diff --git a/tools/camera/CAMERA_annotateDiffreport.r b/tools/camera/CAMERA_annotateDiffreport.r index d99e58c88d..653c27f2e6 100644 --- a/tools/camera/CAMERA_annotateDiffreport.r +++ b/tools/camera/CAMERA_annotateDiffreport.r @@ -5,9 +5,9 @@ cat("\tSESSION INFO\n") # Import the different functions source_local <- function(fname) { - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep = "/")) + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) } source_local("lib.r") @@ -50,7 +50,7 @@ directory <- retrieveRawfileInTheWorkingDir(singlefile, zipfile) # Because so far CAMERA isn't compatible with the new XCMSnExp object if (exists("xdata")) { - xset <- getxcmsSetObject(xdata) + xset <- getxcmsSetObject(xdata) } cat("\n\n") diff --git a/tools/camera/CAMERA_combinexsAnnos.r b/tools/camera/CAMERA_combinexsAnnos.r index 362e589ac9..a7b95ab2f7 100644 --- a/tools/camera/CAMERA_combinexsAnnos.r +++ b/tools/camera/CAMERA_combinexsAnnos.r @@ -3,11 +3,11 @@ # ----- PACKAGE ----- cat("\tSESSION INFO\n") -#Import the different functions +# Import the different functions source_local <- function(fname) { - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep = "/")) + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) } source_local("lib.r") @@ -18,7 +18,7 @@ cat("\n\n") # ----- ARGUMENTS ----- cat("\tARGUMENTS INFO\n") -args <- parseCommandArgs(evaluate = FALSE) #interpretation of arguments given in command line as an R list of objects +args <- parseCommandArgs(evaluate = FALSE) # interpretation of arguments given in command line as an R list of objects write.table(as.matrix(args), col.names = FALSE, quote = FALSE, sep = "\t") cat("\n\n") @@ -27,7 +27,7 @@ cat("\n\n") # ----- PROCESSING INFILE ----- cat("\tINFILE PROCESSING INFO\n") -#image is an .RData file necessary to use xset variable given by previous tools +# image is an .RData file necessary to use xset variable given by previous tools load(args$image_pos) xaP <- xa @@ -58,16 +58,16 @@ cat("\n\n") cat("\tMAIN PROCESSING INFO\n") cAnnot <- combinexsAnnos_function( - xaP = xaP, xaN = xaN, - diffrepP = diffrepP, diffrepN = diffrepN, - pos = args$pos, tol = args$tol, ruleset = args$ruleset, keep_meta = args$keep_meta, - convertRTMinute = args$convertRTMinute, numDigitsMZ = args$numDigitsMZ, numDigitsRT = args$numDigitsRT, - variableMetadataOutput = "variableMetadata.tsv" + xaP = xaP, xaN = xaN, + diffrepP = diffrepP, diffrepN = diffrepN, + pos = args$pos, tol = args$tol, ruleset = args$ruleset, keep_meta = args$keep_meta, + convertRTMinute = args$convertRTMinute, numDigitsMZ = args$numDigitsMZ, numDigitsRT = args$numDigitsRT, + variableMetadataOutput = "variableMetadata.tsv" ) # ----- EXPORT ----- -#saving R data in .Rdata file to save the variables used in the present tool +# saving R data in .Rdata file to save the variables used in the present tool objects2save <- c("xa", "variableMetadata", "diffrep", "cAnnot", "listOFargs", "zipfile", "singlefile") save(list = objects2save[objects2save %in% ls()], file = "combinexsAnnos.RData") diff --git a/tools/camera/CAMERA_findAdducts.R b/tools/camera/CAMERA_findAdducts.R index 4903a43442..36d2633585 100755 --- a/tools/camera/CAMERA_findAdducts.R +++ b/tools/camera/CAMERA_findAdducts.R @@ -5,9 +5,9 @@ cat("\tSESSION INFO\n") # Import the different functions source_local <- function(fname) { - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep = "/")) + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) } source_local("lib.r") @@ -27,34 +27,34 @@ print(args) # Function to convert "NULL" strings to actual NULL values convertNullString <- function(x) { - if (x == "NULL") { - return(NULL) - } - return(x) + if (x == "NULL") { + return(NULL) + } + return(x) } # Function to convert string to numeric lists convert_psg_list <- function(x) { - # Check if x is NULL or has zero length before further processing - if (is.null(x) || length(x) == 0) { - return(NULL) - } - - # Force conversion to character - x <- as.character(x) - - if (grepl("^[0-9]+$", x)) { - # If the string represents a single numeric value - return(as.numeric(x)) - } else { - # Convert string representation of a list to a numeric vector - # Use a regular expression to split by common separators - return(as.numeric(unlist(strsplit(x, "[,;\\s]+")))) - } + # Check if x is NULL or has zero length before further processing + if (is.null(x) || length(x) == 0) { + return(NULL) + } + + # Force conversion to character + x <- as.character(x) + + if (grepl("^[0-9]+$", x)) { + # If the string represents a single numeric value + return(as.numeric(x)) + } else { + # Convert string representation of a list to a numeric vector + # Use a regular expression to split by common separators + return(as.numeric(unlist(strsplit(x, "[,;\\s]+")))) + } } for (arg in names(args)) { - args[[arg]] <- convertNullString(args[[arg]]) + args[[arg]] <- convertNullString(args[[arg]]) } # Convert only the 'psg_list' element in args @@ -65,7 +65,7 @@ print(sapply(args, class)) # Check if the image file exists if (!file.exists(args$image)) { - stop("The RData file does not exist: ", args$image) + stop("The RData file does not exist: ", args$image) } # ----- PROCESSING INFILE ----- @@ -88,41 +88,41 @@ args <- rawFilePath$args print(paste("singlefile :", singlefile)) if (!is.null(singlefile)) { - directory <- retrieveRawfileInTheWorkingDir(singlefile, zipfile) + directory <- retrieveRawfileInTheWorkingDir(singlefile, zipfile) } # Check if the 'rules' argument in 'args' is NULL if (is.null(args$rules)) { - # If 'args$rules' is NULL, set 'rulset' to NULL - args$rulset <- NULL + # If 'args$rules' is NULL, set 'rulset' to NULL + args$rulset <- NULL } else { - # Try to read the rules file with different delimiters - delimiters <- c(";", "\t", ",") # List of possible delimiters - success <- FALSE # Flag to check if reading was successful - - for (sep in delimiters) { - # Attempt to read the rules file with the current separator - args$rulset <- read.table(args$rules, header = TRUE, sep = sep) - - # Check if the number of columns is at least 4 - if (ncol(args$rulset) >= 4) { - success <- TRUE # Mark success if the format is correct - break # Exit the loop if the file was read successfully + # Try to read the rules file with different delimiters + delimiters <- c(";", "\t", ",") # List of possible delimiters + success <- FALSE # Flag to check if reading was successful + + for (sep in delimiters) { + # Attempt to read the rules file with the current separator + args$rulset <- read.table(args$rules, header = TRUE, sep = sep) + + # Check if the number of columns is at least 4 + if (ncol(args$rulset) >= 4) { + success <- TRUE # Mark success if the format is correct + break # Exit the loop if the file was read successfully + } + } + + # If reading the rules file failed for all delimiters + if (!success) { + # Display an error message if the file is not well formatted + error_message <- "The rules file appears to be improperly formatted. Accepted column separators are ;, tab, and ,." + print(error_message) + stop(error_message) # Stop execution with an error } - } - - # If reading the rules file failed for all delimiters - if (!success) { - # Display an error message if the file is not well formatted - error_message <- "The rules file appears to be improperly formatted. Accepted column separators are ;, tab, and ,." - print(error_message) - stop(error_message) # Stop execution with an error - } } # Verify that the object xa is loaded if (!exists("xa")) { - stop("The object xa was not found in the RData file.") + stop("The object xa was not found in the RData file.") } print("Loaded xa object:") @@ -139,26 +139,26 @@ print(xa) peakList <- getPeaklist(xa, intval = args$intval) if (length(phenoData@data$sample_name) == 1) { - peakList$name <- make.unique(paste0("M", round(peakList[, "mz"], 0), "T", round(peakList[, "rt"], 0)), "_") - variableMetadata <- peakList[, c("name", setdiff(names(peakList), "name"))] - variableMetadata <- formatIonIdentifiers(variableMetadata, numDigitsRT = args$numDigitsRT, numDigitsMZ = args$numDigitsMZ) + peakList$name <- make.unique(paste0("M", round(peakList[, "mz"], 0), "T", round(peakList[, "rt"], 0)), "_") + variableMetadata <- peakList[, c("name", setdiff(names(peakList), "name"))] + variableMetadata <- formatIonIdentifiers(variableMetadata, numDigitsRT = args$numDigitsRT, numDigitsMZ = args$numDigitsMZ) } else { - names_default <- groupnames(xa@xcmsSet, mzdec = 0, rtdec = 0) # Names without decimals - names_custom <- groupnames(xa@xcmsSet, mzdec = args$numDigitsMZ, rtdec = args$numDigitsRT) # Names with "x" decimals - - variableMetadata <- data.frame( - name = names_default, - name_custom = names_custom, - stringsAsFactors = FALSE - ) - variableMetadata <- cbind(variableMetadata, peakList[, !(make.names(colnames(peakList)) %in% c(make.names(sampnames(xa@xcmsSet))))]) + names_default <- groupnames(xa@xcmsSet, mzdec = 0, rtdec = 0) # Names without decimals + names_custom <- groupnames(xa@xcmsSet, mzdec = args$numDigitsMZ, rtdec = args$numDigitsRT) # Names with "x" decimals + + variableMetadata <- data.frame( + name = names_default, + name_custom = names_custom, + stringsAsFactors = FALSE + ) + variableMetadata <- cbind(variableMetadata, peakList[, !(make.names(colnames(peakList)) %in% c(make.names(sampnames(xa@xcmsSet))))]) } if (!exists("RTinMinute")) RTinMinute <- FALSE if (args$convertRTMinute && RTinMinute == FALSE) { - RTinMinute <- TRUE - variableMetadata <- RTSecondToMinute(variableMetadata = variableMetadata, convertRTMinute = args$convertRTMinute) + RTinMinute <- TRUE + variableMetadata <- RTSecondToMinute(variableMetadata = variableMetadata, convertRTMinute = args$convertRTMinute) } # Save the extracted peak list as a TSV file named 'variableMetadata.tsv' diff --git a/tools/camera/CAMERA_findIsotopes.R b/tools/camera/CAMERA_findIsotopes.R index 974cc7928c..e25dee80ff 100755 --- a/tools/camera/CAMERA_findIsotopes.R +++ b/tools/camera/CAMERA_findIsotopes.R @@ -5,9 +5,9 @@ cat("\tSESSION INFO\n") # Import the different functions source_local <- function(fname) { - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep = "/")) + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) } source_local("lib.r") @@ -30,7 +30,7 @@ print(sapply(args, class)) # Check if the image file exists if (!file.exists(args$image)) { - stop("The RData file does not exist: ", args$image) + stop("The RData file does not exist: ", args$image) } # ----- PROCESSING INFILE ----- @@ -53,12 +53,12 @@ args <- rawFilePath$args print(paste("singlefile :", singlefile)) if (!is.null(singlefile)) { - directory <- retrieveRawfileInTheWorkingDir(singlefile, zipfile) + directory <- retrieveRawfileInTheWorkingDir(singlefile, zipfile) } # Verify if the xa object is loaded if (!exists("xa")) { - stop("The xa object was not found in the RData file.") + stop("The xa object was not found in the RData file.") } print("xa object loaded:") @@ -66,28 +66,28 @@ print(xa) print("calcIsotopeMatrix") calcIsotopeMatrix <- function(maxiso = 4) { - if (!is.numeric(maxiso)) { - stop("Parameter maxiso is not numeric!\n") - } else if (maxiso < 1 || maxiso > 8) { - stop(paste( - "Parameter maxiso must be between 1 and 8. ", - "Otherwise, use your own IsotopeMatrix.\n" - ), sep = "") - } - - isotopeMatrix <- matrix(NA, 8, 4) - colnames(isotopeMatrix) <- c("mzmin", "mzmax", "intmin", "intmax") - - isotopeMatrix[1, ] <- c(1.000, 1.0040, 1.0, 150) - isotopeMatrix[2, ] <- c(0.997, 1.0040, 0.01, 200) - isotopeMatrix[3, ] <- c(1.000, 1.0040, 0.001, 200) - isotopeMatrix[4, ] <- c(1.000, 1.0040, 0.0001, 200) - isotopeMatrix[5, ] <- c(1.000, 1.0040, 0.00001, 200) - isotopeMatrix[6, ] <- c(1.000, 1.0040, 0.000001, 200) - isotopeMatrix[7, ] <- c(1.000, 1.0040, 0.0000001, 200) - isotopeMatrix[8, ] <- c(1.000, 1.0040, 0.00000001, 200) - - return(isotopeMatrix[1:maxiso, , drop = FALSE]) + if (!is.numeric(maxiso)) { + stop("Parameter maxiso is not numeric!\n") + } else if (maxiso < 1 || maxiso > 8) { + stop(paste( + "Parameter maxiso must be between 1 and 8. ", + "Otherwise, use your own IsotopeMatrix.\n" + ), sep = "") + } + + isotopeMatrix <- matrix(NA, 8, 4) + colnames(isotopeMatrix) <- c("mzmin", "mzmax", "intmin", "intmax") + + isotopeMatrix[1, ] <- c(1.000, 1.0040, 1.0, 150) + isotopeMatrix[2, ] <- c(0.997, 1.0040, 0.01, 200) + isotopeMatrix[3, ] <- c(1.000, 1.0040, 0.001, 200) + isotopeMatrix[4, ] <- c(1.000, 1.0040, 0.0001, 200) + isotopeMatrix[5, ] <- c(1.000, 1.0040, 0.00001, 200) + isotopeMatrix[6, ] <- c(1.000, 1.0040, 0.000001, 200) + isotopeMatrix[7, ] <- c(1.000, 1.0040, 0.0000001, 200) + isotopeMatrix[8, ] <- c(1.000, 1.0040, 0.00000001, 200) + + return(isotopeMatrix[1:maxiso, , drop = FALSE]) } isotopeMatrix <- calcIsotopeMatrix(args$maxiso) @@ -103,26 +103,26 @@ print(xa) peakList <- getPeaklist(xa, intval = args$intval) if (length(phenoData@data$sample_name) == 1) { - peakList$name <- make.unique(paste0("M", round(peakList[, "mz"], 0), "T", round(peakList[, "rt"], 0)), "_") - variableMetadata <- peakList[, c("name", setdiff(names(peakList), "name"))] - variableMetadata <- formatIonIdentifiers(variableMetadata, numDigitsRT = args$numDigitsRT, numDigitsMZ = args$numDigitsMZ) + peakList$name <- make.unique(paste0("M", round(peakList[, "mz"], 0), "T", round(peakList[, "rt"], 0)), "_") + variableMetadata <- peakList[, c("name", setdiff(names(peakList), "name"))] + variableMetadata <- formatIonIdentifiers(variableMetadata, numDigitsRT = args$numDigitsRT, numDigitsMZ = args$numDigitsMZ) } else { - names_default <- groupnames(xa@xcmsSet, mzdec = 0, rtdec = 0) # Names without decimals - names_custom <- groupnames(xa@xcmsSet, mzdec = args$numDigitsMZ, rtdec = args$numDigitsRT) # Names with "x" decimals - - variableMetadata <- data.frame( - name = names_default, - name_custom = names_custom, - stringsAsFactors = FALSE - ) - variableMetadata <- cbind(variableMetadata, peakList[, !(make.names(colnames(peakList)) %in% c(make.names(sampnames(xa@xcmsSet))))]) + names_default <- groupnames(xa@xcmsSet, mzdec = 0, rtdec = 0) # Names without decimals + names_custom <- groupnames(xa@xcmsSet, mzdec = args$numDigitsMZ, rtdec = args$numDigitsRT) # Names with "x" decimals + + variableMetadata <- data.frame( + name = names_default, + name_custom = names_custom, + stringsAsFactors = FALSE + ) + variableMetadata <- cbind(variableMetadata, peakList[, !(make.names(colnames(peakList)) %in% c(make.names(sampnames(xa@xcmsSet))))]) } if (!exists("RTinMinute")) RTinMinute <- FALSE if (args$convertRTMinute && RTinMinute == FALSE) { - RTinMinute <- TRUE - variableMetadata <- RTSecondToMinute(variableMetadata = variableMetadata, convertRTMinute = args$convertRTMinute) + RTinMinute <- TRUE + variableMetadata <- RTSecondToMinute(variableMetadata = variableMetadata, convertRTMinute = args$convertRTMinute) } # Saves the extracted peak list as a TSV file named 'variableMetadata.tsv' diff --git a/tools/camera/CAMERA_groupCorr.R b/tools/camera/CAMERA_groupCorr.R index f0180f581a..951ae59a98 100755 --- a/tools/camera/CAMERA_groupCorr.R +++ b/tools/camera/CAMERA_groupCorr.R @@ -5,9 +5,9 @@ cat("\tSESSION INFO\n") # Import the different functions source_local <- function(fname) { - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep = "/")) + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) } source_local("lib.r") @@ -27,34 +27,34 @@ print(args) # Function to convert "NULL" strings to actual NULL values convertNullString <- function(x) { - if (x == "NULL") { - return(NULL) - } - return(x) + if (x == "NULL") { + return(NULL) + } + return(x) } # Function to convert string to numeric lists convert_psg_list <- function(x) { - # Check if x is NULL or has zero length before further processing - if (is.null(x) || length(x) == 0) { - return(NULL) - } - - # Force conversion to character - x <- as.character(x) - - if (grepl("^[0-9]+$", x)) { - # If the string represents a single numeric value - return(as.numeric(x)) - } else { - # Convert string representation of a list to a numeric vector - # Use a regular expression to split by common separators - return(as.numeric(unlist(strsplit(x, "[,;\\s]+")))) - } + # Check if x is NULL or has zero length before further processing + if (is.null(x) || length(x) == 0) { + return(NULL) + } + + # Force conversion to character + x <- as.character(x) + + if (grepl("^[0-9]+$", x)) { + # If the string represents a single numeric value + return(as.numeric(x)) + } else { + # Convert string representation of a list to a numeric vector + # Use a regular expression to split by common separators + return(as.numeric(unlist(strsplit(x, "[,;\\s]+")))) + } } for (arg in names(args)) { - args[[arg]] <- convertNullString(args[[arg]]) + args[[arg]] <- convertNullString(args[[arg]]) } # Convert only the 'psg_list' element in args @@ -65,7 +65,7 @@ print(sapply(args, class)) # Check if the image file exists if (!file.exists(args$image)) { - stop("The RData file does not exist: ", args$image) + stop("The RData file does not exist: ", args$image) } # ----- PROCESSING INFILE ----- @@ -88,12 +88,12 @@ args <- rawFilePath$args print(paste("singlefile :", singlefile)) if (!is.null(singlefile)) { - directory <- retrieveRawfileInTheWorkingDir(singlefile, zipfile) + directory <- retrieveRawfileInTheWorkingDir(singlefile, zipfile) } # Ensure the xa object is loaded if (!exists("xa")) { - stop("The xa object was not found in the RData file.") + stop("The xa object was not found in the RData file.") } print("xa object loaded:") @@ -110,26 +110,26 @@ print(xa) peakList <- getPeaklist(xa, intval = args$intval) if (length(phenoData@data$sample_name) == 1) { - peakList$name <- make.unique(paste0("M", round(peakList[, "mz"], 0), "T", round(peakList[, "rt"], 0)), "_") - variableMetadata <- peakList[, c("name", setdiff(names(peakList), "name"))] - variableMetadata <- formatIonIdentifiers(variableMetadata, numDigitsRT = args$numDigitsRT, numDigitsMZ = args$numDigitsMZ) + peakList$name <- make.unique(paste0("M", round(peakList[, "mz"], 0), "T", round(peakList[, "rt"], 0)), "_") + variableMetadata <- peakList[, c("name", setdiff(names(peakList), "name"))] + variableMetadata <- formatIonIdentifiers(variableMetadata, numDigitsRT = args$numDigitsRT, numDigitsMZ = args$numDigitsMZ) } else { - names_default <- groupnames(xa@xcmsSet, mzdec = 0, rtdec = 0) # Names without decimals - names_custom <- groupnames(xa@xcmsSet, mzdec = args$numDigitsMZ, rtdec = args$numDigitsRT) # Names with "x" decimals - - variableMetadata <- data.frame( - name = names_default, - name_custom = names_custom, - stringsAsFactors = FALSE - ) - variableMetadata <- cbind(variableMetadata, peakList[, !(make.names(colnames(peakList)) %in% c(make.names(sampnames(xa@xcmsSet))))]) + names_default <- groupnames(xa@xcmsSet, mzdec = 0, rtdec = 0) # Names without decimals + names_custom <- groupnames(xa@xcmsSet, mzdec = args$numDigitsMZ, rtdec = args$numDigitsRT) # Names with "x" decimals + + variableMetadata <- data.frame( + name = names_default, + name_custom = names_custom, + stringsAsFactors = FALSE + ) + variableMetadata <- cbind(variableMetadata, peakList[, !(make.names(colnames(peakList)) %in% c(make.names(sampnames(xa@xcmsSet))))]) } if (!exists("RTinMinute")) RTinMinute <- FALSE if (args$convertRTMinute == TRUE && RTinMinute == FALSE) { - RTinMinute <- TRUE - variableMetadata <- RTSecondToMinute(variableMetadata = variableMetadata, convertRTMinute = args$convertRTMinute) + RTinMinute <- TRUE + variableMetadata <- RTSecondToMinute(variableMetadata = variableMetadata, convertRTMinute = args$convertRTMinute) } # Save the extracted peak list as a TSV file named 'variableMetadata.tsv' diff --git a/tools/camera/CAMERA_groupFWHM.R b/tools/camera/CAMERA_groupFWHM.R index 6df53f4477..d17901f429 100755 --- a/tools/camera/CAMERA_groupFWHM.R +++ b/tools/camera/CAMERA_groupFWHM.R @@ -5,9 +5,9 @@ cat("\tSESSION INFO\n") # Import the different functions source_local <- function(fname) { - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep = "/")) + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) } source_local("lib.r") @@ -27,19 +27,19 @@ print(args) # Function to convert "NA" strings to actual NA values and string lists to numeric lists convertStringToNumeric <- function(x) { - # Force conversion to character - x <- as.character(x) - - if (x == "NA") { - return(NA) - } else if (grepl("^[0-9]+$", x)) { - # If the string represents a single numeric value - return(as.numeric(x)) - } else { - # Convert string representation of a list to a numeric vector - # Use a regular expression to split by common separators - return(as.numeric(unlist(strsplit(x, "[,;\\s]+")))) - } + # Force conversion to character + x <- as.character(x) + + if (x == "NA") { + return(NA) + } else if (grepl("^[0-9]+$", x)) { + # If the string represents a single numeric value + return(as.numeric(x)) + } else { + # Convert string representation of a list to a numeric vector + # Use a regular expression to split by common separators + return(as.numeric(unlist(strsplit(x, "[,;\\s]+")))) + } } # Convert only the 'sample' element in args @@ -50,7 +50,7 @@ print(sapply(args, class)) # Check if the image file exists if (!file.exists(args$image)) { - stop("The RData file does not exist: ", args$image) + stop("The RData file does not exist: ", args$image) } # ----- PROCESSING INFILE ----- @@ -73,22 +73,22 @@ args <- rawFilePath$args print(paste("singlefile :", singlefile)) if (!is.null(singlefile)) { - directory <- retrieveRawfileInTheWorkingDir(singlefile, zipfile) + directory <- retrieveRawfileInTheWorkingDir(singlefile, zipfile) } # If the xdata object exists, convert it to xcmsSet if (exists("xdata")) { - phenoData <- xdata@phenoData - xset <- getxcmsSetObject(xdata) + phenoData <- xdata@phenoData + xset <- getxcmsSetObject(xdata) } if (!exists("xdata")) stop("\n\nERROR: The RData doesn't contain any object called 'xdata'. This RData should have been created by an old version of XMCS 2.*") # Verification of a group step before doing the fillpeaks job. if (dim(xdata@phenoData@data)[1] > 1) { - if (!hasFeatures(xdata)) stop("You must always do a group step after a retcor. Otherwise it won't work for the groupFWHM step") + if (!hasFeatures(xdata)) stop("You must always do a group step after a retcor. Otherwise it won't work for the groupFWHM step") } else { - print("Only one file in the phenoData keep xset as is") + print("Only one file in the phenoData keep xset as is") } # Convert the xset object to xsAnnotate using CAMERA @@ -108,26 +108,26 @@ xa <- groupFWHM(xsa, sigma = as.numeric(args$sigma), perfwhm = as.numeric(args$p peakList <- getPeaklist(xa, intval = args$intval) if (length(phenoData@data$sample_name) == 1) { - peakList$name <- make.unique(paste0("M", round(peakList[, "mz"], 0), "T", round(peakList[, "rt"], 0)), "_") - variableMetadata <- peakList[, c("name", setdiff(names(peakList), "name"))] - variableMetadata <- formatIonIdentifiers(variableMetadata, numDigitsRT = args$numDigitsRT, numDigitsMZ = args$numDigitsMZ) + peakList$name <- make.unique(paste0("M", round(peakList[, "mz"], 0), "T", round(peakList[, "rt"], 0)), "_") + variableMetadata <- peakList[, c("name", setdiff(names(peakList), "name"))] + variableMetadata <- formatIonIdentifiers(variableMetadata, numDigitsRT = args$numDigitsRT, numDigitsMZ = args$numDigitsMZ) } else { - names_default <- groupnames(xa@xcmsSet, mzdec = 0, rtdec = 0) # Names without decimals - names_custom <- groupnames(xa@xcmsSet, mzdec = args$numDigitsMZ, rtdec = args$numDigitsRT) # Names with "x" decimals - - variableMetadata <- data.frame( - name = names_default, - name_custom = names_custom, - stringsAsFactors = FALSE - ) - variableMetadata <- cbind(variableMetadata, peakList[, !(make.names(colnames(peakList)) %in% c(make.names(sampnames(xa@xcmsSet))))]) + names_default <- groupnames(xa@xcmsSet, mzdec = 0, rtdec = 0) # Names without decimals + names_custom <- groupnames(xa@xcmsSet, mzdec = args$numDigitsMZ, rtdec = args$numDigitsRT) # Names with "x" decimals + + variableMetadata <- data.frame( + name = names_default, + name_custom = names_custom, + stringsAsFactors = FALSE + ) + variableMetadata <- cbind(variableMetadata, peakList[, !(make.names(colnames(peakList)) %in% c(make.names(sampnames(xa@xcmsSet))))]) } if (!exists("RTinMinute")) RTinMinute <- FALSE if (args$convertRTMinute && RTinMinute == FALSE) { - RTinMinute <- TRUE - variableMetadata <- RTSecondToMinute(variableMetadata = variableMetadata, convertRTMinute = args$convertRTMinute) + RTinMinute <- TRUE + variableMetadata <- RTSecondToMinute(variableMetadata = variableMetadata, convertRTMinute = args$convertRTMinute) } # Save the peak list to a TSV file diff --git a/tools/camera/abims_CAMERA_annotateDiffreport.xml b/tools/camera/abims_CAMERA_annotateDiffreport.xml index dd328e1100..17d5cb1645 100644 --- a/tools/camera/abims_CAMERA_annotateDiffreport.xml +++ b/tools/camera/abims_CAMERA_annotateDiffreport.xml @@ -7,10 +7,14 @@ + + + + + + + + = 2) { # Check if the fillpeaks step has been done previously, if it hasn't, there is an error message and the execution is stopped. res <- try(is.null(xset@filled)) - classes <- levels(sampclass(xset)) - for (i in seq_len(length(classes) - 1)) { - for (n in seq_len(length(classes))) { - if (i + n <= length(classes)) { - filebase <- paste(classes[i], class2 = classes[i + n], sep = "-vs-") - - diffrep <- diffreport( - object = xset, class1 = classes[i], class2 = classes[i + n], - filebase = filebase, eicmax = args$eicmax, eicwidth = args$eicwidth, - sortpval = TRUE, value = args$value, h = args$h, w = args$w, mzdec = args$mzdec, missing = 0 - ) - - diffrepOri <- diffrep - # renamming of the column rtmed to rt to fit with camera peaklist function output - colnames(diffrep)[colnames(diffrep) == "rtmed"] <- "rt" - colnames(diffrep)[colnames(diffrep) == "mzmed"] <- "mz" - - # combines results and reorder columns - diffrep <- merge(peakList, diffrep[, c("name", "fold", "tstat", "pvalue")], by.x = "name", by.y = "name", sort = FALSE) - diffrep <- cbind(diffrep[, !(colnames(diffrep) %in% c(sampnames(xa@xcmsSet)))], diffrep[, (colnames(diffrep) %in% c(sampnames(xa@xcmsSet)))]) + # ------ annot ------- + args$calcCiS <- as.logical(args$calcCiS) + args$calcIso <- as.logical(args$calcIso) + args$calcCaS <- as.logical(args$calcCaS) + + # common parameters + args4annotate <- list( + object = xset, + nSlaves = args$nSlaves, sigma = args$sigma, perfwhm = args$perfwhm, + maxcharge = args$maxcharge, maxiso = args$maxiso, minfrac = args$minfrac, + ppm = args$ppm, mzabs = args$mzabs, quick = args$quick, + polarity = args$polarity, max_peaks = args$max_peaks, intval = args$intval + ) - diffrep <- RTSecondToMinute(diffrep, args$convertRTMinute) - diffrep <- formatIonIdentifiers(diffrep, numDigitsRT = args$numDigitsRT, numDigitsMZ = args$numDigitsMZ) + if (args$quick == FALSE) { + args4annotate <- append( + args4annotate, + list( + graphMethod = args$graphMethod, cor_eic_th = args$cor_eic_th, pval = args$pval, + calcCiS = args$calcCiS, calcIso = args$calcIso, calcCaS = args$calcCaS + ) + ) + # no ruleset + if (!is.null(args$multiplier)) { + args4annotate <- append( + args4annotate, + list(multiplier = args$multiplier) + ) + } else { # ruleset + rulset <- read.table(args$rules, h = TRUE, sep = ";") + if (ncol(rulset) < 4) rulset <- read.table(args$rules, h = TRUE, sep = "\t") + if (ncol(rulset) < 4) rulset <- read.table(args$rules, h = TRUE, sep = ",") + if (ncol(rulset) < 4) { + error_message <- "Your ruleset file seems not well formatted. The column separators accepted are ; , and tabulation" + print(error_message) + stop(error_message) + } - if (args$sortpval) { - diffrep <- diffrep[order(diffrep$pvalue), ] - } + args4annotate <- append( + args4annotate, + list(rules = rulset) + ) + } + } - dir.create("tabular", showWarnings = FALSE) - write.table(diffrep, sep = "\t", quote = FALSE, row.names = FALSE, file = paste("tabular/", filebase, "_tsv.tabular", sep = "")) - if (args$eicmax != 0) { - if (args$png2 == "pdf") { - diffreport_png2pdf(filebase) - } - if (args$png2 == "zip") { - diffreport_png2zip() + # launch annotate + xa <- do.call("annotate", args4annotate) + peakList <- getPeaklist(xa, intval = args$intval) + peakList <- cbind(groupnames(xa@xcmsSet), peakList) + colnames(peakList)[1] <- c("name") + + # --- Multi condition : diffreport --- + diffrepOri <- NULL + if (!is.null(args$runDiffreport) && nlevels(sampclass(xset)) >= 2) { + # Check if the fillpeaks step has been done previously, if it hasn't, there is an error message and the execution is stopped. + res <- try(is.null(xset@filled)) + classes <- levels(sampclass(xset)) + for (i in seq_len(length(classes) - 1)) { + for (n in seq_len(length(classes))) { + if (i + n <= length(classes)) { + filebase <- paste(classes[i], class2 = classes[i + n], sep = "-vs-") + + diffrep <- diffreport( + object = xset, class1 = classes[i], class2 = classes[i + n], + filebase = filebase, eicmax = args$eicmax, eicwidth = args$eicwidth, + sortpval = TRUE, value = args$value, h = args$h, w = args$w, mzdec = args$mzdec, missing = 0 + ) + + diffrepOri <- diffrep + + # renamming of the column rtmed to rt to fit with camera peaklist function output + colnames(diffrep)[colnames(diffrep) == "rtmed"] <- "rt" + colnames(diffrep)[colnames(diffrep) == "mzmed"] <- "mz" + + # combines results and reorder columns + diffrep <- merge(peakList, diffrep[, c("name", "fold", "tstat", "pvalue")], by.x = "name", by.y = "name", sort = FALSE) + diffrep <- cbind(diffrep[, !(colnames(diffrep) %in% c(sampnames(xa@xcmsSet)))], diffrep[, (colnames(diffrep) %in% c(sampnames(xa@xcmsSet)))]) + + diffrep <- RTSecondToMinute(diffrep, args$convertRTMinute) + diffrep <- formatIonIdentifiers(diffrep, numDigitsRT = args$numDigitsRT, numDigitsMZ = args$numDigitsMZ) + + if (args$sortpval) { + diffrep <- diffrep[order(diffrep$pvalue), ] + } + + dir.create("tabular", showWarnings = FALSE) + write.table(diffrep, sep = "\t", quote = FALSE, row.names = FALSE, file = paste("tabular/", filebase, "_tsv.tabular", sep = "")) + + if (args$eicmax != 0) { + if (args$png2 == "pdf") { + diffreport_png2pdf(filebase) + } + if (args$png2 == "zip") { + diffreport_png2zip() + } + } + } } - } } - } - } - if (args$tabular2 == "zip") { - diffreport_tabular2zip() + if (args$tabular2 == "zip") { + diffreport_tabular2zip() + } } - } - - # --- variableMetadata --- - variableMetadata <- peakList[, !(make.names(colnames(peakList)) %in% c(make.names(sampnames(xa@xcmsSet))))] - variableMetadata <- RTSecondToMinute(variableMetadata, args$convertRTMinute) - variableMetadata <- formatIonIdentifiers(variableMetadata, numDigitsRT = args$numDigitsRT, numDigitsMZ = args$numDigitsMZ) - # if we have 2 conditions, we keep stat of diffrep - if (!is.null(args$runDiffreport) && nlevels(sampclass(xset)) == 2) { - variableMetadata <- merge(variableMetadata, diffrep[, c("name", "fold", "tstat", "pvalue")], by.x = "name", by.y = "name", sort = FALSE) - if (exists("args[[\"sortpval\"]]")) { - variableMetadata <- variableMetadata[order(variableMetadata$pvalue), ] + + # --- variableMetadata --- + variableMetadata <- peakList[, !(make.names(colnames(peakList)) %in% c(make.names(sampnames(xa@xcmsSet))))] + variableMetadata <- RTSecondToMinute(variableMetadata, args$convertRTMinute) + variableMetadata <- formatIonIdentifiers(variableMetadata, numDigitsRT = args$numDigitsRT, numDigitsMZ = args$numDigitsMZ) + # if we have 2 conditions, we keep stat of diffrep + if (!is.null(args$runDiffreport) && nlevels(sampclass(xset)) == 2) { + variableMetadata <- merge(variableMetadata, diffrep[, c("name", "fold", "tstat", "pvalue")], by.x = "name", by.y = "name", sort = FALSE) + if (exists("args[[\"sortpval\"]]")) { + variableMetadata <- variableMetadata[order(variableMetadata$pvalue), ] + } } - } - variableMetadataOri <- variableMetadata - write.table(variableMetadata, sep = "\t", quote = FALSE, row.names = FALSE, file = variableMetadataOutput) + variableMetadataOri <- variableMetadata + write.table(variableMetadata, sep = "\t", quote = FALSE, row.names = FALSE, file = variableMetadataOutput) - return(list("xa" = xa, "diffrep" = diffrepOri, "variableMetadata" = variableMetadataOri)) + return(list("xa" = xa, "diffrep" = diffrepOri, "variableMetadata" = variableMetadataOri)) } @@ -246,108 +246,108 @@ combinexsAnnos_function <- function( xaP, xaN, diffrepP = NULL, diffrepN = NULL, pos = TRUE, tol = 2, ruleset = NULL, keep_meta = TRUE, convertRTMinute = FALSE, numDigitsMZ = 0, numDigitsRT = 0, variableMetadataOutput = "variableMetadata.tsv") { - # Load the two Rdata to extract the xset objects from positive and negative mode - cat("\tObject xset from positive mode\n") - print(xaP) - cat("\n") - - cat("\tObject xset from negative mode\n") - print(xaN) - cat("\n") - - cat("\n") - cat("\tCombining...\n") - # Convert the string to numeric for creating matrix - row <- as.numeric(strsplit(ruleset, ",")[[1]][1]) - column <- as.numeric(strsplit(ruleset, ",")[[1]][2]) - ruleset <- cbind(row, column) - # Test if the file comes from an older version tool - if ((!is.null(xaP)) && (!is.null(xaN))) { - # Launch the combinexsannos function from CAMERA - cAnnot <- combinexsAnnos(xaP, xaN, pos = pos, tol = tol, ruleset = ruleset) - } else { - stop("You must relauch the CAMERA.annotate step with the lastest version.") - } - - if (pos) { - xa <- xaP - mode <- "neg. Mode" - } else { - xa <- xaN - mode <- "pos. Mode" - } - - peakList <- getPeaklist(xa) - peakList <- cbind(groupnames(xa@xcmsSet), peakList) - colnames(peakList)[1] <- c("name") - variableMetadata <- cbind(peakList, cAnnot[, c("isotopes", "adduct", "pcgroup", mode)]) - variableMetadata <- variableMetadata[, !(colnames(variableMetadata) %in% c(sampnames(xa@xcmsSet)))] - - # Test if there are more than two classes (conditions) - if (nlevels(sampclass(xaP@xcmsSet)) == 2 && (!is.null(diffrepN)) && (!is.null(diffrepP))) { - diffrepP <- diffrepP[, c("name", "fold", "tstat", "pvalue")] - colnames(diffrepP) <- paste("P.", colnames(diffrepP), sep = "") - diffrepN <- diffrepN[, c("name", "fold", "tstat", "pvalue")] - colnames(diffrepN) <- paste("N.", colnames(diffrepN), sep = "") - - variableMetadata <- merge(variableMetadata, diffrepP, by.x = "name", by.y = "P.name") - variableMetadata <- merge(variableMetadata, diffrepN, by.x = "name", by.y = "N.name") - } - - rownames(variableMetadata) <- NULL - # TODO: checker colnames(variableMetadata)[1:2] = c("name", "mz/rt"); - - variableMetadata <- RTSecondToMinute(variableMetadata, convertRTMinute) - variableMetadata <- formatIonIdentifiers(variableMetadata, numDigitsRT = numDigitsRT, numDigitsMZ = numDigitsMZ) - - # If the user want to keep only the metabolites which match a difference - if (keep_meta) { - variableMetadata <- variableMetadata[variableMetadata[, c(mode)] != "", ] - } - - # Write the output into a tsv file - write.table(variableMetadata, sep = "\t", quote = FALSE, row.names = FALSE, file = variableMetadataOutput) - return(variableMetadata) + # Load the two Rdata to extract the xset objects from positive and negative mode + cat("\tObject xset from positive mode\n") + print(xaP) + cat("\n") + + cat("\tObject xset from negative mode\n") + print(xaN) + cat("\n") + + cat("\n") + cat("\tCombining...\n") + # Convert the string to numeric for creating matrix + row <- as.numeric(strsplit(ruleset, ",")[[1]][1]) + column <- as.numeric(strsplit(ruleset, ",")[[1]][2]) + ruleset <- cbind(row, column) + # Test if the file comes from an older version tool + if ((!is.null(xaP)) && (!is.null(xaN))) { + # Launch the combinexsannos function from CAMERA + cAnnot <- combinexsAnnos(xaP, xaN, pos = pos, tol = tol, ruleset = ruleset) + } else { + stop("You must relauch the CAMERA.annotate step with the lastest version.") + } + + if (pos) { + xa <- xaP + mode <- "neg. Mode" + } else { + xa <- xaN + mode <- "pos. Mode" + } + + peakList <- getPeaklist(xa) + peakList <- cbind(groupnames(xa@xcmsSet), peakList) + colnames(peakList)[1] <- c("name") + variableMetadata <- cbind(peakList, cAnnot[, c("isotopes", "adduct", "pcgroup", mode)]) + variableMetadata <- variableMetadata[, !(colnames(variableMetadata) %in% c(sampnames(xa@xcmsSet)))] + + # Test if there are more than two classes (conditions) + if (nlevels(sampclass(xaP@xcmsSet)) == 2 && (!is.null(diffrepN)) && (!is.null(diffrepP))) { + diffrepP <- diffrepP[, c("name", "fold", "tstat", "pvalue")] + colnames(diffrepP) <- paste("P.", colnames(diffrepP), sep = "") + diffrepN <- diffrepN[, c("name", "fold", "tstat", "pvalue")] + colnames(diffrepN) <- paste("N.", colnames(diffrepN), sep = "") + + variableMetadata <- merge(variableMetadata, diffrepP, by.x = "name", by.y = "P.name") + variableMetadata <- merge(variableMetadata, diffrepN, by.x = "name", by.y = "N.name") + } + + rownames(variableMetadata) <- NULL + # TODO: checker colnames(variableMetadata)[1:2] = c("name", "mz/rt"); + + variableMetadata <- RTSecondToMinute(variableMetadata, convertRTMinute) + variableMetadata <- formatIonIdentifiers(variableMetadata, numDigitsRT = numDigitsRT, numDigitsMZ = numDigitsMZ) + + # If the user want to keep only the metabolites which match a difference + if (keep_meta) { + variableMetadata <- variableMetadata[variableMetadata[, c(mode)] != "", ] + } + + # Write the output into a tsv file + write.table(variableMetadata, sep = "\t", quote = FALSE, row.names = FALSE, file = variableMetadataOutput) + return(variableMetadata) } # This function get the raw file path from the arguments getRawfilePathFromArguments <- function(singlefile, zipfile, args) { - if (!is.null(args$zipfile)) zipfile <- args$zipfile - if (!is.null(args$zipfilePositive)) zipfile <- args$zipfilePositive - if (!is.null(args$zipfileNegative)) zipfile <- args$zipfileNegative - - if (!is.null(args$singlefile_galaxyPath)) { - singlefile_galaxyPaths <- args$singlefile_galaxyPath - singlefile_sampleNames <- args$singlefile_sampleName - } - if (!is.null(args$singlefile_galaxyPathPositive)) { - singlefile_galaxyPaths <- args$singlefile_galaxyPathPositive - singlefile_sampleNames <- args$singlefile_sampleNamePositive - } - if (!is.null(args$singlefile_galaxyPathNegative)) { - singlefile_galaxyPaths <- args$singlefile_galaxyPathNegative - singlefile_sampleNames <- args$singlefile_sampleNameNegative - } - if (exists("singlefile_galaxyPaths")) { - singlefile_galaxyPaths <- unlist(strsplit(singlefile_galaxyPaths, ",")) - singlefile_sampleNames <- unlist(strsplit(singlefile_sampleNames, ",")) - - singlefile <- NULL - for (singlefile_galaxyPath_i in seq_len(length(singlefile_galaxyPaths))) { - singlefile_galaxyPath <- singlefile_galaxyPaths[singlefile_galaxyPath_i] - singlefile_sampleName <- singlefile_sampleNames[singlefile_galaxyPath_i] - singlefile[[singlefile_sampleName]] <- singlefile_galaxyPath + if (!is.null(args$zipfile)) zipfile <- args$zipfile + if (!is.null(args$zipfilePositive)) zipfile <- args$zipfilePositive + if (!is.null(args$zipfileNegative)) zipfile <- args$zipfileNegative + + if (!is.null(args$singlefile_galaxyPath)) { + singlefile_galaxyPaths <- args$singlefile_galaxyPath + singlefile_sampleNames <- args$singlefile_sampleName + } + if (!is.null(args$singlefile_galaxyPathPositive)) { + singlefile_galaxyPaths <- args$singlefile_galaxyPathPositive + singlefile_sampleNames <- args$singlefile_sampleNamePositive + } + if (!is.null(args$singlefile_galaxyPathNegative)) { + singlefile_galaxyPaths <- args$singlefile_galaxyPathNegative + singlefile_sampleNames <- args$singlefile_sampleNameNegative + } + if (exists("singlefile_galaxyPaths")) { + singlefile_galaxyPaths <- unlist(strsplit(singlefile_galaxyPaths, ",")) + singlefile_sampleNames <- unlist(strsplit(singlefile_sampleNames, ",")) + + singlefile <- NULL + for (singlefile_galaxyPath_i in seq_len(length(singlefile_galaxyPaths))) { + singlefile_galaxyPath <- singlefile_galaxyPaths[singlefile_galaxyPath_i] + singlefile_sampleName <- singlefile_sampleNames[singlefile_galaxyPath_i] + singlefile[[singlefile_sampleName]] <- singlefile_galaxyPath + } + } + for (argument in c( + "zipfile", "zipfilePositive", "zipfileNegative", + "singlefile_galaxyPath", "singlefile_sampleName", + "singlefile_galaxyPathPositive", "singlefile_sampleNamePositive", + "singlefile_galaxyPathNegative", "singlefile_sampleNameNegative" + )) { + args[[argument]] <- NULL } - } - for (argument in c( - "zipfile", "zipfilePositive", "zipfileNegative", - "singlefile_galaxyPath", "singlefile_sampleName", - "singlefile_galaxyPathPositive", "singlefile_sampleNamePositive", - "singlefile_galaxyPathNegative", "singlefile_sampleNameNegative" - )) { - args[[argument]] <- NULL - } - return(list(zipfile = zipfile, singlefile = singlefile, args = args)) + return(list(zipfile = zipfile, singlefile = singlefile, args = args)) } @@ -355,37 +355,37 @@ getRawfilePathFromArguments <- function(singlefile, zipfile, args) { # - if zipfile: unzip the file with its directory tree # - if singlefiles: set symlink with the good filename retrieveRawfileInTheWorkingDir <- function(singlefile, zipfile) { - if (!is.null(singlefile) && (length("singlefile") > 0)) { - for (singlefile_sampleName in names(singlefile)) { - singlefile_galaxyPath <- singlefile[[singlefile_sampleName]] - if (!file.exists(singlefile_galaxyPath)) { - error_message <- paste("Cannot access the sample:", singlefile_sampleName, "located:", singlefile_galaxyPath, ". Please, contact your administrator ... if you have one!") - print(error_message) - stop(error_message) - } - - file.symlink(singlefile_galaxyPath, singlefile_sampleName) - } - directory <- "." - } - if (!is.null(zipfile) && (zipfile != "")) { - if (!file.exists(zipfile)) { - error_message <- paste("Cannot access the Zip file:", zipfile, ". Please, contact your administrator ... if you have one!") - print(error_message) - stop(error_message) + if (!is.null(singlefile) && (length("singlefile") > 0)) { + for (singlefile_sampleName in names(singlefile)) { + singlefile_galaxyPath <- singlefile[[singlefile_sampleName]] + if (!file.exists(singlefile_galaxyPath)) { + error_message <- paste("Cannot access the sample:", singlefile_sampleName, "located:", singlefile_galaxyPath, ". Please, contact your administrator ... if you have one!") + print(error_message) + stop(error_message) + } + + file.symlink(singlefile_galaxyPath, singlefile_sampleName) + } + directory <- "." } + if (!is.null(zipfile) && (zipfile != "")) { + if (!file.exists(zipfile)) { + error_message <- paste("Cannot access the Zip file:", zipfile, ". Please, contact your administrator ... if you have one!") + print(error_message) + stop(error_message) + } - # unzip - suppressWarnings(unzip(zipfile, unzip = "unzip")) + # unzip + suppressWarnings(unzip(zipfile, unzip = "unzip")) - # get the directory name - filesInZip <- unzip(zipfile, list = TRUE) - directories <- unique(unlist(lapply(strsplit(filesInZip$Name, "/"), function(x) x[1]))) - directories <- directories[!(directories %in% c("__MACOSX")) & file.info(directories)$isdir] - directory <- "." - if (length(directories) == 1) directory <- directories + # get the directory name + filesInZip <- unzip(zipfile, list = TRUE) + directories <- unique(unlist(lapply(strsplit(filesInZip$Name, "/"), function(x) x[1]))) + directories <- directories[!(directories %in% c("__MACOSX")) & file.info(directories)$isdir] + directory <- "." + if (length(directories) == 1) directory <- directories - cat("files_root_directory\t", directory, "\n") - } - return(directory) + cat("files_root_directory\t", directory, "\n") + } + return(directory) } diff --git a/tools/camera/macros.xml b/tools/camera/macros.xml index 92ed1580e7..b63fb1d122 100644 --- a/tools/camera/macros.xml +++ b/tools/camera/macros.xml @@ -1,7 +1,7 @@ 1.48.0 - 1 + 2 r-snow diff --git a/tools/correlation_analysis/abims_correlation_analysis.xml b/tools/correlation_analysis/abims_correlation_analysis.xml index 1c01862fad..62eb4be4af 100644 --- a/tools/correlation_analysis/abims_correlation_analysis.xml +++ b/tools/correlation_analysis/abims_correlation_analysis.xml @@ -1,4 +1,4 @@ - + to highlight ion correlations considering PC-groups @@ -7,6 +7,9 @@ r-reshape r-mass + + + Rscript '$__tool_directory__/correlation_analysis.r' @@ -106,7 +109,7 @@ (cond_input_type['select_input_type'] == 'select_input_other') - + (cond_input_type['select_input_type'] == 'select_input_other') diff --git a/tools/correlation_analysis/correlation_analysis.r b/tools/correlation_analysis/correlation_analysis.r index 013de27b3c..ac5b62ddaf 100644 --- a/tools/correlation_analysis/correlation_analysis.r +++ b/tools/correlation_analysis/correlation_analysis.r @@ -1,46 +1,45 @@ #!/usr/local/public/bin/Rscript --vanilla --slave --no-site-file -#For questions: Antoine Gravot (Protocole conception) and Misharl Monsoor (for galaxy wrapper and R script). +# For questions: Antoine Gravot (Protocole conception) and Misharl Monsoor (for galaxy wrapper and R script). -#Load the different libraries -library(batch) #necessary for parseCommandArgs function -library(reshape) #necessary for using melt function +# Load the different libraries +library(batch) # necessary for parseCommandArgs function +library(reshape) # necessary for using melt function library(MASS) # necessary for using the write.matrix() -#interpretation of arguments given in command line as an R list of objects +# interpretation of arguments given in command line as an R list of objects list_arguments <- parseCommandArgs(evaluate = FALSE) -cat("\nJob starting time:\n", format(Sys.time(), "%a %d %b %Y %X"), +cat( + "\nJob starting time:\n", format(Sys.time(), "%a %d %b %Y %X"), "\n\n--------------------------------------------------------------------", - "\nParameters used in 'Metabolites Correlation Analysis':\n\n") + "\nParameters used in 'Metabolites Correlation Analysis':\n\n" +) print(list_arguments) cat("--------------------------------------------------------------------\n\n") -#The main function of this script that will execute all the other functions +# The main function of this script that will execute all the other functions main_function <- function(sorting, variable_metadata, data_matrix, sample_metadata, corrdel, param_correlation, param_cytoscape, matrix_corr, user_matrix_corr, corr_method) { - - if (sorting == 1) { - ####Executing the sorting function#### + #### Executing the sorting function#### cat("\nExecuting the sorting function\n") - #Read the tsv annotateDiffreport file and don't modify the columns name (check.names = FALSE) + # Read the tsv annotateDiffreport file and don't modify the columns name (check.names = FALSE) variable_metadata_input <- read.csv(variable_metadata, header = T, sep = "\t", dec = ".", check.names = FALSE) data_matrix_input <- read.csv(data_matrix, header = T, sep = "\t", dec = ".", check.names = FALSE) first_column_variable <- toString(names(variable_metadata_input)[1]) first_column_datamatrix <- toString(names(data_matrix_input)[1]) - #@TODO merge + # @TODO merge input_tsv <- cbind(variable_metadata_input, data_matrix_input[, !(colnames(data_matrix_input) %in% c(first_column_datamatrix))]) - #Load the sample.info from the xcmsSet + # Load the sample.info from the xcmsSet sample_metadata_info_tsv <- read.table(sample_metadata, header = T, sep = "\t", dec = ",", check.names = FALSE) - #Extract the samples name from the sample.info file generated from the xcmsSet step in ABIMS Workflow4Metabo. + # Extract the samples name from the sample.info file generated from the xcmsSet step in ABIMS Workflow4Metabo. samples_name <- as.vector(t(sample_metadata_info_tsv[1])) output_tsv <- sorting(input_tsv, samples_name) # output now if corrdel == 0 if (corrdel == 0) { - output_tsv_vm <- output_tsv[, which(!(colnames(output_tsv) %in% samples_name))] - write.table(output_tsv_vm[, c(3:ncol(output_tsv_vm), 2, 1)], sep = "\t", quote = FALSE, col.names = TRUE, row.names = FALSE, file = "sorted_table.tsv") + output_tsv_vm <- output_tsv[, which(!(colnames(output_tsv) %in% samples_name))] + write.table(output_tsv_vm[, c(3:ncol(output_tsv_vm), 2, 1)], sep = "\t", quote = FALSE, col.names = TRUE, row.names = FALSE, file = "sorted_table.tsv") } - } if (corrdel == 1) { cat("\nExecuting the corr_matrix_del function\n") @@ -50,84 +49,80 @@ main_function <- function(sorting, variable_metadata, data_matrix, sample_metada if (matrix_corr == 1) { cat("\nExecuting the corr_matrix function\n") corr_matrix_user(user_matrix_corr, param_cytoscape, corr_method) - } - } -#The sorting function will sort the dataframe by rt column. -#Then it creates a "signal_moy" column which contains the mean values of the signal values of the sample by row. -#It finally creates a table tsv format "sorted_table.tsv". +# The sorting function will sort the dataframe by rt column. +# Then it creates a "signal_moy" column which contains the mean values of the signal values of the sample by row. +# It finally creates a table tsv format "sorted_table.tsv". sorting <- function(input_tsv, samples_name) { - - #Sort by rt column + # Sort by rt column new_input <- input_tsv[order(input_tsv$rt), ] - #Compute the mean operation of all the signal values of the sample by row, and put the results in a new column "signal_moy" + # Compute the mean operation of all the signal values of the sample by row, and put the results in a new column "signal_moy" new_input["signal_moy"] <- data.frame(Means = rowMeans(new_input[, colnames(new_input) %in% samples_name])) - #Rearrange the data frame in order to have the columns "signal_moy" and "pcgroup" at the beginning of the table + # Rearrange the data frame in order to have the columns "signal_moy" and "pcgroup" at the beginning of the table new_input <- cbind(new_input["signal_moy"], new_input["pcgroup"], new_input[, !(colnames(new_input) %in% c("signal_moy", "pcgroup"))]) - #Sort the "signal_moy" column data frame by pcgroup + # Sort the "signal_moy" column data frame by pcgroup new_input <- new_input[order(new_input$pcgroup, -new_input$signal_moy), ] - #Write the data frame to a file named "sorted_table.tsv" - #Erase the rownames of the table + # Write the data frame to a file named "sorted_table.tsv" + # Erase the rownames of the table rownames(new_input) <- NULL - #Suppress rows which contains Nas + # Suppress rows which contains Nas new_input <- new_input[complete.cases(new_input$pcgroup), ] return(new_input) } corr_matrix_del <- function(output_tsv, samples_name, param_correlation, param_cytoscape, corr_method) { - - #statmatrix, a dataframe which contains only the columns "name" and the values of the intensity for all the samples + # statmatrix, a dataframe which contains only the columns "name" and the values of the intensity for all the samples first_column <- toString(names(output_tsv)[3]) statmatrix <- output_tsv[(names(output_tsv) %in% c(first_column, samples_name))] statmatrix2 <- output_tsv[(names(output_tsv) %in% c(first_column, "signal_moy", samples_name))] n <- statmatrix[, first_column] - #transpose all but the first column (name) + # transpose all but the first column (name) statmatrix <- as.data.frame(t(statmatrix[, -1])) - #Rename the columns of the dataframe "statmatrix" + # Rename the columns of the dataframe "statmatrix" colnames(statmatrix) <- n - #Transform dataframe to matrix before doing the cor function + # Transform dataframe to matrix before doing the cor function corr_transpo <- data.matrix(statmatrix) - #Create a matrix which contains only the data of the samples without the other columns + # Create a matrix which contains only the data of the samples without the other columns statmatrix <- output_tsv[(names(output_tsv) %in% c(samples_name))] - #Add the rownames previously + # Add the rownames previously rownames(statmatrix) <- make.unique(as.vector(output_tsv[, first_column])) - #Do the cor step, with the transposed statmatrix (metabolites as columns) + # Do the cor step, with the transposed statmatrix (metabolites as columns) corr_transpo <- cor(t(as.matrix(statmatrix)), method = corr_method) - #Add the columns "signal_moy", "pcgroup" and "name" to the final correlation matrix output + # Add the columns "signal_moy", "pcgroup" and "name" to the final correlation matrix output new_dataframe <- cbind(output_tsv["signal_moy"], output_tsv["pcgroup"], output_tsv[names(output_tsv)[3]], corr_transpo) - #Add a new column "suppress" which indicates if the metabolite will be removed from the analysis (because it is correlated with the other metabolite) + # Add a new column "suppress" which indicates if the metabolite will be removed from the analysis (because it is correlated with the other metabolite) new_dataframe["suppress"] <- "" - #Take the pcgroup names + # Take the pcgroup names pcgroups <- unique(new_dataframe$pcgroup) - #Remove NAs from the pcgroups vector + # Remove NAs from the pcgroups vector pcgroups <- pcgroups[which(pcgroups != "NA")] - #Creates a pcgroups list + # Creates a pcgroups list list_data <- vector(mode = "list", length = length(pcgroups)) - #Will do the following steps by pcgroup + # Will do the following steps by pcgroup for (pcgroup in pcgroups) { - #Select a subset data frame for each pcgroup + # Select a subset data frame for each pcgroup subset_data <- new_dataframe[new_dataframe$pcgroup == pcgroup, ] - #Stores the metabolites names for each pcgroup into a matrix one dimension. + # Stores the metabolites names for each pcgroup into a matrix one dimension. pc_group_elements <- t(as.matrix(subset_data[3])) - #uses the matrix "pc_group_elements" containing the metabolites to have the correlation data frame "subset_data2" by pcgroup + # uses the matrix "pc_group_elements" containing the metabolites to have the correlation data frame "subset_data2" by pcgroup subset_data2 <- subset_data[names(subset_data) %in% c(names(output_tsv)[3], pc_group_elements)] - #We keep the first metabolites for each pcgroup that correspond to the metabolites with the highest amount of signal + # We keep the first metabolites for each pcgroup that correspond to the metabolites with the highest amount of signal first_metabolite <- pc_group_elements[1] - #Remove the first metabolite from the pc_group_elements matrix + # Remove the first metabolite from the pc_group_elements matrix pc_group_elements <- pc_group_elements[, c(seq_len(length(pc_group_elements)))] - #print (pc_group_elements) + # print (pc_group_elements) for (metabolite in pc_group_elements) { metabolite_dataframe <- subset_data2[subset_data2[, first_column] == metabolite, ] - #retrives metabolite index from the vector object "pc_group_elements" + # retrives metabolite index from the vector object "pc_group_elements" index_metabolite <- as.numeric(which(pc_group_elements == metabolite, arr.ind = TRUE)) for (f in 2:index_metabolite - 1) { if (metabolite != first_metabolite) { value_intensity <- t(data.matrix(metabolite_dataframe[, pc_group_elements[f]])) value_intensity <- value_intensity[1:1] - #If one value is >0.75, it means that the metabolite est correlated with at least one previous metabolite in the table, so the boolean variable is set to true. + # If one value is >0.75, it means that the metabolite est correlated with at least one previous metabolite in the table, so the boolean variable is set to true. if (value_intensity > param_correlation) { new_dataframe$suppress[new_dataframe[, first_column] == metabolite] <- "DEL" } @@ -135,39 +130,39 @@ corr_matrix_del <- function(output_tsv, samples_name, param_correlation, param_c } } } - #The dataframe with the column "suppress" at the begginning + # The dataframe with the column "suppress" at the begginning new_dataframe_suppression <- cbind(new_dataframe[first_column], new_dataframe["suppress"], output_tsv["rt"], new_dataframe["signal_moy"], new_dataframe[, !(colnames(new_dataframe) %in% c(first_column, "suppress", "pcgroup", "signal_moy"))]) - #remove all the rows which have the "suppress" data and keep only the selected metabolites + # remove all the rows which have the "suppress" data and keep only the selected metabolites selected_metabolite_dataframe <- new_dataframe_suppression[new_dataframe_suppression$suppress != "DEL", ] - #Keep the metabolites selected in a list + # Keep the metabolites selected in a list metabolites_selected_list <- as.vector(t(selected_metabolite_dataframe[1])) - #Add the other columns to keep + # Add the other columns to keep metabolites_selected_list <- c(metabolites_selected_list, first_column, "rt", "signal_moy") - #Keep the metabolites selected to keep only the columns + # Keep the metabolites selected to keep only the columns selected_metabolite_dataframe <- selected_metabolite_dataframe[, colnames(selected_metabolite_dataframe) %in% metabolites_selected_list] - #Export to siff table format for visualization in cytoscape for the selected metabolites + # Export to siff table format for visualization in cytoscape for the selected metabolites siff_table <- melt(selected_metabolite_dataframe[, !colnames(selected_metabolite_dataframe) %in% c("pcgroup", "rt", "signal_moy")]) - #Remove the values equal to 1 (correlation between two metabolite identical) + # Remove the values equal to 1 (correlation between two metabolite identical) siff_table <- siff_table[siff_table$value != 1, ] - #Keep only the values corresponding to the param_cytoscape + # Keep only the values corresponding to the param_cytoscape siff_table <- siff_table[siff_table$value >= param_cytoscape, ] - #Change the order of the columns + # Change the order of the columns siff_table <- cbind(siff_table[first_column], siff_table["value"], siff_table["variable"]) - #Join the two datasets to keep only the selected metabolite, with all the information about the intensity value for statistics analysis in the next step of the workflow. + # Join the two datasets to keep only the selected metabolite, with all the information about the intensity value for statistics analysis in the next step of the workflow. joined_dataframe <- merge(statmatrix2, selected_metabolite_dataframe[first_column], by = first_column) - #Order by the signal intensity + # Order by the signal intensity joined_dataframe <- joined_dataframe[order(-joined_dataframe$signal_moy), ] - #Transposition of the dataframe + # Transposition of the dataframe joined_dataframe <- joined_dataframe[, !(colnames(joined_dataframe) %in% c("signal_moy"))] - #Write the different tables into files + # Write the different tables into files write.table(selected_metabolite_dataframe, sep = "\t", quote = FALSE, col.names = TRUE, row.names = FALSE, file = "correlation_matrix_selected.tsv") write.table(siff_table, sep = "\t", quote = FALSE, col.names = FALSE, row.names = FALSE, file = "siff_table.tsv") output_tsv_vm <- output_tsv[, which(!(colnames(output_tsv) %in% samples_name))] @@ -175,39 +170,41 @@ corr_matrix_del <- function(output_tsv, samples_name, param_correlation, param_c output_tsv_vm <- merge(x = output_tsv_vm, y = new_dataframe[, c(3, which(colnames(new_dataframe) == "suppress"))], by.x = 1, by.y = 1, sort = FALSE) output_tsv_vm <- output_tsv_vm[order(output_tsv_vm$ori.or), ][, -c(which(colnames(output_tsv_vm) == "ori.or"))] write.table(output_tsv_vm, sep = "\t", quote = FALSE, col.names = TRUE, row.names = FALSE, file = "sorted_table.tsv") - } corr_matrix_user <- function(user_matrix_corr, param_cytoscape, corr_method) { - #read the input table + # read the input table input_tsv <- read.csv(user_matrix_corr, header = T, sep = "\t", dec = ".", check.names = FALSE) n <- input_tsv$HD - #transpose all but the first column (name) + # transpose all but the first column (name) statmatrix_corr <- as.data.frame(t(input_tsv[, -1])) - #Rename the columns of the dataframe "statmatrix" + # Rename the columns of the dataframe "statmatrix" colnames(statmatrix_corr) <- n - #Do the cor step, with the transposed statmatrix. + # Do the cor step, with the transposed statmatrix. corr_transpo <- cor(t(as.matrix(statmatrix_corr)), method = corr_method) - #Write the matrix to a tsv file + # Write the matrix to a tsv file write.table(corr_transpo, sep <- "\t", quote = FALSE, col.names = NA, file = "correlation_matrix.tsv") - #Export to siff table format for visualization in cytoscape for the selected conditions + # Export to siff table format for visualization in cytoscape for the selected conditions siff_table <- melt(corr_transpo) - #Remove the values equal to 1 (correlation between two metabolite identical) + # Remove the values equal to 1 (correlation between two metabolite identical) siff_table <- siff_table[siff_table$value != 1, ] - #Keep only the values corresponding to the param_cytoscape + # Keep only the values corresponding to the param_cytoscape siff_table <- siff_table[siff_table$value >= param_cytoscape, ] - #Change the order of the columns + # Change the order of the columns siff_table <- cbind(Node1 = siff_table["X1"], interaction = siff_table["value"], Node2 = siff_table["X2"]) - #Write the siff table + # Write the siff table write.table(siff_table, sep = "\t", quote = FALSE, col.names = FALSE, row.names = FALSE, file = "siff_table.tsv") - } do.call(main_function, list_arguments) -cat("\n--------------------------------------------------------------------", - "\nInformation about R (version, Operating System, attached or loaded packages):\n\n") +cat( + "\n--------------------------------------------------------------------", + "\nInformation about R (version, Operating System, attached or loaded packages):\n\n" +) sessionInfo() -cat("--------------------------------------------------------------------\n", - "\nJob ending time:\n", format(Sys.time(), "%a %d %b %Y %X")) +cat( + "--------------------------------------------------------------------\n", + "\nJob ending time:\n", format(Sys.time(), "%a %d %b %Y %X") +) diff --git a/tools/genform/genform.xml b/tools/genform/genform.xml index 4eac4b208d..0b9004d24f 100644 --- a/tools/genform/genform.xml +++ b/tools/genform/genform.xml @@ -1,10 +1,11 @@ - + Generation of molecular formulas by high-resolution MS and MS/MS data 8 genform + bc - @@ -302,7 +302,6 @@ genform - diff --git a/tools/ipo/ipo4retgroup.r b/tools/ipo/ipo4retgroup.r index 16a56d6978..21e03a2f28 100755 --- a/tools/ipo/ipo4retgroup.r +++ b/tools/ipo/ipo4retgroup.r @@ -7,9 +7,9 @@ cat("\tSESSION INFO\n") # Import the different functions source_local <- function(fname) { - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep = "/")) + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) } source_local("lib.r") @@ -27,8 +27,8 @@ cat("\tARGUMENTS PROCESSING INFO\n") samplebyclass <- 2 if (!is.null(args$samplebyclass)) { - samplebyclass <- args$samplebyclass - args$samplebyclass <- NULL + samplebyclass <- args$samplebyclass + args$samplebyclass <- NULL } cat("\n\n") @@ -43,7 +43,7 @@ args$image <- NULL # Because so far CAMERA isn't compatible with the new XCMSnExp object if (exists("xdata")) { - xset <- getxcmsSetObject(xdata) + xset <- getxcmsSetObject(xdata) } if (!exists("xset")) stop("\n\nERROR: The RData doesn't contain any object called 'xdata' which is provided by the tool: MSnbase readMSData") diff --git a/tools/ipo/ipo4retgroup.xml b/tools/ipo/ipo4retgroup.xml index 2f2e2d706e..05bc92044d 100644 --- a/tools/ipo/ipo4retgroup.xml +++ b/tools/ipo/ipo4retgroup.xml @@ -1,4 +1,4 @@ - + IPO optimization process for xcms.groupChromPeaks and xcms.adjustRtime @@ -7,7 +7,10 @@ - + + + + + IPO optimization process for xcms.findChromPeaks @@ -7,6 +7,10 @@ + + + + 0) { - #random selection of N files for IPO in each class - classes <- unique(basename(dirname(files))) - mzmlfile <- NULL - for (class_i in classes){ - files_class_i <- files[files_classes == class_i] - if (samplebyclass > length(files_class_i)) { - mzmlfile <- c(mzmlfile, files_class_i) - } else { - mzmlfile <- c(mzmlfile, sample(files_class_i, samplebyclass)) - } + setwd(directory) + + files <- list.files(".", recursive = TRUE) # "KO/ko15.CDF" "KO/ko16.CDF" "WT/wt15.CDF" "WT/wt16.CDF" + files <- files[!files %in% c("conda_activate.log", "log.txt")] + files_classes <- basename(dirname(files)) # "KO", "KO", "WT", "WT" + + mzmlfile <- files + if (samplebyclass > 0) { + # random selection of N files for IPO in each class + classes <- unique(basename(dirname(files))) + mzmlfile <- NULL + for (class_i in classes) { + files_class_i <- files[files_classes == class_i] + if (samplebyclass > length(files_class_i)) { + mzmlfile <- c(mzmlfile, files_class_i) + } else { + mzmlfile <- c(mzmlfile, sample(files_class_i, samplebyclass)) + } + } } - } - #@TODO: else, must we keep the RData to been use directly by group? + # @TODO: else, must we keep the RData to been use directly by group? - cat("\t\tSamples used:\n") - print(mzmlfile) + cat("\t\tSamples used:\n") + print(mzmlfile) - peakpickingParameters <- getDefaultXcmsSetStartingParams(args$method) #get default parameters of IPO + peakpickingParameters <- getDefaultXcmsSetStartingParams(args$method) # get default parameters of IPO - # filter args to only get releavant parameters and complete with those that are not declared - peakpickingParametersUser <- c(args[names(args) %in% names(peakpickingParameters)], peakpickingParameters[!(names(peakpickingParameters) %in% names(args))]) - peakpickingParametersUser$verbose.columns <- TRUE - resultPeakpicking <- optimizeXcmsSet(mzmlfile, peakpickingParametersUser, nSlaves = args$nSlaves, subdir = "./IPO_results") #some images generated by IPO + # filter args to only get releavant parameters and complete with those that are not declared + peakpickingParametersUser <- c(args[names(args) %in% names(peakpickingParameters)], peakpickingParameters[!(names(peakpickingParameters) %in% names(args))]) + peakpickingParametersUser$verbose.columns <- TRUE + resultPeakpicking <- optimizeXcmsSet(mzmlfile, peakpickingParametersUser, nSlaves = args$nSlaves, subdir = "./IPO_results") # some images generated by IPO - # export - peakpicking_best_params <- resultPeakpicking$best_settings$parameters[!(names(resultPeakpicking$best_settings$parameters) %in% c("nSlaves", "verbose.columns"))] - write.table(t(as.data.frame(peakpicking_best_params)), file = parametersOutput, sep = "\t", row.names = TRUE, col.names = FALSE, quote = FALSE) #can be read by user + # export + peakpicking_best_params <- resultPeakpicking$best_settings$parameters[!(names(resultPeakpicking$best_settings$parameters) %in% c("nSlaves", "verbose.columns"))] + write.table(t(as.data.frame(peakpicking_best_params)), file = parametersOutput, sep = "\t", row.names = TRUE, col.names = FALSE, quote = FALSE) # can be read by user - return(resultPeakpicking$best_settings$xset) + return(resultPeakpicking$best_settings$xset) } ## ## This function launch IPO functions to get the best parameters for group and retcor ## ipo4retgroup <- function(xset, directory, parametersOutput, args, samplebyclass = 4) { - setwd(directory) - - files <- list.files(".", recursive = TRUE) # "KO/ko15.CDF" "KO/ko16.CDF" "WT/wt15.CDF" "WT/wt16.CDF" - files <- files[!files %in% c("conda_activate.log", "log.txt")] - files_classes <- basename(dirname(files)) # "KO", "KO", "WT", "WT" - - retcorGroupParameters <- getDefaultRetGroupStartingParams(args$retcorMethod) #get default parameters of IPO - print(retcorGroupParameters) - # filter args to only get releavant parameters and complete with those that are not declared - retcorGroupParametersUser <- c(args[names(args) %in% names(retcorGroupParameters)], retcorGroupParameters[!(names(retcorGroupParameters) %in% names(args))]) - print("retcorGroupParametersUser") - print(retcorGroupParametersUser) - resultRetcorGroup <- optimizeRetGroup(xset, retcorGroupParametersUser, nSlaves = args$nSlaves, subdir = "../IPO_results") #some images generated by IPO - - # export - resultRetcorGroup_best_params <- resultRetcorGroup$best_settings - write.table(t(as.data.frame(resultRetcorGroup_best_params)), file = parametersOutput, sep = "\t", row.names = TRUE, col.names = FALSE, quote = FALSE) #can be read by user + setwd(directory) + + files <- list.files(".", recursive = TRUE) # "KO/ko15.CDF" "KO/ko16.CDF" "WT/wt15.CDF" "WT/wt16.CDF" + files <- files[!files %in% c("conda_activate.log", "log.txt")] + files_classes <- basename(dirname(files)) # "KO", "KO", "WT", "WT" + + retcorGroupParameters <- getDefaultRetGroupStartingParams(args$retcorMethod) # get default parameters of IPO + print(retcorGroupParameters) + # filter args to only get releavant parameters and complete with those that are not declared + retcorGroupParametersUser <- c(args[names(args) %in% names(retcorGroupParameters)], retcorGroupParameters[!(names(retcorGroupParameters) %in% names(args))]) + print("retcorGroupParametersUser") + print(retcorGroupParametersUser) + resultRetcorGroup <- optimizeRetGroup(xset, retcorGroupParametersUser, nSlaves = args$nSlaves, subdir = "../IPO_results") # some images generated by IPO + + # export + resultRetcorGroup_best_params <- resultRetcorGroup$best_settings + write.table(t(as.data.frame(resultRetcorGroup_best_params)), file = parametersOutput, sep = "\t", row.names = TRUE, col.names = FALSE, quote = FALSE) # can be read by user } # This function check if XML contains special caracters. It also checks integrity and completness. -#@author Misharl Monsoor misharl.monsoor@sb-roscoff.fr ABiMS TEAM +# @author Misharl Monsoor misharl.monsoor@sb-roscoff.fr ABiMS TEAM checkXmlStructure <- function(directory) { - cat("Checking XML structure...\n") - - cmd <- paste0("IFS=$'\n'; for xml in $(find '", directory, "' -not -name '\\.*' -not -path '*conda-env*' -type f -iname '*.*ml*'); do if [ $(xmllint --nonet --noout \"$xml\" 2> /dev/null; echo $?) -gt 0 ]; then echo $xml;fi; done;") - capture <- system(cmd, intern = TRUE) + cat("Checking XML structure...\n") - if (length(capture) > 0) { - write("\n\nERROR: The following mzXML or mzML file(s) are incorrect, please check these files first:", stderr()) - write(capture, stderr()) - stop("ERROR: xcmsSet cannot continue with incorrect mzXML or mzML files") - } + cmd <- paste0("IFS=$'\n'; for xml in $(find '", directory, "' -not -name '\\.*' -not -path '*conda-env*' -type f -iname '*.*ml*'); do if [ $(xmllint --nonet --noout \"$xml\" 2> /dev/null; echo $?) -gt 0 ]; then echo $xml;fi; done;") + capture <- system(cmd, intern = TRUE) + if (length(capture) > 0) { + write("\n\nERROR: The following mzXML or mzML file(s) are incorrect, please check these files first:", stderr()) + write(capture, stderr()) + stop("ERROR: xcmsSet cannot continue with incorrect mzXML or mzML files") + } } # This function get the raw file path from the arguments -#@author Gildas Le Corguille lecorguille@sb-roscoff.fr +# @author Gildas Le Corguille lecorguille@sb-roscoff.fr getRawfilePathFromArguments <- function(singlefile, zipfile, args, prefix = "") { - if (!(prefix %in% c("", "Positive", "Negative", "MS1", "MS2"))) stop("prefix must be either '', 'Positive', 'Negative', 'MS1' or 'MS2'") - - if (!is.null(args[[paste0("zipfile", prefix)]])) zipfile <- args[[paste0("zipfile", prefix)]] - - if (!is.null(args[[paste0("singlefile_galaxyPath", prefix)]])) { - singlefile_galaxyPaths <- args[[paste0("singlefile_galaxyPath", prefix)]] - singlefile_sampleNames <- args[[paste0("singlefile_sampleName", prefix)]] - } - if (exists("singlefile_galaxyPaths")) { - singlefile_galaxyPaths <- unlist(strsplit(singlefile_galaxyPaths, "\\|")) - singlefile_sampleNames <- unlist(strsplit(singlefile_sampleNames, "\\|")) - - singlefile <- NULL - for (singlefile_galaxyPath_i in seq_along(length(singlefile_galaxyPaths))) { - singlefile_galaxyPath <- singlefile_galaxyPaths[singlefile_galaxyPath_i] - singlefile_sampleName <- singlefile_sampleNames[singlefile_galaxyPath_i] - # In case, an url is used to import data within Galaxy - singlefile_sampleName <- tail(unlist(strsplit(singlefile_sampleName, "/")), n = 1) - singlefile[[singlefile_sampleName]] <- singlefile_galaxyPath + if (!(prefix %in% c("", "Positive", "Negative", "MS1", "MS2"))) stop("prefix must be either '', 'Positive', 'Negative', 'MS1' or 'MS2'") + + if (!is.null(args[[paste0("zipfile", prefix)]])) zipfile <- args[[paste0("zipfile", prefix)]] + + if (!is.null(args[[paste0("singlefile_galaxyPath", prefix)]])) { + singlefile_galaxyPaths <- args[[paste0("singlefile_galaxyPath", prefix)]] + singlefile_sampleNames <- args[[paste0("singlefile_sampleName", prefix)]] + } + if (exists("singlefile_galaxyPaths")) { + singlefile_galaxyPaths <- unlist(strsplit(singlefile_galaxyPaths, "\\|")) + singlefile_sampleNames <- unlist(strsplit(singlefile_sampleNames, "\\|")) + + singlefile <- NULL + for (singlefile_galaxyPath_i in seq_along(length(singlefile_galaxyPaths))) { + singlefile_galaxyPath <- singlefile_galaxyPaths[singlefile_galaxyPath_i] + singlefile_sampleName <- singlefile_sampleNames[singlefile_galaxyPath_i] + # In case, an url is used to import data within Galaxy + singlefile_sampleName <- tail(unlist(strsplit(singlefile_sampleName, "/")), n = 1) + singlefile[[singlefile_sampleName]] <- singlefile_galaxyPath + } } - } - return(list(zipfile = zipfile, singlefile = singlefile)) + return(list(zipfile = zipfile, singlefile = singlefile)) } # This function retrieve the raw file in the working directory # - if zipfile: unzip the file with its directory tree # - if singlefiles: set symlink with the good filename -#@author Gildas Le Corguille lecorguille@sb-roscoff.fr +# @author Gildas Le Corguille lecorguille@sb-roscoff.fr retrieveRawfileInWD <- function(singlefile, zipfile) { - if (!is.null(singlefile) && (length("singlefile") > 0)) { - for (singlefile_sampleName in names(singlefile)) { - singlefile_galaxyPath <- singlefile[[singlefile_sampleName]] - if (!file.exists(singlefile_galaxyPath)) { - error_message <- paste("Cannot access the sample:", singlefile_sampleName, "located:", singlefile_galaxyPath, ". Please, contact your administrator ... if you have one!") - print(error_message) - stop(error_message) - } - - if (!suppressWarnings(try(file.link(singlefile_galaxyPath, singlefile_sampleName), silent = TRUE))) - file.copy(singlefile_galaxyPath, singlefile_sampleName) - + if (!is.null(singlefile) && (length("singlefile") > 0)) { + for (singlefile_sampleName in names(singlefile)) { + singlefile_galaxyPath <- singlefile[[singlefile_sampleName]] + if (!file.exists(singlefile_galaxyPath)) { + error_message <- paste("Cannot access the sample:", singlefile_sampleName, "located:", singlefile_galaxyPath, ". Please, contact your administrator ... if you have one!") + print(error_message) + stop(error_message) + } + + if (!suppressWarnings(try(file.link(singlefile_galaxyPath, singlefile_sampleName), silent = TRUE))) { + file.copy(singlefile_galaxyPath, singlefile_sampleName) + } + } + directory <- "." } - directory <- "." - - } - if (!is.null(zipfile) && (zipfile != "")) { - if (!file.exists(zipfile)) { - error_message <- paste( - "Cannot access the Zip file:", - zipfile, - ". Please, contact your administrator ... if you have one!" - ) - print(error_message) - stop(error_message) + if (!is.null(zipfile) && (zipfile != "")) { + if (!file.exists(zipfile)) { + error_message <- paste( + "Cannot access the Zip file:", + zipfile, + ". Please, contact your administrator ... if you have one!" + ) + print(error_message) + stop(error_message) + } + + # unzip + suppressWarnings(unzip(zipfile, unzip = "unzip")) + + # get the directory name + suppressWarnings(filesInZip <- unzip(zipfile, list = TRUE)) + directories <- unique(unlist(lapply(strsplit(filesInZip$Name, "/"), function(x) x[1]))) + directories <- directories[!(directories %in% c("__MACOSX")) & file.info(directories)$isdir] + directory <- "." + if (length(directories) == 1) directory <- directories + + cat("files_root_directory\t", directory, "\n") } - - #unzip - suppressWarnings(unzip(zipfile, unzip = "unzip")) - - #get the directory name - suppressWarnings(filesInZip <- unzip(zipfile, list = TRUE)) - directories <- unique(unlist(lapply(strsplit(filesInZip$Name, "/"), function(x) x[1]))) - directories <- directories[!(directories %in% c("__MACOSX")) & file.info(directories)$isdir] - directory <- "." - if (length(directories) == 1) directory <- directories - - cat("files_root_directory\t", directory, "\n") - - } - return(directory) + return(directory) } # This function retrieve a xset like object -#@author Gildas Le Corguille lecorguille@sb-roscoff.fr +# @author Gildas Le Corguille lecorguille@sb-roscoff.fr getxcmsSetObject <- function(xobject) { - # XCMS 1.x - if (class(xobject) == "xcmsSet") - return(xobject) - # XCMS 3.x - if (class(xobject) == "XCMSnExp") { - # Get the legacy xcmsSet object - suppressWarnings(xset <- as(xobject, "xcmsSet")) - if (!is.null(xset@phenoData$sample_group)) - sampclass(xset) <- xset@phenoData$sample_group - else - sampclass(xset) <- "." - return(xset) - } + # XCMS 1.x + if (class(xobject) == "xcmsSet") { + return(xobject) + } + # XCMS 3.x + if (class(xobject) == "XCMSnExp") { + # Get the legacy xcmsSet object + suppressWarnings(xset <- as(xobject, "xcmsSet")) + if (!is.null(xset@phenoData$sample_group)) { + sampclass(xset) <- xset@phenoData$sample_group + } else { + sampclass(xset) <- "." + } + return(xset) + } } diff --git a/tools/isoplot/.shed.yml b/tools/isoplot/.shed.yml index 17c666d4e2..34846d8925 100644 --- a/tools/isoplot/.shed.yml +++ b/tools/isoplot/.shed.yml @@ -3,5 +3,6 @@ owner: workflow4metabolomics description: Isoplot is a software for the visualisation of MS data from C13 labelling experiments categories: [Metabolomics, Visualization] long_description: Isoplot is a software for the visualisation of MS data from C13 labelling experiments. It takes as input corrected MS data from IsoCor in csv or tsv format. Isoplot outputs static and interactive plots that are then used for quality assessment, biological interpretation, project reports, published papers and so forth. +homepage_url: https://github.com/llegregam/Isoplot remote_repository_url: https://github.com/llegregam/Isoplot/tree/main type: unrestricted diff --git a/tools/isoplot/isoplot.xml b/tools/isoplot/isoplot.xml index bc94921fab..08d54c012e 100644 --- a/tools/isoplot/isoplot.xml +++ b/tools/isoplot/isoplot.xml @@ -1,9 +1,14 @@ - + + + 1.3.1 + - isoplot + jinja2 + zlib + isoplot - $data_output 2> '$log_output' +-g > '$data_output' 2> '$log_output' ]]> diff --git a/tools/kmd_hmdb_data_plot/kmd_hmdb_data_plot.xml b/tools/kmd_hmdb_data_plot/kmd_hmdb_data_plot.xml index 78ce8b3d6b..28e941b16a 100644 --- a/tools/kmd_hmdb_data_plot/kmd_hmdb_data_plot.xml +++ b/tools/kmd_hmdb_data_plot/kmd_hmdb_data_plot.xml @@ -5,7 +5,7 @@ macro.xml 1.0.0 - 0 + 2 topic_0091 @@ -14,11 +14,16 @@ operation_3803 + click python pandas plotly kmd_hmdb_api_client + + + + - + @@ -162,7 +167,9 @@ So don't hesitate to select a lot of parameters for X and Y! ]]> - + + 10.1093/nar/gkab1062 + 1) - stop("Several 'grouping level' for random effect not implemented yet.") - - - ## extracting information from mfl models ------------------------------------------------------------- - # data - df <- mfl@frame - responseC <- names(df)[1] - unitC <- names(mfl@flist)[1] - # observations - yVn <- df[, responseC] - nobsN <- length(yVn) - # units - idunitVc <- levels(mfl@flist[[1]]) - nunitN <- length(unique(idunitVc)) - #X - xMN <- mfl@pp$X - pN <- ncol(xMN) - #Z - zMN <- t(as.matrix(mfl@pp$Zt)) - # Estimated covariance matrix of random effects (Gam) - aux <- VarCorr(mfl)[[1]] ## assuming only one level of grouping - aux2 <- attr(aux, "stddev") - gMN <- attr(aux, "correlation") * (aux2 %*% t(aux2)) - gammaMN <- as.matrix(kronecker(diag(nunitN), gMN)) - q <- dim(gMN)[1] - # Estimated covariance matrix of conditonal error (homoskedastic conditional independance model) - sigsqN <- attr(VarCorr(mfl), "sc")^2 - rMN <- sigsqN * diag(nobsN) - # Estimated covariance matrix of Y - vMN <- (zMN %*% gammaMN %*% t(zMN)) + rMN - invvMN <- MASS::ginv(vMN) - # H and Q matrix - hMN <- MASS::ginv(t(xMN) %*% invvMN %*% xMN) - qMN <- invvMN - invvMN %*% xMN %*% (hMN) %*% t(xMN) %*% invvMN - # eblue and eblup - eblueVn <- mfl@beta - eblupVn <- gammaMN %*% t(zMN) %*% invvMN %*% (yVn - xMN %*% eblueVn) ## equivalent de ranef(mfl) - rownames(eblupVn) <- colnames(zMN) - ## Calculs of matrices and vectors used in graph diagnosics --------------------------------------------- - ## Marginal and individual predictions, residuals and variances - marpredVn <- xMN %*% eblueVn - marresVn <- yVn - marpredVn - marvarMN <- vMN - xMN %*% hMN %*% t(xMN) - condpredVn <- marpredVn + zMN %*% eblupVn - condresVn <- yVn - condpredVn - condvarMN <- rMN %*% qMN %*% rMN - ## Analysis of marginal and conditional residuals - stmarresVn <- stcondresVn <- rep(0, nobsN) - lesverVn <- rep(0, nunitN) - names(lesverVn) <- idunitVc - for (i in 1:nunitN) { - idxiVn <- which(df[, unitC] == idunitVc[i]) ## position des observations du sujet i - miN <- length(idxiVn) - ## standardization of marginal residual - stmarresVn[idxiVn] <- as.vector(solve(sqrtmF(marvarMN[idxiVn, idxiVn])) %*% marresVn[idxiVn]) - ##Standardized Lessafre and Verbeke's measure - auxMN <- diag(1, ncol = miN, nrow = miN) - stmarresVn[idxiVn] %*% t(stmarresVn[idxiVn]) - lesverVn[i] <- sum(diag(auxMN %*% t(auxMN))) - ## standardization of conditional residual - stcondresVn[idxiVn] <- as.vector(solve(sqrtmF(condvarMN[idxiVn, idxiVn])) %*% condresVn[idxiVn]) - } - lesverVn <- lesverVn / sum(lesverVn) - ## Least confounded conditional residuals - ## EBLUP analysis (Mahalanobis' distance) - varbMN <- gammaMN %*% t(zMN) %*% qMN %*% zMN %*% gammaMN - mdistVn <- rep(0, nunitN) - qm <- q - 1 - for (j in 1:nunitN) { - gbi <- varbMN[(q * j - qm):(q * j), (q * j - qm):(q * j)] - eblupi <- eblupVn[(q * j - qm):(q * j)] - mdistVn[j] <- t(eblupi) %*% ginv(gbi) %*% eblupi - } - names(mdistVn) <- levels(mfl@flist[[1]]) - ## output ---------------------------------------------- - return(list( - data = df, - q = q, - eblue = eblueVn, - eblup = eblupVn, - marginal.prediction = marpredVn, - conditional.prediction = condpredVn, - std.marginal.residuals = stmarresVn, - std.conditional.residuals = stcondresVn, - mahalanobis.distance = mdistVn, - std.mahalanobis.distance = mdistVn / sum(mdistVn), - std.lesaffreverbeke.measure = lesverVn - )) -} - - -#' Wrapper function for diagnostic plots of 'lmer' linear mixed models -#' -#' (W4M mixmod) -#' -#' @param mfl A linear mixed model fitted via lmer or a data frame containing data -#' @param title aa -#' @param outlier.limit aa -#' @param pvalCutof aa -#' @param resC aa -#' @param uniC aa -#' @param fixC aa -#' @param lest.confounded Not used yet. -#' @return NULL -#' @author Natacha Lenuzza -#' @examples -#' print("hello !") -#' -#' @export diagmflF - - -diagmflF <- function(mfl, - title = "", - outlier.limit = 3, - pvalCutof = 0.05, - resC = "vd", - uniC = "subject", - timC = "time", - fixC = "fixfact", - least.confounded = FALSE) { - ## diagnostics - diagLs <- lmer.computeDiag(mfl) - ## plots - blank <- rectGrob(gp = gpar(col = "white")) - rectspacer <- rectGrob(height = unit(0.1, "npc"), gp = gpar(col = "grey")) - grid.arrange(blank, - plot_linearity(diagLs, hlimitN = outlier.limit, plotL = FALSE, - label_factor = c(uniC, fixC, timC)), - blank, - plot_conditionalResiduals(diagLs, hlimitN = outlier.limit, plotL = FALSE, - label_factor = c(uniC, fixC, timC)), - blank, - plot_condresQQplot(diagLs, plotL = FALSE), - blank, - plot_lesaffreVeerbeke(diagLs, plotL = FALSE), - blank, - plot_randomEffect(mfl, plotL = FALSE)[[1]], - blank, - plot_mahalanobisKhi2(diagLs, plotL = FALSE), - blank, - plot_mahalanobis(diagLs, plotL = FALSE), - blank, - blank, - blank, - top = textGrob(title, gp = gpar(fontsize = 40, font = 4)), - layout_matrix = matrix(c(rep(1, 7), - 2, 3, rep(4, 3), 20, 21, - rep(5, 7), - 6:12, - rep(13, 7), - 14:18, rep(19, 2)), - ncol = 7, nrow = 6, byrow = TRUE), - heights = c(0.1 / 3, 0.3, 0.1 / 3, 0.3, 0.1 / 3, 0.3), - widths = c(0.22, 0.04, 0.22, 0.04, 0.22, 0.04, 0.22)) -} - -####################################################################################################### -## Raw data time courses -####################################################################################################### - -#' Visualization of raw time course -#' -#' Une -#' -#' @param mfl A linear mixed model fitted via lmer or a data frame containing data -#' @param responseC Name of the 'response' variable -#' @param timeC Name of the 'time' variable -#' @param subjectC Name of the 'subject' variable -#' @param fixfactC Name of the 'fixed factor' variable (e.g.treatment) -#' @param offset_subject Boolean indicating if an offset value (subject's mean) should substracted to each data point. Default is FALSE -#' @param plotL Boolean -#' @param colorType One of NA, FIXFACT or SUBJECT -#' @param shapeType One of NA, FIXFACT or SUBJECT -#' @param lineType One of NA, FIXFACT or SUBJECT -#' @return A plot -#' @author Natacha Lenuzza -#' @examples -#' print("hello !") -#' -#' @export plot_timeCourse - - - -plot_timeCourse <- function(mfl, - responseC, - timeC, - subjectC, - fixfactC = NULL, - offset_subject = FALSE, - plotL = TRUE, - colorType = NA, ## subject, fixfact, none or NA - shapeType = NA, ## subject, fixfact, none or NA - lineType = NA ## subject, fixfact, none or NA -) { - ## Data ----- - if (class(mfl) %in% c("merModLmerTest", "lmerMod", "lmerModLmerTest")) { - DF <- mfl@frame - } else if (class(mfl) == "data.frame") { - DF <- mfl - } else { - stop("'mfl' argument must be a linear mixed effect model or a data frame.") - } - ## Format data ----- - if (is.null(fixfactC)) { - DF <- DF[, c(responseC, timeC, subjectC)] - colnames(DF) <- c("DV", "TIME", "SUBJECT") - meanDF <- aggregate(DF$DV, - by = list(SUBJECT = DF$SUBJECT, - TIME = DF$TIME), - FUN = mean, - na.rm = TRUE) - colnames(meanDF) <- c("SUBJECT", "TIME", "DV") - meanDF$GROUP <- meanDF$SUBJECT - } else{ - DF <- DF[, c(responseC, fixfactC, timeC, subjectC)] - colnames(DF) <- c("DV", "FIXFACT", "TIME", "SUBJECT") - meanDF <- aggregate(DF$DV, - by = list(SUBJECT = DF$SUBJECT, - TIME = DF$TIME, - FIXFACT = DF$FIXFACT), - FUN = mean, - na.rm = TRUE) - colnames(meanDF) <- c("SUBJECT", "TIME", "FIXFACT", "DV") - meanDF$GROUP <- paste(meanDF$SUBJECT, meanDF$FIXFACT, sep = "_") - } - ## Offset ----- - if (offset_subject) { - offsetMN <- aggregate(DF$DV, by = list(DF$SUBJECT), mean, na.rm = TRUE) - offsetVn <- offsetMN[, 2] - names(offsetVn) <- offsetMN[, 1] - rm(offsetMN) - DF$DV <- DF$DV - offsetVn[DF$SUBJECT] - meanDF$DV <- meanDF$DV - offsetVn[as.character(meanDF$SUBJECT)] - } - ## Graphical parameters ----- - xlabC <- timeC - ylabC <- responseC - titC <- "Individual time-courses" - if (offset_subject) { - ylabC <- paste(ylabC, "minus 'within-subject' empirical mean") - titC <- paste(titC, "('within-subject' empirical mean offset)") - } - ## color - if (is.na(colorType)) { ## automaticatical attribution - if (is.null(fixfactC)) { - colorType <- "SUBJECT" - } else { - colorType <- "FIXFACT" - } - colTxt <- paste(", colour=", colorType) - } else if (colorType == "none") { - colTxt <- "" - } else { - colTxt <- paste(", colour=", colorType) - } - ## lineType - if (is.na(lineType)) { ## automaticatical attribution - if (is.null(fixfactC)) { - linTxt <- "" - } else { - linTxt <- paste(", linetype=", - ifelse(colorType == "SUBJECT", "FIXFACT", "SUBJECT")) - } - } else if (lineType == "none") { - linTxt <- "" - } else { - linTxt <- paste(", linetype=", lineType) - } - ## shapeType - if (is.na(shapeType)) { ## automaticatical attribution - if (is.null(fixfactC)) { - shaTxt <- "" - } else { - shaTxt <- paste(", shape=", - ifelse(colorType == "SUBJECT", "FIXFACT", "SUBJECT")) - } - } else if (shapeType == "none") { - shaTxt <- "" - } else { - shaTxt <- paste(", shape=", shapeType) - } - ## aes mapping - txtMap <- paste("aes(x = TIME, y = DV", - colTxt, shaTxt, ")", sep = "") - txtLineMap <- paste("aes(x = TIME, y = DV, group = GROUP ", - colTxt, linTxt, ")", sep = "") - ## plot and output - p <- ggplot(data = DF, mapping = eval(parse(text = txtMap))) + - ggtitle(titC) + - xlab(xlabC) + ylab(ylabC) + - theme(legend.position = "left", - plot.title = element_text(size = rel(1.2), face = "bold")) + - geom_point() + - geom_line(eval(parse(text = txtLineMap)), data = meanDF) + - theme_bw() + - NULL - if (plotL) plot(p) - invisible(p) -} - -####################################################################################################### -## Post-hoc estimate -####################################################################################################### -#' Visualization of fixed effects (post-hoc estimates) -#' -#' Description -#' -#' @param mfl A linear mixed model fitted via lmer or a data frame containing data -#' @param pvalCutof User pvalue cut of -#' @param plotL Boolean -#' @param titC Title of the plot -#' @return A plot -#' @author Natacha Lenuzza -#' @examples -#' print("hello !") -#' -#' @export plot_posthoc -plot_posthoc <- function(mfl, pvalCutof = 0.05, plotL = TRUE, titC = "Post-hoc estimates") { - ddlsm1 <- as.data.frame(difflsmeans(mfl, test.effs = NULL)) - colnames(ddlsm1)[ncol(ddlsm1)] <- "pvalue" - ddlsm1$Significance <- rep("NS", nrow(ddlsm1)) - ## modif JF pour tenir compte du seuil de pvalues defini par le user - ddlsm1$Significance[which(ddlsm1$pvalue < pvalCutof)] <- paste("p-value < ", pvalCutof, sep = "") - ddlsm1$Significance[which(ddlsm1$pvalue < pvalCutof / 5)] <- paste("p-value < ", pvalCutof / 5, sep = "") - ddlsm1$Significance[which(ddlsm1$pvalue < pvalCutof / 10)] <- paste("p-value < ", pvalCutof / 10, sep = "") - ddlsm1$levels <- rownames(ddlsm1) - ddlsm1$term <- sapply(rownames(ddlsm1), function(namC) { - strsplit(namC, split = " ", fixed = TRUE)[[1]][1] - }) - colValue <- c("grey", "yellow", "orange", "red") - names(colValue) <- c("NS", - paste("p-value < ", pvalCutof, sep = ""), - paste("p-value < ", pvalCutof / 5, sep = ""), - paste("p-value < ", pvalCutof / 10, sep = "")) - p <- ggplot(ddlsm1, aes(x = levels, y = Estimate)) + - facet_grid(facets = ~term, ddlsm1, scales = "free", space = "free") + - geom_bar(aes(fill = Significance), stat = "identity") + - theme(axis.text.x = element_text(angle = 90, hjust = 1)) + - scale_fill_manual(values = colValue) + - geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.25) + - ggtitle(titC) + xlab("") + - NULL - if (plotL) plot(p) - invisible(p) -} - -####################################################################################################### -## Visualisation des effets aléatoires -####################################################################################################### -#' Visualization of random effects -#' -#' Equivalent of dotplot(ranef) -#' -#' @param mfl A linear mixed model fitted via lmer or a data frame containing data -#' @param plotL Logical -#' @return A plot -#' @author Natacha Lenuzza -#' @examples -#' print("hello !") -#' -#' @export plot_randomEffect - - -plot_randomEffect <- function(mfl, plotL = TRUE) { - ## Estimation et format des effets aléatoires - randomEffect <- ranef(mfl, condVar = TRUE) - DF <- data.frame(randomEffect = rep(names(randomEffect), - times = sapply(seq_len(length(randomEffect)), - function(lsi) { - return(length(unlist(randomEffect[[lsi]])))}))) - DF$condVar <- DF$estimate <- DF$x2 <- DF$x1 <- rep(NA, nrow(DF)) - for (rafC in names(randomEffect)) { - eff <- randomEffect[[rafC]] - DF$x1[which(DF$randomEffect == rafC)] <- rep(colnames(eff), each = nrow(eff)) - DF$x2[which(DF$randomEffect == rafC)] <- rep(rownames(eff), ncol(eff)) - DF$estimate[which(DF$randomEffect == rafC)] <- unlist(eff) - condvar <- attr(randomEffect[[rafC]], "postVar") - se <- NULL - for (coli in seq_len(ncol(eff))) { - se <- c(se, - sapply(seq_len(nrow(eff)), - function(i) { - return(condvar[coli, coli, i])})) - } - DF$condVar[which(DF$randomEffect == rafC)] <- se - } - DF$se <- sqrt(DF$condVar) - DF$lower <- DF$estimate - 1.96 * DF$se - DF$upper <- DF$estimate + 1.96 * DF$se - ## Plot - plotLs <- vector("list", length(randomEffect)) - names(plotLs) <- names(randomEffect) - for (pi in seq_len(length(plotLs))) { - subDF <- DF[DF$randomEffect == names(plotLs)[pi], ] - subDF <- subDF[order(subDF$x1, subDF$estimate, decreasing = FALSE), ] - p <- ggplot(data = subDF, - mapping = aes(x = estimate, y = reorder(x2, estimate)) - ) + - geom_point(size = 3) + - geom_segment(aes(xend = lower, yend = x2)) + - geom_segment(aes(xend = upper, yend = x2)) + - facet_wrap(~x1, ncol = length(unique(subDF$x1))) + - ylab("") + xlab("") + - ggtitle(paste("Random effect - ", names(plotLs)[pi], sep = "")) + - theme(legend.position = "none", plot.title = element_text(size = rel(1.2), face = "bold")) + - geom_vline(xintercept = 0, linetype = "dashed") + - theme_bw() - plotLs[[pi]] <- p - if (plotL) plot(p) - } - invisible(plotLs) -} - -####################################################################################################### -## Linearité des effets et outlying observations -####################################################################################################### -#' Linarity of the fixed effect with regard to the continuous time -#' -#' @param diagLs diagnostic list -#' @param hlimitN Limit value for outliers (e.g.2 or 3) -#' @param plotL Boolean -#' @param label_factor Column of observation names used to label outlying values -#' @return A plot -#' @author Natacha Lenuzza -#' @examples -#' print("hello !") -#' -#' @export plot_linearity -#' - -plot_linearity <- function(diagLs, hlimitN, plotL = TRUE, label_factor = NULL) { - df <- cbind.data.frame(diagLs$data, - marginal.prediction = diagLs$marginal.prediction, - standardized.marginal.residuals = diagLs$std.marginal.residuals) - # outlier annotation - df$outliers <- rep("", nrow(df)) - outidx <- which(abs(df$standardized.marginal.residuals) > hlimitN) - df[outidx, "outliers"] <- (seq_len(nrow(df)))[outidx] - if (length(label_factor) >= 1) { - df[outidx, "outliers"] <- paste(df[outidx, "outliers"], - df[outidx, label_factor[1]], - sep = "_") - if (length(label_factor) > 1) { - for (li in 2:length(label_factor)) { - df[outidx, "outliers"] <- paste(df[outidx, "outliers"], - df[outidx, label_factor[li]], - sep = ".") - } - } - } - p <- ggplot(data = df, - aes(x = marginal.prediction, - y = standardized.marginal.residuals)) + - geom_point(size = 2) + - geom_hline(yintercept = 0, col = "grey") + - geom_smooth(aes(x = marginal.prediction, - y = standardized.marginal.residuals), data = df, se = FALSE, col = "blue", method = "loess") + - ggtitle("Linearity of effects/outlying obervations") + - xlab("Marginal predictions") + - ylab("Standardized marginal residuals") + - theme(legend.position = "none", plot.title = element_text(size = rel(1.2), face = "bold")) + - geom_hline(yintercept = c(-1, 1) * hlimitN, linetype = "dashed") + - geom_text(aes(label = outliers), hjust = 0, vjust = 0) - if (plotL) plot(p) - invisible(p) -} - - -####################################################################################################### -## EBLUP -####################################################################################################### - - -#' Mahalanobis distance -#' -#' @param diagLs diagnostic list -#' @param plotL Boolean -#' @return A plot -#' @author Natacha Lenuzza -#' @examples -#' print("hello !") -#' -#' @export plot_mahalanobis -#' - - -plot_mahalanobis <- function(diagLs, plotL = TRUE) { - unitDf <- data.frame(unit = names(diagLs$std.mahalanobis.distance), - mal = diagLs$std.mahalanobis.distance) - ## Outlying subjects - p <- - ggplot(aes(y = mal, x = unit), data = unitDf) + - geom_point(size = 3) + - ylab("Standardized Mahalanobis distance") + - geom_vline(xintercept = 0, linetype = "dashed") + - theme(legend.position = "none", plot.title = element_text(size = rel(1.2), face = "bold")) + - geom_hline(yintercept = 2 * mean(unitDf$mal), linetype = "dashed") + - geom_text(aes(label = unit), - data = unitDf[unitDf$mal > 2 * mean(unitDf$mal), ], - hjust = 1, vjust = 0) + - ggtitle("Outlying unit") + - xlab("unit") - if (plotL) plot(p) - invisible(p) -} - - - - - - -#' Mahalanobis distance (Chi2) -#' -#' @param diagLs diagnostic list -#' @param plotL aa -#' @return A plot -#' @author Natacha Lenuzza -#' @examples -#' print("hello !") -#' -#' @export plot_mahalanobisKhi2 -#' - - -plot_mahalanobisKhi2 <- function(diagLs, plotL = TRUE) { - unitDf <- data.frame(unit = names(diagLs$std.mahalanobis.distance), - mal = diagLs$mahalanobis.distance) - p <- qqplotF(x = unitDf$mal, - distribution = "chisq", - df = diagLs$q, - line.estimate = NULL, - conf = 0.95) + - xlab("Chi-squared quantiles") + - ylab("Mahalanobis distance") + - ggtitle("Normality of random effect") + - theme(legend.position = "none", plot.title = element_text(size = rel(1.2), face = "bold")) - if (plotL) plot(p) - invisible(p) -} - - - - - - - -####################################################################################################### -## Residus conditionels -####################################################################################################### - -## Presence of outlying observations and homoscedacity of residuals - -#' Homoskedacity of conditionalresiduals -#' -#' @param diagLs diagnostic list -#' @param hlimitN Limit value for outliers (e.g.2 or 3) -#' @param plotL Boolean -#' @param label_factor Column of observation names used to label outlying values -#' @return A plot -#' @author Natacha Lenuzza -#' @examples -#' print("hello !") -#' -#' @export plot_conditionalResiduals -#' - - -plot_conditionalResiduals <- function(diagLs, hlimitN, plotL = TRUE, label_factor = NULL) { - df <- cbind.data.frame(diagLs$data, - conditional.prediction = diagLs$conditional.prediction, - standardized.conditional.residuals = diagLs$std.conditional.residuals) - # outlier annotation - df$outliers <- rep("", nrow(df)) - outidx <- which(abs(df$standardized.conditional.residuals) > hlimitN) - df[outidx, "outliers"] <- (seq_len(nrow(df)))[outidx] - if (length(label_factor) >= 1) { - df[outidx, "outliers"] <- paste(df[outidx, "outliers"], - df[outidx, label_factor[1]], - sep = "_") - if (length(label_factor) > 1) { - for (li in 2:length(label_factor)) { - df[outidx, "outliers"] <- paste(df[outidx, "outliers"], - df[outidx, label_factor[li]], - sep = ".") - } - } - } - p <- ggplot(data = df, - aes(x = conditional.prediction, - y = standardized.conditional.residuals)) + - geom_point(size = 2) + - geom_hline(yintercept = 0, col = "grey") + - geom_smooth(aes(x = conditional.prediction, - y = standardized.conditional.residuals), - data = df, se = FALSE, col = "blue", method = "loess") + - ggtitle("Homoscedasticity of conditional residuals/outlying observations") + - xlab("Individual predictions") + - ylab("Standardized conditional residuals") + - theme(legend.position = "none", plot.title = element_text(size = rel(1.2), face = "bold")) + - geom_hline(yintercept = c(-1, 1) * hlimitN, linetype = "dashed") + - geom_text(aes(label = outliers), hjust = 0, vjust = 0) - if (plotL) plot(p) - invisible(p) -} - - - - -#' Normality of conditionalresiduals -#' -#' @param diagLs diagnostic list -#' @param plotL aa -#' @return A plot -#' @author Natacha Lenuzza -#' @examples -#' print("hello !") -#' -#' @export plot_condresQQplot -#' - - -plot_condresQQplot <- function(diagLs, plotL = TRUE) { - df <- cbind.data.frame(diagLs$data, - conditional.prediction = diagLs$conditional.prediction, - standardized.conditional.residuals = diagLs$std.conditional.residuals) - p <- qqplotF(x = df$standardized.conditional.residuals, - distribution = "norm", - line.estimate = NULL, - conf = 0.95) + - xlab("Standard normal quantiles") + - ylab("Standardized conditional residual quantiles") + - ggtitle("Normality of conditional error") + - theme(legend.position = "none", plot.title = element_text(size = rel(1.2), face = "bold")) - if (plotL) plot(p) - invisible(p) -} - - - - - -####################################################################################################### -## Within-units covariance structure -####################################################################################################### - - -#' Lesaffre-Veerbeke measure -#' -#' @param diagLs diagnostic list -#' @param plotL aa -#' @return A plot -#' @author Natacha Lenuzza -#' @examples -#' print("hello !") -#' -#' @export plot_lesaffreVeerbeke -#' - - -plot_lesaffreVeerbeke <- function(diagLs, plotL = TRUE) { - unitDf <- data.frame(unit = names(diagLs$std.lesaffreverbeke.measure), - lvm = diagLs$std.lesaffreverbeke.measure) - p <- ggplot(data = unitDf, - aes(x = unit, - y = lvm)) + - geom_point(size = 2) + - theme(legend.position = "none") + - xlab("units") + - ylab("Standardized Lesaffre-Verbeke measure") + - geom_hline(yintercept = 2 * mean(unitDf$lvm), linetype = "dashed") + - geom_text(aes(label = unit), - data = unitDf[unitDf$lvm > 2 * mean(unitDf$lvm), ], - hjust = 0, vjust = 0) + - ggtitle("Within-units covariance matrice") + - theme(legend.position = "none", plot.title = element_text(size = rel(1.2), face = "bold")) - if (plotL) plot(p) - invisible(p) -} - -##-------------------------------------------------------------------------------------------------## -## Helpers -##-------------------------------------------------------------------------------------------------## - - -## square root of a matrix -## From Rocha, Singer and Nobre - -#' square root of a matrix (Rocha) -#' -#' Description -#' -#' @param mat Matrix -#' @return A list -#' @author Natacha Lenuzza -#' @examples -#' print("hello !") -#' -#' @export sqrt.matrix - -sqrt.matrix <- function(mat) { - mat <- as.matrix(mat) # new line of code - singular_dec <- svd(mat, LINPACK = F) - U <- singular_dec$u - V <- singular_dec$v - D <- diag(singular_dec$d) - sqrtmatrix <- U %*% sqrt(D) %*% t(V) -} - - -## square root of a matrix -## http://www.cs.toronto.edu/~jepson/csc420/notes/introSVD.pdf (page 6) -## (for matMN a n x n matrix that symetric and non-negative definite) - -#' square root of a matrix (Rocha) -#' -#' @param mat Matrix -#' @return A list -#' @author Natacha Lenuzza -#' @examples -#' print("hello !") -#' -#' @export sqrtmF - -sqrtmF <- function(matMN) { - matMN <- as.matrix(matMN) - ## check that matMN is symetric: if (!all(t(matMN == matMN))) stop("matMN must be symetric.") - svd_dec <- svd(matMN) - invisible(svd_dec$u %*% sqrt(diag(svd_dec$d)) %*% t(svd_dec$v)) -} - - -## qqplotF -## adapted from https://gist.github.com/rentrop/d39a8406ad8af2a1066c - -qqplotF <- function(x, - distribution = "norm", ..., - line.estimate = NULL, - conf = 0.95, - labels = names(x)) { - q.function <- eval(parse(text = paste0("q", distribution))) - d.function <- eval(parse(text = paste0("d", distribution))) - x <- na.omit(x) - ord <- order(x) - n <- length(x) - P <- ppoints(length(x)) - daf <- data.frame(ord.x = x[ord], z = q.function(P, ...)) - if (is.null(line.estimate)) { - Q.x <- quantile(daf$ord.x, c(0.25, 0.75)) - Q.z <- q.function(c(0.25, 0.75), ...) - b <- diff(Q.x) / diff(Q.z) - coef <- c(Q.x[1] - b * Q.z[1], b) - } else { - coef <- coef(line.estimate(ord.x ~ z)) - } - zz <- qnorm(1 - (1 - conf) / 2) - SE <- (coef[2] / d.function(daf$z, ...)) * sqrt(P * (1 - P) / n) - fit.value <- coef[1] + coef[2] * daf$z - daf$upper <- fit.value + zz * SE - daf$lower <- fit.value - zz * SE - if (!is.null(labels)) { - daf$label <- ifelse(daf$ord.x > daf$upper | daf$ord.x < daf$lower, labels[ord], "") - } - p <- ggplot(daf, aes(x = z, y = ord.x)) + - geom_point() + - geom_abline(intercept = coef[1], slope = coef[2], col = "red") + - geom_line(aes(x = z, y = lower), daf, col = "red", linetype = "dashed") + - geom_line(aes(x = z, y = upper), daf, col = "red", linetype = "dashed") + - xlab("") + ylab("") - if (!is.null(labels)) p <- p + geom_text(aes(label = label)) - return(p) -} - - -## histogramm -histF <- function(x, sd_x = NULL, breaks = "scott") { - if (is.null(sd_x)) - sd_x <- sd(x) - ## Bandwith estimation (default is Scott) - if (!breaks %in% c("sqrt", "sturges", "rice", "scott", "fd")) - breaks <- "scott" - if (breaks %in% c("sqrt", "sturges", "rice")) { - k <- switch(breaks, - sqrt = sqrt(length(x)), - sturges = floor(log2(x)) + 1, - rice = floor(2 * length(x) ^ (1 / 3)) - ) - bw <- diff(range(x)) / k - }else{ - bw <- switch(breaks, - scott = 3.5 * sd_x / length(x) ^ (1 / 3), - fd = diff(range(x)) / (2 * IQR(x) / length(x) ^ (1 / 3)) - ) - } - - - daf <- data.frame(x = x) - ## graph - return(ggplot(data = daf, aes(x)) + - geom_histogram(aes(y = ..density..), - col = "black", fill = "grey", binwidth = bw) + - geom_density(size = 1.2, - col = "blue", - linetype = "blank", - fill = rgb(0, 0, 1, 0.1)) + - stat_function(fun = dnorm, - args = list(mean = 0, sd = sd_x), - col = "blue", size = 1.2) + - theme(legend.position = "none") + - xlab("")) -} - - -plot.res.Lmixed <- function(mfl, df, title = "", pvalCutof = 0.05) { - - ## define subscript of the different columns depending if we have only time (ncol(df)=3) or not - if (ncol(df) > 3) { - varidx <- 4 - ffidx <- 1 - timidx <- 2 - individx <- 3 - } else { - varidx <- 3 - ffidx <- 1 - timidx <- 1 - individx <- 2 - } - nameVar <- colnames(df)[varidx] - fflab <- colnames(df)[ffidx] - ## Individual time-course - rawPlot <- - ggplot(data = df, aes(x = df[[timidx]], y = df[[varidx]], colour = df[[ffidx]], group = df[[individx]])) + - geom_point() + - geom_line() + ggtitle("Individual time-courses (raw data)") + - ylab(nameVar) + - xlab(label = colnames(df)[2]) + - theme(legend.title = element_blank(), legend.position = "none", plot.title = element_text(size = rel(1.2), face = "bold")) - ## Boxplot of fixed factor - bPlot <- - ggplot(data = df, aes(y = df[[varidx]], x = df[[ffidx]], color = df[[ffidx]])) + - geom_boxplot(outlier.colour = "red", outlier.shape = 8, outlier.size = 4) + - ggtitle(paste("Boxplot by ", fflab, sep = "")) + xlab("") + ylab("") + - theme(legend.title = element_blank(), plot.title = element_text(size = rel(1.2), face = "bold")) - ## Post-hoc estimates - ddlsm1 <- mfl - ddlsm1$name <- rownames(ddlsm1) - ddlsm1$Significance <- rep("NS", nrow(ddlsm1)) - ## modif JF pour tenir compte du seuil de pvalues defini par le user - options("scipen" = 100, "digits" = 5) - pvalCutof <- as.numeric(pvalCutof) - bs <- 0.05; bm <- 0.01; bi <- 0.005 - if (pvalCutof > bm) { - bs <- pvalCutof - } else - if (pvalCutof < bm & pvalCutof > bi) { - bm <- pvalCutof; bs <- pvalCutof - } else - if (pvalCutof < bi) { - bi <- pvalCutof; bm <- pvalCutof; bs <- pvalCutof - } - lbs <- paste("p-value < ", bs, sep = "") - lbm <- paste("p-value < ", bm, sep = "") - lbi <- paste("p-value < ", bi, sep = "") - cols <- paste("p-value < ", bs, sep = "") - colm <- paste("p-value < ", bm, sep = "") - coli <- paste("p-value < ", bi, sep = "") - valcol <- c("grey", "yellow", "orange", "red") - names(valcol) <- c("NS", lbs, lbm, lbi) - ddlsm1$Significance[which(ddlsm1$p.value <= bs)] <- lbs - ddlsm1$Significance[which(ddlsm1$p.value < bs & ddlsm1$p.value >= bm)] <- lbm - ddlsm1$Significance[which(ddlsm1$p.value < bi)] <- lbi - ddlsm1$levels <- rownames(ddlsm1) - ddlsm1$term <- sapply(rownames(ddlsm1), function(namC) { - strsplit(namC, split = " ", fixed = TRUE)[[1]][1] - }) - - phPlot <- - ggplot(ddlsm1, aes(x = levels, y = Estimate)) + - facet_grid(facets = ~term, ddlsm1, scales = "free", space = "free") + - geom_bar(aes(fill = Significance), stat = "identity") + - theme(axis.text.x = element_text(angle = 90, hjust = 1)) + - scale_fill_manual( - values = valcol) + - geom_errorbar(aes(ymin = Lower.CI, ymax = Upper.CI), width = 0.25) + - ggtitle("Post-hoc estimates ") + xlab("") + - theme(plot.title = element_text(size = rel(1.2), face = "bold")) - - ## Final plotting - grid.arrange(arrangeGrob(rawPlot, bPlot, ncol = 2), - phPlot, nrow = 2, - top = textGrob(title, gp = gpar(fontsize = 32, font = 4)) - ) - -} +#' Calcul des grandeurs "diagnostiques" +#' +#' Script adapte de http://www.ime.unicamp.br/~cnaber/residdiag_nlme_v22.R pour fonctionner +#' avec un modele lmer (et non lme), des sujets avec des identifiants non numeriques, +#' et des observations non ordonnees sujet par sujet (dernier point a verifier.) +#' +#' @detail Les graphiques, les calculs associés et les notations utilisees dans le script suivent +#' l'article de Singer et al (2016) Graphical Tools for detedcting departures from linear +#' mixed model assumptions and some remedial measures, International Statistical Review +#' (doi:10.1111/insr.12178) +#' +#' @param mfl A linear mixed model fitted via lmer or a data frame containing data +#' @return A list +#' @author Natacha Lenuzza +#' @examples +#' print("hello !") +#' +#' @export lmer.computeDiag + + + +lmer.computeDiag <- function(mfl) { + ## Check arguments --------------------------------------------------------- + + if (length(mfl@flist) > 1) { + stop("Several 'grouping level' for random effect not implemented yet.") + } + + + ## extracting information from mfl models ------------------------------------------------------------- + # data + df <- mfl@frame + responseC <- names(df)[1] + unitC <- names(mfl@flist)[1] + # observations + yVn <- df[, responseC] + nobsN <- length(yVn) + # units + idunitVc <- levels(mfl@flist[[1]]) + nunitN <- length(unique(idunitVc)) + # X + xMN <- mfl@pp$X + pN <- ncol(xMN) + # Z + zMN <- t(as.matrix(mfl@pp$Zt)) + # Estimated covariance matrix of random effects (Gam) + aux <- VarCorr(mfl)[[1]] ## assuming only one level of grouping + aux2 <- attr(aux, "stddev") + gMN <- attr(aux, "correlation") * (aux2 %*% t(aux2)) + gammaMN <- as.matrix(kronecker(diag(nunitN), gMN)) + q <- dim(gMN)[1] + # Estimated covariance matrix of conditonal error (homoskedastic conditional independance model) + sigsqN <- attr(VarCorr(mfl), "sc")^2 + rMN <- sigsqN * diag(nobsN) + # Estimated covariance matrix of Y + vMN <- (zMN %*% gammaMN %*% t(zMN)) + rMN + invvMN <- MASS::ginv(vMN) + # H and Q matrix + hMN <- MASS::ginv(t(xMN) %*% invvMN %*% xMN) + qMN <- invvMN - invvMN %*% xMN %*% (hMN) %*% t(xMN) %*% invvMN + # eblue and eblup + eblueVn <- mfl@beta + eblupVn <- gammaMN %*% t(zMN) %*% invvMN %*% (yVn - xMN %*% eblueVn) ## equivalent de ranef(mfl) + rownames(eblupVn) <- colnames(zMN) + ## Calculs of matrices and vectors used in graph diagnosics --------------------------------------------- + ## Marginal and individual predictions, residuals and variances + marpredVn <- xMN %*% eblueVn + marresVn <- yVn - marpredVn + marvarMN <- vMN - xMN %*% hMN %*% t(xMN) + condpredVn <- marpredVn + zMN %*% eblupVn + condresVn <- yVn - condpredVn + condvarMN <- rMN %*% qMN %*% rMN + ## Analysis of marginal and conditional residuals + stmarresVn <- stcondresVn <- rep(0, nobsN) + lesverVn <- rep(0, nunitN) + names(lesverVn) <- idunitVc + for (i in 1:nunitN) { + idxiVn <- which(df[, unitC] == idunitVc[i]) ## position des observations du sujet i + miN <- length(idxiVn) + ## standardization of marginal residual + stmarresVn[idxiVn] <- as.vector(solve(sqrtmF(marvarMN[idxiVn, idxiVn])) %*% marresVn[idxiVn]) + ## Standardized Lessafre and Verbeke's measure + auxMN <- diag(1, ncol = miN, nrow = miN) - stmarresVn[idxiVn] %*% t(stmarresVn[idxiVn]) + lesverVn[i] <- sum(diag(auxMN %*% t(auxMN))) + ## standardization of conditional residual + stcondresVn[idxiVn] <- as.vector(solve(sqrtmF(condvarMN[idxiVn, idxiVn])) %*% condresVn[idxiVn]) + } + lesverVn <- lesverVn / sum(lesverVn) + ## Least confounded conditional residuals + ## EBLUP analysis (Mahalanobis' distance) + varbMN <- gammaMN %*% t(zMN) %*% qMN %*% zMN %*% gammaMN + mdistVn <- rep(0, nunitN) + qm <- q - 1 + for (j in 1:nunitN) { + gbi <- varbMN[(q * j - qm):(q * j), (q * j - qm):(q * j)] + eblupi <- eblupVn[(q * j - qm):(q * j)] + mdistVn[j] <- t(eblupi) %*% ginv(gbi) %*% eblupi + } + names(mdistVn) <- levels(mfl@flist[[1]]) + ## output ---------------------------------------------- + return(list( + data = df, + q = q, + eblue = eblueVn, + eblup = eblupVn, + marginal.prediction = marpredVn, + conditional.prediction = condpredVn, + std.marginal.residuals = stmarresVn, + std.conditional.residuals = stcondresVn, + mahalanobis.distance = mdistVn, + std.mahalanobis.distance = mdistVn / sum(mdistVn), + std.lesaffreverbeke.measure = lesverVn + )) +} + + +#' Wrapper function for diagnostic plots of 'lmer' linear mixed models +#' +#' (W4M mixmod) +#' +#' @param mfl A linear mixed model fitted via lmer or a data frame containing data +#' @param title aa +#' @param outlier.limit aa +#' @param pvalCutof aa +#' @param resC aa +#' @param uniC aa +#' @param fixC aa +#' @param lest.confounded Not used yet. +#' @return NULL +#' @author Natacha Lenuzza +#' @examples +#' print("hello !") +#' +#' @export diagmflF + + +diagmflF <- function(mfl, + title = "", + outlier.limit = 3, + pvalCutof = 0.05, + resC = "vd", + uniC = "subject", + timC = "time", + fixC = "fixfact", + least.confounded = FALSE) { + ## diagnostics + diagLs <- lmer.computeDiag(mfl) + ## plots + blank <- rectGrob(gp = gpar(col = "white")) + rectspacer <- rectGrob(height = unit(0.1, "npc"), gp = gpar(col = "grey")) + grid.arrange(blank, + plot_linearity(diagLs, + hlimitN = outlier.limit, plotL = FALSE, + label_factor = c(uniC, fixC, timC) + ), + blank, + plot_conditionalResiduals(diagLs, + hlimitN = outlier.limit, plotL = FALSE, + label_factor = c(uniC, fixC, timC) + ), + blank, + plot_condresQQplot(diagLs, plotL = FALSE), + blank, + plot_lesaffreVeerbeke(diagLs, plotL = FALSE), + blank, + plot_randomEffect(mfl, plotL = FALSE)[[1]], + blank, + plot_mahalanobisKhi2(diagLs, plotL = FALSE), + blank, + plot_mahalanobis(diagLs, plotL = FALSE), + blank, + blank, + blank, + top = textGrob(title, gp = gpar(fontsize = 40, font = 4)), + layout_matrix = matrix( + c( + rep(1, 7), + 2, 3, rep(4, 3), 20, 21, + rep(5, 7), + 6:12, + rep(13, 7), + 14:18, rep(19, 2) + ), + ncol = 7, nrow = 6, byrow = TRUE + ), + heights = c(0.1 / 3, 0.3, 0.1 / 3, 0.3, 0.1 / 3, 0.3), + widths = c(0.22, 0.04, 0.22, 0.04, 0.22, 0.04, 0.22) + ) +} + +####################################################################################################### +## Raw data time courses +####################################################################################################### + +#' Visualization of raw time course +#' +#' Une +#' +#' @param mfl A linear mixed model fitted via lmer or a data frame containing data +#' @param responseC Name of the 'response' variable +#' @param timeC Name of the 'time' variable +#' @param subjectC Name of the 'subject' variable +#' @param fixfactC Name of the 'fixed factor' variable (e.g.treatment) +#' @param offset_subject Boolean indicating if an offset value (subject's mean) should substracted to each data point. Default is FALSE +#' @param plotL Boolean +#' @param colorType One of NA, FIXFACT or SUBJECT +#' @param shapeType One of NA, FIXFACT or SUBJECT +#' @param lineType One of NA, FIXFACT or SUBJECT +#' @return A plot +#' @author Natacha Lenuzza +#' @examples +#' print("hello !") +#' +#' @export plot_timeCourse + + + +plot_timeCourse <- function(mfl, + responseC, + timeC, + subjectC, + fixfactC = NULL, + offset_subject = FALSE, + plotL = TRUE, + colorType = NA, ## subject, fixfact, none or NA + shapeType = NA, ## subject, fixfact, none or NA + lineType = NA ## subject, fixfact, none or NA +) { + ## Data ----- + if (class(mfl) %in% c("merModLmerTest", "lmerMod", "lmerModLmerTest")) { + DF <- mfl@frame + } else if (class(mfl) == "data.frame") { + DF <- mfl + } else { + stop("'mfl' argument must be a linear mixed effect model or a data frame.") + } + ## Format data ----- + if (is.null(fixfactC)) { + DF <- DF[, c(responseC, timeC, subjectC)] + colnames(DF) <- c("DV", "TIME", "SUBJECT") + meanDF <- aggregate(DF$DV, + by = list( + SUBJECT = DF$SUBJECT, + TIME = DF$TIME + ), + FUN = mean, + na.rm = TRUE + ) + colnames(meanDF) <- c("SUBJECT", "TIME", "DV") + meanDF$GROUP <- meanDF$SUBJECT + } else { + DF <- DF[, c(responseC, fixfactC, timeC, subjectC)] + colnames(DF) <- c("DV", "FIXFACT", "TIME", "SUBJECT") + meanDF <- aggregate(DF$DV, + by = list( + SUBJECT = DF$SUBJECT, + TIME = DF$TIME, + FIXFACT = DF$FIXFACT + ), + FUN = mean, + na.rm = TRUE + ) + colnames(meanDF) <- c("SUBJECT", "TIME", "FIXFACT", "DV") + meanDF$GROUP <- paste(meanDF$SUBJECT, meanDF$FIXFACT, sep = "_") + } + ## Offset ----- + if (offset_subject) { + offsetMN <- aggregate(DF$DV, by = list(DF$SUBJECT), mean, na.rm = TRUE) + offsetVn <- offsetMN[, 2] + names(offsetVn) <- offsetMN[, 1] + rm(offsetMN) + DF$DV <- DF$DV - offsetVn[DF$SUBJECT] + meanDF$DV <- meanDF$DV - offsetVn[as.character(meanDF$SUBJECT)] + } + ## Graphical parameters ----- + xlabC <- timeC + ylabC <- responseC + titC <- "Individual time-courses" + if (offset_subject) { + ylabC <- paste(ylabC, "minus 'within-subject' empirical mean") + titC <- paste(titC, "('within-subject' empirical mean offset)") + } + ## color + if (is.na(colorType)) { ## automaticatical attribution + if (is.null(fixfactC)) { + colorType <- "SUBJECT" + } else { + colorType <- "FIXFACT" + } + colTxt <- paste(", colour=", colorType) + } else if (colorType == "none") { + colTxt <- "" + } else { + colTxt <- paste(", colour=", colorType) + } + ## lineType + if (is.na(lineType)) { ## automaticatical attribution + if (is.null(fixfactC)) { + linTxt <- "" + } else { + linTxt <- paste( + ", linetype=", + ifelse(colorType == "SUBJECT", "FIXFACT", "SUBJECT") + ) + } + } else if (lineType == "none") { + linTxt <- "" + } else { + linTxt <- paste(", linetype=", lineType) + } + ## shapeType + if (is.na(shapeType)) { ## automaticatical attribution + if (is.null(fixfactC)) { + shaTxt <- "" + } else { + shaTxt <- paste( + ", shape=", + ifelse(colorType == "SUBJECT", "FIXFACT", "SUBJECT") + ) + } + } else if (shapeType == "none") { + shaTxt <- "" + } else { + shaTxt <- paste(", shape=", shapeType) + } + ## aes mapping + txtMap <- paste("aes(x = TIME, y = DV", + colTxt, shaTxt, ")", + sep = "" + ) + txtLineMap <- paste("aes(x = TIME, y = DV, group = GROUP ", + colTxt, linTxt, ")", + sep = "" + ) + ## plot and output + p <- ggplot(data = DF, mapping = eval(parse(text = txtMap))) + + ggtitle(titC) + + xlab(xlabC) + + ylab(ylabC) + + theme( + legend.position = "left", + plot.title = element_text(size = rel(1.2), face = "bold") + ) + + geom_point() + + geom_line(eval(parse(text = txtLineMap)), data = meanDF) + + theme_bw() + + NULL + if (plotL) plot(p) + invisible(p) +} + +####################################################################################################### +## Post-hoc estimate +####################################################################################################### +#' Visualization of fixed effects (post-hoc estimates) +#' +#' Description +#' +#' @param mfl A linear mixed model fitted via lmer or a data frame containing data +#' @param pvalCutof User pvalue cut of +#' @param plotL Boolean +#' @param titC Title of the plot +#' @return A plot +#' @author Natacha Lenuzza +#' @examples +#' print("hello !") +#' +#' @export plot_posthoc +plot_posthoc <- function(mfl, pvalCutof = 0.05, plotL = TRUE, titC = "Post-hoc estimates") { + ddlsm1 <- as.data.frame(difflsmeans(mfl, test.effs = NULL)) + colnames(ddlsm1)[ncol(ddlsm1)] <- "pvalue" + ddlsm1$Significance <- rep("NS", nrow(ddlsm1)) + ## modif JF pour tenir compte du seuil de pvalues defini par le user + ddlsm1$Significance[which(ddlsm1$pvalue < pvalCutof)] <- paste("p-value < ", pvalCutof, sep = "") + ddlsm1$Significance[which(ddlsm1$pvalue < pvalCutof / 5)] <- paste("p-value < ", pvalCutof / 5, sep = "") + ddlsm1$Significance[which(ddlsm1$pvalue < pvalCutof / 10)] <- paste("p-value < ", pvalCutof / 10, sep = "") + ddlsm1$levels <- rownames(ddlsm1) + ddlsm1$term <- sapply(rownames(ddlsm1), function(namC) { + strsplit(namC, split = " ", fixed = TRUE)[[1]][1] + }) + colValue <- c("grey", "yellow", "orange", "red") + names(colValue) <- c( + "NS", + paste("p-value < ", pvalCutof, sep = ""), + paste("p-value < ", pvalCutof / 5, sep = ""), + paste("p-value < ", pvalCutof / 10, sep = "") + ) + p <- ggplot(ddlsm1, aes(x = levels, y = Estimate)) + + facet_grid(facets = ~term, ddlsm1, scales = "free", space = "free") + + geom_bar(aes(fill = Significance), stat = "identity") + + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + scale_fill_manual(values = colValue) + + geom_errorbar(aes(ymin = lower, ymax = upper), width = 0.25) + + ggtitle(titC) + + xlab("") + + NULL + if (plotL) plot(p) + invisible(p) +} + +####################################################################################################### +## Visualisation des effets aléatoires +####################################################################################################### +#' Visualization of random effects +#' +#' Equivalent of dotplot(ranef) +#' +#' @param mfl A linear mixed model fitted via lmer or a data frame containing data +#' @param plotL Logical +#' @return A plot +#' @author Natacha Lenuzza +#' @examples +#' print("hello !") +#' +#' @export plot_randomEffect + + +plot_randomEffect <- function(mfl, plotL = TRUE) { + ## Estimation et format des effets aléatoires + randomEffect <- ranef(mfl, condVar = TRUE) + DF <- data.frame(randomEffect = rep(names(randomEffect), + times = sapply( + seq_len(length(randomEffect)), + function(lsi) { + return(length(unlist(randomEffect[[lsi]]))) + } + ) + )) + DF$condVar <- DF$estimate <- DF$x2 <- DF$x1 <- rep(NA, nrow(DF)) + for (rafC in names(randomEffect)) { + eff <- randomEffect[[rafC]] + DF$x1[which(DF$randomEffect == rafC)] <- rep(colnames(eff), each = nrow(eff)) + DF$x2[which(DF$randomEffect == rafC)] <- rep(rownames(eff), ncol(eff)) + DF$estimate[which(DF$randomEffect == rafC)] <- unlist(eff) + condvar <- attr(randomEffect[[rafC]], "postVar") + se <- NULL + for (coli in seq_len(ncol(eff))) { + se <- c( + se, + sapply( + seq_len(nrow(eff)), + function(i) { + return(condvar[coli, coli, i]) + } + ) + ) + } + DF$condVar[which(DF$randomEffect == rafC)] <- se + } + DF$se <- sqrt(DF$condVar) + DF$lower <- DF$estimate - 1.96 * DF$se + DF$upper <- DF$estimate + 1.96 * DF$se + ## Plot + plotLs <- vector("list", length(randomEffect)) + names(plotLs) <- names(randomEffect) + for (pi in seq_len(length(plotLs))) { + subDF <- DF[DF$randomEffect == names(plotLs)[pi], ] + subDF <- subDF[order(subDF$x1, subDF$estimate, decreasing = FALSE), ] + p <- ggplot( + data = subDF, + mapping = aes(x = estimate, y = reorder(x2, estimate)) + ) + + geom_point(size = 3) + + geom_segment(aes(xend = lower, yend = x2)) + + geom_segment(aes(xend = upper, yend = x2)) + + facet_wrap(~x1, ncol = length(unique(subDF$x1))) + + ylab("") + + xlab("") + + ggtitle(paste("Random effect - ", names(plotLs)[pi], sep = "")) + + theme(legend.position = "none", plot.title = element_text(size = rel(1.2), face = "bold")) + + geom_vline(xintercept = 0, linetype = "dashed") + + theme_bw() + plotLs[[pi]] <- p + if (plotL) plot(p) + } + invisible(plotLs) +} + +####################################################################################################### +## Linearité des effets et outlying observations +####################################################################################################### +#' Linarity of the fixed effect with regard to the continuous time +#' +#' @param diagLs diagnostic list +#' @param hlimitN Limit value for outliers (e.g.2 or 3) +#' @param plotL Boolean +#' @param label_factor Column of observation names used to label outlying values +#' @return A plot +#' @author Natacha Lenuzza +#' @examples +#' print("hello !") +#' +#' @export plot_linearity +#' + +plot_linearity <- function(diagLs, hlimitN, plotL = TRUE, label_factor = NULL) { + df <- cbind.data.frame(diagLs$data, + marginal.prediction = diagLs$marginal.prediction, + standardized.marginal.residuals = diagLs$std.marginal.residuals + ) + # outlier annotation + df$outliers <- rep("", nrow(df)) + outidx <- which(abs(df$standardized.marginal.residuals) > hlimitN) + df[outidx, "outliers"] <- (seq_len(nrow(df)))[outidx] + if (length(label_factor) >= 1) { + df[outidx, "outliers"] <- paste(df[outidx, "outliers"], + df[outidx, label_factor[1]], + sep = "_" + ) + if (length(label_factor) > 1) { + for (li in 2:length(label_factor)) { + df[outidx, "outliers"] <- paste(df[outidx, "outliers"], + df[outidx, label_factor[li]], + sep = "." + ) + } + } + } + p <- ggplot( + data = df, + aes( + x = marginal.prediction, + y = standardized.marginal.residuals + ) + ) + + geom_point(size = 2) + + geom_hline(yintercept = 0, col = "grey") + + geom_smooth(aes( + x = marginal.prediction, + y = standardized.marginal.residuals + ), data = df, se = FALSE, col = "blue", method = "loess") + + ggtitle("Linearity of effects/outlying obervations") + + xlab("Marginal predictions") + + ylab("Standardized marginal residuals") + + theme(legend.position = "none", plot.title = element_text(size = rel(1.2), face = "bold")) + + geom_hline(yintercept = c(-1, 1) * hlimitN, linetype = "dashed") + + geom_text(aes(label = outliers), hjust = 0, vjust = 0) + if (plotL) plot(p) + invisible(p) +} + + +####################################################################################################### +## EBLUP +####################################################################################################### + + +#' Mahalanobis distance +#' +#' @param diagLs diagnostic list +#' @param plotL Boolean +#' @return A plot +#' @author Natacha Lenuzza +#' @examples +#' print("hello !") +#' +#' @export plot_mahalanobis +#' + + +plot_mahalanobis <- function(diagLs, plotL = TRUE) { + unitDf <- data.frame( + unit = names(diagLs$std.mahalanobis.distance), + mal = diagLs$std.mahalanobis.distance + ) + ## Outlying subjects + p <- + ggplot(aes(y = mal, x = unit), data = unitDf) + + geom_point(size = 3) + + ylab("Standardized Mahalanobis distance") + + geom_vline(xintercept = 0, linetype = "dashed") + + theme(legend.position = "none", plot.title = element_text(size = rel(1.2), face = "bold")) + + geom_hline(yintercept = 2 * mean(unitDf$mal), linetype = "dashed") + + geom_text(aes(label = unit), + data = unitDf[unitDf$mal > 2 * mean(unitDf$mal), ], + hjust = 1, vjust = 0 + ) + + ggtitle("Outlying unit") + + xlab("unit") + if (plotL) plot(p) + invisible(p) +} + + + + + + +#' Mahalanobis distance (Chi2) +#' +#' @param diagLs diagnostic list +#' @param plotL aa +#' @return A plot +#' @author Natacha Lenuzza +#' @examples +#' print("hello !") +#' +#' @export plot_mahalanobisKhi2 +#' + + +plot_mahalanobisKhi2 <- function(diagLs, plotL = TRUE) { + unitDf <- data.frame( + unit = names(diagLs$std.mahalanobis.distance), + mal = diagLs$mahalanobis.distance + ) + p <- qqplotF( + x = unitDf$mal, + distribution = "chisq", + df = diagLs$q, + line.estimate = NULL, + conf = 0.95 + ) + + xlab("Chi-squared quantiles") + + ylab("Mahalanobis distance") + + ggtitle("Normality of random effect") + + theme(legend.position = "none", plot.title = element_text(size = rel(1.2), face = "bold")) + if (plotL) plot(p) + invisible(p) +} + + + + + + + +####################################################################################################### +## Residus conditionels +####################################################################################################### + +## Presence of outlying observations and homoscedacity of residuals + +#' Homoskedacity of conditionalresiduals +#' +#' @param diagLs diagnostic list +#' @param hlimitN Limit value for outliers (e.g.2 or 3) +#' @param plotL Boolean +#' @param label_factor Column of observation names used to label outlying values +#' @return A plot +#' @author Natacha Lenuzza +#' @examples +#' print("hello !") +#' +#' @export plot_conditionalResiduals +#' + + +plot_conditionalResiduals <- function(diagLs, hlimitN, plotL = TRUE, label_factor = NULL) { + df <- cbind.data.frame(diagLs$data, + conditional.prediction = diagLs$conditional.prediction, + standardized.conditional.residuals = diagLs$std.conditional.residuals + ) + # outlier annotation + df$outliers <- rep("", nrow(df)) + outidx <- which(abs(df$standardized.conditional.residuals) > hlimitN) + df[outidx, "outliers"] <- (seq_len(nrow(df)))[outidx] + if (length(label_factor) >= 1) { + df[outidx, "outliers"] <- paste(df[outidx, "outliers"], + df[outidx, label_factor[1]], + sep = "_" + ) + if (length(label_factor) > 1) { + for (li in 2:length(label_factor)) { + df[outidx, "outliers"] <- paste(df[outidx, "outliers"], + df[outidx, label_factor[li]], + sep = "." + ) + } + } + } + p <- ggplot( + data = df, + aes( + x = conditional.prediction, + y = standardized.conditional.residuals + ) + ) + + geom_point(size = 2) + + geom_hline(yintercept = 0, col = "grey") + + geom_smooth( + aes( + x = conditional.prediction, + y = standardized.conditional.residuals + ), + data = df, se = FALSE, col = "blue", method = "loess" + ) + + ggtitle("Homoscedasticity of conditional residuals/outlying observations") + + xlab("Individual predictions") + + ylab("Standardized conditional residuals") + + theme(legend.position = "none", plot.title = element_text(size = rel(1.2), face = "bold")) + + geom_hline(yintercept = c(-1, 1) * hlimitN, linetype = "dashed") + + geom_text(aes(label = outliers), hjust = 0, vjust = 0) + if (plotL) plot(p) + invisible(p) +} + + + + +#' Normality of conditionalresiduals +#' +#' @param diagLs diagnostic list +#' @param plotL aa +#' @return A plot +#' @author Natacha Lenuzza +#' @examples +#' print("hello !") +#' +#' @export plot_condresQQplot +#' + + +plot_condresQQplot <- function(diagLs, plotL = TRUE) { + df <- cbind.data.frame(diagLs$data, + conditional.prediction = diagLs$conditional.prediction, + standardized.conditional.residuals = diagLs$std.conditional.residuals + ) + p <- qqplotF( + x = df$standardized.conditional.residuals, + distribution = "norm", + line.estimate = NULL, + conf = 0.95 + ) + + xlab("Standard normal quantiles") + + ylab("Standardized conditional residual quantiles") + + ggtitle("Normality of conditional error") + + theme(legend.position = "none", plot.title = element_text(size = rel(1.2), face = "bold")) + if (plotL) plot(p) + invisible(p) +} + + + + + +####################################################################################################### +## Within-units covariance structure +####################################################################################################### + + +#' Lesaffre-Veerbeke measure +#' +#' @param diagLs diagnostic list +#' @param plotL aa +#' @return A plot +#' @author Natacha Lenuzza +#' @examples +#' print("hello !") +#' +#' @export plot_lesaffreVeerbeke +#' + + +plot_lesaffreVeerbeke <- function(diagLs, plotL = TRUE) { + unitDf <- data.frame( + unit = names(diagLs$std.lesaffreverbeke.measure), + lvm = diagLs$std.lesaffreverbeke.measure + ) + p <- ggplot( + data = unitDf, + aes( + x = unit, + y = lvm + ) + ) + + geom_point(size = 2) + + theme(legend.position = "none") + + xlab("units") + + ylab("Standardized Lesaffre-Verbeke measure") + + geom_hline(yintercept = 2 * mean(unitDf$lvm), linetype = "dashed") + + geom_text(aes(label = unit), + data = unitDf[unitDf$lvm > 2 * mean(unitDf$lvm), ], + hjust = 0, vjust = 0 + ) + + ggtitle("Within-units covariance matrice") + + theme(legend.position = "none", plot.title = element_text(size = rel(1.2), face = "bold")) + if (plotL) plot(p) + invisible(p) +} + +## -------------------------------------------------------------------------------------------------## +## Helpers +## -------------------------------------------------------------------------------------------------## + + +## square root of a matrix +## From Rocha, Singer and Nobre + +#' square root of a matrix (Rocha) +#' +#' Description +#' +#' @param mat Matrix +#' @return A list +#' @author Natacha Lenuzza +#' @examples +#' print("hello !") +#' +#' @export sqrt.matrix + +sqrt.matrix <- function(mat) { + mat <- as.matrix(mat) # new line of code + singular_dec <- svd(mat, LINPACK = F) + U <- singular_dec$u + V <- singular_dec$v + D <- diag(singular_dec$d) + sqrtmatrix <- U %*% sqrt(D) %*% t(V) +} + + +## square root of a matrix +## http://www.cs.toronto.edu/~jepson/csc420/notes/introSVD.pdf (page 6) +## (for matMN a n x n matrix that symetric and non-negative definite) + +#' square root of a matrix (Rocha) +#' +#' @param mat Matrix +#' @return A list +#' @author Natacha Lenuzza +#' @examples +#' print("hello !") +#' +#' @export sqrtmF + +sqrtmF <- function(matMN) { + matMN <- as.matrix(matMN) + ## check that matMN is symetric: if (!all(t(matMN == matMN))) stop("matMN must be symetric.") + svd_dec <- svd(matMN) + invisible(svd_dec$u %*% sqrt(diag(svd_dec$d)) %*% t(svd_dec$v)) +} + + +## qqplotF +## adapted from https://gist.github.com/rentrop/d39a8406ad8af2a1066c + +qqplotF <- function(x, + distribution = "norm", ..., + line.estimate = NULL, + conf = 0.95, + labels = names(x)) { + q.function <- eval(parse(text = paste0("q", distribution))) + d.function <- eval(parse(text = paste0("d", distribution))) + x <- na.omit(x) + ord <- order(x) + n <- length(x) + P <- ppoints(length(x)) + daf <- data.frame(ord.x = x[ord], z = q.function(P, ...)) + if (is.null(line.estimate)) { + Q.x <- quantile(daf$ord.x, c(0.25, 0.75)) + Q.z <- q.function(c(0.25, 0.75), ...) + b <- diff(Q.x) / diff(Q.z) + coef <- c(Q.x[1] - b * Q.z[1], b) + } else { + coef <- coef(line.estimate(ord.x ~ z)) + } + zz <- qnorm(1 - (1 - conf) / 2) + SE <- (coef[2] / d.function(daf$z, ...)) * sqrt(P * (1 - P) / n) + fit.value <- coef[1] + coef[2] * daf$z + daf$upper <- fit.value + zz * SE + daf$lower <- fit.value - zz * SE + if (!is.null(labels)) { + daf$label <- ifelse(daf$ord.x > daf$upper | daf$ord.x < daf$lower, labels[ord], "") + } + p <- ggplot(daf, aes(x = z, y = ord.x)) + + geom_point() + + geom_abline(intercept = coef[1], slope = coef[2], col = "red") + + geom_line(aes(x = z, y = lower), daf, col = "red", linetype = "dashed") + + geom_line(aes(x = z, y = upper), daf, col = "red", linetype = "dashed") + + xlab("") + + ylab("") + if (!is.null(labels)) p <- p + geom_text(aes(label = label)) + return(p) +} + + +## histogramm +histF <- function(x, sd_x = NULL, breaks = "scott") { + if (is.null(sd_x)) { + sd_x <- sd(x) + } + ## Bandwith estimation (default is Scott) + if (!breaks %in% c("sqrt", "sturges", "rice", "scott", "fd")) { + breaks <- "scott" + } + if (breaks %in% c("sqrt", "sturges", "rice")) { + k <- switch(breaks, + sqrt = sqrt(length(x)), + sturges = floor(log2(x)) + 1, + rice = floor(2 * length(x)^(1 / 3)) + ) + bw <- diff(range(x)) / k + } else { + bw <- switch(breaks, + scott = 3.5 * sd_x / length(x)^(1 / 3), + fd = diff(range(x)) / (2 * IQR(x) / length(x)^(1 / 3)) + ) + } + + + daf <- data.frame(x = x) + ## graph + return(ggplot(data = daf, aes(x)) + + geom_histogram(aes(y = ..density..), + col = "black", fill = "grey", binwidth = bw + ) + + geom_density( + size = 1.2, + col = "blue", + linetype = "blank", + fill = rgb(0, 0, 1, 0.1) + ) + + stat_function( + fun = dnorm, + args = list(mean = 0, sd = sd_x), + col = "blue", size = 1.2 + ) + + theme(legend.position = "none") + + xlab("")) +} + + +plot.res.Lmixed <- function(mfl, df, title = "", pvalCutof = 0.05) { + ## define subscript of the different columns depending if we have only time (ncol(df)=3) or not + if (ncol(df) > 3) { + varidx <- 4 + ffidx <- 1 + timidx <- 2 + individx <- 3 + } else { + varidx <- 3 + ffidx <- 1 + timidx <- 1 + individx <- 2 + } + nameVar <- colnames(df)[varidx] + fflab <- colnames(df)[ffidx] + ## Individual time-course + rawPlot <- + ggplot(data = df, aes(x = df[[timidx]], y = df[[varidx]], colour = df[[ffidx]], group = df[[individx]])) + + geom_point() + + geom_line() + + ggtitle("Individual time-courses (raw data)") + + ylab(nameVar) + + xlab(label = colnames(df)[2]) + + theme(legend.title = element_blank(), legend.position = "none", plot.title = element_text(size = rel(1.2), face = "bold")) + ## Boxplot of fixed factor + bPlot <- + ggplot(data = df, aes(y = df[[varidx]], x = df[[ffidx]], color = df[[ffidx]])) + + geom_boxplot(outlier.colour = "red", outlier.shape = 8, outlier.size = 4) + + ggtitle(paste("Boxplot by ", fflab, sep = "")) + + xlab("") + + ylab("") + + theme(legend.title = element_blank(), plot.title = element_text(size = rel(1.2), face = "bold")) + ## Post-hoc estimates + ddlsm1 <- mfl + ddlsm1$name <- rownames(ddlsm1) + ddlsm1$Significance <- rep("NS", nrow(ddlsm1)) + ## modif JF pour tenir compte du seuil de pvalues defini par le user + options("scipen" = 100, "digits" = 5) + pvalCutof <- as.numeric(pvalCutof) + bs <- 0.05 + bm <- 0.01 + bi <- 0.005 + if (pvalCutof > bm) { + bs <- pvalCutof + } else if (pvalCutof < bm & pvalCutof > bi) { + bm <- pvalCutof + bs <- pvalCutof + } else if (pvalCutof < bi) { + bi <- pvalCutof + bm <- pvalCutof + bs <- pvalCutof + } + lbs <- paste("p-value < ", bs, sep = "") + lbm <- paste("p-value < ", bm, sep = "") + lbi <- paste("p-value < ", bi, sep = "") + cols <- paste("p-value < ", bs, sep = "") + colm <- paste("p-value < ", bm, sep = "") + coli <- paste("p-value < ", bi, sep = "") + valcol <- c("grey", "yellow", "orange", "red") + names(valcol) <- c("NS", lbs, lbm, lbi) + ddlsm1$Significance[which(ddlsm1$p.value <= bs)] <- lbs + ddlsm1$Significance[which(ddlsm1$p.value < bs & ddlsm1$p.value >= bm)] <- lbm + ddlsm1$Significance[which(ddlsm1$p.value < bi)] <- lbi + ddlsm1$levels <- rownames(ddlsm1) + ddlsm1$term <- sapply(rownames(ddlsm1), function(namC) { + strsplit(namC, split = " ", fixed = TRUE)[[1]][1] + }) + + phPlot <- + ggplot(ddlsm1, aes(x = levels, y = Estimate)) + + facet_grid(facets = ~term, ddlsm1, scales = "free", space = "free") + + geom_bar(aes(fill = Significance), stat = "identity") + + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + + scale_fill_manual( + values = valcol + ) + + geom_errorbar(aes(ymin = Lower.CI, ymax = Upper.CI), width = 0.25) + + ggtitle("Post-hoc estimates ") + + xlab("") + + theme(plot.title = element_text(size = rel(1.2), face = "bold")) + + ## Final plotting + grid.arrange(arrangeGrob(rawPlot, bPlot, ncol = 2), + phPlot, + nrow = 2, + top = textGrob(title, gp = gpar(fontsize = 32, font = 4)) + ) +} diff --git a/tools/mixmodel4repeated_measures/mixmodel.xml b/tools/mixmodel4repeated_measures/mixmodel.xml index 18b263f947..641036d86e 100755 --- a/tools/mixmodel4repeated_measures/mixmodel.xml +++ b/tools/mixmodel4repeated_measures/mixmodel.xml @@ -1,4 +1,4 @@ - + ANOVA for repeated measures statistics @@ -8,6 +8,11 @@ r-gridExtra bioconductor-multtest + + + + + nfp <- 4 - res[c((nfp - 2):nfp), ] <- raov[, 6] - rownames(res)[c((nfp - 2):nfp)] <- namesFactprinc - - #################### Residuals diagnostics for significants variables ######################### - ### Il at least 1 factor is significant and visu=TRUE NL graphics add to pdf - ## ajout JF du passage de la valeur de p-value cutoff - if (length(which(raov[, 6] <= pvalCutof)) > 0 & visu == "yes") { - diagmflF(mfl, title = tit, pvalCutof = pvalCutof, least.confounded = least.confounded, - outlier.limit = outlier.limit) - } - - # pvalue of fixed factor comparisons - nresf <- nresT - res[(nfp + 1):(nfp + nct), ] <- ddlsm1[c(1:nct), 9] - res[(nfp + nct + 1):(nfp + nct + ncff), ] <- ddlsm1[(nct + 1):(nct + ncff), 9] - rownames(res)[(nfp + 1):(nfp + nct + ncff)] <- namesFactPval - res[(nfp + nct + ncff + 1):(nfp + nresf), ] <- ddlsm1[(nct + ncff + 1):(nresT), 9] - rownames(res)[(nfp + nct + ncff + 1):(nfp + nresT)] <- namespvalues - # Estimate of the difference between levels of factors - res[(nfp + nresf + 1):(nfp + nresf + nct), ] <- ddlsm1[c(1:nct), 3] - res[(nfp + nresf + nct + 1):(nfp + nresf + nct + ncff), ] <- ddlsm1[(nct + 1):(nct + ncff), 3] - rownames(res)[(nfp + nresf + 1):(nfp + nresf + nct + ncff)] <- namesFactEstim - res[(nfp + nresf + nct + ncff + 1):(nfp + 2 * nresf), ] <- ddlsm1[(nct + ncff + 1):(nresT), 3] - rownames(res)[(nfp + nresf + nct + ncff + 1):(nfp + 2 * nresf)] <- namesEstimate - # lower CI of the difference between levels of factors - nresf <- nresf + nresT - res[(nfp + nresf + 1):(nfp + nresf + nct), ] <- ddlsm1[c(1:nct), 7] - res[(nfp + nresf + nct + 1):(nfp + nresf + nct + ncff), ] <- ddlsm1[(nct + 1):(nct + ncff), 7] - rownames(res)[(nfp + nresf + 1):(nfp + nresf + nct + ncff)] <- namesFactLowerCI - res[(nfp + nresf + nct + ncff + 1):(nfp + 2 * nresf), ] <- ddlsm1[(nct + ncff + 1):(nresf), 7] - rownames(res)[(nfp + nresf + nct + ncff + 1):(nfp + nresf + (nresf / 2))] <- namesLowerCI - # Upper CI of the difference between levels of factors - nresf <- nresf + nresT - res[(nfp + nresf + 1):(nfp + nresf + nct), ] <- ddlsm1[c(1:nct), 8] - res[(nfp + nresf + nct + 1):(nfp + nresf + nct + ncff), ] <- ddlsm1[(nct + 1):(nct + ncff), 8] - rownames(res)[(nfp + nresf + 1):(nfp + nresf + nct + ncff)] <- namesFactUpperCI - res[(nfp + nresf + nct + ncff + 1):(nfp + nresf + (nresT)), ] <- ddlsm1[(nct + ncff + 1):(nresT), 8] - rownames(res)[(nfp + nresf + nct + ncff + 1):(nfp + nresf + (nresT))] <- namesUpperCI - - } else { - ## one of the subject has only one time, subject can't be a random variable - ## A repeated measure could be run instead function lme of package nlme, in next version? - res[1, ] <- NA - cat("\n Computing impossible for feature ", tit, ": at least one subject has only one time.\n") - } - tres <- data.frame(t(res)) - rownames(tres)[1] <- nameVar - cres <- list(tres, rn, fn) - return(cres) -} - -############################################################################################################## -lmRepeated1FF <- function(ids, ifixfact = 0, itime, isubject, ivd, ndim, nameVar = colnames(ids)[[ivd]], - dffOption, pvalCutof = 0.05) { - ### function to perform linear mixed model with factor Time + random factor subject - ### based on lmerTest package providing functions giving the same results as SAS proc mixed - - - if (!is.numeric(ids[[ivd]])) stop("Dependant variable is not numeric") - if (!is.factor(ids[[itime]])) stop("Repeated factor is not a factor") - if (!is.factor(ids[[isubject]])) stop("Random factor is not a factor") - # Could be interesting here to add an experience plan check to give back a specific error message - # in case time points are missing for some individuals - - time <- ids[[itime]] - subject <- ids[[isubject]] - vd <- ids[[ivd]] ## dependant variables (quatitative) - - # nfp : nombre de facteurs principaux + model infos + normality test - nfp <- ndim[1] - # nlt number of time levels; nct number of comparisons of the time factor - nlt <- ndim[4] - nct <- ndim[5] - # number of all lmer results - nresT <- nct - ## initialization of the result vector (1 line) - res <- data.frame(array(rep(NA, (nfp + 4 * nresT)))) - colnames(res)[1] <- "resultLM" - - ### if at least one subject have data for only 1 time, mixed model is not possible and variable must be skip - ### after excluding NA, table function is used to seek subjects with only 1 data - ids <- ids[!is.na(ids[[ivd]]), ] - skip <- length(which(table(ids[[isubject]]) == 1)) - - if (skip == 0) { - - mfl <- lmer(vd ~ time + (1 | subject), ids) # lmer remix - rsum <- summary(mfl, ddf = dffOption) - ## test Shapiro Wilks on the residus of the model - rShapiro <- shapiro.test(rsum$residuals) - raov <- anova(mfl, ddf = dffOption) - ## Sum of square : aov$'Sum Sq', Mean square : aov$`Mean Sq`, proba : aov$`Pr(>F)` - - ## Test of all differences estimates between levels as SAS proc mixed. - ## results are in diffs.lsmeans.table dataframe - ## test.effs=NULL perform all pairs comparisons including interaction effect - dlsm1 <- data.frame(difflsmeans(mfl, test.effs = NULL)) - ddlsm1 <- dlsm1 - ## save rownames and factor names - rn <- rownames(ddlsm1) - fn <- ddlsm1[, c(1, 2)] - ## writing the results on a single line - namesFactEstim <- paste("estimate ", rownames(ddlsm1)[c(1:(nct))], sep = "") - namesFactPval <- paste("pvalue ", rownames(ddlsm1)[c(1:(nct))], sep = "") - namesFactprinc <- "pval_time" - namesLowerCI <- paste("lowerCI ", rownames(ddlsm1)[c(1:(nct))], sep = "") - namesUpperCI <- paste("upperCI ", rownames(ddlsm1)[c(1:(nct))], sep = "") - - ### lmer results on 1 vector - # pvalue of shapiro Wilks test of the residuals - res[1, ] <- rShapiro$p.value; rownames(res)[1] <- "Shapiro.pvalue.residuals" - res[2, ] <- rsum$varcor$subject[1] ;rownames(res)[2] <- "Subject.Variance" - res[3, ] <- rsum$devcomp$cmp[7] ; rownames(res)[3] <- "REML" - - ### factor time pvalue results + shapiro test - res[nfp, ] <- raov[, 6] - rownames(res)[nfp] <- namesFactprinc - - # pvalues of time factor levels comparisons - res[(nfp + 1):(nfp + nct), ] <- ddlsm1[c(1:nct), 9] - rownames(res)[(nfp + 1):(nfp + nct)] <- namesFactPval - - # Estimates of time factor levels - nresf <- nresT - res[(nfp + nresf + 1):(nfp + nresf + nct), ] <- ddlsm1[c(1:nct), 3] - rownames(res)[(nfp + nresf + 1):(nfp + nresf + nct)] <- namesFactEstim - - # Lower CI of the difference between levels of factors - # nresf is incremeted - nresf <- nresf + nresT - res[(nfp + nresf + 1):(nfp + nresf + nct), ] <- ddlsm1[c(1:nct), 7] - rownames(res)[(nfp + nresf + 1):(nfp + nresf + nct)] <- namesLowerCI - # Lower CI of the difference between levels of factors - # nresf is incremeted - nresf <- nresf + nresT - res[(nfp + nresf + 1):(nfp + nresf + nct), ] <- ddlsm1[c(1:nct), 8] - rownames(res)[(nfp + nresf + 1):(nfp + nresf + nct)] <- namesUpperCI - - - } else { - ## one of the subject has only one time, subject can't be a random variable - ## A repeated measure could be run instead function lme of package nlme, next version - res[1, ] <- NA - cat("\n Computing impossible for feature ", colnames(ids)[4], ": at least one subject has only one time.\n") - } - tres <- data.frame(t(res)) - rownames(tres)[1] <- nameVar - cres <- list(tres, rn, fn) - return(cres) - -} - -############################################################################################################## -defColRes <- function(ids, ifixfact, itime) { - ## define the size of the result file depending on the numbers of levels of the fixed and time factor. - ## Numbers of levels define the numbers of comparisons with pvalue and estimate of the difference. - ## The result file also contains the pvalue of the fixed factor, time factor and interaction - ## plus Shapiro normality test. This is define by nfp - ## subscript of fixed factor=0 means no other fixed factor than "time" - if (ifixfact > 0) { - nfp <- 6 # shapiro + subject variance + REML + time + fixfact + interaction - time <- ids[[itime]] - fixfact <- ids[[ifixfact]] - - cat("\n Levels fixfact: ", levels(fixfact)) - cat("\n Levels time: ", levels(time)) - - # ncff number of comparisons of the fixed factor (nlff number of levels of fixed factor) - nlff <- length(levels(fixfact)) - ncff <- (nlff * (nlff - 1)) / 2 - # nct number of comparison of the time factor (nlt number of levels of time factor) - nlt <- length(levels(time)) - nct <- (nlt * (nlt - 1)) / 2 - # nci number of comparison of the interaction - nli <- nlff * nlt - nci <- (nli * (nli - 1)) / 2 - ndim <- c(NA, NA, NA, NA, NA, NA, NA) - - ndim[1] <- nfp # pvalues of fixed factor, time factor and interaction (3columns) and shapiro test pvalue - ndim[2] <- nlff # number of levels of fixed factor - ndim[3] <- ncff # number of comparisons (2by2) of the fixed factor - ndim[4] <- nlt # number of levels of time factor - ndim[5] <- nct # number of comparisons (2by2) of the time factor - ndim[6] <- nli # number of levels of interaction - ndim[7] <- nci # number of comparisons (2by2) of the interaction - - } - else { - nfp <- 4 # Mandatory columns: shapiro + subject variance + REML + time - time <- ids[[itime]] - # nct number of comparison of the time factor - nlt <- length(levels(time)) - nct <- (nlt * (nlt - 1)) / 2 - ndim <- c(NA, NA, NA, NA, NA, NA, NA) - - ndim[1] <- nfp # pvalues of shapiro test + subject variance + REML + time factor - ndim[4] <- nlt # number of levels of time factor - ndim[5] <- nct # number of comparisons (2by2) of the time factor - } - return(ndim) -} - -############################################################################################################## -lmixedm <- function(datMN, - samDF, - varDF, - fixfact, time, subject, - logtr = "none", - pvalCutof = 0.05, - pvalcorMeth = c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none")[7], - dffOption, - visu = "no", - least.confounded = FALSE, - outlier.limit = 3, - pdfC, - pdfE - ) { - sampids <- samDF - dataMatrix <- datMN - varids <- varDF - - options("scipen" = 50, "digits" = 5) - pvalCutof <- as.numeric(pvalCutof) - - cat("\n dff computation method=", dffOption) - ### Function running lmer function on a set of variables described in - ### 3 different dataframes as used by W4M - ### results are merge with the metadata variables varids - ### ifixfact, itime, isubject are subscripts of the dependant variables. if only time factor the ifixfat is set to 0 and no diag is performed (visu="no") - if (fixfact == "none") { - ifixfact <- 0 ; visu <- "no" - } else ifixfact <- which(colnames(sampids) == fixfact) - itime <- which(colnames(sampids) == time) - isubject <- which(colnames(sampids) == subject) - - lmmds <- dataMatrix - if (logtr != "log10" & logtr != "log2") logtr <- "none" - if (logtr == "log10") lmmds <- log10(lmmds + 1) - if (logtr == "log2") lmmds <- log2(lmmds + 1) - - dslm <- cbind(sampids, lmmds) - - nvar <- ncol(lmmds) - firstvar <- ncol(sampids) + 1 - lastvar <- firstvar + ncol(lmmds) - 1 - - if (ifixfact > 0) dslm[[ifixfact]] <- factor(dslm[[ifixfact]]) - dslm[[itime]] <- factor(dslm[[itime]]) - dslm[[isubject]] <- factor(dslm[[isubject]]) - ## call defColres to define the numbers of test and so the number of columns of results - ## depends on whether or not there is a fixed factor with time. If only time factor ifixfact=0 - if (ifixfact > 0) { - ndim <- defColRes(dslm[, c(ifixfact, itime)], ifixfact = 1, itime = 2) - nColRes <- ndim[1] + (4 * (ndim[3] + ndim[5] + ndim[7])) - firstpval <- ndim[1] - 2 - lastpval <- ndim[1] + ndim[3] + ndim[5] + ndim[7] - } else { - ndim <- defColRes(dslm[, itime], ifixfact = 0, itime = 1) - nColRes <- ndim[1] + (4 * (ndim[5])) - firstpval <- ndim[1] - lastpval <- ndim[1] + ndim[5] - } - ## initialisation of the result file - resLM <- data.frame(array(rep(NA, nvar * nColRes), dim = c(nvar, nColRes))) - rownames(resLM) <- rownames(varids) - - ## PDF initialisation - if (visu == "yes") { - pdf(pdfC, onefile = TRUE, height = 15, width = 30) - par(mfrow = c(1, 3)) - } - - - for (i in firstvar:lastvar) { - - subds <- dslm[, c(ifixfact, itime, isubject, i)] - - tryCatch({ - if (ifixfact > 0) - reslmer <- lmRepeated2FF(subds, ifixfact = 1, itime = 2, isubject = 3, ivd = 4, ndim = ndim, visu = visu, - tit = colnames(dslm)[i], pvalCutof = pvalCutof, - dffOption = dffOption, least.confounded = least.confounded, - outlier.limit = outlier.limit) - else - reslmer <- lmRepeated1FF(subds, ifixfact = 0, itime = 1, isubject = 2, ivd = 3, ndim = ndim, - pvalCutof = pvalCutof, dffOption = dffOption) - - resLM[i - firstvar + 1, ] <- reslmer[[1]] - }, error = function(e) { - cat("\nERROR with ", rownames(resLM)[i - firstvar + 1], ": ", conditionMessage(e), "\n") - } - ) - - } - - if (exists("reslmer")) { - colnames(resLM) <- colnames(reslmer[[1]]) - labelRow <- reslmer[[2]] - factorRow <- reslmer[[3]] - } else { - stop("\n- - - - -\nModel computation impossible for every single variables in the dataset: no result returned.\n- - - - -\n") - } - - if (visu == "yes") dev.off() - - - ## pvalue correction with p.adjust library multtest - ## Possible methods of pvalue correction - AdjustMeth <- c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none") - if (length(which(pvalcorMeth == AdjustMeth)) == 0) pvalcorMeth <- "none" - - if (pvalcorMeth != "none") { - for (k in firstpval:lastpval) { - resLM[[k]] <- p.adjust(resLM[[k]], method = pvalcorMeth, n = dim(resLM[k])[[1]]) - - } - } - - ## for each variables, set pvalues to NA and estimates = 0 when pvalue of factor > pvalCutof value define by user - if (ifixfact > 0) { - ## time effect - resLM[which(resLM[, firstpval] > pvalCutof), c((lastpval + 1):(lastpval + ndim[5]))] <- 0 - resLM[which(resLM[, firstpval] > pvalCutof), c((ndim[1] + 1):(ndim[1] + ndim[5]))] <- NA - ## treatment effect - resLM[which(resLM[, firstpval + 1] > pvalCutof), c((lastpval + ndim[5] + 1):(lastpval + ndim[5] + ndim[3]))] <- 0 - resLM[which(resLM[, firstpval + 1] > pvalCutof), c((ndim[1] + ndim[5] + 1):(ndim[1] + ndim[5] + ndim[3]))] <- NA - ## interaction effect - resLM[which(resLM[, firstpval + 2] > pvalCutof), c((lastpval + ndim[5] + ndim[3] + 1):(lastpval + ndim[5] + ndim[3] + ndim[7]))] <- 0 - resLM[which(resLM[, firstpval + 2] > pvalCutof), c((ndim[1] + ndim[5] + ndim[3] + 1):(ndim[1] + ndim[5] + ndim[3] + ndim[7]))] <- NA - } else { - ## time effect only - resLM[which(resLM[, firstpval] > pvalCutof), c((lastpval + 1):(lastpval + ndim[5]))] <- 0 - resLM[which(resLM[, firstpval] > pvalCutof), c((firstpval + 1):(firstpval + ndim[5]))] <- NA - } - - ## for each variable, estimates plots are performed if at least one factor is significant after p-value correction - pdf(pdfE, onefile = TRUE, height = 15, width = 30) - - ## for each variable (in row) - for (i in seq_len(nrow(resLM))) { - - ## if any fixed factor + time factor - if (ifixfact > 0) - - ## if any main factor after p-value correction is significant -> plot estimates and time course - if (length(which(resLM[i, c(4:6)] < pvalCutof)) > 0) { - - ## Plot of time course by fixfact : data prep with factors and quantitative var to be plot - subv <- dslm[, colnames(dslm) == rownames(resLM)[i]] - subds <- data.frame(dslm[[ifixfact]], dslm[[itime]], dslm[[isubject]], subv) - libvar <- c(fixfact, time, subject) - colnames(subds) <- c(libvar, rownames(resLM)[i]) - - ## Plot of estimates with error bars for all fixed factors and interaction - rddlsm1 <- t(resLM[i, ]) - pval <- rddlsm1[substr(rownames(rddlsm1), 1, 6) == "pvalue"] - esti <- rddlsm1[substr(rownames(rddlsm1), 1, 6) == "estima"] - loci <- rddlsm1[substr(rownames(rddlsm1), 1, 6) == "lowerC"] - upci <- rddlsm1[substr(rownames(rddlsm1), 1, 6) == "UpperC"] - rddlsm1 <- data.frame(pval, esti, loci, upci, factorRow) - colnames(rddlsm1) <- c("p.value", "Estimate", "Lower.CI", "Upper.CI", colnames(factorRow)) - rownames(rddlsm1) <- labelRow - - ## function for plotting these 2 graphs - plot.res.Lmixed(rddlsm1, subds, title = rownames(resLM)[i], pvalCutof = pvalCutof) - - } - - ## if only a time factor - if (ifixfact == 0) - - ## if time factor after p-value correction is significant -> plot time course - if (length(which(resLM[i, 4] < pvalCutof)) > 0) { - - ## Plot of time course : data prep with factors and quantitative var to be plot - subv <- dslm[, colnames(dslm) == rownames(resLM)[i]] - subds <- data.frame(dslm[[itime]], dslm[[isubject]], subv) - libvar <- c(time, subject) - colnames(subds) <- c(libvar, rownames(resLM)[i]) - - ## Plot of estimates with error bars for all fixed factors and interaction - rddlsm1 <- t(resLM[i, ]) - pval <- rddlsm1[substr(rownames(rddlsm1), 1, 6) == "pvalue"] - esti <- rddlsm1[substr(rownames(rddlsm1), 1, 6) == "estima"] - loci <- rddlsm1[substr(rownames(rddlsm1), 1, 6) == "lowerC"] - upci <- rddlsm1[substr(rownames(rddlsm1), 1, 6) == "upperC"] - rddlsm1 <- data.frame(pval, esti, loci, upci, factorRow) - colnames(rddlsm1) <- c("p.value", "Estimate", "Lower.CI", "Upper.CI", colnames(factorRow)) - rownames(rddlsm1) <- labelRow - - ## function for plotting these 2 graphs - plot.res.Lmixed(rddlsm1, subds, title = rownames(resLM)[i], pvalCutof = pvalCutof) - - } - - } - dev.off() - - ## return result file with pvalues and estimates (exclude confidence interval used for plotting) - iCI <- which(substr(colnames(resLM), 4, 7) == "erCI") - resLM <- resLM[, - iCI] - resLM <- cbind(varids, resLM) - return(resLM) -} +####### R functions to perform linear mixed model for repeated measures +####### on a multi var dataset using 3 files as used in W4M +############################################################################################################## +lmRepeated2FF <- function(ids, ifixfact, itime, isubject, ivd, ndim, nameVar = colnames(ids)[[ivd]], + pvalCutof = 0.05, dffOption, visu = visu, tit = "", least.confounded = FALSE, outlier.limit = 3) { + ### function to perform linear mixed model with 1 Fixed factor + Time + random factor subject + ### based on lmerTest package providing functions giving the same results as SAS proc mixed + options(scipen = 50, digits = 5) + + if (!is.numeric(ids[[ivd]])) stop("Dependant variable is not numeric") + if (!is.factor(ids[[ifixfact]])) stop("fixed factor is not a factor") + if (!is.factor(ids[[itime]])) stop("Repeated factor is not a factor") + if (!is.factor(ids[[isubject]])) stop("Random factor is not a factor") + + ## factors + time <- ids[[itime]] + fixfact <- ids[[ifixfact]] + subject <- ids[[isubject]] + # dependant variables + vd <- ids[[ivd]] + + # argument of the function instead of re re-running ndim <- defColRes(ids, ifixfact, itime) + # nfp : number of main factors + model infos (REML, varSubject) + normality test + nfp <- ndim[1] + # ncff number of comparison of the fixed factor + nlff <- ndim[2] + ncff <- ndim[3] + # nct number of comparison of the time factor + nlt <- ndim[4] + nct <- ndim[5] + # nci number of comparison of the interaction + nli <- ndim[6] + nci <- ndim[7] + # number of all lmer results + nresT <- ncff + nct + nci + ## initialization of the result vector (1 line) + ## 4 * because nresf for : pvalues + Etimates + lower CI + Upper CI + res <- data.frame(array(rep(NA, (nfp + 4 * nresT)))) + colnames(res)[1] <- "resultLM" + + ### if at least one subject have data for only 1 time, mixed model is not possible and variable must be skip + ### after excluding NA, table function is used to seek subjects with only 1 data + ids <- ids[!is.na(ids[[ivd]]), ] + skip <- length(which(table(ids[[isubject]]) == 1)) + + if (skip == 0) { + mfl <- lmer(vd ~ time + fixfact + time:fixfact + (1 | subject), ids) # lmer remix + + rsum <- summary(mfl, ddf = dffOption) + ## test Shapiro Wilks on the residus of the model + rShapiro <- shapiro.test(rsum$residuals) + raov <- anova(mfl, ddf = dffOption) + dlsm1 <- data.frame(difflsmeans(mfl, test.effs = NULL)) + ddlsm1 <- dlsm1 + ## save rownames and factor names + rn <- rownames(ddlsm1) + fn <- ddlsm1[, c(1, 2)] + ## writing the results on a single line + namesFactEstim <- paste("estimate ", rownames(ddlsm1)[c(1:(nct + ncff))], sep = "") + namesFactPval <- paste("pvalue ", rownames(ddlsm1)[c(1:(nct + ncff))], sep = "") + namesInter <- rownames(ddlsm1)[-c(1:(nct + ncff))] + namesEstimate <- paste("estimate ", namesInter) + namespvalues <- paste("pvalue ", namesInter) + namesFactprinc <- c("pval_time", "pval_trt", "pval_inter") + namesFactEstim <- paste("estimate ", rownames(ddlsm1)[c(1:(nct + ncff))], sep = "") + + namesFactLowerCI <- paste("lowerCI ", rownames(ddlsm1)[c(1:(nct + ncff))], sep = "") + namesLowerCI <- paste("lowerCI ", namesInter, sep = "") + + namesFactUpperCI <- paste("UpperCI ", rownames(ddlsm1)[c(1:(nct + ncff))], sep = "") + namesUpperCI <- paste("UpperCI ", namesInter, sep = "") + + + ### lmer results on 1 vector row + # pvalue of shapiro Wilks test of the residuals + res[1, ] <- rShapiro$p.value + rownames(res)[1] <- "Shapiro.pvalue.residuals" + res[2, ] <- rsum$varcor$subject[1] + rownames(res)[2] <- "Subject.Variance" + res[3, ] <- rsum$devcomp$cmp[7] + rownames(res)[3] <- "REML" + ### 3 principal factors pvalues results + shapiro test => nfp <- 4 + res[c((nfp - 2):nfp), ] <- raov[, 6] + rownames(res)[c((nfp - 2):nfp)] <- namesFactprinc + + #################### Residuals diagnostics for significants variables ######################### + ### Il at least 1 factor is significant and visu=TRUE NL graphics add to pdf + ## ajout JF du passage de la valeur de p-value cutoff + if (length(which(raov[, 6] <= pvalCutof)) > 0 & visu == "yes") { + diagmflF(mfl, + title = tit, pvalCutof = pvalCutof, least.confounded = least.confounded, + outlier.limit = outlier.limit + ) + } + + # pvalue of fixed factor comparisons + nresf <- nresT + res[(nfp + 1):(nfp + nct), ] <- ddlsm1[c(1:nct), 9] + res[(nfp + nct + 1):(nfp + nct + ncff), ] <- ddlsm1[(nct + 1):(nct + ncff), 9] + rownames(res)[(nfp + 1):(nfp + nct + ncff)] <- namesFactPval + res[(nfp + nct + ncff + 1):(nfp + nresf), ] <- ddlsm1[(nct + ncff + 1):(nresT), 9] + rownames(res)[(nfp + nct + ncff + 1):(nfp + nresT)] <- namespvalues + # Estimate of the difference between levels of factors + res[(nfp + nresf + 1):(nfp + nresf + nct), ] <- ddlsm1[c(1:nct), 3] + res[(nfp + nresf + nct + 1):(nfp + nresf + nct + ncff), ] <- ddlsm1[(nct + 1):(nct + ncff), 3] + rownames(res)[(nfp + nresf + 1):(nfp + nresf + nct + ncff)] <- namesFactEstim + res[(nfp + nresf + nct + ncff + 1):(nfp + 2 * nresf), ] <- ddlsm1[(nct + ncff + 1):(nresT), 3] + rownames(res)[(nfp + nresf + nct + ncff + 1):(nfp + 2 * nresf)] <- namesEstimate + # lower CI of the difference between levels of factors + nresf <- nresf + nresT + res[(nfp + nresf + 1):(nfp + nresf + nct), ] <- ddlsm1[c(1:nct), 7] + res[(nfp + nresf + nct + 1):(nfp + nresf + nct + ncff), ] <- ddlsm1[(nct + 1):(nct + ncff), 7] + rownames(res)[(nfp + nresf + 1):(nfp + nresf + nct + ncff)] <- namesFactLowerCI + res[(nfp + nresf + nct + ncff + 1):(nfp + 2 * nresf), ] <- ddlsm1[(nct + ncff + 1):(nresf), 7] + rownames(res)[(nfp + nresf + nct + ncff + 1):(nfp + nresf + (nresf / 2))] <- namesLowerCI + # Upper CI of the difference between levels of factors + nresf <- nresf + nresT + res[(nfp + nresf + 1):(nfp + nresf + nct), ] <- ddlsm1[c(1:nct), 8] + res[(nfp + nresf + nct + 1):(nfp + nresf + nct + ncff), ] <- ddlsm1[(nct + 1):(nct + ncff), 8] + rownames(res)[(nfp + nresf + 1):(nfp + nresf + nct + ncff)] <- namesFactUpperCI + res[(nfp + nresf + nct + ncff + 1):(nfp + nresf + (nresT)), ] <- ddlsm1[(nct + ncff + 1):(nresT), 8] + rownames(res)[(nfp + nresf + nct + ncff + 1):(nfp + nresf + (nresT))] <- namesUpperCI + } else { + ## one of the subject has only one time, subject can't be a random variable + ## A repeated measure could be run instead function lme of package nlme, in next version? + res[1, ] <- NA + cat("\n Computing impossible for feature ", tit, ": at least one subject has only one time.\n") + } + tres <- data.frame(t(res)) + rownames(tres)[1] <- nameVar + cres <- list(tres, rn, fn) + return(cres) +} + +############################################################################################################## +lmRepeated1FF <- function(ids, ifixfact = 0, itime, isubject, ivd, ndim, nameVar = colnames(ids)[[ivd]], + dffOption, pvalCutof = 0.05) { + ### function to perform linear mixed model with factor Time + random factor subject + ### based on lmerTest package providing functions giving the same results as SAS proc mixed + + + if (!is.numeric(ids[[ivd]])) stop("Dependant variable is not numeric") + if (!is.factor(ids[[itime]])) stop("Repeated factor is not a factor") + if (!is.factor(ids[[isubject]])) stop("Random factor is not a factor") + # Could be interesting here to add an experience plan check to give back a specific error message + # in case time points are missing for some individuals + + time <- ids[[itime]] + subject <- ids[[isubject]] + vd <- ids[[ivd]] ## dependant variables (quatitative) + + # nfp : nombre de facteurs principaux + model infos + normality test + nfp <- ndim[1] + # nlt number of time levels; nct number of comparisons of the time factor + nlt <- ndim[4] + nct <- ndim[5] + # number of all lmer results + nresT <- nct + ## initialization of the result vector (1 line) + res <- data.frame(array(rep(NA, (nfp + 4 * nresT)))) + colnames(res)[1] <- "resultLM" + + ### if at least one subject have data for only 1 time, mixed model is not possible and variable must be skip + ### after excluding NA, table function is used to seek subjects with only 1 data + ids <- ids[!is.na(ids[[ivd]]), ] + skip <- length(which(table(ids[[isubject]]) == 1)) + + if (skip == 0) { + mfl <- lmer(vd ~ time + (1 | subject), ids) # lmer remix + rsum <- summary(mfl, ddf = dffOption) + ## test Shapiro Wilks on the residus of the model + rShapiro <- shapiro.test(rsum$residuals) + raov <- anova(mfl, ddf = dffOption) + ## Sum of square : aov$'Sum Sq', Mean square : aov$`Mean Sq`, proba : aov$`Pr(>F)` + + ## Test of all differences estimates between levels as SAS proc mixed. + ## results are in diffs.lsmeans.table dataframe + ## test.effs=NULL perform all pairs comparisons including interaction effect + dlsm1 <- data.frame(difflsmeans(mfl, test.effs = NULL)) + ddlsm1 <- dlsm1 + ## save rownames and factor names + rn <- rownames(ddlsm1) + fn <- ddlsm1[, c(1, 2)] + ## writing the results on a single line + namesFactEstim <- paste("estimate ", rownames(ddlsm1)[c(1:(nct))], sep = "") + namesFactPval <- paste("pvalue ", rownames(ddlsm1)[c(1:(nct))], sep = "") + namesFactprinc <- "pval_time" + namesLowerCI <- paste("lowerCI ", rownames(ddlsm1)[c(1:(nct))], sep = "") + namesUpperCI <- paste("upperCI ", rownames(ddlsm1)[c(1:(nct))], sep = "") + + ### lmer results on 1 vector + # pvalue of shapiro Wilks test of the residuals + res[1, ] <- rShapiro$p.value + rownames(res)[1] <- "Shapiro.pvalue.residuals" + res[2, ] <- rsum$varcor$subject[1] + rownames(res)[2] <- "Subject.Variance" + res[3, ] <- rsum$devcomp$cmp[7] + rownames(res)[3] <- "REML" + + ### factor time pvalue results + shapiro test + res[nfp, ] <- raov[, 6] + rownames(res)[nfp] <- namesFactprinc + + # pvalues of time factor levels comparisons + res[(nfp + 1):(nfp + nct), ] <- ddlsm1[c(1:nct), 9] + rownames(res)[(nfp + 1):(nfp + nct)] <- namesFactPval + + # Estimates of time factor levels + nresf <- nresT + res[(nfp + nresf + 1):(nfp + nresf + nct), ] <- ddlsm1[c(1:nct), 3] + rownames(res)[(nfp + nresf + 1):(nfp + nresf + nct)] <- namesFactEstim + + # Lower CI of the difference between levels of factors + # nresf is incremeted + nresf <- nresf + nresT + res[(nfp + nresf + 1):(nfp + nresf + nct), ] <- ddlsm1[c(1:nct), 7] + rownames(res)[(nfp + nresf + 1):(nfp + nresf + nct)] <- namesLowerCI + # Lower CI of the difference between levels of factors + # nresf is incremeted + nresf <- nresf + nresT + res[(nfp + nresf + 1):(nfp + nresf + nct), ] <- ddlsm1[c(1:nct), 8] + rownames(res)[(nfp + nresf + 1):(nfp + nresf + nct)] <- namesUpperCI + } else { + ## one of the subject has only one time, subject can't be a random variable + ## A repeated measure could be run instead function lme of package nlme, next version + res[1, ] <- NA + cat("\n Computing impossible for feature ", colnames(ids)[4], ": at least one subject has only one time.\n") + } + tres <- data.frame(t(res)) + rownames(tres)[1] <- nameVar + cres <- list(tres, rn, fn) + return(cres) +} + +############################################################################################################## +defColRes <- function(ids, ifixfact, itime) { + ## define the size of the result file depending on the numbers of levels of the fixed and time factor. + ## Numbers of levels define the numbers of comparisons with pvalue and estimate of the difference. + ## The result file also contains the pvalue of the fixed factor, time factor and interaction + ## plus Shapiro normality test. This is define by nfp + ## subscript of fixed factor=0 means no other fixed factor than "time" + if (ifixfact > 0) { + nfp <- 6 # shapiro + subject variance + REML + time + fixfact + interaction + time <- ids[[itime]] + fixfact <- ids[[ifixfact]] + + cat("\n Levels fixfact: ", levels(fixfact)) + cat("\n Levels time: ", levels(time)) + + # ncff number of comparisons of the fixed factor (nlff number of levels of fixed factor) + nlff <- length(levels(fixfact)) + ncff <- (nlff * (nlff - 1)) / 2 + # nct number of comparison of the time factor (nlt number of levels of time factor) + nlt <- length(levels(time)) + nct <- (nlt * (nlt - 1)) / 2 + # nci number of comparison of the interaction + nli <- nlff * nlt + nci <- (nli * (nli - 1)) / 2 + ndim <- c(NA, NA, NA, NA, NA, NA, NA) + + ndim[1] <- nfp # pvalues of fixed factor, time factor and interaction (3columns) and shapiro test pvalue + ndim[2] <- nlff # number of levels of fixed factor + ndim[3] <- ncff # number of comparisons (2by2) of the fixed factor + ndim[4] <- nlt # number of levels of time factor + ndim[5] <- nct # number of comparisons (2by2) of the time factor + ndim[6] <- nli # number of levels of interaction + ndim[7] <- nci # number of comparisons (2by2) of the interaction + } else { + nfp <- 4 # Mandatory columns: shapiro + subject variance + REML + time + time <- ids[[itime]] + # nct number of comparison of the time factor + nlt <- length(levels(time)) + nct <- (nlt * (nlt - 1)) / 2 + ndim <- c(NA, NA, NA, NA, NA, NA, NA) + + ndim[1] <- nfp # pvalues of shapiro test + subject variance + REML + time factor + ndim[4] <- nlt # number of levels of time factor + ndim[5] <- nct # number of comparisons (2by2) of the time factor + } + return(ndim) +} + +############################################################################################################## +lmixedm <- function(datMN, + samDF, + varDF, + fixfact, time, subject, + logtr = "none", + pvalCutof = 0.05, + pvalcorMeth = c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none")[7], + dffOption, + visu = "no", + least.confounded = FALSE, + outlier.limit = 3, + pdfC, + pdfE) { + sampids <- samDF + dataMatrix <- datMN + varids <- varDF + + options("scipen" = 50, "digits" = 5) + pvalCutof <- as.numeric(pvalCutof) + + cat("\n dff computation method=", dffOption) + ### Function running lmer function on a set of variables described in + ### 3 different dataframes as used by W4M + ### results are merge with the metadata variables varids + ### ifixfact, itime, isubject are subscripts of the dependant variables. if only time factor the ifixfat is set to 0 and no diag is performed (visu="no") + if (fixfact == "none") { + ifixfact <- 0 + visu <- "no" + } else { + ifixfact <- which(colnames(sampids) == fixfact) + } + itime <- which(colnames(sampids) == time) + isubject <- which(colnames(sampids) == subject) + + lmmds <- dataMatrix + if (logtr != "log10" & logtr != "log2") logtr <- "none" + if (logtr == "log10") lmmds <- log10(lmmds + 1) + if (logtr == "log2") lmmds <- log2(lmmds + 1) + + dslm <- cbind(sampids, lmmds) + + nvar <- ncol(lmmds) + firstvar <- ncol(sampids) + 1 + lastvar <- firstvar + ncol(lmmds) - 1 + + if (ifixfact > 0) dslm[[ifixfact]] <- factor(dslm[[ifixfact]]) + dslm[[itime]] <- factor(dslm[[itime]]) + dslm[[isubject]] <- factor(dslm[[isubject]]) + ## call defColres to define the numbers of test and so the number of columns of results + ## depends on whether or not there is a fixed factor with time. If only time factor ifixfact=0 + if (ifixfact > 0) { + ndim <- defColRes(dslm[, c(ifixfact, itime)], ifixfact = 1, itime = 2) + nColRes <- ndim[1] + (4 * (ndim[3] + ndim[5] + ndim[7])) + firstpval <- ndim[1] - 2 + lastpval <- ndim[1] + ndim[3] + ndim[5] + ndim[7] + } else { + ndim <- defColRes(dslm[, itime], ifixfact = 0, itime = 1) + nColRes <- ndim[1] + (4 * (ndim[5])) + firstpval <- ndim[1] + lastpval <- ndim[1] + ndim[5] + } + ## initialisation of the result file + resLM <- data.frame(array(rep(NA, nvar * nColRes), dim = c(nvar, nColRes))) + rownames(resLM) <- rownames(varids) + + ## PDF initialisation + if (visu == "yes") { + pdf(pdfC, onefile = TRUE, height = 15, width = 30) + par(mfrow = c(1, 3)) + } + + + for (i in firstvar:lastvar) { + subds <- dslm[, c(ifixfact, itime, isubject, i)] + + tryCatch( + { + if (ifixfact > 0) { + reslmer <- lmRepeated2FF(subds, + ifixfact = 1, itime = 2, isubject = 3, ivd = 4, ndim = ndim, visu = visu, + tit = colnames(dslm)[i], pvalCutof = pvalCutof, + dffOption = dffOption, least.confounded = least.confounded, + outlier.limit = outlier.limit + ) + } else { + reslmer <- lmRepeated1FF(subds, + ifixfact = 0, itime = 1, isubject = 2, ivd = 3, ndim = ndim, + pvalCutof = pvalCutof, dffOption = dffOption + ) + } + + resLM[i - firstvar + 1, ] <- reslmer[[1]] + }, + error = function(e) { + cat("\nERROR with ", rownames(resLM)[i - firstvar + 1], ": ", conditionMessage(e), "\n") + } + ) + } + + if (exists("reslmer")) { + colnames(resLM) <- colnames(reslmer[[1]]) + labelRow <- reslmer[[2]] + factorRow <- reslmer[[3]] + } else { + stop("\n- - - - -\nModel computation impossible for every single variables in the dataset: no result returned.\n- - - - -\n") + } + + if (visu == "yes") dev.off() + + + ## pvalue correction with p.adjust library multtest + ## Possible methods of pvalue correction + AdjustMeth <- c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none") + if (length(which(pvalcorMeth == AdjustMeth)) == 0) pvalcorMeth <- "none" + + if (pvalcorMeth != "none") { + for (k in firstpval:lastpval) { + resLM[[k]] <- p.adjust(resLM[[k]], method = pvalcorMeth, n = dim(resLM[k])[[1]]) + } + } + + ## for each variables, set pvalues to NA and estimates = 0 when pvalue of factor > pvalCutof value define by user + if (ifixfact > 0) { + ## time effect + resLM[which(resLM[, firstpval] > pvalCutof), c((lastpval + 1):(lastpval + ndim[5]))] <- 0 + resLM[which(resLM[, firstpval] > pvalCutof), c((ndim[1] + 1):(ndim[1] + ndim[5]))] <- NA + ## treatment effect + resLM[which(resLM[, firstpval + 1] > pvalCutof), c((lastpval + ndim[5] + 1):(lastpval + ndim[5] + ndim[3]))] <- 0 + resLM[which(resLM[, firstpval + 1] > pvalCutof), c((ndim[1] + ndim[5] + 1):(ndim[1] + ndim[5] + ndim[3]))] <- NA + ## interaction effect + resLM[which(resLM[, firstpval + 2] > pvalCutof), c((lastpval + ndim[5] + ndim[3] + 1):(lastpval + ndim[5] + ndim[3] + ndim[7]))] <- 0 + resLM[which(resLM[, firstpval + 2] > pvalCutof), c((ndim[1] + ndim[5] + ndim[3] + 1):(ndim[1] + ndim[5] + ndim[3] + ndim[7]))] <- NA + } else { + ## time effect only + resLM[which(resLM[, firstpval] > pvalCutof), c((lastpval + 1):(lastpval + ndim[5]))] <- 0 + resLM[which(resLM[, firstpval] > pvalCutof), c((firstpval + 1):(firstpval + ndim[5]))] <- NA + } + + ## for each variable, estimates plots are performed if at least one factor is significant after p-value correction + pdf(pdfE, onefile = TRUE, height = 15, width = 30) + + ## for each variable (in row) + for (i in seq_len(nrow(resLM))) { + ## if any fixed factor + time factor + if (ifixfact > 0) { + ## if any main factor after p-value correction is significant -> plot estimates and time course + if (length(which(resLM[i, c(4:6)] < pvalCutof)) > 0) { + ## Plot of time course by fixfact : data prep with factors and quantitative var to be plot + subv <- dslm[, colnames(dslm) == rownames(resLM)[i]] + subds <- data.frame(dslm[[ifixfact]], dslm[[itime]], dslm[[isubject]], subv) + libvar <- c(fixfact, time, subject) + colnames(subds) <- c(libvar, rownames(resLM)[i]) + + ## Plot of estimates with error bars for all fixed factors and interaction + rddlsm1 <- t(resLM[i, ]) + pval <- rddlsm1[substr(rownames(rddlsm1), 1, 6) == "pvalue"] + esti <- rddlsm1[substr(rownames(rddlsm1), 1, 6) == "estima"] + loci <- rddlsm1[substr(rownames(rddlsm1), 1, 6) == "lowerC"] + upci <- rddlsm1[substr(rownames(rddlsm1), 1, 6) == "UpperC"] + rddlsm1 <- data.frame(pval, esti, loci, upci, factorRow) + colnames(rddlsm1) <- c("p.value", "Estimate", "Lower.CI", "Upper.CI", colnames(factorRow)) + rownames(rddlsm1) <- labelRow + + ## function for plotting these 2 graphs + plot.res.Lmixed(rddlsm1, subds, title = rownames(resLM)[i], pvalCutof = pvalCutof) + } + } + + ## if only a time factor + if (ifixfact == 0) { + ## if time factor after p-value correction is significant -> plot time course + if (length(which(resLM[i, 4] < pvalCutof)) > 0) { + ## Plot of time course : data prep with factors and quantitative var to be plot + subv <- dslm[, colnames(dslm) == rownames(resLM)[i]] + subds <- data.frame(dslm[[itime]], dslm[[isubject]], subv) + libvar <- c(time, subject) + colnames(subds) <- c(libvar, rownames(resLM)[i]) + + ## Plot of estimates with error bars for all fixed factors and interaction + rddlsm1 <- t(resLM[i, ]) + pval <- rddlsm1[substr(rownames(rddlsm1), 1, 6) == "pvalue"] + esti <- rddlsm1[substr(rownames(rddlsm1), 1, 6) == "estima"] + loci <- rddlsm1[substr(rownames(rddlsm1), 1, 6) == "lowerC"] + upci <- rddlsm1[substr(rownames(rddlsm1), 1, 6) == "upperC"] + rddlsm1 <- data.frame(pval, esti, loci, upci, factorRow) + colnames(rddlsm1) <- c("p.value", "Estimate", "Lower.CI", "Upper.CI", colnames(factorRow)) + rownames(rddlsm1) <- labelRow + + ## function for plotting these 2 graphs + plot.res.Lmixed(rddlsm1, subds, title = rownames(resLM)[i], pvalCutof = pvalCutof) + } + } + } + dev.off() + + ## return result file with pvalues and estimates (exclude confidence interval used for plotting) + iCI <- which(substr(colnames(resLM), 4, 7) == "erCI") + resLM <- resLM[, -iCI] + resLM <- cbind(varids, resLM) + return(resLM) +} diff --git a/tools/mixmodel4repeated_measures/mixmodel_wrapper.R b/tools/mixmodel4repeated_measures/mixmodel_wrapper.R index 4525f59f30..3c74e6aa4f 100755 --- a/tools/mixmodel4repeated_measures/mixmodel_wrapper.R +++ b/tools/mixmodel4repeated_measures/mixmodel_wrapper.R @@ -1,200 +1,208 @@ -#!/usr/bin/env Rscript - -library(lme4) ## mixed model computing -library(Matrix) -library(MASS) -library(lmerTest) ## computing pvalue and lsmeans from results of lme4 package -library(multtest) ## multiple testing -library(ggplot2) -library(gridExtra) -library(grid) - -source_local <- function(fname) { - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep = "/")) -} - -source_local("mixmodel_script.R") -source_local("diagmfl.R") - -parse_args <- function() { - args <- commandArgs() - start <- which(args == "--args")[1] + 1 - if (is.na(start)) { - return(list()) - } - seq_by2 <- seq(start, length(args), by = 2) - result <- as.list(args[seq_by2 + 1]) - names(result) <- args[seq_by2] - return(result) -} - -argVc <- unlist(parse_args()) - -##------------------------------ -## Initializing -##------------------------------ - -## options -##-------- - -strAsFacL <- options()$stringsAsFactors -options(stringsAsFactors = FALSE) - -## constants -##---------- - -modNamC <- "mixmodel" ## module name - -topEnvC <- environment() -flagC <- "\n" - -## functions -##---------- - -flgF <- function(tesC, - envC = topEnvC, - txtC = NA) { ## management of warning and error messages - - tesL <- eval(parse(text = tesC), envir = envC) - - if (!tesL) { - - sink(NULL) - stpTxtC <- ifelse(is.na(txtC), - paste0(tesC, " is FALSE"), - txtC) - - stop(stpTxtC, - call. = FALSE) - - } - -} ## flgF - -## log file -##--------- - -sink(argVc["information"]) - -cat("\nStart of the '", modNamC, "' Galaxy module call: ", format(Sys.time(), "%a %d %b %Y %X"), "\n", sep = "") -cat("\nParameters used:\n\n") -print(argVc) -cat("\n\n") - -## loading -##-------- - -datMN <- t(as.matrix(read.table(argVc["dataMatrix_in"], - check.names = FALSE, - header = TRUE, - row.names = 1, - sep = "\t"))) - -samDF <- read.table(argVc["sampleMetadata_in"], - check.names = FALSE, - header = TRUE, - row.names = 1, - sep = "\t") - -varDF <- read.table(argVc["variableMetadata_in"], - check.names = FALSE, - header = TRUE, - row.names = 1, - sep = "\t") - - -## checking -##--------- - -flgF("identical(rownames(datMN), rownames(samDF))", txtC = "Column names of the dataMatrix are not identical to the row names of the sampleMetadata; check your data with the 'Check Format' module in the 'Quality Control' section") -flgF("identical(colnames(datMN), rownames(varDF))", txtC = "Row names of the dataMatrix are not identical to the row names of the variableMetadata; check your data with the 'Check Format' module in the 'Quality Control' section") - -flgF("argVc['time'] %in% colnames(samDF)", txtC = paste0("Required time factor '", argVc["time"], "' could not be found in the column names of the sampleMetadata")) -flgF("argVc['subject'] %in% colnames(samDF)", txtC = paste0("Required subject factor '", argVc["subject"], "' could not be found in the column names of the sampleMetadata")) - -flgF("mode(samDF[, argVc['time']]) %in% c('character', 'numeric')", txtC = paste0("The '", argVc["time"], "' column of the sampleMetadata should contain either number only, or character only")) -flgF("mode(samDF[, argVc['subject']]) %in% c('character', 'numeric')", txtC = paste0("The '", argVc["subject"], "' column of the sampleMetadata should contain either number only, or character only")) - -flgF("argVc['adjC'] %in% c('holm', 'hochberg', 'hommel', 'bonferroni', 'BH', 'BY', 'fdr', 'none')") -flgF("argVc['trf'] %in% c('none', 'log10', 'log2')") - -flgF("0 <= as.numeric(argVc['thrN']) && as.numeric(argVc['thrN']) <= 1", txtC = "(corrected) p-value threshold must be between 0 and 1") -flgF("argVc['diaR'] %in% c('no', 'yes')") - - -##------------------------------ -## Formating -##------------------------------ - -if (argVc["dff"] == "Satt") { - dffmeth <- "Satterthwaite" -} else { - dffmeth <- "Kenward-Roger" -} - - -##------------------------------ -## Computation -##------------------------------ - - -varDFout <- lmixedm(datMN = datMN, - samDF = samDF, - varDF = varDF, - fixfact = argVc["fixfact"], - time = argVc["time"], - subject = argVc["subject"], - logtr = argVc["trf"], - pvalCutof = argVc["thrN"], - pvalcorMeth = argVc["adjC"], - dffOption = dffmeth, - visu = argVc["diaR"], - least.confounded = FALSE, - outlier.limit = 3, - pdfC = argVc["out_graph_pdf"], - pdfE = argVc["out_estim_pdf"] - ) - - - - -##------------------------------ -## Rounding -##------------------------------ - -if (argVc["rounding"] == "yes") { - varDFout[, which(!(colnames(varDFout) %in% colnames(varDF)))] <- apply(varDFout[, which(!(colnames(varDFout) %in% colnames(varDF)))], 2, round, digits = as.numeric(argVc["decplaces"])) -} - -##------------------------------ -## Ending -##------------------------------ - - -## saving -##-------- - -varDFout <- cbind.data.frame(variableMetadata = rownames(varDFout), - varDFout) - -write.table(varDFout, - file = argVc["variableMetadata_out"], - quote = FALSE, - row.names = FALSE, - sep = "\t") - -## closing -##-------- - -cat("\n\nEnd of '", modNamC, "' Galaxy module call: ", - as.character(Sys.time()), "\n", sep = "") -cat("\nInformation about R (version, Operating System, attached or loaded packages):\n\n") -sessionInfo() - -sink() - -options(stringsAsFactors = strAsFacL) - -rm(list = ls()) +#!/usr/bin/env Rscript + +library(lme4) ## mixed model computing +library(Matrix) +library(MASS) +library(lmerTest) ## computing pvalue and lsmeans from results of lme4 package +library(multtest) ## multiple testing +library(ggplot2) +library(gridExtra) +library(grid) + +source_local <- function(fname) { + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) +} + +source_local("mixmodel_script.R") +source_local("diagmfl.R") + +parse_args <- function() { + args <- commandArgs() + start <- which(args == "--args")[1] + 1 + if (is.na(start)) { + return(list()) + } + seq_by2 <- seq(start, length(args), by = 2) + result <- as.list(args[seq_by2 + 1]) + names(result) <- args[seq_by2] + return(result) +} + +argVc <- unlist(parse_args()) + +## ------------------------------ +## Initializing +## ------------------------------ + +## options +## -------- + +strAsFacL <- options()$stringsAsFactors +options(stringsAsFactors = FALSE) + +## constants +## ---------- + +modNamC <- "mixmodel" ## module name + +topEnvC <- environment() +flagC <- "\n" + +## functions +## ---------- + +flgF <- function(tesC, + envC = topEnvC, + txtC = NA) { ## management of warning and error messages + + tesL <- eval(parse(text = tesC), envir = envC) + + if (!tesL) { + sink(NULL) + stpTxtC <- ifelse(is.na(txtC), + paste0(tesC, " is FALSE"), + txtC + ) + + stop(stpTxtC, + call. = FALSE + ) + } +} ## flgF + +## log file +## --------- + +sink(argVc["information"]) + +cat("\nStart of the '", modNamC, "' Galaxy module call: ", format(Sys.time(), "%a %d %b %Y %X"), "\n", sep = "") +cat("\nParameters used:\n\n") +print(argVc) +cat("\n\n") + +## loading +## -------- + +datMN <- t(as.matrix(read.table(argVc["dataMatrix_in"], + check.names = FALSE, + header = TRUE, + row.names = 1, + sep = "\t" +))) + +samDF <- read.table(argVc["sampleMetadata_in"], + check.names = FALSE, + header = TRUE, + row.names = 1, + sep = "\t" +) + +varDF <- read.table(argVc["variableMetadata_in"], + check.names = FALSE, + header = TRUE, + row.names = 1, + sep = "\t" +) + + +## checking +## --------- + +flgF("identical(rownames(datMN), rownames(samDF))", txtC = "Column names of the dataMatrix are not identical to the row names of the sampleMetadata; check your data with the 'Check Format' module in the 'Quality Control' section") +flgF("identical(colnames(datMN), rownames(varDF))", txtC = "Row names of the dataMatrix are not identical to the row names of the variableMetadata; check your data with the 'Check Format' module in the 'Quality Control' section") + +flgF("argVc['time'] %in% colnames(samDF)", txtC = paste0("Required time factor '", argVc["time"], "' could not be found in the column names of the sampleMetadata")) +flgF("argVc['subject'] %in% colnames(samDF)", txtC = paste0("Required subject factor '", argVc["subject"], "' could not be found in the column names of the sampleMetadata")) + +flgF("mode(samDF[, argVc['time']]) %in% c('character', 'numeric')", txtC = paste0("The '", argVc["time"], "' column of the sampleMetadata should contain either number only, or character only")) +flgF("mode(samDF[, argVc['subject']]) %in% c('character', 'numeric')", txtC = paste0("The '", argVc["subject"], "' column of the sampleMetadata should contain either number only, or character only")) + +flgF("argVc['adjC'] %in% c('holm', 'hochberg', 'hommel', 'bonferroni', 'BH', 'BY', 'fdr', 'none')") +flgF("argVc['trf'] %in% c('none', 'log10', 'log2')") + +flgF("0 <= as.numeric(argVc['thrN']) && as.numeric(argVc['thrN']) <= 1", txtC = "(corrected) p-value threshold must be between 0 and 1") +flgF("argVc['diaR'] %in% c('no', 'yes')") + + +## ------------------------------ +## Formating +## ------------------------------ + +if (argVc["dff"] == "Satt") { + dffmeth <- "Satterthwaite" +} else { + dffmeth <- "Kenward-Roger" +} + + +## ------------------------------ +## Computation +## ------------------------------ + + +varDFout <- lmixedm( + datMN = datMN, + samDF = samDF, + varDF = varDF, + fixfact = argVc["fixfact"], + time = argVc["time"], + subject = argVc["subject"], + logtr = argVc["trf"], + pvalCutof = argVc["thrN"], + pvalcorMeth = argVc["adjC"], + dffOption = dffmeth, + visu = argVc["diaR"], + least.confounded = FALSE, + outlier.limit = 3, + pdfC = argVc["out_graph_pdf"], + pdfE = argVc["out_estim_pdf"] +) + + + + +## ------------------------------ +## Rounding +## ------------------------------ + +if (argVc["rounding"] == "yes") { + varDFout[, which(!(colnames(varDFout) %in% colnames(varDF)))] <- apply(varDFout[, which(!(colnames(varDFout) %in% colnames(varDF)))], 2, round, digits = as.numeric(argVc["decplaces"])) +} + +## ------------------------------ +## Ending +## ------------------------------ + + +## saving +## -------- + +varDFout <- cbind.data.frame( + variableMetadata = rownames(varDFout), + varDFout +) + +write.table(varDFout, + file = argVc["variableMetadata_out"], + quote = FALSE, + row.names = FALSE, + sep = "\t" +) + +## closing +## -------- + +cat("\n\nEnd of '", modNamC, "' Galaxy module call: ", + as.character(Sys.time()), "\n", + sep = "" +) +cat("\nInformation about R (version, Operating System, attached or loaded packages):\n\n") +sessionInfo() + +sink() + +options(stringsAsFactors = strAsFacL) + +rm(list = ls()) diff --git a/tools/ms2snoop/.shed.yml b/tools/ms2snoop/.shed.yml index 02051b3e37..c47bd9a235 100644 --- a/tools/ms2snoop/.shed.yml +++ b/tools/ms2snoop/.shed.yml @@ -1,6 +1,7 @@ categories: [Metabolomics] description: '[W4M][Utils] Extraction et nettoyage des spectre MS1/2 post-traitement MSPurity.' homepage_url: http://workflow4metabolomics.org +remote_repository_url: https://github.com/workflow4metabolomics/tools-metabolomics long_description: 'Extraction des spectres MS1 et adduits associées + Extraction + nettoyage automatisé des spectres MS2 Extraction des métadonnées des fichiers RAW Export en fichier idéalement en JSON' diff --git a/tools/ms2snoop/MS2snoop.R b/tools/ms2snoop/MS2snoop.R index fcd6a15425..7363cdbbcb 100644 --- a/tools/ms2snoop/MS2snoop.R +++ b/tools/ms2snoop/MS2snoop.R @@ -15,281 +15,276 @@ get_version <- function() { - cmd <- commandArgs(trailingOnly = FALSE) - root <- dirname(gsub("--file=", "", cmd[grep("--file=", cmd)])) - readme <- readLines(file.path(root, "README.md")) - version_line <- readme[grepl(" * **@version**: ", readme, fixed = TRUE)] - return(gsub(".*: ", "", version_line)) + cmd <- commandArgs(trailingOnly = FALSE) + root <- dirname(gsub("--file=", "", cmd[grep("--file=", cmd)])) + readme <- readLines(file.path(root, "README.md")) + version_line <- readme[grepl(" * **@version**: ", readme, fixed = TRUE)] + return(gsub(".*: ", "", version_line)) } defaults <- list( - MS2SNOOP_VERSION = get_version(), - MISSING_PARAMETER_ERROR = 1, - BAD_PARAMETER_VALUE_ERROR = 2, - MISSING_INPUT_FILE_ERROR = 3, - NO_ANY_RESULT_ERROR = 255, - DEFAULT_PRECURSOR_PATH = NULL, - DEFAULT_FRAGMENTS_PATH = NULL, - DEFAULT_COMPOUNDS_PATH = NULL, - DEFAULT_OUTPUT_PATH = "compound_fragments_result.txt", - DEFAULT_TOLMZ = 0.01, - DEFAULT_TOLRT = 20, - DEFAULT_MZDECIMAL = 3, - DEFAULT_R_THRESHOLD = 0.85, - DEFAULT_MINNUMBERSCAN = 8, - DEFAULT_SEUIL_RA = 0.05, - DEFAULT_FRAGMENTS_MATCH_DELTA = 10, - DEFAULT_FRAGMENTS_MATCH_DELTA_UNIT = "ppm", - DEFAULT_PDF_PATH = "" + MS2SNOOP_VERSION = get_version(), + MISSING_PARAMETER_ERROR = 1, + BAD_PARAMETER_VALUE_ERROR = 2, + MISSING_INPUT_FILE_ERROR = 3, + NO_ANY_RESULT_ERROR = 255, + DEFAULT_PRECURSOR_PATH = NULL, + DEFAULT_FRAGMENTS_PATH = NULL, + DEFAULT_COMPOUNDS_PATH = NULL, + DEFAULT_OUTPUT_PATH = "compound_fragments_result.txt", + DEFAULT_TOLMZ = 0.01, + DEFAULT_TOLRT = 20, + DEFAULT_MZDECIMAL = 3, + DEFAULT_R_THRESHOLD = 0.85, + DEFAULT_MINNUMBERSCAN = 8, + DEFAULT_SEUIL_RA = 0.05, + DEFAULT_FRAGMENTS_MATCH_DELTA = 10, + DEFAULT_FRAGMENTS_MATCH_DELTA_UNIT = "ppm", + DEFAULT_PDF_PATH = "" ) env <- globalenv() for (default in names(defaults)) { - assign(default, defaults[[default]], envir = env) - lockBinding(default, env) + assign(default, defaults[[default]], envir = env) + lockBinding(default, env) } ######################################################################## get_formulas <- function( - mzref, - spectra, - processing_parameters, - background = !TRUE, - show_sirius_outputs = !TRUE -) { - if (is.vector(mzref) && length(mzref) > 1) { - return(lapply( - mzref, - function(mz) { - return(get_formulas( - mzref = mz, - spectra = spectra, - processing_parameters = processing_parameters, - background = background + mzref, + spectra, + processing_parameters, + background = !TRUE, + show_sirius_outputs = !TRUE) { + if (is.vector(mzref) && length(mzref) > 1) { + return(lapply( + mzref, + function(mz) { + return(get_formulas( + mzref = mz, + spectra = spectra, + processing_parameters = processing_parameters, + background = background + )) + } )) - } - )) - } - input <- sprintf( - "%s-%s.ms", - gsub("[[:space:]]", "_", processing_parameters$c_name), - mzref - ) - create_ms_file(input, mzref, spectra, processing_parameters) - output <- sprintf( - "out/%s-%s.out", - gsub("[[:space:]]", "_", processing_parameters$c_name), - mzref - ) - command <- sprintf( - paste( - "sirius", - "--noCite", - "--noSummaries", - "--loglevel=WARNING", - "-i='%s'", - "-o='%s'", - "tree", - "2>&1" - ), - input, - output - ) - if (!show_sirius_outputs) { - ## loglevel is not taken into account during - ## sirius startup, so we filter outputs... - command <- paste(command, "| grep '^(WARNING|SEVERE)'") - } - verbose_catf( - ">> Sirius is running %swith the command: %s\n", - if (background) "in the background " else "", - command - ) - system( - command, - wait = !background, - ignore.stdout = background, - ignore.stderr = background - ) - return(extract_sirius_results(output, spectra$mz, processing_parameters)) + } + input <- sprintf( + "%s-%s.ms", + gsub("[[:space:]]", "_", processing_parameters$c_name), + mzref + ) + create_ms_file(input, mzref, spectra, processing_parameters) + output <- sprintf( + "out/%s-%s.out", + gsub("[[:space:]]", "_", processing_parameters$c_name), + mzref + ) + command <- sprintf( + paste( + "sirius", + "--noCite", + "--noSummaries", + "--loglevel=WARNING", + "-i='%s'", + "-o='%s'", + "tree", + "2>&1" + ), + input, + output + ) + if (!show_sirius_outputs) { + ## loglevel is not taken into account during + ## sirius startup, so we filter outputs... + command <- paste(command, "| grep '^(WARNING|SEVERE)'") + } + verbose_catf( + ">> Sirius is running %swith the command: %s\n", + if (background) "in the background " else "", + command + ) + system( + command, + wait = !background, + ignore.stdout = background, + ignore.stderr = background + ) + return(extract_sirius_results(output, spectra$mz, processing_parameters)) } create_ms_file <- function( - path, - mzref, - spectra, - processing_parameters -) { - file_content <- paste( - sprintf(">compound %s", processing_parameters$c_name), - sprintf(">ionization %s", processing_parameters$ionization), - sprintf(">parentmass %s", mzref), - sprintf(">formula %s", processing_parameters$elemcomposition), - sep = "\n" - ) - displayed_file_content <- sprintf( - "%s\n>collision\n%s", - file_content, - paste( - sprintf( - "%s %s", - spectra[1:3, "mz"], - spectra[1:3, "intensities"] - ), - collapse = "\n" + path, + mzref, + spectra, + processing_parameters) { + file_content <- paste( + sprintf(">compound %s", processing_parameters$c_name), + sprintf(">ionization %s", processing_parameters$ionization), + sprintf(">parentmass %s", mzref), + sprintf(">formula %s", processing_parameters$elemcomposition), + sep = "\n" ) - ) - if (nrow(spectra) > 3) { displayed_file_content <- sprintf( - "%s\n[... %s more rows of mz and intensities ...]", - displayed_file_content, - nrow(spectra) - 3 + "%s\n>collision\n%s", + file_content, + paste( + sprintf( + "%s %s", + spectra[1:3, "mz"], + spectra[1:3, "intensities"] + ), + collapse = "\n" + ) + ) + if (nrow(spectra) > 3) { + displayed_file_content <- sprintf( + "%s\n[... %s more rows of mz and intensities ...]", + displayed_file_content, + nrow(spectra) - 3 + ) + } + catf( + ">> MS file created for %s with content:\n%s\n", + processing_parameters$c_name, + displayed_file_content ) - } - catf( - ">> MS file created for %s with content:\n%s\n", - processing_parameters$c_name, - displayed_file_content - ) - file_content <- sprintf( - "%s\n\n>collision\n%s", - file_content, - paste( - paste(spectra$mz, spectra$intensities), - collapse = "\n" + file_content <- sprintf( + "%s\n\n>collision\n%s", + file_content, + paste( + paste(spectra$mz, spectra$intensities), + collapse = "\n" + ) ) - ) - cat(file_content, file = path, append = FALSE) + cat(file_content, file = path, append = FALSE) } extract_sirius_results <- function( - output, - mz_list, - processing_parameters -) { - - delta <- processing_parameters$fragment_match_delta - delta_unit <- tolower(processing_parameters$fragment_match_delta_unit) - - output <- list.dirs(output, recursive = FALSE)[[1]] - - spectra_out_dir <- sprintf("%s/spectra", output) - spectra_filename <- sprintf( - "%s/%s", - spectra_out_dir, - list.files(spectra_out_dir)[[1]] - ) - - trees_out_dir <- sprintf("%s/trees", output) - trees_filename <- sprintf( - "%s/%s", - trees_out_dir, - list.files(trees_out_dir)[[1]] - ) - - if (!is.null(spectra_filename)) { - sirius_results <- get_csv_or_tsv(spectra_filename) - } else { - return(rep(NA, length(mz_list))) - } - if (!is.null(trees_filename)) { - extracted_ppm <- extract_sirius_ppm(trees_filename) - sirius_results <- cbind(sirius_results, extracted_ppm) - } else { - return(rep(NA, length(mz_list))) - } - - fragment_matchings <- data.frame( - formula = NA, - ppm = NA, - mz = mz_list, - error = NA - ) - - sirius_results <- filter_sirius_with_delta( - sirius_results = sirius_results, - original_mz = fragment_matchings$mz, - delta = delta, - delta_unit = delta_unit - ) - - for (index in seq_len(nrow(sirius_results))) { - result <- sirius_results[index, ] - filter <- order(abs(fragment_matchings$mz - result$mz))[1] - fragment_matchings[filter, "formula"] <- result$formula - fragment_matchings[filter, "ppm"] <- result$ppm - catf( - "[OK] Fragment with m/z=%s matches %s with a difference of %s ppm\n", - fragment_matchings[filter, "mz"], result$formula, result$ppm + output, + mz_list, + processing_parameters) { + delta <- processing_parameters$fragment_match_delta + delta_unit <- tolower(processing_parameters$fragment_match_delta_unit) + + output <- list.dirs(output, recursive = FALSE)[[1]] + + spectra_out_dir <- sprintf("%s/spectra", output) + spectra_filename <- sprintf( + "%s/%s", + spectra_out_dir, + list.files(spectra_out_dir)[[1]] + ) + + trees_out_dir <- sprintf("%s/trees", output) + trees_filename <- sprintf( + "%s/%s", + trees_out_dir, + list.files(trees_out_dir)[[1]] ) - } - return(fragment_matchings) + + if (!is.null(spectra_filename)) { + sirius_results <- get_csv_or_tsv(spectra_filename) + } else { + return(rep(NA, length(mz_list))) + } + if (!is.null(trees_filename)) { + extracted_ppm <- extract_sirius_ppm(trees_filename) + sirius_results <- cbind(sirius_results, extracted_ppm) + } else { + return(rep(NA, length(mz_list))) + } + + fragment_matchings <- data.frame( + formula = NA, + ppm = NA, + mz = mz_list, + error = NA + ) + + sirius_results <- filter_sirius_with_delta( + sirius_results = sirius_results, + original_mz = fragment_matchings$mz, + delta = delta, + delta_unit = delta_unit + ) + + for (index in seq_len(nrow(sirius_results))) { + result <- sirius_results[index, ] + filter <- order(abs(fragment_matchings$mz - result$mz))[1] + fragment_matchings[filter, "formula"] <- result$formula + fragment_matchings[filter, "ppm"] <- result$ppm + catf( + "[OK] Fragment with m/z=%s matches %s with a difference of %s ppm\n", + fragment_matchings[filter, "mz"], result$formula, result$ppm + ) + } + return(fragment_matchings) } filter_sirius_with_delta <- function( - sirius_results, - original_mz, - delta, - delta_unit -) { - if (is.numeric(delta) && !is.na(delta) && delta > 0) { - if (delta_unit == "ppm") { - filter <- abs(sirius_results$ppm) <= delta - fine <- which(filter) - not_fine <- which(!filter) - catf( - paste( - "[KO] fragment %s (m/z=%s) eleminated because ppm=%s is greater", - "than delta=%s\n" - ), - sirius_results[not_fine, ]$formula, - sirius_results[not_fine, ]$mz, - sirius_results[not_fine, ]$ppm, - delta - ) - sirius_results <- sirius_results[fine, ] - } else if (delta_unit == "mz") { - differences <- sapply( - sirius_results$mz, - function(mz) min(abs(original_mz - mz)) - ) - fine <- which(sapply( - sirius_results$mz, - function(mz) any(abs(original_mz - mz) <= delta) - )) - not_fine <- which(sapply( - sirius_results$mz, - function(mz) all(abs(original_mz - mz) > delta) - )) - catf( - paste( - "[KO] fragment %s eleminated because mz difference=%s is", - "greater than delta=%s\n" - ), - sirius_results[not_fine, ]$formula, - differences[not_fine], - delta - ) - sirius_results <- sirius_results[fine, ] + sirius_results, + original_mz, + delta, + delta_unit) { + if (is.numeric(delta) && !is.na(delta) && delta > 0) { + if (delta_unit == "ppm") { + filter <- abs(sirius_results$ppm) <= delta + fine <- which(filter) + not_fine <- which(!filter) + catf( + paste( + "[KO] fragment %s (m/z=%s) eleminated because ppm=%s is greater", + "than delta=%s\n" + ), + sirius_results[not_fine, ]$formula, + sirius_results[not_fine, ]$mz, + sirius_results[not_fine, ]$ppm, + delta + ) + sirius_results <- sirius_results[fine, ] + } else if (delta_unit == "mz") { + differences <- sapply( + sirius_results$mz, + function(mz) min(abs(original_mz - mz)) + ) + fine <- which(sapply( + sirius_results$mz, + function(mz) any(abs(original_mz - mz) <= delta) + )) + not_fine <- which(sapply( + sirius_results$mz, + function(mz) all(abs(original_mz - mz) > delta) + )) + catf( + paste( + "[KO] fragment %s eleminated because mz difference=%s is", + "greater than delta=%s\n" + ), + sirius_results[not_fine, ]$formula, + differences[not_fine], + delta + ) + sirius_results <- sirius_results[fine, ] + } } - } - return(sirius_results) + return(sirius_results) } extract_sirius_ppm <- function(path) { - json <- file(path, "r") - suppressWarnings(json_lines <- readLines(json)) - close(json) - json_lines <- json_lines[ - grepl("\\s+\"(massDeviation|recalibratedMass)\" :", json_lines) - ] - json_lines <- gsub("^\\s+\"[^\"]+\" : \"?", "", json_lines) - ppms <- json_lines[seq(1, length(json_lines), 2)] - mz <- json_lines[seq(2, length(json_lines), 2)] - ppms <- as.numeric(gsub(" ppm .*", "", ppms)) - mz <- as.numeric(gsub(",$", "", mz)) - ordered <- order(mz) - return(list(ppm = ppms[ordered], recalibrated_mz = mz[ordered])) + json <- file(path, "r") + suppressWarnings(json_lines <- readLines(json)) + close(json) + json_lines <- json_lines[ + grepl("\\s+\"(massDeviation|recalibratedMass)\" :", json_lines) + ] + json_lines <- gsub("^\\s+\"[^\"]+\" : \"?", "", json_lines) + ppms <- json_lines[seq(1, length(json_lines), 2)] + mz <- json_lines[seq(2, length(json_lines), 2)] + ppms <- as.numeric(gsub(" ppm .*", "", ppms)) + mz <- as.numeric(gsub(",$", "", mz)) + ordered <- order(mz) + return(list(ppm = ppms[ordered], recalibrated_mz = mz[ordered])) } #' @title plot_pseudo_spectra @@ -308,161 +303,161 @@ extract_sirius_ppm <- function(path) { #' ions in next with intensities #' fid file id when several a precursor has been detected in several files plot_pseudo_spectra <- function( - x, - fid, - sum_int, - vmz, - cor_abs_int, - refcol, - meaned_mz, - processing_parameters -) { - ## du fait de la difference de nombre de colonne entre la dataframe qui - ## inclue les scans en 1ere col, mzRef se decale de 1 - refcol <- refcol - 1 - ## compute relative intensities max=100% - rel_int <- sum_int[-1] - rel_int <- rel_int / max(rel_int) - - if (processing_parameters$do_pdf) { - ## define max value on vertical axis (need to increase in order to plot the - ## label of fragments) - ymax <- max(rel_int) + 0.2 * max(rel_int) - - par(mfrow = c(2, 1)) - plot(vmz, rel_int, type = "h", ylim = c(0, ymax), - main = processing_parameters$c_name - ) - ## low correl coef. will be display in grey - cor_low <- which(round(cor_abs_int, 2) < processing_parameters$r_threshold) - - lbmzcor <- sprintf("%s(r=%s)", vmz, round(cor_abs_int, 2)) - - if (length(cor_low) > 0) { - text( - vmz[cor_low], - rel_int[cor_low], - lbmzcor[cor_low], - cex = 0.5, - col = "grey", - srt = 90, - adj = 0 - ) - if (length(vmz) - length(cor_low) > 1) { + x, + fid, + sum_int, + vmz, + cor_abs_int, + refcol, + meaned_mz, + processing_parameters) { + ## du fait de la difference de nombre de colonne entre la dataframe qui + ## inclue les scans en 1ere col, mzRef se decale de 1 + refcol <- refcol - 1 + ## compute relative intensities max=100% + rel_int <- sum_int[-1] + rel_int <- rel_int / max(rel_int) + + if (processing_parameters$do_pdf) { + ## define max value on vertical axis (need to increase in order to plot the + ## label of fragments) + ymax <- max(rel_int) + 0.2 * max(rel_int) + + par(mfrow = c(2, 1)) + plot(vmz, rel_int, + type = "h", ylim = c(0, ymax), + main = processing_parameters$c_name + ) + ## low correl coef. will be display in grey + cor_low <- which(round(cor_abs_int, 2) < processing_parameters$r_threshold) + + lbmzcor <- sprintf("%s(r=%s)", vmz, round(cor_abs_int, 2)) + + if (length(cor_low) > 0) { + text( + vmz[cor_low], + rel_int[cor_low], + lbmzcor[cor_low], + cex = 0.5, + col = "grey", + srt = 90, + adj = 0 + ) + if (length(vmz) - length(cor_low) > 1) { + text( + vmz[-c(refcol, cor_low)], + rel_int[-c(refcol, cor_low)], + lbmzcor[-c(refcol, cor_low)], + cex = 0.6, + col = 1, + srt = 90, + adj = 0 + ) + } + } else { + if (length(vmz) > 1) { + text( + vmz[-c(refcol)], + rel_int[-c(refcol)], + lbmzcor[-c(refcol)], + cex = 0.6, + col = 1, + srt = 90, + adj = 0 + ) + } + } + text( - vmz[-c(refcol, cor_low)], - rel_int[-c(refcol, cor_low)], - lbmzcor[-c(refcol, cor_low)], - cex = 0.6, - col = 1, - srt = 90, - adj = 0 + vmz[refcol], + rel_int[refcol], + lbmzcor[refcol], + cex = 0.8, + col = 2, + srt = 90, + adj = 0 ) - } + } + + ## prepare result file + cor_valid <- (round(cor_abs_int, 2) >= processing_parameters$r_threshold) + + do_sirius <- TRUE + verbose_catf("Checking sirius parameters...\n") + if (is.null(processing_parameters$ionization)) { + do_sirius <- FALSE + verbose_catf("[KO] No ionization passed in parameter.\n") } else { - if (length(vmz) > 1) { - text( - vmz[-c(refcol)], - rel_int[-c(refcol)], - lbmzcor[-c(refcol)], - cex = 0.6, - col = 1, - srt = 90, - adj = 0 + verbose_catf("[OK] Ionization=%s.\n", processing_parameters$ionization) + } + if (is.na(processing_parameters$elemcomposition)) { + do_sirius <- FALSE + verbose_catf("[KO] Elemental composition is NA.\n") + } else if (length(processing_parameters$elemcomposition) < 1) { + do_sirius <- FALSE + verbose_catf("[KO] No elemental composition is provided.\n") + } else if (processing_parameters$elemcomposition == "") { + do_sirius <- FALSE + verbose_catf("[KO] Elemental composition is an empty string.\n") + } else { + verbose_catf( + "[OK] Elemental composition=%s.\n", + processing_parameters$elemcomposition ) - } } - text( - vmz[refcol], - rel_int[refcol], - lbmzcor[refcol], - cex = 0.8, - col = 2, - srt = 90, - adj = 0 - ) - } - - ## prepare result file - cor_valid <- (round(cor_abs_int, 2) >= processing_parameters$r_threshold) - - do_sirius <- TRUE - verbose_catf("Checking sirius parameters...\n") - if (is.null(processing_parameters$ionization)) { - do_sirius <- FALSE - verbose_catf("[KO] No ionization passed in parameter.\n") - } else { - verbose_catf("[OK] Ionization=%s.\n", processing_parameters$ionization) - } - if (is.na(processing_parameters$elemcomposition)) { - do_sirius <- FALSE - verbose_catf("[KO] Elemental composition is NA.\n") - } else if (length(processing_parameters$elemcomposition) < 1) { - do_sirius <- FALSE - verbose_catf("[KO] No elemental composition is provided.\n") - } else if (processing_parameters$elemcomposition == "") { - do_sirius <- FALSE - verbose_catf("[KO] Elemental composition is an empty string.\n") - } else { - verbose_catf( - "[OK] Elemental composition=%s.\n", - processing_parameters$elemcomposition - ) - } - - cp_res_length <- length(vmz) - ppm <- rep(NA, cp_res_length) - formulas <- rep(NA, cp_res_length) - if (do_sirius) { - verbose_catf("Everything is ok, preparing for sirius.\n") - formulas <- get_formulas( - mzref = processing_parameters$mzref, - spectra = data.frame(mz = meaned_mz, intensities = sum_int[-1]), - processing_parameters = processing_parameters - ) - if (nrow(formulas) == 0) { - catf("No formula found.\n") + cp_res_length <- length(vmz) + ppm <- rep(NA, cp_res_length) + formulas <- rep(NA, cp_res_length) + if (do_sirius) { + verbose_catf("Everything is ok, preparing for sirius.\n") + formulas <- get_formulas( + mzref = processing_parameters$mzref, + spectra = data.frame(mz = meaned_mz, intensities = sum_int[-1]), + processing_parameters = processing_parameters + ) + if (nrow(formulas) == 0) { + catf("No formula found.\n") + } else { + ppm <- formulas$ppm + formulas <- formulas$formula + catf( + "Found %s formula for %s fragments\n", + length(formulas[which(!(is.na(formulas)))]), + cp_res_length + ) + } } else { - ppm <- formulas$ppm - formulas <- formulas$formula - catf( - "Found %s formula for %s fragments\n", - length(formulas[which(!(is.na(formulas)))]), - cp_res_length - ) + verbose_catf("Sirius cannot be run.\n") } - } else { - verbose_catf("Sirius cannot be run.\n") - } - cp_res <- data.frame( - rep(processing_parameters$c_name, cp_res_length), - rep(processing_parameters$inchikey, cp_res_length), - rep(processing_parameters$elemcomposition, cp_res_length), - formulas, - meaned_mz, - ppm, - rep(fid, cp_res_length), - cor_abs_int, - sum_int[-1], - rel_int, - cor_valid - ) - - colnames(cp_res) <- c( - "compoundName", - "inchikey", - "elemcomposition", - "fragment", - "fragment_mz", - "ppm", - "fileid", - "CorWithPrecursor", - "AbsoluteIntensity", - "relativeIntensity", - "corValid" - ) - return(cp_res) + cp_res <- data.frame( + rep(processing_parameters$c_name, cp_res_length), + rep(processing_parameters$inchikey, cp_res_length), + rep(processing_parameters$elemcomposition, cp_res_length), + formulas, + meaned_mz, + ppm, + rep(fid, cp_res_length), + cor_abs_int, + sum_int[-1], + rel_int, + cor_valid + ) + + colnames(cp_res) <- c( + "compoundName", + "inchikey", + "elemcomposition", + "fragment", + "fragment_mz", + "ppm", + "fileid", + "CorWithPrecursor", + "AbsoluteIntensity", + "relativeIntensity", + "corValid" + ) + return(cp_res) } #' @@ -481,753 +476,750 @@ plot_pseudo_spectra <- function( #' @description #' function for extraction of fragments corresponding to precursors #' detected by MSPurity -extract_fragments <- function( ## nolint cyclocomp_linter - precursors, - fragments, - processing_parameters -) { - ## filter precursor in the precursors file based on mz and rt in the - ## compound list - catf("processing %s\n", processing_parameters$c_name) - verbose_catf("===\n") - param <- processing_parameters - selected_precursors <- which( - (abs(precursors$precurMtchMZ - param$mzref) <= param$tolmz) - & (abs(precursors$precurMtchRT - param$rtref) <= param$tolrt) - ) - rm(param) - - verbose_catf( - "> %s precursors selected with mz=%s±%s and rt=%s±%s\n", - length(selected_precursors), - processing_parameters$mzref, - processing_parameters$tolmz, - processing_parameters$rtref, - processing_parameters$tolrt - ) - - ## check if there is the precursor in the file - - if (length(selected_precursors) < 1) { - cat("> non detected in precursor file\n") - show_end_processing() - return(NULL) - } - - precursors <- precursors[selected_precursors, ] - - ## check if fragments corresponding to precursor are found in several - ## files (collision energy) - ## this lead to a processing for each fileid - file_ids <- as.character(sort(unique(precursors$fileid))) - if (length(file_ids) > 1) { - catf("> several files detected for this compounds :\n") - } else if (length(file_ids) < 1 || nrow(precursors) < 1) { - return(data.frame()) - } - - res_comp <- data.frame() - for (curent_file_id in file_ids) { - curent_precursors <- precursors[precursors$fileid == curent_file_id, ] - selected_fragments <- fragments[ - fragments$grpid %in% as.character(curent_precursors$grpid) - & fragments$fileid == curent_file_id, - ] - filtered_fragments <- selected_fragments[ - selected_fragments$ra > processing_parameters$seuil_ra, - ] - if (nrow(filtered_fragments) != 0) { - res_comp_by_file <- process_file( - curent_file_id = curent_file_id, - precursor_mz = curent_precursors$mz, - filtered_fragments = filtered_fragments, - processing_parameters = processing_parameters - ) - if (!is.null(res_comp_by_file)) { - res_comp <- rbind(res_comp, res_comp_by_file) - } - } else { - catf("No fragment found for in fragment file\n") +extract_fragments <- function( + ## nolint cyclocomp_linter + precursors, + fragments, + processing_parameters) { + ## filter precursor in the precursors file based on mz and rt in the + ## compound list + catf("processing %s\n", processing_parameters$c_name) + verbose_catf("===\n") + param <- processing_parameters + selected_precursors <- which( + (abs(precursors$precurMtchMZ - param$mzref) <= param$tolmz) & + (abs(precursors$precurMtchRT - param$rtref) <= param$tolrt) + ) + rm(param) + + verbose_catf( + "> %s precursors selected with mz=%s±%s and rt=%s±%s\n", + length(selected_precursors), + processing_parameters$mzref, + processing_parameters$tolmz, + processing_parameters$rtref, + processing_parameters$tolrt + ) + + ## check if there is the precursor in the file + + if (length(selected_precursors) < 1) { + cat("> non detected in precursor file\n") + show_end_processing() + return(NULL) } - } - return(unique(res_comp)) + + precursors <- precursors[selected_precursors, ] + + ## check if fragments corresponding to precursor are found in several + ## files (collision energy) + ## this lead to a processing for each fileid + file_ids <- as.character(sort(unique(precursors$fileid))) + if (length(file_ids) > 1) { + catf("> several files detected for this compounds :\n") + } else if (length(file_ids) < 1 || nrow(precursors) < 1) { + return(data.frame()) + } + + res_comp <- data.frame() + for (curent_file_id in file_ids) { + curent_precursors <- precursors[precursors$fileid == curent_file_id, ] + selected_fragments <- fragments[ + fragments$grpid %in% as.character(curent_precursors$grpid) & + fragments$fileid == curent_file_id, + ] + filtered_fragments <- selected_fragments[ + selected_fragments$ra > processing_parameters$seuil_ra, + ] + if (nrow(filtered_fragments) != 0) { + res_comp_by_file <- process_file( + curent_file_id = curent_file_id, + precursor_mz = curent_precursors$mz, + filtered_fragments = filtered_fragments, + processing_parameters = processing_parameters + ) + if (!is.null(res_comp_by_file)) { + res_comp <- rbind(res_comp, res_comp_by_file) + } + } else { + catf("No fragment found for in fragment file\n") + } + } + return(unique(res_comp)) } process_file <- function( - curent_file_id, - precursor_mz, - filtered_fragments, - processing_parameters -) { - mznominal <- round(x = filtered_fragments$mz, digits = 0) - meaned_mz <- round( - aggregate( - data.frame( - mz = filtered_fragments$mz, - mznominal = mznominal - ), - list(mznominal), - FUN = mean - )$mz, - digits = processing_parameters$mzdecimal - ) - filtered_fragments <- data.frame(filtered_fragments, mznominal) - - ## creation of cross table row=scan col=mz X=ra - - vmz <- as.character(sort(unique(filtered_fragments$mznominal))) - - ds_abs_int <- create_ds_abs_int(vmz, filtered_fragments) - - if (global_debug) { - print(ds_abs_int) - } - - ## elimination of mz with less than min_number_scan scans (user defined - ## parameter) - xmz <- rep(NA, ncol(ds_abs_int) - 1) - sum_int <- rep(NA, ncol(ds_abs_int)) - nbxmz <- 0 - nb_scan_check <- min(nrow(ds_abs_int), processing_parameters$min_number_scan) - - for (j in 2:ncol(ds_abs_int)) { - sum_int[j] <- sum(ds_abs_int[j], na.rm = TRUE) - if (sum(!is.na(ds_abs_int[[j]])) < nb_scan_check) { - nbxmz <- nbxmz + 1 - xmz[nbxmz] <- j + curent_file_id, + precursor_mz, + filtered_fragments, + processing_parameters) { + mznominal <- round(x = filtered_fragments$mz, digits = 0) + meaned_mz <- round( + aggregate( + data.frame( + mz = filtered_fragments$mz, + mznominal = mznominal + ), + list(mznominal), + FUN = mean + )$mz, + digits = processing_parameters$mzdecimal + ) + filtered_fragments <- data.frame(filtered_fragments, mznominal) + + ## creation of cross table row=scan col=mz X=ra + + vmz <- as.character(sort(unique(filtered_fragments$mznominal))) + + ds_abs_int <- create_ds_abs_int(vmz, filtered_fragments) + + if (global_debug) { + print(ds_abs_int) } - } - - xmz <- xmz[-which(is.na(xmz))] - if (length(xmz) > 0) { - ds_abs_int <- ds_abs_int[, -c(xmz)] - sum_int <- sum_int[-c(xmz)] - ## liste des mz keeped decale de 1 avec ds_abs_int - vmz <- as.numeric(vmz[-c(xmz - 1)]) - meaned_mz <- meaned_mz[-c(xmz - 1)] - } - - ## mz of precursor in data precursor to check correlation with - mz_prec <- paste0( - "mz", - round(mean(precursor_mz), processing_parameters$mzdecimal) - ) - ## reference ion for correlation computing = precursor OR maximum - ## intensity ion in precursor is not present - refcol <- which(colnames(ds_abs_int) == mz_prec) - if (length(refcol) == 0) { - refcol <- which(sum_int == max(sum_int, na.rm = TRUE)) - } - - if (processing_parameters$do_pdf) { - start_pdf(processing_parameters, curent_file_id) - } - - ## Pearson correlations between absolute intensities computing - cor_abs_int <- rep(NA, length(vmz)) - - if (length(refcol) > 0) { - for (i in 2:length(ds_abs_int)) { - cor_abs_int[i - 1] <- stats::cor( - x = ds_abs_int[[refcol]], - y = ds_abs_int[[i]], - use = "pairwise.complete.obs", - method = "pearson" - ) - debug_catf( - "Correlation between %s and %s: %s\n", - paste(ds_abs_int[[refcol]], collapse = ";"), - paste(ds_abs_int[[i]], collapse = ";"), - paste(cor_abs_int[i - 1], collapse = ";") - ) - if (processing_parameters$do_pdf) { - pdf_plot_ds_abs_int( - processing_parameters$c_name, - ds_abs_int, - refcol, - i, - round(cor_abs_int[i - 1], 2) - ) - } + + ## elimination of mz with less than min_number_scan scans (user defined + ## parameter) + xmz <- rep(NA, ncol(ds_abs_int) - 1) + sum_int <- rep(NA, ncol(ds_abs_int)) + nbxmz <- 0 + nb_scan_check <- min(nrow(ds_abs_int), processing_parameters$min_number_scan) + + for (j in 2:ncol(ds_abs_int)) { + sum_int[j] <- sum(ds_abs_int[j], na.rm = TRUE) + if (sum(!is.na(ds_abs_int[[j]])) < nb_scan_check) { + nbxmz <- nbxmz + 1 + xmz[nbxmz] <- j + } } - ## plot pseudo spectra - res_comp_by_file <- plot_pseudo_spectra( - x = ds_abs_int, - fid = curent_file_id, - sum_int = sum_int, - vmz = vmz, - cor_abs_int = cor_abs_int, - refcol = refcol, - meaned_mz = meaned_mz, - processing_parameters = processing_parameters - ) - catf( - "%s has been processed and %s fragments have been found.\n", - processing_parameters$c_name, - nrow(res_comp_by_file) + + xmz <- xmz[-which(is.na(xmz))] + if (length(xmz) > 0) { + ds_abs_int <- ds_abs_int[, -c(xmz)] + sum_int <- sum_int[-c(xmz)] + ## liste des mz keeped decale de 1 avec ds_abs_int + vmz <- as.numeric(vmz[-c(xmz - 1)]) + meaned_mz <- meaned_mz[-c(xmz - 1)] + } + + ## mz of precursor in data precursor to check correlation with + mz_prec <- paste0( + "mz", + round(mean(precursor_mz), processing_parameters$mzdecimal) ) - } else { - res_comp_by_file <- NULL - cat(">> non detected in fragments file \n") - } - show_end_processing() - if (processing_parameters$do_pdf) { - end_pdf() - } - return(res_comp_by_file) + ## reference ion for correlation computing = precursor OR maximum + ## intensity ion in precursor is not present + refcol <- which(colnames(ds_abs_int) == mz_prec) + if (length(refcol) == 0) { + refcol <- which(sum_int == max(sum_int, na.rm = TRUE)) + } + + if (processing_parameters$do_pdf) { + start_pdf(processing_parameters, curent_file_id) + } + + ## Pearson correlations between absolute intensities computing + cor_abs_int <- rep(NA, length(vmz)) + + if (length(refcol) > 0) { + for (i in 2:length(ds_abs_int)) { + cor_abs_int[i - 1] <- stats::cor( + x = ds_abs_int[[refcol]], + y = ds_abs_int[[i]], + use = "pairwise.complete.obs", + method = "pearson" + ) + debug_catf( + "Correlation between %s and %s: %s\n", + paste(ds_abs_int[[refcol]], collapse = ";"), + paste(ds_abs_int[[i]], collapse = ";"), + paste(cor_abs_int[i - 1], collapse = ";") + ) + if (processing_parameters$do_pdf) { + pdf_plot_ds_abs_int( + processing_parameters$c_name, + ds_abs_int, + refcol, + i, + round(cor_abs_int[i - 1], 2) + ) + } + } + ## plot pseudo spectra + res_comp_by_file <- plot_pseudo_spectra( + x = ds_abs_int, + fid = curent_file_id, + sum_int = sum_int, + vmz = vmz, + cor_abs_int = cor_abs_int, + refcol = refcol, + meaned_mz = meaned_mz, + processing_parameters = processing_parameters + ) + catf( + "%s has been processed and %s fragments have been found.\n", + processing_parameters$c_name, + nrow(res_comp_by_file) + ) + } else { + res_comp_by_file <- NULL + cat(">> non detected in fragments file \n") + } + show_end_processing() + if (processing_parameters$do_pdf) { + end_pdf() + } + return(res_comp_by_file) } create_ds_abs_int <- function(vmz, filtered_fragments) { - verbose_catf( - ">> fragments: %s\n", - paste(vmz, collapse = " ") - ) - ds_abs_int <- create_int_mz(vmz[1], filtered_fragments) - for (mz in vmz[-1]) { - int_mz <- create_int_mz(mz, filtered_fragments) - ds_abs_int <- merge( - x = ds_abs_int, - y = int_mz, - by.x = 1, - by.y = 1, - all.x = TRUE, - all.y = TRUE + verbose_catf( + ">> fragments: %s\n", + paste(vmz, collapse = " ") ) - } - return(ds_abs_int) + ds_abs_int <- create_int_mz(vmz[1], filtered_fragments) + for (mz in vmz[-1]) { + int_mz <- create_int_mz(mz, filtered_fragments) + ds_abs_int <- merge( + x = ds_abs_int, + y = int_mz, + by.x = 1, + by.y = 1, + all.x = TRUE, + all.y = TRUE + ) + } + return(ds_abs_int) } create_int_mz <- function(mz, filtered_fragments) { - ## absolute intensity - int_mz <- filtered_fragments[ - filtered_fragments$mznominal == mz, - c("acquisitionNum", "i") - ] - colnames(int_mz)[2] <- paste0("mz", mz) - ## average intensities of mass in duplicate scans - comp_scans <- aggregate(x = int_mz, by = list(int_mz[[1]]), FUN = mean) - return(comp_scans[, -1]) + ## absolute intensity + int_mz <- filtered_fragments[ + filtered_fragments$mznominal == mz, + c("acquisitionNum", "i") + ] + colnames(int_mz)[2] <- paste0("mz", mz) + ## average intensities of mass in duplicate scans + comp_scans <- aggregate(x = int_mz, by = list(int_mz[[1]]), FUN = mean) + return(comp_scans[, -1]) } show_end_processing <- function() { - verbose_catf("==========\n") - cat("\n") + verbose_catf("==========\n") + cat("\n") } start_pdf <- function(processing_parameters, curent_file_id) { - if (!dir.exists(processing_parameters$pdf_path)) { - dir.create(processing_parameters$pdf_path, recursive = TRUE) - } - pdf( - file = sprintf( - "%s/%s_processing_file%s.pdf", - processing_parameters$pdf_path, - processing_parameters$c_name, - curent_file_id - ), - width = 8, - height = 11 - ) - par(mfrow = c(3, 2)) + if (!dir.exists(processing_parameters$pdf_path)) { + dir.create(processing_parameters$pdf_path, recursive = TRUE) + } + pdf( + file = sprintf( + "%s/%s_processing_file%s.pdf", + processing_parameters$pdf_path, + processing_parameters$c_name, + curent_file_id + ), + width = 8, + height = 11 + ) + par(mfrow = c(3, 2)) } pdf_plot_ds_abs_int <- function(c_name, ds_abs_int, refcol, i, r_coef) { - plot( - ds_abs_int[[refcol]], - ds_abs_int[[i]], - xlab = colnames(ds_abs_int)[refcol], - ylab = colnames(ds_abs_int)[i], - main = sprintf( - "%s corr coeff r=%s", c_name, r_coef + plot( + ds_abs_int[[refcol]], + ds_abs_int[[i]], + xlab = colnames(ds_abs_int)[refcol], + ylab = colnames(ds_abs_int)[i], + main = sprintf( + "%s corr coeff r=%s", c_name, r_coef + ) ) - ) } end_pdf <- function() { - dev.off() + dev.off() } set_global <- function(var, value) { - assign(var, value, envir = globalenv()) + assign(var, value, envir = globalenv()) } set_debug <- function() { - set_global("global_debug", TRUE) + set_global("global_debug", TRUE) } unset_debug <- function() { - set_global("global_debug", FALSE) + set_global("global_debug", FALSE) } set_verbose <- function() { - set_global("global_verbose", TRUE) + set_global("global_verbose", TRUE) } unset_verbose <- function() { - set_global("global_verbose", FALSE) + set_global("global_verbose", FALSE) } verbose_catf <- function(...) { - if (global_verbose) { - cat(sprintf(...), sep = "") - } + if (global_verbose) { + cat(sprintf(...), sep = "") + } } debug_catf <- function(...) { - if (global_debug) { - cat(sprintf(...), sep = "") - } + if (global_debug) { + cat(sprintf(...), sep = "") + } } catf <- function(...) { - cat(sprintf(...), sep = "") + cat(sprintf(...), sep = "") } create_parser <- function() { - parser <- optparse::OptionParser() - parser <- optparse::add_option( - parser, - c("-v", "--verbose"), - action = "store_true", - default = FALSE, - help = paste( - "[default %default]", - "Print extra output" + parser <- optparse::OptionParser() + parser <- optparse::add_option( + parser, + c("-v", "--verbose"), + action = "store_true", + default = FALSE, + help = paste( + "[default %default]", + "Print extra output" + ) ) - ) - parser <- optparse::add_option( - parser, - c("-V", "--version"), - action = "store_true", - default = FALSE, - help = "Prints version and exits" - ) - parser <- optparse::add_option( - parser, - c("-d", "--debug"), - action = "store_true", - default = FALSE, - help = paste( - "[default %default]", - "Print debug outputs" + parser <- optparse::add_option( + parser, + c("-V", "--version"), + action = "store_true", + default = FALSE, + help = "Prints version and exits" ) - ) - parser <- optparse::add_option( - parser, - c("-o", "--output"), - type = "character", - default = DEFAULT_OUTPUT_PATH, - action = "store", - help = "Path to the output file [default %default]" - ) - parser <- optparse::add_option( - parser, - c("-p", "--precursors"), - type = "character", - default = DEFAULT_PRECURSOR_PATH, - action = "store", - help = "Path to the precursors file [default %default]" - ) - parser <- optparse::add_option( - parser, - c("-f", "--fragments"), - type = "character", - default = DEFAULT_FRAGMENTS_PATH, - action = "store", - help = "Path to the fragments file [default %default]" - ) - parser <- optparse::add_option( - parser, - c("-c", "--compounds"), - type = "character", - default = DEFAULT_COMPOUNDS_PATH, - action = "store", - help = "Path to the compounds file [default %default]" - ) - parser <- optparse::add_option( - parser, - c("--tolmz"), - type = "numeric", - action = "store", - default = DEFAULT_TOLMZ, - metavar = "number", - help = paste( - "[default %default]", - "Tolerance for MZ (in Dalton) to match the standard in the compounds" + parser <- optparse::add_option( + parser, + c("-d", "--debug"), + action = "store_true", + default = FALSE, + help = paste( + "[default %default]", + "Print debug outputs" + ) ) - ) - parser <- optparse::add_option( - parser, - c("--tolrt"), - type = "integer", - action = "store", - default = DEFAULT_TOLRT, - metavar = "number", - help = paste( - "[default %default]", - "RT (in seconds) to match the standard in the compounds" + parser <- optparse::add_option( + parser, + c("-o", "--output"), + type = "character", + default = DEFAULT_OUTPUT_PATH, + action = "store", + help = "Path to the output file [default %default]" ) - ) - parser <- optparse::add_option( - parser, - c("--seuil_ra"), - type = "numeric", - action = "store", - default = DEFAULT_SEUIL_RA, - metavar = "number", - help = paste( - "[default %default]", - "relative intensity threshold" - ), - ) - parser <- optparse::add_option( - parser, - c("--mzdecimal"), - type = "integer", - default = DEFAULT_MZDECIMAL, - action = "store", - help = paste( - "[default %default]", - "Number of decimal to write for MZ" - ), - metavar = "number" - ) - parser <- optparse::add_option( - parser, - c("--r_threshold"), - type = "integer", - default = DEFAULT_R_THRESHOLD, - action = "store", - help = paste( - "[default %default]", - "R-Pearson correlation threshold between precursor and fragment", - "absolute intensity" - ), - metavar = "number" - ) - parser <- optparse::add_option( - parser, - c("--min_number_scan"), - type = "numeric", - action = "store", - default = DEFAULT_MINNUMBERSCAN, - help = paste( - "[default %default]", - "Fragments are kept if there are found in a minimum number", - "of min_number_scan scans" - ), - metavar = "number" - ) - parser <- optparse::add_option( - parser, - c("--pdf_path"), - type = "character", - default = DEFAULT_PDF_PATH, - help = paste( - "[default %default]", - "PDF files output path" + parser <- optparse::add_option( + parser, + c("-p", "--precursors"), + type = "character", + default = DEFAULT_PRECURSOR_PATH, + action = "store", + help = "Path to the precursors file [default %default]" ) - ) - parser <- optparse::add_option( - parser, - c("--ionization"), - type = "character", - action = "store", - default = "None", - help = paste( - "[default %default]", - "Which ionization to use for sirius" - ), - metavar = "character" - ) - parser <- optparse::add_option( - parser, - c("--fragment_match_delta"), - type = "numeric", - action = "store", - default = DEFAULT_FRAGMENTS_MATCH_DELTA, - help = paste( - "[default %default]", - "Fragment match delta" - ), - metavar = "numeric" - ) - parser <- optparse::add_option( - parser, - c("--fragment_match_delta_unit"), - type = "character", - action = "store", - default = DEFAULT_FRAGMENTS_MATCH_DELTA_UNIT, - help = paste( - "[default %default]", - "Fragment match delta" - ), - metavar = "character" - ) - return(parser) -} - -stop_with_status <- function(msg, status) { - sink(stderr()) - message(sprintf("Error: %s", msg)) - message(sprintf("Error code: %s", status)) - sink(NULL) - base::quit(status = status) -} - -check_args_validity <- function(args) { ## nolint cyclocomp_linter - if (length(args$output) == 0 || nchar(args$output[1]) == 0) { - stop_with_status( - "Missing output parameters. Please set it with --output.", - MISSING_PARAMETER_ERROR + parser <- optparse::add_option( + parser, + c("-f", "--fragments"), + type = "character", + default = DEFAULT_FRAGMENTS_PATH, + action = "store", + help = "Path to the fragments file [default %default]" ) - } - if (length(args$precursors) == 0 || nchar(args$precursors[1]) == 0) { - stop_with_status( - "Missing precursors parameters. Please set it with --precursors.", - MISSING_PARAMETER_ERROR + parser <- optparse::add_option( + parser, + c("-c", "--compounds"), + type = "character", + default = DEFAULT_COMPOUNDS_PATH, + action = "store", + help = "Path to the compounds file [default %default]" ) - } - if (length(args$fragments) == 0 || nchar(args$fragments[1]) == 0) { - stop_with_status( - "Missing fragments parameters. Please set it with --fragments.", - MISSING_PARAMETER_ERROR + parser <- optparse::add_option( + parser, + c("--tolmz"), + type = "numeric", + action = "store", + default = DEFAULT_TOLMZ, + metavar = "number", + help = paste( + "[default %default]", + "Tolerance for MZ (in Dalton) to match the standard in the compounds" + ) + ) + parser <- optparse::add_option( + parser, + c("--tolrt"), + type = "integer", + action = "store", + default = DEFAULT_TOLRT, + metavar = "number", + help = paste( + "[default %default]", + "RT (in seconds) to match the standard in the compounds" + ) + ) + parser <- optparse::add_option( + parser, + c("--seuil_ra"), + type = "numeric", + action = "store", + default = DEFAULT_SEUIL_RA, + metavar = "number", + help = paste( + "[default %default]", + "relative intensity threshold" + ), + ) + parser <- optparse::add_option( + parser, + c("--mzdecimal"), + type = "integer", + default = DEFAULT_MZDECIMAL, + action = "store", + help = paste( + "[default %default]", + "Number of decimal to write for MZ" + ), + metavar = "number" ) - } - if (length(args$compounds) == 0 || nchar(args$compounds[1]) == 0) { - stop_with_status( - "Missing compounds parameters. Please set it with --compounds.", - MISSING_PARAMETER_ERROR + parser <- optparse::add_option( + parser, + c("--r_threshold"), + type = "integer", + default = DEFAULT_R_THRESHOLD, + action = "store", + help = paste( + "[default %default]", + "R-Pearson correlation threshold between precursor and fragment", + "absolute intensity" + ), + metavar = "number" ) - } - if (!file.exists(args$precursors)) { - stop_with_status( - sprintf( - "Precursors file %s does not exist or cannot be accessed.", - args$precursors - ), - MISSING_INPUT_FILE_ERROR + parser <- optparse::add_option( + parser, + c("--min_number_scan"), + type = "numeric", + action = "store", + default = DEFAULT_MINNUMBERSCAN, + help = paste( + "[default %default]", + "Fragments are kept if there are found in a minimum number", + "of min_number_scan scans" + ), + metavar = "number" ) - } - if (!file.exists(args$fragments)) { - stop_with_status( - sprintf( - "Fragments file %s does not exist or cannot be accessed.", - args$fragments - ), - MISSING_INPUT_FILE_ERROR + parser <- optparse::add_option( + parser, + c("--pdf_path"), + type = "character", + default = DEFAULT_PDF_PATH, + help = paste( + "[default %default]", + "PDF files output path" + ) ) - } - if (!file.exists(args$compounds)) { - stop_with_status( - sprintf( - "Compounds file %s does not exist or cannot be accessed.", - args$compounds - ), - MISSING_INPUT_FILE_ERROR + parser <- optparse::add_option( + parser, + c("--ionization"), + type = "character", + action = "store", + default = "None", + help = paste( + "[default %default]", + "Which ionization to use for sirius" + ), + metavar = "character" ) - } - if (in_galaxy_env()) { - check_galaxy_args_validity(args) - } + parser <- optparse::add_option( + parser, + c("--fragment_match_delta"), + type = "numeric", + action = "store", + default = DEFAULT_FRAGMENTS_MATCH_DELTA, + help = paste( + "[default %default]", + "Fragment match delta" + ), + metavar = "numeric" + ) + parser <- optparse::add_option( + parser, + c("--fragment_match_delta_unit"), + type = "character", + action = "store", + default = DEFAULT_FRAGMENTS_MATCH_DELTA_UNIT, + help = paste( + "[default %default]", + "Fragment match delta" + ), + metavar = "character" + ) + return(parser) } -in_galaxy_env <- function() { - sysvars <- Sys.getenv() - sysvarnames <- names(sysvars) - return( - "_GALAXY_JOB_HOME_DIR" %in% sysvarnames - || "_GALAXY_JOB_TMP_DIR" %in% sysvarnames - || "GALAXY_MEMORY_MB" %in% sysvarnames - || "GALAXY_MEMORY_MB_PER_SLOT" %in% sysvarnames - || "GALAXY_SLOTS" %in% sysvarnames - ) +stop_with_status <- function(msg, status) { + sink(stderr()) + message(sprintf("Error: %s", msg)) + message(sprintf("Error code: %s", status)) + sink(NULL) + base::quit(status = status) } -check_galaxy_args_validity <- function(args) { - if (!file.exists(args$output)) { - stop_with_status( - sprintf( - "Output file %s does not exist or cannot be accessed.", - args$output - ), - MISSING_INPUT_FILE_ERROR +check_args_validity <- function(args) { ## nolint cyclocomp_linter + if (length(args$output) == 0 || nchar(args$output[1]) == 0) { + stop_with_status( + "Missing output parameters. Please set it with --output.", + MISSING_PARAMETER_ERROR + ) + } + if (length(args$precursors) == 0 || nchar(args$precursors[1]) == 0) { + stop_with_status( + "Missing precursors parameters. Please set it with --precursors.", + MISSING_PARAMETER_ERROR + ) + } + if (length(args$fragments) == 0 || nchar(args$fragments[1]) == 0) { + stop_with_status( + "Missing fragments parameters. Please set it with --fragments.", + MISSING_PARAMETER_ERROR + ) + } + if (length(args$compounds) == 0 || nchar(args$compounds[1]) == 0) { + stop_with_status( + "Missing compounds parameters. Please set it with --compounds.", + MISSING_PARAMETER_ERROR + ) + } + if (!file.exists(args$precursors)) { + stop_with_status( + sprintf( + "Precursors file %s does not exist or cannot be accessed.", + args$precursors + ), + MISSING_INPUT_FILE_ERROR + ) + } + if (!file.exists(args$fragments)) { + stop_with_status( + sprintf( + "Fragments file %s does not exist or cannot be accessed.", + args$fragments + ), + MISSING_INPUT_FILE_ERROR + ) + } + if (!file.exists(args$compounds)) { + stop_with_status( + sprintf( + "Compounds file %s does not exist or cannot be accessed.", + args$compounds + ), + MISSING_INPUT_FILE_ERROR + ) + } + if (in_galaxy_env()) { + check_galaxy_args_validity(args) + } +} + +in_galaxy_env <- function() { + sysvars <- Sys.getenv() + sysvarnames <- names(sysvars) + return( + "_GALAXY_JOB_HOME_DIR" %in% sysvarnames || + "_GALAXY_JOB_TMP_DIR" %in% sysvarnames || + "GALAXY_MEMORY_MB" %in% sysvarnames || + "GALAXY_MEMORY_MB_PER_SLOT" %in% sysvarnames || + "GALAXY_SLOTS" %in% sysvarnames ) - } } -get_csv_or_tsv <- function( - path, - sep_stack = c("\t", ",", ";"), - sep_names = c("tab", "comma", "semicolon"), - header = TRUE, - quote = "\"" -) { - sep <- determine_csv_or_tsv_sep( - path = path, - sep_stack = sep_stack, - header = header, - quote = quote - ) - verbose_catf( - "%s separator has been determined for %s.\n", - sep_names[sep_stack == sep], - path - ) - return(read.table( - file = path, - sep = sep, - header = header, - quote = quote - )) +check_galaxy_args_validity <- function(args) { + if (!file.exists(args$output)) { + stop_with_status( + sprintf( + "Output file %s does not exist or cannot be accessed.", + args$output + ), + MISSING_INPUT_FILE_ERROR + ) + } } -determine_csv_or_tsv_sep <- function( - path, - sep_stack = c("\t", ",", ";"), - header = TRUE, - quote = "\"" -) { - count <- -1 - best_sep <- sep_stack[1] - for (sep in sep_stack) { - tryCatch({ - table <- read.table( +get_csv_or_tsv <- function( + path, + sep_stack = c("\t", ",", ";"), + sep_names = c("tab", "comma", "semicolon"), + header = TRUE, + quote = "\"") { + sep <- determine_csv_or_tsv_sep( + path = path, + sep_stack = sep_stack, + header = header, + quote = quote + ) + verbose_catf( + "%s separator has been determined for %s.\n", + sep_names[sep_stack == sep], + path + ) + return(read.table( file = path, sep = sep, header = header, - quote = quote, - nrows = 1 - ) - if (ncol(table) > count) { - count <- ncol(table) - best_sep <- sep - } - }) - } - return(best_sep) + quote = quote + )) +} + +determine_csv_or_tsv_sep <- function( + path, + sep_stack = c("\t", ",", ";"), + header = TRUE, + quote = "\"") { + count <- -1 + best_sep <- sep_stack[1] + for (sep in sep_stack) { + tryCatch({ + table <- read.table( + file = path, + sep = sep, + header = header, + quote = quote, + nrows = 1 + ) + if (ncol(table) > count) { + count <- ncol(table) + best_sep <- sep + } + }) + } + return(best_sep) } uniformize_columns <- function(df) { - cols <- colnames(df) - for (func in c(tolower)) { - cols <- func(cols) - } - colnames(df) <- cols - return(df) + cols <- colnames(df) + for (func in c(tolower)) { + cols <- func(cols) + } + colnames(df) <- cols + return(df) } handle_galaxy_param <- function(args) { - for (param in names(args)) { - if (is.character(args[[param]])) { - args[[param]] <- gsub("__ob__", "[", args[[param]]) - args[[param]] <- gsub("__cb__", "]", args[[param]]) + for (param in names(args)) { + if (is.character(args[[param]])) { + args[[param]] <- gsub("__ob__", "[", args[[param]]) + args[[param]] <- gsub("__cb__", "]", args[[param]]) + } } - } - return(args) + return(args) } zip_pdfs <- function(processing_parameters) { - if (processing_parameters$do_pdf) { - if ((zip <- Sys.getenv("R_ZIPCMD", "zip")) == "") { - catf("R could not fin the zip executable. Trying luck: zip = \"zip\"") - zip <- "zip" - } else { - catf("Found zip executable at %s .", zip) + if (processing_parameters$do_pdf) { + if ((zip <- Sys.getenv("R_ZIPCMD", "zip")) == "") { + catf("R could not fin the zip executable. Trying luck: zip = \"zip\"") + zip <- "zip" + } else { + catf("Found zip executable at %s .", zip) + } + utils::zip( + processing_parameters$pdf_zip_path, + processing_parameters$pdf_path, + zip = zip + ) } - utils::zip( - processing_parameters$pdf_zip_path, - processing_parameters$pdf_path, - zip = zip - ) - } } main <- function(args) { - if (args$version) { - catf("%s\n", MS2SNOOP_VERSION) - base::quit(status = 0) - } - if (in_galaxy_env()) { - print(sessionInfo()) - cat("\n\n") - } - check_args_validity(args) - args <- handle_galaxy_param(args) - if (args$ionization == "None") { - args$ionization <- NULL - } - if (args$debug) { - set_debug() - } - if (args$verbose) { - set_verbose() - } - precursors <- get_csv_or_tsv(args$precursors) - fragments <- get_csv_or_tsv(args$fragments) - compounds <- get_csv_or_tsv(args$compounds) - - compounds <- uniformize_columns(compounds) - mandatory_columns <- c( - "compound_name", - "mz", - "rtsec", - "inchikey" - ) - presents <- mandatory_columns %in% colnames(compounds) - if (!all(presents)) { - stop_with_status( - sprintf( - "Some columns are missing: %s", - paste(mandatory_columns[which(!presents)], collapse = ", ") - ), - BAD_PARAMETER_VALUE_ERROR + if (args$version) { + catf("%s\n", MS2SNOOP_VERSION) + base::quit(status = 0) + } + if (in_galaxy_env()) { + print(sessionInfo()) + cat("\n\n") + } + check_args_validity(args) + args <- handle_galaxy_param(args) + if (args$ionization == "None") { + args$ionization <- NULL + } + if (args$debug) { + set_debug() + } + if (args$verbose) { + set_verbose() + } + precursors <- get_csv_or_tsv(args$precursors) + fragments <- get_csv_or_tsv(args$fragments) + compounds <- get_csv_or_tsv(args$compounds) + + compounds <- uniformize_columns(compounds) + mandatory_columns <- c( + "compound_name", + "mz", + "rtsec", + "inchikey" ) - } - - res_all <- data.frame() - processing_parameters <- list( - min_number_scan = args$min_number_scan, - mzdecimal = args$mzdecimal, - r_threshold = args$r_threshold, - seuil_ra = args$seuil_ra, - tolmz = args$tolmz, - tolrt = args$tolrt, - ionization = args$ionization, - do_pdf = nchar(args$pdf_path) > 0, - pdf_zip_path = args$pdf_path, - pdf_path = tempdir(), - fragment_match_delta = args$fragment_match_delta, - fragment_match_delta_unit = args$fragment_match_delta_unit - ) - for (i in seq_len(nrow(compounds))) { - processing_parameters$mzref <- compounds[["mz"]][i] - processing_parameters$rtref <- compounds[["rtsec"]][i] - processing_parameters$c_name <- compounds[["compound_name"]][i] - processing_parameters$inchikey <- compounds[["inchikey"]][i] - processing_parameters$elemcomposition <- compounds[["elemcomposition"]][i] - res_cor <- extract_fragments( - precursors = precursors, - fragments = fragments, - processing_parameters = processing_parameters + presents <- mandatory_columns %in% colnames(compounds) + if (!all(presents)) { + stop_with_status( + sprintf( + "Some columns are missing: %s", + paste(mandatory_columns[which(!presents)], collapse = ", ") + ), + BAD_PARAMETER_VALUE_ERROR + ) + } + + res_all <- data.frame() + processing_parameters <- list( + min_number_scan = args$min_number_scan, + mzdecimal = args$mzdecimal, + r_threshold = args$r_threshold, + seuil_ra = args$seuil_ra, + tolmz = args$tolmz, + tolrt = args$tolrt, + ionization = args$ionization, + do_pdf = nchar(args$pdf_path) > 0, + pdf_zip_path = args$pdf_path, + pdf_path = tempdir(), + fragment_match_delta = args$fragment_match_delta, + fragment_match_delta_unit = args$fragment_match_delta_unit ) - if (!is.null(res_cor)) { - res_all <- rbind(res_all, res_cor) + for (i in seq_len(nrow(compounds))) { + processing_parameters$mzref <- compounds[["mz"]][i] + processing_parameters$rtref <- compounds[["rtsec"]][i] + processing_parameters$c_name <- compounds[["compound_name"]][i] + processing_parameters$inchikey <- compounds[["inchikey"]][i] + processing_parameters$elemcomposition <- compounds[["elemcomposition"]][i] + res_cor <- extract_fragments( + precursors = precursors, + fragments = fragments, + processing_parameters = processing_parameters + ) + if (!is.null(res_cor)) { + res_all <- rbind(res_all, res_cor) + } + } + + if (nrow(res_all) == 0) { + stop_with_status("No result at all!", NO_ANY_RESULT_ERROR) } - } - - if (nrow(res_all) == 0) { - stop_with_status("No result at all!", NO_ANY_RESULT_ERROR) - } - - write.table( - x = res_all, - file = args$output, - sep = "\t", - row.names = FALSE - ) - zip_pdfs(processing_parameters) - unlink(processing_parameters$pdf_path, recursive = TRUE) + + write.table( + x = res_all, + file = args$output, + sep = "\t", + row.names = FALSE + ) + zip_pdfs(processing_parameters) + unlink(processing_parameters$pdf_path, recursive = TRUE) } global_debug <- FALSE diff --git a/tools/ms2snoop/MS2snoop.xml b/tools/ms2snoop/MS2snoop.xml index 07837ac4e2..74ef8e1aa2 100644 --- a/tools/ms2snoop/MS2snoop.xml +++ b/tools/ms2snoop/MS2snoop.xml @@ -19,6 +19,9 @@ sirius-csifingerid zip + + + @@ -155,7 +158,7 @@ - + @@ -168,7 +171,7 @@ - + @@ -182,7 +185,7 @@ - + @@ -196,7 +199,7 @@ - + @@ -211,7 +214,7 @@ - + @@ -254,7 +257,7 @@ - + @@ -267,7 +270,7 @@ - + @@ -310,7 +313,7 @@ Description MS2snoop use results of msPurity to find spectra of standards listed in a compounds file. Check the fragments and precursor using correlation of -among the different scans of fragments +among the different scans of fragments ----------------- Workflow position @@ -359,4 +362,7 @@ Input files @CHANGELOG@ ]]> + + 10.18129/B9.bioc.msPurity + diff --git a/tools/ms2snoop/macros.xml b/tools/ms2snoop/macros.xml index af8fb25afe..cc4b97be72 100644 --- a/tools/ms2snoop/macros.xml +++ b/tools/ms2snoop/macros.xml @@ -26,13 +26,13 @@ msPurity.frag4feature Compounds file | A tabulated, comma or semicolon separated values file whith compounds. Its format is as follow: - | + | | At least 4 columns: | **compound_name**: The name of the compound | **mz**: The m/z ratio | **rtsec**: The retention time in seconds | **inchikey**: The compound's inchi key - | + | | An optional column can be added: | **elemcomposition**: The elemental composition of the molecule @@ -66,10 +66,10 @@ compound_fragments_result.tsv | tabular output | Array with p rows (corresponding to the fragments for the different compounds of the compounds file) - | Last column "corvalid" is a boolean for validated fragments + | Last column "corvalid" is a boolean for validated fragments processing_file.pdf - | pdf output + | pdf output | For each compound of the compounds file, Graph of the all fragments with parents (or most intense peak if parent is not detected in the fragments. At the end the pdf file a graph of the spectra with @@ -103,7 +103,7 @@ Changelog/News mzref and the rtref to be the 1st, the 2nd and the 3rd columns respectively, and the header was somewhat ignored in the compound file. - | + | | Now, we'd like to add some new columns, some of them are optional. This new format of file implies a better definition of the columns order, @@ -114,7 +114,7 @@ Changelog/News | - **elemcomposition** [OPTIONAL] ; | - **mz** [MANDATORY] ; | - **rtsec** [MANDATORY]. - | + | | These names can be written indiferentially in lower and/or upper case. The new definition being incompatible with the older one, that was more flexible, but also more error-prone. @@ -139,7 +139,7 @@ Changelog/News 2.2.1 - 0 + 2 @@ -236,4 +236,4 @@ Changelog/News - \ No newline at end of file + diff --git a/tools/mzxmlshaper/mzXMLShaper.R b/tools/mzxmlshaper/mzXMLShaper.R index bbf7fb6ced..f9983b16a0 100644 --- a/tools/mzxmlshaper/mzXMLShaper.R +++ b/tools/mzxmlshaper/mzXMLShaper.R @@ -15,8 +15,10 @@ library("tools") args <- W4MRUtils::parse_args(args = commandArgs()) -cat("\n\n--------------------------------------------------------------------", - "\nParameters used by the 'mz(X)MLShaper' tool:\n\n") +cat( + "\n\n--------------------------------------------------------------------", + "\nParameters used by the 'mz(X)MLShaper' tool:\n\n" +) cat("--------------------------------------------------------------------\n\n") print(args) @@ -32,15 +34,19 @@ pks <- mzR::spectra(spectrum) hdr <- mzR::header(spectrum) if (outputfileformat == "mzml") { - writeMSData(pks, file = outputfilename, outformat = "mzml", header = hdr) + writeMSData(pks, file = outputfilename, outformat = "mzml", header = hdr) } if (outputfileformat == "mzXml") { - writeMSData(pks, file = outputfilename, outformat = "mzxml", header = hdr) + writeMSData(pks, file = outputfilename, outformat = "mzxml", header = hdr) } -cat("\n--------------------------------------------------------------------", - "\nInformation about R (version, Operating System, attached or loaded packages):\n\n") +cat( + "\n--------------------------------------------------------------------", + "\nInformation about R (version, Operating System, attached or loaded packages):\n\n" +) sessionInfo() -cat("--------------------------------------------------------------------\n", - "\nJob ending time:\n", format(Sys.time(), "%a %d %b %Y %X")) +cat( + "--------------------------------------------------------------------\n", + "\nJob ending time:\n", format(Sys.time(), "%a %d %b %Y %X") +) diff --git a/tools/mzxmlshaper/mzXMLShaper.xml b/tools/mzxmlshaper/mzXMLShaper.xml index c58fa14b92..340976a3ba 100644 --- a/tools/mzxmlshaper/mzXMLShaper.xml +++ b/tools/mzxmlshaper/mzXMLShaper.xml @@ -1,4 +1,4 @@ - + conversion into XCMS-supported mz(X)ML file formats @@ -8,6 +8,9 @@ bioconductor-mzr r-w4mrutils + + + 10.18129/B9.bioc.mzR - \ No newline at end of file + diff --git a/tools/nmr_annotation/DrawSpec.R b/tools/nmr_annotation/DrawSpec.R index c775b4d102..b38ccc4226 100644 --- a/tools/nmr_annotation/DrawSpec.R +++ b/tools/nmr_annotation/DrawSpec.R @@ -1,74 +1,72 @@ -drawSpec <- function (X, startP = -1, endP = -1, groupLabel = NULL, useLog = -1, highBound = -1, lowBound = -1, - xlab = NULL, ylab = NULL, main = NULL, nAxisPos = 4, offside = 0) -{ - groupLabel_name = groupLabel - X = as.data.frame(X) -# colnames(X) = c(1:ncol(X)) - X = as.matrix(X) - if (highBound != -1) { - for (i in 1:nrow(X)) { - myIndex = which(X[i, ] > highBound) - X[i, myIndex] = highBound +drawSpec <- function(X, startP = -1, endP = -1, groupLabel = NULL, useLog = -1, highBound = -1, lowBound = -1, + xlab = NULL, ylab = NULL, main = NULL, nAxisPos = 4, offside = 0) { + groupLabel_name <- groupLabel + X <- as.data.frame(X) + # colnames(X) = c(1:ncol(X)) + X <- as.matrix(X) + if (highBound != -1) { + for (i in 1:nrow(X)) { + myIndex <- which(X[i, ] > highBound) + X[i, myIndex] <- highBound + } } - } - if (lowBound != -1) { - for (i in 1:nrow(X)) { - myIndex = which(X[i, ] < lowBound) - X[i, myIndex] = lowBound + if (lowBound != -1) { + for (i in 1:nrow(X)) { + myIndex <- which(X[i, ] < lowBound) + X[i, myIndex] <- lowBound + } } - } - if (is.null(groupLabel)) { - groupLabel = c(1:nrow(X)) - groupLabel = as.factor(groupLabel) - } - else { - levels(groupLabel) = c(1:length(levels(groupLabel))) - } - if (startP == -1) - startP = 1 - if (endP == -1) - endP = ncol(X) - if (is.null(xlab)) { - xlab = "index" - } - if (is.null(ylab)) { - ylab = "intensity" - } - if (is.null(main)) { - main = paste(" ", startP + offside, "-", endP + offside) - } - GraphRange <- c(startP:endP) - yn <- X[, GraphRange] - if (useLog != -1) - yn = log(yn) - if (length(yn) > ncol(X)) - { - plot(yn[1, ], ylim = c(min(yn), max(yn)), type = "n", ylab = ylab, xlab = xlab, main = main, xaxt = "n") - tempVal = trunc(length(GraphRange)/nAxisPos) - xPos = c(0:nAxisPos) * tempVal - axis(1, at = xPos, labels = colnames(X)[xPos + startP + offside]) - for (i in 1:length(levels(groupLabel))) - { - groupLabelIdx = which(groupLabel == levels(groupLabel)[i]) - color <- palette(rainbow(length(levels(groupLabel)))) - for (j in 1:length(groupLabelIdx)) - { - lines(yn[groupLabelIdx[j], ], col = color[i]) - } + if (is.null(groupLabel)) { + groupLabel <- c(1:nrow(X)) + groupLabel <- as.factor(groupLabel) + } else { + levels(groupLabel) <- c(1:length(levels(groupLabel))) } - if (!is.null(groupLabel_name)) - { - legendPos = "topleft" - legend(legendPos, levels(groupLabel_name), col = as.integer(levels(groupLabel)), text.col = "black", pch = c(19, 19), bg = "gray90") + if (startP == -1) { + startP <- 1 } - } - if (length(yn) == ncol(X)) - { - plot(yn, ylim = c(min(yn), max(yn)), type = "n", ylab = ylab, xlab = xlab, main = main, xaxt = "n") - tempVal = trunc(length(GraphRange)/nAxisPos) - xPos = c(0:nAxisPos) * tempVal -# axis(1, at = xPos, labels = xPos + startP + offside) - axis(1, at = xPos, labels = colnames(X)[xPos + startP + offside]) - lines(yn) - } -} \ No newline at end of file + if (endP == -1) { + endP <- ncol(X) + } + if (is.null(xlab)) { + xlab <- "index" + } + if (is.null(ylab)) { + ylab <- "intensity" + } + if (is.null(main)) { + main <- paste(" ", startP + offside, "-", endP + offside) + } + GraphRange <- c(startP:endP) + yn <- X[, GraphRange] + if (useLog != -1) { + yn <- log(yn) + } + if (length(yn) > ncol(X)) { + plot(yn[1, ], ylim = c(min(yn), max(yn)), type = "n", ylab = ylab, xlab = xlab, main = main, xaxt = "n") + tempVal <- trunc(length(GraphRange) / nAxisPos) + xPos <- c(0:nAxisPos) * tempVal + axis(1, at = xPos, labels = colnames(X)[xPos + startP + offside]) + for (i in 1:length(levels(groupLabel))) + { + groupLabelIdx <- which(groupLabel == levels(groupLabel)[i]) + color <- palette(rainbow(length(levels(groupLabel)))) + for (j in 1:length(groupLabelIdx)) + { + lines(yn[groupLabelIdx[j], ], col = color[i]) + } + } + if (!is.null(groupLabel_name)) { + legendPos <- "topleft" + legend(legendPos, levels(groupLabel_name), col = as.integer(levels(groupLabel)), text.col = "black", pch = c(19, 19), bg = "gray90") + } + } + if (length(yn) == ncol(X)) { + plot(yn, ylim = c(min(yn), max(yn)), type = "n", ylab = ylab, xlab = xlab, main = main, xaxt = "n") + tempVal <- trunc(length(GraphRange) / nAxisPos) + xPos <- c(0:nAxisPos) * tempVal + # axis(1, at = xPos, labels = xPos + startP + offside) + axis(1, at = xPos, labels = colnames(X)[xPos + startP + offside]) + lines(yn) + } +} diff --git a/tools/nmr_annotation/asics_wrapper.R b/tools/nmr_annotation/asics_wrapper.R index 00841fcc1f..c05d76df41 100644 --- a/tools/nmr_annotation/asics_wrapper.R +++ b/tools/nmr_annotation/asics_wrapper.R @@ -6,78 +6,76 @@ runExampleL <- FALSE -##------------------------------ +## ------------------------------ ## Options -##------------------------------ +## ------------------------------ strAsFacL <- options()$stringsAsFactors -options(stringsAsFactors=FALSE) +options(stringsAsFactors = FALSE) -##------------------------------ +## ------------------------------ ## Libraries loading -##------------------------------ +## ------------------------------ # ParseCommandArgs function -library(batch) -library(ASICS) +library(batch) +library(ASICS) # R script call -source_local <- function(fname) -{ -argv <- commandArgs(trailingOnly=FALSE) -base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) -source(paste(base_dir, fname, sep="/")) +source_local <- function(fname) { + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) } -#Import the different functions +# Import the different functions source_local("DrawSpec.R") -##------------------------------ +## ------------------------------ ## Errors ????????????????????? -##------------------------------ +## ------------------------------ -##------------------------------ +## ------------------------------ ## Constants -##------------------------------ +## ------------------------------ topEnvC <- environment() flagC <- "\n" -##------------------------------ +## ------------------------------ ## Script -##------------------------------ -if(!runExampleL) - argLs <- parseCommandArgs(evaluate=FALSE) +## ------------------------------ +if (!runExampleL) { + argLs <- parseCommandArgs(evaluate = FALSE) +} # Standards loading load(argLs[["standards"]]) ## Parameters Loading -##------------------- +## ------------------- # Inputs ## Spectrum to annotate -zipfile= argLs[["zipfile"]] -directory=unzip(zipfile, list=F) -directory=paste(getwd(),strsplit(directory[1],"/")[[1]][2],sep="/") +zipfile <- argLs[["zipfile"]] +directory <- unzip(zipfile, list = F) +directory <- paste(getwd(), strsplit(directory[1], "/")[[1]][2], sep = "/") -##Exclusion zone(s) +## Exclusion zone(s) exclusionZones <- argLs[["zone_exclusion_choices.choice"]] exclusionZonesBorders <- NULL -if (!is.null(argLs$zone_exclusion_left)) -{ - for(i in which(names(argLs)=="zone_exclusion_left")) - { -# exclusionZonesBorders <- c(exclusionZonesBorders,list(c(argLs[[i]],argLs[[i+1]]))) - exclusionZonesBorders <- c(exclusionZonesBorders,argLs[[i]],argLs[[i+1]]) - } - exclusionZonesBorders <- matrix(exclusionZonesBorders, byrow=T, ncol=2) +if (!is.null(argLs$zone_exclusion_left)) { + for (i in which(names(argLs) == "zone_exclusion_left")) + { + # exclusionZonesBorders <- c(exclusionZonesBorders,list(c(argLs[[i]],argLs[[i+1]]))) + exclusionZonesBorders <- c(exclusionZonesBorders, argLs[[i]], argLs[[i + 1]]) + } + exclusionZonesBorders <- matrix(exclusionZonesBorders, byrow = T, ncol = 2) } -if (is.null(argLs$zone_exclusion_left)) -{ - exclusionZonesBorders <- matrix(c(0,0), byrow=T, ncol=2) +if (is.null(argLs$zone_exclusion_left)) { + exclusionZonesBorders <- matrix(c(0, 0), byrow = T, ncol = 2) } ## Maximal allowed shift shift <- argLs[["shift"]] @@ -85,12 +83,11 @@ shift <- argLs[["shift"]] ## Graphical zone(s) graphicalZones <- argLs[["zone_graphical_choices.choice"]] graphicalZonesBorders <- NULL -if (!is.null(argLs$zone_exclusion_left)) -{ - for(i in which(names(argLs)=="zone_graphical_left")) - { - graphicalZonesBorders <- c(graphicalZonesBorders,list(c(argLs[[i]],argLs[[i+1]]))) - } +if (!is.null(argLs$zone_exclusion_left)) { + for (i in which(names(argLs) == "zone_graphical_left")) + { + graphicalZonesBorders <- c(graphicalZonesBorders, list(c(argLs[[i]], argLs[[i + 1]]))) + } } # Outputs @@ -101,98 +98,104 @@ graphOut <- argLs[["graphOut"]] sink(logOut) cat("\tPACKAGE INFO\n") # pkgs=c("batch", "ASICS") -pkgs=c("batch", "ASICS") -for(pkg in pkgs) { - suppressPackageStartupMessages( stopifnot( library(pkg, quietly=TRUE, logical.return=TRUE, character.only=TRUE))) - cat(pkg,"\t",as.character(packageVersion(pkg)),"\n",sep="") +pkgs <- c("batch", "ASICS") +for (pkg in pkgs) { + suppressPackageStartupMessages(stopifnot(library(pkg, quietly = TRUE, logical.return = TRUE, character.only = TRUE))) + cat(pkg, "\t", as.character(packageVersion(pkg)), "\n", sep = "") } cat("\n") ## Checking arguments -##------------------- +## ------------------- error.stock <- "\n" -if(length(error.stock) > 1) - stop(error.stock) - - +if (length(error.stock) > 1) { + stop(error.stock) +} + + ## Computation -##------------ -# annotation.Asics <- ASICS(directory, exclusion.areas=matrix(exclusionZonesBorders, byrow=T, ncol=2), -# max.shift=shift, which.spectra="last", library.metabolites=NULL, +## ------------ +# annotation.Asics <- ASICS(directory, exclusion.areas=matrix(exclusionZonesBorders, byrow=T, ncol=2), +# max.shift=shift, which.spectra="last", library.metabolites=NULL, # threshold.noise=0.02, seed=1234, nb.iter.signif=400) -annotation.Asics <- ASICS(directory, exclusion.areas=exclusionZonesBorders, - max.shift=shift, which.spectra="last", library.metabolites=NULL, - threshold.noise=0.02, seed=1234, nb.iter.signif=400) +annotation.Asics <- ASICS(directory, + exclusion.areas = exclusionZonesBorders, + max.shift = shift, which.spectra = "last", library.metabolites = NULL, + threshold.noise = 0.02, seed = 1234, nb.iter.signif = 400 +) ## Saving -##------- +## ------- # Identified metabolites metabolites.estimation <- present_metabolites(annotation.Asics) -colnames(metabolites.estimation) <- c("Metabolite",colnames(metabolites.estimation)[-1]) -write.table(metabolites.estimation,file=argLs$proportionEstimation,row.names=FALSE,quote=FALSE,sep="\t") +colnames(metabolites.estimation) <- c("Metabolite", colnames(metabolites.estimation)[-1]) +write.table(metabolites.estimation, file = argLs$proportionEstimation, row.names = FALSE, quote = FALSE, sep = "\t") ## Graphical display -##------------------ +## ------------------ # Raw and annotated spectra comparison -pdf(graphOut,onefile=TRUE) +pdf(graphOut, onefile = TRUE) ## Graphical output: overlay of raw and estimated spectra -ppm.metabolites.estimation <- data.frame(round(ppm_grid(annotation.Asics),3), - original_mixture(annotation.Asics)) +ppm.metabolites.estimation <- data.frame( + round(ppm_grid(annotation.Asics), 3), + original_mixture(annotation.Asics) +) colnames(ppm.metabolites.estimation) <- c("PPM", "EstimatedProportion") -ppm.metabolites.estimation <- ppm.metabolites.estimation[order(ppm.metabolites.estimation[,1],decreasing=T), ] +ppm.metabolites.estimation <- ppm.metabolites.estimation[order(ppm.metabolites.estimation[, 1], decreasing = T), ] -mix <- data.frame(t(ppm.metabolites.estimation[,2])) -colnames(mix) <- ppm.metabolites.estimation[,1] -ppm <- ppm.metabolites.estimation[,1] +mix <- data.frame(t(ppm.metabolites.estimation[, 2])) +colnames(mix) <- ppm.metabolites.estimation[, 1] +ppm <- ppm.metabolites.estimation[, 1] -estimatedMix <- data.frame(round(ppm_grid(annotation.Asics),3), reconstituted_mixture(annotation.Asics)) -colnames(estimatedMix) <- c("PPM","EstimatedProportion") -estimatedMix <- estimatedMix[order(estimatedMix[,1],decreasing=T), ] -estimatedMix <- estimatedMix[,2] +estimatedMix <- data.frame(round(ppm_grid(annotation.Asics), 3), reconstituted_mixture(annotation.Asics)) +colnames(estimatedMix) <- c("PPM", "EstimatedProportion") +estimatedMix <- estimatedMix[order(estimatedMix[, 1], decreasing = T), ] +estimatedMix <- estimatedMix[, 2] ## Whole spectra GraphRange <- 1:ncol(mix) -tempVal <- trunc(length(GraphRange)/10) +tempVal <- trunc(length(GraphRange) / 10) xPos <- c(10:0) * tempVal -plot(1:ncol(mix), mix, type='l', xlab="", main="", xaxt="n", ylab="") -axis(1, at=xPos, labels=colnames(mix)[xPos + 1]) -lines(estimatedMix, col="red") -legend("topleft",legend=c("Real Mixture","Estimated Composition"),lty=c(1,1),col=c("black","red")) +plot(1:ncol(mix), mix, type = "l", xlab = "", main = "", xaxt = "n", ylab = "") +axis(1, at = xPos, labels = colnames(mix)[xPos + 1]) +lines(estimatedMix, col = "red") +legend("topleft", legend = c("Real Mixture", "Estimated Composition"), lty = c(1, 1), col = c("black", "red")) ## Zoomed spectral window depending on user-selected zone(s) graphical.zone.length <- length(graphicalZonesBorders) -if (graphical.zone.length != 0) - - # par(mfrow=c(2,1)) -for (g in 1:graphical.zone.length) - { - print(g) - plot(1:length((which(round(as.numeric(colnames(mix)),2) == graphicalZonesBorders[[g]][1])[1]):(which(round(as.numeric(colnames(mix)),2) == max(graphicalZonesBorders[[g]][2],0.5))[1])), - mix[(which(round(as.numeric(colnames(mix)),2) == graphicalZonesBorders[[g]][1])[1]):(which(round(as.numeric(colnames(mix)),2) == max(graphicalZonesBorders[[g]][2],0.5))[1])], type='l', xlab="", ylab="Intensity", main="", xaxt="n") - lines(estimatedMix[(which(round(as.numeric(colnames(mix)),2) == graphicalZonesBorders[[g]][1])[1]):(which(round(as.numeric(colnames(mix)),2) == max(graphicalZonesBorders[[g]][2],0.5))[1])],col="red") - - xPos <- 1 - nAxisPos <- 4 - startP <- length(nAxisPos) - endP <- length((which(round(as.numeric(colnames(mix)),2) == graphicalZonesBorders[[g]][1])[1]):(which(round(as.numeric(colnames(mix)),2) == max(graphicalZonesBorders[[g]][2],0.5))[1])) - GraphRange <- c(startP:endP) - tempVal <- trunc(length(GraphRange)/nAxisPos) - xPos <- c(0:nAxisPos) * tempVal - noms <- ppm.metabolites.estimation[xPos + which(ppm == round(graphicalZonesBorders[[g]][1],1))[1],1] - axis(1, at=xPos, labels=noms) - } +if (graphical.zone.length != 0) { + # par(mfrow=c(2,1)) + for (g in 1:graphical.zone.length) + { + print(g) + plot(1:length((which(round(as.numeric(colnames(mix)), 2) == graphicalZonesBorders[[g]][1])[1]):(which(round(as.numeric(colnames(mix)), 2) == max(graphicalZonesBorders[[g]][2], 0.5))[1])), + mix[(which(round(as.numeric(colnames(mix)), 2) == graphicalZonesBorders[[g]][1])[1]):(which(round(as.numeric(colnames(mix)), 2) == max(graphicalZonesBorders[[g]][2], 0.5))[1])], + type = "l", xlab = "", ylab = "Intensity", main = "", xaxt = "n" + ) + lines(estimatedMix[(which(round(as.numeric(colnames(mix)), 2) == graphicalZonesBorders[[g]][1])[1]):(which(round(as.numeric(colnames(mix)), 2) == max(graphicalZonesBorders[[g]][2], 0.5))[1])], col = "red") + + xPos <- 1 + nAxisPos <- 4 + startP <- length(nAxisPos) + endP <- length((which(round(as.numeric(colnames(mix)), 2) == graphicalZonesBorders[[g]][1])[1]):(which(round(as.numeric(colnames(mix)), 2) == max(graphicalZonesBorders[[g]][2], 0.5))[1])) + GraphRange <- c(startP:endP) + tempVal <- trunc(length(GraphRange) / nAxisPos) + xPos <- c(0:nAxisPos) * tempVal + noms <- ppm.metabolites.estimation[xPos + which(ppm == round(graphicalZonesBorders[[g]][1], 1))[1], 1] + axis(1, at = xPos, labels = noms) + } +} invisible(dev.off()) ## Ending -##--------------------- -cat("\nEnd of 'NMR annotation' Galaxy module call: ", as.character(Sys.time()), sep="") -options(stringsAsFactors=strAsFacL) -rm(list=ls()) +## --------------------- +cat("\nEnd of 'NMR annotation' Galaxy module call: ", as.character(Sys.time()), sep = "") +options(stringsAsFactors = strAsFacL) +rm(list = ls()) sink() - diff --git a/tools/nmr_annotation/asics_xml.xml b/tools/nmr_annotation/asics_xml.xml index c044f5f873..4c44b7c596 100644 --- a/tools/nmr_annotation/asics_xml.xml +++ b/tools/nmr_annotation/asics_xml.xml @@ -1,11 +1,17 @@ - + Annotation of complex mixture NMR spectra and metabolite proportion estimation + libgfortran r-batch r-asics + + + + + diff --git a/tools/nmr_annotation2d/annotationRmn2D.R b/tools/nmr_annotation2d/annotationRmn2D.R index 13fedd17ad..1f3ab973f0 100644 --- a/tools/nmr_annotation2d/annotationRmn2D.R +++ b/tools/nmr_annotation2d/annotationRmn2D.R @@ -1,223 +1,229 @@ -########################################################################## -# ANNOTATION SPECTRE 2D MATRICE COMPLEXE BASEE SUR UNE SEQUENCE RMN # -# matriceComplexe : data.frame liste couples ppm de la matrice a annoter # -# BdDStandards : objet contenant la base de donnees des composes standards # -# nom_sequence : nom sequence 2D a utiliser pour annotation ("JRES", "COSY", "TOCSY", "HMBC", "HSQC") # -# ppm1Tol : tolerance ppm axe abscisses # -# ppm2Tol : tolerance ppm axe ordonnees # -# nb_ligne_template : preciser le nombre total de ligne de la feuille de calcul a annoter # -####################################################################################################### -annotationRmn2D <- function(matriceComplexe, BdDStandards, nom_sequence, ppm1Tol = 0.01, ppm2Tol = 0.01, - seuil = 0, unicite = "NO") { - ## Longueur de la peak-list de la matrice a annoter - PeakListLength <- length(matriceComplexe[, 1]) - - ## Nombre de metabolites inclus dans BdD de composes standards - nbMetabolitesBdD <- length(BdDStandards) - matrixAnnotation <- data.frame() - allMetabolitesList <- data.frame() - seuil_score <- seuil - - ## Boucle sur les metabolites inclus dans BdD - for (i in seq_len(nbMetabolitesBdD)) { - ## Infos metabolite en cours - iMetabolite <- BdDStandards[[i]] - ppm1M <- iMetabolite[, 1] - ppm2M <- iMetabolite[, 2] - nbPeakMetabolite <- length(ppm1M) - MetaboliteName <- names(BdDStandards[i]) - - ## Initialisation - k <- 0 - presenceScore <- 0 - annotatedPpmRef <- data.frame() - annotatedPpmList <- data.frame() - annotatedPeakLength <- 0 - metabolites <- data.frame() - metabolitesList <- data.frame() - - ## Boucle sur les couples de pics de la matrice a annoter - for (p in seq_len(PeakListLength)) { - ppmAnnotationF1 <- as.numeric(matriceComplexe[p, 3]) - ppmAnnotationF2 <- as.numeric(matriceComplexe[p, 2]) - e <- simpleMessage("end of file") - tryCatch({ - if (!is.na(ppmAnnotationF1)) { - matrixAnnotation <- unique.data.frame(rbind.data.frame(matrixAnnotation, matriceComplexe[p, ])) - } - # Recherche du couple de pics de la matrice la liste des couples du metabolite standard - metaboliteIn <- (ppm1M >= (ppmAnnotationF2 - ppm1Tol) & ppm1M <= (ppmAnnotationF2 + ppm1Tol) & - ppm2M >= (ppmAnnotationF1 - ppm2Tol) & ppm2M <= (ppmAnnotationF1 + ppm2Tol)) - WhichMetaboliteIn <- which(metaboliteIn) - # Si au moins un couple de la matrice a annoter dans liste couples metabolite standard - if (length(WhichMetaboliteIn) > 0) { - for (a in seq_len(length(WhichMetaboliteIn))) { - annotatedPpmList <- data.frame(ppm1 = ppm1M[WhichMetaboliteIn[a]], ppm2 = ppm2M[WhichMetaboliteIn[a]], theoricalLength = nbPeakMetabolite) - annotatedPpmRef <- rbind(annotatedPpmRef, annotatedPpmList) - } - } - }, error = function(e) { - cat("End of file \n"); - }) - } - - # Au - 1 couple de ppm de la matrice complexe annote - if (nrow(annotatedPpmRef) >= 1) { - ## Nombre couples annotes - annotatedPeakLength <- nrow(annotatedPpmRef) - - ## Recherche doublons - annotatedDoublons <- duplicated(annotatedPpmRef) - if (sum(duplicated(annotatedPpmRef)) > 0) { - annotatedPeakLength <- nrow(annotatedPpmRef) - sum(duplicated(annotatedPpmRef)) - annotatedPpmRef <- annotatedPpmRef[-duplicated(annotatedPpmRef), ] - } - presenceScore <- round(annotatedPeakLength / nbPeakMetabolite, 2) - } - - ## Conservation metabolites dont score > seuil - if (presenceScore > seuil_score) { - metabolites <- data.frame(Metabolite = MetaboliteName, score = presenceScore) - metabolitesList <- cbind.data.frame(annotatedPpmRef, metabolites) - allMetabolitesList <- rbind.data.frame(allMetabolitesList, metabolitesList) - } - } - - # Initialisation - commonPpm <- data.frame() - commonPpmList <- data.frame() - metaboliteAdd <- data.frame() - metaboliteAddList <- data.frame() - commonMetabolitesList <- data.frame() - commonMetabolitesPpmList <- data.frame() - commonMetabolitesPpmAllList1 <- data.frame() - commonMetabolitesPpmAllList <- data.frame() - listeTotale_2D_unicite <- allMetabolitesList[, 1:4] - allMetabolitesList <- allMetabolitesList[, -3] - metabolitesAllUnicite <- data.frame() - - ## Boucle sur tous couples annotes - for (j in seq_len(length(allMetabolitesList$ppm1))) { - ## Boucle sur metabolites dans BdD composes standards - for (i in seq_len(nbMetabolitesBdD)) { - ppmMetaboliteBdD <- BdDStandards[[i]] - ppm1M <- ppmMetaboliteBdD[, 1] - ppm2M <- ppmMetaboliteBdD[, 2] - # Nombre de couples metabolite - nbPeakMetabolite <- length(ppm1M) - MetaboliteName <- names(BdDStandards[i]) - - metabolitesInAll <- (ppm1M >= (allMetabolitesList[j, 1] - ppm1Tol) & ppm1M <= (allMetabolitesList[j, 1] + ppm1Tol) & - ppm2M >= (allMetabolitesList[j, 2] - ppm2Tol) & ppm2M <= (allMetabolitesList[j, 2] + ppm2Tol)) - WhichMetabolitesInAll <- which(metabolitesInAll) - - if (MetaboliteName != allMetabolitesList[j, 3] & length(WhichMetabolitesInAll) > 0) { - metabolitesAllUnicite <- rbind.data.frame(metabolitesAllUnicite, listeTotale_2D_unicite[j, ]) - commonPpm <- data.frame(ppm1 = allMetabolitesList[j, 1], ppm2 = allMetabolitesList[j, 2]) - commonPpmList <- rbind.data.frame(commonPpmList, commonPpm) - commonPpmList <- unique(commonPpmList) - metaboliteAdd <- data.frame(nom_metabolite = MetaboliteName) - metaboliteAddList <- rbind.data.frame(metaboliteAddList, metaboliteAdd) - commonMetabolitesList <- rbind.data.frame(data.frame(nom_metabolite = allMetabolitesList[j, 3]), metaboliteAddList) - commonMetabolitesPpmList <- cbind.data.frame(commonPpm, commonMetabolitesList) - commonMetabolitesPpmAllList1 <- rbind.data.frame(commonMetabolitesPpmAllList1, commonMetabolitesPpmList) - commonMetabolitesPpmAllList1 <- unique.data.frame(commonMetabolitesPpmAllList1) - } - } - commonMetabolitesPpmAllList <- rbind.data.frame(commonMetabolitesPpmAllList, commonMetabolitesPpmAllList1) - commonMetabolitesPpmAllList <- unique.data.frame(commonMetabolitesPpmAllList) - - #initialisation des data.frame - commonPpm <- data.frame() - metaboliteAdd <- data.frame() - metaboliteAddList <- data.frame() - metabolite_ref <- data.frame() - commonMetabolitesList <- data.frame() - commonMetabolitesPpmList <- data.frame() - commonMetabolitesPpmAllList1 <- data.frame() - } - - unicityAllList <- listeTotale_2D_unicite - if (nrow(listeTotale_2D_unicite) != 0 & nrow(metabolitesAllUnicite) != 0) - unicityAllList <- setdiff(listeTotale_2D_unicite, metabolitesAllUnicite) - - unicitynbCouplesRectif <- data.frame() - for (g in seq_len(nrow(unicityAllList))) { - metaboliteUnicity <- (unicityAllList$Metabolite == unicityAllList$Metabolite[g]) - WhichMetaboliteUnicity <- which(metaboliteUnicity) - nb_occurence <- length(WhichMetaboliteUnicity) - unicitynbCouplesRectif <- rbind.data.frame(unicitynbCouplesRectif, nb_occurence) - } - names(unicitynbCouplesRectif) <- "NbCouplesAnnotes" - unicityAllList <- cbind.data.frame(unicityAllList, unicitynbCouplesRectif) - - unicityAllList <- cbind.data.frame(unicityAllList, score_unicite = unicityAllList$NbCouplesAnnotes / unicityAllList$theoricalLength) - unicityAllList <- unicityAllList[, -3] - unicityAllList <- unicityAllList[, -4] - - unicityAllList <- unicityAllList[unicityAllList$score_unicite > seuil_score, ] - - listeTotale_metabo <- data.frame() - if (nrow(commonPpmList) != 0) { - for (o in seq_len(length(commonPpmList[, 1]))) { - tf6 <- (commonMetabolitesPpmAllList$ppm1 == commonPpmList[o, 1] & commonMetabolitesPpmAllList$ppm2 == commonPpmList[o, 2]) - w6 <- which(tf6) - - for (s in seq_len(length(w6))) { - metaboliteAdd <- data.frame(nom_metabolite = commonMetabolitesPpmAllList[w6[s], 3]) - commonMetabolitesList <- paste(commonMetabolitesList, metaboliteAdd[1, ], sep = " ") - } - liste_metabo_ppm <- cbind.data.frame(ppm1 = commonPpmList[o, 1], ppm2 = commonPpmList[o, 2], commonMetabolitesList) - listeTotale_metabo <- rbind.data.frame(listeTotale_metabo, liste_metabo_ppm) - commonMetabolitesList <- data.frame() - } - } - - # Representation graphique - if (nom_sequence == "HSQC" | nom_sequence == "HMBC") { - atome <- "13C" - indice_positif <- 1 - indice_negatif <- -10 - } else { - atome <- "1H" - indice_positif <- 0.5 - indice_negatif <- -0.5 - } - - matriceComplexe <- matrixAnnotation - ppm1 <- as.numeric(matriceComplexe[, 2]) - ppm2 <- as.numeric(matriceComplexe[, 3]) - - if (unicite == "NO") { - listeTotale_2D_a_utiliser <- allMetabolitesList - d1.ppm <- allMetabolitesList$ppm1 - d2.ppm <- allMetabolitesList$ppm2 - } else { - listeTotale_2D_a_utiliser <- unicityAllList - d1.ppm <- listeTotale_2D_a_utiliser$ppm1 - d2.ppm <- listeTotale_2D_a_utiliser$ppm2 - } - - if (nrow(listeTotale_2D_a_utiliser) > 0) { - ## Taches de correlations - # Matrice biologique + Annotations - maxX <- max(round(max(as.numeric(matriceComplexe[, 2]))) + 0.5, round(max(as.numeric(matriceComplexe[, 2])))) - maxY <- max(round(max(as.numeric(matriceComplexe[, 3]))) + indice_positif, round(max(as.numeric(matriceComplexe[, 3])))) - probability.score <- as.factor(round(listeTotale_2D_a_utiliser[, 4], 2)) - lgr <- length(unique(probability.score)) - sp <- ggplot(matriceComplexe, aes(x = ppm1, y = ppm2)) - sp <- sp + geom_point(size = 2) + scale_x_reverse(breaks = seq(maxX, 0, -0.5)) + - scale_y_reverse(breaks = seq(maxY, 0, indice_negatif)) + - xlab("1H chemical shift (ppm)") + ylab(paste(atome, " chemical shift (ppm)")) + ggtitle(nom_sequence) + - geom_text(data = listeTotale_2D_a_utiliser, aes(d1.ppm, d2.ppm, label = str_to_lower(substr(listeTotale_2D_a_utiliser[, 3], 1, 3)), col = probability.score), - size = 4, hjust = 0, nudge_x = 0.02, vjust = 0, nudge_y = 0.2) + scale_colour_manual(values = viridis(lgr)) - print(sp) - } - - # Liste des resultats (couples pmm / metabolite / score) + liste ppms metabolites communs - if (unicite == "NO") { - return(list(liste_resultat = allMetabolitesList, listing_ppm_commun = listeTotale_metabo)) - } else { - return(list(liste_resultat_unicite = unicityAllList, listing_ppm_commun_affichage = listeTotale_metabo)) - } -} +########################################################################## +# ANNOTATION SPECTRE 2D MATRICE COMPLEXE BASEE SUR UNE SEQUENCE RMN # +# matriceComplexe : data.frame liste couples ppm de la matrice a annoter # +# BdDStandards : objet contenant la base de donnees des composes standards # +# nom_sequence : nom sequence 2D a utiliser pour annotation ("JRES", "COSY", "TOCSY", "HMBC", "HSQC") # +# ppm1Tol : tolerance ppm axe abscisses # +# ppm2Tol : tolerance ppm axe ordonnees # +# nb_ligne_template : preciser le nombre total de ligne de la feuille de calcul a annoter # +####################################################################################################### +annotationRmn2D <- function(matriceComplexe, BdDStandards, nom_sequence, ppm1Tol = 0.01, ppm2Tol = 0.01, + seuil = 0, unicite = "NO") { + ## Longueur de la peak-list de la matrice a annoter + PeakListLength <- length(matriceComplexe[, 1]) + + ## Nombre de metabolites inclus dans BdD de composes standards + nbMetabolitesBdD <- length(BdDStandards) + matrixAnnotation <- data.frame() + allMetabolitesList <- data.frame() + seuil_score <- seuil + + ## Boucle sur les metabolites inclus dans BdD + for (i in seq_len(nbMetabolitesBdD)) { + ## Infos metabolite en cours + iMetabolite <- BdDStandards[[i]] + ppm1M <- iMetabolite[, 1] + ppm2M <- iMetabolite[, 2] + nbPeakMetabolite <- length(ppm1M) + MetaboliteName <- names(BdDStandards[i]) + + ## Initialisation + k <- 0 + presenceScore <- 0 + annotatedPpmRef <- data.frame() + annotatedPpmList <- data.frame() + annotatedPeakLength <- 0 + metabolites <- data.frame() + metabolitesList <- data.frame() + + ## Boucle sur les couples de pics de la matrice a annoter + for (p in seq_len(PeakListLength)) { + ppmAnnotationF1 <- as.numeric(matriceComplexe[p, 3]) + ppmAnnotationF2 <- as.numeric(matriceComplexe[p, 2]) + e <- simpleMessage("end of file") + tryCatch( + { + if (!is.na(ppmAnnotationF1)) { + matrixAnnotation <- unique.data.frame(rbind.data.frame(matrixAnnotation, matriceComplexe[p, ])) + } + # Recherche du couple de pics de la matrice la liste des couples du metabolite standard + metaboliteIn <- (ppm1M >= (ppmAnnotationF2 - ppm1Tol) & ppm1M <= (ppmAnnotationF2 + ppm1Tol) & + ppm2M >= (ppmAnnotationF1 - ppm2Tol) & ppm2M <= (ppmAnnotationF1 + ppm2Tol)) + WhichMetaboliteIn <- which(metaboliteIn) + # Si au moins un couple de la matrice a annoter dans liste couples metabolite standard + if (length(WhichMetaboliteIn) > 0) { + for (a in seq_len(length(WhichMetaboliteIn))) { + annotatedPpmList <- data.frame(ppm1 = ppm1M[WhichMetaboliteIn[a]], ppm2 = ppm2M[WhichMetaboliteIn[a]], theoricalLength = nbPeakMetabolite) + annotatedPpmRef <- rbind(annotatedPpmRef, annotatedPpmList) + } + } + }, + error = function(e) { + cat("End of file \n") + } + ) + } + + # Au - 1 couple de ppm de la matrice complexe annote + if (nrow(annotatedPpmRef) >= 1) { + ## Nombre couples annotes + annotatedPeakLength <- nrow(annotatedPpmRef) + + ## Recherche doublons + annotatedDoublons <- duplicated(annotatedPpmRef) + if (sum(duplicated(annotatedPpmRef)) > 0) { + annotatedPeakLength <- nrow(annotatedPpmRef) - sum(duplicated(annotatedPpmRef)) + annotatedPpmRef <- annotatedPpmRef[-duplicated(annotatedPpmRef), ] + } + presenceScore <- round(annotatedPeakLength / nbPeakMetabolite, 2) + } + + ## Conservation metabolites dont score > seuil + if (presenceScore > seuil_score) { + metabolites <- data.frame(Metabolite = MetaboliteName, score = presenceScore) + metabolitesList <- cbind.data.frame(annotatedPpmRef, metabolites) + allMetabolitesList <- rbind.data.frame(allMetabolitesList, metabolitesList) + } + } + + # Initialisation + commonPpm <- data.frame() + commonPpmList <- data.frame() + metaboliteAdd <- data.frame() + metaboliteAddList <- data.frame() + commonMetabolitesList <- data.frame() + commonMetabolitesPpmList <- data.frame() + commonMetabolitesPpmAllList1 <- data.frame() + commonMetabolitesPpmAllList <- data.frame() + listeTotale_2D_unicite <- allMetabolitesList[, 1:4] + allMetabolitesList <- allMetabolitesList[, -3] + metabolitesAllUnicite <- data.frame() + + ## Boucle sur tous couples annotes + for (j in seq_len(length(allMetabolitesList$ppm1))) { + ## Boucle sur metabolites dans BdD composes standards + for (i in seq_len(nbMetabolitesBdD)) { + ppmMetaboliteBdD <- BdDStandards[[i]] + ppm1M <- ppmMetaboliteBdD[, 1] + ppm2M <- ppmMetaboliteBdD[, 2] + # Nombre de couples metabolite + nbPeakMetabolite <- length(ppm1M) + MetaboliteName <- names(BdDStandards[i]) + + metabolitesInAll <- (ppm1M >= (allMetabolitesList[j, 1] - ppm1Tol) & ppm1M <= (allMetabolitesList[j, 1] + ppm1Tol) & + ppm2M >= (allMetabolitesList[j, 2] - ppm2Tol) & ppm2M <= (allMetabolitesList[j, 2] + ppm2Tol)) + WhichMetabolitesInAll <- which(metabolitesInAll) + + if (MetaboliteName != allMetabolitesList[j, 3] & length(WhichMetabolitesInAll) > 0) { + metabolitesAllUnicite <- rbind.data.frame(metabolitesAllUnicite, listeTotale_2D_unicite[j, ]) + commonPpm <- data.frame(ppm1 = allMetabolitesList[j, 1], ppm2 = allMetabolitesList[j, 2]) + commonPpmList <- rbind.data.frame(commonPpmList, commonPpm) + commonPpmList <- unique(commonPpmList) + metaboliteAdd <- data.frame(nom_metabolite = MetaboliteName) + metaboliteAddList <- rbind.data.frame(metaboliteAddList, metaboliteAdd) + commonMetabolitesList <- rbind.data.frame(data.frame(nom_metabolite = allMetabolitesList[j, 3]), metaboliteAddList) + commonMetabolitesPpmList <- cbind.data.frame(commonPpm, commonMetabolitesList) + commonMetabolitesPpmAllList1 <- rbind.data.frame(commonMetabolitesPpmAllList1, commonMetabolitesPpmList) + commonMetabolitesPpmAllList1 <- unique.data.frame(commonMetabolitesPpmAllList1) + } + } + commonMetabolitesPpmAllList <- rbind.data.frame(commonMetabolitesPpmAllList, commonMetabolitesPpmAllList1) + commonMetabolitesPpmAllList <- unique.data.frame(commonMetabolitesPpmAllList) + + # initialisation des data.frame + commonPpm <- data.frame() + metaboliteAdd <- data.frame() + metaboliteAddList <- data.frame() + metabolite_ref <- data.frame() + commonMetabolitesList <- data.frame() + commonMetabolitesPpmList <- data.frame() + commonMetabolitesPpmAllList1 <- data.frame() + } + + unicityAllList <- listeTotale_2D_unicite + if (nrow(listeTotale_2D_unicite) != 0 & nrow(metabolitesAllUnicite) != 0) { + unicityAllList <- setdiff(listeTotale_2D_unicite, metabolitesAllUnicite) + } + + unicitynbCouplesRectif <- data.frame() + for (g in seq_len(nrow(unicityAllList))) { + metaboliteUnicity <- (unicityAllList$Metabolite == unicityAllList$Metabolite[g]) + WhichMetaboliteUnicity <- which(metaboliteUnicity) + nb_occurence <- length(WhichMetaboliteUnicity) + unicitynbCouplesRectif <- rbind.data.frame(unicitynbCouplesRectif, nb_occurence) + } + names(unicitynbCouplesRectif) <- "NbCouplesAnnotes" + unicityAllList <- cbind.data.frame(unicityAllList, unicitynbCouplesRectif) + + unicityAllList <- cbind.data.frame(unicityAllList, score_unicite = unicityAllList$NbCouplesAnnotes / unicityAllList$theoricalLength) + unicityAllList <- unicityAllList[, -3] + unicityAllList <- unicityAllList[, -4] + + unicityAllList <- unicityAllList[unicityAllList$score_unicite > seuil_score, ] + + listeTotale_metabo <- data.frame() + if (nrow(commonPpmList) != 0) { + for (o in seq_len(length(commonPpmList[, 1]))) { + tf6 <- (commonMetabolitesPpmAllList$ppm1 == commonPpmList[o, 1] & commonMetabolitesPpmAllList$ppm2 == commonPpmList[o, 2]) + w6 <- which(tf6) + + for (s in seq_len(length(w6))) { + metaboliteAdd <- data.frame(nom_metabolite = commonMetabolitesPpmAllList[w6[s], 3]) + commonMetabolitesList <- paste(commonMetabolitesList, metaboliteAdd[1, ], sep = " ") + } + liste_metabo_ppm <- cbind.data.frame(ppm1 = commonPpmList[o, 1], ppm2 = commonPpmList[o, 2], commonMetabolitesList) + listeTotale_metabo <- rbind.data.frame(listeTotale_metabo, liste_metabo_ppm) + commonMetabolitesList <- data.frame() + } + } + + # Representation graphique + if (nom_sequence == "HSQC" | nom_sequence == "HMBC") { + atome <- "13C" + indice_positif <- 1 + indice_negatif <- -10 + } else { + atome <- "1H" + indice_positif <- 0.5 + indice_negatif <- -0.5 + } + + matriceComplexe <- matrixAnnotation + ppm1 <- as.numeric(matriceComplexe[, 2]) + ppm2 <- as.numeric(matriceComplexe[, 3]) + + if (unicite == "NO") { + listeTotale_2D_a_utiliser <- allMetabolitesList + d1.ppm <- allMetabolitesList$ppm1 + d2.ppm <- allMetabolitesList$ppm2 + } else { + listeTotale_2D_a_utiliser <- unicityAllList + d1.ppm <- listeTotale_2D_a_utiliser$ppm1 + d2.ppm <- listeTotale_2D_a_utiliser$ppm2 + } + + if (nrow(listeTotale_2D_a_utiliser) > 0) { + ## Taches de correlations + # Matrice biologique + Annotations + maxX <- max(round(max(as.numeric(matriceComplexe[, 2]))) + 0.5, round(max(as.numeric(matriceComplexe[, 2])))) + maxY <- max(round(max(as.numeric(matriceComplexe[, 3]))) + indice_positif, round(max(as.numeric(matriceComplexe[, 3])))) + probability.score <- as.factor(round(listeTotale_2D_a_utiliser[, 4], 2)) + lgr <- length(unique(probability.score)) + sp <- ggplot(matriceComplexe, aes(x = ppm1, y = ppm2)) + sp <- sp + geom_point(size = 2) + scale_x_reverse(breaks = seq(maxX, 0, -0.5)) + + scale_y_reverse(breaks = seq(maxY, 0, indice_negatif)) + + xlab("1H chemical shift (ppm)") + ylab(paste(atome, " chemical shift (ppm)")) + ggtitle(nom_sequence) + + geom_text( + data = listeTotale_2D_a_utiliser, aes(d1.ppm, d2.ppm, label = str_to_lower(substr(listeTotale_2D_a_utiliser[, 3], 1, 3)), col = probability.score), + size = 4, hjust = 0, nudge_x = 0.02, vjust = 0, nudge_y = 0.2 + ) + scale_colour_manual(values = viridis(lgr)) + print(sp) + } + + # Liste des resultats (couples pmm / metabolite / score) + liste ppms metabolites communs + if (unicite == "NO") { + return(list(liste_resultat = allMetabolitesList, listing_ppm_commun = listeTotale_metabo)) + } else { + return(list(liste_resultat_unicite = unicityAllList, listing_ppm_commun_affichage = listeTotale_metabo)) + } +} diff --git a/tools/nmr_annotation2d/annotationRmn2DGlobale.R b/tools/nmr_annotation2d/annotationRmn2DGlobale.R index 58a5fd9d12..18a6c09151 100644 --- a/tools/nmr_annotation2d/annotationRmn2DGlobale.R +++ b/tools/nmr_annotation2d/annotationRmn2DGlobale.R @@ -1,147 +1,158 @@ -################################################################################################### -# ANNOTATION SPECTRE 2D MATRICE COMPLEXE BASEE SUR UNE (OU PLUSIEURS) SEQUENCE(s) # -# template : dataframe contenant la liste des couples de deplacements chimiques de la matrice complexe a annoter # -# cosy : 1 si sequence a utiliser / 0 sinon # -# hmbc : 1 si sequence a utiliser / 0 sinon # -# hsqc : 1 si sequence a utiliser / 0 sinon # -# jres : 1 si sequence a utiliser / 0 sinon # -# tocsy : 1 si sequence a utiliser / 0 sinon # -# tolPpm1 : tolerance autorisee autour de la valeur1 du couple de deplacements chimiques # -# tolPpm2HJRes : tolerance autorisee autour de la valeur2 du couple de deplacements chimiques si H dans dimension 2 # -# tolPpm2C : tolerance autorisee autour de la valeur2 du couple de deplacements chimiques si C dans dimension 2 # -# seuil : valeur du score de presence en dela de laquelle les metabolites annotes ne sont pas retenus # -# unicite : boolean pour ne retenir que les ... # -################################################################################################### -## CALCUL MOYENNE SANS VALEUR(S) MANQUANTE(S) -mean.rmNa <- function(x) { - mean(x, na.rm = TRUE) -} - -annotationRmn2DGlobale <- function(template, tolPpm1 = 0.01, tolPpm2HJRes = 0.002, tolPpm2C = 0.5, cosy = 1, hmbc = 1, hsqc = 1, jres = 1, tocsy = 1, seuil, unicite = "NO") { - ## Initialisation - options(max.print = 999999999) - annotationCOSY <- data.frame() - annotationHMBC <- data.frame() - annotationHSQC <- data.frame() - annotationJRES <- data.frame() - annotationTOCSY <- data.frame() - - dataCOSY <- "NA" - dataHMBC <- "NA" - dataHSQC <- "NA" - dataJRES <- "NA" - dataTOCSY <- "NA" - - ## Application seuil seulement si annotation avec 1 seule sequence - seuilPls2D <- seuil - - if (cosy == 1) { - matrice.cosy <- read.xlsx(template, sheet = "COSY", startRow = 2, colNames = TRUE, rowNames = FALSE, cols = 1:3, na.strings = "NA") - matrice.cosy <- matrice.cosy[matrice.cosy$peak.index != "x", ] - annotationCOSY <- annotationRmn2D(matrice.cosy, BdDReference_COSY, "COSY", ppm1Tol = tolPpm1, ppm2Tol = tolPpm1, seuil = seuilPls2D, unicite = unicite) - dataCOSY <- data.frame(Metabolite = str_to_lower(annotationCOSY$liste_resultat$Metabolite), score.COSY = annotationCOSY$liste_resultat$score) - dataCOSY <- unique.data.frame(dataCOSY) - } - - if (hmbc == 1) { - matrice.hmbc <- read.xlsx(template, sheet = "HMBC", startRow = 2, colNames = TRUE, rowNames = FALSE, cols = 1:3, na.strings = "NA") - matrice.hmbc <- matrice.hmbc[matrice.hmbc$peak.index != "x", ] - annotationHMBC <- annotationRmn2D(matrice.hmbc, BdDReference_HMBC, "HMBC", ppm1Tol = tolPpm1, ppm2Tol = tolPpm2C, seuil = seuilPls2D, unicite = unicite) - dataHMBC <- data.frame(Metabolite = str_to_lower(annotationHMBC$liste_resultat$Metabolite), score.HMBC = annotationHMBC$liste_resultat$score) - dataHMBC <- unique.data.frame(dataHMBC) - } - - if (hsqc == 1) { - matrice.hsqc <- read.xlsx(template, sheet = "HSQC", startRow = 2, colNames = TRUE, rowNames = FALSE, cols = 1:3, na.strings = "NA") - matrice.hsqc <- matrice.hsqc[matrice.hsqc$peak.index != "x", ] - annotationHSQC <- annotationRmn2D(matrice.hsqc, BdDReference_HSQC, "HSQC", ppm1Tol = tolPpm1, ppm2Tol = tolPpm2C, seuil = seuilPls2D, unicite = unicite) - dataHSQC <- data.frame(Metabolite = str_to_lower(annotationHSQC$liste_resultat$Metabolite), score.HSQC = annotationHSQC$liste_resultat$score) - dataHSQC <- unique.data.frame(dataHSQC) - } - - if (jres == 1) { - matrice.jres <- read.xlsx(template, sheet = "JRES", startRow = 2, colNames = TRUE, rowNames = FALSE, cols = 1:3, na.strings = "NA") - matrice.jres <- matrice.jres[matrice.jres$peak.index != "x", ] - annotationJRES <- annotationRmn2D(matrice.jres, BdDReference_JRES, "JRES", ppm1Tol = tolPpm1, ppm2Tol = tolPpm2HJRes, seuil = seuilPls2D, unicite = unicite) - dataJRES <- data.frame(Metabolite = str_to_lower(annotationJRES$liste_resultat$Metabolite), score.JRES = annotationJRES$liste_resultat$score) - dataJRES <- unique.data.frame(dataJRES) - } - - if (tocsy == 1) { - matrice.tocsy <- read.xlsx(template, sheet = "TOCSY", startRow = 2, colNames = TRUE, rowNames = FALSE, cols = 1:3, na.strings = "NA") - matrice.tocsy <- matrice.tocsy[matrice.tocsy$peak.index != "x", ] - annotationTOCSY <- annotationRmn2D(matrice.tocsy, BdDReference_TOCSY, "TOCSY", ppm1Tol = tolPpm1, ppm2Tol = tolPpm1, seuil = seuilPls2D, unicite = unicite) - dataTOCSY <- data.frame(Metabolite = str_to_lower(annotationTOCSY$liste_resultat$Metabolite), score.TOCSY = annotationTOCSY$liste_resultat$score) - dataTOCSY <- unique.data.frame(dataTOCSY) - } - - seqCombiMeanScoreSeuil <- data.frame() - seqCombiMeanScoreSeuilFiltre <- data.frame() - - ## CONCATENATION RESULTATS DIFFERENTES SEQUENCES - data2D <- list(dataCOSY, dataHMBC, dataHSQC, dataJRES, dataTOCSY) - whichSequenceNaN <- which((data2D != "NA")) - data2D <- data2D[whichSequenceNaN] - sequencesCombination <- data.frame(data2D[1]) - seqCombiMeanScore <- sequencesCombination - - ## Si une seule sequence et seuil sur score = filtre applique dans la fonction annotationRmn2D - if (length(data2D) >= 2) { - ## CONCATENATION SCORE PAR SEQUENCE - for (l in 2:length(data2D)) - sequencesCombination <- merge.data.frame(sequencesCombination, data2D[l], by = "Metabolite", all.x = TRUE, all.y = TRUE) - - ## Replacement of NA values due to mis annotation - for (m in seq_len(nrow(sequencesCombination))) { - COSYcompound <- sort(names(BdDReference_COSY)) - HMBCcompound <- sort(names(BdDReference_HMBC)) - HSQCcompound <- sort(names(BdDReference_HSQC)) - JREScompound <- sort(names(BdDReference_JRES)) - TOCSYcompound <- sort(names(BdDReference_TOCSY)) - - if (is.na(sequencesCombination[m, 2])) { - compound <- as.character(sequencesCombination[m, 1]) - for (c in seq_len(length(COSYcompound))) - if (str_to_lower(compound) == str_to_lower(COSYcompound[c])) - sequencesCombination[m, 2] <- 0 - } - - if (is.na(sequencesCombination[m, 3])) { - compound <- as.character(sequencesCombination[m, 1]) - for (c in seq_len(length(HMBCcompound))) - if (str_to_lower(compound) == str_to_lower(HMBCcompound[c])) - sequencesCombination[m, 3] <- 0 - } - - if (is.na(sequencesCombination[m, 4])) { - compound <- as.character(sequencesCombination[m, 1]) - for (c in seq_len(length(HSQCcompound))) - if (str_to_lower(compound) == str_to_lower(HSQCcompound[c])) - sequencesCombination[m, 4] <- 0 - } - - if (is.na(sequencesCombination[m, 5])) { - compound <- as.character(sequencesCombination[m, 1]) - for (c in seq_len(length(JREScompound))) - if (str_to_lower(compound) == str_to_lower(JREScompound[c])) - sequencesCombination[m, 5] <- 0 - } - - if (is.na(sequencesCombination[m, 6])) { - compound <- as.character(sequencesCombination[m, 1]) - for (c in seq_len(length(TOCSYcompound))) - if (str_to_lower(compound) == str_to_lower(TOCSYcompound[c])) - sequencesCombination[m, 6] <- 0 - } - } - - ## SCORE MOYEN (sans prise en compte valeurs manquantes) - meanScore <- round(apply(sequencesCombination[, -1], 1, FUN = mean.rmNa), 2) - seqCombiMeanScore <- cbind.data.frame(sequencesCombination, averageScore = meanScore) - - ## SUPPRESSION METABOLITE AVEC SCORE MOYEN < SEUIL - seqCombiMeanScoreSeuilFiltre <- seqCombiMeanScore[seqCombiMeanScore$averageScore > seuil, ] - } - - return(list(COSY = annotationCOSY, HMBC = annotationHMBC, HSQC = annotationHSQC, JRES = annotationJRES, TOCSY = annotationTOCSY, combination = seqCombiMeanScoreSeuilFiltre)) -} +################################################################################################### +# ANNOTATION SPECTRE 2D MATRICE COMPLEXE BASEE SUR UNE (OU PLUSIEURS) SEQUENCE(s) # +# template : dataframe contenant la liste des couples de deplacements chimiques de la matrice complexe a annoter # +# cosy : 1 si sequence a utiliser / 0 sinon # +# hmbc : 1 si sequence a utiliser / 0 sinon # +# hsqc : 1 si sequence a utiliser / 0 sinon # +# jres : 1 si sequence a utiliser / 0 sinon # +# tocsy : 1 si sequence a utiliser / 0 sinon # +# tolPpm1 : tolerance autorisee autour de la valeur1 du couple de deplacements chimiques # +# tolPpm2HJRes : tolerance autorisee autour de la valeur2 du couple de deplacements chimiques si H dans dimension 2 # +# tolPpm2C : tolerance autorisee autour de la valeur2 du couple de deplacements chimiques si C dans dimension 2 # +# seuil : valeur du score de presence en dela de laquelle les metabolites annotes ne sont pas retenus # +# unicite : boolean pour ne retenir que les ... # +################################################################################################### +## CALCUL MOYENNE SANS VALEUR(S) MANQUANTE(S) +mean.rmNa <- function(x) { + mean(x, na.rm = TRUE) +} + +annotationRmn2DGlobale <- function(template, tolPpm1 = 0.01, tolPpm2HJRes = 0.002, tolPpm2C = 0.5, cosy = 1, hmbc = 1, hsqc = 1, jres = 1, tocsy = 1, seuil, unicite = "NO") { + ## Initialisation + options(max.print = 999999999) + annotationCOSY <- data.frame() + annotationHMBC <- data.frame() + annotationHSQC <- data.frame() + annotationJRES <- data.frame() + annotationTOCSY <- data.frame() + + dataCOSY <- "NA" + dataHMBC <- "NA" + dataHSQC <- "NA" + dataJRES <- "NA" + dataTOCSY <- "NA" + + ## Application seuil seulement si annotation avec 1 seule sequence + seuilPls2D <- seuil + + if (cosy == 1) { + matrice.cosy <- read.xlsx(template, sheet = "COSY", startRow = 2, colNames = TRUE, rowNames = FALSE, cols = 1:3, na.strings = "NA") + matrice.cosy <- matrice.cosy[matrice.cosy$peak.index != "x", ] + annotationCOSY <- annotationRmn2D(matrice.cosy, BdDReference_COSY, "COSY", ppm1Tol = tolPpm1, ppm2Tol = tolPpm1, seuil = seuilPls2D, unicite = unicite) + dataCOSY <- data.frame(Metabolite = str_to_lower(annotationCOSY$liste_resultat$Metabolite), score.COSY = annotationCOSY$liste_resultat$score) + dataCOSY <- unique.data.frame(dataCOSY) + } + + if (hmbc == 1) { + matrice.hmbc <- read.xlsx(template, sheet = "HMBC", startRow = 2, colNames = TRUE, rowNames = FALSE, cols = 1:3, na.strings = "NA") + matrice.hmbc <- matrice.hmbc[matrice.hmbc$peak.index != "x", ] + annotationHMBC <- annotationRmn2D(matrice.hmbc, BdDReference_HMBC, "HMBC", ppm1Tol = tolPpm1, ppm2Tol = tolPpm2C, seuil = seuilPls2D, unicite = unicite) + dataHMBC <- data.frame(Metabolite = str_to_lower(annotationHMBC$liste_resultat$Metabolite), score.HMBC = annotationHMBC$liste_resultat$score) + dataHMBC <- unique.data.frame(dataHMBC) + } + + if (hsqc == 1) { + matrice.hsqc <- read.xlsx(template, sheet = "HSQC", startRow = 2, colNames = TRUE, rowNames = FALSE, cols = 1:3, na.strings = "NA") + matrice.hsqc <- matrice.hsqc[matrice.hsqc$peak.index != "x", ] + annotationHSQC <- annotationRmn2D(matrice.hsqc, BdDReference_HSQC, "HSQC", ppm1Tol = tolPpm1, ppm2Tol = tolPpm2C, seuil = seuilPls2D, unicite = unicite) + dataHSQC <- data.frame(Metabolite = str_to_lower(annotationHSQC$liste_resultat$Metabolite), score.HSQC = annotationHSQC$liste_resultat$score) + dataHSQC <- unique.data.frame(dataHSQC) + } + + if (jres == 1) { + matrice.jres <- read.xlsx(template, sheet = "JRES", startRow = 2, colNames = TRUE, rowNames = FALSE, cols = 1:3, na.strings = "NA") + matrice.jres <- matrice.jres[matrice.jres$peak.index != "x", ] + annotationJRES <- annotationRmn2D(matrice.jres, BdDReference_JRES, "JRES", ppm1Tol = tolPpm1, ppm2Tol = tolPpm2HJRes, seuil = seuilPls2D, unicite = unicite) + dataJRES <- data.frame(Metabolite = str_to_lower(annotationJRES$liste_resultat$Metabolite), score.JRES = annotationJRES$liste_resultat$score) + dataJRES <- unique.data.frame(dataJRES) + } + + if (tocsy == 1) { + matrice.tocsy <- read.xlsx(template, sheet = "TOCSY", startRow = 2, colNames = TRUE, rowNames = FALSE, cols = 1:3, na.strings = "NA") + matrice.tocsy <- matrice.tocsy[matrice.tocsy$peak.index != "x", ] + annotationTOCSY <- annotationRmn2D(matrice.tocsy, BdDReference_TOCSY, "TOCSY", ppm1Tol = tolPpm1, ppm2Tol = tolPpm1, seuil = seuilPls2D, unicite = unicite) + dataTOCSY <- data.frame(Metabolite = str_to_lower(annotationTOCSY$liste_resultat$Metabolite), score.TOCSY = annotationTOCSY$liste_resultat$score) + dataTOCSY <- unique.data.frame(dataTOCSY) + } + + seqCombiMeanScoreSeuil <- data.frame() + seqCombiMeanScoreSeuilFiltre <- data.frame() + + ## CONCATENATION RESULTATS DIFFERENTES SEQUENCES + data2D <- list(dataCOSY, dataHMBC, dataHSQC, dataJRES, dataTOCSY) + whichSequenceNaN <- which((data2D != "NA")) + data2D <- data2D[whichSequenceNaN] + sequencesCombination <- data.frame(data2D[1]) + seqCombiMeanScore <- sequencesCombination + + ## Si une seule sequence et seuil sur score = filtre applique dans la fonction annotationRmn2D + if (length(data2D) >= 2) { + ## CONCATENATION SCORE PAR SEQUENCE + for (l in 2:length(data2D)) { + sequencesCombination <- merge.data.frame(sequencesCombination, data2D[l], by = "Metabolite", all.x = TRUE, all.y = TRUE) + } + + ## Replacement of NA values due to mis annotation + for (m in seq_len(nrow(sequencesCombination))) { + COSYcompound <- sort(names(BdDReference_COSY)) + HMBCcompound <- sort(names(BdDReference_HMBC)) + HSQCcompound <- sort(names(BdDReference_HSQC)) + JREScompound <- sort(names(BdDReference_JRES)) + TOCSYcompound <- sort(names(BdDReference_TOCSY)) + + if (is.na(sequencesCombination[m, 2])) { + compound <- as.character(sequencesCombination[m, 1]) + for (c in seq_len(length(COSYcompound))) { + if (str_to_lower(compound) == str_to_lower(COSYcompound[c])) { + sequencesCombination[m, 2] <- 0 + } + } + } + + if (is.na(sequencesCombination[m, 3])) { + compound <- as.character(sequencesCombination[m, 1]) + for (c in seq_len(length(HMBCcompound))) { + if (str_to_lower(compound) == str_to_lower(HMBCcompound[c])) { + sequencesCombination[m, 3] <- 0 + } + } + } + + if (is.na(sequencesCombination[m, 4])) { + compound <- as.character(sequencesCombination[m, 1]) + for (c in seq_len(length(HSQCcompound))) { + if (str_to_lower(compound) == str_to_lower(HSQCcompound[c])) { + sequencesCombination[m, 4] <- 0 + } + } + } + + if (is.na(sequencesCombination[m, 5])) { + compound <- as.character(sequencesCombination[m, 1]) + for (c in seq_len(length(JREScompound))) { + if (str_to_lower(compound) == str_to_lower(JREScompound[c])) { + sequencesCombination[m, 5] <- 0 + } + } + } + + if (is.na(sequencesCombination[m, 6])) { + compound <- as.character(sequencesCombination[m, 1]) + for (c in seq_len(length(TOCSYcompound))) { + if (str_to_lower(compound) == str_to_lower(TOCSYcompound[c])) { + sequencesCombination[m, 6] <- 0 + } + } + } + } + + ## SCORE MOYEN (sans prise en compte valeurs manquantes) + meanScore <- round(apply(sequencesCombination[, -1], 1, FUN = mean.rmNa), 2) + seqCombiMeanScore <- cbind.data.frame(sequencesCombination, averageScore = meanScore) + + ## SUPPRESSION METABOLITE AVEC SCORE MOYEN < SEUIL + seqCombiMeanScoreSeuilFiltre <- seqCombiMeanScore[seqCombiMeanScore$averageScore > seuil, ] + } + + return(list(COSY = annotationCOSY, HMBC = annotationHMBC, HSQC = annotationHSQC, JRES = annotationJRES, TOCSY = annotationTOCSY, combination = seqCombiMeanScoreSeuilFiltre)) +} diff --git a/tools/nmr_annotation2d/annotationRmn2DWrapper.R b/tools/nmr_annotation2d/annotationRmn2DWrapper.R index 0274314eab..aca2a50caa 100755 --- a/tools/nmr_annotation2d/annotationRmn2DWrapper.R +++ b/tools/nmr_annotation2d/annotationRmn2DWrapper.R @@ -1,304 +1,354 @@ -#!/usr/local/public/bin/Rscript --vanilla --slave --no-site-file - -## 201919016 2DNmrAnnotation_1.0.0.R -## Marie Tremblay-Franco -## MetaboHUB: The French Infrastructure for Metabolomics and Fluxomics -## marie.tremblay-franco@inrae.fr - -runExampleL <- FALSE - -if (runExampleL) { -##------------------------------ -## Example of arguments -##------------------------------ -} - - -##------------------------------ -## Options -##------------------------------ -strAsFacL <- options()$stringsAsFactors -options(stringsAsFactors = FALSE) -options(digits = 8, scipen = 3) - -##------------------------------ -## Constants -##------------------------------ -topEnvC <- environment() -flagC <- "\n" - - -##------------------------- -## Input parameters reading -##------------------------- - -##------------------------------ -## R libraries laoding -##------------------------------ -library(batch) -library(dplyr) -library(ggplot2) -library(openxlsx) -library(stringr) -library(tidyr) -library(curl) -library(jsonlite) -library(stringi) - -if (!runExampleL) - argLs <- parseCommandArgs(evaluate = FALSE) -logFile <- argLs[["logOut"]] -sink(logFile) - -cat("\tPACKAGE INFO\n") -sessionInfo() - -##------------------------------ -## Functions -##------------------------------ -source_local <- function(fname) { - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep = "/")) -} -#Import the different functions -source_local("annotationRmn2D.R") -source_local("annotationRmn2DGlobale.R") -source_local("viridis.R") - -## Input parameter values -fileToAnnotate <- argLs[[1]] - # Constraints values -ph <- argLs$pH -field <- argLs$magneticField - - # Chosen sequence(s) -cosy <- 0 -hmbc <- 0 -hsqc <- 0 -jres <- 0 -tocsy <- 0 - -if (argLs$cosy_2dsequences == "yes") { - cosy <- 1 - peakforestSpectra <- readLines(curl("https://metabohub.peakforest.org/rest/v1/spectra/nmr2d/search?query=cosy&token=9131jq9l8gsjn1j14t351h716u&max=500")) - peakforestSpectra <- fromJSON(peakforestSpectra, simplifyDataFrame = TRUE) - if (ph != 0) - peakforestSpectra <- peakforestSpectra[peakforestSpectra$sampleNMRTubeConditionsMetadata$potentiaHydrogenii == ph, ] - if (field != 0) - peakforestSpectra <- peakforestSpectra[peakforestSpectra$analyzerNMRSpectrometerDevice$magneticFieldStrenght == field, ] - - if (nrow(peakforestSpectra) != 0) { - BdDReference_COSY <- peakforestSpectra$peaks - names(BdDReference_COSY) <- str_split(peakforestSpectra[, 2], simplify = TRUE, pattern = ";")[, 1] - names(BdDReference_COSY) <- enc2utf8(names(BdDReference_COSY)) - names(BdDReference_COSY) <- str_replace_all(names(BdDReference_COSY), "\u00e9", "e") - - for (k in seq_len(length(BdDReference_COSY))) { - peakforestSpectra_df <- data.frame(ppm.dim1 = BdDReference_COSY[[k]][, 2], ppm.dim2 = BdDReference_COSY[[k]][, 1], - BdDReference_COSY[[k]][, 3:ncol(BdDReference_COSY[[k]])]) - BdDReference_COSY[[k]] <- peakforestSpectra_df - } - } else { - stop("No COSY spectra correspond to requested pH and/or magnetic field", call. = FALSE) - } - rm(peakforestSpectra) - rm(peakforestSpectra_df) -} - -if (argLs$hmbc_2dsequences == "yes") { - hmbc <- 1 - peakforestSpectra <- readLines(curl("https://metabohub.peakforest.org/rest/v1/spectra/nmr2d/search?query=hmbc&token=9131jq9l8gsjn1j14t351h716u&max=500")) - peakforestSpectra <- fromJSON(peakforestSpectra, simplifyDataFrame = TRUE) - if (ph != 0) - peakforestSpectra <- peakforestSpectra[peakforestSpectra$sampleNMRTubeConditionsMetadata$potentiaHydrogenii == ph, ] - if (field != 0) - peakforestSpectra <- peakforestSpectra[peakforestSpectra$analyzerNMRSpectrometerDevice$magneticFieldStrenght == field, ] - - if (nrow(peakforestSpectra) != 0) { - - BdDReference_HMBC <- peakforestSpectra$peaks - names(BdDReference_HMBC) <- str_split(peakforestSpectra[, 2], simplify = TRUE, pattern = ";")[, 1] - names(BdDReference_HMBC) <- enc2utf8(names(BdDReference_HMBC)) - names(BdDReference_HMBC) <- str_replace_all(names(BdDReference_HMBC), "\u00e9", "e") - - peakforestSpectra_df <- data.frame() - for (k in seq_len(length(BdDReference_HMBC))) { - peakforestSpectra_df <- data.frame(ppm.dim1 = BdDReference_HMBC[[k]][, 2], ppm.dim2 = BdDReference_HMBC[[k]][, 1], - BdDReference_HMBC[[k]][, 3:ncol(BdDReference_HMBC[[k]])]) - BdDReference_HMBC[[k]] <- peakforestSpectra_df - } - } else { - stop("No HMBC spectra correspond to requested pH and/or magnetic field", call. = FALSE) - } - rm(peakforestSpectra) - rm(peakforestSpectra_df) -} - -if (argLs$hsqc_2dsequences == "yes") { - hsqc <- 1 - peakforestSpectra <- readLines(curl("https://metabohub.peakforest.org/rest/v1/spectra/nmr2d/search?query=hsqc&token=9131jq9l8gsjn1j14t351h716u&max=500")) - peakforestSpectra <- fromJSON(peakforestSpectra, simplifyDataFrame = TRUE) - - if (ph != 0) - peakforestSpectra <- peakforestSpectra[peakforestSpectra$sampleNMRTubeConditionsMetadata$potentiaHydrogenii == ph, ] - if (field != 0) - peakforestSpectra <- peakforestSpectra[peakforestSpectra$analyzerNMRSpectrometerDevice$magneticFieldStrenght == field, ] - - if (nrow(peakforestSpectra) != 0) { - BdDReference_HSQC <- peakforestSpectra$peaks - names(BdDReference_HSQC) <- str_split(peakforestSpectra[, 2], simplify = TRUE, pattern = ";")[, 1] - names(BdDReference_HSQC) <- enc2utf8(names(BdDReference_HSQC)) - names(BdDReference_HSQC) <- str_replace_all(names(BdDReference_HSQC), "\u00e9", "e") - - for (k in seq_len(length(BdDReference_HSQC))) { - peakforestSpectra_df <- data.frame(ppm.dim1 = BdDReference_HSQC[[k]][, 2], ppm.dim2 = BdDReference_HSQC[[k]][, 1], - BdDReference_HSQC[[k]][, 3:ncol(BdDReference_HSQC[[k]])]) - BdDReference_HSQC[[k]] <- peakforestSpectra_df - } - } else { - stop("No HSQC spectra correspond to requested pH and/or magnetic field", call. = FALSE) - } - rm(peakforestSpectra) - rm(peakforestSpectra_df) -} - -if (argLs$jres_2dsequences == "yes") { - jres <- 1 - peakforestSpectra <- readLines(curl("https://metabohub.peakforest.org/rest/v1/spectra/nmr2d/search?query=jres&token=9131jq9l8gsjn1j14t351h716u&max=500")) - peakforestSpectra <- fromJSON(peakforestSpectra, simplifyDataFrame = TRUE) - - if (ph != 0) - peakforestSpectra <- peakforestSpectra[peakforestSpectra$sampleNMRTubeConditionsMetadata$potentiaHydrogenii == ph, ] - if (field != 0) - peakforestSpectra <- peakforestSpectra[peakforestSpectra$analyzerNMRSpectrometerDevice$magneticFieldStrenght == field, ] - - if (nrow(peakforestSpectra) != 0) { - BdDReference_JRES <- peakforestSpectra$peaks - names(BdDReference_JRES) <- str_split(peakforestSpectra[, 2], simplify = TRUE, pattern = ";")[, 1] - names(BdDReference_JRES) <- enc2utf8(names(BdDReference_JRES)) - names(BdDReference_JRES) <- str_replace_all(names(BdDReference_JRES), "\u00e9", "e") - - for (k in seq_len(length(BdDReference_JRES))) { - peakforestSpectra_df <- data.frame(ppm.dim1 = BdDReference_JRES[[k]][, 2], ppm.dim2 = BdDReference_JRES[[k]][, 1], - BdDReference_JRES[[k]][, 3:ncol(BdDReference_JRES[[k]])]) - BdDReference_JRES[[k]] <- peakforestSpectra_df - } - } else { - stop("No JRES spectra correspond to requested pH and/or magnetic field", call. = FALSE) - } - rm(peakforestSpectra) - rm(peakforestSpectra_df) -} - -if (argLs$tocsy_2dsequences == "yes") { - tocsy <- 1 - peakforestSpectra <- readLines(curl("https://metabohub.peakforest.org/rest/v1/spectra/nmr2d/search?query=tocsy&token=9131jq9l8gsjn1j14t351h716u&max=500")) - peakforestSpectra <- fromJSON(peakforestSpectra, simplifyDataFrame = TRUE) - - if (ph != 0) - peakforestSpectra <- peakforestSpectra[peakforestSpectra$sampleNMRTubeConditionsMetadata$potentiaHydrogenii == ph, ] - if (field != 0) - peakforestSpectra <- peakforestSpectra[peakforestSpectra$analyzerNMRSpectrometerDevice$magneticFieldStrenght == field, ] - - if (nrow(peakforestSpectra) != 0) { - BdDReference_TOCSY <- peakforestSpectra$peaks - names(BdDReference_TOCSY) <- str_split(peakforestSpectra[, 2], simplify = TRUE, pattern = ";")[, 1] - names(BdDReference_TOCSY) <- enc2utf8(names(BdDReference_TOCSY)) - names(BdDReference_TOCSY) <- str_replace_all(names(BdDReference_TOCSY), "\u00e9", "e") - - for (k in seq_len(length(BdDReference_TOCSY))) { - peakforestSpectra_df <- data.frame(ppm.dim1 = BdDReference_TOCSY[[k]][, 2], ppm.dim2 = BdDReference_TOCSY[[k]][, 1], - BdDReference_TOCSY[[k]][, 3:ncol(BdDReference_TOCSY[[k]])]) - BdDReference_TOCSY[[k]] <- peakforestSpectra_df - } - } else { - stop("No TOCSY spectra correspond to requested pH and/or magnetic field", call. = FALSE) - } - rm(peakforestSpectra) - rm(peakforestSpectra_df) -} - -if (argLs$cosy_2dsequences == "no" & argLs$hmbc_2dsequences == "no" & argLs$hsqc_2dsequences == "no" & argLs$jres_2dsequences == "no" & argLs$tocsy_2dsequences == "no") - stop("No chosen sequence. You have to choose at least 1 sequence", call. = FALSE) - - - # User database - - - # Allowed chemical shifts -tolPpm1 <- argLs$tolppm1 -tolPpm2HJRes <- argLs$tolppmJRES -tolPpm2C <- argLs$tolppm2 - # Threshold to remove metabolites (probability score < threshold) -seuil <- argLs$threshold -# Remove metabolites when multiple assignations? -unicite <- str_to_upper(argLs$unicity) - -## Output paramater values -AnnotationGraph <- argLs[["AnnotationGraph"]] - -print(argLs) - -## ANNOTATION -st0 <- Sys.time() -pdf(AnnotationGraph, onefile = TRUE) -annotationMelange <- annotationRmn2DGlobale(fileToAnnotate, tolPpm1 = tolPpm1, tolPpm2HJRes = tolPpm2HJRes, - tolPpm2C = tolPpm2C, cosy = cosy, hmbc = hmbc, hsqc = hsqc, - jres = jres, tocsy = tocsy, seuil = seuil, unicite = unicite) -dev.off() - -if (cosy == 1) { - write.table(annotationMelange$COSY$liste_resultat, file = argLs[["annotationCOSY"]], quote = FALSE, - row.names = FALSE, sep = "\t") - if (nrow(annotationMelange$COSY$listing_ppm_commun) != 0) - write.table(annotationMelange$COSY$listing_ppm_commun, file = argLs[["ppmCommunCOSY"]], quote = FALSE, - row.names = FALSE, sep = "\t") -} - -if (hmbc == 1) { - write.table(annotationMelange$HMBC$liste_resultat, file = argLs[["annotationHMBC"]], quote = FALSE, - row.names = FALSE, sep = "\t") - if (nrow(annotationMelange$HMBC$listing_ppm_commun) != 0) - write.table(annotationMelange$HMBC$listing_ppm_commun, file = argLs[["ppmCommunHMBC"]], quote = FALSE, - row.names = FALSE, sep = "\t") -} - -if (hsqc == 1) { - write.table(annotationMelange$HSQC$liste_resultat, file = argLs[["annotationHSQC"]], quote = FALSE, - row.names = FALSE, sep = "\t") - if (nrow(annotationMelange$HSQC$listing_ppm_commun) != 0) - write.table(annotationMelange$HSQC$listing_ppm_commun, file = argLs[["ppmCommunHSQC"]], quote = FALSE, - row.names = FALSE, sep = "\t") -} - -if (jres == 1) { - write.table(annotationMelange$JRES$liste_resultat, file = argLs[["annotationJRES"]], quote = FALSE, - row.names = FALSE, sep = "\t") - if (nrow(annotationMelange$JRES$listing_ppm_commun) != 0) - write.table(annotationMelange$JRES$listing_ppm_commun, file = argLs[["ppmCommunJRES"]], quote = FALSE, - row.names = FALSE, sep = "\t") -} - -if (tocsy == 1) { - write.table(annotationMelange$TOCSY$liste_resultat, file = argLs[["annotationTOCSY"]], quote = FALSE, - row.names = FALSE, sep = "\t") - if (nrow(annotationMelange$TOCSY$listing_ppm_commun) != 0) - write.table(annotationMelange$TOCSY$listing_ppm_commun, file = argLs[["ppmCommunTOCSY"]], quote = FALSE, - row.names = FALSE, sep = "\t") -} - -## Combinaison de sequences -if (cosy + jres + hmbc + hsqc + tocsy > 1) { - write.table(annotationMelange$combination, file = argLs[["annotationCombination"]], quote = FALSE, - row.names = FALSE, sep = "\t") -} -st1 <- Sys.time() -print(st1 - st0) - -## Ending -##-------- -cat("\nEnd of '2D NMR annotation' Galaxy module call: ", as.character(Sys.time()), sep = "") -sink() -options(stringsAsFactors = strAsFacL) -rm(list = ls()) +#!/usr/local/public/bin/Rscript --vanilla --slave --no-site-file + +## 201919016 2DNmrAnnotation_1.0.0.R +## Marie Tremblay-Franco +## MetaboHUB: The French Infrastructure for Metabolomics and Fluxomics +## marie.tremblay-franco@inrae.fr + +runExampleL <- FALSE + +if (runExampleL) { + ## ------------------------------ + ## Example of arguments + ## ------------------------------ +} + + +## ------------------------------ +## Options +## ------------------------------ +strAsFacL <- options()$stringsAsFactors +options(stringsAsFactors = FALSE) +options(digits = 8, scipen = 3) + +## ------------------------------ +## Constants +## ------------------------------ +topEnvC <- environment() +flagC <- "\n" + + +## ------------------------- +## Input parameters reading +## ------------------------- + +## ------------------------------ +## R libraries laoding +## ------------------------------ +library(batch) +library(dplyr) +library(ggplot2) +library(openxlsx) +library(stringr) +library(tidyr) +library(curl) +library(jsonlite) +library(stringi) + +if (!runExampleL) { + argLs <- parseCommandArgs(evaluate = FALSE) +} +logFile <- argLs[["logOut"]] +sink(logFile) + +cat("\tPACKAGE INFO\n") +sessionInfo() + +## ------------------------------ +## Functions +## ------------------------------ +source_local <- function(fname) { + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) +} +# Import the different functions +source_local("annotationRmn2D.R") +source_local("annotationRmn2DGlobale.R") +source_local("viridis.R") + +## Input parameter values +fileToAnnotate <- argLs[[1]] +# Constraints values +ph <- argLs$pH +field <- argLs$magneticField + +# Chosen sequence(s) +cosy <- 0 +hmbc <- 0 +hsqc <- 0 +jres <- 0 +tocsy <- 0 + +if (argLs$cosy_2dsequences == "yes") { + cosy <- 1 + peakforestSpectra <- readLines(curl("https://metabohub.peakforest.org/rest/v1/spectra/nmr2d/search?query=cosy&token=9131jq9l8gsjn1j14t351h716u&max=500")) + peakforestSpectra <- fromJSON(peakforestSpectra, simplifyDataFrame = TRUE) + if (ph != 0) { + peakforestSpectra <- peakforestSpectra[peakforestSpectra$sampleNMRTubeConditionsMetadata$potentiaHydrogenii == ph, ] + } + if (field != 0) { + peakforestSpectra <- peakforestSpectra[peakforestSpectra$analyzerNMRSpectrometerDevice$magneticFieldStrenght == field, ] + } + + if (nrow(peakforestSpectra) != 0) { + BdDReference_COSY <- peakforestSpectra$peaks + names(BdDReference_COSY) <- str_split(peakforestSpectra[, 2], simplify = TRUE, pattern = ";")[, 1] + names(BdDReference_COSY) <- enc2utf8(names(BdDReference_COSY)) + names(BdDReference_COSY) <- str_replace_all(names(BdDReference_COSY), "\u00e9", "e") + + for (k in seq_len(length(BdDReference_COSY))) { + peakforestSpectra_df <- data.frame( + ppm.dim1 = BdDReference_COSY[[k]][, 2], ppm.dim2 = BdDReference_COSY[[k]][, 1], + BdDReference_COSY[[k]][, 3:ncol(BdDReference_COSY[[k]])] + ) + BdDReference_COSY[[k]] <- peakforestSpectra_df + } + } else { + stop("No COSY spectra correspond to requested pH and/or magnetic field", call. = FALSE) + } + rm(peakforestSpectra) + rm(peakforestSpectra_df) +} + +if (argLs$hmbc_2dsequences == "yes") { + hmbc <- 1 + peakforestSpectra <- readLines(curl("https://metabohub.peakforest.org/rest/v1/spectra/nmr2d/search?query=hmbc&token=9131jq9l8gsjn1j14t351h716u&max=500")) + peakforestSpectra <- fromJSON(peakforestSpectra, simplifyDataFrame = TRUE) + if (ph != 0) { + peakforestSpectra <- peakforestSpectra[peakforestSpectra$sampleNMRTubeConditionsMetadata$potentiaHydrogenii == ph, ] + } + if (field != 0) { + peakforestSpectra <- peakforestSpectra[peakforestSpectra$analyzerNMRSpectrometerDevice$magneticFieldStrenght == field, ] + } + + if (nrow(peakforestSpectra) != 0) { + BdDReference_HMBC <- peakforestSpectra$peaks + names(BdDReference_HMBC) <- str_split(peakforestSpectra[, 2], simplify = TRUE, pattern = ";")[, 1] + names(BdDReference_HMBC) <- enc2utf8(names(BdDReference_HMBC)) + names(BdDReference_HMBC) <- str_replace_all(names(BdDReference_HMBC), "\u00e9", "e") + + peakforestSpectra_df <- data.frame() + for (k in seq_len(length(BdDReference_HMBC))) { + peakforestSpectra_df <- data.frame( + ppm.dim1 = BdDReference_HMBC[[k]][, 2], ppm.dim2 = BdDReference_HMBC[[k]][, 1], + BdDReference_HMBC[[k]][, 3:ncol(BdDReference_HMBC[[k]])] + ) + BdDReference_HMBC[[k]] <- peakforestSpectra_df + } + } else { + stop("No HMBC spectra correspond to requested pH and/or magnetic field", call. = FALSE) + } + rm(peakforestSpectra) + rm(peakforestSpectra_df) +} + +if (argLs$hsqc_2dsequences == "yes") { + hsqc <- 1 + peakforestSpectra <- readLines(curl("https://metabohub.peakforest.org/rest/v1/spectra/nmr2d/search?query=hsqc&token=9131jq9l8gsjn1j14t351h716u&max=500")) + peakforestSpectra <- fromJSON(peakforestSpectra, simplifyDataFrame = TRUE) + + if (ph != 0) { + peakforestSpectra <- peakforestSpectra[peakforestSpectra$sampleNMRTubeConditionsMetadata$potentiaHydrogenii == ph, ] + } + if (field != 0) { + peakforestSpectra <- peakforestSpectra[peakforestSpectra$analyzerNMRSpectrometerDevice$magneticFieldStrenght == field, ] + } + + if (nrow(peakforestSpectra) != 0) { + BdDReference_HSQC <- peakforestSpectra$peaks + names(BdDReference_HSQC) <- str_split(peakforestSpectra[, 2], simplify = TRUE, pattern = ";")[, 1] + names(BdDReference_HSQC) <- enc2utf8(names(BdDReference_HSQC)) + names(BdDReference_HSQC) <- str_replace_all(names(BdDReference_HSQC), "\u00e9", "e") + + for (k in seq_len(length(BdDReference_HSQC))) { + peakforestSpectra_df <- data.frame( + ppm.dim1 = BdDReference_HSQC[[k]][, 2], ppm.dim2 = BdDReference_HSQC[[k]][, 1], + BdDReference_HSQC[[k]][, 3:ncol(BdDReference_HSQC[[k]])] + ) + BdDReference_HSQC[[k]] <- peakforestSpectra_df + } + } else { + stop("No HSQC spectra correspond to requested pH and/or magnetic field", call. = FALSE) + } + rm(peakforestSpectra) + rm(peakforestSpectra_df) +} + +if (argLs$jres_2dsequences == "yes") { + jres <- 1 + peakforestSpectra <- readLines(curl("https://metabohub.peakforest.org/rest/v1/spectra/nmr2d/search?query=jres&token=9131jq9l8gsjn1j14t351h716u&max=500")) + peakforestSpectra <- fromJSON(peakforestSpectra, simplifyDataFrame = TRUE) + + if (ph != 0) { + peakforestSpectra <- peakforestSpectra[peakforestSpectra$sampleNMRTubeConditionsMetadata$potentiaHydrogenii == ph, ] + } + if (field != 0) { + peakforestSpectra <- peakforestSpectra[peakforestSpectra$analyzerNMRSpectrometerDevice$magneticFieldStrenght == field, ] + } + + if (nrow(peakforestSpectra) != 0) { + BdDReference_JRES <- peakforestSpectra$peaks + names(BdDReference_JRES) <- str_split(peakforestSpectra[, 2], simplify = TRUE, pattern = ";")[, 1] + names(BdDReference_JRES) <- enc2utf8(names(BdDReference_JRES)) + names(BdDReference_JRES) <- str_replace_all(names(BdDReference_JRES), "\u00e9", "e") + + for (k in seq_len(length(BdDReference_JRES))) { + peakforestSpectra_df <- data.frame( + ppm.dim1 = BdDReference_JRES[[k]][, 2], ppm.dim2 = BdDReference_JRES[[k]][, 1], + BdDReference_JRES[[k]][, 3:ncol(BdDReference_JRES[[k]])] + ) + BdDReference_JRES[[k]] <- peakforestSpectra_df + } + } else { + stop("No JRES spectra correspond to requested pH and/or magnetic field", call. = FALSE) + } + rm(peakforestSpectra) + rm(peakforestSpectra_df) +} + +if (argLs$tocsy_2dsequences == "yes") { + tocsy <- 1 + peakforestSpectra <- readLines(curl("https://metabohub.peakforest.org/rest/v1/spectra/nmr2d/search?query=tocsy&token=9131jq9l8gsjn1j14t351h716u&max=500")) + peakforestSpectra <- fromJSON(peakforestSpectra, simplifyDataFrame = TRUE) + + if (ph != 0) { + peakforestSpectra <- peakforestSpectra[peakforestSpectra$sampleNMRTubeConditionsMetadata$potentiaHydrogenii == ph, ] + } + if (field != 0) { + peakforestSpectra <- peakforestSpectra[peakforestSpectra$analyzerNMRSpectrometerDevice$magneticFieldStrenght == field, ] + } + + if (nrow(peakforestSpectra) != 0) { + BdDReference_TOCSY <- peakforestSpectra$peaks + names(BdDReference_TOCSY) <- str_split(peakforestSpectra[, 2], simplify = TRUE, pattern = ";")[, 1] + names(BdDReference_TOCSY) <- enc2utf8(names(BdDReference_TOCSY)) + names(BdDReference_TOCSY) <- str_replace_all(names(BdDReference_TOCSY), "\u00e9", "e") + + for (k in seq_len(length(BdDReference_TOCSY))) { + peakforestSpectra_df <- data.frame( + ppm.dim1 = BdDReference_TOCSY[[k]][, 2], ppm.dim2 = BdDReference_TOCSY[[k]][, 1], + BdDReference_TOCSY[[k]][, 3:ncol(BdDReference_TOCSY[[k]])] + ) + BdDReference_TOCSY[[k]] <- peakforestSpectra_df + } + } else { + stop("No TOCSY spectra correspond to requested pH and/or magnetic field", call. = FALSE) + } + rm(peakforestSpectra) + rm(peakforestSpectra_df) +} + +if (argLs$cosy_2dsequences == "no" & argLs$hmbc_2dsequences == "no" & argLs$hsqc_2dsequences == "no" & argLs$jres_2dsequences == "no" & argLs$tocsy_2dsequences == "no") { + stop("No chosen sequence. You have to choose at least 1 sequence", call. = FALSE) +} + + +# User database + + +# Allowed chemical shifts +tolPpm1 <- argLs$tolppm1 +tolPpm2HJRes <- argLs$tolppmJRES +tolPpm2C <- argLs$tolppm2 +# Threshold to remove metabolites (probability score < threshold) +seuil <- argLs$threshold +# Remove metabolites when multiple assignations? +unicite <- str_to_upper(argLs$unicity) + +## Output paramater values +AnnotationGraph <- argLs[["AnnotationGraph"]] + +print(argLs) + +## ANNOTATION +st0 <- Sys.time() +pdf(AnnotationGraph, onefile = TRUE) +annotationMelange <- annotationRmn2DGlobale(fileToAnnotate, + tolPpm1 = tolPpm1, tolPpm2HJRes = tolPpm2HJRes, + tolPpm2C = tolPpm2C, cosy = cosy, hmbc = hmbc, hsqc = hsqc, + jres = jres, tocsy = tocsy, seuil = seuil, unicite = unicite +) +dev.off() + +if (cosy == 1) { + write.table(annotationMelange$COSY$liste_resultat, + file = argLs[["annotationCOSY"]], quote = FALSE, + row.names = FALSE, sep = "\t" + ) + if (nrow(annotationMelange$COSY$listing_ppm_commun) != 0) { + write.table(annotationMelange$COSY$listing_ppm_commun, + file = argLs[["ppmCommunCOSY"]], quote = FALSE, + row.names = FALSE, sep = "\t" + ) + } +} + +if (hmbc == 1) { + write.table(annotationMelange$HMBC$liste_resultat, + file = argLs[["annotationHMBC"]], quote = FALSE, + row.names = FALSE, sep = "\t" + ) + if (nrow(annotationMelange$HMBC$listing_ppm_commun) != 0) { + write.table(annotationMelange$HMBC$listing_ppm_commun, + file = argLs[["ppmCommunHMBC"]], quote = FALSE, + row.names = FALSE, sep = "\t" + ) + } +} + +if (hsqc == 1) { + write.table(annotationMelange$HSQC$liste_resultat, + file = argLs[["annotationHSQC"]], quote = FALSE, + row.names = FALSE, sep = "\t" + ) + if (nrow(annotationMelange$HSQC$listing_ppm_commun) != 0) { + write.table(annotationMelange$HSQC$listing_ppm_commun, + file = argLs[["ppmCommunHSQC"]], quote = FALSE, + row.names = FALSE, sep = "\t" + ) + } +} + +if (jres == 1) { + write.table(annotationMelange$JRES$liste_resultat, + file = argLs[["annotationJRES"]], quote = FALSE, + row.names = FALSE, sep = "\t" + ) + if (nrow(annotationMelange$JRES$listing_ppm_commun) != 0) { + write.table(annotationMelange$JRES$listing_ppm_commun, + file = argLs[["ppmCommunJRES"]], quote = FALSE, + row.names = FALSE, sep = "\t" + ) + } +} + +if (tocsy == 1) { + write.table(annotationMelange$TOCSY$liste_resultat, + file = argLs[["annotationTOCSY"]], quote = FALSE, + row.names = FALSE, sep = "\t" + ) + if (nrow(annotationMelange$TOCSY$listing_ppm_commun) != 0) { + write.table(annotationMelange$TOCSY$listing_ppm_commun, + file = argLs[["ppmCommunTOCSY"]], quote = FALSE, + row.names = FALSE, sep = "\t" + ) + } +} + +## Combinaison de sequences +if (cosy + jres + hmbc + hsqc + tocsy > 1) { + write.table(annotationMelange$combination, + file = argLs[["annotationCombination"]], quote = FALSE, + row.names = FALSE, sep = "\t" + ) +} +st1 <- Sys.time() +print(st1 - st0) + +## Ending +## -------- +cat("\nEnd of '2D NMR annotation' Galaxy module call: ", as.character(Sys.time()), sep = "") +sink() +options(stringsAsFactors = strAsFacL) +rm(list = ls()) diff --git a/tools/nmr_annotation2d/annotationRmn2D_xml.xml b/tools/nmr_annotation2d/annotationRmn2D_xml.xml index 56db47aed9..049f46af81 100644 --- a/tools/nmr_annotation2d/annotationRmn2D_xml.xml +++ b/tools/nmr_annotation2d/annotationRmn2D_xml.xml @@ -1,4 +1,4 @@ - + Annotation of complex mixture bidimensional NMR spectra @@ -11,14 +11,25 @@ r-tidyr r-curl r-jsonlite - r-stringi + r-stringi - + + + + + + + + + + + + - + + ]]> @@ -237,7 +248,9 @@ - +
+ +
diff --git a/tools/nmr_annotation2d/viridis.R b/tools/nmr_annotation2d/viridis.R index 3ba9829df8..6eaf63bb73 100644 --- a/tools/nmr_annotation2d/viridis.R +++ b/tools/nmr_annotation2d/viridis.R @@ -1,26 +1,38 @@ -viridis <- function(n, alpha = 1, begin = 0, end = 1, direction = 1, option = "D") { - if (begin < 0 | begin > 1 | end < 0 | end > 1) { - stop("begin and end must be in [0,1]") - } - if (abs(direction) != 1) { - stop("direction must be 1 or -1") - } - if (direction == -1) { - tmp <- begin - begin <- end - end <- tmp - } - option <- switch(EXPR = option, A = "A", magma = "A", - B = "B", inferno = "B", C = "C", plasma = "C", - D = "D", viridis = "D", E = "E", cividis = "E", { - warning(paste0("Option '", option, "' does not exist. Defaulting to 'viridis'.")) - "D" - }) - map <- viridisLite::viridis.map[viridisLite::viridis.map$opt == - option, ] - map_cols <- grDevices::rgb(map$R, map$G, map$B) - fn_cols <- grDevices::colorRamp(map_cols, space = "Lab", - interpolate = "spline") - cols <- fn_cols(seq(begin, end, length.out = n)) / 255 - grDevices::rgb(cols[, 1], cols[, 2], cols[, 3], alpha = alpha) -} +viridis <- function(n, alpha = 1, begin = 0, end = 1, direction = 1, option = "D") { + if (begin < 0 | begin > 1 | end < 0 | end > 1) { + stop("begin and end must be in [0,1]") + } + if (abs(direction) != 1) { + stop("direction must be 1 or -1") + } + if (direction == -1) { + tmp <- begin + begin <- end + end <- tmp + } + option <- switch(EXPR = option, + A = "A", + magma = "A", + B = "B", + inferno = "B", + C = "C", + plasma = "C", + D = "D", + viridis = "D", + E = "E", + cividis = "E", + { + warning(paste0("Option '", option, "' does not exist. Defaulting to 'viridis'.")) + "D" + } + ) + map <- viridisLite::viridis.map[viridisLite::viridis.map$opt == + option, ] + map_cols <- grDevices::rgb(map$R, map$G, map$B) + fn_cols <- grDevices::colorRamp(map_cols, + space = "Lab", + interpolate = "spline" + ) + cols <- fn_cols(seq(begin, end, length.out = n)) / 255 + grDevices::rgb(cols[, 1], cols[, 2], cols[, 3], alpha = alpha) +} diff --git a/tools/nmr_preprocessing/DrawFunctions.R b/tools/nmr_preprocessing/DrawFunctions.R index c16fd2c29e..62c1764565 100644 --- a/tools/nmr_preprocessing/DrawFunctions.R +++ b/tools/nmr_preprocessing/DrawFunctions.R @@ -3,266 +3,284 @@ require(gridExtra) require(reshape2) -Draw <- function(Signal_data, type.draw = c("signal", "pca"), output = c("default", - "window", "png", "pdf"), dirpath = ".", filename = "%003d", height = 480, +Draw <- function(Signal_data, type.draw = c("signal", "pca"), output = c( + "default", + "window", "png", "pdf" + ), dirpath = ".", filename = "%003d", height = 480, width = 640, pdf.onefile = TRUE, ...) { - - # Data initialisation and checks ---------------------------------------------- - type.draw <- match.arg(type.draw) - output <- match.arg(output) - fullpath <- paste(file.path(dirpath, filename), output, sep = ".") - createFile <- TRUE - createWindow <- FALSE - - # Drawing -------------------------------------------------------------------- - # output - switch(output, default = { - createFile <- FALSE - }, window = { - createWindow <- TRUE - createFile <- FALSE - }, png = { - grDevices::png(fullpath, width, height) - }, pdf = { - grDevices::pdf(fullpath, width = width/72, height = height/72, - onefile = pdf.onefile) - }, { - stop("Unknown output type.") - }) - - # Drawing type (signal/spectrum or PCA) - funs <- list(signal = DrawSignal, pca = DrawPCA) - if (type.draw %in% names(funs)) { - fun <- funs[[type.draw]] - } else { - stop(paste("Unknown type:", type.draw)) - } - - # Plot finalisation ---------------------------------------------- - if (is.vector(Signal_data)) { - Signal_data <- vec2mat(Signal_data) - } - fun(Signal_data, createWindow = createWindow, ...) - if (createFile) { - grDevices::dev.off() - } + # Data initialisation and checks ---------------------------------------------- + type.draw <- match.arg(type.draw) + output <- match.arg(output) + fullpath <- paste(file.path(dirpath, filename), output, sep = ".") + createFile <- TRUE + createWindow <- FALSE + + # Drawing -------------------------------------------------------------------- + # output + switch(output, + default = { + createFile <- FALSE + }, + window = { + createWindow <- TRUE + createFile <- FALSE + }, + png = { + grDevices::png(fullpath, width, height) + }, + pdf = { + grDevices::pdf(fullpath, + width = width / 72, height = height / 72, + onefile = pdf.onefile + ) + }, + { + stop("Unknown output type.") + } + ) + + # Drawing type (signal/spectrum or PCA) + funs <- list(signal = DrawSignal, pca = DrawPCA) + if (type.draw %in% names(funs)) { + fun <- funs[[type.draw]] + } else { + stop(paste("Unknown type:", type.draw)) + } + + # Plot finalisation ---------------------------------------------- + if (is.vector(Signal_data)) { + Signal_data <- vec2mat(Signal_data) + } + fun(Signal_data, createWindow = createWindow, ...) + if (createFile) { + grDevices::dev.off() + } } ##### DrawSignal -DrawSignal <- function(Signal_data, subtype = c("stacked", "together", - "separate", "diffmean", "diffmedian", "diffwith"), - ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, - xlab = "rowname", RowNames = NULL, row = 1, num.stacked = 4, +DrawSignal <- function(Signal_data, subtype = c( + "stacked", "together", + "separate", "diffmean", "diffmedian", "diffwith" + ), + ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, + xlab = "rowname", RowNames = NULL, row = 1, num.stacked = 4, main = NULL, createWindow) { - # nticks - - # Data initialisation and checks ---------------------------------------------- - - subtype <- match.arg(subtype) - vec <- is.vector(Signal_data) - if (vec) { - Signal_data <- vec2mat(Signal_data) - } - - n <- nrow(Signal_data) - m <- ncol(Signal_data) - - if (n < num.stacked){ - num.stacked <- n - } - - scale <- colnames(Signal_data) - - num.plot <- sum(ReImModArg) - - Var <- rowname <- value <- NULL # only for R CMD check - - # Drawing array - if (num.plot <= 0) { - stop("Nothing selected in ReImModArg.") - } else if (num.plot <= 2) { - if (vertical) { - nrow <- num.plot - ncol <- 1 - } else { - nrow <- 1 - ncol <- num.plot - } - } else { - nrow <- 2 - ncol <- 2 - } - - # RowNames - if (is.null(RowNames)) { - RowNames <- rownames(Signal_data) - if (is.null(RowNames)) { - RowNames <- 1:n + # nticks + + # Data initialisation and checks ---------------------------------------------- + + subtype <- match.arg(subtype) + vec <- is.vector(Signal_data) + if (vec) { + Signal_data <- vec2mat(Signal_data) } - } else { - if (!is.vector(RowNames)) { - stop("RowNames is not a vector") + + n <- nrow(Signal_data) + m <- ncol(Signal_data) + + if (n < num.stacked) { + num.stacked <- n } - if (length(RowNames) != n) { - stop(paste("RowNames has length", length(RowNames), "and there are", n, "FIDs.")) + + scale <- colnames(Signal_data) + + num.plot <- sum(ReImModArg) + + Var <- rowname <- value <- NULL # only for R CMD check + + # Drawing array + if (num.plot <= 0) { + stop("Nothing selected in ReImModArg.") + } else if (num.plot <= 2) { + if (vertical) { + nrow <- num.plot + ncol <- 1 + } else { + nrow <- 1 + ncol <- num.plot + } + } else { + nrow <- 2 + ncol <- 2 } - } - - if (n == 1) { - RowNames <- deparse(substitute(Signal_data)) - } - - elements <- list() - if (ReImModArg[1]) { - elements[["Re"]] <- Re(Signal_data) - rownames(elements[["Re"]]) <- RowNames - } - if (ReImModArg[2]) { - elements[["Im"]] <- Im(Signal_data) - rownames(elements[["Im"]]) <- RowNames - } - if (ReImModArg[3]) { - elements[["Mod"]] <- Mod(Signal_data) - rownames(elements[["Mod"]]) <- RowNames - } - if (ReImModArg[4]) { - elements[["Arg"]] <- Arg(Signal_data) - rownames(elements[["Arg"]]) <- RowNames - } - - - - - # Drawing -------------------------------------------------------------------- - - y = x = NULL # only for R CMD check - - - # SEPARATE or STACKED =============== - if (subtype == "separate" | subtype == "stacked") { - - i <- 1 - while (i <= n) { - if (createWindow) { - grDevices::dev.new(noRStudioGD = TRUE) - } - if (subtype == "separate") { - # The other uses gridExtra to do that - graphics::par(mfrow = c(nrow, ncol)) - } - plots <- list() - if (subtype == "separate") { - last <- i - } else { - last <- min(i + num.stacked - 1, n) - } - for (name in names(elements)) { - if (subtype == "separate") { - if (n == 1) { - df <- data.frame(x = as.numeric(scale), y = elements[[name]]) - } else {df <- data.frame(x = as.numeric(scale), y = elements[[name]][i, ]) - } - - plots[[name]] <- ggplot2::ggplot(data = df, ggplot2::aes(x = x, y = y)) + - ggplot2::geom_line(size = 1) + - ggplot2::theme(legend.position = "none") + - ggplot2::labs(x = xlab, y = name) + - ggplot2::ggtitle(RowNames[i]) + - ggplot2::theme_bw() - - if ((df[1, "x"] - df[(dim(df)[1]), "x"]) > 0) { - plots[[name]] <- plots[[name]] + ggplot2::scale_x_reverse() - } - - } else { - - if (n == 1 ) { - melted <- data.frame(rowname = rep(name, m), - Var = as.numeric(scale), value = elements[[name]][i,]) - } else if (last==i){ - melted <- data.frame(rowname = rep(rownames(elements[[name]])[i], m), - Var = as.numeric(scale), value = elements[[name]][i,]) - } else {melted <- reshape2::melt(elements[[name]][i:last, ], - varnames = c("rowname", "Var")) - } - - - plots[[name]] <- ggplot2::ggplot(data = melted, ggplot2::aes(x = Var, y = value)) + - ggplot2::geom_line(size = 0.3) + - ggplot2::facet_grid(rowname ~ ., scales = "free_y") + - ggplot2::theme(legend.position = "none") + - ggplot2::labs(x = xlab, y = name) + - ggplot2::ggtitle(label = main) + - ggplot2::theme_bw() - - if ((melted[1, "Var"] - melted[(dim(melted)[1]), "Var"]) > 0) { - plots[[name]] <- plots[[name]] + ggplot2::scale_x_reverse() - } + + # RowNames + if (is.null(RowNames)) { + RowNames <- rownames(Signal_data) + if (is.null(RowNames)) { + RowNames <- 1:n + } + } else { + if (!is.vector(RowNames)) { + stop("RowNames is not a vector") + } + if (length(RowNames) != n) { + stop(paste("RowNames has length", length(RowNames), "and there are", n, "FIDs.")) } - } - - if (subtype == "stacked") { - do.call(gridExtra::grid.arrange, c(plots, list(nrow = nrow, ncol = ncol))) - } - - i <- last + 1 } - } else if (subtype %in% c("together", "diffmean", "diffmedian", "diffwith")) { - - # TOGHETER or DIFFMEAN or DIFFMEDIAN or DIFFWITH =============== - - rainbow_colors <- grDevices::rainbow(n) - - if (createWindow) { - grDevices::dev.new(noRStudioGD = TRUE) + + if (n == 1) { + RowNames <- deparse(substitute(Signal_data)) + } + + elements <- list() + if (ReImModArg[1]) { + elements[["Re"]] <- Re(Signal_data) + rownames(elements[["Re"]]) <- RowNames + } + if (ReImModArg[2]) { + elements[["Im"]] <- Im(Signal_data) + rownames(elements[["Im"]]) <- RowNames } - graphics::par(mfrow = c(nrow, ncol)) - - plots <- list() - - # Loop for Re, Im, Mod and Arg - for (name in names(elements)) { - # Get this part of the signal - element <- elements[[name]] - - # Express the signal according to a reference if asked by `subtype' - if (subtype == "diffmean") { - element <- sweep(element, MARGIN = 2, colMeans(element), `-`) - } else if (subtype == "diffmedian") { - element <- sweep(element, MARGIN = 2, matrixStats::colMedians(element), `-`) - } else if (subtype == "diffwith") { - element <- sweep(element, MARGIN = 2, element[row, ], `-`) - if (row == 1 & n > 1) { - # Since we use plot on the first row and lines on the following, the y - # scale is calculated at the first row so if the first row is all 0, it - # causes problems - tmp <- element[1, ] - element[1, ] <- element[2, ] - element[2, ] <- tmp + if (ReImModArg[3]) { + elements[["Mod"]] <- Mod(Signal_data) + rownames(elements[["Mod"]]) <- RowNames + } + if (ReImModArg[4]) { + elements[["Arg"]] <- Arg(Signal_data) + rownames(elements[["Arg"]]) <- RowNames + } + + + + + # Drawing -------------------------------------------------------------------- + + y <- x <- NULL # only for R CMD check + + + # SEPARATE or STACKED =============== + if (subtype == "separate" | subtype == "stacked") { + i <- 1 + while (i <= n) { + if (createWindow) { + grDevices::dev.new(noRStudioGD = TRUE) + } + if (subtype == "separate") { + # The other uses gridExtra to do that + graphics::par(mfrow = c(nrow, ncol)) + } + plots <- list() + if (subtype == "separate") { + last <- i + } else { + last <- min(i + num.stacked - 1, n) + } + for (name in names(elements)) { + if (subtype == "separate") { + if (n == 1) { + df <- data.frame(x = as.numeric(scale), y = elements[[name]]) + } else { + df <- data.frame(x = as.numeric(scale), y = elements[[name]][i, ]) + } + + plots[[name]] <- ggplot2::ggplot(data = df, ggplot2::aes(x = x, y = y)) + + ggplot2::geom_line(size = 1) + + ggplot2::theme(legend.position = "none") + + ggplot2::labs(x = xlab, y = name) + + ggplot2::ggtitle(RowNames[i]) + + ggplot2::theme_bw() + + if ((df[1, "x"] - df[(dim(df)[1]), "x"]) > 0) { + plots[[name]] <- plots[[name]] + ggplot2::scale_x_reverse() + } + } else { + if (n == 1) { + melted <- data.frame( + rowname = rep(name, m), + Var = as.numeric(scale), value = elements[[name]][i, ] + ) + } else if (last == i) { + melted <- data.frame( + rowname = rep(rownames(elements[[name]])[i], m), + Var = as.numeric(scale), value = elements[[name]][i, ] + ) + } else { + melted <- reshape2::melt(elements[[name]][i:last, ], + varnames = c("rowname", "Var") + ) + } + + + plots[[name]] <- ggplot2::ggplot(data = melted, ggplot2::aes(x = Var, y = value)) + + ggplot2::geom_line(size = 0.3) + + ggplot2::facet_grid(rowname ~ ., scales = "free_y") + + ggplot2::theme(legend.position = "none") + + ggplot2::labs(x = xlab, y = name) + + ggplot2::ggtitle(label = main) + + ggplot2::theme_bw() + + if ((melted[1, "Var"] - melted[(dim(melted)[1]), "Var"]) > 0) { + plots[[name]] <- plots[[name]] + ggplot2::scale_x_reverse() + } + } + } + + if (subtype == "stacked") { + do.call(gridExtra::grid.arrange, c(plots, list(nrow = nrow, ncol = ncol))) + } + + i <- last + 1 + } + } else if (subtype %in% c("together", "diffmean", "diffmedian", "diffwith")) { + # TOGHETER or DIFFMEAN or DIFFMEDIAN or DIFFWITH =============== + + rainbow_colors <- grDevices::rainbow(n) + + if (createWindow) { + grDevices::dev.new(noRStudioGD = TRUE) + } + graphics::par(mfrow = c(nrow, ncol)) + + plots <- list() + + # Loop for Re, Im, Mod and Arg + for (name in names(elements)) { + # Get this part of the signal + element <- elements[[name]] + + # Express the signal according to a reference if asked by `subtype' + if (subtype == "diffmean") { + element <- sweep(element, MARGIN = 2, colMeans(element), `-`) + } else if (subtype == "diffmedian") { + element <- sweep(element, MARGIN = 2, matrixStats::colMedians(element), `-`) + } else if (subtype == "diffwith") { + element <- sweep(element, MARGIN = 2, element[row, ], `-`) + if (row == 1 & n > 1) { + # Since we use plot on the first row and lines on the following, the y + # scale is calculated at the first row so if the first row is all 0, it + # causes problems + tmp <- element[1, ] + element[1, ] <- element[2, ] + element[2, ] <- tmp + } + } + + + melted <- reshape2::melt(elements[[name]], varnames = c("rowname", "Var")) + + + plots[[name]] <- ggplot2::ggplot(melted, ggplot2::aes( + x = Var, + y = value, group = rowname, colour = rowname + )) + + ggplot2::geom_line() + + ggplot2::labs(x = xlab, y = name) + + ggplot2::scale_colour_discrete(name = NULL) + + ggplot2::ggtitle(main) + + if ((melted[1, "Var"] - melted[(dim(melted)[1]), "Var"]) > + 0) { + plots[[name]] <- plots[[name]] + ggplot2::scale_x_reverse() + } + + do.call(gridExtra::grid.arrange, c(plots, list( + nrow = nrow, + ncol = ncol + ))) } - } - - - melted <- reshape2::melt(elements[[name]], varnames = c("rowname", "Var")) - - - plots[[name]] <- ggplot2::ggplot(melted, ggplot2::aes(x = Var, - y = value, group = rowname, colour = rowname)) + ggplot2::geom_line() + - ggplot2::labs(x = xlab, y = name) + ggplot2::scale_colour_discrete(name = NULL) + - ggplot2::ggtitle(main) - - if ((melted[1, "Var"] - melted[(dim(melted)[1]), "Var"]) > - 0) { - plots[[name]] <- plots[[name]] + ggplot2::scale_x_reverse() - } - - do.call(gridExtra::grid.arrange, c(plots, list(nrow = nrow, - ncol = ncol))) } - } - - } diff --git a/tools/nmr_preprocessing/NmrPreprocessing_script.R b/tools/nmr_preprocessing/NmrPreprocessing_script.R index 78f0a847e9..b0ecbc08f4 100644 --- a/tools/nmr_preprocessing/NmrPreprocessing_script.R +++ b/tools/nmr_preprocessing/NmrPreprocessing_script.R @@ -2,1243 +2,1287 @@ # Internal functions ## ========================== -# beginTreatment -beginTreatment <- function(name, Signal_data = NULL, Signal_info = NULL, +# beginTreatment +beginTreatment <- function(name, Signal_data = NULL, Signal_info = NULL, force.real = FALSE) { - - cat("Begin", name, "\n") - - - # Formatting the Signal_data and Signal_info ----------------------- - - vec <- is.vector(Signal_data) - if (vec) { - Signal_data <- vec2mat(Signal_data) - } - if (is.vector(Signal_info)) { - Signal_info <- vec2mat(Signal_info) - } - if (!is.null(Signal_data)) { - if (!is.matrix(Signal_data)) { - stop("Signal_data is not a matrix.") + cat("Begin", name, "\n") + + + # Formatting the Signal_data and Signal_info ----------------------- + + vec <- is.vector(Signal_data) + if (vec) { + Signal_data <- vec2mat(Signal_data) } - if (!is.complex(Signal_data) && !is.numeric(Signal_data)) { - stop("Signal_data contains non-numerical values.") + if (is.vector(Signal_info)) { + Signal_info <- vec2mat(Signal_info) } - } - if (!is.null(Signal_info) && !is.matrix(Signal_info)) { - stop("Signal_info is not a matrix.") - } - - - Original_data <- Signal_data - - # Extract the real part of the spectrum --------------------------- - - if (force.real) { - if (is.complex(Signal_data)) { - Signal_data <- Re(Signal_data) - } else { - # The signal is numeric Im(Signal_data) is zero anyway so let's avoid - # using complex(real=...,imaginary=0) which would give a complex signal - # in endTreatment() - force.real <- FALSE + if (!is.null(Signal_data)) { + if (!is.matrix(Signal_data)) { + stop("Signal_data is not a matrix.") + } + if (!is.complex(Signal_data) && !is.numeric(Signal_data)) { + stop("Signal_data contains non-numerical values.") + } + } + if (!is.null(Signal_info) && !is.matrix(Signal_info)) { + stop("Signal_info is not a matrix.") + } + + + Original_data <- Signal_data + + # Extract the real part of the spectrum --------------------------- + + if (force.real) { + if (is.complex(Signal_data)) { + Signal_data <- Re(Signal_data) + } else { + # The signal is numeric Im(Signal_data) is zero anyway so let's avoid + # using complex(real=...,imaginary=0) which would give a complex signal + # in endTreatment() + force.real <- FALSE + } } - } - - - # Return the formatted data and metadata entries -------------------- - - return(list(start = proc.time(), vec = vec, force.real = force.real, - Original_data = Original_data, Signal_data = Signal_data, Signal_info = Signal_info)) + + + # Return the formatted data and metadata entries -------------------- + + return(list( + start = proc.time(), vec = vec, force.real = force.real, + Original_data = Original_data, Signal_data = Signal_data, Signal_info = Signal_info + )) } -# endTreatment +# endTreatment endTreatment <- function(name, begin_info, Signal_data) { - - # begin_info: object outputted from beginTreatment - - - # Formatting the entries and printing process time ----------------------- - end_time <- proc.time() # record it as soon as possible - start_time <- begin_info[["start"]] - delta_time <- end_time - start_time - delta <- delta_time[] - cat("End", name, "\n") - cat("It lasted", round(delta["user.self"], 3), "s user time,", round(delta["sys.self"],3), - "s system time and", round(delta["elapsed"], 3), "s elapsed time.\n") - - - if (begin_info[["force.real"]]) { - # The imaginary part is left untouched - i <- complex(real = 0, imaginary = 1) - Signal_data <- Signal_data + i * Im(begin_info[["Original_data"]]) - } - - if (begin_info[["vec"]]) { - Signal_data <- Signal_data[1, ] - } - - # Return the formatted data and metadata entries -------------------- - return(Signal_data) -} + # begin_info: object outputted from beginTreatment -# checkArg -checkArg <- function(arg, checks, can.be.null=FALSE) { - check.list <- list(bool=c(is.logical, "a boolean"), - int =c(function(x){x%%1==0}, "an integer"), - num =c(is.numeric, "a numeric"), - str =c(is.character, "a string"), - pos =c(function(x){x>0}, "positive"), - pos0=c(function(x){x>=0}, "positive or zero"), - l1 =c(function(x){length(x)==1}, "of length 1") - ) - if (is.null(arg)) { - if (!can.be.null) { - stop(deparse(substitute(arg)), " is null.") - } - } else { - if (is.matrix(arg)) { - stop(deparse(substitute(arg)), " is not scalar.") + + # Formatting the entries and printing process time ----------------------- + end_time <- proc.time() # record it as soon as possible + start_time <- begin_info[["start"]] + delta_time <- end_time - start_time + delta <- delta_time[] + cat("End", name, "\n") + cat( + "It lasted", round(delta["user.self"], 3), "s user time,", round(delta["sys.self"], 3), + "s system time and", round(delta["elapsed"], 3), "s elapsed time.\n" + ) + + + if (begin_info[["force.real"]]) { + # The imaginary part is left untouched + i <- complex(real = 0, imaginary = 1) + Signal_data <- Signal_data + i * Im(begin_info[["Original_data"]]) } - for (c in checks) { - if (!check.list[[c]][[1]](arg)) { - stop(deparse(substitute(arg)), " is not ", check.list[[c]][[2]], ".") - } + + if (begin_info[["vec"]]) { + Signal_data <- Signal_data[1, ] } - } + + # Return the formatted data and metadata entries -------------------- + return(Signal_data) } -# getArg -getArg <- function(arg, info, argname, can.be.absent=FALSE) { - if (is.null(arg)) { - start <- paste("impossible to get argument", argname, "it was not given directly and"); - if (!is.matrix(info)) { - stop(paste(start, "the info matrix was not given")) - } - if (!(argname %in% colnames(info))) { - if (can.be.absent) { - return(NULL) - } else { - stop(paste(start, "is not in the info matrix")) - } - } - if (nrow(info) < 1) { - stop(paste(start, "the info matrix has no row")) +# checkArg +checkArg <- function(arg, checks, can.be.null = FALSE) { + check.list <- list( + bool = c(is.logical, "a boolean"), + int = c(function(x) { + x %% 1 == 0 + }, "an integer"), + num = c(is.numeric, "a numeric"), + str = c(is.character, "a string"), + pos = c(function(x) { + x > 0 + }, "positive"), + pos0 = c(function(x) { + x >= 0 + }, "positive or zero"), + l1 = c(function(x) { + length(x) == 1 + }, "of length 1") + ) + if (is.null(arg)) { + if (!can.be.null) { + stop(deparse(substitute(arg)), " is null.") + } + } else { + if (is.matrix(arg)) { + stop(deparse(substitute(arg)), " is not scalar.") + } + for (c in checks) { + if (!check.list[[c]][[1]](arg)) { + stop(deparse(substitute(arg)), " is not ", check.list[[c]][[2]], ".") + } + } } - arg <- info[1,argname] - if (is.na(arg)) { - stop(paste(start, "it is NA in the info matrix")) +} + +# getArg +getArg <- function(arg, info, argname, can.be.absent = FALSE) { + if (is.null(arg)) { + start <- paste("impossible to get argument", argname, "it was not given directly and") + if (!is.matrix(info)) { + stop(paste(start, "the info matrix was not given")) + } + if (!(argname %in% colnames(info))) { + if (can.be.absent) { + return(NULL) + } else { + stop(paste(start, "is not in the info matrix")) + } + } + if (nrow(info) < 1) { + stop(paste(start, "the info matrix has no row")) + } + arg <- info[1, argname] + if (is.na(arg)) { + stop(paste(start, "it is NA in the info matrix")) + } } - } - return(arg) + return(arg) } # binarySearch binarySearch <- function(a, target, lower = TRUE) { - # search the index i in a such that a[i] == target - # if it doesn't exists and lower, it searches the closer a[i] such that a[i] < target - # if !lower, it seraches the closer a[i] such that a[i] > target - # a should be monotone but can be increasing or decreasing - - # if a is increasing INVARIANT: a[amin] < target < a[amax] - N <- length(a) - if ((a[N] - target) * (a[N] - a[1]) <= 0) { - return(N) - } - if ((a[1] - target) * (a[N] - a[1]) >= 0) { - return(1) - } - amin <- 1 - amax <- N - while (amin + 1 < amax) { - amid <- floor((amin + amax)/2) - if ((a[amid] - target) * (a[amax] - a[amid]) < 0) { - amin <- amid - } else if ((a[amid] - target) * (a[amax] - a[amid]) > 0) { - amax <- amid + # search the index i in a such that a[i] == target + # if it doesn't exists and lower, it searches the closer a[i] such that a[i] < target + # if !lower, it seraches the closer a[i] such that a[i] > target + # a should be monotone but can be increasing or decreasing + + # if a is increasing INVARIANT: a[amin] < target < a[amax] + N <- length(a) + if ((a[N] - target) * (a[N] - a[1]) <= 0) { + return(N) + } + if ((a[1] - target) * (a[N] - a[1]) >= 0) { + return(1) + } + amin <- 1 + amax <- N + while (amin + 1 < amax) { + amid <- floor((amin + amax) / 2) + if ((a[amid] - target) * (a[amax] - a[amid]) < 0) { + amin <- amid + } else if ((a[amid] - target) * (a[amax] - a[amid]) > 0) { + amax <- amid + } else { + # a[amid] == a[amax] or a[amid] == target In both cases, a[amid] == + # target + return(amid) + } + } + if (xor(lower, a[amin] > a[amax])) { + # (lower && a[amin] < a[amax]) || (!lower && a[min] > a[max]) + # If increasing and we want the lower, we take amin + # If decreasing and we want the bigger, we take amin too + return(amin) } else { - # a[amid] == a[amax] or a[amid] == target In both cases, a[amid] == - # target - return(amid) + return(amax) } - } - if (xor(lower, a[amin] > a[amax])) { - # (lower && a[amin] < a[amax]) || (!lower && a[min] > a[max]) - # If increasing and we want the lower, we take amin - # If decreasing and we want the bigger, we take amin too - return(amin) - } else { - return(amax) - } } # Interpol Interpol <- function(t, y) { - # y: sample - # t : warping function - - m <- length(y) - # t <= m-1 - # because if t > m-1, y[ti+1] will be NA when we compute g - valid <- 1 <= t & t <= m-1 # FIXME it was '<' in Bubble v2 - s <- (1:m)[valid] - ti <- floor(t[s]) - tr <- t[s] - ti - g <- y[ti + 1] - y[ti] - f <- y[ti] + tr * g - list(f=f, s=s, g=g) + # y: sample + # t : warping function + + m <- length(y) + # t <= m-1 + # because if t > m-1, y[ti+1] will be NA when we compute g + valid <- 1 <= t & t <= m - 1 # FIXME it was '<' in Bubble v2 + s <- (1:m)[valid] + ti <- floor(t[s]) + tr <- t[s] - ti + g <- y[ti + 1] - y[ti] + f <- y[ti] + tr * g + list(f = f, s = s, g = g) } # vec2mat vec2mat <- function(vec) { - return(matrix(vec, nrow = 1, dimnames = list(c(1), names(vec)))) - + return(matrix(vec, nrow = 1, dimnames = list(c(1), names(vec)))) } # binarySearch binarySearch <- function(a, target, lower = TRUE) { - # search the index i in a such that a[i] == target - # if it doesn't exists and lower, it searches the closer a[i] such that a[i] < target - # if !lower, it seraches the closer a[i] such that a[i] > target - # a should be monotone but can be increasing or decreasing - - # if a is increasing INVARIANT: a[amin] < target < a[amax] - N <- length(a) - if ((a[N] - target) * (a[N] - a[1]) <= 0) { - return(N) - } - if ((a[1] - target) * (a[N] - a[1]) >= 0) { - return(1) - } - amin <- 1 - amax <- N - while (amin + 1 < amax) { - amid <- floor((amin + amax)/2) - if ((a[amid] - target) * (a[amax] - a[amid]) < 0) { - amin <- amid - } else if ((a[amid] - target) * (a[amax] - a[amid]) > 0) { - amax <- amid + # search the index i in a such that a[i] == target + # if it doesn't exists and lower, it searches the closer a[i] such that a[i] < target + # if !lower, it seraches the closer a[i] such that a[i] > target + # a should be monotone but can be increasing or decreasing + + # if a is increasing INVARIANT: a[amin] < target < a[amax] + N <- length(a) + if ((a[N] - target) * (a[N] - a[1]) <= 0) { + return(N) + } + if ((a[1] - target) * (a[N] - a[1]) >= 0) { + return(1) + } + amin <- 1 + amax <- N + while (amin + 1 < amax) { + amid <- floor((amin + amax) / 2) + if ((a[amid] - target) * (a[amax] - a[amid]) < 0) { + amin <- amid + } else if ((a[amid] - target) * (a[amax] - a[amid]) > 0) { + amax <- amid + } else { + # a[amid] == a[amax] or a[amid] == target In both cases, a[amid] == + # target + return(amid) + } + } + if (xor(lower, a[amin] > a[amax])) { + # (lower && a[amin] < a[amax]) || (!lower && a[min] > a[max]) + # If increasing and we want the lower, we take amin + # If decreasing and we want the bigger, we take amin too + return(amin) } else { - # a[amid] == a[amax] or a[amid] == target In both cases, a[amid] == - # target - return(amid) + return(amax) } - } - if (xor(lower, a[amin] > a[amax])) { - # (lower && a[amin] < a[amax]) || (!lower && a[min] > a[max]) - # If increasing and we want the lower, we take amin - # If decreasing and we want the bigger, we take amin too - return(amin) - } else { - return(amax) - } } # indexInterval -indexInterval <- function (a, from, to, inclusive=TRUE) { - # If inclusive and from <= to, we need to take the lower - # If not inclusive and from > to, we need to take the lower too - lowerFrom <- xor(inclusive, from > to) - fromIndex <- binarySearch(a, from, lowerFrom) - toIndex <- binarySearch(a, to, !lowerFrom) - return(fromIndex:toIndex) +indexInterval <- function(a, from, to, inclusive = TRUE) { + # If inclusive and from <= to, we need to take the lower + # If not inclusive and from > to, we need to take the lower too + lowerFrom <- xor(inclusive, from > to) + fromIndex <- binarySearch(a, from, lowerFrom) + toIndex <- binarySearch(a, to, !lowerFrom) + return(fromIndex:toIndex) } ## ========================== -# GroupDelayCorrection +# GroupDelayCorrection ## ========================== GroupDelayCorrection <- function(Fid_data, Fid_info = NULL, group_delay = NULL) { - - # Data initialisation and checks ---------------------------------------------- - + begin_info <- beginTreatment("GroupDelayCorrection", Fid_data, Fid_info) Fid_data <- begin_info[["Signal_data"]] dimension_names <- dimnames(Fid_data) Fid_info <- begin_info[["Signal_info"]] checkArg(group_delay, c("num", "pos0"), can.be.null = TRUE) # if Fid_info and group_delay are NULL, getArg will generate an error - + group_delay <- getArg(group_delay, Fid_info, "GRPDLY", can.be.absent = TRUE) - + if (is.null(group_delay)) { - - # See DetermineBrukerDigitalFilter.m in matNMR MATLAB library - group_delay_matrix <- matrix(c(44.75, 46, 46.311, 33.5, 36.5, 36.53, 66.625, - 48, 47.87, 59.0833, 50.1667, 50.229, 68.5625, 53.25, 53.289, 60.375, - 69.5, 69.551, 69.5313, 72.25, 71.6, 61.0208, 70.1667, 70.184, 70.0156, - 72.75, 72.138, 61.3438, 70.5, 70.528, 70.2578, 73, 72.348, 61.5052, 70.6667, - 70.7, 70.3789, 72.5, 72.524, 61.5859, 71.3333, NA, 70.4395, 72.25, NA, - 61.6263, 71.6667, NA, 70.4697, 72.125, NA, 61.6465, 71.8333, NA, 70.4849, - 72.0625, NA, 61.6566, 71.9167, NA, 70.4924, 72.0313, NA), nrow = 21, - ncol = 3, byrow = TRUE, dimnames = list(c(2, 3, 4, 6, 8, 12, 16, 24, - 32, 48, 64, 96, 128, 192, 256, 384, 512, 768, 1024, 1536, 2048), - c(10, 11, 12))) - decim <- Fid_info[1, "DECIM"] - dspfvs <- Fid_info[1, "DSPFVS"] - if (!(toString(decim) %in% rownames(group_delay_matrix))) { - stop(paste("Invalid DECIM", decim, "it should be one of", rownames(group_delay_matrix))) - } - if (!(toString(dspfvs) %in% colnames(group_delay_matrix))) { - stop(paste("Invalid DSPFVS", dspfvs, "it should be one of", colnames(group_delay_matrix))) - } - group_delay <- group_delay_matrix[toString(decim), toString(dspfvs)] - if (is.na(group_delay)) { - stop(paste("Invalid DECIM", decim, "for DSPFVS", dspfvs)) - } + # See DetermineBrukerDigitalFilter.m in matNMR MATLAB library + group_delay_matrix <- matrix( + c( + 44.75, 46, 46.311, 33.5, 36.5, 36.53, 66.625, + 48, 47.87, 59.0833, 50.1667, 50.229, 68.5625, 53.25, 53.289, 60.375, + 69.5, 69.551, 69.5313, 72.25, 71.6, 61.0208, 70.1667, 70.184, 70.0156, + 72.75, 72.138, 61.3438, 70.5, 70.528, 70.2578, 73, 72.348, 61.5052, 70.6667, + 70.7, 70.3789, 72.5, 72.524, 61.5859, 71.3333, NA, 70.4395, 72.25, NA, + 61.6263, 71.6667, NA, 70.4697, 72.125, NA, 61.6465, 71.8333, NA, 70.4849, + 72.0625, NA, 61.6566, 71.9167, NA, 70.4924, 72.0313, NA + ), + nrow = 21, + ncol = 3, byrow = TRUE, dimnames = list( + c( + 2, 3, 4, 6, 8, 12, 16, 24, + 32, 48, 64, 96, 128, 192, 256, 384, 512, 768, 1024, 1536, 2048 + ), + c(10, 11, 12) + ) + ) + decim <- Fid_info[1, "DECIM"] + dspfvs <- Fid_info[1, "DSPFVS"] + if (!(toString(decim) %in% rownames(group_delay_matrix))) { + stop(paste("Invalid DECIM", decim, "it should be one of", rownames(group_delay_matrix))) + } + if (!(toString(dspfvs) %in% colnames(group_delay_matrix))) { + stop(paste("Invalid DSPFVS", dspfvs, "it should be one of", colnames(group_delay_matrix))) + } + group_delay <- group_delay_matrix[toString(decim), toString(dspfvs)] + if (is.na(group_delay)) { + stop(paste("Invalid DECIM", decim, "for DSPFVS", dspfvs)) + } } m <- ncol(Fid_data) - n <- nrow(Fid_data) - + n <- nrow(Fid_data) + # GroupDelayCorrection ---------------------------------------------- - + # We do the shifting in the Fourier domain because the shift can be non-integer. # That way we automatically have the circular behaviour of the shift and the # interpolation if it is non-integer. - + Spectrum <- t(stats::mvfft(t(Fid_data))) - + # Spectrum <- FourierTransform(Fid_data, Fid_info) - p <- ceiling(m/2) + p <- ceiling(m / 2) new_index <- c((p + 1):m, 1:p) - Spectrum <- Spectrum[,new_index] + Spectrum <- Spectrum[, new_index] Spectrum <- matrix(data = Spectrum, ncol = m, nrow = n) - - Omega <- (0:(m - 1))/m + + Omega <- (0:(m - 1)) / m i <- complex(real = 0, imaginary = 1) - - if (n>1) { - Spectrum <- sweep(Spectrum, MARGIN = 2, exp(i * group_delay * 2 * pi * Omega), `*`) - Spectrum <- Spectrum[,new_index] - }else { - Spectrum <- Spectrum* exp(i * group_delay * 2 * pi * Omega) - Spectrum <- Spectrum[new_index] - Spectrum <- matrix(data = Spectrum, ncol = m, nrow = n) + + if (n > 1) { + Spectrum <- sweep(Spectrum, MARGIN = 2, exp(i * group_delay * 2 * pi * Omega), `*`) + Spectrum <- Spectrum[, new_index] + } else { + Spectrum <- Spectrum * exp(i * group_delay * 2 * pi * Omega) + Spectrum <- Spectrum[new_index] + Spectrum <- matrix(data = Spectrum, ncol = m, nrow = n) } - - - Fid_data <- t(stats::mvfft(t(Spectrum), inverse = TRUE))/m + + + Fid_data <- t(stats::mvfft(t(Spectrum), inverse = TRUE)) / m colnames(Fid_data) <- dimension_names[[2]] rownames(Fid_data) <- dimension_names[[1]] - + # Data finalisation ---------------------------------------------- - + return(endTreatment("GroupDelayCorrection", begin_info, Fid_data)) - } +} ## ========================== -# SolventSuppression +# SolventSuppression ## ========================== -SolventSuppression <- function(Fid_data, lambda.ss = 1e+06, ptw.ss = TRUE, +SolventSuppression <- function(Fid_data, lambda.ss = 1e+06, ptw.ss = TRUE, plotSolvent = F, returnSolvent = F) { - - # Data initialisation and checks ---------------------------------------------- - - begin_info <- beginTreatment("SolventSuppression", Fid_data) - Fid_data <- begin_info[["Signal_data"]] - checkArg(ptw.ss, c("bool")) - checkArg(lambda.ss, c("num", "pos0")) - - - # difsm function definition for the smoother ----------------------------------- - - if (ptw.ss) { - # Use of the function in ptw that smoothes signals with a finite difference - # penalty of order 2 - difsm <- ptw::difsm - } else { - # Or manual implementation based on sparse matrices for large data series (cf. - # Eilers, 2003. 'A perfect smoother') - difsm <- function(y, d = 2, lambda) { - - m <- length(y) - # Sparse identity matrix m x m - E <- Matrix::Diagonal(m) - D <- Matrix::diff(E, differences = d) - A <- E + lambda.ss * Matrix::t(D) %*% D - # base::chol does not take into account that A is sparse and is extremely slow - C <- Matrix::chol(A) - x <- Matrix::solve(C, Matrix::solve(Matrix::t(C), y)) - return(as.numeric(x)) + # Data initialisation and checks ---------------------------------------------- + + begin_info <- beginTreatment("SolventSuppression", Fid_data) + Fid_data <- begin_info[["Signal_data"]] + checkArg(ptw.ss, c("bool")) + checkArg(lambda.ss, c("num", "pos0")) + + + # difsm function definition for the smoother ----------------------------------- + + if (ptw.ss) { + # Use of the function in ptw that smoothes signals with a finite difference + # penalty of order 2 + difsm <- ptw::difsm + } else { + # Or manual implementation based on sparse matrices for large data series (cf. + # Eilers, 2003. 'A perfect smoother') + difsm <- function(y, d = 2, lambda) { + m <- length(y) + # Sparse identity matrix m x m + E <- Matrix::Diagonal(m) + D <- Matrix::diff(E, differences = d) + A <- E + lambda.ss * Matrix::t(D) %*% D + # base::chol does not take into account that A is sparse and is extremely slow + C <- Matrix::chol(A) + x <- Matrix::solve(C, Matrix::solve(Matrix::t(C), y)) + return(as.numeric(x)) + } } - } - - # Solvent Suppression ---------------------------------------------- - - n <- dim(Fid_data)[1] - if (returnSolvent) { - SolventRe <- Fid_data - SolventIm <- Fid_data - } - for (i in 1:n) { - FidRe <- Re(Fid_data[i, ]) - FidIm <- Im(Fid_data[i, ]) - solventRe <- difsm(y = FidRe, lambda = lambda.ss) - solventIm <- difsm(y = FidIm, lambda = lambda.ss) - - if (plotSolvent) { - m <- length(FidRe) - graphics::plot(1:m, FidRe, type = "l", col = "red") - graphics::lines(1:m, solventRe, type = "l", col = "blue") - graphics::plot(1:m, FidIm, type = "l", col = "red") - graphics::lines(1:m, solventIm, type = "l", col = "blue") + + # Solvent Suppression ---------------------------------------------- + + n <- dim(Fid_data)[1] + if (returnSolvent) { + SolventRe <- Fid_data + SolventIm <- Fid_data } - FidRe <- FidRe - solventRe - FidIm <- FidIm - solventIm - Fid_data[i, ] <- complex(real = FidRe, imaginary = FidIm) + for (i in 1:n) { + FidRe <- Re(Fid_data[i, ]) + FidIm <- Im(Fid_data[i, ]) + solventRe <- difsm(y = FidRe, lambda = lambda.ss) + solventIm <- difsm(y = FidIm, lambda = lambda.ss) + + if (plotSolvent) { + m <- length(FidRe) + graphics::plot(1:m, FidRe, type = "l", col = "red") + graphics::lines(1:m, solventRe, type = "l", col = "blue") + graphics::plot(1:m, FidIm, type = "l", col = "red") + graphics::lines(1:m, solventIm, type = "l", col = "blue") + } + FidRe <- FidRe - solventRe + FidIm <- FidIm - solventIm + Fid_data[i, ] <- complex(real = FidRe, imaginary = FidIm) + if (returnSolvent) { + SolventRe[i, ] <- solventRe + SolventIm[i, ] <- solventIm + } + } + + + # Data finalisation ---------------------------------------------- + + Fid_data <- endTreatment("SolventSuppression", begin_info, Fid_data) if (returnSolvent) { - SolventRe[i, ] <- solventRe - SolventIm[i, ] <- solventIm + return(list(Fid_data = Fid_data, SolventRe = SolventRe, SolventIm = SolventIm)) + } else { + return(Fid_data) } - } - - - # Data finalisation ---------------------------------------------- - - Fid_data <- endTreatment("SolventSuppression", begin_info, Fid_data) - if (returnSolvent) { - return(list(Fid_data = Fid_data, SolventRe = SolventRe, SolventIm = SolventIm)) - } else { - return(Fid_data) - } } ## ========================== -# Apodization +# Apodization # ============================= -Apodization <- function(Fid_data, Fid_info = NULL, DT = NULL, - type.apod = c("exp","cos2", "blockexp", "blockcos2", - "gauss", "hanning", "hamming"), phase = 0, rectRatio = 1/2, +Apodization <- function(Fid_data, Fid_info = NULL, DT = NULL, + type.apod = c( + "exp", "cos2", "blockexp", "blockcos2", + "gauss", "hanning", "hamming" + ), phase = 0, rectRatio = 1 / 2, gaussLB = 1, expLB = 1, plotWindow = F, returnFactor = F) { - - # Data initialisation and checks ---------------------------------------------- - begin_info <- beginTreatment("Apodization", Fid_data, Fid_info) - Fid_data <- begin_info[["Signal_data"]] - Fid_info <- begin_info[["Signal_info"]] - # Data check - type.apod <- match.arg(type.apod) - checkArg(DT, c("num", "pos"), can.be.null = TRUE) - checkArg(phase, c("num")) - - # Apodization ---------------------------------------------- - DT <- getArg(DT, Fid_info, "DT") # Dwell Time - m <- ncol(Fid_data) - t <- (1:m) * DT # Time - rectSize <- ceiling(rectRatio * m) - gaussLB <- (gaussLB/(sqrt(8 * log(2)))) - # Define the types of apodization: - switch(type.apod, exp = { - # exponential - Factor <- exp(-expLB * t) - }, cos2 = { - # cos^2 - c <- cos((1:m) * pi/(2 * m) - phase * pi/2) - Factor <- c * c - }, blockexp = { - # block and exponential - Factor <- c(rep.int(1, rectSize), rep.int(0, m - rectSize)) - # | rectSize | 1 ___________ | \ 0 \____ - Factor[(rectSize + 1):m] <- exp(-expLB * t[1:(m - rectSize)]) - }, blockcos2 = { - # block and cos^2 - Factor <- c(rep.int(1, rectSize), rep.int(0, m - rectSize)) - c <- cos((1:(m - rectSize)) * pi/(2 * (m - rectSize))) - Factor[(rectSize + 1):m] <- c * c - }, gauss = { - # gaussian - Factor <- exp(-(gaussLB * t)^2/2) - Factor <- Factor/max(Factor) - }, hanning = { - # Hanning - Factor <- 0.5 + 0.5 * cos((1:m) * pi/m - phase * pi) - }, hamming = { - # Hamming - Factor <- 0.54 + 0.46 * cos((1:m) * pi/m - phase * pi) - }) - if (plotWindow) { - graphics::plot(1:m, Factor, "l") - # dev.off() # device independent, it is the responsability of the - # caller to do it - } - # Apply the apodization factor on the spectra - Fid_data <- sweep(Fid_data, MARGIN = 2, Factor, `*`) - - # Data finalisation ---------------------------------------------- - Fid_data <- endTreatment("Apodization", begin_info, Fid_data) - if (returnFactor) { - return(list(Fid_data = Fid_data, Factor = Factor)) - } else { - return(Fid_data) - } + # Data initialisation and checks ---------------------------------------------- + begin_info <- beginTreatment("Apodization", Fid_data, Fid_info) + Fid_data <- begin_info[["Signal_data"]] + Fid_info <- begin_info[["Signal_info"]] + # Data check + type.apod <- match.arg(type.apod) + checkArg(DT, c("num", "pos"), can.be.null = TRUE) + checkArg(phase, c("num")) + + # Apodization ---------------------------------------------- + DT <- getArg(DT, Fid_info, "DT") # Dwell Time + m <- ncol(Fid_data) + t <- (1:m) * DT # Time + rectSize <- ceiling(rectRatio * m) + gaussLB <- (gaussLB / (sqrt(8 * log(2)))) + # Define the types of apodization: + switch(type.apod, + exp = { + # exponential + Factor <- exp(-expLB * t) + }, + cos2 = { + # cos^2 + c <- cos((1:m) * pi / (2 * m) - phase * pi / 2) + Factor <- c * c + }, + blockexp = { + # block and exponential + Factor <- c(rep.int(1, rectSize), rep.int(0, m - rectSize)) + # | rectSize | 1 ___________ | \ 0 \____ + Factor[(rectSize + 1):m] <- exp(-expLB * t[1:(m - rectSize)]) + }, + blockcos2 = { + # block and cos^2 + Factor <- c(rep.int(1, rectSize), rep.int(0, m - rectSize)) + c <- cos((1:(m - rectSize)) * pi / (2 * (m - rectSize))) + Factor[(rectSize + 1):m] <- c * c + }, + gauss = { + # gaussian + Factor <- exp(-(gaussLB * t)^2 / 2) + Factor <- Factor / max(Factor) + }, + hanning = { + # Hanning + Factor <- 0.5 + 0.5 * cos((1:m) * pi / m - phase * pi) + }, + hamming = { + # Hamming + Factor <- 0.54 + 0.46 * cos((1:m) * pi / m - phase * pi) + } + ) + if (plotWindow) { + graphics::plot(1:m, Factor, "l") + # dev.off() # device independent, it is the responsability of the + # caller to do it + } + # Apply the apodization factor on the spectra + Fid_data <- sweep(Fid_data, MARGIN = 2, Factor, `*`) + + # Data finalisation ---------------------------------------------- + Fid_data <- endTreatment("Apodization", begin_info, Fid_data) + if (returnFactor) { + return(list(Fid_data = Fid_data, Factor = Factor)) + } else { + return(Fid_data) + } } ## ==================================================== -# FourierTransform +# FourierTransform ## ==================================================== -# fftshift1D2D +# fftshift1D2D fftshift1D2D <- function(x) { - vec <- F - if (is.vector(x)) { - x <- vec2mat(x) - vec <- T - } - m <- dim(x)[2] - p <- ceiling(m/2) - new_index <- c((p + 1):m, 1:p) - y <- x[, new_index, drop = vec] + vec <- F + if (is.vector(x)) { + x <- vec2mat(x) + vec <- T + } + m <- dim(x)[2] + p <- ceiling(m / 2) + new_index <- c((p + 1):m, 1:p) + y <- x[, new_index, drop = vec] } # FourierTransform FourierTransform <- function(Fid_data, Fid_info = NULL, SW_h = NULL, SW = NULL, O1 = NULL, reverse.axis = TRUE) { - - # Data initialisation and checks ---------------------------------------------- - begin_info <- beginTreatment("FourierTransform", Fid_data, Fid_info) - Fid_data <- begin_info[["Signal_data"]] - Fid_info <- begin_info[["Signal_info"]] - - m <- ncol(Fid_data) - n <- nrow(Fid_data) - - if (is.null(SW_h)) { - SW_h <- getArg(SW_h, Fid_info, "SW_h") - } - - if (is.null(SW)) { - SW <- getArg(SW, Fid_info, "SW") # Sweep Width in ppm (semi frequency scale in ppm) - } - - - if (is.null(O1)) { - O1 <- getArg(O1, Fid_info, "O1") - } - - - checkArg(reverse.axis, c("bool")) - - # Fourier Transformation ---------------------------------------------- - # mvfft does the unnormalized fourier transform (see ?mvfft), so we need divide - # by m. It does not matter a lot in our case since the spectrum will be - # normalized. - - # FT - RawSpect_data <- fftshift1D2D(t(stats::mvfft(t(Fid_data)))) - # recover the frequencies values - f <- ((0:(m - 1)) - floor(m/2)) * Fid_info[1, "SW_h"]/(m-1) - - if(reverse.axis == TRUE) { - revind <- rev(1:m) - RawSpect_data <- RawSpect_data[,revind] # reverse the spectrum - } - - RawSpect_data <- matrix(RawSpect_data, nrow = n, ncol = m) - colnames(RawSpect_data) <- f - rownames(RawSpect_data) <- rownames(Fid_data) - - # PPM conversion ---------------------------------------------- - - # The Sweep Width has to be the same since the column names are the same - - ppmInterval <- SW/(m-1) - - O1index = round((m+1)/2+O1*(m - 1) / SW_h) - - end <- O1index - m - start <- O1index -1 - ppmScale <- (start:end) * ppmInterval - RawSpect_data <- matrix(RawSpect_data, nrow = n, ncol = -(end - start) + 1, dimnames = - list(rownames(RawSpect_data), ppmScale)) - - - # Data finalisation ---------------------------------------------- - return(endTreatment("FourierTransform", begin_info, RawSpect_data)) -} + # Data initialisation and checks ---------------------------------------------- + begin_info <- beginTreatment("FourierTransform", Fid_data, Fid_info) + Fid_data <- begin_info[["Signal_data"]] + Fid_info <- begin_info[["Signal_info"]] -## ==================================================== -# InternalReferencing -## ==================================================== + m <- ncol(Fid_data) + n <- nrow(Fid_data) -InternalReferencing <- function(Spectrum_data, Fid_info, method = c("max", "thres"), - range = c("nearvalue", "all", "window"), ppm.value = 0, - direction = "left", shiftHandling = c("zerofilling", "cut", - "NAfilling", "circular"), c = 2, pc = 0.02, fromto.RC = NULL, - ppm.ir = TRUE, rowindex_graph = NULL) { - - - - # Data initialisation and checks ---------------------------------------------- - - begin_info <- beginTreatment("InternalReferencing", Spectrum_data, Fid_info) - Spectrum_data <- begin_info[["Signal_data"]] - Fid_info <- begin_info[["Signal_info"]] - - - ######## Check input arguments - - range <- match.arg(range) - shiftHandling <- match.arg(shiftHandling) - method <- match.arg(method) - plots <- NULL - - checkArg(ppm.ir, c("bool")) - checkArg(unlist(fromto.RC), c("num"), can.be.null = TRUE) - checkArg(pc, c("num")) - checkArg(ppm.value, c("num")) - checkArg(rowindex_graph, "num", can.be.null = TRUE) - - # fromto.RC : if range == "window", - # fromto.RC defines the spectral window where to search for the peak - if (!is.null(fromto.RC)) { - diff <- diff(unlist(fromto.RC))[1:length(diff(unlist(fromto.RC)))%%2 !=0] - for (i in 1:length(diff)) { - if (diff[i] >= 0) { - fromto <- c(fromto.RC[[i]][2], fromto.RC[[i]][1]) - fromto.RC[[i]] <- fromto - } - } - } - - - # findTMSPpeak function ---------------------------------------------- - # If method == "tresh", findTMSPpeak will find the position of the first - # peak (from left or right) which is higher than a predefined threshold - # and is computed as: c*(cumulated_mean/cumulated_sd) - findTMSPpeak <- function(ft, c = 2, direction = "left") { - ft <- Re(ft) # extraction de la partie réelle - N <- length(ft) - if (direction == "left") { - newindex <- rev(1:N) - ft <- rev(ft) - } - thres <- 99999 - i <- 1000 # Start at point 1000 to find the peak - vect <- ft[1:i] - - while (vect[i] <= (c * thres)) { - cumsd <- stats::sd(vect) - cummean <- mean(vect) - thres <- cummean + 3 * cumsd - i <- i + 1 - vect <- ft[1:i] - } - if (direction == "left") { - v <- newindex[i] - } else {v <- i} - - if (is.na(v)) { - warning("No peak found, need to lower the threshold.") - return(NA) - } else { - # recherche dans les 1% de points suivants du max trouve pour etre au sommet du - # pic - d <- which.max(ft[v:(v + N * 0.01)]) - new.peak <- v + d - 1 # nouveau pic du TMSP si d > 0 - - if (names(which.max(ft[v:(v + N * 0.01)])) != names(which.max(ft[v:(v + N * 0.03)]))) { - # recherche dans les 3% de points suivants du max trouve pour eviter un faux - # positif - warning("the TMSP peak might be located further away, increase the threshold to check.") - } - return(new.peak) + if (is.null(SW_h)) { + SW_h <- getArg(SW_h, Fid_info, "SW_h") } - } - - - # Define the search zone ---------------------------------------- - - n <- nrow(Spectrum_data) - m <- ncol(Spectrum_data) - - # The Sweep Width (SW) has to be the same since the column names are the same - SW <- Fid_info[1, "SW"] # Sweep Width in ppm - ppmInterval <- SW/(m-1) # size of a ppm interval - - # range: How the search zone is defined ("all", "nearvalue" or "window") - if (range == "all") { - - Data <- Spectrum_data - - } else { # range = "nearvalue" or "window" - # Need to define colindex (column indexes) to apply indexInterval on it - - if (range == "nearvalue") { - - fromto.RC <- list(c(-(SW * pc)/2 + ppm.value, (SW * pc)/2 + ppm.value)) # automatic fromto values in ppm - colindex <- as.numeric(colnames(Spectrum_data)) - - } else { - # range == "window" - # fromto.RC is already user-defined - if (ppm.ir == TRUE) { - colindex <- as.numeric(colnames(Spectrum_data)) - } else { - colindex <- 1:m - } - } - - # index intervals taking into account the different elements in the list fromto.RC - Int <- vector("list", length(fromto.RC)) - for (i in 1:length(fromto.RC)) { - Int[[i]] <- indexInterval(colindex, from = fromto.RC[[i]][1], - to = fromto.RC[[i]][2], inclusive = TRUE) + + if (is.null(SW)) { + SW <- getArg(SW, Fid_info, "SW") # Sweep Width in ppm (semi frequency scale in ppm) } - - # define Data as the cropped spectrum including the index intervals - # outside the research zone, the intensities are set to the minimal - # intensity of the research zone - - if (n > 1){ - Data <- apply(Re(Spectrum_data[,unlist(Int)]),1, function(x) rep(min(x), m)) - Data <- t(Data) - Data[,unlist(Int)] <- Re(Spectrum_data[,unlist(Int)]) - } else { - Data <- rep(min(Re(Spectrum_data)) ,m) - Data[unlist(Int)] <- Re(Spectrum_data[unlist(Int)]) + + + if (is.null(O1)) { + O1 <- getArg(O1, Fid_info, "O1") } - - } - - - # Apply the peak location search method ('thres' or 'max') on spectra - # ----------------------------------------------------------------------- - - if (method == "thres") { - TMSPpeaks <- apply(Data, 1, findTMSPpeak, c = c, direction = direction) - } else { # method == "max - TMSPpeaks <- apply(Re(Data), 1, which.max) - } - - - # Shift spectra according to the TMSPpeaks found -------------------------------- - # Depends on the shiftHandling - - # TMSPpeaks is a column index - maxpeak <- max(TMSPpeaks) # max accross spectra - minpeak <- min(TMSPpeaks) # min accross spectra - - - if (shiftHandling %in% c("zerofilling", "NAfilling", "cut")) { - fill <- NA - if (shiftHandling == "zerofilling") { - fill <- 0 + + + checkArg(reverse.axis, c("bool")) + + # Fourier Transformation ---------------------------------------------- + # mvfft does the unnormalized fourier transform (see ?mvfft), so we need divide + # by m. It does not matter a lot in our case since the spectrum will be + # normalized. + + # FT + RawSpect_data <- fftshift1D2D(t(stats::mvfft(t(Fid_data)))) + # recover the frequencies values + f <- ((0:(m - 1)) - floor(m / 2)) * Fid_info[1, "SW_h"] / (m - 1) + + if (reverse.axis == TRUE) { + revind <- rev(1:m) + RawSpect_data <- RawSpect_data[, revind] # reverse the spectrum } - - start <- maxpeak - 1 - end <- minpeak - m - - # ppm values of each interval for the whole spectral range of the spectral matrix + + RawSpect_data <- matrix(RawSpect_data, nrow = n, ncol = m) + colnames(RawSpect_data) <- f + rownames(RawSpect_data) <- rownames(Fid_data) + + # PPM conversion ---------------------------------------------- + + # The Sweep Width has to be the same since the column names are the same + + ppmInterval <- SW / (m - 1) + + O1index <- round((m + 1) / 2 + O1 * (m - 1) / SW_h) + + end <- O1index - m + start <- O1index - 1 ppmScale <- (start:end) * ppmInterval - - # check if ppm.value is in the ppmScale interval - if(ppm.value < min(ppmScale) | ppm.value > max(ppmScale)) { - warning("ppm.value = ", ppm.value, " is not in the ppm interval [", - round(min(ppmScale),2), ",", round(max(ppmScale),2), "], and is set to its default ppm.value 0") - ppm.value = 0 - } - - # if ppm.value != 0, ppmScale is adapted - ppmScale <- ppmScale + ppm.value - - # create the spectral matrix with realigned spectra - Spectrum_data_calib <- matrix(fill, nrow = n, ncol = -(end - start) + 1, - dimnames = list(rownames(Spectrum_data), ppmScale)) - - # fills in Spectrum_data_calib with shifted spectra - for (i in 1:n) { - shift <- (1 - TMSPpeaks[i]) + start - Spectrum_data_calib[i, (1 + shift):(m + shift)] <- Spectrum_data[i, ] + RawSpect_data <- matrix(RawSpect_data, + nrow = n, ncol = -(end - start) + 1, dimnames = + list(rownames(RawSpect_data), ppmScale) + ) + + + # Data finalisation ---------------------------------------------- + return(endTreatment("FourierTransform", begin_info, RawSpect_data)) +} + +## ==================================================== +# InternalReferencing +## ==================================================== + +InternalReferencing <- function(Spectrum_data, Fid_info, method = c("max", "thres"), + range = c("nearvalue", "all", "window"), ppm.value = 0, + direction = "left", shiftHandling = c( + "zerofilling", "cut", + "NAfilling", "circular" + ), c = 2, pc = 0.02, fromto.RC = NULL, + ppm.ir = TRUE, rowindex_graph = NULL) { + # Data initialisation and checks ---------------------------------------------- + + begin_info <- beginTreatment("InternalReferencing", Spectrum_data, Fid_info) + Spectrum_data <- begin_info[["Signal_data"]] + Fid_info <- begin_info[["Signal_info"]] + + + ######## Check input arguments + + range <- match.arg(range) + shiftHandling <- match.arg(shiftHandling) + method <- match.arg(method) + plots <- NULL + + checkArg(ppm.ir, c("bool")) + checkArg(unlist(fromto.RC), c("num"), can.be.null = TRUE) + checkArg(pc, c("num")) + checkArg(ppm.value, c("num")) + checkArg(rowindex_graph, "num", can.be.null = TRUE) + + # fromto.RC : if range == "window", + # fromto.RC defines the spectral window where to search for the peak + if (!is.null(fromto.RC)) { + diff <- diff(unlist(fromto.RC))[1:length(diff(unlist(fromto.RC))) %% 2 != 0] + for (i in 1:length(diff)) { + if (diff[i] >= 0) { + fromto <- c(fromto.RC[[i]][2], fromto.RC[[i]][1]) + fromto.RC[[i]] <- fromto + } + } } - - if (shiftHandling == "cut") { - Spectrum_data_calib = as.matrix(stats::na.omit(t(Spectrum_data_calib))) - Spectrum_data_calib = t(Spectrum_data_calib) - base::attr(Spectrum_data_calib, "na.action") <- NULL + + + # findTMSPpeak function ---------------------------------------------- + # If method == "tresh", findTMSPpeak will find the position of the first + # peak (from left or right) which is higher than a predefined threshold + # and is computed as: c*(cumulated_mean/cumulated_sd) + findTMSPpeak <- function(ft, c = 2, direction = "left") { + ft <- Re(ft) # extraction de la partie réelle + N <- length(ft) + if (direction == "left") { + newindex <- rev(1:N) + ft <- rev(ft) + } + thres <- 99999 + i <- 1000 # Start at point 1000 to find the peak + vect <- ft[1:i] + + while (vect[i] <= (c * thres)) { + cumsd <- stats::sd(vect) + cummean <- mean(vect) + thres <- cummean + 3 * cumsd + i <- i + 1 + vect <- ft[1:i] + } + if (direction == "left") { + v <- newindex[i] + } else { + v <- i + } + + if (is.na(v)) { + warning("No peak found, need to lower the threshold.") + return(NA) + } else { + # recherche dans les 1% de points suivants du max trouve pour etre au sommet du + # pic + d <- which.max(ft[v:(v + N * 0.01)]) + new.peak <- v + d - 1 # nouveau pic du TMSP si d > 0 + + if (names(which.max(ft[v:(v + N * 0.01)])) != names(which.max(ft[v:(v + N * 0.03)]))) { + # recherche dans les 3% de points suivants du max trouve pour eviter un faux + # positif + warning("the TMSP peak might be located further away, increase the threshold to check.") + } + return(new.peak) + } } - - - } else { - # circular - start <- 1 - maxpeak - end <- m - maxpeak - - ppmScale <- (start:end) * ppmInterval - - # check if ppm.value in is the ppmScale interval - if(ppm.value < min(ppmScale) | ppm.value > max(ppmScale)) { - warning("ppm.value = ", ppm.value, " is not in the ppm interval [", - round(min(ppmScale),2), ",", round(max(ppmScale),2), "], and is set to its default ppm.value 0") - ppm.value = 0 + + + # Define the search zone ---------------------------------------- + + n <- nrow(Spectrum_data) + m <- ncol(Spectrum_data) + + # The Sweep Width (SW) has to be the same since the column names are the same + SW <- Fid_info[1, "SW"] # Sweep Width in ppm + ppmInterval <- SW / (m - 1) # size of a ppm interval + + # range: How the search zone is defined ("all", "nearvalue" or "window") + if (range == "all") { + Data <- Spectrum_data + } else { # range = "nearvalue" or "window" + # Need to define colindex (column indexes) to apply indexInterval on it + + if (range == "nearvalue") { + fromto.RC <- list(c(-(SW * pc) / 2 + ppm.value, (SW * pc) / 2 + ppm.value)) # automatic fromto values in ppm + colindex <- as.numeric(colnames(Spectrum_data)) + } else { + # range == "window" + # fromto.RC is already user-defined + if (ppm.ir == TRUE) { + colindex <- as.numeric(colnames(Spectrum_data)) + } else { + colindex <- 1:m + } + } + + # index intervals taking into account the different elements in the list fromto.RC + Int <- vector("list", length(fromto.RC)) + for (i in 1:length(fromto.RC)) { + Int[[i]] <- indexInterval(colindex, + from = fromto.RC[[i]][1], + to = fromto.RC[[i]][2], inclusive = TRUE + ) + } + + # define Data as the cropped spectrum including the index intervals + # outside the research zone, the intensities are set to the minimal + # intensity of the research zone + + if (n > 1) { + Data <- apply(Re(Spectrum_data[, unlist(Int)]), 1, function(x) rep(min(x), m)) + Data <- t(Data) + Data[, unlist(Int)] <- Re(Spectrum_data[, unlist(Int)]) + } else { + Data <- rep(min(Re(Spectrum_data)), m) + Data[unlist(Int)] <- Re(Spectrum_data[unlist(Int)]) + } } - - # if ppm.value != 0, ppmScale is adapted - ppmScale <- ppmScale + ppm.value - - # create the spectral matrix with realigned spectra - Spectrum_data_calib <- matrix(nrow=n, ncol=end-start+1, - dimnames=list(rownames(Spectrum_data), ppmScale)) - - # fills in Spectrum_data_calib with shifted spectra - for (i in 1:n) { - shift <- (maxpeak-TMSPpeaks[i]) - Spectrum_data_calib[i,(1+shift):m] <- Spectrum_data[i,1:(m-shift)] - if (shift > 0) { - Spectrum_data_calib[i,1:shift] <- Spectrum_data[i,(m-shift+1):m] - } + + + # Apply the peak location search method ('thres' or 'max') on spectra + # ----------------------------------------------------------------------- + + if (method == "thres") { + TMSPpeaks <- apply(Data, 1, findTMSPpeak, c = c, direction = direction) + } else { # method == "max + TMSPpeaks <- apply(Re(Data), 1, which.max) } - } - - - - - # Plot of the spectra (depending on rowindex_graph) --------------------------------------------------- - - ppm = xstart = value = xend = Legend = NULL # only for R CMD check - - - # with the search zone for TMSP and the location of the peaks just found - if (!is.null(rowindex_graph)) { - - if (range == "window") { - if (ppm.ir == TRUE) { - fromto <- fromto.RC - } else { - fromto <- list() - idcol <- as.numeric(colnames(Spectrum_data)) - for (i in 1:length(fromto.RC)) { - fromto[[i]] <- as.numeric(colnames(Spectrum_data))[fromto.RC[[i]]] + + + # Shift spectra according to the TMSPpeaks found -------------------------------- + # Depends on the shiftHandling + + # TMSPpeaks is a column index + maxpeak <- max(TMSPpeaks) # max accross spectra + minpeak <- min(TMSPpeaks) # min accross spectra + + + if (shiftHandling %in% c("zerofilling", "NAfilling", "cut")) { + fill <- NA + if (shiftHandling == "zerofilling") { + fill <- 0 + } + + start <- maxpeak - 1 + end <- minpeak - m + + # ppm values of each interval for the whole spectral range of the spectral matrix + ppmScale <- (start:end) * ppmInterval + + # check if ppm.value is in the ppmScale interval + if (ppm.value < min(ppmScale) | ppm.value > max(ppmScale)) { + warning( + "ppm.value = ", ppm.value, " is not in the ppm interval [", + round(min(ppmScale), 2), ",", round(max(ppmScale), 2), "], and is set to its default ppm.value 0" + ) + ppm.value <- 0 + } + + # if ppm.value != 0, ppmScale is adapted + ppmScale <- ppmScale + ppm.value + + # create the spectral matrix with realigned spectra + Spectrum_data_calib <- matrix(fill, + nrow = n, ncol = -(end - start) + 1, + dimnames = list(rownames(Spectrum_data), ppmScale) + ) + + # fills in Spectrum_data_calib with shifted spectra + for (i in 1:n) { + shift <- (1 - TMSPpeaks[i]) + start + Spectrum_data_calib[i, (1 + shift):(m + shift)] <- Spectrum_data[i, ] + } + + if (shiftHandling == "cut") { + Spectrum_data_calib <- as.matrix(stats::na.omit(t(Spectrum_data_calib))) + Spectrum_data_calib <- t(Spectrum_data_calib) + base::attr(Spectrum_data_calib, "na.action") <- NULL } - } } else { - fromto <- fromto.RC + # circular + start <- 1 - maxpeak + end <- m - maxpeak + + ppmScale <- (start:end) * ppmInterval + + # check if ppm.value in is the ppmScale interval + if (ppm.value < min(ppmScale) | ppm.value > max(ppmScale)) { + warning( + "ppm.value = ", ppm.value, " is not in the ppm interval [", + round(min(ppmScale), 2), ",", round(max(ppmScale), 2), "], and is set to its default ppm.value 0" + ) + ppm.value <- 0 + } + + # if ppm.value != 0, ppmScale is adapted + ppmScale <- ppmScale + ppm.value + + # create the spectral matrix with realigned spectra + Spectrum_data_calib <- matrix( + nrow = n, ncol = end - start + 1, + dimnames = list(rownames(Spectrum_data), ppmScale) + ) + + # fills in Spectrum_data_calib with shifted spectra + for (i in 1:n) { + shift <- (maxpeak - TMSPpeaks[i]) + Spectrum_data_calib[i, (1 + shift):m] <- Spectrum_data[i, 1:(m - shift)] + if (shift > 0) { + Spectrum_data_calib[i, 1:shift] <- Spectrum_data[i, (m - shift + 1):m] + } + } + } + + + + + # Plot of the spectra (depending on rowindex_graph) --------------------------------------------------- + + ppm <- xstart <- value <- xend <- Legend <- NULL # only for R CMD check + + + # with the search zone for TMSP and the location of the peaks just found + if (!is.null(rowindex_graph)) { + if (range == "window") { + if (ppm.ir == TRUE) { + fromto <- fromto.RC + } else { + fromto <- list() + idcol <- as.numeric(colnames(Spectrum_data)) + for (i in 1:length(fromto.RC)) { + fromto[[i]] <- as.numeric(colnames(Spectrum_data))[fromto.RC[[i]]] + } + } + } else { + fromto <- fromto.RC + } + + # TMSPloc in ppm + TMSPloc <- as.numeric(colnames(Spectrum_data))[TMSPpeaks[rowindex_graph]] + + # num plot per window + num.stacked <- 6 + + # rectanglar bands of color for the search zone + rects <- data.frame( + xstart = sapply(fromto, function(x) x[[1]]), + xend = sapply(fromto, function(x) x[[2]]), + Legend = "Peak search zone and location" + ) + + # vlines for TMSP peak + addlines <- data.frame(rowname = rownames(Spectrum_data)[rowindex_graph], TMSPloc) + + nn <- length(rowindex_graph) + i <- 1 + j <- 1 + plots <- vector(mode = "list", length = ceiling(nn / num.stacked)) + + while (i <= nn) { + last <- min(i + num.stacked - 1, nn) + + melted <- reshape2::melt(Re(Spectrum_data[i:last, ]), + varnames = c("rowname", "ppm") + ) + + plots[[j]] <- ggplot2::ggplot() + + ggplot2::theme_bw() + + ggplot2::geom_line( + data = melted, + ggplot2::aes(x = ppm, y = value) + ) + + ggplot2::geom_rect(data = rects, ggplot2::aes( + xmin = xstart, xmax = xend, + ymin = -Inf, ymax = Inf, fill = Legend + ), alpha = 0.4) + + ggplot2::facet_grid(rowname ~ ., scales = "free_y") + + ggplot2::theme(legend.position = "none") + + ggplot2::geom_vline( + data = addlines, ggplot2::aes(xintercept = TMSPloc), + color = "red", show.legend = TRUE + ) + + ggplot2::ggtitle("Peak search zone and location") + + ggplot2::theme(legend.position = "top", legend.text = ggplot2::element_text()) + + + + if ((melted[1, "ppm"] - melted[(dim(melted)[1]), "ppm"]) > 0) { + plots[[j]] <- plots[[j]] + ggplot2::scale_x_reverse() + } + + i <- last + 1 + j <- j + 1 + } + + plots } - - # TMSPloc in ppm - TMSPloc <- as.numeric(colnames(Spectrum_data))[TMSPpeaks[rowindex_graph]] - - # num plot per window - num.stacked <- 6 - - # rectanglar bands of color for the search zone - rects <- data.frame(xstart = sapply(fromto, function(x) x[[1]]), - xend = sapply(fromto, function(x) x[[2]]), - Legend = "Peak search zone and location") - - # vlines for TMSP peak - addlines <- data.frame(rowname = rownames(Spectrum_data)[rowindex_graph],TMSPloc) - - nn <- length(rowindex_graph) - i <- 1 - j <- 1 - plots <- vector(mode = "list", length = ceiling(nn/num.stacked)) - - while (i <= nn) { - - last <- min(i + num.stacked - 1, nn) - - melted <- reshape2::melt(Re(Spectrum_data[i:last, ]), - varnames = c("rowname", "ppm")) - - plots[[j]] <- ggplot2::ggplot() + ggplot2::theme_bw() + - ggplot2::geom_line(data = melted, - ggplot2::aes(x = ppm, y = value)) + - ggplot2::geom_rect(data = rects, ggplot2::aes(xmin = xstart, xmax = xend, - ymin = -Inf, ymax = Inf, fill = Legend), alpha = 0.4) + - ggplot2::facet_grid(rowname ~ ., scales = "free_y") + - ggplot2::theme(legend.position = "none") + - ggplot2::geom_vline(data = addlines, ggplot2::aes(xintercept = TMSPloc), - color = "red", show.legend = TRUE) + - ggplot2::ggtitle("Peak search zone and location") + - ggplot2::theme(legend.position = "top", legend.text = ggplot2::element_text()) - - - - if ((melted[1, "ppm"] - melted[(dim(melted)[1]), "ppm"]) > 0) { - plots[[j]] <- plots[[j]] + ggplot2::scale_x_reverse() - } - - i <- last + 1 - j <- j + 1 + + + # Return the results ---------------------------------------------- + Spectrum_data <- endTreatment("InternalReferencing", begin_info, Spectrum_data_calib) + + if (is.null(plots)) { + return(Spectrum_data) + } else { + return(list(Spectrum_data = Spectrum_data, plots = plots)) } - - plots - } - - - # Return the results ---------------------------------------------- - Spectrum_data <- endTreatment("InternalReferencing", begin_info, Spectrum_data_calib) - - if (is.null(plots)) { - return(Spectrum_data) - } else { - return(list(Spectrum_data = Spectrum_data, plots = plots)) - } - } ## ==================================================== -# ZeroOrderPhaseCorrection +# ZeroOrderPhaseCorrection ## ==================================================== -ZeroOrderPhaseCorrection <- function(Spectrum_data, type.zopc = c("rms", "manual", "max"), - plot_rms = NULL, returnAngle = FALSE, createWindow = TRUE, - angle = NULL, plot_spectra = FALSE, - ppm.zopc = TRUE, exclude.zopc = list(c(5.1,4.5))) { - - - # Data initialisation and checks ---------------------------------------------- - - # Entry arguments definition: - # plot_rms : graph of rms criterion returnAngle : if TRUE, returns avector of - # optimal angles createWindow : for plot_rms plots angle : If angle is not NULL, - # spectra are rotated according to the angle vector values - # plot_spectra : if TRUE, plot rotated spectra - - - - begin_info <- beginTreatment("ZeroOrderPhaseCorrection", Spectrum_data) - Spectrum_data <- begin_info[["Signal_data"]] - n <- nrow(Spectrum_data) - m <- ncol(Spectrum_data) - - rnames <- rownames(Spectrum_data) - - # Check input arguments - type.zopc <- match.arg(type.zopc) - checkArg(ppm.zopc, c("bool")) - checkArg(unlist(exclude.zopc), c("num"), can.be.null = TRUE) - - - # type.zopc in c("max", "rms") ----------------------------------------- - if (type.zopc %in% c("max", "rms")) { - # angle is found by optimization - - # rms function to be optimised - rms <- function(ang, y, meth = c("max", "rms")) { - # if (debug_plot) { graphics::abline(v=ang, col='gray60') } - roty <- y * exp(complex(real = 0, imaginary = ang)) # spectrum rotation - Rey <- Re(roty) - - if (meth == "rms") { - ReyPos <- Rey[Rey >= 0] # select positive intensities - POSss <- sum((ReyPos)^2, na.rm = TRUE) # SS for positive intensities - ss <- sum((Rey)^2, na.rm = TRUE) # SS for all intensities - return(POSss/ss) # criterion : SS for positive values / SS for all intensities - } else { - maxi <- max(Rey, na.rm = TRUE) - return(maxi) - } - } - - - # Define the interval where to search for (by defining Data) - if (is.null(exclude.zopc)) { - Data <- Spectrum_data - } else { - - # if ppm.zopc == TRUE, then exclude.zopc is in the colnames values, else, in the column - # index - if (ppm.zopc == TRUE) { - colindex <- as.numeric(colnames(Spectrum_data)) - } else { - colindex <- 1:m - } - - # Second check for the argument exclude.zopc - diff <- diff(unlist(exclude.zopc))[1:length(diff(unlist(exclude.zopc)))%%2 !=0] - for (i in 1:length(diff)) { - if (ppm.zopc == TRUE & diff[i] >= 0) { - stop(paste("Invalid region removal because from <= to in ppm.zopc")) - } else if (ppm.zopc == FALSE & diff[i] <= 0) {stop(paste("Invalid region removal because from >= to in column index"))} - } - - - Int <- vector("list", length(exclude.zopc)) - for (i in 1:length(exclude.zopc)) { - Int[[i]] <- indexInterval(colindex, from = exclude.zopc[[i]][1], - to = exclude.zopc[[i]][2], inclusive = TRUE) - } - - vector <- rep(1, m) - vector[unlist(Int)] <- 0 - if (n > 1) { - Data <- sweep(Spectrum_data, MARGIN = 2, FUN = "*", vector) # Cropped_Spectrum - } else { - Data <- Spectrum_data * vector - } # Cropped_Spectrum - } - - - # angles computation - Angle <- c() - for (k in 1:n) - { - # The function is rms is periodic (period 2pi) and it seems that there is a phase - # x such that rms is unimodal (i.e. decreasing then increasing) on the interval - # [x; x+2pi]. However, if we do the optimization for example on [x-pi; x+pi], - # instead of being decreasing then increasing, it might be increasing then - # decreasing in which case optimize, thinking it is a valley will have to choose - # between the left or the right of this hill and if it chooses wrong, it will end - # up at like x-pi while the minimum is close to x+pi. - - # Supposing that rms is unimodal, the classical 1D unimodal optimization will - # work in either [-pi;pi] or [0;2pi] (this is not easy to be convinced by that I - # agree) and we can check which one it is simply by the following trick - - f0 <- rms(0, Data[k, ],meth = type.zopc) - fpi <- rms(pi, Data[k, ], meth = type.zopc) - if (f0 < fpi) { - interval <- c(-pi, pi) - } else { - interval <- c(0, 2 * pi) - } - - # graphs of rms criteria - debug_plot <- F # rms should not plot anything now, only when called by optimize - if (!is.null(plot_rms) && rnames[k] %in% plot_rms) { - x <- seq(min(interval), max(interval), length.out = 100) - y <- rep(1, 100) - for (K in (1:100)) { - y[K] <- rms(x[K], Data[k, ], meth = type.zopc) +ZeroOrderPhaseCorrection <- function(Spectrum_data, type.zopc = c("rms", "manual", "max"), + plot_rms = NULL, returnAngle = FALSE, createWindow = TRUE, + angle = NULL, plot_spectra = FALSE, + ppm.zopc = TRUE, exclude.zopc = list(c(5.1, 4.5))) { + # Data initialisation and checks ---------------------------------------------- + + # Entry arguments definition: + # plot_rms : graph of rms criterion returnAngle : if TRUE, returns avector of + # optimal angles createWindow : for plot_rms plots angle : If angle is not NULL, + # spectra are rotated according to the angle vector values + # plot_spectra : if TRUE, plot rotated spectra + + + + begin_info <- beginTreatment("ZeroOrderPhaseCorrection", Spectrum_data) + Spectrum_data <- begin_info[["Signal_data"]] + n <- nrow(Spectrum_data) + m <- ncol(Spectrum_data) + + rnames <- rownames(Spectrum_data) + + # Check input arguments + type.zopc <- match.arg(type.zopc) + checkArg(ppm.zopc, c("bool")) + checkArg(unlist(exclude.zopc), c("num"), can.be.null = TRUE) + + + # type.zopc in c("max", "rms") ----------------------------------------- + if (type.zopc %in% c("max", "rms")) { + # angle is found by optimization + + # rms function to be optimised + rms <- function(ang, y, meth = c("max", "rms")) { + # if (debug_plot) { graphics::abline(v=ang, col='gray60') } + roty <- y * exp(complex(real = 0, imaginary = ang)) # spectrum rotation + Rey <- Re(roty) + + if (meth == "rms") { + ReyPos <- Rey[Rey >= 0] # select positive intensities + POSss <- sum((ReyPos)^2, na.rm = TRUE) # SS for positive intensities + ss <- sum((Rey)^2, na.rm = TRUE) # SS for all intensities + return(POSss / ss) # criterion : SS for positive values / SS for all intensities + } else { + maxi <- max(Rey, na.rm = TRUE) + return(maxi) + } } - if (createWindow == TRUE) { - grDevices::dev.new(noRStudioGD = FALSE) + + + # Define the interval where to search for (by defining Data) + if (is.null(exclude.zopc)) { + Data <- Spectrum_data + } else { + # if ppm.zopc == TRUE, then exclude.zopc is in the colnames values, else, in the column + # index + if (ppm.zopc == TRUE) { + colindex <- as.numeric(colnames(Spectrum_data)) + } else { + colindex <- 1:m + } + + # Second check for the argument exclude.zopc + diff <- diff(unlist(exclude.zopc))[1:length(diff(unlist(exclude.zopc))) %% 2 != 0] + for (i in 1:length(diff)) { + if (ppm.zopc == TRUE & diff[i] >= 0) { + stop(paste("Invalid region removal because from <= to in ppm.zopc")) + } else if (ppm.zopc == FALSE & diff[i] <= 0) { + stop(paste("Invalid region removal because from >= to in column index")) + } + } + + + Int <- vector("list", length(exclude.zopc)) + for (i in 1:length(exclude.zopc)) { + Int[[i]] <- indexInterval(colindex, + from = exclude.zopc[[i]][1], + to = exclude.zopc[[i]][2], inclusive = TRUE + ) + } + + vector <- rep(1, m) + vector[unlist(Int)] <- 0 + if (n > 1) { + Data <- sweep(Spectrum_data, MARGIN = 2, FUN = "*", vector) # Cropped_Spectrum + } else { + Data <- Spectrum_data * vector + } # Cropped_Spectrum + } + + + # angles computation + Angle <- c() + for (k in 1:n) + { + # The function is rms is periodic (period 2pi) and it seems that there is a phase + # x such that rms is unimodal (i.e. decreasing then increasing) on the interval + # [x; x+2pi]. However, if we do the optimization for example on [x-pi; x+pi], + # instead of being decreasing then increasing, it might be increasing then + # decreasing in which case optimize, thinking it is a valley will have to choose + # between the left or the right of this hill and if it chooses wrong, it will end + # up at like x-pi while the minimum is close to x+pi. + + # Supposing that rms is unimodal, the classical 1D unimodal optimization will + # work in either [-pi;pi] or [0;2pi] (this is not easy to be convinced by that I + # agree) and we can check which one it is simply by the following trick + + f0 <- rms(0, Data[k, ], meth = type.zopc) + fpi <- rms(pi, Data[k, ], meth = type.zopc) + if (f0 < fpi) { + interval <- c(-pi, pi) + } else { + interval <- c(0, 2 * pi) + } + + # graphs of rms criteria + debug_plot <- F # rms should not plot anything now, only when called by optimize + if (!is.null(plot_rms) && rnames[k] %in% plot_rms) { + x <- seq(min(interval), max(interval), length.out = 100) + y <- rep(1, 100) + for (K in (1:100)) { + y[K] <- rms(x[K], Data[k, ], meth = type.zopc) + } + if (createWindow == TRUE) { + grDevices::dev.new(noRStudioGD = FALSE) + } + graphics::plot(x, y, + main = paste( + "Criterion maximization \n", + rownames(Data)[k] + ), ylim = c(0, 1.1), + ylab = "positiveness criterion", xlab = "angle " + ) + debug_plot <- T + } + + # Best angle + best <- stats::optimize(rms, + interval = interval, maximum = TRUE, + y = Data[k, ], meth = type.zopc + ) + ang <- best[["maximum"]] + + + if (debug_plot) { + graphics::abline(v = ang, col = "black") + graphics::text(x = (ang + 0.1 * ang), y = (y[ang] - 0.1 * y[ang]), labels = round(ang, 3)) + } + + # Spectrum rotation + Spectrum_data[k, ] <- Spectrum_data[k, ] * exp(complex(real = 0, imaginary = ang)) + Angle <- c(Angle, ang) + } + } else { + # type.zopc is "manual" ------------------------------------------------------- + # if Angle is already specified and no optimisation is needed + Angle <- angle + + if (!is.vector(angle)) { + stop("angle is not a vector") + } + + if (!is.numeric(angle)) { + stop("angle is not a numeric") + } + + if (length(angle) != n) { + stop(paste("angle has length", length(angle), "and there are", n, "spectra to rotate.")) + } + for (k in 1:n) { + Spectrum_data[k, ] <- Spectrum_data[k, ] * exp(complex(real = 0, imaginary = -angle[k])) } - graphics::plot(x, y, main = paste("Criterion maximization \n", - rownames(Data)[k]), ylim = c(0, 1.1), - ylab = "positiveness criterion", xlab = "angle ") - debug_plot <- T - } - - # Best angle - best <- stats::optimize(rms, interval = interval, maximum = TRUE, - y = Data[k,], meth = type.zopc) - ang <- best[["maximum"]] - - - if (debug_plot) { - graphics::abline(v = ang, col = "black") - graphics::text(x = (ang+0.1*ang), y = (y[ang]-0.1*y[ang]), labels = round(ang, 3)) - } - - # Spectrum rotation - Spectrum_data[k, ] <- Spectrum_data[k, ] * exp(complex(real = 0, imaginary = ang)) - Angle <- c(Angle, ang) - } - - - - - } else { - # type.zopc is "manual" ------------------------------------------------------- - # if Angle is already specified and no optimisation is needed - Angle <- angle - - if (!is.vector(angle)) { - stop("angle is not a vector") - } - - if (!is.numeric(angle)) { - stop("angle is not a numeric") - } - - if (length(angle) != n) { - stop(paste("angle has length", length(angle), "and there are", n, "spectra to rotate.")) } - for (k in 1:n) { - Spectrum_data[k, ] <- Spectrum_data[k, ] * exp(complex(real = 0, imaginary = - angle[k])) + + + # Draw spectra + if (plot_spectra == TRUE) { + nn <- ceiling(n / 4) + i <- 1 + for (k in 1:nn) { + if (createWindow == TRUE) { + grDevices::dev.new(noRStudioGD = FALSE) + } + graphics::par(mfrow = c(4, 2)) + while (i <= n) { + last <- min(i + 4 - 1, n) + graphics::plot(Re(Spectrum_data[i, ]), + type = "l", ylab = "intensity", + xlab = "Index", main = paste0(rownames(Spectrum_data)[i], " - Real part") + ) + graphics::plot(Im(Spectrum_data[i, ]), + type = "l", ylab = "intensity", + xlab = "Index", main = paste0(rownames(Spectrum_data)[i], " - Imaginary part") + ) + i <- i + 1 + } + i <- last + 1 + } } - } - - - # Draw spectra - if (plot_spectra == TRUE) { - nn <- ceiling(n/4) - i <- 1 - for (k in 1:nn) { - if (createWindow == TRUE) { - grDevices::dev.new(noRStudioGD = FALSE) - } - graphics::par(mfrow = c(4, 2)) - while (i <= n) { - last <- min(i + 4 - 1, n) - graphics::plot(Re(Spectrum_data[i, ]), type = "l", ylab = "intensity", - xlab = "Index", main = paste0(rownames(Spectrum_data)[i], " - Real part")) - graphics::plot(Im(Spectrum_data[i, ]), type = "l", ylab = "intensity", - xlab = "Index", main = paste0(rownames(Spectrum_data)[i], " - Imaginary part")) - i <- i + 1 - } - i <- last + 1 + + + # Data finalisation ---------------------------------------------- + + Spectrum_data <- endTreatment("ZeroOrderPhaseCorrection", begin_info, Spectrum_data) + if (returnAngle) { + return(list(Spectrum_data = Spectrum_data, Angle = Angle)) + } else { + return(Spectrum_data) } - } - - - # Data finalisation ---------------------------------------------- - - Spectrum_data <- endTreatment("ZeroOrderPhaseCorrection", begin_info, Spectrum_data) - if (returnAngle) { - return(list(Spectrum_data = Spectrum_data, Angle = Angle)) - } else { - return(Spectrum_data) - } } ## ==================================================== -# Baseline Correction +# Baseline Correction ## ==================================================== -BaselineCorrection <- function(Spectrum_data, ptw.bc = TRUE, maxIter = 42, - lambda.bc = 1e+07, p.bc = 0.05, eps = 1e-08, - ppm.bc = TRUE, exclude.bc = list(c(5.1,4.5)), +BaselineCorrection <- function(Spectrum_data, ptw.bc = TRUE, maxIter = 42, + lambda.bc = 1e+07, p.bc = 0.05, eps = 1e-08, + ppm.bc = TRUE, exclude.bc = list(c(5.1, 4.5)), returnBaseline = F) { - - # Data initialisation ---------------------------------------------- - begin_info <- beginTreatment("BaselineCorrection", Spectrum_data, force.real = T) - Spectrum_data <- begin_info[["Signal_data"]] - p <- p.bc - lambda <- lambda.bc - n <- dim(Spectrum_data)[1] - m <- dim(Spectrum_data)[2] - - - # Data check - checkArg(ptw.bc, c("bool")) - checkArg(maxIter, c("int", "pos")) - checkArg(lambda, c("num", "pos0")) - checkArg(p.bc, c("num", "pos0")) - checkArg(eps, c("num", "pos0")) - checkArg(returnBaseline, c("bool")) - checkArg(ppm.bc, c("bool")) - checkArg(unlist(exclude.bc), c("num"), can.be.null = TRUE) - - # Define the interval where to search for (by defining Data) - if (is.null(exclude.bc)) { - exclude_index <- NULL - } else { - # if ppm.bc == TRUE, then exclude.bc is in the colnames values, else, in the column - # index - if (ppm.bc == TRUE) { - colindex <- as.numeric(colnames(Spectrum_data)) - } else { - colindex <- 1:m - } - - Int <- vector("list", length(exclude.bc)) - for (i in 1:length(exclude.bc)) { - Int[[i]] <- indexInterval(colindex, from = exclude.bc[[i]][1], - to = exclude.bc[[i]][2], inclusive = TRUE) - } - exclude_index <- unlist(Int) - } - - # Baseline Correction implementation definition ---------------------- - - # 2 Ways: either use the function asysm from the ptw package or by - # built-in functions - if (ptw.bc) { - asysm <- ptw::asysm - } else { - difsmw <- function(y, lambda, w, d) { - # Weighted smoothing with a finite difference penalty cf Eilers, 2003. - # (A perfect smoother) - # y: signal to be smoothed - # lambda: smoothing parameter - # w: weights (use0 zeros for missing values) - # d: order of differences in penalty (generally 2) - m <- length(y) - W <- Matrix::Diagonal(x=w) - E <- Matrix::Diagonal(m) - D <- Matrix::diff(E, differences = d) - C <- Matrix::chol(W + lambda * t(D) %*% D) - x <- Matrix::solve(C, Matrix::solve(t(C), w * y)) - return(as.numeric(x)) - - } - asysm <- function(y, lambda, p, eps, exclude_index) { - # Baseline estimation with asymmetric least squares - # y: signal - # lambda: smoothing parameter (generally 1e5 to 1e8) - # p: asymmetry parameter (generally 0.001) - # d: order of differences in penalty (generally 2) - # eps: 1e-8 in ptw package - m <- length(y) - w <- rep(1, m) - i <- 1 - repeat { - z <- difsmw(y, lambda, w, d = 2) - w0 <- w - p_vect <- rep((1-p), m) # if y <= z + eps - p_vect[y > z + eps | y < 0] <- p # if y > z + eps | y < 0 - if(!is.null(exclude_index)){ - p_vect[exclude_index] <- 0 # if exclude area + # Data initialisation ---------------------------------------------- + begin_info <- beginTreatment("BaselineCorrection", Spectrum_data, force.real = T) + Spectrum_data <- begin_info[["Signal_data"]] + p <- p.bc + lambda <- lambda.bc + n <- dim(Spectrum_data)[1] + m <- dim(Spectrum_data)[2] + + + # Data check + checkArg(ptw.bc, c("bool")) + checkArg(maxIter, c("int", "pos")) + checkArg(lambda, c("num", "pos0")) + checkArg(p.bc, c("num", "pos0")) + checkArg(eps, c("num", "pos0")) + checkArg(returnBaseline, c("bool")) + checkArg(ppm.bc, c("bool")) + checkArg(unlist(exclude.bc), c("num"), can.be.null = TRUE) + + # Define the interval where to search for (by defining Data) + if (is.null(exclude.bc)) { + exclude_index <- NULL + } else { + # if ppm.bc == TRUE, then exclude.bc is in the colnames values, else, in the column + # index + if (ppm.bc == TRUE) { + colindex <- as.numeric(colnames(Spectrum_data)) + } else { + colindex <- 1:m } - - w <- p_vect - # w <- p * (y > z + eps | y < 0) + (1 - p) * (y <= z + eps) - - if (sum(abs(w - w0)) == 0) { - break + + Int <- vector("list", length(exclude.bc)) + for (i in 1:length(exclude.bc)) { + Int[[i]] <- indexInterval(colindex, + from = exclude.bc[[i]][1], + to = exclude.bc[[i]][2], inclusive = TRUE + ) + } + exclude_index <- unlist(Int) + } + + # Baseline Correction implementation definition ---------------------- + + # 2 Ways: either use the function asysm from the ptw package or by + # built-in functions + if (ptw.bc) { + asysm <- ptw::asysm + } else { + difsmw <- function(y, lambda, w, d) { + # Weighted smoothing with a finite difference penalty cf Eilers, 2003. + # (A perfect smoother) + # y: signal to be smoothed + # lambda: smoothing parameter + # w: weights (use0 zeros for missing values) + # d: order of differences in penalty (generally 2) + m <- length(y) + W <- Matrix::Diagonal(x = w) + E <- Matrix::Diagonal(m) + D <- Matrix::diff(E, differences = d) + C <- Matrix::chol(W + lambda * t(D) %*% D) + x <- Matrix::solve(C, Matrix::solve(t(C), w * y)) + return(as.numeric(x)) } - i <- i + 1 - if (i > maxIter) { - warning("cannot find Baseline estimation in asysm") - break + asysm <- function(y, lambda, p, eps, exclude_index) { + # Baseline estimation with asymmetric least squares + # y: signal + # lambda: smoothing parameter (generally 1e5 to 1e8) + # p: asymmetry parameter (generally 0.001) + # d: order of differences in penalty (generally 2) + # eps: 1e-8 in ptw package + m <- length(y) + w <- rep(1, m) + i <- 1 + repeat { + z <- difsmw(y, lambda, w, d = 2) + w0 <- w + p_vect <- rep((1 - p), m) # if y <= z + eps + p_vect[y > z + eps | y < 0] <- p # if y > z + eps | y < 0 + if (!is.null(exclude_index)) { + p_vect[exclude_index] <- 0 # if exclude area + } + + w <- p_vect + # w <- p * (y > z + eps | y < 0) + (1 - p) * (y <= z + eps) + + if (sum(abs(w - w0)) == 0) { + break + } + i <- i + 1 + if (i > maxIter) { + warning("cannot find Baseline estimation in asysm") + break + } + } + return(z) } - } - return(z) } - } - - # Baseline estimation ---------------------------------------------- - Baseline <- matrix(NA, nrow = nrow(Spectrum_data), ncol = ncol(Spectrum_data)) - - # for (k in 1:n) { - # Baseline[k, ] <- asysm(y = Spectrum_data[k, ], lambda = lambda, p = p, eps = eps) - - if (ptw.bc ){ - Baseline <- apply(Spectrum_data,1, asysm, lambda = lambda, p = p, - eps = eps) - }else { - Baseline <- apply(Spectrum_data,1, asysm, lambda = lambda, p = p, - eps = eps, exclude_index = exclude_index) - } - - - Spectrum_data <- Spectrum_data - t(Baseline) - # } - - # Data finalisation ---------------------------------------------- - Spectrum_data <- endTreatment("BaselineCorrection", begin_info, Spectrum_data) # FIXME create removeImaginary filter ?? - - if (returnBaseline) { - return(list(Spectrum_data = Spectrum_data, Baseline = Baseline)) - } else { - return(Spectrum_data) - } + + # Baseline estimation ---------------------------------------------- + Baseline <- matrix(NA, nrow = nrow(Spectrum_data), ncol = ncol(Spectrum_data)) + + # for (k in 1:n) { + # Baseline[k, ] <- asysm(y = Spectrum_data[k, ], lambda = lambda, p = p, eps = eps) + + if (ptw.bc) { + Baseline <- apply(Spectrum_data, 1, asysm, + lambda = lambda, p = p, + eps = eps + ) + } else { + Baseline <- apply(Spectrum_data, 1, asysm, + lambda = lambda, p = p, + eps = eps, exclude_index = exclude_index + ) + } + + + Spectrum_data <- Spectrum_data - t(Baseline) + # } + + # Data finalisation ---------------------------------------------- + Spectrum_data <- endTreatment("BaselineCorrection", begin_info, Spectrum_data) # FIXME create removeImaginary filter ?? + + if (returnBaseline) { + return(list(Spectrum_data = Spectrum_data, Baseline = Baseline)) + } else { + return(Spectrum_data) + } } ## ==================================================== -# NegativeValuesZeroing +# NegativeValuesZeroing ## ==================================================== NegativeValuesZeroing <- function(Spectrum_data) { - # Data initialisation and checks ---------------------------------------------- - begin_info <- beginTreatment("NegativeValuesZeroing", Spectrum_data, force.real = T) - Spectrum_data <- begin_info[["Signal_data"]] - - # NegativeValuesZeroing ---------------------------------------------- - Spectrum_data[Spectrum_data < 0] <- 0 - - # Data finalisation ---------------------------------------------- - return(endTreatment("NegativeValuesZeroing", begin_info, Spectrum_data)) -} + # Data initialisation and checks ---------------------------------------------- + begin_info <- beginTreatment("NegativeValuesZeroing", Spectrum_data, force.real = T) + Spectrum_data <- begin_info[["Signal_data"]] + # NegativeValuesZeroing ---------------------------------------------- + Spectrum_data[Spectrum_data < 0] <- 0 + # Data finalisation ---------------------------------------------- + return(endTreatment("NegativeValuesZeroing", begin_info, Spectrum_data)) +} diff --git a/tools/nmr_preprocessing/NmrPreprocessing_wrapper.R b/tools/nmr_preprocessing/NmrPreprocessing_wrapper.R index 85a63d3384..a364c94abb 100644 --- a/tools/nmr_preprocessing/NmrPreprocessing_wrapper.R +++ b/tools/nmr_preprocessing/NmrPreprocessing_wrapper.R @@ -3,24 +3,24 @@ ## 170116_NmrPreprocessing.R ## Manon Martin and Marie Tremblay-Franco -##====================================================== -##====================================================== +## ====================================================== +## ====================================================== # Preamble -##====================================================== -##====================================================== +## ====================================================== +## ====================================================== runExampleL <- FALSE -##------------------------------ +## ------------------------------ ## Options -##------------------------------ +## ------------------------------ strAsFacL <- options()$stringsAsFactors options(stringsAsFactors = FALSE) -##------------------------------ +## ------------------------------ ## Libraries laoding -##------------------------------ +## ------------------------------ library(batch) library(ptw) library(Matrix) @@ -30,36 +30,36 @@ library(reshape2) # R script call -source_local <- function(fname) -{ - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep="/")) +source_local <- function(fname) { + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) } -#Import the different functions +# Import the different functions source_local("NmrPreprocessing_script.R") source_local("DrawFunctions.R") -##------------------------------ +## ------------------------------ ## Script -##------------------------------ +## ------------------------------ runExampleL <- FALSE -if(!runExampleL) - argLs <- parseCommandArgs(evaluate=FALSE) +if (!runExampleL) { + argLs <- parseCommandArgs(evaluate = FALSE) +} sink(argLs$logOut) -##------------------------------ +## ------------------------------ ## Errors ????????????????????? -##------------------------------ +## ------------------------------ -##------------------------------ +## ------------------------------ ## Constants -##------------------------------ +## ------------------------------ topEnvC <- environment() flagC <- "\n" @@ -73,11 +73,11 @@ flagC <- "\n" cat("\nStart of 'Preprocessing' Galaxy module call: ", as.character(Sys.time()), "\n", sep = "") -##====================================================== -##====================================================== +## ====================================================== +## ====================================================== ## Parameters Loading -##====================================================== -##====================================================== +## ====================================================== +## ====================================================== # graphical inputs FirstOPCGraph <- argLs[["FirstOPCGraph"]] @@ -91,115 +91,115 @@ FinalGraph <- argLs[["FinalGraph"]] # 1rst order phase correction ------------------------ - # Inputs - ## Data matrix -Fid_data0 <- read.table(argLs[["dataMatrixFid"]],header=TRUE, check.names=FALSE, sep='\t') +# Inputs +## Data matrix +Fid_data0 <- read.table(argLs[["dataMatrixFid"]], header = TRUE, check.names = FALSE, sep = "\t") # Fid_data0 <- Fid_data0[,-1] Fid_data0 <- as.matrix(Fid_data0) - ## Samplemetadata -samplemetadataFid <- read.table(argLs[["sampleMetadataFid"]],check.names=FALSE,header=TRUE,sep="\t") +## Samplemetadata +samplemetadataFid <- read.table(argLs[["sampleMetadataFid"]], check.names = FALSE, header = TRUE, sep = "\t") samplemetadataFid <- as.matrix(samplemetadataFid) # water and solvent(s) correction ------------------------ - # Inputs +# Inputs lambda <- argLs[["lambda"]] # apodization ----------------------------------------- - # Inputs -phase=0 -rectRatio=1/2 -gaussLB=1 -expLB=1 +# Inputs +phase <- 0 +rectRatio <- 1 / 2 +gaussLB <- 1 +expLB <- 1 apodization <- argLs[["apodizationMethod"]] -if (apodization=='exp'){ - expLB <- argLs[["expLB"]] - } else if (apodization=='cos2'){ - phase <- argLs[["phase"]] - } else if (apodization=='hanning'){ - phase <- argLs[["phase"]] - } else if (apodization=='hamming'){ - phase <- argLs[["phase"]] - } else if (apodization=='blockexp'){ - rectRatio <- argLs[["rectRatio"]] - expLB <- argLs[["expLB"]] - } else if (apodization=='blockcos2'){ - rectRatio <- argLs[["rectRatio"]] - } else if (apodization=='gauss'){ - rectRatio <- argLs[["rectRatio"]] - gaussLB <- argLs[["gaussLB"]] - } +if (apodization == "exp") { + expLB <- argLs[["expLB"]] +} else if (apodization == "cos2") { + phase <- argLs[["phase"]] +} else if (apodization == "hanning") { + phase <- argLs[["phase"]] +} else if (apodization == "hamming") { + phase <- argLs[["phase"]] +} else if (apodization == "blockexp") { + rectRatio <- argLs[["rectRatio"]] + expLB <- argLs[["expLB"]] +} else if (apodization == "blockcos2") { + rectRatio <- argLs[["rectRatio"]] +} else if (apodization == "gauss") { + rectRatio <- argLs[["rectRatio"]] + gaussLB <- argLs[["gaussLB"]] +} # Fourier transform ---------------------------------- - # Inputs +# Inputs # Zero Order Phase Correction ------------------------------- - # Inputs +# Inputs -angle = NULL -excludeZOPC = NULL +angle <- NULL +excludeZOPC <- NULL zeroOrderPhaseMethod <- argLs[["zeroOrderPhaseMethod"]] - -if (zeroOrderPhaseMethod=='manual'){ - angle <- argLs[["angle"]] + +if (zeroOrderPhaseMethod == "manual") { + angle <- argLs[["angle"]] } excludeZoneZeroPhase <- argLs[["excludeZoneZeroPhase.choice"]] -if (excludeZoneZeroPhase == 'YES') { - excludeZoneZeroPhaseList <- list() - for(i in which(names(argLs)=="excludeZoneZeroPhase_left")) { - excludeZoneZeroPhaseLeft <- argLs[[i]] - excludeZoneZeroPhaseRight <- argLs[[i+1]] - excludeZoneZeroPhaseList <- c(excludeZoneZeroPhaseList,list(c(excludeZoneZeroPhaseLeft,excludeZoneZeroPhaseRight))) - } - excludeZOPC <- excludeZoneZeroPhaseList +if (excludeZoneZeroPhase == "YES") { + excludeZoneZeroPhaseList <- list() + for (i in which(names(argLs) == "excludeZoneZeroPhase_left")) { + excludeZoneZeroPhaseLeft <- argLs[[i]] + excludeZoneZeroPhaseRight <- argLs[[i + 1]] + excludeZoneZeroPhaseList <- c(excludeZoneZeroPhaseList, list(c(excludeZoneZeroPhaseLeft, excludeZoneZeroPhaseRight))) + } + excludeZOPC <- excludeZoneZeroPhaseList } # Internal referencering ---------------------------------- # Inputs -shiftTreshold = 2 # c -ppm = TRUE -shiftReferencingRangeList = NULL # fromto.RC -pctNearValue = 0.02 # pc -rowindex_graph = NULL -ppm_ref = 0 # ppm.ref - -# +shiftTreshold <- 2 # c +ppm <- TRUE +shiftReferencingRangeList <- NULL # fromto.RC +pctNearValue <- 0.02 # pc +rowindex_graph <- NULL +ppm_ref <- 0 # ppm.ref + +# # shiftReferencing <- argLs[["shiftReferencing"]] # print(shiftReferencing) -# +# # if (shiftReferencing=="YES") # { -# +# # shiftReferencingMethod <- argLs[["shiftReferencingMethod"]] -# +# # if (shiftReferencingMethod == "thres") { # shiftTreshold <- argLs[["shiftTreshold"]] # } shiftReferencingRange <- argLs[["shiftReferencingRange"]] -if (shiftReferencingRange == "near0"){ - pctNearValue <- argLs[["pctNearValue"]] +if (shiftReferencingRange == "near0") { + pctNearValue <- argLs[["pctNearValue"]] } -if (shiftReferencingRange == "window"){ - shiftReferencingRangeList <- list() - for(i in which(names(argLs)=="shiftReferencingRangeLeft")) - { - shiftReferencingRangeLeft <- argLs[[i]] - shiftReferencingRangeRight <- argLs[[i+1]] - shiftReferencingRangeList <- c(shiftReferencingRangeList,list(c(shiftReferencingRangeLeft,shiftReferencingRangeRight))) - } +if (shiftReferencingRange == "window") { + shiftReferencingRangeList <- list() + for (i in which(names(argLs) == "shiftReferencingRangeLeft")) + { + shiftReferencingRangeLeft <- argLs[[i]] + shiftReferencingRangeRight <- argLs[[i + 1]] + shiftReferencingRangeList <- c(shiftReferencingRangeList, list(c(shiftReferencingRangeLeft, shiftReferencingRangeRight))) + } } shiftHandling <- argLs[["shiftHandling"]] @@ -212,30 +212,30 @@ ppmvalue <- argLs[["ppmvalue"]] # Baseline Correction ------------------------------- - # Inputs -lambdaBc <- argLs[["lambdaBc"]] -pBc <- argLs[["pBc"]] -epsilon <- argLs[["epsilon"]] +# Inputs +lambdaBc <- argLs[["lambdaBc"]] +pBc <- argLs[["pBc"]] +epsilon <- argLs[["epsilon"]] -excludeBC = NULL +excludeBC <- NULL excludeZoneBC <- argLs[["excludeZoneBC.choice"]] -if (excludeZoneBC == 'YES') { - excludeZoneBCList <- list() - for(i in which(names(argLs)=="excludeZoneBC_left")) { - excludeZoneBCLeft <- argLs[[i]] - excludeZoneBCRight <- argLs[[i+1]] - excludeZoneBCList <- c(excludeZoneBCList,list(c(excludeZoneBCLeft,excludeZoneBCRight))) - } - excludeBC <- excludeZoneBCList +if (excludeZoneBC == "YES") { + excludeZoneBCList <- list() + for (i in which(names(argLs) == "excludeZoneBC_left")) { + excludeZoneBCLeft <- argLs[[i]] + excludeZoneBCRight <- argLs[[i + 1]] + excludeZoneBCList <- c(excludeZoneBCList, list(c(excludeZoneBCLeft, excludeZoneBCRight))) + } + excludeBC <- excludeZoneBCList } # transformation of negative values ------------------------------- - # Inputs +# Inputs NegativetoZero <- argLs[["NegativetoZero"]] - # Outputs +# Outputs nomGraphe <- argLs[["graphOut"]] # dataMatrixOut <- argLs[["dataMatrixOut"]] log <- argLs[["logOut"]] @@ -243,18 +243,19 @@ log <- argLs[["logOut"]] ## Checking arguments -##------------------- +## ------------------- error.stock <- "\n" -if(length(error.stock) > 1) - stop(error.stock) - +if (length(error.stock) > 1) { + stop(error.stock) +} + -##====================================================== -##====================================================== +## ====================================================== +## ====================================================== ## Computation -##====================================================== -##====================================================== +## ====================================================== +## ====================================================== pdf(nomGraphe, onefile = TRUE, width = 13, height = 13) @@ -262,35 +263,43 @@ pdf(nomGraphe, onefile = TRUE, width = 13, height = 13) Fid_data <- GroupDelayCorrection(Fid_data0, Fid_info = samplemetadataFid, group_delay = NULL) if (FirstOPCGraph == "YES") { - title = "FIDs after Group Delay Correction" - DrawSignal(Fid_data, subtype = "stacked", - ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, - xlab = "Frequency", num.stacked = 4, - main = title, createWindow=FALSE) + title <- "FIDs after Group Delay Correction" + DrawSignal(Fid_data, + subtype = "stacked", + ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, + xlab = "Frequency", num.stacked = 4, + main = title, createWindow = FALSE + ) } # SolventSuppression --------------------------------- Fid_data <- SolventSuppression(Fid_data, lambda.ss = lambda, ptw.ss = TRUE, plotSolvent = F, returnSolvent = F) - + if (SSGraph == "YES") { - title = "FIDs after Solvent Suppression " - DrawSignal(Fid_data, subtype = "stacked", - ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, - xlab = "Frequency", num.stacked = 4, - main = title, createWindow=FALSE) + title <- "FIDs after Solvent Suppression " + DrawSignal(Fid_data, + subtype = "stacked", + ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, + xlab = "Frequency", num.stacked = 4, + main = title, createWindow = FALSE + ) } -# Apodization --------------------------------- -Fid_data <- Apodization(Fid_data, Fid_info = samplemetadataFid, DT = NULL, - type.apod = apodization, phase = phase, rectRatio = rectRatio, gaussLB = gaussLB, expLB = expLB, plotWindow = F, returnFactor = F) +# Apodization --------------------------------- +Fid_data <- Apodization(Fid_data, + Fid_info = samplemetadataFid, DT = NULL, + type.apod = apodization, phase = phase, rectRatio = rectRatio, gaussLB = gaussLB, expLB = expLB, plotWindow = F, returnFactor = F +) if (ApodGraph == "YES") { - title = "FIDs after Apodization" - DrawSignal(Fid_data, subtype = "stacked", - ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, - xlab = "Frequency", num.stacked = 4, - main = title, createWindow=FALSE) + title <- "FIDs after Apodization" + DrawSignal(Fid_data, + subtype = "stacked", + ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, + xlab = "Frequency", num.stacked = 4, + main = title, createWindow = FALSE + ) } @@ -299,97 +308,113 @@ Spectrum_data <- FourierTransform(Fid_data, Fid_info = samplemetadataFid, revers if (FTGraph == "YES") { - title = "Fourier transformed spectra" - DrawSignal(Spectrum_data, subtype = "stacked", - ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, - xlab = "Frequency", num.stacked = 4, - main = title, createWindow=FALSE) + title <- "Fourier transformed spectra" + DrawSignal(Spectrum_data, + subtype = "stacked", + ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, + xlab = "Frequency", num.stacked = 4, + main = title, createWindow = FALSE + ) } # ZeroOrderPhaseCorrection --------------------------------- -Spectrum_data <- ZeroOrderPhaseCorrection(Spectrum_data, type.zopc = zeroOrderPhaseMethod, - plot_rms = NULL, returnAngle = FALSE, - createWindow = TRUE,angle = angle, - plot_spectra = FALSE, - ppm.zopc = TRUE, exclude.zopc = excludeZOPC) +Spectrum_data <- ZeroOrderPhaseCorrection(Spectrum_data, + type.zopc = zeroOrderPhaseMethod, + plot_rms = NULL, returnAngle = FALSE, + createWindow = TRUE, angle = angle, + plot_spectra = FALSE, + ppm.zopc = TRUE, exclude.zopc = excludeZOPC +) # InternalReferencing --------------------------------- # if (shiftReferencing=="YES") { -Spectrum_data <- InternalReferencing(Spectrum_data, samplemetadataFid, method = "max", range = shiftReferencingRange, - ppm.value = ppmvalue, shiftHandling = shiftHandling, ppm.ir = TRUE, - fromto.RC = shiftReferencingRangeList, pc = pctNearValue) +Spectrum_data <- InternalReferencing(Spectrum_data, samplemetadataFid, + method = "max", range = shiftReferencingRange, + ppm.value = ppmvalue, shiftHandling = shiftHandling, ppm.ir = TRUE, + fromto.RC = shiftReferencingRangeList, pc = pctNearValue +) if (SRGraph == "YES") { - title = "Spectra after Shift Referencing" - DrawSignal(Spectrum_data, subtype = "stacked", - ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, - xlab = "Frequency", num.stacked = 4, - main = title, createWindow=FALSE) + title <- "Spectra after Shift Referencing" + DrawSignal(Spectrum_data, + subtype = "stacked", + ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, + xlab = "Frequency", num.stacked = 4, + main = title, createWindow = FALSE + ) } # } if (ZeroOPCGraph == "YES") { -title = "Spectra after Zero Order Phase Correction" -DrawSignal(Spectrum_data, subtype = "stacked", - ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, - xlab = "Frequency", num.stacked = 4, - main = title, createWindow=FALSE) + title <- "Spectra after Zero Order Phase Correction" + DrawSignal(Spectrum_data, + subtype = "stacked", + ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, + xlab = "Frequency", num.stacked = 4, + main = title, createWindow = FALSE + ) } -# BaselineCorrection --------------------------------- -Spectrum_data <- BaselineCorrection(Spectrum_data, ptw.bc = TRUE, lambda.bc = lambdaBc, - p.bc = pBc, eps = epsilon, ppm.bc = TRUE, - exclude.bc = excludeBC, - returnBaseline = F) +# BaselineCorrection --------------------------------- +Spectrum_data <- BaselineCorrection(Spectrum_data, + ptw.bc = TRUE, lambda.bc = lambdaBc, + p.bc = pBc, eps = epsilon, ppm.bc = TRUE, + exclude.bc = excludeBC, + returnBaseline = F +) if (BCGraph == "YES") { -title = "Spectra after Baseline Correction" -DrawSignal(Spectrum_data, subtype = "stacked", - ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, - xlab = "Frequency", num.stacked = 4, - main = title, createWindow=FALSE) + title <- "Spectra after Baseline Correction" + DrawSignal(Spectrum_data, + subtype = "stacked", + ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, + xlab = "Frequency", num.stacked = 4, + main = title, createWindow = FALSE + ) } # NegativeValuesZeroing --------------------------------- -if (NegativetoZero=="YES") { - Spectrum_data <- NegativeValuesZeroing(Spectrum_data) +if (NegativetoZero == "YES") { + Spectrum_data <- NegativeValuesZeroing(Spectrum_data) } if (FinalGraph == "YES") { - title = "Final preprocessed spectra" - DrawSignal(Spectrum_data, subtype = "stacked", - ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, - xlab = "Frequency", num.stacked = 4, - main = title, createWindow=FALSE) + title <- "Final preprocessed spectra" + DrawSignal(Spectrum_data, + subtype = "stacked", + ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, + xlab = "Frequency", num.stacked = 4, + main = title, createWindow = FALSE + ) } invisible(dev.off()) -data_variable <- matrix(NA, nrow = 1, ncol = dim(Spectrum_data)[2], dimnames = list("ID", NULL)) +data_variable <- matrix(NA, nrow = 1, ncol = dim(Spectrum_data)[2], dimnames = list("ID", NULL)) colnames(data_variable) <- colnames(Spectrum_data) -data_variable[1,] <- colnames(data_variable) +data_variable[1, ] <- colnames(data_variable) -##====================================================== -##====================================================== +## ====================================================== +## ====================================================== ## Saving -##====================================================== -##====================================================== +## ====================================================== +## ====================================================== # Data Matrix -write.table(round(t(Re(Spectrum_data)),6), file=argLs$dataMatrix, quote=FALSE, row.names=TRUE, sep="\t", col.names=TRUE) +write.table(round(t(Re(Spectrum_data)), 6), file = argLs$dataMatrix, quote = FALSE, row.names = TRUE, sep = "\t", col.names = TRUE) # Variable metadata -write.table(data_variable,file=argLs$variableMetadata, quote=FALSE, row.names=TRUE, sep="\t", col.names=TRUE) +write.table(data_variable, file = argLs$variableMetadata, quote = FALSE, row.names = TRUE, sep = "\t", col.names = TRUE) # log file # write.table(t(data.frame(argLs)), file = argLs$logOut, col.names = FALSE, quote=FALSE) diff --git a/tools/nmr_preprocessing/NmrPreprocessing_xml.xml b/tools/nmr_preprocessing/NmrPreprocessing_xml.xml index 5671c7448a..437177bc8f 100644 --- a/tools/nmr_preprocessing/NmrPreprocessing_xml.xml +++ b/tools/nmr_preprocessing/NmrPreprocessing_xml.xml @@ -1,4 +1,4 @@ - + Preprocessing of 1D NMR spectra @@ -6,9 +6,15 @@ + libgfortran r-ptw r-matrix + + + + + @@ -161,13 +167,13 @@
- + + + + + + @@ -235,9 +241,9 @@
- + + @@ -364,7 +370,13 @@
- + + + + + + + diff --git a/tools/nmr_preprocessing/ReadFids_script.R b/tools/nmr_preprocessing/ReadFids_script.R index 9d8918adff..6534be32a2 100644 --- a/tools/nmr_preprocessing/ReadFids_script.R +++ b/tools/nmr_preprocessing/ReadFids_script.R @@ -8,64 +8,68 @@ # vec2mat ============================================================================== vec2mat <- function(vec) { - return(matrix(vec, nrow = 1, dimnames = list(c(1), names(vec)))) + return(matrix(vec, nrow = 1, dimnames = list(c(1), names(vec)))) } # ReadFid ============================================================================== ReadFid <- function(path) { - - # Read 1D FID using Bruker XWinNMR and TopSpin format. It is inspired of the - # matNMR matlab library which deals with 2D FID and also other formats - # Read also the parameters in the acqus file - - paramFile <- file.path(path, "acqus") - # BYTEORDA: 0 -> Little Endian 1 -> Big Endian - params <- readParams(paramFile, c("TD", "BYTORDA", "DIGMOD", "DECIM", "DSPFVS", - "SW_h", "SW", "O1")) - - if (params[["DSPFVS"]] >= 20) { - # The group delay first order phase correction is given directly from version 20 - grpdly <- readParams(paramFile, c("GRPDLY")) - params[["GRPDLY"]] <- grpdly[["GRPDLY"]] - } - TD <- params[["TD"]] - endianness <- if (params$BYTORDA) - "big" else "little" - if (TD%%2 != 0) { - stop(paste("Only even numbers are allowed for size in TD because it is complex - data with the real and imaginary part for each element.", - "The TD value is in the", paramFile, "file")) - } - - # Interpret params Dwell Time, time between 2 data points in the FID - params[["DT"]] <- 1/(2 * params[["SW_h"]]) - - # Read fid - fidFile <- file.path(path, "fid") - fidOnDisk <- readBin(fidFile, what = "int", n = TD, size = 4L, endian = endianness) - - # Real size that is on disk (it should be equal to TD2, except for TopSpin/Bruker - # (which is our case) according to matNMR as just discussed - TDOnDisk <- length(fidOnDisk) - if (TDOnDisk < TD) { - warning("Size is smaller than expected, the rest is filled with zero so the size is the same for every fid") - fidGoodSize <- sapply(vector("list", length = TD), function(x) 0) - fidGoodSize[1:TDOnDisk] <- fidOnDisk - - } else if (TDOnDisk > TD) { - warning("Size is bigger than expected, the rest ignored so the size is the same for every fid") - fidGoodSize <- fidOnDisk(1:TD) - - } else { - fidGoodSize <- fidOnDisk - } - - fidRePart <- fidGoodSize[seq(from = 1, to = TD, by = 2)] - fidImPart <- fidGoodSize[seq(from = 2, to = TD, by = 2)] - fid <- complex(real = fidRePart, imaginary = fidImPart) - - return(list(fid = fid, params = params)) + # Read 1D FID using Bruker XWinNMR and TopSpin format. It is inspired of the + # matNMR matlab library which deals with 2D FID and also other formats + # Read also the parameters in the acqus file + + paramFile <- file.path(path, "acqus") + # BYTEORDA: 0 -> Little Endian 1 -> Big Endian + params <- readParams(paramFile, c( + "TD", "BYTORDA", "DIGMOD", "DECIM", "DSPFVS", + "SW_h", "SW", "O1" + )) + + if (params[["DSPFVS"]] >= 20) { + # The group delay first order phase correction is given directly from version 20 + grpdly <- readParams(paramFile, c("GRPDLY")) + params[["GRPDLY"]] <- grpdly[["GRPDLY"]] + } + TD <- params[["TD"]] + endianness <- if (params$BYTORDA) { + "big" + } else { + "little" + } + if (TD %% 2 != 0) { + stop(paste( + "Only even numbers are allowed for size in TD because it is complex + data with the real and imaginary part for each element.", + "The TD value is in the", paramFile, "file" + )) + } + + # Interpret params Dwell Time, time between 2 data points in the FID + params[["DT"]] <- 1 / (2 * params[["SW_h"]]) + + # Read fid + fidFile <- file.path(path, "fid") + fidOnDisk <- readBin(fidFile, what = "int", n = TD, size = 4L, endian = endianness) + + # Real size that is on disk (it should be equal to TD2, except for TopSpin/Bruker + # (which is our case) according to matNMR as just discussed + TDOnDisk <- length(fidOnDisk) + if (TDOnDisk < TD) { + warning("Size is smaller than expected, the rest is filled with zero so the size is the same for every fid") + fidGoodSize <- sapply(vector("list", length = TD), function(x) 0) + fidGoodSize[1:TDOnDisk] <- fidOnDisk + } else if (TDOnDisk > TD) { + warning("Size is bigger than expected, the rest ignored so the size is the same for every fid") + fidGoodSize <- fidOnDisk(1:TD) + } else { + fidGoodSize <- fidOnDisk + } + + fidRePart <- fidGoodSize[seq(from = 1, to = TD, by = 2)] + fidImPart <- fidGoodSize[seq(from = 2, to = TD, by = 2)] + fid <- complex(real = fidRePart, imaginary = fidImPart) + + return(list(fid = fid, params = params)) } @@ -73,16 +77,16 @@ ReadFid <- function(path) { # getDirsContainingFid ============================================================================== getDirsContainingFid <- function(path) { - subdirs <- dir(path, full.names = TRUE) - if (length(subdirs) > 0) { - cond <- sapply(subdirs, function(x) { - content <- dir(x) - # subdirs must contain fid, acqu and acqus files - return("fid" %in% content && "acqu" %in% content && "acqus" %in% content) - }) - subdirs <- subdirs[cond] - } - return(subdirs) + subdirs <- dir(path, full.names = TRUE) + if (length(subdirs) > 0) { + cond <- sapply(subdirs, function(x) { + content <- dir(x) + # subdirs must contain fid, acqu and acqus files + return("fid" %in% content && "acqu" %in% content && "acqus" %in% content) + }) + subdirs <- subdirs[cond] + } + return(subdirs) } @@ -92,133 +96,145 @@ getDirsContainingFid <- function(path) { # beginTreatment ============================================================================== -beginTreatment <- function(name, Signal_data = NULL, Signal_info = NULL, +beginTreatment <- function(name, Signal_data = NULL, Signal_info = NULL, force.real = FALSE) { - - cat("Begin", name, "\n") - - - # Formatting the Signal_data and Signal_info ----------------------- - - vec <- is.vector(Signal_data) - if (vec) { - Signal_data <- vec2mat(Signal_data) - } - if (is.vector(Signal_info)) { - Signal_info <- vec2mat(Signal_info) - } - if (!is.null(Signal_data)) { - if (!is.matrix(Signal_data)) { - stop("Signal_data is not a matrix.") + cat("Begin", name, "\n") + + + # Formatting the Signal_data and Signal_info ----------------------- + + vec <- is.vector(Signal_data) + if (vec) { + Signal_data <- vec2mat(Signal_data) } - if (!is.complex(Signal_data) && !is.numeric(Signal_data)) { - stop("Signal_data contains non-numerical values.") + if (is.vector(Signal_info)) { + Signal_info <- vec2mat(Signal_info) } - } - if (!is.null(Signal_info) && !is.matrix(Signal_info)) { - stop("Signal_info is not a matrix.") - } - - - Original_data <- Signal_data - - # Extract the real part of the spectrum --------------------------- - - if (force.real) { - if (is.complex(Signal_data)) { - Signal_data <- Re(Signal_data) - } else { - # The signal is numeric Im(Signal_data) is zero anyway so let's avoid - # using complex(real=...,imaginary=0) which would give a complex signal - # in endTreatment() - force.real <- FALSE + if (!is.null(Signal_data)) { + if (!is.matrix(Signal_data)) { + stop("Signal_data is not a matrix.") + } + if (!is.complex(Signal_data) && !is.numeric(Signal_data)) { + stop("Signal_data contains non-numerical values.") + } + } + if (!is.null(Signal_info) && !is.matrix(Signal_info)) { + stop("Signal_info is not a matrix.") + } + + + Original_data <- Signal_data + + # Extract the real part of the spectrum --------------------------- + + if (force.real) { + if (is.complex(Signal_data)) { + Signal_data <- Re(Signal_data) + } else { + # The signal is numeric Im(Signal_data) is zero anyway so let's avoid + # using complex(real=...,imaginary=0) which would give a complex signal + # in endTreatment() + force.real <- FALSE + } } - } - - - # Return the formatted data and metadata entries -------------------- - - return(list(start = proc.time(), vec = vec, force.real = force.real, - Original_data = Original_data, Signal_data = Signal_data, Signal_info = Signal_info)) + + + # Return the formatted data and metadata entries -------------------- + + return(list( + start = proc.time(), vec = vec, force.real = force.real, + Original_data = Original_data, Signal_data = Signal_data, Signal_info = Signal_info + )) } # endTreatment ============================================================================== endTreatment <- function(name, begin_info, Signal_data) { - end_time = proc.time() # record it as soon as possible - start_time = begin_info[["start"]] - delta_time = end_time - start_time - delta = delta_time[] - cat("End", name, "\n") - cat("It lasted", - round(delta["user.self"], 3), "s user time,", - round(delta["sys.self"] , 3), "s system time and", - round(delta["elapsed"] , 3), "s elapsed time.\n") - if (begin_info[["force.real"]]) { - # The imaginary part is left untouched - i <- complex(real=0, imaginary=1) - Signal_data = Signal_data + i * Im(begin_info[["Original_data"]]) - } - if (begin_info[["vec"]]) { - Signal_data = Signal_data[1,] - } - return(Signal_data) + end_time <- proc.time() # record it as soon as possible + start_time <- begin_info[["start"]] + delta_time <- end_time - start_time + delta <- delta_time[] + cat("End", name, "\n") + cat( + "It lasted", + round(delta["user.self"], 3), "s user time,", + round(delta["sys.self"], 3), "s system time and", + round(delta["elapsed"], 3), "s elapsed time.\n" + ) + if (begin_info[["force.real"]]) { + # The imaginary part is left untouched + i <- complex(real = 0, imaginary = 1) + Signal_data <- Signal_data + i * Im(begin_info[["Original_data"]]) + } + if (begin_info[["vec"]]) { + Signal_data <- Signal_data[1, ] + } + return(Signal_data) } # checkArg ============================================================================== -checkArg <- function(arg, checks, can.be.null=FALSE) { - check.list <- list(bool=c(is.logical, "a boolean"), - int =c(function(x){x%%1==0}, "an integer"), - num =c(is.numeric, "a numeric"), - str =c(is.character, "a string"), - pos =c(function(x){x>0}, "positive"), - pos0=c(function(x){x>=0}, "positive or zero"), - l1 =c(function(x){length(x)==1}, "of length 1") - ) - if (is.null(arg)) { - if (!can.be.null) { - stop(deparse(substitute(arg)), " is null.") - } - } else { - if (is.matrix(arg)) { - stop(deparse(substitute(arg)), " is not scalar.") - } - for (c in checks) { - if (!check.list[[c]][[1]](arg)) { - stop(deparse(substitute(arg)), " is not ", check.list[[c]][[2]], ".") - } +checkArg <- function(arg, checks, can.be.null = FALSE) { + check.list <- list( + bool = c(is.logical, "a boolean"), + int = c(function(x) { + x %% 1 == 0 + }, "an integer"), + num = c(is.numeric, "a numeric"), + str = c(is.character, "a string"), + pos = c(function(x) { + x > 0 + }, "positive"), + pos0 = c(function(x) { + x >= 0 + }, "positive or zero"), + l1 = c(function(x) { + length(x) == 1 + }, "of length 1") + ) + if (is.null(arg)) { + if (!can.be.null) { + stop(deparse(substitute(arg)), " is null.") + } + } else { + if (is.matrix(arg)) { + stop(deparse(substitute(arg)), " is not scalar.") + } + for (c in checks) { + if (!check.list[[c]][[1]](arg)) { + stop(deparse(substitute(arg)), " is not ", check.list[[c]][[2]], ".") + } + } } - } } # getArg ============================================================================== -getArg <- function(arg, info, argname, can.be.absent=FALSE) { - if (is.null(arg)) { - start <- paste("impossible to get argument", argname, "it was not given directly and"); - if (!is.matrix(info)) { - stop(paste(start, "the info matrix was not given")) - } - if (!(argname %in% colnames(info))) { - if (can.be.absent) { - return(NULL) - } else { - stop(paste(start, "is not in the info matrix")) - } - } - if (nrow(info) < 1) { - stop(paste(start, "the info matrix has no row")) +getArg <- function(arg, info, argname, can.be.absent = FALSE) { + if (is.null(arg)) { + start <- paste("impossible to get argument", argname, "it was not given directly and") + if (!is.matrix(info)) { + stop(paste(start, "the info matrix was not given")) + } + if (!(argname %in% colnames(info))) { + if (can.be.absent) { + return(NULL) + } else { + stop(paste(start, "is not in the info matrix")) + } + } + if (nrow(info) < 1) { + stop(paste(start, "the info matrix has no row")) + } + arg <- info[1, argname] + if (is.na(arg)) { + stop(paste(start, "it is NA in the info matrix")) + } } - arg <- info[1,argname] - if (is.na(arg)) { - stop(paste(start, "it is NA in the info matrix")) - } - } - return(arg) + return(arg) } @@ -229,32 +245,36 @@ getArg <- function(arg, info, argname, can.be.absent=FALSE) { # Get the name of the signal from the title file or fromt the name of the subdirectory getTitle <- function(path, l, subdirs) { - title <- NULL - title_file <- file.path(file.path(file.path(path, "pdata"), "1"), "title") - if (file.exists(title_file)) { - lines <- readLines(title_file, warn = FALSE) - if (length(lines) >= 1) { - first_line <- gsub("^\\s+|\\s+$", "", lines[l]) - if (nchar(first_line) >= 1) { - title <- first_line - } else { - warning(paste("The", l ,"line of the title file is blank for directory ", - path, "and the (sub)dirs names are used instead")) - } + title <- NULL + title_file <- file.path(file.path(file.path(path, "pdata"), "1"), "title") + if (file.exists(title_file)) { + lines <- readLines(title_file, warn = FALSE) + if (length(lines) >= 1) { + first_line <- gsub("^\\s+|\\s+$", "", lines[l]) + if (nchar(first_line) >= 1) { + title <- first_line + } else { + warning(paste( + "The", l, "line of the title file is blank for directory ", + path, "and the (sub)dirs names are used instead" + )) + } + } else { + warning(paste("The title file is empty for directory ", path, "and the (sub)dirs names are used instead")) + } } else { - warning(paste("The title file is empty for directory ", path, "and the (sub)dirs names are used instead")) + warning(paste("Title file doesn't exists for directory ", path, "\n the (sub)dirs names are used instead")) + } + if (is.null(title)) { + if (subdirs) { + separator <- .Platform$file.sep + path_elem <- strsplit(path, separator)[[1]] + title <- paste(path_elem[length(path_elem) - 1], path_elem[length(path_elem)], sep = "_") + } else { + title <- basename(path) + } } - } else { - warning(paste("Title file doesn't exists for directory ", path, "\n the (sub)dirs names are used instead")) - } - if (is.null(title)) { - if(subdirs) { - separator <- .Platform$file.sep - path_elem <- strsplit(path,separator)[[1]] - title <- paste(path_elem[length(path_elem)-1], path_elem[length(path_elem)], sep = "_") - } else{title <- basename(path)} - } - return(title) + return(title) } @@ -264,41 +284,40 @@ getTitle <- function(path, l, subdirs) { # Read parameter values for Fid_info in the ReadFids function readParams <- function(file, paramsName) { - - isDigit <- function(c) { - return(suppressWarnings(!is.na(as.numeric(c)))) - } - lines <- readLines(file) - params <- sapply(paramsName, function(x) NULL) - - for (paramName in paramsName) { - # Find the line with the parameter I add a '$' '=' in the pattern so that for - # example 'TD0' is not found where I look for 'TD' and LOCSW and WBSW when I look - # for 'SW' - pattern <- paste("\\$", paramName, "=", sep = "") - occurences <- grep(pattern, lines) - if (length(occurences) == 0L) { - stop(paste(file, "has no field", pattern)) + isDigit <- function(c) { + return(suppressWarnings(!is.na(as.numeric(c)))) } - if (length(occurences) > 1L) { - warning(paste(file, "has more that one field", pattern, " I take the first one")) + lines <- readLines(file) + params <- sapply(paramsName, function(x) NULL) + + for (paramName in paramsName) { + # Find the line with the parameter I add a '$' '=' in the pattern so that for + # example 'TD0' is not found where I look for 'TD' and LOCSW and WBSW when I look + # for 'SW' + pattern <- paste("\\$", paramName, "=", sep = "") + occurences <- grep(pattern, lines) + if (length(occurences) == 0L) { + stop(paste(file, "has no field", pattern)) + } + if (length(occurences) > 1L) { + warning(paste(file, "has more that one field", pattern, " I take the first one")) + } + line <- lines[occurences[1]] + + # Cut beginning and end of the line '##$TD= 65536' -> '65536' + igual <- as.numeric(regexpr("=", line)) + + first <- igual + while (first <= nchar(line) & !isDigit(substr(line, first, first))) { + first <- first + 1 + } + last <- nchar(line) + while (last > 0 & !isDigit(substr(line, last, last))) { + last <- last - 1 + } + params[paramName] <- as.numeric(substr(line, first, last)) } - line <- lines[occurences[1]] - - # Cut beginning and end of the line '##$TD= 65536' -> '65536' - igual = as.numeric(regexpr("=", line)) - - first <- igual - while (first <= nchar(line) & !isDigit(substr(line, first, first))) { - first <- first + 1 - } - last <- nchar(line) - while (last > 0 & !isDigit(substr(line, last, last))) { - last <- last - 1 - } - params[paramName] <- as.numeric(substr(line, first, last)) - } - return(params) + return(params) } @@ -306,99 +325,112 @@ readParams <- function(file, paramsName) { # ReadFids ============================================================================== ReadFids <- function(path, l = 1, subdirs = FALSE, dirs.names = FALSE) { - - # Data initialisation and checks ---------------------------------------------- - begin_info <- beginTreatment("ReadFids") - checkArg(path, c("str")) - checkArg(l, c("pos")) - if (file.exists(path) == FALSE) { - stop(paste("Invalid path:", path)) - } - - - # Extract the FIDs and their info ---------------------------------------------- - - if (subdirs == FALSE) { - fidDirs <- getDirsContainingFid(path) - n <- length(fidDirs) - if (n == 0L) { - stop(paste("No valid fid in", path)) - } - if (dirs.names) { - separator <- .Platform$file.sep - path_elem <- strsplit(fidDirs,separator) - fidNames <- sapply(path_elem, function(x) x[[length(path_elem[[1]])]]) - }else {fidNames <- sapply(X = fidDirs, FUN = getTitle, l = l, subdirs = subdirs, USE.NAMES = F)} - - for (i in 1:n) { - fidList <- ReadFid(fidDirs[i]) - fid <- fidList[["fid"]] - info <- fidList[["params"]] - m <- length(fid) - if (i == 1) { - Fid_data <- matrix(nrow = n, ncol = m, dimnames = list(fidNames, - info[["DT"]] * (0:(m - 1)))) - Fid_info <- matrix(nrow = n, ncol = length(info), dimnames = list(fidNames, - names(info))) - } - Fid_data[i, ] <- fid - Fid_info[i, ] <- unlist(info) + # Data initialisation and checks ---------------------------------------------- + begin_info <- beginTreatment("ReadFids") + checkArg(path, c("str")) + checkArg(l, c("pos")) + if (file.exists(path) == FALSE) { + stop(paste("Invalid path:", path)) } - - } else { - maindirs <- dir(path, full.names = TRUE) # subdirectories - Fid_data <- numeric() - Fid_info <- numeric() - - fidDirs <- c() - for (j in maindirs) { - fd <- getDirsContainingFid(j) # recoved FIDs from subdirectories - n <- length(fd) - if (n > 0L) { - fidDirs <- c(fidDirs, fd) - } else {warning(paste("No valid fid in",j ))} + + + # Extract the FIDs and their info ---------------------------------------------- + + if (subdirs == FALSE) { + fidDirs <- getDirsContainingFid(path) + n <- length(fidDirs) + if (n == 0L) { + stop(paste("No valid fid in", path)) + } + if (dirs.names) { + separator <- .Platform$file.sep + path_elem <- strsplit(fidDirs, separator) + fidNames <- sapply(path_elem, function(x) x[[length(path_elem[[1]])]]) + } else { + fidNames <- sapply(X = fidDirs, FUN = getTitle, l = l, subdirs = subdirs, USE.NAMES = F) + } + + for (i in 1:n) { + fidList <- ReadFid(fidDirs[i]) + fid <- fidList[["fid"]] + info <- fidList[["params"]] + m <- length(fid) + if (i == 1) { + Fid_data <- matrix(nrow = n, ncol = m, dimnames = list( + fidNames, + info[["DT"]] * (0:(m - 1)) + )) + Fid_info <- matrix(nrow = n, ncol = length(info), dimnames = list( + fidNames, + names(info) + )) + } + Fid_data[i, ] <- fid + Fid_info[i, ] <- unlist(info) + } + } else { + maindirs <- dir(path, full.names = TRUE) # subdirectories + Fid_data <- numeric() + Fid_info <- numeric() + + fidDirs <- c() + for (j in maindirs) { + fd <- getDirsContainingFid(j) # recoved FIDs from subdirectories + n <- length(fd) + if (n > 0L) { + fidDirs <- c(fidDirs, fd) + } else { + warning(paste("No valid fid in", j)) + } + } + + if (dirs.names == TRUE) { + if (length(fidDirs) != length(dir(path))) { # at least one subdir contains more than 1 FID + separator <- .Platform$file.sep + path_elem <- strsplit(fidDirs, separator) + fidNames <- sapply(path_elem, function(x) { + paste(x[[length(path_elem[[1]]) - 1]], + x[[length(path_elem[[1]])]], + sep = "_" + ) + }) + } else { + fidNames <- dir(path) + } + } else { + fidNames <- sapply(X = fidDirs, FUN = getTitle, l = l, subdirs = subdirs, USE.NAMES = F) + } + + for (i in 1:length(fidNames)) { + fidList <- ReadFid(fidDirs[i]) + fid <- fidList[["fid"]] + info <- fidList[["params"]] + m <- length(fid) + if (i == 1) { + Fid_data <- matrix(nrow = length(fidNames), ncol = m, dimnames = list( + fidNames, + info[["DT"]] * (0:(m - 1)) + )) + Fid_info <- matrix(nrow = length(fidNames), ncol = length(info), dimnames = list( + fidNames, + names(info) + )) + } + Fid_data[i, ] <- fid + Fid_info[i, ] <- unlist(info) + } } - - if (dirs.names==TRUE) { - if (length(fidDirs)!= length(dir(path))) { # at least one subdir contains more than 1 FID - separator <- .Platform$file.sep - path_elem <- strsplit(fidDirs,separator) - fidNames <- sapply(path_elem, function(x) paste(x[[length(path_elem[[1]])-1]], - x[[length(path_elem[[1]])]], sep = "_")) - }else {fidNames <- dir(path)} - - } else {fidNames <- sapply(X = fidDirs, FUN = getTitle, l = l, subdirs = subdirs, USE.NAMES = F)} - - for (i in 1:length(fidNames)) { - fidList <- ReadFid(fidDirs[i]) - fid <- fidList[["fid"]] - info <- fidList[["params"]] - m <- length(fid) - if (i == 1) { - Fid_data <- matrix(nrow = length(fidNames), ncol = m, dimnames = list(fidNames, - info[["DT"]] * (0:(m - 1)))) - Fid_info <- matrix(nrow = length(fidNames), ncol = length(info), dimnames = list(fidNames, - names(info))) - } - Fid_data[i, ] <- fid - Fid_info[i, ] <- unlist(info) + + # Check for non-unique IDs ---------------------------------------------- + NonnuniqueIds <- sum(duplicated(row.names(Fid_data))) + cat("dim Fid_data: ", dim(Fid_data), "\n") + cat("IDs: ", rownames(Fid_data), "\n") + cat("non-unique IDs?", NonnuniqueIds, "\n") + if (NonnuniqueIds > 0) { + warning("There are duplicated IDs: ", Fid_data[duplicated(Fid_data)]) } - - - } - - # Check for non-unique IDs ---------------------------------------------- - NonnuniqueIds <- sum(duplicated(row.names(Fid_data))) - cat("dim Fid_data: ", dim(Fid_data), "\n") - cat("IDs: ", rownames(Fid_data), "\n") - cat("non-unique IDs?", NonnuniqueIds, "\n") - if (NonnuniqueIds > 0) { - warning("There are duplicated IDs: ", Fid_data[duplicated(Fid_data)]) - } - - - # Return the results ---------------------------------------------- - return(list(Fid_data = endTreatment("ReadFids", begin_info, Fid_data), Fid_info = Fid_info)) - -} + + # Return the results ---------------------------------------------- + return(list(Fid_data = endTreatment("ReadFids", begin_info, Fid_data), Fid_info = Fid_info)) +} diff --git a/tools/nmr_preprocessing/ReadFids_wrapper.R b/tools/nmr_preprocessing/ReadFids_wrapper.R index 0310396982..88b022fe54 100644 --- a/tools/nmr_preprocessing/ReadFids_wrapper.R +++ b/tools/nmr_preprocessing/ReadFids_wrapper.R @@ -4,78 +4,78 @@ ## Manon Martin ## manon.martin@uclouvain.be -##====================================================== -##====================================================== +## ====================================================== +## ====================================================== # Preamble -##====================================================== -##====================================================== +## ====================================================== +## ====================================================== runExampleL <- FALSE -##------------------------------ +## ------------------------------ ## Options -##------------------------------ +## ------------------------------ strAsFacL <- options()$stringsAsFactors options(stringsAsFactors = FALSE) -options(warn=1) +options(warn = 1) -##------------------------------ +## ------------------------------ ## Libraries laoding -##------------------------------ -library(batch) +## ------------------------------ +library(batch) library(ggplot2) library(gridExtra) library(reshape2) # R script call -source_local <- function(fname) -{ - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep="/")) +source_local <- function(fname) { + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) } -#Import the different functions +# Import the different functions source_local("ReadFids_script.R") source_local("DrawFunctions.R") -##------------------------------ +## ------------------------------ ## Errors ????????????????????? -##------------------------------ +## ------------------------------ -##------------------------------ +## ------------------------------ ## Constants -##------------------------------ +## ------------------------------ topEnvC <- environment() flagC <- "\n" -##------------------------------ +## ------------------------------ ## Script -##------------------------------ -if(!runExampleL) - argLs <- parseCommandArgs(evaluate=FALSE) +## ------------------------------ +if (!runExampleL) { + argLs <- parseCommandArgs(evaluate = FALSE) +} sink(argLs$logOut) -##====================================================== -##====================================================== +## ====================================================== +## ====================================================== ## Parameters Loading -##====================================================== -##====================================================== +## ====================================================== +## ====================================================== - ## Inputs - # Path - ## Bruker FIDs -fileType="Bruker" -zipfile= argLs[["fidzipfile"]] -directory=unzip(zipfile, list=F) -path=paste(getwd(),strsplit(directory[1],"/")[[1]][2],sep="/") +## Inputs +# Path +## Bruker FIDs +fileType <- "Bruker" +zipfile <- argLs[["fidzipfile"]] +directory <- unzip(zipfile, list = F) +path <- paste(getwd(), strsplit(directory[1], "/")[[1]][2], sep = "/") # other inputs from ReadFids -l = argLs[["title_line"]] +l <- argLs[["title_line"]] subdirs <- argLs[["subdirectories"]] dirs.names <- argLs[["dirs_names"]] @@ -85,33 +85,34 @@ dirs.names <- argLs[["dirs_names"]] # sampleMetadata <- argLs[["sampleMetadata"]] logOut <- argLs[["logOut"]] nomGraphe <- argLs[["graphOut"]] - + ## Checking arguments -##------------------- +## ------------------- error.stock <- "\n" -if(length(error.stock) > 1) - stop(error.stock) +if (length(error.stock) > 1) { + stop(error.stock) +} -##====================================================== -##====================================================== +## ====================================================== +## ====================================================== ## Computation -##====================================================== -##====================================================== -sink(logOut,append=TRUE) +## ====================================================== +## ====================================================== +sink(logOut, append = TRUE) -if(length(warnings())>0){ # or !is.null(warnings()) - print("something happened") +if (length(warnings()) > 0) { # or !is.null(warnings()) + print("something happened") } ## Starting cat("\nStart of 'ReadFids' Galaxy module call: ", as.character(Sys.time()), "\n\n", sep = "") -outputs <- ReadFids(path = path, l=l, subdirs = subdirs, dirs.names = dirs.names) +outputs <- ReadFids(path = path, l = l, subdirs = subdirs, dirs.names = dirs.names) data_matrix <- outputs[["Fid_data"]] # Data matrix data_sample <- outputs[["Fid_info"]] # Sample metadata @@ -119,24 +120,26 @@ data_sample <- outputs[["Fid_info"]] # Sample metadata pdf(nomGraphe, onefile = TRUE, width = 13, height = 13) -title = "Raw FID data" -DrawSignal(data_matrix, subtype = "stacked", - ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, - xlab = "Frequency", num.stacked = 4, - main = title, createWindow=FALSE) +title <- "Raw FID data" +DrawSignal(data_matrix, + subtype = "stacked", + ReImModArg = c(TRUE, FALSE, FALSE, FALSE), vertical = T, + xlab = "Frequency", num.stacked = 4, + main = title, createWindow = FALSE +) invisible(dev.off()) -##====================================================== -##====================================================== +## ====================================================== +## ====================================================== ## Saving -##====================================================== -##====================================================== +## ====================================================== +## ====================================================== # Data matrix -write.table(data_matrix,file=argLs$dataMatrix, quote=FALSE, row.names=TRUE, sep="\t", col.names=TRUE) +write.table(data_matrix, file = argLs$dataMatrix, quote = FALSE, row.names = TRUE, sep = "\t", col.names = TRUE) # Sample metadata -write.table(data_sample,file=argLs$sampleMetadata, quote=FALSE, row.names=TRUE, sep="\t", col.names=TRUE) +write.table(data_sample, file = argLs$sampleMetadata, quote = FALSE, row.names = TRUE, sep = "\t", col.names = TRUE) # log file # write.table(t(data.frame(argLs)), file = argLs$logOut, col.names = FALSE, quote=FALSE) @@ -155,4 +158,4 @@ sink() options(stringsAsFactors = strAsFacL) -rm(list = ls()) \ No newline at end of file +rm(list = ls()) diff --git a/tools/nmr_preprocessing/ReadFids_xml.xml b/tools/nmr_preprocessing/ReadFids_xml.xml index 86bbfc082b..e5d5448fa6 100644 --- a/tools/nmr_preprocessing/ReadFids_xml.xml +++ b/tools/nmr_preprocessing/ReadFids_xml.xml @@ -1,4 +1,4 @@ - + Read Bruker NMR raw files @@ -6,6 +6,11 @@ + + + + + diff --git a/tools/nmr_preprocessing/test-data/NMR_Preprocessing_dataMatrix.tabular b/tools/nmr_preprocessing/test-data/NMR_Preprocessing_dataMatrix.tabular deleted file mode 100644 index e4e0721ec6..0000000000 --- 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28579.653059 1314.68256 --5.19421597721003 65781.747835 49719.359859 30551.160911 76563.257749 22312.879838 --5.19482670219265 40546.527346 39404.169528 30953.688296 37758.383497 14716.159696 --5.19543742717528 96635.639323 21153.157015 17329.606483 31263.971066 18364.627422 --5.1960481521579 53464.340403 14047.252219 7830.376234 50954.001658 23405.927065 --5.19665887714052 64668.329998 25794.148604 11417.593308 53826.440061 0 --5.19726960212315 64590.046266 58655.173351 25998.562628 0 8678.616335 --5.19788032710577 80791.206499 26066.829463 7825.774513 1325.124817 24510.686979 --5.19849105208839 44677.346326 43369.20512 24380.591202 60493.397465 0 --5.19910177707102 55448.028927 45047.792169 15478.617571 25591.842599 10883.072517 --5.19971250205364 19522.297048 30138.8797 53966.549594 16890.860396 15763.412002 --5.20032322703626 40051.95591 30671.334027 3376.39537 59708.916221 3183.026308 --5.20093395201889 12685.50857 73651.091156 24014.335888 33453.956944 9599.118737 --5.20154467700151 86664.648429 68689.544873 19657.315325 49582.30862 27390.286575 --5.20215540198413 70147.727035 38848.295702 34913.610679 38908.150225 14431.993234 --5.20276612696676 71132.135281 57452.650476 20807.315273 81167.206697 5997.118461 --5.20337685194938 96203.012131 82638.743758 5449.976133 73974.750438 3653.366394 --5.203987576932 69327.305011 80120.712928 36288.029619 67222.071114 25396.665227 --5.20459830191462 36296.957432 83069.674111 12371.421043 11275.273975 11973.374989 --5.20520902689725 74695.4295 79398.930542 31185.377915 58856.338414 17299.103947 --5.20581975187987 3211.239482 65868.059543 42756.479668 70797.478324 9001.965584 --5.20643047686249 7105.856533 66981.526916 52871.641339 71620.404541 28802.212692 --5.20704120184512 39017.36301 91828.661028 36514.551732 77919.837961 20933.576529 --5.20765192682774 62699.76765 67611.148797 41826.1733 28233.849323 13591.727002 --5.20826265181036 72195.499781 99441.118036 40953.900022 58352.777315 14293.75835 --5.20887337679299 73436.785493 99254.604186 33049.976301 32424.753365 26805.124417 --5.20948410177561 26989.602008 92682.503805 27335.074051 21827.796186 16062.938275 --5.21009482675823 51434.834307 95799.344434 50527.016787 73690.205953 8567.548884 --5.21070555174086 85509.511367 89425.382227 43238.876828 70051.276331 12665.804364 --5.21131627672348 102822.943425 103272.941106 18856.055942 87898.760769 30005.615962 --5.2119270017061 35912.855875 104206.344357 35694.473544 73891.146581 19355.43137 --5.21253772668873 62468.124386 114273.139463 8101.812685 51917.442435 0 --5.21314845167135 66553.031111 93130.874451 20895.863647 103795.201705 11045.412893 --5.21375917665397 53529.244462 122604.270141 56254.286804 79138.11952 28394.313375 --5.2143699016366 76777.870058 139339.688146 55795.986706 114007.067861 16416.989989 --5.21498062661922 87343.538217 139417.131966 41548.287371 48901.259066 35765.061465 --5.21559135160184 134376.402222 123784.519485 65088.404866 96010.004818 27851.231928 --5.21620207658446 110398.716299 135340.968436 67820.297567 163884.59935 15549.133317 --5.21681280156709 94719.832963 98439.519016 74404.036631 66343.129291 11486.596934 --5.21742352654971 77458.993866 125545.79072 57606.947453 72707.310092 15715.334629 --5.21803425153233 114782.143052 119837.192763 72920.080972 116797.686405 29247.140598 --5.21864497651496 104390.19186 134116.91302 18556.171689 124759.126211 12888.183822 --5.21925570149758 122508.383272 146918.686771 54925.216891 107658.438015 29523.200169 --5.2198664264802 100984.375867 112925.918113 43469.967405 61097.641379 26659.504725 --5.22047715146283 121244.681201 115568.116341 74418.008355 133904.944345 31476.021545 --5.22108787644545 95492.138209 113321.364591 41019.96692 76947.770414 5686.645329 --5.22169860142807 95746.006154 114108.511928 62765.614419 79703.563319 28143.730408 --5.2223093264107 65167.805146 143017.871246 79753.272518 134138.935527 15828.600113 --5.22292005139332 76232.676063 158155.78001 64355.02733 130550.55296 10972.069483 --5.22353077637594 39513.559138 133432.052645 78585.33843 94604.67302 24000.548272 --5.22414150135857 100788.060473 151592.55936 73633.588072 99659.703826 29790.322293 --5.22475222634119 90733.914786 132182.593608 56800.360801 129667.91989 41635.378802 --5.22536295132381 72054.198713 164397.164937 52492.108627 113571.820311 4351.434202 --5.22597367630644 97395.794519 162706.603928 66323.420709 125015.897185 23244.282805 --5.22658440128906 72239.294145 187777.971768 88127.734811 84392.782361 4374.024649 --5.22719512627168 80170.002331 146058.841924 77647.21693 139254.682873 21808.729605 --5.22780585125431 89543.26773 177776.183084 69545.56343 109517.12696 5177.19751 --5.22841657623693 57494.500232 154857.451331 64277.843009 47242.514689 28966.265866 --5.22902730121955 49784.792536 177355.523207 67276.427857 118185.829721 14990.215956 --5.22963802620218 82091.914529 169953.1797 83524.056125 101572.093811 13338.54061 --5.2302487511848 68709.162289 179468.076431 61888.709673 124955.606009 30568.714124 --5.23085947616742 46785.897343 168554.803953 77159.290517 145300.786494 7842.166664 --5.23147020115004 35805.92775 184402.035985 75064.431185 95995.886342 20962.600401 --5.23208092613267 49663.818141 192694.720669 44325.357693 130868.687572 22379.625682 --5.23269165111529 88776.054829 156815.358793 96997.946251 126347.851106 10890.318024 --5.23330237609791 65410.407657 136984.455643 81996.141767 151575.60582 15230.800861 --5.23391310108054 67569.366232 190492.444327 47139.131475 116950.815303 0 --5.23452382606316 40166.875766 156406.715218 38253.271008 107543.642545 15530.596971 --5.23513455104578 25837.407641 148877.98073 54664.787125 82636.983843 11077.776388 --5.23574527602841 51644.090578 171898.479512 62444.12625 88887.725956 0 --5.23635600101103 0 168274.130156 55697.445233 103798.572923 0 --5.23696672599365 0 144523.773995 55288.762151 106073.227849 8824.242922 --5.23757745097628 0 167243.776449 77491.944542 79404.366635 0 --5.2381881759589 0 163484.769057 57582.266016 44248.087032 6117.065798 --5.23879890094152 0 151664.041336 41750.009234 91968.557069 0 --5.23940962592415 0 147258.620094 39430.282189 57015.926643 0 --5.24002035090677 812043.565709 82927.345515 0 6190.743301 0 --5.24063107588939 815748.054046 61483.703824 0 5500.392145 0 --5.24124180087202 819446.322696 81749.33671 4368.559129 0 187711.210227 --5.24185252585464 823140.438734 64686.665829 0 0 188473.903958 --5.24246325083726 826832.059516 0 0 0 189236.026933 --5.24307397581988 830522.430824 0 0 1034867.843743 189997.864151 --5.24368470080251 834212.385026 0 571394.702131 1040949.40358 190759.605988 diff --git a/tools/normalization/DrawSpec.R b/tools/normalization/DrawSpec.R index c775b4d102..b38ccc4226 100644 --- a/tools/normalization/DrawSpec.R +++ b/tools/normalization/DrawSpec.R @@ -1,74 +1,72 @@ -drawSpec <- function (X, startP = -1, endP = -1, groupLabel = NULL, useLog = -1, highBound = -1, lowBound = -1, - xlab = NULL, ylab = NULL, main = NULL, nAxisPos = 4, offside = 0) -{ - groupLabel_name = groupLabel - X = as.data.frame(X) -# colnames(X) = c(1:ncol(X)) - X = as.matrix(X) - if (highBound != -1) { - for (i in 1:nrow(X)) { - myIndex = which(X[i, ] > highBound) - X[i, myIndex] = highBound +drawSpec <- function(X, startP = -1, endP = -1, groupLabel = NULL, useLog = -1, highBound = -1, lowBound = -1, + xlab = NULL, ylab = NULL, main = NULL, nAxisPos = 4, offside = 0) { + groupLabel_name <- groupLabel + X <- as.data.frame(X) + # colnames(X) = c(1:ncol(X)) + X <- as.matrix(X) + if (highBound != -1) { + for (i in 1:nrow(X)) { + myIndex <- which(X[i, ] > highBound) + X[i, myIndex] <- highBound + } } - } - if (lowBound != -1) { - for (i in 1:nrow(X)) { - myIndex = which(X[i, ] < lowBound) - X[i, myIndex] = lowBound + if (lowBound != -1) { + for (i in 1:nrow(X)) { + myIndex <- which(X[i, ] < lowBound) + X[i, myIndex] <- lowBound + } } - } - if (is.null(groupLabel)) { - groupLabel = c(1:nrow(X)) - groupLabel = as.factor(groupLabel) - } - else { - levels(groupLabel) = c(1:length(levels(groupLabel))) - } - if (startP == -1) - startP = 1 - if (endP == -1) - endP = ncol(X) - if (is.null(xlab)) { - xlab = "index" - } - if (is.null(ylab)) { - ylab = "intensity" - } - if (is.null(main)) { - main = paste(" ", startP + offside, "-", endP + offside) - } - GraphRange <- c(startP:endP) - yn <- X[, GraphRange] - if (useLog != -1) - yn = log(yn) - if (length(yn) > ncol(X)) - { - plot(yn[1, ], ylim = c(min(yn), max(yn)), type = "n", ylab = ylab, xlab = xlab, main = main, xaxt = "n") - tempVal = trunc(length(GraphRange)/nAxisPos) - xPos = c(0:nAxisPos) * tempVal - axis(1, at = xPos, labels = colnames(X)[xPos + startP + offside]) - for (i in 1:length(levels(groupLabel))) - { - groupLabelIdx = which(groupLabel == levels(groupLabel)[i]) - color <- palette(rainbow(length(levels(groupLabel)))) - for (j in 1:length(groupLabelIdx)) - { - lines(yn[groupLabelIdx[j], ], col = color[i]) - } + if (is.null(groupLabel)) { + groupLabel <- c(1:nrow(X)) + groupLabel <- as.factor(groupLabel) + } else { + levels(groupLabel) <- c(1:length(levels(groupLabel))) } - if (!is.null(groupLabel_name)) - { - legendPos = "topleft" - legend(legendPos, levels(groupLabel_name), col = as.integer(levels(groupLabel)), text.col = "black", pch = c(19, 19), bg = "gray90") + if (startP == -1) { + startP <- 1 } - } - if (length(yn) == ncol(X)) - { - plot(yn, ylim = c(min(yn), max(yn)), type = "n", ylab = ylab, xlab = xlab, main = main, xaxt = "n") - tempVal = trunc(length(GraphRange)/nAxisPos) - xPos = c(0:nAxisPos) * tempVal -# axis(1, at = xPos, labels = xPos + startP + offside) - axis(1, at = xPos, labels = colnames(X)[xPos + startP + offside]) - lines(yn) - } -} \ No newline at end of file + if (endP == -1) { + endP <- ncol(X) + } + if (is.null(xlab)) { + xlab <- "index" + } + if (is.null(ylab)) { + ylab <- "intensity" + } + if (is.null(main)) { + main <- paste(" ", startP + offside, "-", endP + offside) + } + GraphRange <- c(startP:endP) + yn <- X[, GraphRange] + if (useLog != -1) { + yn <- log(yn) + } + if (length(yn) > ncol(X)) { + plot(yn[1, ], ylim = c(min(yn), max(yn)), type = "n", ylab = ylab, xlab = xlab, main = main, xaxt = "n") + tempVal <- trunc(length(GraphRange) / nAxisPos) + xPos <- c(0:nAxisPos) * tempVal + axis(1, at = xPos, labels = colnames(X)[xPos + startP + offside]) + for (i in 1:length(levels(groupLabel))) + { + groupLabelIdx <- which(groupLabel == levels(groupLabel)[i]) + color <- palette(rainbow(length(levels(groupLabel)))) + for (j in 1:length(groupLabelIdx)) + { + lines(yn[groupLabelIdx[j], ], col = color[i]) + } + } + if (!is.null(groupLabel_name)) { + legendPos <- "topleft" + legend(legendPos, levels(groupLabel_name), col = as.integer(levels(groupLabel)), text.col = "black", pch = c(19, 19), bg = "gray90") + } + } + if (length(yn) == ncol(X)) { + plot(yn, ylim = c(min(yn), max(yn)), type = "n", ylab = ylab, xlab = xlab, main = main, xaxt = "n") + tempVal <- trunc(length(GraphRange) / nAxisPos) + xPos <- c(0:nAxisPos) * tempVal + # axis(1, at = xPos, labels = xPos + startP + offside) + axis(1, at = xPos, labels = colnames(X)[xPos + startP + offside]) + lines(yn) + } +} diff --git a/tools/normalization/NmrNormalization_script.R b/tools/normalization/NmrNormalization_script.R index 34c3f308f7..c64abb5885 100644 --- a/tools/normalization/NmrNormalization_script.R +++ b/tools/normalization/NmrNormalization_script.R @@ -1,158 +1,153 @@ -################################################################################################################# -# SPECTRA NORMALIZATION FROM SPECTRAL DATA # -# User : Galaxy # -# Original data : -- # -# Starting date : 20-10-2014 # -# Version 1 : 27-01-2015 # -# Version 2 : 27-02-2015 # -# # -# Input files: # -# - Data matrix containing bucketed and integrated spectra to normalize # -# - Sample metadata matrix containing at least biological factor of interest # -# - Scaling method: Total intensity/Probabilistic Quotient Normalization # -# - Control group: name of control to compute median reference spectra # -# - Graph: normalization result representation # -################################################################################################################# -NmrNormalization <- function(dataMatrix,scalingMethod=c("None","Total","PQN","BioFactor"),sampleMetadata=NULL, - bioFactor=NULL,ControlGroup=NULL,graph=c("None","Overlay","One_per_individual"), - nomFichier=NULL,savLog.txtC=NULL) -{ - - ## Option - ##--------------- - strAsFacL <- options()$stringsAsFactors - options(stingsAsFactors = FALSE) - options(warn = -1) - - - ## Constants - ##--------------- - topEnvC <- environment() - flgC <- "\n" - - ## Log file (in case of integration into Galaxy) - ##---------------------------------------------- - if(!is.null(savLog.txtC)) - sink(savLog.txtC, append = TRUE) - - ## Functions definition - ##--------------------- - ################################################################################################################# - # Total intensity normalization - # Input parameters - # - data : bucketed spectra (rows=buckets; columns=samples) - ################################################################################################################# - NmrBrucker_total <- function(data) - { - # Total intensity normalization - data.total <- apply(data,2,sum) - data.normalized <- data[,1]/data.total[1] - for (i in 2:ncol(data)) - data.normalized <- cbind(data.normalized,data[,i]/data.total[i]) - colnames(data.normalized) <- colnames(data) - rownames(data.normalized) <- rownames(data) - return(data.normalized) - } - - - ################################################################################################################# - # Biological factor normalization - # Input parameters - # - data : bucketed spectra (rows=buckets; columns=samples) - # - sampleMetadata : dataframe with biological factor of interest measured for each invidual - # - bioFactor : name of the column cotaining the biological factor of interest - ################################################################################################################# - NmrBrucker_bioFact <- function(data,sampleMetadata,bioFactor) - { - # Total intensity normalization - data.normalized <- data[,1]/bioFactor[1] - for (i in 2:ncol(data)) - data.normalized <- cbind(data.normalized,data[,i]/bioFactor[i]) - colnames(data.normalized) <- colnames(data) - rownames(data.normalized) <- rownames(data) - return(data.normalized) - } - - - ################################################################################################################# - # Probabilistic quotient normalization (PQN) - # Input parameters - # - data : bucketed spectra (rows=buckets; columns=samples) - # - sampleMetadata : dataframe with treatment group of inviduals - # - pqnFactor : number of the column cotaining the biological facor of interest - # - nomControl : name of the treatment group - ################################################################################################################# - NmrBrucker_pqn <- function(data,sampleMetadata,pqnFactor,nomControl) - { - # Total intensity normalization - data.total <- apply(data,2,sum) - data.normalized <- data[,1]/data.total[1] - for (i in 2:ncol(data)) - data.normalized <- cbind(data.normalized,data[,i]/data.total[i]) - colnames(data.normalized) <- colnames(data) - rownames(data.normalized) <- rownames(data) - - # Reference spectrum - # Recuperation spectres individus controle - control.spectra <- data.normalized[,sampleMetadata[,pqnFactor]==nomControl] - spectrum.ref <- apply(control.spectra,1,median) - for (j in 1:length(spectrum.ref)) - { - if (spectrum.ref[j] == 0) - spectrum.ref[j] <- mean(control.spectra[j, ]) - if (spectrum.ref[j] == 0) - spectrum.ref[j] <- 10^(-24) - } - - # Ratio between normalized and reference spectra - data.normalized.ref <- data.normalized/spectrum.ref - - # Median ratio - data.normalized.ref.median <- apply(data.normalized.ref,1,median) - for (j in 1:length(data.normalized.ref.median)) - if (data.normalized.ref.median[j] == 0 | is.na(data.normalized.ref.median[j]) | data.normalized.ref.median == "NaN" | data.normalized.ref.median == "NA") - data.normalized.ref.median[j] <- mean(data.normalized.ref[j, ]) - - # Normalization - data.normalizedPQN <- data.normalized[,1]/data.normalized.ref.median - for (i in 2:ncol(data)) - data.normalizedPQN <- cbind(data.normalizedPQN,data.normalized[,i]/data.normalized.ref.median) - colnames(data.normalizedPQN) <- colnames(data) - rownames(data.normalizedPQN) <- rownames(data) - - return(data.normalizedPQN) - } - - - ## Tests - if (scalingMethod=="QuantitativeVariable") - { - if(mode(sampleMetadata[,bioFactor]) == "character") - bioFact <- factor(sampleMetadata[,bioFactor]) - else - bioFact <- sampleMetadata[,bioFactor] - } - - ## Spectra scaling depending on the user choice - if (scalingMethod == "None") - { - NormalizedBucketedSpectra <- dataMatrix - } - else if (scalingMethod == "Total") - { - NormalizedBucketedSpectra <- NmrBrucker_total(dataMatrix) - } - else if (scalingMethod == "PQN") - { - NormalizedBucketedSpectra <- NmrBrucker_pqn(dataMatrix,sampleMetadata,bioFactor,ControlGroup) - } - else if (scalingMethod == "QuantitativeVariable") - { - NormalizedBucketedSpectra <- NmrBrucker_bioFact(dataMatrix,sampleMetadata,bioFact) - } - - ## OUTPUTS - return(list(NormalizedBucketedSpectra)) - -} - +################################################################################################################# +# SPECTRA NORMALIZATION FROM SPECTRAL DATA # +# User : Galaxy # +# Original data : -- # +# Starting date : 20-10-2014 # +# Version 1 : 27-01-2015 # +# Version 2 : 27-02-2015 # +# # +# Input files: # +# - Data matrix containing bucketed and integrated spectra to normalize # +# - Sample metadata matrix containing at least biological factor of interest # +# - Scaling method: Total intensity/Probabilistic Quotient Normalization # +# - Control group: name of control to compute median reference spectra # +# - Graph: normalization result representation # +################################################################################################################# +NmrNormalization <- function(dataMatrix, scalingMethod = c("None", "Total", "PQN", "BioFactor"), sampleMetadata = NULL, + bioFactor = NULL, ControlGroup = NULL, graph = c("None", "Overlay", "One_per_individual"), + nomFichier = NULL, savLog.txtC = NULL) { + ## Option + ## --------------- + strAsFacL <- options()$stringsAsFactors + options(stingsAsFactors = FALSE) + options(warn = -1) + + + ## Constants + ## --------------- + topEnvC <- environment() + flgC <- "\n" + + ## Log file (in case of integration into Galaxy) + ## ---------------------------------------------- + if (!is.null(savLog.txtC)) { + sink(savLog.txtC, append = TRUE) + } + + ## Functions definition + ## --------------------- + ################################################################################################################# + # Total intensity normalization + # Input parameters + # - data : bucketed spectra (rows=buckets; columns=samples) + ################################################################################################################# + NmrBrucker_total <- function(data) { + # Total intensity normalization + data.total <- apply(data, 2, sum) + data.normalized <- data[, 1] / data.total[1] + for (i in 2:ncol(data)) { + data.normalized <- cbind(data.normalized, data[, i] / data.total[i]) + } + colnames(data.normalized) <- colnames(data) + rownames(data.normalized) <- rownames(data) + return(data.normalized) + } + + + ################################################################################################################# + # Biological factor normalization + # Input parameters + # - data : bucketed spectra (rows=buckets; columns=samples) + # - sampleMetadata : dataframe with biological factor of interest measured for each invidual + # - bioFactor : name of the column cotaining the biological factor of interest + ################################################################################################################# + NmrBrucker_bioFact <- function(data, sampleMetadata, bioFactor) { + # Total intensity normalization + data.normalized <- data[, 1] / bioFactor[1] + for (i in 2:ncol(data)) { + data.normalized <- cbind(data.normalized, data[, i] / bioFactor[i]) + } + colnames(data.normalized) <- colnames(data) + rownames(data.normalized) <- rownames(data) + return(data.normalized) + } + + + ################################################################################################################# + # Probabilistic quotient normalization (PQN) + # Input parameters + # - data : bucketed spectra (rows=buckets; columns=samples) + # - sampleMetadata : dataframe with treatment group of inviduals + # - pqnFactor : number of the column cotaining the biological facor of interest + # - nomControl : name of the treatment group + ################################################################################################################# + NmrBrucker_pqn <- function(data, sampleMetadata, pqnFactor, nomControl) { + # Total intensity normalization + data.total <- apply(data, 2, sum) + data.normalized <- data[, 1] / data.total[1] + for (i in 2:ncol(data)) { + data.normalized <- cbind(data.normalized, data[, i] / data.total[i]) + } + colnames(data.normalized) <- colnames(data) + rownames(data.normalized) <- rownames(data) + + # Reference spectrum + # Recuperation spectres individus controle + control.spectra <- data.normalized[, sampleMetadata[, pqnFactor] == nomControl] + spectrum.ref <- apply(control.spectra, 1, median) + for (j in 1:length(spectrum.ref)) + { + if (spectrum.ref[j] == 0) { + spectrum.ref[j] <- mean(control.spectra[j, ]) + } + if (spectrum.ref[j] == 0) { + spectrum.ref[j] <- 10^(-24) + } + } + + # Ratio between normalized and reference spectra + data.normalized.ref <- data.normalized / spectrum.ref + + # Median ratio + data.normalized.ref.median <- apply(data.normalized.ref, 1, median) + for (j in 1:length(data.normalized.ref.median)) { + if (data.normalized.ref.median[j] == 0 | is.na(data.normalized.ref.median[j]) | data.normalized.ref.median == "NaN" | data.normalized.ref.median == "NA") { + data.normalized.ref.median[j] <- mean(data.normalized.ref[j, ]) + } + } + + # Normalization + data.normalizedPQN <- data.normalized[, 1] / data.normalized.ref.median + for (i in 2:ncol(data)) { + data.normalizedPQN <- cbind(data.normalizedPQN, data.normalized[, i] / data.normalized.ref.median) + } + colnames(data.normalizedPQN) <- colnames(data) + rownames(data.normalizedPQN) <- rownames(data) + + return(data.normalizedPQN) + } + + + ## Tests + if (scalingMethod == "QuantitativeVariable") { + if (mode(sampleMetadata[, bioFactor]) == "character") { + bioFact <- factor(sampleMetadata[, bioFactor]) + } else { + bioFact <- sampleMetadata[, bioFactor] + } + } + + ## Spectra scaling depending on the user choice + if (scalingMethod == "None") { + NormalizedBucketedSpectra <- dataMatrix + } else if (scalingMethod == "Total") { + NormalizedBucketedSpectra <- NmrBrucker_total(dataMatrix) + } else if (scalingMethod == "PQN") { + NormalizedBucketedSpectra <- NmrBrucker_pqn(dataMatrix, sampleMetadata, bioFactor, ControlGroup) + } else if (scalingMethod == "QuantitativeVariable") { + NormalizedBucketedSpectra <- NmrBrucker_bioFact(dataMatrix, sampleMetadata, bioFact) + } + + ## OUTPUTS + return(list(NormalizedBucketedSpectra)) +} diff --git a/tools/normalization/NmrNormalization_wrapper.R b/tools/normalization/NmrNormalization_wrapper.R index 2063775aea..4fb9883444 100755 --- a/tools/normalization/NmrNormalization_wrapper.R +++ b/tools/normalization/NmrNormalization_wrapper.R @@ -9,165 +9,165 @@ runExampleL <- FALSE -##------------------------------ +## ------------------------------ ## Options -##------------------------------ +## ------------------------------ strAsFacL <- options()$stringsAsFactors options(stringsAsFactors = FALSE) -options(scipen=999) +options(scipen = 999) -##------------------------------ +## ------------------------------ ## Libraries laoding -##------------------------------ +## ------------------------------ # For parseCommandArgs function -library(batch) +library(batch) # Constants argv <- commandArgs(trailingOnly = FALSE) -script.path <- sub("--file=","",argv[grep("--file=",argv)]) +script.path <- sub("--file=", "", argv[grep("--file=", argv)]) prog.name <- basename(script.path) # Print help -if (length(grep('-h', argv)) >0) { - cat("Usage:", prog.name, - "dataMatrix myDataMatrix.tsv", - "scalingMethod PQN|QuantitativeVariable", - "graphType None|Overlay|One_per_individual", - "logOut myLog.txt", - "dataMatrixOut myDataMatrixOutput.tsv", - "graphOut myGraph.pdf", - "\n") - quit(status = 0) +if (length(grep("-h", argv)) > 0) { + cat( + "Usage:", prog.name, + "dataMatrix myDataMatrix.tsv", + "scalingMethod PQN|QuantitativeVariable", + "graphType None|Overlay|One_per_individual", + "logOut myLog.txt", + "dataMatrixOut myDataMatrixOutput.tsv", + "graphOut myGraph.pdf", + "\n" + ) + quit(status = 0) } # R script call -source_local <- function(fname) -{ - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep="/")) +source_local <- function(fname) { + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) } -#Import the different functions +# Import the different functions source_local("NmrNormalization_script.R") source_local("DrawSpec.R") -##------------------------------ +## ------------------------------ ## Errors ????????????????????? -##------------------------------ +## ------------------------------ -##------------------------------ +## ------------------------------ ## Constants -##------------------------------ +## ------------------------------ topEnvC <- environment() flagC <- "\n" -##------------------------------ +## ------------------------------ ## Script -##------------------------------ -if(!runExampleL) - argLs <- parseCommandArgs(evaluate=FALSE) +## ------------------------------ +if (!runExampleL) { + argLs <- parseCommandArgs(evaluate = FALSE) +} ## Parameters Loading -##------------------- - # Inputs -data <- read.table(argLs[["dataMatrix"]], check.names=FALSE, header=TRUE, sep="\t", row.names=1) +## ------------------- +# Inputs +data <- read.table(argLs[["dataMatrix"]], check.names = FALSE, header = TRUE, sep = "\t", row.names = 1) names <- rownames(data) - ## Add a test to check if all values are numercical -if (!all(vapply(data, is.numeric, FUN.VALUE = FALSE))) - stop("Data are not numeric") - ## Integer conversion to avoid stack overflow when computin the sum +## Add a test to check if all values are numercical +if (!all(vapply(data, is.numeric, FUN.VALUE = FALSE))) { + stop("Data are not numeric") +} +## Integer conversion to avoid stack overflow when computin the sum data <- as.data.frame(lapply(data, as.numeric)) rownames(data) <- names scaling <- argLs[["scalingMethod"]] graphique <- argLs[["graphType"]] -if (scaling=='PQN') -{ - metadataSample <- read.table(argLs[["sampleMetadata"]],check.names=FALSE,header=TRUE,sep="\t") - factor<- argLs[["factor"]] - ControlGroup <- argLs[["controlGroup"]] +if (scaling == "PQN") { + metadataSample <- read.table(argLs[["sampleMetadata"]], check.names = FALSE, header = TRUE, sep = "\t") + factor <- argLs[["factor"]] + ControlGroup <- argLs[["controlGroup"]] } -if (scaling=='QuantitativeVariable') -{ - metadataSample <- read.table(argLs[["sampleMetadata"]],check.names=FALSE,header=TRUE,sep="\t") - factor <- argLs[["factor"]] +if (scaling == "QuantitativeVariable") { + metadataSample <- read.table(argLs[["sampleMetadata"]], check.names = FALSE, header = TRUE, sep = "\t") + factor <- argLs[["factor"]] } - # Outputs +# Outputs nomGraphe <- argLs[["graphOut"]] dataMatrixOut <- argLs[["dataMatrixOut"]] log <- argLs[["logOut"]] ## Checking R packages -##-------------------- +## -------------------- sink(log) cat("\tPACKAGE INFO\n") -pkgs=c("batch") -for(pkg in pkgs) { - suppressPackageStartupMessages( stopifnot( library(pkg, quietly=TRUE, logical.return=TRUE, character.only=TRUE))) - cat(pkg,"\t",as.character(packageVersion(pkg)),"\n",sep="") +pkgs <- c("batch") +for (pkg in pkgs) { + suppressPackageStartupMessages(stopifnot(library(pkg, quietly = TRUE, logical.return = TRUE, character.only = TRUE))) + cat(pkg, "\t", as.character(packageVersion(pkg)), "\n", sep = "") } cat("\n") ## Checking arguments -##------------------- +## ------------------- error.stock <- "\n" -if(length(error.stock) > 1) - stop(error.stock) - - +if (length(error.stock) > 1) { + stop(error.stock) +} + + ## Computation -##------------ -NormalizationResults <- NmrNormalization(dataMatrix=data,scalingMethod=scaling,sampleMetadata=metadataSample, - bioFactor=factor,ControlGroup=ControlGroup, - graph=graphique,nomFichier=nomGraphe,savLog.txtC=log) +## ------------ +NormalizationResults <- NmrNormalization( + dataMatrix = data, scalingMethod = scaling, sampleMetadata = metadataSample, + bioFactor = factor, ControlGroup = ControlGroup, + graph = graphique, nomFichier = nomGraphe, savLog.txtC = log +) data_normalized <- NormalizationResults[[1]] ## Graphical outputs -##------------------ -if (graphique != "None") -{ - # Graphic Device opening - pdf(nomGraphe,onefile=TRUE) - - if (graphique == "Overlay") - { - # Global spectral window - spectra <- data.frame(t(data_normalized)) - drawSpec(spectra,xlab="", ylab="Intensity", main="") - } - else - { - for (i in 1:ncol(data_normalized)) - { - spectra <- t(data_normalized[,i]) - drawSpec(spectra,xlab="", ylab="Intensity", main=colnames(data_normalized)[i]) +## ------------------ +if (graphique != "None") { + # Graphic Device opening + pdf(nomGraphe, onefile = TRUE) + + if (graphique == "Overlay") { + # Global spectral window + spectra <- data.frame(t(data_normalized)) + drawSpec(spectra, xlab = "", ylab = "Intensity", main = "") + } else { + for (i in 1:ncol(data_normalized)) + { + spectra <- t(data_normalized[, i]) + drawSpec(spectra, xlab = "", ylab = "Intensity", main = colnames(data_normalized)[i]) + } } - } - dev.off() + dev.off() } ## Saving -##------- - # Data -data_normalized <- cbind(rownames(data_normalized),data_normalized) -colnames(data_normalized) <- c("Variable",colnames(data_normalized)[-1]) -write.table(data_normalized,file=argLs$dataMatrixOut,quote=FALSE,row.names=FALSE,sep="\t") +## ------- +# Data +data_normalized <- cbind(rownames(data_normalized), data_normalized) +colnames(data_normalized) <- c("Variable", colnames(data_normalized)[-1]) +write.table(data_normalized, file = argLs$dataMatrixOut, quote = FALSE, row.names = FALSE, sep = "\t") ## Ending -##--------------------- +## --------------------- cat("\nEnd of 'Normalization' Galaxy module call: ", as.character(Sys.time()), sep = "") sink() options(stringsAsFactors = strAsFacL) diff --git a/tools/normalization/NmrNormalization_xml.xml b/tools/normalization/NmrNormalization_xml.xml index 45cb84d915..50fc8e899a 100644 --- a/tools/normalization/NmrNormalization_xml.xml +++ b/tools/normalization/NmrNormalization_xml.xml @@ -1,10 +1,15 @@ - + Normalization of (preprocessed) spectra r-batch + + + + + @@ -85,7 +90,7 @@ - + diff --git a/tools/w4mconcatenate/concat.xml b/tools/w4mconcatenate/concat.xml index aa74348dde..7c5df0019c 100644 --- a/tools/w4mconcatenate/concat.xml +++ b/tools/w4mconcatenate/concat.xml @@ -1,31 +1,36 @@ - + to merge two metadata tables r-base r-dplyr r-w4mrutils + + + + + - - + + - + - + - + - + @@ -73,22 +78,22 @@ - + - + - - - + + + - + - + - + @@ -99,14 +104,14 @@ - + - - + + - + @@ -116,51 +121,51 @@ - + - + - + 10.1093/bioinformatics/btu813 - + - + - \ No newline at end of file + diff --git a/tools/w4mconcatenate/concatenation.R b/tools/w4mconcatenate/concatenation.R index 013e5e38ec..44dea83608 100644 --- a/tools/w4mconcatenate/concatenation.R +++ b/tools/w4mconcatenate/concatenation.R @@ -1,352 +1,337 @@ if (FALSE) { - rm(list = ls()) - - DM1 <- data.frame(data = c("5d_-kkcùf", "npèt", "5PY4(*3"), - `j 785` = c(0.356426723610756, 0.801750949101246, 0.875199970420953), - `y54j 68y4j6` = c(0.380152310071702, 0.535593104115636, 0.0825428101366147), - `5-6 4` = c(0.0306944207412024, 0.258351312473067, 0.253659010703906), - hrrrrh = c(0.334137638848017, 0.599475573145688, 0.507762246807195), - `5h -` = c(0.298147485608469, 0.0763319665667417, 0.856444177031262)) - - DM2 <- data.frame(data = c("5d_-kkcùf", "npèt", "5PY4(*3"), - `j 785` = c(0.356426723610756, 0.801750949101246, 0.875199970420953), - `y54j 68y4j6` = c(0.380152310071702, 0.535593104115636, 0.0825428101366147), - `5-6 4` = c(0.0306944207412024, 0.258351312473067, 0.253659010703906), - hrrrrh = c(0.334137638848017, 0.599475573145688, 0.507762246807195), - `5h -` = c(0.298147485608469, 0.0763319665667417, 0.856444177031262)) - - M1 <- data.frame(samplename = c("j 785", "y54j 68y4j6", "5-6 4", "hrrrrh", "5h -"), - ABD = c(19, 24, 2, 3, "y"), E = c(9, "p0", 45, 24, 29), - AAA = c("r", "bg", "il", "d", "b"), - fp = c("pj", "z", "e", "r", "t"), - uv = c("s", "d", "f", "s", "d")) - - M2 <- data.frame(samplename = c("j 785", "y54j 68y4j6", "5-6 4", "hrrrrh", "5h -"), - ABD = c(19, 24, 2, 3, "y"), E = c(9, "ici", 45, 24, 29), - AAA = c("r", "bg", "il", "d", "b"), - fp = c("pj", "z", "e", "r", "t"), - uv = c("s", "d", "f", "s", "d")) - type <- "sample" - concatenation <- "unique" - tab1 <- "tab1" - tab2 <- "tab2" - choice_keep <- "oui" - keep <- 0 - concat(DM1, M1, DM2, M2, type, tab1, tab2, concatenation, choice_keep, keep) + rm(list = ls()) + + DM1 <- data.frame( + data = c("5d_-kkcùf", "npèt", "5PY4(*3"), + `j 785` = c(0.356426723610756, 0.801750949101246, 0.875199970420953), + `y54j 68y4j6` = c(0.380152310071702, 0.535593104115636, 0.0825428101366147), + `5-6 4` = c(0.0306944207412024, 0.258351312473067, 0.253659010703906), + hrrrrh = c(0.334137638848017, 0.599475573145688, 0.507762246807195), + `5h -` = c(0.298147485608469, 0.0763319665667417, 0.856444177031262) + ) + + DM2 <- data.frame( + data = c("5d_-kkcùf", "npèt", "5PY4(*3"), + `j 785` = c(0.356426723610756, 0.801750949101246, 0.875199970420953), + `y54j 68y4j6` = c(0.380152310071702, 0.535593104115636, 0.0825428101366147), + `5-6 4` = c(0.0306944207412024, 0.258351312473067, 0.253659010703906), + hrrrrh = c(0.334137638848017, 0.599475573145688, 0.507762246807195), + `5h -` = c(0.298147485608469, 0.0763319665667417, 0.856444177031262) + ) + + M1 <- data.frame( + samplename = c("j 785", "y54j 68y4j6", "5-6 4", "hrrrrh", "5h -"), + ABD = c(19, 24, 2, 3, "y"), E = c(9, "p0", 45, 24, 29), + AAA = c("r", "bg", "il", "d", "b"), + fp = c("pj", "z", "e", "r", "t"), + uv = c("s", "d", "f", "s", "d") + ) + + M2 <- data.frame( + samplename = c("j 785", "y54j 68y4j6", "5-6 4", "hrrrrh", "5h -"), + ABD = c(19, 24, 2, 3, "y"), E = c(9, "ici", 45, 24, 29), + AAA = c("r", "bg", "il", "d", "b"), + fp = c("pj", "z", "e", "r", "t"), + uv = c("s", "d", "f", "s", "d") + ) + type <- "sample" + concatenation <- "unique" + tab1 <- "tab1" + tab2 <- "tab2" + choice_keep <- "oui" + keep <- 0 + concat(DM1, M1, DM2, M2, type, tab1, tab2, concatenation, choice_keep, keep) } ################################################################################################################# concat <- function(DM1, M1, DM2, M2, type, tab1, tab2, concatenation, choice_keep, keep) { + # DM1/DM2 = data.frame containing data Matrix + # M1/M2 = data.frame containing sample Metadata or variable Metadata + # type = "sample" or "variable" depending on Metadata content + # tab1/tab2 = Suffix for Metadata 1/2 + # concatenation = type of concatenation + # choice_keep = choice of keeping columns with the same no or keeping just one + # keep = keep the column in M1 or M2 + # returns the concatenated metadata and the two Data Matrix + identifiers_1 <- colnames(M1)[1] + identifiers_2 <- colnames(M2)[1] + err.stock <- NULL - #DM1/DM2 = data.frame containing data Matrix - #M1/M2 = data.frame containing sample Metadata or variable Metadata - #type = "sample" or "variable" depending on Metadata content - #tab1/tab2 = Suffix for Metadata 1/2 - #concatenation = type of concatenation - #choice_keep = choice of keeping columns with the same no or keeping just one - #keep = keep the column in M1 or M2 - #returns the concatenated metadata and the two Data Matrix - - identifiers_1 <- colnames(M1)[1] - identifiers_2 <- colnames(M2)[1] - - err.stock <- NULL - - #Concatenation------------------------------------------------------------------ - - #If Metadatas is Sample_Metadata we transpose - if (type == "sample") { - - rownames(DM1) <- DM1[, 1] - corner_DM1 <- colnames(DM1)[1] - DM1 <- DM1[, -1, drop = FALSE] - DM1 <- t(DM1) - DM1 <- data.frame(sample = row.names(DM1), DM1, check.names = FALSE) - rownames(DM1) <- NULL - - rownames(DM2) <- DM2[, 1] - corner_DM2 <- colnames(DM2)[1] - DM2 <- DM2[, -1, drop = FALSE] - DM2 <- t(DM2) - DM2 <- data.frame(sample = row.names(DM2), DM2, check.names = FALSE) - rownames(DM2) <- NULL - } - - #Add order of sample and Sort by order - - - M1$order1 <- seq(1, nrow(M1)) - M2$order2 <- seq(nrow(M1) + 1, nrow(M2) + nrow(M1)) - - M1_bf <- M1[order(M1[, 1]), ] - M2_bf <- M2[order(M2[, 1]), ] + # Concatenation------------------------------------------------------------------ + # If Metadatas is Sample_Metadata we transpose + if (type == "sample") { + rownames(DM1) <- DM1[, 1] + corner_DM1 <- colnames(DM1)[1] + DM1 <- DM1[, -1, drop = FALSE] + DM1 <- t(DM1) + DM1 <- data.frame(sample = row.names(DM1), DM1, check.names = FALSE) + rownames(DM1) <- NULL + + rownames(DM2) <- DM2[, 1] + corner_DM2 <- colnames(DM2)[1] + DM2 <- DM2[, -1, drop = FALSE] + DM2 <- t(DM2) + DM2 <- data.frame(sample = row.names(DM2), DM2, check.names = FALSE) + rownames(DM2) <- NULL + } - #Check the variables in common and extract them. + # Add order of sample and Sort by order - same <- check_features(M1_bf, M2_bf) - same <- same[- which(same == identifiers_1)] + M1$order1 <- seq(1, nrow(M1)) + M2$order2 <- seq(nrow(M1) + 1, nrow(M2) + nrow(M1)) - #Check that shared variables have the same values. - #If not, they are renamed or deleted according to the parameters chosen by the user. - result2 <- compare_same_columns(M1_bf, M2_bf, same, choice_keep, keep, tab1, tab2) - M1 <- result2$M1 - M2 <- result2$M2 + M1_bf <- M1[order(M1[, 1]), ] + M2_bf <- M2[order(M2[, 1]), ] - #Unique-------------------------------------------------------------------------- - if (concatenation == "unique") { - #Table match check - #We verify that the individuals are all the same - err.stock <- match2_bis(M1, M2, type) - check_err(err.stock) - M_merge <- merge(M1, M2, by = 1) - } + # Check the variables in common and extract them. - #Intersection-------------------------------------------------------------------- - if (concatenation == "intersection") { + same <- check_features(M1_bf, M2_bf) + same <- same[-which(same == identifiers_1)] - #select individuals in common - sample_common <- intersect(M1[, 1], M2[, 1]) + # Check that shared variables have the same values. + # If not, they are renamed or deleted according to the parameters chosen by the user. + result2 <- compare_same_columns(M1_bf, M2_bf, same, choice_keep, keep, tab1, tab2) + M1 <- result2$M1 + M2 <- result2$M2 - #if the list of individuals in common is null, an error message is sent - if (length(sample_common) == 0) { - err.stock <- c(err.stock, "\nThere are no individuals in common \n") - check_err(err.stock) + # Unique-------------------------------------------------------------------------- + if (concatenation == "unique") { + # Table match check + # We verify that the individuals are all the same + err.stock <- match2_bis(M1, M2, type) + check_err(err.stock) + M_merge <- merge(M1, M2, by = 1) } - #if the list of individuals in common is less than 5, then a Warning message is sent - if (length(sample_common) < 5) { - cat("\nWarning: Less than 5 individuals in common\n") - } - M_merge <- merge(M1, M2, by = 1) - } - #Union -------------------------------------------------------------------------- - if (concatenation == "union") { - #select common ids - id_common <- intersect(M1[, 1], M2[, 1]) + # Intersection-------------------------------------------------------------------- - if (is.null(id_common)) { - cat("\nT Warning : there are no individuals in common\n") - } - - M2_common <- M2[M2[, 1] %in% id_common, ] - #Store rows with individuals belonging only to M2 - M2_specifique <- M2[! M2[, 1] %in% id_common, ] - #Merge the two tables only with the samples not in common - M_merge <- bind_rows(M1, M2_specifique) - col_names <- colnames(M2_common) - col_names <- col_names[- which(col_names == identifiers_2)] - feature_common <- check_features(M_merge, M2_bf) - #Check if M_merge and M2_bf have columns in common. If so, complete the table with the values not taken. - if (!is.null(feature_common)) { - - identifiers_3 <- M2_specifique[, 1] - #We select the value in M2_bf, the M2 table before undergoing any changes, then insert it in the M_merge table. - for (feature in feature_common) { - for (id in identifiers_3) { - - index_row <- which(M2_bf[, 1] == id) - index_col <- which(colnames(M2_bf) == feature) - new_value <- M2_bf[index_row, index_col] - index_row <- which(M_merge[, 1] == id) - index_col <- which(colnames(M_merge) == feature) - M_merge[index_row, index_col] <- new_value + if (concatenation == "intersection") { + # select individuals in common + sample_common <- intersect(M1[, 1], M2[, 1]) + # if the list of individuals in common is null, an error message is sent + if (length(sample_common) == 0) { + err.stock <- c(err.stock, "\nThere are no individuals in common \n") + check_err(err.stock) } - } - } - #Fill in the table with common values - for (col in col_names) { - for (id in id_common) { - index_row <- which(M2_common[, 1] == id) - index_col <- which(colnames(M2_common) == col) - new_value <- M2_common[index_row, index_col] - index_row <- which(M_merge[, 1] == id) - index_col <- which(colnames(M_merge) == col) - M_merge[index_row, index_col] <- new_value - - } + # if the list of individuals in common is less than 5, then a Warning message is sent + if (length(sample_common) < 5) { + cat("\nWarning: Less than 5 individuals in common\n") + } + M_merge <- merge(M1, M2, by = 1) } - } - M_merge_sort <- M_merge[order(M_merge$order1, M_merge$order2), ] - M_merge_sort <- M_merge_sort[, - which(colnames(M_merge_sort) == "order1")] - M_merge_sort <- M_merge_sort[, - which(colnames(M_merge_sort) == "order2")] - #DataMatrix --------------------------------------------------------------------- - - colnames_1 <- colnames(DM1) - colnames_2 <- colnames(DM2) - #Unique ------------------------------------------------------------------------- - if (concatenation == "unique") { + # Union -------------------------------------------------------------------------- + if (concatenation == "union") { + # select common ids + id_common <- intersect(M1[, 1], M2[, 1]) - if (type == "sample") { + if (is.null(id_common)) { + cat("\nT Warning : there are no individuals in common\n") + } - rownames(DM1) <- DM1[, 1] - DM1 <- DM1[, -1] - DM1 <- t(DM1) - DM1 <- data.frame(sample = row.names(DM1), DM1, check.names = FALSE) - colnames(DM1)[1] <- corner_DM1 - rownames(DM1) <- NULL - - rownames(DM2) <- DM2[, 1] - DM2 <- DM2[, -1, drop = FALSE] - DM2 <- t(DM2) - DM2 <- data.frame(sample = row.names(DM2), DM2, check.names = FALSE) - colnames(DM2)[1] <- corner_DM2 - rownames(DM2) <- NULL + M2_common <- M2[M2[, 1] %in% id_common, ] + # Store rows with individuals belonging only to M2 + M2_specifique <- M2[!M2[, 1] %in% id_common, ] + # Merge the two tables only with the samples not in common + M_merge <- bind_rows(M1, M2_specifique) + col_names <- colnames(M2_common) + col_names <- col_names[-which(col_names == identifiers_2)] + feature_common <- check_features(M_merge, M2_bf) + # Check if M_merge and M2_bf have columns in common. If so, complete the table with the values not taken. + if (!is.null(feature_common)) { + identifiers_3 <- M2_specifique[, 1] + # We select the value in M2_bf, the M2 table before undergoing any changes, then insert it in the M_merge table. + for (feature in feature_common) { + for (id in identifiers_3) { + index_row <- which(M2_bf[, 1] == id) + index_col <- which(colnames(M2_bf) == feature) + new_value <- M2_bf[index_row, index_col] + index_row <- which(M_merge[, 1] == id) + index_col <- which(colnames(M_merge) == feature) + M_merge[index_row, index_col] <- new_value + } + } + } + # Fill in the table with common values + for (col in col_names) { + for (id in id_common) { + index_row <- which(M2_common[, 1] == id) + index_col <- which(colnames(M2_common) == col) + new_value <- M2_common[index_row, index_col] + index_row <- which(M_merge[, 1] == id) + index_col <- which(colnames(M_merge) == col) + M_merge[index_row, index_col] <- new_value + } + } } - result <- list(M_merge_sort = M_merge_sort, DM1 = DM1, DM2 = DM2) - return(result) - } - - #Intersection-------------------------------------------------------------------- - - if (concatenation == "intersection") { - - id_in_common <- intersect(DM1[, 1], DM2[, 1]) - - DM1_filter <- subset(DM1, DM1[, 1] %in% id_in_common) - DM2_filter <- subset(DM2, DM2[, 1] %in% id_in_common) - - if (type == "sample") { - - rownames(DM1_filter) <- DM1_filter[, 1] - DM1_filter <- DM1_filter[, -1] - DM1_filter <- t(DM1_filter) - DM1_filter <- data.frame(sample = row.names(DM1_filter), DM1_filter, check.names = FALSE) - colnames(DM1_filter)[1] <- corner_DM1 - rownames(DM1_filter) <- NULL - - rownames(DM2_filter) <- DM2_filter[, 1] - DM2_filter <- DM2_filter[, -1, drop = FALSE] - DM2_filter <- t(DM2_filter) - DM2_filter <- data.frame(sample = row.names(DM2_filter), DM2_filter, check.names = FALSE) - colnames(DM2_filter)[1] <- corner_DM2 - rownames(DM2_filter) <- NULL + M_merge_sort <- M_merge[order(M_merge$order1, M_merge$order2), ] + M_merge_sort <- M_merge_sort[, -which(colnames(M_merge_sort) == "order1")] + M_merge_sort <- M_merge_sort[, -which(colnames(M_merge_sort) == "order2")] + # DataMatrix --------------------------------------------------------------------- + + colnames_1 <- colnames(DM1) + colnames_2 <- colnames(DM2) + # Unique ------------------------------------------------------------------------- + + if (concatenation == "unique") { + if (type == "sample") { + rownames(DM1) <- DM1[, 1] + DM1 <- DM1[, -1] + DM1 <- t(DM1) + DM1 <- data.frame(sample = row.names(DM1), DM1, check.names = FALSE) + colnames(DM1)[1] <- corner_DM1 + rownames(DM1) <- NULL + + rownames(DM2) <- DM2[, 1] + DM2 <- DM2[, -1, drop = FALSE] + DM2 <- t(DM2) + DM2 <- data.frame(sample = row.names(DM2), DM2, check.names = FALSE) + colnames(DM2)[1] <- corner_DM2 + rownames(DM2) <- NULL + } + result <- list(M_merge_sort = M_merge_sort, DM1 = DM1, DM2 = DM2) + return(result) } - result <- list(M_merge_sort = M_merge_sort, DM1 = DM1_filter, DM2 = DM2_filter) - return(result) - } - - #Union -------------------------------------------------------------------------- - if (concatenation == "union") { + # Intersection-------------------------------------------------------------------- - common_individuals <- intersect(DM1[, 1], DM2[, 1]) - common_columns <- intersect(colnames_1, colnames_2) - #check whether there are individuals or variables in common - if (is.null(common_individuals) || is.null(common_columns)) { + if (concatenation == "intersection") { + id_in_common <- intersect(DM1[, 1], DM2[, 1]) - comparison_result <- FALSE - #If the individuals in common take the same values for all variables, then comparison_result=TRUE - } else { + DM1_filter <- subset(DM1, DM1[, 1] %in% id_in_common) + DM2_filter <- subset(DM2, DM2[, 1] %in% id_in_common) - DM1_common <- subset(DM1, DM1[, 1] %in% common_individuals) - DM2_common <- subset(DM2, DM2[, 1] %in% common_individuals) - DM1_common <- DM1_common[, common_columns, drop = FALSE] - DM2_common <- DM2_common[, common_columns, drop = FALSE] - - for (col in common_columns) { - comparison_result <- identical(DM1_common$col, DM2_common$col) - } + if (type == "sample") { + rownames(DM1_filter) <- DM1_filter[, 1] + DM1_filter <- DM1_filter[, -1] + DM1_filter <- t(DM1_filter) + DM1_filter <- data.frame(sample = row.names(DM1_filter), DM1_filter, check.names = FALSE) + colnames(DM1_filter)[1] <- corner_DM1 + rownames(DM1_filter) <- NULL + rownames(DM2_filter) <- DM2_filter[, 1] + DM2_filter <- DM2_filter[, -1, drop = FALSE] + DM2_filter <- t(DM2_filter) + DM2_filter <- data.frame(sample = row.names(DM2_filter), DM2_filter, check.names = FALSE) + colnames(DM2_filter)[1] <- corner_DM2 + rownames(DM2_filter) <- NULL + } + result <- list(M_merge_sort = M_merge_sort, DM1 = DM1_filter, DM2 = DM2_filter) + return(result) } - if (comparison_result) { - - - DM1$order1 <- seq(1, nrow(DM1)) - DM2$order2 <- seq(nrow(DM1) + 1, nrow(DM2) + nrow(DM1)) - DM1_sort <- DM1[order(DM1[, 1]), ] - DM2_sort <- DM2[order(DM2[, 1]), ] - id_in_common <- intersect(DM1[, 1], DM2[, 1]) - DM1_filter <- subset(DM1, DM1[, 1] %in% id_in_common) - DM2_filter <- subset(DM2, DM2[, 1] %in% id_in_common) - different_DM2 <- colnames_2[! colnames_2 %in% colnames_1] - DM2_specifique <- DM2[! DM2[, 1] %in% id_in_common, ] - #Merge the two tables only with the samples not in common - DM1_merge <- bind_rows(DM1, DM2_specifique) - - - #Deletion of columns present only in DM2 - DM1_merge <- DM1_merge[, ! names(DM1_merge) %in% different_DM2] - different_DM1 <- colnames_1[! colnames_1 %in% colnames_2] - DM1_specifique <- DM1[! DM1[, 1] %in% id_in_common, ] - #Merge the two tables only with the samples not in common - DM2_merge <- bind_rows(DM2, DM1_specifique) - #Deletion of columns present only in DM2 - DM2_merge <- DM2_merge[, ! names(DM2_merge) %in% different_DM1] - #DM2_merge - - - DM1_merge_sort <- DM1_merge[order(DM1_merge$order1, DM1_merge$order2), ] - DM1_merge_sort <- DM1_merge_sort[, - which(colnames(DM1_merge_sort) == "order1")] - DM1_merge_sort <- DM1_merge_sort[, - which(colnames(DM1_merge_sort) == "order2")] - - DM2_merge_sort <- DM2_merge[order(DM2_merge$order1, DM2_merge$order2), ] - DM2_merge_sort <- DM2_merge_sort[, - which(colnames(DM2_merge_sort) == "order1")] - DM2_merge_sort <- DM2_merge_sort[, - which(colnames(DM2_merge_sort) == "order2")] - - - if (type == "sample") { - - rownames(DM1_merge_sort) <- DM1_merge_sort[, 1] - DM1_merge_sort <- DM1_merge_sort[, -1] - DM1_merge_sort <- t(DM1_merge_sort) - DM1_merge_sort <- data.frame(sample = row.names(DM1_merge_sort), DM1_merge_sort, check.names = FALSE) - colnames(DM1_merge_sort)[1] <- corner_DM1 - rownames(DM1_merge_sort) <- NULL - - rownames(DM2_merge_sort) <- DM2_merge_sort[, 1] - DM2_merge_sort <- DM2_merge_sort[, -1, drop = FALSE] - DM2_merge_sort <- t(DM2_merge_sort) - DM2_merge_sort <- data.frame(sample = row.names(DM2_merge_sort), DM2_merge_sort, check.names = FALSE) - colnames(DM2_merge_sort)[1] <- corner_DM2 - rownames(DM2_merge_sort) <- NULL - } - - result <- list(M_merge_sort = M_merge_sort, DM1 = DM1_merge_sort, DM2 = DM2_merge_sort) - return(result) - - } else { - #selects line ids that are in DM2 and not in DM1 - id_diff_1 <- setdiff(DM2[, 1], DM1[, 1]) - #we store them in a dataframe - row_add_1 <- data.frame(id = id_diff_1) - #renames columns with their names in DM1 - colnames(row_add_1)[1] <- colnames(DM1)[1] - #Merge - DM1_add <- bind_rows(DM1, row_add_1) - id_diff_2 <- setdiff(DM1[, 1], DM2[, 1]) - row_add_2 <- data.frame(id = id_diff_2) - colnames(row_add_2)[1] <- colnames(DM2)[1] - DM2_add <- bind_rows(DM2, row_add_2) - - if (type == "sample") { - rownames(DM1_add) <- DM1_add[, 1] - DM1_add <- DM1_add[, -1] - DM1_add <- t(DM1_add) - DM1_add <- data.frame(sample = row.names(DM1_add), DM1_add, check.names = FALSE) - colnames(DM1_add)[1] <- corner_DM1 - rownames(DM1_add) <- NULL - rownames(DM2_add) <- DM2_add[, 1] - DM2_add <- DM2_add[, -1, drop = FALSE] - DM2_add <- t(DM2_add) - DM2_add <- data.frame(sample = row.names(DM2_add), DM2_add, check.names = FALSE) - colnames(DM2_add)[1] <- corner_DM2 - rownames(DM2_add) <- NULL - } - result <- list(M_merge_sort = M_merge_sort, DM1 = DM1_add, DM2 = DM2_add) - return(result) + # Union -------------------------------------------------------------------------- + + if (concatenation == "union") { + common_individuals <- intersect(DM1[, 1], DM2[, 1]) + common_columns <- intersect(colnames_1, colnames_2) + # check whether there are individuals or variables in common + if (is.null(common_individuals) || is.null(common_columns)) { + comparison_result <- FALSE + # If the individuals in common take the same values for all variables, then comparison_result=TRUE + } else { + DM1_common <- subset(DM1, DM1[, 1] %in% common_individuals) + DM2_common <- subset(DM2, DM2[, 1] %in% common_individuals) + DM1_common <- DM1_common[, common_columns, drop = FALSE] + DM2_common <- DM2_common[, common_columns, drop = FALSE] + + for (col in common_columns) { + comparison_result <- identical(DM1_common$col, DM2_common$col) + } + } + if (comparison_result) { + DM1$order1 <- seq(1, nrow(DM1)) + DM2$order2 <- seq(nrow(DM1) + 1, nrow(DM2) + nrow(DM1)) + DM1_sort <- DM1[order(DM1[, 1]), ] + DM2_sort <- DM2[order(DM2[, 1]), ] + id_in_common <- intersect(DM1[, 1], DM2[, 1]) + DM1_filter <- subset(DM1, DM1[, 1] %in% id_in_common) + DM2_filter <- subset(DM2, DM2[, 1] %in% id_in_common) + different_DM2 <- colnames_2[!colnames_2 %in% colnames_1] + DM2_specifique <- DM2[!DM2[, 1] %in% id_in_common, ] + # Merge the two tables only with the samples not in common + DM1_merge <- bind_rows(DM1, DM2_specifique) + + + # Deletion of columns present only in DM2 + DM1_merge <- DM1_merge[, !names(DM1_merge) %in% different_DM2] + different_DM1 <- colnames_1[!colnames_1 %in% colnames_2] + DM1_specifique <- DM1[!DM1[, 1] %in% id_in_common, ] + # Merge the two tables only with the samples not in common + DM2_merge <- bind_rows(DM2, DM1_specifique) + # Deletion of columns present only in DM2 + DM2_merge <- DM2_merge[, !names(DM2_merge) %in% different_DM1] + # DM2_merge + + + DM1_merge_sort <- DM1_merge[order(DM1_merge$order1, DM1_merge$order2), ] + DM1_merge_sort <- DM1_merge_sort[, -which(colnames(DM1_merge_sort) == "order1")] + DM1_merge_sort <- DM1_merge_sort[, -which(colnames(DM1_merge_sort) == "order2")] + + DM2_merge_sort <- DM2_merge[order(DM2_merge$order1, DM2_merge$order2), ] + DM2_merge_sort <- DM2_merge_sort[, -which(colnames(DM2_merge_sort) == "order1")] + DM2_merge_sort <- DM2_merge_sort[, -which(colnames(DM2_merge_sort) == "order2")] + + + if (type == "sample") { + rownames(DM1_merge_sort) <- DM1_merge_sort[, 1] + DM1_merge_sort <- DM1_merge_sort[, -1] + DM1_merge_sort <- t(DM1_merge_sort) + DM1_merge_sort <- data.frame(sample = row.names(DM1_merge_sort), DM1_merge_sort, check.names = FALSE) + colnames(DM1_merge_sort)[1] <- corner_DM1 + rownames(DM1_merge_sort) <- NULL + + rownames(DM2_merge_sort) <- DM2_merge_sort[, 1] + DM2_merge_sort <- DM2_merge_sort[, -1, drop = FALSE] + DM2_merge_sort <- t(DM2_merge_sort) + DM2_merge_sort <- data.frame(sample = row.names(DM2_merge_sort), DM2_merge_sort, check.names = FALSE) + colnames(DM2_merge_sort)[1] <- corner_DM2 + rownames(DM2_merge_sort) <- NULL + } + + result <- list(M_merge_sort = M_merge_sort, DM1 = DM1_merge_sort, DM2 = DM2_merge_sort) + return(result) + } else { + # selects line ids that are in DM2 and not in DM1 + id_diff_1 <- setdiff(DM2[, 1], DM1[, 1]) + # we store them in a dataframe + row_add_1 <- data.frame(id = id_diff_1) + # renames columns with their names in DM1 + colnames(row_add_1)[1] <- colnames(DM1)[1] + # Merge + DM1_add <- bind_rows(DM1, row_add_1) + id_diff_2 <- setdiff(DM1[, 1], DM2[, 1]) + row_add_2 <- data.frame(id = id_diff_2) + colnames(row_add_2)[1] <- colnames(DM2)[1] + DM2_add <- bind_rows(DM2, row_add_2) + + if (type == "sample") { + rownames(DM1_add) <- DM1_add[, 1] + DM1_add <- DM1_add[, -1] + DM1_add <- t(DM1_add) + DM1_add <- data.frame(sample = row.names(DM1_add), DM1_add, check.names = FALSE) + colnames(DM1_add)[1] <- corner_DM1 + rownames(DM1_add) <- NULL + rownames(DM2_add) <- DM2_add[, 1] + DM2_add <- DM2_add[, -1, drop = FALSE] + DM2_add <- t(DM2_add) + DM2_add <- data.frame(sample = row.names(DM2_add), DM2_add, check.names = FALSE) + colnames(DM2_add)[1] <- corner_DM2 + rownames(DM2_add) <- NULL + } + result <- list(M_merge_sort = M_merge_sort, DM1 = DM1_add, DM2 = DM2_add) + return(result) + } } - } } diff --git a/tools/w4mconcatenate/concatenation_wrapper.R b/tools/w4mconcatenate/concatenation_wrapper.R index 98d3c673a7..17e1cabe5a 100644 --- a/tools/w4mconcatenate/concatenation_wrapper.R +++ b/tools/w4mconcatenate/concatenation_wrapper.R @@ -1,5 +1,5 @@ rm(list = ls()) -#Chargement des library et des ressources +# Chargement des library et des ressources library("W4MRUtils") library(dplyr) @@ -12,14 +12,16 @@ source_local("fonctions_auxiliaires.R") para <- W4MRUtils::parse_args(args = commandArgs()) -cat("\nJob starting time:\n", format(Sys.time(), "%a %d %b %Y %X"), +cat( + "\nJob starting time:\n", format(Sys.time(), "%a %d %b %Y %X"), "\n\n--------------------------------------------------------------------", - "\nParameters used by the 'W4M concatenate' tool:\n\n") + "\nParameters used by the 'W4M concatenate' tool:\n\n" +) print(para) cat("--------------------------------------------------------------------\n\n") -#Lancement de l'outil +# Lancement de l'outil A <- W4MRUtils::import2(para$dataMatrix_1, para$metadata_1, para$type, disable_comm = FALSE) B <- W4MRUtils::import2(para$dataMatrix_2, para$metadata_2, para$type, disable_comm = FALSE) @@ -37,8 +39,12 @@ write.table(result_tables[[1]], file = para$metadata_out, sep = "\t", row.names write.table(result_tables[[2]], file = para$dataMatrix_1_out, sep = "\t", row.names = FALSE, quote = FALSE) write.table(result_tables[[3]], file = para$dataMatrix_2_out, sep = "\t", row.names = FALSE, quote = FALSE) -cat("\n--------------------------------------------------------------------", - "\nInformation about R (version, Operating System, attached or loaded packages):\n\n") +cat( + "\n--------------------------------------------------------------------", + "\nInformation about R (version, Operating System, attached or loaded packages):\n\n" +) sessionInfo() -cat("--------------------------------------------------------------------\n", - "\nJob ending time:\n", format(Sys.time(), "%a %d %b %Y %X")) +cat( + "--------------------------------------------------------------------\n", + "\nJob ending time:\n", format(Sys.time(), "%a %d %b %Y %X") +) diff --git a/tools/w4mconcatenate/fonctions_auxiliaires.R b/tools/w4mconcatenate/fonctions_auxiliaires.R index c3a4139a76..8bb1b00923 100644 --- a/tools/w4mconcatenate/fonctions_auxiliaires.R +++ b/tools/w4mconcatenate/fonctions_auxiliaires.R @@ -1,18 +1,18 @@ #-------------------------------------------------------------------------------------------------------------------------------------------------------------- check_features <- function(M1, M2) { - #M1/M2 = data.frame containing sampleMetadata or variableMetadata - #check the variables in the 2 matrices . - #returns the names of the columns in the two metadata + # M1/M2 = data.frame containing sampleMetadata or variableMetadata + # check the variables in the 2 matrices . + # returns the names of the columns in the two metadata - colnames_1 <- colnames(M1) - colnames_2 <- colnames(M2) - samecolumns <- intersect(colnames_1, colnames_2) + colnames_1 <- colnames(M1) + colnames_2 <- colnames(M2) + samecolumns <- intersect(colnames_1, colnames_2) - if (is.null(samecolumns)) { - cat("\nWarning: There are no features in common \n") - } + if (is.null(samecolumns)) { + cat("\nWarning: There are no features in common \n") + } - return(samecolumns) + return(samecolumns) } #--------------------------------------------------------------------------------------------------------------------------------------------------------------------------- @@ -20,139 +20,128 @@ check_features <- function(M1, M2) { #--------------------------------------------------------------------------------------------------------------------------------------------------------------------------- compare_same_columns <- function(M1, M2, same, choice_keep, Keep, tab1, tab2) { + # M1/M2 = data.frame containing sampleMetadata or variableMetadata + # same = list of column names with the same name in M1 and M2 + # choice_keep = choice of keeping columns with the same no or keeping just one + # keep = keep the column in M1 or M2 + # tab1/tab2 = Suffix for Metadata 1/2 - #M1/M2 = data.frame containing sampleMetadata or variableMetadata - #same = list of column names with the same name in M1 and M2 - #choice_keep = choice of keeping columns with the same no or keeping just one - #keep = keep the column in M1 or M2 - #tab1/tab2 = Suffix for Metadata 1/2 + # Check that the variables in the 2 matrices have the same values and + # If not, they are renamed or deleted according to the parameters chosen by the user. + # returns the two modified metadata - #Check that the variables in the 2 matrices have the same values and - #If not, they are renamed or deleted according to the parameters chosen by the user. - #returns the two modified metadata + compare_results <- list() + non_identical_columns_v <- c() - compare_results <- list() - non_identical_columns_v <- c() + # Creation of 2 sub-tables with shared individuals and variables + common_individuals <- intersect(M1[, 1], M2[, 1]) + common_columns <- intersect(colnames(M1), colnames(M2)) - #Creation of 2 sub-tables with shared individuals and variables - common_individuals <- intersect(M1[, 1], M2[, 1]) - common_columns <- intersect(colnames(M1), colnames(M2)) + M1_common <- subset(M1, M1[, 1] %in% common_individuals) + M2_common <- subset(M2, M2[, 1] %in% common_individuals) - M1_common <- subset(M1, M1[, 1] %in% common_individuals) - M2_common <- subset(M2, M2[, 1] %in% common_individuals) + M1_common <- M1_common[, common_columns] + M2_common <- M2_common[, common_columns] - M1_common <- M1_common[, common_columns] - M2_common <- M2_common[, common_columns] + common_columns <- common_columns[-1] # delete id column - common_columns <- common_columns[-1]# delete id column + for (col_name in common_columns) { + # Check that the columns are identical, then delete them from M2 + if (!identical(M1_common[[col_name]], M2_common[[col_name]])) { + non_identical_columns_v <- c(non_identical_columns_v, col_name) - for (col_name in common_columns) { - #Check that the columns are identical, then delete them from M2 - if (!identical(M1_common[[col_name]], M2_common[[col_name]])) { + # otherwise store the columns where the values are not the same in non_identical_columns + } else { + M2 <- M2[, -which(colnames(M2) == col_name)] + } + } - non_identical_columns_v <- c(non_identical_columns_v, col_name) + # if the list of columns that do not take the same values is null, we return M1/M2 + if (is.null(non_identical_columns_v)) { + result <- list(M1 = M1, M2 = M2) - #otherwise store the columns where the values are not the same in non_identical_columns + return(result) } else { - M2 <- M2[, -which(colnames(M2) == col_name)] + for (non_identical_columns in non_identical_columns_v) { + # If we decide to keep the 2 columns and they do not take the same values, we change their names by adding a suffix. + + if (choice_keep == "yes") { # keep both columns and give them a new name + + new_name <- paste(tab1, non_identical_columns, sep = "_") + colnames(M1)[colnames(M1) == non_identical_columns] <- new_name + + new_name <- paste(tab2, non_identical_columns, sep = "_") + colnames(M2)[colnames(M2) == non_identical_columns] <- new_name + } + + if (choice_keep == "no") { # Keep only one and delete the other + if (Keep == 1) { + M2 <- M2[, -which(colnames(M2) == non_identical_columns)] + new_name <- paste(tab1, non_identical_columns, sep = "_") + colnames(M1)[colnames(M1) == non_identical_columns] <- new_name + } + if (Keep == 2) { + M1 <- M1[, -which(colnames(M1) == non_identical_columns)] + new_name <- paste(tab2, non_identical_columns, sep = "_") + colnames(M2)[colnames(M2) == non_identical_columns] <- new_name + } + } + } } - } - - #if the list of columns that do not take the same values is null, we return M1/M2 - if (is.null(non_identical_columns_v)) { result <- list(M1 = M1, M2 = M2) - return(result) - - - } else { - - for (non_identical_columns in non_identical_columns_v) { - - #If we decide to keep the 2 columns and they do not take the same values, we change their names by adding a suffix. - - if (choice_keep == "yes") {#keep both columns and give them a new name - - new_name <- paste(tab1, non_identical_columns, sep = "_") - colnames(M1)[colnames(M1) == non_identical_columns] <- new_name - - new_name <- paste(tab2, non_identical_columns, sep = "_") - colnames(M2)[colnames(M2) == non_identical_columns] <- new_name - - } - - if (choice_keep == "no") {#Keep only one and delete the other - if (Keep == 1) { - M2 <- M2[, -which(colnames(M2) == non_identical_columns)] - new_name <- paste(tab1, non_identical_columns, sep = "_") - colnames(M1)[colnames(M1) == non_identical_columns] <- new_name - - } - if (Keep == 2) { - M1 <- M1[, -which(colnames(M1) == non_identical_columns)] - new_name <- paste(tab2, non_identical_columns, sep = "_") - colnames(M2)[colnames(M2) == non_identical_columns] <- new_name - } - - } - } - } - - - result <- list(M1 = M1, M2 = M2) - return(result) - } #--------------------------------------------------------------------------------------------------------------------------------------------------------------------------- #--------------------------------------------------------------------------------------------------------------------------------------------------------------------------- match2_bis <- function(Metadata_1, Metadata_2, Mtype) { - - - #Metadata1/Metadata2 = data.frame containing sampleMetadata or variableMetadata - #Mtype = "sample" or "variable" depending on Metadata content - #To check if metadata1 and metadata2 match regarding identifiers - #returns a vector containing an error message if the identifiers are not all the same in the two metadatas - err.stock <- NULL#error vector - - - id2 <- Metadata_1[, 1] - id1 <- Metadata_2[, 1] - - if (length(which(id1 %in% id2)) != length(id1) || length(which(id2 %in% id1)) != length(id2)) { - err.stock <- c("\n", Mtype, "Metadata_1 and ", Mtype, "Metadata_2 do not match regarding Metadata_2 identifiers.") - if (length(which(id1 %in% id2)) != length(id1)) { - if (length(which(! (id1 %in% id2))) < 4) { - err.stock <- c(err.stock, "\n The ") - } else { - err.stock <- c(err.stock, "\n For example, the ") - } - err.stock <- c(err.stock, "following identifiers found in the ", Mtype, "Metadata_1 file\n", - " do not appear in the ", Mtype, " Metadata_2 file:\n") - identif <- id1[which(! (id1 %in% id2))][seq_len(min(3, length(which(! (id1 %in% id2)))))] - err.stock <- c(err.stock, " ", paste(identif, collapse = "\n "), "\n") - } - if (length(which(id2 %in% id1)) != length(id2)) { - if (length(which(! (id2 %in% id1))) < 4) { - err.stock <- c(err.stock, "\n The ") - } else { - err.stock <- c(err.stock, "\n For example, the ") - } - err.stock <- c(err.stock, "following identifiers found in the ", Mtype, " Metadata_2 file\n", - " do not appear in the", Mtype, " Metadata_1 file:\n") - identif <- id2[which(! (id2 %in% id1))][seq_len(min(3, length(which(! (id2 %in% id1)))))] - err.stock <- c(err.stock, " ", paste(identif, collapse = "\n "), "\n") + # Metadata1/Metadata2 = data.frame containing sampleMetadata or variableMetadata + # Mtype = "sample" or "variable" depending on Metadata content + # To check if metadata1 and metadata2 match regarding identifiers + # returns a vector containing an error message if the identifiers are not all the same in the two metadatas + err.stock <- NULL # error vector + + + id2 <- Metadata_1[, 1] + id1 <- Metadata_2[, 1] + + if (length(which(id1 %in% id2)) != length(id1) || length(which(id2 %in% id1)) != length(id2)) { + err.stock <- c("\n", Mtype, "Metadata_1 and ", Mtype, "Metadata_2 do not match regarding Metadata_2 identifiers.") + if (length(which(id1 %in% id2)) != length(id1)) { + if (length(which(!(id1 %in% id2))) < 4) { + err.stock <- c(err.stock, "\n The ") + } else { + err.stock <- c(err.stock, "\n For example, the ") + } + err.stock <- c( + err.stock, "following identifiers found in the ", Mtype, "Metadata_1 file\n", + " do not appear in the ", Mtype, " Metadata_2 file:\n" + ) + identif <- id1[which(!(id1 %in% id2))][seq_len(min(3, length(which(!(id1 %in% id2)))))] + err.stock <- c(err.stock, " ", paste(identif, collapse = "\n "), "\n") + } + if (length(which(id2 %in% id1)) != length(id2)) { + if (length(which(!(id2 %in% id1))) < 4) { + err.stock <- c(err.stock, "\n The ") + } else { + err.stock <- c(err.stock, "\n For example, the ") + } + err.stock <- c( + err.stock, "following identifiers found in the ", Mtype, " Metadata_2 file\n", + " do not appear in the", Mtype, " Metadata_1 file:\n" + ) + identif <- id2[which(!(id2 %in% id1))][seq_len(min(3, length(which(!(id2 %in% id1)))))] + err.stock <- c(err.stock, " ", paste(identif, collapse = "\n "), "\n") + } + err.stock <- c(err.stock, "\nPlease check your data.\n") } - err.stock <- c(err.stock, "\nPlease check your data.\n") - } - - return(err.stock) + return(err.stock) } diff --git a/tools/xcms/abims_xcms_fillPeaks.xml b/tools/xcms/abims_xcms_fillPeaks.xml index d224550a08..7d349a3280 100644 --- a/tools/xcms/abims_xcms_fillPeaks.xml +++ b/tools/xcms/abims_xcms_fillPeaks.xml @@ -8,10 +8,14 @@ + + + + + + + + - + + + + + + + + bioconductor-camera r-batch - + + + + + + + + 1) { + if (length(unique(xdata$sample_group)) < 10) { + group_colors <- brewer.pal(length(unique(xdata$sample_group)), "Set1") + } else { + group_colors <- hcl.colors(length(unique(xdata$sample_group)), palette = "Dark 3") + } names(group_colors) <- unique(xdata$sample_group) - plotAdjustedRtime(xdata, col = group_colors[xdata$sample_group]) - legend("topright", legend = names(group_colors), col = group_colors, cex = 0.8, lty = 1) - } + col_per_samp <- as.character(xdata$sample_group) + for (i in seq_len(length(group_colors))) { + col_per_samp[col_per_samp == (names(group_colors)[i])] <- group_colors[i] + } - # Color by sample - plotAdjustedRtime(xdata, col = rainbow(length(xdata@phenoData@data$sample_name))) - legend("topright", legend = xdata@phenoData@data$sample_name, col = rainbow(length(xdata@phenoData@data$sample_name)), cex = 0.8, lty = 1) + xlim <- c(min(featureDefinitions(xdata)$rtmin), max(featureDefinitions(xdata)$rtmax)) + for (i in seq_len(nrow(featureDefinitions(xdata)))) { + mzmin <- featureDefinitions(xdata)[i, ]$mzmin + mzmax <- featureDefinitions(xdata)[i, ]$mzmax + plotChromPeakDensity(xdata, param = param, mz = c(mzmin, mzmax), col = col_per_samp, pch = 16, xlim = xlim, main = paste(round(mzmin, mzdigit), round(mzmax, mzdigit))) + legend("topright", legend = names(group_colors), col = group_colors, cex = 0.8, lty = 1) + } - dev.off() + dev.off() } -#@author G. Le Corguille -# value: intensity values to be used into, maxo or intb -getPeaklistW4M <- function(xdata, intval = "into", convertRTMinute = FALSE, numDigitsMZ = 4, numDigitsRT = 0, naTOzero = TRUE, variableMetadataOutput, dataMatrixOutput, sampleNamesList) { - dataMatrix <- featureValues(xdata, method = "medret", value = intval) - colnames(dataMatrix) <- make.names(tools::file_path_sans_ext(colnames(dataMatrix))) - dataMatrix <- cbind(name = groupnames(xdata), dataMatrix) - variableMetadata <- featureDefinitions(xdata) - colnames(variableMetadata)[1] <- "mz" - colnames(variableMetadata)[4] <- "rt" - variableMetadata <- data.frame(name = groupnames(xdata), variableMetadata) +# @author G. Le Corguille +# Draw the plotChromPeakDensity 3 per page in a pdf file +getPlotAdjustedRtime <- function(xdata) { + pdf(file = "raw_vs_adjusted_rt.pdf", width = 16, height = 12) - variableMetadata <- RTSecondToMinute(variableMetadata, convertRTMinute) - variableMetadata <- formatIonIdentifiers(variableMetadata, numDigitsRT = numDigitsRT, numDigitsMZ = numDigitsMZ) - dataMatrix <- naTOzeroDataMatrix(dataMatrix, naTOzero) + # Color by group + if (length(unique(xdata$sample_group)) < 10) { + group_colors <- brewer.pal(length(unique(xdata$sample_group)), "Set1") + } else { + group_colors <- hcl.colors(length(unique(xdata$sample_group)), palette = "Dark 3") + } + if (length(group_colors) > 1) { + names(group_colors) <- unique(xdata$sample_group) + plotAdjustedRtime(xdata, col = group_colors[xdata$sample_group]) + legend("topright", legend = names(group_colors), col = group_colors, cex = 0.8, lty = 1) + } - # FIX: issue when the vector at peakidx is too long and is written in a new line during the export - variableMetadata[, "peakidx"] <- vapply(variableMetadata[, "peakidx"], FUN = paste, FUN.VALUE = character(1), collapse = ",") + # Color by sample + plotAdjustedRtime(xdata, col = rainbow(length(xdata@phenoData@data$sample_name))) + legend("topright", legend = xdata@phenoData@data$sample_name, col = rainbow(length(xdata@phenoData@data$sample_name)), cex = 0.8, lty = 1) - write.table(variableMetadata, file = variableMetadataOutput, sep = "\t", quote = FALSE, row.names = FALSE) - write.table(dataMatrix, file = dataMatrixOutput, sep = "\t", quote = FALSE, row.names = FALSE) + dev.off() +} +# @author G. Le Corguille +# value: intensity values to be used into, maxo or intb +getPeaklistW4M <- function(xdata, intval = "into", convertRTMinute = FALSE, numDigitsMZ = 4, numDigitsRT = 0, naTOzero = TRUE, variableMetadataOutput, dataMatrixOutput, sampleNamesList) { + dataMatrix <- featureValues(xdata, method = "medret", value = intval) + colnames(dataMatrix) <- make.names(tools::file_path_sans_ext(colnames(dataMatrix))) + dataMatrix <- cbind(name = groupnames(xdata), dataMatrix) + variableMetadata <- featureDefinitions(xdata) + colnames(variableMetadata)[1] <- "mz" + colnames(variableMetadata)[4] <- "rt" + variableMetadata <- data.frame(name = groupnames(xdata), variableMetadata) + + variableMetadata <- RTSecondToMinute(variableMetadata, convertRTMinute) + variableMetadata <- formatIonIdentifiers(variableMetadata, numDigitsRT = numDigitsRT, numDigitsMZ = numDigitsMZ) + dataMatrix <- naTOzeroDataMatrix(dataMatrix, naTOzero) + + # FIX: issue when the vector at peakidx is too long and is written in a new line during the export + variableMetadata[, "peakidx"] <- vapply(variableMetadata[, "peakidx"], FUN = paste, FUN.VALUE = character(1), collapse = ",") + + write.table(variableMetadata, file = variableMetadataOutput, sep = "\t", quote = FALSE, row.names = FALSE) + write.table(dataMatrix, file = dataMatrixOutput, sep = "\t", quote = FALSE, row.names = FALSE) } -#@author G. Le Corguille +# @author G. Le Corguille # It allow different of field separators getDataFrameFromFile <- function(filename, header = TRUE) { - myDataFrame <- read.table(filename, header = header, sep = ";", stringsAsFactors = FALSE) - if (ncol(myDataFrame) < 2) myDataFrame <- read.table(filename, header = header, sep = "\t", stringsAsFactors = FALSE) - if (ncol(myDataFrame) < 2) myDataFrame <- read.table(filename, header = header, sep = ",", stringsAsFactors = FALSE) - if (ncol(myDataFrame) < 2) { - error_message <- "Your tabular file seems not well formatted. The column separators accepted are ; , and tabulation" - print(error_message) - stop(error_message) - } - return(myDataFrame) + myDataFrame <- read.table(filename, header = header, sep = ";", stringsAsFactors = FALSE) + if (ncol(myDataFrame) < 2) myDataFrame <- read.table(filename, header = header, sep = "\t", stringsAsFactors = FALSE) + if (ncol(myDataFrame) < 2) myDataFrame <- read.table(filename, header = header, sep = ",", stringsAsFactors = FALSE) + if (ncol(myDataFrame) < 2) { + error_message <- "Your tabular file seems not well formatted. The column separators accepted are ; , and tabulation" + print(error_message) + stop(error_message) + } + return(myDataFrame) } -#@author G. Le Corguille +# @author G. Le Corguille # Draw the BPI and TIC graphics # colored by sample names or class names getPlotChromatogram <- function(chrom, xdata, pdfname = "Chromatogram.pdf", aggregationFun = "max") { + if (aggregationFun == "sum") { + type <- "Total Ion Chromatograms" + } else { + type <- "Base Peak Intensity Chromatograms" + } - if (aggregationFun == "sum") - type <- "Total Ion Chromatograms" - else - type <- "Base Peak Intensity Chromatograms" - - adjusted <- "Raw" - if (hasAdjustedRtime(xdata)) - adjusted <- "Adjusted" + adjusted <- "Raw" + if (hasAdjustedRtime(xdata)) { + adjusted <- "Adjusted" + } - main <- paste(type, ":", adjusted, "data") + main <- paste(type, ":", adjusted, "data") - pdf(pdfname, width = 16, height = 10) + pdf(pdfname, width = 16, height = 10) - # Color by group - if (length(unique(xdata$sample_group)) < 10) { - group_colors <- brewer.pal(length(unique(xdata$sample_group)), "Set1") - } else { - group_colors <- hcl.colors(length(unique(xdata$sample_group)), palette = "Dark 3") - } - if (length(group_colors) > 1) { - names(group_colors) <- unique(xdata$sample_group) - plot(chrom, col = group_colors[chrom$sample_group], main = main, peakType = "none") - legend("topright", legend = names(group_colors), col = group_colors, cex = 0.8, lty = 1) - } + # Color by group + if (length(unique(xdata$sample_group)) < 10) { + group_colors <- brewer.pal(length(unique(xdata$sample_group)), "Set1") + } else { + group_colors <- hcl.colors(length(unique(xdata$sample_group)), palette = "Dark 3") + } + if (length(group_colors) > 1) { + names(group_colors) <- unique(xdata$sample_group) + plot(chrom, col = group_colors[chrom$sample_group], main = main, peakType = "none") + legend("topright", legend = names(group_colors), col = group_colors, cex = 0.8, lty = 1) + } - # Color by sample - plot(chrom, col = rainbow(length(xdata@phenoData@data$sample_name)), main = main, peakType = "none") - legend("topright", legend = xdata@phenoData@data$sample_name, col = rainbow(length(xdata@phenoData@data$sample_name)), cex = 0.8, lty = 1) + # Color by sample + plot(chrom, col = rainbow(length(xdata@phenoData@data$sample_name)), main = main, peakType = "none") + legend("topright", legend = xdata@phenoData@data$sample_name, col = rainbow(length(xdata@phenoData@data$sample_name)), cex = 0.8, lty = 1) - dev.off() + dev.off() } # Get the polarities from all the samples of a condition -#@author Misharl Monsoor misharl.monsoor@sb-roscoff.fr ABiMS TEAM -#@author Gildas Le Corguille lecorguille@sb-roscoff.fr ABiMS TEAM +# @author Misharl Monsoor misharl.monsoor@sb-roscoff.fr ABiMS TEAM +# @author Gildas Le Corguille lecorguille@sb-roscoff.fr ABiMS TEAM getSampleMetadata <- function(xdata = NULL, sampleMetadataOutput = "sampleMetadata.tsv") { - cat("Creating the sampleMetadata file...\n") - - #Create the sampleMetada dataframe - sampleMetadata <- xdata@phenoData@data - rownames(sampleMetadata) <- NULL - colnames(sampleMetadata) <- c("sample_name", "class") - - sampleNamesOrigin <- sampleMetadata$sample_name - sampleNamesMakeNames <- make.names(sampleNamesOrigin) - - if (any(duplicated(sampleNamesMakeNames))) { - write("\n\nERROR: Usually, R has trouble to deal with special characters in its column names, so it rename them using make.names().\nIn your case, at least two columns after the renaming obtain the same name, thus XCMS will collapse those columns per name.", stderr()) - for (sampleName in sampleNamesOrigin) { - write(paste(sampleName, "\t->\t", make.names(sampleName)), stderr()) + cat("Creating the sampleMetadata file...\n") + + # Create the sampleMetada dataframe + sampleMetadata <- xdata@phenoData@data + rownames(sampleMetadata) <- NULL + colnames(sampleMetadata) <- c("sample_name", "class") + + sampleNamesOrigin <- sampleMetadata$sample_name + sampleNamesMakeNames <- make.names(sampleNamesOrigin) + + if (any(duplicated(sampleNamesMakeNames))) { + write("\n\nERROR: Usually, R has trouble to deal with special characters in its column names, so it rename them using make.names().\nIn your case, at least two columns after the renaming obtain the same name, thus XCMS will collapse those columns per name.", stderr()) + for (sampleName in sampleNamesOrigin) { + write(paste(sampleName, "\t->\t", make.names(sampleName)), stderr()) + } + stop("\n\nERROR: One or more of your files will not be import by xcmsSet. It may due to bad characters in their filenames.") } - stop("\n\nERROR: One or more of your files will not be import by xcmsSet. It may due to bad characters in their filenames.") - } - if (!all(sampleNamesOrigin == sampleNamesMakeNames)) { - cat("\n\nWARNING: Usually, R has trouble to deal with special characters in its column names, so it rename them using make.names()\nIn your case, one or more sample names will be renamed in the sampleMetadata and dataMatrix files:\n") - for (sampleName in sampleNamesOrigin) { - cat(paste(sampleName, "\t->\t", make.names(sampleName), "\n")) + if (!all(sampleNamesOrigin == sampleNamesMakeNames)) { + cat("\n\nWARNING: Usually, R has trouble to deal with special characters in its column names, so it rename them using make.names()\nIn your case, one or more sample names will be renamed in the sampleMetadata and dataMatrix files:\n") + for (sampleName in sampleNamesOrigin) { + cat(paste(sampleName, "\t->\t", make.names(sampleName), "\n")) + } } - } - sampleMetadata$sample_name <- sampleNamesMakeNames - - - #For each sample file, the following actions are done - for (fileIdx in seq_len(length(fileNames(xdata)))) { - #Check if the file is in the CDF format - if (!mzR:::netCDFIsFile(fileNames(xdata))) { - - # If the column isn't exist, with add one filled with NA - if (is.null(sampleMetadata$polarity)) sampleMetadata$polarity <- NA - - #Extract the polarity (a list of polarities) - polarity <- fData(xdata)[fData(xdata)$fileIdx == fileIdx, "polarity"] - #Verify if all the scans have the same polarity - uniq_list <- unique(polarity) - if (length(uniq_list) > 1) { - polarity <- "mixed" - } else { - polarity <- as.character(uniq_list) - } - - #Set the polarity attribute - sampleMetadata$polarity[fileIdx] <- polarity + sampleMetadata$sample_name <- sampleNamesMakeNames + + + # For each sample file, the following actions are done + for (fileIdx in seq_len(length(fileNames(xdata)))) { + # Check if the file is in the CDF format + if (!mzR:::netCDFIsFile(fileNames(xdata))) { + # If the column isn't exist, with add one filled with NA + if (is.null(sampleMetadata$polarity)) sampleMetadata$polarity <- NA + + # Extract the polarity (a list of polarities) + polarity <- fData(xdata)[fData(xdata)$fileIdx == fileIdx, "polarity"] + # Verify if all the scans have the same polarity + uniq_list <- unique(polarity) + if (length(uniq_list) > 1) { + polarity <- "mixed" + } else { + polarity <- as.character(uniq_list) + } + + # Set the polarity attribute + sampleMetadata$polarity[fileIdx] <- polarity + } } - } - - write.table(sampleMetadata, sep = "\t", quote = FALSE, row.names = FALSE, file = sampleMetadataOutput) - - return(list("sampleNamesOrigin" = sampleNamesOrigin, "sampleNamesMakeNames" = sampleNamesMakeNames)) + write.table(sampleMetadata, sep = "\t", quote = FALSE, row.names = FALSE, file = sampleMetadataOutput) + return(list("sampleNamesOrigin" = sampleNamesOrigin, "sampleNamesMakeNames" = sampleNamesMakeNames)) } # This function will compute MD5 checksum to check the data integrity -#@author Gildas Le Corguille lecorguille@sb-roscoff.fr +# @author Gildas Le Corguille lecorguille@sb-roscoff.fr getMd5sum <- function(files) { - cat("Compute md5 checksum...\n") - library(tools) - return(as.matrix(md5sum(files))) + cat("Compute md5 checksum...\n") + library(tools) + return(as.matrix(md5sum(files))) } # This function retrieve the raw file in the working directory # - if zipfile: unzip the file with its directory tree # - if singlefiles: set symlink with the good filename -#@author Gildas Le Corguille lecorguille@sb-roscoff.fr +# @author Gildas Le Corguille lecorguille@sb-roscoff.fr retrieveRawfileInTheWorkingDir <- function(singlefile, zipfile, args, prefix = "") { - - if (!(prefix %in% c("", "Positive", "Negative", "MS1", "MS2"))) stop("prefix must be either '', 'Positive', 'Negative', 'MS1' or 'MS2'") - - # single - if the file are passed in the command arguments -> refresh singlefile - if (!is.null(args[[paste0("singlefile_galaxyPath", prefix)]])) { - singlefile_galaxyPaths <- unlist(strsplit(args[[paste0("singlefile_galaxyPath", prefix)]], "\\|")) - singlefile_sampleNames <- unlist(strsplit(args[[paste0("singlefile_sampleName", prefix)]], "\\|")) - - singlefile <- NULL - for (singlefile_galaxyPath_i in seq_len(length(singlefile_galaxyPaths))) { - singlefile_galaxyPath <- singlefile_galaxyPaths[singlefile_galaxyPath_i] - singlefile_sampleName <- singlefile_sampleNames[singlefile_galaxyPath_i] - # In case, an url is used to import data within Galaxy - singlefile_sampleName <- tail(unlist(strsplit(singlefile_sampleName, "/")), n = 1) - singlefile[[singlefile_sampleName]] <- singlefile_galaxyPath + if (!(prefix %in% c("", "Positive", "Negative", "MS1", "MS2"))) stop("prefix must be either '', 'Positive', 'Negative', 'MS1' or 'MS2'") + + # single - if the file are passed in the command arguments -> refresh singlefile + if (!is.null(args[[paste0("singlefile_galaxyPath", prefix)]])) { + singlefile_galaxyPaths <- unlist(strsplit(args[[paste0("singlefile_galaxyPath", prefix)]], "\\|")) + singlefile_sampleNames <- unlist(strsplit(args[[paste0("singlefile_sampleName", prefix)]], "\\|")) + + singlefile <- NULL + for (singlefile_galaxyPath_i in seq_len(length(singlefile_galaxyPaths))) { + singlefile_galaxyPath <- singlefile_galaxyPaths[singlefile_galaxyPath_i] + singlefile_sampleName <- singlefile_sampleNames[singlefile_galaxyPath_i] + # In case, an url is used to import data within Galaxy + singlefile_sampleName <- tail(unlist(strsplit(singlefile_sampleName, "/")), n = 1) + singlefile[[singlefile_sampleName]] <- singlefile_galaxyPath + } + } + # zipfile - if the file are passed in the command arguments -> refresh zipfile + if (!is.null(args[[paste0("zipfile", prefix)]])) { + zipfile <- args[[paste0("zipfile", prefix)]] } - } - # zipfile - if the file are passed in the command arguments -> refresh zipfile - if (!is.null(args[[paste0("zipfile", prefix)]])) - zipfile <- args[[paste0("zipfile", prefix)]] - - # single - if (!is.null(singlefile) && (length("singlefile") > 0)) { - files <- vector() - for (singlefile_sampleName in names(singlefile)) { - singlefile_galaxyPath <- singlefile[[singlefile_sampleName]] - if (!file.exists(singlefile_galaxyPath)) { - error_message <- paste("Cannot access the sample:", singlefile_sampleName, "located:", singlefile_galaxyPath, ". Please, contact your administrator ... if you have one!") - print(error_message) - stop(error_message) - } - if (!suppressWarnings(try(file.link(singlefile_galaxyPath, singlefile_sampleName), silent = TRUE))) - file.copy(singlefile_galaxyPath, singlefile_sampleName) - files <- c(files, singlefile_sampleName) + # single + if (!is.null(singlefile) && (length("singlefile") > 0)) { + files <- vector() + for (singlefile_sampleName in names(singlefile)) { + singlefile_galaxyPath <- singlefile[[singlefile_sampleName]] + if (!file.exists(singlefile_galaxyPath)) { + error_message <- paste("Cannot access the sample:", singlefile_sampleName, "located:", singlefile_galaxyPath, ". Please, contact your administrator ... if you have one!") + print(error_message) + stop(error_message) + } + + if (!suppressWarnings(try(file.link(singlefile_galaxyPath, singlefile_sampleName), silent = TRUE))) { + file.copy(singlefile_galaxyPath, singlefile_sampleName) + } + files <- c(files, singlefile_sampleName) + } } - } - # zipfile - if (!is.null(zipfile) && (zipfile != "")) { - if (!file.exists(zipfile)) { - error_message <- paste("Cannot access the Zip file:", zipfile, ". Please, contact your administrator ... if you have one!") - print(error_message) - stop(error_message) + # zipfile + if (!is.null(zipfile) && (zipfile != "")) { + if (!file.exists(zipfile)) { + error_message <- paste("Cannot access the Zip file:", zipfile, ". Please, contact your administrator ... if you have one!") + print(error_message) + stop(error_message) + } + suppressWarnings(unzip(zipfile, unzip = "unzip")) + + # get the directory name + suppressWarnings(filesInZip <- unzip(zipfile, list = TRUE)) + directories <- unique(unlist(lapply(strsplit(filesInZip$Name, "/"), function(x) x[1]))) + directories <- directories[!(directories %in% c("__MACOSX")) & file.info(directories)$isdir] + directory <- "." + if (length(directories) == 1) directory <- directories + + cat("files_root_directory\t", directory, "\n") + + filepattern <- c("[Cc][Dd][Ff]", "[Nn][Cc]", "([Mm][Zz])?[Xx][Mm][Ll]", "[Mm][Zz][Dd][Aa][Tt][Aa]", "[Mm][Zz][Mm][Ll]") + filepattern <- paste(paste("\\.", filepattern, "$", sep = ""), collapse = "|") + info <- file.info(directory) + listed <- list.files(directory[info$isdir], pattern = filepattern, recursive = TRUE, full.names = TRUE) + files <- c(directory[!info$isdir], listed) + exists <- file.exists(files) + files <- files[exists] } - suppressWarnings(unzip(zipfile, unzip = "unzip")) - - #get the directory name - suppressWarnings(filesInZip <- unzip(zipfile, list = TRUE)) - directories <- unique(unlist(lapply(strsplit(filesInZip$Name, "/"), function(x) x[1]))) - directories <- directories[!(directories %in% c("__MACOSX")) & file.info(directories)$isdir] - directory <- "." - if (length(directories) == 1) directory <- directories - - cat("files_root_directory\t", directory, "\n") - - filepattern <- c("[Cc][Dd][Ff]", "[Nn][Cc]", "([Mm][Zz])?[Xx][Mm][Ll]", "[Mm][Zz][Dd][Aa][Tt][Aa]", "[Mm][Zz][Mm][Ll]") - filepattern <- paste(paste("\\.", filepattern, "$", sep = ""), collapse = "|") - info <- file.info(directory) - listed <- list.files(directory[info$isdir], pattern = filepattern, recursive = TRUE, full.names = TRUE) - files <- c(directory[!info$isdir], listed) - exists <- file.exists(files) - files <- files[exists] - - } - return(list(zipfile = zipfile, singlefile = singlefile, files = files)) + return(list(zipfile = zipfile, singlefile = singlefile, files = files)) } # This function retrieve a xset like object -#@author Gildas Le Corguille lecorguille@sb-roscoff.fr +# @author Gildas Le Corguille lecorguille@sb-roscoff.fr getxcmsSetObject <- function(xobject) { - # XCMS 1.x - if (class(xobject) == "xcmsSet") - return(xobject) - # XCMS 3.x - if (class(xobject) == "XCMSnExp") { - # Get the legacy xcmsSet object - suppressWarnings(xset <- as(xobject, "xcmsSet")) - if (!is.null(xset@phenoData$sample_group)) - sampclass(xset) <- xset@phenoData$sample_group - else - sampclass(xset) <- "." - return(xset) - } + # XCMS 1.x + if (class(xobject) == "xcmsSet") { + return(xobject) + } + # XCMS 3.x + if (class(xobject) == "XCMSnExp") { + # Get the legacy xcmsSet object + suppressWarnings(xset <- as(xobject, "xcmsSet")) + if (!is.null(xset@phenoData$sample_group)) { + sampclass(xset) <- xset@phenoData$sample_group + } else { + sampclass(xset) <- "." + } + return(xset) + } } diff --git a/tools/xcms/macros_xcms.xml b/tools/xcms/macros_xcms.xml index a84099fb7b..4c76317226 100644 --- a/tools/xcms/macros_xcms.xml +++ b/tools/xcms/macros_xcms.xml @@ -2,7 +2,7 @@ 3.12.0 - 1 + 2 21.09 diff --git a/tools/xcms/msnbase_readmsdata.r b/tools/xcms/msnbase_readmsdata.r index f8a06bf4ad..17e455ea88 100755 --- a/tools/xcms/msnbase_readmsdata.r +++ b/tools/xcms/msnbase_readmsdata.r @@ -9,11 +9,11 @@ sink(log_file, type = "output") # ----- PACKAGE ----- cat("\tSESSION INFO\n") -#Import the different functions +# Import the different functions source_local <- function(fname) { - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep = "/")) + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) } source_local("lib.r") @@ -24,7 +24,7 @@ cat("\n\n") # ----- ARGUMENTS ----- cat("\tARGUMENTS INFO\n") -args <- parseCommandArgs(evaluate = FALSE) #interpretation of arguments given in command line as an R list of objects +args <- parseCommandArgs(evaluate = FALSE) # interpretation of arguments given in command line as an R list of objects write.table(as.matrix(args), col.names = FALSE, quote = FALSE, sep = "\t") cat("\n\n") @@ -83,8 +83,8 @@ cat("\t\tphenoData\n") print(raw_data@phenoData@data) cat("\n\n") -#saving R data in .Rdata file to save the variables used in the present tool -objects2save <- c("raw_data", "zipfile", "singlefile", "md5sumList", "sampleNamesList") #, "chromTIC", "chromBPI") +# saving R data in .Rdata file to save the variables used in the present tool +objects2save <- c("raw_data", "zipfile", "singlefile", "md5sumList", "sampleNamesList") # , "chromTIC", "chromBPI") save(list = objects2save[objects2save %in% ls()], file = "readmsdata.RData") diff --git a/tools/xcms/msnbase_readmsdata.xml b/tools/xcms/msnbase_readmsdata.xml index 1c4fa5fae6..9741955ad6 100644 --- a/tools/xcms/msnbase_readmsdata.xml +++ b/tools/xcms/msnbase_readmsdata.xml @@ -1,4 +1,4 @@ - + Imports mass-spectrometry data files @@ -7,10 +7,14 @@ + + + + + + + + diff --git a/tools/xcms/xcms_fillpeaks.r b/tools/xcms/xcms_fillpeaks.r index 9618b06d58..6ae401e0e4 100755 --- a/tools/xcms/xcms_fillpeaks.r +++ b/tools/xcms/xcms_fillpeaks.r @@ -9,11 +9,11 @@ sink(log_file, type = "output") # ----- PACKAGE ----- cat("\tSESSION INFO\n") -#Import the different functions +# Import the different functions source_local <- function(fname) { - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep = "/")) + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) } source_local("lib.r") @@ -24,7 +24,7 @@ cat("\n\n") # ----- ARGUMENTS ----- cat("\tARGUMENTS INFO\n") -args <- parseCommandArgs(evaluate = FALSE) #interpretation of arguments given in command line as an R list of objects +args <- parseCommandArgs(evaluate = FALSE) # interpretation of arguments given in command line as an R list of objects write.table(as.matrix(args), col.names = FALSE, quote = FALSE, sep = "\t") cat("\n\n") @@ -32,7 +32,7 @@ cat("\n\n") # ----- PROCESSING INFILE ----- cat("\tARGUMENTS PROCESSING INFO\n") -#saving the specific parameters +# saving the specific parameters method <- "FillChromPeaks" if (!is.null(args$convertRTMinute)) convertRTMinute <- args$convertRTMinute @@ -47,11 +47,11 @@ cat("\n\n") # ----- ARGUMENTS PROCESSING ----- cat("\tINFILE PROCESSING INFO\n") -#image is an .RData file necessary to use xset variable given by previous tools +# image is an .RData file necessary to use xset variable given by previous tools load(args$image) if (!exists("xdata")) stop("\n\nERROR: The RData doesn't contain any object called 'xdata'. This RData should have been created by an old version of XMCS 2.*") -#Verification of a group step before doing the fillpeaks job. +# Verification of a group step before doing the fillpeaks job. if (!hasFeatures(xdata)) stop("You must always do a group step after a retcor. Otherwise it won't work for the fillpeaks step") # Handle infiles @@ -83,7 +83,7 @@ register(SerialParam()) xdata <- fillChromPeaks(xdata, param = fillChromPeaksParam) if (exists("intval")) { - getPeaklistW4M(xdata, intval, convertRTMinute, numDigitsMZ, numDigitsRT, naTOzero, "variableMetadata.tsv", "dataMatrix.tsv") + getPeaklistW4M(xdata, intval, convertRTMinute, numDigitsMZ, numDigitsRT, naTOzero, "variableMetadata.tsv", "dataMatrix.tsv") } cat("\n\n") @@ -100,8 +100,8 @@ xset <- getxcmsSetObject(xdata) print(xset) cat("\n\n") -#saving R data in .Rdata file to save the variables used in the present tool -objects2save <- c("xdata", "zipfile", "singlefile", "md5sumList", "sampleNamesList") #, "chromTIC", "chromBPI", "chromTIC_adjusted", "chromBPI_adjusted") +# saving R data in .Rdata file to save the variables used in the present tool +objects2save <- c("xdata", "zipfile", "singlefile", "md5sumList", "sampleNamesList") # , "chromTIC", "chromBPI", "chromTIC_adjusted", "chromBPI_adjusted") save(list = objects2save[objects2save %in% ls()], file = "fillpeaks.RData") cat("\n\n") diff --git a/tools/xcms/xcms_group.r b/tools/xcms/xcms_group.r index dd534bedc1..de01ba83ef 100755 --- a/tools/xcms/xcms_group.r +++ b/tools/xcms/xcms_group.r @@ -9,11 +9,11 @@ sink(log_file, type = "output") # ----- PACKAGE ----- cat("\tSESSION INFO\n") -#Import the different functions +# Import the different functions source_local <- function(fname) { - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep = "/")) + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) } source_local("lib.r") @@ -24,7 +24,7 @@ cat("\n\n") # ----- ARGUMENTS ----- cat("\tARGUMENTS INFO\n") -args <- parseCommandArgs(evaluate = FALSE) #interpretation of arguments given in command line as an R list of objects +args <- parseCommandArgs(evaluate = FALSE) # interpretation of arguments given in command line as an R list of objects write.table(as.matrix(args), col.names = FALSE, quote = FALSE, sep = "\t") cat("\n\n") @@ -32,7 +32,7 @@ cat("\n\n") # ----- PROCESSING INFILE ----- cat("\tARGUMENTS PROCESSING INFO\n") -#saving the specific parameters +# saving the specific parameters method <- args$method if (!is.null(args$convertRTMinute)) convertRTMinute <- args$convertRTMinute @@ -47,7 +47,7 @@ cat("\n\n") # ----- ARGUMENTS PROCESSING ----- cat("\tINFILE PROCESSING INFO\n") -#image is an .RData file necessary to use xset variable given by previous tools +# image is an .RData file necessary to use xset variable given by previous tools load(args$image) if (!exists("xdata")) stop("\n\nERROR: The RData doesn't contain any object called 'xdata'. This RData should have been created by an old version of XMCS 2.*") @@ -83,7 +83,7 @@ cat("\t\tDRAW GRAPHICS\n") getPlotChromPeakDensity(xdata, param = groupChromPeaksParam) if (exists("intval")) { - getPeaklistW4M(xdata, intval, convertRTMinute, numDigitsMZ, numDigitsRT, naTOzero, "variableMetadata.tsv", "dataMatrix.tsv") + getPeaklistW4M(xdata, intval, convertRTMinute, numDigitsMZ, numDigitsRT, naTOzero, "variableMetadata.tsv", "dataMatrix.tsv") } cat("\n\n") @@ -100,8 +100,8 @@ xset <- getxcmsSetObject(xdata) print(xset) cat("\n\n") -#saving R data in .Rdata file to save the variables used in the present tool -objects2save <- c("xdata", "zipfile", "singlefile", "md5sumList", "sampleNamesList") #, "chromTIC", "chromBPI", "chromTIC_adjusted", "chromBPI_adjusted") +# saving R data in .Rdata file to save the variables used in the present tool +objects2save <- c("xdata", "zipfile", "singlefile", "md5sumList", "sampleNamesList") # , "chromTIC", "chromBPI", "chromTIC_adjusted", "chromBPI_adjusted") save(list = objects2save[objects2save %in% ls()], file = "group.RData") cat("\n\n") diff --git a/tools/xcms/xcms_merge.r b/tools/xcms/xcms_merge.r index 801b1aad53..70f4db014a 100755 --- a/tools/xcms/xcms_merge.r +++ b/tools/xcms/xcms_merge.r @@ -1,10 +1,10 @@ #!/usr/bin/env Rscript -#Import the different functions +# Import the different functions source_local <- function(fname) { - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep = "/")) + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) } source_local("lib.r") @@ -12,7 +12,7 @@ pkgs <- c("xcms", "batch") loadAndDisplayPackages(pkgs) cat("\n\n") -args <- parseCommandArgs(evaluate = FALSE) #interpretation of arguments given in command line as an R list of objects +args <- parseCommandArgs(evaluate = FALSE) # interpretation of arguments given in command line as an R list of objects cat("\tXSET MERGING...\n") @@ -43,6 +43,6 @@ print(xset) cat("\n\n") cat("\tSAVE RData\n") -#saving R data in .Rdata file to save the variables used in the present tool -objects2save <- c("xdata", "zipfile", "singlefile", "md5sumList", "sampleNamesList") #, "chromTIC", "chromBPI") +# saving R data in .Rdata file to save the variables used in the present tool +objects2save <- c("xdata", "zipfile", "singlefile", "md5sumList", "sampleNamesList") # , "chromTIC", "chromBPI") save(list = objects2save[objects2save %in% ls()], file = "merged.RData") diff --git a/tools/xcms/xcms_merge.xml b/tools/xcms/xcms_merge.xml index 3a74cd0437..2797ef4129 100644 --- a/tools/xcms/xcms_merge.xml +++ b/tools/xcms/xcms_merge.xml @@ -7,10 +7,14 @@ + + + + macros_xcms.xml + + + + ") writehtml("

___ XCMS analysis summary using Workflow4Metabolomics ___

") # to pass the planemo shed_test if (user_email != "test@bx.psu.edu") { - if (!is.null(user_email)) writehtml("By: ", user_email, " - ") - writehtml("Date: ", format(Sys.time(), "%y%m%d-%H:%M:%S")) + if (!is.null(user_email)) writehtml("By: ", user_email, " - ") + writehtml("Date: ", format(Sys.time(), "%y%m%d-%H:%M:%S")) } writehtml("
") writehtml("

Samples used:

") writehtml("
") if (all(getSampleNames(xobject) == sampleNamesList$sampleNamesMakeNames)) { - sampleNameHeaderHtml <- paste0("") - sampleNameHtml <- paste0("") + sampleNameHeaderHtml <- paste0("") + sampleNameHtml <- paste0("") } else { - sampleNameHeaderHtml <- paste0("") - sampleNameHtml <- paste0("") + sampleNameHeaderHtml <- paste0("") + sampleNameHtml <- paste0("") } if (!exists("md5sumList")) { - md5sumHeaderHtml <- "" - md5sumHtml <- "" - md5sumLegend <- "" + md5sumHeaderHtml <- "" + md5sumHtml <- "" + md5sumLegend <- "" } else if (is.null(md5sumList$removalBadCharacters)) { - md5sumHeaderHtml <- paste0("") - md5sumHtml <- paste0("") - md5sumLegend <- "
*The program md5sum is designed to verify data integrity. So you can check if the data were uploaded correctly or if the data were changed during the process." + md5sumHeaderHtml <- paste0("") + md5sumHtml <- paste0("") + md5sumLegend <- "
*The program md5sum is designed to verify data integrity. So you can check if the data were uploaded correctly or if the data were changed during the process." } else { - md5sumHeaderHtml <- paste0("") - md5sumHtml <- paste0("") - md5sumLegend <- "
*The program md5sum is designed to verify data integrity. So you can check if the data were uploaded correctly or if the data were changed during the process.
**Because some bad characters (eg: accent) were removed from your original file, the checksum have changed too.
" + md5sumHeaderHtml <- paste0("") + md5sumHtml <- paste0("") + md5sumLegend <- "
*The program md5sum is designed to verify data integrity. So you can check if the data were uploaded correctly or if the data were changed during the process.
**Because some bad characters (eg: accent) were removed from your original file, the checksum have changed too.
" } writehtml("", sampleNameHeaderHtml, "", md5sumHeaderHtml, "") @@ -155,43 +159,44 @@ writehtml("
sample", getSampleNames(xobject), "sample", getSampleNames(xobject), "samplesample renamed", getSampleNames(xobject), "", sampleNamesList$sampleNamesMakeNames, "samplesample renamed", getSampleNames(xobject), "", sampleNamesList$sampleNamesMakeNames, "md5sum*", md5sumList$origin, "md5sum*", md5sumList$origin, "md5sum*md5sum** after bad characters removal", md5sumList$origin, "", md5sumList$removalBadCharacters, "md5sum*md5sum** after bad characters removal", md5sumList$origin, "", md5sumList$removalBadCharacters, "
filename
") writehtml("") # XCMS 3.x if (class(xobject) == "XCMSnExp") { - xcmsFunction <- NULL - params <- NULL - for (processHistoryItem in processHistory(xobject)) { - if ((xcmsFunction == processType(processHistoryItem)) && equalParams(params, processParam(processHistoryItem))) - next - timestamp <- processDate(processHistoryItem) - xcmsFunction <- processType(processHistoryItem) - params <- processParam(processHistoryItem) - writehtml("") - } + xcmsFunction <- NULL + params <- NULL + for (processHistoryItem in processHistory(xobject)) { + if ((xcmsFunction == processType(processHistoryItem)) && equalParams(params, processParam(processHistoryItem))) { + next + } + timestamp <- processDate(processHistoryItem) + xcmsFunction <- processType(processHistoryItem) + params <- processParam(processHistoryItem) + writehtml("") + } } # CAMERA and retrocompatability XCMS 1.x if (exists("listOFlistArguments")) { - for (tool in names(listOFlistArguments)) { - listOFlistArgumentsDisplay <- listOFlistArguments[[tool]][!(names(listOFlistArguments[[tool]]) %in% argBlacklist)] - - timestamp <- strsplit(tool, "_")[[1]][1] - xcmsFunction <- strsplit(tool, "_")[[1]][2] - writehtml("") - line_begin <- "" - for (arg in names(listOFlistArgumentsDisplay)) { - writehtml(line_begin, "") - line_begin <- "" + for (tool in names(listOFlistArguments)) { + listOFlistArgumentsDisplay <- listOFlistArguments[[tool]][!(names(listOFlistArguments[[tool]]) %in% argBlacklist)] + + timestamp <- strsplit(tool, "_")[[1]][1] + xcmsFunction <- strsplit(tool, "_")[[1]][2] + writehtml("") + line_begin <- "" + for (arg in names(listOFlistArgumentsDisplay)) { + writehtml(line_begin, "") + line_begin <- "" + } } - } } writehtml("
timestamp***functionargumentvalue
", timestamp, "", xcmsFunction, "
")
-    writeraw(htmlOutput, params)
-    writehtml("
", timestamp, "", xcmsFunction, "
")
+        writeraw(htmlOutput, params)
+        writehtml("
", timestamp, "", xcmsFunction, "", arg, "", unlist(listOFlistArgumentsDisplay[arg][1]), "
", timestamp, "", xcmsFunction, "", arg, "", unlist(listOFlistArgumentsDisplay[arg][1]), "
") writehtml("
***timestamp format: DD MM dd hh:mm:ss YYYY or yymmdd-hh:mm:ss") writehtml("
") if (class(xobject) == "XCMSnExp") { - writehtml("

Informations about the XCMSnExp object:

") - writehtml("
")
-  writeraw(htmlOutput, xobject)
-  writehtml("
") + writehtml("

Informations about the XCMSnExp object:

") + writehtml("
")
+    writeraw(htmlOutput, xobject)
+    writehtml("
") } writehtml("

Informations about the xcmsSet object:

") @@ -204,10 +209,10 @@ writehtml("") # CAMERA if (exists("xa")) { - writehtml("

Informations about the CAMERA object:

") - writehtml("
") - writehtml("Number of pcgroup: ", length(xa@pspectra)) - writehtml("
") + writehtml("

Informations about the CAMERA object:

") + writehtml("
") + writehtml("Number of pcgroup: ", length(xa@pspectra)) + writehtml("
") } writehtml("

Citations:

") diff --git a/tools/xcms/xcms_xcmsSet.r b/tools/xcms/xcms_xcmsSet.r index fbf2b146e2..4e654f11a0 100755 --- a/tools/xcms/xcms_xcmsSet.r +++ b/tools/xcms/xcms_xcmsSet.r @@ -9,11 +9,11 @@ sink(log_file, type = "output") # ----- PACKAGE ----- cat("\tSESSION INFO\n") -#Import the different functions +# Import the different functions source_local <- function(fname) { - argv <- commandArgs(trailingOnly = FALSE) - base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) - source(paste(base_dir, fname, sep = "/")) + argv <- commandArgs(trailingOnly = FALSE) + base_dir <- dirname(substring(argv[grep("--file=", argv)], 8)) + source(paste(base_dir, fname, sep = "/")) } source_local("lib.r") @@ -24,7 +24,7 @@ cat("\n\n") # ----- ARGUMENTS ----- cat("\tARGUMENTS INFO\n") -args <- parseCommandArgs(evaluate = FALSE) #interpretation of arguments given in command line as an R list of objects +args <- parseCommandArgs(evaluate = FALSE) # interpretation of arguments given in command line as an R list of objects write.table(as.matrix(args), col.names = FALSE, quote = FALSE, sep = "\t") cat("\n\n") @@ -33,14 +33,14 @@ cat("\n\n") # ----- PROCESSING INFILE ----- cat("\tARGUMENTS PROCESSING INFO\n") -#saving the commun parameters +# saving the commun parameters BPPARAM <- MulticoreParam(1) if (!is.null(args$BPPARAM)) { - BPPARAM <- MulticoreParam(args$BPPARAM) + BPPARAM <- MulticoreParam(args$BPPARAM) } register(BPPARAM) -#saving the specific parameters +# saving the specific parameters if (!is.null(args$filterAcquisitionNum)) filterAcquisitionNumParam <- args$filterAcquisitionNum if (!is.null(args$filterRt)) filterRtParam <- args$filterRt if (!is.null(args$filterMz)) filterMzParam <- args$filterMz @@ -49,9 +49,9 @@ if (!is.null(args$peaklist)) peaklistParam <- args$peaklist method <- args$method if (!is.null(args$roiList)) { - cat("\t\troiList provided\n") - args$roiList <- list(getDataFrameFromFile(args$roiList)) - print(args$roiList) + cat("\t\troiList provided\n") + args$roiList <- list(getDataFrameFromFile(args$roiList)) + print(args$roiList) } cat("\n\n") @@ -59,7 +59,7 @@ cat("\n\n") # ----- INFILE PROCESSING ----- cat("\tINFILE PROCESSING INFO\n") -#image is an .RData file necessary to use xset variable given by previous tools +# image is an .RData file necessary to use xset variable given by previous tools load(args$image) if (!exists("raw_data")) stop("\n\nERROR: The RData doesn't contain any object called 'raw_data' which is provided by the tool: MSnbase readMSData") @@ -79,12 +79,12 @@ cat("\tMAIN PROCESSING INFO\n") cat("\t\tCOMPUTE\n") cat("\t\t\tApply filter[s] (if asked)\n") -if (exists("filterAcquisitionNumParam")) raw_data <- filterAcquisitionNum(raw_data, filterAcquisitionNumParam[1]:filterAcquisitionNumParam[2]) +if (exists("filterAcquisitionNumParam")) raw_data <- filterAcquisitionNum(raw_data, filterAcquisitionNumParam[1]:filterAcquisitionNumParam[2]) if (exists("filterRtParam")) raw_data <- filterRt(raw_data, filterRtParam) if (exists("filterMzParam")) raw_data <- filterMz(raw_data, filterMzParam) -#Apply this filter only if file contain MS and MSn +# Apply this filter only if file contain MS and MSn if (length(unique(msLevel(raw_data))) != 1) { - raw_data <- filterMsLevel(raw_data, msLevel = 1) + raw_data <- filterMsLevel(raw_data, msLevel = 1) } cat("\t\t\tChromatographic peak detection\n") @@ -103,10 +103,10 @@ sampleNamesList <- getSampleMetadata(xdata = xdata, sampleMetadataOutput = "samp # Create a chromPeaks table if required if (exists("peaklistParam")) { - if (peaklistParam) { - cat("\nCreating the chromatographic peaks' table...\n") - write.table(chromPeaks(xdata), file = "chromPeak_table.tsv", sep = "\t", quote = FALSE, row.names = FALSE) - } + if (peaklistParam) { + cat("\nCreating the chromatographic peaks' table...\n") + write.table(chromPeaks(xdata), file = "chromPeak_table.tsv", sep = "\t", quote = FALSE, row.names = FALSE) + } } cat("\n\n") @@ -123,7 +123,7 @@ xset <- getxcmsSetObject(xdata) print(xset) cat("\n\n") -#saving R data in .Rdata file to save the variables used in the present tool +# saving R data in .Rdata file to save the variables used in the present tool objects2save <- c("xdata", "zipfile", "singlefile", "md5sumList", "sampleNamesList") save(list = objects2save[objects2save %in% ls()], file = "xcmsSet.RData")