<tool id="mass_spectrometry_imaging_qc" name="MSI Qualitycontrol" version="1.10.0.3">
    <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 &&
            ln -s '${infile.extra_files_path}/ibd' infile.ibd &&
        #elif $infile.ext == 'analyze75'
            ln -s '${infile.extra_files_path}/hdr' infile.hdr &&
            ln -s '${infile.extra_files_path}/img' infile.img &&
            ln -s '${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[

################################# load libraries and read file #################

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


#if $infile.ext == 'imzml'
    #if str($processed_cond.processed_file) == "processed":
        msidata <- readImzML('infile', mass.accuracy=$processed_cond.accuracy, units.accuracy = "$processed_cond.units")
    #else
        msidata <- readImzML('infile')
    #end if
#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))
## Range m/z
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 m/z (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)
## 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
processinginfo = processingData(msidata)
centroidedinfo = processinginfo@centroided

## if TRUE write processinginfo if FALSE 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 and filter tabular file with m/z ###########################

### reading calibrant file: 

#if $calibrant_file:

    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)} ## if there is just one column dublicate it to have a names column

    ### calculate how many input calibrant m/z 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 = as.data.frame(matrix(, nrow = 0, ncol = 2))
    number_calibrants_in = 0
    number_calibrants_valid = 0

#end if

## rename input dataframe and extract m/z
colnames(inputcalibrants) = c("m/z", "name")
inputcalibrantmasses = inputcalibrants[,1]


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

pdf("qualitycontrol.pdf", fonts = "Times", pointsize = 12)
plot(0,type='n',axes=FALSE,ann=FALSE)

## if no filename is given, name of file in Galaxy history is used

#if not $filename:
    #set $filename = $infile.display_name
#end if

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",
               "Intensities > 0",
               "Number of empty spectra",
               "Median TIC", 
               "Median # peaks per spectrum",
               "Normalization", 
               "Smoothing",
               "Baseline reduction",
               "Peak picking",
               "Centroided", 
               paste0("calibrants (#valid/#input) 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(medTIC),
           paste0(medpeaks),
           paste0(normalizationinfo),
           paste0(smoothinginfo),
           paste0(baselinereductioninfo),
           paste0(peakpickinginfo),
           paste0(centroidedinfo), 
           paste0(number_calibrants_valid, " / ", number_calibrants_in))

property_df = data.frame(properties, values)

grid.table(property_df, rows= NULL)

####################### II) x-y images #######################################
##############################################################################
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=""){
      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)}

    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\nnumber"))

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

    ## matrix with calibrants in columns and in rows if there is peak intensity in range or not
    pixelmatrix = matrix(ncol=ncol(msidata), nrow = 0) 

    ## plot only possible when there is at least one valid calibrant
    if (length(inputcalibrantmasses) != 0){

    ## calculate plusminus values in m/z for each calibrant
    plusminusvalues = rep($plusminus_ppm/1000000, length(inputcalibrantmasses))*inputcalibrantmasses

    ## filter for m/z window of each calibrant and calculate if sum of peak intensities > 0
    for (mass in 1:length(inputcalibrantmasses)){

        filtered_data = msidata[mz(msidata) >= inputcalibrantmasses[mass]-plusminusvalues[mass] & mz(msidata) <= inputcalibrantmasses[mass]+plusminusvalues[mass],]

        if (nrow(filtered_data) > 1 & sum(spectra(filtered_data)) > 0){

            ## intensity of all m/z > 0
            intensity_sum = colSums(spectra(filtered_data)) > 0
        }else if(nrow(filtered_data) == 1 & sum(spectra(filtered_data)) > 0){

            ## intensity of only m/z > 0
            intensity_sum = spectra(filtered_data) > 0
        }else{
            intensity_sum = rep(FALSE, ncol(filtered_data))}

        ## for each pixel add sum of intensity in the given m/z range
        pixelmatrix = rbind(pixelmatrix, intensity_sum)
    }

        ## for each pixel count TRUE (each calibrant m/z range with intensity > 0 is TRUE)
        countvector= as.factor(colSums(pixelmatrix))
        countdf= cbind(coord(msidata)[,1:2], countvector) ## add pixel coordinates to counts
        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(paste0("Number of calibrants per pixel (±",$plusminus_ppm, " ppm)")) +
          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 tabular output with spectrum values
        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:
        #for $foldchanges in $calibrantratio:
            mass1 = $foldchanges.mass1
            mass2 = $foldchanges.mass2
            distance = $foldchanges.distance

            ### if user did not write a label use input m/z as label
            #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

            ### filter msidata for given m/z range (for both input m/z)
            filtered_data1 = msidata[mz(msidata) >= mass1-distance & mz(msidata) <= mass1+distance,]
            filtered_data2 = msidata[mz(msidata) >= mass2-distance & mz(msidata) <= mass2+distance,]

            ### find m/z in the two given ranges with the highest mean intensity
            ### this two m/z will be used to calculate the fold change (red line in plot)
            maxmassrow1 = rowMeans(spectra(filtered_data1))
            maxmass1 = mz(filtered_data1)[which.max(maxmassrow1)]
            maxmassrow2 = rowMeans(spectra(filtered_data2))
            maxmass2 = mz(filtered_data2)[which.max(maxmassrow2)]

            ### plot legend: chosen value in blue, distance in blue, max m/z in red
            ### m/z range for each plot (fixed range of 5 Da)
            ### xlim does not work because it does not adjust for the max. intensities within the range
            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 for first m/z 
            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(3,6,3))
            abline(v=maxmass1, col="red", lty=5)

            ### plot for second m/z 
            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(3,6,3))
            abline(v=maxmass2, col="red", lty=5)
            title("Control of fold change plot", outer=TRUE)

            ### filter spectra for max m/z to have two vectors, which can be divided
            ### plot spatial distribution of fold change
            ### only possible when there are intensities > 0 in both given m/z ranges

            if (length(maxmass1)>0&length(maxmass2)>0){
                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"))}

        #end for
    #end if

    #################### 4) m/z heatmaps #######################################

    par(mfrow=c(1,1), mar=c(5.1, 4.1, 4.1, 2.1), mgp=c(3, 1, 0), las=0)
    if (length(inputcalibrants[,1]) != 0){
        for (mass in 1:length(inputcalibrants[,1])){


            image(msidata, mz=inputcalibrants[,1][mass], plusminus=plusminusvalues[mass], 
            main= paste0(inputcalibrants[,2][mass], ": ", round(inputcalibrants[,1][mass], digits = 2)," (±",$plusminus_ppm, " ppm)"),
            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 tabular output with spectrum values
    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 tabular output with spectrum values
    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]

    ## append list for optional tabular output with spectrum values
    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))

    ########################## 9) number of peaks per spectrum #################
    ## 9a) scatterplot
    plot_colorByDensity(pixels(msidata), peaksperpixel, ylab = "", xlab = "", main="Number of peaks per spectrum")
    title(xlab="Spectra index", line=3)
    title(ylab="Number of peaks", line=4)
    abline(v=abline_vector, lty = 3)

    ## 9b) histogram

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

    ## 9c) additional histogram to show subsample contributions
    ## only when samples were combined before (combined_sample)
    if (!is.null(levels(msidata\$combined_sample))){

        df_9 = data.frame(peaksperpixel, msidata\$combined_sample)
        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 ###########################

    ## 10a)density scatterplot
    par(mfrow = c(2,1), mar=c(5,6,4,2))
    plot_colorByDensity(pixels(msidata), TICs,  ylab = "", xlab = "", main="TIC per spectrum")
    title(xlab="Spectra index", line=3)
    title(ylab = "Total ion chromatogram intensity", line=4)
    abline(v=abline_vector, lty = 3)

    ## 10b) histogram
    hist(log(TICs), main="", las=1, xlab = "log(TIC per spectrum)", ylab="")
    title(main= "TIC per spectrum", line=2)
    title(ylab="Frequency = # spectra", line=4)
    abline(v=median(log(TICs[TICs>0])), col="blue")    

    ## 10c) additional histogram to show subsample contributions
    ## only when samples were combined before (combined_sample)
    if (!is.null(levels(msidata\$combined_sample))){
        df_10 = data.frame(log(TICs), msidata\$combined_sample)
        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) properties over m/z ####################
    ############################################################################
    print("propverties over m/z")

    ########################## 11) 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))
    hist(mz(msidata), xlab = "m/z", main="Histogram of m/z values")

    ########################## 12) Number of peaks per m/z #####################

    peakspermz = rowSums(spectra(msidata)[] > 0 )

    par(mfrow = c(2,1), mar=c(5,6,4,4.5))
    ## 12a) scatterplot
    plot_colorByDensity(mz(msidata),peakspermz, main= "Number of peaks per m/z", ylab ="")
    title(xlab="m/z", 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)

    ## 12b) histogram
    hist(peakspermz, main="", las=1, ylab="", xlab="")
    title(ylab = "Frequency", line=4)
    title(main="Number of peaks per m/z", xlab = "Number of peaks per m/z", line=2)
    abline(v=median(peakspermz), col="blue") 

    ########################## 13) Sum of intensities per m/z ##################

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

    par(mfrow = c(2,1), mar=c(5,6,4,2))
    ## 13a) scatterplot
    plot_colorByDensity(mz(msidata),mzTIC,  main= "Sum of intensities per m/z", ylab ="")
    title(xlab="m/z", line=2.5)
    title(ylab="Intensity sum", line=4)

    ## 13b) histogram
    hist(log(mzTIC), main="", xlab = "", las=1, ylab="")
    title(main="Sum of intensities per m/z", line=2, ylab="")
    title(xlab = "log (sum of intensities per m/z)")
    title(ylab = "Frequency", line=4)
    abline(v=median(log(mzTIC[mzTIC>0])), col="blue")

    ################################## V) intensity plots ########################
    ############################################################################
    print("intensity plots")
    ########################## 14) Intensity distribution ######################

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

    ## 14a) Median intensity over spectra
    medianint_spectra = apply(spectra(msidata), 2, median)
    plot(medianint_spectra, main="Median intensity per spectrum",las=1, xlab="Spectra index", ylab="")
    title(ylab="Median spectrum intensity", line=4)
    abline(v=abline_vector, lty = 3)

    ## 14b) histogram: 
    hist(log2(spectra(msidata)[]), main="", xlab = "", ylab="", las=1)
    title(main="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")

    ## 14c) histogram to show subsample contribution
    ## only for previously combined samples
    if (!is.null(levels(msidata\$combined_sample))){

    df_13 = data.frame(matrix(,ncol=2, nrow=0))
    for (subsample in levels(msidata\$combined_sample)){
        log2_int_subsample = log2(spectra(msidata)[,msidata\$combined_sample==subsample])
        df_subsample = data.frame(as.numeric(log2_int_subsample))
        df_subsample\$sample_name = subsample
        df_13 = rbind(df_13, df_subsample)}
    df_13\$sample_name = as.factor(df_13\$sample_name)
    colnames(df_13) = c("logint", "sample_name")

    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)

    ## 14d) 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))
    }

    ################################## VI) Mass spectra and m/z accuracy ########################
    ############################################################################
    print("Mass spectra and m/z accuracy")

    ############################ 15) Mass spectra ##############################

    par(mfrow = c(2, 2), cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
    pixels_for_plot = c(round(length(pixelnumber)/2, , digits=0), round(length(pixelnumber)/4, , digits=0), round(length(pixelnumber)/4*3, , digits=0))
    plot(msidata, pixel = 1:length(pixelnumber), main= "Average spectrum")
    plot(msidata, pixel = pixels_for_plot[1], main=paste0("Spectrum at ", rownames(coord(msidata)[pixels_for_plot[1],1:2])))
    plot(msidata, pixel = pixels_for_plot[2], main= paste0("Spectrum at ", rownames(coord(msidata)[pixels_for_plot[2],1:2])))
    plot(msidata, pixel = pixels_for_plot[3], main= paste0("Spectrum at ", rownames(coord(msidata)[pixels_for_plot[3],1:2])))

    #################### 16) Zoomed in mass spectra for calibrants##############

    count = 1
    differencevector = numeric()
    differencevector2 = vector()

    if (length(inputcalibrantmasses) != 0){

    ### calculate plusminus values in m/z for each calibrant, this is used for all following plots
    plusminusvalues = rep($plusminus_ppm/1000000, length(inputcalibrantmasses))*inputcalibrantmasses

        for (mass in 1:length(inputcalibrantmasses)){

            ### define the plot window with xmin und xmax
            minmasspixel = features(msidata, mz=inputcalibrantmasses[mass]-1)
            maxmasspixel = features(msidata, mz=inputcalibrantmasses[mass]+3)

            ### find m/z with the highest mean intensity in m/z range (red line in plot 16) and calculate ppm difference for plot 17
            filtered_data = msidata[mz(msidata) >= inputcalibrantmasses[mass]-plusminusvalues[mass] & mz(msidata) <= inputcalibrantmasses[mass]+plusminusvalues[mass],]
            if (nrow(filtered_data) > 0 & sum(spectra(filtered_data)) > 0){
                maxmassrow = rowMeans(spectra(filtered_data)) ## for each m/z average intensity is calculated
                maxvalue = mz(filtered_data)[which.max(maxmassrow)] ### m/z with highest average intensity in m/z range
                mzdifference = maxvalue - inputcalibrantmasses[mass] ### difference: theoretical value - closest m/z value
            ppmdifference = mzdifference/inputcalibrantmasses[mass]*1000000 ### calculate ppm for accuracy measurement
            }else{
                ppmdifference = NA
                maxvalue = NA}
            differencevector[mass] = round(ppmdifference, digits=2)

            ### find m/z closest to inputcalibrant and calculate ppm difference for plot 18
            mznumber = features(msidata, mz = inputcalibrantmasses[mass]) ### gives featurenumber which is closest to given m/z
            mzvalue = mz(msidata)[mznumber] ### gives closest m/z
            mzdifference2 = mzvalue - inputcalibrantmasses[mass] 
            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))

        ### plot the ppm difference calculated above: theor. m/z value to highest m/z value: 

        calibrant_names = as.character(inputcalibrants[,2])
        diff_df = data.frame(differencevector, calibrant_names)

        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 ###########

        ### plot the ppm difference calculated above theor. m/z value to closest m/z value: 

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

    mycolours = c("darkgrey", "darkblue", "blue", "green" , "red", "orange", "yellow", "magenta", "olivedrab1", "lightseagreen")
    count = 1
    ppm_df = as.data.frame(matrix(,ncol=0, nrow = ncol(msidata)))
    for (calibrant in inputcalibrantmasses){
        ### find m/z with the highest mean intensity in m/z range, if no m/z in the range, all ppm differences will be NA
        filtered_data = msidata[mz(msidata) >= calibrant-plusminusvalues[count] & mz(msidata) <= calibrant+plusminusvalues[count],]

        if (nrow(filtered_data) > 0){
            ### filtered for m/z range, now go through it pixel by pixel to find max peak in each spectrum
            ppm_vector = numeric()
            for (pixel_count in 1:ncol(filtered_data)){
                mz_max = mz(filtered_data)[which.max(spectra(filtered_data)[,pixel_count])]

                mzdiff = mz_max - calibrant
                ppmdiff = mzdiff/calibrant*1000000 

                ### if maximum intensity in m/z range was 0 set ppm diff to NA (not shown in plot)
                if (max(spectra(filtered_data)[,pixel_count]) == 0){
                ppmdiff = NA}
                ppm_vector[pixel_count] = ppmdiff}
        }else{ppm_vector = rep(NA, ncol(msidata))}

        ppm_df = cbind(ppm_df, ppm_vector)
        count=count+1}

        if (sum(is.na(ppm_df)) == ncol(ppm_df)*nrow(ppm_df)){
            print("plot 19: no peaks in the chosen region, repeat with higher ppm range")
        }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)"/>
        <conditional name="processed_cond">
            <param name="processed_file" type="select" label="Is the input file a processed imzML file ">
                <option value="no_processed" selected="True">not a processed imzML</option>
                <option value="processed">processed imzML</option>
            </param>
            <when value="no_processed"/>
            <when value="processed">
                <param name="accuracy" type="float" value="50" label="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="Unit of the mass accuracy" help="either m/z or ppm">
                    <option value="mz" >mz</option>
                    <option value="ppm" selected="True" >ppm</option>
                </param>
            </when>
        </conditional>
        <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="plusminus_ppm" value="50" type="float" label="ppm range" help="Will be added in both directions to input calibrant m/z"/>
        <repeat name="calibrantratio" title="Plot fold change of two m/z" min="0" max="10">
            <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 output with spectra information"/>
    </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>
            <conditional name="processed_cond">
                <param name="processed_file" value="processed"/>
                <param name="accuracy" value="200"/>
                <param name="units" value="ppm"/>
            </conditional>
            <param name="calibrant_file" value="inputcalibrantfile1.txt"/>
            <param name="plusminus_ppm" value="100"/>
            <param name="filename" value="Testfile_imzml"/>
            <repeat name="calibrantratio">
                <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="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="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="calibrant_file" value="inputcalibrantfile2.txt"/>
            <param name="filename" value="Testfile_rdata"/>
            <output name="plots" file="QC_empty_spectra.pdf" compare="sim_size" delta="20000"/>
        </test>
    </tests>
    <help>
        <![CDATA[
Cardinal is an R package that implements statistical & computational tools for analyzing mass spectrometry imaging datasets. `More information on Cardinal <http://cardinalmsi.org//>`_

This tool uses Cardinal to read files and create a quality control report with descriptive plots for mass spectrometry imaging 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 <https://ms-imaging.org/wp/imzml/>`_
- Analyze7.5 (upload hdr, img and t2m file via the "composite" function)
- Cardinal "MSImageSet" data (with variable name "msidata", saved as .RData)

Options: 

- internal calibrants are used for m/z heatmaps (x-y grid), heatmap of number of calibrants per spectrum (x-y grid), zoomed in mass spectra, m/z accuracy
- 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 valid calibrants), 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 mass spectra tool. 

----------------------------------------------------------------------------------------------------------------------------------------------------

Overview of the QC report plots:

- (comb): this plots will only be drawn if several files were combined into one file with the msi_combine tool
- (cal): this plots will only be drawn if a tabular file with at least one valid calibrant m/z is provided
- (FC): this plots will only be drawn if the optional fold change image is selected
- Vertical lines in histograms represent median values. In density scatter plots the colour changes from blue to green, yellow and red the more points are overlayed.

- Overview of file properties: Numbers and ranges for m/z features and pixels are given. Median and range across all intensity values are provided. Intensities > 0 gives the percentage of m/z-pixel pairs with an intensity above zero. The number of empty spectra (TIC = 0), the median number of peaks (intensities > 0) per spectra as well as the median TIC (total ion chromatogram) are given. The processing status of the file is provided as well as the number of valid calibrants from the provided tabular file.> 0 (Intensities > 0). 

x-y images (pixel/spectra information):

- (comb) Spatial orientation of combined data: All pixels of a combined file have the same colour. 
- Pixel order: Shows the order of the pixels in the provided file. Depending on the instrument this can represent the acquisition order.
- (cal) Number of calibrants per pixel: In every spectrum the calibrant m/z window (calibrant m/z plusminus 'ppm range') is searched for peaks (intensity > 0). Calibrants are considered present in a spectrum when they have at least one peak in their m/z window. 
- (FC) Control of fold change plot: For both input m/z a zoomed in average spectrum is drawn with the input m/z as blue dashed line, the m/z range as blue dotted lines and the maximum intensity in the m/z window with a red line. 
- (FC) Fold change image: For each spectrum the intensities of the two optimal m/z features (red lines in control plots) are divided and log2 transformed to obtain the fold change, which is then plotted as a heatmap. 
- (cal) Intensity heatmaps for the m/z value that is closest to the calibrant m/z. The intensities are averaged within the calibrant m/z window (ppm range). 
- Number of peaks per spectrum: For each spectrum the number of m/z values with intensity > 0 is calculated and plotted as heatmap.
- Total ion chromatogram: For each spectrum all intensities are summed up to obtain the TIC which is plotted as heatmap. 
- Most abundant m/z in each spectrum: For each spectrum the m/z value with the highest intensity is plotted. 
- PCA for two components: Result of a principal component analysis (PCA) for two components is given. The loading plot depicts the contribution of each m/z value and the x-y image represents the differences between the pixels. 

Properties over spectra/pixels: 

- Number of peaks per spectrum: Scatter plot and histogram showing the number of intensities > 0 for each spectrum. Dotted lines in the scatter plot separate spectra of combined samples.
- (comb) Number of peaks per spectrum and sample: Same histogram as in plot before but with colours to show the contribution of each combined sample.
- TIC per spectrum: Scatter plot and histogram showing the sum of all intensities per spectrum (TIC). Dotted lines in the scatter plot separate spectra of combined samples.
- (comb) TIC per spectrum and sample: Same histogram as in plot before but with colours to show the contribution of each combined sample. 

Properties over m/z features: 

- Histogram of m/z values: Histogram of all m/z values (complete m/z axis)
- Number of peaks per m/z: Scatter plot and histogram giving the number of intensities > 0 for each m/z. 
- Sum of intensities per m/z: Scatter plot and histogram of the sum of all intensities per m/z. 

Intensity plots: 

- Median intensity per spectrum: Scatter plot in which each point represents the median intensity for one spectrum. Dotted lines in the scatter plot separate spectra of combined samples.
- Log2-transformed intensities: Histogram of log2-transformed intensities. 
- (comb) log2-transformed intensities per sample: Same histogram as before but with colours to show the contribution of each combined sample. 
- (comb) Mean intensities per m/z and sample: For all pixels of a sample the mean intensity for each m/z is calculated and shown as boxplot. 

Mass spectra and m/z accuracy: 

- Mass spectra over the full m/z range: First plot shows the average intensities over all spectra. The other three mass spectra are from single pixels (spectra). 
- (cal) For each calibrant four zoomed in mass spectrum are drawn: The first shows the average intensities over all spectra and the other three are single mass spectra. The theoretical calibrant m/z (taken from the input file) is represented by the dashed blue line. The dotted blue lines show the given ppm range. The green line is the m/z value that is closest to the theoretical calibrant and the red line is the m/z with the highest average intensity in the m/z window.
- (comb) Average spectrum per sample: For each calibrant a zoomed in mass spectrum is plotted this time with the average intensities for each combined sample separately. 
- (cal) Difference m/z with max. average intensity vs. theor. calibrant m/z: The difference in ppm between the m/z with the highest average intensity and the theoretical m/z are plotted for each calibrant. This corresponds to the difference between the dashed blue line and the red line in the zoomed in mass spectra. 
- (cal) Difference closest measured m/z vs. theor. calibrant m/z: The difference in ppm between the closest m/z value and the theoretical m/z values are plotted for each calibrant. This corresponds to the difference between the dashed blue line and the green line in the zoomed in mass spectra. 
- (cal) Difference m/z with max. average intensity vs. theor. m/z (per spectrum): For each spectrum the ppm difference between the m/z with the highest average intensity and the theoretical m/z are plotted. The calibrants have different plotting colours. Dashed lines separate spectra of combined samples. 


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