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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<h1 class=\"alert alert-info\">HNSCC HPV- Cohort <small> <i class=\"icon-repeat\"></i> Integrative Prognositic Screen</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": [
      "from Processing.Screen import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Setup Survival Screen Functions"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def surv_test(s, surv, cov_df):\n",
      "    s = s.dropna()\n",
      "    try:\n",
      "        return get_cox_ph_ms(surv, s, cov_df, return_val='LR', interactions='just_feature')\n",
      "    except:\n",
      "        return pd.Series(index=['LR','feature_p', 'fmla', 'hazzard'])\n",
      "\n",
      "\n",
      "def run_screen(screen, filters, covariates):\n",
      "    cov_df = pd.concat(covariates, axis=1)\n",
      "    keepers_o = screen.get_patient_set(filters)\n",
      "    cutoff = max(np.ceil(len(keepers_o) * .05), 10)\n",
      "    df = screen.get_data(keepers_o, cutoff)\n",
      "\n",
      "    univariate = cox_screen(df, surv)\n",
      "    vec = univariate.LR.p.sort_index()\n",
      "    univariate = pd.concat([univariate['hazard'], corrections(vec)], \n",
      "                           keys=['hazard', 'p'], axis=1)\n",
      "    #hits = univariate[univariate['q_bh'] < .2].index\n",
      "    hits = univariate.index\n",
      "    \n",
      "    full = df.ix[hits].apply(surv_test, args=(surv, cov_df,), axis=1)\n",
      "    vec = full.LR.ix[univariate.index].sort_index()\n",
      "    full = pd.concat([full[['fmla']], corrections(vec)],\n",
      "                            keys=['fmla','p'], axis=1)\n",
      "    hits = true_index(full.p.bh_all.order() < .1)\n",
      "    res = full.sort([('p','uncorrected')]).head()\n",
      "\n",
      "    return res, full, univariate, keepers_o, df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Screen for Univariate Markers of Survival"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "screen = Screen(mut, cn, rna, mirna, clinical.binary_df, surv, keepers_o)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "res, full, univariate, keepers_o, df = run_screen(screen, filters=[hpv], \n",
      "                                                  covariates=[old, age])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(mut.df.ix[:, keepers_o].sum(1) > 0).value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 78,
       "text": [
        "True     12088\n",
        "False     1070\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "(878, 250)"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='pool_size'>To analyze the resulting pool of 879 events...</div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.groupby(level=0).size()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "clinical     13\n",
        "cna          74\n",
        "mirna       238\n",
        "mutation    121\n",
        "rna         432\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len([i for i in df.ix['mutation'].index if i not in run.gene_sets])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "33"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len([i for i in df.ix['mutation'].index if i in run.gene_sets])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "88"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len([i for i in df.ix['rna'].index if i not in run.gene_sets])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "294"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len([i for i in df.ix['rna'].index if i in run.gene_sets])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "138"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(full.p.bh_all < .1).groupby(level=0).apply(pd.value_counts).unstack()"
     ],
     "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>False</th>\n",
        "      <th>True</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>clinical</th>\n",
        "      <td>   5</td>\n",
        "      <td>  8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cna</th>\n",
        "      <td>  62</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mirna</th>\n",
        "      <td> 226</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mutation</th>\n",
        "      <td> 107</td>\n",
        "      <td> 14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rna</th>\n",
        "      <td> 376</td>\n",
        "      <td> 56</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "          False  True \n",
        "clinical      5      8\n",
        "cna          62     12\n",
        "mirna       226     12\n",
        "mutation    107     14\n",
        "rna         376     56\n",
        "\n",
        "[5 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(full.p.bh_all < .1).value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "False    776\n",
        "True     102\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rr = cox_screen(df, surv)\n",
      "haz = rr['hazard'][['exp(coef)','lower .95','upper .95']]\n",
      "p = rr.LR.p\n",
      "uni = haz.join(p)\n",
      "uni['PASS'] = uni.p < .05"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "multi = full.reset_index().sort(['level_0',('p','uncorrected')]).set_index(['level_0','level_1'])\n",
      "multi.index.names = ['data_type','event']\n",
      "multi['p'] = multi['p'].clip_upper(1.)\n",
      "multi.columns = multi.columns.droplevel(0)\n",
      "multi['PASS'] = multi.bh_all < .1\n",
      "f = pd.concat([uni, multi], keys=['Univariate','Multivariate'], axis=1)\n",
      "f[('BOTH','PASS')] = f.Univariate.PASS & f.Multivariate.PASS\n",
      "f = f.sort([('BOTH','PASS'),('Multivariate','uncorrected')], ascending=[False, True])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fd = f.copy()\n",
      "fd.index = pd.MultiIndex.from_tuples([(i[0], i[1].replace('_',' ')) for i in fd.index])\n",
      "fd[('Multivariate','fmla')] = fd.Multivariate.astype(str).fmla.str.replace('\\n','')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 112
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fd.to_csv(FIGDIR + 'supplemental_table1.csv', float_format='%.2e')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(f.Univariate.PASS & f.Multivariate.PASS).value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 93,
       "text": [
        "False    796\n",
        "True      82\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hits = full.ix[ti((f.Univariate.PASS & f.Multivariate.PASS))]\n",
      "hits = hits.sort([('p','uncorrected')])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 99
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='mut_ranking'>Among mutation events, TP53 had the highest survival association overall.</div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_cox_ph(surv, df.ix['mutation'].ix['TP53'], print_desc=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        coef exp(coef) se(coef)   z      p\n",
        "feature 1.07      2.91    0.333 3.2 0.0014\n",
        "\n",
        "Likelihood ratio test=13.6  on 1 df, p=0.000228  n= 250, number of events= 102 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "exp(1.07), exp(1.07) - exp(1.07 - .333)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "(2.9153794999769969, 0.82572236974094038)"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hits.ix['mutation'].ix['TP53']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "fmla  fmla           Surv(days, event) ~ feature + old\\n\n",
        "p     uncorrected                                0.00017\n",
        "      bh_within                                   0.0103\n",
        "      bh_all                                     0.00875\n",
        "      bonf_all                                     0.149\n",
        "      bonf_within                                 0.0206\n",
        "      two_step                                    0.0514\n",
        "Name: TP53, dtype: object"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hits.ix['mutation']['p'].sort('uncorrected').head(4)"
     ],
     "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>uncorrected</th>\n",
        "      <th>bh_within</th>\n",
        "      <th>bh_all</th>\n",
        "      <th>bonf_all</th>\n",
        "      <th>bonf_within</th>\n",
        "      <th>two_step</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>TP53</th>\n",
        "      <td> 1.70e-04</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.15</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 0.05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>MUC5B</th>\n",
        "      <td> 6.34e-04</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 0.56</td>\n",
        "      <td> 0.08</td>\n",
        "      <td> 0.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>KEGG_CELL_CYCLE</th>\n",
        "      <td> 8.16e-04</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 0.72</td>\n",
        "      <td> 0.10</td>\n",
        "      <td> 0.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>REACTOME_SIGNALLING_TO_RAS</th>\n",
        "      <td> 2.62e-03</td>\n",
        "      <td> 0.06</td>\n",
        "      <td> 0.05</td>\n",
        "      <td> 2.30</td>\n",
        "      <td> 0.32</td>\n",
        "      <td> 0.30</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "                            uncorrected  bh_within  bh_all  bonf_all  bonf_within  two_step\n",
        "TP53                           1.70e-04       0.01    0.01      0.15         0.02      0.05\n",
        "MUC5B                          6.34e-04       0.02    0.02      0.56         0.08      0.12\n",
        "KEGG_CELL_CYCLE                8.16e-04       0.02    0.02      0.72         0.10      0.12\n",
        "REACTOME_SIGNALLING_TO_RAS     2.62e-03       0.06    0.05      2.30         0.32      0.30\n",
        "\n",
        "[4 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='cn_hits'>Among copy-number alterations, the most significant survival association was observed with heterozygous chromosomal deletions on the 3p arm.</div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_cox_ph(surv, df.ix['cna'].ix['del_3p14.2'], print_desc=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        coef exp(coef) se(coef)    z       p\n",
        "feature 1.26      3.54    0.369 3.43 0.00061\n",
        "\n",
        "Likelihood ratio test=16.7  on 1 df, p=4.32e-05  n= 250, number of events= 102 \n",
        "\n"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "exp(1.26), exp(1.26) - exp(1.26 - .369)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "(3.5254214873653824, 1.0878554884534357)"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "hits.ix['cna']['p'].sort('uncorrected').head(4)"
     ],
     "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>uncorrected</th>\n",
        "      <th>bh_within</th>\n",
        "      <th>bh_all</th>\n",
        "      <th>bonf_all</th>\n",
        "      <th>bonf_within</th>\n",
        "      <th>two_step</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>del_3p14.2</th>\n",
        "      <td> 5.74e-06</td>\n",
        "      <td> 3.28e-04</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 4.25e-04</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>del_3p25.3</th>\n",
        "      <td> 8.86e-06</td>\n",
        "      <td> 3.28e-04</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 6.56e-04</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>del_3p14.3</th>\n",
        "      <td> 3.05e-05</td>\n",
        "      <td> 6.10e-04</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.03</td>\n",
        "      <td> 2.26e-03</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>del_3p12.2</th>\n",
        "      <td> 3.30e-05</td>\n",
        "      <td> 6.10e-04</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.03</td>\n",
        "      <td> 2.44e-03</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 25,
       "text": [
        "            uncorrected  bh_within  bh_all  bonf_all  bonf_within  two_step\n",
        "del_3p14.2     5.74e-06   3.28e-04       0      0.01     4.25e-04         0\n",
        "del_3p25.3     8.86e-06   3.28e-04       0      0.01     6.56e-04         0\n",
        "del_3p14.3     3.05e-05   6.10e-04       0      0.03     2.26e-03         0\n",
        "del_3p12.2     3.30e-05   6.10e-04       0      0.03     2.44e-03         0\n",
        "\n",
        "[4 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hits.ix['cna']['p'].iloc[0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "uncorrected    5.74e-06\n",
        "bh_within      3.28e-04\n",
        "bh_all         1.68e-03\n",
        "bonf_all       5.04e-03\n",
        "bonf_within    4.25e-04\n",
        "two_step       1.64e-03\n",
        "Name: del_3p14.2, dtype: float64"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(df.ix['cna'].ix['del_3p14.2'], surv, figsize=(6,4))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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WncSqy2p6JwYEBEClUmHDhg3IyclBeHg4QkJCuE4dABASEoItW7bg/PnzKCwsxNKlS/Hm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\nuSeey/u75N0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8785450>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='other_hits'>Among expression events, the most significant were the microRNA mir-3170 and the P4HA1 gene expression.</div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hits.ix['rna']['p'].sort('uncorrected').head(3)"
     ],
     "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>uncorrected</th>\n",
        "      <th>bh_within</th>\n",
        "      <th>bh_all</th>\n",
        "      <th>bonf_all</th>\n",
        "      <th>bonf_within</th>\n",
        "      <th>two_step</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>SIG_PIP3_SIGNALING_IN_B_LYMPHOCYTES</th>\n",
        "      <td> 8.84e-07</td>\n",
        "      <td> 3.82e-04</td>\n",
        "      <td> 7.77e-04</td>\n",
        "      <td> 7.77e-04</td>\n",
        "      <td> 3.82e-04</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>P4HA1</th>\n",
        "      <td> 1.79e-05</td>\n",
        "      <td> 2.91e-03</td>\n",
        "      <td> 2.90e-03</td>\n",
        "      <td> 1.57e-02</td>\n",
        "      <td> 7.72e-03</td>\n",
        "      <td> 0.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>REACTOME_THROMBOXANE_SIGNALLING_THROUGH_TP_RECEPTOR</th>\n",
        "      <td> 2.26e-05</td>\n",
        "      <td> 2.91e-03</td>\n",
        "      <td> 2.90e-03</td>\n",
        "      <td> 1.99e-02</td>\n",
        "      <td> 9.78e-03</td>\n",
        "      <td> 0.01</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "                                                     uncorrected  bh_within    bh_all  bonf_all  bonf_within  two_step\n",
        "SIG_PIP3_SIGNALING_IN_B_LYMPHOCYTES                     8.84e-07   3.82e-04  7.77e-04  7.77e-04     3.82e-04      0.00\n",
        "P4HA1                                                   1.79e-05   2.91e-03  2.90e-03  1.57e-02     7.72e-03      0.01\n",
        "REACTOME_THROMBOXANE_SIGNALLING_THROUGH_TP_RECEPTOR     2.26e-05   2.91e-03  2.90e-03  1.99e-02     9.78e-03      0.01\n",
        "\n",
        "[3 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(df.ix['rna'].ix['SIG_PIP3_SIGNALING_IN_B_LYMPHOCYTES'], surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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69ev4/fff8b///Q9ZWVlo2bKl0CGRA5BOmYz616/CuUkT+B09DNm8aKPPqS9L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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x87da310>"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hits.ix['mirna']['p'].sort('uncorrected').head(4)"
     ],
     "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>uncorrected</th>\n",
        "      <th>bh_within</th>\n",
        "      <th>bh_all</th>\n",
        "      <th>bonf_all</th>\n",
        "      <th>bonf_within</th>\n",
        "      <th>two_step</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>hsa-mir-3170</th>\n",
        "      <td> 3.24e-05</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.03</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>hsa-mir-411</th>\n",
        "      <td> 2.04e-04</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.18</td>\n",
        "      <td> 0.05</td>\n",
        "      <td> 0.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>hsa-mir-100</th>\n",
        "      <td> 3.30e-04</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.29</td>\n",
        "      <td> 0.08</td>\n",
        "      <td> 0.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>hsa-mir-629</th>\n",
        "      <td> 4.04e-04</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.35</td>\n",
        "      <td> 0.10</td>\n",
        "      <td> 0.12</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "              uncorrected  bh_within  bh_all  bonf_all  bonf_within  two_step\n",
        "hsa-mir-3170     3.24e-05       0.01    0.00      0.03         0.01      0.04\n",
        "hsa-mir-411      2.04e-04       0.02    0.01      0.18         0.05      0.12\n",
        "hsa-mir-100      3.30e-04       0.02    0.01      0.29         0.08      0.12\n",
        "hsa-mir-629      4.04e-04       0.02    0.01      0.35         0.10      0.12\n",
        "\n",
        "[4 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "survival_and_stats(df.ix['mirna'].ix['hsa-mir-3170'].dropna(), surv, figsize=(6,4))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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ff2Pw4MHYtm0b9u/fj6ioKG6Axx9//IEPPvgAWVlZGDZsGGJiYuDiorlMAjUn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\nJUlMCCGEy5IkJoQQwmVJEhNCCOGy/gfWyYFd6jP5rgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x89004d0>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='clin_hits'>Finally, the clinical event with greatest prognostic ability was extra-capsular spread.</div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hits.ix['clinical']['p'].sort('uncorrected').head(4)"
     ],
     "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>uncorrected</th>\n",
        "      <th>bh_within</th>\n",
        "      <th>bh_all</th>\n",
        "      <th>bonf_all</th>\n",
        "      <th>bonf_within</th>\n",
        "      <th>two_step</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>recent_smoker</th>\n",
        "      <td> 4.70e-06</td>\n",
        "      <td> 6.11e-05</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 0.00</td>\n",
        "      <td> 6.11e-05</td>\n",
        "      <td> 3.06e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>pre_2000</th>\n",
        "      <td> 7.78e-05</td>\n",
        "      <td> 5.06e-04</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.07</td>\n",
        "      <td> 1.01e-03</td>\n",
        "      <td> 2.53e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>spread</th>\n",
        "      <td> 1.79e-04</td>\n",
        "      <td> 7.77e-04</td>\n",
        "      <td> 0.01</td>\n",
        "      <td> 0.16</td>\n",
        "      <td> 2.33e-03</td>\n",
        "      <td> 3.88e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lymph_n2+</th>\n",
        "      <td> 4.52e-04</td>\n",
        "      <td> 1.47e-03</td>\n",
        "      <td> 0.02</td>\n",
        "      <td> 0.40</td>\n",
        "      <td> 5.87e-03</td>\n",
        "      <td> 7.34e-03</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 116,
       "text": [
        "               uncorrected  bh_within  bh_all  bonf_all  bonf_within  two_step\n",
        "recent_smoker     4.70e-06   6.11e-05    0.00      0.00     6.11e-05  3.06e-04\n",
        "pre_2000          7.78e-05   5.06e-04    0.01      0.07     1.01e-03  2.53e-03\n",
        "spread            1.79e-04   7.77e-04    0.01      0.16     2.33e-03  3.88e-03\n",
        "lymph_n2+         4.52e-04   1.47e-03    0.02      0.40     5.87e-03  7.34e-03\n",
        "\n",
        "[4 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Searching for Association Among Prognostic Biomarkers"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It has previously been shown that genetic alterations often act in redundant or synergistic mechanisms to confer a growth advantage in the tumor.  Under the hypothesis that such prognostic biomarkers might act in concert, we next examined the 78 primary events for pairwise association. \n",
      "\n",
      "* We are only testing across different data-layers\n",
      "* We test each combination once \n",
      "* We limit redundant tests by filtering \n",
      "* We isolate biologically interesting cases for followup"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = df.replace({'no': False, 'yes':True})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hit_df = df.ix[hits.index].astype(float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 118
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Looking for associations within data types"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* For association testing we filter down features that are highly redundant within a datatype\n",
      "* This is done mainly to ease interpretation as well as for power purposes.  We only look at associations that would beat the entire test space anyway. \n",
      "* Its easy to see that the redundant things we are filtering here are relatively trivial"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fet_within = pd.DataFrame({(a, b): fisher_exact_test(v1, v2)\n",
      "                           for a, v1 in hit_df.iterrows()\n",
      "                           for b, v2 in hit_df.iterrows()\n",
      "                           if a[0] == b[0]\n",
      "                           and a[1] > b[1]}).T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fet_within.sort('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 style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>odds_ratio</th>\n",
        "      <th>p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>((cna, del_3p14.3), (cna, del_3p14.2))</th>\n",
        "      <td> 8976.00</td>\n",
        "      <td> 9.28e-47</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>((cna, del_11q23.1), (cna, del_11q14.2))</th>\n",
        "      <td>  147.54</td>\n",
        "      <td> 7.00e-39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>((cna, del_3p25.3), (cna, del_3p14.3))</th>\n",
        "      <td>  468.22</td>\n",
        "      <td> 7.23e-37</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>((cna, del_3p25.3), (cna, del_3p14.2))</th>\n",
        "      <td>  273.00</td>\n",
        "      <td> 2.03e-34</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>((cna, del_3p14.2), (cna, del_3p12.2))</th>\n",
        "      <td>  225.17</td>\n",
        "      <td> 4.28e-33</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 120,
       "text": [
        "                                          odds_ratio         p\n",
        "((cna, del_3p14.3), (cna, del_3p14.2))       8976.00  9.28e-47\n",
        "((cna, del_11q23.1), (cna, del_11q14.2))      147.54  7.00e-39\n",
        "((cna, del_3p25.3), (cna, del_3p14.3))        468.22  7.23e-37\n",
        "((cna, del_3p25.3), (cna, del_3p14.2))        273.00  2.03e-34\n",
        "((cna, del_3p14.2), (cna, del_3p12.2))        225.17  4.28e-33\n",
        "\n",
        "[5 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Here we tag these redundant features and remove them from the test space. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = {b for i, (a, v1) in enumerate(hit_df.iterrows())\n",
      "       for j, (b, v2) in enumerate(hit_df.iterrows())\n",
      "       if (i < j)\n",
      "       and a[0] == b[0]\n",
      "       and np.log2(fisher_exact_test(v1, v2)['odds_ratio']) > 4}\n",
      "hit_df = hit_df.ix[[i for i in hit_df.index if i not in s]] #to keep sorted order"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 121
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Looking for associations across data types"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are 5 choose 2 = 10 different combinations of data-types.  We are looking for associations which can not be explained trivially, for example a copy number event associated with expression  of a nearby gene or mirna isn't very interesting. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hit_df.groupby(level=0).size()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 122,
       "text": [
        "clinical     6\n",
        "cna          7\n",
        "mirna        9\n",
        "mutation     5\n",
        "rna         30\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import itertools as itertools"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fet= {}\n",
      "for dtypes in itertools.combinations(hit_df.index.levels[0], 2):\n",
      "    fet[dtypes] = pd.DataFrame({(a[1], b[1]): fisher_exact_test(v1, v2)\n",
      "                                for a, v1 in hit_df.iterrows()\n",
      "                                for b, v2 in hit_df.iterrows()\n",
      "                                if a[0] != b[0]\n",
      "                                if a[0] == dtypes[0]\n",
      "                                if b[0] == dtypes[1]}).T\n",
      "fet = pd.concat(fet)\n",
      "fet.index = pd.MultiIndex.from_tuples([(i[0], i[2][0], i[1], i[2][1]) for i in fet.index])\n",
      "fet['p bonf.'] = fet['p'] * len(fet)\n",
      "fet['PASS'] = (fet['p bonf.'] < .01)\n",
      "fet = fet.sort(['PASS','p'], ascending=[0,1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fet.PASS.value_counts()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 127,
       "text": [
        "False    1046\n",
        "True       33\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clinical.binary_df.ix['stage_iv'].value_counts(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 139,
       "text": [
        "False    222\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 139
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 146,
       "text": [
        "(878, 250)"
       ]
      }
     ],
     "prompt_number": 146
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.crosstab(combo, clinical.binary_df.ix['stage_iv'])"
     ],
     "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>stage_iv</th>\n",
        "      <th>False</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TP53</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>TP53</th>\n",
        "      <td>  22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>both</th>\n",
        "      <td> 157</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>del_3p14.2</th>\n",
        "      <td>  22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>neither</th>\n",
        "      <td>  21</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 141,
       "text": [
        "stage_iv    False\n",
        "TP53             \n",
        "TP53           22\n",
        "both          157\n",
        "del_3p14.2     22\n",
        "neither        21\n",
        "\n",
        "[4 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 141
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fet.to_csv(FIGDIR + 'supplemental_table2.csv', float_format='%.2e')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 128
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Top hits across each data-type combination."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "f3 = fet.groupby(level=[0,2]).apply(lambda s: s.sort('p').head(1))\n",
      "f3.index = f3.index.droplevel([0,1])\n",
      "f3['p_corrected'] = f3['p'] * len(fet)\n",
      "f3.index = pd.MultiIndex.from_tuples([tuple((i.replace('_',' ') for i in s))\n",
      "                                      for s in f3.index])\n",
      "f3.sort('p')"
     ],
     "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></th>\n",
        "      <th></th>\n",
        "      <th>odds_ratio</th>\n",
        "      <th>p</th>\n",
        "      <th>p bonf.</th>\n",
        "      <th>PASS</th>\n",
        "      <th>p_corrected</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>cna</th>\n",
        "      <th>del 3p14.2</th>\n",
        "      <th>rna</th>\n",
        "      <th>BIOCARTA AKAP13 PATHWAY</th>\n",
        "      <td> 15.94</td>\n",
        "      <td> 4.53e-14</td>\n",
        "      <td> 4.88e-11</td>\n",
        "      <td>  True</td>\n",
        "      <td> 4.88e-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">clinical</th>\n",
        "      <th rowspan=\"2\" valign=\"top\">recent smoker</th>\n",
        "      <th>cna</th>\n",
        "      <th>del 3p14.2</th>\n",
        "      <td>  7.38</td>\n",
        "      <td> 1.72e-08</td>\n",
        "      <td> 1.85e-05</td>\n",
        "      <td>  True</td>\n",
        "      <td> 1.85e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rna</th>\n",
        "      <th>ST GAQ PATHWAY</th>\n",
        "      <td>  4.73</td>\n",
        "      <td> 7.95e-08</td>\n",
        "      <td> 8.58e-05</td>\n",
        "      <td>  True</td>\n",
        "      <td> 8.58e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mirna</th>\n",
        "      <th>hsa-mir-3170</th>\n",
        "      <th>rna</th>\n",
        "      <th>SIG PIP3 SIGNALING IN B LYMPHOCYTES</th>\n",
        "      <td>  0.20</td>\n",
        "      <td> 2.11e-07</td>\n",
        "      <td> 2.27e-04</td>\n",
        "      <td>  True</td>\n",
        "      <td> 2.27e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cna</th>\n",
        "      <th>del 3p14.2</th>\n",
        "      <th>mutation</th>\n",
        "      <th>TP53</th>\n",
        "      <td>  6.59</td>\n",
        "      <td> 3.56e-07</td>\n",
        "      <td> 3.84e-04</td>\n",
        "      <td>  True</td>\n",
        "      <td> 3.84e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mutation</th>\n",
        "      <th>TP53</th>\n",
        "      <th>rna</th>\n",
        "      <th>QARS</th>\n",
        "      <td>  0.17</td>\n",
        "      <td> 7.12e-07</td>\n",
        "      <td> 7.68e-04</td>\n",
        "      <td>  True</td>\n",
        "      <td> 7.68e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cna</th>\n",
        "      <th>del 3p14.2</th>\n",
        "      <th>mirna</th>\n",
        "      <th>hsa-mir-3170</th>\n",
        "      <td>  5.43</td>\n",
        "      <td> 5.15e-06</td>\n",
        "      <td> 5.56e-03</td>\n",
        "      <td>  True</td>\n",
        "      <td> 5.56e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>clinical</th>\n",
        "      <th>invasion</th>\n",
        "      <th>mirna</th>\n",
        "      <th>hsa-mir-656</th>\n",
        "      <td>  4.28</td>\n",
        "      <td> 5.02e-05</td>\n",
        "      <td> 5.42e-02</td>\n",
        "      <td> False</td>\n",
        "      <td> 5.42e-02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mirna</th>\n",
        "      <th>hsa-mir-570</th>\n",
        "      <th>mutation</th>\n",
        "      <th>REACTOME SIGNALLING TO RAS</th>\n",
        "      <td>  0.23</td>\n",
        "      <td> 4.63e-04</td>\n",
        "      <td> 4.99e-01</td>\n",
        "      <td> False</td>\n",
        "      <td> 4.99e-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>clinical</th>\n",
        "      <th>lymph n2+</th>\n",
        "      <th>mutation</th>\n",
        "      <th>TP53</th>\n",
        "      <td>  3.38</td>\n",
        "      <td> 3.14e-03</td>\n",
        "      <td> 3.39e+00</td>\n",
        "      <td> False</td>\n",
        "      <td> 3.39e+00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "                                                                     odds_ratio         p   p bonf.   PASS  p_corrected\n",
        "cna      del 3p14.2    rna      BIOCARTA AKAP13 PATHWAY                   15.94  4.53e-14  4.88e-11   True     4.88e-11\n",
        "clinical recent smoker cna      del 3p14.2                                 7.38  1.72e-08  1.85e-05   True     1.85e-05\n",
        "                       rna      ST GAQ PATHWAY                             4.73  7.95e-08  8.58e-05   True     8.58e-05\n",
        "mirna    hsa-mir-3170  rna      SIG PIP3 SIGNALING IN B LYMPHOCYTES        0.20  2.11e-07  2.27e-04   True     2.27e-04\n",
        "cna      del 3p14.2    mutation TP53                                       6.59  3.56e-07  3.84e-04   True     3.84e-04\n",
        "mutation TP53          rna      QARS                                       0.17  7.12e-07  7.68e-04   True     7.68e-04\n",
        "cna      del 3p14.2    mirna    hsa-mir-3170                               5.43  5.15e-06  5.56e-03   True     5.56e-03\n",
        "clinical invasion      mirna    hsa-mir-656                                4.28  5.02e-05  5.42e-02  False     5.42e-02\n",
        "mirna    hsa-mir-570   mutation REACTOME SIGNALLING TO RAS                 0.23  4.63e-04  4.99e-01  False     4.99e-01\n",
        "clinical lymph n2+     mutation TP53                                       3.38  3.14e-03  3.39e+00  False     3.39e+00\n",
        "\n",
        "[10 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "3p14.2 Deletion is associated with change of the AKAP13 pathay.  Included is RHOA and PRKAR2A which are both on 3p. Not very interesting..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = subplots(1,2, figsize=(10,4))\n",
      "violin_plot_pandas(df.ix['cna'].ix['del_3p14.2'], rna.pathways.ix['BIOCARTA_AKAP13_PATHWAY'], \n",
      "                   ax=axs[0])\n",
      "rna.loadings.ix['BIOCARTA_AKAP13_PATHWAY'].order().plot(kind='bar', ax=axs[1])\n",
      "for ax in axs:\n",
      "    prettify_ax(ax)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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MKdx/fwAXXBDHnDkm/GVn1xdxJYCLuzneNQDml/F45RUOP/hBPJuhCk4qbekT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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x86034d0>"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.crosstab(df.ix['clinical'].ix['recent_smoker'], df.ix['cna'].ix['del_3p14.2'])"
     ],
     "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>del_3p14.2</th>\n",
        "      <th>0.0</th>\n",
        "      <th>1.0</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>recent_smoker</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>False</th>\n",
        "      <td> 32</td>\n",
        "      <td>  52</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>True </th>\n",
        "      <td> 12</td>\n",
        "      <td> 144</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>2 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "del_3p14.2      0    1\n",
        "recent_smoker         \n",
        "False          32   52\n",
        "True           12  144\n",
        "\n",
        "[2 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Interesting trend between smoking and GAQ signaling, but looks more like a phenotype of smoking rather than having to do with tumor progression. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, axs = subplots(1,2, figsize=(10,4))\n",
      "violin_plot_pandas(df.ix['clinical'].ix['recent_smoker'], \n",
      "                   rna.pathways.ix['SIG_PIP3_SIGNALING_IN_B_LYMPHOCYTES'], ax=axs[0])\n",
      "rna.loadings.ix['SIG_PIP3_SIGNALING_IN_B_LYMPHOCYTES'].order().plot(kind='bar', ax=axs[1])\n",
      "for ax in axs:\n",
      "    prettify_ax(ax)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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I+y2SxNFIV+EKBoNJTS120eu18HpdYFl24IMJIYTkDNmRrlgs1r083WQyIRKJZCSoYidJ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\nMEwZ+D+B0bowtB8PzQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8d65cd0>"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='p53_3p'>TP53 mutation seems to be highly overlapping with 3p14.2 deletion.</div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "venn_pandas(df.ix['mutation'].ix['TP53'], df.ix['cna'].ix['del_3p14.2']);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vwKSBhrLgdtQRNSLxGXGD1usPWo+vuPoojAntUd6+zg6MeshPUUfzVcNLXpIZ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\n27Do9cxOTqXXLfLmDvVyXxV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aGhwOB2pra7F582YAuMrpPB5PSOveT5w4gd7e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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8662cd0>"
       ]
      }
     ],
     "prompt_number": 47
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(keepers_o)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 48,
       "text": [
        "250"
       ]
      }
     ],
     "prompt_number": 48
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Adjusting for the test space we get a p-value of ~10<sup>-4</sup>."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fisher_exact_test(df.ix['mutation'].ix['TP53'], df.ix['cna'].ix['del_3p14.2'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 49,
       "text": [
        "odds_ratio    6.59e+00\n",
        "p             3.56e-07\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fisher_exact_test(df.ix['mutation'].ix['TP53'], df.ix['cna'].ix['del_3p14.2']).p * len(fet)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "0.00038427707515341651"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "* We show here that 3p14.2 is highly overlapping with TP53 mutation\n",
      "* Does this have any implications towards prognosis?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "combo = combine(df.ix['mutation'].ix['TP53'] > 0, df.ix['cna'].ix['del_3p14.2'])\n",
      "survival_and_stats(combo, clinical.survival.survival_5y, figsize=(6,4))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zY1tyjvGxpc5j8vLAZGcj3rERCp8/R96+0ktfMXI5MmbPUTqWvX4DZC9f4s3w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\nYxiGYbQWS2IMwzCM1mJJjGEYhtFa/wenPtgbiq2OyAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x8c05cd0>"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_surv_fit_lr(surv, combo)"
     ],
     "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> 19.3</td>\n",
        "      <td> 0.000236</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>both</th>\n",
        "      <td> 179</td>\n",
        "      <td> 87</td>\n",
        "      <td> 1.9</td>\n",
        "      <td>  1.5</td>\n",
        "      <td> 2.84</td>\n",
        "      <td> 0.339</td>\n",
        "      <td> 0.255</td>\n",
        "      <td>  0.45</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>del_3p14.2</th>\n",
        "      <td>  26</td>\n",
        "      <td>  7</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 2.96</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 0.621</td>\n",
        "      <td> 0.419</td>\n",
        "      <td> 0.921</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TP53</th>\n",
        "      <td>  23</td>\n",
        "      <td>  5</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 4.49</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  0.62</td>\n",
        "      <td> 0.374</td>\n",
        "      <td>     1</td>\n",
        "      <td>     </td>\n",
        "      <td>         </td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>neither</th>\n",
        "      <td>  22</td>\n",
        "      <td>  3</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 4.71</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 0.709</td>\n",
        "      <td> 0.444</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": 52,
       "text": [
        "                Stats          Median Survival             5y Survival               Log-Rank          \n",
        "           # Patients # Events          Median Lower Upper        Surv  Lower  Upper     chi2         p\n",
        "                                                                                         19.3  0.000236\n",
        "both              179       87             1.9   1.5  2.84       0.339  0.255   0.45                   \n",
        "del_3p14.2         26        7             NaN  2.96   NaN       0.621  0.419  0.921                   \n",
        "TP53               23        5             NaN  4.49   NaN        0.62  0.374      1                   \n",
        "neither            22        3             NaN  4.71   NaN       0.709  0.444      1                   \n",
        "\n",
        "[5 rows x 10 columns]"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<b>There is a lot of literature out there implicating TP53 with chromosomal instability... are we picking up an artifact of this?  </b>  \n",
      "\n",
      "* Short answer is no\n",
      "* Long answer is in <a href='copy_number_exploration.ipynb'>copy_number_exporation notebook</a>.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "two_hit = combo == 'both'\n",
      "p53_mut = df.ix['mutation'].ix['TP53']\n",
      "del_3p = df.ix['cna'].ix['del_3p14.2']\n",
      "two_hit.name = 'TP53_3p'\n",
      "survival_and_stats(two_hit, surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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we+stB0RMRO7GKZvrn3vuOYwYMQKPPvqoaImdzfVUqDDJm3K1MCXfgt/smQCA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\nM8YYY0bq/wAW4XajdxcuqgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xc5a4850>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div id='prog_interaction'>Although the prognostic effect of TP53 mutation in HNSCC has been well characterized, this aggregate event was significantly more predictive of survival than TP53 mutation alone.</div>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m1 = get_cox_ph(surv, two_hit, [age, old], interactions='None', print_desc=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         coef exp(coef) se(coef)    z       p\n",
        "feature 1.203      3.33   0.2814 4.28 1.9e-05\n",
        "old     0.262      1.30   0.0873 3.00 2.7e-03\n",
        "\n",
        "Likelihood ratio test=29.1  on 2 df, p=4.9e-07  n= 250, number of events= 102 \n",
        "   (128 observations deleted due to missingness)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m2 = get_cox_ph(surv, p53_mut, [age, old], interactions='None', print_desc=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "        coef exp(coef) se(coef)    z      p\n",
        "feature 1.09      2.97    0.334 3.26 0.0011\n",
        "old     0.23      1.26    0.087 2.64 0.0082\n",
        "\n",
        "Likelihood ratio test=19.6  on 2 df, p=5.44e-05  n= 250, number of events= 102 \n",
        "   (128 observations deleted due to missingness)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LR_test(m1, m2, df=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "0.0021463124501954031"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m3 = get_cox_ph(surv, del_3p, [age, old], interactions='None', print_desc=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "         coef exp(coef) se(coef)    z       p\n",
        "feature 1.403      4.07   0.3726 3.76 0.00017\n",
        "old     0.294      1.34   0.0879 3.35 0.00082\n",
        "\n",
        "Likelihood ratio test=26.1  on 2 df, p=2.18e-06  n= 250, number of events= 102 \n",
        "   (128 observations deleted due to missingness)\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "LR_test(m1, m3, df=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "0.084060129965569844"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cox(two_hit.ix[true_index(p53_mut==1)].ix[ti(old==False)], surv)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "hazard       exp(coef)      5.58\n",
        "             exp(-coef)     0.18\n",
        "             lower .95      1.37\n",
        "             upper .95     22.74\n",
        "LR           stat          10.37\n",
        "             df             1.00\n",
        "             p              0.00\n",
        "concordance  stat           0.55\n",
        "             se             0.02\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "surv_test(two_hit.ix[true_index(p53_mut==1)], surv, [age, old])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "LR                                       0.00239\n",
        "feature_p                                 0.0109\n",
        "fmla         Surv(days, event) ~ feature + old\\n\n",
        "hazzard                                     3.25\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "surv_test(two_hit.ix[true_index(del_3p==1)], surv, [age, old])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "LR                                                      0.0186\n",
        "feature_p                                                 0.05\n",
        "fmla         Surv(days, event) ~ feature + old + feature:old\\n\n",
        "hazzard                                                   2.25\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Mutation Events Overlapping / Mutually Exclusive with TP53-3p"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r = screen_feature(two_hit, fisher_exact_test, df.ix['mutation'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r.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>odds_ratio</th>\n",
        "      <th>p</th>\n",
        "      <th>q</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>TP53</th>\n",
        "      <td>  inf</td>\n",
        "      <td> 3.26e-34</td>\n",
        "      <td> 3.94e-32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>KEGG_CELL_CYCLE</th>\n",
        "      <td>  inf</td>\n",
        "      <td> 2.92e-13</td>\n",
        "      <td> 1.77e-11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>REACTOME_SOS_MEDIATED_SIGNALLING</th>\n",
        "      <td> 0.11</td>\n",
        "      <td> 2.50e-06</td>\n",
        "      <td> 1.01e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>REACTOME_SHC_MEDIATED_SIGNALLING</th>\n",
        "      <td> 0.12</td>\n",
        "      <td> 7.72e-06</td>\n",
        "      <td> 2.34e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CASP8</th>\n",
        "      <td> 0.13</td>\n",
        "      <td> 2.32e-05</td>\n",
        "      <td> 5.61e-04</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 69,
       "text": [
        "                                  odds_ratio         p         q\n",
        "TP53                                     inf  3.26e-34  3.94e-32\n",
        "KEGG_CELL_CYCLE                          inf  2.92e-13  1.77e-11\n",
        "REACTOME_SOS_MEDIATED_SIGNALLING        0.11  2.50e-06  1.01e-04\n",
        "REACTOME_SHC_MEDIATED_SIGNALLING        0.12  7.72e-06  2.34e-04\n",
        "CASP8                                   0.13  2.32e-05  5.61e-04\n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(r)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "121"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r.ix['REACTOME_SOS_MEDIATED_SIGNALLING'].p * len(r)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "0.00030230943613944141"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "r.ix['CASP8'].p * len(r)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "0.0028054320178764786"
       ]
      }
     ],
     "prompt_number": 72
    }
   ],
   "metadata": {}
  }
 ]
}