1 Load package

visit gitlab for installation instructions https://gitlab.com/ferroao/idiogramFISH

#load package
library(idiogramFISH) 

2 Get your chromosome size data

Initially you have to open your chromosome data as a data.frame.

# 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)

setwd("~/folder/subfolder")

mydfChrSize<-read.csv("somefile.csv")

colnames(mydfChrSize)<-c("OTU", "chrName","chrSize")

3 Get marks general data

This data.frame is optional for ver. > 1.0.0

packageVersion("idiogramFISH")

Open or make your mark data as a data.frame. This data.frame has the marks present in all karyotypes with color and style, without position info. If style column is not present it will be filled with square 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)

For fixing column names use:

colnames(mydfMarkColor)<-c("markName", "markColor","style") 
# if style column not present it will be filled with "square"

4 Get marks positions data

Open or write your mark positions as 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)

For fixing column names use something like:

colnames(mydfMarkColor)<-c("OTU", "chrName","markName","markPos","markSize") 

5 Plotting

function plotIdiogramsHolo deprecated after ver. > 1.5.1

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=8 )
# png("mydfChrSizeHolo.png", width=600, height=400)
par(mar=c(1,4,1,1)) # bottom left top right

# function `plotIdiogramsHolo` deprecated after ver. > 1.5.1

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
              
              rulerPos=-1,                 # position of ruler
              ruler.tck=-0.01,             # size and orient. of tick of ruler
              rulerNumberPos=.9,           # ruler's number position
              
              xlimLeftMod=2.2,             # modify xlim left argument
              ylimBotMod=.1                # modify ylim bottom argument
              ,legendWidth=1               # width of legend
              ,legendHeight=.7             # height of legend item 
              ,asp=1                       # y x aspect
)
dev.off()

For ver. > 1.0.0 there is no need to add 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)

par(mar=c(1,4,1,1)) 
# function plotIdiogramsHolo was deprecated
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=F,               # add OTU name
              rulerPos=-.7,               # ruler position
              ruler.tck=-0.01,            # ruler ticks size and orient.
              rulerNumberPos=.9,          # position of numbers of ruler
              xlimLeftMod=1,              # modify left xlim arg.
              xlimRightMod=3,             # modify right xlim arg.
              ylimBotMod=.2               # modify bottom ylim
              ,asp=1                      # y x aspect
)

6 Example with several species (OTUs)

To illustrate this, we will load some data.frames from the package

• Chromosome sizes

data(bigdfChrSizeHolo)

• Mark characteristics, does not require OTU

data(dfMarkColor) 

• Mark position

data(bigdfMarkPosHolo)

library(idiogramFISH)

# fig.width=6, fig.height=6
png("bigdfChrSizeHolo.png", width=600, height=600)
par(mar=c(1,1,1,1))

plotIdiograms(dfChrSize=bigdfChrSizeHolo, # df of chr. sizes
              dfMarkColor=dfMarkColor,    # df of mark style
              dfMarkPos=bigdfMarkPosHolo, # df of marks' position
              
              markDistType="cen",         # measure towards center of mark
              roundness=6,                # vertices roundness of chr. and marks 
              
              karHeiSpace = 4,            # karyotype height including spacing
              karSepar=TRUE,              # reduce vertical space among karyotypes 
              amoSepar = 1,               # separation among karyotypes
              distTextChr=.5,             # distance text to chr.
              
              legendWidth = 1             # width of legend labels
              
              ,chrId="simple",            # numbering of chr., not using "original" name
              
              indexIdTextSize=.9,         # font size of chr names and indices
              markLabelSize=.9,           # font size of legends
              rulerNumberSize=.9,         # font size of ruler
              ruler.tck= -.004,           # tick length and orient.
              
              ylimBotMod=.4               # modify ylim bottom argument
              ,asp=1                      # y x aspect
)
dev.off()

7 Using bases instead of micrometers

Create some data in millions of bases:

# transform previous data.frames for simplicity
bigdfChrSizeHoloMb <- bigdfChrSizeHolo
bigdfChrSizeHoloMb$chrSize <- bigdfChrSizeHoloMb$chrSize * 980000
bigdfMarkPosHoloMb <- bigdfMarkPosHolo
bigdfMarkPosHoloMb$markPos <- bigdfMarkPosHoloMb$markPos * 980000
bigdfMarkPosHoloMb$markSize<- bigdfMarkPosHoloMb$markSize * 980000

png("bigdfChrSizeHolo2.png", width=700, height=600)
par(mar=c(1,1,1,1))

plotIdiograms(dfChrSize=bigdfChrSizeHoloMb,  # df of chr. sizes
              dfMarkColor=dfMarkColor,       # df of mark style
              dfMarkPos=bigdfMarkPosHoloMb,  # df of mark positions
              
              markDistType="cen",            # distance to mark is to its center
              ylabline = -8, # <- NEW        # modifies position of y axis title (Mb)
              roundness=4,                   # vertices roundness of chr. and marks 
              distTextChr = .5,              # separ. chr. to text
              
              karHeight = 2,                 # rel. karyotype height
              karHeiSpace = 4,               # karyotype height including spacing
              karSepar=TRUE,                 # reduce spacing among karyotypes 
              amoSepar = 1,                  # depends on karSepar, amount of sep.
              
              chrId="simple",                # chr. names not "original"
              indexIdTextSize=.9,            # font size of chr names and indices
              karIndex = FALSE,              # do not add karyotype asymmetry index
              
              markLabelSize=.9,              # font size of legend
              rulerNumberSize=.9,            # font size of ruler
              rulerPos = 0,                  # position of ruler
              ruler.tck= -.004,              # ruler tick length and orient.
              
              legendWidth = 1.2,             # width of legends
              
              xlimLeftMod = 1,               # modify left argument of xlim
              ylimBotMod=.4                  # modify bottom argument of ylim
              ,asp=1)                        # y x aspect
dev.off()

8 GISH of holocentric chromosomes

You need the data.frame of chr. sizes, and a d.f. of marks

parentalAndHybHoloChrSize

dfAlloParentMarksHolo

# svg("gish.svg",width=8,height=7 )
# png("mydfChrSize.png", width=600, height=500)
plotIdiograms(dfChrSize = parentalAndHybHoloChrSize,  # d.f. of chr. sizes
              dfMarkPos = dfAlloParentMarksHolo,      # d.f. of marks' positions
              chrColor  = "gray",          # chr. color
              cenColor  = NULL,            # cen. color when GISH
              
              karHeight = 3,               # karyotype heigth without spacing
              karHeiSpace=5,               # karyotype height including spacing
              distTextChr = 0.8,           # separation among chr. and text
              
              rulerPos=-1.8,               # ruler position
              ruler.tck=-0.01,             # ruler tick orientation and length
              rulerNumberSize=0.7          # ruler font size
              ,legend=""                   # no legend
              
              ,xlimRightMod = 0            # xlim right arg. modif.
              ,asp=1                       # y x aspect
)

# dev.off()