analyses/pre-post-activation-cell-counts/Cell counts pre- and post- activation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n",
      "Attaching package: ‘tidyr’\n",
      "\n",
      "The following object is masked from ‘package:magrittr’:\n",
      "\n",
      "    extract\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library('readr')\n",
    "library('magrittr')\n",
    "library('dplyr')\n",
    "library('tidyr')\n",
    "library('ggplot2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Donor</th><th scope=col>Day</th><th scope=col>Condition</th><th scope=col>Cell_count_avg</th><th scope=col>Cell_count_sd</th><th scope=col>Live_percent_avg</th><th scope=col>Live_percent_sd</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Donor 8       </td><td>Day 1         </td><td>Thawed        </td><td>2100000       </td><td> 14142.136    </td><td>98.0          </td><td>0.0000000     </td></tr>\n",
       "\t<tr><td>Donor 8       </td><td>Day 2         </td><td>Pre-activation</td><td>2340000       </td><td> 14142.136    </td><td>94.0          </td><td>0.0000000     </td></tr>\n",
       "\t<tr><td>Donor 8       </td><td>Day 4         </td><td>Debeaded      </td><td>2075000       </td><td> 77781.746    </td><td>96.5          </td><td>0.7071068     </td></tr>\n",
       "\t<tr><td>Donor 9       </td><td>Day 1         </td><td>Thawed        </td><td>1715000       </td><td>  7071.068    </td><td>95.5          </td><td>0.7071068     </td></tr>\n",
       "\t<tr><td>Donor 9       </td><td>Day 2         </td><td>Pre-activation</td><td>1820000       </td><td> 14142.136    </td><td>94.0          </td><td>0.0000000     </td></tr>\n",
       "\t<tr><td>Donor 9       </td><td>Day 4         </td><td>Debeaded      </td><td>1270000       </td><td> 14142.136    </td><td>90.5          </td><td>0.7071068     </td></tr>\n",
       "\t<tr><td>Donor 10      </td><td>Day 1         </td><td>Thawed        </td><td> 818000       </td><td> 53740.115    </td><td>94.0          </td><td>0.0000000     </td></tr>\n",
       "\t<tr><td>Donor 10      </td><td>Day 2         </td><td>Pre-activation</td><td> 601000       </td><td> 12727.922    </td><td>90.0          </td><td>0.0000000     </td></tr>\n",
       "\t<tr><td>Donor 10      </td><td>Day 4         </td><td>Debeaded      </td><td> 478000       </td><td> 36769.553    </td><td>85.5          </td><td>3.5355339     </td></tr>\n",
       "\t<tr><td>Donor 11      </td><td>Day 1         </td><td>Thawed        </td><td>2580000       </td><td>296984.848    </td><td>97.0          </td><td>1.4142136     </td></tr>\n",
       "\t<tr><td>Donor 11      </td><td>Day 2         </td><td>Pre-activation</td><td>2320000       </td><td>325269.119    </td><td>88.5          </td><td>4.9497475     </td></tr>\n",
       "\t<tr><td>Donor 11      </td><td>Day 4         </td><td>Debeaded      </td><td>2580000       </td><td>311126.984    </td><td>90.0          </td><td>0.0000000     </td></tr>\n",
       "\t<tr><td>Donor 14      </td><td>Day 1         </td><td>Thawed        </td><td>3250000       </td><td>127279.221    </td><td>97.0          </td><td>1.4142136     </td></tr>\n",
       "\t<tr><td>Donor 14      </td><td>Day 2         </td><td>Pre-activation</td><td>2975000       </td><td> 35355.339    </td><td>95.0          </td><td>0.0000000     </td></tr>\n",
       "\t<tr><td>Donor 14      </td><td>Day 4         </td><td>Debeaded      </td><td>2635000       </td><td> 91923.882    </td><td>94.5          </td><td>2.1213203     </td></tr>\n",
       "\t<tr><td>Donor 15      </td><td>Day 1         </td><td>Thawed        </td><td>2965000       </td><td>  7071.068    </td><td>95.5          </td><td>0.7071068     </td></tr>\n",
       "\t<tr><td>Donor 15      </td><td>Day 2         </td><td>Pre-activation</td><td>2830000       </td><td>466690.476    </td><td>94.5          </td><td>0.7071068     </td></tr>\n",
       "\t<tr><td>Donor 15      </td><td>Day 4         </td><td>Debeaded      </td><td>2335000       </td><td> 49497.475    </td><td>90.0          </td><td>1.4142136     </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       " Donor & Day & Condition & Cell\\_count\\_avg & Cell\\_count\\_sd & Live\\_percent\\_avg & Live\\_percent\\_sd\\\\\n",
       "\\hline\n",
       "\t Donor 8        & Day 1          & Thawed         & 2100000        &  14142.136     & 98.0           & 0.0000000     \\\\\n",
       "\t Donor 8        & Day 2          & Pre-activation & 2340000        &  14142.136     & 94.0           & 0.0000000     \\\\\n",
       "\t Donor 8        & Day 4          & Debeaded       & 2075000        &  77781.746     & 96.5           & 0.7071068     \\\\\n",
       "\t Donor 9        & Day 1          & Thawed         & 1715000        &   7071.068     & 95.5           & 0.7071068     \\\\\n",
       "\t Donor 9        & Day 2          & Pre-activation & 1820000        &  14142.136     & 94.0           & 0.0000000     \\\\\n",
       "\t Donor 9        & Day 4          & Debeaded       & 1270000        &  14142.136     & 90.5           & 0.7071068     \\\\\n",
       "\t Donor 10       & Day 1          & Thawed         &  818000        &  53740.115     & 94.0           & 0.0000000     \\\\\n",
       "\t Donor 10       & Day 2          & Pre-activation &  601000        &  12727.922     & 90.0           & 0.0000000     \\\\\n",
       "\t Donor 10       & Day 4          & Debeaded       &  478000        &  36769.553     & 85.5           & 3.5355339     \\\\\n",
       "\t Donor 11       & Day 1          & Thawed         & 2580000        & 296984.848     & 97.0           & 1.4142136     \\\\\n",
       "\t Donor 11       & Day 2          & Pre-activation & 2320000        & 325269.119     & 88.5           & 4.9497475     \\\\\n",
       "\t Donor 11       & Day 4          & Debeaded       & 2580000        & 311126.984     & 90.0           & 0.0000000     \\\\\n",
       "\t Donor 14       & Day 1          & Thawed         & 3250000        & 127279.221     & 97.0           & 1.4142136     \\\\\n",
       "\t Donor 14       & Day 2          & Pre-activation & 2975000        &  35355.339     & 95.0           & 0.0000000     \\\\\n",
       "\t Donor 14       & Day 4          & Debeaded       & 2635000        &  91923.882     & 94.5           & 2.1213203     \\\\\n",
       "\t Donor 15       & Day 1          & Thawed         & 2965000        &   7071.068     & 95.5           & 0.7071068     \\\\\n",
       "\t Donor 15       & Day 2          & Pre-activation & 2830000        & 466690.476     & 94.5           & 0.7071068     \\\\\n",
       "\t Donor 15       & Day 4          & Debeaded       & 2335000        &  49497.475     & 90.0           & 1.4142136     \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Donor | Day | Condition | Cell_count_avg | Cell_count_sd | Live_percent_avg | Live_percent_sd | \n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Donor 8        | Day 1          | Thawed         | 2100000        |  14142.136     | 98.0           | 0.0000000      | \n",
       "| Donor 8        | Day 2          | Pre-activation | 2340000        |  14142.136     | 94.0           | 0.0000000      | \n",
       "| Donor 8        | Day 4          | Debeaded       | 2075000        |  77781.746     | 96.5           | 0.7071068      | \n",
       "| Donor 9        | Day 1          | Thawed         | 1715000        |   7071.068     | 95.5           | 0.7071068      | \n",
       "| Donor 9        | Day 2          | Pre-activation | 1820000        |  14142.136     | 94.0           | 0.0000000      | \n",
       "| Donor 9        | Day 4          | Debeaded       | 1270000        |  14142.136     | 90.5           | 0.7071068      | \n",
       "| Donor 10       | Day 1          | Thawed         |  818000        |  53740.115     | 94.0           | 0.0000000      | \n",
       "| Donor 10       | Day 2          | Pre-activation |  601000        |  12727.922     | 90.0           | 0.0000000      | \n",
       "| Donor 10       | Day 4          | Debeaded       |  478000        |  36769.553     | 85.5           | 3.5355339      | \n",
       "| Donor 11       | Day 1          | Thawed         | 2580000        | 296984.848     | 97.0           | 1.4142136      | \n",
       "| Donor 11       | Day 2          | Pre-activation | 2320000        | 325269.119     | 88.5           | 4.9497475      | \n",
       "| Donor 11       | Day 4          | Debeaded       | 2580000        | 311126.984     | 90.0           | 0.0000000      | \n",
       "| Donor 14       | Day 1          | Thawed         | 3250000        | 127279.221     | 97.0           | 1.4142136      | \n",
       "| Donor 14       | Day 2          | Pre-activation | 2975000        |  35355.339     | 95.0           | 0.0000000      | \n",
       "| Donor 14       | Day 4          | Debeaded       | 2635000        |  91923.882     | 94.5           | 2.1213203      | \n",
       "| Donor 15       | Day 1          | Thawed         | 2965000        |   7071.068     | 95.5           | 0.7071068      | \n",
       "| Donor 15       | Day 2          | Pre-activation | 2830000        | 466690.476     | 94.5           | 0.7071068      | \n",
       "| Donor 15       | Day 4          | Debeaded       | 2335000        |  49497.475     | 90.0           | 1.4142136      | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "   Donor    Day   Condition      Cell_count_avg Cell_count_sd Live_percent_avg\n",
       "1  Donor 8  Day 1 Thawed         2100000         14142.136    98.0            \n",
       "2  Donor 8  Day 2 Pre-activation 2340000         14142.136    94.0            \n",
       "3  Donor 8  Day 4 Debeaded       2075000         77781.746    96.5            \n",
       "4  Donor 9  Day 1 Thawed         1715000          7071.068    95.5            \n",
       "5  Donor 9  Day 2 Pre-activation 1820000         14142.136    94.0            \n",
       "6  Donor 9  Day 4 Debeaded       1270000         14142.136    90.5            \n",
       "7  Donor 10 Day 1 Thawed          818000         53740.115    94.0            \n",
       "8  Donor 10 Day 2 Pre-activation  601000         12727.922    90.0            \n",
       "9  Donor 10 Day 4 Debeaded        478000         36769.553    85.5            \n",
       "10 Donor 11 Day 1 Thawed         2580000        296984.848    97.0            \n",
       "11 Donor 11 Day 2 Pre-activation 2320000        325269.119    88.5            \n",
       "12 Donor 11 Day 4 Debeaded       2580000        311126.984    90.0            \n",
       "13 Donor 14 Day 1 Thawed         3250000        127279.221    97.0            \n",
       "14 Donor 14 Day 2 Pre-activation 2975000         35355.339    95.0            \n",
       "15 Donor 14 Day 4 Debeaded       2635000         91923.882    94.5            \n",
       "16 Donor 15 Day 1 Thawed         2965000          7071.068    95.5            \n",
       "17 Donor 15 Day 2 Pre-activation 2830000        466690.476    94.5            \n",
       "18 Donor 15 Day 4 Debeaded       2335000         49497.475    90.0            \n",
       "   Live_percent_sd\n",
       "1  0.0000000      \n",
       "2  0.0000000      \n",
       "3  0.7071068      \n",
       "4  0.7071068      \n",
       "5  0.0000000      \n",
       "6  0.7071068      \n",
       "7  0.0000000      \n",
       "8  0.0000000      \n",
       "9  3.5355339      \n",
       "10 1.4142136      \n",
       "11 4.9497475      \n",
       "12 0.0000000      \n",
       "13 1.4142136      \n",
       "14 0.0000000      \n",
       "15 2.1213203      \n",
       "16 0.7071068      \n",
       "17 0.7071068      \n",
       "18 1.4142136      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prepost_act_counts <- read_tsv(\n",
    "    \"counts.tsv\",\n",
    "    col_types=cols(\n",
    "      Donor=col_factor(levels=c(\"D8\", \"D9\", \"D10\", \"D11\", \"D14\", \"D15\")),\n",
    "      Condition=col_factor(levels=c(\"Thawed\", \"Pre-activation\", \"Debeaded\")),\n",
    "      Day=col_factor(levels=c(\"D1\", \"D2\", \"D4\")),\n",
    "      Count=col_factor(levels=c(\"1\", \"2\")),\n",
    "      `Total volume (ml)` = col_double(),\n",
    "      `Live count per ml` = col_double(),\n",
    "      `Live fraction` = col_double(),\n",
    "      `Total cells` = col_double()\n",
    "    ) \n",
    ") %>%\n",
    "mutate(\n",
    "    `Day`=factor(`Day`, labels=c(\"Day 1\", \"Day 2\", \"Day 4\")),\n",
    "    `Donor`=factor(`Donor`, labels=c(\"Donor 8\", \"Donor 9\", \"Donor 10\", \"Donor 11\", \"Donor 14\", \"Donor 15\"))\n",
    ") %>%\n",
    "group_by(`Donor`, `Day`, `Condition`) %>%\n",
    "summarize(\n",
    "    `Cell_count_avg`=mean(`Live count per ml`),\n",
    "    `Cell_count_sd`=sd(`Live count per ml`),\n",
    "    `Live_percent_avg`=mean(`Live fraction`) * 100,\n",
    "    `Live_percent_sd`=sd(`Live fraction`) * 100\n",
    ")\n",
    "\n",
    "prepost_act_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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0ViCwgggAAC\nCCCAAAKlCaxbNtsWf/dGzCIr5k9275fO/bdtLdgYM6/J7sdZ7cZtY6bxBgEE4gsQIMV3YSoC\nCCCAAAIIIBBYgdULv7Kpr/eJW76fpo41/USng2qOIkCKBuE1AqUIECCVgsMsBBBAAAEEEEAg\niAL1W3W09uc8n3TRGvyyPAkBBJITIEBKzomlEEAAAQQQQACBwAjUqNfKatQ7OzDloSAIZJMA\ngzRk096kLggggAACCCCAAAIIIJCWQMoBkh4UW7lyZfMeGFv0fVqlYWUEEEAAAQQQQAABBBBA\noBwFUu5id/DBB9vmzZsjRT700ENj3kdm8AIBBBBAAAEEEEAAAQQQCJlAyi1IIasfxUUAAQQQ\nQAABBBBAAAEEkhZIuQVJOb/00ks2dOhQmzdvnm3cuNEKCwuLbXDlypXFpjEBAQQQQAABBBBA\nAAEEEAiyQMoB0qRJk+zss8+26tWr23777WdNmjSJ3I8U5IpSNgQQQAABBBBAAAEEEEAgkUDK\nAdILL7xg1apVsy+++MLatGmTKH/mI4AAAggggAACCCCAAAKhEUj5HqSFCxda+/btCY5Cs4sp\nKAIIIIAAAggggAACCCQrkHKApOBIrUcbNmxIdhsshwACCCCAAAIIIIAAAgiEQiDlAKlHjx7W\nvHlzu/nmmxneOxS7mEIigAACCCCAAAIIIIBAsgIp34P03nvvWePGje2uu+6y4cOHW8uWLa1m\nzZrFtvf1118Xm8YEBBBAAAEEEEAAAQQQQCDIAikHSBq+Oz8/3zp06BDkelE2BBBAAAEEEEAA\nAQQQQCBlgZQDpIsvvtj0Q0IAAQQQQAABBBBAAAEEsk0g4T1IP/74o61duzatek+ZMiWt9VkZ\nAQQQQAABBBBAAAEEECgLgYQB0rRp09yQ3iNGjHBd65It1NatW23MmDF24IEH2hVXXJHsaiyH\nAAIIIIAAAggggAACCJSbQMIA6fjjj7dRo0bZ0KFD3eh1PXv2tLfeesuWLVsWU+jCwkKbP3++\nTZgwwYYMGWJt27Y1LXvqqafaxIkTY5blDQIIIIAAAggggAACCCAQRIGk7kE6+uij7dtvv7WH\nHnrI7r33Xnv88cddXapXr+5GsatRo4bNmTPH1q9f76ZXqFDBzjvvPBs0aJC1aNEiiPWmTAgg\ngAACCCCAAAIIIIBAMYGkAiStpSDo6quvtt69e9uHH35oU6dONXW/08+iRYts//33d8FSly5d\n7KSTTrJmzZoV2xgTEEAAAQQQQAABBBBAAIEgCyQdIHmVyMvLsyOPPNL9eNP4iwACCCCAAAII\nIIAAAghkg0DCe5CyoZLUAQEEEEAAAQQQQAABBBBIRoAAKRkllkEAAQQQQAABBBBAAIGcECBA\nyondTCURQAABBBBAAIHcFFi+fLm98cYb9vHHH9vq1avLFSE/P9/mzZsXGdhs06ZN7v2GDRtK\nLFcyy5S4MjO2S4AAabvYWAkBBBBAAAEEEEAgqAJ6/MzNN99s++67rzVu3NhOPPFE69SpkxtE\n7JprrjEFHeWRPvvsM9tpp53sxRdfdJvXo3D0XgGclzQQ2siRI7237nE5RZeJzOSFLwIESL6w\nkikCCCCAAAIIIIBAeQlcfvnldsstt1jTpk3d8zknT55sI0aMsI4dO9qwYcPsrLPOsq1bt5ZX\n8SLbbdSokXXt2tWV05t40EEHmcrrpXjLePP4649AyqPYPf300+6ZSIMHD45boldeecWuvPJK\nmzlzpuk5SSQEEEAAAQQQQAABBMpK4B//+Ic9+OCDdsUVV9h9990X2ewf/vAHu/TSS11L0r/+\n9S8bO3asde/ePTK/PF60b9/e3nnnnZhNb9myJeZ9vGViFuBNxgWSCpCWLl1qmzdvdhv/8ssv\n7dNPP7WffvqpWGG0jJoIf/zxR9d0SYBUjIgJCCCAAAIIIIAAAj4JqFXo/vvvt3322ce1HBXd\nTMWKFe3RRx+1Sy65xNatWxcze+PGja5rm7rBKZ/99tvPevXqZfXq1Yss98gjj1j9+vWtc+fO\n9uSTT5qW1bM/zzzzTBd4RRb87cUHH3xgr7/+ui1cuND0rNBddtklZpE5c+bYM888Y2effbY1\nbNjQHnjgAVP3wM8//9xuuukmu+iii0z3LXnL7LnnnpH1/ShvJPMcf5FUgPTEE0/YddddF0PV\nsmXLmPfRb/TQWH14SAgggAACCCCAAAIIlJXAt99+6wKf0047zapWrRp3swqeJk2aFDPv559/\ntkMPPdT094gjjnDrqovevffea6+++qqp25uS7g1SvjfeeKNb9sADD7TXXnvNtVSNGTPGTj/9\n9Ei+d911l/Xt29cFWrvuuqtdffXV1rp168h8vVCANHDgQBfQaRvvv/++m79o0SL3Wl0B58+f\nH1nGC5D8KG9MwXL8TVIB0lVXXWVq7isoKLD33nvPjbbRo0ePYnR6iKwCI0XRJAQQQAABBBBA\nAAEEylLgk08+cZvzAolkt92zZ09bvHixqcVHXfGUZs2aZX/84x9N57zqQaXzXKUPP/zQ/v73\nv7t7nNRbavbs2aZAaejQoZEASfmoq99ll13mWrQqVKjgWpEOO+wwl0e8XzvvvLM7z1YrlwaV\nUEuXkgKkoinT5S2af66/TypAqly5svXv399Z7bHHHjZ9+nTX7JfreNQfAQQQQAABBBBAIDgC\nP/zwgyuMBmdINi1YsMDeeust6927dyQ40rq7776760Gllp///ve/roucpqsF6bbbbrMqVaro\nrbVp08a1En3//ffuvX7pnvxKlSrZrbfeagqOlNQVr1+/fnbxxRe799v7y4/ybm9ZsnW9lEex\nUx9JNTmSEEAAAQQQQAABBBAIksDee+/tivPdd98lXawZM2a4Zb2Wo+gVDz74YPdWg495qVWr\nVpHgyJvWpEkT0z1BXvr6669Nt6M0aNDAm+T+el31Yiam+MaP8qZYhKxfPOUASSIvvfSSuxGt\nRYsWbserW13Rn6yXo4IIIIAAAggggAACgRJQVzcl9XYqLQ0fPjzy7CE9SFapTp06xVapVauW\nm6bbTLxUo0YN72Xkr1qJNLiCl5Snt643TX+LBkzR85J97Ud5k912riyXVBe7aAzd1KZWJPW5\n1Ogeipi9psPo5XiNAAIIIIAAAggggEBZCrRt29Zq167tRo7T4Afxgh51w+vTp48bjOGEE04w\nDaCg5HXPiy6vN00DkKWStLy62RVNGlwh3eRHedMtU7atn3KA9MILL1i1atXsiy++cH0usw2E\n+iCAAAIIIIAAAgiEU0ADHOjZRxpY4bzzznMj0EVfyNcjaTRPrT1/+9vfXCXbtWvnekJp2G49\nOyl6+ccff9wtk2qApK55ym/8+PF2yimnRDA10l2ipHuXvMfrxFvWj/LG204uT0u5i53GcdcD\nq3RDGgkBBBBAAAEEEEAAgSAJ/PWvf7Urr7zS9DBYdbm7/vrrXWvOPffcY7pHSQMuKDjSUOBK\n6gqnQRd08V/TPv74Y/ccIj0rSUN833HHHTHPQkqmrhdeeKFpxDr9ffbZZ23atGl2++23R0am\nKy0P3baiUaP1TKR4I9j5Ud7SypOL81JuQVJwpCbLDRs2WLw+mJlE1ANn1aVP/TU7deoUty9n\nOtvTQ8C++uor109Vo/N16NChWHZ+l6HYBpmAAAIIIIAAAgggkJbAkCFDXPe6hx9+2AU4XmYa\nYEHDZytwiU4ajlu3j+i5nzrnVNIodsOGDTM97ibVpBHuXn75ZdNw3HrYqwZwaN68uT311FOm\nZxuVlm644QY3RPjll1/uzrV32GGHYotnurzFNpDjEyr80sT4+x1lSWAsW7bMRcRqLowe4jCJ\nVVNaRE8M1sO49LAu9dfUU4R1Q52i6kwkBUeXXnppZEz6jz76yD0VWUM5eimdMixZssQ9hdnL\nK97f2v2ujTc51NPW/nNIsfIfMKVrsWlhn/Blhwlxq3DZk83iTg/rxAd6LExYdA1bmiitXLnS\nNm3aVOpiuXI8CCHbjolcOR607xIdE8kcD8pHvTFKSxwPpekEex7Hw+/7RxfS69at+/uEcni1\nbds200NX9YwjDS6me+cTJbXaqJubAppMJAVHOpf17h1KJk+dp65YscIaNWoU0+Uv3rqZLm+8\nbeTatJRbkNTk17hxY9PTgRWwaAjDmjVrFnPT8Ibbm9Rq88QTT7inF6vPpx5Sq2BG/Tb1N5Wk\noRDfeecd19Qavd7YsWPdk5aVp8o/b948O//8892DuXSDXybLEL1dXiOAAAIIIIAAAgiUjYDu\nSVKgk0qwo1amTCa1TKUSHGnbCtB0vp1MynR5k9lmti+T8j1IuhKs1hx1R9t3331d9zc9MKvo\nTzpwn376qfsgezfE6cnFxx13nE2Y8PtV+/Xr17sm0muuucY9hOuzzz6Lu0ldMZg4cWKxeXoK\ncteuXSPBXevWrV2/VG8byZShWKZMQAABBBBAAAEEEEAAgVALpNyCpKf/pvsE4ERi6nqgZtDo\npMhf3fvUVKoATX061fJz0kknmVqJ+vbt634USCWTtI2iVxP0Xl3jlBKVQVckvKTWJ++hXd40\nDYGu0f5yLeVKnalnap9sHS+5YhYtkyt1zpV6at9mqq6Zyif68xb017lS51ypZzLHQ/RocEH/\nfFI+BKIFUg6Qolf267X6ihYdt15j2is4Wr16tb322mumh2TpHiWN5HHqqaeamhc12sexxx5r\nP/30k2tVUvnWrl1rq1atMo1EoqTuc71793bBVtFt6P2sWbPcconKEH0vlLodDho0yK3n/Xrr\nrbcsUV/0dd7CWfQ32iWLqlWsKtSzGEmpEypXrpxwUBeOh1IJAz0zV44H7YRM1TVRPhwPgf7I\nl1q4RPu21JVDNjNRXTWgFwmBMAoEMkDSyZTuO4pO3nvd8KfWGo1sN2rUqMgiS5cuNXX/UwuQ\ngqljjjnGzZs9e7ab7r1Xf07169QVbS9PLxO99+6nSlQGbx39VXdDjXoSnTR6yZo1a6InFX99\n34PFp2V4ivq9qi4KFFMcj2O7ShKvzrOOnLRdeaWykvap9p1aF/Xjd4pXT23zqcv93rK5QEPd\nTksqQyZLkOgjrG0VvdAQb/s6thKWtwyOBx2XurqrL+2ix3+8cqc7raQ6l8Uxof2iOpbFCUpJ\n9SyL40H7U/t13bp17iJauvss0fqJjolkjgdtoySzyPbL4HjQ/xF9p2oAldKeuRIpU5ovSqpz\nWRwP+n7Q976+C/1OJdWzLI4Hfd/re1+DAhQUFPhd1V8+x6Vvghak0n2YG1yBlAMkDXd47733\nJqyRup1tb9KIHd6Ti7089A9HVyp0r5P+welLMfrA06gkeiCY/gFqxJTTTz/drTrxl/uPJk+e\nHHnv5acAq+g/Sm3DG0oxURm8fPR3r732cj/R0xSo6T6p8k46cdA/TJ0kqQUuW5PqqC9AfSEE\nwd1PZ6/7RlDqmcwJoUbjSTSKnZ9mXt76nyE/laUsAmlvu+XxV/tF7kH5nPhloIsj+j+nfVoW\nJ4SJ6pHM8aA8grBf9H2qACkX/m8qaNDxHwT3RJ+hdOarnvpRwFsWF0cSlVWfr7JMuhAcfW5Y\nlttmW9klkHKApMBB48JHJ30Ja9Q3BUUKYs4999zo2Sm/3nnnnU1d1HT1U1e4lPSALe++JP3V\ncIm9evVyAZHmq1vd1KlTk36Q1y677OLyPPHEE7W6S9OnT7czzjjDvU5Uht9W4Q8CCCCAAAII\nIIBAQAR0Mbgsegh41dUFGlL2CaQcIP3lL38x/cRL//vf/9w9QInuvYm3bvS0o48+2h588EEb\nPXq0G3pbrUlvvPGG9e/f3y3WrVs3e/31191Q4HrYlq4E6+G1TZs2daPdReelJyZ760VPVyA0\nYMAAN8hDu3btbNy4ce6KywknnOAWS1SG6Lx4jQACCCCAAAIIIFD+AmpF2tzrgjIrSJWnRpfZ\ntthQ2QmkHCCVVjS1ylx//fVuEAQNv62uD9uT1Ox/66232i233OKCJDUXn3baaZEnGyugUXCj\n5zApiFL3qvbt21ufPn2KbU4tXvopmjp27GjnnHOO6SnFWl+tUnpysQZ9UEpUhqL58R4BBBBA\nAAEEEEAAAQTCL5DRAEkcGk1O9/ZocIQ99thju4UOOOAAe+WVV9yTjzWwgu4tik56hpF+NDhD\nvXr1XJATPT+Z1xdeeKG7b0n3HsULohKVIZltsAwCCCCAAAIIIIAAAgiERyCjAZJuCNRQ22o5\n2nHHHTOioG5zpaVknzJcUh7qOxovOIpePlEZopflNQIIIIAAAggggAACCIRXIOUA6dFHH7XH\nHnusWI01Co4GadDziXr06JHwmSfFMmACAggggAACCCCAAAIIIFDOAikHSBo6Mt4wmWo10oAI\nelDrlVdeWc7VYvMIIIAAAggggAACCCCAQOoCKQdIGtRAPyQEEEAAAQQQQAABBBBAINsEUg6Q\nPACNMT/xl4ewfvfdd+4hc/vvv7/pRwMmkBBAAAEEEEAAAQQQQACBMApsV4D0+eefu/uMvv32\n22J1vuOOO+wf//hHselMQAABBBBAAAEEEEAgmwVmzJhhY8eOLbWKeiTO7bffbn/9619tp512\nKnXZ8pp5//332x/+8Af3U15lKM/tphwgrVq1yvSgVrUgDRs2zA4++GD37CA9zPXxxx93D2Wt\nVq2aXXXVVeVZL7aNAAIIIIAAAggggECZCixbtszef//9yDY//vhja9Kkie26666Radddd53d\nfPPN9sc//jGwAdJ9993nbqlRkJSLKeUASaPYKUj64osvbPfdd4+Y7bvvvnbKKafYJZdcYg8+\n+CABUkSGFwgggAACCCCAAAK5IHD44Yfbv//970hVFRidffbZph5WXtq0aZP3kr8BFYh9+moS\nhfz666/tyCOPjAmOole7+OKL3UNif/755+jJvEYAAQQQQAABBBBAAIHfBFavXm19+/Z1I0Bf\nffXVtmjRohibf/3rX3bhhRdaly5d7C9/+Yu9+eabbv64ceNiAi41XPTq1csmTZoUWV/LDB8+\nPPL+6aeftrPOOss1Ztx9992uJ1hk5i8v3nnnHevZs6edeeaZ9vbbb0fPysnXKbcgaThvDfVd\nUvLmbd26taRFcmJ61apVbdu2beVeV3WF1AN89UDcbE4VKlRw9SwsLDR18czmpGNM+zVM9axc\nuXIgdok+Hzoe9H8sTH7bg6d66n9QttdTdVRd8/Ly3H7dHqvyWCcI+6VixYo59X9T3xNBcPfz\n8+Z9FwalrkH53++neTp56x4kBUDHHXecPfTQQ/bf//7XdJ+/0ogRI6xfv36m7nhqmBg/fryd\ncMIJ9umnn1r16tVt4MCB1qdPH/fcUbVYjRw50p3rderUya0/dOhQd0uM3ujxO88++6ypEaN2\n7do2ePBgt61XXnnFLfvWW2/Zn/70J/vzn/9s7du3d2XSc01zOaUcIAnu2muvdTuoaL9EnXwI\nvVGjRtaqVatcdrW6desGqv41atQIVHkoTPoCYdqntWrVSr/C5IBAKQJhOh5Ujfr165dSG2b5\nJRC2z4lfDuQbDIHLLrvMBg0a5Aqj82a13qxZs8bq1KljS5YsMQU5CmqUFLw0btzYPvnkEzdN\nwacCquOPP961/uyzzz72n//8xy2r4EbLaWyAWbNmmQZcGDVqlHXv3t3NP+OMM6xNmzZu/SOO\nOMIFWv3797cBAwa4+Wppir6Nxk3MsV8pB0gXXXSRG5xB0aya8xQkaUdqkIYnn3zS3ZukHUJC\nAAEEEEAAAQQQQACB+AIdO3aMzDjooIPca92iovPqW265xRYuXGjqKjdz5kzTLS4bN2403b+k\nXkrHHnus6wqnAGnChAl25513ui50Wl8tSm3btnU/ajlSA8aUKVNcHt4GdeHys88+cy1Gs2fP\ntqOOOsqbZTvvvDMBUkQjyRdq1vvoo49cP8Xovo1aXVfE1CR4wQUXJJkbiyGAAAIIIIAAAggg\nkHsCCoS8pG6RSgpmlO655x7XxU4tQ4ceeqip1Ucj4nlJA6Op9WnOnDm2dOlS10WuXbt29t57\n79nrr7/u3mtZ3Z+kLsgKqrxtaPrf/vY322uvvWzt2rWuO7a67kenXO8emXILkvCaN2/ubhRb\nsGCBabx3NeVplA7tGLrSRH+8eI0AAggggAACCCCAQPICainSvUdDhgxxgYzW1L39559/fuT+\n9hNPPNHdK6TeWxouXAHN0UcfbbqfSD8adEFpt912s4KCAjv55JPNuz9JeT311FOulWiHHXYw\n/Wh59Q5TUsD1zTffuNe5+qvi9lRcN8VquO/p06db165d7ZxzzrEff/zRTjrppMgIG9uTL+sg\ngAACCCCAAAIIIJDLAmrxadiwoRvVzhuIpnfv3pafn++62MlG89WypJYmBUZK+vv8889bzZo1\nrUOHDm5a586dXVc73V80bdo0t76ewaQAzGvB6tGjhxvEYeLEibZy5Uq76aabIi1ZLpMc/JVy\ngKQo9MADD3Q3iKlZz0saFUr9GxXRqr8jCQEEEEAAAQQQQAABBFITUGuQBj1TsKOHzKqFRz20\n1CDx5ZdfRjJTN7v169e7YcA10WsB6tatW6Q7nfJ69dVX3YiV6q6ngdR0j9IzzzzjXmu922+/\n3TV46By+WbNmph5iOtfP5VThl76Ov3Z2TFJBN4JpOEINNyjI6LRixQo3QoZuJNNNYhpClIQA\nAggggAACCCCAgN8COqVVi8vGC//i96Yi+dd6anTktR8vFKwoQFKrUrpJz13SvUZqfYqXNADE\nunXrIoFTvGVyZVrKEYyiUA0JWDQ4EliDBg3cUIGLFy+277//PlcMqScCCCCAAAIIIIAAAhkX\naNmyZUaCIxVMj6ApKTjSfD0nTC1MJLOUAyShlTayhYIkpWx/MKmrJL8QQAABBBBAAAEEEEAg\nqwRSbq/TzV6PPPKIG+pbN4dFJzVr3nXXXa6/ZK4/KFaj+23evDmah9cIZKXAfvvtl7Beek6a\nmvZJCGS7QDLHgwzUFZ2EQLYLqLVCLSAkBMImkHKApAdTHXzwwe5GMD1pd//997fatWvbTz/9\nZC+++KJ7mNXo0f72xwwbMuVFAAEEEEAAAQQQQACBcAikHCBpFA0N1HDxxRe7UTGiR6xTq5He\nd+/ePRy1p5QIIIAAAggggAACCCCAQJRAygGS1tVNXE8//bQbI12DMaj1aOedd7YWLVpEhhWM\n2gYvEUAAAQQQQAABBBBAAIFQCGxXgOTVrEKFCrbLLru4H28afxFAAAEEEEAAAQQQKA8BnZvm\nPfxYmWyax9mUCXO5bCStAKlcSsxGEUAAAQQQQAABBBCII6CgRT2dSAikI0CAlI4e6yKAAAII\nIIAAAggERkAjKuuBp2WRCMbKQrl8tkGAVD7ubBUBBBBAAAEEEEAgwwKFhYW2y7t/yHCuJWe3\n6JRvS57JnNAKbNeDYkNbWwqOAAIIIIAAAggggAACCJQiQAtSKTjM2n6Bzz//3KZNmxbJoGrV\nqlanTh3r0KGDNWrUKDI9iC9+/vln++STT+y0006LW7ytW7fac889Z6eccoqrU9yFmIhAlMDy\n5cvt9ddfj0xRtwwdD7vuuqvttddekelBfJHM5/2LL75wDwLWg8RJCCQSyMbjYd26de57o2jd\njzjiCKtcuXLRybxHAIGACyQMkBYtWmR/+tOfUq6GTjBJuSugE6aXX37Z9txzT4ewceNG02dp\n+PDhdv7559uf//znQOLoS+7GG2+0KlWqlBggPfLII/bSSy9Zly5dCJACuReDVyidEOrRCG3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bbtWqlV122WUu2Cp8DC1///33brtEeahXr57bTv+bM2eO\nvfnmm5FlPbj88sutWrVqMeuKLDz7QpFV+bjil+O/y8diFVumMdcVuzrAKxO8h5MsmS4ocD4U\nYIXlnMi/80F1mJ5zgvOB86FAIMiP4p8PGzZsCHLhyHuIBXwZIOlq89atW2OqxVuuXr26azHS\nzHbPP/98ZJulS5e6SRzUAqRgqmPHju65WbNmufXe8m677WYVKlQw/WDz9untRMtel7tEefBe\no3979uwZOZ63XvtX975cpzp16rgr9/JR61u+JtWX3hPqeqmxZfmcFJxXrlzZF+8vOe++++4J\nudXqq4sVuU46vzUbpiZ82bx5c66zk9Hjq15UxnRObpPRDJdy5/q81/eCLpoV/kwv5S7L9LJk\nzgcdwA/fD/oc0eeJPjP12ZnPST069L2s78J8Tmqp1/f+mjVrbOPGjTkvaqKeAznPIBlAoAQB\nXwZIu+66q82dOzcmyzrZ9UGuyRr0Q0snXbly5SLbNGjQwHr16uU+APXh0KNHD/ecxh9NmjQp\nsuy9QD+mC/9g0zEaNmzoNkmUB28/+lc/uApPQa5AzU9NywqO8jlA8sq2Y8eOvC5n9PvOK3P0\nOj8/9kN+9f5QCsv7JAzlDGqdcj5k99PKe5/4wT2TJffKGYZzP5OO7BsBXwZIzZs3t7feestd\nDdQEDUpTp06NjEvS+KSff/7ZLrjgAhcQ6XlN1T1lypSk+4e3aNHC7bNr1656uUu6H5Jag5QS\n5cFtxP8QQAABBBBAAAEEEEAgrwQKZhrwUbH+8pe/uNy88MILrjXgxx9/tHHjxrn7IumJE044\nwU2moKnA1QqkyRt0HyPdVFZdraKTZqbr379/9Cr3WIHQO++84+6zpCstr7zyiuuO0qVLF/d8\nojwU2SErEEAAAQQQQAABBBBAIPACvmxBUjc6TbJw6623moIkDWY96aST7IgjjnDgrVu3tptu\nuskeeugh97yCokMOOcT69OlTpELUVU5/hdNhhx1mp556ql166aUuqFKr1A033BDpKpcoD4X3\nxzICCCCAAAIIIIAAAggEX8CXAZJYDz74YHfjVg1k1cQKGlwZnXQPI/1pwKWmmSzcchS9bUmP\nzz33XDduSWOPiguiEuWhpP2yHgEEEEAAAQQQQAABBIIp4NsAyeNMNCOQgqeyJM3iU1xwFL3P\nRHmI3pbHCCCAAAIIIIAAAgggEFyB2GaZ4JaDnCOAAAIIIIAAAggggAACZRYgQCozITtAAAEE\nEEAAAQQQQACBfBEgQMqXmqQcCCCAAAIIIIAAAgggUGYBAqQyE7IDBBBAAAEEEEAAAQQQyBcB\nAqR8qUnKgQACCCCAAAIIIIAAAmUWIEAqMyE7QAABBBBAAAEEEEAAgXwRIEDKl5qkHAgggAAC\nCCCAAAIIIFBmAQKkMhOyAwQQQAABBBBAAAEEEMgXgXI7dqZ8KQzlKCrw9NNP25QpU2zAgAFW\nq1atohvkyZo5c+bYQw89ZEcffbSdcMIJeVKq4ouhcqq8999/f/EbsLZEgfHjx9ubb75pF154\nobVu3brE7YL+xJYtW6xv377WsmVLu/TSS4NenLj5Hz16tE2cONGuvPJKa9q0adxteTJWYPr0\n6fb4449bly5drEOHDrFP5tnSXXfdZatXr7Y777wzz0oWW5zPP//cXnzxRfvb3/5mRxxxROyT\nLCGAQNICtCAlTRXMDb/66isbN26c/frrr8EsQJK5XrlypSunvvDzPX3yySeurFzbSL2mZ82a\n5eyWLFmS+osD9Irt27e7cn722WcBynXpsjp16lRX1lWrVpVuByF+lc4DfT/ovMj39OGHH9rb\nb7+d78W0BQsWuDqdP39+3peVAiKQSQECpEzqsm8EEEAAAQQQQAABBBAIlAABUqCqi8wigAAC\nCCCAAAIIIIBAJgUqZnLn7Dv3Ahp3VL9+fStfPr9j4UqVKrly1qhRI/foGc5BnTp1XFnLlSuX\n4SPl3+6rV6/u7CpXrpx/hYsqkd4bOu/1Xsn3VLNmTVdWfQaQUhPwPjd1XuR7qlevnmlsXr6n\nKlWquPOhatWq+V5UyodARgWYpCGjvOwcAQQQQAABBBBAAAEEgiSQ380KQaoJ8ooAAggggAAC\nCCCAAAI5FyBAynkVkAEEEEAAAQQQQAABBBDwiwABkl9qgnwggAACCCCAAAIIIIBAzgWYpCHn\nVVByBl5++WVbu3ZtZAMNRt5tt93syCOPtGwNSNa9IzTRw8EHHxzJR7offPnll/btt99GdqtB\npnXr1rVDDz3UlTfyRAYe6H4xupHu119/bbvvvru1b9/edPxMpGXLltlrr70W2XWFChWsdu3a\ntvfee9v+++8fWZ/pB14+zjrrLFMegpL8cD4sXLjQdB8q3YQxU8mrH2//2X6f/Pzzz+7Gqzqu\nbjTZuHFjLytp/9cPdep5B+18yOXnpvdGCMP5sGHDBnfO67zQ5/Qf/vAHr/hp/9cPdapC6bzU\nd75uNE1CIKwCTNLg45o/+eSTTT/g99hjD9NNQXUz1EWLFrkf1QMGDMj4j2oFDX369LELLrjA\nTj/99IxJDRkyxH0ge0GCvpB++eUXW79+vZ1zzjl2xhlnZOTY+mF0/vnnu4DowAMPtE8//dQU\nhP7rX/9yxuk+6MyZM93xWrdubdWqVXMzKi1dutR0s8aDDjrI7r77bsv0zEN6H1199dWmu62/\n++67FqTZ3HJ9Pqxbt84uvvhi93558skn0/32iOwvl++TG2+80SZNmmTt2rWzOXPm2Lx58+y2\n226zww8/PJK/dD7IdZ0G+XzI1eemV/9hOB/eeustu/fee+2AAw4wzfSniyPdunVzn6GeQzr/\nzXWdqiyvv/663XPPPXb99ddb586d01k89oVAoARoQfJ5dXXs2NEuvPDCSC51t/hrrrnGBg4c\naM8884z70I48maYHW7dutWHDhrm/bE0l3aBBA3vwwQdjSqAvi8cff9zatGmTkat2o0aNclfH\nH330UXfcjRs32kknnWQjRoxwQWFMZtK40Ldv35grc1999ZX179/flI8rr7wyjUcquiuVedq0\naUWfCMiaXJwPolHQoB8NOv+aN2+eFa1sv08UmKnFWFePdT4q3XrrrfbQQw9lLEDSMXJVpzp2\n0M+HXHxuyi0M54MuTj777LN20UUXRVqMdX4ocDjxxBNjPsNlkq6UqzpV/hcsWOC+c7PVQyVd\nZuwHgUwIMAYpE6oZ3Ke6nt11112m5v6xY8dGjvTxxx/bnXfeaZdffrm74vvZZ5+557Zt22aD\nBw92XcgiG+988MEHH9jw4cOjV0Uejxs3zt544w274447rGnTppH12X6gL6bf//739sQTT0QO\nvXnzZnvhhRdMV7oVTDzwwAOutUkb6MqXgpvotHjxYvfDVq1RhZOuCJ555pmR1WrV2XfffZ1t\nZGUWHvzxj390AdmYMWNMLUpeSmedap9qEdAXvlpB8iWVdD7o/FDAfdVVV7ngU+913QOlNOeD\nurkqgD3uuOPstNNOyxldce+TeOfDrFmz3HtfgX900kWH//73v9Gr3GO1UJ933nmR4Egr1c1G\nrblqaclWykadqiz5eD6oXJn+3PTz+aDyl/S5mer5sGLFCmvbtq116NBBu3XJ62quz5dspuLq\ntKTPOOUr1e9CvUYXRnXhVV1N9V2YrYujOjYJAT8KECD5sVYS5Ek3gGzYsKHNnj3bbTl69Gh3\npVdjBfQjbtOmTa6Vafr06W6MyfLly4sEDk8//XSJR/nzn/9sL730kh122GElbpOtJ9Tt7scf\nf4wcTl/b0pUAAA8fSURBVD941TVMPxaVP7W+KCjU1T59qOvHX3Qw9Oabb5q+GIu7gayCo+gy\n6gtx8uTJtt9++0WOl60HXvdCr6zprlMFB2oNUGukumzmUyp8PuiHg77k9UOuU6dOLsgfOnSo\nC7Q1ribV80Hvq5EjR7rukRUr5rbRvfD7JN75oHoeP368G0/k1be6zKl1WJ8fhZPOhegLBnpe\n55q6hGb7x1Km6zSfzwfVWyY/N/18PsT73Ez1fNh1113dRTgF7F7S+aDPkFatWnmrsvZvdJ3G\n+4xThlL9LtRrdPFMFw179OihRRICoRcgQAroW2Cfffax+fPnu9zryu///d//uR+F6jN8yy23\nuLE0U6dOdc936dLF1KK0Zs0at6yAYe7cua5rS3HF14+TXP8Q9PKlcmpMksYLrV692nQ39Jtu\nusl1cdD4hUsvvdS1+Kjrk8ZNaEzN+++/773c3n77bRc0RlaU8EBX4uXWrFkzt+8SNsvYag2G\nLV++vP3000/uGOmuU7XCqevG8ccfn7Ey5HLH0eeDDI855hjX6qPuW2ox0+QbpT0fdC7onPBD\nin6fJDof9GPn6KOPdkGSl3eNqdB4t2QmXlBr7DfffOMuQHivz+a/mazTMJwPmfrc9Ov5oPdm\nvM/Nsp4PuiCp8akaj6sJfbKdor8LE33Gpfpd+N1339m///1v95mZ7Ysh2XbkeAgkK5Dby6HJ\n5pLtigjoy08TCiipW4wCCHWb0xXiH374wX799VfTj36lP/3pT27Sgf/85z/ux79+JGmGOF0h\n83vyWoNUVk1goMkpFOCpG6C+JPQDTknl3WWXXewvf/mL+0HYtWtX04e+uthpXbykwPG6665z\nAeT999+ftRkCo/OkblBqBctEnao7lVrSdIUwX1P0+aCWEHWFUbl1IUB/mh1K7w+lIJ8P0e+T\nOnXqxD0fVFZdHLniiitMraO6uKAWJX1eJEpqYVZX1ttvvz0nV8uVv0zVaRjOh2x8biZ6D2Xj\n+ejzQcdL9F1Y2vNBs6z269fPXXhJ5vzJRNmj6zTRZ5xmYk32u1DnmbrWqSeGZsklIYDAbwK0\nIAXwnaDxAAoOvK5S6v6j1hR1ndEVNF0t148hL+mKn7oa6ceRxmBMmDDB/XDynvfzvwr4dPVe\nwZGCIHUpUquRujpoXXT/cJVDX4DqJqexPGo9UndBTaVdUlJgeckll7j+1w8//HDOgkaVUykT\ndaqrnrp6qlnyrr322siYrhtuuCGm+1VJRn5fX/h80AUCnQ8ak6cJKX73u9+5VhOvHEE/H1QO\nvU+SOR8UKOpqt84XnRe6GKBWpZKSgnTN2qXWo0GDBrnzp6RtM7k+k3Wa7+eD6iXTn5uZrPtU\n9p3q52aq54Py8tFHH7mLDCeccILruq6W/lyk6DpN9Bmn/CX7XahbT+h7UL8L9P2gPwVj+gx4\n5JFHclFUjomALwRoQfJFNaSWCf3w19TQ6j6kH0mPPfaYCxp69uzpdqQgSFeEogdWa2zSiy++\naGpF0mBMBQ5+Twr2NBGF1y1s4sSJbsyRPri9Lg6aVUjJK6vGS6ibnLrZqUVNV/1KSmpd+sc/\n/uF+QKt7Xabuf1TS8aPXK7hVtyfN2JfuOlVrmiy9pP7r6m6mCSm8VhXvuSD+G30+KP9q/dhz\nzz3dBB4aL6CkK8D68e+lIJ4Pynv0++S9995LeD6ou4y63ep8UJdcBUcKlktK+txQq6w+U1q0\naFHSZhlfn8k6zffzIdOfmxmv/BQOEH0+JPO5mer5oHPMa11RgJSrVLhOk/mMS/a7UGNuC489\nVIu7vo/22muvXBWZ4yKQcwECpJxXQfwMKBDSDxYFABpnM2PGDDc1rX706ANQwY662qgLjX4A\nqludpovWIGR9YXhJUxPrg1CtJMcee6zv7n+jvHrd5dRtQjcg1HTDKtvf//53Vwy1JCn405eF\nAiTNruXNcBddVv0Aev75593AcnUlLCndd999bn+66adcvaQWp0xO5azplHWFTnWnli4Fcpo2\nV/eb0Q961XU669QLML3yaWILdVHs1auX794HXh5L+jfR+aDX6X2iIFDvCQ1W1hVgBQhNmjSJ\n7Nbv54Mymuh9kuz5oGDwueeec63OmqijpKRumO+88467Sq4JLrzzUdtrgLgXbJb0+tKuz3ad\n5tP5kIvPzdLWc1lfl+h8SPZzM9nzQZO5aMZYXVRQoBB9Pmh210xdXEq2ThN9xsk7me9CzRSr\nv+ikXikax6SeJyQEwipAgOTzmteVVP0paSY2XdXRTGTeTDPqLqRB6Lqi9Oqrr7rAqHv37i4I\n0lid6KQmd3Wb0ReE35ICBU00oaR7MKgvtMaK6CaxXndBdY/QB776Sqt7ncqu7nEqk8rqBTVq\nWdM9lBT4aJvikr5cdGNYJe0vOum42memkr50lXQ1U2Xbe++93VTs3h3a86VOM+GX6HzQMU85\n5RQ386F+CGvSDg1u1vtE7wkFpt6Mhn4+H1SORO+TZM+HRo0amW6ErPe8XlNS0j2BlNTFrnCS\ne7yWp8Lbp7IcpjpNxSWZbbP9uZlMnjK1TaLzIdnPzWTPB10w0PgcdT3TX3RSzwR9F2UiJVOn\nyX7GJfNdmIkysE8E8kGg3M6rLtm7wUU+iPm4DLoSq6taJQUF6mv8yiuvBH6wvlrHNINXSZNM\nqIVJN3zV1M7qbhfkFJY6zUQd6T2iYLukH/ZhOR9kq4kaFCSdffbZmaDO2j7DUqeZAOVzs0A1\nLOdDPn0XFtQejxDIjkDxl9ezc2yOkmYBTeNcXNKVY4230axUmqI06Ek/eosLjtQ6oIGsCgI1\nlXHQgyPVU1jqNBPvSXVTLC6F5XxQ2dWyqq5JGoOlmysHPYWlTjNRT3xuhud8yMfvwkycE+wT\ngXgCBEjxdPLkOQ001cxNGnvUrVu3PClV0WLo6nLv3r3dWJPBgwcX3SCP1oSlTjNRZWGy08yF\nmvHymmuuydiYiUzUUar7DFOdpmqTaPswfW6G5XwIU50men/zPAKlFaCLXWnlAvY6DfzM5Sxt\n2eIKSznlGaaypvv9ExY7TWqiXtQldbtNt2su9xeWOs2EcVjsOB8y8e5hnwjkpwABUn7WK6VC\nAAEEEEAAAQQQQACBUgiUL8VreAkCCCCAAAIIIIAAAgggkJcCBEh5Wa0UCgEEEEAAAQQQQAAB\nBEojQIBUGjVegwACCCCAAAIIIIAAAnkpwCx2eVmtFAoBBPJBYPv27TZ//vyYolSoUMHq169v\n1apVi1nPAgIIIIAAAgikR4BJGtLjyF4QQACBtAssXbq0xHth1apVy84880y79tprrWnTpmk/\nNjtEAAEEEEAgrAIESGGtecqNAAK+F/ACpD/+8Y929tlnu/xqquK1a9fapEmTbOzYsVavXj2b\nMmWK7bHHHr4vDxlEAAEEEEAgCAJ0sQtCLZFHBBAItcA+++xj//d//1fEYNCgQda3b18755xz\n7O2337Zy5coV2YYVCCCAAAIIIJCaAAFSal5sjQACCPhG4Oqrr7bZs2fbkCFDbMKECdaxY8dI\n3saNG2cTJ060WbNmWd26da1NmzZ2wQUXWM2aNW3NmjV233332cEHH2wnnnhi5DV6sGjRIre/\nbt26Wdu2bWOeYwEBBBBAAIEwCDCLXRhqmTIigEDeCpx++umubN9++22kjFrXtWtXGz16tO3Y\nscPeeOMNu/LKK+0Pf/iDbd682WrXrm1jxoyxyy67zD0feeHOB0OHDrWBAwe6iSCi1/MYAQQQ\nQACBsAgQIIWlpiknAgjkpUCLFi1cuaZNm+b+fe+992z48OGu693MmTPtlVdesZ9//tkuvvhi\n15qkrnhK6panGfI++OADt+z977nnnrN27dqZt19vPf8igAACCCAQFgECpLDUNOVEAIG8FKhR\no4Yr18KFC92/zZs3dwHS9ddfHymvxiaddNJJblkTPyiplalSpUr2/PPPu2X974svvrAZM2ZE\nJoSIPMEDBBBAAAEEQiTAGKQQVTZFRQCB/BOYO3euK1STJk3cv3vttZfpT8HOV199ZdOnT3d/\nn332mXteXeyUGjRo4LrhjRo1yh5++GGrWrWqqfVIAdff/vY3tw3/QwABBBBAIIwCtCCFsdYp\nMwII5I3ADz/84MridYnTBAxHHXWUHXrooW7ckQKlli1bmiZ0KJzUzW716tVuuvAtW7bYSy+9\nZD169HATORTelmUEEEAAAQTCIkALUlhqmnIigEBeCjz22GOuXJqUQUld6zR73RNPPGFnnXWW\n60an9WopUtKkDV7q0qWLa0kaOXKk6cazy5Ytc6/xnudfBBBAAAEEwihAgBTGWqfMCCAQeAEF\nOg8++KC9++671rlzZzvooINcmdRiVL169ZjgSE9oJjulrVu3un/1v4oVK1qvXr3s8ccfdwFS\ns2bNrH379pHneYAAAggggEAYBQiQwljrlBkBBAIlMHny5EgXOQU4CxYssO+++840S12rVq3s\n6aefjpRHgdKkSZPsuuuus969e9vy5ctt2LBh9uKLL7pt1KUuOqmb3eDBg+3ZZ5+1/v37c7PZ\naBweI4AAAgiEUqDczquQBf0tQklAoRFAAAF/CmjGOU2mEJ00I53WNW3a1E2moJu//v/27thW\nYRgIAOiJSEiUWYCGJmGDZINMxCDZJDMg0TABI6SgpET/O7JQJAT0yaOKiF3c8zUnx76yLF9D\n7vd7nE6nGIYhxnGMoiii67ro+z7ato26rqddp9eE/4fUEPZ6vUY6z3Q4HOavPBMgQIAAgdUJ\nKJBWt+QCJkBgDQLP5zNut1ukz+Z2u93XkJumie12+9YT6eskLwkQIECAwEIFfGK30IUVFgEC\n6xbYbDZRVdVPhPP5HOkK8Hk/pJ+TDCBAgAABAgsWsIO04MUVGgECBD4JpFvu0tmk1CvpeDzG\n5XKZLm34NN7/BAgQIEBgLQL6IK1lpcVJgACBmcB+v4/H4zGdY0rnldKNdn4ECBAgQIBAhB0k\nWUCAAAECBAgQIECAAIEsYAdJKhAgQIAAAQIECBAgQCALKJCkAgECBAgQIECAAAECBLKAAkkq\nECBAgAABAgQIECBAIAsokKQCAQIECBAgQIAAAQIEsoACSSoQIECAAAECBAgQIEAgCyiQpAIB\nAgQIECBAgAABAgSygAJJKhAgQIAAAQIECBAgQCALKJCkAgECBAgQIECAAAECBLLAHxMCqYE4\nY3x0AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prepost_act_counts %>%\n",
    "ggplot(aes(x=`Day`, y=`Cell_count_avg`, group=`Donor`, fill=`Condition`)) +\n",
    "    geom_col() +\n",
    "    geom_errorbar(\n",
    "        aes(ymin=`Cell_count_avg`-`Cell_count_sd`, ymax=`Cell_count_avg`+`Cell_count_sd`),\n",
    "        width=0.2\n",
    "    ) +\n",
    "    facet_wrap(~`Donor`, ncol=3) +\n",
    "    ylab('Average cell count (n=3)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>Condition</th><th scope=col>Day</th><th scope=col>Avg_FC</th><th scope=col>Avg_FC_sd</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>Thawed        </td><td>Day 1         </td><td>100.00000     </td><td> 0.00000      </td></tr>\n",
       "\t<tr><td>Pre-activation</td><td>Day 2         </td><td> 94.65512     </td><td>13.37398      </td></tr>\n",
       "\t<tr><td>Debeaded      </td><td>Day 4         </td><td> 81.85437     </td><td>15.72448      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       " Condition & Day & Avg\\_FC & Avg\\_FC\\_sd\\\\\n",
       "\\hline\n",
       "\t Thawed         & Day 1          & 100.00000      &  0.00000      \\\\\n",
       "\t Pre-activation & Day 2          &  94.65512      & 13.37398      \\\\\n",
       "\t Debeaded       & Day 4          &  81.85437      & 15.72448      \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "Condition | Day | Avg_FC | Avg_FC_sd | \n",
       "|---|---|---|\n",
       "| Thawed         | Day 1          | 100.00000      |  0.00000       | \n",
       "| Pre-activation | Day 2          |  94.65512      | 13.37398       | \n",
       "| Debeaded       | Day 4          |  81.85437      | 15.72448       | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  Condition      Day   Avg_FC    Avg_FC_sd\n",
       "1 Thawed         Day 1 100.00000  0.00000 \n",
       "2 Pre-activation Day 2  94.65512 13.37398 \n",
       "3 Debeaded       Day 4  81.85437 15.72448 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normalize_by_D1 <- function(x) {\n",
    "    x / x[1]\n",
    "}\n",
    "\n",
    "norm_counts <-\n",
    "    prepost_act_counts %>%\n",
    "    group_by(`Donor`) %>%\n",
    "    mutate(`Norm_value`=normalize_by_D1(`Cell_count_avg`)) %>%\n",
    "    group_by(`Condition`, `Day`) %>%\n",
    "    summarize(\n",
    "        `Avg_FC`=mean(`Norm_value`)*100,\n",
    "        `Avg_FC_sd`=sd(`Norm_value`)*100\n",
    "    )\n",
    "\n",
    "norm_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CSfF7ofPUKcEpYDlAWrNmjVkQduzYsTJx4kQpWLCgZMqU6T6dHTt2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1ym3Xh1aNGjXc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\nAQQQQACB3C3w/wEqJoumed15wgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "norm_counts %>%\n",
    "    ggplot(aes(x=`Condition`, y=`Avg_FC`, fill=`Day`)) +\n",
    "    geom_col() +\n",
    "    geom_errorbar(\n",
    "        aes(ymin=`Avg_FC`-`Avg_FC_sd`, ymax=`Avg_FC`+`Avg_FC_sd`),\n",
    "        width=0.2,\n",
    "    ) + \n",
    "    ylim(0, 120) +\n",
    "    ylab('Average cell count per condition/day (%)\\n(normalized to thawed count across 6 donors)')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}