{
 "metadata": {
  "name": "",
  "signature": "sha256:3845a55a0fd4d9374975997cea01dcc4a61e81b62c43cbcf4d377bcf647728d3"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<h1 class=\"alert alert-info\">HNSCC HPV- Cohort <small>  <i class=\"icon-zoom-in\"></i>  3p Deletion Exploration</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": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "importing IPython notebook from Imports.ipynb\n",
        "Populating the interactive namespace from numpy and matplotlib"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "changing to source dirctory"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "populating namespace with data"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rppa = cancer.load_data('RPPA')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Exploration of Chromosomal Instability"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* We know chromosomal instability has a large role in cancer, lets see if we can find any good correlates \n",
      "* For chromosomal instability I'm using the total number of amplified or deleted lesions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_by_gene = FH.get_gistic_gene_matrix(run.data_path, cancer.name)\n",
      "#cn.features.index = cn.features.index.droplevel(2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cin = (cn_by_gene.groupby(level='Cytoband').median().abs() >= 1).sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_by_gene.index = map(lambda s: s.split('.')[0], cn_by_gene.index.get_level_values(0))\n",
      "cin = (cn_by_gene.groupby(level=0).median().abs() >= 1).sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cn_by_gene = FH.get_gistic_gene_matrix(run.data_path, cancer.name)\n",
      "cn_by_gene = cn_by_gene.ix[[i for i in cn_by_gene.index if i[2] in rna.df.index]]\n",
      "pct_del = (cn_by_gene < 0).sum() / (1.*len(cn_by_gene))\n",
      "pct_amp = (cn_by_gene > 0).sum() / (1.*len(cn_by_gene))\n",
      "pct_altered = pct_amp + pct_del\n",
      "pct_altered.name = 'CIN'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "draw_survival_curves(pct_altered, surv, hpv.dropna())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAADgCAYAAABb5v/DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhBBCCDHZ/wPa7Ywt3XFgDQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x78a9550>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "violin_plot_pandas(hpv, pct_altered)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEeCAYAAACNLn6mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHwAsQ/ADgGUIfgCwDMEPAJYh+AHAMr8AjDEMHKcIpMUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7d06f50>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = mut.df.ix[(mut.df.sum(1).order() > 5)]\n",
      "tt = lambda a,b: kruskal_pandas(b,a)\n",
      "s = screen_feature(cin, tt, df.clip_upper(1.))\n",
      "s.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 style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>H</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>TP53</th>\n",
        "      <td> 48.88</td>\n",
        "      <td> 2.72e-12</td>\n",
        "      <td> 4.59e-09</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CASP8</th>\n",
        "      <td> 24.06</td>\n",
        "      <td> 9.33e-07</td>\n",
        "      <td> 7.87e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HRAS</th>\n",
        "      <td> 19.74</td>\n",
        "      <td> 8.85e-06</td>\n",
        "      <td> 4.98e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PRDM9</th>\n",
        "      <td> 12.88</td>\n",
        "      <td> 3.31e-04</td>\n",
        "      <td> 1.40e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FAM83G</th>\n",
        "      <td> 11.12</td>\n",
        "      <td> 8.56e-04</td>\n",
        "      <td> 2.89e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PIK3CA</th>\n",
        "      <td> 10.77</td>\n",
        "      <td> 1.03e-03</td>\n",
        "      <td> 2.89e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SPEF2</th>\n",
        "      <td>  9.82</td>\n",
        "      <td> 1.73e-03</td>\n",
        "      <td> 4.16e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PCLO</th>\n",
        "      <td>  9.47</td>\n",
        "      <td> 2.09e-03</td>\n",
        "      <td> 4.40e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DOPEY2</th>\n",
        "      <td>  8.73</td>\n",
        "      <td> 3.14e-03</td>\n",
        "      <td> 5.88e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAPC4</th>\n",
        "      <td>  8.05</td>\n",
        "      <td> 4.54e-03</td>\n",
        "      <td> 7.58e-01</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "            H         p         q\n",
        "TP53    48.88  2.72e-12  4.59e-09\n",
        "CASP8   24.06  9.33e-07  7.87e-04\n",
        "HRAS    19.74  8.85e-06  4.98e-03\n",
        "PRDM9   12.88  3.31e-04  1.40e-01\n",
        "FAM83G  11.12  8.56e-04  2.89e-01\n",
        "PIK3CA  10.77  1.03e-03  2.89e-01\n",
        "SPEF2    9.82  1.73e-03  4.16e-01\n",
        "PCLO     9.47  2.09e-03  4.40e-01\n",
        "DOPEY2   8.73  3.14e-03  5.88e-01\n",
        "SNAPC4   8.05  4.54e-03  7.58e-01\n",
        "\n",
        "[10 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = mut.df.ix[(mut.df.sum(1).order() > 5)]\n",
      "tt = lambda a,b: kruskal_pandas(b,a)\n",
      "s = screen_feature(pct_altered, tt, df.clip_upper(1.))\n",
      "s.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 style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>H</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>TP53</th>\n",
        "      <td> 43.15</td>\n",
        "      <td> 5.06e-11</td>\n",
        "      <td> 8.54e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CASP8</th>\n",
        "      <td> 23.81</td>\n",
        "      <td> 1.06e-06</td>\n",
        "      <td> 8.98e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HRAS</th>\n",
        "      <td> 19.56</td>\n",
        "      <td> 9.73e-06</td>\n",
        "      <td> 5.47e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PRDM9</th>\n",
        "      <td> 13.10</td>\n",
        "      <td> 2.95e-04</td>\n",
        "      <td> 1.24e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FAM83G</th>\n",
        "      <td> 10.82</td>\n",
        "      <td> 1.01e-03</td>\n",
        "      <td> 3.28e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PIK3CA</th>\n",
        "      <td> 10.54</td>\n",
        "      <td> 1.17e-03</td>\n",
        "      <td> 3.28e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>DOPEY2</th>\n",
        "      <td> 10.16</td>\n",
        "      <td> 1.44e-03</td>\n",
        "      <td> 3.44e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PCLO</th>\n",
        "      <td>  9.92</td>\n",
        "      <td> 1.63e-03</td>\n",
        "      <td> 3.44e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SPEF2</th>\n",
        "      <td>  9.60</td>\n",
        "      <td> 1.95e-03</td>\n",
        "      <td> 3.64e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>SNAPC4</th>\n",
        "      <td>  9.41</td>\n",
        "      <td> 2.16e-03</td>\n",
        "      <td> 3.64e-01</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "            H         p         q\n",
        "TP53    43.15  5.06e-11  8.54e-08\n",
        "CASP8   23.81  1.06e-06  8.98e-04\n",
        "HRAS    19.56  9.73e-06  5.47e-03\n",
        "PRDM9   13.10  2.95e-04  1.24e-01\n",
        "FAM83G  10.82  1.01e-03  3.28e-01\n",
        "PIK3CA  10.54  1.17e-03  3.28e-01\n",
        "DOPEY2  10.16  1.44e-03  3.44e-01\n",
        "PCLO     9.92  1.63e-03  3.44e-01\n",
        "SPEF2    9.60  1.95e-03  3.64e-01\n",
        "SNAPC4   9.41  2.16e-03  3.64e-01\n",
        "\n",
        "[10 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Screening for copy-number correlates of chromosomal instability "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* These are all fragile sites usually around large genes ( http://www.sciencedirect.com/science/article/pii/S0304383505008232, http://www.sciencedirect.com/science/article/pii/S0168952512000303 ) "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = cn.features.ix['Deletion']\n",
      "df.index = df.index.get_level_values(0)\n",
      "screen_feature(pct_altered.ix[keepers_o], rev_kruskal, df < 0).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 style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>H</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>5q11.2</th>\n",
        "      <td> 66.52</td>\n",
        "      <td> 3.47e-16</td>\n",
        "      <td> 1.66e-14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1p13.2</th>\n",
        "      <td> 59.27</td>\n",
        "      <td> 1.38e-14</td>\n",
        "      <td> 3.30e-13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3p12.2</th>\n",
        "      <td> 57.34</td>\n",
        "      <td> 3.66e-14</td>\n",
        "      <td> 4.60e-13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5q35.3</th>\n",
        "      <td> 57.25</td>\n",
        "      <td> 3.83e-14</td>\n",
        "      <td> 4.60e-13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5q15</th>\n",
        "      <td> 56.71</td>\n",
        "      <td> 5.06e-14</td>\n",
        "      <td> 4.86e-13</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "            H         p         q\n",
        "5q11.2  66.52  3.47e-16  1.66e-14\n",
        "1p13.2  59.27  1.38e-14  3.30e-13\n",
        "3p12.2  57.34  3.66e-14  4.60e-13\n",
        "5q35.3  57.25  3.83e-14  4.60e-13\n",
        "5q15    56.71  5.06e-14  4.86e-13\n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = cn.features.ix['Amplification']\n",
      "df.index = df.index.get_level_values(0)\n",
      "screen_feature(pct_altered.ix[keepers_o], tt, df > 0).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 style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>H</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>12p13.33</th>\n",
        "      <td> 70.64</td>\n",
        "      <td> 4.28e-17</td>\n",
        "      <td> 1.11e-15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7q21.3</th>\n",
        "      <td> 65.69</td>\n",
        "      <td> 5.29e-16</td>\n",
        "      <td> 6.87e-15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17q12</th>\n",
        "      <td> 58.10</td>\n",
        "      <td> 2.50e-14</td>\n",
        "      <td> 2.16e-13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2q31.2</th>\n",
        "      <td> 55.93</td>\n",
        "      <td> 7.50e-14</td>\n",
        "      <td> 4.88e-13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2q11.2</th>\n",
        "      <td> 55.41</td>\n",
        "      <td> 9.81e-14</td>\n",
        "      <td> 5.10e-13</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "              H         p         q\n",
        "12p13.33  70.64  4.28e-17  1.11e-15\n",
        "7q21.3    65.69  5.29e-16  6.87e-15\n",
        "17q12     58.10  2.50e-14  2.16e-13\n",
        "2q31.2    55.93  7.50e-14  4.88e-13\n",
        "2q11.2    55.41  9.81e-14  5.10e-13\n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = subplots(1,2, figsize=(12,4))\n",
      "violin_plot_pandas(cn.features.ix[('Deletion','5q11.2'), keepers_o].iloc[0], \n",
      "                   pct_altered, ax=axs[0])\n",
      "violin_plot_pandas(cn.features.ix[('Deletion','3p14.2'), keepers_o].iloc[0], \n",
      "                   pct_altered, ax=axs[1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAs0AAAEeCAYAAABxDSidAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ncYGAuxE/hJEAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x79a0f50>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Honing in on 3p"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "del_3p = cn.features.ix[('Deletion','3p14.2')].iloc[0] < 0\n",
      "del_3p = del_3p.ix[keepers_o]\n",
      "del_3p.name = 'del_3p'\n",
      "old.name = 'age_over_75'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Other chromosomal regions associated with 3p loss."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = screen_feature(del_3p, fisher_exact_test, cn.features.ix['Deletion'] < 0)\n",
      "s.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 style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>odds_ratio</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>(3p14.2, Lesion)</th>\n",
        "      <td>     inf</td>\n",
        "      <td> 1.01e-50</td>\n",
        "      <td> 4.83e-49</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(3p14.3, Lesion)</th>\n",
        "      <td> 8976.00</td>\n",
        "      <td> 9.28e-47</td>\n",
        "      <td> 2.23e-45</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(3p25.3, Lesion)</th>\n",
        "      <td>  273.00</td>\n",
        "      <td> 2.03e-34</td>\n",
        "      <td> 3.26e-33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(3p12.2, Lesion)</th>\n",
        "      <td>  225.17</td>\n",
        "      <td> 4.28e-33</td>\n",
        "      <td> 5.14e-32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(18q23, Lesion)</th>\n",
        "      <td>   14.72</td>\n",
        "      <td> 3.40e-13</td>\n",
        "      <td> 3.26e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(18q21.2, Lesion)</th>\n",
        "      <td>    8.26</td>\n",
        "      <td> 4.02e-09</td>\n",
        "      <td> 3.13e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(9p23, Lesion)</th>\n",
        "      <td>    9.21</td>\n",
        "      <td> 4.83e-09</td>\n",
        "      <td> 3.13e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(5q11.2, Lesion)</th>\n",
        "      <td>   10.85</td>\n",
        "      <td> 5.22e-09</td>\n",
        "      <td> 3.13e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(4p16.3, Lesion)</th>\n",
        "      <td>   11.64</td>\n",
        "      <td> 1.36e-08</td>\n",
        "      <td> 7.26e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(5q15, Lesion)</th>\n",
        "      <td>    8.99</td>\n",
        "      <td> 4.02e-08</td>\n",
        "      <td> 1.93e-07</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "                   odds_ratio         p         q\n",
        "(3p14.2, Lesion)          inf  1.01e-50  4.83e-49\n",
        "(3p14.3, Lesion)      8976.00  9.28e-47  2.23e-45\n",
        "(3p25.3, Lesion)       273.00  2.03e-34  3.26e-33\n",
        "(3p12.2, Lesion)       225.17  4.28e-33  5.14e-32\n",
        "(18q23, Lesion)         14.72  3.40e-13  3.26e-12\n",
        "(18q21.2, Lesion)        8.26  4.02e-09  3.13e-08\n",
        "(9p23, Lesion)           9.21  4.83e-09  3.13e-08\n",
        "(5q11.2, Lesion)        10.85  5.22e-09  3.13e-08\n",
        "(4p16.3, Lesion)        11.64  1.36e-08  7.26e-08\n",
        "(5q15, Lesion)           8.99  4.02e-08  1.93e-07\n",
        "\n",
        "[10 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* Looking at thresholding on the 3p chromosomal arm.\n",
      "* The GISTIC2 threshold is -.1, which is nearly optimal here... best to not mess with it."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "args = [run.data_path] + [cancer.name]\n",
      "f = '{}/analyses/{}/CopyNumber_Gistic2/all_lesions.conf_99.txt'.format(*args)\n",
      "lesions = pd.read_table(f, index_col=0)\n",
      "lesions = lesions.set_index('Descriptor').select(lambda s: s.startswith('TCGA'), 1)\n",
      "lesions = lesions.groupby(lambda s: s[:12],1).first()\n",
      "lesions.index = map(str.strip, lesions.index)\n",
      "\n",
      "levels = lesions.ix['3p14.2'].ix[2].ix[keepers_o]\n",
      "calls = lesions.ix['3p14.2'].ix[0].ix[keepers_o]\n",
      "calls.name = 'del_3p'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#del_3p = lesions.ix['3p14.2'].ix[0] > 0\n",
      "p53_mut = mut.features.ix['TP53']>0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = subplots(1, 2, figsize=(12,4))\n",
      "levels.hist(bins=50, ax=axs[0])\n",
      "s = pd.DataFrame({c: log_rank(levels < c, surv) for c in levels.order()[2:-2]})\n",
      "s.ix['p'].plot(ax=axs[1])\n",
      "axs[0].set_xlabel('Copy Number')\n",
      "axs[1].set_xlabel('Copy Number')\n",
      "axs[1].set_ylabel('p-value')\n",
      "axs[1].set_yscale('log')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAsMAAAEUCAYAAADOXztIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7ae9390>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Extended Data Figure 2: Chromosome 3p Characterization"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Extended Data Figure 2a: HPV- Patients"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "colors = rcParams['axes.color_cycle']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "coords = pd.read_csv('../Extra_Data/gene_coords.csv', index_col=0)\n",
      "\n",
      "cn_by_gene = FH.get_gistic_gene_matrix(run.data_path, cancer.name)\n",
      "cn_by_gene = cn_by_gene.reset_index(level=[0,1])\n",
      "cn_by_gene = cn_by_gene.join(coords).set_index(['chrom','start', 'Cytoband','Locus ID'], append=True)\n",
      "cn_by_gene = cn_by_gene.ix[:,keepers_o].dropna(axis=1)\n",
      "\n",
      "ch_3 = cn_by_gene.xs('3', level='chrom')\n",
      "p_arm = pd.Series(ch_3.index.get_level_values('Cytoband').map(lambda s: 'p' in s), ch_3.index)\n",
      "p_arm = ch_3[p_arm].sortlevel('start')\n",
      "p_arm = p_arm.ix[:-1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#df = rna.df.xs('01', 1, 1).ix[p_arm.index.get_level_values(0)].dropna()#\n",
      "#df = rna.features.ix['real'].ix[p_arm.index.get_level_values(0)].dropna()\n",
      "df = rna.df.xs('01', 1, 1).ix[p_arm.index.get_level_values(0)].dropna()\n",
      "#df = df.ix[screen_feature(del_3p, kruskal_pandas, df).p < .05]\n",
      "df = df.ix[:,keepers_o].dropna(1)\n",
      "test = lambda s: get_cox_ph_ms(surv, s, [age, old], interactions='just_first')\n",
      "exp = df.apply(test, axis=1)\n",
      "exp = exp.sort_index()\n",
      "exp.index = p_arm.sort_index().ix[exp.index].index\n",
      "\n",
      "exp2 = -exp.LR.map(log10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8,4))\n",
      "ax1 = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)\n",
      "ax2 = plt.subplot2grid((7,1), (3,0), rowspan=3, colspan=1)\n",
      "ax3 = plt.subplot2grid((7,1), (6,0), rowspan=1, colspan=1)\n",
      "\n",
      "bands = p_arm.reset_index().groupby('Cytoband')\n",
      "bands = bands.first().start.order()\n",
      "starts = p_arm.index.get_level_values('start')\n",
      "for i,(idx,v) in enumerate(bands.iteritems()):\n",
      "    try:\n",
      "        r = plt.Rectangle((v,0), bands.iloc[i+1]-v, 1, lw=2, \n",
      "                          alpha=.5, fc=colors[i%2])\n",
      "    except:\n",
      "        r = plt.Rectangle((v,0), starts[-1] - v, 1, lw=2, \n",
      "                          alpha=.5, fc=colors[i%2])\n",
      "    ax3.add_artist(r)\n",
      "ax3.set_xticks(bands)\n",
      "b = [{0:s,1:''}[(i%2)] for i,s in enumerate(bands.index)]\n",
      "ax3.set_xticklabels(b, rotation=270+45, ha='left')\n",
      "\n",
      "ax1.scatter(starts, (p_arm < -.1).sum(1), s=40, c=colors[2], \n",
      "            edgecolor='grey', lw=.5, alpha=.5)\n",
      "ax2.scatter(exp2.index.get_level_values('start'), exp2, s=40, \n",
      "            c=colors[4], edgecolor='grey', alpha=.7)\n",
      "\n",
      "#ax1.set_ybound(215,255)\n",
      "ax1.set_ylabel('# of Patients\\nwith Deletion\\n\\n', ha='center', va='center')\n",
      "ax2.set_ybound(-.5,4.)\n",
      "ax2.set_ylabel('mRNA Survival\\nAssociation\\n(Log10 p)\\n\\n\\n', ha='center', va='center')\n",
      "ax3.set_yticks([])\n",
      "fig.tight_layout()\n",
      "for ax in [ax1, ax2, ax3]:\n",
      "    ax.set_xbound(starts[0], \n",
      "                  starts[-1])\n",
      "    ax.set_xticks(bands)\n",
      "\n",
      "for n, ss in exp2.order().tail(2).iteritems():\n",
      "    ax2.annotate(n[0], (n[1], ss), xytext=(15, -5), \n",
      "                 textcoords='offset points', \n",
      "                 arrowprops=dict(arrowstyle=\"->\"))\n",
      "ax1.set_xticklabels('')\n",
      "ax2.set_xticklabels('')\n",
      "ax2.set_yticks([0,1,2,3,4,5])\n",
      "fig.tight_layout()\n",
      "fig.savefig('/cellar/users/agross/figures/supp_2a.pdf', transparent=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAEZCAYAAACabV/4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xadaad50>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Extended Data Figure 2b: HPV+ Patients"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "levels = lesions.ix['3p14.2'].ix[2]\n",
      "calls = lesions.ix['3p14.2'].ix[0]\n",
      "calls.name = 'del_3p'\n",
      "\n",
      "cn_by_gene = FH.get_gistic_gene_matrix(run.data_path, cancer.name)\n",
      "cn_by_gene = cn_by_gene.reset_index(level=[0,1])\n",
      "cn_by_gene = cn_by_gene.join(coords).set_index(['chrom','start', 'Cytoband','Locus ID'], append=True)\n",
      "cn_by_gene = cn_by_gene.ix[:,true_index(hpv==True)].dropna(axis=1)\n",
      "\n",
      "ch_3 = cn_by_gene.xs('3', level='chrom')\n",
      "p_arm = pd.Series(ch_3.index.get_level_values('Cytoband').map(lambda s: 'p' in s), ch_3.index)\n",
      "p_arm = ch_3[p_arm].sortlevel('start')\n",
      "p_arm = p_arm.ix[:-1] #bad annotation"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rna_all = cancer.load_data('mRNASeq')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#df = rna_all.features.ix['real'].ix[p_arm.index.get_level_values(0)].dropna()\n",
      "df = rna_all.df.xs('01', 1, 1).ix[p_arm.index.get_level_values(0)].dropna()\n",
      "df = df.ix[:,true_index(hpv.dropna())]\n",
      "#df = df.ix[screen_feature(del_3p, kruskal_pandas, df).p < .05]\n",
      "\n",
      "test = lambda s: get_cox_ph_ms(surv, s, [age, old], interactions='just_first')\n",
      "exp = df.apply(test, axis=1)\n",
      "\n",
      "exp = exp.sort_index()\n",
      "exp.index = p_arm.sort_index().ix[exp.index].index\n",
      "\n",
      "exp2 = -exp.LR.map(log10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8,4))\n",
      "ax1 = plt.subplot2grid((7,1), (0,0), rowspan=3, colspan=1)\n",
      "ax2 = plt.subplot2grid((7,1), (3,0), rowspan=3, colspan=1)\n",
      "ax3 = plt.subplot2grid((7,1), (6,0), rowspan=1, colspan=1)\n",
      "\n",
      "bands = p_arm.reset_index().groupby('Cytoband')\n",
      "bands = bands.first().start.order()\n",
      "starts = p_arm.index.get_level_values('start')\n",
      "for i,(idx,v) in enumerate(bands.iteritems()):\n",
      "    try:\n",
      "        r = plt.Rectangle((v,0), bands.iloc[i+1]-v, 1, lw=2, \n",
      "                          alpha=.5, fc=colors[i%2])\n",
      "    except:\n",
      "        r = plt.Rectangle((v,0), starts[-1] - v, 1, lw=2, \n",
      "                          alpha=.5, fc=colors[i%2])\n",
      "    ax3.add_artist(r)\n",
      "ax3.set_xticks(bands)\n",
      "b = [{0:s,1:''}[(i%2)] for i,s in enumerate(bands.index)]\n",
      "ax3.set_xticklabels(b, rotation=270+45, ha='left')\n",
      "\n",
      "ax1.scatter(starts, (p_arm < -.1).sum(1), s=40, c=colors[2], \n",
      "            edgecolor='grey', alpha=1)\n",
      "ax2.scatter(exp2.index.get_level_values('start'), exp2, s=40, \n",
      "            c=colors[4], edgecolor='grey', alpha=1)\n",
      "\n",
      "#ax1.set_ybound(215,255)\n",
      "ax1.set_ylabel('# of Patients\\nwith Deletion\\n\\n', ha='center', va='center')\n",
      "ax2.set_ybound(-.5,3)\n",
      "ax2.set_ylabel('mRNA Survival\\nAssociation\\n(Log10 p)\\n\\n\\n', ha='center', va='center')\n",
      "ax3.set_yticks([])\n",
      "fig.tight_layout()\n",
      "for ax in [ax1, ax2, ax3]:\n",
      "    ax.set_xbound(starts[0], \n",
      "                  starts[-1])\n",
      "    ax.set_xticks(bands)\n",
      "\n",
      "for n, ss in exp2.order().tail(2).iteritems():\n",
      "    ax2.annotate(n[0], (n[1], ss), xytext=(15, -5), \n",
      "                 textcoords='offset points', \n",
      "                 arrowprops=dict(arrowstyle=\"->\"))\n",
      "ax1.set_xticklabels('')\n",
      "ax2.set_xticklabels('')\n",
      "ax2.set_yticks([0,1,2,3,4,5])\n",
      "fig.tight_layout()\n",
      "fig.savefig('/cellar/users/agross/figures/supp_2b.pdf', transparent=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAEZCAYAAACabV/4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noVAoFN2O/w8XJUsuloPl+AAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xfa0b290>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Extended Data Figure 3: TP53 Association with Chromosomal Instability"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t1 = screen_feature(pct_del.ix[keepers_o], rev_kruskal, mut.df.ix[mut.df.ix[:,keepers_o].sum(1) > 5]>0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t1.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 style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>H</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>HRAS</th>\n",
        "      <td> 22.17</td>\n",
        "      <td> 2.49e-06</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CASP8</th>\n",
        "      <td> 21.31</td>\n",
        "      <td> 3.91e-06</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TP53</th>\n",
        "      <td> 21.20</td>\n",
        "      <td> 4.14e-06</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>FBN1</th>\n",
        "      <td> 12.34</td>\n",
        "      <td> 4.44e-04</td>\n",
        "      <td> 0.13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>COL4A4</th>\n",
        "      <td> 11.35</td>\n",
        "      <td> 7.54e-04</td>\n",
        "      <td> 0.18</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PRDM9</th>\n",
        "      <td>  8.64</td>\n",
        "      <td> 3.28e-03</td>\n",
        "      <td> 0.42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>OR8K5</th>\n",
        "      <td>  8.25</td>\n",
        "      <td> 4.08e-03</td>\n",
        "      <td> 0.42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PON1</th>\n",
        "      <td>  8.10</td>\n",
        "      <td> 4.43e-03</td>\n",
        "      <td> 0.42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AFF3</th>\n",
        "      <td>  7.93</td>\n",
        "      <td> 4.85e-03</td>\n",
        "      <td> 0.42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CDH10</th>\n",
        "      <td>  7.85</td>\n",
        "      <td> 5.08e-03</td>\n",
        "      <td> 0.42</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "            H         p     q\n",
        "HRAS    22.17  2.49e-06  0.00\n",
        "CASP8   21.31  3.91e-06  0.00\n",
        "TP53    21.20  4.14e-06  0.00\n",
        "FBN1    12.34  4.44e-04  0.13\n",
        "COL4A4  11.35  7.54e-04  0.18\n",
        "PRDM9    8.64  3.28e-03  0.42\n",
        "OR8K5    8.25  4.08e-03  0.42\n",
        "PON1     8.10  4.43e-03  0.42\n",
        "AFF3     7.93  4.85e-03  0.42\n",
        "CDH10    7.85  5.08e-03  0.42\n",
        "\n",
        "[10 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t1 = screen_feature(pct_altered.ix[keepers_o], rev_kruskal, \n",
      "                    cn.features.ix['Deletion'] < 0)\n",
      "t2 = screen_feature(pct_altered.ix[keepers_o], rev_kruskal, \n",
      "                    cn.features.ix['Amplification'] > 0)\n",
      "del t1['q']\n",
      "t1['p_bonf'] = t1.p * len(t1)\n",
      "del t2['q']\n",
      "t2['p_bonf'] = t2.p * len(t2)\n",
      "pd.concat([t1.head(6), t2.head(6)], keys=['Deletion', 'Amplification'])"
     ],
     "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></th>\n",
        "      <th></th>\n",
        "      <th>H</th>\n",
        "      <th>p</th>\n",
        "      <th>p_bonf</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"6\" valign=\"top\">Deletion</th>\n",
        "      <th>(5q11.2, Lesion)</th>\n",
        "      <td> 66.52</td>\n",
        "      <td> 3.47e-16</td>\n",
        "      <td> 1.66e-14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(1p13.2, Lesion)</th>\n",
        "      <td> 59.27</td>\n",
        "      <td> 1.38e-14</td>\n",
        "      <td> 6.60e-13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(3p12.2, Lesion)</th>\n",
        "      <td> 57.34</td>\n",
        "      <td> 3.66e-14</td>\n",
        "      <td> 1.76e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(5q35.3, Lesion)</th>\n",
        "      <td> 57.25</td>\n",
        "      <td> 3.83e-14</td>\n",
        "      <td> 1.84e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(5q15, Lesion)</th>\n",
        "      <td> 56.71</td>\n",
        "      <td> 5.06e-14</td>\n",
        "      <td> 2.43e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(3p14.2, Lesion)</th>\n",
        "      <td> 54.49</td>\n",
        "      <td> 1.56e-13</td>\n",
        "      <td> 7.51e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"6\" valign=\"top\">Amplification</th>\n",
        "      <th>(12p13.33, Lesion)</th>\n",
        "      <td> 70.64</td>\n",
        "      <td> 4.28e-17</td>\n",
        "      <td> 1.11e-15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(7q21.3, Lesion)</th>\n",
        "      <td> 65.69</td>\n",
        "      <td> 5.29e-16</td>\n",
        "      <td> 1.37e-14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(17q12, Lesion)</th>\n",
        "      <td> 58.10</td>\n",
        "      <td> 2.50e-14</td>\n",
        "      <td> 6.49e-13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(2q31.2, Lesion)</th>\n",
        "      <td> 55.93</td>\n",
        "      <td> 7.50e-14</td>\n",
        "      <td> 1.95e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(2q11.2, Lesion)</th>\n",
        "      <td> 55.41</td>\n",
        "      <td> 9.81e-14</td>\n",
        "      <td> 2.55e-12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(12q15, Lesion)</th>\n",
        "      <td> 49.86</td>\n",
        "      <td> 1.65e-12</td>\n",
        "      <td> 4.29e-11</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>12 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "                                      H         p    p_bonf\n",
        "Deletion      (5q11.2, Lesion)    66.52  3.47e-16  1.66e-14\n",
        "              (1p13.2, Lesion)    59.27  1.38e-14  6.60e-13\n",
        "              (3p12.2, Lesion)    57.34  3.66e-14  1.76e-12\n",
        "              (5q35.3, Lesion)    57.25  3.83e-14  1.84e-12\n",
        "              (5q15, Lesion)      56.71  5.06e-14  2.43e-12\n",
        "              (3p14.2, Lesion)    54.49  1.56e-13  7.51e-12\n",
        "Amplification (12p13.33, Lesion)  70.64  4.28e-17  1.11e-15\n",
        "              (7q21.3, Lesion)    65.69  5.29e-16  1.37e-14\n",
        "              (17q12, Lesion)     58.10  2.50e-14  6.49e-13\n",
        "              (2q31.2, Lesion)    55.93  7.50e-14  1.95e-12\n",
        "              (2q11.2, Lesion)    55.41  9.81e-14  2.55e-12\n",
        "              (12q15, Lesion)     49.86  1.65e-12  4.29e-11\n",
        "\n",
        "[12 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='segs_p53'> \n",
      "\n",
      "<b>Chromosomal segments associated with TP53 mutation in HPV- pateints.  </b>\n",
      "<p>In text as : \"While mutation of TP53 has previously been associated with chromosomal instability, we find that TP53 associates with 3p chromosomal loss far more frequently than it does with deletions in other chromosomal regions with similar fragile sites, suggesting a selective advantage of this combination of events for tumor cells.\"</p></div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t3 = screen_feature(p53_mut.ix[keepers_o], fisher_exact_test, \n",
      "                    cn.features.ix['Deletion'] < 0).head(5)\n",
      "t4 = screen_feature(p53_mut.ix[keepers_o], fisher_exact_test, \n",
      "                    cn.features.ix['Amplification'] > 0).head()\n",
      "pd.concat([t3, t4], keys=['Deletion', 'Amplification'])"
     ],
     "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></th>\n",
        "      <th></th>\n",
        "      <th>odds_ratio</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">Deletion</th>\n",
        "      <th>(3p14.3, Lesion)</th>\n",
        "      <td> 6.59</td>\n",
        "      <td> 3.56e-07</td>\n",
        "      <td> 8.55e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(3p14.2, Lesion)</th>\n",
        "      <td> 6.59</td>\n",
        "      <td> 3.56e-07</td>\n",
        "      <td> 8.55e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(3p25.3, Lesion)</th>\n",
        "      <td> 5.49</td>\n",
        "      <td> 1.81e-06</td>\n",
        "      <td> 2.89e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(3p12.2, Lesion)</th>\n",
        "      <td> 5.07</td>\n",
        "      <td> 6.28e-06</td>\n",
        "      <td> 7.54e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(10p15.3, Lesion)</th>\n",
        "      <td> 4.00</td>\n",
        "      <td> 6.74e-04</td>\n",
        "      <td> 6.47e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"5\" valign=\"top\">Amplification</th>\n",
        "      <th>(3q26.33, Lesion)</th>\n",
        "      <td> 6.35</td>\n",
        "      <td> 9.00e-08</td>\n",
        "      <td> 2.34e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(8q24.21, Lesion)</th>\n",
        "      <td> 4.24</td>\n",
        "      <td> 3.98e-05</td>\n",
        "      <td> 5.18e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(12p13.33, Lesion)</th>\n",
        "      <td> 3.21</td>\n",
        "      <td> 2.63e-03</td>\n",
        "      <td> 2.28e-02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(9p24.1, Lesion)</th>\n",
        "      <td> 0.40</td>\n",
        "      <td> 8.88e-03</td>\n",
        "      <td> 5.77e-02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>(18p11.31, Lesion)</th>\n",
        "      <td> 2.61</td>\n",
        "      <td> 1.69e-02</td>\n",
        "      <td> 8.51e-02</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "                                  odds_ratio         p         q\n",
        "Deletion      (3p14.3, Lesion)          6.59  3.56e-07  8.55e-06\n",
        "              (3p14.2, Lesion)          6.59  3.56e-07  8.55e-06\n",
        "              (3p25.3, Lesion)          5.49  1.81e-06  2.89e-05\n",
        "              (3p12.2, Lesion)          5.07  6.28e-06  7.54e-05\n",
        "              (10p15.3, Lesion)         4.00  6.74e-04  6.47e-03\n",
        "Amplification (3q26.33, Lesion)         6.35  9.00e-08  2.34e-06\n",
        "              (8q24.21, Lesion)         4.24  3.98e-05  5.18e-04\n",
        "              (12p13.33, Lesion)        3.21  2.63e-03  2.28e-02\n",
        "              (9p24.1, Lesion)          0.40  8.88e-03  5.77e-02\n",
        "              (18p11.31, Lesion)        2.61  1.69e-02  8.51e-02\n",
        "\n",
        "[10 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Association of percentage of genes amplified or deleted TP53 mutation in HPV- pateints."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kruskal_pandas(p53_mut.ix[keepers_o], pct_del)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "H    2.12e+01\n",
        "p    4.14e-06\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = subplots(2,1, figsize=(4,6))\n",
      "p53_mut = mut.features.ix['TP53'].ix[keepers_o].map({0:'TP53_WT', 1:'TP53_mut'})\n",
      "violin_plot_pandas(p53_mut, pct_del, ax=axs[0])\n",
      "axs[0].set_ylabel('% of Genes Deleted')\n",
      "violin_plot_pandas(p53_mut, pct_amp, ax=axs[1])\n",
      "axs[1].set_ylabel('% of Genes Amplified')\n",
      "for ax in axs:\n",
      "    prettify_ax(ax)\n",
      "    ax.set_xlabel('')\n",
      "fig.tight_layout()\n",
      "fig.savefig('/cellar/users/agross/figures/p53_cin_supp.pdf', transparent=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAARUAAAGnCAYAAACZ7MOcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAwNDWGGECgMDQ1j5f5aZBb7cxCQkAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xffac8d0>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Is the 3p deletion more predictive than chromosomal instability?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* It seems like the 3p14 effect is stronger than an numerical chromosomal instability\n",
      "* From text: \"While the (3p14.2) deletion event tracked with chromosomal instability as a whole, in multivariate modeling we found that this event was responsible for the majority of the prognostic impact.\""
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "violin_plot_pandas(del_3p, pct_altered)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEeCAYAAACNLn6mAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1025d6d0>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Across All Patients"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "del_3p.name = 'del_3p'\n",
      "pct_del.name = 'CIN'\n",
      "fmla = 'Surv(days, event) ~ del_3p + CIN + age_over_75 + age'\n",
      "m1 = get_cox_ph(surv, covariates=[del_3p, pct_del, old, age], formula=fmla, \n",
      "                print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "               coef exp(coef) se(coef)      z      p\n",
        "del_3p       0.5015     1.651    0.160  3.136 0.0017\n",
        "CIN          0.0634     1.065    0.119  0.531 0.6000\n",
        "age_over_75  0.3120     1.366    0.119  2.629 0.0086\n",
        "age         -0.0375     0.963    0.136 -0.276 0.7800\n",
        "\n",
        "Likelihood ratio test=26.4  on 4 df, p=2.57e-05  n= 250, number of events= 102 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fmla = 'Surv(days, event) ~ CIN + age_over_75 + age'\n",
      "m2 = get_cox_ph(surv, covariates=[del_3p, pct_del, old, age], formula=fmla, \n",
      "                print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "               coef exp(coef) se(coef)      z      p\n",
        "CIN          0.2833     1.328   0.0969  2.925 0.0034\n",
        "age_over_75  0.2618     1.299   0.1168  2.241 0.0250\n",
        "age         -0.0352     0.965   0.1329 -0.265 0.7900\n",
        "\n",
        "Likelihood ratio test=14.3  on 3 df, p=0.00253  n= 250, number of events= 102 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LR_test(m1, m2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 100,
       "text": [
        "0.00048909784102555808"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "In TP53 Mutated Patients"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "del_3p.name = 'del_3p'\n",
      "p53_mut = mut.features.ix['TP53'].ix[keepers_o]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fmla = 'Surv(days, event) ~ del_3p + CIN + age_over_75 + age'\n",
      "m1 = get_cox_ph(surv, covariates=[del_3p.ix[ti(p53_mut==1)], pct_del, old, age], formula=fmla, \n",
      "                print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "               coef exp(coef) se(coef)      z      p\n",
        "del_3p       0.4081     1.504    0.157  2.599 0.0094\n",
        "CIN         -0.0794     0.924    0.128 -0.619 0.5400\n",
        "age_over_75  0.2561     1.292    0.125  2.050 0.0400\n",
        "age         -0.0115     0.989    0.137 -0.084 0.9300\n",
        "\n",
        "Likelihood ratio test=13.8  on 4 df, p=0.00779  n= 202, number of events= 92 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fmla = 'Surv(days, event) ~ CIN + age_over_75 + age'\n",
      "m2 = get_cox_ph(surv, covariates=[del_3p.ix[ti(p53_mut==1)], pct_del, old, age], formula=fmla, \n",
      "                print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "               coef exp(coef) se(coef)      z     p\n",
        "CIN          0.0836     1.087    0.110  0.760 0.450\n",
        "age_over_75  0.2179     1.243    0.123  1.778 0.075\n",
        "age         -0.0148     0.985    0.132 -0.112 0.910\n",
        "\n",
        "Likelihood ratio test=4.85  on 3 df, p=0.183  n= 202, number of events= 92 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LR_test(m1, m2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "0.0027052080739890017"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "In TP53 Wild-Type Patients"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fmla = 'Surv(days, event) ~ del_3p + CIN + age_over_75 + age'\n",
      "m1 = get_cox_ph(surv, covariates=[del_3p.ix[ti(p53_mut==0)], pct_del, old, age], formula=fmla, \n",
      "                print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "              coef exp(coef) se(coef)      z    p\n",
        "del_3p      -0.663     0.515    0.753 -0.881 0.38\n",
        "CIN          1.435     4.201    0.559  2.569 0.01\n",
        "age_over_75  0.800     2.226    0.425  1.883 0.06\n",
        "age         -0.546     0.579    0.559 -0.977 0.33\n",
        "\n",
        "Likelihood ratio test=14.7  on 4 df, p=0.00529  n= 48, number of events= 10 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fmla = 'Surv(days, event) ~ CIN + age_over_75 + age'\n",
      "m2 = get_cox_ph(surv, covariates=[del_3p.ix[ti(p53_mut==0)], pct_del, old, age], formula=fmla, \n",
      "                print_desc=True, interactions=False);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "              coef exp(coef) se(coef)      z      p\n",
        "CIN          1.054     2.870    0.323  3.268 0.0011\n",
        "age_over_75  0.896     2.451    0.418  2.143 0.0320\n",
        "age         -0.486     0.615    0.559 -0.869 0.3800\n",
        "\n",
        "Likelihood ratio test=13.9  on 3 df, p=0.00305  n= 48, number of events= 10 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LR_test(m1, m2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "0.36082942654866224"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Figure 3c: Multivariate models with 3p and CIN"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pct_del.name = 'CIN'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = subplots(3, 1, figsize=(3.5,3.5), sharey=True, sharex=False)\n",
      "\n",
      "fmla = 'Surv(days, event) ~ del_3p + CIN + age_over_75 + age'\n",
      "pts = [keepers_o, ti(p53_mut==1), ti(p53_mut==0)]\n",
      "color_list = ['grey', '#a1d99b','#9ecae1']\n",
      "\n",
      "for i, pt in enumerate(pts):\n",
      "    ax = axs[i]\n",
      "    m1 = get_cox_ph(surv, covariates=[del_3p.ix[pt], pct_del, old, age], formula=fmla, \n",
      "                    print_desc=False, interactions=False);\n",
      "    ci = convert_robj(robjects.r.summary(m1)[7])\n",
      "    ci = ci.ix[['del_3p','CIN']]\n",
      "    ci.index = ['3p', 'CIN']\n",
      "    ci = ci.ix[['CIN', '3p']]\n",
      "    haz = ci['exp(coef)']\n",
      "    for j,h in enumerate(haz):\n",
      "        ax.scatter(h, j, marker='s', s=100, color=color_list[i], \n",
      "                   edgecolors=['black'], zorder=10)\n",
      "        ax.plot(*zip(*((ci.iloc[j]['lower .95'],j), (ci.iloc[j]['upper .95'],j))), \n",
      "                lw=3, ls='-', marker='o', dash_joinstyle='bevel', color=color_list[i])\n",
      "    ax.axvline(1, ls='--', color='black')\n",
      "    ax.set_xscale('log')\n",
      "    ax.set_ybound(-.5,1.5)\n",
      "    ax.set_xticks([.25,.5, .8, 1, 1.5, 2,4, 8])\n",
      "    ax.set_xticklabels([.25,.5, .8, 1, 1.5, 2, 4, 8])\n",
      "    ax.set_yticks([0,1])\n",
      "    ax.set_yticklabels(['CIN', '3p'])\n",
      "    prettify_ax(ax)\n",
      "axs[0].set_xbound(.66,2.5)\n",
      "axs[1].set_xbound(.66,2.5)\n",
      "axs[2].set_xbound(.25,20)\n",
      "axs[2].set_xticks([.5, 1, 2,4, 8])\n",
      "axs[2].set_xticklabels([.5, 1, 2, 4, 8])\n",
      "axs[2].set_xlabel('Hazard Ratio')\n",
      "fig.tight_layout()\n",
      "fig.savefig('/cellar/users/agross/figures/fig2c.pdf', transparent=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPAAAADyCAYAAABznUiZAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7aae690>"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figdir = '/cellar/users/agross/figures/'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df1 = pd.concat([del_3p, pct_del, old, age, surv[:,'days'], surv[:,'event']],\n",
      "                 keys=['3p','CIN','age_over_75','age',  'Days to Death/Censoring', 'Death Indicator'],\n",
      "                 axis=1).dropna()\n",
      "df1.to_csv(figdir + 'fig2c_all.csv')\n",
      "df1.ix[ti(p53_mut>0)].to_csv(figdir + 'fig2c_TP53_mut.csv')\n",
      "df1.ix[ti(p53_mut==0)].to_csv(figdir + 'fig2c_TP53_WT.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 118
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Alternate Multivariate Model with the Kitchen Sink"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = subplots(3, 1, figsize=(3.5,4.5), sharey=True, sharex=True)\n",
      "\n",
      "fmla = 'Surv(days, event) ~ del_3p + CIN + age_over_75'\n",
      "fmla2 = 'Surv(days, event) ~ CIN + age_over_75'\n",
      "pts = [keepers_o, ti(p53_mut==1), ti(p53_mut==0)]\n",
      "\n",
      "color_list = ['grey', '#a1d99b','#9ecae1']\n",
      "\n",
      "for i, pt in enumerate(pts):\n",
      "    ax = axs[i]\n",
      "    m1 = get_cox_ph(surv, covariates=[del_3p.ix[pt], pct_del, old, age], formula=fmla, \n",
      "                    print_desc=False, interactions=False);\n",
      "    m2 = get_cox_ph(surv, covariates=[del_3p.ix[pt], pct_del, old, age], formula=fmla2, \n",
      "                print_desc=False, interactions=False);\n",
      "    print LR_test(m1, m2)\n",
      "    ci = convert_robj(robjects.r.summary(m1)[7])\n",
      "    #ci.index = ['3p14.2', 'CIN', 'age > 75', 'age','year','spread']\n",
      "    #ci = ci.ix[['year', 'spread', 'age','age > 75', 'CIN', '3p14.2']]\n",
      "    haz = ci['exp(coef)']\n",
      "    for j,h in enumerate(haz):\n",
      "        ax.scatter(h, j, marker='s', s=100, color=color_list[i], \n",
      "                   edgecolors=['black'], zorder=10)\n",
      "        ax.plot(*zip(*((ci.iloc[j]['lower .95'],j), (ci.iloc[j]['upper .95'],j))), \n",
      "                lw=3, ls='-', marker='o', dash_joinstyle='bevel', color=color_list[i])\n",
      "    ax.axvline(1, ls='--', color='black')\n",
      "    ax.set_xscale('log')\n",
      "    ax.set_xbound(.5,3.5)\n",
      "    ax.set_ybound(-.5,len(ci.index) - .5)\n",
      "    ax.set_xticks([.67, 1, 1.5, 2,3])\n",
      "    ax.set_xticklabels([.67, 1, 1.5, 2, 3])\n",
      "    ax.set_yticks(range(len(ci.index)))\n",
      "    ax.set_yticklabels(ci.index)\n",
      "    prettify_ax(ax)\n",
      "axs[2].set_xlabel('Hazard Ratio')\n",
      "fig.tight_layout()\n",
      "fig.savefig('/cellar/users/agross/figures/fig2c_alt.pdf', transparent=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.00049071280384\n",
        "0.00269718222184"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.418877206975"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPAAAAE6CAYAAAAoUd/0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nu+HsAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xd9d2c50>"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Even more significant in the presence of confounding variables such as age and year of diagnosis."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "year = clinical.binary_df.ix['pre_2000']\n",
      "year.name = 'year'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = subplots(3, 1, figsize=(3.5,4.5), sharey=True, sharex=True)\n",
      "\n",
      "fmla = 'Surv(days, event) ~ del_3p + CIN + age_over_75 + age + year'\n",
      "fmla2 = 'Surv(days, event) ~ CIN + age_over_75 + age + year'\n",
      "pts = [keepers_o, ti(p53_mut==1), ti(p53_mut==0)]\n",
      "\n",
      "color_list = ['grey', '#a1d99b','#9ecae1']\n",
      "\n",
      "for i, pt in enumerate(pts):\n",
      "    ax = axs[i]\n",
      "    m1 = get_cox_ph(surv, covariates=[del_3p.ix[pt], pct_del, old, age, \n",
      "                                      year == False], formula=fmla, \n",
      "                    print_desc=False, interactions=False);\n",
      "    m2 = get_cox_ph(surv, covariates=[del_3p.ix[pt], pct_del, old, age, \n",
      "                                  year == False], formula=fmla2, \n",
      "                print_desc=False, interactions=False);\n",
      "    print LR_test(m1, m2)\n",
      "    ci = convert_robj(robjects.r.summary(m1)[7])\n",
      "    #ci.index = ['3p14.2', 'CIN', 'age > 75', 'age','year','spread']\n",
      "    #ci = ci.ix[['year', 'spread', 'age','age > 75', 'CIN', '3p14.2']]\n",
      "    haz = ci['exp(coef)']\n",
      "    for j,h in enumerate(haz):\n",
      "        ax.scatter(h, j, marker='s', s=100, color=color_list[i], \n",
      "                   edgecolors=['black'], zorder=10)\n",
      "        ax.plot(*zip(*((ci.iloc[j]['lower .95'],j), (ci.iloc[j]['upper .95'],j))), \n",
      "                lw=3, ls='-', marker='o', dash_joinstyle='bevel', color=color_list[i])\n",
      "    ax.axvline(1, ls='--', color='black')\n",
      "    ax.set_xscale('log')\n",
      "    ax.set_xbound(.5,3.5)\n",
      "    ax.set_ybound(-.5,len(ci.index) - .5)\n",
      "    ax.set_xticks([.67, 1, 1.5, 2,3])\n",
      "    ax.set_xticklabels([.67, 1, 1.5, 2, 3])\n",
      "    ax.set_yticks(range(len(ci.index)))\n",
      "    ax.set_yticklabels(ci.index)\n",
      "    prettify_ax(ax)\n",
      "axs[2].set_xlabel('Hazard Ratio')\n",
      "fig.tight_layout()\n",
      "fig.savefig('/cellar/users/agross/figures/fig2c_alt.pdf', transparent=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.000985887091617\n",
        "0.00265660814229"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.286580014929"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAPAAAAE6CAYAAAAoUd/0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMIHAYP4/NeKL+sBGRjYAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f93550>"
       ]
      }
     ],
     "prompt_number": 120
    }
   ],
   "metadata": {}
  }
 ]
}