Introduction
There are two packages available in Bioconductor for visualizing genomic data: rtracklayer and Gviz. rtracklayer provides an interface to genome browsers and associated annotation tracks. Gviz plots data and annotation information along genomic coordinates. TrackViewer is a light-weighted visualization tool for generating neat figures for publication. It utilizes Gviz, is easy to use, and has a low memory and cpu consumption.
library(Gviz)
library(rtracklayer)
library(trackViewer)
extdata <- system.file("extdata", package="trackViewer",
mustWork=TRUE)
gr <- GRanges("chr11", IRanges(122929275, 122930122), strand="-")
fox2 <- importScore(file.path(extdata, "fox2.bed"), format="BED",
ranges=gr)
fox2$dat <- coverageGR(fox2$dat)
viewTracks(trackList(fox2), gr=gr, autoOptimizeStyle=TRUE, newpage=FALSE)
dt <- DataTrack(range=fox2$dat[strand(fox2$dat)=="-"] ,
genome="hg19", type="hist", name="fox2",
window=-1, chromosome="chr11",
fill.histogram="black", col.histogram="NA",
background.title="white",
col.frame="white", col.axis="black",
col="black", col.title="black")
plotTracks(dt, from=122929275, to=122930122, strand="-")
TrackViewer not only has the functionalities to plot the figures generated by Gviz, as shown in Figure above, but also provides additional plotting styles as shown in Figure below. The mimimalist design requires minimum input from users while retaining the flexibility to change output style easily.
gr <- GRanges("chr1", IRanges(c(1, 6, 10), c(3, 6, 12)), score=c(3, 4, 1))
dt <- DataTrack(range=gr, data="score", type="hist")
plotTracks(dt, from=2, to=11)
tr <- new("track", dat=gr, type="data", format="BED")
viewTracks(trackList(tr), chromosome="chr1", start=2, end=11)
It requires huge memory space to handle big wig files. To solve this problem, trackViewer rewrote the import function to import whole file first and parse it later when plot. trackViewer provides higher import speed (21 min vs. over 180 min) and acceptable memory cost (5.32G vs. over 10G) for a half giga wig file (GSM917672) comparing to Gviz.
Operators
If there are two tracks and we want to draw the two track by adding or substract one from another, we can try operators.
newtrack <- repA
## must keep same format for dat and dat2
newtrack <- parseWIG(newtrack, "chr11", 122929275, 122930122)
newtrack$dat2 <- newtrack$dat
newtrack$dat <- fox2$dat2
setTrackStyleParam(newtrack, "color", c("blue", "red"))
viewTracks(trackList(newtrack, trs),
gr=gr, viewerStyle=viewerStyle, operator="+")
viewTracks(trackList(newtrack, trs), gr=gr, viewerStyle=viewerStyle, operator="-")
Or try GRoperator before view tracks.
newtrack$dat <- GRoperator(newtrack$dat, newtrack$dat2, col="score", operator="-")
newtrack$dat2 <- GRanges()
viewTracks(trackList(newtrack, trs), gr=gr, viewerStyle=viewerStyle)
Lolliplot
Lolliplot is a mutation distribution graphics tool.
SNP <- c(10, 12, 1400, 1402)
sample.gr <- GRanges("chr1", IRanges(SNP, width=1, names=paste0("snp", SNP)))
features <- GRanges("chr1", IRanges(c(1, 501, 1001),
width=c(120, 400, 405),
names=paste0("block", 1:3)))
lolliplot(sample.gr, features)
## more SNPs
SNP <- c(10, 100, 105, 108, 400, 410, 420, 600, 700, 805, 840, 1400, 1402)
sample.gr <- GRanges("chr1", IRanges(SNP, width=1, names=paste0("snp", SNP)))
lolliplot(sample.gr, features)
## define the range
lolliplot(sample.gr, features, ranges = GRanges("chr1", IRanges(104, 109)))
Change lolliplot colors
Change the color of features.
features$fill <- c("#FF8833", "#51C6E6", "#DFA32D")
lolliplot(sample.gr, features)
Change the color of lollipop.
sample.gr$color <- sample.int(6, length(SNP), replace=TRUE)
sample.gr$border <- sample(c("gray80", "gray30"), length(SNP), replace=TRUE)
lolliplot(sample.gr, features)
Add index labels in the node
sample.gr$label <- as.character(1:length(sample.gr))
sample.gr$label.col <- "white"
lolliplot(sample.gr, features)
Change the height of features
features$height <- c(0.02, 0.05, 0.08)
lolliplot(sample.gr, features)
## keep the height by giving the unit
features$height <- list(unit(1/16, "inches"),
unit(3, "mm"),
unit(12, "points"))
lolliplot(sample.gr, features)
Multiple transcripts in the features
The metadata ‘featureLayerID’ are used for drawing features in different layers.
features.mul <- rep(features, 2)
features.mul$height[4:6] <- list(unit(1/8, "inches"),
unit(0.5, "lines"),
unit(.2, "char"))
features.mul$fill <- c("#FF8833", "#F9712A", "#DFA32D",
"#51C6E6", "#009DDA", "#4B9CDF")
end(features.mul)[5] <- end(features.mul[5])+50
features.mul$featureLayerID <-
paste("tx", rep(1:2, each=length(features)), sep="_")
names(features.mul) <-
paste(features.mul$featureLayerID,
rep(1:length(features), 2), sep="_")
lolliplot(sample.gr, features.mul)
## one name per transcripts
names(features.mul) <- features.mul$featureLayerID
lolliplot(sample.gr, features.mul)
Change the height of lolliplot
#Note: the score value is integer less than 10
sample.gr$score <- sample.int(5, length(sample.gr), replace = TRUE)
lolliplot(sample.gr, features)
##remove yaxis
lolliplot(sample.gr, features, yaxis=FALSE)
#Try score value greater than 10
sample.gr$score <- sample.int(20, length(sample.gr), replace=TRUE)
lolliplot(sample.gr, features)
#Try float numeric score
sample.gr$score <- runif(length(sample.gr))*10
lolliplot(sample.gr, features)
# score should not be smaller than 1
Customize xaxis label positions
xaxis <- c(1, 200, 400, 701, 1000, 1200, 1402) ## define the position
lolliplot(sample.gr, features, xaxis=xaxis)
names(xaxis) <- xaxis # define the labels
names(xaxis)[4] <- "center"
lolliplot(sample.gr, features, xaxis=xaxis)
Customize yaxis label positions
yaxis <- c(0, 5) ## define the position
lolliplot(sample.gr, features, yaxis=yaxis)
yaxis <- c(0, 5, 10, 15)
names(yaxis) <- yaxis # define the labels
names(yaxis)[3] <- "yaxis"
lolliplot(sample.gr, features, yaxis=yaxis)
Add legend
legend <- 1:6 ## legend fill color
names(legend) <- paste0("legend", letters[1:6]) ## legend labels
lolliplot(sample.gr, features, legend=legend)
## use list to define more attributes. see ?grid::gpar to get more details.
legend <- list(labels=paste0("legend", LETTERS[1:6]),
col=palette()[6:1],
fill=palette()[legend])
lolliplot(sample.gr, features, legend=legend)
## if you have multiple tracks, try to set the legend by list.
## see more in section [Plot multiple samples](#plot-multiple-samples)
legend <- list(legend)
lolliplot(sample.gr, features, legend=legend)
Control the labels
Use can control the paramters of labels by name the metadata start as label.parameter. such as label.parameter.rot, label.parameter.gp. The parameter is used for .
sample.gr.rot <- sample.gr
sample.gr.rot$label.parameter.rot <- 45
lolliplot(sample.gr.rot, features, legend=legend)
sample.gr.rot$label.parameter.rot <- 60
sample.gr.rot$label.parameter.gp <- gpar(col="brown")
lolliplot(sample.gr.rot, features, legend=legend)
If you want to change the text in ylab, please try to set the labels in ylab. lolliplot does not support the parameters for title and xlab. If you want to add title and xlab, please try to add them by .
lolliplot(sample.gr.rot, features, legend=legend, ylab="y label here")
grid.text("x label here", x=.5, y=.01, just="bottom")
grid.text("title here", x=.5, y=.98, just="top",
gp=gpar(cex=1.5, fontface="bold"))
Change the type of lolliplot
Google pin
lolliplot(sample.gr, features, type="pin")
sample.gr$color <- lapply(sample.gr$color, function(.ele) c(.ele, sample.int(6, 1)))
sample.gr$border <- sample.int(6, length(SNP), replace=TRUE)
lolliplot(sample.gr, features, type="pin")
Pie plot
sample.gr$score <- NULL ## must be removed, because pie will consider all the numeric columns except column "color", "fill", "lwd", "id" and "id.col".
sample.gr$label <- NULL
sample.gr$label.col <- NULL
x <- sample.int(100, length(SNP))
sample.gr$value1 <- x
sample.gr$value2 <- 100 - x
## the length of color should be no less than the values number
sample.gr$color <- rep(list(c("#87CEFA", '#98CE31')), length(SNP))
sample.gr$border <- "gray30"
lolliplot(sample.gr, features, type="pie")
Plot multiple samples
multiple layers
SNP2 <- sample(4000:8000, 30)
x2 <- sample.int(100, length(SNP2), replace=TRUE)
sample2.gr <- GRanges("chr3", IRanges(SNP2, width=1, names=paste0("snp", SNP2)),
value1=x2, value2=100-x2)
sample2.gr$color <- rep(list(c('#DB7575', '#FFD700')), length(SNP2))
sample2.gr$border <- "gray30"
features2 <- GRanges("chr3", IRanges(c(5001, 5801, 7001),
width=c(500, 500, 405),
names=paste0("block", 4:6)),
fill=c("orange", "gray30", "lightblue"),
height=unit(c(0.5, 0.3, 0.8), "cm"))
legends <- list(list(labels=c("WT", "MUT"), fill=c("#87CEFA", '#98CE31')),
list(labels=c("WT", "MUT"), fill=c('#DB7575', '#FFD700')))
lolliplot(list(A=sample.gr, B=sample2.gr),
list(x=features, y=features2),
type="pie", legend=legends)
Different layouts are also be possible.
sample2.gr$score <- sample2.gr$value1 ## circle layout need score column
lolliplot(list(A=sample.gr, B=sample2.gr),
list(x=features, y=features2),
type=c("pie", "circle"), legend=legends)
pie.stack layout
rand.id <- sample.int(length(sample.gr), 3*length(sample.gr), replace=TRUE)
rand.id <- sort(rand.id)
sample.gr.mul.patient <- sample.gr[rand.id]
## pie.stack require metadata "stack.factor", and the metadata can not be
## stack.factor.order or stack.factor.first
len.max <- max(table(rand.id))
stack.factors <- paste0("patient", formatC(1:len.max,
width=nchar(as.character(len.max)),
flag="0"))
sample.gr.mul.patient$stack.factor <-
unlist(lapply(table(rand.id), sample, x=stack.factors))
sample.gr.mul.patient$value1 <-
sample.int(100, length(sample.gr.mul.patient), replace=TRUE)
sample.gr.mul.patient$value2 <- 100 - sample.gr.mul.patient$value1
patient.color.set <- as.list(as.data.frame(rbind(rainbow(length(stack.factors)),
"#FFFFFFFF"),
stringsAsFactors=FALSE))
names(patient.color.set) <- stack.factors
sample.gr.mul.patient$color <-
patient.color.set[sample.gr.mul.patient$stack.factor]
legend <- list(labels=stack.factors, col="gray80",
fill=sapply(patient.color.set, `[`, 1))
lolliplot(sample.gr.mul.patient, features, type="pie.stack",
legend=legend, dashline.col="gray")
caterpillar layout
Metadata SNPsideID is used to trigger caterpillar layout. SNPsideID must be ‘top’ or ‘bottom’.
sample.gr$SNPsideID <- sample(c("top", "bottom"),
length(sample.gr),
replace=TRUE)
lolliplot(sample.gr, features, type="pie",
legend=legends[[1]])
## two layers
sample2.gr$SNPsideID <- "top"
idx <- sample.int(length(sample2.gr), 15)
sample2.gr$SNPsideID[idx] <- "bottom"
sample2.gr$color[idx] <- '#FFD700'
lolliplot(list(A=sample.gr, B=sample2.gr),
list(x=features.mul, y=features2),
type=c("pie", "circle"), legend=legends)
Methylation data
library(rtracklayer)
session <- browserSession()
query <- ucscTableQuery(session, track="HAIB Methyl RRBS",
range=GRangesForUCSCGenome("hg19", seqnames(gr), ranges(gr)))
tableName(query) <- tableNames(query)[1]
methy <- track(query)
methy <- GRanges(methy)
lolliplot(methy, features, ranges=gr, type="pin")
Change node size
In above sample, some of the nodes overlap each other. To change the node size, cex prameter could be used.
methy$lwd <- .5
lolliplot(methy, features, ranges=gr, type="pin", cex=.5)
lolliplot(methy, features, ranges=gr, type="circle", cex=.5)
methy$score2 <- max(methy$score) - methy$score
lolliplot(methy, features, ranges=gr, type="pie", cex=.5)
Dandelion Plot
Sometimes, there are too many SNPs invoved in one gene. If you want to plot such as more than handred SNPs, Dandelion plot may be the good chioce. Please note, the height of the dandelion means the desity of the SNPs.
dandelion.plot(methy, features, ranges=gr, type="pin")
change the type of Dandelion plot
There are one more type for dandelion plot, type “fan”. The area of the fan indicate the percentage of methylation or rate of mutation.
methy$color <- 3
methy$border <- "gray"
## score info is required, the score must be a number in [0, 1]
m <- max(methy$score)
methy$score <- methy$score/m
dandelion.plot(methy, features, ranges=gr, type="fan")
methy$color <- rep(list(c(3, 5)), length(methy))
methy$score2 <- methy$score2/m
legends <- list(list(labels=c("s1", "s2"), fill=c(3, 5)))
dandelion.plot(methy, features, ranges=gr, type="pie", legend=legends)
change the number of dandelions
## want less
dandelion.plot(methy, features, ranges=gr, type="circle", maxgaps=1/10)
## want more
dandelion.plot(methy, features, ranges=gr, type="circle", maxgaps=1/100)
