6.[GO set] subhierarchy_GO_example.ipynb

{
 "cells": [
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   "cell_type": "code",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['GO:0031330', 'GO:0031329', 'GO:0044248', 'GO:0044237', 'GO:0009987', 'GO:0008150', 'GO:0008152', 'GO:0008150', 'GO:0009056', 'GO:0008152', 'GO:0008150', 'GO:0009894', 'GO:0019222', 'GO:0050789', 'GO:0008150', 'GO:0065007', 'GO:0008150', 'GO:0008152', 'GO:0008150', 'GO:0009056', 'GO:0008152', 'GO:0008150', 'GO:0031323', 'GO:0044237', 'GO:0009987', 'GO:0008150', 'GO:0008152', 'GO:0008150', 'GO:0050794', 'GO:0009987', 'GO:0008150', 'GO:0050789', 'GO:0008150', 'GO:0065007', 'GO:0008150', 'GO:0019222', 'GO:0050789', 'GO:0008150', 'GO:0065007', 'GO:0008150', 'GO:0008152', 'GO:0008150', 'GO:0031324', 'GO:0044237', 'GO:0009987', 'GO:0008150', 'GO:0008152', 'GO:0008150', 'GO:0048523', 'GO:0009987', 'GO:0008150', 'GO:0048519', 'GO:0008150', 'GO:0050789', 'GO:0008150', 'GO:0065007', 'GO:0008150', 'GO:0050794', 'GO:0009987', 'GO:0008150', 'GO:0050789', 'GO:0008150', 'GO:0065007', 'GO:0008150', 'GO:0009892', 'GO:0048519', 'GO:0008150', 'GO:0050789', 'GO:0008150', 'GO:0065007', 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      "959\n"
     ]
    }
   ],
   "source": [
    "## find all parents of GO:0010898, and the lineage all the way to the root \n",
    "# load a sif network, where showing child-parent relationship\n",
    "import pandas as pd\n",
    "\n",
    "def load_ont(filename):\n",
    "    data = []\n",
    "    with open(filename, 'r') as f:\n",
    "        for line in f:\n",
    "            columns = line.strip().split('\\t')  # tab-separated values\n",
    "            if len(columns) >= 3:\n",
    "                parent = columns[0] # based on collapsed_go.symbol, the first column is parent\n",
    "                child = columns[1] # second column is child\n",
    "                interaction = columns[2] # third column is interaction type\n",
    "                if interaction == 'default':\n",
    "                    data.append({'child': child, 'parent': parent}) # Add child and parent to a dataframe\n",
    "    df = pd.DataFrame(data)\n",
    "    return df\n",
    "             \n",
    "\n",
    "def get_ancestors(child_node, edges):\n",
    "    ancestors = []\n",
    "    # Find the parents of the current node. In the 'edges' DataFrame, \n",
    "    # this is done by looking up rows where the 'child' column matches \n",
    "    # the current node, and then getting the corresponding values in the 'parent' column.\n",
    "    parents = edges.loc[edges['child'] == child_node, 'parent'].values\n",
    "\n",
    "    for parent in parents:\n",
    "        if parent not in ancestors:  # Check if the parent is already in the ancestors list\n",
    "            ancestors.append(parent)  # If not, add it to the list\n",
    "            ancestors.extend(get_ancestors(parent, edges))  # Recursively find the parent's ancestors\n",
    "\n",
    "    return ancestors\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    GO_ID = 'GO:0010897'\n",
    "    go_network = load_ont('./data/GO_BP/collapsed_go.symbol')\n",
    "    ancestors = get_ancestors(GO_ID, go_network)\n",
    "    print(ancestors)\n",
    "    print(len(ancestors))\n",
    "    filtered_network = go_network[(go_network['child'].isin(ancestors+[GO_ID]))]\n",
    "    GO_ID_name = GO_ID.replace(':', '_')\n",
    "    filtered_network.to_csv(f'./data/GO_term_analysis/{GO_ID_name}_subhierarchy.txt', sep= '\\t', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "97 41\n"
     ]
    }
   ],
   "source": [
    "print(len(filtered_network), len(set(ancestors)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42, 4)\n"
     ]
    }
   ],
   "source": [
    "# make a nodes attribute table for all nodes in the subnetwork\n",
    "GO_ID = 'GO:0010897'\n",
    "GO_ID_name = GO_ID.replace(':', '_')\n",
    "# load the GO term annotation file\n",
    "import pandas as pd\n",
    "df_csv = pd.read_csv('./data/go_terms.csv', index_col=0)\n",
    "\n",
    "terms_in_subnetwork = set(filtered_network['child'].tolist() + filtered_network['parent'].tolist())\n",
    "\n",
    "sub_nodes = df_csv.loc[df_csv['GO'].isin(terms_in_subnetwork), :]\n",
    "\n",
    "# sub_nodes.head()\n",
    "print(sub_nodes.shape)\n",
    "\n",
    "sub_nodes.to_csv(f'./data/GO_term_analysis/{GO_ID_name}_subhierarchy_nodes.txt', sep='\\t', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GO</th>\n",
       "      <th>Genes</th>\n",
       "      <th>Gene_Count</th>\n",
       "      <th>Term_Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>GO:0071704</td>\n",
       "      <td>COMT ASPSCR1 CWF19L1 SERPINI2 ACTR6 ATP5IF1 CA...</td>\n",
       "      <td>10459</td>\n",
       "      <td>organic substance metabolic process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>GO:0090207</td>\n",
       "      <td>NR1H2 SIRT1 KAT5 APOA5 APOA4 PANK2 CTDNEP1 CNE...</td>\n",
       "      <td>42</td>\n",
       "      <td>regulation of triglyceride metabolic process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>GO:0044242</td>\n",
       "      <td>HAO1 PLA2G7 HEXA PHYH HAO2 HEXB ABCD4 MGLL SMP...</td>\n",
       "      <td>223</td>\n",
       "      <td>cellular lipid catabolic process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>616</th>\n",
       "      <td>GO:0009892</td>\n",
       "      <td>TRIM33 ZNF256 ZNF692 SERPINI2 CBFA2T3 ACTR6 AT...</td>\n",
       "      <td>2806</td>\n",
       "      <td>negative regulation of metabolic process</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1025</th>\n",
       "      <td>GO:0019216</td>\n",
       "      <td>HPGD PRKAG2 ADRA2A TREM2 APOA5 TWIST1 ATP1A1 A...</td>\n",
       "      <td>303</td>\n",
       "      <td>regulation of lipid metabolic process</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              GO                                              Genes  \\\n",
       "121   GO:0071704  COMT ASPSCR1 CWF19L1 SERPINI2 ACTR6 ATP5IF1 CA...   \n",
       "312   GO:0090207  NR1H2 SIRT1 KAT5 APOA5 APOA4 PANK2 CTDNEP1 CNE...   \n",
       "516   GO:0044242  HAO1 PLA2G7 HEXA PHYH HAO2 HEXB ABCD4 MGLL SMP...   \n",
       "616   GO:0009892  TRIM33 ZNF256 ZNF692 SERPINI2 CBFA2T3 ACTR6 AT...   \n",
       "1025  GO:0019216  HPGD PRKAG2 ADRA2A TREM2 APOA5 TWIST1 ATP1A1 A...   \n",
       "\n",
       "      Gene_Count                              Term_Description  \n",
       "121        10459           organic substance metabolic process  \n",
       "312           42  regulation of triglyceride metabolic process  \n",
       "516          223              cellular lipid catabolic process  \n",
       "616         2806      negative regulation of metabolic process  \n",
       "1025         303         regulation of lipid metabolic process  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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