06-ExemplePositionNoyau.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPORTATION DES DIFFERENTS MODULES\n",
    "\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# LOADING THE FILE\n",
    "\n",
    "# Create a variable that contains the path to the file. \n",
    "# The path to place is either absolute (i.e. from the root of a disk)\n",
    "#filename = 'D:/Documents/Code/CoursePython/datas/positionCore.csv\n",
    "# or relative (i.e. from the folder where the file is located)\n",
    "filename = 'datas/positionNoyau.csv'\n",
    "data = pd.read_csv(filename, sep=';', decimal=b',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Condition</th>\n",
       "      <th>Apoptotic</th>\n",
       "      <th>Apical</th>\n",
       "      <th>Basal</th>\n",
       "      <th>Fichier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ctrl</td>\n",
       "      <td>1</td>\n",
       "      <td>13.10</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2015-02-11 sqhGFP Shot Dcp1_p1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ctrl</td>\n",
       "      <td>0</td>\n",
       "      <td>11.50</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2015-02-11 sqhGFP Shot Dcp1_p1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ctrl</td>\n",
       "      <td>0</td>\n",
       "      <td>7.39</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2015-02-11 sqhGFP Shot Dcp1_p1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ctrl</td>\n",
       "      <td>0</td>\n",
       "      <td>5.55</td>\n",
       "      <td>22.4</td>\n",
       "      <td>2015-02-11 sqhGFP Shot Dcp1_p1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ctrl</td>\n",
       "      <td>0</td>\n",
       "      <td>4.51</td>\n",
       "      <td>22.7</td>\n",
       "      <td>2015-02-11 sqhGFP Shot Dcp1_p1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Condition  Apoptotic  Apical  Basal                         Fichier\n",
       "0      ctrl          1   13.10   14.2  2015-02-11 sqhGFP Shot Dcp1_p1\n",
       "1      ctrl          0   11.50   15.5  2015-02-11 sqhGFP Shot Dcp1_p1\n",
       "2      ctrl          0    7.39   20.2  2015-02-11 sqhGFP Shot Dcp1_p1\n",
       "3      ctrl          0    5.55   22.4  2015-02-11 sqhGFP Shot Dcp1_p1\n",
       "4      ctrl          0    4.51   22.7  2015-02-11 sqhGFP Shot Dcp1_p1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Allows you to see the first 5 rows of the table\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Apoptotic</th>\n",
       "      <th>Apical</th>\n",
       "      <th>Basal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>134.000000</td>\n",
       "      <td>134.000000</td>\n",
       "      <td>134.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.186567</td>\n",
       "      <td>8.593410</td>\n",
       "      <td>16.192388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.391025</td>\n",
       "      <td>4.425846</td>\n",
       "      <td>5.946123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.080000</td>\n",
       "      <td>5.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.420000</td>\n",
       "      <td>11.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.670000</td>\n",
       "      <td>16.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>11.345000</td>\n",
       "      <td>20.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.460000</td>\n",
       "      <td>29.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Apoptotic      Apical       Basal\n",
       "count  134.000000  134.000000  134.000000\n",
       "mean     0.186567    8.593410   16.192388\n",
       "std      0.391025    4.425846    5.946123\n",
       "min      0.000000    1.080000    5.410000\n",
       "25%      0.000000    5.420000   11.025000\n",
       "50%      0.000000    7.670000   16.250000\n",
       "75%      0.000000   11.345000   20.575000\n",
       "max      1.000000   22.460000   29.600000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Provides basic information about the numeric columns.\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1- Add a column that calculates the normalized position of the core (pos = apical/(apical+basal))\n",
    "data['normalized_position'] = data.Apical/(data.Apical+data.Basal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Condition</th>\n",
       "      <th>Apoptotic</th>\n",
       "      <th>Apical</th>\n",
       "      <th>Basal</th>\n",
       "      <th>Fichier</th>\n",
       "      <th>normalized_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ctrl</td>\n",
       "      <td>1</td>\n",
       "      <td>13.10</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2015-02-11 sqhGFP Shot Dcp1_p1</td>\n",
       "      <td>0.479853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ctrl</td>\n",
       "      <td>0</td>\n",
       "      <td>11.50</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2015-02-11 sqhGFP Shot Dcp1_p1</td>\n",
       "      <td>0.425926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ctrl</td>\n",
       "      <td>0</td>\n",
       "      <td>7.39</td>\n",
       "      <td>20.2</td>\n",
       "      <td>2015-02-11 sqhGFP Shot Dcp1_p1</td>\n",
       "      <td>0.267851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ctrl</td>\n",
       "      <td>0</td>\n",
       "      <td>5.55</td>\n",
       "      <td>22.4</td>\n",
       "      <td>2015-02-11 sqhGFP Shot Dcp1_p1</td>\n",
       "      <td>0.198569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ctrl</td>\n",
       "      <td>0</td>\n",
       "      <td>4.51</td>\n",
       "      <td>22.7</td>\n",
       "      <td>2015-02-11 sqhGFP Shot Dcp1_p1</td>\n",
       "      <td>0.165748</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Condition  Apoptotic  Apical  Basal                         Fichier  \\\n",
       "0      ctrl          1   13.10   14.2  2015-02-11 sqhGFP Shot Dcp1_p1   \n",
       "1      ctrl          0   11.50   15.5  2015-02-11 sqhGFP Shot Dcp1_p1   \n",
       "2      ctrl          0    7.39   20.2  2015-02-11 sqhGFP Shot Dcp1_p1   \n",
       "3      ctrl          0    5.55   22.4  2015-02-11 sqhGFP Shot Dcp1_p1   \n",
       "4      ctrl          0    4.51   22.7  2015-02-11 sqhGFP Shot Dcp1_p1   \n",
       "\n",
       "   normalized_position  \n",
       "0             0.479853  \n",
       "1             0.425926  \n",
       "2             0.267851  \n",
       "3             0.198569  \n",
       "4             0.165748  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can see this new column\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x242493d3be0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2- Display the boxplot according to the Condition column, and according to the Condition and Apoptotic columns.\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "ax = plt.axes()\n",
    "data.boxplot(column='normalized_position', by=['Condition', 'Apoptotic'], ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stat=787.000, p=0.000\n",
      "Probably different distributions\n"
     ]
    }
   ],
   "source": [
    "# 3- Do the statistical tests to see if there are significant differences or not. \n",
    "\n",
    "# It is possible to create intermediate variables that contain the filtered/wanted data\n",
    "ctr_apopto = data[(data.Condition=='ctrl') & (data.Apoptotic==1)]['normalized_position']\n",
    "ctr_no_apopto = data[(data.Condition=='ctrl') & (data.Apoptotic==0)]['normalized_position']\n",
    "\n",
    "stat, p = stats.mannwhitneyu(ctr_apopto, ctr_no_apopto)\n",
    "\n",
    "print('stat=%.3f, p=%.3f' % (stat, p))\n",
    "if p > 0.05:\n",
    "    print('Probably the same distribution')\n",
    "else:\n",
    "    print('Probably different distributions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": 2
}