library(echoannot)
To get the full dataset of all fine-mapped Parkinson's Disease loci, you can use the following function:
merged_DT <- echodata::get_Nalls2019_merged()
Annotate SNP-wise fine-mapping results.
Here, we're only annotating a small number of SNPs high-confidence causal SNPs
for demo purposes.
The more SNPs you supply to annotate_snps
, the longer it will take to
query the selected databases for each SNP.
#### Only query high-confidence fine-mapping SNPs from one locus #### dat <- merged_DT[Locus=="LRRK2" & Consensus_SNP==TRUE,] #### Query annotations #### dat_annot <- echoannot::annotate_snps(dat = dat, haploreg_annotation = TRUE, regulomeDB_annotation = TRUE, biomart_annotation = TRUE) knitr::kable(dat_annot)
gg_cs_bin <- echoannot::CS_bin_plot(merged_DT = merged_DT, show_plot = FALSE)
gg_cs_counts <- echoannot::CS_counts_plot(merged_DT = merged_DT, show_plot = FALSE)
gg_epi <- echoannot::peak_overlap_plot( merged_DT = merged_DT, include.NOTT2019_enhancers_promoters = TRUE, include.NOTT2019_PLACseq = TRUE, #### Omit many annotations to save time #### include.NOTT2019_peaks = FALSE, include.CORCES2020_scATACpeaks = FALSE, include.CORCES2020_Cicero_coaccess = FALSE, include.CORCES2020_bulkATACpeaks = FALSE, include.CORCES2020_HiChIP_FitHiChIP_coaccess = FALSE, include.CORCES2020_gene_annotations = FALSE)
Creates one big merged plot using the subfunctions above.
super_plot <- echoannot::super_summary_plot(merged_DT = merged_DT, plot_missense = FALSE)
remove(super_plot, gg_epi, gg_cs_counts, merged_DT)
utils::sessionInfo()
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