high_quality_volcano_plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80ea7abb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from adjustText import adjust_text\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2fa42fdc",
   "metadata": {},
   "outputs": [
    {
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       "      <td>-2.978533</td>\n",
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       "      <td>0.036899</td>\n",
       "      <td>ATAD3B</td>\n",
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       "      <td>THAP3</td>\n",
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       "      <td>MTND2P28</td>\n",
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       "      <td>0.219621</td>\n",
       "      <td>-0.243496</td>\n",
       "      <td>0.807621</td>\n",
       "      <td>0.938042</td>\n",
       "      <td>ACAP3</td>\n",
       "      <td>0.027778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ENSG00000169972</td>\n",
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       "      <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",
       "      <td>0.137935</td>\n",
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       "        Unnamed: 0    baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
       "0  ENSG00000160072  320.481168       -0.492442  0.165330 -2.978533  0.002896   \n",
       "1  ENSG00000041988  103.354977       -0.282331  0.168099 -1.679553  0.093044   \n",
       "2  ENSG00000225630  922.811465        0.733032  0.375566  1.951807  0.050961   \n",
       "3  ENSG00000131584  735.890219       -0.053477  0.219621 -0.243496  0.807621   \n",
       "4  ENSG00000169972  135.304763       -0.154869  0.187044 -0.827982  0.407681   \n",
       "\n",
       "       padj    symbol    nlog10  \n",
       "0  0.036899    ATAD3B  1.432991  \n",
       "1  0.359646     THAP3  0.444125  \n",
       "2  0.256948  MTND2P28  0.590154  \n",
       "3  0.938042     ACAP3  0.027778  \n",
       "4  0.727888     PUSL1  0.137935  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('DE_results.csv').dropna()\n",
    "df['nlog10'] = -np.log10(df.padj)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "dd097b04",
   "metadata": {},
   "outputs": [
    {
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       "      <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>symbol</th>\n",
       "      <th>nlog10</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ENSG00000160072</td>\n",
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       "      <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",
       "      <td>1.432991</td>\n",
       "      <td>nobody_cares</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",
       "      <td>0.444125</td>\n",
       "      <td>nobody_cares</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ENSG00000225630</td>\n",
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       "      <td>-0.243496</td>\n",
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       "      <td>0.938042</td>\n",
       "      <td>ACAP3</td>\n",
       "      <td>0.027778</td>\n",
       "      <td>nobody_cares</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>PUSL1</td>\n",
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       "      <td>nobody_cares</td>\n",
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      "text/plain": [
       "        Unnamed: 0    baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
       "0  ENSG00000160072  320.481168       -0.492442  0.165330 -2.978533  0.002896   \n",
       "1  ENSG00000041988  103.354977       -0.282331  0.168099 -1.679553  0.093044   \n",
       "2  ENSG00000225630  922.811465        0.733032  0.375566  1.951807  0.050961   \n",
       "3  ENSG00000131584  735.890219       -0.053477  0.219621 -0.243496  0.807621   \n",
       "4  ENSG00000169972  135.304763       -0.154869  0.187044 -0.827982  0.407681   \n",
       "\n",
       "       padj    symbol    nlog10         color  \n",
       "0  0.036899    ATAD3B  1.432991  nobody_cares  \n",
       "1  0.359646     THAP3  0.444125  nobody_cares  \n",
       "2  0.256948  MTND2P28  0.590154  nobody_cares  \n",
       "3  0.938042     ACAP3  0.027778  nobody_cares  \n",
       "4  0.727888     PUSL1  0.137935  nobody_cares  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#picked1 and picked2 simulate user lists of genes to label by color\n",
    "picked1 = random.choices(df.symbol.tolist(), weights = df.nlog10.tolist(), k = 250)\n",
    "picked2 = random.choices(df.symbol.tolist(), weights = df.nlog10.tolist(), k = 300)\n",
    "picked2 = [x for x in picked2 if x not in picked1]\n",
    "\n",
    "def map_color(a):\n",
    "    log2FoldChange, symbol, nlog10 = a\n",
    "    \n",
    "    if abs(log2FoldChange) < 1 or nlog10 < 2:\n",
    "        return 'nobody_cares'\n",
    "    if symbol in picked1:\n",
    "        return 'picked1'\n",
    "    if symbol in picked2:\n",
    "        return 'picked2'\n",
    "    \n",
    "    return 'i_care'\n",
    "\n",
    "df['color'] = df[['log2FoldChange', 'symbol', 'nlog10']].apply(map_color, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dde0e1dc",
   "metadata": {},
   "outputs": [
    {
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       "    <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>symbol</th>\n",
       "      <th>nlog10</th>\n",
       "      <th>color</th>\n",
       "      <th>shape</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <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",
       "      <td>1.432991</td>\n",
       "      <td>nobody_cares</td>\n",
       "      <td>not_important</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",
       "      <td>0.444125</td>\n",
       "      <td>nobody_cares</td>\n",
       "      <td>not_important</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",
       "      <td>0.590154</td>\n",
       "      <td>nobody_cares</td>\n",
       "      <td>not_important</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",
       "      <td>0.027778</td>\n",
       "      <td>nobody_cares</td>\n",
       "      <td>not_important</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",
       "      <td>0.137935</td>\n",
       "      <td>nobody_cares</td>\n",
       "      <td>not_important</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        Unnamed: 0    baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
       "0  ENSG00000160072  320.481168       -0.492442  0.165330 -2.978533  0.002896   \n",
       "1  ENSG00000041988  103.354977       -0.282331  0.168099 -1.679553  0.093044   \n",
       "2  ENSG00000225630  922.811465        0.733032  0.375566  1.951807  0.050961   \n",
       "3  ENSG00000131584  735.890219       -0.053477  0.219621 -0.243496  0.807621   \n",
       "4  ENSG00000169972  135.304763       -0.154869  0.187044 -0.827982  0.407681   \n",
       "\n",
       "       padj    symbol    nlog10         color          shape  \n",
       "0  0.036899    ATAD3B  1.432991  nobody_cares  not_important  \n",
       "1  0.359646     THAP3  0.444125  nobody_cares  not_important  \n",
       "2  0.256948  MTND2P28  0.590154  nobody_cares  not_important  \n",
       "3  0.938042     ACAP3  0.027778  nobody_cares  not_important  \n",
       "4  0.727888     PUSL1  0.137935  nobody_cares  not_important  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#picked3 and picked24 simulate user lists of genes to label by shape\n",
    "\n",
    "picked3 = random.choices(df.symbol.tolist(), weights = df.nlog10.tolist(), k = 250)\n",
    "picked4 = random.choices(df.symbol.tolist(), weights = df.nlog10.tolist(), k = 300)\n",
    "picked4 = [x for x in picked4 if x not in picked3]\n",
    "\n",
    "def map_shape(symbol):\n",
    "    if symbol in picked3:\n",
    "        return 'picked3'\n",
    "    if symbol in picked4:\n",
    "        return 'picked4'\n",
    "    \n",
    "    return 'not_important'\n",
    "\n",
    "df['shape'] = df.symbol.map(map_shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "40175321",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (6,6))\n",
    "\n",
    "ax = sns.scatterplot(data = df, x = 'log2FoldChange', y = 'nlog10',\n",
    "                    hue = 'color', hue_order = ['nobody_cares', 'picked1', 'picked2', 'i_care'],\n",
    "                    palette = ['lightgrey', 'orange', 'purple', 'grey'],\n",
    "                    style = 'shape', style_order = ['picked3', 'picked4', 'not_important'],\n",
    "                    markers = ['^', 's', 'o'], \n",
    "                    size = 'baseMean', sizes = (40, 400))\n",
    "\n",
    "ax.axhline(2, zorder = 0, c = 'k', lw = 2, ls = '--')\n",
    "ax.axvline(1, zorder = 0, c = 'k', lw = 2, ls = '--')\n",
    "ax.axvline(-1, zorder = 0, c = 'k', lw = 2, ls = '--')\n",
    "\n",
    "\n",
    "\n",
    "texts = []\n",
    "for i in range(len(df)):\n",
    "    if df.iloc[i].nlog10 > 5 and abs(df.iloc[i].log2FoldChange) > 2:\n",
    "        texts.append(plt.text(x = df.iloc[i].log2FoldChange, y = df.iloc[i].nlog10, s = df.iloc[i].symbol,\n",
    "                             fontsize = 12, weight = 'bold'))\n",
    "        \n",
    "adjust_text(texts, arrowprops = dict(arrowstyle = '-', color = 'k'))\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.legend(loc = 1, bbox_to_anchor = (1.4,1), frameon = False, prop = {'weight':'bold'})\n",
    "\n",
    "for axis in ['bottom', 'left']:\n",
    "    ax.spines[axis].set_linewidth(2)\n",
    "    \n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "ax.tick_params(width = 2)\n",
    "\n",
    "plt.xticks(size = 12, weight = 'bold')\n",
    "plt.yticks(size = 12, weight = 'bold')\n",
    "\n",
    "plt.xlabel(\"$log_{2}$ fold change\", size = 15)\n",
    "plt.ylabel(\"-$log_{10}$ FDR\", size = 15)\n",
    "\n",
    "plt.savefig('volcano.png', dpi = 300, bbox_inches = 'tight', facecolor = 'white')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1172194b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32795e18",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d160d079",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "639df7a7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a5e6aee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "93e26506",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfce691e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0141edc1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3d0a335",
   "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
}