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-v20240127\")\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",
    ")"
   ]
  },
  {
   "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_55463/2518971873.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"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'resource'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[3], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m entities \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(entity_file, sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      4\u001b[0m relations \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(relation_file, sep\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m node_stat, edge_stat \u001b[38;5;241m=\u001b[39m \u001b[43mbiomedgps2stat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mentities\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrelations\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# Merge two columns: start_entity_type and end_entity_type, the string format is \"start_entity_type:end_entity_type\"\u001b[39;00m\n\u001b[1;32m      8\u001b[0m edge_stat[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msimple_relation_type\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m      9\u001b[0m     edge_stat[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstart_entity_type\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m edge_stat[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mend_entity_type\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m     10\u001b[0m )\n",
      "File \u001b[0;32m~/Documents/Code/BioMedGPS/biomedgps-data/lib/graph.py:531\u001b[0m, in \u001b[0;36mbiomedgps2stat\u001b[0;34m(entities, relations)\u001b[0m\n\u001b[1;32m    520\u001b[0m node_stat \u001b[38;5;241m=\u001b[39m entities\u001b[38;5;241m.\u001b[39mgroupby([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mresource\u001b[39m\u001b[38;5;124m\"\u001b[39m])\u001b[38;5;241m.\u001b[39msize()\u001b[38;5;241m.\u001b[39mreset_index(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    521\u001b[0m node_stat\u001b[38;5;241m.\u001b[39mrename(\n\u001b[1;32m    522\u001b[0m     columns\u001b[38;5;241m=\u001b[39m{\n\u001b[1;32m    523\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mentity_type\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    527\u001b[0m     inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    528\u001b[0m )\n\u001b[1;32m    530\u001b[0m edge_stat \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 531\u001b[0m     \u001b[43mrelations\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mrelation_type\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msource_type\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtarget_type\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mresource\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    532\u001b[0m     \u001b[38;5;241m.\u001b[39msize()\n\u001b[1;32m    533\u001b[0m     \u001b[38;5;241m.\u001b[39mreset_index(name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcount\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m    534\u001b[0m )\n\u001b[1;32m    535\u001b[0m edge_stat\u001b[38;5;241m.\u001b[39mrename(\n\u001b[1;32m    536\u001b[0m     columns\u001b[38;5;241m=\u001b[39m{\n\u001b[1;32m    537\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelation_type\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrelation_type\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    543\u001b[0m     inplace\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m    544\u001b[0m )\n\u001b[1;32m    546\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m node_stat, edge_stat\n",
      "File \u001b[0;32m~/miniconda3/envs/biomedgps-data/lib/python3.10/site-packages/pandas/core/frame.py:8252\u001b[0m, in \u001b[0;36mDataFrame.groupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, observed, dropna)\u001b[0m\n\u001b[1;32m   8249\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou have to supply one of \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mby\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m and \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlevel\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   8250\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_axis_number(axis)\n\u001b[0;32m-> 8252\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataFrameGroupBy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   8253\u001b[0m \u001b[43m    \u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   8254\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   8255\u001b[0m \u001b[43m    \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   8256\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   8257\u001b[0m \u001b[43m    \u001b[49m\u001b[43mas_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mas_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   8258\u001b[0m \u001b[43m    \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   8259\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgroup_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup_keys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   8260\u001b[0m \u001b[43m    \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   8261\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdropna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   8262\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/biomedgps-data/lib/python3.10/site-packages/pandas/core/groupby/groupby.py:931\u001b[0m, in \u001b[0;36mGroupBy.__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, observed, dropna)\u001b[0m\n\u001b[1;32m    928\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropna \u001b[38;5;241m=\u001b[39m dropna\n\u001b[1;32m    930\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m grouper \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 931\u001b[0m     grouper, exclusions, obj \u001b[38;5;241m=\u001b[39m \u001b[43mget_grouper\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    932\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    933\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    934\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    935\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    936\u001b[0m \u001b[43m        \u001b[49m\u001b[43msort\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msort\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    937\u001b[0m \u001b[43m        \u001b[49m\u001b[43mobserved\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobserved\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    938\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdropna\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropna\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    939\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    941\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj \u001b[38;5;241m=\u001b[39m obj\n\u001b[1;32m    942\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39m_get_axis_number(axis)\n",
      "File \u001b[0;32m~/miniconda3/envs/biomedgps-data/lib/python3.10/site-packages/pandas/core/groupby/grouper.py:985\u001b[0m, in \u001b[0;36mget_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, validate, dropna)\u001b[0m\n\u001b[1;32m    983\u001b[0m         in_axis, level, gpr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m, gpr, \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    984\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 985\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(gpr)\n\u001b[1;32m    986\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(gpr, Grouper) \u001b[38;5;129;01mand\u001b[39;00m gpr\u001b[38;5;241m.\u001b[39mkey \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    987\u001b[0m     \u001b[38;5;66;03m# Add key to exclusions\u001b[39;00m\n\u001b[1;32m    988\u001b[0m     exclusions\u001b[38;5;241m.\u001b[39madd(gpr\u001b[38;5;241m.\u001b[39mkey)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'resource'"
     ]
    }
   ],
   "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 = biomedgps2stat(entities, relations)\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",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3a017cd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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,\n",
       "                                           relation_type start_entity_type  \\\n",
       " 0              CTD::IS_ASSOCIATED_WITH:Compound:Disease          Compound   \n",
       " 1              CTD::IS_ASSOCIATED_WITH:Compound:Pathway          Compound   \n",
       " 2     CTD::IS_ASSOCIATED_WITH:Disease:BiologicalProcess           Disease   \n",
       " 3     CTD::IS_ASSOCIATED_WITH:Disease:CellularComponent           Disease   \n",
       " 4     CTD::IS_ASSOCIATED_WITH:Disease:MolecularFunction           Disease   \n",
       " ...                                                 ...               ...   \n",
       " 3510                           STRING::OTHER::Gene:Gene              Gene   \n",
       " 3511                           STRING::PTMOD::Gene:Gene              Gene   \n",
       " 3512                        STRING::REACTION::Gene:Gene              Gene   \n",
       " 3513                   bioarx::DrugHumGen::Compound:Gene          Compound   \n",
       " 3514                     bioarx::HumGenHumGen::Gene:Gene              Gene   \n",
       " \n",
       "         end_entity_type resource  relation_count       simple_relation_type  \n",
       " 0               Disease      CTD         2909558           Compound:Disease  \n",
       " 1               Pathway      CTD         1440442           Compound:Pathway  \n",
       " 2     BiologicalProcess      CTD         2322022  BiologicalProcess:Disease  \n",
       " 3     CellularComponent      CTD          146863  CellularComponent:Disease  \n",
       " 4     MolecularFunction      CTD          254723  Disease:MolecularFunction  \n",
       " ...                 ...      ...             ...                        ...  \n",
       " 3510               Gene   STRING          310666                  Gene:Gene  \n",
       " 3511               Gene   STRING           15113                  Gene:Gene  \n",
       " 3512               Gene   STRING          400426                  Gene:Gene  \n",
       " 3513               Gene   bioarx           18949              Compound:Gene  \n",
       " 3514               Gene   bioarx           57932                  Gene:Gene  \n",
       " \n",
       " [3515 rows x 6 columns])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_stat, edge_stat"
   ]
  },
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   "cell_type": "markdown",
   "id": "334e9880",
   "metadata": {},
   "source": [
    "### Chart 1: Number of nodes and edges in the graph"
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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",
    "\n",
    "fig = go.Figure(\n",
    "    data=[\n",
    "        go.Pie(\n",
    "            labels=[\"Node Count\", \"Edge Count\"],\n",
    "            values=[node_count, edge_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": 6,
   "id": "fb72d991",
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            "linecolor": "white",
            "showbackground": true,
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            "zerolinecolor": "white"
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           "zaxis": {
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            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
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          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
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           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
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           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
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         }
        },
        "title": {
         "text": "Node Count"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
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         "title": {
          "text": "Node Type"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
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         "title": {
          "text": "Count"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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": 7,
   "id": "b6a74ad8",
   "metadata": {},
   "outputs": [
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          "font": {
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           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
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          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
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           "zaxis": {
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            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
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          "shapedefaults": {
           "line": {
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          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "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": {
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           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
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         }
        },
        "title": {
         "text": "Node Count"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
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         "title": {
          "text": "Resource"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
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         "title": {
          "text": "Count"
         }
        }
       }
      }
     },
     "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": 8,
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "563be00e",
   "metadata": {},
   "source": [
    "### Chart 5: Number of relations for each resource"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ed68f98c",
   "metadata": {},
   "outputs": [
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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",
    "fig5.show()"
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   "metadata": {},
   "source": [
    "### Table 1: Number of nodes"
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  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1168e613",
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   "source": [
    "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": 11,
   "id": "5e1ba4a8",
   "metadata": {},
   "outputs": [
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            "Anatomy:Anatomy",
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   "source": [
    "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()"
   ]
  }
 ],
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