R/importFromOpenMSLFQ.R

In hafenr/SECprofiler: Complex feature detection using SEC protein chromatograms.

# Due to: http://stackoverflow.com/questions/24501245/data-table-throws-object-not-found-error
.datatable.aware=TRUE

#' Import peptide profiles from an OpenMS MS1-LFQ DDA data analysis result
#' table.
#' @import data.table
#' @param file.name Quantitative MS data result file. Defaults to
#'     'OpenMS_LFQ_peptides.csv'
#' @param annotation.table Annotation table containing columns 'Name'
#'     (-> raw file name), and 'Fraction'. Fraction should contain the
#'     fraction_number.
#' @return An object of class Traces (peptide level) containing a
#'     'traces.wide', and 'ids' table that can be processed with the
#'     herein contained functions.
#' @export
importFromOpenMSLFQ <- function(file.name='OpenMS_LFQ_peptides.csv',
                                annotation.table='OpenMS_LFQ_annotation.txt') {
  
  # read data & annotation table & clean up
  ##################################################
  # produces funny numbers for one of 88 columns (?)
  # data  <- data.table::fread(file.name, sep='\t', header=TRUE)
  data <- fread(file.name, header=TRUE, integer64='double')
  annotation <- data.table::fread(annotation.table)
  
  # Select Intensity columns of proteotypic peptides 
  ##################################################
  data.s <- data[n_proteins == 1] #remove ambiguos peps
  quantcolumns <- grep('~', names(data.s))
  labelcolumns <- which(names(data.s) %in% c('peptide', 'protein'))
  
  data.s.traces <- data.s[, quantcolumns, with=FALSE]
  data.s.labels <- data.s[, labelcolumns, with=FALSE]
  
  #Replace column headers by fraction number and order ascending
  headers <- names(data.s.traces)
  nruns <- length(names(data.s.traces))
  filenames <- sapply(headers, function(x){ strsplit(x, split='~')[[1]][1] })
  annotation$Name <- toupper(annotation$Name)
  fractions <- sapply(filenames,
                      function(x) {
                          annotation[Name %in% x, 'Fraction', with=FALSE]
                      })
  fractions <- sapply(fractions, function(x) { x[[1]] })
  names(data.s.traces) <- sprintf('%02d', fractions)
  data.s.traces <- data.s.traces[, order(as.numeric(names(data.s.traces))),
                                 with=FALSE]
  
  # Assemble and output result 'Traces' object
  #################################################
  traces.wide <- data.table(protein_id=data.s.labels$protein,
                            peptide_id=data.s.labels$peptide,
                            data.s.traces)
  traces.wide <- traces.wide[grep('DECOY', traces.wide$protein_id,
                                  invert=TRUE)]
  #Revert to clean Uniprot ids, save fasta and name entry information separately
  fasta_id <- traces.wide$protein_id
  # Extract Uniprot id (protein_id) and Uniprot entry name (protein_name) from fasta_id
  protein_id <- sapply(as.character(traces.wide$protein_id),
                       function(x) { strsplit(x, split='\\|')[[1]][2] })
  protein_name <- sapply(as.character(traces.wide$protein_id),
                         function(x) { strsplit(x, split='\\|')[[1]][3] })
  traces.wide$protein_id <- protein_id
  setorder(traces.wide, protein_id)
   
  # traces.long <- melt(traces.wide,
  #                     id.vars=c('protein_id','peptide_id'),
  #                     value.name='Intensity', 
  #                     variable.name='fraction_number')
  labels <- data.table(fasta_id=fasta_id, protein_id=protein_id,
                       protein_name=protein_name,
                       peptide_id=traces.wide$peptide_id)
  setorder(labels, protein_id)
  
  result <- list(traces.wide=traces.wide, ids=labels)
  class(result) <- 'Traces'

  return(result)
}