{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "359e043c-b552-4000-b378-4e0f5b2d4d55",
   "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": "0e7fecdb",
   "metadata": {},
   "source": [
    "# Changing Units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bf22d1d6-9689-45d4-a500-b4092381d88e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# General jupyter instructions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1c904c06",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "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": 4,
   "id": "3d0bc307",
   "metadata": {},
   "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": "c58e7a2d",
   "metadata": {},
   "source": [
    "## Using bases instead of micrometers - no. cen.\n",
    "\n",
    "Create some data in millions of bases:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "383fb8f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "bigdfChrSizeHoloMb <- bigdfChrSizeHolo # included in idiogramFISH\n",
    "bigdfChrSizeHoloMb$chrSize <- bigdfChrSizeHoloMb$chrSize * 98000000\n",
    "\n",
    "bigdfMarkPosHoloMb <- bigdfMarkPosHolo\n",
    "bigdfMarkPosHoloMb$markPos <- bigdfMarkPosHoloMb$markPos * 98000000\n",
    "bigdfMarkPosHoloMb$markSize<- bigdfMarkPosHoloMb$markSize * 98000000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0e93f4e",
   "metadata": {},
   "source": [
    "### Plotting\n",
    "\n",
    "In the plot length is shown in Mb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3edfc525",
   "metadata": {
    "attributes": {
     "classes": [],
     "echo": "TRUE,",
     "eval": "FALSE",
     "fig.height": "6,",
     "fig.width": "6,",
     "id": "",
     "message": "FALSE,dev='png',",
     "results": "\"hide\","
    }
   },
   "outputs": [
    {
     "data": {
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kYIERQUZPhb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\nAtEDACpJ7awQABu7fPny4sWLn3nmG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VVVV54sTbv5bIeQKUV/GHuWLfMhXACQAAwBt+c0IAUFPOnDnDva979+595MiR9+/fV3PM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\neWAAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 750,
       "width": 750
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# png(\"bigdfChrSizeHolo2.png\", width=700, height=600)\n",
    "# par(mar=c(1,1,1,1))\n",
    "par(mar=rep(0,4)) \n",
    "\n",
    "options(repr.plot.width = 1500/200, repr.plot.height = 1500/200, repr.plot.res = 200)\n",
    "suppressMessages(\n",
    "plotIdiograms(dfChrSize=bigdfChrSizeHoloMb,  # chr. size data.frame\n",
    "              dfMarkColor=dfMarkColor,       # df of mark style\n",
    "              dfMarkPos=bigdfMarkPosHoloMb,  # df of mark positions\n",
    "              \n",
    "              markDistType=\"cen\",            # distance to mark is to its center\n",
    "              squareness=4,                  # vertices squareness of chr. and marks \n",
    "              distTextChr = .5,              # separ. chr. to text\n",
    "              \n",
    "              karHeight = 2,                 # rel. karyotype height\n",
    "              karHeiSpace = 4,               # karyotype height including spacing\n",
    "              karSepar=TRUE,                 # reduce spacing among karyotypes \n",
    "              amoSepar = 1,                  # depends on karSepar, amount of sep.\n",
    "              \n",
    "              chrId=\"simple\",                # chr. names not \"original\"\n",
    "              chrSize=TRUE,                  # show chr. size under chr.\n",
    "              indexIdTextSize=.9,            # font size of chr names and indices\n",
    "              karIndex = FALSE,              # do not add karyotype asymmetry index\n",
    "              \n",
    "              rulerNumberSize=.9,            # font size of ruler\n",
    "              rulerPos = 0,                  # position of ruler\n",
    "              ruler.tck= -.004,              # ruler tick length and orient.\n",
    "              xPosRulerTitle = 3.5,          # modifies position of ruler title (Mb)\n",
    "              \n",
    "              markLabelSize=.9,              # font size of legend\n",
    "              legendWidth = 1.2,             # width of legends\n",
    "              \n",
    "              xlimLeftMod = 1,               # modify left argument of xlim\n",
    "              ylimBotMod=.4                  # modify bottom argument of ylim\n",
    "              ,chromatids=FALSE              # do not show chromatids\n",
    "              )        \n",
    ")\n",
    "#dev.off()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f1c2f0b",
   "metadata": {},
   "source": [
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec8807a5",
   "metadata": {},
   "source": [
    "For another example see:\n",
    "https://stackoverflow.com/questions/33727432/how-to-plot-positions-along-a-chromosome-graphic/57153497#57153497\n",
    "\n",
    "\n",
    "## Using `threshold` to fix scale\n",
    "\n",
    "The default value of `35` for `threshold` may shrink one of the OTUs of this example more than expected. In this case `threshold` must be bigger."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cee969cd",
   "metadata": {
    "attributes": {
     "classes": [],
     "echo": "TRUE,",
     "fig.height": "7,",
     "fig.width": "7,",
     "id": "",
     "message": "FALSE,dev='png',",
     "results": "\"hide\",",
     "warning": "FALSE"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7u6VLl0rHe/bs0bSvW7fu1q1bQoinn35a7+LEL774onSQkJBQS9qG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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 750,
       "width": 750
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig.width=7, fig.height=7\n",
    "bigdfOfChrSize3_100Mb<-bigdfOfChrSize3Mb\n",
    "bigdfOfChrSize3_100Mb$chrSize<-bigdfOfChrSize3Mb$chrSize*33\n",
    "\n",
    "bigdfOfMarks3_100Mb<-bigdfOfMarks3Mb\n",
    "bigdfOfMarks3_100Mb$markPos<-bigdfOfMarks3_100Mb$markPos*33\n",
    "bigdfOfMarks3_100Mb$markSize<-bigdfOfMarks3_100Mb$markSize*33\n",
    "\n",
    "par(mar=rep(0,4))\n",
    "options(repr.plot.width = 1500/200, repr.plot.height = 1500/200, repr.plot.res = 200)\n",
    "suppressMessages(\n",
    "plotIdiograms(dfChrSize   = bigdfOfChrSize3_100Mb,  # chr. size data.frame\n",
    "              dfMarkPos   = bigdfOfMarks3_100Mb,    # mark position df\n",
    "              \n",
    "              chrWidth=.6,                # width of chr.\n",
    "              chrSpacing = .6,            # space among chr.\n",
    "              karHeight = 3,              # kar. height without interspace\n",
    "              karHeiSpace = 5,            # vertical size of karyotype including spacer\n",
    "              amoSepar =2,                # separ. among kar.\n",
    "              \n",
    "              indexIdTextSize=.6,         # font size of chr. name and indices\n",
    "              markLabelSize=.7,           # font size of mark legends\n",
    "              distTextChr = .65,          # separation among chr. names and indices\n",
    "              \n",
    "              fixCenBorder = TRUE         # use chrColor as border color of cen. or cen. marks\n",
    "              ,legendWidth = 1.5          # legend items width\n",
    "              \n",
    "              ,xPosRulerTitle = 3.5       # position of Mb (title) in ruler               \n",
    "              ,rulerPos= 0,               # ruler position\n",
    "              ruler.tck=-0.005,           # ticks of ruler size and orientation\n",
    "              rulerNumberPos =.7,         # position of numbers in ruler\n",
    "              rulerNumberSize=.7,         # font size of ruler numbers\n",
    "              rulerInterval = 1.5,        # ruler interval for micrometeres\n",
    "              rulerIntervalMb = 50,       # ruler interval for Mb\n",
    "\n",
    "              ylimBotMod = 0.4,           # modify ylim bottom argument\n",
    "              ylimTopMod = 0              # modify ylim top argument\n",
    "              ,chromatids=FALSE           # do not show chromatids\n",
    "              \n",
    "              ,threshold = 90             # this will allow to not to shrink data greater than 350 Mb\n",
    ")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c6cd5e",
   "metadata": {},
   "source": [
    "## Plot data in micrometers and bases\n",
    "\n",
    "Info in number of bases can be combined in the same plot with info. in micrometers.\n",
    "\n",
    "Here the new mark style `cenStyle` is used to add centromeres to \"holocen.\" (genomes).\n",
    "\n",
    "To make the rules fit better, having less excess of length over chr., use `ceilingFactor`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ad01fb4e",
   "metadata": {
    "attributes": {
     "classes": [],
     "comment": "\"#\"",
     "echo": "TRUE,",
     "fig.height": "10,",
     "fig.width": "10,",
     "id": "",
     "message": "FALSE,dev='png',",
     "warning": "FALSE,collapse=TRUE,"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBy default 5S are plotted as dots, to change this behavior make your own dfMarkColor data.frame\u001b[39m\n",
      "\n",
      "\u001b[34mNot enough colors in mycolor parameter, will be recycled\u001b[39m\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 8 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>markName</th><th scope=col>markColor</th><th scope=col>style</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>5S       </td><td>red           </td><td>dots    </td></tr>\n",
       "\t<tr><td>45S      </td><td>chartreuse3   </td><td>square  </td></tr>\n",
       "\t<tr><td>DAPI     </td><td>dodgerblue    </td><td>square  </td></tr>\n",
       "\t<tr><td>CMA      </td><td>darkgoldenrod1</td><td>square  </td></tr>\n",
       "\t<tr><td>cDAPI    </td><td>dodgerblue    </td><td>cenStyle</td></tr>\n",
       "\t<tr><td>cCMA     </td><td>darkgoldenrod1</td><td>cenStyle</td></tr>\n",
       "\t<tr><td>sim. cen.</td><td>NA            </td><td>cenStyle</td></tr>\n",
       "\t<tr><td>S58A     </td><td>red           </td><td>upArrow </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 8 × 3\n",
       "\\begin{tabular}{lll}\n",
       " markName & markColor & style\\\\\n",
       " <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 5S        & red            & dots    \\\\\n",
       "\t 45S       & chartreuse3    & square  \\\\\n",
       "\t DAPI      & dodgerblue     & square  \\\\\n",
       "\t CMA       & darkgoldenrod1 & square  \\\\\n",
       "\t cDAPI     & dodgerblue     & cenStyle\\\\\n",
       "\t cCMA      & darkgoldenrod1 & cenStyle\\\\\n",
       "\t sim. cen. & NA             & cenStyle\\\\\n",
       "\t S58A      & red            & upArrow \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 8 × 3\n",
       "\n",
       "| markName &lt;chr&gt; | markColor &lt;chr&gt; | style &lt;chr&gt; |\n",
       "|---|---|---|\n",
       "| 5S        | red            | dots     |\n",
       "| 45S       | chartreuse3    | square   |\n",
       "| DAPI      | dodgerblue     | square   |\n",
       "| CMA       | darkgoldenrod1 | square   |\n",
       "| cDAPI     | dodgerblue     | cenStyle |\n",
       "| cCMA      | darkgoldenrod1 | cenStyle |\n",
       "| sim. cen. | NA             | cenStyle |\n",
       "| S58A      | red            | upArrow  |\n",
       "\n"
      ],
      "text/plain": [
       "  markName  markColor      style   \n",
       "1 5S        red            dots    \n",
       "2 45S       chartreuse3    square  \n",
       "3 DAPI      dodgerblue     square  \n",
       "4 CMA       darkgoldenrod1 square  \n",
       "5 cDAPI     dodgerblue     cenStyle\n",
       "6 cCMA      darkgoldenrod1 cenStyle\n",
       "7 sim. cen. NA             cenStyle\n",
       "8 S58A      red            upArrow "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IqKGjJkyGBaKNqR0WjMz88vLCx0VjwaGRkZExPj6enplGoAAABwKwSjAADg/zQ1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\nvHnznn/++crKysWLF48cOTIlJSUpKenMmTPd1Fy1atXq1asrKio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sXF1d1c4CAAAAgJmi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\nGwDuCOpaAAAgAElEQVS0JZ9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UuXRkRE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\nBQAZ2bBhg+q/TE1NNba5evWqEKJLly6Nd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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 800,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig.width=10, fig.height=10\n",
    "# modify data in millions to hundreds of millions of Mb\n",
    "bigdfOfChrSize3_100Mb<-bigdfOfChrSize3Mb[1:8,]\n",
    "bigdfOfChrSize3_100Mb$chrSize<-bigdfOfChrSize3_100Mb$chrSize*100\n",
    "\n",
    "bigdfOfMarks3_100Mb<-bigdfOfMarks3Mb\n",
    "bigdfOfMarks3_100Mb$markPos <-bigdfOfMarks3_100Mb$markPos *100\n",
    "bigdfOfMarks3_100Mb$markSize<-bigdfOfMarks3_100Mb$markSize*100\n",
    "\n",
    "# merge data.frames in micrometers and number of bases\n",
    "mixedThreeSpChrSize <- plyr::rbind.fill(bigdfOfChrSize[1:8,], bigdfOfChrSize3_100Mb)\n",
    "# sort by OTU name\n",
    "mixedThreeSpChrSize <- mixedThreeSpChrSize[order(mixedThreeSpChrSize$OTU),]\n",
    "\n",
    "# add cenStyle marks to simulate centromeres in karyo. in Mb (holocen.)\n",
    "# compare rulers\n",
    "bigdfSimCenMarks<- bigdfOfChrSize3_100Mb\n",
    "bigdfSimCenMarks$markPos<-bigdfSimCenMarks$chrSize/2\n",
    "\n",
    "bigdfSimCenMarks$markName<-\"sim. cen.\"\n",
    "bigdfSimCenMarks$chrSize<-NULL\n",
    "\n",
    "# merge marks in micrometers and bases\n",
    "mixedThreeSpMarks <- plyr::rbind.fill(bigdfOfMarks , bigdfOfMarks3_100Mb,bigdfSimCenMarks)\n",
    "\n",
    "# remove cenStyle mark info.\n",
    "mixedThreeSpMarks<-mixedThreeSpMarks[which(!( mixedThreeSpMarks$OTU %in% \"Species 2 genome\" & \n",
    "                          mixedThreeSpMarks$chrName %in% c(1,4) &\n",
    "                          mixedThreeSpMarks$markName %in% \"sim. cen.\") ),]\n",
    "\n",
    "# constric. marks\n",
    "mixedThreeSpMarks[which(mixedThreeSpMarks$OTU %in% \"Species 2 genome\" & \n",
    "                          mixedThreeSpMarks$chrName %in% c(1,4) ),]$markName<-c(\"cDAPI\",\"cCMA\")\n",
    "                        \n",
    "# add arrow mark\n",
    "mixedThreeSpMarks <- dplyr::bind_rows(mixedThreeSpMarks , mixedThreeSpMarks[nrow(mixedThreeSpMarks),] )\n",
    "mixedThreeSpMarks[nrow(mixedThreeSpMarks),]$markName<-\"S58A\"\n",
    "mixedThreeSpMarks[nrow(mixedThreeSpMarks),]$markPos<-.7e+08\n",
    "mixedThreeSpMarks[nrow(mixedThreeSpMarks),]$markSize<-.7e+08\n",
    "  \n",
    "dfMarkColorAndStyle<-makedfMarkColorMycolors(unique(mixedThreeSpMarks$markName),\n",
    "                                                            c(\"red\",\"chartreuse3\",\"dodgerblue\",\"darkgoldenrod1\",\"dodgerblue\",\"darkgoldenrod1\",\"black\")\n",
    ")\n",
    "\n",
    "# d.f. of marks'styles\n",
    "\n",
    "dfMarkColorAndStyle$style[5:7]<-\"cenStyle\"\n",
    "dfMarkColorAndStyle$markColor[7]<-NA\n",
    "dfMarkColorAndStyle$style[8]<-\"upArrow\"\n",
    "\n",
    "dfMarkColorAndStyle\n",
    "\n",
    "par(mar=rep(0,4))\n",
    "options(repr.plot.width = 1800/200, repr.plot.height = 1600/200, repr.plot.res = 200)\n",
    "suppressMessages(\n",
    "plotIdiograms(dfChrSize   = mixedThreeSpChrSize,  # chr. size data.frame\n",
    "              dfMarkPos   = mixedThreeSpMarks,    # mark position df\n",
    "              dfMarkColor = dfMarkColorAndStyle,\n",
    "              \n",
    "              chrWidth=.6,                # width of chr.\n",
    "              chrSpacing = .6,            # space among chr.\n",
    "              karHeight = 3,              # kar. height without interspace\n",
    "              karHeiSpace = 5,            # vertical size of karyotype including spacer\n",
    "              amoSepar =2,                # separ. among kar.\n",
    "              \n",
    "              indexIdTextSize=.6,         # font size of chr. name and indices\n",
    "              markLabelSize=.7,           # font size of mark legends\n",
    "              distTextChr = .65,          # separation among chr. names and indices\n",
    "              lwd.mimicCen = 1.5,         # constric. line width\n",
    "              \n",
    "              legendWidth = 1.5,          # legend items width\n",
    "              fixCenBorder = TRUE,        # use chrColor as border color of cen. or cen. marks\n",
    "              \n",
    "              xPosRulerTitle = 4.5,       # position of Mb (title) in ruler\n",
    "              rulerPos= 0,                # ruler position\n",
    "              ruler.tck=-0.005,           # ticks of ruler size and orientation\n",
    "              rulerNumberPos =.7,         # position of numbers in ruler\n",
    "              rulerNumberSize=.7,         # font size of ruler numbers\n",
    "              rulerInterval = 1.5,        # ruler interval for micrometeres\n",
    "              rulerIntervalMb = 150,      # ruler interval for Mb\n",
    "              ceilingFactor = 1,          # affects rounding for ruler max. value\n",
    "              \n",
    "              ylimBotMod = 0.4,           # modify ylim bottom argument\n",
    "              ylimTopMod = 0              # modify ylim top argument\n",
    "              ,holocenNotAsChromatids = TRUE # do not use chromatids in holocen.\n",
    "              ,pattern=\"^c\"               # regex pattern to remove from mark names\n",
    "              ,remSimiMarkLeg = TRUE      # remove pseudoduplicate names arising from pattern removal\n",
    ")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14c4c8c5",
   "metadata": {},
   "source": [
    "</div>\n",
    "\n",
    "Let's explore those data.frames\n",
    "\n",
    "<div class=\"pre-scrollx\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "38d6358e",
   "metadata": {
    "attributes": {
     "classes": [],
     "eval": "F",
     "id": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>OTU</th><th scope=col>chrName</th><th scope=col>shortArmSize</th><th scope=col>longArmSize</th><th scope=col>chrSize</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;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>Species 1       </td><td>1</td><td>1.5</td><td>2.0</td><td>     NA</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Species 1       </td><td>2</td><td>2.0</td><td>2.5</td><td>     NA</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Species 1       </td><td>3</td><td>1.0</td><td>2.0</td><td>     NA</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>Species 1 genome</td><td>1</td><td> NA</td><td> NA</td><td>3.5e+08</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>Species 1 genome</td><td>2</td><td> NA</td><td> NA</td><td>4.5e+08</td></tr>\n",
       "\t<tr><th scope=row>11</th><td>Species 1 genome</td><td>3</td><td> NA</td><td> NA</td><td>2.5e+08</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & OTU & chrName & shortArmSize & longArmSize & chrSize\\\\\n",
       "  & <chr> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Species 1        & 1 & 1.5 & 2.0 &      NA\\\\\n",
       "\t2 & Species 1        & 2 & 2.0 & 2.5 &      NA\\\\\n",
       "\t3 & Species 1        & 3 & 1.0 & 2.0 &      NA\\\\\n",
       "\t9 & Species 1 genome & 1 &  NA &  NA & 3.5e+08\\\\\n",
       "\t10 & Species 1 genome & 2 &  NA &  NA & 4.5e+08\\\\\n",
       "\t11 & Species 1 genome & 3 &  NA &  NA & 2.5e+08\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| <!--/--> | OTU &lt;chr&gt; | chrName &lt;chr&gt; | shortArmSize &lt;dbl&gt; | longArmSize &lt;dbl&gt; | chrSize &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 1 | Species 1        | 1 | 1.5 | 2.0 |      NA |\n",
       "| 2 | Species 1        | 2 | 2.0 | 2.5 |      NA |\n",
       "| 3 | Species 1        | 3 | 1.0 | 2.0 |      NA |\n",
       "| 9 | Species 1 genome | 1 |  NA |  NA | 3.5e+08 |\n",
       "| 10 | Species 1 genome | 2 |  NA |  NA | 4.5e+08 |\n",
       "| 11 | Species 1 genome | 3 |  NA |  NA | 2.5e+08 |\n",
       "\n"
      ],
      "text/plain": [
       "   OTU              chrName shortArmSize longArmSize chrSize\n",
       "1  Species 1        1       1.5          2.0              NA\n",
       "2  Species 1        2       2.0          2.5              NA\n",
       "3  Species 1        3       1.0          2.0              NA\n",
       "9  Species 1 genome 1        NA           NA         3.5e+08\n",
       "10 Species 1 genome 2        NA           NA         4.5e+08\n",
       "11 Species 1 genome 3        NA           NA         2.5e+08"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(mixedThreeSpChrSize,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb87a10d",
   "metadata": {},
   "source": [
    "</div>\n",
    "\n",
    "<div class=\"verysmall\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96b05146",
   "metadata": {},
   "source": [
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c90b4b03",
   "metadata": {
    "attributes": {
     "classes": [],
     "eval": "F",
     "id": ""
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 11 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>OTU</th><th scope=col>chrName</th><th scope=col>markName</th><th scope=col>chrRegion</th><th scope=col>markDistCen</th><th scope=col>markSize</th><th scope=col>markPos</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;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><th scope=row>1</th><td>Species 1       </td><td>1</td><td>5S       </td><td>p </td><td>0.5</td><td>1e+00</td><td>      NA</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Species 1       </td><td>1</td><td>45S      </td><td>q </td><td>0.5</td><td>1e+00</td><td>      NA</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Species 1       </td><td>2</td><td>45S      </td><td>p </td><td>1.0</td><td>1e+00</td><td>      NA</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Species 1       </td><td>3</td><td>DAPI     </td><td>q </td><td>1.0</td><td>1e+00</td><td>      NA</td></tr>\n",
       "\t<tr><th scope=row>13</th><td>Species 1 genome</td><td>1</td><td>5S       </td><td>NA</td><td> NA</td><td>1e+08</td><td>2.50e+08</td></tr>\n",
       "\t<tr><th scope=row>14</th><td>Species 1 genome</td><td>1</td><td>45S      </td><td>NA</td><td> NA</td><td>1e+08</td><td>5.00e+07</td></tr>\n",
       "\t<tr><th scope=row>15</th><td>Species 1 genome</td><td>2</td><td>45S      </td><td>NA</td><td> NA</td><td>1e+08</td><td>3.50e+08</td></tr>\n",
       "\t<tr><th scope=row>16</th><td>Species 1 genome</td><td>3</td><td>DAPI     </td><td>NA</td><td> NA</td><td>1e+08</td><td>0.00e+00</td></tr>\n",
       "\t<tr><th scope=row>25</th><td>Species 1 genome</td><td>1</td><td>sim. cen.</td><td>NA</td><td> NA</td><td>   NA</td><td>1.75e+08</td></tr>\n",
       "\t<tr><th scope=row>26</th><td>Species 1 genome</td><td>2</td><td>sim. cen.</td><td>NA</td><td> NA</td><td>   NA</td><td>2.25e+08</td></tr>\n",
       "\t<tr><th scope=row>27</th><td>Species 1 genome</td><td>3</td><td>sim. cen.</td><td>NA</td><td> NA</td><td>   NA</td><td>1.25e+08</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 11 × 7\n",
       "\\begin{tabular}{r|lllllll}\n",
       "  & OTU & chrName & markName & chrRegion & markDistCen & markSize & markPos\\\\\n",
       "  & <chr> & <chr> & <chr> & <chr> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t1 & Species 1        & 1 & 5S        & p  & 0.5 & 1e+00 &       NA\\\\\n",
       "\t2 & Species 1        & 1 & 45S       & q  & 0.5 & 1e+00 &       NA\\\\\n",
       "\t3 & Species 1        & 2 & 45S       & p  & 1.0 & 1e+00 &       NA\\\\\n",
       "\t4 & Species 1        & 3 & DAPI      & q  & 1.0 & 1e+00 &       NA\\\\\n",
       "\t13 & Species 1 genome & 1 & 5S        & NA &  NA & 1e+08 & 2.50e+08\\\\\n",
       "\t14 & Species 1 genome & 1 & 45S       & NA &  NA & 1e+08 & 5.00e+07\\\\\n",
       "\t15 & Species 1 genome & 2 & 45S       & NA &  NA & 1e+08 & 3.50e+08\\\\\n",
       "\t16 & Species 1 genome & 3 & DAPI      & NA &  NA & 1e+08 & 0.00e+00\\\\\n",
       "\t25 & Species 1 genome & 1 & sim. cen. & NA &  NA &    NA & 1.75e+08\\\\\n",
       "\t26 & Species 1 genome & 2 & sim. cen. & NA &  NA &    NA & 2.25e+08\\\\\n",
       "\t27 & Species 1 genome & 3 & sim. cen. & NA &  NA &    NA & 1.25e+08\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 11 × 7\n",
       "\n",
       "| <!--/--> | OTU &lt;chr&gt; | chrName &lt;chr&gt; | markName &lt;chr&gt; | chrRegion &lt;chr&gt; | markDistCen &lt;dbl&gt; | markSize &lt;dbl&gt; | markPos &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 1 | Species 1        | 1 | 5S        | p  | 0.5 | 1e+00 |       NA |\n",
       "| 2 | Species 1        | 1 | 45S       | q  | 0.5 | 1e+00 |       NA |\n",
       "| 3 | Species 1        | 2 | 45S       | p  | 1.0 | 1e+00 |       NA |\n",
       "| 4 | Species 1        | 3 | DAPI      | q  | 1.0 | 1e+00 |       NA |\n",
       "| 13 | Species 1 genome | 1 | 5S        | NA |  NA | 1e+08 | 2.50e+08 |\n",
       "| 14 | Species 1 genome | 1 | 45S       | NA |  NA | 1e+08 | 5.00e+07 |\n",
       "| 15 | Species 1 genome | 2 | 45S       | NA |  NA | 1e+08 | 3.50e+08 |\n",
       "| 16 | Species 1 genome | 3 | DAPI      | NA |  NA | 1e+08 | 0.00e+00 |\n",
       "| 25 | Species 1 genome | 1 | sim. cen. | NA |  NA |    NA | 1.75e+08 |\n",
       "| 26 | Species 1 genome | 2 | sim. cen. | NA |  NA |    NA | 2.25e+08 |\n",
       "| 27 | Species 1 genome | 3 | sim. cen. | NA |  NA |    NA | 1.25e+08 |\n",
       "\n"
      ],
      "text/plain": [
       "   OTU              chrName markName  chrRegion markDistCen markSize markPos \n",
       "1  Species 1        1       5S        p         0.5         1e+00          NA\n",
       "2  Species 1        1       45S       q         0.5         1e+00          NA\n",
       "3  Species 1        2       45S       p         1.0         1e+00          NA\n",
       "4  Species 1        3       DAPI      q         1.0         1e+00          NA\n",
       "13 Species 1 genome 1       5S        NA         NA         1e+08    2.50e+08\n",
       "14 Species 1 genome 1       45S       NA         NA         1e+08    5.00e+07\n",
       "15 Species 1 genome 2       45S       NA         NA         1e+08    3.50e+08\n",
       "16 Species 1 genome 3       DAPI      NA         NA         1e+08    0.00e+00\n",
       "25 Species 1 genome 1       sim. cen. NA         NA            NA    1.75e+08\n",
       "26 Species 1 genome 2       sim. cen. NA         NA            NA    2.25e+08\n",
       "27 Species 1 genome 3       sim. cen. NA         NA            NA    1.25e+08"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mixedThreeSpMarks[which(mixedThreeSpMarks$OTU %in% c(\"Species 1\",\"Species 1 genome\") ),] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb089ec1",
   "metadata": {},
   "source": [
    "<div class=\"verysmall\">"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b3cb16f",
   "metadata": {},
   "source": [
    "## Use cM as units\n",
    "\n",
    "Info in cM can be combined in the same plot with info. in micrometers.\n",
    "\n",
    "To make the rules fit better, having less excess of length over chr., use `ceilingFactor`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "95cb3955",
   "metadata": {
    "attributes": {
     "classes": [],
     "echo": "TRUE,",
     "fig.height": "12,",
     "fig.width": "9,",
     "id": "",
     "message": "FALSE,dev='png',",
     "results": "\"hide\",",
     "warning": "FALSE"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32mBy default 5S are plotted as dots, to change this behavior make your own dfMarkColor data.frame\u001b[39m\n",
      "\n",
      "\u001b[34mNot enough colors in mycolor parameter, will be recycled\u001b[39m\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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TAKALAuFy9eFEJMnjy5ybhh5NKlSxboqSVqtfrQoUM9e/YcOXLkzJkzhRBGPE2/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\nALaFYBQAgLakpaVdunQpJiam4Xn5xmbPnr1+/fqkpCRfX9+0tDQ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      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 800,
       "width": 900
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig.width=10, fig.height=10\n",
    "# merge data.frames in micrometers and cM\n",
    "bigdfOfChrSize3cM<-bigdfOfChrSize3Mb[1:8,]\n",
    "bigdfOfChrSize3cM$chrSize<-bigdfOfChrSize3cM$chrSize/100000\n",
    "mixedThreeSpChrSize <- plyr::rbind.fill(bigdfOfChrSize[1:8,], bigdfOfChrSize3cM)\n",
    "\n",
    "# sort by OTU name\n",
    "mixedThreeSpChrSize <- mixedThreeSpChrSize[order(mixedThreeSpChrSize$OTU),]\n",
    "\n",
    "# create data with cM. markSize col. is not necessary because style is cM\n",
    "bigdfOfMarks3cM<-bigdfOfMarks3Mb\n",
    "bigdfOfMarks3cM$markPos<-bigdfOfMarks3Mb$markPos/100000\n",
    "bigdfOfMarks3cM$markSize<-NA\n",
    "# As we want only the cM idiograms to be plotted as cM (lines), change mark names \n",
    "bigdfOfMarks3cM$markName<-paste0(\"cM\",bigdfOfMarks3cM$markName)\n",
    "\n",
    "# d.f of all marks\n",
    "mixedThreeSpMarks <- plyr::rbind.fill(bigdfOfMarks , bigdfOfMarks3cM)\n",
    "\n",
    "# create a data.frame with mark characteristics\n",
    "mixedDfMarkStyle  <- makedfMarkColorMycolors(unique(mixedThreeSpMarks$markName), \n",
    "                                                            c(\"red\",\"chartreuse3\",\"dodgerblue\",\"darkgoldenrod1\")\n",
    "                                                            )\n",
    "\n",
    "# mark names of cM marks with \"cM\" style (lines): not dots, not squares\n",
    "mixedDfMarkStyle[which(mixedDfMarkStyle$markName %in% \n",
    "                         grep(\"cM\", mixedDfMarkStyle$markName, value=TRUE) ) ,]$style<-\"cM\"\n",
    "\n",
    "par(mar=rep(0,4))\n",
    "options(repr.plot.width = 1800/200, repr.plot.height = 1600/200, repr.plot.res = 200)\n",
    "suppressMessages(\n",
    "plotIdiograms(dfChrSize   = mixedThreeSpChrSize,  # chr. size data.frame\n",
    "              dfMarkPos   = mixedThreeSpMarks,    # mark position data.frame\n",
    "              dfMarkColor = mixedDfMarkStyle,     # mark style data.frame\n",
    "              \n",
    "              chrWidth=.6,                # width of chr.\n",
    "              chrSpacing = .7,            # space among chr.\n",
    "              \n",
    "              specialOTUNames = bigdfOfMarks3cM$OTU, # OTUs in this object will have different ruler units \n",
    "              specialyTitle = \"cM\",       # ruler title for specialOTUNames\n",
    "              specialChrWidth = .2,       # modify chr width of OTUs in specialOTUNames\n",
    "              specialChrSpacing = 1.1,    # modify chr spacing of OTUs in specialOTUNames\n",
    "\n",
    "              karHeight = 3,              # kar. height without interspace\n",
    "              karHeiSpace = 6,            # vertical size of karyotype including spacer\n",
    "              amoSepar = 3,               # separ. among kar.\n",
    "              \n",
    "              chrSize=TRUE,               # show chr. size under chr.\n",
    "              indexIdTextSize=.6,         # font size of chr. name and indices\n",
    "              distTextChr = .85,          # separation among chr. names and indices\n",
    "              \n",
    "              protruding = 1,             # extension of cM mark type\n",
    "              pattern = \"cM\",             # regex pattern to remove from mark names\n",
    "              markLabelSize=.7            # font size of mark legends\n",
    "              ,legendWidth = 2            # legend items width\n",
    "              ,fixCenBorder = TRUE        # use chrColor as border color of cen. or cen. marks\n",
    "              ,lwd.cM = 2                 # thickness of cM marks \n",
    "              ,holocenNotAsChromatids=TRUE# do not use chromatids in holocen. kar.\n",
    "              \n",
    "              ,xPosRulerTitle = 4         # position of Mb or cM (title) in ruler               \n",
    "              ,rulerPos= 0,               # ruler position\n",
    "              ruler.tck=-0.005,           # ticks of ruler size and orientation\n",
    "              rulerNumberPos =.7,         # position of numbers in ruler\n",
    "              rulerNumberSize=0.7,        # font size of ruler numbers\n",
    "              rulerIntervalcM = 12,       # ruler interval for OTU in specialOTUnames and MbThreshold not met\n",
    "              ceilingFactor = 1,          # affects max. value in ruler. See also rulerInterval\n",
    "              \n",
    "              ylimBotMod = 0.4,           # modify ylim bottom argument\n",
    "              ylimTopMod = 0              # modify ylim top argument\n",
    ")\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
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  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
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   "version": "4.1.1"
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