Exp_fin_8_PPI_Centrality.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here, we create a dictionary out of the dataframe that has as a key the exposures and as \n",
    "#values the set of the genes that they perturb\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",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of exposures is 9887\n",
      "The total number of perturbed genes is 25580\n"
     ]
    }
   ],
   "source": [
    "#Here, we remove the elements that do not perturb any genes:       \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",
    "print(\"The number of exposures is %s\" %len(chem_gene_cleaned))\n",
    "print(\"The total number of perturbed genes is %s\" %len(set(tot_gene_list)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The number of genes is pretty high, because there are included also: pseudogenes, lncRNA,\n",
    "#miRNA and other non coding elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's build also the vice-versa: the gene-chem dictionary\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\"]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "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\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "chem_vocabolary = pd.read_csv(\"input/CTD/CTD_chemicals_cleaned.txt\",delimiter= '\\t',\n",
    "           skipinitialspace=True)\n",
    "chem_voc_dict = {}\n",
    "for i,v in chem_vocabolary.iterrows():\n",
    "    try:\n",
    "        chem_voc_dict[v[\"ChemicalID\"]] = v[\"ParentIDs\"].split(\"|\")\n",
    "    except:\n",
    "        chem_voc_dict[v[\"ChemicalID\"]] = v[\"ParentIDs\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections.abc import Mapping\n",
    "# Empty directed graph\n",
    "G = nx.DiGraph()\n",
    "\n",
    "for k in chem_voc_dict.keys():\n",
    "    try:\n",
    "        for v in chem_voc_dict[k]:\n",
    "            G.add_edge(k, v)\n",
    "    except:\n",
    "        G.add_edge(k,\"D\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here, we group all the chemicals in 12 categories, obtained extrapoling the\n",
    "#second layer of the chemical ontology\n",
    "classes_list=[]\n",
    "for i in G.nodes():\n",
    "    if len(nx.predecessor(G, i))==2:\n",
    "        classes_list.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's clean the list from the nan elements\n",
    "classes_list_cleaned = [x for x in classes_list if str(x) != 'nan']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here, we import the merged PPI\n",
    "ppi = pd.read_csv(\"input/PPI/autocore_symbol_lcc.csv\",delimiter= ',',\n",
    "           skipinitialspace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_ppi = nx.from_pandas_edgelist(ppi, 'symbol1', 'symbol2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18815\n",
      "482935\n"
     ]
    }
   ],
   "source": [
    "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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here, we filter the exposures, considering only the ones that target proteins\n",
    "#in the PPI\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",
    "bipartite_G = nx.Graph()\n",
    "exposures_list = []\n",
    "genes_list = []\n",
    "for k,v in chem_gene_dictio_cleaned_ppi.items():\n",
    "    exposures_list.append(k)\n",
    "    for gene in v:\n",
    "        genes_list.append(gene)   \n",
    "for gene in set(genes_list):\n",
    "    bipartite_G.add_node(gene,s=\"^\", bipartite='gene')   \n",
    "for exposure in set(exposures_list):\n",
    "    bipartite_G.add_node(exposure,s=\"o\", bipartite='exposure')\n",
    "    for genes in chem_gene_dictio_cleaned_ppi[exposure]:\n",
    "        bipartite_G.add_edges_from([(exposure,genes)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.bipartite.is_bipartite(bipartite_G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27875"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bipartite_G.number_of_nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "537985"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bipartite_G.number_of_edges()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of considered genes is 18023\n"
     ]
    }
   ],
   "source": [
    "print(\"The number of considered genes is %s\" %len(set(genes_list)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "gene_chem_cleaned_ppi={}\n",
    "\n",
    "for gene, exp_list in gene_chem_cleaned.items():\n",
    "    if gene in G_ppi_lcc.nodes():\n",
    "        gene_chem_cleaned_ppi[gene]=exp_list\n",
    "    else:\n",
    "        pass\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PearsonRResult(statistic=0.23729929599830776, pvalue=3.928859999516407e-229)\n",
      "SignificanceResult(statistic=0.3904600122770663, pvalue=0.0)\n"
     ]
    }
   ],
   "source": [
    "import scipy\n",
    "gene_degreeppi=[]\n",
    "gene_numexp=[]\n",
    "for gene,explist in gene_chem_cleaned_ppi.items():\n",
    "    gene_degreeppi.append(nx.degree(G_ppi_lcc,gene))\n",
    "    gene_numexp.append(len(explist))\n",
    "\n",
    "print(scipy.stats.pearsonr(gene_degreeppi,gene_numexp))  # Pearson's r\n",
    "print(scipy.stats.spearmanr(gene_degreeppi,gene_numexp)) # Spearman's rho"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy\n",
    "from matplotlib.pyplot import figure\n",
    "from scipy import stats\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "slope, intercept, r, p, stderr = scipy.stats.linregress(gene_degreeppi, gene_numexp)\n",
    "\n",
    "ax.plot(gene_degreeppi,gene_numexp, linewidth=0, marker='s',c='k', label='protein')\n",
    "y=[]\n",
    "for i in gene_degreeppi:\n",
    "    g=(intercept + slope * i)\n",
    "    y.append(g)\n",
    "line = \"Regression line\" \n",
    "\n",
    "ax.plot(gene_degreeppi, y, label=line)\n",
    "#ax.set_xlabel('Degree in the PPI', fontsize=18)\n",
    "#ax.set_ylabel('Number of exposures',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",
    "# plt.title('Correlation between Resnik and Genetic overlap in %s' %met )\n",
    "ax.legend(facecolor='white',fontsize=36)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's group in 5 boxplots sorted by their degree\n",
    "#Let's create a dataframe that contains these values\n",
    "column_names = [\"N_exp\", \"Degree\"]\n",
    "\n",
    "deg_nexp_df = pd.DataFrame(columns = column_names)\n",
    "deg_nexp_df['N_exp']=gene_numexp\n",
    "deg_nexp_df['Degree']=gene_degreeppi\n",
    "\n",
    "deg_nexp_df_sort_by_degree=deg_nexp_df.sort_values(\"Degree\")\n",
    "\n",
    "cat_list=[]\n",
    "for i in range(len(deg_nexp_df_sort_by_degree)):\n",
    "    if i<len(deg_nexp_df_sort_by_degree)/5:\n",
    "        cat_list.append(\"1st quintile\")\n",
    "    elif i>len(deg_nexp_df_sort_by_degree)/5 and i<2*len(deg_nexp_df_sort_by_degree)/5:\n",
    "        cat_list.append(\"2nd quintile\")\n",
    "    elif i>2*len(deg_nexp_df_sort_by_degree)/5 and i<3*len(deg_nexp_df_sort_by_degree)/5:\n",
    "        cat_list.append(\"3rd quintile\")\n",
    "    elif i>3*len(deg_nexp_df_sort_by_degree)/5 and i<4*len(deg_nexp_df_sort_by_degree)/5:\n",
    "        cat_list.append(\"4th quintile\")\n",
    "    else:\n",
    "        cat_list.append(\"5th quintile\")\n",
    "        \n",
    "deg_nexp_df_sort_by_degree[\"Group\"]=cat_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/0b/z0_jtrl111x5sn006y224x0w0000gq/T/ipykernel_13991/1513413221.py:21: UserWarning: FixedFormatter should only be used together with 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_nexp_df_sort_by_degree,x=\"Group\", y=\"N_exp\",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",
    "g.set_yscale(\"log\")\n",
    "#plt.savefig('output/Figures/Fig3B.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MannwhitneyuResult(statistic=4252373.0, pvalue=1.4816184563223326e-142)\n",
      "MannwhitneyuResult(statistic=3428533.5, pvalue=2.656620944640691e-264)\n",
      "MannwhitneyuResult(statistic=3038017.0, pvalue=0.0)\n",
      "MannwhitneyuResult(statistic=2483973.0, pvalue=0.0)\n",
      "MannwhitneyuResult(statistic=5503155.5, pvalue=2.5154526315110236e-29)\n",
      "MannwhitneyuResult(statistic=5026032.5, pvalue=2.51274711250499e-62)\n",
      "MannwhitneyuResult(statistic=4218498.5, pvalue=1.297666721572126e-146)\n",
      "MannwhitneyuResult(statistic=6014537.0, pvalue=4.9513724643969e-08)\n",
      "MannwhitneyuResult(statistic=5124020.5, pvalue=2.66864604684785e-54)\n",
      "MannwhitneyuResult(statistic=5573351.5, pvalue=1.5075442800920064e-25)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='1st quintile']['N_exp'].tolist(), deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='2nd quintile']['N_exp'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='1st quintile']['N_exp'].tolist(), deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='3rd quintile']['N_exp'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='1st quintile']['N_exp'].tolist(), deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='4th quintile']['N_exp'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='1st quintile']['N_exp'].tolist(), deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='5th quintile']['N_exp'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='2nd quintile']['N_exp'].tolist(), deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='3rd quintile']['N_exp'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='2nd quintile']['N_exp'].tolist(), deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='4th quintile']['N_exp'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='2nd quintile']['N_exp'].tolist(), deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='5th quintile']['N_exp'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='3rd quintile']['N_exp'].tolist(), deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='4th quintile']['N_exp'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='3rd quintile']['N_exp'].tolist(), deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='5th quintile']['N_exp'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n",
    "print(scipy.stats.mannwhitneyu(deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='4th quintile']['N_exp'].tolist(), deg_nexp_df_sort_by_degree[deg_nexp_df_sort_by_degree['Group']=='5th quintile']['N_exp'].tolist(), use_continuity=True, alternative='two-sided'))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Furthermore, I will compute a z-score distribution of the median degree of the perturbed genes, comparing with random\n",
    "#genest of the same size\n",
    "\n",
    "import math\n",
    "exp_mediandegree_zscore={}\n",
    "\n",
    "for exp,geneset in chem_gene_dictio_cleaned_ppi.items():\n",
    "    gene_degreeppi=[]\n",
    "    for gene in geneset:\n",
    "        gene_degreeppi.append(nx.degree(G_ppi_lcc,gene))   \n",
    "    median_gene=np.median(gene_degreeppi)\n",
    "        \n",
    "    S = 1000\n",
    "    median_degree_rnd=[]\n",
    "    for s in range(S):\n",
    "        gene_degreeppi_random=[]\n",
    "        gene_sample = rd.sample(G_ppi_lcc.nodes(),len(geneset))\n",
    "        for gene in gene_sample:\n",
    "            gene_degreeppi_random.append(nx.degree(G_ppi_lcc,gene)) \n",
    "        median_gene_random=np.median(gene_degreeppi_random)\n",
    "        median_degree_rnd.append(median_gene_random)\n",
    "    mu = np.mean(median_degree_rnd)\n",
    "    std = np.std(median_degree_rnd)\n",
    "\n",
    "    z = round(((median_gene-mu)/std),3)\n",
    "\n",
    "    exp_mediandegree_zscore[exp]=z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9852"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(exp_mediandegree_zscore)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.53590712545676\n"
     ]
    }
   ],
   "source": [
    "z_zscore_list=[]\n",
    "for chem,z_score in exp_mediandegree_zscore.items():\n",
    "    z_zscore_list.append(float(z_score))\n",
    "print(np.mean(z_zscore_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('output/exp_mediandegree_zscore.pickle', 'wb') as handle:\n",
    "    pk.dump(exp_mediandegree_zscore, handle, protocol=pk.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('output/exp_mediandegree_zscore.pickle', 'rb') as handle:\n",
    "    exp_mediandegree_zscore = pk.load(handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5888\n"
     ]
    }
   ],
   "source": [
    "notsignificantdegreeexp_list=[]\n",
    "for k,v in exp_mediandegree_zscore.items():\n",
    "    if float(v)<2:\n",
    "        notsignificantdegreeexp_list.append(k)\n",
    "print(len(notsignificantdegreeexp_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5976451481932602\n"
     ]
    }
   ],
   "source": [
    "print(len(notsignificantdegreeexp_list)/len(exp_mediandegree_zscore))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "exp_mediandegree_zscore_cleaned=[]\n",
    "for z in exp_mediandegree_zscore.values():\n",
    "    if str(z)!=\"nan\":\n",
    "        exp_mediandegree_zscore_cleaned.append(z)\n",
    "        \n",
    "fig, ax = plt.subplots(figsize=(15,15))\n",
    "plt.hist(exp_mediandegree_zscore_cleaned, bins=50, alpha=0.5)\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",
    "#plt.xlabel('z-score',fontsize=22)\n",
    "#plt.ylabel('Frequency', fontsize=22)\n",
    "plt.yscale('log')\n",
    "plt.xticks(fontsize=36) \n",
    "plt.yticks(fontsize=36)\n",
    "plt.vlines(x=2,ymin=0,ymax=4000,color='r',label='statistically significance')\n",
    "#plt.title('Distribution of median degree z-score of geneset perturbed by each exposures', fontsize=28)\n",
    "#plt.savefig('output/Figures/Fig3C.pdf',format='pdf',dpi=300, bbox_inches = \"tight\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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