R/comp_count_max_error.R

In YasinEl/mzRAPP: Benchmark Dataset Generation and Non-Targeted Data Pre-Processing Assessment

#' count_errors_max
#'
#' @param dt dt
#'
#'
#' @keywords internal
count_errors_max <- function(dt){
  theMolecule <- unique(dt$molecule_b)
  theAdduct  <- unique(dt$adduct_b)

  dt <- dt[main_peak=='TRUE' | is.na(main_peak), c('sample_id_b', 'isoab_b', 'feature_id_g', 'molecule_b', 'adduct_b', 'peak_area_g', 'peak_area_ug')]

  #Generate peak status Column
  ## -1 = Peak not found in g or ug (peakpicking error)
  ## -2 = Peak not found in g (grouping error)
  ## -3 = Different Peak in g and ug
  dt <- dt[, peak_status := ifelse((is.na(peak_area_g)) & (is.na(peak_area_ug)), "Lost_b.PP",
                                   ifelse((is.na(peak_area_g)) & (!is.na(peak_area_ug)), 'Lost_b.A',
                                          ifelse((!is.na(peak_area_g)) & (!is.na(peak_area_ug)) & (peak_area_g != peak_area_ug), feature_id_g, feature_id_g)))] # put -3 here


  dt[, sample_id_b := as.character(sample_id_b)]
  dt[, isoab_b := as.character(isoab_b)]
  dt[, peak_status := as.character(peak_status)]

  dt <- data.table::dcast(dt, sample_id_b ~ isoab_b, value.var='peak_status', fun.aggregate = function(x) paste(x, collapse = ""))

  theReturn <- count_alignment_errors(dt, get_main_UT_groups(dt))
  return(theReturn)
}