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#' @importFrom magrittr '%>%'
RawDataExtraction <- function(data, MoleculeArray, logEnvironment, verbose) {
if(verbose){message(date(), " :: processing raw data ...")}
for (i in 2:ncol(data)) {
data[, i] <- as.numeric(data[, i])
}
# remove rows that are ALL NA (=> don't remove individual NAs !)
data <- data[!apply(apply(data, 1, is.na), 2, sum) == ncol(data), ]
# remove columns that are all NA
data <- data[, !apply(apply(data, 2, is.na), 2, sum) == nrow(data)]
rownames(data) <- as.character(data[, "Measurements/Samples"])
data <- as.matrix(data[, -which(colnames(data) %in% "Measurements/Samples"), drop = FALSE])
txt_extracted <- rownames(data)
# Extraction of measurement locations from the imported data to check for presence.
MoleculesTotal <- length(MoleculeArray)
MoleculeName <- names(MoleculeArray)
MoleculeLocationList <- list()
NumberTransitionsList <- list()
TransitionLocationExpectedList <- list()
TransitionLocationList <- list()
MissingMoleculesList <- list()
MissingTransitionsList <- list()
for (MoleculeNo in seq_len(MoleculesTotal)) {
MoleculeData <- MoleculeArray[[MoleculeNo]] # entire 'MoleculeInfo' data for current molecule
TransitionsExpected <- MoleculeData[["TransitionsExpected"]] # from CalculateTransitions()
NumberTransitions <- nrow(TransitionsExpected)
# Check: Do the measurement labels in the first column of the data match the theoretically expected measurement labels?
MissingTransitions.vec <- vector()
TransitionLocationExpected.vec <- vector()
for (TransitionNo in seq_len(NumberTransitions)) {
tmpIdx <- stringr::str_detect(txt_extracted, paste0("^", rownames(TransitionsExpected)[TransitionNo], "$")) %>% which
MissingTransitions.vec[TransitionNo] <- 0
if (length(tmpIdx) == 0) {
TransitionLocationExpected.vec[TransitionNo] <- 0
MissingTransitions.vec[TransitionNo] <- 1
notification <- stringr::str_c("In measurement data file: The expected measurement ID '", rownames(TransitionsExpected)[TransitionNo], "' could not be found in the measurement data file.\nCorrection will be performed, however the results may be less accurate. Please check your input file for typos.",
"\nBe especially careful when considering fraction and mean enrichment values from molecules with missing measurements.")
errorHandler(notification, logEnvironment, "warning", verbose=verbose)
} else {
TransitionLocationExpected.vec[TransitionNo] <- tmpIdx
}
} #TransitionNo
if (sum(TransitionLocationExpected.vec) == 0) {
notification <- stringr::str_c("In measurement data file: None of the expected measurement IDs found for molecule '", names(MoleculeArray[MoleculeNo]),
"'.")
errorHandler(notification, logEnvironment, "error", verbose=verbose)
}
names(TransitionLocationExpected.vec) <- rownames(TransitionsExpected)
names(MissingTransitions.vec) <- rownames(TransitionsExpected)
# If measurement IDs are missing, a new list of Measurement IDs is produced containing only those that were actually found in the input data. This list
# is then used for further computations.
Transitions <- TransitionsExpected[MissingTransitions.vec == 0, , drop = FALSE]
TransitionLocation <- TransitionLocationExpected.vec[MissingTransitions.vec == 0]
TransitionLocationExpectedList[[MoleculeNo]] <- TransitionLocationExpected.vec
TransitionLocationList[[MoleculeNo]] <- TransitionLocation
MoleculeArray[[MoleculeNo]][["Transitions"]] <- Transitions
MoleculeArray[[MoleculeNo]][["MissingTransitions"]] <- MissingTransitions.vec
MoleculeArray[[MoleculeNo]][["TransitionLocationExpected"]] <- TransitionLocationExpected.vec
MoleculeArray[[MoleculeNo]][["TransitionLocation"]] <- TransitionLocation
} #MoleculeNo
if(verbose){message(date(), " :: processing raw data [OK]\n")}
return(list(MoleculeInfo = MoleculeArray, dataRaw = data))
} #RawDataExtraction
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