analysis-final.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import data_processing as data\n",
    "import HBA_analysis as hba\n",
    "import plotting\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "negraes = data.get_genelist('negraes')\n",
    "duncan = data.get_genelist('duncan')\n",
    "watson_coding = data.get_genelist('watson_protein_coding')\n",
    "lutterAN = data.get_genelist('lutterAN')\n",
    "lutterBN = data.get_genelist('lutterBN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed HBA brain dataset found locally. Loading from data/processed/adult_brainarea_vs_genes_exp_reannotator_donors_10021-9861-14380-15697-15496-12876.tsv\n",
      "Processed HBA brain dataset found locally. Loading from data/processed/fetal_brainarea_vs_genes_exp_reannotator_donors_12566-12690-12840-14751.tsv\n"
     ]
    }
   ],
   "source": [
    "adult_exp = data.get_dataset(dataset='adult', probes_strategy='reannotator')\n",
    "fetal_exp = data.get_dataset(dataset='fetal', probes_strategy='reannotator', \n",
    "                             selected_donor_ids=['12566', '12690', '12840', '14751'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results with Negraes gene list\n",
    "## Adult human brain data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You submitted a gene list with 360 genes.\n",
      "    289 of those genes are present in the reference dataset.\n",
      "    Genes not found in our reference data: ['AC093850.2' 'ACTG1P11' 'ADGRE4P' 'ADGRF5P1' 'ARMC4P1' 'C16orf47'\n",
      " 'C4orf50' 'CASC16' 'EEF1DP5' 'ERICH5' 'FAM27E4' 'FOXD4L1' 'HCG24' 'HLA-U'\n",
      " 'HMGN2P30' 'HOXB' 'IMPA1P' 'ITM2BP1' 'KRR1P1' 'KRT8P50' 'LDHAP3' 'LDHAP7'\n",
      " 'LINC00106' 'LINC00441' 'LINC00565' 'LINC00920' 'LINC00941' 'LINC01123'\n",
      " 'LINC01252' 'LINC01340' 'LINC01549' 'LMNTD1' 'LMNTD2' 'MICF'\n",
      " 'MIR4435-2HG' 'MIR4458HG' 'MIR4712' 'MRPS31P2' 'MTND4P24' 'MYO5BP2'\n",
      " 'NEFHP1' 'OGFRP1' 'OR7E12P' 'PARP4P1' 'PGBD4P3' 'PPIEL' 'PSMD10P1'\n",
      " 'PSPHP1' 'RN7SKP234' 'RNA5SP195' 'RNA5SP323' 'RNU6-1095P' 'RNU6-125P'\n",
      " 'RNU6-476P' 'RNU6-703P' 'RP4-747G18.5' 'RPL21P43' 'RPL23AP69' 'RPL39P5'\n",
      " 'RPSAP53' 'SETP17' 'SLC10A5P1' 'SLC9A7P1' 'SNAI1P1' 'TIMM9P2' 'TPT1P10'\n",
      " 'TRPC6P' 'TTC6' 'UBE2Q2P6' 'VTRNA1-3' 'ZSWIM5P2']\n"
     ]
    }
   ],
   "source": [
    "results_adult_negraes = hba.generate_stats_table(exp_df=adult_exp, gene_list=negraes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "brain_areas = ['lateral parabrachial nucleus', 'medial parabrachial nucleus',\n",
    "               'paraventricular nucleus of the hypothalamus', 'arcuate nucleus of the hypothalamus',\n",
    "               'raphe nuclei of medulla', 'pontine raphe nucleus', 'subcallosal cingulate gyrus', 'nucleus accumbens', \n",
    "               'ventral tegmental area', 'bed  nucleus of stria terminalis', 'central nucleus']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "brain_area_labels = ['lateral parabrachial nucleus **', 'medial parabrachial nucleus **',\n",
    "               'paraventricular nucleus of the hypothalamus', 'arcuate nucleus of the hypothalamus',\n",
    "               'raphe nuclei of medulla **', 'pontine raphe nucleus **', 'subcallosal cingulate gyrus', 'nucleus accumbens', \n",
    "               'ventral tegmental area **', 'bed  nucleus of stria terminalis', 'central nucleus']\n",
    "sns.set_style(\"white\")\n",
    "plotting.make_violins(adult_exp, brain_areas, negraes, brain_area_labels, output='adult_negraes_violins.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>AUROC</th>\n",
       "      <th>p</th>\n",
       "      <th>pFDR</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lateral parabrachial nucleus</td>\n",
       "      <td>0.579242</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>medial parabrachial nucleus</td>\n",
       "      <td>0.570097</td>\n",
       "      <td>0.000041</td>\n",
       "      <td>0.000253</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>paraventricular nucleus of the hypothalamus</td>\n",
       "      <td>0.522800</td>\n",
       "      <td>0.182426</td>\n",
       "      <td>0.345142</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>arcuate nucleus of the hypothalamus</td>\n",
       "      <td>0.515532</td>\n",
       "      <td>0.363715</td>\n",
       "      <td>0.520171</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>raphe nuclei of medulla</td>\n",
       "      <td>0.564662</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000804</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pontine raphe nucleus</td>\n",
       "      <td>0.573857</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>subcallosal cingulate gyrus</td>\n",
       "      <td>0.493245</td>\n",
       "      <td>0.692814</td>\n",
       "      <td>0.781519</td>\n",
       "      <td>137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nucleus accumbens</td>\n",
       "      <td>0.522924</td>\n",
       "      <td>0.180057</td>\n",
       "      <td>0.345142</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ventral tegmental area</td>\n",
       "      <td>0.572880</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bed  nucleus of stria terminalis</td>\n",
       "      <td>0.514136</td>\n",
       "      <td>0.408418</td>\n",
       "      <td>0.567384</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>central nucleus</td>\n",
       "      <td>0.507887</td>\n",
       "      <td>0.644633</td>\n",
       "      <td>0.751532</td>\n",
       "      <td>106.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                AUROC         p      pFDR  \\\n",
       "lateral parabrachial nucleus                 0.579242  0.000004  0.000031   \n",
       "medial parabrachial nucleus                  0.570097  0.000041  0.000253   \n",
       "paraventricular nucleus of the hypothalamus  0.522800  0.182426  0.345142   \n",
       "arcuate nucleus of the hypothalamus          0.515532  0.363715  0.520171   \n",
       "raphe nuclei of medulla                      0.564662  0.000156  0.000804   \n",
       "pontine raphe nucleus                        0.573857  0.000016  0.000121   \n",
       "subcallosal cingulate gyrus                  0.493245  0.692814  0.781519   \n",
       "nucleus accumbens                            0.522924  0.180057  0.345142   \n",
       "ventral tegmental area                       0.572880  0.000020  0.000147   \n",
       "bed  nucleus of stria terminalis             0.514136  0.408418  0.567384   \n",
       "central nucleus                              0.507887  0.644633  0.751532   \n",
       "\n",
       "                                              rank  \n",
       "lateral parabrachial nucleus                   1.0  \n",
       "medial parabrachial nucleus                    9.0  \n",
       "paraventricular nucleus of the hypothalamus   75.0  \n",
       "arcuate nucleus of the hypothalamus           93.0  \n",
       "raphe nuclei of medulla                       15.0  \n",
       "pontine raphe nucleus                          2.0  \n",
       "subcallosal cingulate gyrus                  137.0  \n",
       "nucleus accumbens                             74.0  \n",
       "ventral tegmental area                         4.0  \n",
       "bed  nucleus of stria terminalis              96.0  \n",
       "central nucleus                              106.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_adult_negraes.loc[brain_areas]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetal brain data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subgenual cingulate and central amygdala have multiple subsamples\n",
    "subgenual_cingulate_cortex = ['IZ in subgenual (subcallosal) cingulate cortex', 'VZ in subgenual cingulate neocortex',\n",
    "                              'SZ in subgenual cingulate cortex', 'SP in subgenual (subcallosal) cingulate cortex',\n",
    "                              'outer CP in subgenual (subcallosal) cingulate cortex',\n",
    "                              'inner CP in subgenual (subcallosal) cingulate cortex']\n",
    "\n",
    "central_amygdala = ['central nuclear group',\n",
    "                    'medial subdivision of central nucleus']\n",
    "\n",
    "# generate new columns that are aggregates of smaller sampled areas\n",
    "fetal_exp['subgenual_cingulate_cortex'] = fetal_exp.loc[:, subgenual_cingulate_cortex].mean(axis=1)\n",
    "fetal_exp['central_amygdala'] = fetal_exp.loc[:, central_amygdala].mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# slight differences in naming conventions in comparison to adult...\n",
    "fetal_brain_areas = ['lateral parabrachial nucleus', 'medial parabrachial nucleus',\n",
    "                     'paraventricular nucleus of hypothalamus', 'arcuate nucleus of hypothalamus',\n",
    "                     'dorsal raphe nucleus', 'raphe magnus nucleus', 'raphe obscurus nucleus', 'subgenual_cingulate_cortex', \n",
    "                     'core of nucleus accumbens', 'ventral tegmental area', 'bed nucleus of stria terminalis', \n",
    "                      'central_amygdala', 'solitary nucleus']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fetal_labels = ['lateral parabrachial nucleus **', 'medial parabrachial nucleus **', \n",
    "          'paraventricular nucleus of hypothalamus', 'arcuate nucleus of hypothalamus',\n",
    "          'dorsal raphe nucleus **', 'raphe magnus nucleus **', 'raphe obscurus nucleus **', 'subgenual cingulate cortex **',\n",
    "          'core of nucleus accumbens',\n",
    "          'ventral tegmental area **', 'bed nucleus of stria terminalis', \n",
    "          'cental amygdala **', 'solitary nucleus **']\n",
    "\n",
    "plotting.make_violins(fetal_exp, fetal_brain_areas, negraes, brain_area_labels=fetal_labels, output='fetal_negraes_violins.pdf')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results with Watson gene list\n",
    "## Adult human data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You submitted a gene list with 107 genes.\n",
      "    105 of those genes are present in the reference dataset.\n",
      "    Genes not found in our reference data: ['XXcos-LUCA11.5' 'RP11-949J7.8']\n"
     ]
    }
   ],
   "source": [
    "results_adult_watson_protein_coding = hba.generate_stats_table(exp_df=adult_exp, gene_list=watson_coding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>AUROC</th>\n",
       "      <th>p</th>\n",
       "      <th>pFDR</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lateral parabrachial nucleus</td>\n",
       "      <td>0.431583</td>\n",
       "      <td>0.015419</td>\n",
       "      <td>0.157477</td>\n",
       "      <td>215.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>medial parabrachial nucleus</td>\n",
       "      <td>0.485963</td>\n",
       "      <td>0.619205</td>\n",
       "      <td>0.816225</td>\n",
       "      <td>168.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>paraventricular nucleus of the hypothalamus</td>\n",
       "      <td>0.528956</td>\n",
       "      <td>0.305265</td>\n",
       "      <td>0.689554</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>arcuate nucleus of the hypothalamus</td>\n",
       "      <td>0.603365</td>\n",
       "      <td>0.000252</td>\n",
       "      <td>0.014642</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>raphe nuclei of medulla</td>\n",
       "      <td>0.471105</td>\n",
       "      <td>0.306278</td>\n",
       "      <td>0.689554</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pontine raphe nucleus</td>\n",
       "      <td>0.527921</td>\n",
       "      <td>0.322873</td>\n",
       "      <td>0.689554</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>subcallosal cingulate gyrus</td>\n",
       "      <td>0.560881</td>\n",
       "      <td>0.031119</td>\n",
       "      <td>0.267397</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nucleus accumbens</td>\n",
       "      <td>0.456595</td>\n",
       "      <td>0.124344</td>\n",
       "      <td>0.496342</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ventral tegmental area</td>\n",
       "      <td>0.493528</td>\n",
       "      <td>0.818764</td>\n",
       "      <td>0.918407</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bed  nucleus of stria terminalis</td>\n",
       "      <td>0.545678</td>\n",
       "      <td>0.105815</td>\n",
       "      <td>0.496342</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>central nucleus</td>\n",
       "      <td>0.493574</td>\n",
       "      <td>0.820014</td>\n",
       "      <td>0.918407</td>\n",
       "      <td>152.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                AUROC         p      pFDR  \\\n",
       "lateral parabrachial nucleus                 0.431583  0.015419  0.157477   \n",
       "medial parabrachial nucleus                  0.485963  0.619205  0.816225   \n",
       "paraventricular nucleus of the hypothalamus  0.528956  0.305265  0.689554   \n",
       "arcuate nucleus of the hypothalamus          0.603365  0.000252  0.014642   \n",
       "raphe nuclei of medulla                      0.471105  0.306278  0.689554   \n",
       "pontine raphe nucleus                        0.527921  0.322873  0.689554   \n",
       "subcallosal cingulate gyrus                  0.560881  0.031119  0.267397   \n",
       "nucleus accumbens                            0.456595  0.124344  0.496342   \n",
       "ventral tegmental area                       0.493528  0.818764  0.918407   \n",
       "bed  nucleus of stria terminalis             0.545678  0.105815  0.496342   \n",
       "central nucleus                              0.493574  0.820014  0.918407   \n",
       "\n",
       "                                              rank  \n",
       "lateral parabrachial nucleus                 215.0  \n",
       "medial parabrachial nucleus                  168.0  \n",
       "paraventricular nucleus of the hypothalamus   55.0  \n",
       "arcuate nucleus of the hypothalamus            1.0  \n",
       "raphe nuclei of medulla                      189.0  \n",
       "pontine raphe nucleus                         58.0  \n",
       "subcallosal cingulate gyrus                    7.0  \n",
       "nucleus accumbens                            200.0  \n",
       "ventral tegmental area                       153.0  \n",
       "bed  nucleus of stria terminalis              18.0  \n",
       "central nucleus                              152.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_adult_watson_protein_coding.loc[brain_areas]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetal human data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You submitted a gene list with 107 genes.\n",
      "    105 of those genes are present in the reference dataset.\n",
      "    Genes not found in our reference data: ['XXcos-LUCA11.5' 'RP11-949J7.8']\n"
     ]
    }
   ],
   "source": [
    "results_fetal_watson_coding = hba.generate_stats_table(exp_df=fetal_exp, gene_list=watson_coding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>AUROC</th>\n",
       "      <th>p</th>\n",
       "      <th>pFDR</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lateral parabrachial nucleus</td>\n",
       "      <td>0.451671</td>\n",
       "      <td>0.087055</td>\n",
       "      <td>0.191078</td>\n",
       "      <td>406.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>medial parabrachial nucleus</td>\n",
       "      <td>0.448790</td>\n",
       "      <td>0.069807</td>\n",
       "      <td>0.164364</td>\n",
       "      <td>414.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>paraventricular nucleus of hypothalamus</td>\n",
       "      <td>0.529829</td>\n",
       "      <td>0.290917</td>\n",
       "      <td>0.470922</td>\n",
       "      <td>173.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>arcuate nucleus of hypothalamus</td>\n",
       "      <td>0.491424</td>\n",
       "      <td>0.761400</td>\n",
       "      <td>0.858323</td>\n",
       "      <td>303.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>dorsal raphe nucleus</td>\n",
       "      <td>0.420370</td>\n",
       "      <td>0.004811</td>\n",
       "      <td>0.030725</td>\n",
       "      <td>477.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>raphe magnus nucleus</td>\n",
       "      <td>0.546348</td>\n",
       "      <td>0.100800</td>\n",
       "      <td>0.214875</td>\n",
       "      <td>127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>raphe obscurus nucleus</td>\n",
       "      <td>0.422812</td>\n",
       "      <td>0.006278</td>\n",
       "      <td>0.035734</td>\n",
       "      <td>471.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>subgenual_cingulate_cortex</td>\n",
       "      <td>0.484943</td>\n",
       "      <td>0.593968</td>\n",
       "      <td>0.730821</td>\n",
       "      <td>321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>core of nucleus accumbens</td>\n",
       "      <td>0.559212</td>\n",
       "      <td>0.036041</td>\n",
       "      <td>0.103055</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ventral tegmental area</td>\n",
       "      <td>0.506054</td>\n",
       "      <td>0.830282</td>\n",
       "      <td>0.917028</td>\n",
       "      <td>249.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bed nucleus of stria terminalis</td>\n",
       "      <td>0.461253</td>\n",
       "      <td>0.170107</td>\n",
       "      <td>0.311362</td>\n",
       "      <td>386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>central_amygdala</td>\n",
       "      <td>0.449974</td>\n",
       "      <td>0.076523</td>\n",
       "      <td>0.173743</td>\n",
       "      <td>410.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>solitary nucleus</td>\n",
       "      <td>0.550961</td>\n",
       "      <td>0.071178</td>\n",
       "      <td>0.166833</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            AUROC         p      pFDR   rank\n",
       "lateral parabrachial nucleus             0.451671  0.087055  0.191078  406.0\n",
       "medial parabrachial nucleus              0.448790  0.069807  0.164364  414.0\n",
       "paraventricular nucleus of hypothalamus  0.529829  0.290917  0.470922  173.0\n",
       "arcuate nucleus of hypothalamus          0.491424  0.761400  0.858323  303.0\n",
       "dorsal raphe nucleus                     0.420370  0.004811  0.030725  477.0\n",
       "raphe magnus nucleus                     0.546348  0.100800  0.214875  127.0\n",
       "raphe obscurus nucleus                   0.422812  0.006278  0.035734  471.0\n",
       "subgenual_cingulate_cortex               0.484943  0.593968  0.730821  321.0\n",
       "core of nucleus accumbens                0.559212  0.036041  0.103055   95.0\n",
       "ventral tegmental area                   0.506054  0.830282  0.917028  249.0\n",
       "bed nucleus of stria terminalis          0.461253  0.170107  0.311362  386.0\n",
       "central_amygdala                         0.449974  0.076523  0.173743  410.0\n",
       "solitary nucleus                         0.550961  0.071178  0.166833  116.0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_fetal_watson_coding.loc[fetal_brain_areas]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results with Duncan gene list\n",
    "## The following cells should have the stats (pvalues) done with R, since the gene list contains only 6 genes (<20)\n",
    "due to known approximation in SciPy:\n",
    "https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mannwhitneyu.html\n",
    "\n",
    "## Adult human data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You submitted a gene list with 6 genes.\n",
      "    6 of those genes are present in the reference dataset.\n",
      "    Genes not found in our reference data: []\n"
     ]
    }
   ],
   "source": [
    "results_adult_duncan = hba.generate_stats_table(exp_df=adult_exp, gene_list=duncan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\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>AUROC</th>\n",
       "      <th>p</th>\n",
       "      <th>pFDR</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lateral parabrachial nucleus</td>\n",
       "      <td>0.738253</td>\n",
       "      <td>0.043252</td>\n",
       "      <td>0.856897</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>medial parabrachial nucleus</td>\n",
       "      <td>0.582003</td>\n",
       "      <td>0.486636</td>\n",
       "      <td>0.997377</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>paraventricular nucleus of the hypothalamus</td>\n",
       "      <td>0.475195</td>\n",
       "      <td>0.833351</td>\n",
       "      <td>0.997377</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>arcuate nucleus of the hypothalamus</td>\n",
       "      <td>0.504817</td>\n",
       "      <td>0.967428</td>\n",
       "      <td>0.997377</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>raphe nuclei of medulla</td>\n",
       "      <td>0.549609</td>\n",
       "      <td>0.673868</td>\n",
       "      <td>0.997377</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pontine raphe nucleus</td>\n",
       "      <td>0.571354</td>\n",
       "      <td>0.544962</td>\n",
       "      <td>0.997377</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>subcallosal cingulate gyrus</td>\n",
       "      <td>0.544105</td>\n",
       "      <td>0.708294</td>\n",
       "      <td>0.997377</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nucleus accumbens</td>\n",
       "      <td>0.588514</td>\n",
       "      <td>0.452709</td>\n",
       "      <td>0.997377</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ventral tegmental area</td>\n",
       "      <td>0.687860</td>\n",
       "      <td>0.110993</td>\n",
       "      <td>0.975949</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bed  nucleus of stria terminalis</td>\n",
       "      <td>0.539991</td>\n",
       "      <td>0.734426</td>\n",
       "      <td>0.997377</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>central nucleus</td>\n",
       "      <td>0.531859</td>\n",
       "      <td>0.786968</td>\n",
       "      <td>0.997377</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                AUROC         p      pFDR  \\\n",
       "lateral parabrachial nucleus                 0.738253  0.043252  0.856897   \n",
       "medial parabrachial nucleus                  0.582003  0.486636  0.997377   \n",
       "paraventricular nucleus of the hypothalamus  0.475195  0.833351  0.997377   \n",
       "arcuate nucleus of the hypothalamus          0.504817  0.967428  0.997377   \n",
       "raphe nuclei of medulla                      0.549609  0.673868  0.997377   \n",
       "pontine raphe nucleus                        0.571354  0.544962  0.997377   \n",
       "subcallosal cingulate gyrus                  0.544105  0.708294  0.997377   \n",
       "nucleus accumbens                            0.588514  0.452709  0.997377   \n",
       "ventral tegmental area                       0.687860  0.110993  0.975949   \n",
       "bed  nucleus of stria terminalis             0.539991  0.734426  0.997377   \n",
       "central nucleus                              0.531859  0.786968  0.997377   \n",
       "\n",
       "                                              rank  \n",
       "lateral parabrachial nucleus                   6.0  \n",
       "medial parabrachial nucleus                   48.0  \n",
       "paraventricular nucleus of the hypothalamus  149.0  \n",
       "arcuate nucleus of the hypothalamus          122.0  \n",
       "raphe nuclei of medulla                       70.0  \n",
       "pontine raphe nucleus                         55.0  \n",
       "subcallosal cingulate gyrus                   75.0  \n",
       "nucleus accumbens                             43.0  \n",
       "ventral tegmental area                        14.0  \n",
       "bed  nucleus of stria terminalis              79.0  \n",
       "central nucleus                               85.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_adult_duncan.loc[brain_areas, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetal human data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You submitted a gene list with 6 genes.\n",
      "    6 of those genes are present in the reference dataset.\n",
      "    Genes not found in our reference data: []\n"
     ]
    }
   ],
   "source": [
    "results_fetal_duncan = hba.generate_stats_table(exp_df=fetal_exp, gene_list=duncan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>AUROC</th>\n",
       "      <th>p</th>\n",
       "      <th>pFDR</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lateral parabrachial nucleus</td>\n",
       "      <td>0.492267</td>\n",
       "      <td>0.947719</td>\n",
       "      <td>0.974044</td>\n",
       "      <td>285.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>medial parabrachial nucleus</td>\n",
       "      <td>0.380338</td>\n",
       "      <td>0.310030</td>\n",
       "      <td>0.922964</td>\n",
       "      <td>440.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>paraventricular nucleus of hypothalamus</td>\n",
       "      <td>0.575037</td>\n",
       "      <td>0.524405</td>\n",
       "      <td>0.971767</td>\n",
       "      <td>158.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>arcuate nucleus of hypothalamus</td>\n",
       "      <td>0.706146</td>\n",
       "      <td>0.080311</td>\n",
       "      <td>0.646235</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>dorsal raphe nucleus</td>\n",
       "      <td>0.494208</td>\n",
       "      <td>0.960838</td>\n",
       "      <td>0.979752</td>\n",
       "      <td>283.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>raphe magnus nucleus</td>\n",
       "      <td>0.517775</td>\n",
       "      <td>0.880162</td>\n",
       "      <td>0.971767</td>\n",
       "      <td>247.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>raphe obscurus nucleus</td>\n",
       "      <td>0.422574</td>\n",
       "      <td>0.511287</td>\n",
       "      <td>0.971767</td>\n",
       "      <td>406.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>subgenual_cingulate_cortex</td>\n",
       "      <td>0.478343</td>\n",
       "      <td>0.854247</td>\n",
       "      <td>0.971767</td>\n",
       "      <td>308.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>core of nucleus accumbens</td>\n",
       "      <td>0.521433</td>\n",
       "      <td>0.855736</td>\n",
       "      <td>0.971767</td>\n",
       "      <td>243.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ventral tegmental area</td>\n",
       "      <td>0.697998</td>\n",
       "      <td>0.093005</td>\n",
       "      <td>0.669121</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bed nucleus of stria terminalis</td>\n",
       "      <td>0.472871</td>\n",
       "      <td>0.817992</td>\n",
       "      <td>0.971767</td>\n",
       "      <td>321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>central_amygdala</td>\n",
       "      <td>0.517152</td>\n",
       "      <td>0.884334</td>\n",
       "      <td>0.971767</td>\n",
       "      <td>248.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>solitary nucleus</td>\n",
       "      <td>0.388487</td>\n",
       "      <td>0.344134</td>\n",
       "      <td>0.952982</td>\n",
       "      <td>432.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            AUROC         p      pFDR   rank\n",
       "lateral parabrachial nucleus             0.492267  0.947719  0.974044  285.0\n",
       "medial parabrachial nucleus              0.380338  0.310030  0.922964  440.0\n",
       "paraventricular nucleus of hypothalamus  0.575037  0.524405  0.971767  158.0\n",
       "arcuate nucleus of hypothalamus          0.706146  0.080311  0.646235   37.0\n",
       "dorsal raphe nucleus                     0.494208  0.960838  0.979752  283.0\n",
       "raphe magnus nucleus                     0.517775  0.880162  0.971767  247.0\n",
       "raphe obscurus nucleus                   0.422574  0.511287  0.971767  406.0\n",
       "subgenual_cingulate_cortex               0.478343  0.854247  0.971767  308.0\n",
       "core of nucleus accumbens                0.521433  0.855736  0.971767  243.0\n",
       "ventral tegmental area                   0.697998  0.093005  0.669121   41.0\n",
       "bed nucleus of stria terminalis          0.472871  0.817992  0.971767  321.0\n",
       "central_amygdala                         0.517152  0.884334  0.971767  248.0\n",
       "solitary nucleus                         0.388487  0.344134  0.952982  432.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_fetal_duncan.loc[fetal_brain_areas, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/derek_howard/miniconda3/envs/py37/lib/python3.7/site-packages/ipykernel_launcher.py:14: UserWarning: Tight layout not applied. tight_layout cannot make axes height small enough to accommodate all axes decorations\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x1080 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(5, 1, sharey=True, sharex=True, figsize=(9,15))\n",
    "plotting.make_dist_plot(adult_exp, 'lateral parabrachial nucleus', duncan, gene_text=True, ax=ax1)\n",
    "plotting.make_dist_plot(adult_exp, 'medial parabrachial nucleus', duncan, gene_text=False, ax=ax2)\n",
    "plotting.make_dist_plot(adult_exp, 'ventral tegmental area', duncan, gene_text=False, ax=ax3)\n",
    "plotting.make_dist_plot(fetal_exp, 'ventral tegmental area', duncan, gene_text=False, ax=ax4)\n",
    "plotting.make_dist_plot(fetal_exp, 'arcuate nucleus of hypothalamus', duncan, gene_text=False, ax=ax5)\n",
    "\n",
    "ax1.set_title('Lateral parabrachial nucleus of adult brain')\n",
    "ax2.set_title('Medial parabrachial nucleus of adult brain')\n",
    "ax3.set_title('Ventral tegmental area of adult brain')\n",
    "ax4.set_title('Ventral tegmental area of fetal brain')\n",
    "ax5.set_title('Arcuate nucleus of hypothalamus of fetal brain')\n",
    "ax5.set_xlabel('Expression (z-scored)')\n",
    "fig.tight_layout()\n",
    "#plt.savefig('./figures/fig6-density_plots.pdf', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results with Lutter gene lists\n",
    "## Adult human data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You submitted a gene list with 51 genes.\n",
      "    51 of those genes are present in the reference dataset.\n",
      "    Genes not found in our reference data: []\n"
     ]
    }
   ],
   "source": [
    "results_lutterAN_adult = hba.generate_stats_table(adult_exp, gene_list=lutterAN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>AUROC</th>\n",
       "      <th>p</th>\n",
       "      <th>pFDR</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lateral parabrachial nucleus</td>\n",
       "      <td>0.580136</td>\n",
       "      <td>0.047708</td>\n",
       "      <td>0.593068</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>medial parabrachial nucleus</td>\n",
       "      <td>0.576974</td>\n",
       "      <td>0.057192</td>\n",
       "      <td>0.593068</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>paraventricular nucleus of the hypothalamus</td>\n",
       "      <td>0.524248</td>\n",
       "      <td>0.549105</td>\n",
       "      <td>0.830265</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>arcuate nucleus of the hypothalamus</td>\n",
       "      <td>0.487144</td>\n",
       "      <td>0.750763</td>\n",
       "      <td>0.916722</td>\n",
       "      <td>133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>raphe nuclei of medulla</td>\n",
       "      <td>0.533397</td>\n",
       "      <td>0.409280</td>\n",
       "      <td>0.784478</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pontine raphe nucleus</td>\n",
       "      <td>0.554102</td>\n",
       "      <td>0.181308</td>\n",
       "      <td>0.593068</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>subcallosal cingulate gyrus</td>\n",
       "      <td>0.470767</td>\n",
       "      <td>0.470123</td>\n",
       "      <td>0.790352</td>\n",
       "      <td>170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nucleus accumbens</td>\n",
       "      <td>0.579041</td>\n",
       "      <td>0.050829</td>\n",
       "      <td>0.593068</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ventral tegmental area</td>\n",
       "      <td>0.569608</td>\n",
       "      <td>0.085458</td>\n",
       "      <td>0.593068</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bed  nucleus of stria terminalis</td>\n",
       "      <td>0.601267</td>\n",
       "      <td>0.012347</td>\n",
       "      <td>0.593068</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>central nucleus</td>\n",
       "      <td>0.554766</td>\n",
       "      <td>0.176009</td>\n",
       "      <td>0.593068</td>\n",
       "      <td>38.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                AUROC         p      pFDR  \\\n",
       "lateral parabrachial nucleus                 0.580136  0.047708  0.593068   \n",
       "medial parabrachial nucleus                  0.576974  0.057192  0.593068   \n",
       "paraventricular nucleus of the hypothalamus  0.524248  0.549105  0.830265   \n",
       "arcuate nucleus of the hypothalamus          0.487144  0.750763  0.916722   \n",
       "raphe nuclei of medulla                      0.533397  0.409280  0.784478   \n",
       "pontine raphe nucleus                        0.554102  0.181308  0.593068   \n",
       "subcallosal cingulate gyrus                  0.470767  0.470123  0.790352   \n",
       "nucleus accumbens                            0.579041  0.050829  0.593068   \n",
       "ventral tegmental area                       0.569608  0.085458  0.593068   \n",
       "bed  nucleus of stria terminalis             0.601267  0.012347  0.593068   \n",
       "central nucleus                              0.554766  0.176009  0.593068   \n",
       "\n",
       "                                              rank  \n",
       "lateral parabrachial nucleus                   9.0  \n",
       "medial parabrachial nucleus                   14.0  \n",
       "paraventricular nucleus of the hypothalamus   77.0  \n",
       "arcuate nucleus of the hypothalamus          133.0  \n",
       "raphe nuclei of medulla                       66.0  \n",
       "pontine raphe nucleus                         41.0  \n",
       "subcallosal cingulate gyrus                  170.0  \n",
       "nucleus accumbens                             10.0  \n",
       "ventral tegmental area                        17.0  \n",
       "bed  nucleus of stria terminalis               3.0  \n",
       "central nucleus                               38.0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_lutterAN_adult.loc[brain_areas, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You submitted a gene list with 80 genes.\n",
      "    80 of those genes are present in the reference dataset.\n",
      "    Genes not found in our reference data: []\n"
     ]
    }
   ],
   "source": [
    "results_lutterBN_adult = hba.generate_stats_table(adult_exp, gene_list=lutterBN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>AUROC</th>\n",
       "      <th>p</th>\n",
       "      <th>pFDR</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lateral parabrachial nucleus</td>\n",
       "      <td>0.456652</td>\n",
       "      <td>0.180089</td>\n",
       "      <td>0.611085</td>\n",
       "      <td>195.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>medial parabrachial nucleus</td>\n",
       "      <td>0.423790</td>\n",
       "      <td>0.018439</td>\n",
       "      <td>0.577450</td>\n",
       "      <td>230.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>paraventricular nucleus of the hypothalamus</td>\n",
       "      <td>0.467504</td>\n",
       "      <td>0.314945</td>\n",
       "      <td>0.669195</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>arcuate nucleus of the hypothalamus</td>\n",
       "      <td>0.482017</td>\n",
       "      <td>0.578141</td>\n",
       "      <td>0.808004</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>raphe nuclei of medulla</td>\n",
       "      <td>0.452275</td>\n",
       "      <td>0.139991</td>\n",
       "      <td>0.584880</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>pontine raphe nucleus</td>\n",
       "      <td>0.483489</td>\n",
       "      <td>0.609642</td>\n",
       "      <td>0.826589</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>subcallosal cingulate gyrus</td>\n",
       "      <td>0.501235</td>\n",
       "      <td>0.969550</td>\n",
       "      <td>0.990592</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>nucleus accumbens</td>\n",
       "      <td>0.426941</td>\n",
       "      <td>0.023868</td>\n",
       "      <td>0.577450</td>\n",
       "      <td>227.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ventral tegmental area</td>\n",
       "      <td>0.475317</td>\n",
       "      <td>0.445289</td>\n",
       "      <td>0.769607</td>\n",
       "      <td>163.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bed  nucleus of stria terminalis</td>\n",
       "      <td>0.441367</td>\n",
       "      <td>0.069811</td>\n",
       "      <td>0.577450</td>\n",
       "      <td>219.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>central nucleus</td>\n",
       "      <td>0.439456</td>\n",
       "      <td>0.061175</td>\n",
       "      <td>0.577450</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                AUROC         p      pFDR  \\\n",
       "lateral parabrachial nucleus                 0.456652  0.180089  0.611085   \n",
       "medial parabrachial nucleus                  0.423790  0.018439  0.577450   \n",
       "paraventricular nucleus of the hypothalamus  0.467504  0.314945  0.669195   \n",
       "arcuate nucleus of the hypothalamus          0.482017  0.578141  0.808004   \n",
       "raphe nuclei of medulla                      0.452275  0.139991  0.584880   \n",
       "pontine raphe nucleus                        0.483489  0.609642  0.826589   \n",
       "subcallosal cingulate gyrus                  0.501235  0.969550  0.990592   \n",
       "nucleus accumbens                            0.426941  0.023868  0.577450   \n",
       "ventral tegmental area                       0.475317  0.445289  0.769607   \n",
       "bed  nucleus of stria terminalis             0.441367  0.069811  0.577450   \n",
       "central nucleus                              0.439456  0.061175  0.577450   \n",
       "\n",
       "                                              rank  \n",
       "lateral parabrachial nucleus                 195.0  \n",
       "medial parabrachial nucleus                  230.0  \n",
       "paraventricular nucleus of the hypothalamus  174.0  \n",
       "arcuate nucleus of the hypothalamus          149.0  \n",
       "raphe nuclei of medulla                      200.0  \n",
       "pontine raphe nucleus                        148.0  \n",
       "subcallosal cingulate gyrus                  119.0  \n",
       "nucleus accumbens                            227.0  \n",
       "ventral tegmental area                       163.0  \n",
       "bed  nucleus of stria terminalis             219.0  \n",
       "central nucleus                              221.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_lutterBN_adult.loc[brain_areas, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fetal human data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You submitted a gene list with 51 genes.\n",
      "    51 of those genes are present in the reference dataset.\n",
      "    Genes not found in our reference data: []\n"
     ]
    }
   ],
   "source": [
    "results_lutterAN_fetal = hba.generate_stats_table(fetal_exp, gene_list=lutterAN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\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>AUROC</th>\n",
       "      <th>p</th>\n",
       "      <th>pFDR</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lateral parabrachial nucleus</td>\n",
       "      <td>0.565766</td>\n",
       "      <td>0.104182</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>medial parabrachial nucleus</td>\n",
       "      <td>0.533699</td>\n",
       "      <td>0.405066</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>paraventricular nucleus of hypothalamus</td>\n",
       "      <td>0.522146</td>\n",
       "      <td>0.584269</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>arcuate nucleus of hypothalamus</td>\n",
       "      <td>0.511004</td>\n",
       "      <td>0.785713</td>\n",
       "      <td>0.992928</td>\n",
       "      <td>213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>dorsal raphe nucleus</td>\n",
       "      <td>0.556313</td>\n",
       "      <td>0.164117</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>raphe magnus nucleus</td>\n",
       "      <td>0.547250</td>\n",
       "      <td>0.243033</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>raphe obscurus nucleus</td>\n",
       "      <td>0.522844</td>\n",
       "      <td>0.572479</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>subgenual_cingulate_cortex</td>\n",
       "      <td>0.452176</td>\n",
       "      <td>0.237360</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>465.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>core of nucleus accumbens</td>\n",
       "      <td>0.552371</td>\n",
       "      <td>0.195677</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ventral tegmental area</td>\n",
       "      <td>0.538522</td>\n",
       "      <td>0.341208</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bed nucleus of stria terminalis</td>\n",
       "      <td>0.526378</td>\n",
       "      <td>0.514582</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>central_amygdala</td>\n",
       "      <td>0.523653</td>\n",
       "      <td>0.558955</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>solitary nucleus</td>\n",
       "      <td>0.534512</td>\n",
       "      <td>0.393830</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            AUROC         p      pFDR   rank\n",
       "lateral parabrachial nucleus             0.565766  0.104182  0.987354   27.0\n",
       "medial parabrachial nucleus              0.533699  0.405066  0.987354  109.0\n",
       "paraventricular nucleus of hypothalamus  0.522146  0.584269  0.987354  153.0\n",
       "arcuate nucleus of hypothalamus          0.511004  0.785713  0.992928  213.0\n",
       "dorsal raphe nucleus                     0.556313  0.164117  0.987354   43.0\n",
       "raphe magnus nucleus                     0.547250  0.243033  0.987354   67.0\n",
       "raphe obscurus nucleus                   0.522844  0.572479  0.987354  148.0\n",
       "subgenual_cingulate_cortex               0.452176  0.237360  0.987354  465.0\n",
       "core of nucleus accumbens                0.552371  0.195677  0.987354   51.0\n",
       "ventral tegmental area                   0.538522  0.341208  0.987354   95.0\n",
       "bed nucleus of stria terminalis          0.526378  0.514582  0.987354  129.0\n",
       "central_amygdala                         0.523653  0.558955  0.987354  141.0\n",
       "solitary nucleus                         0.534512  0.393830  0.987354  103.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_lutterAN_fetal.loc[fetal_brain_areas, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You submitted a gene list with 80 genes.\n",
      "    80 of those genes are present in the reference dataset.\n",
      "    Genes not found in our reference data: []\n"
     ]
    }
   ],
   "source": [
    "results_lutterBN_fetal = hba.generate_stats_table(fetal_exp, gene_list=lutterBN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>AUROC</th>\n",
       "      <th>p</th>\n",
       "      <th>pFDR</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>lateral parabrachial nucleus</td>\n",
       "      <td>0.565766</td>\n",
       "      <td>0.104182</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>medial parabrachial nucleus</td>\n",
       "      <td>0.533699</td>\n",
       "      <td>0.405066</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>paraventricular nucleus of hypothalamus</td>\n",
       "      <td>0.522146</td>\n",
       "      <td>0.584269</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>153.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>arcuate nucleus of hypothalamus</td>\n",
       "      <td>0.511004</td>\n",
       "      <td>0.785713</td>\n",
       "      <td>0.992928</td>\n",
       "      <td>213.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>dorsal raphe nucleus</td>\n",
       "      <td>0.556313</td>\n",
       "      <td>0.164117</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>raphe magnus nucleus</td>\n",
       "      <td>0.547250</td>\n",
       "      <td>0.243033</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>raphe obscurus nucleus</td>\n",
       "      <td>0.522844</td>\n",
       "      <td>0.572479</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>subgenual_cingulate_cortex</td>\n",
       "      <td>0.452176</td>\n",
       "      <td>0.237360</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>465.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>core of nucleus accumbens</td>\n",
       "      <td>0.552371</td>\n",
       "      <td>0.195677</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ventral tegmental area</td>\n",
       "      <td>0.538522</td>\n",
       "      <td>0.341208</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bed nucleus of stria terminalis</td>\n",
       "      <td>0.526378</td>\n",
       "      <td>0.514582</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>central_amygdala</td>\n",
       "      <td>0.523653</td>\n",
       "      <td>0.558955</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>141.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>solitary nucleus</td>\n",
       "      <td>0.534512</td>\n",
       "      <td>0.393830</td>\n",
       "      <td>0.987354</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            AUROC         p      pFDR   rank\n",
       "lateral parabrachial nucleus             0.565766  0.104182  0.987354   27.0\n",
       "medial parabrachial nucleus              0.533699  0.405066  0.987354  109.0\n",
       "paraventricular nucleus of hypothalamus  0.522146  0.584269  0.987354  153.0\n",
       "arcuate nucleus of hypothalamus          0.511004  0.785713  0.992928  213.0\n",
       "dorsal raphe nucleus                     0.556313  0.164117  0.987354   43.0\n",
       "raphe magnus nucleus                     0.547250  0.243033  0.987354   67.0\n",
       "raphe obscurus nucleus                   0.522844  0.572479  0.987354  148.0\n",
       "subgenual_cingulate_cortex               0.452176  0.237360  0.987354  465.0\n",
       "core of nucleus accumbens                0.552371  0.195677  0.987354   51.0\n",
       "ventral tegmental area                   0.538522  0.341208  0.987354   95.0\n",
       "bed nucleus of stria terminalis          0.526378  0.514582  0.987354  129.0\n",
       "central_amygdala                         0.523653  0.558955  0.987354  141.0\n",
       "solitary nucleus                         0.534512  0.393830  0.987354  103.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_lutterAN_fetal.loc[fetal_brain_areas, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVG mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import SVG"
   ]
  },
  {
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414.98874,207.77781 416.88574,208.27781 C 418.78274,208.77781 420.31074,211.52781 421.31074,213.90281 C 422.31274,216.27781 423.26074,220.90281 422.89174,222.52781 C 422.52274,224.15281 418.99274,222.90281 419.09874,223.65281 C 419.20374,224.40281 423.20774,225.40281 423.52374,227.02781 C 423.83974,228.65281 422.99774,232.59081 420.99474,233.40281 C 418.99274,234.21581 414.46174,231.59081 411.51174,231.90281 C 408.56074,232.21581 401.76374,234.59081 403.29174,235.27781 C 404.81974,235.96481 418.20274,234.77781 420.67874,236.02781 C 423.15574,237.27781 419.41474,241.34081 418.14974,242.77781 C 416.88574,244.21481 413.56574,243.65281 413.09174,244.65281 C 412.61774,245.65281 415.93674,248.15281 415.30474,248.77781 C 414.67274,249.40281 409.98374,247.34081 409.29874,248.40281 C 408.61374,249.46581 411.56374,253.71581 411.19474,255.15281 C 410.82674,256.59081 408.19174,255.09081 407.08574,257.02781 C 405.97874,258.96481 405.24174,266.90281 404.55674,266.77781 C 403.87174,266.65281 403.87174,259.15281 402.97574,256.27781 C 402.07974,253.40281 400.44674,251.27781 399.18174,249.52781 C 397.91774,247.77781 395.28374,244.40281 395.38874,245.77781 C 395.49374,247.15281 398.54974,254.15281 399.81474,257.77781 C 401.07874,261.40281 402.81774,265.15281 402.97574,267.52781 C 403.13374,269.90281 402.23774,271.84081 400.76274,272.02781 C 399.28774,272.21481 395.07274,268.21581 394.12374,268.65281 C 393.17574,269.09081 396.07374,272.90281 395.07274,274.65281 C 394.07174,276.40281 389.90874,278.71581 388.11774,279.15281 C 386.32674,279.59081 384.58774,278.77781 384.32374,277.27781 C 384.06074,275.77781 386.37874,272.09081 386.53674,270.15281 C 386.69474,268.21581 385.27274,267.40281 385.27274,265.65281 C 385.27274,263.90281 387.11674,260.84081 386.53674,259.65281 C 385.95774,258.46581 382.63774,260.84081 381.79474,258.52781 C 380.95174,256.21481 382.11074,247.71481 381.47874,245.77781 C 380.84674,243.84081 379.16074,248.34081 378.00174,246.90281 C 376.84274,245.46581 374.47174,236.77781 374.52374,237.15281 C 374.57674,237.52781 378.26474,247.02781 378.31774,249.15281 C 378.37074,251.27781 374.47174,249.40281 374.83974,249.90281 C 375.20874,250.40281 379.73974,250.77781 380.53074,252.15281 C 381.32074,253.52781 379.16074,256.40281 379.58174,258.15281 C 380.00374,259.90281 382.90174,261.52781 383.05974,262.65281 C 383.21774,263.77781 380.37274,263.90281 380.53074,264.90281 C 380.68874,265.90281 383.63874,266.52781 384.00774,268.65281 C 384.37674,270.77781 382.90174,275.21581 382.74374,277.65281 C 382.58574,280.09081 384.16574,282.77781 383.05974,283.27781 C 381.95274,283.77781 378.26474,280.27781 376.10474,280.65281 C 373.94474,281.02781 371.57374,286.59081 370.09774,285.52781 C 368.62274,284.46481 367.25274,277.27781 367.25274,274.27781 C 367.25274,271.27781 370.36174,269.15281 370.09774,267.52781 C 369.83474,265.90281 366.09374,266.02781 365.67274,264.52781 C 365.25074,263.02781 367.99074,260.15281 367.56874,258.52781 C 367.14774,256.90281 363.51174,256.77781 363.14374,254.77781 C 362.77474,252.77781 365.67274,248.34081 365.35674,246.52781 C 365.03974,244.71481 362.35274,246.40281 361.24674,243.90281 C 360.13974,241.40281 358.87574,231.02781 358.71774,231.52781 C 358.55974,232.02781 360.50874,244.40281 360.29774,246.90281 C 360.08774,249.40281 357.29474,245.09081 357.45274,246.52781 C 357.61074,247.96481 361.08874,253.59081 361.24674,255.52781 C 361.40474,257.46481 357.87474,256.52781 358.40174,258.15281 C 358.92874,259.77781 363.61774,262.96481 364.40774,265.27781 C 365.19774,267.59081 363.88074,271.02781 363.14374,272.02781 C 362.40574,273.02781 359.66574,270.65281 359.98174,271.27781 C 360.29774,271.90281 364.56574,273.96481 365.03974,275.77781 C 365.51474,277.59081 363.19574,280.27781 362.82674,282.15281 C 362.45874,284.02781 363.72274,285.84081 362.82674,287.02781 C 361.93174,288.21481 358.98074,289.84081 357.45274,289.27781 C 355.92474,288.71481 354.34474,283.65281 353.65974,283.65281 C 352.97474,283.65281 354.34474,288.40281 353.34374,289.27781 C 352.34274,290.15281 348.81174,289.96581 347.65274,288.90281 C 346.49374,287.84081 346.86274,282.84081 346.38874,282.90281 C 345.91474,282.96581 345.91474,288.02781 344.80774,289.27781 C 343.70174,290.52781 340.01274,292.34081 339.74974,290.40281 C 339.48674,288.46581 343.06874,280.77781 343.22674,277.65281 C 343.38474,274.52781 340.90874,274.40281 340.69774,271.65281 C 340.48774,268.90281 341.06674,264.21581 341.96274,261.15281 C 342.85874,258.09081 345.75674,256.34081 346.07274,253.27781 C 346.38874,250.21481 343.27974,244.96481 343.85974,242.77781 C 344.43874,240.59081 349.18074,241.21581 349.54974,240.15281 C 349.91874,239.09081 347.23174,238.21581 346.07274,236.40281 C 344.91274,234.59081 343.01674,229.09081 342.59474,229.27781 C 342.17374,229.46481 343.91174,234.52781 343.54374,237.52781 C 343.17474,240.52781 341.48874,246.84081 340.38174,247.27781 C 339.27574,247.71481 337.00974,239.40281 336.90474,240.15281 C 336.79874,240.90281 339.59174,248.71481 339.74974,251.77781 C 339.90774,254.84081 337.95874,256.96481 337.85274,258.52781 C 337.74774,260.09081 339.01174,258.71581 339.11774,261.15281 C 339.22274,263.59081 339.11774,271.15281 338.48574,273.15281 C 337.85274,275.15281 335.11274,272.21581 335.32374,273.15281 C 335.53474,274.09081 339.26674,276.46481 339.74974,278.77781 C 340.23274,281.09081 338.78574,285.11081 338.22074,287.02781 C 337.65574,288.94481 337.21274,289.15281 336.36074,290.27781 C 335.50974,291.40281 334.86474,293.56981 333.11074,293.77781 C 331.35774,293.98581 326.26174,293.71481 325.83974,291.52781 C 325.41874,289.34081 330.10774,283.71581 330.58174,280.65281 C 331.05574,277.59081 328.63274,276.59081 328.68474,273.15281 C 328.73774,269.71581 331.53074,266.52781 330.89774,260.02781 C 330.26574,253.52781 325.73474,236.34081 324.89174,234.15281 C 324.04874,231.96581 327.57874,244.34081 325.83974,246.90281 C 324.10174,249.46581 314.03774,248.77781 314.45974,249.52781 C 314.88074,250.27781 326.15574,249.96581 328.36874,251.40281 C 330.58174,252.84081 329.73874,256.27781 327.73674,258.15281 C 325.73474,260.02781 316.19774,262.15281 316.35574,262.65281 C 316.51374,263.15281 326.89374,260.02781 328.68474,261.15281 C 330.47674,262.27781 328.79074,266.84081 327.10474,269.40281 C 325.41874,271.96581 318.51674,275.59081 318.56874,276.52781 C 318.62174,277.46481 326.41974,274.02781 327.42074,275.02781 C 328.42174,276.02781 327.31574,280.90281 324.57574,282.52781 C 321.83574,284.15281 315.19674,286.52781 310.98174,284.77781 C 306.76674,283.02781 299.60174,274.96481 299.28474,272.02781 C 298.96874,269.09081 308.82174,267.84081 309.08474,267.15281 C 309.34874,266.46581 303.60574,269.46581 300.86574,267.90281 C 298.12574,266.34081 292.33074,259.27781 292.64674,257.77781 C 292.96274,256.27781 303.44774,259.59081 302.76274,258.90281 C 302.07774,258.21581 291.11874,256.02781 288.53674,253.65281 C 285.95474,251.27781 285.48074,245.59081 287.27174,244.65281 C 289.06374,243.71581 299.70674,248.46481 299.28474,248.02781 C 298.86374,247.59081 287.32474,244.46481 284.74274,242.02781 C 282.16174,239.59081 282.84674,236.46581 283.79474,233.40281 C 284.74274,230.34081 290.90774,227.40281 290.43374,223.65281 C 289.95974,219.90281 280.10674,211.34081 280.94974,210.90281 C 281.79274,210.46581 292.90974,218.21481 295.49174,221.02781 C 298.07374,223.84081 297.12474,224.77781 296.43974,227.77781 C 295.75474,230.77781 289.80074,235.15281 291.38174,239.02781 C 292.96274,242.90281 305.34474,248.77781 305.92374,251.02781 C 306.50374,253.27781 294.12174,251.65281 294.85974,252.52781 C 295.59674,253.40281 308.97974,254.34081 310.34974,256.27781 C 311.71974,258.21481 303.07874,263.34081 303.07874,264.15281 C 303.07874,264.96581 308.87474,260.59081 310.34974,261.15281 C 311.82474,261.71581 312.61574,265.02781 311.93074,267.52781 C 311.24574,270.02781 305.39674,274.02781 306.23974,276.15281 C 307.08274,278.27781 315.61874,280.71481 316.98874,280.27781 C 318.35874,279.84081 313.45874,275.59081 314.45974,273.52781 C 315.46074,271.46481 323.25874,269.27781 322.99474,267.90281 C 322.73174,266.52781 314.30174,266.96481 312.87874,265.27781 C 311.45574,263.59081 313.14174,259.46481 314.45974,257.77781 C 315.77674,256.09081 321.51974,255.77781 320.78174,255.15281 C 320.04474,254.52781 312.50974,255.77781 310.03374,254.02781 C 307.55674,252.27781 303.92174,246.40281 305.92374,244.65281 C 307.92574,242.90281 319.56974,244.77781 322.04674,243.52781 C 324.52274,242.27781 320.25474,240.34081 320.78174,237.15281 C 321.30874,233.96581 323.41674,223.09081 325.20774,224.40281 C 326.99874,225.71581 330.31874,238.40281 331.53074,245.02781 C 332.74174,251.65281 332.47874,258.96581 332.47874,264.15281 C 332.47874,269.34081 331.31974,274.02781 331.53074,276.15281 C 331.74074,278.27781 333.84874,274.90281 333.74374,276.90281 C 333.63774,278.90281 330.65174,286.46581 330.89774,288.15281 C 331.14474,289.84081 334.38074,288.02781 335.22274,287.02781 C 336.06674,286.02781 336.36074,284.59081 335.95574,282.15281 C 335.55174,279.71581 332.68974,275.09081 332.79474,272.40281 C 332.89974,269.71581 336.64074,269.84081 336.58874,266.02781 C 336.53574,262.21481 332.79474,253.21481 332.47874,249.52781 C 332.16274,245.84081 335.11274,247.71581 334.69174,243.90281 C 334.26974,240.09081 328.84274,227.65281 329.94974,226.65281 C 331.05574,225.65281 340.43474,238.27781 341.33074,237.90281 C 342.22574,237.52781 334.05974,225.84081 335.32374,224.40281 C 336.58874,222.96581 346.44074,227.09081 348.91774,229.27781 C 351.39374,231.46481 349.23374,235.46481 350.18174,237.52781 C 351.13074,239.59081 354.92374,239.71581 354.60774,241.65281 C 354.29174,243.59081 349.02274,247.02781 348.28474,249.15281 C 347.54774,251.27781 350.91974,251.71581 350.18174,254.40281 C 349.44474,257.09081 343.75374,263.21481 343.85974,265.27781 C 343.96474,267.34081 350.91974,265.71481 350.81374,266.77781 C 350.70874,267.84081 343.43774,270.46581 343.22674,271.65281 C 343.01674,272.84081 349.12874,272.40281 349.54974,273.90281 C 349.97174,275.40281 345.33474,280.15281 345.75574,280.65281 C 346.17774,281.15281 350.07674,276.34081 352.07874,276.90281 C 354.08074,277.46581 357.40074,284.40281 357.76874,284.02781 C 358.13774,283.65281 353.65974,275.84081 354.29174,274.65281 C 354.92374,273.46581 361.82574,277.84081 361.56274,276.90281 C 361.29874,275.96581 353.02674,271.09081 352.71074,269.02781 C 352.39474,266.96481 359.82374,266.21481 359.66574,264.52781 C 359.50774,262.84081 352.13174,260.90281 351.76274,258.90281 C 351.39374,256.90281 357.34774,254.84081 357.45274,252.52781 C 357.55874,250.21481 352.65874,246.90281 352.39474,245.02781 C 352.13174,243.15281 355.23974,244.65281 355.87274,241.27781 C 356.50474,237.90281 354.55474,225.90281 356.18874,224.77781 C 357.82174,223.65281 364.19674,231.40281 365.67274,234.52781 C 367.14774,237.65281 364.56574,241.96481 365.03974,243.52781 C 365.51474,245.09081 368.30674,242.02781 368.51774,243.90281 C 368.72874,245.77781 366.30474,251.21481 366.30474,254.77781 C 366.30474,258.34081 367.35874,263.34081 368.51774,265.27781 C 369.67674,267.21481 372.89074,264.15281 373.25974,266.40281 C 373.62874,268.65281 370.30874,277.40281 370.73074,278.77781 C 371.15174,280.15281 374.36574,274.77781 375.78874,274.65281 C 377.21074,274.52781 378.58074,278.65281 379.26574,278.02781 C 379.95074,277.40281 380.63574,272.77781 379.89774,270.90281 C 379.16074,269.02781 375.26174,268.46481 374.83974,266.77781 C 374.41874,265.09081 377.89574,262.34081 377.36874,260.77781 C 376.84274,259.21481 373.10174,259.15281 371.67874,257.40281 C 370.25674,255.65281 369.36074,253.21481 368.83374,250.27781 C 368.30674,247.34081 367.77974,240.40281 368.51774,239.77781 C 369.25474,239.15281 373.15374,247.59081 373.25974,246.52781 C 373.36474,245.46481 369.09674,236.34081 369.14974,233.40281 C 369.20274,230.46581 371.62574,227.77781 373.57574,228.90281 C 375.52474,230.02781 378.84474,238.21581 380.84674,240.15281 C 382.84874,242.09081 385.11474,238.21481 385.58874,240.52781 C 386.06274,242.84081 383.11274,251.59081 383.69174,254.02781 C 384.27174,256.46481 388.17074,252.40281 389.06574,255.15281 C 389.96174,257.90281 388.48674,269.40281 389.06574,270.52781 C 389.64574,271.65281 391.06774,262.71581 392.54374,261.90281 C 394.01874,261.09081 397.07474,266.15281 397.91774,265.65281 C 398.76074,265.15281 398.65574,260.77781 397.60174,258.90281 C 396.54774,257.02781 392.01674,255.52781 391.59474,254.40281 C 391.17374,253.27781 395.38874,253.71581 395.07274,252.15281 C 394.75674,250.59081 390.01474,246.90281 389.69874,245.02781 C 389.38174,243.15281 390.59374,239.90281 393.17574,240.90281 C 395.75774,241.90281 403.23874,250.52781 405.18874,251.02781 C 407.13774,251.52781 404.02974,244.77781 404.87274,243.90281 C 405.71574,243.02781 409.35074,246.34081 410.24674,245.77781 C 411.14274,245.21481 409.82474,241.65281 410.24674,240.52781 C 410.66774,239.40281 414.25074,238.96481 412.77574,239.02781 C 411.30074,239.09081 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330.26574,253.52781 325.73474,236.34081 324.89174,234.15281 C 324.04874,231.96581 327.57874,244.34081 325.83974,246.90281 C 324.10174,249.46581 314.03774,248.77781 314.45974,249.52781 C 314.88074,250.27781 326.15574,249.96581 328.36874,251.40281 C 330.58174,252.84081 329.73874,256.27781 327.73674,258.15281 C 325.73474,260.02781 316.19774,262.15281 316.35574,262.65281 C 316.51374,263.15281 326.89374,260.02781 328.68474,261.15281 C 330.47674,262.27781 328.79074,266.84081 327.10474,269.40281 C 325.41874,271.96581 318.51674,275.59081 318.56874,276.52781 C 318.62174,277.46481 326.41974,274.02781 327.42074,275.02781 C 328.42174,276.02781 327.31574,280.90281 324.57574,282.52781 C 321.83574,284.15281 315.19674,286.52781 310.98174,284.77781 C 306.76674,283.02781 299.60174,274.96481 299.28474,272.02781 C 298.96874,269.09081 308.82174,267.84081 309.08474,267.15281 C 309.34874,266.46581 303.60574,269.46581 300.86574,267.90281 C 298.12574,266.34081 292.33074,259.27781 292.64674,257.77781 C 292.96274,256.27781 303.44774,259.59081 302.76274,258.90281 C 302.07774,258.21581 291.11874,256.02781 288.53674,253.65281 C 285.95474,251.27781 285.48074,245.59081 287.27174,244.65281 C 289.06374,243.71581 299.70674,248.46481 299.28474,248.02781 C 298.86374,247.59081 287.32474,244.46481 284.74274,242.02781 C 282.16174,239.59081 282.84674,236.46581 283.79474,233.40281 C 284.74274,230.34081 290.90774,227.40281 290.43374,223.65281 C 289.95974,219.90281 280.10674,211.34081 280.94974,210.90281 C 281.79274,210.46581 292.90974,218.21481 295.49174,221.02781 C 298.07374,223.84081 297.12474,224.77781 296.43974,227.77781 C 295.75474,230.77781 289.80074,235.15281 291.38174,239.02781 C 292.96274,242.90281 305.34474,248.77781 305.92374,251.02781 C 306.50374,253.27781 294.12174,251.65281 294.85974,252.52781 C 295.59674,253.40281 308.97974,254.34081 310.34974,256.27781 C 311.71974,258.21481 303.07874,263.34081 303.07874,264.15281 C 303.07874,264.96581 308.87474,260.59081 310.34974,261.15281 C 311.82474,261.71581 312.61574,265.02781 311.93074,267.52781 C 311.24574,270.02781 305.39674,274.02781 306.23974,276.15281 C 307.08274,278.27781 315.61874,280.71481 316.98874,280.27781 C 318.35874,279.84081 313.45874,275.59081 314.45974,273.52781 C 315.46074,271.46481 323.25874,269.27781 322.99474,267.90281 C 322.73174,266.52781 314.30174,266.96481 312.87874,265.27781 C 311.45574,263.59081 313.14174,259.46481 314.45974,257.77781 C 315.77674,256.09081 321.51974,255.77781 320.78174,255.15281 C 320.04474,254.52781 312.50974,255.77781 310.03374,254.02781 C 307.55674,252.27781 303.92174,246.40281 305.92374,244.65281 C 307.92574,242.90281 319.56974,244.77781 322.04674,243.52781 C 324.52274,242.27781 320.25474,240.34081 320.78174,237.15281 C 321.30874,233.96581 323.41674,223.09081 325.20774,224.40281 C 326.99874,225.71581 330.31874,238.40281 331.53074,245.02781 C 332.74174,251.65281 332.47874,258.96581 332.47874,264.15281 C 332.47874,269.34081 331.31974,274.02781 331.53074,276.15281 C 331.74074,278.27781 333.84874,274.90281 333.74374,276.90281 C 333.63774,278.90281 330.65174,286.46581 330.89774,288.15281 C 331.14474,289.84081 334.38074,288.02781 335.22274,287.02781 C 336.06674,286.02781 336.36074,284.59081 335.95574,282.15281 C 335.55174,279.71581 332.68974,275.09081 332.79474,272.40281 C 332.89974,269.71581 336.64074,269.84081 336.58874,266.02781 C 336.53574,262.21481 332.79474,253.21481 332.47874,249.52781 C 332.16274,245.84081 335.11274,247.71581 334.69174,243.90281 C 334.26974,240.09081 328.84274,227.65281 329.94974,226.65281 C 331.05574,225.65281 340.43474,238.27781 341.33074,237.90281 C 342.22574,237.52781 334.05974,225.84081 335.32374,224.40281 C 336.58874,222.96581 346.44074,227.09081 348.91774,229.27781 C 351.39374,231.46481 349.23374,235.46481 350.18174,237.52781 C 351.13074,239.59081 354.92374,239.71581 354.60774,241.65281 C 354.29174,243.59081 349.02274,247.02781 348.28474,249.15281 C 347.54774,251.27781 350.91974,251.71581 350.18174,254.40281 C 349.44474,257.09081 343.75374,263.21481 343.85974,265.27781 C 343.96474,267.34081 350.91974,265.71481 350.81374,266.77781 C 350.70874,267.84081 343.43774,270.46581 343.22674,271.65281 C 343.01674,272.84081 349.12874,272.40281 349.54974,273.90281 C 349.97174,275.40281 345.33474,280.15281 345.75574,280.65281 C 346.17774,281.15281 350.07674,276.34081 352.07874,276.90281 C 354.08074,277.46581 357.40074,284.40281 357.76874,284.02781 C 358.13774,283.65281 353.65974,275.84081 354.29174,274.65281 C 354.92374,273.46581 361.82574,277.84081 361.56274,276.90281 C 361.29874,275.96581 353.02674,271.09081 352.71074,269.02781 C 352.39474,266.96481 359.82374,266.21481 359.66574,264.52781 C 359.50774,262.84081 352.13174,260.90281 351.76274,258.90281 C 351.39374,256.90281 357.34774,254.84081 357.45274,252.52781 C 357.55874,250.21481 352.65874,246.90281 352.39474,245.02781 C 352.13174,243.15281 355.23974,244.65281 355.87274,241.27781 C 356.50474,237.90281 354.55474,225.90281 356.18874,224.77781 C 357.82174,223.65281 364.19674,231.40281 365.67274,234.52781 C 367.14774,237.65281 364.56574,241.96481 365.03974,243.52781 C 365.51474,245.09081 368.30674,242.02781 368.51774,243.90281 C 368.72874,245.77781 366.30474,251.21481 366.30474,254.77781 C 366.30474,258.34081 367.35874,263.34081 368.51774,265.27781 C 369.67674,267.21481 372.89074,264.15281 373.25974,266.40281 C 373.62874,268.65281 370.30874,277.40281 370.73074,278.77781 C 371.15174,280.15281 374.36574,274.77781 375.78874,274.65281 C 377.21074,274.52781 378.58074,278.65281 379.26574,278.02781 C 379.95074,277.40281 380.63574,272.77781 379.89774,270.90281 C 379.16074,269.02781 375.26174,268.46481 374.83974,266.77781 C 374.41874,265.09081 377.89574,262.34081 377.36874,260.77781 C 376.84274,259.21481 373.10174,259.15281 371.67874,257.40281 C 370.25674,255.65281 369.36074,253.21481 368.83374,250.27781 C 368.30674,247.34081 367.77974,240.40281 368.51774,239.77781 C 369.25474,239.15281 373.15374,247.59081 373.25974,246.52781 C 373.36474,245.46481 369.09674,236.34081 369.14974,233.40281 C 369.20274,230.46581 371.62574,227.77781 373.57574,228.90281 C 375.52474,230.02781 378.84474,238.21581 380.84674,240.15281 C 382.84874,242.09081 385.11474,238.21481 385.58874,240.52781 C 386.06274,242.84081 383.11274,251.59081 383.69174,254.02781 C 384.27174,256.46481 388.17074,252.40281 389.06574,255.15281 C 389.96174,257.90281 388.48674,269.40281 389.06574,270.52781 C 389.64574,271.65281 391.06774,262.71581 392.54374,261.90281 C 394.01874,261.09081 397.07474,266.15281 397.91774,265.65281 C 398.76074,265.15281 398.65574,260.77781 397.60174,258.90281 C 396.54774,257.02781 392.01674,255.52781 391.59474,254.40281 C 391.17374,253.27781 395.38874,253.71581 395.07274,252.15281 C 394.75674,250.59081 390.01474,246.90281 389.69874,245.02781 C 389.38174,243.15281 390.59374,239.90281 393.17574,240.90281 C 395.75774,241.90281 403.23874,250.52781 405.18874,251.02781 C 407.13774,251.52781 404.02974,244.77781 404.87274,243.90281 C 405.71574,243.02781 409.35074,246.34081 410.24674,245.77781 C 411.14274,245.21481 409.82474,241.65281 410.24674,240.52781 C 410.66774,239.40281 414.25074,238.96481 412.77574,239.02781 C 411.30074,239.09081 404.55674,241.40281 401.39474,240.90281 C 398.23374,240.40281 392.49074,238.27781 393.80774,236.02781 C 395.12474,233.77781 405.24174,228.77781 409.29874,227.40281 C 413.35574,226.02781 417.72874,228.52781 418.14974,227.77781 C 418.57174,227.02781 411.77474,224.21581 411.82774,222.90281 C 411.87974,221.59081 417.99174,221.27781 418.46574,219.90281 C 418.94074,218.52781 416.99074,214.09081 414.67274,214.65281 C 412.35474,215.21581 406.66374,222.90281 404.55674,223.27781 C 402.44874,223.65281 403.66074,216.21581 402.02774,216.90281 C 400.39374,217.59081 396.49474,228.15281 394.75674,227.40281 C 393.01774,226.65281 392.59574,215.21581 391.59474,212.40281 C 390.59374,209.59081 390.33074,209.40281 388.74974,210.52781 C 387.16974,211.65281 383.84974,218.71581 382.11074,219.15281 C 380.37274,219.59081 379.68774,212.96581 378.31774,213.15281 C 376.94774,213.34081 375.52474,221.15281 373.89174,220.27781 C 372.25874,219.40281 370.67774,210.59081 368.51774,207.90281 C 366.35774,205.21581 360.50874,205.77781 360.93074,204.15281 C 361.35174,202.52781 368.83374,200.02781 371.04674,198.15281 C 373.25974,196.27781 372.78574,192.27781 374.20774,192.90281 C 375.63074,193.52781 378.31774,202.09081 379.58174,201.90281 C 380.84674,201.71581 380.63574,192.21481 381.79474,191.77781 C 382.95374,191.33981 385.58874,199.71481 386.53674,199.27781 C 387.48574,198.84081 385.53574,189.08981 387.48574,189.15281 C 389.43474,189.21581 396.70574,199.90281 398.23374,199.65281 C 399.76174,199.40281 395.65174,189.40281 396.65274,187.65281 C 397.65374,185.90281 402.02774,186.71581 404.24074,189.15281 C 406.45274,191.58981 408.98274,202.02781 409.93074,202.27781 C 410.87874,202.52781 408.82374,191.15281 409.93074,190.65281 C 411.03674,190.15281 415.35774,198.46481 416.56974,199.27781 C 417.78074,200.09081 417.88674,197.34081 417.20174,195.52781 C 416.51674,193.71481 412.30174,189.90281 412.45974,188.40281 C 412.61774,186.90281 418.09774,187.27781 418.14974,186.52781 C 418.20274,185.77781 413.56574,185.15281 412.77574,183.90281 C 411.98574,182.65281 414.56674,178.90281 413.40774,179.02781 C 412.24874,179.15281 408.71874,184.58981 405.82074,184.65281 C 402.92274,184.71581 395.75774,182.02781 396.02074,179.40281 C 396.28474,176.77781 405.60974,171.40281 407.40174,168.90281 C 409.19274,166.40281 408.03374,164.40281 406.76974,164.40281 C 405.50474,164.40281 400.86774,169.40281 399.81474,168.90281 C 398.76074,168.40281 401.02674,163.02781 400.44674,161.40281 C 399.86674,159.77781 396.86374,158.02781 396.33674,159.15281 C 395.80974,160.27781 397.70674,165.65281 397.28574,168.15281 C 396.86374,170.65281 394.96674,174.58981 393.80774,174.15281 C 392.64874,173.71581 390.96274,165.46481 390.33074,165.52781 C 389.69874,165.58981 389.85674,172.21481 390.01474,174.52781 C 390.17274,176.83981 392.17474,177.46581 391.27874,179.40281 C 390.38274,181.33981 385.90474,186.71581 384.63974,186.15281 C 383.37574,185.58981 384.37674,177.71481 383.69174,176.02781 C 383.00674,174.33981 380.95174,174.02781 380.53074,176.02781 C 380.10874,178.02781 382.21674,186.90281 381.16274,188.02781 C 380.10874,189.15281 376.84274,182.21481 374.20774,182.77781 C 371.57374,183.33981 367.67474,191.02781 365.35674,191.40281 C 363.03774,191.77781 360.45574,187.21481 360.29774,185.02781 C 360.13974,182.83981 364.51274,179.15281 364.40774,178.27781 C 364.30274,177.40281 361.14074,180.40281 359.66574,179.77781 C 358.19074,179.15281 354.92374,176.08981 355.55674,174.52781 C 356.18874,172.96481 361.40474,171.15281 363.45974,170.40281 C 365.51474,169.65281 367.62174,171.71481 367.88574,170.02781 C 368.14874,168.33981 363.56474,161.58981 365.03974,160.27781 C 366.51574,158.96481 375.15674,163.02781 376.73674,162.15281 C 378.31774,161.27781 372.99574,155.96481 374.52374,155.02781 C 376.05174,154.08981 383.37574,156.90281 385.90474,156.52781 C 388.43374,156.15281 389.38174,154.46481 389.69874,152.77781 C 390.01474,151.08981 388.85574,147.58981 387.80174,146.40281 C 386.74774,145.21581 384.79874,145.27781 383.37574,145.65281 C 381.95274,146.02781 379.89774,149.46581 379.26574,148.65281 C 378.63374,147.83981 380.37274,141.02781 379.58174,140.77781 C 378.79174,140.52781 376.20974,146.46581 374.52374,147.15281 C 372.83774,147.83981 370.15074,147.21581 369.46574,144.90281 C 368.78074,142.58981 370.78274,134.65281 370.41474,133.27781 C 370.04574,131.90281 368.09574,137.33981 367.25274,136.65281 C 366.40974,135.96581 366.19874,129.15281 365.35674,129.15281 C 364.51274,129.15281 362.08974,133.58981 362.19474,136.65281 C 362.30074,139.71581 366.98974,145.58981 365.98874,147.52781 C 364.98774,149.46481 357.29474,146.15281 356.18874,148.27781 C 355.08174,150.40281 360.13974,158.46481 359.34974,160.27781 C 358.55974,162.08981 352.39474,157.40281 351.44674,159.15281 C 350.49774,160.90281 354.29174,169.02781 353.65974,170.77781 C 353.02674,172.52781 349.49674,168.46581 347.65274,169.65281 C 345.80874,170.83981 343.91174,176.96581 342.59474,177.90281 C 341.27774,178.83981 340.22374,176.96481 339.74974,175.27781 C 339.27574,173.58981 340.80374,169.08981 339.74974,167.77781 C 338.69574,166.46481 334.74474,168.40281 333.42674,167.40281 C 332.10974,166.40281 330.63474,163.46481 331.84674,161.77781 C 333.05874,160.08981 339.43374,159.46481 340.69774,157.27781 C 341.96274,155.08981 338.59074,150.52781 339.43374,148.65281 C 340.27674,146.77781 345.17674,147.96481 345.75574,146.02781 C 346.33574,144.08981 343.06874,139.27781 342.91074,137.02781 C 342.75274,134.77781 343.64874,132.77781 344.80774,132.52781 C 345.96674,132.27781 348.23274,136.46481 349.86574,135.52781 C 351.49874,134.58981 354.97674,128.02781 354.60774,126.90281 C 354.23874,125.77781 348.96974,128.96481 347.65274,128.77781 C 346.33574,128.58981 346.12474,126.96481 346.70474,125.77781 C 347.28374,124.58981 351.28874,122.52781 351.13074,121.65281 C 350.97274,120.77781 347.23174,119.71481 345.75574,120.52781 C 344.28074,121.33981 343.12174,126.71481 342.27874,126.52781 C 341.43574,126.33981 341.69874,120.40281 340.69774,119.40281 C 339.69674,118.40281 336.74674,119.27781 336.27274,120.52781 C 335.79774,121.77781 337.00974,125.15281 337.85274,126.90281 C 338.69574,128.65281 341.01474,129.71481 341.33074,131.02781 C 341.64674,132.33981 340.75074,134.27781 339.74974,134.77781 C 338.74874,135.27781 335.16574,133.02781 335.32374,134.02781 C 335.48174,135.02781 340.48774,138.71481 340.69774,140.77781 C 340.90874,142.83981 337.74774,143.77781 336.58874,146.40281 C 335.42974,149.02781 335.06074,155.33981 333.74374,156.52781 C 332.42574,157.71481 329.52774,155.96481 328.68474,153.52781 C 327.84174,151.08981 329.31774,144.33981 328.68474,141.90281 C 328.05274,139.46581 324.99674,140.40281 324.89174,138.90281 C 324.78674,137.40281 328.26374,135.08981 328.05274,132.90281 C 327.84174,130.71581 324.36474,128.90281 323.62674,125.77781 C 322.88974,122.65281 324.68074,115.90281 323.62674,114.15281 C 322.57374,112.40281 318.25274,113.65281 317.30474,115.27781 C 316.35574,116.90281 316.67274,120.83981 317.93674,123.90281 C 319.20174,126.96581 324.25974,131.08981 324.89174,133.65281 C 325.52374,136.21581 321.15074,136.77781 321.73074,139.27781 C 322.30974,141.77781 327.42074,145.21581 328.36874,148.65281 C 329.31774,152.08981 328.68474,158.77781 327.42074,159.90281 C 326.15574,161.02781 322.94174,155.71581 320.78174,155.40281 C 318.62174,155.08981 315.51274,158.90281 314.45974,158.02781 C 313.40574,157.15281 315.72374,151.58981 314.45974,150.15281 C 313.19474,148.71581 307.50474,150.96581 306.87274,149.40281 C 306.23974,147.83981 310.29674,143.21481 310.66574,140.77781 C 311.03474,138.33981 310.50774,136.58981 309.08474,134.77781 C 307.66274,132.96481 302.39374,131.90281 302.13074,129.90281 C 301.86674,127.90281 307.39874,124.71481 307.50474,122.77781 C 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style=\"font-size:40px;font-style:normal;font-weight:normal;fill:#000000;fill-opacity:1;stroke:none;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;font-family:Arial\" transform=\"scale(0.52) translate(0,80)\" x=\"221.29668\" y=\"23.017452\"><tspan id=\"tspan2445\" style=\"font-size: 20px; fill: #777777;\" x=\"221.29668\" y=\"23.017452\">\n",
       "TH\n",
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       "MES\n",
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       "MET\n",
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       "MY\n",
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      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVG(filename='./figures/human_diagram.svg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supplemental\n",
    "Tables with expression levels of disease gene lists in the areas of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#union of gene_lists\n",
    "genelist_union = pd.concat([negraes, watson_coding, duncan, lutterAN])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "duplicated = genelist_union[genelist_union.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32          AMT\n",
       "16        GIPC3\n",
       "19        IFIT3\n",
       "38      SCGB1A1\n",
       "44    TNFRSF10A\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duplicated genes across gene lists\n",
    "- AMT (in Negraes/Watson coding)\n",
    "https://www.genecards.org/cgi-bin/carddisp.pl?gene=AMT\n",
    "- TNFRSF10A (Negraes and Lutter)\n",
    "http://www.genecards.org/cgi-bin/carddisp.pl?gene=TNFRSF10A\n",
    "- SCGB1A1 (Negraes and Lutter)\n",
    "http://www.genecards.org/cgi-bin/carddisp.pl?gene=SCGB1A1\n",
    "- IFIT3 (Negraes and Lutter)\n",
    "http://www.genecards.org/cgi-bin/carddisp.pl?gene=IFIT3\n",
    "- GIPC3 (Negraes and Lutter)\n",
    "http://www.genecards.org/cgi-bin/carddisp.pl?gene=GIPC3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "genelist_union = genelist_union.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(519,)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genelist_union.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_supp_table(gene_list):\n",
    "    supp_table_adult = adult_exp.loc[adult_exp.index.isin(gene_list), brain_areas]\n",
    "    supp_table_fetal = fetal_exp.loc[fetal_exp.index.isin(gene_list), fetal_brain_areas]\n",
    "    supp_table = pd.concat([supp_table_adult, supp_table_fetal.add_prefix('fetal_')], axis=1)\n",
    "    \n",
    "    return supp_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "supp_table_lists_union = generate_supp_table(genelist_union)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "supp_table_lists_union.insert(0, 'Negraes et al. Table S5', supp_table_lists_union.index.isin(negraes))\n",
    "supp_table_lists_union.insert(1, 'Watson et al. protein coding Table S6', supp_table_lists_union.index.isin(watson_coding))\n",
    "supp_table_lists_union.insert(2, 'Duncan et al. rs4622308', supp_table_lists_union.index.isin(duncan))\n",
    "supp_table_lists_union.insert(3, 'Lutter et al. Table S3.RestrictedEating', supp_table_lists_union.index.isin(lutterAN))\n",
    "supp_table_lists_union.insert(4, 'Lutter et al. Table S4.BingeEating', supp_table_lists_union.index.isin(lutterBN))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Negraes et al. Table S5</th>\n",
       "      <th>Watson et al. protein coding Table S6</th>\n",
       "      <th>Duncan et al. rs4622308</th>\n",
       "      <th>Lutter et al. Table S3.RestrictedEating</th>\n",
       "      <th>Lutter et al. Table S4.BingeEating</th>\n",
       "      <th>lateral parabrachial nucleus</th>\n",
       "      <th>medial parabrachial nucleus</th>\n",
       "      <th>paraventricular nucleus of the hypothalamus</th>\n",
       "      <th>arcuate nucleus of the hypothalamus</th>\n",
       "      <th>raphe nuclei of medulla</th>\n",
       "      <th>...</th>\n",
       "      <th>fetal_arcuate nucleus of hypothalamus</th>\n",
       "      <th>fetal_dorsal raphe nucleus</th>\n",
       "      <th>fetal_raphe magnus nucleus</th>\n",
       "      <th>fetal_raphe obscurus nucleus</th>\n",
       "      <th>fetal_subgenual_cingulate_cortex</th>\n",
       "      <th>fetal_core of nucleus accumbens</th>\n",
       "      <th>fetal_ventral tegmental area</th>\n",
       "      <th>fetal_bed nucleus of stria terminalis</th>\n",
       "      <th>fetal_central_amygdala</th>\n",
       "      <th>fetal_solitary nucleus</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gene_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",
       "      <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",
       "      <td>ABCA12</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.559354</td>\n",
       "      <td>-0.712071</td>\n",
       "      <td>-0.594848</td>\n",
       "      <td>-0.172974</td>\n",
       "      <td>-0.682818</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.071422</td>\n",
       "      <td>0.000910</td>\n",
       "      <td>-0.498300</td>\n",
       "      <td>-0.662835</td>\n",
       "      <td>0.946788</td>\n",
       "      <td>0.223315</td>\n",
       "      <td>0.901787</td>\n",
       "      <td>-0.307584</td>\n",
       "      <td>-1.182149</td>\n",
       "      <td>-0.871649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ABCC6P2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.612350</td>\n",
       "      <td>1.020678</td>\n",
       "      <td>0.920739</td>\n",
       "      <td>0.857978</td>\n",
       "      <td>0.330344</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.832635</td>\n",
       "      <td>-0.405025</td>\n",
       "      <td>-0.736371</td>\n",
       "      <td>-1.526651</td>\n",
       "      <td>-0.027777</td>\n",
       "      <td>0.174622</td>\n",
       "      <td>0.107702</td>\n",
       "      <td>-0.102345</td>\n",
       "      <td>-1.540131</td>\n",
       "      <td>-1.575547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ACTG1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.410066</td>\n",
       "      <td>-1.046665</td>\n",
       "      <td>0.169894</td>\n",
       "      <td>-0.666537</td>\n",
       "      <td>0.461756</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.013390</td>\n",
       "      <td>0.634428</td>\n",
       "      <td>0.119645</td>\n",
       "      <td>-0.615935</td>\n",
       "      <td>-0.531451</td>\n",
       "      <td>1.025118</td>\n",
       "      <td>0.126828</td>\n",
       "      <td>0.255389</td>\n",
       "      <td>0.206124</td>\n",
       "      <td>0.075387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ACYP2</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.787990</td>\n",
       "      <td>0.518638</td>\n",
       "      <td>0.435496</td>\n",
       "      <td>-0.285587</td>\n",
       "      <td>0.852885</td>\n",
       "      <td>...</td>\n",
       "      <td>0.916150</td>\n",
       "      <td>1.632514</td>\n",
       "      <td>1.600224</td>\n",
       "      <td>1.487197</td>\n",
       "      <td>0.447047</td>\n",
       "      <td>0.036608</td>\n",
       "      <td>0.783924</td>\n",
       "      <td>0.168733</td>\n",
       "      <td>0.313144</td>\n",
       "      <td>0.966486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ADAM20</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0.154131</td>\n",
       "      <td>1.043086</td>\n",
       "      <td>0.197874</td>\n",
       "      <td>-0.705747</td>\n",
       "      <td>0.311954</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.695882</td>\n",
       "      <td>-0.841844</td>\n",
       "      <td>-1.960607</td>\n",
       "      <td>-1.985197</td>\n",
       "      <td>0.157226</td>\n",
       "      <td>0.261494</td>\n",
       "      <td>-1.111818</td>\n",
       "      <td>-0.443833</td>\n",
       "      <td>1.047889</td>\n",
       "      <td>0.830027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZNF589</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.449572</td>\n",
       "      <td>-0.624372</td>\n",
       "      <td>-0.906556</td>\n",
       "      <td>-0.194309</td>\n",
       "      <td>-0.971752</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.513900</td>\n",
       "      <td>-1.008418</td>\n",
       "      <td>-0.062754</td>\n",
       "      <td>-1.370383</td>\n",
       "      <td>0.654766</td>\n",
       "      <td>0.069444</td>\n",
       "      <td>-1.047053</td>\n",
       "      <td>-0.416086</td>\n",
       "      <td>-0.454460</td>\n",
       "      <td>-0.197103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZNF596</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-0.472164</td>\n",
       "      <td>-0.300559</td>\n",
       "      <td>-0.113749</td>\n",
       "      <td>0.239661</td>\n",
       "      <td>-0.569624</td>\n",
       "      <td>...</td>\n",
       "      <td>1.020297</td>\n",
       "      <td>-0.227560</td>\n",
       "      <td>-1.426584</td>\n",
       "      <td>-0.936864</td>\n",
       "      <td>0.743878</td>\n",
       "      <td>-0.059895</td>\n",
       "      <td>-1.280994</td>\n",
       "      <td>0.287349</td>\n",
       "      <td>0.307728</td>\n",
       "      <td>-0.284733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZNF716</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0.861883</td>\n",
       "      <td>0.118554</td>\n",
       "      <td>-0.238531</td>\n",
       "      <td>-1.798348</td>\n",
       "      <td>-0.212845</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.617640</td>\n",
       "      <td>-0.774815</td>\n",
       "      <td>-1.404310</td>\n",
       "      <td>-0.414257</td>\n",
       "      <td>0.331522</td>\n",
       "      <td>-0.075734</td>\n",
       "      <td>-0.918150</td>\n",
       "      <td>0.721085</td>\n",
       "      <td>-2.186585</td>\n",
       "      <td>-1.581105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZNF728</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1.100070</td>\n",
       "      <td>-0.583210</td>\n",
       "      <td>-0.130536</td>\n",
       "      <td>-1.410760</td>\n",
       "      <td>-0.085622</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.899287</td>\n",
       "      <td>-1.184358</td>\n",
       "      <td>-1.089989</td>\n",
       "      <td>0.048029</td>\n",
       "      <td>0.320742</td>\n",
       "      <td>-0.268932</td>\n",
       "      <td>-0.840413</td>\n",
       "      <td>0.006821</td>\n",
       "      <td>-1.641763</td>\n",
       "      <td>-1.016277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ZNF835</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>-1.222222</td>\n",
       "      <td>-1.346992</td>\n",
       "      <td>-0.973511</td>\n",
       "      <td>-0.150693</td>\n",
       "      <td>-0.988008</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019190</td>\n",
       "      <td>-1.590880</td>\n",
       "      <td>-0.754773</td>\n",
       "      <td>-1.224084</td>\n",
       "      <td>0.269044</td>\n",
       "      <td>0.938955</td>\n",
       "      <td>-0.818466</td>\n",
       "      <td>-0.073363</td>\n",
       "      <td>-0.569350</td>\n",
       "      <td>0.111231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>446 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Negraes et al. Table S5  Watson et al. protein coding Table S6  \\\n",
       "gene_symbol                                                                   \n",
       "ABCA12                          True                                  False   \n",
       "ABCC6P2                         True                                  False   \n",
       "ACTG1                          False                                  False   \n",
       "ACYP2                          False                                   True   \n",
       "ADAM20                          True                                  False   \n",
       "...                              ...                                    ...   \n",
       "ZNF589                         False                                   True   \n",
       "ZNF596                          True                                  False   \n",
       "ZNF716                         False                                  False   \n",
       "ZNF728                          True                                  False   \n",
       "ZNF835                          True                                  False   \n",
       "\n",
       "             Duncan et al. rs4622308  Lutter et al. Table S3.RestrictedEating  \\\n",
       "gene_symbol                                                                     \n",
       "ABCA12                         False                                    False   \n",
       "ABCC6P2                        False                                    False   \n",
       "ACTG1                          False                                     True   \n",
       "ACYP2                          False                                    False   \n",
       "ADAM20                         False                                    False   \n",
       "...                              ...                                      ...   \n",
       "ZNF589                         False                                    False   \n",
       "ZNF596                         False                                    False   \n",
       "ZNF716                         False                                     True   \n",
       "ZNF728                         False                                    False   \n",
       "ZNF835                         False                                    False   \n",
       "\n",
       "             Lutter et al. Table S4.BingeEating  lateral parabrachial nucleus  \\\n",
       "gene_symbol                                                                     \n",
       "ABCA12                                    False                     -0.559354   \n",
       "ABCC6P2                                   False                     -0.612350   \n",
       "ACTG1                                     False                     -0.410066   \n",
       "ACYP2                                     False                      0.787990   \n",
       "ADAM20                                    False                      0.154131   \n",
       "...                                         ...                           ...   \n",
       "ZNF589                                    False                     -0.449572   \n",
       "ZNF596                                    False                     -0.472164   \n",
       "ZNF716                                    False                      0.861883   \n",
       "ZNF728                                    False                      1.100070   \n",
       "ZNF835                                    False                     -1.222222   \n",
       "\n",
       "             medial parabrachial nucleus  \\\n",
       "gene_symbol                                \n",
       "ABCA12                         -0.712071   \n",
       "ABCC6P2                         1.020678   \n",
       "ACTG1                          -1.046665   \n",
       "ACYP2                           0.518638   \n",
       "ADAM20                          1.043086   \n",
       "...                                  ...   \n",
       "ZNF589                         -0.624372   \n",
       "ZNF596                         -0.300559   \n",
       "ZNF716                          0.118554   \n",
       "ZNF728                         -0.583210   \n",
       "ZNF835                         -1.346992   \n",
       "\n",
       "             paraventricular nucleus of the hypothalamus  \\\n",
       "gene_symbol                                                \n",
       "ABCA12                                         -0.594848   \n",
       "ABCC6P2                                         0.920739   \n",
       "ACTG1                                           0.169894   \n",
       "ACYP2                                           0.435496   \n",
       "ADAM20                                          0.197874   \n",
       "...                                                  ...   \n",
       "ZNF589                                         -0.906556   \n",
       "ZNF596                                         -0.113749   \n",
       "ZNF716                                         -0.238531   \n",
       "ZNF728                                         -0.130536   \n",
       "ZNF835                                         -0.973511   \n",
       "\n",
       "             arcuate nucleus of the hypothalamus  raphe nuclei of medulla  \\\n",
       "gene_symbol                                                                 \n",
       "ABCA12                                 -0.172974                -0.682818   \n",
       "ABCC6P2                                 0.857978                 0.330344   \n",
       "ACTG1                                  -0.666537                 0.461756   \n",
       "ACYP2                                  -0.285587                 0.852885   \n",
       "ADAM20                                 -0.705747                 0.311954   \n",
       "...                                          ...                      ...   \n",
       "ZNF589                                 -0.194309                -0.971752   \n",
       "ZNF596                                  0.239661                -0.569624   \n",
       "ZNF716                                 -1.798348                -0.212845   \n",
       "ZNF728                                 -1.410760                -0.085622   \n",
       "ZNF835                                 -0.150693                -0.988008   \n",
       "\n",
       "             ...  fetal_arcuate nucleus of hypothalamus  \\\n",
       "gene_symbol  ...                                          \n",
       "ABCA12       ...                              -0.071422   \n",
       "ABCC6P2      ...                              -0.832635   \n",
       "ACTG1        ...                              -0.013390   \n",
       "ACYP2        ...                               0.916150   \n",
       "ADAM20       ...                              -0.695882   \n",
       "...          ...                                    ...   \n",
       "ZNF589       ...                              -1.513900   \n",
       "ZNF596       ...                               1.020297   \n",
       "ZNF716       ...                              -0.617640   \n",
       "ZNF728       ...                              -0.899287   \n",
       "ZNF835       ...                               0.019190   \n",
       "\n",
       "             fetal_dorsal raphe nucleus  fetal_raphe magnus nucleus  \\\n",
       "gene_symbol                                                           \n",
       "ABCA12                         0.000910                   -0.498300   \n",
       "ABCC6P2                       -0.405025                   -0.736371   \n",
       "ACTG1                          0.634428                    0.119645   \n",
       "ACYP2                          1.632514                    1.600224   \n",
       "ADAM20                        -0.841844                   -1.960607   \n",
       "...                                 ...                         ...   \n",
       "ZNF589                        -1.008418                   -0.062754   \n",
       "ZNF596                        -0.227560                   -1.426584   \n",
       "ZNF716                        -0.774815                   -1.404310   \n",
       "ZNF728                        -1.184358                   -1.089989   \n",
       "ZNF835                        -1.590880                   -0.754773   \n",
       "\n",
       "             fetal_raphe obscurus nucleus  fetal_subgenual_cingulate_cortex  \\\n",
       "gene_symbol                                                                   \n",
       "ABCA12                          -0.662835                          0.946788   \n",
       "ABCC6P2                         -1.526651                         -0.027777   \n",
       "ACTG1                           -0.615935                         -0.531451   \n",
       "ACYP2                            1.487197                          0.447047   \n",
       "ADAM20                          -1.985197                          0.157226   \n",
       "...                                   ...                               ...   \n",
       "ZNF589                          -1.370383                          0.654766   \n",
       "ZNF596                          -0.936864                          0.743878   \n",
       "ZNF716                          -0.414257                          0.331522   \n",
       "ZNF728                           0.048029                          0.320742   \n",
       "ZNF835                          -1.224084                          0.269044   \n",
       "\n",
       "             fetal_core of nucleus accumbens  fetal_ventral tegmental area  \\\n",
       "gene_symbol                                                                  \n",
       "ABCA12                              0.223315                      0.901787   \n",
       "ABCC6P2                             0.174622                      0.107702   \n",
       "ACTG1                               1.025118                      0.126828   \n",
       "ACYP2                               0.036608                      0.783924   \n",
       "ADAM20                              0.261494                     -1.111818   \n",
       "...                                      ...                           ...   \n",
       "ZNF589                              0.069444                     -1.047053   \n",
       "ZNF596                             -0.059895                     -1.280994   \n",
       "ZNF716                             -0.075734                     -0.918150   \n",
       "ZNF728                             -0.268932                     -0.840413   \n",
       "ZNF835                              0.938955                     -0.818466   \n",
       "\n",
       "             fetal_bed nucleus of stria terminalis  fetal_central_amygdala  \\\n",
       "gene_symbol                                                                  \n",
       "ABCA12                                   -0.307584               -1.182149   \n",
       "ABCC6P2                                  -0.102345               -1.540131   \n",
       "ACTG1                                     0.255389                0.206124   \n",
       "ACYP2                                     0.168733                0.313144   \n",
       "ADAM20                                   -0.443833                1.047889   \n",
       "...                                            ...                     ...   \n",
       "ZNF589                                   -0.416086               -0.454460   \n",
       "ZNF596                                    0.287349                0.307728   \n",
       "ZNF716                                    0.721085               -2.186585   \n",
       "ZNF728                                    0.006821               -1.641763   \n",
       "ZNF835                                   -0.073363               -0.569350   \n",
       "\n",
       "             fetal_solitary nucleus  \n",
       "gene_symbol                          \n",
       "ABCA12                    -0.871649  \n",
       "ABCC6P2                   -1.575547  \n",
       "ACTG1                      0.075387  \n",
       "ACYP2                      0.966486  \n",
       "ADAM20                     0.830027  \n",
       "...                             ...  \n",
       "ZNF589                    -0.197103  \n",
       "ZNF596                    -0.284733  \n",
       "ZNF716                    -1.581105  \n",
       "ZNF728                    -1.016277  \n",
       "ZNF835                     0.111231  \n",
       "\n",
       "[446 rows x 29 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "supp_table_lists_union"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#supp_table_lists_union.to_csv('./results/supp_expression_table.csv')"
   ]
  }
 ],
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