{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d1f56416-391c-4842-9d59-1f88776d30c6",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "# Comparison of Preranked Gene Set Enrichment analysis(GSEA), traditional GSEA and fGSEA in Python and R\n",
    "\n",
    "Author: Shakira Agata \n",
    "\n",
    "This Jupyter notebook describes the steps for the execution and comparison of methods: preranked GSEA, traditional GSEA and fGSEA (Fast Gene Set Enrichment Analysis). My goal was to identify the potential difference in outcomes when using three different methods of GSEA: preranked GSEA from GSEApy, traditional GSEA from GSEApy and fGSEA from Bioconductor(1,2). I did this by comparing the outcomes in identified pathways, calculated Enrichment Scores (ES) and Normalized Enrichment Scores for the three methods. The comparison was made using the data for Ag+ and control at 24 hours from ArrayExpress dataset: E-MEXP-3583. This dataset looked at  the effect of Ag+ and AgNPs on the gene expression in a human lung epithelial cell line A549. \n",
    "\n",
    "This notebook is subdivided into the following two sections:\n",
    "\n",
    "* Section 1: Preranked GSEA\n",
    "  * Section 1.1: System preparation\n",
    "  * Section 1.2: Generation of the RNK file\n",
    "  * Section 1.3: Selection of the geneset\n",
    "  * Section 1.4: Execution of GSEA\n",
    "* Section 2: Traditional GSEA\n",
    "  * Section 2.1: Generation of expression data frame\n",
    "  * Section 2.2: Generation of the CLS file\n",
    "  * Section 2.3: Selection of the geneset\n",
    "  * Section 2.4: Execution of GSEA\n",
    "* Section 3: fGSEA\n",
    "  * Section 3.1 Magic R extension and system preparation\n",
    "  * Section 3.2 Execution of fGSEA\n",
    "* Section 4: Session information for Python and R\n",
    "  * Section 4.1: Python session information\n",
    "  * Section 4.2: R session information"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4205aa02-0287-4d9a-9d08-f57dc3b114c3",
   "metadata": {},
   "source": [
    "## Section 1: Preranked GSEA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6046535-4348-4aea-8277-a4ec3de935fb",
   "metadata": {},
   "source": [
    "In this section, the preranked GSEA method will be used which allows users to select the ranking method for the comparison of two group of samples (e.g control and treatment). In this case, the differential gene expression results for the comparison of Ag+ to the control at 24h was used to allow for the calculation of the ranking by the log2FC. This ranking method is used in the formal documentation of GSEA and can be used as option in fGSEA and Clusterprofiler GSEA in Rstudio (1)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36049303-1f2e-485d-b064-8399053c25f9",
   "metadata": {},
   "source": [
    "### Section 1.1: System preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f724e382-75b0-4810-8490-ccc80c36da5c",
   "metadata": {},
   "source": [
    "**Step 1.** The necessary packages for using this pipeline are first installed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c6a4210f-1bc7-48d5-aadd-4a322f79be52",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from gseapy.plot import gseaplot\n",
    "import gseapy as gp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from gseapy import dotplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5afcdb41-97a8-487a-8122-3fa1349aef8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: watermark in c:\\users\\shaki\\anaconda3\\lib\\site-packages (2.4.3)\n",
      "Requirement already satisfied: ipython>=6.0 in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from watermark) (8.25.0)\n",
      "Requirement already satisfied: importlib-metadata>=1.4 in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from watermark) (7.0.1)\n",
      "Requirement already satisfied: setuptools in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from watermark) (69.5.1)\n",
      "Requirement already satisfied: zipp>=0.5 in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from importlib-metadata>=1.4->watermark) (3.17.0)\n",
      "Requirement already satisfied: decorator in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from ipython>=6.0->watermark) (5.1.1)\n",
      "Requirement already satisfied: jedi>=0.16 in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from ipython>=6.0->watermark) (0.18.1)\n",
      "Requirement already satisfied: matplotlib-inline in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from ipython>=6.0->watermark) (0.1.6)\n",
      "Requirement already satisfied: prompt-toolkit<3.1.0,>=3.0.41 in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from ipython>=6.0->watermark) (3.0.43)\n",
      "Requirement already satisfied: pygments>=2.4.0 in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from ipython>=6.0->watermark) (2.15.1)\n",
      "Requirement already satisfied: stack-data in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from ipython>=6.0->watermark) (0.2.0)\n",
      "Requirement already satisfied: traitlets>=5.13.0 in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from ipython>=6.0->watermark) (5.14.3)\n",
      "Requirement already satisfied: colorama in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from ipython>=6.0->watermark) (0.4.6)\n",
      "Requirement already satisfied: parso<0.9.0,>=0.8.0 in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from jedi>=0.16->ipython>=6.0->watermark) (0.8.3)\n",
      "Requirement already satisfied: wcwidth in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from prompt-toolkit<3.1.0,>=3.0.41->ipython>=6.0->watermark) (0.2.5)\n",
      "Requirement already satisfied: executing in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from stack-data->ipython>=6.0->watermark) (0.8.3)\n",
      "Requirement already satisfied: asttokens in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from stack-data->ipython>=6.0->watermark) (2.0.5)\n",
      "Requirement already satisfied: pure-eval in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from stack-data->ipython>=6.0->watermark) (0.2.2)\n",
      "Requirement already satisfied: six in c:\\users\\shaki\\anaconda3\\lib\\site-packages (from asttokens->stack-data->ipython>=6.0->watermark) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[notice] A new release of pip is available: 24.3.1 -> 25.0.1\n",
      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
     ]
    }
   ],
   "source": [
    "pip install watermark"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1aed12e-3d61-480f-9f55-537c1261e1a5",
   "metadata": {},
   "source": [
    "### Section 1.2: Generation of the RNK file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c34b56ea-2da2-44cc-86a4-c50c5a1bf6b4",
   "metadata": {},
   "source": [
    "**Step 2.** Next, you load the data from E-MEXP3583 into a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1a2bb5d5-a245-4ddd-92ff-5e4f7ea483ce",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>GeneID</th>\n",
       "      <th>meanExpr</th>\n",
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       "      <th>log2FC SE</th>\n",
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       "      <td>-0.000033</td>\n",
       "      <td>0.167015</td>\n",
       "      <td>9.998260e-01</td>\n",
       "      <td>0.999923</td>\n",
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       "      <td>0.199487</td>\n",
       "      <td>9.999336e-01</td>\n",
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       "      <td>0.999985</td>\n",
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       "<p>20518 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "       GeneID   meanExpr    log2FC  log2FC SE       p-value  adjpvalue\n",
       "0        4490   9.925881  3.935193   0.206737  9.842270e-11   0.000002\n",
       "1        4495   9.561522  4.109887   0.243653  3.924060e-10   0.000004\n",
       "2       79974   6.381454  2.532636   0.174021  2.094514e-09   0.000011\n",
       "3        4489  12.843381  2.498127   0.172052  2.150589e-09   0.000011\n",
       "4        1638   6.429810  2.349836   0.170834  3.954792e-09   0.000016\n",
       "...       ...        ...       ...        ...           ...        ...\n",
       "20513  391257   6.462645 -0.000055   0.146291  9.996750e-01   0.999870\n",
       "20514   10078   8.351476 -0.000037   0.128166  9.997478e-01   0.999894\n",
       "20515   84328   9.130185 -0.000033   0.167015  9.998260e-01   0.999923\n",
       "20516  253018   7.162925 -0.000015   0.199487  9.999336e-01   0.999982\n",
       "20517  440737  10.365751  0.000003   0.187502  9.999848e-01   0.999985\n",
       "\n",
       "[20518 rows x 6 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv('topTable_Ag._.1.3_24 - H2O.control_.0.0_24-try.csv').dropna()\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f891100-c995-471c-b37e-97f7a8e0d204",
   "metadata": {},
   "source": [
    "**Step 3.** The dataframe is reformatted so that the geneID and log2FC columns are kept. In this case, the log2FC used as the rank and thus the name of the column:log2FC is changed to 'Rank'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "89616dfd-4268-4c44-abb3-60d725ce5143",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>0.000003</td>\n",
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       "  </tbody>\n",
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       "<p>20518 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       GeneID      Rank\n",
       "0        4490  3.935193\n",
       "1        4495  4.109887\n",
       "2       79974  2.532636\n",
       "3        4489  2.498127\n",
       "4        1638  2.349836\n",
       "...       ...       ...\n",
       "20513  391257 -0.000055\n",
       "20514   10078 -0.000037\n",
       "20515   84328 -0.000033\n",
       "20516  253018 -0.000015\n",
       "20517  440737  0.000003\n",
       "\n",
       "[20518 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2= df1[['GeneID','log2FC']]\n",
    "df3= df2.rename(columns={'log2FC':'Rank'})\n",
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcbaff2c-2607-437d-95a8-f9c22119ea3a",
   "metadata": {},
   "source": [
    "**Step 4.** The datatype of the first column of df3 is changed to a string as needed for the GSEA function we will use in section 1.4. The 'Rank' column will remain unnchanged as rank needs to remain numeric for the GSEA function. We also remove duplicates as duplicates will raise errors when running GSEA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7dc7c84e-8062-4555-9ef8-8b4250f597af",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3['GeneID']=df3['GeneID'].astype(str)\n",
    "ranking=df3.drop_duplicates(subset=\"GeneID\", keep=\"first\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adf297bd-c555-4d59-9163-e6669254c311",
   "metadata": {},
   "source": [
    "### Section 1.3 Selection of the geneset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91b49c9c-5782-4d4e-9d32-0cd29b01355c",
   "metadata": {},
   "source": [
    "**Step 5.** Here, you can select the geneset of interest. The command below displays the available options. In this notebook, the ENTREZ ID version of WikiPathways 2024 Human will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dedb7357-8e01-41cb-b123-ca7b79f83a11",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = gp.get_library_name()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b985905-0a0f-414b-b7ce-f607ed4df438",
   "metadata": {},
   "source": [
    "### Section 1.4: Execution of preranked GSEA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef28afc-5e6d-4d70-bf74-7a5fb9006df6",
   "metadata": {},
   "source": [
    "**Step 6.** Preranked GSEA can be executed by selection of the RNK file and gene set. The seed, min_size, max_size and permutation number were set to those below in accordance with the formal documentation of GSEAPY. The permutation type was set to 'gene_set' as we have less than 15 samples and verbose was set to TRUE to get more insight into the GSEA process(1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "87903865-6c3f-484d-83fe-6218373e9009",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-03-04 11:19:47,130 [INFO] Parsing data files for GSEA.............................\n",
      "2025-03-04 11:19:47,195 [INFO] 0000 gene_sets have been filtered out when max_size=1000 and min_size=5\n",
      "2025-03-04 11:19:47,195 [INFO] 0830 gene_sets used for further statistical testing.....\n",
      "2025-03-04 11:19:47,199 [INFO] Start to run GSEA...Might take a while..................\n",
      "2025-03-04 11:20:38,521 [INFO] Congratulations. GSEApy runs successfully................\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Preranked_GSEA= gp.prerank(rnk=ranking, \n",
    "                     gene_sets='c2.cp.wikipathways.v2024.1.Hs.entrez.gmt', \n",
    "                     min_size=5, \n",
    "                     max_size=1000, \n",
    "                     permutation_type='gene_set',\n",
    "                     seed=6, \n",
    "                     permutation_num=1000, \n",
    "                     verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "028dfde3-19d8-4baf-8359-6f59ea4da211",
   "metadata": {},
   "source": [
    "**Step 7.** In the next step, the results are displayed and visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2f671dca-2d43-4423-b89a-a2710e258eb4",
   "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>Name</th>\n",
       "      <th>Term</th>\n",
       "      <th>ES</th>\n",
       "      <th>NES</th>\n",
       "      <th>NOM p-val</th>\n",
       "      <th>FDR q-val</th>\n",
       "      <th>FWER p-val</th>\n",
       "      <th>Tag %</th>\n",
       "      <th>Gene %</th>\n",
       "      <th>Lead_genes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>prerank</td>\n",
       "      <td>WP_COPPER_HOMEOSTASIS</td>\n",
       "      <td>0.758562</td>\n",
       "      <td>2.676049</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12/53</td>\n",
       "      <td>2.46%</td>\n",
       "      <td>4495;4490;4501;4489;4494;4496;4493;84560;4500;...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>prerank</td>\n",
       "      <td>WP_ZINC_HOMEOSTASIS</td>\n",
       "      <td>0.816562</td>\n",
       "      <td>2.670822</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15/37</td>\n",
       "      <td>5.27%</td>\n",
       "      <td>4495;4490;4501;4489;4494;7780;4496;7779;4493;8...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>prerank</td>\n",
       "      <td>WP_GENES_RELATED_TO_PRIMARY_CILIUM_DEVELOPMENT...</td>\n",
       "      <td>-0.546802</td>\n",
       "      <td>-2.576323</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60/101</td>\n",
       "      <td>27.17%</td>\n",
       "      <td>55764;8100;132320;23322;57539;23059;79659;5508...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>prerank</td>\n",
       "      <td>WP_BARDETBIEDL_SYNDROME</td>\n",
       "      <td>-0.540727</td>\n",
       "      <td>-2.417239</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42/86</td>\n",
       "      <td>20.25%</td>\n",
       "      <td>55764;132320;2736;5311;5727;57539;23059;79659;...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>prerank</td>\n",
       "      <td>WP_CILIOPATHIES</td>\n",
       "      <td>-0.470576</td>\n",
       "      <td>-2.415103</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78/181</td>\n",
       "      <td>20.38%</td>\n",
       "      <td>1063;55764;132320;4751;2736;5311;4867;219844;2...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Name                                               Term        ES  \\\n",
       "0  prerank                              WP_COPPER_HOMEOSTASIS  0.758562   \n",
       "1  prerank                                WP_ZINC_HOMEOSTASIS  0.816562   \n",
       "2  prerank  WP_GENES_RELATED_TO_PRIMARY_CILIUM_DEVELOPMENT... -0.546802   \n",
       "3  prerank                            WP_BARDETBIEDL_SYNDROME -0.540727   \n",
       "4  prerank                                    WP_CILIOPATHIES -0.470576   \n",
       "\n",
       "        NES NOM p-val FDR q-val FWER p-val   Tag %  Gene %  \\\n",
       "0  2.676049       0.0       0.0        0.0   12/53   2.46%   \n",
       "1  2.670822       0.0       0.0        0.0   15/37   5.27%   \n",
       "2 -2.576323       0.0       0.0        0.0  60/101  27.17%   \n",
       "3 -2.417239       0.0       0.0        0.0   42/86  20.25%   \n",
       "4 -2.415103       0.0       0.0        0.0  78/181  20.38%   \n",
       "\n",
       "                                          Lead_genes  \n",
       "0  4495;4490;4501;4489;4494;4496;4493;84560;4500;...  \n",
       "1  4495;4490;4501;4489;4494;7780;4496;7779;4493;8...  \n",
       "2  55764;8100;132320;23322;57539;23059;79659;5508...  \n",
       "3  55764;132320;2736;5311;5727;57539;23059;79659;...  \n",
       "4  1063;55764;132320;4751;2736;5311;4867;219844;2...  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Preranked_GSEA.res2d.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eb89955d-38e0-4f23-bd9c-7d2c6de5e8f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shaki\\anaconda3\\Lib\\site-packages\\gseapy\\plot.py:738: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df[self.colname] = df[self.colname].replace(0, np.nan).bfill()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax1 = dotplot(Preranked_GSEA.res2d,\n",
    "             column=\"FDR q-val\",\n",
    "             title='Preranked GSEA',\n",
    "             cmap=plt.cm.viridis,\n",
    "             size=6, # adjust dot size\n",
    "             figsize=(4,5), cutoff=0.25, show_ring=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34c4eb19-bdd2-4d73-9537-5d70a9754885",
   "metadata": {},
   "source": [
    "## Section 2: Traditional GSEA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44f0197d-8ccd-4979-9ef0-2c221dc64fdf",
   "metadata": {},
   "source": [
    "In this section, traditional GSEA will be executed. This method allows for 5 ranking methods including: signal_to_noise, t_test, ratio_of_classes, diff_of_classes and log2_ratio_of_classes. In order to use this method, the following two files have to be prepared beforehand."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff6b17ef-a66c-409c-8677-003ed52c8394",
   "metadata": {},
   "source": [
    "### Section 2.1 Generation of expression data frame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d731a02-d103-4c71-84d1-8e6dd1263e94",
   "metadata": {},
   "source": [
    "**Step 8.** Gene expression data dataframe will be generated in this step. The gene expression data file is a expression value table in which the sample names are placed horizontal and the gene names are placed vertical. This data was loaded and manipulated to meet GSEA guidelines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cc719813-4ef0-486f-9bb9-3e1444260927",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv('TraditionalGSEA-EMEXP3583.csv')\n",
    "df.to_csv('TraditionalGSEA-EMEXP3583-version2.txt', sep='\\t',index=False)\n",
    "df2 = pd.read_csv(\"TraditionalGSEA-EMEXP3583-version2.txt\",sep=\"\\t\", index_col=0)\n",
    "df2 = df2.loc[:, ['M225-05', 'M225-01L', 'M225-03','M225-07']] \n",
    "TraditionalGSEA= df2.rename(columns={\"M225-05\": \"Control\", \"M225-01L\": \"Control\",\"M225-03\": \"Treatment\",\"M225-07\": \"Treatment\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ed8146b5-59ef-4262-a9c3-98c5803b0908",
   "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>Control</th>\n",
       "      <th>Control</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Treatment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EntrezID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7.941181</td>\n",
       "      <td>8.006905</td>\n",
       "      <td>7.990053</td>\n",
       "      <td>8.326014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.473060</td>\n",
       "      <td>4.561733</td>\n",
       "      <td>4.546220</td>\n",
       "      <td>4.573649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>10.246258</td>\n",
       "      <td>10.481636</td>\n",
       "      <td>10.297045</td>\n",
       "      <td>10.176861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>11.149919</td>\n",
       "      <td>11.145671</td>\n",
       "      <td>11.090569</td>\n",
       "      <td>11.116654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>10.274072</td>\n",
       "      <td>10.243285</td>\n",
       "      <td>10.226056</td>\n",
       "      <td>10.054899</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>9991</th>\n",
       "      <td>10.900741</td>\n",
       "      <td>10.551433</td>\n",
       "      <td>10.444575</td>\n",
       "      <td>10.473755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>5.068302</td>\n",
       "      <td>5.348781</td>\n",
       "      <td>5.664488</td>\n",
       "      <td>5.512333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>9.888579</td>\n",
       "      <td>10.014469</td>\n",
       "      <td>9.710480</td>\n",
       "      <td>9.798821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>7.720305</td>\n",
       "      <td>7.575626</td>\n",
       "      <td>7.660303</td>\n",
       "      <td>7.523620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>8.247325</td>\n",
       "      <td>8.484476</td>\n",
       "      <td>8.669681</td>\n",
       "      <td>8.838813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20518 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Control    Control  Treatment  Treatment\n",
       "EntrezID                                            \n",
       "1          7.941181   8.006905   7.990053   8.326014\n",
       "10         4.473060   4.561733   4.546220   4.573649\n",
       "100       10.246258  10.481636  10.297045  10.176861\n",
       "1000      11.149919  11.145671  11.090569  11.116654\n",
       "10000     10.274072  10.243285  10.226056  10.054899\n",
       "...             ...        ...        ...        ...\n",
       "9991      10.900741  10.551433  10.444575  10.473755\n",
       "9992       5.068302   5.348781   5.664488   5.512333\n",
       "9993       9.888579  10.014469   9.710480   9.798821\n",
       "9994       7.720305   7.575626   7.660303   7.523620\n",
       "9997       8.247325   8.484476   8.669681   8.838813\n",
       "\n",
       "[20518 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TraditionalGSEA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2c0ea48-ddd6-4e16-bb39-e2af718c1296",
   "metadata": {},
   "source": [
    "### Section 2.2 Generation of the CLS file"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "053c38f2-9ceb-441c-8a45-d650efa5023a",
   "metadata": {},
   "source": [
    "**Step 9.** The CLS file was created according to the GSEA guidelines. Based on the expression data, the following structure of the cls file was created and saved with a cls extension:\n",
    "\n",
    "CLS file structure:\n",
    "\n",
    "* First line : 4 2 1\n",
    "* Second line: # Control Treatment\n",
    "* Third line: 'Control' 'Control' 'Treatment' 'Treatment'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6043a3a9-99c5-40b5-be44-eb2d0171be75",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"clsfile.cls\", \"w\") as file:\n",
    "    file.write(\"4 2 1\\n\")\n",
    "    file.write(\"# Control Treatment \\n\")\n",
    "    file.write(\"'Control' 'Control' 'Treatment' 'Treatment'\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff12456-b424-499f-9baf-3a61a5c2376c",
   "metadata": {},
   "source": [
    "### Section 2.3 Selection of the geneset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8bd23ec-1ebb-4eba-bc70-943cdc50fc70",
   "metadata": {},
   "source": [
    "In this section, the geneset of interest can be selected. In this notebook, the ENTREZ ID version of WikiPathways 2024 Human will be used."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91cd4df6-00e1-4f0c-acaa-2aef0be811a3",
   "metadata": {},
   "source": [
    "### Section 2.4 Execution of traditional GSEA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6f38973-371d-42bb-bf5f-1357b166b615",
   "metadata": {},
   "source": [
    "**Step 10.** Lastly traditional GSEA was executed. This requires setting the EntrezID as the index and maintenance of the same parameters as for preranked GSEA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b8b8730d-47e4-417a-9c48-01574c1e9889",
   "metadata": {},
   "outputs": [],
   "source": [
    "TraditionalGSEA.reset_index('EntrezID', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7ed1b75f-29ae-4ecb-824a-77576928a0e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "TraditionalGSEA['EntrezID'] = TraditionalGSEA['EntrezID'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "5b568be3-c6a0-465e-890d-614f8ffccf6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-03-04 11:20:41,812 [INFO] Parsing data files for GSEA.............................\n",
      "2025-03-04 11:20:41,887 [INFO] 0000 gene_sets have been filtered out when max_size=1000 and min_size=5\n",
      "2025-03-04 11:20:41,887 [INFO] 0830 gene_sets used for further statistical testing.....\n",
      "2025-03-04 11:20:41,887 [INFO] Start to run GSEA...Might take a while..................\n",
      "2025-03-04 11:21:42,060 [INFO] Congratulations. GSEApy ran successfully.................\n",
      "\n"
     ]
    }
   ],
   "source": [
    "GSEA = gp.gsea(data=TraditionalGSEA, \n",
    "                 gene_sets='c2.cp.wikipathways.v2024.1.Hs.entrez.gmt', \n",
    "                 cls= \"clsfile.cls\", \n",
    "                 min_size=5, \n",
    "                 max_size=1000,\n",
    "                 permutation_num=1000, \n",
    "                 permutation_type='gene_set',\n",
    "                 outdir=None,  \n",
    "                 method='log2_ratio_of_classes',\n",
    "                 threads=4, \n",
    "                 verbose=True,\n",
    "                 seed= 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "db514fc0-4e6f-4ed6-af63-d1a1852f05e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Name                                               Term        ES  \\\n",
      "0  gsea  WP_GENES_RELATED_TO_PRIMARY_CILIUM_DEVELOPMENT...  0.557377   \n",
      "1  gsea                                WP_ZINC_HOMEOSTASIS -0.775016   \n",
      "2  gsea                            WP_BARDETBIEDL_SYNDROME   0.50323   \n",
      "3  gsea                              WP_COPPER_HOMEOSTASIS -0.696479   \n",
      "4  gsea  WP_REGULATION_OF_SISTER_CHROMATID_SEPARATION_A...  0.720957   \n",
      "\n",
      "        NES NOM p-val FDR q-val FWER p-val   Tag %  Gene %  \\\n",
      "0  2.580045       0.0       0.0        0.0  50/101  21.25%   \n",
      "1 -2.319458       0.0       0.0        0.0   13/37   4.76%   \n",
      "2  2.290378       0.0  0.002076      0.002   40/86  19.31%   \n",
      "3  -2.19289       0.0       0.0        0.0   12/53   7.55%   \n",
      "4  2.130938       0.0  0.020764      0.029    4/14   4.81%   \n",
      "\n",
      "                                          Lead_genes  \n",
      "0  8100;55764;132320;23322;79659;57539;23059;1328...  \n",
      "1  4495;4490;4501;4489;7780;4496;4494;4493;84560;...  \n",
      "2  55764;132320;79659;2736;57539;5311;23059;13288...  \n",
      "3  4495;4490;4501;4489;4496;4494;4493;84560;4500;...  \n",
      "4                                 1062;9232;9700;699  \n"
     ]
    }
   ],
   "source": [
    "print(GSEA.res2d.head())  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb1262c-c209-4a39-80ed-f1926b2e54be",
   "metadata": {},
   "source": [
    "**Step 11.** In the next step, the results are displayed and visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "02120c66-cb75-4f17-ab58-4dbfb7bc3c5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shaki\\anaconda3\\Lib\\site-packages\\gseapy\\plot.py:738: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  df[self.colname] = df[self.colname].replace(0, np.nan).bfill()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax2 = dotplot(GSEA.res2d,\n",
    "             column=\"FDR q-val\",\n",
    "             title='Traditional GSEA',\n",
    "             cmap=plt.cm.viridis,\n",
    "             size=6, # adjust dot size\n",
    "             figsize=(4,5), cutoff=0.25, show_ring=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e57d6a92-2103-4e50-af79-5e1991e184c6",
   "metadata": {},
   "source": [
    "## Section 3: fGSEA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d8d7eab-215d-4303-be0a-2590a2377f4c",
   "metadata": {},
   "source": [
    "In this section, fast GSEA (fGSEA) will be executed which is a package developed by BioConductor. To be able to use this package in this notebook, rpy2 was employed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38d69bf9-c496-412a-8dc7-8f2c8cf5f462",
   "metadata": {},
   "source": [
    "### Section 3.1 Magic R extension and system preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c5a25e9-858a-48f2-bad2-df4663df3500",
   "metadata": {},
   "source": [
    "**Step 12.** We first activate rpy2 in this notebook. If a Windows laptop is used, the the warning 'quartz' may appear which can be ignored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6df2ceba-37c3-423b-a148-96bd6cf95fd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shaki\\anaconda3\\Lib\\site-packages\\rpy2\\robjects\\packages.py:367: UserWarning: The symbol 'quartz' is not in this R namespace/package.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "965d860c-9762-43d1-958b-c8ae475c82d9",
   "metadata": {},
   "source": [
    "**Step 13**. Next, the needed packages for fGSEA are installed. Dpending on the user's background and possible prior experience with R, notifications for updating packages may appear. These notifications can be answered at users discretion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "98c0298a-5afd-4c77-907d-f248035f6285",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Bioconductor version 3.20 (BiocManager 1.30.25), R 4.4.1 (2024-06-14 ucrt)\n",
      "\n",
      "R[write to console]: Bioconductor version 3.20 (BiocManager 1.30.25), R 4.4.1 (2024-06-14 ucrt)\n",
      "\n",
      "R[write to console]: Installation paths not writeable, unable to update packages\n",
      "  path: C:/Program Files/R/R-4.4.1/library\n",
      "  packages:\n",
      "    class, cluster, foreign, KernSmooth, Matrix, nlme, nnet, rpart, spatial,\n",
      "    survival\n",
      "\n",
      "R[write to console]: Old packages: 'aplot', 'Biostrings', 'cli', 'cpp11', 'ggnewscale', 'igraph',\n",
      "  'IRanges', 'jsonlite', 'MASS', 'processx', 'R.utils', 'readxl', 'recolorize',\n",
      "  'rlang', 'S4Vectors', 'terra', 'tinytex', 'xml2', 'XVector', 'zlibbioc'\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Update all/some/none? [a/s/n]: "
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " n\n"
     ]
    }
   ],
   "source": [
    "%%R \n",
    "if (!require(\"BiocManager\", quietly = TRUE))\n",
    "    install.packages(\"BiocManager\")\n",
    "\n",
    "BiocManager::install(\"clusterProfiler\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "79f29980-c0cf-465a-9d0d-e43c8b30e371",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Bioconductor version 3.20 (BiocManager 1.30.25), R 4.4.1 (2024-06-14 ucrt)\n",
      "\n",
      "R[write to console]: Installation paths not writeable, unable to update packages\n",
      "  path: C:/Program Files/R/R-4.4.1/library\n",
      "  packages:\n",
      "    class, cluster, foreign, KernSmooth, Matrix, nlme, nnet, rpart, spatial,\n",
      "    survival\n",
      "\n",
      "R[write to console]: Old packages: 'aplot', 'Biostrings', 'cli', 'cpp11', 'ggnewscale', 'igraph',\n",
      "  'IRanges', 'jsonlite', 'MASS', 'processx', 'R.utils', 'readxl', 'recolorize',\n",
      "  'rlang', 'S4Vectors', 'terra', 'tinytex', 'xml2', 'XVector', 'zlibbioc'\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Update all/some/none? [a/s/n]: "
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " n\n"
     ]
    }
   ],
   "source": [
    "%%R \n",
    "if (!require(\"BiocManager\", quietly = TRUE))\n",
    "    install.packages(\"BiocManager\")\n",
    "\n",
    "BiocManager::install(\"fgsea\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "6594daca-aa40-483f-b62d-f520b6be3f3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Installing packages into 'C:/Users/shaki/AppData/Local/R/win-library/4.4'\n",
      "(as 'lib' is unspecified)\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Please select a CRAN mirror for use in this session ---\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: trying URL 'https://mirror.lyrahosting.com/CRAN/bin/windows/contrib/4.4/readr_2.1.5.zip'\n",
      "\n",
      "R[write to console]: Content type 'application/zip'\n",
      "R[write to console]:  length 1223048 bytes (1.2 MB)\n",
      "\n",
      "R[write to console]: downloaded 1.2 MB\n",
      "\n",
      "\n",
      "R[write to console]: trying URL 'https://mirror.lyrahosting.com/CRAN/bin/windows/contrib/4.4/ggplot2_3.5.1.zip'\n",
      "\n",
      "R[write to console]: Content type 'application/zip'\n",
      "R[write to console]:  length 5021782 bytes (4.8 MB)\n",
      "\n",
      "R[write to console]: downloaded 4.8 MB\n",
      "\n",
      "\n",
      "R[write to console]: trying URL 'https://mirror.lyrahosting.com/CRAN/bin/windows/contrib/4.4/data.table_1.17.0.zip'\n",
      "\n",
      "R[write to console]: Content type 'application/zip'\n",
      "R[write to console]:  length 2910072 bytes (2.8 MB)\n",
      "\n",
      "R[write to console]: downloaded 2.8 MB\n",
      "\n",
      "\n",
      "R[write to console]: trying URL 'https://mirror.lyrahosting.com/CRAN/bin/windows/contrib/4.4/dplyr_1.1.4.zip'\n",
      "\n",
      "R[write to console]: Content type 'application/zip'\n",
      "R[write to console]:  length 1590307 bytes (1.5 MB)\n",
      "\n",
      "R[write to console]: downloaded 1.5 MB\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "package 'readr' successfully unpacked and MD5 sums checked\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Warning:\n",
      "R[write to console]:  cannot remove prior installation of package 'readr'\n",
      "\n",
      "R[write to console]: Warning:\n",
      "R[write to console]:  restored 'readr'\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "package 'ggplot2' successfully unpacked and MD5 sums checked\n",
      "package 'data.table' successfully unpacked and MD5 sums checked\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Warning:\n",
      "R[write to console]:  cannot remove prior installation of package 'data.table'\n",
      "\n",
      "R[write to console]: Warning:\n",
      "R[write to console]:  restored 'data.table'\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "package 'dplyr' successfully unpacked and MD5 sums checked\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: Warning:\n",
      "R[write to console]:  cannot remove prior installation of package 'dplyr'\n",
      "\n",
      "R[write to console]: Warning:\n",
      "R[write to console]:  restored 'dplyr'\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The downloaded binary packages are in\n",
      "\tC:\\Users\\shaki\\AppData\\Local\\Temp\\RtmpYdIkNC\\downloaded_packages\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "install.packages(c (\"readr\",\"ggplot2\",\"data.table\",\"dplyr\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d9e720b-b073-4f7d-995a-67eea55d94f8",
   "metadata": {},
   "source": [
    "**Step 14**. Next, the installed libraries are loaded prior to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9d3c6052-705b-460a-8378-2f523608f168",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "R[write to console]: data.table 1.17.0 using 4 threads (see ?getDTthreads).  \n",
      "R[write to console]: Latest news: r-datatable.com\n",
      "\n",
      "R[write to console]: \n",
      "\n",
      "R[write to console]: clusterProfiler v4.14.6 Learn more at https://yulab-smu.top/contribution-knowledge-mining/\n",
      "\n",
      "Please cite:\n",
      "\n",
      "S Xu, E Hu, Y Cai, Z Xie, X Luo, L Zhan, W Tang, Q Wang, B Liu, R Wang,\n",
      "W Xie, T Wu, L Xie, G Yu. Using clusterProfiler to characterize\n",
      "multiomics data. Nature Protocols. 2024, 19(11):3292-3320\n",
      "\n",
      "R[write to console]: \n",
      "Attaching package: 'clusterProfiler'\n",
      "\n",
      "\n",
      "R[write to console]: The following object is masked from 'package:stats':\n",
      "\n",
      "    filter\n",
      "\n",
      "\n",
      "R[write to console]: \n",
      "Attaching package: 'dplyr'\n",
      "\n",
      "\n",
      "R[write to console]: The following objects are masked from 'package:data.table':\n",
      "\n",
      "    between, first, last\n",
      "\n",
      "\n",
      "R[write to console]: The following objects are masked from 'package:stats':\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "R[write to console]: The following objects are masked from 'package:base':\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "library(fgsea)\n",
    "library(ggplot2)\n",
    "library(data.table)\n",
    "library(readr)\n",
    "library(clusterProfiler)\n",
    "library(dplyr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66bf2666-5ecf-4ae1-9735-2064221fdf54",
   "metadata": {},
   "source": [
    "### Section 3.2 Execution of fGSEA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88147c66-fab6-4308-8b1f-33b39785032f",
   "metadata": {},
   "source": [
    "**Step 15**. fGSEA can be executed by first loading the data. At this step, the rankData variable name will be created which contains the GeneID and log2FC column of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "a641a580-5e57-4de8-ade7-81a2aa1c78e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    4490     4495    79974     4489     1638     4501 \n",
      "3.935193 4.109887 2.532636 2.498127 2.349836 2.727981 \n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "data <- read_csv('topTable_Ag._.1.3_24 - H2O.control_.0.0_24-try.csv',show_col_types = FALSE)\n",
    "rankData <- data$log2FC\n",
    "names(rankData) <- data$GeneID\n",
    "head(rankData)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c693d9c-ff0a-4914-a979-4f61741f1729",
   "metadata": {},
   "source": [
    "**Step 16**. Next, the geneset of interest is read into the variable name: 'pathway'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "da3cebb1-77f4-4328-bc30-cfcb67de5751",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%R\n",
    "pathway <- gmtPathways(\"./c2.cp.wikipathways.v2024.1.Hs.entrez.gmt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8f475bc-7b04-486f-a90f-f9bc2fd7dcad",
   "metadata": {},
   "source": [
    "**Step 17**. Lastly, fgGSEA is run and results are displayed. Based on the NES and ES scores, we can see strong similarity to the values observed for the preranked GSEA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "dcacff9f-8194-439d-8f3d-dcbc9a87288f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                           pathway         pval\n",
      "                                                            <char>        <num>\n",
      " 1:                                          WP_COPPER_HOMEOSTASIS 1.537901e-12\n",
      " 2:                                            WP_ZINC_HOMEOSTASIS 5.055497e-13\n",
      " 3: WP_GENES_RELATED_TO_PRIMARY_CILIUM_DEVELOPMENT_BASED_ON_CRISPR 1.766840e-11\n",
      " 4:                                        WP_BARDETBIEDL_SYNDROME 2.574977e-09\n",
      " 5:                                                WP_CILIOPATHIES 8.496336e-13\n",
      " 6:                               WP_RETINOBLASTOMA_GENE_IN_CANCER 4.165935e-07\n",
      " 7:                                            WP_JOUBERT_SYNDROME 3.479830e-06\n",
      " 8:                                                  WP_CELL_CYCLE 4.005527e-07\n",
      " 9:                                           WP_CILIARY_LANDSCAPE 1.492812e-08\n",
      "10:       WP_OVERVIEW_OF_PROINFLAMMATORY_AND_PROFIBROTIC_MEDIATORS 2.309469e-05\n",
      "            padj   log2err         ES       NES  size  leadingEdge\n",
      "           <num>     <num>      <num>     <num> <int>       <list>\n",
      " 1: 3.649953e-10 0.9101197  0.7585618  2.699857    53 4495, 44....\n",
      " 2: 3.024696e-10 0.9214260  0.8165622  2.665558    37 4495, 44....\n",
      " 3: 3.144975e-09 0.8634154 -0.5468024 -2.521632   101 55764, 8....\n",
      " 4: 3.666768e-07 0.7749390 -0.5407270 -2.449247    86 55764, 1....\n",
      " 5: 3.024696e-10 0.9214260 -0.4705761 -2.370628   181 1063, 55....\n",
      " 6: 3.707682e-05 0.6749629 -0.4887608 -2.226432    87 891, 241....\n",
      " 7: 2.752932e-04 0.6272567 -0.4782106 -2.104594    76 4646, 48....\n",
      " 8: 3.707682e-05 0.6749629 -0.4340094 -2.068247   119 891, 534....\n",
      " 9: 1.771470e-06 0.7337620 -0.3807739 -1.945516   210 3934, 55....\n",
      "10: 1.644342e-03 0.5756103  0.4737143  1.925449   124 6375, 63....\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "fgseaRes <- fgseaMultilevel(pathway, \n",
    "                  rankData, \n",
    "                  minSize=10, \n",
    "                  maxSize = 500)\n",
    "\n",
    "fgseaRes %>% \n",
    "  arrange(desc(abs(NES))) %>% \n",
    "  top_n(10, -padj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "addd81bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "\n",
    "topPathwaysUp <- fgseaRes[ES > 0][head(order(pval), n=10), pathway]\n",
    "topPathwaysDown <- fgseaRes[ES < 0][head(order(pval), n=10), pathway]\n",
    "topPathways <- c(topPathwaysUp, rev(topPathwaysDown))\n",
    "plotGseaTable(pathway[topPathways], rankData, fgseaRes, gseaParam=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5352847c-209e-4473-9e6b-b0891f0dcfbe",
   "metadata": {},
   "source": [
    "**Conclusion:** *Using both methods of GSEA in GSEApy revealed that the ranking method affects the pattern in NES scores. Besides the debate on the best ranking method, the internal ranking in the traditional GSEA yields a different pattern compared to the explicit usage of log2FC in preranked GSEA. To further confirm the findings, fGSEA was executed which yielded NES scores and ES scores similar to preranked GSEA. The usage of logFC explicitly in preranked GSEA appears more in line with findings from other GSEA packages and descriptions of GSEA tutorials.*"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e879b628-82df-4b2d-9ab7-980e3e5d3fff",
   "metadata": {},
   "source": [
    "## Section 4. Session information for Python and R"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59d86c35-8031-4416-a84c-c72b17d415a2",
   "metadata": {},
   "source": [
    "In this section, the session information for both Python and R used in this notebook will be displayed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12c95b12-9792-4595-b1ad-4895d9cf25d8",
   "metadata": {},
   "source": [
    "### Section 4.1: Python session information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6fc0fb6a-efa3-4c82-81b2-062ecf260b4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: 2025-03-04T11:33:17.306769+01:00\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.12.3\n",
      "IPython version      : 8.25.0\n",
      "\n",
      "Compiler    : MSC v.1938 64 bit (AMD64)\n",
      "OS          : Windows\n",
      "Release     : 11\n",
      "Machine     : AMD64\n",
      "Processor   : Intel64 Family 6 Model 140 Stepping 1, GenuineIntel\n",
      "CPU cores   : 8\n",
      "Architecture: 64bit\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2d4a03-7796-4b47-86b2-35cb4ec6d16a",
   "metadata": {},
   "source": [
    "### Section 4.2: R session information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "0eb5ef28-b3b9-4d53-9646-5636b963e9f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R version 4.4.1 (2024-06-14 ucrt)\n",
      "Platform: x86_64-w64-mingw32/x64\n",
      "Running under: Windows 11 x64 (build 26100)\n",
      "\n",
      "Matrix products: default\n",
      "\n",
      "\n",
      "locale:\n",
      "[1] LC_COLLATE=Dutch_Netherlands.1252  LC_CTYPE=Dutch_Netherlands.1252   \n",
      "[3] LC_MONETARY=Dutch_Netherlands.1252 LC_NUMERIC=C                      \n",
      "[5] LC_TIME=Dutch_Netherlands.1252    \n",
      "\n",
      "time zone: Europe/Amsterdam\n",
      "tzcode source: internal\n",
      "\n",
      "attached base packages:\n",
      "[1] tools     stats     graphics  grDevices utils     datasets  methods  \n",
      "[8] base     \n",
      "\n",
      "other attached packages:\n",
      "[1] dplyr_1.1.4            clusterProfiler_4.14.6 readr_2.1.5           \n",
      "[4] data.table_1.17.0      ggplot2_3.5.1          fgsea_1.32.2          \n",
      "[7] BiocManager_1.30.25   \n",
      "\n",
      "loaded via a namespace (and not attached):\n",
      " [1] tidyselect_1.2.1        farver_2.1.2            blob_1.2.4             \n",
      " [4] R.utils_2.12.3          Biostrings_2.73.2       lazyeval_0.2.2         \n",
      " [7] fastmap_1.2.0           digest_0.6.37           lifecycle_1.0.4        \n",
      "[10] KEGGREST_1.46.0         tidytree_0.4.6          RSQLite_2.3.9          \n",
      "[13] magrittr_2.0.3          compiler_4.4.1          rlang_1.1.4            \n",
      "[16] igraph_2.1.3            ggtangle_0.0.6          labeling_0.4.3         \n",
      "[19] bit_4.5.0.1             gson_0.1.0              plyr_1.8.9             \n",
      "[22] RColorBrewer_1.1-3      aplot_0.2.4             BiocParallel_1.40.0    \n",
      "[25] purrr_1.0.4             withr_3.0.2             BiocGenerics_0.52.0    \n",
      "[28] R.oo_1.27.0             grid_4.4.1              stats4_4.4.1           \n",
      "[31] GOSemSim_2.32.0         enrichplot_1.26.6       colorspace_2.1-1       \n",
      "[34] GO.db_3.20.0            scales_1.3.0            cli_3.6.3              \n",
      "[37] crayon_1.5.3            treeio_1.30.0           generics_0.1.3         \n",
      "[40] ggtree_3.14.0           httr_1.4.7              reshape2_1.4.4         \n",
      "[43] tzdb_0.4.0              ape_5.8-1               DBI_1.2.3              \n",
      "[46] qvalue_2.38.0           cachem_1.1.0            DOSE_4.0.0             \n",
      "[49] stringr_1.5.1           zlibbioc_1.51.2         splines_4.4.1          \n",
      "[52] parallel_4.4.1          ggplotify_0.1.2         AnnotationDbi_1.68.0   \n",
      "[55] XVector_0.45.0          vctrs_0.6.5             yulab.utils_0.2.0      \n",
      "[58] Matrix_1.7-1            jsonlite_1.8.9          gridGraphics_0.5-1     \n",
      "[61] IRanges_2.39.2          hms_1.1.3               patchwork_1.3.0        \n",
      "[64] S4Vectors_0.43.2        ggrepel_0.9.6           bit64_4.6.0-1          \n",
      "[67] tidyr_1.3.1             snow_0.4-4              glue_1.8.0             \n",
      "[70] codetools_0.2-20        cowplot_1.1.3           stringi_1.8.4          \n",
      "[73] gtable_0.3.6            GenomeInfoDb_1.42.3     UCSC.utils_1.2.0       \n",
      "[76] munsell_0.5.1           tibble_3.2.1            pillar_1.10.1          \n",
      "[79] GenomeInfoDbData_1.2.13 R6_2.6.1                vroom_1.6.5            \n",
      "[82] lattice_0.22-6          Biobase_2.66.0          R.methodsS3_1.8.2      \n",
      "[85] png_0.1-8               memoise_2.0.1           ggfun_0.1.8            \n",
      "[88] Rcpp_1.0.14             fastmatch_1.1-6         nlme_3.1-166           \n",
      "[91] fs_1.6.5                pkgconfig_2.0.3        \n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "sessionInfo()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7820111c-2716-4cec-b48b-0599f274fa1c",
   "metadata": {},
   "source": [
    "**References:**\n",
    "\n",
    "1. Zhuoqing Fang, Xinyuan Liu, Gary Peltz, GSEApy: a comprehensive package for performing gene set enrichment analysis in Python, Bioinformatics, Volume 39, Issue January 2023, btac757, https://doi.org/10.1093/bioinformatics/btac757\n",
    "2. Korotkevich G, Sukhov V, Sergushichev A (2019). “Fast gene set enrichment analysis.” bioRxiv. doi:10.1101/060012, http://biorxiv.org/content/early/2016/06/20/060012.\n"
   ]
  }
 ],
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