{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tumor growth modeling\n",
    "This practical session is intended to explore tumor growth data and interpret it using mathematical models. It is divided into three parts (only the first two parts will be covered in this session):\n",
    "1. Analysis of the data by basic plots\n",
    "2. Fitting and comparing tumor growth models to the data in order to understand **tumor growth laws**\n",
    "3. Using the model(s) to **predict** future tumor growth with only a limited number of initial data points\n",
    "\n",
    "The data provided consists of measurements of tumor volumes from tumors implanted subcutaneously in the back of mice. The cells are from a murine lung cancer cell line (Lewis Lung Carcinoma). The volumes were computed from two one dimensional measures recorded using a caliper (the length $L$ and width $w$) and using the formula $V = \\frac{1}{2}L\\times w^2$. Volumes are given in mm$^3$ as a function of days following injection of the cells ($10^6$ cells $\\simeq$ 1 mm$^3$ injected on day 0.\n",
    "\n",
    "Are you ready to start your exploration? \n",
    "\n",
    "Good luck on your adventure! :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "% matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data file ``data_table.xlsx`` as a pandas Dataframe and display it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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",
       "        vertical-align: top;\n",
       "    }\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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>54.886377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.809022</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>59.408036</td>\n",
       "      <td>89.231295</td>\n",
       "      <td>55.348590</td>\n",
       "      <td>59.853289</td>\n",
       "      <td>46.502387</td>\n",
       "      <td>94.727972</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.567329</td>\n",
       "      <td>26.880016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>50.021795</td>\n",
       "      <td>85.662278</td>\n",
       "      <td>157.351502</td>\n",
       "      <td>56.175814</td>\n",
       "      <td>58.988502</td>\n",
       "      <td>55.155193</td>\n",
       "      <td>126.094176</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.293694</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>309.519493</td>\n",
       "      <td>261.572775</td>\n",
       "      <td>221.698580</td>\n",
       "      <td>103.330909</td>\n",
       "      <td>179.664759</td>\n",
       "      <td>251.222209</td>\n",
       "      <td>333.136619</td>\n",
       "      <td>201.165288</td>\n",
       "      <td>126.820889</td>\n",
       "      <td>144.151300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>324.895878</td>\n",
       "      <td>412.265650</td>\n",
       "      <td>327.492254</td>\n",
       "      <td>155.320341</td>\n",
       "      <td>309.787647</td>\n",
       "      <td>341.175444</td>\n",
       "      <td>538.680724</td>\n",
       "      <td>316.382967</td>\n",
       "      <td>222.661158</td>\n",
       "      <td>193.401925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>450.842120</td>\n",
       "      <td>488.450738</td>\n",
       "      <td>461.437963</td>\n",
       "      <td>167.958671</td>\n",
       "      <td>470.571322</td>\n",
       "      <td>438.335387</td>\n",
       "      <td>669.929621</td>\n",
       "      <td>338.240373</td>\n",
       "      <td>244.726914</td>\n",
       "      <td>281.171233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>572.450814</td>\n",
       "      <td>618.854795</td>\n",
       "      <td>641.444986</td>\n",
       "      <td>219.378690</td>\n",
       "      <td>480.930314</td>\n",
       "      <td>859.952765</td>\n",
       "      <td>762.527617</td>\n",
       "      <td>411.958788</td>\n",
       "      <td>333.629836</td>\n",
       "      <td>294.886207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>664.336606</td>\n",
       "      <td>798.997212</td>\n",
       "      <td>746.868414</td>\n",
       "      <td>412.378378</td>\n",
       "      <td>488.777983</td>\n",
       "      <td>854.727952</td>\n",
       "      <td>923.717646</td>\n",
       "      <td>586.667016</td>\n",
       "      <td>367.475268</td>\n",
       "      <td>391.884141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1151.693754</td>\n",
       "      <td>1218.721058</td>\n",
       "      <td>1359.458389</td>\n",
       "      <td>584.192211</td>\n",
       "      <td>1021.721046</td>\n",
       "      <td>1143.279505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>991.881984</td>\n",
       "      <td>805.778850</td>\n",
       "      <td>744.954870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1338.383299</td>\n",
       "      <td>1415.471856</td>\n",
       "      <td>1626.874371</td>\n",
       "      <td>762.074018</td>\n",
       "      <td>1278.530775</td>\n",
       "      <td>1645.406820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1219.899900</td>\n",
       "      <td>1030.034281</td>\n",
       "      <td>990.331786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>1522.807849</td>\n",
       "      <td>1410.149208</td>\n",
       "      <td>2063.912472</td>\n",
       "      <td>880.325721</td>\n",
       "      <td>1377.381845</td>\n",
       "      <td>1950.482691</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1833.096551</td>\n",
       "      <td>1272.818884</td>\n",
       "      <td>1085.314905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1897.073737</td>\n",
       "      <td>1524.126579</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1040.074430</td>\n",
       "      <td>1538.922047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2131.605693</td>\n",
       "      <td>1555.359077</td>\n",
       "      <td>1331.189667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1935.415344</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1335.136464</td>\n",
       "      <td>1958.560253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1671.148523</td>\n",
       "      <td>1641.333918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1850.447333</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1992.067465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2079.445167</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             1            2            3            4            5   \\\n",
       "5           NaN    54.886377          NaN          NaN    68.809022   \n",
       "6           NaN    59.408036    89.231295    55.348590    59.853289   \n",
       "7     50.021795    85.662278   157.351502    56.175814    58.988502   \n",
       "11   309.519493   261.572775   221.698580   103.330909   179.664759   \n",
       "12   324.895878   412.265650   327.492254   155.320341   309.787647   \n",
       "13   450.842120   488.450738   461.437963   167.958671   470.571322   \n",
       "14   572.450814   618.854795   641.444986   219.378690   480.930314   \n",
       "15   664.336606   798.997212   746.868414   412.378378   488.777983   \n",
       "18  1151.693754  1218.721058  1359.458389   584.192211  1021.721046   \n",
       "19  1338.383299  1415.471856  1626.874371   762.074018  1278.530775   \n",
       "20  1522.807849  1410.149208  2063.912472   880.325721  1377.381845   \n",
       "21  1897.073737  1524.126579          NaN  1040.074430  1538.922047   \n",
       "22          NaN  1935.415344          NaN  1335.136464  1958.560253   \n",
       "24          NaN          NaN          NaN  1850.447333          NaN   \n",
       "25          NaN          NaN          NaN  2079.445167          NaN   \n",
       "\n",
       "             6           7            8            9            10  \n",
       "5           NaN         NaN          NaN          NaN          NaN  \n",
       "6     46.502387   94.727972          NaN    18.567329    26.880016  \n",
       "7     55.155193  126.094176          NaN    73.293694          NaN  \n",
       "11   251.222209  333.136619   201.165288   126.820889   144.151300  \n",
       "12   341.175444  538.680724   316.382967   222.661158   193.401925  \n",
       "13   438.335387  669.929621   338.240373   244.726914   281.171233  \n",
       "14   859.952765  762.527617   411.958788   333.629836   294.886207  \n",
       "15   854.727952  923.717646   586.667016   367.475268   391.884141  \n",
       "18  1143.279505         NaN   991.881984   805.778850   744.954870  \n",
       "19  1645.406820         NaN  1219.899900  1030.034281   990.331786  \n",
       "20  1950.482691         NaN  1833.096551  1272.818884  1085.314905  \n",
       "21          NaN         NaN  2131.605693  1555.359077  1331.189667  \n",
       "22          NaN         NaN          NaN  1671.148523  1641.333918  \n",
       "24          NaN         NaN          NaN          NaN  1992.067465  \n",
       "25          NaN         NaN          NaN          NaN          NaN  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('data_table.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the time vector. It is in days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([5, 6, 7, 11, 12, 13, 14, 15, 18, 19, 20, 21, 22, 24, 25], dtype='int64')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time = df.index\n",
    "time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the growth of the first three mice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Volume (mm^3)')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1119f3908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1119f32b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113d967f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mouse 1\n",
    "plt.figure(1)\n",
    "plt.plot(time, df[1], 'o')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Volume (mm^3)')\n",
    "\n",
    "# Mouse 2\n",
    "plt.figure(2)\n",
    "plt.plot(time, df[2], 'o')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Volume (mm^3)')\n",
    "\n",
    "# Mouse 3\n",
    "plt.figure(3)\n",
    "plt.plot(time, df[3], 'o')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Volume (mm^3)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot all tumor growth on the same panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e43a470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for mouse in df.columns:\n",
    "    plt.plot(time, df[mouse], 'o')\n",
    "    plt.xlabel('Days')\n",
    "    plt.ylabel('Volume (mm^3)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the average of the data with error bars as standard deviations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Volume (mm^3)')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113f13630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate columns with mean\n",
    "df['mean'] = df.mean(axis=1)\n",
    "# Generate columns with std\n",
    "df['std'] = df.std(axis=1)\n",
    "df[['mean']].plot(fmt='o', yerr=df['std'])\n",
    "plt.legend(loc='upper left')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Volume (mm^3)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the individual plots, what tumor growth pattern/equation would you suggest? How could you simply graphically test it? What do you conclude?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Volume (mm^3)')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/benzekry/anaconda3/lib/python3.6/site-packages/matplotlib/scale.py:111: RuntimeWarning: invalid value encountered in less_equal\n",
      "  out[a <= 0] = -1000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ad2c908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11adedba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aee7b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ac9da58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exponential growth\n",
    "# Should be linear in log scale\n",
    "# Mouse 1\n",
    "plt.figure(1)\n",
    "plt.semilogy(time, df[1], 'o')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Volume (mm^3)')\n",
    "\n",
    "# Mouse 2\n",
    "plt.figure(2)\n",
    "plt.semilogy(time, df[2], 'o')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Volume (mm^3)')\n",
    "\n",
    "# Mouse 3\n",
    "plt.figure(3)\n",
    "plt.semilogy(time, df[3], 'o')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Volume (mm^3)')\n",
    "\n",
    "# Mouse 4\n",
    "plt.figure(4)\n",
    "plt.semilogy(time, df[4], 'o')\n",
    "plt.xlabel('Days')\n",
    "plt.ylabel('Volume (mm^3)')"
   ]
  },
  {
   "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}