{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d35135aa-d369-42af-b089-9ec5758231be",
   "metadata": {},
   "source": [
    "### Notebook index\n",
    "\n",
    "[3 Minimal examples](03-minimal.ipynb)  \n",
    "[4 Plotting chromosomes](04-plotting.ipynb)   \n",
    "[5 Multiple OTUs](05-multiple.ipynb)    \n",
    "[6 Changing units](06-units.ipynb)  \n",
    "[7 GISH](07-gish.ipynb)  \n",
    "[8 Groups](08-groups.ipynb)  \n",
    "[9 Circular Plots](09-circular.ipynb)  \n",
    "[10 Plotting alongside phylogeny](10-phylogeny.ipynb)  \n",
    "[11 Citrus](11-citrushelp.ipynb)  \n",
    "[12 Human Karyotype](12-human.ipynb)  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8df326e",
   "metadata": {},
   "source": [
    "# Groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "712b46b8-3664-4143-ac76-fda25d26cb2e",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# General jupyter instructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "74e6eb5d-3ca4-48b2-910e-74b9202ec2d4",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": [],
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# press Ctrl + Enter to RUN active cell\n",
    "\n",
    "# SHOW keyboard shortcuts Ctrl + M then H (only jupyter notebooks and colab)\n",
    "\n",
    "# Ctrl + D (or X) DELETE a line of code\n",
    "\n",
    "# press Ctrl + M to escape from cell (opposite to ENTER)\n",
    "\n",
    "# then !\n",
    "# B to ADD cell below or A above\n",
    "# M to turn code cell into MARKDOWN\n",
    "# Y turn markdown to CODE\n",
    "# O to toggle OUTPUT\n",
    "# D D to DELETE cell\n",
    "\n",
    "# If in jupyter-lab:\n",
    "# collapse cell clicking in the blue left bar\n",
    "# use the settings menu to use the dark theme\n",
    "\n",
    "# if in colab:\n",
    "# remove bar at 80 in tools, preferences, 80 -> 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0f61d0f9-f67d-4a75-9ad6-448e927b2e2b",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "version 2.0.6 See help: ?idiogramFISH - browseVignettes('idiogramFISH') - https://ferroao.gitlab.io/manualidiogramfish\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# install if necessary\n",
    "\n",
    "if (system.file(package = \"idiogramFISH\") == '') {\n",
    "  install.packages(\"idiogramFISH\")\n",
    "}\n",
    "\n",
    "library(idiogramFISH)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f78c5714-1268-48e5-9fe1-d9382c97b1ab",
   "metadata": {},
   "source": [
    "## monocentrics\n",
    "\n",
    "### Adding the column `group`\n",
    "\n",
    "Open your chromosome data - Chr. size - as data.frame and add column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f32ff8b4",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>chrName</th><th scope=col>shortArmSize</th><th scope=col>longArmSize</th><th scope=col>group</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1</td><td>3.0</td><td>5.0</td><td> 1</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1</td><td>3.2</td><td>5.5</td><td> 1</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>1</td><td>3.5</td><td>4.8</td><td> 1</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>4</td><td>1.0</td><td>3.0</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>5</td><td>3.0</td><td>5.0</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>X</td><td>4.0</td><td>6.0</td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & chrName & shortArmSize & longArmSize & group\\\\\n",
       "  & <chr> & <dbl> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & 1 & 3.0 & 5.0 &  1\\\\\n",
       "\t2 & 1 & 3.2 & 5.5 &  1\\\\\n",
       "\t3 & 1 & 3.5 & 4.8 &  1\\\\\n",
       "\t4 & 4 & 1.0 & 3.0 & NA\\\\\n",
       "\t5 & 5 & 3.0 & 5.0 & NA\\\\\n",
       "\t6 & X & 4.0 & 6.0 & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 4\n",
       "\n",
       "| <!--/--> | chrName &lt;chr&gt; | shortArmSize &lt;dbl&gt; | longArmSize &lt;dbl&gt; | group &lt;int&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1 | 1 | 3.0 | 5.0 |  1 |\n",
       "| 2 | 1 | 3.2 | 5.5 |  1 |\n",
       "| 3 | 1 | 3.5 | 4.8 |  1 |\n",
       "| 4 | 4 | 1.0 | 3.0 | NA |\n",
       "| 5 | 5 | 3.0 | 5.0 | NA |\n",
       "| 6 | X | 4.0 | 6.0 | NA |\n",
       "\n"
      ],
      "text/plain": [
       "  chrName shortArmSize longArmSize group\n",
       "1 1       3.0          5.0          1   \n",
       "2 1       3.2          5.5          1   \n",
       "3 1       3.5          4.8          1   \n",
       "4 4       1.0          3.0         NA   \n",
       "5 5       3.0          5.0         NA   \n",
       "6 X       4.0          6.0         NA   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Example data.frame written in R, use\n",
    "dfwithgroups <-read.table(text=\"\n",
    "      chrName shortArmSize longArmSize group\n",
    "1        1            3           5     1\n",
    "2        1            3.2         5.5   1\n",
    "3        1            3.5         4.8   1\n",
    "4        4            1           3     NA\n",
    "5        5            3           5     NA\n",
    "6        X            4           6     NA\", header=TRUE, stringsAsFactors=F)\n",
    "dfwithgroups        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d0d82a4",
   "metadata": {},
   "source": [
    "</div>\n",
    "<div class=\"verysmall\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e3e872",
   "metadata": {},
   "source": [
    "### Heteromorphic pairs\n",
    "\n",
    "It can be used to plot heteromorphic pairs, see pair 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6cdc912f",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 5 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>chrName</th><th scope=col>shortArmSize</th><th scope=col>longArmSize</th><th scope=col>group</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1A</td><td>3</td><td>5</td><td> 1</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1B</td><td>3</td><td>5</td><td> 1</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>2 </td><td>1</td><td>3</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>5</th><td>3 </td><td>3</td><td>5</td><td>NA</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>4 </td><td>4</td><td>6</td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 5 × 4\n",
       "\\begin{tabular}{r|llll}\n",
       "  & chrName & shortArmSize & longArmSize & group\\\\\n",
       "  & <chr> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t1 & 1A & 3 & 5 &  1\\\\\n",
       "\t2 & 1B & 3 & 5 &  1\\\\\n",
       "\t4 & 2  & 1 & 3 & NA\\\\\n",
       "\t5 & 3  & 3 & 5 & NA\\\\\n",
       "\t6 & 4  & 4 & 6 & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 5 × 4\n",
       "\n",
       "| <!--/--> | chrName &lt;chr&gt; | shortArmSize &lt;int&gt; | longArmSize &lt;int&gt; | group &lt;int&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 1 | 1A | 3 | 5 |  1 |\n",
       "| 2 | 1B | 3 | 5 |  1 |\n",
       "| 4 | 2  | 1 | 3 | NA |\n",
       "| 5 | 3  | 3 | 5 | NA |\n",
       "| 6 | 4  | 4 | 6 | NA |\n",
       "\n"
      ],
      "text/plain": [
       "  chrName shortArmSize longArmSize group\n",
       "1 1A      3            5            1   \n",
       "2 1B      3            5            1   \n",
       "4 2       1            3           NA   \n",
       "5 3       3            5           NA   \n",
       "6 4       4            6           NA   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfwithHetero<-read.table(text=\"\n",
    "       chrName shortArmSize longArmSize group\n",
    "1        1A           3           5     1\n",
    "2        1B           3           5     1\n",
    "4        2            1           3     NA\n",
    "5        3            3           5     NA\n",
    "6        4            4           6     NA\", header=TRUE, stringsAsFactors=FALSE)\n",
    "dfwithHetero"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32775d21",
   "metadata": {},
   "source": [
    "</div>\n",
    "<div class=\"verysmall\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f911b460",
   "metadata": {},
   "source": [
    "</div>\n",
    "\n",
    "Open or write your mark positions as a data.frame. This data.frame has the marks present in all karyotypes with position info.\n",
    "<div class=\"fold s\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3f9ecef",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 4 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>chrName</th><th scope=col>markName</th><th scope=col>chrRegion</th><th scope=col>markSize</th><th scope=col>markDistCen</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>1A</td><td>5S  </td><td>p</td><td>1</td><td>0.9</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>1B</td><td>45S </td><td>p</td><td>1</td><td>0.9</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>2 </td><td>CMA </td><td>q</td><td>1</td><td>1.0</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>3 </td><td>DAPI</td><td>q</td><td>1</td><td>1.0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 4 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & chrName & markName & chrRegion & markSize & markDistCen\\\\\n",
       "  & <chr> & <chr> & <chr> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & 1A & 5S   & p & 1 & 0.9\\\\\n",
       "\t2 & 1B & 45S  & p & 1 & 0.9\\\\\n",
       "\t3 & 2  & CMA  & q & 1 & 1.0\\\\\n",
       "\t4 & 3  & DAPI & q & 1 & 1.0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 4 × 5\n",
       "\n",
       "| <!--/--> | chrName &lt;chr&gt; | markName &lt;chr&gt; | chrRegion &lt;chr&gt; | markSize &lt;int&gt; | markDistCen &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 1 | 1A | 5S   | p | 1 | 0.9 |\n",
       "| 2 | 1B | 45S  | p | 1 | 0.9 |\n",
       "| 3 | 2  | CMA  | q | 1 | 1.0 |\n",
       "| 4 | 3  | DAPI | q | 1 | 1.0 |\n",
       "\n"
      ],
      "text/plain": [
       "  chrName markName chrRegion markSize markDistCen\n",
       "1 1A      5S       p         1        0.9        \n",
       "2 1B      45S      p         1        0.9        \n",
       "3 2       CMA      q         1        1.0        \n",
       "4 3       DAPI     q         1        1.0        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfOfMarksHetero<-read.table(text=\n",
    "\"     chrName markName chrRegion markSize markDistCen\n",
    "1       1A       5S       p        1         0.9\n",
    "2       1B      45S       p        1         0.9\n",
    "3       2       CMA       q        1         1.0\n",
    "4       3      DAPI       q        1         1.0\", header=TRUE, stringsAsFactors=FALSE)\n",
    "dfOfMarksHetero"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10e20555",
   "metadata": {},
   "source": [
    "</div>\n",
    "<div class=\"verysmall\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f5bfffc",
   "metadata": {},
   "source": [
    "</div>\n",
    "\n",
    "Plot\n",
    "\n",
    "<div class=\"fold s\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "596167d0",
   "metadata": {
    "attributes": {
     "classes": [],
     "dev": "'png'",
     "echo": "TRUE,",
     "fig.height": "8,",
     "fig.width": "13.5,",
     "id": "",
     "message": "FALSE,",
     "results": "\"hide\","
    },
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 675
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "require(idiogramFISH)\n",
    "# svg(\"dfwithHetero.svg\",width=13.5,height=8 )\n",
    "par(mar=rep(0,4)) \n",
    "\n",
    "dfwithHetero$OTU<-\"hetero\"\n",
    "dfwithgroups$OTU<-\"first\"\n",
    "both<-plyr::rbind.fill(dfwithHetero,dfwithgroups)\n",
    "dfOfMarksHetero$OTU<-\"hetero\"\n",
    "\n",
    "options(repr.plot.width = 13.5, repr.plot.height = 8, repr.plot.res = 100)\n",
    "suppressMessages(\n",
    "plotIdiograms(dfChrSize=both,    # chr. sizes\n",
    "              dfMarkPos=dfOfMarksHetero, # position of marks\n",
    "              karHeiSpace = 4,\n",
    "              \n",
    "              chrId=\"original\",          # chr. name in df.\n",
    "              chrIndex = \"\",             # do not add chr. indices\n",
    "              morpho=\"\",                 # do not add chr. morphologies  \n",
    "              karIndex = FALSE,          # do not add karyotype indices\n",
    "              distTextChr = .8,          # distance from text to chr.\n",
    "              \n",
    "              markDistType=\"cen\",        # mark position measured to center of mark\n",
    "              orderChr = \"name\",       # order chr. by name\n",
    "              \n",
    "              ruler=FALSE                # do not plot ruler\n",
    "              \n",
    "              ,ylimBotMod = 1            # modify ylim bottom argument\n",
    "              ,legendWidth = 1           # width of legend\n",
    ")\n",
    ")\n",
    "# dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e73a1825",
   "metadata": {},
   "source": [
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9efccd",
   "metadata": {},
   "source": [
    "## Holocentrics\n",
    "\n",
    "Let's modify some data.frames to add a group\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fdae85a7",
   "metadata": {
    "attributes": {
     "classes": [],
     "echo": "TRUE,",
     "fig.height": "4.5,",
     "fig.width": "7,",
     "id": "",
     "message": "FALSE,dev='png'",
     "results": "\"hide\","
    },
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "data(\"dfChrSizeHolo\")\n",
    "data(\"dfMarkPosHolo\")\n",
    "dfMarkPosHoloHetero<-dfMarkPosHolo\n",
    "dfMarkPosHoloHetero$chrName<-c(3,3,\"1A\",2,\"1B\",\"1B\")\n",
    "dfMarkPosHoloHetero$OTU<-\"heteromorphic\"\n",
    "\n",
    "dfChrSizeHoloHetero<-dfChrSizeHolo\n",
    "dfChrSizeHoloHetero$chrName<-c(\"1A\",\"1B\",2,3)\n",
    "dfChrSizeHoloHetero$OTU<-\"heteromorphic\"\n",
    "\n",
    "# Adding the group column\n",
    "dfChrSizeHoloHetero$group<-c(1,1,NA,NA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bb76edf",
   "metadata": {},
   "source": [
    "### Creating a new data.frame for holocentrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "60211f33",
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 7 × 4</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>OTU</th><th scope=col>chrName</th><th scope=col>chrSize</th><th scope=col>group</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>Species name</td><td>1</td><td>3.1</td><td> 1</td></tr>\n",
       "\t<tr><td>Species name</td><td>1</td><td>3.2</td><td> 1</td></tr>\n",
       "\t<tr><td>Species name</td><td>1</td><td>3.3</td><td> 1</td></tr>\n",
       "\t<tr><td>Species name</td><td>1</td><td>3.4</td><td> 1</td></tr>\n",
       "\t<tr><td>Species name</td><td>2</td><td>4.0</td><td>NA</td></tr>\n",
       "\t<tr><td>Species name</td><td>3</td><td>5.0</td><td>NA</td></tr>\n",
       "\t<tr><td>Species name</td><td>4</td><td>6.0</td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 7 × 4\n",
       "\\begin{tabular}{llll}\n",
       " OTU & chrName & chrSize & group\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t Species name & 1 & 3.1 &  1\\\\\n",
       "\t Species name & 1 & 3.2 &  1\\\\\n",
       "\t Species name & 1 & 3.3 &  1\\\\\n",
       "\t Species name & 1 & 3.4 &  1\\\\\n",
       "\t Species name & 2 & 4.0 & NA\\\\\n",
       "\t Species name & 3 & 5.0 & NA\\\\\n",
       "\t Species name & 4 & 6.0 & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 7 × 4\n",
       "\n",
       "| OTU &lt;chr&gt; | chrName &lt;dbl&gt; | chrSize &lt;dbl&gt; | group &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| Species name | 1 | 3.1 |  1 |\n",
       "| Species name | 1 | 3.2 |  1 |\n",
       "| Species name | 1 | 3.3 |  1 |\n",
       "| Species name | 1 | 3.4 |  1 |\n",
       "| Species name | 2 | 4.0 | NA |\n",
       "| Species name | 3 | 5.0 | NA |\n",
       "| Species name | 4 | 6.0 | NA |\n",
       "\n"
      ],
      "text/plain": [
       "  OTU          chrName chrSize group\n",
       "1 Species name 1       3.1      1   \n",
       "2 Species name 1       3.2      1   \n",
       "3 Species name 1       3.3      1   \n",
       "4 Species name 1       3.4      1   \n",
       "5 Species name 2       4.0     NA   \n",
       "6 Species name 3       5.0     NA   \n",
       "7 Species name 4       6.0     NA   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfChrSizeHoloGroup <- data.frame(OTU=\"Species name\", \n",
    "                                  chrName = c(1, 1, 1, 1, 2, 3, 4), \n",
    "                                  chrSize = c(3.1, 3.2, 3.3, 3.4, 4, 5, 6), \n",
    "                                  group = c(1, 1, 1, 1, NA, NA, NA) \n",
    "                                )\n",
    "dfChrSizeHoloGroup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b8ffbe",
   "metadata": {},
   "source": [
    "</div>\n",
    "<div class=\"verysmall\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d56d14ea",
   "metadata": {},
   "source": [
    "</div>\n",
    "\n",
    "<div class=\"fold s\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2dede0c8",
   "metadata": {
    "attributes": {
     "classes": [],
     "echo": "TRUE,",
     "fig.height": "4.5,",
     "fig.width": "7,",
     "id": "",
     "message": "FALSE,dev='png'",
     "results": "\"hide\","
    },
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 675
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mar=rep(0,4)) \n",
    "mergedChrSize<-plyr::rbind.fill(dfChrSizeHoloGroup,dfChrSizeHoloHetero)\n",
    "\n",
    "options(repr.plot.width = 13.5, repr.plot.height = 8, repr.plot.res = 100)\n",
    "suppressMessages(\n",
    "plotIdiograms(dfChrSize=mergedChrSize,      # data.frame of chr. sizes\n",
    "              dfMarkPos=dfMarkPosHoloHetero,# d.f. of marks\n",
    "              orderChr = \"name\",            # order chr. by name\n",
    "              karIndex = FALSE,             # do not add karyotype indices\n",
    "              addOTUName = TRUE,            # add OTU name\n",
    "              karHeiSpace = 4,              # height of kar. with spacing\n",
    "              \n",
    "              ruler=FALSE,                  # no ruler\n",
    "              \n",
    "              xlimLeftMod=-1,               # modify left argument of xlim\n",
    "              xlimRightMod=0,               # modify right argument of xlim\n",
    "              ylimBotMod=1.3                # modify bottom argument of ylim\n",
    "              ,xModifier = 100              # separ. among chromatids\n",
    ")\n",
    ")"
   ]
  }
 ],
 "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": "4.1.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}