Osmotic_Stress_RNAseq_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e60c8c0d",
   "metadata": {},
   "source": [
    "## Analysis of Osmotic Stress RNA-seq data\n",
    "\n",
    "This document is a reproducible record of the various steps in the analysis of osmotic stress RNA-seq data for RPE-1 cells."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a1797ca",
   "metadata": {},
   "source": [
    "### Necessary Modules and R Packages\n",
    "\n",
    "Apart from `r-base` and `r-essentials` set of packages the following sets of additional packages are necessary.\n",
    "\n",
    "- NMF\n",
    "- ggplot2\n",
    "- locfit (there may be some complexities in anaconda install)\n",
    "\n",
    "From [Bioconductor](https://bioconductor.org) the following are needed (installed with `BiocManager::install()`)\n",
    "\n",
    "- DESeq2\n",
    "- limma\n",
    "- vsn\n",
    "- gplots"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97b2d805",
   "metadata": {},
   "source": [
    "### Load relevant libraries\n",
    "\n",
    "First we will load the necessary libraries as below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dd072772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol>\n",
       "\t<li>TRUE</li>\n",
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       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
       "\t<li>TRUE</li>\n",
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      "text/latex": [
       "\\begin{enumerate}\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
       "\\item TRUE\n",
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      "text/markdown": [
       "1. TRUE\n",
       "2. TRUE\n",
       "3. TRUE\n",
       "4. TRUE\n",
       "5. TRUE\n",
       "6. TRUE\n",
       "\n",
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      "text/plain": [
       "[[1]]\n",
       "[1] TRUE\n",
       "\n",
       "[[2]]\n",
       "[1] TRUE\n",
       "\n",
       "[[3]]\n",
       "[1] TRUE\n",
       "\n",
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       "\n",
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       "[1] TRUE\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Libraries <- c(\"limma\",\"vsn\",\"gplots\",\"ggplot2\",\"DESeq2\",\"stringr\")\n",
    "suppressMessages(lapply(Libraries, require, character.only=T))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d6e2682",
   "metadata": {},
   "source": [
    "### Loading Count Data\n",
    "\n",
    "Next we will load the count data from the tab separated file as below and inspect it. Then we will rename the rows based on gene ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5e6a6578",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>st_gene_id</th><th scope=col>gene_id</th><th scope=col>gene_symbol</th><th scope=col>read_count_Media_0h_1</th><th scope=col>read_count_Media_0h_2</th><th scope=col>read_count_Media_0h_3</th><th scope=col>read_count_Media_6h_1</th><th scope=col>read_count_Media_6h_2</th><th scope=col>read_count_Media_6h_3</th><th scope=col>read_count_Media_12h_1</th><th scope=col>...</th><th scope=col>read_count_Hyper_12h_3</th><th scope=col>read_count_Hyper_24h_1</th><th scope=col>read_count_Hyper_24h_2</th><th scope=col>read_count_Hyper_24h_3</th><th scope=col>read_count_Hyper_48h_1</th><th scope=col>read_count_Hyper_48h_2</th><th scope=col>read_count_Hyper_48h_3</th><th scope=col>read_count_Hyper_72h_1</th><th scope=col>read_count_Hyper_72h_2</th><th scope=col>read_count_Hyper_72h_3</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>G9606_9266  </td><td>     2323   </td><td>FLT3LG      </td><td> 273        </td><td> 292.0      </td><td> 221        </td><td> 218        </td><td> 207        </td><td> 219        </td><td> 267        </td><td>...         </td><td> 276        </td><td> 350        </td><td> 307        </td><td> 298        </td><td> 244        </td><td> 342        </td><td> 235        </td><td> 291        </td><td> 260.00     </td><td> 325        </td></tr>\n",
       "\t<tr><td>G9606_9392  </td><td>    90459   </td><td>ERI1        </td><td> 535        </td><td> 611.0      </td><td> 520        </td><td> 590        </td><td> 539        </td><td> 526        </td><td> 570        </td><td>...         </td><td> 401        </td><td> 507        </td><td> 486        </td><td> 378        </td><td> 507        </td><td> 425        </td><td> 511        </td><td> 339        </td><td> 366.00     </td><td> 334        </td></tr>\n",
       "\t<tr><td>G9606_4009  </td><td>    79595   </td><td>SAP130      </td><td>3580        </td><td>4097.0      </td><td>3234        </td><td>3931        </td><td>4068        </td><td>3823        </td><td>4216        </td><td>...         </td><td>3136        </td><td>3152        </td><td>3098        </td><td>2977        </td><td>3611        </td><td>3324        </td><td>3384        </td><td>2956        </td><td>2775.00     </td><td>2763        </td></tr>\n",
       "\t<tr><td>G9606_24999 </td><td>100129098   </td><td>LOC100129098</td><td>   0        </td><td>   0.0      </td><td>   0        </td><td>   1        </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>   0.00     </td><td>   0        </td></tr>\n",
       "\t<tr><td>G9606_4181  </td><td>    10927   </td><td>SPIN1       </td><td>1809        </td><td>1690.0      </td><td>1607        </td><td>2423        </td><td>1866        </td><td>1927        </td><td>2343        </td><td>...         </td><td>2636        </td><td>2022        </td><td>1906        </td><td>1731        </td><td>2707        </td><td>2232        </td><td>2312        </td><td>2084        </td><td>2193.00     </td><td>1778        </td></tr>\n",
       "\t<tr><td>G9606_16727 </td><td>     8879   </td><td>SGPL1       </td><td>1619        </td><td>1526.1      </td><td>1642        </td><td>1636        </td><td>1613        </td><td>1526        </td><td>1719        </td><td>...         </td><td>1911        </td><td>1634        </td><td>1604        </td><td>1450        </td><td>1779        </td><td>1359        </td><td>1418        </td><td>1634        </td><td>1480.99     </td><td>1519        </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
       " st\\_gene\\_id & gene\\_id & gene\\_symbol & read\\_count\\_Media\\_0h\\_1 & read\\_count\\_Media\\_0h\\_2 & read\\_count\\_Media\\_0h\\_3 & read\\_count\\_Media\\_6h\\_1 & read\\_count\\_Media\\_6h\\_2 & read\\_count\\_Media\\_6h\\_3 & read\\_count\\_Media\\_12h\\_1 & ... & read\\_count\\_Hyper\\_12h\\_3 & read\\_count\\_Hyper\\_24h\\_1 & read\\_count\\_Hyper\\_24h\\_2 & read\\_count\\_Hyper\\_24h\\_3 & read\\_count\\_Hyper\\_48h\\_1 & read\\_count\\_Hyper\\_48h\\_2 & read\\_count\\_Hyper\\_48h\\_3 & read\\_count\\_Hyper\\_72h\\_1 & read\\_count\\_Hyper\\_72h\\_2 & read\\_count\\_Hyper\\_72h\\_3\\\\\n",
       "\\hline\n",
       "\t G9606\\_9266  &      2323     & FLT3LG        &  273          &  292.0        &  221          &  218          &  207          &  219          &  267          & ...           &  276          &  350          &  307          &  298          &  244          &  342          &  235          &  291          &  260.00       &  325         \\\\\n",
       "\t G9606\\_9392  &     90459     & ERI1          &  535          &  611.0        &  520          &  590          &  539          &  526          &  570          & ...           &  401          &  507          &  486          &  378          &  507          &  425          &  511          &  339          &  366.00       &  334         \\\\\n",
       "\t G9606\\_4009  &     79595     & SAP130        & 3580          & 4097.0        & 3234          & 3931          & 4068          & 3823          & 4216          & ...           & 3136          & 3152          & 3098          & 2977          & 3611          & 3324          & 3384          & 2956          & 2775.00       & 2763         \\\\\n",
       "\t G9606\\_24999 & 100129098     & LOC100129098  &    0          &    0.0        &    0          &    1          &    0          &    0          &    0          & ...           &    0          &    0          &    0          &    0          &    0          &    0          &    0          &    0          &    0.00       &    0         \\\\\n",
       "\t G9606\\_4181  &     10927     & SPIN1         & 1809          & 1690.0        & 1607          & 2423          & 1866          & 1927          & 2343          & ...           & 2636          & 2022          & 1906          & 1731          & 2707          & 2232          & 2312          & 2084          & 2193.00       & 1778         \\\\\n",
       "\t G9606\\_16727 &      8879     & SGPL1         & 1619          & 1526.1        & 1642          & 1636          & 1613          & 1526          & 1719          & ...           & 1911          & 1634          & 1604          & 1450          & 1779          & 1359          & 1418          & 1634          & 1480.99       & 1519         \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| st_gene_id | gene_id | gene_symbol | read_count_Media_0h_1 | read_count_Media_0h_2 | read_count_Media_0h_3 | read_count_Media_6h_1 | read_count_Media_6h_2 | read_count_Media_6h_3 | read_count_Media_12h_1 | ... | read_count_Hyper_12h_3 | read_count_Hyper_24h_1 | read_count_Hyper_24h_2 | read_count_Hyper_24h_3 | read_count_Hyper_48h_1 | read_count_Hyper_48h_2 | read_count_Hyper_48h_3 | read_count_Hyper_72h_1 | read_count_Hyper_72h_2 | read_count_Hyper_72h_3 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| G9606_9266   |      2323    | FLT3LG       |  273         |  292.0       |  221         |  218         |  207         |  219         |  267         | ...          |  276         |  350         |  307         |  298         |  244         |  342         |  235         |  291         |  260.00      |  325         |\n",
       "| G9606_9392   |     90459    | ERI1         |  535         |  611.0       |  520         |  590         |  539         |  526         |  570         | ...          |  401         |  507         |  486         |  378         |  507         |  425         |  511         |  339         |  366.00      |  334         |\n",
       "| G9606_4009   |     79595    | SAP130       | 3580         | 4097.0       | 3234         | 3931         | 4068         | 3823         | 4216         | ...          | 3136         | 3152         | 3098         | 2977         | 3611         | 3324         | 3384         | 2956         | 2775.00      | 2763         |\n",
       "| G9606_24999  | 100129098    | LOC100129098 |    0         |    0.0       |    0         |    1         |    0         |    0         |    0         | ...          |    0         |    0         |    0         |    0         |    0         |    0         |    0         |    0         |    0.00      |    0         |\n",
       "| G9606_4181   |     10927    | SPIN1        | 1809         | 1690.0       | 1607         | 2423         | 1866         | 1927         | 2343         | ...          | 2636         | 2022         | 1906         | 1731         | 2707         | 2232         | 2312         | 2084         | 2193.00      | 1778         |\n",
       "| G9606_16727  |      8879    | SGPL1        | 1619         | 1526.1       | 1642         | 1636         | 1613         | 1526         | 1719         | ...          | 1911         | 1634         | 1604         | 1450         | 1779         | 1359         | 1418         | 1634         | 1480.99      | 1519         |\n",
       "\n"
      ],
      "text/plain": [
       "  st_gene_id  gene_id   gene_symbol  read_count_Media_0h_1\n",
       "1 G9606_9266       2323 FLT3LG        273                 \n",
       "2 G9606_9392      90459 ERI1          535                 \n",
       "3 G9606_4009      79595 SAP130       3580                 \n",
       "4 G9606_24999 100129098 LOC100129098    0                 \n",
       "5 G9606_4181      10927 SPIN1        1809                 \n",
       "6 G9606_16727      8879 SGPL1        1619                 \n",
       "  read_count_Media_0h_2 read_count_Media_0h_3 read_count_Media_6h_1\n",
       "1  292.0                 221                   218                 \n",
       "2  611.0                 520                   590                 \n",
       "3 4097.0                3234                  3931                 \n",
       "4    0.0                   0                     1                 \n",
       "5 1690.0                1607                  2423                 \n",
       "6 1526.1                1642                  1636                 \n",
       "  read_count_Media_6h_2 read_count_Media_6h_3 read_count_Media_12h_1 ...\n",
       "1  207                   219                   267                   ...\n",
       "2  539                   526                   570                   ...\n",
       "3 4068                  3823                  4216                   ...\n",
       "4    0                     0                     0                   ...\n",
       "5 1866                  1927                  2343                   ...\n",
       "6 1613                  1526                  1719                   ...\n",
       "  read_count_Hyper_12h_3 read_count_Hyper_24h_1 read_count_Hyper_24h_2\n",
       "1  276                    350                    307                  \n",
       "2  401                    507                    486                  \n",
       "3 3136                   3152                   3098                  \n",
       "4    0                      0                      0                  \n",
       "5 2636                   2022                   1906                  \n",
       "6 1911                   1634                   1604                  \n",
       "  read_count_Hyper_24h_3 read_count_Hyper_48h_1 read_count_Hyper_48h_2\n",
       "1  298                    244                    342                  \n",
       "2  378                    507                    425                  \n",
       "3 2977                   3611                   3324                  \n",
       "4    0                      0                      0                  \n",
       "5 1731                   2707                   2232                  \n",
       "6 1450                   1779                   1359                  \n",
       "  read_count_Hyper_48h_3 read_count_Hyper_72h_1 read_count_Hyper_72h_2\n",
       "1  235                    291                    260.00               \n",
       "2  511                    339                    366.00               \n",
       "3 3384                   2956                   2775.00               \n",
       "4    0                      0                      0.00               \n",
       "5 2312                   2084                   2193.00               \n",
       "6 1418                   1634                   1480.99               \n",
       "  read_count_Hyper_72h_3\n",
       "1  325                  \n",
       "2  334                  \n",
       "3 2763                  \n",
       "4    0                  \n",
       "5 1778                  \n",
       "6 1519                  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "read.counts <- read.table(\"gene_expression_organized_2.txt\", sep = \"\", header = T)\n",
    "head(read.counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5960eea4",
   "metadata": {},
   "source": [
    "### Preprocess the data\n",
    "\n",
    "Next we will remove some of the duplicate gene names in the data and assign the rownames to the table. We will also remove the non-numerical data so that we can import it directly to DESeq."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d10001b",
   "metadata": {},
   "outputs": [],
   "source": [
    "read.counts <- read.counts[!duplicated(read.counts$gene_symbol),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4b3d05b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "row.names(read.counts) <- read.counts$gene_symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "723d80f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>read_count_Media_0h_1</th><th scope=col>read_count_Media_0h_2</th><th scope=col>read_count_Media_0h_3</th><th scope=col>read_count_Media_6h_1</th><th scope=col>read_count_Media_6h_2</th><th scope=col>read_count_Media_6h_3</th><th scope=col>read_count_Media_12h_1</th><th scope=col>read_count_Media_12h_2</th><th scope=col>read_count_Media_12h_3</th><th scope=col>read_count_Media_24h_1</th><th scope=col>...</th><th scope=col>read_count_Hyper_12h_3</th><th scope=col>read_count_Hyper_24h_1</th><th scope=col>read_count_Hyper_24h_2</th><th scope=col>read_count_Hyper_24h_3</th><th scope=col>read_count_Hyper_48h_1</th><th scope=col>read_count_Hyper_48h_2</th><th scope=col>read_count_Hyper_48h_3</th><th scope=col>read_count_Hyper_72h_1</th><th scope=col>read_count_Hyper_72h_2</th><th scope=col>read_count_Hyper_72h_3</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>FLT3LG</th><td> 273   </td><td> 292.0 </td><td> 221   </td><td> 218   </td><td> 207   </td><td> 219   </td><td> 267   </td><td> 207   </td><td> 206   </td><td> 233   </td><td>...    </td><td> 276   </td><td> 350   </td><td> 307   </td><td> 298   </td><td> 244   </td><td> 342   </td><td> 235   </td><td> 291   </td><td> 260.00</td><td> 325   </td></tr>\n",
       "\t<tr><th scope=row>ERI1</th><td> 535   </td><td> 611.0 </td><td> 520   </td><td> 590   </td><td> 539   </td><td> 526   </td><td> 570   </td><td> 474   </td><td> 531   </td><td> 577   </td><td>...    </td><td> 401   </td><td> 507   </td><td> 486   </td><td> 378   </td><td> 507   </td><td> 425   </td><td> 511   </td><td> 339   </td><td> 366.00</td><td> 334   </td></tr>\n",
       "\t<tr><th scope=row>SAP130</th><td>3580   </td><td>4097.0 </td><td>3234   </td><td>3931   </td><td>4068   </td><td>3823   </td><td>4216   </td><td>4030   </td><td>3418   </td><td>3635   </td><td>...    </td><td>3136   </td><td>3152   </td><td>3098   </td><td>2977   </td><td>3611   </td><td>3324   </td><td>3384   </td><td>2956   </td><td>2775.00</td><td>2763   </td></tr>\n",
       "\t<tr><th scope=row>LOC100129098</th><td>   0   </td><td>   0.0 </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>...    </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>   0.00</td><td>   0   </td></tr>\n",
       "\t<tr><th scope=row>SPIN1</th><td>1809   </td><td>1690.0 </td><td>1607   </td><td>2423   </td><td>1866   </td><td>1927   </td><td>2343   </td><td>2001   </td><td>1988   </td><td>2393   </td><td>...    </td><td>2636   </td><td>2022   </td><td>1906   </td><td>1731   </td><td>2707   </td><td>2232   </td><td>2312   </td><td>2084   </td><td>2193.00</td><td>1778   </td></tr>\n",
       "\t<tr><th scope=row>SGPL1</th><td>1619   </td><td>1526.1 </td><td>1642   </td><td>1636   </td><td>1613   </td><td>1526   </td><td>1719   </td><td>1493   </td><td>1579   </td><td>1422   </td><td>...    </td><td>1911   </td><td>1634   </td><td>1604   </td><td>1450   </td><td>1779   </td><td>1359   </td><td>1418   </td><td>1634   </td><td>1480.99</td><td>1519   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
       "  & read\\_count\\_Media\\_0h\\_1 & read\\_count\\_Media\\_0h\\_2 & read\\_count\\_Media\\_0h\\_3 & read\\_count\\_Media\\_6h\\_1 & read\\_count\\_Media\\_6h\\_2 & read\\_count\\_Media\\_6h\\_3 & read\\_count\\_Media\\_12h\\_1 & read\\_count\\_Media\\_12h\\_2 & read\\_count\\_Media\\_12h\\_3 & read\\_count\\_Media\\_24h\\_1 & ... & read\\_count\\_Hyper\\_12h\\_3 & read\\_count\\_Hyper\\_24h\\_1 & read\\_count\\_Hyper\\_24h\\_2 & read\\_count\\_Hyper\\_24h\\_3 & read\\_count\\_Hyper\\_48h\\_1 & read\\_count\\_Hyper\\_48h\\_2 & read\\_count\\_Hyper\\_48h\\_3 & read\\_count\\_Hyper\\_72h\\_1 & read\\_count\\_Hyper\\_72h\\_2 & read\\_count\\_Hyper\\_72h\\_3\\\\\n",
       "\\hline\n",
       "\tFLT3LG &  273    &  292.0  &  221    &  218    &  207    &  219    &  267    &  207    &  206    &  233    & ...     &  276    &  350    &  307    &  298    &  244    &  342    &  235    &  291    &  260.00 &  325   \\\\\n",
       "\tERI1 &  535    &  611.0  &  520    &  590    &  539    &  526    &  570    &  474    &  531    &  577    & ...     &  401    &  507    &  486    &  378    &  507    &  425    &  511    &  339    &  366.00 &  334   \\\\\n",
       "\tSAP130 & 3580    & 4097.0  & 3234    & 3931    & 4068    & 3823    & 4216    & 4030    & 3418    & 3635    & ...     & 3136    & 3152    & 3098    & 2977    & 3611    & 3324    & 3384    & 2956    & 2775.00 & 2763   \\\\\n",
       "\tLOC100129098 &    0    &    0.0  &    0    &    1    &    0    &    0    &    0    &    0    &    0    &    0    & ...     &    0    &    0    &    0    &    0    &    0    &    0    &    0    &    0    &    0.00 &    0   \\\\\n",
       "\tSPIN1 & 1809    & 1690.0  & 1607    & 2423    & 1866    & 1927    & 2343    & 2001    & 1988    & 2393    & ...     & 2636    & 2022    & 1906    & 1731    & 2707    & 2232    & 2312    & 2084    & 2193.00 & 1778   \\\\\n",
       "\tSGPL1 & 1619    & 1526.1  & 1642    & 1636    & 1613    & 1526    & 1719    & 1493    & 1579    & 1422    & ...     & 1911    & 1634    & 1604    & 1450    & 1779    & 1359    & 1418    & 1634    & 1480.99 & 1519   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | read_count_Media_0h_1 | read_count_Media_0h_2 | read_count_Media_0h_3 | read_count_Media_6h_1 | read_count_Media_6h_2 | read_count_Media_6h_3 | read_count_Media_12h_1 | read_count_Media_12h_2 | read_count_Media_12h_3 | read_count_Media_24h_1 | ... | read_count_Hyper_12h_3 | read_count_Hyper_24h_1 | read_count_Hyper_24h_2 | read_count_Hyper_24h_3 | read_count_Hyper_48h_1 | read_count_Hyper_48h_2 | read_count_Hyper_48h_3 | read_count_Hyper_72h_1 | read_count_Hyper_72h_2 | read_count_Hyper_72h_3 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| FLT3LG |  273    |  292.0  |  221    |  218    |  207    |  219    |  267    |  207    |  206    |  233    | ...     |  276    |  350    |  307    |  298    |  244    |  342    |  235    |  291    |  260.00 |  325    |\n",
       "| ERI1 |  535    |  611.0  |  520    |  590    |  539    |  526    |  570    |  474    |  531    |  577    | ...     |  401    |  507    |  486    |  378    |  507    |  425    |  511    |  339    |  366.00 |  334    |\n",
       "| SAP130 | 3580    | 4097.0  | 3234    | 3931    | 4068    | 3823    | 4216    | 4030    | 3418    | 3635    | ...     | 3136    | 3152    | 3098    | 2977    | 3611    | 3324    | 3384    | 2956    | 2775.00 | 2763    |\n",
       "| LOC100129098 |    0    |    0.0  |    0    |    1    |    0    |    0    |    0    |    0    |    0    |    0    | ...     |    0    |    0    |    0    |    0    |    0    |    0    |    0    |    0    |    0.00 |    0    |\n",
       "| SPIN1 | 1809    | 1690.0  | 1607    | 2423    | 1866    | 1927    | 2343    | 2001    | 1988    | 2393    | ...     | 2636    | 2022    | 1906    | 1731    | 2707    | 2232    | 2312    | 2084    | 2193.00 | 1778    |\n",
       "| SGPL1 | 1619    | 1526.1  | 1642    | 1636    | 1613    | 1526    | 1719    | 1493    | 1579    | 1422    | ...     | 1911    | 1634    | 1604    | 1450    | 1779    | 1359    | 1418    | 1634    | 1480.99 | 1519    |\n",
       "\n"
      ],
      "text/plain": [
       "             read_count_Media_0h_1 read_count_Media_0h_2 read_count_Media_0h_3\n",
       "FLT3LG        273                   292.0                 221                 \n",
       "ERI1          535                   611.0                 520                 \n",
       "SAP130       3580                  4097.0                3234                 \n",
       "LOC100129098    0                     0.0                   0                 \n",
       "SPIN1        1809                  1690.0                1607                 \n",
       "SGPL1        1619                  1526.1                1642                 \n",
       "             read_count_Media_6h_1 read_count_Media_6h_2 read_count_Media_6h_3\n",
       "FLT3LG        218                   207                   219                 \n",
       "ERI1          590                   539                   526                 \n",
       "SAP130       3931                  4068                  3823                 \n",
       "LOC100129098    1                     0                     0                 \n",
       "SPIN1        2423                  1866                  1927                 \n",
       "SGPL1        1636                  1613                  1526                 \n",
       "             read_count_Media_12h_1 read_count_Media_12h_2\n",
       "FLT3LG        267                    207                  \n",
       "ERI1          570                    474                  \n",
       "SAP130       4216                   4030                  \n",
       "LOC100129098    0                      0                  \n",
       "SPIN1        2343                   2001                  \n",
       "SGPL1        1719                   1493                  \n",
       "             read_count_Media_12h_3 read_count_Media_24h_1 ...\n",
       "FLT3LG        206                    233                   ...\n",
       "ERI1          531                    577                   ...\n",
       "SAP130       3418                   3635                   ...\n",
       "LOC100129098    0                      0                   ...\n",
       "SPIN1        1988                   2393                   ...\n",
       "SGPL1        1579                   1422                   ...\n",
       "             read_count_Hyper_12h_3 read_count_Hyper_24h_1\n",
       "FLT3LG        276                    350                  \n",
       "ERI1          401                    507                  \n",
       "SAP130       3136                   3152                  \n",
       "LOC100129098    0                      0                  \n",
       "SPIN1        2636                   2022                  \n",
       "SGPL1        1911                   1634                  \n",
       "             read_count_Hyper_24h_2 read_count_Hyper_24h_3\n",
       "FLT3LG        307                    298                  \n",
       "ERI1          486                    378                  \n",
       "SAP130       3098                   2977                  \n",
       "LOC100129098    0                      0                  \n",
       "SPIN1        1906                   1731                  \n",
       "SGPL1        1604                   1450                  \n",
       "             read_count_Hyper_48h_1 read_count_Hyper_48h_2\n",
       "FLT3LG        244                    342                  \n",
       "ERI1          507                    425                  \n",
       "SAP130       3611                   3324                  \n",
       "LOC100129098    0                      0                  \n",
       "SPIN1        2707                   2232                  \n",
       "SGPL1        1779                   1359                  \n",
       "             read_count_Hyper_48h_3 read_count_Hyper_72h_1\n",
       "FLT3LG        235                    291                  \n",
       "ERI1          511                    339                  \n",
       "SAP130       3384                   2956                  \n",
       "LOC100129098    0                      0                  \n",
       "SPIN1        2312                   2084                  \n",
       "SGPL1        1418                   1634                  \n",
       "             read_count_Hyper_72h_2 read_count_Hyper_72h_3\n",
       "FLT3LG        260.00                 325                  \n",
       "ERI1          366.00                 334                  \n",
       "SAP130       2775.00                2763                  \n",
       "LOC100129098    0.00                   0                  \n",
       "SPIN1        2193.00                1778                  \n",
       "SGPL1        1480.99                1519                  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "read.counts <- read.counts[,-c(1:3)]\n",
    "head(read.counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5a004b7",
   "metadata": {},
   "source": [
    "### Preparation for DESeq Analysis\n",
    "\n",
    "Next we have to prepare the data for analysis using `DESeq2`. For this first we have to modify the names of the columns and create a factor vector from the column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3fb1e03d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Media_0h_1'</li>\n",
       "\t<li>'Media_0h_2'</li>\n",
       "\t<li>'Media_0h_3'</li>\n",
       "\t<li>'Media_6h_1'</li>\n",
       "\t<li>'Media_6h_2'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Media\\_0h\\_1'\n",
       "\\item 'Media\\_0h\\_2'\n",
       "\\item 'Media\\_0h\\_3'\n",
       "\\item 'Media\\_6h\\_1'\n",
       "\\item 'Media\\_6h\\_2'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Media_0h_1'\n",
       "2. 'Media_0h_2'\n",
       "3. 'Media_0h_3'\n",
       "4. 'Media_6h_1'\n",
       "5. 'Media_6h_2'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"Media_0h_1\" \"Media_0h_2\" \"Media_0h_3\" \"Media_6h_1\" \"Media_6h_2\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "read.counts.orig.names <- names(read.counts)\n",
    "names(read.counts) <- gsub(\"read_count_(.*)\", \"\\\\1\", read.counts.orig.names)\n",
    "names(read.counts)[1:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ac7d6d",
   "metadata": {},
   "source": [
    "### Creating condition vectors for samples and time\n",
    "\n",
    "The next step is to create separate condition vectors for different samples (osmolarity) and time intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1617d83f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>Media</li>\n",
       "\t<li>Media</li>\n",
       "\t<li>Media</li>\n",
       "\t<li>Media</li>\n",
       "\t<li>Media</li>\n",
       "</ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<ol class=list-inline>\n",
       "\t\t<li>'Hyper'</li>\n",
       "\t\t<li>'Hypo'</li>\n",
       "\t\t<li>'Iso'</li>\n",
       "\t\t<li>'Media'</li>\n",
       "\t</ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item Media\n",
       "\\item Media\n",
       "\\item Media\n",
       "\\item Media\n",
       "\\item Media\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item 'Hyper'\n",
       "\\item 'Hypo'\n",
       "\\item 'Iso'\n",
       "\\item 'Media'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. Media\n",
       "2. Media\n",
       "3. Media\n",
       "4. Media\n",
       "5. Media\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. 'Hyper'\n",
       "2. 'Hypo'\n",
       "3. 'Iso'\n",
       "4. 'Media'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] Media Media Media Media Media\n",
       "Levels: Hyper Hypo Iso Media"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_info <- data.frame(condition = gsub(\"([A-Za-z]+)_.*\", \"\\\\1\", names(read.counts)), row.names = names(read.counts))\n",
    "sample_info[1:5,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c31acbdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>0h</li>\n",
       "\t<li>0h</li>\n",
       "\t<li>0h</li>\n",
       "\t<li>6h</li>\n",
       "\t<li>6h</li>\n",
       "</ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<ol class=list-inline>\n",
       "\t\t<li>'0h'</li>\n",
       "\t\t<li>'12h'</li>\n",
       "\t\t<li>'24h'</li>\n",
       "\t\t<li>'48h'</li>\n",
       "\t\t<li>'6h'</li>\n",
       "\t\t<li>'72h'</li>\n",
       "\t</ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 0h\n",
       "\\item 0h\n",
       "\\item 0h\n",
       "\\item 6h\n",
       "\\item 6h\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item '0h'\n",
       "\\item '12h'\n",
       "\\item '24h'\n",
       "\\item '48h'\n",
       "\\item '6h'\n",
       "\\item '72h'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 0h\n",
       "2. 0h\n",
       "3. 0h\n",
       "4. 6h\n",
       "5. 6h\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. '0h'\n",
       "2. '12h'\n",
       "3. '24h'\n",
       "4. '48h'\n",
       "5. '6h'\n",
       "6. '72h'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 0h 0h 0h 6h 6h\n",
       "Levels: 0h 12h 24h 48h 6h 72h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Time information\n",
    "time_info <- data.frame(condition = gsub(\"[A-Za-z]+_([0-9]+h)_[0-9]\", \"\\\\1\", names(read.counts)), row.names = names(read.counts))\n",
    "time_info[1:5,]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f79df1a1",
   "metadata": {},
   "source": [
    "### Import Data to DESeq2 and inspect\n",
    "\n",
    "Next we are ready to import the data using DESeq2 and inspect to make sure that the import is successful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3aeb4e90",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "converting counts to integer mode\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DataFrame with 6 rows and 1 column\n",
       "           condition\n",
       "            <factor>\n",
       "Media_0h_1     Media\n",
       "Media_0h_2     Media\n",
       "Media_0h_3     Media\n",
       "Media_6h_1     Media\n",
       "Media_6h_2     Media\n",
       "Media_6h_3     Media"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DataFrame with 6 rows and 0 columns"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Media_0h_1</th><th scope=col>Media_0h_2</th><th scope=col>Media_0h_3</th><th scope=col>Media_6h_1</th><th scope=col>Media_6h_2</th><th scope=col>Media_6h_3</th><th scope=col>Media_12h_1</th><th scope=col>Media_12h_2</th><th scope=col>Media_12h_3</th><th scope=col>Media_24h_1</th><th scope=col>...</th><th scope=col>Hyper_12h_3</th><th scope=col>Hyper_24h_1</th><th scope=col>Hyper_24h_2</th><th scope=col>Hyper_24h_3</th><th scope=col>Hyper_48h_1</th><th scope=col>Hyper_48h_2</th><th scope=col>Hyper_48h_3</th><th scope=col>Hyper_72h_1</th><th scope=col>Hyper_72h_2</th><th scope=col>Hyper_72h_3</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>FLT3LG</th><td> 273</td><td> 292</td><td> 221</td><td> 218</td><td> 207</td><td> 219</td><td> 267</td><td> 207</td><td> 206</td><td> 233</td><td>... </td><td> 276</td><td> 350</td><td> 307</td><td> 298</td><td> 244</td><td> 342</td><td> 235</td><td> 291</td><td> 260</td><td> 325</td></tr>\n",
       "\t<tr><th scope=row>ERI1</th><td> 535</td><td> 611</td><td> 520</td><td> 590</td><td> 539</td><td> 526</td><td> 570</td><td> 474</td><td> 531</td><td> 577</td><td>... </td><td> 401</td><td> 507</td><td> 486</td><td> 378</td><td> 507</td><td> 425</td><td> 511</td><td> 339</td><td> 366</td><td> 334</td></tr>\n",
       "\t<tr><th scope=row>SAP130</th><td>3580</td><td>4097</td><td>3234</td><td>3931</td><td>4068</td><td>3823</td><td>4216</td><td>4030</td><td>3418</td><td>3635</td><td>... </td><td>3136</td><td>3152</td><td>3098</td><td>2977</td><td>3611</td><td>3324</td><td>3384</td><td>2956</td><td>2775</td><td>2763</td></tr>\n",
       "\t<tr><th scope=row>LOC100129098</th><td>   0</td><td>   0</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>... </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>SPIN1</th><td>1809</td><td>1690</td><td>1607</td><td>2423</td><td>1866</td><td>1927</td><td>2343</td><td>2001</td><td>1988</td><td>2393</td><td>... </td><td>2636</td><td>2022</td><td>1906</td><td>1731</td><td>2707</td><td>2232</td><td>2312</td><td>2084</td><td>2193</td><td>1778</td></tr>\n",
       "\t<tr><th scope=row>SGPL1</th><td>1619</td><td>1526</td><td>1642</td><td>1636</td><td>1613</td><td>1526</td><td>1719</td><td>1493</td><td>1579</td><td>1422</td><td>... </td><td>1911</td><td>1634</td><td>1604</td><td>1450</td><td>1779</td><td>1359</td><td>1418</td><td>1634</td><td>1481</td><td>1519</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
       "  & Media\\_0h\\_1 & Media\\_0h\\_2 & Media\\_0h\\_3 & Media\\_6h\\_1 & Media\\_6h\\_2 & Media\\_6h\\_3 & Media\\_12h\\_1 & Media\\_12h\\_2 & Media\\_12h\\_3 & Media\\_24h\\_1 & ... & Hyper\\_12h\\_3 & Hyper\\_24h\\_1 & Hyper\\_24h\\_2 & Hyper\\_24h\\_3 & Hyper\\_48h\\_1 & Hyper\\_48h\\_2 & Hyper\\_48h\\_3 & Hyper\\_72h\\_1 & Hyper\\_72h\\_2 & Hyper\\_72h\\_3\\\\\n",
       "\\hline\n",
       "\tFLT3LG &  273 &  292 &  221 &  218 &  207 &  219 &  267 &  207 &  206 &  233 & ...  &  276 &  350 &  307 &  298 &  244 &  342 &  235 &  291 &  260 &  325\\\\\n",
       "\tERI1 &  535 &  611 &  520 &  590 &  539 &  526 &  570 &  474 &  531 &  577 & ...  &  401 &  507 &  486 &  378 &  507 &  425 &  511 &  339 &  366 &  334\\\\\n",
       "\tSAP130 & 3580 & 4097 & 3234 & 3931 & 4068 & 3823 & 4216 & 4030 & 3418 & 3635 & ...  & 3136 & 3152 & 3098 & 2977 & 3611 & 3324 & 3384 & 2956 & 2775 & 2763\\\\\n",
       "\tLOC100129098 &    0 &    0 &    0 &    1 &    0 &    0 &    0 &    0 &    0 &    0 & ...  &    0 &    0 &    0 &    0 &    0 &    0 &    0 &    0 &    0 &    0\\\\\n",
       "\tSPIN1 & 1809 & 1690 & 1607 & 2423 & 1866 & 1927 & 2343 & 2001 & 1988 & 2393 & ...  & 2636 & 2022 & 1906 & 1731 & 2707 & 2232 & 2312 & 2084 & 2193 & 1778\\\\\n",
       "\tSGPL1 & 1619 & 1526 & 1642 & 1636 & 1613 & 1526 & 1719 & 1493 & 1579 & 1422 & ...  & 1911 & 1634 & 1604 & 1450 & 1779 & 1359 & 1418 & 1634 & 1481 & 1519\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Media_0h_1 | Media_0h_2 | Media_0h_3 | Media_6h_1 | Media_6h_2 | Media_6h_3 | Media_12h_1 | Media_12h_2 | Media_12h_3 | Media_24h_1 | ... | Hyper_12h_3 | Hyper_24h_1 | Hyper_24h_2 | Hyper_24h_3 | Hyper_48h_1 | Hyper_48h_2 | Hyper_48h_3 | Hyper_72h_1 | Hyper_72h_2 | Hyper_72h_3 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| FLT3LG |  273 |  292 |  221 |  218 |  207 |  219 |  267 |  207 |  206 |  233 | ...  |  276 |  350 |  307 |  298 |  244 |  342 |  235 |  291 |  260 |  325 |\n",
       "| ERI1 |  535 |  611 |  520 |  590 |  539 |  526 |  570 |  474 |  531 |  577 | ...  |  401 |  507 |  486 |  378 |  507 |  425 |  511 |  339 |  366 |  334 |\n",
       "| SAP130 | 3580 | 4097 | 3234 | 3931 | 4068 | 3823 | 4216 | 4030 | 3418 | 3635 | ...  | 3136 | 3152 | 3098 | 2977 | 3611 | 3324 | 3384 | 2956 | 2775 | 2763 |\n",
       "| LOC100129098 |    0 |    0 |    0 |    1 |    0 |    0 |    0 |    0 |    0 |    0 | ...  |    0 |    0 |    0 |    0 |    0 |    0 |    0 |    0 |    0 |    0 |\n",
       "| SPIN1 | 1809 | 1690 | 1607 | 2423 | 1866 | 1927 | 2343 | 2001 | 1988 | 2393 | ...  | 2636 | 2022 | 1906 | 1731 | 2707 | 2232 | 2312 | 2084 | 2193 | 1778 |\n",
       "| SGPL1 | 1619 | 1526 | 1642 | 1636 | 1613 | 1526 | 1719 | 1493 | 1579 | 1422 | ...  | 1911 | 1634 | 1604 | 1450 | 1779 | 1359 | 1418 | 1634 | 1481 | 1519 |\n",
       "\n"
      ],
      "text/plain": [
       "             Media_0h_1 Media_0h_2 Media_0h_3 Media_6h_1 Media_6h_2 Media_6h_3\n",
       "FLT3LG        273        292        221        218        207        219      \n",
       "ERI1          535        611        520        590        539        526      \n",
       "SAP130       3580       4097       3234       3931       4068       3823      \n",
       "LOC100129098    0          0          0          1          0          0      \n",
       "SPIN1        1809       1690       1607       2423       1866       1927      \n",
       "SGPL1        1619       1526       1642       1636       1613       1526      \n",
       "             Media_12h_1 Media_12h_2 Media_12h_3 Media_24h_1 ... Hyper_12h_3\n",
       "FLT3LG        267         207         206         233        ...  276       \n",
       "ERI1          570         474         531         577        ...  401       \n",
       "SAP130       4216        4030        3418        3635        ... 3136       \n",
       "LOC100129098    0           0           0           0        ...    0       \n",
       "SPIN1        2343        2001        1988        2393        ... 2636       \n",
       "SGPL1        1719        1493        1579        1422        ... 1911       \n",
       "             Hyper_24h_1 Hyper_24h_2 Hyper_24h_3 Hyper_48h_1 Hyper_48h_2\n",
       "FLT3LG        350         307         298         244         342       \n",
       "ERI1          507         486         378         507         425       \n",
       "SAP130       3152        3098        2977        3611        3324       \n",
       "LOC100129098    0           0           0           0           0       \n",
       "SPIN1        2022        1906        1731        2707        2232       \n",
       "SGPL1        1634        1604        1450        1779        1359       \n",
       "             Hyper_48h_3 Hyper_72h_1 Hyper_72h_2 Hyper_72h_3\n",
       "FLT3LG        235         291         260         325       \n",
       "ERI1          511         339         366         334       \n",
       "SAP130       3384        2956        2775        2763       \n",
       "LOC100129098    0           0           0           0       \n",
       "SPIN1        2312        2084        2193        1778       \n",
       "SGPL1        1418        1634        1481        1519       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DESeq.ds <- DESeqDataSetFromMatrix(countData=round(read.counts), colData=sample_info, design = ~ condition)\n",
    "head(colData(DESeq.ds))\n",
    "head(rowData(DESeq.ds))\n",
    "head(assay(DESeq.ds, \"counts\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f35ae4c1",
   "metadata": {},
   "source": [
    "### Collapsing technical replicates\n",
    "\n",
    "Since there are three technical replicates for each different sample we will use the `collapseReplicates()` function in DESeq2 to collapse them and verify that the transformation was correct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "11060470",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>Media_0h</li>\n",
       "\t<li>Media_0h</li>\n",
       "\t<li>Media_0h</li>\n",
       "\t<li>Media_6h</li>\n",
       "\t<li>Media_6h</li>\n",
       "\t<li>Media_6h</li>\n",
       "\t<li>Media_12h</li>\n",
       "\t<li>Media_12h</li>\n",
       "\t<li>Media_12h</li>\n",
       "\t<li>Media_24h</li>\n",
       "\t<li>Media_24h</li>\n",
       "\t<li>Media_24h</li>\n",
       "\t<li>Media_48h</li>\n",
       "\t<li>Media_48h</li>\n",
       "\t<li>Media_48h</li>\n",
       "\t<li>Media_72h</li>\n",
       "\t<li>Media_72h</li>\n",
       "\t<li>Media_72h</li>\n",
       "\t<li>Iso_6h</li>\n",
       "\t<li>Iso_6h</li>\n",
       "\t<li>Iso_6h</li>\n",
       "\t<li>Iso_12h</li>\n",
       "\t<li>Iso_12h</li>\n",
       "\t<li>Iso_12h</li>\n",
       "\t<li>Iso_24h</li>\n",
       "\t<li>Iso_24h</li>\n",
       "\t<li>Iso_24h</li>\n",
       "\t<li>Iso_48h</li>\n",
       "\t<li>Iso_48h</li>\n",
       "\t<li>Iso_48h</li>\n",
       "\t<li>Iso_72h</li>\n",
       "\t<li>Iso_72h</li>\n",
       "\t<li>Iso_72h</li>\n",
       "\t<li>Hypo_6h</li>\n",
       "\t<li>Hypo_6h</li>\n",
       "\t<li>Hypo_6h</li>\n",
       "\t<li>Hypo_12h</li>\n",
       "\t<li>Hypo_12h</li>\n",
       "\t<li>Hypo_12h</li>\n",
       "\t<li>Hypo_24h</li>\n",
       "\t<li>Hypo_24h</li>\n",
       "\t<li>Hypo_24h</li>\n",
       "\t<li>Hypo_48h</li>\n",
       "\t<li>Hypo_48h</li>\n",
       "\t<li>Hypo_48h</li>\n",
       "\t<li>Hypo_72h</li>\n",
       "\t<li>Hypo_72h</li>\n",
       "\t<li>Hypo_72h</li>\n",
       "\t<li>Hyper_6h</li>\n",
       "\t<li>Hyper_6h</li>\n",
       "\t<li>Hyper_6h</li>\n",
       "\t<li>Hyper_12h</li>\n",
       "\t<li>Hyper_12h</li>\n",
       "\t<li>Hyper_12h</li>\n",
       "\t<li>Hyper_24h</li>\n",
       "\t<li>Hyper_24h</li>\n",
       "\t<li>Hyper_24h</li>\n",
       "\t<li>Hyper_48h</li>\n",
       "\t<li>Hyper_48h</li>\n",
       "\t<li>Hyper_48h</li>\n",
       "\t<li>Hyper_72h</li>\n",
       "\t<li>Hyper_72h</li>\n",
       "\t<li>Hyper_72h</li>\n",
       "</ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<ol class=list-inline>\n",
       "\t\t<li>'Hyper_12h'</li>\n",
       "\t\t<li>'Hyper_24h'</li>\n",
       "\t\t<li>'Hyper_48h'</li>\n",
       "\t\t<li>'Hyper_6h'</li>\n",
       "\t\t<li>'Hyper_72h'</li>\n",
       "\t\t<li>'Hypo_12h'</li>\n",
       "\t\t<li>'Hypo_24h'</li>\n",
       "\t\t<li>'Hypo_48h'</li>\n",
       "\t\t<li>'Hypo_6h'</li>\n",
       "\t\t<li>'Hypo_72h'</li>\n",
       "\t\t<li>'Iso_12h'</li>\n",
       "\t\t<li>'Iso_24h'</li>\n",
       "\t\t<li>'Iso_48h'</li>\n",
       "\t\t<li>'Iso_6h'</li>\n",
       "\t\t<li>'Iso_72h'</li>\n",
       "\t\t<li>'Media_0h'</li>\n",
       "\t\t<li>'Media_12h'</li>\n",
       "\t\t<li>'Media_24h'</li>\n",
       "\t\t<li>'Media_48h'</li>\n",
       "\t\t<li>'Media_6h'</li>\n",
       "\t\t<li>'Media_72h'</li>\n",
       "\t</ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item Media\\_0h\n",
       "\\item Media\\_0h\n",
       "\\item Media\\_0h\n",
       "\\item Media\\_6h\n",
       "\\item Media\\_6h\n",
       "\\item Media\\_6h\n",
       "\\item Media\\_12h\n",
       "\\item Media\\_12h\n",
       "\\item Media\\_12h\n",
       "\\item Media\\_24h\n",
       "\\item Media\\_24h\n",
       "\\item Media\\_24h\n",
       "\\item Media\\_48h\n",
       "\\item Media\\_48h\n",
       "\\item Media\\_48h\n",
       "\\item Media\\_72h\n",
       "\\item Media\\_72h\n",
       "\\item Media\\_72h\n",
       "\\item Iso\\_6h\n",
       "\\item Iso\\_6h\n",
       "\\item Iso\\_6h\n",
       "\\item Iso\\_12h\n",
       "\\item Iso\\_12h\n",
       "\\item Iso\\_12h\n",
       "\\item Iso\\_24h\n",
       "\\item Iso\\_24h\n",
       "\\item Iso\\_24h\n",
       "\\item Iso\\_48h\n",
       "\\item Iso\\_48h\n",
       "\\item Iso\\_48h\n",
       "\\item Iso\\_72h\n",
       "\\item Iso\\_72h\n",
       "\\item Iso\\_72h\n",
       "\\item Hypo\\_6h\n",
       "\\item Hypo\\_6h\n",
       "\\item Hypo\\_6h\n",
       "\\item Hypo\\_12h\n",
       "\\item Hypo\\_12h\n",
       "\\item Hypo\\_12h\n",
       "\\item Hypo\\_24h\n",
       "\\item Hypo\\_24h\n",
       "\\item Hypo\\_24h\n",
       "\\item Hypo\\_48h\n",
       "\\item Hypo\\_48h\n",
       "\\item Hypo\\_48h\n",
       "\\item Hypo\\_72h\n",
       "\\item Hypo\\_72h\n",
       "\\item Hypo\\_72h\n",
       "\\item Hyper\\_6h\n",
       "\\item Hyper\\_6h\n",
       "\\item Hyper\\_6h\n",
       "\\item Hyper\\_12h\n",
       "\\item Hyper\\_12h\n",
       "\\item Hyper\\_12h\n",
       "\\item Hyper\\_24h\n",
       "\\item Hyper\\_24h\n",
       "\\item Hyper\\_24h\n",
       "\\item Hyper\\_48h\n",
       "\\item Hyper\\_48h\n",
       "\\item Hyper\\_48h\n",
       "\\item Hyper\\_72h\n",
       "\\item Hyper\\_72h\n",
       "\\item Hyper\\_72h\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item 'Hyper\\_12h'\n",
       "\\item 'Hyper\\_24h'\n",
       "\\item 'Hyper\\_48h'\n",
       "\\item 'Hyper\\_6h'\n",
       "\\item 'Hyper\\_72h'\n",
       "\\item 'Hypo\\_12h'\n",
       "\\item 'Hypo\\_24h'\n",
       "\\item 'Hypo\\_48h'\n",
       "\\item 'Hypo\\_6h'\n",
       "\\item 'Hypo\\_72h'\n",
       "\\item 'Iso\\_12h'\n",
       "\\item 'Iso\\_24h'\n",
       "\\item 'Iso\\_48h'\n",
       "\\item 'Iso\\_6h'\n",
       "\\item 'Iso\\_72h'\n",
       "\\item 'Media\\_0h'\n",
       "\\item 'Media\\_12h'\n",
       "\\item 'Media\\_24h'\n",
       "\\item 'Media\\_48h'\n",
       "\\item 'Media\\_6h'\n",
       "\\item 'Media\\_72h'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. Media_0h\n",
       "2. Media_0h\n",
       "3. Media_0h\n",
       "4. Media_6h\n",
       "5. Media_6h\n",
       "6. Media_6h\n",
       "7. Media_12h\n",
       "8. Media_12h\n",
       "9. Media_12h\n",
       "10. Media_24h\n",
       "11. Media_24h\n",
       "12. Media_24h\n",
       "13. Media_48h\n",
       "14. Media_48h\n",
       "15. Media_48h\n",
       "16. Media_72h\n",
       "17. Media_72h\n",
       "18. Media_72h\n",
       "19. Iso_6h\n",
       "20. Iso_6h\n",
       "21. Iso_6h\n",
       "22. Iso_12h\n",
       "23. Iso_12h\n",
       "24. Iso_12h\n",
       "25. Iso_24h\n",
       "26. Iso_24h\n",
       "27. Iso_24h\n",
       "28. Iso_48h\n",
       "29. Iso_48h\n",
       "30. Iso_48h\n",
       "31. Iso_72h\n",
       "32. Iso_72h\n",
       "33. Iso_72h\n",
       "34. Hypo_6h\n",
       "35. Hypo_6h\n",
       "36. Hypo_6h\n",
       "37. Hypo_12h\n",
       "38. Hypo_12h\n",
       "39. Hypo_12h\n",
       "40. Hypo_24h\n",
       "41. Hypo_24h\n",
       "42. Hypo_24h\n",
       "43. Hypo_48h\n",
       "44. Hypo_48h\n",
       "45. Hypo_48h\n",
       "46. Hypo_72h\n",
       "47. Hypo_72h\n",
       "48. Hypo_72h\n",
       "49. Hyper_6h\n",
       "50. Hyper_6h\n",
       "51. Hyper_6h\n",
       "52. Hyper_12h\n",
       "53. Hyper_12h\n",
       "54. Hyper_12h\n",
       "55. Hyper_24h\n",
       "56. Hyper_24h\n",
       "57. Hyper_24h\n",
       "58. Hyper_48h\n",
       "59. Hyper_48h\n",
       "60. Hyper_48h\n",
       "61. Hyper_72h\n",
       "62. Hyper_72h\n",
       "63. Hyper_72h\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. 'Hyper_12h'\n",
       "2. 'Hyper_24h'\n",
       "3. 'Hyper_48h'\n",
       "4. 'Hyper_6h'\n",
       "5. 'Hyper_72h'\n",
       "6. 'Hypo_12h'\n",
       "7. 'Hypo_24h'\n",
       "8. 'Hypo_48h'\n",
       "9. 'Hypo_6h'\n",
       "10. 'Hypo_72h'\n",
       "11. 'Iso_12h'\n",
       "12. 'Iso_24h'\n",
       "13. 'Iso_48h'\n",
       "14. 'Iso_6h'\n",
       "15. 'Iso_72h'\n",
       "16. 'Media_0h'\n",
       "17. 'Media_12h'\n",
       "18. 'Media_24h'\n",
       "19. 'Media_48h'\n",
       "20. 'Media_6h'\n",
       "21. 'Media_72h'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] Media_0h  Media_0h  Media_0h  Media_6h  Media_6h  Media_6h  Media_12h\n",
       " [8] Media_12h Media_12h Media_24h Media_24h Media_24h Media_48h Media_48h\n",
       "[15] Media_48h Media_72h Media_72h Media_72h Iso_6h    Iso_6h    Iso_6h   \n",
       "[22] Iso_12h   Iso_12h   Iso_12h   Iso_24h   Iso_24h   Iso_24h   Iso_48h  \n",
       "[29] Iso_48h   Iso_48h   Iso_72h   Iso_72h   Iso_72h   Hypo_6h   Hypo_6h  \n",
       "[36] Hypo_6h   Hypo_12h  Hypo_12h  Hypo_12h  Hypo_24h  Hypo_24h  Hypo_24h \n",
       "[43] Hypo_48h  Hypo_48h  Hypo_48h  Hypo_72h  Hypo_72h  Hypo_72h  Hyper_6h \n",
       "[50] Hyper_6h  Hyper_6h  Hyper_12h Hyper_12h Hyper_12h Hyper_24h Hyper_24h\n",
       "[57] Hyper_24h Hyper_48h Hyper_48h Hyper_48h Hyper_72h Hyper_72h Hyper_72h\n",
       "21 Levels: Hyper_12h Hyper_24h Hyper_48h Hyper_6h Hyper_72h ... Media_72h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Bio_Factors <- factor(gsub(\"([a-zA-z]+_[0-9]+h)_[0-9]\", \"\\\\1\", colnames(DESeq.ds)))\n",
    "Bio_Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a5934354",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame with 21 rows and 1 column\n",
       "          condition\n",
       "           <factor>\n",
       "Hyper_12h     Hyper\n",
       "Hyper_24h     Hyper\n",
       "Hyper_48h     Hyper\n",
       "Hyper_6h      Hyper\n",
       "Hyper_72h     Hyper\n",
       "...             ...\n",
       "Media_12h     Media\n",
       "Media_24h     Media\n",
       "Media_48h     Media\n",
       "Media_6h      Media\n",
       "Media_72h     Media"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Hyper_12h</th><th scope=col>Hyper_24h</th><th scope=col>Hyper_48h</th><th scope=col>Hyper_6h</th><th scope=col>Hyper_72h</th><th scope=col>Hypo_12h</th><th scope=col>Hypo_24h</th><th scope=col>Hypo_48h</th><th scope=col>Hypo_6h</th><th scope=col>Hypo_72h</th><th scope=col>...</th><th scope=col>Iso_24h</th><th scope=col>Iso_48h</th><th scope=col>Iso_6h</th><th scope=col>Iso_72h</th><th scope=col>Media_0h</th><th scope=col>Media_12h</th><th scope=col>Media_24h</th><th scope=col>Media_48h</th><th scope=col>Media_6h</th><th scope=col>Media_72h</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>FLT3LG</th><td>  952</td><td> 955 </td><td>  821</td><td>  390</td><td> 876 </td><td>  772</td><td> 878 </td><td> 922 </td><td> 698 </td><td> 862 </td><td>...  </td><td> 820 </td><td> 821 </td><td>  783</td><td> 934 </td><td>  786</td><td>  680</td><td>  622</td><td>  773</td><td>  644</td><td> 989 </td></tr>\n",
       "\t<tr><th scope=row>ERI1</th><td> 1276</td><td>1371 </td><td> 1443</td><td> 1378</td><td>1039 </td><td> 1288</td><td>1095 </td><td> 826 </td><td>1530 </td><td> 928 </td><td>...  </td><td>1534 </td><td>1309 </td><td> 1577</td><td>1111 </td><td> 1666</td><td> 1575</td><td> 1797</td><td> 1702</td><td> 1655</td><td>1093 </td></tr>\n",
       "\t<tr><th scope=row>SAP130</th><td>10041</td><td>9227 </td><td>10319</td><td>12369</td><td>8494 </td><td>11424</td><td>6901 </td><td>5640 </td><td>9221 </td><td>5322 </td><td>...  </td><td>9216 </td><td>8926 </td><td>10466</td><td>7131 </td><td>10911</td><td>11664</td><td>10125</td><td>10269</td><td>11822</td><td>7312 </td></tr>\n",
       "\t<tr><th scope=row>LOC100129098</th><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><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>    1</td><td>   2 </td></tr>\n",
       "\t<tr><th scope=row>SPIN1</th><td> 7867</td><td>5659 </td><td> 7251</td><td> 7649</td><td>6055 </td><td> 6487</td><td>5447 </td><td>4471 </td><td>5436 </td><td>4698 </td><td>...  </td><td>6265 </td><td>5809 </td><td> 5676</td><td>5740 </td><td> 5106</td><td> 6332</td><td> 6694</td><td> 6616</td><td> 6216</td><td>5662 </td></tr>\n",
       "\t<tr><th scope=row>SGPL1</th><td> 5341</td><td>4688 </td><td> 4556</td><td> 3729</td><td>4634 </td><td> 8625</td><td>5603 </td><td>4819 </td><td>6731 </td><td>5894 </td><td>...  </td><td>4573 </td><td>4146 </td><td> 4873</td><td>4519 </td><td> 4787</td><td> 4791</td><td> 4412</td><td> 3941</td><td> 4775</td><td>4427 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & Hyper\\_12h & Hyper\\_24h & Hyper\\_48h & Hyper\\_6h & Hyper\\_72h & Hypo\\_12h & Hypo\\_24h & Hypo\\_48h & Hypo\\_6h & Hypo\\_72h & ... & Iso\\_24h & Iso\\_48h & Iso\\_6h & Iso\\_72h & Media\\_0h & Media\\_12h & Media\\_24h & Media\\_48h & Media\\_6h & Media\\_72h\\\\\n",
       "\\hline\n",
       "\tFLT3LG &   952 &  955  &   821 &   390 &  876  &   772 &  878  &  922  &  698  &  862  & ...   &  820  &  821  &   783 &  934  &   786 &   680 &   622 &   773 &   644 &  989 \\\\\n",
       "\tERI1 &  1276 & 1371  &  1443 &  1378 & 1039  &  1288 & 1095  &  826  & 1530  &  928  & ...   & 1534  & 1309  &  1577 & 1111  &  1666 &  1575 &  1797 &  1702 &  1655 & 1093 \\\\\n",
       "\tSAP130 & 10041 & 9227  & 10319 & 12369 & 8494  & 11424 & 6901  & 5640  & 9221  & 5322  & ...   & 9216  & 8926  & 10466 & 7131  & 10911 & 11664 & 10125 & 10269 & 11822 & 7312 \\\\\n",
       "\tLOC100129098 &     0 &    0  &     0 &     0 &    0  &     0 &    0  &    0  &    0  &    0  & ...   &    0  &    0  &     0 &    0  &     0 &     0 &     0 &     0 &     1 &    2 \\\\\n",
       "\tSPIN1 &  7867 & 5659  &  7251 &  7649 & 6055  &  6487 & 5447  & 4471  & 5436  & 4698  & ...   & 6265  & 5809  &  5676 & 5740  &  5106 &  6332 &  6694 &  6616 &  6216 & 5662 \\\\\n",
       "\tSGPL1 &  5341 & 4688  &  4556 &  3729 & 4634  &  8625 & 5603  & 4819  & 6731  & 5894  & ...   & 4573  & 4146  &  4873 & 4519  &  4787 &  4791 &  4412 &  3941 &  4775 & 4427 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Hyper_12h | Hyper_24h | Hyper_48h | Hyper_6h | Hyper_72h | Hypo_12h | Hypo_24h | Hypo_48h | Hypo_6h | Hypo_72h | ... | Iso_24h | Iso_48h | Iso_6h | Iso_72h | Media_0h | Media_12h | Media_24h | Media_48h | Media_6h | Media_72h |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| FLT3LG |   952 |  955  |   821 |   390 |  876  |   772 |  878  |  922  |  698  |  862  | ...   |  820  |  821  |   783 |  934  |   786 |   680 |   622 |   773 |   644 |  989  |\n",
       "| ERI1 |  1276 | 1371  |  1443 |  1378 | 1039  |  1288 | 1095  |  826  | 1530  |  928  | ...   | 1534  | 1309  |  1577 | 1111  |  1666 |  1575 |  1797 |  1702 |  1655 | 1093  |\n",
       "| SAP130 | 10041 | 9227  | 10319 | 12369 | 8494  | 11424 | 6901  | 5640  | 9221  | 5322  | ...   | 9216  | 8926  | 10466 | 7131  | 10911 | 11664 | 10125 | 10269 | 11822 | 7312  |\n",
       "| LOC100129098 |     0 |    0  |     0 |     0 |    0  |     0 |    0  |    0  |    0  |    0  | ...   |    0  |    0  |     0 |    0  |     0 |     0 |     0 |     0 |     1 |    2  |\n",
       "| SPIN1 |  7867 | 5659  |  7251 |  7649 | 6055  |  6487 | 5447  | 4471  | 5436  | 4698  | ...   | 6265  | 5809  |  5676 | 5740  |  5106 |  6332 |  6694 |  6616 |  6216 | 5662  |\n",
       "| SGPL1 |  5341 | 4688  |  4556 |  3729 | 4634  |  8625 | 5603  | 4819  | 6731  | 5894  | ...   | 4573  | 4146  |  4873 | 4519  |  4787 |  4791 |  4412 |  3941 |  4775 | 4427  |\n",
       "\n"
      ],
      "text/plain": [
       "             Hyper_12h Hyper_24h Hyper_48h Hyper_6h Hyper_72h Hypo_12h Hypo_24h\n",
       "FLT3LG         952      955        821       390     876        772     878    \n",
       "ERI1          1276     1371       1443      1378    1039       1288    1095    \n",
       "SAP130       10041     9227      10319     12369    8494      11424    6901    \n",
       "LOC100129098     0        0          0         0       0          0       0    \n",
       "SPIN1         7867     5659       7251      7649    6055       6487    5447    \n",
       "SGPL1         5341     4688       4556      3729    4634       8625    5603    \n",
       "             Hypo_48h Hypo_6h Hypo_72h ... Iso_24h Iso_48h Iso_6h Iso_72h\n",
       "FLT3LG        922      698     862     ...  820     821      783   934   \n",
       "ERI1          826     1530     928     ... 1534    1309     1577  1111   \n",
       "SAP130       5640     9221    5322     ... 9216    8926    10466  7131   \n",
       "LOC100129098    0        0       0     ...    0       0        0     0   \n",
       "SPIN1        4471     5436    4698     ... 6265    5809     5676  5740   \n",
       "SGPL1        4819     6731    5894     ... 4573    4146     4873  4519   \n",
       "             Media_0h Media_12h Media_24h Media_48h Media_6h Media_72h\n",
       "FLT3LG         786      680       622       773       644     989     \n",
       "ERI1          1666     1575      1797      1702      1655    1093     \n",
       "SAP130       10911    11664     10125     10269     11822    7312     \n",
       "LOC100129098     0        0         0         0         1       2     \n",
       "SPIN1         5106     6332      6694      6616      6216    5662     \n",
       "SGPL1         4787     4791      4412      3941      4775    4427     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DESeq.ds.coll <- collapseReplicates(DESeq.ds, Bio_Factors)\n",
    "colData(DESeq.ds.coll)\n",
    "head(assay(DESeq.ds.coll, \"counts\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b46054bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>12h</li>\n",
       "\t<li>24h</li>\n",
       "\t<li>48h</li>\n",
       "\t<li>6h</li>\n",
       "\t<li>72h</li>\n",
       "</ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<ol class=list-inline>\n",
       "\t\t<li>'0h'</li>\n",
       "\t\t<li>'12h'</li>\n",
       "\t\t<li>'24h'</li>\n",
       "\t\t<li>'48h'</li>\n",
       "\t\t<li>'6h'</li>\n",
       "\t\t<li>'72h'</li>\n",
       "\t</ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 12h\n",
       "\\item 24h\n",
       "\\item 48h\n",
       "\\item 6h\n",
       "\\item 72h\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item '0h'\n",
       "\\item '12h'\n",
       "\\item '24h'\n",
       "\\item '48h'\n",
       "\\item '6h'\n",
       "\\item '72h'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 12h\n",
       "2. 24h\n",
       "3. 48h\n",
       "4. 6h\n",
       "5. 72h\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. '0h'\n",
       "2. '12h'\n",
       "3. '24h'\n",
       "4. '48h'\n",
       "5. '6h'\n",
       "6. '72h'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 12h 24h 48h 6h  72h\n",
       "Levels: 0h 12h 24h 48h 6h 72h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_info.coll <- data.frame(condition = gsub(\"[A-Za-z]+_([0-9]+h)\", \"\\\\1\", colnames(DESeq.ds.coll)), row.names = colnames(DESeq.ds.coll))\n",
    "time_info.coll[1:5,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "32d98acc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame with 21 rows and 2 columns\n",
       "          condition condition\n",
       "           <factor>  <factor>\n",
       "Hyper_12h     Hyper       12h\n",
       "Hyper_24h     Hyper       24h\n",
       "Hyper_48h     Hyper       48h\n",
       "Hyper_6h      Hyper        6h\n",
       "Hyper_72h     Hyper       72h\n",
       "...             ...       ...\n",
       "Media_12h     Media       12h\n",
       "Media_24h     Media       24h\n",
       "Media_48h     Media       48h\n",
       "Media_6h      Media        6h\n",
       "Media_72h     Media       72h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DESeq.ds.coll <- DESeq.ds.coll\n",
    "colData(DESeq.ds.coll) <- cbind(colData(DESeq.ds.coll), time_info.coll)\n",
    "colData(DESeq.ds.coll)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d0d32a6",
   "metadata": {},
   "source": [
    "### Normalization of counts\n",
    "\n",
    "Next is the important step of normalization of counts. To do this we will first remove any gene with zero counts and then check to make sure that column counts are consistent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d7716ab6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>Hyper_12h</dt>\n",
       "\t\t<dd>76776790</dd>\n",
       "\t<dt>Hyper_24h</dt>\n",
       "\t\t<dd>79798273</dd>\n",
       "\t<dt>Hyper_48h</dt>\n",
       "\t\t<dd>78977730</dd>\n",
       "\t<dt>Hyper_6h</dt>\n",
       "\t\t<dd>76210729</dd>\n",
       "\t<dt>Hyper_72h</dt>\n",
       "\t\t<dd>76982339</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[Hyper\\textbackslash{}\\_12h] 76776790\n",
       "\\item[Hyper\\textbackslash{}\\_24h] 79798273\n",
       "\\item[Hyper\\textbackslash{}\\_48h] 78977730\n",
       "\\item[Hyper\\textbackslash{}\\_6h] 76210729\n",
       "\\item[Hyper\\textbackslash{}\\_72h] 76982339\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "Hyper_12h\n",
       ":   76776790Hyper_24h\n",
       ":   79798273Hyper_48h\n",
       ":   78977730Hyper_6h\n",
       ":   76210729Hyper_72h\n",
       ":   76982339\n",
       "\n"
      ],
      "text/plain": [
       "Hyper_12h Hyper_24h Hyper_48h  Hyper_6h Hyper_72h \n",
       " 76776790  79798273  78977730  76210729  76982339 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DESeq.ds.coll <- DESeq.ds.coll[ rowSums(counts(DESeq.ds.coll)) > 0, ]\n",
    "DESeq.ds.coll <- na.omit(DESeq.ds.coll)\n",
    "colSums(counts(DESeq.ds.coll))[1:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64209654",
   "metadata": {},
   "source": [
    "To perform the normalization we will first use `estimateSizeFactors` from DESeq2 and then use `counts()` to normalize it. Finally we will do a log2 transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "60833bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "DESeq.ds.coll <- estimateSizeFactors(DESeq.ds.coll)\n",
    "counts.coll.sf_normalized <- counts(DESeq.ds.coll, normalized=T)\n",
    "log.norm.counts.coll <- log2(counts.coll.sf_normalized + 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5e1598d",
   "metadata": {},
   "source": [
    "Next we can visualize the unnormalized and normalized data to make sure that normalization was done properly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "dc76dd86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xzq3gt2cOgeQUiXpzFqIy3dElKfltJ2K/tg5ZFy4pdigTUbR4R+krNPf3N06fRl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\ntZfZkD0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      "text/plain": [
       "Plot with title “log2-transformed read counts”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow=c(2,1))\n",
    "boxplot(counts.coll.sf_normalized, notch=T, main=\"untransformed read counts\", ylab=\"read counts\")\n",
    "boxplot(log.norm.counts.coll, notch=T, main=\"log2-transformed read counts\", ylab=\"log2(read counts)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c661e18",
   "metadata": {},
   "source": [
    "### Visual Exploration and Homoskedasticity Test\n",
    "\n",
    "Next we will explore the data visually by doing pairwise plots to make sure the read counts are similar between replicates. Also we will do a `meanSdplot()` to check the homoskedasticity of the data (i.e. all variables have similar variance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3c2eecfe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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F5zrmKNlB1OXRZ/oxvJNYR0UUO7owhS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rTRkCaUt0eFml5oJ6TpcnazCznqsG0wuW\nZOdp4/X5yiOBFRSPNX9n3zh9Dl8Vmg/ybCP9t84+ISB76vEZWvsFexlTl2GM1emMuN/3NNKd\nef/byxNY/MnCOUFUbpPK5r1rLgEEoGM/cyO/5zr6HZMA2j1ctmSdMrdIntJFoAZzUlQNhud+\nnXL6FCtyGmcHPfHyKs/as01ilO6QdaYdBVoEtJUfAZ5AVWjTiY78yytCkZOvdGfej3P/rw99\nqvSe3NEKfyELzJo6IwQtgLSGPd+CYP5ftA1la3cRl9nRDkkeie+58ZAuBa7B8BwHi1PbC/f0\nIW4DAt8mF+ow0rEf8N1lw0Id2nAr6Fc/PJcKHA30F/+5G0IBkO7Mm32DJ9S1Lu0q12tVPnEo\nDkDlU4IT20Xp+L2pI7w0CBLKBy4+XzSxkrz7rtnxVCNMQWswNjY3BVSDnbHWgbeSccldnPFz\npmdiKwjqMFIx2DuBS5VxTXIHZhpo9naJSaC+bXNXtZMVJc+82eH6q1dHQ5V9GqCTmLui3GEw\nCexDh3To6pHQCoYaWjNZ+uuSUqyA+B1d9FD6/XqyrdQmiDqMNK15m1dM2jIlvKO0gJAPu2hm\nVCjlM7ZlZ03iqU31j7gbQgFQ9Mx74RTuCgNuhqIuedqlVtou2Ms9E1qBUIuR2kFFdoI5TziU\nDadl7rF/AnUYCV9lp1FMWcPLQnP/aaBD1t3x0ETe7X/syJ0wv5ZyGoPavUHmuppp4yuEtdEY\noqjcBYR0IyjQF9dRoan9QtKUqO6yqMVIhSHR0dTQZYguaJfUcT0blRipmM4uSknuMQ0x5943\nYyFUmS2H5U+YQTCee364Pwdnh2gv451QMZK26Ftxd4z8UJg45r90dt5WUVBmeixW2EjTuQUH\nhr8+qs6rzSiLQcvPc0SaiL1VO9gL3eutxJ6gKjDSgt07Fxc3m7hFIRGgMmzioA7eBoO2WRGq\nuE+vFfiXH9yN4fmRO2HurednifSBRRj/uZ95tfXKmd0BuVNKbDT9+fqG9ec2Xv5KoZ1SlTVS\nphd1FeMM9hwLLZ1UoaClU6l1UE7qEAVQ3kh/QKFCrDLJCJrahsQHAnRn7tIDGImocxbqH6NG\n/uHxSiXMwnjbSI971V5fsKJoGW8aURTbK4BoCkzfp2/Cc7n1u44OUmR9SGWvSD9wI0SWfLQk\nJOqE066ONHNh6tYl1raKzOPJe6UOUQCFjZTTe+ajtxbPrx6nNTekKYQqVqCYRy2if5nYsu2E\nbm+1/AC/n3xwlAKL8yp+LzAVQBsMM/kbAVsntaYipN2hdew+DV1Amb3pFNblDLcuwz2r3xUa\n8pBenj4ldWTPQmEjnYEg9qkIADc8CCyXbLo4ekmGgNwLCHEolzA5LToxjyvLlHytVlqZaky+\nUF5GbmsB76I675IbcZv4iWXKjTzy4RWpAxREWSP9SZVhn+6ZIdXfYSJ2Ftsn/ym0VZ8N2Yx0\nusznQh8u4yr7JY16MBqMCHwp5v5Rj7w1PmELOzTjGuyuNSh/290QCoByCXPbaH2Q9aAnzMSR\ntI/G1ozJXpcMAwC+e4Absw0OKt502F2eZaTrCd2Yx0VTi2gDfO3jGlArnYZeK3VY+SCbkVbB\na66LcKvZ2RfM5M+/LfyM/J1RKTjgbggFQLmEeRSVXDGcClhi7fCXlQqIrUuBXmdb29oQBpBm\nmp2x4fcLUkfnCsWrvBg/QFyC8HgzRmoOcELqqPJDNiNlb3KxNN0ZvTV9Z8W4hkZuijmy7dKB\noPOJtQnDRmreq1Kjf4wCa6PLnjB2a9wPik4yg/dmbf2TBmRMOoOAomxLfxui2A2Zt0gd2bNQ\n1kg5c9mZaUnhNZk7acRdkvwoFFDcr8FjPHe71IE9E+Vb7dYC8r6RpB0FT1AGb4MGjaCiOegt\nJbry5U6YcTDH9uqeX0SCF3POnRPM1ehaXXISZWu49ZZyC3ywKGukPyAR40xvinoiWU7gAxAv\ndWDPRBkjtazoWFrrZnREubeTy5YL9bGrw+7SYU58514hn/J0sDWs+n6MN8V+7W4cz4fcCbPI\neyX3nH3mcWJahXDa4vVrMbYKgzrs5be75B72l0zcJu8+UU+iiJF+Xmx78bjXJ5/c6RFXpixC\nzEWaohGikC4itG7Ohrm9FRz6jRUyUnak4Zr94ATSepVEZi94oj0TlT5Pg5Xd8t1yG+OZMNDd\nOJ4P2RPGNhTqPZiDKA0c+wAWBOTRI4zx06R00IGPoqMzlTBSjh/YB9K1ghFP1l16sWGB7Au2\n5UWZK9LVv51e+4QmxYcb9GFaHc2cYZiqL4V6NY0OCzJpCtUpM/rY0rQFTKns/TIPkZctYTLy\nTrea5b/OaA00XJ8bsKG0lqnZadjuNXbf2HJmQNM7GEI0JRQafcijyBVptmMt4pVVW6cGJibo\nKL1GF2pEiPayDKk5ZPfC/sptH8uh/D3S9/yAqTyU+Jzvh7TAWDxXmVkDciXMZqpLnncHosWU\nhu1xxbdp27wsvosN2Htr/ShYIHVcz0bpVrs2AFbzk/kCwVIHlS+KG+lIn6FLPiwUHENpfMsH\nJBg0EX7RJnOQV8V2LRqXW3PgpzmjZ3e85W4EBUGuhNnrlbfO+olpjc7EVd4e6LlmKSZrbHU8\npAfrbMMKqeN6NnLpkvPHvTzv3t/HXHIunDn6Xd3Adl2LxoTo/CzMLRJQFB1SwZiozCgPZxQ3\nUh8YjRNtbQy5510WAzJS13COL3ijS+5GUBDkSphDgU5bqDVKvjczYEtmdr0kTfT5sN625Q8R\n3ZNbRoimdD38kXaG1IE9E7l0+THPQFSMu8MSnBlsQCU3A03lrcJ8RxWROqb8UdpI+9Z1OI9/\nqxIQqDX6Gs31zaZCEbX9CgdaWi/+7OsFOVtOj5793Tx3AygQciXMb9DO/k5Oouna+7AMZ8cZ\nwXgQ1bLnSx2w1e+qMWeZd6UO7JnIpcuJQlPzvLsgcS/Gr1Uu9t7ZwrERgRqnXSgMf6f1lDqm\n/FHYSOdpC0zGmB3Jm/csE8LeUf8EoaDMdloy3gv8zbei7HnjGMaj38rJPo0/HDmiS7MqpsBS\nlcM0uhCLke2ljkYWS+H5px5WLXxE0bYGZe6RmhS7j/HjtLpM7W6aH3MuoZOYSp05N2W8ldxg\nLBeFjfSoRuO0HRhXYSp0QYwk7H+goa163+7sp3+XblXmb3d/voDInTB9YTjOCURnMb6lMfpQ\ntm0E8vIuOzrmsNRxPRtFmr/jTNcYXbzDM9nd3lnyTNNSpu7/BLIZaaOpreCnq1NMxuBKtCXZ\nHEIjClFJCCE6zqtL2cYtsnH32o/xvhLfuBtBQZA7Ya7MuYHx7yvwhU8eDBj7Sd9lk8olVisf\n4uWbEhZo1ND69ubYUq91+vrBJx+d/WEXxttfrrF2S8ayj5dvLv9swpxe7SKC0soE+FQymnUa\nL0tEsebLcLe3pA4qX2Qz0mCIEPy0H1fNdZxeNHx7QzR4me/mhGsv4XmgSA1YqWbeItCc3dL9\nICNYHozcBWoYxkmATh16uUZ/5/jyHbKZXgiebvqGoqcBlFgfJw+yGelx37x/69U5XBvvim2b\nqvWe0rX6p6M6VS1VvsErbeqnxTYJbjHz6PifTvy9+MTi369nrlsv61Z9NpQyUl+v5eEJKcEP\n8G6a7Y+1LfOHIl/RBEVTwRsxHhBT/f6dysOljs4VsuvCdrQuHsO/njdh0MTePaOCUlP8gipq\nTVqNtzmufM8fc1q8pnB3rIxGWgPN87xbFDoyj3tBM5Tf0jG3vss/Da8Jumv+0BDgFTwehrgb\nQgGQK2HO1+bXrJ7qx/zgoFZZrbVzMf6wbfYIqyYwVKPhNp9gNxZ4t8bvTKkO70kdVX7IbaTF\nVufNAq6FWrn+e2RPlpp68/fWWVLHlD+yGenP4nn/2t4Gdv79bWvRPeBdDlHpZnYQotbgz6jD\n3EluHu9X5XFj04ykmHF4Q8rP7oZQAORKmBXQmDsqB7VwTgh9zgplcHYgfbHIE/WXYOiH8U2d\nWYmrszNyG2ksOO96dEajfUKXWKBmyD0OUwhlW+1WzZw2umpc13HRFj9vS+tKAbGVipYK+KCb\nt6XWFXxohCIjGnKRK2Eyv+TbnM71uYrxvl/xzvrX7j/auRqf6lZ7xPhX64d7NynrV9JiKrdm\nzv4r+Na2rdlYUVnkr9o5r/u4bGb9SWMHtencf1r9wmWDw19Liqo5onu/H77PztnuYrabbChq\npJwArnEBPXmWYRiFW4IyPbE25EqYrTTfljIFLcI5aX43hlDfhCUYqHPVmUodLmVrfAF+2b/X\nYZyp+kz0UugixN2n84Tnx3Wg0DZadpS9In0xaljf4oHN+/kbTDp97RTvwJSoeO+OzXT6Mufx\nrnf/dfeX3UGuhNll+IA7mqNl7pXSw26N062JK+5NX2oA4I2r2npMkIkbPtVBO9W3wXyt5JE9\nC0UHrXprQZNoQJTJ1+4hGlF+qaf/SZsidVj5IJuRbvT5nTs62Mu+GfeE6QuGVKqdkmzR+RbX\nIj8a0aFGb73eb/nYMK7fAE/t3UcxM8mWMLbZRfcWMi+yH+MHO3Mys/f+duLX+Nn/+7T1wDFf\nr56/vPWEee2Xrlx1myl7d2/WdSWWsLCjiJF2/8o/71jfb1KPBYvblYiOqBmSlly6cOiMWklf\n4gdfXpQ6qHyRzUhfQz3uqDfYmjLxHVrnL3ihjvEGLTtu5qEewSJ3f72gyJ0wVaAJ99yb+ZMz\naW4A75MDG/oyH38EU5rDL0JfJxOK9CMFUufZ50wLBehpXU4ufmKAqxLIZqT/xh3ijs6Mtk+O\nXfLtj/NavVWrZrRvQl2rJsGMvEsEh/v7JW5anMZH9fX4cYoNo5I7YQZoJ3LPixN3YRxjLkEB\n5csmDe2rodkWX1pnZtvJVyZs+DRJyZUQFbkijejMDzAc3DkyJbDyK4yfQAsUohGkatErmWcb\nKdGumxf5jDT+0JMfLP2OedhaO9g/uEV7vTY0RIMM71SKalrp3Trvrx63bFC9YxhfHqvUsn9y\nJ0z2Sebh3H6cxbrk0IZJWxZu3b1v39zuk7fvunB4y8zNEx/k/PHn31d2Hzt7H99QZm1IDrl0\neTTuJHd095cHGJ/YjW+sfbz3+NF9JXqMqvfuG0WLVSkXn/Ba6fgxvzzE2esvrFW6zU6Bqp2D\n27TuPsapgrW7MHbUUA3mVKTYpAG5jTQCpuFsLRx9F77Ej/KKwY1wgIDpfBcbqpYVYVZuARS5\ndBkAYdzRQBiHc0I0FzvDdJ2f35OrBwFMxL9BMRAexykn8jU29HxqObbx7Fbf3xY26AxlKyNk\nMiKg6kZ7l4xtnPzq7J7jW6Zsxfiv7kqtaSG3kb4K+gnjUEPGnGDmz9Zzg4NoLdsvQGnCuMMy\nPxm8whI0XoF9cJ1SynXLytYtYO7MHa0rtwPjd5pkflvhcLMe3aoDZUC5c5GYJ0OZnfhS+rAK\nyu5oziJ783fWt/xz9t/4h/Wnt39/ZOuAn7YtnT5l/pCR05aOmPjFnKUb9mw6+Meh/fzeAg+U\nao9Rem0CtSKbLlnO7z1i6ng4Kyc7Mwtn3bp7//btW7ceXcv8737Wo6wnSiqF7EaKhgrcc18Y\n93T7izNGrlgV+qS7v18w5EqY4wmTHG+1Kfegs/bzao0QGDRA+dC5J17EVGnQzqJ9/k4c5erb\n5EIuXfZ5D3K85aO5EgiNEyp4a/2L5+m99wH60FGLsnOGeWQ3UhXb1OoZfj8AMHWWp5cQAnZJ\nC4qK5Iq9FX7Z3d8vGHIlzE9OTbc5MfrLqdDeHGivujgzAaVtgYZSR5Ufcumyiepsfydbj/7y\ngrJ+8VqkDc97+wiwYxf1htQhiUDpNRtUi2xVmAP3HW9dPYNvL8/+59LCkWu3tlqxZ9Ww3p1r\nvf11yw+nLx85exY+cRMfUmrrWDuy6XLVaWbEzXM483t8637GhX8fXVm26cflK5dP+3nO6cX7\nbh6/gCM3wtkAABoUSURBVPE1ZSff88hmpFNR/BSak4MvYVyvBD48OGPu4kbFG7WsGBlmNqWW\nahrmHdqhabEWFFU+NXFurbls2R/HKjfNRLZBqyudJkof2IS3N77cqtv+zWvnlSlbJD6xUgNL\n5SrGQp26Fq0xs837U/ZgvHpqhtSBPROl7h3/9+bVlCoY1+w/2eITVtLbu7JfbIJ/VN1BcSMX\nH5Q6IjHIZqR6oOOOesNotnn3xw4wgdaBCyygZy0UD3+4++MFRu5pFCw5IdQ5KyQD+NLCt4+m\nByeBm8elHEoZKR4qAWSME1KluNQRiUE2Ix0P4/evPNbvIsavpOCD/a7OmF87ufarpUKCvAzJ\nxRoEGQNb1yvcmKLSUmKnV5vJll014sW/Ip2v7bTVxkets9po5+pMA9o0TeA37KbZhgZkMCAq\nSm+Jfhs/qpX4k9SBPROljPRJ1E6NF75uKZoOwO5yY0I0jShrMbrMm19IHZEYyD2SC0jztzBE\nF2FkM9L1npsdb5WNwZ31y5JS21fWGWlvK4389CYTDZS+fR8fn1Uxtb8t/0lPdrD4KOVWFZU7\nYc6OuYJxlXh8cuyN6o2TQ0qW8YuO9dNQ2rDW5tBiZfwSXp+O743fL3VM+SOXLneGOO1N/s00\nPClqb6k6+K7WovMq648Q0lERdaJjE3SKLV7xBIoMEWLvkbQQItSPpAFIBEgAPTTC+AZlUGwL\nE7mN9D4Mw5kAszrAQBc3jvtXQC2pY8ofuXRZ7pQv7CpC3uyWy3drPaUKLXU4IpHNSPcmOq1s\n2LoanhC0vXbTEZ0Cw71iEs2GIkHh4V6UPmjUlORCuyv33tp6xYSjTLkvV7n4TumR20hHepzB\nONGK9/S8lFQy0BgWqbdY2W42r3SNKShC71u+f/at3pukjil/5NLlVv//Od76ciIeE7KzaDV8\nl/bSGopbmSuSBgVXCQ2P0gyQOhyRkHskF5B7AWGILsLIZqSsn69yRxeG3MR49Ty8v/V1jIe3\nLlU9yD844c24gLQoU0yfzrOrtuvYvujct8c2PbmFHzqz/bi7v18wZOtHmnyGO8r45C7Gy8bh\n2ZYTzTrWTK7aumizSqXLV6lbqUnX1K7zf+kzbtqeefs3fv7/YCFEd8lrpHvsqoYbl+P16RmV\nW9Qpm1LNHBJdumX1Tm8uTKzTadGc/dOyr3xyz9VXyYhsRvoeXuWOSkFjnEnBfh8ox94TuCTE\nyG3BfIkOUqYJXK6EGQzh3FFFaICzANa6HoCoZdf/Myq67yWW30iDYDzOZvKFAquQKEYYWQJe\nlzokEchmpH+q8r80xrKUcVPEoxaaaRh727ZEsj1ptH5Aa2jw15uprnUC2EtYVtN+7v5+wZBt\nxz6vN7mjkaaZGPvrrmsh+ikrIe9ISmssa6laxFJH6qjyQ24j/VpqG8ZFA+/FQFsnQQAhA/Nk\ntlTy3jnQpIaOJHKP5AJyLyAM0UUYYiQXkIQRhugijGxGutVvB3c0M5hfqCIVBmgN3rSfvwYh\nFBRMBxfz0yAISyy7v0/Vxq0HVvoI7/3gr4rj8Lb+t90NoEDItsUjXZU7GqifgnG8FQ/1W6sz\n6iluZBCiDPwtEwqfXviVQx98jccou+8llk+Xs6XncEfDTcxzqBeOg/eSK1NI72M0MAmD2PWt\nI8a8PuZm3+0Yf6WCXlnZjJTbweYPifzXwtPz7zlsfW5a3AqagxHXB2WmEcuVMDGAuCMT+HAd\nshaIFVLFF6ANxN2kDC/L2t/vQCB3ZAIrfgwwXihfdADToD7zL0Chpl0nZDPSnaH7uKOFURu4\n58ow3uDtqw2J0FOIjozWRZcNYV7FFa9+ZED9Nl1H1R6HDw25UHM63jNMmSk4ciXMAW9+Atu8\ngNUYVy+EFxY+5B8WZEI6L0TRxmCaomiEdGV+rPXWuelb8JffSx1Vfsily5XqS7mjadZlGCf6\n4RIwvmwDDeUVajGzVyREafSFZnWdcWfYHxivmSx1TPlD7pFcQO4FhCG6CEOM5AKSMMIQXYSR\nzUiZM/ilHg9UucRcr/vh2V5nqjXsXrVll7Dwxg3CKtYsVDm+TOVi3Ua9t272Xxd+m7EG52w9\nylQGM7cotNaqXAnzh6YRdzTf+gPGDYrilvQ3Bm8fncGLHU/mq/ONiEtp2KnVm4OljkYssi0Q\nOewod3Tpowx2nhbuotkVXjzEGpRoCAgMDI4OTazVqvfk+dukDkY0shnpI1sPvpa5eXwA8L3L\nHnwE/uwmWyd+AW90Ci+Eru7+fsGQK2H0wAtMgdZ2Uy2sy0apwxGJXLp8CKHcUXmowzbCLBPW\nhT4vdTRikW9Xc2Mb7igZ6mKsozJoCBbKFxoFm5r0i9IH3T1bskbl//D+VIXW/pMrYbrY1h2r\nwC6ib6Iee0M6OK8UjyjKy9/XLzFT6nBEIpcu//Ppzh19YlmAsRd9k4IY23nWpgui9P6RTdUi\nC7lHcgW5FxCG6CIMMZILSMIIQ3QRhhjJBSRhhCG6CCObkR6V51e/WeZ1CONXS+E10ZdfffPc\n4ZS6Sf6lylh8qhZPCarZYOXCRfU+bvbptzkYZ29TbGskDrkSZjYK4o5qozcx9tbgEuhjdvqn\nf7DGl0LI9FYFXXjhYeUr1AitOEIV9wNy6ZJRmv+hH2N2YRzvi5tRK2ijlqL0VKjZqNOHtelc\nIaXd9o6rv70gdTzikM1IUbbWKeY2EV8H6MMtzxBMC7fcrcT4K2jv7g+7hVwJg+y6AL4P0Eio\ndcr2zjqpIxKDXLqUBYo70oGR1aWVYKudN1BOywIqimxG6gpe3BHFPgMcNUBRgLdK5RXGZNSy\nK39b/8T4cLGF7v6wW8iVMJ1Byx1FQSlWHOwLDdgxmVotaJgnlB6C9NbmoaHR+rBW95/5dTIh\nly5LqNLcUQ3Uis2X6xp24UynVjuDF4WMPbzq1PtO6njEQe6RXEDuBYQhughDjOQCkjDCEF2E\nIUZyAUkYYYguwhAjuYAkjDBEF2FkM9K14HHc0QzTJoz99HiwYVfJymY6rRAKCfHz0ftaChVu\nU7d0bMs9bQ/t++zUv7OG3MOXflZw4xu5EqakbWKfEQLYJjxMQRC7dD43Soi7sdZ4BfecUWQ9\n/2+W9nosdVj5IJcu7yN+95IEVInRBWETasbPQ2L/0/l7mYq1LTdIqS3vBZDNSH5Ozd/ngd2j\nQ3hwpgF8TRDVCaArbgir3f3tgiN/8zfC/wLjIqHm7xCoxv+bFNgpdVj5oES3wBV2ox+hfOkj\ndSzikc1IvWzN31aI55q/dexaznk7S2jmFEzX07TtEPnxynDzWrygmoKdbXIlzEfIwB0VR7W4\nM68FNaJ1AezW3Yii2MeQlMqfb2h5iv83O+Yqt9ENj1y6pNuu1N4QYrtSx3BrNXBXaqSlKP8y\noc0OSR2LeMg9kgvIvYAwRBdhiJFcQBJGGKKLMMRILiAJIwzRRRhiJBeQhBGG6CIMMZILSMII\nQ3QRRj4jFf6TP4xj/uuUhq+F42otlvXsv7bBsrkffbFvxKQxWy8fz9q4dvW9i7f/fsCUuahs\n+5RcCVPdNsqZBgPXakdBmEbHt08xTxpN0BtBvj1vDBy46NIA5VdBxPLpsgElcEe1UU+M/XQ4\nCg2lGJAWIZOfX3KP1Kljjo1qfkrqYEQjm5Fy+wWYBMH7AdIE+gUQlOI6kiyoOMZroLe7P+wW\nSvSX3AAIe0oX9nAa46px0EXqiMSgRP/aHeF+xwhgu1JUgmxG0joZiVGnFXpaGB31KvMYYI7T\nNsR4u2Wsuz/sFvJfkYy2K1JEnitSMHtFujlo0Mt2RYqw9SNR7C6xACcQaO3nFfZ0i0JDBwVp\nh0kdjGjkq9rZanb8836Mj+IrGF/OwFfv4ev3sm/fvfcIZ+JHmQ9wdk4WW0TB4UEs5F5AGKKL\nMKSxwQUkYYQhughDjOQCkjDCEF2EIUZyAUkYYYguwhAjuYAkjDBEF2GIkVxAEkYYooswxEgu\nIAkjDNFFGPmMpLN1g7DrIfYuybyNy7+6duLbqwe8vuiz+bOmLll++spvh1dfPHHqr7v/4Uc3\n3P1Vd5ErYXTO/SXcvBsDNzXWNkMW6YH6PKPzOnyiY/sBHc9e7LhV6rDyQS5deiMNd+SFQhlx\n2BmyzdkJWkjj5VO+XFyQpXK9zhN//0XqWMSjyMiGXcDO6nOxrQuAb9iDigalZxErMbLhIjuu\n42ldrF9DfTwCQA+fzoOWUoeVD0qMbDjBrSb6dL54U6rZ1UU+I1F5RzY0FhjZwO62y/box6Y+\nbhZyzd2fdRPZevARzR1pkYUb2UCj4vzaBOziBEjnHaex7Lg/5Ri+Nmnq0kk370z+S+qw8kH+\nKzXFJs0JBHTuyAaaZh60Bt+S73VRer6wA/mqdotsh9wyHr9hvBlvP4f/OYEvH314+9b1K3fu\nPsJ3H93NepT5OJuRJ8vdX3UXci8gDNFFGNLY4AKSMMIQXYQhRnIBSRhhiC7CECO5gCSMMEQX\nYYiRXEASRhiiizDESC4gCSMM0UUYYiQXkIQRhugiDDGSC0jCCEN0EYYYyQUkYYQhughDjOQC\nkjDCEF2Ekc1I31L8IvqlqI8x1tB4MHVQ71UjJirNGB8ZEhY8Znnb4Sd79BsxfMXJn/f/gfGj\nPdzYhiP/uvvrBUWuhKFtQ2EQOxQGoD3iRkrlriDEvNYCpGTik141pY5HHHLpYrQvCqOxLQoT\njngoOrJHtLk1PjhIseQQQPZBq+wgxO+49BAca8d+QsGveDBMZgqfRGnu/npBUWJw5lEXgzOp\nDLwILFLHIw4lBvMef1IX9iVuCzOlDuU5kM1IT5x5m+Q58zLQAOHss66wOaLwX3hl4kam8K0S\n77n76wVFroQZQ/HbusRTdZkrNYUrUN9pjV50SEmfIoVLvT30lR7TO3Xfx3w+/6TU8YhDDVek\niG6RzBXpwICX8or0/w1yLyAM0UUYYiQXkIQRhugiDDGSC0jCCEN0EYYYyQUkYYQhughDjOQC\nkjDCEF2EIUZyAUkYYYguwhAjuYAkjDBEF2GIkVxAEkYYooswxEguIAkjDNFFGGIkF5CEEYbo\nIgwxkgtIwghDdBGGGMkFJGGEIboIQ4zkApIwwhBdhCFGcgFJGGGILsIQI7mAJIwwRBdh5DOS\npgj39CW7ZrxRh9dosF9409hyA4Lrv9Gg95x3vp3w/elVx9Ze2Hnu/GP8j+JLf8uWMBrnxeIB\nJtoXi+fnscVtqjM/tk5yREvlFeGRSxeLbXMBDdLZNhfg5yPRxYdO+HXkuzNeq71jyh2pQ3kO\nFJkh+xm7RYnwti7slkDN1kN7d3/UbZSYIfudwAzZzlCJnQKJzkgdjkiUmCH70xO6mLiX0TBL\n6lCeAxm3dcmdIctt6+L75AxZbpIsBRpk8u1/wH+Euz/qNnIlTKhtQy09MnEbamlRUYqikC60\npI/VPzh0xo2vL4z9aerIdVJHIxblNmDT8Duvmeu9PqVJxY4JEZ93vCh1KM8BuUdyAbkXEIbo\nIgwxkgtIwghDdBGGGMkFJGGEIboIQ4zkApIwwhBdhCFGcgFJGGGILsIQI7mAJIwwRBdhiJFc\nQBJGGKKLMMRILiAJIwzRRRhiJBeQhBGG6CIMMZILSMIIQ3QRhhjJBSRhhCG6CEOM5AKSMMIQ\nXYQhRnIBSRhhiC7CECO5gCSMMEQXYYiRXEASRhiiizDESC4gCSMM0UUYYiQXkIQRhugiDDGS\nC0jCCEN0EYYYyQUkYYQhughDjOQCkjDCEF2EIUZyAUkYYYguwhAjuYAkjDBEF2GIkVxAEkYY\nooswxEguIAkjDNFFGGIkF5CEEYboIgwxkgtIwghDdBHGY0ba0iU9tVLPPQKfvKjCiIPoIszL\np4s4I832f3vWoskdLV8+/dGLKowoiC7CvIS6iDNSxEHuaXus461Hp3mCOWGM7LYlDDRouG06\nKNBx23Qg4J8o32Cf4ls2vlOn1JXP5nElc0b+yH/RuqEzFor+gzyFhxKm4LrQSONlDJr4zeoK\nLS7cGLhvdechj/DmIQ82Mk9KorAulAbVT6yJ8fXOnTJmLcInB1zwSDju4ykjmfn9ex57Od76\nOHeXiXD2yHn7kqoC25dwpFZgHnpqNdzGNwchhv+iUkDp7j/H3+QRPJQwz6EL4CHsjhxP6OIb\nCTBiMbwaD/AbrgQ/lYe1HgmsoCihy+Qn8+UuXgAwkdI9eBtGeiQc9/GUkcpNYR+zxqY73npo\nO8PU7MEeOc4wWhdXpBCf1N83vlun9NV5n3Mlc0blXpGGzVz0HH+SZ/BQwjyHLoJXpE++XZ3+\n+oWbg/5Y03nIY+aK9HDj0BfiilRQXZyvSLPZK5JatnbxlJH2R4bXbFg5IP7o0x81e/u5o1IB\nHkoYooswL6EuIlvtHq2bNmbaxkyBT15UYcRBdBHm5dPF7X6kF1UYdyG6CPOi6kKMJBFEF2Fe\nVF2IkSSC6CLMi6oLMZJEEF2EeVF1IUaSCKKLMC+qLsRIEkF0EeZF1YUYSSKILsK8qLoQI0kE\n0UWYF1UXYiSJILoI86LqQowkEUQXYV5UXYiRJILoIsyLqgsxkkQQXYR5UXUhRpIIooswL6ou\nxEgSQXQR5kXVhRhJIoguwryouhAjSQTRRZgXVRdiJIkgugjzoupCjCQRRBdhXlRdiJEkgugi\nzIuqCzGSRBBdhHlRdSFGkgiiizAvqi7uG+mVuSyN3mgrijZVxJVr26KmyIKNGoosWGv4XDu+\n0ifM8+nSrK7Iv6Jaa3HlxMrSpuacuSrWpUFjceXEpkubKm3EFaw9bO5z6eK2kcbEcSBaKwoN\naMQVpJG4clqKElkQWePsFNrl7t/tYV0okX+uaP3EyqKBqDgV64JE/hli00W0fs7ZIkYXt41k\nw7xaXLnDkCGu4GeJIn+5Y3uRBQvPFlnQk4jVZWg1ceXuwF5xBbu2FlfuBFwSV9CziNWlRS9x\n5T6PF1fuPPwprmDR6eLK5UKMJC3ESMIQI7mAGEkYYiRhiJFcQIwkDDGSMMRILiBGEoYYSRhi\nJBcQIwlDjCQMMZILiJGEIUYShhjJBZ1PiSt3vYXInbR29xX5ywsXiCzY/38iC3oSsbr8PFZc\nuazXr4kruOQzceVutXggrqBnEavLrK/Eldvzvrhy91vcEVdw4DZx5XLxlJEIhJcaYiQCwQMQ\nIxEIHoAYiUDwAMRIBIIHIEYiEDwAMRKB4AGIkQgED0CMRCB4AGIkAsEDECMRCB7AM0baX84c\nKWpIqEWn1+s35ldqkXWkuC/lC+b/pdvL+0QMfY4wPYVCuoiURSlVxP+kMulSIF08YqRHkZ9m\n7ff7Pf+C2egfEV/Xp3nVkaK+lC+Y/5fetn6W82fgCtFhegqFdBEpi1KqiNdFmXQpmC4eMdK6\ncObh7S75F7wBt0V83W7cZKSoL+UL5v+lGfOZh8ZjRIfpKRTSRaQsSqkiXhdl0qVgunjESNNq\nMg+z0vMveBpaRySNzcm3HPsHi/pStqC4L70WtFt0mJ5CMV1Ey6KEKuJ1US5dnl8XjxhpTEPm\nYVFq/gXPdVibdSBsbr7l2D9Y1JeyBUV96bVyQ8WH6SkU00WsLIqoIl4XxdKlALp45opUmXmY\nXElk6eH18y3CnWLEfClbUMyXHkiY+rxhegDFdBEpizKqPOdPKpAuBdHFI0baGJCFccce+Re8\nupt5GNw033LsHyzqS9mCIr50X/ja5wnTUyimizhZFFJF/E8qlC4F0sUjRnocO/bxDsue/Avu\nMW7CB4OX5luO/YNFfSlbMP8vvR+36rnC9BSK6SJKFqVUEf+TyqRLwXTxTD/S4QqmGFHLr39Z\n2Jo4Lb9Cej2l0TcV8aW2gvl+6begZ2glPkxPoYwuImVRTBXxuiiSLgXThYxsIBA8ADESgeAB\niJEIBA9AjEQgeABiJALBAxAjEQgegBiJQPAAxEgEggcgRiIQPAAxEoHgAYiRCAQPQIxEIHgA\nYiQCwQMQIxEIHoAYiUDwAMRIBIIHIEYiEDwAMRKB4AGIkQgED0CMRCB4AGIkAsEDECMRCB5A\nWSP5f88+pk8UUZTfkoPfcSMDMqSNS3lEKvOSqWKnnEthlFJE5UbKyba94LfksO248RKkjDgj\nvWyq2HFpJMUUUYmRyk9gnlM+P4NmVU9+8wHelZ6UMgufo2b4HbWV5LfksO24kQFLi1qbPFQs\nbBlwpczWcoWShueeXV46VewwRsp+Ny4h9VesFkVUYqR5hTE+ZLp9Hobih6lTbwcsxRdDNl2D\ngdmOsrkLoF8L2p0Bb2fdiFimRMRy4UKZGz6rcEas0/qfL5cqdhgjrY25j7e9idWiiMJG8glm\n0E7Ed7y24YFt8Hn4i13gfFUk89n73TLgiFNZm0DsjhsZcArjRmMUCVkmXCjzbTTzWb+WjnIv\nlyp2GCMd9p59kXmlFkUUNtLCywxlmQpvh444dh2TLjcxnlB1ji46OjqkRQZccyrLC8TtuMHV\nfe1bdLyQuFBmehnms/HVHOVeLlXssPdIm1r4lfgVq0URlVTt8FbzxshsJl1OYvzha6sTuE/z\n3itygvA7brwEKeNCme+imHf7tnKUe7lUscM3NmQvMd1XiyJqMRJOihuMmXR5H99Jmn0ncCV+\n2Gfj00ay7bjxEqSMC2Vu+n6PL0eucJR7uVSxwxhpQefH+IDuvloUUY2RxrP12fMws3h454d4\nV3p8TM8Hzkbit+Sw7bjxEqSMK2W2lS1U+BN7qZdNFTuMke60jYwtvhKrRRHVjGz4sgpm0+U/\npeNQH0SZ/w+oxUhXE9dgNl3uKh2I6iDK/L9AJUYa4fcx+/RUuhxN5ZmnRFCqQEgZosqTKK+I\nSoxEIPz/hhiJQPAAxEgEggcgRiIQPAAxEoHgAYiRCAQPQIxEIHgAYiQCwQMQIxEIHoAYiUDw\nAMRIBIIHIEYiEDwAMRKB4AGIkQgED0CMRCB4AGIkAsEDECMRCB6AGIlA8AD/B1BculxOOMM3\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Normalized log2(read counts) - Hypo”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mfrow = c(1,3))\n",
    "plot(log.norm.counts.coll[,1:2], cex=.1, main = \"Normalized log2(read counts) - Media\")\n",
    "plot(log.norm.counts.coll[,6:7], cex=.1, main = \"Normalized log2(read counts) - Iso\")\n",
    "plot(log.norm.counts.coll[,11:12], cex=.1, main = \"Normalized log2(read counts) - Hypo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eb01a64",
   "metadata": {},
   "source": [
    "Next we can plot the `meanSdplot()` as below with `ggplot2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f53a03da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ABURo+dn6oyxqkL6boDAABVosfOH7nvnKPrDgAAVIlg53e05DayHQAAqIxgBwAA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h3u26/+9196ativUUXIir9kW7/ouu6ubR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\nFkubd1jZtE2B1V7ZcwQAAACAsgJiV8S1h3YihP5uel1lTwQoR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DM2cXqPYcMffPuXVt0aXD7z5uIPPv9mUrvjuxFCJotV3CwXqTWxM1ks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\nx7fja3M/r0mr45WRXXB5JuFxlfSJjdEFQGmseGuElmChYYtl5yWQVhdbnf0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ouXw675d3ntCziJ3OsxOh80RyS7pgd+HCBSKa8S/xeT9x\nIx0Pwe5qyZ4ZpadgPWdbabyTrmsqzXbbFk4VbVw227FUx0gzmbS7pTTesUinsh0SpDpGGu/4\nSCckjXcsYTCy2S505Xqzayd2lzZ3bvaJ28BL4x0n6dYqm+1ES+DKZruIJIFJ4520hiQn0pBk\nBWDZbCdNTtJ4x0c6IWm8k/7AZbMdS3WXyyTxTvoekA6SRTpGNtuxVMfbtnDq6OfXimqaJZuS\nxjs+0gmld7lW9MjOt54WdZeWzXbs2LTZbFar1e/3SzuPSded1hPvdJ5SdP5Hze2o7w7BLpkl\nabDjsXiX6KmOEOxaip0cVc6/V5vtVFarZ/GOTdRJsXgnjXQMy2QfPHcnP9Uhi8U7aaqTbkr6\n65xh2U4a6Zj3Zgxk7cWkIYNh8S6k3EKATd1JI93lGkG2k6Y6hsU7la4GLN7JxLVLhNlOpYzF\nO2mqY1i8U5kMY9nu7cnX3v+6Ygc8Fu9UfuAs3kkj3eWaS9lO5T0gHK001TEs3okinRCbupNG\nustDEvzApamOYfGOTdTJbOpSvBMdmCzYERHLduVr54vSodBVxbtf3mH2qs5OohoEu2SW1MHO\nSBDsWlvL/qpu2XZI64oqT0/vVD39yra+/PC9C7ZoltnStDuHpti1O4NtePSGiWuPaZaZdPQi\nU79yd2lD2l1KichE2mV6mozp6Yu1blLPh1Ye1ixTybVMhNNuk7jp6WH3LtiqY3fa58Zmv/aQ\nTCZdH91WSX5M/bHvNWssDu2jgOQymTDY8VQincp2WixWpxRZCHbJDF+eAEh2d8xd3Za705Pq\njG3CmqNtuTs9qQ4ADAPBDgAAAMAgEOwAAAAADALBDgAAAMAgEOwAAAAADALBDgAAAMAgEOwA\nAAAADALBDqBN6VmYqsWLV7XMyd0fteXufvxouWbN2ondY7W70//+XLPm1J5PYrW7dZN6atbU\n7P44VrvTw1v9g2bN5mfviNXuPMcPataoLNB9tfQsLOc/dzJWuwNo/7BAsUFggeK2obSm6NVG\nMT2dJYc+/opSzVdL57DbSp0nvlgyg91WWamOpbpr756mVFP50d/Z7b7jnpGtqdlzOax0G/Yb\npU39tP3/+BtFdz+qVHN8xz/Z7e63jZPfXfnlKFYw5F7ZmtUPdBr9wtv87bz+tyvtjqW6rS8/\nPPk9+dYLP3+7k93O7XerbM3pfZeXi+vYd4jS7liqy7tFcUhHtqxht5We3fkf9rLbKp1/Lxz9\nhr/hLrhOqaauaj+77ercXbbmzP7t7HZ2UX+lTXlPXFx4OeOaPko1LNUdLluv5917073/LVtT\nsflNdltlbWGW6kQHqXSBYvVNxXBpYqFYnVJEsEBxMkOwMwgEuzYjPRG3+BQs2pTsdkTxThjp\nGOlvR+HvRUYU72Qn6kTxThjpGGm2E6Y6RhTvWKQTEsU7YaRjpNlOmOp40vRzev826aZE8U52\noq7LoF8L764a2/GuP4pHJc12wlTHiOKd7ESdKN4JIx0jfXbCVMeI4h2LdEKieCeMdIw02wlT\nHa9i85sjn7li5pVFOiFRvJOdqOt0wzDRI9J3rzTbCVMdI8pkshN17ChTCnbS7VCrpTpeDE8p\nDIJdMkOwMwgEuzbGn4t/+flXZ2dJPt7JpjqGj3eykY7hs93Wlx9WnzLkO4zJpjqGj3eykY7h\ns51spGNYtpNNdQwf76SRTogPQLKRjmHZTv3yKx/vhBN1Uny8k410DMt26pdf+Xgnm+oY/tnJ\nRjqGz3abnh6m0kH4iyUz+F4UsqmO4eOdNNIJ8VN3spGOYdlO/fIrH+/U3718vJONdAzLZOqX\nXw+XrVcJdsJNtWqkE4rVKYWHYJfMEOwMAsEucTkcDrPZHAgoto2PLZ3NavXIVL7cxoRD2s1M\nicienqNZY03T7nvLmtCriIY4PUOyOLV3Fw5qv2oqre6FAnWnNWtSs/P0bEqPkI6R+07/pFmj\ns1Wrnk+5xSrT6HyHH/3XRvVgl9AQ7JIZvjwBAAAAYBAIdgAAAAAGgWAHAAAAYBAIdgAAAAAG\ngWAHAAAAYBAIdgAAAAAGgWAHAAAAYBAIdgDJJVbNag+Xrf961f9ollV++LpmTfna+Zo1O996\nevtfFbuQ8bb/9dEtL43X3NTWlx/WU/PJHxUbo/E++eNvvnx1quam1Bfd1V9TvnZ+rDalp+ar\npXOO71inXlOx+U096/fqqYlhf+R22I4ZoC1hgWKDwALFiauNFyhmZNdxFf3C09PIsmTyi7I1\nfOzLzMxMSUk5f/78wInzpDWiSPer6YtkN7XzrafZbVEnK0YU+1iXWCFR7JNtoSuKfdJ+Yjw+\n9tntdovFcmvpUtmaLX+ZKLwr2w1ClLFkf0p05Q9KT39VnbtT6kfM9zjJzMy0WCxdhvxWtkbU\nAUK2xaoo0rVSX1Ql6u9wzc4TCQ0LFCczBDuVQ7DaAAAMYElEQVSDQLBLXPEKdqSvWa2oTKlG\nGO+EM3ks2JEktchO1ImynTDSCQnjndJMnijbyU7mibKd0mSeMN4JZ/L4YMeHg9HPr728rysj\nHSMKW0ozZ8IflNJ0pnBTStvRuTthvBO2reODHX9OFnZrVWrqJcp2shN1Ot9ysaKyOwS7dgjB\nLibaItj5fL6lS5eWl5eHQqF+/fo98cQTubm5SsUIdi2DYJe44hjseL1ve0jz96ueGj7bia7P\nCoMdb+DEeZrXXn81fZFSpGP4bKd5fXb0C29rXp+9Y+5qzeuzfLYTXZ8VBruLu3t+rVKqY0Y8\n+YbmxVA+22n+oPRsSk/N0MdfkXYiFgY70teqlYhuGfd7zWuvet5OMSS7OwS7dgjBLibaItjN\nnz+/trZ25syZDodj5cqVZ86cWbx4sdks//E+BLuWQbBLXHEPdq1KGuyMRBrsjEQU7AwGwa4d\nQrCLiVb/8kRtbe2ePXtKS0sLCwu7du06a9asmpqaioqK1t4vAAAAQLKxtPYOKisrbTZbjx49\n+Lsul6ugoKCysrK4uJh/pLGxkeM4/rbJZDKZTK03mNbefhzxz8uoz45n1GdnuiTeA2kVxn5n\nGvu14xn12eG1A6Nq9WDn9XrT09OFb6+MjAyPx8PuvvTSS5988gl/Oysra8uWLa06npycnFbd\nfnylpqbGewityNivHX8x3aiM/do5HI54D6EV4bVLXNnZ2fEeAsRBqwc7kvzRILrk36tXr0GD\nBvG3XS4Xm72LOavVGo1GQ6FQK20/vvjPLEYikXgPpFVYLBaTydR67434wmuXuMxms8lkCofD\n8R5IqzD2a2cymcxmM167dsVqtcZ7CEbQ6sEuMzPT6/VGo1EW7zweT1ZWFiuYMmXKlClT2N3W\n/vKEcLLQSJLhyxNGfe2S4csTRn3tkuHLE0Z97ZLhyxP8L994j+Uq4MsTMdHqX54oKiriOK6q\nqoq/6/F4qqure/fu3dr7BQAAAEg2rR7ssrKyhg4dumTJkqqqqurq6ldffbWwsLBv376tvV8A\nAACAZNMWn7GbOXPmsmXL5s2bF4lEiouLZ82aha/qAAAAAMRcWwS71NTUp5566qmnnmqDfQEA\nAAAkrVa/FAsAAAAAbQPBDgAAAMAgEOwAAAAADALBDgAAAMAgEOwAAAAADALBDgAAAMAgEOwA\nAAAADALBDgAAAMAgEOwAAAAADALBDgAAAMAgEOwAAAAADALBDgAAAMAgEOwAAAAADALBDgAA\nAMAgEOwAAAAADALBDgAAAMAgTNFoNN5jaCNjx4612+3r1q2L90Dgqk2aNCkQCGzcuDHeA4Gr\nNm3atNOnT3/00UfxHghctRkzZhw6dGjbtm3xHghctdmzZx84cODjjz+22+3xHgu0NUu8B9B2\nfD4fx3HxHgW0hM/n8/v98R4FtITf729oaIj3KKAlAoGA1+uN9yigJRobG71eb/JM3IAQLsUC\nAAAAGASCHQAAAIBBJNGl2OHDh1ssSfR8jeTWW28NBoPxHgW0RElJSV1dXbxHAS0xcODATp06\nxXsU0BL9+/fPyMgwmzF3k4yS6MsTAAAAAMaGOA8AAABgEAh2AAAAAAaRFJ858/l8S5cuLS8v\nD4VC/fr1e+KJJ3Jzc+M9KNCltLT02LFj7K7D4diwYUP8hgPaampqFi1aVFVVtWnTJvYgjsGE\nIPva4Rhs/+rq6v7xj38cOHCA47gePXpMmTKlqKiIcNwlq6T4jN38+fNra2tnzpzpcDhWrlx5\n5syZxYsX41OlCWHq1Kn3339/SUkJf9dsNmdnZ8d3SKBix44dy5cvLy4uLisrE4YDHIPtn9Jr\nh2Ow/Zs9e7bdbn/sscecTueaNWu++eabZcuWORwOHHfJyfgvcG1t7Z49e0pLSwsLC7t27Tpr\n1qyampqKiop4jwt0aWho6Ny5c4dL8BulneM4buHChSwE8HAMJgTZ145wDLZ7DQ0NnTp1mjlz\nZs+ePfPy8h555BGPx3PixAkcd0nL+JdiKysrbTZbjx49+Lsul6ugoKCysrK4uDi+AwNNHMcF\ng8Fdu3atWrXK7/f36tVrypQp+fn58R4XKBo1ahQRHTlyRPggjsGEIPva4Rhs/9LT0+fOncvu\nnj9/3mQyZWdn47hLWsafsfN6venp6SaTiT2SkZHh8XjiOCTQKRAIZGZmBgKBGTNmPPfcc6FQ\n6Pnnn0dvsYSDYzBx4RhMLA0NDUuWLLn33ns7dOiA4y5pGX/GjoiE72wiSoaPFRpDRkbG6tWr\n2d25c+dOnjx5586dd955ZxxHBS2AYzBB4RhMICdPnvzzn/988803T5s2jX8Ex11yMn6wy8zM\n5Hshs7e4x+PJysqK76igBRwOR4cOHc6fPx/vgcDVwTFoGDgG262KiooFCxZMmDDhnnvu4R/B\ncZe0jH8ptqioiOO4qqoq/q7H46muru7du3d8RwV6HD9+/PXXX+c4jr/b2Nh49uzZvLy8+I4K\nrhaOwcSFYzAhHDx4cMGCBc888wxLdYTjLokZf8YuKytr6NChS5YsKS0ttdvty5cvLyws7Nu3\nb7zHBdqys7N37doVCoUeeuihcDi8evVqt9s9ZMiQeI8LFF24cCEcDjc0NBBRbW0tEblcLhyD\nCUH2tcMx2P41Nze/9tpr9913X7du3fgXjnDcJbekWMcuEAgsW7Zs165dkUikuLh4+vTpmI5O\nFFVVVatWraqsrLRarX369Jk6dSq6krdnjz766NmzZ0WP3HfffTgG2z+l1w7HYDtXUVHxhz/8\nQfTg7373u3vuuQfHXXJKimAHAAAAkAyM/xk7AAAAgCSBYAcAAABgEAh2AAAAAAaBYAcAAABg\nEAh2AAAAAAaBYAcAAABgEAh2AAAAAAaBYAcAAABgEAh2AAAAAAaBYAcAAABgEAh2AAAAAAaB\nYAcAAABgEAh2ABAzI0aMGDZs2O7duwcOHOhwOLp06bJw4cJQKPTCCy/k5+enp6fffvvtR44c\nYfVfffXV6NGj3W630+ksLi5esWKFcGvr168vKSnJyspyu90DBgxYv369aEfffvvtqFGjXC5X\nbm7u+PHjz50713ZPFQCgXUKwA4CYsVgsJ06cmDt37qJFi/bv33/TTTfNmTNn7NixoVBo+/bt\nGzZsKC8vnzZtGl9cVlY2cuRIjuPWrFnz/vvvl5SUTJs2beHChfy/btiwYfz48QUFBWvWrFm3\nbl3Hjh3Hjx//4Ycfsh1VV1dPnTp11qxZ33333WuvvfbOO+88+eST8XnaAADthikajcZ7DABg\nEHfcccfnn3++d+/eW265hYi+/PLLESNGDB48+Ouvv+YLJkyY8O677zY2NqakpAwYMKCuru7Q\noUN2u53/1zFjxnzxxRenT592Op2vvPLK1q1bP/jgA6vVSkRerzcnJ+fBBx9cs2YN21FZWdmI\nESP4/zt69Oj9+/fX1tbG4WkDALQbmLEDgFhyuVx8qiOi/Px8Iho2bBj71/z8fI7j/H5/bW3t\nvn377rrrrmg02nTJ3Xff7fF49u3bR0Rz5sz59NNP+VRHRG63u3PnzidOnGCbcjqdLNURUdeu\nXevq6trgCQIAtGcIdgAQSzk5Oey2xWKRfSQSiVRXVxPR3/72N6fA9OnTiaimpoaI6uvrZ8+e\nff3112dkZDgcDofDcfLkyUgkwjaVm5sr3G9KSgquPwAAWOI9AABIRiaTiYimTJny+OOPi/6p\nsLCQiB588MGysrI//elPI0eOdLvdJpPp9ttvj8NAAQASCoIdAMRBt27diCgSiZSUlEj/9dSp\nU5999llpaelzzz3HPxIKhTweT5sOEQAgAeFSLADEQXZ29qBBgzZt2lRfX88eXL169bx580Kh\nkNfrpSuv4S5fvtzv94fD4TiMFQAgcWDGDgDiY8GCBaNHjx42bNizzz7bqVOnHTt2vPzyy5Mm\nTbJYLL169SooKFi6dOnNN9+clZX1/vvvHzhwYOTIkXv37t22bdvgwYPjPXYAgHYKM3YAEB8j\nRozYtm1bXl7ejBkzxowZ895777344ovLli0jIqvVunHjxm7duo0fP/6BBx7wer0bN26cPXu2\n3W6fNGnSqVOn4j12AIB2CuvYAQAAABgEZuwAAAAADALBDgAAAMAgEOwAAAAADALBDgAAAMAg\nEOwAAAAADALBDgAAAMAgEOwAAAAADALBDgAAAMAgEOwAAAAADALBDgAAAMAgEOwAAAAADALB\nDgAAAMAgEOwAAAAADOL/AUU6z2FsD2bvAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msd_plot.coll <- meanSdPlot(log.norm.counts.coll, ranks=F, plot=F)\n",
    "msd_plot.coll$gg + ggtitle(\"sequencing depth normalized log2(read counts)\") + ylab(\"standard deviation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df26651d",
   "metadata": {},
   "source": [
    "### Decreasing heteroskedasticity\n",
    "\n",
    "As we can see from the plot above the counts are indeed very heteroskedastic. The `rlog()` function in DESeq2 returns values that are both normalized for sequencing depth and transformed to log scale. So we can use this for our purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "33d5de1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "DESeq.rlog.coll <- rlog(DESeq.ds.coll, blind=T)\n",
    "rlog.norm.counts.coll <- assay(DESeq.rlog.coll)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "044f099c",
   "metadata": {},
   "outputs": [],
   "source": [
    "colnames(colData(DESeq.rlog.coll))[1] <- \"Treatment\"\n",
    "colnames(colData(DESeq.rlog.coll))[2] <- \"Time\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36fee17d",
   "metadata": {},
   "source": [
    "Now we can plot the meanSdplot as before and compare with the previous data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5902409e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BwXAACgpqam/kZ1OhKn0+m7UU1mkZqpRBfhGLF7+umnT5w4MX/+/CVLlqSkpDz1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\n54b5VTvMkbDP5nh7xtmvz172ZfGkWA8NDNCantu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YTO/USsdgbvfuPVfoRD2yeDG11TGY3qmVjsHcTpMWorMAABa+SURBVK10DN7t1FbH\nYHqnUDoeNnWnVjoG73Zqq2MwvWMTdWqY3qmVjsHc7qXLR3/3rte0ypjeqa2OwfRO7U8M5m1q\npWO8tuq0q1/vMlwV0zuF0vGwqTud9fBup7Y6BtM7NlGnhumd2rEGhsS5ndrqGEzv1FbHYHqn\nsznmdmqlY2x94S4WL6a2OgbTO7XSMZjbqZWOwbvdUU3UqZG57MN/jeiUMb2TmaiD2I1YIHZK\nZPL7CCFjz77csMZVUGZYY5GIYHr35oXLHt4hMypDZAKIBkd0E0KIORwubaouP/RVec1X5TVf\nj63fZz2Sm3Q4u6hi3KyKcbN3Tzi5LU15TMp8IfZ1SyV6yQSV9mpfLOY2125Ys+XZ1XMuu8Ow\nzDAykuh+R/NIfuqisjnJbclkGTkz84xrVJ8KIbZk5QyuYEipmYY1VolGOW/cuvTChz6WGZUh\nMkFkJrNUiFzoyB9IOsh8esMBv8zmZAj2GX959nUZR+19/Oeb5191t2FZOBQwrNn6wl2GNZIM\n5UEXEyB2IxbcYzcMWPrHT80SMazRZVRrXXnNVxMPfV1e89X42r1Of/9ZJ2S2HBw1ft/oKV+U\nTq0YP7s5I3+IBzYEyJyEoki0TjBAkmhZHZDktJ/+MdZDAGAEAbED/WS5W8trv5lQu7e8fu+k\ng1+mc/Hb7Wk5FeVzvho786uxM6uKJ/XZnURuCg0AAAAAQwnEbuTi6u0ur/tmYs1X5TXfTKz9\nOqdz4Cbu1oz8T09a/O3oqd+OnlpZMsWT5IrhOAEAAAAgCcRuRJDm6SpsrR3VWlfYXFPUWlvY\nUlvYWpfuGbgBzp2Svm3Sgn2lU74tnVI5ZnpHWnYMRwsAAACAYwNil2hkdLcXttYVtdRQkytq\nqS1srXP1uvmasNnckpG/v6j8QOGEb0un7Cud0pA98KtS9Y8nAAAAADAsgNgNY1y97lFt9aPa\n6gta68c07i9trC5srU3x9vA1IbOlObOgsmRSQ3ZxQ3ZRQ1ZhQ3ZRTf6YPqR4AQAAAAkHxC7e\nsYaC2e7W/O72/PaGvM7G3I7G3I6m/PaGgvbDjsE/XwhabI3ZhV+NnVmfW3o4p/hwTvHh3JLm\nzFFBy8C7LNPuBAAAAADDFIidkr0b1xq2n5h3w0M6zVSPDZe3J7ezKb+jMa+jMa+zOa+zKa+j\nKa+jMbu71ayysT67syG7qCGn+HBOSX1uyeGcksO5xS0ZBWG5jln6bP/zrXN+qpnPQ3lxRalO\nU+WjYv3tyxSBE8Ka7971f1HZ3JZnVxt2M5HsYxctZD5yMjXR3ZxOitFRsfnRlYrACWEN6549\nBLy26rSh7Hiy7j/mXvwnzf7MlHduO+vsu96MyuY23P1DReDEsdVI8vGfbzbsZiLZxw4AEBVG\nSoPiGz4d+F3nowt7hPXXbxzQ3I3/8wOt1c674SG6oON2GWUDiUNB7tqoNRTMcbfmdrUUdDbm\ndjbndbXkdTYXdDbmdbWwMC5GxGTqSM1qdGU1puU0ped2FpU3ZY1qycxvySzoShnUPLb1m4GR\n5EyeJxzS/ncGAqzGLrlCa+Q7n+oXGh232/PS79jy1EtuEtb87cflbPniR8U7at0NA0PVcbv1\nt/fnLui43du/voAtL/r5o8KaTY/cwJZ1TjNbnl1NF3Tcbs+rf/T7+9vA6iRPaP1zITJhJ0O/\nOS294xvfa0Vw8r1kddxu86Mr6YKO273/h6vZslbyxNu/vpAtn3efZrLIG7cupQs6bvfaqtPY\nslYZX/P9/9UMO3n1xv4wDB23W/cfc1NTU71ebzAYpBGxgs394gy2LLMHdLxtw90/lK8hhLAA\nFQX1W//FlnMmztVa1cd/vpkuyBx0RO7jFEW0DoR9m18OH/mLOloH3RCDBsUjlhEhdrzVUdRu\nx1sdQ6F3TOl4FHr31urz/+2FA5ndbXkdjbmdzbmdTXkdjdlth3O7WvK7mrK6202qHd5nczRl\n5Ddn5LVkFjTT/zLymzPzD4VCAYuyr70ixpFXOoba7Xiro2x5dvXlLw4KTmBKx6PQO17pGGq3\n462OonY73uoYCr1jSsej0Dte6Rhqt+OtjqE40/BnF4ZC775+/f8lJye73W4mdkQu6lESmXji\nqGzO5XL5/f6xC79vuB6ZkEr1yVh4GlboHVM6HoXe8UrHUJsN7zQMhd4xpeNReBuva1o1WmUK\nvWNKx6PQu3X/0e9DTOwIIWq3462OIrkHFOrG69pR1ajdjrc6hkLvmNLxyBx0kh+naKE+oBp2\nvd3d3R0efKlE5tiMKyB2I5ahELuenp4nnnhi27ZtwWBw2rRpP/vZz/LyNJOIoit2136gd62Z\n6p1Q6RjM7dRWl+bpzOloyu1oyG1vSNm/O6+no8TvzelqzulssaqyccJmc7srqykjvyU9tzk9\ntyUjrykjvzk9tzkjr5ObfrPYnYSQv/24/IIHNmkN6f9uWvRvfz1ENKyOQfVOrXQ8dOpOqHQM\n5nZCq2NQvVMrHSM5Ofm7f9hINJSOwdxOaHUMqndCq2NQvRMqHYOdZoQnGAbVu+1r70lOTlaL\nHYV+7x//171MsuTxb46Knd/vl9wc1Tv9hEp6PtY/BzO3E1odg+qd0OoYVG62PfzvnZ2dVInU\nULcTKh0PVTehrh1VDXU7odIxmNsxqyODxa5/cw9uJiKl46F7QKh0DOZtQmNTlOnXUL0TKh2D\nuZ3Q6hj0uNM/6GQ+TlGEP6DS09PVYkeid4wPDRC7EctQiN2aNWtaW1tvvPFGp9P57LPPNjY2\nPvzww2aNu8Gi+ClUT9SpkYlPMNXszW+ry287XNBWl99aX9BWn99aX9BW5xD9205XZmtGfktG\nXlNmQUtGfmtGXnNmQUtGflt6bshsCXq7DTcnmZ3q62g2rOmq1cwUHxjwwa9kNieTHKp/1k9O\nTg6Hwz7f8M6r0BG74QUTu1gP5LhISUlJSkrSEbvhglrs4hOZGz35i5jDFC2xG15A7EYsJ/zH\nE62trVu3bn3ooYfGjh1LCFm1atWVV15ZUVExa9asE73po8Xu9+W11OQ21+Q1H8pvPpTXfCiv\nuSav+ZCrR2lafpujMae4OWtUa0ZBS2ZBc/ao1oz8ppSM5swCv9Uek8EDAAAAAJxwsausrLTb\n7WVlZfShy+UqKSmprKyMidild7U4vd0pni6ntzurvSGrtT6z+VBuS21OW3122+E0t/Ivm5DF\n1pJTXFVU3pRT3Jhd1JRT1JRV1JhT1JEm+Ksi2OcdkhcBAAAAACDmhIud2+1OTU01mUzsmfT0\n9K6uLvbwmWee2bat/6YTl8v129/+NnobDyke//4X87PaDqvrglZ7W3ZhXXF5S05Jc97o5vzR\nzbmlzXmj27ILw2ZLb0td9IaUyKSnp+v8X3rx3eFwDNVwTgj0VdArgLEey3FhsVisVmsCvApC\niMvlircfgR0tFovFYrEM91dBSUtLG+4vxGq1pqamxnoUx4vVaiVGX8sgIRmKPna81RFCFMf8\n/v37t27t/xFAZmamzab8HehxoBS7rQsuTPZ0+pwub3JqZ2ZBW3Zxc0ZuW3ZxZ0ZeZPAgwTEg\n895ZEiKvjJ6GYz2K40XrPtdhBz2BDXfwdsQVUT0TxZKEeSFAnhN+BGZkZLjd7kgkwvSuq6sr\nMzOTFaxZs2bNmjXsYVTv9FT+eOKZ65SNNGV+PAEk0X/v8OOJuAI/nogrhsuPJ2Rob29PgJ8d\n4McTMQE/nogKJ/xvxPLy8kAgUFVVRR92dXXV1tZOmjTpRG8XAAAAAGCkccLFLjMz89RTT33k\nkUeqqqpqa2sfeOCB8ePHT5069URvFwAAAABgpDEUd3XceOON48aN+9WvfnXzzTc7nc4777zT\nNCQ3tD19lsF1jUcX9jx+pvG1j+cvKYhKzV+vKHv5upMMy/7vpkVRqdlw9yX6/T8pMs02ZWr0\nm9gBABKA4dKbF4CRTOJHil39nlhe+VQxrfAJhfb96JVGdY1C6RQ5XYy/XlHGln/45B5hjUL7\nhOETCqVbtvoV4ao23H0JW9aKaFRon7D1qOJ7XCY5VAvcYxdX4B67uGJ43WOn1aa4fsebDocD\n99jFCbjHbsSS+GLX29tLBqdQqINiKQq9E07mKdxOa6KO1zte6Xh4vdOayVO4ndZEHa93vNLx\n8HqnNZMnE4aocDv5iTqIXVwBsYsrhpfYUfivC/pdkZqaCrGLHyB2I5aRInbkiNtpWR3j+o1W\nw+uzP3ql0fDaK3U7Latj/PDJPfrXZ51O57Lfbdjwy2X6SrRs9StaSseQyWckhEw68zLDCy4n\nnXv90V57hdjFFRC7uGI4ih2F/7qA2MUVELsRywgSu2GK0+l0uVw9PT0JoEQQu/gBYhdXDF+x\n44HYxRUQuxFLgrTEBAAAAAAAEDsAAAAAgAQBYgcAAAAAkCBA7AAAAAAAEgSIHQAAAABAggCx\nAwAAAABIECB2AAAAAAAJAsQOAAAAACBBgNgBAAAAACQIEDsAAAAAgAQBYgcAAAAAkCBA7AAA\nAAAAEgSIHQAAAABAggCxAwAAAABIECB2AAAAAAAJAsQOAAAAACBBgNgBAAAAACQIEDsAAAAA\ngAQBYgcAAAAAkCBA7AAAAAAAEgSIHQAAAABAggCxAwAAAABIECB2AAAAAAAJAsQOAAAAACBB\ngNgBAAAAACQIpkgkEusxAD3eeuut+++//+abb16+fHmsxwLISy+99PTTT69Zs2bhwoWxHgsg\nDz/88GuvvfbYY49NnDgx1mMB5De/+c2mTZvWrl2bl5cX67EA8p//+Z9ffPHFO++8Y7VaYz0W\nMKRgxi7eCQQCbrfb7/fHeiCAEEL6+vrcbncwGIz1QAAhR96OU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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "msd_plot2.coll <- meanSdPlot(rlog.norm.counts.coll, ranks=F, plot=F)\n",
    "msd_plot2.coll$gg + ggtitle(\"rlog-transformed read counts\") + ylab(\"standard deviation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70c4e440",
   "metadata": {},
   "source": [
    "We can see from the plot above that the above operations have reduced heteroskedasticity of data. So we will now use this transformed data for our analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fcc3eb9",
   "metadata": {},
   "source": [
    "### Correlation Analysis and Clustering\n",
    "\n",
    "Next we can do a simple correlation analysis and generate simple cluster diagram to identify any association between the mediums of different osmolarities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9b25ea23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AExJKBP7V2G9PGpnPLMfPqGjPWG1PiW\n95GSWKfGT+/ohs5Y5Og3isTKnw+RLIFIbjAmoE/tXcb08amc8qx8+saM9YbU+GyRrFPjp3d0\nY2fMc/QbRWLlz4dIlkAkNxgT0DftXYb08amc8sx8+saM9dmp8TkiWabGz+zoxlz2pjn6c0Ri\n5M+HSJZAJAAkAJEAkABEAkACEAkACUAkACQAkQCQAEQCQAIQCQAJQCQAJACRAJAARAJAAhAJ\nAAlAJAAkAJEAkABEAkACbkXa8eHrK6V0BIAo41ika/XUgvSuVoSQw5bI6w8AkcSxSGSS9mM2\nKf/tOX3JXvUSewRABHEn0v6t9OSHzxWOlNchAKKIK5HWkymJ6d/hpVcg5rgUaU5i+qpSaf0B\nIJK4O7Vr88fE9Nj9ZHUHgGjiXKTTF9VvnHzAj9rkpy2GSewRABHEuUhJnqL0782K3uPPD4DK\nOBZp9q3XXDRq2LGvUXpXp2dl9giACCIhRGgrXpQDYo8bkRpXzp8797XV0voCQGRxLtLGie2T\nl0k1036U2CEAoohjkdZ2Jl1GXT1z5pQzOpLuW2R2CYDo4ViksSWPpqZ231lwkaTeABBRHItU\nNSYzPaKTjK4AEF0ci1R6fWZ6KkKEQMxxLFLN8Mz04M4yugJAdHEs0sSCG3ckp7ZOJlfI6g4A\n0cSxSFt6ksp+oyecP7JvBTnmJ5ldAiB6OL+PtP2W2kL9NlJx71kNEjsEQBRxFSK0Y/niJfW7\nZHUFgOiCECEAJIAQIQAk4E2I0Fc/OyBNdeVul30EIPR4EyK0a859aS4nO513D4Bo4H2I0LsQ\nCaiP9yFCEAnEAO9DhCASiAHehwhBJBADvA8RgkggBngfIgSRQAzwPkQIIoEY4P0b+6In0rb5\nILSsDXrvsAAi5fNQYWsQUkrrgt47LJAh0ooBAxil0RNpNh74DS2jRwfdAwtkiLSUsGqBSEAe\nSou0fdkyRilEAvJQWiQ2EAnIQ0mRxB7sg0hAHgqKJPpgH0QC8lBPJOHc3xAJyEM9kYRzf0Mk\nIA/1RFL4wT6IFF7UE0nhB/sgUnhRTySFH+yDSOFFPZEUfrAPIoUX9URS+ME+iBRe1BNJ4Qf7\nIFJ4UVAkquyDfRApvKgpkhAQCcgDIkUIiBReIFKEgEjhBSJFCIgUXiBShIBI4QUiRQiIFF4g\nUoSASOEFIkUIiBReIFKEgEjhBSJFCIgUXiBShIBI4QUiRQiIFF4gUoSASOEFIkUIiBReIFKE\ngEjhBSJFCIgUXiBShIBI4QUiRQiIFF4gUoSASOEFIkUIiBReIFKEgEjhBSJFCIgUXiBShIBI\n4QUiRQiIFF4gUoSASOEFIkUIiBReIFKEgEjhBSJFCIgUXiBShIBI4QUiRQiIFF4gUoSASOEF\nIkUIiBReIFKEgEjhBSJFCIgUXiBShIBI4QUiRQiIFF4gUoSASOEFIkUIiBReIFKEgEjhRWGR\nvp/0BbMcIgF5KCzSGvI8sxwiAXmoJ1JdE6eT4+vqGDNCJCAP9UQiBhgzQiQgD/VEmljU/eXN\nOp+RxzZvZswIkYA81BOJLupReOFWimsk4CcKikQbbqjo9AJEAn6iokiUrhhARnwDkYB/qCmS\nts+1aT0TIgHfUFUkuv40ApGAbygrEqUvXvo5sxwiAXkoKVLjyvlz5762mjMXRALyUFCkjRPb\nJ2/G1kz7kTUfRALyUE+ktZ1Jl1FXz5w55YyOpPsWxowQCchDPZHGljyamtp9Z8FFjBkhEpCH\neiJVjclMj+jEmBEiAXmoJ1Lp9ZnpqaWMGSESkId6ItUMz0wPZu15EAnIQz2RJhbcuCM5tXUy\nuYIxI0QC8lBPpC09SWW/0RPOH9m3ghzzE2NGiATkoZ5IdPsttYX6baTi3rMaWPNBJCAPBUXS\n2LF88ZL6XZyZIBKQh5IiIUQI+I2CIiFECPiPeiIxQ4Q2nH5Kmv4QCUhDPZGYIULfXzkpzZkQ\nCUhDPZEQIgQCQD2RECIEAkA9kRAiBAJAPZEQIgQCQD2RECIEAkA9kRAiBAJAQZEoQoSA76gp\nkhAQCcgDIkUIiBRelBZpxYABjFKIBOShtEhL8aIx4BNKi7R92TJGKUQC8lBaJDYQCchDSZHw\nYB/wGwVFwoN9wH/UEwm5v0EAqCcScn+DAFBPJDzYBwJAPZHwYB8IAPVEwoN9IADUEwkP9oEA\nUE8kPNgHAkA9kfBgHwgABUWieLAP+I6aIgkBkYA8IFKEgEjhBSJFCIgUXiBShIBI4QUiRQiI\nFF4gUoSASOEFIkUIiBReIFKEgEjhBSJFCIgUXiBShIBI4QUiRQiIFF4gUoSASOEFIkUIiBRe\nIFKEgEjhBSJFCIgUDB/ewKe2VmCmDwPoPETKByIFw+i2tVxqavjztA3iqAWR8oFIwSDrtC2Q\n0z+IlA9ECgaIxAQiATEgEhOIBMSASEwgEhADIjGBSEAMiMQEIgExIBITiATEgEhMIBIQAyIx\ngUhADIjEBCIBMSASE4gExIBITCASEAMiMYFIQAyIxAQiATEgEhOIBMSASEwgEhADIjGBSEAM\niMQEIgExIBITiATEgEhMIBIQAyIxgUhADIjEBCIBMeIt0u4vFm1nzgCRgBgxFem9wYcOXUzr\nDyWkxZ2s+SASECOeIi0rIyWkckXvit/9tgV5njEjRAJixFOkESVzd6867LSCtyhd3vw4xowQ\nCYgRT5E6n6n9eJ301adHt2bMCJGAGPEUqfwa7cf3pE6fnlzKmBEiATHiKdL+I/WfLa/Qf57W\nhTEjRAJixFOk0WVvN00uLD2DMSNEAmLEU6T65gV/SE6dVVryCWNGiATEiKdIdFG/KcmJXx64\ngDUfRAJixFSkNOvYxRAJiBFbkRpXzp8797XVnLkgEhAjpiJtnNieJKiZ9iNrPogExIinSGs7\nky6jrp45c8oZHUn3LYwZIRIQI54ijS15NDW1+86CixgzQiQgRjxFqhqTmR7RiTEjRAJixFOk\n0usz01MRIgTcE0+RaoZnpgez9jyIBMSIp0gTC27ckZzaOplcwZgRIgEx4inSlp6kst/oCeeP\n7FtBjvmJMSNEAmLEUyS6/ZbaQv02UnHvWQ2s+SASECOmImnsWL54Sf0uzkwQCYgRW5EQIgRk\nElORECIE5BJPkZghQj9eMynNmRAJCBFPkZghQutOOC7N/5AdzrsXCBApGOIpEkKEgGTiKRJC\nhIBk4ikSQoSAZOIpEkKEgGTiKRJChIBk4ikSQoSAZGIqEkWIEJBKfEUSAiIBMSASE4gExIi9\nSCsGDGCUQiQgRuxFWkpYtUAkIEbsRdq+bBmjFCIBMWIvEhuIBMSIrUh4sA/IJKYi4cE+IJd4\nioTc30Ay8RQJub+BZOIpEh7sA5KJp0h4sA9IJp4i4cE+IJl4ioQH+4Bk4ikSHuwDkomnSHiw\nD0gmpiJRPNgHpBJfkYSASEAMiMQEIgExIBITiATEgEhMIBIQAyIxgUhADIjEBCIBMSASE4gE\nxIBITCASEAMiMZEt0hVEAZpvkrpN1AAiMZEt0ujj53vMiw963cLfySqp20QNIBIT6SIFsZkk\nswoimQCRmECkfCCSGRCJCUTKByKZAZGYQKR8IJIZEIkJRMoHIpkBkZhApHwgkhkQiQlEygci\nmQGRmECkfCCSGRCJCUTKByKZAZGYQKR8IJIZEIkJRMoHIpkBkZhApHwgkhkQiQlEygcimQGR\nmECkfCCSGRCJCUTKByKZAZGYQKR8IJIZEIkJRMoHIpkBkZhApHwgkhkQiQlEygcimQGRmECk\nfCCSGRCJCUTKByKZAZGYQKR8IJIZEIkJRMoHIpkBkZhApHwgkhkQiQlEygcimQGRmECkfCCS\nGRCJCUTKByKZAZGYQKR8IJIZEIkJRMoHIpkBkb5bxSiESPlAJDNiKtJbv6mpvW1XYnISqxaI\nlA9EMiOeIi0tIc1KyNHf6tMQySYQyYx4inRyyTONO24p6fUjhUi2gUhmxFOkTmfpP18rHbwH\nItkGIpkRT5HKr078epBcApFsA5HMiKdIBw9J/v4DuRki2QUimRFPkSYW3NWg/24cRS67ECLZ\nAyKZEU+RNu5PBiYmGi8kBCLZAyKZEU+R6KYJl6Smnu4CkewBkcyIqUiiQKR8IJIZsRWpceX8\nuXNfW82ZCyLlA5HMiKlIGye2Jwlqpv3Img8i5QORzIinSGs7ky6jrp45c8oZHUn3LYwZIVI+\nEMmMeIo0tuTR1NTuOwsuYswIkfKBSGbEU6SqMZnpEZ0YM0KkfCCSGfEUqfT6zPTUUsaMECkf\niGRGPEWqGZ6ZHtyZMSNEygcimRFPkSYW3LgjObV1MrmCMSNEygcimRFPkbb0JJX9Rk84f2Tf\nCnLMT4wZIVI+EMmMeIpEt99SW6jfRiruPauBNR9EygcimRFTkTR2LF+8pH4XZyZ1RVp4g1Mm\nkUmOl10Y9Gp7RmxFinuI0OgOxzmkX/t+ThftEJrVl05MRUKIUCA9Cc/qSyeeIjFDhHY/+0Sa\nayFS5Bv1h3iKxAwRWlXVOk0l2eG8eyaEZ0+CSHJRRqS3v01NLHyavyBChCCSZJQRiTyTmriJ\nJUYKhAhBJMmoIVL9vHnk6nkJ5h7enL8gQoQgkmTUEGkGyWI4a5EkCBGCSJJRQyS69jly1owE\nM5/i3WSlCBGiEEk2iohE6aD37SyJECGIJBdlRLJNzEOEIJJclBGp8YFBPbolkdgCRIp+o/6g\njEi3EFLaMonEFiBS9Bv1B2VE6nrgx47qWDFgAKMUIkW/UX9QRqTyPzmrY2lMc39DJLkoI1LN\nDc7q2L5sGaMUIkW/UX9QRqRrawXuH9kGIkW/UX9QQqR6jZUj+zzzaX0CoWVj/2AfRJKKEiIR\nIwJL4sE+iCQXJUSqM8JfELm/IZJklBDJNsj9DZEkE0+R8GAfRJKMMiL1PKKJ3kNu3MxZEA/2\nQSTJKCPSvnsRQoq0f2UlhNSsZS+IB/sgkmSUEemnk46Z9z3d+kq/UQ0bbys6m70gHuyDSJJR\nRqTzj9md+L372GsoHb8fe0E82AeRJKOMSO1mpSb+WkPpfSWcJfFgH0SSizIilU1NTcwoo/Sa\n9vyF8WBfTBr1B2VEqm2VfNb8k/Y/o4vaDZbUAkSKfqP+oIxI/ygkXQedetKhBeR++quy9yS1\nIC7S6idEOPZYodl4IYASgEhyUUYk+savy/UB8COfoPQBaW8PEReprrS1ABUVInOVCkQ5uQUi\nyUUdkSjd/fWXqyU/SyEukswN4MfGhEhyUUKkdd9p/zJIbAEiRb9Rf1BCJDLQ8CiFxBYgUvQb\n9QclRBoxQ/uXQWILECn6jfqDEiJ5B0SKfqP+oJJIG5fwor5tA5Gi36g/qCPSgl6EzKN06Ksy\nW4BI0W/UH5QRaWFp5UBNpA1VZR9IbAEiRb9Rf1BGpCHVa9bpR6T11cMktgCRot+oPygjUtsZ\nNCESnd5BYgsQKfqN+oMyIhU/lBJpNuvRcbtApOg36g/KiLTf5JRIdTUSW1BXpPHjvW8jD4jk\nWz22yBbpnJaLdZG+nVpwnsQW1BXp+++9byMPiORbPbbIFmlddVE30uPQMlL9jcQW1BUpEJRd\nMYVEouvHtyGE7H3uepktQCSpKLtiKolEaePaeplHIx2IJBVlV0wtkTwAIklF2RVTRaTuBiS2\nAJGkouyKqSKS7de6iAKRpKLsiqki0tYEpC75W2ILEEkqyq6YKiKlPsq/xwiRpKLsikEkDhBJ\nKsquGETiAJGkouyKQSQOEEkqyq4YROIAkaSi7IpBJA4QSSrKrpgqIl2TgNQmf0tsASJJRdkV\nU0Uk3JCNBsqumCoiPWRAYgsQSSrKrpgqInkGRJKKsisGkTioKFLjA4N6dEviZTNmQCTf6rEF\nRHLCLYSUtkziZTNmQCTf6rEFRHJC1wM/9rJ6FhDJt3psAZGcUP4nL2tnApF8q8cWEMkJNTd4\nWTsTiORbPbaASE64tlby+0HFgUi+1WMLKSJtqmcUqiZSvcbKkX2e+bQ+gVfNWAGRfKvHFlJE\nmsSqRTWRiGcxIEJAJN/qsQVEskudEa+asQIi+VaPLSBSxFB2xeIqUm0WVXEUqeGTd1d53YYJ\nEMm3emzhWKTCwrI0RfES6R39HQN3ttUukLq94V0rFkAk3+qxhWORJlVmBqzidWr3ZmllI72P\ntBh+3sDC0g89a8YCiORbPbZwLNKuw3ql76XES6QBVSspre68TptcWHGiZ81YAJF8q8cWzgcb\nPq+4rGkyXiK1upzSLeSOxPS4Vp41YwFE8q0eW7gYtfv+26apBTMYsyknUvOrKN1R8FRielq5\nZ81YAJF8q8cWCBGyTZ+u2yjtfbk+ubtnD8+asQAi+VaPLdyI1Lhy/ty5r63mzKWcSPNI7csN\nS6oe3rnrgwHkL541YwFE8q0eWzgXaePE9skgmZppP7LmU04kOqeSVHSrIUVFpODyRu+aMQci\n+VaPLRyLtLYz6TLq6pkzp5zRkXTfwpjRO5G2rbTmd79jFG6z104eG24+oaZFWZvaC5e6rMgB\nEMm3emzhWKSxJY+mpnbfWXARY0bvRDqPOETmS9sp3fXoRqn1sYFIvtVjC8ciVY3JTI/oxJjR\nO5FGM446y5czjlZyN/Rm8rbU+thAJN/qsYVjkUqvz0xPLWXM6KFIDjeY5A0NkSQRT5Fqhmem\nB3dmzAiRpAKRfKvHFo5Fmlhw447k1NbJ5ArGjBBJKhDJt3ps4VikLT1JZb/RE84f2beCHPMT\nY0aIJBWI5Fs9tnB+H2n7LbWF+hBYce9ZDaz5IJJUIJJv9djCVYjQjuWLl9Tz8ulAJKlAJN/q\nsUWUQ4QgklrEVKTgQ4QgklrEUyR2iNDqzO3PJyGSTCCSb/XYwpsQoRUF2RE5OwSrhEgCQCTf\n6rGFRyFC33+X5iX1jkg7n/wk61PD89+5rM8OECnN4hvMqa21KFjsTb8TIETIAXtKp7mswTkQ\nKTN/21pTamrM/97Wy02HECEn9O+/x20VToFIvs1vC4QIOWHT6IGPLEISfcnEU6QIhAitnW/O\n8cdbFKwVbRlJ9L0gniJFIESorrS1KeXl5n8vFc6If+qZY5FEXzoxFYmGPkQoVBtaFpHopDPi\nK5IQioq0cclme7VLAiL5Nr8tIJKj+Rf0ImQepUNftdeCBCCSb/PbQoZIKwYMYJSqKNLC0sqB\nmkgbqso+sNeEeyCSb/PbQhOXbWwAACAASURBVIZIS5ljVyqKNKR6zTr9iLS+epi9JtwDkXyb\n3xYyRNq+bBmjVEWR2s6gCZHo9A72mnAPRPJtflvgGsnJ/MUPpUSazQqO8gSFRFrIi5FbyF5e\nGZHC/mCfdxt6v8kpkepq7DXhHoVEGt3hOAOHHGL83CFKtzecixT+B/u829DntFysi/Tt1AK5\nSVsFUEkkl/9/aogUgtzfwW3oddVF3UiPQ8tI9Tf2mnAPRBIudzu/LRyLFILc3wFu6PXj22jH\n4r3PXW+n/pfGSaBrVxm1vGRvy3gDRNIJQ+7vIDd049p6u0ej0Z1Pcc/RR0uopHMoDmsQSScC\nD/Z5uqFXvz3/vXUe1u8p4egJRNKJwIN9Hm7oWZ0T4yzdHveofo8JR08gkk4EHuzzbkPfTVoM\nHHfB2L4lZI4n9XtNOHoCkXQi8GCfdxu6a69kvpMV1Yd4Ur/XhKMnEClB+B/s825Dl8xNTdxY\n5kn9XhOOnkCkJmL7YF+711MTszt6Ur/XhKMnEMkWKoo0timgYfDFntTvNeHoCUSyhYoirT9y\n+DOfrf5i7qD+X67RkF6/14SjJxDJFiqKlPOWdOn1e004egKRbKGiSCefOiIb6fV7TTh6ApFs\noaJIzgjH7qsTjp5AJFuoKNKR97Li3d3X7zXh6AlEsoWKIhWTitNfsZ3+Oxy7r044egKRbOGd\nSAsWyK1PfP5N9w0oIp0m20z8HY7dVyccPYFItvBOJNn12Zp/wz39ikif+3/wsD/eEY6eQCRb\nKCqSxvo7epJm45Z7Vr93hKMnEMkWyoq07YlTmpGakuLJohdL4dh9dcLRE4hkC0VFeufsvUjF\nWW/S1b8l13nUH+8IR08gki1UFOmr6w4i5LC7kmPgv67yqD/eEY6eQCRbqChSIWl57kdNH26v\n8Kg/3hGOnkAkW6goUt852zIflj/rUX+8Ixw9gUi2UFEkZ4Rj99UJR08gki2UE6m7AQ/74x3h\n6AlEsoVyIjl6hMJJf7wjHD2BSLZQTqStCUhd8reH/fGOcPQEItlCOZGSkPH2Krdbv6eEoydu\nRRk3Tm57roBIDueHSK5xK9Jmm6/DhkgOgUhWhKMnsk/d3LbnCojkcH6I5BqIZIvgRBozhj+P\n4/Yhkmsgki2CE+nzz+3ND5H8BSLZwguRGh8Y1KNbEoe9ctH+NQlIbfK3/Pp9IBw9gUi28EKk\nWwgpbZnEYa907q410rZtzh/uNl0sFDdk5wf52r/5ctYBItnCC5G6Hvixw94Y2st5Hf1FF+W8\nrt68Pw8ZsNOexP9Id2//6907DO/7g0i28EKk8j857Iyt9sL8HxnkyZmstqO8/fOIpkg1Nzjs\njK32wvwfCZHkt+eKaIp0bS3vZTIy2gvzfyREkt+eK6InUr3GypF9nvm0PoGLnkX5P1IFkcZz\n7iDwyu0CkQw4exOEk/bcbvgrCAfWq3c5qCDSFk7eZ165XSCSgTojLnrmtUijj59v4KmnjJ+P\nd1G/HyL9+QBzKistCv7sfZ/cAJE8wnORPKzfj201+qj7TJk61fzvR4XiNq81EMma71bZXkS8\nvdiL5GXQbwBApBze+k1N7W3JYbtJYb5GgkjhAiIZWVpCmpWQo7/VpyGSh0AkG0RQpJNLnmnc\ncUtJrx+p4iItN78USdC7N6NQPK+/q/65nd85zoKWIZKRTmfpP18rHbxHcZHGWo2OabRta11W\nOdZdv0X753Z+5zgLWg6tSI0r58+d+9pqzlzSRSq/OvHrQXKJ4iI5bd+vyAOv2uXjLGg5pCJt\nnNg+eVuxZtqPrPmsRbqNd3/iNtPFDh6S/P0HcjNEkrmc23r8E8lZ0HI4RVrbmXQZdfXMmVPO\n6Ei6s25BW4uUe58i9/6ExX2JiQV3Nei/G0eRyy4077/YOTREkluPfyI5C1oOp0hjSx5NTe2+\ns+AixowMkZztSBv3JwMTE40XWoUIiZ1DQyS59fgnkrOg5XCKVJWVWWREJ8aM0kWimyZckpp6\nuot5/8XOoSGS3Hr8EUk8aPke3hPQ90jslmORSq/PTE8tZcwoXyQ+YufQEEluPf6IJB607PAJ\naIfdcrpgzfDM9ODOjBk9FWmT+TeS2Dl01EW6khddnsuVcvvvdn5niActe/3/a8CxSBMLbtyR\nnNo6mfk8gKciWYzaiZ1DR12k3OjyJp580vzvdqPNwymSONEQaUtPUtlv9ITzR/atIMf8xJjR\nb5HEz6EjL1LIxPBbpO8nfcEsj4ZIdPsttYX6+UJx71kNrPn8FsnGOTRECnR+t6whzzPLIyKS\nxo7li5fU806ipIuUPe5Sld//8JxDqy7SDa2NlJXl/MHySpV3n49Znv6vPZ0cr8A1EnUfIuRw\nRQsLy9IUuek/RHLZ/rFPGLjzTuPnYy2X593nY5aH5ozD2CvHS7oPEXK4opMqMxc+vBAh5oN/\nqot0Iy8E68ag+se7z8csn1jU/eXNOp+Rx5ivSIqGSDJChJyt6K7DeqVPJ01FEn3wT3WRckOw\nrr3W3qPh3vWPd5+PXb6oR+GF+itH1bhGCi5EiH5ecVnTpJkowg/+KS9SaMt59/k45Q03VHR6\nQRWRAgwRot9/2zS1YEZ+qfCDfxApqHLefT7ufcAVA8iIb9QQKTwhQrse3Wj4LPzgH0QKopx3\nn0/wPuDsNq1nKiFSOEKEdDaTtw2fhR/8g0hBlPPu84neB1x/GlFCpHCECOnkiiT84B9ECqKc\nd59P/D7gi5eyX8kYDZHCESKkkyuSwIN/Utv3rH7VyyUQ6O0NA45FCkeIkE6uSAIP/klt37P6\nVS+nTBEaPnnXojAstzcMOBeJBhUilEuuSAIP/klt37P6VS3nifDOedqPO9tq34Ld3jApDs3t\nDQNuRAooRCiXPJFEgUiBlPNEeLO0spHeR1oMP29gYemH+eWhub1hwLlIgYUI5ZIr0qpvLWb0\nqH3P6le0nCfCgKqVlFZ3XqdNLqw4Mb88NLc3DDgWiRkitLI8ewTT6uTPI5FIxXSx1BgQKZBy\nngitLqd0C7kjMT2uVX55aG5vGHAsEjNEqHFB5rnMVx63qsIrkToXHvKqyIIQKZByngjNr9Ku\nvgueSkxPK88vD83tDQOORRIOEbLGK5Emvd+NDP7It/Y9q1/Rcp4Ifbpuo7T35frk7p498stD\nc3vDgGORhEOErPFMJNpwayvS58Hv/Gnfs/oVLeeJMI/UvtywpOrhnbs+GED+kl8emtsbBhyL\nJBwiZI3jFd355CdZnxqeNxqjiaTZdVUbUvw/Y//IehsjRAqknCvCnEpS0a2GFBWRgssbTcrD\ncnvDgGORhEOErHG8ontKpzEWS4hE6bY5JzZjf2PddBO7fV45j5DuyIGXc0XYcPMJNS3K2tRe\nuJTdQJLcoGXX/XOEY5GEQ4Sscb6i/fvvsV4sJZLGro8eu8t+t6QR1h05LOWCWIqSwvI+YjRE\nEg4Rssb5im4aPfCRRVZh9hmRAiboHTXs5dQywacB3g33qItEBUOErHG+osww+7Ipjnskl6B3\n1LCXU7H3W8VAJJc4X9FTzxwrEGafgndq4BlB76hhL6cQSQ4+rajjWDy3BL2jhr2cQiQjKwYM\ncLScqxXduISViSmbwER64gl35UHv6B6VcxJ85hEfkZYyB5mtcbGiC3oRMo/SoSKhQIGJ5Jag\nRbjN/NWjbuu3m+AzPiJtX7bM0XLOV3RhaeVATaQNVWUf8JuBSA7LeTis306CT534iOQU5ys6\npHrNOv2ItL56GL8ZiOSwnIfD+rkJPnNQXySxB/uscb6ibWfQhEh0egd+MxDJYTkPp/VzEnzm\norpIog/2WeN8RYsfSok0WyBcFiI5LOfhuH52gs9cFBdJOPe3Nc5XdL/JKZHqavjNRFYk3k2y\nyIpkxFkI0Mb353+QHLfNDVoWbj8cIgnn/rbG+Yqe03KxLtK3UwvO4zcTWZHWrGGXKyKSE1He\nOkI/FyoYwBzlioZIgT7Yt666qBvpcWgZqf6G30xkReKhsEgcUT4oLeo9ZsKYwwv2+n/e908M\nxyJJeLDv9tsdl68f30bb0Hufu16gGYjksNzr9lOY/P/wRDmpbdKwxXuf7n3/xHAskoQH+1zR\nuLbe8mjEfvBPGdQViSdKm6mpiWvbu2g/HCJJeLDPFavfnv/eOosy9oN/yqCuSDxRimenJh4u\ncdF+OESS8GCfC2Z1Toy8d7PIUMR88I+6fBlwaFBXJJ4oHS9OTUzpyKh45kx2w7xyWzi/j+T+\nwT7n3E1aDBx3wdi+JWSOaTnzwT/q8mXAoUFdkXiijC55Wv/V+HTzsUJN+ICrECGXD/Y5p2uv\n5GXPiupDTMt579dx9TLg0PDYY+zy6IrEE+U/7UhV/5P6tScdvhJqwgeCjLVzTsnc1MSNZabl\nvAf/3L0MOCpEVySuKF+Naql9SbY5e61QC34QTZHavZ6amM06R7bG5cuAI0J0RRIQpXFtvdVY\nUyBEU6SxTQENgy+2moX54J/rlwFHggiLxBeFNWobBNEUaf2Rw5/5bPUXcwf1/3LNGrNIGsaD\nf5JeBswkHKOCXos0a5aLcl6sHFsUzqhtAERTJN4Le1kP/sl6GTCLcIwKOhfJc9F5sXIcUXij\ntgEQTZFOPnVENnnlrAf/5L0M2JpwjArec4/Tcq9F54UA8UThjdoGQDRF4mHrwT8PiPqooNei\n80KAeKLwRm0DIJoiHXkv+wkosQf/vp/0BauSnZ8s3u6gbzpRHxX0WnReCBBPFLejth4QTZGK\nScXprzCigMQe/FtDnjf9+2vHdD5hIX25IyGVnJMjK6I+Kui16LwQIJ4oAqO2fhNNkTbdN6CI\ndJpsOaTGfPAvfflzOjne7BroXyWkkjR/t7LTmb9tQ/5pu29+jAp6jdei80KAeKJwR239J5oi\naWy4p18R6XP/D6aFzAf/eKNyp+z7Cd3Yv9Mvtao3df617Y7JGBUMMmjWD9F5IUA8UWSMrEom\nFJ1wyPo7epJm45abFjEe/JtY1P3lzTqfkcc2m9y1rZmu/XiP3KtPT29tu1cyRgWDDJr1Yyfl\nhQDx+sAbtQ2AYESScsNy2xOnNCM1JcWTTS+WGA/+LepReOFWanmNVP5QpuyBYuv2PSTIoFkZ\nw/9cOCFAIRSFRzAiSbhh+c7Ze5GKs96kq39LrjMrZ90Zb7ihotMLliIdoD8V+ApJ5Oud3I65\nHkx4o36M8rAPj0vAVawcb9Q2AIIRye0Ny6+uO4iQw+5Kbs1fV+XPwAshWTGAjPjGQqTR+7y+\n4+NDf1a9mtIv2p7M7Ic5vFE//qhgGIbHGz55d5WH1TNDgLi3NzijtgEQjEhub1gWkpbnftT0\n4faKvHKBEJLZbVrPNBdpRQtNwdaf1TQ/5ojSovfZHTGDN+onMCoY7PD4O/qY2Z1t9S+iNzxq\ngvNFxxOFN2obQKxjMCK5vWHZd862zIflz+aVi4SQrD+NWNxH+njEEaO/oJ8fRUiXf7D7YQpv\n1I9THvjw+JullY30PtJi+HkDC0s/9KQJ7hPOHFEoZ9Q2gFjHYETy+oalWAjJi5d+zqzlhw2O\nGueN+nHKAx8eH1C1ktLqzvp518KKE+XXT4W+6JiiJLEetQ0g1tF/kVzfsOxuwLQN4RCS71Yx\nCjkhRFbwRv045YEPj7e6nNIt5I7E9LhW8uunol90jNsbOoxR2wBiHf0XyfUNS4FvbPad8bd+\nU1N7W/KQx3wbglUIEQfeqJ+EUUFPh8ebX0XpjoKnEtPTyj1pQuiLjn17gzlqG0Cso/8iub5h\nuTUBqUv+Nm2DeWd8aQlpVkKOTrwRwUwkXggRD96on/CooPUR0dPh8T5dtQvQ3pfrk7t79vCk\nCYFYOaYovFHbAGIdIxvZQMazCllHrZNLnmnccUtJL/1lNGYiub2zzxv1Ex4VtD4iejo8Po/U\nvtywpOrhnbs+GED+YjqL21ExXggQTxTWqG1AsY4Bi8S8RmGWM0Vi3hnvdJb+87XSwXvMReKF\nEHHhjfpxygWOiN4Oj8+pJBXdakhRESm4vNF0DrejYrzTd97tDdaoretLB2cEIRLvGkXoGoYp\nEpPyqxO/HiSXWNTPCSEShjfqZ1HO+Y8W+UZ1ecTYcPMJNS3K2tReuNSi525HxXghQLzbGyz8\neALahABE4l2j8MqTMEVi3hk/eEjy9x/IzRb1s0OIhOGN+lmUc46IIt+oUu+jmLwILNAngAVG\nbQMgAJF41yi88iRMkZh3xicW3JXIsdw4ilx2oUX9rBAiYXjLW5Wzj4gi36hS76OYpMtyOyrG\n/KLjiSJ6aibyhDPv0kKcAETiXaPwypMwRWLeGd+4PxmYmGi8kPEfYR1CxIN3jcO/BnJ9RJR6\nxDARye2oGPOLjicKd9SWF8soevvDFgGIxLtG4ZVfk4DUJn9btcK4M75pwiWpqae7WK+/dQgR\nB96OIPKNyjki8t4nL/U+So5IMkbFmF90Arc3KOuLlBfLKHbpYJcAROJdo/DKhUddOHfGm7B8\nGXAqhIj3suBceKN+YqOCjCMi/33yUu+j5IgkaVSMFwLEG0yyLufFOopdOtglAJF41yi88ocM\nWLfDvjOewfHr563gjfqJjQpaHhE575OXfh8lZ/3ljYoxv+ici8SLdRS7dLBLACLxrlHErmF4\n8B78yyBdJO41juA1kEVQLed98pKOGBm8egcv+4vOuUjcWEfe7Q9HBHEfiXeNInYNw4L74F82\n8kXij/qJjgqajSpx3icv/T4Ka/2dB/3yvuici8SLZeTf/nBCZEOEmPDujBvwQiT+qB+jnDOq\nJOF98rbIW3/3Qb8CX3TOReLFMorc/rBPACKt+tZduQC27ox7IxJ31M+ynDeqZOd98owjhvCj\n5LnrLyHoV+CLzloU3qgtL5ZRzqVDXn9lVWSjyYrpzDEjXrlsPBKJP+pnUc4bVeK+T553xLD5\nKHnu+ksI+mV+0fFE4dbPi3V0f+lgQhAidS48xOS9RcLlPOyGkHgmksP6eaNKvPfJ844Ydh8l\nt9s/t0G/PFFER22dPuHsjCBEmvR+NzL4I8fl3Polj0r5LRJ3VInzPnneEUPsUXLrF4G5Dfrl\nfdEJi8LDYrBDwqWDCYGIRBtubUX6PGj2ojaBch5id8YzhE0kkVGl7PfJ554a8o4YIo+Ss14E\n5jbo1+4XnWOs2vfk0iEYkbS956o2pPh/xv7xz/bLBVsRfswibCLZHVWye0QTeJSc+SIwt0G/\ndr/o7MIb7HB76WBOUCJRum3Oic1Mv5F45YKtRFYku6NKdo9oAo+SM18EJifo1/nzZDx4Rzy3\nlw4WrUquT6TJSU1Tuz567C775YKtMP6jdj75Sdan/JcB88rtYVtUm6NKdo9oAo+Ss18EJiXo\n1zuReIMdbi8dzAlUJGflgq0w/qP2lE5jLssrt4fbIx4vaNb2EY3/KDnvRWBiMPMGeicSb7BD\nzqVDXq2yKhKnbIq7cjGY/1H9+7MDWXnltnArkvwjGvdRct6LwDJNcYJeHebccAlnsEPKpUNe\nrbIqcg7vG9fuYwxJ2A/+jR74yCLW8zKcclv4LpJNTLYv70VgaYLIuSECa7BDzqVDXq2yKnKO\n7B1F4ME/3vCr1OHZIEXiHTEslue9CCyNRzk3JGA92CHn0iGvVi8qtYdskQTuU5x65lhm9DOv\n3Bas/m9d561IItHNZstzXgTGrJ93Q1jsCWfXWA52yLl0yEVBkaTdGXeFdWRANg915I8KBiES\n60VgtVlUOci54dsNWcEnnJ1dOuSioEhibFzCDgPjlbNhRQYkar/j0os0xu9bya8rEJF2a/92\nfviR2aBeYWFZmiIHOTf8/qLzZ/+KqUgLehEyj9KhVne4eeUcmJEBGqv2SX0fl1xvWm7A7qkh\n74ghUP/u84ZT+uWBhBxtEnkwqTJz4eUk54bfQCTBcgcsLK0cqImyoarsA0flPJiRARq/r7xj\nAZn96pRqEVFZ6292asg7YgjUP4NcSunAwgnnF07Pn3/XYb3SsWrmj3l48eCccyCSYLkDhlSv\nWacfcdZXD3NUzoMdGUBp9STaQBZR+vk+b/ErM1t/1qkh74ghUP+h2gHpvwXjKB3zC5MFPq+4\njFW/Nw/OOQciCZY7oO0MmhCFTu/gqJwHLzKg9D66h7ynTVx5LL8yk/VnnhryjhgC9be4h9K/\nkgWU3mGa0vj79IMIC2aYFHvy4JxzIJJguQOKH0qJMts85wGvnAcvMqDDNEorntAmHuYMNlgM\nj7NPDTlHjFxM6q+8l9LTm++k9M4W/OX9GRVzDkQSLHfAfpNTotTVOCrnwYsMGNZxAe35qx2U\n/i/njX0Ww+OcU0POESMHs1O7M+jayt9qE/97sKPlbZV7DUSiYjcsHXBOy8W6KN9OLTjPUTkP\nXmTA++W96e1kv2E9yO/NK+AMj9s6NWQdESy273RyVBV5jdI5pZeZLWUEIukEJpK8G5YOWFdd\n1I30OLSMVH/jqJwLLzJg0Sy657JyUjDEfB/nDY8Lnxrq2B3109g+uqLVTXo7h25yV79Iudco\nLZLMG5ZOWD++jdb+3ueud1jOxzoyIM32f2+zKOENjwufGurYHfXL4t2d/OohUoJgRJJ6w9IZ\n2o7OPNrwyjlYRwas077/12UwXZo3PM49NczG7qifXSCSTjAiSb1h6YzVb89/j3XI4JUzYUUG\n6DdZeLFm3GsgzqmhAdujfjYJp0i8SwexSwtxghFJ6g1LJ8zqnNiLuz3usJwDKzJgxAztH+P9\nqTpi10DWp4YGTHZk5vYdaMBR/bbKvYB36cArt08wIkm9YemAu0mLgeMuGNu3hMxxVM6DHRnA\nh3UNJHJqaMBkR2ZuX7vR2SEUiXfpwCt3QDAiSbth6ZCuvZJH8xXVhzgq58GJDODCugYSOTU0\nYLIjM7fvFwYc1W+r3AN4lw68cgcEI5K0G5YOKZmbmrixzFE5D1ZkwBEGeppXwLgGEjk1NGCy\nI0vdviEUiXfpwCt3QDAiub5h6ZJ2r6cmZpsfEXnlPFiRAUUaxSX6Gbr2r3X++40yCF4D8TDZ\nkaVu3xCKxLt0kJMlyUBA95Fc3rB0y9imgIXBFzsq58GLDPiu7/n/2k5/ePf0/vmvrrR9DcTD\nbEd2v339HhWzBe/SQThLkjiBRTa4umHplvVHDn/ms9VfzB3U/8s1GrbLefAiA8aekpo4MT8n\nBO8aSOjUsAlmiJWL7ev/qJgteJcOwlmSxAlKJHc3LN1iHJiS8J5VC6wiA/ZpSnB6+z55Zbxr\nIPFTQ2oaAiRj+wYwKmYL3qWDcJYkcYIRye0NS7ecfOqIEaydlVfulrKmgIJJzgYzWKeGCRgh\nQDK2bwCjYvbgXTqIZkkSJxiR3N6wjDqHtXkv8fudlgIvQjOBdWqowwoBkrF9AxgVswvv0kHg\n0sIWwYjk9oalW4681+KbXLCciUBkwAtFZP/jTjpuf1LwRF6ZyDUQ69RQx+sQqwBGxeziaQiY\nCcGI5PaGpVuKScXprzDSe/PKmYhcX719QrlWVnrsS/lFItdAvFNDr0OsAhgVs4fHIWAmBCOS\n6xuWLtl034Ai0mmyZT5fXjkTsciAPV9/ucbsxZUJeNdAvFNDVgiQjO0bwKiYLbwOATMhoFM7\nKTcsXbHhnn5FpM/9PzgtdwszASXvGoh1aqjDCgGSsX0DGBWzhdchYCYEI5KbG5byWH9HT9Js\n3HLH5S7gJKDkXQOxTg11eCFArrev/6NitvA6BMyEYERyc8NSGtueOKUZqSkpnmxxMcQrdwEv\nAaXA8Djz1JAXAiRh+/o9KmYLr0PATAg2+YmTG5aSeOfsvUjFWW/S1b8l1zkpdwUvAaXA8Dg7\nNzknBMj99mXkBhcq9xavQ8BMCEEWIRPc3rDk8dV1BxFy2F3J85pfV9kudwsvASXvGkgsN7l1\nCJDb7cvMDS5Q7jVeh4CZEE6R3N6w5FFIWp6bfq317RW2y93CTUDJuQZinRqKhAC53b7M3OAC\n5V7jVwhYdpUyKrGHyxuWMug7J+u7evmztsvdIpCAknkNxDo1FAkBcrt9eTfUg77hHkAIWBBv\nNXd3w1IFBBJQMq+BWKeGQiFALrcv74Z60DfcAyAAkSTcsHRFdwP2yyXATUDJuQZym5ucuty+\nvNzgtnKHe4CnIWDmhPMaibp9Yx4L3hFR/vlzPpwElLzhcbe5yanL7cvLDW4rd7gHeBoCZk5I\nRXL5xjwmWxOQuuRv++VyYCag5A2Ps04NhUKAXG5f3g11W7nDPcDTEDBzwimS2zfmCcB7Pb3X\nr69nwhseZ50aioQAud2+vBvqtnKHe4PfIWDhFMntG/MECE6kBgOms3CvgTinhrwQIFnbl5cb\nXCh3uGf4GgIWTpHcvjFPgOBEErgGE7gGYp4a8kKAfNi+weNvCFg4RZIwKsUjQJGKDh95Ont4\n2u37mXghQD5s36DxOwQsnCJJGJXiEZxIF7cj1f/3KXMW5vC4wKkhLwTI1fbl3VC3mzvcAwII\nAQunSG6/kQUIcLCh4R/Dy0jt7ayUcqxrIIFTQ14IkKvtG4bbBxwCCAELp0iu35jH4poEpDb5\n2365DL67+0hSMuyZXdZzWF8DCZwa8kKAXG1f3g11u7nDPSCAELBwiiThjXnWhOQbdfmV1WTv\nCxbbX1Dg1JAbAuTl9o0nIRXJ9RvzGDxkwH65NBrfONJMVO41kMCpIT8EyLvtGzTBhICFViTl\n2f3PU5uTA/+YXyByROSfGnoXYhV2gjnjCKFIAqNS0efzyzuQyjGmybIEroF0mKeGrBAg1bdv\nMCFgIRQpBKM+HrP5nsNJwbFzfjIvFbkGSmBxakg5IUDqb18dv0dlQ7ghBb+RI8u8EeXkgKmr\nrGcQugayPjWknBAg1bdvEogk/o0cUUjRUde/8HwT5vNwr4EYp4aUEwKk+vZNApFEv5Eji+Cp\nFeMaiH1qSHkhQIpv3yQQKQF/VCq6CA+vW1wDcU8NBUKAVN6+SSBSE45vWCqC9fA4/9RQJARI\n8e0LkTJYj0qpD2t4C9+FrwAACxZJREFUnH9qKBYCpOj2DSYELLwbkjUqpTjsayCRU0OBECBl\nty9uyGbDHpVSGoFrIAE4IUAKb99gQsBCKRJ3VEppRIbH3RHv7esNIRRJzjdydHF76sELAYr7\n9vWGEIrk/TdyuHF76sG9Roj59vWGMIoUi1gw7+CFAGH7ekEIN6RvzwMpCi8ECNvXC0IoEnBJ\nLEKAwgZEUhL1Q4DCBkRSFcVDgMIGRFIXRUOAwgk2tKooGwIUTiCSmigcAhROIJKCIATIfyCS\nciAEKAggknIgBCgIIJJyIAQoCLChlQMhQEEAkQCQAEQCQAIQCQAJQCQAJACRAJAARAJAAhAJ\nAAlAJAAkAJEAkABEAkACEAkACUAkACQAkQCQAEQCQAIQCQAJQCQAJACRAJAARAJAAhAJAAlA\nJAAkAJEAkABEAkACEAkACUAkACQAkQCQAEQCQAIQCQAJQCQAJACRAJAARAJAAhAJAAlAJAAk\nAJEAkABEAkACEAkACUAkACQAkQCQAEQCQAIQCQAJQCQAJACRAJAARAJAAhAJAAlAJAAkAJEA\nkABEAkACEAkACUAkACQAkQCQAEQCQAIQCQAJQCQAJACRAJAARAJAAhAJAAlAJAAkAJEAkABE\nAkACEAkACUAkACQAkQCQAEQCQAIQCQAJQCQAJACRAJAARAJAAhAJAAlAJAAkAJEAkABEAkAC\nEAkACUAkACQAkQCQAEQCQAIQCQAJQCQAJACRAJAARAJAAhAJAAlAJAAkAJEAkABEAkACEAkA\nCUAkACQAkQCQAEQCQAIQCQAJQCQAJACRAJAARAJAAhAJAAlAJAAkAJF8pOgISkeQdb60NYKs\ncbaQowUBRPIRXaQZA7/L/tOMeo/agkj+ApF8RBfJyFoyz6O2IJK/QCQfyRfpOW9E2rMTIvkM\nRPKFf/Ys36duc3HqGmnHzF/u1eIXM/fQQUTjbUrfP3nfspozV2kz/p5sm1JdfvCtjdr06rPa\nlR9y0y5tat05nUr2Hvphurrfk+/P3bviqCXbLqpq3nuxsalTCzb1L30y6cN/Rncs2fukhfqf\nn+9V3v7Cbfv2TM+XqT0zF0RyDETyg3eK2lw3a3jfkpRIY8gZ99zzO3I+ff8scvUz39JF5R2n\n3vd/LdptonQsOXn0q68NIH/VTvs6VEy4aTAZRen66pZXPjh9v9I3m+obSwZf+dbtJZ2Hn7/g\ngb3a7TS0dSYZ3W/aJwkfvmrX/IqHrmld+halbxS2umbWMUP2Sh8TM7VnzQWRHAOR/OAE8p72\n8xySEqnZUfofL/3dbjojcWp331ELtJ93kDsorSPDtcl/k0GUnk1e1iYHkWV0fJF+1PmqsldT\nfXVkvPZzGBmq/ZxA3jG0NZYM3EOTTowiz2hTH+tnlL8m71Pa8CuSFilTe9ZcEMkxEMkH9lTs\nr//6V5NIrTp8kyqZkb5G2tOwgFyqK/Ki/qm8B6Wtq/Wpla9vpG26rdMZSDamZq4j87Wfl5GH\ntJ+3kacMjdWRR/Rfmg+NLav0M0Tam2zcU95Vn3oxI1K69qy5IJJzIJIPfE2O039tbxLpRtLy\n9w/8V/9TUqQ9d/Vqpl8tXaRr8Jn+95bd6LrkQlQf2mtiSeovdeRz7ec15HXt51/Io4bG6sgi\n/Zfmw1rSP/GXMeT9r/VDHKXfp0XKrj09F0RyDkTygS/JSYnfBU03ZF8Z1pwU/Ka+SaTLSZ/H\n3l00OylS4saSJlJ9aiGqTfWYl2Rz6i/Jua7RxylMRErUoPnQVMMEMv9LcmpiMj1umF17ei6I\n5ByI5ANrkl//P5BMZMPON84uPGhnUqTt5fvt0P70qlGkdaRfavFvyKE5FYqJ1FTDGPLB6qQt\nW7OOSP1yprS5IJJzIJIPNJQmrpHeI4YQofPJh0mR/k2G6Z+vMopEW7fTr13+3x2f0r3LvtX/\nuCFdoZhItE37xNXP4QWbdxYlXHwl6xopXXtmLojkHIjkB8cmRtZOT4n0bsc5+h/PJx/RmWQu\npdsKDtM+fl6jD8VliTQ2YchpZCk9l0zVpjZUDdWOXktXUGGRztZrp0sKBlB6RMGnlO7up/cg\nWUWm9qy5IJJjIJIfvFjQctLtJ/ZvnRRpZ7fS/73rzjGFRzfSp8jhNy2kJ5Hxj0zeZ37Jfn/f\nmiXS1+3Lzr9xMBmp30cqPPtv06vLXqV0GdH2d1GR/tuh+ZRHrtu78mNKHyNVN/z12FFlRzRV\nkak9ay6I5BiI5AuP/aJ0n7GbO/VMntptuPig5nv94vqtlO76XbP9nqYbztinxTFv0WtbdFiX\nJRJddWa78kNu1G+3rju3U0n7Ie9TmyLRr8Z0KG53mj7CR2d1La2esqukd1MVWbVn5oJIjoFI\ncWIjGRJ0F1QFIsWDB47RgyNuIzOD7oiqQCQFaNicYZf5LB+UVU2bdW5RzWbzYuAWiKQAz5MM\nj1rM8/aJ7Ur2rfuvr/2KExBJAb57O8NG/uzAAyASABKASABIACIBIAGIBIAEIJLHpAINjIhG\nD/xf6WL+TDYQa3dK6UKprcYCiOQxNkUy5Ln7R8Ft+q/jJubMtJKMpVcknl63SW67OVn1UvXu\n7lPzrf26Yw5E8hh7Ihny3P3U/nBKd1M6dAptNMy1gVxAryPL7Hcmp93crHpN9X5ZdLb9umMO\nRPIYeyIZ8tzNJM9T+ude9w+b+Kcu72fPtY38gd5GVtnvTE67uVn10vWeUeKg8ngDkTymjqyc\n3rm8qzFRnb5DDyJ6uE6DHoqdl+cuQWOVnrDkw0t+SYr7TP23odLi6+n9ZJN5brq8nHcnk/Xj\n2pcefDdNiZROktfUWiZrXlO9S8jF/m0hNYBIHlNHRtbecMMB5IHsVHI5IuXmuUuymExI/H58\n772W51R631K6/I4G89x0eTnvTiVHjn/t6V5kVrLdTJK8VGtZWfNS9dLG9l3920JqAJE8po70\n2aVn4jIkqssRKTfPXZIbEk+u0hV7PTTusJ2mdZvmpsvLeTcikSrvu+adkyJlJclLtpaXNY/q\nD/Oulr4l1AYieUxdYhenpYZEdTkimeW50zVJDCf86Vd0Q4c3TOs2zU2Xl/NuRLIHA8lXyUQO\nmSR5ydbysuZpXJ3I9AXEgUgeU0c+1X8ZEtXlipSb5y7JsFSaFO1otN20avPcdHk570YkU+WN\n0y6HEsnuMknyEq3lZ83TuJ08Lmf1YwNE8hizRHW5IuXmuUtyLNnGrto8N13eY+gjkqdpFyTz\n1mUnyUu0lp81T+NBcp/rNY8XEMljzBLVZYn0UzKBgiHPXYqhvHf7meemMxHpC71sLHlPbzc7\nSV6itfyseVQ/Ij1md0VjDkTyGNNEdbpIw8h67dNnKZFoVp67FGdzb7ma5qYzESlxjdRX81Jv\nNytJXrK1vKx5VL9Ges3VWscPiOQxponq9B36XPKW9ulSTaTcPHcpZmRNa+xcmndn1zQ3nYlI\ng/ZQuqKkG021m06Sl2wt6w9pTif/kboV1AcieYxpojp9h36Z9Jz35qUDWvaneXnuknyYuo+U\nop4cnVu3aW46E5H6H3f3TdX6yVryPlI6SV6ytaw/NNHY/kDPNoiiQCSPMU1Ul4gwmN2tov34\nHzr2oXl57pLs2eeg7BC7etI3r3Kz3HQmIn15UcfSn/+NNkU2pJPkpVrL/KGJJeQCDzaF0kCk\n8DKdPJf98YGhVjOycZDx8ffFK5y1FV8gUnj5cZ/Dsz8Ov81ZNfZFWlE01llTMQYihZjnCm7N\nfNg27XtntdgWafevqjc5ayrGQKQwI+UJWdsiXVXygftW4wZEAkACEAkACUAkACTw/wGjrvF9\nItIBAgAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “rlog transformed read counts\n",
       "distance: Pearson corr”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distance.m_rlog.coll <- as.dist(1 - cor(rlog.norm.counts.coll, method=\"pearson\"))\n",
    "plot(hclust(distance.m_rlog.coll), labels=colnames(rlog.norm.counts.coll), main = \"rlog transformed read counts\\ndistance: Pearson corr\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce85f4ea",
   "metadata": {},
   "source": [
    "### Principal Components Analysis\n",
    "\n",
    "Next we can perform Principal Components analysis to identify if groups of genes have something in common. This can be done most easily with `plotPCA()` function in `ggplot2` for a DESeq2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ee68bbf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FC+szWZTLzTfK+yRIHw8PDs7GxVVd1/j7OysiIiItwdbr/99ttvv117nJGRsWfP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FnRucO1u38sqXDP34oizKblnTsEuCoA8COncH9h6Ylq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\nAKATBDsAAACdINgBAADoBMEOAABAJ/4/g/wdslVSLFsAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Collapsed components\n",
    "P.coll <- plotPCA(DESeq.rlog.coll, \"Treatment\")\n",
    "P.coll <- P.coll + theme_bw() + ggtitle(\"Rlog transformed counts\")\n",
    "print(P.coll)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "16f6c7af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IJgBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDs\nAAAAFIJgBwAAoBAEOwAAAIUg2AEAACgEwQ4AAEAhCHYAAAAKQbADAABQCIIdAACAQhDsAAAA\nFIJgBwAAoBAEOwAAAIUg2AEAACiE1idLPX/+/IIFC06ePLl582bHxIKCgmXLlh08eNBqtTZv\n3vzRRx+Ni4vzSXkAAABVkQ/22O3Zs2fmzJmJiYklpr/++uvnzp177rnnFixYoNFonn32Wbvd\n7v3yAAAAqigfBDuLxTJ//vx27do5T8zMzDxw4MDEiROTkpISExMnT558/vz5X3/91fvlAQAA\nVFE+CHbdu3ePjY0tMfHPP/8MDAysW7eu/DQkJKRmzZp//vmn16sDAACoqnxzjl1peXl5oaGh\nKpXKMSU8PDw3N9fx9Isvvjh+/LjjqXyU1mazFRYWerNOf2az2ex2Ox+Ig8ViEUIUFxebzWZf\n1+Iv7HZ7UVGRr6vwF5IkCbqRf6MbKYFupDQPdSNms5nzryqFvwQ7IYRzqhP/9LkO33777fbt\n2+XH4eHhMTExQgi73W40Gr1WYZVgtVp9XYJ/MZlMvi7Bv/AnUwLdSGl0IyXQjZTgiT8Zq9Vq\ns9kqfbbXIX8JdhEREXl5eZIkOeJdbm5uZGSko8G4ceNGjBghPy4sLHzttdeEEFqtNiQkxPvV\n+iez2Wyz2YKCgnxdiL8wGo0mkykkJESr9Zfvuc+V3jV+PZMkKTc3l27EGd1ICXQjpXmoG9Hr\n9XzIlcJfPsSGDRtaLJaTJ082aNBACJGbm5uSktK4cWNHg4SEhISEBPlxTk6O/JVSqVR8Dxxs\nNpskSXwgDmq1Wgih0Wj4TBzkPxmCnUw+LEA34oxupAS6kdI81I2o1Wq6pkrhg4sncnJyMjMz\n8/PzhRCZmZmZmZnFxcWRkZEdO3ZctGjRyZMnU1JSXnvttaSkpGbNmnm/PAAAgCrKB/+CTJs2\nLT09XX784IMPCiEeeuihgQMHTpgw4Z133vnvf/9rt9tbtWo1efJkwjsAAID7fBDsli9fXuZ0\ng8EwadKkSZMmebkeAAAAZeBesQAAAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAA\nhSDYAQAAKATBDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUgmAHAACgEAQ7AAAAhSDY\nAQAAKATBDgAAQCEIdgAAAApBsAMAAFAIgh0AAIBCEOwAAAAUQuvrAuCCdOncJ6SyTTQAACAA\nSURBVBfPfFyYe1ySrMFhDeNqDapeZ6hKzVYDAABlICL4KVNR2s/f3HM5/XvHlPzsXy+e+fiv\n315q1XVDcHgjH9YGAAD8E4di/ZHFlP3D9i7Oqc6hIOfID593Nhac9X5VAADAzxHs/NGxH6cW\n5Z280qvm4vTf9z3izXoAAECVQLDzO+bijLSTa1y3yTy/Iz/nN+/UAwAAqgqCnd/JvviNJNnK\nbZaV9qUXigEAAFUIwc7vmIwX3GuW5ulKAABA1UKw8zvagPBKbAYAAK4fBDu/Ex7Txq1msW09\nXQkAAKhaCHZ+JySiaURsO9dtDKH1ouO7eaceAABQVRDs/FGTWxZpNEGuGrRbrFIHeK0eAABQ\nJRDs/FF4zM0tuq7XBISUfkmt0TXv+G5sjT7erwoAAPg5gl1JhbbyhxrxgriaAzoO/LVGUrI2\n8O+LJDRaQ3yduzv0/zGxwYO+rQ0AAPgn7hX7t8+zcxafv7D7cl6hzaZXqzuFh41NiL8rNlrl\nu5IMofVu6LSqud1iMl6UJKsuKEGt0fmuHAAA4O8IdsJsl8acOLn6YrpjSrHd/mXO5S9zLg+K\niVrbpGGwRuPD8lTqAH1wTR8WAAAAqgqCnRj35ynnVOfsk8zsEUdPbGrexIf77VBVSJdtth+N\n9nNmySSpIjWa5npNM73gqwMA8KLrPdh9eznv3QuXXDT4JDN7Y0bW4Nhor5WEqkcSls/yLNvy\nhUVyTLPuKlDXDAh8KEqdwPXLAAAvud4vnnj7wsVy2yxNK78NrmfmDy5bNuc5pzqZPcVieinD\nft7ik6oAANeh6z3YfZ+bV36bvPLb4Lpl+8lo/aZAiJKpTiYZ7eZl2cLu5aIAANepKnkoVpIk\nu90uhLDb7Uaj8VpmlW21ltumyGbPLiwMUvt7CLZardf+gSiJ1WoVQphMJqsbW7nCpC2XhRDi\nyifT2dMsxv25olWg52pwnyRJRqNRpeLUPyGEkCRJVEY3oiR0IyV4pxupWux2e3FxcaXP1mKx\nyL/suEZVMtg5u8afqFitNs9azsB1YVqNwacXxrpJ/ij4zS5BpVJ58DPJs0upbgx8eNSiau0v\nQ9V49gOpmvhAHOhGysRfjTMPfUn4hCtLlQx2KpVKrVYLIdRqtV6vv5ZZdY2MOOXy4gkhRNeI\n8GtcineYTCaVSlUlSvUOm81msVgCAwMDAirn8gV7msX+p1kqsKmC1eoknToxwH7B7M7/rerL\nks4/tovRaNTr9XSgMkmSCgsLr70bURK6kRIqvRtRAA91I1qtVu33R8aqhCoZ7CrRhBrVXV8V\nK4T4T43q3ikGfst+0WpZm2M7ZnKeqE4K1PYKdev9AQQpAIA3XO/puGVI8IxaiS4aPFy9Ws/I\nCK/VAz9kP2M2zU0vkeqEEPaTZvOKbHdCm7om/+sDALzheg92Qojn69Z+qnbZ2W58jepLGtb3\ncj3wLybJtCRLKrrCKb0myZ19cZq2hkqtCQCAsl3vh2KFEGqVeL5u7XtiY5acv7A7Ny/DbIkM\n0HYKD3s0Ib5dmHsH2qBclq8LpMuuLo+QLJJKq5KsZQ93IoTQdglW12CPHQDAGwh2f2sRErys\nUZKvq4Dfsf3kxrgPoWpVsSQZy9irp7lRHziMQ/kAAC/hUCzginSp/MGrpFybfmacpnWQ89+T\nKkITeG+EbkKM0HLlBADAS9hjB1QCVZxW92i0VGSXzlsko6SK1qgTAq48aDEAwO+YzebAQL8Y\nTP5asMcOcEVVrfx/ftRxWvkvSWVQqxvoNDfq1TVIdQDgF+x2+5w5c2rVqqXX62+66aadO3dO\nnDjRMTBhhw4dunbt+tlnn9WsWbNjx47yxM8//7xz586hoaFBQUHNmzd/7bXX5BvVCCFatmzZ\nsmVL5/nfcccdMTEx8uO2bdt27Nhx9+7dbdu2DQoKio6OHjVqVG5urrfWVQj22AGuaVoH2U+b\ny2lzExe9AoCfmjt37jPPPDNkyJDRo0dfvHjxoYceio+P1+n+vhtQYGBgTk7OE088MWXKlPr1\n6wshNm/efNddd3Xq1GnVqlWhoaEbNmyYMmVKWlra/Pnzy11WQEDAqVOnJk+e/Morr9SrV2/7\n9u2TJ0/OycnZvHmzZ1fSCcEOcCWgW4j1qwIp54oXxqpC1dpeId4sCQDgJkmSFi1a1Lx583Xr\n1sl3y0hKSurYsWNwcLDcQKvVHj58eOPGjXfeeac8ZcaMGYmJiTt37pTDX+/evTMzM994440Z\nM2ZER0e7XpxKpbp06dIHH3zQvXt3IcT48eMPHz68bNmyc+fO1apVy4Pr6YRDsYBLOpVuXLTK\ncIW/FJ1KNzZaFczfEQD4o3Pnzl26dKlXr16Oe6B16NChadOmzm20Wu2AAQPkx2lpaceOHevT\np49jl54Qol+/fhaLZf/+/e4sMSgoqEuXLo6nt956qxDi0KFD17gi7uMHCSiHuk6gfmacpomu\n5PQknX5GnLphyekAAD9x6dIlIUT16v+6NWijRo2cn8bGxmq1fx/APH/+vBAiMfFfty1ISEgQ\nQly4cMGdJVarVk2j0TieyqffZWRkVKD4iuFQLFA+VTWt7vFY+wWL/YRZyrepQtTqJJ06kWGH\nAcCvyRc9OHbXyUo8db4SVn7JbP7XqdVlzuRKnFOd471qtff2oxHsAHepqweoqxPmAKDKkM+K\nK7HD7MSJE1dqX7NmTSFESkqK88TU1FTxz248tVpttf5rfFN5p6DDhQsX7Ha7I8mlpaUJIapV\nq3YNK3F1OBQLAACUqW7duuHh4bt373ZM+eGHH44cOXKl9tWqVbvhhhu2bt1qNP7vtkObN282\nGAzt27cXQkRGRjofk7148eKvv/7qPIeioqKdO3c6nn7xxRdqtbpNmzaVsjruINgBAABl0mg0\nDzzwwA8//DBq1KgdO3asXLny/vvv79Spk4u3zJ07Nycnp1evXv/3f/+3ZcuW4cOHf/7557Nm\nzQoLCxNC9OjRIzMzc+7cuampqfv37x86dGiJM/Zq1KgxYcKEJUuW7Ny5c8qUKR999NHQoUPj\n4+M9u55OOBQLAAAU6+WXX7ZarevXr1+/fv3NN9+8du3a119//ZdffrlS+379+n322WcvvPBC\ncnKy1Wpt2rTpihUrRo0aJb/6+OOPZ2RkLFmyZM6cOY0bN37hhRe2b9++Zs0ax9tDQ0Pfe++9\nxx577NChQ3q9/qGHHlqwYIHHV9KJyjGYchWSk5MzYcKEhQsXBgQEhIeH+7ocf2EymaxWq2Ns\nHhQWFhqNxvDwcMcI48jJyYmIiHDzFGDFkyQpKyuLbsQZ3UgJdCOleagbeffddyMiIgYPHly5\nsy1Tr169/vjjD/kC2MrVqVOnzMzMY8eOVfqc3cehWAAAoFgLFy4cPHiw44qH3NzcAwcOlLgt\nmJJwKBYAAChWbGysfGOJhx9+uKioaMGCBQUFBVOnTvV1XZ5CsAMAAIo1fPhwu92+YMGC4cOH\nS5LUqlWrbdu2devWzdd1eQrBDgAAKNl999133333eWFBe/fu9cJSXOMcOwAAAIUg2AEAACgE\nwQ4AAEAhriLYGY3GvXv3rl+/PjMzUwhR4l5pAAAA8C13g928efPi4+NvvfXWYcOGnTx5Ugjx\n9NNPP/jggzabzZPlAQAAwF1uBbvly5c/8cQTXbt2Xbp0qWNio0aN3nvvvXnz5nmsNgAAAFwF\nt4Ld4sWLx44d+8knnyQnJzsmjhw5ctq0ae+9957HagMAAMBVcCvYHTt2rMzbt3Xp0uX06dOV\nXRIAAAAqwq1gFxAQYDQaS0+/dOkS90UGAADwE24Fu7Zt277++usmk8l54uXLl+fNm9euXTvP\nFAYAAICr49YtxZ5++ukePXo0bdr0tttuE0IsW7Zs6dKlmzdvLioqcr6cAgAAAD7k1h67zp07\n79ixIyIi4q233hJCrFy5cvXq1Y0aNdq5c2fHjh09XCEAAADc4tYeOyFE9+7dDx06lJmZmZKS\nolKpateuHRkZ6dHKAAAAcFXcHaD4woULixYtiomJadWqVcuWLa1W67PPPnvhwgWPFgcAAAD3\nuRXsjh8/3rp166lTpzqmFBUVPf300zfddJN8FwoAAICqymKWCgt9XUTlcOtQ7JNPPhkSEvLp\np586ptSuXfuPP/4YOHDgk08+uWHDBo+VBwAA4BnFxdY9X9t/PihlZQohRKBO3biptmtPVY2a\nnliaVqvdsGHDHXfc4YmZO7i1x27Pnj0zZ85s06aN88QmTZpMmzbt22+/9UxhAAAAniJdvGB+\n/SXbl59LmZl/TzKb7Id/Nr8xz/btV9cy55ycnPvvvz8yMjI4OLhv375nzpy59mrd59Yeu8LC\nQp1OV8abtdrCytt1OXHiROeV1+v1H330UWXNHAAAQCbl51lWvCnl5gohhKrkq9Ztm4XBoLm5\ngiP1Jicnp6am7ty5MyQkZPr06f379//11181Gs21lewut4Jdq1atVq9ePWzYMLX6f3v4CgsL\nly5d2rJly8oqpaCgYMyYMY4Rj52XBQAAUFlsX2z7O9WVRRLCunWzpumNwmC42jmnpKRs2bLl\np59+atWqlRBi1apV1apV27VrV+/evYUQWVlZffr02b17d0xMzPPPPz9y5MhrWYsyuRXsZs2a\n1b9//6ZNm/bq1atatWrFxcWpqalbtmy5fPnytm3bKquU/Pz8+Pj4mJiYypohAABASRaz7ecf\nXbyuEkIYi2y//aK5pcPVzvvHH38MCgpy7PaKjIxs0qTJwYMH5WC3cOHCpUuXbtq0ac6cOWPH\njh08eHBwcHCF1uGK3Ap2ffr02bJly4wZMxYvXuyY2KJFi/fee+/222+vlDosFovJZNq3b9/q\n1asLCwvr168/atSohIQER4MjR45cvHhRfmw2m+12uxDCbreXuNHZ9cxisfCBOLPZbOKfj8XX\ntfgLSZJMJpNKVerAw3VJkiRBN/JvdCMl0I2U5qFuxGq1eu1DtqedFxZLuc2kc6fF1Qe7jIyM\nqKgo588nNjY2PT1dfnzfffd16NBBCDFmzJiXXnrpzJkzzZo1u9pFuObuAMV9+/bt27dvRkZG\namqqEKJmzZqVu2utqKgoIiKiqKho/PjxarX6ww8/nDFjxptvvulIsuvWrdu+fbv8ODw8XF66\nzWbLz8+vxDIUwGw2+7oE/1JUVOTrEvxLQUGBr0vwL3QjpdGNlEA3UoInuhGz2SzHaG8oNrrT\nSqrodi+ReiVJckxJSkqSHwQFBQkhjEa3Krkq7gY7WWxsbGxsbKUXIYQIDw9fs2aN4+n06dOT\nk5P37t0r351WCNG7d+8GDRrIj+12+xdffCGE0Gg0er3eE/VURfK/O4GBgb4uxF+YzWaLxaLX\n6712yqr/MxqNer2ePXYySZKKioroRpzRjZRAN1Kah7qRgIAAr33IqpBQt5qFhlVg5tWqVcvM\nzHQOc+np6dWqVZMfe+H6AbeC3aVLlyZOnLhr167s7Gz54IWz0lOunV6vj4mJycrKckzp3Llz\n586d5cc5OTlffvmlEEKtVsuZF0IIk8lktVr5QBzsdrvFYtHpdAEBAb6uxV8UFxcHBQUR7GRy\nsKMbcUY3UgLdSGke6kYCAgK8dtGkqnoNlSFYKipnWA91UsMKzLxt27Ymk+nHH3+UB4nLyMg4\nevSofPjVO9wKdhMnTty4cWO3bt1q1KjhoUB99uzZLVu2PPLII/Ifj9FoTE9Pr169uieWBQAA\nrl9qtbpDZ9uXn7toooqKVje9oQLzrl69+j333PPQQw+tWLEiKCjoscceu+mmmxx7przArWD3\n1Vdfvffee8OGDfNcHVFRUfv27bNarcOGDbPZbGvWrAkLC2vfvr3nlggAAK6CSbJnWVUFkgiR\nREDV3vGv7drTfux3KfXcFV7Wau8ZIbRXd7qawzvvvDNp0qQePXpYrdbevXuvWrXKm8dJ3Cq6\nqKjI0xkrNDR0zpw5q1evnjx5ckBAQNOmTV988UVOfAEAwOfsf5ktW/JsR03CJgUKYQw0aloE\nBQwMU8dXMPr4XkBAwOhHrR+stv95rMQrquBg7bCR6npJFZ53WFjYypUrV65cWWK61Wp1PI6P\nj/fEmWzCzWB38803//bbb7Vr1/ZEBQ5JSUnPPfecRxcBAACuivXLAvP6y/+aZJZsB4tsvxp1\nD0VpWlXVMzJVhuCAh8bZ//jN9vOP0oU0YS5WRcWoGzfV3NJJVOXTTN0Kdm+88caYMWPCwsK8\neZDYf+zJ+WVH1g/nii+FaQ0tQhvcGdclJiDC10UBAOBxtoNFJVOdg1kyvZOtnxarrluFL6NW\nN72hYufS+S23gt2ECRMuXbrUpUsXg8FQergTL9/d1pv+Mp4feeTZ7y4fdp445cQbc+o9/Fht\nD55xCACA71kk80dXvO/W3w3WXdbPiPNWQSifW8FOrVbXr1/fMaredeLPopSOB8dkmEv+p5Jv\nLXr8xMIU06XXGk7ySWEAAHiB7Y9i6XI5gwbb/zLbL1jU1RkOxl+4Fex2795d5vTCwsK0tLRK\nrcdf2CX7sN9mlU51DgvOrusWedOA2E7erAoAAK+xny3/vltyM4Kd/7imwQD379/ftWvXSqrE\nv2zL/P6nvOOu28z5613vFAMAgPdJxe7dvNXNZvAKdy9U3rZt24cffnju3DnHPXptNtvvv/+u\n0+k8VpsvbcnYW26bn/KOXzBlVtdV5j1zAQDwE6oIt25JoIrkfmt+xK1gt27dunvvvVer1cbH\nx6empiYkJOTm5hYWFnbr1m3KlCmeLtEnzhRfKLeNJKTTxgsEOwCAImma6i3C5cUTQgitSt1Q\nmbt4qii3DsXOnz+/b9++2dnZKSkpOp1u165dly9ffuutt7RabZcuXTxdok/o1G6dLqBXV+Fr\nvAEAcEGdGKBpVs6dArRdglVBXrrHK9zh1sY4ceLEuHHjQkNDHVO0Wu3YsWNbtGgxffp0j9Xm\nS82D65fbRqcOaBhcywvFAADgE4EjI1XhVzzSqq4REHhnuDfrQbncCnZqtdpxm7PAwMD8/Hz5\n8cCBAzdu3Oip0nxqWHzPctsMiL01RFOFB6cGAMA1VZRGPz1WXauMo1ia5nrd1Fihq9o3jVUe\nt4Jd48aNV65cabFYhBAJCQnffPONPD0rK8sR8hSmRWiDUQn9XTQI1gQ9X/8Rr9UDAIBPqGK1\n+v9W0z0arW1vUNcPtNfRaLsE66bE6ibFqEI4COt33Lp4YtKkScOHD8/Pz9++ffttt902a9as\n1NTU6Ojot99+u0WLFp4u0VeWNJl6rvjiruwfS79k0Og/vOHZRhyHBQBcD1RC0zpI0zpICJGT\nkxMcEeE4jgd/41awu/fee9Vq9blz54QQzzzzzNGjR9944w0hRM2aNRcuXOjZAn0nSK3b3vr1\n+WfXzj/zQZbl78uCVELVM7rNqw0n3hBS/kl4AAAA3uTuOHZDhw6VH0RGRn7xxRdpaWl5eXn1\n69cPCFDyYNNalebJOiOn1h5xMO9oanG6QaNrFdoogfFNAACAX3I32JWQkJCQkJBQuaX4La1K\n0z68ueC6HwAA4N9cBbvGjRsnJyfPmDGjcePGLpodO3assqsCAADwBospq+DyHzabMSi4dnB4\nI1+Xc61cBbuIiIigoCD5gbfqAQAA8IaCnCPHf5qRmfq5JNnkKYbQevVufCqxwSghvHd1iFar\n3bBhwx133FE5c3Px2v79+0s8AAAAUID0lE9/3X2vzVrkPLEo/68j343OuvDljbe+p1JV/B64\nx48fT05O/vHHH61Wq2NiWlratGnTdu7caTKZWrZsOW/evLZt21Z8Ba6g/BFozGZzmzZttm7d\nWunLBgAA8L6CnCOlU53Dhb8+/PPnWRWe+fr167t169aoUcmjuoMGDUpNTf3iiy8OHTqUkJDQ\nv3//wsLCCi/lSsoPdoGBgWlpaSdPnqz0ZQMAAHjf8UNPXinVyc4cedVYcKZiMzeZTPv377/z\nzjudJ2ZnZ9epU2fZsmUtW7ZMSkp6+eWXMzIyjhw5Ir+alZXVp08fg8FQq1atNWvWVGy5MrfG\njH777beXL1++adMm5z2KAAAAVY7FlJV5frvrNna7+eKZjyo2/5EjR9aqVfIWBlFRUR9//LFj\nN9758+fVanWNGjXkpwsXLpw1a1Z2dvaIESPGjh17LXvy3Ap28+bN02g0d911V1BQUEJCQp1/\nq/CyAQAAvCz/8u+OqyVcyMv+1UMFZGdnjx49euLEiYmJifKU++67r0OHDnq9fsyYMUaj8cyZ\nMxWeuVvj2Fmt1sjIyB49elR4MQAAAP7AbjVWYrOrdezYsQEDBvTs2fPVV191TExKSpIfyKOR\nGI0VX7Rbwe67774rc3phYWFaWlqFlw0AAOBl+hC3bvUeFFK70he9a9euoUOHPvPMMxMmTHCe\nrla7dQTVHdc0o/3793ft2rWSKgEAAPC4kPAmQSF1y20Wm9i3cpe7d+/eIUOGvP/++yVSXeVy\n95Zi27Zt+/DDD8+dO2e32+UpNpvt999/1+l0HqsNAACg8tW/ceaR7x920SAitl10Qs+Kzfzi\nxYtWqzUrK0sIkZqaKoSIiIjQaDTJycmTJ09u3ry5PFEIERkZGRwcXLGlXIlbwW7dunX33nuv\nVquNj49PTU1NSEjIzc0tLCzs1q3blClTKrcgAAAAj0psODrrwpcXTq8v89VAfcyNnd+v8M0n\n2rVrd/bsWflxzZo1hRALFiy44YYb/vrrr9mzZ8+ePdvRctGiRZW+986tYDd//vy+ffuuW7cu\nNDRUr9fv2rUrKSlp+fLlGzdu7NKlS+UWhKorzWS+aDaHajX19HqNyns3YwEA4Cqpbuy8Niik\nzpnfF9jtZucXImJvubHzWkNo/QrP+krXtEqSVOZ057Hk4uPjr9TMTW4FuxMnTsyZMyc0NPR/\nb9Nqx44de+rUqenTpy9ZsuRaKkBVZ5fEiouXXks5f7To76t4YgMCHoiPm1E7MVLr7rF+AAC8\nSaXSNLzppZqNHrlw+qOCnN+slnxDaL3YxL7RCT29eaPYSufW765arVb9swMmMDAwPz9ffjxw\n4MAhQ4YQ7K5nxXb7kN+Pb8nKdp6YYbHMSzm/ISPr8xubNjIE+ao2AABcCwqpW++G6b6uojK5\ndVVs48aNV65cabFYhBAJCQnffPONPD0rK8sR8nB9GnviVIlU53C6uLjfb3/kWcsfBBIAAFQK\nt/bYTZo0afjw4fn5+du3b7/ttttmzZqVmpoaHR399ttvt2jRwtMlwm/tz8tffTHdRYNTxuJ5\nKeefq+vWiEEAAOAauRXs7r33XrVafe7cOSHEM888c/To0TfeeEMIUbNmzYULF3q2QPixNS5T\nnWz1xfRn69aqwmcrAIDfkIT4vbDoTHFxiEbTLNgQGxDg64rgd9wKdjabbejQofLjyMjIL774\nIi0tLS8vr379+gF8q65jhwoKym2TYjJlmC1xgXxPAKDiJCHeTrs491zquWKTPEWjUvWOjHi5\nfp0bgg2+rQ1+xa1z7GrWrDllypRffvnFMSUhIaFx48akuutcgc3uTrN8G6fZAUDFWSVp6O/H\nHz1xypHqhBA2Sfo8O6fdT79uzszyYW3wN24Fu9q1ay9YsKBVq1bNmzd/+eWXU1JSPF0WqoRE\nXWC5bbQqVfXA8psBAK5k5l9nP87ILPOlIpt9xNETRwqLvFwS/JZbwW7fvn1nzpyZN2+ewWB4\n8skna9eu3a1btxUrVuTl5Xm6Pviz3pER5bbpFB5m0FTavY0B4Hpzurh44fk0Fw2KbPYn/zrj\nrXLg79z9xa1Vq9bUqVMPHDhw+vTpl156qaCgYPTo0dWqVXOce4fr0Ojq1aICyjlNc3qtGt4p\nBgAU6aP0TLO9nFsRbM++nGmxeKce+LmrvjFAnTp1nnjiiWnTpn3yySdTp0796KOP1q8v+1Zr\nHiXfcEOSJAtf5X/YbDa73e7NDyRYiHeT6t559M8rNXi0erUeoSG+2kZ2u138+1YtkP9kVNzw\nTQhBN1IW73cjfs4fupFf8su/TM0mSYdz828NDy235bXzUDdis9mu8VZakF1dsLPZbHv27Nmw\nYcOmTZvS0tKioqIefvhhD1XmgiRJ8h+bzWYrLi72fgH+yW632+12L38gPQxB/9eg7qNnUtIt\n/+r4AtWqKfFx0+Or+XADyX2x2Wwm2zlIkmQymcpvdz3x/l+NP5N/XPlAHPyhGym0uLXoXFNx\ncbE3rmj0UDditVrlX3ZcI7eCndVq/frrrzds2LB58+b09HSDwTBgwIDhw4f36dPHJxfGqlQq\njUYjhNBqtc53sL3OmUwmq9UaHBzs5eXeFRraO77a+5cyvsy5fN5kjgrQtgkNub9aXP0gvZcr\nKaGwsNBoNBoMBi7fdsjJyQkJCWGPnUz+fdJoNHQjDr7qRvyWP3Qj9UOCxeXccps1jYwM9cot\nHD3Ujeh0OvmXHdfIrWBXrVq17OxsrVbbq1ev4cOH33nnnfzZw1mIRjM2IX5sQryvCwEApekX\nHfl6qquLJ4QQSUF6bswNmVvBrmnTpvfee++QIUNiYmI8XRAAAHDoERnRLix0f56rO7M/Vbum\n1+qBn3Mr2O3Zs8fTdfiJApPYeUT8fFak5wmVSiREipvqiB5Nhe6qLzIBAKASqIRY26Rh+58P\np5vLvqjlvmqxyfFxXq4KfovA8j9H08RbX4kCp1NCT2eI0xni66PiPz1FrWjfVQYAuI7VC9Lv\nb33jfUdPfJ/7r/12OrX6iZo1nq5Tk9NmK8wq2Xbn/Hyk4FSRzVQ7KL5XVNvYwPKHaPVnBLu/\nnckUC3cKc1nXHmUViPnbxexBIibE62UBACBEXb1+b6sbv87J/Sw752xxcZBa0yLEMCQupqZO\n5+vSqrCPL3019cQb54ovOaYEqLQPJw56pcH4YI33zlnUarUbNmy44447KmVu3BJACCEkIVbv\nLTvVyQqKxQf7vFgQAAD/phKie2T4/Pp1Pm7WeE2TBlNq1iDVXYuXzqwZw2Y27wAAIABJREFU\ncvgp51QnhLBI1jdT/u/Wg2PzrIUVnnNaWtqIESPi4uLCw8O7dOly4MCBEg1WrVqlUqk2b95c\n4UW4QLATQoi/0sXZ8u6h/Os5kVX+IJEAAMDf7cw6MOPPt6706s/5J8YcfanCMx80aFBqauoX\nX3xx6NChhISE/v37Fxb+LyZeunTpySefDAry1B7Bqwt2x48ff/HFF8eNGzdu3LiXX3757Nmz\nHirLy05cLL+NJMSfl8pvBgAA/NxTJ5e6brD+4pc/55+owJyzs7Pr1KmzbNmyli1bJiUlvfzy\nyxkZGUeOHHE0GD9+/MiRI8PCwpzflZWV1adPH4PBUKtWrTVr1lRguQ5XEewWLVrUpEmTDz74\n4NSpU6dOnVqxYkX9+vVXr159LYv3E/nujbLuZjMAAOC3zhVfOph3tNxmG9O/qcDMo6KiPv74\n40aNGslPz58/r1ara9T4+7bpGzdu/OWXX+bMmVPiXQsXLpw1a1Z2dvaIESPGjh3rvIfval1F\nsHvhhRe2bdt25MiRHTt27Nix4/jx4+vWrXvqqacqvGz/EeLeLRJCfXwnBQAAcK3+LEpxp9nx\nwms9LJmdnT169OiJEycmJiYKIXJyciZMmLB8+fLSx2Hvu+++Dh066PX6MWPGGI3GM2fOVHih\nroLdkCFDLl7830FKk8nUunVr5wa33nprdnZ2hZftPxpWK7+NSogGbjQDAAA4duzYLbfc0qVL\nl1dffVWe8vjjjw8YMKBr166lGyclJckP5MxnNBorvFxXw51kZ2c3adJk3rx5o0ePVqlUvXv3\n7tOnz4MPPpiYmKhSqVJSUt59992+fftWeNn+o36cqBUtzrm8fqJFLRHNcCcAAFRxDQxu3aij\nUXDtCi9i165dQ4cOfeaZZyZMmCBP2blz59dff3348OEy26vVlXYxq6sZffnllwsWLHjyySe7\ndet24sSJZcuWtW7d+rnnnrvzzjsHDx48b968Xr16rVixorJK8SGVSjzQSQReOeWG6MTwdl4s\nCAAAeEYtfbU2YU3KbXZXXNeKzX/v3r1Dhgx5//33HalOCLFixYpLly7Vq1cvJiYmJiYmPT19\n5MiRgwcPrtgiXCgnIT7wwANHjx6tUaNGixYtFi1a9NZbb126dMlkMpnN5rNnz77yyislLuuo\nuurEiIm9REhZQwJFh4ipfURMqNdrAgAAHvBC0ljXDYZU69EqtGEF5mw0GpOTkydPnty8efPU\nfxQWFi5ZsuTPP//85R8xMTELFix4++23K1S+K+Xv+ouNjV27du2mTZvefffd1q1b79+/PzAw\nsBL3GfqPpgnixXtE/5YiIUJoNSJQI2pHi7tvFs/fxf3EAABQjl7RbV9MevRKr7YMbbCs6ZMV\nm/P333//119/zZ49u6aTlStXRkVFJTpRq9XR0dExMTEVXYMrcveWYrfffvvvv/8+e/bszp07\njxkzZu7cuaGhCtyFFaITd90k7rrJ13UAAABPmlF3ZJIhccqJN1Kcbj6hVWkerjHolYYTQip6\nS7EePXpIklRuM+eLU63W/935Kj4+3p23u1B+sPvjjz9SU1MlSapZs+b8+fOHDx/+8MMPN23a\ndMmSJQMHDryWZQMAAPjKPdW63xHXeXfOz4fzTxbYjHWDqveOvqVaYJSv67omroLdwYMHk5OT\njx793yB+TZo0WbZs2YEDBxYsWHDvvff27dt30aJF8fHxnq8TAACgkgWotD2j2vSMauPrQiqN\nq1PlRo8enZycnJKSYrFYzGbzyZMn+/TpM3ToUJVKNXXq1CNHjuTn5zdpUv51JQAAAPACV8Eu\nIyNj+vTpiYmJWq02ICCgfv36r7zySkZGRlZWlhCibt2627dvX7x4sbdKBQAAgCuugl3t2rXn\nzp178eJFSZLsdntqaurs2bOjoqKio/93jeiIESM8XyQAAADK5+ocu2XLlg0ePHjmzJlqtVqS\nJEmS6tWr98EHHyhyrBMAAICqzlWwu/HGG0+cOHH48GH5qtjExMQWLVqoVCqvFQcAAAD3lTPc\niUqlatGiRYsWLcp8NSUl5ddff+3fv78HCgMAAMDVuaaDqjt27BgwYEBllQIAAIBrwdlyAAAA\nCuHqUOzevXtdv/nkyZOVWgwAAAAqzlWwu/XWW71WBwAAAK6Rq2DXunXr9PT0p59++koN9u7d\nu3r1ag9UBQAAgKvmKtitXbu2devWVqt17NixV2pDsAMAAPATri6eaNy48auvvvrYY4/99ttv\nXisIAAAAFVPOOHaPPvpoy5YtDQZDma82btx49OjRHqgKAAAAV62cYCeEaN++/ZVe6tSpU6dO\nnSq1HgAAAC85W2z6KCPzt4KiIruttl7XNyqye2RElb7FVvnBTpKkgoKC0NDQ0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DRUVdxP5fXXX4+KikpKSurTp0+jRo0efvjh0tJS71SG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TWwganAb2rNgHHnjg8uXLZVqGDh1aVFS0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\nAACATBDsAAAAZIJgBwAAIBP/H9RnYkq5AowVAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#PCA by time\n",
    "#Collapsed components\n",
    "P.coll.time <- plotPCA(DESeq.rlog.coll, \"Time\")\n",
    "P.coll.time <- P.coll.time + theme_bw() + ggtitle(\"Rlog transformed counts - By Time\")\n",
    "print(P.coll.time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d29c0c9",
   "metadata": {},
   "source": [
    "### Differential Gene Expression Analysis\n",
    "\n",
    "Next we will perform a differential gene expression analysis between different samples and time points to identify list of genes of interest. We will use the `limma` package for this. But first we need to get the proper list of gene symbols"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79e2b4ed",
   "metadata": {},
   "source": [
    "To use the `limma` package we have to first create a design matrix for different sample conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5e08c136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>Hyper</li>\n",
       "\t<li>Hyper</li>\n",
       "\t<li>Hyper</li>\n",
       "\t<li>Hyper</li>\n",
       "\t<li>Hyper</li>\n",
       "\t<li>Hypo</li>\n",
       "\t<li>Hypo</li>\n",
       "\t<li>Hypo</li>\n",
       "\t<li>Hypo</li>\n",
       "\t<li>Hypo</li>\n",
       "\t<li>Iso</li>\n",
       "\t<li>Iso</li>\n",
       "\t<li>Iso</li>\n",
       "\t<li>Iso</li>\n",
       "\t<li>Iso</li>\n",
       "\t<li>Media</li>\n",
       "\t<li>Media</li>\n",
       "\t<li>Media</li>\n",
       "\t<li>Media</li>\n",
       "\t<li>Media</li>\n",
       "\t<li>Media</li>\n",
       "</ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<ol class=list-inline>\n",
       "\t\t<li>'Hyper'</li>\n",
       "\t\t<li>'Hypo'</li>\n",
       "\t\t<li>'Iso'</li>\n",
       "\t\t<li>'Media'</li>\n",
       "\t</ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item Hyper\n",
       "\\item Hyper\n",
       "\\item Hyper\n",
       "\\item Hyper\n",
       "\\item Hyper\n",
       "\\item Hypo\n",
       "\\item Hypo\n",
       "\\item Hypo\n",
       "\\item Hypo\n",
       "\\item Hypo\n",
       "\\item Iso\n",
       "\\item Iso\n",
       "\\item Iso\n",
       "\\item Iso\n",
       "\\item Iso\n",
       "\\item Media\n",
       "\\item Media\n",
       "\\item Media\n",
       "\\item Media\n",
       "\\item Media\n",
       "\\item Media\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item 'Hyper'\n",
       "\\item 'Hypo'\n",
       "\\item 'Iso'\n",
       "\\item 'Media'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. Hyper\n",
       "2. Hyper\n",
       "3. Hyper\n",
       "4. Hyper\n",
       "5. Hyper\n",
       "6. Hypo\n",
       "7. Hypo\n",
       "8. Hypo\n",
       "9. Hypo\n",
       "10. Hypo\n",
       "11. Iso\n",
       "12. Iso\n",
       "13. Iso\n",
       "14. Iso\n",
       "15. Iso\n",
       "16. Media\n",
       "17. Media\n",
       "18. Media\n",
       "19. Media\n",
       "20. Media\n",
       "21. Media\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. 'Hyper'\n",
       "2. 'Hypo'\n",
       "3. 'Iso'\n",
       "4. 'Media'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] Hyper Hyper Hyper Hyper Hyper Hypo  Hypo  Hypo  Hypo  Hypo  Iso   Iso  \n",
       "[13] Iso   Iso   Iso   Media Media Media Media Media Media\n",
       "Levels: Hyper Hypo Iso Media"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "treatment <- factor(colData(DESeq.rlog.coll)[,1])\n",
    "treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "19902e2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>12h</li>\n",
       "\t<li>24h</li>\n",
       "\t<li>48h</li>\n",
       "\t<li>6h</li>\n",
       "\t<li>72h</li>\n",
       "\t<li>12h</li>\n",
       "\t<li>24h</li>\n",
       "\t<li>48h</li>\n",
       "\t<li>6h</li>\n",
       "\t<li>72h</li>\n",
       "\t<li>12h</li>\n",
       "\t<li>24h</li>\n",
       "\t<li>48h</li>\n",
       "\t<li>6h</li>\n",
       "\t<li>72h</li>\n",
       "\t<li>0h</li>\n",
       "\t<li>12h</li>\n",
       "\t<li>24h</li>\n",
       "\t<li>48h</li>\n",
       "\t<li>6h</li>\n",
       "\t<li>72h</li>\n",
       "</ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<ol class=list-inline>\n",
       "\t\t<li>'0h'</li>\n",
       "\t\t<li>'12h'</li>\n",
       "\t\t<li>'24h'</li>\n",
       "\t\t<li>'48h'</li>\n",
       "\t\t<li>'6h'</li>\n",
       "\t\t<li>'72h'</li>\n",
       "\t</ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 12h\n",
       "\\item 24h\n",
       "\\item 48h\n",
       "\\item 6h\n",
       "\\item 72h\n",
       "\\item 12h\n",
       "\\item 24h\n",
       "\\item 48h\n",
       "\\item 6h\n",
       "\\item 72h\n",
       "\\item 12h\n",
       "\\item 24h\n",
       "\\item 48h\n",
       "\\item 6h\n",
       "\\item 72h\n",
       "\\item 0h\n",
       "\\item 12h\n",
       "\\item 24h\n",
       "\\item 48h\n",
       "\\item 6h\n",
       "\\item 72h\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item '0h'\n",
       "\\item '12h'\n",
       "\\item '24h'\n",
       "\\item '48h'\n",
       "\\item '6h'\n",
       "\\item '72h'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 12h\n",
       "2. 24h\n",
       "3. 48h\n",
       "4. 6h\n",
       "5. 72h\n",
       "6. 12h\n",
       "7. 24h\n",
       "8. 48h\n",
       "9. 6h\n",
       "10. 72h\n",
       "11. 12h\n",
       "12. 24h\n",
       "13. 48h\n",
       "14. 6h\n",
       "15. 72h\n",
       "16. 0h\n",
       "17. 12h\n",
       "18. 24h\n",
       "19. 48h\n",
       "20. 6h\n",
       "21. 72h\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. '0h'\n",
       "2. '12h'\n",
       "3. '24h'\n",
       "4. '48h'\n",
       "5. '6h'\n",
       "6. '72h'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 12h 24h 48h 6h  72h 12h 24h 48h 6h  72h 12h 24h 48h 6h  72h 0h  12h 24h 48h\n",
       "[20] 6h  72h\n",
       "Levels: 0h 12h 24h 48h 6h 72h"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "timeinterval <- factor(colData(DESeq.rlog.coll)[,2])\n",
    "timeinterval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4c7c796e",
   "metadata": {},
   "outputs": [],
   "source": [
    "design <- model.matrix(~treatment)\n",
    "design.time <- model.matrix(~timeinterval)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "302ae360",
   "metadata": {},
   "source": [
    "Next we can use the `lmFit` function for the fitting and `eBayes` for making an adjustment to the p-values based on Bayes criteria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fa1f3a73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>logFC</th><th scope=col>AveExpr</th><th scope=col>t</th><th scope=col>P.Value</th><th scope=col>adj.P.Val</th><th scope=col>B</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>SULT1C2</th><td> 1.0742645  </td><td> 3.107667   </td><td> 42.69572   </td><td>2.270905e-19</td><td>4.120331e-15</td><td>32.06089    </td></tr>\n",
       "\t<tr><th scope=row>SMCO3</th><td> 2.6705288  </td><td> 4.271397   </td><td> 26.42827   </td><td>1.009646e-15</td><td>9.159506e-12</td><td>25.62794    </td></tr>\n",
       "\t<tr><th scope=row>ARRDC4</th><td> 2.3257376  </td><td> 7.178200   </td><td> 21.74768   </td><td>2.927424e-14</td><td>1.398710e-10</td><td>22.65086    </td></tr>\n",
       "\t<tr><th scope=row>SAP25</th><td> 1.4338343  </td><td> 8.649229   </td><td> 21.68196   </td><td>3.083576e-14</td><td>1.398710e-10</td><td>22.60357    </td></tr>\n",
       "\t<tr><th scope=row>SLC16A6</th><td> 2.7239224  </td><td> 7.554396   </td><td> 20.07045   </td><td>1.156849e-13</td><td>4.061454e-10</td><td>21.38827    </td></tr>\n",
       "\t<tr><th scope=row>SDSL</th><td> 2.6604948  </td><td> 8.991761   </td><td> 19.89565   </td><td>1.343073e-13</td><td>4.061454e-10</td><td>21.24968    </td></tr>\n",
       "\t<tr><th scope=row>MYCL</th><td> 2.3611312  </td><td> 5.180822   </td><td> 19.70741   </td><td>1.579415e-13</td><td>4.093844e-10</td><td>21.09888    </td></tr>\n",
       "\t<tr><th scope=row>OGA</th><td> 1.4784577  </td><td>12.460437   </td><td> 19.22658   </td><td>2.405160e-13</td><td>5.454903e-10</td><td>20.70616    </td></tr>\n",
       "\t<tr><th scope=row>KIFC3</th><td>-0.6196548  </td><td>12.580597   </td><td>-18.89271   </td><td>3.239211e-13</td><td>5.877424e-10</td><td>20.42693    </td></tr>\n",
       "\t<tr><th scope=row>RIMKLB</th><td> 1.1876937  </td><td> 9.602819   </td><td> 18.84904   </td><td>3.369041e-13</td><td>5.877424e-10</td><td>20.39000    </td></tr>\n",
       "\t<tr><th scope=row>BIN1</th><td>-0.6578851  </td><td>13.303305   </td><td>-18.76000   </td><td>3.650995e-13</td><td>5.877424e-10</td><td>20.31441    </td></tr>\n",
       "\t<tr><th scope=row>GPCPD1</th><td> 2.0767657  </td><td> 9.522482   </td><td> 18.59826   </td><td>4.228724e-13</td><td>5.877424e-10</td><td>20.17607    </td></tr>\n",
       "\t<tr><th scope=row>HMGN4</th><td> 0.6606982  </td><td>11.407409   </td><td> 18.59733   </td><td>4.232283e-13</td><td>5.877424e-10</td><td>20.17528    </td></tr>\n",
       "\t<tr><th scope=row>ZNF44</th><td> 1.3872292  </td><td> 8.179178   </td><td> 18.46515   </td><td>4.776275e-13</td><td>5.877424e-10</td><td>20.06123    </td></tr>\n",
       "\t<tr><th scope=row>IRF2BP2</th><td> 0.7270478  </td><td>12.585985   </td><td> 18.44645   </td><td>4.858982e-13</td><td>5.877424e-10</td><td>20.04502    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & logFC & AveExpr & t & P.Value & adj.P.Val & B\\\\\n",
       "\\hline\n",
       "\tSULT1C2 &  1.0742645   &  3.107667    &  42.69572    & 2.270905e-19 & 4.120331e-15 & 32.06089    \\\\\n",
       "\tSMCO3 &  2.6705288   &  4.271397    &  26.42827    & 1.009646e-15 & 9.159506e-12 & 25.62794    \\\\\n",
       "\tARRDC4 &  2.3257376   &  7.178200    &  21.74768    & 2.927424e-14 & 1.398710e-10 & 22.65086    \\\\\n",
       "\tSAP25 &  1.4338343   &  8.649229    &  21.68196    & 3.083576e-14 & 1.398710e-10 & 22.60357    \\\\\n",
       "\tSLC16A6 &  2.7239224   &  7.554396    &  20.07045    & 1.156849e-13 & 4.061454e-10 & 21.38827    \\\\\n",
       "\tSDSL &  2.6604948   &  8.991761    &  19.89565    & 1.343073e-13 & 4.061454e-10 & 21.24968    \\\\\n",
       "\tMYCL &  2.3611312   &  5.180822    &  19.70741    & 1.579415e-13 & 4.093844e-10 & 21.09888    \\\\\n",
       "\tOGA &  1.4784577   & 12.460437    &  19.22658    & 2.405160e-13 & 5.454903e-10 & 20.70616    \\\\\n",
       "\tKIFC3 & -0.6196548   & 12.580597    & -18.89271    & 3.239211e-13 & 5.877424e-10 & 20.42693    \\\\\n",
       "\tRIMKLB &  1.1876937   &  9.602819    &  18.84904    & 3.369041e-13 & 5.877424e-10 & 20.39000    \\\\\n",
       "\tBIN1 & -0.6578851   & 13.303305    & -18.76000    & 3.650995e-13 & 5.877424e-10 & 20.31441    \\\\\n",
       "\tGPCPD1 &  2.0767657   &  9.522482    &  18.59826    & 4.228724e-13 & 5.877424e-10 & 20.17607    \\\\\n",
       "\tHMGN4 &  0.6606982   & 11.407409    &  18.59733    & 4.232283e-13 & 5.877424e-10 & 20.17528    \\\\\n",
       "\tZNF44 &  1.3872292   &  8.179178    &  18.46515    & 4.776275e-13 & 5.877424e-10 & 20.06123    \\\\\n",
       "\tIRF2BP2 &  0.7270478   & 12.585985    &  18.44645    & 4.858982e-13 & 5.877424e-10 & 20.04502    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | logFC | AveExpr | t | P.Value | adj.P.Val | B |\n",
       "|---|---|---|---|---|---|---|\n",
       "| SULT1C2 |  1.0742645   |  3.107667    |  42.69572    | 2.270905e-19 | 4.120331e-15 | 32.06089     |\n",
       "| SMCO3 |  2.6705288   |  4.271397    |  26.42827    | 1.009646e-15 | 9.159506e-12 | 25.62794     |\n",
       "| ARRDC4 |  2.3257376   |  7.178200    |  21.74768    | 2.927424e-14 | 1.398710e-10 | 22.65086     |\n",
       "| SAP25 |  1.4338343   |  8.649229    |  21.68196    | 3.083576e-14 | 1.398710e-10 | 22.60357     |\n",
       "| SLC16A6 |  2.7239224   |  7.554396    |  20.07045    | 1.156849e-13 | 4.061454e-10 | 21.38827     |\n",
       "| SDSL |  2.6604948   |  8.991761    |  19.89565    | 1.343073e-13 | 4.061454e-10 | 21.24968     |\n",
       "| MYCL |  2.3611312   |  5.180822    |  19.70741    | 1.579415e-13 | 4.093844e-10 | 21.09888     |\n",
       "| OGA |  1.4784577   | 12.460437    |  19.22658    | 2.405160e-13 | 5.454903e-10 | 20.70616     |\n",
       "| KIFC3 | -0.6196548   | 12.580597    | -18.89271    | 3.239211e-13 | 5.877424e-10 | 20.42693     |\n",
       "| RIMKLB |  1.1876937   |  9.602819    |  18.84904    | 3.369041e-13 | 5.877424e-10 | 20.39000     |\n",
       "| BIN1 | -0.6578851   | 13.303305    | -18.76000    | 3.650995e-13 | 5.877424e-10 | 20.31441     |\n",
       "| GPCPD1 |  2.0767657   |  9.522482    |  18.59826    | 4.228724e-13 | 5.877424e-10 | 20.17607     |\n",
       "| HMGN4 |  0.6606982   | 11.407409    |  18.59733    | 4.232283e-13 | 5.877424e-10 | 20.17528     |\n",
       "| ZNF44 |  1.3872292   |  8.179178    |  18.46515    | 4.776275e-13 | 5.877424e-10 | 20.06123     |\n",
       "| IRF2BP2 |  0.7270478   | 12.585985    |  18.44645    | 4.858982e-13 | 5.877424e-10 | 20.04502     |\n",
       "\n"
      ],
      "text/plain": [
       "        logFC      AveExpr   t         P.Value      adj.P.Val    B       \n",
       "SULT1C2  1.0742645  3.107667  42.69572 2.270905e-19 4.120331e-15 32.06089\n",
       "SMCO3    2.6705288  4.271397  26.42827 1.009646e-15 9.159506e-12 25.62794\n",
       "ARRDC4   2.3257376  7.178200  21.74768 2.927424e-14 1.398710e-10 22.65086\n",
       "SAP25    1.4338343  8.649229  21.68196 3.083576e-14 1.398710e-10 22.60357\n",
       "SLC16A6  2.7239224  7.554396  20.07045 1.156849e-13 4.061454e-10 21.38827\n",
       "SDSL     2.6604948  8.991761  19.89565 1.343073e-13 4.061454e-10 21.24968\n",
       "MYCL     2.3611312  5.180822  19.70741 1.579415e-13 4.093844e-10 21.09888\n",
       "OGA      1.4784577 12.460437  19.22658 2.405160e-13 5.454903e-10 20.70616\n",
       "KIFC3   -0.6196548 12.580597 -18.89271 3.239211e-13 5.877424e-10 20.42693\n",
       "RIMKLB   1.1876937  9.602819  18.84904 3.369041e-13 5.877424e-10 20.39000\n",
       "BIN1    -0.6578851 13.303305 -18.76000 3.650995e-13 5.877424e-10 20.31441\n",
       "GPCPD1   2.0767657  9.522482  18.59826 4.228724e-13 5.877424e-10 20.17607\n",
       "HMGN4    0.6606982 11.407409  18.59733 4.232283e-13 5.877424e-10 20.17528\n",
       "ZNF44    1.3872292  8.179178  18.46515 4.776275e-13 5.877424e-10 20.06123\n",
       "IRF2BP2  0.7270478 12.585985  18.44645 4.858982e-13 5.877424e-10 20.04502"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit <- lmFit(rlog.norm.counts.coll, design)\n",
    "fit <- eBayes(fit)\n",
    "topTable(fit, coef=2, adjust=\"fdr\", number=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16883d74",
   "metadata": {},
   "source": [
    "We can get the full list of genes and write it to an output file for future analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "858bff45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5jYpUuX+vTpY9xnzZo17777rn6T4XbGxo0bd+rUKSEhoXv37hXcH9MQ7AAAgHnb\nvXt3ycYOHToYXw+3cOHChQsXluyvUqkePHhgeBsQEGBY2a5kWYVCYdz4xM8tc1OV4ho7AAAA\nmSDYAQAAyATBDgAAQCYIdgAAADJBsAMAAJAJgh0AAIBMsNyJ+cnIyLhx40axVXP07t+/f+rU\nqVdffbX6ZwUAAGocwc78vPrqq5cvX27Xrl3JTYcPH9avdl2mGKlnVdqq3hUl+VNXfpC6YFup\nCwrvsruUW2epC56WumAnqQs+J3VBITpKXfBwnsQFraQu2FnqgkKIw1IXdJa6oOTyKvhY0dLU\nkfpHc1/iesJK8r8lYDKCnfnx9PSMiXlyMFu6dGmTJk2qeT4AAOAZwTV2AAAAMkGwAwAAkAmC\nHQAAgEwQ7AAAAGSCYAcAACATBDsAAACZINgBAADzNnjwYMWTpKamGvqMHDlSoVCsXLmy9LFW\nVlYtW7YMCwu7e/fu0/o0bNjQ398/ISHB0EGr1X7yySetW7dWqVQeHh6LFy/WaDRVvddPxDp2\nAADA7Lm7u69du7ZYo6urq/5FTk7Orl27OnbsuGHDhrCwsJJjo6Ki9K8LCwuTk5OXLFmSmJh4\n6tQpS0vLkn2ysrLWr1/fq1evf//73/379xdCzJ49e9myZQsXLvTx8fnxxx8/+ugjnU73wQcf\nVNHOloJgBwAAzJ6dnV3v3r2ftnXr1q3W1tYRERFdu3ZNTU1t3759sbE9evQwvO3bt6+rq2tQ\nUNCBAwcCAgKe2Gfo0KFt27ZdsWJF//79NRrNqlWrpkyZ8v777wshunXrdvbs2c2bN9dIsONU\nLAAAkLn169cPGzbMz8/Pw8PjaU9vMubj4yOEyMzMfFoHS0vL559/PiMjQwihVCqTkpJmzJhh\n2Ors7Hzr1i0pJl5uBDsAACBnaWlpx44de+ONN4QQo0ePjo2NLSoqKn3I+fPnhRAtWrQopU96\nerqjo6MQQqlUtmzZsl69evp2rVYbHx/v5+cnzezLiVOxslJYWLhr1660tDTjRgsLi5EjR+p/\n+QAAkCWtVpubm2vcolAo1Gq1ECI6Otrd3V2ftEaPHj179uz4+HjDOVY9w70Ojx8/TkpKCg0N\nbdWqVc+ePZ/Y548//lizZk1SUlJ0dHTJmcyfPz8lJWX9+vWS7Vt5EOxkpXHjxklJSefOnTNu\nVCqVXbp0IdgBAGQsJSWldu3axi0WFhYajUar1W7cuPHNN9/UxzJHR0d/f/+YmDp2LQ0AACAA\nSURBVBjjYHf69GkrKyvDW4VC0bNnz6ioKMOdEyX71KlTZ9WqVfqjgMbmzZu3aNGibdu2tWvX\nTtodNBHBTlbS09NregoAANSAli1bFjtIplAohBDx8fGZmZnz5s2bN2+eYZOtrW1OTo69vb3+\nrYeHx6ZNm/SvIyMjt2/fHhcX16hRI+Nqxn3q16/v4uKiVP7pejatVvuPf/xj48aNe/bsefnl\nl6XeP1MR7AAAgNlTq9VPvKwtOjra19d31apVhhaNRtOzZ89t27aFhIToW2xtbb29vfWvPTw8\nvvnmm8mTJxtiXMk+TzRlypRt27b98MMP+hsvagrBDgAAyFN2dvbu3buXL19eLJMFBATExMQY\ngp0xe3v7ZcuWBQcHjx49ul+/fiZ+0KZNm7788svDhw//7W9/k2DelcBdsQAAQJ62bNlSWFg4\ndOjQYu1BQUFHjx592vVLo0aN6tat2/jx4/Py8kz5lPz8/JkzZw4cOPDhw4eJRvLz8yu7A+VH\nsAMAAPIUHR3drVs3BweHYu0DBw60sbEpZUG7zz///Nq1a3PmzDHlU3777bfMzMwdO3b4/9nv\nv/9eqdlXCKdiAQCAedu9e/cT248fP/7Edjs7O8PRuCeO7dChw+PHj8usr+fp6anT6UydaxXj\niB0AAIBMEOwAAABkgmAHAAAgEwQ7AAAAmSDYAQAAyATBDgAAQCYIdgAAADLBOnZ/UR9JXVAl\ndUEhxDdSF+wudcFsqQuKMMkrCmEtdcHSHpZYIYelLviH1AWFONxb6ooPJa732ErigoebSlxQ\nCCHUEtfLkLheFfzyXJe6oBB/SP4XruQ/6zNSF4TJOGIHAAAgEwQ7AAAAmSDYAQAAyATBDgAA\nQCYIdgAAADJBsAMAAJAJgh0AAIBMsI5dddNqtfHx8Q0bNjSl86NHj6p6PgAAQDYIdtUtOjr6\nzTffLNeQ06dP+/j4VNF8AAAwdw0bNrxz506xRrVanZubK4QYPHjwnj17Nm3aNGLECMPW3Nzc\n2rVr7927d+DAgYY+JSunpKS0b9/e8PbRo0cdO3a8c+fO7du3i/UsZVN1IthVNycnJ2tr65s3\nb5rSuaCgoGnTpp06darqWQEAYL6+/vrrwsJCw9vc3NxRo0b16dPH0GJhYTF9+vRXXnlFrX7q\n80/c3d3Xrl1brNHV1dX4bXh4eHp6ep06dUoOL2VTdSLY1Yx69eqZ0i0/P7+qZwIAgLnr1q2b\n8duxY8fa2dl9+eWXhpZBgwYlJiYuXrx44cKFTytiZ2fXu3dpTw88d+7c8uXLx4wZs2vXLtM3\nVTNungAAAPLx9ddfR0dHf/XVV40aNTI0qtXqefPmffLJJ5cvX65YWZ1O984777z11lslT6OV\nsqn6EewAAIBM3Lhx4+233x4/fnz//v2N2zUazfjx493d3d97772KVY6MjMzIyAgPDy/XpurH\nqVi5admyZX5+vo2NjaHF0tIyKiqqa9euNTgrAACqmk6nGzNmTMOGDZcvX15yk6Wl5Weffdan\nT58ffvihV69eJYdrtVr9zRYGCoVCf03ezZs3Z8yYERMTY2dnV2xUKZtqBMFObh4+fNivX78u\nXboYWhQKRZs2bWpwSgAAVIOVK1cePHjw2LFjKpXqiR169+79yiuvTJo0KTk5ueTWlJSU2rVr\nG7dYWFhoNBohxKRJk3r16hUYGFhyVCmbagTBTm5sbW27d+8+duzYmp4IAADV59y5czNnzpw7\nd663t3cp3T755JN27dqtWbOm5D+ULVu2XL9+vXGLQqEQQuzbty8+Pv7s2bMlq5WyqaYQ7AAA\ngHkrLCwcOXKkl5fXzJkzS+/p7u4+efLkOXPmvPrqq8U2qdVqPz+/kkO2b9+enZ3dvHlz/Vud\nTqfVai0tLRctWvTrr78+bdP06dMrvVsVQbADAADm7cMPP7x06VJycrKFhUWZnWfNmhUTE7Nw\n4UKl0qRbSBcuXGh8y0VcXNzq1auPHDni4ODw6NGjp22qwF5IgmAHAADM2OHDhz/99NMxY8ak\np6enp6cbb+rUqZPxoid6dnZ2ixcvNv0pUM2aNWvWrJnhraOjo4WFheFxFKVsqhEEOwAAYMYO\nHz6s1Wq/+uqrr776qtimXbt2DR48uOSQ0aNHr169+ueff66WCVYrhU6nq+k5/LXs378/MDDw\n0aNHpnTOz89XqVTHjx83/Vmxbm5uH330UZk3T1xXKEwsaKIn34BUOd9IXbCV1AWzpS7Yv6XU\nFYUQ1lIXLO265Ar545kvKISoK3XBh1IXtJK6YFOpCwohnvowp2eG5L8816UuKKrgL1zJf9Zn\nJK6nO0tWMRULFAMAAMgEwQ4AAEAmCHYAAAAyQbADAACQCYIdAACATBDsAAAAZIJ17OQgJCTE\n1tZWrVYLIbKysi5fvlzTMwIAADWAYCcHcXFx7u7uTZo0EUIUFhbeuXOnzCE7pZ5DVax45SJ1\nQcmXnZN+ubT/J3lFIb6rgprSknyvJV9pTwixSuJ6daX+dcyWfKW9w1IXFNIvB6iS+mvMc5a4\noPQrIIoqWBtP8p91Vew1TEOwk4PatWt/+OGHw4cPF0K4ubl5e1fFP2sAAOBZxzV2AAAAMkGw\nAwAAkAmCHQAAgEwQ7AAAAGSCYAcAACATBDsAAACZINgBAACzp9PpYmNju3fvXrduXbVa3aZN\nmxkzZty6dcu4T1FR0ezZsy0sLJYsWWJoTE1NVTzJxYsXyzWBq1event729jYfPLJJ4GBgcWq\njRkzRt8tPz//ww8/dHd3V6lUHh4eixYt0mg0ld77/8M6dgAAwOyNGzcuOjp6+PDh48ePV6lU\np06d+vzzz7dt23bo0KEWLVoIIW7evPn666/fvHnT0vJP4cfV1TUhIcG4ZcuWLXv27HF0dCzX\nBL766qvz58//8MMPrVq12rt374ABA6ZNm2bY6uDgoH8RGhq6d+/edevWtWvX7uTJk+PGjcvJ\nyTEOmpVEsAMAAOYtOjo6Ojp65cqVoaGh+pZBgwYFBwd7e3vPnDlz8+bNQojY2NgGDRp88803\nxRKbWq3u0aOH4e3du3eHDRv2ySef6B/UaYqioiKlUnnnzh1nZ+eXXnpJCPHgwQNPT0/jsnpa\nrXbr1q3Tpk0bOHCgEMLV1fXgwYNxcXESBjtOxQIAAPO2YsUKLy8vQ6rTc3d3T0hI+OKLL/Rv\nX3vttR07dtSuXbv0UvPnz2/RokVwcLD+bWFh4fTp05s3b25tbd2iRYuZM2c+fvxYv6l+/fqf\nfvppYGCgjY1Nq1atPv/883PnzikUioULF+bk5DzxgxQKhVartbKyMrRYW1vrdLoK73hJBDsA\nAGDGsrOzU1JSXnnllZKbPD09DQHLycmpzFJXr15ds2bN4sWLFQqFvmXChAlffvnlxx9/fO7c\nuSVLlqxevXry5Mn6TdbW1uvXr+/YsePhw4d//vnnt956q23btvfu3Xv//fcfPHhgZ2dXsr5C\noRg3btyXX3559uxZIcTp06e3bNny1ltvVWzHn4hTsXKj0WiuXLmSlJRkaFEoFB06dDD+/wEA\nALJx8+ZNnU7n4uJS+VLLly/v0KFD37599W/v3LmzYcOGefPmvf7660KIli1bpqamrlixYtmy\nZSqVytLS0traOjw8XN/Z2tpaqVTWrVtXCPHgwYOTJ0/6+PikpqY2adJkyJAhc+fO1Ue9zz77\n7O7du+3bt7ewsCgqKnr33XdnzZpV+ZkbEOzk5t69e/Pnz58/f75x4zfffBMYGFhTUwIAoOro\nj1xotdpK1snNzY2Ojl6+fLmh5fTp0xqNxs/Pz9Di4+NTUFBw/vx5Ly8vIYS3t3fJOlqt1tra\n+uLFi9OmTXN1dT1x4sTs2bMvX768c+dOIcQHH3xw8ODBzZs3t2/fPjk5+b333mvYsOGCBQsq\nOXkDgp3c3Lt378GDB8YtlpaWZV5SAACAmXJwcFAqlWlpaZWs8+9//zsvLy8oKMjQkpOTI4So\nV6+eoUX/Wt9ebJOBUqm8d++e4W2XLl0sLCwmTZqUmZmp0+mWL18eExOjPwTYoUOHvLy8iRMn\nhoWFNWrUqJLz/++nS1IFzw5LS8t6f0aqAwDImFqt9vX13blzZ1FRUbFNe/fu1R8nM8X+/ft9\nfX3151L16tSpI4QwTmn61/p203Xq1EkIceXKlQsXLmi12ueee86wycPDQ6PRXL58uVwFS0Gw\nAwAA5m3SpElpaWlz5swxbkxLSwsJCTE92CUkJLzwwgvGLZ6enlZWVkeOHDG0/PTTT2q1um3b\ntqXUSUtLGzZsWEpKiqHl+PHjQghXV1dnZ2chxPnz5w2bfvvtN/0mEydZJk7FAgAA8xYUFJSY\nmBgeHn7q1Knhw4fb29snJSVFRES4ubmtXLlS3+fUqVP6U6hFRUXp6emJiYlCiBdffFGlUgkh\ntFrttWvXWrZsaVy2Xr16ISEhy5cvb9OmzQsvvHD06NHVq1dPnDjRxsamlMm4uLj88ssvf//7\n38PDw5s1a/bTTz8tWLAgODi4WbNmQoiXX3555syZ9vb2zz33XGpq6oIFC4KCgqQ6DysIdgAA\nQAZWr17do0ePtWvXvvfee/n5+W5ubtOnTw8LCzOsMzxhwoQTJ07oX0dGRkZGRgohfv311zZt\n2gghbt26VVRUVPIc67/+9S97e/tJkyZlZWU5OTlNnz79ww8/LH0m1tbWCQkJH3zwQWho6P37\n993c3ObOnTtp0iT91ri4uPnz54eFhV2/ft3JySk4OFjau2IV0i6LhzLt378/MDDw0aNHpnTO\nz89XqVTHjx/38fExbs/LyzOu0Lp161WrVg0fPtz0aaz63wo9UmkqbTkhhBCS/f/lfx5KXfAP\nqQu+ca/sPuX2XRXUlNb/k7qgqcvFl8cqievVzZa4YHbdsvuUs6LUBYUQUk9SJfUk85wlLij5\nLgtRBX+Xle+ZqCaQeq9198gqpuKInVmqU6dOsWcGf/fdd+UKdgAAQH4IdmbJ2tp6+fLlhpV1\n+vTpY1hNEQAA/GUR7MySQqFo2bKl4eYd/WrXNTslAABQ40gDAAAAMkGwAwAAkAmCHQAAgEwQ\n7AAAAGSCYAcAACAT3BX7FyX5yqMnpS4ohHhN6oLXpS4o/bLMGZJXFKJQ6oLPld2lhqmkL2n1\nzK8n3FnqGUq+/rYQIkPy9YQlrlcFFaviD7Xk6zxLXE/kVcXq1jANR+wAAABkgmAHAAAgEwQ7\nAAAAmSDYAQAAyATBDgAAQCYIdgAAADJBsAMAAJAJgh0AADB7hw4dCgwMbN68ea1atRwdHQcN\nGvTTTz+V7DZy5EiFQrFy5cpi7YMHD1YYadiwob+/f0JCgqGDVqv95JNPWrdurVKpPDw8Fi9e\nrNFo9JsCAwMVfzZmzJgq29EysEAxAAAwb999992AAQOCgoKioqIaNWp09erV5cuX9+rV69ix\nY56enoZuOTk5u3bt6tix44YNG8LCwooVcXd3j4qK0r/Oyspav359r169/v3vf/fv318IMXv2\n7GXLli1cuNDHx+fHH3/86KOPdDrdBx98IIR48ODBgAEDpk2bZijl4OBQ5fv8FAQ7AABg3tat\nW9e2bdu4uDj9Wy8vr759+3bu3DkxMdE42G3dutXa2joiIqJr166pqant27c3LmJnZ9ejRw/D\n26FDh7Zt23bFihX9+/fXaDSrVq2aMmXK+++/L4To1q3b2bNnN2/ebAh2np6exmNrEMEOAACY\nN41GY2VlZdyiUqlOnz5drNv69euHDRvm5+fn4eERExPz8ccfl1LT0tLy+eefT05OFkIolcqk\npKQGDRoYtjo7O+/fv1//Oicnp3bt2tLsSaVxjR0AADBvgYGBycnJQ4YMOX78eFFR0RP7pKWl\nHTt27I033hBCjB49OjY29mk9DdLT0x0dHYUQSqWyZcuW9erV07drtdr4+Hg/Pz/92wcPHtjZ\n2Um2M5XDETs5yMvLi4yMPHjw4BO3KpXKKVOmeHh4VPOsAACoHuPGjbt+/frSpUt37dpVu3Zt\nPz+/wMDA4OBg47wVHR3t7u6uT2OjR4+ePXt2fHx8QECAcR3D/RB//PHHmjVrkpKSoqOjS37c\n/PnzU1JS1q9fr3/74MGDkydP+vj4pKamNmnSZMiQIXPnzq2pqMcROzl44YUXrK2t7z3do0eP\nanqOAABUoVmzZmVlZe3Zs2fcuHEZGRkTJkxo1aqV4WysVqvduHHjqFGjNBqNRqNxdHT09/eP\niYkxrnD69Gmr/2natOnKlStXrVqlP8JnbN68eYsWLYqLi2vXrp2+srW19cWLF8PCwuLj40ND\nQ7/44ouSo6oNR+zkwPh+bAAA/ppUKtWgQYMGDRokhDh69OiQIUPCwsIOHTokhIiPj8/MzJw3\nb968efMM/W1tbXNycuzt7fVvPTw8Nm3apH9dv359FxcXpfJPx7+0Wu0//vGPjRs37tmz5+WX\nX9Y3KpXKe/fuGfp06dLFwsJi0qRJmZmZTk5OVbm7T0awAwAA5u3GjRtqtdoQ0YQQfn5+QUFB\nhmNy0dHRvr6+q1atMnTQaDQ9e/bctm1bSEiIvsXW1tbb27uUT5kyZcq2bdt++OEHHx+fUrp1\n6tRJCHHlyhWCXcVdvHixWKx+Zt24cUOn05nYWb9Tvr6+JTcdPnx4wIABUs4MAAAzlJWV1aJF\ni8mTJy9btsy4/cKFC/pbH7Kzs3fv3r18+fJiuS0gICAmJsYQ7Eq3adOmL7/88vDhw3/729+M\n29PS0j788MPZs2d36NBB33L8+HEhhKura2V2qsLkEOwyMzNbtWpV07OoErVq1dq7d6+NjU2x\n9oEDBxpuxgEA4K+sSZMmU6dO/fjjj2/fvj1kyJAGDRpkZWXFxsbu379/w4YNQogtW7YUFhYO\nHTq02MCgoKARI0akp6e7ubmV/hH5+fkzZ84cOHDgw4cPExMTDe0+Pj4uLi6//PLL3//+9/Dw\n8GbNmv30008LFiwIDg5u1qyZ1DtqEjkEO4VCIYT44YcfXFxcanouZTty5Mjbb79tev+BAweW\nbLS0tDSXI5QAAFS1pUuXdujQISoqKiQkJDs728HBwdPTMzExsXv37kKI6Ojobt26lXwaxMCB\nA21sbGJiYubOnVt6/d9++y0zM3PHjh07duwwbr9w4ULLli0TEhI++OCD0NDQ+/fvu7m5zZ07\nd9KkSZLuXznIIdjpNW/evMzE/Sy4ePFiTU8BAAC5GTVq1KhRo564SX9utCQ7O7u8vDz96927\nd5dS3NPTs5TLqJo3b75x40aTZ1q1OOoDAAAgEwQ7AAAAmSDYAQAAyATBDgAAQCYIdgAAADJB\nsAMAAJAJgh0AAIBMyGcdO5TL7CipK/aWuqAQMd9IXbGT1AX/kLjeYk+JC4oq2GnJV2K0euYL\nCiFCSnuAZIWclLjeseckLtjynMQFhRBSz1FIPkcrqf9Q15W4nhBCXFdJXTFb6oKSzxAm44gd\nAACATBDsAAAAZIJgBwAAIBMEOwAAAJkg2AEAAMgEwQ4AAEAmCHYAAAAyQbADAACQCYIdAAAw\nb4MHD1Y8SUBAgBBi69atCoXi22+/LTYqNDTUzs7u2rVrhpaRI0cqFIqVK1c+7YMePXrUunXr\nhg0bVtGOVB5PngAAAGbP3d197dq1xRr1CWz48OGRkZGTJ0/u3bt3rVq19JtSU1PXrFmzaNGi\n5s2b61tycnJ27drVsWPHDRs2hIWFPfFTwsPD09PT69SpU2X7UVkEOwAAYPbs7Ox6937q0y0j\nIiI6duz46aef/vOf/9S3hIWFtW7desqUKYY+W7dutba2joiI6Nq1a2pqavv27YsVOXfu3PLl\ny8eMGbNr166q2AVJcCoWAADIXOvWradOnRoeHp6ZmSmE2LFjR0JCwurVq62s/u/h0uvXrx82\nbJifn5+Hh0dMTEyxCjqd7p133nnrrbc6dZL8EdxSItgBAACzp9PpCkrQ6XSGDh999FGDBg3e\nf//9/Pz8adOmBQcHd+/e3bA1LS3t2LFjb7zxhhBi9OjRsbGxRUVFxvUjIyMzMjLCw8OrbY8q\nhmBnlgoKCgYOHPjEC0VLsrS0/M9//lPTUwYAoAqdOXPGtoQff/zR0EGlUn322WdbtmwZPnz4\n/fv3ly9fbjw8Ojra3d3dz89PCDF69OisrKz4+HjD1ps3b86YMSMiIsLOzq7a9qhiuMbOLG3f\nvl2lUllYWJjSWaFQdOnSpaqnBABADWrVqlXJ86ft2rUzfvvqq6++/PLLe/fujYiIaNy4saFd\nq9Vu3LjxzTff1Gg0QghHR0d/f/+YmBj9TbVCiEmTJvXq1SswMLCKd0ICBDuz9Oqrr9b0FAAA\neIaoVCpfX98yuw0ZMuQ///nPkCFDjBvj4+MzMzPnzZs3b948Q6OtrW1OTo69vf2+ffvi4+PP\nnj0r/aSrAMEOAAD8pUVHR/v6+q5atcrQotFoevbsuW3btpCQkO3bt2dnZxtWRdHpdFqt1tLS\nctGiRdOnT6+hKT8VwQ4AAPx1ZWdn7969e/ny5d7e3sbtAQEBMTExISEhCxcufO+99wztcXFx\nq1evPnLkiIODQ7VPtmwEOwAAYPZyc3MPHDhQsr1nz55KZWm3im7ZsqWwsHDo0KHF2oOCgkaM\nGJGenu7m5tasWTNDu6Ojo4WFRclV7p4RBDsAAGD2Ll261KdPn5Lt+fn5NjY2pQyMjo7u1q1b\nycNvAwcOtLGxiYmJmTt3roTzrGoEOwAAYN52795tYs+QkJCQkBDjluPHjz+xp52dXV5eXsn2\niRMnTpw4sbwzrDasYwcAACATBDsAAACZINgBAADIBMEOAABAJgh2AAAAMkGwAwAAkAmCHQAA\ngEywjt1fVHJI2X3KpZHE9YQQ4hupC/aWuuBpqQv+PUrqikKIw1IXdJa64B9SF6wK66Qu2FLq\ngg8lrndR8h+0EKKV1AWzJa73uKnEBfOuS1ywKuTVlbpiodQFYTKO2AEAAMgEwQ4AAEAmCHYA\nAAAyQbADAACQCYIdAACATBDsAAAAZIJgBwAAIBMEOwAAIBMjR45UKBQrV64s1j548GCFESsr\nq5YtW4aFhd29e7fCfdRqdZs2bUJDQ69cuVLs43Q6XWxsbPfu3evWravvNmPGjFu3bpWc8KNH\nj1q3bt2wYUOpvgEWKAYAAHKQk5Oza9eujh07btiwISwsrNhWd3f3qKj/rgJfWFiYnJy8ZMmS\nxMTEU6dOWVpaVqBPXl5eSkrKl19+uWHDhp07d/bp08fwWePGjYuOjh4+fPj48eNVKtWpU6c+\n//zzbdu2HTp0qEWLFsazCg8PT09Pr1OnjlRfAsEOAADIwdatW62trSMiIrp27Zqamtq+fXvj\nrXZ2dj169DC87du3r6ura1BQ0IEDBwICAirWp3///hMnThwwYEBQUFBaWlqjRo2EENHR0dHR\n0StXrgwNDdV3GzRoUHBwsLe398yZMzdv3myof+7cueXLl48ZM2bXrl1SfQmcigUAAHKwfv36\nYcOG+fn5eXh4xMTElNnfx8dHCJGZmVmZPmq1et26dTk5OYZDfStWrPDy8jKkOj13d/eEhIQv\nvvjC0KLT6d5555233nqrU6dOZU7VdAQ7AABg9tLS0o4dO/bGG28IIUaPHh0bG1tUVFT6kPPn\nzwship0brUAfd3f39u3bHz58WAiRnZ2dkpLyyiuvlOzm6elZu3Ztw9vIyMiMjIzw8PDSJ1le\nnIqViR9++OH8+fONGzd+4ta+fftKeP4eAIBnTXR0tLu7u5+fnxBi9OjRs2fPjo+PN5w/1dNo\nNPoXjx8/TkpKCg0NbdWqVc+ePcvbpyRnZ+erV68KIW7evKnT6VxcXErvf/PmzRkzZsTExNjZ\n2ZVjJ01AsJOJ6dOnX7x48Wm31TRo0KDMX0oAAMyUVqvduHHjm2++qY9ljo6O/v7+MTExxsHu\n9OnTVlZWhrcKhaJnz55RUVGGuyJM7PNEBQUFNjY2Qgj9cK1WW3r/SZMm9erVKzAwsBw7aRqC\nnUw899xzXl5ekZGRNT0RAACqW3x8fGZm5rx58+bNm2dotLW1zcnJsbe317/18PDYtGmT/nVk\nZOT27dvj4uL0tzsYmNLniS5cuODt7S2EcHBwUCqVaWlppXTet29ffHz82bNny7OLpuIaOwAA\nYN6io6N9fX3/n5Fjx44JIbZt22boY2tr6/0/y5Ytq1Wr1uTJk4vVMaVPSSdOnLhy5Ur//v2F\nEGq12tfXd+fOnSWv8Nu7d+/OnTuFENu3b8/Ozm7evLmlpaWlpeWkSZPu3LljaWn58ccfV/J7\nEAQ7AABg1rKzs3fv3j1q1ChvI76+vgEBAU+7N9be3n7ZsmWbN2/+/vvvn1bWlD76Tx8/fryz\ns3NQUJC+ZdKkSWlpaXPmzDHulpaWFhISog92CxcuPHPmTPL/zJw5s27dusnJyePGjSv3zpfA\nqVgAAGDGtmzZUlhYOHTo0GLtQUFBI0aMSE9Pd3NzKzlq1KhRkZGR48ePT01NValUT6z8xD65\nubmJiYlCiIKCgtTU1IiIiPv373/77bdqtdrwuYmJieHh4adOnRo+fLi9vX1SUlJERISbm5v+\nkRjNmjVr1qyZ4VMcHR0tLCyKrbpXYRyxAwAAZiw6Orpbt24ODg7F2gcOHGhjY1PKgnaff/75\ntWvXih1aK7PPpUuX/P39/f39AwMDIyIi+vXrd/Lkyc6dOxuPWr169datWwsKCt57771Ro0bt\n2bNn+vTphw8flvDRYU+j0Ol0Vf0ZVe333393cnJKS0tr1apVTc+lbPv37w8MDHz06JG0ZYOD\ng21sbEy/eeK0QiHtBMq+srT8vpG6YG+pC56WuuDfo6SuKIQ4LHVBZ6kL/iF1waqwTuqCkn+N\nj6UuWBUk/0s6W+qCTaUueF3qglUhT+qChRLX010w+6xSbThiBwAAIBMEOwAAAJkg2AEAAMgE\nwQ4AAEAmCHYAAAAyQbADAACQCRYorhn37t2TtmBhYWGtWrWkrQkAAMwLQTOdqAAAIABJREFU\nwa66ZWZmFhYW1q9fX/LKJddmBAAAfykEu+o2ZsyYZs2aSb729D//+c9GjcqxSLBa2o8Xwkrq\ngkKIllIXvCB1QemXZZZ8yWMh/UqhIk7qgpJ/j1WxXvZoqQtKvrKu5Os8V8XXKPkqynWkLnhR\n6oJVsf625D+aJz9PqxIk/7nAZAS76qZUKvv16yd52SZNmtjY2EheFgAAmBFungAAAJAJgh0A\nAIBMEOwAAABkgmAHAAAgEwQ7AAAAmSDYAQAAyATBDgAAmL1Dhw4FBgY2b968Vq1ajo6OgwYN\n+umnnwxbBw8e7Onp+bSxOp0uNja2e/fudevWVavVbdq0mTFjxq1bt4z7FBUVzZ4928LCYsmS\nJcWGnzlzxt/fX6VSOTo6Tpo0qbCw+PKhI0eOVCgUK1euLPnRZY4tL4IdAAAwb999913Pnj3t\n7OyioqKOHTu2Zs2au3fv9urVKzk52ZTh48aNCw4OdnR0XLt2bVxc3GuvvbZu3TofH5+rV6/q\nO9y8ebN3797bt2+3tCy+AHBmZqa/v3+zZs3i4+M//fTT2NjYGTNmGHfIycnZtWtXx44dN2zY\nUN6xFcACxQAAwLytW7eubdu2cXH/fTCOl5dX3759O3funJiYWMqBOr3o6Ojo6OiVK1eGhobq\nWwYNGhQcHOzt7T1z5szNmzcLIWJjYxs0aPDNN984OjoWG7506VInJ6eYmBilUvnSSy85ODjk\n5uYad9i6dau1tXVERETXrl1TU1Pbt29v+tgKINgBAADzptForKz+9GxLlUp1+rRJT2lcsWKF\nl5eXIdXpubu7JyQkuLu769++9tpr06ZNe+Lw3bt3h4aGKpX/PQXq7+9frMP69euHDRvm5+fn\n4eERExPz8ccfmz62AjgVCwAAzFtgYGBycvKQIUOOHz9eVFRk+sDs7OyUlJRXXnml5CZPT8/a\ntWvrXzs5OT1x+L179zIzMxs3bjxmzJhGjRo1bdp0ypQpjx49MnRIS0s7duzYG2+8IYQYPXp0\nbGysYXpljq0YjtjJRHp6ekZGRp8+fUpuUiqV4eHh3t7e1T8rAACqwbhx465fv7506dJdu3bV\nrl3bz88vMDAwODjYzs6u9IE3b97U6XQuLi4V+1z9DRazZ8+eMGHCxIkTT548qT+wt2LFCn2H\n6Ohod3d3Pz8/IcTo0aNnz54dHx8fEBBgytiKIdjJxMiRI48fP960adOSmywsLBo1alT9UwIA\noNrMmjVr6tSpBw4cOHjwYHx8/IQJE+bPn//dd9916tSplFH6E7harbZiH/r48WMhRP/+/WfO\nnCmE8Pb2/uOPPxYuXBgeHq5SqbRa7caNG998802NRiOEcHR09Pf3j4mJ0Qe70sdWbD6CYCcb\nEyZMmDBhQk3PAgCAGqNSqQYNGjRo0CAhxNGjR4cMGRIWFnbo0KFShjg4OCiVyrS0tIp9ov5c\n7QsvvGBoeemllwoLCy9dutShQ4f4+PjMzMx58+bNmzfP0MHW1jYnJ8fe3r70sRWbj+AaOwAA\nYO5u3LiRk5Nj3OLn5xcUFPTLL7+UPlCtVvv6+u7cubPklXl79+7duXNn6cOdnJxsbW1v375t\naNHXqVWrlhAiOjra19f3/xk5duyYEGLbtm1ljq0wgh0AADBjWVlZLVq0WLBgQbH2CxculFyd\npKRJkyalpaXNmTPHuDEtLS0kJKTMYGdhYdGrV6+vv/7a0JKYmKhWq11dXbOzs3fv3j1q1Chv\nI76+vgEBATExMaWPLXPOpeBULAAAMGNNmjSZOnXqxx9/fPv27SFDhjRo0CArKys2Nnb//v3G\nawLn5uYeOHDAeGDbtm2bNWsWFBSUmJgYHh5+6tSp4cOH29vbJyUlRUREuLm5GZ4VcerUKf0R\nwaKiovT09MTERCHEiy++qFKpZs2a1bVr17Fjx4aEhPzyyy+ffvrpzJkzraystmzZUlhYOHTo\n0GKzDQoKGjFiRHp6upub29PGVubbUOh0usqMfxb8/vvvTk5OaWlprVq1qum5mI2LCoW0BetI\nW04IIYRJCxCVx2OpC6qlLtg9tOw+5faH1AVPSl1Q8nt7quJmIclrZktdUPIfdFV8jZL/IazU\nv4BPclHqgpL/XEQV/Ggk/xqlLqj7qeysEhsbGxUV9euvv2ZnZzs4OHh6ek6dOrV79+76rYMH\nD96zZ0+xIWvWrHn33Xf1r7dt27Z27dozZ87k5+e7ubmNGDEiLCxMrf7vX/O+vr4nTpwoNvzX\nX39t06aNEOL777//5z//efbs2YYNG44dO3bevHkWFha+vr62trYJCQnFRuXm5jZu3Hj69Olz\n58592thyfTnFEOz+ogh2kiDYSYNgJwmCnSQIds9kQVOCHfSKn4r9/+zde0BM+f8/8Pd0b6ZS\nIUJJtcnGKuwmsiH1iajWfiR9krSxi1XC0u5GrBLrziZWrZBaVmRZrKIIuZUorIR1L5ekcm2a\n+f1x9jff2Smp6X2amdPz8dfM+7zPc15n5jRe5vKeysrKgwcPtm3bViHVyEf6g4cAAAAArZZs\nYxcVFbV69WqFlNJMd+/exSt2AAAA0JrJfivW1ta2e/fuYpVy7949QoiZmZki7kAAAAAAZYHl\nTgAAAAA4Ao0dAAAAAEegsQMAAADgCDR2AAAAAByBX55opV4ouoDGoL5cWj/agW9pB7JiAO1A\nb9qBf9MOvE07kJD2GygHUv8bfNmJdiL1Fd0IIdSLpL4wnvIvEUdYOGrqqypSX+QTGg2v2AEA\nAABwBBo7AAAAAI5AYwcAAADAEWjsAAAAADgCjR0AAAAAR6CxAwAAAOAINHYAAAAAHIHGDgAA\nAIAj0NgBAACACgsICOC9w/Lly8vLy01NTceMGSO9y8uXL7t16+bu7s5c9fHx4fF4KSkp0nOq\nq6t5PN7+/fuZq5GRkRoaGtLzJdq1azdkyJCsrCz2j/X98MsTAAAAoMK+/fbboKAg5nJwcLCV\nldX333/PXLWxsTE2Nt64caO3t3daWtrnn3/OjM+fP//p06fHjh2ThKirq8+ZM8fb21sgaNTv\nZlhZWSUkJDCXy8rKNm/e7Orqun///hEjRtA6LvmgsQMAAAAVZmdnZ2dnx1zm8/kdOnQYNmyY\n9AQvL6/x48dPmzZtyJAhxsbG+fn5q1evjouLMzc3l56TnZ0dGxsbHR3dmBvV09MbPHiw5Orn\nn3/eo0ePVatWKbyxw1uxAAAAwHFr165VV1efMWNGbW3tpEmTXFxcJk+eLD1BIBAsXLhwxYoV\nt27dkiNfQ0PDwcHh9m0Wfqm6qZUougBoFKFQeP36dW1tbTn2VVdXNzc35/F41KsCAABQCYaG\nhps2bfL09Kyuri4uLi4sLJT5Z1EoFE6ZMmXjxo2zZs3avXu3HDdx8+ZNU1NTSvXKD42davjy\nyy9/+eUXuXdPT0/39vamWA8AAIBqGTFihJ+f36+//rp48WILCwuZrWKxWENDY/Xq1W5ubkeO\nHHF1dX1voFAoZC48fvw4Pj4+Ly8vKSmJdtVNhsZONdja2vbs2XPv3r1y7Mvj8bp27Uq9JAAA\nABVSVVV16tQpPp+fnp4+Z84cdXX1unOGDRvm7e0dFhZWUFDQcNrFixc1NTUlV9u0abNu3boJ\nEyZQLrrp0NipBjU1NW1tbUtLS0UXAgAAoJLCw8N5PF5OTs6AAQNWr149a9aseqetWLHCzs4u\nPj5+4sSJDaTZ2Nhs376duWxsbGxhYaGmphTfW0BjBwAAABx36NChX3755eDBg3369ImMjJw3\nb563t7e1tXXdmVZWVjNmzIiKivrss88aCNTV1e3Xrx9r9cpPKbpLAAAAAJZUVFSEhIQEBgb+\n5z//IYTMnTvX0tIyJCRELBbXOz8yMlJHRyc6OlpJXoRrEtWrGAAAAKDxwsLCampqVq5cyVzV\n1NTctGnT8ePHN27cWO98PT292NhYyfrDqgWNHQAAAHDWvn37tm7dum7dOmNjY8mgk5PTlClT\n5syZc/fu3Xr3CgwM7Nu3r0gkaqkyqcFn7AAAAIAj/vrrL5mRUaNG1fuWa1xcXFxcHHM5PT1d\nZiuPxztz5oz0SHR0tORHKerOVx54xQ4AAACAI9DYAQAAAHAEGjsAAAAAjkBjBwAAAMARaOwA\nAAAAOAKNHQAAAABHYLkTpRAUFJSVldW+fft3Tbhz587Lly9bsiQAAABQOWjslEJVVVXbtm3H\njBnzrgmHDx++du0axVvMpZhFCCGkE+1AQshw2oEf0A48SDuQCKgnEpJJO3AY7UBD2oHv/C+S\n/B5TP+rrtAMf0A78kHYgGx7TDqR+N7KB+hOuFu1A6o8LNBoaO6VgbGzM5/Pnzp37rgkaGhrP\nnz9vyZIAAABA5eAzdgAAAAAcgcYOAAAAgCPQ2AEAAABwBBo7AAAAAI5AYwcAAADAEWjsAAAA\nADgCjR0AAACoNh8fH3t7e5nB8+fP83i8/fv3S+bweLwlS5bITKuqqtLV1eXxeE+ePJEMpqSk\nDB061MjISEdHp2vXrgEBAQUFBTK3yOPxUlJSpAerq6ulb1HizZs33bt3b9euXXOOsZHQ2AEA\nAECrwOfzt2/fLjOYnp7O4/GkRwIDAwMCAkxNTePj49PS0sLDw/Pz852cnA4fPiw9TV1dfc6c\nOS9evHjv7cbExNy8ebP59TcGGjsAAABoFQYMGFBUVFRYWCg9mJqa6ujoKLmamJi4bdu2n3/+\nefv27X5+fp6enjNmzDh//nzPnj1/+OEH6R29vLxevnwZGxvb8I1euXJl+fLlQUFB9I6jIWjs\nAAAAoFUwMTHp27ev9PunT548ycjIcHd3l4ysXr3ayckpJCREekc+n5+dnX3ixAnpQYFAsHDh\nwhUrVty6detdtygWi7/88stJkyb17t2b3nE0BI0dAAAAqLza2tqKf6uurq47x9fXNzU1VSwW\nMyO7du0yNzd3cHBgrlZUVBQVFY0aNapuvkAg+2PeQqFwypQpVlZWs2bNeldVmzZtun37dkxM\njPwH1kT4rVjV8PTp09LS0qVLl8q3+//+978uXbrQLQkAAEB5FBUVGRkZvXfauHHjIiIiTp48\n6ezsTAhJTU0dN26cZGtpaSkhpGvXrpIRoVD4+vVryVVdXV11dXXmslgs1tDQWL16tZub25Ej\nR1xdXWVuq7S0dO7cuVu3btXT02vGkTUNGjvV0LFjR21t7czMTPl2HzJkCBo7AADgMCsrq4SE\nBOmRa9euffXVVzLTzMzMBg0alJKS4uzsfP/+/ZycnPXr19+9e5fZqqmpSQgRiUSS+cnJyRMn\nTpRc3bNnj4+Pj3TgsGHDvL29w8LCZL42SwgJCwtzdXWt9/U/9qCxUw2hoaGhoaGKrgIAAEBJ\n6enpDR48WGak3pn+/v7ff//9mjVrduzY0bNnTzs7O0ljZ2pqqqamVlJSIpk8YsSInJwcQsjz\n589HjhxZb+CKFSvs7Ozi4+OlW8ADBw5kZGRcvny5eYfVZPiMHQAAALQiY8aMqayszMrKSk1N\n9ff3l97E5/MHDhy4a9cuyYfwTExMnJ2dnZ2dpb85K8PKymrGjBlRUVEVFRWSwd9++62iosLM\nzExDQ0NDQyMsLOzp06caGho//vgjS8fFQGMHAAAArYixsbGHh8eWLVvy8vL8/Pxkts6cOfPy\n5cuLFi2SGT979mwDmZGRkTo6OtHR0Wpq/3RW0dHRly5dKvj/IiIiDA0NCwoKgoODKR5LXXgr\nFgAAAFoXf3//gIAAJycnCwsLmU0+Pj6zZ8+Oioo6e/bs2LFj27Vr9/DhwwMHDjCfrnNzc6s3\nUE9PLzY29osvvpCMdO7cuXPnzpKrpqam6urqPXv2ZOFo/gWv2AEAAEDr4uXlpaurK/19WGnL\nli3bv3+/WCyeM2eOj4/PvHnzCCH79u3bs2dP3UVPJAIDA/v27Sv9xQuFwCt2AAAAoNrS09Pr\nDvbr10/yUTmZOXw+v6qqSnLVw8NDeiYhxNPT09PTs0m3yOPxzpw58675X3/99ddff91AIC14\nxQ4AAACAI9DYAQAAAHAEGjsAAAAAjkBjBwAAAMARaOwAAAAAOAKNHQAAAABHoLEDAAAA4Ais\nY9dKtacdaEg7kLBQ5BXagfTxWcj8inbgcdqBf9MOrKEdSAg5RjvwnaucysuFdmDF+6c0GfWH\nRpN2IPW/QepPZISQx7QDqRdJ/XGBRsMrdgAAAAAcgcYOAAAAgCPQ2AEAAABwBBo7AAAAAI5A\nYwcAAADAEWjsAAAAADgCjR0AAAAAR6CxAwAAAOAINHYAAACg8sRi8bZt2wYPHmxkZMTn821s\nbMLDw+/du1d3ZkpKytChQ42MjHR0dLp27RoQEFBQUFBv5v/+9z8ej7d27VqWa6cJjR0AAACo\nNrFYHBAQEBQUZGZmFh8f/9tvv02ePHnPnj0ODg7nzp2TnhkYGBgQEGBqahofH5+WlhYeHp6f\nn+/k5HT48GGZzMrKyj179nz00UdbtmxpwUNpLvykGAAAAKi2+Pj4lJSUbdu2BQQEMCOenp4h\nISGDBg3y9/cvLCzU0dEhhCQmJm7btm3Tpk0hISGSfSdPnuzi4vLDDz+4u7tLZ+7YsUNLSysu\nLm7QoEFFRUU9e/ZsySOSG16xAwAAANW2atUqV1dXSVfHMDQ0XL16dUlJyYEDB5iR1atXOzk5\nSXd1hBA+n5+dnX3ixAmZzM2bN48ZM8bZ2dnGxmbr1q2s1k8RGjsAAABQYQ8ePCgpKfH29q67\naejQoQYGBpmZmYSQioqKoqKiUaNG1Z0mEAhkRoqLi3NzcydMmEAICQwMTE5Orq2tZaF2+tDY\ncUpCQoK+vr7Vv1lbW2dlZSm6NAAAAFY8ePCAENK1a9e6m3g8nrm5OTOhtLRUZppQKKyWIt26\nJSUlWVlZOTs7E0ICAwPLysoyMjLYPhAq8Bk7rtHU1Jw7d670iLq6eu/evRVVDwAAAKs0NTUJ\nISKRqN6tIpFIXV293mnJyckTJ06UXN2zZ4+Pjw8zZ9u2bV988YVQKCSEmJqaDhkyZOvWrR4e\nHmweBx1o7DhFV1eXz+dPnjxZ0YUAAAC0kE6dOhFCbty4UXeTSCS6c+fOsGHDCCGmpqZqamol\nJSWSrSNGjMjJySGEPH/+fOTIkZLxjIyMe/fuLVy4cOHChZJBXV3dyspKAwMD9g6ECrwVCwAA\nACqsffv2H330UVpaWt1NR48era6uHj58OCGEz+cPHDhw165dYrGY2WpiYuLs7Ozs7Ozo6Ci9\nV1JSUv/+/c9Jyc3NJYTs3LmT/aNpLjR2AAAAoNpmzJiRm5sbHx8vPVhRUREeHt67d2/mFTtC\nyMyZMy9fvrxo0SKZ3c+ePSu9V3p6ekBAQD8p/fv39/DwUInvxuKtWAAAAFBtEydOzMnJmTZt\n2vHjx729vQ0MDK5cuRIXF1dTU3P48GENjX+6HR8fn9mzZ0dFRZ09e3bs2LHt2rV7+PDhgQMH\nmE/Xubm5EUJ+/fXXt2/ffv755zI34evr6+/vf/PmTUtLy5Y+vKbAK3YAAACg8n755ZeUlJSy\nsrLp06f/97//3bRpk6+vb35+/ocffig9bdmyZfv37xeLxXPmzPHx8Zk3bx4hZN++fXv27GEW\nPUlKSvr00087duwokz9y5EgdHR3lf9EOr9gBAAAAF/j5+fn5+b13mqenp6en57u2nj59ut5x\nPT29ly9fyl9cS8ErdgAAAAAcgcYOAAAAgCPQ2AEAAABwBBo7AAAAAI7gzpcnDh48WFBQoOgq\n5HTlyhU+n6/oKgAAAEC1caGx09TU1NXVXblyJfNjcKrowYMHxsbGiq4CAAAAVBsXGjsTExOV\n+AZyAyZNmvT69Ws5dpwyZUptba2kKczLy3v27BnV0gAAAEBlcKGxa83S09NNTU2tra2Zq48e\nPRIKhY3ZMZN2JZq0AwkhR2gHjqcdSP//E/OoJxJynHagNe3A6bQDb9MOJKRTPb9C2SyGFZQD\nqf9V/005jxBCDFnIpIv6Z2Kel7x/TlN1pR1YQ7vIGsp50ARo7FRb+/btJ06cOH36P/8wbt++\nfe7cuYotCQAAABQF34oFAAAA4Ag0dgAAAAAcgcYOAAAAgCPQ2AEAAABwBBo7AAAAAI5AYwcA\nAADAEWjsAAAAQLVFRkZqaPyzgpuPjw9PiqamprW1dWhoaHl5uWS+zByJoqIiQohIJFqxYkX3\n7t35fL6NjU1sbKxkjVjpHbW0tCwsLIKDg2/fvv2u5Hbt2g0ZMiQrK0syoYFwKrCOHQAAAHCK\nlZVVQkICc/nt27cFBQVLlizJzs7Oz8+X9H9WVlYbNmyQ2bFbt26EkPnz5y9btiw6OtrR0fHk\nyZPz5s0Ti8XfffedTHhNTc1ff/0VHR196tSpK1euqKmp1b31srKyzZs3u7q67t+/f8SIEe8N\nbz40dgAAAMApenp6gwcPllx1d3fv1q2br69vZmamh4eHZM6wYcPq7isUCtetWxceHv7NN98Q\nQj799NPLly+npKRIei/pcDc3N11d3UmTJl27dq1Hjx713vrnn3/eo0ePVatWjRgx4r3hzYfG\nDgAAADjO0dGREHLv3r33zlRTU8vLy2vbtq1kpGvXrocPH37XfG1tbUKIjo7OuyZoaGg4ODgU\nFBTIES4HNHYAAADAcX/99RchxNzc/L0z1dTUJL/ATggRiUQZGRnOzs7Sc5hPxb19+/bSpUuL\nFy/28fFh3sN9l5s3b5qamjYyvJnQ2HGKUCh88+ZNZua/fgxcTU1twIABDfxnAgAAgGMk30io\nqanJy8ubPn36Bx98MHToUMkEkUhUXV0tvQuPxxMIBDI5P/zwQ2Fh4ebNmyUjFy9e1NTUlFwd\nOnToxo0b33Xrjx8/jo+Pz8vLS0pKqltk3fDmQ2PHKU+fPn369Kmbm5v0oJqa2u+//+7p6amo\nqgAAAFqSTO/F4/GGDh2akJAg+eYEIaSwsFBfX196L3V1dZkvqC5cuHDx4sU7d+60s7OTDNrY\n2Gzfvp0QIhKJHjx4sGnTJgcHh6NHj3bv3r3eW2/Tps26desmTJggU2S94c2Hxo5TZs6cOXPm\nTEVXAQAAoEiS3osQsmnTpt9++y01NbV9+/bSc6ytrWVeKuPxeJLLIpFo2rRp27Zt27t37/Dh\nw6Wn6erq9uvXT3J15MiRdnZ28+bN27lzZ91bNzY2trCwkHxh9r3hzYfGDgAAADhFuveysbH5\n/fffZ8yYIWm2GAKBoIEPt4WHh+/cufPIkSPMty4aoKGhYWdnV1hYWO+tNzNcDligGAAAADjL\nwMBg2bJlKSkpf/75ZyN32b59+88//3zo0KHGNF5CofDSpUudOnViI1wOeMUOAAAAuCwgIGDT\npk1TpkwpKiri8/kNT3716lVERMTIkSNfvHiRnZ0tGXd0dNTV1SWEVFdXM+NisbisrCwpKenm\nzZurV69uTCXvDW8+NHYAAADAcT/99FOfPn2ioqKWLVvW8Mxr167du3dv165du3btkh6/fv06\ns1LJjRs3hgwZwgyamJj07t07KyvLxcWlMWW8N7z50NipjNevX9+5c0f6Gz2EkDdv3ojFYkWV\nBAAAoAyio6Ojo6OZy+np6XUn9OrVq6amRnK13jkMe3v7Bv5hbWDHxkxoOJwKNHYqY8iQIadP\nn647np6eHhoa2vL1AAAAgLJBY6cy7OzsTExMVq1aJT3o4eHh7e2tqJIAAABAqaCxUxnq6up6\nenqWlpbSgzo6OjKr4wAAAECrhZ4AAAAAgCPQ2AEAAABwBBo7AAAAAI5AYwcAAADAEWjsAAAA\nADgC34ptpTYU0E58TDuQEGJBOe/bCsqBRJNy3goe5UBCyN+0A0toB/amHfg37UDCwlGfpx1I\n2lPO+5SFP+qa909pmlzagY39vc9G60E7kBDygHbgS9qBH9IOhMbDK3YAAAAAHIHGDgAAAIAj\n0NgBAAAAcAQaOwAAAACOQGMHAAAAwBGyjZ1YLBaJRAopBQAAAACaQ7axu3Tp0v379xVSCgAA\nAAA0h2xj99FHH3Xu3FkhpQAAAADIwcfHx97eXnpk3759GhoaS5cuZa5GRkZqaGhIJvPqU1RU\nxEwQi8XJyckuLi6GhoYCgcDW1nbu3LmPHj2ShL948SIiIqJbt266uro2NjYxMTFCobBFDvT9\nZBco5vF4amr44B0AAACoqjNnzvj5+U2bNm3u3Ln1TrCystqwYYPMYLdu3ZgLwcHBSUlJY8eO\nnTJlCp/Pz8/P/+mnn3bu3Hns2DFzc3NCSFBQ0LFjx2JjY21sbE6ePBkZGfn69etFixaxelCN\nhF+eAAAAAO64fv36yJEjvby8Vq9e/a45enp6w4YNq3dTUlJSUlLS2rVrp0+fzox4eXmNHz++\nX79+ERERKSkp5eXlGRkZa9asmTBhAiFk0KBBRUVFaWlpaOwAAAAAaHr06JGHh4e9vf2WLVt4\nPHl+pXHVqlV9+vSRdHUMKyurrKwsKysrQoixsXFFxb9+oVJXV1d53u1UljoAAAAAmuPFixee\nnp6Ghoa7d+/W0tKSI6GioqKwsNDb27vuJnt7e319femRmpqaR48ebdmyZfv27bNmzZKzaNrw\nip1qe/r06ebNm3NychqYo6GhERkZ+eGH+FFmAADgLKFQOGbMmPPnzy9dulSmA6tLJBJVV1dL\nj/B4PIFAUFpaKhaLLSwsGnOLw4cPP3LkSJs2beLi4iZOnCh35XShsVNtgwcPrqmpMTIyamAO\nvhADAACcd/nyZR0dna+//joyMnLgwIEDBw5sYHJhYaFM86euri4UCjU1NQkhjVzQd926dXfv\n3s3Ozp46deqzZ89mzpzZnPppQWOn2rZv367oEgAAABTPwsIiJydHW1v76tWrY8aMycvLMzU1\nfddka2vrzZs3S48wH8jr2LGjmppacXFxY26xR48ePXr0cHd3FwgE3377bVBQkLGxcTOPovnw\nQg4AAACovDZt2jBfYkhJSeHxeGPGjKmpqXnXZIFA4PxvzCt8AoGBec68AAAgAElEQVSgf//+\naWlptbW1Mrvs27cvLS2NEHLv3r3ExMTnz59LNvXr1+/t27e3b99m58iaBo0dAAAAcIeJicmO\nHTvOnDkj33ujYWFhxcXFUVFR0oPFxcUhISFMY/fs2bOQkJC9e/dKthYUFBCpZfAUC2/FAgAA\nAKc4OzsvXrx4zpw5n3zyyfjx45u0r6+vb3Z2dkxMTH5+/tixYw0MDPLy8uLi4iwtLdeuXUsI\n6dWrl4eHR3h4+IsXL3r27Jmfnx8TExMYGGhoaMjO0TQNGjsAAADgmtmzZ588efLLL7/s1auX\nzK+Nvdf69esHDx68YcOGWbNmvXr1ytLScs6cOaGhoQKBgJmwY8eORYsWLVu27OHDh2ZmZqGh\nod999x0LByEPNHYAAACg2tLT02VGeDye9GB0dHR0dPS7Jtfl6+vr6+v7rq0GBgbLli1btmyZ\nXMWyC5+xAwAAAOAINHYAAAAAHIHGDgAAAIAj8Bk7pXD58uW8vLw//vijgTlVVVXt27dvsZIA\nAABA5aCxUwo//vhjQUFBhw4dGpizdu1abW3tFisJAAAAVA4aO6XALHvd8JzMzEyZXywGAAAA\nkIbP2AEAAABwBF6xA0qev39Kk12hHWhNO1BAOe8+5TxCCMmlHRhIO1AlPjp6SNEFvJ8m5bwS\nynmEsPBYU1/pn/qTxEvagYQQLdqBD2gHXqQdCI2HV+wAAAAAOAKNHQAAAABHoLEDAAAA4Ag0\ndgAAAAAcgcYOAAAAgCPQ2AEAAABwBBo7AAAAAI5AYwcAAADAEWjsAAAAQOUdO3Zs1KhRZmZm\n2trapqamXl5ep06dkmz18fHh8XhLliyR2auqqkpXV5fH4z158kQymJKSMnToUCMjIx0dna5d\nuwYEBBQUFEjvNWrUKN6/BQUFsXlwTYDGDgAAAFTboUOHhg4dqqenl5CQkJubGx8fX15e7urq\nKt2Q8fn87du3y+yYnp7O4/GkRwIDAwMCAkxNTePj49PS0sLDw/Pz852cnA4fPiyZU1VV5enp\nmSUlIiKC1QNsPPykGAAAAKi2xMTEHj16pKamMlf79Onj7u7u5OSUnZ1tb2/PDA4YMCAzM7Ow\nsLBXr16SHVNTUx0dHbOzsyU527Zt27RpU0hIiGTO5MmTXVxcfvjhB3d3d2akqqrK3t5+8ODB\n7B9Zk+EVOwAAAFBtQqFQU/Nfv5fM5/MvXrw4Y8YMyYiJiUnfvn1TUlIkI0+ePMnIyJC0a4SQ\n1atXOzk5SXd1TFR2dvaJEyckI5WVlfr6+vQPgwY0dgAAAKDaRo0aVVBQMHr06NOnT9fW1tY7\np7a21tfXNzU1VSwWMyO7du0yNzd3cHBgrlZUVBQVFY0aNaruvgKBQPpqVVWVnp4e1SOgBo0d\nlxkaGvJ4PDU1tT/++EPRtQAAALAlODh40aJFGRkZTk5ORkZGI0aMiI+Pr66ulpk2bty4O3fu\nnDx5krmampo6btw4ydbS0lJCSNeuXSUjQqGwWoqkZayqqjp//ryjo6NAILC0tJw9e3bd21IU\nNHZcZmRkFB4efvTo0WHDhim6FgAAABZFRkaWlZXt3bs3ODj49u3bU6dO/eCDDy5evCg9x8zM\nbNCgQcy7sffv38/JyZFu7Jg3c0UikWQkOTlZX8q+ffuYCVpaWiUlJaGhoRkZGdOnT9+4ceOE\nCRNa6DjfB1+e4DJNTc0ePXoo56c7AQAA6OLz+V5eXl5eXoSQEydOjB49OjQ09NixY9Jz/P39\nv//++zVr1uzYsaNnz552dnZ3795lNpmamqqpqZWUlEgmjxgxIicnhxDy/PnzkSNHMoNqamrP\nnj2TzBkwYIC6unpYWNi9e/e6dOnC9jG+F16xAwAAANX28OHDyspK6RFnZ2dfX98LFy7IzBwz\nZkxlZWVWVlZqaqq/v7/0Jj6fP3DgwF27dkk+hGdiYuLs7Ozs7Ozo6NjArffu3ZsQ8vfffzf/\nQJoPjR0AAACosLKyMnNz80WLFsmMX79+3dTUVGbQ2NjYw8Njy5YteXl5fn5+Mltnzpx5+fLl\nulFnz56VXC4uLh4zZkxhYaFk5PTp04SQbt26NfNAqMBbsQAAAKDCOnToMHPmzB9//PHJkyej\nR49u27ZtWVlZcnLy4cOHt2zZUne+v79/QECAk5OThYWFzCYfH5/Zs2dHRUWdPXt27Nix7dq1\ne/jw4YEDB/bs2ePj4+Pm5kYIsbCwuHDhwn//+9+YmJjOnTufOnVq0aJF48eP79y5cwsc7Huh\nsQMAAADVtnTp0l69eiUkJISEhFRUVHTs2NHe3j47O9vFxaXuZC8vL11dXemvTUhbtmzZ4MGD\n169fP2fOnPLy8nbt2jk5Oe3bt2/EiBHMBC0traysrO+++2769OnPnz+3tLRcsGBBWFgYi4fX\nFGjsAAAAQOUFBAQEBAS8a2t6errkMp/Pr6qqklz18PCQfKiO4enp6enp2cBtmZmZbdu2rRnF\nsgifsQMAAADgCDR2AAAAAByBxg4AAACAI9DYAQAAAHAEGjsAAAAAjkBjBwAAAMARWO5EZdy6\ndauwsJBZHbGR/v777/Pnz0+aNIm9qgAAAEB5oLFTGT4+Pm3btu3atWvjd8nNzTU0NKx/myad\nqv7PcNqBhJBdtAMf0A58STnvNuU8QgiJoB14nXbgsfdPaRo2TsYXtAPf8Zcpvxe0T2/qfy6E\nkAragbT/BOljo0LqZ6OAdiD1f2Gg8dDYqYypU6dOnTq1Sbvs3r3b2tqapXoAAABA2eAzdgAA\nAAAcgcYOAAAAgCPQ2AEAAABwBBo7AAAAAI5AYwcAAADAEWjsAAAAADgCjR0AAACoNh8fH97/\np6WlZWFhERwcfPv2/60NGhkZqaGhIT05JSVFOqG6uprH4+3fv18yUltbO3/+fHV19SVLlrTM\nUVCBdewAAABA5VlZWSUkJBBCampq/vrrr+jo6FOnTl25ckVNrZ7XsNTV1efMmePt7S0Q1L88\nc2lp6bhx40pLSyXtoKrAK3YAAACg8vT09AYPHjx48GA3N7fp06fHxMRcu3bt2rVr9U728vJ6\n+fJlbGzsu9KSk5Pbtm179uxZTU0V+x0NNHYAAADANdra2oQQHR2dercKBIKFCxeuWLHi1q1b\n9U7w8/PbtWuXvr4+iyWyA40dAAAAcIFQKBQKhS9fvjx9+vTixYt9fHy6dev2rplTpkyxsrKa\nNWtWvRO6dOnCZqUsQmPHZa9evTp+/HhCQsLTp08VXQsAAACLLl68qKmpqampKRAInJycOnXq\ntHHjxndNFovFGhoaq1ev3rNnz5EjR1qyTrahseMybW3to0ePLl26tKioSNG1AAAAsMjGxubc\nuXPnzp07c+bMnj17dHR0HBwc3vUZO8awYcO8vb3DwsKEQmGL1ck2FfuuBzRJSUmJoksAAABo\nCbq6uv369ZNcHTlypJ2d3bx583bu3NnAXitWrLCzs4uPj584cSL7NbYEvGIHAAAAXKOhoWFn\nZ1dYWNjwNCsrqxkzZkRFRVVUVLRMYWxDYwcAAABcIxQKL1261KlTp/fOjIyM1NHRiY6OrnfF\nO5WDt2IBAABA5VVXV2dnZxNCxGJxWVlZUlLSzZs3V69e/d4d9fT0YmNjv/jiC+nB/Pz8yspK\nQkhtbe3NmzeZ5E8++YTP57NRPEVo7AAAAEDl3bhxY8iQIcxlExOT3r17Z2Vlubi4NGbfwMDA\n9evXnz17VjIyderUM2fOMJc3bdq0adMmQsjVq1dtbW1pF04ZGjsAAABQbenp6Q1PiI6Ojo6O\nftdkHo8naeMYp0+fplheS+LC28kAAAAAQNDYAQAAAHAGGjsAAAAAjkBjBwAAAMARaOwAAAAA\nOAKNHQAAAABHoLEDAAAA4Ag0dgAAAAAcgQWKW6m/7SgHdqWcRwghB2kHltAO/JR2oD31RELS\ntGgnUv9BnQeU89IMKQcSQogm5Tz6vzdeQzuQhV9Oeqn0P7N+nPrfywvagYSQD2gHPqYdSP1s\nhEbDK3YAAAAAHIHGDgAAAIAj0NgBAAAAcAQaOwAAAACOQGMHAAAAwBFo7AAAAAA4Ao0dAAAA\nAEegsQMAAADV5uPjw+PxlixZIjNeVVWlq6vL4/GePHmyY8cOHo/3xx9/yMyZPn26np7e3bt3\nmatisTg5OdnFxcXQ0FAgENja2s6dO/fRo0fSt2Vvb8/q4TQHGjsAAABQeXw+f/v27TKD6enp\nPB6PuTx27FhXV9cZM2a8efNGMqGoqCg+Pn7+/PlmZmbMSHBw8Pjx401NTTds2JCamurn55eY\nmOjo6Hjnzp2WOZBmQmMHAAAAKm/AgAFFRUWFhYXSg6mpqY6OjpKrcXFxd+7cWblypWQkNDS0\ne/fu4eHhzNWkpKSkpKS1a9f++uuvfn5+Xl5eCxYsOHPmTEVFRURERMscSDOhsQMAAACVZ2Ji\n0rdv35SUFMnIkydPMjIy3N3dJSPdu3efOXNmTEzMvXv3CCG7du3Kyspav369puY/Pxq4atWq\nPn36TJ8+XTrZysoqKytr48aNLXIczYXGDgAAAFRebW2tr69vamqqWCxmRnbt2mVubu7g4CA9\nbd68eW3btv3mm29evXo1e/bs8ePHu7i4MJsqKioKCwu9vb3rhtvb2+vr67N9CFRoKLoAkJO9\nvf2bN28EAsF7Z6qrq69Zs6Z///4tUBUAAICijBs3LiIi4uTJk87OzoSQ1NTUcePGyczh8/mr\nV68ePXr0ixcvnj9/vnz5csmm0tJSsVhsYWHRkjVTh8ZOVZWWlg4aNKhfv37vncnj8czNzVug\nJAAAAAUyMzMbNGhQSkqKs7Pz/fv3c3Jy1q9fL/m6q8Rnn302fPjwffv2xcXFmZiYSMaZN2RF\nIlGLFk0bGjtVJRAIhg8fHhwcrOhCAAAAlIW/v//333+/Zs2aHTt29OzZ087Orm5jRwgZPXr0\nwYMHR48eLT3YsWNHNTW14uLiliqWFfiMHQAAAHDEmDFjKisrs7KyUlNT/f39m7SvQCDo379/\nWlpabW2tzKZ9+/alpaXRK5NFaOwAAACAI4yNjT08PLZs2ZKXl+fn59fU3cPCwoqLi6OioqQH\ni4uLQ0JCVKWxw1uxAAAAwB3+/v4BAQFOTk5yfA3C19c3Ozs7JiYmPz9/7NixBgYGeXl5cXFx\nlpaWa9eulUyrrq7OzMyU3rFHjx6dO3dufvHNh8YOAAAAuMPLy0tXV7fu92Ebaf369YMHD96w\nYcOsWbNevXplaWk5Z86c0NBQ6WUobty44ebmJr1XfHz8V1991ay6KUFjBwAAAKotPT1dcpnP\n51dVVUmuenh4SFa2kwgJCQkJCXlXmq+vr6+vb2NuSwnhM3YAAAAAHIHGDgAAAIAj0NgBAAAA\ncAQaOwAAAACOQGMHAAAAwBFo7AAAAAA4AsudKNjp06cTEhI++OCDpu74+PHje/fusVESAAAA\nqCg0dgq2devWX3/91dbWtqk7vnz58s6dO3Lfbhu593yHCtqBhJAHtAN70w6soR1ItKgnEqJJ\nO/Al7cD2tAPpPzDsZCq5xyxkGtIOVP7HRfD+KU1G/cmR+rMEKA4aOwWzsrKytbU9f/68HDsO\nGDCAjZIAAABAReEzdgAAAAAcgcYOAAAAgCPQ2AEAAABwBBo7AAAAAI5AYwcAAADAEWjsAAAA\nADgCjR0AAACovGPHjo0aNcrMzExbW9vU1NTLy+vUqVOSrT4+Pjweb8mSJTJ7VVVV6erq8ni8\nJ0+eMCNisTg5OdnFxcXQ0FAgENja2s6dO/fRo0fSe4nF4m3btg0ePNjIyIjP59vY2ISHh8v8\nasCLFy8iIiK6deumq6trY2MTExMjFArZOfR/QWMHAAAAqu3QoUNDhw7V09NLSEjIzc2Nj48v\nLy93dXUtKCiQzOHz+du3b5fZMT09ncfjSY8EBwePHz/e1NR0w4YNqampfn5+iYmJjo6Okh8F\nEIvFAQEBQUFBZmZm8fHxv/322+TJk/fs2ePg4HDu3DlJTlBQ0C+//BIZGXn48OHg4OCoqKiF\nCxeydgf8HyxQDAAAAKotMTGxR48eqampzNU+ffq4u7s7OTllZ2fb29szgwMGDMjMzCwsLOzV\nq5dkx9TUVEdHx+zsbOZqUlJSUlLS2rVrp0+fzox4eXmNHz++X79+ERERKSkphJD4+PiUlJRt\n27YFBAQwczw9PUNCQgYNGuTv719YWKijo1NeXp6RkbFmzZoJEyYQQgYNGlRUVJSWlrZo0SK2\n7wq8YgcAAACqTSgUamr+65fR+Hz+xYsXZ8yYIRkxMTHp27cv05wxnjx5kpGR4e7uLhlZtWpV\nnz59JF0dw8rKKisra+PGjZI5rq6ukq6OYWhouHr16pKSkgMHDhBCjI2NKyoqmK6Ooaurq6bW\nEk0XGjsAAABQbaNGjSooKBg9evTp06dra2vrnVNbW+vr65uamioWi5mRXbt2mZubOzg4MFcr\nKioKCwu9vb3r7mtvb6+vr08IefDgQUlJSb1zhg4damBgkJmZKT1YU1Pz6NGjLVu2bN++fdas\nWc05xkbCW7GqSiQSPX78+ObNm42ZbGFh0TL/UQAAAGh5wcHBDx48WLp06Z49e/T19Z2dnUeN\nGjV+/Hg9PT3paePGjYuIiDh58qSzszMhJDU1ddy4cZKtpaWlYrHYwsKigRt68OABIaRr1651\nN/F4PHNzc2aCxPDhw48cOdKmTZu4uLiJEyc24xAbC//Yq6rHjx9HRERYNc6+ffsUXS8AAACL\nIiMjy8rK9u7dGxwcfPv27alTp37wwQcXL16UnmNmZjZo0CDm3dj79+/n5ORIN3bMm7kikaiB\nW2l4jkgkUldXlx5Zt27dn3/+OXXq1KlTp65cuVLeg2sCvGKnqu7duyd5Mfm9jIyMWC0GAABA\n4fh8vpeXl5eXFyHkxIkTo0ePDg0NPXbsmPQcf3//77//fs2aNTt27OjZs6ednd3du3eZTR07\ndlRTUysuLm7gJjp16kQIuXHjRt1NIpHozp07w4YNkx7s0aNHjx493N3dBQLBt99+GxQUZGxs\n3MzDbBhesVNVhoaGRo2m6GIBAABY9PDhw8rKSukRZ2dnX1/fCxcuyMwcM2ZMZWVlVlZWamqq\nv7+/9CaBQNC/f/+0tLS6n9Lbt29fWloaIaR9+/YfffQRc1nG0aNHq6urhw8fTgi5d+9eYmLi\n8+fPJVv79ev39u3b27dvN+MoGwWNHQAAAKiwsrIyc3PzuiuJXL9+3dTUVGbQ2NjYw8Njy5Yt\neXl5fn5+MlvDwsKKi4ujoqKkB4uLi0NCQiTN3IwZM5il8qTnVFRUhIeH9+7dm3nF7tmzZyEh\nIXv37pVMYFbU69atm/zH2Th4KxYAAABUWIcOHWbOnPnjjz8+efJk9OjRbdu2LSsrS05OPnz4\n8JYtW+rO9/f3DwgIcHJyqvs9CV9f3+zs7JiYmPz8/LFjxxoYGOTl5cXFxVlaWq5du5aZM3Hi\nxJycnGnTph0/ftzb29vAwODKlStxcXE1NTWHDx/W0NAghPTq1cvDwyM8PPzFixc9e/bMz8+P\niYkJDAw0NDRk+c5AYwcAAAAqbunSpb169UpISAgJCamoqOjYsaO9vX12draLi0vdyV5eXrq6\nutJfm5C2fv36wYMHb9iwYdasWa9evbK0tJwzZ05oaKhAIJDM+eWXX9zd3X/++efp06e/ePHC\nzMzM19d31qxZJiYmkjk7duxYtGjRsmXLHj58aGZmFhoa+t1331E/8Lp4Mh/A37hx46pVq/76\n668WuG0ghKxYsSI1NfX8+fMtfLsV//4FFeVUz0cYmucD2oF82oEfD3v/nCbTfP+UpqmhHaj8\nFbZOL1jIpP5qBR5rKpT+b1Cc0dgvC4LsK3ZXr169du0aTxX+1ecMmcWyAQAAAOQj29gtWLDg\nk08+kX4tEVj166+/njx5UtFVAAAAABfINnaGhoYy3/4FVl28eJH5pgwAAABAM2G5EwAAAACO\nQGMHAAAAwBFo7AAAAAA4Ao0dAAAAAEegsQMAAADgCPzyRCt1lXbgY9qBhJDhtAMf0A5k/adh\nlJPyL7xYwUIm9Qdb6ZeEJYL3T1E85T8b2aD8yzK3zsdFOeAVOwAAAACOQGMHAAAAwBFo7AAA\nAAA4Ao0dAAAAAEegsQMAAADgCDR2AAAAAByBxg4AAACAI9DYAQAAgMo7duzYqFGjzMzMtLW1\nTU1Nvby8Tp06Jdnq7Ow8bNgwmV3+/vtvHo+XlJTEXPXx8bG3t5eZExAQwHuH5cuXM3Nqa2vn\nz5+vrq6+ZMkSmd3FYnFycrKLi4uhoaFAILC1tZ07d+6jR48kE0Qi0YoVK7p3787n821sbGJj\nY4VCYXPuByxQDAAAAKrt0KFDnp6evr6+CQkJ7du3v3PnzvLly11dXXNzc+v2ak3y7bffBgUF\nMZeDg4OtrKy+//575qqNjQ0hpLS0dNy4caWlpRoa9fRUwcHBSUlJY8eOnTJlCp/Pz8/P/+mn\nn3bu3Hns2DFzc3NCyPz585ctWxYdHe3o6Hjy5Ml58+aJxeLvvvtO7oLR2AEAAIBqS0xM7NGj\nR2pqKnO1T58+7u7uTk5O2dnZzWzs7Ozs7OzsmMt8Pr9Dhw4yr/wlJye3bdv2999/NzU1ldk3\nKSkpKSlp7dq106dPZ0a8vLzGjx/fr1+/iIiIlJQUoVC4bt268PDwb775hhDy6aefXr58OSUl\nBY0dAAAAtF5CoVBT818/ZMbn8y9evNgCN+3n5zd79ux6N61atapPnz6Sro5hZWWVlZVlZWVF\nCFFTU8vLy2vbtq1ka9euXQ8fPtycevAZOwAAAFBto0aNKigoGD169OnTp2tra1vyprt06VLv\neEVFRWFhobe3d91N9vb2+vr6hBA1NTVra2sjIyNmXCQSZWRkODs7N6cevGKnYI8fP75//35E\nRAR7N6GmpjZ58mQLCwv2bgIAAECBgoODHzx4sHTp0j179ujr6zs7O48aNWr8+PF6enqKKqm0\ntFQsFjfpH98ffvihsLBw8+bNzbldNHYKZmFhoa+vn5eXx95NqKurl5eXo7EDAAAOi4yMnDlz\nZmZm5tGjRzMyMqZOnfrDDz8cOnSod+/eCqmHeWtYJBI1cv7ChQsXL168c+dOyUf65IPGTsG+\n+uqrr776StFVAAAAqDw+n+/l5eXl5UUIOXHixOjRo0NDQ48dO0YIUVdXr6mpkZn/+vVrQoiW\nlhYbxXTs2FFNTa24uPi9M0Ui0bRp07Zt27Z3797hw4c383bxGTsAAABQbQ8fPqysrJQecXZ2\n9vX1vXDhAnPV1NT077//ltnr+vXr5N0fkmsmgUDQv3//tLS0up/527dvX1pamuRqeHj4zp07\njxw50vyujqCxAwAAAJVWVlZmbm6+aNEimfHr169LliAZMWLEnTt3/vzzT8lWkUi0atWqDh06\nODk5sVRYWFhYcXFxVFSU9GBxcXFISIiksdu+ffvPP/986NAhR0dHKjeKt2IBAABAhXXo0GHm\nzJk//vjjkydPRo8e3bZt27KysuTk5MOHD2/ZsoWZ4+/vn5iYOHr06JkzZ/bt2/fJkyebN28+\nc+bMb7/9Jr1OSnV1dWZmpnR4jx49Onfu3MCt5+fnMy8W1tbW3rx5Mzs7mxDyySef8Pl8X1/f\n7OzsmJiY/Pz8sWPHGhgY5OXlxcXFWVparl27lhDy6tWriIiIkSNHvnjxgtmR4ejoqKurK9+9\nwROLxfLtCSotl8ejG/iYbhwhhJB+tAMf0A40pB34gewP3tCg+f4pXMPG6Uj9wab+uMh+fEgp\ntcKzkQ3UH2ulf1zEB97fqyQnJyckJFy9erWioqJjx4729vYzZ850cXGRTHj9+vXixYt37Nhx\n+/ZtPT29/v37f/vttwMHDpRM8PHx2bt3r0xsfHy85KPwtra29vb2v/76q/SE/v37nzlzRmav\nq1ev2traMpd37ty5YcOGS5cuvXr1ytLS0t/fPzQ0VCAQEEIKCgocHBzqHsv169etra3fe8j1\nQmPXSqGxowKNnZJCY6e0WuHZyAY0dvBu+IwdAAAAAEegsQMAAADgCDR2AAAAAByBxg4AAACA\nI9DYAQAAAHAEGjsAAAAAjkBjBwAAAMARWMcOAAAAgCPwih0AAAAAR6CxAwAAAOAINHYAAAAA\nHIHGDgAAAIAj0NgBAAAAcAQaOwAAAACOQGMHAAAAwBFo7AAAAAA4Ao0dAAAAAEegsQMAAADg\nCA1FFwBK6tmzZ8uXLz9//nx5ebnM786dP38egZwpUvkDVaJI5Q9UiSKVP1AlimydRw0SaOyg\nfiEhIfv37x8xYkSXLl14PB4ClSSzFQaykdkKA9nIbIWBbGQqfyAbmWwUCf8QA9THwMDgt99+\nQ6CyZbbCQDYyW2EgG5mtMJCNTOUPZCOTjSKBgc/YQf20tbWtra0RqGyZrTCQjcxWGMhGZisM\nZCNT+QPZyGSjSGCoL1iwQNE1gDKqrKw8d+6ch4cHApUqsxUGspHZCgPZyGyFgWxkKn8gG5ls\nFAkMnvjfH1qEVk7S6KurqycnJ7dr127gwIF8Pr/eOQhU3SKVP1AlilT+QJUoUvkDVaLI1nnU\nUBcaO/iXjh07vndOaWkpAls4sxUGspHZCgPZyGyFgWxkKn8gG5lsFAky0NgBAAAAcAS+PAHv\nJxQKHzx4UFtbSz1ZJBK9fv1aCQOfPXv24sWL5ufISElJef78Oa00lookVB8XuodMWuXZyEYg\n9ZOH+uPCxgNN/WxkKZM077EWiUR37ty5dOnSpUuX7t69y97LN0r+fNt6KfZLuaBsRCLR8uXL\nP/nkk48//njTpk1isXjdunUGBgaEEB0dnfDw8Ddv3jQ1c9OmTa6urr169Zo6dSrzLCORlZUl\nx0lIN7C2tjY2NjYzM5O5vHTp0g4dOjB/Hd26dfv555+bWl4D1NXVCwsL5diRjSKpPy71kvuQ\nxa3ybGQjkPrJQ/1xYeOBrldzzkY2Mqk/1seOHRsxYoSurjbz1/cAABz6SURBVK70v/J6enr/\n/e9/L1y4oAxFtuTzbauFxg7+Zfny5VpaWv/973//97//6evrf//99wYGBj/88ENKSsqCBQsM\nDQ1jYmKaFLhmzRpCiJubW3BwcLdu3QwNDY8ePSrZKsfzAvXAb7/9ViAQHDhwQCwWR0ZGamlp\nhYaG7tixIyUlZdKkSerq6suWLWtSoFgstnsHQoi1tTVzWbFFUr8bqR+yuFWejdQDxSycPNQf\nF+qBbJyN1DOpP9a//fabmprayJEj4+Pj//zzz9zc3Nzc3IMHD65Zs+bTTz/V0tI6dOhQkwLZ\nKJKN51uQgcYO/qV79+5bt25lLp86dYoQwvwHmrFjxw5bW9umBs6fP5+5/Pr166+++kpXV/f4\n8ePMiBzPC9QDO3XqtHnzZuZyly5dVq5cKb01Nja2a9euTQpkMjU0NIKDgxf9m5qa2vTp05nL\nii2SjbuR7iGLW+XZSD1QzM7JQ/1xoRvIxtlIPZP6Y/3hhx9GRka+a+uMGTMcHByaFMhGkWw8\n34IMNHbwL7q6urdu3WIui0QidXX1S5cuSbbeuHFDV1e3qYHHjh2THvn666/btGlTUFAglut5\ngXqgjo7OqVOnmMvGxsbnz5+X3pqTk6Otrd2kQLFY/Pz586+//prP5y9YsOD169eScbnftaFe\nJPW7kfohi1vl2Ug9UMzOyUP9caEbyMbZSD2T+mOtpaV1+vTpd23Nzc2V46lMJZ5vQQa+PAH/\nYmJiUlxczFy+du1abW3ttWvXJFuLi4tNTU2bFNi5c+e8vDzpkbVr144YMcLd3f3ixYtyVEg9\nsF+/fomJicxlNze3EydOSG/dunVrjx49mpppYGCwbt267OzsvXv3fvTRR8zTX3NQL5L63Uj9\nkEmrPBupBxIWTh7qjwv1QDbORuqZ1B/rTp06nT179l1bT58+3blzZ4UXycbzLchSdGcJymX2\n7NmdO3desmTJihUrrK2tx4wZY2Zmtnv37qtXr+7evdvS0jIsLKxJgYsXL9bR0YmKinr27Jlk\nsKamJigoiM/n+/v7N/UkpB6Yl5dnaGj48ccfr1mzZtu2bRYWFuHh4du2bVu5cuXAgQM1NDT+\n+OOPJgVKEwqFK1eu1NfXDwwMfPTokdz/uadeJPW7UYLWIYtb5dnIxuNC/eSh/rhQD5SgeDZS\nz6T+WC9btkxbW3vOnDk5OTkPHjyorq6urq5+8OBBdnZ2eHi4pqbmTz/9pPAiWX2+BQYaO/iX\n6urqoKAggUDA5/NDQ0NramrCwsJ4PB7z3wA3NzfpP+/GqKmp+eabbwQCQUlJicymn3/+mVms\nUrGBYrH49u3bU6dOlfnvrI6OjqenZ25ublPT6rpz5463t3fbtm15PJ7c/67QLZKNu1EalUNu\nhWcjS48L3ZOH+uNCPVAGlbOReiYbj/XatWvrfVnO0tJS8sk2hRfJ9vMtYIFiqIdIJOLxeJIn\n1lu3bl2/fr1r1642NjaSwSZ5+/athoaGmprsW/8vXry4evVqv379FB7IePbs2aNHj2pqavT1\n9bt06aKuri5fTr3S09PT0tJiY2O7dOnSnByKRbJ0N0pQOeRWeDay97hQPHmoPy7UA2XQ+gOk\nm0n9sRaLxdeuXSspKWFW1zM0NOzevbu1tbV85bFUJIPV59vWDI0dNEpKSoqnp2ebNm2auuPr\n16+1tbWpPC83Rm1t7atXr/T09NgINzQ0LCgosLCwaGaOSCR6+/atjo4OjaJkUSmS+t1I95CV\n52xU/sAmaebJI/fj0mKBDDb+AKlnqsRTmUoU2QrhyxPQKIGBgXfv3pVjR11d3V69emVkZFAv\nqV45OTn6+votc1sNS0hIGDZs2EcffTRt2rR79+5Jbzp+/LjMCqLKRr67scUOWXnORuUPbEly\nPy5sBLJxNrbkH7XyPJU1QCWKbIU0FF0AKJeePXvWO15bW/vZZ59pa2sTQoqKipqU2atXr9Gj\nRzs7Oy9evNjBwYFClUpv7dq1YWFhbm5uH3/88cGDB1NSUnbv3j1kyBBF18UiNg5ZJc5G5Q+k\njvrjQj2QjbOxFf5Rg4pCYwf/wnzoITAwsFu3btLjUVFRw4cPNzExkSPzyy+/XL58eWRk5Mcf\nf+zu7j5p0iRPT08tLS35KkxISGhgq/QqCQq0fv36+fPnL1y4kBDy5s2bGTNmeHp6/vnnn4MG\nDVJ0af+gfjeyccjKfzaqRCB11B8X6oFsnI3UM1XiqUwligRZCv7yBigZ6otwEkKysrKYy1ev\nXp04caKOjo6ent7IkSPnz5+/devWvXv3NimwMR8/kqPOxmjTpo1kGdWGsbHMbCM1skjqdyMb\nh6z8Z6PyBzZJI08e6o+L8q/9y0amSjyVqUSRIAONHdTj7NmzDg4ONjY2kp8FpPJPKaO8vPyX\nX34ZN24c81X5pj4vfPPNN7169Xr+/HlNfTIzM5Xhicba2lrmp3JEItG4ceNMTEwKCgqUobGj\nfjeyd8jKfDYqf2CTNOmfUoqPC/VANs5G6pkq8VSmEkWCDDR2UD9ai3DW/YdK2qtXr27evNmk\nwJqaGmdnZ39//3q3KkPPJGZz+V9aRVK/G1k9ZKU9G5U/sEma+k8p9eV/lXbtXzYyVeKpTCWK\nBBlo7KAhzV+Es+F/qORz//792NjYu3fv1t10/vz5vn370r05iSY9G7K6/G8DGl8k3buxBQ5Z\nCc9G5Q9sEvn+KaW+/K9yrv3LRqbyP5WJVaRIkIbGDt5vz549AQEB9f5hv9eFCxeqqqqol6QQ\nTX2iefPmTW1tbd3x6urqc+fOyVdDTU2NSCRqYIJinw3ZOGQZSnU2Kn9gkzTn5GnO48JSIBtn\nYwuc4S1AJXomlShSOaGxAzlR/6tTnsDS0tLr168/evRIZjw3N1f6k91UNKnIt2/fqqurnzp1\nqoE51ItUnselJTO5HdhiZ7hSHXXLBLKRSSXw8ePH7u7uzGWFP5W9C9tFthJY7gTgHyKRKDo6\nOj4+vrS0lBkxNzefPXv29OnTmav9+/dXXHWEEKKpqenq6pqRkeHk5PSuOQovEpSW8p/hQEtW\nVlZGRkZ5eTlzVSwWX758ubCwkLmqJA+0ShSpitDYAfwjOjo6NjZ2woQJn3zyiZGRUXl5+YkT\nJ2bPns3j8b7++mtFV/ePUaNGbdy48c8//+zXr5+RkZH0pgULFiioKFANKnGGQ/MlJiZOmjSp\nW7dud+7csbKyqqioKCsr+/TTT3/55RdFl/Z/VKJIVaXolwxBVSnn2w3NCezatWtiYqLMYHx8\nvI2NDcWqZDS1SAMDg7Zt23aoj5JU2PKBbGRyMrDlz3BlOOoWDmQjs6mBtra269evF4vFHTp0\nePjwoVgszszMdHZ2vnHjBsWqZKhEka0EXrED+Mf9+/c//fRTmcH//Oc/oaGhCqmnXs+fP1d0\nCaCqVOIMh+a7deuWq6srIYTH49XW1hJCXF1dX758OXnyZGblOWWgEkWqKDVFFwCgLJiFRmUG\nCwsL27dvr5B6GvbmzZuHDx/W1NQouhBQGap1hoPc2rRp8+jRI0KIvr7+rVu3mME+ffrk5uYq\ntK5/UYkiVRQaO4B/jB079ssvv1y5cuXZs2evX79+9uzZVatWBQcHM0uPKo+dO3c6OjoKBIJO\nnTrduHGjoqJiwoQJr1+/VnRdoOxU5QyHZnJzc5s0adL9+/f79+8/ZcqU06dPFxcXx8bGKlUH\nrxJFqii8FQvwj9jY2GfPns2ZM4d5X4AQoqmpOXHixJiYGMUWJi0xMXHy5Mmff/75xIkTmXfQ\nXr9+febMmcjIyOXLlyu6OlBqKnGGQ/OtXLlywoQJb968WbBggaenJ/Mlem1t7cTEREWX9n9U\nokgVhcYO4B/a2tqbN29evnz5xYsXKysr27RpY29vL/PNU4VbunTpypUrw8LCCCEzZswghHTs\n2PGnn3764osv5GvshEKhurp6Y37qW1GBbGSyUaTyU4kzHJrPxMTk4MGDzOWLFy/m5+c/f/68\nd+/ekp8bVgYqUaSqUvS3N0DZUV/LVMkDRSLR/fv3r1+/znxRixZaRWpqal6/fp25rK2tffXq\nVbFYfOvWLS0tLTmqor7iMRtLKKtEkRKsrv3b/OVbX758Kdnr+vXr6enpVH4rQsmPmu0KxSys\nrKu0S/W25PrtIB+eWCxWdG8Jyui9a5lyL7CysnLWrFk7d+6srKxkRoyNjQMCAmJjY/l8vpIU\n2aVLl9jY2PHjxxNCdHR0CgoKbG1td+3aNWvWrNu3b8sR+J///GfgwIHz58+Xr54WCGQjk3og\n9QeavHv5Vrm/GX3u3DkPD4+9e/c6Ozvv3r177NixQqGQz+cfOHDAxcVFjkDlP2o2KqReJK3A\n5cuXu7m59e7du4EX72fPni1fhbTuSVaLBAbeioX6UV/LVPkDJ0+enJWVFRYW9uGHH/L5/Kqq\nqgsXLmzcuLGiomLLli1yBLJRZGBg4JQpU27dujV06FCxWHz16tXjx49HRkZOnjxZvgqpr3jM\nxhLKyl8k9QeajeVb58yZ89lnn/Xr148QMmvWLD8/v/Xr1y9cuHD+/PnHjh2TI1D5j5qNNZmp\nF0krMDo6um3btr17946Ojn7XHLl7Jlr3JKtFwj8U+XIhKDHqa5kqf6BAIDh//rzM4O7du/X0\n9OQLFLNQ5Nu3b7/55htdXV3Jn7CmpuaUKVNqamrkC6S+4jEbSygrf5HUH2g2lm/V19cvKioS\ni8UXLlwghFy+fFksFl+5csXQ0FC+QOU/ajbWZKZepEos1auQ9dtBPmjsoH4aGhqSz3JJ3Lx5\nU1NTk6uBbdq0EQqFMoMlJSVGRkbyBYpZKJJRUVFx9uzZvXv3Hj16tLy8vDlRQAX1B1pbW/va\ntWtisbhjx4737t1jBn///XdXV1e5i9TX1//rr7/EYvHSpUvNzc2Zwb/++ksgEMgXqPxHzcYf\nIPUi2XisqWPpqQzYgLdioX7MWqbW1tbSg81Zy1T5A728vHbu3Dlu3DjpwV9//dXPz0++QDaK\nZBgYGHTu3NnIyEhPT4/WtxrfvHlTXl7erl07TU1N5QxkI5NWIPUHmlm+1cbGhlm+tXPnzqTZ\ny7f26tUrLi4uICAgLi5uzJgxzOAff/zxwQcfyBeo/EfNxh8g9SJpBTo7Ozc8oaam5syZM/IV\nSeueZLVIYKCxg/oxa5neuXPH2dnZyMjo2bNnJ0+ejImJmThxIlcDHRwcQkNDN2/e3LdvXz09\nvfLy8jNnzty8efOrr75asmQJMyciIkKxRbLxDY+dO3euWLEiLy+vtrb26tWrHTt2DAsL27hx\no46OjpIEKn+R1B9oZvnWzMxMZvnWTZs2GRsbr127tjkdyeLFi0eNGrVu3TpbW9u5c+cSQn7/\n/fe5c+fK/eEw5T9q6hWyUSStQAsLC+YCj8c7cuSIUCh0cHBo06ZNeXn5uXPnjIyMfHx85KuQ\n0LsnWS0S/qHolwxBSb1+/TooKEhdXV1yqmhqak6ePPnNmzdcDeTz+QYGBm0apPAix44da2Ji\nMm/evNTU1L179yYnJ8+aNUtPTy8wMFC+wISEBDU1tTFjxsTHx2tqal69evXhw4fdu3efNWuW\nkgSqRJHUH+iysjIPD48bN27cuHHD1taWydTW1k5OTpYvkFFdXX3x4kXJshQlJSVHjhyRO035\nj5p6hWwUST0wNjY2ODj47du3kpGXL19+/vnny5Ytky9QzMI9yUaRwMByJ9CQp0+f0l3LVPkD\n2UCxSD09vWPHjvXt21d6cM+ePYGBgVVVVXIE2tjYTJs2jVnxWLJ+SmZm5hdffCHf+inUA1Wl\nSMLa2fj27VtlXr5V+Y+avWcJ6g8NlUBTU9PMzEw7OzvpwXPnzvn4+Ny/f7855VG8J9krEvBW\nLDTE2NjY1tb25cuXtD7LpcyBHh4eQUFBPj4+zXnHsF4Ui9TQ0LC3t5cZ/Oijj+T+iNjff//t\n6ekpM2htbS1ZrUrhgWxkslHkq1ev9PT0hg4dSggpKSk5fvx43759u3TpIneghJaWVv/+/eXe\nXU9P771zqqur5QtX2qOWYK9CQq9IuoHPnj17+vRpYwabhO49yVKRQNDYwbtQ/yyX8geKRKLx\n48fr6emNHTt2woQJzG8XNhP1Iql/w8PExCQ3N1fmM9Hnz5+X+9UC6oEqUSSttX/ZWL5V7gXb\n3kuZj5puhWwUyd5Ru7i4TJgwITIy0t7e3tDQ8OXLlxcuXIiMjBwwYIAcaQzqq1uzUST8Q9Hv\nBYOSov5ZLuUPFIvFT58+TUxMHD58uJaWVvfu3RcvXtzMH1yiXuTKlSvbtWvn5ub2/9q7v5Cm\n3y8O4J+pm6iY30qhDMSsWJNlVLavKcQUMUwj0qgZmxGaYYQXRZDMi6TyFyI4ykBDpexim4WJ\ndRGNGBlkhRH4sxaKzPIfmn8WJlpi+12Mn6ip6XYe95ztvK5GrsP7TKqHj885Xbly5fr16xcv\nXkxISNi8eXNxcfF//m9VBQsLC4OCgoqLi1+9eiWRSBoaGqqqqsLCwrRarXMJwQuiCKlUKnNy\nciYnJ+12e2RkpFqtdpzpDx48uKo6ISEhtbW1jhdQFz3Z4b9rqIQsQrLruqenJzExccG/9QqF\nwpXFeICfJLuQxIEOdmRx4Nt6+S8419jYWF1dXVpamkQiSUlJaWpq+v37Nw8hwSc8wDcegxdE\nERJ89y8K/HfNf0J2ent7TSZTQ0PDs2fPrFari9UYfZKwIYkD/SiWLA78Lhf/BecaGBiwWq1f\nv34ViUQzMzMnTpzYu3dvfX29Y8WUG0NOTEw49xuXIhaLS0tLtVptR0fHwMBAcHCwi3eiwQti\nCenn5ycIwvPnzyMiIqKjowVB8PHxmZ6edqUm//jvmv+EjGzZsiU0NBRw7yOLTxI8JBHojh1Z\nCvhdLv4LCoIwNDSk1+sfPHjw/v17uVyek5Oj0Wg2btzY399/7NgxtVptNpvdG5LRhAf4xmMW\nK5R5Dgm1+xfX+lb+uwbcyQwekun3GnzvI/h2axYhiQOtOyGLKy8vLykp2bNnz5/beiUSieM9\nq9rWy3/Bw4cPm0ymgICAkydP5uTkLJhNa2lpSUxMnJqaWnlBFiFTUlLMZjPghAf/Qy0oQr58\n+fLIkSPj4+M7d+5sbm4OCwtramrKzMysra3VaDQrr6NWqx0vllnfqtPpnEjIAv9dQyVkEZJd\n1zU1NXl5eZmZmUlJSQUFBW1tbf/8849SqUxPT19mUGN5gJ8ku5DEgQ52ZHFBQUF+fn4ikWiZ\n99hsNk8qGB8fn5OTo1KpgoKC/vzqt2/f6urqLl26tPKCLEIKgjA6OtrY2Pjo0aMXL15s3br1\n9OnTGo3G6aUDKpXKbDafO3cuOjo6MDBwfHz8w4cPVVVVGRkZ9+/f56EglpATExNdXV1SqdTf\n318QhK6uri9fvjh2Qzjh5s2bnZ2dlZWVsz+fmpyc1Gg0cXFxzk1KMsJ/17AJWYQEL8hoTSPs\nJ8koJBEEmoolXu+/K+DujIsDmfBAMdSCIiSsTZs2Oe6qz/Xu3bvw8HC35FkbKLoGDwleUCwW\nd3Z2Ol77+/tbLBa73W61WiUSiXMFWUAREim6Y0cWB36Xi9uCu3bt+ut77M4+2Ga39FgAmvBA\nMdTCbUh2u395Xt/Kf9dMdzKDf2vACwKuaWT3SbJYeEkc6GBHFge+rZfbgo8fP559PTExoVar\nr127JpfLXYwHG3Iu2AkPFEMt3IZkt/uX5/Wt/HfNLqHA4FsDXjA7Ozs/P99qtSYlJdntdovF\n0tzcXFRUlJeXt9pS7D5JwJBkAbpjR5YEe5cLRUGbzbZ+/Xqz2axUKp0uwjQk+IQH/0MtWELC\n6u3tzc7OXnBGVygUer0+KirKXalYQ9E1eEjwgtPT01qttqKiYnJy0vErYrE4Nzf31q1bjpUl\nPEAREik62JG/s9lsT548MRqNJpNJqVReuHAhPT19+YEApAVZHOxgQ4JPePA/1MKiJouQLPT1\n9VkslvHx8cDAQKlUGhkZ6e5EawFF1+AhwQt+//4dcE0jIyhCokPnYvJ3UNt6ERVkwZWQ7e3t\njhd3794VBMFqtf75HrlcHhYWttq5XfCNx+AFWdRkEZIF71zfiqJr8JDgBUNCQvbv3+96HaZQ\nhMTHraMbhGuDg4M6nW7fvn2CIMjl8vLy8uHhYbvd3tfXp1AolEql5xUcGxsTBMFsNq/2N7IO\nye7P8qFDh/R6veO/gAQBXpBFTRYhwRmNRoVC4evrKwiCxWIZGxvLzs7mPLPrUHQNHhKk4MYV\ncDohFBQhsaMndmRxc+9yVVRUzL3LFR4ertPp/vz/m7EXZAEqJLsJD26HWtCFhDW7vvXMmTMF\nBQWCIExNTb19+7aoqMiD17ei6Bo8JFTBkZGRgICAuLi45OTkDRs2OJFkDaAIiZ67T5aEUwcO\nHKiurv7x48eiXx0aGiorK/OMgv/OERsbKwiCTCb7d75VBWMRci7wx4ojIyM1NTWpqakSiUQq\nlZaUlPT09HBVEEtIQDt27NDpdI7Xs1u+TCZTRESEW3OxhaJr8JBQBS0Wy9WrV2Uyma+vb3Jy\ncnV19ejoqHOR2EEREjsaniDzzN7lWsaqHhTxXzA9Pf2v73n69OnKCwoMQs7FbsKD26EWdCFd\nJ5FIPn365NjyNbuXv7u7WyqV/vz5073Z2EHRNXhI8IJtbW0Gg6G+vr6npyclJUWlUh09enQl\nG+nWEoqQSNGPYsk84Nt6+S+42kPbSjBdeswOiqEWFCFd553rW1F0DR4SvGBMTExMTExJSUlr\na6vBYCgsLDx79mxaWppKpcrMzHSuJjgUIZGigx2ZB/wuF/8FWUARchbsxmMWBbGEBOSd61tR\ndA0ekl3XsbGxsbGxKpWqrKzs4cOHjY2N09PTLtYEhyIkMm77ITDhHvhdLv4LssB516mpqX5+\nfsHBwbm5uS0tLQu++vr1a39/f/cWxBIS1q9fvy5fvhwQEDD7d7VYLM7Pz5+ennZvMKZQdA0e\nklHX7e3tWq02KipKJBIlJCTcuXNnaGjIlYIsoAiJDj2xIwSBueO0MzMzgiCcP39+3bp1c9/z\n5s0bJyrbbLbKysqlNh5v3779xo0b7i2IJSQssVhcWlqq1Wq9an0riq7BQ8IW7O7uNhgMer2+\nra1t9+7deXl5WVlZERERTsdjAUVIvGh4giwJ/JI+/wVZAAkJPuHB/1ALi5pMh1oIca+Kigq9\nXt/S0iKVSo8fP37q1CmZTObuUAuhCIkdHezIkvg/h3nPwQ7cSqY+V/WXA3hBFjVZhIQVGhr6\n1/cMDw+vQZK1hKJr8JDgBUUiUWBgYHx8fEREhOO5/p/u3bu38oIsoAiJHf0olhBvhGKoBUVI\nWN65vhVF1+AhwQumpaU5XgwODrpejREUIbGjJ3ZkngV3uVpbW2UymSt3ufgvyAKKkLNQPElF\nEdJ1nz9/NhqNRqOxo6MjMTFRpVJlZGTwdskMHIquwUOi6JpgRAc7Mg/4XS7+C7KAIuQsFGcm\nFCEBeef6VhRdg4dE0TVBhA52hHg7FGcmFCFZcKxvra+vHx4e9p71rSi6Bg+JomvCPzrYEeLt\nUJyZUIRkp7W11bG+1cfHx3vWt6LoGjwkiq4Jz3zcHYAQQsjiPn78WFRUtG3bNoVC0dvbe/v2\n7f7+fneHYg5F1+AhUXRNUKAndoR4IxRDLShCsrBgfWtWVpY3rG9F0TV4SBRdE1xo3Qkh3mjB\nDq3ZHQT8FGRRk0VIWAvWtxoMBm9Y34qia/CQKLomGNETO0II4YV3rm9F0TV4SBRdE4zoiR0h\nhPDCO9e3ougaPCSKrglG9MSOEEIIIcRD0FQsIYQQQoiHoIMdIYQQQoiHoIMdIYQQQoiHoIMd\nIYQQQoiHoIMdIYQQQoiHoIMdIYQQQoiHoIMdIYQQQoiHoIMdIYQQQoiHoIMdIYQQQoiHoIMd\nIYQQQoiH+B8A9Ol2SxvXEQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diffexpIDs <- as.character(rownames(topTable(fit, coef=2, number=20)))\n",
    "diffexprows <- which(rownames(rlog.norm.counts.coll) %in% diffexpIDs)\n",
    "heatmap(rlog.norm.counts.coll[diffexprows,], col=greenred(100), Rowv=F, scale=\"row\", labCol=colnames(rlog.norm.counts.coll))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3366290d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>logFC</th><th scope=col>AveExpr</th><th scope=col>t</th><th scope=col>P.Value</th><th scope=col>adj.P.Val</th><th scope=col>B</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>COL5A3</th><td>-0.50540596 </td><td> 4.5696554  </td><td>-5.775256   </td><td>3.027702e-05</td><td>0.4136213   </td><td> 1.37934641 </td></tr>\n",
       "\t<tr><th scope=row>LOC284898</th><td>-0.06160494 </td><td>-1.6222767  </td><td>-5.355074   </td><td>6.803529e-05</td><td>0.4136213   </td><td> 0.86984262 </td></tr>\n",
       "\t<tr><th scope=row>CA2</th><td>-0.06098101 </td><td>-1.6222767  </td><td>-5.352415   </td><td>6.838977e-05</td><td>0.4136213   </td><td> 0.86651472 </td></tr>\n",
       "\t<tr><th scope=row>FLJ20712</th><td>-0.17164638 </td><td> 1.1672927  </td><td>-4.737872   </td><td>2.325062e-04</td><td>0.9995744   </td><td> 0.06351506 </td></tr>\n",
       "\t<tr><th scope=row>EDARADD</th><td>-0.08957431 </td><td>-0.8153277  </td><td>-4.018042   </td><td>1.022954e-03</td><td>0.9995744   </td><td>-0.95532761 </td></tr>\n",
       "\t<tr><th scope=row>SLC24A5</th><td>-0.11153128 </td><td>-0.3462480  </td><td>-3.985657   </td><td>1.094420e-03</td><td>0.9995744   </td><td>-1.00282761 </td></tr>\n",
       "\t<tr><th scope=row>SLC23A1</th><td>-0.06575288 </td><td>-1.2100798  </td><td>-3.970930   </td><td>1.128570e-03</td><td>0.9995744   </td><td>-1.02446834 </td></tr>\n",
       "\t<tr><th scope=row>BTLA</th><td>-0.08977437 </td><td>-0.4747555  </td><td>-3.873732   </td><td>1.382640e-03</td><td>0.9995744   </td><td>-1.16789049 </td></tr>\n",
       "\t<tr><th scope=row>BHLHA15</th><td>-0.26457929 </td><td> 2.1497676  </td><td>-3.749752   </td><td>1.792294e-03</td><td>0.9995744   </td><td>-1.35221410 </td></tr>\n",
       "\t<tr><th scope=row>FOLR1</th><td>-0.07798853 </td><td>-0.7389603  </td><td>-3.705433   </td><td>1.966713e-03</td><td>0.9995744   </td><td>-1.41844126 </td></tr>\n",
       "\t<tr><th scope=row>ARHGEF5</th><td> 0.51848124 </td><td> 8.5779319  </td><td> 3.697851   </td><td>1.998218e-03</td><td>0.9995744   </td><td>-1.42978792 </td></tr>\n",
       "\t<tr><th scope=row>KIAA1210</th><td>-0.09512721 </td><td> 0.4030249  </td><td>-3.649377   </td><td>2.211954e-03</td><td>0.9995744   </td><td>-1.50243345 </td></tr>\n",
       "\t<tr><th scope=row>YIF1A</th><td>-0.37657682 </td><td>12.6074668  </td><td>-3.616020   </td><td>2.372217e-03</td><td>0.9995744   </td><td>-1.55252654 </td></tr>\n",
       "\t<tr><th scope=row>SND1</th><td>-0.21111977 </td><td>14.2912700  </td><td>-3.590385   </td><td>2.503239e-03</td><td>0.9995744   </td><td>-1.59107443 </td></tr>\n",
       "\t<tr><th scope=row>MAP3K8</th><td> 0.51676251 </td><td> 7.6753650  </td><td> 3.477874   </td><td>3.169417e-03</td><td>0.9995744   </td><td>-1.76073447 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & logFC & AveExpr & t & P.Value & adj.P.Val & B\\\\\n",
       "\\hline\n",
       "\tCOL5A3 & -0.50540596  &  4.5696554   & -5.775256    & 3.027702e-05 & 0.4136213    &  1.37934641 \\\\\n",
       "\tLOC284898 & -0.06160494  & -1.6222767   & -5.355074    & 6.803529e-05 & 0.4136213    &  0.86984262 \\\\\n",
       "\tCA2 & -0.06098101  & -1.6222767   & -5.352415    & 6.838977e-05 & 0.4136213    &  0.86651472 \\\\\n",
       "\tFLJ20712 & -0.17164638  &  1.1672927   & -4.737872    & 2.325062e-04 & 0.9995744    &  0.06351506 \\\\\n",
       "\tEDARADD & -0.08957431  & -0.8153277   & -4.018042    & 1.022954e-03 & 0.9995744    & -0.95532761 \\\\\n",
       "\tSLC24A5 & -0.11153128  & -0.3462480   & -3.985657    & 1.094420e-03 & 0.9995744    & -1.00282761 \\\\\n",
       "\tSLC23A1 & -0.06575288  & -1.2100798   & -3.970930    & 1.128570e-03 & 0.9995744    & -1.02446834 \\\\\n",
       "\tBTLA & -0.08977437  & -0.4747555   & -3.873732    & 1.382640e-03 & 0.9995744    & -1.16789049 \\\\\n",
       "\tBHLHA15 & -0.26457929  &  2.1497676   & -3.749752    & 1.792294e-03 & 0.9995744    & -1.35221410 \\\\\n",
       "\tFOLR1 & -0.07798853  & -0.7389603   & -3.705433    & 1.966713e-03 & 0.9995744    & -1.41844126 \\\\\n",
       "\tARHGEF5 &  0.51848124  &  8.5779319   &  3.697851    & 1.998218e-03 & 0.9995744    & -1.42978792 \\\\\n",
       "\tKIAA1210 & -0.09512721  &  0.4030249   & -3.649377    & 2.211954e-03 & 0.9995744    & -1.50243345 \\\\\n",
       "\tYIF1A & -0.37657682  & 12.6074668   & -3.616020    & 2.372217e-03 & 0.9995744    & -1.55252654 \\\\\n",
       "\tSND1 & -0.21111977  & 14.2912700   & -3.590385    & 2.503239e-03 & 0.9995744    & -1.59107443 \\\\\n",
       "\tMAP3K8 &  0.51676251  &  7.6753650   &  3.477874    & 3.169417e-03 & 0.9995744    & -1.76073447 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | logFC | AveExpr | t | P.Value | adj.P.Val | B |\n",
       "|---|---|---|---|---|---|---|\n",
       "| COL5A3 | -0.50540596  |  4.5696554   | -5.775256    | 3.027702e-05 | 0.4136213    |  1.37934641  |\n",
       "| LOC284898 | -0.06160494  | -1.6222767   | -5.355074    | 6.803529e-05 | 0.4136213    |  0.86984262  |\n",
       "| CA2 | -0.06098101  | -1.6222767   | -5.352415    | 6.838977e-05 | 0.4136213    |  0.86651472  |\n",
       "| FLJ20712 | -0.17164638  |  1.1672927   | -4.737872    | 2.325062e-04 | 0.9995744    |  0.06351506  |\n",
       "| EDARADD | -0.08957431  | -0.8153277   | -4.018042    | 1.022954e-03 | 0.9995744    | -0.95532761  |\n",
       "| SLC24A5 | -0.11153128  | -0.3462480   | -3.985657    | 1.094420e-03 | 0.9995744    | -1.00282761  |\n",
       "| SLC23A1 | -0.06575288  | -1.2100798   | -3.970930    | 1.128570e-03 | 0.9995744    | -1.02446834  |\n",
       "| BTLA | -0.08977437  | -0.4747555   | -3.873732    | 1.382640e-03 | 0.9995744    | -1.16789049  |\n",
       "| BHLHA15 | -0.26457929  |  2.1497676   | -3.749752    | 1.792294e-03 | 0.9995744    | -1.35221410  |\n",
       "| FOLR1 | -0.07798853  | -0.7389603   | -3.705433    | 1.966713e-03 | 0.9995744    | -1.41844126  |\n",
       "| ARHGEF5 |  0.51848124  |  8.5779319   |  3.697851    | 1.998218e-03 | 0.9995744    | -1.42978792  |\n",
       "| KIAA1210 | -0.09512721  |  0.4030249   | -3.649377    | 2.211954e-03 | 0.9995744    | -1.50243345  |\n",
       "| YIF1A | -0.37657682  | 12.6074668   | -3.616020    | 2.372217e-03 | 0.9995744    | -1.55252654  |\n",
       "| SND1 | -0.21111977  | 14.2912700   | -3.590385    | 2.503239e-03 | 0.9995744    | -1.59107443  |\n",
       "| MAP3K8 |  0.51676251  |  7.6753650   |  3.477874    | 3.169417e-03 | 0.9995744    | -1.76073447  |\n",
       "\n"
      ],
      "text/plain": [
       "          logFC       AveExpr    t         P.Value      adj.P.Val B          \n",
       "COL5A3    -0.50540596  4.5696554 -5.775256 3.027702e-05 0.4136213  1.37934641\n",
       "LOC284898 -0.06160494 -1.6222767 -5.355074 6.803529e-05 0.4136213  0.86984262\n",
       "CA2       -0.06098101 -1.6222767 -5.352415 6.838977e-05 0.4136213  0.86651472\n",
       "FLJ20712  -0.17164638  1.1672927 -4.737872 2.325062e-04 0.9995744  0.06351506\n",
       "EDARADD   -0.08957431 -0.8153277 -4.018042 1.022954e-03 0.9995744 -0.95532761\n",
       "SLC24A5   -0.11153128 -0.3462480 -3.985657 1.094420e-03 0.9995744 -1.00282761\n",
       "SLC23A1   -0.06575288 -1.2100798 -3.970930 1.128570e-03 0.9995744 -1.02446834\n",
       "BTLA      -0.08977437 -0.4747555 -3.873732 1.382640e-03 0.9995744 -1.16789049\n",
       "BHLHA15   -0.26457929  2.1497676 -3.749752 1.792294e-03 0.9995744 -1.35221410\n",
       "FOLR1     -0.07798853 -0.7389603 -3.705433 1.966713e-03 0.9995744 -1.41844126\n",
       "ARHGEF5    0.51848124  8.5779319  3.697851 1.998218e-03 0.9995744 -1.42978792\n",
       "KIAA1210  -0.09512721  0.4030249 -3.649377 2.211954e-03 0.9995744 -1.50243345\n",
       "YIF1A     -0.37657682 12.6074668 -3.616020 2.372217e-03 0.9995744 -1.55252654\n",
       "SND1      -0.21111977 14.2912700 -3.590385 2.503239e-03 0.9995744 -1.59107443\n",
       "MAP3K8     0.51676251  7.6753650  3.477874 3.169417e-03 0.9995744 -1.76073447"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fit.time <- lmFit(rlog.norm.counts.coll, design.time)\n",
    "fit.time <- eBayes(fit.time)\n",
    "topTable(fit.time, coef=2, adjust=\"fdr\", number=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "378ca23e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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ACQCYIdAACATBDsAAAAZIJgBwAA6rAN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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diffexpIDs.time <- as.character(rownames(topTable(fit.time, coef=2, number=20)))\n",
    "diffexprows.time <- which(rownames(rlog.norm.counts.coll) %in% diffexpIDs.time)\n",
    "heatmap(rlog.norm.counts.coll[diffexprows.time,], col=greenred(100), Rowv=F, scale=\"row\", labCol=colnames(rlog.norm.counts.coll))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60bcefae",
   "metadata": {},
   "source": [
    "### DEG Analysis with negative binomial test\n",
    "\n",
    "Now we will do this same analysis with negative binomial distribution supplied by DESeq2 package. This will be done on the unnnormalized data as DESeq will perform the normalization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7fea40ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Hyper_12h</th><th scope=col>Hyper_24h</th><th scope=col>Hyper_48h</th><th scope=col>Hyper_6h</th><th scope=col>Hyper_72h</th><th scope=col>Hypo_12h</th><th scope=col>Hypo_24h</th><th scope=col>Hypo_48h</th><th scope=col>Hypo_6h</th><th scope=col>Hypo_72h</th><th scope=col>...</th><th scope=col>Iso_24h</th><th scope=col>Iso_48h</th><th scope=col>Iso_6h</th><th scope=col>Iso_72h</th><th scope=col>Media_0h</th><th scope=col>Media_12h</th><th scope=col>Media_24h</th><th scope=col>Media_48h</th><th scope=col>Media_6h</th><th scope=col>Media_72h</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>FLT3LG</th><td>  952</td><td> 955 </td><td>  821</td><td>  390</td><td> 876 </td><td>  772</td><td> 878 </td><td> 922 </td><td> 698 </td><td> 862 </td><td>...  </td><td> 820 </td><td> 821 </td><td>  783</td><td> 934 </td><td>  786</td><td>  680</td><td>  622</td><td>  773</td><td>  644</td><td> 989 </td></tr>\n",
       "\t<tr><th scope=row>ERI1</th><td> 1276</td><td>1371 </td><td> 1443</td><td> 1378</td><td>1039 </td><td> 1288</td><td>1095 </td><td> 826 </td><td>1530 </td><td> 928 </td><td>...  </td><td>1534 </td><td>1309 </td><td> 1577</td><td>1111 </td><td> 1666</td><td> 1575</td><td> 1797</td><td> 1702</td><td> 1655</td><td>1093 </td></tr>\n",
       "\t<tr><th scope=row>SAP130</th><td>10041</td><td>9227 </td><td>10319</td><td>12369</td><td>8494 </td><td>11424</td><td>6901 </td><td>5640 </td><td>9221 </td><td>5322 </td><td>...  </td><td>9216 </td><td>8926 </td><td>10466</td><td>7131 </td><td>10911</td><td>11664</td><td>10125</td><td>10269</td><td>11822</td><td>7312 </td></tr>\n",
       "\t<tr><th scope=row>LOC100129098</th><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><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>    1</td><td>   2 </td></tr>\n",
       "\t<tr><th scope=row>SPIN1</th><td> 7867</td><td>5659 </td><td> 7251</td><td> 7649</td><td>6055 </td><td> 6487</td><td>5447 </td><td>4471 </td><td>5436 </td><td>4698 </td><td>...  </td><td>6265 </td><td>5809 </td><td> 5676</td><td>5740 </td><td> 5106</td><td> 6332</td><td> 6694</td><td> 6616</td><td> 6216</td><td>5662 </td></tr>\n",
       "\t<tr><th scope=row>SGPL1</th><td> 5341</td><td>4688 </td><td> 4556</td><td> 3729</td><td>4634 </td><td> 8625</td><td>5603 </td><td>4819 </td><td>6731 </td><td>5894 </td><td>...  </td><td>4573 </td><td>4146 </td><td> 4873</td><td>4519 </td><td> 4787</td><td> 4791</td><td> 4412</td><td> 3941</td><td> 4775</td><td>4427 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllll}\n",
       "  & Hyper\\_12h & Hyper\\_24h & Hyper\\_48h & Hyper\\_6h & Hyper\\_72h & Hypo\\_12h & Hypo\\_24h & Hypo\\_48h & Hypo\\_6h & Hypo\\_72h & ... & Iso\\_24h & Iso\\_48h & Iso\\_6h & Iso\\_72h & Media\\_0h & Media\\_12h & Media\\_24h & Media\\_48h & Media\\_6h & Media\\_72h\\\\\n",
       "\\hline\n",
       "\tFLT3LG &   952 &  955  &   821 &   390 &  876  &   772 &  878  &  922  &  698  &  862  & ...   &  820  &  821  &   783 &  934  &   786 &   680 &   622 &   773 &   644 &  989 \\\\\n",
       "\tERI1 &  1276 & 1371  &  1443 &  1378 & 1039  &  1288 & 1095  &  826  & 1530  &  928  & ...   & 1534  & 1309  &  1577 & 1111  &  1666 &  1575 &  1797 &  1702 &  1655 & 1093 \\\\\n",
       "\tSAP130 & 10041 & 9227  & 10319 & 12369 & 8494  & 11424 & 6901  & 5640  & 9221  & 5322  & ...   & 9216  & 8926  & 10466 & 7131  & 10911 & 11664 & 10125 & 10269 & 11822 & 7312 \\\\\n",
       "\tLOC100129098 &     0 &    0  &     0 &     0 &    0  &     0 &    0  &    0  &    0  &    0  & ...   &    0  &    0  &     0 &    0  &     0 &     0 &     0 &     0 &     1 &    2 \\\\\n",
       "\tSPIN1 &  7867 & 5659  &  7251 &  7649 & 6055  &  6487 & 5447  & 4471  & 5436  & 4698  & ...   & 6265  & 5809  &  5676 & 5740  &  5106 &  6332 &  6694 &  6616 &  6216 & 5662 \\\\\n",
       "\tSGPL1 &  5341 & 4688  &  4556 &  3729 & 4634  &  8625 & 5603  & 4819  & 6731  & 5894  & ...   & 4573  & 4146  &  4873 & 4519  &  4787 &  4791 &  4412 &  3941 &  4775 & 4427 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Hyper_12h | Hyper_24h | Hyper_48h | Hyper_6h | Hyper_72h | Hypo_12h | Hypo_24h | Hypo_48h | Hypo_6h | Hypo_72h | ... | Iso_24h | Iso_48h | Iso_6h | Iso_72h | Media_0h | Media_12h | Media_24h | Media_48h | Media_6h | Media_72h |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| FLT3LG |   952 |  955  |   821 |   390 |  876  |   772 |  878  |  922  |  698  |  862  | ...   |  820  |  821  |   783 |  934  |   786 |   680 |   622 |   773 |   644 |  989  |\n",
       "| ERI1 |  1276 | 1371  |  1443 |  1378 | 1039  |  1288 | 1095  |  826  | 1530  |  928  | ...   | 1534  | 1309  |  1577 | 1111  |  1666 |  1575 |  1797 |  1702 |  1655 | 1093  |\n",
       "| SAP130 | 10041 | 9227  | 10319 | 12369 | 8494  | 11424 | 6901  | 5640  | 9221  | 5322  | ...   | 9216  | 8926  | 10466 | 7131  | 10911 | 11664 | 10125 | 10269 | 11822 | 7312  |\n",
       "| LOC100129098 |     0 |    0  |     0 |     0 |    0  |     0 |    0  |    0  |    0  |    0  | ...   |    0  |    0  |     0 |    0  |     0 |     0 |     0 |     0 |     1 |    2  |\n",
       "| SPIN1 |  7867 | 5659  |  7251 |  7649 | 6055  |  6487 | 5447  | 4471  | 5436  | 4698  | ...   | 6265  | 5809  |  5676 | 5740  |  5106 |  6332 |  6694 |  6616 |  6216 | 5662  |\n",
       "| SGPL1 |  5341 | 4688  |  4556 |  3729 | 4634  |  8625 | 5603  | 4819  | 6731  | 5894  | ...   | 4573  | 4146  |  4873 | 4519  |  4787 |  4791 |  4412 |  3941 |  4775 | 4427  |\n",
       "\n"
      ],
      "text/plain": [
       "             Hyper_12h Hyper_24h Hyper_48h Hyper_6h Hyper_72h Hypo_12h Hypo_24h\n",
       "FLT3LG         952      955        821       390     876        772     878    \n",
       "ERI1          1276     1371       1443      1378    1039       1288    1095    \n",
       "SAP130       10041     9227      10319     12369    8494      11424    6901    \n",
       "LOC100129098     0        0          0         0       0          0       0    \n",
       "SPIN1         7867     5659       7251      7649    6055       6487    5447    \n",
       "SGPL1         5341     4688       4556      3729    4634       8625    5603    \n",
       "             Hypo_48h Hypo_6h Hypo_72h ... Iso_24h Iso_48h Iso_6h Iso_72h\n",
       "FLT3LG        922      698     862     ...  820     821      783   934   \n",
       "ERI1          826     1530     928     ... 1534    1309     1577  1111   \n",
       "SAP130       5640     9221    5322     ... 9216    8926    10466  7131   \n",
       "LOC100129098    0        0       0     ...    0       0        0     0   \n",
       "SPIN1        4471     5436    4698     ... 6265    5809     5676  5740   \n",
       "SGPL1        4819     6731    5894     ... 4573    4146     4873  4519   \n",
       "             Media_0h Media_12h Media_24h Media_48h Media_6h Media_72h\n",
       "FLT3LG         786      680       622       773       644     989     \n",
       "ERI1          1666     1575      1797      1702      1655    1093     \n",
       "SAP130       10911    11664     10125     10269     11822    7312     \n",
       "LOC100129098     0        0         0         0         1       2     \n",
       "SPIN1         5106     6332      6694      6616      6216    5662     \n",
       "SGPL1         4787     4791      4412      3941      4775    4427     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DESeq.ds.coll.2 <- collapseReplicates(DESeq.ds, Bio_Factors)\n",
    "head(assay(DESeq.ds.coll.2, \"counts\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77119770",
   "metadata": {},
   "source": [
    "#### DEG with respect to conditions\n",
    "\n",
    "First as before we will do the DEG with respect to the conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6eff88ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "colData(DESeq.ds.coll.2)$condition <- factor(colData(DESeq.ds.coll.2)$condition, levels = c(\"Iso\",\"Media\",\"Hypo\",\"Hyper\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1ecf2fc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>Hyper</li>\n",
       "\t<li>Hyper</li>\n",
       "\t<li>Hyper</li>\n",
       "\t<li>Hyper</li>\n",
       "\t<li>Hyper</li>\n",
       "\t<li>Hypo</li>\n",
       "\t<li>Hypo</li>\n",
       "\t<li>Hypo</li>\n",
       "\t<li>Hypo</li>\n",
       "\t<li>Hypo</li>\n",
       "\t<li>Iso</li>\n",
       "\t<li>Iso</li>\n",
       "\t<li>Iso</li>\n",
       "\t<li>Iso</li>\n",
       "\t<li>Iso</li>\n",
       "\t<li>Media</li>\n",
       "\t<li>Media</li>\n",
       "\t<li>Media</li>\n",
       "\t<li>Media</li>\n",
       "\t<li>Media</li>\n",
       "\t<li>Media</li>\n",
       "</ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<ol class=list-inline>\n",
       "\t\t<li>'Iso'</li>\n",
       "\t\t<li>'Media'</li>\n",
       "\t\t<li>'Hypo'</li>\n",
       "\t\t<li>'Hyper'</li>\n",
       "\t</ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item Hyper\n",
       "\\item Hyper\n",
       "\\item Hyper\n",
       "\\item Hyper\n",
       "\\item Hyper\n",
       "\\item Hypo\n",
       "\\item Hypo\n",
       "\\item Hypo\n",
       "\\item Hypo\n",
       "\\item Hypo\n",
       "\\item Iso\n",
       "\\item Iso\n",
       "\\item Iso\n",
       "\\item Iso\n",
       "\\item Iso\n",
       "\\item Media\n",
       "\\item Media\n",
       "\\item Media\n",
       "\\item Media\n",
       "\\item Media\n",
       "\\item Media\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item 'Iso'\n",
       "\\item 'Media'\n",
       "\\item 'Hypo'\n",
       "\\item 'Hyper'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. Hyper\n",
       "2. Hyper\n",
       "3. Hyper\n",
       "4. Hyper\n",
       "5. Hyper\n",
       "6. Hypo\n",
       "7. Hypo\n",
       "8. Hypo\n",
       "9. Hypo\n",
       "10. Hypo\n",
       "11. Iso\n",
       "12. Iso\n",
       "13. Iso\n",
       "14. Iso\n",
       "15. Iso\n",
       "16. Media\n",
       "17. Media\n",
       "18. Media\n",
       "19. Media\n",
       "20. Media\n",
       "21. Media\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. 'Iso'\n",
       "2. 'Media'\n",
       "3. 'Hypo'\n",
       "4. 'Hyper'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] Hyper Hyper Hyper Hyper Hyper Hypo  Hypo  Hypo  Hypo  Hypo  Iso   Iso  \n",
       "[13] Iso   Iso   Iso   Media Media Media Media Media Media\n",
       "Levels: Iso Media Hypo Hyper"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colData(DESeq.ds.coll.2)$condition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e131523c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "estimating size factors\n",
      "estimating dispersions\n",
      "gene-wise dispersion estimates\n",
      "mean-dispersion relationship\n",
      "final dispersion estimates\n",
      "fitting model and testing\n"
     ]
    }
   ],
   "source": [
    "DESeq.ds.coll.2 <- DESeq(DESeq.ds.coll.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b84eb723",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "out of 18144 with nonzero total read count\n",
      "adjusted p-value < 0.05\n",
      "LFC > 0.50 (up)    : 146, 0.8%\n",
      "LFC < -0.50 (down) : 34, 0.19%\n",
      "outliers [1]       : 21, 0.12%\n",
      "low counts [2]     : 2462, 14%\n",
      "(mean count < 1)\n",
      "[1] see 'cooksCutoff' argument of ?results\n",
      "[2] see 'independentFiltering' argument of ?results\n",
      "\n"
     ]
    }
   ],
   "source": [
    "DGE.results = results(DESeq.ds.coll.2, independentFiltering = T, alpha = 0.05, lfcThreshold = 0.5)\n",
    "summary(DGE.results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fef46855",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'DACT3'</li>\n",
       "\t<li>'TP53AIP1'</li>\n",
       "\t<li>'GPER1'</li>\n",
       "\t<li>'ATP1A3'</li>\n",
       "\t<li>'CYP2C19'</li>\n",
       "\t<li>'TMEM107'</li>\n",
       "\t<li>'NFATC2'</li>\n",
       "\t<li>'CHD7'</li>\n",
       "\t<li>'GSTO2'</li>\n",
       "\t<li>'PXYLP1'</li>\n",
       "\t<li>'GDNF'</li>\n",
       "\t<li>'FNDC9'</li>\n",
       "\t<li>'P2RY8'</li>\n",
       "\t<li>'CCDC69'</li>\n",
       "\t<li>'THEGL'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'DACT3'\n",
       "\\item 'TP53AIP1'\n",
       "\\item 'GPER1'\n",
       "\\item 'ATP1A3'\n",
       "\\item 'CYP2C19'\n",
       "\\item 'TMEM107'\n",
       "\\item 'NFATC2'\n",
       "\\item 'CHD7'\n",
       "\\item 'GSTO2'\n",
       "\\item 'PXYLP1'\n",
       "\\item 'GDNF'\n",
       "\\item 'FNDC9'\n",
       "\\item 'P2RY8'\n",
       "\\item 'CCDC69'\n",
       "\\item 'THEGL'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'DACT3'\n",
       "2. 'TP53AIP1'\n",
       "3. 'GPER1'\n",
       "4. 'ATP1A3'\n",
       "5. 'CYP2C19'\n",
       "6. 'TMEM107'\n",
       "7. 'NFATC2'\n",
       "8. 'CHD7'\n",
       "9. 'GSTO2'\n",
       "10. 'PXYLP1'\n",
       "11. 'GDNF'\n",
       "12. 'FNDC9'\n",
       "13. 'P2RY8'\n",
       "14. 'CCDC69'\n",
       "15. 'THEGL'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"DACT3\"    \"TP53AIP1\" \"GPER1\"    \"ATP1A3\"   \"CYP2C19\"  \"TMEM107\" \n",
       " [7] \"NFATC2\"   \"CHD7\"     \"GSTO2\"    \"PXYLP1\"   \"GDNF\"     \"FNDC9\"   \n",
       "[13] \"P2RY8\"    \"CCDC69\"   \"THEGL\"   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(rownames(subset(DGE.results, padj<0.05)), n=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "57780f2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ive9vTTLVH7zWUdPrzg4HzhnMLCztdd13xwXovV2DuxAwAAgAtE7NQgYhcBzkCd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Mbxdk201R+UjYrwJgENSIWJ8lWE4P9naH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",
      "text/plain": [
       "Plot with title “Osmotic conditions”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotMA(DGE.results, alpha=0.05, main=\"Osmotic conditions\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "600c813b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Intercept'</li>\n",
       "\t<li>'condition_Media_vs_Iso'</li>\n",
       "\t<li>'condition_Hypo_vs_Iso'</li>\n",
       "\t<li>'condition_Hyper_vs_Iso'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Intercept'\n",
       "\\item 'condition\\_Media\\_vs\\_Iso'\n",
       "\\item 'condition\\_Hypo\\_vs\\_Iso'\n",
       "\\item 'condition\\_Hyper\\_vs\\_Iso'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Intercept'\n",
       "2. 'condition_Media_vs_Iso'\n",
       "3. 'condition_Hypo_vs_Iso'\n",
       "4. 'condition_Hyper_vs_Iso'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"Intercept\"              \"condition_Media_vs_Iso\" \"condition_Hypo_vs_Iso\" \n",
       "[4] \"condition_Hyper_vs_Iso\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resultsNames(DESeq.ds.coll.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f0a2c6c",
   "metadata": {},
   "source": [
    "To obtain a better estimate of the fit we can use shrunken log2 fold changes, which remove the noise associated with log2 fold changes from low count genes without requiring arbitrary filtering thresholds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8250c6d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "using 'normal' for LFC shrinkage, the Normal prior from Love et al (2014).\n",
      "\n",
      "Note that type='apeglm' and type='ashr' have shown to have less bias than type='normal'.\n",
      "See ?lfcShrink for more details on shrinkage type, and the DESeq2 vignette.\n",
      "Reference: https://doi.org/10.1093/bioinformatics/bty895\n"
     ]
    },
    {
     "ename": "ERROR",
     "evalue": "Error in fitGLMsWithPrior(object = object, betaTol = betaTol, maxit = maxit, : betaPriorVar should have length 6 to match: (Intercept), condition6h, condition12h, condition24h, condition48h, condition72h\n",
     "output_type": "error",
     "traceback": [
      "Error in fitGLMsWithPrior(object = object, betaTol = betaTol, maxit = maxit, : betaPriorVar should have length 6 to match: (Intercept), condition6h, condition12h, condition24h, condition48h, condition72h\nTraceback:\n",
      "1. lfcShrink(DESeq.ds.coll.2, coef = \"condition_Hypo_vs_Iso\")",
      "2. nbinomWaldTest(dds, betaPrior = TRUE, betaPriorVar = betaPriorVar, \n .     modelMatrix = modelMatrix, modelMatrixType = modelMatrixType, \n .     quiet = TRUE)",
      "3. fitGLMsWithPrior(object = object, betaTol = betaTol, maxit = maxit, \n .     useOptim = useOptim, useQR = useQR, betaPriorVar = betaPriorVar, \n .     modelMatrix = modelMatrix, minmu = minmu)",
      "4. stop(paste(\"betaPriorVar should have length\", p, \"to match:\", \n .     paste(colnames(modelMatrix), collapse = \", \")))"
     ]
    }
   ],
   "source": [
    "res.iso_vs_hypo.lfc <- lfcShrink(DESeq.ds.coll.2, coef = \"condition_Hypo_vs_Iso\")\n",
    "plotMA(res.iso_vs_hypo.lfc, alpha=0.05, main=\"Iso_vs_Hypo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c9ecde",
   "metadata": {},
   "source": [
    "### Heatmap of DEGs\n",
    "\n",
    "Next we can sort the differentially expressed values and plot them as below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "a16e3693",
   "metadata": {},
   "outputs": [],
   "source": [
    "DGE.results.sorted <- DGE.results[order(DGE.results$padj < 0.05),]\n",
    "DGE.results.names <- head(rownames(subset(DGE.results.sorted, padj < 0.05)), n=20)\n",
    "#hm.mat_iso_vs_hypo.deg <- log.norm.counts.coll[iso_vs_hypo.deg.names,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ab05401d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jp27JiSktKyZUvTOm+88cavv/6anJzcqlUrY+OGDRvatWuXmJhYdNy0tLSAgIDA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tWbJE3IdNnz7d0tJy2bJlzFVra2vmgqWl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\n9UoAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap(rlog.norm.counts.coll[DGE.results.names,], col=greenred(100), Rowv=T, Colv = T, scale=\"row\", labCol=colnames(rlog.norm.counts.coll))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7e91f8c",
   "metadata": {},
   "source": [
    "#### DEG with respect to time\n",
    "\n",
    "We can next perform the DEG with respect to time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "ad1d74e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame with 21 rows and 2 columns\n",
       "          condition       sizeFactor\n",
       "           <factor>        <numeric>\n",
       "Hyper_12h       12h 1.07085236249724\n",
       "Hyper_24h       24h 1.03423140666106\n",
       "Hyper_48h       48h 1.07214377749886\n",
       "Hyper_6h         6h 0.92936852023517\n",
       "Hyper_72h       72h 1.02800877673977\n",
       "...             ...              ...\n",
       "Media_12h       12h 1.03619250779122\n",
       "Media_24h       24h 1.01671816203189\n",
       "Media_48h       48h  1.0425339657361\n",
       "Media_6h         6h 1.03032416109285\n",
       "Media_72h       72h 1.00419152457885"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DESeq.ds.coll.3 <- collapseReplicates(DESeq.ds, Bio_Factors)\n",
    "colData(DESeq.ds.coll.2)$condition <- factor(timeinterval, levels = c(\"0h\",\"6h\",\"12h\",\"24h\",\"48h\",\"72h\"))\n",
    "colData(DESeq.ds.coll.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "342bfe8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "estimating size factors\n",
      "estimating dispersions\n",
      "gene-wise dispersion estimates\n",
      "mean-dispersion relationship\n",
      "final dispersion estimates\n",
      "fitting model and testing\n"
     ]
    }
   ],
   "source": [
    "DESeq.ds.coll.3 <- DESeq(DESeq.ds.coll.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bac13185",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "out of 18144 with nonzero total read count\n",
      "adjusted p-value < 0.05\n",
      "LFC > 0.50 (up)    : 15, 0.083%\n",
      "LFC < -0.50 (down) : 169, 0.93%\n",
      "outliers [1]       : 21, 0.12%\n",
      "low counts [2]     : 0, 0%\n",
      "(mean count < 0)\n",
      "[1] see 'cooksCutoff' argument of ?results\n",
      "[2] see 'independentFiltering' argument of ?results\n",
      "\n"
     ]
    }
   ],
   "source": [
    "DGE.results.time = results(DESeq.ds.coll.3, independentFiltering = T, alpha = 0.05, lfcThreshold = 0.5)\n",
    "summary(DGE.results.time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9ed1dbdb",
   "metadata": {},
   "outputs": [],
   "source": [
    "DGE.results.time.sorted <- DGE.results.time[order(DGE.results.time$padj < 0.05),]\n",
    "DGE.results.time.names <- head(rownames(subset(DGE.results.time.sorted, padj < 0.05)), n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "165b6c50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DJBsAMAAJAJgh0AAIBMEOwAAIBMvPba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\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "heatmap(rlog.norm.counts.coll[DGE.results.time.names,], col=greenred(100), Rowv=T, Colv = T, scale=\"row\", labCol=colnames(rlog.norm.counts.coll))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d0636644",
   "metadata": {},
   "outputs": [],
   "source": [
    "DGE.results.time.names"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}