NB6_significance_decipher_210419.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluate the enrichment of Decipher shared phenotype patients on our ASD-CHD interactome\n",
    "\n",
    "Update 19-12-12: found all damaging SNVs/indels in DECIPHER, by looking up karyotype for abnormal nervous and cardiovascular systems.  Use this to make an ROC curve\n",
    "\n",
    "Update 20-04-28: make sure results can be replicated from supplemental tables, add more description\n",
    "\n",
    "Update 21-04-19: update for consistency with revision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import random\n",
    "\n",
    "\n",
    "# latex rendering of text in graphs\n",
    "import matplotlib as mpl\n",
    "from matplotlib import rcParams\n",
    "rcParams['font.family'] = 'sans-serif'\n",
    "rcParams['font.sans-serif'] = ['Arial']\n",
    "\n",
    "sns.set_style('white')\n",
    "\n",
    "import sys\n",
    "\n",
    "% matplotlib inline\n",
    "\n",
    "sns.set_style(\"ticks\", {\"xtick.major.size\": 15, \"ytick.major.size\": 15})\n",
    "plt.rcParams['svg.fonttype'] = 'none'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the ASD-CHD network genes, (z_ASD-CHD>=3; 844 genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "844\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene</th>\n",
       "      <th>ASD_seed</th>\n",
       "      <th>CHD_seed</th>\n",
       "      <th>dual_phenotype_snv_indel_DECIPHERv11.1</th>\n",
       "      <th>dual_phenotype_small_CNV_less1MB_DECIPHERv11.1</th>\n",
       "      <th>PCGC/PHN dual phenotype dDNV</th>\n",
       "      <th>sum_dual_pheno_damaging_variants (DECIPHER 11.1, PCGC/PHN)</th>\n",
       "      <th>z_both</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene</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>KMT2A</th>\n",
       "      <td>KMT2A</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>19</td>\n",
       "      <td>52.658388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KANSL1</th>\n",
       "      <td>KANSL1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18</td>\n",
       "      <td>7.389539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CDK13</th>\n",
       "      <td>CDK13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16</td>\n",
       "      <td>65.097038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PTPN11</th>\n",
       "      <td>PTPN11</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15</td>\n",
       "      <td>3.995214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ANKRD11</th>\n",
       "      <td>ANKRD11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15</td>\n",
       "      <td>3.766040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            gene  ASD_seed  CHD_seed  dual_phenotype_snv_indel_DECIPHERv11.1  \\\n",
       "gene                                                                           \n",
       "KMT2A      KMT2A         0         1                                    17.0   \n",
       "KANSL1    KANSL1         0         0                                     3.0   \n",
       "CDK13      CDK13         0         1                                    15.0   \n",
       "PTPN11    PTPN11         0         1                                    14.0   \n",
       "ANKRD11  ANKRD11         0         0                                    14.0   \n",
       "\n",
       "         dual_phenotype_small_CNV_less1MB_DECIPHERv11.1  \\\n",
       "gene                                                      \n",
       "KMT2A                                               1.0   \n",
       "KANSL1                                             14.0   \n",
       "CDK13                                               1.0   \n",
       "PTPN11                                              0.0   \n",
       "ANKRD11                                             1.0   \n",
       "\n",
       "         PCGC/PHN dual phenotype dDNV  \\\n",
       "gene                                    \n",
       "KMT2A                             1.0   \n",
       "KANSL1                            1.0   \n",
       "CDK13                             0.0   \n",
       "PTPN11                            1.0   \n",
       "ANKRD11                           0.0   \n",
       "\n",
       "         sum_dual_pheno_damaging_variants (DECIPHER 11.1, PCGC/PHN)     z_both  \n",
       "gene                                                                            \n",
       "KMT2A                                                   19           52.658388  \n",
       "KANSL1                                                  18            7.389539  \n",
       "CDK13                                                   16           65.097038  \n",
       "PTPN11                                                  15            3.995214  \n",
       "ANKRD11                                                 15            3.766040  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ASD_CHD_df = pd.read_excel('data/supplemental_tables_cell_systems_210416.xlsx',\n",
    "                           sheet_name='Table S5',skiprows=1)\n",
    "ASD_CHD_df.index=ASD_CHD_df['gene']\n",
    "print(len(ASD_CHD_df))\n",
    "display(ASD_CHD_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "190"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of dual-phenotype genes harboring at least one damaging variant (from DECIPHER v9.25, SNVs/INDELS/smallCNVs, + PCGC/PHN dual-phenotype dDNVs)\n",
    "# but MLL2 is included here... set to nan because it is a duplicate of KMT2A in PCnet\n",
    "sum(ASD_CHD_df['sum_dual_pheno_damaging_variants (DECIPHER 11.1, PCGC/PHN)']>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the DECIPHER all dual-condition results\n",
    "\n",
    "DECIPHER all dual-condition results come from DECIPHER v11.1 (manually aqcuired from the webtool), and include only  SNVs/indels which were classified as likely LOF or protein changing. We were not able to collect comprehensive CNV information from the DECIPHER webtool.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "911.0\n",
      "437\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>chr</th>\n",
       "      <th>gene</th>\n",
       "      <th>num_SNVs_indels</th>\n",
       "      <th>num_likely_LOF</th>\n",
       "      <th>num_PC</th>\n",
       "      <th>num_annotated_reg_region</th>\n",
       "      <th>num_UTR</th>\n",
       "      <th>num_ncRNA</th>\n",
       "      <th>num_other</th>\n",
       "      <th>num_LOF_PC</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene</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>GNB1</th>\n",
       "      <td>1</td>\n",
       "      <td>GNB1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CAMTA1</th>\n",
       "      <td>1</td>\n",
       "      <td>CAMTA1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RERE</th>\n",
       "      <td>1</td>\n",
       "      <td>RERE</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MTHFR</th>\n",
       "      <td>1</td>\n",
       "      <td>MTHFR</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MTOR</th>\n",
       "      <td>1</td>\n",
       "      <td>MTOR</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       chr    gene  num_SNVs_indels  num_likely_LOF  num_PC  \\\n",
       "gene                                                          \n",
       "GNB1     1    GNB1                1             0.0     1.0   \n",
       "CAMTA1   1  CAMTA1                1             1.0     0.0   \n",
       "RERE     1    RERE                3             0.0     3.0   \n",
       "MTHFR    1   MTHFR                1             0.0     1.0   \n",
       "MTOR     1    MTOR                1             0.0     1.0   \n",
       "\n",
       "        num_annotated_reg_region  num_UTR  num_ncRNA  num_other  num_LOF_PC  \n",
       "gene                                                                         \n",
       "GNB1                         0.0      0.0        0.0        0.0         1.0  \n",
       "CAMTA1                       0.0      0.0        0.0        0.0         1.0  \n",
       "RERE                         0.0      0.0        0.0        0.0         3.0  \n",
       "MTHFR                        0.0      0.0        0.0        0.0         1.0  \n",
       "MTOR                         0.0      0.0        0.0        0.0         1.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dec_kary_shared = pd.read_excel('data/DECIPHER_karyotypes_9.31.xlsx',sheet_name='ASD-CHD')\n",
    "# dec_kary_shared = pd.read_excel('/Users/brinrosenthal/Google Drive/UCSD/CCBB_tickets/Ideker_17_05_neuro/manuscript/tables_19_01/DECIPHER_karyotypes_210129.xlsx',\n",
    "#                                sheet_name = 'ASD-CHD_210129')\n",
    "\n",
    "# dec_kary_shared = pd.read_excel('/Users/brinrosenthal/Google Drive/UCSD/CCBB_tickets/Ideker_17_05_neuro/manuscript/CELL_SYSTEMS/supplemental_tables_cell_systems_210212.xlsx',\n",
    "#                            sheet_name='DECIPHER_11.1_DETAILED')\n",
    "\n",
    "dec_kary_shared = pd.read_excel('data/supplemental_tables_cell_systems_210416.xlsx',\n",
    "                           sheet_name='Table S6',skiprows=1)\n",
    "\n",
    "dec_kary_shared.index=dec_kary_shared['gene']\n",
    "dec_kary_shared = dec_kary_shared.fillna(0)\n",
    "dec_kary_shared['num_LOF_PC']=dec_kary_shared['num_likely_LOF']+dec_kary_shared['num_PC']\n",
    "print(dec_kary_shared['num_LOF_PC'].sum())\n",
    "print(len(dec_kary_shared))\n",
    "dec_kary_shared.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "437\n",
      "911.0\n",
      "191\n"
     ]
    }
   ],
   "source": [
    "print(len(dec_kary_shared))\n",
    "print(sum(dec_kary_shared['num_LOF_PC']))\n",
    "print(sum(dec_kary_shared['num_LOF_PC']>1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'# dual-phenotype variants')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LFy+Snp5OzZo1FW0YDAaTQfjk5GSOHj2Ks7MzVlYlZyAsW7aMX3/9VfxXtD8ATp48ycqVK+nYsSO3b9822bcXLlygevXqGAwG1q9fz549e/jPf/5D9erVS/y7hIQE8vLy2L59OxUqVCh23zdv3ryn75EYKfV3ScqT5cuXY21tTW5uLh9//LEoHzNmDE2bNsXGxkb8vnLlSrKzs8nNzaVKlSq8++67AMycOZMqVaqIgTFlyhTeffddsrOzadGiheJ6x44dY/z48axdu5YxY8aI8pCQECwtLcnPz0en0zF8+HA2btzI9evXyc7OJigoSNQ9dOgQjo6O4ndXV1dVeZ2cnKhTpw5Nmzbl8OHDCjkOHjyo+N3X15dNmzYpviEREBAAFA7mSZMmAYUvDKAp293XcHd3Z9CgQUyYMIHWrVsr/v1///sfBoOBM2fO0LRpU6ZMmQLA4sWLycvLU/RnrVq1cHV1LTYAt23bRqtWrQgLCyMjIwMPDw/y8/MZP348n332GXZ2dqLu2bNnuX79Op9++ilLly4FoE6dOixatIhhw4Zx+/ZtUXfJkiX079+f5ORkDh48yPjx44HCySU2NpZ//etfom54eDjvv/8+9vb2BAUFMWvWLKDw058WFhZCcb7zzjuq9waF47Bz586EhoaKcQUwb948dDodOTk5REdHs23bNoYMGYJer2ft2rWKTMoffviBmJgY0tLSmD17NgBffvklzz77LL179y72fA4fPszOnTtp0qQJrq6u4hwaGxsbhdIaMmQIjRs3RqfTKWQODQ3lxo0bjBs3ji1btjBz5kwA4uPj2bp1q6LuvWCWCsPBwYG//vpLMVigcDBfv35d8Y1We3t7mjRpQqdOnQgODhblPXr0wMbGRqEctL7tWqVKFWxtbYtp/fz8fIYPHw7AihUrALh9+zY2NjbFZqfk5GROnz4tBp2rq6uqvPHx8eh0OubNm0dKSoqijV9//RV3d3dq166tkKFhw4bUr19fUdfKyor58+djZWUlBqiWbAcOHECn04kZFwqVUUpKCrNnz6ZatWpCUXp5eWFlZcUzzzzDm2++qei7OnXqKJbkzp8/z/z587lx4wbz588XAzEnJ4eXXnqJo0eP8sMPPzBy5Eju3LlDRkYGQ4YMYfTo0eJ61tbW1KtXj+zsbGE1VKlShffee49FixaRlJQkrlehQgUcHR1xdHTkyJEjovzw4cNYWFiQlpYmXoiiVkHdunVF3WvXrlG9enVmzJjBunXrNO8NwNnZGV9fX86fP68oDwwM5Ny5c4SFhbFs2TIA/v77b1avXs3AgQNp3LixqPvbb7/h4uKCg4ODYnzd/eyhcO/WG2+8gYeHB4MHDwYKx9W0adMoKChg+fLljBs3DijcppGeno6np6eijaIT0qFDhwD48MMP8fT0pHr16mI83ysmXZJHQfPmzWnXrh3W1tZs2LABHx8fACpVqiRMrF9//RVnZ2eSk5Pp3Lkz8fHxZGZmipfzueee49lnn1W0u2nTJmJiYoiNjSU2NlaYurt27SIqKoqbN28SFRUlXJU9e/bg5OREamoqv/32Gz4+Pnh5eeHp6clff/3Ftm3bhEuRlZWFv78/7u7uQgZLS0uio6OJj4+nbt26WFpa0q1bN7p164a3tzfdunVTyNemTRscHBxwc3PD2dlZ9IW9vT21atWiatWqom6dOnXIysrC3t4ePz8/KlWqpCnb9evXqVChAhUqVMDDw0P028svv4yvry/NmjUT7p2Liwvp6emcPXuWtm3bius1atSIGjVqKFwBBwcH6tevj4eHBy4uLkJmDw8P7ty5Q/Pmzalfvz6Ojo40bdoUOzs7Tpw4wZtvvimsjGPHjmEwGIiOjkan09GmTRucnZ1xcHDA29ub27dv4+HhAUBERAQvvPAC+fn5REZGCvn69OmDv78/3t7e5OfnU79+fS5dusS+ffuIioqiWrVqoo3ExEQSEhJo2LAhFy9epEWLFqr3BoWHR505c4bff/+dM2fOiHERFhZGbGwsY8aMwdbWFmtra958800cHR25cOECv/zyi6jr4+NDrVq1OHPmDHv37sXb25vw8HDS0tKIjo5WuMaurq5EREQQExODh4cHDg4OXL58mYKCAgwGAwcPHhTuedWqValZsybXr19Hp9MJZWRs++TJkyQmJtKtWzfy8vI4e/YsCQkJvPTSSybdoZIwSwtj7dq1XL9+nXfffZe//vpLtc6JEydo06aN8NMbNmxIw4YNxb+rmfdas/WgQYOws7Pjxx9/xMvLS5R7eHgIq8E44Pbu3Uv9+vU5d+6cUGRQGOuYMWMG3bt3p3379gAsWLCA1157jdzcXBYuXMi0adOEq2SkqHn4ww8/0LBhQ4Vrs2bNGtVZITQ0lN69ewsfd8KECUChUu3bt6+i7rlz5ygoKOCvv/7iueeeo3Xr1jRt2pTFixdTUFBAnz59xKa/y5cvs3//fvLz8xWbAefNm0dqaio1atQQs3hWVhY6nY6jR4+SlJQk3JsVK1Zga2uLs7MzAwcOBCAoKIj09HT69OnDsmXLhKvp5uZG/fr12bx5s+i3iIgIGjVqxNatWxXJgoMGDSI0NBS9Xi9mYCi0aLZv386hQ4d49dVXgUIlkpubS0FBAdu2bRN1W7VqxZUrVwgLCxNbHhYuXEh6ejr/+te/6NGjh6i7atUqoDAWVbSN9PR0LCwshIUyZswYvv76a44dO0Z2dra4DyjcsW18nq+88goAPXv2VIwdI+7u7ri7u5ORkcH27dupX78+sbGx7Nu3jypVqjB69GhR99NPP6V3795s3boVKysrAgMDARg1ahSVK1dmy5YtYod5165d6dq1KwkJCYSEhPDee+8Vu3ZpMUuFoeXvGn11KAwigbYvp2beDxo0iIyMDMVMDYUKSs3vS09PF4rr559/BuDPP//k7NmzTJ8+nTVr1ohZPDc3l08++YTg4GAxYGrWrCkUjXEnoJqrZCQpKYnk5GTxNwD79+8nPT29mH9tbW0tvhNj/EKdVl9YWlpiZ2fHW2+9JY5c3LRpE1OnTqWgoIAFCxYwbdo00cdTp05Fr9fzv//9T5QHBgYSHBzMiBEjhGzt27cnODiYBg0aiAEL8Oyzz/LOO+8ojnesV68eLi4udO3alfj4eFGemJhIXFycoj/T09Np2bIl586dU7il3377Lbdu3RLPw9/fH4DBgwczdepUcnJy8PPzAwpN+a+//pqYmBjFi2a876L3V7lyZSZNmkRISIjieWi10aJFC1xcXLh27Rq5ubkArF69Gjc3NywtLQkJCRH9dPToUXJzc/nPf/5DWFgYnp6eHD58GCcnJ9GeMWBddJIzulG1atWiUqVKwhUxUqNGDeLj4xk4cKAYmwD79u3jzp07vPHGG4SHh+Pu7s4XX3xB9+7dSUlJue9vJJulwjD6u2+//TZjxowR/m6HDh2Awk83Gn/W8uUmTJhAamqqwi05efIke/fuJT8/n65du9KqVStA3e8DdcWVlZXF5cuXWbp0qSIYmZaWxoULF0hNTeXChQu4urpy4sQJoeROnToFIM4HUePUqVNYWlqKvwkICKB9+/aMGjWqWN2hQ4dy5MgRLCwsxGDW6gtbW1t69epFfHy88OuNJriFhYViEGmVG/u9KAMHDqRZs2ZUrVqVsLAwEZCNjo4mLCxM3H9AQIBQ8PPmzePSpUuiDbX+NLpHAwYMEDEC0J5IVqxYwfbt24mOjsbV1ZV27doxYcIEJk2ahJ2dnaI/1O4vPj6egwcPcvHiRQ4ePCgmgfHjx/Pf//63WBuHDx/G29sbZ2dngoKCaNeuHfDP+CyqELOyssjMzGT//v3iK+pq8S5Qn+SMLtv8+fOBfyzSrKwsjh8/LpIqjZw9e1asgCUnJwOFK15OTk44OTlx9OhR7gezVBgvvPCC6LT/+7//E+V79+7F19eXkJAQnJycePfddykoKBAPs6hfe/fpYGPGjOHnn39m8uTJQGEA1agwjIFIYwDPiJriatu2rTB369SpI+p6eHgQGxuLm5ubGLhGS6WogtuyZQt//vkneXl5uLi40K9fP9GG0bc8dOgQJ0+eJCAgQNV0hcIVkTt37pCbm8sff/xRYl/06tULULpt0dHRzJ8/H4PBoHiBtcrnzZtHTk6OYuAuWrRIVbaZM2diMBjw9vYWL78xuGplZUVCQoKoq9afbm5ufPLJJxQUFNCpU6cSnwcUKoGhQ4ei1+sJCwujXbt2dOrUiaioKBISEtiyZYtw09Tur0+fPuh0Onr37k1eXp643pdffsnu3buJjY1VtFHUTTKOU6MygELL1Mj48eOJjo4mLy+P1157DSh0j43PpChqk9yIESNU0xnGjRvHsWPH+PPPPxVnzCxatEgsnRqtkvz8fPHvRX++F8xSYagtUUKhprxy5Qr9+vXj119/BQof6siRI9m5cyd6vV68JGrLXEVdkaJxhJkzZ2JjY0NkZKTCHVBTXBEREQwbNgxAkZ/Ru3dv/v77b7Zs2cKVK1fo27evqoJLTk5m4sSJAIrZE+Czzz4T0Xfj5xzeeecdWrZsKV4m4wxTtWpVGjdurFgd0uqLcePG8dxzzyncmpkzZ4qVk6Jolb/22mtUr16d5ORkoQSOHDmiqvy2bNlC//79SUlJEcufRl97+PDh7N69m5EjR2r2Z7t27bCysiI7O1uxw1lrIgkMDKRRo0bcvHlTJPr16tWLyMhILC0tOXTokHjZjf1ZlAMHDjBmzBhu3rzJsmXLRBAyNzeXzp0706ZNG27evCnqN2jQgIULF6LX60Xg3M/Pj+nTp7N3714RT4LCoKfx+R06dIgpU6aoxrsArl69yrZt2xRxpYiICMUqm3G8ablA3t7excZLz549Wbx4MXq9XlVRlQWzVBhaJtutW7fYt28fY8eO5dtvvwVg8uTJTJw4kX79+imSoNSWuYzLgAaDQWHGffrpp6oBOTXFdfz4cYVpbpzl1HJH1BScXq/nzp07GAyGYi9lWFgYqampTJkyBUtLS6AwhnHw4EESExOpVauWqFu7dm06depEfHy8eIm0+kIt/+TAgQMcOXJEmNNGd0Kr3JjX4ODgIPIatJSf1vKnGmr9uWrVKtWXQWsicXV15dy5c4wcOVJYUL/99huXL1/GysqKAQMGiL9RewFHjRrF9OnTqVixoiLvZ/78+bz33nvk5uby1VdfYVxQ7NKliwi2G5/rN998w4ABA/D29lZYZvv27ePQoUOK56cW74J/4kdF40pa4w3UXSC16yUnJ+Pj48PevXu5du2aYnGgrJilwtAy2UaNGiXMcOPKwZIlS6hRowabN28mKipKzMCjR4+mZcuWfPPNN2JWHTNmDNWrV6dKlSps3bpVtKsVkFNTXB07duStt94qJpta7ohRwY0bN04oOAsLC0JDQ4HiMQG16LuVlRWVKlXi5s2bCnP577//ZubMmfTt21f0lVZfuLm5kZ6eTlpamsgUDAgIIDMzUygEI1rlankNRuUHKJRfTk4OOTk5GAwGYaqvXr2atm3bMm/ePM6cOSMsDK3+NL4MRT9toTWRpKWlUaNGDQwGg4gfHT9+nNq1a/PCCy8I1xPUFZRx6T4lJYV9+/bx73//W9yzg4MDDg4OiizSohhX6xwcHDhx4gT+/v5cuHBB/Lu1tXWx52eMdxn/b7wPtfhKx44dGTx4MD/88AMRERGiXS0XSO16MTExpKSkMG3aNEJCQu7rXBqzVBgnT55UvLjGgb9y5cpiGXaBgYHExsYCKAJTK1euJCYmBh8fH5FpuXr1auzs7EhPTxeDwng9KB6QU1Ncb731Fnq9XjxAY0ZlXFwclStX5tSpU0yfPp25c+dy69YtEY0/duwYM2bMKDFKXXT2MNKlSxc8PT3x8vJSDPSrV68ye/ZsVqxYIcziUaNGYWtri5WVleKE9pCQEKysrMjLyxMZqwcOHODSpUsiMm+0SLTKq1WrxvLlyzEYDMKisbCwIDg4mN27dyusl0GDBrFmzRoMBoNY/mzVqhXNmjVj9OjRrFy5ssT+LPoyFN2nojWRVKtWDTs7O3r27MmcOXNwdXVlxowZZGRkEBISQlBQEF9//TWgrqC6dOkirI6ffvpJlBcUFBAZGYler1fIVHRLZZDtAAAImUlEQVS1zhjMbty4MTdv3uTq1auKGVzt+dWuXZuoqCg2bNggMjpBPb5iaWnJkiVLimXvarlAatczTphJSUmkpqYW67+yYJYKo3379iQmJlK7dm06d+4sytUy7BYtWkSzZs3Izc3lp59+EiayWlpvo0aNSEpKIigoSJG8ohWQU1NcWunXRpnffvttYa7OmTNHyLt+/XoAkXhjMBgUfrEWGzZsAIpbI4BwbYy+/+zZs3FyciIlJYXGjRuLGIZaxurFixdp06YNp0+fxs7OTigGrfKGDRvy6quvYjAYhAK2t7fn+vXrtG/fnunTpwu5jh07RuvWrWnTpg1bt27l9ddfx8vLiw4dOjB37lzFCodafzo6OjJ9+nRSUlIUh05rTSQZGRnEx8fz+++/i/TyoKAgatSoQZcuXRR5B66urpw9exZ3d3f27NlD9+7dSU5OZseOHeTn5ytchBEjRnDs2DEsLCx44403RHnR1TrjnpGi47Soy7NhwwZhERmfoZq8oB5fyczMVM3e1XKB1K7XrVs3rK2tefbZZ8X4u1fMUmF069YNg8HAzp07GTp0KJs2bQLUfXxbW1uRMWl8GUA9rffEiRMYDAa2bNkC/OOfawXk1BSXVvq1msxq8oaGhuLu7k5WVhZnzpzh008/LbEvNm7cSEJCAvXr11fkVvzyyy+cPXuWpk2b0q9fP7777juaNGnCpUuXmDRpkmKTknGp12AwCPM1KytLJPYUjT9olR8+fJgmTZoA/wQnMzMzqVixYrEMyYSEBBITE2nTpo1YdXJzc8Pd3R0HBweR26HVn9evX2fu3LksX75c4QpoTSRF08uN/Vz0GkWJjY3FysoKd3d34e6cOHFC5JGsWbNGrEzNnTtXPKsdO3aIZ+Xs7KzY89GoUSPVa4F6YFhNXlCPrxjT6nfu3Mns2bPFypuWC6R2vY0bN6oGw+8Fs1QYn3/+Obdv36Zt27aKZBo1H9+4dg6FWYpGjObdxYsXSUtLY8qUKYwfPx5bW1sAxfKpFmpKoGbNmvTo0YOtW7cqHqCazGry1q5dmyNHjtC3b19FtqIWaglTUGieG5WccdBdunQJvV4vXDSjdaCWsaq11Kb1s5rvrzWYU1NTsbKyEn46/GNZOTo6iv4ANPvzzp076HQ6xUqU1kRiYWGBXq8nNDTU5A5U4yYz405iQGHxFL1nrWc1fPhwBg0apNjzoYVaYFhLXq0Ap1r2rpYLpHY9rWD4vWCWCmPr1q307duXEydOEBERwYwZM4DCDiwoKGD37t0iAOTm5kZaWhpXr14VW6ZB3bzbvHkz06dP59SpU3z99dfC7zb6oWlpaQrzTk0JWFtbs2PHDuLi4sRyIBSap1WqVOH06dOcPn1akSdwN66urrRo0aLYtnk1jAPbGFw01lcb+N26dVNtz8bGhpo1awoXAdBcatMq1wpOqg3mzZs3U6VKFbZv345Op+P999/XvD+1/tTpdCIwHBoaKvpRayLR6mc1/vjjD2rVqkWHDh1wcXFh7Nix5OXlsXjxYmxsbIptG1B7VhMnTmTPnj2cP38eX1/fYscaFMX4jIquimnJq9XHami5QGrX0wqG3wtmqTAmTpzInTt3iI2NVeyjUPN3tUxoNfNuyJAhTJ48GU9PT8WX6OfPn0+lSpWwtrZWBAvVFFfXrl3ZsGEDfn5+PPPMM6JuaQdtXl4e+fn5+Pv706JFC81t6EbefvttqlevTmhoKHv27OG7774D1Gep/fv3ExAQUMxEvnz5MklJSbRp00YsJzdo0EB1T4FWeefOnVmwYAH5+fkEBAQoktbuZuHChSQlJVGrVi3VMzmKotafWn2pNZGUBScnJ3r06EHr1q3F5GBhYYGdnR1vvvmmiM9AoftSVLkYD7Mxrng988wz9OvXr8QPABm3lOfm5pKVlVWibGrxlbKidj1jAlcJx/eWGrNUGMZB9O9//1vxUqr5u2UxoQ8cOIC9vT1//PGHIpVZK1ioprjWrl0LFAbgTp48WabZDbQDhVqouR7G+7mbWbNmsXTpUm7dukXFihXFTFLURbjXKHlYWBiBgYEYDAZWrFhR4n0bB/p3332ncB3UKEt/ak0kZaFatWrC9zcGUI3WmnGZ2GhJFFUuRZd3iwbfjQl2D0JmtfhKWVG73iuvvMJLL71EcnIyzZs3L/OYLYpZKgytQaTm75ZkQl+4cIF+/fqJZccOHTrQsWPHYqctaQUL1RTX/XQ2oBko1ELN9dBi+/btFBQUMHXqVMUqUFlcBC1q1aolIu+mTmzSch3UKEt/ak0kZaGo79+gQQNAaa0VlUlNuYB68P1ByFyWZ12W6/Xq1Ytx48axfPlyxSa6e8EsFYbWIFLzd7VM6IyMDD755BNFnsLGjRuL5XGAdrDwfq0JNbQChVqUJaCXmZmJTqdj8eLFilTtsrgIWmRnZ/PLL79gYWFh0rRVi+c8CB7E81Dz/bXaUVMuoB7MfhAyq8VXyora9YzB47uDyPeCyY8xmxNZWVls2LCBZs2a0apVqxJv/PPPP2fYsGGsXbuWIUOGiLpGU3LkyJFii7Fxv8nVq1fFNvDHkWXLlikChHcPuO+++46vvvqqRBdBi3nz5hEVFUVOTg42NjaK8yEkD4a5c+fSo0cPEhMTuXTp0n1bA0bu3rP0xLkkWpRFW6tF2rVMSa3A6eOG0Zy9m7K4CFoU9duNeSqSB4uWC3S/PCgLDx4zhVGWG1erm56ejl6vp2/fvooVige5/fdRUlRJFnVfHoSLUBa/XXJvaLlA5sRj5ZLcL19++SXXr18nJydHsUJx8eJFduzYIQKn97Ob70nlQZq1kseXp0phrFq1ioKCAnJzc+/rXEOJ5GnlqVIYgFihiIuLM7lCIZFIlDx1CkMikdw7pcsekkgkEqTCkEgkZUAqDIlEUmqkwpBIJKVGKgyJRFJqpMKQSCSl5v8BwU+UVlj8+y4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x101.52 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar-plot genes harboring most variants\n",
    "# sns.set_style('whitegrid')\n",
    "sns.set_style(\"ticks\", {\"xtick.major.size\": 15, \"ytick.major.size\": 15})\n",
    "# dual_plot =ASD_CHD_df[ASD_CHD_df['sum_damaging_variants (DECIPHER 9.25, PCGC/PHN)']>3]['sum_damaging_variants (DECIPHER 9.25, PCGC/PHN)']\n",
    "dual_plot =ASD_CHD_df[ASD_CHD_df['sum_damaging_variants (DECIPHER 11.1, PCGC/PHN)']>3]['sum_damaging_variants (DECIPHER 11.1, PCGC/PHN)']\n",
    "dual_plot=dual_plot.sort_values(ascending=False)\n",
    "plt.figure(figsize=(4,1.41))\n",
    "plt.bar(np.arange(len(dual_plot)),dual_plot,color='black')\n",
    "tmp=plt.xticks(np.arange(len(dual_plot)),dual_plot.index.tolist(),fontsize=6,rotation='vertical',va='top')\n",
    "plt.xlim([-1,len(dual_plot)])\n",
    "plt.yticks([0,5,10,15],fontsize=9)\n",
    "plt.ylabel('# dual-phenotype variants',fontsize=8)\n",
    "\n",
    "# plt.savefig('/Users/brinrosenthal/Google Drive/UCSD/CCBB_tickets/Ideker_17_05_neuro/manuscript/figures_revisions_2102/Figure3/barchart_dualpheno_g2_11.1.png',dpi=300,bbox_inches='tight')\n",
    "# plt.savefig('/Users/brinrosenthal/Google Drive/UCSD/CCBB_tickets/Ideker_17_05_neuro/manuscript/figures_revisions_2102/Figure3/barchart_dualpheno_g2_11.1.svg',dpi=300,bbox_inches='tight')\n",
    "\n",
    "# plt.savefig('../../manuscript/figures_1911/Figure3/Figure3_final assets/barchart_dualpheno_g2.png',dpi=300,bbox_inches='tight')\n",
    "# plt.savefig('../../manuscript/figures_1911/Figure3/Figure3_final assets/barchart_dualpheno_g2.svg',dpi=300,bbox_inches='tight',)\n",
    "# plt.savefig('../../manuscript/figures_1911/Figure3/barchart_dualpheno_g2.svg',dpi=300,bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gene\n",
       "KMT2A      19\n",
       "KANSL1     18\n",
       "CDK13      16\n",
       "PTPN11     15\n",
       "ANKRD11    15\n",
       "CREBBP     14\n",
       "KMT2D      14\n",
       "KAT6A      14\n",
       "MAPT       13\n",
       "NSD1       10\n",
       "ARID1B     10\n",
       "NF1        10\n",
       "EHMT1      10\n",
       "EP300       9\n",
       "DYRK1A      8\n",
       "MED13L      8\n",
       "CHD7        7\n",
       "CHD4        7\n",
       "NOTCH1      7\n",
       "ADNP        7\n",
       "MECP2       6\n",
       "ZEB2        6\n",
       "KAT6B       5\n",
       "POGZ        5\n",
       "KMT2C       5\n",
       "BRAF        5\n",
       "WAC         5\n",
       "SETD5       5\n",
       "FOXP1       5\n",
       "SRCAP       5\n",
       "NALCN       4\n",
       "SON         4\n",
       "KDM6A       4\n",
       "BPTF        4\n",
       "SETD1A      4\n",
       "ATRX        4\n",
       "SOS1        4\n",
       "KDM5B       4\n",
       "PIK3CA      4\n",
       "Name: sum_damaging_variants (DECIPHER 11.1, PCGC/PHN), dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dual_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DECIPHER ROC curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "80\n",
      "19701\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>zscore_ASD</th>\n",
       "      <th>zscore_CHD</th>\n",
       "      <th>zprod_both</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1BG</th>\n",
       "      <td>-1.483328</td>\n",
       "      <td>0.316803</td>\n",
       "      <td>-0.469922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1CF</th>\n",
       "      <td>-0.927115</td>\n",
       "      <td>0.571386</td>\n",
       "      <td>-0.529741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M</th>\n",
       "      <td>-2.837494</td>\n",
       "      <td>0.598259</td>\n",
       "      <td>-1.697556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2ML1</th>\n",
       "      <td>-1.119579</td>\n",
       "      <td>1.554647</td>\n",
       "      <td>-1.740550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3GALT2</th>\n",
       "      <td>-0.807590</td>\n",
       "      <td>3.459356</td>\n",
       "      <td>-2.793741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         zscore_ASD  zscore_CHD  zprod_both\n",
       "gene                                       \n",
       "A1BG      -1.483328    0.316803   -0.469922\n",
       "A1CF      -0.927115    0.571386   -0.529741\n",
       "A2M       -2.837494    0.598259   -1.697556\n",
       "A2ML1     -1.119579    1.554647   -1.740550\n",
       "A3GALT2   -0.807590    3.459356   -2.793741"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the full z-score results for ASD and CHD\n",
    "\n",
    "num_reps = 5000\n",
    "focal_interactome='PCnet'\n",
    "rand_method = 'degree_binning' # can be 'degree_binning' or 'degree_ks_test' (deprecated)\n",
    "\n",
    "z_ASD = pd.read_csv('data/z_score_results/z_ASD_'+str(num_reps)+'_reps'+focal_interactome+'_'+rand_method+'.tsv',\n",
    "                sep='\\t',names=['gene','zscore'],index_col='gene')\n",
    "z_CHD = pd.read_csv('data/z_score_results/z_CHD_'+str(num_reps)+'_reps'+focal_interactome+'_'+rand_method+'.tsv',\n",
    "                       sep='\\t',names=['gene','zscore'],index_col='gene')\n",
    "\n",
    "# z_ASD = z_ASD['zscore'].copy()\n",
    "# z_CHD = z_CHD['zscore'].copy()\n",
    "\n",
    "z_ASD.head()\n",
    "\n",
    "# multiply by the sign (+1 if both +, -1 if either -). \n",
    "z_ASDCHD = z_ASD['zscore'].abs()*z_CHD['zscore'].abs()*(((np.sign(z_ASD['zscore'])>0)&(np.sign(z_CHD['zscore'])>0))*1.0-.5)*2\n",
    "\n",
    "\n",
    "z_ASDCHD.head()\n",
    "\n",
    "\n",
    "\n",
    "seed_genes = ASD_CHD_df[(ASD_CHD_df['ASD_seed']==1)|(ASD_CHD_df['CHD_seed']==1)].index.tolist()\n",
    "print(len(seed_genes))\n",
    "non_seed_genes = list(np.setdiff1d(z_ASDCHD.index.tolist(),seed_genes))\n",
    "z_ASDCHD_noseeds = z_ASDCHD.loc[non_seed_genes]\n",
    "print(len(z_ASDCHD_noseeds))\n",
    "z_ASDCHD_noseeds.max()\n",
    "\n",
    "z_comb = z_ASD.join(z_CHD,lsuffix='_ASD',rsuffix='_CHD')\n",
    "z_comb['zprod_both']=z_ASDCHD\n",
    "z_comb.head()\n",
    "\n",
    "z_comb_no_seeds = z_comb.loc[non_seed_genes]\n",
    "z_comb_no_seeds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94\n",
      "334.0\n"
     ]
    }
   ],
   "source": [
    "focal_genes_temp = list(np.intersect1d(z_ASDCHD[z_ASDCHD>3].index.tolist(),dec_kary_shared.index.tolist()))\n",
    "print(len(focal_genes_temp))\n",
    "print(dec_kary_shared['num_LOF_PC'].loc[focal_genes_temp].dropna().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def calc_FPR_TPR(z_ASDCHD, both_DECIPHER,thresh_list):\n",
    "\n",
    "    TP_both_list,FP_both_list,TN_both_list,FN_both_list = [],[],[],[]\n",
    "\n",
    "#     thresh_list = np.arange(0,30)\n",
    "#     thresh_list = np.linspace(-17,400,1000)\n",
    "#     thresh_list = np.linspace(0,2,1000)\n",
    "    for thresh_temp in thresh_list:\n",
    "\n",
    "        both_predicted = z_ASDCHD[z_ASDCHD>=thresh_temp].index.tolist()\n",
    "\n",
    "        # ------ both -------\n",
    "        num_TP_both = len(np.intersect1d(both_DECIPHER,both_predicted))\n",
    "        num_FP_both = len(both_predicted)-num_TP_both\n",
    "        num_FN_both = len(both_DECIPHER)-num_TP_both\n",
    "        # number true negatives is everything that wasn't TP, FN, or FP\n",
    "        num_TN_both = len(z_ASDCHD)-num_TP_both-num_FP_both-num_FN_both \n",
    "\n",
    "        TP_both_list.append(num_TP_both)\n",
    "        FP_both_list.append(num_FP_both)\n",
    "        TN_both_list.append(num_TN_both)\n",
    "        FN_both_list.append(num_FN_both)\n",
    "\n",
    "        # P is number of true positives in the data\n",
    "        P_both = len(both_DECIPHER)\n",
    "        # N is number of true negatives in the data (# number of interactome genes- P..?)\n",
    "        N_both = len(z_ASDCHD)-P_both \n",
    "        # (TPR = sensitivity/ recall)\n",
    "        TPR_both = np.divide(TP_both_list,float(P_both))\n",
    "        FPR_both = np.divide(FP_both_list,float(N_both))\n",
    "\n",
    "        \n",
    "    return(TPR_both,FPR_both)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "191\n",
      "186\n",
      "160\n",
      "\n",
      "AUC all:\n",
      "0.7048441971426768\n",
      "\n",
      "AUC seeds excluded:\n",
      "0.6591106519625403\n",
      "0\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-d6aba0d8699b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 51\u001b[0;31m     \u001b[0mTPR_both_rand\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mFPR_both_rand\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcalc_FPR_TPR\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz_ASDCHDrand\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mboth_DECIPHER\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mthresh_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     52\u001b[0m     \u001b[0mauc_both_rand\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mauc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTPR_both_rand\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mFPR_both_rand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-12-b4d8126c21d1>\u001b[0m in \u001b[0;36mcalc_FPR_TPR\u001b[0;34m(z_ASDCHD, both_DECIPHER, thresh_list)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mthresh_temp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mthresh_list\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m         \u001b[0mboth_predicted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mz_ASDCHD\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mz_ASDCHD\u001b[0m\u001b[0;34m>=\u001b[0m\u001b[0mthresh_temp\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0;31m# ------ both -------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brinrosenthal/anaconda3/envs/ipykernel_py2/lib/python2.7/site-packages/pandas/core/ops.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, other, axis)\u001b[0m\n\u001b[1;32m   1772\u001b[0m             \u001b[0mres_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1773\u001b[0m             return self._constructor(res_values, index=self.index,\n\u001b[0;32m-> 1774\u001b[0;31m                                      name=res_name, dtype='bool')\n\u001b[0m\u001b[1;32m   1775\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1776\u001b[0m     \u001b[0mwrapper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop_name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brinrosenthal/anaconda3/envs/ipykernel_py2/lib/python2.7/site-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[1;32m    262\u001b[0m                                       raise_cast_failure=True)\n\u001b[1;32m    263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 264\u001b[0;31m                 \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSingleBlockManager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m         \u001b[0mgeneric\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNDFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brinrosenthal/anaconda3/envs/ipykernel_py2/lib/python2.7/site-packages/pandas/core/internals/managers.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, block, axis, do_integrity_check, fastpath)\u001b[0m\n\u001b[1;32m   1479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1480\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBlock\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1481\u001b[0;31m             \u001b[0mblock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_block\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplacement\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mslice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1483\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblocks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mblock\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brinrosenthal/anaconda3/envs/ipykernel_py2/lib/python2.7/site-packages/pandas/core/internals/blocks.pyc\u001b[0m in \u001b[0;36mmake_block\u001b[0;34m(values, placement, klass, ndim, dtype, fastpath)\u001b[0m\n\u001b[1;32m   3086\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mklass\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3087\u001b[0m         \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3088\u001b[0;31m         \u001b[0mklass\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_block_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3089\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3090\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mklass\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mDatetimeTZBlock\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_datetime64tz_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/brinrosenthal/anaconda3/envs/ipykernel_py2/lib/python2.7/site-packages/pandas/core/internals/blocks.pyc\u001b[0m in \u001b[0;36mget_block_type\u001b[0;34m(values, dtype)\u001b[0m\n\u001b[1;32m   3046\u001b[0m     \"\"\"\n\u001b[1;32m   3047\u001b[0m     \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3048\u001b[0;31m     \u001b[0mvtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3049\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3050\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mis_sparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# make an ROC curve sweeping z score\n",
    "from sklearn.metrics import roc_curve,auc\n",
    "thresh_list = np.linspace(-17,400,2000)\n",
    "\n",
    "# -------- set threshold for number of variants observed per gene (1 used in main text, 0 used in supplement ) --------\n",
    "dec_thresh=1 \n",
    "# ------------------------------------------------\n",
    "\n",
    "both_DECIPHER = dec_kary_shared[dec_kary_shared['num_LOF_PC']>dec_thresh].index.tolist()\n",
    "print(len(both_DECIPHER))\n",
    "# make sure the genes are able to be recovered\n",
    "both_DECIPHER = list(np.intersect1d(z_ASDCHD.index.tolist(),both_DECIPHER))\n",
    "print(len(both_DECIPHER))\n",
    "\n",
    "TPR_both,FPR_both=calc_FPR_TPR(z_comb['zprod_both'], both_DECIPHER,thresh_list)\n",
    "\n",
    "both_DECIPHER_noseeds = list(np.setdiff1d(both_DECIPHER,seed_genes))\n",
    "print(len(both_DECIPHER_noseeds))\n",
    "TPR_both_noseeds,FPR_both_noseeds=calc_FPR_TPR(z_comb_no_seeds['zprod_both'], both_DECIPHER_noseeds,thresh_list)\n",
    "# TPR_both\n",
    "\n",
    "auc_both = auc(FPR_both,TPR_both)\n",
    "print('\\nAUC all:')\n",
    "print(auc_both)\n",
    "auc_both_noseeds = auc(FPR_both_noseeds,TPR_both_noseeds)\n",
    "print('\\nAUC seeds excluded:')\n",
    "print(auc_both_noseeds)\n",
    "\n",
    "\n",
    "\n",
    "auc_both_rand=[]\n",
    "# randomize xeno_df\n",
    "for r in np.arange(100):\n",
    "#     print(r)\n",
    "    if (r%10)==0:\n",
    "        print(r)\n",
    "\n",
    "    z_ASDrand = z_ASD.copy(deep=True)\n",
    "    rand_index=z_ASDrand.index.tolist()\n",
    "    np.random.shuffle(rand_index)\n",
    "    z_ASDrand.index=rand_index\n",
    "    z_CHDrand = z_CHD.copy(deep=True)\n",
    "    rand_index=z_CHDrand.index.tolist()\n",
    "    np.random.shuffle(rand_index)\n",
    "    z_CHDrand.index=rand_index\n",
    "    z_ASDrand.head()\n",
    "\n",
    "    z_ASDCHDrand = z_ASDrand['zscore'].abs()*z_CHDrand['zscore'].abs()*(((np.sign(z_ASDrand['zscore'])>0)&(np.sign(z_CHDrand['zscore'])>0))*1.0-.5)*2\n",
    "\n",
    "    \n",
    "    TPR_both_rand,FPR_both_rand=calc_FPR_TPR(z_ASDCHDrand, both_DECIPHER,thresh_list)\n",
    "    auc_both_rand.append(auc(TPR_both_rand,FPR_both_rand))\n",
    "\n",
    "    \n",
    "print(np.mean(auc_both_rand))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11.483111070490256\n",
      "8.020149377308533e-31\n",
      "9.504023449205095\n",
      "1.0096687025639668e-21\n"
     ]
    },
    {
     "data": {
      "image/png": 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Uoxl9fHwwGAxK9WYBWYpki4wE9BhKrNATWYl079491Gq15Gs5mbtbzFt9KjyKrObYfPXVV0yePJm7d+8C8PDhQ0muO3HiRBwcHLh9+7Yk15MjshKprKwMV1dX8vLygK5NaqTA0dERPz8/RSQLyKpqu3//vs3WcAoICFBEsoBsRGpubqahocFmy/QFBARQXl4+Jje6sQbZiFRRUQHYblW5gIAAOjo6KC8vt8n1RzuyEamkpASQbmTkNzE3KRQWFtrk+pKzdWvXf1byybM/5pNnfzzo4mQjUnFxMSB9G5KZwMBAHB0duX79uk2uP9qRjUhFRUUEBgYybtw4m1zf1dWV8PBwrl69apPrj3ZkI1JxcTFhYWE2LWPu3LlcvXpV6XPrBVmI1NHRQXFxMeHh4TYt5/HHH0en0426fVmGA1mIVFBQQFtb25AWaLeGJUuWAHD69GmbljMakYVIFy9eBCAqKsqm5UyaNImIiAhyc3NtWs5oRBYinT9/npCQEPz9/W1eVmxsLOfPn1eWuvkGo1oknU7HrVu3OHXqFMuWLZOsk7YvjEYj3/ve9zCZTLz55pvKFKVujGqR9Ho9u3fvpqOjg4kTJ9p8NmxLSwu3b98mLCyMgwcPKgPdujGqRWpvb+df//oXQUFBhIaGDlu5K1asQKfTceLEiWErc6QzakUSBIGdO3dSV1dHbGzssO6VEhkZSVBQEHv37qW5uXnYyh3JjFqRXnnlFd577z2efPJJZs2aNaxlq1QqVq9eTVVVFa+++uqwlj1SGXUimUwmtmzZQkZGBs899xzx8fF2iSMsLIw1a9awd+9ezpw5Y5cYRhI2FclkMrFjxw4SEhJ4/vnnxaEeg6WhoYGf/vSn7Nu3jxdeeIE33nhj2Lal6I0dO3YQERFBfHw8ly9ftlscIwGb/hXOnj1LR0cHR48eZcuWLbzxxhsDOl8QBJqamigsLGTv3r3MmjWL48ePs3//fg4cOGBXiaBrT7fc3Fx8fX2Jjo4mLS2NGzdu0NjYOOb642z6l7h+/brYrTBv3jxu3bo1oPOTkpLQaDRERkby0ksvMW3aNC5fvszmzZttEe6gCAoK4tKlS8THx7N7926ioqLw8vJizZo19g5tWFEJNvzqbN26le9///tER0cDsGzZMs6ePdtjEazuW0iUlZUxdepUW4XTJzqdbsQue2zv2O7fvy8uvG8Jm84icXd3p6WlRXxtMpkeWUktISGBhIQEW4bRL3FxcRw7dsyuMfTFSI6tOzat2h5//HHy8/OBrh56Ww/zULAfNs1ITz/9NBcvXmT16tUIgsDrr79uy+IU7IhNRXJwcCAjI8OWRUiCvatWS4zk2Lpj05tthbHDqGvZVhiZKCIpSIKsFpGwhMlk4pVXXqGkpAQXFxcyMzP51re+JX6emZnJjRs3UKvVABw8eBAPD48RE9+///1v3nrrLaBrU8X09PRhHfHQL8IYIS8vT0hLSxMEQRC++OIL4Ve/+lWPz1evXi18/fXX9ghNEATL8TU1NQk//OEPxfjeeecdu8baG2OmarPUXWMymaioqGDHjh2sXr2af/zjHyMqvi+++ILw8HB27dpFYmIivr6+NlssY7CMmaqtubkZd3d38bWjoyNGoxEnJydaW1tJSkpi/fr1dHZ2sm7dOiIjI3nsscdGRHw6nY4rV65w/Phxxo8fz9q1a5k3b55dupP6YsxkJEvdNW5ubqxbtw43Nzfc3d1ZtGgR//nPf0ZMfF5eXsyePRs/Pz/UajXz588X1zoYKYwZkSx115SXl5OYmEhnZycGg4EbN24M+6hLS/FFRkZSWlpKfX09RqORmzdv2nx6+kAZMw2S5qei0tJSsbsmPz+f4OBgnnzySbKzszl16hTOzs6sXLly2IeB9BffP//5Tw4fPgx0TT7YuHHjsMbXH2NGJAXbMmaqNgXbooikIAmKSAqSoIikIAmKSAqSoIg0BBoaGli8eLG4QOmGDRvEzwoKCsjKygLg0KFD/Pa3vyUlJYXt27fLcuv3MdNFYguOHj1KSkoK7777bp+LfJ07d462tjZ27twJdK3l9ODBA0JCQoYxUtujZKRBYjAYOH36NLGxsXR2dlJaWvrIMSqViv/+978sWLBAfG/ZsmWykwgUkQZNbm4uJpOJjIwMOjs7yc7OpqOjA5PJBHTt7ebt7c2MGTP47LPPxPOOHz8uy+ndSsv2IElKSmL//v34+fkhCAJr1qxh1apV5OXlodFoMBqNvPbaa3h6erJ//37Ky8txcXHBzc2Nbdu24eLiYu9fQVIUkRQkQanaFCRBEUlBEhSRFCRBEUlBEhSRFCRBEUlBEv4fWgRQvmHLW8AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 129.6x129.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "sns.set_style('white')\n",
    "sns.set_style(\"ticks\", {\"xtick.major.size\": 15, \"ytick.major.size\": 15})\n",
    "# from scipy.special import ndtr\n",
    "plt.figure(figsize=(1.8,1.8))\n",
    "sns.distplot(auc_both_rand,label='permuted labels',color='k')\n",
    "z_both = (auc_both-np.mean(auc_both_rand))/np.std(auc_both_rand)\n",
    "print(z_both)\n",
    "print(norm.sf(z_both))\n",
    "z_both_noseeds = (auc_both_noseeds-np.mean(auc_both_rand))/np.std(auc_both_rand)\n",
    "print(z_both_noseeds)\n",
    "print(norm.sf(z_both_noseeds))\n",
    "plt.vlines(auc_both,0,4,color='#A81012',label='z_ASD-CHD')\n",
    "plt.vlines(auc_both_noseeds,0,4,color='#FF4F4F',label='z_ASD-CHD\\nno seeds')\n",
    "plt.legend(loc='upper right',fontsize=6)\n",
    "plt.xlabel('AUC',fontsize=8)\n",
    "plt.ylabel('count',fontsize=8)\n",
    "# plt.savefig('../../manuscript/figures_1911/Supplement/DECIPHER_SNV_INDEL_AUC_'+str(dec_thresh)+'.png',dpi=300,bbox_inches='tight')\n",
    "# plt.savefig('../../manuscript/figures_1911/Supplement/DECIPHER_SNV_INDEL_AUC_'+str(dec_thresh)+'.svg',dpi=300,bbox_inches='tight')\n",
    "\n",
    "# plt.savefig('/Users/brinrosenthal/Google Drive/UCSD/CCBB_tickets/Ideker_17_05_neuro/manuscript/figures_revisions_2102/Figure3/DECIPHER_11.1_SNV_INDEL_AUC_'+str(dec_thresh)+'.png',dpi=300,bbox_inches='tight')\n",
    "# plt.savefig('/Users/brinrosenthal/Google Drive/UCSD/CCBB_tickets/Ideker_17_05_neuro/manuscript/figures_revisions_2102/Figure3/DECIPHER_11.1_SNV_INDEL_AUC_'+str(dec_thresh)+'.svg',dpi=300,bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>FPR_both</th>\n",
       "      <th>FPR_both_noseeds</th>\n",
       "      <th>FPR_both_rand</th>\n",
       "      <th>TPR_both</th>\n",
       "      <th>TPR_both_noseeds</th>\n",
       "      <th>TPR_both_rand</th>\n",
       "      <th>thresh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.038223</td>\n",
       "      <td>0.036032</td>\n",
       "      <td>0.019576</td>\n",
       "      <td>0.220903</td>\n",
       "      <td>0.148052</td>\n",
       "      <td>0.014252</td>\n",
       "      <td>3.026013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    FPR_both  FPR_both_noseeds  FPR_both_rand  TPR_both  TPR_both_noseeds  \\\n",
       "96  0.038223          0.036032       0.019576  0.220903          0.148052   \n",
       "\n",
       "    TPR_both_rand    thresh  \n",
       "96       0.014252  3.026013  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 129.6x129.6 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve,auc\n",
    "sns.set_style('white')\n",
    "sns.set_style(\"ticks\", {\"xtick.major.size\": 15, \"ytick.major.size\": 15})\n",
    "threshlist = np.linspace(-17,400,2000)\n",
    "ROC_df = pd.DataFrame({'thresh':threshlist,\n",
    "                      'FPR_both':FPR_both,'TPR_both':TPR_both,\n",
    "                      'FPR_both_noseeds':FPR_both_noseeds,'TPR_both_noseeds':TPR_both_noseeds,\n",
    "                      'FPR_both_rand':FPR_both_rand,'TPR_both_rand':TPR_both_rand})\n",
    "ROC_df.head()\n",
    "plt.figure(figsize=(1.8,1.8))\n",
    "plt.plot(FPR_both_noseeds,TPR_both_noseeds,'.-',color='#FF4F4F',label='$z_{ASD-CHD}$, no seeds',lw=.5,ms=2)\n",
    "plt.plot(FPR_both,TPR_both,'.-',color='#A81012',label='$z_{ASD-CHD}$, all',lw=.5,ms=2)\n",
    "\n",
    "\n",
    "# circle z=3 \n",
    "ROC_z3 = ROC_df[(ROC_df['thresh']>2.9)&(ROC_df['thresh']<3.1)]\n",
    "display(ROC_z3)\n",
    "plt.plot(ROC_z3['FPR_both'],ROC_z3['TPR_both'],'o',ms=4,color='k',fillstyle='none',label='$z=3$')\n",
    "plt.plot(ROC_z3['FPR_both_noseeds'],ROC_z3['TPR_both_noseeds'],'o',ms=4,color='k',fillstyle='none',label='')\n",
    "\n",
    "# plt.plot(FPR_both_rand,TPR_both_rand,'k.-')\n",
    "plt.plot([0,1],[0,1],'k--',alpha=.5)\n",
    "plt.legend(loc='upper left', fontsize=6,frameon=False)\n",
    "plt.xlabel('DECIPHER false positive rate',fontsize=8)\n",
    "plt.ylabel('DECIPHER true positive rate',fontsize=8)\n",
    "plt.xticks(fontsize=8)\n",
    "plt.yticks(fontsize=8)\n",
    "\n",
    "plt.xlim([-.03,1.1])\n",
    "plt.ylim([-.03,1.1])\n",
    "\n",
    "# plt.savefig('/Users/brinrosenthal/Google Drive/UCSD/CCBB_tickets/Ideker_17_05_neuro/manuscript/figures_revisions_2102/Figure3/DECIPHER_11.1_SNV_INDEL_ROC_'+str(dec_thresh)+'.png',dpi=300,bbox_inches='tight')\n",
    "# plt.savefig('/Users/brinrosenthal/Google Drive/UCSD/CCBB_tickets/Ideker_17_05_neuro/manuscript/figures_revisions_2102/Figure3/DECIPHER_11.1_SNV_INDEL_ROC_'+str(dec_thresh)+'.svg',dpi=300,bbox_inches='tight')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}