R/norm_rel_TIC.R

In wesleyburr/subMaldi: Organization and Processing of MALDI-MS Datasets

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# Last Updated: January 28, 2021
# Author: Kristen Yeh, Sophie Castel
# Title: subMALDI: Normalization Method - TIC
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# This method evaluates the sum of all intensities of each spectrum in a set.
# If the sum is not equivalent between spectra, multiplies the spectra by
# normalization factor.
# Each peak in each spectrum is then divided by the normalized TIC.

# Divide intensity of each peak in spectrum by sum of all intensities
.norm_TIC <- function(y, all_y){
  return(y/all_y) # single value
}

norm_rel_TIC <- function(dat, mass_dat, spectra_cols){
  
  # ---------------------
  # LOGICAL CHECKS
  # ---------------------
  full_mz <- Intensity <- Spectrum <- NULL
  
  stopifnot(
    is.character(mass_dat),
    is.character(spectra_cols),
    mass_dat %in% colnames(dat),
    all(spectra_cols %in% colnames(dat))
  )    
  
  
  mz <- dat[[mass_dat]]
  
  spectra <- lapply(spectra_cols, function(x){dat[x]})
  i <- do.call(what = data.frame, args = c(spectra))
  
  if(length(spectra_cols) == 1){
    i_sum <- sum(i)
  } else{
    i_sum <- apply(i, 2, FUN = sum)
  }
  
  max_sum <- max(i_sum)
  
  for(j in 1:length(spectra_cols)){
    
    if(i_sum[j] == max_sum){
      
      other <- i_sum[-j]
      other_index <- which(i_sum %in% other)
      
      factors <- rep(1,length(spectra_cols))
      
      factors[other_index] = i_sum[j]/i_sum[other_index]
      
    }
  }
  
  i_mult <- t(t(i)*factors)
  
  i_rel_TIC<- t(t(i_mult)*max_sum^-1) # i_mult / max_sum matrix multiplication
  
  dat <- data.frame(mz, i_rel_TIC)
  colnames(dat) <- c("full_mz", spectra_cols)
  
  return(dat)
}

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