hidecan is an R package for generating HIDECAN plots, which are visualisations summarising the results of one or more Genome-wide association study (GWAS) and transcriptomics differential expression (DE) analysis, alongside candidate genes of interest.
hidecan is available on the CRAN and can be installed via:
Alternatively, you can install the development version of
hidecan from GitHub with:
# install.packages("devtools") devtools::install_github("PlantandFoodResearch/hidecan")
The hidecan package works as follows:
it takes as an input one of more data-frames containing GWAS results, differential expression results and list of candidate genes of interest;
it computes the length of each chromosome based on the genomic position of the markers and genes provided in the input data;
it filters the datasets to retain significant markers or differentially expressed genes, according to a threshold on their score and/or log2-fold change. The fold-change is set by the user, and can be different for GWAS and differential expression results.
it displays the position of the significant markers and genes alongside candidate genes (HIDECAN plot). The plot can be customised by the user via a number of parameters (e.g. legend position or label size).
The wrapper function
hidecan_plot() performs all of these steps. Its use is demonstrated below with an example dataset:
library(hidecan) ## Getting an example dataset x <- get_example_data() hidecan_plot( gwas_list = x[["GWAS"]], ## data-frame of GWAS results de_list = x[["DE"]], ## data-frame of DE results can_list = x[["CAN"]], ## data-frame of candidate genes score_thr_gwas = -log10(0.0001), ## sign. threshold for GWAS score_thr_de = -log10(0.05), ## sign. threshold for DE log2fc_thr = 0, ## log2FC threshold for DE label_size = 2 ## label size for candidate genes )
If using HIDECAN, please cite:
Angelin-Bonnet, O., Vignes, M., Biggs, P. J., Baldwin, S., & Thomson, S. (2023). Visual integration of GWAS and differential expression results with the hidecan R package. bioRxiv, 2023-03. https://doi.org/10.1101/2023.03.30.535015