{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "47ab9d3d-5a84-4b1c-a5e0-03e14abf4524",
   "metadata": {
    "tags": [
     "remove_cell"
    ]
   },
   "outputs": [],
   "source": [
    "# Setting up the Colab environment. DO NOT EDIT!\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "try:\n",
    "    import otter\n",
    "\n",
    "except ImportError:\n",
    "    ! pip install -q otter-grader==4.0.0\n",
    "    import otter\n",
    "\n",
    "if not os.path.exists('walkthrough-tests'):\n",
    "    zip_files = [f for f in os.listdir() if f.endswith('.zip')]\n",
    "    assert len(zip_files)>0, 'Could not find any zip files!'\n",
    "    assert len(zip_files)==1, 'Found multiple zip files!'\n",
    "    ! unzip {zip_files[0]}\n",
    "\n",
    "grader = otter.Notebook(colab=True,\n",
    "                        tests_dir = 'walkthrough-tests')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "739d080a-ad24-4687-bb6e-aab37952233a",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Walkthrough"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e920c6ea-e620-4f58-ba02-15bf10430cce",
   "metadata": {},
   "source": [
    "## Learning Objectives\n",
    "At the end of this learning activity you will be able to:\n",
    "\n",
    "* Practice summarize observations by sample using `groupby`.\n",
    "* Measure the uncertainty of the estimate of the mean.\n",
    "* Distinguish when to use parametric and non-parametric estimates of error.\n",
    "* Practice merging two dataframes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e3d5466",
   "metadata": {
    "id": "59f3483e-0a2a-4217-8185-c5557dda482e"
   },
   "source": [
    "This week we will start looking at the imaging data we discussed with Dr. Gaskill.\n",
    "In this experiment, they used pH responsive beads that flouresce when in the low pH environment of the phagasome.\n",
    "With this technology, they exposed cells to different levels of dopamine and measured the uptake of these beads.\n",
    "They did this using a _high content_ imager which automates the process of scanning a plate, detecting cell boundaries, and _spots_ of flourescing beads."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ad9f689-472f-479d-a638-8d7bd4e5f097",
   "metadata": {},
   "source": [
    "This imager returns a giant spreadsheet where each cell is a row and the columns are the cell area, bead count, and intensity.\n",
    "This dataset of a single 96-well plate has over **315,000 cells** measured across 60 samples of 20 conditions performed in triplicate."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3294db5",
   "metadata": {},
   "source": [
    "---------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "02d710d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "00ed4f31-4ec4-47fb-9d37-1885d9cae14b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .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>Well</th>\n",
       "      <th>Field</th>\n",
       "      <th>Cell_Number</th>\n",
       "      <th>Top</th>\n",
       "      <th>Left</th>\n",
       "      <th>XCentroid</th>\n",
       "      <th>YCentroid</th>\n",
       "      <th>ObjectAreaCh1</th>\n",
       "      <th>ObjectTotalIntenCh1</th>\n",
       "      <th>ObjectAvgIntenCh1</th>\n",
       "      <th>ObjectVarIntenCh1</th>\n",
       "      <th>SpotCountCh2</th>\n",
       "      <th>SpotTotalAreaCh2</th>\n",
       "      <th>SpotAvgAreaCh2</th>\n",
       "      <th>SpotTotalIntenCh2</th>\n",
       "      <th>SpotAvgIntenCh2</th>\n",
       "      <th>TotalIntenCh2</th>\n",
       "      <th>AvgIntenCh2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>127</td>\n",
       "      <td>634</td>\n",
       "      <td>703.418228</td>\n",
       "      <td>197.382766</td>\n",
       "      <td>14889</td>\n",
       "      <td>40514018</td>\n",
       "      <td>2721.070455</td>\n",
       "      <td>1428.303950</td>\n",
       "      <td>5</td>\n",
       "      <td>122</td>\n",
       "      <td>24.400000</td>\n",
       "      <td>113038</td>\n",
       "      <td>926.540984</td>\n",
       "      <td>1151084</td>\n",
       "      <td>77.311035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>203</td>\n",
       "      <td>53</td>\n",
       "      <td>119.211656</td>\n",
       "      <td>272.750579</td>\n",
       "      <td>14670</td>\n",
       "      <td>36697977</td>\n",
       "      <td>2501.566258</td>\n",
       "      <td>1270.956156</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>531102</td>\n",
       "      <td>36.203272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>477</td>\n",
       "      <td>595</td>\n",
       "      <td>664.088627</td>\n",
       "      <td>538.774772</td>\n",
       "      <td>14341</td>\n",
       "      <td>38322709</td>\n",
       "      <td>2672.248030</td>\n",
       "      <td>1543.100214</td>\n",
       "      <td>16</td>\n",
       "      <td>382</td>\n",
       "      <td>23.875000</td>\n",
       "      <td>823276</td>\n",
       "      <td>2155.172775</td>\n",
       "      <td>3011914</td>\n",
       "      <td>210.021198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>488</td>\n",
       "      <td>302</td>\n",
       "      <td>389.107857</td>\n",
       "      <td>581.589321</td>\n",
       "      <td>25302</td>\n",
       "      <td>59364634</td>\n",
       "      <td>2346.242748</td>\n",
       "      <td>939.256416</td>\n",
       "      <td>21</td>\n",
       "      <td>324</td>\n",
       "      <td>15.428571</td>\n",
       "      <td>259496</td>\n",
       "      <td>800.913580</td>\n",
       "      <td>1265323</td>\n",
       "      <td>50.008814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B2</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>713</td>\n",
       "      <td>717</td>\n",
       "      <td>808.196306</td>\n",
       "      <td>790.656465</td>\n",
       "      <td>22414</td>\n",
       "      <td>57729816</td>\n",
       "      <td>2575.614170</td>\n",
       "      <td>1724.895666</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>26.345231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Well  Field  Cell_Number  Top  Left   XCentroid   YCentroid  ObjectAreaCh1  \\\n",
       "0   B2      1            1  127   634  703.418228  197.382766          14889   \n",
       "1   B2      1            2  203    53  119.211656  272.750579          14670   \n",
       "2   B2      1            3  477   595  664.088627  538.774772          14341   \n",
       "3   B2      1            4  488   302  389.107857  581.589321          25302   \n",
       "4   B2      1            5  713   717  808.196306  790.656465          22414   \n",
       "\n",
       "   ObjectTotalIntenCh1  ObjectAvgIntenCh1  ObjectVarIntenCh1  SpotCountCh2  \\\n",
       "0             40514018        2721.070455        1428.303950             5   \n",
       "1             36697977        2501.566258        1270.956156             0   \n",
       "2             38322709        2672.248030        1543.100214            16   \n",
       "3             59364634        2346.242748         939.256416            21   \n",
       "4             57729816        2575.614170        1724.895666             0   \n",
       "\n",
       "   SpotTotalAreaCh2  SpotAvgAreaCh2  SpotTotalIntenCh2  SpotAvgIntenCh2  \\\n",
       "0               122       24.400000             113038       926.540984   \n",
       "1                 0        0.000000                  0         0.000000   \n",
       "2               382       23.875000             823276      2155.172775   \n",
       "3               324       15.428571             259496       800.913580   \n",
       "4                 0        0.000000                  0         0.000000   \n",
       "\n",
       "   TotalIntenCh2  AvgIntenCh2  \n",
       "0        1151084    77.311035  \n",
       "1         531102    36.203272  \n",
       "2        3011914   210.021198  \n",
       "3        1265323    50.008814  \n",
       "4         590502    26.345231  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cell_level_data = pd.read_csv('pHrodo_DMEM.csv')\n",
    "cell_level_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e9864ba-0314-4aff-bae4-178b457b4fb6",
   "metadata": {},
   "source": [
    "## Sumarize by sample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e8d395-418e-44b4-9b08-debbbe19f716",
   "metadata": {},
   "source": [
    "### Q1: How many cells are in each well?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b836dd30-86df-4a60-a65a-d08f3b0ed740",
   "metadata": {
    "tags": [
     "otter_assign_solution_cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     60.000000\n",
       "mean     525.966667\n",
       "std      133.318145\n",
       "min      257.000000\n",
       "25%      418.250000\n",
       "50%      528.000000\n",
       "75%      641.000000\n",
       "max      794.000000\n",
       "Name: Cell_Number, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use `groupby` to count the number of cells per well\n",
    "\n",
    "cells_per_well = cell_level_data.groupby('Well')['Cell_Number'].count() # SOLUTION\n",
    "cells_per_well.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf1e3c4e",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "grader.check(\"q1_cells_per_well\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8cd107f4-2051-41d1-8435-fbe27537d479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cells_per_well.plot(kind='box')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11def79e-e815-41c9-abee-550b4afa6501",
   "metadata": {},
   "source": [
    "The count ranges from 257 to 794 with an average of 525 cells per well."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "129f75e6-f68f-4561-9be9-3628f17e7034",
   "metadata": {},
   "source": [
    "## Measuring phagocytosis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e823934d-691e-4523-8ea9-38e84e791cbc",
   "metadata": {},
   "source": [
    "Each cell can take up 0 or more pH beads.\n",
    "Our biological question is whether dopamine changes the amount of beads that are taken up by the cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1c754d5e-646b-41e2-982f-8720ce91424f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='SpotCountCh2', ylabel='Percent'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = cell_level_data,\n",
    "             x = 'SpotCountCh2',\n",
    "             bins = np.arange(0, 100),\n",
    "             stat = 'percent')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e469f666-fddb-473a-a7b4-6fd26858e6ae",
   "metadata": {},
   "source": [
    "From our graph, we can see that most cells took up 0 beads and then about 10% took up 1, ~5% took up two, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad7b9c5f-a1ac-4c75-9364-b78de949a694",
   "metadata": {},
   "source": [
    "We hypothesize that dopamine treatment will increase the average number of beads taken up by cells."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c6cdd477-0923-42bb-a54c-1af140c2984d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='SpotCountCh2', ylabel='Well'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visually\n",
    "sns.barplot(data = cell_level_data,\n",
    "            y = 'Well',\n",
    "            x = 'SpotCountCh2')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47f5bb2f-13c0-4afb-8c8d-80b0a79ed601",
   "metadata": {},
   "source": [
    "The length of the bars indicates the average number of spots per cell while the black hashes indicate the 95% CI of that estimate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9742a3d3-bddc-4d40-b60f-7287a760603d",
   "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>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Well</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B10</th>\n",
       "      <td>4.138889</td>\n",
       "      <td>0.290394</td>\n",
       "      <td>612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B11</th>\n",
       "      <td>2.692828</td>\n",
       "      <td>0.200705</td>\n",
       "      <td>739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B2</th>\n",
       "      <td>4.599343</td>\n",
       "      <td>0.326940</td>\n",
       "      <td>609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B3</th>\n",
       "      <td>4.391667</td>\n",
       "      <td>0.312008</td>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B4</th>\n",
       "      <td>3.464491</td>\n",
       "      <td>0.301991</td>\n",
       "      <td>521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean       sem  count\n",
       "Well                           \n",
       "B10   4.138889  0.290394    612\n",
       "B11   2.692828  0.200705    739\n",
       "B2    4.599343  0.326940    609\n",
       "B3    4.391667  0.312008    720\n",
       "B4    3.464491  0.301991    521"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numerically\n",
    "well_level_data = cell_level_data.groupby('Well')['SpotCountCh2'].agg(['mean', 'sem', 'count'])\n",
    "well_level_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bcb719a-126f-49fe-8460-f58dc4d95404",
   "metadata": {},
   "source": [
    "## Decoding samples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3adfeda-e84f-45d9-9590-8d7ba0d8c75e",
   "metadata": {},
   "source": [
    "Up to now we've been treating all of our without knowing which treatment they came from.\n",
    "Now that we've collapsed our data to a single representative number for each sample, we can merge with our plate map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b21f12bd-5d1b-415e-b169-4c099a1b2026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>well</th>\n",
       "      <th>pHrodo_conc_ug</th>\n",
       "      <th>DA_Tx</th>\n",
       "      <th>replicate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>veh</td>\n",
       "      <td>Rep1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>veh</td>\n",
       "      <td>Rep2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>veh</td>\n",
       "      <td>Rep3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>DA06</td>\n",
       "      <td>Rep1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>DA06</td>\n",
       "      <td>Rep2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  well pHrodo_conc_ug DA_Tx replicate\n",
       "0   B2            5.0   veh      Rep1\n",
       "1   C2            5.0   veh      Rep2\n",
       "2   D2            5.0   veh      Rep3\n",
       "3   B3            5.0  DA06      Rep1\n",
       "4   C3            5.0  DA06      Rep2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load in plate map\n",
    "plate_map = pd.read_csv('plate_map.csv')\n",
    "\n",
    "# Treat concentration as a category instead of a number\n",
    "plate_map['pHrodo_conc_ug'] = pd.Categorical(plate_map['pHrodo_conc_ug'])\n",
    "\n",
    "plate_map.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b4a0fd3-6e72-4935-96de-8bc49156346e",
   "metadata": {},
   "source": [
    "This function helps visualize how the plate is layed out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7a13ec1e-fbf4-4031-a41e-dddfd6ce1014",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>col</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",
       "      <th>11</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>row</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>5.0-veh-Rep1</td>\n",
       "      <td>5.0-DA06-Rep1</td>\n",
       "      <td>5.0-DA07-Rep1</td>\n",
       "      <td>5.0-DA08-Rep1</td>\n",
       "      <td>5.0-DA09-Rep1</td>\n",
       "      <td>5.0-DA10-Rep1</td>\n",
       "      <td>5.0-DA11-Rep1</td>\n",
       "      <td>5.0-DA12-Rep1</td>\n",
       "      <td>5.0-DA13-Rep1</td>\n",
       "      <td>5.0-DA14-Rep1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>5.0-veh-Rep2</td>\n",
       "      <td>5.0-DA06-Rep2</td>\n",
       "      <td>5.0-DA07-Rep2</td>\n",
       "      <td>5.0-DA08-Rep2</td>\n",
       "      <td>5.0-DA09-Rep2</td>\n",
       "      <td>5.0-DA10-Rep2</td>\n",
       "      <td>5.0-DA11-Rep2</td>\n",
       "      <td>5.0-DA12-Rep2</td>\n",
       "      <td>5.0-DA13-Rep2</td>\n",
       "      <td>5.0-DA14-Rep2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>5.0-veh-Rep3</td>\n",
       "      <td>5.0-DA06-Rep3</td>\n",
       "      <td>5.0-DA07-Rep3</td>\n",
       "      <td>5.0-DA08-Rep3</td>\n",
       "      <td>5.0-DA09-Rep3</td>\n",
       "      <td>5.0-DA10-Rep3</td>\n",
       "      <td>5.0-DA11-Rep3</td>\n",
       "      <td>5.0-DA12-Rep3</td>\n",
       "      <td>5.0-DA13-Rep3</td>\n",
       "      <td>5.0-DA14-Rep3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>7.5-veh-Rep1</td>\n",
       "      <td>7.5-DA06-Rep1</td>\n",
       "      <td>7.5-DA07-Rep1</td>\n",
       "      <td>7.5-DA08-Rep1</td>\n",
       "      <td>7.5-DA09-Rep1</td>\n",
       "      <td>7.5-DA10-Rep1</td>\n",
       "      <td>7.5-DA11-Rep1</td>\n",
       "      <td>7.5-DA12-Rep1</td>\n",
       "      <td>7.5-DA13-Rep1</td>\n",
       "      <td>7.5-DA14-Rep1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>7.5-veh-Rep2</td>\n",
       "      <td>7.5-DA06-Rep2</td>\n",
       "      <td>7.5-DA07-Rep2</td>\n",
       "      <td>7.5-DA08-Rep2</td>\n",
       "      <td>7.5-DA09-Rep2</td>\n",
       "      <td>7.5-DA10-Rep2</td>\n",
       "      <td>7.5-DA11-Rep2</td>\n",
       "      <td>7.5-DA12-Rep2</td>\n",
       "      <td>7.5-DA13-Rep2</td>\n",
       "      <td>7.5-DA14-Rep2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>7.5-veh-Rep3</td>\n",
       "      <td>7.5-DA06-Rep3</td>\n",
       "      <td>7.5-DA07-Rep3</td>\n",
       "      <td>7.5-DA08-Rep3</td>\n",
       "      <td>7.5-DA09-Rep3</td>\n",
       "      <td>7.5-DA10-Rep3</td>\n",
       "      <td>7.5-DA11-Rep3</td>\n",
       "      <td>7.5-DA12-Rep3</td>\n",
       "      <td>7.5-DA13-Rep3</td>\n",
       "      <td>7.5-DA14-Rep3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "col            2              3              4              5              6   \\\n",
       "row                                                                             \n",
       "B    5.0-veh-Rep1  5.0-DA06-Rep1  5.0-DA07-Rep1  5.0-DA08-Rep1  5.0-DA09-Rep1   \n",
       "C    5.0-veh-Rep2  5.0-DA06-Rep2  5.0-DA07-Rep2  5.0-DA08-Rep2  5.0-DA09-Rep2   \n",
       "D    5.0-veh-Rep3  5.0-DA06-Rep3  5.0-DA07-Rep3  5.0-DA08-Rep3  5.0-DA09-Rep3   \n",
       "E    7.5-veh-Rep1  7.5-DA06-Rep1  7.5-DA07-Rep1  7.5-DA08-Rep1  7.5-DA09-Rep1   \n",
       "F    7.5-veh-Rep2  7.5-DA06-Rep2  7.5-DA07-Rep2  7.5-DA08-Rep2  7.5-DA09-Rep2   \n",
       "G    7.5-veh-Rep3  7.5-DA06-Rep3  7.5-DA07-Rep3  7.5-DA08-Rep3  7.5-DA09-Rep3   \n",
       "\n",
       "col             7              8              9              10             11  \n",
       "row                                                                             \n",
       "B    5.0-DA10-Rep1  5.0-DA11-Rep1  5.0-DA12-Rep1  5.0-DA13-Rep1  5.0-DA14-Rep1  \n",
       "C    5.0-DA10-Rep2  5.0-DA11-Rep2  5.0-DA12-Rep2  5.0-DA13-Rep2  5.0-DA14-Rep2  \n",
       "D    5.0-DA10-Rep3  5.0-DA11-Rep3  5.0-DA12-Rep3  5.0-DA13-Rep3  5.0-DA14-Rep3  \n",
       "E    7.5-DA10-Rep1  7.5-DA11-Rep1  7.5-DA12-Rep1  7.5-DA13-Rep1  7.5-DA14-Rep1  \n",
       "F    7.5-DA10-Rep2  7.5-DA11-Rep2  7.5-DA12-Rep2  7.5-DA13-Rep2  7.5-DA14-Rep2  \n",
       "G    7.5-DA10-Rep3  7.5-DA11-Rep3  7.5-DA12-Rep3  7.5-DA13-Rep3  7.5-DA14-Rep3  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fancy_pivot(df):\n",
    "    import re\n",
    "    # Extract row letters and column numbers from the 'well' column\n",
    "    df['row'] = df['well'].apply(lambda x: re.match(r'([A-H])', x).group(1))\n",
    "    df['col'] = df['well'].apply(lambda x: int(re.match(r'[A-H]([0-9]{1,2})', x).group(1)))\n",
    "\n",
    "    # Concatenate all other columns as 'V1-V2-V3' format\n",
    "    value_columns = [col for col in df.columns if col not in ['well', 'row', 'col']]\n",
    "    df['values'] = df[value_columns].astype(str).agg('-'.join, axis=1)\n",
    "\n",
    "    # Create pivot table with aggfunc as 'first'\n",
    "    pivot_table = df.pivot_table(index='row', columns='col', values='values', aggfunc='first')\n",
    "\n",
    "    return pivot_table\n",
    "\n",
    "\n",
    "fancy_pivot(plate_map)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "569c18b4-0f28-4be4-b61a-54b200b5b225",
   "metadata": {},
   "source": [
    "Merge the plate map with the well level aggregates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "02500ff4-8b7b-4e81-95df-be71987fd3e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>well</th>\n",
       "      <th>pHrodo_conc_ug</th>\n",
       "      <th>DA_Tx</th>\n",
       "      <th>replicate</th>\n",
       "      <th>row</th>\n",
       "      <th>col</th>\n",
       "      <th>values</th>\n",
       "      <th>mean</th>\n",
       "      <th>sem</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>veh</td>\n",
       "      <td>Rep1</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0-veh-Rep1</td>\n",
       "      <td>4.599343</td>\n",
       "      <td>0.326940</td>\n",
       "      <td>609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>veh</td>\n",
       "      <td>Rep2</td>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0-veh-Rep2</td>\n",
       "      <td>4.737265</td>\n",
       "      <td>0.426620</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>D2</td>\n",
       "      <td>5.0</td>\n",
       "      <td>veh</td>\n",
       "      <td>Rep3</td>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "      <td>5.0-veh-Rep3</td>\n",
       "      <td>3.623116</td>\n",
       "      <td>0.350467</td>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>DA06</td>\n",
       "      <td>Rep1</td>\n",
       "      <td>B</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0-DA06-Rep1</td>\n",
       "      <td>4.391667</td>\n",
       "      <td>0.312008</td>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>DA06</td>\n",
       "      <td>Rep2</td>\n",
       "      <td>C</td>\n",
       "      <td>3</td>\n",
       "      <td>5.0-DA06-Rep2</td>\n",
       "      <td>5.070039</td>\n",
       "      <td>0.538362</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  well pHrodo_conc_ug DA_Tx replicate row  col         values      mean  \\\n",
       "0   B2            5.0   veh      Rep1   B    2   5.0-veh-Rep1  4.599343   \n",
       "1   C2            5.0   veh      Rep2   C    2   5.0-veh-Rep2  4.737265   \n",
       "2   D2            5.0   veh      Rep3   D    2   5.0-veh-Rep3  3.623116   \n",
       "3   B3            5.0  DA06      Rep1   B    3  5.0-DA06-Rep1  4.391667   \n",
       "4   C3            5.0  DA06      Rep2   C    3  5.0-DA06-Rep2  5.070039   \n",
       "\n",
       "        sem  count  \n",
       "0  0.326940    609  \n",
       "1  0.426620    373  \n",
       "2  0.350467    398  \n",
       "3  0.312008    720  \n",
       "4  0.538362    257  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_level_data = pd.merge(plate_map, well_level_data,\n",
    "                             left_on = 'well', right_index = True)\n",
    "sample_level_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3a82e3f-f88b-4bc2-be37-880b1853ff5c",
   "metadata": {},
   "source": [
    "Now we can visualize the well level aggregates by the treatment condition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3548353f-915b-4ebf-99ce-383be5fa07ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'mean(bead count)')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(data = sample_level_data,\n",
    "            hue = 'pHrodo_conc_ug',\n",
    "            x = 'DA_Tx',\n",
    "            y = 'mean', errorbar=('se', 2), alpha=0.5)\n",
    "\n",
    "sns.stripplot(data = sample_level_data,\n",
    "            hue = 'pHrodo_conc_ug',\n",
    "            x = 'DA_Tx',\n",
    "            y = 'mean', dodge=True, legend=False, ax=ax)\n",
    "\n",
    "ax.set_ylabel('mean(bead count)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed272060-a08a-4727-8fe6-65c5c15ca6ff",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "source": [
    "### Q2: Describe the graph\n",
    "\n",
    "_Points:_ 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b676cf4b-352c-4330-8ed5-1ed93665597a",
   "metadata": {
    "tags": [
     "otter_assign_solution_cell"
    ]
   },
   "outputs": [],
   "source": [
    "# Which experimental condition (pHrodo_conc_ug) had less noise in the measurement?\n",
    "# Answer 5.0 or 7.5\n",
    "q2a = 5.0 # SOLUTION\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "476b7c14-fc25-4476-ab45-a95d16d536b3",
   "metadata": {
    "tags": [
     "otter_assign_solution_cell"
    ]
   },
   "source": [
    "**q2a reasoning**: *your answer here*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "26f35773-65be-4762-a85c-0d715fb3e47c",
   "metadata": {
    "tags": [
     "otter_assign_solution_cell"
    ]
   },
   "outputs": [],
   "source": [
    "# Does this graph show evidence that dopamine increases the amount of beads phagocytosed?\n",
    "# Anwser 'yes' or 'no'\n",
    "q2b = 'no' # SOLUTION\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc0bf51e-e546-4816-ac77-955018c24cbe",
   "metadata": {
    "tags": [
     "otter_assign_solution_cell"
    ]
   },
   "source": [
    "**q2b reasoning**: *your answer here*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "517941e2",
   "metadata": {
    "deletable": false,
    "editable": false
   },
   "outputs": [],
   "source": [
    "grader.check(\"q2_graph\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f1c0c96-003b-48d5-820e-d163f03dc29d",
   "metadata": {},
   "source": [
    "In the next few weeks well cover strategies to quantify our hypothesis using techniques like ANOVAs and multiple regression."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fff3c2b-4168-44f0-ba3c-be8f3c1f0326",
   "metadata": {},
   "source": [
    "---------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8bd13ea-c6aa-48c9-90e5-19d0d21970e4",
   "metadata": {
    "tags": [
     "remove_cell"
    ]
   },
   "source": [
    "## Submission\n",
    "\n",
    "You do not need to submit this walkthrough notebook.\n",
    "Simply complete the quiz."
   ]
  }
 ],
 "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": "3.9.13"
  },
  "otter": {
   "assignment_name": "Module07_walkthrough"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}