SMG-epi-blasticidin-plot-ratios.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5618354b-6d2b-4107-bdaf-76387afa00cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: ../jupyter_figures: File exists\n"
     ]
    }
   ],
   "source": [
    "import sys, os, glob, warnings, logging\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sw_plotting import change_bar_width\n",
    "from sw_utilities import tukeyTest\n",
    "\n",
    "# logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.DEBUG)\n",
    "# logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.INFO)\n",
    "logging.basicConfig(stream=sys.stdout, format='%(asctime)s - %(levelname)s - %(message)s', level=logging.ERROR)\n",
    "\n",
    "# ignore warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# plotting configuration\n",
    "font = {'family' : 'Arial',\n",
    "        'size'   : 7}\n",
    "matplotlib.rc('font', **font)\n",
    "plt.rcParams['svg.fonttype'] = 'none'\n",
    "\n",
    "# Make a folder if it is not already there to store exported figures\n",
    "!mkdir ../jupyter_figures\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "08f8a42d-ed83-4593-9c54-bf5734b58820",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the ratio data\n",
    "\n",
    "data_folder = '../data/220601-X-188-blast-test/220601-X-188-no-blast-40x-output'\n",
    "df = pd.read_csv(os.path.join(data_folder, 'ratio_per_sample_no_blast.csv'),\n",
    "                 header=None)\n",
    "df.columns = ['ratio_pos']\n",
    "df['groups'] = ['-blast']*len(df)\n",
    "df1 = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "80468c14-00f7-4271-a491-7168985bb56a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the ratio data\n",
    "\n",
    "data_folder = '../data/220601-X-188-blast-test/220601-X-188-add-blast-40x-output'\n",
    "df = pd.read_csv(os.path.join(data_folder, 'ratio_per_sample_blast.csv'),\n",
    "                 header=None)\n",
    "df.columns = ['ratio_pos']\n",
    "df['groups'] = ['+blast']*len(df)\n",
    "df2 = df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5005eefa-7865-454e-a40e-e450500839c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df1, df2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e18ba04e-03e2-4732-9f18-59c88392ccf7",
   "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",
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       "    }\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>ratio_pos</th>\n",
       "      <th>groups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.671772</td>\n",
       "      <td>-blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.235474</td>\n",
       "      <td>-blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.744713</td>\n",
       "      <td>-blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.671441</td>\n",
       "      <td>-blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.287152</td>\n",
       "      <td>-blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.586357</td>\n",
       "      <td>-blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.663912</td>\n",
       "      <td>-blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.429020</td>\n",
       "      <td>-blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.479491</td>\n",
       "      <td>-blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.302711</td>\n",
       "      <td>-blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.831008</td>\n",
       "      <td>+blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.637241</td>\n",
       "      <td>+blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.946612</td>\n",
       "      <td>+blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.788679</td>\n",
       "      <td>+blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.660300</td>\n",
       "      <td>+blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.809470</td>\n",
       "      <td>+blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.869688</td>\n",
       "      <td>+blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.787843</td>\n",
       "      <td>+blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.910314</td>\n",
       "      <td>+blast</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.801980</td>\n",
       "      <td>+blast</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ratio_pos  groups\n",
       "0    0.671772  -blast\n",
       "1    0.235474  -blast\n",
       "2    0.744713  -blast\n",
       "3    0.671441  -blast\n",
       "4    0.287152  -blast\n",
       "5    0.586357  -blast\n",
       "6    0.663912  -blast\n",
       "7    0.429020  -blast\n",
       "8    0.479491  -blast\n",
       "9    0.302711  -blast\n",
       "10   0.831008  +blast\n",
       "11   0.637241  +blast\n",
       "12   0.946612  +blast\n",
       "13   0.788679  +blast\n",
       "14   0.660300  +blast\n",
       "15   0.809470  +blast\n",
       "16   0.869688  +blast\n",
       "17   0.787843  +blast\n",
       "18   0.910314  +blast\n",
       "19   0.801980  +blast"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index(drop=True, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5bea4686-605f-4486-8489-c15bd2193cee",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ratio_pos_percent'] = df['ratio_pos']*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7f52fdbf-f1a1-41f9-abeb-b7a95840aaab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>groups</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>+blast</th>\n",
       "      <td>10.0</td>\n",
       "      <td>80.431357</td>\n",
       "      <td>9.756598</td>\n",
       "      <td>63.724138</td>\n",
       "      <td>78.805181</td>\n",
       "      <td>80.572516</td>\n",
       "      <td>86.001823</td>\n",
       "      <td>94.661191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-blast</th>\n",
       "      <td>10.0</td>\n",
       "      <td>50.720426</td>\n",
       "      <td>18.626392</td>\n",
       "      <td>23.547401</td>\n",
       "      <td>33.428803</td>\n",
       "      <td>53.292392</td>\n",
       "      <td>66.955898</td>\n",
       "      <td>74.471299</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        count       mean        std        min        25%        50%  \\\n",
       "groups                                                                 \n",
       "+blast   10.0  80.431357   9.756598  63.724138  78.805181  80.572516   \n",
       "-blast   10.0  50.720426  18.626392  23.547401  33.428803  53.292392   \n",
       "\n",
       "              75%        max  \n",
       "groups                        \n",
       "+blast  86.001823  94.661191  \n",
       "-blast  66.955898  74.471299  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('groups').describe()['ratio_pos_percent']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bbd96679-37d4-4998-8383-b139fd03e155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 300x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make plot for figure\n",
    "\n",
    "fig_width, fig_height = 1.0, 2.0 # inches\n",
    "line_width = .8\n",
    "output_prefix = '220601-X-188-blast-ratios'\n",
    "outputFigPath = '../jupyter_figures/' + output_prefix + '.svg'\n",
    "# outputFigPath = '../jupyter_figures/' + output_prefix + '.tif'\n",
    "\n",
    "fig = plt.figure(figsize=(fig_width,fig_height), dpi=300)\n",
    "ax = fig.add_axes([0.45, 0.15, 0.5, 0.8])\n",
    "\n",
    "sns.swarmplot(data = df,\n",
    "              x = 'groups', y = 'ratio_pos_percent',\n",
    "              color='#ff00ff',\n",
    "              size = 3\n",
    "             )\n",
    "sns.barplot(data = df,\n",
    "            x = 'groups', y = 'ratio_pos_percent',\n",
    "            ci = 'sd',\n",
    "            color=\".7\", # This a nicer looking lighter gray to my own taste\n",
    "            errwidth=.7, errcolor=\"k\", capsize=.2)\n",
    "\n",
    "change_bar_width(ax, .5)\n",
    "\n",
    "# rotate x tick labels if necessary\n",
    "x_labels = ax.get_xticklabels()\n",
    "ax.set_xticklabels(labels=x_labels, rotation=45, ha=\"right\")\n",
    "\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel(\"Transdued cells %\")\n",
    "plt.ylim([0,100])\n",
    "\n",
    "# ax.get_legend().remove()\n",
    "\n",
    "for o in fig.findobj():\n",
    "    o.set_clip_on(False)\n",
    "for o in ax.findobj():\n",
    "    o.set_clip_on(False)\n",
    "\n",
    "plt.savefig(outputFigPath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "24d7fc0d-17e8-4eba-bca1-dfea5829dde7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary of test:\n",
      "  Multiple Comparison of Means - Tukey HSD, FWER=0.05 \n",
      "=====================================================\n",
      "group1 group2 meandiff p-adj  lower    upper   reject\n",
      "-----------------------------------------------------\n",
      "+blast -blast -29.7109 0.001 -43.6806 -15.7412   True\n",
      "-----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " 0.001\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fcb69d96700>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tukeyTest(df['ratio_pos_percent'], df['groups'])"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "788cf6af-ddcb-419c-be15-5f7c1b8be9fc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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