bb_extraction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import random\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import plotly.io as pio\n",
    "pio.templates.default = 'plotly_white'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranks = pd.read_pickle(\"data/gene_dependency_ranks_above_0-5.pkl\")\n",
    "#ranks = pd.read_csv(\"/home/ccoquide/Bureau/PhD/CNWW/PROJECT/GIT/cancer_networks/data/gene_dependency_ranks_above_0-5.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ACH-000004', 'ACH-000005', 'ACH-000007', 'ACH-000009', 'ACH-000011',\n",
       "       'ACH-000012', 'ACH-000013', 'ACH-000014', 'ACH-000015', 'ACH-000017',\n",
       "       ...\n",
       "       'ACH-001736', 'ACH-001737', 'ACH-001740', 'ACH-001745', 'ACH-001750',\n",
       "       'ACH-001765', 'ACH-001814', 'ACH-001838', 'ACH-001956', 'ACH-001957'],\n",
       "      dtype='object', length=625)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_matrix = ranks[ranks.columns].corr()\n",
    "correlation_matrix.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ACH-000004</th>\n",
       "      <th>ACH-000005</th>\n",
       "      <th>ACH-000007</th>\n",
       "      <th>ACH-000009</th>\n",
       "      <th>ACH-000011</th>\n",
       "      <th>ACH-000012</th>\n",
       "      <th>ACH-000013</th>\n",
       "      <th>ACH-000014</th>\n",
       "      <th>ACH-000015</th>\n",
       "      <th>ACH-000017</th>\n",
       "      <th>...</th>\n",
       "      <th>ACH-001736</th>\n",
       "      <th>ACH-001737</th>\n",
       "      <th>ACH-001740</th>\n",
       "      <th>ACH-001745</th>\n",
       "      <th>ACH-001750</th>\n",
       "      <th>ACH-001765</th>\n",
       "      <th>ACH-001814</th>\n",
       "      <th>ACH-001838</th>\n",
       "      <th>ACH-001956</th>\n",
       "      <th>ACH-001957</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ACH-000004</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.670811</td>\n",
       "      <td>0.634086</td>\n",
       "      <td>0.668743</td>\n",
       "      <td>0.627837</td>\n",
       "      <td>0.683202</td>\n",
       "      <td>0.674804</td>\n",
       "      <td>0.645831</td>\n",
       "      <td>0.678091</td>\n",
       "      <td>0.644843</td>\n",
       "      <td>...</td>\n",
       "      <td>0.674436</td>\n",
       "      <td>0.515364</td>\n",
       "      <td>0.682904</td>\n",
       "      <td>0.679575</td>\n",
       "      <td>0.653029</td>\n",
       "      <td>0.684327</td>\n",
       "      <td>0.600411</td>\n",
       "      <td>0.645787</td>\n",
       "      <td>0.694980</td>\n",
       "      <td>0.647552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACH-000005</th>\n",
       "      <td>0.670811</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.595817</td>\n",
       "      <td>0.612021</td>\n",
       "      <td>0.618527</td>\n",
       "      <td>0.637441</td>\n",
       "      <td>0.647392</td>\n",
       "      <td>0.605953</td>\n",
       "      <td>0.637074</td>\n",
       "      <td>0.631191</td>\n",
       "      <td>...</td>\n",
       "      <td>0.645563</td>\n",
       "      <td>0.508010</td>\n",
       "      <td>0.619845</td>\n",
       "      <td>0.642149</td>\n",
       "      <td>0.633958</td>\n",
       "      <td>0.655382</td>\n",
       "      <td>0.576940</td>\n",
       "      <td>0.615256</td>\n",
       "      <td>0.655533</td>\n",
       "      <td>0.635836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACH-000007</th>\n",
       "      <td>0.634086</td>\n",
       "      <td>0.595817</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.689916</td>\n",
       "      <td>0.659210</td>\n",
       "      <td>0.689017</td>\n",
       "      <td>0.686473</td>\n",
       "      <td>0.656473</td>\n",
       "      <td>0.689560</td>\n",
       "      <td>0.664908</td>\n",
       "      <td>...</td>\n",
       "      <td>0.662469</td>\n",
       "      <td>0.506101</td>\n",
       "      <td>0.665620</td>\n",
       "      <td>0.682570</td>\n",
       "      <td>0.668785</td>\n",
       "      <td>0.687120</td>\n",
       "      <td>0.623574</td>\n",
       "      <td>0.662435</td>\n",
       "      <td>0.697441</td>\n",
       "      <td>0.678311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACH-000009</th>\n",
       "      <td>0.668743</td>\n",
       "      <td>0.612021</td>\n",
       "      <td>0.689916</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.674617</td>\n",
       "      <td>0.745205</td>\n",
       "      <td>0.724943</td>\n",
       "      <td>0.690675</td>\n",
       "      <td>0.731248</td>\n",
       "      <td>0.682845</td>\n",
       "      <td>...</td>\n",
       "      <td>0.678239</td>\n",
       "      <td>0.542141</td>\n",
       "      <td>0.690460</td>\n",
       "      <td>0.706106</td>\n",
       "      <td>0.697572</td>\n",
       "      <td>0.726958</td>\n",
       "      <td>0.637741</td>\n",
       "      <td>0.685768</td>\n",
       "      <td>0.739834</td>\n",
       "      <td>0.726810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACH-000011</th>\n",
       "      <td>0.627837</td>\n",
       "      <td>0.618527</td>\n",
       "      <td>0.659210</td>\n",
       "      <td>0.674617</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.689373</td>\n",
       "      <td>0.700604</td>\n",
       "      <td>0.676628</td>\n",
       "      <td>0.684779</td>\n",
       "      <td>0.670969</td>\n",
       "      <td>...</td>\n",
       "      <td>0.672168</td>\n",
       "      <td>0.552894</td>\n",
       "      <td>0.666295</td>\n",
       "      <td>0.684159</td>\n",
       "      <td>0.700965</td>\n",
       "      <td>0.712906</td>\n",
       "      <td>0.643263</td>\n",
       "      <td>0.688556</td>\n",
       "      <td>0.730965</td>\n",
       "      <td>0.731748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 625 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ACH-000004  ACH-000005  ACH-000007  ACH-000009  ACH-000011  \\\n",
       "ACH-000004    1.000000    0.670811    0.634086    0.668743    0.627837   \n",
       "ACH-000005    0.670811    1.000000    0.595817    0.612021    0.618527   \n",
       "ACH-000007    0.634086    0.595817    1.000000    0.689916    0.659210   \n",
       "ACH-000009    0.668743    0.612021    0.689916    1.000000    0.674617   \n",
       "ACH-000011    0.627837    0.618527    0.659210    0.674617    1.000000   \n",
       "\n",
       "            ACH-000012  ACH-000013  ACH-000014  ACH-000015  ACH-000017  ...  \\\n",
       "ACH-000004    0.683202    0.674804    0.645831    0.678091    0.644843  ...   \n",
       "ACH-000005    0.637441    0.647392    0.605953    0.637074    0.631191  ...   \n",
       "ACH-000007    0.689017    0.686473    0.656473    0.689560    0.664908  ...   \n",
       "ACH-000009    0.745205    0.724943    0.690675    0.731248    0.682845  ...   \n",
       "ACH-000011    0.689373    0.700604    0.676628    0.684779    0.670969  ...   \n",
       "\n",
       "            ACH-001736  ACH-001737  ACH-001740  ACH-001745  ACH-001750  \\\n",
       "ACH-000004    0.674436    0.515364    0.682904    0.679575    0.653029   \n",
       "ACH-000005    0.645563    0.508010    0.619845    0.642149    0.633958   \n",
       "ACH-000007    0.662469    0.506101    0.665620    0.682570    0.668785   \n",
       "ACH-000009    0.678239    0.542141    0.690460    0.706106    0.697572   \n",
       "ACH-000011    0.672168    0.552894    0.666295    0.684159    0.700965   \n",
       "\n",
       "            ACH-001765  ACH-001814  ACH-001838  ACH-001956  ACH-001957  \n",
       "ACH-000004    0.684327    0.600411    0.645787    0.694980    0.647552  \n",
       "ACH-000005    0.655382    0.576940    0.615256    0.655533    0.635836  \n",
       "ACH-000007    0.687120    0.623574    0.662435    0.697441    0.678311  \n",
       "ACH-000009    0.726958    0.637741    0.685768    0.739834    0.726810  \n",
       "ACH-000011    0.712906    0.643263    0.688556    0.730965    0.731748  \n",
       "\n",
       "[5 rows x 625 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_matrix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "T_Links={}\n",
    "N=len(correlation_matrix.columns)\n",
    "Cell=correlation_matrix.columns\n",
    "ww=open(\"data/cell_lines_corr_above0_5.dat\",\"w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "for j in range(N):\n",
    "    for i in range(j+1,N):\n",
    "        T_Links[str(j)+\",\"+str(i)]=correlation_matrix[Cell[j]][Cell[i]]\n",
    "        ww.write(str(j)+\"\\t\"+str(i)+\"\\t\"+str(correlation_matrix[Cell[j]][Cell[i]])+\"\\n\")\n",
    "ww.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_bb(G,alp):\n",
    "    keep_graph = nx.Graph()\n",
    "    for i in G:\n",
    "        neigh=G[i]\n",
    "        k=len(neigh)\n",
    "        if k>1:\n",
    "            W=sum(G.get_edge_data(i,j)['weight'] for j in neigh)\n",
    "            for j in neigh:\n",
    "                p=1.0*G.get_edge_data(i,j)['weight']/W\n",
    "                if (1-p)**(k-1) < alp:\n",
    "                    keep_graph.add_edge(i,j, weight = G.get_edge_data(i,j))\n",
    "    return keep_graph, 1.0*len(keep_graph.edges())/(1.0*len(G.edges())),nx.number_connected_components(keep_graph) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "G=nx.Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_nodes_from(range(N))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "A=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in list(T_Links.keys()):\n",
    "    A.append((int(i.split(\",\")[0]), int(i.split(',')[1]), {'weight': T_Links[i]}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "G.add_edges_from(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.27 5.12820512821e-06 1.0\n",
      "0.271 5.12820512821e-06 1.0\n",
      "0.272 5.12820512821e-06 1.0\n",
      "0.273 5.12820512821e-06 1.0\n",
      "0.274 5.12820512821e-06 1.0\n",
      "0.275 5.12820512821e-06 1.0\n",
      "0.276 5.12820512821e-06 1.0\n",
      "0.277 5.12820512821e-06 1.0\n",
      "0.278 1.02564102564e-05 1.0\n",
      "0.279 1.02564102564e-05 1.0\n",
      "0.28 1.02564102564e-05 1.0\n",
      "0.281 1.53846153846e-05 1.0\n",
      "0.282 1.53846153846e-05 1.0\n",
      "0.283 1.53846153846e-05 1.0\n",
      "0.284 1.53846153846e-05 1.0\n",
      "0.285 1.53846153846e-05 1.0\n",
      "0.286 1.53846153846e-05 1.0\n",
      "0.287 1.53846153846e-05 1.0\n",
      "0.288 1.53846153846e-05 1.0\n",
      "0.289 1.53846153846e-05 1.0\n",
      "0.29 1.53846153846e-05 1.0\n",
      "0.291 1.53846153846e-05 1.0\n",
      "0.292 1.53846153846e-05 1.0\n",
      "0.293 1.53846153846e-05 1.0\n",
      "0.294 1.53846153846e-05 1.0\n",
      "0.295 1.53846153846e-05 1.0\n",
      "0.296 1.53846153846e-05 1.0\n",
      "0.297 2.5641025641e-05 3.0\n",
      "0.298 2.5641025641e-05 3.0\n",
      "0.299 2.5641025641e-05 3.0\n",
      "0.3 3.07692307692e-05 4.0\n",
      "0.301 3.58974358974e-05 5.0\n",
      "0.302 3.58974358974e-05 5.0\n",
      "0.303 3.58974358974e-05 5.0\n",
      "0.304 3.58974358974e-05 5.0\n",
      "0.305 4.10256410256e-05 6.0\n",
      "0.306 5.12820512821e-05 7.0\n",
      "0.307 6.15384615385e-05 8.0\n",
      "0.308 6.15384615385e-05 8.0\n",
      "0.309 7.69230769231e-05 10.0\n",
      "0.31 8.71794871795e-05 11.0\n",
      "0.311 9.23076923077e-05 11.0\n",
      "0.312 0.000102564102564 11.0\n",
      "0.313 0.000117948717949 11.0\n",
      "0.314 0.000128205128205 11.0\n",
      "0.315 0.000164102564103 14.0\n",
      "0.316 0.000189743589744 15.0\n",
      "0.317 0.000215384615385 16.0\n",
      "0.318 0.000307692307692 14.0\n",
      "0.319 0.000353846153846 14.0\n",
      "0.32 0.000451282051282 17.0\n",
      "0.321 0.000579487179487 20.0\n",
      "0.322 0.000764102564103 19.0\n",
      "0.323 0.000953846153846 15.0\n",
      "0.324 0.00133333333333 14.0\n",
      "0.325 0.00166666666667 14.0\n",
      "0.326 0.00214871794872 10.0\n",
      "0.327 0.00276923076923 10.0\n",
      "0.328 0.00372820512821 8.0\n",
      "0.329 0.00471794871795 7.0\n",
      "0.33 0.00613846153846 4.0\n",
      "0.331 0.00789230769231 4.0\n",
      "0.332 0.0103179487179 3.0\n",
      "0.333 0.0131794871795 2.0\n",
      "0.334 0.0166923076923 1.0\n",
      "0.335 0.0211230769231 1.0\n",
      "0.336 0.0269179487179 1.0\n",
      "0.337 0.0337948717949 1.0\n",
      "0.338 0.0419487179487 1.0\n",
      "0.339 0.0514615384615 1.0\n"
     ]
    }
   ],
   "source": [
    "alp=np.arange(0.27,0.34,0.001)\n",
    "y1=alp.copy()\n",
    "y2=alp.copy()\n",
    "for i in np.arange(alp.size):\n",
    "    rez=extract_bb(G,alp[i])\n",
    "    y1[i]=rez[1]#fraction of remaining links\n",
    "    y2[i]=rez[2]#number of connected components \n",
    "    print(alp[i], y1[i], y2[i])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.,   1.,   1.,   1.,   1.,   1.,   1.,   1.,   1.,   1.,   1.,\n",
       "         1.,   1.,   1.,   1.,   1.,   1.,   1.,   1.,   1.,   1.,   1.,\n",
       "         1.,   1.,   1.,   1.,   1.,   3.,   3.,   3.,   4.,   5.,   5.,\n",
       "         5.,   5.,   6.,   7.,   8.,   8.,  10.,  11.,  11.,  11.,  11.,\n",
       "        11.,  14.,  15.,  16.,  14.,  14.,  17.,  20.,  19.,  15.,  14.,\n",
       "        14.,  10.,  10.,   8.,   7.,   4.,   4.,   3.,   2.,   1.,   1.,\n",
       "         1.,   1.,   1.,   1.])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f69b84cf6d8>]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f6981b491d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(alp,y1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f697ab744a8>]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f698195f668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(alp,y2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are: 126 nodes\n",
      "there are: 186 links\n",
      "15 connected commponent\n"
     ]
    }
   ],
   "source": [
    "alp=\"0-323\"#alpha value with - instead of . (for output name automatic change)\n",
    "bb,dens,comp=extract_bb(G,float(alp.replace('-','.')))\n",
    "print(\"there are:\", len(bb.nodes()), \"nodes\")\n",
    "print(\"there are:\", len(bb.edges()), \"links\")\n",
    "print(comp, \"connected commponent\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "ww=open(\"data/cell_lines_corr-above0-5-\"+alp+\".net\",\"w\")\n",
    "ww.write(\"#SOURCE\\t#TARGET\\t#WEIGHT\\n\")\n",
    "Link_tab=nx.get_edge_attributes(bb,'weight')\n",
    "for i in bb.edges():\n",
    "    ww.write(str(i[0]+1)+\"\\t\"+str(i[1]+1)+\"\\t\"+str(Link_tab[i][\"weight\"])+\"\\n\")\n",
    "ww.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "info=open(\"data/cell_info.txt\",'r').read()\n",
    "info=info.split(\"\\n\")\n",
    "info.remove(\"\")\n",
    "header=info[0].split(\"\\t\")[0]+\"\\t\"+info[0].split(\"\\t\")[5]+\"\\t\"+info[0].split(\"\\t\")[37]#information related to DepMap_ID/Cancer_Type/Gender\n",
    "info.remove(info[0])\n",
    "dic={}\n",
    "subtype=open(\"data/cell_lines_corr-above0-5-0-323-subtypes.nodes\", \"r\").read()\n",
    "subtype=subtype.split(\"\\n\")\n",
    "subtype.remove(\"\")\n",
    "subtype.remove(subtype[0])\n",
    "for i in info:\n",
    "    tmp=i.split(\"\\t\")\n",
    "    dic[tmp[0]]=[tmp[5],tmp[-1], \"\"]#cancer type, gender, cancer subtype\n",
    "for i in subtype:\n",
    "    tmp=i.split(\"\\t\")\n",
    "    dic[tmp[1]][2]=tmp[-1]\n",
    "for i in Cell:\n",
    "    if i not in dic.keys():\n",
    "        dic[i]=[\"NA\", \"NA\", \"NA\"]\n",
    "ww=open(\"data/cell_lines_corr-above0-5-\"+alp+\".nodes\",\"w\")\n",
    "ww.write(\"Node_ID\\t\"+header+\"\\t\"+\"Cancer_subtype\"+\"\\n\")\n",
    "for i in sorted(list(bb.nodes())):\n",
    "    ww.write(str(i+1)+\"\\t\"+Cell[i]+\"\\t\"+dic[Cell[i]][0]+\"\\t\"+dic[Cell[i]][1]+\"\\t\"+dic[Cell[i]][-1]+\"\\n\")    \n",
    "ww.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing cancer_type-cancer_type links and node statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "net=open(\"data/cell_lines_corr-above0-5-\"+alp+\".net\",\"r\").read()\n",
    "net=net.split(\"\\n\")\n",
    "net.remove(\"\")\n",
    "net.remove(net[0])\n",
    "link_class={}\n",
    "Nl=len(net)\n",
    "C={}\n",
    "ww1=open(\"data/cell_lines_corr-above0-5-\"+alp+\".linkclass.dat\",\"w\")\n",
    "ww2=open(\"data/cell_lines_corr-above0-5-\"+alp+\".nodeclass.dat\",\"w\")\n",
    "for i in Cell:\n",
    "    for j in Cell:        \n",
    "        link_class[dic[i][0]+\":\"+dic[j][0]]=0\n",
    "        C[dic[i][0]]=0\n",
    "        C[dic[j][0]]=0\n",
    "LIST=[]\n",
    "for l in net:\n",
    "    tmp=l.split(\"\\t\")[0:2]\n",
    "    link_class[dic[Cell[int(tmp[0])-1]][0]+\":\"+dic[Cell[int(tmp[1])-1]][0]]+=1\n",
    "    if tmp[0] not in LIST:\n",
    "        C[dic[Cell[int(tmp[0])-1]][0]]+=1\n",
    "        LIST.append(tmp[0])\n",
    "    if tmp[1] not in LIST:\n",
    "        C[dic[Cell[int(tmp[1])-1]][0]]+=1\n",
    "        LIST.append(tmp[1])\n",
    "N=len(LIST)\n",
    "rez={}\n",
    "LIST=[]\n",
    "cncer=[]\n",
    "W=0\n",
    "for i in link_class.keys():\n",
    "    if i.split(\":\")[0] not in cncer:\n",
    "        cncer.append(i.split(\":\")[0])\n",
    "    if i.split(\":\")[1] not in cncer:\n",
    "        cncer.append(i.split(\":\")[1])\n",
    "    W+=link_class[i]\n",
    "for i in range(len(cncer)):\n",
    "    for j in range(i,len(cncer)):\n",
    "        I=cncer[i]\n",
    "        J=cncer[j]\n",
    "        LIST.append(I+\":\"+J)\n",
    "        if I!=J:\n",
    "            rez[I+\":\"+J]=link_class[I+\":\"+J]+link_class[J+\":\"+I]\n",
    "        if I==J:\n",
    "            rez[I+\":\"+J]=link_class[I+\":\"+J]\n",
    "link_class=[]\n",
    "link_class_name=[]\n",
    "for i in LIST:\n",
    "    if rez[i]>0:\n",
    "        z=0\n",
    "        link_class_name.append(i)\n",
    "        link_class.append(rez[i])\n",
    "tmp=sorted(range(len(link_class)), key=lambda k : link_class[k], reverse=True)\n",
    "ww1.write(\"#SOURCE\\t#TARGET\\t#LINK\\n\")\n",
    "for i in tmp:\n",
    "    ww1.write(link_class_name[i].replace(\":\",\"\\t\")+\"\\t\"+str(link_class[i])+\"\\n\")\n",
    "ww1.close()\n",
    "LIST=[[],[]]\n",
    "for i in C.keys():\n",
    "    if C[i]>0:        \n",
    "        LIST[0].append(i)\n",
    "        LIST[1].append(C[i])\n",
    "tmp=sorted(range(len(LIST[0])), key=lambda k : LIST[1][k], reverse=True)\n",
    "ww2.write(\"#CANCER\"+\"\\t\"+\"#OCCURANCE\"+\"\\n\")\n",
    "for i in tmp:\n",
    "    ww2.write(LIST[0][i]+\"\\t\"+str(LIST[1][i])+\"\\n\")\n",
    "ww2.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    " ##Computing cancer_subtype-cancer_subtype links and node statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "stlink_class={}\n",
    "stC={}\n",
    "ww1=open(\"data/cell_lines_corr-above0-5-\"+alp+\"-subtypes.linkclass.dat\",\"w\")\n",
    "ww2=open(\"data/cell_lines_corr-above0-5-\"+alp+\"-subtypes.nodeclass.dat\",\"w\")\n",
    "for i in Cell:\n",
    "    for j in Cell:        \n",
    "        stlink_class[dic[i][-1]+\":\"+dic[j][-1]]=0\n",
    "        stC[dic[i][-1]]=0\n",
    "        stC[dic[j][-1]]=0\n",
    "stLIST=[]\n",
    "for l in net:\n",
    "    tmp=l.split(\"\\t\")[0:2]\n",
    "    stlink_class[dic[Cell[int(tmp[0])-1]][-1]+\":\"+dic[Cell[int(tmp[1])-1]][-1]]+=1\n",
    "    if tmp[0] not in stLIST:\n",
    "        stC[dic[Cell[int(tmp[0])-1]][-1]]+=1\n",
    "        stLIST.append(tmp[0])\n",
    "    if tmp[1] not in stLIST:\n",
    "        stC[dic[Cell[int(tmp[1])-1]][-1]]+=1\n",
    "        stLIST.append(tmp[1])\n",
    "N=len(stLIST)\n",
    "strez={}\n",
    "stLIST=[]\n",
    "stcncer=[]\n",
    "W=0\n",
    "for i in stlink_class.keys():\n",
    "    if i.split(\":\")[0] not in stcncer:\n",
    "        stcncer.append(i.split(\":\")[0])\n",
    "    if i.split(\":\")[1] not in stcncer:\n",
    "        stcncer.append(i.split(\":\")[1])\n",
    "    W+=stlink_class[i]\n",
    "for i in range(len(stcncer)):\n",
    "    for j in range(i,len(stcncer)):\n",
    "        I=stcncer[i]\n",
    "        J=stcncer[j]\n",
    "        stLIST.append(I+\":\"+J)\n",
    "        if I!=J:\n",
    "            strez[I+\":\"+J]=stlink_class[I+\":\"+J]+stlink_class[J+\":\"+I]\n",
    "        if I==J:\n",
    "            strez[I+\":\"+J]=stlink_class[I+\":\"+J]\n",
    "stlink_class=[]\n",
    "stlink_class_name=[]\n",
    "for i in stLIST:\n",
    "    if strez[i]>0:\n",
    "        z=0\n",
    "        stlink_class_name.append(i)\n",
    "        stlink_class.append(strez[i])\n",
    "tmp=sorted(range(len(stlink_class)), key=lambda k : stlink_class[k], reverse=True)\n",
    "ww1.write(\"#SOURCE\\t#TARGET\\t#LINK\\n\")\n",
    "for i in tmp:\n",
    "    ww1.write(stlink_class_name[i].replace(\":\",\"\\t\")+\"\\t\"+str(stlink_class[i])+\"\\n\")\n",
    "ww1.close()\n",
    "stLIST=[[],[]]\n",
    "for i in stC.keys():\n",
    "    if stC[i]>0:        \n",
    "        stLIST[0].append(i)\n",
    "        stLIST[1].append(stC[i])\n",
    "tmp=sorted(range(len(stLIST[0])), key=lambda k : stLIST[1][k], reverse=True)\n",
    "ww2.write(\"#CANCER_Subtype\"+\"\\t\"+\"#OCCURANCE\"+\"\\n\")\n",
    "for i in tmp:\n",
    "    ww2.write(stLIST[0][i]+\"\\t\"+str(stLIST[1][i])+\"\\n\")\n",
    "ww2.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Homophiliness cancer type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 80.0  % cancer types having heterophylic connections\n"
     ]
    }
   ],
   "source": [
    "a=open(\"data/cell_lines_corr-above0-5-\"+alp+\".linkclass.dat\",\"r\").read()\n",
    "a=a.split(\"\\n\")\n",
    "a.remove(\"\")\n",
    "a.remove(a[0])\n",
    "b=open(\"data/cell_lines_corr-above0-5-\"+alp+\".nodeclass.dat\",\"r\").read()\n",
    "b=b.split(\"\\n\")\n",
    "b.remove(\"\")\n",
    "b.remove(b[0])\n",
    "HOM={}\n",
    "DIFF={}\n",
    "LINK=[]\n",
    "BAL={}\n",
    "ww=open(\"data/cell_lines_corr-above0-5-\"+alp+\".homophiliness\",\"w\")\n",
    "for i in a:\n",
    "    tmp=i.split(\"\\t\")\n",
    "    HOM[tmp[0]]=0\n",
    "    HOM[tmp[1]]=0\n",
    "    DIFF[tmp[0]]=0\n",
    "    DIFF[tmp[1]]=0\n",
    "for i in a:\n",
    "    tmp=i.split(\"\\t\")\n",
    "    if tmp[0] != tmp[1]:\n",
    "        DIFF[tmp[0]]+=int(tmp[2])\n",
    "        DIFF[tmp[1]]+=int(tmp[2])\n",
    "    else:\n",
    "        HOM[tmp[0]]+=int(tmp[2])\n",
    "LIST=[[],[]]\n",
    "for i in a:\n",
    "    tmp=i.split(\"\\t\")\n",
    "    BAL[tmp[0]]=(1.0*(HOM[tmp[0]]-DIFF[tmp[0]]))/(1.0*(HOM[tmp[0]]+DIFF[tmp[0]]))\n",
    "    if tmp[0] not in LIST[0]:\n",
    "        LIST[0].append(tmp[0])\n",
    "        LIST[1].append(BAL[tmp[0]])\n",
    "    BAL[tmp[1]]=(1.0*(HOM[tmp[1]]-DIFF[tmp[1]]))/(1.0*(HOM[tmp[1]]+DIFF[tmp[1]]))\n",
    "    if tmp[1] not in LIST[0]:\n",
    "        LIST[0].append(tmp[1])\n",
    "        LIST[1].append(BAL[tmp[1]])\n",
    "C={}\n",
    "for i in b:\n",
    "    tmp=i.split(\"\\t\")\n",
    "    C[tmp[0]]=tmp[1]\n",
    "tmp=sorted(range(len(LIST[0])), key=lambda k : LIST[1][k], reverse=True)\n",
    "ww.write(\"#CANCER\"+\"\\t\"+\"#HOMOPHILINESS\"+\"\\t\"+\"#NODE_OCCURANCE\"+\"\\n\")\n",
    "w=0.0\n",
    "for i in tmp:\n",
    "    ww.write(LIST[0][i]+\"\\t\"+str(LIST[1][i])+\"\\t\"+C[LIST[0][i]]+\"\\n\")    \n",
    "    if(LIST[1][i]<0.0):\n",
    "        w+=1.0/len(tmp)\n",
    "ww.close()\n",
    "print(\"there are\", round(w*100,2), \" % cancer types having heterophylic connections\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "##HOMOPHILINESS cancer subtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 85.29  % cancer subtypes having heterophylic connections\n"
     ]
    }
   ],
   "source": [
    "a=open(\"data/cell_lines_corr-above0-5-\"+alp+\"-subtypes.linkclass.dat\",\"r\").read()\n",
    "a=a.split(\"\\n\")\n",
    "a.remove(\"\")\n",
    "a.remove(a[0])\n",
    "b=open(\"data/cell_lines_corr-above0-5-\"+alp+\"-subtypes.nodeclass.dat\",\"r\").read()\n",
    "b=b.split(\"\\n\")\n",
    "b.remove(\"\")\n",
    "b.remove(b[0])\n",
    "HOM={}\n",
    "DIFF={}\n",
    "LINK=[]\n",
    "BAL={}\n",
    "ww=open(\"data/cell_lines_corr-above0-5-\"+alp+\"-subtypes.homophiliness\",\"w\")\n",
    "for i in a:\n",
    "    tmp=i.split(\"\\t\")\n",
    "    HOM[tmp[0]]=0\n",
    "    HOM[tmp[1]]=0\n",
    "    DIFF[tmp[0]]=0\n",
    "    DIFF[tmp[1]]=0\n",
    "for i in a:\n",
    "    tmp=i.split(\"\\t\")\n",
    "    if tmp[0] != tmp[1]:\n",
    "        DIFF[tmp[0]]+=int(tmp[2])\n",
    "        DIFF[tmp[1]]+=int(tmp[2])\n",
    "    else:\n",
    "        HOM[tmp[0]]+=int(tmp[2])\n",
    "LIST=[[],[]]\n",
    "for i in a:\n",
    "    tmp=i.split(\"\\t\")\n",
    "    BAL[tmp[0]]=(1.0*(HOM[tmp[0]]-DIFF[tmp[0]]))/(1.0*(HOM[tmp[0]]+DIFF[tmp[0]]))\n",
    "    if tmp[0] not in LIST[0]:\n",
    "        LIST[0].append(tmp[0])\n",
    "        LIST[1].append(BAL[tmp[0]])\n",
    "    BAL[tmp[1]]=(1.0*(HOM[tmp[1]]-DIFF[tmp[1]]))/(1.0*(HOM[tmp[1]]+DIFF[tmp[1]]))\n",
    "    if tmp[1] not in LIST[0]:\n",
    "        LIST[0].append(tmp[1])\n",
    "        LIST[1].append(BAL[tmp[1]])\n",
    "C={}\n",
    "for i in b:\n",
    "    tmp=i.split(\"\\t\")\n",
    "    C[tmp[0]]=tmp[1]\n",
    "tmp=sorted(range(len(LIST[0])), key=lambda k : LIST[1][k], reverse=True)\n",
    "ww.write(\"#CANCER_Subtype\"+\"\\t\"+\"#HOMOPHILINESS\"+\"\\t\"+\"#NODE_OCCURANCE\"+\"\\n\")\n",
    "w=0.0\n",
    "for i in tmp:\n",
    "    ww.write(LIST[0][i]+\"\\t\"+str(LIST[1][i])+\"\\t\"+C[LIST[0][i]]+\"\\n\")    \n",
    "    if(LIST[1][i]<0.0):\n",
    "        w+=1.0/len(tmp)\n",
    "ww.close()\n",
    "print(\"there are\", round(w*100,2), \" % cancer subtypes having heterophylic connections\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cell_Line Homophily\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6\t17\t0.817612181314\n",
      "6\tACH-000012\tLung Cancer\tFemale\tNSCLC-Adenocarcinoma\n",
      "there are 46.83  % cell_lines having heterophylic connections\n"
     ]
    }
   ],
   "source": [
    "ww=open(\"data/cell_lines_corr-above0-5\"+alp+\"-cell.homophily\",\"w\")\n",
    "ww.write(\"Node_ID\\tDepMap_ID\\t#HOMOPHILINESS\\n\")\n",
    "a=open(\"data/cell_lines_corr-above0-5-\"+alp+\".net\",\"r\").read()\n",
    "b=open(\"data/cell_lines_corr-above0-5-\"+alp+\".nodes\",\"r\").read()\n",
    "a=a.split(\"\\n\")\n",
    "b=b.split(\"\\n\")\n",
    "a.remove(\"\")\n",
    "a.remove(a[0])\n",
    "b.remove(\"\")\n",
    "b.remove(b[0])\n",
    "print(a[0])\n",
    "print(b[0])\n",
    "w=0.0\n",
    "D={}\n",
    "H={}#Homophily\n",
    "h={}#Heterophily\n",
    "B={}\n",
    "for i in b:\n",
    "    tmp=i.split(\"\\t\")\n",
    "    D[tmp[0]]=[tmp[1],tmp[2]]\n",
    "    H[tmp[0]]=0.0\n",
    "    h[tmp[0]]=0.0\n",
    "    B[tmp[0]]=0.0\n",
    "for i in a:\n",
    "    tmp=i.split(\"\\t\")\n",
    "    if(D[tmp[0]][1]==D[tmp[1]][1]):\n",
    "        H[tmp[0]]+=1\n",
    "        H[tmp[1]]+=1\n",
    "    else:\n",
    "        h[tmp[0]]+=1\n",
    "        h[tmp[1]]+=1\n",
    "for i in D.keys():\n",
    "    B[i]=(H[i]-h[i])/(H[i]+h[i])\n",
    "    if B[i]<0.0:\n",
    "        w+=1.0/len(b)\n",
    "for i in D.keys():\n",
    "    ww.write(D[i][0]+\"\\t\"+D[i][1]+\"\\t\"+str(B[i])+\"\\n\")\n",
    "ww.close()\n",
    "print(\"there are\", round(w*100,2), \" % cell_lines having heterophylic connections\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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   "source": [
    "pd.DataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>#CANCER</th>\n",
       "      <th>Homophiliness</th>\n",
       "      <th>Number of Cell Lines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Kidney Cancer</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Leukemia</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Head and Neck Cancer</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lymphoma</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Neuroblastoma</td>\n",
       "      <td>-0.157895</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                #CANCER  Homophiliness  Number of Cell Lines\n",
       "0         Kidney Cancer       1.000000                     4\n",
       "1              Leukemia       0.875000                    15\n",
       "2  Head and Neck Cancer       0.200000                     6\n",
       "3              Lymphoma       0.000000                     5\n",
       "4         Neuroblastoma      -0.157895                     7"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdata = pd.read_csv(\"data/cell_lines_corr-above0-5-\"+alp+\".homophiliness\",sep=\"\\t\")\n",
    "cdata=cdata.rename(columns={'#HOMOPHILINESS':'Homophiliness', '#NODE_OCCURANCE':'Number of Cell Lines'})\n",
    "cdata.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
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          "Breast Cancer",
          "Esophageal Cancer",
          "Lung Cancer",
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          "Brain Cancer",
          "Colon/Colorectal Cancer",
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    "fig.write_image(\"figures/node-count_vs_homophily.svg\")\n",
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       "           #CANCER_Subtype  Homophiliness  Number of Cell Lines\n",
       "0     Non-Hodgkins, B-cell       1.000000                     2\n",
       "1                      ALL       0.666667                     7\n",
       "2                      AML       0.636364                     8\n",
       "3  Squamous Cell Carcinoma       0.250000                     9\n",
       "4     Non-Hodgkins, T-cell       0.000000                     2"
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    "stcdata = pd.read_csv(\"data/cell_lines_corr-above0-5-\"+alp+\"-subtypes.homophiliness\",sep=\"\\t\")\n",
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\"bgcolor\": \"white\", \"caxis\": {\"gridcolor\": \"#DFE8F3\", \"linecolor\": \"#A2B1C6\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"#EBF0F8\", \"linecolor\": \"#EBF0F8\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"#EBF0F8\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"#EBF0F8\", \"linecolor\": \"#EBF0F8\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"#EBF0F8\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Number of Cell Lines\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"Homophiliness\"}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('b369ce57-9a1e-47e6-bb7a-d4df7f4cc22f');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
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       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
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       "    x.observe(notebookContainer, {childList: true});\n",
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       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
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       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.scatter(stcdata, x='Number of Cell Lines', y='Homophiliness',text=\"#CANCER_Subtype\")\n",
    "fig.update_traces(textposition='top center',marker={'size':12})\n",
    "fig.write_image(\"figures/node-count_vs_homophily-subtypes.svg\")\n",
    "fig.write_image(\"figures/node-count_vs_homophily-subtypes.png\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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