comparison msi_qualitycontrol.xml @ 4:fef8bd551236 draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/msi_qualitycontrol commit edbf2a6cb50fb04d0db56a7557a64e3bb7a0806a

3:f6aa0cff777c

<tool id="mass_spectrometry_imaging_qc" name="MSI Qualitycontrol" version="1.7.0.2">
    <description>
        mass spectrometry imaging QC
    </description>
    <requirements>
        <requirement type="package" version="1.7.0">bioconductor-cardinal</requirement>

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)

  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]

               "Normalization", 
               "Smoothing",
               "Baseline reduction",
               "Peak picking",
               "Centroided", 
                paste0("# valid masses in ", "$filename"))

values = c(paste0(maxfeatures), 
           paste0(minmz, " - ", maxmz), 
           paste0(pixelcount), 
           paste0(minimumx, " - ", maximumx),  

           paste0(normalizationinfo),
           paste0(smoothinginfo),
           paste0(baselinereductioninfo),
           paste0(peakpickinginfo),
           paste0(centroidedinfo), 
           paste0(length(inputmasses), "/", length(calibrant_list[,1])+length(input_list[,1])))


property_df = data.frame(properties, values)

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

    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

    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,] 

          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:

            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)

            ### 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)

    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=$plusminusinDalton, 
          main= paste0("3", LETTERS[mass], ") ", inputnames[mass], " (", round(inputmasses[mass], digits = 2)," ± ", $plusminusinDalton, " 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

     + 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() 

    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)##########
    ##############################################################################

    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)

    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))

    </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"/>
            <param name="filenameratioplot" type="text" optional="true" label="Title" help="Optional title for fold change plot."/>

        <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>

4:fef8bd551236

<tool id="mass_spectrometry_imaging_qc" name="MSI Qualitycontrol" version="1.7.0.3">
    <description>
        mass spectrometry imaging QC
    </description>
    <requirements>
        <requirement type="package" version="1.7.0">bioconductor-cardinal</requirement>

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)

  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)

#else
    inputpeptides = data.frame(0,0)
    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)
#else
    inputcalibrants = data.frame(0,0)
    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]

               "Normalization", 
               "Smoothing",
               "Baseline reduction",
               "Peak picking",
               "Centroided", 
               paste0("# peptides in ", "$peptide_file.display_name"), 
               paste0("# calibrants in ", "$calibrant_file.display_name"))

values = c(paste0(maxfeatures), 
           paste0(minmz, " - ", maxmz), 
           paste0(pixelcount), 
           paste0(minimumx, " - ", maximumx),  

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

    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

    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,] 

          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:

            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)

            ### 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)

    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 outside the mass range")}

    ## 4) Number of peaks per pixel - image

     + 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() 

    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))


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

    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)

    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))

    </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."/>

        <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="peptide_file" value="inputpeptides.csv" ftype="txt"/>
            <param name="calibrant_file" ftype="txt" value="inputcalibrantfile1.txt"/>
            <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>