{
"cells": [
{
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"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": "bb97bc3c-692a-404e-bc62-6ba0e442397f",
"metadata": {},
"source": [
"# Plotting alongside phylogeny"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ce53f14c-feba-4855-af7a-bcb5901803cb",
"metadata": {},
"outputs": [],
"source": [
"# General jupyter instructions"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "206c3e31-cc39-40e9-a4c4-4173d9ae6312",
"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": 3,
"id": "b1a73a58-ae83-41c5-852a-788c9c1149f5",
"metadata": {
"tags": []
},
"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": "07fb5d02",
"metadata": {},
"source": [
"This guide shows the files to plot idiograms alongside a phylogeny "
]
},
{
"cell_type": "markdown",
"id": "5237bca2",
"metadata": {},
"source": [
"## ggtree of iqtree and monocentrics\n",
"\n",
"idiogramFISH comes with two trees and data.frames with chr. and marks' data for the correspondent OTUs, first we will plot a tree produced with iqtree [@Nguyen2015c]\n",
"\n",
"### Load the iqtree:\n",
"\n",
"We will use phytools for that [@phytools2012]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "16c53995-9e2f-4e6a-83de-9e7de73f4b51",
"metadata": {},
"outputs": [],
"source": [
"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) {\n",
" install.packages(\"BiocManager\")\n",
" }\n",
"if (system.file(package = \"ggtree\") == '') {\n",
" BiocManager::install(\"ggtree\")\n",
" }\n",
"if (system.file(package = \"treeio\") == '') {\n",
" BiocManager::install(\"treeio\")\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a75501e5-c11d-4bdd-a993-38ea9c34fc2a",
"metadata": {},
"outputs": [],
"source": [
"neededPkg <- c(\"grid\",\"ggpubr\",\"phytools\",\"plyr\")\n",
"invisible(\n",
"lapply(neededPkg, function(pkg) {\n",
" if (system.file(package = pkg) == '') {\n",
" install.packages(pkg)\n",
" }\n",
"})\n",
")"
]
},
{
"cell_type": "markdown",
"id": "50124e62",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "fa501ddf",
"metadata": {
"attributes": {
"classes": [],
"eval": "packageCheck",
"id": "",
"message": "FALSE,"
}
},
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"source": [
"suppressPackageStartupMessages({\n",
"require(ggplot2)\n",
"require(phytools)\n",
"require(ggpubr)\n",
"require(grid) #pushViewport\n",
"require(ggtree)\n",
"})\n",
"# list.files(system.file('extdata', package = 'my_package') )\n",
"\n",
"# find path of iqtree file\n",
"iqtreeFile <- system.file(\"extdata\", \"eightSpIqtree.treefile\", package = \"idiogramFISH\")\n",
"\n",
"# load file as phylo object\n",
"iqtreephylo <- read.newick(iqtreeFile) # phytools\n",
"\n",
"# transform tree\n",
"iqtreephyloUM <- force.ultrametric(iqtreephylo, method= \"extend\") # phytools\n"
]
},
{
"cell_type": "markdown",
"id": "8f614092",
"metadata": {},
"source": [
"### Make a ggtree [@ggtree2018]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "20fd7f79",
"metadata": {
"attributes": {
"classes": [],
"eval": "packageCheck",
"id": "",
"message": "FALSE,"
}
},
"outputs": [],
"source": [
"ggtreeOf8 <- ggtree(iqtreephyloUM) + geom_tiplab(size=6)"
]
},
{
"cell_type": "markdown",
"id": "c9b2761d",
"metadata": {},
"source": [
"Modify optionally graphical parameters with ggplot and ggpubr: [@ggplot22016; @R-ggpubr]\n",
"\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "3a528431",
"metadata": {
"attributes": {
"classes": [],
"id": "",
"message": "FALSE,eval=packageCheck"
}
},
"outputs": [],
"source": [
"gbuil2 <- ggplot_build(ggtreeOf8) # get ggplot_built\n",
"gtgbuild <- ggplot_gtable(gbuil2) # get gtable from ggplot_built\n",
"gtgbuild$layout$clip[gtgbuild$layout$name == \"panel\"] <- \"off\" # modify gtable\n",
"ggtreeOf8b <- as_ggplot(gtgbuild) # back to ggplot\n",
"gtgbuildgg2 <- ggtreeOf8b + theme(plot.margin = unit(c(1,9.5,3,1.5), \"cm\") ) # top right bottom left - modify margins"
]
},
{
"cell_type": "markdown",
"id": "6721720c",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"id": "40eee22b-0130-40a6-87ec-dd480591d86b",
"metadata": {
"attributes": {
"classes": [],
"id": "",
"message": "FALSE,eval=packageCheck"
}
},
"source": [
"### Order OTUs in data.frame of chr. data\n",
"\n",
"Apply order of phylogeny to the data.frame"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "5d7865c1-e981-45e1-9e13-aa217f568b35",
"metadata": {},
"outputs": [],
"source": [
"# Let's get the order of species in tree\n",
"ggtreeOf8TIPS <- ggtreeOf8$data[which(ggtreeOf8$data$isTip),]\n",
"desiredOrder <- rev(ggtreeOf8TIPS[order(ggtreeOf8TIPS$y),]$label)\n",
"\n",
"# make a vector without missing OTUs\n",
"desiredFiltered <- intersect(desiredOrder,allChrSizeSample$OTU)\n",
"\n",
"# establish desired order\n",
"allChrSizeSample$OTU <- factor(allChrSizeSample$OTU, levels = desiredFiltered)\n",
"\n",
"# order\n",
"allChrSizeSample <- allChrSizeSample[order(allChrSizeSample$OTU),]"
]
},
{
"cell_type": "markdown",
"id": "23b8794e",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "a81a17de",
"metadata": {
"attributes": {
"classes": [],
"id": "",
"message": "FALSE,eval=packageCheck"
}
},
"outputs": [],
"source": [
"# Establish position of OTUs before missing data OTUs\n",
"matchres <- match(desiredOrder,desiredFiltered)\n",
"matchres[is.na(matchres)] <- \"R\"\n",
"reps <- rle(matchres)\n",
"posOTUsBeforeMissing <- as.numeric(matchres[which(matchres==\"R\")-1][which(matchres[which(matchres==\"R\")-1]!=\"R\")] )\n",
"\n",
"# This are the OTUs that come before missing chr. data OTUs\n",
"BeforeMissing <- desiredFiltered[posOTUsBeforeMissing]\n",
"\n",
"# This is the amount of missing OTUs, spaces to add (ghost karyotypes)\n",
"valuesOfMissRepsBefore <- reps$lengths[which(reps$values==\"R\")]"
]
},
{
"cell_type": "markdown",
"id": "81997b30",
"metadata": {},
"source": [
"### Plotting\n",
"\n",
"Now we are ready to plot adding those arguments for `addMissingOTUAfter` and `missOTUspacings`\n"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "9a7ac530",
"metadata": {
"attributes": {
"classes": [],
"comment": "NA,",
"echo": "T",
"id": "",
"message": "FALSE,",
"results": "\"hide\",eval=FALSE,"
}
},
"outputs": [
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1WiaTlZWV9evXj1LM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\nz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"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
"height": 1750,
"width": 1900
}
},
"output_type": "display_data"
}
],
"source": [
"# plot to png file\n",
"#png(file=\"firstplot.png\" ,width=962,height=962 )\n",
"\n",
"options(repr.plot.width = 3800/300, repr.plot.height = 3500/300, repr.plot.res = 300)\n",
"\n",
"par(omi=rep(0,4), \n",
" mar=c(0,1,2,1), \n",
" mfrow=c(1,2) ) # one row two columns\n",
"\n",
"par(fig=c(0,.3,0,1)) # location of left ghost plot\n",
"\n",
"plot.new() # ghost plot to the left\n",
"par(fig=c(.3,1,0,1)) # location of right plot\n",
"\n",
"suppressMessages(\n",
"plotIdiograms(dfChrSize = allChrSizeSample, # data.frame of Chr. Sizes\n",
" dfMarkPos = allMarksSample, # d.f. of Marks (inc. cen. marks) \n",
" dfMarkColor = mydfMaColor, # d.f. of mark characteristics\n",
" \n",
" squareness = 4, # squareness of vertices\n",
" lwd.chr=.5, # width of lines\n",
" orderChr = \"name\", # order chr. by name\n",
" centromereSize = 1.3, # apparent cen. size\n",
" chrWidth =.75, # width of chr.\n",
" chrSpacing = .25, # horizontal spacing of chr.\n",
" indexIdTextSize=.4, # font size of indices and chr. names\n",
" \n",
" karHeight = 4.8, # karyotype vertical relative size without spacing\n",
" karHeiSpace = 6.5, # karyotype vertical relative size with spacing\n",
" \n",
" nameChrIndexPos=4, # move the name of chr. indexes to left\n",
" morpho=\"both\", # add chr. morphology\n",
" chrIndex = \"both\", # add chr. indices\n",
" karIndex = TRUE, # add karyotype indices\n",
" yTitle= \"\", # remove units title of ruler\n",
" \n",
" markLabelSpacer = 0 # spaces from rightmost chr. to legend\n",
" \n",
" ,ylimTopMod = -.1 # modify ylim top margin\n",
" ,ylimBotMod=2.6 # modify ylim bottom margin\n",
" \n",
" ,rulerPos = -0.5 # position of rulers\n",
" ,rulerNumberSize = .35 # font size of ruler number\n",
" ,rulerNumberPos = .4 # position of ruler numbers\n",
" ,ruler.tck = -.004 # tick size and orient.\n",
" \n",
" ,addMissingOTUAfter = BeforeMissing # OTUs after which there are ghost karyotypes - empty spaces\n",
" ,missOTUspacings = valuesOfMissRepsBefore # number of ghost karyotypes\n",
")\n",
")\n",
"\n",
"# plot to the left the ggtree\n",
"pushViewport(viewport(layout = grid.layout(1, 2)))\n",
"pushViewport(viewport(layout.pos.col = 1, layout.pos.row = 1))\n",
"print(gtgbuildgg2,newpage=F) \n",
"\n",
"# close png\n",
"#dev.off()"
]
},
{
"cell_type": "markdown",
"id": "6b51647d",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"id": "5c26ad96",
"metadata": {},
"source": [
"## plot of revBayes tree and holocentrics\n",
"\n",
"Now we are going to plot a tree from revBayes [@Hohna2017]\n",
"\n",
"### First, load the revBayes tree: {-}\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a2957c47",
"metadata": {
"attributes": {
"classes": [],
"eval": "packageCheck",
"id": "",
"message": "FALSE,"
}
},
"outputs": [],
"source": [
"suppressPackageStartupMessages({\n",
"require(ggplot2)\n",
"require(phytools)\n",
"require(ggpubr)\n",
"require(grid) #pushViewport\n",
"require(ggtree)\n",
"require(treeio)\n",
"})\n",
"# find path of iqtree file\n",
"revBayesFile <- system.file(\"extdata\", \"revBayesTutorial.tree\", package = \"idiogramFISH\")\n",
"\n",
"# load file as phylo object\n",
"revBayesPhylo <- read.beast(revBayesFile) # ggtree or treeio\n",
"\n",
"# transform tree\n",
"revBayesPhyloUM <- force.ultrametric(revBayesPhylo@phylo, method= \"extend\") # phytools"
]
},
{
"cell_type": "markdown",
"id": "0d62562e",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"id": "234c7be1-3725-4c66-bcc7-67c962ba752a",
"metadata": {},
"source": [
"### Order OTUs in data.frame of chr. data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "c8980547",
"metadata": {
"attributes": {
"classes": [],
"id": "",
"message": "FALSE,eval=packageCheck"
}
},
"outputs": [],
"source": [
"# Get order of OTUs in tree\n",
"is_tip <- revBayesPhyloUM$edge[,2] <= length(revBayesPhyloUM$tip.label)\n",
"ordered_tips <- revBayesPhyloUM$edge[is_tip, 2]\n",
"desiredorderRevB <- rev(revBayesPhyloUM$tip.label[ordered_tips])\n",
"\n",
"# Ceate some holocentrics' data\n",
"\n",
"allChrSizeSampleHolo <- allChrSizeSample\n",
"allChrSizeSampleHolo <- allChrSizeSampleHolo[,c(\"OTU\",\"chrName\",\"longArmSize\")]\n",
"colnames(allChrSizeSampleHolo)[which(names(allChrSizeSampleHolo)==\"longArmSize\")]<-\"chrSize\"\n",
"\n",
"allMarksSampleHolo <- allMarksSample\n",
"allMarksSampleHolo <- allMarksSampleHolo[which(allMarksSampleHolo$chrRegion!=\"cen\"),]\n",
"allMarksSampleHolo <- allMarksSampleHolo[c(\"OTU\",\"chrName\",\"markName\",\"markDistCen\",\"markSize\")]\n",
"colnames(allMarksSampleHolo)[which(names(allMarksSampleHolo)==\"markDistCen\")] <- \"markPos\"\n",
"allMarksSampleHolo[which(allMarksSampleHolo$markName==\"5S\"),]$markSize <- .5\n",
"\n",
"# Apply order of phylogeny to data.frame\n",
"\n",
"# make a vector without missing OTUs\n",
"desiredFiltered <- intersect(desiredorderRevB,allChrSizeSampleHolo$OTU)\n",
"\n",
"# establish desired order\n",
"allChrSizeSampleHolo$OTU <- factor(allChrSizeSampleHolo$OTU, levels = desiredFiltered)\n",
"\n",
"# order\n",
"allChrSizeSampleHolo <- allChrSizeSampleHolo[order(allChrSizeSampleHolo$OTU),]"
]
},
{
"cell_type": "markdown",
"id": "7315c19e",
"metadata": {},
"source": [
"\n",
"\n",
"Now we have to establish where are the OTUs in the tree, that don't have chr. data\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "8411ade8",
"metadata": {
"attributes": {
"classes": [],
"id": "",
"message": "FALSE,eval=packageCheck"
}
},
"outputs": [],
"source": [
"# Establish position of OTUs before missing data OTUs\n",
"matchres <- match(desiredorderRevB,desiredFiltered)\n",
"matchres[is.na(matchres)] <- \"R\"\n",
"reps <- rle(matchres)\n",
"posOTUsBeforeMissing <- as.numeric(matchres[which(matchres==\"R\")-1][which(matchres[which(matchres==\"R\")-1]!=\"R\")] )\n",
"\n",
"# This are the OTUs that come before missing chr. data OTUs\n",
"BeforeMissingPlot2 <- desiredFiltered[posOTUsBeforeMissing]\n",
"\n",
"# This is the amount of missing OTUs, spaces to add (ghost karyotypes)\n",
"valuesOfMissRepsBeforePlot2 <- reps$lengths[which(reps$values==\"R\")]"
]
},
{
"cell_type": "markdown",
"id": "dc12ca1f",
"metadata": {},
"source": [
"### Plotting\n",
"\n",
"Now we are ready to plot adding those arguments for `addMissingOTUAfter` and `missOTUspacings`\n"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "0b53a868",
"metadata": {
"attributes": {
"classes": [],
"comment": "NA,",
"echo": "TRUE",
"id": "",
"message": "FALSE,",
"results": "\"hide\",eval=FALSE,"
}
},
"outputs": [
{
"data": {
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"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
"height": 1750,
"width": 1750
}
},
"output_type": "display_data"
}
],
"source": [
"# plot to png file\n",
"#png(file=paste0(\"secondplot.png\" ),width=962,height=700)\n",
"\n",
"options(repr.plot.width = 3500/300, repr.plot.height = 3500/300, repr.plot.res = 300)\n",
"\n",
"{\n",
"par(omi=rep(0,4) , mar=c(0,0,0,0), mfrow=c(1,2))\n",
"# left figure / phylogeny from 0 to 1 \n",
"par(fig=c(0,.3, 0.15,0.85)) # l r bottom top\n",
"par(mar=c(2,0,2,0)) # b l t r\n",
"\n",
"plot(revBayesPhyloUM)\n",
"par(fig=c(0.27,1,0,1), new=TRUE)\n",
"par(mar=c(0,0,0,0)) # b l t r\n",
"\n",
"suppressMessages(\n",
"plotIdiograms(allChrSizeSampleHolo, # chr. size data.frame\n",
" dfMarkPos = allMarksSampleHolo, # data.frame of marks' positions\n",
" dfMarkColor = mydfMaColor, # d.f. of mark characteristics\n",
" \n",
" squareness = 4, # vertices squareness\n",
" karHeight = 2.8, # karyotype height\n",
" karHeiSpace = 4.5, # vertical size of kar. including spacing\n",
" yTitle=\"\",\n",
"\n",
" karIndex = TRUE, # add karyotype index\n",
" indexIdTextSize=.4 # font size of indices and chr. names\n",
" \n",
" ,addMissingOTUAfter = BeforeMissingPlot2 # add ghost OTUs after these names\n",
" ,missOTUspacings = valuesOfMissRepsBeforePlot2 # how many ghosts, respectively\n",
" ,lwd.chr=.5 # line width\n",
"\n",
" ,markLabelSpacer = 0 # dist. of legend to rightmost chr.\n",
" ,legendWidth = 2.3 # width of square or dots of legend\n",
" \n",
" ,rulerPos = -0.5 # position of ruler\n",
" ,rulerNumberSize = .35 # font size of number of ruler\n",
" ,rulerNumberPos = .4 # position of ruler number\n",
" ,ruler.tck=-.004 # tick of ruler size and orient.\n",
"\n",
" ,ylimTopMod = -4 # modify ylim of top\n",
" ,ylimBotMod = -4 # modify ylim of bottom\n",
" ,xlimRightMod = 3 # modify xlim right argument\n",
" ,xModifier = 100 # separ. among chromatids\n",
" )\n",
")\n",
"}\n",
"# close png\n",
"#dev.off()"
]
},
{
"cell_type": "markdown",
"id": "617e3651",
"metadata": {},
"source": [
"
"
]
},
{
"cell_type": "markdown",
"id": "f5efb234",
"metadata": {},
"source": [
"## plot of revBayes tree and holocentrics and monocentrics\n",
"\n",
"Create data.frames with both types of karyotypes [@plyr2011]\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "9e3139ee",
"metadata": {
"attributes": {
"classes": [],
"id": "",
"message": "FALSE,eval=packageCheck"
}
},
"outputs": [],
"source": [
"suppressPackageStartupMessages(\n",
"require(plyr)\n",
")\n",
"# Select this OTU from the monocen.\n",
"monosel<-c(\"Species_F\",\"Species_C\",\"Species_A\")\n",
"# chr.\n",
"allChrSizeSampleSel <- allChrSizeSample [which(allChrSizeSample$OTU %in% monosel ),]\n",
"# marks\n",
"allMarksSampleSel <- allMarksSample [which(allMarksSample$OTU %in% monosel ),]\n",
"\n",
"# Select the others from the holocen.\n",
"holosel <- setdiff(unique(allChrSizeSampleHolo$OTU),monosel)\n",
"# chr.\n",
"allChrSizeSampleHoloSel <- allChrSizeSampleHolo[which(allChrSizeSampleHolo$OTU %in% holosel ),]\n",
"# marks\n",
"allMarksSampleHoloSel <- allMarksSampleHolo [which(allMarksSampleHolo$OTU %in% holosel ),]\n",
"\n",
"# merge chr d.fs\n",
"mixChrSize <- plyr::rbind.fill(allChrSizeSampleSel,allChrSizeSampleHoloSel)\n",
"\n",
"# merge marks' d.fs\n",
"mixMarks <- plyr::rbind.fill(allMarksSampleSel,allMarksSampleHoloSel)"
]
},
{
"cell_type": "markdown",
"id": "25ed00e6",
"metadata": {},
"source": [
"\n",
"\n",
"Order data.frame and determine missing karyotypes\n",
"\n",
""
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "3e1a8b5c",
"metadata": {
"attributes": {
"classes": [],
"id": "",
"message": "FALSE,eval=packageCheck"
}
},
"outputs": [],
"source": [
"# make a vector without missing OTUs\n",
"desiredFiltered <- intersect(desiredorderRevB, mixChrSize$OTU)\n",
"\n",
"# establish desired order\n",
"mixChrSize$OTU <- factor(mixChrSize$OTU, levels = desiredFiltered)\n",
"\n",
"# order data.frame\n",
"mixChrSize <- mixChrSize[order(mixChrSize$OTU),]\n",
"\n",
"# Establish position of OTUs before missing data OTUs\n",
"matchres <- match(desiredorderRevB,desiredFiltered)\n",
"matchres[is.na(matchres)] <- \"R\"\n",
"reps <- rle(matchres)\n",
"posOTUsBeforeMissing <- as.numeric(matchres[which(matchres==\"R\")-1][which(matchres[which(matchres==\"R\")-1]!=\"R\")] )\n",
"\n",
"# This are the OTUs that come before missing chr. data OTUs\n",
"BeforeMissingPlot2 <- desiredFiltered[posOTUsBeforeMissing]\n",
"\n",
"# This is the amount of missing OTUs, spaces to add (ghost karyotypes)\n",
"valuesOfMissRepsBeforePlot2 <- reps$lengths[which(reps$values==\"R\")]"
]
},
{
"cell_type": "markdown",
"id": "c46adbb4",
"metadata": {},
"source": [
"### Plotting\n",
"\n",
"Now we are ready to plot adding those arguments for `addMissingOTUAfter` and `missOTUspacings`\n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "67c0319f",
"metadata": {
"attributes": {
"classes": [],
"comment": "NA,",
"echo": "TRUE",
"id": "",
"message": "FALSE,",
"results": "\"hide\",eval=FALSE,"
}
},
"outputs": [
{
"data": {
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HDvXO1PTkQVUqlclk8jMP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\nzh5FqImSiVxK4F6rnZyc3NzcOBbCM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gnE7n5ORk5veDVCpVKpVsvU3+BrLPa2VlZW5uLvPnolAoOjo6+G+zPjMzs7i4mPn54XB4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\nu3fv2LFjaGgoq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"text/plain": [
"plot without title"
]
},
"metadata": {
"image/png": {
"height": 1500,
"width": 1750
}
},
"output_type": "display_data"
}
],
"source": [
"# plot to png file\n",
"#png(file=paste0(\"thirdplot.png\" ),width=1100,height=1000)\n",
"\n",
"options(repr.plot.width = 3500/300, repr.plot.height = 3000/300, repr.plot.res = 300)\n",
"\n",
"{\n",
" par(omi=rep(0,4) , mar=c(0,0,0,0), mfrow=c(1,2))\n",
" par(fig=c(0,.25,0,1)) \n",
" par(mar=c(1,0,0,0))\n",
" \n",
" plot(revBayesPhyloUM)\n",
" par(fig=c(0.25,1,0,1), new=TRUE)\n",
" par(mar=c(0,0,0,0))\n",
" \n",
"suppressMessages(\n",
"plotIdiograms(mixChrSize, # chr. size data.frame\n",
" dfMarkPos = mixMarks, # data.frame of marks' positions (inc. cen. marks)\n",
" dfMarkColor = mydfMaColor, # d.f. of mark characteristics\n",
" \n",
" origin=\"b\", # position measured from bottom of chr.\n",
" \n",
" karHeight = 2.8, # vertical size of kar. including spacing\n",
" karHeiSpace = 4.5, # vertical size of kar. including spacing\n",
" squareness = 5, # vertices squareness\n",
" chrSpacing = .25, # horizontal spacing among chr.\n",
" yTitle = \"\",\n",
" \n",
" karIndex = TRUE # add karyotype index\n",
" ,indexIdTextSize=.4 # font size of indices and chr. names\n",
" ,distTextChr=0.7 # dist. among chr. and chr. name\n",
" \n",
" ,addMissingOTUAfter = BeforeMissingPlot2 # add ghost OTUs after these names\n",
" ,missOTUspacings = valuesOfMissRepsBeforePlot2 # how many ghosts, respectively\n",
" ,lwd.chr=.5 # line width\n",
" \n",
" ,markLabelSpacer = 0 # dist. of legend to rightmost chr.\n",
" ,legendWidth = 2 # width of square or dots of legend\n",
" \n",
" ,ylimTopMod = -1 # modify ylim of top\n",
" ,ylimBotMod = -2 # modify ylim of bottom\n",
" \n",
" ,rulerPos = -0.5 # position of ruler\n",
" ,rulerNumberSize = .35 # font size of number of ruler\n",
" ,rulerNumberPos = .4 # position of ruler number\n",
" ,ruler.tck=-.004 # ruler tick size and orient.\n",
" ,OTUfont=3 # italics\n",
" ,OTUfamily=\"Courier New\" # for OTU name\n",
" \n",
" ,xModMonoHoloRate = 5 # factor (quotient) to shrink separation of chromatids of holocen.\n",
" \n",
")\n",
")\n",
"}\n",
"# close png\n",
"#dev.off()"
]
},
{
"cell_type": "markdown",
"id": "9a61a1f1",
"metadata": {
"tags": []
},
"source": [
"
"
]
}
],
"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
}