Count windows above a cutoff

For a given set of genomic windows, count (per sample) how many windows exceed a chosen threshold using the selected normalisation/measure.

countWindowsAboveCutoff(
  qseaSet,
  GRanges,
  samples = NULL,
  cutoff = 0,
  normMethod = "nrpm"
)

Arguments

qseaSet

qseaSet. A qseaSet object containing methylation-enriched sequencing data. Default: none (must be supplied).

GRanges

GenomicRanges::GRanges. Windows to count over. Default: none (must be supplied).

samples

character() or NULL. Sample names to include, or a single string used as a pattern to match sample names. If NULL, all samples are used. Default: NULL.

cutoff

numeric(1). Threshold; windows with value >= cutoff are counted. Default: 0.

normMethod

character(1). Measure to use. One of "nrpm" or "beta". Default: "nrpm".

Value

A tibble with one row per sample containing:

• sample_name

• numOverCutoff — number of windows >= cutoff

• totalWindowsUsed — number of windows evaluated followed by columns from the sample table (left-joined).

Details

The function builds a window × sample matrix over GRanges for the chosen normMethod, optionally restricts to samples, and counts, per sample, the number of windows with value >= cutoff. It also reports how many windows were evaluated (totalWindowsUsed). Results are joined with the sample table.

See also

getDataTable(), summariseAcrossWindows()

Other window-summaries: addSummaryAcrossWindows(), summariseAcrossWindows()

Examples

data(exampleTumourNormal, package = "mesa")

#' # Count windows with NRPM >= 1 over the first 100 regions
countWindowsAboveCutoff(exampleTumourNormal, 
                               qsea::getRegions(exampleTumourNormal)[1:100], 
                               cutoff = 1, 
                               normMethod = "nrpm")
#> obtaining raw values for 10 samples in 100 windows
#> deriving nrpm values...
#> creating table...
#> Joining with `by = join_by(sample_name)`
#> # A tibble: 10 × 11
#>    sample_name numOverCutoff totalWindowsUsed group  patient type  tumour tissue
#>    <chr>               <dbl>            <int> <chr>  <chr>   <chr> <chr>  <chr> 
#>  1 Colon1_N                6              100 Colon… Colon1  Norm… Normal Colon 
#>  2 Colon1_T                6              100 Colon… Colon1  CRC   Tumour Colon 
#>  3 Colon2_N                8              100 Colon… Colon2  Norm… Normal Colon 
#>  4 Colon2_T               11              100 Colon… Colon2  CRC   Tumour Colon 
#>  5 Lung1_N                13              100 Lung1… Lung1   Norm… Normal Lung  
#>  6 Lung1_T                 6              100 Lung1… Lung1   LUAD  Tumour Lung  
#>  7 Lung2_N                15              100 Lung2… Lung2   Norm… Normal Lung  
#>  8 Lung2_T                 9              100 Lung2… Lung2   LUSC  Tumour Lung  
#>  9 Lung3_N                14              100 Lung3… Lung3   Norm… Normal Lung  
#> 10 Lung3_T                11              100 Lung3… Lung3   LUAD  Tumour Lung  
#> # ℹ 3 more variables: age <dbl>, gender <chr>, stage <chr>