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Imported QFeatures Reading from Norman
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,10 +1,11 @@ | ||
| Package: MetFamily | ||
| Type: Package | ||
| Title: MetFamily: Discovering Regulated Metabolite Families in Untargeted Metabolomics Studies | ||
| Version: 0.99.3 | ||
| Date: 2024-03-02 | ||
| Version: 0.99.4 | ||
| Date: 2024-08-22 | ||
| Author: c( person("Hendrik", "Treutler, role = c("aut"), email = "[email protected]"), | ||
| person("Khabat", "Vahabi, role = c("aut"), email = "[email protected]"), | ||
| person("Norman", "Storz", role = c("aut"), email = "[email protected]"), | ||
| person(given = "Steffen", family = "Neumann", email = "[email protected]", | ||
| role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7899-7192")) ) | ||
| Depends: | ||
|
|
@@ -37,6 +38,7 @@ Imports: | |
| graphics, | ||
| grDevices, | ||
| methods, | ||
| QFeatures, | ||
| stats, | ||
| utils | ||
| Remotes: decisionpatterns/searchable | ||
|
|
@@ -63,5 +65,8 @@ Collate: | |
| 'TreeAlgorithms.R' | ||
| 'Plots.R' | ||
| 'R_packages.R' | ||
| parsePeakAbundanceMatrixQF.R | ||
| readMSDial.R | ||
| readMetaboScape.R | ||
| RoxygenNote: 7.2.3 | ||
| Encoding: UTF-8 | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,175 @@ | ||
| #' Title | ||
| #' | ||
| #' @param qfeatures Object of type QFeatures | ||
| #' @param doPrecursorDeisotoping | ||
| #' @param mzDeviationInPPM_precursorDeisotoping | ||
| #' @param mzDeviationAbsolute_precursorDeisotoping | ||
| #' @param maximumRtDifference | ||
| #' @param progress boolean whether or not to show a progress bar | ||
| #' | ||
| #' @return | ||
| #' @export | ||
| #' | ||
| #' @examples | ||
| parsePeakAbundanceMatrixQF <- function(qfeatures, | ||
| doPrecursorDeisotoping, | ||
| mzDeviationInPPM_precursorDeisotoping, | ||
| mzDeviationAbsolute_precursorDeisotoping, | ||
| maximumRtDifference, | ||
| progress=FALSE) | ||
| { | ||
| ## read file | ||
|
|
||
| if(!is.na(progress)) { | ||
| if(progress) { | ||
| incProgress(amount = 0.1, detail = paste("Parsing MS1 file content...", sep = "")) | ||
| } else { | ||
| print(paste("Parsing MS1 file content...", sep = "")) | ||
| } | ||
| } | ||
|
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| cols_to_exclude <- c("Reference RT","Reference m/z","Comment", | ||
| "Manually modified for quantification", | ||
| "Total score","RT similarity","Average","Stdev") | ||
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| cols_to_keep <- which(!colnames(rowData(qfeatures))[[1]] %in% cols_to_exclude) | ||
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| dataFrame <- cbind(rowData(qfeatures)[[1]][,cols_to_keep] ,assay(qfeatures)) | ||
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| ncol(rowData(qfeatures)[[1]]) | ||
| numberOfPrecursors <- nrow(dataFrame) | ||
| numberOfPrecursorsPrior <- numberOfPrecursors | ||
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| if(nrow(colData(qfeatures))>0){ | ||
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| dataColumnStartEndIndeces <- 1 | ||
| numberOfDataColumns <- nrow(colData(qfeatures)) | ||
| sampleClass <- colData(qfeatures)$Class | ||
| sampleType <- colData(qfeatures)$Type | ||
| sampleInjectionOrder <- colData(qfeatures)$"Injection order" | ||
| batchID <- NULL | ||
| if(! is.null(colData(qfeatures)$BatchID)) | ||
| batchID <- colData(qfeatures)$BatchID | ||
|
|
||
| } else { | ||
| dataColumnStartEndIndeces <- NULL | ||
| numberOfDataColumns <- 0 | ||
| sampleClass <- NULL | ||
| sampleType <- NULL | ||
| sampleInjectionOrder <- NULL | ||
| batchID <- NULL | ||
| } | ||
|
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||
| commaNumbers <- sum(grepl(x = dataFrame$"Average Mz", pattern = "^(\\d+,\\d+$)|(^\\d+$)")) | ||
| decimalSeparatorIsComma <- commaNumbers == nrow(dataFrame) | ||
| if(decimalSeparatorIsComma){ | ||
| if(!is.null(dataFrame$"Average Rt(min)")) dataFrame$"Average Rt(min)" <- gsub(x = gsub(x = dataFrame$"Average Rt(min)", pattern = "\\.", replacement = ""), pattern = ",", replacement = ".") | ||
| if(!is.null(dataFrame$"Average Mz")) dataFrame$"Average Mz" <- gsub(x = gsub(x = dataFrame$"Average Mz", pattern = "\\.", replacement = ""), pattern = ",", replacement = ".") | ||
| if(!is.null(dataFrame$"Fill %")) dataFrame$"Fill %" <- gsub(x = gsub(x = dataFrame$"Fill %", pattern = "\\.", replacement = ""), pattern = ",", replacement = ".") | ||
| if(!is.null(dataFrame$"Dot product")) dataFrame$"Dot product" <- gsub(x = gsub(x = dataFrame$"Dot product", pattern = "\\.", replacement = ""), pattern = ",", replacement = ".") | ||
| if(!is.null(dataFrame$"Reverse dot product")) dataFrame$"Reverse dot product" <- gsub(x = gsub(x = dataFrame$"Reverse dot product", pattern = "\\.", replacement = ""), pattern = ",", replacement = ".") | ||
| if(!is.null(dataFrame$"Fragment presence %")) dataFrame$"Fragment presence %" <- gsub(x = gsub(x = dataFrame$"Fragment presence %", pattern = "\\.", replacement = ""), pattern = ",", replacement = ".") | ||
|
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| ## replace -1 by 0 | ||
| if(numberOfDataColumns > 0) { | ||
| for(colIdx in dataColumnStartEndIndeces[[1]]:dataColumnStartEndIndeces[[2]]){ | ||
| dataFrame[ , colIdx] <- gsub(x = gsub(x = dataFrame[ , colIdx], pattern = "\\.", replacement = ""), pattern = ",", replacement = ".") | ||
| } | ||
| } | ||
| } | ||
|
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| ################### | ||
| ## column formats | ||
| if(!is.null(dataFrame$"Average Rt(min)")) dataFrame$"Average Rt(min)" <- as.numeric(dataFrame$"Average Rt(min)") | ||
| if(!is.null(dataFrame$"Average Mz")) dataFrame$"Average Mz" <- as.numeric(dataFrame$"Average Mz") | ||
| if(!is.null(dataFrame$"Fill %")) dataFrame$"Fill %" <- as.numeric(dataFrame$"Fill %") | ||
| if(!is.null(dataFrame$"MS/MS included")) dataFrame$"MS/MS included" <- as.logical(dataFrame$"MS/MS included") | ||
| if(!is.null(dataFrame$"Dot product")) dataFrame$"Dot product" <- as.numeric(dataFrame$"Dot product") | ||
| if(!is.null(dataFrame$"Reverse dot product")) dataFrame$"Reverse dot product" <- as.numeric(dataFrame$"Reverse dot product") | ||
| if(!is.null(dataFrame$"Fragment presence %")) dataFrame$"Fragment presence %" <- as.numeric(dataFrame$"Fragment presence %") | ||
|
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| ##################### | ||
| ## sorted by m/z (needed for deisotoping) | ||
| if(!is.null(dataFrame$"Average Mz")) | ||
| dataFrame <- dataFrame[order(dataFrame$"Average Mz"), ] | ||
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| ## replace -1 by 0 | ||
| if(numberOfDataColumns > 0){ | ||
| for(colIdx in dataColumnStartEndIndeces[[1]]:dataColumnStartEndIndeces[[2]]){ | ||
| dataFrame[ , colIdx] <- as.numeric(dataFrame[ , colIdx]) | ||
| if(!is.na(sum(dataFrame[,colIdx] == -1))) | ||
| dataFrame[(dataFrame[,colIdx] == -1),colIdx] <- 0 | ||
| } | ||
| } | ||
|
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| ## deisotoping | ||
| numberOfRemovedIsotopePeaks <- 0 | ||
| if(doPrecursorDeisotoping & !is.null(dataFrame$"Average Mz")){ | ||
| if(!is.na(progress)) if(progress) incProgress(amount = 0, detail = paste("Precursor deisotoping...", sep = "")) else print(paste("Precursor deisotoping...", sep = "")) | ||
| distance13Cminus12C <- 1.0033548378 | ||
|
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| ## mark isotope precursors | ||
| precursorsToRemove <- vector(mode = "logical", length = numberOfPrecursors) | ||
|
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| if(numberOfDataColumns > 0){ | ||
| intensities <- dataFrame[ , dataColumnStartEndIndeces[[1]]:dataColumnStartEndIndeces[[2]]] | ||
| medians <- apply(X = as.matrix(intensities), MARGIN = 1, FUN = median) | ||
| } | ||
|
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| for(precursorIdx in seq_len(numberOfPrecursors)){ | ||
| if((precursorIdx %% (as.integer(numberOfPrecursors/10))) == 0) | ||
| if(!is.na(progress)) if(progress) incProgress(amount = 0.0, detail = paste("Precursor deisotoping ", precursorIdx, " / ", numberOfPrecursors, sep = "")) else print(paste("Precursor deisotoping ", precursorIdx, " / ", numberOfPrecursors, sep = "")) | ||
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| mzError <- dataFrame$"Average Mz"[[precursorIdx]] * mzDeviationInPPM_precursorDeisotoping / 1000000 | ||
| mzError <- max(mzError, mzDeviationAbsolute_precursorDeisotoping) | ||
|
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| ## RT difference <= maximumRtDifference | ||
| validPrecursorsInRt <- abs(dataFrame$"Average Rt(min)"[[precursorIdx]] - dataFrame$"Average Rt(min)"[-precursorIdx]) <= maximumRtDifference | ||
|
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| ## MZ difference around 1.0033548378 (first isotope) or 1.0033548378 * 2 (second isotope) | ||
| validPrecursorsInMz1 <- abs((dataFrame$"Average Mz"[[precursorIdx]] - distance13Cminus12C * 1) - dataFrame$"Average Mz"[-precursorIdx]) <= mzError | ||
| validPrecursorsInMz2 <- abs((dataFrame$"Average Mz"[[precursorIdx]] - distance13Cminus12C * 2) - dataFrame$"Average Mz"[-precursorIdx]) <= mzError | ||
| validPrecursorsInMz <- validPrecursorsInMz1 | validPrecursorsInMz2 | ||
|
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| ## intensity gets smaller in the isotope spectrum | ||
| if(numberOfDataColumns > 0){ | ||
| validPrecursorsInIntensity <- (medians[-precursorIdx] - medians[[precursorIdx]]) > 0 | ||
| } else { | ||
| validPrecursorsInIntensity <- TRUE | ||
| } | ||
|
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| if(any(validPrecursorsInRt & validPrecursorsInMz & validPrecursorsInIntensity)) | ||
| precursorsToRemove[[precursorIdx]] <- TRUE | ||
| } | ||
|
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| ## remove isotopes | ||
| dataFrame <- dataFrame[!precursorsToRemove, ] | ||
|
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| numberOfRemovedIsotopePeaks <- sum(precursorsToRemove) | ||
| numberOfPrecursors <- nrow(dataFrame) | ||
| } | ||
|
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| if(!is.na(progress)) if(progress) incProgress(amount = 0, detail = paste("Boxing...", sep = "")) else print(paste("Boxing...", sep = "")) | ||
| returnObj <- list() | ||
| returnObj$dataFrame <- dataFrame | ||
|
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| ## meta | ||
| returnObj$oldFormat <- oldFormat | ||
| returnObj$numberOfPrecursors <- numberOfPrecursors | ||
| returnObj$dataColumnStartEndIndeces <- dataColumnStartEndIndeces | ||
| returnObj$numberOfDataColumns <- numberOfDataColumns | ||
|
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| ## group anno | ||
| returnObj$sampleClass <- sampleClass | ||
| returnObj$sampleType <- sampleType | ||
| returnObj$sampleInjectionOrder <- sampleInjectionOrder | ||
| returnObj$batchID <- batchID | ||
|
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| ## misc | ||
| returnObj$numberOfPrecursorsPrior <- numberOfPrecursorsPrior | ||
| returnObj$numberOfRemovedIsotopePeaks <- numberOfRemovedIsotopePeaks | ||
|
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| return (returnObj) | ||
| } |
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|---|---|---|
| @@ -0,0 +1,91 @@ | ||
| #' Read MS-DIAL Output File into a QFeatures Object | ||
| #' | ||
| #' This function reads the output file from MS-DIAL and | ||
| #' converts it into a QFeatures object. | ||
| #' | ||
| #' @param file A string with the path to the MS-DIAL output file. | ||
| #' @param version A character string specifying the version of MS-DIAL used to generate the file. | ||
| #' This parameter is currently not used. | ||
| #' | ||
| #' @return A QFeatures object containing: | ||
| #' \itemize{ | ||
| #' \item An assay named "exampleAssay" with the metabolite counts. | ||
| #' \item Row data (feature metadata) extracted from the input file. | ||
| #' \item Column data (sample metadata) extracted from the input file. | ||
| #' } | ||
| #' | ||
| #' @export | ||
| #' | ||
| #' @examples | ||
| #' \dontrun{ | ||
| #' # Assuming you have an MS-DIAL output file named "Metabolite_profile_showcase.txt" in a "data" directory: | ||
| #' qf <- readMSDial("data/Metabolite_profile_showcase.txt") | ||
| #' | ||
| #' # Examine the structure of the resulting QFeatures object | ||
| #' qf | ||
| #' | ||
| #' # Access the assay data | ||
| #' assay(qf[["exampleAssay"]]) | ||
| #' | ||
| #' # Access the row data (feature metadata) | ||
| #' rowData(qf[["exampleAssay"]]) | ||
| #' | ||
| #' # Access the column data (sample metadata) | ||
| #' colData(qf) | ||
| #' } | ||
| #' | ||
| #' @importFrom QFeatures QFeatures | ||
| #' @importFrom SummarizedExperiment SummarizedExperiment | ||
| #' @importFrom S4Vectors DataFrame | ||
| #' | ||
| #' @seealso | ||
| #' \code{\link[QFeatures]{QFeatures}} for more information on the QFeatures class. | ||
| #' \code{\link[SummarizedExperiment]{SummarizedExperiment}} for details on the underlying data structure. | ||
| #' | ||
| #' @note | ||
| #' | ||
| #' | ||
| #' @references | ||
| #' | ||
| readMSDial <- function(file, version){ | ||
| table <- read.table(file, fill = TRUE, sep = "\t", | ||
| quote = "", header = FALSE) | ||
|
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| # Identify the starting row and column of the data | ||
| startRow <- which(table[, 1] != "")[1] | ||
| startCol <- which(table[1, ] != "")[1] | ||
| ##TODO: version dependent error message if startRow or startCol are not as expected. | ||
|
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| # Split the table in parts | ||
| colDataRaw <- table[1:startRow, startCol:ncol(table)] | ||
| rowDataRaw <- table[startRow:nrow(table), 1:(startCol)] | ||
| countsRaw <- table[startRow:nrow(table), startCol:ncol(table)] | ||
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| # Extract ids and counts data | ||
| ids <- rowDataRaw[-1, 1] | ||
| counts <- as.matrix(countsRaw[-1, -1]) | ||
| counts <- matrix(as.numeric(counts), nrow = nrow(counts), ncol = ncol(counts)) | ||
| colnames(counts) <- as.character(countsRaw[1, -1]) | ||
| rownames(counts) <- ids | ||
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| # Ensure row names of colData match counts column names | ||
| colData <- DataFrame(t(colDataRaw[-nrow(colDataRaw), -1])) | ||
| rownames(colData) <- as.character(colDataRaw[nrow(colDataRaw), -1]) | ||
| colnames(colData) <- as.character(colDataRaw[-nrow(colDataRaw), 1]) | ||
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| # Ensure row names of rowData match counts row names | ||
| rowData <- DataFrame(rowDataRaw[-1, ], row.names = ids) | ||
| colnames(rowData) <- as.character(rowDataRaw[1,]) | ||
|
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| # Create SummarizedExperiment object | ||
|
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| sumExp <- SummarizedExperiment(assays = list(counts = counts), | ||
| rowData = rowData, | ||
| colData = colData) | ||
| ##TODO: Metadata with data source and version | ||
|
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| # Create QFeatures object | ||
| qf <- QFeatures(list(exampleAssay = sumExp), colData = colData(sumExp)) | ||
| qf | ||
| ##TODO: name | ||
| } |
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