integrate with sourceDB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Integrate resources to replicate hetionet (AlzKB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install hetio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add rel used in alzkb even we didn't update this time\n",
    "# add scores to rel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/__init__.py:11: FutureWarning: \n",
      "PACKAGE HAS BEEN RELOCATED:\n",
      "v0.2.11 is the last release before the hetio package is renamed to hetnetpy.\n",
      "Consider switching to the hetnetpy package or specifying hetio<=0.2.10 to silence this warning.\n",
      "Future releases and development will only occur for the hetnetpy package.\n",
      "\n",
      "  warnings.warn(message, FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "import pandas\n",
    "import seaborn\n",
    "\n",
    "import hetio.hetnet\n",
    "import hetio.readwrite\n",
    "import hetio.stats\n",
    "\n",
    "#from utils import rawgit, obo_iri\n",
    "def obo_iri(identifier):\n",
    "    return 'http://purl.obolibrary.org/obo/' + identifier.replace(':', '_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#function\n",
    "def rawgit(handle, repo, commit, *args):\n",
    "    \"\"\"Returns url for a raw file in a github reposotory.\"\"\"\n",
    "    url_head = 'https://raw.githubusercontent.com'\n",
    "    return '/'.join((url_head, handle, repo, commit) + args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the metagraph and instantiate the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "kind_to_abbev = {\n",
    "    \n",
    "    # metanodes\n",
    "    'Compound': 'C',\n",
    "    'Disease': 'D',\n",
    "    'Gene': 'G',\n",
    "    'Anatomy': 'A',\n",
    "    'Symptom': 'S',\n",
    "    'Side Effect': 'SE',\n",
    "    #'Pathway': 'PW',\n",
    "    'Pharmacologic Class': 'PC',\n",
    "    'Biological Process': 'BP',\n",
    "    'Cellular Component': 'CC',\n",
    "    'Molecular Function': 'MF',\n",
    "    \n",
    "    # metaedges\n",
    "    'treats': 't',\n",
    "    'palliates': 'p',\n",
    "    'binds': 'b',\n",
    "    'expresses': 'e',\n",
    "    'regulates': 'r',\n",
    "    'upregulates': 'u',\n",
    "    'downregulates': 'd',\n",
    "    'interacts': 'i',\n",
    "    'includes': 'i',\n",
    "    'covaries': 'c',\n",
    "    'regulates': 'r',\n",
    "    'participates': 'p',\n",
    "    'resembles': 'r',\n",
    "    'associates': 'a',\n",
    "    'localizes': 'l',\n",
    "    'presents': 'p',\n",
    "    'causes': 'c',\n",
    "}\n",
    "\n",
    "metaedge_tuples = [ #only those we're updating this time\n",
    "    ('Compound', 'Disease', 'treats', 'both'),\n",
    "    ('Compound', 'Disease', 'palliates', 'both'),\n",
    "    ('Compound', 'Gene', 'binds', 'both'),\n",
    "    ('Compound', 'Gene', 'upregulates', 'both'),\n",
    "    ('Compound', 'Gene', 'downregulates', 'both'),\n",
    "    #('Compound', 'Compound', 'resembles', 'both'),\n",
    "    ('Compound', 'Side Effect', 'causes', 'both'),\n",
    "    ('Pharmacologic Class', 'Compound', 'includes', 'both'),\n",
    "    #('Anatomy', 'Gene', 'expresses', 'both'),\n",
    "    ('Anatomy', 'Gene', 'upregulates', 'both'),\n",
    "    ('Anatomy', 'Gene', 'downregulates', 'both'),\n",
    "    ('Gene', 'Gene', 'interacts', 'both'),\n",
    "    ('Gene', 'Gene', 'covaries', 'both'),\n",
    "    ('Gene', 'Gene', 'regulates', 'forward'),\n",
    "    #('Gene', 'Pathway', 'participates', 'both'),\n",
    "    ('Gene', 'Biological Process', 'participates', 'both'),\n",
    "    ('Gene', 'Cellular Component', 'participates', 'both'),\n",
    "    ('Gene', 'Molecular Function', 'participates', 'both'),\n",
    "    ('Disease', 'Disease', 'resembles', 'both'),\n",
    "    #('Disease', 'Gene', 'associates', 'both'),\n",
    "    #('Disease', 'Gene', 'upregulates', 'both'),\n",
    "    #('Disease', 'Gene', 'downregulates', 'both'),\n",
    "    ('Disease', 'Anatomy', 'localizes', 'both'),\n",
    "    ('Disease', 'Symptom', 'presents', 'both'),\n",
    "]\n",
    "metagraph = hetio.hetnet.MetaGraph.from_edge_tuples(metaedge_tuples, kind_to_abbev)\n",
    "graph = hetio.hetnet.Graph(metagraph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "path ='../data'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gene Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>tax_id</th>\n",
       "      <th>GeneID</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>chromosome</th>\n",
       "      <th>map_location</th>\n",
       "      <th>type_of_gene</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9606</td>\n",
       "      <td>1</td>\n",
       "      <td>A1BG</td>\n",
       "      <td>19</td>\n",
       "      <td>19q13.43</td>\n",
       "      <td>protein-coding</td>\n",
       "      <td>alpha-1-B glycoprotein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9606</td>\n",
       "      <td>2</td>\n",
       "      <td>A2M</td>\n",
       "      <td>12</td>\n",
       "      <td>12p13.31</td>\n",
       "      <td>protein-coding</td>\n",
       "      <td>alpha-2-macroglobulin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tax_id  GeneID Symbol chromosome map_location    type_of_gene  \\\n",
       "0    9606       1   A1BG         19     19q13.43  protein-coding   \n",
       "1    9606       2    A2M         12     12p13.31  protein-coding   \n",
       "\n",
       "              description  \n",
       "0  alpha-1-B glycoprotein  \n",
       "1   alpha-2-macroglobulin  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'EntrezGene/genes-human.tsv')\n",
    "gene_df = pandas.read_table(url)\n",
    "gene_df = gene_df[gene_df.type_of_gene == 'protein-coding']\n",
    "coding_genes = set(gene_df.GeneID)\n",
    "gene_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in gene_df.iterrows():\n",
    "    if row.type_of_gene != 'protein-coding':\n",
    "        continue\n",
    "    data = {\n",
    "        'description': row['description'],\n",
    "        'source': 'Entrez Gene',\n",
    "        'url': 'http://identifiers.org/ncbigene/{}'.format(row.GeneID),\n",
    "        'license': 'CC0 1.0',\n",
    "    }\n",
    "    if pandas.notnull(row['chromosome']):\n",
    "        data['chromosome'] = row['chromosome']\n",
    "    graph.add_node(kind = 'Gene', identifier=row.GeneID, name=row.Symbol, data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Disease Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>doid</th>\n",
       "      <th>name</th>\n",
       "      <th>source</th>\n",
       "      <th>pathophysiology</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DOID:2531</td>\n",
       "      <td>hematologic cancer</td>\n",
       "      <td>DOcancerslim</td>\n",
       "      <td>neoplastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DOID:1319</td>\n",
       "      <td>brain cancer</td>\n",
       "      <td>DOcancerslim</td>\n",
       "      <td>neoplastic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        doid                name        source pathophysiology\n",
       "0  DOID:2531  hematologic cancer  DOcancerslim      neoplastic\n",
       "1  DOID:1319        brain cancer  DOcancerslim      neoplastic"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'DiseaseOntology/slim-terms.tsv')\n",
    "disease_df = pandas.read_table(url)\n",
    "disease_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in disease_df.iterrows():\n",
    "    data = {\n",
    "        'source': 'Disease Ontology',\n",
    "        'url': obo_iri(row.doid),\n",
    "        'license': 'CC BY 3.0',\n",
    "    }\n",
    "    graph.add_node(kind='Disease', identifier=row.doid, name=row['name'], data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compound Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>drugbank_id</th>\n",
       "      <th>name</th>\n",
       "      <th>type</th>\n",
       "      <th>groups</th>\n",
       "      <th>atc_codes</th>\n",
       "      <th>categories</th>\n",
       "      <th>inchikey</th>\n",
       "      <th>inchi</th>\n",
       "      <th>description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DB00006</td>\n",
       "      <td>Bivalirudin</td>\n",
       "      <td>small molecule</td>\n",
       "      <td>approved|investigational</td>\n",
       "      <td>B01AE06</td>\n",
       "      <td>Amino Acids, Peptides, and Proteins|Anticoagul...</td>\n",
       "      <td>OIRCOABEOLEUMC-GEJPAHFPSA-N</td>\n",
       "      <td>InChI=1S/C98H138N24O33/c1-5-52(4)82(96(153)122...</td>\n",
       "      <td>Bivalirudin is a synthetic 20 residue peptide ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DB00007</td>\n",
       "      <td>Leuprolide</td>\n",
       "      <td>small molecule</td>\n",
       "      <td>approved|investigational</td>\n",
       "      <td>L02AE51|L02AE02</td>\n",
       "      <td>Adrenal Cortex Hormones|Agents Causing Muscle ...</td>\n",
       "      <td>GFIJNRVAKGFPGQ-LIJARHBVSA-N</td>\n",
       "      <td>InChI=1S/C59H84N16O12/c1-6-63-57(86)48-14-10-2...</td>\n",
       "      <td>Leuprolide is a synthetic 9-residue peptide an...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  drugbank_id         name            type                    groups  \\\n",
       "0     DB00006  Bivalirudin  small molecule  approved|investigational   \n",
       "1     DB00007   Leuprolide  small molecule  approved|investigational   \n",
       "\n",
       "         atc_codes                                         categories  \\\n",
       "0          B01AE06  Amino Acids, Peptides, and Proteins|Anticoagul...   \n",
       "1  L02AE51|L02AE02  Adrenal Cortex Hormones|Agents Causing Muscle ...   \n",
       "\n",
       "                      inchikey  \\\n",
       "0  OIRCOABEOLEUMC-GEJPAHFPSA-N   \n",
       "1  GFIJNRVAKGFPGQ-LIJARHBVSA-N   \n",
       "\n",
       "                                               inchi  \\\n",
       "0  InChI=1S/C98H138N24O33/c1-5-52(4)82(96(153)122...   \n",
       "1  InChI=1S/C59H84N16O12/c1-6-63-57(86)48-14-10-2...   \n",
       "\n",
       "                                         description  \n",
       "0  Bivalirudin is a synthetic 20 residue peptide ...  \n",
       "1  Leuprolide is a synthetic 9-residue peptide an...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'DrugBank/drugbank-slim.tsv')\n",
    "compound_df = pandas.read_table(url)\n",
    "compound_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in compound_df.iterrows():\n",
    "    url = 'http://www.drugbank.ca/drugs/' + row.drugbank_id\n",
    "    data = {\n",
    "        'source': 'DrugBank',\n",
    "        'inchikey': row.inchikey,\n",
    "        'inchi': row.inchi, 'url': url,\n",
    "        'license': 'CC BY-NC 4.0',\n",
    "    }\n",
    "    graph.add_node(kind='Compound', identifier=row.drugbank_id, name=row['name'], data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anotomy nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>uberon_id</th>\n",
       "      <th>uberon_name</th>\n",
       "      <th>mesh_id</th>\n",
       "      <th>mesh_name</th>\n",
       "      <th>bto_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UBERON:0000002</td>\n",
       "      <td>uterine cervix</td>\n",
       "      <td>D002584</td>\n",
       "      <td>Cervix Uteri</td>\n",
       "      <td>BTO:0001421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UBERON:0000004</td>\n",
       "      <td>nose</td>\n",
       "      <td>D009666</td>\n",
       "      <td>Nose</td>\n",
       "      <td>BTO:0000840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        uberon_id     uberon_name  mesh_id     mesh_name       bto_id\n",
       "0  UBERON:0000002  uterine cervix  D002584  Cervix Uteri  BTO:0001421\n",
       "1  UBERON:0000004            nose  D009666          Nose  BTO:0000840"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'Uberon/hetio-slim.tsv')\n",
    "uberon_df = pandas.read_table(url)\n",
    "uberon_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in uberon_df.iterrows():\n",
    "    data = {\n",
    "        'source': 'Uberon',\n",
    "        'url': obo_iri(row['uberon_id']),\n",
    "        'license': 'CC BY 3.0',\n",
    "    }\n",
    "    for xref in 'mesh_id', 'bto_id':\n",
    "        if pandas.notnull(row[xref]):\n",
    "            data[xref] = row[xref]\n",
    "    graph.add_node(kind='Anatomy', identifier=row['uberon_id'], name=row['uberon_name'], data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Symptom Nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mesh_id</th>\n",
       "      <th>mesh_name</th>\n",
       "      <th>in_hsdn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>D000006</td>\n",
       "      <td>Abdomen, Acute</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D000067329</td>\n",
       "      <td>Obesity, Metabolically Benign</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mesh_id                      mesh_name  in_hsdn\n",
       "0     D000006                 Abdomen, Acute        1\n",
       "1  D000067329  Obesity, Metabolically Benign        0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'MeSH/symptoms.tsv')\n",
    "symptom_df = pandas.read_table(url)\n",
    "symptom_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in symptom_df.iterrows():\n",
    "    url = 'http://identifiers.org/mesh/{}'.format(row.mesh_id)\n",
    "    data = {\n",
    "        'source': 'MeSH',\n",
    "        'url': url,\n",
    "        'license': 'CC0 1.0',\n",
    "    }\n",
    "    graph.add_node(kind='Symptom', identifier=row.mesh_id, name=row.mesh_name, data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pharmacologic Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>class_id</th>\n",
       "      <th>class_name</th>\n",
       "      <th>class_source</th>\n",
       "      <th>class_type</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>M0000499</td>\n",
       "      <td>Adrenocorticotropic Hormone</td>\n",
       "      <td>FDA</td>\n",
       "      <td>Chemical Structure</td>\n",
       "      <td>http://purl.bioontology.org/ontology/NDFRT/M00...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>M0000713</td>\n",
       "      <td>Alkaloids</td>\n",
       "      <td>FDA</td>\n",
       "      <td>Chemical Structure</td>\n",
       "      <td>http://purl.bioontology.org/ontology/NDFRT/M00...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     class_id                   class_name class_source          class_type  \\\n",
       "639  M0000499  Adrenocorticotropic Hormone          FDA  Chemical Structure   \n",
       "640  M0000713                    Alkaloids          FDA  Chemical Structure   \n",
       "\n",
       "                                                   url  \n",
       "639  http://purl.bioontology.org/ontology/NDFRT/M00...  \n",
       "640  http://purl.bioontology.org/ontology/NDFRT/M00...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'DrugCentral/classes.tsv')\n",
    "class_df = pandas.read_table(url)\n",
    "class_types = {'Physiologic Effect', 'Mechanism of Action', 'Chemical/Ingredient', 'Chemical Structure'}\n",
    "class_df = class_df.query(\"class_type in @class_types\")\n",
    "class_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in class_df.itertuples():\n",
    "    data = {\n",
    "        'source': '{} via DrugCentral'.format(row.class_source),\n",
    "        'class_type': row.class_type,\n",
    "        'license': 'CC BY 4.0',\n",
    "        'url': row.url,\n",
    "    }\n",
    "    graph.add_node(kind='Pharmacologic Class', identifier=row.class_id, name=row.class_name, data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>drugbank_id</th>\n",
       "      <th>drugbank_name</th>\n",
       "      <th>class_id</th>\n",
       "      <th>class_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DB11791</td>\n",
       "      <td>Capmatinib</td>\n",
       "      <td>N0000182138</td>\n",
       "      <td>Cytochrome P450 1A2 Inhibitors</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DB11791</td>\n",
       "      <td>Capmatinib</td>\n",
       "      <td>N0000185503</td>\n",
       "      <td>P-Glycoprotein Inhibitors</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  drugbank_id drugbank_name     class_id                      class_name\n",
       "1     DB11791    Capmatinib  N0000182138  Cytochrome P450 1A2 Inhibitors\n",
       "2     DB11791    Capmatinib  N0000185503       P-Glycoprotein Inhibitors"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'DrugCentral/drug-to-class.tsv')\n",
    "drug_class_df = pandas.read_table(url)\n",
    "drug_class_df = drug_class_df.query(\"class_id in @class_df.class_id\")\n",
    "drug_class_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for row in drug_class_df.itertuples():\n",
    "    data = {\n",
    "        'source': 'DrugCentral',\n",
    "        'license': 'CC BY 4.0',\n",
    "        'unbiased': False,\n",
    "    }\n",
    "    source_id = 'Pharmacologic Class', row.class_id\n",
    "    target_id = 'Compound', row.drugbank_id\n",
    "    graph.add_edge(source_id, target_id, 'includes', 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gene Ontology Domains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>go_id</th>\n",
       "      <th>go_name</th>\n",
       "      <th>go_domain</th>\n",
       "      <th>tax_id</th>\n",
       "      <th>annotation_type</th>\n",
       "      <th>size</th>\n",
       "      <th>gene_ids</th>\n",
       "      <th>gene_symbols</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GO:0000002</td>\n",
       "      <td>mitochondrial genome maintenance</td>\n",
       "      <td>biological_process</td>\n",
       "      <td>9606</td>\n",
       "      <td>inferred</td>\n",
       "      <td>26</td>\n",
       "      <td>142|291|1763|1890|2021|4205|4358|4976|5428|624...</td>\n",
       "      <td>PARP1|SLC25A4|DNA2|TYMP|ENDOG|MEF2A|MPV17|OPA1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GO:0000009</td>\n",
       "      <td>alpha-1,6-mannosyltransferase activity</td>\n",
       "      <td>molecular_function</td>\n",
       "      <td>9606</td>\n",
       "      <td>inferred</td>\n",
       "      <td>2</td>\n",
       "      <td>55650|79087</td>\n",
       "      <td>PIGV|ALG12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        go_id                                 go_name           go_domain  \\\n",
       "0  GO:0000002        mitochondrial genome maintenance  biological_process   \n",
       "1  GO:0000009  alpha-1,6-mannosyltransferase activity  molecular_function   \n",
       "\n",
       "   tax_id annotation_type  size  \\\n",
       "0    9606        inferred    26   \n",
       "1    9606        inferred     2   \n",
       "\n",
       "                                            gene_ids  \\\n",
       "0  142|291|1763|1890|2021|4205|4358|4976|5428|624...   \n",
       "1                                        55650|79087   \n",
       "\n",
       "                                        gene_symbols  \n",
       "0  PARP1|SLC25A4|DNA2|TYMP|ENDOG|MEF2A|MPV17|OPA1...  \n",
       "1                                         PIGV|ALG12  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'GeneOntology/GO_annotations-9606-inferred-allev.tsv')\n",
    "go_df = pandas.read_table(url)\n",
    "go_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in go_df.iterrows():\n",
    "    genes = coding_genes & set(map(int, row.gene_ids.split('|')))\n",
    "    if 2 > len(genes) or len(genes) > 1000:\n",
    "        continue\n",
    "    kind = row['go_domain'].replace('_', ' ').title()\n",
    "    data = {'source': 'Gene Ontology', 'url': obo_iri(row.go_id), 'license': 'CC BY 4.0'}\n",
    "    target = graph.add_node(kind=kind, identifier=row['go_id'], name=row['go_name'], data=data)\n",
    "    target_id = target.get_id()\n",
    "    for gene in genes:\n",
    "        source_id = 'Gene', gene\n",
    "        data = {'source': 'NCBI gene2go', 'unbiased': False, 'license': 'CC BY 4.0'}\n",
    "        graph.add_edge(source_id, target_id, 'participates', 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MEDLINE cooccurrence data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "medline_data = {\n",
    "    'source': 'MEDLINE cooccurrence',\n",
    "    'unbiased': False,\n",
    "    'license': 'CC0 1.0',\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Symptom edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>doid_code</th>\n",
       "      <th>doid_name</th>\n",
       "      <th>mesh_id</th>\n",
       "      <th>mesh_name</th>\n",
       "      <th>cooccurrence</th>\n",
       "      <th>expected</th>\n",
       "      <th>enrichment</th>\n",
       "      <th>odds_ratio</th>\n",
       "      <th>p_fisher</th>\n",
       "      <th>n_source</th>\n",
       "      <th>n_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>D000647</td>\n",
       "      <td>Amnesia</td>\n",
       "      <td>489</td>\n",
       "      <td>19.641482</td>\n",
       "      <td>24.896288</td>\n",
       "      <td>48.294582</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10199</td>\n",
       "      <td>1042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>D004314</td>\n",
       "      <td>Down Syndrome</td>\n",
       "      <td>1102</td>\n",
       "      <td>56.021580</td>\n",
       "      <td>19.670991</td>\n",
       "      <td>34.268557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10199</td>\n",
       "      <td>2972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    doid_code            doid_name  mesh_id      mesh_name  cooccurrence  \\\n",
       "0  DOID:10652  Alzheimer's disease  D000647        Amnesia           489   \n",
       "1  DOID:10652  Alzheimer's disease  D004314  Down Syndrome          1102   \n",
       "\n",
       "    expected  enrichment  odds_ratio  p_fisher  n_source  n_target  \n",
       "0  19.641482   24.896288   48.294582       0.0     10199      1042  \n",
       "1  56.021580   19.670991   34.268557       0.0     10199      2972  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'MEDLINE/disease-symptom-cooccurrence.tsv')\n",
    "disease_symptom_df = pandas.read_table(url)\n",
    "disease_symptom_df = disease_symptom_df[disease_symptom_df.p_fisher < 0.005]\n",
    "disease_symptom_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in disease_symptom_df.iterrows():\n",
    "    source_id = 'Disease', row.doid_code\n",
    "    target_id = 'Symptom', row.mesh_id\n",
    "    data = medline_data.copy()\n",
    "    data['p_fisher'] = row.p_fisher\n",
    "    graph.add_edge(source_id, target_id, 'presents', 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disease-localization edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>doid_code</th>\n",
       "      <th>doid_name</th>\n",
       "      <th>uberon_id</th>\n",
       "      <th>uberon_name</th>\n",
       "      <th>cooccurrence</th>\n",
       "      <th>expected</th>\n",
       "      <th>enrichment</th>\n",
       "      <th>odds_ratio</th>\n",
       "      <th>p_fisher</th>\n",
       "      <th>n_source</th>\n",
       "      <th>n_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>UBERON:0000955</td>\n",
       "      <td>brain</td>\n",
       "      <td>21745</td>\n",
       "      <td>3071.649615</td>\n",
       "      <td>7.079258</td>\n",
       "      <td>27.695942</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30744</td>\n",
       "      <td>97944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>UBERON:0000956</td>\n",
       "      <td>cerebral cortex</td>\n",
       "      <td>3590</td>\n",
       "      <td>594.986283</td>\n",
       "      <td>6.033753</td>\n",
       "      <td>8.029408</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30744</td>\n",
       "      <td>18972</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    doid_code            doid_name       uberon_id      uberon_name  \\\n",
       "0  DOID:10652  Alzheimer's disease  UBERON:0000955            brain   \n",
       "1  DOID:10652  Alzheimer's disease  UBERON:0000956  cerebral cortex   \n",
       "\n",
       "   cooccurrence     expected  enrichment  odds_ratio  p_fisher  n_source  \\\n",
       "0         21745  3071.649615    7.079258   27.695942       0.0     30744   \n",
       "1          3590   594.986283    6.033753    8.029408       0.0     30744   \n",
       "\n",
       "   n_target  \n",
       "0     97944  \n",
       "1     18972  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'MEDLINE/disease-uberon-cooccurrence.tsv')\n",
    "disease_anatomy_df = pandas.read_table(url)\n",
    "disease_anatomy_df = disease_anatomy_df[disease_anatomy_df.p_fisher < 0.005]\n",
    "disease_anatomy_df = disease_anatomy_df[disease_anatomy_df.uberon_id.isin(uberon_df['uberon_id'])]\n",
    "disease_anatomy_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in disease_anatomy_df.iterrows():\n",
    "    source_id = 'Disease', row.doid_code\n",
    "    target_id = 'Anatomy', row.uberon_id\n",
    "    data = medline_data.copy()\n",
    "    data['p_fisher'] = row.p_fisher\n",
    "    graph.add_edge(source_id, target_id, 'localizes', 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disease-disease similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>doid_code_0</th>\n",
       "      <th>doid_name_0</th>\n",
       "      <th>doid_code_1</th>\n",
       "      <th>doid_name_1</th>\n",
       "      <th>cooccurrence</th>\n",
       "      <th>expected</th>\n",
       "      <th>enrichment</th>\n",
       "      <th>odds_ratio</th>\n",
       "      <th>p_fisher</th>\n",
       "      <th>n_source</th>\n",
       "      <th>n_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>DOID:14330</td>\n",
       "      <td>Parkinson's disease</td>\n",
       "      <td>2521</td>\n",
       "      <td>1562.065225</td>\n",
       "      <td>1.613889</td>\n",
       "      <td>1.651428</td>\n",
       "      <td>8.873145e-115</td>\n",
       "      <td>104084</td>\n",
       "      <td>73639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>DOID:11949</td>\n",
       "      <td>Creutzfeldt-Jakob disease</td>\n",
       "      <td>157</td>\n",
       "      <td>116.223134</td>\n",
       "      <td>1.350850</td>\n",
       "      <td>1.361746</td>\n",
       "      <td>1.558560e-04</td>\n",
       "      <td>104084</td>\n",
       "      <td>5479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  doid_code_0          doid_name_0 doid_code_1                doid_name_1  \\\n",
       "0  DOID:10652  Alzheimer's disease  DOID:14330        Parkinson's disease   \n",
       "1  DOID:10652  Alzheimer's disease  DOID:11949  Creutzfeldt-Jakob disease   \n",
       "\n",
       "   cooccurrence     expected  enrichment  odds_ratio       p_fisher  n_source  \\\n",
       "0          2521  1562.065225    1.613889    1.651428  8.873145e-115    104084   \n",
       "1           157   116.223134    1.350850    1.361746   1.558560e-04    104084   \n",
       "\n",
       "   n_target  \n",
       "0     73639  \n",
       "1      5479  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'MEDLINE/disease-disease-cooccurrence.tsv')\n",
    "disease_similarity_df = pandas.read_table(url)\n",
    "disease_similarity_df = disease_similarity_df[-disease_similarity_df[['doid_code_0', 'doid_code_1']].apply(frozenset, 1).duplicated()]\n",
    "disease_similarity_df = disease_similarity_df[disease_similarity_df.p_fisher < 0.005]\n",
    "disease_similarity_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>doid_code_0</th>\n",
       "      <th>doid_name_0</th>\n",
       "      <th>doid_code_1</th>\n",
       "      <th>doid_name_1</th>\n",
       "      <th>cooccurrence</th>\n",
       "      <th>expected</th>\n",
       "      <th>enrichment</th>\n",
       "      <th>odds_ratio</th>\n",
       "      <th>p_fisher</th>\n",
       "      <th>n_source</th>\n",
       "      <th>n_target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>DOID:14330</td>\n",
       "      <td>Parkinson's disease</td>\n",
       "      <td>2521</td>\n",
       "      <td>1562.065225</td>\n",
       "      <td>1.613889</td>\n",
       "      <td>1.651428</td>\n",
       "      <td>8.873145e-115</td>\n",
       "      <td>104084</td>\n",
       "      <td>73639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>DOID:11949</td>\n",
       "      <td>Creutzfeldt-Jakob disease</td>\n",
       "      <td>157</td>\n",
       "      <td>116.223134</td>\n",
       "      <td>1.350850</td>\n",
       "      <td>1.361746</td>\n",
       "      <td>1.558560e-04</td>\n",
       "      <td>104084</td>\n",
       "      <td>5479</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  doid_code_0          doid_name_0 doid_code_1                doid_name_1  \\\n",
       "0  DOID:10652  Alzheimer's disease  DOID:14330        Parkinson's disease   \n",
       "1  DOID:10652  Alzheimer's disease  DOID:11949  Creutzfeldt-Jakob disease   \n",
       "\n",
       "   cooccurrence     expected  enrichment  odds_ratio       p_fisher  n_source  \\\n",
       "0          2521  1562.065225    1.613889    1.651428  8.873145e-115    104084   \n",
       "1           157   116.223134    1.350850    1.361746   1.558560e-04    104084   \n",
       "\n",
       "   n_target  \n",
       "0     73639  \n",
       "1      5479  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disease_similarity_df[disease_similarity_df['doid_name_0']==\"Alzheimer's disease\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in disease_similarity_df.iterrows():\n",
    "    source_id = 'Disease', row.doid_code_0\n",
    "    target_id = 'Disease', row.doid_code_1\n",
    "    data = medline_data.copy()\n",
    "    data['p_fisher'] = row.p_fisher\n",
    "    graph.add_edge(source_id, target_id, 'resembles', 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anatomy-gene differential expression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>GeneID</th>\n",
       "      <th>uberon_id</th>\n",
       "      <th>direction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37241</th>\n",
       "      <td>2</td>\n",
       "      <td>UBERON:0000002</td>\n",
       "      <td>upregulates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37249</th>\n",
       "      <td>18</td>\n",
       "      <td>UBERON:0000002</td>\n",
       "      <td>downregulates</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       GeneID       uberon_id      direction\n",
       "37241       2  UBERON:0000002    upregulates\n",
       "37249      18  UBERON:0000002  downregulates"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commit = '08ba54e83ee8e28dec22b4351d29e23f1d034d30'\n",
    "url = rawgit('dhimmel', 'bgee', commit, 'data/diffex.tsv.gz')\n",
    "diffex_df = pandas.read_table(url, compression='gzip')\n",
    "diffex_df = pandas.melt(diffex_df, id_vars='GeneID', var_name='uberon_id', value_name='direction')\n",
    "diffex_df = diffex_df.query(\"direction != 0\")\n",
    "diffex_df = diffex_df[diffex_df.uberon_id.isin(uberon_df.uberon_id) & diffex_df.GeneID.isin(coding_genes)]\n",
    "diffex_df = diffex_df.replace({'direction': {-1: 'downregulates', 1: 'upregulates'}})\n",
    "diffex_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in diffex_df.iterrows():\n",
    "    source_id = 'Gene', row['GeneID']\n",
    "    target_id = 'Anatomy', row['uberon_id']\n",
    "    data = {'source': 'Bgee', 'unbiased': True}\n",
    "    graph.add_edge(source_id, target_id, row['direction'], 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compound bindings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>drugbank_id</th>\n",
       "      <th>entrez_gene_id</th>\n",
       "      <th>sources</th>\n",
       "      <th>pubmed_ids</th>\n",
       "      <th>actions</th>\n",
       "      <th>affinity_nM</th>\n",
       "      <th>license</th>\n",
       "      <th>urls</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DB00006</td>\n",
       "      <td>2147</td>\n",
       "      <td>DrugBank (target)|DrugCentral (IUPHAR)</td>\n",
       "      <td>11060732|11504570|11752352|11833835|11923794|1...</td>\n",
       "      <td>inhibitor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC BY-NC 4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DB00006</td>\n",
       "      <td>4353</td>\n",
       "      <td>DrugBank (enzyme)</td>\n",
       "      <td>18701766</td>\n",
       "      <td>inhibitor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC BY-NC 4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  drugbank_id  entrez_gene_id                                 sources  \\\n",
       "0     DB00006            2147  DrugBank (target)|DrugCentral (IUPHAR)   \n",
       "1     DB00006            4353                       DrugBank (enzyme)   \n",
       "\n",
       "                                          pubmed_ids    actions  affinity_nM  \\\n",
       "0  11060732|11504570|11752352|11833835|11923794|1...  inhibitor          NaN   \n",
       "1                                           18701766  inhibitor          NaN   \n",
       "\n",
       "        license urls  \n",
       "0  CC BY-NC 4.0  NaN  \n",
       "1  CC BY-NC 4.0  NaN  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = os.path.join(path, 'BindingDB/CbG-binding.tsv')\n",
    "binding_df = pandas.read_table(url)\n",
    "binding_df = binding_df.merge(compound_df[['drugbank_id']])\n",
    "binding_df = binding_df[binding_df.entrez_gene_id.isin(coding_genes)]\n",
    "binding_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in binding_df.iterrows():\n",
    "    source_id = 'Compound', row.drugbank_id\n",
    "    target_id = 'Gene', row.entrez_gene_id\n",
    "    data = {'unbiased': False}\n",
    "    # singular fields\n",
    "    for key in 'affinity_nM', 'license':\n",
    "        value = row[key]\n",
    "        if pandas.notnull(value):\n",
    "            data[key] = value\n",
    "    # compound fields\n",
    "    for key in 'sources', 'pubmed_ids', 'actions', 'urls':\n",
    "        value = row[key]\n",
    "        if pandas.notnull(value):\n",
    "            data[key] = value.split('|')\n",
    "    data['source'] = row['sources']\n",
    "    graph.add_edge(source_id, target_id, 'binds', 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Protein Interactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>gene_0</th>\n",
       "      <th>gene_1</th>\n",
       "      <th>sources</th>\n",
       "      <th>unbiased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>310</td>\n",
       "      <td>hetio-dag</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1026</td>\n",
       "      <td>hetio-dag</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gene_0  gene_1    sources  unbiased\n",
       "0       1     310  hetio-dag         0\n",
       "1       1    1026  hetio-dag         0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commit = 'f6a7edbc8de6ba2d7fe1ef3fee4d89e5b8d0b900'\n",
    "url = rawgit('dhimmel', 'ppi', commit, 'data/ppi-hetio-ind.tsv')\n",
    "ppi_df = pandas.read_table(url)\n",
    "ppi_df = ppi_df[ppi_df.gene_0.isin(coding_genes) & ppi_df.gene_1.isin(coding_genes)]\n",
    "# Consider allowing self-interactions in future versions of Hetionet by removing following \n",
    "# line of code. See https://github.com/dhimmel/hetio/issues/10\n",
    "ppi_df = ppi_df.query(\"gene_0 != gene_1\")\n",
    "ppi_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in ppi_df.iterrows():\n",
    "    source_id = 'Gene', row.gene_0\n",
    "    target_id = 'Gene', row.gene_1\n",
    "    data = {\n",
    "        'sources': row.sources.split('|'),\n",
    "        'unbiased': bool(row.unbiased),\n",
    "    }\n",
    "    data['source'] = 'Hetionet - '+row.sources\n",
    "    graph.add_edge(source_id, target_id, 'interacts', 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evolutionary rate covariation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>source_entrez</th>\n",
       "      <th>target_entrez</th>\n",
       "      <th>correlation</th>\n",
       "      <th>n_ucsc_genes</th>\n",
       "      <th>gene_pair</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>9</td>\n",
       "      <td>3295</td>\n",
       "      <td>0.77623</td>\n",
       "      <td>1</td>\n",
       "      <td>(9, 3295)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>9</td>\n",
       "      <td>23313</td>\n",
       "      <td>0.79472</td>\n",
       "      <td>1</td>\n",
       "      <td>(9, 23313)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    source_entrez  target_entrez  correlation  n_ucsc_genes   gene_pair\n",
       "29              9           3295      0.77623             1   (9, 3295)\n",
       "46              9          23313      0.79472             1  (9, 23313)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commit = '757733f77a89499439c887acb88456e011c5322e'\n",
    "url = rawgit('dhimmel', 'erc', commit, 'data/erc_mam33-entrez-gt-0.6.tsv.gz')\n",
    "erc_df = pandas.read_table(url, compression='gzip')\n",
    "erc_df = erc_df[erc_df.correlation >= 0.75]\n",
    "erc_df = erc_df[erc_df.source_entrez.isin(coding_genes) & erc_df.target_entrez.isin(coding_genes)]\n",
    "# Drop self-loops (see https://github.com/dhimmel/erc/issues/2)\n",
    "erc_df = erc_df.query(\"source_entrez != target_entrez\")\n",
    "# Drop duplicate gene pair(s) (see https://github.com/dhimmel/erc/issues/1)\n",
    "erc_df['gene_pair'] = list(map(frozenset, zip(erc_df.source_entrez, erc_df.target_entrez)))\n",
    "erc_df = erc_df.drop_duplicates('gene_pair', keep='last')\n",
    "erc_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in erc_df.iterrows():\n",
    "    source_id = 'Gene', row.source_entrez\n",
    "    target_id = 'Gene', row.target_entrez\n",
    "    data = {\n",
    "        'source': 'Hetionet - ERC',\n",
    "        'unbiased': True,\n",
    "    }\n",
    "    data['correlation']=row.correlation\n",
    "    graph.add_edge(source_id, target_id, 'covaries', 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indications from the PharmacotherapyDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>doid_id</th>\n",
       "      <th>drugbank_id</th>\n",
       "      <th>disease</th>\n",
       "      <th>drug</th>\n",
       "      <th>category</th>\n",
       "      <th>n_curators</th>\n",
       "      <th>n_resources</th>\n",
       "      <th>kind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>DB00843</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>Donepezil</td>\n",
       "      <td>DM</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>treats</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DOID:10652</td>\n",
       "      <td>DB00674</td>\n",
       "      <td>Alzheimer's disease</td>\n",
       "      <td>Galantamine</td>\n",
       "      <td>DM</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>treats</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      doid_id drugbank_id              disease         drug category  \\\n",
       "0  DOID:10652     DB00843  Alzheimer's disease    Donepezil       DM   \n",
       "1  DOID:10652     DB00674  Alzheimer's disease  Galantamine       DM   \n",
       "\n",
       "   n_curators  n_resources    kind  \n",
       "0           2            1  treats  \n",
       "1           1            4  treats  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commit = '11d535ba0884ee56c3cd5756fdfb4985f313bd80'\n",
    "url = rawgit('dhimmel', 'indications', commit, 'catalog/indications.tsv')\n",
    "indication_df = pandas.read_table(url)\n",
    "categories = {'DM', 'SYM'}\n",
    "indication_df = indication_df.query(\"category in @categories\").copy()\n",
    "indication_df['kind'] = indication_df.category.map({'DM': 'treats', 'SYM': 'palliates'})\n",
    "indication_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1145, 8)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indication_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1129, 8)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indication_df=indication_df[indication_df['drugbank_id'].isin(compound_df['drugbank_id'].to_list())]\n",
    "indication_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in indication_df.iterrows():\n",
    "    source_id = 'Disease', row.doid_id\n",
    "    target_id = 'Compound', row.drugbank_id\n",
    "    data = {'source': 'Hetionet - PharmacotherapyDB', 'unbiased': False, 'license': 'CC0 1.0'}\n",
    "    graph.add_edge(source_id, target_id, row['kind'], 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LINCS L1000 relationships"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "commit = 'abcb12f942f93e3ee839e5e3593f930df2c56845'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_l1000_df(df, n):\n",
    "    \"\"\"\n",
    "    Filter LINCS L1000 differentially expression genes to at most `n` genes\n",
    "    per perturbagen-direction-status combination.\n",
    "    \"\"\"\n",
    "    df = df.groupby(['perturbagen', 'direction', 'status']).apply(\n",
    "        lambda x: x.nlargest(n, 'nlog10_bonferroni_pval')).reset_index(drop=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LINCS compound-gene dysregulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>perturbagen</th>\n",
       "      <th>entrez_gene_id</th>\n",
       "      <th>z_score</th>\n",
       "      <th>symbol</th>\n",
       "      <th>status</th>\n",
       "      <th>direction</th>\n",
       "      <th>nlog10_bonferroni_pval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40550</th>\n",
       "      <td>DB09020</td>\n",
       "      <td>3895</td>\n",
       "      <td>4.077</td>\n",
       "      <td>KTN1</td>\n",
       "      <td>measured</td>\n",
       "      <td>up</td>\n",
       "      <td>1.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40551</th>\n",
       "      <td>DB09020</td>\n",
       "      <td>23368</td>\n",
       "      <td>4.062</td>\n",
       "      <td>PPP1R13B</td>\n",
       "      <td>measured</td>\n",
       "      <td>up</td>\n",
       "      <td>1.323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      perturbagen  entrez_gene_id  z_score    symbol    status direction  \\\n",
       "40550     DB09020            3895    4.077      KTN1  measured        up   \n",
       "40551     DB09020           23368    4.062  PPP1R13B  measured        up   \n",
       "\n",
       "       nlog10_bonferroni_pval  \n",
       "40550                   1.351  \n",
       "40551                   1.323  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = rawgit('dhimmel', 'lincs', commit, 'data/consensi/signif/dysreg-drugbank.tsv')\n",
    "l1000_df = pandas.read_table(url)\n",
    "l1000_df = l1000_df.query(\"perturbagen in @compound_df.drugbank_id and entrez_gene_id in @coding_genes\")\n",
    "l1000_df = filter_l1000_df(l1000_df, n=125)\n",
    "l1000_df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapper = {'up': 'upregulates', 'down': 'downregulates'}\n",
    "for row in l1000_df.itertuples():\n",
    "    source_id = 'Compound', row.perturbagen\n",
    "    target_id = 'Gene', row.entrez_gene_id\n",
    "    data = {\n",
    "        'source': 'Hetionet - LINCS L1000',\n",
    "        'z_score': round(row.z_score, 3),\n",
    "        'method': row.status,\n",
    "        'unbiased': True,\n",
    "    }\n",
    "    kind = mapper[row.direction]\n",
    "    graph.add_edge(source_id, target_id, kind, 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LINCS genetic perturbations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = rawgit('dhimmel', 'lincs', commit, 'data/consensi/signif/dysreg-knockdown.tsv')\n",
    "l1000_kd_df = filter_l1000_df(pandas.read_table(url), n=50)\n",
    "\n",
    "url = rawgit('dhimmel', 'lincs', commit, 'data/consensi/signif/dysreg-overexpression.tsv')\n",
    "l1000_oe_df = filter_l1000_df(pandas.read_table(url), n=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>perturbagen</th>\n",
       "      <th>entrez_gene_id</th>\n",
       "      <th>z_score</th>\n",
       "      <th>symbol</th>\n",
       "      <th>status</th>\n",
       "      <th>direction</th>\n",
       "      <th>nlog10_bonferroni_pval</th>\n",
       "      <th>kind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>7422</td>\n",
       "      <td>-7.823</td>\n",
       "      <td>VEGFA</td>\n",
       "      <td>imputed</td>\n",
       "      <td>down</td>\n",
       "      <td>10.475</td>\n",
       "      <td>knockdown downregulates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6382</td>\n",
       "      <td>-6.402</td>\n",
       "      <td>SDC1</td>\n",
       "      <td>imputed</td>\n",
       "      <td>down</td>\n",
       "      <td>6.002</td>\n",
       "      <td>knockdown downregulates</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   perturbagen  entrez_gene_id  z_score symbol   status direction  \\\n",
       "0            2            7422   -7.823  VEGFA  imputed      down   \n",
       "1            2            6382   -6.402   SDC1  imputed      down   \n",
       "\n",
       "   nlog10_bonferroni_pval                     kind  \n",
       "0                  10.475  knockdown downregulates  \n",
       "1                   6.002  knockdown downregulates  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapper = {'up': 'knockdown upregulates', 'down': 'knockdown downregulates'}\n",
    "l1000_kd_df['kind'] = l1000_kd_df.direction.map(lambda x: mapper[x])\n",
    "\n",
    "mapper = {'up': 'overexpression upregulates', 'down': 'overexpression downregulates'}\n",
    "l1000_oe_df['kind'] = l1000_oe_df.direction.map(lambda x: mapper[x])\n",
    "\n",
    "l1000_genetic_df = pandas.concat([l1000_kd_df, l1000_oe_df])\n",
    "l1000_genetic_df = l1000_genetic_df.query('perturbagen in @coding_genes and entrez_gene_id in @coding_genes')\n",
    "l1000_genetic_df = l1000_genetic_df.query('perturbagen != entrez_gene_id')\n",
    "\n",
    "l1000_genetic_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "for (pert, gene), df in l1000_genetic_df.groupby(['perturbagen', 'entrez_gene_id'], sort=False):\n",
    "    source_id = 'Gene', pert\n",
    "    target_id = 'Gene', gene\n",
    "    method, = df.status.unique()\n",
    "    data = {'source': 'Hetionet - LINCS L1000', 'subtypes': list(df.kind), 'method': method, 'unbiased': True}\n",
    "    data['z_score']=df.loc[df.index[0], 'z_score']\n",
    "    graph.add_edge(source_id, target_id, 'regulates', 'forward', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Side Effects - SIDER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>umls_cui_from_meddra</th>\n",
       "      <th>side_effect_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C0863129</td>\n",
       "      <td>AGEP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C0001849</td>\n",
       "      <td>AIDS dementia complex</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  umls_cui_from_meddra       side_effect_name\n",
       "0             C0863129                   AGEP\n",
       "1             C0001849  AIDS dementia complex"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "commit = 'be3adebc0d845baaddb907a880890cb5e85f5801'\n",
    "url = rawgit('dhimmel', 'SIDER4', commit, 'data/side-effect-terms.tsv')\n",
    "side_effect_df = pandas.read_table(url)\n",
    "side_effect_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in side_effect_df.iterrows():\n",
    "    umls_id = row['umls_cui_from_meddra']\n",
    "    data = {\n",
    "        'source': 'Hetionet - UMLS via SIDER 4.1',\n",
    "        'url': 'http://identifiers.org/umls/{}'.format(umls_id),\n",
    "        'license': 'CC BY-NC-SA 4.0',\n",
    "    }\n",
    "    graph.add_node(kind='Side Effect', identifier=umls_id, name=row['side_effect_name'], data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>drugbank_id</th>\n",
       "      <th>drugbank_name</th>\n",
       "      <th>umls_cui_from_meddra</th>\n",
       "      <th>side_effect_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6398</th>\n",
       "      <td>DB01048</td>\n",
       "      <td>Abacavir</td>\n",
       "      <td>C0001849</td>\n",
       "      <td>AIDS dementia complex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6399</th>\n",
       "      <td>DB01048</td>\n",
       "      <td>Abacavir</td>\n",
       "      <td>C0232487</td>\n",
       "      <td>Abdominal discomfort</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     drugbank_id drugbank_name umls_cui_from_meddra       side_effect_name\n",
       "6398     DB01048      Abacavir             C0001849  AIDS dementia complex\n",
       "6399     DB01048      Abacavir             C0232487   Abdominal discomfort"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = rawgit('dhimmel', 'SIDER4', commit, 'data/side-effects.tsv')\n",
    "sider_df = pandas.read_table(url)\n",
    "sider_df = sider_df[sider_df.drugbank_id.isin(compound_df.drugbank_id)]\n",
    "sider_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i, row in sider_df.iterrows():\n",
    "    umls_id = row.umls_cui_from_meddra\n",
    "    source_id = 'Compound', row.drugbank_id\n",
    "    target_id = 'Side Effect', umls_id\n",
    "    data = {\n",
    "        'source': 'Hetionet - SIDER 4.1',\n",
    "        'url': 'http://sideeffects.embl.de/se/{}/'.format(umls_id),\n",
    "        'unbiased': False,\n",
    "        'license': 'CC BY-NC-SA 4.0',\n",
    "    }\n",
    "    graph.add_edge(source_id, target_id, 'causes', 'both', data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network visualizations and stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export node degree tables\n",
    "hetio.stats.degrees_to_excel(graph, os.path.join(path,'hetionet-custom-degrees.xlsx'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/axisgrid.py:337: UserWarning: The `size` parameter has been renamed to `height`; please update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1600 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1200 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x2400 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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RKYGBRERESmAgERGREhhIRESkBAYSEREpgYFERERKYCAREZESGEhERKQEBhIRESmBgUREREpgIBERkRIYSEREpAQGEhERKYGBRERESmAgERGREhhIRESkBAYSEREpgYFERERKYCAREZESGEhERKQEBhIRESmBgUREREpgIBERkRIYSEREpAQGEhERKYGBRERESmAgERGREhhIRESkBAYSEREpgYFERERKYCAREZESGEhERKQEBhIRESmBgUREREq4oUCKi4uDTqdDbGys1iYimD9/Pjw9PWFvb49hw4Zh3759FverqKhATEwM3Nzc4OjoiMjISJw4ccKipri4GNHR0TAYDDAYDIiOjsbZs2dvZLhERKSwRgdSZmYmVq5ciR49eli0L168GEuWLMHSpUuRmZkJo9GI0NBQlJaWajWxsbFISEhAfHw8UlNTUVZWhoiICFRVVWk1UVFRMJlMSExMRGJiIkwmE6Kjoxs7XCIiUlyjAqmsrAyPPfYYVq1ahbZt22rtIoL33nsPL7/8Mh566CH4+/vjk08+wfnz57F+/XoAgNlsxkcffYR33nkHI0aMQO/evfHpp58iOzsbP/zwAwAgJycHiYmJ+PDDDxEcHIzg4GCsWrUKmzZtQm5u7k04bCIiUk2jAum5557D6NGjMWLECIv2vLw8FBYWIiwsTGvT6/UYOnQo0tLSAABZWVm4ePGiRY2npyf8/f21mm3btsFgMCAoKEir6d+/PwwGg1ZTU0VFBUpKSiw2IiJqPqwbeof4+Hjs3LkTmZmZtfoKCwsBAB4eHhbtHh4eOHbsmFZja2trcWZVXVN9/8LCQri7u9fav7u7u1ZTU1xcHBYsWNDQwyEiIkU06AwpPz8fM2bMwKeffgo7O7t663Q6ncVtEanVVlPNmrrqr7afuXPnwmw2a1t+fv5VH4+IiNTSoEDKyspCUVERAgMDYW1tDWtra6SkpOBf//oXrK2ttTOjmmcxRUVFWp/RaERlZSWKi4uvWnP69Olaj3/mzJlaZ1/V9Ho9nJ2dLTYiImo+GhRIISEhyM7Ohslk0ra+ffviscceg8lkgre3N4xGI5KSkrT7VFZWIiUlBQMGDAAABAYGwsbGxqKmoKAAe/fu1WqCg4NhNpuxfft2rSYjIwNms1mrISKiO0uDriE5OTnB39/fos3R0RGurq5ae2xsLBYuXIguXbqgS5cuWLhwIRwcHBAVFQUAMBgMmDhxImbNmgVXV1e4uLjg+eefR0BAgLZIwtfXFyNHjsSkSZOwYsUKAMDkyZMREREBHx+fGz5oIiJST4MXNVzL7NmzUV5ejqlTp6K4uBhBQUHYsmULnJyctJp3330X1tbWGD9+PMrLyxESEoI1a9bAyspKq1m3bh2mT5+urcaLjIzE0qVLb/ZwiYhIEToRkaYexK1QUlICg8EAs9nc6OtJ6zOO19sXFdShsUMjIqI68LPsiIhICQwkIiJSAgOJiIiUwEAiIiIlMJCIiEgJDCQiIlICA4mIiJTAQCIiIiUwkIiISAkMJCIiUgIDiYiIlMBAIiIiJTCQiIhICQwkIiJSAgOJiIiUwEAiIiIlMJCIiEgJDCQiIlICA4mIiJTAQCIiIiUwkIiISAkMJCIiUgIDiYiIlMBAIiIiJTCQiIhICQwkIiJSAgOJiIiUwEAiIiIlMJCIiEgJDCQiIlICA4mIiJTAQCIiIiUwkIiISAkMJCIiUgIDiYiIlMBAIiIiJTCQiIhICQwkIiJSAgOJiIiUwEAiIiIlMJCIiEgJDCQiIlICA4mIiJTAQCIiIiUwkIiISAkMJCIiUgIDiYiIlMBAIiIiJTCQiIhICQwkIiJSAgOJiIiUwEAiIiIlNCiQli9fjh49esDZ2RnOzs4IDg7Gd999p/WLCObPnw9PT0/Y29tj2LBh2Ldvn8U+KioqEBMTAzc3Nzg6OiIyMhInTpywqCkuLkZ0dDQMBgMMBgOio6Nx9uzZxh8lEREpr0GBdPfdd+PNN9/Ejh07sGPHDtx///144IEHtNBZvHgxlixZgqVLlyIzMxNGoxGhoaEoLS3V9hEbG4uEhATEx8cjNTUVZWVliIiIQFVVlVYTFRUFk8mExMREJCYmwmQyITo6+iYdMhERqUgnInIjO3BxccFbb72Fp59+Gp6enoiNjcWcOXMA/HE25OHhgUWLFuGZZ56B2WxGu3btsHbtWkyYMAEAcOrUKXh5eWHz5s0IDw9HTk4O/Pz8kJ6ejqCgIABAeno6goOD8csvv8DHx+e6xlVSUgKDwQCz2QxnZ+dGHdv6jOP19kUFdWjUPomIqG6NvoZUVVWF+Ph4nDt3DsHBwcjLy0NhYSHCwsK0Gr1ej6FDhyItLQ0AkJWVhYsXL1rUeHp6wt/fX6vZtm0bDAaDFkYA0L9/fxgMBq2GiIjuPNYNvUN2djaCg4Nx4cIFtG7dGgkJCfDz89PCwsPDw6Lew8MDx44dAwAUFhbC1tYWbdu2rVVTWFio1bi7u9d6XHd3d62mLhUVFaioqNBul5SUNPTQiIioCTX4DMnHxwcmkwnp6el49tln8eSTT2L//v1av06ns6gXkVptNdWsqav+WvuJi4vTFkEYDAZ4eXld7yEREZECGhxItra26Ny5M/r27Yu4uDj07NkT//znP2E0GgGg1llMUVGRdtZkNBpRWVmJ4uLiq9acPn261uOeOXOm1tnXlebOnQuz2axt+fn5DT00IiJqQjf8PiQRQUVFBTp16gSj0YikpCStr7KyEikpKRgwYAAAIDAwEDY2NhY1BQUF2Lt3r1YTHBwMs9mM7du3azUZGRkwm81aTV30er22HL16IyKi5qNB15BeeukljBo1Cl5eXigtLUV8fDySk5ORmJgInU6H2NhYLFy4EF26dEGXLl2wcOFCODg4ICoqCgBgMBgwceJEzJo1C66urnBxccHzzz+PgIAAjBgxAgDg6+uLkSNHYtKkSVixYgUAYPLkyYiIiLjuFXZERNT8NCiQTp8+jejoaBQUFMBgMKBHjx5ITExEaGgoAGD27NkoLy/H1KlTUVxcjKCgIGzZsgVOTk7aPt59911YW1tj/PjxKC8vR0hICNasWQMrKyutZt26dZg+fbq2Gi8yMhJLly69GcdLRESKuuH3IamK70MiImpe+Fl2RESkBAYSEREpgYFERERKYCAREZESGEhERKQEBhIRESmBgUREREpgIBERkRIYSEREpAQGEhERKYGBRERESmAgERGREhhIRESkBAYSEREpgYFERERKYCAREZESGEhERKQEBhIRESmBgUREREpgIBERkRIYSEREpAQGEhERKYGBRERESmAgERGREhhIRESkBAYSEREpgYFERERKYCAREZESGEhERKQEBhIRESmBgUREREpgIBERkRIYSEREpAQGEhERKYGBRERESmAgERGREhhIRESkBAYSEREpgYFERERKYCAREZESGEhERKQEBhIRESmBgUREREpgIBERkRIYSEREpAQGEhERKYGBRERESmAgERGREhhIRESkBAYSEREpgYFERERKYCAREZESGhRIcXFx6NevH5ycnODu7o6xY8ciNzfXokZEMH/+fHh6esLe3h7Dhg3Dvn37LGoqKioQExMDNzc3ODo6IjIyEidOnLCoKS4uRnR0NAwGAwwGA6Kjo3H27NnGHSURESmvQYGUkpKC5557Dunp6UhKSsKlS5cQFhaGc+fOaTWLFy/GkiVLsHTpUmRmZsJoNCI0NBSlpaVaTWxsLBISEhAfH4/U1FSUlZUhIiICVVVVWk1UVBRMJhMSExORmJgIk8mE6Ojom3DIRESkIp2ISGPvfObMGbi7uyMlJQVDhgyBiMDT0xOxsbGYM2cOgD/Ohjw8PLBo0SI888wzMJvNaNeuHdauXYsJEyYAAE6dOgUvLy9s3rwZ4eHhyMnJgZ+fH9LT0xEUFAQASE9PR3BwMH755Rf4+Phcc2wlJSUwGAwwm81wdnZu1PGtzzheb19UUIdG7ZOIiOp2Q9eQzGYzAMDFxQUAkJeXh8LCQoSFhWk1er0eQ4cORVpaGgAgKysLFy9etKjx9PSEv7+/VrNt2zYYDAYtjACgf//+MBgMWg0REd1ZrBt7RxHBzJkzMWjQIPj7+wMACgsLAQAeHh4WtR4eHjh27JhWY2tri7Zt29aqqb5/YWEh3N3daz2mu7u7VlNTRUUFKioqtNslJSWNPDIiImoKjT5DmjZtGvbs2YP/+Z//qdWn0+ksbotIrbaaatbUVX+1/cTFxWkLIAwGA7y8vK7nMIiISBGNCqSYmBh8/fXX2Lp1K+6++26t3Wg0AkCts5iioiLtrMloNKKyshLFxcVXrTl9+nStxz1z5kyts69qc+fOhdls1rb8/PzGHBoRETWRBgWSiGDatGnYuHEjfvrpJ3Tq1Mmiv1OnTjAajUhKStLaKisrkZKSggEDBgAAAgMDYWNjY1FTUFCAvXv3ajXBwcEwm83Yvn27VpORkQGz2azV1KTX6+Hs7GyxERFR89Gga0jPPfcc1q9fj//93/+Fk5OTdiZkMBhgb28PnU6H2NhYLFy4EF26dEGXLl2wcOFCODg4ICoqSqudOHEiZs2aBVdXV7i4uOD5559HQEAARowYAQDw9fXFyJEjMWnSJKxYsQIAMHnyZERERFzXCjsiImp+GhRIy5cvBwAMGzbMon316tV46qmnAACzZ89GeXk5pk6diuLiYgQFBWHLli1wcnLS6t99911YW1tj/PjxKC8vR0hICNasWQMrKyutZt26dZg+fbq2Gi8yMhJLly5tzDESEVEzcEPvQ1IZ34dERNS88LPsiIhICQwkIiJSAgOJiIiUwEAiIiIlMJCIiEgJDCQiIlICA4mIiJTAQCIiIiUwkIiISAkMJCIiUgIDiYiIlMBAIiIiJTCQiIhICQwkIiJSAgOJiIiUwEAiIiIlMJCIiEgJDCQiIlICA4mIiJTAQCIiIiUwkIiISAkMJCIiUgIDiYiIlMBAIiIiJTCQiIhICQwkIiJSAgOJiIiUwEAiIiIlMJCIiEgJDCQiIlICA4mIiJTAQCIiIiUwkIiISAkMJCIiUgIDiYiIlMBAIiIiJTCQiIhICQwkIiJSAgOJiIiUwEAiIiIlMJCIiEgJDCQiIlICA4mIiJTAQCIiIiUwkIiISAkMJCIiUgIDiYiIlMBAIiIiJTCQiIhICQwkIiJSAgOJiIiUwEAiIiIlMJCIiEgJDQ6k//u//8OYMWPg6ekJnU6Hr776yqJfRDB//nx4enrC3t4ew4YNw759+yxqKioqEBMTAzc3Nzg6OiIyMhInTpywqCkuLkZ0dDQMBgMMBgOio6Nx9uzZBh8gERE1Dw0OpHPnzqFnz55YunRpnf2LFy/GkiVLsHTpUmRmZsJoNCI0NBSlpaVaTWxsLBISEhAfH4/U1FSUlZUhIiICVVVVWk1UVBRMJhMSExORmJgIk8mE6OjoRhwiERE1BzoRkUbfWadDQkICxo4dC+CPsyNPT0/ExsZizpw5AP44G/Lw8MCiRYvwzDPPwGw2o127dli7di0mTJgAADh16hS8vLywefNmhIeHIycnB35+fkhPT0dQUBAAID09HcHBwfjll1/g4+NzzbGVlJTAYDDAbDbD2dm5Uce3PuN4vX1RQR0atU8iIqrbTb2GlJeXh8LCQoSFhWlter0eQ4cORVpaGgAgKysLFy9etKjx9PSEv7+/VrNt2zYYDAYtjACgf//+MBgMWk1NFRUVKCkpsdiIiKj5uKmBVFhYCADw8PCwaPfw8ND6CgsLYWtri7Zt2161xt3dvdb+3d3dtZqa4uLitOtNBoMBXl5eN3w8RER0+9ySVXY6nc7itojUaqupZk1d9Vfbz9y5c2E2m7UtPz+/ESMnIqKmclMDyWg0AkCts5iioiLtrMloNKKyshLFxcVXrTl9+nSt/Z85c6bW2Vc1vV4PZ2dni42IiJqPmxpInTp1gtFoRFJSktZWWVmJlJQUDBgwAAAQGBgIGxsbi5qCggLs3btXqwkODobZbMb27du1moyMDJjNZq2GiIjuLNYNvUNZWRkOHTqk3c7Ly4PJZIKLiws6dOiA2NhYLFy4EF26dEGXLl2wcOFCODg4ICoqCgBgMBgwceJEzJo1C66urnBxccHzzz+PgIAAjBgxAgDg6+uLkSNHYtKkSVixYgUAYPLkyYiIiLiuFXZERNT8NDiQduzYgeHDh2u3Z86cCQB48sknsWbNGsyePRvl5eWYOnUqiouLERQUhC1btsDJyUm7z7vvvgtra2uMHz8e5eXlCAkJwZo1a2BlZaXVrFu3DtOnT9dW40VGRtb73iciImr+buh9SCrj+5CIiJoXfpYdEREpgYFERERKYCAREZESGEhERKQEBhIRESmBgUREREpgIBERkRIYSEREpAQGEhERKYGBRERESmAgERGREhhIRESkBAYSEREpgYFERERKYCAREZESGEhERKQEBhIRESmBgUREREpgIBERkRIYSEREpAQGEhERKYGBRERESmAgERGREhhIRESkBAYSEREpgYFERERKYCAREZESGEhERKQEBhIRESmBgUREREpgIBERkRIYSEREpATrph5Ac7U+43id7VFBHW7zSIiI7gw8QyIiIiUwkIiISAkMJCIiUgIDiYiIlMBAIiIiJTCQiIhICQwkIiJSAgOJiIiUwEAiIiIlMJCIiEgJDCQiIlICA4mIiJTAQCIiIiXw075vsvo+BRzgJ4ETEV0Nz5CIiEgJDCQiIlICA4mIiJTAa0i3Eb9lloiofjxDIiIiJSh/hrRs2TK89dZbKCgoQPfu3fHee+9h8ODBTT2sm4or84iIFA+kzz77DLGxsVi2bBkGDhyIFStWYNSoUdi/fz86dGgZf6j5Mh8RtRQ6EZGmHkR9goKC0KdPHyxfvlxr8/X1xdixYxEXF3fV+5aUlMBgMMBsNsPZ2blRj3+1M5fmiCFGRCpT9gypsrISWVlZePHFFy3aw8LCkJaW1kSjat5uZsAy3IjoZlM2kH799VdUVVXBw8PDot3DwwOFhYW16isqKlBRUaHdNpvNAP44U2qs8+dKG33fO92HP+1r6iFQI4zv63XT9vX5jvzb8jhUv+b0HDg5OUGn0121RtlAqlbzAESkzoOKi4vDggULarV7ean1pBA1pUl32ONQ/VR7Dq7n8omygeTm5gYrK6taZ0NFRUW1zpoAYO7cuZg5c6Z2+/Lly/j999/h6up6zVSuS0lJCby8vJCfn9/oa1B3Is5L3TgvtXFO6tZS58XJyemaNcoGkq2tLQIDA5GUlIQHH3xQa09KSsIDDzxQq16v10Ov11u0tWnT5obH4ezs3KJ+aK4X56VunJfaOCd147zUpmwgAcDMmTMRHR2Nvn37Ijg4GCtXrsTx48cxZcqUph4aERHdZEoH0oQJE/Dbb7/h1VdfRUFBAfz9/bF582Z07NixqYdGREQ3mdKBBABTp07F1KlTb/vj6vV6zJs3r9bLgC0d56VunJfaOCd147zUT+k3xhIRUcvBD1clIiIlMJCIiEgJDCQiIlICA6key5YtQ6dOnWBnZ4fAwED8/PPPTT2kWyYuLg79+vWDk5MT3N3dMXbsWOTm5lrUiAjmz58PT09P2NvbY9iwYdi3z/LjgyoqKhATEwM3Nzc4OjoiMjISJ06cuJ2HcsvExcVBp9MhNjZWa2upc3Ly5Ek8/vjjcHV1hYODA3r16oWsrCytv6XNy6VLl/DKK6+gU6dOsLe3h7e3N1599VVcvnxZq2lpc9JoQrXEx8eLjY2NrFq1Svbv3y8zZswQR0dHOXbsWFMP7ZYIDw+X1atXy969e8VkMsno0aOlQ4cOUlZWptW8+eab4uTkJBs2bJDs7GyZMGGCtG/fXkpKSrSaKVOmyF133SVJSUmyc+dOGT58uPTs2VMuXbrUFId102zfvl3uuece6dGjh8yYMUNrb4lz8vvvv0vHjh3lqaeekoyMDMnLy5MffvhBDh06pNW0tHl5/fXXxdXVVTZt2iR5eXnyxRdfSOvWreW9997TalranDQWA6kO9913n0yZMsWirVu3bvLiiy820Yhur6KiIgEgKSkpIiJy+fJlMRqN8uabb2o1Fy5cEIPBIP/+979FROTs2bNiY2Mj8fHxWs3JkyelVatWkpiYeHsP4CYqLS2VLl26SFJSkgwdOlQLpJY6J3PmzJFBgwbV298S52X06NHy9NNPW7Q99NBD8vjjj4tIy5yTxuJLdjVUf+1FWFiYRXtL+tqL6k9Kd3FxAQDk5eWhsLDQYk70ej2GDh2qzUlWVhYuXrxoUePp6Ql/f/9mPW/PPfccRo8ejREjRli0t9Q5+frrr9G3b1+MGzcO7u7u6N27N1atWqX1t8R5GTRoEH788UccOHAAALB7926kpqbiT3/6E4CWOSeNpfwbY2+3hn7txZ1GRDBz5kwMGjQI/v7+AKAdd11zcuzYMa3G1tYWbdu2rVXTXOctPj4eO3fuRGZmZq2+ljonR44cwfLlyzFz5ky89NJL2L59O6ZPnw69Xo8nnniiRc7LnDlzYDab0a1bN1hZWaGqqgpvvPEGHn30UQAt92elMRhI9bjer72400ybNg179uxBampqrb7GzElznbf8/HzMmDEDW7ZsgZ2dXb11LWlOgD8+Rb9v375YuHAhAKB3797Yt28fli9fjieeeEKra0nz8tlnn+HTTz/F+vXr0b17d5hMJsTGxsLT0xNPPvmkVteS5qSx+JJdDQ392os7SUxMDL7++mts3boVd999t9ZuNBoB4KpzYjQaUVlZieLi4nprmpOsrCwUFRUhMDAQ1tbWsLa2RkpKCv71r3/B2tpaO6aWNCcA0L59e/j5+Vm0+fr64vjxP76NuCX+rLzwwgt48cUX8cgjjyAgIADR0dH429/+hri4OAAtc04ai4FUw5Vfe3GlpKQkDBgwoIlGdWuJCKZNm4aNGzfip59+QqdOnSz6O3XqBKPRaDEnlZWVSElJ0eYkMDAQNjY2FjUFBQXYu3dvs5y3kJAQZGdnw2QyaVvfvn3x2GOPwWQywdvbu8XNCQAMHDiw1lsCDhw4oH3gcUv8WTl//jxatbL8U2plZaUt+26Jc9JoTbSYQmnVy74/+ugj2b9/v8TGxoqjo6McPXq0qYd2Szz77LNiMBgkOTlZCgoKtO38+fNazZtvvikGg0E2btwo2dnZ8uijj9a5bPXuu++WH374QXbu3Cn333//HbVs9cpVdiItc062b98u1tbW8sYbb8jBgwdl3bp14uDgIJ9++qlW09Lm5cknn5S77rpLW/a9ceNGcXNzk9mzZ2s1LW1OGouBVI8PPvhAOnbsKLa2ttKnTx9tCfSdCECd2+rVq7Way5cvy7x588RoNIper5chQ4ZIdna2xX7Ky8tl2rRp4uLiIvb29hIRESHHjx+/zUdz69QMpJY6J9988434+/uLXq+Xbt26ycqVKy36W9q8lJSUyIwZM6RDhw5iZ2cn3t7e8vLLL0tFRYVW09LmpLH4ad9ERKQEXkMiIiIlMJCIiEgJDCQiIlICA4mIiJTAQCIiIiUwkIiISAkMJCIiUgIDiYiIlMBAIrpFhg0bZvGV50R0dQwkIiJSAgOJqJm6ePFiUw+B6KZiIBHdBOfOncMTTzyB1q1bo3379njnnXcs+isrKzF79mzcddddcHR0RFBQEJKTky1qVq1aBS8vLzg4OODBBx/EkiVL0KZNG61//vz56NWrFz7++GN4e3tDr9dDRGA2mzF58mS4u7vD2dkZ999/P3bv3m2x72+++QaBgYGws7ODt7c3FixYgEuXLt2q6SBqFAYS0U3wwgsvYOvWrUhISMCWLVuQnJyMrKwsrf8vf/kL/vOf/yA+Ph579uzBuHHjMHLkSBw8eBAA8J///AdTpkzBjBkzYDKZEBoaijfeeKPW4xw6dAiff/45NmzYAJPJBAAYPXo0CgsLsXnzZmRlZaFPnz4ICQnB77//DgD4/vvv8fjjj2P69OnYv38/VqxYgTVr1tS5f6Im1cSfNk7U7JWWloqtra3Ex8drbb/99pvY29vLjBkz5NChQ6LT6eTkyZMW9wsJCZG5c+eKiMiECRNk9OjRFv2PPfaYGAwG7fa8efPExsZGioqKtLYff/xRnJ2d5cKFCxb3vffee2XFihUiIjJ48GBZuHChRf/atWulffv2jT9oolvAuqkDkai5O3z4MCorKxEcHKy1ubi4wMfHBwCwc+dOiAi6du1qcb+Kigq4uroCAHJzc/Hggw9a9N93333YtGmTRVvHjh3Rrl077XZWVhbKysq0/VQrLy/H4cOHtZrMzEyLM6KqqipcuHAB58+fh4ODQ2MPneimYiAR3SC5xleKXb58GVZWVsjKyoKVlZVFX+vWrbV96HS6a+7X0dGx1r7bt29f63oUAO360+XLl7FgwQI89NBDtWrs7OyuOnai24mBRHSDOnfuDBsbG6Snp6NDhw4AgOLiYhw4cABDhw5F7969UVVVhaKiIgwePLjOfXTr1g3bt2+3aNuxY8c1H7tPnz4oLCyEtbU17rnnnnprcnNz0blz54YdGNFtxkAiukGtW7fGxIkT8cILL8DV1RUeHh54+eWX0arVH2uGunbtisceewxPPPEE3nnnHfTu3Ru//vorfvrpJwQEBOBPf/oTYmJiMGTIECxZsgRjxozBTz/9hO+++67WWVNNI0aMQHBwMMaOHYtFixbBx8cHp06dwubNmzF27Fj07dsX//jHPxAREQEvLy+MGzcOrVq1wp49e5CdnY3XX3/9dkwR0XXhKjuim+Ctt97CkCFDEBkZiREjRmDQoEEIDAzU+levXo0nnngCs2bNgo+PDyIjI5GRkQEvLy8AwMCBA/Hvf/8bS5YsQc+ePZGYmIi//e1v13xJTafTYfPmzRgyZAiefvppdO3aFY888giOHj0KDw8PAEB4eDg2bdqEpKQk9OvXD/3798eSJUvQsWPHWzchRI2gk2u9AE5ETWLSpEn45Zdf8PPPPzf1UIhuC75kR6SIt99+G6GhoXB0dMR3332HTz75BMuWLWvqYRHdNjxDIlLE+PHjkZycjNLSUnh7eyMmJgZTpkxp6mER3TYMJCIiUgIXNRARkRIYSEREpAQGEhERKYGBRERESmAgERGREhhIRESkBAYSEREpgYFERERKYCAREZES/h//irg8YFhVSAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create and save degree distribution vizualizations\n",
    "# Commented out due to https://github.com/ContinuumIO/anaconda-issues/issues/368\n",
    "hetio.stats.plot_degrees(graph, os.path.join(path,'hetionet-custom-degrees.pdf'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:80: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n"
     ]
    },
    {
     "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>metanode</th>\n",
       "      <th>abbreviation</th>\n",
       "      <th>metaedges</th>\n",
       "      <th>nodes</th>\n",
       "      <th>unconnected_nodes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anatomy</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>652</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Biological Process</td>\n",
       "      <td>BP</td>\n",
       "      <td>1</td>\n",
       "      <td>12322</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Cellular Component</td>\n",
       "      <td>CC</td>\n",
       "      <td>1</td>\n",
       "      <td>1695</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Compound</td>\n",
       "      <td>C</td>\n",
       "      <td>7</td>\n",
       "      <td>2778</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Disease</td>\n",
       "      <td>D</td>\n",
       "      <td>5</td>\n",
       "      <td>137</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gene</td>\n",
       "      <td>G</td>\n",
       "      <td>11</td>\n",
       "      <td>20608</td>\n",
       "      <td>1486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Molecular Function</td>\n",
       "      <td>MF</td>\n",
       "      <td>1</td>\n",
       "      <td>3460</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Pharmacologic Class</td>\n",
       "      <td>PC</td>\n",
       "      <td>1</td>\n",
       "      <td>478</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Side Effect</td>\n",
       "      <td>SE</td>\n",
       "      <td>1</td>\n",
       "      <td>5734</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Symptom</td>\n",
       "      <td>S</td>\n",
       "      <td>1</td>\n",
       "      <td>505</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              metanode abbreviation  metaedges  nodes  unconnected_nodes\n",
       "3              Anatomy            A          3    652                101\n",
       "6   Biological Process           BP          1  12322                  0\n",
       "8   Cellular Component           CC          1   1695                  0\n",
       "2             Compound            C          7   2778                505\n",
       "1              Disease            D          5    137                  9\n",
       "0                 Gene            G         11  20608               1486\n",
       "7   Molecular Function           MF          1   3460                  0\n",
       "5  Pharmacologic Class           PC          1    478                  0\n",
       "9          Side Effect           SE          1   5734                 41\n",
       "4              Symptom            S          1    505                 35"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of metanodes and cooresponding nodes\n",
    "metanode_df = hetio.stats.get_metanode_df(graph)\n",
    "metanode_df.to_csv(os.path.join(path,'hetionet-custom-metanodes.tsv'), sep='\\t', index=False)\n",
    "metanode_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48369"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total number of nodes\n",
    "metanode_df.nodes.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n",
      "/opt/anaconda3/lib/python3.9/site-packages/hetio/stats.py:97: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.\n",
      "  series = pandas.Series()\n"
     ]
    },
    {
     "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>metaedge</th>\n",
       "      <th>abbreviation</th>\n",
       "      <th>edges</th>\n",
       "      <th>source_nodes</th>\n",
       "      <th>target_nodes</th>\n",
       "      <th>unbiased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anatomy - downregulates - Gene</td>\n",
       "      <td>AdG</td>\n",
       "      <td>201798</td>\n",
       "      <td>64</td>\n",
       "      <td>15544</td>\n",
       "      <td>201798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anatomy - upregulates - Gene</td>\n",
       "      <td>AuG</td>\n",
       "      <td>169742</td>\n",
       "      <td>64</td>\n",
       "      <td>16084</td>\n",
       "      <td>169742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Compound - binds - Gene</td>\n",
       "      <td>CbG</td>\n",
       "      <td>25733</td>\n",
       "      <td>2167</td>\n",
       "      <td>2705</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Compound - causes - Side Effect</td>\n",
       "      <td>CcSE</td>\n",
       "      <td>138807</td>\n",
       "      <td>1073</td>\n",
       "      <td>5693</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Compound - downregulates - Gene</td>\n",
       "      <td>CdG</td>\n",
       "      <td>21490</td>\n",
       "      <td>746</td>\n",
       "      <td>2839</td>\n",
       "      <td>21490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Compound - palliates - Disease</td>\n",
       "      <td>CpD</td>\n",
       "      <td>381</td>\n",
       "      <td>214</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Compound - treats - Disease</td>\n",
       "      <td>CtD</td>\n",
       "      <td>748</td>\n",
       "      <td>383</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Compound - upregulates - Gene</td>\n",
       "      <td>CuG</td>\n",
       "      <td>19062</td>\n",
       "      <td>715</td>\n",
       "      <td>3197</td>\n",
       "      <td>19062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Disease - localizes - Anatomy</td>\n",
       "      <td>DlA</td>\n",
       "      <td>4335</td>\n",
       "      <td>121</td>\n",
       "      <td>537</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Disease - presents - Symptom</td>\n",
       "      <td>DpS</td>\n",
       "      <td>3758</td>\n",
       "      <td>121</td>\n",
       "      <td>470</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Disease - resembles - Disease</td>\n",
       "      <td>DrD</td>\n",
       "      <td>250</td>\n",
       "      <td>87</td>\n",
       "      <td>81</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Gene - covaries - Gene</td>\n",
       "      <td>GcG</td>\n",
       "      <td>61633</td>\n",
       "      <td>9024</td>\n",
       "      <td>9514</td>\n",
       "      <td>61633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gene - interacts - Gene</td>\n",
       "      <td>GiG</td>\n",
       "      <td>147042</td>\n",
       "      <td>9516</td>\n",
       "      <td>14035</td>\n",
       "      <td>15510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gene - participates - Biological Process</td>\n",
       "      <td>GpBP</td>\n",
       "      <td>548342</td>\n",
       "      <td>16061</td>\n",
       "      <td>12322</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Gene - participates - Cellular Component</td>\n",
       "      <td>GpCC</td>\n",
       "      <td>88885</td>\n",
       "      <td>12241</td>\n",
       "      <td>1695</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Gene - participates - Molecular Function</td>\n",
       "      <td>GpMF</td>\n",
       "      <td>104777</td>\n",
       "      <td>14213</td>\n",
       "      <td>3460</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Gene &gt; regulates &gt; Gene</td>\n",
       "      <td>Gr&gt;G</td>\n",
       "      <td>265558</td>\n",
       "      <td>4629</td>\n",
       "      <td>7044</td>\n",
       "      <td>265558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Pharmacologic Class - includes - Compound</td>\n",
       "      <td>PCiC</td>\n",
       "      <td>1948</td>\n",
       "      <td>478</td>\n",
       "      <td>1202</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     metaedge abbreviation   edges  \\\n",
       "0              Anatomy - downregulates - Gene          AdG  201798   \n",
       "1                Anatomy - upregulates - Gene          AuG  169742   \n",
       "2                     Compound - binds - Gene          CbG   25733   \n",
       "3             Compound - causes - Side Effect         CcSE  138807   \n",
       "4             Compound - downregulates - Gene          CdG   21490   \n",
       "5              Compound - palliates - Disease          CpD     381   \n",
       "6                 Compound - treats - Disease          CtD     748   \n",
       "7               Compound - upregulates - Gene          CuG   19062   \n",
       "8               Disease - localizes - Anatomy          DlA    4335   \n",
       "9                Disease - presents - Symptom          DpS    3758   \n",
       "10              Disease - resembles - Disease          DrD     250   \n",
       "11                     Gene - covaries - Gene          GcG   61633   \n",
       "12                    Gene - interacts - Gene          GiG  147042   \n",
       "13   Gene - participates - Biological Process         GpBP  548342   \n",
       "14   Gene - participates - Cellular Component         GpCC   88885   \n",
       "15   Gene - participates - Molecular Function         GpMF  104777   \n",
       "16                    Gene > regulates > Gene         Gr>G  265558   \n",
       "17  Pharmacologic Class - includes - Compound         PCiC    1948   \n",
       "\n",
       "    source_nodes  target_nodes  unbiased  \n",
       "0             64         15544    201798  \n",
       "1             64         16084    169742  \n",
       "2           2167          2705         0  \n",
       "3           1073          5693         0  \n",
       "4            746          2839     21490  \n",
       "5            214            48         0  \n",
       "6            383            76         0  \n",
       "7            715          3197     19062  \n",
       "8            121           537         0  \n",
       "9            121           470         0  \n",
       "10            87            81         0  \n",
       "11          9024          9514     61633  \n",
       "12          9516         14035     15510  \n",
       "13         16061         12322         0  \n",
       "14         12241          1695         0  \n",
       "15         14213          3460         0  \n",
       "16          4629          7044    265558  \n",
       "17           478          1202         0  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary of metaedges and cooresponding edges\n",
    "metaedge_df = hetio.stats.get_metaedge_df(graph)\n",
    "\n",
    "# Calculate number of unbiased edges\n",
    "rows = list()\n",
    "for metaedge, edges in graph.get_metaedge_to_edges(exclude_inverts=True).items():\n",
    "    unbiased = sum(edge.data['unbiased'] for edge in edges)\n",
    "    rows.append({'metaedge': str(metaedge), 'unbiased': unbiased})\n",
    "\n",
    "metaedge_df = metaedge_df.merge(pandas.DataFrame(rows))\n",
    "metaedge_df.to_csv(os.path.join(path,'hetionet-custom-metaedges.tsv'), sep='\\t', index=False)\n",
    "metaedge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Summary of different styles for representing each metaedge\n",
    "#metaedge_style_df = hetio.stats.get_metaedge_style_df(metagraph)\n",
    "#metaedge_style_df.to_csv('data/summary/metaedge-styles.tsv', sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1804289"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of edges in the network\n",
    "metaedge_df.edges.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Save graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "#modify the functions to save properties of node and rel\n",
    "#https://github.com/hetio/hetnetpy/blob/main/hetnetpy/readwrite.py\n",
    "import collections\n",
    "import operator\n",
    "import pandas as pd\n",
    "\n",
    "def write_nodetable(graph):\n",
    "    \"\"\"Write a tabular encoding of the graph nodes.\"\"\"\n",
    "    rows = list()\n",
    "    for node in graph.node_dict.values():\n",
    "        row = collections.OrderedDict()\n",
    "        row[\"kind\"] = node.metanode.identifier\n",
    "        row[\"id\"] = str(node)\n",
    "        row[\"name\"] = node.name\n",
    "        row[\"sourceDB\"] = node.data['source']\n",
    "        rows.append(row)\n",
    "    rows.sort(key=operator.itemgetter(\"kind\", \"id\"))\n",
    "    fieldnames = [\"id\", \"name\", \"kind\", \"sourceDB\"]\n",
    "    df_nodes_tsv = pd.DataFrame(rows, columns=fieldnames)\n",
    "    print(df_nodes_tsv.shape)\n",
    "    return df_nodes_tsv\n",
    "\n",
    "\n",
    "def write_edgetable(graph):\n",
    "    \"\"\"Write a tsv of the graph edges.\"\"\"\n",
    "    rows = list()\n",
    "    edge_properties=[\"sourceDB\", \"unbiased\", \"affinity_nM\", \"z_score\", \"p_fisher\", \"correlation\"]\n",
    "    fieldnames =[\"source\", \"metaedge\", \"target\"]\n",
    "    fieldnames = fieldnames+edge_properties\n",
    "    metaedge_to_edges = graph.get_metaedge_to_edges(exclude_inverts=True)\n",
    "    for metaedge, edges in metaedge_to_edges.items():\n",
    "        for edge in edges:\n",
    "            row = collections.OrderedDict()\n",
    "            row[\"source\"] = edge.source\n",
    "            row[\"metaedge\"] = edge.metaedge.abbrev\n",
    "            row[\"target\"] = edge.target\n",
    "            for pro in edge_properties:\n",
    "                if pro =='sourceDB':\n",
    "                    if 'source' in edge.data.keys():\n",
    "                        row[pro]=edge.data['source']\n",
    "                    else:\n",
    "                        row[pro]=None\n",
    "                else:\n",
    "                    if pro in edge.data.keys():\n",
    "                        row[pro]=edge.data[pro]\n",
    "                    else:\n",
    "                        row[pro]=None\n",
    "            rows.append(row)\n",
    "    df_edges_tsv = pd.DataFrame(rows, columns=fieldnames)\n",
    "    print(df_edges_tsv.shape)\n",
    "    return df_edges_tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(48369, 4)\n",
      "(1804289, 9)\n"
     ]
    }
   ],
   "source": [
    "# Write nodes to a table\n",
    "#hetio.readwrite.write_nodetable(graph, os.path.join(path,'nodes.tsv'))\n",
    "df_nodes_tsv = write_nodetable(graph)\n",
    "df_nodes_tsv.to_csv(os.path.join(path,'hetionet-custom-nodes.tsv'), sep=\"\\t\", header=True, index=False) \n",
    "\n",
    "# Write edges to a table\n",
    "#hetio.readwrite.write_sif(graph, os.path.join(path,'edges.sif.gz'))\n",
    "df_edges_tsv = write_edgetable(graph)\n",
    "df_edges_tsv.to_csv(os.path.join(path,'hetionet-custom-edges.tsv'), sep=\"\\t\", header=True, index=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/sg/983t9lp12bsfyjv9ylh6002m0000gt/T/ipykernel_91920/3904201155.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  df_edges_tsv.groupby('metaedge')[\"sourceDB\",\"unbiased\", \"affinity_nM\", \"z_score\", \"p_fisher\", \"correlation\"].nunique()#.add_prefix('num_').reset_index()\n"
     ]
    },
    {
     "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>sourceDB</th>\n",
       "      <th>unbiased</th>\n",
       "      <th>affinity_nM</th>\n",
       "      <th>z_score</th>\n",
       "      <th>p_fisher</th>\n",
       "      <th>correlation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>metaedge</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AdG</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AuG</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CbG</th>\n",
       "      <td>246</td>\n",
       "      <td>1</td>\n",
       "      <td>1720</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CcSE</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CdG</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8568</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CpD</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CtD</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CuG</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8063</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DlA</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3732</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DpS</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3567</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DrD</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>179</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GcG</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GiG</th>\n",
       "      <td>62</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GpBP</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GpCC</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GpMF</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gr&gt;G</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>15718</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PCiC</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          sourceDB  unbiased  affinity_nM  z_score  p_fisher  correlation\n",
       "metaedge                                                                 \n",
       "AdG              1         1            0        0         0            0\n",
       "AuG              1         1            0        0         0            0\n",
       "CbG            246         1         1720        0         0            0\n",
       "CcSE             1         1            0        0         0            0\n",
       "CdG              1         1            0     8568         0            0\n",
       "CpD              1         1            0        0         0            0\n",
       "CtD              1         1            0        0         0            0\n",
       "CuG              1         1            0     8063         0            0\n",
       "DlA              1         1            0        0      3732            0\n",
       "DpS              1         1            0        0      3567            0\n",
       "DrD              1         1            0        0       179            0\n",
       "GcG              1         1            0        0         0        14904\n",
       "GiG             62         2            0        0         0            0\n",
       "GpBP             1         1            0        0         0            0\n",
       "GpCC             1         1            0        0         0            0\n",
       "GpMF             1         1            0        0         0            0\n",
       "Gr>G             1         1            0    15718         0            0\n",
       "PCiC             1         1            0        0         0            0"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_edges_tsv.groupby('metaedge')[\"sourceDB\",\"unbiased\", \"affinity_nM\", \"z_score\", \"p_fisher\", \"correlation\"].nunique()#.add_prefix('num_').reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_edges_tsv[['metaedge','sourceDB']].drop_duplicates()\n",
    "#df_edges_tsv['sourceDB'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>kind</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anatomy</td>\n",
       "      <td>Uberon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>652</th>\n",
       "      <td>Biological Process</td>\n",
       "      <td>Gene Ontology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12974</th>\n",
       "      <td>Cellular Component</td>\n",
       "      <td>Gene Ontology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14669</th>\n",
       "      <td>Compound</td>\n",
       "      <td>DrugBank</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17447</th>\n",
       "      <td>Disease</td>\n",
       "      <td>Disease Ontology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17584</th>\n",
       "      <td>Gene</td>\n",
       "      <td>Entrez Gene</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38192</th>\n",
       "      <td>Molecular Function</td>\n",
       "      <td>Gene Ontology</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41652</th>\n",
       "      <td>Pharmacologic Class</td>\n",
       "      <td>FDA via DrugCentral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42130</th>\n",
       "      <td>Side Effect</td>\n",
       "      <td>Hetionet - UMLS via SIDER 4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47864</th>\n",
       "      <td>Symptom</td>\n",
       "      <td>MeSH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      kind                         source\n",
       "0                  Anatomy                         Uberon\n",
       "652     Biological Process                  Gene Ontology\n",
       "12974   Cellular Component                  Gene Ontology\n",
       "14669             Compound                       DrugBank\n",
       "17447              Disease               Disease Ontology\n",
       "17584                 Gene                    Entrez Gene\n",
       "38192   Molecular Function                  Gene Ontology\n",
       "41652  Pharmacologic Class            FDA via DrugCentral\n",
       "42130          Side Effect  Hetionet - UMLS via SIDER 4.1\n",
       "47864              Symptom                           MeSH"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nodes_tsv[['kind','source']].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write graph as json\n",
    "hetio.readwrite.write_graph(graph, os.path.join(path,'hetionet-custom-hetnet.json.bz2'))\n",
    "\n",
    "#read graph\n",
    "#graph = hetio.readwrite.read_graph(path, formatting=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Barplots of metaedge and metanode counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = seaborn.barplot(x='metanode', y='nodes', data=metanode_df.sort_values('nodes'))\n",
    "for tick in ax.get_xticklabels():\n",
    "    tick.set_rotation(90)\n",
    "ax.set_xlabel(''); ax.set_ylabel('nodes');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = seaborn.barplot(x='metaedge', y='edges', data=metaedge_df.sort_values('edges'))\n",
    "for tick in ax.get_xticklabels():\n",
    "    tick.set_rotation(90)\n",
    "ax.set_xlabel(''); ax.set_ylabel('edges');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}