diff dimsPredictPuritySingle.R @ 0:fc9bb7e49a3a draft

planemo upload for repository https://github.com/computational-metabolomics/mspurity-galaxy commit 2948ce35fa7fffe5a64711cb30be971031e79019-dirty

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/dimsPredictPuritySingle.R	Fri May 24 09:02:25 2019 -0400
@@ -0,0 +1,136 @@
+library(msPurity)
+library(optparse)
+print(sessionInfo())
+
+option_list <- list(
+  make_option(c("--mzML_file"), type="character"),
+  make_option(c("--peaks_file"), type="character"),
+  make_option(c("-o", "--out_dir"), type="character"),
+  make_option("--minOffset", default=0.5),
+  make_option("--maxOffset", default=0.5),
+  make_option("--ilim", default=0.05),
+  make_option("--ppm", default=4),
+  make_option("--dimspy", action="store_true"),
+  make_option("--sim", action="store_true"),
+  make_option("--remove_nas", action="store_true"),
+  make_option("--iwNorm", default="none", type="character"),
+  make_option("--file_num_dimspy", default=1),
+  make_option("--exclude_isotopes", action="store_true"),
+  make_option("--isotope_matrix", type="character")
+)
+
+# store options
+opt<- parse_args(OptionParser(option_list=option_list))
+
+print(sessionInfo())
+print(opt)
+
+if (is.null(opt$dimspy)){
+
+  df <- read.table(opt$peaks_file, header = TRUE, sep='\t')
+  filename = NA
+  mzml_file <- opt$mzML_file
+}else{
+  indf <- read.table(opt$peaks_file,
+                     header = TRUE, sep='\t', stringsAsFactors = FALSE)
+  
+
+  if (file.exists(opt$mzML_file)){
+     mzml_file <- opt$mzML_file
+  }else{
+     
+     filename = colnames(indf)[8:ncol(indf)][opt$file_num_dimspy]
+     print(filename)
+     # check if the data file is mzML or RAW (can only use mzML currently) so
+     # we expect an mzML file of the same name in the same folder
+     indf$i <- indf[,colnames(indf)==filename]
+     indf[,colnames(indf)==filename] <- as.numeric(indf[,colnames(indf)==filename])
+
+     filename = sub("raw", "mzML", filename, ignore.case = TRUE)
+     print(filename)
+
+     mzml_file <- file.path(opt$mzML_file, filename)
+
+  }	
+  
+  df <- indf[4:nrow(indf),]
+  if ('blank_flag' %in% colnames(df)){
+      df <- df[df$blank_flag==1,]
+  }
+  colnames(df)[colnames(df)=='m.z'] <- 'mz'
+
+  if ('nan' %in% df$mz){
+    df[df$mz=='nan',]$mz <- NA
+  }
+  df$mz <- as.numeric(df$mz)
+	
+
+
+
+}
+
+if (!is.null(opt$remove_nas)){
+  df <- df[!is.na(df$mz),]
+}
+
+if (is.null(opt$isotope_matrix)){
+    im <- NULL
+}else{
+    im <- read.table(opt$isotope_matrix,
+                     header = TRUE, sep='\t', stringsAsFactors = FALSE)
+}
+
+if (is.null(opt$exclude_isotopes)){
+    isotopes <- FALSE
+}else{
+    isotopes <- TRUE
+}
+
+
+
+if (is.null(opt$sim)){
+    sim=FALSE
+}else{
+    sim=TRUE
+}
+
+minOffset = as.numeric(opt$minOffset)
+maxOffset = as.numeric(opt$maxOffset)
+
+
+
+if (opt$iwNorm=='none'){
+    iwNorm = FALSE
+    iwNormFun = NULL
+}else if (opt$iwNorm=='gauss'){
+    iwNorm = TRUE
+    iwNormFun = msPurity::iwNormGauss(minOff=-minOffset, maxOff=maxOffset)
+}else if (opt$iwNorm=='rcosine'){
+    iwNorm = TRUE
+    iwNormFun = msPurity::iwNormRcosine(minOff=-minOffset, maxOff=maxOffset)
+}else if (opt$iwNorm=='QE5'){
+    iwNorm = TRUE
+    iwNormFun = msPurity::iwNormQE.5()
+}
+
+print('FIRST ROWS OF PEAK FILE')
+print(head(df))
+print(mzml_file)
+predicted <- msPurity::dimsPredictPuritySingle(df$mz,
+                                     filepth=mzml_file,
+                                     minOffset=minOffset,
+                                     maxOffset=maxOffset,
+                                     ppm=opt$ppm,
+                                     mzML=TRUE,
+                                     sim = sim,
+                                     ilim = opt$ilim,
+                                     isotopes = isotopes,
+                                     im = im,
+                                     iwNorm = iwNorm,
+                                     iwNormFun = iwNormFun
+                                     )
+predicted <- cbind(df, predicted)
+
+print(head(predicted))
+print(file.path(opt$out_dir, 'dimsPredictPuritySingle_output.tsv'))
+write.table(predicted, file.path(opt$out_dir, 'dimsPredictPuritySingle_output.tsv'), row.names=FALSE, sep='\t')