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2 Plotting chromosomes II

This guide shows the files to plot idiograms of karyotypes of holocentrics and optionally marks.

2.1 Load package

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

#load package
library(idiogramFISH)

2.2 Get your chromosome size data

Initially you have to put your chromosome data in a data.frame.

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:

setwd("~/folder/subfolder")

Open your chromosome data as data.frame importing it from a .csv (read.csv) or .xls file (readxl).

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

For fixing column names use:

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

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2.3 Get marks general data

This data.frame is optional for ver. > 1.0.0

packageVersion("idiogramFISH")

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:

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

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2.4 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:

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

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2.5 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=6 )
# png("mydChrSizeHolo.png", width=600, height=300)
par(mar=c(0,4,0,1)) # 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
              
              xlimLeftMod= 1,              # modify xlim left argument
              ylimTopMod= -1,              # modify ylim top argument
              ylimBotMod= -2               # modify ylim bottom argument
              ,rulerPos = 0
              ,ruler.tck = -0.01
              
              ,legendWidth=1               # width of legend
              ,legendHeight=.7             # height of legend item 
              #,asp=1                      # y x aspect
)

# dev.off() # closes png or svg

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)

# par(mar=c(1,4,1,1)) 
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
)

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2.6 Example with several species (OTUs)

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

Chromosome sizes

data(bigdfChrSizeHolo)

OTU	chrName	chrSize
species one	1	5.1
species one	2	5.0
species one	3	4.9
species one	4	5.2
species two	1	3.0
species two	2	4.0
species two	3	2.0
species two	4	5.0
species three	1	1.5
species three	2	2.0
species three	3	6.0
species three	4	8.0

Mark characteristics, does not require OTU

d.f optional for ver. > 1.0.0

data(dfMarkColor)

markName	markColor	style
5S	red	dots
45S	green	square
DAPI	blue	square
CMA	yellow	square

Mark position

data(bigdfMarkPosHolo)

OTU	chrName	markName	markPos	markSize
species two	3	5S	1.0	0.5
species two	3	DAPI	2.0	0.5
species two	1	45S	2.0	0.5
species two	2	DAPI	2.0	0.5
species two	4	CMA	2.0	0.5
species two	4	5S	0.5	0.5

2.6.1 Plotting

library(idiogramFISH)

# fig.width=6, fig.height=6
png("bigdfChrSizeHolo.png", width=600, height=600)
# par(mar=c(1,1,1,1))
par(mar=rep(0,4)) 

plotIdiograms(dfChrSize=bigdfChrSizeHolo, # chr. size data.frame
              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
              
              rulerPos=0,
              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()

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2.7 Using bases instead of micrometers

Create some data in millions of bases:

# transform previous data.frames for simplicity
bigdfChrSizeHoloMb <- bigdfChrSizeHolo
bigdfChrSizeHoloMb$chrSize <- bigdfChrSizeHoloMb$chrSize * 98000000
bigdfMarkPosHoloMb <- bigdfMarkPosHolo
bigdfMarkPosHoloMb$markPos <- bigdfMarkPosHoloMb$markPos * 98000000
bigdfMarkPosHoloMb$markSize<- bigdfMarkPosHoloMb$markSize * 98000000

Plotting

In the plot length is shown in Mb

png("bigdfChrSizeHolo2.png", width=700, height=600)
# par(mar=c(1,1,1,1))
par(mar=rep(0,4)) 

plotIdiograms(dfChrSize=bigdfChrSizeHoloMb,  # chr. size data.frame
              dfMarkColor=dfMarkColor,       # df of mark style
              dfMarkPos=bigdfMarkPosHoloMb,  # df of mark positions
              
              markDistType="cen",            # distance to mark is to its center
              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
              
              rulerNumberSize=.9,            # font size of ruler
              rulerPos = 0,                  # position of ruler
              ruler.tck= -.004,              # ruler tick length and orient.
              ylabline = -6, # <- NEW        # modifies position of ruler title (Mb)
              
              markLabelSize=.9,              # font size of legend
              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()

For another example see: https://stackoverflow.com/questions/33727432/how-to-plot-positions-along-a-chromosome-graphic/57153497#57153497

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2.8 GISH of holocentric chromosomes

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

Chr. sizes:

parentalAndHybHoloChrSize

OTU	chrName	chrSize
Parental 1	7	4
Parental 1	4	2
Parental 1	5	6
Parental 1	6	7
Parental 2	1	4
Parental 2	2	5
Parental 2	3	3
Allopolyploid	7	4
Allopolyploid	2	5
Allopolyploid	3	3
Allopolyploid	4	2
Allopolyploid	5	6
Allopolyploid	6	7

Marks’ positions data

dfAlloParentMarksHolo

	OTU	chrName	markName	chrRegion
1	Allopolyploid	7	Parental 1	w
4	Allopolyploid	2	Parental 2	w
5	Allopolyploid	3	Parental 2	w
6	Allopolyploid	4	Parental 1	w
7	Allopolyploid	5	Parental 1	w
8	Allopolyploid	6	Parental 1	w
9	Parental 1	6	Parental 1	w
10	Parental 1	5	Parental 1	w
11	Parental 1	7	Parental 1	w
12	Parental 1	4	Parental 1	w
13	Parental 2	2	Parental 2	w
14	Parental 2	1	Parental 2	w
15	Parental 2	3	Parental 2	w

Plotting

# svg("gish.svg",width=8,height=7 )
par(mar=c(0,0,0,0)) 
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 height without spacing
              karHeiSpace=5,               # karyotype height including spacing
              distTextChr = 0.8            # separation among chr. and text
              
              ,ruler=FALSE                 # no ruler
              ,legend=""                   # no legend
              
              ,xlimRightMod = 0            # xlim right arg. modif.
              #,asp=1                      # y x aspect
)

# dev.off()

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2.9 Holocentrics in groups

Let’s modify some data.frames to add a group

data("dfChrSizeHolo")
data("dfMarkPosHolo")
dfMarkPosHoloHetero<-dfMarkPosHolo
dfMarkPosHoloHetero$chrName<-c(3,3,"1A",2,"1B","1B")
dfMarkPosHoloHetero$OTU<-"heteromorphic"

dfChrSizeHoloHetero<-dfChrSizeHolo
dfChrSizeHoloHetero$chrName<-c("1A","1B",2,3)
dfChrSizeHoloHetero$OTU<-"heteromorphic"

# Adding the group column
dfChrSizeHoloHetero$group<-c(1,1,NA,NA)

Creating a new data.frame for holocentrics

dfChrSizeHoloGroup<-data.frame(OTU="Species name", 
                               chrName=c(1,1,1,1,2,3,4), 
                               chrSize=c(3.1,3.2,3.3,3.4,4,5,6), 
                               group=c(1,1,1,1,NA,NA,NA) 
                               )

OTU	chrName	chrSize	group
Species name	1	3.1	1
Species name	1	3.2	1
Species name	1	3.3	1
Species name	1	3.4	1
Species name	2	4.0	NA
Species name	3	5.0	NA
Species name	4	6.0	NA

par(mar=rep(0,4)) 
mergedChrSize<-plyr::rbind.fill(dfChrSizeHoloGroup,dfChrSizeHoloHetero)

plotIdiograms(dfChrSize=mergedChrSize,      # data.frame of chr. sizes
              dfMarkPos=dfMarkPosHoloHetero,# d.f. of marks
              orderBySize = FALSE,          # do not order chr. by size
              karIndex = FALSE,             # do not add karyotype indices
              addOTUName = TRUE,            # add OTU name
              karHeiSpace = 4,              # height of kar. with spacing
              
              ruler=FALSE,                  # no ruler
              
              xlimLeftMod=-1,               # modify left argument of xlim
              xlimRightMod=0,               # modify right argument of xlim
              ylimBotMod=1.3                # modify bottom argument of ylim
)