VariantQuantileThresholding_Combined — VariantQuantileThresholding

We get variants of interest using the quantile thresholding. This function combines the functions VariantSelection_Quantile, VariantSelection_Group and VariantSelection_TopCells. If you use top_cells and top_VAF, you have to only supply one quantil value (quantiles = 0.9, thresholds = 0). This function is adapted from the Peter van Galen. Source: https://github.com/petervangalen/MAESTER-2021

Usage

VariantQuantileThresholding_Combined(
  SE,
  min_coverage = 2,
  quantiles = c(0.1, 0.9),
  thresholds = c(0.1, 0.9),
  top_cells = NULL,
  top_VAF = NULL,
  min_quality = NULL,
  mean_allele_frequency = 0,
  group_of_interest = NULL,
  group1 = NULL,
  group2 = NULL,
  group_factor = NULL,
  remove_nocall = TRUE,
  verbose = TRUE
)

Arguments

SE: SummarizedExperiment object.
min_coverage: Minimum coverage needed.
quantiles: The lower and upper quantile you want to use.
thresholds: The VAF thresholds you want to use for the quantiles.
top_cells: The number of cells with at least top_VAF percent for a variant.
top_VAF: The VAF for the top cells.
min_quality: The minimum quality you want for the Variants of Interest. Can be ignored by setting it to NULL.
mean_allele_frequency: The minimum mean allele frequency. Default = 0
group_of_interest: The column data that divides the cells.
group1: The first group of interest. If set, the quantiles are only calculated for this group.
group2: The second group of interest.
group_factor: How much higher has the mean allele frequency to be in group 1 when compared to group 2?
remove_nocall: Should NoCall cells (consensus = 0) be disregarded during the analysis?
verbose: Should the function be verbose? Default = TRUE