R/normalize.R

In CeMOS-Mannheim/MALDIcellassay: Automated MALDI Cell Assays Using Dose-Response Curve Fitting

#' Normalize spectra and peaks
#' 
#' @details
#' Typically MassSpectrum lists should be provided as `spec`, still its possible to provide peaks for both arguments `spec` and `peaks` in the case that no raw data (continuous spectra) is available.
#' In this case the operations performed on `spec` and `peaks` are equal.
#' 
#'
#' @param spec     List of MALDIquant::MassSpectrum or MALDIquant::MassPeaks objects
#' @param peaks    List of MALDIquant::MassPeaks
#' @param normMeth Character, normalization method. Options are "TIC", "median" and "mz".
#' @param normMz   Numeric, mz used to normalize.
#' @param normTol  Numeric, tolerance around normMz.
#'
#' @return
#' List of lists of normalized MALDIquant::MassSpectrum, normalized MALDIquant::MassPeaks,
#' normalization factors as well as indicies of spectra containing the `normMz` in case of `normMeth = "mz"`,
#'
#' @export
#' @importFrom MALDIquant totalIonCurrent
#' @examples
#' data(Blank2022spec)
#' data(Blank2022peaks)
#' norm <- normalize(Blank2022spec, Blank2022peaks, normMeth = "mz", normMz = 760.585, normTol = 0.1)
#' 
#' # normalization factors
#' norm$factor 
normalize <- function(spec, peaks, normMeth, normMz, normTol) {
  nm <- names(spec)
  stopifnot(!is.null(nm))
  stopifnot(!is.na(as.numeric(nm)))

  switch(normMeth,
         "TIC" = {
           if(isMassPeaksList(spec)) {
             tic <- purrr::map_dbl(spec, 
                                   function(x) 
                                     sum(x@intensity)
               )
           } else {
             tic <- purrr::map_dbl(spec, totalIonCurrent)  
           }
           

           spec <- normalizeByFactor(spec, tic)
           peaks <- normalizeByFactor(peaks, tic)

           norm_fac <- tic
           included_specIdx <- 1:length(spec)
         },
         "median" = {
           median <- vapply(spec,
                            FUN = function(x)
                              median(x@intensity),
                            numeric(1))

           spec <- normalizeByFactor(spec, median)
           peaks <- normalizeByFactor(peaks, median)

           norm_fac <- median
           included_specIdx <- 1:length(spec)
         },
         "mz" = {
           mzNorm <- getNormFactors(
             peaks = peaks,
             targetMz = normMz,
             tol = normTol,
             allowNoMatch = TRUE,
             tolppm = FALSE
           )

           norm_fac <- mzNorm$norm_factor
           spec <- normalizeByFactor(spec[mzNorm$specIdx], mzNorm$norm_factor)
           peaks <- normalizeByFactor(peaks[mzNorm$specIdx], mzNorm$norm_factor)
           included_specIdx <- mzNorm$specIdx

           u_nm <- unique(nm)
           u_fil <- unique(nm[included_specIdx])
           if(length(u_nm) != length(u_fil)) {
             # stop if a single condition got filtered completely
             label_removed <- u_nm[which(!(u_nm %in% u_fil))]

             stop("Could not find ", normMz, " in all spectra with label ",
                  paste0(label_removed, collapse = ", "),
                  ".\n Consider increasing tol.\n")
           }
         },
         "none" = {
           norm_fac <- c("norm_factor" = 0)
           included_specIdx <- 1:length(spec)
         }
  )

  return(list(spec = spec,
              peaks = peaks,
              factor = norm_fac,
              idx = included_specIdx))
}