comparison msi_qualitycontrol.xml @ 10:3eee933c27cf draft

planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_qualitycontrol commit 37da74ed68228b16efbdbde776e7c38cc06eb5d5

9:963c7ec00141

<tool id="mass_spectrometry_imaging_qc" name="MSI Qualitycontrol" version="1.10.0.1">
    <description>
        mass spectrometry imaging QC
    </description>
    <requirements>
        <requirement type="package" version="1.10.0">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 $infile.ext == 'imzml'
            ln -s '${infile.extra_files_path}/imzml' infile.imzML &&

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

#if $infile.ext == 'imzml'
    msidata = readImzML('infile')
#elif $infile.ext == 'analyze75'
    msidata = readAnalyze('infile')
#else
    load('infile.RData')
#end if

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

## Number of features (m/z)
maxfeatures = length(features(msidata))

percpeaks = round(npeaks/numpeaks*100, digits=2)
## Number of empty TICs
TICs = colSums(spectra(msidata)[]) 
NumemptyTIC = sum(TICs == 0)
## Median TIC
medTIC = median(TICs)
## Median peaks per spectrum
medpeaks = median(colSums(spectra(msidata)[]>0))
print(cor(TICs,colSums(spectra(msidata)[]>0), method="pearson"))

## Processing informations

  peakpickinginfo='FALSE'
} else {
  peakpickinginfo=processinginfo@peakPicking
}


############## Read and filter tabular file with m/z ###########################

### reading peptide file: 

#if $peptide_file:

title(main=paste("$filename"))

################# I) file properties in numbers ################################
################################################################################
    print("properties in numbers")

properties = c("Number of m/z features",
               "Range of m/z values [Da]",
               "Number of pixels", 
               "Range of x coordinates", 
               "Range of y coordinates",
               "Range of intensities", 
               "Median of intensities",

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


property_df = data.frame(properties, values)

grid.table(property_df, rows= NULL)

    ####################### II) images in x-y grid ###############################
    ##############################################################################
    print("x-y images")
if (npeaks > 0){

    ## function for density plots
    plot_colorByDensity = function(x1,x2,
                                   ylim=c(min(x2),max(x2)),
                                   xlim=c(min(x1),max(x1)),
                                   xlab="",ylab="",main=""){

      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)}

    abline_vector= -100000 ## will be filled for samples in case data is combined

    ################### 0) overview for combined data ###########################

    ### only for previously combined data, same plot as in combine QC pdf
    if (!is.null(levels(msidata\$combined_sample))){
        position_df = cbind(coord(msidata)[,1:2], msidata\$combined_sample)
        colnames(position_df)[3] = "sample_name"

        combine_plot = ggplot(position_df, aes(x=x, y=y, fill=sample_name))+
               geom_tile() +
               coord_fixed()+
               ggtitle("Spatial orientation of combined data")+
               theme_bw()+
               theme(text=element_text(family="ArialMT", face="bold", size=15))+
               theme(legend.position="bottom",legend.direction="vertical")+
               guides(fill=guide_legend(ncol=4,byrow=TRUE))
        coord_labels = aggregate(cbind(x,y)~sample_name, data=position_df, mean)
        coord_labels\$file_number = gsub( "_.*$", "", coord_labels\$sample_name)
        for(file_count in 1:nrow(coord_labels))
        {combine_plot = combine_plot + annotate("text",x=coord_labels[file_count,"x"],
        y=coord_labels[file_count,"y"],label=toString(coord_labels[file_count,4]))}
        print(combine_plot)

        ### find max pixelnumber per subsample to later draw ablines
        pixel_name_df = data.frame(pixels(msidata), msidata\$combined_sample)
        colnames(pixel_name_df) = c("pixel_number", "pixel_name")
        last_pixel = aggregate(pixel_number~pixel_name, data = pixel_name_df, max)
        pixel_vector = last_pixel[,2]
        abline_vector = pixel_vector[1:length(levels(msidata\$combined_sample))-1]
        print(abline_vector)
        }


    ################### 1) Pixel order image ###################################

    pixelnumber = 1:pixelcount
    pixelxyarray=cbind(coord(msidata)[,1:2],pixelnumber)

    print(ggplot(pixelxyarray, aes(x=x, y=y, fill=pixelnumber))
     + geom_tile() + coord_fixed()
     + ggtitle("Pixel order")
     +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]

        countvector= as.factor(colSums(pixelmatrix>0))
        countdf= cbind(coord(msidata)[,1:2], 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("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 m/z were not provided or outside the m/z range")}

    ########################## 3) fold change image ###########################

    #if $calibrantratio:

                mass1vector = spectra(msidata)[features(msidata, mz = maxmass1),]
                mass2vector = spectra(msidata)[features(msidata, mz = maxmass2),]
                foldchange= log2(mass1vector/mass2vector)
                fcmatrix = cbind(foldchange, coord(msidata)[,1:2])

                print(ggplot(fcmatrix, 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"))
            }else{
                plot(0,type='n',axes=FALSE,ann=FALSE)
                title(main=paste("At least one m/z range did not contain any intensity value > 0,\n therefore no foldchange plot could be drawn"))}

    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(inputnames[mass], " (", round(inputmasses[mass], digits = 2)," ± ", $plusminus_dalton, " Da)"),
            contrast.enhance = "histogram")
        }
    } else {print("4) The input peptide and calibrant m/z were not provided or outside the m/z range")}

    #################### 5) Number of peaks per pixel - image ##################

    ## here every intensity value > 0 counts as pixel
    peaksperpixel = colSums(spectra(msidata)[]> 0)
    peakscoordarray=cbind(coord(msidata)[,1:2], peaksperpixel)

    print(ggplot(peakscoordarray, aes(x=x, y=y, fill=peaksperpixel), colour=colo)
     + geom_tile() + coord_fixed() 
     + ggtitle("Number of peaks per spectrum")
     + 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"))

    ############################### 6) TIC image ###############################

    TICcoordarray=cbind(coord(msidata)[,1:2], 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("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"))

    ############################### 7) Most abundant m/z image #################

    highestmz = apply(spectra(msidata)[],2,which.max) 
    highestmz_matrix = cbind(coord(msidata)[,1:2],mz(msidata)[highestmz])
    colnames(highestmz_matrix)[3] = "highestmzinDa"

    print(ggplot(highestmz_matrix, aes(x=x, y=y, fill=highestmzinDa))
    + geom_tile() + coord_fixed() 
    + ggtitle("Most abundant m/z in each spectrum")
    + 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 m/z 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]

    print(head(sort(table(round(highestmz_matrix\$highestmzinDa, digits=0)), decreasing=TRUE)))

    ########################## 8) pca image for two components #################

    pca = PCA(msidata, ncomp=2) 
    par(mfrow = c(2,1))
    plot(pca, col=c("black", "darkgrey"), main="PCA for two components")
    image(pca, col=c("black", "white"), strip=FALSE)

    ################## III) properties over spectra index ##########
    ##############################################################################
    print("properties over pixels")
    par(mfrow = c(2,1), mar=c(5,6,4,2))

        colnames(df_9) = c("Npeaks", "sample_name")

        hist_9 = ggplot(df_9, aes(x=Npeaks, fill=sample_name)) +
        geom_histogram()+ theme_bw()+
        theme(text=element_text(family="ArialMT", face="bold", size=12))+
        labs(title="Number of peaks per spectrum and sample", x="Number of peaks per spectrum", y = "Frequency = # spectra") +
        geom_vline(xintercept = median(peaksperpixel), size = 1, colour = "black",linetype = "dashed")
        print(hist_9)}

    ########################## 10) TIC per spectrum ###########################

        colnames(df_10) = c("TICs", "sample_name")

        hist_10 = ggplot(df_10, aes(x=TICs, fill=sample_name)) +
        geom_histogram()+ theme_bw()+
        theme(text=element_text(family="ArialMT", face="bold", size=12))+
        labs(title="TIC per spectrum and sample", x="log(TIC per spectrum)", y = "Frequency = # spectra") +
        geom_vline(xintercept = median(log(TICs[TICs>0])), size = 1, colour = "black",linetype = "dashed")
        print(hist_10)}

    ################################## IV) changes over m/z ####################
    ############################################################################

    hist_13 = ggplot(df_13, aes(x=logint, fill=sample_name)) +
    geom_histogram()+ theme_bw()+
    theme(text=element_text(family="ArialMT", face="bold", size=12))+
    labs(title="Log2-transformed intensities per sample", x="log2 intensities", y = "Frequency") +
    geom_vline(xintercept = median(log2(spectra(msidata)[(spectra(msidata)>0)])), size = 1, colour = "black",linetype = "dashed")
    print(hist_13)

    ## 13d) boxplots to visualize in a different way the intensity distributions
    par(mfrow = c(1,1), cex.axis=1.3, cex.lab=1.3, mar=c(13.1,4.1,5.1,2.1))

    mean_matrix = matrix(,ncol=0, nrow = nrow(msidata))
    for (subsample in levels(msidata\$combined_sample)){
        mean_mz_sample = colMeans(spectra(msidata)[,msidata\$combined_sample==subsample])
        mean_matrix = cbind(mean_matrix, mean_mz_sample)}
    boxplot(mean_matrix, ylab = "mean intensity per m/z", names=levels(msidata\$combined_sample), main="Mean intensities per m/z and sample", las=2)
    }

    ########################## 14) Histogram on m/z values #####################

    par(mfrow = c(1, 1), cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))

            ppmdifference2 = mzdifference2/inputcalibrantmasses[mass]*1000000 
            differencevector2[mass] = round(ppmdifference2, digits=2)

            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(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,6,3))
            abline(v=c(maxvalue), col="red", lty=5)
            abline(v=c(mzvalue), col="green2", lty=5)
            plot(msidata[minmasspixel:maxmasspixel,], pixel = pixels_for_plot[1], main=paste0("Spectrum at ", rownames(coord(msidata)[pixels_for_plot[1],1:2])))
            abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,6,3))
            abline(v=c(maxvalue), col="red", lty=5)
            abline(v=c(mzvalue), col="green2", lty=5)
            plot(msidata[minmasspixel:maxmasspixel,], pixel = pixels_for_plot[2], main= paste0("Spectrum at ", rownames(coord(msidata)[pixels_for_plot[2],1:2])))
            abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,6,3))
            abline(v=c(maxvalue), col="red", lty=5)
            abline(v=c(mzvalue), col="green2", lty=5)
            plot(msidata[minmasspixel:maxmasspixel,], pixel = pixels_for_plot[3], main= paste0("Spectrum at ", rownames(coord(msidata)[pixels_for_plot[3],1:2])))
            abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,6,3))
            abline(v=c(maxvalue), col="red", lty=5)
            abline(v=c(mzvalue), col="green2", lty=5)
            title(paste0("theor. m/z: ", inputcalibrants[count,1]), col.main="blue", outer=TRUE, line=0, adj=0.074)
            title(paste0("most abundant m/z: ", round(maxvalue, digits=4)), col.main="red", outer=TRUE, line=0, adj=0.49)
            title(paste0("closest m/z: ", round(mzvalue, digits=4)), col.main="green2", outer=TRUE, line=0, adj=0.93)
            count=count+1
        }

    ######### 17) ppm difference input calibrant m/z and m/z with max intensity in given m/z range#########

        par(mfrow = c(1, 1))

        if (sum(is.na(diff_df[,1])) == nrow(diff_df)){
            print("plot 17: no peaks in the chosen region, repeat with higher ppm range")
        }else{

        diff_plot=ggplot(data=diff_df, aes(x=calibrant_names, y=differencevector)) + geom_bar(stat="identity", fill = "darkgray") + theme_minimal() +
        labs(title="Difference m/z with max. average intensity vs. theoretical calibrant m/z", x="calibrants", y = "Difference in ppm")+
        geom_text(aes(label=differencevector), vjust=-0.3, size=3.5, col="blue")

        print(diff_plot)}

    ######### 18) ppm difference input calibrant m/z and closest m/z ###########

        differencevector2 = round(differencevector2, digits=2)
        calibrant_names = as.character(inputcalibrants[,2])
        diff_df = data.frame(differencevector2, calibrant_names)

        diff_plot=ggplot(data=diff_df, aes(x=calibrant_names, y=differencevector2)) + geom_bar(stat="identity", fill = "darkgray") + theme_minimal() +
        labs(title="Difference closest measured m/z vs. theoretical calibrant m/z", x="calibrants", y = "Difference in ppm")+
        geom_text(aes(label=differencevector2), vjust=-0.3, size=3.5, col="blue")

        print(diff_plot)

    #################### 19) ppm difference over pixels #####################

        }else{

    ### plot ppm differences over pixels (spectra index)

    par(mar=c(4.1, 4.1, 4.1, 7.5))
    plot(0,0,type="n", ylim=c(min(ppm_df, na.rm=TRUE),max(ppm_df, na.rm=TRUE)), xlim = c(1,ncol(filtered_data)),xlab = "Spectra index", ylab = "m/z difference in ppm", main="Difference m/z with max. average intensity vs. theoretical m/z\n(per spectrum)") 

    for (each_cal in 1:ncol(ppm_df)){
        lines(ppm_df[,each_cal], col=mycolours[each_cal], type="p")}
    legend("topright", inset=c(-0.25,0), xpd = TRUE, bty="n", legend=inputcalibrantmasses, col=mycolours[1:ncol(ppm_df)],lty=1)
    abline(v=abline_vector, lty = 3)}

    }else{print("16+17+18+19) The inputcalibrant m/z were not provided or outside the m/z 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 as header in the quality report, if nothing given input dataset name is used"/>
        <param name="calibrant_file" type="data" optional="true" format="tabular"
            label="File with internal calibrants" help="first column: m/z, second column: name (optional), tabular file"/>
        <param name="peptide_file" type="data" optional="true" format="tabular" label="File with m/z of interest"
            help="first column: m/z, second column: name (optional), tabular file"/>

            <param name="mass1" value="1111" type="float" label="M/z 1" help="First m/z"/>
            <param name="mass2" value="2222" type="float" label="M/z 2" help="Second m/z"/>
            <param name="distance" value="0.25" type="float" label="M/z range" help="Plusminus m/z window added to input m/z. In both m/z ranges the maximum intensity is used to calculate the fold change"/>
            <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 = "$infile.display_name QC_report"/>
    </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.txt"/>
            <param name="calibrant_file" value="inputcalibrantfile1.txt"/>
            <param name="plusminus_dalton" value="0.25"/>
            <param name="plusminus_ppm" value="100"/>
            <param name="filename" value="Testfile_imzml"/>

                <param name="mass1" value="328.9"/>
                <param name="mass2" value="398.8"/>
                <param name="distance" value="0.25"/>
                <param name="filenameratioplot" value = "Ratio of mass1 (328.9) / mass2 (398.8)"/>
            </repeat>
            <output name="plots" file="QC_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="calibrant_file" value="inputcalibrantfile2.txt"/>
            <param name="plusminus_dalton" value="0.5"/>
            <param name="filename" value="Testfile_analyze75"/>
            <output name="plots" file="QC_analyze75.pdf" compare="sim_size" delta="20000"/>
        </test>
        <test>
            <param name="infile" value="123_combined.RData" ftype="rdata"/>
            <param name="plusminus_dalton" value="0.2"/>
            <param name="filename" value="Testfile_rdata"/>
            <output name="plots" file="QC_rdata.pdf" compare="sim_size" delta="20000"/>
        </test>
        <test>
            <param name="infile" value="empty_spectra.rdata" ftype="rdata"/>
            <param name="peptide_file" value="inputpeptides.txt"/>
            <param name="calibrant_file" value="inputcalibrantfile2.txt"/>
            <param name="plusminus_dalton" value="0.1"/>
            <param name="filename" value="Testfile_rdata"/>

- optional fold change plot: draws a heatmap (x-y grid) for the fold change of two m/z (log2(intensity ratio))

Output: 

- quality control report as pdf with key numbers and descriptive plots describing the mass spectrometry imaging data

Tip: 

- For additional m/z heatmaps use the MSI ion images tool and to plot more mass spectra use the MSI massspectra tool. 

10:3eee933c27cf

<tool id="mass_spectrometry_imaging_qc" name="MSI Qualitycontrol" version="1.10.0.2">
    <description>
        mass spectrometry imaging QC
    </description>
    <requirements>
        <requirement type="package" version="1.10.0">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>
        <requirement type="package" version="0.5.0">r-scales</requirement>
    </requirements>
    <command detect_errors="exit_code">
    <![CDATA[
        #if $infile.ext == 'imzml'
            ln -s '${infile.extra_files_path}/imzml' infile.imzML &&

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

#if $infile.ext == 'imzml'
    msidata <- readImzML('infile', mass.accuracy=$accuracy, units.accuracy = "$units")
#elif $infile.ext == 'analyze75'
    msidata = readAnalyze('infile')
#else
    load('infile.RData')
#end if

## create full matrix to make processed imzML files compatible with segmentation
iData(msidata) <- iData(msidata)[] 

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

## Number of features (m/z)
maxfeatures = length(features(msidata))

percpeaks = round(npeaks/numpeaks*100, digits=2)
## Number of empty TICs
TICs = colSums(spectra(msidata)[]) 
NumemptyTIC = sum(TICs == 0)
## Median TIC
medTIC = round(median(TICs), digits=2)
## Median peaks per spectrum
medpeaks = median(colSums(spectra(msidata)[]>0))
print(cor(TICs,colSums(spectra(msidata)[]>0), method="pearson"))

## Processing informations

  peakpickinginfo='FALSE'
} else {
  peakpickinginfo=processinginfo@peakPicking
}

############## Read and filter tabular file with m/z ###########################

### reading peptide file: 

#if $peptide_file:

title(main=paste("$filename"))

################# I) file properties in numbers ################################
################################################################################
print("properties in numbers")

properties = c("Number of m/z features",
               "Range of m/z values",
               "Number of pixels", 
               "Range of x coordinates", 
               "Range of y coordinates",
               "Range of intensities", 
               "Median of intensities",

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

property_df = data.frame(properties, values)

grid.table(property_df, rows= NULL)

####################### II) images in x-y grid ###############################
##############################################################################
print("x-y images")

if (npeaks > 0){
    ## function for density plots
    plot_colorByDensity = function(x1,x2,
                                   ylim=c(min(x2),max(x2)),
                                   xlim=c(min(x1),max(x1)),
                                   xlab="",ylab="",main=""){

      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)}

    abline_vector= -100000 ## will be filled for samples in case data is combined

    ## start list for optional spectrum values output
    spectrum_list = list()
    list_count = 1

    ################### 0) overview for combined data ###########################

    ### only for previously combined data, same plot as in combine QC pdf

    if (!is.null(levels(msidata\$combined_sample))){
        number_combined = length(levels(msidata\$combined_sample))

        ## the more combined_samples a file has the smaller will be the legend
        if (number_combined<20){
            legend_size = 10
            cex_boxplot = 1
        }else if (number_combined>20 && number_combined<40){
            legend_size = 9
            cex_boxplot = 0.8
        }else if (number_combined>40 && number_combined<60){
            legend_size = 8
            cex_boxplot = 0.6
        }else if (number_combined>60 && number_combined<100){
            legend_size = 7
            cex_boxplot = 0.5
        }else{
            legend_size = 6
            cex_boxplot = 0.3
        }

        position_df = cbind(coord(msidata)[,1:2], msidata\$combined_sample)
        colnames(position_df)[3] = "sample_name"

        combine_plot = ggplot(position_df, aes(x=x, y=y, fill=sample_name))+
               geom_tile() +
               coord_fixed()+
               ggtitle("Spatial orientation of combined data")+
               theme_bw()+
               theme(plot.title = element_text(hjust = 0.5))+
               theme(text=element_text(family="ArialMT", face="bold", size=12))+
               theme(legend.position="bottom",legend.direction="vertical")+
               theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = legend_size))+
               guides(fill=guide_legend(ncol=5,byrow=TRUE))
        coord_labels = aggregate(cbind(x,y)~sample_name, data=position_df, mean)
        coord_labels\$file_number = gsub( "_.*$", "", coord_labels\$sample_name)
        for(file_count in 1:nrow(coord_labels))
            {combine_plot = combine_plot + annotate("text",x=coord_labels[file_count,"x"],
            y=coord_labels[file_count,"y"],label=toString(coord_labels[file_count,4]))}

        print(combine_plot)

        ### find max pixelnumber per subsample to later draw ablines 
        pixel_name_df = data.frame(pixels(msidata), msidata\$combined_sample)
        colnames(pixel_name_df) = c("pixel_number", "pixel_name")
        last_pixel = aggregate(pixel_number~pixel_name, data = pixel_name_df, max)
        pixel_vector = last_pixel[,2]
        abline_vector = pixel_vector[1:number_combined-1]
        print(abline_vector)
    }

    ################### 1) Pixel order image ###################################

    pixelnumber = 1:pixelcount
    pixelxyarray=cbind(coord(msidata)[,1:2],pixelnumber)

    print(ggplot(pixelxyarray, aes(x=x, y=y, fill=pixelnumber))+
     geom_tile() + coord_fixed()+
     ggtitle("Pixel order") + theme_bw()+
     theme(plot.title = element_text(hjust = 0.5))+
     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 = "Pixel number"))

    ################ 2) Number of calibrants per spectrum ######################

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

        countvector= as.factor(colSums(pixelmatrix>0))
        countdf= cbind(coord(msidata)[,1:2], 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("Number of calibrants per pixel") +
          theme_bw() +
          theme(plot.title = element_text(hjust = 0.5))+
          theme(text=element_text(family="ArialMT", face="bold", size=12))+
          scale_fill_manual(values = mycolours[1:length(countvector)], 
                                na.value = "black", name = "# calibrants"))

        ## append list for optional spectrum values output
        colnames(countdf)[3] = "Number of Calibrants"
        spectrum_list[[list_count]] = countdf
        list_count = list_count+1

    }else{print("2) The inputcalibrant m/z were not provided or outside the m/z range")}

    ########################## 3) fold change image ###########################

    #if $calibrantratio:

                mass1vector = spectra(msidata)[features(msidata, mz = maxmass1),]
                mass2vector = spectra(msidata)[features(msidata, mz = maxmass2),]
                foldchange= log2(mass1vector/mass2vector)
                fcmatrix = cbind(foldchange, coord(msidata)[,1:2])

                print(ggplot(fcmatrix, aes(x=x, y=y, fill=foldchange), colour=colo)+
                 geom_tile() + coord_fixed()+
                 ggtitle("$label")+
                 theme_bw()+
                 theme(plot.title = element_text(hjust = 0.5))+
                 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"))
            }else{
                plot(0,type='n',axes=FALSE,ann=FALSE)
                title(main=paste("At least one m/z range did not contain any intensity value > 0,\n therefore no foldchange plot could be drawn"))}

    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(inputnames[mass], " (", round(inputmasses[mass], digits = 2)," ± ", $plusminus_dalton, " Da)"),
            contrast.enhance = "histogram", ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy))
        }
    } else {print("4) The input peptide and calibrant m/z were not provided or outside the m/z range")}

    #################### 5) Number of peaks per pixel - image ##################

    ## here every intensity value > 0 counts as pixel
    peaksperpixel = colSums(spectra(msidata)[]> 0)
    peakscoordarray=cbind(coord(msidata)[,1:2], peaksperpixel)

    print(ggplot(peakscoordarray, aes(x=x, y=y, fill=peaksperpixel), colour=colo)+
     geom_tile() + coord_fixed() +
     ggtitle("Number of peaks per spectrum")+
     theme_bw() +
     theme(plot.title = element_text(hjust = 0.5))+
     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"))

    ## append list for optional spectrum values output
    colnames(peakscoordarray)[3] = "Number of Peaks"
    spectrum_list[[list_count]] = peakscoordarray
    list_count = list_count+1

    ############################### 6) TIC image ###############################

    TICcoordarray=cbind(coord(msidata)[,1:2], 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("Total Ion Chromatogram")+
     theme_bw() +
     theme(plot.title = element_text(hjust = 0.5))+
     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"))

    ## append list for optional spectrum values output
    colnames(TICcoordarray)[3] = "TIC per spectrum"
    spectrum_list[[list_count]] = TICcoordarray
    list_count = list_count+1

    ############################### 7) Most abundant m/z image #################

    highestmz = apply(spectra(msidata)[],2,which.max) 
    highestmz_matrix = cbind(coord(msidata)[,1:2],mz(msidata)[highestmz])
    colnames(highestmz_matrix)[3] = "highestmzinDa"

    print(ggplot(highestmz_matrix, aes(x=x, y=y, fill=highestmzinDa))+
    geom_tile() + coord_fixed() +
    ggtitle("Most abundant m/z in each spectrum")+
    theme_bw() +
    theme(plot.title = element_text(hjust = 0.5))+
    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 m/z 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]

    print(head(sort(table(round(highestmz_matrix\$highestmzinDa, digits=0)), decreasing=TRUE)))

    ## append list for optional spectrum values output
    colnames(highestmz_matrix)[3] = "Most abundant m/z"
    spectrum_list[[list_count]] = highestmz_matrix

    ########################## 8) pca image for two components #################

    pca = PCA(msidata, ncomp=2) 
    par(mfrow = c(2,1))
    plot(pca, col=c("black", "darkgrey"), main="PCA for two components")
    image(pca, col=c("black", "white"), strip=FALSE, ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy))

    ################## III) properties over spectra index ##########
    ##############################################################################
    print("properties over pixels")
    par(mfrow = c(2,1), mar=c(5,6,4,2))

        colnames(df_9) = c("Npeaks", "sample_name")

        hist_9 = ggplot(df_9, aes(x=Npeaks, fill=sample_name)) +
        geom_histogram()+ theme_bw()+
        theme(text=element_text(family="ArialMT", face="bold", size=12))+
        theme(plot.title = element_text(hjust = 0.5))+
        theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = legend_size))+
        theme(legend.position="bottom",legend.direction="vertical")+
        labs(title="Number of peaks per spectrum and sample", x="Number of peaks per spectrum", y = "Frequency = # spectra") +
        guides(fill=guide_legend(ncol=5,byrow=TRUE))+
        geom_vline(xintercept = median(peaksperpixel), size = 1, colour = "black",linetype = "dashed")
        print(hist_9)}

    ########################## 10) TIC per spectrum ###########################

        colnames(df_10) = c("TICs", "sample_name")

        hist_10 = ggplot(df_10, aes(x=TICs, fill=sample_name)) +
        geom_histogram()+ theme_bw()+
        theme(text=element_text(family="ArialMT", face="bold", size=12))+
        theme(plot.title = element_text(hjust = 0.5))+
        theme(legend.position="bottom",legend.direction="vertical")+
        theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = legend_size))+
        labs(title="TIC per spectrum and sample", x="log(TIC per spectrum)", y = "Frequency = # spectra") +
        guides(fill=guide_legend(ncol=5,byrow=TRUE))+
        geom_vline(xintercept = median(log(TICs[TICs>0])), size = 1, colour = "black",linetype = "dashed")
        print(hist_10)}

    ################################## IV) changes over m/z ####################
    ############################################################################

    hist_13 = ggplot(df_13, aes(x=logint, fill=sample_name)) +
    geom_histogram()+ theme_bw()+
    theme(text=element_text(family="ArialMT", face="bold", size=12))+
    labs(title="Log2-transformed intensities per sample", x="log2 intensities", y = "Frequency") +
    theme(plot.title = element_text(hjust = 0.5))+
    theme(legend.position="bottom",legend.direction="vertical")+
    theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = legend_size))+
    guides(fill=guide_legend(ncol=5,byrow=TRUE))+
    geom_vline(xintercept = median(log2(spectra(msidata)[(spectra(msidata)>0)])), size = 1, colour = "black",linetype = "dashed")
    print(hist_13)

    ## 13d) boxplots to visualize in a different way the intensity distributions
    par(mfrow = c(1,1), cex.axis=1.3, cex.lab=1.3, mar=c(13.1,4.1,5.1,2.1))

    mean_matrix = matrix(,ncol=0, nrow = nrow(msidata))
    for (subsample in levels(msidata\$combined_sample)){
        mean_mz_sample = rowMeans(spectra(msidata)[,msidata\$combined_sample==subsample])
        mean_matrix = cbind(mean_matrix, mean_mz_sample)}

    boxplot(log2(mean_matrix), ylab = "log2 mean intensity per m/z", main="Mean intensities per m/z and sample", xaxt = "n")
    (axis(1, at = c(1:number_combined), labels=levels(msidata\$combined_sample), cex.axis=cex_boxplot, las=2))
    }

    ########################## 14) Histogram on m/z values #####################

    par(mfrow = c(1, 1), cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))

            ppmdifference2 = mzdifference2/inputcalibrantmasses[mass]*1000000 
            differencevector2[mass] = round(ppmdifference2, digits=2)

            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(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,1,3))
            abline(v=c(maxvalue), col="red", lty=2)
            abline(v=c(mzvalue), col="green2", lty=4)
            plot(msidata[minmasspixel:maxmasspixel,], pixel = pixels_for_plot[1], main=paste0("Spectrum at ", rownames(coord(msidata)[pixels_for_plot[1],1:2])))
            abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,1,3))
            abline(v=c(maxvalue), col="red", lty=2)
            abline(v=c(mzvalue), col="green2", lty=4)
            plot(msidata[minmasspixel:maxmasspixel,], pixel = pixels_for_plot[2], main= paste0("Spectrum at ", rownames(coord(msidata)[pixels_for_plot[2],1:2])))
            abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,1,3))
            abline(v=c(maxvalue), col="red", lty=2)
            abline(v=c(mzvalue), col="green2", lty=4)
            plot(msidata[minmasspixel:maxmasspixel,], pixel = pixels_for_plot[3], main= paste0("Spectrum at ", rownames(coord(msidata)[pixels_for_plot[3],1:2])))
            abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,1,3))
            abline(v=c(maxvalue), col="red", lty=2)
            abline(v=c(mzvalue), col="green2", lty=4)
            title(paste0("theor. m/z: ", inputcalibrants[count,1]), col.main="blue", outer=TRUE, line=0, adj=0.074)
            title(paste0("most abundant m/z: ", round(maxvalue, digits=4)), col.main="red", outer=TRUE, line=0, adj=0.49)
            title(paste0("closest m/z: ", round(mzvalue, digits=4)), col.main="green2", outer=TRUE, line=0, adj=0.93)

            ### 16b) one large extra plot with different colours for different samples (for combined_sample only)

            if (!is.null(levels(msidata\$combined_sample))){
                if (number_combined < 10){
                    key_zoomed = TRUE
                }else{key_zoomed = FALSE}
                par(mfrow = c(1, 1))
                plot(msidata[minmasspixel:maxmasspixel,], pixel=1:ncol(msidata),main="average spectrum per sample",
                pixel.groups=msidata\$combined_sample, key=key_zoomed, col=hue_pal()(number_combined),superpose=TRUE)
                abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="black", lty=c(3,1,3))
            count=count+1
            }
        }

    ######### 17) ppm difference input calibrant m/z and m/z with max intensity in given m/z range#########

        par(mfrow = c(1, 1))

        if (sum(is.na(diff_df[,1])) == nrow(diff_df)){
            print("plot 17: no peaks in the chosen region, repeat with higher ppm range")
        }else{

        diff_plot=ggplot(data=diff_df, aes(x=calibrant_names, y=differencevector)) + geom_bar(stat="identity", fill = "darkgray") + theme_minimal() +
        labs(title="Difference m/z with max. average intensity vs. theor. calibrant m/z", x="calibrants", y = "Difference in ppm")+
        theme(plot.title = element_text(hjust = 0.5))+theme(text=element_text(family="ArialMT", face="bold", size=12))+
        geom_text(aes(label=differencevector), vjust=-0.3, size=3.5, col="blue")

        print(diff_plot)}

    ######### 18) ppm difference input calibrant m/z and closest m/z ###########

        differencevector2 = round(differencevector2, digits=2)
        calibrant_names = as.character(inputcalibrants[,2])
        diff_df = data.frame(differencevector2, calibrant_names)

        diff_plot=ggplot(data=diff_df, aes(x=calibrant_names, y=differencevector2)) + geom_bar(stat="identity", fill = "darkgray") + theme_minimal() +
        labs(title="Difference closest measured m/z vs. theor. calibrant m/z", x="calibrants", y = "Difference in ppm")+
        theme(plot.title = element_text(hjust = 0.5))+theme(text=element_text(family="ArialMT", face="bold", size=12))+
        geom_text(aes(label=differencevector2), vjust=-0.3, size=3.5, col="blue")

        print(diff_plot)

    #################### 19) ppm difference over pixels #####################

        }else{

    ### plot ppm differences over pixels (spectra index)

    par(mar=c(4.1, 4.1, 4.1, 7.5))
    plot(0,0,type="n", ylim=c(min(ppm_df, na.rm=TRUE),max(ppm_df, na.rm=TRUE)), xlim = c(1,ncol(filtered_data)),xlab = "Spectra index", ylab = "m/z difference in ppm", main="Difference m/z with max. average intensity vs. theor. m/z\n(per spectrum)") 

    for (each_cal in 1:ncol(ppm_df)){
        lines(ppm_df[,each_cal], col=mycolours[each_cal], type="p")}
    legend("topright", inset=c(-0.25,0), xpd = TRUE, bty="n", legend=inputcalibrantmasses, col=mycolours[1:ncol(ppm_df)],lty=1)
    abline(v=abline_vector, lty = 3)}

    }else{print("16+17+18+19) The inputcalibrant m/z were not provided or outside the m/z range")}

dev.off()

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

## tabular output of spectra values

#if $pixel_output:
    print("pixel list")
    pixel_df = Reduce(function(...) merge(..., by=c("x", "y"), all=T), spectrum_list)
    write.table(pixel_df, file="$pixel_tabular_output", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
#end if



    ]]></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="accuracy" type="float" value="50" label="Only for processed imzML files: enter mass accuracy to which the m/z values will be binned" help="This should be set to the native accuracy of the mass spectrometer, if known"/>
        <param name="units" display="radio" type="select" label="Only for processed imzML files: unit of the mass accuracy" help="either m/z or ppm">
            <option value="mz" >mz</option>
            <option value="ppm" selected="True" >ppm</option>
        </param>
        <param name="filename" type="text" value="" optional="true" label="Title" help="will appear as header in the quality report, if nothing given input dataset name is used"/>
        <param name="calibrant_file" type="data" optional="true" format="tabular"
            label="File with internal calibrants" help="first column: m/z, second column: name (optional), tabular file"/>
        <param name="peptide_file" type="data" optional="true" format="tabular" label="File with m/z of interest"
            help="first column: m/z, second column: name (optional), tabular file"/>

            <param name="mass1" value="1111" type="float" label="M/z 1" help="First m/z"/>
            <param name="mass2" value="2222" type="float" label="M/z 2" help="Second m/z"/>
            <param name="distance" value="0.25" type="float" label="M/z range" help="Plusminus m/z window added to input m/z. In both m/z ranges the maximum intensity is used to calculate the fold change"/>
            <param name="filenameratioplot" type="text" optional="true" label="Title" help="Optional title for fold change plot."/>
        </repeat>
        <param name="pixel_output" type="boolean" display="radio" label="Tabular with spectra information" help="Values for each spectrum (pixel) in x-y grid images"/>
    </inputs>
    <outputs>
        <data format="pdf" name="plots" from_work_dir="qualitycontrol.pdf" label = "$infile.display_name QC_report"/>
        <data format="tabular" name="pixel_tabular_output" label="$infile.display_name spectra information">
            <filter>pixel_output</filter>
        </data>
    </outputs>
    <tests>
        <test expect_num_outputs="2">
            <param name="infile" value="" ftype="imzml">
                <composite_data value="Example_Processed.imzML"/>
                <composite_data value="Example_Processed.ibd"/>
            </param>
            <param name="accuracy" value="200"/>
            <param name="units" value="ppm"/>
            <param name="peptide_file" value="inputpeptides.txt"/>
            <param name="calibrant_file" value="inputcalibrantfile1.txt"/>
            <param name="plusminus_dalton" value="0.25"/>
            <param name="plusminus_ppm" value="100"/>
            <param name="filename" value="Testfile_imzml"/>

                <param name="mass1" value="328.9"/>
                <param name="mass2" value="398.8"/>
                <param name="distance" value="0.25"/>
                <param name="filenameratioplot" value = "Ratio of mass1 (328.9) / mass2 (398.8)"/>
            </repeat>
            <param name="pixel_output" value="True"/>
            <output name="pixel_tabular_output" file="spectra_info_imzml.txt"/>
            <output name="plots" file="QC_imzml.pdf" compare="sim_size" delta="20000"/>
        </test>
        <test expect_num_outputs="1">
            <param name="infile" value="" ftype="analyze75">
                <composite_data value="Analyze75.hdr"/>
                <composite_data value="Analyze75.img"/>
                <composite_data value="Analyze75.t2m"/>
            </param>

            <param name="calibrant_file" value="inputcalibrantfile2.txt"/>
            <param name="plusminus_dalton" value="0.5"/>
            <param name="filename" value="Testfile_analyze75"/>
            <output name="plots" file="QC_analyze75.pdf" compare="sim_size" delta="20000"/>
        </test>
        <test expect_num_outputs="2">
            <param name="infile" value="123_combined.RData" ftype="rdata"/>
            <param name="plusminus_dalton" value="0.2"/>
            <param name="filename" value="Testfile_rdata"/>
            <param name="pixel_output" value="True"/>
            <output name="pixel_tabular_output" file="spectra_info_123_combi.txt"/>
            <output name="plots" file="QC_rdata.pdf" compare="sim_size" delta="20000"/>
        </test>
        <test expect_num_outputs="1">
            <param name="infile" value="empty_spectra.rdata" ftype="rdata"/>
            <param name="peptide_file" value="inputpeptides.txt"/>
            <param name="calibrant_file" value="inputcalibrantfile2.txt"/>
            <param name="plusminus_dalton" value="0.1"/>
            <param name="filename" value="Testfile_rdata"/>

- optional fold change plot: draws a heatmap (x-y grid) for the fold change of two m/z (log2(intensity ratio))

Output: 

- quality control report as pdf with key numbers and descriptive plots describing the mass spectrometry imaging data
- optional spectra information as tabular file with numbers of calibrants (needs input calibrant file), numbers of peaks, TIC and most abundant m/z in each spectrum

Tip: 

- For additional m/z heatmaps use the MSI ion images tool and to plot more mass spectra use the MSI massspectra tool.