{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "eb3d38f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JinmiaoChenLab/DISCOtoolkit_py/blob/main/docs/scEnrichment.ipynb)\n",
    "# scEnrichment\n",
    "\n",
    "In this tutorial, we will provide a quick guideline for applying the scEnrichment feature of discotoolkit on DEGs (Differentially Expressed Genes). Follow these steps:\n",
    "\n",
    "1. First, we load the DEGs from Example 1, available on the [DISCO](https://disco.bii.a-star.edu.sg/v1/) website.\n",
    "2. Finally, we convert the retrieved data into a Pandas DataFrame, which will serve as the input for the `dt.CELLiD_enrichment` function. By running this function, we can obtain the desired enrichment analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ea0bd44c-44a6-4761-af8b-115838fb99df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for google colab\n",
    "# pip install discotoolkit\n",
    "# import package\n",
    "\n",
    "import discotoolkit as dt\n",
    "import scanpy as sc\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# adding ignore warning to clean the code\n",
    "import warnings\n",
    "\n",
    "# Ignore all warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "sns.set_theme(rc={'figure.dpi': 300})"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "d3b7c407",
   "metadata": {},
   "outputs": [],
   "source": [
    "The user can input either a gene list or a gene list with log fold change as the input to the `dt.CELLiD_enrichment` function.\n",
    "\n",
    "Example of the DEGs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bbf2ff61",
   "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>gene</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PRSS1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CTRB2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CELA3A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CTRB1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>REG1B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     gene\n",
       "0   PRSS1\n",
       "1   CTRB2\n",
       "2  CELA3A\n",
       "3   CTRB1\n",
       "4   REG1B"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# testing DEG from DEGs of acinar cell(Peng, Junya et al.)\n",
    "# Reference is in DISCO website\n",
    "\n",
    "test_genes = {\"gene\":[\"PRSS1\", \"CTRB2\", \"CELA3A\", \"CTRB1\", \"REG1B\", \"CLPS\", \"CPB1\", \"CPA1\", \"PLA2G1B\", \"REG3A\", \"CTRC\"],\n",
    "            #   \"fc\":[5.236605052, 5.179753462, 4.724315075, 4.702706704, 4.65145949, 4.513887613, 4.351968886, 4.311988687, 4.272185339, 4.253882194, 4.208933992]\n",
    "              }\n",
    "\n",
    "deg_df = pd.DataFrame(test_genes)\n",
    "\n",
    "deg_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4cc90757",
   "metadata": {},
   "outputs": [],
   "source": [
    "Now that we have our data ready, we can proceed to run the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "be892376",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:Downloading gene set reference...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:root:Comparing the ranked gene list to reference gene sets...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done  40 tasks      | elapsed:    0.3s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 161 tasks      | elapsed:    0.9s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 364 tasks      | elapsed:    1.6s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 647 tasks      | elapsed:    2.9s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 1012 tasks      | elapsed:    4.3s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 1253 out of 1253 | elapsed:    5.5s finished\n"
     ]
    }
   ],
   "source": [
    "# get enrichment analysis result using dt.CELLiD_enrichment\n",
    "results = dt.CELLiD_enrichment(deg_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "77ac62f6",
   "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>pval</th>\n",
       "      <th>or</th>\n",
       "      <th>name</th>\n",
       "      <th>gene</th>\n",
       "      <th>background</th>\n",
       "      <th>overlap</th>\n",
       "      <th>geneset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>700.394</td>\n",
       "      <td>Marker of Acinar cell in pancreas_cell</td>\n",
       "      <td>CTRC,CELA3A,REG1B,REG3A,CPB1,CLPS,CPA1,PRSS1,P...</td>\n",
       "      <td>4228</td>\n",
       "      <td>11</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>609.570</td>\n",
       "      <td>Marker of Acinar cell in PDAC_pancreas</td>\n",
       "      <td>CTRC,CELA3A,CPB1,CLPS,CPA1,PRSS1,PLA2G1B,CTRB2...</td>\n",
       "      <td>4105</td>\n",
       "      <td>11</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
       "      <td>317.643</td>\n",
       "      <td>Marker of Paneth cell in intestine</td>\n",
       "      <td>REG1B,REG3A</td>\n",
       "      <td>4493</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>278.933</td>\n",
       "      <td>Marker of Acinar cell in type_1_diabetes_pancreas</td>\n",
       "      <td>CTRC,CELA3A,REG1B,CPB1,CLPS,CPA1,PRSS1,PLA2G1B...</td>\n",
       "      <td>1104</td>\n",
       "      <td>11</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>159.589</td>\n",
       "      <td>Marker of Acinar cell in type_2_diabetes_pancreas</td>\n",
       "      <td>CTRC,CELA3A,REG1B,REG3A,CPB1,CLPS,CPA1,PRSS1,P...</td>\n",
       "      <td>1543</td>\n",
       "      <td>11</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.0</td>\n",
       "      <td>154.783</td>\n",
       "      <td>type 1 diabetes vs control for T cell in type_...</td>\n",
       "      <td>REG1B,PRSS1,REG3A,CTRB2,CLPS,CTRB1,CELA3A,PLA2...</td>\n",
       "      <td>1104</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>117.889</td>\n",
       "      <td>type 1 diabetes vs control for Perivascular ce...</td>\n",
       "      <td>REG3A,REG1B,PRSS1,CELA3A,CTRB2,CLPS,PLA2G1B,CP...</td>\n",
       "      <td>1104</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>109.330</td>\n",
       "      <td>type 1 diabetes vs control for Delta cell in t...</td>\n",
       "      <td>REG3A,REG1B,PRSS1,CLPS,CELA3A,PLA2G1B,CTRB1,CTRB2</td>\n",
       "      <td>1104</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.0</td>\n",
       "      <td>89.915</td>\n",
       "      <td>type 1 diabetes vs control for Alpha cell in t...</td>\n",
       "      <td>REG3A,REG1B,PRSS1,CTRB2,CELA3A,CTRB1,CLPS,PLA2...</td>\n",
       "      <td>1104</td>\n",
       "      <td>9</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.0</td>\n",
       "      <td>39.280</td>\n",
       "      <td>type 1 diabetes vs control for Fibroblast in t...</td>\n",
       "      <td>REG1B,REG3A,CTRB2,PRSS1,CTRB1,PLA2G1B,CPB1,CEL...</td>\n",
       "      <td>1104</td>\n",
       "      <td>10</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    pval       or                                               name  \\\n",
       "9    0.0  700.394             Marker of Acinar cell in pancreas_cell   \n",
       "3    0.0  609.570             Marker of Acinar cell in PDAC_pancreas   \n",
       "22   0.0  317.643                 Marker of Paneth cell in intestine   \n",
       "13   0.0  278.933  Marker of Acinar cell in type_1_diabetes_pancreas   \n",
       "0    0.0  159.589  Marker of Acinar cell in type_2_diabetes_pancreas   \n",
       "21   0.0  154.783  type 1 diabetes vs control for T cell in type_...   \n",
       "20   0.0  117.889  type 1 diabetes vs control for Perivascular ce...   \n",
       "16   0.0  109.330  type 1 diabetes vs control for Delta cell in t...   \n",
       "14   0.0   89.915  type 1 diabetes vs control for Alpha cell in t...   \n",
       "18   0.0   39.280  type 1 diabetes vs control for Fibroblast in t...   \n",
       "\n",
       "                                                 gene background overlap  \\\n",
       "9   CTRC,CELA3A,REG1B,REG3A,CPB1,CLPS,CPA1,PRSS1,P...       4228      11   \n",
       "3   CTRC,CELA3A,CPB1,CLPS,CPA1,PRSS1,PLA2G1B,CTRB2...       4105      11   \n",
       "22                                        REG1B,REG3A       4493       2   \n",
       "13  CTRC,CELA3A,REG1B,CPB1,CLPS,CPA1,PRSS1,PLA2G1B...       1104      11   \n",
       "0   CTRC,CELA3A,REG1B,REG3A,CPB1,CLPS,CPA1,PRSS1,P...       1543      11   \n",
       "21  REG1B,PRSS1,REG3A,CTRB2,CLPS,CTRB1,CELA3A,PLA2...       1104       9   \n",
       "20  REG3A,REG1B,PRSS1,CELA3A,CTRB2,CLPS,PLA2G1B,CP...       1104       9   \n",
       "16  REG3A,REG1B,PRSS1,CLPS,CELA3A,PLA2G1B,CTRB1,CTRB2       1104       8   \n",
       "14  REG3A,REG1B,PRSS1,CTRB2,CELA3A,CTRB1,CLPS,PLA2...       1104       9   \n",
       "18  REG1B,REG3A,CTRB2,PRSS1,CTRB1,PLA2G1B,CPB1,CEL...       1104      10   \n",
       "\n",
       "   geneset  \n",
       "9       81  \n",
       "3       89  \n",
       "22      43  \n",
       "13      55  \n",
       "0      117  \n",
       "21      31  \n",
       "20      38  \n",
       "16      29  \n",
       "14      47  \n",
       "18     143  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the top 10 results\n",
    "results.head(10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "749c7dd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "We can visualize the results using a horizontal barplot with the help of the `seaborn` library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b0861d14",
   "metadata": {},
   "outputs": [
    {
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2q3Kev/zyy0ntVq1axR//+Mfo3bv3NuteuXJljBs3Lp588sliH6+K98Ty2FE+cwAAAAAAoLYRNAIAAACo5dLT0+PUU0+Nf/7znxERkZeXF2+++Wb069ev2O1HjBhRZGxF9evXLy688MJShxaaNWsWv/nNb2K33XZLutH9tddei5tuuilatGhRpuOvWbMm0tLSYsiQIZW26s3rr78e119/fdKv13fs2DEeffTR2H333bc59q9//WtkZ2cn2q1atYonnnhiu0GXpk2bxk033RQdO3aMO++8M9E/duzY+MUvfhHf+973SlX75pr79u0b999/f9SpU6dU4yrqwQcfLNJ3xx13bHMlnV122SX+/ve/x3XXXRevvfZaoj83NzeeeuqpGDx4cNL2Bx10UFLI67XXXksKGu29994pC9NVloKCgrjuuuuS5lZExL777htZWVlJfe3atYv//Oc/xd5oXZy6devGmWeeGfvss0+cf/75iVUhsrOzY+zYsXHaaaeVudaIiEsvvbTIudhSZmZm/PnPf46zzjorZs2alegfNWrUdm/6vv/++yMvLy/R7tChQzzxxBPbXe2sRYsWceedd0b79u2T5t6wYcPi6quvTgqp7b333rH33ntHRMTXX3+ddHP6LrvskpI5UxWvVUXcf//9sXbt2qS+M888M26//fZthtciIho2bBj9+vWLfv36xfz587d5jMo+lzujmj63UqGynmNlXPtVOc+3XvnuD3/4w3ZDRhHffZ8488wz44wzzogFCxYUebwq3hPLY0f5zAEAAAAAgNomvboLAAAAAKDitr4BdOTIkcVuN2/evJg+fXqiffjhh0fbtm0rfPz27duXawWh888/Pw4//PBEe926dfHOO++Uq4Zzzjmn0kJGTz75ZPz85z9PCoLst99+8fzzz283ZJSdnR3Dhw9P6rv33nvLtJrORRddlLRqz8aNG2PYsGGlHh8R0aZNm7jzzjurLGT04YcfxieffJLUd9ppp20zZLRZ3bp146677op27dol9b/44ouJm453BBs3bow333wzzjrrrJg8eXKRxy+99NIifRkZGaUOGW2pa9eucfXVVyf1jRs3rsz7iYg4+OCD47rrrtvudnXr1o1rrrkmqW/BggWxdOnSEsd88803MXbs2EQ7PT09/vSnP233hu8t/fSnP01ajaewsDBGjx5d6vGpVJmvVUXMmjUr3n777aS+nj17xh133LHdkNHWOnbsWGz/jnYua5qaOrdSqTY8x6qc57m5uUkrUjZq1Cjpu0FppKWlVcmKhqnkMwcAAAAAAKqeoBEAAADADqBr166JX6KPiBg/fnzSCjqbbbmaUUTRgFJ12HpFpS2DUKWVnp4eV111VapKSti4cWPcddddcdddd8XGjRsT/ccdd1w89dRT0bJly+3uY8yYMUkBpYMPPjiOOOKIMtdy/vnnJ7WLC6Zsy49+9KPIzMws83HLa8vVECK+O0eluVF4syZNmhQJxuTk5JT5eVenp59+OgYPHlzkz89+9rO48MILo2fPnnHVVVfF3Llzi4w94ogj4oQTTkhpPVtfax9//HG59nP11VdHenrp/mn5+9//fjRo0CCpb8vVJrY2YsSI2LBhQ6J99NFHJ93AXRppaWlx3nnnJfVNmjSpTPtIlcp8rSpi65vg09LS4tZbb01pEHFHO5c1TU2dW6lUG55jVc7zrZ9fRCQde0flMwcAAAAAAKqeoBEAAADADmLL0ND69etjzJgxSY9v2rQpRo0alWg3atQo5UGG8th6RaDy3Bjcs2fPIqvfVNSaNWvi5z//eTz55JNJ/QMHDoy//vWv0ahRo1LtZ+ubTY8//vhy1XPggQcmtcsaEtk6ZFLZPvroo6R27969o3379mXaR79+/Yq8zlvvtyabOnVqjBo1qsif1157LSZNmhR5eXnFjuvevXs88MADKa+nZcuW0bRp00R74cKFsXLlyjLtIysrK3r16lXq7evXrx977rlnUt+SJUtK3D5V18tBBx2U1C5vqKoiKvu1qoitA3u9e/cucuyK2pHOZU1Tk+dWqtSW51iV87xhw4ZJ33UKCgri5ZdfLtfxagufOQAAAAAAUD3qVncBAAAAAKTGKaecEn/84x8Tv8w+cuTIuPDCCxOPT548ORYsWJBo9+nTJzIyMiqllg0bNsTs2bNjzpw58eWXX8aqVasiPz8/CgsLY9OmTUnbbh10WLFiRZmPt//++1eo3q1lZ2fHlVdemRRqSUtLi1/+8pdx6aWXlmlfH3zwQVJ7woQJMW/evIj4Lvy19euxuV3S/262du3ayMvLiyZNmmy3hnbt2kXbtm3LVHdFzZgxI6l98MEHl3kfGRkZ0b1795g6dWqi75NPPqlwbTVVWlpaXHDBBTF48OBo2LBhqcd98803MWvWrJg7d27k5OTE6tWro6CgoMiciYhYt25dUnvFihVJ4aPt2X///SMtLa3U20dEkZW/SgpYbdiwIaZNm5bU98YbbySuw7JcL4WFhUnbrVixIjZt2lTm2iuiMl+rili3bl2R66h3794pPcaOdi5rmpo6t1KpNjzH6pjnxx9/fDz99NOJ9u9+97v47LPP4oILLojddtutws+ppvGZAwAAAAAA1UPQCAAAAGAH0bp16+jVq1dMmDAhIiKmT58e8+bNiz322CMiIkaMGJG0/ZYrIKXK/Pnz47HHHovXXnstli9fXq59rFq1qsxjOnfuXK5jFeerr76KW2+9Nb766qtEX/369eOee+6Jfv36lWlfhYWFsWzZsqS+9957LyV1RkTk5OSUKmiUytenNFavXh1r1qxJ6ttrr73Kta9u3bolBY3KO69qqvT09Nhjjz3i+OOPj7PPPjs6duxYqnEFBQUxdOjQGDVqVMyePbvcxy/rikatWrUq8zG2DjQWFBQUu11OTk6Rx955550yH684GzZsiFWrVpUpVFVRlflaVcSKFSti/fr1SX09evRI6TF2tHNZ09TUuZVKteE5Vsc8v+yyy2LMmDGRk5OT2G7o0KExdOjQ6NKlS/Tq1SsOOuig2GeffUr9eVKT+cwBAAAAAIDqIWgEAAAAsAPp379/ImgU8d2qRr/4xS9i7dq18dprryX6W7dunfIVLB555JF46KGHivyqe1nl5+eXeUxmZmaFjrml2267LTZu3JhoN2vWLB5++OE45JBDyryv3NzclNVVnLVr15Zqu1S+PqVRXHilWbNm5drX1uPKGoypTgMHDix2ta369etHZmZmNG3aNDp37lzmlcUmTpwYN954Y3z77bcVrrGsN+KXZxW0rVd02PL62tLmG+crS2mvl1SpzNeqIop7nZs3b17px0ilqj6XNU1NnVupVBueY3XM83bt2sXDDz8cV199dWRnZyc9NmfOnJgzZ0489dRTEfHdd71DDz00jjvuuDjuuOOicePGlVpvZfCZAwAAAAAA1UPQCAAAAGAHcsIJJ0Tjxo0TYZ2RI0fGz3/+83jzzTeTVgo65ZRTok6dOik77j333BOPPfZYyvZXVo0aNUrZvra+ITUzMzPat29frn2VZ3WmypDK16c0iguvNGzYsFz72rr2mr4Kx5YOPvjgOPnkk1O6z/Hjx8dVV11V4UDfZps2bUrJflIhLy+vukvYKRR3DaX6PcK5ZGdQXfP84IMPjpEjR8Zf//rXGDZsWJEVBDdbunRpjBkzJsaMGRMZGRkxcODAuPzyy0u1EuLOwPsUAAAAAACUTNAIAAAAYAfSqFGj+MEPfhDDhw+PiIgFCxbE1KlTY8SIEUnb9e/fP2XH/N///ldsyKh+/frRvXv32GuvvaJ169bRvHnzaNCgQdSrVy/p1+YXLVoU999/f8rqqajTTjstRo8enQgcffPNNzFw4MB44oknYrfddivTvor7Jf5rr7223MGlrbVt2zYl+0m14m5iXr16dbn2tfWNwFW9OlNNkpeXF7/+9a+LDRntueee0a1bt9hll12iZcuW0ahRo6hXr16RQOGdd95Z6SttlVdx18tvfvObyMrKSsn+mzZtmpL91HbFXUOpDkU6l+wMqnOet27dOm699dYYPHhwjBs3LiZOnBiTJ0+OBQsWFLv96tWr4x//+Ee88sor8Y9//CM6d+6ckhprM+9TAAAAAABQMkEjAAAAgB1M//79E0GjiIjHH388JkyYkGh37do19t5775Qd7+9//3tSu379+nHNNdfEeeedV6pQyLRp02pU0OiMM86IY445Jn71q1/F+vXrI+K7MNQFF1wQjz32WHTt2rXU+2revHmRvr322iuOPfbYlNVbEzVr1qxI37Jly8q1r6VLl2533zuL//u//yvyevTr1y+uvfba6NSpU6n2cffdd1dGaSlR3PXSo0ePOOSQQ6qhmh1XcTfRL1myJKXHcC7ZGdSEed6kSZM4/fTT4/TTT4+I7z5rp02bFh9++GG89957MWPGjKTtv/nmmxg0aFCMHj262KDNzqQmnD8AAAAAAKip0qu7AAAAAABS6/DDD09a6WbcuHGJwEzEdyv2pMqqVati8uTJSX233HJLXHbZZaVeeaa8AZTKdPLJJ8df/vKXqF+/fqJv6dKlceGFF8Ynn3xS6v3Ur18/WrVqldS39U2/O6IGDRoUuYG3vM9763Ht2rUrd1213ZtvvpnUPv744+P+++8vdcho3bp1NXY1o4jvAjCNGjVK6tsZrpeqlpWVVeT9+aOPPkr5MZxLdnQ1cZ63atUqjj/++Bg8eHC8/PLL8frrr8fJJ5+ctM3ChQvj6aefrqYKa46aeP4AAAAAAKCmEDQCAAAA2MGkp6fHKaecUuJjp556asqO9fXXXyeFmDIyMuKMM84o0z4++OCDlNWTSn369Im//e1vSTeh5uTkxMUXX1ymmg899NCk9uuvv56yGmuyAw44IKk9fvz4Mu/j22+/jTlz5mxzvzuTL774Iql94YUXlmn8tGnTYuPGjaksKaXq1KkTBx98cFLfG2+8UenHTUtLq/Rj1CTp6elFXuetQ2wVVV3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mxnm6jn9THZ8AAAAAANAQTSbRCOorLy8vTjrppIS6DR9q2ZhPPvkk3n333YS6DduqizPOOKPOSXD5+flx8cUXJ9QtW7Ys3njjjRqXHzNmTFRUVFSVDzjggISbd3WRl5cXJ598ckLdW2+9Va82mqJnn302oVxcXBynnXZandc/5JBD6n0sMuGoo45KKH/wwQcNakc/ry6dcbds2bJa3Te3nWmdO3eudsN+w+ShjVm3bl2MGzcuoW748OE1Lpvt+2HDhyny8vLit7/9bZ1/2akuNnyQokWLFtUefNmU4uLiOPvssxPq5s+fH1OmTKlXHOeff37k59dtCrr//vtXO3bf/EbgdDv77LOjVatWdV4+W/Z5tsqW/VPf49qUpHI8ZmK+lC19KllSeXxydQ6Vasma/6ZKOuYL+kZq5fI8qC5cZ6md8ZEartPUj3FSnes0jZfKftVYyRo7qfw7no553sbU9zNxro3z+hz/pjo+AQAAAACgISQasVkbPnx4tG7duqr86aef1umG5oYPygwYMCB23nnnem07Ly8vjjnmmHqts9dee8UWW2yRUDdp0qQal93wxtwhhxxSr219bcNvf822h+0yYfLkyQnlI488Mpo3b16vNo499thkhpQSnTp1ivbt21eV582bF8uXL69XG/p5zdIZd6tWraJbt25V5TVr1sSjjz7aoO2lyobj4YUXXqhTX3vuuedixYoVVeVWrVrF0KFDa1w22/fDxIkTE8r77LNP7Ljjjkndxvvvv59QPuSQQ6KoqKhebRx77LHVHnLZsN1NKSoqir333rvOy7do0aLafli4cGGd10+mgoKCjfavjcmGfZ7NsmH/NOS4NhWpHo+ZmC9lQ59KllQfn1ydQ6VaMua/qZSO+YK+kTq5PA+qD9dZamd8JJ/rNHVnnNTMdZrGSXW/aqxkjJ1U/x1PxzyvJg35TJxr47w+x78pjk8AAAAAAGioZpkOADKpffv2ceSRR8bDDz9cVTdq1KjYa6+9NrrO2rVrY8yYMQl1DfmW3e222y46d+5cr3Xy8/Nj4MCB8cwzz1TV1fQN6xUVFdVuzD733HNVD0lWVlZGZWVlwutflzf8b2lpacJyS5YsicrKysjLy6tX7E3F2rVrY8aMGQl1G94UrYuGrJMsX3zxRUybNi1mzpwZS5cujVWrVsWaNWuq9YmIiLKysoTykiVLEm7M1kY/ry4TcR9yyCFx//33V5V/85vfxPTp0+N//ud/Yvvtt2/0e2qsgw46KIqKimLp0qUR8d9fKnryySerfbPphjb85aPvfOc70a5du40un637oaysrFof32effZK6jZrOXbvvvnu92ykuLo6ePXvGxx9/XFVXnwdBvvWtb9V7XHXq1CmhvHLlynqtnyw9e/bcZP/aULbs82yVLfunvsc1lc4888zo3bt3g9fv2bNnvZZP5XjMxHwpW/pUsqTy+OTqHKox0jn/TZV0zBc2x76RTrk8D6oP11n+H+MjPVyncZ2msVynabxU9qtNSefYSeXf8XTM8zamvp+Js2mcp+r4N7XxCQAAAAAADSXRiM3eqaeemvAAzHPPPReLFy+O4uLiGpcfN25cwjfeFRYWxpFHHlnv7e600071DzYievXqlXADdtGiRdWWWbp0aaxZsyah7uWXX27Q9jZUUVERK1asyIqH7TJh8eLFsX79+oS6Xr161budnj17RkFBQVRUVCQrtE1as2ZN3HvvvfHEE08kPDhbX/X9tk/9vLpMxH3OOefEk08+WZXIU1FREffee2/ce++9sdNOO8Xee+8du+66a/Tv3z+6d++elFjqo0WLFnHUUUfFvffeW1U3evToTSYaLViwIF5//fWEuuOOO26T28nW/bBkyZIoLy9PqKvvt7fXZunSpdXOXQ1NaOjdu3fCeWTx4sV1Xre+Dx5FRLRt2zahvOH4SZcePXrUa/ls2efZKlv2T32Payrttddesf/++6dte6kcj5mYL2VLn0qWVB6fXJ1D1Vem5r+pkq75wubQNzIll+dB9eU6S/0YH43jOo3rNI3lOk3jpbJfbShTYyeVf8fTMc/bmIZc68jkOE/H8W9q4xMAAAAAABpKohGbvX79+sW3vvWtqm/iKysri0ceeSTOPvvsGpcfNWpUQnnYsGHRunXrem+3Q4cO9Q+2hvVquin29U2wVFm3bl1K289mNe3vhhzLgoKCaNeuXSxbtiwZYW3ShAkT4he/+EXMnz+/0W3V98E2/by6TMTdrVu3+Nvf/hbnn39+tYelP/nkk/jkk0/innvuiYiILl26xODBg+Pggw+Ogw8+ONq0aZPSeL82fPjwhESjSZMmxcyZMzf6Cx2PPfZYwsNkW2211Sa/JT0ie/dDTX2iY8eOSd1GTeeaho7PoqKiWtvemA0fsqmLDb/JdsOHCNOlvg+0Zcs+z1bZsn825wd6UzkeMzFfypY+lSypPD65Ooeqj0zOf1MlHfOFzaFvZFIuz4Pqy3WW+tvcx0djuE7jOk1juU7TeKnsV9+UybGT7vl5sud5G1Pfz8SZHOfpOv5NbXwCAAAAAEBD5XyiUWVlZTz44IMNXj8vLy+GDx8eLVq0SGJU5JpTTjml6gGYiIh///vfcdZZZ1W7GTht2rR4//33E+pOOumkBm2zVatWSVlv7dq11ZZZuXJlg9qmdjXd7GzMsUz1AyyvvPJKnHfeeVFaWpqU9iorK+u1vH5eXabi3m233eLxxx+Pv/71rzF69Oga92lERElJSTz55JPx5JNPRtu2bePUU0+Nc889N9q1a5fS+Pr16xe9e/eO6dOnV9U99thjcemll9a4/OjRoxPKw4YNq3bOrkk27oeaHm5oyIOVm1LT+0zl+GyK6ru/7PNNy5b909BtsmmZmC9lS5/KBbk6h6qrTM9/UyUd84Wm3jdIL9dZSBfXaVynaSzXaRovHfPuTI+dVErHPG9j6nvsMjVe0n38m9L4BAAAAACAhsqKRKOVK1dW+/bSoqKiOO6442pdt6KiIn772982avvLly+Pc845p1FtkNuGDh0aV199ddU38n3++ecxYcKE2GeffRKW27Cf7rbbbrHjjjs2aJurVq1Kyno13bSq6RsWL7vssmrfzt5Qvv0/0apVq6KwsLDebaX6xuzKlSvjl7/8ZY03YHfcccfo06dPbLXVVtGpU6do3bp1NG/ePAoKChKWu+qqqxr1kI1+Xl0m4+7SpUv89re/jZEjR8bzzz8fEyZMiIkTJ8bcuXNrXH7VqlVx++23x7hx4+L222/f6K8LJcvw4cPj6quvriqPGTMmLrnkksjPz09Y7v3334/Zs2dXlfPy8uLYY4+t83aybT/UdP5YsWJFUrdR0xhq6Pjc8NzVkPPf5sA+3zT7p2nLxHxJn6q7XJ1D1UU2zH9TJR3zhabcN0g/11nqx/hoONdp6sc4qc51msZLZb+KyI6xk0rpmOclSybGS6aOf1MZnwAAAAAA0FBZkWj04IMPxrXXXptQ9/vf/z5t27/lllti2LBh0bVr17Rtk+zSokWLOO644+LOO++sqnvwwQcTHoBZs2ZNPPHEEwnrNfRbdiMiFi1alJT1arr51rFjx2p1O++8c+y+++4N2ib/T003fhctWhTdunWrVztr1qyJ1atXJyusGj388MNRUlKSUDd06NC45JJLYtttt61TG99M+mgI/by6bIi7Xbt2MWzYsBg2bFhE/Hd/T5o0Kd577714/fXX48MPP0xY/osvvoizzjorxo4dW+MDBcly9NFHx3XXXRdlZWUREbFgwYJ47bXXYr/99ktY7tFHH00o77777nXu09+ULfuhpocxFi5cmLT2I2oeQyUlJdG3b996t1WX8Yl9Xhv7p2nLxHxJn6q7bJiLpEo2zH9TJR3zhabcN0g/11lIF9dp6sc4qS4b4s6W6xMNlcp+FZEdYyeV0jHPS5ZMjJdMH/9cH58AAAAAANBQ+bUvklplZWVxzz33JNQNGjQoTjjhhLTFsHr16vjzn/+ctu2RnU4++eSEX8148cUXE252PvnkkwnfJNixY8c4/PDDG7y9jz76KCnrbbXVVtWWKSoqitatWyfUbXjDi4bp1KlTtGrVKqGuIcfyo48+isrKymSFVaMXXnghoXzIIYfEDTfcUOcbsGVlZY3+pk/9vLpsjLtz585xyCGHxMiRI+PRRx+NZ599No488siEZebNmxf3339/SuMoLi6OAw44IKFu9OjRCeV169bFU089lVA3fPjwpGw/U/uhqKio2sNx77//flK30bFjx2rnrqlTpzaorQ3765ZbbtnguJoy+3zT7J+mLRPzJX2q7rJxLpIs2TD/TZV0zBeact8gM1xnIR1cp6kf46S6bIw7W67T1FUq+1VEdoydVErHPC9ZMjFesu3459r4BAAAAACAhsp4otGzzz4bCxYsSKi74oorIi8vL61xjB07NqtvNpEoFf2je/fuMWTIkKpyWVlZwq9mjBo1KmH5Y489Nlq0aNHg7S1atCimT59er3VWr14d7733XkLdt771rWrLFRQUxG677ZZQ99xzz9U/SKpp1qxZ9OvXL6Hutddeq3c7r7/+erJC2qjZs2cnlL///e/Xa/1JkybF+vXrGxVDU+znjT3/5ML43G677eJPf/pTHHzwwQn1L774Ysq3feyxxyaUn3/++YSHD59//vlYvnx5VblNmzZx2GGHpSSWdO2H/Pz8an1iw4coGqtZs2ax8847J9S98sor9W5n5syZMW/evIS6msZnJqV7Drkxm9M+bwj7p2nLxHwpU30qW8459ZELc5GGyob5b6qkY77QlPsGtXOd5f/Jps+f1M51mvppiuPEdZrMS2W/isiOsZNK6ZjnJUsmxku2H/9sH58AAAAAANBQWZFo9E277bZb9O/fv8Ht5efnx6WXXlqnf926datab926dTF27NgGb5f02vDBk/Ly8qS0e8oppySUR40aFZWVlfHRRx/F5MmTq+rz8vLipJNOavT2HnrooXotP27cuFi1alVC3aB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      "text/plain": [
       "<Figure size 1800x4500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# setting the theme to whitegrid\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "# Initialize the matplotlib figure\n",
    "f, ax = plt.subplots(figsize=(6, 15))\n",
    "\n",
    "# plot only the top 20 results\n",
    "sns.barplot(x=\"or\", y=\"name\", data=results.head(20), color=\"b\")\n",
    "\n",
    "# adding axis title\n",
    "ax.set(ylabel=\"Gene sets\",\n",
    "       xlabel=\"Odds Ratio\")\n",
    "\n",
    "# make the plot look cleaner and nicer\n",
    "sns.despine(left=True, bottom=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "disco",
   "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": "3.12.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}