Visualization Of Cnv With Genomic Coordinates In R Ggplot2 Or Ggbio
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10.9 years ago

Hi all,

I came through a heatmap of CNVs from Illumina Genome Studio which has four samples (see attachment). On Y axis they have genomic coordinates and on X axis the samples. Red denotes amplification and blue denotes deletion. I was wondering how can we make similar heatmap in R for CNV data or expression data? I know a little basis of ggplot and ggbio but I don't know how to make heatmap with genomic coordinates on Y axis. There are fix length of each chromosomes defined say chr-1 is 250 million base-pairs (units), chr-2 is 240 M, chr-3 is 200 M etc. Now there are segments of copy number changes with start position and end position for which heatmap is to be made. Now for chr-1 of size proportional to 250M, we need red block at location proportional to base pair 23432-to-25925 and another red block at 34564-to-44572 etc... similarly for each chromosome.

CNV data is in segments:

chr start end copy-number-T1 copy-number-T2

chr1 23432 25925 4 3

chr1 34564 44572 5 5

chr1 78463 85634 3 4

chr2 1375364 1378364 1 2

chr2 1463723 1469367 4 6

chr2 1678573 1683642 2 5

etc....

Thanks in advance.

CNV heatmap

r visualization copynumber • 14k views
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if you could make a picture visible and provide an example of your data, hacking R code for you will be much easier

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Sorry my image hosting site link broke. Updated the question with new image link. Thanks.

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Thank you. I answered below. In my example each of 23 chromosomes is divided into 20 segments which are then colored by a random value. I plot these using scale_fill_identity (discrete) so labels are getting crowded and I don't know how to make them nice. If you can use continuous scale, then things will be easier.

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5
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10.9 years ago
Pavel Senin ★ 1.9k

I can help with something like this, but I don't know how to prettify the Y axis labels - all chromosomes in my example are of length 20, so labels are getting overcrowded and I disabled them.

-- edit: fixed labels

enter image description here

require(ggplot2)
require(reshape)

data <- data.frame(matrix(rnorm(2*20*4)-0.5, nrow=2*20, byrow=F))
names(data) <- paste("T",1:4,sep="")
data$chromosome=factor(1:(2*20),1:(2*20),
  labels=paste(rep(paste("chr",1:2,sep=""),each=20),1:20,sep="."))
data$labels=matrix(rbind(paste("chr",1:2,sep=""),
  matrix(rep("",19*2),nrow=19)),byrow=T,nrow=20*2)

dm <- melt(data,id.var=c("chromosome","labels"))

dm$fill <- rep("white",2*20*4)
dm$fill[dm$value < -0.5] <- "red"
dm$fill[dm$value > 0.5] <- "blue"

p <- ggplot(dm, aes(variable, chromosome, fill=fill)) + geom_tile()
p + scale_fill_identity(expand=c(0,0),guide = "legend",
                        labels=c("deletion","amplification","none")) +
  scale_y_discrete(breaks=dm$chromosome,labels=dm$labels) +
  ggtitle("Example for 2 chromosomes")

-- edit2, with your data, just changed two values to negatives:

require(ggplot2)
require(reshape)

# four samples, here genome is linear, 2M long, each bin is 1000bp
# matrix rows = bins, matrix columns = samples
data <- matrix(rep(rep(0,2000000/1000),4),ncol=4)

# the intervals from your sample
intervals=data.frame(chromosome=c("chr1","chr1","chr1","chr2","chr2","chr2"),
                    start=c(23432,34564,78463,1375364,1463723,1678573),
                    end=c(25925,44572,85634,1378364,1469367,1683642),
                    t1=c(4,5,3,-1,4,2),
                    t2=c(3,5,4,2,-6,5))

# transfer the values from data frame into the matrix, probably can code a single function
mark_t1 <- function(a,b,c){ data[ (a/1000) : (b/1000), 1] <<- c}
apply(intervals[,c('start','end','t1')], 1 , function(x) mark_t1(x[1],x[2],x[3]))

mark_t2 <- function(a,b,c){ data[ (a/1000) : (b/1000), 2] <<- c}
apply(intervals[,c('start','end','t2')], 1 , function(x) mark_t2(x[1],x[2],x[3]))

# converting the matrix into a data frame
data <- as.data.frame(data)
names(data) <- paste("T",1:4,sep="")

# add bins column
data$bin=seq(1,2000000,by=1000)
dm <- melt(data,id.var=c("bin"))

# color the bins according to their values
dm$fill <- "white"
dm$fill[dm$value < -0.5] <- "red"
dm$fill[dm$value > 0.5] <- "blue"

# plot as red-white-blue rectangles, by using bins and continuous scale, it's easy to
# place Chromosome or even Gene markers
#
p <- ggplot(dm, aes(variable, bin, fill=fill)) + geom_tile()
p + scale_fill_identity(expand=c(0,0),guide = "legend",
                        labels=c("amplification","deletion","none")) +
  scale_y_continuous(breaks=c(0,1400000,2000000),labels=c("Chr 1","Chr 2","Chr 3")) +
  ggtitle("Example for 2 chromosomes") +
  theme_bw() +
  theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank(),
       panel.border = element_blank(),panel.background = element_blank(),
        axis.title.x= element_blank(),axis.title.y = element_blank())

enter image description here

or just like a heatmap plot:

#plot as white-to-blue heatmap
p2 <- ggplot(dm, aes(variable, bin, fill=value)) + geom_tile()
p2 + scale_y_continuous(breaks=c(0,1400000,2000000),labels=c("Chr 1","Chr 2","Chr 3")) +
  scale_fill_gradient(low = "white", high = "steelblue") +
  theme_bw() + ggtitle("Example for 2 chromosomes") +
  theme(panel.grid.major = element_blank(),panel.grid.minor = element_blank(),
        panel.border = element_blank(),panel.background = element_blank(),
        axis.title.x= element_blank(),axis.title.y = element_blank())

enter image description here

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Thanks you very much Senin. That is awesome. I am able to use this code to generate plot from simulated data. According to your code, there is a constant increment on Y-axis but in real data, there is a start position and end position given in data set. I have updated question with more data.

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code might not be optimal, but have no more time, happy new year!

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thank you very much Senin. Wish you happy new year and enjoy the time.... Will try out with the code and post my working code.

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Have a look into geom_segment in stead of geom_tile to make the y-axis fit with start and end positions of your segments

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10.9 years ago
PoGibas 5.1k

Why not circos? Of course, if you want "classical" heatmap you can stick with your idea...
Otherwise, CNV looks really nice in circos:

enter image description here

Moreover, tutorials are very detailed and they have CNV recipe special for you.

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Circos is good and I actually have one per sample but it's not fitting with the requirement of visualization for this particular figure. By the way thanks for the CNV recipe link.

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looks interesting and novel, i really like the rearrangements part. but how it'll look like for 10 samples?

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Here is example how it looks for 2 samples (you just add an additional circle) http://genomebiology.com/2010/11/8/R82/figure/F1?highres=y

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It looks good for 2-3 sample. But as you increase # of samples, the inner circle will become so small that it's almost difficult to compare with other samples. That is why I made individual circos for each sample.

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10.9 years ago

Hi Senin, your code gave the exact visualization required. But when I tried with the full data set , chromosome sorting is unusual. chr1 is followed by chr10, chr11 etc (plot1). I got to learn that factor level can be explicitly specified so I tried that(plot2) but NAs are automatically added for each empty field in lable which I think are coming in the axis label.

cnv.exp <- read.delim("output.txt", header=T)

cnv.exp$sample1 = as.numeric(cnv.exp$sample1)

cnv.exp$label = as.factor(cnv.exp$label)

dm = melt(cnv.exp, id.var=c("chromosome","label"))

dm$fill = rep("white",25055)

dm$fill[dm$value > 2] <- "red"

dm$fill[dm$value < 2] <- "blue"

p <- ggplot(dm, aes(variable, chromosome, fill=fill)) + geom_tile()

p + scale_fill_identity(expand=c(0,0),guide = "legend", label=c("deletion","amplification","none")) + scale_y_discrete(breaks=dm$chromosome,labels=dm$label) + ggtitle("plot1")

head(cnv.exp)

chromosome sample1 label

1 chr.1.1.761584 2 chr1

2 chr.1.761585.762902 3

3 chr.1.762903.803449 2

4 chr.1.803450.812182 3

5 chr.1.812183.861119 2

6 chr.1.861120.879961 3

str(cnv.exp)

'data.frame': 25055 obs. of 3 variables:

$ chromosome: Factor w/ 25045 levels "chr.1.1.761584",..: 1 2443 2448 2474 2478 2513 2527 2526 2540 2547 ...

$ sample1 : num 2 3 2 3 2 3 2 3 2 3 ...

$ label : Factor w/ 23 levels "","chr1","chr10",..: 2 1 1 1 1 1 1 1 1 1 ...

without NA

cnv.exp <- read.delim("output.txt", header=T)

cnv.exp$sample1 = as.numeric(cnv.exp$sample1)

cnv.exp$label = factor(cnv.exp$label, levels = c("chr.1","chr.2","chr.3", "chr.4","chr.5","chr.6", "chr7","chr8","chr9", "chr10","chr11","chr12", "chr13","chr14","chr15", "chr16","chr17","chr18", "chr19","chr20", "chr21","chr22"), labels = c("chr1","chr2","chr3","chr4","chr5", "chr6","chr7","chr8","chr9","chr10", "chr11","chr12","chr13","chr14","chr15", "chr16","chr17","chr18","chr19","chr20", "chr21","chr22"))

dm = melt(cnv.exp, id.var=c("chromosome","label"))

dm$fill = rep("white",25055)

dm$fill[dm$value > 2] <- "red"

dm$fill[dm$value < 2] <- "blue"

p <- ggplot(dm, aes(variable, chromosome, fill=fill)) + geom_tile()

p + scale_fill_identity(expand=c(0,0),guide = "legend", label=c("deletion","amplification","none")) + scale_y_discrete(breaks=dm$chromosome,labels=dm$label) + ggtitle("plot2")

head(cnv.exp)

chromosome sample1 label

1 chr.1.1.761584 2 <na>

2 chr.1.761585.762902 3 <na>

3 chr.1.762903.803449 2 <na>

4 chr.1.803450.812182 3 <na>

5 chr.1.812183.861119 2 <na>

6 chr.1.861120.879961 3 <na>

str(cnv.exp)

'data.frame': 25055 obs. of 3 variables:

$ chromosome: Factor w/ 25045 levels "chr.1.1.761584",..: 1 2443 2448 2474 2478 2513 2527 2526 2540 2547 ...

$ sample1 : num 2 3 2 3 2 3 2 3 2 3 ...

$ label : Factor w/ 22 levels "chr1","chr2",..: NA NA NA NA NA NA NA NA NA NA ...

with NA:

enter image description here

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Hi there: (1) I am not getting why is there so many labels on the Y axis, moreover, I don't know which scale discrete or continuous you are using. Nevertheless, for both scales, you can specify two vectors - labels and breaks which will make those pretty. (2) Since labels are not sorted right, I would assume that your scale is discrete, - there are two options available, one is to switch to continuous, second is to fix the factor - probably it doesn't work for you

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I have updated the post with exact code and data structure. I got to learn that levels can be explicitly specified for factors but when I an doing so, NAs are added for empty fields which I think are coming up in the labels.

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Hi there! Hope you had an awesome weekend. So, I would guess that there is a bit of misunderstanding between us. You see, when you call scale_y_discrete(breaks=dm$chromosome,labels=dm$label) ggplot will populate Y scale labels with all the stuff you have in your dataframe -- bunch of labels where some were set, and some were not -- but not with what you want. i.e. problem is in the way data structures are populated and in the plotting instructions. If you will look on my example below edit2 - which I think is better than the first one - you'll find that the whole genome is imagined as a continuous canvas of an arbitrary width with length from 0 to the total length of sum of all chromosomes; then, each interval where some phenomenon occurs is imagined as a "tile" - with start, stop, and color; the plotting instruction places all colored tiles onto blank canvas; the labels along the canvas are then populated manually, not in automatic fashion - moreover, you can customize them the way you want. Let me know if you'd need more information.

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5.9 years ago
Shixiang ▴ 100

copynumber package maybe a good option, please see http://127.0.0.1:11794/library/copynumber/doc/copynumber.pdf

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