library(echolocatoR)
Using pre-merged data for vignette speed.
merged_DT <- echodata::get_Nalls2019_merged()
get_SNPgroup_counts()
Get the number of SNPs for each SNP group per locus.
It also prints the mean number of SNPs for each SNP group across all loci.
NOTE: You will need to make sure to set merge_finemapping_results(minimum_support=1)
in the above step to get accurate counts for all SNP groups.
snp_groups <- echodata::get_SNPgroup_counts(merged_DT = merged_DT)
get_CS_counts()
County the number of tool-specific and UCS Credible Set SNPs per locus.
UCS_counts <- echodata::get_CS_counts(merged_DT = merged_DT) knitr::kable(UCS_counts)
...$plot
and the ...$data
used to make the plot. snp_filter
allows user to use any filtering argument (supplied as a string)
to subset the data they want to use in the plot/data. If you ran colocalization tests with echolocatoR
(via catalogueR
)
you can use those results to
come up with a top QTL nominated gene for each locus
(potentially implicating that gene in your phenotype).
coloc_res <- echodata::get_Nalls2019_coloc()
super_plot <- echoannot::super_summary_plot(merged_DT = merged_DT, coloc_results = coloc_res, plot_missense = FALSE)
utils::sessionInfo()
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