figure-example-enrichment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.lines as mlines\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,8))\n",
    "pd.DataFrame({'Cholesterol': np.random.lognormal(0.5, 0.1, 100),\n",
    "              'Fatty-acids': np.random.lognormal(0.7, 0.05, 100),\n",
    "              'BCAAs metabolism': np.random.lognormal(0.25, 0.15, 100),\n",
    "              'Glycolysis/Gluconeogenesis': np.random.lognormal(0.05, 0.05, 100)}).plot.box(ax=ax, \n",
    "                                                                                            boxprops=dict(linewidth=2.5),\n",
    "                                                                                            medianprops=dict(linewidth=2.5));\n",
    "ax.axhline(-np.log10(0.05), linestyle='--', color='k')\n",
    "ax.set_xlabel('Pathways', fontsize=14)\n",
    "ax.set_ylabel('-log10(p-value) (pathway enrichment)', fontsize=14);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}