<tool id="mass_spectrometry_imaging_qc" name="MSI Qualitycontrol" version="1.7.0.4">
    <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 Imaging dataset

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

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



###################################### 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
}

### Read tabular file with peptide masses for plots and heatmap images: 

#if $peptide_file:

    input_list = read.delim("$peptide_file", header = FALSE, na.strings=c("","NA"), stringsAsFactors = FALSE)
        if (ncol(input_list) == 1)
        {
            input_list = cbind(input_list, input_list)
        }

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

#else
    ###inputpeptides = data.frame(0,0)
    inputpeptides = as.data.frame(matrix(, nrow = 0, ncol = 2))
    number_peptides_in = 0
    number_peptides_valid = 0
#end if

colnames(inputpeptides) = c("mz", "name")

#if $calibrant_file:
    ### Read tabular file with calibrant masses: 
    calibrant_list = read.delim("$calibrant_file", header = FALSE, na.strings=c("","NA"), stringsAsFactors = FALSE)
        if (ncol(calibrant_list) == 1)
        {
            calibrant_list = cbind(calibrant_list, calibrant_list)
         }
        ### calculate how many input calibrant masses are valid: 
        inputcalibrants = calibrant_list[calibrant_list[,1]>minmz & calibrant_list[,1]<maxmz,]
        number_calibrants_in = length(calibrant_list[,1])
        number_calibrants_valid = length(inputcalibrants[,1])
#else
    ###inputcalibrants = data.frame(0,0)

    inputcalibrants = as.data.frame(matrix(, nrow = 0, ncol = 2))

    number_calibrants_in = 0
    number_calibrants_valid = 0
#end if

colnames(inputcalibrants) = c("mz", "name")

### 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", 
               paste0("# peptides in \n", "$peptide_file.display_name"), 
               paste0("# calibrants in \n", "$calibrant_file.display_name"))

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(number_peptides_valid, " / " , number_peptides_in),
           paste0(number_calibrants_valid, " / ", number_calibrants_in))


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)
    }


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

    ## 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-$plusminus_dalton)
          calibrantfeaturemax = features(msidata, mz=calibrantmz+$plusminus_dalton)

            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", "olivedrab1", "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 not provided or 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

            #if not str($foldchanges.filenameratioplot).strip():
                #set $label = "Fold change %s Da / %s Da" % ($foldchanges.mass1, $foldchanges.mass2)
            #else:
                #set $label = $foldchanges.filenameratioplot
            #end if

            ### 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)

            ### find mass in the given range with the highest intensity (will be plotted in red)

                if (mzrowdown1 == mzrowup1)
                {
                         maxmass1 = mz(msidata)[ mzrowdown1]
                }else{ ### for all masses in the massrange calculate mean intensity over all pixels and take mass which has highest mean
                        maxmassrow1 = rowMeans(spectra(msidata)[mzrowdown1:mzrowup1,])
                        maxmass1 = mz(msidata)[mzrowdown1:mzrowup1][which.max(maxmassrow1)] 
                }
                if (mzrowdown2 == mzrowup2)
                {
                    maxmass2 = mz(msidata)[mzrowup2]
                }else{
                    maxmassrow2 = rowMeans(spectra(msidata)[mzrowdown2:mzrowup2,])
                    maxmass2 = mz(msidata)[mzrowdown2:mzrowup2][which.max(maxmassrow2)]
                }

            ### plot the part which was chosen, with chosen value in blue, distance in blue, maxmass in red, xlim fixed to 5 Da window
            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)
             + geom_tile() + coord_fixed()
             + ggtitle("$label")
             + 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:

    par(mfrow=c(1,1), mar=c(5.1, 4.1, 4.1, 2.1), mgp=c(3, 1, 0), las=0)
    if (length(inputmasses) != 0)
    {   for (mass in 1:length(inputmasses))
        {
          image(msidata, mz=inputmasses[mass], plusminus=$plusminus_dalton, 
          main= paste0("3", LETTERS[mass], ") ", inputnames[mass], " (", round(inputmasses[mass], digits = 2)," ± ", $plusminus_dalton, " Da)"),
                contrast.enhance = "histogram", ylim=c(maximumy+2, 0))
        }
    } else {print("3) The inputpeptide masses were not provided or 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(maximumy+2, 0),  strip=FALSE)


    ############################# 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") 


    ################################## 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 = $plusminus_dalton
    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(length(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 not provided or 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="peptide_file" type="data" optional="true" format="tabular" label="Text file with peptidemasses and names"
            help="first column peptide m/z, second column peptide name, tab separated file"/>
        <param name="calibrant_file" type="data" optional="true" format="tabular"
            label="Internal calibrants"
            help="Used for plot number of calibrant per spectrum and for zoomed in mass spectra"/>
        <param name="plusminus_dalton" 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"/>
            <param name="filenameratioplot" type="text" optional="true" label="Title" help="Optional title for fold change plot."/>
        </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="peptide_file" value="inputpeptides.csv" ftype="csv"/>
            <param name="calibrant_file" ftype="txt" value="inputcalibrantfile1.txt"/>
            <param name="plusminus_dalton" 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"/>
                <param name="filenameratioplot" value = "Ratio of mass1 (111) / mass2 (222)"/>
            </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="peptide_file" value="inputpeptides.txt" ftype="txt"/>
            <param name="calibrant_file" ftype="txt" value="inputcalibrantfile2.txt"/>
            <param name="plusminus_dalton" 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="plusminus_dalton" 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="peptide_file" value="inputpeptides.txt" ftype="txt"/>
            <param name="calibrant_file" ftype="txt" value="inputcalibrantfile2.txt"/>
            <param name="plusminus_dalton" 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. The output of this tool contains key values and plots of the imaging data as pdf. 
For additional beautiful heatmap images use the MSI ion images tool and to plot more mass spectra use the MSI massspectra tool. 

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)



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