Lung_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lung Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import random\n",
    "import scipy\n",
    "from scipy import stats\n",
    "import statsmodels.stats.multitest as ssm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# settings for editable pdfs\n",
    "plt.rcParams['pdf.fonttype'] = 42\n",
    "plt.rcParams['ps.fonttype'] = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # set size of tick labels\n",
    "# plt.rc('xtick', labelsize=15)\n",
    "# plt.rc('ytick', labelsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load data, preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load MAF data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jamie/opt/anaconda3/envs/singlecell/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3071: DtypeWarning: Columns (35,36,53) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "# load genie version 8.1\n",
    "master_mut = pd.read_csv(\"R_script/cleaned_data/GENIE8.1_master_list_clinical_gen.tsv\", sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load panels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "DFCI_1 = pd.read_csv('R_script/Panels/DFCI_1.txt', sep='\\t')\n",
    "DFCI_2 = pd.read_csv('R_script/Panels/DFCI_2.txt', sep='\\t')\n",
    "DFCI_3 = pd.read_csv('R_script/Panels/DFCI_3.txt', sep='\\t')\n",
    "DFCI_3_1 = DFCI_3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "DFCI_1['SEQ_ASSAY_ID'] = 'DFCI-ONCOPANEL-1'\n",
    "DFCI_2['SEQ_ASSAY_ID'] = 'DFCI-ONCOPANEL-2'\n",
    "DFCI_3['SEQ_ASSAY_ID'] = 'DFCI-ONCOPANEL-3'\n",
    "DFCI_3_1['SEQ_ASSAY_ID'] = 'DFCI-ONCOPANEL-3.1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "MSK_341 = pd.read_csv('R_script/Panels/IMPACT_341.txt', sep='\\t')\n",
    "MSK_410 = pd.read_csv('R_script/Panels/IMPACT_410.txt', sep='\\t')\n",
    "MSK_468 = pd.read_csv('R_script/Panels/IMPACT_468.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "MSK_341['SEQ_ASSAY_ID'] = 'MSK-IMPACT-341'\n",
    "MSK_410['SEQ_ASSAY_ID'] = 'MSK-IMPACT-410'\n",
    "MSK_468['SEQ_ASSAY_ID'] = 'MSK-IMPACT-468'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_panels = pd.concat([DFCI_1, DFCI_2, DFCI_3, DFCI_3_1, MSK_341, MSK_410, MSK_468]).groupby(\n",
    "    'Hugo_Symbol')['SEQ_ASSAY_ID'].apply(list).to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define Variables of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancers = ['NSCLC', 'LUAD', 'LUSC']\n",
    "\n",
    "centers = ['MSK', 'DFCI']\n",
    "\n",
    "panels = ['DFCI-ONCOPANEL-1', 'DFCI-ONCOPANEL-2', 'DFCI-ONCOPANEL-3', 'DFCI-ONCOPANEL-3.1', \n",
    "                        'MSK-IMPACT-341', 'MSK-IMPACT-410', 'MSK-IMPACT-468']\n",
    "\n",
    "races = ['Asian', 'Black', 'White']\n",
    "\n",
    "sample_types = ['Primary', 'Metastasis']\n",
    "\n",
    "variants = ['Missense_Mutation', 'Splice_Site', 'Nonsense_Mutation', 'Frame_Shift_Del', \n",
    "                         'Frame_Shift_Ins', 'Nonstop_Mutation', 'In_Frame_Del']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter MAF data by variables of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "master_mut['SEQ_ASSAY_ID'] = master_mut['SEQ_ASSAY_ID'].replace({'MSK-IMPACT341':'MSK-IMPACT-341', \n",
    "                                                                 'MSK-IMPACT410':'MSK-IMPACT-410', \n",
    "                                                                 'MSK-IMPACT468':'MSK-IMPACT-468'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (NOTE From Scanner's code: do not subset by Variant_Classification yet as that will skew our denominator and demographic numbers)\n",
    "\n",
    "filt_mut = master_mut[(master_mut['ONCOTREE_CODE'].isin(cancers)) & (master_mut['SEQ_ASSAY_ID'].isin(panels)) & (\n",
    "    master_mut['PRIMARY_RACE'].isin(races) & master_mut['SAMPLE_TYPE'].isin(sample_types))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create dictionaries separated by primary/metstasis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Non-filtered by variants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "mut_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "mut_dict['prim'] = filt_mut[filt_mut['SAMPLE_TYPE'] == 'Primary']\n",
    "mut_dict['met'] = filt_mut[filt_mut['SAMPLE_TYPE'] == 'Metastasis']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter by variants of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using this data for subsequent analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_mut_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# variants to include\n",
    "variants = ['Missense_Mutation', 'Splice_Site', 'Nonsense_Mutation', 'Frame_Shift_Del', \n",
    "                         'Frame_Shift_Ins', 'Nonstop_Mutation', 'In_Frame_Del']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_filt_mut = filt_mut[filt_mut['Variant_Classification'].isin(variants)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_mut_dict['prim'] =  var_filt_mut[var_filt_mut['SAMPLE_TYPE'] == 'Primary']\n",
    "var_mut_dict['met'] = var_filt_mut[var_filt_mut['SAMPLE_TYPE'] == 'Metastasis']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demographic Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# looking at non-filtered by variant data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "race_demo_dict = {}\n",
    "panel_demo_dict = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overall (Primary + Metastasis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6578"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total number of unique patients\n",
    "len(filt_mut.groupby('PATIENT_ID').nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "race_demo_dict['overall'] = filt_mut.groupby('PRIMARY_RACE')['PATIENT_ID'].nunique()\n",
    "panel_demo_dict['overall'] = filt_mut.groupby('SEQ_ASSAY_ID')['PATIENT_ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRIMARY_RACE\n",
       "Asian     516\n",
       "Black     301\n",
       "White    5761\n",
       "Name: PATIENT_ID, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by race\n",
    "race_demo_dict['overall']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('Lung Cancer', size=15)\n",
    "plt.pie(race_demo_dict['overall'], \n",
    "        labels=['Asian', 'Black', 'White'],\n",
    "       autopct='%1.1f%%',\n",
    "       textprops = dict(rotation_mode = 'anchor', \n",
    "#                         va='center', \n",
    "#                         ha='center', \n",
    "                        size=15));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SEQ_ASSAY_ID\n",
       "DFCI-ONCOPANEL-1       435\n",
       "DFCI-ONCOPANEL-2      1097\n",
       "DFCI-ONCOPANEL-3       725\n",
       "DFCI-ONCOPANEL-3.1     313\n",
       "MSK-IMPACT-341         321\n",
       "MSK-IMPACT-410        1217\n",
       "MSK-IMPACT-468        2741\n",
       "Name: PATIENT_ID, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by panels\n",
    "panel_demo_dict['overall']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Primary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4219"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total primary\n",
    "len(mut_dict['prim'].groupby('PATIENT_ID').nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "race_demo_dict['prim'] = mut_dict['prim'].groupby('PRIMARY_RACE')['PATIENT_ID'].nunique()\n",
    "panel_demo_dict['prim'] = mut_dict['prim'].groupby('SEQ_ASSAY_ID')['PATIENT_ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRIMARY_RACE\n",
       "Asian     321\n",
       "Black     180\n",
       "White    3718\n",
       "Name: PATIENT_ID, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by race\n",
    "race_demo_dict['prim']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SEQ_ASSAY_ID\n",
       "DFCI-ONCOPANEL-1       253\n",
       "DFCI-ONCOPANEL-2       678\n",
       "DFCI-ONCOPANEL-3       448\n",
       "DFCI-ONCOPANEL-3.1     160\n",
       "MSK-IMPACT-341         199\n",
       "MSK-IMPACT-410         746\n",
       "MSK-IMPACT-468        1794\n",
       "Name: PATIENT_ID, dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by panel\n",
    "panel_demo_dict['prim']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metastasis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2625"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total metastasis\n",
    "len(mut_dict['met'].groupby('PATIENT_ID').nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "race_demo_dict['met'] = mut_dict['met'].groupby('PRIMARY_RACE')['PATIENT_ID'].nunique()\n",
    "panel_demo_dict['met'] = mut_dict['met'].groupby('SEQ_ASSAY_ID')['PATIENT_ID'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PRIMARY_RACE\n",
       "Asian     236\n",
       "Black     136\n",
       "White    2253\n",
       "Name: PATIENT_ID, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by race\n",
    "race_demo_dict['met']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SEQ_ASSAY_ID\n",
       "DFCI-ONCOPANEL-1       182\n",
       "DFCI-ONCOPANEL-2       432\n",
       "DFCI-ONCOPANEL-3       281\n",
       "DFCI-ONCOPANEL-3.1     156\n",
       "MSK-IMPACT-341         124\n",
       "MSK-IMPACT-410         497\n",
       "MSK-IMPACT-468        1049\n",
       "Name: PATIENT_ID, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by panel\n",
    "panel_demo_dict['met']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate mutation frequency (for all genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove patients with duplicate tumors sequenced by multiple panels\n",
    "# choose sample sequenced by larger panel\n",
    "\n",
    "# mut_tbl: mutation table in MAF format\n",
    "def remove_duplicates(mut_tbl):\n",
    "    # group by patient and panel to find the unique sample IDs\n",
    "    df = mut_tbl.groupby(['PATIENT_ID', 'SEQ_ASSAY_ID'])['SAMPLE_ID'].unique().to_frame()\n",
    "    # choose the last panel for each patient\n",
    "    samples = np.concatenate(df.groupby('PATIENT_ID').tail(1)['SAMPLE_ID'])\n",
    "    # filter out the duplicates\n",
    "    cleaned_mut = mut_tbl[mut_tbl['SAMPLE_ID'].isin(samples)]\n",
    "    \n",
    "    return cleaned_mut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# count patients sequenced by each panel by race\n",
    "\n",
    "# mut_tbl: mutation table in MAF format\n",
    "# combined_panels: table with genes as rows, lists of panels that sequenced that gene as column\n",
    "# race_category: name of race column\n",
    "def calc_seq_counts(mut_tbl, combined_panels, race_category):\n",
    "    # first remove duplicates to get accurate sequenced count\n",
    "    mut_tbl = remove_duplicates(mut_tbl)\n",
    "    \n",
    "    # count number of patients for each panel\n",
    "    panel_race_counts = mut_tbl.groupby(['SEQ_ASSAY_ID', race_category])[\n",
    "        'PATIENT_ID'].nunique().reset_index().pivot_table(index='SEQ_ASSAY_ID', columns=race_category, \n",
    "                                                          values='PATIENT_ID', fill_value=0)\n",
    "    # count number of patients by race sequenced for one gene\n",
    "    def sum_gene_counts(row):\n",
    "        count = np.sum(panel_race_counts[panel_race_counts.index.isin(row)], axis=0)\n",
    "        return count\n",
    "    \n",
    "    # apply function to all genes\n",
    "    seq_cnt = combined_panels['SEQ_ASSAY_ID'].apply(sum_gene_counts)\n",
    "    \n",
    "    return seq_cnt\n",
    "#     return panel_race_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate mutation frequency\n",
    "\n",
    "# mut_tbl: MAF mutation table\n",
    "# var_mut_tbl: mut_tbl filtered by variants we excluded\n",
    "# combined_panels: table with genes as rows, lists of panels that sequenced that gene as column\n",
    "# race_category: name of race column\n",
    "def calc_mut_freq(mut_tbl, var_mut_tbl, combined_panels, race_category):\n",
    "    \n",
    "    # calculate number of patients sequenced in each race category for all genes in panels\n",
    "    seq_cnt = calc_seq_counts(mut_tbl, combined_panels, race_category)\n",
    "    \n",
    "    # remove duplicates before counting mutations\n",
    "    var_mut_tbl = remove_duplicates(var_mut_tbl)\n",
    "    \n",
    "    # calculate number of patients w/ mutation in each race category for each gene in MAF table\n",
    "    mut_cnt = var_mut_tbl.groupby(['Hugo_Symbol', race_category])['PATIENT_ID'].nunique().reset_index().pivot_table(\n",
    "        index='Hugo_Symbol', columns=race_category, values='PATIENT_ID', fill_value=0)\n",
    "    \n",
    "    mut_seq = mut_cnt.join(seq_cnt, how='outer', lsuffix='_mut', rsuffix='_seq')\n",
    "    \n",
    "    # find any missing genes that weren't included in panel but still had mutations\n",
    "    # don't know why these are included in the panel in GENIE but not elsewhere\n",
    "    # total for DFCI is especially wrong (looking directly at the genomic_information.txt file)\n",
    "    missing = mut_seq[mut_seq['Asian_seq'].isna()]\n",
    "    \n",
    "    # remove missing genes\n",
    "    mut_seq = mut_seq[~mut_seq.index.isin(missing.index)]\n",
    "    \n",
    "#     # fill NaN mutation values with zero\n",
    "#     mut_seq = mut_seq.fillna(0)\n",
    "\n",
    "    # remove NaN mutation values (genes that were sequenced but have no mutation counts)\n",
    "    mut_seq = mut_seq.dropna()\n",
    "    \n",
    "    # compute mutation frequency\n",
    "    mut_columns = mut_seq.columns[mut_seq.columns.str.contains('_mut')]\n",
    "    seq_columns = mut_seq.columns[mut_seq.columns.str.contains('_seq')]\n",
    "    \n",
    "    mut_freq = mut_seq[mut_columns] / mut_seq[seq_columns].values * 100\n",
    "    mut_freq.columns = mut_freq.columns.str.replace('_mut', '')\n",
    "    \n",
    "    return mut_seq, mut_freq\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "mut_freq_dict = {}\n",
    "mut_seq_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(mut_dict.items(), var_mut_dict.items()):\n",
    "    mut_seq_dict[k1], mut_freq_dict[k1] = calc_mut_freq(v1, v2, combined_panels, 'PRIMARY_RACE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Significance testing functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fisher's Test and FDR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# performs Fisher's exact test for one gene between two races and calculates 95% confidence interval\n",
    "\n",
    "# row: gene row\n",
    "# group1: name of first race\n",
    "# group2: name of second race\n",
    "def fisher_one_gene(row, group1, group2):\n",
    "    seq_group1 = row[group1 + '_seq']\n",
    "    seq_group2 = row[group2 + '_seq']\n",
    "    \n",
    "    mut_group1 = row[group1 + '_mut']\n",
    "    mut_group2 = row[group2 + '_mut']\n",
    "    \n",
    "    a = mut_group1\n",
    "    b = mut_group2\n",
    "    c = seq_group1 - a\n",
    "    d = seq_group2 - b\n",
    "    \n",
    "    cont_tbl = pd.DataFrame(np.array([[a, c],\n",
    "                                     [b, d]]), index=[group1, group2], columns=['Mutation', 'No Mutation'])\n",
    "    \n",
    "    odds_ratio, p_val = scipy.stats.fisher_exact(cont_tbl)\n",
    "    \n",
    "    # calculate 95% confidence interval    \n",
    "    SE = np.sqrt(1/a + 1/c + 1/b + 1/d) \n",
    "    \n",
    "    c1 = round(odds_ratio * np.exp(-1.96 * SE), 3)\n",
    "    c2 = round(odds_ratio * np.exp(1.96 * SE), 3)\n",
    "    \n",
    "    CI = (c1, c2)\n",
    "    \n",
    "    return cont_tbl, odds_ratio, p_val, CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# performs Fisher's exact test for all genes between two races and computes FDR\n",
    "\n",
    "# mut_tbl: table with genes as rows and *_mut/*_seq counts as columns (* is name of race group)\n",
    "# group1: name of first race\n",
    "# group2: name of second race\n",
    "\n",
    "def fisher_all_genes(mut_tbl, group1, group2):\n",
    "    fisher = mut_tbl.apply(lambda x: fisher_one_gene(x, group1, group2), axis=1)\n",
    "    \n",
    "    fisher_tbl = pd.DataFrame(fisher.to_list(), index=fisher.index).iloc[:,1:].rename(\n",
    "        columns={1:group1 + '_' + group2 + '_odds_ratio', \n",
    "                 2:group1 + '_' + group2 + '_pval', \n",
    "                 3:group1 + '_' + group2 + '_CI'})\n",
    "    fdr = ssm.multipletests(fisher_tbl[group1 + '_' + group2 + '_pval'], method='fdr_bh')[1]\n",
    "    fisher_tbl.insert(2, group1 + '_' + group2 + '_fdr', fdr)\n",
    "    \n",
    "    return fisher_tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# performs Fisher's exact test for all race comparisons, and computes FDR\n",
    "\n",
    "# mut_tbl: table with genes as rows and *_mut/*_seq counts as columns (* is name of race group)\n",
    "# groups: list of tuples with race comparisons\n",
    "def fisher_fdr_all_groups(mut_tbl, groups):\n",
    "    fisher_tbl = pd.DataFrame()\n",
    "    for group in groups:\n",
    "        fisher_tbl = pd.concat([fisher_tbl, fisher_all_genes(mut_tbl, group[0], group[1])], axis=1)\n",
    "\n",
    "    return fisher_tbl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# change if doing different comparisons\n",
    "groups = [('Black', 'White'), \n",
    "          ('Black', 'Asian'), \n",
    "          ('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Significant genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finds significant genes based on FDR cutoff specified\n",
    "\n",
    "# pvals: table of p-values, genes as rows, race comparisons as columns\n",
    "# mut_freq: table of mutation frequencies, genes as rows, races as columns\n",
    "# fdr_cutoff: float\n",
    "def find_sig_genes(pvals, mut_freq, fdr_cutoff):\n",
    "    cols = pvals.columns[pvals.columns.str.contains('fdr')]\n",
    "    pval_cols = pvals[cols]    \n",
    "    \n",
    "    sig_pval = pval_cols[(pval_cols <= fdr_cutoff).any(1)]\n",
    "    sig_genes = sig_pval.index\n",
    "    sig_pval = pvals.loc[sig_genes]\n",
    "    \n",
    "    sig_pct = mut_freq.loc[sig_genes]\n",
    "    \n",
    "    return sig_genes.to_list(), sig_pval, sig_pct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# finds significant genes for all groups\n",
    "\n",
    "# race_pairs: list of tuples with race comparisons\n",
    "# pvals: table of p-values, genes as rows, race comparisons as columns\n",
    "# mut_freq: table of mutation frequencies, genes as rows, races as columns\n",
    "# fdr_cutoff: float\n",
    "def find_sig_genes_groups(race_pairs, pvals, mut_freq, fdr_cutoff):\n",
    "    sig_pval_dict = {}\n",
    "    sig_freq_dict = {}\n",
    "    sig_gene_list = []\n",
    "    for pair in race_pairs:\n",
    "        pair_cols = ['_'.join(pair) + '_pval', '_'.join(pair) + '_fdr', \n",
    "                     '_'.join(pair) + '_odds_ratio', '_'.join(pair) + '_CI']\n",
    "        pair_pval = pvals[pair_cols]\n",
    "        pair_mut_freq = mut_freq[[pair[0], pair[1]]]\n",
    "        sig_genes, sig_pval_dict[pair], sig_freq_dict[pair] = find_sig_genes(pair_pval, pair_mut_freq, fdr_cutoff)\n",
    "        sig_gene_list = sig_gene_list + sig_genes\n",
    "    \n",
    "    # remove duplicates\n",
    "    sig_gene_list = set(sig_gene_list)\n",
    "    \n",
    "    return sig_gene_list, sig_pval_dict, sig_freq_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Composition plot functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variant Composition Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate variant composition for genes of interest\n",
    "\n",
    "# mut_tbl: MAF table\n",
    "# genes: list of genes of interest\n",
    "def variant_comp(mut_tbl, genes):\n",
    "    # filter mut_tbl by genes of interest\n",
    "    filt_mut = mut_tbl[mut_tbl['Hugo_Symbol'].isin(genes)]\n",
    "    comp_mut = filt_mut.groupby(['Hugo_Symbol', 'Variant_Classification'])['PATIENT_ID'].nunique().reset_index().pivot_table(\n",
    "        index='Hugo_Symbol', columns='Variant_Classification', values='PATIENT_ID', fill_value=0)\n",
    "    pct_mut = comp_mut.div(comp_mut.sum(axis=1), axis=0) * 100\n",
    "    \n",
    "    return comp_mut, pct_mut"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Panel Composition Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate panel composition for genes of interest\n",
    "\n",
    "# mut_tbl: MAF table\n",
    "# genes: list of genes of interest\n",
    "def panel_comp(mut_tbl, genes):\n",
    "    # filter mut_tbl by genes of interest\n",
    "    filt_mut = mut_tbl[mut_tbl['Hugo_Symbol'].isin(genes)]\n",
    "    comp_mut = filt_mut.groupby(['Hugo_Symbol', 'SEQ_ASSAY_ID'])['PATIENT_ID'].nunique().reset_index().pivot_table(\n",
    "        index='Hugo_Symbol', columns='SEQ_ASSAY_ID', values='PATIENT_ID', fill_value=0)\n",
    "    pct_mut = comp_mut.div(comp_mut.sum(axis=1), axis=0) * 100\n",
    "    \n",
    "    return comp_mut, pct_mut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top Mutations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Find genes most frequently altered overall and by race"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate mutations most common in all patients, count number of patients with that mutation\n",
    "\n",
    "# mut_tbl: MAF table\n",
    "def common_mut_overall(mut_tbl):\n",
    "    common_mut = mut_tbl.groupby('Hugo_Symbol')['PATIENT_ID'].nunique().sort_values(ascending=False)\n",
    "    \n",
    "    return common_mut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# most common mutations for each race\n",
    "\n",
    "# mut_tbl: MAF table\n",
    "# race_category: race column name\n",
    "# races: list of races of interest\n",
    "def common_mut_race(mut_tbl, race_category, races):    \n",
    "    common_dict = {}\n",
    "    \n",
    "    for race in races:\n",
    "        race_tbl = mut_tbl[mut_tbl[race_category] == race]\n",
    "        common_tbl = common_mut_overall(race_tbl)\n",
    "        common_dict[race] = common_tbl\n",
    "        \n",
    "    return common_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_mut_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in var_mut_dict.items():\n",
    "    common_mut_dict[key] = common_mut_overall(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_mut_race_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in var_mut_dict.items():\n",
    "    common_mut_race_dict[key] = common_mut_race(value, 'PRIMARY_RACE', ['Asian', 'Black', 'White'])   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 20 mutations overall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hugo_Symbol\n",
       "TP53       2080\n",
       "KRAS       1414\n",
       "EGFR        925\n",
       "STK11       625\n",
       "KEAP1       551\n",
       "KMT2D       418\n",
       "RBM10       394\n",
       "ATM         375\n",
       "NF1         369\n",
       "PTPRD       333\n",
       "CDKN2A      307\n",
       "FAT1        301\n",
       "ARID1A      293\n",
       "PIK3CA      292\n",
       "SMARCA4     288\n",
       "SETD2       278\n",
       "ATRX        261\n",
       "NTRK3       253\n",
       "EPHA3       248\n",
       "BRAF        233\n",
       "Name: PATIENT_ID, dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top 20 primary\n",
    "common_mut_dict['prim'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hugo_Symbol\n",
       "TP53       1600\n",
       "KRAS        755\n",
       "EGFR        666\n",
       "KEAP1       423\n",
       "STK11       374\n",
       "SMARCA4     285\n",
       "KMT2D       270\n",
       "ATM         269\n",
       "NF1         239\n",
       "ARID1A      236\n",
       "PTPRD       231\n",
       "FAT1        200\n",
       "CDKN2A      199\n",
       "RBM10       190\n",
       "SETD2       189\n",
       "EPHA5       180\n",
       "APC         177\n",
       "RB1         163\n",
       "ALK         159\n",
       "PIK3CA      158\n",
       "Name: PATIENT_ID, dtype: int64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# top 20 metastatic\n",
    "common_mut_dict['met'].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_mut_seq_dict = {}\n",
    "top_mut_freq_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in common_mut_dict.items():\n",
    "    top_20 = value.head(20).index\n",
    "    top_mut_seq_dict[key] = mut_seq_dict[key].loc[top_20]\n",
    "    top_mut_freq_dict[key] = mut_freq_dict[key].loc[top_20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TumorPortal genes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subset mutation data by tumor portal genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load tumor portal genes\n",
    "genes = pd.read_csv('R_script/cleaned_data/sig_mut_genes_lung.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove TumorPortal genes that are not in data\n",
    "prim_genes = genes[genes['Hugo_Symbol'].isin(mut_seq_dict['prim'].index)]  \n",
    "met_genes = genes[genes['Hugo_Symbol'].isin(mut_seq_dict['met'].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27 23 23\n"
     ]
    }
   ],
   "source": [
    "# check genes are same among prim and met data\n",
    "# if numbers are different, need to change downstream analysis\n",
    "print(len(genes), len(prim_genes), len(met_genes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hugo_Symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Hugo_Symbol\n",
       "0          True\n",
       "1          True\n",
       "2          True\n",
       "3          True\n",
       "4          True\n",
       "5          True\n",
       "6          True\n",
       "7          True\n",
       "8          True\n",
       "9          True\n",
       "10         True\n",
       "11         True\n",
       "12         True\n",
       "13         True\n",
       "14         True\n",
       "15         True\n",
       "17         True\n",
       "18         True\n",
       "19         True\n",
       "23         True\n",
       "24         True\n",
       "25         True\n",
       "26         True"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prim_genes == met_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from here assuming that Tumor Portal genes are the same in prim and met datasets\n",
    "# both prim and met have the same tumor portal genes of interest\n",
    "# if not all True in printouts above, need to modify code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "tp_mut_seq_dict = {}\n",
    "tp_mut_freq_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(mut_seq_dict.items(), mut_freq_dict.items()):\n",
    "    tp_mut_seq_dict[k1] = v1.loc[prim_genes.iloc[:,0]]\n",
    "    tp_mut_freq_dict[k1] = v2.loc[prim_genes.iloc[:,0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 20 + Tumor Portal Combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine Top 20 + TP data together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use these for prevalence bar charts\n",
    "comb_mut_seq_dict = {}\n",
    "comb_mut_freq_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(top_mut_seq_dict.items(), tp_mut_seq_dict.items()):\n",
    "    comb_mut_seq_dict[k1] = pd.concat([v1, v2]).drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(top_mut_freq_dict.items(), tp_mut_freq_dict.items()):\n",
    "    comb_mut_freq_dict[k1] = pd.concat([v1, v2]).drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fisher's Test and FDR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# outer join Top 20 primary and metastatic genes together for FDR calculation\n",
    "# so we can compare genes from both in FDR scatter plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ATRX', 'NTRK3', 'EPHA3'], dtype='object', name='Hugo_Symbol')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for visualization, these are the genes from Top 20 primary not in Top 20 metastasis\n",
    "comb_mut_seq_dict['prim'][~comb_mut_seq_dict['prim'].index.isin(comb_mut_seq_dict['met'].index)].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['EPHA5', 'APC', 'ALK'], dtype='object', name='Hugo_Symbol')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_mut_seq_dict['met'][~comb_mut_seq_dict['met'].index.isin(comb_mut_seq_dict['prim'].index)].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb_genes = comb_mut_seq_dict['prim'].index | comb_mut_seq_dict['met'].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb_pm_mut_seq_dict = {}\n",
    "comb_pm_mut_freq_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(mut_seq_dict.items(), mut_freq_dict.items()):\n",
    "    comb_pm_mut_seq_dict[k1] = v1.loc[comb_genes]\n",
    "    comb_pm_mut_freq_dict[k1] = v2.loc[comb_genes]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb_pval_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-40-ce1fee6a180c>:24: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  SE = np.sqrt(1/a + 1/c + 1/b + 1/d)\n",
      "<ipython-input-40-ce1fee6a180c>:27: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  c2 = round(odds_ratio * np.exp(1.96 * SE), 3)\n",
      "<ipython-input-40-ce1fee6a180c>:26: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  c1 = round(odds_ratio * np.exp(-1.96 * SE), 3)\n"
     ]
    }
   ],
   "source": [
    "for key, value in comb_pm_mut_seq_dict.items():\n",
    "    comb_pval_dict[key] = fisher_fdr_all_groups(value, groups)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Significant genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb_sig_pval_dict = {}\n",
    "comb_sig_freq_dict = {}\n",
    "comb_sig_genes_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(comb_pval_dict.items(), comb_pm_mut_freq_dict.items()):\n",
    "    comb_sig_genes_dict[k1], comb_sig_pval_dict[k1], comb_sig_freq_dict[k1] = find_sig_genes_groups(\n",
    "        groups, v1, v2, 0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Primary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Black',\n",
       "  'White'):              Black_White_pval  Black_White_fdr  Black_White_odds_ratio  \\\n",
       " Hugo_Symbol                                                              \n",
       " EGFR                 0.000211         0.006949                1.907088   \n",
       " \n",
       "              Black_White_CI  \n",
       " Hugo_Symbol                  \n",
       " EGFR         (1.374, 2.647)  ,\n",
       " ('Black',\n",
       "  'Asian'):              Black_Asian_pval  Black_Asian_fdr  Black_Asian_odds_ratio  \\\n",
       " Hugo_Symbol                                                              \n",
       " EGFR                 0.000002         0.000053                0.390824   \n",
       " KEAP1                0.008120         0.038281                2.529027   \n",
       " KRAS                 0.000113         0.001242                2.555234   \n",
       " NF1                  0.000262         0.002158                3.644970   \n",
       " NFE2L2               0.001557         0.010278               14.883721   \n",
       " PTPRD                0.000046         0.000758                4.089151   \n",
       " TP53                 0.003840         0.021121                1.742657   \n",
       " \n",
       "               Black_Asian_CI  \n",
       " Hugo_Symbol                   \n",
       " EGFR          (0.266, 0.575)  \n",
       " KEAP1         (1.304, 4.904)  \n",
       " KRAS          (1.594, 4.096)  \n",
       " NF1           (1.807, 7.354)  \n",
       " NFE2L2       (1.846, 119.99)  \n",
       " PTPRD         (2.077, 8.052)  \n",
       " TP53          (1.205, 2.521)  ,\n",
       " ('Asian',\n",
       "  'White'):              Asian_White_pval  Asian_White_fdr  Asian_White_odds_ratio  \\\n",
       " Hugo_Symbol                                                              \n",
       " ATM              7.020723e-04     2.574265e-03                0.410036   \n",
       " ATRX             2.358733e-03     6.515855e-03                0.368751   \n",
       " CDKN2A           9.497207e-03     2.410830e-02                0.475003   \n",
       " EGFR             1.166554e-38     3.849628e-37                4.879664   \n",
       " FAT1             3.801265e-04     1.568022e-03                0.368090   \n",
       " KEAP1            6.429030e-07     5.303950e-06                0.329366   \n",
       " KMT2D            7.550658e-05     4.983434e-04                0.371341   \n",
       " KRAS             4.607974e-20     7.603157e-19                0.249479   \n",
       " NF1              2.254292e-03     6.515855e-03                0.434779   \n",
       " NFE2L2           2.882463e-04     1.358875e-03                0.081683   \n",
       " PTPRD            2.369402e-03     6.515855e-03                0.448863   \n",
       " SMARCA4          1.874836e-04     1.031160e-03                0.284690   \n",
       " STK11            7.208635e-09     7.929498e-08                0.278122   \n",
       " \n",
       "              Asian_White_CI  \n",
       " Hugo_Symbol                  \n",
       " ATM          (0.233, 0.722)  \n",
       " ATRX         (0.181, 0.753)  \n",
       " CDKN2A       (0.263, 0.856)  \n",
       " EGFR         (3.861, 6.167)  \n",
       " FAT1         (0.199, 0.681)  \n",
       " KEAP1          (0.2, 0.542)  \n",
       " KMT2D        (0.215, 0.641)  \n",
       " KRAS         (0.177, 0.351)  \n",
       " NF1          (0.247, 0.766)  \n",
       " NFE2L2       (0.011, 0.586)  \n",
       " PTPRD        (0.259, 0.778)  \n",
       " SMARCA4      (0.133, 0.608)  \n",
       " STK11        (0.167, 0.463)  }"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_pval_dict['prim']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Black',\n",
       "  'White'): PRIMARY_RACE      Black      White\n",
       " Hugo_Symbol                       \n",
       " EGFR          30.555556  18.746638,\n",
       " ('Black',\n",
       "  'Asian'): PRIMARY_RACE      Black      Asian\n",
       " Hugo_Symbol                       \n",
       " EGFR          30.555556  52.959502\n",
       " KEAP1         12.571429   5.379747\n",
       " KRAS          26.111111  12.149533\n",
       " NF1           13.333333   4.049844\n",
       " NFE2L2         4.444444   0.311526\n",
       " PTPRD         16.875000   4.729730\n",
       " TP53          58.333333  44.548287,\n",
       " ('Asian',\n",
       "  'White'): PRIMARY_RACE      Asian      White\n",
       " Hugo_Symbol                       \n",
       " ATM            4.049844   9.332975\n",
       " ATRX           2.492212   6.481980\n",
       " CDKN2A         3.738318   7.557827\n",
       " EGFR          52.959502  18.746638\n",
       " FAT1           3.780069   9.643606\n",
       " KEAP1          5.379747  14.721104\n",
       " KMT2D          4.430380  11.098332\n",
       " KRAS          12.149533  35.664336\n",
       " NF1            4.049844   8.848843\n",
       " NFE2L2         0.311526   3.684777\n",
       " PTPRD          4.729730   9.958791\n",
       " SMARCA4        2.180685   7.261969\n",
       " STK11          4.984424  15.868747}"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_freq_dict['prim']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRIMARY_RACE</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PTPRD</th>\n",
       "      <td>4.729730</td>\n",
       "      <td>16.875000</td>\n",
       "      <td>9.958791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDKN2A</th>\n",
       "      <td>3.738318</td>\n",
       "      <td>7.222222</td>\n",
       "      <td>7.557827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KRAS</th>\n",
       "      <td>12.149533</td>\n",
       "      <td>26.111111</td>\n",
       "      <td>35.664336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATM</th>\n",
       "      <td>4.049844</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>9.332975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EGFR</th>\n",
       "      <td>52.959502</td>\n",
       "      <td>30.555556</td>\n",
       "      <td>18.746638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMARCA4</th>\n",
       "      <td>2.180685</td>\n",
       "      <td>5.555556</td>\n",
       "      <td>7.261969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KEAP1</th>\n",
       "      <td>5.379747</td>\n",
       "      <td>12.571429</td>\n",
       "      <td>14.721104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KMT2D</th>\n",
       "      <td>4.430380</td>\n",
       "      <td>9.714286</td>\n",
       "      <td>11.098332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP53</th>\n",
       "      <td>44.548287</td>\n",
       "      <td>58.333333</td>\n",
       "      <td>49.166218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATRX</th>\n",
       "      <td>2.492212</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>6.481980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NF1</th>\n",
       "      <td>4.049844</td>\n",
       "      <td>13.333333</td>\n",
       "      <td>8.848843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAT1</th>\n",
       "      <td>3.780069</td>\n",
       "      <td>7.947020</td>\n",
       "      <td>9.643606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFE2L2</th>\n",
       "      <td>0.311526</td>\n",
       "      <td>4.444444</td>\n",
       "      <td>3.684777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STK11</th>\n",
       "      <td>4.984424</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>15.868747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRIMARY_RACE      Asian      Black      White\n",
       "Hugo_Symbol                                  \n",
       "PTPRD          4.729730  16.875000   9.958791\n",
       "CDKN2A         3.738318   7.222222   7.557827\n",
       "KRAS          12.149533  26.111111  35.664336\n",
       "ATM            4.049844   6.666667   9.332975\n",
       "EGFR          52.959502  30.555556  18.746638\n",
       "SMARCA4        2.180685   5.555556   7.261969\n",
       "KEAP1          5.379747  12.571429  14.721104\n",
       "KMT2D          4.430380   9.714286  11.098332\n",
       "TP53          44.548287  58.333333  49.166218\n",
       "ATRX           2.492212   6.666667   6.481980\n",
       "NF1            4.049844  13.333333   8.848843\n",
       "FAT1           3.780069   7.947020   9.643606\n",
       "NFE2L2         0.311526   4.444444   3.684777\n",
       "STK11          4.984424  10.000000  15.868747"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_mut_freq_dict['prim'].loc[comb_sig_genes_dict['prim']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_White_odds_ratio</th>\n",
       "      <th>Black_White_pval</th>\n",
       "      <th>Black_White_fdr</th>\n",
       "      <th>Black_White_CI</th>\n",
       "      <th>Black_Asian_odds_ratio</th>\n",
       "      <th>Black_Asian_pval</th>\n",
       "      <th>Black_Asian_fdr</th>\n",
       "      <th>Black_Asian_CI</th>\n",
       "      <th>Asian_White_odds_ratio</th>\n",
       "      <th>Asian_White_pval</th>\n",
       "      <th>Asian_White_fdr</th>\n",
       "      <th>Asian_White_CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PTPRD</th>\n",
       "      <td>1.835468</td>\n",
       "      <td>0.010411</td>\n",
       "      <td>0.114524</td>\n",
       "      <td>(1.193, 2.825)</td>\n",
       "      <td>4.089151</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000758</td>\n",
       "      <td>(2.077, 8.052)</td>\n",
       "      <td>0.448863</td>\n",
       "      <td>2.369402e-03</td>\n",
       "      <td>6.515855e-03</td>\n",
       "      <td>(0.259, 0.778)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDKN2A</th>\n",
       "      <td>0.952138</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>(0.535, 1.696)</td>\n",
       "      <td>2.004491</td>\n",
       "      <td>0.091470</td>\n",
       "      <td>0.182758</td>\n",
       "      <td>(0.894, 4.492)</td>\n",
       "      <td>0.475003</td>\n",
       "      <td>9.497207e-03</td>\n",
       "      <td>2.410830e-02</td>\n",
       "      <td>(0.263, 0.856)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KRAS</th>\n",
       "      <td>0.637476</td>\n",
       "      <td>0.008340</td>\n",
       "      <td>0.114524</td>\n",
       "      <td>(0.454, 0.895)</td>\n",
       "      <td>2.555234</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.001242</td>\n",
       "      <td>(1.594, 4.096)</td>\n",
       "      <td>0.249479</td>\n",
       "      <td>4.607974e-20</td>\n",
       "      <td>7.603157e-19</td>\n",
       "      <td>(0.177, 0.351)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATM</th>\n",
       "      <td>0.693907</td>\n",
       "      <td>0.289656</td>\n",
       "      <td>0.822802</td>\n",
       "      <td>(0.382, 1.259)</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>0.205425</td>\n",
       "      <td>0.322811</td>\n",
       "      <td>(0.755, 3.792)</td>\n",
       "      <td>0.410036</td>\n",
       "      <td>7.020723e-04</td>\n",
       "      <td>2.574265e-03</td>\n",
       "      <td>(0.233, 0.722)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EGFR</th>\n",
       "      <td>1.907088</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>0.006949</td>\n",
       "      <td>(1.374, 2.647)</td>\n",
       "      <td>0.390824</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>(0.266, 0.575)</td>\n",
       "      <td>4.879664</td>\n",
       "      <td>1.166554e-38</td>\n",
       "      <td>3.849628e-37</td>\n",
       "      <td>(3.861, 6.167)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMARCA4</th>\n",
       "      <td>0.751198</td>\n",
       "      <td>0.460958</td>\n",
       "      <td>0.963085</td>\n",
       "      <td>(0.392, 1.439)</td>\n",
       "      <td>2.638655</td>\n",
       "      <td>0.068621</td>\n",
       "      <td>0.181591</td>\n",
       "      <td>(0.987, 7.057)</td>\n",
       "      <td>0.284690</td>\n",
       "      <td>1.874836e-04</td>\n",
       "      <td>1.031160e-03</td>\n",
       "      <td>(0.133, 0.608)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KEAP1</th>\n",
       "      <td>0.832976</td>\n",
       "      <td>0.510396</td>\n",
       "      <td>0.963085</td>\n",
       "      <td>(0.528, 1.315)</td>\n",
       "      <td>2.529027</td>\n",
       "      <td>0.008120</td>\n",
       "      <td>0.038281</td>\n",
       "      <td>(1.304, 4.904)</td>\n",
       "      <td>0.329366</td>\n",
       "      <td>6.429030e-07</td>\n",
       "      <td>5.303950e-06</td>\n",
       "      <td>(0.2, 0.542)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KMT2D</th>\n",
       "      <td>0.861874</td>\n",
       "      <td>0.710127</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>(0.517, 1.437)</td>\n",
       "      <td>2.320976</td>\n",
       "      <td>0.031472</td>\n",
       "      <td>0.115397</td>\n",
       "      <td>(1.115, 4.831)</td>\n",
       "      <td>0.371341</td>\n",
       "      <td>7.550658e-05</td>\n",
       "      <td>4.983434e-04</td>\n",
       "      <td>(0.215, 0.641)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP53</th>\n",
       "      <td>1.447484</td>\n",
       "      <td>0.017854</td>\n",
       "      <td>0.129621</td>\n",
       "      <td>(1.069, 1.96)</td>\n",
       "      <td>1.742657</td>\n",
       "      <td>0.003840</td>\n",
       "      <td>0.021121</td>\n",
       "      <td>(1.205, 2.521)</td>\n",
       "      <td>0.830619</td>\n",
       "      <td>1.163428e-01</td>\n",
       "      <td>1.919657e-01</td>\n",
       "      <td>(0.66, 1.045)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATRX</th>\n",
       "      <td>1.030528</td>\n",
       "      <td>0.876859</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>(0.566, 1.878)</td>\n",
       "      <td>2.794643</td>\n",
       "      <td>0.030718</td>\n",
       "      <td>0.115397</td>\n",
       "      <td>(1.12, 6.971)</td>\n",
       "      <td>0.368751</td>\n",
       "      <td>2.358733e-03</td>\n",
       "      <td>6.515855e-03</td>\n",
       "      <td>(0.181, 0.753)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NF1</th>\n",
       "      <td>1.584756</td>\n",
       "      <td>0.045905</td>\n",
       "      <td>0.216408</td>\n",
       "      <td>(1.016, 2.472)</td>\n",
       "      <td>3.644970</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.002158</td>\n",
       "      <td>(1.807, 7.354)</td>\n",
       "      <td>0.434779</td>\n",
       "      <td>2.254292e-03</td>\n",
       "      <td>6.515855e-03</td>\n",
       "      <td>(0.247, 0.766)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAT1</th>\n",
       "      <td>0.808883</td>\n",
       "      <td>0.571221</td>\n",
       "      <td>0.963085</td>\n",
       "      <td>(0.443, 1.478)</td>\n",
       "      <td>2.197515</td>\n",
       "      <td>0.072059</td>\n",
       "      <td>0.181591</td>\n",
       "      <td>(0.946, 5.106)</td>\n",
       "      <td>0.368090</td>\n",
       "      <td>3.801265e-04</td>\n",
       "      <td>1.568022e-03</td>\n",
       "      <td>(0.199, 0.681)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFE2L2</th>\n",
       "      <td>1.215753</td>\n",
       "      <td>0.544729</td>\n",
       "      <td>0.963085</td>\n",
       "      <td>(0.586, 2.521)</td>\n",
       "      <td>14.883721</td>\n",
       "      <td>0.001557</td>\n",
       "      <td>0.010278</td>\n",
       "      <td>(1.846, 119.99)</td>\n",
       "      <td>0.081683</td>\n",
       "      <td>2.882463e-04</td>\n",
       "      <td>1.358875e-03</td>\n",
       "      <td>(0.011, 0.586)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STK11</th>\n",
       "      <td>0.589077</td>\n",
       "      <td>0.034952</td>\n",
       "      <td>0.192237</td>\n",
       "      <td>(0.359, 0.966)</td>\n",
       "      <td>2.118056</td>\n",
       "      <td>0.041027</td>\n",
       "      <td>0.135388</td>\n",
       "      <td>(1.052, 4.265)</td>\n",
       "      <td>0.278122</td>\n",
       "      <td>7.208635e-09</td>\n",
       "      <td>7.929498e-08</td>\n",
       "      <td>(0.167, 0.463)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Black_White_odds_ratio  Black_White_pval  Black_White_fdr  \\\n",
       "Hugo_Symbol                                                              \n",
       "PTPRD                      1.835468          0.010411         0.114524   \n",
       "CDKN2A                     0.952138          1.000000         1.000000   \n",
       "KRAS                       0.637476          0.008340         0.114524   \n",
       "ATM                        0.693907          0.289656         0.822802   \n",
       "EGFR                       1.907088          0.000211         0.006949   \n",
       "SMARCA4                    0.751198          0.460958         0.963085   \n",
       "KEAP1                      0.832976          0.510396         0.963085   \n",
       "KMT2D                      0.861874          0.710127         1.000000   \n",
       "TP53                       1.447484          0.017854         0.129621   \n",
       "ATRX                       1.030528          0.876859         1.000000   \n",
       "NF1                        1.584756          0.045905         0.216408   \n",
       "FAT1                       0.808883          0.571221         0.963085   \n",
       "NFE2L2                     1.215753          0.544729         0.963085   \n",
       "STK11                      0.589077          0.034952         0.192237   \n",
       "\n",
       "             Black_White_CI  Black_Asian_odds_ratio  Black_Asian_pval  \\\n",
       "Hugo_Symbol                                                             \n",
       "PTPRD        (1.193, 2.825)                4.089151          0.000046   \n",
       "CDKN2A       (0.535, 1.696)                2.004491          0.091470   \n",
       "KRAS         (0.454, 0.895)                2.555234          0.000113   \n",
       "ATM          (0.382, 1.259)                1.692308          0.205425   \n",
       "EGFR         (1.374, 2.647)                0.390824          0.000002   \n",
       "SMARCA4      (0.392, 1.439)                2.638655          0.068621   \n",
       "KEAP1        (0.528, 1.315)                2.529027          0.008120   \n",
       "KMT2D        (0.517, 1.437)                2.320976          0.031472   \n",
       "TP53          (1.069, 1.96)                1.742657          0.003840   \n",
       "ATRX         (0.566, 1.878)                2.794643          0.030718   \n",
       "NF1          (1.016, 2.472)                3.644970          0.000262   \n",
       "FAT1         (0.443, 1.478)                2.197515          0.072059   \n",
       "NFE2L2       (0.586, 2.521)               14.883721          0.001557   \n",
       "STK11        (0.359, 0.966)                2.118056          0.041027   \n",
       "\n",
       "             Black_Asian_fdr   Black_Asian_CI  Asian_White_odds_ratio  \\\n",
       "Hugo_Symbol                                                             \n",
       "PTPRD               0.000758   (2.077, 8.052)                0.448863   \n",
       "CDKN2A              0.182758   (0.894, 4.492)                0.475003   \n",
       "KRAS                0.001242   (1.594, 4.096)                0.249479   \n",
       "ATM                 0.322811   (0.755, 3.792)                0.410036   \n",
       "EGFR                0.000053   (0.266, 0.575)                4.879664   \n",
       "SMARCA4             0.181591   (0.987, 7.057)                0.284690   \n",
       "KEAP1               0.038281   (1.304, 4.904)                0.329366   \n",
       "KMT2D               0.115397   (1.115, 4.831)                0.371341   \n",
       "TP53                0.021121   (1.205, 2.521)                0.830619   \n",
       "ATRX                0.115397    (1.12, 6.971)                0.368751   \n",
       "NF1                 0.002158   (1.807, 7.354)                0.434779   \n",
       "FAT1                0.181591   (0.946, 5.106)                0.368090   \n",
       "NFE2L2              0.010278  (1.846, 119.99)                0.081683   \n",
       "STK11               0.135388   (1.052, 4.265)                0.278122   \n",
       "\n",
       "             Asian_White_pval  Asian_White_fdr  Asian_White_CI  \n",
       "Hugo_Symbol                                                     \n",
       "PTPRD            2.369402e-03     6.515855e-03  (0.259, 0.778)  \n",
       "CDKN2A           9.497207e-03     2.410830e-02  (0.263, 0.856)  \n",
       "KRAS             4.607974e-20     7.603157e-19  (0.177, 0.351)  \n",
       "ATM              7.020723e-04     2.574265e-03  (0.233, 0.722)  \n",
       "EGFR             1.166554e-38     3.849628e-37  (3.861, 6.167)  \n",
       "SMARCA4          1.874836e-04     1.031160e-03  (0.133, 0.608)  \n",
       "KEAP1            6.429030e-07     5.303950e-06    (0.2, 0.542)  \n",
       "KMT2D            7.550658e-05     4.983434e-04  (0.215, 0.641)  \n",
       "TP53             1.163428e-01     1.919657e-01   (0.66, 1.045)  \n",
       "ATRX             2.358733e-03     6.515855e-03  (0.181, 0.753)  \n",
       "NF1              2.254292e-03     6.515855e-03  (0.247, 0.766)  \n",
       "FAT1             3.801265e-04     1.568022e-03  (0.199, 0.681)  \n",
       "NFE2L2           2.882463e-04     1.358875e-03  (0.011, 0.586)  \n",
       "STK11            7.208635e-09     7.929498e-08  (0.167, 0.463)  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_pval_dict['prim'].loc[comb_sig_genes_dict['prim']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Asian vs White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian_White_pval</th>\n",
       "      <th>Asian_White_fdr</th>\n",
       "      <th>Asian_White_odds_ratio</th>\n",
       "      <th>Asian_White_CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ATM</th>\n",
       "      <td>7.020723e-04</td>\n",
       "      <td>2.574265e-03</td>\n",
       "      <td>0.410036</td>\n",
       "      <td>(0.233, 0.722)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATRX</th>\n",
       "      <td>2.358733e-03</td>\n",
       "      <td>6.515855e-03</td>\n",
       "      <td>0.368751</td>\n",
       "      <td>(0.181, 0.753)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDKN2A</th>\n",
       "      <td>9.497207e-03</td>\n",
       "      <td>2.410830e-02</td>\n",
       "      <td>0.475003</td>\n",
       "      <td>(0.263, 0.856)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EGFR</th>\n",
       "      <td>1.166554e-38</td>\n",
       "      <td>3.849628e-37</td>\n",
       "      <td>4.879664</td>\n",
       "      <td>(3.861, 6.167)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAT1</th>\n",
       "      <td>3.801265e-04</td>\n",
       "      <td>1.568022e-03</td>\n",
       "      <td>0.368090</td>\n",
       "      <td>(0.199, 0.681)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KEAP1</th>\n",
       "      <td>6.429030e-07</td>\n",
       "      <td>5.303950e-06</td>\n",
       "      <td>0.329366</td>\n",
       "      <td>(0.2, 0.542)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KMT2D</th>\n",
       "      <td>7.550658e-05</td>\n",
       "      <td>4.983434e-04</td>\n",
       "      <td>0.371341</td>\n",
       "      <td>(0.215, 0.641)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KRAS</th>\n",
       "      <td>4.607974e-20</td>\n",
       "      <td>7.603157e-19</td>\n",
       "      <td>0.249479</td>\n",
       "      <td>(0.177, 0.351)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NF1</th>\n",
       "      <td>2.254292e-03</td>\n",
       "      <td>6.515855e-03</td>\n",
       "      <td>0.434779</td>\n",
       "      <td>(0.247, 0.766)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFE2L2</th>\n",
       "      <td>2.882463e-04</td>\n",
       "      <td>1.358875e-03</td>\n",
       "      <td>0.081683</td>\n",
       "      <td>(0.011, 0.586)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTPRD</th>\n",
       "      <td>2.369402e-03</td>\n",
       "      <td>6.515855e-03</td>\n",
       "      <td>0.448863</td>\n",
       "      <td>(0.259, 0.778)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMARCA4</th>\n",
       "      <td>1.874836e-04</td>\n",
       "      <td>1.031160e-03</td>\n",
       "      <td>0.284690</td>\n",
       "      <td>(0.133, 0.608)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STK11</th>\n",
       "      <td>7.208635e-09</td>\n",
       "      <td>7.929498e-08</td>\n",
       "      <td>0.278122</td>\n",
       "      <td>(0.167, 0.463)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Asian_White_pval  Asian_White_fdr  Asian_White_odds_ratio  \\\n",
       "Hugo_Symbol                                                              \n",
       "ATM              7.020723e-04     2.574265e-03                0.410036   \n",
       "ATRX             2.358733e-03     6.515855e-03                0.368751   \n",
       "CDKN2A           9.497207e-03     2.410830e-02                0.475003   \n",
       "EGFR             1.166554e-38     3.849628e-37                4.879664   \n",
       "FAT1             3.801265e-04     1.568022e-03                0.368090   \n",
       "KEAP1            6.429030e-07     5.303950e-06                0.329366   \n",
       "KMT2D            7.550658e-05     4.983434e-04                0.371341   \n",
       "KRAS             4.607974e-20     7.603157e-19                0.249479   \n",
       "NF1              2.254292e-03     6.515855e-03                0.434779   \n",
       "NFE2L2           2.882463e-04     1.358875e-03                0.081683   \n",
       "PTPRD            2.369402e-03     6.515855e-03                0.448863   \n",
       "SMARCA4          1.874836e-04     1.031160e-03                0.284690   \n",
       "STK11            7.208635e-09     7.929498e-08                0.278122   \n",
       "\n",
       "             Asian_White_CI  \n",
       "Hugo_Symbol                  \n",
       "ATM          (0.233, 0.722)  \n",
       "ATRX         (0.181, 0.753)  \n",
       "CDKN2A       (0.263, 0.856)  \n",
       "EGFR         (3.861, 6.167)  \n",
       "FAT1         (0.199, 0.681)  \n",
       "KEAP1          (0.2, 0.542)  \n",
       "KMT2D        (0.215, 0.641)  \n",
       "KRAS         (0.177, 0.351)  \n",
       "NF1          (0.247, 0.766)  \n",
       "NFE2L2       (0.011, 0.586)  \n",
       "PTPRD        (0.259, 0.778)  \n",
       "SMARCA4      (0.133, 0.608)  \n",
       "STK11        (0.167, 0.463)  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_pval_dict['prim'][('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRIMARY_RACE</th>\n",
       "      <th>Asian</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ATM</th>\n",
       "      <td>4.049844</td>\n",
       "      <td>9.332975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATRX</th>\n",
       "      <td>2.492212</td>\n",
       "      <td>6.481980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDKN2A</th>\n",
       "      <td>3.738318</td>\n",
       "      <td>7.557827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EGFR</th>\n",
       "      <td>52.959502</td>\n",
       "      <td>18.746638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FAT1</th>\n",
       "      <td>3.780069</td>\n",
       "      <td>9.643606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KEAP1</th>\n",
       "      <td>5.379747</td>\n",
       "      <td>14.721104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KMT2D</th>\n",
       "      <td>4.430380</td>\n",
       "      <td>11.098332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KRAS</th>\n",
       "      <td>12.149533</td>\n",
       "      <td>35.664336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NF1</th>\n",
       "      <td>4.049844</td>\n",
       "      <td>8.848843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFE2L2</th>\n",
       "      <td>0.311526</td>\n",
       "      <td>3.684777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTPRD</th>\n",
       "      <td>4.729730</td>\n",
       "      <td>9.958791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMARCA4</th>\n",
       "      <td>2.180685</td>\n",
       "      <td>7.261969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STK11</th>\n",
       "      <td>4.984424</td>\n",
       "      <td>15.868747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRIMARY_RACE      Asian      White\n",
       "Hugo_Symbol                       \n",
       "ATM            4.049844   9.332975\n",
       "ATRX           2.492212   6.481980\n",
       "CDKN2A         3.738318   7.557827\n",
       "EGFR          52.959502  18.746638\n",
       "FAT1           3.780069   9.643606\n",
       "KEAP1          5.379747  14.721104\n",
       "KMT2D          4.430380  11.098332\n",
       "KRAS          12.149533  35.664336\n",
       "NF1            4.049844   8.848843\n",
       "NFE2L2         0.311526   3.684777\n",
       "PTPRD          4.729730   9.958791\n",
       "SMARCA4        2.180685   7.261969\n",
       "STK11          4.984424  15.868747"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_freq_dict['prim'][('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Black vs White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_White_pval</th>\n",
       "      <th>Black_White_fdr</th>\n",
       "      <th>Black_White_odds_ratio</th>\n",
       "      <th>Black_White_CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EGFR</th>\n",
       "      <td>0.000211</td>\n",
       "      <td>0.006949</td>\n",
       "      <td>1.907088</td>\n",
       "      <td>(1.374, 2.647)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Black_White_pval  Black_White_fdr  Black_White_odds_ratio  \\\n",
       "Hugo_Symbol                                                              \n",
       "EGFR                 0.000211         0.006949                1.907088   \n",
       "\n",
       "             Black_White_CI  \n",
       "Hugo_Symbol                  \n",
       "EGFR         (1.374, 2.647)  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_pval_dict['prim'][('Black', 'White')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRIMARY_RACE</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EGFR</th>\n",
       "      <td>30.555556</td>\n",
       "      <td>18.746638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRIMARY_RACE      Black      White\n",
       "Hugo_Symbol                       \n",
       "EGFR          30.555556  18.746638"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_freq_dict['prim'][('Black', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Black vs Asian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_Asian_pval</th>\n",
       "      <th>Black_Asian_fdr</th>\n",
       "      <th>Black_Asian_odds_ratio</th>\n",
       "      <th>Black_Asian_CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EGFR</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.390824</td>\n",
       "      <td>(0.266, 0.575)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KEAP1</th>\n",
       "      <td>0.008120</td>\n",
       "      <td>0.038281</td>\n",
       "      <td>2.529027</td>\n",
       "      <td>(1.304, 4.904)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KRAS</th>\n",
       "      <td>0.000113</td>\n",
       "      <td>0.001242</td>\n",
       "      <td>2.555234</td>\n",
       "      <td>(1.594, 4.096)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NF1</th>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.002158</td>\n",
       "      <td>3.644970</td>\n",
       "      <td>(1.807, 7.354)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFE2L2</th>\n",
       "      <td>0.001557</td>\n",
       "      <td>0.010278</td>\n",
       "      <td>14.883721</td>\n",
       "      <td>(1.846, 119.99)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTPRD</th>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.000758</td>\n",
       "      <td>4.089151</td>\n",
       "      <td>(2.077, 8.052)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP53</th>\n",
       "      <td>0.003840</td>\n",
       "      <td>0.021121</td>\n",
       "      <td>1.742657</td>\n",
       "      <td>(1.205, 2.521)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Black_Asian_pval  Black_Asian_fdr  Black_Asian_odds_ratio  \\\n",
       "Hugo_Symbol                                                              \n",
       "EGFR                 0.000002         0.000053                0.390824   \n",
       "KEAP1                0.008120         0.038281                2.529027   \n",
       "KRAS                 0.000113         0.001242                2.555234   \n",
       "NF1                  0.000262         0.002158                3.644970   \n",
       "NFE2L2               0.001557         0.010278               14.883721   \n",
       "PTPRD                0.000046         0.000758                4.089151   \n",
       "TP53                 0.003840         0.021121                1.742657   \n",
       "\n",
       "              Black_Asian_CI  \n",
       "Hugo_Symbol                   \n",
       "EGFR          (0.266, 0.575)  \n",
       "KEAP1         (1.304, 4.904)  \n",
       "KRAS          (1.594, 4.096)  \n",
       "NF1           (1.807, 7.354)  \n",
       "NFE2L2       (1.846, 119.99)  \n",
       "PTPRD         (2.077, 8.052)  \n",
       "TP53          (1.205, 2.521)  "
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_pval_dict['prim'][('Black', 'Asian')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRIMARY_RACE</th>\n",
       "      <th>Black</th>\n",
       "      <th>Asian</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EGFR</th>\n",
       "      <td>30.555556</td>\n",
       "      <td>52.959502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KEAP1</th>\n",
       "      <td>12.571429</td>\n",
       "      <td>5.379747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KRAS</th>\n",
       "      <td>26.111111</td>\n",
       "      <td>12.149533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NF1</th>\n",
       "      <td>13.333333</td>\n",
       "      <td>4.049844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NFE2L2</th>\n",
       "      <td>4.444444</td>\n",
       "      <td>0.311526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTPRD</th>\n",
       "      <td>16.875000</td>\n",
       "      <td>4.729730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TP53</th>\n",
       "      <td>58.333333</td>\n",
       "      <td>44.548287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRIMARY_RACE      Black      Asian\n",
       "Hugo_Symbol                       \n",
       "EGFR          30.555556  52.959502\n",
       "KEAP1         12.571429   5.379747\n",
       "KRAS          26.111111  12.149533\n",
       "NF1           13.333333   4.049844\n",
       "NFE2L2         4.444444   0.311526\n",
       "PTPRD         16.875000   4.729730\n",
       "TP53          58.333333  44.548287"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_freq_dict['prim'][('Black', 'Asian')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metastasis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Black',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       " Index: [],\n",
       " ('Black',\n",
       "  'Asian'):              Black_Asian_pval  Black_Asian_fdr  Black_Asian_odds_ratio  \\\n",
       " Hugo_Symbol                                                              \n",
       " CDKN2A           5.899167e-03     3.893450e-02                3.228571   \n",
       " EGFR             8.321387e-09     2.746058e-07                0.270893   \n",
       " KEAP1            3.372996e-03     2.782722e-02                3.068584   \n",
       " KRAS             2.586793e-04     2.845473e-03                2.967075   \n",
       " STK11            3.766518e-07     6.214755e-06                9.403540   \n",
       " \n",
       "               Black_Asian_CI  \n",
       " Hugo_Symbol                   \n",
       " CDKN2A        (1.433, 7.272)  \n",
       " EGFR          (0.172, 0.426)  \n",
       " KEAP1         (1.439, 6.545)  \n",
       " KRAS           (1.658, 5.31)  \n",
       " STK11        (3.484, 25.382)  ,\n",
       " ('Asian',\n",
       "  'White'):              Asian_White_pval  Asian_White_fdr  Asian_White_odds_ratio  \\\n",
       " Hugo_Symbol                                                              \n",
       " ATM              1.233981e-02     4.072139e-02                0.486687   \n",
       " ATRX             7.905554e-03     3.261041e-02                0.326685   \n",
       " EGFR             8.578416e-33     2.830877e-31                5.410483   \n",
       " EPHA3            1.865606e-03     8.973007e-03                0.251500   \n",
       " EPHA5            1.903365e-03     8.973007e-03                0.313354   \n",
       " KEAP1            1.339714e-08     1.105264e-07                0.237676   \n",
       " KRAS             9.378035e-14     1.547376e-12                0.240061   \n",
       " PIK3CA           1.009361e-02     3.700990e-02                1.863975   \n",
       " SMARCA4          1.894294e-03     8.973007e-03                0.419748   \n",
       " STK11            9.571444e-11     1.052859e-09                0.119706   \n",
       " \n",
       "              Asian_White_CI  \n",
       " Hugo_Symbol                  \n",
       " ATM          (0.274, 0.865)  \n",
       " ATRX         (0.133, 0.805)  \n",
       " EGFR         (4.094, 7.151)  \n",
       " EPHA3        (0.092, 0.687)  \n",
       " EPHA5        (0.137, 0.717)  \n",
       " KEAP1        (0.132, 0.429)  \n",
       " KRAS         (0.155, 0.373)  \n",
       " PIK3CA       (1.179, 2.947)  \n",
       " SMARCA4      (0.231, 0.761)  \n",
       " STK11        (0.049, 0.292)  }"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_pval_dict['met']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Black',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Black, White]\n",
       " Index: [],\n",
       " ('Black',\n",
       "  'Asian'): PRIMARY_RACE      Black      Asian\n",
       " Hugo_Symbol                       \n",
       " CDKN2A        12.500000   4.237288\n",
       " EGFR          28.676471  59.745763\n",
       " KEAP1         14.393939   5.194805\n",
       " KRAS          24.264706   9.745763\n",
       " STK11         16.911765   2.118644,\n",
       " ('Asian',\n",
       "  'White'): PRIMARY_RACE      Asian      White\n",
       " Hugo_Symbol                       \n",
       " ATM            5.508475  10.696849\n",
       " ATRX           2.118644   6.213937\n",
       " EGFR          59.745763  21.526853\n",
       " EPHA3          1.941748   7.298881\n",
       " EPHA5          2.912621   8.737347\n",
       " KEAP1          5.194805  18.735026\n",
       " KRAS           9.745763  31.025300\n",
       " PIK3CA        10.169492   5.725699\n",
       " SMARCA4        5.084746  11.318242\n",
       " STK11          2.118644  15.312916}"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_freq_dict['met']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Asian vs White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian_White_pval</th>\n",
       "      <th>Asian_White_fdr</th>\n",
       "      <th>Asian_White_odds_ratio</th>\n",
       "      <th>Asian_White_CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ATM</th>\n",
       "      <td>1.233981e-02</td>\n",
       "      <td>4.072139e-02</td>\n",
       "      <td>0.486687</td>\n",
       "      <td>(0.274, 0.865)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATRX</th>\n",
       "      <td>7.905554e-03</td>\n",
       "      <td>3.261041e-02</td>\n",
       "      <td>0.326685</td>\n",
       "      <td>(0.133, 0.805)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EGFR</th>\n",
       "      <td>8.578416e-33</td>\n",
       "      <td>2.830877e-31</td>\n",
       "      <td>5.410483</td>\n",
       "      <td>(4.094, 7.151)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPHA3</th>\n",
       "      <td>1.865606e-03</td>\n",
       "      <td>8.973007e-03</td>\n",
       "      <td>0.251500</td>\n",
       "      <td>(0.092, 0.687)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPHA5</th>\n",
       "      <td>1.903365e-03</td>\n",
       "      <td>8.973007e-03</td>\n",
       "      <td>0.313354</td>\n",
       "      <td>(0.137, 0.717)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KEAP1</th>\n",
       "      <td>1.339714e-08</td>\n",
       "      <td>1.105264e-07</td>\n",
       "      <td>0.237676</td>\n",
       "      <td>(0.132, 0.429)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KRAS</th>\n",
       "      <td>9.378035e-14</td>\n",
       "      <td>1.547376e-12</td>\n",
       "      <td>0.240061</td>\n",
       "      <td>(0.155, 0.373)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PIK3CA</th>\n",
       "      <td>1.009361e-02</td>\n",
       "      <td>3.700990e-02</td>\n",
       "      <td>1.863975</td>\n",
       "      <td>(1.179, 2.947)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMARCA4</th>\n",
       "      <td>1.894294e-03</td>\n",
       "      <td>8.973007e-03</td>\n",
       "      <td>0.419748</td>\n",
       "      <td>(0.231, 0.761)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STK11</th>\n",
       "      <td>9.571444e-11</td>\n",
       "      <td>1.052859e-09</td>\n",
       "      <td>0.119706</td>\n",
       "      <td>(0.049, 0.292)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Asian_White_pval  Asian_White_fdr  Asian_White_odds_ratio  \\\n",
       "Hugo_Symbol                                                              \n",
       "ATM              1.233981e-02     4.072139e-02                0.486687   \n",
       "ATRX             7.905554e-03     3.261041e-02                0.326685   \n",
       "EGFR             8.578416e-33     2.830877e-31                5.410483   \n",
       "EPHA3            1.865606e-03     8.973007e-03                0.251500   \n",
       "EPHA5            1.903365e-03     8.973007e-03                0.313354   \n",
       "KEAP1            1.339714e-08     1.105264e-07                0.237676   \n",
       "KRAS             9.378035e-14     1.547376e-12                0.240061   \n",
       "PIK3CA           1.009361e-02     3.700990e-02                1.863975   \n",
       "SMARCA4          1.894294e-03     8.973007e-03                0.419748   \n",
       "STK11            9.571444e-11     1.052859e-09                0.119706   \n",
       "\n",
       "             Asian_White_CI  \n",
       "Hugo_Symbol                  \n",
       "ATM          (0.274, 0.865)  \n",
       "ATRX         (0.133, 0.805)  \n",
       "EGFR         (4.094, 7.151)  \n",
       "EPHA3        (0.092, 0.687)  \n",
       "EPHA5        (0.137, 0.717)  \n",
       "KEAP1        (0.132, 0.429)  \n",
       "KRAS         (0.155, 0.373)  \n",
       "PIK3CA       (1.179, 2.947)  \n",
       "SMARCA4      (0.231, 0.761)  \n",
       "STK11        (0.049, 0.292)  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_pval_dict['met'][('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRIMARY_RACE</th>\n",
       "      <th>Asian</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ATM</th>\n",
       "      <td>5.508475</td>\n",
       "      <td>10.696849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ATRX</th>\n",
       "      <td>2.118644</td>\n",
       "      <td>6.213937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EGFR</th>\n",
       "      <td>59.745763</td>\n",
       "      <td>21.526853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPHA3</th>\n",
       "      <td>1.941748</td>\n",
       "      <td>7.298881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EPHA5</th>\n",
       "      <td>2.912621</td>\n",
       "      <td>8.737347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KEAP1</th>\n",
       "      <td>5.194805</td>\n",
       "      <td>18.735026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KRAS</th>\n",
       "      <td>9.745763</td>\n",
       "      <td>31.025300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PIK3CA</th>\n",
       "      <td>10.169492</td>\n",
       "      <td>5.725699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMARCA4</th>\n",
       "      <td>5.084746</td>\n",
       "      <td>11.318242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STK11</th>\n",
       "      <td>2.118644</td>\n",
       "      <td>15.312916</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRIMARY_RACE      Asian      White\n",
       "Hugo_Symbol                       \n",
       "ATM            5.508475  10.696849\n",
       "ATRX           2.118644   6.213937\n",
       "EGFR          59.745763  21.526853\n",
       "EPHA3          1.941748   7.298881\n",
       "EPHA5          2.912621   8.737347\n",
       "KEAP1          5.194805  18.735026\n",
       "KRAS           9.745763  31.025300\n",
       "PIK3CA        10.169492   5.725699\n",
       "SMARCA4        5.084746  11.318242\n",
       "STK11          2.118644  15.312916"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_freq_dict['met'][('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Black vs White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_White_pval</th>\n",
       "      <th>Black_White_fdr</th>\n",
       "      <th>Black_White_odds_ratio</th>\n",
       "      <th>Black_White_CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_pval_dict['met'][('Black', 'White')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRIMARY_RACE</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black, White]\n",
       "Index: []"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_freq_dict['met'][('Black', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Black vs Asian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_Asian_pval</th>\n",
       "      <th>Black_Asian_fdr</th>\n",
       "      <th>Black_Asian_odds_ratio</th>\n",
       "      <th>Black_Asian_CI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CDKN2A</th>\n",
       "      <td>5.899167e-03</td>\n",
       "      <td>3.893450e-02</td>\n",
       "      <td>3.228571</td>\n",
       "      <td>(1.433, 7.272)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EGFR</th>\n",
       "      <td>8.321387e-09</td>\n",
       "      <td>2.746058e-07</td>\n",
       "      <td>0.270893</td>\n",
       "      <td>(0.172, 0.426)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KEAP1</th>\n",
       "      <td>3.372996e-03</td>\n",
       "      <td>2.782722e-02</td>\n",
       "      <td>3.068584</td>\n",
       "      <td>(1.439, 6.545)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KRAS</th>\n",
       "      <td>2.586793e-04</td>\n",
       "      <td>2.845473e-03</td>\n",
       "      <td>2.967075</td>\n",
       "      <td>(1.658, 5.31)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STK11</th>\n",
       "      <td>3.766518e-07</td>\n",
       "      <td>6.214755e-06</td>\n",
       "      <td>9.403540</td>\n",
       "      <td>(3.484, 25.382)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Black_Asian_pval  Black_Asian_fdr  Black_Asian_odds_ratio  \\\n",
       "Hugo_Symbol                                                              \n",
       "CDKN2A           5.899167e-03     3.893450e-02                3.228571   \n",
       "EGFR             8.321387e-09     2.746058e-07                0.270893   \n",
       "KEAP1            3.372996e-03     2.782722e-02                3.068584   \n",
       "KRAS             2.586793e-04     2.845473e-03                2.967075   \n",
       "STK11            3.766518e-07     6.214755e-06                9.403540   \n",
       "\n",
       "              Black_Asian_CI  \n",
       "Hugo_Symbol                   \n",
       "CDKN2A        (1.433, 7.272)  \n",
       "EGFR          (0.172, 0.426)  \n",
       "KEAP1         (1.439, 6.545)  \n",
       "KRAS           (1.658, 5.31)  \n",
       "STK11        (3.484, 25.382)  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_pval_dict['met'][('Black', 'Asian')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PRIMARY_RACE</th>\n",
       "      <th>Black</th>\n",
       "      <th>Asian</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hugo_Symbol</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CDKN2A</th>\n",
       "      <td>12.500000</td>\n",
       "      <td>4.237288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EGFR</th>\n",
       "      <td>28.676471</td>\n",
       "      <td>59.745763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KEAP1</th>\n",
       "      <td>14.393939</td>\n",
       "      <td>5.194805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KRAS</th>\n",
       "      <td>24.264706</td>\n",
       "      <td>9.745763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>STK11</th>\n",
       "      <td>16.911765</td>\n",
       "      <td>2.118644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PRIMARY_RACE      Black      Asian\n",
       "Hugo_Symbol                       \n",
       "CDKN2A        12.500000   4.237288\n",
       "EGFR          28.676471  59.745763\n",
       "KEAP1         14.393939   5.194805\n",
       "KRAS          24.264706   9.745763\n",
       "STK11         16.911765   2.118644"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_sig_freq_dict['met'][('Black', 'Asian')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutation Prevalence Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Primary Genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = comb_mut_freq_dict['prim'].sort_values(['Asian'], ascending=False)\n",
    "ax = df.plot(kind='barh', figsize=(10, 15), width=0.8)\n",
    "hfont = {'fontname':'Helvetica'}\n",
    "plt.title('Primary Tumor Mutations in Lung Cancer', fontsize=20, **hfont)\n",
    "plt.xlabel('Patients with Mutation (%)', fontsize=15, **hfont)\n",
    "plt.ylabel('Gene', fontsize=15, **hfont)\n",
    "plt.xticks(size=15, **hfont)\n",
    "plt.yticks(size=15, **hfont)\n",
    "plt.xlim((0, 100))\n",
    "ax.invert_yaxis()\n",
    "plt.legend(prop={'family':'Helvetica', 'size': 15}, loc='lower right')\n",
    "plt.tight_layout()\n",
    "# plt.savefig('figures/lung/Top20+Tumor_Portal/lung_top20+TP_prim.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metastatic Genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = comb_mut_freq_dict['met'].sort_values(['Asian'], ascending=False)\n",
    "ax = df.plot(kind='barh', figsize=(10, 15), width=0.8)\n",
    "hfont = {'fontname':'Helvetica'}\n",
    "plt.title('Metastatic Tumor Mutations in Lung Cancer', fontsize=20, **hfont)\n",
    "plt.xlabel('Patients with Mutation (%)', fontsize=15, **hfont)\n",
    "plt.ylabel('Gene', fontsize=15, **hfont)\n",
    "plt.xticks(size=15, **hfont)\n",
    "plt.yticks(size=15, **hfont)\n",
    "plt.xlim((0, 100))\n",
    "ax.invert_yaxis()\n",
    "plt.legend(prop={'family':'Helvetica', 'size': 15}, loc='lower right')\n",
    "plt.tight_layout()\n",
    "# plt.savefig('figures/lung/Top20+Tumor_Portal/lung_top20+TP_met.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Variant Composition Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb_var_comp_cnt_dict = {}\n",
    "comb_var_comp_pct_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(var_mut_dict.items(), comb_mut_freq_dict.items()):\n",
    "    comb_var_comp_cnt_dict[k1], comb_var_comp_pct_dict[k1] = variant_comp(v1, v2.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Primary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = comb_var_comp_pct_dict['prim'].plot(kind='bar', stacked=True, figsize=(30, 15))\n",
    "chartBox = ax.get_position()\n",
    "ax.set_position([chartBox.x0, chartBox.y0, chartBox.width*0.6, chartBox.height])\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(1.3, 1), ncol=2, prop={'size':20})\n",
    "plt.title(\"Variant Composition for Primary Lung Tumor Mutations\", size=35)\n",
    "plt.xlabel('Gene', size=25)\n",
    "plt.ylabel('Percent', size=25)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metastasis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pW6wckiRJkiRJkiRJ6hArh4bgObveNOoQJC2Er1NJkurhe6okSZIktZ+VQ5IkSZIkSZIkSR1i5ZAktYhrUUmSJEmSJElqmpVDkiRJkiRJkiRJHWLlkCQBM+49fcqPMWfqYUjSwGbNnjvqECRJkiRJ0mLKyiFJkiRJkiRJkqQOsXJIkiRJUm2sxpUkSZKk9rNySJIkSZIkSZIkqUNMDkmSJEmSJEmSJHWIbeWGwNYakiRJkiRJkiSpLUwOSZIkSVM08+Cpn1bPqiEOSZIkSZIGYVs5SZIkSZIkSZKkDjE5JEmSJEmSJEmS1CEmhyRJkiRJkiRJkjrE5JAkSZIkSZIkSVKHTH3lXElSbf7+6yNHHYIkSZI0FNu/5pNT+vk59YQhSZLUSVYOSZIkSZIkSZIkdYjJIUmSJEmSJEmSpA6xrZw0IrNmzx11CJIkSZIkSZKkDjI5JEmSJE0DMw+e+qn9rBrikCRJkiS1n23lJEmSJEmSJEmSOsTkkCRJkiRJkiRJUoeYHJIkSZIkSZIkSeqQzieHIuIZEfHFiLgpIu6LiDkR8ZmIWHHUsUmSJEmSJEmSJNVt6qvWLsYiYg3gEuApwLeA64AXAwcA20XESzLzthGGKEmSJEmSJEmSVKuuVw59npIY2j8zX5OZB2Xm1sDRwNrAR0canSRJkiRJkiRJUs06WzkUEc8GtgXmAMf13H0YsB/w5oh4X2bePeTwpE75+6+PHHUIkiRJkiRJktQZXa4c2rr6/oPMnDf+jsz8O/BTYBngX4YdmCRJkiRJkiRJUlO6nBxau/r+mwnu/7/q+1rJiQzhAAAgAElEQVRDiEWSJEmSJEmSJGkoIjNHHcNIRMQJwL7Avpl5Up/7Pwp8APhAZn6s5779KG3noCSZrp9iOCsDt07xMerQhjjaEAO0I442xADtiKMNMUA74mhDDNCOOIxhvjbE0YYYoB1xtCEGaEccbYgB2hFHG2KAdsTRhhigHXG0IQZoRxxtiAHaEUcbYoB2xFFHDKtl5pPrCEaSJGk66eyaQwOI6vujsmeZeQJwQm0DRVyRmRvU9XiLcxxtiKEtcbQhhrbE0YYY2hJHG2JoSxzG0K442hBDW+JoQwxtiaMNMbQljjbE0JY42hBDW+JoQwxtiaMNMbQljjbE0JY42hCDJEnSdNXltnJ3VN+Xn+D+5Xq2kyRJkiRJkiRJWux1OTk01gpuojWF/qn6PtGaRJIkSZIkSZIkSYudLieHzq++bxsRj/g7RMQTgZcA9wA/G0IstbWom6I2xNGGGKAdcbQhBmhHHG2IAdoRRxtigHbEYQzztSGONsQA7YijDTFAO+JoQwzQjjjaEAO0I442xADtiKMNMUA74mhDDNCOONoQA7QjjjbEIEmSNC1F5qOW1OmMiPg+sC2wf2YeM+72TwMHAl/IzLeNKj5JkiRJkiRJkqS6dT05tAZwCfAU4FvAr4GNgK0o7eQ2yczbRhehJEmSJEmSJElSvTqdHAKIiGcCRwDbASsBfwLOAj6UmbePMjZJkiRJkiRJkqS6dT451EYRsWxm3jXqOCRJkiRJkiRJ0vSzxKgDmO4i4lWT3H4Z4LsNhbPYiYilRh2DJEmSJHVFRDw2Il4YEetGRIw6HkmSJDXD5FDzzoiIjQfZMCKWBs4GXlJ3EBHxzog4OCIes6Dxq23eXvf4kxURa0bEUcDvRx2L5ouI50TE0aOOQ2o7E9vqkojYPSLWHXUcWnQRsd6oY5A0XBGxXkR8JyKuj4ivVevxEhHbAr8DLgeuAm6MiE1HGaskSZKaYVu5hkXE3cA/gM0y87oFbPcY4JvADsAPM/PlNcawCfAT4MOZefhCtj0UOAzYJDMvrSuGQVR/g52BfYEtgQAeyswJE1qLMMaNi/BjmZlr1BXD4iYiHgvMBPYDNgHIzCVHGhQQEetQntO7jDoWaUxErEk5hu2emasOcdz1gK0ox82fZOYVwxpb/XVpn0TEPODwzDxi1LFocFW1+m6U9/cXZWajSe2IeA7wYuDJQAK3AJdm5vVNjttG1Tnvs4EVKH+LO4AbM/OBkQamzoiIpwO/ApYbd/NsYBvgSuAe4P8BTwc2BO4EnpuZfxxyqJIkSWqQlUPNmwksD5wbEU/rt0FELAmcQUkMXQS8uuYY9gDuAj45wLafAv4OvKXmGCYUEWtHxCeBPwL/Q5lMmwscDqxe83AzgNWq74N+1R3DwyLixojYv+e2Z0XE5k2NOaiIeF5EfA74E3AqpaJtNnDIiONaLSJOAWYBrx3iuE+KiFdExMyI2GBIY+4UEZ+NiE9FxDYL2G6PiDhvGDEtIIZTIuLBmh/zhxFxYEQ8qc7HrVtEPCYido2IHwPXA/8GrFzzGJtHxGkR8S997jucMpHzKcpx/tKI+HSd4y+qiFiryeNZRKwfEUdFxPkR8cuImBUR50XExyLi+U2NW4292O2TiHj/qI8VbRERm0TE7g0+fmuP31WrqOMp7+8nUCZ+b2hwvE0j4ufAL4EvAkcBHwdOAa6NiMsHrbJvWkS8urpQqqnHnxkR51POy68FLqFMwF8L3FUdv1px0Ut13vOsBh536eq9/VsRcWZE7FN9Fuq37QGLeGHXlEXElREx0nPe8SLiExHx2xof8r2UxNC+lCTl7pTPSKdTzmXWyczXZuZGwNurbfef4LGGqunjtyRJUqdkpl8NfwF7AvOAXwAr9NwXlITIPMoHxCc0MP61wDcmsf2ZwLUN/02WBt4IXAg8VP3+91bfv0pV1dbAuPOA+4CvAy8Hlhzkq8G/wzzg0J7bDqNUTI3iufp4YK/quTi2X+ZRWkpsPYTxNwZ+TLk68TbgLGDN6r7HUSZZx54nfwTeVfP42wLP7nP7R6pxHxr39Uvg+Q39HQL4Ws8+eIjSdnKFPtuP7DkzLoZT6o5h3O99D/DfwOaj/B37xLd29Zz8y7h9NRs4FHhGzWMdUz0Hl++5fbNq3AeA04DjxsWzQwv+RrU/L6rHXRL4PPBgz+tk/OvlQeBzDb6fLHb7pMH98aj3srZ/Nfi3aOXxG1iWUiF0xbj3sbur52hjx1Zg++oYPq8a7yLgK5SLoi6qbptXbbPNNH5eLEE5vx17XtxFucjlYuCn1b/vGvd8OaOpY9ck/xYP1vyYj6F0M+h9fVwBrNZn+1GeE88DThjlPuizP2r7W1A+l57Xc9v3q/3x0j7bXwNcNeq/QxN/C7/88ssvv/zyy68uf7kmwhBk5qkR8VTgP4GzI2KbzLyvuvskSkuPq4DtMvPuBkJ4FvDtSWx/AyVxUruI+GfK5MSbgBUpkyhXUipTvkJpMXJHZjbV73DLavzXAjtRJnJPAk7JzJsbGrP1ImJ9ypWDb6BcGTi2X06hTIBenpmNXtlcrVfxY0oSaMyOwAur1ojfBtajPEeOAj6fmffWHMb3gA8BD7dGiogjgA9QJnovqcZ/IfDPwA8j4nmZeWvNcewFvI6y5tbx1dh7AK8ELo6IrTPzLzWP2VazgVUpz83dIuJ64ETgS5l5+7CDibI23C6U48imlNfK/dX3/wV2bej4tTGl/dIdPbe/ldKSaP/M/K8qxmMokzh7Ad9tIJY2+CTwNsrf/mvABZSEcQBPA7amvIbeSUng/HsDMbhP1E+rjt8RsSHlePV64AmU18jPgRcB/5OZ+zU49hOAkykJgSOAT2bmXT3bLEt5fX4A+GJErJWZ9zQV0wi9m/Le8f8oFdgXZeZD4zeoqme2oFyQsgvlnONzQ46zV9T8eO+mVKKPVVY+AOxN+dzx04jYMjMbq2IbExGnDbjpluO2zczco6mYRmA14Ic9t/0CeBll//T6GbBr00FJkiRpuEwODUlmHhkRq1I+FJ0RETsDx1ImEX5FuVryzoaGX5IyUTWoeTTQcjAiLqZMpgVwM3A0JSnzq3Hb1D3sI2TmRcBFETHWPmFfStLuiIj4DmXS+dwGk1OtEhH7UK01wPz9cjLj9ks1qTkM/05JDH2hiiGq2PamXGX6LMqE8BG9k0s1esQTMCJWorQI+zPwisy8qrp9KeCzlDYbBwIfrDmOvYC/ARuOTSJGxNGUpNh7gR9VE4x1J6UeFhEXTfJH1mokkHJV+2eZ/3p9HuV58J8RcSbwhcz8SUNjP6wFie2n0T+psDXlyvsTx27IzOsi4vuUVlG1iojJvjfUflCPsq7TuymLZW+X/dcr+WJEfAQ4FzgwIo7PzLpbE418n1TJ68l4QZ3j91hhsi2oMnNuXYNHxLMn+SNPrGvsHm04fi9HOVbtC6zL/Pf3E6je36OsE9W0XYCnAh/MzI/126B6Pz80Iu4FPkxJrH25rgAiYutJ/khTa8W9BbgO2Coz7++3QZUsOi8itgKuppz/jDo5VLfdKO0Mtxh3QdzXI+JdlM8F50fEFg0cr3u9ifLZaEHvUQmsWX2N/b+25NAkElRjNqlr7MoSlOTceHcBZOZf+2x/G4+8gKs2LTp+S5IkdY7JoSHKzAMi4imUqzd/CawD/B/wsoavgL8FWGMS268JNDFpsQkl8XQUcEhmDmNioq/M/BvlA/fnqvUi9qWsD/Vq4A8RcTJwzAQfjqaTEyj75BvAl4Dv9V7JOkSbUq7Cf/u42y6Psqj7iyiTS0cOOaaXAo+lPF+vGrsxMx+MiPcA21HWCqs7OfR84Ovjry6v9su/RsRc4DOUCcatGnyObsrCJ056NZIYqSozjgGOqV6vb6VMOo6vJjoBOK2JY2kbEtvAStXY4+N6KmXi9QeZ2bve0/9RFpWuWxsWS38zZV/sOUFiCHg4IbMHcD5lInCyiZSFacM+OYSWvE6BA6qvycRR53noDTT3u01GG47fN1HaxD4AfJOSxB7F+/v2wO2UCpGF+RQlebYDNSaHgB8xuedFTHL7Qa0JHDtRYmi8zLwvIs4G3lVnANXzbzJWrHP8ytrA6b2dEjLz2Ii4iXKxxXlVguh3DYw/5h5K69EPUlr69QrgPOAcBluzdVEMkqDqVedz81ZglZ7b/gBcNsH2q1Bez01oy/FbkiSpc0wODd/ulAmll1HaJW09hHZmlwPbRMTyfdrfPEJELE+ZvPpRA3HcQPlw/H5gp4g4FfhyZt7UwFgDy8yfAT+rJvvfQPmgeBjlqs2zRxnbkARlMuu5lFYzfxpRHE+lrAXV6yeU5NDJww0HgBmUD6s/6L0jMx+oFpWe2cC4S9Mz6Txu3M9FxEOUZMkPI+JlDYwP8FfKJMGOA27/SUq7xkaNe70eQEkSjF0Z/yngYxFxZma+qeZh25DYvodHT+K8sPp+FY92H2Xiq25BadM26PvWysAyNcewCfDrzLxwYRtm5oURcS0l2Vm3NuyTeyjt9D464Pb7UP/V52PupFTMjEpW4/9iwO3XAZ7SQBxtOH4vQzlmHQscnZl/bGichVmX0j5t0ITIRZT2sXV6iHKR1PcH3H5TYLJVDIO4F3jSJLZ/UvUzdXrGIvxM3RP2SzBBgiEzvxERr6e0Cj0/IraoeezxXkBJQn6CcuHAUb3v7dVFH38a5L1mEf2dcp71jgG3P4iyNmZdrqXn9ZaZJzPx+fZalIrdJrTl+C1JktQ5JocaFhH91mlZlnISfB/w5T5XnGdmvrTGML5CWV/nOMpVagtyLKVU/ys1jg9AZq4VEVtSrvp/DfAx4CMR8UPK2jbfqnvMQUXE4ymtTHanfHhO4B9DGn7LnufAllVM/0H/qwkzMz9c09ibUvbH63jk/jgVOGuQCZ0aLU2ZXOx1J0Bm3jLEWMaMTeROtD7ELZS46/ZHShu9vjLzuIh4DPBpyoTXTxuI4SpKtczcQVqlRURTrf76qtpwHgccFxEvpjyPX09pWVN3cqgNie3rgO0jYqlxFSmvoByrLumz/TNpJtH7e+CuzHzuIBtHxCmU42qd1qFPwnYBLqWZKqo27JNZwJqZ+aVBNq7eg5tKDh2dmXVXZ03GjQCZudUgGzf03IR2HL9PphwPDwQOiIgfUc6zhv2+vgqTW2Prt1TnQDX6DbBsZu41yMbV86KJ5NClwOsj4vPjK5EniOFFlLVd6k5K3Az8OTMHai9Zvde9ueYYfs/8Nm2PkplnRcQbgdMplTsX1Dz+2Di/qdaz/CBlrclXR8QeC6pGbcA1wHqDJp8iYs+ax78A2C8ill7YcSEiVqe8d3y65hjGtOX4LUmS1Dkmh5q35QLuW6f66lX3VXpnUiaqdouIZ1J6uj98JWe1wPrmlPY0mwE/zcwza44BgMy8ALggIp4E7Em5ink7ykK0f6P87nVfZT6hiHghpfJgN2A5ytoyRwInDaHf+Zgt6f88+VDP/8daTyRlH05ZZl4CXBIR+zO/CuPh/RERX61jnMXMjIjYvPr346vvq9L/aslVaabFxixggR+QM/MzEfFYSlKviXVErq5iWAf49QDbN95XbSKZeRlwWVX998YGHr8Nie2vAx8Hzo6I4ylX8O4N3MGjF5SGsuD3IPttsq4CdoiIx2bmfQ08/iBWYOKEbT8300x7pDbsk6uBDSPimZn5+5ofe3FzFfDaiFi2wXXpBjHy43dm7hsRB1KOh/tS3tO3Be6IiK9Q2sgOwxPpf9HHRO6iXEBVp6so578rVC2FR+UjlGTPJdU++D4lcTVW0b885RiyHSUxtCSDVwQO6ipg656E9oI00ebrSmDbBcWQmf9bJVBPo5lE3dg4D1HWHP1uNdZV1YVZnx7S2qNXAy+JiDUy87dDGO8RMvMTlMqpQTxEqQ6/sqFw2nL8liRJ6hyTQ80b6AqoJmVmRsTOlA+im1XfH4yI2ygf/FamPBeCchXb64YQ0+2Uq88+HRGbURZ637mKYbdqnZmTKFfn1zr5Xi3UPDZhsh7z24adCJw95J78vQmgkajaDR4LHBsRGzF/TZe3VZtsHxHvo6zp0mQFz2siYkbPbesDRMQX+2yfmbl3zTHswfwFh8eScVvSfzJtXWBOzeNDudL6NRHxisw8Z6KNMvOoKrn7IeqfxDmXslbZcgNufyJlbZeRycy/A8c39NgXMNrE9jGUJPbYmFCen/+amY9oPVS9hmdUP1O3ayitBteltCxdmKD+xOETKO3UBnUfzeybNuyTyykVIs+hXJG/MBfXPH6bjJ2/rMdg1ThNPDehHcdvqgnWLwBfiIj1Ke/nuwJvr/6dwBoRsUqD7Y2XZPK/25I1x3ANpWXwCxjsPeo2YLJr8yxUZl4SEa+jvFfuyfzzjF5BWQtm38z8fzWHcQ3lWPXc6t8L09TrYzfKOeaEXQoy8/QqQXRyQ3GMH+uK6mKxoygJ/50iYqBKsym6kPK57BmUqrmFOYtmzjkXKjPn0sDrYpy2HL8lSZI6J4ZzYZTaoGqd9l5KUqS35clcyoLuR2fmZCbdahMRK1A+LO9D+eAKcG9m1japV7Uh2IVSEfJH4IvAyV5x/WgR8URKe659KJMqSWmzdnZm7tLAeIuyjktmZm0TSRFx2AR3XZ2Z3+rZdj3KlY6fy8z31BVD9dhPojxPr6+SEgvbfg9gRma2ItlYl+r1+s3MbOXaXz2J7cdRXiPX0lxi+wmUNlEbUyYwv9pv8jki3kpJWPxbZt5QcwzrUCYXv5uZ/zfA9itRWjrVtk5Bdaw4fNAWZtXr+tA6jxXjHnvk+6QNJrtPGorh6cAGwOWjXMuwzcfviFiGMjG/H7BhdfODwDmUiukJk1mLON48SpvaUwf8kb2A3Wt+X1+ekpj93Ygrh4CHz612oVw8tjalYghKBdH1lFZqX68udqh77A0pibJTMnOha7tExNrAU+tcc6d6Dm5GaW+30ARVVbW72qCtM6cqIl5K+WywEuWzwsmZud8wxu6ythy/JUmSusjkUMtExLuBrTKz0YXdI+IZlJZYAdyUmX9ocrzJioiNqdbCyczaWoxUExUPAN+hVEYMVCWUmf2qVkYiItYb5AN1zWO+iLI/dgOWaWiSdaKraBdoWBMGvSJiWcrkwe1NTOKMWkQ8C/hbtbaPFmAYiW3NVx3Hr66+BvECYN0mjluLoyba9rQhOaTJiYjnU97X30Bp1VjrxRbVGPOYXOVQNBGHNBlVQvFoynvHmZn5kRGHNHRVkntjSqXujZl5xYhDkiRJUkNMDrXM2AKbfjAuImK5Oienx1WnDPrEb8VERc/Vvi/KzJG0hKyukt8tM08axfhtFBErZ+ato46jbhHxEPChEVcCPCYzHxjV+IuiqcT2IsTxHGC/zDxwVDE0pQ1VhouiiX0SEa/KzG9PYvtlgHMzc/OFbqyhiIjjgF0zc6URjf84Sru5fTJz05of+wIWoWXeoIvSa3Kq9RTnVC3CNEmTWKtpsRAR2wI39K6xGhEfAf4VeMy4m39NOf+fNcQQJUmSNAQmh1qmqeRQRLyYsvDxiykf1H8GfDAzB1kzYtpYQNuwBRpVu66qB/p+lMTQspRk1W8yc51RxKP5qtYj+wE7Zubjhzz26ymv5SWAC3pb3tU0xsgrASLiLuAnlDY7P87MphZCXmTVGmYr9E621Z3YHjCWxwIzKc/LTQAaqvJbAXg3j3w/Oa5au6xxi1OVYdP7JCLuBl42yNok1fo236VUJ9d9jvFF4KzxLSAjYi1gnX5tIat9uEdmbl1jDLtTWoAutF1Wm3hR0GhVr9G1KO+nvxzyupND1YaLPsaLiB0pa4C+GHgy5f3kFuBS4L/rbnO4qCJiTUpL7t0zc9URxvEkSpvWWpJ7/Z4PEXEEcAily8JllP3xQko78r8Az2vigqjF9fgtSZI0HYyk+kDDVV2tfD6ld/aYlwGbRMQGmXndaCJbsIh4P/DyOiePFoc1Wap2ZW+gTCa+oLr5XuB/KGsCXNTQuCsCDy1oQrtqNTajiRgi4lBKoqOR368OEfEU4C2U9mGrU5J1jUyIR8Q+wAcoEyaXUxYRnw38gNKvn2r8/SPibOC1Of2y/UtR1rfZFiAi/kY5lv0Y+NEga94MwYHAofQsoj7MxFBEPI9yvHgTZf2KAG6kLORd91grUCaM1mD+YtCvAPaIiI2GsabHqFpJTsYw9wlwdkRstqD38mph928AWwM/bCCGPSkLpY9PBO1Gn9dGZQawRc0xnAocDjw8udhEEmpxEBFLAGdQJtvfNFEFZpUw/DKlsm7XIYY4VBGxKmVyf+z99L8zc171PnsUpa0ewK0R8Y7MPHNEoT4sIj5BeV9fo86HrfGxFllVhf41yjpsvTE9gXJ8mBkR5wCvzxGshVodM3emPG+2pMQ56sThp4A3U9/n90f87as1Av8N+DPwisy8qrp9KeCzlPPQA4EP1jT+eKfi8VuSJGkklhh1ABqKf6ckhr4EbAT8C2UyYBng/SOMa2HWoebJo4g4tGqr0ToRsWFEnAjcBPwX5Uq9Kykf3v4nM3dvKCmzUURcA9wK/DUiLo6IjSbYfC/K5HwTDqd8AB8f2wERcWPfrYcoIraNiK8Dvwc+Cjwb+CllQvRpDYy3HXACZYLkCZS/y9eB9wKbA5dQ+uGfAtwJ7EhJWk03KwDbAEdSEhLLAa8FjgWui4i5EXFKRLypmvzrjIh4fETsFRGXANcA76L8vX5BqSRZMzM/1sDQ7wfWpLSYeT9wEGUR9TVp9/tJ40a0T2ZSkk/nRkTfY1FELElJFOwAXAS8uuYY2mwG9SehFgc7V1/fWlBrzsy8HzgL2CUidh5WcBOJiKUi4p01P+YzKOdShwHvpLxvnlC11PoCsCLwV+BBSvLojGqdxVFbmfL8nY6+AGxPOe/8COWCtecA/1z9+z+B2ygXHhw/zMAiYu2I+CTwR8pFWVsBcynnqKsPM5YJNJngeynwWOCQscQQQNVK7z2UC5R2aHD8XjPo5vFbkiRpqEwOdcMWwFWZuVdmXp6Zl2XmHpTFvLccbWhDdzgt+p0jYrmIeEdEXEVpzbQ3cDdl4v/5mblhw+OvAfwIeD6lOukuSuujiyLirU2OPaAVgNVGMXBErBoRH6ySU9+jJCVuo3wwPyUzN8/M0xq6ovVdlJYeO1MSIjtT2t68j9K+a7PM/NfM3JtSXfYPYPcG4hhpJVJm3puZP87MD2bmxsCTKImwzwKzgKcDe1AS33+IiGsj4nOji7h5EbF+tUbJTcBJlGT/VZQ2bwCXZ+Z5DYbwSsqk2Ysz8xOZ+XFKS6A/Aa9qcNzWGuU+qdou7Udp+XNuVdk1PrYATgN2orzHvCIz720iFrXKTMrz8SsDbHsG5TX9hkYjWoAo9gB+A9R9DD8IWAX4DrA/8G3K++VhlMTtP2XmypQLpt5LqXSbdmu1tUVErE95rv2C0qLs0Mw8LzOvz8zrqn8fAjwP+CXwpohYt+GYlo6IN0bEhcC1lOfBcpTzvf8Fnp2ZR2TmH5qMowVmUM77ftB7R5VkPp9SNSxJkqRpxLZy3fA04Jt9br8AeMewgqj6WE/GCxa+yWLvJkpV1wOUfXQq8L0h9rw/iFKZ8gHg45QPhTOBY4DPV4vvHjekWEaumkjdnjLZugPlGHk/8/fNudX/m94/GwDnZObY6/abVXuVVwOfGb9hZs6JiLNo5mrOAyNir0lsnzW3wOl98L9TJvi+Aw+3QNm6+tqWUm24NmUCcFqp2h/tB7yIMmF1M6VF2SmZ+atqm2OGEMrqwGmZ+Y+xGzLzrqq1YRMJyglFxAsoyf4HgR9k5vUTbPdq4NWZWWt1XVv2SWaeGhFPpVxtf3ZEbJOZ91V3n0Rp73YVsF1m3t10PGqFDYHzBmk1mpkZEedRKiRqV7WsfVcV0wOUdeSOH0tSRsQrKecfa1c/0u98dSq2BX6RmWMVc8dWF+T8C2X9rd8CVOddn6mOF5v1f6hFFxGnTfJHNqk7hsqo28++oYph98y8ZaKNMvMv1To0VzE/mVSriPhn5rf/XJFyHL+Scr73Fcp6O3c01bI3Iia7dtCKTcQxzoPV979McP8twNINxyBJkqQhMznUsCiLNE/Gpg2EsTT910W5A3hMA+NN5BDKB8LJtEQY9YfYpi0DzKO0yTo6M/845PFfCvw0M48cd9tXI+IySrXM5yLiwcz8wpDjGpU5wDN45ATB6Zl5+9gGJX/UuJUorbrGG/v/nD7b/47SWqpuKzB/LYZBDPv1+qxxX08Z8thjguGs43AC5VjxDUql1DCTyOMtQ1mPoNfNPHJdu0ZVbX/GX92fEfF54H192mitT6kwq7v1Ylv2CZl5ZNVa8d2Utlg7U95X9gJ+BWwzzHWwNHJPBSZT5fBHGjiGRsTKlJagqzH/OPkaYMeI2IbSMuwt1X3fAQ7NzKtrDuMZwIk9t10IrEtJPPS6Eti45higJCDacA58eEQcPpkYMrPOz4sbAVdm5kKTPZl5TUT8nJLIq1VEXEzZz2OJ/aMZl9ivtql72F7PWISfqfs5MWNcu+2x9/BVKeeVvVYFbu9zuyRJkhZjJoeat+ci/Mx0TYjcQ5mA+OiA2+9Dc1dOtsXJwOspk5wHRMSPKP3wz6rWAmja0yhr2TxCZs6OiM0oLSSOqxJETSyk3jbPpEz2HgkcMe7q+2FbkvJ6Ge9eePjq5l4P0EyC4jOUFm6tEBHrML9SaEvmX+n7J+BsyvO1qTWxJjK29tMjRMTKmXlrzWMFpQXkc4GfU37vzomInShtf+4Evsr8FozvBJ4fEa8YX9nUdDi0ZJ9k5gER8RTKe8ovKZV0/0dZ76grE3ojP39ahHXyVm4gjPspa4cM6rGU11HdDqK0qrqGsn5LAG+mtDs+h1LVcylwQGZe1sD4AI+jtIQd76/wcDVqrztp5sKpv1MSdoNW7B9E+fvUbbLnCnWfW/wTpbXfoH5OMy1LN6Gc74X5Z2EAACAASURBVB1FWWNnXgNjLMzNwJ8zc6BOCRFxKuX1U6c9qi8o+zop51df6rPtuvS/QKkuIz9+S5IkdZHJoeZNpiVTk14TETN6blsfJqxuymo9kzrNAtbMzH4fOB4lIrakmeTQ+KvkBpKZFzUQB5m5b0QcCLwR2Bd4OWUy4I6I+Ar9P5zV6U4mOA5k5i0RsRXlCtsvREQTE0e9VoiIZ43/P0BEPJMJJigyc7JtORbkx5TEw0HAOyLiDODUzLy0xjEWJ3/LzH5Xjw5FRKzG/GTQVpSrVoPS8uRHlNaY50/UTmwYMvMOxlVmRsRLKW1qdqTeSppNgbcCrwM+BnwkIn5IqW4bVjJ5zPpVu59H3AYQEW+mz2s1MyfbUmlB3k5Jlm40tu8j4mBKsv11wHciYochrK3Tpn0yZndK5eHLKIuHb52ZNw9p7N7nxYKeE+s3FEPfqoiImKiiq+6qCCjJkMmqe1L0JmC9SWy/XvUzddueUoGw0djrISKOBa4DtqGsd/TGptp2tcw1wHqZeeEgG0fEng3FcXhmTrbNc52Wp7QnG9StTK6CeVA3AGsC7wd2qhIvX87MJl4HE7kK2Lpq4fzgQreu/zjxoQlu/1vvDRGxHuW43eS6jm04fkuSJHWOJ1QNGzQRMiYilqCZK+TWZ+LJmD373JZA3cmhq4ENI+KZmfn7mh97MsZfJTeIpMHXSmbeBXyBkoBZH3gbsCtlAvRt1fhrRMQqDUzyzaGslzFRbLdUk90XUiZe+7VgqdMB1VevORNsX+u+ycxtImJ1yuT+HpSJ3/0i4npKdch/1zXWAHqTmDMAqoqu3onWGUOKadjGrsC/DbiIqjIoM68dXUiPVlVrvIVS7bg6Zf/0a+W5yDLzEuCSiNifcuXwvsB2lITy3yLiq3WOtxCvrr56BSUx0k+dyaEXAt8YnxSsKgBmRsSngfcA346IVzZZ/deGfVKtFdNrWcqx8T7gy31aI2VmvrSBcPo9LyZ6ToxdoV63UVdFQDkGjNpPgTdHxJqZecOCNoyINSmVAqc2EMcMygUWDydKM/OeiPgO5fzmP4aUGGpD8ulq4CURscbYOkcd9XhKZdugHqBUf9UqM9eqLkJ7K6XV4fgE/ynAt+oes49rKO8Xz63+vTC1Hq8yc6LkUD+/pRzbmqxCbcPxW5IkqXNMDrVEdYX8PpRKo1UpbaXq0pbqpcsp7W6eAwySHLq4oTjm0mxbhEVW9dp/W0S8l7KI+H6URZy3An4fEecAJ2XmOTUNeQFw4IIST5n5p4jYmpIg2oDmJlnmNvjYA8vM2cDBEXEIZaJzP8pV+EdSFnxPYJWIWLrhyoB+Scyg7LNeTU20Tsokrn4d+CEpv9cvKK1lrqBccd4KEbEt5fnxKsr7aVCOWycB/9vEmFWl0rGURdU3okxs7UKZaAXYPiLeB5y2oMW+p6DpasZBLEv/9RDIzPdWVxm/DzirWly+USPeJ1su4L51qq9eTRwrRv68yMwlRh0DwCirLcc5nnLu978R8fLM7LvAfEQ8GfgasARlDa26PZ7SOqvXWDyTbcG3qA6MiPHnwmNVyf3Gb6JKBco51GaUdWYGSQ6dRUvPVaeLzLwAuCAinkS5UG4fxiX4KcfKZRoM4RuUxNegx+SP0aeN7TBUF7Ld1eDjt+L4LUmS1EXRjU4O7RQRS/LIyeclKB8QfpSZLx9hXEsAr8rMYVw1N1QRMY/Rt9SYlIh4PmWy8Q2USYvMzFqShxGxCXAm8OnM/MRCtn0WZXLjWXWNv7iofvd9KZMHT6e8Tv8KfBk4OTN/WfN4F7AIE7iZuVWNMQz8WqmuPN8X2D0zV60xht2Y31Zudcrf5C7gJ5QqovMys+lqtt6YVqVUCe3N/EXW/0xZAP6UBtpxDhLTEymLne8DvIDyd3oQODszdxl2PE2LiNmUfT/h3zoijqZUIZ5DSS4eNMzj1rD2SURssSg/N2hrKy2+IuK/KOcOt1Gqk8+jrHmTlATFWAvMlYDjM/OdDcTQ930kIg4DDh3Ga7KKYbJqO89qkzacA1cxzGHwxNcMYLVh7Y+qOns/yjp2Y4mbaykXfXx5uq3fFhErAO8GXkz5XX8GHFdd9CBJkqQOMDk0AhHxbMqE0Z7AKtXNt1I+vJ88qqtOe6uX/GDcLhHxOEq7ub0zc7NRx9NF49o+jq0PtSQwbzr2PK8mnedMdDyKiMdQJk/2pVQvBPBQZjaxkPfY8elllETRlpQKy6Rc3Xsh85NFv2pg7KCsnbEfsAOlSuh+4DuUVkznVv8/KTP3q3v8yYiIF1EmhHcDlmnDcTwiVs7MW2t8vO8Ca2XmmgvZ7jhKe85/AI8f1d+ijftE019ELAV8nnJeN9HJfgAnAu/IzInW9ZhKDPMoFTBn9dy1E2Vdtj1peI2y6r1j0lpSAVarNpwDLy7Juippsgfl9fPc6uZ7M7O2SqKIWCIzJ/X3iIjNMvMnNY2/AnAZsAbzX4dJWY9po8x81NpDbRER2wP7ZeZOo45FkiRpcWdyaEiqD+k7USYXt6JUCd0PfJsywTqSScW2Vi81ZbIfjNtWRVW1sfpwZm406li6LiKeTkmM7JWZizT5tDiKiLWpqoQoV5wHpcXXKcAXM/MPQ4rjOZQr37cGNgdWrO66JTOfWvNYv6NcaR/AlZSE0OnjryCuji0jTw6NiYgnAG/JzGNGGMNYZcKOmfn4Gh/33cBngS0WNkkWESdSqrxGXgnQhn0ynVXv15M22cnZAWNZGngnJZH9IPA9SmXhoxIwEXEAcEBmPrvuOKrH35jS4vAllKQ6wJ8oLTBPqNbOakR1XFxQYqrvfaN+rU5XVaLsb6OsClkcqx2r19Bbgddl5rI1Pu4pmTlw6++I+Bfg+5m5fE3jfwx4P6Uy6kuU1+SewNrAxzPz4DrGqUt13j1Wuf1M8FghSZJUB5NDDYuIf6JMpO4BrEyfycVRTCqOunopIlYBXktpY/BkygTBLcClwDcn6o9fw7gDJYdGUUVV9Tx/MDPv7HPfxpT1bjaH6fthqFr/4PHA78cWqa4mEvpNJlyVmd8eZnz9REQMaUHtkakmOnehTPRvSjmO3Q8sTVlbZ9dR/Q2qyYJtgAOB59NAEqA6bswDjgKOyMz7JtimFcmhiFgDOAKY2VQl1wLGfgpl8mYfSivAAO7IzBUX+IOTG+NplDY4l2Zmb0VCv+0PA2ZMZhKubqPcJ8MQEbsvys/VXCGyKJUvWXflZ1VVeR6wCY+8Gv8qYOfe86thtlgbtohYpPVRRvlahXIu0sD6YFrMRcRy/c7Rp/B484CjBknCRMT6lArpJ9Z1zIqIWZR21Wtn5j+q25alrO34t8x8Xh3jTEVVuf0Kyvnndsxfk/dC4MTM/MqoYpMkSZoupl0rpBa6njIp8BfgaMqVo7W3PRrEAqqXvkGpXvpWZh46hDj+HTiM0su7t53IHsCnIuLQzPx0A8P/jtKGql9cE1ZRNRDH+HF3Bj5O6as+9mHtrZl5aTXR+nnKfgvgaqC2fRQRi9RapInnSZUcuwG4IjNfOu6uLSnPl153RsTqmfnXumOZjCaTItVaR08Grs+yGPBYMuQQSmJ1CcpkwX/W2bZr3Pj/THk9vIlSmTM+uf0VSkL3jmEmhiJiJcrxa2tK5dBYa7Gxq9B/0cCwP67GOwh4R0ScAZyamZc2MNYCVeshbAg8AFyc49ZcioinAh+iJP0fA9w0xLi2pTxXXkU5twhKZcJJlARibTLzJmDgK5oz80N1jt+rDfskInZifpXKuZn5wwm22wPYIzO3rjmEU5ncOmljr9fakkPA7ycRw7KUyscmvJtSpXMl8CnK82JvSivSn0bElpl5Q0Njt8qokzyTFRHLUyop3gUsV/Njjzx5GRE3LmIMa9QYw3mU9886X/uLGss7Kfv5k5n5wATbLA28D/hrZh5fcwg/B/49Im5aUEVpRDwP+CGwPKWCqS6rA6eNJYYAMvOuiDibUh0+MhHxTMpFJm8Bnsb8z4sXUyr2fzuq2CRJkqYbk0PDkcB3ga+PIjE0ieqlYcTyfuBj1X8vBi4A/ljF9DTKpO9LgE9UFRmfqnP8zFy9T0wjq6KqJhW/xiOTZOsC34uILSltB58J/Ao4LDO/UXMIh1Cen4/q+d/H+Em3JpKIbwSeSP9J36RMPI9ZkZJQeyNwbF0BVJMWk5U9yaw64liS8vp8Q3XTnRGxJ3AFpT/8quM2Xxd4ZURsUPMVrRcDG1OeGzfTJ7ldLuhsVnUV6xbMTwY9r4ppbPDfUpI351HWHKo9SZaZ20TE6pTn4B6UyZn9IuJ6Sju9/657zF5Vcv9M4JU9t38iMw+KiDdREsnLUvbXkUDdE1m9Ma3K/BYvq1H2yZ+Bp1KeK3s3NO6NwNGjbs/Whn1SXVX9VcoFHmOvifdExDnA7n3WjJhB/0rMOjxIWYfr2oYef4Eyc8bCtqmqet4NfLC6aU4DoexGadu2RWbeXd329Yh4F+U4en5EbJGZizJRX4uIeCxlHZUHgVnTvfoVHq4IfxElWXdZZt487r7HUapP/5VyfvGPvg8yxRCqsRupjB/QjEX4mbqfG1tSzv1HKiI2AT5HadXcNzEEkJn3V8eN4yLiqpovCtkeuAQ4OiJuzsyv9YlzLUpiaCXgPZl5Yo3jL0N5z+51M6WKf6iq1qA7Uj6zbkupErof+CblXOvbwHUmhiRJkuplcqh5/0GZPNsL2LOaTDwV+HJm/mlIMbSieqlqBXQ4cBuwS2ZeMMF2W1OuNP9wRJzexN+pLVVUwHsoEwYHAydXt72N0n7oPMqk4ruA45tYF4HyvBzEEyixrkn9EwVjtqN86Lus352ZefL4/0fEG4AdqDE5RJm06Ds8EyfQmvh7vJWS+Lqb8vpdm1KB8TXgScBHKS0YV6Ssa7Eh5crWfhVWi2oT5rdSO6Sh598CRcQlwAaUCYKxv/9NlGqpHwM/zszfDyOWzJwNHBwRh/DICsMjKS0fE1glIpbOzPsbCOGdlKqcuyntVIIyyf9vEXE35dj6d0p10zGZeU8DMYwlI7an/P47UM4jxiZvTgXOrf5f++L248xg/hpTo/T/27v3eMvnsv/jrzfjUMQ45czoVupWiJL8xCgaJaekUjJOqdyVEMlxMJK7gygkyVBuciMkIufDXVIUEZVIjkXJIceZ6/fH9d1mWbP2nr33fE+z1/v5eKzHzHzXd63vNevwXWt9rs/nutrwnOwMfIBcNfNtcvB5Mpmwul7SO6sq09rlGrLs6dbAq4GTgbMj4tkajj0skrYjJ6esAvwL2I8cHC7bauTEm6c7N0bEtyQ9SK66vLJIEFU5+WRN8rPitcBvgS9GxN3FKr/TyVWpAPdJ+lhEXF9RHOPJhNy65HnyF8DxUWPfG0nHAXsw83PkeUn7RMQJxUSc08i+cs+R/cyO6nlHc24cOSHqZOCs7tdIDWaZINXHJgNPAV8dxr5fA/Ymf8+VlhyKiEclTSITRKdL+ntEXDVwfTEp5Qpy4toBEVHF+aoVJE0lP8+WocckxmKfxuIzMzMzG9MiwpcaLmQ5kXPIH54zyAGcnwAfLP79nQqPPYOcHXoKsN4Q+1QWQ3GM/cgBw22Gse/7i5g+X3IMryVXnDxSxDKDXI3xaWDxuh6LjngeAH7WY/sVRXy71hHHEPGJXBXwQPG4PATsXtGx/kr2bOnefigwvcf275K9icqMYaMel1OL56LXdRuRs8PLfixuIJOoKxb/XpFczfYc8KmufRcA7iN7MJUZwx+K53w68Huy1M5yXfvUce56jFydsQfw+qqONcr4VgKOKF67A4/Vo2QS/o0lH+vn5ID2azq2rUYmH6aTAynL1PB//stg586aXxeHtOD5b/w5Aa4D/gG8umPbvOSA5wyyzOKSHdf1PJ+WFMuqZDL5oeL//0/gm8AaDT9P6xfP1fTiHHoMsFiFx3uKLPU52PVbkwnUPxfn9tKfE2B5soTujI7L3cBriu0PkZNhbiyuexxYvoLHYnzxWTK9I47p5KSH8TU9/5OZ+T34d+RK7BeLy2TgWfI7+Qndn3Elx7EyMBW4v3gM/kUmdNep43Foy6VF5+87gPNGsP+5wB0VxbJW8Xp4HFir2LYCcE/xeB1e4XNxHllCrvPyo+I1+rEe1+1YYSwvkhO+Vh9in1p+n/niiy+++OKLL77006XxAPrtQs6o3Z/srTLwI3k6WSqqkh+IZPmUuzuOdweZqFm2Y586kkM/Ae4ewf53AxeVHMPAY/AQOXg2yw+QOn98FANEX+6x/egizloGTwaJbTNyYHE6Odh1GLBQhcd7BpjaY/tW5Gq37u1HAv+u4XGobDB1iGM+3v1/ZmaSatke+58EPFlBHBPJWe7PMDOpfTGwHTB/1e8VYG1AdT72o4xznuJ1elHxGM0AXqz6NVFsP614Xaxb0/91YADnSGCBIfbph+RQ489JEcMsSfXius8Wj9VvKJIhdZzPyNUR25KryF7s+I6za5WfIT3iWJWclDOQmDibjkRehcf9Pblqaqh9tivOFX8kV5GUnRz6WvF/3pXsqbJD8Vz8gkwILdqx7yeKfY+u4LE4qrjv3wH7kt897yiek6Nqeh1cRSaA3t6xbUMyUfgiWVrwTTW+LgdKZ/24eA1MB24BPgUsUlccI4h3qZLvry3n76dG8ponv5c/VWE8G5PftR4ky2sPTND5SoXH7Pwd2n0Z9LqKYhk45j+BE4G3DRKvk0O++OKLL7744osvJV9cVq5mkeVdvgx8WdK7yNI8W5Glk34p6VZyoOf4Eo95JHBkUbrg42QZnC8X2y4jB7LqsDpw7Qj2vwF4RwVxBA32gOoyjt617f8NELP2i6icpDXIxNm7yMdqGllWrOoyiNOBBbs3RsQFwAU99p+f6krcNe1V5GqUTvcDDPI8PETWji9VZOnHqyUtTvbk2o1MGk4iB6WjiuN2HP/mkd5G0uJRlCCpS2TJvQuACyQtT55ny27E/ipyhVi3gbJUvyn5eIO5guz/tD+wh6SzyObiZfZhmFu04TmZn1wJO4uIOE7SdHL1zs8kbVJDPETEi+Qs+3OLPi8DPf2+A3xd0mYR8fOqjl+crw4lkx7zk6uG9omIX1R1zC43A++WNK54LGYREf9b9DE5nVzNU7ZNgatjZjnWH0j6GFkK893RUdItIk6StAfZ4+MLJcfxPnLl8bpRNL2XdAJwJ/ldtFePwbKtAfyo8zUXEddKOp8sybhLRNxWQxwDx54BXAhcWJRb3pUsV3Y82W/zbODgiHigrph6kbQo+Xr4NJlgLNPEEZYIi4g4ouQY5mVk3yFnkIm9SkTEVZJ2JCfkXEuu3D8xIvat6pjk+act36NXZuZ3p4G+jn8gJ0adHhG9eiOZmZmZWQmcHGpQRFwBXCFpSXLgZFdgTbIGfmnJoY7jXQpcKunV5A/R3cjeEZuRPw7WkrRORPy67GMXFiMHsYfrQbK/Spna0AOqlYpBiiPJGcbzApeRZf1+V1MIfwNeN4L9V6PZxs5VEjmjuVPPQcZCpT/ui2TL18mB3XeQSe1tyTi3L3pbfJd8H5WWmCkSD58cbpJU0ubkAPTyZcUwUhHxgKTvAueXfNeidx+f6cVxq+hzNIuI2LTog7A7WZJpYBDnLnIQ5wd1xAFsLWnCCPaPiNi15Bja8Jw8QJY37Ckiji+SEF8HLiUnXdQmsqfOwUX/sJPI9+ZSQ99qdCTNT/bG+yKwKLn6eP+IOLeK4w3hYmB7cnXQmYPtFBH/Uzw3pzB4T7vRWplsYt/pVjI51Cvp/gvgwyXHANnj5vSBxBBARDwl6UKyRFUdFiVX63f7Y/FnZYnK2YmIB8n+mlPJ7+PfJs+r55Pv7UoUSdt1yJVLv4yIRzquWxDYC/g8+b291wSmObURg/d47BTkeyPIEq5l+jvwHyPYf1WybGxpJHWfu28kJ2ftR668/O8e+xARvSYljFhE7FTG/ZQhIu4HDpV0GNnPcHfyPTEwmbH7fGZmZmZmJXFyqAUi4lHyx8BXi8a4u1V8vNpXLxUWJht3D9czwEJlBtCiVVSddiqe904TACRd2WP/iIh3lXVwSQuTs0P3IleB3ArsFxGXlXWMYboJeK+kxSLin0PtKGkxcvXCT2qJzF4SEdcB10n6DDmItRu5KvAYsoRQmSuJPgisL2nnIpnek6SFiuPvSpYKatoRZK3+sj9jx/cYKBoPIGlFegwwlzWI1HWf9wBflHQQ+dmxOzno/GXgS+RA3tKS5q8wQbIm2adhuIJ8fZSt6efkNrIc0aAi4huSFiDfn28u8dhDKiYc7FJcViZLe/2A3smJMtxFJsr+QSaJjo+IXsm7qv2IHNSc7Uz3iDhN0l/Ix6dM85AD/52eKo7Z6/P1MXqs3C3BK+n9ODwCvKKC4/XS67FgYFtEPFNTHD0V549dyUlLK5Dfk0tNQnQd7ziyh9/Auel5SftExAnFd9HTijieA44lzxtlu6a4NOkmYFNJi3aupOulWEW1KXB5yTHcS+/JPUGu0P7zINeV8t2iWKn0m4i4tYz7K0Oxsu4i4KLiM2Q38jNks2KX7SQ9B5wSEXWtmDYzMzMb05wcapmBMk41Hq/O1UujmRlb9mxaoBWrqDpNKC69TOyxrbRVIpI+CUwhZ3I/RJYPOS0imigzcSY50/oESR+ZTQzHkwNL/1NLZM3oLrsyEUDSwcz6vphY9sFnN2hQrOY5FjhW0tvJFSQfKDmM/YHDyffqN8lVAC9L/kjagFz99xqyjNfHSo5htKo4d+1ZXHq5t8e20gaReikG3s8DzisGOD9Ofo4sT5aTelDS98lBnLJXIA5WbrJuTT8nF5OrqDaPiEGT5RFxdLGy5jAqXGkoaR7yuR8oQTmOTGDtSa4sHHIQdg6tzMyVBp8HPj+M0lUREaUmZopVMpeOYP+ryzx+4VFg6a5t95O9n3pZmkyqjVVtKZ0FgKR5ycT6x8mkwzxk0nQq8D8R8VRFx51Mfs+bQfbGErkK+zhJT5Or++Yt/pxarGyqwtURcXhF9z1cZwLbkN8nd5jNvt8iy4gOuhJwlO6j2dfmNPI3QGuSQ52K19/hko4gP092BzYH/ossa3tzRLy1yRjNzMzMxgI1MwZsbTaweikiZvdjaaT3O4NMfF09zJtsDGwYEfOWGcdgulZRDfSzqWoV1cAxNxrN7SKilBmXxXMSZMmVbzLM8iER8b0yjt8jnuuA9cl660cA10XEC8V185FNpA8kS5L8X0RU0ZOqO6ZDyebJtbwOi2POGMHuL5VdKTPGIoYpnQM4xcDS5Ih45yC3WSQinigrhuI+1wTOAP4TuB3YISJ+W7wevkSuEIBsFj1lsB4fdZJ0KrBjyc/HvYxiECkiVikrhuEokgNbkIOek8iBxhkRUVpCpNdrswlteE6K/jrbAXcNJ8lQvIcnRMRhZcVQ3O8qzFz9sCy5+uEs4OSIGCwhUaoRnjdfEhGV9RFpiqSfAMtGxNrD3P86YL6IWK/kOGaQJdK6y2xuA2xJJpN7ra47veQYRvo+jTLPWR2xvJZMnE4GXg08QU5yOTkibin7eD2OfxXwdmDjgR5MkjYkSxDOSyYQt6iyB1OLzt8CriMfj+vJ75zXDqx2LZLpGwIHkf1Pb4iIDRsKtxJteS5GQtLS5GfNruRnWW3fzc3MzMzGKieHrDZtGPAejq5VVKs1EUNdup6T4ZwMKn1OJC1Lzrh+YxHPi2S5mwCWJGehC/gd2VS71Aa1knolvdYiV9MNVnKw9F4mRUJqxMoc8B0kOVR7oqw47gJkybLPkqWAvk6uTngj2Tdix4i4sc6YhlJFcmhuJGl5igbTZa7OmBsHtMY6SQPl234FnAycGREjKSM7ZhTlYKd1JjgkvQ14W0Qc12P/PYE9I+I1JcawLznZZfXZlXYsEnt/Ar4eEfuWFUNx30MlZjTYdRVMdBixspOGRWJmILnwC/J98sM6y9pJegy4LCK279r+Q3Ll76YR0auccZkxtOb8XSQaLgXWYOjvnL8FJhVlucs8/krA42VPqhnB8VvzXIyGpE0iouxSf2ZmZmZ9x2XlrE6lzlSuSt09oBrWquckIh6StB6wL1nub0VgmY5d/ko27/5aRQOPO43iutJ7mZQ9q39uV5SS26uYDf9jskcWwInA55vuGWG9RcQDwJSiwbQBkt4QEb+v4H5XAt5Kno9uioi/ln2M2YVAJm6XBQ4BDmmipFtLTGTWFdKbkY/LLMkhskdV2aXtvgJ8ZZi7TwfeTzW9oE6n4ZJuLVoZthH5HrmIXAX7GrJ321C3iYgY1WSRQSxKJgK7/bH48+clHqv1IuKRoizu3uREhpV4+XfO+4DvAMdU9D3jHvJ7+FyZnGmaE0NmZmZm5XByyGozNw54190Dqlsx835tsh79/0XE38u8/zY+J0W/hsOAw4qG7suQA48P1TDguXPF92+jJGkF4ABggY7Na5Gvj3saCaoP9epFVZTfWbDX7OeidOZGjMHBL0lTI+KgEey/KtnQfPmS4/ga2dNnYJQ5JB1T9iqQYZiPbGRvc5GIuA+4T9IbKrjvncq+z7ncfGRJva0ZXl+6AMpMDs1DJqi6vQBQ00SL08j+gK1Q/J+PBI4svmcsSz43D0bE/RUfvpK+qiM0vphcMGzFOaMSksYBbyJXcf1usP6jRcnhNcssQWlmZmbWr5wcMmuYpDXIvilLATdRrIopGrDux8z36QuSvhgRx5R47KVGmnCStFeZMQylSAbVNgM+IgYrHddqkl4PHBER2zUdSxUkfZRsCL0o8L/kyqHDgI8Bv5W0d0R8t8Ljj2jgBFi4BTEAlQziTGPWBtZfJFdF9CoFNbG4rszk0GFAKX3X5tABkh6IiBNnt2Px/F3Jy2elzzFJHwH2IgeR72Rmg/m9i2bdZTdQ76lFqzOMViUuZ0km97G2TD5peiXXiB4HSYsA46tMSAwokkE9E0KSNgWmRsTbqo6jZnsWl+EKKho/91t97AAAIABJREFUkLQVWW5xiWLT/cX3u3N77L41+d3CySEzMzOzOeTkkFmDikH964GFyEG99wJrSzoLOJBs6n0bsBiwClnq7rcl1oS/RNJGwy3RJulTZMm90pNDRVPke4c7AFAk1dbq51mDklYmB+p3IGcEl63RQSRJ44GTyF4IT5B9fH5QXD1Z0oXF9SdJ2gLYrezVdYV7afixGGUMlQ3iNKlFKx7vAY6T9EhEnDfYTsUK0KvIVTVHlhzDruQM60kRcVVxvE2AS4rrakkOWeuMJHG5MhUkLgvTmDWZ3JdaNPlkiqQpva7o6B3WKSKitM+R4hhTIuKIjm0fAj4UEe/vcZO9GHzyQVkxLQ68OMjq17cDX2Jmv6ix5gng8aaDkPRmcvLPOLLM4QvAfwJnSzo6Ig5oMj4zMzOzsWzMDRpZe0k6ZBQ3i84fkGPQ/uQqg28BlwGbAp8G/oMcTHx/RPwLQNLWwLnF9WUlh9YGzpO0eUS8ONSOkiYXcf6zpGN3u4qu2uuSvgDsFxFL9Nh/GyqYNShpXeAoYF1ycP0XwIERcVOZxxlGHG8HppJ9RF4AriP76/xJ0oLFdZ8G5gceKmIuW89BpEEGkKDkQSQyMTowsL5Td1nBiDhX0g3AqcAWwG2Sdo+IC0uMAbLvQNPJoV4xjCdXU/2l/nCa06LPkknADcAZkiZFxLXdOxQNz68gk/vHRMTBJcewBnD+QGIIsg+DpAvIVVvWn0aSuLySahKX1k4jLWVWdukz9bjP1wNblXyc2QcibQv8NzCh+PdtwCci4kZJrwZOIL9riiyFN5rPntlp+rvFMRHRhrKv+5LjEh8dWPEq6W3A94EvSBoXEfs1GaCZmZnZWOXkkNVpCvkjaLh11lX8OZaTQxsBN0TEZ4t/XyRpbWB9YOeBxBBARJwv6RKgzJIWp5KlTk4DPjrYTpI+DJwCPEk21q5Cr9fFguQAeC2KngtXAa/o2LwJsL6kt0TEnTXFsQY5oLxgx+YtyVVl6wM/BtYE/g4cDZwQEc9WEUrF+8/OksDeEfGNwXaIiIeB90jag2zAfh4lf7ZFxIQy76+sGIrE3cERsUrtATVrCsP/LBlQ+mdJkah9L9mX7vxiFeZtA9dLWoIs1fU64NsRsU+Zxy8sBtzVY/udZNkd609tSFzaCEhagDxXzEP2WhlsEsaoufzjTJLeAZzNyz9H1iBX1E8kv2etCNwOHDpUknUO7SVpJKX2IiL+o6JYmrQhcGlnKdQiSbcu+VzsI+lFryAyMzMzK5+TQ1antpQCapNlycHsTr8kk0O399j/DuDdJR7/42Svow8XM4z37t6hWLF0OvAMsHlE/KrE47fNfmRi6DRyxqiA/yJ723yB+noG7Ecmhk4ik3ICdifLRF0HrESW9zs8Ip6qIoCWDCKtExF3DGfHiDhB0uXkLNN+0fSM4ya9SA4YXQrMaCqIiPi1pG2Ai8hBxfUj4j5Ji5KrQVcHpkXEHhWFMFSD+TY0O+9XE6WXPfwTASQdzKzPy8SyD96SxKV1kbQsM7933QT8ICJmSNqNnOgxMBnmUUl7DNJrxcrxOfK9+EXyexbAJ8nV61eSq/o/Tb4/qvyMGc/IJkGN1c/9pcjVWS8TEY9LmgRcTK4geiEiDq09OjMzM7MxzMkhq02L+kS0yfzAv7q2PQEQEc/02P9pSqy7XgxKfJCcPbynpIcj4r8Hrpe0GXAWORC7VUTcUNaxW2oj4Jauhsm/lPQm6i3RtAFwY0R8qmPbTZLWBNYhy9x9ucZ4GjHcxFDH/n8oVlbZ2HYasB1Z7uct5MDeqUUz8doVZdx2As4ALpX0HrLXz5uBH5JJ3UpDqPj+beQm0vszo/t7UOcq6VK1IHE5YLyklUZyg+H2HpybSFqBTAi9mpnP+YaSziYnggj4B/AqcqD8LEnrRcSvGwp5rFsPuCIiju7YNlXSxuR7d/eIOKXnLcv1DeDYGo7Tdo+RCblZRMS/i2T3T4GDJD1fa2RmZmZmY5yTQ2Z9LiKelfQ+4HrgKEkPRcT3Jb2Tmauato2IsvoctdlywI96bL8aqHoArdMywDk9tl9HJofqGLCYK1VRimekA5sdsYy5Ac5C92DveABJKzLrqojSy0JGxM6SPgvsAOxGDrgfIumnwMnARRXP9O4V01lFma5jyJJu8wMXAjtERNXJm0YbzNssWjMRpgWJS4A9i8twBWPz98n+wNLkisefkSVrdwTeANwKfCAi7pY0L/AZ4OvAXuR5zsq3FNAr8fYrMjlU16qtxyOir/oGDuLPDFE2uyNB9DNyddfddQVmZmZmNtaNxR9fNkZIej1wRERs13QsFWt81ndE/LMo2/Bz4LvFIO8BwHzAhyLikkYDrE+vlVwU2+arOY4nemwfWFX29xpjaVyRiFgKuGugjF7RSP0gYF2ytNZVwJci4tEKQriXkb9Px+oAJww+2HtvXQFExJPAicCJktYhSzVtD7wXeFjS94DvRcQ9NcZ0bJEg2p+c4bxdFcnKHpruDWYd2rZKuuHEJeTn1uM1HKft3g3cGhFbFf/+lqRbyBUsG0fE3fDSBIdvSNoKeEczofaFccC/e2z/N2Q5s3rDqV9LygcPuBw4WNJrIuLPvXaIiKeK3ypXkBOlGv/9ZGZmZjYWjNWBK5uLSVqZbDi+AznoO9b1nPU9yIzvykTE/ZLeTa4gOoLs47FjhU14e4ZR47Gs5YoZ1NOAjxSbnihmwf+K7M21bMfuawDvk/SWiOiVWJsT9zHra3M8sCjQbzN+ez0WjSrKLv1a0t5kgmg34EBgf0mbRcQVVRx3iHN0AJOAZ7v6zhThlrdqp2WDe9ZSDSYuAY6JiMNrOlabrUCubOx0DfnZdUuP/W8G3l51UA3ZSdLEjn9PAJDUa4X6hBriseb9iPzcfA9w/GA7RcQTkjYt9p9QT2hmZmZmY5uTQ1YrSW8HpgJvJRtmXwd8vmievGBx3afJma0PAUc1FWuNRjqLu7SBWUk79tj8v8Du5My8+XrtExGnlxVDlzYkyraWNKFr21pFHN/rsX9ERBWledoSR5M+AXyU7LV1F7Aa8F3gbGBx4EjgRmAx4L/I88o+QKnNiiNiQve24nV6cESsUuax2q7XY9EWRdmZH5J92ZYvLgtVeMjRrMDxqp0xrvjM/E1E3NpgDI0nLm0WC5J9VTr9E15aCdntCepdsVynCfQe2J84yP5VTUjoTlLB0ImqiIh3VRRLX4uI3zLMZGixqmvjaiMyMzMz6x/+EWi1kbQGmXBYsGPzlsDaRRP5HwNrAn8HjgZOiIhnaw+0Ri2Y9T2NwX90b1pceqkqOdRYoqzDWsWll50GiaGKpExb4mjSR8nBs7Ui4q9FucNbyJUhn4uIEwd2LJp6/5E8p5SaHBpEratnhkqQDjUQ3IbBXklviIjfV3yMt5Gl5T5IJoQeJCcbXF/VMVtw/h6xoufM7hGxTdOxjGHTyNXPLyWHJE0GJkfEO2uKwYlLa6s2DepPYPDVJxN7bCv7c39jYNDSp5K2BN5JvjevjYi6+iCZmZmZWR9pfNDI+sp+ZGLoJOAU8sfO7uSA9nXASsBXgcMH+opY5U6nJSWiWjLQunPTARTaEkfTVgd+FBF/BSgSRD8mm3if37ljRDwn6RJmlqAba1ox2CtpakQcNIL9VyV7CSxfQSyLAR8jk4Wrk6UwLyZLN10cETPKPubcqOjPtQv5Wbtiw+H0qwnARnUdrCWfpzarVnzfalJEXNN0DIU2JKkWAX4g6eDux0XSqeR3nYHP8U9LOj8itq07yDpI6tlnqMMMsnfZb4FpEXFd9VGZmZmZ9Qcnh6xOGwA3RsSnOrbdJGlNsrHogRHx5WZC608RsVPTMbRJRJzWdAzQnjha4FXAX7u23Q8QEQ/12P8h4JVVB9WEFg32HiDpgc5VW4ORtBJwJbBMmQFI2ohcJfR+csLBvcAhwPcGeV30HWXNsM3JCRibkaX2IHucdPc9MbN67CWpc/LHeBh0YHx8PSG1S9dqmWuq6nvZkiTVlsDaZHncl0h6HzCZLKl7DPAkeS7fWtL2EXFm3YHWYMIw91ubLAf45Yg4sMJ4zMzMzPqGk0NWp2WAc3psv45MDp1Sbzhm1nICXuza1v3vTn0/K7sG9wDHSXpkqEG7YrXKVWQT9iNLjuEqsmfdRWSi42cUz72kQZNo/bCSqCi9uBu5Umg5Zs46vx7YOSLubio26z8DSe0iUfxW8n1608Bq0D40nt5JnwmD7D/mPtMkbQHsS/bs614tM41cDdq61TKSxpOlIY8t8W7XBX7eo4T2LuRzv3NEnFMc//vA3WS53bGYHJpd/8Z5gCWB9cnXz/6Sro2ISyuPzMzMzGyMc3LI6jQ/2WC32xMAEfH3esOxXiStDCxF/jD9e0Tc13BItZG0IXDvcP/Pxaq3NSOi1B5MbYnDrIdJwA3AGZImRcS13TtIWprsL7cKcExEHFxBHOOArYvLcARj9DtPkRDbklxN9W5yldDzwI+AU8l+fnc6MWRNkPRV4HPMHPAPScdExL4NhtWE2Q1+94uhVsvsSMtWy0h6RxHHtsACQJnJoWWAn/fYviFZQu2lHkMR8bCknwD/r8Tjt0ZE/GUYu91DVpw4F7gd+BTg5JCZmZnZHBqTAyVmNjKSlgQOALYHXt113SPAGcBREfGPBsKr01XAYcDhAxskfQHYLyKW6LH/1mQ5q7KTMm2Jow0mZoWsmf8GkHQws/bUmVhPSP0rIv4k6b3A1cD5kjaKiNsGrpe0BNlj6HXAtyNinwrCuI8xOKN+NCRNJXuULUO+H24GpgH/M3C+7nr/WD0afX1Kmj6Km0VElPq7QNJHgL3Jx+NO8jW6GrC3pJvHaHmsnoY5+N0PWr9aRtLiwE5kwv115Ov2SeD7JR9qMeBl36uLVXaLAz+OiO7zyD1kcq2vRcT9ki4ANmk6FjMzM7OxwMkhq9vWkiZ0bVsLQNL3euwfEbFr1UH1M0mvJcsyrcjMMl6PFX9fnBx03BvYVtImETG7prFzs16jqAtSf+3/tsTRBhPpnfQ5rOvfQT5upQ/KDjXQOsR1pQ+0tkVE/FrSNmRZt0skrR8R90laFLgMWJ1sGL1HRcefUMX9zqUOIBt1nwicGBG3NxyPpSmSpnRvrPF8MZqMYBVZxF3J7xSTIuIqAEmbAJcU1/VNcmg0JC01BlfVt3a1jKSNyYTQNmS1AwF/Br4EnBUR/y75kE+SpVc7rVP8ecsgt+lOqvWrvwC9JkuZmZmZ2QiNyYEra7W1iksvO/XYFuQAglWgKEd0BrASuRJgKnB9RDxfXL8A8A7gQGAj4AdkvW+zOnQngJrSloHW1oiIyyXtRJ4/LpX0HnKg983AD2nZeVvSGyLi903HUYEgezF8FJhX0rSIuHE2t7HqjfT9X+r5YqDXTwusAZw/kBiCl84dF+CVnoMqEu1fAD4NLNJwOGVr1WoZSUuRvz92A1Yl34uPkN939wGujIhek9fKcBuwuaSFI+KpYts25Hn9+h77rwI8VFEsc5tFgGeaDsLMzMxsLHByyOq0c9MB2CzeDbwFOBvYvvtHeUQ8B1wu6QpywHdbSZtGxM/qD9X6TUS0IjnUooHWVomIs4r+QseQJaPmBy4EdugxwFcaSVMj4qAR7L8qWepu+apiatDK5Ez3nYFPALtL+gPZa+j0iHi4yeD6kc8XL7MYcFeP7Xcy/H5hY0rR13Ed4AXglxHxSMd1CwJ7AZ8nH7uyV6q0QStWyxQr2HYnE0/zM7NP2zTgkoiYLqmKsqidzgBOAq6RdBpZwu6jwMNkeeHOeAVsQO9VV/1oU3qfW8zMzMxshJwcstpExGkj2b9Y1bJFReFY2hZ4DvjMUIO5ERGSPk3+iP4AWYbOzPpcRBxbJIj2B34KbBcRo+l3MhIHSHogIk6c3Y7FjPQryVJGY05E3A8cKukw4L3kYOd7gC8DR0ryubqPFa//t5IrEW6KiL/WHMI8ZBKk2wuM8dWVvUg6DtiDmf/35yXtExEnSJoInEYmTp4DjgWOaiTQarVltcxlxTFvoatPW41OAd4PTCKrKoh8b+zZ43P0XeTn2OW1RtgykhYDvkL2Ljuw4XDMzMzMxgQnh6x1ilmVu5EzoZcF5m02ojFtbeCG4dS0j4i/Sbq+uI1Z5STtCPwmIm5tOhZLQ/VMIQe4ns0Jzi+/ruR+KvcAx0l6JCLOG2wnScuTs69XAI4s8fitExEzyB5QF0lajvwM3QXYrNhlO0nPAadExG8aCtNqJOmrwOeYmYgIScdExL41h1LZKsK5iaTJZJm4GcDvyedlNfJc9jS5gmTe4s+pEfFgU7FWrE2rZYLssfko8HRFxxj84BEzJG0ObE+WbH4MOG+Qc/SSZMLwwhpDrI2kK2ezyzxkj6HXAfMBtwPHVR2XmZmZWT9wcshaQdK8wFbkrOdNyB8BQZ/PkKvBivSeqTmY28kfsWNZWway2hJHk6YBU4CXkkPFANvkiHhnQzH1uzb0X5oE3ACcIWlSRFw7ywFzNdMV5KzzYyLi4JJjaK1iUPlwSUeQyaHdgc2B/wL2kHRzRLy1yRjHsmLV84gVCb6yYvgIsDf5OXInMxMRexfP/5llHWsYpkia0uuKQZLNZSeT22InsnTZxhHxcwBJG5IrsU8B7ge2iIjbGouwHm1ZLfMxsiTnpuTvjicknQmcVmfftuJ9f0ZxGWq/s4CzagmqGROHud9z5Aq7fSJiLJZdNDMzM6udKmwLYDZbkl5DznDeCVi62PwoOavwlIj4S0Oh9QVJzwJHR8Shw9z/MGC/iHhFtZE1Q9IMRpGUiYhSV7e1JY6mFY/DlIg4vGPbocAhY+3/aiMjaR3ganJQcaPOAVVJSxTXrQ58OyL2aCLGNimSZbsWlwl+/1RniNV1Qyk1IVL0CXwHMCkiriq2bQJcAlwTEZuUdazZxDGqhNdY7Nsk6THgsojYvmv7D8lyvZtGxOxWT4wJRQJ1tqtlJH0YeBvw1Yh4oKJYXkcm0HckV+cMJFRPI8tzfjcidq/i2DaTpI1ms8sM4F/AXUU/VDMzMzMryVicmWctJ2kcWV98d2BjcpXQ88B5ZA+cCyLikOYi7CvzAyMZyJpR3GYsG+kqh6oy7G2Jw6x1IuLXkrYhS6ldImn9iLhP0qJkL4nVgWlODKWi6f2XgC8VSQKrzl8Z/vl4YbJUUtnWAM4fSAwBRMTlki5g+DP059hYTPLMgUWBP/XY/sfiz6pKp7VOm1bLRMQfgM9L+iL5G2R3YCOy31MAG0ga+G3yYpWx9LOIuKbpGMzMzMz6lZNDVhtJryVLOEwmZ+cJuJmORrCjnWVqc8RJhUJbBrLaEodZmxWD3TuRA4yXSnoPcCbwZuCH5CoZ6xIRLtdaoYiYMLt9JM0HfIaZDdXvLTmMxYC7emy/E9i65GPZ8MxDrnTs9gJARDxTbzjWKSJeIBNRZ0n6DzJJNBl4PXA28Kik0xvo2WVmZmZmViknh6xOd5GJiL8BxwCnRsTtzYZkDNEPwMyszSLirKJk2jHkwPf8ZMPuHaIP6uZK2nE0t4uI08uOxYZH0nbkqoRVyDJJ+1F+Y/WhEhFl9wCz4Rvz56SxICLuBr4g6UAymbo72f9ob8DJITMzMzMbU5wcsroFcDFwjhNDreHyZdZmfr3ZkCLi2CJBtD/wU2C7Ho3Nx6ppjOw9omJ/J4dqJml94GvAusCLZELo8Ij4Z0WH9LmzfQadjDNIr6pSe1HZyBRl5M4BzpG0Cl6NamZmZmZjkPpgYq21RDEDbxdytmyQK4mmAd+PiIeKfWbg5q9mxkvng5F+SHkwbYwbZBB1dsbk66J4j7xA9l66Y7i3i4iDKwvKXkbSqmRj+23I5Nw5wP4R8ecKj+lzZ8uMtmyyy8yamZmZmVmVnByy2kmaRPYe2gKYD5hONhA/jaz37eSQmXkwzXry62ImSVcBGxb//D/gZODsiHi2uagMQNLiwKHAJ8hyhz8H9omIX9RwbL9HzIYgaaXR3C4i7is7FjMzMzOzJjk5ZI2R9GpyJdFuwGuYOcv118CnIuLXTcXWLyQtAFwHPAlsVjTk7bXf/MAlwELAOwbbz8zM6lWsTPk4sCPwauAJ4AfAyRFxa5Ox9aPi8/JzwBeBRYG7yZVC5zYamJm9xKvrzMzMzMySk0PWCpLeRTZ83YqcYRvAreQqouObjG0sk7QLOdN8i4i4eDb7bkb2i9olIqbVEJ6ZmQ2TpHHkZ+jHgU3IEma/Bk4CzoqIpxsMr29IugdYCfgHcARwfB/1wDKbK3SU5LyZ7AE2LBHxjsqCMjMzMzNrgJND1iqSlgR2Ipu+rkbO0pu30aDGMEkXAatFxGuHuf9dwJ8iYvNqIzMzs9GStDK5KncnYDngKXJ16M+bjKsfdKxI+Cfw72HeLCJi5eqiMrNOkp4AFgYeBE4FTomIexsNyszMzMysAU4OWWtJmgjsFhE7NB3LWCXpAeDiiPj4MPc/GXhvRCxfbWRmZjanJL2HXDm0PLBNRFzYcEhjnvv9mLWfpIWA7ckk+rrADOBycjX9BREx7NVEZmZmZmZzM/8QtdaKiKudGKrcksAjI9j/EWCJimIxM7M5JGk5SQdJ+jNwEXnO/gFZPskqFhHzjObSdNxm/SQino6I70bEesBawIlkkuhs4AFJR0sa1qp6MzMzM7O5mX+MmvW3Z8iyGsO1MPBsRbGYmdkoSJpH0paSLgTuBQ4HngT2BJaLiMkRcX+TMZqZtVFE3BoRnyFLcO4E/AHYF7hT0pVOEpmZmZnZWOaycmZ9TNJtwBMR8f+Guf/1wCIRsUa1kZmZ2exIWoXs0bczsCzwNHAWcHJE/LLJ2MzM5laS1iNXEbkkp5mZmZmNaeOaDsDMGnU1sIekt0TEr4baUdI6wPrAN+sIzMzMZutPxZ+/Ag4FzoyIpxuMx8xsriVpA+DjwLbAK4G/APc1GpSZmZmZWYW8csisj0laDbgd+Cvw3oj4/SD7vR64GFgReGNE3FVflGZm1oukGcALjKx3XETEyhWFZGY2V5G0BDAZ2A1YDXgR+DHwHeBn4R/LZmZmZjaGOTlk1uckHQJMAZ4HzgGuBO4HAlgBeBc5g3IB4JCImNpMpGZm1qlIDo1YRLjnpJn1NUmbkKuEtgLmJ1difheYFhF/azI2MzMzM7O6ODlkZkg6gCxJNB+ZFHrZ1eTM9CkRcVTdsZmZmZmZlUXS3cAE4DngR2SftqubjMnMzMzMrAlODpkZAJJWBnYB/h/Z2FzAg8D1wKkR8ZcGwzMzMzMzm2MdJTkvBx4b5s0iIiZXF5WZmZmZWf2cHDIzMzMzM7O+MMqSnBER85YejJmZmZlZg8Y1HYCZmZmZmZlZTTZuOgAzMzMzszbwyiEzMzMzMzMzMzMzM7M+4pVDZmZmZmZmZoCkLYF3kv03r4mI8xoOyczMzMysEl45ZGZmZmZmZn1B0hbAvsDBEXFN13XTgI+RiSGAAM6PiG1rDdLMzMzMrAbzNB2AmZmZmZmZWU22BNYGbuzcKOl9wI7Av4GpwBeAPwNbS9q+7iDNzMzMzKrmlUNmZmZmZmbWFyT9FvhbRGzatf08YCvgQxFxTrFtGeBu4KqIeF/twZqZmZmZVcgrh8zMzMzMzKxfDCR8um0IPA6cO7AhIh4GfgK8uZ7QzMzMzMzq4+SQmZmZmZmZ9YvFgH90bpC0ErA4cH3MWlrjHmCJmmIzMzMzM6uNk0NmZmZmZmbWL54EVujatk7x5y2D3ObZ6sIxMzMzM2uGk0NmZmZmZmbWL24DNpe0cMe2bYAAru+x/yrAQ3UEZmZmZmZWJyeHzMzMzMzMrF+cQZaWu0bSZyV9C/go8DBwVeeOkgRsANxRe5RmZmZmZhUb13QAZmZmZmZmZjU5BXg/MAlYCxDwArBnREzv2vddwDLA5bVGaGZmZmZWAyeHzMzMzMzMrC9ExAxJmwPbA+sDjwHnRcRveuy+JHAscGGNIZqZmZmZ1UIR0XQMZmZmZmZmZmZmZmZmVhP3HDIzMzMzMzMzMzMzM+sjTg6ZmZmZmZmZmZmZmZn1ESeHzMzMzMzMzMzMzMzM+si4pgMwMzMzG4qkeYCtgC2A9YClgUWAp4AHgFuAnwI/jognmorTzMzMzMzMzGxuoYhoOgYzMzOzniS9DTgNWK1j83TgX8BCwAId258ApkTEMfVFaGZmZmZmZmY293FZOTMzM2slSVsD15KJoceAg4E3AvNFxBIRsSC5iugDwAXAwsCHGgrXzMzMzMzMzGyu4ZVDZmZm1jqSXg/cRCZ8bgXeExEPzuY2qwO7RcReNYRoZmZmZmZmZjbX8sohMzMza6OpZGLoaWCb2SWGACLi9sESQ5IWlPRZSddIelTS85IelnS+pM0Gu09JUVwmSnqVpKmS7pT0jKTHJF1UlL4bUnH7MyXdJ+lZSf+S9EtJ+0laaHa3NzMzMzMzMzMrk1cOmZmZWatIWhZ4ABDw7Yj41Bze32uBnwCvLTYF2Z9o0Y7dToyIPXrcduCL0keAw4FVgWeBGcAri+teALaIiEt73H4ccCKwW8fmp4BXAPMW/74LmBQRfxnxf87MzMzMzMzMbBS8csjMzMzaZmMyMQRw4ZzckaTxwGVkYuhKYEPgFRExHhgP7E0maz4lac8h7up44HngncBC5KqmdcnEznzASZJ6fa/6KpkYegTYA1giIl5FJoc2Bm4heyqdN8jtzczMzMzMzMxK55VDZmZm1iqSpgIHFv9cLiIemoP7+grweTIxNCkiXuyxzzbAecCjwLKd+3SsHPo78MaI+FvXbd9E9kQC2CAibui47o3Fdc8A60XEbT2O/SrgDmAFsnze+aP9v5qZmZmZmZmZDZdnqJqZmVnbLNHx93/02kHSqkXPoF6X9Yt9BOxS3ORrvRJDhfPJMnNLAusMss93uhNDAEXC557in2t0Xb0ruQLqJ70SQ8XtnyyODzBpkGObmZmZmZnzCVkLAAADO0lEQVSZmZVqXNMBmJmZmY3COGDpQa6bv/jzP4HFi79PkzRjiPtbuPhzZeDGHtf32jbgQWCVjmMN2KD48z2SHh7msc3MzMzMzMzMKufkkJmZmbXNYx1/XxyYpaxcRNzJzL5ESJrAzBU8A5br+PtSwzz2KwfZ/uQQtxlYkTTfIMdfmJkJoNEc28zMzMzMzMysVE4OmZmZWdvc0fH3teiRHBqmeTv+vkxEPDL6kObo+PtHxNE1H9vMzMzMzMzMbFDuOWRmZmZtcxUQxd+3nIP76Szl9qY5uJ85PX4TxzYzMzMzMzMzG5STQ2ZmZtYqEfEQcF7xz49JWmWUd/U74Ini7x+e48BG7obiz80lDaesnJmZmZmZmZlZLZwcMjMzszY6CHgaWAg4X9Jys9l/FhHxIvC94p+TJW0w1P6SFh9xlEM7mVwBNR74ymyOPZ8TSGZmZmZmZmZWFyeHzMzMrHUi4k5gB+B5YA3gVkkHSVpdkgb2k7SIpM2Abw5yV0cAd5N9Fn8qaW9JS3XcflFJm0k6Dbiu5P/Db4BvFP/8pKT/lbTWQPyS5pW0pqSDixjXKvP4ZmZmZmZmZmaDUUTMfi8zMzOzBkhaD5gGrNaxeTrwODAfsEjH9ieB/wa+GhHPdtzHKsCPgDU79n2cnCTTefs/RcRru44/8EVp44i4epAYrwY2Ag6LiCld180LfBX4XMfmZ8lVUYuSSasBG0TEDZiZmZmZmZmZVWzc7HcxMzMza0ZE/ELSfwJbA1sA6wFLk4mVp4DfAzcDlwHnRsTTPe7jHklvAbYHPgisAyxJJpnuAX4D/Li4lB3/dGAvSacDnySTSCsU8f8T+APwM+D8iPht2cc3MzMzMzMzM+vFK4fMzMzMzMzMzMzMzMz6iHsOmZmZmZmZmZmZmZmZ9REnh8zMzMzMzMzMzMzMzPqIk0NmZmZmZmZmZmZmZmZ9xMkhMzMzMzMzMzMzMzOzPuLkkJmZmZmZmZmZmZmZWR9xcsjMzMzMzMzMzMzMzKyPODlkZmZmZmZmZmZmZmbWR5wcMjMzMzMzMzMzMzMz6yNODpmZmZmZmZmZmZmZmfURJ4fMzMzMzMzMzMzMzMz6yP8H55iF42p8rCwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = comb_var_comp_pct_dict['met'].plot(kind='bar', stacked=True, figsize=(30, 15))\n",
    "chartBox = ax.get_position()\n",
    "ax.set_position([chartBox.x0, chartBox.y0, chartBox.width*0.6, chartBox.height])\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(1.3, 1), ncol=2, prop={'size':20})\n",
    "plt.title(\"Variant Composition for Metastatic Lung Tumor Mutations\", size=35)\n",
    "plt.xlabel('Gene', size=25)\n",
    "plt.ylabel('Percent', size=25)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Panel Composition Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb_panel_comp_cnt_dict = {}\n",
    "comb_panel_comp_pct_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(var_mut_dict.items(), comb_mut_freq_dict.items()):\n",
    "    comb_panel_comp_cnt_dict[k1], comb_panel_comp_pct_dict[k1] = panel_comp(v1, v2.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Primary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = comb_panel_comp_pct_dict['prim'].plot(kind='bar', stacked=True, figsize=(30, 15))\n",
    "chartBox = ax.get_position()\n",
    "ax.set_position([chartBox.x0, chartBox.y0, chartBox.width*0.6, chartBox.height])\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(1.3, 1), ncol=2, prop={'size':20})\n",
    "plt.title(\"Panel Composition for Primary Lung Tumor Mutations\", size=35)\n",
    "plt.xlabel('Gene', size=25)\n",
    "plt.ylabel('Percent', size=25)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metastasis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = comb_panel_comp_pct_dict['met'].plot(kind='bar', stacked=True, figsize=(30, 15))\n",
    "chartBox = ax.get_position()\n",
    "ax.set_position([chartBox.x0, chartBox.y0, chartBox.width*0.6, chartBox.height])\n",
    "ax.legend(loc='upper center', bbox_to_anchor=(1.3, 1), ncol=2, prop={'size':20})\n",
    "plt.title(\"Panel Composition for Metastatic Lung Tumor Mutations\", size=35)\n",
    "plt.xlabel('Gene', size=25)\n",
    "plt.ylabel('Percent', size=25)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main Figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "from adjustText import adjust_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_prim = np.log10(comb_pval_dict['prim'][['Black_White_fdr', 'Black_Asian_fdr', 'Asian_White_fdr']])\n",
    "x_met = -np.log10(comb_pval_dict['met'][['Black_White_fdr', 'Black_Asian_fdr', 'Asian_White_fdr']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "def label_outliers(x, y, labels, x_min, y_min):\n",
    "    # label points that pass thresholds\n",
    "    x_pass = []\n",
    "    y_pass = []\n",
    "    genes_pass = []\n",
    "    for a, b, c in zip(x, y, labels):\n",
    "        if abs(a) >= x_min and abs(b) >= y_min:\n",
    "            x_pass.append(a)\n",
    "            y_pass.append(b)\n",
    "            genes_pass.append(c)\n",
    "            \n",
    "    return list(zip(x_pass, y_pass, genes_pass))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_y_prim_AW = comb_pm_mut_freq_dict['prim']['Asian'] - comb_pm_mut_freq_dict['prim']['White']\n",
    "split_y_prim_BW = comb_pm_mut_freq_dict['prim']['Black'] - comb_pm_mut_freq_dict['prim']['White']\n",
    "split_y_prim_BA = comb_pm_mut_freq_dict['prim']['Black'] - comb_pm_mut_freq_dict['prim']['Asian']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_y_met_AW = comb_pm_mut_freq_dict['met']['Asian'] - comb_pm_mut_freq_dict['met']['White']\n",
    "split_y_met_BW = comb_pm_mut_freq_dict['met']['Black'] - comb_pm_mut_freq_dict['met']['White']\n",
    "split_y_met_BA = comb_pm_mut_freq_dict['met']['Black'] - comb_pm_mut_freq_dict['met']['Asian']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Split y-axis\n",
    "\n",
    "fig = plt.figure(figsize=(14, 12))\n",
    "# fig.patch.set_facecolor('white')\n",
    "\n",
    "plt.scatter(x_prim['Black_White_fdr'], split_y_prim_BW, label='Black vs. White', s=230, c='tab:olive', edgecolors='black')\n",
    "plt.scatter(x_prim['Asian_White_fdr'], split_y_prim_AW, label='Asian vs. White', s=230, c='tab:pink', edgecolors='black')\n",
    "plt.scatter(x_prim['Black_Asian_fdr'], split_y_prim_BA, label='Black vs. Asian', s=230, c='tab:cyan', edgecolors='black')\n",
    "\n",
    "plt.scatter(x_met['Black_White_fdr'], split_y_met_BW, s=230, c='tab:olive', edgecolors='black')\n",
    "plt.scatter(x_met['Asian_White_fdr'], split_y_met_AW, s=230, c='tab:pink', edgecolors='black')\n",
    "plt.scatter(x_met['Black_Asian_fdr'], split_y_met_BA, s=230, c='tab:cyan', edgecolors='black')\n",
    "\n",
    "plt.axvline(x=0, c='black')\n",
    "plt.axhline(y=0, c='black')\n",
    "\n",
    "# plot significance lines\n",
    "plt.axvline(x=-np.log10(0.05), c='red', linestyle='dashed', linewidth=3.5)\n",
    "plt.axvline(x=np.log10(0.05), c='red', linestyle='dashed', linewidth=3.5)\n",
    "\n",
    "hfont = {'fontname':'Helvetica'}\n",
    "plt.title('Lung Cancer', size=30, **hfont)\n",
    "plt.xlabel('-log10(FDR)', size=30, **hfont)\n",
    "plt.ylabel('Difference in Mutation Frequency (%)', size=30, **hfont)\n",
    "plt.xlim(-40, 40)\n",
    "# plt.ylim(0, 20)\n",
    "\n",
    "# plt.xticks(np.arange(-3, 3.5, 0.5))\n",
    "plt.xticks(fontsize=30)\n",
    "plt.yticks(fontsize=30)\n",
    "plt.tick_params('both', length=10, width=2, which='major')\n",
    "\n",
    "plt.legend(\n",
    "#     bbox_to_anchor=(1.05, 1), \n",
    "           loc='lower right', prop={\"family\":\"Helvetica\", 'size':20})\n",
    "\n",
    "x = pd.concat([x_prim['Black_White_fdr'], x_prim['Asian_White_fdr'], x_prim['Black_Asian_fdr'],\n",
    "          x_met['Black_White_fdr'], x_met['Asian_White_fdr'], x_met['Black_Asian_fdr']]).to_list()\n",
    "\n",
    "y = pd.concat([split_y_prim_BW, split_y_prim_AW, split_y_prim_BA, \n",
    "               split_y_met_BW, split_y_met_AW, split_y_met_BA]).to_list()\n",
    "\n",
    "labels = split_y_prim_BW.index.to_list() + split_y_prim_AW.index.to_list() + split_y_prim_BA.index.to_list() +\\\n",
    "            split_y_met_BW.index.to_list() + split_y_met_AW.index.to_list() + split_y_met_BA.index.to_list()\n",
    "\n",
    "outliers = label_outliers(x, y, labels, 0, 9)\n",
    "\n",
    "texts = [plt.text(x=a, y=b, s=label, size=30, **hfont) for a,b,label in outliers]\n",
    "adjust_text(texts, arrowprops=dict(arrowstyle=\"-\", color='k', lw=1.75))\n",
    "\n",
    "# plt.savefig(\"figures/lung/lung_FDR_scatter_v2.pdf\", bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rearrange order of columns\n",
    "pval_columns = ['Black_White_pval', 'Black_Asian_pval', 'Asian_White_pval',\n",
    "                'Black_White_fdr', 'Black_Asian_fdr', 'Asian_White_fdr',\n",
    "                'Black_White_odds_ratio', 'Black_Asian_odds_ratio', 'Asian_White_odds_ratio',\n",
    "                'Black_White_CI', 'Black_Asian_CI', 'Asian_White_CI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Mutation counts and sequenced counts\n",
    "# comb_pm_mut_seq_dict['prim'].to_csv(\n",
    "#     'data_tables/lung/lung_mut_seq_prim.txt', sep='\\t')\n",
    "\n",
    "# comb_pm_mut_seq_dict['met'].to_csv(\n",
    "#     'data_tables/lung/lung_mut_seq_met.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Mutation frequency\n",
    "# comb_pm_mut_freq_dict['prim'].to_csv(\n",
    "#     'data_tables/lung/lung_mut_freq_prim.txt', sep='\\t')\n",
    "\n",
    "# comb_pm_mut_freq_dict['met'].to_csv(\n",
    "#     'data_tables/lung/lung_mut_freq_met.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # p-values and fdr\n",
    "# comb_pval_dict['prim'][pval_columns].to_csv(\n",
    "#     'data_tables/lung/lung_pval_prim.txt', sep='\\t')\n",
    "\n",
    "# comb_pval_dict['met'][pval_columns].to_csv(\n",
    "#     'data_tables/lung/lung_pval_met.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DDR Genes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load DDR genes list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using these genes that were sequenced by all 7 panels\n",
    "DDR_genes = pd.DataFrame(['ATM', 'BRCA1', 'BRCA2', 'MSH2', 'MSH6'], columns=['Hugo_Symbol'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove DDR genes not in dataset, if any\n",
    "prim_DDR_genes = DDR_genes[DDR_genes['Hugo_Symbol'].isin(mut_seq_dict['prim'].index)]\n",
    "met_DDR_genes = DDR_genes[DDR_genes['Hugo_Symbol'].isin(mut_seq_dict['met'].index)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 5 5\n"
     ]
    }
   ],
   "source": [
    "# check if number of genes changed\n",
    "print(len(DDR_genes), len(prim_DDR_genes), len(met_DDR_genes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hugo_Symbol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Hugo_Symbol\n",
       "0         True\n",
       "1         True\n",
       "2         True\n",
       "3         True\n",
       "4         True"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prim_DDR_genes == met_DDR_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from here assuming prim_DDR genes are the same as met_DDR\n",
    "# assuming both prim and met have the same DDR genes\n",
    "# if not all True in printouts above, need to modify"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "DDR_mut_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in mut_dict.items():\n",
    "    DDR_mut_dict[key] = value[value['Hugo_Symbol'].isin(prim_DDR_genes.iloc[:,0])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter by variants of interest (use this for all genes calculation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "DDR_var_mut_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in DDR_mut_dict.items():\n",
    "    DDR_var_mut_dict[key] = value[value['Variant_Classification'].isin(variants)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate mutation frequency (all genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using all patients as common denominator\n",
    "# use this mutation frequency for the combined graphs\n",
    "# using variant filtered mutations\n",
    "# count the number of unique patients with mutations in the DDR genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "DDR_all_mut_cnt_dict = {}\n",
    "DDR_all_mut_freq_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prim prim\n",
      "met met\n"
     ]
    }
   ],
   "source": [
    "for (k1, v1), (k2, v2) in zip(DDR_var_mut_dict.items(), {k: race_demo_dict[k] for k in ['prim', 'met']}.items()):\n",
    "    # checking order of prim and met is consistent\n",
    "    print(k1, k2)\n",
    "    mut_cnt = v1.groupby('PRIMARY_RACE')['PATIENT_ID'].nunique()\n",
    "    mut_cnt = mut_cnt.rename('DDR Mutations')\n",
    "    DDR_all_mut_cnt_dict[k1] = mut_cnt\n",
    "    \n",
    "    DDR_all_mut_freq_dict[k1] = (mut_cnt / v2.values * 100).to_frame().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fisher's Test and FDR (all genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note: p-values and FDR are the same here because only testing on hypothesis\n",
    "# we will not adjust p-values for combined genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "DDR_all_pval_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prim prim\n",
      "met met\n"
     ]
    }
   ],
   "source": [
    "for (k1, v1), (k2, v2) in zip(DDR_all_mut_cnt_dict.items(), {k: race_demo_dict[k] for k in ['prim', 'met']}.items()):\n",
    "    print(k1, k2)\n",
    "    df = v1.append(v2).to_frame().transpose()\n",
    "    df.columns = ['Asian_mut', 'Black_mut', 'White_mut', 'Asian_seq', 'Black_seq', 'White_seq']\n",
    "    df.index = ['DDR Mutations']\n",
    "    \n",
    "    DDR_all_pval_dict[k1] = fisher_fdr_all_groups(df, groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'prim':                Black_White_odds_ratio  Black_White_pval  Black_White_fdr  \\\n",
       " DDR Mutations                0.960785          0.921522         0.921522   \n",
       " \n",
       "               Black_White_CI  Black_Asian_odds_ratio  Black_Asian_pval  \\\n",
       " DDR Mutations  (0.65, 1.421)                1.886978          0.018765   \n",
       " \n",
       "                Black_Asian_fdr Black_Asian_CI  Asian_White_odds_ratio  \\\n",
       " DDR Mutations         0.018765  (1.116, 3.19)                0.509166   \n",
       " \n",
       "                Asian_White_pval  Asian_White_fdr  Asian_White_CI  \n",
       " DDR Mutations          0.000175         0.000175  (0.352, 0.737)  ,\n",
       " 'met':                Black_White_odds_ratio  Black_White_pval  Black_White_fdr  \\\n",
       " DDR Mutations                1.155757          0.521226         0.521226   \n",
       " \n",
       "                Black_White_CI  Black_Asian_odds_ratio  Black_Asian_pval  \\\n",
       " DDR Mutations  (0.771, 1.732)                     2.2          0.006087   \n",
       " \n",
       "                Black_Asian_fdr  Black_Asian_CI  Asian_White_odds_ratio  \\\n",
       " DDR Mutations         0.006087  (1.272, 3.806)                0.525344   \n",
       " \n",
       "                Asian_White_pval  Asian_White_fdr  Asian_White_CI  \n",
       " DDR Mutations          0.000968         0.000968  (0.354, 0.781)  }"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_pval_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "prim_met_DDR_pval = pd.concat([DDR_all_pval_dict['prim'], DDR_all_pval_dict['met']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "prim_met_DDR_pval.index = ['Primary', 'Metastasis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prim_met_DDR_pval[pval_columns].to_csv('tables/lung/lung_DDR_all_pval.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Significant mutations (all genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "DDR_all_sig_pval_dict = {}\n",
    "DDR_all_sig_freq_dict = {}\n",
    "DDR_all_sig_genes_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (k1, v1), (k2, v2) in zip(DDR_all_pval_dict.items(), DDR_all_mut_freq_dict.items()):\n",
    "    DDR_all_sig_genes_dict[k1], DDR_all_sig_pval_dict[k1], DDR_all_sig_freq_dict[k1] = find_sig_genes_groups(groups, v1, v2, 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'prim': {('Black',\n",
       "   'White'): Empty DataFrame\n",
       "  Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       "  Index: [],\n",
       "  ('Black',\n",
       "   'Asian'):                Black_Asian_pval  Black_Asian_fdr  Black_Asian_odds_ratio  \\\n",
       "  DDR Mutations          0.018765         0.018765                1.886978   \n",
       "  \n",
       "                Black_Asian_CI  \n",
       "  DDR Mutations  (1.116, 3.19)  ,\n",
       "  ('Asian',\n",
       "   'White'):                Asian_White_pval  Asian_White_fdr  Asian_White_odds_ratio  \\\n",
       "  DDR Mutations          0.000175         0.000175                0.509166   \n",
       "  \n",
       "                 Asian_White_CI  \n",
       "  DDR Mutations  (0.352, 0.737)  },\n",
       " 'met': {('Black',\n",
       "   'White'): Empty DataFrame\n",
       "  Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       "  Index: [],\n",
       "  ('Black',\n",
       "   'Asian'):                Black_Asian_pval  Black_Asian_fdr  Black_Asian_odds_ratio  \\\n",
       "  DDR Mutations          0.006087         0.006087                     2.2   \n",
       "  \n",
       "                 Black_Asian_CI  \n",
       "  DDR Mutations  (1.272, 3.806)  ,\n",
       "  ('Asian',\n",
       "   'White'):                Asian_White_pval  Asian_White_fdr  Asian_White_odds_ratio  \\\n",
       "  DDR Mutations          0.000968         0.000968                0.525344   \n",
       "  \n",
       "                 Asian_White_CI  \n",
       "  DDR Mutations  (0.354, 0.781)  }}"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DDR Primary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Black',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       " Index: [],\n",
       " ('Black',\n",
       "  'Asian'):                Black_Asian_pval  Black_Asian_fdr  Black_Asian_odds_ratio  \\\n",
       " DDR Mutations          0.018765         0.018765                1.886978   \n",
       " \n",
       "               Black_Asian_CI  \n",
       " DDR Mutations  (1.116, 3.19)  ,\n",
       " ('Asian',\n",
       "  'White'):                Asian_White_pval  Asian_White_fdr  Asian_White_odds_ratio  \\\n",
       " DDR Mutations          0.000175         0.000175                0.509166   \n",
       " \n",
       "                Asian_White_CI  \n",
       " DDR Mutations  (0.352, 0.737)  }"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['prim']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Black',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Black, White]\n",
       " Index: [],\n",
       " ('Black',\n",
       "  'Asian'): PRIMARY_RACE       Black      Asian\n",
       " DDR Mutations  17.777778  10.280374,\n",
       " ('Asian',\n",
       "  'White'): PRIMARY_RACE       Asian      White\n",
       " DDR Mutations  10.280374  18.370091}"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_freq_dict['prim']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Asian vs White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian_White_pval</th>\n",
       "      <th>Asian_White_fdr</th>\n",
       "      <th>Asian_White_odds_ratio</th>\n",
       "      <th>Asian_White_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DDR Mutations</th>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.509166</td>\n",
       "      <td>(0.352, 0.737)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Asian_White_pval  Asian_White_fdr  Asian_White_odds_ratio  \\\n",
       "DDR Mutations          0.000175         0.000175                0.509166   \n",
       "\n",
       "               Asian_White_CI  \n",
       "DDR Mutations  (0.352, 0.737)  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['prim'][('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian_White_pval</th>\n",
       "      <th>Asian_White_fdr</th>\n",
       "      <th>Asian_White_odds_ratio</th>\n",
       "      <th>Asian_White_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DDR Mutations</th>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.509166</td>\n",
       "      <td>(0.352, 0.737)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Asian_White_pval  Asian_White_fdr  Asian_White_odds_ratio  \\\n",
       "DDR Mutations          0.000175         0.000175                0.509166   \n",
       "\n",
       "               Asian_White_CI  \n",
       "DDR Mutations  (0.352, 0.737)  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['prim'][('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Black vs White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_White_pval</th>\n",
       "      <th>Black_White_fdr</th>\n",
       "      <th>Black_White_odds_ratio</th>\n",
       "      <th>Black_White_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       "Index: []"
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     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['prim'][('Black', 'White')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_White_pval</th>\n",
       "      <th>Black_White_fdr</th>\n",
       "      <th>Black_White_odds_ratio</th>\n",
       "      <th>Black_White_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       "Index: []"
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     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "DDR_all_sig_pval_dict['prim'][('Black', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Black vs Asian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_Asian_pval</th>\n",
       "      <th>Black_Asian_fdr</th>\n",
       "      <th>Black_Asian_odds_ratio</th>\n",
       "      <th>Black_Asian_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DDR Mutations</th>\n",
       "      <td>0.018765</td>\n",
       "      <td>0.018765</td>\n",
       "      <td>1.886978</td>\n",
       "      <td>(1.116, 3.19)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Black_Asian_pval  Black_Asian_fdr  Black_Asian_odds_ratio  \\\n",
       "DDR Mutations          0.018765         0.018765                1.886978   \n",
       "\n",
       "              Black_Asian_CI  \n",
       "DDR Mutations  (1.116, 3.19)  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['prim'][('Black', 'Asian')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_Asian_pval</th>\n",
       "      <th>Black_Asian_fdr</th>\n",
       "      <th>Black_Asian_odds_ratio</th>\n",
       "      <th>Black_Asian_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DDR Mutations</th>\n",
       "      <td>0.018765</td>\n",
       "      <td>0.018765</td>\n",
       "      <td>1.886978</td>\n",
       "      <td>(1.116, 3.19)</td>\n",
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      ],
      "text/plain": [
       "               Black_Asian_pval  Black_Asian_fdr  Black_Asian_odds_ratio  \\\n",
       "DDR Mutations          0.018765         0.018765                1.886978   \n",
       "\n",
       "              Black_Asian_CI  \n",
       "DDR Mutations  (1.116, 3.19)  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['prim'][('Black', 'Asian')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DDR Metastasis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Black',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       " Index: [],\n",
       " ('Black',\n",
       "  'Asian'):                Black_Asian_pval  Black_Asian_fdr  Black_Asian_odds_ratio  \\\n",
       " DDR Mutations          0.006087         0.006087                     2.2   \n",
       " \n",
       "                Black_Asian_CI  \n",
       " DDR Mutations  (1.272, 3.806)  ,\n",
       " ('Asian',\n",
       "  'White'):                Asian_White_pval  Asian_White_fdr  Asian_White_odds_ratio  \\\n",
       " DDR Mutations          0.000968         0.000968                0.525344   \n",
       " \n",
       "                Asian_White_CI  \n",
       " DDR Mutations  (0.354, 0.781)  }"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['met']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Black',\n",
       "  'White'): Empty DataFrame\n",
       " Columns: [Black, White]\n",
       " Index: [],\n",
       " ('Black',\n",
       "  'Asian'): PRIMARY_RACE       Black      Asian\n",
       " DDR Mutations  24.264706  12.711864,\n",
       " ('Asian',\n",
       "  'White'): PRIMARY_RACE       Asian      White\n",
       " DDR Mutations  12.711864  21.704394}"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_freq_dict['met']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Asian vs White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian_White_pval</th>\n",
       "      <th>Asian_White_fdr</th>\n",
       "      <th>Asian_White_odds_ratio</th>\n",
       "      <th>Asian_White_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DDR Mutations</th>\n",
       "      <td>0.000968</td>\n",
       "      <td>0.000968</td>\n",
       "      <td>0.525344</td>\n",
       "      <td>(0.354, 0.781)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Asian_White_pval  Asian_White_fdr  Asian_White_odds_ratio  \\\n",
       "DDR Mutations          0.000968         0.000968                0.525344   \n",
       "\n",
       "               Asian_White_CI  \n",
       "DDR Mutations  (0.354, 0.781)  "
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['met'][('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian_White_pval</th>\n",
       "      <th>Asian_White_fdr</th>\n",
       "      <th>Asian_White_odds_ratio</th>\n",
       "      <th>Asian_White_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DDR Mutations</th>\n",
       "      <td>0.000968</td>\n",
       "      <td>0.000968</td>\n",
       "      <td>0.525344</td>\n",
       "      <td>(0.354, 0.781)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Asian_White_pval  Asian_White_fdr  Asian_White_odds_ratio  \\\n",
       "DDR Mutations          0.000968         0.000968                0.525344   \n",
       "\n",
       "               Asian_White_CI  \n",
       "DDR Mutations  (0.354, 0.781)  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['met'][('Asian', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Black vs White"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_White_pval</th>\n",
       "      <th>Black_White_fdr</th>\n",
       "      <th>Black_White_odds_ratio</th>\n",
       "      <th>Black_White_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['met'][('Black', 'White')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_White_pval</th>\n",
       "      <th>Black_White_fdr</th>\n",
       "      <th>Black_White_odds_ratio</th>\n",
       "      <th>Black_White_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Black_White_pval, Black_White_fdr, Black_White_odds_ratio, Black_White_CI]\n",
       "Index: []"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['met'][('Black', 'White')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Black vs Asian"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_Asian_pval</th>\n",
       "      <th>Black_Asian_fdr</th>\n",
       "      <th>Black_Asian_odds_ratio</th>\n",
       "      <th>Black_Asian_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DDR Mutations</th>\n",
       "      <td>0.006087</td>\n",
       "      <td>0.006087</td>\n",
       "      <td>2.2</td>\n",
       "      <td>(1.272, 3.806)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Black_Asian_pval  Black_Asian_fdr  Black_Asian_odds_ratio  \\\n",
       "DDR Mutations          0.006087         0.006087                     2.2   \n",
       "\n",
       "               Black_Asian_CI  \n",
       "DDR Mutations  (1.272, 3.806)  "
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['met'][('Black', 'Asian')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_Asian_pval</th>\n",
       "      <th>Black_Asian_fdr</th>\n",
       "      <th>Black_Asian_odds_ratio</th>\n",
       "      <th>Black_Asian_CI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DDR Mutations</th>\n",
       "      <td>0.006087</td>\n",
       "      <td>0.006087</td>\n",
       "      <td>2.2</td>\n",
       "      <td>(1.272, 3.806)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Black_Asian_pval  Black_Asian_fdr  Black_Asian_odds_ratio  \\\n",
       "DDR Mutations          0.006087         0.006087                     2.2   \n",
       "\n",
       "               Black_Asian_CI  \n",
       "DDR Mutations  (1.272, 3.806)  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DDR_all_sig_pval_dict['met'][('Black', 'Asian')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mutation frequency plots (all genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "prim_met_DDR = pd.concat([DDR_all_mut_freq_dict['prim'], DDR_all_mut_freq_dict['met']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "prim_met_DDR.index = ['Primary', 'Metastasis']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prim_met_DDR.to_csv('tables/lung/DDR_all_mut_freq.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (prim_met_DDR).plot(kind='barh', figsize=(8, 5), width=0.5)\n",
    "hfont = {'fontname':'Helvetica'}\n",
    "plt.title('DDR Mutation Frequency in Lung Cancer', fontsize=20, **hfont)\n",
    "plt.xlabel('Patients with Mutation (%)', fontsize=15, **hfont)\n",
    "plt.ylabel('DDR Mutations', fontsize=15, **hfont)\n",
    "plt.xticks(size=15, **hfont)\n",
    "plt.yticks(size=15, **hfont)\n",
    "plt.xlim((0, 100))\n",
    "ax.invert_yaxis()\n",
    "ax.set_yticklabels(['Primary', 'Metastasis'])\n",
    "plt.legend(prop={'family': 'Helvetica', 'size': 15}, loc='lower right')\n",
    "plt.tight_layout()\n",
    "# plt.savefig('figures/Lung/DDR_Actionable/lung_DDR_Action_all_prim.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total mutation counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Count mutations in each patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find genes that were sequenced by all panels, using this as our driver gene list\n",
    "common_genes = combined_panels[combined_panels['SEQ_ASSAY_ID'].apply(len) == 7].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "181"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(common_genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count number of mutations (specified by list) in each patient, separated by primary/metstastasis and by race\n",
    "\n",
    "# removing duplicates:\n",
    "# first choose sample sequenced by later panel\n",
    "# then if still duplicates, choose the sample with the largest mutation count\n",
    "\n",
    "# mut_tbl: mutation table in MAF format\n",
    "# driver_genes: list of driver genes to be counted\n",
    "# races: list of races to separate by\n",
    "def count_mutations(mut_tbl, genes, races):\n",
    "    race_dict = {}\n",
    "    mut = mut_tbl[mut_tbl['Hugo_Symbol'].isin(genes)]\n",
    "    # remove assay duplicates\n",
    "    mut = remove_duplicates(mut)\n",
    "    \n",
    "    for race in races:\n",
    "        # these are patients with >= 1 count\n",
    "        race_mut = mut[mut['PRIMARY_RACE'] == race]\n",
    "        race_mut_cnt = race_mut.groupby(['PATIENT_ID', 'SAMPLE_ID'])['SAMPLE_ID'].count()\n",
    "        \n",
    "        # for remaining duplicates, choose sample with highest mutation count\n",
    "        race_mut_cnt = race_mut_cnt.groupby('PATIENT_ID').max()\n",
    "        \n",
    "        # find patients with zero counts and add them back in\n",
    "        zero_IDs = mut_tbl[(mut_tbl['PRIMARY_RACE'] == race) & \n",
    "                           ~(mut_tbl['PATIENT_ID'].isin(race_mut_cnt.index))]['PATIENT_ID'].unique()\n",
    "        zero_mut_cnt = pd.Series(index=zero_IDs, data=0)\n",
    "        \n",
    "        total_cnt = pd.concat([race_mut_cnt, zero_mut_cnt], axis=0)\n",
    "        total_cnt = total_cnt.rename('Mutation Count')\n",
    "\n",
    "        race_dict[race] = total_cnt\n",
    "    \n",
    "    return race_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in var_mut_dict.items():\n",
    "    driver_dict[key] = count_mutations(value, common_genes, races)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mean and Median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "median_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in driver_dict.items():\n",
    "    means = []\n",
    "    medians = []\n",
    "    for race in races:\n",
    "        medians.append(np.median(value[race]))\n",
    "        means.append(np.mean(value[race]))\n",
    "    df = pd.DataFrame(np.array([means, medians]), index=['Mean', 'Median'], columns=races)\n",
    "    median_dict[key] = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>3.768987</td>\n",
       "      <td>5.494444</td>\n",
       "      <td>5.178204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Asian     Black     White\n",
       "Mean    3.768987  5.494444  5.178204\n",
       "Median  3.000000  4.000000  4.000000"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_dict['prim']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>4.394068</td>\n",
       "      <td>5.536765</td>\n",
       "      <td>5.785937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Asian     Black     White\n",
       "Mean    4.394068  5.536765  5.785937\n",
       "Median  4.000000  4.000000  5.000000"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_dict['met']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "# median_dict['prim'].to_csv('tables/lung/lung_prim_median_count.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "# median_dict['met'].to_csv('tables/lung/lung_met_median_count.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate significance (Wilcoxon Rank Sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate if distribution of mutation counts is different between races, including outliers\n",
    "# using Wilcoxon because mutation counts of each race is not normally distributed (tested using shapiro-wilk test)\n",
    "# also calculate percent difference of median count between races"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver_pval_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculates the confidence interval of difference of medians for wilcoxon rank sum test\n",
    "# taken from here: https://gist.github.com/meetbash/aa5dbbeae746a173c5421ac57b45fe70#file-non_param_paired_ci-py\n",
    "\n",
    "def non_param_unpaired_CI(sample1, sample2, conf):\n",
    "    n1 = len(sample1)  \n",
    "    n2 = len(sample2)  \n",
    "    alpha = 1-conf      \n",
    "    N = stats.norm.ppf(1 - alpha/2) \n",
    "    # The confidence interval for the difference between the two population\n",
    "    # medians is derived through the n x m differences.\n",
    "    diffs = sorted([i-j for i in sample1 for j in sample2])\n",
    "    # the Kth smallest to the Kth largest of the n x m differences then determine \n",
    "    # the confidence interval, where K is:\n",
    "    k = np.math.ceil(n1*n2/2 - (N * (n1*n2*(n1+n2+1)/12)**0.5))\n",
    "    CI = (round(diffs[k-1],3), round(diffs[len(diffs)-k],3))\n",
    "    return CI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in driver_dict.items():\n",
    "    df = pd.DataFrame(index=['Rank Sum Stat', 'p-value', 'CI', 'Mean Percent Difference'])\n",
    "    for group in groups:\n",
    "        pct_diff = (np.mean(value[group[0]]) - np.mean(value[group[1]])) / np.mean(value[group[1]]) * 100\n",
    "        CI = non_param_unpaired_CI(value[group[0]], value[group[1]], 0.95)\n",
    "        df[group[0] + '_' + group[1]] = list(stats.ranksums(value[group[0]], value[group[1]])) + [CI] + [pct_diff]\n",
    "    driver_pval_dict[key] = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Primary Stats:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_White</th>\n",
       "      <th>Black_Asian</th>\n",
       "      <th>Asian_White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rank Sum Stat</th>\n",
       "      <td>-0.0629917</td>\n",
       "      <td>4.41931</td>\n",
       "      <td>-7.5015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.949773</td>\n",
       "      <td>9.90166e-06</td>\n",
       "      <td>6.30894e-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CI</th>\n",
       "      <td>(0, 0)</td>\n",
       "      <td>(1, 2)</td>\n",
       "      <td>(-1, -1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Percent Difference</th>\n",
       "      <td>6.10714</td>\n",
       "      <td>45.7804</td>\n",
       "      <td>-27.2144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Black_White  Black_Asian  Asian_White\n",
       "Rank Sum Stat            -0.0629917      4.41931      -7.5015\n",
       "p-value                    0.949773  9.90166e-06  6.30894e-14\n",
       "CI                           (0, 0)       (1, 2)     (-1, -1)\n",
       "Mean Percent Difference     6.10714      45.7804     -27.2144"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Primary Stats:')\n",
    "driver_pval_dict['prim']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metastatic Stats:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Black_White</th>\n",
       "      <th>Black_Asian</th>\n",
       "      <th>Asian_White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rank Sum Stat</th>\n",
       "      <td>-0.837427</td>\n",
       "      <td>2.68065</td>\n",
       "      <td>-5.56049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.402353</td>\n",
       "      <td>0.00734792</td>\n",
       "      <td>2.69022e-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CI</th>\n",
       "      <td>(-1, 0)</td>\n",
       "      <td>(0, 1)</td>\n",
       "      <td>(-1, -1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Percent Difference</th>\n",
       "      <td>-4.30651</td>\n",
       "      <td>26.0054</td>\n",
       "      <td>-24.0561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Black_White Black_Asian  Asian_White\n",
       "Rank Sum Stat             -0.837427     2.68065     -5.56049\n",
       "p-value                    0.402353  0.00734792  2.69022e-08\n",
       "CI                          (-1, 0)      (0, 1)     (-1, -1)\n",
       "Mean Percent Difference    -4.30651     26.0054     -24.0561"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Metastatic Stats:')\n",
    "driver_pval_dict['met']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "# driver_pval_dict['prim'].to_csv('tables/lung/lung_prim_count_stats.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# driver_pval_dict['met'].to_csv('tables/lung/lung_met_count_stats.txt', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver_count_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in driver_dict.items():\n",
    "    dfs = []\n",
    "    for race in races:\n",
    "        df = pd.DataFrame(value[race])\n",
    "        df['Race'] = race\n",
    "        if key == 'prim':\n",
    "            df['Sample Type'] = 'Primary'\n",
    "        else:\n",
    "            df['Sample Type'] = 'Metastasis'\n",
    "        dfs.append(df)\n",
    "    driver_count_dict[key] = pd.concat(dfs)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "driver_df = pd.concat([driver_count_dict['prim'], driver_count_dict['met']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try a log scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding 1 to prevent infinity values\n",
    "driver_df['log10(Mutation Count)'] = np.log10(driver_df['Mutation Count'] + 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "''"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 12))\n",
    "ax = sns.boxplot(data=driver_df[['log10(Mutation Count)', 'Race', 'Sample Type']], \n",
    "                 hue='Race', x='Sample Type', y='log10(Mutation Count)', notch=True, fliersize=12,\n",
    "#                 width=\n",
    "                )\n",
    "# plt.ylim(-0.1, 3)\n",
    "# ax.set_xlim(-0.55, 1.55)\n",
    "sns.despine(ax=ax)\n",
    "\n",
    "hfont = {'fontname':'Helvetica'}\n",
    "plt.title('Lung Cancer', size=30, **hfont)\n",
    "plt.xlabel('Sample Type', size=30, **hfont)\n",
    "plt.ylabel('Mutation Count', size=30, **hfont)\n",
    "plt.xticks(fontsize=30, **hfont)\n",
    "ax.set_yticks([np.log10(1), np.log10(11), np.log10(101)])\n",
    "ax.set_yticklabels(['0', '$10^1$', '$10^2$'])\n",
    "plt.yticks(fontsize=30, **hfont)\n",
    "plt.tick_params('y', length=10, width=2, which='major')\n",
    "\n",
    "\n",
    "plt.legend(loc='upper right', prop={\"family\":\"Helvetica\", 'size':20})\n",
    "# plt.savefig(\"figures/lung/lung_boxplot_v3.pdf\", bbox_inches='tight')\n",
    ";"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Try removing patients with EGFR mutations to see if that affects the distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'EGFR' in common_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_EGFR_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_EGFR_mut_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in var_mut_dict.items():\n",
    "    EGFR_IDs = value[value['Hugo_Symbol'] == 'EGFR']['PATIENT_ID'].unique()\n",
    "    no_EGFR_mut_dict[key] = value[~value['PATIENT_ID'].isin(EGFR_IDs)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in no_EGFR_mut_dict.items():\n",
    "    no_EGFR_dict[key] = count_mutations(value, common_genes[~common_genes.isin(['EGFR'])], races)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_EGFR_median_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in no_EGFR_dict.items():\n",
    "    means = []\n",
    "    medians = []\n",
    "    for race in races:\n",
    "        medians.append(np.median(value[race]))\n",
    "        means.append(np.mean(value[race]))\n",
    "    df = pd.DataFrame(np.array([means, medians]), index=['Mean', 'Median'], columns=races)\n",
    "    no_EGFR_median_dict[key] = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>4.068493</td>\n",
       "      <td>5.544</td>\n",
       "      <td>5.31988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000</td>\n",
       "      <td>4.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Asian  Black    White\n",
       "Mean    4.068493  5.544  5.31988\n",
       "Median  3.000000  4.000  4.00000"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_EGFR_median_dict['prim']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mean</th>\n",
       "      <td>4.43617</td>\n",
       "      <td>5.773196</td>\n",
       "      <td>5.992622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Median</th>\n",
       "      <td>3.00000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Asian     Black     White\n",
       "Mean    4.43617  5.773196  5.992622\n",
       "Median  3.00000  5.000000  5.000000"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_EGFR_median_dict['met']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_EGFR_pval_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in no_EGFR_dict.items():\n",
    "    df = pd.DataFrame(index=['Rank Sum Stat', 'p-value', 'Mean Percent Difference'])\n",
    "    for group in groups:\n",
    "        pct_diff = (np.mean(value[group[0]]) - np.mean(value[group[1]])) / np.mean(value[group[1]]) * 100\n",
    "        df[group[0] + '_' + group[1]] = list(stats.ranksums(value[group[0]], value[group[1]])) + [pct_diff]\n",
    "    no_EGFR_pval_dict[key] = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "# still see significant differences after removing patients with EGFR mutations from the counts\n",
    "# but less significant as before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Black_White</th>\n",
       "      <th>Black_Asian</th>\n",
       "      <th>Asian_White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rank Sum Stat</th>\n",
       "      <td>-0.492141</td>\n",
       "      <td>2.828183</td>\n",
       "      <td>-4.920800e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.622619</td>\n",
       "      <td>0.004681</td>\n",
       "      <td>8.619098e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Percent Difference</th>\n",
       "      <td>4.212879</td>\n",
       "      <td>36.266667</td>\n",
       "      <td>-2.352284e+01</td>\n",
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      "text/plain": [
       "                         Black_White  Black_Asian   Asian_White\n",
       "Rank Sum Stat              -0.492141     2.828183 -4.920800e+00\n",
       "p-value                     0.622619     0.004681  8.619098e-07\n",
       "Mean Percent Difference     4.212879    36.266667 -2.352284e+01"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_EGFR_pval_dict['prim']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Rank Sum Stat</th>\n",
       "      <td>-0.651835</td>\n",
       "      <td>2.406056</td>\n",
       "      <td>-4.090307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p-value</th>\n",
       "      <td>0.514507</td>\n",
       "      <td>0.016126</td>\n",
       "      <td>0.000043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mean Percent Difference</th>\n",
       "      <td>-3.661605</td>\n",
       "      <td>30.139188</td>\n",
       "      <td>-25.972801</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "                         Black_White  Black_Asian  Asian_White\n",
       "Rank Sum Stat              -0.651835     2.406056    -4.090307\n",
       "p-value                     0.514507     0.016126     0.000043\n",
       "Mean Percent Difference    -3.661605    30.139188   -25.972801"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_EGFR_pval_dict['met']"
   ]
  }
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