idiogramFISH: Idiograms with Marks and Karyotype Indices
Introduction
The goal of idiogramFISH is to plot idiograms of karyotypes, plasmids and circular chr. having a set of data.frames for chromosome data and optionally marks’ data (plotIdiograms function) (Roa and PC Telles, 2020). Idiograms can also be plotted in concentric circles. Separated chromatids can be visible when not in a circular plot.
Six styles of marks are available: square (squareLeft), dots, cM (cMLeft), cenStyle, upArrow, downArrow; its legend (label) can be drawn inline or to the right of karyotypes. It is possible to calculate also chromosome and karyotype indexes (Romero-Zarco, 1986; Watanabe et al., 1999) and classify chromosome morphology in the categories of Levan (1964), and Guerra (1986).
IdiogramFISH was written in R (R Core Team, 2019) and also uses crayon (Csárdi, 2017), tidyr (Wickham and Henry, 2020) and dplyr packages (Wickham et al., 2019a). Documentation was written with R-packages roxygen2 (Wickham et al., 2018), usethis (Wickham and Bryan, 2019), bookdown (Xie, 2016), knitr (Xie, 2015), pkgdown (Wickham and Hesselberth, 2019), Rmarkdown (Xie et al., 2018), rvcheck (Yu, 2019a), badger (Yu, 2019b), kableExtra (Zhu, 2019), rmdformats (Barnier, 2020) and RCurl (Temple Lang and CRAN team, 2019). For some vignette figures, packages rentrez (Winter, 2017), plyr (Wickham, 2011), phytools (Revell, 2012), ggtree (Yu et al., 2018), ggplot2 (Wickham, 2016) and ggpubr (Kassambara, 2019) were used.
Installation instructions
You can install idiogramFISH from CRAN with:
Or the devel. version
Install Gitlab dev. ver. with devtools (Wickham et al., 2019b):
Attention windows users, please install Rtools and git
Vignettes use a lua filter, so you would need pandoc ver. > 2. rmarkdown::pandoc_version()
# This installs package devtools, necessary for installing the dev version
install.packages("devtools")
url <- "https://gitlab.com/ferroao/idiogramFISH"
# Necessary packages for vignettes:
list.of.packages <- c(
"plyr",
"knitr",
"kableExtra",
"rmdformats",
"rmarkdown",
"RCurl",
"rvcheck",
"badger",
"rentrez"
)
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
# Linux with vignettes and Windows
devtools::install_git(url = url,build_vignettes = TRUE, force=TRUE)
# Mac with vignettes
devtools::install_git(url = url, build_opts=c("--no-resave-data","--no-manual") )
Need help?
Manual in Bookdown style
Documentation in Pkgdown style
Vignettes:
Online:
Launch vignettes from R for the installed version:
Minimal examples
Monocentrics:
Define your plotting window size with something like par(pin=c(10,6)), or with svg(), png(), etc. Add chromosome morphology according to Guerra (1986) or (Levan et al., 1964)
library(idiogramFISH)
data(dfOfChrSize) # chromsome data
data(dfMarkColor) # mark general data
data(dfOfMarks2) # mark position data (inc. cen.)
# column Mbp not for plotting purposes
dfOfChrSize$Mbp<-(dfOfChrSize$shortArmSize+dfOfChrSize$longArmSize)*100
svg("dfOfChrSize.svg",width=10,height=6 )
# png("dfOfChrSize.png", width=500, height=400)
plotIdiograms(dfChrSize =dfOfChrSize, # data.frame of chr. size
dfMarkColor=dfMarkColor, # d.f of mark style <- Optional
dfMarkPos=dfOfMarks2, # df of mark positions (includes cen. marks)
karHeight=5, # kar. height
chrWidth = 1.2, # chr. width
chrSpacing = 1, # space among chr.
morpho="Guerra", # chr. morpho. classif. (Guerra, Levan, both, "" ) ver. >= 1.12 only
chrIndex="CI", # cen. pos. (CI, AR, both, "" ) ver. >= 1.12 only
chrSize = TRUE, # add chr. sizes under chr.
chrSizeMbp = TRUE, # add Mbp sizes under chr. (see above)
rulerPos= 0, # position of ruler
ruler.tck=-0.01, # size and orientation of ruler ticks
rulerNumberSize=.8 # font size of rulers
,xPosRulerTitle = 3 # pos of ruler title
,legendWidth=1 # width of legend items
,fixCenBorder = TRUE # use chrColor as border color of cen. or cen. marks
,distTextChr = 1.2 # chr. text separation
,xlimLeftMod = 2 # xlim left param.
,ylimBotMod = 0 # modify ylim bottom argument
,ylimTopMod = 0 # modify ylim top argument
)
dev.off() # close svg()
Let’s explore the data.frames for monocentrics:
If only one species, column OTU is optional
| chrName | shortArmSize | longArmSize | Mbp |
|---|---|---|---|
| 1 | 3 | 4 | 700 |
| 2 | 4 | 5 | 900 |
| 3 | 2 | 3 | 500 |
| X | 1 | 2 | 300 |
| markName | markColor | style |
|---|---|---|
| 5S | red | dots |
| 45S | green | square |
| DAPI | blue | square |
| CMA | yellow | square |
p, q and w marks can have empty columns markDistCen and markSize since v. 1.9.1 to plot whole arms (p, q) and whole chr. w.
| chrName | markName | chrRegion | markSize | markDistCen |
|---|---|---|---|---|
| 1 | 5S | p | 1 | 0.5 |
| 1 | 45S | q | 1 | 0.5 |
| X | 45S | p | NA | NA |
| 3 | DAPI | q | 1 | 1.0 |
| 1 | DAPI | cen | NA | NA |
| X | CMA | cen | NA | NA |
Holocentrics:
library(idiogramFISH)
# load some package data.frames - optional
data(dfChrSizeHolo, dfMarkColor, dfMarkPosHolo)
# column Mbp not for plotting purposes
dfChrSizeHolo$Mbp<-dfChrSizeHolo$chrSize*100
# svg("testing.svg",width=14,height=8 )
par(mar = c(0, 0, 0, 0), omi=rep(0,4) )
plotIdiograms(dfChrSize =dfChrSizeHolo, # data.frame of chr. size
dfMarkColor=dfMarkColor, # df of mark style
dfMarkPos =dfMarkPosHolo, # df of mark positions
addOTUName=FALSE, # do not add OTU names
distTextChr = 1, # chr. name distance to chr.
chrSize = TRUE, # show chr. size under chr.
chrSizeMbp = TRUE, # show chr. size in Mbp under chr. requires Mbp column
rulerPos=-0.1, # position of ruler
rulerNumberPos=.9 # position of numbers of rulers
,xPosRulerTitle = 3 # pos. of ruler title (units)
,xlimLeftMod=2 # modify xlim left argument of plot
,ylimBotMod=.2 # modify ylim bottom argument of plot
,legendHeight=.5 # height of legend labels
,legendWidth = 1.2 # width of legend labels
,xModifier = .025 # separ. among chromatids
) 
Let’s explore the data.frames for holocentrics:
- chromosome data, if only 1 species, column OTU is optional
| chrName | chrSize | Mbp |
|---|---|---|
| 1 | 3 | 300 |
| 2 | 4 | 400 |
| 3 | 2 | 200 |
| 4 | 5 | 500 |
- mark general data
| markName | markColor | style |
|---|---|---|
| 5S | red | dots |
| 45S | green | square |
| DAPI | blue | square |
| CMA | yellow | square |
- mark position data, if only 1 species, column OTU is optional (mandatory if in d.f of Chr. Size)
| chrName | markName | markPos | markSize |
|---|---|---|---|
| 3 | 5S | 1.0 | 0.5 |
| 3 | DAPI | 1.5 | 0.5 |
| 1 | 45S | 2.0 | 0.5 |
| 2 | DAPI | 2.0 | 0.5 |
| 4 | CMA | 2.0 | 0.5 |
| 4 | 5S | 0.5 | 0.5 |
Plotting both mono. and holo.
Available only for ver. > 1.5.1
Merge data.frames with plyr (Wickham, 2011)
# chromosome data, if only 1 species, column OTU is optional
require(plyr)
dfOfChrSize$OTU <- "Species mono"
dfChrSizeHolo$OTU <- "Species holo"
monoholoCS <- plyr::rbind.fill(dfOfChrSize,dfChrSizeHolo)
dfOfMarks2$OTU <-"Species mono"
dfOfMarks2[which(dfOfMarks2$markName=="5S"),]$markSize<-.7
dfMarkPosHolo$OTU <-"Species holo"
monoholoMarks <- plyr::rbind.fill(dfOfMarks2,dfMarkPosHolo)library(idiogramFISH)
#svg("testing.svg",width=14,height=10 )
png("monoholoCS.png", width=700, height=600)
par(mar=rep(0,4))
plotIdiograms(dfChrSize = monoholoCS, # data.frame of chr. size
dfMarkColor= dfMarkColor, # df of mark style
dfMarkPos = monoholoMarks,# df of mark positions, includes cen. marks
chrSize = TRUE, # show chr. size under chr.
squareness = 4, # vertices squareness
roundedCen = FALSE, # triangular cen.
addOTUName = TRUE, # add OTU names
OTUTextSize = .7, # font size of OTU
distTextChr = .5, # separ. among chr. and text and among chr. name and indices
karHeiSpace = 4, # karyotype height inc. spacing
karIndexPos = .2, # move karyotype index
legendHeight= 1, # height of legend labels
legendWidth = 1, # width of legend labels
fixCenBorder = TRUE, # use chrColor as border color of cen. or cen. marks
rulerPos= 0, # position of ruler
ruler.tck=-0.02, # size and orientation of ruler ticks
rulerNumberPos=.9, # position of numbers of rulers
xPosRulerTitle = 3.5, # ruler title (units) position
xlimLeftMod=1, # modify xlim left argument of plot
xlimRightMod=3, # modify xlim right argument of plot
ylimBotMod= .2 # modify ylim bottom argument of plot
,chromatids=FALSE # do not show separ. chromatids
# for Circular Plot, add:
# ,circularPlot = TRUE # circularPlot
# ,shrinkFactor = .9 # percentage 1 = 100% of circle with chr.
# ,circleCenter = 3 # X coordinate of circleCenter (affects legend pos.)
# ,chrLabelSpacing = .9 # chr. names spacing
# ,OTUsrt = 0 # angle for OTU name (or number)
# ,OTUplacing = "number" # Use number and legend instead of name
# ,OTULabelSpacerx = -1.5 # modify position of OTU label, when OTUplacing="number" or "simple"
# ,OTUlegendHeight = 1.5 # space among OTU names when in legend - OTUplacing
)
dev.off() # close png
Citation
To cite idiogramFISH in publications, please use:
Roa F, Telles MPC (2020) idiogramFISH: Idiograms with Marks and Karyotype Indices, Universidade Federal de Goiás. Brazil. R-package. version 1.16.1 https://ferroao.gitlab.io/manualidiogramfish/. doi:10.5281/zenodo.3579417
To write citation to file:
1 Plotting chromosomes
This guide shows how to plot idiograms of measured karyotypes and optionally marks.
1.1 Load package
visit gitlab for installation instructions https://gitlab.com/ferroao/idiogramFISH
1.2 Plot monocentrics
Initially you have to put your chromosome data in a data.frame.
From scratch:
# Example data.frame to write in R, use: (column OTU is optional if only 1 OTU)
mydfChrSize<-read.table(text=
" OTU chrName shortArmSize longArmSize
\"Species one\" 1 1.5 2.0
\"Species one\" 2 2.0 2.5
\"Species one\" 3 1.0 1.5
\"Species one\" B 2.0 3.5" , header=TRUE, stringsAsFactors=FALSE,fill=TRUE)| OTU | chrName | shortArmSize | longArmSize |
|---|---|---|---|
| Species one | 1 | 1.5 | 2.0 |
| Species one | 2 | 2.0 | 2.5 |
| Species one | 3 | 1.0 | 1.5 |
| Species one | B | 2.0 | 3.5 |
loading saved data:
If you use RStudio, in the menu “Session”, use “Set working directory” for choosing your desired folder or:
Open your chromosome data data.frame importing it from a .csv (read.csv) or .xls file (readxl).
Editing a data.frame:
For fixing column names use:
Get marks general data
This data.frame is optional.
Put your mark data in a data.frame. This data.frame has the marks present in all karyotypes without position info. If the style column is not present, param. defaultStyleMark = "square" will be used during plotting.
Note the cenStyle to make constrictions, mimicking centromeres, and upArrow and downArrow styles.
# From scratch:
mydfMarkColor<-read.table(text=
" markName markColor style
5S red dots
45S green square
gene1 orange upArrow
gene2 salmon downArrow
gene3 \"#056522\" cMLeft
DAPI blue cM
\"cB mark\" black cenStyle
CMA yellow square
\"B mark\" black square" , header=TRUE, stringsAsFactors=FALSE,fill=TRUE)| markName | markColor | style |
|---|---|---|
| 5S | red | dots |
| 45S | green | square |
| gene1 | orange | upArrow |
| gene2 | salmon | downArrow |
| gene3 | #056522 | cMLeft |
| DAPI | blue | cM |
| cB mark | black | cenStyle |
| CMA | yellow | square |
| B mark | black | square |
For fixing column names use:
Get marks positions data
Open or write your mark positions in a data.frame. This data.frame has the marks present in all karyotypes with position info. This data.frame has also the centromeric marks present in all karyotypes.
# We will use column OTU if data.frame because chromosome size df has it
mydfOfMarks<-read.table(text=
" OTU chrName markName chrRegion markSize markDistCen
\"Species one\" 1 45S p NA NA # no measure means whole arm for square marks
\"Species one\" 1 5S q 0.5 0.5
\"Species one\" B \"B mark\" w NA NA # w for whole chromosome for square marks
\"Species one\" B \"cB mark\" q NA 1.0
\"Species one\" 2 45S p 1 1.0
\"Species one\" 2 gene1 q 0.5 1.0
\"Species one\" 2 gene2 q 0.5 2.0
\"Species one\" 3 DAPI q NA 1
\"Species one\" 3 gene3 p NA .5
\"Species one\" 1 DAPI cen
\"Species one\" 3 CMA cen", header=TRUE, stringsAsFactors=FALSE,fill=TRUE)| OTU | chrName | markName | chrRegion | markSize | markDistCen |
|---|---|---|---|---|---|
| Species one | 1 | 45S | p | NA | NA |
| Species one | 1 | 5S | q | 0.5 | 0.5 |
| Species one | B | B mark | w | NA | NA |
| Species one | B | cB mark | q | NA | 1.0 |
| Species one | 2 | 45S | p | 1.0 | 1.0 |
| Species one | 2 | gene1 | q | 0.5 | 1.0 |
| Species one | 2 | gene2 | q | 0.5 | 2.0 |
| Species one | 3 | DAPI | q | NA | 1.0 |
| Species one | 3 | gene3 | p | NA | 0.5 |
| Species one | 1 | DAPI | cen | NA | NA |
| Species one | 3 | CMA | cen | NA | NA |
For fixing column names use something like:
Add some text to the right
For ver. > 1.7
# We will use column note to add a note to the right of the karyotype of the OTU in column OTU
notesdf<-read.table(text=
" OTU note
\"Species one\" \"Author notes\" ", header=TRUE, stringsAsFactors=FALSE,fill=TRUE)For adding just the OTU name (from column OTU of data.frame of chr. size) to the right, use OTUasNote=TRUE
Plotting
You can plot without marks (use only 1st data.frame), but we will use all 4 data.frames created. By default the function will calculate indices (Romero-Zarco, 1986; Watanabe et al., 1999) and morphological categories of Guerra (1986) and Levan (1964). Use parameters chrIndex and morpho of the function plotIdiograms to modify that. See ?plotIdiograms.
The cM style of mark always adds the name as if legend="inline", even when legend="aside" (default).
# svg("mydfChrSize.svg",width=12,height=6 )
par(mar = c(0, 0, 0, 0))
plotIdiograms(dfChrSize= mydfChrSize, # chr. size data.frame
dfMarkPos= mydfOfMarks, # mark position data.frame (inc. cen.)
dfMarkColor=mydfMarkColor, # mark style d.f.
chrSpacing=.6, # separ. among chr.
distTextChr = .7, # separation among text and chr. names and ind.
orderChr = "name", # order chr. by name
karHeiSpace=2 # vertical size of karyotype including spacer
,arrowhead = .5 # proportion of head of arrow
,fixCenBorder = TRUE # use chrColor as border color of cen. or cen. marks
,legendWidth = .8 # legend item width
,legendHeight = .5 # legend item height
,markLabelSpacer = 2 # legend spacer
,lwd.mimicCen = 2.5 # constric. mark line width
,rulerPos=0 # ruler position
,ruler.tck=-0.01 # ticks of ruler size and orientation
,xPosRulerTitle = 2.5 # ruler units pos.
,markPer = "45S" # show mark % of chr. under kar.
,showMarkPos = TRUE # show position of marks under kar. See bToRemove
,bToRemove = c("B mark","cB mark") # bands to remove from pos. calc. See showMarkPos
,notes=notesdf # data.frame with notes
,notesTextSize = 1.3 # font size of notes
,notesPos = .2 # space from chr. (right) to note
,ylimBotMod = 2 # modify ylim bottom argument
,ylimTopMod = 0 # modify ylim top argument
,xlimLeftMod = 2 # modify left xlim
,xlimRightMod = 3 # modify right xlim
,lwd.cM = 2 # thickness of cM marks
,pattern = "^c" # regex pattern to remove from mark names
,remSimiMarkLeg=TRUE # remove pseudoduplicated mark names from legend (same after pattern removal)
# ,legend="inline" # legend next to chr.
# ,bannedMarkName = "cB mark" # remove from legends
)
Vertices when centromereSize=0 are rounded:
png("mydfChrSize2.png", width=550, height=550)
par(mar = c(0, 0, 0, 0))
plotIdiograms(dfChrSize = bigdfOfChrSize[1:8,], # chr. size data.frame
dfMarkColor = mydfMarkColor,# mark style df
dfMarkPos = bigdfOfMarks, # mark position df
centromereSize = 0, # <- HERE
squareness=3, # vertices squareness
chrSpacing = .7, # space among chr.
karHeight = 2, # karyotype rel. height
karHeiSpace=4, # vertical size of karyotype including spacer
amoSepar= 2.5, # separation among karyotype
indexIdTextSize=.8, # font size of chr. name and indices
karIndexPos = .1, # position of kar. index
markLabelSize=.7, # font size of mark legends
fixCenBorder = FALSE, # do not use chrColor as border color of cen. or cen. marks
distTextChr = .8, # separation among chr. and ind.
rulerPos= 0, # ruler position
ruler.tck=-0.01, # ticks of ruler size and orientation
xPosRulerTitle = 2.6, # ruler units pos.
xlimLeftMod = 2, # modify xlim left argument
ylimBotMod = 0.4, # modify ylim bottom argument
ylimTopMod = 0 # modify ylim top argument
,lwd.cM = 2 # thickness of cM marks
)
dev.off()
There is no need to add dfMarkColor and you can also use the parameter mycolors (optional too), to establish marks’ colors. Colors are assigned depending on the order of marks, i.e.:
Let’s use the cM style of marks. A protruding line.
cM style does not apply to centromere marks. To make something similar, use centromereSize=0, legend="inline" and fixCenBorder = FALSE.
charVectorCol <- c("tomato3","darkolivegreen4","dfsd","blue","green")
# Modify size of kar. to use rulerInterval and ceilingFactor (>= 1.13)
quo<-9
dfOfChrSize2<-dfOfChrSize
dfOfChrSize2$shortArmSize<-dfOfChrSize$shortArmSize/quo
dfOfChrSize2$longArmSize<-dfOfChrSize$longArmSize/quo
dfOfMarks2b<-dfOfMarks2
dfOfMarks2b$markSize<-dfOfMarks2$markSize/quo
dfOfMarks2b$markDistCen<-dfOfMarks2$markDistCen/quo
png("dfOfChrSizeVector.png", width=1000, height=450)
par(mar=rep(0,4))
plotIdiograms(dfChrSize = dfOfChrSize2, # d.f. of chr. sizes
dfMarkPos = dfOfMarks2b, # d.f. of marks' positions
defaultStyleMark = "cM", # forces "cM" style in d.f dfMarkColor (exc. 5S)
mycolors = charVectorCol, # colors to use
distTextChr = .5, # separ. text and chr.
markLabelSize=.7, # font size for labels (legend)
lwd.cM=2, # width of cM marks
legendWidth=0.9, # legend item width
legendHeight=.5,
rulerPos= 0, # ruler position
ruler.tck=-0.01, # ruler tick orientation and length
rulerNumberSize=.5 # ruler font size
,xPosRulerTitle = 2.8 # ruler units pos.
,xlimRightMod = 1 # modify xlim right arg.
)
dev.off() # close png
1.3 Plot holocentrics
Initially you have to put your chromosome data in a data.frame. For simplicity, I call a holocen. karyotype anything without centromeres. This is recognized by the absence of columns: “shortArmSize”, etc.
From scratch:
# Example data.frame written in R, use: (column OTU is optional if only 1 OTU)
mydfChrSizeHolo<-read.table(text=
" OTU chrName chrSize
\"Species one\" 1 6.5
\"Species one\" 2 5.0
\"Species one\" 3 4.0
\"Species one\" 4 4.0
\"Species one\" X 3.0 " , header=TRUE, stringsAsFactors=FALSE,fill=TRUE)| OTU | chrName | chrSize |
|---|---|---|
| Species one | 1 | 6.5 |
| Species one | 2 | 5.0 |
| Species one | 3 | 4.0 |
| Species one | 4 | 4.0 |
| Species one | X | 3.0 |
or loading saved data:
If you use RStudio, in menu “Session”, open “Set working directory” for choosing your desired folder or:
Open your chromosome data as data.frame importing it from a .csv (read.csv) or .xls file (readxl).
For fixing column names use:
Get marks general data
This data.frame is optional for ver. > 1.0.0
Put your mark data in a data.frame. This data.frame has the marks present in all karyotypes without position info. If the style column is not present, param. defaultStyleMark = "square" will be used during plotting.
# From scratch:
mydfMarkColor<-read.table(text=
" markName markColor style
1 5S red dots
2 45S green square
3 DAPI blue square
4 CMA yellow square" , header=TRUE, stringsAsFactors=FALSE,fill=TRUE)| markName | markColor | style |
|---|---|---|
| 5S | red | dots |
| 45S | green | square |
| DAPI | blue | square |
| CMA | yellow | square |
For fixing column names use:
Get marks positions data
Open or write your mark positions in a data.frame. This data.frame has the marks present in all karyotypes with position info.
# We will use column OTU if data.frame because chromosome size df has it
mydfMarkPosHolo<-read.table(text=
" OTU chrName markName markPos markSize chrRegion
\"Species one\" 4 B NA NA w # whole chromosome mark, use 'w' in col. chrRegion
\"Species one\" 3 DAPI 2.0 0.5
\"Species one\" 1 45S 2.0 0.5
\"Species one\" 2 DAPI 2.0 0.5
\"Species one\" X CMA 2.0 0.5
\"Species one\" X 5S 0.5 0.5
\"Species one\" X 5S 0.5 0.5" , header=TRUE, stringsAsFactors=FALSE,fill=TRUE)| OTU | chrName | markName | markPos | markSize | chrRegion |
|---|---|---|---|---|---|
| Species one | 4 | B | NA | NA | w |
| Species one | 3 | DAPI | 2.0 | 0.5 | |
| Species one | 1 | 45S | 2.0 | 0.5 | |
| Species one | 2 | DAPI | 2.0 | 0.5 | |
| Species one | X | CMA | 2.0 | 0.5 | |
| Species one | X | 5S | 0.5 | 0.5 | |
| Species one | X | 5S | 0.5 | 0.5 |
For fixing column names use something like:
Plotting
You can plot without marks (passing only 1st data.frame), but we will use all 4 data.frames created:
# library(idiogramFISH)
# svg("mydfChrSizeHolo.svg",width=13.5,height=6 )
# png("mydChrSizeHolo.png", width=600, height=300)
par(mar=c(0,0,0,1)) # bottom left top right
plotIdiograms(dfChrSize = mydfChrSizeHolo,# data.frame of chr. sizes
dfMarkColor= mydfMarkColor, # df of mark style
dfMarkPos = mydfMarkPosHolo,# df of mark positions
addOTUName=FALSE, # add OTU names
xlimLeftMod= 2, # modify xlim left argument
ylimTopMod= -1, # modify ylim top argument
ylimBotMod= -2 # modify ylim bottom argument
,rulerPos = 0 # ruler position
,ruler.tck = -0.01 # ruler ticks size and orient.
,xPosRulerTitle=2.6
,legendWidth=1 # width of legend
,legendHeight=.7 # height of legend item
,xModifier=0.01 # separation among chromatids
)
It is not mandatory to use dfMarkColor and you can also use the parameter mycolors (optional too), to establish marks’ colors. When using mycolors, colors are assigned depending on the order of marks, i.e.:
unique(dfMarkPosHolo$markName)
# [1] "5S" "DAPI" "45S" "CMA"
par(mar=rep(0,4))
plotIdiograms(dfChrSize = dfChrSizeHolo, # d.f. of chr. size
dfMarkPos = dfMarkPosHolo, # d.f. of marks' positions
mycolors = c("green","yellow","blue","red"), # colors for marks
addOTUName=FALSE, # do not add OTU name
ruler=FALSE, # do not add ruler
xlimLeftMod=1, # modify left xlim arg.
xlimRightMod=3, # modify right xlim arg.
ylimBotMod=.2 # modify bottom ylim
,chromatids=FALSE # do not show separ. chromatids
)
1.4 Plot holo. and mono. in the same karyotype
To accomplish this, we will use cenStyle marks in a (modified) holocen. karyotype. The ruler is continuous as in holocen.
# mark general characteristics' data.frame:
mydfMarkColor2<-read.table(text=
" markName markColor style
1 5S red dots
2 45S green square
3 DAPI blue square
4 CMA yellow square
5 c45S green cenStyle # <- simulate Cen.
6 c5S red cenStyle
7 cB black cenStyle
8 constr NA cenStyle
9 B black square" , header=TRUE, stringsAsFactors=FALSE,fill=TRUE)
# add new marks to data.frame of marks' position
mydfMarkPosHolo2<-plyr::rbind.fill(mydfMarkPosHolo,
data.frame(OTU="Species one",
chrName=1:4,
markName=c("c45S","c5S","constr","cB"), # <- use new mark
markPos=2.5,
markSize=NA
)
)
# png("mydChrSizeHolo.png", width=600, height=300)
par(mar=c(0,0,0,1)) # bottom left top right
plotIdiograms(dfChrSize = mydfChrSizeHolo, # data.frame of chr. sizes
dfMarkColor= mydfMarkColor2, # df of mark style
dfMarkPos = mydfMarkPosHolo2,# df of mark positions
addOTUName=FALSE, # add OTU names
xlimLeftMod= 2, # modify xlim left argument
ylimTopMod= -1, # modify ylim top argument
ylimBotMod= -2 # modify ylim bottom argument
,rulerPos = 0
,ruler.tck = -0.01
,xPosRulerTitle = 2.6
,legendWidth=1 # width of legend
,legendHeight=.7 # height of legend item
,lwd.mimicCen=2.5 # line width of const. mark
,pattern="^c" # regex pattern to remove from mark names
,remSimiMarkLeg = TRUE # remove pseudoduplicated mark names (got equal after pattern removal)
# ,legend="inline" # legends inline
# ,bannedMarkName = c("c45S","cB") # do not use this names from labels
)
2 Several OTUs
3 Changing Units
5 Using groups
6 Circular plots
7 Plotting alongside phylogeny
8 Citrus - helper functions
9 Human karyotype
10 Functions
References
Guerra M. 1986. Reviewing the chromosome nomenclature of Levan et al. Brazilian Journal of Genetics, 9(4): 741–743
Levan A, Fredga K, Sandberg AA. 1964. Nomenclature for centromeric position on chromosomes Hereditas, 52(2): 201–220. https://doi.org/10.1111/j.1601-5223.1964.tb01953.x. https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1601-5223.1964.tb01953.x
Romero-Zarco C. 1986. A new method for estimating karyotype asymmetry Taxon, 35(3): 526–530. https://onlinelibrary.wiley.com/doi/abs/10.2307/1221906
Watanabe K, Yahara T, Denda T, Kosuge K. 1999. Chromosomal evolution in the genus Brachyscome (Asteraceae, Astereae): statistical tests regarding correlation between changes in karyotype and habit using phylogenetic information Journal of Plant Research, 112: 145–161. http://link.springer.com/article/10.1007/PL00013869
R-packages
Csárdi G. 2017. Crayon: Colored terminal output. R package version 1.3.4. https://CRAN.R-project.org/package=crayon
Kassambara A. 2019. Ggpubr: ’Ggplot2’ based publication ready plots. R package version 0.2.3. https://CRAN.R-project.org/package=ggpubr
R Core Team. 2019. R: A language and environment for statistical computing R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Revell LJ. 2012. Phytools: An r package for phylogenetic comparative biology (and other things). Methods in Ecology and Evolution, 3: 217–223. https://besjournals.onlinelibrary.wiley.com/doi/10.1111/j.2041-210X.2011.00169.x
Roa F, PC Telles M. 2020. idiogramFISH: Idiograms with marks and karyotype indices Universidade Federal de Goiás, UFG, Goiânia. R-package. version 1.12.1. https://doi.org/10.5281/zenodo.3579417. https://ferroao.gitlab.io/manualidiogramfish/
Wickham H. 2011. The split-apply-combine strategy for data analysis Journal of Statistical Software, 40(1): 1–29. https://www.jstatsoft.org/article/view/v040i01
Wickham H. 2016. Ggplot2: Elegant graphics for data analysis Springer-Verlag New York. https://ggplot2.tidyverse.org
Wickham H, François R, Henry L, Müller K. 2019a. Dplyr: A grammar of data manipulation. R package version 0.8.3. https://CRAN.R-project.org/package=dplyr
Wickham H, Henry L. 2020. Tidyr: Tidy messy data. R package version 1.0.2. https://CRAN.R-project.org/package=tidyr
Wickham H, Hester J, Chang W. 2019b. Devtools: Tools to make developing r packages easier. R package version 2.2.1. https://CRAN.R-project.org/package=devtools
Winter DJ. 2017. rentrez: An r package for the ncbi eUtils api The R Journal, 9(2): 520–526
Yu G, Lam TT-Y, Zhu H, Guan Y. 2018. Two methods for mapping and visualizing associated data on phylogeny using ggtree. Molecular Biology and Evolution, 35(2): 3041–3043. https://doi.org/10.1093/molbev/msy194. https://academic.oup.com/mbe/article/35/12/3041/5142656
Documentation
Barnier J. 2020. Rmdformats: HTML output formats and templates for ’rmarkdown’ documents. R package version 0.3.7. https://CRAN.R-project.org/package=rmdformats
Temple Lang D, CRAN team. 2019. RCurl: General network (http/ftp/...) client interface for r. R package version 1.95-4.12. https://CRAN.R-project.org/package=RCurl
Wickham H, Bryan J. 2019. Usethis: Automate package and project setup. R package version 1.5.1. https://CRAN.R-project.org/package=usethis
Wickham H, Danenberg P, Eugster M. 2018. Roxygen2: In-line documentation for r. R package version 6.1.1. https://CRAN.R-project.org/package=roxygen2
Wickham H, Hesselberth J. 2019. Pkgdown: Make static html documentation for a package. R package version 1.4.1. https://CRAN.R-project.org/package=pkgdown
Xie Y. 2015. Dynamic documents with R and knitr Chapman; Hall/CRC, Boca Raton, Florida. ISBN 978-1498716963. http://yihui.name/knitr/
Xie Y. 2016. Bookdown: Authoring books and technical documents with R markdown Chapman; Hall/CRC, Boca Raton, Florida. ISBN 978-1138700109. https://github.com/rstudio/bookdown
Xie Y, Allaire JJ, Grolemund G. 2018. R markdown: The definitive guide Chapman; Hall/CRC, Boca Raton, Florida. ISBN 9781138359338. https://bookdown.org/yihui/rmarkdown
Yu G. 2019a. Rvcheck: R/package version check. R package version 0.1.6. https://CRAN.R-project.org/package=rvcheck
Yu G. 2019b. Badger: Badge for r package. R package version 0.0.6. https://CRAN.R-project.org/package=badger
Zhu H. 2019. KableExtra: Construct complex table with ’kable’ and pipe syntax. R package version 1.1.0. https://CRAN.R-project.org/package=kableExtra