GSEA_in_python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "214054ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install gseapy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d189a185",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from gseapy.plot import gseaplot\n",
    "import gseapy as gp\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "272eccc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "      <th>Gene</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000160072</td>\n",
       "      <td>320.481168</td>\n",
       "      <td>-0.492442</td>\n",
       "      <td>0.165330</td>\n",
       "      <td>-2.978533</td>\n",
       "      <td>0.002896</td>\n",
       "      <td>0.036899</td>\n",
       "      <td>ATAD3B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000041988</td>\n",
       "      <td>103.354977</td>\n",
       "      <td>-0.282331</td>\n",
       "      <td>0.168099</td>\n",
       "      <td>-1.679553</td>\n",
       "      <td>0.093044</td>\n",
       "      <td>0.359646</td>\n",
       "      <td>THAP3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000225630</td>\n",
       "      <td>922.811465</td>\n",
       "      <td>0.733032</td>\n",
       "      <td>0.375566</td>\n",
       "      <td>1.951807</td>\n",
       "      <td>0.050961</td>\n",
       "      <td>0.256948</td>\n",
       "      <td>MTND2P28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000131584</td>\n",
       "      <td>735.890219</td>\n",
       "      <td>-0.053477</td>\n",
       "      <td>0.219621</td>\n",
       "      <td>-0.243496</td>\n",
       "      <td>0.807621</td>\n",
       "      <td>0.938042</td>\n",
       "      <td>ACAP3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000169972</td>\n",
       "      <td>135.304763</td>\n",
       "      <td>-0.154869</td>\n",
       "      <td>0.187044</td>\n",
       "      <td>-0.827982</td>\n",
       "      <td>0.407681</td>\n",
       "      <td>0.727888</td>\n",
       "      <td>PUSL1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>10925</th>\n",
       "      <td>ENSG00000198886</td>\n",
       "      <td>97413.360359</td>\n",
       "      <td>0.131197</td>\n",
       "      <td>0.238091</td>\n",
       "      <td>0.551037</td>\n",
       "      <td>0.581608</td>\n",
       "      <td>0.836573</td>\n",
       "      <td>ND4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10926</th>\n",
       "      <td>ENSG00000198786</td>\n",
       "      <td>45775.084126</td>\n",
       "      <td>0.165326</td>\n",
       "      <td>0.206643</td>\n",
       "      <td>0.800055</td>\n",
       "      <td>0.423679</td>\n",
       "      <td>0.738798</td>\n",
       "      <td>ND5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10927</th>\n",
       "      <td>ENSG00000198695</td>\n",
       "      <td>10271.242782</td>\n",
       "      <td>0.184644</td>\n",
       "      <td>0.222985</td>\n",
       "      <td>0.828053</td>\n",
       "      <td>0.407640</td>\n",
       "      <td>0.727888</td>\n",
       "      <td>ND6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10928</th>\n",
       "      <td>ENSG00000198727</td>\n",
       "      <td>46929.471443</td>\n",
       "      <td>0.271033</td>\n",
       "      <td>0.258781</td>\n",
       "      <td>1.047347</td>\n",
       "      <td>0.294939</td>\n",
       "      <td>0.633176</td>\n",
       "      <td>CYTB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10930</th>\n",
       "      <td>ENSG00000271254</td>\n",
       "      <td>58.543851</td>\n",
       "      <td>0.317405</td>\n",
       "      <td>0.344324</td>\n",
       "      <td>0.921821</td>\n",
       "      <td>0.356622</td>\n",
       "      <td>0.688101</td>\n",
       "      <td>LOC102724250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10813 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unnamed: 0      baseMean  log2FoldChange     lfcSE      stat  \\\n",
       "0      ENSG00000160072    320.481168       -0.492442  0.165330 -2.978533   \n",
       "1      ENSG00000041988    103.354977       -0.282331  0.168099 -1.679553   \n",
       "2      ENSG00000225630    922.811465        0.733032  0.375566  1.951807   \n",
       "3      ENSG00000131584    735.890219       -0.053477  0.219621 -0.243496   \n",
       "4      ENSG00000169972    135.304763       -0.154869  0.187044 -0.827982   \n",
       "...                ...           ...             ...       ...       ...   \n",
       "10925  ENSG00000198886  97413.360359        0.131197  0.238091  0.551037   \n",
       "10926  ENSG00000198786  45775.084126        0.165326  0.206643  0.800055   \n",
       "10927  ENSG00000198695  10271.242782        0.184644  0.222985  0.828053   \n",
       "10928  ENSG00000198727  46929.471443        0.271033  0.258781  1.047347   \n",
       "10930  ENSG00000271254     58.543851        0.317405  0.344324  0.921821   \n",
       "\n",
       "         pvalue      padj          Gene  \n",
       "0      0.002896  0.036899        ATAD3B  \n",
       "1      0.093044  0.359646         THAP3  \n",
       "2      0.050961  0.256948      MTND2P28  \n",
       "3      0.807621  0.938042         ACAP3  \n",
       "4      0.407681  0.727888         PUSL1  \n",
       "...         ...       ...           ...  \n",
       "10925  0.581608  0.836573           ND4  \n",
       "10926  0.423679  0.738798           ND5  \n",
       "10927  0.407640  0.727888           ND6  \n",
       "10928  0.294939  0.633176          CYTB  \n",
       "10930  0.356622  0.688101  LOC102724250  \n",
       "\n",
       "[10813 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('DE_results.csv').dropna()\n",
    "df = df.rename(columns = {'symbol': 'Gene'})\n",
    "#df = df[df.padj < 0.05] it is best practice not to filter\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea48c308",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "      <th>Gene</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000261115</td>\n",
       "      <td>193.721808</td>\n",
       "      <td>4.741075</td>\n",
       "      <td>0.537213</td>\n",
       "      <td>8.825318</td>\n",
       "      <td>1.091489e-18</td>\n",
       "      <td>1.672817e-14</td>\n",
       "      <td>TMEM178B</td>\n",
       "      <td>65.315669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENSG00000145390</td>\n",
       "      <td>2016.886474</td>\n",
       "      <td>2.249586</td>\n",
       "      <td>0.263078</td>\n",
       "      <td>8.551018</td>\n",
       "      <td>1.220076e-17</td>\n",
       "      <td>6.232962e-14</td>\n",
       "      <td>USP53</td>\n",
       "      <td>29.706469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000109846</td>\n",
       "      <td>524.720804</td>\n",
       "      <td>2.398381</td>\n",
       "      <td>0.348668</td>\n",
       "      <td>6.878701</td>\n",
       "      <td>6.040066e-12</td>\n",
       "      <td>9.257006e-09</td>\n",
       "      <td>CRYAB</td>\n",
       "      <td>19.267466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENSG00000106483</td>\n",
       "      <td>7321.978396</td>\n",
       "      <td>3.438204</td>\n",
       "      <td>0.602508</td>\n",
       "      <td>5.706491</td>\n",
       "      <td>1.153292e-08</td>\n",
       "      <td>3.668965e-06</td>\n",
       "      <td>SFRP4</td>\n",
       "      <td>18.688209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000277586</td>\n",
       "      <td>78.098744</td>\n",
       "      <td>3.388113</td>\n",
       "      <td>0.677038</td>\n",
       "      <td>5.004316</td>\n",
       "      <td>5.606066e-07</td>\n",
       "      <td>6.411834e-05</td>\n",
       "      <td>NEFL</td>\n",
       "      <td>14.206417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>10808</th>\n",
       "      <td>ENSG00000203760</td>\n",
       "      <td>107.203668</td>\n",
       "      <td>-1.974640</td>\n",
       "      <td>0.272572</td>\n",
       "      <td>-7.244486</td>\n",
       "      <td>4.340832e-13</td>\n",
       "      <td>1.108793e-09</td>\n",
       "      <td>CENPW</td>\n",
       "      <td>-17.683201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809</th>\n",
       "      <td>ENSG00000170011</td>\n",
       "      <td>53.464280</td>\n",
       "      <td>-4.163618</td>\n",
       "      <td>0.803520</td>\n",
       "      <td>-5.181720</td>\n",
       "      <td>2.198487e-07</td>\n",
       "      <td>3.311684e-05</td>\n",
       "      <td>MYRIP</td>\n",
       "      <td>-18.652804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10810</th>\n",
       "      <td>ENSG00000176208</td>\n",
       "      <td>75.279706</td>\n",
       "      <td>-2.164796</td>\n",
       "      <td>0.304987</td>\n",
       "      <td>-7.097984</td>\n",
       "      <td>1.265898e-12</td>\n",
       "      <td>2.155684e-09</td>\n",
       "      <td>ATAD5</td>\n",
       "      <td>-18.761020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10811</th>\n",
       "      <td>ENSG00000024526</td>\n",
       "      <td>341.005688</td>\n",
       "      <td>-2.632820</td>\n",
       "      <td>0.386997</td>\n",
       "      <td>-6.803209</td>\n",
       "      <td>1.023138e-11</td>\n",
       "      <td>1.425510e-08</td>\n",
       "      <td>DEPDC1</td>\n",
       "      <td>-20.657185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10812</th>\n",
       "      <td>ENSG00000211772</td>\n",
       "      <td>80.031143</td>\n",
       "      <td>-6.688654</td>\n",
       "      <td>1.082359</td>\n",
       "      <td>-6.179698</td>\n",
       "      <td>6.422429e-10</td>\n",
       "      <td>3.785775e-07</td>\n",
       "      <td>TRBC2</td>\n",
       "      <td>-42.953503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10813 rows × 9 columns</p>\n",
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       "            Unnamed: 0     baseMean  log2FoldChange     lfcSE      stat  \\\n",
       "0      ENSG00000261115   193.721808        4.741075  0.537213  8.825318   \n",
       "1      ENSG00000145390  2016.886474        2.249586  0.263078  8.551018   \n",
       "2      ENSG00000109846   524.720804        2.398381  0.348668  6.878701   \n",
       "3      ENSG00000106483  7321.978396        3.438204  0.602508  5.706491   \n",
       "4      ENSG00000277586    78.098744        3.388113  0.677038  5.004316   \n",
       "...                ...          ...             ...       ...       ...   \n",
       "10808  ENSG00000203760   107.203668       -1.974640  0.272572 -7.244486   \n",
       "10809  ENSG00000170011    53.464280       -4.163618  0.803520 -5.181720   \n",
       "10810  ENSG00000176208    75.279706       -2.164796  0.304987 -7.097984   \n",
       "10811  ENSG00000024526   341.005688       -2.632820  0.386997 -6.803209   \n",
       "10812  ENSG00000211772    80.031143       -6.688654  1.082359 -6.179698   \n",
       "\n",
       "             pvalue          padj      Gene       Rank  \n",
       "0      1.091489e-18  1.672817e-14  TMEM178B  65.315669  \n",
       "1      1.220076e-17  6.232962e-14     USP53  29.706469  \n",
       "2      6.040066e-12  9.257006e-09     CRYAB  19.267466  \n",
       "3      1.153292e-08  3.668965e-06     SFRP4  18.688209  \n",
       "4      5.606066e-07  6.411834e-05      NEFL  14.206417  \n",
       "...             ...           ...       ...        ...  \n",
       "10808  4.340832e-13  1.108793e-09     CENPW -17.683201  \n",
       "10809  2.198487e-07  3.311684e-05     MYRIP -18.652804  \n",
       "10810  1.265898e-12  2.155684e-09     ATAD5 -18.761020  \n",
       "10811  1.023138e-11  1.425510e-08    DEPDC1 -20.657185  \n",
       "10812  6.422429e-10  3.785775e-07     TRBC2 -42.953503  \n",
       "\n",
       "[10813 rows x 9 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Rank'] = -np.log10(df.padj)*df.log2FoldChange\n",
    "\n",
    "df = df.sort_values('Rank', ascending = False).reset_index(drop = True)\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9d10aa29",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Gene</th>\n",
       "      <th>Rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TMEM178B</td>\n",
       "      <td>65.315669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>USP53</td>\n",
       "      <td>29.706469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CRYAB</td>\n",
       "      <td>19.267466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SFRP4</td>\n",
       "      <td>18.688209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NEFL</td>\n",
       "      <td>14.206417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>10808</th>\n",
       "      <td>CENPW</td>\n",
       "      <td>-17.683201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10809</th>\n",
       "      <td>MYRIP</td>\n",
       "      <td>-18.652804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10810</th>\n",
       "      <td>ATAD5</td>\n",
       "      <td>-18.761020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10811</th>\n",
       "      <td>DEPDC1</td>\n",
       "      <td>-20.657185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10812</th>\n",
       "      <td>TRBC2</td>\n",
       "      <td>-42.953503</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10813 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Gene       Rank\n",
       "0      TMEM178B  65.315669\n",
       "1         USP53  29.706469\n",
       "2         CRYAB  19.267466\n",
       "3         SFRP4  18.688209\n",
       "4          NEFL  14.206417\n",
       "...         ...        ...\n",
       "10808     CENPW -17.683201\n",
       "10809     MYRIP -18.652804\n",
       "10810     ATAD5 -18.761020\n",
       "10811    DEPDC1 -20.657185\n",
       "10812     TRBC2 -42.953503\n",
       "\n",
       "[10813 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranking = df[['Gene', 'Rank']]\n",
    "ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2b2241a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ARCHS4_Cell-lines',\n",
       " 'ARCHS4_IDG_Coexp',\n",
       " 'ARCHS4_Kinases_Coexp',\n",
       " 'ARCHS4_TFs_Coexp',\n",
       " 'ARCHS4_Tissues',\n",
       " 'Achilles_fitness_decrease',\n",
       " 'Achilles_fitness_increase',\n",
       " 'Aging_Perturbations_from_GEO_down',\n",
       " 'Aging_Perturbations_from_GEO_up',\n",
       " 'Allen_Brain_Atlas_10x_scRNA_2021',\n",
       " 'Allen_Brain_Atlas_down',\n",
       " 'Allen_Brain_Atlas_up',\n",
       " 'Azimuth_Cell_Types_2021',\n",
       " 'BioCarta_2013',\n",
       " 'BioCarta_2015',\n",
       " 'BioCarta_2016',\n",
       " 'BioPlanet_2019',\n",
       " 'BioPlex_2017',\n",
       " 'CCLE_Proteomics_2020',\n",
       " 'CORUM',\n",
       " 'COVID-19_Related_Gene_Sets',\n",
       " 'COVID-19_Related_Gene_Sets_2021',\n",
       " 'Cancer_Cell_Line_Encyclopedia',\n",
       " 'CellMarker_Augmented_2021',\n",
       " 'ChEA_2013',\n",
       " 'ChEA_2015',\n",
       " 'ChEA_2016',\n",
       " 'Chromosome_Location',\n",
       " 'Chromosome_Location_hg19',\n",
       " 'ClinVar_2019',\n",
       " 'DSigDB',\n",
       " 'Data_Acquisition_Method_Most_Popular_Genes',\n",
       " 'DepMap_WG_CRISPR_Screens_Broad_CellLines_2019',\n",
       " 'DepMap_WG_CRISPR_Screens_Sanger_CellLines_2019',\n",
       " 'Descartes_Cell_Types_and_Tissue_2021',\n",
       " 'DisGeNET',\n",
       " 'Disease_Perturbations_from_GEO_down',\n",
       " 'Disease_Perturbations_from_GEO_up',\n",
       " 'Disease_Signatures_from_GEO_down_2014',\n",
       " 'Disease_Signatures_from_GEO_up_2014',\n",
       " 'DrugMatrix',\n",
       " 'Drug_Perturbations_from_GEO_2014',\n",
       " 'Drug_Perturbations_from_GEO_down',\n",
       " 'Drug_Perturbations_from_GEO_up',\n",
       " 'ENCODE_Histone_Modifications_2013',\n",
       " 'ENCODE_Histone_Modifications_2015',\n",
       " 'ENCODE_TF_ChIP-seq_2014',\n",
       " 'ENCODE_TF_ChIP-seq_2015',\n",
       " 'ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X',\n",
       " 'ESCAPE',\n",
       " 'Elsevier_Pathway_Collection',\n",
       " 'Enrichr_Libraries_Most_Popular_Genes',\n",
       " 'Enrichr_Submissions_TF-Gene_Coocurrence',\n",
       " 'Enrichr_Users_Contributed_Lists_2020',\n",
       " 'Epigenomics_Roadmap_HM_ChIP-seq',\n",
       " 'FANTOM6_lncRNA_KD_DEGs',\n",
       " 'GO_Biological_Process_2013',\n",
       " 'GO_Biological_Process_2015',\n",
       " 'GO_Biological_Process_2017',\n",
       " 'GO_Biological_Process_2017b',\n",
       " 'GO_Biological_Process_2018',\n",
       " 'GO_Biological_Process_2021',\n",
       " 'GO_Cellular_Component_2013',\n",
       " 'GO_Cellular_Component_2015',\n",
       " 'GO_Cellular_Component_2017',\n",
       " 'GO_Cellular_Component_2017b',\n",
       " 'GO_Cellular_Component_2018',\n",
       " 'GO_Cellular_Component_2021',\n",
       " 'GO_Molecular_Function_2013',\n",
       " 'GO_Molecular_Function_2015',\n",
       " 'GO_Molecular_Function_2017',\n",
       " 'GO_Molecular_Function_2017b',\n",
       " 'GO_Molecular_Function_2018',\n",
       " 'GO_Molecular_Function_2021',\n",
       " 'GTEx_Aging_Signatures_2021',\n",
       " 'GTEx_Tissue_Expression_Down',\n",
       " 'GTEx_Tissue_Expression_Up',\n",
       " 'GWAS_Catalog_2019',\n",
       " 'GeneSigDB',\n",
       " 'Gene_Perturbations_from_GEO_down',\n",
       " 'Gene_Perturbations_from_GEO_up',\n",
       " 'Genes_Associated_with_NIH_Grants',\n",
       " 'Genome_Browser_PWMs',\n",
       " 'HDSigDB_Human_2021',\n",
       " 'HDSigDB_Mouse_2021',\n",
       " 'HMDB_Metabolites',\n",
       " 'HMS_LINCS_KinomeScan',\n",
       " 'HomoloGene',\n",
       " 'HuBMAP_ASCT_plus_B_augmented_w_RNAseq_Coexpression',\n",
       " 'HuBMAP_ASCTplusB_augmented_2022',\n",
       " 'HumanCyc_2015',\n",
       " 'HumanCyc_2016',\n",
       " 'Human_Gene_Atlas',\n",
       " 'Human_Phenotype_Ontology',\n",
       " 'InterPro_Domains_2019',\n",
       " 'Jensen_COMPARTMENTS',\n",
       " 'Jensen_DISEASES',\n",
       " 'Jensen_TISSUES',\n",
       " 'KEA_2013',\n",
       " 'KEA_2015',\n",
       " 'KEGG_2013',\n",
       " 'KEGG_2015',\n",
       " 'KEGG_2016',\n",
       " 'KEGG_2019_Human',\n",
       " 'KEGG_2019_Mouse',\n",
       " 'KEGG_2021_Human',\n",
       " 'Kinase_Perturbations_from_GEO_down',\n",
       " 'Kinase_Perturbations_from_GEO_up',\n",
       " 'L1000_Kinase_and_GPCR_Perturbations_down',\n",
       " 'L1000_Kinase_and_GPCR_Perturbations_up',\n",
       " 'LINCS_L1000_Chem_Pert_down',\n",
       " 'LINCS_L1000_Chem_Pert_up',\n",
       " 'LINCS_L1000_Ligand_Perturbations_down',\n",
       " 'LINCS_L1000_Ligand_Perturbations_up',\n",
       " 'Ligand_Perturbations_from_GEO_down',\n",
       " 'Ligand_Perturbations_from_GEO_up',\n",
       " 'MAGMA_Drugs_and_Diseases',\n",
       " 'MCF7_Perturbations_from_GEO_down',\n",
       " 'MCF7_Perturbations_from_GEO_up',\n",
       " 'MGI_Mammalian_Phenotype_2013',\n",
       " 'MGI_Mammalian_Phenotype_2017',\n",
       " 'MGI_Mammalian_Phenotype_Level_3',\n",
       " 'MGI_Mammalian_Phenotype_Level_4',\n",
       " 'MGI_Mammalian_Phenotype_Level_4_2019',\n",
       " 'MGI_Mammalian_Phenotype_Level_4_2021',\n",
       " 'MSigDB_Computational',\n",
       " 'MSigDB_Hallmark_2020',\n",
       " 'MSigDB_Oncogenic_Signatures',\n",
       " 'Microbe_Perturbations_from_GEO_down',\n",
       " 'Microbe_Perturbations_from_GEO_up',\n",
       " 'Mouse_Gene_Atlas',\n",
       " 'NCI-60_Cancer_Cell_Lines',\n",
       " 'NCI-Nature_2015',\n",
       " 'NCI-Nature_2016',\n",
       " 'NIH_Funded_PIs_2017_AutoRIF_ARCHS4_Predictions',\n",
       " 'NIH_Funded_PIs_2017_GeneRIF_ARCHS4_Predictions',\n",
       " 'NIH_Funded_PIs_2017_Human_AutoRIF',\n",
       " 'NIH_Funded_PIs_2017_Human_GeneRIF',\n",
       " 'NURSA_Human_Endogenous_Complexome',\n",
       " 'OMIM_Disease',\n",
       " 'OMIM_Expanded',\n",
       " 'Old_CMAP_down',\n",
       " 'Old_CMAP_up',\n",
       " 'Orphanet_Augmented_2021',\n",
       " 'PFOCR_Pathways',\n",
       " 'PPI_Hub_Proteins',\n",
       " 'PanglaoDB_Augmented_2021',\n",
       " 'Panther_2015',\n",
       " 'Panther_2016',\n",
       " 'Pfam_Domains_2019',\n",
       " 'Pfam_InterPro_Domains',\n",
       " 'PheWeb_2019',\n",
       " 'PhenGenI_Association_2021',\n",
       " 'Phosphatase_Substrates_from_DEPOD',\n",
       " 'ProteomicsDB_2020',\n",
       " 'RNA-Seq_Disease_Gene_and_Drug_Signatures_from_GEO',\n",
       " 'RNAseq_Automatic_GEO_Signatures_Human_Down',\n",
       " 'RNAseq_Automatic_GEO_Signatures_Human_Up',\n",
       " 'RNAseq_Automatic_GEO_Signatures_Mouse_Down',\n",
       " 'RNAseq_Automatic_GEO_Signatures_Mouse_Up',\n",
       " 'Rare_Diseases_AutoRIF_ARCHS4_Predictions',\n",
       " 'Rare_Diseases_AutoRIF_Gene_Lists',\n",
       " 'Rare_Diseases_GeneRIF_ARCHS4_Predictions',\n",
       " 'Rare_Diseases_GeneRIF_Gene_Lists',\n",
       " 'Reactome_2013',\n",
       " 'Reactome_2015',\n",
       " 'Reactome_2016',\n",
       " 'SILAC_Phosphoproteomics',\n",
       " 'SubCell_BarCode',\n",
       " 'SysMyo_Muscle_Gene_Sets',\n",
       " 'TF-LOF_Expression_from_GEO',\n",
       " 'TF_Perturbations_Followed_by_Expression',\n",
       " 'TG_GATES_2020',\n",
       " 'TRANSFAC_and_JASPAR_PWMs',\n",
       " 'TRRUST_Transcription_Factors_2019',\n",
       " 'Table_Mining_of_CRISPR_Studies',\n",
       " 'TargetScan_microRNA',\n",
       " 'TargetScan_microRNA_2017',\n",
       " 'Tissue_Protein_Expression_from_Human_Proteome_Map',\n",
       " 'Tissue_Protein_Expression_from_ProteomicsDB',\n",
       " 'Transcription_Factor_PPIs',\n",
       " 'UK_Biobank_GWAS_v1',\n",
       " 'Virus-Host_PPI_P-HIPSTer_2020',\n",
       " 'VirusMINT',\n",
       " 'Virus_Perturbations_from_GEO_down',\n",
       " 'Virus_Perturbations_from_GEO_up',\n",
       " 'WikiPathway_2021_Human',\n",
       " 'WikiPathways_2013',\n",
       " 'WikiPathways_2015',\n",
       " 'WikiPathways_2016',\n",
       " 'WikiPathways_2019_Human',\n",
       " 'WikiPathways_2019_Mouse',\n",
       " 'dbGaP',\n",
       " 'huMAP',\n",
       " 'lncHUB_lncRNA_Co-Expression',\n",
       " 'miRTarBase_2017']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp.get_library_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f665339",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_res = gp.prerank(rnk = ranking, gene_sets = 'GO_Biological_Process_2021', seed = 6, permutation_num = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "60a0dc53",
   "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>Term</th>\n",
       "      <th>fdr</th>\n",
       "      <th>es</th>\n",
       "      <th>nes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>double-strand break repair (GO:0006302)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.856192</td>\n",
       "      <td>-1.756167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>regulation of cell cycle process (GO:0010564)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.900505</td>\n",
       "      <td>-1.761875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mitotic sister chromatid segregation (GO:0000070)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.925483</td>\n",
       "      <td>-1.806771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DNA metabolic process (GO:0006259)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.888812</td>\n",
       "      <td>-1.955188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>cellular response to DNA damage stimulus (GO:0...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.822049</td>\n",
       "      <td>-1.749057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>mitochondrial calcium ion homeostasis (GO:0051...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.324106</td>\n",
       "      <td>0.518098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>positive regulation of protein serine/threonin...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.367671</td>\n",
       "      <td>-0.716632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>membrane protein proteolysis (GO:0033619)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.413640</td>\n",
       "      <td>-0.716520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>positive regulation of dendrite morphogenesis ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.272526</td>\n",
       "      <td>0.491416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021</th>\n",
       "      <td>regulation of synapse organization (GO:0050807)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.263382</td>\n",
       "      <td>0.439428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2022 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   Term  fdr        es  \\\n",
       "0               double-strand break repair (GO:0006302)  0.0 -0.856192   \n",
       "1         regulation of cell cycle process (GO:0010564)  0.0 -0.900505   \n",
       "2     mitotic sister chromatid segregation (GO:0000070)  0.0 -0.925483   \n",
       "3                    DNA metabolic process (GO:0006259)  0.0 -0.888812   \n",
       "4     cellular response to DNA damage stimulus (GO:0...  0.0 -0.822049   \n",
       "...                                                 ...  ...       ...   \n",
       "2017  mitochondrial calcium ion homeostasis (GO:0051...  1.0  0.324106   \n",
       "2018  positive regulation of protein serine/threonin...  1.0 -0.367671   \n",
       "2019          membrane protein proteolysis (GO:0033619)  1.0 -0.413640   \n",
       "2020  positive regulation of dendrite morphogenesis ...  1.0  0.272526   \n",
       "2021    regulation of synapse organization (GO:0050807)  1.0  0.263382   \n",
       "\n",
       "           nes  \n",
       "0    -1.756167  \n",
       "1    -1.761875  \n",
       "2    -1.806771  \n",
       "3    -1.955188  \n",
       "4    -1.749057  \n",
       "...        ...  \n",
       "2017  0.518098  \n",
       "2018 -0.716632  \n",
       "2019 -0.716520  \n",
       "2020  0.491416  \n",
       "2021  0.439428  \n",
       "\n",
       "[2022 rows x 4 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = []\n",
    "\n",
    "for term in list(pre_res.results):\n",
    "    out.append([term,\n",
    "               pre_res.results[term]['fdr'],\n",
    "               pre_res.results[term]['es'],\n",
    "               pre_res.results[term]['nes']])\n",
    "\n",
    "out_df = pd.DataFrame(out, columns = ['Term','fdr', 'es', 'nes']).sort_values('fdr').reset_index(drop = True)\n",
    "out_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "03d7503c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'double-strand break repair (GO:0006302)'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "term_to_graph = out_df.iloc[0].Term\n",
    "term_to_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6ed09187",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x396 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gseaplot(pre_res.ranking, term = term_to_graph, **pre_res.results[term_to_graph])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "968fd462",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38cd40d9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40034503",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Example of user set of genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0591bf0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#this block is just me making a enriched set of genes in the DE upregulated genes\n",
    "#not required for GSEA\n",
    "import random\n",
    "\n",
    "def a_thing(x):\n",
    "    if x < 0:\n",
    "        return 1\n",
    "    else:\n",
    "        return x\n",
    "df['weights'] = df.Rank.map(a_thing)\n",
    "\n",
    "a_list = random.choices(df.Gene.values, k = 100, weights = df.weights.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa1047d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_set = {'set_name1' : a_list}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e34daff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre_res = gp.prerank(rnk = ranking, gene_sets = user_set, seed = 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b06c517",
   "metadata": {},
   "outputs": [],
   "source": [
    "gseaplot(pre_res.ranking, term = 'set_name1', **pre_res.results['set_name1']) #ofname = 'saved_fig.png'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43cd3dd6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}