rna-seq.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "measured-place",
   "metadata": {},
   "source": [
    "# Análise de RNA-Seq usando a linguagem R\n",
    "\n",
    "Vamos aqui analisar os genes diferencialmente expressos em células de glândulas mamárias de camundongos fêmeas em duas situações: grávidas e lactantes.\n",
    "O desenho do experimento original tem, células dos subtipos basal e luminal em três condições: virgens, grávidas e lactantes.\n",
    "O artigo original do trabalho (Fu, 2015) pode ser encontrado em https://www.ncbi.nlm.nih.gov/pubmed/25730472.\n",
    "Os dados do experimento estão depositados em https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60450.\n",
    "\n",
    "Fu NY, Rios AC, Pal B, Soetanto R et al. EGF-mediated induction of Mcl-1 at the switch to lactation is essential for alveolar cell survival. Nat Cell Biol 2015 Apr;17(4):365-75."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "closing-award",
   "metadata": {},
   "source": [
    "### Passo 01: Instalar os pacotes R necessários"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "about-slave",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Bioconductor version 3.10 (BiocManager 1.30.10), R 3.6.3 (2020-02-29)\n",
      "Installing package(s) 'limma', 'edgeR', 'Glimma', 'data.table', 'org.Mm.eg.db',\n",
      "  'statmod'\n",
      "Warning message in install.packages(...):\n",
      "“installation of package ‘edgeR’ had non-zero exit status”Warning message in install.packages(...):\n",
      "“installation of package ‘Glimma’ had non-zero exit status”Updating HTML index of packages in '.Library'\n",
      "Making 'packages.html' ... done\n"
     ]
    }
   ],
   "source": [
    "if (!requireNamespace(\"BiocManager\", quietly = TRUE))\n",
    "    install.packages(\"BiocManager\")\n",
    "BiocManager::install(c(\"limma\",\"edgeR\",\"Glimma\",\"data.table\",\"org.Mm.eg.db\", \"statmod\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adopted-tsunami",
   "metadata": {},
   "source": [
    "### Passo 02: Carregar os pacotes R instalados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "lesbian-korean",
   "metadata": {},
   "outputs": [],
   "source": [
    "suppressMessages(library(limma))\n",
    "suppressMessages(library(Glimma))\n",
    "suppressMessages(library(edgeR))\n",
    "suppressMessages(library(data.table))\n",
    "suppressMessages(library(org.Mm.eg.db))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "optimum-truth",
   "metadata": {},
   "source": [
    "### Passo 03: Download e leitura dos arquivos\n",
    "Os arquivos são baixados e lidos como um dataframe chamado dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "limited-curtis",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>27179</li>\n",
       "\t<li>12</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 27179\n",
       "\\item 12\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 27179\n",
       "2. 12\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 27179    12"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>MCL1.DG</th><th scope=col>MCL1.DH</th><th scope=col>MCL1.DI</th><th scope=col>MCL1.DJ</th><th scope=col>MCL1.DK</th><th scope=col>MCL1.DL</th><th scope=col>MCL1.LA</th><th scope=col>MCL1.LB</th><th scope=col>MCL1.LC</th><th scope=col>MCL1.LD</th><th scope=col>MCL1.LE</th><th scope=col>MCL1.LF</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>497097</th><td>438</td><td>300</td><td> 65</td><td>237</td><td>354</td><td>287</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>100503874</th><td>  1</td><td>  0</td><td>  1</td><td>  1</td><td>  0</td><td>  4</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>100038431</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>  0</td><td>  0</td></tr>\n",
       "\t<tr><th scope=row>19888</th><td>  1</td><td>  1</td><td>  0</td><td>  0</td><td>  0</td><td>  0</td><td> 10</td><td>  3</td><td> 10</td><td>  2</td><td>  0</td><td>  0</td></tr>\n",
       "\t<tr><th scope=row>20671</th><td>106</td><td>182</td><td> 82</td><td>105</td><td> 43</td><td> 82</td><td> 16</td><td> 25</td><td> 18</td><td>  8</td><td>  3</td><td> 10</td></tr>\n",
       "\t<tr><th scope=row>27395</th><td>309</td><td>234</td><td>337</td><td>300</td><td>290</td><td>270</td><td>560</td><td>464</td><td>489</td><td>328</td><td>307</td><td>342</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & MCL1.DG & MCL1.DH & MCL1.DI & MCL1.DJ & MCL1.DK & MCL1.DL & MCL1.LA & MCL1.LB & MCL1.LC & MCL1.LD & MCL1.LE & MCL1.LF\\\\\n",
       "\\hline\n",
       "\t497097 & 438 & 300 &  65 & 237 & 354 & 287 &   0 &   0 &   0 &   0 &   0 &   0\\\\\n",
       "\t100503874 &   1 &   0 &   1 &   1 &   0 &   4 &   0 &   0 &   0 &   0 &   0 &   0\\\\\n",
       "\t100038431 &   0 &   0 &   0 &   0 &   0 &   0 &   0 &   0 &   0 &   0 &   0 &   0\\\\\n",
       "\t19888 &   1 &   1 &   0 &   0 &   0 &   0 &  10 &   3 &  10 &   2 &   0 &   0\\\\\n",
       "\t20671 & 106 & 182 &  82 & 105 &  43 &  82 &  16 &  25 &  18 &   8 &   3 &  10\\\\\n",
       "\t27395 & 309 & 234 & 337 & 300 & 290 & 270 & 560 & 464 & 489 & 328 & 307 & 342\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | MCL1.DG | MCL1.DH | MCL1.DI | MCL1.DJ | MCL1.DK | MCL1.DL | MCL1.LA | MCL1.LB | MCL1.LC | MCL1.LD | MCL1.LE | MCL1.LF |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 497097 | 438 | 300 |  65 | 237 | 354 | 287 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
       "| 100503874 |   1 |   0 |   1 |   1 |   0 |   4 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
       "| 100038431 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |   0 |\n",
       "| 19888 |   1 |   1 |   0 |   0 |   0 |   0 |  10 |   3 |  10 |   2 |   0 |   0 |\n",
       "| 20671 | 106 | 182 |  82 | 105 |  43 |  82 |  16 |  25 |  18 |   8 |   3 |  10 |\n",
       "| 27395 | 309 | 234 | 337 | 300 | 290 | 270 | 560 | 464 | 489 | 328 | 307 | 342 |\n",
       "\n"
      ],
      "text/plain": [
       "          MCL1.DG MCL1.DH MCL1.DI MCL1.DJ MCL1.DK MCL1.DL MCL1.LA MCL1.LB\n",
       "497097    438     300      65     237     354     287       0       0    \n",
       "100503874   1       0       1       1       0       4       0       0    \n",
       "100038431   0       0       0       0       0       0       0       0    \n",
       "19888       1       1       0       0       0       0      10       3    \n",
       "20671     106     182      82     105      43      82      16      25    \n",
       "27395     309     234     337     300     290     270     560     464    \n",
       "          MCL1.LC MCL1.LD MCL1.LE MCL1.LF\n",
       "497097      0       0       0       0    \n",
       "100503874   0       0       0       0    \n",
       "100038431   0       0       0       0    \n",
       "19888      10       2       0       0    \n",
       "20671      18       8       3      10    \n",
       "27395     489     328     307     342    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# AMOSTRAS\n",
    "# Download das amostras\n",
    "url <- \"https://ftp.ncbi.nlm.nih.gov/geo/series/GSE60nnn/GSE60450/suppl/GSE60450_Lactation-GenewiseCounts.txt.gz\"\n",
    "download.file(url, \"GSE60450_Lactation.txt.gz\")\n",
    "# Ler tabela de gene counts id dos genes do NCBI Entrez\n",
    "dados <- read.delim(\"GSE60450_Lactation.txt.gz\", row.names=\"EntrezGeneID\", stringsAsFactors = TRUE)\n",
    "colnames(dados) <- substring(colnames(dados),1,7)# nomes de colunas abreviados para 7 caracteres\n",
    "dados <- dados[,-(1)] # remove a columna 1: Length/\n",
    "dim(dados) # mostrar as dimensoes do dataframe dados\n",
    "head(dados) # mostrar primeiras linhas do dataframe dados"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "opened-judge",
   "metadata": {},
   "source": [
    "### Passo 04: criar uma dataframe auxiliar com os dados do desenho do experimento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "acoustic-lease",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Arquivo</th><th scope=col>Amostra</th><th scope=col>Celula</th><th scope=col>Status</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>column_01</th><td>MCL1.LA_BC2CTUACXX_GATCAG_L001_R1</td><td>MCL1.LA                          </td><td>basal                            </td><td>virgem                           </td></tr>\n",
       "\t<tr><th scope=row>column_02</th><td>MCL1.LB_BC2CTUACXX_TGACCA_L001_R1</td><td>MCL1.LB                          </td><td>luminal                          </td><td>virgem                           </td></tr>\n",
       "\t<tr><th scope=row>column_03</th><td>MCL1.LC_BC2CTUACXX_GCCAAT_L001_R1</td><td>MCL1.LC                          </td><td>luminal                          </td><td>gravida                          </td></tr>\n",
       "\t<tr><th scope=row>column_04</th><td>MCL1.LD_BC2CTUACXX_GGCTAC_L001_R1</td><td>MCL1.LD                          </td><td>luminal                          </td><td>gravida                          </td></tr>\n",
       "\t<tr><th scope=row>column_05</th><td>MCL1.LE_BC2CTUACXX_TAGCTT_L001_R1</td><td>MCL1.LE                          </td><td>luminal                          </td><td>lactante                         </td></tr>\n",
       "\t<tr><th scope=row>column_06</th><td>MCL1.LF_BC2CTUACXX_CTTGTA_L001_R1</td><td>MCL1.LF                          </td><td>luminal                          </td><td>lactante                         </td></tr>\n",
       "\t<tr><th scope=row>column_07</th><td>MCL1.DG_BC2CTUACXX_ACTTGA_L002_R1</td><td>MCL1.DG                          </td><td>luminal                          </td><td>virgem                           </td></tr>\n",
       "\t<tr><th scope=row>column_08</th><td>MCL1.DH_BC2CTUACXX_CAGATC_L002_R1</td><td>MCL1.DH                          </td><td>basal                            </td><td>virgem                           </td></tr>\n",
       "\t<tr><th scope=row>column_09</th><td>MCL1.DI_BC2CTUACXX_ACAGTG_L002_R1</td><td>MCL1.DI                          </td><td>basal                            </td><td>gravida                          </td></tr>\n",
       "\t<tr><th scope=row>column_10</th><td>MCL1.DJ_BC2CTUACXX_CGATGT_L002_R1</td><td>MCL1.DJ                          </td><td>basal                            </td><td>gravida                          </td></tr>\n",
       "\t<tr><th scope=row>column_11</th><td>MCL1.DK_BC2CTUACXX_TTAGGC_L002_R1</td><td>MCL1.DK                          </td><td>basal                            </td><td>lactante                         </td></tr>\n",
       "\t<tr><th scope=row>column_12</th><td>MCL1.DL_BC2CTUACXX_ATCACG_L002_R1</td><td>MCL1.DL                          </td><td>basal                            </td><td>lactante                         </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & Arquivo & Amostra & Celula & Status\\\\\n",
       "\\hline\n",
       "\tcolumn\\_01 & MCL1.LA\\_BC2CTUACXX\\_GATCAG\\_L001\\_R1 & MCL1.LA                                   & basal                                     & virgem                                   \\\\\n",
       "\tcolumn\\_02 & MCL1.LB\\_BC2CTUACXX\\_TGACCA\\_L001\\_R1 & MCL1.LB                                   & luminal                                   & virgem                                   \\\\\n",
       "\tcolumn\\_03 & MCL1.LC\\_BC2CTUACXX\\_GCCAAT\\_L001\\_R1 & MCL1.LC                                   & luminal                                   & gravida                                  \\\\\n",
       "\tcolumn\\_04 & MCL1.LD\\_BC2CTUACXX\\_GGCTAC\\_L001\\_R1 & MCL1.LD                                   & luminal                                   & gravida                                  \\\\\n",
       "\tcolumn\\_05 & MCL1.LE\\_BC2CTUACXX\\_TAGCTT\\_L001\\_R1 & MCL1.LE                                   & luminal                                   & lactante                                 \\\\\n",
       "\tcolumn\\_06 & MCL1.LF\\_BC2CTUACXX\\_CTTGTA\\_L001\\_R1 & MCL1.LF                                   & luminal                                   & lactante                                 \\\\\n",
       "\tcolumn\\_07 & MCL1.DG\\_BC2CTUACXX\\_ACTTGA\\_L002\\_R1 & MCL1.DG                                   & luminal                                   & virgem                                   \\\\\n",
       "\tcolumn\\_08 & MCL1.DH\\_BC2CTUACXX\\_CAGATC\\_L002\\_R1 & MCL1.DH                                   & basal                                     & virgem                                   \\\\\n",
       "\tcolumn\\_09 & MCL1.DI\\_BC2CTUACXX\\_ACAGTG\\_L002\\_R1 & MCL1.DI                                   & basal                                     & gravida                                  \\\\\n",
       "\tcolumn\\_10 & MCL1.DJ\\_BC2CTUACXX\\_CGATGT\\_L002\\_R1 & MCL1.DJ                                   & basal                                     & gravida                                  \\\\\n",
       "\tcolumn\\_11 & MCL1.DK\\_BC2CTUACXX\\_TTAGGC\\_L002\\_R1 & MCL1.DK                                   & basal                                     & lactante                                 \\\\\n",
       "\tcolumn\\_12 & MCL1.DL\\_BC2CTUACXX\\_ATCACG\\_L002\\_R1 & MCL1.DL                                   & basal                                     & lactante                                 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Arquivo | Amostra | Celula | Status |\n",
       "|---|---|---|---|---|\n",
       "| column_01 | MCL1.LA_BC2CTUACXX_GATCAG_L001_R1 | MCL1.LA                           | basal                             | virgem                            |\n",
       "| column_02 | MCL1.LB_BC2CTUACXX_TGACCA_L001_R1 | MCL1.LB                           | luminal                           | virgem                            |\n",
       "| column_03 | MCL1.LC_BC2CTUACXX_GCCAAT_L001_R1 | MCL1.LC                           | luminal                           | gravida                           |\n",
       "| column_04 | MCL1.LD_BC2CTUACXX_GGCTAC_L001_R1 | MCL1.LD                           | luminal                           | gravida                           |\n",
       "| column_05 | MCL1.LE_BC2CTUACXX_TAGCTT_L001_R1 | MCL1.LE                           | luminal                           | lactante                          |\n",
       "| column_06 | MCL1.LF_BC2CTUACXX_CTTGTA_L001_R1 | MCL1.LF                           | luminal                           | lactante                          |\n",
       "| column_07 | MCL1.DG_BC2CTUACXX_ACTTGA_L002_R1 | MCL1.DG                           | luminal                           | virgem                            |\n",
       "| column_08 | MCL1.DH_BC2CTUACXX_CAGATC_L002_R1 | MCL1.DH                           | basal                             | virgem                            |\n",
       "| column_09 | MCL1.DI_BC2CTUACXX_ACAGTG_L002_R1 | MCL1.DI                           | basal                             | gravida                           |\n",
       "| column_10 | MCL1.DJ_BC2CTUACXX_CGATGT_L002_R1 | MCL1.DJ                           | basal                             | gravida                           |\n",
       "| column_11 | MCL1.DK_BC2CTUACXX_TTAGGC_L002_R1 | MCL1.DK                           | basal                             | lactante                          |\n",
       "| column_12 | MCL1.DL_BC2CTUACXX_ATCACG_L002_R1 | MCL1.DL                           | basal                             | lactante                          |\n",
       "\n"
      ],
      "text/plain": [
       "          Arquivo                           Amostra Celula  Status  \n",
       "column_01 MCL1.LA_BC2CTUACXX_GATCAG_L001_R1 MCL1.LA basal   virgem  \n",
       "column_02 MCL1.LB_BC2CTUACXX_TGACCA_L001_R1 MCL1.LB luminal virgem  \n",
       "column_03 MCL1.LC_BC2CTUACXX_GCCAAT_L001_R1 MCL1.LC luminal gravida \n",
       "column_04 MCL1.LD_BC2CTUACXX_GGCTAC_L001_R1 MCL1.LD luminal gravida \n",
       "column_05 MCL1.LE_BC2CTUACXX_TAGCTT_L001_R1 MCL1.LE luminal lactante\n",
       "column_06 MCL1.LF_BC2CTUACXX_CTTGTA_L001_R1 MCL1.LF luminal lactante\n",
       "column_07 MCL1.DG_BC2CTUACXX_ACTTGA_L002_R1 MCL1.DG luminal virgem  \n",
       "column_08 MCL1.DH_BC2CTUACXX_CAGATC_L002_R1 MCL1.DH basal   virgem  \n",
       "column_09 MCL1.DI_BC2CTUACXX_ACAGTG_L002_R1 MCL1.DI basal   gravida \n",
       "column_10 MCL1.DJ_BC2CTUACXX_CGATGT_L002_R1 MCL1.DJ basal   gravida \n",
       "column_11 MCL1.DK_BC2CTUACXX_TTAGGC_L002_R1 MCL1.DK basal   lactante\n",
       "column_12 MCL1.DL_BC2CTUACXX_ATCACG_L002_R1 MCL1.DL basal   lactante"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Informação das amostras do experimento para classificacao posterior\n",
    "column_head <- c(\"Arquivo\", \"Amostra\", \"Celula\", \"Status\")\n",
    "column_01 <- c(\"MCL1.LA_BC2CTUACXX_GATCAG_L001_R1\",    \"MCL1.LA\",    \"basal\",   \"virgem\")\n",
    "column_02 <- c(\"MCL1.LB_BC2CTUACXX_TGACCA_L001_R1\",    \"MCL1.LB\",  \"luminal\",   \"virgem\")\n",
    "column_03 <- c(\"MCL1.LC_BC2CTUACXX_GCCAAT_L001_R1\",    \"MCL1.LC\",  \"luminal\", \"gravida\")\n",
    "column_04 <- c(\"MCL1.LD_BC2CTUACXX_GGCTAC_L001_R1\",    \"MCL1.LD\",  \"luminal\", \"gravida\")\n",
    "column_05 <- c(\"MCL1.LE_BC2CTUACXX_TAGCTT_L001_R1\",    \"MCL1.LE\",  \"luminal\",  \"lactante\")\n",
    "column_06 <- c(\"MCL1.LF_BC2CTUACXX_CTTGTA_L001_R1\",    \"MCL1.LF\",  \"luminal\",  \"lactante\")\n",
    "column_07 <- c(\"MCL1.DG_BC2CTUACXX_ACTTGA_L002_R1\",    \"MCL1.DG\",  \"luminal\",   \"virgem\")\n",
    "column_08 <- c(\"MCL1.DH_BC2CTUACXX_CAGATC_L002_R1\",    \"MCL1.DH\",    \"basal\",   \"virgem\")\n",
    "column_09 <- c(\"MCL1.DI_BC2CTUACXX_ACAGTG_L002_R1\",    \"MCL1.DI\",    \"basal\", \"gravida\")\n",
    "column_10 <- c(\"MCL1.DJ_BC2CTUACXX_CGATGT_L002_R1\",    \"MCL1.DJ\",    \"basal\", \"gravida\")\n",
    "column_11 <- c(\"MCL1.DK_BC2CTUACXX_TTAGGC_L002_R1\",    \"MCL1.DK\",    \"basal\",  \"lactante\")\n",
    "column_12 <- c(\"MCL1.DL_BC2CTUACXX_ATCACG_L002_R1\",    \"MCL1.DL\",    \"basal\",  \"lactante\")\n",
    "info_amostras <- data.frame(rbind(\n",
    "  column_01, \n",
    "  column_02, \n",
    "  column_03, \n",
    "  column_04, \n",
    "  column_05, \n",
    "  column_06, \n",
    "  column_07, \n",
    "  column_08, \n",
    "  column_09, \n",
    "  column_10, \n",
    "  column_11, \n",
    "  column_12))\n",
    "colnames(info_amostras) <- column_head\n",
    "info_amostras"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fundamental-abraham",
   "metadata": {},
   "source": [
    "### Passo 05: Criação do objeto DGEList\n",
    "O objeto DGEList é a forma com a qual o pacote EdgeR organiza os dados para serem analisados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "impaired-dover",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criacao de objeto DGEListcom a partir do dataframe dados\n",
    "dgList <- DGEList(counts=dados, genes=rownames(dados))\n",
    "# Adicionando os grupos do experimento ao DGEList\n",
    "group <- paste(info_amostras$Celula, info_amostras$Status, sep=\".\")\n",
    "dgList$samples$group <- factor(group)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cardiovascular-flight",
   "metadata": {},
   "source": [
    "### Passo 06: Anotação dos genes\n",
    "A primeira coluna dos genes (ver passo 3) contém números que são os identificadores de cada gene no NCBI. Usando o pacote org.Mm.eg.db podemos mapear esses números para as respectivas anotações de genes de *Mus musculus*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fifteen-flavor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'ACCNUM'</li>\n",
       "\t<li>'ALIAS'</li>\n",
       "\t<li>'ENSEMBL'</li>\n",
       "\t<li>'ENSEMBLPROT'</li>\n",
       "\t<li>'ENSEMBLTRANS'</li>\n",
       "\t<li>'ENTREZID'</li>\n",
       "\t<li>'ENZYME'</li>\n",
       "\t<li>'EVIDENCE'</li>\n",
       "\t<li>'EVIDENCEALL'</li>\n",
       "\t<li>'GENENAME'</li>\n",
       "\t<li>'GO'</li>\n",
       "\t<li>'GOALL'</li>\n",
       "\t<li>'IPI'</li>\n",
       "\t<li>'MGI'</li>\n",
       "\t<li>'ONTOLOGY'</li>\n",
       "\t<li>'ONTOLOGYALL'</li>\n",
       "\t<li>'PATH'</li>\n",
       "\t<li>'PFAM'</li>\n",
       "\t<li>'PMID'</li>\n",
       "\t<li>'PROSITE'</li>\n",
       "\t<li>'REFSEQ'</li>\n",
       "\t<li>'SYMBOL'</li>\n",
       "\t<li>'UNIGENE'</li>\n",
       "\t<li>'UNIPROT'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'ACCNUM'\n",
       "\\item 'ALIAS'\n",
       "\\item 'ENSEMBL'\n",
       "\\item 'ENSEMBLPROT'\n",
       "\\item 'ENSEMBLTRANS'\n",
       "\\item 'ENTREZID'\n",
       "\\item 'ENZYME'\n",
       "\\item 'EVIDENCE'\n",
       "\\item 'EVIDENCEALL'\n",
       "\\item 'GENENAME'\n",
       "\\item 'GO'\n",
       "\\item 'GOALL'\n",
       "\\item 'IPI'\n",
       "\\item 'MGI'\n",
       "\\item 'ONTOLOGY'\n",
       "\\item 'ONTOLOGYALL'\n",
       "\\item 'PATH'\n",
       "\\item 'PFAM'\n",
       "\\item 'PMID'\n",
       "\\item 'PROSITE'\n",
       "\\item 'REFSEQ'\n",
       "\\item 'SYMBOL'\n",
       "\\item 'UNIGENE'\n",
       "\\item 'UNIPROT'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'ACCNUM'\n",
       "2. 'ALIAS'\n",
       "3. 'ENSEMBL'\n",
       "4. 'ENSEMBLPROT'\n",
       "5. 'ENSEMBLTRANS'\n",
       "6. 'ENTREZID'\n",
       "7. 'ENZYME'\n",
       "8. 'EVIDENCE'\n",
       "9. 'EVIDENCEALL'\n",
       "10. 'GENENAME'\n",
       "11. 'GO'\n",
       "12. 'GOALL'\n",
       "13. 'IPI'\n",
       "14. 'MGI'\n",
       "15. 'ONTOLOGY'\n",
       "16. 'ONTOLOGYALL'\n",
       "17. 'PATH'\n",
       "18. 'PFAM'\n",
       "19. 'PMID'\n",
       "20. 'PROSITE'\n",
       "21. 'REFSEQ'\n",
       "22. 'SYMBOL'\n",
       "23. 'UNIGENE'\n",
       "24. 'UNIPROT'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"ACCNUM\"       \"ALIAS\"        \"ENSEMBL\"      \"ENSEMBLPROT\"  \"ENSEMBLTRANS\"\n",
       " [6] \"ENTREZID\"     \"ENZYME\"       \"EVIDENCE\"     \"EVIDENCEALL\"  \"GENENAME\"    \n",
       "[11] \"GO\"           \"GOALL\"        \"IPI\"          \"MGI\"          \"ONTOLOGY\"    \n",
       "[16] \"ONTOLOGYALL\"  \"PATH\"         \"PFAM\"         \"PMID\"         \"PROSITE\"     \n",
       "[21] \"REFSEQ\"       \"SYMBOL\"       \"UNIGENE\"      \"UNIPROT\"     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'select()' returned 1:1 mapping between keys and columns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       " TRUE \n",
       "27179 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>ENTREZID</th><th scope=col>SYMBOL</th><th scope=col>GENENAME</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>497097                               </td><td>Xkr4                                 </td><td>X-linked Kx blood group related 4    </td></tr>\n",
       "\t<tr><td>100503874                            </td><td>Gm19938                              </td><td>predicted gene, 19938                </td></tr>\n",
       "\t<tr><td>100038431                            </td><td>Gm10568                              </td><td>predicted gene 10568                 </td></tr>\n",
       "\t<tr><td>19888                                </td><td>Rp1                                  </td><td>retinitis pigmentosa 1 (human)       </td></tr>\n",
       "\t<tr><td>20671                                </td><td>Sox17                                </td><td>SRY (sex determining region Y)-box 17</td></tr>\n",
       "\t<tr><td>27395                                </td><td>Mrpl15                               </td><td>mitochondrial ribosomal protein L15  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " ENTREZID & SYMBOL & GENENAME\\\\\n",
       "\\hline\n",
       "\t 497097                                & Xkr4                                  & X-linked Kx blood group related 4    \\\\\n",
       "\t 100503874                             & Gm19938                               & predicted gene, 19938                \\\\\n",
       "\t 100038431                             & Gm10568                               & predicted gene 10568                 \\\\\n",
       "\t 19888                                 & Rp1                                   & retinitis pigmentosa 1 (human)       \\\\\n",
       "\t 20671                                 & Sox17                                 & SRY (sex determining region Y)-box 17\\\\\n",
       "\t 27395                                 & Mrpl15                                & mitochondrial ribosomal protein L15  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| ENTREZID | SYMBOL | GENENAME |\n",
       "|---|---|---|\n",
       "| 497097                                | Xkr4                                  | X-linked Kx blood group related 4     |\n",
       "| 100503874                             | Gm19938                               | predicted gene, 19938                 |\n",
       "| 100038431                             | Gm10568                               | predicted gene 10568                  |\n",
       "| 19888                                 | Rp1                                   | retinitis pigmentosa 1 (human)        |\n",
       "| 20671                                 | Sox17                                 | SRY (sex determining region Y)-box 17 |\n",
       "| 27395                                 | Mrpl15                                | mitochondrial ribosomal protein L15   |\n",
       "\n"
      ],
      "text/plain": [
       "  ENTREZID  SYMBOL  GENENAME                             \n",
       "1 497097    Xkr4    X-linked Kx blood group related 4    \n",
       "2 100503874 Gm19938 predicted gene, 19938                \n",
       "3 100038431 Gm10568 predicted gene 10568                 \n",
       "4 19888     Rp1     retinitis pigmentosa 1 (human)       \n",
       "5 20671     Sox17   SRY (sex determining region Y)-box 17\n",
       "6 27395     Mrpl15  mitochondrial ribosomal protein L15  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pacote com anotacoes de genes\n",
    "columns(org.Mm.eg.db)\n",
    "anotacoes <- select(org.Mm.eg.db,keys=rownames(dgList$counts),columns=c(\"ENTREZID\",\"SYMBOL\",\"GENENAME\"))\n",
    "table(anotacoes$ENTREZID==rownames(dgList$counts))\n",
    "# Adicionando as anotacoes dos genes ao Objeto DGEList\n",
    "dgList$genes <- anotacoes\n",
    "head(dgList$genes) # primeiras linhas dos nomes de genes mapeados para os IDs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "pleasant-yorkshire",
   "metadata": {},
   "source": [
    "### Passo 07: Filtragem\n",
    "\n",
    "Há muitos genes que não são expressos ou tem uma quantidade significativas de reads.\n",
    "É possível estabelecer critérios e selecionar apenas genes expressos que atendam a tais critérios.\n",
    "Neste exemplo vamos utilizar uma métrica chamada *count per million (cpm)* para selecionar apenas genes expressos `> 1 cpm` que estejam em pelo menos 2 amostras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "posted-elephant",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>MCL1.DG</th><th scope=col>MCL1.DH</th><th scope=col>MCL1.DI</th><th scope=col>MCL1.DJ</th><th scope=col>MCL1.DK</th><th scope=col>MCL1.DL</th><th scope=col>MCL1.LA</th><th scope=col>MCL1.LB</th><th scope=col>MCL1.LC</th><th scope=col>MCL1.LD</th><th scope=col>MCL1.LE</th><th scope=col>MCL1.LF</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>497097</th><td>18.85684388</td><td>13.77543859</td><td> 2.69700983</td><td>10.45648006</td><td>16.442685  </td><td>14.3389690 </td><td> 0.0000000 </td><td> 0.0000000 </td><td> 0.0000000 </td><td> 0.00000000</td><td> 0.0000000 </td><td> 0.0000000 </td></tr>\n",
       "\t<tr><th scope=row>100503874</th><td> 0.04305215</td><td> 0.00000000</td><td> 0.04149246</td><td> 0.04412017</td><td> 0.000000  </td><td> 0.1998463 </td><td> 0.0000000 </td><td> 0.0000000 </td><td> 0.0000000 </td><td> 0.00000000</td><td> 0.0000000 </td><td> 0.0000000 </td></tr>\n",
       "\t<tr><th scope=row>100038431</th><td> 0.00000000</td><td> 0.00000000</td><td> 0.00000000</td><td> 0.00000000</td><td> 0.000000  </td><td> 0.0000000 </td><td> 0.0000000 </td><td> 0.0000000 </td><td> 0.0000000 </td><td> 0.00000000</td><td> 0.0000000 </td><td> 0.0000000 </td></tr>\n",
       "\t<tr><th scope=row>19888</th><td> 0.04305215</td><td> 0.04591813</td><td> 0.00000000</td><td> 0.00000000</td><td> 0.000000  </td><td> 0.0000000 </td><td> 0.4903857 </td><td> 0.1381969 </td><td> 0.4496078 </td><td> 0.09095771</td><td> 0.0000000 </td><td> 0.0000000 </td></tr>\n",
       "\t<tr><th scope=row>20671</th><td> 4.56352843</td><td> 8.35709941</td><td> 3.40238163</td><td> 4.63261775</td><td> 1.997275  </td><td> 4.0968483 </td><td> 0.7846171 </td><td> 1.1516411 </td><td> 0.8092940 </td><td> 0.36383085</td><td> 0.1213404 </td><td> 0.4055595 </td></tr>\n",
       "\t<tr><th scope=row>27395</th><td>13.30311589</td><td>10.74484210</td><td>13.98295863</td><td>13.23605071</td><td>13.469996  </td><td>13.4896224 </td><td>27.4615975 </td><td>21.3744588 </td><td>21.9858214 </td><td>14.91706476</td><td>12.4171715 </td><td>13.8701357 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & MCL1.DG & MCL1.DH & MCL1.DI & MCL1.DJ & MCL1.DK & MCL1.DL & MCL1.LA & MCL1.LB & MCL1.LC & MCL1.LD & MCL1.LE & MCL1.LF\\\\\n",
       "\\hline\n",
       "\t497097 & 18.85684388 & 13.77543859 &  2.69700983 & 10.45648006 & 16.442685   & 14.3389690  &  0.0000000  &  0.0000000  &  0.0000000  &  0.00000000 &  0.0000000  &  0.0000000 \\\\\n",
       "\t100503874 &  0.04305215 &  0.00000000 &  0.04149246 &  0.04412017 &  0.000000   &  0.1998463  &  0.0000000  &  0.0000000  &  0.0000000  &  0.00000000 &  0.0000000  &  0.0000000 \\\\\n",
       "\t100038431 &  0.00000000 &  0.00000000 &  0.00000000 &  0.00000000 &  0.000000   &  0.0000000  &  0.0000000  &  0.0000000  &  0.0000000  &  0.00000000 &  0.0000000  &  0.0000000 \\\\\n",
       "\t19888 &  0.04305215 &  0.04591813 &  0.00000000 &  0.00000000 &  0.000000   &  0.0000000  &  0.4903857  &  0.1381969  &  0.4496078  &  0.09095771 &  0.0000000  &  0.0000000 \\\\\n",
       "\t20671 &  4.56352843 &  8.35709941 &  3.40238163 &  4.63261775 &  1.997275   &  4.0968483  &  0.7846171  &  1.1516411  &  0.8092940  &  0.36383085 &  0.1213404  &  0.4055595 \\\\\n",
       "\t27395 & 13.30311589 & 10.74484210 & 13.98295863 & 13.23605071 & 13.469996   & 13.4896224  & 27.4615975  & 21.3744588  & 21.9858214  & 14.91706476 & 12.4171715  & 13.8701357 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | MCL1.DG | MCL1.DH | MCL1.DI | MCL1.DJ | MCL1.DK | MCL1.DL | MCL1.LA | MCL1.LB | MCL1.LC | MCL1.LD | MCL1.LE | MCL1.LF |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 497097 | 18.85684388 | 13.77543859 |  2.69700983 | 10.45648006 | 16.442685   | 14.3389690  |  0.0000000  |  0.0000000  |  0.0000000  |  0.00000000 |  0.0000000  |  0.0000000  |\n",
       "| 100503874 |  0.04305215 |  0.00000000 |  0.04149246 |  0.04412017 |  0.000000   |  0.1998463  |  0.0000000  |  0.0000000  |  0.0000000  |  0.00000000 |  0.0000000  |  0.0000000  |\n",
       "| 100038431 |  0.00000000 |  0.00000000 |  0.00000000 |  0.00000000 |  0.000000   |  0.0000000  |  0.0000000  |  0.0000000  |  0.0000000  |  0.00000000 |  0.0000000  |  0.0000000  |\n",
       "| 19888 |  0.04305215 |  0.04591813 |  0.00000000 |  0.00000000 |  0.000000   |  0.0000000  |  0.4903857  |  0.1381969  |  0.4496078  |  0.09095771 |  0.0000000  |  0.0000000  |\n",
       "| 20671 |  4.56352843 |  8.35709941 |  3.40238163 |  4.63261775 |  1.997275   |  4.0968483  |  0.7846171  |  1.1516411  |  0.8092940  |  0.36383085 |  0.1213404  |  0.4055595  |\n",
       "| 27395 | 13.30311589 | 10.74484210 | 13.98295863 | 13.23605071 | 13.469996   | 13.4896224  | 27.4615975  | 21.3744588  | 21.9858214  | 14.91706476 | 12.4171715  | 13.8701357  |\n",
       "\n"
      ],
      "text/plain": [
       "          MCL1.DG     MCL1.DH     MCL1.DI     MCL1.DJ     MCL1.DK   MCL1.DL   \n",
       "497097    18.85684388 13.77543859  2.69700983 10.45648006 16.442685 14.3389690\n",
       "100503874  0.04305215  0.00000000  0.04149246  0.04412017  0.000000  0.1998463\n",
       "100038431  0.00000000  0.00000000  0.00000000  0.00000000  0.000000  0.0000000\n",
       "19888      0.04305215  0.04591813  0.00000000  0.00000000  0.000000  0.0000000\n",
       "20671      4.56352843  8.35709941  3.40238163  4.63261775  1.997275  4.0968483\n",
       "27395     13.30311589 10.74484210 13.98295863 13.23605071 13.469996 13.4896224\n",
       "          MCL1.LA    MCL1.LB    MCL1.LC    MCL1.LD     MCL1.LE    MCL1.LF   \n",
       "497097     0.0000000  0.0000000  0.0000000  0.00000000  0.0000000  0.0000000\n",
       "100503874  0.0000000  0.0000000  0.0000000  0.00000000  0.0000000  0.0000000\n",
       "100038431  0.0000000  0.0000000  0.0000000  0.00000000  0.0000000  0.0000000\n",
       "19888      0.4903857  0.1381969  0.4496078  0.09095771  0.0000000  0.0000000\n",
       "20671      0.7846171  1.1516411  0.8092940  0.36383085  0.1213404  0.4055595\n",
       "27395     27.4615975 21.3744588 21.9858214 14.91706476 12.4171715 13.8701357"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>MCL1.DG</th><th scope=col>MCL1.DH</th><th scope=col>MCL1.DI</th><th scope=col>MCL1.DJ</th><th scope=col>MCL1.DK</th><th scope=col>MCL1.DL</th><th scope=col>MCL1.LA</th><th scope=col>MCL1.LB</th><th scope=col>MCL1.LC</th><th scope=col>MCL1.LD</th><th scope=col>MCL1.LE</th><th scope=col>MCL1.LF</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>497097</th><td> TRUE</td><td> TRUE</td><td> TRUE</td><td> TRUE</td><td> TRUE</td><td> TRUE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td></tr>\n",
       "\t<tr><th scope=row>100503874</th><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td></tr>\n",
       "\t<tr><th scope=row>100038431</th><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td></tr>\n",
       "\t<tr><th scope=row>19888</th><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td></tr>\n",
       "\t<tr><th scope=row>20671</th><td> TRUE</td><td> TRUE</td><td> TRUE</td><td> TRUE</td><td> TRUE</td><td> TRUE</td><td>FALSE</td><td> TRUE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td><td>FALSE</td></tr>\n",
       "\t<tr><th scope=row>27395</th><td> TRUE</td><td> TRUE</td><td> TRUE</td><td> TRUE</td><td> TRUE</td><td> TRUE</td><td> TRUE</td><td> TRUE</td><td> TRUE</td><td> TRUE</td><td> TRUE</td><td> TRUE</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & MCL1.DG & MCL1.DH & MCL1.DI & MCL1.DJ & MCL1.DK & MCL1.DL & MCL1.LA & MCL1.LB & MCL1.LC & MCL1.LD & MCL1.LE & MCL1.LF\\\\\n",
       "\\hline\n",
       "\t497097 &  TRUE &  TRUE &  TRUE &  TRUE &  TRUE &  TRUE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE\\\\\n",
       "\t100503874 & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE\\\\\n",
       "\t100038431 & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE\\\\\n",
       "\t19888 & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE & FALSE\\\\\n",
       "\t20671 &  TRUE &  TRUE &  TRUE &  TRUE &  TRUE &  TRUE & FALSE &  TRUE & FALSE & FALSE & FALSE & FALSE\\\\\n",
       "\t27395 &  TRUE &  TRUE &  TRUE &  TRUE &  TRUE &  TRUE &  TRUE &  TRUE &  TRUE &  TRUE &  TRUE &  TRUE\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | MCL1.DG | MCL1.DH | MCL1.DI | MCL1.DJ | MCL1.DK | MCL1.DL | MCL1.LA | MCL1.LB | MCL1.LC | MCL1.LD | MCL1.LE | MCL1.LF |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 497097 |  TRUE |  TRUE |  TRUE |  TRUE |  TRUE |  TRUE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |\n",
       "| 100503874 | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |\n",
       "| 100038431 | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |\n",
       "| 19888 | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE | FALSE |\n",
       "| 20671 |  TRUE |  TRUE |  TRUE |  TRUE |  TRUE |  TRUE | FALSE |  TRUE | FALSE | FALSE | FALSE | FALSE |\n",
       "| 27395 |  TRUE |  TRUE |  TRUE |  TRUE |  TRUE |  TRUE |  TRUE |  TRUE |  TRUE |  TRUE |  TRUE |  TRUE |\n",
       "\n"
      ],
      "text/plain": [
       "          MCL1.DG MCL1.DH MCL1.DI MCL1.DJ MCL1.DK MCL1.DL MCL1.LA MCL1.LB\n",
       "497097     TRUE    TRUE    TRUE    TRUE    TRUE    TRUE   FALSE   FALSE  \n",
       "100503874 FALSE   FALSE   FALSE   FALSE   FALSE   FALSE   FALSE   FALSE  \n",
       "100038431 FALSE   FALSE   FALSE   FALSE   FALSE   FALSE   FALSE   FALSE  \n",
       "19888     FALSE   FALSE   FALSE   FALSE   FALSE   FALSE   FALSE   FALSE  \n",
       "20671      TRUE    TRUE    TRUE    TRUE    TRUE    TRUE   FALSE    TRUE  \n",
       "27395      TRUE    TRUE    TRUE    TRUE    TRUE    TRUE    TRUE    TRUE  \n",
       "          MCL1.LC MCL1.LD MCL1.LE MCL1.LF\n",
       "497097    FALSE   FALSE   FALSE   FALSE  \n",
       "100503874 FALSE   FALSE   FALSE   FALSE  \n",
       "100038431 FALSE   FALSE   FALSE   FALSE  \n",
       "19888     FALSE   FALSE   FALSE   FALSE  \n",
       "20671     FALSE   FALSE   FALSE   FALSE  \n",
       "27395      TRUE    TRUE    TRUE    TRUE  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cpm <- cpm(dgList) # gerando o objeto counts por milhao\n",
    "head(cpm) # primeiras linhas de cpm para cada gene em todas as amostras\n",
    "mascara_cpm_maior_que_1 <- cpm > 1 # quantos sao maiores que 1 cpm?\n",
    "head(mascara_cpm_maior_que_1) # primeiras linhas da máscara para identificar pelo menos 2 amostras com cpm > 1\n",
    "mantidos <- rowSums(mascara_cpm_maior_que_1) >= 2 # manteremos apenas aqueles que tempelo menos 2 TRUE\n",
    "dgList_filtrada <- dgList[mantidos,]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "choice-dakota",
   "metadata": {},
   "source": [
    "### Passo 08: Normalização\n",
    "\n",
    "A normalização visa transformar os dados para uma escala diferente evitando vieses por ordem de grandeza.\n",
    "O pacote EdgeR usa trimmed mean of M-values (TMM) para normalização."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "august-visit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9eurW156aWXvvrqqz179pR05Dt37hS/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\nAQeEk3C/LBBuAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Dados normalizados”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dgList_filtrada <- calcNormFactors(dgList_filtrada, method=\"TMM\") # normalizacao com TMM\n",
    "# Plot para visualizar os dados nomalizados\n",
    "par(mfrow=c(1,1))\n",
    "colors <- rep(c(\"darkgreen\", \"red\", \"blue\"), 2)\n",
    "plotMDS(dgList_filtrada, col=colors[dgList_filtrada$samples$group])\n",
    "title(\"Dados normalizados\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "desperate-snake",
   "metadata": {},
   "source": [
    "# Passo 09: Expressão diferencial\n",
    "\n",
    "Aqui, o primeiro passo será criar uma matriz com o desenho do experimento.\n",
    "Em seguida uma matriz de contraste entre os doois casos que analisaremos: basal lactante e basal grávida.\n",
    "Depois disso, será possível calcular a dispersão e a distribuição binomial negativa, o que nos dará a taxa de *false discovery rate* (FDR)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "romance-cycling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>basal.gravida</th><th scope=col>basal.lactante</th><th scope=col>basal.virgem</th><th scope=col>luminal.gravida</th><th scope=col>luminal.lactante</th><th scope=col>luminal.virgem</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>0</td><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>0</td><td>1</td><td>0</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       " basal.gravida & basal.lactante & basal.virgem & luminal.gravida & luminal.lactante & luminal.virgem\\\\\n",
       "\\hline\n",
       "\t 0 & 0 & 1 & 0 & 0 & 0\\\\\n",
       "\t 0 & 0 & 0 & 0 & 0 & 1\\\\\n",
       "\t 0 & 0 & 0 & 1 & 0 & 0\\\\\n",
       "\t 0 & 0 & 0 & 1 & 0 & 0\\\\\n",
       "\t 0 & 0 & 0 & 0 & 1 & 0\\\\\n",
       "\t 0 & 0 & 0 & 0 & 1 & 0\\\\\n",
       "\t 0 & 0 & 0 & 0 & 0 & 1\\\\\n",
       "\t 0 & 0 & 1 & 0 & 0 & 0\\\\\n",
       "\t 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\t 1 & 0 & 0 & 0 & 0 & 0\\\\\n",
       "\t 0 & 1 & 0 & 0 & 0 & 0\\\\\n",
       "\t 0 & 1 & 0 & 0 & 0 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| basal.gravida | basal.lactante | basal.virgem | luminal.gravida | luminal.lactante | luminal.virgem |\n",
       "|---|---|---|---|---|---|\n",
       "| 0 | 0 | 1 | 0 | 0 | 0 |\n",
       "| 0 | 0 | 0 | 0 | 0 | 1 |\n",
       "| 0 | 0 | 0 | 1 | 0 | 0 |\n",
       "| 0 | 0 | 0 | 1 | 0 | 0 |\n",
       "| 0 | 0 | 0 | 0 | 1 | 0 |\n",
       "| 0 | 0 | 0 | 0 | 1 | 0 |\n",
       "| 0 | 0 | 0 | 0 | 0 | 1 |\n",
       "| 0 | 0 | 1 | 0 | 0 | 0 |\n",
       "| 1 | 0 | 0 | 0 | 0 | 0 |\n",
       "| 1 | 0 | 0 | 0 | 0 | 0 |\n",
       "| 0 | 1 | 0 | 0 | 0 | 0 |\n",
       "| 0 | 1 | 0 | 0 | 0 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "   basal.gravida basal.lactante basal.virgem luminal.gravida luminal.lactante\n",
       "1  0             0              1            0               0               \n",
       "2  0             0              0            0               0               \n",
       "3  0             0              0            1               0               \n",
       "4  0             0              0            1               0               \n",
       "5  0             0              0            0               1               \n",
       "6  0             0              0            0               1               \n",
       "7  0             0              0            0               0               \n",
       "8  0             0              1            0               0               \n",
       "9  1             0              0            0               0               \n",
       "10 1             0              0            0               0               \n",
       "11 0             1              0            0               0               \n",
       "12 0             1              0            0               0               \n",
       "   luminal.virgem\n",
       "1  0             \n",
       "2  1             \n",
       "3  0             \n",
       "4  0             \n",
       "5  0             \n",
       "6  0             \n",
       "7  1             \n",
       "8  0             \n",
       "9  0             \n",
       "10 0             \n",
       "11 0             \n",
       "12 0             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Desenho da matriz com os dados de forma que eles repreentem o experimento\n",
    "desenho_do_experimento <- model.matrix(~ 0 + group)\n",
    "colnames(desenho_do_experimento) <- levels(dgList_filtrada$samples$group)\n",
    "desenho_do_experimento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "alive-wright",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6QX0okDedMGBwd/9dVXJmdaqZ6qexAl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8cP/990+fPn3QoEEZGRkHDhxYsWLFxo0b\n8/LyJkyYEECWhN/w66eyFVUeZ/6cuZaiXcvgMsmoWAuWvaV/XL+ArZL60Xg8ESUR07P6vFI5\ntkxKT1oMOQF3s4aMKkEQBtbCrmnTpqtXrx47duyMGTM2bdoUr1a79t133/3EE080a9bM5wyJ\nINBfVfQkvq31TQZLEeZYC9pKxvC3jM2I5oN8SWGjdlWsT0Sq/5JvkN6VgV1V7ZWYDjPJm3lN\ngM4OEVq0fnkiKytr6tSpU6ZM2bVr1759+7Kzs/Pz89PT0/1OjnCMXcdFUd68+0pzNrW0uGzB\nBIya9s/JbD8hbtSkIjFG4dlqImxzQ0jy8covNLYMhqRfBEEQASAWdlVVVfzBSCTSrl27du3a\nMWXS0tJ8So4IEsX8pxZqUdPvmcruyfDQlArDPO1gJ6JdfSyM4FPH3YR1ucQs65SH2s59EE2S\n3SAkCCI1EAu72rVt/IYsfZCFAd7WslVdx80ylhQV5f1Y0hUu0jkI6/m8G/BmOD/+odkdE2dj\n6Hd5giAIwkAs4G655ZaA8yAcoNhtxtyOoFhmdbC3yby85eBWSsbk069oJGC3ik5FftAUVYJM\nm3FDLVfMfc1KIdx9SiAMBq0m4cwziQaQIAhPEAu7WbNmBZwH4RLHDpb5teaCKbPk6maRMZjV\nK51WZHc8WFb0CaZpy7HVL+kTmt8liHBC68gEkTJYP8fOzI8//lhSUvLDDz/4lA1hC9ndi0KN\nYqB+YpytKdnZs8cYk8+u26df2MA8UEz31V1Q2GOOn2KlGHlFDsHvFXPTR3DdcXbTDFzv/3PT\nhSRqVIHmAMo+TMLWnVAlQxDhREvYHT9+fOzYsdnZ2Y0aNerUqVPTpk2zsrLGjBlz4sQJv/Mj\nzHj4IRs9j/lP/bYMteFGdgjnEkWj+pLIcqB0VgA98TAcnzLHOyY1C8sSMw7Gzo5ZAdvKQXhm\n7Q6FITFt1WIysdWWA1JbasgEH0EQocX6JolTp07169evuLg4Ozt75MiRWVlZBw4cWLNmzbhx\n4+bOnbt27dp69eoFkCgBR1vZzH9CqaUUEk1/w5na8HO5oYqXng4aUuwS00e/in5zws1qOvsR\nvd1751jM6YfVr8K8cEDwK4wkg3yFhpcgLLEWdmPGjCkuLn7sscceffTROnXqxA5WVlaOHz9+\n/PjxY8eOnTRpks9JEh6gnvid1ZXhbIuVY2dL7eQ5dhMd1GJad9y0rcxt7R3kjwvzNPt25oNu\nhsWlrPepUaNpl03QHrXEQuNPEAbWS7FLliy57LLLxo0bZ6g6AOnp6ePGjevbt+/ixYv9TI9w\njs5nHGPjRUw4WILRrCJbkmPScN+cTj5uEtbEPKqaVfR9Pver88K2mJV6RQLeIhwoT5azFS0i\nCRccw7b1LQwk1xkkCP+wFnYlJSWFhYXCt3r27Llt2zavUyK8R3MOEM7omvHNTTjwkMwR9Jdc\nEcgNmLYGhO+FbFT152aFoybLQaeAwumUKW+rTF1hDJFiP4AMtWurxhOFpL5IEq7DEp6A35Cq\nIwgD66XYdu3abdmyRfjWli1b2rdv73VKhPdYrsf5FF9dXrG3Tx1QU8y5WZ0xmlAEkaWhk577\nLhiuKj9huzytjjtlLuw4DZlbqdAl7hdS/cbbVhx8mQmh7gngKxlB1EysHbuBAwd++umnU6ZM\nYY6//PLLy5cvHzBggD+JEbbR8WkCaEUfSydMrQ88XLNTIExS3ZBlp9ysyfJZOZgdnfmyqG71\nGb0wd8c/Z0iRrewcqWvByrn0iZQ3zwiCSCzWjt3EiRM/+OCD+++/f/r06YMGDcrIyDhw4MCK\nFSs2btyYl5c3YcKEALIkdBCaNw6wnBEVSktdUT8B2ZJcpPrmPwdrvrK2mNdqoaDZR6EnET3/\nJGdNY0+noQAwjwyj5MwvAvBg9J2eVPWEUqNTqdELgggh1sKuadOmq1evHjt27IwZMzZt2hSv\nVrv23Xff/cQTTzRr1sznDAkbuBc6TDF+tlYoHsef1HY1gX55D6d2Zlj8Xt0OIcz2QVsaVxbN\nbnX3ZqfdMp7Db7VMVCYEQaQk1sIOQFZW1tSpU6dMmbJr1659+/ZlZ2fn5+enp6f7nRyRcJxp\nQf0dcuaNYrxRx4dyoFzV+ag1qyx/nWKKgPqzuIf2p7O66hwcuJgGzgzm1BNAqdcjgiASi1TY\nLV26FMCAAQMqKyuNg1lZWVlZWQAqKioqKipiBxs2bOhzkgTg4pu9jtKSRTZPvToTvHCqZpwe\nGfqyTOYa8m8xktETFANla5HaD4dSWNEBwooO3DVPLGQPW0wxyO0jCIJHKuyGDBkCoLy8PDMz\nUx2CPlaCwb9xtoxsOT0rpJvsnk1mTnLfO0/WRh0rA7X/pO9O8VO1G7Gib53aqhhOXO5H9Fsk\nOTuP+n4zQRBEDKmw69GjB4DatWvfddddAeZDhAsHS7FmNKcl42YCWShhHJ17HdTpyWAWizUj\nOHanhHvXNMP6jaWby+/IhKQjfuRgxs2Ks+Z+TZi+lpCoIgginEiF3fr162Mvpk+fHlQyhAfo\n3O5gN4LLWuaFWqEOkOVpaVwZB3nLkDmuieayr+a2MwfTf5BywY1adbm4rMZzN9dui5pVbJV3\n7AQ7qEUQRE1G6+YJM5988klJSUl+fn7//v1pd11ikU2uATwjQ3PrG1OFea2vfoQFGPEaOf9w\nNZmo1Z+JhbWYm39lUsDZzrlwzt86cseld8UocgeXrnvzjP/KISsmfB1CfDUUya0kiJAjFXaH\nDx+ePHnyunXr6tate9NNN915550nT54cMWJE7KYKAG3btp0xY8Y111wTVKoEi0zDuZ/nNEua\nZ2Vbi6FCTcbLKVtTiKEM3NwsIgsLbrR1BLTm4qxX2Xq+S0y9jB4baj+a0F+O94owK5WECyk+\ngYSnRBCEArGwKy8vLywsLC8vj/25cOHC77777sCBA0uXLu3bt+/1119/5MiRV199deTIkRs2\nbOjQoUOACRPVSNTHq84KHWPICddhFX6VIRrUN9Uy5p9Ozo5nKfXKo1e6SmE6Oi7pJgE1ttZq\nHUfztoof2NU6yatTQzLgBEHIEAu7MWPGlJeX33rrrY8//nhaWtozzzzz0EMP1a5d++c///nb\nb78d+8i+5557evToMXHixFdffTXYnAm3aE66sm1tMAky4T45nWd/qImafp5BnarlNi9n64lM\nLdk2QT5ny8iyg+FB3zDTsfS86qm+wPUPxbWUpCotKRIgCMIW4t+KXb58eUZGxvTp0zt27Niu\nXbsXXnghPz+/srJy3LhxxtR18cUXX3HFFQsWLAgwW8IzZJO3zvKizge9pSazjGOoKGfukdEX\nsziTrVzLpmpzEM1e8wnoYGwQlGkUYTQ3M64st8h5LCPobEpzqQks01AX8HyzqfsemUN5EsfA\nk5XxYEiWPAkiSRELux07dvTq1atu3bqxP9PS0mJPP+natau5WFZWVllZmd8pEp5jliwRE0YB\ns8Lg32JC6Rs5/KKqy5lbs7DhL9rViHwVS5loLqmQjPxBtYp1oAOE6ZnTlslcprAz48395K0f\nwQ+R5G3AABryUHQSBJHUiJdiz54927x5c/ORpk2b8sXoi1dSIJuVFduz+Lc0V29lQSzr2l3p\nU+PHlcmoUm8nUZ2FS09aNILwq8zChvQbtSv+7C6y261u+a6aAJZ3NRfuU4+a0EeCSCC2H3dC\nhJmI5E4FRWHND1kPt0w50w0GshndPE0y7zpQpbLI7uFtS+GfluUtkfmC+k0z7p2tHBTdYcI6\n9gJ1rnCdYjzBKA8Pv8kQBEEYkLBLWdSbw3jpo7BwoC1unCk/x9aFOVU3GlGtAPQXjoVNCFWU\n3VDqs6M4rggl/NMxnuhObyuqR8nBNUYQBBF+pMJuy5YtL7zwgvFncXExAPMR4yARHviFNn10\n1gH5zXa8qSMLDvldpTI7kHd3ZInJUmLK8GaPIQo1d/u5sf0cW5WWFR2rOmc5yLD79SD4lUcH\np8BZhnavFq/kI8lQgiCkwm7dunXr1q1jDv7xj3/0OR/CGn2nwf3CmaKYZhVjwdeWR2XWB2YR\nxsxbdsdBZlapc2PkrOYio36GahyIg4TM7vwYWrqMsHOVJtf+s8D8S5/iEPqQmCbChljYTZky\nJeA8CBnC+cxyknOzaCgUVYpWotxzSRQbuXS2cwkPCt079aqibBCYuvqKQSZPdSL49LnPrC3a\nastbqaQjmh2vAutcz/qXq61GEz5hhyQNQgadGiJsiIXdf/zHfwScR83hxx9x4gQyMpxHcDl7\nKVZF3W+Qd7bTX3/fmHnRll/Xg0l7GTaheuVXZ3FQvUAsK+YTCvniZplYs7pjLehycDwZW51V\ne513AyYkaRAEkSzQzROBcvAgCgpw+DDq1kXbtmjTBrm5F160aYM2bZCZWa2KH2s6QvOMcX2c\nrZwafzK3bmjenaA4zvuCCgVmuVark5U+LvWxh007q6vv8KH6ndc6dQPTJe7XoBNbPTCSJU+C\nIJxBwi5QfvgBR48CwJkz+PZbfPutoEzdunGFZ0i9nJz4/zMyUIt7pLRdE0V/2VFdQLijK1r9\nNghLX0Q2GevMPQ7221k2oU5VsxVNI1DRijq4ZRxYWafOpna+X8zp099L5/ikW2aoGU3tuSbE\nlbSL4+EKm6QjoUkQ3kLCLlA6dsTq1Vi9Gnv3Yt8+lJZi716UlaGi4kKZM2ewYwd27BBUT09H\nZuYFwZebiz/96VdAUWkpsrKQni5uVCYXbG32Z254FC6PwmTRaaKWMnxDOqkKl3T1eypMQ7Ok\nOngA2/8VG9rM2FLMsor6Sld2X4XwQrKMponLoQ7/vRqyf4+KgwRB1ARI2AXNZZfhssuqHYlG\nUV4eV3gxqRfTfPv2Yd8+nDlzoWRlJfbswZ495tr/AJCXh2KXm70AACAASURBVEgEmZlo0wbZ\n2cjNRXZ23OTLzsaJE7o2hn4ZYQHhEbVKkE3k+nO8h2aYf/ihNdW6jVk2tYvQS7PM0Cw1LAvz\neHgqQ2UC+XrtKf4F+dSiJT6tIRAEoQkJu8QTiSArC1lZ4ncPHEBZGfbtw+7dKCvDnj1x5bd3\nL06evFAsGsX+/di/XxykWTO0aYOOHTF+fLR3bxw+jJYtVYt0/Gsztnw+KGWBY1dJnSG0t4L5\nrQBk8V22aOlWerKgaV6AtrW4rNnfMPtMIUnDkmTJ0yWhUuqOSY1eEOGHhF14iU1yGRnIyEBh\noaDAkSNxk6+sLK72Yv+VluLYsWoljx3DsWPYvBnz58eP5Oejd+/4f716oVmz+HHhPiphYopb\nWc3ygtmKZ9lf9REGS+vRvDQs21Tn4fKfOsPgP9A9bNHxxkE+DZe3FesUE16WxvGA5aP+zr/U\nILV7RxDhh4RdSNFRG82bo3lzdOkieOvUqQvG3t692L8fpaUoLsY33yD2qbtrF3btwltvxdpC\nhw7o1Qu9eqF3bwBNge8tW7c1DautNVvH1dja6qezhT8kM3FC0nDpp/pRN7kaBXePUQIzSWpS\nY9BSoxdE+CFhF1JcfgTUr4+LL8bFF7PHv/8ea9fiiy+wbh2+/DJ+i0Y0iu3bsX07/vGPWKmj\nwPZI5B/AaGDdsWMX/DwjN4Xn4WB9Td+/cePhOTCE1Ou5snsCFJgDujnFfug8ofJg/FfL1n26\n3dVbPM9Ep4Ph6T5BEKkNCbuaRdOmGDwYgwfH//zuO3zxBYYPfwTonZNzw/nbMiLAxcDFwK8A\nXHQR2rdHr17o2RO9eqGwULA/Tzad66+a6c/9nqyjmX0UvmnFsznctK6zlCnzCwNwEGWr0ry6\nVatnI4izG6VlJEoduhRtpOcIgggYEnaphq37Xlu0wPDhF8ofPowvvsCXX+LLL/H226VAbqx8\n7PEr//xnvHpuLgoL0bMnevZEYSHatLHQBI43RTlTipZoDpGRuY43qZObg/U4hcLzA0vr1FKJ\n6pRxtiiZdAopeCUanltPCIJIICTsajrmmaBlS/z0p4Y9kwe0WrDg4LBhfwV65uXduHt3vFhp\nKUpLL9yH0bo1evbEQw9F//lPFBaiQwfIdse5nOpsVbRsSyE6ZTrVCOjTgqPdiVld3tKJ1Ixm\nvjeWOajeH6nW5foruR7iQPoEc7nawrIXwlPva0oEQYQHEnaphgN7THa76PkXE2MvjhzBhg1Y\nuxbr1+PLLy/ch3HwIBYswIIF8YqNG6N7d/TogVdfjfbogYoK1KnjV0ecYRZnMYT3+SrmQoWm\nYWCCyDplXr40J6BTUWEo+nrDr77xxmSSwIVLx9sEw4PinNKKMEEQIGGXktidmXTW1wA0b47B\ngy8cP34cGzZg/Xps2IB167B1a/z3M44fx8qVWLkyXis9HX36YNWqW0+fnuXT7GLLn+D9J7NA\n0dQfTChFebtLrrbQUVTGa14QyPJnBkGoJBzvOQsJCkkXcpxtbBAScglLEIQzSNilIJbLbXbv\nLRWWb9IkEo1GBwyI/3n6NDZvxvr1cam3aRNOnACAykqsWgVgVl4e7roLTz6ZC+xRJ6lO3jJh\n9SqhYdfJzDDGyZOlKjM4PcTWUpplAZdLipYLtSmgEsKZvObysYPxD2d/CYJwCQm7GopstrAU\nfLzuMb/bq5dxMA3o8MYb226++WngFiDzwAE8+STS0kpHjMA77wyNRqVb8czBLdcumX1yEY2f\n0pJ5Vy6liWwl19btLExKfMLOTpwQHZWsRt/iSjoNYWs3nsu7FjzZsmn3AvMQW8FT6Q6PFPgy\nQ6QktRKdAOEXkfOYDxqrkDL3Rb3uxizSMVTfdFUFlPzqV4hG/wLkzZmDyy8HgKoqvPMOgEWd\nOuGZZ3D4cLXWmUZ1XDEmZ1nX1JGZ4zLVaBkW3BApMreLQuPKTpyssIMClipQc6zM8ZNi6dNX\nYqMhHDrjYNT0UJ5wYuufSSpRYztOhBwSdjUanWmeOaIjm7gXZ37zG3z2GYCed92Fhg0B4Jtv\n8Je/ICcHt9yCFSsQiQguRYUIMNSkLAdbc6H+uqowpqwtmRNj2IrGa0sVa/RIoQA0UZugfDTF\nSNpq18G1JKvIvOuH6HE/pDpZBaYMwiNBjO8/YZaqBJHUkLBLWRTSR6YnDOPNUB52WxS2ZSrw\n5fTp2LsXL7yASy4BgNOnMWcOBg1C587nnn46euiQoAuWjartN1kyDuCHhRlGc6Pm18JpzKgi\na0hY0o3GUnTfLKCdmX+a14y6F+rgsoBqL1PdkGX8MOC5LEt4f8MjNAki9SBhl9w4+4A2PlUj\nJszvCl/zf6qbkL3VrBn+8IdIcTE++QS/+Q3q1gWArVvxl7+gbVvcdBMWLMC5c/HCZg2q2bQB\nP69bBuGrmI+obUJFHEMxo/rg2+2Ug+mQz19d2NYpNitXnfLC1zqFnYkARXq2nFcFzuzMMOiq\nxCZAEIR/kLCr0ZitJqG2U091OhOhYl1v4EDMmYO9e/HMM3EDr6ICb72F4cORn4/HHsP27U46\nZTTEt+4MRRDZWwqvVDOsJ2tVtuSLzGLUUcNGMbVDHHuheVLUskyza4oTpFNMJ0kHUlu/ZMIl\nIEEQSQcJu+TGpXAxT7SWU6DLCUmmaVq2xJ//jOJirFyJ229HgwYAsGcPJkzAT34CYMWMGdHj\nx3VbYTqlrwmMxHSmfM0ZV+jbWdYyt2tUtzvHq50kPg3eIdM32PihVqgrZ1csH1AYR3ict2Ah\nXxwPwMoKphWCIGosJOxqNGpjSTEDGeIpKnoAiqKi4q2iIrz2Gvbvx7RpAFYDiEYBDLjjDmRm\n4tZbsWgRqqqsQ5ltSKGCcSBV3cALC0vhG8OcqjHUZqnHRxAKIA/7q+P2CWG+PKh1qqbrZgl/\nNerbkDp5aibgGP6kEwRBWELCLjlw/MmulgK22jKMGcvVtEh1zNUtm2vSBHffDaA/cOl//Rey\nswHg5EnMno1rr0VuLv7yF2zYIO6gZkcsMWLaMr0UxXjfTqGn7QY0vyvLkBklB/6fMKwsmvlS\n4UsKvxJ4hcKQi1R/sIhOAgG4a7ZOuk8jFirhGKpkCCIZIWGXsphnUOaguqLdmYzRE/xb+t6M\nuWI0WvzUUygtxYcf4te/BnAKQFkZnnkGhYWIRDZHIo/s2mUr03iLllpT5nKZJbL+9KOQcbzg\n48sIKyqSV2g+/kxZqj3jKrJUaYoeGWJL89IS+qzCxBQ95a98naYtsRVH3xd0lolXnXIgXkl+\nEURoIWGXHDj45OWFAr8wpyO5GPvKiKBIyVxA1qJQd/KkpWH4cPy//4fvv6//6qu48krUil+z\nBcCT7dvj8sujL7wQ3b9fmj+fmy2VELW51qyP4yCWXTAkF1PLeJepyBQQtuVAJ/E6D5ygVGtl\nRSvGqVFnYu4db9ExXfbja48wJab7npiClv+cFf8i3LfuIWHLhyCSDhJ2NR1Gt8UQOm06zp9x\n3GzhMJMrH5lXTjxNmuDOOyMff4xz53InT0bXrrGAWL0af/gDsrOrrr4a06Zd+CkLN9ODpigU\nDp1lZKOKZa2I8kdvLdPT8SM1qyiCMNX57xKQa0o1OoV1xDF/YZvfMp8ObyUFf4oValsH/mQ5\nOF9e4TiarWuAIAgHkLBLfdR2lND7QfWZm58OZfE15w9Z08L43Ft7HnwQmzbhq6/w178C2AkA\nSPv4Y9x7L7KyMHQopk1D7FnHmnYOlOKDV1e80WJLfkE0L9q1rIRhjddmAW2W17xVpk5Av1FN\n8yxismmFKp/vhSKgjsFm/o5hWddXuyhqwvMWPZdu3kJ6jiACg4RdiiNc62FmNV5qREyY4xjl\nhWrPUv3IkPkQMnsj1laXLnjySQAd1qzBH/+Itm0B4OxZLF4cV3jAQuB68420QvhWFEKWsXYU\n2lRHc6jbVShvRXBeLfHjyYg8WQK2EOavk63s+0MACFv3SoKEXGkFjOU/Ga8gBUkQJOwIFuGE\npPg4ZgwYYS1eJsrme00dYBQGon374rnnsHs3Pv0UDzwAoAyIPRhlKDCvQwc89VR8idaBKSXr\nkfo445kp0DS6hG3JKpoNVHM+ijNlidDxsjue3k7qOt8uZIVt1U15UqbvqdELgnAJCbsUR6bS\nFOJAaPbA/qc/o2zU06q56ajp17fMx40EGKMldrxWLVxxBV58MQLkrFyJBx5As2YAsHs3HnoI\nOTm4/XZ8/rl476CO4DC3zufMF1N3zTjowNcRDimDbHiZuvonVOjGqXMwCpjVpK3O2p2n3ZuO\nsK8+U0BMpIy5mDIdIQg3JLewO3fuXElJSXFx8dmzZxOdSxIj1Hb8R6T6Q5Mx54TH3XzmKjQQ\nZ8ZUFRXhhRewdy+mTo3fZnH6NP72N/TpA+Bz4PZTp+xpGrPdpW4dnIRV9Eizdc08dUIlJJoz\nQ4hR8141obD0bKXnvqIbUsZjIwjCc5JG2D322GMzZsww/qysrJwwYULjxo07depUUFDQsGHD\nO++88+jRownMMEnhrTu7Xo46ZqT6c86MgzKnUOia6M+d5pING+Lee7FpE5Yvxy9/ifT02OHL\ngNcaNDgCPF1SEpUtKRpHdGZQdRnG52Nkn6Ku7C1+NGRiWp0zU9IyE7tmW5DKw5Zm1TRZwwxZ\nUwRByEgaYTdhwoRZs2YZf/7+979/7LHH6tWrd+ONN9577729evWaMWNGv379Tp06lcAkkwiz\ncOHfkhVWHDGQzTfM+qna6NJJklnTVHRk0CD84x/YtQtPPBH/KQugOfCfnToBWASMBGrzcczZ\nCnvEdE2YrfGaz1AdVia8dM6XDrJ+CZswdLmttsxa1sH6ptoo1Ykgk8VqG5W/1B20ThAEkShq\nJzoBJ3z11VfTp0/v06fPBx980LJly9jBmTNn3nbbbU8++eSECRMSm17C4TWBrEBEdIOnzjyq\nOdfa0i5+TKjmDrZpEwFQURGdPx8vv4yPP0Y0CuAa4JrsbNx5J/bsUQ2C5agyIow5rtC7OsGZ\nd5lBE2ZrVw8Zmcii2VV1Op3iUdfSj8kPrGxMeA9V9q5dvOqLZlvk4RFEjNOnUVaGsjLs3Yv9\n+7FnD8rLsXcvysqQl4f33kODBolO0U+SUth99tln0Wj0ueeeM1QdgFGjRk2bNm3hwoUk7NSY\npxO7+oxxs/iAtnIwXquXX40CioaYwhHTr5HyOd94I268Edu2oXPn54HbgIvKyjB+PCZOxPDh\nuPdeDB+OtDRpE/o4s6nUdflBUJTUTIApqSOAdOJrrgj7rXtcKjM31d2PT8B4qzUJwlcOHYqL\ntn37qmm4/fsvPKme59tvUVKCHj0CTDRwklLYlZWVAejWrRtzvGvXrubl2hqL5oRqWdI8q5nl\nkQNLg5kgeV2oP6MofCOhXuTDGm1Fo388dQr/+AemTsW//oWqKrz/Pt5/H8AeYEZp6ZicHCdT\nu9rZEnpIlk3wFqCwa45Rd5Mxd2HVR52YwvIQOcfCryJqRSg89c5sZvNXBe2uOMSyFZ3vNm6a\ns+vLBoyi+yRJU5LKSpSXX5BuMdFm/Hn6tG6cBg3Qpg2yspCTg379UlzVIUmF3cUXXwxg586d\nXWN3PJ6nvLy8efPmCUoq7DAaQrjuqZ65Nddqzeu8+umpbRumjDHXKiZCfilQqKLq18ftt+P2\n2xGJFAL3Nmnyux9+AJADjGnXDsOGAbj+7FnUtvqHojmvMKqOeUtznVcnuK0ZTlP96BiKMJ0d\naCgV/UVVW3ilw/jLmLm8hU6nfyIjAOEVZm2k6H6Y0ybUnDghlW7l5dA8sbVqISMDbdogOxtt\n28Y1XFYW2rRBmzbxR1/VHJJJ2JWUlEyYMKFz586dOnVq1arVuHHj3nzzTePf+erVq99///2f\n//zniU0yWTALI+EsKPRLNCObm7Cs68AOkWkXXnborx5Go+sBnDiBN97AtGn44gtUVeGDDwDM\ny83F7bfjjjvQsaOWc8brY7ULJUzVLrbG0HIB1MjH5G7aS09f25mr2IrvSRzNrZOa32osy6ib\ncy9zbV0GMidbs3pCCHl6hIzDh1FWhtLSuGKLvdi3D3v24Phx3SD16sVFW24usrPRps2FPzMz\nrb9+1xySZr9tbm7u3r17mWw//PDD4cOHA7j//vunT58eiUTWrFnTvXt3b5t+5ZVXfve73x0/\nfrxRo0beRg4MflHVrLoU++XBzfQBozZIwOkPzWiW727YgOnT8T//8z3Q9Py7GDQId96JX/wC\n9etrNeF4S6KtKVZmTDrG2aZJTaWo34qbC089CA6uHHWQMOD+32li/6UTyU40euEGBbNu27cP\ne/faWDZt0QLZ2cjJYaVbdjZatPCzAzapqKioW7fuqlWr+vfvn+hcWJJG4paWlp46dWr79u3f\nnKekpCT9/KPJ3nrrrU6dOk2fPt1zVZcUaG5+j2HLnGMUoeaCoHnFREdFOdiMFfOQ1PvtHJhM\nxusePfB//y/+53+ygZuKil5ftQrRKJYvx/LluPXWY7/7XbM77sBll1kEEWaoI+nMr2X6Q7iy\nbLnCLmyRKaA4KbKDijKOVYKRhucSytv03ASxhfvFejWk5whLzp27sOltzx7s3XvhRVkZKiq0\ngqSlISMjrtXatkXbttWUnOJrM6FJ0gg7APXr1+/atSuzry7GypUrO3ToEHxKYUAobhQTsGJR\nyc2EJ1QwltvgZLmZjwjnTjeSRZ9o9MfzoS75z//cMmsWDh4E0GzqVEydii5dcMcd+O1v0bq1\nNAG+U5YS3M386rK/DJY+HHN2bMlKTUdNkYCvKscSv/e6mVtRf4chCG+JeW8xubZnzwUZt2cP\nysqg+TNPderEdVtMseXkxLe75eYiI4OWTf0lRUa3xqo6ITKtoO/qMRU1bQmmsM5am5GYMW85\nnsA8XIsUsfWZZyIVFdE6dUb+27+9/dFHqKrC11/jz3/Gn/9cef316f/n/+CnPzV+3OJCSrwD\nJ8wZ9mWQM8XDF1C0yOtydY94/WHXiIWd/XzO3DLHC46W34u8Qqhrk9pOC9uyNRHj4EHs3Yu9\ne7F7d/xFaak97y226S0m1wzdFnuRmelz9oScFBF2NZnAttTI7hvgjxhLpYpojAgQSg1jGU7R\nlvAIsxzsHlP8d957D2VlmDkTr7+ObdsApM+fj/nz0aoVfvMbjBqFnj3FiQkHhF9Rlc2C6l2G\nOijKW3qHdlUX35DOBkd9zN8ibI2Dy7VXW5CgiUEjkEC+/x579sQVG6PhNPe91asX99tycpCT\nE188jR1p1crn7AlHJM3NE5YcO3bsqquuArB+/Xr9Wrt27erbt29lZaWizJkzZ06ePHnixImG\nDRu6zTIcmE01Ty4ATyYwfYfPsjzfL39sPKxahddfx5tv4ocfLhzs3Rtr1/YD/gW5DlOrZCH8\nTRK2qgur6NurikxQ/VYby9Vb2bvCNDTX3PWT9zyOT5eWHzg73Y5b8SM4IeT06fhS6Z492L07\nvopaWmrjntM6deKKLTe32os2baRbTWo4Yb55Imk+kiz57rvvYj9EYatHVVVVH3744WnlN5et\nW7c+/vjjZ86cqVOnjtssw4fjHWxMecfToeVkoxAN+nnq1JX1QsNqajB79o9/+xsWLz4X+/3l\ntDRUVb14/PgDjRsnTM3Y0o46dprCUzQPHX9m1QFlrx30Qo3OVeqgjGY3/cbWNeO3GE0isZtE\nRKPYvz8u12ImnPH6wAGtCGlpyMxEXl7ceMvJuSDjMjNB2zhtQcIuCCorK1esWAFg8ODB3kb+\n7LPPioqKQivs9Ffu4JGVpT+FxOCnfM0gwlCOvSWZOpHNdpYDy1eMRHKAe+vXf/TUKQDIzUVp\n6b8BH+hrBZdSQG0KqnsEyZkSVmT6LryKLBUPg6VI1Szgk6rgr94YHrZFXhcB4ORJ7N4dF22l\npfHXsbVUza1vGRlx6ZabW03DZWXRXQueEWZhlzonOT093XNJlxTIpgE/thzx8zGjSJiAjJ7g\n35K1wpQUSSgbN3+o32JemCdXYXVhLyLxbYV7AJSU4N//HcuWobQUwPs334yDBwXLGeYuMGPo\nWJrIlJllLZ3NajLX0/x/iAaWj6OZqvnKMQ8OH9zBZWzLkGaOODg7Oo2mgKQjbapPeXlcscXU\nmyHmFL9zaqZhw7j3FvvP/LpePZ9TJ8JNsgq7aDR65MiRioqKjIyMWrVqJTqdpMHlkpxZfzAa\ni6/ipi1e26lDKSJYCjWGaPXf5IBcgkRF92fEyixZgtdew+jROHIEb7yBRYvw7LMYNerCYofQ\nsjLC2umoDYQJo7omM/pufkvts9pVpba8SVvXlWJUFQnYklz6F61+2u4Jz9JnSNIIDxUV8VsW\nSkuxa9cFJad570JaGrKykJcX995yc5Gbi7w8tG0L+vlMQkaSCbtPPvlk+vTpy5cvP3jwYOyO\nh7S0tMzMzKKiovvuu2/QoEGJTjCkyPZyGe+aj+vMtUJ/TtgoX0ZowyiqM03z7eqLUUV5WXOM\nUaQObnqBO++MAJnAC8AvjxzB7bdjzhxMnYr27dn8+d5ZJsaXYapbhrIla/iwUdHyq/5QgzuV\nlmnoi3JbVqXwODNQCjWvI3yFcWxBNlj4OXkSu3bFjbeYjNu9G7t2Yf9+nDtnXb1RI+TlXRBw\nOTnIz48/wpd5iBJBWBKW73mWRKPRUaNGzZ49G0B2dnZOTk6LFi0AHDlyZN++fXv27AEwatSo\n119/XfNbuz4h32PHINxspJ5UPP+6r5Z9tvZLuSkDbjTUWSnEooOZ1Whu/vzoffdh3z4AaNAA\n48fjD39AWppFAvxxnSVLy5SEKkptTHqrSIzvGIwI0/mqYNmWXWGnCAJuD4AsN83hciPOfDLk\n9FMiZWlw7Fhcuu3aFVdyMQ136JBW9divYxn/xdRbbi7Zb8kH7bHzgMmTJ8+ePXvYsGGTJk3q\n0aMH825xcfGECRNmzpzZpUuX0aNHJyTDhCNzUCzRlFAOtJQllpu6IJFlbnSquUc6FqBwkVEt\ns2J+ZOz1iBG4/vomwORate47eRL/+Z944w387/+iWzc2JcVCp6wLRhlmCVVYUm1qav6pQNM4\nVMdUq0xLNK9PzfQ0bWzmQtI/j5okXFElPIHAMM7dkSMXpFvsRez1999bB0lPR9u2cfstPz/+\nIvb/unV97wJBJI1jd/nllx89evTrr7+uLbmrJxqNXnnllWfPnl21apW3TQfp2Hn1tV7T/1Ds\npvI2NyaC0Fa0bMVzJ89WQzKXi1d7whMBYOXK6F13YevW2F+Vjz6a/uij8Q96W36VorC+qLVs\nxRlq+0po4ME0jO59O8v0zH+6b0Uhpu3GJ1fMJ2PSkspKbNuGzZvx9deYMOHt7t1H7txZ7cmU\nMurVq6bb8vKQn4/8fGRlCSx5IsUgx84DiouLb7jhBpmqAxCJRK644oopU6YEmZXnePi1XuHi\nxDBcIrOdI5N6fBzhnG0uz/8pzIQpoBgBtUNmawlePc7R6lvrhHaXbDo3jyov/jZswPjx+O//\nRmVl+oQJmDsX06ejqEiwO00nPYW45HOT6S1PZlPz6bMlUCyNLlut80d4a9NuKAc4q+65prF1\nZsMgK4NpvaoKO3bgq6/iSm7zZpSUwPSI+pEbN7JVGjZEfj7atYurN+M/+tUsIpwkjbArKCj4\n/PPPq6qq0uRfhdasWVNQUBBkVqFF5yNS6EiBW1ey1SgvJc3rkuYjvHeiljWWbp9ZGwllKNMc\n31lhAUsUY8W/Va9eBMDGjdG77sIXX2DLFlxxxbn77681cSJijzKWpWQc5N0gc5cthzHK7UJT\nWLbqVUtZQ8LEZEEUFR2cAv6If8qGv7CTmuSSgLbYswebN2PTpriM27IFscdM8qSloV07dO4c\n13Ax+y0vDy1bBpsxQbgjaYTdiBEjHn744euvv37y5MldunRh3v3mm2/Gjx+/bNmySZMmJSS9\ncKL48OXVg6YSEh5hCpvFh1nqCbWjLIJsCU9hHFo6XozuEZZnlJMbJ0mWA4DVq/H88/jLX04C\nDV56CS+9tBu4NhpdoFOdt1r5jqjVG49CpSlGSebmQqREFfBjbllY53TYsqz4nioakn1/UCfD\nR/N18dGnyCGXdMeOxTXcV1/h66/x9dc4dkxcMhJBfj4KCtClCwoKcOmluOQS1K8fbLoE4QNJ\ns8cuGo3efvvtM2fOBJCbm5ubm9u8efNIJHL06NG9e/fu2LEDwG9/+9tZs2bZWpLTIYF3xfr3\n5Vi4DcjWlMkfFC4+asZkitnqOCMyjGQ0uyCcaxVL0lAKGv3kI5HI9u3Rjh2XAVfHjtx2G559\n1uL+OOHyq34ClrmpXTp9dBpypnLsXqU+/fOxG1ZniZywS0UFtm7FV19h06a4ktuzR1o4Kwtd\nu6KgAAUF6NoVl1yCxo0DzJVILcK8xy7JPlaWL18+bdq02HPsqqqqAKSlpbVu3XrAgAH33nvv\n1Vdf7UejyfW4E03UK6F2VZFioRByYcS/KzziwBeR5SDsGq9B1erNXF6mRJm3+NemtmoBdzZr\nNv28r3DgjTcyfvUriw4yXdNXwDIVG/vT8WKoZqr678oStltS1opPtp8sArSvZzcXv2bMpGbv\n3riGi/1/61bz9rhqNG0aF3BduqBLF3TtihYtgs2VSGnCLOySZik2xpVXXnnllVcCOHfu3KFD\nh6LRaOvWremXJxSoJwaz8aO2TxgfS7YOK1u5U2QiXJNVr+LJykSjUXVnZW8p3DtUVz/mJpi1\nTqMwOIWkyCcaPQdg3z78x39g/nwAGTffjJtv/hC4LxrdpdMLmaBRu3pMXUZueiICosrbdyzN\nPNlyJ3M6+Gjmt2St2OqgS2Ek+8civOpSQ355xalT2LwZGzfGZdzGjThyRFwyPR2dOqFrV3Tr\nFpdxeXnB5koQoSHJhJ1BrVq1MjIyEp1FEiOc0VFdmWku12q6R1GNhVqdBVDIxY2m06aoy8eX\nSR8+JqMkItWfZsc0zfzZpg3mzUMk8mvgeSAD+GnD8vYhHQAAIABJREFUhrueew4PPBB/boJQ\nW+jYOUKBLhwBWEkxSESVAgcyhTeDZc3JfDjNyJp4q7TsajgHZ1wRJPzLwfv2xWXchg3YtAkl\nJaiqEpds2xZduqB7d3TrhoICXHIJUmhBhSBcEaJ/0qHls88+u6ao6Gh5efIuxTZv3hzAEdO3\nXeZI8/Mbu44cOWK8ZS5jPsgfYYoZoZgjTBNMQHXyTP7CYuoOqqvzDWlWkZUxvyUsJjwp3+44\nmtX+n2dwFxAB0LuwavrzP/boWsUX1sxQP2FoDLUwDU+aVpRx06h+cJenmy/j1SnjqzsOIhsB\n5iJ0P9SaVFSgeFvaps1pG79O27i59oav0r47Ipbm9etFCy45173gbLeCqq6XVnXvUtX8Ipq5\niERSUVFxUWbm4lAuxZKws+azzz77uqjonkSnQdQcVmDgvXhlKzoDqI2zf8azT+CJ+pA8pIEg\nkoTv0GIDemxE99h/xbi0EuJfQs3Bnm7Y1A2bemBDN2y6GN+kQeLdEUSCeAXoGkphl6xLsQSR\nwgzEivUonIi/PoUHK1Dnv/Ffc/GLqfjdECxJdGoEocs51NqOjhvQYwN6bEK3jei+F22FJevi\nTAE2d8fGbtjUHRu7Y2NzBOQaEkTqQcJOi3eAO598UvFs5JDz0EMPTZ482UH5hx56KHZk8uTJ\n5tdMTOYtcxzZQQNzNMs81QGNJHWCqOMoihmFFa0YaZhHUhYHoiGtB1Q89NCt+Mfm3EVrSrO/\nRYdrsGhUz83Nvrzyhclufw3ZGG1zB805Q28MzWXsXmOQXAm2GrXVFtNNB0H4aF4FdJYDRFeO\nf61Y8mNF+lflrTbsz9hQ1nrj/tZflbf6sUJsyLVudLJ3m/1dMw91zz7UNfPQpa0P14rE1o4y\ngUzgWs86QBD+UFVV9c4jj3RNdBpCaCnWmhR73IniwRA6tyDIYsZeqG+zYI4LH7ni+HZF9SNX\nFOXN7/J3Bjjbcc83p98F5haTc+cwdSoeftj48cpDwJ+j0VnC/PnMhWUg2lCv8xgXvi33j9Jw\n8wAXZ614Hl//IvFjuDwJ64z9+7FhAzZuxMMPv9mp0y+/+QbnzgmKpaWhUyd0747u3VFYiO7d\nQXe+EckOPe6ECAWMkJLN8catkeZimk/N0K8iLKMvIvmHWSjumTWXUT9CRXFbpez5L3wCOjei\n8rXAdcrQdvfdhxEjcP/9mDcPQCtg1rXXAmjP569oSHj2jft2FXWFIk9ntDWxvBVXDSNDhTcv\nK157gn5A9ZcNx205Hjpbdauq8M032LABGzZg/Xps3IgDB4w3f7lt24WSTZqgWzf06oUuXVBY\niIIC1KvnIEGCIJxAwq5moRBSfnskEKkxRRXZEbXdaJkPqk//tkZA9tQSzQepOLNVYsHbtsU7\n72DuXDzwAMrKsGgRGjT4NhJ5EHg2Gq0U5m+ZkmycmTGRPQhGWNcNvNbkC/BvybqpfvSJZVjN\nd83FZMmoC3vu3tlCXffcOWzciM8/j4u5TZtw8qQwDeTloVu3uBvXrRvat4f28BME4TEk7GoQ\nsolf/ZQ1fStFEceBK6Ne3rK1ICWUJryskYUyD5RCWxhvySIrBKvmDP2LX+Caa/Dgg5g2LTbF\nPgX8+osvcNll1kuuOm0Jx1wmkngL0IHCsFSNfHmF7DP/aUtjKdA0nm313ZMvUS5VnWIkS0qw\ndCmWLsXHH4sfCJyejoICdOuGHj3QvTt69kSzZs6yIAjCe0jYJRnutwdZemBMAfW7/J9qxWA+\nonZfeD3Biy1GS+ksJsqmQ50lPFgNi9CkFEoH3gkT6iSzIDYCvvwybr0VRUWbgQKgx+WX4/e/\nx4QJaNRIGl/RtPmgjocnjMMId1sul/Cg7KozV2EO2vL5zL2wzLMmsH9/XMwtWYK9e9l3mzVD\n164oLESPHigsxCWXoG5dcRz3H1AEQbiEhF2Nw5gI9VVIDGYzExMNnB5iliAVARViS22ouF9B\nsxVZXx/wfXe2jAuR2RmLeeZM9L//G08+idOn8cILePttTJqEoUPRurVUkDH2kqYw4hHaVPy1\nYXkK9AdZaCHrZK6/IOsAN2Z2wolGoydOYPlyLFmCJUuweTNbIDMTV1+NwYMxaBA6dEhEigRB\nOIKEXZKhWM6zFUSxbqU/GfNyUOafKRwyw5dSNGe5emgYTjrroYy3BG5A+GlY0xNiWmdaQXVB\nLAwljCwbvcceiwA/ueqqbR9/jD17cMstANCpU7SoCJHIHUBndRd4Ia6ZlfpEmAswelRfiuln\nwgw+c64DkFN8wpa+ryc4i3n2LD7/HIsX44knVqanX1FZWe3dxo1x1VVxPVdQ4GSfnOW1QRCE\n35CwS2Icf1AqpiJLU4rfFCX71NY06owXik9/XvkpFuxkaRiSThFB3RAvTWQt8oJDNBIwJ8AL\nINmQVj9esmwZXnsNd9xxGGgJYNs2bNsGYAaAVq1w+eUoKkJREYB6wGlZGsLE+N4JJa/QP5ON\nc9RkGNu6gIXBZRFk50snuPBdhY+o/prkHk/cvpISLF6MxYvx8cfGc3Piqq5OHfTrhyFDMHgw\n+vRBbdGc4FKZkaQjiCAhYZdSWM4B/Iqe0N9SCxGhUSesZcyvzEwsW72VZcus7gnnUXNbmmYJ\nb1kZ8c1Kl3H4mAGUjQnTESOaMEmZjJP5jnyxO+7IBfoA/YErLrrop0ePAsDhw3jvPbz3Xqzg\nMWDdX/6CK65A//5o3braYAqD6+hmWbZRk4fKRHM2zcuUtKU012nOpfKw5XcaMGpJIej5ijrf\nGY4cwdKlWLwY06fvBvKqR0CXLhgyBEOGYNAgNGxokSopM4JIIkjYpTiKVUX1PC0syS91MeKA\nn9ohmXotJZ0iMSYNXqHqrI7xB2VjIoupLq/QMcIRM1tfkOsY4UHTWfgEwOHD0bS0LlOnfr1q\nFT77DN9+GytVF+j/zDN45hkA6NgRwN9eeQVA16qqTWlpgnz4/sraFYpU8/jw16HCbOOFr05F\ny5KyfwvmkrIrVtaE8JuGPp6LzrNnsWYNFi7EokVYtw5V8Z9Xjau67GwMGYKZM2+NRpds2rRf\nM6ZLu44giIAJ3ZbeEJLUvzyhbyQIl7p41DJRMXFarnYJ3+VX02SiTR1cWNecPN9xporMB7Jr\n1SjksqZVI5QjsnwikQiQCRQB/fv1+/O6dWC2VQEAvgfW3377lfn5aNcO+fnIz0deXm2gSp0V\nf3b4ZIzCHn7U2PLkhEnyBcyRHfyTgaSz6svDK820cycWLsTChfj4Y3z/PfPmj8OHN7zmGgwd\nioICLxs1SC7ll1zZEqGFfnmCSBhqIaUuI1RUsviWK4mWTctEodDeMx9kDDCZxBQeiZlDwi4z\n+sncLlNLYYvy3Rc6UjJz0bHliWrjXA7MjUbfAnDqFBo0GDh58oqHHnqvZcvrDh+OFWgKXPn6\n69Wqp6efrazcAey66y7k5qJdO4waNXD37hVt2sD4zWThkJrTE7pZMjko64iigLC60IiVGYG2\nNKICmapzFk2HkyexfDkWLsSCBSgpYZJBYSFiYm7w4BYffXTmww+tL7bqEWx8XbH7xYZEFUH4\nCgm7VIY3ohRrTEL1AG5uFvoxLqdDRg1AY5oXak2zAmMSNqQYPyCy1TRhSnx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LikyEp0D4xUNYjBlbYUPCd42KhvqUd1ELdQ4MHuokvyUX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\n8cUXlZWVw4YNixq4x/U56ptuuumUU07p0qVLR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      "text/plain": [
       "Plot with title “Dispersão de genes nas condições: basal lactante e basal grávida”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exemplo: contraste entre basal lactante e basal grávida\n",
    "matriz_de_contraste <- makeContrasts(basal.lactante-basal.gravida, levels=desenho_do_experimento)\n",
    "# Calcular a dispersao\n",
    "dgList_filtrada <- estimateDisp(dgList_filtrada, desenho_do_experimento, robust=TRUE)\n",
    "plotBCV(dgList_filtrada)\n",
    "title(\"Dispersão de genes nas condições: basal lactante e basal grávida\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "sporting-centre",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl>\n",
       "\t<dt>$table</dt>\n",
       "\t\t<dd><table>\n",
       "<thead><tr><th></th><th scope=col>ENTREZID</th><th scope=col>SYMBOL</th><th scope=col>GENENAME</th><th scope=col>logFC</th><th scope=col>logCPM</th><th scope=col>F</th><th scope=col>PValue</th><th scope=col>FDR</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>13645</th><td>13645                                                                          </td><td>Egf                                                                            </td><td>epidermal growth factor                                                        </td><td> 5.361744                                                                      </td><td>3.829733                                                                       </td><td>743.4969                                                                       </td><td>5.259573e-09                                                                   </td><td>7.728942e-05                                                                   </td></tr>\n",
       "\t<tr><th scope=row>13358</th><td>13358                                                                          </td><td>Slc25a1                                                                        </td><td>solute carrier family 25 (mitochondrial carrier, citrate transporter), member 1</td><td> 4.138133                                                                      </td><td>7.502629                                                                       </td><td>473.0680                                                                       </td><td>2.993017e-08                                                                   </td><td>1.836789e-04                                                                   </td></tr>\n",
       "\t<tr><th scope=row>22051</th><td>22051                                                                          </td><td>Trip6                                                                          </td><td>thyroid hormone receptor interactor 6                                          </td><td>-2.335107                                                                      </td><td>5.845660                                                                       </td><td>446.0500                                                                       </td><td>3.749825e-08                                                                   </td><td>1.836789e-04                                                                   </td></tr>\n",
       "\t<tr><th scope=row>11941</th><td>11941                                                                          </td><td>Atp2b2                                                                         </td><td>ATPase, Ca++ transporting, plasma membrane 2                                   </td><td> 7.372591                                                                      </td><td>6.623890                                                                       </td><td>380.6524                                                                       </td><td>7.157124e-08                                                                   </td><td>2.111141e-04                                                                   </td></tr>\n",
       "\t<tr><th scope=row>72027</th><td>72027                                                                          </td><td>Slc39a4                                                                        </td><td>solute carrier family 39 (zinc transporter), member 4                          </td><td>-4.334606                                                                      </td><td>4.264431                                                                       </td><td>362.5695                                                                       </td><td>8.283912e-08                                                                   </td><td>2.111141e-04                                                                   </td></tr>\n",
       "\t<tr><th scope=row>74051</th><td>74051                                                                          </td><td>Steap2                                                                         </td><td>six transmembrane epithelial antigen of prostate 2                             </td><td> 3.237002                                                                      </td><td>4.940290                                                                       </td><td>358.8140                                                                       </td><td>8.619834e-08                                                                   </td><td>2.111141e-04                                                                   </td></tr>\n",
       "\t<tr><th scope=row>11416</th><td>11416                                                                          </td><td>Slc33a1                                                                        </td><td>solute carrier family 33 (acetyl-CoA transporter), member 1                    </td><td> 2.613230                                                                      </td><td>4.715439                                                                       </td><td>309.8140                                                                       </td><td>1.508570e-07                                                                   </td><td>3.061273e-04                                                                   </td></tr>\n",
       "\t<tr><th scope=row>64291</th><td>64291                                                                          </td><td>Osbpl1a                                                                        </td><td>oxysterol binding protein-like 1A                                              </td><td> 2.554938                                                                      </td><td>6.108691                                                                       </td><td>301.8088                                                                       </td><td>1.666566e-07                                                                   </td><td>3.061273e-04                                                                   </td></tr>\n",
       "\t<tr><th scope=row>213006</th><td>213006                                                                         </td><td>Mfsd4a                                                                         </td><td>major facilitator superfamily domain containing 4A                             </td><td> 2.567247                                                                      </td><td>4.402297                                                                       </td><td>286.8873                                                                       </td><td>2.020840e-07                                                                   </td><td>3.285520e-04                                                                   </td></tr>\n",
       "\t<tr><th scope=row>100705</th><td>100705                                                                         </td><td>Acacb                                                                          </td><td>acetyl-Coenzyme A carboxylase beta                                             </td><td> 4.325864                                                                      </td><td>6.597597                                                                       </td><td>276.0288                                                                       </td><td>2.339727e-07                                                                   </td><td>3.285520e-04                                                                   </td></tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</dd>\n",
       "\t<dt>$adjust.method</dt>\n",
       "\t\t<dd>'BH'</dd>\n",
       "\t<dt>$comparison</dt>\n",
       "\t\t<dd>'-1*basal.gravida 1*basal.lactante'</dd>\n",
       "\t<dt>$test</dt>\n",
       "\t\t<dd>'glm'</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description}\n",
       "\\item[\\$table] \\begin{tabular}{r|llllllll}\n",
       "  & ENTREZID & SYMBOL & GENENAME & logFC & logCPM & F & PValue & FDR\\\\\n",
       "\\hline\n",
       "\t13645 & 13645                                                                           & Egf                                                                             & epidermal growth factor                                                         &  5.361744                                                                       & 3.829733                                                                        & 743.4969                                                                        & 5.259573e-09                                                                    & 7.728942e-05                                                                   \\\\\n",
       "\t13358 & 13358                                                                           & Slc25a1                                                                         & solute carrier family 25 (mitochondrial carrier, citrate transporter), member 1 &  4.138133                                                                       & 7.502629                                                                        & 473.0680                                                                        & 2.993017e-08                                                                    & 1.836789e-04                                                                   \\\\\n",
       "\t22051 & 22051                                                                           & Trip6                                                                           & thyroid hormone receptor interactor 6                                           & -2.335107                                                                       & 5.845660                                                                        & 446.0500                                                                        & 3.749825e-08                                                                    & 1.836789e-04                                                                   \\\\\n",
       "\t11941 & 11941                                                                           & Atp2b2                                                                          & ATPase, Ca++ transporting, plasma membrane 2                                    &  7.372591                                                                       & 6.623890                                                                        & 380.6524                                                                        & 7.157124e-08                                                                    & 2.111141e-04                                                                   \\\\\n",
       "\t72027 & 72027                                                                           & Slc39a4                                                                         & solute carrier family 39 (zinc transporter), member 4                           & -4.334606                                                                       & 4.264431                                                                        & 362.5695                                                                        & 8.283912e-08                                                                    & 2.111141e-04                                                                   \\\\\n",
       "\t74051 & 74051                                                                           & Steap2                                                                          & six transmembrane epithelial antigen of prostate 2                              &  3.237002                                                                       & 4.940290                                                                        & 358.8140                                                                        & 8.619834e-08                                                                    & 2.111141e-04                                                                   \\\\\n",
       "\t11416 & 11416                                                                           & Slc33a1                                                                         & solute carrier family 33 (acetyl-CoA transporter), member 1                     &  2.613230                                                                       & 4.715439                                                                        & 309.8140                                                                        & 1.508570e-07                                                                    & 3.061273e-04                                                                   \\\\\n",
       "\t64291 & 64291                                                                           & Osbpl1a                                                                         & oxysterol binding protein-like 1A                                               &  2.554938                                                                       & 6.108691                                                                        & 301.8088                                                                        & 1.666566e-07                                                                    & 3.061273e-04                                                                   \\\\\n",
       "\t213006 & 213006                                                                          & Mfsd4a                                                                          & major facilitator superfamily domain containing 4A                              &  2.567247                                                                       & 4.402297                                                                        & 286.8873                                                                        & 2.020840e-07                                                                    & 3.285520e-04                                                                   \\\\\n",
       "\t100705 & 100705                                                                          & Acacb                                                                           & acetyl-Coenzyme A carboxylase beta                                              &  4.325864                                                                       & 6.597597                                                                        & 276.0288                                                                        & 2.339727e-07                                                                    & 3.285520e-04                                                                   \\\\\n",
       "\\end{tabular}\n",
       "\n",
       "\\item[\\$adjust.method] 'BH'\n",
       "\\item[\\$comparison] '-1*basal.gravida 1*basal.lactante'\n",
       "\\item[\\$test] 'glm'\n",
       "\\end{description}\n"
      ],
      "text/markdown": [
       "$table\n",
       ":   \n",
       "| <!--/--> | ENTREZID | SYMBOL | GENENAME | logFC | logCPM | F | PValue | FDR |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| 13645 | 13645                                                                           | Egf                                                                             | epidermal growth factor                                                         |  5.361744                                                                       | 3.829733                                                                        | 743.4969                                                                        | 5.259573e-09                                                                    | 7.728942e-05                                                                    |\n",
       "| 13358 | 13358                                                                           | Slc25a1                                                                         | solute carrier family 25 (mitochondrial carrier, citrate transporter), member 1 |  4.138133                                                                       | 7.502629                                                                        | 473.0680                                                                        | 2.993017e-08                                                                    | 1.836789e-04                                                                    |\n",
       "| 22051 | 22051                                                                           | Trip6                                                                           | thyroid hormone receptor interactor 6                                           | -2.335107                                                                       | 5.845660                                                                        | 446.0500                                                                        | 3.749825e-08                                                                    | 1.836789e-04                                                                    |\n",
       "| 11941 | 11941                                                                           | Atp2b2                                                                          | ATPase, Ca++ transporting, plasma membrane 2                                    |  7.372591                                                                       | 6.623890                                                                        | 380.6524                                                                        | 7.157124e-08                                                                    | 2.111141e-04                                                                    |\n",
       "| 72027 | 72027                                                                           | Slc39a4                                                                         | solute carrier family 39 (zinc transporter), member 4                           | -4.334606                                                                       | 4.264431                                                                        | 362.5695                                                                        | 8.283912e-08                                                                    | 2.111141e-04                                                                    |\n",
       "| 74051 | 74051                                                                           | Steap2                                                                          | six transmembrane epithelial antigen of prostate 2                              |  3.237002                                                                       | 4.940290                                                                        | 358.8140                                                                        | 8.619834e-08                                                                    | 2.111141e-04                                                                    |\n",
       "| 11416 | 11416                                                                           | Slc33a1                                                                         | solute carrier family 33 (acetyl-CoA transporter), member 1                     |  2.613230                                                                       | 4.715439                                                                        | 309.8140                                                                        | 1.508570e-07                                                                    | 3.061273e-04                                                                    |\n",
       "| 64291 | 64291                                                                           | Osbpl1a                                                                         | oxysterol binding protein-like 1A                                               |  2.554938                                                                       | 6.108691                                                                        | 301.8088                                                                        | 1.666566e-07                                                                    | 3.061273e-04                                                                    |\n",
       "| 213006 | 213006                                                                          | Mfsd4a                                                                          | major facilitator superfamily domain containing 4A                              |  2.567247                                                                       | 4.402297                                                                        | 286.8873                                                                        | 2.020840e-07                                                                    | 3.285520e-04                                                                    |\n",
       "| 100705 | 100705                                                                          | Acacb                                                                           | acetyl-Coenzyme A carboxylase beta                                              |  4.325864                                                                       | 6.597597                                                                        | 276.0288                                                                        | 2.339727e-07                                                                    | 3.285520e-04                                                                    |\n",
       "\n",
       "\n",
       "$adjust.method\n",
       ":   'BH'\n",
       "$comparison\n",
       ":   '-1*basal.gravida 1*basal.lactante'\n",
       "$test\n",
       ":   'glm'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "Coefficient:  -1*basal.gravida 1*basal.lactante \n",
       "       ENTREZID  SYMBOL\n",
       "13645     13645     Egf\n",
       "13358     13358 Slc25a1\n",
       "22051     22051   Trip6\n",
       "11941     11941  Atp2b2\n",
       "72027     72027 Slc39a4\n",
       "74051     74051  Steap2\n",
       "11416     11416 Slc33a1\n",
       "64291     64291 Osbpl1a\n",
       "213006   213006  Mfsd4a\n",
       "100705   100705   Acacb\n",
       "                                                                              GENENAME\n",
       "13645                                                          epidermal growth factor\n",
       "13358  solute carrier family 25 (mitochondrial carrier, citrate transporter), member 1\n",
       "22051                                            thyroid hormone receptor interactor 6\n",
       "11941                                     ATPase, Ca++ transporting, plasma membrane 2\n",
       "72027                            solute carrier family 39 (zinc transporter), member 4\n",
       "74051                               six transmembrane epithelial antigen of prostate 2\n",
       "11416                      solute carrier family 33 (acetyl-CoA transporter), member 1\n",
       "64291                                                oxysterol binding protein-like 1A\n",
       "213006                              major facilitator superfamily domain containing 4A\n",
       "100705                                              acetyl-Coenzyme A carboxylase beta\n",
       "           logFC   logCPM        F       PValue          FDR\n",
       "13645   5.361744 3.829733 743.4969 5.259573e-09 7.728942e-05\n",
       "13358   4.138133 7.502629 473.0680 2.993017e-08 1.836789e-04\n",
       "22051  -2.335107 5.845660 446.0500 3.749825e-08 1.836789e-04\n",
       "11941   7.372591 6.623890 380.6524 7.157124e-08 2.111141e-04\n",
       "72027  -4.334606 4.264431 362.5695 8.283912e-08 2.111141e-04\n",
       "74051   3.237002 4.940290 358.8140 8.619834e-08 2.111141e-04\n",
       "11416   2.613230 4.715439 309.8140 1.508570e-07 3.061273e-04\n",
       "64291   2.554938 6.108691 301.8088 1.666566e-07 3.061273e-04\n",
       "213006  2.567247 4.402297 286.8873 2.020840e-07 3.285520e-04\n",
       "100705  4.325864 6.597597 276.0288 2.339727e-07 3.285520e-04"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "       -1*basal.gravida 1*basal.lactante\n",
       "Down                                1341\n",
       "NotSig                             12166\n",
       "Up                                  1188"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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qVKlRo4cGCkcwEAAADgU0EK7AghN9xwQ8mSJSOdCwAAAAA/CkxVrKRbt24XL16M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GdjPYGd4pYhjyAcEur1kOX7Wcu9gwPsPH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\nAAAAAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “Log-fold change e abundância média (basal lactantes e grávidas)”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calcular a distribuicao negativa binomial com método quasi-likelihood \n",
    "fit <- glmQLFit(dgList_filtrada, desenho_do_experimento, robust=TRUE)\n",
    "res <- glmQLFTest(fit, contrast = matriz_de_contraste)\n",
    "topTags(res) # mostrar mais representativos\n",
    "#  genes identificados com false dicovery range (FDR) de até 5% \n",
    "genes_fdr_5 <- decideTestsDGE(res)\n",
    "summary(genes_fdr_5)\n",
    "plotMD(res, status=genes_fdr_5, main=\"Log-fold change e abundância média (basal lactantes e grávidas)\")"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}