1B-2.[Omics_revamped]run_gProfiler.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c64a0bfd-4de3-44dd-a77c-a72c7b47f645",
   "metadata": {
    "vscode": {
     "languageId": "raw"
    }
   },
   "source": [
    "runs g:profiler for 'omics gene set\n",
    "\n",
    "using g:Profiler API\n",
    "\n",
    "https://biit.cs.ut.ee/gprofiler/page/apis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "96571de6-823d-4010-9b5c-949615ba060a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3e522d64-5c7d-4335-baaa-804f1eeeaca6",
   "metadata": {},
   "outputs": [],
   "source": [
    "adj_pval_thresh = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5467d3a5-6097-4a81-a0cd-a58b609bf5fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95ad027a",
   "metadata": {},
   "source": [
    "Need the file update the gene symbol, run [this notebook](1B-1[Omics_revamped]Gene_symbol_update.ipynb) for the data/omics_revamped_LLM_updated_genes_DF.tsv File"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4006f12-345b-48b7-8880-4b54e8d9f8b5",
   "metadata": {},
   "source": [
    "## Read in naming file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7111f86e-d3b5-4424-ab64-926e4a707e5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputFile = 'data/omics_revamped_LLM_updated_genes_DF.tsv'\n",
    "outputFile = 'data/omics_revamped_LLM_gprofiler_new_gene_name_DF.tsv' #save for the updated gene names\n",
    "\n",
    "\n",
    "geneSeparator = \" \"\n",
    "\n",
    "genesCol = 'updated GeneList' # 'GeneList'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "671d9293-4553-4ed4-9799-114961eb5cbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(300, 9)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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</th>\n",
       "      <th>GeneSetID</th>\n",
       "      <th>GeneSetName</th>\n",
       "      <th>GeneList</th>\n",
       "      <th>updated GeneList</th>\n",
       "      <th>n_Genes</th>\n",
       "      <th>LLM Name</th>\n",
       "      <th>LLM Analysis</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NeST</td>\n",
       "      <td>Cluster1-10</td>\n",
       "      <td>Cluster1-10</td>\n",
       "      <td>CTRL HSD17B14 KIAA0232 PAQR8 PLA2G1B RNF145 SG...</td>\n",
       "      <td>CTRL HSD17B14 KIAA0232 PAQR8 PLA2G1B RNF145 SG...</td>\n",
       "      <td>12</td>\n",
       "      <td>Lipid Metabolism and Membrane Dynamics</td>\n",
       "      <td>1. CTRL, or carboxyl-terminal esterase/lipase,...</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NeST</td>\n",
       "      <td>Cluster1-11</td>\n",
       "      <td>Cluster1-11</td>\n",
       "      <td>LMF1 MFHAS1 MR1 PLA2G1B RASL11A RNF145 SLC2A6 ...</td>\n",
       "      <td>LMF1 MFHAS1 MR1 PLA2G1B RASL11A RNF145 SLC2A6 ...</td>\n",
       "      <td>12</td>\n",
       "      <td>Lipid Metabolism and Membrane Trafficking</td>\n",
       "      <td>1. LMF1 (Lipase Maturation Factor 1) is crucia...</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NeST</td>\n",
       "      <td>Cluster1-12</td>\n",
       "      <td>Cluster1-12</td>\n",
       "      <td>AMY2B CNPY2 EGFL7 LDLR LPL LRP8 LRPAP1 MYLIP P...</td>\n",
       "      <td>AMY2B CNPY2 EGFL7 LDLR LPL LRP8 LRPAP1 MYLIP P...</td>\n",
       "      <td>12</td>\n",
       "      <td>Lipid Metabolism and Receptor-Mediated Endocyt...</td>\n",
       "      <td>1. AMY2B (Amylase, alpha 2B) is an enzyme that...</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NeST</td>\n",
       "      <td>Cluster1-13</td>\n",
       "      <td>Cluster1-13</td>\n",
       "      <td>ACO1 CDKAL1 CDX2 CIAO1 CITED2 FAM96A FAM96B FB...</td>\n",
       "      <td>ACO1 CDKAL1 CDX2 CIAO1 CITED2 CIAO2A CIAO2B FB...</td>\n",
       "      <td>16</td>\n",
       "      <td>Iron Metabolism and Regulation in Pancreatic F...</td>\n",
       "      <td>1. ACO1, also known as aconitase 1, is a key e...</td>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NeST</td>\n",
       "      <td>Cluster1-14</td>\n",
       "      <td>Cluster1-14</td>\n",
       "      <td>DTWD1 DTWD2 POLD2 POLD4 POLH POLI RAD18 REV1 R...</td>\n",
       "      <td>DTWD1 DTWD2 POLD2 POLD4 POLH POLI RAD18 REV1 R...</td>\n",
       "      <td>15</td>\n",
       "      <td>DNA Damage Tolerance and Repair Pathway</td>\n",
       "      <td>1. DTWD1 and DTWD2 are proteins containing DTW...</td>\n",
       "      <td>0.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Source    GeneSetID  GeneSetName  \\\n",
       "0   NeST  Cluster1-10  Cluster1-10   \n",
       "1   NeST  Cluster1-11  Cluster1-11   \n",
       "2   NeST  Cluster1-12  Cluster1-12   \n",
       "3   NeST  Cluster1-13  Cluster1-13   \n",
       "4   NeST  Cluster1-14  Cluster1-14   \n",
       "\n",
       "                                            GeneList  \\\n",
       "0  CTRL HSD17B14 KIAA0232 PAQR8 PLA2G1B RNF145 SG...   \n",
       "1  LMF1 MFHAS1 MR1 PLA2G1B RASL11A RNF145 SLC2A6 ...   \n",
       "2  AMY2B CNPY2 EGFL7 LDLR LPL LRP8 LRPAP1 MYLIP P...   \n",
       "3  ACO1 CDKAL1 CDX2 CIAO1 CITED2 FAM96A FAM96B FB...   \n",
       "4  DTWD1 DTWD2 POLD2 POLD4 POLH POLI RAD18 REV1 R...   \n",
       "\n",
       "                                    updated GeneList  n_Genes  \\\n",
       "0  CTRL HSD17B14 KIAA0232 PAQR8 PLA2G1B RNF145 SG...       12   \n",
       "1  LMF1 MFHAS1 MR1 PLA2G1B RASL11A RNF145 SLC2A6 ...       12   \n",
       "2  AMY2B CNPY2 EGFL7 LDLR LPL LRP8 LRPAP1 MYLIP P...       12   \n",
       "3  ACO1 CDKAL1 CDX2 CIAO1 CITED2 CIAO2A CIAO2B FB...       16   \n",
       "4  DTWD1 DTWD2 POLD2 POLD4 POLH POLI RAD18 REV1 R...       15   \n",
       "\n",
       "                                            LLM Name  \\\n",
       "0             Lipid Metabolism and Membrane Dynamics   \n",
       "1          Lipid Metabolism and Membrane Trafficking   \n",
       "2  Lipid Metabolism and Receptor-Mediated Endocyt...   \n",
       "3  Iron Metabolism and Regulation in Pancreatic F...   \n",
       "4            DNA Damage Tolerance and Repair Pathway   \n",
       "\n",
       "                                        LLM Analysis  Score  \n",
       "0  1. CTRL, or carboxyl-terminal esterase/lipase,...   0.85  \n",
       "1  1. LMF1 (Lipase Maturation Factor 1) is crucia...   0.85  \n",
       "2  1. AMY2B (Amylase, alpha 2B) is an enzyme that...   0.88  \n",
       "3  1. ACO1, also known as aconitase 1, is a key e...   0.88  \n",
       "4  1. DTWD1 and DTWD2 are proteins containing DTW...   0.92  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genesets_LLM = pd.read_csv(inputFile, sep = \"\\t\") \n",
    "print(genesets_LLM.shape)\n",
    "genesets_LLM.head() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "44cbc132-9812-4652-a2d4-ad2c313fcfeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "LLM_DF = genesets_LLM.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aaaf87a1-171f-4b94-a7f1-70a75b3cea17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "300"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nGeneSets = LLM_DF.shape[0]\n",
    "nGeneSets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "75409c4f-0bfd-48de-afc1-292a0fce9da1",
   "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</th>\n",
       "      <th>GeneSetID</th>\n",
       "      <th>GeneSetName</th>\n",
       "      <th>GeneList</th>\n",
       "      <th>updated GeneList</th>\n",
       "      <th>n_Genes</th>\n",
       "      <th>LLM Name</th>\n",
       "      <th>LLM Analysis</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Source, GeneSetID, GeneSetName, GeneList, updated GeneList, n_Genes, LLM Name, LLM Analysis, Score]\n",
       "Index: []"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LLM_repeated_DF = LLM_DF[LLM_DF[['Source', 'GeneSetID', 'GeneSetName', 'GeneList']].duplicated() == True]\n",
    "LLM_repeated_DF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f66485e2-8b7e-4feb-b638-d5c893b92f46",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Go through each system and run g:Profiler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ea2d86be-1de9-4019-8e4f-a5269a4b851f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Each system will have multiple rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "60b89dd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>Term</th>\n",
       "      <th>GO ID</th>\n",
       "      <th>Adjusted P-value</th>\n",
       "      <th>intersection_size</th>\n",
       "      <th>term_size</th>\n",
       "      <th>query_size</th>\n",
       "      <th>intersections</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>peptidyl-pyroglutamic acid biosynthetic proces...</td>\n",
       "      <td>GO:0017186</td>\n",
       "      <td>0.105748</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>HIST3H2A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CD8-positive, alpha-beta T cell lineage commit...</td>\n",
       "      <td>GO:0043375</td>\n",
       "      <td>0.105748</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>PUSL1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>diphosphoinositol polyphosphate catabolic process</td>\n",
       "      <td>GO:0071544</td>\n",
       "      <td>0.105748</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>HIST1H2BH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>inositol phosphate catabolic process</td>\n",
       "      <td>GO:0071545</td>\n",
       "      <td>0.105748</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>HIST1H2BH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cellular response to dsDNA</td>\n",
       "      <td>GO:1990786</td>\n",
       "      <td>0.105748</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>EPG5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>negative regulation of macromolecule biosynthe...</td>\n",
       "      <td>GO:0010558</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2773</td>\n",
       "      <td>20</td>\n",
       "      <td>CCDC85B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>cellular process</td>\n",
       "      <td>GO:0009987</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>14</td>\n",
       "      <td>19708</td>\n",
       "      <td>20</td>\n",
       "      <td>ACYP1,CAMTA1,CCDC85B,EPG5,GPR75,HIST1H2BH,HIST...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>gene expression</td>\n",
       "      <td>GO:0010467</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>9214</td>\n",
       "      <td>20</td>\n",
       "      <td>CAMTA1,CCDC85B,HIST1H4K,PUSL1,QPCTL,RCCD1,SNX22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>establishment of localization in cell</td>\n",
       "      <td>GO:0051649</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1972</td>\n",
       "      <td>20</td>\n",
       "      <td>EPG5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>negative regulation of biological process</td>\n",
       "      <td>GO:0048519</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>5874</td>\n",
       "      <td>20</td>\n",
       "      <td>CCDC85B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>445 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  Term       GO ID  \\\n",
       "0    peptidyl-pyroglutamic acid biosynthetic proces...  GO:0017186   \n",
       "1    CD8-positive, alpha-beta T cell lineage commit...  GO:0043375   \n",
       "2    diphosphoinositol polyphosphate catabolic process  GO:0071544   \n",
       "3                 inositol phosphate catabolic process  GO:0071545   \n",
       "4                           cellular response to dsDNA  GO:1990786   \n",
       "..                                                 ...         ...   \n",
       "440  negative regulation of macromolecule biosynthe...  GO:0010558   \n",
       "441                                   cellular process  GO:0009987   \n",
       "442                                    gene expression  GO:0010467   \n",
       "443              establishment of localization in cell  GO:0051649   \n",
       "444          negative regulation of biological process  GO:0048519   \n",
       "\n",
       "     Adjusted P-value intersection_size term_size query_size  \\\n",
       "0            0.105748                 1         2         20   \n",
       "1            0.105748                 1         2         20   \n",
       "2            0.105748                 1         1         20   \n",
       "3            0.105748                 1         2         20   \n",
       "4            0.105748                 1         2         20   \n",
       "..                ...               ...       ...        ...   \n",
       "440          1.000000                 1      2773         20   \n",
       "441          1.000000                14     19708         20   \n",
       "442          1.000000                 7      9214         20   \n",
       "443          1.000000                 1      1972         20   \n",
       "444          1.000000                 1      5874         20   \n",
       "\n",
       "                                         intersections  \n",
       "0                                             HIST3H2A  \n",
       "1                                                PUSL1  \n",
       "2                                            HIST1H2BH  \n",
       "3                                            HIST1H2BH  \n",
       "4                                                 EPG5  \n",
       "..                                                 ...  \n",
       "440                                            CCDC85B  \n",
       "441  ACYP1,CAMTA1,CCDC85B,EPG5,GPR75,HIST1H2BH,HIST...  \n",
       "442    CAMTA1,CCDC85B,HIST1H4K,PUSL1,QPCTL,RCCD1,SNX22  \n",
       "443                                               EPG5  \n",
       "444                                            CCDC85B  \n",
       "\n",
       "[445 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "import requests\n",
    "\n",
    "def query_gprofiler(query_genes) -> pd.DataFrame:\n",
    "    res_df = pd.DataFrame(columns=['Term', 'GO ID', 'Adjusted P-value', 'intersection_size',  'term_size', 'query_size', 'intersections'])\n",
    "    r = requests.post(\n",
    "        url='https://biit.cs.ut.ee/gprofiler/api/gost/profile/',\n",
    "        json={\n",
    "            'organism': 'hsapiens',\n",
    "            'query': query_genes,\n",
    "            'sources': ['GO:BP'], # query only GO BP\n",
    "            'user_threshold': 0.05, \n",
    "            'significance_threshold_method': 'fdr', # using fdr for adjusted p \n",
    "            'all_results': True # get all results, not just significant ones\n",
    "        }\n",
    "        )\n",
    "    result = r.json()['result']\n",
    "    # Check if the result is not empty\n",
    "    if result:\n",
    "        # Extract relevant fields and append to the DataFrame\n",
    "        for entry in result:\n",
    "            # The elements in this list correspond to query genes and are in the same order as the genes\n",
    "            intersection_genes = [\n",
    "                query_genes[i] for i, intersect in enumerate(entry['intersections']) if intersect\n",
    "            ]\n",
    "            # print(entry['intersections'])\n",
    "            newrow ={\n",
    "                'Term': entry['name'],\n",
    "                'GO ID': entry['native'],\n",
    "                'Adjusted P-value': entry['p_value'],\n",
    "                'intersection_size': entry['intersection_size'],\n",
    "                'term_size': entry['term_size'],\n",
    "                'query_size': entry['query_size'],\n",
    "                'intersections': ','.join(intersection_genes) # Convert list to a space-separated string\n",
    "            }\n",
    "            res_df = pd.concat([res_df, pd.DataFrame([newrow])], ignore_index=True)\n",
    "    else:\n",
    "        raise ValueError('No results found for the query genes')\n",
    "    return res_df.sort_values('Adjusted P-value', ascending=True).reset_index(drop=True)\n",
    "\n",
    "# one example running case\n",
    "# query_genes = LLM_DF['GeneList'][0].split(geneSeparator)\n",
    "# print(query_genes )\n",
    "# res_df = query_gprofiler(query_genes)\n",
    "# res_df\n",
    "genes = [\n",
    "    \"ACBD6\", \"ACRV1\", \"ACYP1\", \"C4orf48\", \"CAMTA1\", \"CCDC85B\", \"CTRL\", \"EPG5\", \n",
    "    \"GPR75\", \"HIST1H2AH\", \"HIST1H2AI\", \"HIST1H2BH\", \"HIST1H3F\", \"HIST1H4A\", \n",
    "    \"HIST1H4K\", \"HIST3H2A\", \"NRM\", \"NUDT3\", \"OGFOD2\", \"PRR22\", \"PUSL1\", \"QPCTL\", \n",
    "    \"RCCD1\", \"SNX22\", \"ST20\", \"SYCE1L\", \"TCTEX1D2\", \"TMSB15B\", \"TOX\", \"TOX2\", \n",
    "    \"ZNF236\", \"ZNF726\"\n",
    "]\n",
    "query_gprofiler(genes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f8897f4d-be79-401d-826c-63173909dba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Source',\n",
       " 'GeneSetID',\n",
       " 'GeneSetName',\n",
       " 'GeneList',\n",
       " 'updated GeneList',\n",
       " 'n_Genes',\n",
       " 'LLM Name',\n",
       " 'LLM Analysis',\n",
       " 'Score',\n",
       " 'Term',\n",
       " 'GO ID',\n",
       " 'Adjusted P-value',\n",
       " 'intersection_size',\n",
       " 'term_size',\n",
       " 'query_size',\n",
       " 'intersections']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expandedColumns = list(LLM_DF.columns)\n",
    "additionalCols = ['Term', 'GO ID', 'Adjusted P-value', 'intersection_size', 'term_size', 'query_size', 'intersections']\n",
    "expandedColumns.extend(additionalCols)\n",
    "expandedColumns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "63805a5e-982a-4ff4-8011-1cec7684452f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize the expanded DataFrame\n",
    "expanded_LLM_genes_DF = pd.DataFrame(columns=expandedColumns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9368f41a-5b50-461a-8f2d-10214e61d92f",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "# Loop over all gene sets \n",
    "for geneSetInd in range(nGeneSets):\n",
    "    print(geneSetInd)\n",
    "    geneSetRow = LLM_DF.iloc[geneSetInd]\n",
    "    geneSetGenes = geneSetRow[genesCol].split(geneSeparator)\n",
    "\n",
    "    # Perform gProfiler enrichment analysis get the full table\n",
    "    sigRes_DF = query_gprofiler(geneSetGenes)\n",
    "\n",
    "    # thresholding\n",
    "\n",
    "    sigRes_DF = sigRes_DF.sort_values('Adjusted P-value').reset_index(drop=True)\n",
    "    \n",
    "    if sigRes_DF.shape[0] == 0: # failed enrichment analysis\n",
    "        \n",
    "        print([geneSetInd, ' has not enirchment'])\n",
    "        #continue # skip to next gene set\n",
    "        sigRes_DF.loc[0, ['Term', 'GO ID', 'Adjusted P-value', 'intersection_size', 'term_size', 'query_size', 'intersections']] = 'NaN'\n",
    "  \n",
    "        \n",
    "    else:\n",
    "\n",
    "        if sigRes_DF.loc[0, 'Adjusted P-value'] > adj_pval_thresh: # nothing significant, return the first on the list\n",
    "            sigRes_DF = sigRes_DF.loc[[0],:]\n",
    "        else: # there are sig. terms, return all\n",
    "            sigRes_DF = sigRes_DF[sigRes_DF['Adjusted P-value'] <=adj_pval_thresh]\n",
    "        \n",
    "    # Doing what should be a simple colbind \n",
    "    geneSetRow_DF = geneSetRow.to_frame().T\n",
    "    geneSetRow_repeated_DF = geneSetRow_DF.loc[geneSetRow_DF.index.repeat(sigRes_DF.shape[0])]\n",
    "    geneSetRow_repeated_DF = geneSetRow_repeated_DF.reset_index()\n",
    "    \n",
    "    # system_Row_sigRes = systemRow_repeated_DF.join(sigRes_DF)\n",
    "    geneSetRowRow_sigRes = pd.concat([geneSetRow_repeated_DF, sigRes_DF],axis=1)\n",
    "    \n",
    "    # Doing what should be a simple  rowbind\n",
    "    if expanded_LLM_genes_DF.shape[0] == 0:\n",
    "        expanded_LLM_genes_DF = geneSetRowRow_sigRes[expandedColumns]\n",
    "    else:\n",
    "        expanded_LLM_genes_DF =  pd.concat([expanded_LLM_genes_DF, geneSetRowRow_sigRes[expandedColumns]],axis=0)\n",
    "    \n",
    "    \n",
    "    expanded_LLM_genes_DF.to_csv(outputFile, sep = \"\\t\", index=False) # Save to not loose work"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4fc8198e",
   "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</th>\n",
       "      <th>GeneSetID</th>\n",
       "      <th>GeneSetName</th>\n",
       "      <th>GeneList</th>\n",
       "      <th>updated GeneList</th>\n",
       "      <th>n_Genes</th>\n",
       "      <th>LLM Name</th>\n",
       "      <th>LLM Analysis</th>\n",
       "      <th>Score</th>\n",
       "      <th>Term</th>\n",
       "      <th>GO ID</th>\n",
       "      <th>Adjusted P-value</th>\n",
       "      <th>intersection_size</th>\n",
       "      <th>term_size</th>\n",
       "      <th>query_size</th>\n",
       "      <th>intersections</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NeST</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>22</td>\n",
       "      <td>Wnt/Planar Cell Polarity Signaling</td>\n",
       "      <td>1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...</td>\n",
       "      <td>0.92</td>\n",
       "      <td>non-canonical Wnt signaling pathway</td>\n",
       "      <td>GO:0035567</td>\n",
       "      <td>1.708941e-27</td>\n",
       "      <td>14</td>\n",
       "      <td>70</td>\n",
       "      <td>22</td>\n",
       "      <td>CELSR1,CELSR2,CELSR3,FZD1,FZD2,FZD3,FZD5,FZD6,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NeST</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>22</td>\n",
       "      <td>Wnt/Planar Cell Polarity Signaling</td>\n",
       "      <td>1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...</td>\n",
       "      <td>0.92</td>\n",
       "      <td>cell-cell signaling by wnt</td>\n",
       "      <td>GO:0198738</td>\n",
       "      <td>4.033949e-20</td>\n",
       "      <td>16</td>\n",
       "      <td>454</td>\n",
       "      <td>22</td>\n",
       "      <td>APC2,CELSR1,CELSR2,CELSR3,FUZ,FZD1,FZD2,FZD3,F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NeST</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>22</td>\n",
       "      <td>Wnt/Planar Cell Polarity Signaling</td>\n",
       "      <td>1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...</td>\n",
       "      <td>0.92</td>\n",
       "      <td>Wnt signaling pathway</td>\n",
       "      <td>GO:0016055</td>\n",
       "      <td>4.033949e-20</td>\n",
       "      <td>16</td>\n",
       "      <td>452</td>\n",
       "      <td>22</td>\n",
       "      <td>APC2,CELSR1,CELSR2,CELSR3,FUZ,FZD1,FZD2,FZD3,F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NeST</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>22</td>\n",
       "      <td>Wnt/Planar Cell Polarity Signaling</td>\n",
       "      <td>1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...</td>\n",
       "      <td>0.92</td>\n",
       "      <td>Wnt signaling pathway, planar cell polarity pa...</td>\n",
       "      <td>GO:0060071</td>\n",
       "      <td>3.728262e-19</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>22</td>\n",
       "      <td>CELSR1,CELSR2,CELSR3,FZD1,FZD2,FZD3,FZD6,FZD7,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NeST</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>22</td>\n",
       "      <td>Wnt/Planar Cell Polarity Signaling</td>\n",
       "      <td>1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...</td>\n",
       "      <td>0.92</td>\n",
       "      <td>canonical Wnt signaling pathway</td>\n",
       "      <td>GO:0060070</td>\n",
       "      <td>8.454424e-15</td>\n",
       "      <td>12</td>\n",
       "      <td>305</td>\n",
       "      <td>22</td>\n",
       "      <td>APC2,FUZ,FZD1,FZD2,FZD3,FZD5,FZD6,FZD7,FZD8,FZ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>NeST</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>22</td>\n",
       "      <td>Wnt/Planar Cell Polarity Signaling</td>\n",
       "      <td>1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...</td>\n",
       "      <td>0.92</td>\n",
       "      <td>negative regulation of mitochondrial outer mem...</td>\n",
       "      <td>GO:1901029</td>\n",
       "      <td>4.861368e-02</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>FZD9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>NeST</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>22</td>\n",
       "      <td>Wnt/Planar Cell Polarity Signaling</td>\n",
       "      <td>1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...</td>\n",
       "      <td>0.92</td>\n",
       "      <td>nephron tubule epithelial cell differentiation</td>\n",
       "      <td>GO:0072160</td>\n",
       "      <td>4.861368e-02</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>FAT4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>NeST</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>22</td>\n",
       "      <td>Wnt/Planar Cell Polarity Signaling</td>\n",
       "      <td>1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...</td>\n",
       "      <td>0.92</td>\n",
       "      <td>leukocyte activation</td>\n",
       "      <td>GO:0045321</td>\n",
       "      <td>4.861368e-02</td>\n",
       "      <td>4</td>\n",
       "      <td>946</td>\n",
       "      <td>22</td>\n",
       "      <td>FZD5,FZD7,FZD8,FZD9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>NeST</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>22</td>\n",
       "      <td>Wnt/Planar Cell Polarity Signaling</td>\n",
       "      <td>1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...</td>\n",
       "      <td>0.92</td>\n",
       "      <td>midbrain dopaminergic neuron differentiation</td>\n",
       "      <td>GO:1904948</td>\n",
       "      <td>4.861368e-02</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>FZD1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>NeST</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>Cluster2-191</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...</td>\n",
       "      <td>22</td>\n",
       "      <td>Wnt/Planar Cell Polarity Signaling</td>\n",
       "      <td>1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...</td>\n",
       "      <td>0.92</td>\n",
       "      <td>regulation of cytoskeleton organization</td>\n",
       "      <td>GO:0051493</td>\n",
       "      <td>4.947489e-02</td>\n",
       "      <td>3</td>\n",
       "      <td>515</td>\n",
       "      <td>22</td>\n",
       "      <td>APC2,CELSR1,FZD10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>343 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Source     GeneSetID   GeneSetName  \\\n",
       "0     NeST  Cluster2-191  Cluster2-191   \n",
       "1     NeST  Cluster2-191  Cluster2-191   \n",
       "2     NeST  Cluster2-191  Cluster2-191   \n",
       "3     NeST  Cluster2-191  Cluster2-191   \n",
       "4     NeST  Cluster2-191  Cluster2-191   \n",
       "..     ...           ...           ...   \n",
       "338   NeST  Cluster2-191  Cluster2-191   \n",
       "339   NeST  Cluster2-191  Cluster2-191   \n",
       "340   NeST  Cluster2-191  Cluster2-191   \n",
       "341   NeST  Cluster2-191  Cluster2-191   \n",
       "342   NeST  Cluster2-191  Cluster2-191   \n",
       "\n",
       "                                              GeneList  \\\n",
       "0    APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...   \n",
       "1    APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...   \n",
       "2    APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...   \n",
       "3    APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...   \n",
       "4    APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...   \n",
       "..                                                 ...   \n",
       "338  APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...   \n",
       "339  APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...   \n",
       "340  APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...   \n",
       "341  APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...   \n",
       "342  APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...   \n",
       "\n",
       "                                      updated GeneList n_Genes  \\\n",
       "0    APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...      22   \n",
       "1    APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...      22   \n",
       "2    APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...      22   \n",
       "3    APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...      22   \n",
       "4    APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...      22   \n",
       "..                                                 ...     ...   \n",
       "338  APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...      22   \n",
       "339  APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...      22   \n",
       "340  APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...      22   \n",
       "341  APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...      22   \n",
       "342  APC2 CELSR1 CELSR2 CELSR3 FAT4 FREM2 FUZ FZD1 ...      22   \n",
       "\n",
       "                               LLM Name  \\\n",
       "0    Wnt/Planar Cell Polarity Signaling   \n",
       "1    Wnt/Planar Cell Polarity Signaling   \n",
       "2    Wnt/Planar Cell Polarity Signaling   \n",
       "3    Wnt/Planar Cell Polarity Signaling   \n",
       "4    Wnt/Planar Cell Polarity Signaling   \n",
       "..                                  ...   \n",
       "338  Wnt/Planar Cell Polarity Signaling   \n",
       "339  Wnt/Planar Cell Polarity Signaling   \n",
       "340  Wnt/Planar Cell Polarity Signaling   \n",
       "341  Wnt/Planar Cell Polarity Signaling   \n",
       "342  Wnt/Planar Cell Polarity Signaling   \n",
       "\n",
       "                                          LLM Analysis Score  \\\n",
       "0    1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...  0.92   \n",
       "1    1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...  0.92   \n",
       "2    1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...  0.92   \n",
       "3    1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...  0.92   \n",
       "4    1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...  0.92   \n",
       "..                                                 ...   ...   \n",
       "338  1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...  0.92   \n",
       "339  1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...  0.92   \n",
       "340  1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...  0.92   \n",
       "341  1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...  0.92   \n",
       "342  1. The proteins APC2, CELSR1, CELSR2, CELSR3, ...  0.92   \n",
       "\n",
       "                                                  Term       GO ID  \\\n",
       "0                  non-canonical Wnt signaling pathway  GO:0035567   \n",
       "1                           cell-cell signaling by wnt  GO:0198738   \n",
       "2                                Wnt signaling pathway  GO:0016055   \n",
       "3    Wnt signaling pathway, planar cell polarity pa...  GO:0060071   \n",
       "4                      canonical Wnt signaling pathway  GO:0060070   \n",
       "..                                                 ...         ...   \n",
       "338  negative regulation of mitochondrial outer mem...  GO:1901029   \n",
       "339     nephron tubule epithelial cell differentiation  GO:0072160   \n",
       "340                               leukocyte activation  GO:0045321   \n",
       "341       midbrain dopaminergic neuron differentiation  GO:1904948   \n",
       "342            regulation of cytoskeleton organization  GO:0051493   \n",
       "\n",
       "     Adjusted P-value intersection_size term_size query_size  \\\n",
       "0        1.708941e-27                14        70         22   \n",
       "1        4.033949e-20                16       454         22   \n",
       "2        4.033949e-20                16       452         22   \n",
       "3        3.728262e-19                10        50         22   \n",
       "4        8.454424e-15                12       305         22   \n",
       "..                ...               ...       ...        ...   \n",
       "338      4.861368e-02                 1        15         22   \n",
       "339      4.861368e-02                 1        15         22   \n",
       "340      4.861368e-02                 4       946         22   \n",
       "341      4.861368e-02                 1        15         22   \n",
       "342      4.947489e-02                 3       515         22   \n",
       "\n",
       "                                         intersections  \n",
       "0    CELSR1,CELSR2,CELSR3,FZD1,FZD2,FZD3,FZD5,FZD6,...  \n",
       "1    APC2,CELSR1,CELSR2,CELSR3,FUZ,FZD1,FZD2,FZD3,F...  \n",
       "2    APC2,CELSR1,CELSR2,CELSR3,FUZ,FZD1,FZD2,FZD3,F...  \n",
       "3    CELSR1,CELSR2,CELSR3,FZD1,FZD2,FZD3,FZD6,FZD7,...  \n",
       "4    APC2,FUZ,FZD1,FZD2,FZD3,FZD5,FZD6,FZD7,FZD8,FZ...  \n",
       "..                                                 ...  \n",
       "338                                               FZD9  \n",
       "339                                               FAT4  \n",
       "340                                FZD5,FZD7,FZD8,FZD9  \n",
       "341                                               FZD1  \n",
       "342                                  APC2,CELSR1,FZD10  \n",
       "\n",
       "[343 rows x 16 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expanded_LLM_genes_DF.loc[expanded_LLM_genes_DF[\"GeneSetID\"] == 'Cluster2-191']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d90bb93e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53452, 16)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove NAN values\n",
    "expanded_LLM_genes_DF = expanded_LLM_genes_DF.dropna(subset=['Term'])\n",
    "\n",
    "expanded_LLM_genes_DF.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0390f34d-f3c1-4957-a556-7c7c4525d616",
   "metadata": {},
   "outputs": [],
   "source": [
    "expanded_LLM_genes_DF.to_csv(outputFile, sep = \"\\t\", index=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a1567471-ae53-4733-a905-1749aa8805cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#\n",
    "use_update_name = pd.read_csv('data/omics_revamped_LLM_gprofiler_new_gene_name_DF.tsv', sep = \"\\t\")\n",
    "use_default_name = pd.read_csv('data/omics_revamped_LLM_gprofiler_default_gene_name_DF.tsv', sep = \"\\t\")\n",
    "\n",
    "use_update_name.equals(use_default_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1ccd9897",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "128\n"
     ]
    }
   ],
   "source": [
    "# if the Terms are different\n",
    "print(use_update_name['Term'].equals(use_default_name['Term']))\n",
    "#if not, check the difference\n",
    "diff = use_update_name[~use_update_name['Term'].isin(use_default_name['Term'])].index\n",
    "print(len(diff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "99134f27",
   "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>GeneSetID</th>\n",
       "      <th>LLM Name</th>\n",
       "      <th>Term</th>\n",
       "      <th>Adjusted P-value</th>\n",
       "      <th>intersection_size</th>\n",
       "      <th>term_size</th>\n",
       "      <th>intersections</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>Cluster1-13</td>\n",
       "      <td>Iron Metabolism and Regulation in Pancreatic F...</td>\n",
       "      <td>protein maturation by [4Fe-4S] cluster transfer</td>\n",
       "      <td>0.000882</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>CIAO2A,CIAO2B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1401</th>\n",
       "      <td>Cluster1-3</td>\n",
       "      <td>Mitochondrial Biogenesis and Function</td>\n",
       "      <td>mitochondrial RNA 5'-end processing</td>\n",
       "      <td>0.002310</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>KHDRBS1,PFDN5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>Cluster1-3</td>\n",
       "      <td>Mitochondrial Biogenesis and Function</td>\n",
       "      <td>tRNA 5'-leader removal</td>\n",
       "      <td>0.017892</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>KHDRBS1,SRSF10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1477</th>\n",
       "      <td>Cluster1-3</td>\n",
       "      <td>Mitochondrial Biogenesis and Function</td>\n",
       "      <td>tRNA 5'-end processing</td>\n",
       "      <td>0.024982</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>KHDRBS1,SRSF10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>Cluster1-9</td>\n",
       "      <td>Protein Quality Control and Intracellular Traf...</td>\n",
       "      <td>arsenite transport</td>\n",
       "      <td>0.019149</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>GET3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53054</th>\n",
       "      <td>Ebolavirus(ZEBOV)_6day_PBMCs_rNAPc2treated</td>\n",
       "      <td>System of unrelated proteins</td>\n",
       "      <td>regulation of extrathymic T cell differentiation</td>\n",
       "      <td>0.164711</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>FOXD2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53097</th>\n",
       "      <td>HCoV-EMC2012_24Hour</td>\n",
       "      <td>System of unrelated proteins</td>\n",
       "      <td>regulation of ribosomal subunit export from nu...</td>\n",
       "      <td>0.136722</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>RIOK2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53098</th>\n",
       "      <td>HHV-8_72Hour</td>\n",
       "      <td>System of unrelated proteins</td>\n",
       "      <td>epithelial fluid transport</td>\n",
       "      <td>0.313999</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>AHCYL1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53107</th>\n",
       "      <td>SARS-CoV MA15_Day1</td>\n",
       "      <td>System of unrelated proteins</td>\n",
       "      <td>protein N-linked glycosylation via arginine</td>\n",
       "      <td>0.223802</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>KRTCAP2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53109</th>\n",
       "      <td>SARS-CoV MA15_Day4-TNFRsf1b KO</td>\n",
       "      <td>System of unrelated proteins</td>\n",
       "      <td>purine deoxyribonucleotide salvage</td>\n",
       "      <td>0.173444</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>FAM199X</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        GeneSetID  \\\n",
       "473                                   Cluster1-13   \n",
       "1401                                   Cluster1-3   \n",
       "1465                                   Cluster1-3   \n",
       "1477                                   Cluster1-3   \n",
       "1997                                   Cluster1-9   \n",
       "...                                           ...   \n",
       "53054  Ebolavirus(ZEBOV)_6day_PBMCs_rNAPc2treated   \n",
       "53097                         HCoV-EMC2012_24Hour   \n",
       "53098                                HHV-8_72Hour   \n",
       "53107                          SARS-CoV MA15_Day1   \n",
       "53109              SARS-CoV MA15_Day4-TNFRsf1b KO   \n",
       "\n",
       "                                                LLM Name  \\\n",
       "473    Iron Metabolism and Regulation in Pancreatic F...   \n",
       "1401               Mitochondrial Biogenesis and Function   \n",
       "1465               Mitochondrial Biogenesis and Function   \n",
       "1477               Mitochondrial Biogenesis and Function   \n",
       "1997   Protein Quality Control and Intracellular Traf...   \n",
       "...                                                  ...   \n",
       "53054                       System of unrelated proteins   \n",
       "53097                       System of unrelated proteins   \n",
       "53098                       System of unrelated proteins   \n",
       "53107                       System of unrelated proteins   \n",
       "53109                       System of unrelated proteins   \n",
       "\n",
       "                                                    Term  Adjusted P-value  \\\n",
       "473      protein maturation by [4Fe-4S] cluster transfer          0.000882   \n",
       "1401                 mitochondrial RNA 5'-end processing          0.002310   \n",
       "1465                              tRNA 5'-leader removal          0.017892   \n",
       "1477                              tRNA 5'-end processing          0.024982   \n",
       "1997                                  arsenite transport          0.019149   \n",
       "...                                                  ...               ...   \n",
       "53054   regulation of extrathymic T cell differentiation          0.164711   \n",
       "53097  regulation of ribosomal subunit export from nu...          0.136722   \n",
       "53098                         epithelial fluid transport          0.313999   \n",
       "53107        protein N-linked glycosylation via arginine          0.223802   \n",
       "53109                 purine deoxyribonucleotide salvage          0.173444   \n",
       "\n",
       "       intersection_size  term_size   intersections  \n",
       "473                    2          7   CIAO2A,CIAO2B  \n",
       "1401                   2          4   KHDRBS1,PFDN5  \n",
       "1465                   2         13  KHDRBS1,SRSF10  \n",
       "1477                   2         16  KHDRBS1,SRSF10  \n",
       "1997                   1          1            GET3  \n",
       "...                  ...        ...             ...  \n",
       "53054                  1          2           FOXD2  \n",
       "53097                  1          1           RIOK2  \n",
       "53098                  1         12          AHCYL1  \n",
       "53107                  1          1         KRTCAP2  \n",
       "53109                  1          3         FAM199X  \n",
       "\n",
       "[128 rows x 7 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "use_update_name.loc[diff, ['GeneSetID', 'LLM Name', 'Term', 'Adjusted P-value', 'intersection_size', 'term_size','intersections']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8c393a6b",
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>GeneSetID</th>\n",
       "      <th>LLM Name</th>\n",
       "      <th>Term</th>\n",
       "      <th>Adjusted P-value</th>\n",
       "      <th>intersection_size</th>\n",
       "      <th>term_size</th>\n",
       "      <th>intersections</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>Cluster1-13</td>\n",
       "      <td>Iron Metabolism and Regulation in Pancreatic F...</td>\n",
       "      <td>regulation of metabolic process</td>\n",
       "      <td>0.000551</td>\n",
       "      <td>13</td>\n",
       "      <td>7158</td>\n",
       "      <td>ACO1,CDX2,CIAO1,CITED2,FAM96A,FAM96B,FBXL5,IRE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1401</th>\n",
       "      <td>Cluster1-3</td>\n",
       "      <td>Mitochondrial Biogenesis and Function</td>\n",
       "      <td>positive regulation of cellular catabolic process</td>\n",
       "      <td>0.001802</td>\n",
       "      <td>8</td>\n",
       "      <td>332</td>\n",
       "      <td>ERAL1,GFM2,HIST1H1B,NOB1,RRP1B,SLC25A5,SSB,TXN2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>Cluster1-3</td>\n",
       "      <td>Mitochondrial Biogenesis and Function</td>\n",
       "      <td>respiratory chain complex III assembly</td>\n",
       "      <td>0.014871</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>BCS1L,TPR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1477</th>\n",
       "      <td>Cluster1-3</td>\n",
       "      <td>Mitochondrial Biogenesis and Function</td>\n",
       "      <td>regulation of metabolic process</td>\n",
       "      <td>0.017982</td>\n",
       "      <td>44</td>\n",
       "      <td>7158</td>\n",
       "      <td>AATF,ABT1,AURKAIP1,COQ3,COX17,CWC22,DDX49,DIEX...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>Cluster1-9</td>\n",
       "      <td>Protein Quality Control and Intracellular Traf...</td>\n",
       "      <td>stress granule assembly</td>\n",
       "      <td>0.016232</td>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>DNAJC14,TRAPPC11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53054</th>\n",
       "      <td>BRD-A42737819_-666_MCF7_6.0_h_10.0_um</td>\n",
       "      <td>System of unrelated proteins</td>\n",
       "      <td>anatomical structure development</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>37</td>\n",
       "      <td>5899</td>\n",
       "      <td>1060P11.3,AKR1B1,ANXA5,ARG2,CKAP4,COCH,CXCL8,D...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53097</th>\n",
       "      <td>BRD-A42737819_-666_MCF7_6.0_h_10.0_um</td>\n",
       "      <td>System of unrelated proteins</td>\n",
       "      <td>regulation of apoptotic process</td>\n",
       "      <td>0.004777</td>\n",
       "      <td>14</td>\n",
       "      <td>1463</td>\n",
       "      <td>1060P11.3,AKR1B1,ALDH1A2,ANXA5,ARG2,BTG1,PEG3,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53098</th>\n",
       "      <td>BRD-A42737819_-666_MCF7_6.0_h_10.0_um</td>\n",
       "      <td>System of unrelated proteins</td>\n",
       "      <td>cellular response to lipid</td>\n",
       "      <td>0.004777</td>\n",
       "      <td>9</td>\n",
       "      <td>609</td>\n",
       "      <td>AKR1B1,CCND2,COCH,ITM2A,PIP,TRIM29,DNAJA3,KAZN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53107</th>\n",
       "      <td>BRD-A42737819_-666_MCF7_6.0_h_10.0_um</td>\n",
       "      <td>System of unrelated proteins</td>\n",
       "      <td>innate immune response</td>\n",
       "      <td>0.005656</td>\n",
       "      <td>11</td>\n",
       "      <td>965</td>\n",
       "      <td>ANXA5,CCND2,DPYSL2,FCGBP,PIP,TIPARP,COL3A1,HIS...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53109</th>\n",
       "      <td>BRD-A42737819_-666_MCF7_6.0_h_10.0_um</td>\n",
       "      <td>System of unrelated proteins</td>\n",
       "      <td>regulation of tumor necrosis factor production</td>\n",
       "      <td>0.005693</td>\n",
       "      <td>5</td>\n",
       "      <td>163</td>\n",
       "      <td>ANXA5,CCND2,PIP,TRIM29,SLC39A8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   GeneSetID  \\\n",
       "473                              Cluster1-13   \n",
       "1401                              Cluster1-3   \n",
       "1465                              Cluster1-3   \n",
       "1477                              Cluster1-3   \n",
       "1997                              Cluster1-9   \n",
       "...                                      ...   \n",
       "53054  BRD-A42737819_-666_MCF7_6.0_h_10.0_um   \n",
       "53097  BRD-A42737819_-666_MCF7_6.0_h_10.0_um   \n",
       "53098  BRD-A42737819_-666_MCF7_6.0_h_10.0_um   \n",
       "53107  BRD-A42737819_-666_MCF7_6.0_h_10.0_um   \n",
       "53109  BRD-A42737819_-666_MCF7_6.0_h_10.0_um   \n",
       "\n",
       "                                                LLM Name  \\\n",
       "473    Iron Metabolism and Regulation in Pancreatic F...   \n",
       "1401               Mitochondrial Biogenesis and Function   \n",
       "1465               Mitochondrial Biogenesis and Function   \n",
       "1477               Mitochondrial Biogenesis and Function   \n",
       "1997   Protein Quality Control and Intracellular Traf...   \n",
       "...                                                  ...   \n",
       "53054                       System of unrelated proteins   \n",
       "53097                       System of unrelated proteins   \n",
       "53098                       System of unrelated proteins   \n",
       "53107                       System of unrelated proteins   \n",
       "53109                       System of unrelated proteins   \n",
       "\n",
       "                                                    Term  Adjusted P-value  \\\n",
       "473                      regulation of metabolic process          0.000551   \n",
       "1401   positive regulation of cellular catabolic process          0.001802   \n",
       "1465              respiratory chain complex III assembly          0.014871   \n",
       "1477                     regulation of metabolic process          0.017982   \n",
       "1997                             stress granule assembly          0.016232   \n",
       "...                                                  ...               ...   \n",
       "53054                   anatomical structure development          0.000221   \n",
       "53097                    regulation of apoptotic process          0.004777   \n",
       "53098                         cellular response to lipid          0.004777   \n",
       "53107                             innate immune response          0.005656   \n",
       "53109     regulation of tumor necrosis factor production          0.005693   \n",
       "\n",
       "       intersection_size  term_size  \\\n",
       "473                   13       7158   \n",
       "1401                   8        332   \n",
       "1465                   2         12   \n",
       "1477                  44       7158   \n",
       "1997                   2         31   \n",
       "...                  ...        ...   \n",
       "53054                 37       5899   \n",
       "53097                 14       1463   \n",
       "53098                  9        609   \n",
       "53107                 11        965   \n",
       "53109                  5        163   \n",
       "\n",
       "                                           intersections  \n",
       "473    ACO1,CDX2,CIAO1,CITED2,FAM96A,FAM96B,FBXL5,IRE...  \n",
       "1401     ERAL1,GFM2,HIST1H1B,NOB1,RRP1B,SLC25A5,SSB,TXN2  \n",
       "1465                                           BCS1L,TPR  \n",
       "1477   AATF,ABT1,AURKAIP1,COQ3,COX17,CWC22,DDX49,DIEX...  \n",
       "1997                                    DNAJC14,TRAPPC11  \n",
       "...                                                  ...  \n",
       "53054  1060P11.3,AKR1B1,ANXA5,ARG2,CKAP4,COCH,CXCL8,D...  \n",
       "53097  1060P11.3,AKR1B1,ALDH1A2,ANXA5,ARG2,BTG1,PEG3,...  \n",
       "53098  AKR1B1,CCND2,COCH,ITM2A,PIP,TRIM29,DNAJA3,KAZN...  \n",
       "53107  ANXA5,CCND2,DPYSL2,FCGBP,PIP,TIPARP,COL3A1,HIS...  \n",
       "53109                     ANXA5,CCND2,PIP,TRIM29,SLC39A8  \n",
       "\n",
       "[128 rows x 7 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "use_default_name.loc[diff, ['GeneSetID', 'LLM Name', 'Term', 'Adjusted P-value', 'intersection_size', 'term_size','intersections']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e352054a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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