gget_enrichr_with_background_genes.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pachterlab/gget_examples/blob/main/gget_enrichr_with_background_genes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Example notebook showing how to use `gget enrichr` with a list of background genes\n",
        "\n",
        "Written by Chi Hoang on August 9, 2023."
      ],
      "metadata": {
        "id": "GEfdSsNju70K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# For pretty plot\n",
        "%config InlineBackend.figure_format='retina'"
      ],
      "metadata": {
        "id": "Z4Q24M1Nu318"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Install and import gget:"
      ],
      "metadata": {
        "id": "NWmt4Mp_u4AQ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "aBBhBNoGpObC"
      },
      "outputs": [],
      "source": [
        "!pip install -U -q gget\n",
        "import gget"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Show `gget enrichr` manual:"
      ],
      "metadata": {
        "id": "lrzZkqVtvJua"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "help(gget.enrichr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VVWHMoHNpdKj",
        "outputId": "414bf9b1-65ed-4a5a-ed30-8b2894da3866"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on function enrichr in module gget.gget_enrichr:\n",
            "\n",
            "enrichr(genes, database, background_list=None, background=False, ensembl=False, ensembl_bkg=False, plot=False, figsize=(10, 10), ax=None, json=False, save=False, verbose=True)\n",
            "    Perform an enrichment analysis on a list of genes using Enrichr (https://maayanlab.cloud/Enrichr/).\n",
            "    \n",
            "    Args:\n",
            "    - genes             List of Entrez gene symbols to perform enrichment analysis on, passed as a list of strings, e.g. ['PHF14', 'RBM3', 'MSL1', 'PHF21A'].\n",
            "                        Set 'ensembl = True' to input a list of Ensembl gene IDs, e.g. ['ENSG00000106443', 'ENSG00000102317', 'ENSG00000188895'].\n",
            "    - database          Database to use as reference for the enrichment analysis.\n",
            "                        Supported shortcuts (and their default database):\n",
            "                        'pathway' (KEGG_2021_Human)\n",
            "                        'transcription' (ChEA_2016)\n",
            "                        'ontology' (GO_Biological_Process_2021)\n",
            "                        'diseases_drugs' (GWAS_Catalog_2019)\n",
            "                        'celltypes' (PanglaoDB_Augmented_2021)\n",
            "                        'kinase_interactions' (KEA_2015)\n",
            "                        or any database listed under Gene-set Library at: https://maayanlab.cloud/Enrichr/#libraries\n",
            "    - background_list   List of gene names/Ensembl IDs to be used as background genes. (Default: None)\n",
            "    - background        If True, use set of 20,625 default background genes from https://maayanlab.cloud/Enrichr/. (Default: False)\n",
            "    \n",
            "    - ensembl     Define as 'True' if 'genes' is a list of Ensembl gene IDs. (Default: False)\n",
            "    - ensembl_bkg Define as 'True' if 'background_list' is a list of Ensembl gene IDs. (Default: False)\n",
            "    - plot        True/False whether to provide a graphical overview of the first 15 results. (Default: False)\n",
            "    - figsize     (width, height) of plot in inches. (Default: (10,10))\n",
            "    - ax          Pass a matplotlib axes object for further customization of the plot. (Default: None)\n",
            "    - json        If True, returns results in json format instead of data frame. (Default: False)\n",
            "    - save        True/False whether to save the results in the local directory. (Default: False)\n",
            "    - verbose     True/False whether to print progress information. Default True.\n",
            "    \n",
            "    Returns a data frame with the Enrichr results.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let’s consider a hypothetical microarray experiment, in which the RNA expression of 50 liver cell marker genes is measured."
      ],
      "metadata": {
        "id": "1m6aGpekqKOb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# List of liver genes tested in experiment\n",
        "liver_genes = [\"ALB\",\"Got1\",\"Gpt\",\"Gsta2\",\"Cdo1\",\"Csad\",\"Nr0b2\",\"Hsd17b9\",\"Hsd17b2\",\"Ste\",\"Ces2\",\"Hpcl2\",\"Ces3\",\"Hao2\",\"Fabp7\",\"Amacr\",\"RoDHII\",\"Rdh3\",\"Gck\",\"Pck1\",\"Pc\",\"Slc37a4\",\"Rnase4\",\"Enpp2\",\"Cyp3a13\",\"Aldh1a1\",\"Cyp1a2\",\"Cyp2c\",\"Fmo1\",\"Cyp17a1\",\"Cyp3a11\",\"Adh4\",\"Bdh\",\"Cml1\",\"Lbp\",\"Igfbp2\",\"Inhbe\",\"Ghr\",\"Igfbp1\",\"Dio1\",\"Afp\",\"Obp3 \",\"Obp3\",\"Mt1a\",\"Lcn2\",\"Ela1\",\"Es2\",\"Spin2a\",\"Krt2-8\",\"Krt1-18\"]\n"
      ],
      "metadata": {
        "id": "uVqlOvbEpkrT"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We find that 5 of our liver genes are up-regulated (for simplicity, we will pretend these are the first 5 genes in our liver_genes list). Without specifying a background gene list, an enrichment analysis of these 5 genes against a cell type database (PanglaoDB_Augmented_2021) results in significant P-values for several different types of liver cells:"
      ],
      "metadata": {
        "id": "CndtdpMCqbVd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "liver_genes[:5]"
      ],
      "metadata": {
        "id": "q875DpjIvZAd",
        "outputId": "d4844683-2043-4015-8ad6-06150c9b9f3c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ALB', 'Got1', 'Gpt', 'Gsta2', 'Cdo1']"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "enrichr_df = gget.enrichr(liver_genes[:5], database=\"celltypes\", plot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gtiibBjyqHqm",
        "outputId": "388867c0-0183-45b5-f66c-14e40ca568e0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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FLFy4MEnStm3bWkcwNmjQICeddFLOOuusJMmgQYOW2/aCCy7Ik08+mRdffDHTpk3L8ccfn8mTJxfu941vfCP9+/ev8X4r46ijjqrx/A477LBC7bfffvvC9rJGuH9aRUVFhg8fnpEjR2bChAmZNWtWYW3kTxs5cmSt/X0RfO1rX6vxAxdJcuihh6Zdu3aZMmVKFixYkJdeeikHH3zw51RhzapO6X3cccfV6Zr99tuvMPPB888/n1122WWF7/vJVN+fWNXT5ld9rt69e9dpqvD99tuvsP3888+v0nqq6tixY+HfxD//+c/5yU9+ssrvMX78+BrPjxw50qhvAAAAAOquyXr1ez18gQm+oR4MHTo0Q4cOrbHNeuutlyuvvLLayOFl2XXXXWu9X9WRpj169EijRrX/1d9zzz2rXV9ZWbnMQKxhw4b561//mp122imzZs2qFpK3b9++2rrfq1LVoHpZWrduXdhu2bJl2rdvX2P7Nm3aFLarjjL9tLKyslx11VX54x//WOvawJ/49Iccvqh23333Wts0bNgw3bt3L4SxI0aMWCOC748//jhvv/12kiVT3td1GvY333yzsF1bwLo8661X/QfdT5YsWFVeeumlwvagQYOqTau+PFWnbV/Z56qLY445pjAa/ac//WmeeOKJ9OnTJwcccEA6dOiwSu5RWz8TJ05cJfcBAAAA4Aui8bpJu24rN915u52WXA+sFME3rCFKS0uz/vrrZ8cdd8z++++fvn371mlK47pM/ztlypTC9mabbVanejp16lTYXrRoUWbPnp0WLVoss+3mm2+ea6+9ttoU7SUlJbntttuqBcqrUm0jh6uG+7W1/XT75Y3cXrhwYb72ta/l8ccfr2OVS3x6xO4X1aabbrrC7ap+79anquHnokWLcs0116xwH9OnT1+pe7do0SKNGjUqTHc+Y8aMlepneT788MPC9qOPPrrC16/sc9VF//7989hjj+Wf//xnkuSpp57KU089lWTJ98nee++dfffdN1//+tfTtm3b1VbHF9Hkaa0yb2HTasdmz12nnqoBAAAAKDLdvpM8efqKX7fTd1Z9LfAFYo1vqAe//OUvU1lZWe1r9uzZGTduXB588MGceeaZdV7Hd511ag8iqo4Qbd68eZ36/XS72sLbT6+fvfHGG2e33Xar071WRl2mY16ZtjW58MILC6F3SUlJjj322Pz973/P6NGjM3PmzCxatKjae/qJqttfZOuuW7dPKlb93ltTPjSwKtYar7pO94qq+oGVqqPIV4XP+myfrHu+OjRs2DD33Xdfbrzxxmy77bbVzr3//vu544470r9//2yyySbp379/pk2bttpq+aIZ9U7nPD9q+2pfo96peV17AAAAAP6/bfok69Q+aK2addolXeu2xCKwbIJv+AKouibw3Llz63TNp9t9errlqqZOnZoTTjih2rEPP/wwP/zhD+te5Bpu4cKFufrqqwv7t9xyS+66664cffTR6dq1a1q0aJHGjRsXzq8pge3qVFFRsULt582bV6d2Vb/3avq+q6sVrXNZqobxLVq0WOqDK3X5uuWWW1b6/nvttVdh+5VXXvksj7KUqs923333rdSzrU4lJSU5+eST88Ybb2TMmDG5/vrrc8IJJ2SLLbYotFm8eHFuuumm9OjRY42ZJQAAAACAL7AmpcnX/pE0bFp722RJu6/dt+Q6YKUJvuELoOp06O+//36drhk3blxhu0mTJjUGkP379y9MBb3lllumYcOGSZIbb7yxMEVxsRsyZEhh5Px2222Xvn371ti+Luskr2mqTvdel9HJKzpSuK7fe1XXjF7W9NVVP2CwOupclg033LCwPWvWrDqH+KvKfvvtV9h+77338uKLL66yvqs+26RJk1ZZv6tDly5dcsopp+SWW27Ju+++mzFjxuTHP/5x4d+cd999t87rrwMAAADAatVh7+SoJ2of+b1Ou+SoJ5MOe9XcDqiV4Bu+AHbeeefC9pAhQ+o0NXHVYG3nnXde7nTh1113XR544IEkS6Zd/9e//pVf/OIXhfP9+/evtoZwsar6DDvssEOt7Z999tnVWc5qUXUN948//rjGtosWLcrbb7+9Qv2//PLLtbYpLy/P0KFDC/u77LLLUm1WpM4k+c9//lPHCpdv4403TseOHQv7qzJ4roujjz662ocALr/88lXWd9UlCV544YVV1u/noUuXLrnsssuqhd0PPvhgPVYEAAAAAFV02DvpPzY54Lqk3U7Vz7Xbacnx/mOF3rCKCL7hC2CPPfZI06ZLplSZMmVKHn744RrbV1RU5C9/+Uthv+po06o+GW35icsuuyxdu3bN+eefn9133z3JkmCyb9++Rb/OdYMG//fPZW2jfSsqKnL99dev7pJWuU6dOhW2R44cWWPbBx98MAsWLFih/h988MHMmjWrxjaPPfZYPvrooyRJs2bNCt9HK1vnggUL8q9//WuF6lyeww47rLB97bXXrpI+62qdddbJmWeeWdj/xz/+kX/84x8r3M/cuXOXCu2rPtd9992XyZMnr3yh9eRrX/taYbsY6wcAAABgLdakNNnx1KTviOTMucnpk5b82XfEkuOmN4dVRvANXwCtWrXKscceW9g/55xzalyD+k9/+lNhlGyDBg1y6qmnLtVm0aJFOe644woh8OGHH57vfOc7SZKGDRvmr3/9a2F69KeeeiqXXXbZKnue+lB1PeFnnnmmxumzL7nkkowaNerzKGuVqjryt6b1qGfNmpWf/vSnK9z/rFmz8vOf/3y55+fOnZtzzz23sH/UUUelZcuWNdb50EMPZerUqcvt84ILLqjx/Io466yzClNq33///Su0ZveqmEL83HPPrTYC/vjjj1+hUP/1119Pz5498/jjj1c73qNHj/Tq1StJMn/+/Bx//PFZtGhRnfpctGhRpk+fXucaVlRd37uq0+NvsMEGq6scAAAAAPhsGq+bNN9wyZ/AKif4hi+ICy64IKWlSz459vbbb+eggw7K2LFjq7WpqKjIlVdeWW0U9/e+971qI2w/8Ytf/CKvvvpqkmSjjTbKTTfdVO38FltskT/96U+F/fPOO6/W0blrsp133jnt27dPsmTN6KOPPnqpKdwXLlyYCy64ID/96U/TvHnz+ijzMznuuOMK23fddVe19+8Tb731Vvbbb7+8++67hVkE6qpJkya55ppr8tOf/nSpYPWDDz7IV7/61bz55ptJloxw/uUvf7nMfrp3757OnTsnSebMmZPevXsvFb7Omzcv55xzTi655JIVrnN5OnfuXG0a/5NOOilnn332csPZsrKyPP744zn++OOrLTewspo2bZp77rmnEOzOnz8/RxxxRPr27ZvRo0cv85rKysoMHTo0J5xwQrp165bXX399me2uvvrqwr8PTzzxRL785S/nlVdeWW4tb7/9dn7961+nU6dOq3V69E033TSnnXZannnmmVRUVCyzzbBhw3LGGWcU9g855JDVVg8AAAAAAGuuRvVdAPD56Ny5c2688cb06dMn5eXleemll7L11ltn7733TufOnTNnzpw899xz+eCDDwrX9OzZM3/4wx+W6uvpp58ujOAuKSnJX/7yl7Rr126pdn379s0jjzySu+++uzBCfPjw4VlnnXVW34OuJg0aNMivf/3rnHTSSUmWhINdunTJHnvskc022ywff/xxBg8eXAhgr7/++vTp06c+S15he+21V7761a8WpsI/44wzcs0116Rnz54pKSnJmDFj8vLLL6eioiL9+vXL//73vzzzzDN17v+iiy7Keeedl9///ve56aab0qtXr7Ru3TrvvfdeBg8eXC0Mv+KKK7Llllsus5+SkpJcfPHFOeaYY5IkTz75ZDbffPN85StfSdu2bTNp0qQ8++yzmTFjRjbZZJN873vfy3nnnfcZXpn/88tf/jLjxo3LrbfemsrKylx22WW5+uqr86UvfSmdO3fOuuuum1mzZmXcuHF57bXXMnfu3CTJ+uuvv0ruv8UWW+SVV17J4Ycfntdffz0VFRW5/fbbc/vtt6dTp07Zcccd07Zt25SXl2fSpEkZOXLkUlN/fzITQ1Xbb7997rzzzhx77LGZN29eXnnllfTs2TOdO3fOLrvskjZt2mTBggX56KOP8tprr1X7d2J1mj9/fq6//vpcf/31WW+99bLTTjtls802S/PmzTN16tS89dZbeeONNwrt27VrlwEDBnwutQEAAAAAsGYRfMMXyLHHHpvmzZunf//+mTx5csrKyjJo0KAMGjRoqba9e/fOjTfemGbNmlU7Pm3atPTt27cw+vKMM87IwQcfvNx7/vnPf85LL72U999/P6NHj85ZZ531ua+PvKqceOKJeeedd/Lb3/42yZKpuZ944olqbZo1a5Yrrrgixx13XNEF30ly22235aCDDsqwYcOSLBnh/dZbb1Vrc/LJJ+eaa67JQQcdtEJ9d+/ePffcc0/69u2bqVOn5t57712qTbNmzXL55Zcvc3r9qo4++uhceOGFhVHhM2fOzH333VetzdZbb51//OMfGTp06ArVWZOSkpLccsst2XXXXfPLX/4y06dPz6JFi/Liiy8utXZ21Wv23HPPVVZDp06d8tJLL+WPf/xjLr/88syYMSNJMm7cuIwbN26513Xr1i0DBgzIEUccsczzhx12WF588cWcfPLJGT58eJLk3XffzbvvvltjLR06dFjZR6lVaWlp5syZkySZPXt2nnvuuTz33HPLbNutW7fcdddd2WSTTVZbPV8km200Oes0XVjt2PyFTfPepA3rqSIAAAAAgJoJvuEL5rDDDss777yTm2++OQ899FDeeOONTJ06Neuss0422WST7Lvvvunbt2+1dZSrOvXUUwujPbfffvv8/ve/r/F+rVq1yu2335599903FRUVGThwYA455JAcfvjhq/zZPg+/+c1vcsghh+RPf/pTnn/++UyZMiXrrbdeOnTokIMPPjgnn3xyttpqq/ouc6W1adMmL774Ym688cbceeedeeO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\n"
          },
          "metadata": {
            "image/png": {
              "width": 991,
              "height": 983
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can also inspect the dataframe containing all results:"
      ],
      "metadata": {
        "id": "TZL8vg-2unyW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "enrichr_df"
      ],
      "metadata": {
        "id": "69s5sAjWun6w",
        "outputId": "7e2cd640-ff0e-4e2f-c3a8-0d122ad0bf90",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    rank                         path_name     p_val     z_score  \\\n",
              "0      1                  Follicular Cells  0.000297  122.759259   \n",
              "1      2                      Hepatoblasts  0.000318  118.351190   \n",
              "2      3             Proximal Tubule Cells  0.000658   81.617284   \n",
              "3      4                       Enterocytes  0.001005   65.651741   \n",
              "4      5                       Hepatocytes  0.001189   60.200913   \n",
              "5      6           Airway Epithelial Cells  0.025488   48.757353   \n",
              "6      7             Noradrenergic Neurons  0.025733   48.281553   \n",
              "7      8                    Foveolar Cells  0.028180   43.986726   \n",
              "8      9                         Tanycytes  0.029889   41.406250   \n",
              "9     10                      Acinar Cells  0.030621   40.390244   \n",
              "10    11  Oligodendrocyte Progenitor Cells  0.030865   40.062500   \n",
              "11    12            Hepatic Stellate Cells  0.032570   37.908397   \n",
              "12    13                    Cholangiocytes  0.033057   37.334586   \n",
              "13    14                   Principal Cells  0.033544   36.777778   \n",
              "14    15                     Epsilon Cells  0.034030   36.237226   \n",
              "15    16                        Adipocytes  0.051414   23.667464   \n",
              "\n",
              "    combined_score overlapping_genes  adj_p_val                  database  \n",
              "0       997.217612      [GSTA2, ALB]   0.002548  PanglaoDB_Augmented_2021  \n",
              "1       952.964050      [GSTA2, ALB]   0.002548  PanglaoDB_Augmented_2021  \n",
              "2       598.008573      [GSTA2, ALB]   0.003507  PanglaoDB_Augmented_2021  \n",
              "3       453.196785     [GSTA2, CDO1]   0.003805  PanglaoDB_Augmented_2021  \n",
              "4       405.425154      [GSTA2, ALB]   0.003805  PanglaoDB_Augmented_2021  \n",
              "5       178.916489           [GSTA2]   0.036299  PanglaoDB_Augmented_2021  \n",
              "6       176.708864             [ALB]   0.036299  PanglaoDB_Augmented_2021  \n",
              "7       156.995603           [GSTA2]   0.036299  PanglaoDB_Augmented_2021  \n",
              "8       145.346969            [CDO1]   0.036299  PanglaoDB_Augmented_2021  \n",
              "9       140.803434           [GSTA2]   0.036299  PanglaoDB_Augmented_2021  \n",
              "10      139.343111            [CDO1]   0.036299  PanglaoDB_Augmented_2021  \n",
              "11      129.811804             [ALB]   0.036299  PanglaoDB_Augmented_2021  \n",
              "12      127.292906             [ALB]   0.036299  PanglaoDB_Augmented_2021  \n",
              "13      124.856946            [CDO1]   0.036299  PanglaoDB_Augmented_2021  \n",
              "14      122.500053             [ALB]   0.036299  PanglaoDB_Augmented_2021  \n",
              "15       70.241447            [CDO1]   0.051414  PanglaoDB_Augmented_2021  "
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              "      <th></th>\n",
              "      <th>rank</th>\n",
              "      <th>path_name</th>\n",
              "      <th>p_val</th>\n",
              "      <th>z_score</th>\n",
              "      <th>combined_score</th>\n",
              "      <th>overlapping_genes</th>\n",
              "      <th>adj_p_val</th>\n",
              "      <th>database</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Follicular Cells</td>\n",
              "      <td>0.000297</td>\n",
              "      <td>122.759259</td>\n",
              "      <td>997.217612</td>\n",
              "      <td>[GSTA2, ALB]</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Hepatoblasts</td>\n",
              "      <td>0.000318</td>\n",
              "      <td>118.351190</td>\n",
              "      <td>952.964050</td>\n",
              "      <td>[GSTA2, ALB]</td>\n",
              "      <td>0.002548</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Proximal Tubule Cells</td>\n",
              "      <td>0.000658</td>\n",
              "      <td>81.617284</td>\n",
              "      <td>598.008573</td>\n",
              "      <td>[GSTA2, ALB]</td>\n",
              "      <td>0.003507</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Enterocytes</td>\n",
              "      <td>0.001005</td>\n",
              "      <td>65.651741</td>\n",
              "      <td>453.196785</td>\n",
              "      <td>[GSTA2, CDO1]</td>\n",
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              "      <th>4</th>\n",
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              "      <td>Hepatocytes</td>\n",
              "      <td>0.001189</td>\n",
              "      <td>60.200913</td>\n",
              "      <td>405.425154</td>\n",
              "      <td>[GSTA2, ALB]</td>\n",
              "      <td>0.003805</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Airway Epithelial Cells</td>\n",
              "      <td>0.025488</td>\n",
              "      <td>48.757353</td>\n",
              "      <td>178.916489</td>\n",
              "      <td>[GSTA2]</td>\n",
              "      <td>0.036299</td>\n",
              "      <td>PanglaoDB_Augmented_2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>Noradrenergic Neurons</td>\n",
              "      <td>0.025733</td>\n",
              "      <td>48.281553</td>\n",
              "      <td>176.708864</td>\n",
              "      <td>[ALB]</td>\n",
              "      <td>0.036299</td>\n",
              "      <td>PanglaoDB_Augmented_2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Foveolar Cells</td>\n",
              "      <td>0.028180</td>\n",
              "      <td>43.986726</td>\n",
              "      <td>156.995603</td>\n",
              "      <td>[GSTA2]</td>\n",
              "      <td>0.036299</td>\n",
              "      <td>PanglaoDB_Augmented_2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>Tanycytes</td>\n",
              "      <td>0.029889</td>\n",
              "      <td>41.406250</td>\n",
              "      <td>145.346969</td>\n",
              "      <td>[CDO1]</td>\n",
              "      <td>0.036299</td>\n",
              "      <td>PanglaoDB_Augmented_2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>Acinar Cells</td>\n",
              "      <td>0.030621</td>\n",
              "      <td>40.390244</td>\n",
              "      <td>140.803434</td>\n",
              "      <td>[GSTA2]</td>\n",
              "      <td>0.036299</td>\n",
              "      <td>PanglaoDB_Augmented_2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
              "      <td>Oligodendrocyte Progenitor Cells</td>\n",
              "      <td>0.030865</td>\n",
              "      <td>40.062500</td>\n",
              "      <td>139.343111</td>\n",
              "      <td>[CDO1]</td>\n",
              "      <td>0.036299</td>\n",
              "      <td>PanglaoDB_Augmented_2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
              "      <td>Hepatic Stellate Cells</td>\n",
              "      <td>0.032570</td>\n",
              "      <td>37.908397</td>\n",
              "      <td>129.811804</td>\n",
              "      <td>[ALB]</td>\n",
              "      <td>0.036299</td>\n",
              "      <td>PanglaoDB_Augmented_2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>Cholangiocytes</td>\n",
              "      <td>0.033057</td>\n",
              "      <td>37.334586</td>\n",
              "      <td>127.292906</td>\n",
              "      <td>[ALB]</td>\n",
              "      <td>0.036299</td>\n",
              "      <td>PanglaoDB_Augmented_2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>14</td>\n",
              "      <td>Principal Cells</td>\n",
              "      <td>0.033544</td>\n",
              "      <td>36.777778</td>\n",
              "      <td>124.856946</td>\n",
              "      <td>[CDO1]</td>\n",
              "      <td>0.036299</td>\n",
              "      <td>PanglaoDB_Augmented_2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>Epsilon Cells</td>\n",
              "      <td>0.034030</td>\n",
              "      <td>36.237226</td>\n",
              "      <td>122.500053</td>\n",
              "      <td>[ALB]</td>\n",
              "      <td>0.036299</td>\n",
              "      <td>PanglaoDB_Augmented_2021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>Adipocytes</td>\n",
              "      <td>0.051414</td>\n",
              "      <td>23.667464</td>\n",
              "      <td>70.241447</td>\n",
              "      <td>[CDO1]</td>\n",
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          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This makes sense, since our 5 genes are liver markers after all. However, in the context of the 50 total liver genes tested, only 5 liver genes coming back positive does not seem like much. Indeed, when adding the correct background gene list for this experiment, the enrichment results come back not significant:"
      ],
      "metadata": {
        "id": "QXq1ZyQFqczw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "enrichr_df_bkg = gget.enrichr(liver_genes[:5], database=\"celltypes\", background_list=liver_genes, plot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "W5gAKKMZqByT",
        "outputId": "39cbbdab-2f54-4134-d1a6-4ab72383f890"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 991,
              "height": 983
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "enrichr_df_bkg"
      ],
      "metadata": {
        "id": "pLq0HrU5uy2A",
        "outputId": "c1fb6f32-9fb4-436b-f100-c2d6760ae034",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    rank                         path_name     p_val    z_score  \\\n",
              "0      1                      Hepatoblasts  0.046749  14.000000   \n",
              "1      2                       Enterocytes  0.074530   9.111111   \n",
              "2      3            Hepatic Stellate Cells  0.102041        inf   \n",
              "3      4                         Tanycytes  0.102041        inf   \n",
              "4      5                   Principal Cells  0.102041        inf   \n",
              "5      6  Oligodendrocyte Progenitor Cells  0.102041        inf   \n",
              "6      7                    Foveolar Cells  0.102041        inf   \n",
              "7      8             Proximal Tubule Cells  0.143011   5.200000   \n",
              "8      9                  Follicular Cells  0.143011   5.200000   \n",
              "9     10           Airway Epithelial Cells  0.195578  10.750000   \n",
              "10    11                    Cholangiocytes  0.195578  10.750000   \n",
              "11    12                       Hepatocytes  0.266726   3.000000   \n",
              "12    13                      Acinar Cells  0.281155   5.250000   \n",
              "13    14             Noradrenergic Neurons  0.281155   5.250000   \n",
              "14    15                     Epsilon Cells  0.281155   5.250000   \n",
              "15    16                        Adipocytes  0.430480   2.500000   \n",
              "\n",
              "    combined_score overlapping_genes  adj_p_val                  database  \n",
              "0        42.881535      [GSTA2, ALB]   0.233236  PanglaoDB_Augmented_2021  \n",
              "1        23.657465     [GSTA2, CDO1]   0.233236  PanglaoDB_Augmented_2021  \n",
              "2              inf             [ALB]   0.233236  PanglaoDB_Augmented_2021  \n",
              "3              inf            [CDO1]   0.233236  PanglaoDB_Augmented_2021  \n",
              "4              inf            [CDO1]   0.233236  PanglaoDB_Augmented_2021  \n",
              "5              inf            [CDO1]   0.233236  PanglaoDB_Augmented_2021  \n",
              "6              inf           [GSTA2]   0.233236  PanglaoDB_Augmented_2021  \n",
              "7        10.113139      [GSTA2, ALB]   0.254242  PanglaoDB_Augmented_2021  \n",
              "8        10.113139      [GSTA2, ALB]   0.254242  PanglaoDB_Augmented_2021  \n",
              "9        17.541813           [GSTA2]   0.284477  PanglaoDB_Augmented_2021  \n",
              "10       17.541813             [ALB]   0.284477  PanglaoDB_Augmented_2021  \n",
              "11        3.964597      [GSTA2, ALB]   0.299898  PanglaoDB_Augmented_2021  \n",
              "12        6.661465           [GSTA2]   0.299898  PanglaoDB_Augmented_2021  \n",
              "13        6.661465             [ALB]   0.299898  PanglaoDB_Augmented_2021  \n",
              "14        6.661465             [ALB]   0.299898  PanglaoDB_Augmented_2021  \n",
              "15        2.107137            [CDO1]   0.430480  PanglaoDB_Augmented_2021  "
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              "      <th></th>\n",
              "      <th>rank</th>\n",
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              "      <th>overlapping_genes</th>\n",
              "      <th>adj_p_val</th>\n",
              "      <th>database</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Hepatoblasts</td>\n",
              "      <td>0.046749</td>\n",
              "      <td>14.000000</td>\n",
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              "      <td>[GSTA2, CDO1]</td>\n",
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              "      <th>5</th>\n",
              "      <td>6</td>\n",
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              "      <td>0.102041</td>\n",
              "      <td>inf</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>Foveolar Cells</td>\n",
              "      <td>0.102041</td>\n",
              "      <td>inf</td>\n",
              "      <td>inf</td>\n",
              "      <td>[GSTA2]</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Proximal Tubule Cells</td>\n",
              "      <td>0.143011</td>\n",
              "      <td>5.200000</td>\n",
              "      <td>10.113139</td>\n",
              "      <td>[GSTA2, ALB]</td>\n",
              "      <td>0.254242</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
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              "      <td>0.195578</td>\n",
              "      <td>10.750000</td>\n",
              "      <td>17.541813</td>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>11</td>\n",
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              "      <td>17.541813</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>12</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
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              "      <td>5.250000</td>\n",
              "      <td>6.661465</td>\n",
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              "      <th>13</th>\n",
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              "      <th>15</th>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "pZdfYChruzcJ"
      },
      "execution_count": 9,
      "outputs": []
    }
  ]
}