R/MAF_filter.R

In AbbVie-ComputationalGenomics/POEMColoc: Implement POEM colocalization method

#' Remove low minor allele frequency positions from dataset
#'
#' @param dataset
#' POEM_output format dataset
#' Specifically this is a coloc format dataset with the following changes
#' top_pos giving the position of the top p-value
#' pos vector giving positions
#' chr giving chromosome
#' snp is mandatory
#' MAF is mandatory
#' imputation class that is either all or top
#' Unlike coloc, length of pvalues or beta L is permitted to be zero
#' @param min_MAF
#' minimum minor allele frequency to use
#' @return
#' POEM_output format dataset
#' @examples
#' # example with p-values
#' dataset_full <- list(pos = c(1, 2, 3, 4, 5), MAF = c(0.14, 0.15, 0.25, 0.2, 0.4), N=1000, type ="quant", pvalues = c(2 * 10^-8, 4 * 10^-8, 2 * 10^-4, 0.6, 0.03), chr= "Z", imputation_class = "all", top_pos = 1, snp = as.character(1:5))
#' MAF_filter(dataset = dataset_full, min_MAF = 0.1)
#' MAF_filter(dataset = dataset_full, min_MAF = 0.2)
#' # example that should warn
#' dataset_full$MAF[2] <- 0.77
#' MAF_filter(dataset = dataset_full, min_MAF = 0.1)
#' # example with empty that should be TRUE
#' dataset_empty <- list(pos = integer(0), MAF = numeric(0), N=1000, type ="quant", pvalues = numeric(0), chr= "Z", imputation_class = "all", top_pos = 1, snp = character(0))
#' MAF_filter(dataset = dataset_empty, min_MAF = 0.1)

MAF_filter <- function(dataset, min_MAF) {
  if (any(dataset$MAF > 0.5)) {
    warning("Minor allele frequency greater than 0.5 may produced undesired behavior")
  }
  keep <- dataset$MAF >= min_MAF
  dataset_filter(dataset, keep)
}