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The goal of gtexr is to provide a convenient R interface to the GTEx Portal API V2.
New to R? Try out the ⭐shiny app⭐.
You can install this package from CRAN:
install.packages("gtexr")Or you can install the development version of gtexr from GitHub with:
# install.packages("devtools")
devtools::install_github("rmgpanw/gtexr")Get general information about the GTEx service:
library(gtexr)
get_service_info()
#> # A tibble: 1 × 9
#> id name version organization_name organization_url description contactUrl
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 org.g… GTEx… 2.0.0 GTEx Project https://gtexpor… This servi… https://g…
#> # ℹ 2 more variables: documentationUrl <chr>, environment <chr>Retrieve eQTL genes for whole blood tissue:
get_eqtl_genes("Whole_Blood")
#> Warning: ! Total number of items (12360) exceeds maximum page size (250).
#> ℹ Try increasing `itemsPerPage`.
#>
#> ── Paging info ─────────────────────────────────────────────────────────────────
#> • numberOfPages = 50
#> • page = 0
#> • maxItemsPerPage = 250
#> • totalNumberOfItems = 12360
#> # A tibble: 250 × 10
#> tissueSiteDetailId ontologyId datasetId empiricalPValue gencodeId geneSymbol
#> <chr> <chr> <chr> <dbl> <chr> <chr>
#> 1 Whole_Blood UBERON:001… gtex_v8 1.05e- 9 ENSG0000… WASH7P
#> 2 Whole_Blood UBERON:001… gtex_v8 1.06e-25 ENSG0000… RP11-34P1…
#> 3 Whole_Blood UBERON:001… gtex_v8 6.31e- 2 ENSG0000… CICP27
#> 4 Whole_Blood UBERON:001… gtex_v8 8.71e- 9 ENSG0000… RP11-34P1…
#> 5 Whole_Blood UBERON:001… gtex_v8 6.01e-20 ENSG0000… RP11-34P1…
#> 6 Whole_Blood UBERON:001… gtex_v8 6.96e- 9 ENSG0000… RP11-34P1…
#> 7 Whole_Blood UBERON:001… gtex_v8 3.10e- 4 ENSG0000… RP11-34P1…
#> 8 Whole_Blood UBERON:001… gtex_v8 1.92e- 3 ENSG0000… ABC7-4304…
#> 9 Whole_Blood UBERON:001… gtex_v8 1.58e- 3 ENSG0000… RP11-34P1…
#> 10 Whole_Blood UBERON:001… gtex_v8 7.82e- 2 ENSG0000… AP006222.2
#> # ℹ 240 more rows
#> # ℹ 4 more variables: log2AllelicFoldChange <dbl>, pValue <dbl>,
#> # pValueThreshold <dbl>, qValue <dbl>Retrieve significant eQTLs for one or more genes:
get_significant_single_tissue_eqtls(gencodeId = c("ENSG00000132693.12",
"ENSG00000203782.5"))
#>
#> ── Paging info ─────────────────────────────────────────────────────────────────
#> • numberOfPages = 1
#> • page = 0
#> • maxItemsPerPage = 250
#> • totalNumberOfItems = 249
#> # A tibble: 249 × 13
#> snpId pos snpIdUpper variantId geneSymbol pValue geneSymbolUpper
#> <chr> <int> <chr> <chr> <chr> <dbl> <chr>
#> 1 rs12128960 159343657 RS12128960 chr1_1593… CRP 8.52e-5 CRP
#> 2 rs12132451 159344052 RS12132451 chr1_1593… CRP 7.92e-5 CRP
#> 3 rs12136402 159347493 RS12136402 chr1_1593… CRP 7.92e-5 CRP
#> 4 rs10908709 159350390 RS10908709 chr1_1593… CRP 7.92e-5 CRP
#> 5 rs10908710 159351189 RS10908710 chr1_1593… CRP 7.92e-5 CRP
#> 6 rs11265178 159359256 RS11265178 chr1_1593… CRP 9.62e-5 CRP
#> 7 rs35532309 159360755 RS35532309 chr1_1593… CRP 6.11e-5 CRP
#> 8 rs6692378 159369451 RS6692378 chr1_1593… CRP 1.17e-6 CRP
#> 9 rs10908714 159370563 RS10908714 chr1_1593… CRP 1.80e-5 CRP
#> 10 rs6656924 159372915 RS6656924 chr1_1593… CRP 1.00e-6 CRP
#> # ℹ 239 more rows
#> # ℹ 6 more variables: datasetId <chr>, tissueSiteDetailId <chr>,
#> # ontologyId <chr>, chromosome <chr>, gencodeId <chr>, nes <dbl>These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.