Hello,
I am trying to plot some SNPs (variants) in a genomic range with Gviz but the labels overlap and I can't figure out where the problem is. They are close by but the ranges themselves do not overlap but their labels do. I want them to be one under the other. I have tried various parameter values for stacking in AnnotationTrack but it doesn't help. Here is a code snippet:
strack <- AnnotationTrack(current_SNPs, name="GWAS SNPs",
stacking="full",
group=current_SNPs$SNPS, groupAnnotation="group",
fill=Purple, col=Purple)
grbtrack <- AnnotationTrack(current_GRB, name="GRB human:dog",
id=current_GRB$targets, fontcolor.feature="black",
fill=Grey, col=Grey)
displayPars(grbtrack) <- list(featureAnnotation="id")
plotTracks(list(grbtrack, strack),
sizes = c(1,1),
background.title="azure3", fontcolor.title="black")
The two granges objects:
> current_GRB
GRanges object with 1 range and 7 metadata columns:
seqnames ranges strand | ENSEMBLGeneID geneName originalSupport
<Rle> <IRanges> <Rle> | <character> <character> <character>
[1] chr1 213297131-215179503 * | ENSG00000117707 PROX1 0.876
normalisedSupport prediction ucscCoordinates.hg19 targets
<character> <character> <character> <character>
[1] 1 target chr1:214156524-21421.. PROX1
> current_SNPs
GRanges object with 2 ranges and 2 metadata columns:
seqnames ranges strand | SNPS SNP.coords
<Rle> <IRanges> <Rle> | <character> <character>
[1] chr1 213937382 * | rs1891058 <NA>
[2] chr1 213944467-213944489 * | rs147340715 1:213944467-213944489
Doesn't look like there's great customization options in the Gviz documentation. Your best bet might be to zoom in on the SNPs in PROX1 to make them bigger.
Arranging items on the plotting canvas is relatively straight forward as long as there are no overlaps between individual regions or groups of regions. Those inevitably cause overplotting which could seriously obfuscate the information on the plot. A logical solution to this problem is to stack overlapping items in separate horizontal lines to accommodate all of them. This involves some optimization, and the Gviz package automatically tries to come up with the most compact arrangement.
Its a bit of a bigger region than a gene, so that is why its so small. Thanks for the answer. Though, I was bothered by it because in this track, where there are more ranges, Gviz stacks them pretty nicely.
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