{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DESeq2 Analysis of 2015 Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will reproduce the dendrograms from the 2015 data shown in the welcome slides"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load packages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load requisite R packages. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: S4Vectors\n",
      "Loading required package: stats4\n",
      "Loading required package: BiocGenerics\n",
      "Loading required package: parallel\n",
      "\n",
      "Attaching package: ‘BiocGenerics’\n",
      "\n",
      "The following objects are masked from ‘package:parallel’:\n",
      "\n",
      "    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n",
      "    clusterExport, clusterMap, parApply, parCapply, parLapply,\n",
      "    parLapplyLB, parRapply, parSapply, parSapplyLB\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    IQR, mad, xtabs\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    anyDuplicated, append, as.data.frame, cbind, colnames, do.call,\n",
      "    duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect,\n",
      "    is.unsorted, lapply, lengths, Map, mapply, match, mget, order,\n",
      "    paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,\n",
      "    Reduce, rownames, sapply, setdiff, sort, table, tapply, union,\n",
      "    unique, unsplit, which, which.max, which.min\n",
      "\n",
      "\n",
      "Attaching package: ‘S4Vectors’\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    colMeans, colSums, expand.grid, rowMeans, rowSums\n",
      "\n",
      "Loading required package: IRanges\n",
      "Loading required package: GenomicRanges\n",
      "Loading required package: GenomeInfoDb\n",
      "Loading required package: SummarizedExperiment\n",
      "Loading required package: Biobase\n",
      "Welcome to Bioconductor\n",
      "\n",
      "    Vignettes contain introductory material; view with\n",
      "    'browseVignettes()'. To cite Bioconductor, see\n",
      "    'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
      "\n",
      "\n",
      "---------------------\n",
      "Welcome to dendextend version 1.5.2\n",
      "Type citation('dendextend') for how to cite the package.\n",
      "\n",
      "Type browseVignettes(package = 'dendextend') for the package vignette.\n",
      "The github page is: https://github.com/talgalili/dendextend/\n",
      "\n",
      "Suggestions and bug-reports can be submitted at: https://github.com/talgalili/dendextend/issues\n",
      "Or contact: <tal.galili@gmail.com>\n",
      "\n",
      "\tTo suppress this message use:  suppressPackageStartupMessages(library(dendextend))\n",
      "---------------------\n",
      "\n",
      "\n",
      "Attaching package: ‘dendextend’\n",
      "\n",
      "The following object is masked from ‘package:stats’:\n",
      "\n",
      "    cutree\n",
      "\n",
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "The following object is masked from ‘package:Biobase’:\n",
      "\n",
      "    combine\n",
      "\n",
      "The following objects are masked from ‘package:GenomicRanges’:\n",
      "\n",
      "    intersect, setdiff, union\n",
      "\n",
      "The following object is masked from ‘package:GenomeInfoDb’:\n",
      "\n",
      "    intersect\n",
      "\n",
      "The following objects are masked from ‘package:IRanges’:\n",
      "\n",
      "    collapse, desc, intersect, regroup, setdiff, slice, union\n",
      "\n",
      "The following objects are masked from ‘package:S4Vectors’:\n",
      "\n",
      "    first, intersect, rename, setdiff, setequal, union\n",
      "\n",
      "The following objects are masked from ‘package:BiocGenerics’:\n",
      "\n",
      "    combine, intersect, setdiff, union\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(DESeq2)\n",
    "library(tools)\n",
    "library(dendextend)\n",
    "library(dplyr)\n",
    "library(RColorBrewer)\n",
    "options(width=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare for Data Import"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First set the directory under which the HTSeq count files are stored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "datadir<-\"~/work/HTS_SummerCourse_2017/Materials/Statistics/08032017/Data/2015\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, put the filenames into a data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "phdata<-data.frame(fname=list.files(path=datadir,pattern=\"*.csv\"),stringsAsFactors=FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is always a good idea to check the dimension of the file you have read in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>30</li>\n",
       "\t<li>1</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 30\n",
       "\\item 1\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 30\n",
       "2. 1\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 30  1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dim(phdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract the label from the filename. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "phdata <- phdata %>% transmute(sample=substr(fname,1,4),fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add some design info to the data frame. We will add the treatment factor (the first character of the sample string: 7 or 8), the replicate id (the second character in the \n",
    "\n",
    "Note that tools::md5sum will add the MD5 signature for each of the HTSeq count files. You should keep track of these for the purpose of conducting reproducible analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>sample</th><th scope=col>fname</th><th scope=col>trt</th><th scope=col>repl</th><th scope=col>team</th><th scope=col>md5</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>7A_E    </td><td>7A_E.csv</td><td>7       </td><td>A       </td><td>E       </td><td>NA      </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>7A_G    </td><td>7A_G.csv</td><td>7       </td><td>A       </td><td>G       </td><td>NA      </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>7A_K    </td><td>7A_K.csv</td><td>7       </td><td>A       </td><td>K       </td><td>NA      </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>7A_N    </td><td>7A_N.csv</td><td>7       </td><td>A       </td><td>N       </td><td>NA      </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>7A_P    </td><td>7A_P.csv</td><td>7       </td><td>A       </td><td>P       </td><td>NA      </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>7B_E    </td><td>7B_E.csv</td><td>7       </td><td>B       </td><td>E       </td><td>NA      </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & sample & fname & trt & repl & team & md5\\\\\n",
       "\\hline\n",
       "\t1 & 7A\\_E     & 7A\\_E.csv & 7          & A          & E          & NA        \\\\\n",
       "\t2 & 7A\\_G     & 7A\\_G.csv & 7          & A          & G          & NA        \\\\\n",
       "\t3 & 7A\\_K     & 7A\\_K.csv & 7          & A          & K          & NA        \\\\\n",
       "\t4 & 7A\\_N     & 7A\\_N.csv & 7          & A          & N          & NA        \\\\\n",
       "\t5 & 7A\\_P     & 7A\\_P.csv & 7          & A          & P          & NA        \\\\\n",
       "\t6 & 7B\\_E     & 7B\\_E.csv & 7          & B          & E          & NA        \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "  sample    fname trt repl team  md5\n",
       "1   7A_E 7A_E.csv   7    A    E <NA>\n",
       "2   7A_G 7A_G.csv   7    A    G <NA>\n",
       "3   7A_K 7A_K.csv   7    A    K <NA>\n",
       "4   7A_N 7A_N.csv   7    A    N <NA>\n",
       "5   7A_P 7A_P.csv   7    A    P <NA>\n",
       "6   7B_E 7B_E.csv   7    B    E <NA>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phdata <- phdata %>% \n",
    "                mutate(trt=as.factor(substr(sample,1,1)),\n",
    "                       repl=substr(sample,2,2),\n",
    "                       team=substr(sample,4,4),\n",
    "                       md5=tools::md5sum(file.path(datadir,fname)))\n",
    "head(phdata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we import the counts. Note that the first argument is the sample table while the second is the directory storing the count files. The last argument specifies the design. More on this later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dds<-DESeqDataSetFromHTSeqCount(sampleTable=phdata,directory=datadir,design=~ trt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimate library specific size factors and gene specific dispersion paramaters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimate Size factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dds<-estimateSizeFactors(dds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estimate dispersion parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "gene-wise dispersion estimates\n",
      "mean-dispersion relationship\n",
      "final dispersion estimates\n"
     ]
    }
   ],
   "source": [
    "dds<-estimateDispersions(dds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspect object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "class: DESeqDataSet \n",
       "dim: 4444 30 \n",
       "metadata(1): version\n",
       "assays(2): counts mu\n",
       "rownames(4444): gene0 gene1 ... gene998 gene999\n",
       "rowData names(9): baseMean baseVar ... dispOutlier dispMAP\n",
       "colnames(30): 7A_E 7A_G ... 8C_N 8C_P\n",
       "colData names(5): trt repl team md5 sizeFactor"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create dendrograms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this tutorial, we will use t he expression data based on rlog transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rlogexp<-assay(rlog(dds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create the dendrogram object using hierarchical clustering using complete linkage (method=\"complete\"). \n",
    "\n",
    "Note that you have you transpose the expression matrix as R assumes that the samples are along the rows and that the genes are across the columns. This is accomplished using the t() function. Next, you create the distance matrix for this expression matrix using the dist() function. Afterwards, you conduct hierachical clustering on the basis of complete linkage using this distance matrix usin the dist() function. Finally, the results from the clustering is converted into a dendrogram class using the as.dendrogram() function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dend <- rlogexp %>%\n",
    "                t %>% \n",
    "                dist %>% \n",
    "                hclust(method=\"complete\") %>% \n",
    "                as.dendrogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look as a plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8PJK9KKOQfq2wOL5lsqEYl84fPh5BVpxvpxD7Z0WyoRiXzh8+HkFWnl3LE70uDi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\nP4uAgx8zmFeSAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dend %>% plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we will add some eye candy to this dendrogram. We will add the treatment label as symbols and the group/team labels as colors. Note that one can extract the labels from the dendrogram using the labels() function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'8A_N'</li>\n",
       "\t<li>'8A_K'</li>\n",
       "\t<li>'8B_K'</li>\n",
       "\t<li>'8C_K'</li>\n",
       "\t<li>'8C_E'</li>\n",
       "\t<li>'8A_E'</li>\n",
       "\t<li>'8B_E'</li>\n",
       "\t<li>'8B_N'</li>\n",
       "\t<li>'8C_N'</li>\n",
       "\t<li>'8B_P'</li>\n",
       "\t<li>'8A_P'</li>\n",
       "\t<li>'8C_P'</li>\n",
       "\t<li>'8A_G'</li>\n",
       "\t<li>'7B_G'</li>\n",
       "\t<li>'8B_G'</li>\n",
       "\t<li>'7A_G'</li>\n",
       "\t<li>'7C_G'</li>\n",
       "\t<li>'8C_G'</li>\n",
       "\t<li>'7B_N'</li>\n",
       "\t<li>'7A_N'</li>\n",
       "\t<li>'7C_N'</li>\n",
       "\t<li>'7A_K'</li>\n",
       "\t<li>'7B_K'</li>\n",
       "\t<li>'7C_K'</li>\n",
       "\t<li>'7B_E'</li>\n",
       "\t<li>'7A_P'</li>\n",
       "\t<li>'7B_P'</li>\n",
       "\t<li>'7C_P'</li>\n",
       "\t<li>'7A_E'</li>\n",
       "\t<li>'7C_E'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '8A\\_N'\n",
       "\\item '8A\\_K'\n",
       "\\item '8B\\_K'\n",
       "\\item '8C\\_K'\n",
       "\\item '8C\\_E'\n",
       "\\item '8A\\_E'\n",
       "\\item '8B\\_E'\n",
       "\\item '8B\\_N'\n",
       "\\item '8C\\_N'\n",
       "\\item '8B\\_P'\n",
       "\\item '8A\\_P'\n",
       "\\item '8C\\_P'\n",
       "\\item '8A\\_G'\n",
       "\\item '7B\\_G'\n",
       "\\item '8B\\_G'\n",
       "\\item '7A\\_G'\n",
       "\\item '7C\\_G'\n",
       "\\item '8C\\_G'\n",
       "\\item '7B\\_N'\n",
       "\\item '7A\\_N'\n",
       "\\item '7C\\_N'\n",
       "\\item '7A\\_K'\n",
       "\\item '7B\\_K'\n",
       "\\item '7C\\_K'\n",
       "\\item '7B\\_E'\n",
       "\\item '7A\\_P'\n",
       "\\item '7B\\_P'\n",
       "\\item '7C\\_P'\n",
       "\\item '7A\\_E'\n",
       "\\item '7C\\_E'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '8A_N'\n",
       "2. '8A_K'\n",
       "3. '8B_K'\n",
       "4. '8C_K'\n",
       "5. '8C_E'\n",
       "6. '8A_E'\n",
       "7. '8B_E'\n",
       "8. '8B_N'\n",
       "9. '8C_N'\n",
       "10. '8B_P'\n",
       "11. '8A_P'\n",
       "12. '8C_P'\n",
       "13. '8A_G'\n",
       "14. '7B_G'\n",
       "15. '8B_G'\n",
       "16. '7A_G'\n",
       "17. '7C_G'\n",
       "18. '8C_G'\n",
       "19. '7B_N'\n",
       "20. '7A_N'\n",
       "21. '7C_N'\n",
       "22. '7A_K'\n",
       "23. '7B_K'\n",
       "24. '7C_K'\n",
       "25. '7B_E'\n",
       "26. '7A_P'\n",
       "27. '7B_P'\n",
       "28. '7C_P'\n",
       "29. '7A_E'\n",
       "30. '7C_E'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"8A_N\" \"8A_K\" \"8B_K\" \"8C_K\" \"8C_E\" \"8A_E\" \"8B_E\" \"8B_N\" \"8C_N\" \"8B_P\" \"8A_P\" \"8C_P\" \"8A_G\"\n",
       "[14] \"7B_G\" \"8B_G\" \"7A_G\" \"7C_G\" \"8C_G\" \"7B_N\" \"7A_N\" \"7C_N\" \"7A_K\" \"7B_K\" \"7C_K\" \"7B_E\" \"7A_P\"\n",
       "[27] \"7B_P\" \"7C_P\" \"7A_E\" \"7C_E\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels(dend)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now use this information to create a treatment label vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>19</li>\n",
       "\t<li>19</li>\n",
       "\t<li>19</li>\n",
       "\t<li>19</li>\n",
       "\t<li>19</li>\n",
       "\t<li>19</li>\n",
       "\t<li>19</li>\n",
       "\t<li>19</li>\n",
       "\t<li>19</li>\n",
       "\t<li>19</li>\n",
       "\t<li>19</li>\n",
       "\t<li>19</li>\n",
       "\t<li>19</li>\n",
       "\t<li>2</li>\n",
       "\t<li>19</li>\n",
       "\t<li>2</li>\n",
       "\t<li>2</li>\n",
       "\t<li>19</li>\n",
       "\t<li>2</li>\n",
       "\t<li>2</li>\n",
       "\t<li>2</li>\n",
       "\t<li>2</li>\n",
       "\t<li>2</li>\n",
       "\t<li>2</li>\n",
       "\t<li>2</li>\n",
       "\t<li>2</li>\n",
       "\t<li>2</li>\n",
       "\t<li>2</li>\n",
       "\t<li>2</li>\n",
       "\t<li>2</li>\n",
       "</ol>\n"
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      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 19\n",
       "\\item 19\n",
       "\\item 19\n",
       "\\item 19\n",
       "\\item 19\n",
       "\\item 19\n",
       "\\item 19\n",
       "\\item 19\n",
       "\\item 19\n",
       "\\item 19\n",
       "\\item 19\n",
       "\\item 19\n",
       "\\item 19\n",
       "\\item 2\n",
       "\\item 19\n",
       "\\item 2\n",
       "\\item 2\n",
       "\\item 19\n",
       "\\item 2\n",
       "\\item 2\n",
       "\\item 2\n",
       "\\item 2\n",
       "\\item 2\n",
       "\\item 2\n",
       "\\item 2\n",
       "\\item 2\n",
       "\\item 2\n",
       "\\item 2\n",
       "\\item 2\n",
       "\\item 2\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 19\n",
       "2. 19\n",
       "3. 19\n",
       "4. 19\n",
       "5. 19\n",
       "6. 19\n",
       "7. 19\n",
       "8. 19\n",
       "9. 19\n",
       "10. 19\n",
       "11. 19\n",
       "12. 19\n",
       "13. 19\n",
       "14. 2\n",
       "15. 19\n",
       "16. 2\n",
       "17. 2\n",
       "18. 19\n",
       "19. 2\n",
       "20. 2\n",
       "21. 2\n",
       "22. 2\n",
       "23. 2\n",
       "24. 2\n",
       "25. 2\n",
       "26. 2\n",
       "27. 2\n",
       "28. 2\n",
       "29. 2\n",
       "30. 2\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 19 19 19 19 19 19 19 19 19 19 19 19 19  2 19  2  2 19  2  2  2  2  2  2  2  2  2  2  2  2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trtlab<-c(2,19)[factor(substr(labels(dend),1,1))]\n",
    "trtlab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and then a group label vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "\t<li>'#377EB8'</li>\n",
       "\t<li>'#984EA3'</li>\n",
       "\t<li>'#984EA3'</li>\n",
       "\t<li>'#984EA3'</li>\n",
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       "11. '#FF7F00'\n",
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       "13. '#377EB8'\n",
       "14. '#377EB8'\n",
       "15. '#377EB8'\n",
       "16. '#377EB8'\n",
       "17. '#377EB8'\n",
       "18. '#377EB8'\n",
       "19. '#984EA3'\n",
       "20. '#984EA3'\n",
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       "22. '#4DAF4A'\n",
       "23. '#4DAF4A'\n",
       "24. '#4DAF4A'\n",
       "25. '#E41A1C'\n",
       "26. '#FF7F00'\n",
       "27. '#FF7F00'\n",
       "28. '#FF7F00'\n",
       "29. '#E41A1C'\n",
       "30. '#E41A1C'\n",
       "\n",
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      "text/plain": [
       " [1] \"#984EA3\" \"#4DAF4A\" \"#4DAF4A\" \"#4DAF4A\" \"#E41A1C\" \"#E41A1C\" \"#E41A1C\" \"#984EA3\" \"#984EA3\"\n",
       "[10] \"#FF7F00\" \"#FF7F00\" \"#FF7F00\" \"#377EB8\" \"#377EB8\" \"#377EB8\" \"#377EB8\" \"#377EB8\" \"#377EB8\"\n",
       "[19] \"#984EA3\" \"#984EA3\" \"#984EA3\" \"#4DAF4A\" \"#4DAF4A\" \"#4DAF4A\" \"#E41A1C\" \"#FF7F00\" \"#FF7F00\"\n",
       "[28] \"#FF7F00\" \"#E41A1C\" \"#E41A1C\""
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grplab<-brewer.pal(5,\"Set1\")[factor(substr(labels(dend),4,4))]\n",
    "grplab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First add the treatment label. The symbol use for each leaf is controlled by \"leaves_pch\" (you can use label_cex and leaves_cex to control the relative size of the labels or leaves)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V/oiJQEkYJQ9IWOSEkQKQhFX+iIlASR\nglD0hY5ISRApCEVf6IiUBJGCUPSFjkhJECkIRV/oiJQEkYJQ9IWOSEkQKQhFX+iIlASRmsIf\nXjXTdYN5/4uex0ekVoNITaFDefIVv+MjUqtBpKbQdf1TRtaadz/V5TlLRGo1iNQUfEcZOh+6\n/xSIhEhFzIfuPwUiIVIR86H7T4FIiFTEfOj+UyASIhUxH7r/FIiESEXMh+4/BSIhUhHzoftP\ngUiIVMR86P5TIBIiFTEfuv8UiIRIRcyH7j8FIiFSEfOh+0+BSIhUxHzo/lMgEiIVMR+6/xSI\nhEhFzIfuPwUiIVIR86H7T4FIiFTEfOj+UyASIhUxH7r/FIiESEXMh+4/BSIhUhHzoftPgUiI\nVMR86P5TIBIiFTEfuv8UiIRIRcyH7j8FItmJdP6lqy0ZcbJt5WnPNX1udQktAiK1mpyINGLq\nTEs6p9lWdny56XOrS2gREKnV5ESkZlyYkC8CQ4uASK0GkZpCaBEQqdUgUlMILQIitRpEagqh\nRUCkVoNITSG0CIjUahCpKYQWAZFaDSI1hdAiIFKrQaSmEFoERGo1iNQUQouASK0GkZpCaBEQ\nqdUgUlMILQIitRpEagqhRUCkVoNITSG0CIjUahCpKYQWAZFaDSI1hdAiIFKrQaSmEFoERGo1\niNQUQouASK0GkZpCaBEQqdX4ibRlw8a+RjWIVMB86P5TtLFIa5ft1a2U6pp6wePGOkQqYD50\n/ynaVqS+s5Tqnrlg0YJZ45U6vddQiUgFzIfuP0XbinSlOui+nvJW7/3z1WpDJSIVMB+6/xRt\nK9KcSW/WtnumTzVUIlIB86H7T9G2Io1bMuTByk5DJSIVMB+6/xRtK9LcyYN3pN4ZUwyViFTA\nfOj+U7StSFcPfo20br5aZahEpALmQ/efom1F6luhVPesIxYfOXu8Uqf2GCoRqYD50P2naFuR\nomj9udPGKqVGT1n5qLEOkQqYD91/ijYWqZ/NT+vf2fDiMfNrHKTeatwRIuUrH7r/FG0uUom3\nX9SotPmTF9c4hTtS8fKh+0/RviL96dqzTrq2p+d/dKrtT3zRVMhLuwLmQ/efom1F2vju0tdH\nauln1G5/OUVNet1QiUgFzIfuP0XbivQxtfTBxz6qRn/wrajvKvUxQyUiFTAfuv8UbSvSvvv1\nRlHfAepnpe2+6dMNlYhUwHzo/lO0rUijl/X/u3jgO3KndBkqEamA+dD9p2hbkfac2f/vTeeV\nH8yfYKhEpALmQ/efom1F+pC6prb9YMcxhkpEKmA+dP8p2lakZ7rVhLPKW//ntM7hPzRUIlIB\n86H7T9G2IkXPnr773PLGaWryt02FiFTAfKv7n3PMCWZ22aVBwTFzZEfkis87GwbeqvrQw6Zf\nNEekQuZb3X/nwrPNzJnToGCh6XfiWgAfx9UUQotQNJH8+7tjjMQ4soNITSG0CIjUahCpKYQW\nAZFaDSI1hdAiIFKrQaSmEFoERGo1iNQUWi3Clf9+U4ztPhp/fNlrfuPxHV/z+0OkMojkl+/Y\ndc8YwycmHl/rNx7f8TW/P0Qqg0h++Ub1+Vv40v0hUhlE8ssjEiKVQSS/PCIhUhlE8ssjEiKV\nQSS/PCIhUhlE8ssjEiKVQSS/PCIhUhlE8ssjEiKVQSS/PCIhUhlE8stLi/Tde+41MvLT5v03\nmP4+icD40iBSGUTyy0uL1KE8+ZLs+BuDSGUQyS8vLVLext8YRCqDSH55REKkMojkl0ckRCqD\nSH55REKkMojkl0ckRCqDSH55REKkMojkl0ckRCqDSH55REKkMojkl0ckRCqDSH55REKkMojk\nl0ckRCpjdSJ/dpP5nZLJN1Ze5lJ99y3eM3Wdj2AekRCpjNWJnOH7TkojHd4zdZ2PYB6REKmM\n1Yls6l9tk70QeVuIiNRsEKkCIvnUhz4eIlVAJL88IiFSGUTyyyMSIpXZ1kV6KsHo6+OPH/c8\nHiI1G0SqEFakhr/a3eCvSSASIpXZ1kXqStyB1iXuUF2eoiBSs0GkCmFF8hUBkRCpDCKF3e9b\nH/p4iFQBkcLu960PfTxEqoBIYff71oc+HiJVQKSw+33rQx8PkSogUtj9vvWhj4dIFRAp7H7f\n+tDHQ6QKiBR2v2996OMhUoWWiPQ/jz67PocPN+w8e+5jbodCpNYeD5EqtESkebufUJ/5Eww7\nTxjxL26HQqTWHg+RKrREJI98sxcGIvkdD5EqIFLY/b71oY+HSBUQKex+3/rQx0OkCogUdr9v\nfejjIVIFRAq737c+9PEQqQIihd3vWx/6eIhUAZHC7vetD308RKqASGH3+9aHPh4iVUCksPt9\n60MfD5EqIFLY/b71oY+HSBUQKex+3/rQx0OkCogUdr9vfUuO99L/e6g+a0YZdj70g62ZpuEA\nIlmBSGZGNvhYvmFfFzjegR5/a+Qat/m4g0hWIJKZ0Z+J/7Wpz9wVfzzqNoHjnbb0VQPPmnY2\n+lxAfxDJCkRq7vGbff39vwZrBCJZUfSFWPTxl0GkxkWIFHZ/s+sRyR9EEqhHpAiREMm/HpEi\nREIk/3pEihAJkfzrESlCJETyr0ekCJEQyb8ekSJEQiT/ekSKEAmR/OsRKUIkRPKvR6QIkRDJ\nvx6RIkQqrEgv/7z2V8VHD/7V8V/ZRPO2EBEpc9QSRKqP/vdfOmyieVuIiJQ5agki1WfZ4O+/\n/La2dYPVrzTnbSEiUuaoJYjkWG/32QB5W4iN9s87Jf73oIYfHn980P8PO74yiNS4CJHC7u9c\nGBdn8qL44xFfCzu+MojUuAiR2N8QRGpchEjsbwgiNS5CJPY3BJEaFyES+xuCSI2LEIn9DUGk\nxkWIxP6GtLFIWzZs7GtUg0gC9dv6/jJtKtLaZXt1K6W6pl7wuLEOkQTqt/X9ZdpSpL6zlOqe\nuWDRglnjlTq911CJSAL12/r+Mm0p0pXqoPt6ylu9989Xqw2V2UU6Y9/5MXbYIf543zPcxoxI\nhd1fpi1FmjPpzdp2z/SphsrsIh07c3WMJUvij2ce6zZmRCrs/jJtKdK4JUMerOw0VGYXqdGJ\nExHDtb5AIt20svacM+Lk6talH23Z8QX3l2lLkeZOHrwj9c6YYqhEJIH6TPvfs9PMKp3TqltT\nR7Ts+IL7y7SlSFcPfo20br5aZahEJIH6TPsdxq/N3/EP11XZ7pzq1lWXio3PYX+ZthSpb4VS\n3bOOWHzk7PFKndpjqEQkgfogIh08bs8qHbtWt3YNckcr05YiRdH6c6eNVUqNnrLyUWMdIgnU\nBxHJNy+4v/547MizSP1sflr/zobfza29Op+5t3qrcUeI1IT9iOTWvxceIm1+/LXK1vO/Tex6\nc83gN6n/O3ck/3pEqjceA8//sPZXzUetqW49+IBbJ7ZkFumXhw1TwxY/V96eY+qFl3YC9YhU\nbzwGpmf/FCh3soq0Yax6z0kT1eQN/Q8QKQkiye6vPx4DQ/4K+guOnwLlTlaRlqqvRdHW89V7\nt0aIlAaRSvzdISdUGT67unXsodZ5q/G41ttdP3eyirRH+XxsXaK+GuVWpNsGfw5SZvr0+ON/\nuC1D/4hknV+wV+1TiP58cXVrofZdMNuuSJ1Ly/97Ydwur+VWpCE/BykzZkz88biDM/SPSC3L\nW/XnWp83kfaYPPBD2KvVcVvzKlJT9iNSy/JW/bnW502kC9Wi3/X/v+8odcEfESkJIgnkrfpz\nrc+bSK/vp9Ru/X+Y4eWD1Y7diJQAkQTyVv251udNpGjT6hnjH+nfeOOTuylESiAi0mvrdX9W\n5jfW+dAiIJIjvb/5v4a9iJS1fob9DxRzKYLgu8/LHHhg4ruuDU5y4UQyg0hZ65ctrt2Gflrb\nur7LOh9aBN/8nEbfdZ2j6yrT8f1BJMf9LRQp9ELOZd53PyLlZD8ihc377keknOzXX4hJ+82M\nMewv4o93vNN8qOIs5NB53/2IJLT/3p1rq3vs2Nrmzvfa5vUXYtSH418HH3N5/PHIb5qHWpyF\nHDrvux+RhPbf2Fn7Tam/+qvaZueNtvkWvvs5lws5dN53PyIJ7S/QrxGEHn8u8777EUloPyIV\nO++7H5GE9rdwIXzv04MfZ7WiunX15S07flvmffcjktD+MD9Q7JhY+zirbeedCYgkyrYrEvkm\n5H33I5LQ/tALgbxf3nc/IgntD70QyPvlffcjUpWNtQ+EWTr4KTEbdZW5XAjk/fK++xGpwj9p\nf7dA/ZNtPvRCIO+X992PSBVuH137+Mw776xtjr7dNh96IZD3y/9oj9pfbNxxx9rmHj/yO74/\nhRMp9IUkHzb/H9tdXGX+/Nrmdv/hd3x/EIk8eQEQiTx5ARCJPHkBEIk8eQEQiTx5ARCJPHkB\nEIk8eQEQiTx5ARCJPHkBEIk8eQEQiTx5ARCJPHkBEIk8eQEQiTx5ARCJPHkBEIk8eQEQiTx5\nARCJPHkBEIk8eQEQiTx5ARCJPHkBEIk8eQEQiTx5ARCJPHkBEIk8eQEQiTx5ARCJPHkBEIk8\neQEQiTx5ARCJPHkBEIk8eQEQiTx5ARCJPHkBEIk8eQEQiTx5ARCJPHkBEIk8eQEQiTx5ARCJ\nPHkBEIk8eQEQiTx5ARCJPHkBEIk8eQEQiTx5ARCJPHkBEIk8eQEQiTx5ARCJPHkBEIk8eQEQ\niTx5ARCJPHkBEIk8eQEQiTx5ARCJPHkBEIk8eQEQiTx5ARCJPHkBEIk8eQEQiTx5ARCJPHkB\nEIk8eQEQiTx5ARCJPHkBEIk8eQEQiTx5ARCJPHkBEIk8eQEQiTx5ARCJPHkB/ETasmFjX6Ma\nRCKf+7w/2UVau2yvbqVU19QLHjfWIRL53Of9ySpS31lKdc9csGjBrPFKnd5rqEQk8rnP+5NV\npCvVQff1lLd675+vVhsqEYl87vP+ZBVpzqQ3a9s906caKhGJfO7z/mQVadySIQ9WdhoqEYl8\n7vP+ZBVp7uTBO1LvjCmGSkQin/u8P1lFunrwa6R189UqQyUikc993p/M37VboVT3rCMWHzl7\nvFKn9hgqEYl87vP+ZP850vpzp41VSo2esvJRYx0ikc993h+/dzZsflr/zoYNe+9Z48/UW407\nCn0iyW/beX/8RKrH21+7rsZF3JHI5z3vT3NEGgov7cjnPu8PIpEnLwAikScvQFaRuuMYKhGJ\nfO7z/mQV6SuzlNr93TUMlYhEPvd5fzK/tOs5Un3bqhCRyOc+70/2r5FuRyTy7ZL3J7tIvxtz\nm1UdIpHPfd4fvmtHnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68\nAIhEnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68AIhEnrwAiESe\nvACIRJ68AIhEnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68AIhE\nnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68AIhEnrwAiESevACI\nRJ68AIhEnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68AIhEnrwA\niESevACIRJ68AIhEnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68\nAIhEnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68AIhEnrwAiESe\nvACIRJ68AIhEnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68AIhE\nnrwAiESevACIRJ68AIhEnrwAiESevACIRJ68AIhEnrwAiJTf/DvfaZvfNPyyVuUvG76p/fIC\nIFJu8+uGDVtnmb9ITUqf5Kbk/zRJXdR+eQH8RNqyYWNfoxoLkTatHXnJhmTj1ocOPfShrcnW\nDZeMXJt6SmnLfN+hu+9+cOrsavNPjhy5/edbk7987MjOX7VbXoLsIq1dtle3Uqpr6gWPG+sa\ni/S/+/sZfvLGWOPD+5ca1f4Pxxo3njy81Nj9pW0gH31t9JITuv4t3qbPR8cdMmbFuBcSjc3I\nRy91rxhz2PGJxsLnJcgqUt9ZpQsyc8GiBbPGK3V6r6GyoUhfVAO8f+jT9y+7Bxq7hz59bD2s\nUnp12+ejLe/41PLln5z0x6hhPvre8IfG3Paus2JtTclH0Zn7fmfMIx13xxuLnhchq0hXqoPu\n6ylv9d4/X602VDYS6fVxlaurvjGkdVG1cfGQxm9UG8e93u756O8m/XH58jfe8cmoUT7qedc5\npa+x1g5/MGp2Pnqk47ulr7HO3uftqI3yMmQVac6kN2vbPdOnGiobifSt6tVVJw02vj2q2jhq\nyKRPrJXe0ub56Jmuf+//ZsXXR/02apCPrtr+hf5vVnzgkL5m56PDPtj/zYrf7/DFqI3yMmQV\nadySIQ9Wdib2/mbnHWuMU2b/r6xd3UMHG5+vNarnB1sPqTVe2eb5aMncvuiMM0pfMZ8QNci/\nutMXoqj77uipkTc2O39D5xPR3d1RdMWOL7dPXoisIs2dPHhH6p0xJbF36/fvrXHP1809/XPt\n6h492Li51jhsyKvco2qt/9Lm+R8NX1eyseTgwx0/aJA/b2rpjv9Y6cnq4j/f0tz8G7t/vHSv\nfaz0394fbp+8EFlFunrwa6R189UqjxH8unZ1h/Yyo9o4Y0jjqlrpk+2d3zrz9Orm8um9xvwv\nRtxZ2dq022XNzV86sfqNxXs6at+pLXpeiszftVuhVPesIxYfOXu8Uqf2+Ayh+sp9wh+GNN5c\nveTfHNL4yoRK44ltnv+yWrm6wkr1ZWN+wc7VytXzxjzXzPxzY+bVSnde0C55KbL/HGn9udPG\nlq7J6CkrH/UbwqZ55Ys78aex1s929Dd2fDbW+NOJ5dJ5m9o8v2bmENYY80cPqZz9SDPzj8we\n0nh0u+Sl8Htnw+anG7+zoTFbbz7n8JOueDXR+tjFRx998WOJxj9ccdLh59yceL9Ae+a1aPMO\n+OahHs1/rx3ANgAiAQiASAACIBKAAIgEIAAiAQiQR5Hi351927URWob+/G+TlyqPIp059KT/\n7EDXxjiP3evV2IgtD9+2Pv2+Dm2rvrTQ6M9/xks15Pzbn7+GJ1XuUpsJK9JOcapjWlZ7e1TP\nZzuVa2M08Yz+f689p/xgsRXVi40AAAwhSURBVHJt1I9KP9Trd1BKvSv5Y1dtq760zCMVLFrt\nG21K9ZOyn7/+/NtfKv35dzh/2kaHSy1HWJH2HsIIVR3L4eqkypPMf85W23/VtTEa+GW6ysmq\nnjP7Rv2otI33KvW+pVPVxPi7ErSt+tLoP8+7sDSMCuZW+0brUv35t5+//vzbXyr9+bc/f/qT\n6nCp5cjLS7tnPqA6/76y/cZCtaj/lwF7rxipFj7r3Ogtkn5UusZ5/b/V+vaJ6vJYgbZVX3rP\nOLW8NIz9LzxU7fDt542t9o0upcaZNpy//vzbXyr9+bc/f/qTug2L1POFMWre4KcbvPVBdeyb\n0a8PVdtf35ehUUqkxKg0jbvu1//verUsVqJt1Ta+PKZjzR8HhvGtsacZW+0bXUpNM7WYv/78\n218q/fm3P3/687/tirRuutrl34a++7VnqTriyi618JkoS6OQSKlRpRtV+QNpemIfzFCnVdv4\nCfXF2jCuUneZWu0bXUoNM7WZf52LYn2p9Off/vzVOf/bqEivnTtMnZ340qH3TBV/knNpFBFJ\nN6pUY+UKai9kolXbOHv8W7XGnnHHm1rtG11K687Ubv5RnYtie6nqiGR9/kznf1sTqe+GXdW7\nfpJu/ht1YupDviwbBUTSjird6CvShPeV/zfwHaW5E02t9o0upfXOv+X8y626i2J5qRBJjCeP\nVF2f0/14ru8T6pTUDwjsGv1F0o5K0+gr0tj3D3nw3u1MrfaNLqV1zr/t/MtoL4rdpUIkIf70\nmVHquKeTrWsG2FcdW/6/a2OkJp1YYpI6ceB/ro36UWkb1W7H91P53/GmVm3j9B0GX+j0dU+K\nDK32jQ6l+vNvP3/9+be/VPrzb3/+6px/+0stR1iR9lHqnLsGqY4pjmujd147Km2j7/GXqeon\nd0TRrbXPtdO22jc6lOrPf+vmHzovSFiR9LP7YhzXxujBOK6NDhfikTiRoVXb+PPhE6s/0Xl2\nV/WDyNBq3+hQ6rsQ9eff/lLpz7/9+dOff4dLLUdYkT4dp17Z3270anTNa0dlN1TX439E7XhV\n/wffvbJqjFpR26VttW+0L9VPqoXzz1s+O6G/2ZBAP7udkn9pwK3RO29/IVyP3/e3HUq94+D+\nD/f5m8GvwrWt9o0upTaTauL885bPTs5ECn0iW5//xYrJpaU98ZQHYju1rfaNLqWe42+vfHYQ\nKXz+zec2Vb+jNuRpXttq3+hS6jn+NspnB5HIkxcAkciTFwCRyJMXAJHIkxcAkciTFwCRyJMX\nAJHIkxcg/yJtbcaJdOrU7/hNGX8u8/VmGvb8uV3qzORJJM2U+x74yOQoumbI32HtufPqW1/s\n36g09r3U/+8tPyj//metUt9q3WmChheibj52KOOgDEcKK5LlQkyfVMtLpas09GrfaSpvcf6z\nkxuRNBei79FP7DH4nuQXz3/vUd+IXtiv1NJ1Ra3kqr2P6f//8aprzeAnEOpbrTutYnHNjPn4\noUyDqhFIJN8ngvhMHS6Vw/lzuP7avNX5z04+REpciDLr/37vUtMeF1feHP/sLv3v3v9f/019\n8HMrx6lbKrmT1IiP9m/cdHiHqv0heH2rdacO16xOXncow6Cq+L40ypAXeSJIzNThUjmcP4fr\nr81bnH8v8iBS8kKUeHLVAf3n7eB1tV/rPE2d8cQTfz1Cfa60/fOR7xlo/JaaXf37u09PV3dE\nplbrTh2umT6vPVS9QVWxeGlUvzGV19480o0CTwTpmTpcKofz53D9tflG59+X4CJpLsSGz88q\ntUy9aJ06Z7Buj8k9pVU0Wf2+/8F7uwca5w37Ra3giWHHRIZW+04drpk+rz2UfqgVbF4amW4e\nibz25qFr9H0i0M7U4VI5nD+H6+9+/gUIK5L+QpSa9v/UY6VLOLS1Y2H/vwsHxruoMuzJ+w6J\nzZwaGVrtO3W4Zvq89lD6oZaxemlkuHkk89qbh7bR94lAO1OHS+Vw/hyuv+v5FyH4r5prLoRS\nC39SfiYc2qr9FJjRC4fEjhwdGVrtO3W4ZnU+BUd3KP1Q7V8a1bt5aPLam4e20feJQDtTh0vl\ncP4crr/T+ZcitEi6C7FsnFJ/8fHSlWx4It+952BB3277RYZW+069F4L2UNpBObw00jbq89qb\nh/6O4vlEoJ2pw6VyOH8O19/+/AsSViT9hYje+ObiUUrt++mGJ3Kp+mWt4AG1JDK1WnfqvRC0\nh9IOyuGlkbZRn9fePBzuqA4LUTdTh0vlcP4crr/9+Rck8DcbtBein43/etQIpXb+2CPVVyzq\ngP6PQztArRn430DjrWrf6u95vrK7ujkytVp36rIQtHntobSDcnhppG/U5rU3D4c7qttCTM3U\n4VI5nD+H66/N1y+VIfh37TQXosLL17xvmFLvvGzgkfbjoPo+pP7sqt/3RX0vfWGCWlL9XpS+\n1bpTl4WgzWsPpR2Uw0sjbaM+r7156O+onk8E2pk6XCqH8+dw/bV5Y6kA4UWKUhdikGevmGX+\nCLW3P1YKjt2rtJ7Uh/9Ui+lbrTu1v2b6vPZQ+kHZvzTS3zy0ee3No95LS68nAv1M7S+Vw/lz\nuf7aUTUo9SUXIkXxKcf4dd1PuxvgiY8cMEp17n3uzyxaLTvNdM3qMeRQ+kFZvjSqe/NI57U3\nD22j9xNBnZnqaXBR9GS7/tp8puPbkheRIt0pe/bHr6TL0o19b6X+tk/dVutONRguhD5vO1Sb\nl0amm0cir715ZH1pY1yI19yk60jfOjCK+Pwd8vadupTKkROR4ktuy+rjj/tOtHlxacEc8oSx\nUZ/Xtrp3aiGCPp9lqI1eGjW4ecTu6Nqbh67RV4TS/Nani7St2k4d8vadupTKEVgk7ZJ7fZ/S\nw+G3LVJ/ec4haseXDI0OC9mhU3sR9HmXoQ6h4UujBgzNa2+e6UZvESbt3XnZG1atejsc8tad\nupTKEVYk/ZK7UH1iw4MHbqduK21/RZ1raHRYyPadOoigzzsMtUrsNmV+8hxaaqrUvopJ3lF8\nRZjz1qUjd/3HNyxa9Z065K07dSmVI6xI+oX4zpl9/d9ZOqp/u2/G/oZGh4Vs36mDCPq8w1C1\ntyn9k6euVF9pL6K/CFH0q6PVrpc91bC1jh0OedtOXUrlCCuSfiGOPrX0zxZ1fvnByV2GRoeF\nbN+pgwj6vP1Q9bcp7ZrVltZ5RrYXUUCEKPrJAqXm/uPDPcbWunY45O06dSmVI6xI+oW495A1\nFx20v6HRYSHbd+oggj5vP1T9bUq7ZrWldZ6R7UUUESGK1p3drVTXYaZWgx0OeZtOXUrlCCuS\nfiFeoC555sEDO9Ttpe2vqvMMjQ4L2b5TBxH0efuh6u+o2jWrLa33jGwv4sD/fUWIojduOnOK\nMrUa7XDIN+7UpVSOsCLpF+Kr00pPnmN+uo9acM6hasLLhkaHhWzfqYMI+rz9UPV3VO2a1ZbW\nf0a2F3EAXxH6eTaVH9LawA6HfKNOXUrlCCuSfiFGmy49ZvGPo2cOLe18/1OmRoeFbN+pi7Pa\nvP1Q9XdU7ZrVlpqeke1FrOIrQhVtq4UdDnlTpy6lcgT+OZJ+IVbo+/WPX00l4o32C9mhUwdn\nTZ1aDFVvp3bNakvNz8j2IsaxX4gvv6bLa1u1nTrk7Tt1KZUjH+9s0C9E37xsr0bnM3aqt1O7\nZrWljZ6RG4noK4ID+kN54tBpU44/hHyIVCB87YyjtVO/ZnWlNncUk4gONHshFh1EygNxO41r\nNlaqr7QXEcRApPbDXkQQA5EABEAkAAEQCUAARAIQAJEABEAkAAEQCUAARAIQAJEABEAkAAEQ\nCUAARAIQAJEABEAkAAEQCUAARAIQAJEABEAkAAEQCUAARAIQAJEABEAkAAEQCUAARAIQAJEA\nBEAkAAEQCUAARAIQAJEABEAkAAEQCUAARAIQAJEABEAkAAEQCUAARAIQAJEABEAkAAEQCUAA\nRAIQAJEABEAkAAEQCUAARAIQAJEABEAkAAEQCUAARAIQAJEABEAkAAEQCUAARAIQAJEABEAk\nAAEQCUAARAIQAJEABEAkAAEQCUAARAIQAJEABEAkAAEQCUAARAIQAJEABEAkAAEQCUAARAIQ\nAJEABEAkAAEQCUAARAIQAJEABEAkAAEQCUAARAIQAJEABEAkAAEQCUAARAIQAJEABPgvLJN9\nLWv7z74AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dend %>% \n",
    "    set(\"labels_cex\",1) %>% \n",
    "    set(\"leaves_pch\",trtlab) %>% \n",
    "    set(\"leaves_cex\",1) %>% \n",
    "    plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, lets add the group label to the above. The color of each label is controlled by \"labels_col\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rR8+XJhB4ey9Ai7AQMGXHfddf369avud+xmz57985//fM6cOXfeeWdcQwKkqFOn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\nYZ6j+Ue+Slg33XfGs4Ofs2xx5rmQY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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dend %>% \n",
    "    set(\"labels_cex\",1) %>% \n",
    "    set(\"labels_col\",grplab) %>% \n",
    "    set(\"leaves_pch\",trtlab) %>% \n",
    "    set(\"leaves_cex\",1) %>% \n",
    "    plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also add a title to the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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09SUpLaEwEAADQL2gs7xZgxYw4dOjRmzJiEhAS1ZwEAAGgWtHHxxE3dcccd69ev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kCBZ2CKHBgqJAVqusbWrxthdbCXu62Wmd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\nAElFTkSuQmCC",
      "text/plain": [
       "Plot with title “2015 Student Data”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dend %>% \n",
    "    set(\"labels_cex\",1) %>% \n",
    "    set(\"labels_col\",grplab) %>% \n",
    "    set(\"leaves_pch\",trtlab) %>% \n",
    "    set(\"leaves_cex\",1) %>% \n",
    "    plot(main=\"2015 Student Data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Repeat the analysis using expression data based on limma::voom with HC with single linkage:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get expression matrix using voom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "voomexp<-limma::voom(assay(dds))$E"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create dendrogram object based on single linkage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dend <- voomexp %>% \n",
    "        t %>% \n",
    "        dist %>% \n",
    "        hclust(method=\"single\") %>% \n",
    "        as.dendrogram"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ptvvnqNyWRSbnTr1s1xRb2rx2jU5gBa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\ngg==",
      "text/plain": [
       "Plot with title “2015 Student Data”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dend %>% \n",
    "    set(\"labels_cex\",1) %>% \n",
    "    set(\"labels_col\",grplab) %>% \n",
    "    set(\"leaves_pch\",trtlab) %>% \n",
    "    set(\"leaves_cex\",1) %>% \n",
    "    plot(main=\"2015 Student Data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Session Information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.3.1 (2016-06-21)\n",
       "Platform: x86_64-pc-linux-gnu (64-bit)\n",
       "Running under: Debian GNU/Linux 8 (jessie)\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8       \n",
       " [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C              \n",
       "[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "attached base packages:\n",
       " [1] tools     parallel  stats4    stats     graphics  grDevices utils     datasets  methods  \n",
       "[10] base     \n",
       "\n",
       "other attached packages:\n",
       " [1] RColorBrewer_1.1-2         dplyr_0.5.0                dendextend_1.5.2          \n",
       " [4] DESeq2_1.14.1              SummarizedExperiment_1.4.0 Biobase_2.34.0            \n",
       " [7] GenomicRanges_1.26.4       GenomeInfoDb_1.10.3        IRanges_2.8.2             \n",
       "[10] S4Vectors_0.12.2           BiocGenerics_0.20.0       \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] viridis_0.4.0        viridisLite_0.2.0    jsonlite_1.1         splines_3.3.1       \n",
       " [5] Formula_1.2-2        assertthat_0.1       latticeExtra_0.6-28  robustbase_0.92-7   \n",
       " [9] RSQLite_1.0.0        backports_1.1.0      lattice_0.20-34      limma_3.30.13       \n",
       "[13] uuid_0.1-2           digest_0.6.10        XVector_0.14.1       checkmate_1.8.3     \n",
       "[17] colorspace_1.3-1     htmltools_0.3.5      Matrix_1.2-7.1       plyr_1.8.4          \n",
       "[21] XML_3.98-1.9         genefilter_1.56.0    zlibbioc_1.20.0      mvtnorm_1.0-6       \n",
       "[25] xtable_1.8-2         scales_0.4.1         whisker_0.3-2        BiocParallel_1.8.2  \n",
       "[29] htmlTable_1.9        tibble_1.2           annotate_1.52.1      ggplot2_2.2.1       \n",
       "[33] repr_0.7             nnet_7.3-12          lazyeval_0.2.0       survival_2.41-3     \n",
       "[37] magrittr_1.5         crayon_1.3.1         mclust_5.3           memoise_1.0.0       \n",
       "[41] evaluate_0.10        MASS_7.3-45          class_7.3-14         foreign_0.8-67      \n",
       "[45] data.table_1.10.4    trimcluster_0.1-2    stringr_1.0.0        kernlab_0.9-25      \n",
       "[49] munsell_0.4.3        locfit_1.5-9.1       cluster_2.0.5        AnnotationDbi_1.36.2\n",
       "[53] fpc_2.1-10           grid_3.3.1           RCurl_1.95-4.8       pbdZMQ_0.2-3        \n",
       "[57] IRkernel_0.7         htmlwidgets_0.9      bitops_1.0-6         base64enc_0.1-3     \n",
       "[61] gtable_0.2.0         flexmix_2.3-14       DBI_0.5-1            R6_2.2.0            \n",
       "[65] gridExtra_2.2.1      prabclus_2.2-6       knitr_1.15.1         Hmisc_4.0-3         \n",
       "[69] modeltools_0.2-21    stringi_1.1.2        IRdisplay_0.4.3      Rcpp_0.12.8         \n",
       "[73] geneplotter_1.52.0   rpart_4.1-10         acepack_1.4.1        DEoptimR_1.0-8      \n",
       "[77] diptest_0.75-7      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  }
 ],
 "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": "3.3.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}