R package to visualize complex genomic rearrangements by plotting copy number profiles and structural variants.
If you use ReConPlot please cite our paper
ReConPlot – an R package for the visualization and interpretation of genomic rearrangements
Jose Espejo Valle-Inclán, Isidro Cortés-Ciriano
Bioinformatics, Volume 39, Issue 12, December 2023, btad719, https://doi.org/10.1093/bioinformatics/btad719
You can clone this repository and install it using the following command in the command line:
R CMD INSTALL ReConPlot/
Or use devtools to install directly from GitHub within R:
> devtools::install_github("cortes-ciriano-lab/ReConPlot")
ReConPlot needs a ggplot version >=3.4.0.
Please take a look at the detailed tutorial and documentation of the package for examples and best practices for using ReConPlot to generate publication-quality figures.
First, load the package
library(ggplot2)
library(ReConPlot)
You will need three data frames:
- SV data (with columns chr1, pos1, chr2, pos2 and strands (+- notation)
- CN data (with columns chr, start, end, copyNumber and minorAlleleCopyNumber)
- Chromosome selection with genomic region(s) to plot (with columns chr, start, end)
Any extra column in the data frames will not be read.
#SV data
print(head(sv_data))
sample chr1 pos1 chr2 pos2 filter homlen homseq inslen strands
Test chr1 203476803 chr1 204967317 PASS . . . -+
Test chr1 203476817 chr1 204967172 PASS . . . +-
Test chr1 203507815 chr9 37245409 PASS . . . +-
Test chr1 203509239 chr12 69072120 PASS 3 GTA . ++
Test chr1 203509728 chr12 69071885 PASS . . . -+
Test chr1 203509991 chr1 203522717 PASS 1 A . +-
#CN data
print(head(cn_data))
chr start end copyNumber minorAlleleCopyNumber
chr1 1 9631965 2 1
chr1 9631966 9631966 3 1
chr1 9631967 11239516 2 1
chr1 11239517 11239533 3 1
chr1 11239534 22578082 2 1
chr1 22578083 27086500 2 1
#Chromosome selection
chrs=c("chr1")
chr_selection = data.frame(
chr=chrs,
start=rep(0 ,length(chrs)),
end=rep (250000000, length(chrs))
)
print(chr_selection)
chr start end
chr1 0 2.5e+08
You can then generate the plot with the ReconPlot function:
p = ReConPlot(sv_data,
cn_data,
chr_selection=chr_selection,
legend_SV_types=T,
pos_SVtype_description=1000000,
scale_separation_SV_type_labels=1/23,
title="Example")
print(p)
The ReCon plots are ggplot objects and can be modified after generation as such, and they can be easily saved to a PDF file. In our experience, the following dimensions work well for publication-quality figures and written reports. If using an annotation plot in combination with the ReCon plot, the height might need to be increased.
ggsave(filename = "example_ReConPlot.pdf", plot = p, width = 19, height = 5, units = "cm")
For advanced usage, please visit the tutorial.
If you have any comments or suggestions please raise an issue or contact us:
Jose Espejo Valle-Inclan: [email protected]
Isidro Cortes-Ciriano: [email protected]
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