R/compactOutput_pmolCalc.R

In ELELAB/lipidQ: Lipidomics Analysis Tool

#' @title Make Compact Output Containing Pico Mol Calculations
#' @author André Vidas Olsen
#' @description compactOutput_pmolCalc saves a compact version of data.frame
#' NAME, CLASS_PMOL_SUBT_PMOL_*, and MOL_PCT_CLASS_SUBT_PMOL_* columns
#' @param data data formatted by the use of the mergeDataSet function from
#' LipidQ.
#' @param userSpecifiedColnames the column names template file containing user
#' specified column names for the input data.
#' @return a compact data output comprising of pmol and pmol related columns
#' @export
#' @examples
#' # load user specified column names file.
#' userSpecifiedColnames <- read.table(system.file("extdata/LipidQ_DataBase",
#'  "userSpecifiedColnames.csv", package = "lipidQ"),
#'  stringsAsFactors = FALSE, header = TRUE, sep = ",")
#'
#' # load pmolCalculatedDataSet.csv made by using the pmolCalc() function
#' pmolCalculatedDataSet <- read.table(system.file("extdata/dataTables/checks",
#'  "pmolCalculatedDataSet.csv", package = "lipidQ"),
#'  stringsAsFactors = FALSE, header = TRUE, sep = ",")
#'
#' # make compact output from pmolCalculatedDataSet
#' compact_output <- compactOutput_pmolCalc(pmolCalculatedDataSet,
#'  userSpecifiedColnames = userSpecifiedColnames)
compactOutput_pmolCalc <- function(data, userSpecifiedColnames = NULL){

  # get colnames for data
  dataColnames <- checkColnames(userSpecifiedColnames)


  # create the new data set initialized with SUM_COMPOSITION
  classPmol_molPctClass <- data.frame(x = data[,dataColnames$SUM_COMPOSITION],
                                      stringsAsFactors = FALSE)
  colnames(classPmol_molPctClass) <- dataColnames$SUM_COMPOSITION[1]


  # remove numbers in the SUM_COMPOSITION column, so only the class name is used
  # for SUM_COMPOSITION.
  for(i in 1:nrow(data)){
    classPmol_molPctClass[i, dataColnames$SUM_COMPOSITION] <-
      unlist(strsplit(classPmol_molPctClass[i,dataColnames$SUM_COMPOSITION],
                      " "))[[1]]
  }

  # insert CLASS_PMOL_SUBT_PMOL_*, and MOL_PCT_CLASS_SUBT_PMOL_* columns
  classPmol_molPctClass <- cbind( classPmol_molPctClass,
    data[,c(colnames(data)[grep("^CLASS_PMOL_SUBT_PMOL_",colnames(data))])] )
  classPmol_molPctClass <- cbind( classPmol_molPctClass,
    data[,c(colnames(data)[grep("^MOL_PCT_CLASS_SUBT_PMOL_",colnames(data))])] )



  #### sum filtered values for each classes for each sample after BLNK
  #### subtraction

  # find all unique class names (without numbers)
  exData <- data[-grep("is",data[,dataColnames$SUM_COMPOSITION]),]
  classNames <- gsub("^(\\w+.)[[:space:]].*", "\\1",data[1:nrow(exData),
                dataColnames$SUM_COMPOSITION])
  classNames <-unique(classNames)

  FILTERED_names <- colnames(data)[grep("^FILTERED",colnames(data))]

  # store all sumClassValue to be used later in mol% class calculation
  sumClassValueList <- matrix(numeric(), nrow = length(classNames),
                              ncol = length(FILTERED_names))
  for(i in 1:length(classNames)){

    for(j in 1:length(FILTERED_names)){
      # sum values for each class
      sumClassValue <- sum(data[grep(paste0("^",classNames[i]," "),
          data[,dataColnames$SUM_COMPOSITION]),FILTERED_names[j]],na.rm = TRUE)

      sumClassValueList[i,j] <- sumClassValue

      # add sum value to all species with the same class
      data[grep(paste0("^",classNames[i]," "),
          data[,dataColnames$SUM_COMPOSITION]),
          paste0("CLASS_",FILTERED_names[j])] <- sumClassValue
    }
  }


  # Combine ^CLASS_PMOL_SUBT_PMOL_ , ^MOL_PCT_CLASS_SUBT_PMOL_ & ^CLASS_FILTERED
  # together
  classPmol_molPctClass <- cbind( classPmol_molPctClass,
              data[,c(colnames(data)[grep("^CLASS_FILTERED",colnames(data))])] )

  # remove rows containing duplicates of SUM_COMPOSITION
  classPmol_molPctClass <- classPmol_molPctClass[!duplicated(
    classPmol_molPctClass[,dataColnames$SUM_COMPOSITION]),]


  return(classPmol_molPctClass)

}