Visualization

embed images in ggplot2 via subview and annotate a phylogenetic tree with images using inset function

I extended the subview function to support embed image file in a ggplot object.

set.seed(123)
d = data.frame(x=rnorm(10), y=rnorm(10))

imgfile <- tempfile(, fileext=".png")
download.file("https://avatars1.githubusercontent.com/u/626539?v=3&u=e731426406dd3f45a73d96dd604bc45ae2e7c36f&s=140",
              destfile=imgfile, mode='wb')

p = ggplot(d, aes(x, y))
subview(p, imgfile, x=d$x[1], y=d$y[1]) + geom_point(size=5)

Font Awesome supported in emojifont

CRAN_Status_Badge

emojifont is available in CRAN, you can use the following command to install it.

install.packages("emojifont")

An example of using emoji font in R plot is showed below:

covplot supports GRangesList

To answer the issue, I extend the covplot function to support viewing coverage of a list of GRanges objects or bed files.

library(ChIPseeker)
files <- getSampleFiles()
peak=GenomicRanges::GRangesList(CBX6=readPeakFile(files[[4]]),
                                CBX7=readPeakFile(files[[5]]))

p <- covplot(peak)
print(p)

use emoji font in R

I have played with emoji in R for a while. My solution of using it is different from what implemented in emoGG.

emoGG is a good attemp to add emoji in ggplot2. It render emoji picture (png) and creat a layer, geom_emoji, to add emoji.

In my opinion, emoji should be treated as ordinary font in user interface, albeit it maynot be true internally.

It would be more flexible if we can use emoji as ordinary font and in this way user don’t need to learn extra stuff.

use simplify to remove redundancy of enriched GO terms

To simplify enriched GO result, we can use slim version of GO and use enricher function to analyze.

Another strategy is to use GOSemSim to calculate similarity of GO terms and remove those highly similar terms by keeping one representative term. To make this feature available to clusterProfiler users, I develop a simplify method to reduce redundant GO terms from output of enrichGO function.

require(clusterProfiler)
data(geneList, package="DOSE")
de <- names(geneList)[abs(geneList) > 2]
bp <- enrichGO(de, ont="BP")
enrichMap(bp)

subview

I implemented a function, subview, in ggtree that make it easy to embed a subplot in ggplot.

An example is shown below:

library(ggplot2)
library(ggtree)

dd <- data.frame(x=LETTERS[1:3], y=1:3)
pie <- ggplot(dd, aes(x=1, y, fill=x)) + 
    geom_bar(stat="identity", width=1) + 
    coord_polar(theta="y") + theme_tree() + 
    xlab(NULL) + ylab(NULL) + 
    theme_transparent()

x <- sample(2:9)
y <- sample(2:9)
width <- sample(seq(0.05, 0.15, length.out=length(x)))
height <- width

p <- ggplot(data=data.frame(x=c(0, 10), y=c(0, 10)), aes(x, y))+geom_blank()
print(p)
for (i in seq_along(x)) {
    p %<>% subview(pie, x[i], y[i], width[i], height[i])
    print(p)
}

upsetplot in ChIPseeker

ChIPseeker is an R package for ChIP peak annotation, comparison and visualization.

We have implemented several visualization methods, including vennpie that was designed for viewing annotation overlap as shown below:

dotplot for enrichment result

This is a feature request from clusterProfiler user. It’s similar to what I implemented in clusterProfiler for comparing biological themes. For comparing different enrichment results, the x-axis represent different gene clusters while for a single enrichment result, the x-axis can be gene count or gene ratio. This is actually similar to traditional barplot, with dot position as bar height and dot color as bar color. But dotplot can represent one more feature nicely by dot size and it can be a good alternative to barplot.

an example of drawing beast tree using ggtree

FigTree is designed for viewing beast output as demonstrated by their example data:

viewing and annotating phylogenetic tree with ggtree

When I need to annotate nucleotide substitutions in the phylogenetic tree, I found that all the software are designed to display the tree but not annotating it. Some of them may support annotating the tree with specific data such as bootstrap values, but they are restricted to a few supported data types. It is hard/impossible to inject user specific data.