Hi Tengfei and others

Just wondering if you know of some code based examples where people
have used ggbio based circular plots for overlaying different
expression based datasets.

I have RNA, miRNA and methylation datasets that I would love to
present it users in a single plot using some sort of a circular plot
as my first attempt.

pointers appreciated.

(i did send this earlier on the mailing list but assuming this forum is the new way to go).. I do believe this would be a very useful resource ..look fwd to reusable BioC help.

Thanks!
-Abhi

Hello Abhi. ggbio plots are highly customizable and maybe is hard to find an example that does exactly what you want. A very useful starting point is the vignette of ggbio.

Now, the following example does not display data in circular plots but in a karyogram (which may also fit your needs). However, it illustrates the logic behind ggbio and it should not be very hard to adjust it for your data and circular plots.

# Load requried libraries
library(ggbio)
library(GenomicRanges)

# The custom ggbiosave function
ggbiosave <- function (filename = default_name(plot), plot = last_plot(), 
    device = default_device(filename), path = NULL, scale = 1, 
    width = par("din")[1], height = par("din")[2], units = c("in", 
        "cm", "mm"), dpi = 300, limitsize = TRUE, ...) 
{
    #if (!inherits(plot, "ggplot") & !is(plot, "Tracks")) 
    #    stop("plot should be a ggplot2 plot or tracks object")
    eps <- ps <- function(..., width, height) grDevices::postscript(..., 
        width = width, height = height, onefile = FALSE, horizontal = FALSE, 
        paper = "special")
    tex <- function(..., width, height) grDevices::pictex(..., 
        width = width, height = height)
    pdf <- function(..., version = "1.4") grDevices::pdf(..., 
        version = version)
    svg <- function(...) grDevices::svg(...)
    wmf <- function(..., width, height) grDevices::win.metafile(..., 
        width = width, height = height)
    emf <- function(..., width, height) grDevices::win.metafile(..., 
        width = width, height = height)
    png <- function(..., width, height) grDevices::png(..., width = width, 
        height = height, res = dpi, units = "in")
    jpg <- jpeg <- function(..., width, height) grDevices::jpeg(..., 
        width = width, height = height, res = dpi, units = "in")
    bmp <- function(..., width, height) grDevices::bmp(..., width = width, 
        height = height, res = dpi, units = "in")
    tiff <- function(..., width, height) grDevices::tiff(..., 
        width = width, height = height, res = dpi, units = "in")
    default_name <- function(plot) {
        paste(digest.ggplot(plot), ".pdf", sep = "")
    }
    default_device <- function(filename) {
        pieces <- strsplit(filename, "\\.")[[1]]
        ext <- tolower(pieces[length(pieces)])
        match.fun(ext)
    }
    units <- match.arg(units)
    convert_to_inches <- function(x, units) {
        x <- switch(units, `in` = x, cm = x/2.54, mm = x/2.54/10)
    }
    convert_from_inches <- function(x, units) {
        x <- switch(units, `in` = x, cm = x * 2.54, mm = x * 
            2.54 * 10)
    }
    if (!missing(width)) {
        width <- convert_to_inches(width, units)
    }
    if (!missing(height)) {
        height <- convert_to_inches(height, units)
    }
    if (missing(width) || missing(height)) {
        message("Saving ", prettyNum(convert_from_inches(width * 
            scale, units), digits = 3), " x ", prettyNum(convert_from_inches(height * 
            scale, units), digits = 3), " ", units, " image")
    }
    width <- width * scale
    height <- height * scale
    if (limitsize && (width >= 50 || height >= 50)) {
        stop("Dimensions exceed 50 inches (height and width are specified in inches/cm/mm, not pixels).", 
            " If you are sure you want these dimensions, use 'limitsize=FALSE'.")
    }
    if (!is.null(path)) {
        filename <- file.path(path, filename)
    }
    device(file = filename, width = width, height = height, ...)
    on.exit(capture.output(dev.off()))
    print(plot)
    invisible()
}

# Firstly, we retrieve chromosome information for the organism of initerest to be used
# as seqlengths in our custom datasets. We will come back to this data frame holding
# chromosomal lengths later.
download.file(paste("http://hgdownload.cse.ucsc.edu/goldenPath/mm9/database/chromInfo.txt.gz",sep=""),
  file.path(tempdir(),"chromInfo.txt.gz"))
if (.Platform$OS.type == "unix")
    system(paste("gzip -df",file.path(tempdir(),"chromInfo.txt.gz")))
else
    unzip(file.path(tempdir(),"chromInfo.txt.gz"))
chrom.info <- read.delim(file.path(tempdir(),"chromInfo.txt"),header=FALSE)
chrom.info <- chrom.info[-grep("rand|chrM|hap",chrom.info[,1]),1:2]
rownames(chrom.info) <- chrom.info[,1]

# Then, download cytoband information for the organism of interest to be used for coloring
# the bands of each chromosome
download.file(paste("http://hgdownload.soe.ucsc.edu/goldenPath/mm9/database/cytoBand.txt.gz",sep=""),
  file.path(tempdir(),"cytoBand.txt.gz"))
if (.Platform$OS.type == "unix")
    system(paste("gzip -df",file.path(tempdir(),"cytoBand.txt.gz")))
else
    unzip(file.path(tempdir(),"chromInfo.txt.gz"))

# Create the cytoband information to be used for the respective layer in ggbio
cyto.text <- read.delim(file.path(tempdir(),"cytoBand.txt"),header=FALSE)
names(cyto.text) <- c("chromosome","start","end","cytoband","gieStain")
cyto.info <- makeGRangesFromDataFrame(
    df=cyto.text,
    keep.extra.columns=TRUE,
    seqnames.field="chromosome"
)
seqlengths(cyto.info) <- chrom.info[names(seqlengths(cyto.info)),2]
cyto.info <- keepSeqlevels(cyto.info, paste("chr",c(1:19,"X","Y"),sep=""))

# Now, you have to find a way to format your data in a GRanges object and think
# how you can represent what you want to show in an axes-system suitable for layered
# visualization in ggbio. In the following code, I use a hypothetical file which
# contains CGH aberration data. The first three columns contain genomic co-ordinates
# (chromosome, start, end), the fourth column contains 1 if the respective line
# represents an amplification, or -1 for a deletion. Finally, the fifth column
# contain the name of the aCGH array into which this aberration was discovered.
cgh.data <- read.delim("cgh_aberrations.txt")

# This variable makes sure that there is some space across points in the vertical
# axis that will be created above each chromosome in the karyogram
add <- 0

# Loop through the arrays of our aCGH experiment
for (arr in unique(as.character(cgh.data$array))) {
    ind <- which(cgh.data$array==arr)
    for (i in ind) {
        # Transform 1s and -1s so as to be distinctly visible in the final graphic
        # Maybe not clear now, you have to play with it to understand
        if (cgh.data$aberration[i]==1)
            cgh.data$aberration[i] <- cgh.data$aberration[i] + (3*add + 1)
        else
            cgh.data$aberration[i] <- cgh.data$aberration[i] - (3*add + 1)
    }
    add <- add + 1
}
# To separate amplifications from deletions, we have to draw a horizontal line
# somewhere, which adds one more layer in our final plot.
cgh.data$aberration <- cgh.data$aberration + 55

# After all these transformations, create a GRanges object with the data to display
cgh.data.gr <- makeGRangesFromDataFrame(
    df=cgh.data,
    keep.extra.columns=TRUE,
    seqnames.field="chromosome"
)

# Back to chromosomal lengths. We will create a GRanges object with chromosomal
# lengths and additional data so that we can draw the horizontal line that will
# separate amplifications from deletions.
seqls <- as.numeric(chrom.info[,2])
names(seqls) <- rownames(chrom.info)
chrom.info.gr <- GRanges(
    seqnames=rep(as.character(rownames(chrom.info)),each=2),
    IRanges(
        start=c(
            1,chrom.info[1,2],
            1,chrom.info[2,2],
            1,chrom.info[3,2],
            1,chrom.info[4,2],
            1,chrom.info[5,2],
            1,chrom.info[6,2],
            1,chrom.info[7,2],
            1,chrom.info[8,2],
            1,chrom.info[9,2],
            1,chrom.info[10,2],
            1,chrom.info[11,2],
            1,chrom.info[12,2],
            1,chrom.info[13,2],
            1,chrom.info[14,2],
            1,chrom.info[15,2],
            1,chrom.info[16,2],
            1,chrom.info[17,2],
            1,chrom.info[18,2],
            1,chrom.info[19,2],
            1,chrom.info[20,2],
            1,chrom.info[21,2]
        ),
        end=rep(as.numeric(chrom.info[,2]),each=2)
    ),
    seqlengths=seqls
)
elementMetadata(chrom.info.gr) <- DataFrame(zero=rep(55,length(chrom.info.gr)))

# Now, create the final GRanges object which will hold the scores which you want
# to display in a karyogram. Notice that the main difference with cgh.data.gr is
# a change in the coordinates which place the start and end in the same location
# (middle of aberration), otherwise the result will be very blurry. We also add
# seqlengths which is necessary for the plot.
score.layer <- GRanges(
    seqnames=seqnames(cgh.data.gr),
    IRanges(
        start=round(start(cgh.data.gr) + (end(cgh.data.gr) - start(cgh.data.gr) + 1)/2),
        end=round(start(cgh.data.gr) + (end(cgh.data.gr) - start(cgh.data.gr) + 1)/2),
    ),
    seqlengths=seqlengths(cyto.info)
)
elementMetadata(score.layer) <- elementMetadata(cgh.data.gr)

# and finally, create the karyogram!
p <- ggplot(cyto.info) + 
    theme_bw() +
    layout_karyogram(cytoband=TRUE,rect.height=8) +
    guides(fill=FALSE) +
    layout_karyogram(chrom.info.gr,aes(x=start,y=zero),geom="line",ylim=c(15,100),size=0.1,guide="none") +
    layout_karyogram(score.layer,aes(x=start,y=aberration,color=array),geom="point",size=0.75,ylim=c(15,100)) +
    facet_wrap(~seqnames,nrow=7) +
    theme(axis.title.x=element_blank(),
        axis.title.y=element_blank(),
        axis.text.x=element_text(angle=45,hjust=1,vjust=1.2),
        legend.key=element_blank(),
        legend.title=element_blank(),
        legend.position="bottom") +
    guides(colour=guide_legend(override.aes=list(size=2),nrow=2,byrow=TRUE,keyheight=unit(0.5,"lines")))
ggbiosave(filename=file.path(the.path,"cgh_karyogram_multi.pdf"),plot=p,width=210,height=297,
    dpi=600,units="mm")

The above example will produce something similar to the following plot (also here):