Exp_fin_10_Hazard.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "427f5c0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import itertools as it\n",
    "import random as rd\n",
    "import pickle as pk\n",
    "import os.path\n",
    "import pandas as pd\n",
    "from collections import (defaultdict,Counter)\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics\n",
    "import scipy  \n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e7d9315e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's import the chemical-gene interactions from CTD (downloaded on 5th April 2021)\n",
    "chem_gene_df = pd.read_csv(\"input/CTD/CTD_chem_gene_ixns.tsv\",delimiter= '\\t', skipinitialspace=True)\n",
    "#Here, we filter for only the interactions that regards the H. Sapiens\n",
    "chem_homo = chem_gene_df[(chem_gene_df['Organism'] == 'Homo sapiens')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "643ce773",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here, we import the lists of hub exposures, central module exposures, peripheric exposures and no hub no module exposures\n",
    "hub_exposures_list=[]\n",
    "central_module_exposures_list=[]\n",
    "peripheric_module_exposures_list=[]\n",
    "no_central_no_module_exposures_list=[]\n",
    " \n",
    "# Using readlines()\n",
    "file1 = open('output/Hub_expusres.txt', 'r')\n",
    "Lines = file1.readlines()\n",
    "for line in Lines:\n",
    "    hub_exposures_list.append(line.strip(\"\\n\"))\n",
    "\n",
    "# Using readlines()\n",
    "file1 = open('output/Central_module_exposures.txt', 'r')\n",
    "Lines = file1.readlines()\n",
    "for line in Lines:\n",
    "    central_module_exposures_list.append(line.strip(\"\\n\"))\n",
    "    \n",
    "# Using readlines()\n",
    "file1 = open('output/Peripheric_module_exposures.txt', 'r')\n",
    "Lines = file1.readlines()\n",
    "for line in Lines:\n",
    "    peripheric_module_exposures_list.append(line.strip(\"\\n\"))\n",
    "    \n",
    "# Using readlines()\n",
    "file1 = open('output/No_central_no_module_exposures.txt', 'r')\n",
    "Lines = file1.readlines()\n",
    "for line in Lines:\n",
    "    no_central_no_module_exposures_list.append(line.strip(\"\\n\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c68e0f26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9852"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(hub_exposures_list+central_module_exposures_list+peripheric_module_exposures_list+no_central_no_module_exposures_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5a40a82a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5694"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(hub_exposures_list+central_module_exposures_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5c4e3aaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3370"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(no_central_no_module_exposures_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a20d1b6a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b92d1887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Here, we show the proportion of the chemical classes in the entire CTD \n",
    "fig, ax = plt.subplots(figsize=(10, 20), subplot_kw=dict(aspect=\"equal\"))\n",
    "radius = 1\n",
    "\n",
    "cmap = plt.cm.viridis\n",
    "\n",
    "\n",
    "\n",
    "sizes=[len(hub_exposures_list),len(central_module_exposures_list),len(peripheric_module_exposures_list),len(no_central_no_module_exposures_list)]\n",
    "\n",
    "wedges, texts = ax.pie(sizes, wedgeprops=dict(width=1), startangle=-125)\n",
    "\n",
    "bbox_props = dict(boxstyle=\"square,pad=0.2\", fc=\"w\", ec=\"k\", lw=1.72)\n",
    "kw = dict(arrowprops=dict(arrowstyle=\"-\"),\n",
    "          bbox=bbox_props, zorder=0, va=\"center\")\n",
    "\n",
    "#for i, p in enumerate(wedges):\n",
    "#    ang = (p.theta2 - p.theta1)/2. + p.theta1\n",
    "#    y = np.sin(np.deg2rad(ang))\n",
    "#    x = np.cos(np.deg2rad(ang))\n",
    "#    horizontalalignment = {-1: \"right\", 1: \"left\"}[int(np.sign(x))]\n",
    "#    connectionstyle = \"angle,angleA=0,angleB={}\".format(ang)\n",
    "#    kw[\"arrowprops\"].update({\"connectionstyle\": connectionstyle})\n",
    "#    ax.annotate(labels[i], xy=(x, y), xytext=(1.8*np.sign(x), 2.5*y),\n",
    "                horizontalalignment=horizontalalignment,fontsize=24, **kw)\n",
    "#plt.savefig('output/Figures/Figure3D.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "adbc493f-9788-4171-abb7-8e3640536260",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18815\n",
      "482935\n"
     ]
    }
   ],
   "source": [
    "#Here, we import the merged PPI\n",
    "ppi = pd.read_csv(\"input/PPI/autocore_symbol_lcc.csv\",delimiter= ',',\n",
    "           skipinitialspace=True)\n",
    "G_ppi = nx.from_pandas_edgelist(ppi, 'symbol1', 'symbol2')\n",
    "G_ppi_lcc = G_ppi.subgraph(max(nx.connected_components(G_ppi), key=len))  # extract lcc graph\n",
    "print(G_ppi_lcc.number_of_nodes())\n",
    "print(G_ppi_lcc.number_of_edges())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2472fbe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's build some dictionaries\n",
    "chem_gene = {}\n",
    "for i,v in chem_homo.iterrows():\n",
    "    try:\n",
    "        chem_gene[v[\"ChemicalID\"]] |= {v[\"GeneSymbol\"]}\n",
    "    except KeyError as e:\n",
    "        chem_gene[v[\"ChemicalID\"]] = set([v[\"GeneSymbol\"]])\n",
    "        \n",
    "chem_gene_cleaned = {}\n",
    "tot_gene_list=[]\n",
    "for k,v in chem_gene.items():\n",
    "    if len(v)>0:\n",
    "        chem_gene_cleaned[k]=v\n",
    "        for gene in v:\n",
    "            tot_gene_list.append(gene)           \n",
    "    else:\n",
    "        pass\n",
    "\n",
    "chem_gene_dictio_cleaned_ppi={}\n",
    "for k,v in chem_gene_cleaned.items():\n",
    "    new_list=[]\n",
    "    for gene in v:\n",
    "        if gene in G_ppi_lcc.nodes():\n",
    "            new_list.append(gene)\n",
    "        else:\n",
    "            pass\n",
    "    if len(new_list)>0:\n",
    "        chem_gene_dictio_cleaned_ppi[k]=new_list\n",
    "    else:\n",
    "        pass\n",
    "\n",
    "#Let's build also the vice-versa: the gene-chem \n",
    "gene_chem = {}\n",
    "for i,v in chem_homo.iterrows():\n",
    "    try:\n",
    "        gene_chem[v[\"GeneSymbol\"]] |= {v[\"ChemicalID\"]}\n",
    "    except KeyError as e:\n",
    "        gene_chem[v[\"GeneSymbol\"]] = set([v[\"ChemicalID\"]])\n",
    "        \n",
    "#Here the cleaning process\n",
    "gene_chem_cleaned={}\n",
    "for k,v in gene_chem.items():\n",
    "    if len(v)!=0:\n",
    "        gene_chem_cleaned[k]=v\n",
    "    else:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cd902c6f-7316-4f9c-a6e7-92952d1e85c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's import the median degree z-score\n",
    "with open('output/exp_mediandegree_zscore.pickle', 'rb') as handle:\n",
    "    exp_mediandegree_zscore = pk.load(handle)\n",
    "\n",
    "with open('intermediate/final_exposome_zscore.pickle', 'rb') as handle:\n",
    "    final_exposome_zscore = pk.load(handle)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "07b4c0e3-e021-4e63-ab8f-d9dfa6845651",
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_name = []\n",
    "exp_type = []\n",
    "exp_numgenes = []\n",
    "exp_median_degree = []\n",
    "exp_deg_zscore = []\n",
    "exp_conn_zscore = []\n",
    "\n",
    "for exp in hub_exposures_list:\n",
    "    genelist = chem_gene_dictio_cleaned_ppi[exp]\n",
    "    deg_list = [G_ppi_lcc.degree(gene) for gene in genelist]\n",
    "    exp_name.append(exp)\n",
    "    exp_type.append(\"Hub-exposure\")\n",
    "    exp_numgenes.append(len(genelist))\n",
    "    exp_median_degree.append(np.median(deg_list))\n",
    "    try:\n",
    "        exp_deg_zscore.append(exp_mediandegree_zscore[exp])\n",
    "    except:\n",
    "        exp_deg_zscore.append(\"nan\")\n",
    "    try:\n",
    "        exp_conn_zscore.append(final_exposome_zscore[exp])\n",
    "    except:\n",
    "        exp_conn_zscore.append(\"nan\")\n",
    "    \n",
    "for exp in central_module_exposures_list:\n",
    "    genelist = chem_gene_dictio_cleaned_ppi[exp]\n",
    "    deg_list = [G_ppi_lcc.degree(gene) for gene in genelist]\n",
    "    exp_name.append(exp)\n",
    "    exp_type.append(\"Central-module exposure\")\n",
    "    exp_numgenes.append(len(genelist))\n",
    "    exp_median_degree.append(np.median(deg_list))\n",
    "    try:\n",
    "        exp_deg_zscore.append(exp_mediandegree_zscore[exp])\n",
    "    except:\n",
    "        exp_deg_zscore.append(\"nan\")\n",
    "    try:\n",
    "        exp_conn_zscore.append(final_exposome_zscore[exp])\n",
    "    except:\n",
    "        exp_conn_zscore.append(\"nan\")\n",
    "\n",
    "for exp in peripheric_module_exposures_list:\n",
    "    genelist = chem_gene_dictio_cleaned_ppi[exp]\n",
    "    deg_list = [G_ppi_lcc.degree(gene) for gene in genelist]\n",
    "    exp_name.append(exp)\n",
    "    exp_type.append(\"Peripheral-module exposure\")\n",
    "    exp_numgenes.append(len(genelist))\n",
    "    exp_median_degree.append(np.median(deg_list))\n",
    "    try:\n",
    "        exp_deg_zscore.append(exp_mediandegree_zscore[exp])\n",
    "    except:\n",
    "        exp_deg_zscore.append(\"nan\")\n",
    "    try:\n",
    "        exp_conn_zscore.append(final_exposome_zscore[exp])\n",
    "    except:\n",
    "        exp_conn_zscore.append(\"nan\")\n",
    "\n",
    "for exp in no_central_no_module_exposures_list:\n",
    "    genelist = chem_gene_dictio_cleaned_ppi[exp]\n",
    "    deg_list = [G_ppi_lcc.degree(gene) for gene in genelist]\n",
    "    exp_name.append(exp)\n",
    "    exp_type.append(\"Scattered exposure\")\n",
    "    exp_numgenes.append(len(genelist))\n",
    "    exp_median_degree.append(np.median(deg_list))\n",
    "    try:\n",
    "        exp_deg_zscore.append(exp_mediandegree_zscore[exp])\n",
    "    except:\n",
    "        exp_deg_zscore.append(\"nan\")\n",
    "    try:\n",
    "        exp_conn_zscore.append(final_exposome_zscore[exp])\n",
    "    except:\n",
    "        exp_conn_zscore.append(\"nan\")\n",
    "\n",
    "\n",
    "exp_ppi_df = pd.DataFrame()\n",
    "exp_ppi_df[\"Exposure ID\"] = exp_name\n",
    "exp_ppi_df[\"Type\"] = exp_type\n",
    "exp_ppi_df[\"Number of Perturbed Genes in the PPI\"] = exp_numgenes\n",
    "exp_ppi_df[\"Median Degree in the PPI\"] = exp_median_degree\n",
    "exp_ppi_df[\"Median Degree z-score\"] = exp_deg_zscore\n",
    "exp_ppi_df[\"Connectivity z-score\"] = exp_conn_zscore\n",
    "\n",
    "exp_ppi_df.to_csv(\"output/exp_ppi_df.tsv\",sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "493c4e07-c959-454c-bbed-826d5c6bedd2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92d13a11-1509-41d8-a886-45f7f401f284",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "df6c5cf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's define a dictionary that will convert the chemical ID in chemical name\n",
    "chem_id_cas_conversion = {}\n",
    "for i,v in chem_homo.iterrows():\n",
    "        chem_id_cas_conversion[v[\"ChemicalID\"]] = v[\"CasRN\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8ca1e805",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's define a dictionary that will convert the chemical ID in chemical name\n",
    "chem_id_name_conversion = {}\n",
    "for i,v in chem_gene_df.iterrows():\n",
    "        chem_id_name_conversion[v[\"ChemicalID\"]] = v[\"# ChemicalName\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0e4684f8-6e79-4adc-a25f-8789ec6d5b85",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "with open('output/CTDNameID.txt', 'w') as f:  # Open file in write mode\n",
    "    for chemname, chemid in chem_id_name_conversion.items():  # Iterate over key-value pairs in your dictionary\n",
    "        f.write(str(chemname) + '\\t' + str(chemid) + '\\n')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "749c436f-8576-40a3-96cd-8484daf2e05d",
   "metadata": {},
   "outputs": [],
   "source": [
    "chem_id_name_conversion_inv={}\n",
    "for k,v in chem_id_name_conversion.items():\n",
    "    chem_id_name_conversion_inv[v]=k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cdea31f9-9db5-48a9-b4b3-27ab463fa05e",
   "metadata": {},
   "outputs": [],
   "source": [
    "chem_id_cas_conversion_inv={}\n",
    "for k,v in chem_id_cas_conversion.items():\n",
    "    chem_id_cas_conversion_inv[v]=k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a92009f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We are importing the toxicity data (Hazard) from NORMANN (Susdata0), this was updated at 18.01.2022\n",
    "#(https://www.norman-network.com/nds/SLE/)\n",
    "#We will use the hazard value 'HazScore_EcoChronic_KEMI', which ranges from 0 (no toxic) to 1 (super harmful)\n",
    "#this score was calculated based on the hazard in ecosystem and as chronic exposures and it was reported by\n",
    "#KEMI Market list, a list of the 30418 chemical compounds that are available in the EU market\n",
    "#https://comptox.epa.gov/dashboard/chemical-lists/KEMIMARKET\n",
    "\n",
    "\n",
    "susdat_toxicity_df=pd.read_excel('input/NORMANN/susdat_2022-01-18-104316.xlsx', index_col=0)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "30c30bfe-cf78-4fd0-b65c-4940dcac61aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4668\n"
     ]
    }
   ],
   "source": [
    "susdat_toxicity_ctd_full_dict = {}\n",
    "\n",
    "susdat_toxicity_name_low_dict = {}\n",
    "for i in susdat_toxicity_df['Name'].tolist():\n",
    "    susdat_toxicity_name_low_dict[i.lower()] = i\n",
    "    \n",
    "susdat_toxicity_name_dash_low_dict = {}\n",
    "for i in susdat_toxicity_df['Name_Dashboard'].tolist():\n",
    "    susdat_toxicity_name_dash_low_dict[str(i).lower()] = i\n",
    "\n",
    "susdat_toxicity_name_chem_low_list = {}\n",
    "for i in susdat_toxicity_df['Name_ChemSpider'].tolist():\n",
    "    susdat_toxicity_name_chem_low_list[str(i).lower()] = i\n",
    "\n",
    "susdat_toxicity_name_iupac_low_list = {}\n",
    "for i in susdat_toxicity_df['Name_IUPAC'].tolist():\n",
    "    susdat_toxicity_name_iupac_low_list[str(i).lower()] = i\n",
    "\n",
    "susdat_toxicity_name_chemsyn_low_list = {}\n",
    "for i in susdat_toxicity_df['Synonyms_ChemSpider'].tolist():\n",
    "    susdat_toxicity_name_chemsyn_low_list[str(i).lower()] = i\n",
    "\n",
    "for chem_na,chem_id in chem_id_name_conversion_inv.items():\n",
    "    chem_name = chem_na.lower()\n",
    "    try:\n",
    "        haz=susdat_toxicity_df[susdat_toxicity_df['Name']==susdat_toxicity_name_low_dict[chem_name]]['HazScore_EcoChronic_KEMI'].values[0]\n",
    "        susdat_toxicity_ctd_full_dict[chem_id]=haz\n",
    "    except:\n",
    "        try:\n",
    "            haz=susdat_toxicity_df[susdat_toxicity_df['Name_Dashboard']==susdat_toxicity_name_dash_low_dict[chem_name]]['HazScore_EcoChronic_KEMI'].values[0]\n",
    "            susdat_toxicity_ctd_full_dict[chem_id]=haz\n",
    "        except:\n",
    "            try:\n",
    "                haz=susdat_toxicity_df[susdat_toxicity_df['Name_ChemSpider']==susdat_toxicity_name_chem_low_list[chem_name]]['HazScore_EcoChronic_KEMI'].values[0]\n",
    "                susdat_toxicity_ctd_full_dict[chem_id]=haz\n",
    "            except:\n",
    "                try:\n",
    "                    haz=susdat_toxicity_df[susdat_toxicity_df['Name_IUPAC']==susdat_toxicity_name_iupac_low_list[chem_name]]['HazScore_EcoChronic_KEMI'].values[0]\n",
    "                    susdat_toxicity_ctd_full_dict[chem_id]=haz\n",
    "                except:\n",
    "                    try:\n",
    "                        haz=susdat_toxicity_df[susdat_toxicity_df['Synonyms_ChemSpider']==susdat_toxicity_name_chemsyn_low_list[chem_name]]['HazScore_EcoChronic_KEMI'].values[0]\n",
    "                        susdat_toxicity_ctd_full_dict[chem_id]=haz\n",
    "                    except:\n",
    "                        pass\n",
    "\n",
    "for chem_cas,chem_id in chem_id_cas_conversion_inv.items():\n",
    "    if chem_id not in susdat_toxicity_ctd_full_dict.keys():    \n",
    "        try:\n",
    "            haz=susdat_toxicity_df[susdat_toxicity_df['CAS_RN']==\"CAS_RN:\"+chem_cas]['HazScore_EcoChronic_KEMI'].values[0]\n",
    "            susdat_toxicity_ctd_full_dict[exp]=haz\n",
    "        except:\n",
    "            try:\n",
    "                haz=susdat_toxicity_df[susdat_toxicity_df['CAS_RN_Dashboard']==chem_cas]['HazScore_EcoChronic_KEMI'].values[0]\n",
    "                susdat_toxicity_ctd_full_dict[exp]=haz\n",
    "            except: \n",
    "                pass\n",
    "\n",
    "\n",
    "print(len(susdat_toxicity_ctd_full_dict))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a51b319c-0b31-4205-9fad-7b43c84f1d49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47381242387332523"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(susdat_toxicity_ctd_full_dict)/len(chem_gene_dictio_cleaned_ppi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "33e44a2a-5cea-4650-8e13-2243f207e3b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's see whether there's any preferential chemical class that is missing from the hazard\n",
    "with open('intermediate/d_classes.pickle', 'rb') as handle:\n",
    "    d_classes = pk.load(handle)\n",
    "\n",
    "#Here, we define the fisher test that we will use\n",
    "def overlap_set(setA, setB,background):    \n",
    "    \"\"\"\n",
    "    Accepts to lists\n",
    "    M is the population size (previously N)\n",
    "    n is the number of successes in the population\n",
    "    N is the sample size (previously n)\n",
    "    x is still the number of drawn “successes”\n",
    "    \"\"\"\n",
    "    M= background #total number of exposures in chem_gene_cleaned\n",
    "    n= len(setA)\n",
    "    N= len(setB)\n",
    "    x= len(setA.intersection(setB))\n",
    "    return str(stats.hypergeom.sf(x-1, M, n, N))\n",
    "\n",
    "#Here we define the adjustment\n",
    "from statsmodels.sandbox.stats.multicomp import multipletests\n",
    "def fdr_adjustment(list_of_pvals,alpha):    \n",
    "    return multipletests(list_of_pvals,alpha=alpha,method='fdr_bh')[1] #the benjamin hochberg method is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ff158d3e-7033-41c5-a936-fd654730e976",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "missing_chem = []\n",
    "for chem in chem_gene_dictio_cleaned_ppi.keys():\n",
    "    if chem not in susdat_toxicity_ctd_full_dict.keys():\n",
    "        missing_chem.append(chem)\n",
    "\n",
    "missing_chem_pval = {}\n",
    "missing_chem_fdr = {}\n",
    "\n",
    "for chem_class,chemlist in d_classes.items():\n",
    "    pval = float(overlap_set(set(missing_chem),set(chemlist),len(chem_gene_dictio_cleaned_ppi)))\n",
    "    missing_chem_pval[chem_class] = pval\n",
    "\n",
    "\n",
    "adj_pval=fdr_adjustment(list(missing_chem_pval.values()),0.05)\n",
    "pair_list=list(missing_chem_pval.keys())\n",
    "for i in range(len(adj_pval)):\n",
    "    missing_chem_fdr[pair_list[i]]=adj_pval[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b574467b-0c6d-4b3a-9d08-43daf8ba1773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'D011083': 0.1823854693116027,\n",
       " 'D006571': 0.9340272079494817,\n",
       " 'D008055': 1.8264056031190082e-07,\n",
       " 'D009930': 0.9999999999999923,\n",
       " 'D045424': 1.1447064837752485e-16,\n",
       " 'D007287': 0.6861673254222268,\n",
       " 'D002241': 1.8264056031190082e-07,\n",
       " 'D001685': 1.294065187223938e-10,\n",
       " 'D000602': 3.3641787012528767e-16,\n",
       " 'D020164': 3.3641787012528767e-16,\n",
       " 'D045762': 0.169668210712233,\n",
       " 'D009706': 0.0011436982284394603}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_chem_fdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2b1b9f74-d13f-4962-b006-792bb0e5bc2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the ratio of found exposures with hazard annotation for hub exposures: 0.35435229609321456\n",
      "This is the ratio of found exposures with hazard annotation for central module exposures: 0.40920897284533647\n",
      "This is the ratio of found exposures with hazard annotation for peripheric module exposures: 0.40609137055837563\n",
      "This is the ratio of found exposures with hazard annotation for no module no hub exposures: 0.36112759643916914\n"
     ]
    }
   ],
   "source": [
    "hub_exposures_haz_dict={}\n",
    "for exp in hub_exposures_list:\n",
    "    try:\n",
    "        hub_exposures_haz_dict[exp] = susdat_toxicity_ctd_full_dict[exp]\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "peripheric_module_haz_dict={}\n",
    "for exp in peripheric_module_exposures_list:\n",
    "    try:\n",
    "        peripheric_module_haz_dict[exp] = susdat_toxicity_ctd_full_dict[exp]\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "central_module_haz_dict={}\n",
    "for exp in central_module_exposures_list:\n",
    "    try:\n",
    "        central_module_haz_dict[exp] = susdat_toxicity_ctd_full_dict[exp]\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "no_central_no_module_exposures_dict={}\n",
    "for exp in no_central_no_module_exposures_list:\n",
    "    try:\n",
    "        no_central_no_module_exposures_dict[exp] = susdat_toxicity_ctd_full_dict[exp]\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "print(\"This is the ratio of found exposures with hazard annotation for hub exposures: %s\" %(len(hub_exposures_haz_dict)/len(hub_exposures_list)))\n",
    "print(\"This is the ratio of found exposures with hazard annotation for central module exposures: %s\" %(len(central_module_haz_dict)/len(central_module_exposures_list)))\n",
    "print(\"This is the ratio of found exposures with hazard annotation for peripheric module exposures: %s\" %(len(peripheric_module_haz_dict)/len(peripheric_module_exposures_list)))\n",
    "print(\"This is the ratio of found exposures with hazard annotation for no module no hub exposures: %s\" %(len(no_central_no_module_exposures_dict)/len(no_central_no_module_exposures_list)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "21003a1a-5e84-457a-8295-142ee983d975",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_7320/2399414858.py:9: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  haz_count_df = pd.concat([haz_count_df, pd.DataFrame([new_row])], ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "column_names = [\"Hazard\", \"Group\"]\n",
    "\n",
    "haz_count_df = pd.DataFrame(columns = column_names)\n",
    "\n",
    "for val in list(hub_exposures_haz_dict.values()):\n",
    "    if str(val)!='nan':\n",
    "        new_row={\"Hazard\":val,\"Group\":\"Hub\"}\n",
    "        haz_count_df = pd.concat([haz_count_df, pd.DataFrame([new_row])], ignore_index=True)\n",
    "    \n",
    "for val in list(central_module_haz_dict.values()):\n",
    "    if str(val)!='nan':\n",
    "        new_row={\"Hazard\":val,\"Group\":\"Central Module\"}\n",
    "        haz_count_df = pd.concat([haz_count_df, pd.DataFrame([new_row])], ignore_index=True)\n",
    "    \n",
    "for val in list(peripheric_module_haz_dict.values()):\n",
    "    if str(val)!='nan':\n",
    "        new_row={\"Hazard\":val,\"Group\":\"Peripheral Module\"}\n",
    "        haz_count_df = pd.concat([haz_count_df, pd.DataFrame([new_row])], ignore_index=True)\n",
    "    \n",
    "    \n",
    "for val in list(no_central_no_module_exposures_dict.values()):\n",
    "    if str(val)!='nan':\n",
    "        new_row={\"Hazard\":val,\"Group\":\"Scattered\"}\n",
    "        haz_count_df = pd.concat([haz_count_df, pd.DataFrame([new_row])], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69b755e9-8058-44a9-a7ea-79dc533f11a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "6a6c1eb5-dd81-4ad1-b9b3-a74f656cef00",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_7320/1485264548.py:21: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  g=sns.boxplot(data=haz_count_df,x=\"Group\", y=\"Hazard\",order=[\"Central Module\",\"Hub\",\"Peripheral Module\",\"Scattered\"],palette=my_pal,linewidth=5)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import combinations\n",
    "pairs = list(combinations(list(set(haz_count_df['Group'])), r=2))\n",
    "\n",
    "\n",
    "#Let's visualize as boxplot\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "sns.set(style=\"white\")\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "\n",
    "list_palette=list(sns.color_palette())\n",
    "\n",
    "my_pal = {\"Central Module\":list_palette[1] , \"Hub\": list_palette[0], \"Peripheral Module\":list_palette[2], \"Scattered\":list_palette[3]}\n",
    "\n",
    "\n",
    "g=sns.boxplot(data=haz_count_df,x=\"Group\", y=\"Hazard\",order=[\"Central Module\",\"Hub\",\"Peripheral Module\",\"Scattered\"],palette=my_pal,linewidth=5)\n",
    "\n",
    "ax.grid(False)\n",
    "\n",
    "plt.savefig('output/Figures/Fig3G_v06.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d84656c3-1941-401f-92f6-b51cbcbb1ac0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=226838.5, pvalue=0.02428842344923827)\n",
      "MannwhitneyuResult(statistic=866277.5, pvalue=6.185817331272729e-16)\n",
      "MannwhitneyuResult(statistic=125361.5, pvalue=0.28454259895208334)\n",
      "MannwhitneyuResult(statistic=245288.5, pvalue=1.7545824498236355e-09)\n",
      "MannwhitneyuResult(statistic=282843.5, pvalue=0.00013957768411437826)\n"
     ]
    }
   ],
   "source": [
    "#Let's make some statistics\n",
    "import scipy\n",
    "\n",
    "hub_haz_list_cleaned = [i for i in list(hub_exposures_haz_dict.values()) if str(i)!='nan']\n",
    "nocentral_nomodule_haz_list_cleaned = [i for i in list(no_central_no_module_exposures_dict.values()) if str(i)!='nan']\n",
    "peripheric_module_haz_list_cleaned = [i for i in list(peripheric_module_haz_dict.values()) if str(i)!='nan']\n",
    "central_module_haz_list_cleaned = [i for i in list(central_module_haz_dict.values()) if str(i)!='nan']\n",
    "\n",
    "\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(hub_haz_list_cleaned, nocentral_nomodule_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(central_module_haz_list_cleaned, nocentral_nomodule_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(peripheric_module_haz_list_cleaned, nocentral_nomodule_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(central_module_haz_list_cleaned, peripheric_module_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(hub_haz_list_cleaned, central_module_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f41cf139-2012-4a33-9c54-185b8af6ce69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.31"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "susdat_toxicity_df[susdat_toxicity_df['Name']=='Endrin ']['HazScore_EcoChronic_KEMI'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "04655144-9f19-4470-805d-c00524b4fe65",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "C027667 0.15\n",
      "C027667 0.15\n",
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      "C030735 0.1\n",
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      "C023844 0.14\n",
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      "C023844 0.14\n",
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      "D011441 0.16\n",
      "D011441 0.16\n",
      "D011441 0.16\n",
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      "C015559 0.2\n",
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      "D014508 0.18\n",
      "D014508 0.18\n",
      "D014508 0.18\n",
      "D014508 0.18\n",
      "D014508 0.18\n",
      "D014867 0.03\n",
      "D014867 0.03\n",
      "D019820 0.05\n",
      "D019820 0.05\n",
      "D019820 0.05\n"
     ]
    }
   ],
   "source": [
    "for exp,haz in hub_exposures_haz_dict.items():\n",
    "    if haz<=0.3:\n",
    "        if len(chem_gene_dictio_cleaned_ppi[exp])<20:\n",
    "            if len(set(chem_gene_dictio_cleaned_ppi['D004732'])&set(chem_gene_dictio_cleaned_ppi[exp]))<1:\n",
    "                if len(set(chem_gene_dictio_cleaned_ppi['D014641'])&set(chem_gene_dictio_cleaned_ppi[exp]))<1:\n",
    "                    G_ppi_sub = G_ppi.subgraph(chem_gene_dictio_cleaned_ppi[exp]) \n",
    "                    if G_ppi_sub.number_of_edges()>4:\n",
    "                        for gene in chem_gene_dictio_cleaned_ppi[exp]:\n",
    "                            if nx.degree(G_ppi,gene)>50:\n",
    "                                print(exp,haz)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "cdc3e2c2-714f-4a35-a282-3961064ea50a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# open file in write mode\n",
    "with open('output/Methylmethacrylate.txt', 'w') as fp:\n",
    "    for item in chem_gene_dictio_cleaned_ppi['D020366']:\n",
    "        # write each item on a new line\n",
    "        fp.write(\"%s\\n\" % item)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "475a5338-3046-4784-9271-29b804744f29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# open file in write mode\n",
    "with open('output/Benthiocarb.txt', 'w') as fp:\n",
    "    for item in chem_gene_dictio_cleaned_ppi['C037925']:\n",
    "        # write each item on a new line\n",
    "        fp.write(\"%s\\n\" % item)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "44b8d6af-9102-4315-ae46-fd1748a6a57b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D015104 0.04\n",
      "C035253 0.01\n",
      "D001640 0.1\n",
      "C075375 0.01\n",
      "D006923 0.02\n",
      "D008717 0.01\n",
      "D010852 0.02\n",
      "D012603 0.01\n",
      "D014641 0.01\n"
     ]
    }
   ],
   "source": [
    "for exp,haz in peripheric_module_haz_dict.items():\n",
    "    if haz<=0.2:\n",
    "        if len(chem_gene_dictio_cleaned_ppi[exp])<20 and len(chem_gene_dictio_cleaned_ppi[exp])>3:\n",
    "            if len(set(chem_gene_dictio_cleaned_ppi['D004732'])&set(chem_gene_dictio_cleaned_ppi[exp]))<1:\n",
    "                G_ppi_sub = G_ppi.subgraph(chem_gene_dictio_cleaned_ppi[exp]) \n",
    "                if G_ppi_sub.number_of_edges()>10:\n",
    "                    print(exp,haz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "66bd5f85-727a-44fc-abf4-4a09d4403a04",
   "metadata": {},
   "outputs": [],
   "source": [
    "# open file in write mode\n",
    "with open('output/Vanillic_acid.txt', 'w') as fp:\n",
    "    for item in chem_gene_dictio_cleaned_ppi['D014641']:\n",
    "        # write each item on a new line\n",
    "        fp.write(\"%s\\n\" % item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e6cfaaa2-e433-4277-8b7f-a8d01cc984cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# open file in write mode\n",
    "with open('output/Diflubenzuron.txt', 'w') as fp:\n",
    "    for item in chem_gene_dictio_cleaned_ppi['D004132']:\n",
    "        # write each item on a new line\n",
    "        fp.write(\"%s\\n\" % item)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cbcdd280",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(central_module_haz_list_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "17507b5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.63"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(hub_haz_list_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d041a10c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.57"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(nocentral_nomodule_haz_list_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7ecef0a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.56"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(peripheric_module_haz_list_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "bc75ecf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.13"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(central_module_haz_list_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d22ceb3-7e45-43d5-9658-29267e31ee83",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "80e14082",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(hub_haz_list_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ec9693ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.09"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(nocentral_nomodule_haz_list_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "91823e6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(peripheric_module_haz_list_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a829534b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b3c7c66f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_7320/2142357301.py:3: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  ax = sns.barplot(x=\"Group\", y=\"Hazard\", data=haz_count_df,\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2400x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's visualize as barplots\n",
    "fig, ax = plt.subplots(figsize=(24, 18))\n",
    "ax = sns.barplot(x=\"Group\", y=\"Hazard\", data=haz_count_df,\n",
    "                 palette=\"Blues_d\",estimator=np.mean)\n",
    "#ax.set_yscale(\"log\")\n",
    "ax.set_ylabel(\"Hazard\",fontsize=28)\n",
    "#ax.set_xlabel(\"Group\",fontsize=28)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "167344bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We do not observe any positive correlation between the median degree value and the Hazard, let's try considering\n",
    "#the max degree\n",
    "\n",
    "def max_degree(genelist):\n",
    "    degree_list=[]\n",
    "    for gene in genelist:\n",
    "        degree_list.append(G_ppi_lcc.degree(gene))\n",
    "    max_degree=max(degree_list)\n",
    "    return max_degree\n",
    "\n",
    "chem_max_degree={}\n",
    "for chem, genelist in chem_gene_dictio_cleaned_ppi.items():\n",
    "    chem_max_degree[chem]=max_degree(genelist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e415e0d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonRResult(statistic=0.23893893127169047, pvalue=2.4814094375987696e-42)\n",
      "SignificanceResult(statistic=0.1902223618902743, pvalue=3.5920536381089665e-27)\n"
     ]
    }
   ],
   "source": [
    "#Then, we will make a correlation plot to visualize whether there is a positive correlation between degree and hazard\n",
    "import scipy\n",
    "\n",
    "max_deg_list=[]\n",
    "haz_list=[]\n",
    "for chem,haz in susdat_toxicity_ctd_full_dict.items():\n",
    "    if str(haz)!='nan':\n",
    "        try:\n",
    "            max_deg_list.append(chem_max_degree[chem])\n",
    "            haz_list.append(haz)\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "\n",
    "print(scipy.stats.pearsonr(max_deg_list,haz_list))  # Pearson's r\n",
    "print(scipy.stats.spearmanr(max_deg_list,haz_list)) # Spearman's rho\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "fa961417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import scipy\n",
    "from matplotlib.pyplot import figure\n",
    "from scipy import stats\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "slope, intercept, r, p, stderr = scipy.stats.linregress(max_deg_list, haz_list)\n",
    "\n",
    "ax.plot(max_deg_list,haz_list, linewidth=0, marker='s',c='k', label='Exposure')\n",
    "y=[]\n",
    "for i in max_deg_list:\n",
    "    g=(intercept + slope * i)\n",
    "    y.append(g)\n",
    "line = \"Regression line\" \n",
    "\n",
    "ax.plot(max_deg_list, y, label=line,linewidth=2)\n",
    "#ax.set_xlabel('Max degree value', fontsize=18)\n",
    "#ax.set_ylabel('Hazard',fontsize=18)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "plt.xticks(fontsize=36) \n",
    "plt.yticks(fontsize=36) \n",
    "\n",
    "ax.legend(facecolor='white',fontsize=36)\n",
    "#plt.savefig('output/Figures/Figure3Fv2.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "7e60b3b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_names = [\"Hazard\", \"Degree\"]\n",
    "\n",
    "deg_haz_df = pd.DataFrame(columns = column_names)\n",
    "deg_haz_df['Hazard']=haz_list\n",
    "deg_haz_df['Degree']=max_deg_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c8677526",
   "metadata": {},
   "outputs": [],
   "source": [
    "deg_haz_df_sort_by_degree=deg_haz_df.sort_values(\"Degree\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1763140c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_list=[]\n",
    "for i in range(len(deg_haz_df_sort_by_degree)):\n",
    "    if i<len(deg_haz_df_sort_by_degree)/5:\n",
    "        cat_list.append(\"1st quintile\")\n",
    "    elif i>len(deg_haz_df_sort_by_degree)/5 and i<2*len(deg_haz_df_sort_by_degree)/5:\n",
    "        cat_list.append(\"2nd quintile\")\n",
    "    elif i>2*len(deg_haz_df_sort_by_degree)/5 and i<3*len(deg_haz_df_sort_by_degree)/5:\n",
    "        cat_list.append(\"3rd quintile\")\n",
    "    elif i>3*len(deg_haz_df_sort_by_degree)/5 and i<4*len(deg_haz_df_sort_by_degree)/5:\n",
    "        cat_list.append(\"4th quintile\")\n",
    "    else:\n",
    "        cat_list.append(\"5th quintile\")\n",
    "        \n",
    "deg_haz_df_sort_by_degree[\"Group\"]=cat_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "b6d2b5fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_7320/349719640.py:21: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_yticklabels([round(i,2) for i in ax.get_yticks()], size = 36)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's visualize as boxplot\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above) \n",
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "\n",
    "sns.set_style('white')\n",
    "sns.set(style=\"white\",font_scale = 2)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "g=sns.boxplot(data=deg_haz_df_sort_by_degree,x=\"Group\", y=\"Hazard\",order=['1st quintile','2nd quintile', '3rd quintile','4th quintile',\n",
    " '5th quintile'],linewidth=5)\n",
    "plt.xlabel('',fontsize=22)\n",
    "plt.ylabel('', fontsize=22)\n",
    "#plt.xticks([], fontsize=22)\n",
    "ax.grid(False)\n",
    "ax.set_yticklabels([round(i,2) for i in ax.get_yticks()], size = 36)\n",
    "#ax.set_xticklabels(ax.get_xticks(), size = 36)\n",
    "plt.savefig('output/Figures/Fig3H_v06.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "#g.set_yscale(\"log\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "afe36c20",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=200527.5, pvalue=0.9385109299874242)\n",
      "MannwhitneyuResult(statistic=185296.0, pvalue=0.022861787890550538)\n",
      "MannwhitneyuResult(statistic=166209.5, pvalue=1.7718630825290886e-07)\n",
      "MannwhitneyuResult(statistic=147096.5, pvalue=8.316218729496692e-17)\n",
      "MannwhitneyuResult(statistic=184493.0, pvalue=0.018647836471121417)\n",
      "MannwhitneyuResult(statistic=166064.0, pvalue=1.9991616789068124e-07)\n",
      "MannwhitneyuResult(statistic=147585.5, pvalue=2.233943678621656e-16)\n",
      "MannwhitneyuResult(statistic=181184.5, pvalue=0.004218313651759442)\n",
      "MannwhitneyuResult(statistic=161749.0, pvalue=1.6584198849731648e-09)\n",
      "MannwhitneyuResult(statistic=178189.5, pvalue=0.00046375422377577697)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='1st quintile']['Hazard'].tolist(), deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='2nd quintile']['Hazard'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='1st quintile']['Hazard'].tolist(), deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='3rd quintile']['Hazard'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='1st quintile']['Hazard'].tolist(), deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='4th quintile']['Hazard'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='1st quintile']['Hazard'].tolist(), deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='5th quintile']['Hazard'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='2nd quintile']['Hazard'].tolist(), deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='3rd quintile']['Hazard'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='2nd quintile']['Hazard'].tolist(), deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='4th quintile']['Hazard'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='2nd quintile']['Hazard'].tolist(), deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='5th quintile']['Hazard'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='3rd quintile']['Hazard'].tolist(), deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='4th quintile']['Hazard'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='3rd quintile']['Hazard'].tolist(), deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='5th quintile']['Hazard'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='4th quintile']['Hazard'].tolist(), deg_haz_df_sort_by_degree[deg_haz_df_sort_by_degree['Group']=='5th quintile']['Hazard'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54947c20-7dc2-4ee5-82be-c995a3d61794",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a69eac4-48b7-4366-9443-5faaf6be5dad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c0ba5d2b-67b1-4b16-9976-3291bc76c761",
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_name = []\n",
    "exp_type = []\n",
    "max_deg_list=[]\n",
    "haz_list=[]\n",
    "for chem,haz in susdat_toxicity_ctd_full_dict.items():\n",
    "    if str(haz)!='nan':\n",
    "        try:\n",
    "            max_deg_list.append(chem_max_degree[chem])\n",
    "            haz_list.append(haz)\n",
    "            exp_name.append(chem)\n",
    "            if chem in hub_exposures_list:\n",
    "                exp_type.append(\"Hub-exposure\")\n",
    "            elif chem in central_module_exposures_list:\n",
    "                exp_type.append(\"Central-module exposure\")\n",
    "            elif chem in peripheric_module_exposures_list:\n",
    "                exp_type.append(\"Peripheral-module exposure\")\n",
    "            else:\n",
    "                 exp_type.append(\"Scattered exposure\")\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "exp_haz_df = pd.DataFrame()\n",
    "exp_haz_df[\"Exposure ID\"] = exp_name\n",
    "exp_haz_df[\"Type\"] = exp_type\n",
    "exp_haz_df[\"Max degree in the PPI\"] = max_deg_list\n",
    "exp_haz_df[\"Hazard\"] = haz_list\n",
    "exp_haz_df.to_csv(\"output/exp_haz_df.tsv\",sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "047a93ea-563c-4107-97fb-d4e675932a62",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9321268-68b4-49bf-8c0d-a721fc7a4d0b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26307149-1844-49e0-a7cf-86b248504f98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7a4f492-403e-49da-bcc7-dfc89b5edcd2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9f954fd9-7463-41f2-88c8-2a79e230ce25",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's look at the perturbed cases\n",
    "\n",
    "with open('output/chem_gene_hq.pickle', 'rb') as handle:\n",
    "    chem_gene_hq = pk.load(handle)\n",
    "with open('output/chem_gene_homo_mouse.pickle', 'rb') as handle:\n",
    "    chem_gene_homo_mouse = pk.load(handle)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "86044389-bc43-411f-b3a0-2a54faf4743b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the ratio of found exposures with hazard annotation for hub exposures: 0.5729166666666666\n",
      "This is the ratio of found exposures with hazard annotation for central module exposures: 0.5454545454545454\n",
      "This is the ratio of found exposures with hazard annotation for peripheric module exposures: 0.5642458100558659\n",
      "This is the ratio of found exposures with hazard annotation for no module no hub exposures: 0.4771101573676681\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "#HOMO HQ\n",
    "\n",
    "hub_exposures_list=[]\n",
    "central_module_exposures_list=[]\n",
    "peripheric_module_exposures_list=[]\n",
    "no_central_no_module_exposures_list=[]\n",
    " \n",
    "# Using readlines()\n",
    "file1 = open('output/HomoHQ_Hub_exposures.txt', 'r')\n",
    "Lines = file1.readlines()\n",
    "for line in Lines:\n",
    "    hub_exposures_list.append(line.strip(\"\\n\"))\n",
    "\n",
    "# Using readlines()\n",
    "file1 = open('output/HomoHQ_Central_module_exposures.txt', 'r')\n",
    "Lines = file1.readlines()\n",
    "for line in Lines:\n",
    "    central_module_exposures_list.append(line.strip(\"\\n\"))\n",
    "    \n",
    "# Using readlines()\n",
    "file1 = open('output/HomoHQ_Peripheric_module_exposures.txt', 'r')\n",
    "Lines = file1.readlines()\n",
    "for line in Lines:\n",
    "    peripheric_module_exposures_list.append(line.strip(\"\\n\"))\n",
    "    \n",
    "# Using readlines()\n",
    "file1 = open('output/HomoHQ_No_central_no_module_exposures.txt', 'r')\n",
    "Lines = file1.readlines()\n",
    "for line in Lines:\n",
    "    no_central_no_module_exposures_list.append(line.strip(\"\\n\"))\n",
    "\n",
    "\n",
    "hub_exposures_haz_dict={}\n",
    "for exp in hub_exposures_list:\n",
    "    try:\n",
    "        hub_exposures_haz_dict[exp] = susdat_toxicity_ctd_full_dict[exp]\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "peripheric_module_haz_dict={}\n",
    "for exp in peripheric_module_exposures_list:\n",
    "    try:\n",
    "        peripheric_module_haz_dict[exp] = susdat_toxicity_ctd_full_dict[exp]\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "central_module_haz_dict={}\n",
    "for exp in central_module_exposures_list:\n",
    "    try:\n",
    "        central_module_haz_dict[exp] = susdat_toxicity_ctd_full_dict[exp]\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "no_central_no_module_exposures_dict={}\n",
    "for exp in no_central_no_module_exposures_list:\n",
    "    try:\n",
    "        no_central_no_module_exposures_dict[exp] = susdat_toxicity_ctd_full_dict[exp]\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "print(\"This is the ratio of found exposures with hazard annotation for hub exposures: %s\" %(len(hub_exposures_haz_dict)/len(hub_exposures_list)))\n",
    "print(\"This is the ratio of found exposures with hazard annotation for central module exposures: %s\" %(len(central_module_haz_dict)/len(central_module_exposures_list)))\n",
    "print(\"This is the ratio of found exposures with hazard annotation for peripheric module exposures: %s\" %(len(peripheric_module_haz_dict)/len(peripheric_module_exposures_list)))\n",
    "print(\"This is the ratio of found exposures with hazard annotation for no module no hub exposures: %s\" %(len(no_central_no_module_exposures_dict)/len(no_central_no_module_exposures_list)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fb9e4338-1c06-44a8-a979-5d8919ff87a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_50662/3800223504.py:47: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  g=sns.boxplot(data=haz_count_df,x=\"Group\", y=\"Hazard\",order=[\"Central Module\",\"Hub\",\"Peripheral Module\",\"Scattered\"],palette=my_pal,linewidth=5)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "column_names = [\"Hazard\", \"Group\"]\n",
    "\n",
    "haz_count_df = pd.DataFrame(columns = column_names)\n",
    "\n",
    "for val in list(hub_exposures_haz_dict.values()):\n",
    "    if str(val)!='nan':\n",
    "        new_row={\"Hazard\":val,\"Group\":\"Hub\"}\n",
    "        haz_count_df = pd.concat([haz_count_df, pd.DataFrame([new_row])], ignore_index=True)\n",
    "    \n",
    "for val in list(central_module_haz_dict.values()):\n",
    "    if str(val)!='nan':\n",
    "        new_row={\"Hazard\":val,\"Group\":\"Central Module\"}\n",
    "        haz_count_df = pd.concat([haz_count_df, pd.DataFrame([new_row])], ignore_index=True)\n",
    "    \n",
    "for val in list(peripheric_module_haz_dict.values()):\n",
    "    if str(val)!='nan':\n",
    "        new_row={\"Hazard\":val,\"Group\":\"Peripheral Module\"}\n",
    "        haz_count_df = pd.concat([haz_count_df, pd.DataFrame([new_row])], ignore_index=True)\n",
    "    \n",
    "    \n",
    "for val in list(no_central_no_module_exposures_dict.values()):\n",
    "    if str(val)!='nan':\n",
    "        new_row={\"Hazard\":val,\"Group\":\"Scattered\"}\n",
    "        haz_count_df = pd.concat([haz_count_df, pd.DataFrame([new_row])], ignore_index=True)\n",
    "        \n",
    "        from itertools import combinations\n",
    "pairs = list(combinations(list(set(haz_count_df['Group'])), r=2))\n",
    "\n",
    "\n",
    "#Let's visualize as boxplot\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "sns.set(style=\"white\")\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "\n",
    "list_palette=list(sns.color_palette())\n",
    "\n",
    "my_pal = {\"Central Module\":list_palette[1] , \"Hub\": list_palette[0], \"Peripheral Module\":list_palette[2], \"Scattered\":list_palette[3]}\n",
    "\n",
    "\n",
    "g=sns.boxplot(data=haz_count_df,x=\"Group\", y=\"Hazard\",order=[\"Central Module\",\"Hub\",\"Peripheral Module\",\"Scattered\"],palette=my_pal,linewidth=5)\n",
    "\n",
    "ax.grid(False)\n",
    "\n",
    "plt.savefig('output/Figures/SF7D_v01_post_rev.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8a89ce9d-3f2c-43b3-a7af-954c76ddeafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=47384.0, pvalue=0.00250335288602101)\n",
      "MannwhitneyuResult(statistic=180891.5, pvalue=3.863892199791657e-13)\n",
      "MannwhitneyuResult(statistic=23665.5, pvalue=0.3582765471189281)\n",
      "MannwhitneyuResult(statistic=29416.0, pvalue=6.647327355500882e-06)\n",
      "MannwhitneyuResult(statistic=32547.5, pvalue=0.040038118316432426)\n",
      "MannwhitneyuResult(statistic=7789.5, pvalue=0.005669725286363029)\n"
     ]
    }
   ],
   "source": [
    "#Let's make some statistics\n",
    "import scipy\n",
    "\n",
    "hub_haz_list_cleaned = [i for i in list(hub_exposures_haz_dict.values()) if str(i)!='nan']\n",
    "nocentral_nomodule_haz_list_cleaned = [i for i in list(no_central_no_module_exposures_dict.values()) if str(i)!='nan']\n",
    "peripheric_module_haz_list_cleaned = [i for i in list(peripheric_module_haz_dict.values()) if str(i)!='nan']\n",
    "central_module_haz_list_cleaned = [i for i in list(central_module_haz_dict.values()) if str(i)!='nan']\n",
    "\n",
    "\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(hub_haz_list_cleaned, nocentral_nomodule_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(central_module_haz_list_cleaned, nocentral_nomodule_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(peripheric_module_haz_list_cleaned, nocentral_nomodule_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(central_module_haz_list_cleaned, peripheric_module_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(hub_haz_list_cleaned, central_module_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(hub_haz_list_cleaned, peripheric_module_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64a6bd08-ea9d-4d30-bbde-cc645db892c9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8da915a-7e23-4d5c-9f0c-60e46dc27662",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0e78310a-91be-44b4-a80c-b67bc6be6258",
   "metadata": {},
   "outputs": [],
   "source": [
    "#We do not observe any positive correlation between the median degree value and the Hazard, let's try considering\n",
    "#the max degree\n",
    "\n",
    "def max_degree(genelist):\n",
    "    degree_list=[]\n",
    "    for gene in genelist:\n",
    "        degree_list.append(G_ppi_lcc.degree(gene))\n",
    "    max_degree=max(degree_list)\n",
    "    return max_degree\n",
    "\n",
    "chem_gene_hq_max_degree={}\n",
    "for chem, genelist in chem_gene_hq.items():\n",
    "    n_genelist = set(genelist) & G_ppi_lcc.nodes()\n",
    "    if len(n_genelist)>0:\n",
    "        chem_gene_hq_max_degree[chem]=max_degree(n_genelist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6c6f0446-1a23-4de4-b254-1ddfb11c6c03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonRResult(statistic=0.23906366747534058, pvalue=1.864858100443993e-18)\n",
      "SignificanceResult(statistic=0.2255285929992126, pvalue=1.5150424318012994e-16)\n"
     ]
    }
   ],
   "source": [
    "#Then, we will make a correlation plot to visualize whether there is a positive correlation between degree and hazard\n",
    "import scipy\n",
    "\n",
    "max_deg_list=[]\n",
    "haz_list=[]\n",
    "for chem,haz in susdat_toxicity_ctd_full_dict.items():\n",
    "    if str(haz)!='nan':\n",
    "        try:\n",
    "            max_deg_list.append(chem_gene_hq_max_degree[chem])\n",
    "            haz_list.append(haz)\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "\n",
    "print(scipy.stats.pearsonr(max_deg_list,haz_list))  # Pearson's r\n",
    "print(scipy.stats.spearmanr(max_deg_list,haz_list)) # Spearman's rho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1cecb30b-8270-4a86-8604-b7f2e3b7438c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_50662/2534780583.py:42: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_yticklabels([round(i,2) for i in ax.get_yticks()], size = 36)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "column_names = [\"Hazard\", \"Degree\"]\n",
    "\n",
    "deg_haz_df = pd.DataFrame(columns = column_names)\n",
    "deg_haz_df['Hazard']=haz_list\n",
    "deg_haz_df['Degree']=max_deg_list\n",
    "deg_haz_df_sort_by_degree=deg_haz_df.sort_values(\"Degree\")\n",
    "cat_list=[]\n",
    "for i in range(len(deg_haz_df_sort_by_degree)):\n",
    "    if i<len(deg_haz_df_sort_by_degree)/5:\n",
    "        cat_list.append(\"1st quintile\")\n",
    "    elif i>len(deg_haz_df_sort_by_degree)/5 and i<2*len(deg_haz_df_sort_by_degree)/5:\n",
    "        cat_list.append(\"2nd quintile\")\n",
    "    elif i>2*len(deg_haz_df_sort_by_degree)/5 and i<3*len(deg_haz_df_sort_by_degree)/5:\n",
    "        cat_list.append(\"3rd quintile\")\n",
    "    elif i>3*len(deg_haz_df_sort_by_degree)/5 and i<4*len(deg_haz_df_sort_by_degree)/5:\n",
    "        cat_list.append(\"4th quintile\")\n",
    "    else:\n",
    "        cat_list.append(\"5th quintile\")\n",
    "        \n",
    "deg_haz_df_sort_by_degree[\"Group\"]=cat_list\n",
    "\n",
    "#Let's visualize as boxplot\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above) \n",
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "\n",
    "sns.set_style('white')\n",
    "sns.set(style=\"white\",font_scale = 2)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "g=sns.boxplot(data=deg_haz_df_sort_by_degree,x=\"Group\", y=\"Hazard\",order=['1st quintile','2nd quintile', '3rd quintile','4th quintile',\n",
    " '5th quintile'],linewidth=5)\n",
    "plt.xlabel('',fontsize=22)\n",
    "plt.ylabel('', fontsize=22)\n",
    "#plt.xticks([], fontsize=22)\n",
    "ax.grid(False)\n",
    "ax.set_yticklabels([round(i,2) for i in ax.get_yticks()], size = 36)\n",
    "#ax.set_xticklabels(ax.get_xticks(), size = 36)\n",
    "plt.savefig('output/Figures/SF7F_v01_post_rev.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "#g.set_yscale(\"log\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "729d73e5-94d3-4ea1-bb12-498acd968782",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9ff3be11-78b6-4647-a04e-5d8a580ba8c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6107"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(chem_id_cas_conversion_inv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "37fb835e-668e-451e-9c40-c9f4577c55c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#HOMO MOUSE\n",
    "\n",
    "chem_homo_mouse = chem_gene_df[chem_gene_df['Organism'].isin(['Homo sapiens', 'Mus musculus'])]\n",
    "\n",
    "#Let's define a dictionary that will convert the chemical ID in chemical name\n",
    "chem_id_cas_conversion_mouse = {}\n",
    "for i,v in chem_homo_mouse.iterrows():\n",
    "        chem_id_cas_conversion_mouse[v[\"ChemicalID\"]] = v[\"CasRN\"]\n",
    "\n",
    "#Let's define a dictionary that will convert the chemical ID in chemical name\n",
    "chem_id_name_conversion_mouse = {}\n",
    "for i,v in chem_homo_mouse.iterrows():\n",
    "        chem_id_name_conversion_mouse[v[\"ChemicalID\"]] = v[\"# ChemicalName\"]\n",
    "\n",
    "chem_id_name_conversion_inv_mouse={}\n",
    "for k,v in chem_id_name_conversion_mouse.items():\n",
    "    chem_id_name_conversion_inv_mouse[v]=k\n",
    "\n",
    "chem_id_cas_conversion_inv_mouse={}\n",
    "for k,v in chem_id_cas_conversion_mouse.items():\n",
    "    chem_id_cas_conversion_inv_mouse[v]=k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "10dcccbd-cf11-4040-9046-f339614aaff8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1529\n"
     ]
    }
   ],
   "source": [
    "add_chem_mouse_list = []\n",
    "\n",
    "for chem in chem_gene_homo_mouse.keys():\n",
    "    if chem not in chem_gene_cleaned.keys():\n",
    "        add_chem_mouse_list.append(chem)\n",
    "\n",
    "print(len(add_chem_mouse_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5eefe3a-4301-49d2-9971-1ba71794822b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2baebb43-cc9d-4a86-ba22-124e7c1464c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "685112c9-5f85-47f2-a479-c925c782c159",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8eff90f3-6ecd-4fe5-8072-73927f4ba5df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4668\n"
     ]
    }
   ],
   "source": [
    "#Let's add some exposures coming from mouse\n",
    "\n",
    "for chem_id in add_chem_mouse_list:\n",
    "    chem_name = chem_id_name_conversion_mouse[chem_id].lower()\n",
    "    try:\n",
    "        haz=susdat_toxicity_df[susdat_toxicity_df['Name']==susdat_toxicity_name_low_dict[chem_name]]['HazScore_EcoChronic_KEMI'].values[0]\n",
    "        susdat_toxicity_ctd_full_dict[chem_id]=haz\n",
    "    except:\n",
    "        try:\n",
    "            haz=susdat_toxicity_df[susdat_toxicity_df['Name_Dashboard']==susdat_toxicity_name_dash_low_dict[chem_name]]['HazScore_EcoChronic_KEMI'].values[0]\n",
    "            susdat_toxicity_ctd_full_dict[chem_id]=haz\n",
    "        except:\n",
    "            try:\n",
    "                haz=susdat_toxicity_df[susdat_toxicity_df['Name_ChemSpider']==susdat_toxicity_name_chem_low_list[chem_name]]['HazScore_EcoChronic_KEMI'].values[0]\n",
    "                susdat_toxicity_ctd_full_dict[chem_id]=haz\n",
    "            except:\n",
    "                try:\n",
    "                    haz=susdat_toxicity_df[susdat_toxicity_df['Name_IUPAC']==susdat_toxicity_name_iupac_low_list[chem_name]]['HazScore_EcoChronic_KEMI'].values[0]\n",
    "                    susdat_toxicity_ctd_full_dict[chem_id]=haz\n",
    "                except:\n",
    "                    try:\n",
    "                        haz=susdat_toxicity_df[susdat_toxicity_df['Synonyms_ChemSpider']==susdat_toxicity_name_chemsyn_low_list[chem_name]]['HazScore_EcoChronic_KEMI'].values[0]\n",
    "                        susdat_toxicity_ctd_full_dict[chem_id]=haz\n",
    "                    except:\n",
    "                        pass\n",
    "\n",
    "for chem_id in add_chem_mouse_list:\n",
    "    chem_cas = chem_id_cas_conversion_mouse[chem_id]\n",
    "    if chem_id not in susdat_toxicity_ctd_full_dict.keys():    \n",
    "        try:\n",
    "            haz=susdat_toxicity_df[susdat_toxicity_df['CAS_RN']==\"CAS_RN:\"+chem_cas]['HazScore_EcoChronic_KEMI'].values[0]\n",
    "            susdat_toxicity_ctd_full_dict[exp]=haz\n",
    "        except:\n",
    "            try:\n",
    "                haz=susdat_toxicity_df[susdat_toxicity_df['CAS_RN_Dashboard']==chem_cas]['HazScore_EcoChronic_KEMI'].values[0]\n",
    "                susdat_toxicity_ctd_full_dict[exp]=haz\n",
    "            except: \n",
    "                pass\n",
    "\n",
    "\n",
    "print(len(susdat_toxicity_ctd_full_dict))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9014653-c69a-4bae-84ff-d5835053b09c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04d96ee5-a903-45e9-8bb9-fb0ee87dd45d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "80ecaacc-365c-4ff3-b223-e8744590fd18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the ratio of found exposures with hazard annotation for hub exposures: 0.359792924935289\n",
      "This is the ratio of found exposures with hazard annotation for central module exposures: 0.3920644274209389\n",
      "This is the ratio of found exposures with hazard annotation for peripheric module exposures: 0.3625450180072029\n",
      "This is the ratio of found exposures with hazard annotation for no module no hub exposures: 0.3244005641748942\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "#HOMO Mouse\n",
    "\n",
    "hub_exposures_list=[]\n",
    "central_module_exposures_list=[]\n",
    "peripheric_module_exposures_list=[]\n",
    "no_central_no_module_exposures_list=[]\n",
    " \n",
    "# Using readlines()\n",
    "file1 = open('output/HomoMouse_Hub_exposures.txt', 'r')\n",
    "Lines = file1.readlines()\n",
    "for line in Lines:\n",
    "    hub_exposures_list.append(line.strip(\"\\n\"))\n",
    "\n",
    "# Using readlines()\n",
    "file1 = open('output/HomoMouse_Central_module_exposures.txt', 'r')\n",
    "Lines = file1.readlines()\n",
    "for line in Lines:\n",
    "    central_module_exposures_list.append(line.strip(\"\\n\"))\n",
    "    \n",
    "# Using readlines()\n",
    "file1 = open('output/HomoMouse_Peripheric_module_exposures.txt', 'r')\n",
    "Lines = file1.readlines()\n",
    "for line in Lines:\n",
    "    peripheric_module_exposures_list.append(line.strip(\"\\n\"))\n",
    "    \n",
    "# Using readlines()\n",
    "file1 = open('output/HomoMouse_No_central_no_module_exposures.txt', 'r')\n",
    "Lines = file1.readlines()\n",
    "for line in Lines:\n",
    "    no_central_no_module_exposures_list.append(line.strip(\"\\n\"))\n",
    "\n",
    "\n",
    "hub_exposures_haz_dict={}\n",
    "for exp in hub_exposures_list:\n",
    "    try:\n",
    "        hub_exposures_haz_dict[exp] = susdat_toxicity_ctd_full_dict[exp]\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "peripheric_module_haz_dict={}\n",
    "for exp in peripheric_module_exposures_list:\n",
    "    try:\n",
    "        peripheric_module_haz_dict[exp] = susdat_toxicity_ctd_full_dict[exp]\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "central_module_haz_dict={}\n",
    "for exp in central_module_exposures_list:\n",
    "    try:\n",
    "        central_module_haz_dict[exp] = susdat_toxicity_ctd_full_dict[exp]\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "no_central_no_module_exposures_dict={}\n",
    "for exp in no_central_no_module_exposures_list:\n",
    "    try:\n",
    "        no_central_no_module_exposures_dict[exp] = susdat_toxicity_ctd_full_dict[exp]\n",
    "    except:\n",
    "        pass\n",
    "\n",
    "print(\"This is the ratio of found exposures with hazard annotation for hub exposures: %s\" %(len(hub_exposures_haz_dict)/len(hub_exposures_list)))\n",
    "print(\"This is the ratio of found exposures with hazard annotation for central module exposures: %s\" %(len(central_module_haz_dict)/len(central_module_exposures_list)))\n",
    "print(\"This is the ratio of found exposures with hazard annotation for peripheric module exposures: %s\" %(len(peripheric_module_haz_dict)/len(peripheric_module_exposures_list)))\n",
    "print(\"This is the ratio of found exposures with hazard annotation for no module no hub exposures: %s\" %(len(no_central_no_module_exposures_dict)/len(no_central_no_module_exposures_list)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "74ecad4f-1e2b-4680-be11-09d106177fa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_50662/2648372417.py:47: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  g=sns.boxplot(data=haz_count_df,x=\"Group\", y=\"Hazard\",order=[\"Central Module\",\"Hub\",\"Peripheral Module\",\"Scattered\"],palette=my_pal,linewidth=5)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "column_names = [\"Hazard\", \"Group\"]\n",
    "\n",
    "haz_count_df = pd.DataFrame(columns = column_names)\n",
    "\n",
    "for val in list(hub_exposures_haz_dict.values()):\n",
    "    if str(val)!='nan':\n",
    "        new_row={\"Hazard\":val,\"Group\":\"Hub\"}\n",
    "        haz_count_df = pd.concat([haz_count_df, pd.DataFrame([new_row])], ignore_index=True)\n",
    "    \n",
    "for val in list(central_module_haz_dict.values()):\n",
    "    if str(val)!='nan':\n",
    "        new_row={\"Hazard\":val,\"Group\":\"Central Module\"}\n",
    "        haz_count_df = pd.concat([haz_count_df, pd.DataFrame([new_row])], ignore_index=True)\n",
    "    \n",
    "for val in list(peripheric_module_haz_dict.values()):\n",
    "    if str(val)!='nan':\n",
    "        new_row={\"Hazard\":val,\"Group\":\"Peripheral Module\"}\n",
    "        haz_count_df = pd.concat([haz_count_df, pd.DataFrame([new_row])], ignore_index=True)\n",
    "    \n",
    "    \n",
    "for val in list(no_central_no_module_exposures_dict.values()):\n",
    "    if str(val)!='nan':\n",
    "        new_row={\"Hazard\":val,\"Group\":\"Scattered\"}\n",
    "        haz_count_df = pd.concat([haz_count_df, pd.DataFrame([new_row])], ignore_index=True)\n",
    "        \n",
    "        from itertools import combinations\n",
    "pairs = list(combinations(list(set(haz_count_df['Group'])), r=2))\n",
    "\n",
    "\n",
    "#Let's visualize as boxplot\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "sns.set(style=\"white\")\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "\n",
    "list_palette=list(sns.color_palette())\n",
    "\n",
    "my_pal = {\"Central Module\":list_palette[1] , \"Hub\": list_palette[0], \"Peripheral Module\":list_palette[2], \"Scattered\":list_palette[3]}\n",
    "\n",
    "\n",
    "g=sns.boxplot(data=haz_count_df,x=\"Group\", y=\"Hazard\",order=[\"Central Module\",\"Hub\",\"Peripheral Module\",\"Scattered\"],palette=my_pal,linewidth=5)\n",
    "\n",
    "ax.grid(False)\n",
    "\n",
    "plt.savefig('output/Figures/SF7E_v01_post_rev.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d8da4e40-5acb-409a-a779-e145a7b8d40f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=210165.0, pvalue=0.0008166134450235309)\n",
      "MannwhitneyuResult(statistic=1117489.0, pvalue=1.8014279533075417e-17)\n",
      "MannwhitneyuResult(statistic=136280.0, pvalue=0.9141663683637664)\n",
      "MannwhitneyuResult(statistic=255991.0, pvalue=7.108083242674686e-07)\n",
      "MannwhitneyuResult(statistic=272951.0, pvalue=0.03338477221329437)\n",
      "MannwhitneyuResult(statistic=48229.5, pvalue=0.009159863897277201)\n"
     ]
    }
   ],
   "source": [
    "#Let's make some statistics\n",
    "import scipy\n",
    "\n",
    "hub_haz_list_cleaned = [i for i in list(hub_exposures_haz_dict.values()) if str(i)!='nan']\n",
    "nocentral_nomodule_haz_list_cleaned = [i for i in list(no_central_no_module_exposures_dict.values()) if str(i)!='nan']\n",
    "peripheric_module_haz_list_cleaned = [i for i in list(peripheric_module_haz_dict.values()) if str(i)!='nan']\n",
    "central_module_haz_list_cleaned = [i for i in list(central_module_haz_dict.values()) if str(i)!='nan']\n",
    "\n",
    "\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(hub_haz_list_cleaned, nocentral_nomodule_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(central_module_haz_list_cleaned, nocentral_nomodule_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(peripheric_module_haz_list_cleaned, nocentral_nomodule_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(central_module_haz_list_cleaned, peripheric_module_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(hub_haz_list_cleaned, central_module_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(hub_haz_list_cleaned, peripheric_module_haz_list_cleaned, use_continuity=True, alternative='two-sided'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5fd5889-26fa-46b4-acef-a4285dd96e94",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32a1e951-0304-4de1-8a86-4b562b169e0a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4037b31c-4ad5-4063-a64c-a146fafeda55",
   "metadata": {},
   "outputs": [],
   "source": [
    "chem_gene_homo_mouse_max_degree={}\n",
    "for chem, genelist in chem_gene_homo_mouse.items():\n",
    "    n_genelist = set(genelist) & G_ppi_lcc.nodes()\n",
    "    if len(n_genelist)>0:\n",
    "        chem_gene_homo_mouse_max_degree[chem]=max_degree(n_genelist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "46addd1c-5a9a-48ec-8783-9dbbea0a7a29",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonRResult(statistic=0.2419611304269645, pvalue=1.5246903164864562e-46)\n",
      "SignificanceResult(statistic=0.19672932249872863, pvalue=4.789112517917238e-31)\n"
     ]
    }
   ],
   "source": [
    "#Then, we will make a correlation plot to visualize whether there is a positive correlation between degree and hazard\n",
    "import scipy\n",
    "\n",
    "max_deg_list=[]\n",
    "haz_list=[]\n",
    "for chem,haz in susdat_toxicity_ctd_full_dict.items():\n",
    "    if str(haz)!='nan':\n",
    "        try:\n",
    "            max_deg_list.append(chem_gene_homo_mouse_max_degree[chem])\n",
    "            haz_list.append(haz)\n",
    "        except:\n",
    "            pass\n",
    "\n",
    "\n",
    "print(scipy.stats.pearsonr(max_deg_list,haz_list))  # Pearson's r\n",
    "print(scipy.stats.spearmanr(max_deg_list,haz_list)) # Spearman's rho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3108d3fd-0693-4a2b-a96f-ab1f656880a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_50662/1415533791.py:42: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  ax.set_yticklabels([round(i,2) for i in ax.get_yticks()], size = 36)\n"
     ]
    },
    {
     "data": {
      "image/png": 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KUeIBAAAAFNz27duza9euzM/PHx7btm1btmzZkr6+vgYmY6Uo8QAAAAAKbPv27dm5c2fOPPPMXHrppXnta1+b22+/PZ/61Keyc+fOJFHkrQJrFhYWFhodYjXZtGlTkmTPnj0NTgIAAAAU3ezsbHp7e3PGGWdk+/btaW7+5/1Yc3Nz6evry0MPPZQdO3a4tfYE58UWAAAAAAW1e/fuzM/P59JLL60r8JKkubk5l1xySWq1Wnbv3t2ghKwUJR4AAABAQe3fvz9J8trXvnbR+e7u7rp1nLiUeAAAAAAFtW7duiTJ7bffvuj8+Ph43TpOXEo8AAAAgILavHlzmpqa8qlPfSpzc3N1c3Nzc/n0pz+dUqmUzZs3NyghK0WJBwAAAFBQLS0t2bJlSw4ePJi+vr7ceuutuf/++3Prrbemr68vBw8ezAUXXOClFqtA8zMvAQAAAKBR+vr6kiS7du3K9ddff3i8VCrloosuOjzPiW3NwsLCQqNDrCabNm1KkuzZs6fBSQAAAIDnktnZ2ezevTv79+/PunXrsnnzZjvwVhE78QAAAACeA1paWnLBBRc0OgYN4pl4AAAAAFBwSjwAAAAAKDglHgAAAAAUnBIPAAAAAApOiQcAAAAABafEAwAAAICCU+IBAAAAQMEp8QAAAACg4JR4AAAAAFBwSjwAAAAAKDglHgAAAAAUnBIPAAAAAApOiQcAAAAABafEAwAAAICCU+IBAAAAQMEp8QAAAACg4JR4AAAAAFBwSjwAAAAAKDglHgAAAAAUnBIPAAAAAApOiQcAAAAABafEAwAAAICCU+IBAAAAQMEp8QAAAACg4JR4AAAAAFBwSjwAAAAAKDglHgAAAAAUnBIPAAAAAApOiQcAAAAABafEAwAAAICCU+IBAAAAQMEp8QAAAACg4JR4AAAAAFBwSjwAAAAAKDglHgAAAAAUnBIPAAAAAApOiQcAAAAABafEAwAAAICCU+IBAAAAQMEp8QAAAACg4JR4AAAAAFBwzY0OAAAAAMAzq9VqqVarmZ6eTmtra8rlckqlUqNjsUKUeAAAAAAFV6lUMjo6mqmpqcNjbW1tGRgYSE9PTwOTsVLcTgsAAABQYJVKJcPDw+ns7MzIyEh27NiRkZGRdHZ2Znh4OJVKpdERWQFKPAAAAICCqtVqGR0dTXd3dwYHB9PV1ZW1a9emq6srg4OD6e7uzrZt21Kr1RodleNMiQcAAABQUNVqNVNTU+nt7U1TU32N09TUlN7e3hw4cCDVarVBCVkpSjwAAACAgpqenk6SrF+/ftH5jo6OunWcuJR4AAAAAAXV2tqaJJmcnFx0fu/evXXrOHEp8QAAAAAKqlwup62tLWNjY5mfn6+bm5+fz9jYWNrb21MulxuUkJWixAMAAAAoqFKplIGBgYyPj2doaCgTExOZmZnJxMREhoaGMj4+nv7+/pRKpUZH5Thbs7CwsNDoEKvJpk2bkiR79uxpcBIAAADguaJSqWR0dDRTU1OHx9rb29Pf35+enp4GJmOlNDc6AAAAAABPr6enJ695zWuye/fu7N+/P+vWrcvmzZvT0tLS6GisECUeAAAAQMEtthPvc5/7XAYGBuzEWyU8Ew8AAACgwCqVSoaHh9PZ2ZmRkZHs2LEjIyMj6ezszPDwcCqVSqMjsgKUeAAAAAAFVavVMjo6mu7u7gwODqarqytr165NV1dXBgcH093dnW3btqVWqzU6KseZEg8AAACgoKrVaqamptLb25umpvoap6mpKb29vTlw4ECq1WqDErJSlHgAAAAABTU9PZ0kWb9+/aLzHR0ddes4cSnxAAAAAAqqtbU1STI5Obno/N69e+vWceJS4gEAAAAUVLlcTltbW8bGxjI/P183Nz8/n7GxsbS3t6dcLjcoIStFiQcAAABQUKVSKQMDAxkfH8/Q0FAmJiYyMzOTiYmJDA0NZXx8PP39/SmVSo2OynG2ZmFhYeFYnezuu+/Orl278vWvfz2Tk5N54IEHUiqVctZZZ+W8887L6173uvz8z/98Tj/99GP1lUvy0EMP5c/+7M9y22235Vvf+lYOHjyYhx9+OKeddlrOOuusbNiwIT/5kz+Zf/Wv/lVOPvnk45Jh06ZNSZI9e/Ycl/MDAAAAJ55KpZLR0dFMTU0dHmtvb09/f396enoamIyVckxKvPvuuy+/93u/ly984QvPuPbUU0/N+973vvT19R3xVpXjZW5uLn/8x3+cm266KQ899NAzrj/rrLNy+eWX593vfnfWrFlzTLMo8QAAAIDlqNVqqVarmZ6eTmtra8rlsh14q8hRl3gTExMZGBjIfffdt6TPvf71r8/HPvax47bj7QnT09O54oor8vWvf33Jn33d616X6667Lqeddtoxy6PEAwAAAGCpjqrE++53v5u3ve1tOXjw4BFz5513XtatW5eHHnood911Vx588MEj1rzxjW/Mxz/+8WO+2+0JjzzySN797nfnjjvuOGLulFNOSblczhlnnJEHHngg1Wo1hw4dOmJdT09PbrjhhrS0tByTTEo8AAAAAJaqebkfnJuby/vf//4jCrw3v/nN+c3f/M2sX7/+8Nijjz6az33uc/nwhz9cdzvrV77ylXziE5/IZZddttwYT+u66647osD7kR/5kVx99dW54IIL6oq52dnZjI2N5T/9p/+UmZmZw+OVSiXXX399rrzyyuOSEQAAAACeybJ34v3f//f/nWuvvbZu7LLLLstv//ZvP+Vn9u7dm1/6pV/K97///cNjZ555Zr785S8f85dd3HPPPXnrW9+axx577PBYW1tb/uRP/iQvfvGLn/Jz3/72t3PppZfmgQceODzW0tKSL37xi1m3bt1R57ITDwAAAIClWtabJWZnZ/NHf/RHdWPd3d3ZunXr036uo6MjN9xwQ0466aTDYwcPHsy2bduWE+Np7d69u67AS5Lh4eGnLfCSx28D/vf//t/Xjc3Ozmb37t3HOiIAAAAAPCvLKvG+8IUvZHp6um7s13/915/Vs+1e/vKX5x3veEfd2M0335z5+fnlRHlKf/3Xf113/GM/9mP5qZ/6qWf12c2bN6etra1u7G/+5m+OWTYAAAAAWIpllXif//zn647PO++8vOpVr3rWn3/nO99Zd3z//fdnfHx8OVGe0oEDB+qOzz///Gf92TVr1uTlL3953di99957THIBAAAAwFItucR77LHHctttt9WNvelNb1rSOV7ykpeko6OjbuyLX/ziUqM8rSe/abZWqy3p883N9e/8mJ2dPepMAAAAALAcSy7x/uEf/qHu7a1J8upXv3rJX/zkz9x+++1LPsfTOeuss+qOq9Xqkj5/55131h0fi5daAAAAAMByLLnEu+uuu44Y6+rqWvIXn3feeUec95FHHlnyeZ7Khg0b6o6/973v5S/+4i+e1WcrlUr+8R//sW7sf/lf/pdjFQ0AAAAAlmTJJd7dd99dd3zKKaekvb19yV/85Ntp5+fn873vfW/J53kqv/ALv3DE2DXXXJN9+/Y97ef27duX3/7t364bO/XUU3PRRRcds2wAAAAAsBRLLvGmpqbqjp/8Ftdn6+yzzz5i7J577lnWuRbzyle+Mj/7sz9bN3bgwIH84i/+Ynbt2pXHHnusbm5ubi6f+9zn8ou/+ItHvMTiAx/4QF7wghccs2wAAAAAsBTNz7yk3n333Vd3/ORnzz1bi33un/7pn5Z1rqfyoQ99KPv378//9//9f4fHpqen81u/9Vv53d/93bz85S/PGWeckYceeijVajU/+MEP6j5/0kkn5QMf+EAuvPDCY5oLAAAAAJZiySXeAw88UHd82mmnLeuLTz311CPGHnzwwWWd6+m+4xOf+ET+8A//MNu2bavbfTczM5Px8fFFP9fc3Jy3vOUtef/7358f/dEfPaaZAAAAAGCplnw77ezsbN3xySefvKwvXuxzjz766LLO9XRaWlpy1VVX5cYbb3zWt8S2tLTkzDPPTKlUOuZ5AAAAAGCpjrrEa25e8ma+JFm0IJubm1vWuZ7Ot771rbzrXe9KX1/fEbcCP5WZmZn8yZ/8SX72Z382f/iHf5iFhYVjngsAAAAAnq0lN3C1Wq3uuKlpyT3gU35ufn5+Wed6Kjt27Mjv/d7v1d1G29zcnIsuuiibN2/O+eefn9NPPz0PPvhgvvWtb+XWW2/NLbfccvi/4+zsbP7zf/7PufPOO3Pdddct+78rAAAAAByNJZd4T9559+RS79labNddS0vLss61mN27d+d3fud36nbRdXR05I/+6I/yspe9rG7tC17wgrz+9a/P61//+vT19eV973tf9u7de3j+1ltvzYte9KJcffXVxywfAAAAADxbS95a9uSi7Yd3uS3F8Szx/umf/in//t//+7oCr729PX/yJ39yRIH3ZOedd17+9E//NC984Qvrxrdv356JiYljkg8AAAAAlmLJJd7pp59edzwzM7OsL3744YePGDvllFOWda4n+/SnP52DBw/Wjf3H//gfc/bZZz+rz5911ln5yEc+UjdWq9Xyf/1f/9cxyQcAAAAAS7HkEu/MM8+sO37wwQeX9cWLfe6ss85a1rme7POf/3zd8Ste8Yr81E/91JLO8apXvSo/8zM/Uzf2pS99adk7DwEAAABguZZc4rW1tdUd33///cv64sXeFPuCF7xgWef6YQ888EC+853v1I298Y1vXNa5/tf/9X+tO37kkUfy7W9/e9nZAAAAAGA5llzidXR01B1PTU0t+ny7Z3LPPfccMfaiF71oyed5sv379x8x9qM/+qPLOtdiz89b7PwAAAAAcDwtucR76UtfWnc8NzeX7373u0v+4n/8x3+sOz7rrLPS2tq65PM82WLP2lu7du2yzvUjP/IjR4wdOnRoWecCAAAAgOVacon3L//lvzxi7O///u+X/MXf/OY3647L5fKSz7GYxYq3AwcOLOtcDzzwwBFjz3/+85d1LgAAAABYriWXeK2trTnvvPPqxv76r/96Sec4dOhQ7rjjjrqxnp6epUZZ1GLP1fvWt761rHMt9vy7Z/uGWwAAAAA4VpZc4iXJ61//+rrjPXv25NFHH33Wn//Sl750xPonn3O5zjzzzPzYj/1Y3dgXvvCFzM7OLvlcX/rSl+qOW1tbF31OHgAAAAAcT8sq8bZs2VJ3fPDgwXzmM595Vp+dm5vLjTfeWDf26le/Oi95yUuWE2VRb3rTm47I94lPfGJJ5/jbv/3bfO1rX6sbe8Mb3pCmpmX9TwYAAAAAy7asRuqlL33pEbe/fvSjHz3iFtnFfOhDH8qdd95ZN9bX17ecGE/p0ksvzfOe97y6sf/j//g/8o1vfONZff7AgQO56qqr6sZKpVLe8573HLOMAAAAAPBsLXtb2ZVXXlm3K+3RRx/Nr/zKr+Qv/uIvFl0/OzubD33oQ/nTP/3TuvFXvepVectb3vK037V169acf/75df/ZuXPnU65ft25dLrvssrqxxx57LL/8y7+cm2++OQsLC0/52b/9279Nb2/vES/DuPjii4/pbkEAAAAAeLaal/vBV7ziFXnPe95Td2vswYMHc/nll+cNb3hDfu7nfi4dHR2ZmZnJt771rdx8882ZnJysO8dpp52WD3/4w8tP/zTe//7355vf/GYqlcrhsUcffTS/8zu/kxtvvDEXXXRRzj///JxzzjmZmZnJXXfdlS9/+cv5q7/6qyPO9apXvSr/+//+vx+XnAAAAADwTJZd4iWP78a777776nbFLSws5Ctf+Uq+8pWvPO1nn/e85+UP/uAPsn79+qOJ8JSam5vzsY99LO9973tz++23181973vfy8c+9rFndZ5XvvKVuf7669PS0nI8YgIAAADAMzqqtzQ0NTVlaGgo/+bf/Js0Nz/7PvCcc87J6OhofuZnfuZovv4ZnX766bnpppvynve8Z0n5ksefgfdLv/RL+dSnPpWzzjrrOCUEAAAAgGd21K9abWpqym/8xm9k586deetb35q1a9c+5doXvOAFee9735v/5//5f9Ld3X20X/2slEqlXH311fnCF76QX/qlX8oLXvCCp11/5pln5qKLLsrnP//5fPCDH7QDDwAAAICGW7PwdG95WIZHHnkkX//613Pvvffm/vvvT6lUyvOf//yUy+Wcd955dS/DaJQ777wzd955Z/7pn/4pDz74YE477bQ8//nPz4/92I/lX/yLf5E1a9Yct+/etGlTkmTPnj3H7TsAAAAAOLEc8xKPp6fEAwAAAGCpGr8tDgAAAAB4Wko8AAAAACg4JR4AAAAAFJwSDwAAAAAKTokHAAAAAAWnxAMAAACAgmtudAAAAFaXWq2WarWa6enptLa2plwup1QqNToWABSe39DVTYkHAMCKqVQqGR0dzdTU1OGxtra2DAwMpKenp4HJAKDY/IbidloAAFZEpVLJ8PBwOjs7MzIykh07dmRkZCSdnZ0ZHh5OpVJpdEQAKCS/oSTJmoWFhYVGh1hNNm3alCTZs2dPg5MAAKycWq2Wyy+/PJ2dnRkcHExT0z//XfL8/HyGhoYyOTmZG264wW1BAPBD/IbyBDvxAAA47qrVaqamptLb21v3Lx9J0tTUlN7e3hw4cCDVarVBCQGgmPyG8gQlHgAAx9309HSSZP369YvOd3R01K0DAB7nN5QnKPEAADjuWltbkySTk5OLzu/du7duHQDwOL+hPEGJBwDAcVcul9PW1paxsbHMz8/Xzc3Pz2dsbCzt7e0pl8sNSggAxeQ3lCco8QAAOO5KpVIGBgYyPj6eoaGhTExMZGZmJhMTExkaGsr4+Hj6+/s9kBsAnsRvKE/wdtoV5u20AMBqVqlUMjo6mqmpqcNj7e3t6e/vT09PTwOTAUCx+Q1FibfClHgAwGpXq9VSrVYzPT2d1tbWlMtluwcA4FnwG7q6NTc6AAAAq0upVMqGDRsaHQMAnnP8hq5unokHAAAAAAWnxAMAAACAglPiAQAAAEDBKfEAAAAAoOCUeAAAAABQcEo8AAAAACg4JR4AAAAAFJwSDwAAAAAKTokHAAAAAAXX3OgAAAAAADyzWq2WarWa6enptLa2plwup1QqNToWK0SJBwAAAFBwlUolo6OjmZqaOjzW1taWgYGB9PT0NDAZK8XttAAAAAAFVqlUMjw8nM7OzoyMjGTHjh0ZGRlJZ2dnhoeHU6lUGh2RFaDEAwAAACioWq2W0dHRdHd3Z3BwMF1dXVm7dm26uroyODiY7u7ubNu2LbVardFROc6UeAAAAAAFVa1WMzU1ld7e3jQ11dc4TU1N6e3tzYEDB1KtVhuUkJWixAMAAAAoqOnp6STJ+vXrF53v6OioW8eJS4kHAAAAUFCtra1JksnJyUXn9+7dW7eOE5cSDwAAAKCgyuVy2traMjY2lvn5+bq5+fn5jI2Npb29PeVyuUEJWSlKPAAAAICCKpVKGRgYyPj4eIaGhjIxMZGZmZlMTExkaGgo4+Pj6e/vT6lUanRUjrM1CwsLC40OsZps2rQpSbJnz54GJwEAAACeKyqVSkZHRzM1NXV4rL29Pf39/enp6WlgMlZKc6MDAAAAAPD0enp6snHjxlSr1UxPT6e1tTXlctkOvFVEiQcAAADwHFAqlbJhw4ZGx6BBPBMPAAAAAApOiQcAAAAABafEAwAAAICCU+IBAAAAQMEp8QAAAACg4JR4AAAAAFBwSjwAAAAAKDglHgAAAAAUnBIPAAAAAApOiQcAAAAABdfc6AAAAKwutVot1Wo109PTaW1tTblcTqlUanQsAIBCU+IBALBiKpVKRkdHMzU1dXisra0tAwMD6enpaWAyAIBiczstAAArolKpZHh4OJ2dnRkZGcmOHTsyMjKSzs7ODA8Pp1KpNDoiAEBhKfEAADjuarVaRkdH093dncHBwXR1dWXt2rXp6urK4OBguru7s23bttRqtUZHBQAoJCUeAADHXbVazdTUVHp7e9PUVP9H0KampvT29ubAgQOpVqsNSggAUGxKPAAAjrvp6ekkyfr16xed7+joqFsHAEA9JR4AAMdda2trkmRycnLR+b1799atAwCgnhIPAIDjrlwup62tLWNjY5mfn6+bm5+fz9jYWNrb21MulxuUEACg2JR4AAAcd6VSKQMDAxkfH8/Q0FAmJiYyMzOTiYmJDA0NZXx8PP39/SmVSo2OCgBQSGsWFhYWGh1iNdm0aVOSZM+ePQ1OAgCw8iqVSkZHRzM1NXV4rL29Pf39/enp6WlgMgCAYmtudAAAAFaPnp6ebNy4MdVqNdPT02ltbU25XLYDDwqkVqu5RgEKSIkHAMCKKpVK2bBhQ6NjAItYbLdsW1tbBgYG7JYFaDDPxAMAACCVSiXDw8Pp7OzMyMhIduzYkZGRkXR2dmZ4eDiVSqXREQFWNSUeAADAKler1TI6Opru7u4MDg6mq6sra9euTVdXVwYHB9Pd3Z1t27alVqs1OirAqqXEAwAAWOWq1WqmpqbS29ubpqb6f01sampKb29vDhw4kGq12qCEACjxAAAAVrnp6ekkyfr16xed7+joqFsHwMpT4gEAAKxyra2tSZLJyclF5/fu3Vu3DoCVp8QDAABY5crlctra2jI2Npb5+fm6ufn5+YyNjaW9vT3lcrlBCQFQ4gEAAKxypVIpAwMDGR8fz9DQUCYmJjIzM5OJiYkMDQ1lfHw8/f39KZVKjY4KsGqtWVhYWGh0iNVk06ZNSZI9e/Y0OAkAAEC9SqWS0dHRTE1NHR5rb29Pf39/enp6GpgMgOZGBwAAAKAYenp6snHjxlSr1UxPT6e1tTXlctkOPIACUOIBAABwWKlUyoYNGxodA1hErVZTsq9iSjwAAACAglvsdve2trYMDAy43X2V8GILAAAAgAKrVCoZHh5OZ2dnRkZGsmPHjoyMjKSzszPDw8OpVCqNjsgKUOIBAAAAFFStVsvo6Gi6u7szODiYrq6urF27Nl1dXRkcHEx3d3e2bduWWq3W6KgcZ0o8AAAAgIKqVquZmppKb29vmprqa5ympqb09vbmwIEDqVarDUrISlHiAQAAABTU9PR0kmT9+vWLznd0dNSt48SlxAMAAAAoqNbW1iTJ5OTkovN79+6tW8eJS4kHAAAAUFDlcjltbW0ZGxvL/Px83dz8/HzGxsbS3t6ecrncoISsFCUeAAAAQEGVSqUMDAxkfHw8Q0NDmZiYyMzMTCYmJjI0NJTx8fH09/enVCo1OirH2ZqFhYWFRodYTTZt2pQk2bNnT4OTAAAAAM8VlUolo6OjmZqaOjzW3t6e/v7+9PT0NDAZK6W50QEAAAAAeHo9PT15zWtek927d2f//v1Zt25dNm/enJaWlkZHY4Uo8QAAAAAKbrGdeJ/73OcyMDBgJ94q4Zl4AAAAAAVWqVQyPDyczs7OjIyMZMeOHRkZGUlnZ2eGh4dTqVQaHZEVoMQDAAAAKKharZbR0dF0d3dncHAwXV1dWbt2bbq6ujI4OJju7u5s27YttVqt0VE5zpR4AAAAAAVVrVYzNTWV3t7eNDXV1zhNTU3p7e3NgQMHUq1WG5SQlaLEAwAAACio6enpJMn69esXne/o6Khbx4lLiQcAAABQUK2trUmSycnJRef37t1bt44TlxIPAAAAoKDK5XLa2toyNjaW+fn5urn5+fmMjY2lvb095XK5QQlZKUo8AAAAgIIqlUoZGBjI+Ph4hoaGMjExkZmZmUxMTGRoaCjj4+Pp7+9PqVRqdFSOszULCwsLjQ6xmmzatClJsmfPngYnAQAAAJ4rKpVKRkdHMzU1dXisvb09/f396enpaWAyVkpzowMAAAAA8PR6enqycePGVKvVTE9Pp7W1NeVy2Q68VUSJBwAAAPAcUCqVsmHDhkbHoEE8Ew8AAAAACk6JBwAAAAAFp8QDAAAAgIJT4gEAAABAwSnxAAAAAKDglHgAAAAAUHBKPAAAAAAoOCUeAAAAABScEg8AAAAACk6JBwAAAAAF19zoAAAAABRHrVZLtVrN9PR0WltbUy6XUyqVGh0LYNVT4gEAAJAkqVQqGR0dzdTU1OGxtra2DAwMpKenp4HJAHA7LQAAAKlUKhkeHk5nZ2dGRkayY8eOjIyMpLOzM8PDw6lUKo2OCLCqKfEAAABWuVqtltHR0XR3d2dwcDBdXV1Zu3Zturq6Mjg4mO7u7mzbti21Wq3RUQFWLSUeAADAKletVjM1NZXe3t40NdX/a2JTU1N6e3tz4MCBVKvVBiUEQIkHAACwyk1PTydJ1q9fv+h8R0dH3ToAVp4SDwAAYJVrbW1NkkxOTi46v3fv3rp1AKw8JR4AAMAqVy6X09bWlrGxsczPz9fNzc/PZ2xsLO3t7SmXyw1KCIASDwAAYJUrlUoZGBjI+Ph4hoaGMjExkZmZmUxMTGRoaCjj4+Pp7+9PqVRqdFSAVWvNwsLCQqNDrCabNm1KkuzZs6fBSQAAAOpVKpWMjo5mamrq8Fh7e3v6+/vT09PTwGQANDc6AAAAAMXQ09OTjRs3plqtZnp6Oq2trSmXy3bgARSAEg8AAIDDSqVSNmzY0OgYADyJZ+IBAAAAQMEp8QAAAACg4JR4AAAAAFBwSjwAAAAAKDglHgAAAAAUnBIPAAAAAApOiQcAAAAABafEAwAAAICCU+IBAAAAQME1NzoAAACrS61WS7VazfT0dFpbW1Mul1MqlRodCwAKz2/o6qbEAwBgxVQqlYyOjmZqaurwWFtbWwYGBtLT09PAZABQbH5DcTstAAArolKpZHh4OJ2dnRkZGcmOHTsyMjKSzs7ODA8Pp1KpNDoiABSS31CSZM3CwsJCo0OsJps2bUqS7Nmzp8FJAABWTq1Wy+WXX57Ozs4MDg6mqemf/y55fn4+Q0NDmZyczA033OC2IAD4IX5DeYKdeAAAHHfVajVTU1Pp7e2t+5ePJGlqakpvb28OHDiQarXaoIQAUEx+Q3mCEg8AgONueno6SbJ+/fpF5zs6OurWAQCP8xvKE5R4AAAcd62trUmSycnJRef37t1btw4AeJzfUJ6gxAMA4Lgrl8tpa2vL2NhY5ufn6+bm5+czNjaW9vb2lMvlBiUEgGLyG8oTlHgAABx3pVIpAwMDGR8fz9DQUCYmJjIzM5OJiYkMDQ1lfHw8/f39HsgNAE/iN5QneDvtCvN2WgBgNatUKhkdHc3U1NThsfb29vT396enp6eByQCg2PyGosRbYUo8AGC1q9VqqVarmZ6eTmtra8rlst0DAPAs+A1d3ZobHQAAgNWlVCplw4YNjY4BPAUlARRXrVbLd77znezfvz/r1q3L+eef7/pcRZR4AAAAJFn8dr22trYMDAy4XQ8abPv27dm1a1fdyy22bduWLVu2pK+vr4HJWClebAEAAEAqlUqGh4fT2dmZkZGR7NixIyMjI+ns7Mzw8HAqlUqjI8KqtX379uzcuTNnnHFGrrjiinzyk5/MFVdckTPOOCM7d+7M9u3bGx2RFeCZeCvMM/EAAICiqdVqufzyy9PZ2ZnBwcE0Nf3zfo/5+fkMDQ1lcnIyN9xwg1v3YIXNzs6mt7c3Z5xxRrZv357m5n++qXJubi59fX156KGHsmPHjrS0tDQwKcebnXgAAACrXLVazdTUVHp7e+sKvCRpampKb29vDhw4kGq12qCEsHrt3r078/PzufTSS+sKvCRpbm7OJZdcklqtlt27dzcoIStFiQcAALDKTU9PJ0nWr1+/6HxHR0fdOmDl7N+/P0ny2te+dtH57u7uunWcuJR4AAAAq1xra2uSZHJyctH5vXv31q0DVs66deuSJLfffvui8+Pj43XrOHEp8QAAAFa5crmctra2jI2N1b35Mnn8mXhjY2Npb29PuVxuUEJYvTZv3pympqZ86lOfytzcXN3c3NxcPv3pT6dUKmXz5s0NSshKUeIBAACscqVSKQMDAxkfH8/Q0FAmJiYyMzOTiYmJDA0NZXx8PP39/V5qAQ3Q0tKSLVu25ODBg+nr68utt96a+++/P7feemv6+vpy8ODBXHDBBV5qsQp4O+0K83ZaAACgqCqVSkZHRzM1NXV4rL29Pf39/enp6WlgMmD79u3ZtWtX3W7ZUqmUCy64IH19fQ1MxkpR4q0wJR4AAFBktVot1Wo109PTaW1tTblctgMPCmJ2dja7d+/O/v37s27dumzevNkOvFWk+ZmXAAAAsFqUSqVs2LCh0TGARbS0tOSCCy5odAwaxDPxAAAAAKDglHgAAAAAUHBKPAAAAAAoOCUeAAAAABScEg8AAAAACk6JBwAAAAAFp8QDAAAAgIJT4gEAAABAwSnxAAAAAKDgmhsdAAAAgOKo1WqpVquZnp5Oa2tryuVySqVSo2MBrHpKPAAAAJIklUolo6OjmZqaOjzW1taWgYGB9PT0NDAZAEo8AAAAUqlUMjw8nO7u7lx99dVZv359JicnMzY2luHh4WzdulWRxwnjG9/4Rnbt2pVqtZrZ2dlGx1lVWlpaUi6Xs2XLlrzyla9sdJznlDULCwsLjQ6xmmzatClJsmfPngYnAQAAeFytVsvll1+ezs7ODA4Opqnpnx+fPj8/n6GhoUxOTuaGG25way3Ped/4xjfyu7/7u3nssccaHWVVO+mkk/LBD35QkbcEXmwBAACwylWr1UxNTaW3t7euwEuSpqam9Pb25sCBA6lWqw1KCMfOrl27FHgF8Nhjj2XXrl2NjvGcosQDAABY5aanp5Mk69evX3S+o6Ojbh08l911112NjsD/5J/F0ijxAAAAVrnW1tYkyeTk5KLze/furVsHz2UvfelLGx2B/8k/i6VR4gEAAKxy5XI5bW1tGRsby/z8fN3c/Px8xsbG0t7ennK53KCEcOxs2bIlJ510UqNjrHonnXRStmzZ0ugYzylebLHCvNgCAAAooh9+O21vb286Ojqyd+/ejI2NZXx83NtpOaE8F99OOzc395Rzzc3NK5jk6Hg77fIp8VaYEg8AACiqSqWS0dHRTE1NHR5rb29Pf3+/Ag8a7MILL1y0yGtubs5nP/vZBiRipT13qloAAACOq56enmzcuDHVajXT09NpbW1NuVxOqVRqdDSAVU+JBwAAwGGlUikbNmxodAwAnsSLLQAAAACg4JR4AAAAAFBwSjwAAAAAKDglHgAAAAAUnBIPAAAAAApOiQcAAAAABafEAwAAAICCaz6WJ7v77ruza9eufP3rX8/k5GQeeOCBlEqlnHXWWTnvvPPyute9Lj//8z+f008//Vh+7ZI89NBDufXWW/O1r30tExMTmZqayuzsbE477bScffbZ+fEf//G8/vWvz5ve9KaUSqWG5QQAAACAJ6xZWFhYONqT3Hffffm93/u9fOELX3jGtaeeemre9773pa+vL01NK7cRcHZ2Nn/8x3+c7du3Z2Zm5hnXv/CFL8wHPvCBvPGNbzymOTZt2pQk2bNnzzE9LwAAAHDiuvDCCzM3N3fEeHNzcz772c82IBEr7ahbtImJiVxwwQXPqsBLkocffji///u/n3/7b/9tHnnkkaP9+mdl3759edvb3pbrr7/+WRV4SXLPPffk3/7bf5uPfOQjxzkdAMDqUqvV8s1vfjNf/epX881vfjO1Wq3RkQAACu+obqf97ne/m3e/+905ePDgEXPnnXde1q1bl4ceeih33XVXHnzwwbr5r371q/m1X/u1fPzjH8+aNWuOJsbT2rdvX975znfm3nvvPWLunHPOyUtf+tI0Nzdn3759ufPOO/PkjYk33nhjTjnllPzqr/7qccsIALBaVCqVjI6OZmpq6vBYW1tbBgYG0tPT08BkAADFtuwSb25uLu9///uPKPDe/OY35zd/8zezfv36w2OPPvpoPve5z+XDH/5wHnroocPjX/nKV/KJT3wil1122XJjPK3Z2dm8733vO6LA27BhQ7Zu3ZrXvOY1deP33HNPPvaxj2XXrl1143/4h3+YN7zhDfkX/+JfHJecAACrQaVSyfDwcLq7u3P11Vdn/fr1mZyczNjYWIaHh7N161ZFHgDAU1j27bSf+cxnUq1W68Yuu+yyXH/99XUFXpI873nPS29vb3bu3Jmzzz67bu7jH/94XbF3LH384x8/IuNFF12Uz3zmM0cUeMnjz8H78Ic/nN/+7d+uG5+bm8sf/MEfHJeMAACrQa1Wy+joaLq7uzM4OJiurq6sXbs2XV1dGRwcTHd3d7Zt2+bWWgCAp7CsEm92djZ/9Ed/VDfW3d2drVu3Pu3nOjo6csMNN+Skk046PHbw4MFs27ZtOTGe1tTUVLZv3143tmnTpgwNDdV9/2Iuu+yyI15o8Zd/+Zc5cODAMc8JALAaVKvVTE1Npbe394iXmzU1NaW3tzcHDhw44i9gAQB43LJKvC984QuZnp6uG/v1X//1Z/Vsu5e//OV5xzveUTd28803Z35+fjlRntJnPvOZHDp06PBxa2trhoaGnvUbcd/znvfUHddqtdx2223HNCMAwGrxxJ8dn3zHxhM6Ojrq1gEAUG9ZJd7nP//5uuPzzjsvr3rVq57159/5znfWHd9///0ZHx9fTpRFLSws5L/8l/9SN3b55Zfn+c9//rM+x0/8xE/kBS94QU4//fS8+MUvzite8Yo8+uijxywjAMBq0tramiSZnJxcdH7v3r116wAAqLfkF1s89thjR+xIe9Ob3rSkc7zkJS9JR0fH4T+sJckXv/jFbNy4calxFvXNb36z7tbXJ57JtxRr1qzJ1772teP65lwAgNWiXC6nra0tY2NjGRwcrLs7Yn5+PmNjY2lvb0+5XG5gSgCA4lryTrx/+Id/yMzMTN3Yq1/96iV/8ZM/c/vtty/5HE+lUqnUHff09OS0005b8nkUeAAAx0apVMrAwEDGx8czNDSUiYmJzMzMZGJiIkNDQxkfH09/f39KpVKjowIAFNKSd+LdddddR4x1dXUt+YvPO++8I877yCOP5OSTT17yuZ7sG9/4Rt3xUm71BQDg+Ojp6cnWrVszOjqaq6+++vB4e3t7tm7dmp6engamAwAotiWXeHfffXfd8SmnnJL29vYlf/ETDy9+wvz8fL73ve/lpS996ZLP9WQTExN1x4uVjHfffXcqlUruvvvuPPTQQznttNPS1taW1772tXn1q1/tb4EBAI6Dnp6ebNy4MdVqNdPT02ltbU25XPZnLwCAZ7DkEm9qaqruuK2tbVlffPbZZx8xds899xx1iTc7O5t77723buycc845/H/v2bMnH//4x/PNb37zKc9x1lln5b3vfW/e8Y53pLl5yf8TAQDwNEqlUjZs2NDoGAAAzylLfibefffdV3d81llnLeuLF/vcP/3TPy3rXD9s3759mZ+frxs7++yz84Mf/CDvf//78973vvdpC7zk8bflfuhDH8rFF198RGkJAAAAACttySXeAw88UHe8nBdGJMmpp556xNiDDz64rHP9sOnp6SPGarVaBgYG8oUvfGFJ5/rWt76Vt7/97XVv0QUAAACAlbbke0VnZ2frjpf7IorFPvfoo48u61w/7MklY5L8zu/8Tu64447Dxz/2Yz+Wd73rXenp6ck555yTQ4cO5e67787u3bvzp3/6p3nssccOr923b1/+3b/7d7n55puPyUs3AAAAAGCplrwT78kl3nKfGbfYw4vn5uaWda4fdujQoSPG/t//9/89/H+/5z3vyX/9r/8173znO7N+/fo873nPy5lnnpmf+ImfyDXXXJNbbrklL37xi+s+PzExkY985CNHnQ0AAAAAlmPJJV6tVqs/QdOST/GUn3vys+yW44d30T3Zr/7qr+bqq69+2uLxJS95ST796U/n+c9/ft34zTff7Pl4AAAAADTEkhu4JxdgTy71nq3Fdt21tLQs61w/bGFhYdHxV77ylXn/+9//rM7R3t6ea665pm7s0UcfzWc+85mjzgcAAAAAS7XkEu/JRdvT7Xx7OserxHuqXXbvfe97l7RrcPPmzXnhC19YN/ZXf/VXR5UNAAAAAJZjySXe6aefXnc8MzOzrC9++OGHjxg75ZRTlnWuH7bYyydOOeWU/NRP/dSSzlMqlfK6172ubuzv//7vF33mHgAAAAAcT0su8c4888y64wcffHBZX7zY584666xlneuHPTlfkpx//vk56aSTlnyuf/kv/2Xdca1Wy/e///3lRgMAAACAZVnyq2Xb2trqju+///5lffF99913xNgLXvCCZZ3rhy1WBLa2ti7rXIt97p/+6Z/S0dGxrPMBAPD4X4xWq9VMT0+ntbU15XI5pVKp0bEAAAptySXekwusqampzM3NPe0bXxdzzz33HDH2ohe9aKlxjvDCF74wTU1NdW+6Xez5e8/G8573vCPGZmdnl50NAGC1q1QqGR0dzdTU1OGxtra2DAwMpKenp4HJAACKbcm30770pS+tO56bm8t3v/vdJX/xP/7jP9Ydn3XWWcveMffDWlpajigDDxw4sKxzPfDAA0eMPfmZgAAAPDuVSiXDw8Pp7OzMyMhIduzYkZGRkXR2dmZ4eDiVSqXREQEACmvJJd6TnxOXPP7Ch6X65je/WXdcLpeXfI6n8opXvKLueO/evcvaQfe9733viLFjsVsQAGC1qdVqGR0dTXd3dwYHB9PV1ZW1a9emq6srg4OD6e7uzrZt21Kr1RodFQCgkJZc4rW2tua8886rG/vrv/7rJZ3j0KFDueOOO+rGjuXtEz/5kz9ZdzwzM5O//Mu/XPJ5/u7v/q7ueP369TnttNOOKhsAwGpUrVYzNTWV3t7eNDXV/xG0qakpvb29OXDgQKrVaoMSAgAU25JLvCR5/etfX3e8Z8+ePProo8/681/60peOWP/kcx6NN7zhDUc8HPmWW25Z0jmmp6fzta99rW7sda973VFnAwBYjaanp5M8/peii3niuctPrAMAoN6ySrwtW7bUHR88eDCf+cxnntVn5+bmcuONN9aNvfrVr85LXvKS5URZ1Nlnn503vOENdWNf/OIX89/+23971ue48cYbj7gF9xd+4ReORTwAgFXniWcfT05OLjq/d+/eunUAANRbVon30pe+9IjbXz/60Y8ecYvsYj70oQ/lzjvvrBvr6+tbToyn9Su/8it1xwsLC/n1X//13H///c/42a9+9av5xCc+UTf2mte85ohn7QEA8OyUy+W0tbVlbGws8/PzdXPz8/MZGxtLe3v7MX1OMgDAiWRZJV6SXHnllXXPM3n00UfzK7/yK/mLv/iLRdfPzs7mQx/6UP70T/+0bvxVr3pV3vKWtzztd23dujXnn39+3X927tz5tJ955StfmYsuuqhubO/evXnnO995xEs1ftgtt9ySK664ou4Pl01NTdm6devTfh8AAE+tVCplYGAg4+PjGRoaysTERGZmZjIxMZGhoaGMj4+nv7//iEeiAADwuOblfvAVr3hF3vOe99TdGnvw4MFcfvnlecMb3pCf+7mfS0dHR2ZmZvKtb30rN9988xG3T5x22mn58Ic/vPz0z+ADH/hAvv3tb9e9PXdycjIXX3xxNm3alLe85S350R/90Tz22GO5++67s2vXrvzt3/7tEed53/velw0bNhy3nAAAq0FPT0+2bt2a0dHRXH311YfH29vbs3Xr1mP6ojMAgBPNsku85PHdePfdd1/drriFhYV85StfyVe+8pWn/ezznve8/MEf/MFTPtz4WDjllFPyiU98Ipdffnm+/vWvHx6fn5/Pl770pXzpS196xnO8/e1vz/ve977jlhEAYDXp6enJxo0bU61WMz09ndbW1pTLZTvwAACewbJvp00ev810aGgo/+bf/Js0Nz/7PvCcc87J6OhofuZnfuZovv5ZOe200zI6Opp3vetdS8rY0tKS3/7t387v/d7vZc2aNccxIQDHQ61Wyze/+c189atfzTe/+c3UarVGRwKA5wS/oQDFdFQ78ZLHi7zf+I3fyM///M/n+uuvz1/8xV/k0KFDi659wQtekIsvvjj9/f05/fTTj/arn7W1a9dmcHAwvb29GR0dzVe+8pU88MADi6497bTT8q//9b/Or/zKr+RFL3rRimUE4NipVCoZHR3N1NTU4bG2trYMDAy4XQ8azPUJxeYaBSiuNQsLCwvH8oSPPPJIvv71r+fee+/N/fffn1KplOc///kpl8s577zz6l6G0Shzc3O54447sn///nz/+9/PY489lh/5kR/Jy172smzYsCEtLS3H7bs3bdqUJNmzZ89x+w6A1axSqWR4eDjd3d3p7e3N+vXrMzk5mbGxsYyPj3vuFjSQ6xOKzTUKxXbhhRdmbm7uiPHm5uZ89rOfbUAiVtoxL/F4eko8gOOnVqvl8ssvT2dnZwYHB+v+4mh+fj5DQ0OZnJzMDTfc4PlbsMJcn1BsrlEoPiUejd8WBwDHSLVazdTUVHp7e4/Y+d3U1JTe3t4cOHAg1Wq1QQlh9XJ9QrG5RgGKT4kHwAljeno6SZ7yzecdHR1164CV4/qEYnONAhSfEg+AE0Zra2uSZHJyctH5vXv31q0DVo7rE4rNNQpQfEo8AE4Y5XI5bW1tGRsby/z8fN3c/Px8xsbG0t7ennK53KCEsHq5PqHYXKMAxafEA+CEUSqVMjAwkPHx8QwNDWViYiIzMzOZmJjI0NBQxsfH09/f74Hc0ACuTyg21yhA8Xk77QrzdlqA469SqWR0dDRTU1OHx9rb29Pf35+enp4GJgNcn1BsrlEoLm+nRYm3wpR4J45arZZqtZrp6em0tramXC77m0koENcoFNfs7Gx2796d/fv3Z926ddm8eXNaWloaHQv4nw4dOpSbbrop+/bty7nnnpvLLrssa9eubXQsWPWUeDQ3OgA8Fy32N5RtbW0ZGBjwN5RQEKVSKRs2bGh0DOBJFvsN/dznPuc3FApi+/bt2bVr1+Hn4t1xxx259dZbs2XLlvT19TU4HcDq5pl4sESVSiXDw8Pp7OzMyMhIduzYkZGRkXR2dmZ4eDiVSqXREQGgkPyGQrFt3749O3fuzBlnnJErrrgin/zkJ3PFFVfkjDPOyM6dO7N9+/ZGRwRY1dxOu8LcTvvcVqvVcvnll6ezszODg4NpavrnHnx+fj5DQ0OZnJzMDTfc4LY9APghfkOh2GZnZ9Pb25szzjgj27dvT3PzP9+0NTc3l76+vjz00EPZsWOH29+hQdxOi514sATVajVTU1Pp7e2t+5ePJGlqakpvb28OHDiQarXaoIQAUEx+Q6HYdu/enfn5+Vx66aV1BV7yeEFwySWXpFarZffu3Q1KCIASD5Zgeno6SbJ+/fpF5zs6OurWAQCP8xsKxbZ///4kyWtf+9pF57u7u+vWAbDylHiwBK2trUmSycnJRef37t1btw4AeJzfUCi2devWJUluv/32RefHx8fr1gGw8pR4sATlcjltbW0ZGxs7/MauJ8zPz2dsbCzt7e0pl8sNSggAxeQ3FIpt8+bNaWpqyqc+9akjnrk1NzeXT3/60ymVStm8eXODEgKgxIMlKJVKGRgYyPj4eIaGhjIxMZGZmZlMTExkaGgo4+Pj6e/v90BuAHgSv6FQbC0tLdmyZUsOHjyYvr6+3Hrrrbn//vtz6623pq+vLwcPHswFF1zgpRYADeTttCvM22lPDJVKJaOjo5mamjo81t7env7+/vT09DQwGQAUm99QKLbt27dn165ddTtmS6VSLrjggvT19TUwGeDttCjxVpgS78RRq9VSrVYzPT2d1tbWlMtluwcA4FnwGwrFNjs7m927d2f//v1Zt25dNm/ebAceFIASj+ZnXgIsplar5Tvf+c7hP9ycf/75/gUECuThhx/Oddddl3vvvTfnnHNOrrzyypx66qmNjgXk8V09GzZsaHQM4Cm0tLTkggsuaHQMAJ5EiQfLsNhtBtu2bcuWLVvcZgAFcNVVV+XOO+88fDw5OZl3vOMdednLXpaPfvSjDUwGAACwPF5sAUu0ffv27Ny5M2eccUauuOKKfPKTn8wVV1yRM844Izt37sz27dsbHRFWtScKvDVr1uSNb3xjPvaxj+WNb3xj1qxZkzvvvDNXXXVVoyMCAAAsmRIPlmB2dja7du3KmWeeme3bt+etb31rnv/85+etb31rtm/fnjPPPDO33HJLZmdnGx0VVqWHH374cIG3Y8eOXHXVVfnRH/3RXHXVVdmxY8fhIu/hhx9udFQAAIAlUeLBEuzevTvz8/O59NJL09xcfzd6c3NzLrnkktRqtezevbtBCWF1u+6665Ikb3jDG3LyySfXzZ188sl5/etfX7cOAADguUKJB0uwf//+JMlrX/vaRee7u7vr1gEr6957703y+Ju7FvPEQ7qfWAcAAPBcocSDJVi3bl2S5Pbbb190fnx8vG4dsLLOOeecJMlnP/vZRedvueWWunUAAADPFUo8WILNmzenqakpn/rUpzI3N1c3Nzc3l09/+tMplUrZvHlzgxLC6nbllVcmSf78z/88jzzySN3cI488kq9+9at16wAAAJ4rlHiwBC0tLdmyZUsOHjyYvr6+3Hrrrbn//vtz6623pq+vLwcPHswFF1yQlpaWRkeFVenUU0/Ny172siwsLOTiiy/ORz7ykdx11135yEc+kosvvjgLCwt52ctellNPPbXRUQEAAJZkzcLCwkKjQ6wmmzZtSpLs2bOnwUk4Gtu3b8+uXbsyPz9/eKxUKuWCCy5IX19fA5MBSXLVVVflzjvvPGL8ZS97WT760Y82IBEAABydCy+88Ig7wpLHX7L4VI+T4cTS/MxLgCfr6+vLJZdckt27d2f//v1Zt25dNm/ebAceFMRHP/rRPPzww7nuuuty77335pxzzsmVV15pBx4AAPCcpcSDZWppaTn8pkugeE4++eRccMEFmZ6eTmtra04++eRGRwKA54RarZZqtXr4N7RcLqdUKjU6FsCqp8QD4IRTqVQyOjqaqampw2NtbW0ZGBhIT09PA5MBQLH5DQUoLi+2AOCEUqlUMjw8nM7OzoyMjGTHjh0ZGRlJZ2dnhoeHU6lUGh0RAArJbyhAsSnxADhh1Gq1jI6Opru7O4ODg+nq6sratWvT1dWVwcHBdHd3Z9u2banVao2OCgCF4jcUoPiUeACcMKrVaqamptLb25umpvqfuKampvT29ubAgQOpVqsNSggAxeQ3FKD4lHgAnDCmp6eTJOvXr190vqOjo24dAPA4v6EAxafEA+CE0dramiSZnJxcdH7v3r116wCAx/kNBSg+JR4AJ4xyuZy2traMjY1lfn6+bm5+fj5jY2Npb29PuVxuUEIAKCa/oQDFp8QD4IRRKpUyMDCQ8fHxDA0NZWJiIjMzM5mYmMjQ0FDGx8fT39+fUqnU6KgAUCh+QwGKb83CwsJCo0OsJps2bUqS7Nmzp8FJAE5clUolo6OjmZqaOjzW3t6e/v7+9PT0NDAZABSb31AorgsvvDBzc3NHjDc3N+ezn/1sAxKx0pobHQAAjrWenp685jWvye7du7N///6sW7cumzdvTktLS6OjAUlqtVqq1Wqmp6fT2tqacrlsdw8URE9PTzZu3OgaBSggJR4AJ5zFdhF87nOfy8DAgF0E0GCLXZ9tbW2uTyiQUqmUDRs2NDoGAE+ixAPghFKpVDI8PJzu7u5cffXVWb9+fSYnJzM2Npbh4eFs3bpVUQAN4vpkNfnGN76RXbt2pVqtZnZ2ttFxVpWWlpaUy+Vs2bIlr3zlKxsdB+CY8Uy8FeaZeADHT61Wy+WXX57Ozs4MDg6mqemf3980Pz+foaGhTE5O5oYbbnBbEKww1yeryTe+8Y387u/+bh577LFGR1nVTjrppHzwgx9U5HHC8Ew8vJ0WgBNGtVrN1NRUent76wqCJGlqakpvb28OHDiQarXaoISwerk+WU127dqlwCuAxx57LLt27Wp0DIBjRokHwAljeno6SbJ+/fpF5zs6OurWASvH9clqctdddzU6Av+TfxbAiUSJB8AJo7W1NUkyOTm56PzevXvr1gErx/XJavLSl7600RH4n/yzAE4kSjwAThjlcjltbW0ZGxvL/Px83dz8/HzGxsbS3t6ecrncoISwerk+WU22bNmSk046qdExVr2TTjopW7ZsaXQMgGPG22kBOGGUSqUMDAxkeHg4Q0ND6e3tTUdHR/bu3ZuxsbGMj49n69atHpoPDeD6ZDV55StfmQ9+8IPPybfTLvbQ/Cc0Nz83/vXR22mBE5W3064wb6cFOP4qlUpGR0czNTV1eKy9vT39/f3p6elpYDLA9QnF5u2XUFyuT54bf5UCAEvQ09OTjRs3plqtZnp6Oq2trSmXy3b4QAG4PgEAlkeJB8AJqVQqZcOGDY2OASzC9QkAsHRebAEAAAAABafEAwAAAICCU+IBAAAAQMEp8QAAAACg4JR4AAAAAFBwSjwAAAAAKDglHgAAAAAUnBIPAAAAAApOiQcAAAAABdfc6AAAAKwutVot1Wo109PTaW1tTblcTqlUanQsAIBCU+IBALBiKpVKRkdHMzU1dXisra0tAwMD6enpaWAyAIBiczstAAArolKpZHh4OJ2dnRkZGcmOHTsyMjKSzs7ODA8Pp1KpNDoiAEBhKfEAADjuarVaRkdH093dncHBwXR1dWXt2rXp6urK4OBguru7s23bttRqtUZHBQAoJCUeAADHXbVazdTUVHp7e9PUVP9H0KampvT29ubAgQOpVqsNSggAUGxKPAAAjrvp6ekkyfr16xed7+joqFsHAEA9JR4AAMdda2trkmRycnLR+b1799atAwCgnhIPAIDjrlwup62tLWNjY5mfn6+bm5+fz9jYWNrb21MulxuUEACg2JR4AAAcd6VSKQMDAxkfH8/Q0FAmJiYyMzOTiYmJDA0NZXx8PP39/SmVSo2OCgBQSM2NDgAAwOrQ09OTrVu3ZnR0NFdfffXh8fb29mzdujU9PT0NTAcAUGxKPAAAVkxPT082btyYarWa6enptLa2plwu24EHAPAM3E4LAAAAAAVnJx4AACumUqlkdHQ0U1NTh8fa2toyMDDgdloAgKdhJx4AACuiUqlkeHg4nZ2dGRkZyY4dOzIyMpLOzs4MDw+nUqk0OiIAQGEp8QAAOO5qtVpGR0fT3d2dwcHBdHV1Ze3atenq6srg4GC6u7uzbdu21Gq1RkcFACgkJR4AAMddtVrN1NRUent709RU/0fQpqam9Pb25sCBA6lWqw1KCABQbEo8AACOu+np6STJ+vXrF53v6OioWwcAQD0lHgAAx11ra2uSZHJyctH5vXv31q0DAKCeEg8AgOOuXC6nra0tY2NjmZ+fr5ubn5/P2NhY2tvbUy6XG5QQAKDYlHgAABx3pVIpAwMDGR8fz9DQUCYmJjIzM5OJiYkMDQ1lfHw8/f39KZVKjY4KAFBIzY0OAADA6tDT05OtW7dmdHQ0V1999eHx9vb2bN26NT09PQ1MBwBQbEo8AABWTE9PTzZu3JhqtZrp6em0tramXC7bgQcA8AyUeACckGZnZ7N79+7s378/69aty+bNm9PS0tLoWAAAAMuixAPghLN9+/bs2rWr7uH527Zty5YtW9LX19fAZEClUsno6GimpqYOj7W1tWVgYMDttAAAT8OLLQA4oWzfvj07d+7MGWeckSuuuCKf/OQnc8UVV+SMM87Izp07s3379kZHhFWrUqlkeHg4nZ2dGRkZyY4dOzIyMpLOzs4MDw+nUqk0OiIAQGEp8QA4YczOzmbXrl0588wzs3379rz1rW/N85///Lz1rW/N9u3bc+aZZ+aWW27J7Oxso6PCqlOr1TI6Opru7u4MDg6mq6sra9euTVdXVwYHB9Pd3Z1t27alVqs1OioAQCEp8QA4YezevTvz8/O59NJL09xc/8SI5ubmXHLJJanVatm9e3eDEsLqVa1WMzU1ld7e3jQ11f8RtKmpKb29vTlw4ECq1WqDEgIAFJsSD4ATxv79+5Mkr33taxed7+7urlsHrJzp6ekkyfr16xed7+joqFsHAEA9JR4AJ4x169YlSW6//fZF58fHx+vWASuntbU1STI5Obno/N69e+vWAQBQT4kHwAlj8+bNaWpqyqc+9anMzc3Vzc3NzeXTn/50SqVSNm/e3KCEsHqVy+W0tbVlbGys7s3RSTI/P5+xsbG0t7enXC43KCEAQLEp8QA4YbS0tGTLli05ePBg+vr6cuutt+b+++/Prbfemr6+vhw8eDAXXHBBWlpaGh0VVp1SqZSBgYGMj49naGgoExMTmZmZycTERIaGhjI+Pp7+/v6USqVGRwUAKKTmZ14CAM8dfX19SZJdu3bl+uuvPzxeKpVy0UUXHZ4HVl5PT0+2bt2a0dHRXH311YfH29vbs3Xr1vT09DQwHQBAsSnxADjh9PX15ZJLLsnu3buzf//+rFu3Lps3b7YDDwqgp6cnr3nNa1yfAABLpMQD4ITU0tKSCy64oNExgCepVCoZHR3N1NTU4bHPfe5zGRgYsBMPAOBpeCYeAAArolKpZHh4OJ2dnRkZGcmOHTsyMjKSzs7ODA8Pp1KpNDoiAEBhKfEAADjuarVaRkdH093dncHBwXR1dWXt2rXp6urK4OBguru7s23bttRqtUZHBQAoJCUeAADHXbVazdTUVHp7e9PUVP9H0KampvT29ubAgQOpVqsNSggAUGxKPAAAjrvp6ekkyfr16xed7+joqFsHAEA9JR4AAMdda2trkmRycnLR+b1799atAwCgnhIPAIDjrlwup62tLWNjY5mfn6+bm5+fz9jYWNrb21MulxuUEACg2JR4AAAcd6VSKQMDAxkfH8/Q0FAmJiYyMzOTiYmJDA0NZXx8PP39/SmVSo2OCgBQSM2NDgAAwOrQ09OTrVu3ZnR0NFdfffXh8fb29mzdujU9PT0NTAcAUGxKPAAAVkxPT082btyYarWa6enptLa2plwu24EHAPAMlHgAnJBqtZqSAAqqVqvlO9/5Tvbv359169bl/PPPd30CADwDJR4AJ5xKpZLR0dFMTU0dHmtra8vAwIDb9aDBtm/fnl27dtW93GLbtm3ZsmVL+vr6GpgMAKDYvNgCgBNKpVLJ8PBwOjs7MzIykh07dmRkZCSdnZ0ZHh5OpVJpdERYtbZv356dO3fmjDPOyBVXXJFPfvKTueKKK3LGGWdk586d2b59e6MjAgAUlhIPgBNGrVbL6Ohouru7Mzg4mK6urqxduzZdXV0ZHBxMd3d3tm3bllqt1uiosOrMzs5m165dOfPMM7N9+/a89a1vzfOf//y89a1vzfbt23PmmWfmlltuyezsbKOjAgAUkhIPgBNGtVrN1NRUent709RU/xPX1NSU3t7eHDhwINVqtUEJYfXavXt35ufnc+mll6a5uf6JLs3NzbnkkktSq9Wye/fuBiUEACg2JR4AJ4zp6ekkyfr16xed7+joqFsHrJz9+/cnSV772tcuOt/d3V23DgCAeko8AE4Yra2tSZLJyclF5/fu3Vu3Dlg569atS5Lcfvvti86Pj4/XrQMAoJ4SD4ATRrlcTltbW8bGxurefJkk8/PzGRsbS3t7e8rlcoMSwuq1efPmNDU15VOf+lTm5ubq5ubm5vLpT386pVIpmzdvblBCAIBiU+IBcMIolUoZGBjI+Ph4hoaGMjExkZmZmUxMTGRoaCjj4+Pp7+9PqVRqdFRYdVpaWrJly5YcPHgwfX19ufXWW3P//ffn1ltvTV9fXw4ePJgLLrggLS0tjY4KAFBIzc+8BACeO3p6erJ169aMjo7m6quvPjze3t6erVu3pqenp4HpYHXr6+tLkuzatSvXX3/94fFSqZSLLrro8DwAAEdS4gFwwunp6cmrXvWq3HTTTdm3b1/OPffcXHbZZVm7dm2jo8Gq19fXl3e84x2uTwCAJVLiAXDC2b59e3bt2nX4uXh33HFHbr311mzZssVOH2iwSqWS0dHRTE1NJXn8+vzbv/3bDAwM2CkLAPA0PBMPgBPK9u3bs3Pnzpxxxhm54oor8slPfjJXXHFFzjjjjOzcuTPbt29vdERYtSqVSoaHh9PZ2ZmRkZHs2LEjIyMj6ezszPDwcCqVSqMjAgAUlhIPgBPG7Oxsdu3alTPPPDPbt2/PW9/61jz/+c/PW9/61mzfvj1nnnlmbrnllszOzjY6Kqw6tVoto6Oj6e7uzuDgYLq6urJ27dp0dXVlcHAw3d3d2bZtW2q1WqOjAgAUkhIPgBPG7t27Mz8/n0svvTTNzfVPjGhubs4ll1ySWq2W3bt3NyghrF7VajVTU1Pp7e1NU1P9H0GbmprS29ubAwcOpFqtNighAECxKfEAOGHs378/SfLa17520fnu7u66dcDKmZ6eTpKsX79+0fmOjo66dQAA1FPiAXDCWLduXZLk9ttvX3R+fHy8bh2wclpbW5Mkk5OTi87v3bu3bh0AAPWUeACcMDZv3pympqZ86lOfytzcXN3c3NxcPv3pT6dUKmXz5s0NSgirV7lcTltbW8bGxg6/OfoJ8/PzGRsbS3t7e8rlcoMSAgAUmxIPgBNGS0tLtmzZkoMHD6avry+33npr7r///tx6663p6+vLwYMHc8EFF6SlpaXRUWHVKZVKGRgYyPj4eIaGhjIxMZGZmZlMTExkaGgo4+Pj6e/vT6lUanRUAIBCan7mJQDw3NHX15ck2bVrV66//vrD46VSKRdddNHheWDl9fT0ZOvWrRkdHc3VV199eLy9vT1bt25NT09PA9MBABSbEg+AE05fX18uueSS7N69O/v378+6deuyefNmO/CgAHp6erJx48ZUq9VMT0+ntbU15XLZDjwAgGegxAPghFSr1bJv377s27cvCwsLqdVqjY4EAACwbEo8AE441157bW677bbDx3fccUf+7M/+LBs3bsw111zTwGRApVLJ6OhopqamDo+1tbVlYGDA7bQAAE/Diy0AOKE8UeA1NzfnbW97W2688ca87W1vS3Nzc2677bZce+21jY4Iq1alUsnw8HA6OzszMjKSHTt2ZGRkJJ2dnRkeHk6lUml0RACAwlLiAXDCOHTo0OEC7+abb8673/3urFu3Lu9+97tz8803Hy7yDh061OiosOrUarWMjo6mu7s7g4OD6erqytq1a9PV1ZXBwcF0d3dn27Ztbn0HAHgKSjwAThg33XRTkmTLli1HvMSipaUlF1xwQd06YOVUq9VMTU2lt7c3TU31fwRtampKb29vDhw4kGq12qCEAADFpsQD4ISxb9++JMlb3vKWReff/OY3160DVs709HSSZP369YvOd3R01K0DAKCeEg+AE8a5556bJPniF7+46PyXv/zlunXAymltbU2STE5OLjq/d+/eunUAANRT4gFwwrjsssuSJLt27crs7Gzd3OzsbG655Za6dcDKKZfLaWtry9jYWObn5+vm5ufnMzY2lvb29pTL5QYlBAAoNiUeACeMtWvXZuPGjZmbm8vb3/723HTTTfne976Xm266KW9/+9szNzeXjRs3Zu3atY2OCqtOqVTKwMBAxsfHMzQ0lImJiczMzGRiYiJDQ0MZHx9Pf39/SqVSo6MCABTSmoWFhYVGh1hNNm3alCTZs2dPg5MAnLiuvfba3HbbbUeMb9y4Mddcc00DEgFPqFQqGR0dzdTU1OGx9vb29Pf3p6enp4HJgCS58MILMzc3d8R4c3NzPvvZzzYgEfAE1yfNjQ4AAMfaNddck0OHDuWmm27Kvn37cu655+ayyy6zAw8KoKenJxs3bky1Ws309HRaW1tTLpftwAMAeAZKPFimWq3mX0CgwFpaWvLTP/3Th6/RlpaWRkcC/qdSqZQNGzY0OgYAwHOKEg+WYbFbgdra2jIwMOBWICgA1ygAAHCi8WILWKJKpZLh4eF0dnZmZGQkO3bsyMjISDo7OzM8PJxKpdLoiLCquUYBAIATkRIPlqBWq2V0dDTd3d0ZHBxMV1dX1q5dm66urgwODqa7uzvbtm1LrVZrdFRYlVyjAADAiUqJB0tQrVYzNTWV3t7eNDXVXz5NTU3p7e3NgQMHUq1WG5QQVjfXKAAAcKJS4sESTE9PJ0nWr1+/6HxHR0fdOmBluUYBAIATlRIPlqC1tTVJMjk5uej83r1769YBK8s1CgAAnKiUeLAE5XI5bW1tGRsby/z8fN3c/Px8xsbG0t7ennK53KCEsLq5RgEAgBOVEg+WoFQqZWBgIOPj4xkaGsrExERmZmYyMTGRoaGhjI+Pp7+/P6VSqdFRYVVyjQIAACeq5kYHgOeanp6ebN26NaOjo7n66qsPj7e3t2fr1q3p6elpYDrANQoAAJyIlHiwDD09PXnlK1+Z6667Lvfee2/OOeecXHnllTn11FMbHQ3I49fohg0b8qEPfSjf//73c/bZZ+cDH/hATj/99EZHAwAAWBYlHizDtddem9tuu+3w8eTkZN7xjndk48aNueaaaxqYDEiSq666Knfeeefh4/vuuy+/9Eu/lJe97GX56Ec/2sBkAAAAy+OZeLBETxR4zc3Nedvb3pYbb7wxb3vb29Lc3Jzbbrst1157baMjwqr2RIG3Zs2avPGNb8zHPvaxvPGNb8yaNWty55135qqrrmp0RAAAgCWzEw+W4NChQ4cLvJtvvjktLS1Jkne/+9155zvfmbe//e257bbbcujQoaxdu7bBaWH1efjhhw8XeDt27MjJJ5+c5PFi773vfW8uvvji3HnnnXn44Yfd/g4AADyn2IkHS3DTTTclSbZs2XK4wHtCS0tLLrjggrp1wMq67rrrkiRveMMbDhd4Tzj55JPz+te/vm4dAADAc4USD5Zg3759SZK3vOUti86/+c1vrlsHrKx77703SXLhhRcuOv9E0f7EOgAAgOcKJR4swbnnnpsk+eIXv7jo/Je//OW6dcDKOuecc5Ikn/3sZxedv+WWW+rWAQAAPFco8WAJLrvssiTJrl27Mjs7Wzc3Ozt7uCB4Yh2wsq688sokyZ//+Z/nkUceqZt75JFH8tWvfrVuHQAAwHOFEg+WYO3atdm4cWPm5uby9re/PTfddFO+973v5aabbsrb3/72zM3NZePGjV5qAQ1y6qmn5mUve1kWFhZy8cUX5yMf+UjuuuuufOQjH8nFF1+chYWFvOxlL/NSCwAA4DlnzcLCwkKjQ6wmmzZtSpLs2bOnwUk4Gtdee21uu+22I8Y3btyYa665pgGJgB921VVX5c477zxi/GUve1k++tGPNiARADw3XHjhhZmbmztivLm5+SkfVwGsDNcnzY0OAM9F11xzTQ4dOpSbbrop+/bty7nnnpvLLrvMDjwoiI9+9KN5+OGHc9111+Xee+/NOeeckyuvvNIOPAAA4DlLiQfLtHbt2vzqr/5qo2MAT+HUU0+1MxYKqlarpVqtZnp6Oq2trSmXyymVSo2OBQBQaEo8AABWTKVSyejoaKampg6PtbW1ZWBgID09PQ1MBgBQbF5sAQDAiqhUKhkeHk5nZ2dGRkayY8eOjIyMpLOzM8PDw6lUKo2OCABQWEo8AACOu1qtltHR0XR3d2dwcDBdXV1Zu3Zturq6Mjg4mO7u7mzbti21Wq3RUQEACkmJBwDAcVetVjM1NZXe3t40NdX/EbSpqSm9vb05cOBAqtVqgxICABSbEg8AgONueno6SbJ+/fpF5zs6OurWAQBQT4kHAMBx19ramiSZnJxcdH7v3r116wAAqKfEAwDguCuXy2lra8vY2Fjm5+fr5ubn5zM2Npb29vaUy+UGJQQAKDYlHgAAx12pVMrAwEDGx8czNDSUiYmJzMzMZGJiIkNDQxkfH09/f39KpVKjowIAFFJzowMAALA69PT0ZOvWrRkdHc3VV199eLy9vT1bt25NT09PA9MBABSbEg+WqVarpVqtZnp6Oq2trSmXy3YPQIEcOnQoN910U/bt25dzzz03l112WdauXdvoWLDq9fT05JWvfGWuu+663HvvvTnnnHNy5ZVX5tRTT210NACAQlPiwTJUKpWMjo5mamrq8FhbW1sGBgbsIoACuPbaa3PbbbcdPr7jjjvyZ3/2Z9m4cWOuueaaBiYDnnx9Tk5O5h3veIfrEwDgGXgmHixRpVLJ8PBwOjs7MzIykh07dmRkZCSdnZ0ZHh5OpVJpdERY1Z4oCJqbm/O2t70tN954Y972trelubk5t91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      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "column_names = [\"Hazard\", \"Degree\"]\n",
    "\n",
    "deg_haz_df = pd.DataFrame(columns = column_names)\n",
    "deg_haz_df['Hazard']=haz_list\n",
    "deg_haz_df['Degree']=max_deg_list\n",
    "deg_haz_df_sort_by_degree=deg_haz_df.sort_values(\"Degree\")\n",
    "cat_list=[]\n",
    "for i in range(len(deg_haz_df_sort_by_degree)):\n",
    "    if i<len(deg_haz_df_sort_by_degree)/5:\n",
    "        cat_list.append(\"1st quintile\")\n",
    "    elif i>len(deg_haz_df_sort_by_degree)/5 and i<2*len(deg_haz_df_sort_by_degree)/5:\n",
    "        cat_list.append(\"2nd quintile\")\n",
    "    elif i>2*len(deg_haz_df_sort_by_degree)/5 and i<3*len(deg_haz_df_sort_by_degree)/5:\n",
    "        cat_list.append(\"3rd quintile\")\n",
    "    elif i>3*len(deg_haz_df_sort_by_degree)/5 and i<4*len(deg_haz_df_sort_by_degree)/5:\n",
    "        cat_list.append(\"4th quintile\")\n",
    "    else:\n",
    "        cat_list.append(\"5th quintile\")\n",
    "        \n",
    "deg_haz_df_sort_by_degree[\"Group\"]=cat_list\n",
    "\n",
    "#Let's visualize as boxplot\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# set a grey background (use sns.set_theme() if seaborn version 0.11.0 or above) \n",
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "\n",
    "sns.set_style('white')\n",
    "sns.set(style=\"white\",font_scale = 2)\n",
    "\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(True)\n",
    "ax.spines['left'].set_visible(True)\n",
    "g=sns.boxplot(data=deg_haz_df_sort_by_degree,x=\"Group\", y=\"Hazard\",order=['1st quintile','2nd quintile', '3rd quintile','4th quintile',\n",
    " '5th quintile'],linewidth=5)\n",
    "plt.xlabel('',fontsize=22)\n",
    "plt.ylabel('', fontsize=22)\n",
    "#plt.xticks([], fontsize=22)\n",
    "ax.grid(False)\n",
    "ax.set_yticklabels([round(i,2) for i in ax.get_yticks()], size = 36)\n",
    "#ax.set_xticklabels(ax.get_xticks(), size = 36)\n",
    "plt.savefig('output/Figures/SF7G_v01_post_rev.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "#g.set_yscale(\"log\")\n",
    "\n",
    "plt.show()"
   ]
  }
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