R/DataValidation.R

In eralpdogu/MSstatsQC: Longitudinal system suitability monitoring and quality control for proteomic experiments

#########################################################################################
# here we put a selection of most column names that users use.
#The first element of each vector should be the best name that
# we suggest users to use and  which our code is based on.
#for example "Retention Time" and "Full Width at Half Maximum" which are the first element
# of each vector in the list, are our suggestion so we wrote them in the fisrt place.
best_colnames <- list(
  c("AcquiredTime","Acquired.Time","time"),
  c("MinStartTime","min start time","Min Start Time"),
  c("MaxEndTime", "max end time","Max End Time"),
  c("Precursor","PeptideSequence"),
  c("Annotations","anotations")
)
#### camelCaseSplit function ##############################################################################################
camelCaseSplit <- function(x) {
  # This function get a camelCase word and splits it.
  # Ex : camelCaseSplit("myComputerIsHere") ---> my Computer Is Here
  return(gsub("([a-z])([A-Z])", "\\1 \\L\\2", x, perl = TRUE))
}
#### punc_remove function #################################################################################################
punc_remove <- function(x){
  # This function removes any existing punctuation in your sentence or word
  #and transfer it to space.
  # Ex1: punc_remove("Best.RT") --> "Best RT"     #Ex2: punc_remove("Best_RT") --> "Best RT"
  return(gsub("[[:punct:]///' ]", " ", x))
}
#### clearString function ###############################################################################################
clearString <- function(x){
  # This function, gets a word or setence, Splits it (if it is a camelCase),
  #removes any existing punctuations, and transfer
  # all Upper Case letters to lower case letters.
  # Ex: clearString("myName_isSara.Taheri") --> my name is sara taheri
  return(tolower(punc_remove(camelCaseSplit(x))))
}
#### guessColumnName function ###########################################################################################
# This function receives the data and check the column names of data and changes
#the column names if it is not the
# same names as our suggested sample data to fit our suggested sample data

#################################################################################################
input.sanity.check <- function(data, finalfile) {

  error_message <- ""

  # get the column names and change them to the column names that we want
  #(For ecample we want Retention Time but a user might use RT, this function
  #auotomatically change RT to Retention Time)
  # colnames(data) <- unlist(lapply(colnames(data), function(x)guessColumnName(x)))

  ############## conditions ##############
  # check that the data includes all the requiered columns and if not tell user what column is missing
  required_column_names <- c("Precursor","Annotations")
  if(!("Annotations" %in% colnames(data))) {
    data[,"Annotations"] <- NA
  }
  provided_column_names <- colnames(data)
  if(!all(required_column_names %in% provided_column_names)) {
    missedInput <- which(!(required_column_names %in% provided_column_names))
    error_message <- paste("ERROR : The required input(inputs) : ",
                           paste(required_column_names[missedInput], collapse = ", "),
                           " is(are) not provided in data set. Please add it to your
                           data and try again.\n\n")
  }

  # check that all columns other than Precursor and Acquired Time and
  #Annotations are numeric.
  AfterannoColNum <- (which(colnames(data)=="Annotations")) + 1

  for(i in  AfterannoColNum:ncol(data)) {
    if(is.numeric(data[,i]) == FALSE) {
      error_message <- paste(error_message, "All the values of",
                             colnames(data)[i], "should be numeric and positive.\n\n")
    }
  }

  if(error_message != "") {
    return(paste(error_message))
  }
  # for custom metrics we are checking them to be numeric in QCMetrics in
  # "find_custom_metrics" function and only accepting numeric columns after Annotation

  # if there is any missing value in data replace it with NA
  data[data==""] <- NA
  levels(data$Annotations) = c(levels(data$Annotations), "Not Available")
  data["Annotations"][is.na(data["Annotations"])] <- "Not Available"

  # Define peak assymetry
  # if("MinStartTime" %in% provided_column_names && "MaxEndTime" %in% provided_column_names) {
  #   peakAss <- 2*data$MinStartTime/(data$MaxEndTime+data$MinStartTime)
  #   # locate a new column named "Peak Assymetry" right after the column named "Annotation"
  #   data[,"Peak Assymetry"] <- peakAss
  # }
  print("Your data is ready to go!")
  return(data)
  }