Fraction of thresholded windows overlapping a set of regions

Source: R/qseaExtra.R

For each sample, compute the proportion of windows with counts >= numCountsNeeded that overlap regionsToOverlap, relative to all windows with counts >= numCountsNeeded in that sample.

calculateFractionReadsInGRanges(qseaSet, regionsToOverlap, numCountsNeeded)

Arguments

qseaSet

qseaSet. Input qseaSet. Default: none (must be supplied).

regionsToOverlap

GRanges or data.frame. Regions to consider for overlap. Data frames must be coercible to GRanges (e.g., have seqnames/start/end or chr/window_start/window_end). Default: none (must be supplied).

numCountsNeeded

integer(1). Minimum reads per window for it to count toward the fraction. Default: none (must be supplied).

Value

A tibble with one row per sample containing:

  • sample_name

  • initialOverBackNum — windows >= threshold genome-wide

  • afterOverBackNum — windows >= threshold within regionsToOverlap

  • fraction — afterOverBackNum / initialOverBackNum followed by columns from the sample table (left-joined by sample_name).

Details

Operates on the window × sample count matrix from qsea::getCounts(). Counts are converted to a logical indicator (count >= numCountsNeeded) and summed per sample:

  • initialOverBackNum: windows meeting the threshold genome-wide.

  • afterOverBackNum: windows meeting the threshold within regionsToOverlap (via filterByOverlaps()). The reported fraction is afterOverBackNum / initialOverBackNum.

Note: despite the function name, this computes a window-based fraction using a count threshold, not a direct fraction of raw reads.

Examples

data(exampleTumourNormal, package = "mesa")

# Using the shipped ultra-stable probes (GRCh38) as the region set
exampleTumourNormal %>%
  calculateFractionReadsInGRanges(
    regionsToOverlap = mesa::hg38CpGIslands,
    numCountsNeeded   = 5
  ) %>%
  dplyr::select(sample_name, initialOverBackNum, afterOverBackNum, fraction) 
#> Joining with `by = join_by(sample_name)`
#> # A tibble: 10 × 4
#>    sample_name initialOverBackNum afterOverBackNum fraction
#>    <chr>                    <dbl>            <dbl>    <dbl>
#>  1 Colon1_N                   111               54    0.486
#>  2 Colon1_T                   184               80    0.435
#>  3 Colon2_N                   127               55    0.433
#>  4 Colon2_T                   228               93    0.408
#>  5 Lung1_N                    221               75    0.339
#>  6 Lung1_T                    133               64    0.481
#>  7 Lung2_N                    210               60    0.286
#>  8 Lung2_T                    243              107    0.440
#>  9 Lung3_N                    141               48    0.340
#> 10 Lung3_T                    237              111    0.468

# Define a small GRanges subset from the object's own windows
gr <- exampleTumourNormal %>%
  qsea::getRegions() %>%
  head(n = 100)
  
calculateFractionReadsInGRanges(
      qseaSet           = exampleTumourNormal,
      regionsToOverlap  = gr,
      numCountsNeeded   = 3) %>%
  dplyr::select(sample_name, initialOverBackNum, afterOverBackNum, fraction) 
#> Joining with `by = join_by(sample_name)`
#> # A tibble: 10 × 4
#>    sample_name initialOverBackNum afterOverBackNum fraction
#>    <chr>                    <dbl>            <dbl>    <dbl>
#>  1 Colon1_N                   173                8   0.0462
#>  2 Colon1_T                   269               19   0.0706
#>  3 Colon2_N                   193               14   0.0725
#>  4 Colon2_T                   306               26   0.0850
#>  5 Lung1_N                    314               30   0.0955
#>  6 Lung1_T                    210               16   0.0762
#>  7 Lung2_N                    307               30   0.0977
#>  8 Lung2_T                    319               25   0.0784
#>  9 Lung3_N                    206               23   0.112 
#> 10 Lung3_T                    315               23   0.0730