# HG changeset patch
# User lecorguille
# Date 1520506372 18000
# Node ID 47e953d9da82ccea3ef6b8c53d1f4971e8415704
# Parent  35b9bb3205d80302f8d3c89d55ea4cab10b04af9
planemo upload for repository https://github.com/workflow4metabolomics/xcms commit 49203f8a5271fa5e6bb889e907df71ebf7757309

diff -r 35b9bb3205d8 -r 47e953d9da82 README.rst
--- a/README.rst	Mon Mar 05 09:31:22 2018 -0500
+++ b/README.rst	Thu Mar 08 05:52:52 2018 -0500
@@ -2,6 +2,14 @@
 Changelog/News
 --------------
 
+**Version 3.0.0.0 - 08/03/2018**
+
+- UPGRADE: upgrade the xcms version from 1.46.0 to 3.0.0. So refactoring of a lot of underlining codes and methods
+
+- IMPROVMENT: a new dedicated tool "xcms plot chromatogram" will allow you to get TIC and BPI of your raw data.
+
+- IMPROVMENT: the tool will now generate a sampleMetadata file if any was provided. It will be useful to add some further information for the normalization and statistics steps.
+
 **Version 1.0.1 - 13/02/2017**
 
 - IMPROVMENT: the tool will now raise an error if a sample isn't describe in the sampleMetadata file
@@ -10,4 +18,3 @@
 **Version 1.0.0 - 03/02/2017**
 
 - NEW: a new tool to merge individual xcmsSet outputs to be used by xcms.group
-
diff -r 35b9bb3205d8 -r 47e953d9da82 lib.r
--- a/lib.r	Mon Mar 05 09:31:22 2018 -0500
+++ b/lib.r	Thu Mar 08 05:52:52 2018 -0500
@@ -74,15 +74,19 @@
 #@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)
+
     # Color by group
     group_colors <- brewer.pal(3, "Set1")[1:length(unique(xdata$sample_group))]
     names(group_colors) <- unique(xdata$sample_group)
     plotAdjustedRtime(xdata, col = group_colors[xdata$sample_group])
     legend("topright", legend=names(group_colors), col=group_colors, cex=0.8, lty=1)
+
     # Color by sample
     plotAdjustedRtime(xdata, col = rainbow(length(xdata@phenoData@data$sample_name)))
     legend("topright", legend=xdata@phenoData@data$sample_name, col=rainbow(length(xdata@phenoData@data$sample_name)), cex=0.8, lty=1)
+
     dev.off()
 }
 
@@ -104,255 +108,46 @@
 
 }
 
-#@author Y. Guitton
-getBPC <- function(file,rtcor=NULL, ...) {
-    object <- xcmsRaw(file)
-    sel <- profRange(object, ...)
-    cbind(if (is.null(rtcor)) object@scantime[sel$scanidx] else rtcor ,xcms:::colMax(object@env$profile[sel$massidx,sel$scanidx,drop=FALSE]))
-    #plotChrom(xcmsRaw(file), base=T)
+#@author G. Le Corguille
+getPlotChromatogram <- function(xdata, pdfname="Chromatogram.pdf", aggregationFun = "max") {
+
+    chrom <- chromatogram(xdata, aggregationFun = aggregationFun)
+    if (aggregationFun == "sum")
+        type="Total Ion Chromatograms"
+    else
+        type="Base Peak Intensity Chromatograms"
+
+    adjusted="Raw"
+    if (hasAdjustedRtime(xdata))
+        adjusted="Adjusted"
+
+    main <- paste(type,":",adjusted,"data")
+
+    pdf(pdfname, width=16, height=10)
+
+    # Color by group
+    group_colors <- brewer.pal(3, "Set1")[1:length(unique(xdata$sample_group))]
+    names(group_colors) <- unique(xdata$sample_group)
+    plot(chrom, col = group_colors[chrom$sample_group], main=main)
+    legend("topright", legend=names(group_colors), col=group_colors, cex=0.8, lty=1)
+
+    # Color by sample
+    plot(chrom, col = rainbow(length(xdata@phenoData@data$sample_name)), main=main)
+    legend("topright", legend=xdata@phenoData@data$sample_name, col=rainbow(length(xdata@phenoData@data$sample_name)), cex=0.8, lty=1)
+
+    dev.off()
 }
 
-#@author Y. Guitton
-getBPCs <- function (xcmsSet=NULL, pdfname="BPCs.pdf",rt=c("raw","corrected"), scanrange=NULL) {
-    cat("Creating BIC pdf...\n")
-
-    if (is.null(xcmsSet)) {
-        cat("Enter an xcmsSet \n")
-        stop()
-    } else {
-        files <- filepaths(xcmsSet)
-    }
-
-    phenoDataClass <- as.vector(levels(xcmsSet@phenoData[,"class"])) #sometime phenoData have more than 1 column use first as class
-
-    classnames <- vector("list",length(phenoDataClass))
-    for (i in 1:length(phenoDataClass)){
-        classnames[[i]] <- which( xcmsSet@phenoData[,"class"]==phenoDataClass[i])
-    }
-
-    N <- dim(phenoData(xcmsSet))[1]
-
-    TIC <- vector("list",N)
-
-
-    for (j in 1:N) {
-
-        TIC[[j]] <- getBPC(files[j])
-        #good for raw
-        # seems strange for corrected
-        #errors if scanrange used in xcmsSetgeneration
-        if (!is.null(xcmsSet) && rt == "corrected")
-            rtcor <- xcmsSet@rt$corrected[[j]]
-        else
-            rtcor <- NULL
-
-        TIC[[j]] <- getBPC(files[j],rtcor=rtcor)
-        # TIC[[j]][,1]<-rtcor
-    }
-
-
-
-    pdf(pdfname,w=16,h=10)
-    cols <- rainbow(N)
-    lty <- 1:N
-    pch <- 1:N
-    #search for max x and max y in BPCs
-    xlim <- range(sapply(TIC, function(x) range(x[,1])))
-    ylim <- range(sapply(TIC, function(x) range(x[,2])))
-    ylim <- c(-ylim[2], ylim[2])
-
-
-    ##plot start
-
-    if (length(phenoDataClass)>2){
-        for (k in 1:(length(phenoDataClass)-1)){
-            for (l in (k+1):length(phenoDataClass)){
-                #print(paste(phenoDataClass[k],"vs",phenoDataClass[l],sep=" "))
-                plot(0, 0, type="n", xlim=xlim/60, ylim=ylim, main=paste("Base Peak Chromatograms \n","BPCs_",phenoDataClass[k]," vs ",phenoDataClass[l], sep=""), xlab="Retention Time (min)", ylab="BPC")
-                colvect <- NULL
-                for (j in 1:length(classnames[[k]])) {
-                    tic <- TIC[[classnames[[k]][j]]]
-                    # points(tic[,1]/60, tic[,2], col=cols[i], pch=pch[i], type="l")
-                    points(tic[,1]/60, tic[,2], col=cols[classnames[[k]][j]], pch=pch[classnames[[k]][j]], type="l")
-                    colvect <- append(colvect,cols[classnames[[k]][j]])
-                }
-                for (j in 1:length(classnames[[l]])) {
-                    # i <- class2names[j]
-                    tic <- TIC[[classnames[[l]][j]]]
-                    points(tic[,1]/60, -tic[,2], col=cols[classnames[[l]][j]], pch=pch[classnames[[l]][j]], type="l")
-                    colvect <- append(colvect,cols[classnames[[l]][j]])
-                }
-                legend("topright",paste(basename(files[c(classnames[[k]],classnames[[l]])])), col=colvect, lty=lty, pch=pch)
-            }
-        }
-    }#end if length >2
-
-    if (length(phenoDataClass)==2){
-        k <- 1
-        l <- 2
-        colvect <- NULL
-        plot(0, 0, type="n", xlim=xlim/60, ylim=ylim, main=paste("Base Peak Chromatograms \n","BPCs_",phenoDataClass[k],"vs",phenoDataClass[l], sep=""), xlab="Retention Time (min)", ylab="BPC")
-
-        for (j in 1:length(classnames[[k]])) {
-
-            tic <- TIC[[classnames[[k]][j]]]
-            # points(tic[,1]/60, tic[,2], col=cols[i], pch=pch[i], type="l")
-            points(tic[,1]/60, tic[,2], col=cols[classnames[[k]][j]], pch=pch[classnames[[k]][j]], type="l")
-            colvect<-append(colvect,cols[classnames[[k]][j]])
-        }
-        for (j in 1:length(classnames[[l]])) {
-            # i <- class2names[j]
-            tic <- TIC[[classnames[[l]][j]]]
-            points(tic[,1]/60, -tic[,2], col=cols[classnames[[l]][j]], pch=pch[classnames[[l]][j]], type="l")
-            colvect <- append(colvect,cols[classnames[[l]][j]])
-        }
-        legend("topright",paste(basename(files[c(classnames[[k]],classnames[[l]])])), col=colvect, lty=lty, pch=pch)
-
-    }#end length ==2
-
-    #case where only one class
-    if (length(phenoDataClass)==1){
-        k <- 1
-        ylim <- range(sapply(TIC, function(x) range(x[,2])))
-        colvect <- NULL
-        plot(0, 0, type="n", xlim=xlim/60, ylim=ylim, main=paste("Base Peak Chromatograms \n","BPCs_",phenoDataClass[k], sep=""), xlab="Retention Time (min)", ylab="BPC")
-
-        for (j in 1:length(classnames[[k]])) {
-            tic <- TIC[[classnames[[k]][j]]]
-            # points(tic[,1]/60, tic[,2], col=cols[i], pch=pch[i], type="l")
-            points(tic[,1]/60, tic[,2], col=cols[classnames[[k]][j]], pch=pch[classnames[[k]][j]], type="l")
-            colvect <- append(colvect,cols[classnames[[k]][j]])
-        }
-
-        legend("topright",paste(basename(files[c(classnames[[k]])])), col=colvect, lty=lty, pch=pch)
-
-    }#end length ==1
-
-    dev.off() #pdf(pdfname,w=16,h=10)
-
-    invisible(TIC)
+#@author G. Le Corguille
+getPlotTICs <- function(xdata, pdfname="TICs.pdf") {
+    getPlotChromatogram(xdata, pdfname, aggregationFun = "sum")
 }
 
-
-
-#@author Y. Guitton
-getTIC <- function(file, rtcor=NULL) {
-    object <- xcmsRaw(file)
-    cbind(if (is.null(rtcor)) object@scantime else rtcor, rawEIC(object, mzrange=range(object@env$mz))$intensity)
+#@author G. Le Corguille
+getPlotBPIs <- function(xdata, pdfname="BPIs.pdf") {
+    getPlotChromatogram(xdata, pdfname, aggregationFun = "max")
 }
 
-#overlay TIC from all files in current folder or from xcmsSet, create pdf
-#@author Y. Guitton
-getTICs <- function(xcmsSet=NULL,files=NULL, pdfname="TICs.pdf", rt=c("raw","corrected")) {
-    cat("Creating TIC pdf...\n")
-
-    if (is.null(xcmsSet)) {
-        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, "$", sep=""), collapse="|")
-        if (is.null(files))
-            files <- getwd()
-        info <- file.info(files)
-        listed <- list.files(files[info$isdir], pattern=filepattern, recursive=TRUE, full.names=TRUE)
-        files <- c(files[!info$isdir], listed)
-    } else {
-        files <- filepaths(xcmsSet)
-    }
-
-    phenoDataClass <- as.vector(levels(xcmsSet@phenoData[,"class"])) #sometime phenoData have more than 1 column use first as class
-    classnames <- vector("list",length(phenoDataClass))
-    for (i in 1:length(phenoDataClass)){
-        classnames[[i]] <- which( xcmsSet@phenoData[,"class"]==phenoDataClass[i])
-    }
-
-    N <- length(files)
-    TIC <- vector("list",N)
-
-    for (i in 1:N) {
-        if (!is.null(xcmsSet) && rt == "corrected")
-            rtcor <- xcmsSet@rt$corrected[[i]] else
-        rtcor <- NULL
-        TIC[[i]] <- getTIC(files[i], rtcor=rtcor)
-    }
-
-    pdf(pdfname, w=16, h=10)
-    cols <- rainbow(N)
-    lty <- 1:N
-    pch <- 1:N
-    #search for max x and max y in TICs
-    xlim <- range(sapply(TIC, function(x) range(x[,1])))
-    ylim <- range(sapply(TIC, function(x) range(x[,2])))
-    ylim <- c(-ylim[2], ylim[2])
-
-
-    ##plot start
-    if (length(phenoDataClass)>2){
-        for (k in 1:(length(phenoDataClass)-1)){
-            for (l in (k+1):length(phenoDataClass)){
-                #print(paste(phenoDataClass[k],"vs",phenoDataClass[l],sep=" "))
-                plot(0, 0, type="n", xlim=xlim/60, ylim=ylim, main=paste("Total Ion Chromatograms \n","TICs_",phenoDataClass[k]," vs ",phenoDataClass[l], sep=""), xlab="Retention Time (min)", ylab="TIC")
-                colvect <- NULL
-                for (j in 1:length(classnames[[k]])) {
-                    tic <- TIC[[classnames[[k]][j]]]
-                    # points(tic[,1]/60, tic[,2], col=cols[i], pch=pch[i], type="l")
-                    points(tic[,1]/60, tic[,2], col=cols[classnames[[k]][j]], pch=pch[classnames[[k]][j]], type="l")
-                    colvect <- append(colvect,cols[classnames[[k]][j]])
-                }
-                for (j in 1:length(classnames[[l]])) {
-                    # i=class2names[j]
-                    tic <- TIC[[classnames[[l]][j]]]
-                    points(tic[,1]/60, -tic[,2], col=cols[classnames[[l]][j]], pch=pch[classnames[[l]][j]], type="l")
-                    colvect <- append(colvect,cols[classnames[[l]][j]])
-                }
-                legend("topright",paste(basename(files[c(classnames[[k]],classnames[[l]])])), col=colvect, lty=lty, pch=pch)
-            }
-        }
-    }#end if length >2
-    if (length(phenoDataClass)==2){
-        k <- 1
-        l <- 2
-
-        plot(0, 0, type="n", xlim=xlim/60, ylim=ylim, main=paste("Total Ion Chromatograms \n","TICs_",phenoDataClass[k],"vs",phenoDataClass[l], sep=""), xlab="Retention Time (min)", ylab="TIC")
-        colvect <- NULL
-        for (j in 1:length(classnames[[k]])) {
-            tic <- TIC[[classnames[[k]][j]]]
-            # points(tic[,1]/60, tic[,2], col=cols[i], pch=pch[i], type="l")
-            points(tic[,1]/60, tic[,2], col=cols[classnames[[k]][j]], pch=pch[classnames[[k]][j]], type="l")
-            colvect <- append(colvect,cols[classnames[[k]][j]])
-        }
-        for (j in 1:length(classnames[[l]])) {
-            # i <- class2names[j]
-            tic <- TIC[[classnames[[l]][j]]]
-            points(tic[,1]/60, -tic[,2], col=cols[classnames[[l]][j]], pch=pch[classnames[[l]][j]], type="l")
-            colvect <- append(colvect,cols[classnames[[l]][j]])
-        }
-        legend("topright",paste(basename(files[c(classnames[[k]],classnames[[l]])])), col=colvect, lty=lty, pch=pch)
-
-    }#end length ==2
-
-    #case where only one class
-    if (length(phenoDataClass)==1){
-        k <- 1
-        ylim <- range(sapply(TIC, function(x) range(x[,2])))
-
-        plot(0, 0, type="n", xlim=xlim/60, ylim=ylim, main=paste("Total Ion Chromatograms \n","TICs_",phenoDataClass[k], sep=""), xlab="Retention Time (min)", ylab="TIC")
-        colvect <- NULL
-        for (j in 1:length(classnames[[k]])) {
-            tic <- TIC[[classnames[[k]][j]]]
-            # points(tic[,1]/60, tic[,2], col=cols[i], pch=pch[i], type="l")
-            points(tic[,1]/60, tic[,2], col=cols[classnames[[k]][j]], pch=pch[classnames[[k]][j]], type="l")
-            colvect <- append(colvect,cols[classnames[[k]][j]])
-        }
-
-        legend("topright",paste(basename(files[c(classnames[[k]])])), col=colvect, lty=lty, pch=pch)
-
-    }#end length ==1
-
-    dev.off() #pdf(pdfname,w=16,h=10)
-
-    invisible(TIC)
-}
-
-
 
 # Get the polarities from all the samples of a condition
 #@author Misharl Monsoor misharl.monsoor@sb-roscoff.fr ABiMS TEAM
diff -r 35b9bb3205d8 -r 47e953d9da82 macros.xml
--- a/macros.xml	Mon Mar 05 09:31:22 2018 -0500
+++ b/macros.xml	Thu Mar 08 05:52:52 2018 -0500
@@ -37,6 +37,9 @@
         <validator type="regex" message="The format is '1,2,4,6'" >[0-9, ]+</validator>
     </xml>
 
+    <token name="@INPUT_IMAGE_LABEL@">RData file</token>
+    <token name="@INPUT_IMAGE_HELP@">It contain a xcms3::XCMSnExp object (named xdata)</token>
+
     <!-- zipfile load for planemo test -->
 
     <token name="@COMMAND_FILE_LOAD@">
diff -r 35b9bb3205d8 -r 47e953d9da82 xcms_merge.r
--- a/xcms_merge.r	Mon Mar 05 09:31:22 2018 -0500
+++ b/xcms_merge.r	Thu Mar 08 05:52:52 2018 -0500
@@ -15,16 +15,15 @@
 
 for(image in args$images) {
     load(image)
-
+    print(args)
     # Handle infiles
     if (!exists("singlefile")) singlefile <- NULL
     if (!exists("zipfile")) zipfile <- NULL
     rawFilePath <- getRawfilePathFromArguments(singlefile, zipfile, args)
     zipfile <- rawFilePath$zipfile
     singlefile <- rawFilePath$singlefile
-    args <- rawFilePath$args
     directory <- retrieveRawfileInTheWorkingDirectory(singlefile, zipfile)
-    
+
     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.*")
     cat(sampleNamesList$sampleNamesOrigin,"\n")
     if (!exists("xdata_merged")) {
@@ -70,18 +69,6 @@
 # Create a sampleMetada file
 sampleNamesList <- getSampleMetadata(xdata=xdata, sampleMetadataOutput="sampleMetadata.tsv")
 
-# Get the legacy xcmsSet object
-xset <- getxcmsSetObject(xdata)
-
-cat("\n\n")
-
-
-# -- TIC --
-cat("\t\tGET TIC GRAPH\n")
-#@TODO: one day, use xdata instead of xset to draw the TICs and BPC or a complete other method
-getTICs(xcmsSet=xset, rt="raw", pdfname="TICs.pdf")
-getBPCs(xcmsSet=xset, rt="raw", pdfname="BICs.pdf")
-
 cat("\n\n")
 
 cat("\tXCMSnExp OBJECT INFO\n")
diff -r 35b9bb3205d8 -r 47e953d9da82 xcms_merge.xml
--- a/xcms_merge.xml	Mon Mar 05 09:31:22 2018 -0500
+++ b/xcms_merge.xml	Thu Mar 08 05:52:52 2018 -0500
@@ -21,7 +21,7 @@
     ]]></command>
 
     <inputs>
-        <param name="images" type="data" format="rdata.xcms.raw,rdata" label="xset RData / xcms.xcmsSet file" help="output file from another xcms.xcmsSet" multiple="true" />
+        <param name="images" type="data" format="rdata.xcms.raw,rdata" label="@INPUT_IMAGE_LABEL@" help="@INPUT_IMAGE_HELP@ from multiple findChromPeaks" multiple="true" />
         <param name="sampleMetadata" label="Sample metadata file " format="tabular" type="data" optional="true" help="must contain at least one column with the sample id and one column with the sample class"/>
 
         <expand macro="input_file_load"/>
@@ -32,8 +32,6 @@
         <data name="sampleMetadataOutput" format="tabular" label="xset.merged.sampleMetadata.tsv" from_work_dir="sampleMetadata.tsv">
             <filter>not sampleMetadata</filter>
         </data>
-        <data name="ticsRawPdf" format="pdf" label="xset.merged.xset.TICs_raw.pdf" from_work_dir="TICs.pdf"  />
-        <data name="bpcsRawPdf" format="pdf" label="xset.merged.xset.BPCs_raw.pdf" from_work_dir="BICs.pdf" />
     </outputs>
 
     <tests>
@@ -169,10 +167,14 @@
 Changelog/News
 --------------
 
-**Version 3.0.0.0 - 14/02/2018**
+**Version 3.0.0.0 - 08/03/2018**
 
 - UPGRADE: upgrade the xcms version from 1.46.0 to 3.0.0. So refactoring of a lot of underlining codes and methods
 
+- IMPROVMENT: a new dedicated tool "xcms plot chromatogram" will allow you to get TIC and BPI of your raw data.
+
+- IMPROVMENT: the tool will now generate a sampleMetadata file if any was provided. It will be useful to add some further information for the normalization and statistics steps.
+
 **Version 1.0.1 - 13/02/2017**
 
 - IMPROVMENT: the tool will now raise an error if a sample isn't describe in the sampleMetadata file