library(echoannot)

Import data

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

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)

Summary plots

Credible Set bin plot

gg_cs_bin <- echoannot::CS_bin_plot(merged_DT = merged_DT,
                                    show_plot = FALSE)

Credible Set counts plot

gg_cs_counts <- echoannot::CS_counts_plot(merged_DT = merged_DT, 
                                          show_plot = FALSE)

Epigenomic data

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)

Super summary plot

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) 

Session Info

utils::sessionInfo()



RajLabMSSM/echoannot documentation built on Oct. 26, 2023, 2:41 p.m.