DA_with_edgeR.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9643fa74",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: limma\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(edgeR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8a6ca8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: limma\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(edgeR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "abd345b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#following tutorial here https://bioconductor.org/books/release/OSCA/multi-sample-comparisons.html#differential-abundance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bd6b4605",
   "metadata": {},
   "outputs": [],
   "source": [
    "major_celltype_props <- read.csv(\"compositional_analysis/major_cell_cluster_counts_by_sample.csv\",row.names=1)\n",
    "metadata_2 <- read.csv(\"compositional_analysis/metadata_by_sample.csv\", row.names=1)\n",
    "metadata_2$M.Number <- factor(metadata_2$M.Number)\n",
    "metadata_2$monkey_condition_categories <- factor(metadata_2$monkey_condition_categories)\n",
    "major_celltype_props <- na.omit(major_celltype_props)\n",
    "major_celltype_props <- t(major_celltype_props)\n",
    "\n",
    "metadata_2$M.Number <- factor(metadata_2$M.Number)\n",
    "metadata_2$monkey_condition_categories <- factor(metadata_2$monkey_condition_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "835d8c54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'B.Cells'</li><li>'Endothelial'</li><li>'Fibroblasts'</li><li>'Macrophages.Monocytes'</li><li>'Mast'</li><li>'Neutrophils'</li><li>'Plasma.Cells'</li><li>'Secretory.Epithelial'</li><li>'T1P'</li><li>'T2P'</li><li>'T.NK'</li><li>'cDC'</li><li>'pDC'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'B.Cells'\n",
       "\\item 'Endothelial'\n",
       "\\item 'Fibroblasts'\n",
       "\\item 'Macrophages.Monocytes'\n",
       "\\item 'Mast'\n",
       "\\item 'Neutrophils'\n",
       "\\item 'Plasma.Cells'\n",
       "\\item 'Secretory.Epithelial'\n",
       "\\item 'T1P'\n",
       "\\item 'T2P'\n",
       "\\item 'T.NK'\n",
       "\\item 'cDC'\n",
       "\\item 'pDC'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'B.Cells'\n",
       "2. 'Endothelial'\n",
       "3. 'Fibroblasts'\n",
       "4. 'Macrophages.Monocytes'\n",
       "5. 'Mast'\n",
       "6. 'Neutrophils'\n",
       "7. 'Plasma.Cells'\n",
       "8. 'Secretory.Epithelial'\n",
       "9. 'T1P'\n",
       "10. 'T2P'\n",
       "11. 'T.NK'\n",
       "12. 'cDC'\n",
       "13. 'pDC'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"B.Cells\"               \"Endothelial\"           \"Fibroblasts\"          \n",
       " [4] \"Macrophages.Monocytes\" \"Mast\"                  \"Neutrophils\"          \n",
       " [7] \"Plasma.Cells\"          \"Secretory.Epithelial\"  \"T1P\"                  \n",
       "[10] \"T2P\"                   \"T.NK\"                  \"cDC\"                  \n",
       "[13] \"pDC\"                  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rownames(major_celltype_props)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "42cbd694",
   "metadata": {},
   "outputs": [],
   "source": [
    "props_no_tcells=major_celltype_props[!(row.names(major_celltype_props) %in% c(\"T.NK\")),]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "299f2ff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y.ab <- DGEList(major_celltype_props, samples=metadata_2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "a5676509",
   "metadata": {},
   "outputs": [],
   "source": [
    "design <- model.matrix(~factor(monkey_condition_categories) + factor(treatment), y.ab$samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c92cf0e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "design <- model.matrix(~1 + factor(treatment), y.ab$samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6bfab03e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.7882  0.7882  0.7882  0.7882  0.7882  0.7882 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.ab <- estimateDisp(y.ab, design, trend=\"none\")\n",
    "summary(y.ab$common.dispersion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c94f4c47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.8913  0.8913  0.8913  0.8913  0.8913  0.8913 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit.ab <- glmQLFit(y.ab, design, robust=TRUE, abundance.trend=FALSE)\n",
    "summary(fit.ab$var.prior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "731aaf7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       factor(treatment)IgG\n",
       "Down                      2\n",
       "NotSig                    7\n",
       "Up                        4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res <- glmQLFTest(fit.ab, coef=ncol(design))\n",
    "summary(decideTests(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ed91238d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>pDC</th><td> 1.7491657</td><td>14.13874</td><td>11.043242</td><td>0.001905048</td><td>0.02077620</td></tr>\n",
       "\t<tr><th scope=row>Mast</th><td> 1.5025565</td><td>15.86520</td><td> 9.127676</td><td>0.004364525</td><td>0.02077620</td></tr>\n",
       "\t<tr><th scope=row>Plasma.Cells</th><td> 1.6719252</td><td>14.48735</td><td> 8.916743</td><td>0.004794509</td><td>0.02077620</td></tr>\n",
       "\t<tr><th scope=row>B.Cells</th><td> 1.6618831</td><td>15.16730</td><td> 7.595762</td><td>0.008752969</td><td>0.02844715</td></tr>\n",
       "\t<tr><th scope=row>Secretory.Epithelial</th><td>-3.4491599</td><td>11.20907</td><td> 8.345303</td><td>0.011132015</td><td>0.02894324</td></tr>\n",
       "\t<tr><th scope=row>T1P</th><td>-1.8365540</td><td>12.15945</td><td> 5.929951</td><td>0.019415701</td><td>0.04206735</td></tr>\n",
       "\t<tr><th scope=row>cDC</th><td> 0.6387783</td><td>15.14641</td><td> 4.763031</td><td>0.034924914</td><td>0.06486056</td></tr>\n",
       "\t<tr><th scope=row>Macrophages.Monocytes</th><td>-0.6483051</td><td>18.68741</td><td> 3.456048</td><td>0.070286425</td><td>0.11421544</td></tr>\n",
       "\t<tr><th scope=row>T2P</th><td>-1.1375268</td><td>14.31892</td><td> 2.018332</td><td>0.163121690</td><td>0.23562022</td></tr>\n",
       "\t<tr><th scope=row>Endothelial</th><td>-0.6213582</td><td>14.11567</td><td> 1.548642</td><td>0.220544211</td><td>0.28670747</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'factor(treatment)IgG'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 10 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tpDC &  1.7491657 & 14.13874 & 11.043242 & 0.001905048 & 0.02077620\\\\\n",
       "\tMast &  1.5025565 & 15.86520 &  9.127676 & 0.004364525 & 0.02077620\\\\\n",
       "\tPlasma.Cells &  1.6719252 & 14.48735 &  8.916743 & 0.004794509 & 0.02077620\\\\\n",
       "\tB.Cells &  1.6618831 & 15.16730 &  7.595762 & 0.008752969 & 0.02844715\\\\\n",
       "\tSecretory.Epithelial & -3.4491599 & 11.20907 &  8.345303 & 0.011132015 & 0.02894324\\\\\n",
       "\tT1P & -1.8365540 & 12.15945 &  5.929951 & 0.019415701 & 0.04206735\\\\\n",
       "\tcDC &  0.6387783 & 15.14641 &  4.763031 & 0.034924914 & 0.06486056\\\\\n",
       "\tMacrophages.Monocytes & -0.6483051 & 18.68741 &  3.456048 & 0.070286425 & 0.11421544\\\\\n",
       "\tT2P & -1.1375268 & 14.31892 &  2.018332 & 0.163121690 & 0.23562022\\\\\n",
       "\tEndothelial & -0.6213582 & 14.11567 &  1.548642 & 0.220544211 & 0.28670747\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] 'factor(treatment)IgG'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 10 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| pDC |  1.7491657 | 14.13874 | 11.043242 | 0.001905048 | 0.02077620 |\n",
       "| Mast |  1.5025565 | 15.86520 |  9.127676 | 0.004364525 | 0.02077620 |\n",
       "| Plasma.Cells |  1.6719252 | 14.48735 |  8.916743 | 0.004794509 | 0.02077620 |\n",
       "| B.Cells |  1.6618831 | 15.16730 |  7.595762 | 0.008752969 | 0.02844715 |\n",
       "| Secretory.Epithelial | -3.4491599 | 11.20907 |  8.345303 | 0.011132015 | 0.02894324 |\n",
       "| T1P | -1.8365540 | 12.15945 |  5.929951 | 0.019415701 | 0.04206735 |\n",
       "| cDC |  0.6387783 | 15.14641 |  4.763031 | 0.034924914 | 0.06486056 |\n",
       "| Macrophages.Monocytes | -0.6483051 | 18.68741 |  3.456048 | 0.070286425 | 0.11421544 |\n",
       "| T2P | -1.1375268 | 14.31892 |  2.018332 | 0.163121690 | 0.23562022 |\n",
       "| Endothelial | -0.6213582 | 14.11567 |  1.548642 | 0.220544211 | 0.28670747 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   'factor(treatment)IgG'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  factor(treatment)IgG \n",
       "                           logFC   logCPM         F      PValue        FDR\n",
       "pDC                    1.7491657 14.13874 11.043242 0.001905048 0.02077620\n",
       "Mast                   1.5025565 15.86520  9.127676 0.004364525 0.02077620\n",
       "Plasma.Cells           1.6719252 14.48735  8.916743 0.004794509 0.02077620\n",
       "B.Cells                1.6618831 15.16730  7.595762 0.008752969 0.02844715\n",
       "Secretory.Epithelial  -3.4491599 11.20907  8.345303 0.011132015 0.02894324\n",
       "T1P                   -1.8365540 12.15945  5.929951 0.019415701 0.04206735\n",
       "cDC                    0.6387783 15.14641  4.763031 0.034924914 0.06486056\n",
       "Macrophages.Monocytes -0.6483051 18.68741  3.456048 0.070286425 0.11421544\n",
       "T2P                   -1.1375268 14.31892  2.018332 0.163121690 0.23562022\n",
       "Endothelial           -0.6213582 14.11567  1.548642 0.220544211 0.28670747"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a70bac28",
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- glmQLFTest(fit.ab, coef = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "481a65ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       factor(treatment)CD8b\n",
       "Down                       2\n",
       "NotSig                     9\n",
       "Up                         2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(decideTests(res)) # CD8b vs CD8a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "887a0b33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>pDC</th><td> 2.1741525</td><td>14.13874</td><td>13.963581</td><td>0.0005795507</td><td>0.00753416</td></tr>\n",
       "\t<tr><th scope=row>T1P</th><td>-2.2330899</td><td>12.15945</td><td> 9.036282</td><td>0.0045455672</td><td>0.02954619</td></tr>\n",
       "\t<tr><th scope=row>Secretory.Epithelial</th><td>-3.3179402</td><td>11.20907</td><td> 8.566345</td><td>0.0103022352</td><td>0.03863681</td></tr>\n",
       "\t<tr><th scope=row>cDC</th><td> 0.7774541</td><td>15.14641</td><td> 6.938284</td><td>0.0118882507</td><td>0.03863681</td></tr>\n",
       "\t<tr><th scope=row>Mast</th><td> 1.1209201</td><td>15.86520</td><td> 5.736693</td><td>0.0213627410</td><td>0.05554313</td></tr>\n",
       "\t<tr><th scope=row>Fibroblasts</th><td>-1.4230511</td><td>13.93294</td><td> 3.995464</td><td>0.0524169280</td><td>0.10929489</td></tr>\n",
       "\t<tr><th scope=row>B.Cells</th><td> 1.2844561</td><td>15.16730</td><td> 3.781250</td><td>0.0588510925</td><td>0.10929489</td></tr>\n",
       "\t<tr><th scope=row>Endothelial</th><td>-0.8102104</td><td>14.11567</td><td> 2.401419</td><td>0.1290644148</td><td>0.20972967</td></tr>\n",
       "\t<tr><th scope=row>T2P</th><td>-1.1132809</td><td>14.31892</td><td> 2.051946</td><td>0.1597439942</td><td>0.23074132</td></tr>\n",
       "\t<tr><th scope=row>Neutrophils</th><td>-1.0817010</td><td>12.84734</td><td> 1.247592</td><td>0.2706456441</td><td>0.35183934</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'factor(treatment)CD8b'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 10 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tpDC &  2.1741525 & 14.13874 & 13.963581 & 0.0005795507 & 0.00753416\\\\\n",
       "\tT1P & -2.2330899 & 12.15945 &  9.036282 & 0.0045455672 & 0.02954619\\\\\n",
       "\tSecretory.Epithelial & -3.3179402 & 11.20907 &  8.566345 & 0.0103022352 & 0.03863681\\\\\n",
       "\tcDC &  0.7774541 & 15.14641 &  6.938284 & 0.0118882507 & 0.03863681\\\\\n",
       "\tMast &  1.1209201 & 15.86520 &  5.736693 & 0.0213627410 & 0.05554313\\\\\n",
       "\tFibroblasts & -1.4230511 & 13.93294 &  3.995464 & 0.0524169280 & 0.10929489\\\\\n",
       "\tB.Cells &  1.2844561 & 15.16730 &  3.781250 & 0.0588510925 & 0.10929489\\\\\n",
       "\tEndothelial & -0.8102104 & 14.11567 &  2.401419 & 0.1290644148 & 0.20972967\\\\\n",
       "\tT2P & -1.1132809 & 14.31892 &  2.051946 & 0.1597439942 & 0.23074132\\\\\n",
       "\tNeutrophils & -1.0817010 & 12.84734 &  1.247592 & 0.2706456441 & 0.35183934\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] 'factor(treatment)CD8b'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 10 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| pDC |  2.1741525 | 14.13874 | 13.963581 | 0.0005795507 | 0.00753416 |\n",
       "| T1P | -2.2330899 | 12.15945 |  9.036282 | 0.0045455672 | 0.02954619 |\n",
       "| Secretory.Epithelial | -3.3179402 | 11.20907 |  8.566345 | 0.0103022352 | 0.03863681 |\n",
       "| cDC |  0.7774541 | 15.14641 |  6.938284 | 0.0118882507 | 0.03863681 |\n",
       "| Mast |  1.1209201 | 15.86520 |  5.736693 | 0.0213627410 | 0.05554313 |\n",
       "| Fibroblasts | -1.4230511 | 13.93294 |  3.995464 | 0.0524169280 | 0.10929489 |\n",
       "| B.Cells |  1.2844561 | 15.16730 |  3.781250 | 0.0588510925 | 0.10929489 |\n",
       "| Endothelial | -0.8102104 | 14.11567 |  2.401419 | 0.1290644148 | 0.20972967 |\n",
       "| T2P | -1.1132809 | 14.31892 |  2.051946 | 0.1597439942 | 0.23074132 |\n",
       "| Neutrophils | -1.0817010 | 12.84734 |  1.247592 | 0.2706456441 | 0.35183934 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   'factor(treatment)CD8b'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  factor(treatment)CD8b \n",
       "                          logFC   logCPM         F       PValue        FDR\n",
       "pDC                   2.1741525 14.13874 13.963581 0.0005795507 0.00753416\n",
       "T1P                  -2.2330899 12.15945  9.036282 0.0045455672 0.02954619\n",
       "Secretory.Epithelial -3.3179402 11.20907  8.566345 0.0103022352 0.03863681\n",
       "cDC                   0.7774541 15.14641  6.938284 0.0118882507 0.03863681\n",
       "Mast                  1.1209201 15.86520  5.736693 0.0213627410 0.05554313\n",
       "Fibroblasts          -1.4230511 13.93294  3.995464 0.0524169280 0.10929489\n",
       "B.Cells               1.2844561 15.16730  3.781250 0.0588510925 0.10929489\n",
       "Endothelial          -0.8102104 14.11567  2.401419 0.1290644148 0.20972967\n",
       "T2P                  -1.1132809 14.31892  2.051946 0.1597439942 0.23074132\n",
       "Neutrophils          -1.0817010 12.84734  1.247592 0.2706456441 0.35183934"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "78235a59",
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- glmQLFTest(fit.ab, contrast = c(0,0,0,0,1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "1a86a35c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       1*factor(treatment)CD8b -1*factor(treatment)IgG\n",
       "Down                                                 0\n",
       "NotSig                                              13\n",
       "Up                                                   0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(decideTests(res)) # CD8b vs IgG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "1a5d0006",
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- glmQLFTest(fit.ab, coef = 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "0b1a6cc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       factor(treatment)IgG\n",
       "Down                      2\n",
       "NotSig                    7\n",
       "Up                        4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(decideTests(res)) #IgG vs CD8a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "51b931a2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>pDC</th><td> 1.7491657</td><td>14.13874</td><td>11.043242</td><td>0.001905048</td><td>0.02077620</td></tr>\n",
       "\t<tr><th scope=row>Mast</th><td> 1.5025565</td><td>15.86520</td><td> 9.127676</td><td>0.004364525</td><td>0.02077620</td></tr>\n",
       "\t<tr><th scope=row>Plasma.Cells</th><td> 1.6719252</td><td>14.48735</td><td> 8.916743</td><td>0.004794509</td><td>0.02077620</td></tr>\n",
       "\t<tr><th scope=row>B.Cells</th><td> 1.6618831</td><td>15.16730</td><td> 7.595762</td><td>0.008752969</td><td>0.02844715</td></tr>\n",
       "\t<tr><th scope=row>Secretory.Epithelial</th><td>-3.4491599</td><td>11.20907</td><td> 8.345303</td><td>0.011132015</td><td>0.02894324</td></tr>\n",
       "\t<tr><th scope=row>T1P</th><td>-1.8365540</td><td>12.15945</td><td> 5.929951</td><td>0.019415701</td><td>0.04206735</td></tr>\n",
       "\t<tr><th scope=row>cDC</th><td> 0.6387783</td><td>15.14641</td><td> 4.763031</td><td>0.034924914</td><td>0.06486056</td></tr>\n",
       "\t<tr><th scope=row>Macrophages.Monocytes</th><td>-0.6483051</td><td>18.68741</td><td> 3.456048</td><td>0.070286425</td><td>0.11421544</td></tr>\n",
       "\t<tr><th scope=row>T2P</th><td>-1.1375268</td><td>14.31892</td><td> 2.018332</td><td>0.163121690</td><td>0.23562022</td></tr>\n",
       "\t<tr><th scope=row>Endothelial</th><td>-0.6213582</td><td>14.11567</td><td> 1.548642</td><td>0.220544211</td><td>0.28670747</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'factor(treatment)IgG'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 10 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tpDC &  1.7491657 & 14.13874 & 11.043242 & 0.001905048 & 0.02077620\\\\\n",
       "\tMast &  1.5025565 & 15.86520 &  9.127676 & 0.004364525 & 0.02077620\\\\\n",
       "\tPlasma.Cells &  1.6719252 & 14.48735 &  8.916743 & 0.004794509 & 0.02077620\\\\\n",
       "\tB.Cells &  1.6618831 & 15.16730 &  7.595762 & 0.008752969 & 0.02844715\\\\\n",
       "\tSecretory.Epithelial & -3.4491599 & 11.20907 &  8.345303 & 0.011132015 & 0.02894324\\\\\n",
       "\tT1P & -1.8365540 & 12.15945 &  5.929951 & 0.019415701 & 0.04206735\\\\\n",
       "\tcDC &  0.6387783 & 15.14641 &  4.763031 & 0.034924914 & 0.06486056\\\\\n",
       "\tMacrophages.Monocytes & -0.6483051 & 18.68741 &  3.456048 & 0.070286425 & 0.11421544\\\\\n",
       "\tT2P & -1.1375268 & 14.31892 &  2.018332 & 0.163121690 & 0.23562022\\\\\n",
       "\tEndothelial & -0.6213582 & 14.11567 &  1.548642 & 0.220544211 & 0.28670747\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] 'factor(treatment)IgG'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 10 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| pDC |  1.7491657 | 14.13874 | 11.043242 | 0.001905048 | 0.02077620 |\n",
       "| Mast |  1.5025565 | 15.86520 |  9.127676 | 0.004364525 | 0.02077620 |\n",
       "| Plasma.Cells |  1.6719252 | 14.48735 |  8.916743 | 0.004794509 | 0.02077620 |\n",
       "| B.Cells |  1.6618831 | 15.16730 |  7.595762 | 0.008752969 | 0.02844715 |\n",
       "| Secretory.Epithelial | -3.4491599 | 11.20907 |  8.345303 | 0.011132015 | 0.02894324 |\n",
       "| T1P | -1.8365540 | 12.15945 |  5.929951 | 0.019415701 | 0.04206735 |\n",
       "| cDC |  0.6387783 | 15.14641 |  4.763031 | 0.034924914 | 0.06486056 |\n",
       "| Macrophages.Monocytes | -0.6483051 | 18.68741 |  3.456048 | 0.070286425 | 0.11421544 |\n",
       "| T2P | -1.1375268 | 14.31892 |  2.018332 | 0.163121690 | 0.23562022 |\n",
       "| Endothelial | -0.6213582 | 14.11567 |  1.548642 | 0.220544211 | 0.28670747 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   'factor(treatment)IgG'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  factor(treatment)IgG \n",
       "                           logFC   logCPM         F      PValue        FDR\n",
       "pDC                    1.7491657 14.13874 11.043242 0.001905048 0.02077620\n",
       "Mast                   1.5025565 15.86520  9.127676 0.004364525 0.02077620\n",
       "Plasma.Cells           1.6719252 14.48735  8.916743 0.004794509 0.02077620\n",
       "B.Cells                1.6618831 15.16730  7.595762 0.008752969 0.02844715\n",
       "Secretory.Epithelial  -3.4491599 11.20907  8.345303 0.011132015 0.02894324\n",
       "T1P                   -1.8365540 12.15945  5.929951 0.019415701 0.04206735\n",
       "cDC                    0.6387783 15.14641  4.763031 0.034924914 0.06486056\n",
       "Macrophages.Monocytes -0.6483051 18.68741  3.456048 0.070286425 0.11421544\n",
       "T2P                   -1.1375268 14.31892  2.018332 0.163121690 0.23562022\n",
       "Endothelial           -0.6213582 14.11567  1.548642 0.220544211 0.28670747"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d948dc7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "fb03d29e",
   "metadata": {},
   "source": [
    "# remove t cells from proportions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "50df1207",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y.ab <- DGEList(props_no_tcells, samples=metadata_2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "41b2a7fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$counts</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 12 × 41 of type int</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Array1_28918</th><th scope=col>Array1_29218</th><th scope=col>Array1_29318</th><th scope=col>Array1_29818</th><th scope=col>Array1_30518</th><th scope=col>Array2_28918</th><th scope=col>Array2_29218</th><th scope=col>Array2_29318</th><th scope=col>Array2_29818</th><th scope=col>Array2_30518</th><th scope=col>⋯</th><th scope=col>Array8_28818</th><th scope=col>Array8_29118</th><th scope=col>Array8_29618</th><th scope=col>Array8_30218</th><th scope=col>Array8_30418</th><th scope=col>Array9_28818</th><th scope=col>Array9_29118</th><th scope=col>Array9_29618</th><th scope=col>Array9_30218</th><th scope=col>Array9_30418</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>B.Cells</th><td> 10</td><td> 40</td><td> 36</td><td>  6</td><td> 36</td><td> 13</td><td> 20</td><td>  1</td><td> 65</td><td> 23</td><td>⋯</td><td> 14</td><td> 17</td><td>85</td><td>193</td><td> 18</td><td>  1</td><td> 12</td><td> 39</td><td>129</td><td> 26</td></tr>\n",
       "\t<tr><th scope=row>Endothelial</th><td> 46</td><td>  7</td><td> 31</td><td> 19</td><td>  1</td><td> 13</td><td>  9</td><td> 22</td><td> 16</td><td>  1</td><td>⋯</td><td>  8</td><td>  5</td><td> 6</td><td> 26</td><td>  8</td><td>  8</td><td> 11</td><td>  5</td><td> 12</td><td> 23</td></tr>\n",
       "\t<tr><th scope=row>Fibroblasts</th><td> 33</td><td>  3</td><td>  6</td><td> 57</td><td>  0</td><td>  1</td><td> 41</td><td> 17</td><td>  8</td><td>  5</td><td>⋯</td><td>  6</td><td> 14</td><td> 5</td><td>  9</td><td>  0</td><td>  9</td><td>  5</td><td>  8</td><td>  1</td><td>  9</td></tr>\n",
       "\t<tr><th scope=row>Macrophages.Monocytes</th><td>181</td><td>409</td><td>453</td><td>799</td><td>171</td><td>325</td><td>267</td><td>975</td><td>274</td><td>595</td><td>⋯</td><td>448</td><td>958</td><td>97</td><td>137</td><td>334</td><td>758</td><td>581</td><td>389</td><td>143</td><td>301</td></tr>\n",
       "\t<tr><th scope=row>Mast</th><td>  2</td><td> 23</td><td> 17</td><td> 57</td><td> 29</td><td> 20</td><td> 64</td><td> 16</td><td>181</td><td>114</td><td>⋯</td><td>140</td><td> 41</td><td>26</td><td> 54</td><td> 66</td><td>  7</td><td> 88</td><td> 37</td><td> 21</td><td> 20</td></tr>\n",
       "\t<tr><th scope=row>Neutrophils</th><td> 35</td><td>  0</td><td> 35</td><td>  1</td><td>  6</td><td>  0</td><td>  4</td><td>  0</td><td>  0</td><td>  0</td><td>⋯</td><td>  1</td><td>  1</td><td> 1</td><td>  2</td><td>  0</td><td>  0</td><td>  1</td><td>  1</td><td> 11</td><td>  1</td></tr>\n",
       "\t<tr><th scope=row>Plasma.Cells</th><td>  3</td><td> 16</td><td> 16</td><td> 24</td><td>  4</td><td>  3</td><td> 10</td><td> 19</td><td>120</td><td>  3</td><td>⋯</td><td>  4</td><td> 23</td><td> 9</td><td> 35</td><td>  4</td><td>  6</td><td> 11</td><td> 15</td><td>  9</td><td> 17</td></tr>\n",
       "\t<tr><th scope=row>Secretory.Epithelial</th><td>  2</td><td>  0</td><td>  1</td><td>  0</td><td>  0</td><td>  0</td><td>  0</td><td>  0</td><td>  0</td><td>  0</td><td>⋯</td><td>  0</td><td>  0</td><td> 0</td><td>  0</td><td>  0</td><td>  0</td><td>  0</td><td>  0</td><td>  0</td><td>  0</td></tr>\n",
       "\t<tr><th scope=row>T1P</th><td> 13</td><td>  0</td><td>  3</td><td>  0</td><td>  1</td><td>  4</td><td>  0</td><td> 13</td><td>  1</td><td>  2</td><td>⋯</td><td>  0</td><td>  0</td><td> 4</td><td>  2</td><td>  0</td><td>  0</td><td>  0</td><td>  5</td><td>  0</td><td>  1</td></tr>\n",
       "\t<tr><th scope=row>T2P</th><td>  0</td><td>  2</td><td> 26</td><td> 19</td><td>  5</td><td>  4</td><td>  2</td><td>267</td><td> 20</td><td> 18</td><td>⋯</td><td>  2</td><td> 20</td><td> 0</td><td> 16</td><td>  2</td><td> 30</td><td> 46</td><td>  1</td><td> 10</td><td>  6</td></tr>\n",
       "\t<tr><th scope=row>cDC</th><td> 13</td><td> 23</td><td> 30</td><td> 54</td><td> 19</td><td> 31</td><td> 15</td><td> 19</td><td> 57</td><td> 35</td><td>⋯</td><td> 29</td><td> 68</td><td>21</td><td> 45</td><td> 39</td><td> 10</td><td> 50</td><td> 51</td><td> 40</td><td> 28</td></tr>\n",
       "\t<tr><th scope=row>pDC</th><td>  0</td><td>  6</td><td>  3</td><td> 30</td><td>  6</td><td>  7</td><td>  2</td><td>  0</td><td> 60</td><td>  6</td><td>⋯</td><td> 10</td><td> 45</td><td> 7</td><td> 42</td><td> 14</td><td> 14</td><td> 14</td><td> 12</td><td> 21</td><td>  8</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$samples</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 41 × 15</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>group</th><th scope=col>lib.size</th><th scope=col>norm.factors</th><th scope=col>M.Number</th><th scope=col>treatment</th><th scope=col>infection.dose</th><th scope=col>Tissue.name</th><th scope=col>CFU.granuloma</th><th scope=col>total.thoracic.CFU</th><th scope=col>Array</th><th scope=col>lung.region</th><th scope=col>Gran.State</th><th scope=col>total_cells_elbow</th><th scope=col>monkey_condition_categories</th><th scope=col>tcell.count</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Array1_28918</th><td>1</td><td> 338</td><td>1</td><td>28918</td><td>CD8a</td><td>19</td><td>RLL Gr 2      </td><td>    260</td><td> 5301080</td><td>1</td><td>RLL</td><td>New</td><td>1154</td><td>1</td><td>606</td></tr>\n",
       "\t<tr><th scope=row>Array1_29218</th><td>1</td><td> 529</td><td>1</td><td>29218</td><td>CD8a</td><td>23</td><td>LLL gr 3      </td><td>    390</td><td> 2960670</td><td>1</td><td>LLL</td><td>New</td><td>1219</td><td>2</td><td>611</td></tr>\n",
       "\t<tr><th scope=row>Array1_29318</th><td>1</td><td> 657</td><td>1</td><td>29318</td><td>CD8a</td><td>18</td><td>RUL C         </td><td>  59000</td><td>19228424</td><td>1</td><td>RUL</td><td>New</td><td>1390</td><td>3</td><td>550</td></tr>\n",
       "\t<tr><th scope=row>Array1_29818</th><td>1</td><td>1066</td><td>1</td><td>29818</td><td>IgG </td><td>23</td><td>ACC GR 1      </td><td> 610000</td><td> 4066743</td><td>1</td><td>ACC</td><td>Old</td><td>1583</td><td>1</td><td> 77</td></tr>\n",
       "\t<tr><th scope=row>Array1_30518</th><td>1</td><td> 278</td><td>1</td><td>30518</td><td>CD8b</td><td>18</td><td>RUL Gr 14     </td><td>   2025</td><td> 3895065</td><td>1</td><td>RUL</td><td>New</td><td>1284</td><td>1</td><td>705</td></tr>\n",
       "\t<tr><th scope=row>Array2_28918</th><td>1</td><td> 421</td><td>1</td><td>28918</td><td>CD8a</td><td>19</td><td>Acc Gr 1      </td><td>    580</td><td> 5301080</td><td>2</td><td>ACC</td><td>New</td><td>1073</td><td>1</td><td>506</td></tr>\n",
       "\t<tr><th scope=row>Array2_29218</th><td>1</td><td> 434</td><td>1</td><td>29218</td><td>CD8a</td><td>23</td><td>ACC gr 3      </td><td>    570</td><td> 2960670</td><td>2</td><td>ACC</td><td>New</td><td>1252</td><td>2</td><td>693</td></tr>\n",
       "\t<tr><th scope=row>Array2_29318</th><td>1</td><td>1349</td><td>1</td><td>29318</td><td>CD8a</td><td>18</td><td>LLL 5         </td><td>  65200</td><td>19228424</td><td>2</td><td>LLL</td><td>New</td><td>1503</td><td>3</td><td> 63</td></tr>\n",
       "\t<tr><th scope=row>Array2_29818</th><td>1</td><td> 802</td><td>1</td><td>29818</td><td>IgG </td><td>23</td><td>ACC GR 2      </td><td> 224000</td><td> 4066743</td><td>2</td><td>ACC</td><td>Old</td><td>1542</td><td>1</td><td>124</td></tr>\n",
       "\t<tr><th scope=row>Array2_30518</th><td>1</td><td> 802</td><td>1</td><td>30518</td><td>CD8b</td><td>18</td><td>RLL Gr 44     </td><td>  34800</td><td> 3895065</td><td>2</td><td>RLL</td><td>New</td><td>1212</td><td>1</td><td>323</td></tr>\n",
       "\t<tr><th scope=row>Array3_28918</th><td>1</td><td>1061</td><td>1</td><td>28918</td><td>CD8a</td><td>19</td><td>LUL GR 1      </td><td> 108000</td><td> 5301080</td><td>3</td><td>LUL</td><td>Old</td><td>1262</td><td>1</td><td> 87</td></tr>\n",
       "\t<tr><th scope=row>Array3_29218</th><td>1</td><td> 754</td><td>1</td><td>29218</td><td>CD8a</td><td>23</td><td>RLL 7 Cluster </td><td> 414000</td><td> 2960670</td><td>3</td><td>RLL</td><td>Old</td><td>1219</td><td>2</td><td>245</td></tr>\n",
       "\t<tr><th scope=row>Array3_29318</th><td>1</td><td> 844</td><td>1</td><td>29318</td><td>CD8a</td><td>18</td><td>LML B         </td><td>   4400</td><td>19228424</td><td>3</td><td>LML</td><td>New</td><td>1251</td><td>3</td><td>275</td></tr>\n",
       "\t<tr><th scope=row>Array3_29818</th><td>1</td><td> 463</td><td>1</td><td>29818</td><td>IgG </td><td>23</td><td>ACC GR 15     </td><td>   5680</td><td> 4066743</td><td>3</td><td>ACC</td><td>New</td><td>1136</td><td>1</td><td>358</td></tr>\n",
       "\t<tr><th scope=row>Array3_30518</th><td>1</td><td> 832</td><td>1</td><td>30518</td><td>CD8b</td><td>18</td><td>RLL Gr 38     </td><td> 151800</td><td> 3895065</td><td>3</td><td>RLL</td><td>New</td><td>1284</td><td>1</td><td>259</td></tr>\n",
       "\t<tr><th scope=row>Array4_28918</th><td>1</td><td> 783</td><td>1</td><td>28918</td><td>CD8a</td><td>19</td><td>LUL Gr 4      </td><td>  84000</td><td> 5301080</td><td>4</td><td>LUL</td><td>Old</td><td>1136</td><td>1</td><td>168</td></tr>\n",
       "\t<tr><th scope=row>Array4_29218</th><td>1</td><td> 549</td><td>1</td><td>29218</td><td>CD8a</td><td>23</td><td>RLL gr 17     </td><td>   1470</td><td> 2960670</td><td>4</td><td>RLL</td><td>Old</td><td>1184</td><td>2</td><td>425</td></tr>\n",
       "\t<tr><th scope=row>Array4_29318</th><td>1</td><td> 495</td><td>1</td><td>29318</td><td>CD8a</td><td>18</td><td>LLL 13        </td><td> 149600</td><td>19228424</td><td>4</td><td>LLL</td><td>New</td><td>1211</td><td>3</td><td>559</td></tr>\n",
       "\t<tr><th scope=row>Array4_29818</th><td>1</td><td> 117</td><td>1</td><td>29818</td><td>IgG </td><td>23</td><td>ACC GR 11     </td><td> 224000</td><td> 4066743</td><td>4</td><td>RLL</td><td>Old</td><td> 572</td><td>1</td><td> 63</td></tr>\n",
       "\t<tr><th scope=row>Array4_30518</th><td>1</td><td> 753</td><td>1</td><td>30518</td><td>CD8b</td><td>18</td><td>RLL Gr 35     </td><td> 106800</td><td> 3895065</td><td>4</td><td>RLL</td><td>Old</td><td>1320</td><td>1</td><td>451</td></tr>\n",
       "\t<tr><th scope=row>Array5_28918</th><td>1</td><td> 930</td><td>1</td><td>28918</td><td>CD8a</td><td>19</td><td>LLL Gr 20     </td><td>  17800</td><td> 5301080</td><td>5</td><td>LLL</td><td>New</td><td>1371</td><td>1</td><td> 36</td></tr>\n",
       "\t<tr><th scope=row>Array6_28818</th><td>1</td><td> 909</td><td>1</td><td>28818</td><td>CD8b</td><td>19</td><td>RUL Gr 3      </td><td>  37200</td><td>10562333</td><td>6</td><td>RUL</td><td>New</td><td>1463</td><td>2</td><td> 75</td></tr>\n",
       "\t<tr><th scope=row>Array6_29118</th><td>1</td><td>1100</td><td>1</td><td>29118</td><td>CD8a</td><td>23</td><td>LLL GR 12     </td><td> 184000</td><td> 6466750</td><td>6</td><td>LLL</td><td>Old</td><td>1537</td><td>4</td><td> 79</td></tr>\n",
       "\t<tr><th scope=row>Array6_29618</th><td>1</td><td> 262</td><td>1</td><td>29618</td><td>IgG </td><td>23</td><td>LML gr 1      </td><td> 642000</td><td> 4976735</td><td>6</td><td>LML</td><td>Old</td><td>1379</td><td>2</td><td>565</td></tr>\n",
       "\t<tr><th scope=row>Array6_30218</th><td>1</td><td> 720</td><td>1</td><td>30218</td><td>CD8b</td><td>23</td><td>RLL 25        </td><td>   6200</td><td> 1670412</td><td>6</td><td>RLL</td><td>New</td><td>1449</td><td>3</td><td>510</td></tr>\n",
       "\t<tr><th scope=row>Array6_30418</th><td>1</td><td> 591</td><td>1</td><td>30418</td><td>IgG </td><td>18</td><td>RLL Gr 13     </td><td>  34500</td><td>  633245</td><td>6</td><td>RLL</td><td>Old</td><td>1248</td><td>3</td><td>424</td></tr>\n",
       "\t<tr><th scope=row>Array7_28818</th><td>1</td><td> 313</td><td>1</td><td>28818</td><td>CD8b</td><td>19</td><td>RUL Gr 5      </td><td> 765000</td><td>10562333</td><td>7</td><td>RUL</td><td>Old</td><td> 665</td><td>2</td><td>218</td></tr>\n",
       "\t<tr><th scope=row>Array7_29118</th><td>1</td><td>1119</td><td>1</td><td>29118</td><td>CD8a</td><td>23</td><td>LLL GR 39     </td><td>  10500</td><td> 6466750</td><td>7</td><td>LLL</td><td>New</td><td>1532</td><td>4</td><td>175</td></tr>\n",
       "\t<tr><th scope=row>Array7_29618</th><td>1</td><td> 616</td><td>1</td><td>29618</td><td>IgG </td><td>23</td><td>LLL gr 4      </td><td>2340000</td><td> 4976735</td><td>7</td><td>LLL</td><td>Old</td><td>1210</td><td>2</td><td>391</td></tr>\n",
       "\t<tr><th scope=row>Array7_30218</th><td>1</td><td> 490</td><td>1</td><td>30218</td><td>CD8b</td><td>23</td><td>RLL 15        </td><td>  11200</td><td> 1670412</td><td>7</td><td>RLL</td><td>New</td><td>1499</td><td>3</td><td>742</td></tr>\n",
       "\t<tr><th scope=row>Array7_30418</th><td>1</td><td> 658</td><td>1</td><td>30418</td><td>IgG </td><td>18</td><td>RLL Gr 15     </td><td> 204000</td><td>  633245</td><td>7</td><td>RLL</td><td>Old</td><td>1155</td><td>3</td><td>362</td></tr>\n",
       "\t<tr><th scope=row>Array8_28818</th><td>1</td><td> 662</td><td>1</td><td>28818</td><td>CD8b</td><td>19</td><td>RLL Gr 3      </td><td>  62400</td><td>10562333</td><td>8</td><td>RLL</td><td>Old</td><td>1178</td><td>2</td><td>223</td></tr>\n",
       "\t<tr><th scope=row>Array8_29118</th><td>1</td><td>1192</td><td>1</td><td>29118</td><td>CD8a</td><td>23</td><td>LLL GR 41     </td><td>  56400</td><td> 6466750</td><td>8</td><td>LLL</td><td>Old</td><td>1573</td><td>4</td><td>110</td></tr>\n",
       "\t<tr><th scope=row>Array8_29618</th><td>1</td><td> 261</td><td>1</td><td>29618</td><td>IgG </td><td>23</td><td>LLL gr 9      </td><td> 216000</td><td> 4976735</td><td>8</td><td>LLL</td><td>Old</td><td>1214</td><td>2</td><td>748</td></tr>\n",
       "\t<tr><th scope=row>Array8_30218</th><td>1</td><td> 561</td><td>1</td><td>30218</td><td>CD8b</td><td>23</td><td>RLL 10        </td><td>  75600</td><td> 1670412</td><td>8</td><td>RLL</td><td>Old</td><td>1528</td><td>3</td><td>536</td></tr>\n",
       "\t<tr><th scope=row>Array8_30418</th><td>1</td><td> 485</td><td>1</td><td>30418</td><td>IgG </td><td>18</td><td>RLL Gr 7      </td><td>  17600</td><td>  633245</td><td>8</td><td>RLL</td><td>Old</td><td>1315</td><td>3</td><td>599</td></tr>\n",
       "\t<tr><th scope=row>Array9_28818</th><td>1</td><td> 843</td><td>1</td><td>28818</td><td>CD8b</td><td>19</td><td>RLL Gr 7      </td><td> 535000</td><td>10562333</td><td>9</td><td>RLL</td><td>Old</td><td>1275</td><td>2</td><td> 13</td></tr>\n",
       "\t<tr><th scope=row>Array9_29118</th><td>1</td><td> 819</td><td>1</td><td>29118</td><td>CD8a</td><td>23</td><td>LLL GR 48     </td><td> 588000</td><td> 6466750</td><td>9</td><td>LLL</td><td>Old</td><td>1324</td><td>4</td><td> 76</td></tr>\n",
       "\t<tr><th scope=row>Array9_29618</th><td>1</td><td> 563</td><td>1</td><td>29618</td><td>IgG </td><td>23</td><td>LLL gr 12     </td><td> 618000</td><td> 4976735</td><td>9</td><td>LLL</td><td>Old</td><td>1242</td><td>2</td><td>494</td></tr>\n",
       "\t<tr><th scope=row>Array9_30218</th><td>1</td><td> 397</td><td>1</td><td>30218</td><td>CD8b</td><td>23</td><td>RLL 18        </td><td>   5640</td><td> 1670412</td><td>9</td><td>RLL</td><td>New</td><td>1328</td><td>3</td><td>788</td></tr>\n",
       "\t<tr><th scope=row>Array9_30418</th><td>1</td><td> 440</td><td>1</td><td>30418</td><td>IgG </td><td>18</td><td>RLL Gr 6      </td><td>  11400</td><td>  633245</td><td>9</td><td>RLL</td><td>New</td><td>1291</td><td>3</td><td>406</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$counts] A matrix: 12 × 41 of type int\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & Array1\\_28918 & Array1\\_29218 & Array1\\_29318 & Array1\\_29818 & Array1\\_30518 & Array2\\_28918 & Array2\\_29218 & Array2\\_29318 & Array2\\_29818 & Array2\\_30518 & ⋯ & Array8\\_28818 & Array8\\_29118 & Array8\\_29618 & Array8\\_30218 & Array8\\_30418 & Array9\\_28818 & Array9\\_29118 & Array9\\_29618 & Array9\\_30218 & Array9\\_30418\\\\\n",
       "\\hline\n",
       "\tB.Cells &  10 &  40 &  36 &   6 &  36 &  13 &  20 &   1 &  65 &  23 & ⋯ &  14 &  17 & 85 & 193 &  18 &   1 &  12 &  39 & 129 &  26\\\\\n",
       "\tEndothelial &  46 &   7 &  31 &  19 &   1 &  13 &   9 &  22 &  16 &   1 & ⋯ &   8 &   5 &  6 &  26 &   8 &   8 &  11 &   5 &  12 &  23\\\\\n",
       "\tFibroblasts &  33 &   3 &   6 &  57 &   0 &   1 &  41 &  17 &   8 &   5 & ⋯ &   6 &  14 &  5 &   9 &   0 &   9 &   5 &   8 &   1 &   9\\\\\n",
       "\tMacrophages.Monocytes & 181 & 409 & 453 & 799 & 171 & 325 & 267 & 975 & 274 & 595 & ⋯ & 448 & 958 & 97 & 137 & 334 & 758 & 581 & 389 & 143 & 301\\\\\n",
       "\tMast &   2 &  23 &  17 &  57 &  29 &  20 &  64 &  16 & 181 & 114 & ⋯ & 140 &  41 & 26 &  54 &  66 &   7 &  88 &  37 &  21 &  20\\\\\n",
       "\tNeutrophils &  35 &   0 &  35 &   1 &   6 &   0 &   4 &   0 &   0 &   0 & ⋯ &   1 &   1 &  1 &   2 &   0 &   0 &   1 &   1 &  11 &   1\\\\\n",
       "\tPlasma.Cells &   3 &  16 &  16 &  24 &   4 &   3 &  10 &  19 & 120 &   3 & ⋯ &   4 &  23 &  9 &  35 &   4 &   6 &  11 &  15 &   9 &  17\\\\\n",
       "\tSecretory.Epithelial &   2 &   0 &   1 &   0 &   0 &   0 &   0 &   0 &   0 &   0 & ⋯ &   0 &   0 &  0 &   0 &   0 &   0 &   0 &   0 &   0 &   0\\\\\n",
       "\tT1P &  13 &   0 &   3 &   0 &   1 &   4 &   0 &  13 &   1 &   2 & ⋯ &   0 &   0 &  4 &   2 &   0 &   0 &   0 &   5 &   0 &   1\\\\\n",
       "\tT2P &   0 &   2 &  26 &  19 &   5 &   4 &   2 & 267 &  20 &  18 & ⋯ &   2 &  20 &  0 &  16 &   2 &  30 &  46 &   1 &  10 &   6\\\\\n",
       "\tcDC &  13 &  23 &  30 &  54 &  19 &  31 &  15 &  19 &  57 &  35 & ⋯ &  29 &  68 & 21 &  45 &  39 &  10 &  50 &  51 &  40 &  28\\\\\n",
       "\tpDC &   0 &   6 &   3 &  30 &   6 &   7 &   2 &   0 &  60 &   6 & ⋯ &  10 &  45 &  7 &  42 &  14 &  14 &  14 &  12 &  21 &   8\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$samples] A data.frame: 41 × 15\n",
       "\\begin{tabular}{r|lllllllllllllll}\n",
       "  & group & lib.size & norm.factors & M.Number & treatment & infection.dose & Tissue.name & CFU.granuloma & total.thoracic.CFU & Array & lung.region & Gran.State & total\\_cells\\_elbow & monkey\\_condition\\_categories & tcell.count\\\\\n",
       "  & <fct> & <dbl> & <dbl> & <fct> & <chr> & <int> & <chr> & <int> & <int> & <int> & <chr> & <chr> & <int> & <fct> & <int>\\\\\n",
       "\\hline\n",
       "\tArray1\\_28918 & 1 &  338 & 1 & 28918 & CD8a & 19 & RLL Gr 2       &     260 &  5301080 & 1 & RLL & New & 1154 & 1 & 606\\\\\n",
       "\tArray1\\_29218 & 1 &  529 & 1 & 29218 & CD8a & 23 & LLL gr 3       &     390 &  2960670 & 1 & LLL & New & 1219 & 2 & 611\\\\\n",
       "\tArray1\\_29318 & 1 &  657 & 1 & 29318 & CD8a & 18 & RUL C          &   59000 & 19228424 & 1 & RUL & New & 1390 & 3 & 550\\\\\n",
       "\tArray1\\_29818 & 1 & 1066 & 1 & 29818 & IgG  & 23 & ACC GR 1       &  610000 &  4066743 & 1 & ACC & Old & 1583 & 1 &  77\\\\\n",
       "\tArray1\\_30518 & 1 &  278 & 1 & 30518 & CD8b & 18 & RUL Gr 14      &    2025 &  3895065 & 1 & RUL & New & 1284 & 1 & 705\\\\\n",
       "\tArray2\\_28918 & 1 &  421 & 1 & 28918 & CD8a & 19 & Acc Gr 1       &     580 &  5301080 & 2 & ACC & New & 1073 & 1 & 506\\\\\n",
       "\tArray2\\_29218 & 1 &  434 & 1 & 29218 & CD8a & 23 & ACC gr 3       &     570 &  2960670 & 2 & ACC & New & 1252 & 2 & 693\\\\\n",
       "\tArray2\\_29318 & 1 & 1349 & 1 & 29318 & CD8a & 18 & LLL 5          &   65200 & 19228424 & 2 & LLL & New & 1503 & 3 &  63\\\\\n",
       "\tArray2\\_29818 & 1 &  802 & 1 & 29818 & IgG  & 23 & ACC GR 2       &  224000 &  4066743 & 2 & ACC & Old & 1542 & 1 & 124\\\\\n",
       "\tArray2\\_30518 & 1 &  802 & 1 & 30518 & CD8b & 18 & RLL Gr 44      &   34800 &  3895065 & 2 & RLL & New & 1212 & 1 & 323\\\\\n",
       "\tArray3\\_28918 & 1 & 1061 & 1 & 28918 & CD8a & 19 & LUL GR 1       &  108000 &  5301080 & 3 & LUL & Old & 1262 & 1 &  87\\\\\n",
       "\tArray3\\_29218 & 1 &  754 & 1 & 29218 & CD8a & 23 & RLL 7 Cluster  &  414000 &  2960670 & 3 & RLL & Old & 1219 & 2 & 245\\\\\n",
       "\tArray3\\_29318 & 1 &  844 & 1 & 29318 & CD8a & 18 & LML B          &    4400 & 19228424 & 3 & LML & New & 1251 & 3 & 275\\\\\n",
       "\tArray3\\_29818 & 1 &  463 & 1 & 29818 & IgG  & 23 & ACC GR 15      &    5680 &  4066743 & 3 & ACC & New & 1136 & 1 & 358\\\\\n",
       "\tArray3\\_30518 & 1 &  832 & 1 & 30518 & CD8b & 18 & RLL Gr 38      &  151800 &  3895065 & 3 & RLL & New & 1284 & 1 & 259\\\\\n",
       "\tArray4\\_28918 & 1 &  783 & 1 & 28918 & CD8a & 19 & LUL Gr 4       &   84000 &  5301080 & 4 & LUL & Old & 1136 & 1 & 168\\\\\n",
       "\tArray4\\_29218 & 1 &  549 & 1 & 29218 & CD8a & 23 & RLL gr 17      &    1470 &  2960670 & 4 & RLL & Old & 1184 & 2 & 425\\\\\n",
       "\tArray4\\_29318 & 1 &  495 & 1 & 29318 & CD8a & 18 & LLL 13         &  149600 & 19228424 & 4 & LLL & New & 1211 & 3 & 559\\\\\n",
       "\tArray4\\_29818 & 1 &  117 & 1 & 29818 & IgG  & 23 & ACC GR 11      &  224000 &  4066743 & 4 & RLL & Old &  572 & 1 &  63\\\\\n",
       "\tArray4\\_30518 & 1 &  753 & 1 & 30518 & CD8b & 18 & RLL Gr 35      &  106800 &  3895065 & 4 & RLL & Old & 1320 & 1 & 451\\\\\n",
       "\tArray5\\_28918 & 1 &  930 & 1 & 28918 & CD8a & 19 & LLL Gr 20      &   17800 &  5301080 & 5 & LLL & New & 1371 & 1 &  36\\\\\n",
       "\tArray6\\_28818 & 1 &  909 & 1 & 28818 & CD8b & 19 & RUL Gr 3       &   37200 & 10562333 & 6 & RUL & New & 1463 & 2 &  75\\\\\n",
       "\tArray6\\_29118 & 1 & 1100 & 1 & 29118 & CD8a & 23 & LLL GR 12      &  184000 &  6466750 & 6 & LLL & Old & 1537 & 4 &  79\\\\\n",
       "\tArray6\\_29618 & 1 &  262 & 1 & 29618 & IgG  & 23 & LML gr 1       &  642000 &  4976735 & 6 & LML & Old & 1379 & 2 & 565\\\\\n",
       "\tArray6\\_30218 & 1 &  720 & 1 & 30218 & CD8b & 23 & RLL 25         &    6200 &  1670412 & 6 & RLL & New & 1449 & 3 & 510\\\\\n",
       "\tArray6\\_30418 & 1 &  591 & 1 & 30418 & IgG  & 18 & RLL Gr 13      &   34500 &   633245 & 6 & RLL & Old & 1248 & 3 & 424\\\\\n",
       "\tArray7\\_28818 & 1 &  313 & 1 & 28818 & CD8b & 19 & RUL Gr 5       &  765000 & 10562333 & 7 & RUL & Old &  665 & 2 & 218\\\\\n",
       "\tArray7\\_29118 & 1 & 1119 & 1 & 29118 & CD8a & 23 & LLL GR 39      &   10500 &  6466750 & 7 & LLL & New & 1532 & 4 & 175\\\\\n",
       "\tArray7\\_29618 & 1 &  616 & 1 & 29618 & IgG  & 23 & LLL gr 4       & 2340000 &  4976735 & 7 & LLL & Old & 1210 & 2 & 391\\\\\n",
       "\tArray7\\_30218 & 1 &  490 & 1 & 30218 & CD8b & 23 & RLL 15         &   11200 &  1670412 & 7 & RLL & New & 1499 & 3 & 742\\\\\n",
       "\tArray7\\_30418 & 1 &  658 & 1 & 30418 & IgG  & 18 & RLL Gr 15      &  204000 &   633245 & 7 & RLL & Old & 1155 & 3 & 362\\\\\n",
       "\tArray8\\_28818 & 1 &  662 & 1 & 28818 & CD8b & 19 & RLL Gr 3       &   62400 & 10562333 & 8 & RLL & Old & 1178 & 2 & 223\\\\\n",
       "\tArray8\\_29118 & 1 & 1192 & 1 & 29118 & CD8a & 23 & LLL GR 41      &   56400 &  6466750 & 8 & LLL & Old & 1573 & 4 & 110\\\\\n",
       "\tArray8\\_29618 & 1 &  261 & 1 & 29618 & IgG  & 23 & LLL gr 9       &  216000 &  4976735 & 8 & LLL & Old & 1214 & 2 & 748\\\\\n",
       "\tArray8\\_30218 & 1 &  561 & 1 & 30218 & CD8b & 23 & RLL 10         &   75600 &  1670412 & 8 & RLL & Old & 1528 & 3 & 536\\\\\n",
       "\tArray8\\_30418 & 1 &  485 & 1 & 30418 & IgG  & 18 & RLL Gr 7       &   17600 &   633245 & 8 & RLL & Old & 1315 & 3 & 599\\\\\n",
       "\tArray9\\_28818 & 1 &  843 & 1 & 28818 & CD8b & 19 & RLL Gr 7       &  535000 & 10562333 & 9 & RLL & Old & 1275 & 2 &  13\\\\\n",
       "\tArray9\\_29118 & 1 &  819 & 1 & 29118 & CD8a & 23 & LLL GR 48      &  588000 &  6466750 & 9 & LLL & Old & 1324 & 4 &  76\\\\\n",
       "\tArray9\\_29618 & 1 &  563 & 1 & 29618 & IgG  & 23 & LLL gr 12      &  618000 &  4976735 & 9 & LLL & Old & 1242 & 2 & 494\\\\\n",
       "\tArray9\\_30218 & 1 &  397 & 1 & 30218 & CD8b & 23 & RLL 18         &    5640 &  1670412 & 9 & RLL & New & 1328 & 3 & 788\\\\\n",
       "\tArray9\\_30418 & 1 &  440 & 1 & 30418 & IgG  & 18 & RLL Gr 6       &   11400 &   633245 & 9 & RLL & New & 1291 & 3 & 406\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$counts\n",
       ":   \n",
       "A matrix: 12 × 41 of type int\n",
       "\n",
       "| <!--/--> | Array1_28918 | Array1_29218 | Array1_29318 | Array1_29818 | Array1_30518 | Array2_28918 | Array2_29218 | Array2_29318 | Array2_29818 | Array2_30518 | ⋯ | Array8_28818 | Array8_29118 | Array8_29618 | Array8_30218 | Array8_30418 | Array9_28818 | Array9_29118 | Array9_29618 | Array9_30218 | Array9_30418 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| B.Cells |  10 |  40 |  36 |   6 |  36 |  13 |  20 |   1 |  65 |  23 | ⋯ |  14 |  17 | 85 | 193 |  18 |   1 |  12 |  39 | 129 |  26 |\n",
       "| Endothelial |  46 |   7 |  31 |  19 |   1 |  13 |   9 |  22 |  16 |   1 | ⋯ |   8 |   5 |  6 |  26 |   8 |   8 |  11 |   5 |  12 |  23 |\n",
       "| Fibroblasts |  33 |   3 |   6 |  57 |   0 |   1 |  41 |  17 |   8 |   5 | ⋯ |   6 |  14 |  5 |   9 |   0 |   9 |   5 |   8 |   1 |   9 |\n",
       "| Macrophages.Monocytes | 181 | 409 | 453 | 799 | 171 | 325 | 267 | 975 | 274 | 595 | ⋯ | 448 | 958 | 97 | 137 | 334 | 758 | 581 | 389 | 143 | 301 |\n",
       "| Mast |   2 |  23 |  17 |  57 |  29 |  20 |  64 |  16 | 181 | 114 | ⋯ | 140 |  41 | 26 |  54 |  66 |   7 |  88 |  37 |  21 |  20 |\n",
       "| Neutrophils |  35 |   0 |  35 |   1 |   6 |   0 |   4 |   0 |   0 |   0 | ⋯ |   1 |   1 |  1 |   2 |   0 |   0 |   1 |   1 |  11 |   1 |\n",
       "| Plasma.Cells |   3 |  16 |  16 |  24 |   4 |   3 |  10 |  19 | 120 |   3 | ⋯ |   4 |  23 |  9 |  35 |   4 |   6 |  11 |  15 |   9 |  17 |\n",
       "| Secretory.Epithelial |   2 |   0 |   1 |   0 |   0 |   0 |   0 |   0 |   0 |   0 | ⋯ |   0 |   0 |  0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
       "| T1P |  13 |   0 |   3 |   0 |   1 |   4 |   0 |  13 |   1 |   2 | ⋯ |   0 |   0 |  4 |   2 |   0 |   0 |   0 |   5 |   0 |   1 |\n",
       "| T2P |   0 |   2 |  26 |  19 |   5 |   4 |   2 | 267 |  20 |  18 | ⋯ |   2 |  20 |  0 |  16 |   2 |  30 |  46 |   1 |  10 |   6 |\n",
       "| cDC |  13 |  23 |  30 |  54 |  19 |  31 |  15 |  19 |  57 |  35 | ⋯ |  29 |  68 | 21 |  45 |  39 |  10 |  50 |  51 |  40 |  28 |\n",
       "| pDC |   0 |   6 |   3 |  30 |   6 |   7 |   2 |   0 |  60 |   6 | ⋯ |  10 |  45 |  7 |  42 |  14 |  14 |  14 |  12 |  21 |   8 |\n",
       "\n",
       "\n",
       "$samples\n",
       ":   \n",
       "A data.frame: 41 × 15\n",
       "\n",
       "| <!--/--> | group &lt;fct&gt; | lib.size &lt;dbl&gt; | norm.factors &lt;dbl&gt; | M.Number &lt;fct&gt; | treatment &lt;chr&gt; | infection.dose &lt;int&gt; | Tissue.name &lt;chr&gt; | CFU.granuloma &lt;int&gt; | total.thoracic.CFU &lt;int&gt; | Array &lt;int&gt; | lung.region &lt;chr&gt; | Gran.State &lt;chr&gt; | total_cells_elbow &lt;int&gt; | monkey_condition_categories &lt;fct&gt; | tcell.count &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Array1_28918 | 1 |  338 | 1 | 28918 | CD8a | 19 | RLL Gr 2       |     260 |  5301080 | 1 | RLL | New | 1154 | 1 | 606 |\n",
       "| Array1_29218 | 1 |  529 | 1 | 29218 | CD8a | 23 | LLL gr 3       |     390 |  2960670 | 1 | LLL | New | 1219 | 2 | 611 |\n",
       "| Array1_29318 | 1 |  657 | 1 | 29318 | CD8a | 18 | RUL C          |   59000 | 19228424 | 1 | RUL | New | 1390 | 3 | 550 |\n",
       "| Array1_29818 | 1 | 1066 | 1 | 29818 | IgG  | 23 | ACC GR 1       |  610000 |  4066743 | 1 | ACC | Old | 1583 | 1 |  77 |\n",
       "| Array1_30518 | 1 |  278 | 1 | 30518 | CD8b | 18 | RUL Gr 14      |    2025 |  3895065 | 1 | RUL | New | 1284 | 1 | 705 |\n",
       "| Array2_28918 | 1 |  421 | 1 | 28918 | CD8a | 19 | Acc Gr 1       |     580 |  5301080 | 2 | ACC | New | 1073 | 1 | 506 |\n",
       "| Array2_29218 | 1 |  434 | 1 | 29218 | CD8a | 23 | ACC gr 3       |     570 |  2960670 | 2 | ACC | New | 1252 | 2 | 693 |\n",
       "| Array2_29318 | 1 | 1349 | 1 | 29318 | CD8a | 18 | LLL 5          |   65200 | 19228424 | 2 | LLL | New | 1503 | 3 |  63 |\n",
       "| Array2_29818 | 1 |  802 | 1 | 29818 | IgG  | 23 | ACC GR 2       |  224000 |  4066743 | 2 | ACC | Old | 1542 | 1 | 124 |\n",
       "| Array2_30518 | 1 |  802 | 1 | 30518 | CD8b | 18 | RLL Gr 44      |   34800 |  3895065 | 2 | RLL | New | 1212 | 1 | 323 |\n",
       "| Array3_28918 | 1 | 1061 | 1 | 28918 | CD8a | 19 | LUL GR 1       |  108000 |  5301080 | 3 | LUL | Old | 1262 | 1 |  87 |\n",
       "| Array3_29218 | 1 |  754 | 1 | 29218 | CD8a | 23 | RLL 7 Cluster  |  414000 |  2960670 | 3 | RLL | Old | 1219 | 2 | 245 |\n",
       "| Array3_29318 | 1 |  844 | 1 | 29318 | CD8a | 18 | LML B          |    4400 | 19228424 | 3 | LML | New | 1251 | 3 | 275 |\n",
       "| Array3_29818 | 1 |  463 | 1 | 29818 | IgG  | 23 | ACC GR 15      |    5680 |  4066743 | 3 | ACC | New | 1136 | 1 | 358 |\n",
       "| Array3_30518 | 1 |  832 | 1 | 30518 | CD8b | 18 | RLL Gr 38      |  151800 |  3895065 | 3 | RLL | New | 1284 | 1 | 259 |\n",
       "| Array4_28918 | 1 |  783 | 1 | 28918 | CD8a | 19 | LUL Gr 4       |   84000 |  5301080 | 4 | LUL | Old | 1136 | 1 | 168 |\n",
       "| Array4_29218 | 1 |  549 | 1 | 29218 | CD8a | 23 | RLL gr 17      |    1470 |  2960670 | 4 | RLL | Old | 1184 | 2 | 425 |\n",
       "| Array4_29318 | 1 |  495 | 1 | 29318 | CD8a | 18 | LLL 13         |  149600 | 19228424 | 4 | LLL | New | 1211 | 3 | 559 |\n",
       "| Array4_29818 | 1 |  117 | 1 | 29818 | IgG  | 23 | ACC GR 11      |  224000 |  4066743 | 4 | RLL | Old |  572 | 1 |  63 |\n",
       "| Array4_30518 | 1 |  753 | 1 | 30518 | CD8b | 18 | RLL Gr 35      |  106800 |  3895065 | 4 | RLL | Old | 1320 | 1 | 451 |\n",
       "| Array5_28918 | 1 |  930 | 1 | 28918 | CD8a | 19 | LLL Gr 20      |   17800 |  5301080 | 5 | LLL | New | 1371 | 1 |  36 |\n",
       "| Array6_28818 | 1 |  909 | 1 | 28818 | CD8b | 19 | RUL Gr 3       |   37200 | 10562333 | 6 | RUL | New | 1463 | 2 |  75 |\n",
       "| Array6_29118 | 1 | 1100 | 1 | 29118 | CD8a | 23 | LLL GR 12      |  184000 |  6466750 | 6 | LLL | Old | 1537 | 4 |  79 |\n",
       "| Array6_29618 | 1 |  262 | 1 | 29618 | IgG  | 23 | LML gr 1       |  642000 |  4976735 | 6 | LML | Old | 1379 | 2 | 565 |\n",
       "| Array6_30218 | 1 |  720 | 1 | 30218 | CD8b | 23 | RLL 25         |    6200 |  1670412 | 6 | RLL | New | 1449 | 3 | 510 |\n",
       "| Array6_30418 | 1 |  591 | 1 | 30418 | IgG  | 18 | RLL Gr 13      |   34500 |   633245 | 6 | RLL | Old | 1248 | 3 | 424 |\n",
       "| Array7_28818 | 1 |  313 | 1 | 28818 | CD8b | 19 | RUL Gr 5       |  765000 | 10562333 | 7 | RUL | Old |  665 | 2 | 218 |\n",
       "| Array7_29118 | 1 | 1119 | 1 | 29118 | CD8a | 23 | LLL GR 39      |   10500 |  6466750 | 7 | LLL | New | 1532 | 4 | 175 |\n",
       "| Array7_29618 | 1 |  616 | 1 | 29618 | IgG  | 23 | LLL gr 4       | 2340000 |  4976735 | 7 | LLL | Old | 1210 | 2 | 391 |\n",
       "| Array7_30218 | 1 |  490 | 1 | 30218 | CD8b | 23 | RLL 15         |   11200 |  1670412 | 7 | RLL | New | 1499 | 3 | 742 |\n",
       "| Array7_30418 | 1 |  658 | 1 | 30418 | IgG  | 18 | RLL Gr 15      |  204000 |   633245 | 7 | RLL | Old | 1155 | 3 | 362 |\n",
       "| Array8_28818 | 1 |  662 | 1 | 28818 | CD8b | 19 | RLL Gr 3       |   62400 | 10562333 | 8 | RLL | Old | 1178 | 2 | 223 |\n",
       "| Array8_29118 | 1 | 1192 | 1 | 29118 | CD8a | 23 | LLL GR 41      |   56400 |  6466750 | 8 | LLL | Old | 1573 | 4 | 110 |\n",
       "| Array8_29618 | 1 |  261 | 1 | 29618 | IgG  | 23 | LLL gr 9       |  216000 |  4976735 | 8 | LLL | Old | 1214 | 2 | 748 |\n",
       "| Array8_30218 | 1 |  561 | 1 | 30218 | CD8b | 23 | RLL 10         |   75600 |  1670412 | 8 | RLL | Old | 1528 | 3 | 536 |\n",
       "| Array8_30418 | 1 |  485 | 1 | 30418 | IgG  | 18 | RLL Gr 7       |   17600 |   633245 | 8 | RLL | Old | 1315 | 3 | 599 |\n",
       "| Array9_28818 | 1 |  843 | 1 | 28818 | CD8b | 19 | RLL Gr 7       |  535000 | 10562333 | 9 | RLL | Old | 1275 | 2 |  13 |\n",
       "| Array9_29118 | 1 |  819 | 1 | 29118 | CD8a | 23 | LLL GR 48      |  588000 |  6466750 | 9 | LLL | Old | 1324 | 4 |  76 |\n",
       "| Array9_29618 | 1 |  563 | 1 | 29618 | IgG  | 23 | LLL gr 12      |  618000 |  4976735 | 9 | LLL | Old | 1242 | 2 | 494 |\n",
       "| Array9_30218 | 1 |  397 | 1 | 30218 | CD8b | 23 | RLL 18         |    5640 |  1670412 | 9 | RLL | New | 1328 | 3 | 788 |\n",
       "| Array9_30418 | 1 |  440 | 1 | 30418 | IgG  | 18 | RLL Gr 6       |   11400 |   633245 | 9 | RLL | New | 1291 | 3 | 406 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "An object of class \"DGEList\"\n",
       "$counts\n",
       "                      Array1_28918 Array1_29218 Array1_29318 Array1_29818\n",
       "B.Cells                         10           40           36            6\n",
       "Endothelial                     46            7           31           19\n",
       "Fibroblasts                     33            3            6           57\n",
       "Macrophages.Monocytes          181          409          453          799\n",
       "Mast                             2           23           17           57\n",
       "                      Array1_30518 Array2_28918 Array2_29218 Array2_29318\n",
       "B.Cells                         36           13           20            1\n",
       "Endothelial                      1           13            9           22\n",
       "Fibroblasts                      0            1           41           17\n",
       "Macrophages.Monocytes          171          325          267          975\n",
       "Mast                            29           20           64           16\n",
       "                      Array2_29818 Array2_30518 Array3_28918 Array3_29218\n",
       "B.Cells                         65           23           11           16\n",
       "Endothelial                     16            1           37           60\n",
       "Fibroblasts                      8            5           64           42\n",
       "Macrophages.Monocytes          274          595          728          479\n",
       "Mast                           181          114           51           43\n",
       "                      Array3_29318 Array3_29818 Array3_30518 Array4_28918\n",
       "B.Cells                          6           78            6           19\n",
       "Endothelial                     21            9            3           13\n",
       "Fibroblasts                      4            7            7            9\n",
       "Macrophages.Monocytes          733          170          638          635\n",
       "Mast                             7          119           81           31\n",
       "                      Array4_29218 Array4_29318 Array4_29818 Array4_30518\n",
       "B.Cells                         44           12           27           17\n",
       "Endothelial                      8           11            1            7\n",
       "Fibroblasts                      0            4            0            5\n",
       "Macrophages.Monocytes          332          387           21          547\n",
       "Mast                           117           31           21           84\n",
       "                      Array5_28918 Array6_28818 Array6_29118 Array6_29618\n",
       "B.Cells                          0           10           19           18\n",
       "Endothelial                      5           54           21           33\n",
       "Fibroblasts                      3           16           42           25\n",
       "Macrophages.Monocytes          850          637          808           88\n",
       "Mast                            16           36           59           33\n",
       "                      Array6_30218 Array6_30418 Array7_28818 Array7_29118\n",
       "B.Cells                         83           28           15           22\n",
       "Endothelial                     20            4           16           10\n",
       "Fibroblasts                      5            1            3           13\n",
       "Macrophages.Monocytes          325          421          160          792\n",
       "Mast                            72           64           89           47\n",
       "                      Array7_29618 Array7_30218 Array7_30418 Array8_28818\n",
       "B.Cells                         54           98           19           14\n",
       "Endothelial                     26            8            6            8\n",
       "Fibroblasts                     99            1            0            6\n",
       "Macrophages.Monocytes          232          216          423          448\n",
       "Mast                            32           43          161          140\n",
       "                      Array8_29118 Array8_29618 Array8_30218 Array8_30418\n",
       "B.Cells                         17           85          193           18\n",
       "Endothelial                      5            6           26            8\n",
       "Fibroblasts                     14            5            9            0\n",
       "Macrophages.Monocytes          958           97          137          334\n",
       "Mast                            41           26           54           66\n",
       "                      Array9_28818 Array9_29118 Array9_29618 Array9_30218\n",
       "B.Cells                          1           12           39          129\n",
       "Endothelial                      8           11            5           12\n",
       "Fibroblasts                      9            5            8            1\n",
       "Macrophages.Monocytes          758          581          389          143\n",
       "Mast                             7           88           37           21\n",
       "                      Array9_30418\n",
       "B.Cells                         26\n",
       "Endothelial                     23\n",
       "Fibroblasts                      9\n",
       "Macrophages.Monocytes          301\n",
       "Mast                            20\n",
       "7 more rows ...\n",
       "\n",
       "$samples\n",
       "             group lib.size norm.factors M.Number treatment infection.dose\n",
       "Array1_28918     1      338            1    28918      CD8a             19\n",
       "Array1_29218     1      529            1    29218      CD8a             23\n",
       "Array1_29318     1      657            1    29318      CD8a             18\n",
       "Array1_29818     1     1066            1    29818       IgG             23\n",
       "Array1_30518     1      278            1    30518      CD8b             18\n",
       "             Tissue.name CFU.granuloma total.thoracic.CFU Array lung.region\n",
       "Array1_28918    RLL Gr 2           260            5301080     1         RLL\n",
       "Array1_29218    LLL gr 3           390            2960670     1         LLL\n",
       "Array1_29318       RUL C         59000           19228424     1         RUL\n",
       "Array1_29818    ACC GR 1        610000            4066743     1         ACC\n",
       "Array1_30518   RUL Gr 14          2025            3895065     1         RUL\n",
       "             Gran.State total_cells_elbow monkey_condition_categories\n",
       "Array1_28918        New              1154                           1\n",
       "Array1_29218        New              1219                           2\n",
       "Array1_29318        New              1390                           3\n",
       "Array1_29818        Old              1583                           1\n",
       "Array1_30518        New              1284                           1\n",
       "             tcell.count\n",
       "Array1_28918         606\n",
       "Array1_29218         611\n",
       "Array1_29318         550\n",
       "Array1_29818          77\n",
       "Array1_30518         705\n",
       "36 more rows ...\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.ab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "ef68bf64",
   "metadata": {},
   "outputs": [],
   "source": [
    "#keep <- filterByExpr(y.ab, group=y.ab$samples$treatment)\n",
    "#y.ab <- y.ab[keep,]\n",
    "#summary(keep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "2dbc8a3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "design <- model.matrix(~0+factor(monkey_condition_categories) + factor(treatment), y.ab$samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "60a19ecd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.8128  0.8128  0.8128  0.8128  0.8128  0.8128 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.ab <- estimateDisp(y.ab, design, trend=\"none\")\n",
    "summary(y.ab$common.dispersion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "935788ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AECBAgQIECAAIEVF1AgrTixDyBAgAABAgQIECBAoCsCCqSunCnHSYAAAQIECBAg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\nSUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotBCV(y.ab, cex=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ada4f51c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "b9d221a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.7383  0.7383  0.7383  0.7383  0.7383  0.7383 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit.ab <- glmQLFit(y.ab, design, robust=TRUE, abundance.trend=FALSE)\n",
    "summary(fit.ab$var.prior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "959cb8ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DyD1AfFJAIECBAgQGBsgVl7BqkfUD1nVM8lvbLfzM60KkzO7PTpPsNURU21g5Iv\nJDW9rkad13n/X3m9XdJt9b76/E3yzc77rZ3XuvXvSUlvu0U+XJjUMucm1ees5KNJTfMMUhAm\nobmCNAlnwT4QIECAAAECBFZMYP4GWfXbkv1XbBPjr7gKgo81zdwfj7/osi1xQNb06OSJyaXJ\n2clc8o6knmP6/eRfktrXeyf/mrwrqUKmCqFu+8u8+bfkCckXk7ry9M7kfyXHJ79M9kxOSOqW\nwP+WfCrZI3ltUvugESCwQMAVpAUgPhIgQIAAAQJLEuhzBWn+UfkZPz/kT1xSLMzvtqSjHLzQ\nqFeQqvBZWKD9bqbV9H63vtWVopr35KRa9wrSD/J+U03oaVUgVd/uFafHdz7/SU+felu3752T\nVF9XkIIwCc0VpEk4C/aBAAECBAgQILByAnXl4u+SSbuClFHm5urqylq2upWut92x8+ETee0W\nN935dYtdtSOS11357qr/fDkvvVeUauoP6j9pe1310lThWq2ec+ptW/Khpj2ld6L3ayugQFpb\nf1snQIAAAQIECKywwJVFyJ+t8EamdfXfX7DjN+98ftmC6b0fb9b7Ie9rgIaF7fLOhHWd126x\nVbfxLWz1HJI2QQIKpAk6GXaFAAECBAgQIEBgVQUWXvnpFjb1XNBPF9mTixdMr+eKhrXzOx12\nz2tdNept63s/eL/2AgqktT8H9oAAAQIECBAgQGAyBL7T2Y0qguqLZnvbtfKhbq/rd8Wot1+/\n93Ub3kOSg5PPL+jQvWq1YLKPayXQvey3Vtu3XQIECBAgQIAAAQKTIvC+7EgNmPDcZOGVnRo2\n/MSkBtcat32ms8CzFyy4fz4/dME0H9dYQIG0xifA5gkQIECAAAECBCZG4OTsyZuSKoI+nTwi\neXDy5qRuu6sCqkaoG7edlAVenDwoqSHB7588Jqnpo9yil27aagm4xW61pG2HAAECBAgQIEBg\nGgSekJ38VvK85F87O1xXlf49WTgseGf2SC/1fUl1VaoKrWOTK5L6zqVvJFU8aQQI9AjUbynq\nf7xNPdO8JUCAAAECBAiMK9Dne5DGXYX+PQIH5n2Zdofr7pm1U2/rWaQ9d2oNk7VwDT5RP8su\n5fbDyTqS7I0rSBN3SuwQAQIECBAgQIDAhAjUENyV5W7fXu4VWt/yCXgGafksrYkAAQIECBAg\nQIAAgSkXUCBN+Qm0+wQIECBAgAABAgQILJ+AAmn5LK2JAAECBAgQIECAAIEpF1AgTfkJtPsE\nCBAgQIAAAQIECCyfgAJp+SytiQABAgQIECBAgACBKRdQIE35CbT7BAgQIECAAAECBAgsn4AC\nafksrYkAAQIECBAgQIAAgSkXUCBN+Qm0+wQIECBAgAABAgQILJ+AAmn5LK2JAAECBAgQIECA\nAIEpF1AgTfkJtPsECBAgQIAAAQIECCyfgAJp+SytiQABAgQIECBAgACBKRdQIE35CbT7BAgQ\nIECAAAECBAgsn4ACafksrYkAAQIECBAgQIAAgSkX2DDl+2/3CRAgQIAAAQIErinwW5n0i2tO\nNoXAigjsuiJrXaOVKpDWCN5mCRAgQIAAAQIrIHBR1rk9+cgKrNsqCcyEgAJpJk6zgyRAgAAB\nAgRmROAHOc5dkvUzcrwOczIENmY3LpmMXdn5vVAg7byhNRAgQIAAAQIEJkngiuxMRSNAYAkC\nBmlYAppFCBAgQIAAAQIECBBop4ACqZ3n1VERIECAAAECBAgQILAEAQXSEtAsQoAAAQIECBAg\nQIBAOwUUSO08r46KAAECBAgQIECAAIElCCiQloBmEQIECBAgQIAAAQIE2imgQGrneXVUBAgQ\nIECAAAECBAgsQcAw30tAm9JFbpj9vmNn30/J64+m9DjsNgECBAgQIECAAAECLRc4Ksc3n2xa\ngeOsdb4mqe9DqG1U6v2rk5XYXlarESBAgAABAgQIzJBA/UxZP2PWz7QagWURWMkC6U3Zw25h\ntPC15mkECBAgQIAAAQIEdkZAgbQzemu87LBnrtZn//ZJdlnl/VypAuk2OY6FRdHCz9VHI0CA\nAAECBAgQILBUgVYVSMMKhqUiTdJy18vOvCO5ILk4+WRydNKv3TYTq9+f95s5hdPuOcI+32OE\nProQIECAAAECBAgQmAmBthdIe+QsfjF5eFJXh2pggrsln0lelLS9jfKM0ea2Izg+AgQIECBA\ngAABAqMKtL1AelYgbpS8IKlR3A5O7pB8I3lu8rKkze3LIxzcKH1GWI0uBAgQIECAAAECBAhM\nusCJ2cGfJguHM9870+oqUj2PU0VUtx2aNzXt+d0Jq/S6Us8gzWX/T07qmPql5lUfjQABAgQI\nECBAgMBSBTyDtFS5NVjugGzzs8n2Bdu+KJ8fkHw9+d9J3YLXxlZF0bHJqX0OrqbVvOqjESBA\ngAABAgQIECAQgYVXVtqGcmYO6F5JjUp3+YKDqwEb7p/UVZQ3J2cnlyZta+fkgI5IHpJ0B6f4\nXN6/O6nvQ9IIECBAgAABAgQIEJgRgT/LcdYVknrWaP9FjvmWmX5uUleVnpdU/+cnq9lW6ha7\n1TwG2yJAgAABAgQIEJhNgVbdYtf2U1hXjr6ZVNFTV0t+N+nX6tmjC5PqVzkuWc2mQFpNbdsi\nQIAAAQIECBBYToFWFUhtH8Wubqs7MnlFclayNenX6nmcug3tw/1mmkaAAAECBAgQIECAAIE2\nCoxSENYw4PWFsavZXEFaTW3bIkCAAAECBAgQWE6BVl1BavsgDQtP/I6FE/p8ri+W1QgQIECA\nAAECBAgQmEGBWSuQVvMUVyW9fsQNVl+NAAECBAgQIECAAAECEyXw1OzN15Kn7ORe3TTL16AQ\n3UEfRn2tQSU0AgQIECBAgAABAtMk4Ba7aTpbY+7r9dL/dkm97kz7XhY+PBn1ytCD0ve5yahX\nnNJVI0CAAAECBAgQIECAwMoKLFeBNO5ePjEL1FWm3cddUH8CBAgQIECAAAECayzgCtIan4CV\n3PxPs/KKRoAAAQIECBAgQIDADAqMMuz1DLI4ZAIECBAgQIAAAQIEZlFAgTSLZ90xEyBAgAAB\nAgQIECDQV0CB1JfFRAIECBAgQIAAAQIEZlGg7d+DVIMf7LWEE/v5LHPyEpazCAECBAgQIECA\nAAECUyzQ9gLpaTk3hy7h/ByXZRRIS4CzCAECBAgQIECAAIFpFmh7gXS/nJx3J0cl703ekIzS\nThulkz4ECBAgQIAAAQIECBCYNoHN2eFTki3J7Sd0530P0oSeGLtFgAABAgQIECAwVKBV34M0\nC4M0VGH0+M5p/cehp1cHAgQIECBAgAABAgRmVmAWCqQ6ud9MnpvUgA23TTQCBAgQIECAAAEC\nBAgQmFABt9hN6ImxWwQIECBAgAABAkMF3GI3lEgHAgQIECBAgAABAgQITKHArNxiN4Wnxi4T\nIECAAAECBAgQILDaAgqk1Ra3PQIECBAgQIAAAQIEJlZAgTSxp8aOESBAgAABAgQIECCw2gIK\npNUWtz0CBAgQIECAAAECBCZWQIE0safGjhEgQIAAAQIECBAgsNoCCqTVFrc9AgQIECBAgAAB\nAgQmVkCBNLGnxo4RIECAAAECBAgQILDaAgqk1Ra3PQIECBAgQIAAAQIEJlZAgTSxp8aOESBA\ngAABAgQIECCw2gIKpNUWX8PtXdI016us4S7YNAECBAgQIECAAIGJFlAgTfTpWZ6d29JsetTW\nZvN3Nzebf1Kp95n2yOVZu7UQIECAAAECBAgQIEBgeQWemNXNJ7sv72qbJsXQn2xrNs/3S81b\n7u1ZHwECBAgQIECAwMwJbMoR18+yR7XhyF1BasNZXOQYLm2a/eea+f9vkdlNzas+i803nQAB\nAgQIECBAgMCsCSiQWnzGNzYb7980c5sXP8S5zVf1WbyHOQQIECBAgAABAgRmSUCB1OKzPdfM\nXWfY4aXPfsP6mE+AAAECBAgQIEBgVgQUSC0+0zua5jvDDi99Th/Wx3wCBAgQIECAAAECsyKg\nQGrxmT632frBHN7ZAw7x7E6fAV3MIkCAAAECBAgQIDA7AgqkFp/rGzXNZU1zxSMyqMhF1zzM\nmnbFI67qc825phAgQIAAAQIECBCYRYENs3jQs3TMG5vtn0uVdMj6ZvMzUyjd+apjnzvpimbr\n3+3aNGfOkoVjJUCAAAECBAgQIDBMQIE0TKgF868qhLY8owWH4hAIECBAgAABAgQIrKiAW+xW\nlNfKCRAgQIAAAQIECBCYJgEF0jSdLftKgAABAgQIECBAgMCKCiiQVpTXygkQIECAAAECBAgQ\nmCYBBdI0nS37SoAAAQIECBAgQIDAigookFaU18oJECBAgAABAgQIEJgmAQXSNJ0t+0qAAAEC\nBAgQIECAwIoKKJBWlNfKCRAgQIAAAQIECBCYJgEF0jSdLftKgAABAgQIECBAgMCKCiiQVpTX\nygkQIECAAAECBAgQmCYBBdI0nS37SoAAAQIECBAgQIDAigookFaU18oJECBAgAABAgQIEJgm\nAQXSNJ0t+0qAAAECBAgQIECAwIoKKJBWlNfKCRAgQIAAAQIECBCYJgEF0jSdLftKgAABAgQI\nECBAgMCKCiiQVpTXygkQIECAAAECBAgQmCYBBdI0nS37SoAAAQIECBAgQIDAigookFaU18oJ\nECBAgAABAgQIEJgmAQXSNJ0t+0qAAAECBAgQIECAwIoKKJBWlNfKCRAgQIAAAQIECBCYJgEF\n0jSdLftKgAABAgQIECBAgMCKCmxY0bVbOQECBAgQILDcArtlhUcmuyanJuckGgECBAgsk4Ar\nSMsEaTUECBAgQGAVBJ6ebfw4+UTygeRHyVuSPRKNAAECBAi0RuCJOZL5ZPfWHJEDIUCAAIHl\nFvgfWWH9W9EvJ2b63HJv0PoIECAwosCm9Ku/m44asf9Ed3MFaaJPj50jQIAAAQJXCtQVohcO\nsLhX5j1gwHyzCBAgQGBEgZ0pkOre59smdR90NVc/rnLwXwIECBAgsNwCd8oKh91Gd5/l3qj1\nESBAYBYFllIgHRiodyaXJl9PXppUOz6p325trg8aAQIECBAgsGwCNTDDsDZKn2HrMJ8AAQIz\nL7BhTIEbpP9Xkn2TbyW9fxnXvc/PS45JjkguTzQCBAgQIEBg5wVqtLphbZQ+w9ZhPgECBAiM\nKfCu9K8rR3fuLPfuvH628359XusKUj2g9eTONC+jCRikYTQnvQgQIDDLAvVvcL8BGmraT5O9\nZxnHsRMgsKYCMz1Iwz1D/6rkpD6n4IpMe0FyUXLHPvNNIkCAAAECBJYuUL9M6/fv788y/YFJ\n/furESBAgMBOCoxzi91e2dY+yWkDtrkt877Z6Tegm1kECBAgQIDAmAI/T/+7Jccm90pqsKSv\nJm9Map5GgAABAssgME6BdHG295PkDsk/L7LtKqJunbx2kfkmEyBAgAABAksX2HFu03xor2ZD\nCqL1u843W05NlaQ4WrqnJQkQIHANgXFHsftQ1vCE5I+ThcONXivT3pLUPdD1hXUaAQIECBAg\nsIwC25rNf7pPs+nH65v1H82Dv+/d0Gw+c2uz6W3nN039glIjQIAAgTUQqCLorKQeCK17neuK\n0tnJCUn+fr5yel3q18YTMEjDeF56E5gVgfquuXcm9XdtLhw0701+K9FmUKCKo2R+kXwiJDWa\nrEaAAIG1EGjVIA1LAdwvC9UtdFuSKpS6qQLp6UmNZqeNJ6BAGs9LbwKzIFBf+nlZ0v07tvta\nz3o+dBYAHOOvBTIKw57bmk2XLFIcXVk0Xd5sroEaNAIECKyFwMwXSF30KoQOSurbvffvTvS6\nJAEF0pLYLESgtQL1HXN11ahbFC18rSv49X102owIXN5suM+g4qjmbW02v3JGOBwmAQKTJ9Cq\nAmncZ5C6p6OG+/6N5Izk80m1ev6opmsECBAgsHMC983i1xuwinre5JgB881qmcBcs65GrBvS\n5kfoM2QVZhMgQIBAM26BVFeK3pd8LDmyx6+uJP1+Z/rf9Ez3lgABAgTGF7jxCIsM61O/zfvT\n5EvJj5NTkqclboMOwrS1Hc3WU4fv83wN+a0RIECAwCoLvD3bq/vf/zGpZ5F6273z4TNJ3QpS\nt91powu4xW50Kz0JzILAw3KQC2+rW/j5yQMgNmfeJxdZR/2SS5E0AG9SZ2W0un8dcJvdj/Mg\nsJHsJvXk2S8C7Rdo1S1245yuGh3nl0mNqLRYu0FmbE9esVgH0/sKKJD6sphIYGYF6gfdC5KF\nRVH3c/1dfP0BOscNWLbW8T8GLGvWhApUAZTnjD7Vp0j68dZm4xETutt2iwCB2RCY2QKp/sGu\nf1gH/day/gjUbRw17Lc2uoACaXQrPQnMisCxOdD6hVO3KOq+7si0xw9B+EGf5brL1+sIt2sN\n2YLZayUwt6XZdEwKpVflitIbUhg93ZWjtToVtkuAQI/AzBZIZXBa8poejIVvN2ZCfVfH3y2c\n4fNAAQXSQB4zCcyswJ1z5B9P6msV6vbmk5L7JINaPVvaWwz1e1+j4GkECBAgQGC5BGa6QKrv\nP6rfaD6yj+YemfaGpP4xvl+f+SYtLqBAWtxmkufUF3bW/xMfTd6aPDDRCCyrQIZ3vleuGLwg\n+dt8z839j2tGGlznp9mJfoVRd9q3l3UnrYwAAQIEZl1gpguk6+bsfzGpf2TrH9h3J1UUfTi5\nIKnpb0m08QQUSON5TULv52Qnuj9s9r7WQCYegJ+EMzTl+/Dzptknz5p8YuHzJrmt6pRLBj9/\nVEdez4H2/rlc+P4FU85j9wkQIEBgsgRmukCqU9G9UlTfgVT3wnf/4f1h3j8l8cNhEMZsCqQx\nwda4e93i1P1z3+/1L9Z4/2y+BQIpjD64sDjqfk6RdHIOsQbOWaztkxnfSvr9+fxypu++2IKm\nEyBAgACBJQjMfIHUa7Z3Ptw22at3ovdjCyiQxiZb0wX+I1vv94Nnd9rZa7p3Nj71AjUiWbcY\nWuw1t95VoT6oXTsz65nRXIy68s/reXn9+6R+yaURIECAAIHlFGhVgbRhJ2XqQd//t5PrsDiB\naRO49ZAdri9UvlZSP5hqBJYi0PtF3H2XX9+sqz71/Nti7YLLmg3vWt+s33BF0xyYS/tnzDVX\nvGtjsz136GkECBAgQIDAYgJLKZDukZX9flLPI+2a9LvN402Z/uZEI9BGgWEjgNVAJvU9NRqB\npQqkphna6s/Zoi3DQL8yfzn/UXX49V/065+ypVn34s3N1uctuqAZBAgQIEBgxgV+/e/maBAP\nT7d3jND10yP00YXAtArULXaHDNj5EzNv64D5ZhEYKDDfzH2632+err7QjkX/ns0tek/oFkdX\nX6aGwJt7bkbE+2qKpH9bOM9nAgQIECBAYHyB+h6kuj3jUckNkhqQoV+G/9ueBbVfCXgG6VcU\nU/Gmbp/7btJ95qj39ReZPuwWvKk4SDu5tgIZiOHNiz1/tK3Z9L5Be5dlv7H4spvnc3Xps4OW\nN48AAQIECIwp0KpnkMY59hr1qEatq4d+teUVUCAtr+dqrK1+QfD+pHckxy/m8+1XY+O20X6B\n7zfNLil03phiaMfVi51N7/pZ0+y5mMBxuUiUZa64+jKb56/+edOFiy1vOgECBAgQWIJAqwqk\ncW6xuyxYFyeXLgHNIgTaJvDjfGnna7c083OpkG4+18z9eLdmx1tPaLZ9ve5D1QjsrMBNmuby\n3Kn5B/lz9uJ1zY67ZH3rcuvdybk17puD1n1civY8YFR/Ty9aRGU99Xe5RoAAAQIECCyDwHuz\njhrCeN0yrMsqfi3gCtKvLabiXZ7xeNnVfyP/q9/QfyQ/vdZvUTQCayaQK09vX+TP55VXknKL\nnTsB1uzs2DABAgRaKdCqK0jjnqEaue705N3JXZMDk337pEa300YXUCCNbrXmPfOA+0MG/fCZ\n+S9c8520AzMtkEtPB+U2uwsW+XP641xeqltENQIECBAgsFwCM10gnRLFGuK496H0fu+PWy7t\nGVmPAmmKTnR+6PzIIj94dp/zODeHY6CSKTqnbdzVFOq3yZWkU3r/rObK0adSPN20jcfrmAgQ\nIEBgTQVaVSCN8wxSqX81OWcE/m+N0EcXAlMpkN8I3GJI9XOdfEPstTLUnQfhp/IMt2On86zS\nN3Ikd8zDozfZ0Gw4YHuz/axc2j+rHUfnKAgQIECAwMoJjFsgPXXldsWaCUyLwPz5uUD0G4vv\n7fzWjAFew+FrBNZcIEVRBsTbnmgECBAgQIDAKAIrMdhCfS/S9UbZuD4EplEgV4/eM2S//+OI\nptk2pI/ZBAgQIECAAAECEygw7hWkOoRjkmOTvZONSbW646jWVYMz3CypEZKOSzQCrRM4v9n6\nD9duNj0sQ3sf0ufgztvebP2zPtNNIkCAAAECBAgQaKHAH+SY+g3K0DvtO+lTRZQ2uoBBGka3\nmoieebjoWnkA/p8zUthlVz0Ef+UXc34wD8DXLwg0AgQIECBAgMAsCbRqkIZxT1wNvlCj2P1+\nsn/yi+Qvklskj0wuSF6daOMJKJDG85qY3nmwY5caUjkPJe01MTtlRwgQIECAAAECqyswswVS\nPVu0NXlHj/fH8v59PZ9vn/dXJHfomebtcAEF0nAjPQgQIECAAAECBCZToFUF0jiDNOyR81HP\nHH2m57x8O+97n8P4aj7XLXYP6unjLQECBAgQIECAAAECBKZCYJwCqW6tOy85uOfIqkA6MOkd\nte6sfL5VTx9vCRAgQIAAAQIECBAgMBUC4xRIdUCnJjUAw5H1Ie3/XfXyq0EZ9sznuyQXd6Z7\nIUCAAAECBAgQIECAQGsFDs2R1XNIO5KjkyqwvpfkOfXmhOTcpEa0e0yijS7gGaTRrfQkQIAA\nAQIECBCYLIFWPYO0FNp7ZKEPJzVyXbXDknOS7lDfx+f9uFemsshMNwXSTJ9+B0+AAAECBAgQ\nmGqBmS+Q+p29GuGuCqWD+s00baiAAmkokQ4ECBAgQIAAAQITKtCqAmnDMiHX0N5fWaZ1WQ0B\nAgQIECBAgAABAgTWRGBYgbRP9qqG9q4vgN2e7JvU1aJh7dJ0qGgECBAgQIAAAQIECBBojUCN\nWlfPFh3ROaIzOp+7zxst9vr8Tn8vowm4xW40J70IECBAgAABAgQmT2CmbrH7WPxPTy7snIcP\n5fW6nfeDXv5r0EzzCBAgQIAAAQIECBAgQIDAYgKuIC0mYzoBAgQIECBAgMCkC7TqCtK4w3G/\nKGfnbsncpJ8l+0eAAAECBAgQIECAAIFxBcYtkB6ZDXwq+W7yV8mBiUaAAAECBAgQIECAAIFW\nCIxbID0wR/2SZHPyN8n3kxOTRyW7JhoBAgQIECBAgAABAgRmTqAKq3slb05+kdRodj9PXpsc\nmWjjCXgGaTwvvQkQIECAAAECBCZHoFXPIC0H6+5ZyaOT9yaXJ1Us/XmijS6gQBrdSk8CBAgQ\nIECAAIHJEmhVgTTuLXb9TsXGTKxb7nq/QHZbv46mESBAgAABAgQIECBAYJIFNixx56pK/O3k\n9zqvVSCdl7wmeWPy9UQjQIAAAQIECBAgQIDAVAmMWyAdnaN7TPKwZJ/kiqS+PLaKovcnrhwF\nQSNAgAABAgQIECBAYDYEzshh1jNG30qendwg0XZewDNIO29oDQQIECBAgAABAmsj0KpnkMa9\ngvTWmH84OXlt7G2VAAECBAgQIECAAAECKycwboH0/J5dqe89ulmyW/KFpEazuzTRCBAgQIAA\nAQIECBAgMJUCSxnF7sAc6TuTKoZqMIaXJtWOT16Y1IANGgECBAgQIECAAAECBKZOYNwrSPXM\n0VeSfZN6DqmuHnXbXN48LzkmOSKp70TSCBAgQIAAAQIECBAgMDUC415BekWOrG6tu0tyq6SK\npW47Nm9elNw6eWx3olcCBAgQIECAAAECBAhMi8C4BdI9c2CvSk7qc4A15PcLkouSO/aZbxIB\nAgQIECBAgAABAgQmWmCcAmmvHEl999FpA46ovgfpm51+A7qZRYAAAQIECBAgQIAAgckTGKdA\nuji7/5PkDgMOo4qousXu2wP6mEWAAAECBAgQIECAAIGJFBinQKoD+FDyhOSPkz2S3natfHhL\nsndyYu8M7wkQIECAAAECBAgQINBGgSqCzkrmk3rWqK4onZ2ckJyf1PQ3Jtp4Ak9M97Kr75LS\nCBAgQIAAAQIECEyTwKbsbP0se9Q07fRy7ut+Wdlrky1JQXRTBdLTk/WJNp6AAmk8L70JECBA\ngAABAgQmR2DmC6TuqahC6KDkTsn+3YlelySgQFoSm4UIECBAgAABAgQmQKBVBdK4XxRb/tdN\n6juQ6vXM5PTkZ4lGgAABAgQIECBAgACBmRCoL4f9h+S8pHtLXe/rNzL9MclSCq4sNvPNFaSZ\n/yMAgAABAgQIECAwtQIzdwXp9jlVb0sOTuq5o08m53Te1y12N01qaO83J49OHpRcnmgECBAg\nQIAAAQIECBBolUBdEfp+UleL3pTUF8X2a3fNxK8m1e+f+nUwbaCAK0gDecwkQIAAAQIECBCY\nYIFWXUEa5vy4dKii533JsNHpdkmfKqbq6lGNdKeNLqBAGt1KTwIECBAgQIAAgckSaFWBtG6I\n7X078x+f1yuG9K3C6B+TzcnRQ/qaTYAAAQIECBAgQIAAgYkTGFYg1ZWgC5JRR6n7XucID5i4\nI7VDBAgQIECAAAECBAgQGCIwrEDaN8tfMmQdvbPP7nxwi12vivcECBAgQIAAAQIECEyFwLAC\nqebvGONItnf6zo2xjK4ECBAgQIAAAQIECBCYCIFhBdJE7KSdIECAAAECBAgQIECAwGoIjPLF\nrntlR/7niDvj2aMRoXQjQIAAAQIECBAgQGDyBEYpkK6d3f77ydt1e0SAAAECBAgQIECAAIHl\nFRhWIFVhdJ0lbPLzS1jGIgQIECBAgAABAgQIEFhTgWEF0pvXdO9snAABAgQIECBAgAABAqso\nYJCGVcS2KQIECBAgQIAAAQIEJltAgTTZ58feESBAgAABAgQIECCwigIKpFXEtikCBAgQIECA\nAAECBCZbYNgzSJO990vbu32y2N7J5uSS5OfJpYlGgAABAgQIECBAgMCMC8zKFaTb5zy/Pjk3\nuSD5fvLt5EdJFUnfS16XLGXEviymESBAgAABAgQIECDQBoFZuIL01zlRL+icrLPyenJSRVIV\nRnUlqb7n6cDkScmxyTOStyUaAQIECBAgQIAAAQIEWiXwsBzNfPKh5LABRzaXeXdNvphU/zsl\nq9memI3VdndfzY3aFgECBAgQIECAAIFlENiUddTPskctw7rWfBVLuYJ0j+z17yfXTXZNqrhY\n2N6UCZPwHUrHZD/OSOp1S7JYqxP6meQ+yZnJY5LPJxoBAgQIECBAgAABAjMkMG6B9PDYvGME\nn0+P0Gc1utwuG6lb6gYVR737cWE+fD05oHei9wQIECBAgAABAgQIzIbAuAXS34alRnx7UvLJ\npAY96Nd29Ju4BtN+nG0enmxMto2w/RrhroqqGrBBI0CAAAECBAgQIECAwKIC9XxMFT6vWbTH\n5M14dHapbp97X3LkgN2r2wTvknwh2Z4cnaxm8wzSamrbFgECBAgQIECAwHIKzOwzSJdF8eJk\nmr4z6G3Z33pW6oXJ7yRnJz9Kzk/qWPZKrp3cOLlBUsXRM5PPJRoBAgQIECBAgAABAgQGCrw3\nc6vImLbvTzoo+/z2zr7XFaXeVMF3evLS5EbJWjRXkNZC3TYJECBAgAABAgSWQ6BVV5DGBamr\nMVVMvDu5a3Jgsm+f1Oh2k9rqqlEVQjdP6nuQJqEpkCbhLNgHAgQIECBAgACBpQjMdIF0SsQu\nSnqvwPR7f9xSZGd4GQXSDJ98h06AAAECBAgQmHKBVhVI445i99WcvHNGOIHfGqFP27uU7ai3\nIq5vO4bjI0CAAAECBAgQIEBg+gSeml3+WvKUndz1m2b5GvCh39W1QdNqpECNAAECBAgQIECA\nwDQJzPQVpFFOVF0N2S/56SidJ6zP9bI/9T1I9boz7XtZ+Mik/rCM0h6YTn8xSkd9CBAgQIAA\nAQIECBBYOYFxb7GrPTkmOTapAQ42JtXqe4RqXTU4w82S1yTHJdPWar9rAIrlKO6+PMbB32aM\nvroSIECAAAECBAgQILBCAuMWSH+Q/XjDkH2pUe5OHdJnUmdXYbQcxdGkHp/9IkCAAAECBAgQ\nIEBggMCogwh0V/HsvKkvWH1MckBySfKc5JbJo5ILk48lJyQaAQIECBAgQIAAAQIEWitQzxZt\nTd7Rc4RVDL2v5/Pt8/6K5A4907wdLmCY7+FGehAgQIAAAQIECEymQKsGaRjnCtIeOR/1zNFn\nes7Lt/P+kJ7PNQz4d5IH9UzzlgABAgQIECBAgAABAlMhMM4zSPUFseclB/ccWRVIf5TUqG/d\nZ3fOyvtbJZPQ6srMXkvYkc9nmZOXsJxFCBAgQIAAAQIECBCYIYETc6w/TGoI62p3S+p7fZ5c\nH9L2TH6ZvCmZhFZXtAZ979Bi856/yjvvFrtVBrc5AgQIECBAgACBZRNo1S1241xBKsFnJf+Z\n1NWVuySfTc5IXp7cL7lTsmvyiWQSWu1TDdt9VPLeZNgIfOlyZTut+8YrAQIECBAgQIAAAQIE\nBgncIzM/nNyi0+mwvJ6TdK/GHJ/34zzb1FnNir1szppPSbYkNYjEJDZXkCbxrNgnAgQIECBA\ngACBUQRadQVplAMepU+NcFeF0kGjdF6DPrfONqtAOmkNtj3KJhVIoyjpQ4AAAQIECBAgMIkC\nrSqQduZKT91Kd9uknkeqob3rtrS63W4S2zezU89NasCG2meNAAECBAgQIECAAAECyyJwYNby\nzmRHUrfV1XNI1d6TvDCpW9q08QRcQRrPS28CBAgQIECAAIHJEWjVFaRxB2m4Qc7DV5J9k28l\nuyXdNpc3z0uOSY5ILk80AgQIECBAgAABAgQITI3AuLfYvSJHVrfW1Qh29V1HVSx127F586Kk\nnvd5bHeiVwIECBAgQIAAAQIECEyLwLgF0j1zYK9K+g12UM8hvSCpL5S9Y6IRIECAAAECBAgQ\nIEBgqgTGKZBqgIN9khqMYbG2LTNqQITqpxEgQIDAZApcN7v10uTryelJPVfa/QLwvNUIECBA\ngMDsCoxTIF0cpp8kdxjAVUVU3WL37QF9zCJAgACBtRO4RTZ9avLMpEb1vFnysORzyeMSjQAB\nAgQIzLTAOAVSQX0oeULyx8keSW+7Vj68Jdk7ObF3hvcECBAgMDECx2dPasCdpr7Abpd6c1Wr\nj69LbtL57IUAAQIECBAYQaCKoLOSGt67njWqK0pnJyck5yc1/Y2JNp6AYb7H89KbAIGlCdwu\ni80f1szNf7jZOH95s2l+W7N5/ht5/7hmXf39Xfnrpa3aUgQIECAwwwKtGuZ7Kedxvyz02mRL\n0v0HtV6rQHp6Ur+F1MYTUCCN56U3AQJLE3jw3VIcXdIpjKo46s2Lm/X1d/mblrZqSxEgQIDA\nDAvMfIHUPfdVCB2U3CnZvzvR65IEFEhLYrMQAQLjCOQ7Gu78nUWKo26h9MBm3ZvGWae+BAgQ\nIEAgAq0qkMZ9Bqn3T0AN631G8vnknN4Z3hMgQIDA5Amc3WzIA0b1nd6Ltxc16wd3WHxRcwgQ\nIECAQCsENgw5igMzf/OQPv1m1+12F/SbYRoBAgQIrI3A7s266wzb8i2HFFDDljf/AMiWAABA\nAElEQVSfAAECBAhMu8CwAul9OcBDlnCQx2WZ+tJYjQABAgQmRGCumf9+M6QAykNI6aMRIECA\nAIHZFRhWIHVlfpk3n04u604Y8vqtIfPNJkCAAIFVFtjUbDt1a7Ppq3PN3O37b3p+23yztYYB\n1wgQIECAAIFFBP4k07+T1MhGNaz3m5P/noxaWKWrNoKAQRpGQNKFAIGdF9jSbLp1BmQ4tzso\nw69fN12xtdn45J3fgjUQIECAwAwKtGqQhlHP3+Hp+JLkzKSKpZ8lr0numswl2s4JKJB2zs/S\nBAiMIZBbAg7Y2mx+Va4mfScF0g+3NZvet63ZUH+fawQIECBAYCkCM1kgdaGqGDo6+cekviS2\niqUfJi9NqojSliagQFqam6UIECBAgAABAgTWXmCmC6Re/voepHsm/5TUqHVVLNXteC9Ibpxo\nowsokEa30pMAAQIECBAgQGCyBBRIfc7Hxkx7fHJhUoXScYk2uoACaXQrPQkQIECAAAECBCZL\noFUF0s4OtlDfk/SwTn4rr3ULXt1y97VEI0CAAIHZENg1h/mHyT2S+u68LyavTX6aaAQIECBA\noPUCVRT9aXJKUleLKuckL0/ulBi0IQhjNleQxgTTnQCBiRE4IHtSX+1w5b8H637970J9WXg9\ns6oRIECAQPsFWnUFadTTdaN0rCG/T052JPUPYf1m8NXJ3ZL8m6jthIACaSfwLEqAwJoKfPrw\nZm7+A83G+UubTfMZRnz+lLw/plnX/XdirzXdOxsnQIAAgdUQmKkCqW6Z+HzSLYpqMIYalOFe\nSQ3SoC2PgAJpeRythQCB1RU49F4pjqow+vX3KW3+1fs/a9ZXkeS7lVb3nNgaAQIE1kKgVQXS\nsGeQnhHhQ5Lzkn9PPpZsS3ZPHpAs1r6dGactNtN0AgQIEFhxgfol1g2SiztZ9g3umX8fXt9s\nbDYtcmf1C/N7tM8180ef3Ox43bJv3AoJECBAgMAaCZya7dZvAMfN89dof6d1s64gTeuZs98E\nJk+gfov34qQ7qmjdAfDJ5NBkWduLm3V/2e/KUe+0NzUbatsaAQIECLRbYKauINXtdNdfwvn8\nzBKWsQgBAgQI7JxADZLznkOaufs/OVdvfjNXdi5q5uf+o9lx9zc1Oz63vbnymdEv7dwmfr30\nY5r15/76U/93RzbrarAGjQABAgQIECAwloArSGNx6UyAwCICv/v4DI5weZ9ngj6bgROu1TRf\nXmS5JU3e1my4a+/VokXeP2tJK7cQAQIECEyTQKuuIE0TfJv3VYHU5rPr2AisksBdm7mP9CuO\nuoXLPzUb6nbp+qqGZWm5d27D1mbz6d31X/N10+WXLeP2lmWnrYQAAQIEVkKgVQWS4blX4o+I\ndRIgQGANBJ7UrL/1+kUGTKjdeXS+kSHf6H3j5dq1/9Y0uWtvx+/lMdUaCGJBm98x38w/Ld8g\ne9aCGT4SIECAAIGJFlAgTfTpsXMECBAYXeA2zbqBX9S9McXTPzQblvXv/U3Nti9c0Ww9PMXQ\nW7KnP0p+loLpgymc/lvmvWH0vdeTAAECBAhMhsCwYb4nYy/tBQECBAgMFdivmc/XK8ztP6jj\n7Zvty35FZ5em+W7TbH3soO2aR4AAAQIEpkVgWX+TOC0HbT8JECDQRoHrNs2bBx3X9mb+v3LL\n2/cH9TGPAAECBAjMusC4BdJBAdt3AFqt727Jsn/fxoBtmkWAAAECETih2Xb8fNN8uj/G/LYN\nzY6n9J9nKgECBAgQILBUgTOy4PMHLJxfTl75pbKvHtDHrGsKGMXumiamECCwBIFzmma3jCz3\n6m3Npsu6o8ptbTadmiG577yE1VmEAAECBAiMItCqUeyGPYN084jctUdlz7w/LHl8z7Tu27p6\n1L1y5IsBuypeCRAgsIoCeQDpl02z5Wk/aZpn7dNsukkGUPj5blcNnrCKe2FTBAgQIECgvQJ7\n5dDyC8krrwrlzo2RXi9Jv8MTbXQBV5BGt9KTAAECBAgQIEBgsgRm6gpSfbfFA5Jbdc7By/L6\n2eQ9nc+9LzvyIb+5bL6SnNU7w3sCBAgQIECAAAECBAhMg8CwW+zqGKrgqVQ7IvlM8u76oBEg\nQIAAAQIECBAgQKBNAqMUSL3H+z97Ptw47w9Orp3kiwGvLKIuyKtGgAABAgQIECBAgACBmRGo\n2+1qGNmFzyRtzbSXJwO/yT3ztWsKeAbpmiamECBAgAABAgQITIfATD2DtPCU3CgTTk72Sj6c\nfDX5eVLT7588I9kjqR/465kkjQABAgQIECBAgAABAq0VqGePtiT37HOEGzPtlUldWfJ9G32A\nBkxyBWkAjlkECBAgQIAAAQITLdCqK0j13UXjtLul8+uSj/dZaFum1TNK9TzS3RONAAECBAgQ\nIECAAAECUyUwToG0d46sBmT4xoAj3J55pyX1ZbIaAQIECBAgQIAAAQIEpkpgnFHsLsqRVQ4d\ncIR1ee03k/8c0MesvgK3y9SPP6hp9qtbGDUCBAgQIDDxAo9sXn/kLZsv/ffdmq17X9zsdu5X\nmnv/+webB50+8TtuBwkQWGaBS1JTHJ11fn2Z17s2qxunQKo9rIEZ6nmZDyb/kfS2XfLhVcm+\nySd7Z3g/isDvpdN+rxilpz4ECBAgQGCtBXZvLt7rI82DNybdXTkwA9kevktz6ZbLm90v7U70\nSoDALAjUGG31s+yzZ+Fgr3GMN86UC5MaiOGzSf1A/4LkDckPk5r+rkQbT8AgDeN56U2AAAEC\nayjwyWbja7Y1m+cXy4nN+ses4e7ZNAECqy/QqkEalsJ3QBb6UFLFUG/qt0V/ldSVJG08AQXS\neF56EyBAgMAaCvyo2XT5YsVRTf/PZtNZa7h7Nk2AwOoLtKpAGvcWu+I+O7lfUtfSDk6ul3w/\n+V7i+ZkgaAQIECBAoK0CuWXkOtdr5jYPOr78YHD9QfPNI0CAwCQLjDOK3cLjqGeNKvWlsfsn\nuycaAQIECBAg0GKBf26ay7dfeQPJ4gf5i2a+7jDRCBAgMDMCt8qRfjrpvb2u3m9NXp7MJdp4\nAm6xG89LbwIECBBYQ4ETmw0XD7rF7o3Nhm+u4e7ZNAECqy8w07fY3SjeJyd11ahGtPtq8vOk\npt8/eUZSt97VD/w7Eo0AAQIECBBomcBbmh3PP6JZ97I9+vxO9MxcPXp7s/2pLTtkh0OAAIFF\nBd6dOfWc0T379NiYaa9M6mrSnfvMN2lxAVeQFrcxhwABAgQmUOARzbp/+EKzcUfvlaQPNRu3\n3btpnjCBu2uXCBBYWYFWXUEal+r8LDDou3pq0Idzk78cd8Uz3l+BNON/ABw+AQIEplTgJndv\nmhc+sll3/BFXfQHKflN6HHabAIGdE2hVgTTOKHZ7x+3ayTcG+G3PvNOSwwb0MYsAAQIECBBo\nh8D3P3XlL0XdVd+O0+koCBAogXFGsbso/SuH1oKLtKoefzP5/iLzTSZAgAABAgQIECBAgMDE\nCoxTINVB1MAMdTvYA+rDglZfEPuapIb+/uSCeT4SIECAAAECBAgQIEBg4gXGucWuDubPk/sm\n709OSmoUuwuTGsUuz2U2N0z+LfmPRCNAgAABAgQIECBAgEDrBQ7IEX4oqdHqenNpPv9VUleS\ntPEEDNIwnpfeBAgQIECAAAECkyMws4M0dE/B2Xlzv6S+7+jg5HpJPXP0vaSGANcIECBAgAAB\nAgQIECAwlQLj3mLXe5CX5MOXeif0vN+c94qlHhBvCRAgQIAAAQIECBCYfIFxB2kYdkS7pcNL\nk2cN62g+AQIECBAgQIAAAQIEJk1glAJp/+z0K5PvJDXM9weTurVuYbtPJtR3JD0z2ZkrUwvX\n6zMBAgQIECBAgAABAgQmQqCeM6qR6rqDMVzReX9eXq+fVKuHsl6XdPtU/9sm2ugCBmkY3UpP\nAgQIECBAgACByRJo1SANw2ifnw5V+HwgOSy5dvKSpKb9Y7Jf8tnO51/mtYYBd/UoCGM2BdKY\nYLoTIECAAAECBAhMjMBMFUh1O10N3713D/9c3tdVotOS9yZVLJ2S3DzRliagQFqam6UIECBA\ngAABAgTWXqBVBdKwZ5DqitEZST171G1VEH0tuUXywOT/JEcnpycaAQIECBAgQIAAAQIEplZg\n2O1w9aWvvcVR90B/2HnzT3mt2+o0AgQIECBAgAABAgQITL3AsCtIix3g9s6M1y/WwXQCBAgQ\nIECAAAECBAhMm8BSC6TucV7YfeOVAAECBAgQIECAAAEC0y6wswVSPY+kESBAgAABAgQIECBA\noBUCw55BqoM8IFn4nNGdO0f/pLye33nf+3JSPnyud4L3BAgQIECAAAECBAgQmHaBU3MAdZVo\n3Dx/2g98lfffMN+rDG5zBAgQIECAAAECyybQqmG+h11B+t9hqy+DHbd9YdwF9CdAgAABAgQI\nECBAgMBaCwwrkN4+ZAc3Zn6t4/LE80hDsMwmQIAAAQIECBAgQGCyBXZ2kIaX5PB+mRw+2Ydp\n7wgQIECAAAECBAgQIDBcYGcLpOFb0IMAAQIECBAgQIAAAQJTIqBAmpITZTcJECBAgAABAgQI\nEFh5AQXSyhvbAgECBAgQIECAAAECUyKgQJqSE2U3CRAgQIAAAQIECBBYeYFho9gN24Pj0+HL\nyfeHdTSfAAECBAgQIECAAAECky4wboH0qhzQZclfJNuTL3WSF40AAQIECBAgQIAAAQLTLTDO\nLXabc6iPTR6QVHGkESBAgAABAgQIECBAoFUC4xRIW3Pkv0h2S+ZapeBgCBAgQIAAAQIECBAg\nEIFxCqT59H9wR+19eb1vctNkrz6pq00aAQIECBAgQIAAAQIEWi1wUo7uvKSKpUE5LvO10QWe\nmK7lufvoi+hJgAABAgQIECBAYCIENmUv6mfZoyZib3ZyJ8YdpOHb2d6FI2zztBH66EKAAAEC\nBAgQIECAAIGJEhi3QHrCRO29nSFAgAABAgQIECBAgMAyCozzDNLCze6aCbdNjuzMcHvYQiGf\nCRAgQIAAAQIECBCYKoGlFEgH5gjfmVyafD15aVLt+OSFiQEaSkMjQIAAAQIECBAgQGDqBMa9\nxe4GOcKvJPsm30pqyO9uq6G/n5cckxyRXJ5oBAgQIECAAAECBAgQmBqBca8gvSJHVrfW3SW5\nVVLFUrcdmzcvSm6dPLY70SsBAgQIECBAgAABAgSmRWDcAumeObBXJSf1OcArMu0FyUXJHfvM\nN4kAAQIECBAgQIAAAQITLTBOgVRfCLtPMmgI722Z/81Ov7xoBAgQIECAAAECBAgQmB6BcQqk\ni3NYP0nuMODwqoiqW+zq+5I0AgQIECBAgAABAgQITJXAOAVSHdiHkickf5zskfS2a+XDW5K9\nkxN7Z3hPgAABAgQIECBAgACBNgpUEXRWMp/Us0Z1Rens5ITk/KSmvzHRxhN4YrqXne+SGs9N\nbwIECBAgQIAAgbUX2JRdqJ9lj1r7XVmbPdgvm31tsiUpiG6qQHp6sj7RxhNQII3npTcBAgQI\nECBAgMDkCMx8gdQ9FVUIHZTcKdm/O9HrkgQUSEtisxABAgQIECBAgMAECLSqQBr3i2KrIKpb\n6+pqUQ3rfUYnebmyrct/6zuSqs+pV07xHwIECBAgQIAAAQIECEyJQBU047SPpXMN0LBY25wZ\nn0qetFgH0wkQIECAAAECBAgQIDCpAsOuIN08O37Xnp3fM+8PSx7fM637toqtQzsfLuhO9EqA\nAAECBAgQIECAAIG2CNT3Gp2TdAdiGOX1kvQ/vC0Aq3QcnkFaJWibIUCAAAECBAgQWHaBmXoG\nqb4c9gHJrTqML8vrZ5P3dD73vuzIh18mX0nO6p3hPQECBAgQIECAAAECBNooUAMv1HceDbs1\nr43HvpLH5ArSSupaNwECBAgQIECAwEoKzNQVpF7IGoDhZskuyfbeGd4TIECAAAECBAgQIECg\nDQLjjGK3NQf8i2S3ZK4NB+8YCBAgQIAAAQIECBAg0CswToFUAzQ8uLPw+/J63+SmSQ3ksDB1\ntUkjQIAAAQIECBAgQIBAqwVOytGdlwwbze64Viss/8F5Bmn5Ta2RAAECBAgQIEBgdQRm9hmk\n4v12cuEIzqeN0EcXAgQIECBAgAABAgQITJTAuKPRPWGi9t7OECBAgAABAgQIECBAYBkFxnkG\nadTNrk/H643aWT8CBAgQIECAAAECBAhMisC4V5Bqv49Jjk32TjYm1WpUu1rXrsnNktckxyUa\nAQIECBAgQIAAAQIEpkZg3ALpD3JkbxhydKdnfn2hrEaAAAECBAgQIECAAIGpEhj3Frtn5+gu\nTh6THJBckjwnuWXyqKQGcPhYckKiESBAgAABAgQIECBAoLUC9WxRfVnsO3qOsIqh+k6kbrt9\n3lyR3KE7wetIAob5HolJJwIECBAgQIAAgQkUaNUw3+NcQdojJ6OeOfpMz0mpYb8P6fn81bz/\nTvKgnmneEiBAgAABAgQIECBAYCoEximQLsoR1ZfEHtxzZFUgHZj0jlp3Vj7fqqePtwQIECBA\ngAABAgQIEJgKgXEKpDqgGnyhRrE7sj6k/b+rXq6cVm/3TO6S1HNKGgECBAgQIECAAAECBFot\ncGiOrp5D2pEcnVSB9b3k8qQGZjg3mU9qEAdtdAHPII1upScBAgQIECBAgMBkCbTqGaSl0N4j\nC304uUVn4cPyek5ShVHl+GTcK1NZZKabAmmmT7+DJ0CAAAECBAhMtcDMF0j9zl6NcFeF0kH9\nZpo2VECBNJRIBwIECBAgQIAAgQkVaFWBNO4XxS52Tmpo768sNtN0AgQIECBAgAABAgQITIOA\nW+Gm4SzZRwIECBAgQIAAAQIEVkVg3CtIr8te9Q7pvdhO/mtmVDQCBAgQIECAAAECBAhMjcC4\nBdK9c2Q3GXJ0P8r8Tw/pYzYBAgQIECBAgAABAgQmTmDcAun2OYKFt+XV5xsmt0n+PqkrR/Wq\nESBAgAABAgQIECBAYKYF6nuSaqjvB860wvgHbxS78c0sQYAAAQIECBAgMBkCrRrFbuHVoJ0l\nPjUrODOpW/E0AgQIECBAgAABAgQITJXAchdIm3P0+ybXnSoFO0uAAAECBAgQIECAAIEIjPsM\n0i5ZZq6PXK3nOskLkz2SLyUaAQIECBAgQIAAAQIEpkpg3ALpv3J0w0axOyN9/u9UKdhZAgQI\nECBAgAABAgQIRGDcAukzWeY7feR2ZNrFydeT1ycXJdPS6qGy30y+m1w6LTttPwkQIECAAAEC\nBAgQILBUgUdkwVcmf57crLOSuhXwHUkVdjXy3hXJW5K9k9VuRrFbbXHbI0CAAAECBAgQWC6B\nVo1iNy7Kflngkcmzkxckj0oOSWpwhm7bmDdvTY7qTljD1xqE4r1JFUDdXJj3Byb/1Jn28by+\nLvnPzufP5rXfc1aZvGJNgbRitFZMgAABAgQIECCwwgIzWSDV4AxV9GxLuoVG7+v5mf57SbWn\nJ3U1pgqntW5Pzg7Ufp6Y/E7ytKSekTo9qdsCH5r0tr/Mh+pfReBqNgXSamrbFgECBAgQIECA\nwHIKzFyBtFf0Pp1U4VC3o9VzSP+Q/GnyqqSuwGxJav77k58lkzJIwwezL1W8VYHXbfUltrWv\nH+hO6HmtK05nJXVcq9kUSKupbVsECBAgQIAAAQLLKTBTBVI9j/PVpAqKdyW7Jv3arTPxC0n1\n+2Vy3WQS2jezE7XfvW33fKgrXC/pndjzvorBKvRWsymQVlPbtggQIECAAAECBJZToFUFUl0x\nGdSelJmHJi9PHp5clvRrVYi8uDOjrtbUAAiT0Opq0D2T3itI98vnOu5bJQtbjep3WPKDhTN8\nJkCAAAECBAgQIECAwNdDUFdbrj0CxVfSp57vqatIfzVC/9Xo8pTO/nwirw9OnpP8KKmrYvUM\nUg0y0W1VNL0xqf0/tjtxlV5dQVolaJshQIAAAQIECBBYdoFWXUEapFMjudUVo5MHderMu3Ne\nq+CoqzNVUP1LMgmtip4Tkip6ujk376+X1HNSNa1uDfz35OzO54/mdbWbAmm1xW2PAAECBAgQ\nIEBguQRmpkCq29KqgHjDiHLX7/S7MK/9BkAYcTUr0q2uHtUzR09NDuhs4Vp5fVPys6SOs56d\nekWya7LaTYG02uK2R4AAAQIECBAgsFwCM1MgFdh5SX0/0KjthulYxcZrR11gAvrVVaabJOvX\ncF8USGuIb9MECBAgQIAAAQI7JdCqAqmKg0Gtbq87JDl4UKeeeb/bef/5nmmT/rZuDfx+UrcG\nagQIECBAgAABAgQIEFhU4P6ZU1eE6irSbov2umrG4Xm5PDk36R017qq5s/ffKj7rqtQoeVL6\nlXMNQa4RIECAAAECBAgQmCaBVl1BGgX+LelUP7yfldSob/sk3VYDOdw4eWlSXxb70+T2ybS2\nekbpa0mNfrcz7aZZeFtSbuNEgbQz6pYlQIAAAQIECBBYC4FWFUj1vT/DWl3d2Jo8PvmXTucL\n8lrPJx2YdK8W/SDv75Ocnkxrq9HtbpfU6860M7Lw0Un9YRmlPSCd/nyUjvoQIECAAAECBAgQ\nIDAZAnfLbrw1qeKoe1Wknt/5bvLi5AbJtLflKpDGdTBIw7hi+hMgQIAAAQIECEyKQF0UqPrg\nqEnZobXYjz2z0RqxbtQrJGuxj9O0TQXSNJ0t+0qAAAECBAgQINAr0KoCaZRb7HoPvvv+F3lT\nmcZWz1DtnWxOLkl+nlyaaAQIECBAgAABAgQIzLjAsGG+28JTA0e8PqkR9uoWwe8n305+lFSR\n9L3kdcl1Eo0AAQIECBAgQIAAgRkVWOoVpGni+uvs7As6O1wj8dV3O1WRVIVRXUm6dlKDTTwp\nOTZ5RvK2RCNAgAABAgQIECBAgECrBB6Wo6kHxj6UHDbgyGq48rsmX0yq/52S1WyeQVpNbdsi\nQIAAAQIECBBYToFWPYO0nDCTuK4alrxun6vnjUZp9XzSxclrR+m8jH0USMuIaVUECBAgQIAA\nAQKrKtCqAqntzyDdLn806pa6+hLbUdqF6fT15IBROutDgAABAgQIECBAgEC7BNpeIP04p+vw\nZOOIp62uIFVRVQM4aAQIECBAgAABAgQIzJhA2wukN+d8Hpz8e3LkgHNbzyDdJflwsltyQqIR\nIECAAAECBAgQIDBjAm0fxa5Go7tu8sLkd5Kzkxra+/yknjXaK7l2cuPkBsn25JnJ5xKNAAEC\nBAgQIECAAAECrRQ4KEf19qQKpBqlrjf1JbGnJy9NbpSsRXtiNlr7tPtabNw2CRAgQIAAAQIE\nCOyEQKsGaWj7FaTueT4jbx7Z+VBXjer7j3ZJ6otjL0o0AgQIECBAgAABAgQINLNSIPWe6rq1\nrqIRIECAAAECBAgQIEDgagJtH6ThagfrAwECBAgQIECAAAECBAYJKJAG6ZhHgAABAgQIECBA\ngMBMCSiQZup0O1gCBAgQIECAAAECBAYJKJAG6ZhHgAABAgQIECBAgMBMCSiQZup0O1gCBAgQ\nIECAAAECBAYJKJAG6ZhHgAABAgQIECBAgMBMCSiQZup0O1gCBAgQIECAAAECBAYJKJAG6ZhH\ngAABAgQIECBAgMBMCSiQZup0O1gCBAgQIECAAAECBAYJKJAG6ZhHgAABAgQIECBAgMBMCSiQ\nZup0O1gCBAgQIECAAAECBAYJKJAG6ZhHgAABAgQIECBAgMBMCSiQZup0O1gCBAgQIECAAAEC\nBAYJKJAG6ZhHgAABAgQIECBAgMBMCSiQZup0O1gCBAgQIECAAAECBAYJKJAG6ZhHgAABAgQI\nECBAgMBMCSiQZup0O1gCBAgQIECAAAECBAYJKJAG6ZhHgAABAgQIECBAgMBMCSiQZup0O1gC\nBAgQIECAAAECBAYJKJAG6ZhHgAABAgQIECBAgMBMCSiQZup0O1gCBAgQIECAAAECBAYJKJAG\n6ZhHgAABAgQIECBAgMBMCSiQZup0O1gCBAgQIECAAAECBAYJKJAG6ZhHgAABAgQIECBAgMBM\nCSiQZup0O1gCBAgQIECAAAECBAYJKJAG6ZhHgAABAgQIECBAgMBMCSiQZup0O1gCBAgQIECA\nAAECBAYJKJAG6ZhHgAABAgQIECBAgMBMCSiQZup0O1gCBAgQIECAAAECBAYJKJAG6ZhHgAAB\nAgQIECBAgMBMCSiQZup0O1gCBAgQIECAAAECBAYJKJAG6ZhHgAABAgQIECBAgMBMCSiQZup0\nO1gCBAgQIECAAAECBAYJKJAG6ZhHgAABAgQIECBAgMBMCSiQZup0O1gCBAgQIECAAAECBAYJ\nKJAG6ZhHgAABAgQIECBAgMBMCSiQZup0O1gCBAgQIECAAAECBAYJbBg00zwCBAgQIEBguMBl\nTXOTdc2mp61r5g5pmvlfzjfzHzmz2faGmzfNluFL60GAAAECBAgsFHhiJswnuy+c4TMBAgQI\nTLbA5c3mB2xrNv1yW7N5vjdbm02n/qJprjPZe2/vCBAgsCwCm7KW+ln2qGVZ2xqvxC12a3wC\nbJ4AAQIEplfg0qbZf30z/69NM7frwqOYy9WkXZpNr1843WcCBAgQmGwBBdJknx97R4AAAQIT\nLLCp2fSHKY4GXP2fe2Buv7vxBB+CXSNAgACBBQIKpAUgPhIgQIAAgVEFcj/JrYb1nWs2D+0z\nbB3mEyBAgMDqCSiQVs/alggQIECgdQJzFw07pPXNFUP7DFuH+QQIECCwegIKpNWztiUCBAgQ\naJnAjqb5jyGH9NOvNdu/OKSP2QQIECAwQQIKpAk6GXaFAAECBKZLYJdmywcycNP7++/1/PyO\nZv4ZRzTNtv7zTSVAgACBSRRQIE3iWbFPBAgQIDA1Aj9otj5svtnxDymULu/udJ5NOjNXl47d\n3Gx9Z3eaVwIECBAgQGB0Ad+DNLqVngQIEJhIgZ/ku+y2NhsP39JsOjg7ODeRO2mnCBAgsDIC\nrfoepA0rY2StBAgQIEBgtgSu3zT5WqRtX56to3a0BAgQaJ+AW+zad04dEQECBAgQIECAAAEC\nSxRQIC0RzmIECBAgQIAAAQIECLRPQIHUvnPqiAgQIECAAAECBAgQWKKAAmmJcBYjQIAAAQIE\nCBAgQKB9Agqk9p1TR0SAAAECBAgQIECAwBIFFEhLhLMYAQIECBAgQIAAAQLtE1Agte+cOiIC\nBAgQIECAAAECBJYooEBaIpzFCBAgQIAAAQIECBBon4ACqX3n1BERIECAAAECBAgQILBEAQXS\nEuEsRoAAAQIECBAgQIBA+wQUSO07p46IAAECBAgQIECAAIElCiiQlghnMQIECBAgQIAAAQIE\n2iegQGrfOXVEBAgQIECAAAECBAgsUUCBtEQ4ixEgQIAAAQIECBAg0D6BDe07JEdEgAABAgRW\nXWDXbPHhySHJpclHkpMSjQABAgQIEFiCwBOzzHyy+xKWtQgBAgQIrK1AFUVnJvX3eG/elc+b\nE40AAQJtF9iUA6y//45qw4G6xa4NZ9ExECBAgMBaCeyZDX8wObDPDjw0017WZ7pJBAgQIDDB\nAgqkCT45do0AAQIEJl7gD7KH+w/Yyydn3nUGzDeLAAECBCZMQIE0YSfE7hAgQIDAVAn8/+3d\nCbgsZXkn8IPAZRVQcVc04gIquIDgCooBBx2XOKOjZoGgRNQEH6Mx5nHBGEdx1BgzLsQ1ZmIw\nIS6ZmNERA5gYZRUFUUcUxICCG6Ig3EXu/N97q7TonO7Tl1vnnK5zft/z/G93ffV11de/6ufc\nek9193nIArPdNusftMAYqwkQIEBghgQUSDN0MEyFAAECBAYnsH6KGU8zZorNGEKAAAECSyGg\nQFoKZfsgQIAAgZUqcPoCT6y+0e7sBcZYTYAAAQIzJKBAmqGDYSoECBAgMDiBkzPj8yfM+jVZ\nV0WSRoAAAQIDEVAgDeRAmSYBAgQIzKTAhszqyOTTI7Nbm+VXJv9jpN8iAQIECMy4gD8UO+MH\nyPQIECBAYOYFrsoMD08emLR/KPYzuf/9RCNAgACBgQkokAZ2wEyXAAECBGZW4IuZWUUjQIAA\ngQELeIvdgA+eqRMgQIAAAQIECBAg0K+AAqlfT1sjQIAAAQIECBAgQGDAAgqkAR88UydAgAAB\nAgQIECBAoF8BBVK/nrZGgAABAgQIECBAgMCABRRIAz54pk6AAAECBAgQIECAQL8CCqR+PW2N\nAAECBAgQIECAAIEBCyiQBnzwTJ0AAQIECBAgQIAAgX4FFEj9etoaAQIECBAgQIAAAQIDFlAg\nDfjgmToBAgQIECBAgAABAv0KKJD69bQ1AgQIECBAgAABAgQGLKBAGvDBM3UCBAgQIECAAAEC\nBPoVUCD162lrBAgQIECAAAECBAgMWECBNOCDZ+oECBAgQIAAAQIECPQroEDq19PWCBAgQIAA\nAQIECBAYsIACacAHz9QJECBAgAABAgQIEOhXQIHUr6etESBAgAABAgQIECAwYAEF0oAPnqkT\nIECAAAECBAgQINCvgAKpX09bI0CAAAECBAgQIEBgwAIKpAEfPFMnQIAAAQIECBAgQKBfAQVS\nv562RoAAAQIECBAgQIDAgAUUSAM+eKZOgAABAgQIECBAgEC/Atv1uzlbI0CAAAECBAgQIEBg\nFgSunZu7w/Zzax5ac1k/t+7MXefmrpyFeZkDgWkEjs2gjcku0ww2hgABAgQIECBAgMA4gXPn\n5rZfN7fDn6+fW7Nh/dwOGzdnzYbqq3XjHrcV/Wvy2DqXfdhWbMNDCdxEQIF0Ew4LBAgQIECA\nAAECN1dg3dya9/6yMGoLpM23te7mbnfC41ZUgeQzSBOOtFUECBAgQIAAAQIEhiSwdm7N/baZ\n2+aYcXOudTVm3Hr9c3MKJK8CAgQIECBAgAABAitEIAXQYxd6KtOMWWgbK3m9AmklH13PjQAB\nAgQIECBAYFUJbDO3cceFnvA0Yxbaxkper0BayUfXcyNAgAABAgQIEFhVAhvmbjxvoSc8zZiF\ntrGS1yuQVvLR9dwIECBAgAABAgRWlcBOcxtO2zi38ZxxT7rW1Zhx6/X7DJLXAAECBAgQIECA\nAIGVJLBxw9y6p6YQunD0SVVfrUt/fSW3NkbAH4odA6ObAAECBAgQIECAwBAFdp6bu/zcuXUH\n7De35mn5QoZH1HNIcfTZC+fW/f2B9TdjNQIDEPB3kAZwkEyRAAECBAgQIEBgXgF/B2leFp0E\nCBAgQIAAAQIECBAYuIAvaRj4ATR9AgQIECBAgAABAgT6E1Ag9WdpSwQIECBAgAABAgQIDFxA\ngTTwA2j6BAgQIECAAAECBAj0J6BA6s/SlggQIECAAAECBAgQGLiAAmngB9D0CRAgQIAAAQIE\nCBDoT0CB1J+lLREgQIAAAQIECBAgMHABBdLAD6DpEyBAgAABAgQIECDQn4ACqT9LWyJAgAAB\nAgQIECBAYOACCqSBH0DTJ0CAAAECBAgQIECgPwEFUn+WtkSAAAECBAgQIECAwMAFFEgDP4Cm\nT4AAAQIECBAgQIBAfwIKpP4sbYkAAQIECBAgQIAAgYELKJAGfgBNnwABAgQIECBAgACB/gQU\nSP1Z2hIBAgQIECBAgAABAgMXUCAN/ACaPgECBAgQIECAAAEC/QkokPqztCUCBAgQIECAAAEC\nBAYuoEAa+AE0fQIECBAgQIAAAQIE+hNQIPVnaUsECBAgQIAAAQIECAxcQIE08ANo+gQIECBA\ngAABAgQI9CegQOrP0pYIECBAgAABAgQIEBi4gAJp4AfQ9AkQIECAAAECBAgQ6E9AgdSfpS0R\nIECAAAECBAgQIDBwAQXSwA+g6RMgQIAAAQIECBAg0J/Aai+Qtg3lPZM9+iO1JQIECBAgQIAA\nAQIEhiqwGgqk2+XgnJS8v3OQds/9dybXJRcnP0wuSF6caAQIECBAgAABAgQIEFiRAnvmWV2e\nbEw+0zzD7XN7XtP389yenpySXNb0VeG01IXjsc2+d8mtRoAAAQIECBAgQGBIAmsy2TrfftiQ\nJr1a5/qnzcF6WW53aBBe1PS9K7d3aPrqpg7sW5M6uIcnS9kUSEupbV8ECBAgQIAAAQJ9CiiQ\n+tRc5G19Ptu/JOleEfpIlq9O6krSaKtx307eMLpikZcVSIsMbPMECBAgQIAAAQKLJrCiCqRu\n4bBoYsu44e2y7/OTGztzqLfVVRG0vtPX3q1x30nu1Xa4JUCAAAECBAgQIEBg9Qis9AKpPmt0\neHKbziH9l9y/d3LbTl97t95yd2DypbbDLQECBAgQIECAAAECBFaKQBU7a5N/Tx7VPKmdc/vZ\npL6c4U5NX908MPl6ckOyX7KUzVvsllLbvggQIECAAAECBPoUWFFvsesTZla39duZ2PVJvX2u\nrgy9L3l3s7wut19JrkrqyxlqTBUrS90USEstbn8ECBAgQIAAAQJ9CSiQ+pJcwu3cPvt6fVKf\nPdqQVDHUzbVZPjm5f7IcTYG0HOr2SYAAAQIECBAg0IeAAqkPxWXcxrbZ952Tg5IqiPZIlrsp\nkJb7CNg/AQIECBAgQGBlCdT57puTM5u8KbfVtxhNgbQYqqt8mwqkVf4C8PQJECBAgAABAj0K\nHJxt/SjpvmOq7ldfreu7ragCaaV/i13fB9/2CBAgQIAAAQIECMyywI6Z3CnJreaZZPXVuhqj\nEZhK4HkZVV/kcNxUo8cP2jur6gsgRqv2hZZ3Gr9JawgQIECAAAECBAgsKPDUjFjonLPG9NlW\n1BWk+kOq2i8F6ssc9k/qdmvaJXnwoUm9WKZp982gdyT1R2w1AgQIECBAgAABAjdX4N5TPHCa\nMVNsZmUOUSDd9Li+M4sfSeprv7emVdX++S3YQF1t0ggQIECAAAECBAhsrcAPp9jANGOm2Iwh\nBBZP4GHZdBVV015xWryZ2DIBAgQIECBAgMCQBeqb6tYm495mV+v6/ja7Ooet/dU57eDbavyS\nhvpw2t2T+yT14tgl0QgQIECAAAECBAisBIEr8iReMeGJ1Loao61ygQfl+b8n+V4yXzX9zfT/\nRXLbZDlaVduuIC2HvH0SIECAAAECBFamwFF5Wpcl7blv3a++xWgr6grSYgDN2jZflQl1Xxif\ny/LHkw8ln0jOSr6b1JgfJM9KlropkJZa3P4IECBAgAABAqtD4K55mpXFbAqkxdTtedtPy/aq\n8KlC6METtr1N1h2SnJPU+IcnS9kUSEupbV8ECBAgQIAAAQJ9CiiQ+tRc5G19MNuvt8/tMOV+\n6vNJP0lOmnJ8X8MUSH1J2g4BAgQIECBAgMBSC6yoAmmlf0lD/U2j+rrttVO+Sq7OuAuSvr/Z\nY8rdG0aAAAECBAgQIECAwHIKrPQCqT5bdECy/ZTIdQWpiqqvTTneMAIECBAgQIAAAQIEVpDA\nSi+QPpBjtU/y4eTgCcetPoP0qOSTyc7JxxKNAAECBAgQIECAAIFVJrDdCn++f5Pnd7vktckT\nk/rO98uT+uvB9Vmj3ZJbJ3dL7phsSF6c/FuiESBAgAABAgQIECBAYEUK3CPP6uSkCqT6lrpu\nrsvyxcmbksX+CsTsYt7mSxrmZdFJgAABAgQIECAwAIEV9SUNK/0KUvt6uiR3ntks1FWj3ZMd\nk/rDsdckGgECBAgQIECAAAECBOZWS4HUPdT11rqKRoAAAQIECBAgQIAAgZsIrPQvabjJk7VA\ngAABAgQIECBAgACBSQIKpEk61hEgQIAAAQIECBAgsKoEFEir6nB7sgQIECBAgAABAgQITBJQ\nIE3SsY4AAQIECBAgQIAAgVUloEBaVYfbkyVAgAABAgQIECBAYJKAAmmSjnUECBAgQIAAAQIE\nCKwqAQXSqjrcniwBAgQIECBAgAABApMEVuPfQZrksdzr6q8Qz1rbftYmZD4ECBAgQIDAsgr8\nPHu/cVlnYOezJjCL57A320iBdLPpen3g+mZrP+11qzZGgAABAgQIECBAYOkE1i3drhZvT9ss\n3qZteQsFDsz4Wbta84eZ052Td23hczGcwHIJHJYdPzJ5zXJNwH4JbKHAfTP+BU228KGGE1gW\ngVtnrycmT02uXJYZ2OmsClRxdN6sTs68CPQl8LZs6JS+NmY7BJZA4Pjs44Il2I9dEOhL4HHZ\n0A19bcx2CCyBwN2zj43J3ZZgX3ZBYFkEfEnDsrDbKQECBAgQIECAAAECsyigQJrFo2JOBAgQ\nIECAAAECBAgsi4ACaVnY7ZQAAQIECBAgQIAAgVkUUCDN4lExJwIECBAgQIAAAQIElkVAgbQs\n7HZKgAABAgQIECBAgMAsCiiQZvGomBMBAgQIECBAgAABAssioEBaFnY7JUCAAAECBAgQIEBg\nFgUUSLN4VMyJAAECBAgQIECAAIFlEVAgLQu7nRIgQIAAAQIECBAgMIsCCqRZPCqzM6f1mcq6\n2ZmOmRBYUKBer16zCzIZMEMCXrMzdDBMZSqBOjeo5mftZgf/EiCwygR2z/Pdc5U9Z0932AI7\nZvp3GfZTMPtVJlC/qLzHKnvOnu7wBe45/KfgGRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE\nCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE\nCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE\nCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE\nCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDADAhsOwNzMIXZELh7pvGE5MIJ09k5\n6x6cPCLZI/lJsjbRCCyXwFOy4/o59v0pJnCnjPnV5HvJ9VOMN4TAYghM85q9Q3Z8aHKP5KfJ\ndYlGYLkEFnrN7piJHZA8PNk1+UHy80QjQIDAoAV2y+y/ktR/xOPab2XFVcnGTqpAOj7RCCyH\nwLHZab0eXzzFzquI+lwz/mFTjDeEwGIILPSarZ/FH0m6P2ermP+jxZiMbRKYQmCh1+xh2ca3\nku5r9tIsV79GgACBwQrcKjP/ZFI/3MYVSIdn3Y1J/dCr/6jvn1Rh9LWkHvebiUZgKQWenJ2t\nS+r1N02B9KpmbI1XIAVBW3KBaV6zZ2dW9Rp9XbJfcnRyUVJ9z0g0AkspsNBrdq9M5sfJ1clL\nk/slf5D8MLkmuXuiESBAYHACv5YZfyep/3zXJuMKpNObMUfkttsekoV6bP0HrhFYCoHbZCd/\nndTr7obmdqEC6aCMW5/UW+vqcQqkIGhLJjDta7be4lyvz5NGZnbfpv+MkX6LBBZLYNrX7Esy\ngXrNvmZkIq9u+l8x0m+RAAECMy9wZGZYP9jqvcJPSr6Q/DQZbbdIR/1Ws4qg+T6zVleRNoxZ\nl26NQK8CZ2Vr9br9u6Te9ln3JxVIu2T9xcm/Jm9MavxDE43AUglM+5o9PROq38TX5zlGW71d\nqX4hpRFYCoFpX7MnZjL1M7XOIbqtXq/V//Zup/sEhiRQJ7/a6hSooua1yb2T/z2BoN5aV7+B\nr0vnox+6rP/I75h8a5516dII9C5wXrZYb/l8elJv7ViovSUDbp9UMTX6+l3osdYT6ENg2tfs\nAdlZFfJ1ZXSbpH7m7p9sl5yWnJNoBJZCYNrX7KnNZI4emdRRzXK7fmS1RQIECAxHYNwVpEnP\n4ISsrN8SvWHSIOsILJJA/dayXn/jriDV++dr/TFJtfa3na4gbfbw79ILjHvN7pap1Gv1bUm9\n9fn7zXL11ec5/kuiEVgOgXGv2ZpLvavkj5P6heuXk/oZ+4WkfrH65mT7RCNAgMCgBeqH2k+3\n4BnUb/DrN/JfT3bagscZSqAvgUn/cd8hO6mTzI91dqZA6mC4uywC416z+2Y2VQx9KakrSHVy\nWYVSfcajCqRa97hEI7DUAuNes+089s6dC5J6jbb5Ru7fK9EIDFbAW+wGe+iWdeJHZ+/1Qfk6\nAa3f0vubMkHQZkrgfZlN/Rbz2JmalckQmF9gt6Z7/9z+TlJXRT+avCmpX0ZV+7PNN/4lMDMC\n9dq8MLkuOSjZtbm9MrdfTNrXbu5qBIYloEAa1vGahdm+KpN4f3J5ckjy1UQjMEsCL8hkjkyO\nT+o/7p2btG/32LFZrs95aARmQeC7zSTql05/NTKh07NcJ5z7JHuMrLNIYDkFXpSd/yx5QnJO\nUj9v67aWr0lenmgEBimgQBrkYVuWSdfJ5FuTer9x/QCsr0qut9dpBGZNoP28xocysfoPu83v\nNxOtE87qqy8o0QjMgsB3Mom64vm9eSZT/fWarXbbzTf+JbDsAvVarKtG9cUiPxqZTRVH9QUN\ndUV0r5F1FgkMQqC+HUcjsJBAFdLvTY5O6jMdv57Ub400ArMoUG9Nqg8Mj7ZHpOPBySlJ/Ub+\n6kQjMAsC9SH3+tzGfZK64jn687W+LbRerzVGIzALAvUZ5Do3uN2Yyaxp+uuLHDQCBAgMVuAL\nmfm4L2l4XtZtTD6S+GEXBG0mBJ6UWdTrsj6vMU07MYNqvG+xm0bLmMUQmPSaPS47rNfnq0d2\nvH+Wq4D6x5F+iwSWQmDSa/aiTGBdcuDIRO6c5R8n9VZ8jcAgBVxBGuRhW9JJ3yZ7e12zx91z\n++Exe/+N9F87Zp1uAgQIEJgs8P6sPj45Iam3L1VBdNfktUn9Qe8XJhqBWRKoov6M5FPJG5Kz\nk72TVyZ1vvCsRCMwSAEF0iAP25JO+pHZW/vB4MMm7Ln9APyEIVYRIECAwBiBtek/ODkpeU7y\n/KSuHNVJ5+8llyQagVkS+NdM5tHJO5K6Qt+2+nzyEUl9DkkjQIAAAQIECBAgsNUC9fmNemvd\nLbd6SzZAYGkE6t0mByS+SGRpvO2FAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQ\nIECAwFYJbLNVj/ZgAgQIEFhsgR2yg8c3O/mn3K5b7B0OfPu3yPyfnFybnDoDz2XnzGG/5G7J\nFcmXk2uSLW075QH1OrhXsmtyafLZ5P8lo63G3H+kc2OWb0iuTuoxP066rc4HntJ0XJ/bT3ZX\nznP/ienbLvn35Nx51usiQIAAAQIECBAgsCgCz8hW6+S2Uve1yQI7ZnVZXTx52JKs/a3s5aqk\nPX51+5Pk+GTaVoXLy5IqaLrbqfs/Tz6YVMHUbS/JwujY7vINWX9ysnvnQdt2HnNj7t+1s270\n7gM6Y2v/GgECBAgQIECAAIElE/hU9vTN5IfJGYk2WWBWCqTDM80qNC5N/iipKzpVGH0tqWLl\nN5Np2t9mUI2/JHlz8qvJPsmLkrOSWnd6Ulca29YWSFW8HNXJMblf676d1OM+kbTvJGkLpJpz\nravtj2uvz4oaU1EgjVPST4AAAQIECBAg0LvAXtliXSX4n8m7kzohvW+ijReYlQLp9EyxjtcR\nI1N9SNN/0Uj/fIvHNWOvyO1t5xmwffpOa8Y8t7O+LZBe2Onr3t0jC5cnNb96+1+1tkCqef0o\n+Xx1jmlVrNW4erwCaQySbgIEhitQ7x/WCBAgQGA2BX4706rP1NRv+tcmz0nqpHn0LVqPTt/d\nkn9IRj9bUp+BeVpSnxWpk+m23St3Dkvuk3wrOSO5IOm2Wn+r5B+TY5LbJLWPLyfV9kwem9Q2\n6qT7G8lnk9HtpGuuCpdDk8ck304+ntTVi0cnn06qCOi2aebXHb8199fkwU9N2mLhS7lf8/tZ\nMl/bO51HJndMPpf8n+RJyfrm/i1yu0vyleSfk247Jwv1GaAyq6KkCuD5WtnUlacbk99Ivp+M\nttrfS5O6yrjv6MoJy/Ua+afkd5KDkwuTttVn3D6a1Gvvrkm9brqtxv9K8srkT7or3CdAgAAB\nAgQIECCwmAJ1gnxpUm+t2yGpk+kqIurktoqebnt+Fuq3+S/odjb3f71Z172a8OL0VcFVJ991\nArwhqRP1/57UfttWhdFlyduS2n7l1KTak5M6aa++a5L6XEvdr+3USXu37ZOFq5NaX4+pk/C6\nSvEXSfX9p6Tbpp1f9zHt/SrEapsXtx0L3D6oGVuPqc8H1XNpH39Q7o+2d6aj1ldx8r3mfr0N\n7pvJmclCreZX+6hiclJ7WFbWfr44aVCzrnvMquslST22e8yrv9uqqKoxz2o66/VVy+cnRzT3\nfz+3o+0t6ahjd2BS4z+YaAQIECBAgAABAgQWXeCx2UOdgL69s6cTm75nd/rqbl29uT75fC2M\ntP+b5SpI9mz6n5jb2u5nkjs1fbfM7d8k1X9U01c3VSBV8VTFTV29emZySLJbUsVEFW91olwn\n17skT0nWJj9Ldk+q1dj63M0PksOSajslJyW1v0pdjWnblsyvfUz3dksKpJpHze3a5BlJXfmp\nYqOuJtXzq4K05t+25+ZOzfevknq+1R6RfCep/rOShdoJGVBj37DAwKObcXVctrS9JA+ofbxw\nzAPvm/4qjuvY3rMZ0y2QtktfFbKjr6fyKZP3JPsltQ8FUhA0AgQIECBAgACBxRdoC5aHdHa1\nT+7XSel8X6t8crOuPeGth90xqZPgD9dC06ogqG0c0HY0t3XCX4VNney3VySqQKqxv5t020Oz\n8MlktFCrMe1j6ksJqj0vqW0cVwudVifbX05q3ZGd/i2ZX+dhv7i7JQXSi/Ko2n+9XWy01dWT\nWndCZ8VVuX9ZskOnr+4+PamxZ9XChFbj6grb15OdJoyrVS9LRve/wEN+sbotkKo4fk0nb8r9\nv09uSGrbdTWobd0CqfpOSqqI2qsWmnZobutxj00USA2KGwIECBAgQIAAgcUXaK8IXTTPruq3\n+nWS2i2catgRTf8JtdC0F+e2xv7nZrm2W8t1gr7/PKmrSrX+zkm1ttgZ3dfmtb/8d/vcreLt\nGcnZSW3joKRaXW2o5dvXwkj74yzXurZA2tL5jWxu0+KWFEh/mUfU/u+x6ZE3/ed2zboyqHaX\npMbWW+xGW11xqUJ0UoF0dNbXlbwrk32ThdqzMqD297aFBs6zvi2Q6vGjqQLtG8kxSVsI5+4v\nvqTh/FpIe0xSj61CsW313L+bVDGlQGpV3BIgQIAAAQIECCy6QPuZomuzp8tGUm/9qhPX9yXd\nVldkvp1U8dO2L+ZOndDWCXy1KnRGT5jnWz500+hfFkh7Nsvdm7pSVSfMX03q8zi1nTr5/nFz\n/+DcVjsnqcJgvvacdNbj2gJpS+c33za3pECqYrOuklSBN1/7WTrr+VV7QlJzfXktzNMuT9+4\nAulVWVePvSS5dzJNu08G1WNOm2bwyJi2QHpD+quQafMruT/uuY5eQarXUxVzZybV6jVUb7t7\nay2kKZA2O/iXAIEVKFA/8DQCBAgQmC2BZ2c6dXL8ieZ2dHZ1Raiu1tRv96sgqVYn+h9IXpHU\n1Zvrkwck9baqurpRrd5aVa3eevXGTffm/+fLtLYT0AAABmtJREFUI92jBc4+Wf+55JZJbet/\nJVWMVTH06qQKvLZVkVcn5TX2p21nc7v7yPLNnd/IZqZevC4jt0l2SqrI67Y1Wahiq51TPY9q\nt9588x/+redyxUhvbfvPkuOTsnliclUyTbs4g2qf91tgcP0/XkXc15KnJe18c3fT2yUvrDs3\no9Xr6ZTkd5O9kn2TPZOTE40AAQIECBAgQIDAkglUUVPF0RkT9lgFSY154ciYvbNcJ7ZvTF6X\n1Jj6QH7bqhCo9ee2HSO3B2f5wKSKg2rtW+y6X1RQ/e9IattH1cJIOzXLte7hTf87m+VHNsvd\nm/Z5HNl0bun8uttq71dRU/uvAmOh1s7tofMMrLcg1nY+1qy7fbP86Wa5e1PuNfasTmddgXl/\n0//R3O7cWTft3So+a7t/MOEB/7UZ86nOmJc0faOvj86Q/3B32+Yx53fWPKrpq89qvTe5tLPO\nFaQOhrsECBAgQIAAAQKLJ1BvYaqT4mdP2MWjmzFfnWfMGen7RlIFQveEPYub2ifzb23/8ZsX\nf/FvXalYm3wpad+GNa5A+kTG1DYOSbrtQVm4Nql1hzYrHpDbKsr+Odmh6aubByfrkxrbFki5\nu+nLH6pvmvnV+NG2JQXS4/Lg2tc/JHW1p9s+lIVad1Sn88NN3+Gdvios/q7p73o/r+n7SG5r\nzM1p9ZbDsqu3LnaN2m1VEfejpMYc0Xbmtq8CqUwuT85Mfpi8PmmbAqmVcEuAAAECBAgQILBo\nAlVA1Ino9cnuE/ZSJ65VBNUJ/GNGxtUJffVXjhtZV4v3SWr7lROSOtl/aVLb25AcmLRtXIFU\nVzRq++clz0zqatEfJt9Pav617teStv1p7lRfvd2rrmy9O6nP99T46q9CpW1bMr/2Md3btkCq\nQq2u/syXv+w8oAqYmsPHk5rzk5O271253y2c7p3l7ybrkg8kr03OTtrn8fncr3ab5OqktluF\n4XxzqL5dk4Xa72VAFUCV85MTkxclf520BeboFaa+CqTsYtM33dXzqOxfHU3bL7fV98G2wy0B\nAgQIECBAgACBvgWeng3WSeffTrHhl48Zu0v664scqgDaI5mv7ZPOf0nqykR78ltXCo5Kum1c\ngVRXRN6ZVEHVPv7K3D82qQKr+k5Kuu25Wfi35JqkTvSfn1SBUWMfkXTbtPPrPqa9v2PutHMa\nd1tzbdv2ufOapAqqdnwVi1XIdYujLG5qd8m/JyeXJt9LPpzcI6nHnppUqyKr3dak21ttGr3w\nP3V16LR5tlmOj5/n4X0WSPX2w3oOF43sR4E0AmKRAAECBAgQIEBg+AI75yk8MLlbcnPeBrZb\n8/g75XZcq6sk47b99qyrk+8qiOZrWzu/+bY5rq+Kob2Tu44bkP5xBedts66ex4cmPLaPVWW5\nb1JvTyx7jQABAgQIECBAgACBgQk8J/Ott6Q9e2Ted8jyj5IfJPWFBkNodZXtq8makcnW1aYq\nkF440m+RAAECBAgQIECAAAECNxG4e5bqLX9VCL0xeUpSbw+8IKm36P23ZCjtzzPRKoQ+lRyX\nPCt5T7I++Vxyy0QjQIAAAQIECBAgQIDARIFDsvbcpIqLSn1b3llJfeZqSK2uHL0luS5pn8sV\nuf/BZPdEI0CAAAECBAgQIECAwNQCt87I+yU7Tv2I2RxYX+xQX8ww6fNKszlzsyJAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQI\nECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRW\nqMD/B6sFjdxCdtLwAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotQLDisp(fit.ab, cex=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "5fb1f766",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       factor(treatment)IgG\n",
       "Down                      1\n",
       "NotSig                    6\n",
       "Up                        5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res <- glmQLFTest(fit.ab, coef=ncol(design))\n",
    "summary(decideTests(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "9f170b37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>pDC</th><td> 1.8980108</td><td>14.78002</td><td>13.9122904</td><td>0.0006216318</td><td>0.007214854</td></tr>\n",
       "\t<tr><th scope=row>Plasma.Cells</th><td> 1.7830532</td><td>15.10785</td><td>12.2485818</td><td>0.0012024756</td><td>0.007214854</td></tr>\n",
       "\t<tr><th scope=row>Mast</th><td> 1.5148101</td><td>16.56676</td><td> 9.9822880</td><td>0.0030903297</td><td>0.012361319</td></tr>\n",
       "\t<tr><th scope=row>cDC</th><td> 0.7548637</td><td>15.85529</td><td> 6.9577808</td><td>0.0119741713</td><td>0.029320639</td></tr>\n",
       "\t<tr><th scope=row>B.Cells</th><td> 1.7892351</td><td>16.15946</td><td> 6.9221391</td><td>0.0122169330</td><td>0.029320639</td></tr>\n",
       "\t<tr><th scope=row>Secretory.Epithelial</th><td>-3.4917073</td><td>11.86088</td><td> 7.1241792</td><td>0.0191621937</td><td>0.038324387</td></tr>\n",
       "\t<tr><th scope=row>Macrophages.Monocytes</th><td>-0.4973358</td><td>19.25327</td><td> 4.8209616</td><td>0.0342083068</td><td>0.058642812</td></tr>\n",
       "\t<tr><th scope=row>T1P</th><td>-1.4561087</td><td>12.84958</td><td> 3.4036965</td><td>0.0728285109</td><td>0.109242766</td></tr>\n",
       "\t<tr><th scope=row>Endothelial</th><td>-0.5394923</td><td>14.87738</td><td> 1.0389039</td><td>0.3145004764</td><td>0.419333969</td></tr>\n",
       "\t<tr><th scope=row>Neutrophils</th><td>-0.8073751</td><td>13.74360</td><td> 0.5768519</td><td>0.4522192505</td><td>0.499019018</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'factor(treatment)IgG'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 10 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tpDC &  1.8980108 & 14.78002 & 13.9122904 & 0.0006216318 & 0.007214854\\\\\n",
       "\tPlasma.Cells &  1.7830532 & 15.10785 & 12.2485818 & 0.0012024756 & 0.007214854\\\\\n",
       "\tMast &  1.5148101 & 16.56676 &  9.9822880 & 0.0030903297 & 0.012361319\\\\\n",
       "\tcDC &  0.7548637 & 15.85529 &  6.9577808 & 0.0119741713 & 0.029320639\\\\\n",
       "\tB.Cells &  1.7892351 & 16.15946 &  6.9221391 & 0.0122169330 & 0.029320639\\\\\n",
       "\tSecretory.Epithelial & -3.4917073 & 11.86088 &  7.1241792 & 0.0191621937 & 0.038324387\\\\\n",
       "\tMacrophages.Monocytes & -0.4973358 & 19.25327 &  4.8209616 & 0.0342083068 & 0.058642812\\\\\n",
       "\tT1P & -1.4561087 & 12.84958 &  3.4036965 & 0.0728285109 & 0.109242766\\\\\n",
       "\tEndothelial & -0.5394923 & 14.87738 &  1.0389039 & 0.3145004764 & 0.419333969\\\\\n",
       "\tNeutrophils & -0.8073751 & 13.74360 &  0.5768519 & 0.4522192505 & 0.499019018\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] 'factor(treatment)IgG'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 10 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| pDC |  1.8980108 | 14.78002 | 13.9122904 | 0.0006216318 | 0.007214854 |\n",
       "| Plasma.Cells |  1.7830532 | 15.10785 | 12.2485818 | 0.0012024756 | 0.007214854 |\n",
       "| Mast |  1.5148101 | 16.56676 |  9.9822880 | 0.0030903297 | 0.012361319 |\n",
       "| cDC |  0.7548637 | 15.85529 |  6.9577808 | 0.0119741713 | 0.029320639 |\n",
       "| B.Cells |  1.7892351 | 16.15946 |  6.9221391 | 0.0122169330 | 0.029320639 |\n",
       "| Secretory.Epithelial | -3.4917073 | 11.86088 |  7.1241792 | 0.0191621937 | 0.038324387 |\n",
       "| Macrophages.Monocytes | -0.4973358 | 19.25327 |  4.8209616 | 0.0342083068 | 0.058642812 |\n",
       "| T1P | -1.4561087 | 12.84958 |  3.4036965 | 0.0728285109 | 0.109242766 |\n",
       "| Endothelial | -0.5394923 | 14.87738 |  1.0389039 | 0.3145004764 | 0.419333969 |\n",
       "| Neutrophils | -0.8073751 | 13.74360 |  0.5768519 | 0.4522192505 | 0.499019018 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   'factor(treatment)IgG'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  factor(treatment)IgG \n",
       "                           logFC   logCPM          F       PValue         FDR\n",
       "pDC                    1.8980108 14.78002 13.9122904 0.0006216318 0.007214854\n",
       "Plasma.Cells           1.7830532 15.10785 12.2485818 0.0012024756 0.007214854\n",
       "Mast                   1.5148101 16.56676  9.9822880 0.0030903297 0.012361319\n",
       "cDC                    0.7548637 15.85529  6.9577808 0.0119741713 0.029320639\n",
       "B.Cells                1.7892351 16.15946  6.9221391 0.0122169330 0.029320639\n",
       "Secretory.Epithelial  -3.4917073 11.86088  7.1241792 0.0191621937 0.038324387\n",
       "Macrophages.Monocytes -0.4973358 19.25327  4.8209616 0.0342083068 0.058642812\n",
       "T1P                   -1.4561087 12.84958  3.4036965 0.0728285109 0.109242766\n",
       "Endothelial           -0.5394923 14.87738  1.0389039 0.3145004764 0.419333969\n",
       "Neutrophils           -0.8073751 13.74360  0.5768519 0.4522192505 0.499019018"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "9ed05a78",
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- glmQLFTest(fit.ab, coef = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "a47d6fb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       factor(treatment)CD8b\n",
       "Down                       2\n",
       "NotSig                     7\n",
       "Up                         3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(decideTests(res)) # CD8b vs CD8a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "a3f9a22b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>pDC</th><td> 2.1286548</td><td>14.78002</td><td>14.209943</td><td>0.0005539053</td><td>0.006646864</td></tr>\n",
       "\t<tr><th scope=row>T1P</th><td>-2.2161524</td><td>12.84958</td><td> 7.893985</td><td>0.0077835820</td><td>0.032361162</td></tr>\n",
       "\t<tr><th scope=row>cDC</th><td> 0.8079923</td><td>15.85529</td><td> 7.800867</td><td>0.0080902906</td><td>0.032361162</td></tr>\n",
       "\t<tr><th scope=row>Secretory.Epithelial</th><td>-3.6499283</td><td>11.86088</td><td> 8.628877</td><td>0.0114429104</td><td>0.034328731</td></tr>\n",
       "\t<tr><th scope=row>Mast</th><td> 1.1429792</td><td>16.56676</td><td> 6.172193</td><td>0.0174835922</td><td>0.041960621</td></tr>\n",
       "\t<tr><th scope=row>Fibroblasts</th><td>-1.5343691</td><td>14.62188</td><td> 4.570779</td><td>0.0389919486</td><td>0.077983897</td></tr>\n",
       "\t<tr><th scope=row>B.Cells</th><td> 1.5849288</td><td>16.15946</td><td> 4.239670</td><td>0.0463674431</td><td>0.079487045</td></tr>\n",
       "\t<tr><th scope=row>Endothelial</th><td>-0.9431280</td><td>14.87738</td><td> 2.923312</td><td>0.0954359655</td><td>0.143153948</td></tr>\n",
       "\t<tr><th scope=row>Macrophages.Monocytes</th><td>-0.2796467</td><td>19.25327</td><td> 1.534798</td><td>0.2228910651</td><td>0.297188087</td></tr>\n",
       "\t<tr><th scope=row>T2P</th><td>-0.7363410</td><td>14.80014</td><td> 1.184544</td><td>0.2832645339</td><td>0.310659413</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'factor(treatment)CD8b'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 10 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tpDC &  2.1286548 & 14.78002 & 14.209943 & 0.0005539053 & 0.006646864\\\\\n",
       "\tT1P & -2.2161524 & 12.84958 &  7.893985 & 0.0077835820 & 0.032361162\\\\\n",
       "\tcDC &  0.8079923 & 15.85529 &  7.800867 & 0.0080902906 & 0.032361162\\\\\n",
       "\tSecretory.Epithelial & -3.6499283 & 11.86088 &  8.628877 & 0.0114429104 & 0.034328731\\\\\n",
       "\tMast &  1.1429792 & 16.56676 &  6.172193 & 0.0174835922 & 0.041960621\\\\\n",
       "\tFibroblasts & -1.5343691 & 14.62188 &  4.570779 & 0.0389919486 & 0.077983897\\\\\n",
       "\tB.Cells &  1.5849288 & 16.15946 &  4.239670 & 0.0463674431 & 0.079487045\\\\\n",
       "\tEndothelial & -0.9431280 & 14.87738 &  2.923312 & 0.0954359655 & 0.143153948\\\\\n",
       "\tMacrophages.Monocytes & -0.2796467 & 19.25327 &  1.534798 & 0.2228910651 & 0.297188087\\\\\n",
       "\tT2P & -0.7363410 & 14.80014 &  1.184544 & 0.2832645339 & 0.310659413\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] 'factor(treatment)CD8b'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 10 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| pDC |  2.1286548 | 14.78002 | 14.209943 | 0.0005539053 | 0.006646864 |\n",
       "| T1P | -2.2161524 | 12.84958 |  7.893985 | 0.0077835820 | 0.032361162 |\n",
       "| cDC |  0.8079923 | 15.85529 |  7.800867 | 0.0080902906 | 0.032361162 |\n",
       "| Secretory.Epithelial | -3.6499283 | 11.86088 |  8.628877 | 0.0114429104 | 0.034328731 |\n",
       "| Mast |  1.1429792 | 16.56676 |  6.172193 | 0.0174835922 | 0.041960621 |\n",
       "| Fibroblasts | -1.5343691 | 14.62188 |  4.570779 | 0.0389919486 | 0.077983897 |\n",
       "| B.Cells |  1.5849288 | 16.15946 |  4.239670 | 0.0463674431 | 0.079487045 |\n",
       "| Endothelial | -0.9431280 | 14.87738 |  2.923312 | 0.0954359655 | 0.143153948 |\n",
       "| Macrophages.Monocytes | -0.2796467 | 19.25327 |  1.534798 | 0.2228910651 | 0.297188087 |\n",
       "| T2P | -0.7363410 | 14.80014 |  1.184544 | 0.2832645339 | 0.310659413 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   'factor(treatment)CD8b'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  factor(treatment)CD8b \n",
       "                           logFC   logCPM         F       PValue         FDR\n",
       "pDC                    2.1286548 14.78002 14.209943 0.0005539053 0.006646864\n",
       "T1P                   -2.2161524 12.84958  7.893985 0.0077835820 0.032361162\n",
       "cDC                    0.8079923 15.85529  7.800867 0.0080902906 0.032361162\n",
       "Secretory.Epithelial  -3.6499283 11.86088  8.628877 0.0114429104 0.034328731\n",
       "Mast                   1.1429792 16.56676  6.172193 0.0174835922 0.041960621\n",
       "Fibroblasts           -1.5343691 14.62188  4.570779 0.0389919486 0.077983897\n",
       "B.Cells                1.5849288 16.15946  4.239670 0.0463674431 0.079487045\n",
       "Endothelial           -0.9431280 14.87738  2.923312 0.0954359655 0.143153948\n",
       "Macrophages.Monocytes -0.2796467 19.25327  1.534798 0.2228910651 0.297188087\n",
       "T2P                   -0.7363410 14.80014  1.184544 0.2832645339 0.310659413"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "201fc03c",
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- glmQLFTest(fit.ab, contrast = c(0,0,0,0,1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "af410ec0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       1*factor(treatment)CD8b -1*factor(treatment)IgG\n",
       "Down                                                 0\n",
       "NotSig                                              12\n",
       "Up                                                   0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(decideTests(res)) # CD8b vs IgG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "75e4a664",
   "metadata": {},
   "outputs": [],
   "source": [
    "tnk_celltype_props <- read.csv(\"compositional_analysis/tcell_large_subcluster_counts_by_sample.csv\",row.names=1)\n",
    "metadata <- read.csv(\"compositional_analysis/metadata_by_sample.csv\", row.names=1)\n",
    "\n",
    "tnk_celltype_props <- na.omit(tnk_celltype_props)\n",
    "tnk_celltype_props <- t(tnk_celltype_props)\n",
    "metadata = metadata[colnames(tnk_celltype_props),]\n",
    "\n",
    "metadata$M.Number <- factor(metadata$M.Number)\n",
    "metadata$monkey_condition_categories <- factor(metadata$monkey_condition_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b3ea29ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "y.ab <- DGEList(tnk_celltype_props, samples=metadata)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a630fd30",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0373a113",
   "metadata": {},
   "outputs": [],
   "source": [
    "#keep <- filterByExpr(y.ab, group=y.ab$samples$treatment)\n",
    "#y.ab <- y.ab[keep,]\n",
    "#summary(keep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "52f66f52",
   "metadata": {},
   "outputs": [],
   "source": [
    "design <- model.matrix(~0+factor(monkey_condition_categories) + factor(treatment), y.ab$samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "23c4f142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.2767  0.2767  0.2767  0.2767  0.2767  0.2767 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.ab <- estimateDisp(y.ab, design, trend=\"none\")\n",
    "summary(y.ab$common.dispersion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "abeb3b80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zMvrZ5H+nzysKT/2ai6evbo5JykO6hDJrV+gfrDoBEgQIAAAQIECBBousDT08H/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\nAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotBCV(y.ab, cex=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5971144c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "99fcebb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.9397  0.9397  0.9397  0.9397  0.9397  0.9397 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit.ab <- glmQLFit(y.ab, design, robust=TRUE, abundance.trend=FALSE)\n",
    "summary(fit.ab$var.prior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cbe7d5dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qnDR/3gZtPPZ3g3X2GINx/0WaGLVQQIECBAgAABAgT6I6BA6vCxzsE9fNzwJukz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\nLiO5Fpd+xQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotQLDisp(fit.ab, cex=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e3e4007d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       factor(treatment)IgG\n",
       "Down                      0\n",
       "NotSig                    4\n",
       "Up                        3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res <- glmQLFTest(fit.ab, coef=ncol(design))\n",
    "summary(decideTests(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "201b187c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 7 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Non.cytotoxic.CD8</th><td> 3.6508329</td><td>14.64912</td><td>45.3358727</td><td>3.243809e-08</td><td>2.270667e-07</td></tr>\n",
       "\t<tr><th scope=row>Stem.like</th><td> 1.1318525</td><td>16.71542</td><td>11.8751368</td><td>1.289127e-03</td><td>4.511944e-03</td></tr>\n",
       "\t<tr><th scope=row>Interferon.responding</th><td> 1.8172521</td><td>14.03286</td><td> 8.2318540</td><td>6.375022e-03</td><td>1.487505e-02</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.T.NK</th><td>-0.8946504</td><td>17.96495</td><td> 4.8650581</td><td>3.283974e-02</td><td>5.746955e-02</td></tr>\n",
       "\t<tr><th scope=row>Treg</th><td> 0.6120690</td><td>15.34737</td><td> 3.1847882</td><td>8.143383e-02</td><td>1.140074e-01</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.T.NK</th><td>-0.3310238</td><td>18.27175</td><td> 2.3764899</td><td>1.305544e-01</td><td>1.523134e-01</td></tr>\n",
       "\t<tr><th scope=row>T1T17</th><td> 0.2092938</td><td>18.00529</td><td> 0.4691195</td><td>4.970945e-01</td><td>4.970945e-01</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'factor(treatment)IgG'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 7 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tNon.cytotoxic.CD8 &  3.6508329 & 14.64912 & 45.3358727 & 3.243809e-08 & 2.270667e-07\\\\\n",
       "\tStem.like &  1.1318525 & 16.71542 & 11.8751368 & 1.289127e-03 & 4.511944e-03\\\\\n",
       "\tInterferon.responding &  1.8172521 & 14.03286 &  8.2318540 & 6.375022e-03 & 1.487505e-02\\\\\n",
       "\tProliferating.T.NK & -0.8946504 & 17.96495 &  4.8650581 & 3.283974e-02 & 5.746955e-02\\\\\n",
       "\tTreg &  0.6120690 & 15.34737 &  3.1847882 & 8.143383e-02 & 1.140074e-01\\\\\n",
       "\tCytotoxic.T.NK & -0.3310238 & 18.27175 &  2.3764899 & 1.305544e-01 & 1.523134e-01\\\\\n",
       "\tT1T17 &  0.2092938 & 18.00529 &  0.4691195 & 4.970945e-01 & 4.970945e-01\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] 'factor(treatment)IgG'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 7 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Non.cytotoxic.CD8 |  3.6508329 | 14.64912 | 45.3358727 | 3.243809e-08 | 2.270667e-07 |\n",
       "| Stem.like |  1.1318525 | 16.71542 | 11.8751368 | 1.289127e-03 | 4.511944e-03 |\n",
       "| Interferon.responding |  1.8172521 | 14.03286 |  8.2318540 | 6.375022e-03 | 1.487505e-02 |\n",
       "| Proliferating.T.NK | -0.8946504 | 17.96495 |  4.8650581 | 3.283974e-02 | 5.746955e-02 |\n",
       "| Treg |  0.6120690 | 15.34737 |  3.1847882 | 8.143383e-02 | 1.140074e-01 |\n",
       "| Cytotoxic.T.NK | -0.3310238 | 18.27175 |  2.3764899 | 1.305544e-01 | 1.523134e-01 |\n",
       "| T1T17 |  0.2092938 | 18.00529 |  0.4691195 | 4.970945e-01 | 4.970945e-01 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   'factor(treatment)IgG'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  factor(treatment)IgG \n",
       "                           logFC   logCPM          F       PValue          FDR\n",
       "Non.cytotoxic.CD8      3.6508329 14.64912 45.3358727 3.243809e-08 2.270667e-07\n",
       "Stem.like              1.1318525 16.71542 11.8751368 1.289127e-03 4.511944e-03\n",
       "Interferon.responding  1.8172521 14.03286  8.2318540 6.375022e-03 1.487505e-02\n",
       "Proliferating.T.NK    -0.8946504 17.96495  4.8650581 3.283974e-02 5.746955e-02\n",
       "Treg                   0.6120690 15.34737  3.1847882 8.143383e-02 1.140074e-01\n",
       "Cytotoxic.T.NK        -0.3310238 18.27175  2.3764899 1.305544e-01 1.523134e-01\n",
       "T1T17                  0.2092938 18.00529  0.4691195 4.970945e-01 4.970945e-01"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res,12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02e08db0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cc79adca",
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- glmQLFTest(fit.ab, coef = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a1e6688c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       factor(treatment)CD8b\n",
       "Down                       1\n",
       "NotSig                     3\n",
       "Up                         3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(decideTests(res)) # CD8b vs CD8a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "066e7a5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 7 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Treg</th><td> 1.4022967</td><td>15.34737</td><td>16.290037804</td><td>0.000221099</td><td>0.001547693</td></tr>\n",
       "\t<tr><th scope=row>Stem.like</th><td> 0.9835416</td><td>16.71542</td><td> 8.338434510</td><td>0.006070113</td><td>0.021245395</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.T.NK</th><td>-0.5182300</td><td>18.27175</td><td> 5.723618765</td><td>0.021212447</td><td>0.037378253</td></tr>\n",
       "\t<tr><th scope=row>T1T17</th><td> 0.7350418</td><td>18.00529</td><td> 5.709862503</td><td>0.021359002</td><td>0.037378253</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.T.NK</th><td>-0.7067484</td><td>17.96495</td><td> 2.858036995</td><td>0.098208016</td><td>0.137491223</td></tr>\n",
       "\t<tr><th scope=row>Non.cytotoxic.CD8</th><td> 0.9842635</td><td>14.64912</td><td> 2.392600407</td><td>0.129292200</td><td>0.150840900</td></tr>\n",
       "\t<tr><th scope=row>Interferon.responding</th><td> 0.0558757</td><td>14.03286</td><td> 0.005887857</td><td>0.939194927</td><td>0.939194927</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'factor(treatment)CD8b'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 7 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tTreg &  1.4022967 & 15.34737 & 16.290037804 & 0.000221099 & 0.001547693\\\\\n",
       "\tStem.like &  0.9835416 & 16.71542 &  8.338434510 & 0.006070113 & 0.021245395\\\\\n",
       "\tCytotoxic.T.NK & -0.5182300 & 18.27175 &  5.723618765 & 0.021212447 & 0.037378253\\\\\n",
       "\tT1T17 &  0.7350418 & 18.00529 &  5.709862503 & 0.021359002 & 0.037378253\\\\\n",
       "\tProliferating.T.NK & -0.7067484 & 17.96495 &  2.858036995 & 0.098208016 & 0.137491223\\\\\n",
       "\tNon.cytotoxic.CD8 &  0.9842635 & 14.64912 &  2.392600407 & 0.129292200 & 0.150840900\\\\\n",
       "\tInterferon.responding &  0.0558757 & 14.03286 &  0.005887857 & 0.939194927 & 0.939194927\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] 'factor(treatment)CD8b'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 7 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Treg |  1.4022967 | 15.34737 | 16.290037804 | 0.000221099 | 0.001547693 |\n",
       "| Stem.like |  0.9835416 | 16.71542 |  8.338434510 | 0.006070113 | 0.021245395 |\n",
       "| Cytotoxic.T.NK | -0.5182300 | 18.27175 |  5.723618765 | 0.021212447 | 0.037378253 |\n",
       "| T1T17 |  0.7350418 | 18.00529 |  5.709862503 | 0.021359002 | 0.037378253 |\n",
       "| Proliferating.T.NK | -0.7067484 | 17.96495 |  2.858036995 | 0.098208016 | 0.137491223 |\n",
       "| Non.cytotoxic.CD8 |  0.9842635 | 14.64912 |  2.392600407 | 0.129292200 | 0.150840900 |\n",
       "| Interferon.responding |  0.0558757 | 14.03286 |  0.005887857 | 0.939194927 | 0.939194927 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   'factor(treatment)CD8b'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  factor(treatment)CD8b \n",
       "                           logFC   logCPM            F      PValue         FDR\n",
       "Treg                   1.4022967 15.34737 16.290037804 0.000221099 0.001547693\n",
       "Stem.like              0.9835416 16.71542  8.338434510 0.006070113 0.021245395\n",
       "Cytotoxic.T.NK        -0.5182300 18.27175  5.723618765 0.021212447 0.037378253\n",
       "T1T17                  0.7350418 18.00529  5.709862503 0.021359002 0.037378253\n",
       "Proliferating.T.NK    -0.7067484 17.96495  2.858036995 0.098208016 0.137491223\n",
       "Non.cytotoxic.CD8      0.9842635 14.64912  2.392600407 0.129292200 0.150840900\n",
       "Interferon.responding  0.0558757 14.03286  0.005887857 0.939194927 0.939194927"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1c745041",
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- glmQLFTest(fit.ab, contrast = c(0,0,0,0,1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b28eb7e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       1*factor(treatment)CD8b -1*factor(treatment)IgG\n",
       "Down                                                 2\n",
       "NotSig                                               4\n",
       "Up                                                   1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(decideTests(res)) # CD8b vs IgG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "510f4f46",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 7 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Non.cytotoxic.CD8</th><td>-2.6665694</td><td>14.64912</td><td>25.3656504</td><td>9.124524e-06</td><td>6.387167e-05</td></tr>\n",
       "\t<tr><th scope=row>Interferon.responding</th><td>-1.7613764</td><td>14.03286</td><td> 8.4934383</td><td>5.654031e-03</td><td>1.978911e-02</td></tr>\n",
       "\t<tr><th scope=row>Treg</th><td> 0.7902278</td><td>15.34737</td><td> 5.9245627</td><td>1.919062e-02</td><td>4.477812e-02</td></tr>\n",
       "\t<tr><th scope=row>T1T17</th><td> 0.5257480</td><td>18.00529</td><td> 2.7790128</td><td>1.028277e-01</td><td>1.799485e-01</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.T.NK</th><td>-0.1872062</td><td>18.27175</td><td> 0.7355421</td><td>3.958818e-01</td><td>5.542345e-01</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.T.NK</th><td> 0.1879021</td><td>17.96495</td><td> 0.1991039</td><td>6.577001e-01</td><td>6.684136e-01</td></tr>\n",
       "\t<tr><th scope=row>Stem.like</th><td>-0.1483109</td><td>16.71542</td><td> 0.1860278</td><td>6.684136e-01</td><td>6.684136e-01</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'1*factor(treatment)CD8b -1*factor(treatment)IgG'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 7 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tNon.cytotoxic.CD8 & -2.6665694 & 14.64912 & 25.3656504 & 9.124524e-06 & 6.387167e-05\\\\\n",
       "\tInterferon.responding & -1.7613764 & 14.03286 &  8.4934383 & 5.654031e-03 & 1.978911e-02\\\\\n",
       "\tTreg &  0.7902278 & 15.34737 &  5.9245627 & 1.919062e-02 & 4.477812e-02\\\\\n",
       "\tT1T17 &  0.5257480 & 18.00529 &  2.7790128 & 1.028277e-01 & 1.799485e-01\\\\\n",
       "\tCytotoxic.T.NK & -0.1872062 & 18.27175 &  0.7355421 & 3.958818e-01 & 5.542345e-01\\\\\n",
       "\tProliferating.T.NK &  0.1879021 & 17.96495 &  0.1991039 & 6.577001e-01 & 6.684136e-01\\\\\n",
       "\tStem.like & -0.1483109 & 16.71542 &  0.1860278 & 6.684136e-01 & 6.684136e-01\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] '1*factor(treatment)CD8b -1*factor(treatment)IgG'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 7 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Non.cytotoxic.CD8 | -2.6665694 | 14.64912 | 25.3656504 | 9.124524e-06 | 6.387167e-05 |\n",
       "| Interferon.responding | -1.7613764 | 14.03286 |  8.4934383 | 5.654031e-03 | 1.978911e-02 |\n",
       "| Treg |  0.7902278 | 15.34737 |  5.9245627 | 1.919062e-02 | 4.477812e-02 |\n",
       "| T1T17 |  0.5257480 | 18.00529 |  2.7790128 | 1.028277e-01 | 1.799485e-01 |\n",
       "| Cytotoxic.T.NK | -0.1872062 | 18.27175 |  0.7355421 | 3.958818e-01 | 5.542345e-01 |\n",
       "| Proliferating.T.NK |  0.1879021 | 17.96495 |  0.1991039 | 6.577001e-01 | 6.684136e-01 |\n",
       "| Stem.like | -0.1483109 | 16.71542 |  0.1860278 | 6.684136e-01 | 6.684136e-01 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   '1*factor(treatment)CD8b -1*factor(treatment)IgG'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  1*factor(treatment)CD8b -1*factor(treatment)IgG \n",
       "                           logFC   logCPM          F       PValue          FDR\n",
       "Non.cytotoxic.CD8     -2.6665694 14.64912 25.3656504 9.124524e-06 6.387167e-05\n",
       "Interferon.responding -1.7613764 14.03286  8.4934383 5.654031e-03 1.978911e-02\n",
       "Treg                   0.7902278 15.34737  5.9245627 1.919062e-02 4.477812e-02\n",
       "T1T17                  0.5257480 18.00529  2.7790128 1.028277e-01 1.799485e-01\n",
       "Cytotoxic.T.NK        -0.1872062 18.27175  0.7355421 3.958818e-01 5.542345e-01\n",
       "Proliferating.T.NK     0.1879021 17.96495  0.1991039 6.577001e-01 6.684136e-01\n",
       "Stem.like             -0.1483109 16.71542  0.1860278 6.684136e-01 6.684136e-01"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e5f6752",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dbf1a575",
   "metadata": {},
   "outputs": [],
   "source": [
    "tnk_celltype_props <- read.csv(\"compositional_analysis/cytotoxic_subclusters.csv\",row.names=1)\n",
    "metadata <- read.csv(\"compositional_analysis/metadata_by_sample.csv\", row.names=1)\n",
    "\n",
    "tnk_celltype_props <- na.omit(tnk_celltype_props)\n",
    "tnk_celltype_props <- t(tnk_celltype_props)\n",
    "metadata = metadata[colnames(tnk_celltype_props),]\n",
    "\n",
    "metadata$M.Number <- factor(metadata$M.Number)\n",
    "metadata$monkey_condition_categories <- factor(metadata$monkey_condition_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "da631e89",
   "metadata": {},
   "outputs": [],
   "source": [
    "y.ab <- DGEList(tnk_celltype_props, samples=metadata)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bbbe858",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "688c09ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#keep <- filterByExpr(y.ab, group=y.ab$samples$treatment)\n",
    "#y.ab <- y.ab[keep,]\n",
    "#summary(keep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cd803d73",
   "metadata": {},
   "outputs": [],
   "source": [
    "design <- model.matrix(~0+factor(monkey_condition_categories) + factor(treatment), y.ab$samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d8739c56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.3251  0.3251  0.3251  0.3251  0.3251  0.3251 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.ab <- estimateDisp(y.ab, design, trend=\"none\")\n",
    "summary(y.ab$common.dispersion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "08f67084",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CBAgACBlgk0vUDaPdezbqm7dcTrem22Oy/ZfsTtbUaAAAECBAgQIECAQIMEmv4M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\nAW+xNfcvyWu+AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotBCV(y.ab, cex=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2cfe37e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "55f1b3a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  1.011   1.011   1.011   1.011   1.011   1.011 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit.ab <- glmQLFit(y.ab, design, robust=TRUE, abundance.trend=FALSE)\n",
    "summary(fit.ab$var.prior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "917d0e5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B/Tzi71w60RIECAAAECBAgQWEjgNxZY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\nIEBgQQX+P6BDHpzhrVC/AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotQLDisp(fit.ab, cex=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e444cf01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       factor(treatment)IgG\n",
       "Down                      3\n",
       "NotSig                    2\n",
       "Up                        3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res <- glmQLFTest(fit.ab, coef=ncol(design))\n",
    "summary(decideTests(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "caa505b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 8 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Cytotoxic.6</th><td> 4.3052612</td><td>15.26191</td><td>58.73352313</td><td>1.480407e-10</td><td>1.184325e-09</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.3</th><td>-3.1276923</td><td>17.02351</td><td>49.01032543</td><td>1.698441e-09</td><td>6.793765e-09</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.5</th><td>-3.3471303</td><td>16.88730</td><td>47.12008982</td><td>2.958232e-09</td><td>7.888618e-09</td></tr>\n",
       "\t<tr><th scope=row>T1T17.like.Cytotoxic.7</th><td>-2.7387398</td><td>16.33119</td><td>38.61460177</td><td>4.080353e-08</td><td>8.160706e-08</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.1</th><td> 2.4183986</td><td>17.41599</td><td>32.15425444</td><td>3.503687e-07</td><td>5.605900e-07</td></tr>\n",
       "\t<tr><th scope=row>T1T17.like.Cytotoxic.8</th><td> 1.5003395</td><td>17.88463</td><td> 9.95494535</td><td>2.426561e-03</td><td>3.235415e-03</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.4</th><td> 0.4546043</td><td>17.65244</td><td> 1.58840958</td><td>2.120413e-01</td><td>2.423329e-01</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.2</th><td> 0.1041961</td><td>16.67812</td><td> 0.08308405</td><td>7.740742e-01</td><td>7.740742e-01</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'factor(treatment)IgG'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 8 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tCytotoxic.6 &  4.3052612 & 15.26191 & 58.73352313 & 1.480407e-10 & 1.184325e-09\\\\\n",
       "\tCytotoxic.3 & -3.1276923 & 17.02351 & 49.01032543 & 1.698441e-09 & 6.793765e-09\\\\\n",
       "\tCytotoxic.5 & -3.3471303 & 16.88730 & 47.12008982 & 2.958232e-09 & 7.888618e-09\\\\\n",
       "\tT1T17.like.Cytotoxic.7 & -2.7387398 & 16.33119 & 38.61460177 & 4.080353e-08 & 8.160706e-08\\\\\n",
       "\tCytotoxic.1 &  2.4183986 & 17.41599 & 32.15425444 & 3.503687e-07 & 5.605900e-07\\\\\n",
       "\tT1T17.like.Cytotoxic.8 &  1.5003395 & 17.88463 &  9.95494535 & 2.426561e-03 & 3.235415e-03\\\\\n",
       "\tCytotoxic.4 &  0.4546043 & 17.65244 &  1.58840958 & 2.120413e-01 & 2.423329e-01\\\\\n",
       "\tCytotoxic.2 &  0.1041961 & 16.67812 &  0.08308405 & 7.740742e-01 & 7.740742e-01\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] 'factor(treatment)IgG'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 8 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Cytotoxic.6 |  4.3052612 | 15.26191 | 58.73352313 | 1.480407e-10 | 1.184325e-09 |\n",
       "| Cytotoxic.3 | -3.1276923 | 17.02351 | 49.01032543 | 1.698441e-09 | 6.793765e-09 |\n",
       "| Cytotoxic.5 | -3.3471303 | 16.88730 | 47.12008982 | 2.958232e-09 | 7.888618e-09 |\n",
       "| T1T17.like.Cytotoxic.7 | -2.7387398 | 16.33119 | 38.61460177 | 4.080353e-08 | 8.160706e-08 |\n",
       "| Cytotoxic.1 |  2.4183986 | 17.41599 | 32.15425444 | 3.503687e-07 | 5.605900e-07 |\n",
       "| T1T17.like.Cytotoxic.8 |  1.5003395 | 17.88463 |  9.95494535 | 2.426561e-03 | 3.235415e-03 |\n",
       "| Cytotoxic.4 |  0.4546043 | 17.65244 |  1.58840958 | 2.120413e-01 | 2.423329e-01 |\n",
       "| Cytotoxic.2 |  0.1041961 | 16.67812 |  0.08308405 | 7.740742e-01 | 7.740742e-01 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   'factor(treatment)IgG'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  factor(treatment)IgG \n",
       "                            logFC   logCPM           F       PValue\n",
       "Cytotoxic.6             4.3052612 15.26191 58.73352313 1.480407e-10\n",
       "Cytotoxic.3            -3.1276923 17.02351 49.01032543 1.698441e-09\n",
       "Cytotoxic.5            -3.3471303 16.88730 47.12008982 2.958232e-09\n",
       "T1T17.like.Cytotoxic.7 -2.7387398 16.33119 38.61460177 4.080353e-08\n",
       "Cytotoxic.1             2.4183986 17.41599 32.15425444 3.503687e-07\n",
       "T1T17.like.Cytotoxic.8  1.5003395 17.88463  9.95494535 2.426561e-03\n",
       "Cytotoxic.4             0.4546043 17.65244  1.58840958 2.120413e-01\n",
       "Cytotoxic.2             0.1041961 16.67812  0.08308405 7.740742e-01\n",
       "                                FDR\n",
       "Cytotoxic.6            1.184325e-09\n",
       "Cytotoxic.3            6.793765e-09\n",
       "Cytotoxic.5            7.888618e-09\n",
       "T1T17.like.Cytotoxic.7 8.160706e-08\n",
       "Cytotoxic.1            5.605900e-07\n",
       "T1T17.like.Cytotoxic.8 3.235415e-03\n",
       "Cytotoxic.4            2.423329e-01\n",
       "Cytotoxic.2            7.740742e-01"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res,12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5e559e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "df1b35e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- glmQLFTest(fit.ab, coef = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cc9ac587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       factor(treatment)CD8b\n",
       "Down                       3\n",
       "NotSig                     0\n",
       "Up                         5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(decideTests(res)) # CD8b vs CD8a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ddb5df4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 8 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Cytotoxic.5</th><td>-2.3147865</td><td>16.88730</td><td>26.139093</td><td>2.994144e-06</td><td>1.681235e-05</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.6</th><td> 3.0920327</td><td>15.26191</td><td>25.447203</td><td>4.203088e-06</td><td>1.681235e-05</td></tr>\n",
       "\t<tr><th scope=row>T1T17.like.Cytotoxic.7</th><td>-1.7368286</td><td>16.33119</td><td>17.522676</td><td>8.674163e-05</td><td>2.313110e-04</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.2</th><td> 1.0628108</td><td>16.67812</td><td> 8.841757</td><td>4.121850e-03</td><td>6.718644e-03</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.3</th><td>-1.1668541</td><td>17.02351</td><td> 8.803196</td><td>4.199153e-03</td><td>6.718644e-03</td></tr>\n",
       "\t<tr><th scope=row>T1T17.like.Cytotoxic.8</th><td> 1.3063038</td><td>17.88463</td><td> 7.235964</td><td>9.063379e-03</td><td>1.208450e-02</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.1</th><td> 1.1337924</td><td>17.41599</td><td> 6.820173</td><td>1.117206e-02</td><td>1.276806e-02</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.4</th><td> 0.8539668</td><td>17.65244</td><td> 5.308017</td><td>2.442076e-02</td><td>2.442076e-02</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'factor(treatment)CD8b'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 8 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tCytotoxic.5 & -2.3147865 & 16.88730 & 26.139093 & 2.994144e-06 & 1.681235e-05\\\\\n",
       "\tCytotoxic.6 &  3.0920327 & 15.26191 & 25.447203 & 4.203088e-06 & 1.681235e-05\\\\\n",
       "\tT1T17.like.Cytotoxic.7 & -1.7368286 & 16.33119 & 17.522676 & 8.674163e-05 & 2.313110e-04\\\\\n",
       "\tCytotoxic.2 &  1.0628108 & 16.67812 &  8.841757 & 4.121850e-03 & 6.718644e-03\\\\\n",
       "\tCytotoxic.3 & -1.1668541 & 17.02351 &  8.803196 & 4.199153e-03 & 6.718644e-03\\\\\n",
       "\tT1T17.like.Cytotoxic.8 &  1.3063038 & 17.88463 &  7.235964 & 9.063379e-03 & 1.208450e-02\\\\\n",
       "\tCytotoxic.1 &  1.1337924 & 17.41599 &  6.820173 & 1.117206e-02 & 1.276806e-02\\\\\n",
       "\tCytotoxic.4 &  0.8539668 & 17.65244 &  5.308017 & 2.442076e-02 & 2.442076e-02\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] 'factor(treatment)CD8b'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 8 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Cytotoxic.5 | -2.3147865 | 16.88730 | 26.139093 | 2.994144e-06 | 1.681235e-05 |\n",
       "| Cytotoxic.6 |  3.0920327 | 15.26191 | 25.447203 | 4.203088e-06 | 1.681235e-05 |\n",
       "| T1T17.like.Cytotoxic.7 | -1.7368286 | 16.33119 | 17.522676 | 8.674163e-05 | 2.313110e-04 |\n",
       "| Cytotoxic.2 |  1.0628108 | 16.67812 |  8.841757 | 4.121850e-03 | 6.718644e-03 |\n",
       "| Cytotoxic.3 | -1.1668541 | 17.02351 |  8.803196 | 4.199153e-03 | 6.718644e-03 |\n",
       "| T1T17.like.Cytotoxic.8 |  1.3063038 | 17.88463 |  7.235964 | 9.063379e-03 | 1.208450e-02 |\n",
       "| Cytotoxic.1 |  1.1337924 | 17.41599 |  6.820173 | 1.117206e-02 | 1.276806e-02 |\n",
       "| Cytotoxic.4 |  0.8539668 | 17.65244 |  5.308017 | 2.442076e-02 | 2.442076e-02 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   'factor(treatment)CD8b'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  factor(treatment)CD8b \n",
       "                            logFC   logCPM         F       PValue          FDR\n",
       "Cytotoxic.5            -2.3147865 16.88730 26.139093 2.994144e-06 1.681235e-05\n",
       "Cytotoxic.6             3.0920327 15.26191 25.447203 4.203088e-06 1.681235e-05\n",
       "T1T17.like.Cytotoxic.7 -1.7368286 16.33119 17.522676 8.674163e-05 2.313110e-04\n",
       "Cytotoxic.2             1.0628108 16.67812  8.841757 4.121850e-03 6.718644e-03\n",
       "Cytotoxic.3            -1.1668541 17.02351  8.803196 4.199153e-03 6.718644e-03\n",
       "T1T17.like.Cytotoxic.8  1.3063038 17.88463  7.235964 9.063379e-03 1.208450e-02\n",
       "Cytotoxic.1             1.1337924 17.41599  6.820173 1.117206e-02 1.276806e-02\n",
       "Cytotoxic.4             0.8539668 17.65244  5.308017 2.442076e-02 2.442076e-02"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "cf35f1c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- glmQLFTest(fit.ab, contrast = c(0,0,0,0,1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "7aecaba8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       1*factor(treatment)CD8b -1*factor(treatment)IgG\n",
       "Down                                                 2\n",
       "NotSig                                               4\n",
       "Up                                                   2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(decideTests(res)) # CD8b vs IgG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "03b02b90",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 8 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Cytotoxic.3</th><td> 1.9608382</td><td>17.02351</td><td>18.3752971</td><td>6.106979e-05</td><td>0.0004885584</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.1</th><td>-1.2846062</td><td>17.41599</td><td>10.3089624</td><td>2.055482e-03</td><td>0.0082219289</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.6</th><td>-1.2132285</td><td>15.26191</td><td> 7.1855827</td><td>9.393567e-03</td><td>0.0192849376</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.2</th><td> 0.9586146</td><td>16.67812</td><td> 7.1123757</td><td>9.642469e-03</td><td>0.0192849376</td></tr>\n",
       "\t<tr><th scope=row>T1T17.like.Cytotoxic.7</th><td> 1.0019112</td><td>16.33119</td><td> 4.4112490</td><td>3.957154e-02</td><td>0.0633144668</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.5</th><td> 1.0323439</td><td>16.88730</td><td> 3.6487595</td><td>6.050687e-02</td><td>0.0806758242</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.4</th><td> 0.3993626</td><td>17.65244</td><td> 1.2600919</td><td>2.657466e-01</td><td>0.3037103579</td></tr>\n",
       "\t<tr><th scope=row>T1T17.like.Cytotoxic.8</th><td>-0.1940356</td><td>17.88463</td><td> 0.1686903</td><td>6.826238e-01</td><td>0.6826238031</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'1*factor(treatment)CD8b -1*factor(treatment)IgG'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 8 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tCytotoxic.3 &  1.9608382 & 17.02351 & 18.3752971 & 6.106979e-05 & 0.0004885584\\\\\n",
       "\tCytotoxic.1 & -1.2846062 & 17.41599 & 10.3089624 & 2.055482e-03 & 0.0082219289\\\\\n",
       "\tCytotoxic.6 & -1.2132285 & 15.26191 &  7.1855827 & 9.393567e-03 & 0.0192849376\\\\\n",
       "\tCytotoxic.2 &  0.9586146 & 16.67812 &  7.1123757 & 9.642469e-03 & 0.0192849376\\\\\n",
       "\tT1T17.like.Cytotoxic.7 &  1.0019112 & 16.33119 &  4.4112490 & 3.957154e-02 & 0.0633144668\\\\\n",
       "\tCytotoxic.5 &  1.0323439 & 16.88730 &  3.6487595 & 6.050687e-02 & 0.0806758242\\\\\n",
       "\tCytotoxic.4 &  0.3993626 & 17.65244 &  1.2600919 & 2.657466e-01 & 0.3037103579\\\\\n",
       "\tT1T17.like.Cytotoxic.8 & -0.1940356 & 17.88463 &  0.1686903 & 6.826238e-01 & 0.6826238031\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] '1*factor(treatment)CD8b -1*factor(treatment)IgG'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 8 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Cytotoxic.3 |  1.9608382 | 17.02351 | 18.3752971 | 6.106979e-05 | 0.0004885584 |\n",
       "| Cytotoxic.1 | -1.2846062 | 17.41599 | 10.3089624 | 2.055482e-03 | 0.0082219289 |\n",
       "| Cytotoxic.6 | -1.2132285 | 15.26191 |  7.1855827 | 9.393567e-03 | 0.0192849376 |\n",
       "| Cytotoxic.2 |  0.9586146 | 16.67812 |  7.1123757 | 9.642469e-03 | 0.0192849376 |\n",
       "| T1T17.like.Cytotoxic.7 |  1.0019112 | 16.33119 |  4.4112490 | 3.957154e-02 | 0.0633144668 |\n",
       "| Cytotoxic.5 |  1.0323439 | 16.88730 |  3.6487595 | 6.050687e-02 | 0.0806758242 |\n",
       "| Cytotoxic.4 |  0.3993626 | 17.65244 |  1.2600919 | 2.657466e-01 | 0.3037103579 |\n",
       "| T1T17.like.Cytotoxic.8 | -0.1940356 | 17.88463 |  0.1686903 | 6.826238e-01 | 0.6826238031 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   '1*factor(treatment)CD8b -1*factor(treatment)IgG'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  1*factor(treatment)CD8b -1*factor(treatment)IgG \n",
       "                            logFC   logCPM          F       PValue          FDR\n",
       "Cytotoxic.3             1.9608382 17.02351 18.3752971 6.106979e-05 0.0004885584\n",
       "Cytotoxic.1            -1.2846062 17.41599 10.3089624 2.055482e-03 0.0082219289\n",
       "Cytotoxic.6            -1.2132285 15.26191  7.1855827 9.393567e-03 0.0192849376\n",
       "Cytotoxic.2             0.9586146 16.67812  7.1123757 9.642469e-03 0.0192849376\n",
       "T1T17.like.Cytotoxic.7  1.0019112 16.33119  4.4112490 3.957154e-02 0.0633144668\n",
       "Cytotoxic.5             1.0323439 16.88730  3.6487595 6.050687e-02 0.0806758242\n",
       "Cytotoxic.4             0.3993626 17.65244  1.2600919 2.657466e-01 0.3037103579\n",
       "T1T17.like.Cytotoxic.8 -0.1940356 17.88463  0.1686903 6.826238e-01 0.6826238031"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "195368d6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d72f531b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# T/NK including all subclusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53eee722",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f477c586",
   "metadata": {},
   "outputs": [],
   "source": [
    "tnk_celltype_props <- read.csv(\"compositional_analysis/nkt_all_subcluster_counts_by_sample.csv\",row.names=1)\n",
    "metadata <- read.csv(\"compositional_analysis/metadata_by_sample.csv\", row.names=1)\n",
    "\n",
    "tnk_celltype_props <- na.omit(tnk_celltype_props)\n",
    "tnk_celltype_props <- t(tnk_celltype_props)\n",
    "metadata = metadata[colnames(tnk_celltype_props),]\n",
    "\n",
    "metadata$M.Number <- factor(metadata$M.Number)\n",
    "metadata$monkey_condition_categories <- factor(metadata$monkey_condition_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0453c164",
   "metadata": {},
   "outputs": [],
   "source": [
    "y.ab <- DGEList(tnk_celltype_props, samples=metadata)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d883d4c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "52421578",
   "metadata": {},
   "outputs": [],
   "source": [
    "#keep <- filterByExpr(y.ab, group=y.ab$samples$treatment)\n",
    "#y.ab <- y.ab[keep,]\n",
    "#summary(keep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72e08c9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "design <- model.matrix(~0+factor(monkey_condition_categories) + factor(treatment), y.ab$samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cd44e3c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.3658  0.3658  0.3658  0.3658  0.3658  0.3658 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.ab <- estimateDisp(y.ab, design, trend=\"none\")\n",
    "summary(y.ab$common.dispersion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b1b6f7ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l2Xhj8zLtN6zC/7L0Q/Tkh4fPtK2LY5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\n0Ov8fQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotBCV(y.ab, cex=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "798484de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b5d4abf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  1.114   1.114   1.114   1.114   1.114   1.114 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit.ab <- glmQLFit(y.ab, design, robust=TRUE, abundance.trend=FALSE)\n",
    "summary(fit.ab$var.prior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a6991bf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BAAuRqnYgEAAFSCYgcAAKASFDsAAACVoNgBAACoBMUOAABAJSh2AAAAKkGxAwAA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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotQLDisp(fit.ab, cex=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "80f81b0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       factor(treatment)IgG\n",
       "Down                      5\n",
       "NotSig                    6\n",
       "Up                        6"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res <- glmQLFTest(fit.ab, coef=ncol(design))\n",
    "summary(decideTests(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "adf48074",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 12 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Cytotoxic.4</th><td>-3.2896990</td><td>15.50659</td><td>48.181986</td><td>1.451859e-09</td><td>2.468161e-08</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.6</th><td>-3.6772674</td><td>15.50552</td><td>44.918205</td><td>5.311681e-09</td><td>3.723803e-08</td></tr>\n",
       "\t<tr><th scope=row>Non.cytotoxic.CD8</th><td> 3.6618264</td><td>14.66317</td><td>44.221802</td><td>6.571417e-09</td><td>3.723803e-08</td></tr>\n",
       "\t<tr><th scope=row>T1T17.like.Cytotoxic.8</th><td>-2.9119497</td><td>14.82152</td><td>39.566483</td><td>2.224792e-08</td><td>8.190698e-08</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.5</th><td> 3.9512569</td><td>13.70959</td><td>39.745899</td><td>2.409029e-08</td><td>8.190698e-08</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.Cytotoxic.4</th><td>-2.9107621</td><td>14.14890</td><td>25.867660</td><td>3.115877e-06</td><td>8.828318e-06</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.1</th><td> 2.0904194</td><td>15.83731</td><td>17.972513</td><td>7.073392e-05</td><td>1.717824e-04</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.Cytotoxic.8</th><td>-2.6529343</td><td>13.56750</td><td>15.511707</td><td>1.991185e-04</td><td>4.231268e-04</td></tr>\n",
       "\t<tr><th scope=row>Stem.like</th><td> 1.1475649</td><td>16.70585</td><td> 9.730983</td><td>2.610797e-03</td><td>4.931505e-03</td></tr>\n",
       "\t<tr><th scope=row>T1T17.like.Cytotoxic.7</th><td> 1.1750458</td><td>16.22639</td><td> 9.098640</td><td>3.539330e-03</td><td>6.016861e-03</td></tr>\n",
       "\t<tr><th scope=row>Interferon.responding</th><td> 1.7777839</td><td>14.04001</td><td> 8.170777</td><td>5.679823e-03</td><td>8.777909e-03</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.Cytotoxic.1.2.5.6</th><td>-0.7874275</td><td>15.21678</td><td> 2.798329</td><td>9.906254e-02</td><td>1.403386e-01</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'factor(treatment)IgG'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 12 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tCytotoxic.4 & -3.2896990 & 15.50659 & 48.181986 & 1.451859e-09 & 2.468161e-08\\\\\n",
       "\tCytotoxic.6 & -3.6772674 & 15.50552 & 44.918205 & 5.311681e-09 & 3.723803e-08\\\\\n",
       "\tNon.cytotoxic.CD8 &  3.6618264 & 14.66317 & 44.221802 & 6.571417e-09 & 3.723803e-08\\\\\n",
       "\tT1T17.like.Cytotoxic.8 & -2.9119497 & 14.82152 & 39.566483 & 2.224792e-08 & 8.190698e-08\\\\\n",
       "\tCytotoxic.5 &  3.9512569 & 13.70959 & 39.745899 & 2.409029e-08 & 8.190698e-08\\\\\n",
       "\tProliferating.Cytotoxic.4 & -2.9107621 & 14.14890 & 25.867660 & 3.115877e-06 & 8.828318e-06\\\\\n",
       "\tCytotoxic.1 &  2.0904194 & 15.83731 & 17.972513 & 7.073392e-05 & 1.717824e-04\\\\\n",
       "\tProliferating.Cytotoxic.8 & -2.6529343 & 13.56750 & 15.511707 & 1.991185e-04 & 4.231268e-04\\\\\n",
       "\tStem.like &  1.1475649 & 16.70585 &  9.730983 & 2.610797e-03 & 4.931505e-03\\\\\n",
       "\tT1T17.like.Cytotoxic.7 &  1.1750458 & 16.22639 &  9.098640 & 3.539330e-03 & 6.016861e-03\\\\\n",
       "\tInterferon.responding &  1.7777839 & 14.04001 &  8.170777 & 5.679823e-03 & 8.777909e-03\\\\\n",
       "\tProliferating.Cytotoxic.1.2.5.6 & -0.7874275 & 15.21678 &  2.798329 & 9.906254e-02 & 1.403386e-01\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] 'factor(treatment)IgG'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 12 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Cytotoxic.4 | -3.2896990 | 15.50659 | 48.181986 | 1.451859e-09 | 2.468161e-08 |\n",
       "| Cytotoxic.6 | -3.6772674 | 15.50552 | 44.918205 | 5.311681e-09 | 3.723803e-08 |\n",
       "| Non.cytotoxic.CD8 |  3.6618264 | 14.66317 | 44.221802 | 6.571417e-09 | 3.723803e-08 |\n",
       "| T1T17.like.Cytotoxic.8 | -2.9119497 | 14.82152 | 39.566483 | 2.224792e-08 | 8.190698e-08 |\n",
       "| Cytotoxic.5 |  3.9512569 | 13.70959 | 39.745899 | 2.409029e-08 | 8.190698e-08 |\n",
       "| Proliferating.Cytotoxic.4 | -2.9107621 | 14.14890 | 25.867660 | 3.115877e-06 | 8.828318e-06 |\n",
       "| Cytotoxic.1 |  2.0904194 | 15.83731 | 17.972513 | 7.073392e-05 | 1.717824e-04 |\n",
       "| Proliferating.Cytotoxic.8 | -2.6529343 | 13.56750 | 15.511707 | 1.991185e-04 | 4.231268e-04 |\n",
       "| Stem.like |  1.1475649 | 16.70585 |  9.730983 | 2.610797e-03 | 4.931505e-03 |\n",
       "| T1T17.like.Cytotoxic.7 |  1.1750458 | 16.22639 |  9.098640 | 3.539330e-03 | 6.016861e-03 |\n",
       "| Interferon.responding |  1.7777839 | 14.04001 |  8.170777 | 5.679823e-03 | 8.777909e-03 |\n",
       "| Proliferating.Cytotoxic.1.2.5.6 | -0.7874275 | 15.21678 |  2.798329 | 9.906254e-02 | 1.403386e-01 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   'factor(treatment)IgG'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  factor(treatment)IgG \n",
       "                                     logFC   logCPM         F       PValue\n",
       "Cytotoxic.4                     -3.2896990 15.50659 48.181986 1.451859e-09\n",
       "Cytotoxic.6                     -3.6772674 15.50552 44.918205 5.311681e-09\n",
       "Non.cytotoxic.CD8                3.6618264 14.66317 44.221802 6.571417e-09\n",
       "T1T17.like.Cytotoxic.8          -2.9119497 14.82152 39.566483 2.224792e-08\n",
       "Cytotoxic.5                      3.9512569 13.70959 39.745899 2.409029e-08\n",
       "Proliferating.Cytotoxic.4       -2.9107621 14.14890 25.867660 3.115877e-06\n",
       "Cytotoxic.1                      2.0904194 15.83731 17.972513 7.073392e-05\n",
       "Proliferating.Cytotoxic.8       -2.6529343 13.56750 15.511707 1.991185e-04\n",
       "Stem.like                        1.1475649 16.70585  9.730983 2.610797e-03\n",
       "T1T17.like.Cytotoxic.7           1.1750458 16.22639  9.098640 3.539330e-03\n",
       "Interferon.responding            1.7777839 14.04001  8.170777 5.679823e-03\n",
       "Proliferating.Cytotoxic.1.2.5.6 -0.7874275 15.21678  2.798329 9.906254e-02\n",
       "                                         FDR\n",
       "Cytotoxic.4                     2.468161e-08\n",
       "Cytotoxic.6                     3.723803e-08\n",
       "Non.cytotoxic.CD8               3.723803e-08\n",
       "T1T17.like.Cytotoxic.8          8.190698e-08\n",
       "Cytotoxic.5                     8.190698e-08\n",
       "Proliferating.Cytotoxic.4       8.828318e-06\n",
       "Cytotoxic.1                     1.717824e-04\n",
       "Proliferating.Cytotoxic.8       4.231268e-04\n",
       "Stem.like                       4.931505e-03\n",
       "T1T17.like.Cytotoxic.7          6.016861e-03\n",
       "Interferon.responding           8.777909e-03\n",
       "Proliferating.Cytotoxic.1.2.5.6 1.403386e-01"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res,12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5703d4cd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f5aa22f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- glmQLFTest(fit.ab, coef = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b43ddddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       factor(treatment)CD8b\n",
       "Down                       5\n",
       "NotSig                     9\n",
       "Up                         3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(decideTests(res)) # CD8b vs CD8a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fce959a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Cytotoxic.6</th><td>-2.9446981</td><td>15.50552</td><td>32.492068</td><td>2.978975e-07</td><td>5.064257e-06</td></tr>\n",
       "\t<tr><th scope=row>T1T17.like.Cytotoxic.8</th><td>-2.1502001</td><td>14.82152</td><td>24.159735</td><td>5.439394e-06</td><td>4.623485e-05</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.Cytotoxic.8</th><td>-3.4066080</td><td>13.56750</td><td>21.412043</td><td>1.766335e-05</td><td>1.000923e-04</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.5</th><td> 2.5287815</td><td>13.70959</td><td>13.687874</td><td>4.313720e-04</td><td>1.637856e-03</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.4</th><td>-1.5274822</td><td>15.50659</td><td>13.383283</td><td>4.817224e-04</td><td>1.637856e-03</td></tr>\n",
       "\t<tr><th scope=row>Treg</th><td> 1.3653825</td><td>15.35012</td><td>11.079716</td><td>1.381086e-03</td><td>3.913077e-03</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.Cytotoxic.4</th><td>-1.6327084</td><td>14.14890</td><td> 9.588294</td><td>2.850570e-03</td><td>6.922812e-03</td></tr>\n",
       "\t<tr><th scope=row>Stem.like</th><td> 0.9854649</td><td>16.70585</td><td> 6.671570</td><td>1.183967e-02</td><td>2.515929e-02</td></tr>\n",
       "\t<tr><th scope=row>T1T17</th><td> 0.7570118</td><td>17.95351</td><td> 4.575965</td><td>3.583437e-02</td><td>6.768715e-02</td></tr>\n",
       "\t<tr><th scope=row>T1T17.like.Cytotoxic.7</th><td> 0.7807234</td><td>16.22639</td><td> 3.886960</td><td>5.252733e-02</td><td>8.363736e-02</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'factor(treatment)CD8b'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 10 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tCytotoxic.6 & -2.9446981 & 15.50552 & 32.492068 & 2.978975e-07 & 5.064257e-06\\\\\n",
       "\tT1T17.like.Cytotoxic.8 & -2.1502001 & 14.82152 & 24.159735 & 5.439394e-06 & 4.623485e-05\\\\\n",
       "\tProliferating.Cytotoxic.8 & -3.4066080 & 13.56750 & 21.412043 & 1.766335e-05 & 1.000923e-04\\\\\n",
       "\tCytotoxic.5 &  2.5287815 & 13.70959 & 13.687874 & 4.313720e-04 & 1.637856e-03\\\\\n",
       "\tCytotoxic.4 & -1.5274822 & 15.50659 & 13.383283 & 4.817224e-04 & 1.637856e-03\\\\\n",
       "\tTreg &  1.3653825 & 15.35012 & 11.079716 & 1.381086e-03 & 3.913077e-03\\\\\n",
       "\tProliferating.Cytotoxic.4 & -1.6327084 & 14.14890 &  9.588294 & 2.850570e-03 & 6.922812e-03\\\\\n",
       "\tStem.like &  0.9854649 & 16.70585 &  6.671570 & 1.183967e-02 & 2.515929e-02\\\\\n",
       "\tT1T17 &  0.7570118 & 17.95351 &  4.575965 & 3.583437e-02 & 6.768715e-02\\\\\n",
       "\tT1T17.like.Cytotoxic.7 &  0.7807234 & 16.22639 &  3.886960 & 5.252733e-02 & 8.363736e-02\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] 'factor(treatment)CD8b'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 10 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Cytotoxic.6 | -2.9446981 | 15.50552 | 32.492068 | 2.978975e-07 | 5.064257e-06 |\n",
       "| T1T17.like.Cytotoxic.8 | -2.1502001 | 14.82152 | 24.159735 | 5.439394e-06 | 4.623485e-05 |\n",
       "| Proliferating.Cytotoxic.8 | -3.4066080 | 13.56750 | 21.412043 | 1.766335e-05 | 1.000923e-04 |\n",
       "| Cytotoxic.5 |  2.5287815 | 13.70959 | 13.687874 | 4.313720e-04 | 1.637856e-03 |\n",
       "| Cytotoxic.4 | -1.5274822 | 15.50659 | 13.383283 | 4.817224e-04 | 1.637856e-03 |\n",
       "| Treg |  1.3653825 | 15.35012 | 11.079716 | 1.381086e-03 | 3.913077e-03 |\n",
       "| Proliferating.Cytotoxic.4 | -1.6327084 | 14.14890 |  9.588294 | 2.850570e-03 | 6.922812e-03 |\n",
       "| Stem.like |  0.9854649 | 16.70585 |  6.671570 | 1.183967e-02 | 2.515929e-02 |\n",
       "| T1T17 |  0.7570118 | 17.95351 |  4.575965 | 3.583437e-02 | 6.768715e-02 |\n",
       "| T1T17.like.Cytotoxic.7 |  0.7807234 | 16.22639 |  3.886960 | 5.252733e-02 | 8.363736e-02 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   'factor(treatment)CD8b'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  factor(treatment)CD8b \n",
       "                               logFC   logCPM         F       PValue\n",
       "Cytotoxic.6               -2.9446981 15.50552 32.492068 2.978975e-07\n",
       "T1T17.like.Cytotoxic.8    -2.1502001 14.82152 24.159735 5.439394e-06\n",
       "Proliferating.Cytotoxic.8 -3.4066080 13.56750 21.412043 1.766335e-05\n",
       "Cytotoxic.5                2.5287815 13.70959 13.687874 4.313720e-04\n",
       "Cytotoxic.4               -1.5274822 15.50659 13.383283 4.817224e-04\n",
       "Treg                       1.3653825 15.35012 11.079716 1.381086e-03\n",
       "Proliferating.Cytotoxic.4 -1.6327084 14.14890  9.588294 2.850570e-03\n",
       "Stem.like                  0.9854649 16.70585  6.671570 1.183967e-02\n",
       "T1T17                      0.7570118 17.95351  4.575965 3.583437e-02\n",
       "T1T17.like.Cytotoxic.7     0.7807234 16.22639  3.886960 5.252733e-02\n",
       "                                   FDR\n",
       "Cytotoxic.6               5.064257e-06\n",
       "T1T17.like.Cytotoxic.8    4.623485e-05\n",
       "Proliferating.Cytotoxic.8 1.000923e-04\n",
       "Cytotoxic.5               1.637856e-03\n",
       "Cytotoxic.4               1.637856e-03\n",
       "Treg                      3.913077e-03\n",
       "Proliferating.Cytotoxic.4 6.922812e-03\n",
       "Stem.like                 2.515929e-02\n",
       "T1T17                     6.768715e-02\n",
       "T1T17.like.Cytotoxic.7    8.363736e-02"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6f4f58b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- glmQLFTest(fit.ab, contrast = c(0,0,0,0,1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c595363f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       1*factor(treatment)CD8b -1*factor(treatment)IgG\n",
       "Down                                                 4\n",
       "NotSig                                              12\n",
       "Up                                                   1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(decideTests(res)) # CD8b vs IgG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ea2cbb50",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 10 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Non.cytotoxic.CD8</th><td>-2.7057989</td><td>14.66317</td><td>24.387111</td><td>5.591237e-06</td><td>9.505103e-05</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.4</th><td> 1.7622168</td><td>15.50659</td><td>13.338236</td><td>4.915597e-04</td><td>4.178258e-03</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.1</th><td>-1.4571888</td><td>15.83731</td><td> 9.810121</td><td>2.580384e-03</td><td>1.462218e-02</td></tr>\n",
       "\t<tr><th scope=row>Interferon.responding</th><td>-1.6973790</td><td>14.04001</td><td> 8.191798</td><td>5.621630e-03</td><td>1.959821e-02</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.5</th><td>-1.4224754</td><td>13.70959</td><td> 8.123996</td><td>5.764181e-03</td><td>1.959821e-02</td></tr>\n",
       "\t<tr><th scope=row>Treg</th><td> 0.7746541</td><td>15.35012</td><td> 4.062167</td><td>4.760885e-02</td><td>1.176292e-01</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.Cytotoxic.4</th><td> 1.2780537</td><td>14.14890</td><td> 4.039772</td><td>4.843555e-02</td><td>1.176292e-01</td></tr>\n",
       "\t<tr><th scope=row>Cytotoxic.2</th><td> 0.7119113</td><td>15.01169</td><td> 3.344416</td><td>7.159828e-02</td><td>1.521463e-01</td></tr>\n",
       "\t<tr><th scope=row>T1T17</th><td> 0.5588814</td><td>17.95351</td><td> 2.367628</td><td>1.282833e-01</td><td>2.199297e-01</td></tr>\n",
       "\t<tr><th scope=row>T1T17.like.Cytotoxic.8</th><td> 0.7617495</td><td>14.82152</td><td> 2.353988</td><td>1.293704e-01</td><td>2.199297e-01</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'1*factor(treatment)CD8b -1*factor(treatment)IgG'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 10 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tNon.cytotoxic.CD8 & -2.7057989 & 14.66317 & 24.387111 & 5.591237e-06 & 9.505103e-05\\\\\n",
       "\tCytotoxic.4 &  1.7622168 & 15.50659 & 13.338236 & 4.915597e-04 & 4.178258e-03\\\\\n",
       "\tCytotoxic.1 & -1.4571888 & 15.83731 &  9.810121 & 2.580384e-03 & 1.462218e-02\\\\\n",
       "\tInterferon.responding & -1.6973790 & 14.04001 &  8.191798 & 5.621630e-03 & 1.959821e-02\\\\\n",
       "\tCytotoxic.5 & -1.4224754 & 13.70959 &  8.123996 & 5.764181e-03 & 1.959821e-02\\\\\n",
       "\tTreg &  0.7746541 & 15.35012 &  4.062167 & 4.760885e-02 & 1.176292e-01\\\\\n",
       "\tProliferating.Cytotoxic.4 &  1.2780537 & 14.14890 &  4.039772 & 4.843555e-02 & 1.176292e-01\\\\\n",
       "\tCytotoxic.2 &  0.7119113 & 15.01169 &  3.344416 & 7.159828e-02 & 1.521463e-01\\\\\n",
       "\tT1T17 &  0.5588814 & 17.95351 &  2.367628 & 1.282833e-01 & 2.199297e-01\\\\\n",
       "\tT1T17.like.Cytotoxic.8 &  0.7617495 & 14.82152 &  2.353988 & 1.293704e-01 & 2.199297e-01\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] '1*factor(treatment)CD8b -1*factor(treatment)IgG'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 10 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Non.cytotoxic.CD8 | -2.7057989 | 14.66317 | 24.387111 | 5.591237e-06 | 9.505103e-05 |\n",
       "| Cytotoxic.4 |  1.7622168 | 15.50659 | 13.338236 | 4.915597e-04 | 4.178258e-03 |\n",
       "| Cytotoxic.1 | -1.4571888 | 15.83731 |  9.810121 | 2.580384e-03 | 1.462218e-02 |\n",
       "| Interferon.responding | -1.6973790 | 14.04001 |  8.191798 | 5.621630e-03 | 1.959821e-02 |\n",
       "| Cytotoxic.5 | -1.4224754 | 13.70959 |  8.123996 | 5.764181e-03 | 1.959821e-02 |\n",
       "| Treg |  0.7746541 | 15.35012 |  4.062167 | 4.760885e-02 | 1.176292e-01 |\n",
       "| Proliferating.Cytotoxic.4 |  1.2780537 | 14.14890 |  4.039772 | 4.843555e-02 | 1.176292e-01 |\n",
       "| Cytotoxic.2 |  0.7119113 | 15.01169 |  3.344416 | 7.159828e-02 | 1.521463e-01 |\n",
       "| T1T17 |  0.5588814 | 17.95351 |  2.367628 | 1.282833e-01 | 2.199297e-01 |\n",
       "| T1T17.like.Cytotoxic.8 |  0.7617495 | 14.82152 |  2.353988 | 1.293704e-01 | 2.199297e-01 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   '1*factor(treatment)CD8b -1*factor(treatment)IgG'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  1*factor(treatment)CD8b -1*factor(treatment)IgG \n",
       "                               logFC   logCPM         F       PValue\n",
       "Non.cytotoxic.CD8         -2.7057989 14.66317 24.387111 5.591237e-06\n",
       "Cytotoxic.4                1.7622168 15.50659 13.338236 4.915597e-04\n",
       "Cytotoxic.1               -1.4571888 15.83731  9.810121 2.580384e-03\n",
       "Interferon.responding     -1.6973790 14.04001  8.191798 5.621630e-03\n",
       "Cytotoxic.5               -1.4224754 13.70959  8.123996 5.764181e-03\n",
       "Treg                       0.7746541 15.35012  4.062167 4.760885e-02\n",
       "Proliferating.Cytotoxic.4  1.2780537 14.14890  4.039772 4.843555e-02\n",
       "Cytotoxic.2                0.7119113 15.01169  3.344416 7.159828e-02\n",
       "T1T17                      0.5588814 17.95351  2.367628 1.282833e-01\n",
       "T1T17.like.Cytotoxic.8     0.7617495 14.82152  2.353988 1.293704e-01\n",
       "                                   FDR\n",
       "Non.cytotoxic.CD8         9.505103e-05\n",
       "Cytotoxic.4               4.178258e-03\n",
       "Cytotoxic.1               1.462218e-02\n",
       "Interferon.responding     1.959821e-02\n",
       "Cytotoxic.5               1.959821e-02\n",
       "Treg                      1.176292e-01\n",
       "Proliferating.Cytotoxic.4 1.176292e-01\n",
       "Cytotoxic.2               1.521463e-01\n",
       "T1T17                     2.199297e-01\n",
       "T1T17.like.Cytotoxic.8    2.199297e-01"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5e9980f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# just proliferating subsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc80ce52",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "101eac37",
   "metadata": {},
   "outputs": [],
   "source": [
    "tnk_celltype_props <- read.csv(\"compositional_analysis/nkt_all_subcluster_counts_by_sample.csv\",row.names=1)\n",
    "metadata <- read.csv(\"compositional_analysis/metadata_by_sample.csv\", row.names=1)\n",
    "\n",
    "tnk_celltype_props <- na.omit(tnk_celltype_props)\n",
    "tnk_celltype_props <- t(tnk_celltype_props)[c(\"Proliferating.Cytotoxic.1.2.5.6\",\"Proliferating.Cytotoxic.4\",\"Proliferating.Cytotoxic.8\",\"Proliferating.T1T17\"),]\n",
    "metadata = metadata[colnames(tnk_celltype_props),]\n",
    "\n",
    "metadata$M.Number <- factor(metadata$M.Number)\n",
    "metadata$monkey_condition_categories <- factor(metadata$monkey_condition_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dae4e0de",
   "metadata": {},
   "outputs": [],
   "source": [
    "y.ab <- DGEList(tnk_celltype_props, samples=metadata)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d196796",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "61710d07",
   "metadata": {},
   "outputs": [],
   "source": [
    "#keep <- filterByExpr(y.ab, group=y.ab$samples$treatment)\n",
    "#y.ab <- y.ab[keep,]\n",
    "#summary(keep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2ad7564a",
   "metadata": {},
   "outputs": [],
   "source": [
    "design <- model.matrix(~0+factor(monkey_condition_categories) + factor(treatment), y.ab$samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "23514542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. \n",
       "9.766e-05 9.766e-05 9.766e-05 9.766e-05 9.766e-05 9.766e-05 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.ab <- estimateDisp(y.ab, design, trend=\"none\")\n",
    "summary(y.ab$common.dispersion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "75d1134f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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emb94PGPb+Pi62/fx25s27KnBOaOg3q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\n/D9qk5Nk4H2UfQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotBCV(y.ab, cex=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91b46636",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "da8dbbee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  1.313   1.313   1.313   1.313   1.313   1.313 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit.ab <- glmQLFit(y.ab, design, robust=TRUE, abundance.trend=FALSE)\n",
    "summary(fit.ab$var.prior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c9bd5b19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hQ7AAAAlaDYAQAAqATFDgAAQCUodgAAACpBsQMAAFAJih0AAIBKUOwAAABUgmIH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\nAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotQLDisp(fit.ab, cex=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "31f7d0d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       factor(treatment)IgG\n",
       "Down                      2\n",
       "NotSig                    1\n",
       "Up                        1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res <- glmQLFTest(fit.ab, coef=ncol(design))\n",
    "summary(decideTests(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8d209c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 4 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Proliferating.Cytotoxic.4</th><td>-2.21694830</td><td>16.35294</td><td>24.008814840</td><td>1.227913e-05</td><td>4.911652e-05</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.Cytotoxic.8</th><td>-1.99636101</td><td>15.92515</td><td>19.041003697</td><td>7.162790e-05</td><td>1.432558e-04</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.T1T17</th><td> 0.23633326</td><td>19.53731</td><td> 9.626167676</td><td>4.440940e-03</td><td>5.921253e-03</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.Cytotoxic.1.2.5.6</th><td> 0.01578089</td><td>17.22177</td><td> 0.003216155</td><td>9.550208e-01</td><td>9.550208e-01</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'factor(treatment)IgG'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 4 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tProliferating.Cytotoxic.4 & -2.21694830 & 16.35294 & 24.008814840 & 1.227913e-05 & 4.911652e-05\\\\\n",
       "\tProliferating.Cytotoxic.8 & -1.99636101 & 15.92515 & 19.041003697 & 7.162790e-05 & 1.432558e-04\\\\\n",
       "\tProliferating.T1T17 &  0.23633326 & 19.53731 &  9.626167676 & 4.440940e-03 & 5.921253e-03\\\\\n",
       "\tProliferating.Cytotoxic.1.2.5.6 &  0.01578089 & 17.22177 &  0.003216155 & 9.550208e-01 & 9.550208e-01\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] 'factor(treatment)IgG'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 4 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Proliferating.Cytotoxic.4 | -2.21694830 | 16.35294 | 24.008814840 | 1.227913e-05 | 4.911652e-05 |\n",
       "| Proliferating.Cytotoxic.8 | -1.99636101 | 15.92515 | 19.041003697 | 7.162790e-05 | 1.432558e-04 |\n",
       "| Proliferating.T1T17 |  0.23633326 | 19.53731 |  9.626167676 | 4.440940e-03 | 5.921253e-03 |\n",
       "| Proliferating.Cytotoxic.1.2.5.6 |  0.01578089 | 17.22177 |  0.003216155 | 9.550208e-01 | 9.550208e-01 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   'factor(treatment)IgG'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  factor(treatment)IgG \n",
       "                                      logFC   logCPM            F       PValue\n",
       "Proliferating.Cytotoxic.4       -2.21694830 16.35294 24.008814840 1.227913e-05\n",
       "Proliferating.Cytotoxic.8       -1.99636101 15.92515 19.041003697 7.162790e-05\n",
       "Proliferating.T1T17              0.23633326 19.53731  9.626167676 4.440940e-03\n",
       "Proliferating.Cytotoxic.1.2.5.6  0.01578089 17.22177  0.003216155 9.550208e-01\n",
       "                                         FDR\n",
       "Proliferating.Cytotoxic.4       4.911652e-05\n",
       "Proliferating.Cytotoxic.8       1.432558e-04\n",
       "Proliferating.T1T17             5.921253e-03\n",
       "Proliferating.Cytotoxic.1.2.5.6 9.550208e-01"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res,12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1efbb6c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a1450a3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- glmQLFTest(fit.ab, coef = 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "02428dbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       factor(treatment)CD8b\n",
       "Down                       2\n",
       "NotSig                     1\n",
       "Up                         1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(decideTests(res)) # CD8b vs CD8a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d8f303d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 4 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Proliferating.Cytotoxic.8</th><td>-2.4879325</td><td>15.92515</td><td>19.768350</td><td>0.0000548172</td><td>0.0002192688</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.Cytotoxic.4</th><td>-1.3089309</td><td>16.35294</td><td>13.670519</td><td>0.0005787110</td><td>0.0011574220</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.T1T17</th><td> 0.2668697</td><td>19.53731</td><td>12.060622</td><td>0.0014404001</td><td>0.0019205335</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.Cytotoxic.1.2.5.6</th><td>-0.3215005</td><td>17.22177</td><td> 1.245766</td><td>0.2701563937</td><td>0.2701563937</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'factor(treatment)CD8b'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 4 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tProliferating.Cytotoxic.8 & -2.4879325 & 15.92515 & 19.768350 & 0.0000548172 & 0.0002192688\\\\\n",
       "\tProliferating.Cytotoxic.4 & -1.3089309 & 16.35294 & 13.670519 & 0.0005787110 & 0.0011574220\\\\\n",
       "\tProliferating.T1T17 &  0.2668697 & 19.53731 & 12.060622 & 0.0014404001 & 0.0019205335\\\\\n",
       "\tProliferating.Cytotoxic.1.2.5.6 & -0.3215005 & 17.22177 &  1.245766 & 0.2701563937 & 0.2701563937\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] 'factor(treatment)CD8b'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 4 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Proliferating.Cytotoxic.8 | -2.4879325 | 15.92515 | 19.768350 | 0.0000548172 | 0.0002192688 |\n",
       "| Proliferating.Cytotoxic.4 | -1.3089309 | 16.35294 | 13.670519 | 0.0005787110 | 0.0011574220 |\n",
       "| Proliferating.T1T17 |  0.2668697 | 19.53731 | 12.060622 | 0.0014404001 | 0.0019205335 |\n",
       "| Proliferating.Cytotoxic.1.2.5.6 | -0.3215005 | 17.22177 |  1.245766 | 0.2701563937 | 0.2701563937 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   'factor(treatment)CD8b'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  factor(treatment)CD8b \n",
       "                                     logFC   logCPM         F       PValue\n",
       "Proliferating.Cytotoxic.8       -2.4879325 15.92515 19.768350 0.0000548172\n",
       "Proliferating.Cytotoxic.4       -1.3089309 16.35294 13.670519 0.0005787110\n",
       "Proliferating.T1T17              0.2668697 19.53731 12.060622 0.0014404001\n",
       "Proliferating.Cytotoxic.1.2.5.6 -0.3215005 17.22177  1.245766 0.2701563937\n",
       "                                         FDR\n",
       "Proliferating.Cytotoxic.8       0.0002192688\n",
       "Proliferating.Cytotoxic.4       0.0011574220\n",
       "Proliferating.T1T17             0.0019205335\n",
       "Proliferating.Cytotoxic.1.2.5.6 0.2701563937"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "48d7348d",
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- glmQLFTest(fit.ab, contrast = c(0,0,0,0,1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c64bf10c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       1*factor(treatment)CD8b -1*factor(treatment)IgG\n",
       "Down                                                 0\n",
       "NotSig                                               4\n",
       "Up                                                   0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(decideTests(res)) # CD8b vs IgG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "be0cc9a3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 4 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Proliferating.Cytotoxic.4</th><td> 0.9080174</td><td>16.35294</td><td>2.5017422</td><td>0.1205693</td><td>0.4822773</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.Cytotoxic.1.2.5.6</th><td>-0.3372814</td><td>17.22177</td><td>1.0635147</td><td>0.3078042</td><td>0.6156084</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.Cytotoxic.8</th><td>-0.4915715</td><td>15.92515</td><td>0.4732605</td><td>0.4949439</td><td>0.6599252</td></tr>\n",
       "\t<tr><th scope=row>Proliferating.T1T17</th><td> 0.0305364</td><td>19.53731</td><td>0.1325201</td><td>0.7372464</td><td>0.7372464</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'1*factor(treatment)CD8b -1*factor(treatment)IgG'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 4 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tProliferating.Cytotoxic.4 &  0.9080174 & 16.35294 & 2.5017422 & 0.1205693 & 0.4822773\\\\\n",
       "\tProliferating.Cytotoxic.1.2.5.6 & -0.3372814 & 17.22177 & 1.0635147 & 0.3078042 & 0.6156084\\\\\n",
       "\tProliferating.Cytotoxic.8 & -0.4915715 & 15.92515 & 0.4732605 & 0.4949439 & 0.6599252\\\\\n",
       "\tProliferating.T1T17 &  0.0305364 & 19.53731 & 0.1325201 & 0.7372464 & 0.7372464\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] '1*factor(treatment)CD8b -1*factor(treatment)IgG'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 4 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Proliferating.Cytotoxic.4 |  0.9080174 | 16.35294 | 2.5017422 | 0.1205693 | 0.4822773 |\n",
       "| Proliferating.Cytotoxic.1.2.5.6 | -0.3372814 | 17.22177 | 1.0635147 | 0.3078042 | 0.6156084 |\n",
       "| Proliferating.Cytotoxic.8 | -0.4915715 | 15.92515 | 0.4732605 | 0.4949439 | 0.6599252 |\n",
       "| Proliferating.T1T17 |  0.0305364 | 19.53731 | 0.1325201 | 0.7372464 | 0.7372464 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   '1*factor(treatment)CD8b -1*factor(treatment)IgG'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  1*factor(treatment)CD8b -1*factor(treatment)IgG \n",
       "                                     logFC   logCPM         F    PValue\n",
       "Proliferating.Cytotoxic.4        0.9080174 16.35294 2.5017422 0.1205693\n",
       "Proliferating.Cytotoxic.1.2.5.6 -0.3372814 17.22177 1.0635147 0.3078042\n",
       "Proliferating.Cytotoxic.8       -0.4915715 15.92515 0.4732605 0.4949439\n",
       "Proliferating.T1T17              0.0305364 19.53731 0.1325201 0.7372464\n",
       "                                      FDR\n",
       "Proliferating.Cytotoxic.4       0.4822773\n",
       "Proliferating.Cytotoxic.1.2.5.6 0.6156084\n",
       "Proliferating.Cytotoxic.8       0.6599252\n",
       "Proliferating.T1T17             0.7372464"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "caa61c7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# MACROPHAGE CLUSTERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "00c3016e",
   "metadata": {},
   "outputs": [],
   "source": [
    "major_celltype_props <- read.csv(\"compositional_analysis/mac_subcluster_counts.csv\",row.names=1)\n",
    "metadata_2 <- read.csv(\"compositional_analysis/metadata_by_sample.csv\", row.names=1)\n",
    "metadata_2$M.Number <- factor(metadata_2$M.Number)\n",
    "metadata_2$monkey_condition_categories <- factor(metadata_2$monkey_condition_categories)\n",
    "major_celltype_props <- na.omit(major_celltype_props)\n",
    "major_celltype_props <- t(major_celltype_props)\n",
    "\n",
    "metadata_2$M.Number <- factor(metadata_2$M.Number)\n",
    "metadata_2$monkey_condition_categories <- factor(metadata_2$monkey_condition_categories)\n",
    "metadata_2 <-metadata_2[colnames(major_celltype_props),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "139e9498",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 4 × 40 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Array1_28918</th><th scope=col>Array1_29218</th><th scope=col>Array1_29318</th><th scope=col>Array1_29818</th><th scope=col>Array1_30518</th><th scope=col>Array2_28918</th><th scope=col>Array2_29218</th><th scope=col>Array2_29318</th><th scope=col>Array2_29818</th><th scope=col>Array2_30518</th><th scope=col>⋯</th><th scope=col>Array8_28818</th><th scope=col>Array8_29118</th><th scope=col>Array8_29618</th><th scope=col>Array8_30218</th><th scope=col>Array8_30418</th><th scope=col>Array9_28818</th><th scope=col>Array9_29118</th><th scope=col>Array9_29618</th><th scope=col>Array9_30218</th><th scope=col>Array9_30418</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Alv.Mac</th><td> 46</td><td> 92</td><td> 16</td><td> 87</td><td>18</td><td> 90</td><td>60</td><td>149</td><td> 5</td><td> 97</td><td>⋯</td><td> 69</td><td> 74</td><td> 6</td><td> 1</td><td> 25</td><td> 71</td><td> 24</td><td> 16</td><td>24</td><td> 34</td></tr>\n",
       "\t<tr><th scope=row>Inflam.Mac</th><td>  1</td><td> 53</td><td>127</td><td>163</td><td>47</td><td> 20</td><td>27</td><td>148</td><td>95</td><td> 68</td><td>⋯</td><td> 49</td><td>306</td><td>33</td><td>65</td><td> 45</td><td>141</td><td>154</td><td> 75</td><td>67</td><td> 88</td></tr>\n",
       "\t<tr><th scope=row>Mac</th><td> 19</td><td>112</td><td>144</td><td>435</td><td>74</td><td>171</td><td>89</td><td>573</td><td>85</td><td>371</td><td>⋯</td><td>278</td><td>416</td><td>32</td><td>28</td><td>221</td><td>459</td><td>323</td><td>222</td><td>29</td><td>149</td></tr>\n",
       "\t<tr><th scope=row>Monocyte</th><td>115</td><td>141</td><td>153</td><td> 69</td><td>26</td><td> 39</td><td>83</td><td> 71</td><td>74</td><td> 32</td><td>⋯</td><td> 37</td><td>116</td><td>25</td><td>41</td><td> 38</td><td> 20</td><td> 54</td><td> 57</td><td>21</td><td> 26</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 4 × 40 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & Array1\\_28918 & Array1\\_29218 & Array1\\_29318 & Array1\\_29818 & Array1\\_30518 & Array2\\_28918 & Array2\\_29218 & Array2\\_29318 & Array2\\_29818 & Array2\\_30518 & ⋯ & Array8\\_28818 & Array8\\_29118 & Array8\\_29618 & Array8\\_30218 & Array8\\_30418 & Array9\\_28818 & Array9\\_29118 & Array9\\_29618 & Array9\\_30218 & Array9\\_30418\\\\\n",
       "\\hline\n",
       "\tAlv.Mac &  46 &  92 &  16 &  87 & 18 &  90 & 60 & 149 &  5 &  97 & ⋯ &  69 &  74 &  6 &  1 &  25 &  71 &  24 &  16 & 24 &  34\\\\\n",
       "\tInflam.Mac &   1 &  53 & 127 & 163 & 47 &  20 & 27 & 148 & 95 &  68 & ⋯ &  49 & 306 & 33 & 65 &  45 & 141 & 154 &  75 & 67 &  88\\\\\n",
       "\tMac &  19 & 112 & 144 & 435 & 74 & 171 & 89 & 573 & 85 & 371 & ⋯ & 278 & 416 & 32 & 28 & 221 & 459 & 323 & 222 & 29 & 149\\\\\n",
       "\tMonocyte & 115 & 141 & 153 &  69 & 26 &  39 & 83 &  71 & 74 &  32 & ⋯ &  37 & 116 & 25 & 41 &  38 &  20 &  54 &  57 & 21 &  26\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 4 × 40 of type dbl\n",
       "\n",
       "| <!--/--> | Array1_28918 | Array1_29218 | Array1_29318 | Array1_29818 | Array1_30518 | Array2_28918 | Array2_29218 | Array2_29318 | Array2_29818 | Array2_30518 | ⋯ | Array8_28818 | Array8_29118 | Array8_29618 | Array8_30218 | Array8_30418 | Array9_28818 | Array9_29118 | Array9_29618 | Array9_30218 | Array9_30418 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Alv.Mac |  46 |  92 |  16 |  87 | 18 |  90 | 60 | 149 |  5 |  97 | ⋯ |  69 |  74 |  6 |  1 |  25 |  71 |  24 |  16 | 24 |  34 |\n",
       "| Inflam.Mac |   1 |  53 | 127 | 163 | 47 |  20 | 27 | 148 | 95 |  68 | ⋯ |  49 | 306 | 33 | 65 |  45 | 141 | 154 |  75 | 67 |  88 |\n",
       "| Mac |  19 | 112 | 144 | 435 | 74 | 171 | 89 | 573 | 85 | 371 | ⋯ | 278 | 416 | 32 | 28 | 221 | 459 | 323 | 222 | 29 | 149 |\n",
       "| Monocyte | 115 | 141 | 153 |  69 | 26 |  39 | 83 |  71 | 74 |  32 | ⋯ |  37 | 116 | 25 | 41 |  38 |  20 |  54 |  57 | 21 |  26 |\n",
       "\n"
      ],
      "text/plain": [
       "           Array1_28918 Array1_29218 Array1_29318 Array1_29818 Array1_30518\n",
       "Alv.Mac     46           92           16           87          18          \n",
       "Inflam.Mac   1           53          127          163          47          \n",
       "Mac         19          112          144          435          74          \n",
       "Monocyte   115          141          153           69          26          \n",
       "           Array2_28918 Array2_29218 Array2_29318 Array2_29818 Array2_30518 ⋯\n",
       "Alv.Mac     90          60           149           5            97          ⋯\n",
       "Inflam.Mac  20          27           148          95            68          ⋯\n",
       "Mac        171          89           573          85           371          ⋯\n",
       "Monocyte    39          83            71          74            32          ⋯\n",
       "           Array8_28818 Array8_29118 Array8_29618 Array8_30218 Array8_30418\n",
       "Alv.Mac     69           74           6            1            25         \n",
       "Inflam.Mac  49          306          33           65            45         \n",
       "Mac        278          416          32           28           221         \n",
       "Monocyte    37          116          25           41            38         \n",
       "           Array9_28818 Array9_29118 Array9_29618 Array9_30218 Array9_30418\n",
       "Alv.Mac     71           24           16          24            34         \n",
       "Inflam.Mac 141          154           75          67            88         \n",
       "Mac        459          323          222          29           149         \n",
       "Monocyte    20           54           57          21            26         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "major_celltype_props"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "2a6bdfae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>'Alv.Mac'</li><li>'Inflam.Mac'</li><li>'Mac'</li><li>'Monocyte'</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Alv.Mac'\n",
       "\\item 'Inflam.Mac'\n",
       "\\item 'Mac'\n",
       "\\item 'Monocyte'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Alv.Mac'\n",
       "2. 'Inflam.Mac'\n",
       "3. 'Mac'\n",
       "4. 'Monocyte'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"Alv.Mac\"    \"Inflam.Mac\" \"Mac\"        \"Monocyte\"  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rownames(major_celltype_props)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "25965277",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y.ab <- DGEList(major_celltype_props, samples=metadata_2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2dfa34a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$counts</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A matrix: 4 × 40 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Array1_28918</th><th scope=col>Array1_29218</th><th scope=col>Array1_29318</th><th scope=col>Array1_29818</th><th scope=col>Array1_30518</th><th scope=col>Array2_28918</th><th scope=col>Array2_29218</th><th scope=col>Array2_29318</th><th scope=col>Array2_29818</th><th scope=col>Array2_30518</th><th scope=col>⋯</th><th scope=col>Array8_28818</th><th scope=col>Array8_29118</th><th scope=col>Array8_29618</th><th scope=col>Array8_30218</th><th scope=col>Array8_30418</th><th scope=col>Array9_28818</th><th scope=col>Array9_29118</th><th scope=col>Array9_29618</th><th scope=col>Array9_30218</th><th scope=col>Array9_30418</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Alv.Mac</th><td> 46</td><td> 92</td><td> 16</td><td> 87</td><td>18</td><td> 90</td><td>60</td><td>149</td><td> 5</td><td> 97</td><td>⋯</td><td> 69</td><td> 74</td><td> 6</td><td> 1</td><td> 25</td><td> 71</td><td> 24</td><td> 16</td><td>24</td><td> 34</td></tr>\n",
       "\t<tr><th scope=row>Inflam.Mac</th><td>  1</td><td> 53</td><td>127</td><td>163</td><td>47</td><td> 20</td><td>27</td><td>148</td><td>95</td><td> 68</td><td>⋯</td><td> 49</td><td>306</td><td>33</td><td>65</td><td> 45</td><td>141</td><td>154</td><td> 75</td><td>67</td><td> 88</td></tr>\n",
       "\t<tr><th scope=row>Mac</th><td> 19</td><td>112</td><td>144</td><td>435</td><td>74</td><td>171</td><td>89</td><td>573</td><td>85</td><td>371</td><td>⋯</td><td>278</td><td>416</td><td>32</td><td>28</td><td>221</td><td>459</td><td>323</td><td>222</td><td>29</td><td>149</td></tr>\n",
       "\t<tr><th scope=row>Monocyte</th><td>115</td><td>141</td><td>153</td><td> 69</td><td>26</td><td> 39</td><td>83</td><td> 71</td><td>74</td><td> 32</td><td>⋯</td><td> 37</td><td>116</td><td>25</td><td>41</td><td> 38</td><td> 20</td><td> 54</td><td> 57</td><td>21</td><td> 26</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$samples</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 40 × 15</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>group</th><th scope=col>lib.size</th><th scope=col>norm.factors</th><th scope=col>M.Number</th><th scope=col>treatment</th><th scope=col>infection.dose</th><th scope=col>Tissue.name</th><th scope=col>CFU.granuloma</th><th scope=col>total.thoracic.CFU</th><th scope=col>Array</th><th scope=col>lung.region</th><th scope=col>Gran.State</th><th scope=col>total_cells_elbow</th><th scope=col>monkey_condition_categories</th><th scope=col>tcell.count</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Array1_28918</th><td>1</td><td>181</td><td>1</td><td>28918</td><td>CD8a</td><td>19</td><td>RLL Gr 2      </td><td>    260</td><td> 5301080</td><td>1</td><td>RLL</td><td>New</td><td>1154</td><td>1</td><td>606</td></tr>\n",
       "\t<tr><th scope=row>Array1_29218</th><td>1</td><td>398</td><td>1</td><td>29218</td><td>CD8a</td><td>23</td><td>LLL gr 3      </td><td>    390</td><td> 2960670</td><td>1</td><td>LLL</td><td>New</td><td>1219</td><td>2</td><td>611</td></tr>\n",
       "\t<tr><th scope=row>Array1_29318</th><td>1</td><td>440</td><td>1</td><td>29318</td><td>CD8a</td><td>18</td><td>RUL C         </td><td>  59000</td><td>19228424</td><td>1</td><td>RUL</td><td>New</td><td>1390</td><td>3</td><td>550</td></tr>\n",
       "\t<tr><th scope=row>Array1_29818</th><td>1</td><td>754</td><td>1</td><td>29818</td><td>IgG </td><td>23</td><td>ACC GR 1      </td><td> 610000</td><td> 4066743</td><td>1</td><td>ACC</td><td>Old</td><td>1583</td><td>1</td><td> 77</td></tr>\n",
       "\t<tr><th scope=row>Array1_30518</th><td>1</td><td>165</td><td>1</td><td>30518</td><td>CD8b</td><td>18</td><td>RUL Gr 14     </td><td>   2025</td><td> 3895065</td><td>1</td><td>RUL</td><td>New</td><td>1284</td><td>1</td><td>705</td></tr>\n",
       "\t<tr><th scope=row>Array2_28918</th><td>1</td><td>320</td><td>1</td><td>28918</td><td>CD8a</td><td>19</td><td>Acc Gr 1      </td><td>    580</td><td> 5301080</td><td>2</td><td>ACC</td><td>New</td><td>1073</td><td>1</td><td>506</td></tr>\n",
       "\t<tr><th scope=row>Array2_29218</th><td>1</td><td>259</td><td>1</td><td>29218</td><td>CD8a</td><td>23</td><td>ACC gr 3      </td><td>    570</td><td> 2960670</td><td>2</td><td>ACC</td><td>New</td><td>1252</td><td>2</td><td>693</td></tr>\n",
       "\t<tr><th scope=row>Array2_29318</th><td>1</td><td>941</td><td>1</td><td>29318</td><td>CD8a</td><td>18</td><td>LLL 5         </td><td>  65200</td><td>19228424</td><td>2</td><td>LLL</td><td>New</td><td>1503</td><td>3</td><td> 63</td></tr>\n",
       "\t<tr><th scope=row>Array2_29818</th><td>1</td><td>259</td><td>1</td><td>29818</td><td>IgG </td><td>23</td><td>ACC GR 2      </td><td> 224000</td><td> 4066743</td><td>2</td><td>ACC</td><td>Old</td><td>1542</td><td>1</td><td>124</td></tr>\n",
       "\t<tr><th scope=row>Array2_30518</th><td>1</td><td>568</td><td>1</td><td>30518</td><td>CD8b</td><td>18</td><td>RLL Gr 44     </td><td>  34800</td><td> 3895065</td><td>2</td><td>RLL</td><td>New</td><td>1212</td><td>1</td><td>323</td></tr>\n",
       "\t<tr><th scope=row>Array3_28918</th><td>1</td><td>712</td><td>1</td><td>28918</td><td>CD8a</td><td>19</td><td>LUL GR 1      </td><td> 108000</td><td> 5301080</td><td>3</td><td>LUL</td><td>Old</td><td>1262</td><td>1</td><td> 87</td></tr>\n",
       "\t<tr><th scope=row>Array3_29218</th><td>1</td><td>465</td><td>1</td><td>29218</td><td>CD8a</td><td>23</td><td>RLL 7 Cluster </td><td> 414000</td><td> 2960670</td><td>3</td><td>RLL</td><td>Old</td><td>1219</td><td>2</td><td>245</td></tr>\n",
       "\t<tr><th scope=row>Array3_29318</th><td>1</td><td>695</td><td>1</td><td>29318</td><td>CD8a</td><td>18</td><td>LML B         </td><td>   4400</td><td>19228424</td><td>3</td><td>LML</td><td>New</td><td>1251</td><td>3</td><td>275</td></tr>\n",
       "\t<tr><th scope=row>Array3_29818</th><td>1</td><td>158</td><td>1</td><td>29818</td><td>IgG </td><td>23</td><td>ACC GR 15     </td><td>   5680</td><td> 4066743</td><td>3</td><td>ACC</td><td>New</td><td>1136</td><td>1</td><td>358</td></tr>\n",
       "\t<tr><th scope=row>Array3_30518</th><td>1</td><td>620</td><td>1</td><td>30518</td><td>CD8b</td><td>18</td><td>RLL Gr 38     </td><td> 151800</td><td> 3895065</td><td>3</td><td>RLL</td><td>New</td><td>1284</td><td>1</td><td>259</td></tr>\n",
       "\t<tr><th scope=row>Array4_28918</th><td>1</td><td>617</td><td>1</td><td>28918</td><td>CD8a</td><td>19</td><td>LUL Gr 4      </td><td>  84000</td><td> 5301080</td><td>4</td><td>LUL</td><td>Old</td><td>1136</td><td>1</td><td>168</td></tr>\n",
       "\t<tr><th scope=row>Array4_29218</th><td>1</td><td>320</td><td>1</td><td>29218</td><td>CD8a</td><td>23</td><td>RLL gr 17     </td><td>   1470</td><td> 2960670</td><td>4</td><td>RLL</td><td>Old</td><td>1184</td><td>2</td><td>425</td></tr>\n",
       "\t<tr><th scope=row>Array4_29318</th><td>1</td><td>375</td><td>1</td><td>29318</td><td>CD8a</td><td>18</td><td>LLL 13        </td><td> 149600</td><td>19228424</td><td>4</td><td>LLL</td><td>New</td><td>1211</td><td>3</td><td>559</td></tr>\n",
       "\t<tr><th scope=row>Array4_29818</th><td>1</td><td> 21</td><td>1</td><td>29818</td><td>IgG </td><td>23</td><td>ACC GR 11     </td><td> 224000</td><td> 4066743</td><td>4</td><td>RLL</td><td>Old</td><td> 572</td><td>1</td><td> 63</td></tr>\n",
       "\t<tr><th scope=row>Array4_30518</th><td>1</td><td>533</td><td>1</td><td>30518</td><td>CD8b</td><td>18</td><td>RLL Gr 35     </td><td> 106800</td><td> 3895065</td><td>4</td><td>RLL</td><td>Old</td><td>1320</td><td>1</td><td>451</td></tr>\n",
       "\t<tr><th scope=row>Array5_28918</th><td>1</td><td>806</td><td>1</td><td>28918</td><td>CD8a</td><td>19</td><td>LLL Gr 20     </td><td>  17800</td><td> 5301080</td><td>5</td><td>LLL</td><td>New</td><td>1371</td><td>1</td><td> 36</td></tr>\n",
       "\t<tr><th scope=row>Array6_28818</th><td>1</td><td>602</td><td>1</td><td>28818</td><td>CD8b</td><td>19</td><td>RUL Gr 3      </td><td>  37200</td><td>10562333</td><td>6</td><td>RUL</td><td>New</td><td>1463</td><td>2</td><td> 75</td></tr>\n",
       "\t<tr><th scope=row>Array6_29118</th><td>1</td><td>748</td><td>1</td><td>29118</td><td>CD8a</td><td>23</td><td>LLL GR 12     </td><td> 184000</td><td> 6466750</td><td>6</td><td>LLL</td><td>Old</td><td>1537</td><td>4</td><td> 79</td></tr>\n",
       "\t<tr><th scope=row>Array6_30218</th><td>1</td><td>318</td><td>1</td><td>30218</td><td>CD8b</td><td>23</td><td>RLL 25        </td><td>   6200</td><td> 1670412</td><td>6</td><td>RLL</td><td>New</td><td>1449</td><td>3</td><td>510</td></tr>\n",
       "\t<tr><th scope=row>Array6_30418</th><td>1</td><td>417</td><td>1</td><td>30418</td><td>IgG </td><td>18</td><td>RLL Gr 13     </td><td>  34500</td><td>  633245</td><td>6</td><td>RLL</td><td>Old</td><td>1248</td><td>3</td><td>424</td></tr>\n",
       "\t<tr><th scope=row>Array7_28818</th><td>1</td><td>158</td><td>1</td><td>28818</td><td>CD8b</td><td>19</td><td>RUL Gr 5      </td><td> 765000</td><td>10562333</td><td>7</td><td>RUL</td><td>Old</td><td> 665</td><td>2</td><td>218</td></tr>\n",
       "\t<tr><th scope=row>Array7_29118</th><td>1</td><td>756</td><td>1</td><td>29118</td><td>CD8a</td><td>23</td><td>LLL GR 39     </td><td>  10500</td><td> 6466750</td><td>7</td><td>LLL</td><td>New</td><td>1532</td><td>4</td><td>175</td></tr>\n",
       "\t<tr><th scope=row>Array7_29618</th><td>1</td><td>226</td><td>1</td><td>29618</td><td>IgG </td><td>23</td><td>LLL gr 4      </td><td>2340000</td><td> 4976735</td><td>7</td><td>LLL</td><td>Old</td><td>1210</td><td>2</td><td>391</td></tr>\n",
       "\t<tr><th scope=row>Array7_30218</th><td>1</td><td>211</td><td>1</td><td>30218</td><td>CD8b</td><td>23</td><td>RLL 15        </td><td>  11200</td><td> 1670412</td><td>7</td><td>RLL</td><td>New</td><td>1499</td><td>3</td><td>742</td></tr>\n",
       "\t<tr><th scope=row>Array7_30418</th><td>1</td><td>400</td><td>1</td><td>30418</td><td>IgG </td><td>18</td><td>RLL Gr 15     </td><td> 204000</td><td>  633245</td><td>7</td><td>RLL</td><td>Old</td><td>1155</td><td>3</td><td>362</td></tr>\n",
       "\t<tr><th scope=row>Array8_28818</th><td>1</td><td>433</td><td>1</td><td>28818</td><td>CD8b</td><td>19</td><td>RLL Gr 3      </td><td>  62400</td><td>10562333</td><td>8</td><td>RLL</td><td>Old</td><td>1178</td><td>2</td><td>223</td></tr>\n",
       "\t<tr><th scope=row>Array8_29118</th><td>1</td><td>912</td><td>1</td><td>29118</td><td>CD8a</td><td>23</td><td>LLL GR 41     </td><td>  56400</td><td> 6466750</td><td>8</td><td>LLL</td><td>Old</td><td>1573</td><td>4</td><td>110</td></tr>\n",
       "\t<tr><th scope=row>Array8_29618</th><td>1</td><td> 96</td><td>1</td><td>29618</td><td>IgG </td><td>23</td><td>LLL gr 9      </td><td> 216000</td><td> 4976735</td><td>8</td><td>LLL</td><td>Old</td><td>1214</td><td>2</td><td>748</td></tr>\n",
       "\t<tr><th scope=row>Array8_30218</th><td>1</td><td>135</td><td>1</td><td>30218</td><td>CD8b</td><td>23</td><td>RLL 10        </td><td>  75600</td><td> 1670412</td><td>8</td><td>RLL</td><td>Old</td><td>1528</td><td>3</td><td>536</td></tr>\n",
       "\t<tr><th scope=row>Array8_30418</th><td>1</td><td>329</td><td>1</td><td>30418</td><td>IgG </td><td>18</td><td>RLL Gr 7      </td><td>  17600</td><td>  633245</td><td>8</td><td>RLL</td><td>Old</td><td>1315</td><td>3</td><td>599</td></tr>\n",
       "\t<tr><th scope=row>Array9_28818</th><td>1</td><td>691</td><td>1</td><td>28818</td><td>CD8b</td><td>19</td><td>RLL Gr 7      </td><td> 535000</td><td>10562333</td><td>9</td><td>RLL</td><td>Old</td><td>1275</td><td>2</td><td> 13</td></tr>\n",
       "\t<tr><th scope=row>Array9_29118</th><td>1</td><td>555</td><td>1</td><td>29118</td><td>CD8a</td><td>23</td><td>LLL GR 48     </td><td> 588000</td><td> 6466750</td><td>9</td><td>LLL</td><td>Old</td><td>1324</td><td>4</td><td> 76</td></tr>\n",
       "\t<tr><th scope=row>Array9_29618</th><td>1</td><td>370</td><td>1</td><td>29618</td><td>IgG </td><td>23</td><td>LLL gr 12     </td><td> 618000</td><td> 4976735</td><td>9</td><td>LLL</td><td>Old</td><td>1242</td><td>2</td><td>494</td></tr>\n",
       "\t<tr><th scope=row>Array9_30218</th><td>1</td><td>141</td><td>1</td><td>30218</td><td>CD8b</td><td>23</td><td>RLL 18        </td><td>   5640</td><td> 1670412</td><td>9</td><td>RLL</td><td>New</td><td>1328</td><td>3</td><td>788</td></tr>\n",
       "\t<tr><th scope=row>Array9_30418</th><td>1</td><td>297</td><td>1</td><td>30418</td><td>IgG </td><td>18</td><td>RLL Gr 6      </td><td>  11400</td><td>  633245</td><td>9</td><td>RLL</td><td>New</td><td>1291</td><td>3</td><td>406</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$counts] A matrix: 4 × 40 of type dbl\n",
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & Array1\\_28918 & Array1\\_29218 & Array1\\_29318 & Array1\\_29818 & Array1\\_30518 & Array2\\_28918 & Array2\\_29218 & Array2\\_29318 & Array2\\_29818 & Array2\\_30518 & ⋯ & Array8\\_28818 & Array8\\_29118 & Array8\\_29618 & Array8\\_30218 & Array8\\_30418 & Array9\\_28818 & Array9\\_29118 & Array9\\_29618 & Array9\\_30218 & Array9\\_30418\\\\\n",
       "\\hline\n",
       "\tAlv.Mac &  46 &  92 &  16 &  87 & 18 &  90 & 60 & 149 &  5 &  97 & ⋯ &  69 &  74 &  6 &  1 &  25 &  71 &  24 &  16 & 24 &  34\\\\\n",
       "\tInflam.Mac &   1 &  53 & 127 & 163 & 47 &  20 & 27 & 148 & 95 &  68 & ⋯ &  49 & 306 & 33 & 65 &  45 & 141 & 154 &  75 & 67 &  88\\\\\n",
       "\tMac &  19 & 112 & 144 & 435 & 74 & 171 & 89 & 573 & 85 & 371 & ⋯ & 278 & 416 & 32 & 28 & 221 & 459 & 323 & 222 & 29 & 149\\\\\n",
       "\tMonocyte & 115 & 141 & 153 &  69 & 26 &  39 & 83 &  71 & 74 &  32 & ⋯ &  37 & 116 & 25 & 41 &  38 &  20 &  54 &  57 & 21 &  26\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$samples] A data.frame: 40 × 15\n",
       "\\begin{tabular}{r|lllllllllllllll}\n",
       "  & group & lib.size & norm.factors & M.Number & treatment & infection.dose & Tissue.name & CFU.granuloma & total.thoracic.CFU & Array & lung.region & Gran.State & total\\_cells\\_elbow & monkey\\_condition\\_categories & tcell.count\\\\\n",
       "  & <fct> & <dbl> & <dbl> & <fct> & <chr> & <int> & <chr> & <int> & <int> & <int> & <chr> & <chr> & <int> & <fct> & <int>\\\\\n",
       "\\hline\n",
       "\tArray1\\_28918 & 1 & 181 & 1 & 28918 & CD8a & 19 & RLL Gr 2       &     260 &  5301080 & 1 & RLL & New & 1154 & 1 & 606\\\\\n",
       "\tArray1\\_29218 & 1 & 398 & 1 & 29218 & CD8a & 23 & LLL gr 3       &     390 &  2960670 & 1 & LLL & New & 1219 & 2 & 611\\\\\n",
       "\tArray1\\_29318 & 1 & 440 & 1 & 29318 & CD8a & 18 & RUL C          &   59000 & 19228424 & 1 & RUL & New & 1390 & 3 & 550\\\\\n",
       "\tArray1\\_29818 & 1 & 754 & 1 & 29818 & IgG  & 23 & ACC GR 1       &  610000 &  4066743 & 1 & ACC & Old & 1583 & 1 &  77\\\\\n",
       "\tArray1\\_30518 & 1 & 165 & 1 & 30518 & CD8b & 18 & RUL Gr 14      &    2025 &  3895065 & 1 & RUL & New & 1284 & 1 & 705\\\\\n",
       "\tArray2\\_28918 & 1 & 320 & 1 & 28918 & CD8a & 19 & Acc Gr 1       &     580 &  5301080 & 2 & ACC & New & 1073 & 1 & 506\\\\\n",
       "\tArray2\\_29218 & 1 & 259 & 1 & 29218 & CD8a & 23 & ACC gr 3       &     570 &  2960670 & 2 & ACC & New & 1252 & 2 & 693\\\\\n",
       "\tArray2\\_29318 & 1 & 941 & 1 & 29318 & CD8a & 18 & LLL 5          &   65200 & 19228424 & 2 & LLL & New & 1503 & 3 &  63\\\\\n",
       "\tArray2\\_29818 & 1 & 259 & 1 & 29818 & IgG  & 23 & ACC GR 2       &  224000 &  4066743 & 2 & ACC & Old & 1542 & 1 & 124\\\\\n",
       "\tArray2\\_30518 & 1 & 568 & 1 & 30518 & CD8b & 18 & RLL Gr 44      &   34800 &  3895065 & 2 & RLL & New & 1212 & 1 & 323\\\\\n",
       "\tArray3\\_28918 & 1 & 712 & 1 & 28918 & CD8a & 19 & LUL GR 1       &  108000 &  5301080 & 3 & LUL & Old & 1262 & 1 &  87\\\\\n",
       "\tArray3\\_29218 & 1 & 465 & 1 & 29218 & CD8a & 23 & RLL 7 Cluster  &  414000 &  2960670 & 3 & RLL & Old & 1219 & 2 & 245\\\\\n",
       "\tArray3\\_29318 & 1 & 695 & 1 & 29318 & CD8a & 18 & LML B          &    4400 & 19228424 & 3 & LML & New & 1251 & 3 & 275\\\\\n",
       "\tArray3\\_29818 & 1 & 158 & 1 & 29818 & IgG  & 23 & ACC GR 15      &    5680 &  4066743 & 3 & ACC & New & 1136 & 1 & 358\\\\\n",
       "\tArray3\\_30518 & 1 & 620 & 1 & 30518 & CD8b & 18 & RLL Gr 38      &  151800 &  3895065 & 3 & RLL & New & 1284 & 1 & 259\\\\\n",
       "\tArray4\\_28918 & 1 & 617 & 1 & 28918 & CD8a & 19 & LUL Gr 4       &   84000 &  5301080 & 4 & LUL & Old & 1136 & 1 & 168\\\\\n",
       "\tArray4\\_29218 & 1 & 320 & 1 & 29218 & CD8a & 23 & RLL gr 17      &    1470 &  2960670 & 4 & RLL & Old & 1184 & 2 & 425\\\\\n",
       "\tArray4\\_29318 & 1 & 375 & 1 & 29318 & CD8a & 18 & LLL 13         &  149600 & 19228424 & 4 & LLL & New & 1211 & 3 & 559\\\\\n",
       "\tArray4\\_29818 & 1 &  21 & 1 & 29818 & IgG  & 23 & ACC GR 11      &  224000 &  4066743 & 4 & RLL & Old &  572 & 1 &  63\\\\\n",
       "\tArray4\\_30518 & 1 & 533 & 1 & 30518 & CD8b & 18 & RLL Gr 35      &  106800 &  3895065 & 4 & RLL & Old & 1320 & 1 & 451\\\\\n",
       "\tArray5\\_28918 & 1 & 806 & 1 & 28918 & CD8a & 19 & LLL Gr 20      &   17800 &  5301080 & 5 & LLL & New & 1371 & 1 &  36\\\\\n",
       "\tArray6\\_28818 & 1 & 602 & 1 & 28818 & CD8b & 19 & RUL Gr 3       &   37200 & 10562333 & 6 & RUL & New & 1463 & 2 &  75\\\\\n",
       "\tArray6\\_29118 & 1 & 748 & 1 & 29118 & CD8a & 23 & LLL GR 12      &  184000 &  6466750 & 6 & LLL & Old & 1537 & 4 &  79\\\\\n",
       "\tArray6\\_30218 & 1 & 318 & 1 & 30218 & CD8b & 23 & RLL 25         &    6200 &  1670412 & 6 & RLL & New & 1449 & 3 & 510\\\\\n",
       "\tArray6\\_30418 & 1 & 417 & 1 & 30418 & IgG  & 18 & RLL Gr 13      &   34500 &   633245 & 6 & RLL & Old & 1248 & 3 & 424\\\\\n",
       "\tArray7\\_28818 & 1 & 158 & 1 & 28818 & CD8b & 19 & RUL Gr 5       &  765000 & 10562333 & 7 & RUL & Old &  665 & 2 & 218\\\\\n",
       "\tArray7\\_29118 & 1 & 756 & 1 & 29118 & CD8a & 23 & LLL GR 39      &   10500 &  6466750 & 7 & LLL & New & 1532 & 4 & 175\\\\\n",
       "\tArray7\\_29618 & 1 & 226 & 1 & 29618 & IgG  & 23 & LLL gr 4       & 2340000 &  4976735 & 7 & LLL & Old & 1210 & 2 & 391\\\\\n",
       "\tArray7\\_30218 & 1 & 211 & 1 & 30218 & CD8b & 23 & RLL 15         &   11200 &  1670412 & 7 & RLL & New & 1499 & 3 & 742\\\\\n",
       "\tArray7\\_30418 & 1 & 400 & 1 & 30418 & IgG  & 18 & RLL Gr 15      &  204000 &   633245 & 7 & RLL & Old & 1155 & 3 & 362\\\\\n",
       "\tArray8\\_28818 & 1 & 433 & 1 & 28818 & CD8b & 19 & RLL Gr 3       &   62400 & 10562333 & 8 & RLL & Old & 1178 & 2 & 223\\\\\n",
       "\tArray8\\_29118 & 1 & 912 & 1 & 29118 & CD8a & 23 & LLL GR 41      &   56400 &  6466750 & 8 & LLL & Old & 1573 & 4 & 110\\\\\n",
       "\tArray8\\_29618 & 1 &  96 & 1 & 29618 & IgG  & 23 & LLL gr 9       &  216000 &  4976735 & 8 & LLL & Old & 1214 & 2 & 748\\\\\n",
       "\tArray8\\_30218 & 1 & 135 & 1 & 30218 & CD8b & 23 & RLL 10         &   75600 &  1670412 & 8 & RLL & Old & 1528 & 3 & 536\\\\\n",
       "\tArray8\\_30418 & 1 & 329 & 1 & 30418 & IgG  & 18 & RLL Gr 7       &   17600 &   633245 & 8 & RLL & Old & 1315 & 3 & 599\\\\\n",
       "\tArray9\\_28818 & 1 & 691 & 1 & 28818 & CD8b & 19 & RLL Gr 7       &  535000 & 10562333 & 9 & RLL & Old & 1275 & 2 &  13\\\\\n",
       "\tArray9\\_29118 & 1 & 555 & 1 & 29118 & CD8a & 23 & LLL GR 48      &  588000 &  6466750 & 9 & LLL & Old & 1324 & 4 &  76\\\\\n",
       "\tArray9\\_29618 & 1 & 370 & 1 & 29618 & IgG  & 23 & LLL gr 12      &  618000 &  4976735 & 9 & LLL & Old & 1242 & 2 & 494\\\\\n",
       "\tArray9\\_30218 & 1 & 141 & 1 & 30218 & CD8b & 23 & RLL 18         &    5640 &  1670412 & 9 & RLL & New & 1328 & 3 & 788\\\\\n",
       "\tArray9\\_30418 & 1 & 297 & 1 & 30418 & IgG  & 18 & RLL Gr 6       &   11400 &   633245 & 9 & RLL & New & 1291 & 3 & 406\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$counts\n",
       ":   \n",
       "A matrix: 4 × 40 of type dbl\n",
       "\n",
       "| <!--/--> | Array1_28918 | Array1_29218 | Array1_29318 | Array1_29818 | Array1_30518 | Array2_28918 | Array2_29218 | Array2_29318 | Array2_29818 | Array2_30518 | ⋯ | Array8_28818 | Array8_29118 | Array8_29618 | Array8_30218 | Array8_30418 | Array9_28818 | Array9_29118 | Array9_29618 | Array9_30218 | Array9_30418 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Alv.Mac |  46 |  92 |  16 |  87 | 18 |  90 | 60 | 149 |  5 |  97 | ⋯ |  69 |  74 |  6 |  1 |  25 |  71 |  24 |  16 | 24 |  34 |\n",
       "| Inflam.Mac |   1 |  53 | 127 | 163 | 47 |  20 | 27 | 148 | 95 |  68 | ⋯ |  49 | 306 | 33 | 65 |  45 | 141 | 154 |  75 | 67 |  88 |\n",
       "| Mac |  19 | 112 | 144 | 435 | 74 | 171 | 89 | 573 | 85 | 371 | ⋯ | 278 | 416 | 32 | 28 | 221 | 459 | 323 | 222 | 29 | 149 |\n",
       "| Monocyte | 115 | 141 | 153 |  69 | 26 |  39 | 83 |  71 | 74 |  32 | ⋯ |  37 | 116 | 25 | 41 |  38 |  20 |  54 |  57 | 21 |  26 |\n",
       "\n",
       "\n",
       "$samples\n",
       ":   \n",
       "A data.frame: 40 × 15\n",
       "\n",
       "| <!--/--> | group &lt;fct&gt; | lib.size &lt;dbl&gt; | norm.factors &lt;dbl&gt; | M.Number &lt;fct&gt; | treatment &lt;chr&gt; | infection.dose &lt;int&gt; | Tissue.name &lt;chr&gt; | CFU.granuloma &lt;int&gt; | total.thoracic.CFU &lt;int&gt; | Array &lt;int&gt; | lung.region &lt;chr&gt; | Gran.State &lt;chr&gt; | total_cells_elbow &lt;int&gt; | monkey_condition_categories &lt;fct&gt; | tcell.count &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Array1_28918 | 1 | 181 | 1 | 28918 | CD8a | 19 | RLL Gr 2       |     260 |  5301080 | 1 | RLL | New | 1154 | 1 | 606 |\n",
       "| Array1_29218 | 1 | 398 | 1 | 29218 | CD8a | 23 | LLL gr 3       |     390 |  2960670 | 1 | LLL | New | 1219 | 2 | 611 |\n",
       "| Array1_29318 | 1 | 440 | 1 | 29318 | CD8a | 18 | RUL C          |   59000 | 19228424 | 1 | RUL | New | 1390 | 3 | 550 |\n",
       "| Array1_29818 | 1 | 754 | 1 | 29818 | IgG  | 23 | ACC GR 1       |  610000 |  4066743 | 1 | ACC | Old | 1583 | 1 |  77 |\n",
       "| Array1_30518 | 1 | 165 | 1 | 30518 | CD8b | 18 | RUL Gr 14      |    2025 |  3895065 | 1 | RUL | New | 1284 | 1 | 705 |\n",
       "| Array2_28918 | 1 | 320 | 1 | 28918 | CD8a | 19 | Acc Gr 1       |     580 |  5301080 | 2 | ACC | New | 1073 | 1 | 506 |\n",
       "| Array2_29218 | 1 | 259 | 1 | 29218 | CD8a | 23 | ACC gr 3       |     570 |  2960670 | 2 | ACC | New | 1252 | 2 | 693 |\n",
       "| Array2_29318 | 1 | 941 | 1 | 29318 | CD8a | 18 | LLL 5          |   65200 | 19228424 | 2 | LLL | New | 1503 | 3 |  63 |\n",
       "| Array2_29818 | 1 | 259 | 1 | 29818 | IgG  | 23 | ACC GR 2       |  224000 |  4066743 | 2 | ACC | Old | 1542 | 1 | 124 |\n",
       "| Array2_30518 | 1 | 568 | 1 | 30518 | CD8b | 18 | RLL Gr 44      |   34800 |  3895065 | 2 | RLL | New | 1212 | 1 | 323 |\n",
       "| Array3_28918 | 1 | 712 | 1 | 28918 | CD8a | 19 | LUL GR 1       |  108000 |  5301080 | 3 | LUL | Old | 1262 | 1 |  87 |\n",
       "| Array3_29218 | 1 | 465 | 1 | 29218 | CD8a | 23 | RLL 7 Cluster  |  414000 |  2960670 | 3 | RLL | Old | 1219 | 2 | 245 |\n",
       "| Array3_29318 | 1 | 695 | 1 | 29318 | CD8a | 18 | LML B          |    4400 | 19228424 | 3 | LML | New | 1251 | 3 | 275 |\n",
       "| Array3_29818 | 1 | 158 | 1 | 29818 | IgG  | 23 | ACC GR 15      |    5680 |  4066743 | 3 | ACC | New | 1136 | 1 | 358 |\n",
       "| Array3_30518 | 1 | 620 | 1 | 30518 | CD8b | 18 | RLL Gr 38      |  151800 |  3895065 | 3 | RLL | New | 1284 | 1 | 259 |\n",
       "| Array4_28918 | 1 | 617 | 1 | 28918 | CD8a | 19 | LUL Gr 4       |   84000 |  5301080 | 4 | LUL | Old | 1136 | 1 | 168 |\n",
       "| Array4_29218 | 1 | 320 | 1 | 29218 | CD8a | 23 | RLL gr 17      |    1470 |  2960670 | 4 | RLL | Old | 1184 | 2 | 425 |\n",
       "| Array4_29318 | 1 | 375 | 1 | 29318 | CD8a | 18 | LLL 13         |  149600 | 19228424 | 4 | LLL | New | 1211 | 3 | 559 |\n",
       "| Array4_29818 | 1 |  21 | 1 | 29818 | IgG  | 23 | ACC GR 11      |  224000 |  4066743 | 4 | RLL | Old |  572 | 1 |  63 |\n",
       "| Array4_30518 | 1 | 533 | 1 | 30518 | CD8b | 18 | RLL Gr 35      |  106800 |  3895065 | 4 | RLL | Old | 1320 | 1 | 451 |\n",
       "| Array5_28918 | 1 | 806 | 1 | 28918 | CD8a | 19 | LLL Gr 20      |   17800 |  5301080 | 5 | LLL | New | 1371 | 1 |  36 |\n",
       "| Array6_28818 | 1 | 602 | 1 | 28818 | CD8b | 19 | RUL Gr 3       |   37200 | 10562333 | 6 | RUL | New | 1463 | 2 |  75 |\n",
       "| Array6_29118 | 1 | 748 | 1 | 29118 | CD8a | 23 | LLL GR 12      |  184000 |  6466750 | 6 | LLL | Old | 1537 | 4 |  79 |\n",
       "| Array6_30218 | 1 | 318 | 1 | 30218 | CD8b | 23 | RLL 25         |    6200 |  1670412 | 6 | RLL | New | 1449 | 3 | 510 |\n",
       "| Array6_30418 | 1 | 417 | 1 | 30418 | IgG  | 18 | RLL Gr 13      |   34500 |   633245 | 6 | RLL | Old | 1248 | 3 | 424 |\n",
       "| Array7_28818 | 1 | 158 | 1 | 28818 | CD8b | 19 | RUL Gr 5       |  765000 | 10562333 | 7 | RUL | Old |  665 | 2 | 218 |\n",
       "| Array7_29118 | 1 | 756 | 1 | 29118 | CD8a | 23 | LLL GR 39      |   10500 |  6466750 | 7 | LLL | New | 1532 | 4 | 175 |\n",
       "| Array7_29618 | 1 | 226 | 1 | 29618 | IgG  | 23 | LLL gr 4       | 2340000 |  4976735 | 7 | LLL | Old | 1210 | 2 | 391 |\n",
       "| Array7_30218 | 1 | 211 | 1 | 30218 | CD8b | 23 | RLL 15         |   11200 |  1670412 | 7 | RLL | New | 1499 | 3 | 742 |\n",
       "| Array7_30418 | 1 | 400 | 1 | 30418 | IgG  | 18 | RLL Gr 15      |  204000 |   633245 | 7 | RLL | Old | 1155 | 3 | 362 |\n",
       "| Array8_28818 | 1 | 433 | 1 | 28818 | CD8b | 19 | RLL Gr 3       |   62400 | 10562333 | 8 | RLL | Old | 1178 | 2 | 223 |\n",
       "| Array8_29118 | 1 | 912 | 1 | 29118 | CD8a | 23 | LLL GR 41      |   56400 |  6466750 | 8 | LLL | Old | 1573 | 4 | 110 |\n",
       "| Array8_29618 | 1 |  96 | 1 | 29618 | IgG  | 23 | LLL gr 9       |  216000 |  4976735 | 8 | LLL | Old | 1214 | 2 | 748 |\n",
       "| Array8_30218 | 1 | 135 | 1 | 30218 | CD8b | 23 | RLL 10         |   75600 |  1670412 | 8 | RLL | Old | 1528 | 3 | 536 |\n",
       "| Array8_30418 | 1 | 329 | 1 | 30418 | IgG  | 18 | RLL Gr 7       |   17600 |   633245 | 8 | RLL | Old | 1315 | 3 | 599 |\n",
       "| Array9_28818 | 1 | 691 | 1 | 28818 | CD8b | 19 | RLL Gr 7       |  535000 | 10562333 | 9 | RLL | Old | 1275 | 2 |  13 |\n",
       "| Array9_29118 | 1 | 555 | 1 | 29118 | CD8a | 23 | LLL GR 48      |  588000 |  6466750 | 9 | LLL | Old | 1324 | 4 |  76 |\n",
       "| Array9_29618 | 1 | 370 | 1 | 29618 | IgG  | 23 | LLL gr 12      |  618000 |  4976735 | 9 | LLL | Old | 1242 | 2 | 494 |\n",
       "| Array9_30218 | 1 | 141 | 1 | 30218 | CD8b | 23 | RLL 18         |    5640 |  1670412 | 9 | RLL | New | 1328 | 3 | 788 |\n",
       "| Array9_30418 | 1 | 297 | 1 | 30418 | IgG  | 18 | RLL Gr 6       |   11400 |   633245 | 9 | RLL | New | 1291 | 3 | 406 |\n",
       "\n",
       "\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "An object of class \"DGEList\"\n",
       "$counts\n",
       "           Array1_28918 Array1_29218 Array1_29318 Array1_29818 Array1_30518\n",
       "Alv.Mac              46           92           16           87           18\n",
       "Inflam.Mac            1           53          127          163           47\n",
       "Mac                  19          112          144          435           74\n",
       "Monocyte            115          141          153           69           26\n",
       "           Array2_28918 Array2_29218 Array2_29318 Array2_29818 Array2_30518\n",
       "Alv.Mac              90           60          149            5           97\n",
       "Inflam.Mac           20           27          148           95           68\n",
       "Mac                 171           89          573           85          371\n",
       "Monocyte             39           83           71           74           32\n",
       "           Array3_28918 Array3_29218 Array3_29318 Array3_29818 Array3_30518\n",
       "Alv.Mac              74           25           84           12           32\n",
       "Inflam.Mac          294          175          150           33          161\n",
       "Mac                 248          120          165           90          358\n",
       "Monocyte             96          145          296           23           69\n",
       "           Array4_28918 Array4_29218 Array4_29318 Array4_29818 Array4_30518\n",
       "Alv.Mac              57           77           23            1           19\n",
       "Inflam.Mac           95           59          103            7          196\n",
       "Mac                 405           77          152           11          248\n",
       "Monocyte             60          107           97            2           70\n",
       "           Array5_28918 Array6_28818 Array6_29118 Array6_30218 Array6_30418\n",
       "Alv.Mac              58           20           21            7            6\n",
       "Inflam.Mac           75          117          272          144          179\n",
       "Mac                 636          339          321           80          197\n",
       "Monocyte             37          126          134           87           35\n",
       "           Array7_28818 Array7_29118 Array7_29618 Array7_30218 Array7_30418\n",
       "Alv.Mac              20           28            7           20           28\n",
       "Inflam.Mac           35          328          147           56           71\n",
       "Mac                  72          268           52           83          275\n",
       "Monocyte             31          132           20           52           26\n",
       "           Array8_28818 Array8_29118 Array8_29618 Array8_30218 Array8_30418\n",
       "Alv.Mac              69           74            6            1           25\n",
       "Inflam.Mac           49          306           33           65           45\n",
       "Mac                 278          416           32           28          221\n",
       "Monocyte             37          116           25           41           38\n",
       "           Array9_28818 Array9_29118 Array9_29618 Array9_30218 Array9_30418\n",
       "Alv.Mac              71           24           16           24           34\n",
       "Inflam.Mac          141          154           75           67           88\n",
       "Mac                 459          323          222           29          149\n",
       "Monocyte             20           54           57           21           26\n",
       "\n",
       "$samples\n",
       "             group lib.size norm.factors M.Number treatment infection.dose\n",
       "Array1_28918     1      181            1    28918      CD8a             19\n",
       "Array1_29218     1      398            1    29218      CD8a             23\n",
       "Array1_29318     1      440            1    29318      CD8a             18\n",
       "Array1_29818     1      754            1    29818       IgG             23\n",
       "Array1_30518     1      165            1    30518      CD8b             18\n",
       "             Tissue.name CFU.granuloma total.thoracic.CFU Array lung.region\n",
       "Array1_28918    RLL Gr 2           260            5301080     1         RLL\n",
       "Array1_29218    LLL gr 3           390            2960670     1         LLL\n",
       "Array1_29318       RUL C         59000           19228424     1         RUL\n",
       "Array1_29818    ACC GR 1        610000            4066743     1         ACC\n",
       "Array1_30518   RUL Gr 14          2025            3895065     1         RUL\n",
       "             Gran.State total_cells_elbow monkey_condition_categories\n",
       "Array1_28918        New              1154                           1\n",
       "Array1_29218        New              1219                           2\n",
       "Array1_29318        New              1390                           3\n",
       "Array1_29818        Old              1583                           1\n",
       "Array1_30518        New              1284                           1\n",
       "             tcell.count\n",
       "Array1_28918         606\n",
       "Array1_29218         611\n",
       "Array1_29318         550\n",
       "Array1_29818          77\n",
       "Array1_30518         705\n",
       "35 more rows ...\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.ab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "5edffa77",
   "metadata": {},
   "outputs": [],
   "source": [
    "#keep <- filterByExpr(y.ab, group=y.ab$samples$treatment)\n",
    "#y.ab <- y.ab[keep,]\n",
    "#summary(keep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "eeea3499",
   "metadata": {},
   "outputs": [],
   "source": [
    "#design <- model.matrix(~factor(monkey_condition_categories) + factor(treatment), y.ab$samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "1910bc64",
   "metadata": {},
   "outputs": [],
   "source": [
    "design <- model.matrix(~0 + factor(treatment), y.ab$samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "84e8763b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.3131  0.3131  0.3131  0.3131  0.3131  0.3131 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y.ab <- estimateDisp(y.ab, design, trend=\"none\")\n",
    "summary(y.ab$common.dispersion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8c6a99f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m3SbtX79MXkZcn2SfsYT8p8u/2kmWkXY+31nZ5WIHX68jo5AgQIECBAgMD8Amuu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\nIECAAAECBAgQIECAwEoC/xcnYRGXmQ8+BQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotBCV(y.ab, cex=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df9252fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "687964ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       " 0.9531  0.9531  0.9531  0.9531  0.9531  0.9531 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit.ab <- glmQLFit(y.ab, design, robust=TRUE, abundance.trend=FALSE)\n",
    "summary(fit.ab$var.prior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "7ab24630",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o9GgAABAgQItENgNQqkeixhob8lvjZF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\nAgQIECBAgAABAgQIENhQgf8PfFQcT8OQhFcAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotQLDisp(fit.ab, cex=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f74c3eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       1*factor(treatment)CD8a -1*factor(treatment)CD8b\n",
       "Down                                                  0\n",
       "NotSig                                                4\n",
       "Up                                                    0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res <- glmQLFTest(fit.ab, contrast=c(1,-1,0))\n",
    "summary(decideTests(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "a1474cb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 4 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Alv.Mac</th><td> 0.53130277</td><td>16.62451</td><td>1.53464615</td><td>0.2210600</td><td>0.7927024</td></tr>\n",
       "\t<tr><th scope=row>Monocyte</th><td> 0.26444240</td><td>17.52957</td><td>0.50099837</td><td>0.4822683</td><td>0.7927024</td></tr>\n",
       "\t<tr><th scope=row>Mac</th><td>-0.11266772</td><td>18.78120</td><td>0.16896286</td><td>0.6827489</td><td>0.7927024</td></tr>\n",
       "\t<tr><th scope=row>Inflam.Mac</th><td>-0.09406411</td><td>18.03095</td><td>0.06978987</td><td>0.7927024</td><td>0.7927024</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'1*factor(treatment)CD8b -1*factor(treatment)IgG'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 4 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tAlv.Mac &  0.53130277 & 16.62451 & 1.53464615 & 0.2210600 & 0.7927024\\\\\n",
       "\tMonocyte &  0.26444240 & 17.52957 & 0.50099837 & 0.4822683 & 0.7927024\\\\\n",
       "\tMac & -0.11266772 & 18.78120 & 0.16896286 & 0.6827489 & 0.7927024\\\\\n",
       "\tInflam.Mac & -0.09406411 & 18.03095 & 0.06978987 & 0.7927024 & 0.7927024\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] '1*factor(treatment)CD8b -1*factor(treatment)IgG'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 4 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Alv.Mac |  0.53130277 | 16.62451 | 1.53464615 | 0.2210600 | 0.7927024 |\n",
       "| Monocyte |  0.26444240 | 17.52957 | 0.50099837 | 0.4822683 | 0.7927024 |\n",
       "| Mac | -0.11266772 | 18.78120 | 0.16896286 | 0.6827489 | 0.7927024 |\n",
       "| Inflam.Mac | -0.09406411 | 18.03095 | 0.06978987 | 0.7927024 | 0.7927024 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   '1*factor(treatment)CD8b -1*factor(treatment)IgG'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  1*factor(treatment)CD8b -1*factor(treatment)IgG \n",
       "                 logFC   logCPM          F    PValue       FDR\n",
       "Alv.Mac     0.53130277 16.62451 1.53464615 0.2210600 0.7927024\n",
       "Monocyte    0.26444240 17.52957 0.50099837 0.4822683 0.7927024\n",
       "Mac        -0.11266772 18.78120 0.16896286 0.6827489 0.7927024\n",
       "Inflam.Mac -0.09406411 18.03095 0.06978987 0.7927024 0.7927024"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e7b3683b",
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- glmQLFTest(fit.ab, contrast=c(1,0,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "726c90a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       1*factor(treatment)CD8a -1*factor(treatment)IgG\n",
       "Down                                                 0\n",
       "NotSig                                               2\n",
       "Up                                                   2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(decideTests(res)) # CD8b vs CD8a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "eda6751d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 4 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Alv.Mac</th><td> 0.9891293</td><td>16.62451</td><td>6.010348</td><td>0.01766959</td><td>0.04686297</td></tr>\n",
       "\t<tr><th scope=row>Monocyte</th><td> 0.8207279</td><td>17.52957</td><td>5.457611</td><td>0.02343148</td><td>0.04686297</td></tr>\n",
       "\t<tr><th scope=row>Inflam.Mac</th><td>-0.4093752</td><td>18.03095</td><td>1.571147</td><td>0.21572831</td><td>0.26271509</td></tr>\n",
       "\t<tr><th scope=row>Mac</th><td>-0.2860261</td><td>18.78120</td><td>1.282445</td><td>0.26271509</td><td>0.26271509</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'1*factor(treatment)CD8a -1*factor(treatment)IgG'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 4 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tAlv.Mac &  0.9891293 & 16.62451 & 6.010348 & 0.01766959 & 0.04686297\\\\\n",
       "\tMonocyte &  0.8207279 & 17.52957 & 5.457611 & 0.02343148 & 0.04686297\\\\\n",
       "\tInflam.Mac & -0.4093752 & 18.03095 & 1.571147 & 0.21572831 & 0.26271509\\\\\n",
       "\tMac & -0.2860261 & 18.78120 & 1.282445 & 0.26271509 & 0.26271509\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] '1*factor(treatment)CD8a -1*factor(treatment)IgG'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 4 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Alv.Mac |  0.9891293 | 16.62451 | 6.010348 | 0.01766959 | 0.04686297 |\n",
       "| Monocyte |  0.8207279 | 17.52957 | 5.457611 | 0.02343148 | 0.04686297 |\n",
       "| Inflam.Mac | -0.4093752 | 18.03095 | 1.571147 | 0.21572831 | 0.26271509 |\n",
       "| Mac | -0.2860261 | 18.78120 | 1.282445 | 0.26271509 | 0.26271509 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   '1*factor(treatment)CD8a -1*factor(treatment)IgG'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  1*factor(treatment)CD8a -1*factor(treatment)IgG \n",
       "                logFC   logCPM        F     PValue        FDR\n",
       "Alv.Mac     0.9891293 16.62451 6.010348 0.01766959 0.04686297\n",
       "Monocyte    0.8207279 17.52957 5.457611 0.02343148 0.04686297\n",
       "Inflam.Mac -0.4093752 18.03095 1.571147 0.21572831 0.26271509\n",
       "Mac        -0.2860261 18.78120 1.282445 0.26271509 0.26271509"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "5a8cb7ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- glmQLFTest(fit.ab, contrast=c(0,1,-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "d0eeafbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       1*factor(treatment)CD8b -1*factor(treatment)IgG\n",
       "Down                                                 0\n",
       "NotSig                                               4\n",
       "Up                                                   0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(decideTests(res)) # CD8b vs IgG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "547b3da3",
   "metadata": {},
   "outputs": [],
   "source": [
    "res <- glmQLFTest(fit.ab,  coef = 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b955139b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       factor(treatment)IgG\n",
       "Down                      3\n",
       "NotSig                    1\n",
       "Up                        1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summary(decideTests(res)) #IgG vs CD8a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ed4bc001",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table class=\"dataframe\">\n",
       "<caption>A data.frame: 5 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Inf.Mac</th><td> 1.1503792</td><td>18.33521</td><td>12.648417</td><td>0.0009804507</td><td>0.004902254</td></tr>\n",
       "\t<tr><th scope=row>CD16.MO</th><td>-1.6764240</td><td>16.01412</td><td>10.683568</td><td>0.0022187967</td><td>0.005546992</td></tr>\n",
       "\t<tr><th scope=row>CD14..MO</th><td>-1.4184716</td><td>16.34509</td><td> 7.486028</td><td>0.0092118407</td><td>0.015353068</td></tr>\n",
       "\t<tr><th scope=row>Alv.Mac</th><td>-1.0972233</td><td>17.60552</td><td> 6.064766</td><td>0.0181709296</td><td>0.022713662</td></tr>\n",
       "\t<tr><th scope=row>Mac</th><td> 0.4437221</td><td>18.34385</td><td> 2.250149</td><td>0.1414162029</td><td>0.141416203</td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'factor(treatment)IgG'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] A data.frame: 5 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & logFC & logCPM & F & PValue & FDR\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tInf.Mac &  1.1503792 & 18.33521 & 12.648417 & 0.0009804507 & 0.004902254\\\\\n",
       "\tCD16.MO & -1.6764240 & 16.01412 & 10.683568 & 0.0022187967 & 0.005546992\\\\\n",
       "\tCD14..MO & -1.4184716 & 16.34509 &  7.486028 & 0.0092118407 & 0.015353068\\\\\n",
       "\tAlv.Mac & -1.0972233 & 17.60552 &  6.064766 & 0.0181709296 & 0.022713662\\\\\n",
       "\tMac &  0.4437221 & 18.34385 &  2.250149 & 0.1414162029 & 0.141416203\\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] 'factor(treatment)IgG'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "A data.frame: 5 × 5\n",
       "\n",
       "| <!--/--> | logFC &lt;dbl&gt; | logCPM &lt;dbl&gt; | F &lt;dbl&gt; | PValue &lt;dbl&gt; | FDR &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| Inf.Mac |  1.1503792 | 18.33521 | 12.648417 | 0.0009804507 | 0.004902254 |\n",
       "| CD16.MO | -1.6764240 | 16.01412 | 10.683568 | 0.0022187967 | 0.005546992 |\n",
       "| CD14..MO | -1.4184716 | 16.34509 |  7.486028 | 0.0092118407 | 0.015353068 |\n",
       "| Alv.Mac | -1.0972233 | 17.60552 |  6.064766 | 0.0181709296 | 0.022713662 |\n",
       "| Mac |  0.4437221 | 18.34385 |  2.250149 | 0.1414162029 | 0.141416203 |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   'factor(treatment)IgG'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  factor(treatment)IgG \n",
       "              logFC   logCPM         F       PValue         FDR\n",
       "Inf.Mac   1.1503792 18.33521 12.648417 0.0009804507 0.004902254\n",
       "CD16.MO  -1.6764240 16.01412 10.683568 0.0022187967 0.005546992\n",
       "CD14..MO -1.4184716 16.34509  7.486028 0.0092118407 0.015353068\n",
       "Alv.Mac  -1.0972233 17.60552  6.064766 0.0181709296 0.022713662\n",
       "Mac       0.4437221 18.34385  2.250149 0.1414162029 0.141416203"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topTags(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1428258c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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