdrEgTLzBou9TVa+jZx4kTJY7CW9xGT\nPBERWRU/Pz+pQ7AaTPJEREQyxSRPAIDg4GCpQzALXpf1kOM1kXl4eHhIHYLV4FKzRlAoFBZrayMi\n62bu7wtb/D5KTk62uR+Fxv6fmeSNYIsfKiIyDpM8mYKx/2dW1xMREckUkzwREVmV5ORkqUOwGpwM\nh4jIytnahDgualdoCwukDsMqMMkTEVm5PnN3Sh2CRR1+Z6jUIVgNVtcTERHJFJM8ERGRTDHJExER\nyZTZkrxSqYSDg4PerV27dvd8TFZWlsliePPNN3Ho0CG9fd26ddN7DmdnZ6hUKvHm7OyMjh07Iikp\nCe+//77JYiEiIrI0s5bkr169irKyMvF26dIlcz6dnsuXL+PEiRPo27cvAKCsrAyffPIJ/vzzT73j\niouLodPpxNuwYcPw5ptv4sknn8TmzZuRk5NjsZiJiIhMyeLV9bm5uXj66afh7u6OZs2aYfbs2TWO\nKSkpwYQJE+Du7o7mzZtj+fLl4n1JSUno0KEDVCoVwsPDUVRUVOvzxMbGYty4ceK2p6cnpk+ffteh\nJhs3bkReXh4iIyMBABEREYiLizP2UqmapD17ET55Op6e9CrCJ09H0p69UodERCRrZk3ytU3Bt3jx\nYvj6+uLGjRv45ZdfEBsbizNnzugdEx8fj5KSEty4cQMHDhzAwoULceXKFfz111949tlnkZCQgKys\nLDRp0gRRUVG1PvemTZswcOBAcbugoABlZWV1TgtYVlaGt99+G0uXLhX3hYSEYMOGDcZcOlWTtGcv\noleuR0brp5DVJhQZrZ9C9Mr1TPRERGZk1nHy1df83bhxIyZNmgQvLy9UVlairKwMjo6OyM7OFo8R\nBAGFhYW4du0ajh07ht69e+PChQtwdXXFBx98gHHjxqFfv34AgKioKDz66KN6iRkA0tPTkZeXV681\nh7/44gt07doV3bt3F/d17doVFy9eRF5eHtzd3ev/ApAo/qvvoAgcrbdPETgakTFrcN/PZQCAyOD2\niHysQ43HrvnpAtYkX6yxn8fzeGs8nsiSzJrk09LS4O3trbdv9+7dePrpp+Hg4ID7778fdnZ2evcr\nFAq8+uqryM7Oxvjx45Gbm4tnnnkGy5cvx99//43PPvsM8fHxeo+prKyEUvm/Solr166hWbNm9Yo1\nLi4O7733Xo1YmjZtiqtXr9ZI8tHR0eLfwcHBNrciUn2VC7U3kyiUdrXuJ7JW+X/9jvy03/F9WhNc\n2ecpdTiSyP/rd2j8HpA6DItrjKvjWXzGu+effx5ffvklHn/8cQA1S/sAcPToUbz55ptYvHgxzp8/\nj4iICKxbtw5eXl745z//KfZ6Ly8vR0pKil6CB24n5+r77ubUqVO4dOkShgwZUuO+ysrKWh9TNcnT\nvdkram8mESorLBwJkXlp/B6Axu8BPP3/Jfn58+dLHZLF5acxyTcWZltqVqlU4vr16zVK8l5eXvjs\ns88wePBgrFq1Cm+++SZ27NiBIUOGQKlU4tq1a5g9ezbs7e0RGxuL3NxcDBo0CIsWLYKPjw+GDRuG\nxMRE+Pv7Y8GCBTh69Ch27dql9xzp6ekIDAzUawa4W1zvvfce9u3bh5079aeGrKyshIuLC65fvw4P\nDw9xP5d2rL87bfJVq+wrf9uA+dMn4MnBA+/ySCLrZomlZhvbtLYnVzyPyrJis52/rDAHLVu2NNv5\njRUQEICkpCSznNvY95HZSvJ19WJfvnw5IiMjUVlZiVdeeQUzZszA5MmTkZGRIT4uJiYGkydPRrNm\nzeDk5ITIyEiEhoYCAN555x0MHz4cV69excMPP4y1a9fWeI777rsParUa6enpuO++++4Z15EjR/DI\nI4/U2H/27Fm0bdtWL8GTce4k8s++3oLSSsBRCbzABE8kS56d+6FF0Aiznf+35eNx7Ngxs53fWCtX\nrpQ6hBrMVpKX2htvvAF/f39MnTrV6HPExcUhMzMT77zzjt5+luSJyFC2WJK/sm8dfAdMMNv5D78z\ntFF+B0dHR5utKdfY95Fsp7V97bXXai3l18c333yDadOmmSgiIiIiy5Jtkvf19cVjjz2Gffv2GfX4\nvXv3YuDAgY2y3YeIqDHTtLG9TncAGl2nO0DG1fXmxOp6IjKULVbXm1tjra43J1bXExERkR4meSIi\nIplikiciIpIpJnkiIiKZYpInIiKSKSZ5IiIimWKSJyIikimOkzcCx8kTkaEsMU7e1mjcPZB3K1fq\nMCyq0S1QQ0RElsFCB9WF1fVERGRVkpOTpQ7BajDJExERyRTb5I3ANnkiMpQl2uT5fSR/nLueiIiI\n9DDJExGRVWGbvOGY5ImIiGSKbfJGYBsYERmKbfJkChwnT0Rko+Q0IU4TNzfk5OdLHYZsMMkTEVm5\nK618pQ7BZHwzrtzzmOTkZAQHB5s/GBlgmzwREZFMsU3eCGwDIyJDWaJNXm4leX6/1sRx8kRERKSH\nSZ6IiKwKx8kbjkmeiIhIptgmbwS2yRORodgmXz9sk68d2+SJiIhIj8XGyXfo0AFpaWkAgIqKCtjZ\n2QG4/evk4sWLuO+++0z6fIIg4KmnnsLGjRvh4uIi7p8zZw6OHz+OpKQkAIBWq8Wrr76KrVu3ws3N\nDe+99x7Gjh2LWbNmYfjw4QgKCjJpXESNwdRJk5CWmnrP49QeHvj6u+/g6OhogaiIDMNx8oazWJK/\ncOGC+LdSqcTVq1fh7e1ttuf76quv0LNnT70E/9tvv2HZsmV6b4433ngDeXl5SEtLQ2pqKoKDg/Hw\nww9j+vTpGDduHPbt22e2GImksnHjt1jo5AS14u6VedMK8lBYWAhPT08LRUZycKikBH2dnKQOQ2TL\nPwokr67/4osvMGLECIwcORJhYWHYt28funTpIt6fnJyst71o0SJ4e3vD3d0d8+bNq/O8y5Ytw8SJ\nE8XtsrIyTJ06FS+99JLYrlFUVIT169dj9erVUKvVCAwMxIEDB+Du7g4fHx+4urriwIEDZrhqIuk9\n4uSMgc53vzn8f40bUX0cKi0x6/nrm7BtuTd+o5jWdvv27fjuu+/w1FNP3fWfsX79enzzzTc4fvw4\nFAoFwsLC0LNnTwwbNkzvuAsXLuDmzZto166duO+9997D0KFD0b59e5w5cwYAcOrUKfj4+GDOnDn4\n5ptv0KJFC7z//vvo3r07ACAkJAQbNmxA//79TX/RMpKcmIjEVatgV1qKCkdHhEybhuCQEKnDIiKJ\nfK8rwlZdkdGP79y5swmjAZ566imTns+aNIok37NnT4P+CQkJCZg7d67Yfj99+nRs3LixRpI/dOgQ\nAgMDxe2UlBRs3rwZR48exfr168X92dnZOHfuHCIiIpCRkYGDBw9i5MiROHLkCDp27IgePXogPj7e\nRFcpT8mJidj61iy8kZMr7lvy1iwAYKInslEDnJzxrIvaqMcG38jEli1b7nrMkSNH0Lt3b4PP+dVX\nXxkVixw0iiTv4eFR531Vhwz8/fffGDdunLjikiAIePjhh2s85tq1a2jWrBkAoLKyEi+88AJWrlwJ\nBwcHveNcXV1hb2+P6Oho2NnZ4fHHH8eAAQOwe/dudOzYEV5eXrh69aopLlG2Elet0kvwAPBGTi4W\njxkLf407AMBt5j+heX1mjcfmL12GgmXLa+zn8ZY5nshc3JVKdKj2fVsf9yrJX79+vV6lfaVS8pZp\nyTSKJF+VQqFARUWFuJ2eni7+7eXlheXLl2Po0KEAgNzcXOTk5NQ4h1KpFP+pBQUF+O233zB48GAA\nt3v2V1ZWwtvbG0eOHIEgCCgvLxd7+5eUlMDd/XZyqqysrDPO6Oho8e/g4GCb7dRhV1pa+34ZLX1J\nVB/Jyck23QZsCbb6fWuMRpfkfX19kZaWhjNnzsDDwwMrV64US+4jR47EkiVL8OCDD0KpVGLChAkI\nDg7GrFmz9M7RokULHDp0CADg7u6O0iqJ6Msvv8Q333wjDqHr168foqKiMHfuXBw8eBBHjhzB559/\nDgDIyspCq1atao2zapK3ZRV1DK2q4GQWZKOq/+ifP3++dMFIpK9j4+lZD9j2jwJJZryzs7PDtWvX\n4O3tjS+//BIbNmxAYmKieP/bb7+NlStXolmzZnjttdewevVq/PHHHygvL8ebb76J9evXo6ioCM88\n8ww+/vhj2Nvr/1a5ePEinnjiCVy8eLHGc1d/vqtXr2Ly5MnYv38/2rRpg9jYWAwZMgQA8PHHHyMl\nJQUff/yx3jk4493/1NYmv9jTA8Pff59t8o2Yp9oV+zXuaHKPasz7c27i4tWrHELXAJzxrn4MmfHO\nFofEGfs+ku20tj179sT69evRqVMno88xevRovPjiixg4cKDefiZ5fcmJiUj65BMoS0pQ6eSEJ198\nkQm+kWOStxwm+fphkq8dk3w1GzduxOHDh7FkyRKjHn/z5k2Ehobi8OHDNe5jkidr10yjQRdHRzje\nI8nvz87Gjezsu3aOpbtjkq8fzl1fO2PfR42uTd5URo0ahYSEBBQUFMDNza3ej1+xYgUWLVpkhsiI\npLdz717cuHHjnsfNU6uZ4ImsmGxL8ubEkjwRGYol+fphdX3tuAodERER6WFJ3ggsyRORoViSrx+2\nydeOJXkiIiLSw5K8EViSJyJDWaIkLydN3NyQk59/12PYJm842fauJyKyFSx0UF1YkjcCS/JEZChL\nlOT5fSR/bJMnIiIiPUzyRERkVbjKn+GY5AmAfD80vC7rIcdrIvM4efKk1CFYDSZ5AiDfL1hel/WQ\n4zWRedy6dUvqEKwGkzwREZFMMckTEZFV+euvv6QOwWpwCJ0R5Db5BBGZFyfDIVPgevJEREQkYnU9\nERGRTDHJExERyRSTfD0cP34cAQEBcHV1RWhoKHJycqQOyaRCQ0Nx+PBhqcMwiV27dqF79+5Qq9UY\nOHAgzp07J3VIJrF161a0bdsWbm5uGDx4MC5fvix1SCZz5swZODk5ISsrS+pQTGLAgAFQqVTiLTIy\n0uhzGfLds2vXLnTo0AFubm549tlnUVJS0pDwJWfINbdp00bvNY6JiZEgUtO62/ewUf9jgQxSXl4u\ntG7dWoiPjxfy8vKESZMmCRMmTJA6LJMoLy8Xtm7dKjg4OAiHDx+WOpwGu379uqDRaIQ9e/YIxcXF\nwsKFC4Xu3btLHVaDZWZmitel1WqFmTNnCoMGDZI6LJMoLy8X+vXrJyiVSiEzM1PqcEzivvvuEyoq\nKhp8HkO+e7KzswVPT09h+/btQnZ2tvDEE08Ic+bMafBzS8WQa9bpdELbtm0litD07vU9bOz/mEne\nQPv37xc6deokbqenpwuurq5CaWmphFGZRrdu3QR7e3tBqVTKIsl/8803wuDBg8XtsrIyQalUCrm5\nuRJG1XCbNm0SnnjiCXH7zJkzQtOmTSWMyHQ++OADYdasWYJCoZBFki8qKhI6dOhgknMZ8t2TkJAg\nDB06VNw+cOCA4O/vb5Lnl4Ih1/zHH38Ijz/+uBThmcW9voeN/R+zut5AJ0+eREBAgLjt6+sLlUqF\n1NRUCaMyjZSUFJSVlaF169ZSh2ISwcHBiIuLE7dTUlLg5OQEjUYjYVQNFx4ejp07dwIAysrKkJCQ\ngL59+0ocVcOdP38e69atQ3R0tNShmMylS5dQWlqKhx56CE2bNsWoUaOMboYw5Lun+jE9evTAhQsX\nUFRUZPxFSMiQa7506RIyMjLQpUsXeHt74x//+Ae0Wq0U4ZrEvb6Hjf0fM8kbqKCgoEaS0Gg0yMvL\nkygiqkvz5s3h7+8PAEhMTERISAjmzp0LpdL63+4KhQKJiYlQqVRYunQpnnvuOalDapDKykpERkZi\nxYoVcHJykjock8nNzYW/vz8SEhJw6dIluLq6YuLEiUady5DvnoKCAri5uYnbarUadnZ2Vvv9ZMg1\na7VadOrUCbt27cLp06dx5coVvPHGG5YO1WKM/R/bmzswudBoNMjPz9fbl5+fD3d3d4kioru5desW\npkyZgv3792PZsmUYN26c1CGZTEhICEpLS7F582ZMmDABjz76KJo3by51WEaJi4tDp06d8Oijj4oT\nfQgymLrjkUcewQ8//CBuL168GN7e3tBqtVCr1fU6lyHfPdWP0Wq1qKiosNrvJ0OuefTo0Rg9erS4\nHRMTg6FDh2LVqlUWi9OSjP0fW3/RxkICAwNx6tQpcTs9PR06nU4sMVLjUVpaisGDB8PZ2Rmpqamy\nSfCrV69GbGwsAECpVCIiIgKenp7IyMiQODLj7du3D2vXroVKpYKLiwsAwM/PD1u3bpU4sobZsWMH\nDhw4IG6Xl5fDzs7OqNoKQ757qh9z4sQJtG/fXnxNrY0h17x+/XqcPn1a3C4vL6/3DyhrYuz/mEne\nQEFBQdDpdIiPj8etW7cQFRWFsLAwODg4SB0aVbNhwwY4OzsjISFBr3rL2rVu3RpLlixBSkoKSkpK\n8Nlnn8HOzg5du3aVOjSjffvttyguLoZOp4NOpwMApKWlISwsTOLIGiYrKwvTpk3D5cuXUVBQgFmz\nZiE8PBz29vWvPDXkuyckJARHjx7Fjh07kJ2djZiYGIwZM8aUl2RRhlzz+fPnMWPGDGRmZuLmzZuY\nN28exo4dK2HU5mX0/9iEnQNl7+jRo0L37t0FFxcXYejQoUJ2drbUIZmUn5+fLHrXv/rqq4JSqRTs\n7e3Fm4ODg/D3339LHVqDLVy4UGjVqpXg5uYmBAcHCydPnpQ6JJOS0xC6WbNmCU2bNhVcXV2FiIiI\nBn1f1Pbd8+WXX+r14E9MTBTat28vqNVqYfz48UJxcbEpLkMy97rm0tJS4YUXXhDc3d0FDw8P4R//\n+Ieg0+kkjrrhqn4Pm+J/zLnriYiIZIrV9URERDLFJE9ERCRTTPJEREQyxSRPREQkU0zyZFJ//fUX\nlEolhg8fXuO+kSNHQqlU4u+//67XOaOjozFt2jQAgIODA27cuGGSWAHg448/RpcuXaBSqdC2bVu8\n8847Jp2MJTIyEkuWLDHqsZMmTcL7779f632CICA0NBSFhYXw8fHBZ599pnf/9u3b4eHhgczMTKOe\n+16SkpLqjI1sS22r7Rnzvjx16hQGDhwId3d3NGnSBCNHjkRaWprJ4ty3bx+6d+9u1GOTk5PRpUsX\nk8ViUWYZA0A26/Lly4KDg4Pg5eUlFBYWivu1Wq3g5eUlODo6CmlpafU6Z3R0tPDiiy+aOlRhx44d\nQocOHYSUlBShoqJC+O2334SOHTsKH374ocmfyxiTJk0S3n///VrvW7dunTB37lxBEG4vyNOyZUvx\n9S4rKxM6deokxMbGmjW+3r17y24YKdVfXavt1ed9WVJSIvj6+gqff/65UFJSImRlZQkvvviiEBgY\naPb4DfHTTz8JnTt3ljoMo7AkTyZnZ2eHAQMGYMeOHeK+pKQkDBgwQG/++KNHj+KBBx6ASqXCgAED\nxBJ6Xl4ehg8fDrVajf79+yMjIwMKhQLA7Zne7iz08a9//QvNmzeHWq3GiBEjUFhYCOD2AjULFizA\n/fffDzc3N8ycObPWOJOTkzFs2DB069YNSqUSgYGBeP/998WpI6uXpKtu+/n54cMPP0SLFi2wf/9+\n2NnZ6ZVOgoKCsH79evExMTExePLJJ8X7z549C3d3dxQXF2PZsmXiAhyPP/643gx2Qh21CsuWLRPn\nQh8zZgy6du2KRYsWAQBWrVoFFxcXvPLKK0hKSkKHDh2gUqkQHh4uLmbx119/4bHHHoOrqytatWqF\nlStXiudWKpX45JNP0KRJE2RlZeGDDz5AixYt4OrqikmTJqGsrAwAEBERobcQENkenU4HJyenWteF\nuNv7srrU1FRotVpMnjwZjo6O8PLywpIlS+Dt7Y3i4uIaJemq29HR0Zg6dSoeffRRTJ8+HY8//rje\n5/aTTz7B4MGDsW/fPnTp0gXp6elQKpU1Pq9fffUVTpw4gV69esHFxQXt2rXDt99+a7LXSjJS/8og\nebl8+bLg7OwsfP3118Lo0aPF/ePHjxe+/vprwdnZWUhLSxPy8/MFLy8vYevWrYJWqxXmzp0rhIeH\nC4IgCNOmTRNGjx4t6HQ64dixY4Kbm5swbdo0QRAEcSnSnTt3Cr169RKys7OFnJwcoXfv3sKKFSsE\nQRCEAQMGCJ07dxbS09OFS5cuCR4eHsK+fftqxPrNN98IXl5ewocffigcPXq0xrLB1UvSVbf9/PyE\nIUOGiMvX9u7dW/jiiy8EQRCEjIwMQa1WC4WFheJj/vjjD0GlUomTVyxatEh49tlnhXPnzglt2rQR\n0tPTBa1WK4SFhQmvv/66+HyLFi2qEXdqaqrQunVrvX1nz54VPDw8hN9//13w8vISDh06JFy+fFlo\n2rSp8Msvvwj5+fnC888/L8ycOVMQBEF45plnhIULFwplZWXCzz//LNjZ2Qn5+fniazx58mRBq9UK\np0+fFtq1ayfcvHlTyM7OFvr37y+sW7dOEARBSElJEbp163aPdwTJWUpKitC6dWvhwQcfFDw9PYWI\niAi9yYxqe1/WprCwUGjatKkwdepUISkpqUYNUfWSdNXtefPmCRqNRvj1118FQRCEjz/+WAgODhaP\nHTp0qPDZZ58JycnJ4mNq+7wWFBQIffv2FdauXSuUl5cLX331lbiUM0vyRNWEhobixx9/RElJCUpL\nS7Fnzx6EhoaK92/fvh1BQUF4+umn4eLigtmzZ2P37t0oKSnBtm3b8O9//xvOzs546KGH8PTTT9c4\nf2BgIP773//C3d0deXl5UKvVyMnJAXB7pbaXXnoJvr6+aNu2LXr06FFrP4AxY8Zg+fLl2L17N554\n4gl4eHggPDxc71jhLu3zM2fOhIeHB4DbpdqkpCQAwLZt2/Dkk0/qzaPdpUsXtG3bFj/99BMAYOvW\nrRgzZgy8vb2xa9cutGrVCrm5uXrXUZdDhw4hMDBQb1+nTp3w4osvon///hg2bBiCgoLw9ddfY9y4\ncejXrx/c3NwQFRWFjRs3AgBmzZqF1157DcXFxVAqlRAEAbm5ueL5Zs2aBRcXFxQUFKCoqAi//PIL\nHBwcsHPnTowcORIA0LVrV1y8eNFqVzqjhrvXanu1vS9ro1arceDAAZSXl+PVV1+Fl5cXAgMDDS5J\nh4aGok+fPgCAESNG4Ndff0VhYSEKCgrwyy+/IDw8XO+zXNvn1dXVFR999BHGjRuHwsJCODg43POz\naA2Y5Mks3NzcEBQUhJ07d+KHH35Anz599OaR//vvv5GUlCR21nF3d0dpaSmuXbuGmzdvwsvLSzzW\nz8+vxvmLi4sxYcIEdOrUCa+88opeggKgtyqbk5MTKisra41z/Pjx2LFjB3JycvDzzz+jvLwckydP\nrvXY6gn/ToIHbncq3LNnDyorK7Fly5Za59C+88WSmZmJ8+fPY8iQIRAEATNnzkT79u0xefJkgzol\nXrt2Dc2aNauxf86cOcjPz0dUVBSA26/xJ598Ir7GXbp0wY0bN1BRUYFTp06he/fu6N+/Pz799FPY\n2dnVem19+/ZFVFQUoqOj4eXlhVGjRuH69esAbv+Yatq0Ka5evXrPmEme7qy2161bN7i7u2Px4sXY\ntWuX3rru1d+XdenSpQvWrFmD8+fP49q1a5g4cSKeffZZXL58ucax1T+LVVdia9GiBXr37o09e/aI\nzYRVP6tA3Z/X5ORktGvXDoMHD8aWLVvq/Xo0RkzyZDbh4eHYtGkTNm/ejPDwcL37vLy8EB4eLi5M\notPpcOjQIdx3331o3bq1Xq/aa9eu6T1WEATMnj0bjz32GC5cuIDt27fjgQceqHd8vXr1wubNm8Xt\nHj16YMGCBTh58iSA20msoqJCvP9uCbhdu3bw8/PD7t27cfToUb1aiztfSHeS/LZt2zB8+HDY29tj\n6dKlaNq0KS5fvozdu3dj4MCB94xbqVTW2gZ6p+bgzqpUXl5e+Oc//ym+vgUFBTh06BB0Oh2mTJmC\nnTt34rfffsPq1avrfK4///wTwcHBOHHiBK5evQoPDw/MmTNHvL+uH09kGwxZba/6+7I2K1eu1BuR\n4+3tjX/+85/o2rUrzpw5U+OzmJ6efte47nzWvv/++1oXcant83rhwgUsXLgQJ06cwJEjRxATE3Pv\nF8AKMMmT2YSFhSExMRGJiYk1VhULCQnB3r178fPPP6O0tBSrV6/G+PHjYWdnh3HjxmHBggXIy8vD\n4cOH9RLxHSUlJdDpdCgrK8Pu3buxbds2FBcXi/dX/6VfW7V7SEgIoqKicOLECVRUVOD69etYunQp\nHnvsMQCAr68v9u7di6KiImzfvh2//vrrXa83IiICb7zxBoYMGQJnZ+ca93fv3h0KhQIfffSRWHIo\nKSlBcXExysrKcOTIEXz55ZfiddTVVNCiRQuDqhHDw8Px1Vdf4fTp0yguLkZ0dDRmzZoFQRBQWloK\nrXsvKiIAAAMRSURBVFaL/Px8vP3226isrBRXgavq2LFjmDRpEm7evAkXFxfY2dmhZcuWAG4n+Jyc\nHLRq1eqesZA8mWq1vUGDBuHHH3/EmjVrUFRUhJKSEmzYsAFpaWl46KGH4Ovri7S0NJw5cwZXr17F\nypUrxc64tRk5ciSSkpLw008/1TqcF6j5eS0uLkZFRQV0Oh1u3Lgh1jyUlJTU61oaGyZ5Mrk7H76m\nTZsiMDAQ3bp1Q5MmTfTua9GiBRISEjB16lS4ublhzZo12LRpEwBg9uzZaNasGVq1aoWJEyfqtfEp\nFAooFArMnj0bO3bsQJMmTfD5559j6dKlWLlypbi+dPUvgNq+EObNm4fw8HCMGDECarUavXr1gkaj\nwdq1awEAL7/8MrRaLZo2bYq4uDiMHz/+rtcdERGBP/74o0bJoepzR0REICsrSyyxv/rqq7h8+TLc\n3d0RFRWFZcuWITExETt27BCvtbq+ffuKtQ3VVT0+MDAQ77zzDoYPHw4PDw8cOnQIn3/+Odzc3PDe\ne+9hwIAB6Nq1K/z9/REeHo5JkybVON8zzzyDXr16oUuXLmjevDkKCwvFkvzZs2fRtm3bGlWhZDsm\nT56MYcOGoVevXmjVqhW0Wi1WrVpV47i7JWQA6Ny5M7777jvEx8fDy8sLzZs3x+eff46kpCS0bNkS\n7du3x8yZMxEUFIRHHnlE77NY2+ekVatWaN26NYKCguDq6lprHNU/r/fffz8mT56Mzp07o2/fvhgx\nYgR69eqFiRMn1vlZtAZchY7ICvXs2RPr169Hp06dJIshLi4OmZmZeOeddySLgYjujiV5Iiv01ltv\nIT4+XtIYvvnmG3EmQiJqnJjkiazQqFGjcO7cORQUFEjy/Hv37sXAgQPF9nkiapxYXU9ERCRTLMkT\nERHJFJM8ERGRTDHJExERyRSTPBERkUwxyRMREckUkzwREZFMMckTERHJFJM8ERGRTDHJExERyRST\nPBERkUwxyRMREckUkzwREZFMMckTERHJFJM8ERGRTDHJExERyRSTPBERkUwxyRMREcnU/wE9aX8k\nn1fmLQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x14e07ed0>"
       ]
      }
     ],
     "prompt_number": 184
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Broken down by functional type."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import re as re\n",
      "get_nums = lambda s: re.findall(r'\\d+', s)\n",
      "\n",
      "def is_disruptive(v):\n",
      "    c = v.Variant_Classification\n",
      "    if c != 'Missense':\n",
      "        if 'Ins' in c or 'Del' in c:\n",
      "            return 'InDel'\n",
      "        else:\n",
      "            return v.Variant_Classification.split('_')[0]\n",
      "    else:\n",
      "        s = v.Protein_Change\n",
      "        aa = int(get_nums(s)[0])\n",
      "        if int(aa) in range(163,196):\n",
      "            return 'L2'\n",
      "        if int(aa) in range(236, 252):\n",
      "            return 'L3'\n",
      "    return 'other'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 185
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p53 = maf.ix['TP53'].reset_index()\n",
      "dd = p53.apply(is_disruptive, 1)\n",
      "dd = dd.replace('Synonymous',nan).dropna()\n",
      "others = hit_matrix.columns.diff(p53.Tumor_Sample_Barcode.ix[dd.index])\n",
      "dd.index = p53.Tumor_Sample_Barcode.ix[dd.index]\n",
      "dd = pd.concat([pd.Series('WT', others), dd])\n",
      "dd = dd.ix[keepers]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 186
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s2 = surv.unstack().ix[dd.index]\n",
      "s2.index = range(len(dd))\n",
      "s2 = s2.stack()\n",
      "dd.index = range(len(dd))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 187
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(dd, s2, colors=colors[:6] + ['grey'] + colors[6:], figsize=(7,6))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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XExERkRYmeSIiIolikiciIpIoJnkiIiKJYpInIiKSKCZ5IiIiiWKSJyIikigm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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9090dd0>"
       ]
      }
     ],
     "prompt_number": 188
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By Validation Status.  Validated mutations do seem to have worse prognosis, leading us to think there could be a number of false positives in the mix."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v = maf.ix['TP53'].Validation_Status == 'Valid'\n",
      "v = v.groupby(level=0).sum() > 0\n",
      "v.name = 'valid'\n",
      "v = v.ix[hit_matrix.columns]\n",
      "v = v.fillna('WT')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 189
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "v.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 190,
       "text": [
        "False    37\n",
        "WT       28\n",
        "True      9\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 190
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "draw_survival_curve(v.ix[true_index(clin.HPV=='Negative')], surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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6LcnJyThz5gz27NnD7rOzs0NWVhbb8gAedGdeeuklSKVSpKSkYOpU1XUVqdtC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\nkgchhJP/D8UyiTFDv0daAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd27c690>"
       ]
      }
     ],
     "prompt_number": 191
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "MUC5B mutation status.  Can't really say much here."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(hit_matrix.ix['MUC5B'].ix[true_index(clin.HPV=='Negative')]>0, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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m6NWrl9SltWrff/89SkpKxNHhAJCWloaQkBCJK2v9srKyMG3aNKSmpqKgoADz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\nAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd2b62d0>"
       ]
      }
     ],
     "prompt_number": 192
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Copy Number Data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_tumor = meta.xs('Tumor', level=1)['SNP array ID'].dropna()\n",
      "lu = pd.Series({v:i for i,v in cn_tumor.iteritems()})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 193
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I am loading the GISTIC2 processed data for this cohort.  The data was processed together with the TCGA samples."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "path = '../Extra_Data/UPMC_cohort/'\n",
      "gistic = pd.read_table(path + 'all_thresholded.by_genes.txt', index_col=[0,1, 2])\n",
      "gistic.index = gistic.index.droplevel(1)\n",
      "cn_upmc = pd.DataFrame({i: gistic[t] for i,t in cn_tumor.iteritems() if t in gistic})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 194
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pct_del = (cn_upmc < 0).sum() / (1.*len(cn_upmc))\n",
      "pct_amp = (cn_upmc > 0).sum() / (1.*len(cn_upmc))\n",
      "pct_altered = pct_amp + pct_del\n",
      "pct_altered.name = 'CIN_pct_altered'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 195
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Chromosomal instability in the UPMC cohort."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(to_quants(pct_altered, q=.33, labels=True), surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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CSqXCmjVrRIuFSd6EVaWgTm3n4+ODoUOHorS0VOxQjEpsbCw6d+6MPn36wMrK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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xce848d0>"
       ]
      }
     ],
     "prompt_number": 196
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Taking a look at 3p deletion."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "del_3p = cn_upmc.xs('3p14.2',0,1).median().ix[true_index(clin.HPV=='Negative')].dropna() < 0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 197
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b = combine(p53_mut, del_3p).index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 198
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "keepers = meta['SNP array ID'].notnull() & (meta['Final_74_exome_analysis'] == 1)\n",
      "keepers = keepers.groupby(level=0).sum() \n",
      "keepers = (keepers > 0) & (clin.HPV == 'Negative')\n",
      "keepers = keepers.ix[surv.index.get_level_values(0)].dropna()\n",
      "keepers = keepers.groupby(level=0).last()\n",
      "keepers.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 199,
       "text": [
        "True     49\n",
        "False    25\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 199
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(del_3p, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Dhg2ws7NDXFwcHB0dpQ7HrHh6emLJkiU4deoUiouL8cUXX8DKygo+Pj5Sh1av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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd28e810>"
       ]
      }
     ],
     "prompt_number": 200
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Association of TP53 mutation with 3p deletion"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "len(combine(p53_mut, del_3p))"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fisher_exact_test(p53_mut, del_3p, alternative='greater')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 201,
       "text": [
        "odds_ratio    2.50\n",
        "p             0.17\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 201
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Survival association"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "combo = combine(p53_mut, del_3p)\n",
      "survival_and_stats(combo, surv, figsize=(5,4), title='PITT')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Wd2lpaZg7dy6SkpKQk5ODJUuWwNvbG9raDd8YQUm6DqrqZ02UKzY2Fv/++y90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\nVebo6Mj09fXZiBEjWHp6urJDUmqS/uS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       "text": [
        "<matplotlib.figure.Figure at 0x101d3750>"
       ]
      }
     ],
     "prompt_number": 202
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.crosstab(clin.Alcohol, clin.Alcohol_amt.fillna(0))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>Alcohol_amt</th>\n",
        "      <th>0</th>\n",
        "      <th>.</th>\n",
        "      <th>1</th>\n",
        "      <th>10</th>\n",
        "      <th>12</th>\n",
        "      <th>15</th>\n",
        "      <th>16</th>\n",
        "      <th>2</th>\n",
        "      <th>20</th>\n",
        "      <th>24</th>\n",
        "      <th>27</th>\n",
        "      <th>3</th>\n",
        "      <th>5</th>\n",
        "      <th>6</th>\n",
        "      <th>7</th>\n",
        "      <th>8</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Alcohol</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>.</th>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>no</th>\n",
        "      <td> 15</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yes</th>\n",
        "      <td>  0</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 8</td>\n",
        "      <td> 4</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 4</td>\n",
        "      <td> 11</td>\n",
        "      <td> 3</td>\n",
        "      <td> 9</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 16 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 203,
       "text": [
        "Alcohol_amt   0  .  1  10  12  15  16  2  20  24  27  3  5   6  7  8\n",
        "Alcohol                                                             \n",
        ".             1  0  0   0   0   0   0  0   0   0   0  0  0   0  0  0\n",
        "no           15  0  0   0   0   0   0  0   0   0   0  0  0   0  0  0\n",
        "yes           0  1  1   8   4   3   1  3   2   3   1  4  4  11  3  9\n",
        "\n",
        "[3 rows x 16 columns]"
       ]
      }
     ],
     "prompt_number": 203
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "alch = clin.Alcohol_amt.fillna(0).replace('.', nan).astype(float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 204
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "alch.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 205,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x101bb210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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//alqamp06NChL7z2Z13XNTvj812FQyEtyJwWO+sb7mX93O4c7I/tOxEff+T2q//pGZVn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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xfdf7510>"
       ]
      }
     ],
     "prompt_number": 205
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, (alch.ix[combo.index].dropna() > 10).fillna('M'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>       </td>\n",
        "      <td>      </td>\n",
        "      <td> 6.83</td>\n",
        "      <td> 0.00898</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 39</td>\n",
        "      <td> 19</td>\n",
        "      <td> 5.01</td>\n",
        "      <td>   1.8</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.526</td>\n",
        "      <td>  0.389</td>\n",
        "      <td> 0.712</td>\n",
        "      <td>     </td>\n",
        "      <td>        </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  9</td>\n",
        "      <td>  8</td>\n",
        "      <td>  1.1</td>\n",
        "      <td> 0.953</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.111</td>\n",
        "      <td> 0.0175</td>\n",
        "      <td> 0.705</td>\n",
        "      <td>     </td>\n",
        "      <td>        </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 206,
       "text": [
        "       Stats          Median Survival              5y Survival                Log-Rank         \n",
        "  # Patients # Events          Median  Lower Upper        Surv   Lower  Upper     chi2        p\n",
        "                                                                                  6.83  0.00898\n",
        "0         39       19            5.01    1.8   NaN       0.526   0.389  0.712                  \n",
        "1          9        8             1.1  0.953   NaN       0.111  0.0175  0.705                  \n",
        "\n",
        "[3 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 206
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, clin.Smoking_history.ix[combo.index])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th colspan=\"2\" halign=\"left\">Stats</th>\n",
        "      <th colspan=\"3\" halign=\"left\">Median Survival</th>\n",
        "      <th colspan=\"3\" halign=\"left\">5y Survival</th>\n",
        "      <th colspan=\"2\" halign=\"left\">Log-Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th># Patients</th>\n",
        "      <th># Events</th>\n",
        "      <th>Median</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>Surv</th>\n",
        "      <th>Lower</th>\n",
        "      <th>Upper</th>\n",
        "      <th>chi2</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>   </td>\n",
        "      <td>   </td>\n",
        "      <td>     </td>\n",
        "      <td>     </td>\n",
        "      <td>    </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td>      </td>\n",
        "      <td> 1.14</td>\n",
        "      <td> 0.285</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yes</th>\n",
        "      <td> 41</td>\n",
        "      <td> 24</td>\n",
        "      <td> 1.85</td>\n",
        "      <td> 1.15</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.399</td>\n",
        "      <td> 0.273</td>\n",
        "      <td> 0.585</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>no</th>\n",
        "      <td>  7</td>\n",
        "      <td>  3</td>\n",
        "      <td> 5.01</td>\n",
        "      <td> 1.43</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 0.714</td>\n",
        "      <td> 0.447</td>\n",
        "      <td>     1</td>\n",
        "      <td>     </td>\n",
        "      <td>      </td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 207,
       "text": [
        "         Stats          Median Survival             5y Survival               Log-Rank       \n",
        "    # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2      p\n",
        "                                                                                  1.14  0.285\n",
        "yes         41       24            1.85  1.15   NaN       0.399  0.273  0.585                \n",
        "no           7        3            5.01  1.43   NaN       0.714  0.447      1                \n",
        "\n",
        "[3 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 207
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p2 = p53_mut\n",
      "p2.name = 'TP53'\n",
      "p2 = p2.map({True:'Mut.',False:'WT'})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 208
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "draw_survival_curves(del_3p.map({0:'3p +/+', 1:'3p +/-'}), surv, p2, legend=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rUuRKc44NxprLusDjAT2qVWfcv7tx4wZOnTqF8+fPIz8/H/369dNZgERkuHhs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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x9110a90>"
       ]
      }
     ],
     "prompt_number": 209
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='p53/3p'>This UPMC cohort confirmed that patients with the TP53 / 3p aggregate event have worse prognosis than patients with TP53 mutation alone.</div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_cox_ph(surv, del_3p.ix[true_index(p53_mut)], print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        coef exp(coef) se(coef)    z     p\n",
        "feature 1.92      6.84     1.03 1.87 0.061\n",
        "\n",
        "Likelihood ratio test=6.37  on 1 df, p=0.0116  n= 35, number of events= 20 \n",
        "   (11 observations deleted due to missingness)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 210
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "exp(1.92), exp(1.92) -  exp(1.92 - 1.03)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 211,
       "text": [
        "(6.8209584692907494, 4.3858288180008751)"
       ]
      }
     ],
     "prompt_number": 211
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.crosstab(p53_mut, del_3p.ix[p53_mut.index].fillna('M'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>d</th>\n",
        "      <th>False</th>\n",
        "      <th>True</th>\n",
        "      <th>M</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TP53</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>False</th>\n",
        "      <td> 5</td>\n",
        "      <td>  8</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>True </th>\n",
        "      <td> 7</td>\n",
        "      <td> 28</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>2 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 212,
       "text": [
        "d      False  True   M\n",
        "TP53                  \n",
        "False      5     8   4\n",
        "True       7    28  11\n",
        "\n",
        "[2 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 212
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "two_hit_both = combine(p53_mut>0, del_3p) == 'both'\n",
      "no_cna = true_index(del_3p.ix[true_index(p53_mut==False)].isnull())\n",
      "no_cna = pd.Series(False, index=no_cna)\n",
      "two_hit = two_hit_both.append(no_cna).ix[keepers].dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 213
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(two_hit.ix[keepers].dropna(), surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AfHx8sH37dokjM3+JiYk4cOCAuF1ZWQkbGxvJa0SY5PWkz4Q6RMa0ZcsWODo6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\nSKaY5ImIiGSKSZ6IiEim/j9+ubhzik1wbgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8f31f90>"
       ]
      }
     ],
     "prompt_number": 214
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Coin Tests"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This was a suggestion from a review.  The R [coin](http://cran.r-project.org/web/packages/coin/index.html) package allows for exact permutation tests for log-rank statistics."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load_ext rmagic\n",
      "coin = robjects.packages.importr('coin')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The rmagic extension is already loaded. To reload it, use:\n",
        "  %reload_ext rmagic\n"
       ]
      }
     ],
     "prompt_number": 215
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Test 1: p53/3p Worse Survival than wt"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "th2 = two_hit[true_index(clin.Age_Dx < 75)].dropna()\n",
      "time = robjects.r.c(*list(surv.unstack().days.ix[th2.index]))\n",
      "event = robjects.r.c(*list(surv.unstack().event.ix[th2.index]))\n",
      "g = robjects.r.c(*list(th2*1.))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 216
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i time,event,g\n",
      "\n",
      "exdata <- data.frame(time = time,\n",
      "                     event = event,\n",
      "                     group = factor(g))\n",
      "surv_test(Surv(time, event) ~ group, data=exdata, \n",
      "          distribution=exact(), alternative='less')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\n",
        "\tExact Logrank Test\n",
        "\n",
        "data:  Surv(time, event) by group (0, 1)\n",
        "Z = -1.5691, p-value = 0.05894\n",
        "alternative hypothesis: less\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 217
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Test 2: p53/3p Worse Survival than p53"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = del_3p.ix[true_index(p53_mut)].dropna()*1.\n",
      "f = f.ix[clin.Age_Dx < 77]\n",
      "g = robjects.r.c(*list(f))\n",
      "time = robjects.r.c(*list(surv.unstack().days.ix[f.index]))\n",
      "event = robjects.r.c(*list(surv.unstack().event.ix[f.index]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 218
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cox(f, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 219,
       "text": [
        "hazard       exp(coef)      6.84\n",
        "             exp(-coef)     0.15\n",
        "             lower .95      0.91\n",
        "             upper .95     51.20\n",
        "LR           stat           6.37\n",
        "             df             1.00\n",
        "             p              0.01\n",
        "concordance  stat           0.61\n",
        "             se             0.05\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 219
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i time,event,g\n",
      "\n",
      "exdata <- data.frame(time = time,\n",
      "                     event = event,\n",
      "                     group = factor(g))\n",
      "surv_test(Surv(time, event) ~ group, data=exdata, \n",
      "          distribution=exact(), alternative='less')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\n",
        "\tExact Logrank Test\n",
        "\n",
        "data:  Surv(time, event) by group (0, 1)\n",
        "Z = -2.4025, p-value = 0.009194\n",
        "alternative hypothesis: less\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 220
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Test p53/3p Worst Survival than 3p"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f = p53_mut.ix[true_index(del_3p)].dropna()*1.\n",
      "g = robjects.r.c(*list(f))\n",
      "time = robjects.r.c(*list(surv.unstack().days.ix[f.index]))\n",
      "event = robjects.r.c(*list(surv.unstack().event.ix[f.index]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 221
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%R -i time,event,g\n",
      "\n",
      "exdata <- data.frame(time = time,\n",
      "                     event = event,\n",
      "                     group = factor(g))\n",
      "surv_test(Surv(time, event) ~ group, data=exdata, \n",
      "          distribution=exact(), alternative='less')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "\n",
        "\tExact Logrank Test\n",
        "\n",
        "data:  Surv(time, event) by group (0, 1)\n",
        "Z = 0.0197, p-value = 0.5014\n",
        "alternative hypothesis: less\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 222
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "MIR548K Copy Number"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We do not have expression data for this cohort so we use copy number.  The trend seems to hold here but we do not report this in the manuscript. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_upmc = cn_upmc.ix[:, two_hit.index]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 223
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(cn_upmc.ix['MIR548K'].ix[0].dropna()>0, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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myGS60I0cORKbNm1CWloaPvvsMxQVFcHPzw/PPvus2KGRiOwWhGqNfKeLuv4oIJKySRt+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\n/wESaJuS/uZ6vQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc53eb90>"
       ]
      }
     ],
     "prompt_number": 224
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "st = (two_hit + 1).add(2*(cn_upmc.ix['MIR548K'].ix[0] > 0)).replace(3,0).replace(4,3)\n",
      "st = st.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 225
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mir = cn_upmc.ix['MIR548K'].ix[0].ix[two_hit.index]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 226
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "eq = cn_upmc.ix['MIR548K'].ix[0].ix[two_hit.index]\n",
      "#eq = cn_upmc.xs('11q13.3',0,1).median()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 227
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "st = (two_hit + 1).add(2*(eq > 1)).replace(3,1).replace(4,3)\n",
      "st = st.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 228
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(st, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VMCrbFB4ejt27dwMASkpKEBsbK9bQoNq7cuUKNmzYgKioKKlDsWlXr15FcXEx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\nnR3rAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x105e6510>"
       ]
      }
     ],
     "prompt_number": 229
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_cox_ph(surv, st, print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         coef exp(coef) se(coef)    z     p\n",
        "feature 0.433      1.54    0.243 1.78 0.074\n",
        "\n",
        "Likelihood ratio test=3.14  on 1 df, p=0.0763  n= 48, number of events= 27 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 230
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "HPV+ Combined Patients"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_upmc = pd.DataFrame({i: gistic[t] for i,t in cn_tumor.iteritems() if t in gistic})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 231
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_upmc = pd.DataFrame({i: gistic[t] for i,t in cn_tumor.iteritems() if t in gistic})\n",
      "del_3p_upmc = cn_upmc.xs('3p14.2',0,1).median().ix[true_index(clin.HPV=='Positive')].dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 232
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "surv_combined = pd.concat([surv, clinical.survival.survival_5y])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 233
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_2 = cn_upmc.groupby(level=1).median().ix[:, true_index(clin.HPV=='Positive')].dropna(1, how='all')\n",
      "cn_3 = cn.features.copy().ix[:, ti(clinical.hpv)]\n",
      "cn_3.index = cn_3.index.get_level_values(1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 234
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_4 = cn_2.join(cn_3)\n",
      "cn_4 = cn_4.dropna(1, how='all').dropna(how='any')\n",
      "cn_d = (cn_4 < 0)\n",
      "cn_d = cn_d[cn_d.sum(1) > 0]\n",
      "\n",
      "cn_a = (cn_4 > 0)\n",
      "cn_a = cn_a[cn_a.sum(1) > 0]\n",
      "\n",
      "cn_all = pd.concat([cn_a, cn_d], keys=['Amplification','Deletion'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 235
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cox_screen(cn_all, surv_combined).sort([('LR','p')]).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th colspan=\"4\" halign=\"left\">hazard</th>\n",
        "      <th colspan=\"4\" halign=\"left\">LR</th>\n",
        "      <th colspan=\"2\" halign=\"left\">concordance</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>exp(-coef)</th>\n",
        "      <th>exp(coef)</th>\n",
        "      <th>lower .95</th>\n",
        "      <th>upper .95</th>\n",
        "      <th>df</th>\n",
        "      <th>p</th>\n",
        "      <th>stat</th>\n",
        "      <th>q</th>\n",
        "      <th>se</th>\n",
        "      <th>stat</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Deletion</th>\n",
        "      <th>3p14.2</th>\n",
        "      <td> 0.18</td>\n",
        "      <td> 5.54</td>\n",
        "      <td> 1.51</td>\n",
        "      <td> 20.33</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 8.21</td>\n",
        "      <td> 0.23</td>\n",
        "      <td> 0.08</td>\n",
        "      <td> 0.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"4\" valign=\"top\">Amplification</th>\n",
        "      <th>8q24.21</th>\n",
        "      <td> 6.06</td>\n",
        "      <td> 0.17</td>\n",
        "      <td> 0.04</td>\n",
        "      <td>  0.76</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 7.44</td>\n",
        "      <td> 0.23</td>\n",
        "      <td> 0.08</td>\n",
        "      <td> 0.68</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13q22.1</th>\n",
        "      <td> 0.16</td>\n",
        "      <td> 6.25</td>\n",
        "      <td> 1.82</td>\n",
        "      <td> 21.46</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 7.38</td>\n",
        "      <td> 0.23</td>\n",
        "      <td> 0.06</td>\n",
        "      <td> 0.69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20q11.22</th>\n",
        "      <td> 7.82</td>\n",
        "      <td> 0.13</td>\n",
        "      <td> 0.02</td>\n",
        "      <td>  0.99</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 6.69</td>\n",
        "      <td> 0.23</td>\n",
        "      <td> 0.08</td>\n",
        "      <td> 0.64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8p11.23</th>\n",
        "      <td> 7.71</td>\n",
        "      <td> 0.13</td>\n",
        "      <td> 0.02</td>\n",
        "      <td>  1.02</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 6.44</td>\n",
        "      <td> 0.23</td>\n",
        "      <td> 0.08</td>\n",
        "      <td> 0.60</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 10 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 236,
       "text": [
        "                            hazard                                   LR                    concordance      \n",
        "                        exp(-coef)  exp(coef)  lower .95  upper .95  df     p  stat     q           se  stat\n",
        "Deletion      3p14.2          0.18       5.54       1.51      20.33   1  0.00  8.21  0.23         0.08  0.75\n",
        "Amplification 8q24.21         6.06       0.17       0.04       0.76   1  0.01  7.44  0.23         0.08  0.68\n",
        "              13q22.1         0.16       6.25       1.82      21.46   1  0.01  7.38  0.23         0.06  0.69\n",
        "              20q11.22        7.82       0.13       0.02       0.99   1  0.01  6.69  0.23         0.08  0.64\n",
        "              8p11.23         7.71       0.13       0.02       1.02   1  0.01  6.44  0.23         0.08  0.60\n",
        "\n",
        "[5 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 236
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "del_3p_all = cn_all.ix['Deletion'].ix['3p14.2']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 237
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p53_upmc = hit_matrix.ix['TP53', true_index(clin.HPV=='Positive')].dropna()\n",
      "p53_tcga = mut.features.ix['TP53', true_index(clinical.hpv==True)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 238
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='3p_hpv_OR'>We noted that TP53 mutation is very rare in HPV+ patients, consistent with the idea that mutation confers little selective advantage once TP53 is inactivated by HPV.</div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p53_all = (mut.features.ix['TP53'].append(hit_matrix.ix['TP53']>0)).map({True:'mut',False:'wt'})\n",
      "hpv_all = clinical.hpv.append(clin.HPV=='Positive')\n",
      "pd.crosstab(hpv_all, p53_all)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>TP53</th>\n",
        "      <th>mut</th>\n",
        "      <th>wt</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HPV</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>False</th>\n",
        "      <td> 257</td>\n",
        "      <td> 69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>True </th>\n",
        "      <td>   2</td>\n",
        "      <td> 52</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>2 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 239,
       "text": [
        "TP53   mut  wt\n",
        "HPV           \n",
        "False  257  69\n",
        "True     2  52\n",
        "\n",
        "[2 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 239
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "1 / fisher_exact_test(hpv_all, p53_all)['odds_ratio']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 240,
       "text": [
        "0.010326249625860521"
       ]
      }
     ],
     "prompt_number": 240
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fisher_exact_test(hit_matrix.ix['TP53']>0, clin.HPV=='Positive')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 241,
       "text": [
        "odds_ratio    0.00e+00\n",
        "p             5.14e-06\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 241
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print len(combine(hit_matrix.ix['TP53']>0, clin.HPV=='Positive'))\n",
      "pd.crosstab(hit_matrix.ix['TP53']>0, clin.HPV)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "74\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th>HPV</th>\n",
        "      <th>Negative</th>\n",
        "      <th>Positive</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TP53</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>False</th>\n",
        "      <td> 17</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>True </th>\n",
        "      <td> 46</td>\n",
        "      <td>  0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>2 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 242,
       "text": [
        "HPV    Negative  Positive\n",
        "TP53                     \n",
        "False        17        11\n",
        "True         46         0\n",
        "\n",
        "[2 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 242
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fisher_exact_test(hpv_all, p53_all)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 243,
       "text": [
        "odds_ratio    9.68e+01\n",
        "p             1.17e-27\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 243
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='3p_hpv_surv'>Moreover, the 25 HPV+ patients harboring a 3p deletion had significantly worse prognosis than the 34 HPV+ patients without the 3p event.</div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(del_3p_all, surv_combined)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+wWOPcSQrIiKyTBbTXO/t7S2+ateuDaVSKb6Kiorw0ksvVbquTqdDcHAwpk6dipSUFHh6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\nTUkeAKYkDwBTkgeAKckDwFQSyn1SlW+g5QAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xa2c0ed0>"
       ]
      }
     ],
     "prompt_number": 244
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "log_rank(del_3p_all, surv_combined).ix['p']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 245,
       "text": [
        "0.0038336554312233214"
       ]
      }
     ],
     "prompt_number": 245
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_cox_ph(surv_combined, del_3p_all, print_desc=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        coef exp(coef) se(coef)    z      p\n",
        "feature 1.71      5.54    0.663 2.58 0.0098\n",
        "\n",
        "Likelihood ratio test=8.21  on 1 df, p=0.00417  n= 59, number of events= 13 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 246
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "exp(1.71), exp(1.71) -  exp(1.71 - .663)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 247,
       "text": [
        "(5.5289614776240041, 2.6798704663347901)"
       ]
      }
     ],
     "prompt_number": 247
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "UPMC Validation Cohort Figures"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Figure 3a"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p53_mut.name = 'TP53'\n",
      "del_3p.name = '3p'\n",
      "combo = combine(p53_mut, del_3p)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 248
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots(figsize=(3,2.5))\n",
      "c = {'TP53': colors_th[1], 'both': colors_th[0]}\n",
      "draw_survival_curve(combo[combo.isin(['both','TP53'])], surv, colors=c, ax=ax)\n",
      "ax.legend(loc='lower left', frameon=False)\n",
      "prettify_ax(ax)\n",
      "fig.tight_layout()\n",
      "fig.savefig('/cellar/users/agross/figures/fig3a.pdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/1vsPU5Nvd7569SpKSkpw9+5dODs7AwBiY2ORkZFhsIRh1PP398f8+fONHYZGZtGsrFY2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\nhtETSxqG0RNLGobR0/8H7f9HkeFsN7sAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10c47710>"
       ]
      }
     ],
     "prompt_number": 249
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figdir = '/cellar/users/agross/figures/'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 253
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "c = combo[combo.isin(['both','TP53'])]\n",
      "c = c.replace({'both': 'TP53 and 3p'})\n",
      "df = pd.concat([c, surv[:,'days'], surv[:,'event']],\n",
      "                keys=['Molecular Status', 'Days to Death/Censoring', 'Death Indicator'],\n",
      "                axis=1).dropna()\n",
      "df.to_csv(figdir + 'fig3a.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 254
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots(figsize=(3,2.5))\n",
      "surv_fit = get_surv_fit(surv, combo)\n",
      "tt = surv_fit['5y Survival']\n",
      "b = (tt['Surv']).plot(kind='bar', ax=ax, color=[colors_th[2], colors_th[0], \n",
      "                                                colors_th[3], colors_th[1]],\n",
      "         yerr=[tt.Surv-tt.Lower, tt.Upper-tt.Surv], ecolor='black')\n",
      "ax.set_ylabel('5Y Survival')\n",
      "ax.set_yticks([0, .5, 1.0])\n",
      "bar_labels = ['{}\\n\\n{}'.format(j,int(v)) for j,v in \n",
      "              surv_fit['Stats']['# Patients'].iteritems()]\n",
      "ax.set_xticklabels(bar_labels, rotation=0);\n",
      "prettify_ax(ax)\n",
      "fig.tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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f5TDWYvQWGlOUkpJC7du3JwcHBxo0aBDl5OQYrO0TJ05Qly5dRO3z+uuvU1JSEhERLV26\nlGbNmqWP0p5iaWlJhYWFlJubSzY2NsJ6iURCBQUFLd6er68vWVpakqWlJUkkEuFrS0tL6tChA1lY\nWAjLzs7ONHXqVPrtt9+IiCg+Pp5sbGzI1taWbG1tyc/PT3jdbdu2kZeXF8nlcoqMjKT79++3SL1m\ncxlNYWEhoqKisH37dhQUFKBHjx6YOXOmsctqMonEcDMH1NTUwN3d3WDtXr9+HTU1NcJ5vDt37gjL\nPj4+2Lt3r7B86dIlZGZmIikpSdj32LFjUKlUUKlUuHbt0UnYzMxM/O1vf8ORI0dw+/ZtWFlZYfHi\nxS1Sr9mE5vvvv0dISAgGDRoEOzs7vPHGG7hw4QKmTZuG+fPno2fPnrC3t8fMmTPrnYRtSRqNBjNm\nzICLiwuCgoLw66+/AgDWrl0LDw8PuLi4YMmSJQCANWvWYPfu3fj73/+ODz74AMCjqyiGDBkCe3t7\nhIeHQ6lU6mzz5MmTePHFFxEXFwe5XI5u3boJH8AkJSXBw8MDTk5OWLp0qbCPVCpFQUEBunfvDrVa\nDblcLjy3c+dOeHp6wtPTs968rEePHkWnTp0gk8kQGRkpnG+Ljo7G4sWLERgYiA8//PAZf4KAt7c3\nwsPDhe8hJycHvr6+T213/PhxjBkzBj179oSjoyOioqJw4cKFZ24fgP6OaUxRbW0tERFVV1fTokWL\naMSIERQdHU0eHh6UnZ1NpaWlFBoaSh9++GGLt33ixAmSSCS0YcMG0mg0tGHDBurRowclJydT586d\nKS8vj+7fv0+9e/em9evXExFRdHQ0rV69mogedc9sbGzo1KlTVFlZSeHh4bR8+fImtWthYUEffPAB\nqdVqmjdvHg0cOJCSk5NJoVDQrVu3KC8vj3r27EmHDx8mot+7YTdu3CBbW1vhtSQSCY0bN46USiX9\n5z//ITs7O1Kr1ZSbm0tubm70ww8/UHl5OcXExNC8efOI6FEXs127dvTLL7806ef0ZBcwLCyM/v3v\nfwvLeXl51L17d0pISCAiInd3dxo6dCi5uLhQ3759KT09nYge/a7rft8VFRU0ZcoUmj17dpNq0MWs\nQkNElJqaKvSRP/vsM4qOjqa5c+cKz6ekpNDLL7/c4u2eOHGCXFxc6q1zdXWlgQMH0q5du4R1X331\nFQUGBhLR08c0I0eOFLZbuXIlxcTENKldNzc3YfnUqVPUsWNHGjJkCB06dEhY/+mnn9LUqVOJ6Pc/\n3Nzc3KdCk5GRISxbW1vTrVu3aNWqVTRnzhxhfW5uLnl7ewvfw+LFi3XW+Xgbj4cmNDSUrK2thWOW\nNm3a0JtvvkkqlYpUKhUFBwfTkSNHqLKykrZs2UKenp5UVlYm7L9lyxaSSqVkb29P33//fZPraIzZ\ndM/qDB8+HNXV1di3bx+mTJmCe/fuwcfHR3je1dW1wSuyW8Lj7QCAs7Mzzp49i44dOwrrOnbsKEwK\nLJFI6h1TeHn9PuuTVCoVJhLWpe74BACsra1RW1uLW7duYdKkSZDJZJDJZJg1axbu3Lmj87Uen9W7\nroZbt25h69atwmsFBATg/v370Gg0kEgkDZ6wbiqJRILdu3cLxyz5+fnYunUrbG1tYWtri4yMDERE\nREAmkyE2NhZubm747rvvhP1jY2OhUqmQmJiI0aNH4+HDh82uRfi+n/kVWolt27Zh48aNAB79sseO\nHQs3NzdYW1sjP//3y4pv3LiBDh066KWGx9tRq9XIz89H3759kZOTI6zPycmBt7c3gKevhHg8QM96\ngO7u7o6UlBThj/HevXv45z//qXO/htp1d3fH3LlzhddSKpX48ccfYWFh8Uw11nny51DnypUr2L17\nd711Go0G9vb2WLFiBT777DMAj/5RzJo1CyUlJU06DtTFbELTrl07rF27FllZWVCr1dixYwekUins\n7Ozwr3/9C7/88gsKCgqwfv16vPbaa3qpoaCgABs3boRKpUJSUhL69u2LmJgYrFq1Cnfu3EFRURGW\nL1+OmJgYAIClpaXWX7K2P6SmioyMxNq1a1FYWIiioiJMnDgRBw4cqLeNhYUFNBoNqqqqtL6ORCJB\nZGQk9u7di8uXL6OqqgrLli3DokWLWqTOujYaYmVlhTlz5uDUqVNQq9XYunUrlEol+vfvD3d3d7z/\n/vu4ceMGVCoVVq9ejaCgILi4uDxzPWYzGs2wYcMQGxuLIUOGQKlUolevXkhNTcX69esRFhaGiIgI\nFBUV4a233sKUKVNavH2JRILg4GB89913WLx4MRQKBT7//HN4enoiLy8PCoUCDx8+RGxsrHDv0aBB\ngzBjxgw899xzT3XVnlzW1faTy7Nnz8bNmzcRGBiIyspKTJw4EQsWLKi3/fPPPw+FQoG2bduiqKhI\n6+sGBQVhxYoVGD16NO7cuYOXXnpJeAcQU2dDtTamc+fO2Lp1K2JiYnD37l0EBQXhyy+/hKWlJWbM\nmIHr16+jd+/e0Gg0eOmll3Do0KEmv3ajNVJL/CtoxaZNm4aAgAAsXLjQ2KWwVsJsumeNMfP/G0wk\nDg0Me7adtX5m3z1jTCx+p2FMJA4NYyJxaBgTiUPDmEgcGsZE4tAwJhKHhjGRODSMicShYUwkDg1j\nInFoGBOJQ8OYSBwaxkTi0DAmEoeGMZE4NIyJZLahSU5Oho+PDxwcHNCvXz+kpxtnludjx44hMDAQ\n9vb2+POf/4yrV6+isrISMTExkMvlaNOmDWJiYow2rWJoaKgwnlndPKrGlJiYWK8emUwGa2vrekPk\n6l2LDDnYyty+fZscHR3p4sWLVF1dTVu2bKk32ryh5Ofnk1wup7S0NKqqqqJVq1ZRYGAgJSUl0eDB\ng6m8vJzu379P4eHhtHLlSoPXR0Tk7e1NGo3GKG03xYULF6h79+5UUVFhsDbN8p2G/u8O7+rqakgk\nElRXV9cb5NtQ6gYnHzRoEGxsbLBw4UJcuXIFUqkUGo1GGKGypqbGKPWpVCrY2NhAKjXNP5OHDx8i\nOjoa27ZtM+wMegaLp4lZs2YNSSQSkkgkJJVK6dtvvzV4Dfn5+XTt2jVhOSMjQxhUPDg4WKivS5cu\npFKpDF5fVlYWtWvXjnr27Emurq40duxYvcxP01zbt2+nMWPGGLxdswzN5cuXycnJiY4fP05VVVW0\nYsUK6tSpkzDKvDGkpqaSl5cXvf/++7Ro0SIKDQ2l/Px8un37Nr344otNmiGgpZ0+fZpeeeUVysrK\notLSUoqOjqahQ4cavI6GVFdXU4cOHej8+fMGb9ssQ7NmzRqaOHFivXUODg505coVg9fy4MEDGjdu\nHLVp04b27NlDRETdunWjY8eOCdscOHCAXnjhBYPX9qT79++TRCIRZiEzpi+++IL69OljlLbNZlja\nx9XW1kKtVtdbJ5VK4ejoaNA6qqurER4ejoCAAGRnZwvtazSaevUZozYASE1NhVwuR//+/QE8Ooaw\nsLCAjY2NwWt50s6dOzFmzBijtG2aR3h6FhERgbS0NKSlpUGtVmPz5s3o3LmzMFq/oezfvx+2trbY\ntWtXvVCMGjUKq1evxp07d1BcXIyPP/4YkZGRBq0NeDTlYmxsLHJzc6FUKvHee+8hMjISlpbG/V+r\n0WiQlpaGAQMGGKcAo7y/mYCDBw+Sn58f2djY0MCBA+nGjRsGr+Gvf/0rSaXSehOzWllZ0a1bt+it\nt94iJycncnZ2psWLFxvtY9/33nuP3NzcyMHBgcaOHUvFxcVGqeNxFy5cIDs7O6qpqTFK+zzCJmMi\nmWX3jLFnwaFhTCQODWMicWgYE4lDw5hI/x9Je3rGTMuFqQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xca2b390>"
       ]
      }
     ],
     "prompt_number": 250
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Figure 3b"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = subplots(figsize=(3,2.5))\n",
      "draw_survival_curve(del_3p_all, surv_combined, ax=ax, colors=[colors_th[3], colors_th[2]])\n",
      "ax.legend().set_visible(False)\n",
      "prettify_ax(ax)\n",
      "fig.tight_layout()\n",
      "fig.savefig('/cellar/users/agross/figures/fig3d.pdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x2e62ae10>"
       ]
      }
     ],
     "prompt_number": 255
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.concat([del_3p_all, surv_combined[:,'days'], surv_combined[:,'event']],\n",
      "                keys=['3p Deletion', 'Days to Death/Censoring', 'Death Indicator'],\n",
      "                axis=1).dropna()\n",
      "df.to_csv(figdir + 'fig3ba.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 260
    }
   ],
   "metadata": {}
  }
 ]
}