graph_analysis/visualize.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b47edc00",
   "metadata": {},
   "source": [
    "## Prepare your relation file\n",
    "\n",
    "Prepare your relation file and specify the path in the following cell. The relation file should be a csv/tsv file and the first line should be the header. For the format of the entity & relation file, please refer to the [README](../graph_data/README.md). If you want to build your own entity & relation file, please refer to the [KG README](../graph_data/KG_README.md) for more details.\n",
    "\n",
    "We assume that the relation file is named as `knowledge_graph.tsv`, the entity file is named as `annotated_entities.tsv`, and are located in the `datasets` directory or `you can specify the path in the following cell`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e8ac7158",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "datadir = os.path.join(os.path.dirname(os.getcwd()), \"datasets\", \"biomedgps-v2-20231223\")\n",
    "\n",
    "entity_file = os.path.join(datadir, \"annotated_entities.tsv\")\n",
    "relation_file = os.path.join(datadir, \"knowledge_graph.tsv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3NYRSKGzbNFf",
   "metadata": {
    "id": "3NYRSKGzbNFf"
   },
   "source": [
    "### Install Dependencies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19292c5e",
   "metadata": {},
   "source": [
    "We defined all related functions in `lib/graph.py` module. Before doing the graph analysis, we need to import the module. In addition, we assume that you have followed the instructions in the [README](../README.md) file and have installed all the required dependencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "51cdc6ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "libdir = os.path.join(os.path.dirname(os.getcwd()), \"lib\")\n",
    "sys.path.append(libdir)\n",
    "\n",
    "from graph import (\n",
    "    biomedgps2stat,\n",
    "    make_wide_format,\n",
    "    transposed_array,\n",
    "    snake_case,\n",
    "    gen_layout,\n",
    ")\n",
    "\n",
    "libdir = os.path.join(os.path.dirname(os.getcwd()), \"lib\")\n",
    "sys.path.append(libdir)\n",
    "\n",
    "from lib.graph import get_color\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce8izQ1x0iCT",
   "metadata": {
    "id": "ce8izQ1x0iCT"
   },
   "source": [
    "### Prepare the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b9970475",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/4s/d4nr1sg91ps1k3qz00h28w_r0000gp/T/ipykernel_24745/387943002.py:3: DtypeWarning: Columns (6,8) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  entities = pd.read_csv(entity_file, sep=\"\\t\")\n",
      "/var/folders/4s/d4nr1sg91ps1k3qz00h28w_r0000gp/T/ipykernel_24745/387943002.py:4: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  relations = pd.read_csv(relation_file, sep=\"\\t\")\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "entities = pd.read_csv(entity_file, sep=\"\\t\")\n",
    "relations = pd.read_csv(relation_file, sep=\"\\t\")\n",
    "node_stat, edge_stat, real_node_stat = biomedgps2stat(entities, relations, enable_stat_real_entities=True)\n",
    "\n",
    "# Merge two columns: start_entity_type and end_entity_type, the string format is \"start_entity_type:end_entity_type\"\n",
    "edge_stat[\"simple_relation_type\"] = (\n",
    "    edge_stat[\"start_entity_type\"]   \":\"   edge_stat[\"end_entity_type\"]\n",
    ")\n",
    "\n",
    "# start_entity_type:end_entity_type may be same as end_entity_type:start_entity_type, so we need to merge them\n",
    "edge_stat[\"simple_relation_type\"] = edge_stat[\"simple_relation_type\"].apply(\n",
    "    lambda x: \":\".join(sorted(x.split(\":\")))\n",
    ")\n",
    "\n",
    "edge_stat.to_csv(os.path.join(datadir, \"edge_stat.tsv\"), sep=\"\\t\", index=False)\n",
    "node_stat.to_csv(os.path.join(datadir, \"node_stat.tsv\"), sep=\"\\t\", index=False)\n",
    "\n",
    "if real_node_stat is not None:\n",
    "    real_node_stat.to_csv(os.path.join(datadir, \"real_node_stat.tsv\"), sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3a017cd7",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entity_type</th>\n",
       "      <th>resource</th>\n",
       "      <th>entity_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anatomy</td>\n",
       "      <td>MESH</td>\n",
       "      <td>1315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anatomy</td>\n",
       "      <td>UBERON</td>\n",
       "      <td>15822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BiologicalProcess</td>\n",
       "      <td>GO</td>\n",
       "      <td>30571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BiologicalProcess</td>\n",
       "      <td>Hetionet</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CellularComponent</td>\n",
       "      <td>GO</td>\n",
       "      <td>4485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CellularComponent</td>\n",
       "      <td>Hetionet</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Compound</td>\n",
       "      <td>DrugBank</td>\n",
       "      <td>267869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Compound</td>\n",
       "      <td>MESH</td>\n",
       "      <td>167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Disease</td>\n",
       "      <td>DOID</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Disease</td>\n",
       "      <td>MESH</td>\n",
       "      <td>3518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Disease</td>\n",
       "      <td>MONDO</td>\n",
       "      <td>26582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Gene</td>\n",
       "      <td>ENTREZ</td>\n",
       "      <td>93962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Metabolite</td>\n",
       "      <td>HMDB</td>\n",
       "      <td>247966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MolecularFunction</td>\n",
       "      <td>GO</td>\n",
       "      <td>12502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>MolecularFunction</td>\n",
       "      <td>Hetionet</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pathway</td>\n",
       "      <td>Hetionet</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Pathway</td>\n",
       "      <td>KEGG</td>\n",
       "      <td>1057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Pathway</td>\n",
       "      <td>Reactome</td>\n",
       "      <td>4354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Pathway</td>\n",
       "      <td>WikiPathways</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>PharmacologicClass</td>\n",
       "      <td>Hetionet</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>PharmacologicClass</td>\n",
       "      <td>NDF-RT</td>\n",
       "      <td>45605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>SideEffect</td>\n",
       "      <td>Hetionet</td>\n",
       "      <td>5734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>SideEffect</td>\n",
       "      <td>MedDRA</td>\n",
       "      <td>49561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Symptom</td>\n",
       "      <td>Hetionet</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Symptom</td>\n",
       "      <td>SymptomOntology</td>\n",
       "      <td>1013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           entity_type         resource  entity_count\n",
       "0              Anatomy             MESH          1315\n",
       "1              Anatomy           UBERON         15822\n",
       "2    BiologicalProcess               GO         30571\n",
       "3    BiologicalProcess         Hetionet           387\n",
       "4    CellularComponent               GO          4485\n",
       "5    CellularComponent         Hetionet            32\n",
       "6             Compound         DrugBank        267869\n",
       "7             Compound             MESH           167\n",
       "8              Disease             DOID             1\n",
       "9              Disease             MESH          3518\n",
       "10             Disease            MONDO         26582\n",
       "11                Gene           ENTREZ         93962\n",
       "12          Metabolite             HMDB        247966\n",
       "13   MolecularFunction               GO         12502\n",
       "14   MolecularFunction         Hetionet           143\n",
       "15             Pathway         Hetionet           294\n",
       "16             Pathway             KEGG          1057\n",
       "17             Pathway         Reactome          4354\n",
       "18             Pathway     WikiPathways          1600\n",
       "19  PharmacologicClass         Hetionet             1\n",
       "20  PharmacologicClass           NDF-RT         45605\n",
       "21          SideEffect         Hetionet          5734\n",
       "22          SideEffect           MedDRA         49561\n",
       "23             Symptom         Hetionet           438\n",
       "24             Symptom  SymptomOntology          1013"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_stat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f757cf45",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>relation_type</th>\n",
       "      <th>start_entity_type</th>\n",
       "      <th>end_entity_type</th>\n",
       "      <th>resource</th>\n",
       "      <th>relation_count</th>\n",
       "      <th>simple_relation_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DGIDB::ACTIVATOR::Gene:Compound</td>\n",
       "      <td>Gene</td>\n",
       "      <td>Compound</td>\n",
       "      <td>DGIDB</td>\n",
       "      <td>219</td>\n",
       "      <td>Compound:Gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DGIDB::AGONIST::Gene:Compound</td>\n",
       "      <td>Gene</td>\n",
       "      <td>Compound</td>\n",
       "      <td>DGIDB</td>\n",
       "      <td>1721</td>\n",
       "      <td>Compound:Gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DGIDB::ALLOSTERIC MODULATOR::Gene:Compound</td>\n",
       "      <td>Gene</td>\n",
       "      <td>Compound</td>\n",
       "      <td>CustomKB</td>\n",
       "      <td>1</td>\n",
       "      <td>Compound:Gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DGIDB::ALLOSTERIC MODULATOR::Gene:Compound</td>\n",
       "      <td>Gene</td>\n",
       "      <td>Compound</td>\n",
       "      <td>DGIDB</td>\n",
       "      <td>168</td>\n",
       "      <td>Compound:Gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>DGIDB::ANTAGONIST::Gene:Compound</td>\n",
       "      <td>Gene</td>\n",
       "      <td>Compound</td>\n",
       "      <td>DGIDB</td>\n",
       "      <td>1617</td>\n",
       "      <td>Compound:Gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>treats</td>\n",
       "      <td>Compound</td>\n",
       "      <td>BiologicalProcess</td>\n",
       "      <td>CustomKB</td>\n",
       "      <td>1</td>\n",
       "      <td>BiologicalProcess:Compound</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>treats</td>\n",
       "      <td>Compound</td>\n",
       "      <td>Disease</td>\n",
       "      <td>CustomKB</td>\n",
       "      <td>2</td>\n",
       "      <td>Compound:Disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>treats</td>\n",
       "      <td>Compound</td>\n",
       "      <td>Pathway</td>\n",
       "      <td>CustomKB</td>\n",
       "      <td>1</td>\n",
       "      <td>Compound:Pathway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>treats</td>\n",
       "      <td>Compound</td>\n",
       "      <td>Symptom</td>\n",
       "      <td>CustomKB</td>\n",
       "      <td>1</td>\n",
       "      <td>Compound:Symptom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>treats</td>\n",
       "      <td>Metabolite</td>\n",
       "      <td>Disease</td>\n",
       "      <td>CustomKB</td>\n",
       "      <td>1</td>\n",
       "      <td>Disease:Metabolite</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>192 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  relation_type start_entity_type  \\\n",
       "0               DGIDB::ACTIVATOR::Gene:Compound              Gene   \n",
       "1                 DGIDB::AGONIST::Gene:Compound              Gene   \n",
       "2    DGIDB::ALLOSTERIC MODULATOR::Gene:Compound              Gene   \n",
       "3    DGIDB::ALLOSTERIC MODULATOR::Gene:Compound              Gene   \n",
       "4              DGIDB::ANTAGONIST::Gene:Compound              Gene   \n",
       "..                                          ...               ...   \n",
       "187                                      treats          Compound   \n",
       "188                                      treats          Compound   \n",
       "189                                      treats          Compound   \n",
       "190                                      treats          Compound   \n",
       "191                                      treats        Metabolite   \n",
       "\n",
       "       end_entity_type  resource  relation_count        simple_relation_type  \n",
       "0             Compound     DGIDB             219               Compound:Gene  \n",
       "1             Compound     DGIDB            1721               Compound:Gene  \n",
       "2             Compound  CustomKB               1               Compound:Gene  \n",
       "3             Compound     DGIDB             168               Compound:Gene  \n",
       "4             Compound     DGIDB            1617               Compound:Gene  \n",
       "..                 ...       ...             ...                         ...  \n",
       "187  BiologicalProcess  CustomKB               1  BiologicalProcess:Compound  \n",
       "188            Disease  CustomKB               2            Compound:Disease  \n",
       "189            Pathway  CustomKB               1            Compound:Pathway  \n",
       "190            Symptom  CustomKB               1            Compound:Symptom  \n",
       "191            Disease  CustomKB               1          Disease:Metabolite  \n",
       "\n",
       "[192 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "edge_stat"
   ]
  },
  {
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   "execution_count": 6,
   "id": "311d6bf2",
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>entity_type</th>\n",
       "      <th>resource</th>\n",
       "      <th>entity_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anatomy</td>\n",
       "      <td>MESH</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anatomy</td>\n",
       "      <td>UBERON</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BiologicalProcess</td>\n",
       "      <td>GO</td>\n",
       "      <td>11009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BiologicalProcess</td>\n",
       "      <td>Hetionet</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CellularComponent</td>\n",
       "      <td>GO</td>\n",
       "      <td>1363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CellularComponent</td>\n",
       "      <td>Hetionet</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Compound</td>\n",
       "      <td>DrugBank</td>\n",
       "      <td>15500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Compound</td>\n",
       "      <td>MESH</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Disease</td>\n",
       "      <td>MESH</td>\n",
       "      <td>1514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Disease</td>\n",
       "      <td>MONDO</td>\n",
       "      <td>4014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Gene</td>\n",
       "      <td>ENTREZ</td>\n",
       "      <td>25773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Metabolite</td>\n",
       "      <td>HMDB</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>MolecularFunction</td>\n",
       "      <td>GO</td>\n",
       "      <td>2742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MolecularFunction</td>\n",
       "      <td>Hetionet</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Pathway</td>\n",
       "      <td>Hetionet</td>\n",
       "      <td>294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Pathway</td>\n",
       "      <td>KEGG</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Pathway</td>\n",
       "      <td>Reactome</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Pathway</td>\n",
       "      <td>WikiPathways</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>PharmacologicClass</td>\n",
       "      <td>Hetionet</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>PharmacologicClass</td>\n",
       "      <td>NDF-RT</td>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>SideEffect</td>\n",
       "      <td>Hetionet</td>\n",
       "      <td>5701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Symptom</td>\n",
       "      <td>Hetionet</td>\n",
       "      <td>425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Symptom</td>\n",
       "      <td>SymptomOntology</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           entity_type         resource  entity_count\n",
       "0              Anatomy             MESH             7\n",
       "1              Anatomy           UBERON           400\n",
       "2    BiologicalProcess               GO         11009\n",
       "3    BiologicalProcess         Hetionet           387\n",
       "4    CellularComponent               GO          1363\n",
       "5    CellularComponent         Hetionet            32\n",
       "6             Compound         DrugBank         15500\n",
       "7             Compound             MESH            25\n",
       "8              Disease             MESH          1514\n",
       "9              Disease            MONDO          4014\n",
       "10                Gene           ENTREZ         25773\n",
       "11          Metabolite             HMDB            12\n",
       "12   MolecularFunction               GO          2742\n",
       "13   MolecularFunction         Hetionet           143\n",
       "14             Pathway         Hetionet           294\n",
       "15             Pathway             KEGG             9\n",
       "16             Pathway         Reactome             5\n",
       "17             Pathway     WikiPathways             8\n",
       "18  PharmacologicClass         Hetionet             1\n",
       "19  PharmacologicClass           NDF-RT           344\n",
       "20          SideEffect         Hetionet          5701\n",
       "21             Symptom         Hetionet           425\n",
       "22             Symptom  SymptomOntology            35"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_node_stat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7c96543",
   "metadata": {},
   "source": [
    "## General Graph Analysis and Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "334e9880",
   "metadata": {},
   "source": [
    "### Chart 1: Number of nodes and edges in the graph"
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  },
  {
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   "execution_count": 7,
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   "source": [
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "\n",
    "node_count = node_stat[\"entity_count\"].sum()\n",
    "edge_count = edge_stat[\"relation_count\"].sum()\n",
    "real_node_count = real_node_stat[\"entity_count\"].sum() if real_node_stat is not None else 0\n",
    "\n",
    "fig = go.Figure(\n",
    "    data=[\n",
    "        go.Pie(\n",
    "            labels=[\"Node Count\", \"Edge Count\", \"Real Node Count\"],\n",
    "            values=[node_count, edge_count, real_node_count],\n",
    "            textinfo=\"value\",\n",
    "        )\n",
    "    ]\n",
    ")\n",
    "fig.update_layout(**gen_layout(\"Total Count\", \"Type\", \"Count\", True))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e77b714d",
   "metadata": {},
   "source": [
    "### Chart 2: Number of nodes for each entity type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb72d991",
   "metadata": {},
   "outputs": [
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           "bgcolor": "#E5ECF6",
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          "scene": {
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            "gridwidth": 2,
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            "gridwidth": 2,
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           "line": {
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           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
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           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
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          },
          "title": {
           "x": 0.05
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          "xaxis": {
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           "linecolor": "white",
           "ticks": "",
           "title": {
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           "title": {
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           "zerolinewidth": 2
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         "text": "Node Count"
        },
        "xaxis": {
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         "domain": [
          0,
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          "text": "Node Type"
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        "yaxis": {
         "anchor": "x",
         "domain": [
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         "title": {
          "text": "Count"
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        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "chart2_data = node_stat.groupby([\"resource\", \"entity_type\"]).sum().reset_index()\n",
    "fig2 = px.bar(chart2_data, x=\"entity_type\", y=\"entity_count\", color=\"resource\", barmode=\"stack\")\n",
    "fig2.update_layout(**gen_layout(\"Node Count\", \"Node Type\", \"Count\", True))\n",
    "fig2.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5b79add",
   "metadata": {},
   "source": [
    "### Chart 3: Number of nodes for each resource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b6a74ad8",
   "metadata": {},
   "outputs": [
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            "linecolor": "white",
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           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
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          },
          "scene": {
           "xaxis": {
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            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
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           "yaxis": {
            "backgroundcolor": "#E5ECF6",
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            "gridwidth": 2,
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            "showbackground": true,
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            "gridwidth": 2,
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          },
          "ternary": {
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            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
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           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
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          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
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           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
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           "zerolinecolor": "white",
           "zerolinewidth": 2
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         }
        },
        "title": {
         "text": "Node Count"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
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         "title": {
          "text": "Resource"
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        "yaxis": {
         "anchor": "x",
         "domain": [
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         "title": {
          "text": "Count"
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        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "chart3_data = node_stat.groupby([\"entity_type\"]).sum().reset_index()\n",
    "fig3 = px.bar(\n",
    "    chart3_data, x=\"resource\", y=\"entity_count\", color=\"entity_type\", barmode=\"stack\"\n",
    ")\n",
    "fig3.update_layout(**gen_layout(\"Node Count\", \"Resource\", \"Count\", True))\n",
    "fig3.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1dc3bb9",
   "metadata": {},
   "source": [
    "### Chart 4: Number of relations for each relation type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aa298466",
   "metadata": {},
   "outputs": [
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   "source": [
    "chart4_data = edge_stat.groupby([\"resource\", \"simple_relation_type\"]).sum().reset_index()\n",
    "fig4 = px.bar(\n",
    "    chart4_data,\n",
    "    x=\"simple_relation_type\",\n",
    "    y=\"relation_count\",\n",
    "    color=\"resource\",\n",
    "    barmode=\"stack\",\n",
    ")\n",
    "fig4.update_layout(**gen_layout(\"Edge Count\", \"Relation Type\", \"Count\", True))\n",
    "fig4.show()"
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  },
  {
   "cell_type": "markdown",
   "id": "563be00e",
   "metadata": {},
   "source": [
    "### Chart 5: Number of relations for each resource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ed68f98c",
   "metadata": {},
   "outputs": [
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            "gridwidth": 2,
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            "gridwidth": 2,
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            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
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          "xaxis": {
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           "ticks": "",
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          "yaxis": {
           "automargin": true,
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           "title": {
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           "zerolinewidth": 2
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     },
     "metadata": {},
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    "selected_edge_stat = edge_stat[[\"resource\", \"simple_relation_type\", \"relation_count\"]]\n",
    "chart5_data = selected_edge_stat.groupby([\"simple_relation_type\", \"resource\"]).sum().reset_index()\n",
    "fig5 = px.bar(chart5_data, x=\"resource\", y=\"relation_count\", color=\"simple_relation_type\", barmode=\"stack\")\n",
    "fig5.update_layout(**gen_layout(\"Edge Count\", \"Resource\", \"Count\", True))\n",
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   "source": [
    "### Table 1: Number of nodes"
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          "title": {
           "x": 0.05
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          "xaxis": {
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         "text": "Node Count"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "header_color = \"grey\"\n",
    "row_even_color = \"lightgrey\"\n",
    "row_odd_color = \"white\"\n",
    "\n",
    "node_stat_dicts = node_stat.to_dict(\"records\")\n",
    "data = make_wide_format(node_stat_dicts, \"entity_type\", \"resource\", \"entity_count\")\n",
    "columns = list(set([item[\"resource\"] for item in node_stat_dicts]))\n",
    "cell_values = transposed_array(\n",
    "    [\n",
    "        [item[\"entity_type\"]]   [item.get(snake_case(column), 0) for column in columns]\n",
    "        for item in data\n",
    "    ]\n",
    ")\n",
    "\n",
    "row_num = len(cell_values[0])\n",
    "colors = [row_even_color if i % 2 == 0 else row_odd_color for i in range(row_num)]\n",
    "\n",
    "table = go.Figure(\n",
    "    data=[\n",
    "        go.Table(\n",
    "            header=dict(\n",
    "                values=[\"Node Type\"]   columns,\n",
    "                align=\"center\",\n",
    "                line=dict(width=1, color=\"black\"),\n",
    "                fill=dict(color=header_color),\n",
    "                font=dict(family=\"Arial\", size=12, color=\"white\"),\n",
    "            ),\n",
    "            cells=dict(\n",
    "                values=cell_values,\n",
    "                align=\"center\",\n",
    "                line=dict(color=\"black\", width=1),\n",
    "                fill=dict(color=[colors]),\n",
    "                font=dict(family=\"Arial\", size=11, color=\"black\"),\n",
    "                height=30,\n",
    "            ),\n",
    "        )\n",
    "    ]\n",
    ")\n",
    "\n",
    "table.update_layout(title=\"Node Count\")\n",
    "table.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "423f3357",
   "metadata": {},
   "source": [
    "### Table 2: Number of relations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5e1ba4a8",
   "metadata": {},
   "outputs": [
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           "bgcolor": "#E5ECF6",
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           "ticks": "",
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         "text": "Edge Count"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "import plotly.graph_objects as go\n",
    "\n",
    "header_color = \"grey\"\n",
    "row_even_color = \"lightgrey\"\n",
    "row_odd_color = \"white\"\n",
    "\n",
    "grouped_edge_stat = (\n",
    "    edge_stat.groupby([\"simple_relation_type\", \"resource\"]).sum().reset_index()\n",
    ")\n",
    "edge_stat_dicts = grouped_edge_stat.to_dict(\"records\")\n",
    "data = make_wide_format(\n",
    "    edge_stat_dicts, \"simple_relation_type\", \"resource\", \"relation_count\"\n",
    ")\n",
    "columns = list(set([item[\"resource\"] for item in edge_stat_dicts]))\n",
    "cell_values = transposed_array(\n",
    "    [\n",
    "        [item[\"simple_relation_type\"]]\n",
    "          [item.get(snake_case(column), 0) for column in columns]\n",
    "        for item in data\n",
    "    ]\n",
    ")\n",
    "\n",
    "row_num = len(cell_values[0])\n",
    "colors = [row_even_color if i % 2 == 0 else row_odd_color for i in range(row_num)]\n",
    "\n",
    "table2 = go.Figure(\n",
    "    data=[\n",
    "        go.Table(\n",
    "            header=dict(\n",
    "                values=[\"Relation Type\"]   columns,\n",
    "                align=\"center\",\n",
    "                line=dict(width=1, color=\"black\"),\n",
    "                fill=dict(color=header_color),\n",
    "                font=dict(family=\"Arial\", size=12, color=\"white\"),\n",
    "            ),\n",
    "            cells=dict(\n",
    "                values=cell_values,\n",
    "                align=\"center\",\n",
    "                line=dict(color=\"black\", width=1),\n",
    "                fill=dict(color=[colors]),\n",
    "                font=dict(family=\"Arial\", size=11, color=\"black\"),\n",
    "                height=30,\n",
    "            ),\n",
    "        )\n",
    "    ]\n",
    ")\n",
    "\n",
    "table2.update_layout(title=\"Edge Count\")\n",
    "table2.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91db8821",
   "metadata": {},
   "source": [
    "### Pie Chart 1: Distribution of Entity Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6ddefc3f",
   "metadata": {},
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        },
        "width": 1000
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     "metadata": {},
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   "source": [
    "import plotly.express as px\n",
    "\n",
    "# 准备饼图数据\n",
    "temp_node_stat = node_stat\n",
    "# 准备饼图数据\n",
    "entity_types = temp_node_stat[\"entity_type\"]\n",
    "entity_counts = temp_node_stat[\"entity_count\"]\n",
    "\n",
    "# 绘制饼图\n",
    "fig_pie = px.pie(\n",
    "    temp_node_stat,\n",
    "    names=entity_types,\n",
    "    values=entity_counts,\n",
    "    title=\"Distribution of Entity Types\",\n",
    ")\n",
    "\n",
    "# 修改饼图的标签和显示内容\n",
    "fig_pie.update_traces(\n",
    "    textinfo=\"label value\",  # 显示实体类型标签和总数目\n",
    "    hovertemplate=\"%{label}: %{value} nodes\",  # 悬浮时显示实体类型和总数\n",
    "    textposition=\"outside\",  # 实体类型标签显示在饼图外部\n",
    "    pull=[\n",
    "        0.1 if entity == \"Metabolite\" else 0 for entity in entity_types\n",
    "    ],  # 把较小的部分拉出来\n",
    ")\n",
    "\n",
    "# 调整布局，增加图形尺寸\n",
    "fig_pie.update_layout(\n",
    "    margin=dict(t=0, b=0, l=0, r=0),  # 调整图的边距\n",
    "    height=700,  # 增加图形高度\n",
    "    width=1000,  # 增加图形宽度\n",
    ")\n",
    "\n",
    "# 显示饼图\n",
    "fig_pie.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eee083c5",
   "metadata": {},
   "source": [
    "### Pie Chart 2: Distribution of Node Types in Real Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3ec88e7b",
   "metadata": {},
   "outputs": [
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   "source": [
    "import plotly.express as px\n",
    "\n",
    "# 准备饼图数据\n",
    "temp_node_stat = real_node_stat\n",
    "\n",
    "# 准备饼图数据\n",
    "entity_types = temp_node_stat[\"entity_type\"]\n",
    "entity_counts = temp_node_stat[\"entity_count\"]\n",
    "\n",
    "# 绘制饼图\n",
    "fig_pie = px.pie(\n",
    "    temp_node_stat,\n",
    "    names=entity_types,\n",
    "    values=entity_counts,\n",
    "    title=\"Distribution of Entity Types\",\n",
    ")\n",
    "\n",
    "# 修改饼图的标签和显示内容\n",
    "fig_pie.update_traces(\n",
    "    textinfo=\"label value\",  # 显示实体类型标签和总数目\n",
    "    hovertemplate=\"%{label}: %{value} nodes\",  # 悬浮时显示实体类型和总数\n",
    "    textposition=\"outside\",  # 实体类型标签显示在饼图外部\n",
    "    pull=[\n",
    "        0.1 if entity == \"Metabolite\" else 0 for entity in entity_types\n",
    "    ],  # 把较小的部分拉出来\n",
    ")\n",
    "\n",
    "# 调整布局，增加图形尺寸\n",
    "fig_pie.update_layout(\n",
    "    margin=dict(t=0, b=0, l=0, r=0),  # 调整图的边距\n",
    "    height=700,  # 增加图形高度\n",
    "    width=1000,  # 增加图形宽度\n",
    ")\n",
    "\n",
    "# 显示饼图\n",
    "fig_pie.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9328fff9",
   "metadata": {},
   "source": [
    "### Pie Chart 3: Distribution of Relation Types in Real Graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6cd7bdb2",
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           "bgcolor": "#E5ECF6",
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           "ticks": "",
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        },
        "title": {
         "text": "Relation Type and Count Table"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "# 颜色设置\n",
    "header_color = \"#404040\"\n",
    "row_even_color = \"#f5f5f5\"\n",
    "row_odd_color = \"#e0e0e0\"\n",
    "\n",
    "# 按 simple_relation_type 和 relation_type 分组并计算每个关系的数量\n",
    "grouped_edge_stat = (\n",
    "    edge_stat[edge_stat[\"relation_count\"] > 100].groupby([\"simple_relation_type\", \"relation_type\"]).sum().reset_index()\n",
    ")\n",
    "\n",
    "# Order by the simple_relation_type and relation_count\n",
    "grouped_edge_stat = grouped_edge_stat.sort_values(\n",
    "    by=[\"simple_relation_type\", \"relation_count\"], ascending=[True, False]\n",
    ")\n",
    "\n",
    "# 准备表格数据\n",
    "index = grouped_edge_stat.index.tolist()\n",
    "relation_types = grouped_edge_stat[\"relation_type\"].tolist()\n",
    "simple_relation_types = grouped_edge_stat[\"simple_relation_type\"].tolist()\n",
    "relation_counts = grouped_edge_stat[\"relation_count\"].tolist()\n",
    "\n",
    "# 设置表格的行数\n",
    "row_num = len(relation_types)\n",
    "\n",
    "# 设置每一行的背景色，奇偶行不同\n",
    "colors = [row_even_color if i % 2 == 0 else row_odd_color for i in range(row_num)]\n",
    "\n",
    "# 生成合并的表格\n",
    "table = go.Figure(\n",
    "    data=[\n",
    "        go.Table(\n",
    "            header=dict(\n",
    "                values=[\"#\", \"Simple Relation Type\", \"Relation Type\", \"Count\"],\n",
    "                align=\"center\",\n",
    "                line=dict(width=1, color=\"black\"),\n",
    "                fill=dict(color=header_color),\n",
    "                font=dict(family=\"Arial\", size=14, color=\"white\"),\n",
    "            ),\n",
    "            cells=dict(\n",
    "                values=[index, simple_relation_types, relation_types, relation_counts],\n",
    "                align=\"center\",\n",
    "                line=dict(color=\"black\", width=1),\n",
    "                fill=dict(color=[colors]),\n",
    "                font=dict(family=\"Arial\", size=14, color=\"black\"),\n",
    "                height=30,\n",
    "            ),\n",
    "        )\n",
    "    ]\n",
    ")\n",
    "\n",
    "# 设置表格标题\n",
    "table.update_layout(title=\"Relation Type and Count Table\")\n",
    "\n",
    "# Show all the rows, don't need to scroll\n",
    "table.update_layout(height=1600)\n",
    "\n",
    "table.write_image(os.path.join(datadir, \"relation_type_and_count_table.png\"), height = 3200, width = 1600)\n",
    "\n",
    "# 显示表格\n",
    "table.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fe866d1d",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using regex to filter out the rows with the relation type not matching the pattern \"\\w ::\\w ::\\w :\\w \"\n",
    "filtered_edge_stat = edge_stat[edge_stat[\"relation_type\"].str.contains(r\".*::.*::.*:.*\")]\n",
    "\n",
    "# Group by simple_relation_type and sum the relation_count\n",
    "merged_edge_stat = (\n",
    "    filtered_edge_stat.groupby([\"simple_relation_type\"])[\"relation_count\"]\n",
    "    .sum()\n",
    "    .reset_index()\n",
    ")\n",
    "merged_edge_stat[\"start_entity_type\"] = merged_edge_stat[\"simple_relation_type\"].apply(lambda x: x.split(\":\")[0])\n",
    "merged_edge_stat[\"end_entity_type\"] = merged_edge_stat[\"simple_relation_type\"].apply(lambda x: x.split(\":\")[1])\n",
    "\n",
    "import pygraphviz as pgv\n",
    "import matplotlib.pyplot as plt\n",
    "import PIL.Image as Image\n",
    "\n",
    "# 创建有向图\n",
    "G = pgv.AGraph(strict=False, directed=True)\n",
    "\n",
    "# 添加节点，定义颜色\n",
    "for entity_type in node_stat[\"entity_type\"].unique():\n",
    "    G.add_node(\n",
    "        entity_type,\n",
    "        style=\"filled\",\n",
    "        fillcolor=get_color(entity_type),\n",
    "        fontsize=12,\n",
    "        fontname=\"Arial\",\n",
    "        shape=\"ellipse\",\n",
    "        width=2.0,\n",
    "        height=0.6,\n",
    "    )\n",
    "\n",
    "# 添加边并定义关系标签和数量\n",
    "for _, edge in merged_edge_stat.iterrows():\n",
    "    G.add_edge(\n",
    "        edge[\"start_entity_type\"],\n",
    "        edge[\"end_entity_type\"],\n",
    "        label=edge[\"relation_count\"],\n",
    "        fontsize=10,\n",
    "        fontname=\"Arial\",\n",
    "        dir=\"forward\",\n",
    "        arrowsize=0.8,\n",
    "        penwidth=1.0,\n",
    "        len=0.5,  # 减小边的长度\n",
    "        labeldistance=1.1,\n",
    "        labelangle=-25,\n",
    "        arrowhead=\"vee\",\n",
    "    )\n",
    "\n",
    "# 设置图的整体属性\n",
    "G.graph_attr.update(\n",
    "    {\n",
    "        \"rankdir\": \"LR\",\n",
    "        \"splines\": \"spline\",\n",
    "        \"overlap\": \"false\",\n",
    "        \"sep\": \" 3,3\",  # 减小间隔\n",
    "        \"nodesep\": 0.5,  # 减小同层节点间距\n",
    "        \"ranksep\": 0.4,  # 减小层间距\n",
    "        \"ratio\": \"1\",  # 调整比例使图形更扁\n",
    "        \"size\": \"14,3.5\",  # 略微减小高度\n",
    "        \"concentrate\": \"true\",\n",
    "        \"ordering\": \"out\",  # 优化边的排序\n",
    "    }\n",
    ")\n",
    "\n",
    "# 设置默认的节点和边属性\n",
    "# G.node_attr.update({\"margin\": \"0.12\"})\n",
    "# G.edge_attr.update({\"weight\": \"1.0\"})\n",
    "\n",
    "# 设置图的布局为dot模式，并导出为高分辨率图片\n",
    "G.layout(prog=\"dot\")\n",
    "G.draw(\"/tmp/network_graph_high_res.png\", prog=\"dot\", args=\"-Gdpi=300\")\n",
    "\n",
    "# 显示图片\n",
    "img = Image.open(\"/tmp/network_graph_high_res.png\")\n",
    "plt.figure(figsize=(10, 10))  # 调整显示比例\n",
    "plt.imshow(img)\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  }
 ],
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