omim_vs_orphanet_comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Defaulting to user installation because normal site-packages is not writeable\n",
      "Requirement already satisfied: notebook in /Users/evanc/Library/Python/3.9/lib/python/site-packages (7.2.2)\n",
      "Requirement already satisfied: duckdb in /Users/evanc/Library/Python/3.9/lib/python/site-packages (1.1.3)\n",
      "Requirement already satisfied: pandas in /Users/evanc/Library/Python/3.9/lib/python/site-packages (2.2.3)\n",
      "Requirement already satisfied: fastparquet in /Users/evanc/Library/Python/3.9/lib/python/site-packages (2024.5.0)\n",
      "Requirement already satisfied: semsimian in /Users/evanc/Library/Python/3.9/lib/python/site-packages (0.2.19)\n",
      "Requirement already satisfied: numpy in /Users/evanc/Library/Python/3.9/lib/python/site-packages (2.0.2)\n",
      "\u001b[31mERROR: Could not find a version that satisfies the requirement json (from versions: none)\u001b[0m\n",
      "\u001b[31mERROR: No matching distribution found for json\u001b[0m\n",
      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 24.3.1 is available.\n",
      "You should consider upgrading via the '/Library/Developer/CommandLineTools/usr/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "#%pip install notebook duckdb pandas fastparquet semsimian numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download and unzip the Monarch Graph and Phenio semantic SQL datbabase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2024-11-18 16:34:09--  https://data.monarchinitiative.org/monarch-kg/latest/monarch-kg.duckdb.gz\n",
      "Resolving data.monarchinitiative.org (data.monarchinitiative.org)... 35.208.191.193\n",
      "Connecting to data.monarchinitiative.org (data.monarchinitiative.org)|35.208.191.193|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1628735176 (1.5G) [application/octet-stream]\n",
      "Saving to: ‘monarch-kg.duckdb.gz.1’\n",
      "\n",
      "monarch-kg.duckdb.g 100%[===================>]   1.52G  39.4MB/s    in 44s     \n",
      "\n",
      "2024-11-18 16:34:53 (35.6 MB/s) - ‘monarch-kg.duckdb.gz.1’ saved [1628735176/1628735176]\n",
      "\n",
      "monarch-kg.duckdb already exists -- do you wish to overwrite (y or n)? ^C\n",
      "--2024-11-18 16:38:37--  https://data.monarchinitiative.org/monarch-kg/latest/phenio.db.gz\n",
      "Resolving data.monarchinitiative.org (data.monarchinitiative.org)... 35.208.191.193\n",
      "Connecting to data.monarchinitiative.org (data.monarchinitiative.org)|35.208.191.193|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 2189141060 (2.0G) [application/octet-stream]\n",
      "Saving to: ‘phenio.db.gz’\n",
      "\n",
      "phenio.db.gz         27%[====>               ] 584.00M  61.0MB/s    eta 29s    "
     ]
    }
   ],
   "source": [
    "!wget https://data.monarchinitiative.org/monarch-kg/latest/monarch-kg.duckdb.gz\n",
    "!gunzip monarch-kg.duckdb.gz\n",
    "!wget https://data.monarchinitiative.org/monarch-kg/latest/phenio.db.gz # consider hpo.db.gz for speed\n",
    "!gunzip phenio.db.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import duckdb\n",
    "\n",
    "db = duckdb.connect('monarch-kg.duckdb', read_only=True)\n",
    "db.sql(\"\"\"\n",
    "copy (\n",
    "  select \n",
    "       subject as disease, \n",
    "       list(object) as phenotype, \n",
    "       primary_knowledge_source as source\n",
    "    from edges\n",
    "  where predicate = 'biolink:has_phenotype' \n",
    "       and negated <> True\n",
    "       and primary_knowledge_source in ('infores:orphanet','infores:omim')\n",
    "       and subject in (\n",
    "          select distinct subject\n",
    "            from denormalized_edges\n",
    "            where subject_namespace = 'MONDO'\n",
    "              and predicate = 'biolink:has_phenotype'\n",
    "              and primary_knowledge_source in ('infores:orphanet','infores:omim')\n",
    "            group by all\n",
    "            having count(distinct primary_knowledge_source) > 1\n",
    "       )     \n",
    "    group by all\n",
    ") to 'phenotype_profiles_by_source.parquet';       \n",
    "\"\"\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>disease</th>\n",
       "      <th>phenotype</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MONDO:0011448</td>\n",
       "      <td>[HP:0000822, HP:0000855, HP:0100578, HP:000081...</td>\n",
       "      <td>infores:orphanet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MONDO:0010060</td>\n",
       "      <td>[HP:0000365, HP:0000602, HP:0000648, HP:000125...</td>\n",
       "      <td>infores:orphanet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MONDO:0060578</td>\n",
       "      <td>[HP:0001249, HP:0001263, HP:0008947, HP:010002...</td>\n",
       "      <td>infores:orphanet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MONDO:0007795</td>\n",
       "      <td>[HP:0000252, HP:0000486, HP:0000821, HP:000116...</td>\n",
       "      <td>infores:orphanet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MONDO:0007652</td>\n",
       "      <td>[HP:0004295, HP:0005246, HP:0001824, HP:000201...</td>\n",
       "      <td>infores:orphanet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         disease                                          phenotype  \\\n",
       "0  MONDO:0011448  [HP:0000822, HP:0000855, HP:0100578, HP:000081...   \n",
       "1  MONDO:0010060  [HP:0000365, HP:0000602, HP:0000648, HP:000125...   \n",
       "2  MONDO:0060578  [HP:0001249, HP:0001263, HP:0008947, HP:010002...   \n",
       "3  MONDO:0007795  [HP:0000252, HP:0000486, HP:0000821, HP:000116...   \n",
       "4  MONDO:0007652  [HP:0004295, HP:0005246, HP:0001824, HP:000201...   \n",
       "\n",
       "             source  \n",
       "0  infores:orphanet  \n",
       "1  infores:orphanet  \n",
       "2  infores:orphanet  \n",
       "3  infores:orphanet  \n",
       "4  infores:orphanet  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the phenotype profiles into a pandas dataframe \n",
    "import pandas as pd\n",
    "phenotype_profiles = pd.read_parquet('phenotype_profiles_by_source.parquet')\n",
    "phenotype_profiles.head()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize semsimian from phenio.db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "from semsimian import Semsimian\n",
    "\n",
    "predicates= [\n",
    "    \"rdfs:subClassOf\",\n",
    "    \"BFO:0000050\",\n",
    "    \"UPHENO:0000001\",\n",
    "]\n",
    "\n",
    "semsimian = Semsimian(\n",
    "    spo=None,\n",
    "    predicates=predicates,\n",
    "    pairwise_similarity_attributes=None,\n",
    "    resource_path=\"./phenio.db\",\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Semsimian, generate termset_pairwise_similarity scores for OMIM vs Orphanet, OMIM vs OMIM and Orphanet vs Orphanet using each metric. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = []\n",
    "for disease in phenotype_profiles['disease'].unique():\n",
    "    result = {\n",
    "        'disease': disease\n",
    "    }\n",
    "    # get the omim profile\n",
    "    try:\n",
    "        omim_profile = list(phenotype_profiles[(phenotype_profiles['disease'] == disease) & (phenotype_profiles['source'] == 'infores:omim')]['phenotype'])[0]\n",
    "    except IndexError:\n",
    "        print(f'No OMIM profile for {disease}, skipping')\n",
    "        continue\n",
    "    # get the orphanet profile\n",
    "    try:\n",
    "        orphanet_profile = list(phenotype_profiles[(phenotype_profiles['disease'] == disease) & (phenotype_profiles['source'] == 'infores:orphanet')]['phenotype'])[0]\n",
    "    except IndexError:\n",
    "        print(f'No Orphanet profile for {disease}, skipping')\n",
    "        continue\n",
    "\n",
    "    for metric in ['ancestor_information_content', 'jaccard_similarity', 'phenodigm_score']:                \n",
    "        result[metric] = pairwise_similarity_result = semsimian.termset_pairwise_similarity(\n",
    "            set(omim_profile), set(orphanet_profile), metric\n",
    "        )\n",
    "        result[f\"omim_self_{metric}\"] = semsimian.termset_pairwise_similarity(\n",
    "            set(omim_profile), set(omim_profile), metric\n",
    "        )\n",
    "        result[f\"orphanet_self_{metric}\"] = semsimian.termset_pairwise_similarity(\n",
    "            set(orphanet_profile), set(orphanet_profile), metric\n",
    "        )\n",
    "\n",
    "    results.append(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add scores to a Pandas dataframe.\n",
    "\n",
    "score_df = pd.DataFrame(\n",
    "    [{\n",
    "        # MONDO ID\n",
    "        'disease': r['disease'],\n",
    "\n",
    "        # Symmetrical IC score\n",
    "        'ancestor_information_content': r['ancestor_information_content']['average_score'],\n",
    "\n",
    "        # Unidirectional IC score from subject_best_matches\n",
    "        'average_ancestor_information_content_subject': [\n",
    "            float(entry['ancestor_information_content']) \n",
    "            for entry in r['ancestor_information_content']['subject_best_matches']['similarity'].values()\n",
    "        ],\n",
    "\n",
    "        # Unidirectional IC score from object_best_matches\n",
    "        'average_ancestor_information_content_object': [\n",
    "            float(entry['ancestor_information_content']) \n",
    "            for entry in r['ancestor_information_content']['object_best_matches']['similarity'].values()\n",
    "        ],\n",
    "\n",
    "        # Symmetrical IC score from omim to self\n",
    "        'ancestor_information_content_omim_self': r['omim_self_ancestor_information_content']['average_score'],\n",
    "\n",
    "        # Symmetrical IC score from orphanet to self\n",
    "        'ancestor_information_content_orphanet_self': r['orphanet_self_ancestor_information_content']['average_score'],\n",
    "        \n",
    "        # Same scores for jaccard similarity as outlined above\n",
    "        'jaccard_similarity': r['jaccard_similarity']['average_score'],\n",
    "\n",
    "        'average_jaccard_similarity_subject': [\n",
    "            float(entry['jaccard_similarity']) \n",
    "            for entry in r['ancestor_information_content']['subject_best_matches']['similarity'].values()\n",
    "        ],\n",
    "        'average_jaccard_similarity_object': [\n",
    "            float(entry['jaccard_similarity']) \n",
    "            for entry in r['ancestor_information_content']['object_best_matches']['similarity'].values()\n",
    "        ],\n",
    "        'jaccard_similarity_omim_self': r['omim_self_jaccard_similarity']['average_score'],\n",
    "        'jaccard_similarity_orphanet_self': r['orphanet_self_jaccard_similarity']['average_score'],\n",
    "        \n",
    "        # Same scores for phenodigm score as outlined above\n",
    "        'phenodigm_score': r['phenodigm_score']['average_score'],\n",
    "        'average_phenodigm_score_subject': [\n",
    "            float(entry['phenodigm_score']) \n",
    "            for entry in r['ancestor_information_content']['subject_best_matches']['similarity'].values()\n",
    "        ],\n",
    "        'average_phenodigm_score_object': [\n",
    "            float(entry['phenodigm_score']) \n",
    "            for entry in r['ancestor_information_content']['object_best_matches']['similarity'].values()\n",
    "        ],\n",
    "        'phenodigm_score_omim_self': r['omim_self_phenodigm_score']['average_score'],\n",
    "        'phenodigm_score_orphanet_self': r['orphanet_self_phenodigm_score']['average_score'],\n",
    "\n",
    "    } for r in results]\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Had to parse through the subject_best_matches and object_best_matches to get the score for each match\n",
    "# From the list of scores, calculate the average\n",
    "\n",
    "# IC\n",
    "score_df['average_ancestor_information_content_subject'] = score_df['average_ancestor_information_content_subject'].apply(lambda x: sum(x) / len(x) if x else 0)\n",
    "score_df['average_ancestor_information_content_object'] = score_df['average_ancestor_information_content_object'].apply(lambda x: sum(x) / len(x) if x else 0)\n",
    "\n",
    "# Jaccard\n",
    "score_df['average_jaccard_similarity_subject'] = score_df['average_jaccard_similarity_subject'].apply(lambda x: sum(x) / len(x) if x else 0)\n",
    "score_df['average_jaccard_similarity_object'] = score_df['average_jaccard_similarity_object'].apply(lambda x: sum(x) / len(x) if x else 0)\n",
    "\n",
    "# Phenodigm\n",
    "score_df['average_phenodigm_score_subject'] = score_df['average_phenodigm_score_subject'].apply(lambda x: sum(x) / len(x) if x else 0)\n",
    "score_df['average_phenodigm_score_object'] = score_df['average_phenodigm_score_object'].apply(lambda x: sum(x) / len(x) if x else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\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>disease</th>\n",
       "      <th>ancestor_information_content</th>\n",
       "      <th>average_ancestor_information_content_subject</th>\n",
       "      <th>average_ancestor_information_content_object</th>\n",
       "      <th>ancestor_information_content_omim_self</th>\n",
       "      <th>ancestor_information_content_orphanet_self</th>\n",
       "      <th>jaccard_similarity</th>\n",
       "      <th>average_jaccard_similarity_subject</th>\n",
       "      <th>average_jaccard_similarity_object</th>\n",
       "      <th>jaccard_similarity_omim_self</th>\n",
       "      <th>jaccard_similarity_orphanet_self</th>\n",
       "      <th>phenodigm_score</th>\n",
       "      <th>average_phenodigm_score_subject</th>\n",
       "      <th>average_phenodigm_score_object</th>\n",
       "      <th>phenodigm_score_omim_self</th>\n",
       "      <th>phenodigm_score_orphanet_self</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MONDO:0011448</td>\n",
       "      <td>14.460562</td>\n",
       "      <td>15.984686</td>\n",
       "      <td>12.936439</td>\n",
       "      <td>16.968982</td>\n",
       "      <td>16.785715</td>\n",
       "      <td>0.810782</td>\n",
       "      <td>0.863348</td>\n",
       "      <td>0.671162</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.386308</td>\n",
       "      <td>3.685752</td>\n",
       "      <td>2.924879</td>\n",
       "      <td>4.117371</td>\n",
       "      <td>4.093446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MONDO:0010060</td>\n",
       "      <td>11.748968</td>\n",
       "      <td>10.040206</td>\n",
       "      <td>13.457731</td>\n",
       "      <td>16.351505</td>\n",
       "      <td>13.905611</td>\n",
       "      <td>0.742139</td>\n",
       "      <td>0.484958</td>\n",
       "      <td>0.865253</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.841132</td>\n",
       "      <td>2.163762</td>\n",
       "      <td>3.385636</td>\n",
       "      <td>4.036240</td>\n",
       "      <td>3.720434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MONDO:0060578</td>\n",
       "      <td>14.451857</td>\n",
       "      <td>14.925796</td>\n",
       "      <td>13.977918</td>\n",
       "      <td>15.623298</td>\n",
       "      <td>16.066829</td>\n",
       "      <td>0.908391</td>\n",
       "      <td>0.916342</td>\n",
       "      <td>0.821753</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.585330</td>\n",
       "      <td>3.670703</td>\n",
       "      <td>3.363687</td>\n",
       "      <td>3.944120</td>\n",
       "      <td>3.998657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MONDO:0007795</td>\n",
       "      <td>12.511146</td>\n",
       "      <td>16.100764</td>\n",
       "      <td>8.921529</td>\n",
       "      <td>17.861862</td>\n",
       "      <td>15.479509</td>\n",
       "      <td>0.725424</td>\n",
       "      <td>0.939873</td>\n",
       "      <td>0.453064</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.999887</td>\n",
       "      <td>3.886736</td>\n",
       "      <td>1.995476</td>\n",
       "      <td>4.225051</td>\n",
       "      <td>3.924509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MONDO:0007652</td>\n",
       "      <td>12.790912</td>\n",
       "      <td>14.506511</td>\n",
       "      <td>11.075313</td>\n",
       "      <td>16.392899</td>\n",
       "      <td>17.419737</td>\n",
       "      <td>0.624911</td>\n",
       "      <td>0.769841</td>\n",
       "      <td>0.383253</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.693510</td>\n",
       "      <td>3.335070</td>\n",
       "      <td>2.020433</td>\n",
       "      <td>4.039978</td>\n",
       "      <td>4.170583</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         disease  ancestor_information_content  \\\n",
       "0  MONDO:0011448                     14.460562   \n",
       "1  MONDO:0010060                     11.748968   \n",
       "2  MONDO:0060578                     14.451857   \n",
       "3  MONDO:0007795                     12.511146   \n",
       "4  MONDO:0007652                     12.790912   \n",
       "\n",
       "   average_ancestor_information_content_subject  \\\n",
       "0                                     15.984686   \n",
       "1                                     10.040206   \n",
       "2                                     14.925796   \n",
       "3                                     16.100764   \n",
       "4                                     14.506511   \n",
       "\n",
       "   average_ancestor_information_content_object  \\\n",
       "0                                    12.936439   \n",
       "1                                    13.457731   \n",
       "2                                    13.977918   \n",
       "3                                     8.921529   \n",
       "4                                    11.075313   \n",
       "\n",
       "   ancestor_information_content_omim_self  \\\n",
       "0                               16.968982   \n",
       "1                               16.351505   \n",
       "2                               15.623298   \n",
       "3                               17.861862   \n",
       "4                               16.392899   \n",
       "\n",
       "   ancestor_information_content_orphanet_self  jaccard_similarity  \\\n",
       "0                                   16.785715            0.810782   \n",
       "1                                   13.905611            0.742139   \n",
       "2                                   16.066829            0.908391   \n",
       "3                                   15.479509            0.725424   \n",
       "4                                   17.419737            0.624911   \n",
       "\n",
       "   average_jaccard_similarity_subject  average_jaccard_similarity_object  \\\n",
       "0                            0.863348                           0.671162   \n",
       "1                            0.484958                           0.865253   \n",
       "2                            0.916342                           0.821753   \n",
       "3                            0.939873                           0.453064   \n",
       "4                            0.769841                           0.383253   \n",
       "\n",
       "   jaccard_similarity_omim_self  jaccard_similarity_orphanet_self  \\\n",
       "0                           1.0                               1.0   \n",
       "1                           1.0                               1.0   \n",
       "2                           1.0                               1.0   \n",
       "3                           1.0                               1.0   \n",
       "4                           1.0                               1.0   \n",
       "\n",
       "   phenodigm_score  average_phenodigm_score_subject  \\\n",
       "0         3.386308                         3.685752   \n",
       "1         2.841132                         2.163762   \n",
       "2         3.585330                         3.670703   \n",
       "3         2.999887                         3.886736   \n",
       "4         2.693510                         3.335070   \n",
       "\n",
       "   average_phenodigm_score_object  phenodigm_score_omim_self  \\\n",
       "0                        2.924879                   4.117371   \n",
       "1                        3.385636                   4.036240   \n",
       "2                        3.363687                   3.944120   \n",
       "3                        1.995476                   4.225051   \n",
       "4                        2.020433                   4.039978   \n",
       "\n",
       "   phenodigm_score_orphanet_self  \n",
       "0                       4.093446  \n",
       "1                       3.720434  \n",
       "2                       3.998657  \n",
       "3                       3.924509  \n",
       "4                       4.170583  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show a few rows with the new values\n",
    "score_df[['disease', \n",
    "          'ancestor_information_content', \n",
    "          'average_ancestor_information_content_subject',\n",
    "          'average_ancestor_information_content_object',\n",
    "          'ancestor_information_content_omim_self', \n",
    "          'ancestor_information_content_orphanet_self',\n",
    "          'jaccard_similarity',\n",
    "          'average_jaccard_similarity_subject', \n",
    "          'average_jaccard_similarity_object',\n",
    "          'jaccard_similarity_omim_self', \n",
    "          'jaccard_similarity_orphanet_self',\n",
    "          'phenodigm_score',\n",
    "          'average_phenodigm_score_subject', \n",
    "          'average_phenodigm_score_object',\n",
    "          'phenodigm_score_omim_self',\n",
    "          'phenodigm_score_orphanet_self']\n",
    "        ].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\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>disease</th>\n",
       "      <th>ancestor_information_content</th>\n",
       "      <th>average_ancestor_information_content_subject</th>\n",
       "      <th>average_ancestor_information_content_object</th>\n",
       "      <th>ancestor_information_content_omim_self</th>\n",
       "      <th>ancestor_information_content_orphanet_self</th>\n",
       "      <th>jaccard_similarity</th>\n",
       "      <th>average_jaccard_similarity_subject</th>\n",
       "      <th>average_jaccard_similarity_object</th>\n",
       "      <th>jaccard_similarity_omim_self</th>\n",
       "      <th>jaccard_similarity_orphanet_self</th>\n",
       "      <th>phenodigm_score</th>\n",
       "      <th>average_phenodigm_score_subject</th>\n",
       "      <th>average_phenodigm_score_object</th>\n",
       "      <th>phenodigm_score_omim_self</th>\n",
       "      <th>phenodigm_score_orphanet_self</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MONDO:0011448</td>\n",
       "      <td>14.460562</td>\n",
       "      <td>15.984686</td>\n",
       "      <td>12.936439</td>\n",
       "      <td>16.968982</td>\n",
       "      <td>16.785715</td>\n",
       "      <td>0.810782</td>\n",
       "      <td>0.863348</td>\n",
       "      <td>0.671162</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.386308</td>\n",
       "      <td>3.685752</td>\n",
       "      <td>2.924879</td>\n",
       "      <td>4.117371</td>\n",
       "      <td>4.093446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MONDO:0010060</td>\n",
       "      <td>11.748968</td>\n",
       "      <td>10.040206</td>\n",
       "      <td>13.457731</td>\n",
       "      <td>16.351505</td>\n",
       "      <td>13.905611</td>\n",
       "      <td>0.742139</td>\n",
       "      <td>0.484958</td>\n",
       "      <td>0.865253</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.841132</td>\n",
       "      <td>2.163762</td>\n",
       "      <td>3.385636</td>\n",
       "      <td>4.036240</td>\n",
       "      <td>3.720434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MONDO:0060578</td>\n",
       "      <td>14.451857</td>\n",
       "      <td>14.925796</td>\n",
       "      <td>13.977918</td>\n",
       "      <td>15.623298</td>\n",
       "      <td>16.066829</td>\n",
       "      <td>0.908391</td>\n",
       "      <td>0.916342</td>\n",
       "      <td>0.821753</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.585330</td>\n",
       "      <td>3.670703</td>\n",
       "      <td>3.363687</td>\n",
       "      <td>3.944120</td>\n",
       "      <td>3.998657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MONDO:0007795</td>\n",
       "      <td>12.511146</td>\n",
       "      <td>16.100764</td>\n",
       "      <td>8.921529</td>\n",
       "      <td>17.861862</td>\n",
       "      <td>15.479509</td>\n",
       "      <td>0.725424</td>\n",
       "      <td>0.939873</td>\n",
       "      <td>0.453064</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.999887</td>\n",
       "      <td>3.886736</td>\n",
       "      <td>1.995476</td>\n",
       "      <td>4.225051</td>\n",
       "      <td>3.924509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MONDO:0007652</td>\n",
       "      <td>12.790912</td>\n",
       "      <td>14.506511</td>\n",
       "      <td>11.075313</td>\n",
       "      <td>16.392899</td>\n",
       "      <td>17.419737</td>\n",
       "      <td>0.624911</td>\n",
       "      <td>0.769841</td>\n",
       "      <td>0.383253</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.693510</td>\n",
       "      <td>3.335070</td>\n",
       "      <td>2.020433</td>\n",
       "      <td>4.039978</td>\n",
       "      <td>4.170583</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2092</th>\n",
       "      <td>MONDO:0009746</td>\n",
       "      <td>13.030055</td>\n",
       "      <td>14.175274</td>\n",
       "      <td>11.884835</td>\n",
       "      <td>16.722626</td>\n",
       "      <td>16.275587</td>\n",
       "      <td>0.787127</td>\n",
       "      <td>0.764083</td>\n",
       "      <td>0.659831</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.136199</td>\n",
       "      <td>3.267740</td>\n",
       "      <td>2.768547</td>\n",
       "      <td>4.077862</td>\n",
       "      <td>4.024970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2093</th>\n",
       "      <td>MONDO:0011273</td>\n",
       "      <td>13.046696</td>\n",
       "      <td>13.423890</td>\n",
       "      <td>12.669502</td>\n",
       "      <td>16.775331</td>\n",
       "      <td>16.174108</td>\n",
       "      <td>0.684715</td>\n",
       "      <td>0.682762</td>\n",
       "      <td>0.604381</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.925175</td>\n",
       "      <td>2.980969</td>\n",
       "      <td>2.711878</td>\n",
       "      <td>4.090510</td>\n",
       "      <td>4.011415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2094</th>\n",
       "      <td>MONDO:0009877</td>\n",
       "      <td>11.877996</td>\n",
       "      <td>13.742820</td>\n",
       "      <td>10.013173</td>\n",
       "      <td>15.831883</td>\n",
       "      <td>15.657179</td>\n",
       "      <td>0.652782</td>\n",
       "      <td>0.731532</td>\n",
       "      <td>0.493085</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.724510</td>\n",
       "      <td>3.148632</td>\n",
       "      <td>2.175965</td>\n",
       "      <td>3.954533</td>\n",
       "      <td>3.943137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2095</th>\n",
       "      <td>MONDO:0013572</td>\n",
       "      <td>15.389937</td>\n",
       "      <td>15.842898</td>\n",
       "      <td>14.936976</td>\n",
       "      <td>16.870292</td>\n",
       "      <td>16.549371</td>\n",
       "      <td>0.908019</td>\n",
       "      <td>0.894846</td>\n",
       "      <td>0.843639</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.707692</td>\n",
       "      <td>3.736463</td>\n",
       "      <td>3.527360</td>\n",
       "      <td>4.098533</td>\n",
       "      <td>4.058158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2096</th>\n",
       "      <td>MONDO:0012297</td>\n",
       "      <td>15.210929</td>\n",
       "      <td>15.335664</td>\n",
       "      <td>15.086193</td>\n",
       "      <td>16.481433</td>\n",
       "      <td>16.378604</td>\n",
       "      <td>0.914174</td>\n",
       "      <td>0.891988</td>\n",
       "      <td>0.872031</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.698655</td>\n",
       "      <td>3.674316</td>\n",
       "      <td>3.597319</td>\n",
       "      <td>4.053288</td>\n",
       "      <td>4.042936</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2097 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            disease  ancestor_information_content  \\\n",
       "0     MONDO:0011448                     14.460562   \n",
       "1     MONDO:0010060                     11.748968   \n",
       "2     MONDO:0060578                     14.451857   \n",
       "3     MONDO:0007795                     12.511146   \n",
       "4     MONDO:0007652                     12.790912   \n",
       "...             ...                           ...   \n",
       "2092  MONDO:0009746                     13.030055   \n",
       "2093  MONDO:0011273                     13.046696   \n",
       "2094  MONDO:0009877                     11.877996   \n",
       "2095  MONDO:0013572                     15.389937   \n",
       "2096  MONDO:0012297                     15.210929   \n",
       "\n",
       "      average_ancestor_information_content_subject  \\\n",
       "0                                        15.984686   \n",
       "1                                        10.040206   \n",
       "2                                        14.925796   \n",
       "3                                        16.100764   \n",
       "4                                        14.506511   \n",
       "...                                            ...   \n",
       "2092                                     14.175274   \n",
       "2093                                     13.423890   \n",
       "2094                                     13.742820   \n",
       "2095                                     15.842898   \n",
       "2096                                     15.335664   \n",
       "\n",
       "      average_ancestor_information_content_object  \\\n",
       "0                                       12.936439   \n",
       "1                                       13.457731   \n",
       "2                                       13.977918   \n",
       "3                                        8.921529   \n",
       "4                                       11.075313   \n",
       "...                                           ...   \n",
       "2092                                    11.884835   \n",
       "2093                                    12.669502   \n",
       "2094                                    10.013173   \n",
       "2095                                    14.936976   \n",
       "2096                                    15.086193   \n",
       "\n",
       "      ancestor_information_content_omim_self  \\\n",
       "0                                  16.968982   \n",
       "1                                  16.351505   \n",
       "2                                  15.623298   \n",
       "3                                  17.861862   \n",
       "4                                  16.392899   \n",
       "...                                      ...   \n",
       "2092                               16.722626   \n",
       "2093                               16.775331   \n",
       "2094                               15.831883   \n",
       "2095                               16.870292   \n",
       "2096                               16.481433   \n",
       "\n",
       "      ancestor_information_content_orphanet_self  jaccard_similarity  \\\n",
       "0                                      16.785715            0.810782   \n",
       "1                                      13.905611            0.742139   \n",
       "2                                      16.066829            0.908391   \n",
       "3                                      15.479509            0.725424   \n",
       "4                                      17.419737            0.624911   \n",
       "...                                          ...                 ...   \n",
       "2092                                   16.275587            0.787127   \n",
       "2093                                   16.174108            0.684715   \n",
       "2094                                   15.657179            0.652782   \n",
       "2095                                   16.549371            0.908019   \n",
       "2096                                   16.378604            0.914174   \n",
       "\n",
       "      average_jaccard_similarity_subject  average_jaccard_similarity_object  \\\n",
       "0                               0.863348                           0.671162   \n",
       "1                               0.484958                           0.865253   \n",
       "2                               0.916342                           0.821753   \n",
       "3                               0.939873                           0.453064   \n",
       "4                               0.769841                           0.383253   \n",
       "...                                  ...                                ...   \n",
       "2092                            0.764083                           0.659831   \n",
       "2093                            0.682762                           0.604381   \n",
       "2094                            0.731532                           0.493085   \n",
       "2095                            0.894846                           0.843639   \n",
       "2096                            0.891988                           0.872031   \n",
       "\n",
       "      jaccard_similarity_omim_self  jaccard_similarity_orphanet_self  \\\n",
       "0                              1.0                               1.0   \n",
       "1                              1.0                               1.0   \n",
       "2                              1.0                               1.0   \n",
       "3                              1.0                               1.0   \n",
       "4                              1.0                               1.0   \n",
       "...                            ...                               ...   \n",
       "2092                           1.0                               1.0   \n",
       "2093                           1.0                               1.0   \n",
       "2094                           1.0                               1.0   \n",
       "2095                           1.0                               1.0   \n",
       "2096                           1.0                               1.0   \n",
       "\n",
       "      phenodigm_score  average_phenodigm_score_subject  \\\n",
       "0            3.386308                         3.685752   \n",
       "1            2.841132                         2.163762   \n",
       "2            3.585330                         3.670703   \n",
       "3            2.999887                         3.886736   \n",
       "4            2.693510                         3.335070   \n",
       "...               ...                              ...   \n",
       "2092         3.136199                         3.267740   \n",
       "2093         2.925175                         2.980969   \n",
       "2094         2.724510                         3.148632   \n",
       "2095         3.707692                         3.736463   \n",
       "2096         3.698655                         3.674316   \n",
       "\n",
       "      average_phenodigm_score_object  phenodigm_score_omim_self  \\\n",
       "0                           2.924879                   4.117371   \n",
       "1                           3.385636                   4.036240   \n",
       "2                           3.363687                   3.944120   \n",
       "3                           1.995476                   4.225051   \n",
       "4                           2.020433                   4.039978   \n",
       "...                              ...                        ...   \n",
       "2092                        2.768547                   4.077862   \n",
       "2093                        2.711878                   4.090510   \n",
       "2094                        2.175965                   3.954533   \n",
       "2095                        3.527360                   4.098533   \n",
       "2096                        3.597319                   4.053288   \n",
       "\n",
       "      phenodigm_score_orphanet_self  \n",
       "0                          4.093446  \n",
       "1                          3.720434  \n",
       "2                          3.998657  \n",
       "3                          3.924509  \n",
       "4                          4.170583  \n",
       "...                             ...  \n",
       "2092                       4.024970  \n",
       "2093                       4.011415  \n",
       "2094                       3.943137  \n",
       "2095                       4.058158  \n",
       "2096                       4.042936  \n",
       "\n",
       "[2097 rows x 16 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the df\n",
    "score_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Export the full results as JSON, export the score dataframe as tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "# write results as json\n",
    "with open('phenotype_profile_comparison_results.json', 'w') as f:\n",
    "    json.dump(results, f)\n",
    "\n",
    "# write results as tsv\n",
    "score_df.to_csv('phenotype_profile_comparison_scores.tsv', sep='\\t', index=False)"
   ]
  }
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