R/Normalization.R

In ManonMartin/PEPSNMR: Pre-process 1H-NMR FID signals

#' @export Normalization
Normalization <- function(Spectrum_data, type.norm, fromto.norm = c(3.05, 4.05), 
                          ref.norm = "median", returnFactor = FALSE,verbose = FALSE)  {
  
  # Data initialisation and checks ----------------------------------------------
  checkArg(verbose, c("bool"))
  begin_info <- beginTreatment("Normalization", Spectrum_data, force.real = TRUE,
                               verbose = verbose)
  Spectrum_data <- begin_info[["Signal_data"]]
  # type.norm <- match.arg(type.norm)
  checkArg(fromto.norm, "num")
  
  
  if (missing(type.norm)){
    stop("The \"type.norm\" argument has to be defined by the user")
  }
  
  if (!type.norm %in% c("mean", "pqn", "median", "firstquartile", "peak")){
    stop("type.norm should one of: \"mean\", \"pqn\", 
         \"median\", \"firstquartile\" or \"peak\"")
  }
  
  if (length(ref.norm)==1) {
    if (!ref.norm %in% c("median", "mean") & !is.numeric(ref.norm)) {
      stop("ref.norm is misspecified")
    }
  } else if (length(ref.norm) == dim(Spectrum_data)[2] ) {
     if (!is.numeric(ref.norm)) {
       stop("ref.norm is misspecified")
       }
  } else {stop("ref.norm is misspecified")
    }

  
  if (length(fromto.norm) > 2) {
    warning("fromto.norm has a length > 2, only the first two elements are taken into account")
  }
  
  
  # Normalization ----------------------------------------------
  
  # Normalization factor computation
  switch(type.norm, mean = {
    # mean
    factor <- rowMeans(Spectrum_data, na.rm = TRUE)
  }, median = {
    # median 
    factor <- matrixStats::rowMedians(Spectrum_data, na.rm = TRUE)
  }, firstquartile = {
    # firstquartile
    factor <- matrixStats::rowQuantiles(Spectrum_data, probs=0.25, na.rm = TRUE)
  }, peak = {
    # peak
    ppm <- as.numeric(colnames(Spectrum_data))
    interval <- indexInterval(ppm, fromto.norm[1], fromto.norm[2], inclusive = TRUE)
    Spectrum_dataInZone <- Spectrum_data[, interval, drop = FALSE]
    peakInZone <- which.max(colSums(Spectrum_dataInZone))
    factor <- Spectrum_dataInZone[, peakInZone]
  }, pqn = {
    # pqn
    if (length(ref.norm) == 1) { 
      if (ref.norm == "median"){
        ref.norm <- matrixStats::colMedians(Spectrum_data, na.rm = TRUE)
      } else if (ref.norm == "mean") {
        ref.norm <- matrixStats::colMeans2(Spectrum_data, na.rm = TRUE)
      } else {
        ref.norm <- Spectrum_data[ref.norm, ] 
        }
      }
    quotient <- t(Spectrum_data)/ref.norm 
    
    factor <- quotient.median <- matrixStats::colMedians(quotient, na.rm = TRUE)
  })
  
  invalid <- (factor <= 0)
 
  # invalid factors == 0
   if (TRUE %in% invalid) {
    invalid_factor <- factor[invalid]
    warning("The ", type.norm, "s are ", paste(invalid_factor, collapse = ", "), 
      " for the spectrums ", paste(names(invalid_factor), collapse = ", "), 
      " which is nonpositive, the normalization will not happen for them.")
    factor[invalid] <- 1
   }
  
  
  # Data normalization and finalisation ----------------------------------------------
  if (returnFactor) {
    return(list(Spectrum_data = endTreatment("Normalization", begin_info, Spectrum_data/factor, verbose = verbose), 
                factor = factor))
  } else {return(endTreatment("Normalization", begin_info, Spectrum_data/factor, verbose = verbose))}
  
}