vignettes/finemapping_portal.Rmd
finemapping_portal.Rmd## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2## ⠊⠉⠡⣀⣀⠊⠉⠡⣀⣀⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠
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## ⠊⠉⠡⣀⣀⠊⠉⠡⣀⣀⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠⠊⠉⠢⣀⡠
## ⓞ If you use echolocatoR or any of the echoverse subpackages, please cite:
## ▶ Brian M Schilder, Jack Humphrey, Towfique
## Raj (2021) echolocatoR: an automated
## end-to-end statistical and functional
## genomic fine-mapping pipeline,
## Bioinformatics; btab658,
## https://doi.org/10.1093/bioinformatics/btab658
## ⓞ Please report any bugs/feature requests on GitHub:
## ▶
## https://github.com/RajLabMSSM/echolocatoR/issues
## ⓞ Contributions are welcome!:
## ▶
## https://github.com/RajLabMSSM/echolocatoR/pulls## ## ────────────────────────────────────────────────────────────────────────────────Here we take advantage of the fine-mapping results files already available on the echolocatoR Fine-mapping Portal.
## Limit the number of loci for demo purposes
loci <- echodata::topSNPs_Nalls2019$Locus[1:3] We can query multiple studies, loci, and even data types at once.
When as_datatable=TRUE, all file paths and metadata are
conveniently organized into a data.table.
results_dir <- tempdir()
local_files <- echodata::portal_query(dataset_types="GWAS",
phenotypes = c("parkinson"),
file_types = c("multi_finemap","LD"),
loci = loci,
LD_panels=c("UKB"),
results_dir = results_dir,
as_datatable = TRUE) ## Fetching echolocatoR Fine-mapping Portal study metadata.## + 1 dataset(s) remain after filtering.## + Searching for multi_finemap files...## OK (HTTP 200).+ Searching for LD files...
## OK (HTTP 200).+ 6 unique files identified.
## + Downloading 4 files...
## + Returning table with local file paths.Next, we can gather all of the fine-mapping results generated by
finemap_loci() previously.merge_finemapping_results recursively searches for the
correct files within a hierarchical folder structure and imports only
the multi-finemap files.
merged_DT <- echodata::merge_finemapping_results(dataset = results_dir,
minimum_support = 0,
include_leadSNPs = TRUE,
consensus_thresh = 2)## + Gathering all fine-mapping results from storage...## + 2 multi-finemap files found.## + Importing results... ASXL3## + Importing results... BIN3## Identifying Consensus SNPs...## + support_thresh = 2## + Calculating mean Posterior Probability (mean.PP)...## + 4 fine-mapping methods used.## + 17 Credible Set SNPs identified.## + 6 Consensus SNPs identified.## + Saving merged results ==> /tmp/RtmpJQtSZ8/file217973ffe623merged_results.csv.gz
echodata::results_report(merged_DT)## echolocatoR results report (all loci):## + Overall report:## ++ 2 Loci.## ++ 10605 SNPs.## + Lead SNP report:## ++ 2 lead SNPs.## ++ Lead SNP mean PP = 0.5## + Union Credible Set report:## ++ 17 UCS SNPs.## ++ UCS mean PP = 0.35## ++ 2 UCS SNPs that are also lead SNPs## + Consensus SNP report:## ++ 6 Consensus SNPs.## ++ Consensus SNP mean PP = 0.54## ++ 1 Consensus SNPs that are also lead SNPsNext, we import the a subset of the LD matrices for only the lead SNP.
ld_files <- local_files[file_type=="LD",]
ld_matrices <- lapply(stats::setNames(ld_files$local_file,
ld_files$locus),
function(x){
data.table::fread(x)
})
knitr::kable(head(ld_matrices$ASXL3))| SNP | rs1941685 | rs1941685.1 |
|---|---|---|
| rs12968480 | 0.0396723 | 0.0396723 |
| rs12967667 | 0.0424208 | 0.0424208 |
| rs9945156 | 0.0424096 | 0.0424096 |
| rs1851700 | 0.0419115 | 0.0419115 |
| rs117840441 | 0.0664300 | 0.0664300 |
| rs1523592 | 0.0424137 | 0.0424137 |
Now let’s plot one locus as an example.
locus <- unique(merged_DT$Locus)[1] # Pick the first locus
dat <- merged_DT[Locus==locus,]
LD_matrix = ld_matrices[[locus]]
locus_dir <- file.path(tempdir(),locus)
plt <- echoplot::plot_locus(dat = dat,
LD_matrix = LD_matrix,
LD_reference = "UKB",
locus_dir = locus_dir,
nott_epigenome = TRUE,
nott_regulatory_rects = TRUE,
nott_show_placseq = TRUE,
zoom = c("20x"))
utils::sessionInfo()## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
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## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
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##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] echolocatoR_2.0.3 BiocStyle_2.26.0
##
## loaded via a namespace (and not attached):
## [1] rappdirs_0.3.3 rtracklayer_1.58.0
## [3] GGally_2.1.2 R.methodsS3_1.8.2
## [5] ragg_1.2.4 tidyr_1.2.1
## [7] echoLD_0.99.8 ggplot2_3.3.6
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## [85] coloc_5.1.0.1 rprojroot_2.0.3
## [87] matrixStats_0.62.0 graph_1.76.0
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## [151] Rcpp_1.0.9 GenomicRanges_1.50.0
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## [165] RBGL_1.74.0 expm_0.999-6
## [167] pkgdown_2.0.6 echofinemap_0.99.4
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## [181] DT_0.26 BiocParallel_1.32.0
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## [187] utf8_1.2.2 lattice_0.20-45
## [189] bslib_0.4.0 tibble_3.1.8
## [191] curl_4.3.3 DescTools_0.99.47
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## [197] rmarkdown_2.17 desc_1.4.2
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