Get Credible Sets

Extract credible set assignments at the requested coverage.

Usage

credibleSets(x, ...)

# S4 method for class 'FineMappingResultBase'
credibleSets(
  x,
  studyName = NULL,
  context = NULL,
  trait = NULL,
  method = NULL,
  region = NULL,
  coverage = 0.95,
  minPurity = NULL
)

Arguments

x

A QtlFineMappingResult or GwasFineMappingResult.

...

Class-specific selection arguments plus coverage.

studyName

Character (length 1) or NULL. Restrict the selection to this study; NULL matches all studies.

context

Character (length 1) or NULL. Restrict the selection to this context; NULL matches all contexts.

trait

Character (length 1) or NULL. Restrict the selection to this trait; NULL matches all traits.

method

Character (length 1) or NULL. Restrict the selection to this fine-mapping / weight method; NULL matches all methods.

region

Character (length 1, "chr:start-end") or NULL. Restrict variants to this region; NULL uses the full cis window / all regions.

coverage

Numeric in (0, 1). Credible-set coverage level. Default 0.95.

minPurity

Numeric or NULL. Minimum credible-set purity to retain; NULL applies no purity filter.

Value

A data.frame of credible set information.

Examples

data(qtlFineMappingExample)
credibleSets(qtlFineMappingExample)
#> # A tibble: 42 × 31
#>    study   context   trait  blockId method dataType dataSource standardized
#>    <chr>   <chr>     <chr>  <chr>   <chr>  <chr>    <chr>      <chr>       
#>  1 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#>  2 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#>  3 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#>  4 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#>  5 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#>  6 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#>  7 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#>  8 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#>  9 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#> 10 study_1 context_1 gene_1 NA      susie  NA       NA         NA          
#> # ℹ 32 more rows
#> # ℹ 23 more variables: variant_id <chr>, chrom <chr>, pos <int>, A1 <chr>,
#> #   A2 <chr>, N <dbl>, af <dbl>, posterior_effect <dbl>, posterior_se <dbl>,
#> #   theta <dbl>, pip <dbl>, logBF <dbl>, cs_95 <chr>, cs_70 <chr>, cs_50 <chr>,
#> #   cs_95_purity <dbl>, cs_70_purity <dbl>, cs_50_purity <dbl>,
#> #   within_cs_pip <dbl>, gene <chr>, event <chr>, grange_start <int>,
#> #   grange_end <int>