analyses/tsubset-prevalence/T cell subset variance across donors.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": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bLRo0fbkCFD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yR5T0qNkXX3wh\ngaTJ7LnR+jU2PW3wzTffTHZ1jiwfGYOu5LXXXpNXX31V9OhemzZtotare/D1aIV1r5q2bdua\nozhz5syJmteNCXp06xe/+IU8+uijwdXXFFNNrwcrculBrJj08xTY6okewYwsW7ZsMac9aj9Z\nxToKqKeMuF1ibQ+0TdXFtG/fPvn222/NkQW32+/G+mN9VgOJkTn9zDp1uaSkxJwK5NVtqJ4m\nqN8LgaQvSHzaaaeZsxAWLFgQnJZOD2J9NqvbnujRNI0zkPQFTyfXI7v6udRTlt0utflsapur\ni9mLMemZL3qUz/p9ojHoNlTPUtDtrtslVj/p6ZrXXnutnHnmmXL++edHNbGub0OjQJggJEhp\n8ibQm5fpF/iqVatMi/QUis6dO5vTgPRcbD09RBOM0C/HNGl6lc2wTu8KvS7C+rFiLaQbpXT4\nUWq1J/JvZAwnnniiPPfcc1We7qGnS0Ze56DP9ZTDdCkaU2if1BRTTa+nQ1yRMekpFJMnT455\nvZXepFJPedQvUavMnTvX/HjWz106lMjtgbapupj0FBfdCRHYQ2p+uASONstDDz0k+vmqKyXy\ns3rGGWeIJsP33XefcdGdGrrDwkunHEbGpDvKQm+CqwmT9rteU5GuJfKzWd32RH9gawndhlo3\n9kyXbWiin02Np7qY9XW3S6Ix6emOF154YVizdRuqp9qny3XSkTHpJQB6ynvgbB1z0+2wxgee\nsA2NFOE5CVIavQc6dOgga9euNS3Sc+Qvu+wyCRzKlsCha3nsscfkl7/8pUma0qjJ1Talffv2\n5lx/3bOt8eiRC72WSvc+aVmyZIlJ+vT6mHQtoTFoG3Wjm5kZ+9K98vJycxG5DlIRWvS5/lBL\nl6LvMzXXi8O1VBdTPK+bSlz+LzKm4uLiuFukOyX0Oofhw4dLIsvFvYJazhi6PdAqqmtb4BRX\nsxZNiPS6G91WvPzyy+aas1qu3nOLRX5W9RoWPRIxffp00eslPvzwQ7NNzc3N9UxsoTHpj0/d\nwaQ7aHRbo9c7vvjiiyaWdN6GRn42q9ve6A4mvTZE/4UWHQzH+t4Ine7W40Q+m9rG6mJ2K4bI\n9SYaU+jy+hlbtGiRucY4dLrbj0Nj0u9t7YeqCtvQqmTq7vTYv/TqroerkeuXnI5qp0UvetW9\n3LrXWE8FWrNmjTmt68YbbxQdzMELRU9l0b2bVkw6QIPGFDgHWA499FDR0yn0FJF03vsZGUN1\n7rpnVxNB/fESWvS5dcpd6HS3HgeuczOrtvrFrXbYud7axqQj/elnLHCtkjnyYmebkq0rdHtQ\nU1166qoeGWnZsqWZVUfk0/dj4PoNufzyy8MGqKipLq++HvlZnTJlitmOBq5FMqNq6pH46667\nziRL2t9eKKEx6ZHPwHWcErg+R/71r3+Z/tVRQPWomO4dT9eSyGdTE8DI7afGpclgOiW2iXw2\n07VfIttV25h0gJt//vOfcuutt6bdKb6JxMQ2NPIdwXOOIKXJe0DP29UjLTq6nZa3337bjEw0\nYMAAs9dQ9yT+9re/NUNq6ofeC0WTOi1WTJoU6amCOvrbxRdfbDaqumewur06bscZGUN17dFT\nC3RY5dBh3HX+HTt2mNMnq1s2la9pTGresGHDVK7W0XXVJia9XuzKK680RxnGjRuXViObRW4P\nasLTPe5WcmTNa436ZZ22ZE3369/Qz6rumHnnnXfMzhg98qJ7jwMDwpihiNP1msdY/RIak76u\nR8Sefvppc33nX//6V3ON3fbt29N+Gxrv9kaTQE2GIr/jdBsa+f6O5ZWKaYl+NlPRpmTXUZuY\n9DOmoyjq0SM9nVlPI0ynkmhMbEPTqffSoy0kSOnRD/LGG2+Y61R0L4aWwEhvUUcddI+w7l3z\nynUFU6dONeeSW0PQ6g+TwEhSZthr/bGiRzB0mFC91ipdS2QMNbVTT+vRUwdDi15cbCWJodPd\neKynqehAIKEDFLjRDjvXWZuYdAeEJuq6R15PZU23Erk9qKl9M2bMkPHjx4fNpqe8aNKeLj8s\nwxrnwJPIz2pV21DriIYDTbC9ytCYNB49IqjbzYEDB5pTlvW+LZog6enL6VgS/WzqQESazIZu\nQ3XQBv0xHnpdkpuxJvrZdLOt8a67NjHpcPl62mpgBMOoewHGu14n50s0JrahTvaGN+vmFDsX\n+k0vNtUfL7qHQ2+cqtcZ6Yezf//+5iJHbZKeAqKng7z11lvmsV5AqIexdaSYdLgxYCSbJj76\nw0MTOL0Pkt7cVpMfvX+FdVGxvq6H4/Wi6YKCArMnNCsry9zEM7I+N57HE0NN7Ro8eLD54a0j\nL+mea71GQEfT0iOBqS6aSOv7TIueqqOnMuo9I9Rej0Z6sdgRk57aqSPC9enTR/SopmWkHjoy\noR4FTGWJZ3tQU3v0Jo36udLrjvTHs8akj3Wbokdp/Vbi+axq/+rRFt0BowPg6I0h9Sadeqpv\nOpaaYtLtqI5Wp9cO/vGPfzRHWfR0O/0sa3xuFzs+m7pt0p2E1r31NFnS62/1faw3VE91seOz\nmeo217Q+O2KaPXu2+W2iR971jInQbagOymF959fUFrtetyOmurYNtcvez/WQILnQu7pnQ/9p\n0WtTdM/YqFGj5De/+U2wNXq4Wvdu6yFsHapY9x7qHcX1eol0LPqDU4vusdYE7ogjjhC9BkCv\nhbCK/nDTPYOBew2Y5FBHDNNrqiIvyLXmT/XfeGKoqU16WlPgPkjmQnk9n16PHE2YMCF4HVZN\ny9v5uiaqev2JFm2L/sDQ95DeJDYdk+x4YrcjJv1y11N4dOj1yOHX9fOl79NUlni2BzW1R7ch\nOjiDJkl641A9TUlHcUv1DYpraqddr8fzWf3zn/9sBqnQIX31dFI9CqEjb51zzjl2NcPWeuKJ\nyepjHc1UP9N6k04dlSsdih2fTY1DhwbX7wWNUb8bNMHVhNCNYsdn0412V7dOO2LSHbpa9PdJ\nZNH6U329mB0x1bVtaGS/8TxaICNwFMP9Qeuj28WU/wrol7peQ6BDEqd6o+NUJ+ieRj2q4sc9\n25aZxqfnzes59RQEUiWgR3B1b6q+7/ToLOXH05V1FEm9Z4sekfBD0TMP9FQ7v8QTq090+6lH\nItJpgJtY7WSavwTYhvqrP5OJhgQpGT2WRQABBBBAAAEEEEAAAV8JMEiDr7qTYBBAAAEEEEAA\nAQQQQCAZARKkZPRYFgEEEEAAAQQQQAABBHwlQILkq+4kGAQQQAABBBBAAAEEEEhGgAQpGT2W\nRQABBBBAAAEEEEAAAV8JkCD5qjsJBgEEEEAAAQQQQAABBJIRIEFKRo9lEUAAAQQQQAABBBBA\nwFcC/rgphK+6hGAQQAABewT0Pmpr164Nq0zvLVNUVCQ5OTlh03mCAAIIIIAAAj8KcB8k3gkI\nIICATwU2bdpkbpAaKzy9UfOFF14o48ePlzZt2sSahWkIIIAAAgjUSQESpDrZ7QSNAAJ1QcBK\nkLp16yYXX3yxCbmiokJ27twpH330kcyaNUuaNm0qn3/+ubRu3boukBAjAggggAACNQpwil2N\nRMyAAAIIeFugQ4cOcvnll0cFMXnyZPnLX/4il1xyibzxxhuSkZERNQ8TEEAAAQQQqGsCJEh1\nrceJFwEEEPivwNVXXy2rVq2Shx56SObMmSOnn3560GbPnj3y2GOPySeffCJ61Klz587yu9/9\nTpo0aRKc55FHHjFHoPr27StPPfWUmbdly5YyZMgQ6dWrV3A+68HHH38s06dPl2+++UYOPfRQ\nOfPMM+W0006zXpZly5bJtGnTTDKnbVq9erUMHTpUzjjjjOA8PEAAAQQQQMBpAUaxc1qY+hFA\nAIE0Fhg+fLhp3eLFi4Ot/OGHH+Soo44STaDWrVsn27dvlxtvvFGOOeYY+fTTT4PzaQJ1zz33\nyEknnSQ33XSTrF+/Xh599FHp3bu3zJw5MzifPrjlllvkhBNOkJdeekl0oAg9YtWvXz8ZPXp0\ncL7ly5ebesaMGSM33HCDSbp0fgoCCCCAAAKpFCBBSqU260IAAQTSTKBdu3amRUuXLg227NJL\nL5UNGzbI+++/L2+++aa88sorsmDBAikvLzfXMulfq3zwwQfy61//2sz/zjvvyMKFC80IeXfd\ndZc1i8yfP1/++te/ynnnnWeOEj3//PPmuqexY8fKww8/bI4qBWcOPHj77bdlyZIlUlpaahKz\n0Nd4jAACCCCAgNMCJEhOC1M/AgggkMYCjRo1Mq37/vvvzd/vvvtOXn/9dXM63fHHHx9suV7H\npCPeffHFF/Luu+8Gp2dnZ5ujQ9aw4UcccYQ5HW/NmjXBeZ544glz1Ojvf/+7NGjQwEzX651u\nvfVWM8refffdF5xXH+ipfHoESweQKC4uDnuNJwgggAACCDgtQILktDD1I4AAAmksoNf5aPnZ\nz35m/n755Zfmb2hyZCYE/tNT5LTotUJW0SHCs7KyrKfmb4sWLUSvYbKK1tm2bduoIccbNmxo\nkqnQ+nQZTcYoCCCAAAIIuCVAguSWPOtFAAEE0kDgq6++Mq2wTrXbvHmzeZ6fnx/VusaNG5tp\nBw4cCL6Wm5sbfGw90KNDlZWV1lPROmPVpzNonaH16TS9kS0FAQQQQAABtwRIkNySZ70IIIBA\nGgg8+OCDphUDBw40fw8//HDz1zqyFNpEa1qXLl1CJ9f4WOsMPeUudAGtM9H6QpfnMQIIIIAA\nAnYLkCDZLUp9CCCAgAcE9AjP3XffLXPnzpX+/fsHk5ROnTqZa3902O7Qo0Aakl5LpCXRhObE\nE080R5Fefvlls7z1nw7o8Nlnn0nXrl2tSfxFAAEEEEDAdQHug+R6F9AABBBAwFkBTUR0yG4t\nOgKdDsSggy3osNodO3YMJj76up7ypkNy61DbgwYNMjeS1WuM9J5HmuBMmjQp7F5IukxN5cor\nrzTLX3zxxaI3p9V7JOn6dfphhx0mV111VU1V8DoCCCCAAAIpEyBBShk1K0IAAQTcEdBBEKyB\nEPT6IB1EQQdXuP32282IcTpaXGj5wx/+YIbq1lHrrBu+6sAJU6ZMMUlN6LzxPNYR7ubNmyeX\nXXaZjBo1Sg4ePCg6et7JJ59shvm2BoiIpy7mQQABBBBAwGmBjMApFD9dSev02qgfAQQQQMBT\nAmvXrjVDdLdq1cqWduvodnrdUfv27YNDfttSMZUggAACCCBgkwAJkk2QVIMAAggggAACCCCA\nAALeF2CQBu/3IREggAACCCCAAAIIIICATQIkSDZBUg0CCCCAAAIIIIAAAgh4X4AEyft9SAQI\nIIAAAggggAACCCBgkwAJkk2QVIMAAggggAACCCCAAALeFyBB8n4fEgECCCCAAAIIIIAAAgjY\nJECCZBMk1SCAAAIIIIAAAggggID3BUiQvN+HRIAAAggggAACCCCAAAI2Cfx/9vMhNpt1IY0A\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "read_tsv(\n",
    "    'cd3-cd4-cd8.tsv',\n",
    "    col_types=cols(\n",
    "        `Sample:`=col_character(),\n",
    "        `Donor`=col_factor(levels=c(\"D8\", \"D9\", \"D10\", \"D11\", \"D12\", \"D15\")),\n",
    "        .default=col_double()\n",
    "    )\n",
    ") %>%\n",
    "mutate(\n",
    "    `CD4+ CD8-`=`Live/CD3+/CD4+ CD8- | Freq. of Parent`,\n",
    "    `CD4- CD8+`=`Live/CD3+/CD4- CD8+ | Freq. of Parent`,\n",
    "    `CD4- CD8-`=`Live/CD3+/CD4- CD8- | Freq. of Parent`,\n",
    "    `CD4+ CD8+`=`Live/CD3+/CD4+ CD8+ | Freq. of Parent`\n",
    ") %>%\n",
    "select(`Donor`, `CD4+ CD8-`:`CD4+ CD8+`) %>%\n",
    "gather(\n",
    "    key=\"Population\",\n",
    "    value=\"Frequency\",\n",
    "    `CD4+ CD8-`:`CD4+ CD8+`\n",
    ") %>%\n",
    "ggplot(aes(x=`Donor`, y=`Frequency`, group=`Population`, fill=`Donor`)) +\n",
    "    geom_col() +\n",
    "    facet_wrap(~`Population`) +\n",
    "    ylim(0, 100) +\n",
    "    ylab('Percent Population (%)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VrZJptsYltttZUfHHudPn26PfnkkzZ37lzbfffdbaON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\n/zF1HlczX90F1D6koY8VkOrOlR5BQEIg6gK6OKn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7eG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\nAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "read_tsv(\n",
    "    'naive-eff-central-memory.tsv',\n",
    "    col_types=cols(\n",
    "        `Sample:`=col_character(),\n",
    "        `Donor`=col_factor(levels=c(\"D8\", \"D9\", \"D10\", \"D11\", \"D12\", \"D15\")),\n",
    "        .default=col_double()\n",
    "    )\n",
    ") %>%\n",
    "mutate(\n",
    "    `EMRA: CCR7- CD45RO-`=`Live/CCR7- CD45RO- | Freq. of Parent`,\n",
    "    `EM: CCR7- CD45RO+`=`Live/CCR7- CD45RO+ | Freq. of Parent`,\n",
    "    `Naive: CCR7+ CD45RO-`=`Live/CCR7+ CD45RO- | Freq. of Parent`,\n",
    "    `CM: CCR7+ CD45RO+`=`Live/CCR7+ CD45RO+ | Freq. of Parent`\n",
    ") %>%\n",
    "select(`Donor`, `EMRA: CCR7- CD45RO-`:`CM: CCR7+ CD45RO+`) %>%\n",
    "gather(\n",
    "    key=\"Population\",\n",
    "    value=\"Frequency\",\n",
    "    `EMRA: CCR7- CD45RO-`:`CM: CCR7+ CD45RO+`\n",
    ") %>%\n",
    "ggplot(aes(x=`Donor`, y=`Frequency`, group=`Population`, fill=`Donor`)) +\n",
    "    geom_col() +\n",
    "    facet_wrap(~`Population`) +\n",
    "    ylim(0, 100) +\n",
    "    ylab('Percent Population (%)')"
   ]
  }
 ],
 "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
}