DLD-1-spheroid-bud-count-decoding-plotting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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, plotCountBar\n",
    "from sw_utilities import tukeyTest, getCountDFbyPrefix\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",
    "\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the raw data sets to plot\n",
    "# These are from two experiments with DMSO or ABA treated control or E-cad down spheroids\n",
    "\n",
    "datasetPrefix = \"190926\"\n",
    "df1 = getCountDFbyPrefix(datasetPrefix)\n",
    "\n",
    "datasetPrefix = \"191008\"\n",
    "df2 = getCountDFbyPrefix(datasetPrefix)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Annotate data sets with the experimental conditions\n",
    "\n",
    "# datasetPrefix: \"190926\"\n",
    "# 88 spheroids in wells A1-H11 (last column not accessible for automated imaging)\n",
    "#\n",
    "# Plate layout:\n",
    "# A1-A6: sg-control, DMSO, ***no MG***\n",
    "# A7-A11: sg-control, ABA, ***no MG***\n",
    "# B1-B6: sg-control, DMSO, MG\n",
    "# B7-B11: sg-control, ABA, MG\n",
    "# C1-C6: sg-control mixed with sg1-Ecad (4:1), DMSO, ***no MG***\n",
    "# C7-C11: sg-control mixed with sg1-Ecad (4:1), ABA, ***no MG***\n",
    "# D1-D11: sg-control mixed with sg1-Ecad (4:1), DMSO, MG\n",
    "# E1-G11: sg-control mixed with sg1-Ecad (4:1), ABA, MG\n",
    "# H1-H11: sg-control mixed with ***sg2-Ecad*** (4:1), ABA, MG\n",
    "#\n",
    "# This set of files contains 6, 5, 11, 33 and 11 samples for groups 1-4 and 6:\n",
    "# Group 1: sg-Control only, DMSO, MG\n",
    "# Group 2: sg-Control only, ABA, MG\n",
    "# Group 3: sg-Control mixed with sg1-Ecad (4:1), DMSO, MG\n",
    "# Group 4: sg-Control mixed with sg1-Ecad (4:1), ABA, MG\n",
    "# Group 6: sg-Control mixed with sg2-Ecad (4:1), ABA, MG\n",
    "groups1 = [\"zzz_ignore\"]*11 + [\"1\"]*6 + [\"2\"]*5 + \\\n",
    "            [\"zzz_ignore\"]*11 + [\"3\"]*11 + [\"4\"]*33 + [\"6\"]*11\n",
    "\n",
    "# print(groups1)\n",
    "\n",
    "\n",
    "# datasetPrefix: \"191008\"\n",
    "# 73 spheroids in wells A1-H11 (last column not accessible for automated imaging)\n",
    "# In addition, B1-B3, D1-D6 and G1-G6 were left out when imaging for fluorescence\n",
    "# because they were treated with blebbistatin and were photo-sensitive.\n",
    "# Y: B4-B6, E1-E6 and H1-H6.\n",
    "#\n",
    "# Plate layout:\n",
    "# A1-A3: sg-control, DMSO, ***no MG***\n",
    "# A4-A6: sg-control, ABA, ***no MG*** \n",
    "# A7-A11: sg-control, DMSO, MG\n",
    "# B4-B6: sg-control, ABA, MG, ***Y27632***\n",
    "# B7-B11: sg-control, ABA, MG\n",
    "# C1-C3: sg-control mixed with sg1-Ecad (4:1), DMSO, ***no MG***\n",
    "# C4-C6: sg-control mixed with sg1-Ecad (4:1), ABA, ***no MG***\n",
    "# C7-C11: sg-control mixed with sg1-Ecad (4:1), DMSO, MG\n",
    "# D7-D11 & E7-E11: sg-control mixed with sg1-Ecad (4:1), ABA, MG\n",
    "# E1-E6: sg-control mixed with sg1-Ecad (4:1), ABA, MG, ***Y27632***\n",
    "# F1-F3: sg-control mixed with sg2-Ecad (4:1), DMSO, ***no MG***\n",
    "# F4-F6: sg-control mixed with sg2-Ecad (4:1), ABA, ***no MG***\n",
    "# F7-F11: sg-control mixed with sg2-Ecad (4:1), DMSO, MG\n",
    "# G7-G11 & H7-H11: sg-control mixed with sg2-Ecad (4:1), ABA, MG\n",
    "# H1-H6: sg-control mixed with sg2-Ecad (4:1), ABA, MG, ***Y27632***\n",
    "#\n",
    "# To sum the ones I need to plot:\n",
    "# Group 1: sg-Control only, DMSO, MG\n",
    "# Group 2: sg-Control only, ABA, MG\n",
    "# Group 3: sg-Control mixed with sg1-Ecad (4:1), DMSO, MG\n",
    "# Group 4: sg-Control mixed with sg1-Ecad (4:1), ABA, MG\n",
    "# Group 5: sg-Control mixed with sg2-Ecad (4:1), DMSO, MG\n",
    "# Group 6: sg-Control mixed with sg2-Ecad (4:1), ABA, MG\n",
    "# Other samples will be annotated \"zzz_ignore\" for the purpose of this plotting\n",
    "groups2 = [\"zzz_ignore\"]*6 + [\"1\"]*5 + [\"zzz_ignore\"]*3 + [\"2\"]*5 + \\\n",
    "        [\"zzz_ignore\"]*6 + [\"3\"]*5 + [\"4\"]*5 + [\"zzz_ignore\"]*6 + [\"4\"]*5 + \\\n",
    "        [\"zzz_ignore\"]*6 + [\"5\"]*5 + [\"6\"]*5 + [\"zzz_ignore\"]*6 + [\"6\"]*5\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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",
      "     1      2  -0.3455   0.9 -7.3301  6.6392  False\n",
      "     1      3   -0.233   0.9 -6.4942  6.0282  False\n",
      "     1      4  15.9894 0.001 10.5881 21.3907   True\n",
      "     2      3   0.1125   0.9 -6.3316  6.5566  False\n",
      "     2      4  16.3349 0.001 10.7226 21.9471   True\n",
      "     3      4  16.2224 0.001 11.5411 20.9037   True\n",
      "---------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.9   0.9   0.001 0.9   0.001 0.001]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x1035e6410>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df1,df2])\n",
    "df[\"groups\"] = groups1 + groups2\n",
    "groupsToKeep = [\"1\", \"2\", \"3\", \"4\"]\n",
    "# print(df1and2.groups)\n",
    "dfToPlot = df.loc[df['groups'].isin(groupsToKeep)]\n",
    "\n",
    "# Pass to general df variable to ease the plotting\n",
    "df = dfToPlot\n",
    "df['data_to_compare'] = df.counts\n",
    "df.to_csv('../data_cleaned/' + '190926-191008-dataset-groups1-4-bud-count.csv')\n",
    "# Inspect raw data if curious about how they look\n",
    "# sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# Perform a Tukey test across groups\n",
    "tukeyTest(df.counts, df.groups)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x1a17e6cf90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 210x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 210x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "outputPrefix = \"190916-191008-6dpt-combined-4groups\"\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \"-bud-count-taller-bar.svg\"\n",
    "plotCountBar(df.groups, df.counts, outputFigPath, yMax=21, yTicks=[0,10,20], fig_width=0.7, fig_height=0.9)\n",
    "outputPrefix = \"190916-191008-6dpt-combined-4groups\"\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \"-bud-count-shorter-bar.svg\"\n",
    "plotCountBar(df.groups, df.counts, outputFigPath, yMax=31, yTicks=[0,10,20,30], fig_width=0.7, fig_height=0.9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
      "     1      2  -0.3455    0.9  -8.3623  7.6714  False\n",
      "     1      3   -0.233    0.9  -7.4195  6.9536  False\n",
      "     1      4  15.9894  0.001   9.7899  22.189   True\n",
      "     1      5   2.4545    0.9  -7.4417 12.3508  False\n",
      "     1      6  18.4069  0.001  11.5779  25.236   True\n",
      "     2      3   0.1125    0.9  -7.2839  7.5089  False\n",
      "     2      4  16.3349  0.001   9.8932 22.7765   True\n",
      "     2      5      2.8    0.9  -7.2497 12.8497  False\n",
      "     2      6  18.7524  0.001  11.7028  25.802   True\n",
      "     3      4  16.2224  0.001  10.8493 21.5955   True\n",
      "     3      5   2.6875    0.9  -6.7131 12.0881  False\n",
      "     3      6  18.6399  0.001  12.5512 24.7286   True\n",
      "     4      5 -13.5349  0.001 -22.2044 -4.8654   True\n",
      "     4      6   2.4175 0.6793  -2.4672  7.3022  False\n",
      "     5      6  15.9524  0.001   6.8221 25.0827   True\n",
      "-----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.9        0.9        0.001      0.9        0.001      0.9\n",
      " 0.001      0.9        0.001      0.001      0.9        0.001\n",
      " 0.001      0.67933607 0.001     ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x103688090>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df1,df2])\n",
    "df[\"groups\"] = groups1 + groups2\n",
    "groupsToKeep = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\"]\n",
    "# print(df1and2.groups)\n",
    "dfToPlot = df.loc[df['groups'].isin(groupsToKeep)]\n",
    "\n",
    "# Pass to general df variable to ease the plotting\n",
    "df = dfToPlot\n",
    "df['data_to_compare'] = df.counts\n",
    "df.to_csv('../data_cleaned/' + '190926-191008-dataset-groups1-6-bud-count.csv')\n",
    "\n",
    "# Inspect raw data if curious about how they look\n",
    "# sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# Perform a Tukey test across groups\n",
    "tukeyTest(df.counts, df.groups)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52    6\n",
       "53    4\n",
       "54    3\n",
       "55    4\n",
       "56    3\n",
       "Name: counts, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.counts[df.groups=='5']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x1a17f89690>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "outputPrefix = \"190916-191008-6dpt-combined-6groups\"\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \"-bud-count.svg\"\n",
    "plotCountBar(df.groups, df.counts, outputFigPath, yMax=28, yTicks=[0,10,20], fig_width=1.5, fig_height=0.9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the data and put into a data frame\n",
    "datasetPrefix = \"20191219-6dpt-zStack-integrin-activation\"\n",
    "df = getCountDFbyPrefix(datasetPrefix)\n",
    "\n",
    "# Annotate the data set\n",
    "#\n",
    "# The layout of this data set is straightforward.\n",
    "# For integrin activation test, only Matrigel was added (no ABA)\n",
    "# Integrin activation was done by adding 50 uM MnCl2, 10 ug/mL TS2/16 or both\n",
    "#\n",
    "# Group 1, B2-B11: sg-Control only\n",
    "# Group 2, C2-C6: sg-Control only, 50 uM MnCl2\n",
    "# Group 3, C7-C11: sg-Control only, 10 ug/mL TS2/16\n",
    "# Group 4, D2-D11: sg-Control only, 50 uM MnCl2 and 10 ug/mL TS2/16\n",
    "# Group 5, E2-E11: sg-Control mixed with sg2-Ecad (4:1)\n",
    "# Group 6, F2-F6: sg-Control mixed with sg2-Ecad (4:1), 50 uM MnCl2\n",
    "# Group 7, F7-F11: sg-Control mixed with sg2-Ecad (4:1), 10 ug/mL TS2/16\n",
    "# Group 8, G2-G11: sg-Control mixed with sg2-Ecad (4:1), 50 uM MnCl2 and 10 ug/mL TS2/16\n",
    "\n",
    "groups = [\"1\"]*10 + [\"2\"]*5 + [\"3\"]*5 + [\"4\"]*10 +\\\n",
    "            [\"5\"]*10 + [\"6\"]*5 + [\"7\"]*5 + [\"8\"]*10\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "\n",
    "# # Inspect raw data if curious about how they look\n",
    "# sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# # Perform a Tukey test across groups\n",
    "# tukeyTest(df.counts, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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",
      "     1      5      4.0 0.0077 1.0092  6.9908   True\n",
      "     1      6     10.2  0.001 6.5371 13.8629   True\n",
      "     5      6      6.2  0.001 2.5371  9.8629   True\n",
      "---------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.00765765 0.001      0.001     ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x1a18113710>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupsToKeep = [\"1\", \"5\", \"6\"]\n",
    "dfKeep = df.loc[df['groups'].isin(groupsToKeep)]\n",
    "\n",
    "# Use general df variable to ease the plotting\n",
    "df = dfKeep\n",
    "df['data_to_compare'] = df.counts\n",
    "df.to_csv('../data_cleaned/' + datasetPrefix + '-bud-count.csv')\n",
    "\n",
    "# Inspect raw data if curious about how they look\n",
    "sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# Perform a Tukey test across groups\n",
    "tukeyTest(df.counts, df.groups)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x1a181c3dd0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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23qS7oZmNHWVLDKtk6+tr3n9fbvsPNfsezNYrdCUiszGobIlhqWxdOx7De7P14xHxz5p9lYFgX+5aVGZjTNkSwyPZekrlDUmrAhuQqhi/H+SYLbP1A90NzWzsKFtiuJrU+HiIpEqH8q/l9v86X1jSRsDnSUnj6p5EaDYGlC0xnJqtNwHukTSLNMhrAPcBF0MaoEXST4A/kfo1vAJM73m0ZqNUqRJDRFwDHEu6algBeEu2PQDsHRGvZEUnAXuTukUHcGhE/KPnAZuNUmXrx0BEHC3pKmBP0hgMdwDTI+KuXLHbSZ2fZgLHRsSVPQ/UbBQrXWIAiIgrgCsa7B+QtFxEPFevjJkNXamqEu1wUjDrnhGbGMyse5wYzKzAicHMCpwYzKzAicHMCpwYzKygVIlB0lLNS5lZt5UqMQC/lnS1pJ36HYjZWFa2no8bA5OB1/c7ELOxrGxXDMtm67/1NQqzMa5sieH2bL1+X6MwG+PKlhiOID01eYKk9zUrbGbdUbY2hheA/waOBH4j6SHgBuBh4BmazIQdEUd2PUIb04455piWy86fP3/hup3jjjrqqLbj6rSyJYYrqX75BayaLa1yYjDrgLIlBkgJYbDtZhpeTZhZ68qWGFbvdwDDIWlt4GbgiYhoeqUjaXfSmJYbARNIVaY/At+JiDu7GatZI6VKDBFxf79jGCpJS5JmxVqyhbLjgV8AH6rZtQZpmr49Je0VEed1PFCzFpTtrsSIJGlx4Bxg8xYPOZFqUrgE2Ck79jDgadLVw5mS/qPDoZq1pFRXDHmSliBNZLsN6S/pcsDHIuIuSRsAHwR+GBEP9S9KkLQ8adbtbVssvy5pLgyAXwEfjohK+8hfJP0W+Avp5/0fYOvORmzWXCmvGCR9ALgXOBPYh/Sl25g0XDykmam+DsyWdERfggQkbU/qpVlJCgtaOOxA0u99PvCFXFIAICJuB47LXr7DVw3WD6VLDJI+BlwAvI50V+LFQYpVnqVYgtQZ6oQehbeQpHOA3wBvIN0ROY7WZsOqPCA2MyIerFPm9Nz2B4ccpNkQlSoxSFoF+CEprluBd1N9fiJvGvBpYA4peRwuadNexZl5W7a+D9guIr7e7ABJawArZS9n1isXEXNJ82mAqxLWB6VKDMAhpOrCQ8BWEXF5RMyvLRQR8yPip8DbgSdIyWH/nkaaJuA9HJgaEZe1eMybc9uzm5S9d5BjzHqibInhvaTL8u9GxNPNCkfEvcC3SIlhqy7HVmvLiDgxIgar6tSzcm672ezcD2fr12S3Qs16pmx3JVbL1te1ccy12XrlhqU6LCJaaWisNSm33WzCnHm57WVJ1aYhkbQCaZyLdqw51PPZyFe2xFC5gnm5jWMq3aYLVY4Syv/lf6FJ2efrHDcU+wP9fzLHRoyyVSUqfRI2auOY99QcW2b5q4xmz3bknxMZytWJ2ZCVLTFcRvpCHJh1G24o6yx0EOlLVncS3BLJVx+aDXyb399OO4bZsJWtKvE90kNFbwZ+KenTEfFkbSFJ44BPkBoeJwKvANN7GegQPZvbntikbGV/kLpJD8d04Nw2j1kTuHCY57URqlSJISLulPRl0rMEOwIPS7olV+QrkiaSnitYjurl9jci4tbeRjsk+YfEVmlStrJ/bkS00+ZSEBFzaLPxUmrniXcbbUqVGAAi4tuS5gPfJDW6bUK1Pv7hbF35X7sAOGEEjdx0W267Wav/GoMcY9YTZWtjACAiTgLWJl053EJKDMot9wCnAhuMoKRARDxCtZF0i3rlJE0G1speXtPtuMxqlTIxAETEAxFxRERsQLpyWIHUV2HpiJgSEfuNkOpDrfOz9XaS6vW92Cu3fUGX4zErKG1iyIuIVyLiiYh4rM2ehmV0GqmxdEnglKwhdaFsFKivZi+vi4jrexyfWfnaGPIkvRb4T9Jl9YqkTkyPAf8ALomIZp2ESiciZkmaThqTYQdgpqRvA48Cm5EeJ1+O9LMe2LdAbUwrZWKQtDqpfWEn6l/VPC/ph8CREfFsnTJldSipWrQr6Q5L7chPLwJ7RcRfex2YGZSwKiFpC9IVwS7AOBZtdMwvE0idm2ZJekN/oh2a7OnQDwG7A5cC/yJdITwE/AzYOCLO6V+ENtaV6oohG4/hN1Q795xDGrTk76THq8eRHgbamNRAtzNp0JaLJW3c7/aHiNimzfLnkH5Gs1Ip2xXDEaQnCecDu0bERyPi4oh4NPsr+0JEPBgRF0bEB0m9HxcAU4FP9TFus1GlVFcMwPtJfRZ+EBHnNyscEWdJ2grYF9gTmNHl+MwamjZt2iJT01XWxx9//MIy48eP5/DDD+9LfK0q2xVDZZKWpkkhp3IpvnaHYzFr2/z58xcu9d6v3VdGZbtieJrUhjCuWcGcSruCp6izvhs/vvpQcD4B5N/Pb5dV2RLDn0kNih8hTXDbiu2y9V+6EZBZO8peRWhV2aoSx5MaHveV9PFmhbM5Fw4j9STs+RDyZqNVqRJDRNxImrptHnC6pF9I2kZSZaIZlKwj6TjSEOwTSDM2PSZpjcGWvvwwZiNYqaoSkh7JNhcjdWLaLVtC0jOksSBfDVQqaSK1LRyWLYMJSvZzmpVd2b4wK9W8Vm69XJ1jPKKIWYeVLTEc0+8AzKxkiSEinBjMSqBUicFsuCZPnszAwADAwjXAhAkTFtmeO3duz2MbSZwYbFQZGBhYJCHk37fWOTHYqJK/Mmh0xWCNOTHYqJKvIkycOJGBgQEmTJjAvHnzGhxltUrVwcnMysGJwcwKnBjMrMCJwcwKnBjMrGDE3ZWQtCywJbA0cGNE3NPnkMxGnVJeMUjaTNKpkt5f8/5uwAPARaQh3e6UdFb+sWwzG77SJQZJXyaN5LQPsGnu/amkoeRfRXVuicVIczN4fkezDipVYpC0HnAc6Uu/IFsqDiWNwxCk0aB3Bs7Oyr5b0i69jdZs9CpbG8PnSF/0OcB2EXELQDbx666kpHBdRByQlb9I0nKk+S33oL3Rpc2sjlJdMQDbkL78J1aSQmZLqgO1nFtzzE+y9SbdDc1s7ChbYlglW9dO/f6+3PYfavY9mK1X6EpEZmNQ2RLDUtm6dnr792brxyPinzX7JmXrl7sWldkYU7Y2hkeANwJTgBsAJK0KbECqYvx+kGO2zNYP9CJA669jjml9kK/8FHHtHHfUUUe1HddoU7YrhqtJjY+HSKo8NP+13P5f5wtL2gj4PClpXN2TCM3GgLIlhlOz9SbAPZJmAZ8hffHvAy4GkLS6pJ8AfyL1a3gFmN7zaM1GqVIlhoi4BjiWdNWwAvCWbHsA2DsiXsmKTgL2JnWLDuDQiPhHzwM2G6XK1sZARBwt6SrStPYrAncA0yPirlyx20mdn2YCx0bElT0P1GwUK11iAIiIK4ArGuwfkLRcRDzXw7DMxoxSVSXa4aRg1j0jNjGYWff0pSoh6fIufGxExLZd+FyzMadfbQzbkO4m1JuQNmpe15YbbH/te2Y2RP1KDDOp/0VeDNiCajXnfuBvwOPAfNKtyg2A9bP984AzcZdos47pS2KIiG3q7ZN0Fikp3AHsV+9WpKR1gR+TOkO9Edi+44GajVGlul0p6RPAR0lPTG4REU/WKxsRsyS9C/gr6SGrzwMn9SRQK61p06Yt8oxEZX388ccvLDN+/HgOP/zwvsQ3UpTtrsTnSFWMbzRKChURMQ/4JqmN4ZNdjs1GgPnz5y9c6r1fu8+KSnXFAKybrW9s45hbs/WUDsdiI9D48eMXbucTQP79/LYNrmyJ4UVgGWD5No55Q7Z+tvPh2EjjKkJnlK0qcWe2/lQbxxxEqn7M6nw4ZmNT2RLDz0jtBbtIOk5S3fgkLSXpp8BW2VszehCf2ZhQtqrE6cD+wIbAl4HdJf0KuBmoNEZOJt2i/DDp6UuA30aER4g265BSJYaIeEnSe4FLSB2Y1gAOq1O80hvyN8BuPQjPbMwoW1WCiJgLbAwcCPw9e1s1S5BGb9otInaMiOf7EavZaFWqK4aKiFhAGqptuqQVgbWpDg//GHB7RDzRr/jMRrtSJoa8iHic9JyEmfVI6aoSZtZ/pbpikHTkcI6PiGM7FYvZWFaqxAAczfDGVXBiMOuAsiUGqD94y2ACeJpqHwcz64CyJYY3Ndm/GPBqYFVgO2AfUnL4VER4JiqzDilVYoiIu1ss+jfgQkm/AC4Fzpe0fkQ80r3ozMaOEX1XIpu5ajrwGuCIPodjwOTJk5k4cSITJ05E0sKl8t7EiROZPHlyv8O0JkZ0YshcnK09tFsJDAwMLFzqvV+7z8qnVFWJIaoMArtKX6MwACZMmLBwO58A8u/nt62cRkNi+EC2frqvURgAc+fOXbg9ceJEBgYGmDBhAvPmzetjVNauEZsYJC1DekT7ENKdCd+VMOuQUiUGSQ+0UgxYGliORZ+2/G4XQ7PMMccc03LZ/CjN7Rx31FFHtR2XdVapEgOpf0K7FgD/ld2hGFEkLQ0cDHwEWIuU4O4FzgNOighXj6wvypYYGs1QVRGkBsenSeM8nhMRd3Q7sE6TtDJpQJq31OxaL1s+KWn7iPhnz4OzMa9UiaHRDFWjiaTFgQtJSSGAU4BzSVc/HyQNUrMacJGkjSPimT6F2jZP+DI6lCoxjCGfIY1bCXBwRHw/t+8qSX8B/hdYEzgUGDGV7noTuniSl5FlNHRwGokOyta3AT+o3RkRZ5PGsgT4fHaFMSKMHz9+4VLvfU/4Un4j5j+cpI8Ae5BmnJpHal84IyIu72tgbZI0FZiavfxFNozdYH5O6qMxCXgnqT2i9FxFGB36mhgkCdgF+BjpycoB0iS1MyqNbtncEmcDu9YcvjGwp6SLgI9HxEjpZ7t5bntmg3J/ym1vzQhJDDY69K0qIWkl0hfjXFJyWBfYlNRp6SZJ+2VFvwl8iOJI0ZVlR1LiGCnenNueXa9QRDwGVEa/fnO9cmbd0JfEIGkcqVV+c6pf8H+TBlwRMB44WdIewH5Ueza+kzQewyTSFcRtWfntJW3b4x9jqFbO1i8DjzYpW3mMfOWGpcw6rF9ViT2At5K+8GcAx1bGYpC0CnA8sCepni3gOmDbiHg59xnnS7oSuAl4PfBR4LJe/QDDMClbz4uIZn02Kg8YLDucE0pagTSDVzum5l/Mnp0ububMmTOcUFpy66231t03Vs5f+X3nLNH1E+dFRM8X0tXCAuB3DcpcmZV5Bdi1QbkvZeVu6sfPMoSf/TJSQnyshbLXZmXvHuY5j84+x8vIXXbs5f/TfrUxrJf9sGc1KPOj3Pb1Dcpdl61fP9ygeqRyF6LZ1QJUx7+sd+fCrCv6lRgql7X3NSiTH+at0axTz2brZYYTUA89l62XaqFspcyLXYrFbFD9amOYSPqL2egh/YW3H6Px3JSVv6YjpddMJZG1MlrJxGz91DDPOZ1096cdy5B6Zz5Dahh+EHipjePXJFUZK3Zi0WTfbSP9/Euw6FXwVZ0IqlX97uA0Fi+R78/WS0iaHGkS33oqdyOGNchtRMwBhtJqdl3zIoNLXVQWcXdE1G/V67BRcv6bOhRO29wluvduy22vWa+QpNeRxp2oPcas65wYei/fkLpFg3Jb5rZH3FgTNrL1OzG00jI/qkTEbOCW7OWeGuSaM7NXtn6KHtcvzfrdxnCxpHoNWgs7dEi6p8Fn9LbjR2fMIDUIrk/qhzEtv1PSblSHwz8tInxXwnqq34mhWVffyhXFai2UGUlOI43JsBHwTUnrkHqAvkR6buTzWbn7gG/0I0Ab2/qVGB5gZH6hOyIiXpH0AVIvyKmk7t971hR7GPhARPy71/GZ9SUxRMRq/ThvmUTEI5I2Ig3ashvpsfMlgHuAC4BvRYRn8ba+6HdVYkyLiBdI7QvTmpU166V+35UwsxJyYjCzAicGMytwG4N1y1zgmJrXPv8IoWwgDzOzhVyVMLMCJwYzK3BiMLMCJwYzK3BiMLMCJwYzK3BiMLMCJwYzK3BiMLMCJwYzK3BiMLMCJwYzK3BiMLMCJwbrKklrS3pB0kM9ONcekqLFZUoX43iPpF9KelDSi5LmSvqdpB26dc5Oc2KwrpG0JPATYMkenXKDHp1nUJIWl/Rz4A/Ah4FVSQP8Lg+8D7hI0qkNJhkqDQ/UYl0haXHgHGTlT8MAAAteSURBVGDzHp62khiuJo2+3cgDXTj/aVSnAbgJ+DZphuu1gK+RRgLfF7gL+FYXzt8xHqjFOk7S8sDZwLa5tx+OiFW7fN7HgRWAaRFxRDfPNci5twUuzV5eAuyQn0FM0nKkZLEa8AywUkQ838sY2+GqhHWUpO2Bv1FNCgt6dN6VSEkB4OZenLPGkdn638DutdMKRsTTVGcVezXw7h7G1jZXJaxjJJ0DfCR7GcDxwFbA1j04fb594e89ON9CklYm/ZwA32kwUdBFpBnOn8iW0nJisE56W7a+D9gnIi6TdGWPzl1JDC8Ad/TonBXbAZUGxfPqFYqIx6jOYl5qrkpYJz0CHA5MjYjLenzuDbP1LOA/JP1c0gOSXpI0R9JFWTWnG9bL1s8Dt1XelLS0pDdJev1IuBOR5ysG66QtI6InbQqDqFwxrA1cS/UvOMBkYAdgB0lnAp+OiJc6eO43Z+uHI2KBpA2B/wb+k+p37FFJ04ETa9sfyshXDNYx/UoKkpYi3RIEeBVppvAvkdo2tga+SnVehz2AGR0O4bXZ+mlJHwOuBz7Aon94X0dKFpdKenWHz99xTgw2GqxD9Ut4DbB+RHwrImZmywmkqsbdWZlPSerkXYFlsvUbgJ+SqhQHkZLB0qS+HFdkZbYEftTBc3eF+zFYV2WNj1vTxX4MksaR+gesDtwcEYPO+iRpa+DK7OX/RcSOHTr/3cAa2csXga0i4oaaMosDF1O9TblFRFzTifN3g68YbMSLiFci4u6IuLReUsjKXUX1qmGbDjYIvpDbPrU2KWTnfhn4Qu6tj3Xo3F3hxGBjzS3Z+lXApA595rO57YvqFYqIWcCD2cu3dujcXeHEYGPNQG57iQ595mO57UealK08Zbp8h87dFU4MNuJJ2kDSrpI+10L1YHK2fgWo10OxXbNy282uQirJ6OkOnbsrnBhsNPgiqcfhDKq3LQskjQc2yV7e0sG+DNfntjdtcP7FqMZ3X4fO3RVODDYazMxtN2rU+wTVv+i/7OD5/0B6eArg09ldksHsQGrbgAZtEWXgxGCjwXlUqwVflFQYsEXS+lTHQJgLnNqpk2c9GX+QvVwX+OYg538d8L3c+X/dqfN3gxODjXgR8W/gkOzlMsDVko6UtIWkd0g6jtTxaRKpbWGfBk9ADtVxVNsaDpV0Sdbu8TZJ+wE3AG/M9h8YEc8O+ikl4Q5O1lW96OCUO9eBwHeo/wzQs8C+EXF2l84/Gfg/qk+Z1noFODgiflBnf2n4isFGjYg4mfSk4wzS8GnPA88BtwLTgHW6lRSy888ldX/+JHAZqcrwAjAb+DGw0UhICuArBjMbhK8YzKzAicHMCpwYzKzAicHMCpwYzKzAicHMCpwYzKzAicHMCpwYzKzAicHMCpwYzKzAicHMCpwYzKzAicHMCpwYzKzAicHMCpwYzKzAicHMCpwYzKzAicHMCpwYzKyg3vj7Zi3JZnj6CPAOYG1gOeAlYA5p2PaLgbMj4qm+BWlt8/DxNiSSVgNOBrZvofjTwDER8d1uxmSd48RgbZO0FWnGpWWztx4mzR85E3gMGEeajm17YFdgfFbupIg4uLfR2lA4MVhbJK1NmoexMmvz8cBxEfFCnfJTSRO4vjl7a+eIuLDrgdqwODFYWyRdA7w9e3lwRJzUwjErA7cBrwbujIi1uxiidYDvSljLJO1ANSn8vpWkABARj1Cdgn4tSW/tRnzWOU4M1o7P5baPafPY04HrgCNJ7RAASDpaUki6T9Likk6Q9Jik5yXdJelL+Q+RtKSkfSVdKmmupJckPSrpN5I+KkmDnTx3npA0pV6Qkh7KylxZ8/42ueOnSlpB0slZ3C9mMf9S0maNfgmS1pN0iqQ7sp/xOUmzJf1U0jua/xp7JCK8eGm6ABOBF4EAHiWrhnbgc4/OPvM+4EfZdn75TK7sVNIt0Noy+eVqYHKD8wQwpUE8D2Vlrqx5f5vc8e/LlatdFgAH1fnsTwMvN4n/lE79boez+IrBWrUJsES2fUlk/9M76PWkL841pLsZ7ybdDj0HFrZTXAK8JSt/PrAL8DZgd+CK7P0tgUskLd3h+PJ+CqxCmup+F2Bz4FDgSUDA9yR9NH9A1mg7g3THZjbwmey4rYD9SYkR4LPAIsf2Rb8zk5eRsQD7Uv2rdnQHP/fo3OfeD0ysU+6MXLlD6pT5bq7MCQ3OM9wrhiBd3aimzJuAJ7L9DwETBjn/y8Cqg5x3FVJiCVLi7eu/t3s+Wqtek9ue06igpA2bfNZzETF7kPfPiIh5g3zeSlT/il4S9TtKHQZsR7qqOEDSsVHnNuowPQAcGNk3uiIi7pL0NdKVwSqkKsevst0rZuvngMdrPzAiHpZ0FLAyMKsLMbfFicFala92DtrAByBpceCmJp/1Z9Ilf63r65R/J+kSHNJf6kFFxMuSfgx8m3RrdDPgyiaxDMXPGyScs0hVoHHA+6kmhjuy9bLAOZK+EhG35w+MiO93IdYhcRuDteqZ3PYKXTrHw3XeXye3fUOTz8gnl3Xqlhqev9TbERHPAvdkL9fK7TqD6t2YXYDbsjsT35P0fklLdSfUoXFisFbdldteuV6hiHg5IjTYQmpDaOSZOu+/NrfdsBpTs/81dUsNz6NN9j+RrSvVByLiX8B7gH/kyq0FHAT8FviXpLMlva2TgQ6VE4O16gbSrThIdwy6od6dDrVQpmJcbnsod05a+U683GIM8/NvRsQtwIak398MFk2UE4DdgL9IOry1ULvHicFaEhFPkvoIAKwmaeMenv7J3Hazakx+f/64fJKo20ZCaptoptmVyORs/Vjtjkgui4j9I2I10p2MA4BLc7F9I7u92TdODNaOH+S2v9zD896S227WnXrT3PYdue38X/lB+zhImkTqyNXMBvV2ZJ+xWvbyH7n3l5K0vqRF2j0iYnZETI+I7YD/yt5ejFTt6BsnBmvHeaRuzQAfknRAqwdKWo+h1/mvpPrF/nSDcywOfDJ7+TxwbW7307ntN9T5iFarSLs32Lcn1SuS/FOk9wM3s2hyrfX73HY3O2g15cRgLcvu23+c6iX6yZJOlLRMvWMkTczu7V9L9VHttur+EfEoKSkBvFfSgXWKnkj18e7TavpE5PsG7F/7TEXWV+KEFkPafLAYsquBo7OXf6da9QL4XbZ+h6QP1PncfML5W4uxdEe/e1h5GXkL6VI6/6zAs6RuwnsCW5O6+X4M+DHwFIv2GJwJrJ37rKNz+xr1SFyJdDegUvY8YCdS1eEjwOW5fbeR63WYHb84qdtxpcxFpA5IW5K6Mz9Maly9h9Z6Pi4AfkbqULU58BXg39m++cBmNcdPBV7I9r9I6uuwI6lL986k/g8Lsv3X9v3fuN8BeBmZC6lacCppfMdGDwVVlr8De1DsRtxSYsjKrg3c2eQ8lwEr1jl+a2BeneNeAb5A9UGuK2uOzSeGU0lVk8E+5xngPXXO/yFSFafZ72nQ+Hu5uCphQxIRT0bEZ4HVSX9xLwYeJP01fJF0RfEn0ghPW0bEhhFxZmTfkCGe8w5gPVIr/pXAv0iJ6T5SfX5HYLuIKHQ5zo6/itRdekZ2zEukOwfnZTF+p8VQbgDWB36SHf886anPaaSroT/WOf95wLqkZzpuIV1pzc8+4w/APsAm9eLvJY/gZNYCSdtQfYLzMxFRt2v2aOArBjMrcGIwswInBjMrcGIwswInBjMr8F0JMyvwFYOZFTgxmFmBE4OZFTgxmFmBE4OZFTgxmFmBE4OZFTgxmFmBE4OZFTgxmFmBE4OZFTgxmFmBE4OZFTgxmFmBE4OZFTgxmFmBE4OZFTgxmFmBE4OZFTgxmFmBE4OZFTgxmFmBE4OZFTgxmFmBE4OZFTgxmFmBE4OZFTgxmFmBE4OZFfx/k4C//QVPCQYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 120x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the bar graph to put in figures\n",
    "outputPrefix = \"191219-6dpt-integrin-activation\"\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \"-bud-count.svg\"\n",
    "plotCountBar(df.groups, df.counts, outputFigPath, yMax=25, yTicks=[0,10,20], fig_width=0.4, fig_height=0.9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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",
      "     1      2     -0.6   0.9  -3.6569   2.4569  False\n",
      "     1      3   -19.75 0.001 -22.3973 -17.1027   True\n",
      "     1      4    -19.9 0.001 -22.5473 -17.2527   True\n",
      "     2      3   -19.15 0.001 -21.7973 -16.5027   True\n",
      "     2      4    -19.3 0.001 -21.9473 -16.6527   True\n",
      "     3      4    -0.15   0.9  -2.3115   2.0115  False\n",
      "-----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.9   0.001 0.001 0.001 0.001 0.9  ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x1a18188890>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in the data and put into a data frame\n",
    "datasetPrefix = \"20191219-6dpt-zStack-D193-D267-D301-D2\"\n",
    "df = getCountDFbyPrefix(datasetPrefix)\n",
    "\n",
    "# Annotate the data set\n",
    "#\n",
    "# The layout of this data set is straightforward.\n",
    "# For integrin co-repression, all spheroids were treated with Matrigel and ABA\n",
    "#\n",
    "# Group 1, B2-B11: sg-control mixed with sg1-Ecad (1:1)\n",
    "# Group 2, C2-C11: sg-Itgb1 mixed with sg1-Ecad (1:1)\n",
    "# Group 3, D2-E11: sg-control mixed with sg1-Ecad/sg-Itgb1 (1:1)\n",
    "# Group 4, F2-G11: sg-Itgb1 mixed with sg1-Ecad/sg-Itgb1 (1:1)\n",
    "groups = [\"1\"]*10 + [\"2\"]*10 + [\"3\"]*20 + [\"4\"]*20\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "df['data_to_compare'] = df.counts\n",
    "df.to_csv('../data_cleaned/' + datasetPrefix + '-bud-count.csv')\n",
    "\n",
    "# Inspect raw data if curious about how they look\n",
    "sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# Perform a Tukey test across groups\n",
    "tukeyTest(df.counts, df.groups)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x1a18538550>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 210x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the bar graph to put in figures\n",
    "outputPrefix = \"191219-6dpt-D193-D267-D301-D2-\"\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \"-bud-count.svg\"\n",
    "plotCountBar(df.groups, df.counts, outputFigPath, yMax=26, yTicks=[0,10,20], fig_width=0.7, fig_height=0.9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
      "     1      2  -4.8889 0.0058  -8.6328   -1.145   True\n",
      "     1      3 -30.1056  0.001 -33.6696 -26.5415   True\n",
      "     1      4 -30.7556  0.001 -34.8354 -26.6757   True\n",
      "     2      3 -25.2167  0.001 -28.2496 -22.1838   True\n",
      "     2      4 -25.8667  0.001 -29.4917 -22.2416   True\n",
      "     3      4    -0.65    0.9   -4.089    2.789  False\n",
      "------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.00576642 0.001      0.001      0.001      0.001      0.9       ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x1a181b1e10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in the data and put into a data frame\n",
    "datasetPrefix = \"20191224-6dpt-zStack-D193-D267-D301-D2-integrin-co-modulation-repeat\"\n",
    "df = getCountDFbyPrefix(datasetPrefix)\n",
    "\n",
    "# Annotate the data set\n",
    "#\n",
    "# The layout of this data set is straightforward.\n",
    "# For integrin co-repression, all spheroids were treated with Matrigel and ABA\n",
    "#\n",
    "# Group 1, B2-B11: sg-control mixed with sg1-Ecad (1:1)\n",
    "# Group 2, C2-D11: sg-Itgb1 mixed with sg1-Ecad (1:1)\n",
    "# Group 3, E2-F11: sg-control mixed with sg1-Ecad/sg-Itgb1 (1:1)\n",
    "# Group 4, G2-G11: sg-Itgb1 mixed with sg1-Ecad/sg-Itgb1 (1:1)\n",
    "# ***Note: B4, D2, D6, D7, D9 and D10 images were off-center or off-focus,\n",
    "# so they were removed for segmentation\n",
    "groups = [\"1\"]*9 + [\"2\"]*15 + [\"3\"]*20 + [\"4\"]*10\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "# Inspect raw data if curious about how they look\n",
    "sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# Perform a Tukey test across groups\n",
    "tukeyTest(df.counts, df.groups)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x1a187e0d10>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RLySdKGmN+nKSXilpf+B6YDngFODnkhZp9OnhH8HMxoFKdUeSdH22+2/gVcBngc9KmkVaj2geKVGuwVBH+ACOyj6NeJ5OM+uoqiWUrUiJrjYPZ227YvZpxHN2mllPVS1xnk+b5THMzMpWqcQZEfuVHYOZWTtuODEzK6hSNc6RkLQ0sC2wFHCLV7Y0s16rZI1T0laSzpH0zrrzewH/AK4ELgXukXSRpKXKiNPMxqfKJU5JRwO/A/YH3pg7vz6p8eiVDHVFWoQ0K9KPeh+pmY1XlUqckjYhTRMnYH72qTkCmEBqdZ8G7ApckpV9m6TdehttImkdSadLul3SM5Kel3SfpPMlvWEE9+8tabqkJ7N7/y5pmqR1exG/mRVXtXecHyclwlnAjrV1hSQtCuxBSpo3RcTBWfkrJS0D/BfwIeCHvQxW0oHA13n5BCNrZJ8PSzoxIj7f4N4JpPlD96y7tBbp72EfSftGxGWdjtvMxqZSNU5gMik5nlq3GNu2DK099P26e76TbV/f3dCGk7QLcDYpaT4JfAHYnjSp8qHAQ1nRYyQd0eArTmUoaV4F7JLdeyTwL2AAuFDS67r1ZzCz0alajXPlbHtz3fl35PZ/WXftwWy7QlciaiCrAX+NVDt+AtgqIv6eK3KDpItJY+lfCxwn6YKImJXdvzHwyazs5cB7IyJy9/4UuIH0j8VXSAnZzCqiajXOJbNt/XycO2XbxyPir3XXagu1zetaVC+3PelRHGBKXdIEICKeINUeIdUe8+9gDyH93c8FDs8lzdq9d5He9QK8xbVOs2qpWuJ8JNuuUzshaRVgM9Ij/C8a3LNttv1Hd0MbZuvc/o9blLs6t79Jbn+XbDsjIh6ksfNz+7sXiM3MuqxqifM3pMffwyQNZOf+J3f9B/nCkrYgPfJGdm+v3AD8L2mJj4dblMv//S4BIGkthlbxnNHsxoiYDdydHfpR3axCqvaO8xzSsr+vB+6V9ASwASkx3g/8HBZMYPwFYC/SCKJ5wFm9CjIipgPTR1A0n/BqNcsNcudmtrn/PmC9unvMrGSVSpwRcb2k44Avkhp7ag0+c4D9IuKl7HhZYL/abcAREfGXXsY6Qp/J7f8q266UO9fu9UKtNrucpCUi4oXRBCFpBdJ69EWsPZrfMhsPKpU4ASJiiqTrgH1Ic3DeDZwVEX/LFbuL1Dl+BnBcRFzb80DbkHQwsF12eHNE3JjtL5sr9iytzcntL03q3zoaBwHHjvJeM6tTucQJEBHXANe0uD4oaZmIaJd4SiHp7cBXs8N5pH6dNUvk9tut5vlck/vMrERVaxwasQonzcmkRqwJ2anP52qbMHwYabtJm/Oz289vWsrMeqqSNc5+lc3mdBmpwQpgWkScUlcsn/CXpLX89VG938ycxctHXLWzNnDFGH7TbKHlxNkhkvYDvsnQ3+k3SSt21nsmtz+xzdfWrgdpGOaoZCOWCr0flbyUk1kzffuoXiWSjgHOYyhpngF8rH5EUOaB3P7KDa7T4PrsiOjlyCgza8GJc4wknQyckDs1JSIObZI0Ae7M7bfr8rNWg3vMrGROnGMg6ViG1nOfD3w8Iqa2uiciHmFo5qRtWnz3JKA2J+f1zcqZWe85cY6SpF2BKdnhPOCDEXHOCG+vzRu6o6SVmpTZN7fvGe7NKqRSiVNSu1bmSpD0KuDc3KmDI+KSAl9xLvASqW/m2dk0dfnvXw+oTX58U0TUT7NnZiWqWqv6DyS9EjgtIqrcFeZQhoYw3gTcLGnzNvc8GxEzASLidklnkSYoeTcwQ9KXgUeBrUjj8JchTTt3SBfiN7MxqFri3JKUkFYtO5A2PprbfxNw6wjuuY40w33NEaRx63uQpqnbuq78C8C+EfGH0YdpZt1QqUd10nhsgD+WGkULkpanfTeitiJibkTsSVql89fAP0k1zIeA7wJbRsSlY/0dM+u8qtU47wI2zT43lBxLQ9nM7h3rHZ4lRydIsz5StRrnZ0ndek6U9I52hc3MylC1GufzwJdI83H+RNJDwO9J81I+TZtJMSLii12P0MzGvaolzmsZSo4CVsk+I+XEaWZdV7XECcPfHxZ5l9huijYzs46oWuJcs+wAzMzaqVTijIgH2pcyMytX1VrVzcwqr7KJU9Likt4v6RxJV0n6vaTXZtc2kzRVUpGGIzOzjqhk4pT0LtKa4hcC+wM7kIZj1pak2Iw0nnumpM+WEqSZjVuVS5ySPkCaRu01pFb1Rmvt1MayL07qLH9ij8IzM6tW4pS0MmmtnkWAO4C3MTR+Pe8U4L9J6+gIOErSG3sVp5mNb5VKnMBhpMfxh4DtIuLqiJhbXyibIOM84M1Abez4QT2N1MzGraolzp1IHdm/FhFtV3WMiPuA00iJc7sux2ZmBlQvca6RbW8qcM+N2bbZEhRmZh1VtcRZi6fIUri1YZkve6Q3M+uGqiXO2uqPWxS45+1195qZdVXVEud0Ug3yEEkT2hWWtDHwKdJ70Wu6HJuZGVC9xHk66TF9A+D/SVquUSFJi0rajzQN3UTS5Mdn9ShGMxvnqjbJxz2SjgZOBd4DPCzptlyRYyRNJC1stgxD7zdPiog7ehutmY1XlUqcABHxZUlzgZNJ646/nqG5Nt+bbWsJcz5womd+N7NeqtqjOgARcQawHqnmeRspcSr3uRc4B9jMSdPMeq1yNc6aiPgHafG2z0paFFiWFO9TEdFo/LqZWU9UNnHmRcRLpKGVZmalq3TilPQq4L+AdYEVSZ3cHwP+AlwVEc+XGJ6ZjVOVTJyS1iS939yF5u9hn5P0TeCLEfFMz4Izs3Gvco1DkrYh1Sh3AxZleKNQ/jNA6vx+u6TVyonWzMajSiXObD7On5A6tQu4FNgZWJnUNWkAWJ2UVH+UlVkV+LmkJcqI2czGn0olTlIr+tKkd5l7RMT7I+LnEfFoNgfn8xHxYERcERG7Ax8m9eVcH/hoiXGb2ThStcT5TlKfzW9ExA/bFY6Ii4BvkWqe+3Q5NjMzoHqJs7ZqZdukmXNptl2vw7GYmTVUtcRZm/V90QL31DrDR8tSZmYdUrXE+bts+74C9+yYbW/ocCxmZg1VLXGeQGoYOlDSB9sVlvQ64EjgJcBLBJtZT1QqcUbELcCewBzgfEkXS5osaalaGSUbSToemEHqovQV4DFJazX6lPKHMbOFVqVGDkl6JNtdhNRSvlf2CUlPkyY5/g+gNju8SO82j8w+jQQ9+nNKWg/4M/BERKwygvJ7AweQlgoZAB4GfgV8NSLu6WasZjZ6lUqcwKvrjpXbLtPkHjU531NZB/zvkDrqtys7AbiYVLvOWwv4OLCPpH0j4rKOB2pmY1a1xDm17ABGQ9JipG5RW4/wllMZSppXAWcCs7P7/4f0j8SFku6LiD92OFwzG6NKJc6I6LvEKWl54BJghxGW3xj4ZHZ4OfDeiKh1pbpB0k9JPQSWIb273b6zEZvZWFWqcajfSNoZ+CNDSXP+CG47hPT3Phc4PJc0AYiIu4Djs8O3ZD0HzKxCnDhHSdKlpAlJViM1QB0P/GYEt+6SbWdExINNypyf29991EGaWVc4cY7em7Lt/cCOEfGFdjdkXaNqDWAzmpWLiNnA3dmhH9XNKsaJc/QeAY4C1o+I6SO8Z4Pc/sw2Ze9rcI+ZVUClGof6zLYRMZJ3mnkr5fb/0absw9l2OUlLjGWBOkkrAJMK3rb2aH/PbGHnxDlKo0iakFbqrHm2Tdk5uf2lgVmj+L2ag4Bjx3C/meX4Ub238p3j2y0091yT+8ysZE6cvZWvpbabBi8/Imo0tVsz6xI/qvdW/vF8yTZl89dH/X4zcxbw/YL3rA1cMcbfNVso9V3ilLQ0sC2wFHBLRNxbckhF5JcxntimbO16MDTB86hExCwKviOVKjEFgFklVfJRXdJWks6R9M6683uRWqOvJI0Nv0fSRflp5yrugdz+ym3K1q7Pjoh5XYrHzEahcolT0tGkmeD3B96YO78+aUTNKxlaW30RYG/SUsH94M7cfrvuPrV5RO9sWcrMeq5SiVPSJqShiyI1iOQbRY4gzcMZwDRgV9LkGgLeJmm33kZbXEQ8AjyUHW7TrJykScC62eH13Y7LzIqpVOIkzUUp0vu4LSPiOABJiwJ7kJLmTRFxcERcGREfAH6R3fOhkmIuqraC546SVmpSZt/cfr/Ups3Gjaolzsmk5HhqRNyWO78tQxMZ17cOfyfbvr67oXXMuaQ1kpYAzs7+UVggm0X+89nhTRFxc4/jM7M2qpY4aw0i9cniHbn9X9Zdq80wtEJXIuqwiLid1D0I4N3ADEm7S3qzpMMZmotzLmkKOjOrmKp1R6r1XawfVbNTtn08Iv5ad602jLGfWp6PII1b34M063v9zPEvAPtGxB96HZiZtVe1GmdtsbZ1aickrQJsRnqE/0WDe7bNtu0mzaiMiJgbEXuSegT8GvgnqYb5EPBd0vvdS8uL0MxaqVqN8zfAGsBhkq6IiEHSGjw1P8gXlrQFaRmKYGSTCHdVREwuWP5SUn9UM+sjVatxnpNtXw/cK+l20vK5QZow+OcAktaU9B3gt6R+nS8x9N7QzKyrKpU4I+J64DhS96IVgA2z/UFgv4h4KSu6LLAfadhlAEdExF96HrCZjUtVe1QnIqZIug7YB1iRtITEWRHxt1yxu0id42cAx0XEtT0P1MzGrcolToCIuAa4psX1QUnLRES7yYDNzDquUo/qRThpmllZ+jZxmpmVpZRHdUlXd+FrIyJ26ML3mpkNU9Y7zsmk1vBms+XWLytRX67R9XZLUZiZdURZiXMGzRPdIqQp12qvER4A/gg8ThpdsyxpJNGm2fU5wIX015BLM+tjpSTOViNsJF1ESpp3A59o1tVI0sbAt0md5VcHdu54oGZmDVSqcUjSh4H3k2Y82qZV/8xslqH/BO4hTQLyyV7EaGZWqcRJmsg4gJMi4sl2hSNiDnAy6R3nR7ocm5kZUL3EuXG2vaXAPXdk23ValjIz65CqJc7a+uHLF7hntWz7TMtSZmYdUrXEeU+2/WiBez5Fery/vfPhmJm9XNUS53dJ7yt3k3S8pKbxSVpS0nnAdtmpaT2Iz8yscpN8nA8cBGwOHA3sLely4M9ArbFoEqkL0ntJsycB/DQifoiZWQ9UKnFGxIuSdgKuInVwXws4sknx2miinwB79SA8MzOgeo/qRMRsYEvSCo9/yk6r7hOk2d/3ioj3RMRzZcRqZuNTpWqcNRExn7QUxlmSVgTWY2j538eAuyLiibLiM7PxrZKJMy8iHieNUzczq4TKPaqbmVVdpWqckr44lvsj4rhOxWJm1kylEicwhbHNq+nEaWZdV7XECc0nN24kgH8x1MfTzKzrqpY4X9vm+iLAfwCrADsC+5OS50cj4jddjs3MDKhY4oyIv4+w6B+BKyRdDPwa+KGkTSPike5FZ1a+SZMmMTg4uOA4vz8wMDCs7MDAALNnz+5ZbHn9EudoVSpxFhUR10s6C/g08Fng0JJDMuuqwcHBYUmo/lpV9Euco9XXiTPzc1Li3BknTlvI1dfW2tXkytIvcY7WwpA4a4u0rVxqFGY9UP9IO3HiRAYHBxkYGGDOnDklRfVy/RLnaC0MifNd2fZfpUZhNkpTp04d9b1z585dsB3L9xx77LGjvnc86tvEKekVpCnoDiO1rLtV3cx6olKJU9I/RlIMWApYhuGzJX2ti6GZVcIpp5yyoJYJw2ucJ5xwwrCyEyZM4KijjuppfONFpRInqX9mUfOBz0XE9Z0OptskLUVq0HofsC7pH4D7gMuAMyLCrx9smLlz5w5LnPXXrDeqljhn0H7IZZAahP5FWmfo0oi4u9uBdZqklUgTNm9Yd2mT7PMRSTtHxF97HpxV1oQJE4Yd55Nl/bX647Hyu9ghlUqcETG57Bh6QdJiwBWkpBnA2cD3SbXn3UmTOK8BXClpy4h4uqRQrWL86F0NnlauHAeQ1k0CODQiDoqIayLiuog4FPhgdm1t4IhSIjSzpipV4xxHPpVt7wS+UX8xIi6R9EFSV6tPSvpSRMyrL2dWVQt7I1bf1DglvU/SlZL+Kun3ks6T9J9lx1WUpPWB9bPDi7NlQhr5XrZdFnhr1wMz66BaI1ajxqz6a/3YqFVqjc2wFCgAABBZSURBVFOSgN2AD5BmRhoE/gBMqzWKZGurXwLsUXf7lsA+kq4EPhgR/TIAduvc/owW5X6b29+e1JBk1hfKbMTqhdISp6RXkxpEtq679EbgQEmHRcQ04GRgzxZf9R5SYn1PVwLtvA1y+zObFYqIxyQ9R+qzukGzcmZV1G+P3kWV8qguaVFSq/LWDHVi/zdpQmIBE4AzJX0I+ARDI4PeSpqPc1lSDfTOrPzOknbo8R9jtFbKtvOAR9uUrU2Tt1LLUmbWU2XVOD8EvIGUEC8AjqvNxSlpZeAEYB/Sez4BNwE71DWQ/FDStcCtwKrA+4HpvfoDjMGy2XZORLTrs1qbDWHpsfygpBWASQVvWz9/MHPmUOV41qxZYwmnI+644462ZRznyPVbnPn/HjOL9zSQiOj5h1TbnA/8rEWZa7MyLwF7tCj3mazcrWX8WUbxZ59O+gfjsRGUvTEr+/cx/uaU7Hv88Wdh/bynl/8/LqtVfZPsD3tRizLfyu3f3KLcTdl21bEG1SO1VvQYQdna+kvNWt7NrARlJc7aY+P9Lcrkl9F4okW5Z7LtK8YSUA89m22XHEHZWpkXuhSLmY1CWe84J5JqXK1mNF3QvSginmtRrlYb65c+DbVEP5Jprydm26fG+JtnkXowFPEK0uimp0kNdw8CL44xjrVJr2lqdmH4P5BV4Tg7qxtxLs7wp8zrxvh9hZQ9cmg8PoI+kG0XlzQpIlqtUlVrTR/TInQRMQsYzZv9m9oXGbnUbXeYv0dE+1aJHnOcndXFOG/twHeMSt+MHFqI3JnbX7tZIUmvIfXhrL/HzErmxNl7+YaubVqU2za333dzjZotzMpOnCNpWV6oRMRM4LbscB81eI7J7Jttn6LH72/MrLWy33H+XFKzBocFHVol3dviO3rb8bUzppEabDYl9UM9JX9R0l6k5Y4Bzo0It6qbVUjZibPdUMJajXSNEZTpJ+eS5uTcAjhZ0kakEVQvkiY9+WRW7n7gpDICNLPmykqc/6A/E15HRMRLkt5FGkW0Pml46T51xR4G3hUR/+51fGbWWimJMyLWKON3qyQiHpG0BWlS471I0+otDtwL/Ag4LSKeLDFEM2ui7Ef1cS0inie93zylXVkzq46yW9XNzPqOE6eZWUFOnGZmBfkdp/XSbGBq3XEVOc7O6pc4R0zZRLdmZjZCflQ3MyvIidPMrCAnTjOzgpw4zcwKcuI0MyvIidPMrCAnTjOzgpw4zcwKcuI0MyvIidPMrCAnTjOzgpw4zcwKcuI0MyvIidN6TtJ6kp6X9FDZsdSTtI6k0yXdLumZLM77JJ0v6Q1lxwcgaUNJ35R0r6QXJM2SNEPSxyRVfrlsSUdJiuzTl1Nbelo56ylJSwBXA1sDD0fEKiWHtICkA4GvkxbNa+bEiPh8j0J6GUkHAGfSPMa/ALtGxH29i2rkJK0N3AYslZ2aEBHzSgxpVFzjtJ7JaheXkpJmpUjaBTiblJCeBL4AbE+K9VCgVjs+RtIRJcX4TuCcLMZ/A0cDbwV2Ab6bFdsU+LGkgTJiHIFzGEqafcs1TusJScsDlwA75E5XosYpaVFgJrAG8ASwVUT8va7M8sD1pGWcB4E1I2JWD2MUcHf2+3OALSPinroyRwKnZoeHRMQ3ehXfSEj6KPDtutOucZo1Imln4I8MJc35JYbTyPakpAkwpT5pAkTEE8CR2eEAsFtvQltgW1LSBDilPmlmvkKqLQPs0ZOoRkjSisBp2eETZcbSCU6c1lWSLgV+AqwGBHA88JtSg3q5/KuDH7cod3Vuf5MuxdLMIqS/xwdpEmNEzCfVnAFKr8nX+TqwLOnv8KclxzJmfdmiZX3lTdn2fmD/iJgu6drywmnoBuB/Scnm4Rbl8hWNJboaUZ2IuA64rlWZ7HF+1ezwsa4HNUKS3g28F3ge+DhQWuNapzhxWrc9AnwDOCMiXig7mEYiYjowfQRFt8/tP9ilcMbiIOA12f73ywykRtIrgbOyw+Mj4m8pv/c3J07rtm2zR8iFwWdy+78qLYpMVsNcBtiYlDT3yi79Dji3rLjqnEyqyd8BnFJyLB3jxGldtbAkTUkHA9tlhzdHxI1lxpM5hvTOOO/bwBFVqN1L2ob0aB7AgRExt+SQOsaNQ2ZtSHo78NXscB6pX2cVrNrg3NuAfXsdSL1sBNM3AQHnRMT1JYfUUU6cZi1Imgz8AJiQnfp8RWqbAJcDk0m9Aj5Nep+8OnC6pDNLjAtSA9AGwKPA50qOpeOcOM2ayEbq/AyYmJ2aFhGVeU8XEVdFxHURcUNEfBXYgqHuSAdL2qmMuCRtyFCy/FRE/LuMOLrJidOsAUn7AVcwNDzwm8DBpQU0AtlIpnyM+/Q6BkmLAN8iDQv9SURc1usYesGNQ2Z1JB0DnJA7dQZwWPTH+OTppCGhA/S+kz7Ax4A3Ay8B35S0eYMyy+X2N5P0EvBiRPy1FwF2ghOnWY6kk4GjcqemRMTUsuKpkbQMacjlqyLiF83KRcRLkv5NSpxlTDFXG/CwKKnG3s4fsu0DDA17rTwnTrOMpGMZSprzgYMi4pwSQ8r7Fmn8+YuSlouIOY0KSZoITMoOW42CsjHwO04zQNKuwJTscB7wwQolTYDfZtvFgfe3KPcBhipEIxkN1VERsV9EqNUH+F7ulgnZ+TV6HetYOHHauCfpVQwfaXNwRFxSVjxNXAQ8k+1/SdJq9QUkbcbQtHJPU53RQwsdP6qbpQ7ttcfbm4CbmzRq5D0bETPblOmYiJgt6ShgGvBq4M/Z+9han9KdgE+Suk7VRur0/fRtVeXEaQYfze2/Cbh1BPdcR+p83jMRcbakV5BmcloGOKlBsUHggIi4tJexjTd+VLdxLZvZfeWy4xipiDgN2JzUWHQv8ALwLGmtoZOBdSLi4vIiHB+8dIaZWUGucZqZFeTEaWZWkBOnmVlBTpxmZgU5cZqZFeTEaWZWkBOnmVlBTpxmZgU5cZqZFeTEaWZWkBOnmVlBTpxmZgU5cZqZFeTEaWZWkBOnmVlBTpxmZgU5cZqZFeTEaWZWkBOnmVlBTpxmZgU5cZqZFeR11W2hImlT4H3AW4D1SOuPvwjMAu4Afg5cEhFPlRak9T0vD2wLBUlrAGcCO4+g+L+AqRHxtW7GZAsvJ07re5K2A34MLJ2dehi4DJgBPAYsCqxOSqp7ABOycmdExKG9jdYWBk6c1tckrQf8HnhlduoE4PiIeL5J+fWBHwAbZKd2jYgruh6oLVScOK2vSboeeHN2eGhEnDGCe1YC7gT+A7gnItbrYoi2EHKruvUtSe9mKGn+YiRJEyAiHgFOyw7XlfSGbsRnCy8nTutnH8/tTy147/nATcAXSe9BAZA0RVJIul/SYpJOlPSYpOck/U3SZ/JfImkJSQdK+rWk2ZJelPSopJ9Ier8kNfrx3O+EpHWaBSnpoazMtXXnJ+fuX1/SCpLOzOJ+IYv5/0naqtVfgqRNJJ0t6e7sz/ispJmSzpP0lvZ/jeNURPjjT999gInAC0AAj5K9durA907JvvN+4FvZfv5zQK7s+qQuTvVl8p/fAJNa/E4A67SI56GszLV15yfn7n9Hrlz9Zz7wqSbf/d/AvDbxn92pv9uF6eMap/Wr1wOLZ/tXRZYJOmhVUmK5ntQa/zZSd6dLYcF70quADbPyPwR2A94E7A1ck53fFrhK0lIdji/vPGBlYHoWw9bAEcCTgIDTJb0/f0PWqDaN1ONgJnBAdt92wEGkfzgAPgYMu9dwjdOf/vwABzJUK5rSwe+dkvveB4CJTcpdkCt3WJMyX8uVObHF74y1xhmk2rHqyrwWeCK7/hAw0OD35wGrNPjdlUmJN0j/MJX+v3mVPh45ZP1qudz+rFYFJW3e5ruejYiZDc5fEBFzGnzfqxmqhV0VzTvSHwnsSKqVHizpuGjSTWqM/gEcElnGq4mIv0n6H1LNcmXSI/3l2eUVs+2zwOP1XxgRD0s6FlgJuL0LMfc1J07rV/nXTA0bYAAkLQbc2ua7fkd6pK53c5PybyU94kKq6TUUEfMkfRv4Mqnr01bAtW1iGY3vtUjIF5FeMSwKvJOhxHl3tl0auFTSMRFxV/7GiPh6F2JdKPgdp/Wrp3P7K3TpNx5ucn6j3P7v23xHPvlu1LTU2NzQ7EJEPAPcmx2um7t0AUO9CXYD7sxa1k+X9E5JS3Yn1IWDE6f1q7/l9ldqVigi5kWEGn1I7zBbebrJ+Vfl9lu+Jqi7vlzTUmPzaJvrT2Tb2uM5EfFP4O3AX3Ll1gU+BfwU+KekSyS9qZOBLiycOK1f/Z7U1QZSi3c3NGup1wjK1Cya2x9Ny/9I/j86b4QxzM2fjIjbgM1Jf3/TGP4PyQCwF3CDpKNGFur44cRpfSkiniT1kQRYQ9KWPfz5J3P77V4T5K/n78sn0abvaEnvRttpV5OdlG0fq78QyfSIOCgi1iC1xB8M/DoX20lZ9yXLOHFaP/tGbv/oHv7ubbn9dsM135jbvzu3n68lNuzjKWlZUkf/djZrdiH7jjWyw7/kzi8paVNJw967RsTMiDgrInYEPpedXoT0WG8ZJ07rZ5eRhk0C7Cnp4JHeKGkTRv/O8VqGEt9/t/iNxYCPZIfPATfmLv8rt79ak68Y6SuIvVtc24ehGm1+FqgHgD8z/B+fer/I7XezA3/fceK0vpX1W/wgQ4/AZ0o6VdIrmt0jaWLWt/FGhqaiK/TuMSIeJSVtgJ0kHdKk6KkMTV93bl2f0HzfyIPqx7RnfUVPHGFIWzeKIatNTskO/8TQqw2An2Xbt0h6V5PvzSfkP44wlnHB08pZ35O0GakleOXs1LOkxHYNqWY1nzSEcgdgd9JyGjW/IY0/vzv7rinAsdm11zbpGF9LbLcCr85OXU7q4vMo6dH446T+ngB3Aa+LiMHc/YuRhjqunp36MamB5hnSsM1PA68hDX1cE7guIibn7p/M0LBOSMn/fFK/zTmkkUWfJb0jnQdsFxE35u5fn5RMlyAtLfJN4FekzvCvAd5L6uQv4KaIaDlZyLhT9tAlf/zpxIf02H0OKQm0mrSi9vkT8CFePkxxSq5M06GQWdn1gHva/M50YMUm929PSnKN7nsJOJyhiUaurbt3cq7sOaRH/0bf8zTw9ia/vyfpFUK7v6eG8Y/njx/VbaEQEU9GxMdItbMjSIuyPUiaQekF0ljt35JmiN82IjaPiAsjyyCj/M27gU1IrdDXAv8kJe77Se8T3wPsGBEvG9KY3X8daTjmtOyeF0kt35dlMX51hKH8HtgU+E52/3OkWZtOAdaLiF81+f3LgI1JY+pvI9V252bf8Utgf+D1zeIfz/yobtaH6h7VD4iIpkM/rfNc4zQzK8iJ08ysICdOM7OCnDjNzApy4jQzK8it6mZmBbnGaWZWkBOnmVlBTpxmZgU5cZqZFeTEaWZWkBOnmVlBTpxmZgU5cZqZFeTEaWZWkBOnmVlBTpxmZgU5cZqZFeTEaWZWkBOnmVlBTpxmZgU5cZqZFeTEaWZWkBOnmVlBTpxmZgU5cZqZFeTEaWZWkBOnmVlBTpxmZgU5cZqZFeTEaWZWkBOnmVlBTpxmZgU5cZqZFeTEaWZW0P8HXljX/fjXXOQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 210x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the bar graph to put in figures\n",
    "outputPrefix = \"191224-6dpt-D193-D267-D301-D2-\"\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \"-bud-count.svg\"\n",
    "plotCountBar(df.groups, df.counts, outputFigPath, yMax=38, yTicks=[0,10,20,30], fig_width=0.7, fig_height=0.9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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",
      "     1      2     -0.1    0.9  -4.439   4.239  False\n",
      "     1      3      4.6  0.083 -0.4102  9.6102  False\n",
      "     1      4     11.7  0.001   7.361  16.039   True\n",
      "     2      3      4.7 0.0289   0.361   9.039   True\n",
      "     2      4     11.8  0.001  8.2572 15.3428   True\n",
      "     3      4      7.1  0.001   2.761  11.439   True\n",
      "----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.9        0.08295638 0.001      0.02885341 0.001      0.001     ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x1a184234d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in the data and put into a data frame\n",
    "datasetPrefix = \"20200120-6dpt-integrin-activation-MnCl2\"\n",
    "df = getCountDFbyPrefix(datasetPrefix)\n",
    "\n",
    "# Annotate the data set\n",
    "#\n",
    "# The layout of this data set is straightforward.\n",
    "#\n",
    "# Group 1, B2-B11: sg-Control only\n",
    "# Group 2, C2-D11: sg-Control only, 50 uM MnCl2\n",
    "# Group 3, E2-E11: sg-Control mixed with sg2-Ecad (4:1)\n",
    "# Group 4, F2-G6: sg-Control mixed with sg2-Ecad (4:1), 50 uM MnCl2\n",
    "\n",
    "groups = [\"1\"]*10 + [\"2\"]*20 + [\"3\"]*10 + [\"4\"]*20\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "# Inspect raw data if curious about how they look\n",
    "sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# Perform a Tukey test across groups\n",
    "tukeyTest(df.counts, df.groups)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
      "     1      3      4.6 0.1128 -0.8564 10.0564  False\n",
      "     1      4     11.7  0.001  6.9746 16.4254   True\n",
      "     3      4      7.1 0.0022  2.3746 11.8254   True\n",
      "----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.11279151 0.001      0.00215368]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x1a18528a90>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupsToKeep = [\"1\", \"3\", \"4\"]\n",
    "dfKeep = df.loc[df['groups'].isin(groupsToKeep)]\n",
    "\n",
    "# Use general df variable to ease the plotting\n",
    "df = dfKeep\n",
    "df['data_to_compare'] = df.counts\n",
    "df.to_csv('../data_cleaned/' + datasetPrefix + '-bud-count.csv')\n",
    "\n",
    "# Inspect raw data if curious about how they look\n",
    "sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# Perform a Tukey test across groups\n",
    "tukeyTest(df.counts, df.groups)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x1a1867a710>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 150x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the bar graph to put in figures\n",
    "outputPrefix = \"20200120-6dpt-integrin-activation-MnCl2\"\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \"-bud-count.svg\"\n",
    "plotCountBar(df.groups, df.counts, outputFigPath, yMax=31, yTicks=[0,10,20,30], fig_width=0.5, fig_height=0.9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
      "     1      2     5.55 0.2186  -1.978  13.078  False\n",
      "     1      3     10.8 0.0092  2.1074 19.4926   True\n",
      "     1      4     26.4  0.001  18.872  33.928   True\n",
      "     2      3     5.25 0.2629  -2.278  12.778  False\n",
      "     2      4    20.85  0.001 14.7034 26.9966   True\n",
      "     3      4     15.6  0.001   8.072  23.128   True\n",
      "----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.2186398  0.00915167 0.001      0.26292699 0.001      0.001     ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x1a1806e4d0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in the data and put into a data frame\n",
    "datasetPrefix = \"20200122-8dpt-integrin-activation-MnCl2\"\n",
    "df = getCountDFbyPrefix(datasetPrefix)\n",
    "\n",
    "# Annotate the data set\n",
    "#\n",
    "# The layout of this data set is straightforward.\n",
    "#\n",
    "# Group 1, B2-B11: sg-Control only\n",
    "# Group 2, C2-D11: sg-Control only, 50 uM MnCl2\n",
    "# Group 3, E2-E11: sg-Control mixed with sg2-Ecad (4:1)\n",
    "# Group 4, F2-G6: sg-Control mixed with sg2-Ecad (4:1), 50 uM MnCl2\n",
    "\n",
    "groups = [\"1\"]*10 + [\"2\"]*20 + [\"3\"]*10 + [\"4\"]*20\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "# Inspect raw data if curious about how they look\n",
    "sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# Perform a Tukey test across groups\n",
    "tukeyTest(df.counts, df.groups)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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",
      "     1      3     10.8 0.0032  3.321 18.279   True\n",
      "     1      4     26.4  0.001 19.923 32.877   True\n",
      "     3      4     15.6  0.001  9.123 22.077   True\n",
      "--------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.00321349 0.001      0.001     ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x1a18089c10>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupsToKeep = [\"1\", \"3\", \"4\"]\n",
    "dfKeep = df.loc[df['groups'].isin(groupsToKeep)]\n",
    "\n",
    "# Use general df variable to ease the plotting\n",
    "df = dfKeep\n",
    "df['data_to_compare'] = df.counts\n",
    "df.to_csv('../data_cleaned/' + datasetPrefix + '-bud-count.csv')\n",
    "\n",
    "# Inspect raw data if curious about how they look\n",
    "sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# Perform a Tukey test across groups\n",
    "tukeyTest(df.counts, df.groups)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x1a17f119d0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 150x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the bar graph to put in figures\n",
    "outputPrefix = \"20200122-8dpt-integrin-activation-MnCl2\"\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \"-bud-count.svg\"\n",
    "plotCountBar(df.groups, df.counts, outputFigPath, yMax=38, yTicks=[0,10,20,30], fig_width=0.5, fig_height=0.9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the data and put into a data frame\n",
    "datasetPrefix = \"20200909-6dpt-D193-D267-1to1-MG-LMN-mix-plate1\"\n",
    "df = getCountDFbyPrefix(datasetPrefix)\n",
    "\n",
    "# Annotate the data set\n",
    "#\n",
    "# All spheroids were 1-to-1 mixture of D193 (sg-Control) and D267 (sg1-Ecad) cells\n",
    "#\n",
    "# Group 1, B2-B11: MG alone\n",
    "# Group 2, C2-C11: 5 MG + 1 LMN\n",
    "# Group 3, D2-D11: 2 MG + 1 LMN\n",
    "# Group 4, D2-D11: 1 MG + 1 LMN\n",
    "# Group 5, E2-E11: 1 MG + 2 LMN\n",
    "# Group 6, E2-E11: 1 MG + 5 LMN\n",
    "groups = [\"1\"]*10 + [\"2\"]*10 + [\"3\"]*10 + [\"4\"]*10 + [\"5\"]*10 + [\"6\"]*10\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "df1 = df\n",
    "\n",
    "\n",
    "# Read in the data and put into a data frame\n",
    "datasetPrefix = \"20200909-6dpt-D193-D267-1to1-MG-LMN-mix-plate2-selected\"\n",
    "df = getCountDFbyPrefix(datasetPrefix)\n",
    "\n",
    "# Annotate the data set\n",
    "#\n",
    "# All spheroids were 1-to-1 mixture of D193 (sg-Control) and D267 (sg1-Ecad) cells\n",
    "#\n",
    "# Group 1, B2-B11: LMN alone\n",
    "# Group 2, C2-C11: MG alone (diluted from 9.7 mg/mL, rather than 6 mg/mL)\n",
    "\n",
    "groups = [\"7\"]*10 + [\"8\"]*10\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "df2 = df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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>index</th>\n",
       "      <th>file_name</th>\n",
       "      <th>scrambled_file_name</th>\n",
       "      <th>counts</th>\n",
       "      <th>groups</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Plate000_WellB02_ChannelMono,GFP,RFP_Seq0000.nd2</td>\n",
       "      <td>0a7f2b85856b838896920c032b5160db.nd2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Plate000_WellB03_ChannelMono,GFP,RFP_Seq0001.nd2</td>\n",
       "      <td>eae2cf4d0a05e300e586916c4f7fa2c3.nd2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Plate000_WellB04_ChannelMono,GFP,RFP_Seq0002.nd2</td>\n",
       "      <td>30aa897cd645456df633a9a99880504e.nd2</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Plate000_WellB05_ChannelMono,GFP,RFP_Seq0003.nd2</td>\n",
       "      <td>cd060b1e55f3c6da837db6df128ad4f7.nd2</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Plate000_WellB06_ChannelMono,GFP,RFP_Seq0004.nd2</td>\n",
       "      <td>5980a4022129b02be6773666687f4c1e.nd2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>5</td>\n",
       "      <td>Plate000_WellB07_ChannelMono,GFP,RFP_Seq0005.nd2</td>\n",
       "      <td>65cd9ac2946125592d8bfb2c72346466.nd2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>6</td>\n",
       "      <td>Plate000_WellB08_ChannelMono,GFP,RFP_Seq0006.nd2</td>\n",
       "      <td>d41a394699e4a508d59fbd3a78d079f2.nd2</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>7</td>\n",
       "      <td>Plate000_WellB09_ChannelMono,GFP,RFP_Seq0007.nd2</td>\n",
       "      <td>9f7278d9d4f7c0bfefb866eaff1a1f84.nd2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>8</td>\n",
       "      <td>Plate000_WellB10_ChannelMono,GFP,RFP_Seq0008.nd2</td>\n",
       "      <td>8ad65f4689a0f0f2de3f1e81e67ad8f5.nd2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>9</td>\n",
       "      <td>Plate000_WellB11_ChannelMono,GFP,RFP_Seq0009.nd2</td>\n",
       "      <td>5fc217eb448ca1087451c4d24236cf96.nd2</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>70 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    index                                         file_name  \\\n",
       "0       0  Plate000_WellB02_ChannelMono,GFP,RFP_Seq0000.nd2   \n",
       "1       1  Plate000_WellB03_ChannelMono,GFP,RFP_Seq0001.nd2   \n",
       "2       2  Plate000_WellB04_ChannelMono,GFP,RFP_Seq0002.nd2   \n",
       "3       3  Plate000_WellB05_ChannelMono,GFP,RFP_Seq0003.nd2   \n",
       "4       4  Plate000_WellB06_ChannelMono,GFP,RFP_Seq0004.nd2   \n",
       "..    ...                                               ...   \n",
       "65      5  Plate000_WellB07_ChannelMono,GFP,RFP_Seq0005.nd2   \n",
       "66      6  Plate000_WellB08_ChannelMono,GFP,RFP_Seq0006.nd2   \n",
       "67      7  Plate000_WellB09_ChannelMono,GFP,RFP_Seq0007.nd2   \n",
       "68      8  Plate000_WellB10_ChannelMono,GFP,RFP_Seq0008.nd2   \n",
       "69      9  Plate000_WellB11_ChannelMono,GFP,RFP_Seq0009.nd2   \n",
       "\n",
       "                     scrambled_file_name  counts groups  \n",
       "0   0a7f2b85856b838896920c032b5160db.nd2       9      1  \n",
       "1   eae2cf4d0a05e300e586916c4f7fa2c3.nd2      15      1  \n",
       "2   30aa897cd645456df633a9a99880504e.nd2      16      1  \n",
       "3   cd060b1e55f3c6da837db6df128ad4f7.nd2      16      1  \n",
       "4   5980a4022129b02be6773666687f4c1e.nd2      15      1  \n",
       "..                                   ...     ...    ...  \n",
       "65  65cd9ac2946125592d8bfb2c72346466.nd2       2      7  \n",
       "66  d41a394699e4a508d59fbd3a78d079f2.nd2       4      7  \n",
       "67  9f7278d9d4f7c0bfefb866eaff1a1f84.nd2       1      7  \n",
       "68  8ad65f4689a0f0f2de3f1e81e67ad8f5.nd2       2      7  \n",
       "69  5fc217eb448ca1087451c4d24236cf96.nd2       1      7  \n",
       "\n",
       "[70 rows x 5 columns]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine the data for plotting.\n",
    "# Filter out the 10 spheroids in the second plate that uses different Matrigel stock.\n",
    "df = pd.concat([df1, df2.iloc[:10, :]])\n",
    "df.reset_index(inplace=True)\n",
    "\n",
    "df\n",
    "# # Inspect raw data if curious about how they look\n",
    "# sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# # Perform a Tukey test across groups\n",
    "# tukeyTest(df.counts, df.groups)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "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",
      "     1      2     -4.4 0.1044  -9.2845  0.4845  False\n",
      "     1      3     -5.9 0.0084 -10.7845 -1.0155   True\n",
      "     1      4     -7.4  0.001 -12.2845 -2.5155   True\n",
      "     1      5     -9.1  0.001 -13.9845 -4.2155   True\n",
      "     1      6     -9.4  0.001 -14.2845 -4.5155   True\n",
      "     1      7    -11.3  0.001 -16.1845 -6.4155   True\n",
      "     2      3     -1.5    0.9  -6.3845  3.3845  False\n",
      "     2      4     -3.0 0.5057  -7.8845  1.8845  False\n",
      "     2      5     -4.7  0.067  -9.5845  0.1845  False\n",
      "     2      6     -5.0 0.0415  -9.8845 -0.1155   True\n",
      "     2      7     -6.9 0.0011 -11.7845 -2.0155   True\n",
      "     3      4     -1.5    0.9  -6.3845  3.3845  False\n",
      "     3      5     -3.2 0.4301  -8.0845  1.6845  False\n",
      "     3      6     -3.5 0.3196  -8.3845  1.3845  False\n",
      "     3      7     -5.4  0.021 -10.2845 -0.5155   True\n",
      "     4      5     -1.7    0.9  -6.5845  3.1845  False\n",
      "     4      6     -2.0 0.8627  -6.8845  2.8845  False\n",
      "     4      7     -3.9 0.2029  -8.7845  0.9845  False\n",
      "     5      6     -0.3    0.9  -5.1845  4.5845  False\n",
      "     5      7     -2.2 0.7913  -7.0845  2.6845  False\n",
      "     6      7     -1.9 0.8984  -6.7845  2.9845  False\n",
      "-----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.10444461 0.00839722 0.001      0.001      0.001      0.001\n",
      " 0.9        0.50568994 0.06698521 0.0415031  0.0011252  0.9\n",
      " 0.43014502 0.31961221 0.02099116 0.9        0.86268077 0.20288083\n",
      " 0.9        0.79128039 0.89838069]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7ffa4cd41450>"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot of all groups\n",
    "sns.barplot(df.groups, df.counts)\n",
    "\n",
    "# Inspect raw data if curious about how they look\n",
    "sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# Perform a Tukey test across groups\n",
    "tukeyTest(df.counts, df.groups)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x7ffa4ac87dd0>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vaGZm41OoxB8R+3Y6BjOzXucbn2ZmfaZQV/yNkLQYsBmwMPD3iLivwyGZmXWVQiZ+SRuT5uK/OCJ+n9u/K3AWsEi2KySdD3wyIl5uf6RWaY899mDmzJkA86x32WUXACZOnMi5557bsfjMrICJX9JXSTNuQurC+fts/5qkm78L5IuTZuVcGti2jWFaDTNnzpyb8Cv3m1kxFKqNX9K6pKQvYE62lBxCSvoBnA7sCJyXld1a0k5tDdaqmjhx4tylkf1m1n5Fu+L/LCmRPwVsU3qurqQJwIdJSf+miNg/K3+JpMWB9wN7Ahe2PWKbh5txzIqvUFf8wJak5H5CxcPUN6P87N0LKs75cbZ+W0sjMzPrEUVL/Mtn65sr9v9XbvuPFcceztaTWhKRmVmPKVriXyhbV87HX7px+2RE3F5xrPSg9Vkti8rMrIcULfE/lq1XK+2QtAKwPqkJ6A9VztksWz/U2tDMzHpD0RL/daSbuwdLKk2l+PXc8d/kC0vaEPg86UvhurZEaGbW5YqW+M/M1m8D7pM0lfQQ9QAeAC6D9AAWST8G/gwsCswGTmt7tGZmXahQiT8irgeOJl31TwLWzraHgX0jYnZWdAlgX9K0DQEcEhH/bnvAZmZdqGj9+ImIoyRdC+xNmoP/LuC0iLgnV+xO0uCuKcDREXFN2wM1M+tShUv8ABFxNXD1CMeHJS0eES+1MSwzs55QqKae0Shi0pe0hqRXJD3SYPndJF0p6ZnsvHslnS5p9VbHamb9q2sTf9FImkgaRVx3MhpJC0i6APhf4L2kexYTgVVI01b8Q9IuLQzXzPqYE38TSJofOB/YpMFTTgBKif1yYIfs3EOB54AB4BeS3trcSM3MCtrG300kLU2aJXSrBstPJo09APg18JGIKD1n+AZJvwNuIM1N9F1gi6YGbGZ9z1f84yBpO+BvlJP+nBGKlxxA+ru/Bnwhl/QBiIg7KT+P4N2+6jezZnPiH6PsyV+XAm8ijSU4hsZGD++QradExMM1ypyT2955zEFaWwwNDTE4OMjg4CCS5i6lfYODgwwNDXU6TLO53NQzdu/M1g+QHv14paRrRjpB0irAstnLKbXKRcR0SXcBa+CmnsIbHh5meHi46n6zIipU4pe0UERUzsxZVI8BpwInR0SjzxVcK7c9rU7Z+0mJf6065azDBgYG5m7nk31+f37brNMKlfiB30haFDgxIi7udDB1bBYRjbTp5y2X2643m+ij2XpJSRNH8eXyOpImAaNta1h1rPX1m+nTp8/dHhwcZHh4mIGBAWbMmNHBqMxqK1ri34iUoFbsdCD1jCHpQ/nZAQD1BqDls8ZipMdRjtV+wJHjON/MekjRbu4ulq3/1tEoWic/uKtek9bLNc4zMxuXoiX+O7P1eh2NonXyvxKiZqlENc4zMxuXojX1fBn4HXCspIci4rJOB9Rk+eadhWqWev3xMbfvZ07j9Q+pr2dVoOj3WcxsDIqW+F8B/gc4Arg0m+zsFtKNzheoc5UcEUe0PMLxeTG3PVinbOl4kKZxGLOIeIpR3iOQVL+QmXWloiX+aygndwErZEujip74H8xtL1+nbOn49Ijwg+TNrGmKlvhh3rbt0Vx21mszL4I7ctv1ukuuUuUcM7NxK1riX7nTAbRSRDyWNV+tAGxaq5ykIaA0J//17YitW1x66aUNlZs9e/bcdb1ztt9++3HHZdZNCpX4I+LB+qW63oWk2Tm3kbRcRDxWpcw+ue2L2hKVmfWNonXn7AdnAbNJffPPkDQhf1DSGsDXspc3RcTNbY7Puki9CeI8OZxVU9jEL2lBSbtLOlPS5ZJukfSW7Nj6kr4paTQ3fgshIqaSulcCfBCYImlnSe+S9AXKc/G/RprC2aym0gRxlRPC1dpvBgVr6imRtD1wJuWZLEW6ebtw9np94BvAlyUdGRHHtT/KcTmENG/Ph0lP3qp8ctdMYJ+I+Gu7A7PuUm+COE8OZ9UULvFL+ihpPvrSr5FXeP1gp9JcPguSBnstFhGHtynEcYuI14BdJO0KfBLYEHgD8CRwBXBCRNzewRCtS3iCOBuLQiV+ScsDZ5OS/m3AQaSHm1SOXD2eNC3yt4FJwJckXdTp9vCI2HKU5c8nPavXzKxtitbGfzCpOecRYPOIuCq7Op5HRLwWET8B3gU8TWoK2q+dgZqZdauiJf5tSW3534+I5+oVjoj7gRNJiX/z1oZmZtYbipb4V8rWN43inBuz9XIjljIzM6B4ib8Uz2jmpilN6/C6JiEzM3u9oiX+R7L1hqM4530V55qZ2QiKlvivJF3BHyBpgXqFJU0GDiTdF7i6xbGZmfWEoiX+k0jNPGsB/ydpyWqFJE2QtC9pGudB0hOqTqtW1szM5lWofvwRcbekrwInAB8CHpV0a67I4ZIGSSNdF6fcvv/tiLitrcGamXWpQiV+gIj4jqTXgONIE5m9jfJc+x/J1qWEPwc4tguevGVmVhiFS/wAEXGypIuA/YH3A+swb7PUfcDlwCm+0rd2aeRZAKN5DgD4WQDWGYVM/AAR8RDp4etfzqYuXoIU77MRMd6Hj5uZ9a3CJv68iJhNmprBzMzGqdCJX9JSpKae1YFlSIO0ngD+DVweEa90MDwzs65UyMQvaWVSz54dqN3l9GVJZwNHRMSLbQvOzKzLFa0fP5I2JV3R7wRMIPXgqbYMkAZvTZX0ps5Ea2bWfQqV+LP5+C8lDcoSaa767YDlSV07B4A3k74ULsrKrAhcJmliB0I2M+s6hUr8pF48i5Ha8j8cEbtHxGUR8Xg2B/8rEfFwRFwcETsDe5H68q8JfLyDcZuZdY2iJf4PkAZrnRoRF9YrHBHnAj8kXfnv3eLYzMx6QtES/wrZum7Szyk9unCNJsdiZtaTipb4n8vWE0ZxTmkwV4xYyszMgOIl/r9k6/8exTnbZOsbmhyLmY1gaGiIwcFBBgcHkTR3Ke0bGhrqdIhWQ9ES/7dIN3Y/LWmPeoUlvRU4FJgNHNvi2MwsZ3h4eO7SyH4rjkIN4IqIv0vaBTgXOEfSdsBZwE0R8TKAJAFrA7sDXwAWIj1w/QlJq9R43/vaEb9ZPxkYGJi7nU/ypf3541YshUr8kh7LNucj9dTZNVtC0gukh7S8ASg9nUuktv1Ds6WaoGCf06wXTJ8+fe724OAgw8PDDAwMMGPGjA5GZY0oWkJctuK1cuvFa5yjGvvNeoanhLZmKlri/2anA7DutcceezBzZurklV/vsssuAEycOJFzzz23Y/GZFUWhEn9EOPHbmM2cOXNuwq/cb2ZlhUr8ZuMxcWJ5uqZ8si/tzx8362dO/NYz3Ixj1pii9eM3M7MWc+I3M+szTvxmZn3Gid/MrM/45q6ZdZ2hoaG500TUmi4iP7LY5uXEb2Zdp9YkcJ4YrjFO/GbjVG/EMHjUcLN5grjx6brEL2kxYDNgYeDvnnnTOs0jhtvPE8SNTyETv6SNgY8BF0fE73P7dyVN07xItisknQ98sjRts1m71RsxXLlt1mmFS/ySvgock718HPh9tn9N4BzKUzJDmplzN2BpYNs2hmk2l5twrNsUqjunpHVJSV/AnGwpOYSU9AM4HdgROC8ru7WkndoarJlZlyraFf9nSYn8KWCbiLgVQNIE4MOkpH9TROyflb9E0uLA+4E9gQvbHrGZWZcp1BU/sCUpuZ9QSvqZzSg/iOWCinN+nK3f1tLIzMx6RNGu+JfP1jdX7P+v3PYfK449nK0ntSQisz7WyJO8wE//6jZFu+JfKFu/UrG/dOP2yYi4veLYEtl6VsuiMjPrIUVL/KWHra9W2iFpBWB9UhPQH6qcs1m2fqi1oZmZ9YaiNfVcB6wEHCzp4ogYBr6eO/6bfGFJGwKfJ30pXNeuIM2s9dzM1DpFS/xnAnuRbtTeJ+lpYC1SYn8AuAxA0srAN4BdSSN4ZwGndSBes47yA+ZtLArV1BMR1wNHk7p0TgLWzraHgX0jYnZWdAlgX1LSD+CQiPh32wM267DSdBGV00PU2m8GxbviJyKOknQtsDewDHAXcFpE3JMrdidpcNcU4OiIuKbtgZoVgB8wb2NRuMQPEBFXA1ePcHxY0uIR8VIbwzIrHDfj2FgUqqlnNJz0zczGpmsTv5mZjU1HmnokXdWCt42I2KoF72tm1lM61ca/Jak3jmocj4rXleWqHa/cZ2ZmVXQq8U+hdqKeD9iUcjPUg8DfgCeB10hdOdcH1suOzwB+gadsMDNrSEcSf0RsWeuYpHNJSf8u4HO1umpKmgz8iDTY683Adk0P1MysBxWqO6ekvYDdSTNubhoRz9QqGxFTJb0X+CtpErfPAye3JVAz6+io4U7WPTQ0NPcB77Ue9J5/JnARFSrxkx7EEsC3R0r6JRExQ9JxpDn5P4YTv1nbdPIh852se3h4eJ6En9/fLYqW+Cdn67+P4pzbsvVqI5Yys6bq5KjhTtZdurKH2lf8RVe0xD8TWIT08PRGvSlbv9j8cMyslk6OGu5k3flmnMHBQYaHhxkYGGDGjBkdi2m0ijaA6+5s/fFRnHMgqXloavPDMTPrPUVL/D8l9cnfSdIxkmrGJ2khST8BNs92nd6G+MzMul7RmnrOAfYDNgC+Cuwm6dfAv4DSzd4hUhfOj5Bm7wT4XURc2N5Qzcy6U6ESf0S8Kmlb4HLSAK1VgENrFC+N5r2U9EAWMzNrQKESP0BETJe0Ealr5ydJo3Qrp2yYA/wF+EFEXNDmEM2sx/X6Yx8Ll/gBImIO6VGKp0laBliD9EQugCeAOyPi6U7FZ2bWzQqZ+PMi4knSPD1mZtYERevVY2ZmLVaoK35JR4zn/Ig4ulmxmJn1qkIlfuAoxjevvhO/mVkdRUv8UPvhLNUE8BzlPv5mZlZH0RL/W+ocnw94A7ACsA2pu2cAH4+I61ocm5lZR6eEbpZCJf6IuLfBon8DLpb0S+AK4EJJ60XEY62Lzsyss1NCN0tX9+qJiOtJ/f2XBL7c4XDMrA9MnDhx7tLI/iIq1BX/GF0GfJH06MWDOhyLmfW4ojfjNKKrr/gzpYesL9/RKMzMukQvJP7S5BfPdTIIM7Nu0bVNPZIWIU3hfDCpZ4979ZiZNaBQiV/SQ40UAxYGFs+2RUr8329ZYGZmPaRQiZ/UP3+05gBfyXr4dBVJC5NuSP83sDrpC+x+4FfAyRHxXOeiM7NeVbTEP4X6UzYE6Ybuc6Tn7J4fEXe1OK6mk7Qc6YEza1ccWjdbPiZpu4i4ve3BmVlPK1Tij4gtOx1DO0iaH7iYlPQDOAO4gPTrZWfgAGAl4BJJG0XECx0K1cx6UC/06ulGnyI9NxjgoIjYLyKujohrI+IgYI/s2KrAIR2J0Mx6lhN/ZxyYre8ATq08GBHnkZ4lDPD57BeCmVlTdE3il/Tfki6RdLukWyT9RNJ7Ox3XaElaE1gze/nL7DGT1fwsWy8BvKflgZlZ3+jolaQkATsBHyXNzDkM/BU4vXRTU9J8wHnAhytO3wjYW9IlwB4RMdy2wMdnk9z2lBHK/Tm3vQXpRrCZ2bh17Ipf0rKkxHcBKflPBt5BGpT1D0mfy4oeB+xCuc9+5fIh0hdDt1grtz2tVqGIeAJ4uco5Zmbj0pHEL2kCqVfLJpQT+POkB6oIWAA4RdKewOcoj8x9D2k+/iVIvwDuyMpvJ2mrNn+MsVouW88CHq9TtjTN9HIjljIzG4VONfXsCbydlNB/Dhxdmotf0vLAt4C9Se3cAm4CtoqIWbn3uFDSNcA/gBWB3YEr2/UBxmGJbD0jIuqNWZiRrRcbT4WSJgFDozxtzfyLadPKP04efPDB8YRT02233Va3TCvqbqTefq271/6ti1J3/v+nzIItqbCGTiX+nbP1HyNin/yBiHgU2FfSSsC7SV8OJ1Yk/VLZZyWdSmoOemtrQ26a0mTdrzRQttTUM94JvvcDjhzPG+y4447jDMHMRrAi6SK2LTrVxr8uKaGPNLH1D3PbN49Q7qZsveJ4g2qTUi+eRh4qX3r+cK2eP2Zmo9apxF9qdnhghDL5xzA+PUK5F7P1IuMJqI1eytYLNVC2VKZ7nulmZoXXqaaeQdIV74wRysztnhkRL49QrnQ1vEAT4mqH0hfVQANlB7P1s+Os8zRS76nRWIQ0uvgF0o33h4FXR3H+qqQb+CU7MO+XeSt1qu5+/Myue2x1L8i8rRTXNiuoRnR6RGg/NmGU7hotKGkoIqaPULbUm2dcD5GPiKeAp8Zw6k31i1SXhmjM496IaOxO5jh1qu5+/Myue1x1t61Nv1LXjNztIXfktletVUjSG0nPHag8x8xsXJz42y9/o3rTEcptltvuumcNmFlxdTrxN9KzpadExDTg1uzl3qryezFT6ub6LG1u/zOz3tbpNv7LJNW6YTh3QIOk+0Z4j7YOfGiS00k3XNcDDgOOzx+UtCuwXfbyrIhwrx4za5pOJ/56UxGUfhGs1ECZbnIWaU7+DYHjJK1DGsH8Kmneos9n5R4Avt2JAM2sd3Uq8T9EdybspoiI2ZK2J00xsSZpeoq9K4o9CmwfEc+3Oz4z620dSfwRsVIn6i2SiHhM0oakh7LsSpqWekHgPuAi0jQVz3QuQjPrVZ1u6ulrEfEKqX3/+HplzcyapdO9eszMrM2c+M3M+owTv5lZn3Ebv7XKdOCbFa97ve5+/MyuuzN1j4vqPwTKzMx6iZt6zMz6jBO/mVmfceI3M+szTvxmZn3Gid/MrM848ZuZ9RknfjOzPuPEb2bWZ5z4zcz6jBO/mVmfceI3M+szTvxmZn3Gid/MrM848VtLSVpD0iuSHmlDXatJOknSVEkvZvXeL+kcSW9vcd1rSzpb0n2SZkp6StIUSZ+RtGAr664Rz5ckRbY0ffp1SXvm3r/eslqz689ieJ+k/5P0cPY3ny7p95I+2IK6fjqKz1taHmh2HE0TEV68tGQBJgJ/AQJ4pMV1fRqYmdVVa/lWi+r+VJ26/wWs3Ma/+6rAcK7++VtQxwl1/tb5ZbUm1z0/8LM6dZ5JNu18k+r86Sg+b2m5u13/5qNdPB+/tUR2lfkrYIds16MRsUKL6toBuBAQ8AzwPWAK8BrwduAwoFT3oRHxnSbW/QHg0qzu54H/B9wIvAHYCdg3K3ob8I6IGG5W3SPEdAWwVW7XAhExq8l1/AnYBrgOOLBO8dsj4tUm1v1j4GPZy38A3wHuBVYHvg68JTt2WESc2KQ63wQs2UDRU4BNSYn/QxFxaTPqb7pOf/N46b0FWBq4gnmvflpyxQ9MAO7P6pgOrFojnruzMjOASU2qW7n3fQlYvUqZQ3N/g/3b8Lf/OK+/8mzFFf+T2Xsf1+b/trbKfa4/ARMrji+e++/heWDhNsb2iVxsJ7Tz7zLaxW381lSStgP+RvmKc06Lq9wCWCnbPioi7q0sEBFPkxIwwADpSrwZNqN8dXl8RNxdpcx3Sb9CAD7cpHqrkrQMULrCfbqF9SwLTMpe/qtV9dRwRLZ+HtgtImbmD0bEc8C3s5dvALZuR1CSViT9WwPcQfrlUVhO/NY0ks4nNXu8iXTVcwypKaCVNslt/3aEclflttdtUt3zkT7vw7Xqjog5wLTsZUuaunJ+ACxB+qy/a2E96+e2/9nCeuYhaTlg8+zl9yLimRpFLwHOISXiln0BVvg+6YsG0i+7mSOU7Tg/bN2a6Z3Z+gHgkxFxpaRrWlznDaR29RWAR0col7/ImdiMiiPiWuDakcpIErBi9vKJZtRbo54PAh8BXgE+C3ytVXVRTvyvAHe1sJ5K25Ca1yDdP6oqIp4A9mlLRICkLYCds5e/joir21X3WDnxWzM9BpwKnNyuK56IuBK4soGiW+S2H25RONXsB7wx276gFRVIWhQ4LXt5TETck75vWmaDbD0VeKuk/YH3AMsCz5Fubp8ZEc3+1VH6pfYyqTkFAEkLk774XyHdS2p3j5Vjs/Us4PA21z0mTvzWTJtlTRtFdFhu+0+tqiS7wl8cmExK+rtmh/4CnNWiao8jJb7bgONbVEde6Yp/DVKSz3/LDAEfBD4o6RfAJ6J5PXrWytaPRsQcSRsA/wO8n3Iue1zSaaSbqy2/+JD0HsrNjRfUuM9TOG7jt6YpatLPrkhLbcM3R8SNLazucNLN3CnAbqSk+CNgu1YkIkmbkpp2Avh0RLzW7Doq6luI1G0SYFFS89phpF9UW5CamKZnx/cETm9i9Utl6+ckfRS4GdieeS9g30j6MrhC0htovc/ntr9bs1TBOPFbT5P0PlK/fkg/xQ9qcZUrVtm3NS1oc85GBJ9N+nI5MyKub3YdVaxDOdFeD6wXESdGxJRsOZbUFFTqXfVxSc3qWbNItn4T8BNSk8+BpGS/MOnKu9S+vhnwwybVW1XWt/9D2cvrIuKvrayvmZz4rWdJ2hL4DbBAtutrLb7aB/g1sCUpCX2RdN/jzcBJkk5pcl1fIzV/PA58pcnvXcs/gdVIN1p3jIhnKwtExGOkPu0l9QZ4NWrhbD2J9Atn64j4QUQ8ERGvRMQNwPtIY0gAPiJpk2pv1CSfI40jgS662gc8gMtLaxfgGtowZUOVej/AvNMWnNahzz8JuCcXx7ZNet+1KU8TsUuV4z+lhQO4GoxxWlb/CzRh+gTSPYzSZzpphHKTc+VOaeHnuzer41nS6Oi2/43HuviK33qOpH2BiylfIZ4N7N+JWCLiqYq69x7ve0qaj9SMsSBwaUTU7NrYYbdm60VJ4wvG68Xc9iW1CkXEVMo9t1oyOZ+ktYFVspe/jRbfW2k29+qxniLpcOBbuV0nAwdHdonWIVeSfn0M0JzBY58B3gXMBs7OerdUys8rs76k2cCrEXF7E+pvVH5eombMUJofB/FYnbKPkO63LN2EeqvJzwD6mxbV0TJO/NYzJB0HfCm366iI+GYL61ucNGXDUhHxh1rlImK2pOdJib8ZCbA0UG4C6ZdNPaWbjg9Snt5iTCStT2rjHyLdUB7pC3UoW8+mPG3FeEylPOlfvV8Qpb/zc02ot5oPZOth4I8tqqNlnPitJ0g6knLSnwPsFxFntrjaH5Lm33lV0pIRMaNGbIOUk+BIo4u7wRcpN1ddTY2Ru5IWAN6Wvbw1mtOX/+bc9jtIvYqq1T0f5S6nDzSh3sr3XyCrH+CWiHi52XW0mtv4retJ2hE4Kns5C9ijDUkf4M/ZekFg9xHKfZTyRVYjo4xHFBH7RoRGWkjz1ZcskO1fabx1k8YnlHx0hHJ7Ub4q/78m1Avpyvr5bPsTkibUKPdB0n0FGOFewDisCyyUbd/UgvdvOSd+62qSlmLeEbH7R8R5bar+XMo3HP8n69c9j6xp5ITs5Qu0bvRuu/yKcrPNF7PPNw9J61GeJXQ66aEo4xZpANyp2cvJpBHLlXW/ETgpV3cr2t8n57ZvacH7t5ybeqzbHUS5GeUm4OYaNzvzXoqIaXXK1BUR0yV9iTQ6dVngX9l9htJYgW1JIzsHKY+sbddskS0REc9LOpg0++UiwHWSTiT9kplA6kd/MOkzzyZN1teM9v2SY0iDpiYDh2RfPGeQbuZuRBrbsHxW9oCIeLHqu4zPW3Lbr5sGvCt0uj+pl95eaHE/ftL/8KN9JN41TY7hUFITU636ZgAfbfPf/ae5+lvxIJYDSE84q/WZXyDNl9+KzzZE+nKtVfcsWvjQG+DHubqG2vnv2qzFTT3WtSQtTfnqrmMiPd5vA9LN3vtIA6teAv5Nao5YLSJ+2bEAWyAiTiG1dZ9OGqD2MukzlyaKWyda1OQWEdNJI6M/RvqlMZ00M+c00rxIG0bEqbXfYdxK9w9mZrF0HT9z18ysz/iK38yszzjxm5n1GSd+M7M+48RvZtZnnPjNzPqME7+ZWZ9x4jcz6zNO/GZmfcaJ38yszzjxm5n1GSd+M7M+48RvZtZnnPjNzPqME7+ZWZ9x4jcz6zNO/GZmfcaJ38yszzjxm5n1GSd+M7M+48RvZtZnnPjNzPrM/J0OwKybSFoP+G/g3cAawOLAq8BTwG3AZcB5EfFsp2I0q0cR0ekYzApP0krAKcB2DRR/DvhmRHy/hSGZjZkTv1kdkjYHfgsslu16FPgVMAV4ApgAvJn0pfBhYIGs3MkRcVB7ozWrz4nfbASS1gBuARbNdn0LOCYiXqlRfk3gN8Ba2a4dI+LilgdqNgpO/GYjkHQ98K7s5UERcXID5ywH3AG8Abg7ItZoYYhmo+ZePWY1SPog5aT/h0aSPkBEPAacmL1cXdLbWxGf2Vg58ZvV9tnc9jdHee45wE3AEaT7AABIOkpSSHpA0vySjpX0hKSXJd0j6bD8m0iaKOnTkq6QNF3Sq5Iel3SppN0lqVrluXpC0mq1gpT0SFbmmor9W+bOX1PSJEmnZHHPzGL+P0kbj/RHkLSupDMk3ZV9xpckTZP0E0nvrv9ntFZwd06zKiQNAltnL58gJfGGRcSDwIhJETgD+ETu9WqkHkGlGNYEfg2sXXHesqQbydsB+0naOSKmjya+UVoZuAJYPrdvGeAjwC6SDq72a0jSJ4AzSTe/81bNln0lnQl8Ltzm3Fa+4jer7m3Agtn25S1ITCuSkv71pAS+Nam76Pkw9z7B5ZST/oXATsA7gd2Aq7P9mwGXS1q4yfHl/YSU9K/MYtgEOAR4BhBwkqTd8ydkN8VPJyX9acCnsvM2B/YDHsiKfgaY51xrPV/xm1WXvyF7Xwvefz7gIeB9ETEj23dl7vhxwArZ9hcqxgTcDJwv6fvAQcD6wDeAw1sQJ6Sr+x8Bn8p9Ad4g6bfADcBSwAmSLo6I4ez47qRurbOB90TEI7n3+7OkS4BbgSWAjwG/bFHsVoUTv1l1S+a2nxqpoKQN6rzXSxExrcr+n+eSfv79lqV8FXz5CAPBDgW2If0q2F/S0bW6mY7TQ8ABlb96IuIeSV8nXdkvD/wXqWkK0pcFwEvAk5VvGBGPSjoSWA6Y2oKYbQRO/GbV5ZtBq95ABZA0P/CPOu/1F1KTTKWba5R/D+V28R/WetOImCXpR8B3SF1HNwauqRPLWPxshC+Uc0lNVBOAD1BO/Hdl68VIv04Oj4g78ydGxA9aEKs1wG38ZtW9kNue1KI6Hq2xf53c9i113iP/5bFOzVLjc0OtAxHxIuWmsNVzh35OuTfTTsAdWc+ekyR9QNJCrQnVGuHEb1bdPbnt5WoViohZEaFqC/BgnTpeqLF/qdz2iM1MFceXrFlqfB6vc/zpbF1q3iEi/gO8D/h3rtzqwIHA74D/SDpP0jubGag1xonfrLpbgDnZ9tYjFRyHWj2F1ECZknxXybH0PGokB8xqMIbX8jsj4lZgA9Lf73Tm/SIcAHYl3ST+UkORWtM48ZtVERHPANdlL1eStFEbq38mt12vmSl/PH9e/kug5j0K0r2Beur9khjK1k9UHojkyojYLyJWAt4C7E8aF1CK7dtZ909rEyd+s9pOzW1/tY313prbrjfdwzty23fltvNX6VX7+EtaAhhsIJ71ax3I3mOl7OW/c/sXkrSepHnuO0TEtIg4LSK2Ab6S7Z6P1CxkbeLEb1bbryiP2N1F0v6NnihpXcbe5n4N5cT9iVqFsh5FH8tevgzcmDv8XG77TTXeotEmrN1GOLY35V8U+VlIHwT+xbxfnpX+kNtu5QA0q+DEb1ZD1m99D8pNKKdIOkHSIrXOkTSY9W2/kfJUzqNqe4+Ix0lfOgDbSjqgRtETKE//fFbFmIB83/j9Kuf0ycYKHNtgSJtUiyG7mj8qe/lPyk1jAL/P1u+WtH2N981/ofytwVisCTwts1kdktYn9UQpzVXzEikxX026sp1DmoJhK2Bn0uMYS64jjXi9K3uvo4Ajs2NvqTGwq5SY/0GalwdS//ifk3rYrESaQO492bE7gbfmRs2Wfg1MIz0gBtKDZE4HXiRN+/BF4I2kqRNWBq6NiC1z529JeVoISF9e55D67c8AtgS+TLpHMAvYPCJuzJ2/JunLYCLp0ZRnA38iDeZ6I2men91JvxZuioh68xpZM0WEFy9e6iykZpszSUksGlj+CexJdnGVe5+jcmVWq1PnGsDddeq5ElimxvlbkJJ0tfNmA18gDRAL4JqKc7fMlT2T1HRU7X1eIE07Ua3+XUhNUPX+TlXj99K6xU09Zg2IiGci4jOkq+NDSA9VfxiYmS2PAH8mPaFrs4jYICJ+EVkGHGOddwHrknrBXAP8h/TF8wCpPf1DwDYR8bopEbLzryVN53B6ds6rpJ43v8pi/F6DodwCrAf8ODv/ZdKD5Y8H1oiIP9Wo/1fAZOD7pBvWL5K6fD4B/BH4JPC2WvFb67ipx8xep6Kp51MRUXPqCOs+vuI3M+szTvxmZn3Gid/MrM848ZuZ9RknfjOzPuNePWZmfcZX/GZmfcaJ38yszzjxm5n1GSd+M7M+48RvZtZnnPjNzPqME7+ZWZ9x4jcz6zNO/GZmfcaJ38yszzjxm5n1GSd+M7M+48RvZtZnnPjNzPqME7+ZWZ9x4jcz6zNO/GZmfcaJ38yszzjxm5n1GSd+M7M+48RvZtZnnPjNzPqME7+ZWZ9x4jcz6zNO/GZmfcaJ38yszzjxm5n1GSd+M7M+48RvZtZn/j8a+FDaIba6HQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 270x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the bar graph to put in figures\n",
    "outputPrefix = \"20200909-6dpt-D193-D267-1to1-MG-LMN-mix\"\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \"-bud-count.svg\"\n",
    "plotCountBar(df.groups, df.counts, outputFigPath,\n",
    "             yMax=21, yTicks=[0,10,20],\n",
    "             bar_width=0.7,\n",
    "             fig_width=0.9, fig_height=0.9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
      "  BB-94 Control      9.3  0.001   5.0495 13.5505   True\n",
      "  BB-94 GM-6001   1.1556    0.9  -3.2114  5.5225  False\n",
      "  BB-94     LOX   6.9333  0.001   2.5664 11.3003   True\n",
      "  BB-94     TG5   7.0286  0.001   2.3448 11.7124   True\n",
      "  BB-94    TG50      7.7  0.001   3.4495 11.9505   True\n",
      "Control GM-6001  -8.1444  0.001 -12.5114 -3.7775   True\n",
      "Control     LOX  -2.3667 0.5856  -6.7336  2.0003  False\n",
      "Control     TG5  -2.2714 0.6804  -6.9552  2.4124  False\n",
      "Control    TG50     -1.6 0.8609  -5.8505  2.6505  False\n",
      "GM-6001     LOX   5.7778 0.0047   1.2974 10.2582   True\n",
      "GM-6001     TG5    5.873 0.0082   1.0833 10.6628   True\n",
      "GM-6001    TG50   6.5444  0.001   2.1775 10.9114   True\n",
      "    LOX     TG5   0.0952    0.9  -4.6945   4.885  False\n",
      "    LOX    TG50   0.7667    0.9  -3.6003  5.1336  False\n",
      "    TG5    TG50   0.6714    0.9  -4.0124  5.3552  False\n",
      "-------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.001      0.9        0.001      0.001      0.001      0.001\n",
      " 0.58558905 0.68040083 0.86093171 0.00473182 0.00822474 0.001\n",
      " 0.9        0.9        0.9       ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fa41e6e8fd0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in the data and put into a data frame\n",
    "datasetPrefix = '20210123'\n",
    "df = getCountDFbyPrefix(datasetPrefix)\n",
    "\n",
    "# Annotate the data set\n",
    "# dataset name: 20210123-plate2-stiffness-D193-D267-1to1-7dpt-rows-singleZ\n",
    "# new folder name: 20210123\n",
    "#\n",
    "# All spheroids were 1-to-1 mixture of D193 (sg-Control) and D267 (sg1-Ecad) cells\n",
    "#\n",
    "# B2-B11: Regular formula with 9.0 mg/mL Matrigel.\n",
    "# C2-C11: Regular formula with 9.0 mg/mL Matrigel + LOX 150 ng/mL.\n",
    "# D2-D11: Regular formula with 9.0 mg/mL Matrigel + TG 5 mU/mL.\n",
    "# E2-E11: Regular formula with 9.0 mg/mL Matrigel + TG 50 mU/mL.\n",
    "# F2-F11: Regular formula with 9.0 mg/mL Matrigel + BB-94 (5 μM).\n",
    "# G2-G11: Regular formula with 9.0 mg/mL Matrigel + GM-6001 (25 μM).\n",
    "# excluded: C2, D5, D10, D11, G9\n",
    "# reason: contaminated (C2; LOX), incomplete image (G9) or off-focus (all others)\n",
    "\n",
    "groups = [\"Control\"]*10 + [\"LOX\"]*9 + [\"TG5\"]*7 + [\"TG50\"]*10 + [\"BB-94\"]*10 + [\"GM-6001\"]*9\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "# quick visualization\n",
    "sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# tukey test\n",
    "tukeyTest(df.counts, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x7fa41d99ac10>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 150x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the bar graph to put in figures\n",
    "outputPrefix = \"20210123-7dpt-D193-D267-1to1-BB94-GM6001\"\n",
    "outputFigPath = \"../jupyter_figures/\" + outputPrefix + \"-bud-count.svg\"\n",
    "\n",
    "# Filter out only Control, BB-94 and GM-6001 data to plot\n",
    "df = df[df.groups.isin(['Control', 'BB-94', 'GM-6001'])]\n",
    "\n",
    "plotCountBar(df.groups, df.counts, outputFigPath,\n",
    "             yMax=16, yTicks=[0,5,10,15],\n",
    "             yLabel='Buds per spheroid',\n",
    "             xticklabels_angle=45,\n",
    "             bar_width=0.7,\n",
    "             fig_width=0.5, fig_height=1.0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
      "Anti-β1 Ctrl Ab      6.1 0.0028 2.0022 10.1978   True\n",
      "Anti-β1   No Ab     10.6  0.001 6.5022 14.6978   True\n",
      "Ctrl Ab   No Ab      4.5 0.0292 0.4022  8.5978   True\n",
      "-----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.00277397 0.001      0.02921044]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7f99f74c6850>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in the data and put into a data frame\n",
    "datasetPrefix = '20210125-1-'\n",
    "df = getCountDFbyPrefix(datasetPrefix)\n",
    "\n",
    "# Annotate the data set\n",
    "# dataset name: 20210125-D193-D267-mAb13-mAb16-varying-mixing-ratios-6dpt-singleZ2\n",
    "# new folder name: 20210125-1\n",
    "#\n",
    "# All spheroids were 1-to-1 mixture of D193 (sg-Control) and D267 (sg1-Ecad) cells\n",
    "# Note the D row is the shared control group with another experiment (varying mixing ratios)\n",
    "#\n",
    "# B2-B11: mAb13 (beta1-integrin blocking antibody) 100 μg/mL\n",
    "# C2-C11: mAb16 (alpha5-integrin blocking antibody; serve as isotype-matched control) 100 μg/mL\n",
    "# D2-D11: no antibody control\n",
    "\n",
    "# groups = [\"mAb13\"]*10 + [\"mAb16\"]*10 + [\"noAb\"]*10\n",
    "groups = [\"Anti-β1\"]*10 + [\"Ctrl Ab\"]*10 + [\"No Ab\"]*10\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "# quick visualization\n",
    "sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# tukey test\n",
    "tukeyTest(df.counts, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x7f99f9b94c50>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 90x300 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the bar graph to put in figures\n",
    "outputPrefix = \"20210125-6dpt-D193-D267-1to1-mAb13-mAb16\"\n",
    "outputFigPath = '../jupyter_figures/' + outputPrefix + 'bud-count-bar.svg'\n",
    "\n",
    "plotCountBar(df.groups, df.counts, outputFigPath,\n",
    "             plotting_order=['No Ab', 'Ctrl Ab', 'Anti-β1'],\n",
    "             yMax=21,\n",
    "             yTicks=[0,10,20],\n",
    "             yLabel='Buds per spheroid',\n",
    "             xticklabels_angle=90,\n",
    "             xLabel_off=True,\n",
    "             bar_width=0.8,\n",
    "             fig_width=0.3, fig_height=1.0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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",
      "     e      f  13.5333  0.001 10.5326  16.534   True\n",
      "     e      g  11.9333  0.001  8.9326  14.934   True\n",
      "     e      h      7.2  0.001  4.1993 10.2007   True\n",
      "     f      g     -1.6 0.4974 -4.6007  1.4007  False\n",
      "     f      h  -6.3333  0.001  -9.334 -3.3326   True\n",
      "     g      h  -4.7333  0.001  -7.734 -1.7326   True\n",
      "----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.001      0.001      0.001      0.49742834 0.001      0.001     ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fe0b3a552d0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in the data and put into a data frame\n",
    "datasetPrefix = '20210203'\n",
    "df = getCountDFbyPrefix(datasetPrefix)\n",
    "\n",
    "# Annotate the data set\n",
    "# dataset name: 20210203-D193-D266-varying-mixing-ratios-6dpt\n",
    "# new folder name: 20210203\n",
    "#\n",
    "# sg-Control (D193) : sg2-Cdh1 (D266; stronger guide):\n",
    "# B2-B11 & C2-C6 (15 wells): 1 : 0\n",
    "# C7-C11 & D2-D11 (15 wells): 4 : 1\n",
    "# E2-E11 & F2-F6 (15 wells): 1 : 1\n",
    "# F7-F11 & G2-G11 (15 wells): 0 : 1\n",
    "\n",
    "# groups = [\"D193\"]*15 + [\"25%D266\"]*15 + [\"50%D266\"]*15 + [\"D266\"]*15\n",
    "# groups = [\"1\"]*15 + [\"2\"]*15 + [\"3\"]*15 + [\"4\"]*15\n",
    "groups = [\"e\"]*15 + [\"f\"]*15 + [\"g\"]*15 + [\"h\"]*15\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "# quick visualization\n",
    "sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# tukey test\n",
    "tukeyTest(df.counts, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x7fe0b3d72650>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 210x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the bar graph to put in figures\n",
    "outputPrefix = \"20210203-D193-D266-varying-mixing-ratios-6dpt-\"\n",
    "outputFigPath = '../jupyter_figures/' + outputPrefix + 'bud-count-bar.svg'\n",
    "\n",
    "plotCountBar(df.groups, df.counts, outputFigPath,\n",
    "             yMax=21,\n",
    "             yTicks=[0,10,20],\n",
    "             yLabel='Buds per spheroid',\n",
    "             bar_width=.7,\n",
    "             fig_width=.7, fig_height=.9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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",
      "     1      2  -7.8947  0.001 -10.7896 -4.9999   True\n",
      "     1      3 -11.8947  0.001 -15.5512 -8.2383   True\n",
      "     1      4 -12.0947  0.001  -15.625 -8.5645   True\n",
      "     2      3     -4.0 0.0253   -7.627  -0.373   True\n",
      "     2      4     -4.2 0.0126  -7.6997 -0.7003   True\n",
      "     3      4     -0.2    0.9  -4.3518  3.9518  False\n",
      "-----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.001      0.001      0.001      0.02525077 0.01261139 0.9       ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fcbdd4a9cd0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yQPd645gow/F9k90J4yVpx0F0Qv25fmOcr4ZzIhJWKvUW/PT0wUwb3Y+ogKFXYhwPXjKK9OR4t2N1fgk94KLZkNQXTACGXwgT74Rpj0Gfkc4xfUY6YxEJK2Otuzceys7Otjk5Oa5maE1ldYjogNENMo6UtVBTCdFx9eeryiFGfzmKHA1jzFJrbXbDeb1R2gax0foHTbsY07jQQYUu0oHUViIiPqIzdekYxTvhs0eheBuMuAhOuMjZq774Mcj7EjLGw8m3QHQsfDsXVs2F5AFw2u2Q3Nft9CKepVKX8AuF4PkLoWC9M14zD0I1zhbGr59z5ta9D4WbIHMizL3+0GM3zoebl0BA/4gUaQ+VuoTfjmWHCv2gb15pvE/9m1eheEf9uYL1zuMHjO3YjCI+pdMhCb/ENKDBTqGkPpDUu/U5E3A+fSoi7aJSl/DrPhBO/xl1xZ7cHybeAVN+C9G1O1+i42HK/c7+9eT+tQ80zpp69wwXQov4g/apS8fZsxGKt0PGyc4bogClhbBzOfQdDcHau0hVV0LeYqfce2a5lVbEU7RPXSKvZ1bjkg6mQtbZ9eeiY2HwGZHLJeJjWn4REfERnalLZG1dAnlLIGMCDJzgdhpPq6qpYv7W+RSUFXDOoHPo062P25GkE1CpS+R88Th8+ItD4ykPwCk3u5fH426afxNLdjp3m/zjsj/y/HnPc1zqcS6nErdp+UUiZ9FDDcYPu5PDB77J/6au0AFKq0v525q/uZhIOguVukSODTUY17iTwwdCDV9LoEavp6BSl0g65daWx9Jmo9JGMTptdN04LiqOK4Zd4V4g6TS0pi6Rc8Zd0G+082bpwAkw9By3E3mWMYbZ587m/dz3KSgrYErmFDJTMt2OJZ2APnwkIuJBzX34SMsvIiI+olKX8Mhf53wdrngnbP/auezuQRX7Ie8rqCg5NBeqcY4r3hmZrCI+pjV1OTo1VfDK1bD+A2d87Hlw+V/hs0fgkwecHS6pWTD9beeyuq9dAxXFEJcMl86BXsfC89OgcCOYKDjrF3DG3S7+gUS8Laylbow5G7gSeB+YivMvgRuttdXh/D3Siax++1Chg/Pz0jnwyW8PbWEs3OjsSd+8yCl0cL5/8HPIPM357+D8BfDJAzDqSkjpj4gcubAtvxhjhgInAfHAZdbaWcBCYHITx84yxuQYY3Ly8/PDFUHcULS18dzuNY33pBdthaK81udsTeMbZ4hIm4Wt1K21G6y1Bz8yWFX7fSfQ6IaT1trZ1tpsa212WlpauCKIG4ZfAFGxh8ZRsTBuJvTIrH/cCbX3KW1trkcm9D+pI5KKdAkdtaZ+8J2xvsCuDvod0hn0OgaufgsWP+6MT74Zeg+H6e/Awgdh3zY44WIY8xPnBtTJ/Q9d0GvinRAbBGvh2zcgZYCznh6IcvWPJOJlYd+nbox5AXgJ+BHOrW9usLb5zy9rn7qIyJGL2E0yrLU/qf3xvXA/t4iItEz71EVEfESlLpFTuAme/SHc19P5XrjJ7USeNn/LfKa+MZVxL4zjvi/uo6qmqvUHSZOstex++GHWZY/ju4lnUPTGG25HajeVukTOWzfDls8gVO18f/sWtxN5VmF5IfcsvIftJdsprynntfWv6XrqR6H43XfZ8+RThEpKqM7PZ+ev/jcVGze6HatdVOoSOdu+rD/OW9L0cdKq1XtWUxmqrDe3PH+5O2F8oGzZsvoT1lK2fIU7YY6SSl0iJ2NCy2Nps+N7Hk9sILbe3OHXV5cjkzBmTP0JY0gYM9qVLEdLpS6RM+0xGHQ6BKKd79MeczuRZ6XGp/L7Sb9nQOIAEqITuOzYy7jq+KvcjuVZyeefT8+ZMwkkJhKdnk7f3/wncUOGuB2rXXQ9dRERD9L11EVEugCVuoiIj+h66tIx9m6G/34Qirc513wZOwMqS2HRQ5D3JWSMh4l3Odd+kXbZvG8zs7+ZTUFZARdkXcAFWRe4HcnT9n/8CXtfeZlAQpCeM68nYcQItyO1i0pdwq+mGp67EIq2OOPcBWACsPlT+OZlZ27zIti3HS56wrWYXlZRU8F1H15Hfplz6eovdn5BbFQsUzKnuJzMm0pzcth2yy3OxeWAA4sWkfWPj4hOTXU52ZHT8ouE345lhwr9oFVvOl8N56Rdln6/tK7QD/pw84cupfG+4r9/WFfoAKEDByhZuNDFRO2nUpfwS+nv3JrucN0HOl8N56Rd+nXr12iuf6LuFtVeMf0bv3axTcx5gUpdwi+5H5z1y0PF3nMonHEX/PD3zr1JAeJSnLG0S2ZKJtefeD0B4/xf+JgexzBjxAyXU3lXj8suJWHs2LpxysUXERw3zsVE7ad96tJx9u+C/TuhzygI1J4/VJRA/lpIGwZxie7m84FdB3ZRWF7I8NThGGPcjuN55evWEwgmEJuR4XaUVkXseuoidZL6OF+Hi0uEAY3+dyjt1KdbH/p069P6gdIm8ccd63aEo6blFxERH9GZunSM6gpY+ZqzbXH4BdD7eGf+u38eukfpMee4m9HjyqrLeC/3PQrKCpiSOYXBKYPdjuQZZStWUPLfC4nNGkLy1KmYqCiqduxg37vvEUhIIGXahUQlJ7sds120pi4d4/lpzv50gEAMXP0m5C2Gj39z6Jizf+XcaFqOmLWWqz+4mhX5zuVhYwOxzJk6hxPTTnQ5WedX/MEHbL/jzrotjCnTptHr5pvYdMmlhPbvByB28GAGv/Umgbg4N6O2SNd+kcjZ9e2hQgcIVcGSv8Dnf6p/XMOxtNmK/BV1hQ5QGark5XUvu5jIOwrnPFdvT/q+efMo/OsLdYUOULlpEyWfLHAh3dFTqUv4BZpY1QtEN55v6jhpk4NbGQ8X1fCzAdK0qAavkzEQ3fi1M03MeYFKXcIvfRgc9y+HxtHxcMqtMPHO+sc1HEubjUwbyYS+h24yEowOctVwXU+9LXpef329Yu9+2aX0nD6dqMMuCRA3fDiJZ5zhRryjpjV16Rg11bDufdi3DYb9EHpkOvN5X8LWxTDwZOeiXtJuVaEq5m+dz56yPUweOFlbG49AxYYNlCz6lLisIXSbOBFjDNWFhRT//e8EgkGSp0whkJDgdswWNbemrlIXEfEgvVEqItIFqNRFPCy3KJcvd35JVajK7SieZ0MhSpcupXz1arejHBVtPxDxqPsX31+3jXFA4gCenfqs1tXbqaa4mC3TZ1Cxdi0ASeeeS/9HH/Hk9XR0pi7iQRuLNtbbl76tZBvPrXrOxUTetveVV+oKHWD/Rx9Runixi4naT6Uu4kENb5DR3Jy0TXV+49euqTkvUKmLeNDY9LGNbpRxwRDdo7S9Us4/v97e9aiUFM/uU9eauogHxUTFMGfqHOasmkN+WT4XZl3IpIxJbsfyrISRIxn4zDMUvfoqgWCQ1GuvIap7d7djtYv2qYuIeJD2qYuIdAEqdYm8qjK3E4gQKi+n4UqFrazE1tS4lCg8VOoSObvXwl9Oh/v7ON93r239MSJhVl1QwJZrr2Xd6DFsPHcKBxYvwdbUsPPXv2bd2GzWn3wKhc8/73bMduuQUjfGjDbGLDTGzDHGnNURv0M86J1bYddK5+ddK52xSITtfvAhSr9w9qBX5eWx/a67KHpjLkUvvYytqiK0fz/f//YBytetczlp+3TUmfp4YBdQA6xq+B+NMbOMMTnGmJx8j+4FlXbYsazlsUgElK36tt64pqCA0pyvGh1X/m2j6vKEjir1T4GZwP8D7mr4H621s6212dba7LS0tA6KIJ1O5sSWxyIR0G38hHrjmIwMEs9ssKAQFUVwXKONJZ7QUfvURwM7gb0d+DvEa370OLx7h3Ov0oyT4fw/uJ1IuqD0O+8gVFZGyYIFxGYNoc+vfkX8sGFU5eWx96WXCASDpP3brcQOHOh21HbpkH3qxphTgZuBKuDX1trNzR2rfeoiIkeuuX3qHXIWba39HPi8I55bRESapy2NIiI+ovVuEY9aV7iOx5c/TkF5ARcMuYB/HfavbkfyBFtVRcFfnqBkwQLihmaR9rOfEdO3L/vmvcvel18mEAzS68YbCI4d63bUdlGpi3hQaVUpMz+ayd6KvQB8k/8N3WK6cUGWrtTYmvzHHmPPX54AoHzVKsrXf0fvu+9ix9131x1T+tVXZH30ITHp6W7FbDctv4h40PLdy+sK/aBP8j5xKY23lMz/uN64Ys0ait5+p96cLS/nwGfefFtQpS7iQRnJGRjq32ptUPIgl9J4S2xmZr1xIDmZ+OOOa+I4b76eKnURD8pIyuC2k24jJhADwKi0UVwz4hp3Q3lE+p13EDvIKexAt270ufdeelx5Bd0m1d4UIyqK1BnTCY4Z42LK9tP11EU8bF/FPvZV7GNgsjc/KOMWGwpRuXkzMb17E+jWrW6+ctt2AgnxRPfs6WK6tonoPnURiYyUuBRS4lLcjuE5JhAgbsiQRvOxA/q7kCa8tPwiIuIjOlOXjlFRAsv+Cvu2wYgfw4BssBZWvQl5X0LGeGfemNafS8grzuPNDW8SExXDxcdcTHownf2V+5n73VwKygo4b/B5HN/zeEI2xHu577F6z2qy+2QzeeBkt6N7RkVuLvveeptAMIHul17qiSWYpmhNXTrG01OcC3cBmABc9Rps+RwWPXzomIl3wuR73cnnIdv2b+OyeZexv2o/AGkJacy9cC4z/zGTtYXOjUaiTTRPT3maDzd/yItrX6x77O0n3c71J17vSm4vqdi4kU2XXIotc+7KFdO/P0PmvUMgGHQ5WfN0j1KJnJ0rDhU6gA3BV0/Dl0/WP67hWJr0bu67dYUOkF+WzzPfPlNX6ADVtpqX173M6+tfr/fYl9a+FLGcXlY0d25doQNUbd9OyYIF7gU6Cip1Cb+YJs5uYoIQk9D6cdJIQnRCo7nE2MRGc8HoIHHRcY3mpHWB+MavsUloPOcFKnUJv17HwImXHhrHJsFpt8FZv6x/3Fm/iGwuj5qWNY0BiQPqxsNThzNjxAzOzDizbi45Npnpx0/nxpE31s0FTIAbR92ItK77ZZcR3bt33Thh9GgSJ3rzJi5aU5eOYS3kLnDeKD12CiTWXkPj+9WQtwQyJkDv412N6CWlVaUsyFtATFQMkwZMIjYqlpAN8fmOzykoK2DSgEn0iO8BwJo9a/h2z7eM7T2WISmNt+1J02pKSij5+GMCwSCJkyZhYmLcjtSi5tbUVeoiIh6kN0pFRLoAlbqIiI+o1EVEfESlLiLiIyp1EREfUamLiPiI61sajTH5wBZXQ7RNL6DA7RA+otczfPRahpdXXs9B1tq0hpOul7pXGGNymtoTKu2j1zN89FqGl9dfTy2/iIj4iEpdRMRHVOptN9vtAD6j1zN89FqGl6dfT62pi4j4iM7URUR8RKUuIuIjKvU2MsacbYx5yu0cXmeMmWyMed4Y87oxZpTbebzOGDPWGPOiMeY5Y0zv1h8hLTHGpBtjPH0tcJV6GxhjhgInAfFuZ/GBIDADuB841+UsfhAP3Ay8B5zichZPM8YY4B688WHIZqnU28Bau8Fa+5DbOfzAWjsPp9hvA55zOY7nWWs/A44H7gKWuRzH624E/gaUtXZgZ6ZSl4gyxvQEHgXutdbudjuP1xljxgE5wHnAv7kcx+t+ANwAjDfGXNrawZ1VtNsBpMv5LyANeMAY85a19nW3A3lcMvAMYIA/upzF06y1FwEYY16w1r7mdp720j51EREf0fKLiIiPqNRFRHxEpS4i4iMqdRERH1Gpi4j4iLY0SpdjjBkP3A5U4nx6MBNnW+C1QDnwLbAKGGqtfcoYMweYA/w78AWQC+wEforzic7brbXfR/QPIdIMnalLVzQT51IFj9eOHwGSgI+stTcBE5t53LvW2t/hXN6gL85fCnOAog7MKnJEdKYuXVFs7feDH9IowTnBOTg2QDUQXXs9kJTD5gFigG9wzuhvAuKAtzs4s0ibqNSlK3qq9isGuBh4AfgI+HPt0swnwErgl8Aw4EDt4y43xowE5uKcqc8CaoC1EU0v0gJ9olS6HGPMNOBkoCfwe2vthjY85kxq19g7Np3I0VGpi4j4iN4oFRHxEZW6iIiPqNRFRHxEpS4i4iMqdRERH/kf7ZvE21HvqgoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in the data and put into a data frame\n",
    "datasetPrefix = '20210208'\n",
    "df = getCountDFbyPrefix(datasetPrefix)\n",
    "\n",
    "# Annotate the data set\n",
    "# dataset name: 20210208-D193-D267-4-different-MGs-7dpt\n",
    "# new folder name: 20210208\n",
    "#\n",
    "# All spheroids were 1-to-1 mixture of D193 (sg-Control) and D267 (sg1-Ecad) cells\n",
    "#\n",
    "# B2-C11: MG-1 5% + ABA\n",
    "# D2-E11: MG-2 5% + ABA\n",
    "# F2-F11: MG-3 5% + ABA\n",
    "# G2-G11: MG-4 5% + ABA\n",
    "#\n",
    "# excluded: B9, F9\n",
    "# reason: external fiber presence\n",
    "\n",
    "groups = [\"1\"]*19 + [\"2\"]*20 + [\"3\"]*9 + [\"4\"]*10\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "# quick visualization\n",
    "sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# tukey test\n",
    "tukeyTest(df.counts, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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",
      "     1      2  -7.9263 0.001 -11.2285 -4.6242   True\n",
      "----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " 0.001\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fcbdd4790d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in the data and put into a data frame\n",
    "datasetPrefix = '20210209-1-'\n",
    "df = getCountDFbyPrefix(datasetPrefix)\n",
    "\n",
    "# Annotate the data set\n",
    "# dataset name: 20210209-D193-D267-BB94-7dpt\n",
    "# new folder name: 20210209-1\n",
    "#\n",
    "# All spheroids were 1-to-1 mixture of D193 (sg-Control) and D267 (sg1-Ecad) cells\n",
    "#\n",
    "# D2-E11: Matrigel + ABA\n",
    "# F2-G11: Matrigel + ABA + BB-94 (5 μM)\n",
    "#\n",
    "# excluded: rows B-C; E5\n",
    "# reason: rows B-C are for other experiments (no Matrigel testing); E5 image is incomplete.\n",
    "\n",
    "groups = [\"1\"]*19 + [\"2\"]*20\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "# quick visualization\n",
    "sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# tukey test\n",
    "tukeyTest(df.counts, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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",
      "     1      2  -4.0111 0.2215  -9.3973   1.375  False\n",
      "     1      3  -7.7571 0.0044 -13.5341 -1.9802   True\n",
      "     1      4     -0.4    0.9  -7.3352  6.5352  False\n",
      "     1      5     -8.9 0.0068 -15.8352 -1.9648   True\n",
      "     2      3   -3.746 0.3703  -9.6537  2.1616  False\n",
      "     2      4   3.6111 0.5682  -3.4333 10.6555  False\n",
      "     2      5  -4.8889 0.2834 -11.9333  2.1555  False\n",
      "     3      4   7.3571 0.0496   0.0096 14.7047   True\n",
      "     3      5  -1.1429    0.9  -8.4904  6.2047  False\n",
      "     4      5     -8.5 0.0423 -16.7891 -0.2109   True\n",
      "-----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.22150476 0.0043826  0.9        0.00684701 0.37028485 0.56815032\n",
      " 0.2833891  0.04957685 0.9        0.04233805]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fcbd85ccb90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in the data and put into a data frame\n",
    "datasetPrefix = '20210209-2'\n",
    "df = getCountDFbyPrefix(datasetPrefix)\n",
    "\n",
    "# Annotate the data set\n",
    "# dataset name: 20210209-D193-D267-purified-components-7dpt\n",
    "# new folder name: 20210209-2\n",
    "#\n",
    "# All spheroids were 1-to-1 mixture of D193 (sg-Control) and D267 (sg1-Ecad) cells\n",
    "#\n",
    "# B2-B11: (1 row) LMN-R 7.5% or 450 μg/mL (from 6 mg/mL; R&D 3446-005-01)\n",
    "# C2-C11: (1 row) LMN-R 5% or 300 μg/mL (from 6 mg/mL; R&D 3446-005-01)\n",
    "# D2-D9: (8 wells) LMN-S 450 μg/mL (stock 1.12 mg/mL; desalted from Sigma CC095)\n",
    "# E2-E6: (5 wells) LMN-R 5% or 300 μg/mL + Col4-G 150 μg/mL\n",
    "# F2-F6: (5 wells) LMN-R 5% or 300 μg/mL + Col4-G 150 μg/mL + Nidogen 50 μg/mL + Perlecan 50 μg/mL\n",
    "\n",
    "# excluded: C8, D8, E2, F5\n",
    "# reason: external fiber presence\n",
    "\n",
    "groups = [\"1\"]*10 + [\"2\"]*9 + [\"3\"]*7 + [\"4\"]*4 + [\"5\"]*4\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "# quick visualization\n",
    "sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# tukey test\n",
    "tukeyTest(df.counts, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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",
      "     a      b     14.8  0.001  10.2387 19.3613   True\n",
      "     a      c     20.0  0.001  15.4387 24.5613   True\n",
      "     a      d     13.7  0.001   9.1387 18.2613   True\n",
      "     b      c      5.2 0.0202   0.6387  9.7613   True\n",
      "     b      d     -1.1    0.9  -5.6613  3.4613  False\n",
      "     c      d     -6.3 0.0036 -10.8613 -1.7387   True\n",
      "-----------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.001      0.001      0.001      0.02022216 0.9        0.00362311]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fe0b2fa96d0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read in the data and put into a data frame\n",
    "datasetFolder = '../data/DLD-1-spheroids-bud-count-KM/'\n",
    "datasetPrefix = '20210125-2-'\n",
    "df = getCountDFbyPrefix(datasetPrefix, datasetFolder)\n",
    "\n",
    "# Annotate the data set\n",
    "# dataset name: 20210125-D193-D267-mAb13-mAb16-varying-mixing-ratios-6dpt-singleZ2\n",
    "# new folder name: 20210125-2\n",
    "#\n",
    "# These are D193 and D267 with varying mixing ratios.\n",
    "# Note that the control (row D) is shared by the two experiments.\n",
    "#\n",
    "# D2-D11: sg-Control (D193) : sg1-Cdh1 (D267) = 1:1\n",
    "# E2-E11: sg-Control (D193) : sg1-Cdh1 (D267) = 0:1\n",
    "# F2-F11: sg-Control (D193) : sg1-Cdh1 (D267) = 4:1\n",
    "# G2-G11: sg-Control (D193) : sg1-Cdh1 (D267) = 1:0\n",
    "\n",
    "# groups = [\"3\"]*10 + [\"4\"]*10 + [\"2\"]*10 + [\"1\"]*10\n",
    "groups = [\"c\"]*10 + [\"d\"]*10 + [\"b\"]*10 + [\"a\"]*10\n",
    "\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "# quick visualization\n",
    "sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# tukey test\n",
    "tukeyTest(df.counts, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x7fe0b38c0650>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 210x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the bar graph to put in figures\n",
    "outputPrefix = \"20210125-D193-D267-varying-mixing-ratios-6dpt-\"\n",
    "outputFigPath = '../jupyter_figures/' + outputPrefix + 'bud-count-bar.svg'\n",
    "\n",
    "plotCountBar(df.groups, df.counts, outputFigPath,\n",
    "             yMax=32,\n",
    "             yTicks=[0,15,30],\n",
    "             yLabel='Buds per spheroid',\n",
    "             plotting_order=['a', 'b', 'c', 'd'],\n",
    "             bar_width=.7,\n",
    "             fig_width=.7, fig_height=.9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
      "     1      2    -18.4  0.001 -23.3014 -13.4986   True\n",
      "     1      3     -1.5    0.9  -6.4014   3.4014  False\n",
      "     1      4     -4.3 0.1171  -9.2014   0.6014  False\n",
      "     1      5    -18.3  0.001 -23.2014 -13.3986   True\n",
      "     1      6     -4.8 0.0582  -9.7014   0.1014  False\n",
      "     2      3     16.9  0.001  11.9986  21.8014   True\n",
      "     2      4     14.1  0.001   9.1986  19.0014   True\n",
      "     2      5      0.1    0.9  -4.8014   5.0014  False\n",
      "     2      6     13.6  0.001   8.6986  18.5014   True\n",
      "     3      4     -2.8 0.5398  -7.7014   2.1014  False\n",
      "     3      5    -16.8  0.001 -21.7014 -11.8986   True\n",
      "     3      6     -3.3 0.3626  -8.2014   1.6014  False\n",
      "     4      5    -14.0  0.001 -18.9014  -9.0986   True\n",
      "     4      6     -0.5    0.9  -5.4014   4.4014  False\n",
      "     5      6     13.5  0.001   8.5986  18.4014   True\n",
      "------------------------------------------------------\n",
      "p values of all pair-wise tests:\n",
      " [0.001      0.9        0.1171272  0.001      0.05821015 0.001\n",
      " 0.001      0.9        0.001      0.53976593 0.001      0.36259172\n",
      " 0.001      0.9        0.001     ]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<statsmodels.sandbox.stats.multicomp.TukeyHSDResults at 0x7fe0b1e8ba10>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/fW7R05/T4f7snPgzjYt16v72AP/3pQ08VbqDAWkefrpwPBdMG+l0LDlKm+s3c/N7N1O+p5xpudO4Y9Yd5GW4Z2vaxrfeouaXt+GrqSFr4ULyfvHvJKSlOR1LujhQv5fX7r+bz9Z/Qs6oAs669gfkFIzmzYcfpOzt5aSmZzD7G1cwYc581i5/hfeeegxfSwuTzzybOZd9C5PQP/fQYX0WY8w8Y8xSY8z/Msa8Yox5xBgzKZzPcSweX1XBnz6soM0foLaxlX95Zi2Ve5ucjiVH6aZ3b6J8TzkQnG+/9cNbHU4UPu2Njey84cbgzox+P/tffpm6B7X1bjR665ElweWK1lJbsZ2Xf3MXa5e/ytrlr9Du83GwoZ7//v09bF/7MX9dej/N+/fhb2tl9X/9hb+veLvfch6x1I0xc4wxQ4wxvzbGXNDLuWOBqUAqMB3YBRwEPu3h3MXGmFJjTGltbe2xpz9KayoaQo4DFtbt2Bfx55Uvr7W9lY31G0PG1teudyhN+LVu2oRtCr3BaFm3zqE0ciTVm0P/O2yoqWZHeeh/i9YG2PzRB90eW9XlsZHU2536FcAcoBw480gnWmu3WGt/1XH4LHAN8ELHNbqeu8RaW2ytLc7Jyelr5j4rGR26fDEpwTC1IDvizytfXkpiChMGhy4Rm5o71aE04ZdywngSMjJCxtKmTXMojRzJiPEnhRwPGj6Sgsmh38CVkJjI+JmnYUxotY7s8thI6q3Uk4FFwCpgSB+uWwy0A/VEwbz9xcWjuPq0MQxI81A42Mu9l04hb4DmLGPFXafdxbTcaaQlpTF7xGxumXGL05HCJjEjnZH33kPK8ceTkJFB9kUXMfjb33Y6lvTg9CsWM7bkFJJSUsgbN56v/fCnTDp9AcVfO5/U9Ayyh+VxzvX/wqiTJrHwOz8gKyeXtMwsZlxwKeNPndNvOY+4pNEYMwXYCzQCE6y17/Z6QWP+BDwHnAtY4EZrbf3hzteSRhGRvjvcksYe76KNMcOBO4HJwFrAALlAr185Y629rOO3zx1zWhEROSY9lrq1tgq43Bgz01r7fj9nEummfE85ZXVlFOcWMyZ7jNNxRDo1H2hk6+pVpGZkMLqomIREZ3d/7W2++2xjzDWAH8Ba+63IRxIJ9eiGR/lVafA9+ASTwO2zbuecMec4nEoE9u3exWM/v4Hm/cHVdPkTT+brN9/m6DYkvb1R2mqtvdxa+y0VujihPdDOg2sPrdsO2AAPrH3AwUQih6x57eXOQgeoKFvXbZljf+vtTv10Y0w20ARgrXXPsgOJCQECtLa3how1+5sdSiMSyt/W1m3M19baw5n9p7c79W8B9wJLO36J9CtPgocLT7gwZOzS8Zc6lEYk1KR5C0j8wrccDRo+koJJRc4FovcljQ8TXJY4EPBYa8P+5ZBa0ii9CdgAr2x7hQ11GygZVsK8/HlORxLpVFexnfIVb5OWkcnEeQtIy+ifL3H50lvvGmN+Y639QbiDqdRFRPquT+vUv/CgXxC8U/cA+RHKJiIiYdLbG6VLgUFAHbAj8nFEumtoaeDeNfeyYc8GSnJL+O6U75KWpG0eRHrSW6lfAXy+41YVcHtE04j04KYVN7Fi5wog+CGkRl8jt850z/a7IuHU2+qXwdba71lrv0fwzVKRftXW3sZ7O98LGXuz4k2H0ohEv97u1JOMMT8nWP7efsgjEsKT4GF4xnB2HtjZOVaQVeBgIpHo1tud+hDgTeB5dKcuDjDGcMuMWxiQMgCAod6h3DT9JodTiUSv3u7UG4CVHb/X97+JI2aOmMkbF77BjsYdFGQVkJTg+Bb9IlGrt/93LAMe7DjvD5GPI9KzlMQUjss+zukYIlHviKVurV3JoTv1mLe5ppGsNA+5WalORxEJ4d+7F39tLSnjxjm6w5/Evrj4OXZfs49vPryKjysaSDBw5azR/Pyc/vvOQJEj2bN0KbvvuRd8PpLHHkf+smV4cnOdjiUxqrc3Sl3h0fe383FFAwABCw+9u40NVfuO/CCRfuCr2c3uu38DPh8AbVs+pe4BbS0sxy4uSr1ib/f3eCt7GBPpb76qndDeHjpWqQ9vy7GLi1L/6qRhIcdZqUnMHDvEoTQih6RNnIhn+PCQscwFZzqURtwgLubU543P5Z5Linjyo0oGepP57uljyUr19P5AkQgzHg/5D/+Buvvvx1dVTdY5X2XgRRc5HUti2FFvvRsp2npXRKTvDrf1blxMv4iIxIu4mH4RiWbtjY00PPlkcPrl7IV4S0qcjiR9ULN1C+XvvElqRiaTzzwb74BsR/Oo1EUcZK2l4sqraFm3DoD6xx9n5O/vJ3PuXGeDyVGp3rKRJ275CYF2PwDl77zJol/fT5LHuffsNP0i4qCW8vLOQgfAWhqefMq5QNInZW8t7yx0gIaaairWf+JcIFTqIo5KTE/vNpaQkeFAEjkWyWnddyRPTnX2W7lU6iIOSi4sZMD553ceJ2RmMviqKx1MJH0xZeG5pA8c1HlcWDSNESdOcDCRljSKRIWm1avxVVWTMXsWidnZTseRPmhrbmLrmlLSMrLInzS53zZkO9ySRr1RKhIFvNOmwTSnU8ixSE7zMn7maU7H6KTpFxERF1Gpi4i4SFinX4wx84BvAK8ACwn+pXGNtdZ/xAeKiEhYhO1O3RgzFpgKpAIXWWsXA+8A83s4d7ExptQYU1pbWxuuCCIicS9spW6t3WKt/VXHoa/jf6uBvB7OXWKtLbbWFufk5IQrgohI3IvUnPrnu/7nAbsi9BwiItJFpJY0Pm2MeQgwwNUReg4REeki7KVurb2s47f/Fe5ri4jIkWlJo4iIi6jURURcRKUuIuIiKnURERdRqYuIuIhKXUTERVTqIiIuolIXEXERfUmGiMiXsOFvb1D21nJSMzKYcf4l5I4Z62gelbqIyDHaUrqS1+6/u/O4csN6rvrtMlLTnfvycE2/iIgcoy2rPgg5bm06SGXZOofSBKnURUSOUfawbjuL9zjWn1TqIiLHaMrCrzFi/AQATEICJed9nZyC0Y5m0py6iMgxSvF6ueTWu9hbtZMUr5f07IFOR1Kpi4h8WYOGj3A6QidNv4iIuIhKXUTERVTqIiIuolIXEXERlbqIiIuo1EVEXESlLiLiIip1EREXUamLiLiISl1ExEVU6iIiLqJSFxFxEZW6iIiLqNRFRFxEpS4i4iIqdRERF1Gpi4i4SERK3RhTZIx5xxjziDHm9Eg8h4iIdBepO/XpwC6gHdgQoecQEZEuIlXqK4BvA3cBN3b9h8aYxcaYUmNMaW1tbYQiiIjEn0iVelHHtevp4cutrbVLrLXF1trinJycCEUQEYk/3Qo3TLYD9wE+4NYIPYeIiHQRkVK31r4PvB+Ja4uIyOFpSaNIFGj66CP2vfQS/vp6p6NIjIvU9IuIHKWqm37GvuefByAhM5OC//wjqePHO5xKYpXu1EUc1LZ9e2ehAwQaG9mz7A8OJpJYp1IXcVD7wYPdxgIHDjiQRNxCpS7ioLQJE0idfPKhAWMYeMnFzgWSmKc5dRGH5S9bRsOTT+Krqibr7IV4i4udjiQxTKUu4rDEjAwGX3ml0zHEJTT9IiLiIrpTjzUH6+Ct26GmDI6bB7N+BAmJ8N49sOm/IWcczP0ZZOU5nVREHKBSjzVPXwHb3w3+vnIltB2A5Ax4+46OsQ9h13pY/LZTCUXEQSr1WNK091Chf678xWCpf1HVGmiohOxR/ZdNRKKC5tRjSUomeIeEjg0sDP4KOS8LvIP7K5WIRBGVeixJ9MA5vwJPevA4Mw8W/BLO+DcYkB8cS0qDs++CZK9zOUXEMZp+iTUT/jccNx/qt8HQk4JFD3D9GthdDtn5kJbtaEQRcY5KPRalZkHe5NCxxCTIO7nn80Ukbmj6RUSOSXt7gD1VB/D72kPGG2qaaDngCxk72NDK/j3NIWOtzX7qdx3EWtvrNeXo6U5dRPqsZvt+Xv39Og7uayMlPYkFV05gaH4WL/9uLTXb9pOQaCg5dzTTFhbw9p/+Qfn71WBhTFEOC66awD8+qGbFU5vx+wIMzEvn3O+dTHOjr9s180/SG/59pVIXkT5798lNHNzXBkDrQT9vP7aRsdOGUrNtPwCBdsvKF7eSPiCZ8veqOx+39ZNaNqyo4oNnt+D3BQCorz7IRy9to76mqds1//mXp2CM6edXF9s0/SIifbZvd+hUSuPeFvbVNIWeZGH3Z43dHltX2dhZ6J9r2N3U4zUDfov0jUpdRPpsdFHo5yUKJgxmzNShIWNpmR4mnjaChKQv3GkbOPHUPLJzQ5fcji7K6fGaiR5VVF9p+kVE+mz2xeNI8Xqo2lRPTkEWM84bQ2q6B19rOxs/rMY7IIWSc0YzeEQGX/veZNa8XkG7P8DJ80aRNyabc783mZUvbmVfbTPHTclhyhn5+P2BbteUvjNffOfZCcXFxba0tNTRDCIiscYYs9pa223zff1sIyLiIpp+ccLWt4Pb5A4ZB0X/B5KSoXYTrH08+PH+qVdARk5wA6+PH4Xmejj5YsidAO1+WPcE7OrYenfcgshdU+QItn5Sy85N9QwtyGJcSS4mwVBb0cimj2rwZiVz0qzhpKQlcbChlfL3qmj3BzhxZh4Dcrz429r5+/vV7KttZkxRDsOPzz7sNaVvNP3S3z75M/zl2kPHJ50Hp/8clswFX8fqgex8WPwOLDsT9mwOjiWmwJWvw6ol8Mljhx7/1V9BcvrRXfPqd2DpUV5z+rfD/crFRVa/tp0P/7K18/jk00dy3NShvHD3GgKBYKcMGZXBP11fxBO/XEVTx1LF5LQkLv55CX/780YqyvcGH2xg4eKJNNQ0dbvm7IvH9d+LijGHm37RnXp/W/VQ6HH5i8GdF31fWA7WUAHv/eZQ+QK0t8JHD8HaJ7pcb0n3rXfLXwRvTvdrrujDNVXqcgTr394Zcrzh3SqaG9s6Cx2grvIAa5ZXdBY6QFuzn7VvVh4qdAAbvF5DlyWRG96tYubXx5KYqFnivtC/rf7m6bJ7YkISJGd2Py8lq/tYcgYkJne/Xk/XTEk/umt6DnNNkSNISk7odpyUktjtvOTUHsbSkqDLrIonJbHHa+qDR32nUu9vp90QWqIzroUZ10Dm8ENjI6fDqdfD6DmHxtJzgufO+uGhsYQkmPOTnq/5lWu7X3NmD9c85TDXFDmC6eeO5ot9W/zVQorOyCfFe+iH/8KTh1B0Rj5DRh36STJrSCqTTx/FhFmH/ttM8iQw9ayCHq+ZoDn1PtOcuhMaKuDTtyDnBMifERxr2Q8bXw3Oj487K7ilbqAdNi8Pvql5wkJIGxg8d8dqqFkPo0+DQWMid02RI6jfdZCqzQ0MLcgiJz/402bzgTa2r9uDd0Ay+ScOwiQY2n0Btq+vo709wOiTc/B03NHv3FTPvtpm8k8aTMbAlMNeU3p2uDl1lbqISAzSOnURkTigUhcRcZGIlLoxZqYx5tGOX9mReA4REekuUnfqi4GrgWXAxRF6DhER6SJSpZ5orW0BqoG8rv/QGLPYGFNqjCmtra2NUAQRkfgTqVJvMsakECz0XV3/obV2ibW22FpbnJOTE6EIIiLxJyJLGo0x04DrAA9wtbX2wBHOrQU+C3uIwxsC1PXj8/U3N78+N7820OuLdf39+gqstd3uih1fp97fjDGlPa3tdAs3vz43vzbQ64t10fL6tKRRRMRFVOoiIi4Sj6W+xOkAEebm1+fm1wZ6fbEuKl5f3M2pi4i4WTzeqYuIuJZKXUTEReKu1I0x84wxS53OEW7GmPnGmD8aY54xxkx2Ok+4GWOmGWP+3LGfUK7TecLNGDPUGOPKPaiNMUXGmHeMMY8YY053Ok+4GWNOMsY8aIy53xgz0ek8cVXqxpixwFQg1eksEeAFFgG3AQsczhIJqcB3gP8CTnE4S1iZ4He2/Zj+/RBef5pO8JPl7cAGh7NEwrVAFZAIbHc2SpyVurV2i7X2V07niARr7UsEi/164FGH44SdtfY94CTgRmCNw3HC7RrgMaDZ6SARsgL4NnAXwT8/tykA7gOeBi53OEt8lbqbGWMGA/cAt1hrdzudJ9yMMSVAKXA2wS0o3ORMgruaTjfGXOh0mAgoItg19UDSkU+NSbuBg8BeoqBT3fgvOF7dDeQAdxhj/mKtfcbpQGGWBfyB4PfQ3+dwlrCy1p4PYIz5k7X2aafzRMB2gn9mPuBWZ6NExAPAQwRf348dzqJ16iIibuL4jwoiIhI+KnURERdRqYuIuIhKXUTERVTqIiIuoiWNEneMMdOB7wNtBD/FWUhwueQ3gRagjOAnH8daa5caYx4BHgF+CHwAbCX4pepXEvyk6/ettTX9+iJEDkN36hKPvk1wS4X7O45/A2QCr1trrwVmH+ZxL1tr7yS4DUMewb8UHgEaIphVpE90py7xKLnjfz//kMYBgjc4nx8bwA8kdezLMuAL4xD8QvV1BO/orwVSgBcinFnkqKjUJR4t7fjlAS4A/gS8Dvy+Y2rmLWA98DNgPMGPgANcbIw5GXiO4J36YoKbVP2jX9OLHIE+USpxxxhzHjADGAz8P2vtlqN4zFw65tgjm07ky1Gpi4i4iN4oFRFxEZW6iIiLqNRFRFxEpS4i4iIqdRERF/kfuQKDWLJpD8EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# combine 3 data sets:\n",
    "# 1. before dispase treatment\n",
    "# 2. 1 day after dispase treatment and MG-free culture\n",
    "# 3. 6 days recovery after MG addition\n",
    "\n",
    "datasetFolder = '../data/DLD-1-spheroids-bud-count-KM/'\n",
    "\n",
    "# 20210204-D193-D267-DMSO-or-ABA-all-MG-7dpt\n",
    "# F2-F11: 10 spheroids of D193-D267 mix\n",
    "# G2-G11: 10 spheroids of D267 only\n",
    "#\n",
    "datasetPrefix = '20210204-'\n",
    "df = getCountDFbyPrefix(datasetPrefix, datasetFolder)\n",
    "df1 = df\n",
    "\n",
    "# 20210205-\n",
    "#\n",
    "# * Note well position change because the spheroids were\n",
    "# * cultured in a new plate after dispase treatment\n",
    "#\n",
    "# B2-B11: 10 spheroids of D193-D267 mix\n",
    "# C2-C11: 10 spheroids of D267 only\n",
    "#\n",
    "datasetPrefix = '20210205-'\n",
    "df = getCountDFbyPrefix(datasetPrefix, datasetFolder)\n",
    "df2 = df\n",
    "\n",
    "# 20210211-\n",
    "#\n",
    "# * Recovery was by adding 100x 2x treatment mix on 2/5\n",
    "#\n",
    "# B2-B11: 10 spheroids of D193-D267 mix\n",
    "# C2-C11: 10 spheroids of D267 only\n",
    "#\n",
    "datasetPrefix = '20210211-'\n",
    "df = getCountDFbyPrefix(datasetPrefix, datasetFolder)\n",
    "df3 = df\n",
    "\n",
    "df = pd.concat([df1, df2, df3])\n",
    "\n",
    "# annotate groups\n",
    "groups = [\"1\"]*10 + [\"4\"]*10 + [\"2\"]*10 + [\"5\"]*10 + [\"3\"]*10 + [\"6\"]*10\n",
    "df[\"groups\"] = groups\n",
    "\n",
    "# quick visualization\n",
    "sns.swarmplot(df.groups, df.counts)\n",
    "\n",
    "# tukey test\n",
    "tukeyTest(df.counts, df.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x7fe0b2f10c10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 150x270 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the bar graph to put in figures\n",
    "outputPrefix = \"20210204-11-dispase-recovery-D193-D267-mix-\"\n",
    "outputFigPath = '../jupyter_figures/' + outputPrefix + 'bud-count-bar.svg'\n",
    "\n",
    "# Filter to plot only one data set (the other set is D267 only)\n",
    "df = df[df.groups.isin(['1', '2', '3'])]\n",
    "\n",
    "plotCountBar(df.groups, df.counts, outputFigPath,\n",
    "             yMax=31,\n",
    "             yTicks=[0,10,20,30],\n",
    "             yLabel='Buds per spheroid',\n",
    "#              xticklabels_angle=45,\n",
    "#              xTickLabels=['Before dispase', 'After dispase', 'Recovery 6 day'],\n",
    "#              xLabel_off=True,\n",
    "             bar_width=.7,\n",
    "             fig_width=.5, fig_height=.9)\n"
   ]
  },
  {
   "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": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}