5.1-Link-Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6d7bf9ad",
   "metadata": {},
   "source": [
    "## GDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "879fd325",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json \n",
    "import pandas as pd\n",
    "from neo4j import GraphDatabase\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18f21ce8",
   "metadata": {},
   "source": [
    "### Connect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4f3e16e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "driver = GraphDatabase.driver(uri = \"bolt://localhost:7687\",\\\n",
    "                              auth = (\"neo4j\",\"12341234\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b29a1864",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-17-b734c674563a>:1: ExperimentalWarning: The configuration may change in the future.\n",
      "  driver.verify_connectivity()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Neo4j/5.4.0'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "driver.verify_connectivity()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eccbcb8",
   "metadata": {},
   "source": [
    "### Link Prediction (Proteins to PTM_Types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1a29343d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(56985, 1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"MATCH (p:Protein) RETURN p.UID\"\n",
    "\n",
    "res = []\n",
    "with driver.session() as session:\n",
    "    info = session.run(query)\n",
    "    for item in info:\n",
    "        res.append({\"ID\":item.values()[0]})\n",
    "dfp = pd.DataFrame(res)\n",
    "dfp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0b673aba",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfp.to_csv(\"data/ProteinID.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3f478d37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(570, 1)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"MATCH (ptm:PTM_Type) RETURN ptm.PTMID\"\n",
    "\n",
    "res = []\n",
    "with driver.session() as session:\n",
    "    info = session.run(query)\n",
    "    for item in info:\n",
    "        res.append({\"ID\":item.values()[0]})\n",
    "dfptm = pd.DataFrame(res)\n",
    "dfptm.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "31ba1f1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfptm.to_csv(\"data/PTMID.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "33f4c437",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = []\n",
    "\n",
    "for p in dfp[0:10][\"ID\"]:\n",
    "    for pt in dfptm[0:10][\"ID\"]:\n",
    "    \n",
    "        query = \"MATCH (p:Protein {UID: '\" + p + \"'}) \\\n",
    "        MATCH (ptm:PTM_Type {PTMID: '\" + pt + \"'}) \\\n",
    "        RETURN gds.alpha.linkprediction.totalNeighbors(p, ptm, \\\n",
    "        {relationshipQuery: null})\\\n",
    "        AS Score, p.UID AS Protein, ptm.PTMID AS PTM_Type\"\n",
    "\n",
    "        with driver.session() as session:\n",
    "            info = session.run(query)\n",
    "            for item in info:\n",
    "                res.append({\"Protein\":item.values()[1], \n",
    "                            \"PTM_Type\":item.values()[2],\n",
    "                            \"Score\":item.values()[0]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "626b19b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Protein</th>\n",
       "      <th>PTM_Type</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>P63261</td>\n",
       "      <td>PTM-0306</td>\n",
       "      <td>139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P63261</td>\n",
       "      <td>PTM-0371</td>\n",
       "      <td>133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P63261</td>\n",
       "      <td>PTM-0369</td>\n",
       "      <td>122.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>P63261</td>\n",
       "      <td>PTM-0477</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>P63261</td>\n",
       "      <td>PTM-0336</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Protein  PTM_Type  Score\n",
       "8  P63261  PTM-0306  139.0\n",
       "4  P63261  PTM-0371  133.0\n",
       "3  P63261  PTM-0369  122.0\n",
       "5  P63261  PTM-0477  101.0\n",
       "7  P63261  PTM-0336   96.0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_res = pd.DataFrame(res)\n",
    "df_res = df_res.sort_values(\"Score\", ascending = False)\n",
    "df_res.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "0cc034b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>PTM_Type</th>\n",
       "      <th>PTM-0306</th>\n",
       "      <th>PTM-0312</th>\n",
       "      <th>PTM-0336</th>\n",
       "      <th>PTM-0337</th>\n",
       "      <th>PTM-0368</th>\n",
       "      <th>PTM-0369</th>\n",
       "      <th>PTM-0371</th>\n",
       "      <th>PTM-0450</th>\n",
       "      <th>PTM-0476</th>\n",
       "      <th>PTM-0477</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Protein</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>O42422</th>\n",
       "      <td>65.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>27.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P28317</th>\n",
       "      <td>59.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P30987</th>\n",
       "      <td>70.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>P63261</th>\n",
       "      <td>139.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q38933</th>\n",
       "      <td>57.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q57NN5</th>\n",
       "      <td>57.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q8BWY3</th>\n",
       "      <td>77.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q91YT7</th>\n",
       "      <td>67.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q92785</th>\n",
       "      <td>64.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q9XZD6</th>\n",
       "      <td>59.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "PTM_Type  PTM-0306  PTM-0312  PTM-0336  PTM-0337  PTM-0368  PTM-0369  \\\n",
       "Protein                                                                \n",
       "O42422        65.0      10.0      22.0      22.0      11.0      48.0   \n",
       "P28317        59.0       4.0      16.0      16.0       5.0      42.0   \n",
       "P30987        70.0      15.0      27.0      27.0      16.0      53.0   \n",
       "P63261       139.0      84.0      96.0      96.0      85.0     122.0   \n",
       "Q38933        57.0       2.0      14.0      14.0       3.0      40.0   \n",
       "Q57NN5        57.0       2.0      14.0      14.0       3.0      40.0   \n",
       "Q8BWY3        77.0      22.0      34.0      34.0      23.0      60.0   \n",
       "Q91YT7        67.0      12.0      24.0      24.0      13.0      50.0   \n",
       "Q92785        64.0       9.0      21.0      21.0      10.0      47.0   \n",
       "Q9XZD6        59.0       4.0      16.0      16.0       5.0      42.0   \n",
       "\n",
       "PTM_Type  PTM-0371  PTM-0450  PTM-0476  PTM-0477  \n",
       "Protein                                           \n",
       "O42422        59.0      10.0      19.0      27.0  \n",
       "P28317        53.0       4.0      13.0      21.0  \n",
       "P30987        64.0      15.0      24.0      32.0  \n",
       "P63261       133.0      84.0      93.0     101.0  \n",
       "Q38933        51.0       2.0      11.0      19.0  \n",
       "Q57NN5        51.0       2.0      11.0      19.0  \n",
       "Q8BWY3        71.0      22.0      31.0      39.0  \n",
       "Q91YT7        61.0      12.0      21.0      29.0  \n",
       "Q92785        58.0       9.0      18.0      26.0  \n",
       "Q9XZD6        53.0       4.0      13.0      21.0  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pivot = df_res.pivot(index=\"Protein\", columns=\"PTM_Type\", values=\"Score\")\n",
    "df_pivot.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "e8893b3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize = [10,10])\n",
    "sns.heatmap(df_pivot,cmap=\"Blues\", annot=True, fmt=\".2f\")\n",
    "plt.savefig(\"plots/link-prediction-p2ptm.pdf\")\n",
    "plt.savefig(\"plots/link-prediction-p2ptm.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f607f89",
   "metadata": {},
   "source": [
    "### Link Prediction: Protein to PTM_Type (Specialized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "439eea6b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28631715",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec62b43e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4174a41",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b04b43e2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}