2. Query eQTL Catalogue datasets by region

Choose between tabix (faster for larger queries) or RESTful API (faster for small queries).

eQTL_Catalogue.fetch(
  unique_id,
  quant_method = "ge",
  infer_region = T,
  gwas_data = NULL,
  is_gwas = F,
  nThread = 1,
  use_tabix = T,
  chrom = NULL,
  bp_lower = NULL,
  bp_upper = NULL,
  multithread_tabix = F,
  add_qtl_id = T,
  convert_genes = T,
  verbose = T
)

Arguments

quant_method	eQTL Catalogue actually contains more than just eQTL data. For each dataset, the following kinds of QTLs can be queried: gene expression QTL quant_method="ge" (default) or quant_method="microarray", depending on the dataset. catalogueR will automatically select whichever option is available. exon expression QTL *under construction* quant_method="ex" transcript usage QTL *under construction* quant_method="tx" promoter, splice junction and 3' end usage QTL *under construction* quant_method="txrev"
nThread	The number of CPU cores you want to use to speed up your queries through parallelization.
use_tabix	Tabix is about ~17x faster (default: =T) than the REST API (=F).
bp_lower	Minimum basepair position of the query window.
bp_upper	Maxmimum basepair position of the query window.
verbose	Show more (=T) or fewer (=F) messages.

See also

Other eQTL Catalogue: BST1__Alasoo_2018.macrophage_IFNg, LRRK2__Alasoo_2018.macrophage_IFNg, MEX3C__Alasoo_2018.macrophage_IFNg, eQTL_Catalogue.header, eQTL_Catalogue.iterate_fetch(), eQTL_Catalogue.query(), eQTL_Catalogue.search_metadata(), fetch_restAPI(), fetch_tabix(), merge_gwas_qtl(), meta

Examples

data("meta"); data("BST1");
gwas.qtl <- eQTL_Catalogue.fetch(unique_id=meta$unique_id[1], gwas_data=BST1)
#> [1] "++ Inferring coordinates from gwas_data"
#> Warning: error in running command
#> Warning: File '/var/folders/zq/h7mtybc533b1qzkys_ttgpth0000gn/T//RtmpIjYyRa/file180226875fd26' has size 0. Returning a NULL data.table.
#> Error in dirname(header_path): object 'header_path' not found