<tool id="mass_spectrometry_imaging_qc" name="MSI Qualitycontrol" version="1.7.0.1">
    <description>
        mass spectrometry imaging QC
    </description>
    <requirements>
        <requirement type="package" version="1.7.0">bioconductor-cardinal</requirement>
        <requirement type="package" version="2.2.1">r-ggplot2</requirement>
        <requirement type="package" version="1.1_2">r-rcolorbrewer</requirement>
        <requirement type="package" version="2.2.1">r-gridextra</requirement>
        <requirement type="package" version="2.23_15">r-kernsmooth</requirement>
    </requirements>
    <command detect_errors="exit_code">
    <![CDATA[
        #if $infile.ext == 'imzml'
            cp '${infile.extra_files_path}/imzml' infile.imzML &&
            cp '${infile.extra_files_path}/ibd' infile.ibd &&
        #elif $infile.ext == 'analyze75'
            cp '${infile.extra_files_path}/hdr' infile.hdr &&
            cp '${infile.extra_files_path}/img' infile.img &&
            cp '${infile.extra_files_path}/t2m' infile.t2m &&
        #else
            ln -s '$infile' infile.RData &&
        #end if
        cat '${cardinal_qualitycontrol_script}' &&
        Rscript '${cardinal_qualitycontrol_script}'
    ]]>
    </command>
    <configfiles>
        <configfile name="cardinal_qualitycontrol_script"><![CDATA[

library(Cardinal)
library(ggplot2)
library(RColorBrewer)
library(gridExtra)
library(KernSmooth)

## Read MALDI Imagind dataset

#if $infile.ext == 'imzml'
    msidata <- readMSIData('infile.imzML')
#elif $infile.ext == 'analyze75'
    msidata <- readMSIData('infile.hdr')

#else
    load('infile.RData')
#end if

#if $inputpeptidefile:
    ### Read tabular file with peptide masses for plots and heatmap images: 
    input_list = read.delim("$inputpeptidefile", header = FALSE, na.strings=c("","NA", "#NUM!", "#ZAHL!"), stringsAsFactors = FALSE)
        if (ncol(input_list) == 1)
        {
            input_list = cbind(input_list, input_list)
        }
#else
    input_list = data.frame(0, 0)
#end if
colnames(input_list)[1:2] = c("mz", "name")

#if $inputcalibrants:
    ### Read tabular file with calibrant masses: 
    calibrant_list = read.delim("$inputcalibrants", header = FALSE, na.strings=c("","NA", "#NUM!", "#ZAHL!"), stringsAsFactors = FALSE)
        if (ncol(calibrant_list) == 1)
        {
            calibrant_list = cbind(calibrant_list, calibrant_list)
         }
#else
   calibrant_list = data.frame(0,0)
#end if

colnames(calibrant_list)[1:2] = c("mz", "name")


###################################### file properties in numbers ######################

## Number of features (mz)
maxfeatures = length(features(msidata))
## Range mz
minmz = round(min(mz(msidata)), digits=2)
maxmz = round(max(mz(msidata)), digits=2)
## Number of spectra (pixels)
pixelcount = length(pixels(msidata))
## Range x coordinates
minimumx = min(coord(msidata)[,1])
maximumx = max(coord(msidata)[,1])
## Range y coordinates
minimumy = min(coord(msidata)[,2])
maximumy = max(coord(msidata)[,2])
## Range of intensities
minint = round(min(spectra(msidata)[]), digits=2)
maxint = round(max(spectra(msidata)[]), digits=2)
medint = round(median(spectra(msidata)[]), digits=2)
## Number of intensities > 0
npeaks= sum(spectra(msidata)[]>0)
## Spectra multiplied with mz (potential number of peaks)
numpeaks = ncol(spectra(msidata)[])*nrow(spectra(msidata)[])
## Percentage of intensities > 0
percpeaks = round(npeaks/numpeaks*100, digits=2)
## Number of empty TICs
TICs = colSums(spectra(msidata)[]) 
NumemptyTIC = sum(TICs == 0)

## Processing informations
processinginfo = processingData(msidata)
centroidedinfo = processinginfo@centroided # TRUE or FALSE

## if TRUE write processinginfo if no write FALSE

## normalization
if (length(processinginfo@normalization) == 0) {
  normalizationinfo='FALSE'
} else {
  normalizationinfo=processinginfo@normalization
}
## smoothing
if (length(processinginfo@smoothing) == 0) {
  smoothinginfo='FALSE'
} else {
  smoothinginfo=processinginfo@smoothing
}
## baseline
if (length(processinginfo@baselineReduction) == 0) {
  baselinereductioninfo='FALSE'
} else {
  baselinereductioninfo=processinginfo@baselineReduction
}
## peak picking
if (length(processinginfo@peakPicking) == 0) {
  peakpickinginfo='FALSE'
} else {
  peakpickinginfo=processinginfo@peakPicking
}

### calculate how many input peptide masses are valid: 
inputpeptides = input_list[input_list[,1]>minmz & input_list[,1]<maxmz,]

### calculate how many input calibrant masses are valid: 
inputcalibrants = calibrant_list[calibrant_list[,1]>minmz & calibrant_list[,1]<maxmz,]

### bind inputcalibrants and inputpeptides together, to make heatmap on both lists

inputs_all = rbind(inputcalibrants[,1:2], inputpeptides[,1:2])
inputmasses = inputs_all[,1]
inputnames = inputs_all[,2]


properties = c("Number of mz features",
               "Range of mz values [Da]",
               "Number of pixels", 
               "Range of x coordinates", 
               "Range of y coordinates",
               "Range of intensities", 
               "Median of intensities",
               "Intensities > 0",
               "Number of zero TICs",
               "Preprocessing", 
               "Normalization", 
               "Smoothing",
               "Baseline reduction",
               "Peak picking",
               "Centroided", 
               "# valid input masses")

values = c(paste0(maxfeatures), 
           paste0(minmz, " - ", maxmz), 
           paste0(pixelcount), 
           paste0(minimumx, " - ", maximumx),  
           paste0(minimumy, " - ", maximumy), 
           paste0(minint, " - ", maxint), 
           paste0(medint),
           paste0(percpeaks, " %"), 
           paste0(NumemptyTIC), 
           paste0(" "),
           paste0(normalizationinfo),
           paste0(smoothinginfo),
           paste0(baselinereductioninfo),
           paste0(peakpickinginfo),
           paste0(centroidedinfo), 
           paste0(length(inputmasses)))


property_df = data.frame(properties, values)

######################################## PDF #############################################
##########################################################################################
##########################################################################################

pdf("qualitycontrol.pdf", fonts = "Times", pointsize = 12)
plot(0,type='n',axes=FALSE,ann=FALSE)
#if not $filename:
    #set $filename = $infile.display_name
#end if
title(main=paste("Quality control of MSI data\n\n", "Filename:", "$filename"))

############################# I) numbers ####################################
#############################################################################
grid.table(property_df, rows= NULL)

if (npeaks > 0)
{
    ############################# II) ion images #################################
    ##############################################################################

    ## function without xaxt for plots with automatic x axis
    plot_colorByDensity = function(x1,x2,
                                   ylim=c(min(x2),max(x2)),
                                   xlim=c(min(x1),max(x1)),
                                   xlab="",ylab="",main=""){
      
      df <- data.frame(x1,x2)
      x <- densCols(x1,x2, colramp=colorRampPalette(c("black", "white")))
      df\$dens <- col2rgb(x)[1,] + 1L
      cols <-  colorRampPalette(c("#000099", "#00FEFF", "#45FE4F","#FCFF00", "#FF9400", "#FF3100"))(256)
      df\$col <- cols[df\$dens]
      plot(x2~x1, data=df[order(df\$dens),], 
           ylim=ylim,xlim=xlim,pch=20,col=col,
           cex=1,xlab=xlab,ylab=ylab,las=1,
           main=main)
    }

    ## Variables for plots
    xrange = 1
    yrange = 1
    maxx = max(coord(msidata)[,1])+xrange
    minx = min(coord(msidata)[,1])-xrange
    maxy = max(coord(msidata)[,2])+yrange
    miny = min(coord(msidata)[,2])-yrange

    ############################################################################

    ## 1) Acquisition image

    pixelnumber = 1:pixelcount
    pixelxyarray=cbind(coord(msidata),pixelnumber)

    print(ggplot(pixelxyarray, aes(x=x, y=y, fill=pixelnumber))
     + geom_tile() + coord_fixed()
     + ggtitle("1) Order of Acquisition")
     +theme_bw()
     + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange"), 
                            space = "Lab", na.value = "black", name = "Acq"))

    ## 2) Number of calibrants per spectrum

    pixelmatrix = matrix(ncol=ncol(msidata), nrow=0)
    inputcalibrantmasses = inputcalibrants[,1]

    if (length(inputcalibrantmasses) != 0)
    {   for (calibrantnr in 1:length(inputcalibrantmasses))
        {
          calibrantmz = inputcalibrantmasses[calibrantnr]
          calibrantfeaturemin = features(msidata, mz=calibrantmz-$plusminusinDalton)
          calibrantfeaturemax = features(msidata, mz=calibrantmz+$plusminusinDalton)

            if (calibrantfeaturemin == calibrantfeaturemax)
            {
              
              calibrantintensity = spectra(msidata)[calibrantfeaturemin,] 
              
            }else{
              
              calibrantintensity = colSums(spectra(msidata)[calibrantfeaturemin:calibrantfeaturemax,] )
              
            }
          pixelmatrix = rbind(pixelmatrix, calibrantintensity)
        }

        countvector= as.factor(colSums(pixelmatrix>0))
        countdf= cbind(coord(msidata), countvector)
        mycolours = c("black","grey", "darkblue", "blue", "green" , "red", "yellow", "magenta", "olivedrap1", "lightseagreen")

        print(ggplot(countdf, aes(x=x, y=y, fill=countvector))
           + geom_tile() + coord_fixed() 
          + ggtitle("2) Number of calibrants per pixel")
          + theme_bw() 
          + theme(text=element_text(family="ArialMT", face="bold", size=12))
          + scale_fill_manual(values = mycolours[1:length(countvector)], 
                                na.value = "black", name = "# calibrants"))
    }else{print("2) The inputcalibrant masses were outside the mass range")}


############# new 2b) image of foldchanges (log2 intensity ratios) between two masses in the same spectrum

    #if $calibrantratio:
        #for $foldchanges in $calibrantratio:
            mass1 = $foldchanges.mass1
            mass2 = $foldchanges.mass2
            distance = $foldchanges.distance

            ### find rows which contain masses: 

            mzrowdown1 = features(msidata, mz = mass1-distance)
            mzrowup1 = features(msidata, mz = mass1+distance)
            mzrowdown2 = features(msidata, mz = mass2-distance)
            mzrowup2 = features(msidata, mz = mass2+distance)

            ### lower and upperlimit for the plot
            mzdown1 = features(msidata, mz = mass1-2)
            mzup1 = features(msidata, mz = mass1+3)
            mzdown2 = features(msidata, mz = mass2-2)
            mzup2 = features(msidata, mz = mass2+3)

            ### plot the part which was chosen, with chosen value in blue, distance in blue, maxmass in red, xlim fixed to 5 Da window

                if (mzrowdown1 == mzrowup1)
                {
                        maxmassrow1 = spectra(msidata)[mzrowup1,]
                        maxmass1 = mz(msidata)[mzrowup1][which.max(maxmassrow1)]
                }else{
                        maxmassrow1 = rowMeans(spectra(msidata)[mzrowdown1:mzrowup1,])
                        maxmass1 = mz(msidata)[mzrowdown1:mzrowup1][which.max(maxmassrow1)]
                }
                if (mzrowdown2 == mzrowup2)
                {
                    maxmassrow2 = spectra(msidata)[mzrowup2,]
                    maxmass2 = mz(msidata)[mzrowup2][which.max(maxmassrow2)]
                }else{
                    maxmassrow2 = rowMeans(spectra(msidata)[mzrowdown2:mzrowup2,])
                    maxmass2 = mz(msidata)[mzrowdown2:mzrowup2][which.max(maxmassrow2)]
                }

            par(mfrow=c(2,1), oma=c(0,0,2,0))
            plot(msidata[mzdown1:mzup1,], pixel = 1:pixelcount, main=paste0("average spectrum ", mass1, " Da"))
            abline(v=c(mass1-distance, mass1, mass1+distance), col="blue",lty=c(3,5,3))
            abline(v=maxmass1, col="red", lty=5)

            plot(msidata[mzdown2:mzup2,], pixel = 1:pixelcount, main= paste0("average spectrum ", mass2, " Da"))
            abline(v=c(mass2-distance, mass2, mass2+distance), col="blue", lty=c(3,5,3))
            abline(v=maxmass2, col="red", lty=5)
            title("Control of fold change plot", outer=TRUE)

            ### filter spectra for maxmass to have two vectors, which can be divided

            mass1vector = spectra(msidata)[features(msidata, mz = maxmass1),]
            mass2vector = spectra(msidata)[features(msidata, mz = maxmass2),]

            foldchange = log2(mass1vector/mass2vector)

            ratiomatrix = cbind(foldchange, coord(msidata))

            print(ggplot(ratiomatrix, aes(x=x, y=y, fill=foldchange), colour=colo)
             +scale_y_reverse() + geom_tile() + coord_fixed()
             + ggtitle(paste0("Fold change ", mass1, " Da / ", mass2, " Da"))
             + theme_bw()
             + theme(text=element_text(family="ArialMT", face="bold", size=12))
             + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange")
                                    ,space = "Lab", na.value = "black", name ="FC"))
        #end for
    #end if

    ## 3) Calibrant images:

    if (length(inputmasses) != 0)
    {   for (mass in 1:length(inputmasses))
        {
          image(msidata, mz=inputmasses[mass], plusminus=$plusminusinDalton, 
                main= paste0("3",LETTERS[mass], ") ", inputnames[mass], " (", round(inputmasses[mass], digits = 2), " Da)"), 
                contrast.enhance = "histogram", ylim=c(maxy+1, 0))
        }
    } else {print("3) The inputpeptide masses were outside the mass range")}

    ## 4) Number of peaks per pixel - image

    peaksperpixel = colSums(spectra(msidata)[]> 0)
    peakscoordarray=cbind(coord(msidata), peaksperpixel)

    print(ggplot(peakscoordarray, aes(x=x, y=y, fill=peaksperpixel), colour=colo)
    + geom_tile() + coord_fixed() 
     + ggtitle("4) Number of peaks per pixel")
     + theme_bw() 
     + theme(text=element_text(family="ArialMT", face="bold", size=12))
     + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange") 
                            ,space = "Lab", na.value = "black", name = "# peaks"))

    ## 5) TIC image 
    TICcoordarray=cbind(coord(msidata), TICs)
    colo <- colorRampPalette(
    c("blue", "cyan", "green", "yellow","red"))
    print(ggplot(TICcoordarray, aes(x=x, y=y, fill=TICs), colour=colo)
     + geom_tile() + coord_fixed() 
     + ggtitle("5) Total Ion Chromatogram")
     + theme_bw() 
     + theme(text=element_text(family="ArialMT", face="bold", size=12))
     + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange") 
                            ,space = "Lab", na.value = "black", name = "TIC"))

    ## 6) Most abundant mass image 

    highestmz = apply(spectra(msidata)[],2,which.max) 
    highestmz_matrix = cbind(coord(msidata),mz(msidata)[highestmz])
    colnames(highestmz_matrix)[3] = "highestmzinDa"

    print(ggplot(highestmz_matrix, aes(x=x, y=y, fill=highestmzinDa))
    + geom_tile() + coord_fixed() 
    + ggtitle("6) Most abundant m/z in each pixel")
    + theme_bw() 
    + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange"), space = "Lab", na.value = "black", name = "m/z", 
                           labels = as.character(pretty(highestmz_matrix\$highestmzinDa)[c(1,3,5,7)]),
                           breaks = pretty(highestmz_matrix\$highestmzinDa)[c(1,3,5,7)], limits=c(min(highestmz_matrix\$highestmzinDa), max(highestmz_matrix\$highestmzinDa)))
    + theme(text=element_text(family="ArialMT", face="bold", size=12)))

    ## which mz are highest
    highestmz_peptides = names(sort(table(round(highestmz_matrix\$highestmzinDa, digits=0)), decreasing=TRUE)[1])
    highestmz_pixel = which(round(highestmz_matrix\$highestmzinDa, digits=0) == highestmz_peptides)[1]

    secondhighestmz = names(sort(table(round(highestmz_matrix\$highestmzinDa, digits=0)), decreasing=TRUE)[2]) 
    secondhighestmz_pixel = which(round(highestmz_matrix\$highestmzinDa, digits=0) == secondhighestmz)[1]

    ## 7) pca image for two components
    pca <- PCA(msidata, ncomp=2) 
    par(mfrow = c(2,1))
    plot(pca, col=c("black", "darkgrey"), main="7) PCA for two components")
    image(pca, col=c("black", "white"),ylim=c(maxy+1, 0))


    ############################# III) properties over acquisition (spectra index)##########
    ##############################################################################

    par(mfrow = c(2,1), mar=c(5,6,4,2))

    ## 8a) number of peaks per spectrum - scatterplot
    plot_colorByDensity(pixels(msidata), peaksperpixel, ylab = "", xlab = "", main="8a) Number of peaks per spectrum")
    title(xlab="Spectra index \n (= Acquisition time)", line=3)
    title(ylab="Number of peaks", line=4)

    ## 8b) number of peaks per spectrum - histogram
    hist(peaksperpixel, main="", las=1, xlab = "Number of peaks per spectrum", ylab="") 
    title(main="8b) Number of peaks per spectrum", line=2)
    title(ylab="Frequency = # spectra", line=4)
    abline(v=median(peaksperpixel), col="blue")

    ## 9a) TIC per spectrum -  density scatterplot
    zero=0
    par(mfrow = c(2,1), mar=c(5,6,4,2))
    plot_colorByDensity(pixels(msidata), TICs,  ylab = "", xlab = "", main="9a) TIC per pixel")
    title(xlab="Spectra index \n (= Acquisition time)", line=3)
    title(ylab = "Total ion chromatogram intensity", line=4)

    ## 9b) TIC per spectrum -  histogram
    hist(log(TICs), main="", las=1, xlab = "log(TIC per spectrum)", ylab="")
    title(main= "9b) TIC per spectrum", line=2)
    title(ylab="Frequency = # spectra", line=4)
    abline(v=median(log(TICs[TICs>0])), col="blue") 


    ## 10) intensity of chosen peptides over acquisition (pixel index)

    if (length(inputcalibrants[,1]) != 0)
    {   
        par(mfrow = c(3, 2), oma=c(0,0,2,0))
        intensityvector = vector()
        for (mzvalue in 1:length(inputcalibrants[,1]))
        {
            mznumber = features(msidata, mz = inputcalibrants[,1][mzvalue])
            intensityvector = spectra(msidata)[][mznumber,] 
            plot(intensityvector, main=inputnames[mzvalue], xlab="Spectra index \n (= Acquisition time)")
        }
      title("10) intensity of calibrants over acquisition", outer=TRUE)
    }else{print("10) The inputcalibrant masses were outside the mass range")}

    ################################## IV) changes over mz ############################
    ###################################################################################

    ## 11) Number of peaks per mz
    ## Number of peaks per mz - number across all pixel
    peakspermz = rowSums(spectra(msidata)[] > 0 )

    par(mfrow = c(2,1), mar=c(5,6,4,4.5))
    ## 11a) Number of peaks per mz - scatterplot
    plot_colorByDensity(mz(msidata),peakspermz, main= "11a) Number of peaks for each mz", ylab ="")
    title(xlab="mz in Dalton", line=2.5)
    title(ylab = "Number of peaks", line=4)
    axis(4, at=pretty(peakspermz),labels=as.character(round((pretty(peakspermz)/pixelcount*100), digits=1)), las=1)
    mtext("Coverage of spectra [%]", 4, line=3, adj=1)

    # make plot smaller to fit axis and labels, add second y axis with %
    ## 11b) Number of peaks per mz - histogram
    hist(peakspermz, main="", las=1, ylab="")
    title(ylab = "Frequency", line=4)
    title(main="11b) Number of peaks per mz", xlab = "Number of peaks per mz", line=2)
    abline(v=median(peakspermz), col="blue") 


    ## 12) Sum of intensities per mz

    ## Sum of all intensities for each mz (like TIC, but for mz instead of pixel)
    mzTIC = rowSums(spectra(msidata)[]) # calculate intensity sum for each mz

    par(mfrow = c(2,1), mar=c(5,6,4,2))
    # 12a) sum of intensities per mz - scatterplot
    plot_colorByDensity(mz(msidata),mzTIC,  main= "12a) Sum of all peak intensities for each mz", ylab ="")
    title(xlab="mz in Dalton", line=2.5)
    title(ylab="Intensity sum", line=4)
    # 12b) sum of intensities per mz - histogram
    hist(log(mzTIC), main="", xlab = "", las=1, ylab="")
    title(main="12b) Sum of intensities per mz", line=2, ylab="")
    title(xlab = "log (sum of intensities per mz)")
    title(ylab = "Frequency", line=4)
    abline(v=median(log(mzTIC[mzTIC>0])), col="blue")

    ################################## V) general plots ############################
    ###################################################################################

    ## 13) Intensity distribution

    par(mfrow = c(2,1), mar=c(5,6,4,2))

    ## 13a) Intensity histogram: 
    hist(log2(spectra(msidata)[]), main="", xlab = "", ylab="", las=1)
    title(main="13a) Log2-transformed intensities", line=2)
    title(xlab="log2 intensities")
    title(ylab="Frequency", line=4)
    abline(v=median(log2(spectra(msidata)[(spectra(msidata)>0)])), col="blue")

    ## 13b) Median intensity over spectra
    medianint_spectra = apply(spectra(msidata), 2, median)
    plot(medianint_spectra, main="13b) Median intensity per spectrum",las=1, xlab="Spectra index \n (= Acquisition time)", ylab="")
    title(ylab="Median spectrum intensity", line=4)

    ## 14) Mass spectra 

    par(mfrow = c(2, 2))
    plot(msidata, pixel = 1:length(pixelnumber), main= "Average spectrum")
    plot(msidata, pixel =round(length(pixelnumber)/2, digits=0), main="Spectrum in middle of acquisition")
    plot(msidata, pixel = highestmz_pixel, main= paste0("Spectrum at ", rownames(coord(msidata)[highestmz_pixel,])))
    plot(msidata, pixel = secondhighestmz_pixel, main= paste0("Spectrum at ", rownames(coord(msidata)[secondhighestmz_pixel,])))

    ## 15) Zoomed in mass spectra for calibrants
    plusminusvalue = $plusminusinDalton
    x = 1
    if (length(inputcalibrantmasses) != 0)
    {

        for (calibrant in inputcalibrantmasses)
        {
          minmasspixel = features(msidata, mz=calibrant-1)
          maxmasspixel = features(msidata, mz=calibrant+3)
          par(mfrow = c(2, 2), oma=c(0,0,2,0))
          plot(msidata[minmasspixel:maxmasspixel,], pixel = 1:length(pixelnumber), main= "average spectrum")
          abline(v=c(calibrant-plusminusvalue, calibrant,calibrant+plusminusvalue), col="blue", lty=c(3,5,3))
          plot(msidata[minmasspixel:maxmasspixel,], pixel =round(pixelnumber/2, digits=0), main="pixel in middle of acquisition")
          abline(v=c(calibrant-plusminusvalue, calibrant,calibrant+plusminusvalue), col="blue", lty=c(3,5,3))
          plot(msidata[minmasspixel:maxmasspixel,], pixel = highestmz_pixel,main= paste0("Spectrum at ", rownames(coord(msidata)[highestmz_pixel,])))
          abline(v=c(calibrant-plusminusvalue, calibrant,calibrant+plusminusvalue), col="blue", lty=c(3,5,3))
          plot(msidata[minmasspixel:maxmasspixel,], pixel = secondhighestmz_pixel,  main= paste0("Spectrum at ", rownames(coord(msidata)[secondhighestmz_pixel,])))
          abline(v=c(calibrant-plusminusvalue, calibrant,calibrant+plusminusvalue), col="blue", lty=c(3,5,3))
          title(paste0(inputcalibrants[x,1]), outer=TRUE)
          x=x+1
        }

    }else{print("15) The inputcalibrant masses were outside the mass range")}

dev.off()
}else{
  print("inputfile has no intensities > 0")
dev.off()
}

    ]]></configfile>
    </configfiles>
    <inputs>
        <param name="infile" type="data" format="imzml, rdata, analyze75" label="Inputfile as imzML, Analyze7.5 or Cardinal MSImageSet saved as RData"
            help="Upload composite datatype imzml (ibd+imzML) or analyze75 (hdr+img+t2m) or regular upload .RData (Cardinal MSImageSet)"/>
        <param name="filename" type="text" value="" optional="true" label="Title" help="will appear in the quality report. If nothing given it will take the dataset name."/>
        <param name="inputpeptidefile" type="data" optional="true" format="txt, csv" label="Text file with peptidemasses and names"
            help="first column peptide m/z, second column peptide name, tab separated file"/>
        <param name="inputcalibrants" type="data" optional="true" format="txt,csv"
            label="Internal calibrants"
            help="Used for plot number of calibrant per spectrum and for zoomed in mass spectra"/>
        <param name="plusminusinDalton" value="0.25" type="text" label="Mass range" help="plusminus mass window in Dalton"/>
        <repeat name="calibrantratio" title="Plot fold change of two masses for each spectrum" min="0" max="10">
            <param name="mass1" value="1111" type="float" label="Mass 1" help="First mass in Dalton"/>
            <param name="mass2" value="2222" type="float" label="Mass 2" help="Second mass in Dalton"/>
            <param name="distance" value="0.25" type="float" label="Distance in Dalton" help="Distance in Da used to find peak maximum from input masses in both directions"/>
        </repeat>
    </inputs>
    <outputs>
        <data format="pdf" name="plots" from_work_dir="qualitycontrol.pdf" label = "${tool.name} on $infile.display_name"/>
    </outputs>

    <tests>
        <test>
            <param name="infile" value="" ftype="imzml">
                <composite_data value="Example_Continuous.imzML" />
                <composite_data value="Example_Continuous.ibd" />
            </param>
            <param name="inputpeptidefile" value="inputpeptides.csv" ftype="csv"/>
            <param name="inputcalibrants" ftype="txt" value="inputcalibrantfile1.txt"/>
            <param name="plusminusinDalton" value="0.25"/>
            <param name="filename" value="Testfile_imzml"/>
            <repeat name="calibrantratio">
                <param name="mass1" value="111"/>
                <param name="mass2" value="222"/>
                <param name="distance" value="0.25"/>
            </repeat>
            <output name="plots" file="Testfile_qualitycontrol_imzml.pdf" compare="sim_size" delta="20000"/>
        </test>

        <test>
            <param name="infile" value="" ftype="analyze75">
                <composite_data value="Analyze75.hdr"/>
                <composite_data value="Analyze75.img"/>
                <composite_data value="Analyze75.t2m"/>
            </param>
            <param name="inputpeptidefile" value="inputpeptides.txt" ftype="txt"/>
            <param name="inputcalibrants" ftype="txt" value="inputcalibrantfile2.txt"/>
            <param name="plusminusinDalton" value="0.5"/>
            <param name="filename" value="Testfile_analyze75"/>
            <output name="plots" file="Testfile_qualitycontrol_analyze75.pdf" compare="sim_size" delta="20000"/>
        </test>

        <test>
            <param name="infile" value="preprocessing_results1.RData" ftype="rdata"/>
            <param name="inputpeptidefile" value="inputpeptides.csv" ftype="txt"/>
            <param name="inputcalibrants" ftype="txt" value="inputcalibrantfile1.txt"/>
            <param name="plusminusinDalton" value="0.1"/>
            <param name="filename" value="Testfile_rdata"/>
            <output name="plots" file="Testfile_qualitycontrol_rdata.pdf" compare="sim_size" delta="20000"/>
        </test>
        <test>
            <param name="infile" value="LM8_file16.rdata" ftype="rdata"/>
            <param name="inputpeptidefile" value="inputpeptides.txt" ftype="txt"/>
            <param name="inputcalibrants" ftype="txt" value="inputcalibrantfile2.txt"/>
            <param name="plusminusinDalton" value="0.1"/>
            <param name="filename" value="Testfile_rdata"/>
            <output name="plots" file="LM8_file16output.pdf" compare="sim_size" delta="20000"/>
        </test>
    </tests>
    <help>
        <![CDATA[
Quality control for maldi imaging mass spectrometry data.


Input data: 3 types of input data can be used:

- imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format <http://ms-imaging.org/wp/introduction/>`_
- Analyze7.5 (upload hdr, img and t2m file via the "composite" function)
- Cardinal "MSImageSet" data (with variable name "msidata", saved as .RData)

The output of this tool contains key values and plots of the imaging data as pdf. 

        ]]>
    </help>
    <citations>
        <citation type="doi">10.1093/bioinformatics/btv146</citation>
    </citations>
</tool>