comparison quality_report.xml @ 11:f396c176f366 draft

"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit ca89f8e007c6b17f7c30066729e05b8686ab975a"

10:f365bad862c9

    msidata_coordinates = data.frame(coord(msidata)\$x, coord(msidata)\$y, c(1:ncol(msidata)))
    colnames(msidata_coordinates) = c("x", "y", "pixel_index")
    merged_annotation = merge(msidata_coordinates, annotation_input, by=c("x", "y"), all.x=TRUE)
    merged_annotation[is.na(merged_annotation)] = "NA"
    merged_annotation = merged_annotation[order(merged_annotation\$pixel_index),]
    msidata\$annotation = as.factor(merged_annotation[,4])

#end if

###################### calculation of data properties ################################
@DATA_PROPERTIES_INRAM@

## Median intensities
medint = round(median(spectra(msidata), na.rm=TRUE), digits=2)
## Spectra multiplied with m/z (potential number of peaks)
numpeaks = ncol(msidata)*nrow(msidata)
## Percentage of intensities > 0
percpeaks = round(npeaks/numpeaks*100, digits=2)
## Number of empty TICs
TICs = pixelApply(msidata, sum)
NumemptyTIC = sum(TICs == 0)
## Median und sd TIC
medTIC = round(median(TICs), digits=1)
sdTIC = round(sd(TICs), digits=0)
## Median and sd # peaks per spectrum

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

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

    if (!is.null(levels(msidata\$annotation))){

        number_combined = length(levels(msidata\$annotation))

        position_df = data.frame(coord(msidata)\$x, coord(msidata)\$y, msidata\$annotation)
        colnames(position_df) = c("x", "y","annotation")

        combine_plot = ggplot(position_df, aes(x=x, y=y, fill=annotation))+
               geom_tile() +
               coord_fixed()+
               ggtitle("Spatial orientation of pixel annotations")+

        rm(TICcoordarray)
        gc()

    ############################### 6b) median int image ###############################

    median_int = pixelApply(msidata, median)

    median_coordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, median_int)
    colnames(median_coordarray) = c("x", "y", "median_int")
    print(ggplot(median_coordarray, aes(x=x, y=y, fill=median_int))+
     geom_tile() + coord_fixed() +

        rm(median_coordarray)
        gc()

    ############################### 6c) max int image ###############################

    max_int = pixelApply(msidata, max)

    max_coordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, max_int)
    colnames(max_coordarray) = c("x", "y", "max_int")
    print(ggplot(max_coordarray, aes(x=x, y=y, fill=max_int))+
     geom_tile() + coord_fixed() +

        print(image(pca, run="infile", col=c("black", "white"), strip=FALSE,  ylim= c(maximumy+0.2*maximumy,minimumy-1), layout=FALSE))

        par(oma=c(0,0,0,1))## margin for image legend
        print(image(pca, column = "PC1" , strip=FALSE, superpose = FALSE, main="PC1", col.regions = risk.colors(100), layout=c(2,1), ylim= c(maximumy+0.2*maximumy,minimumy-1)))
        print(image(pca, column = "PC2" , strip=FALSE, superpose = FALSE, main="PC2", col.regions = risk.colors(100), layout=FALSE,  ylim= c(maximumy+0.2*maximumy,minimumy-1)))
    ## remove pca to clean up RAM space
        rm(pca)
        gc()

    #end if

    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)

    if (!is.null(levels(msidata\$annotation))){
        abline(v=abline_vector, lty = 3)}

    ## 9b) histogram

    hist(peaksperpixel, main="", las=1, xlab = "Number of peaks per spectrum", ylab="") 

    title(ylab="Frequency = # spectra", line=4)
    abline(v=median(peaksperpixel), col="blue")

    ## 9c) additional histogram to show contribution of annotation groups

    if (!is.null(levels(msidata\$annotation))){

        df_9 = data.frame(peaksperpixel, msidata\$annotation)
        colnames(df_9) = c("Npeaks", "annotation")

        hist_9 = ggplot(df_9, aes(x=Npeaks, fill=annotation)) +
        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 = 8))+

        plot_colorByDensity(pixels(msidata), TICs,  ylab = "", xlab = "", main="TIC per spectrum")
    }

    title(xlab="Spectra index", line=3)
    title(ylab = "Total ion current intensity", line=4)
    if (!is.null(levels(msidata\$annotation))){
        abline(v=abline_vector, lty = 3)}

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

    ## 10c) additional histogram to show annotation contributions
    if (!is.null(levels(msidata\$annotation))){
        df_10 = data.frame((TICs), msidata\$annotation)
        colnames(df_10) = c("TICs", "annotation")

        hist_10 = ggplot(df_10, aes(x=TICs, fill=annotation)) +
        geom_histogram()+ theme_bw()+

    ########################## 14) Intensity distribution ######################

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

    ## 14a) Median intensity over spectra
    medianint_spectra = pixelApply(msidata, median)
    plot(medianint_spectra, main="Median intensity per spectrum",las=1, xlab="Spectra index", ylab="")
    title(ylab="Median spectrum intensity", line=4)
    if (!is.null(levels(msidata\$annotation))){
        abline(v=abline_vector, lty = 3)}

    ## 14b) histogram: 
    hist(as.matrix(spectra(msidata)), main="", xlab = "", ylab="", las=1)
    title(main="Intensity histogram", line=2)
    title(xlab="intensities")
    title(ylab="Frequency", line=4)
    abline(v=median(as.matrix(spectra(msidata))[(as.matrix(spectra(msidata))>0)], na.rm=TRUE), col="blue")


    ## 14c) histogram to show contribution of annotation groups

    if (!is.null(levels(msidata\$annotation))){

        df_13 = data.frame(matrix(,ncol=2, nrow=0))
        for (subsample in levels(msidata\$annotation)){
            log2_int_subsample = spectra(msidata)[,msidata\$annotation==subsample]
            df_subsample = data.frame(as.numeric(log2_int_subsample))
            df_subsample\$annotation = subsample
            df_13 = rbind(df_13, df_subsample)}
        df_13\$annotation = as.factor(df_13\$annotation)

        labs(title="Intensities per sample", x="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 = 8))+
        guides(fill=guide_legend(ncol=5,byrow=TRUE))+
        geom_vline(xintercept = median(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\$annotation)){
            mean_mz_sample = rowMeans(spectra(msidata)[,msidata\$annotation==subsample],na.rm=TRUE)
            mean_matrix = cbind(mean_matrix, mean_mz_sample)}

        boxplot(log10(mean_matrix), ylab = "Log10 mean intensity per m/z", main="Log10 mean m/z intensities per annotation group", xaxt = "n")
        (axis(1, at = c(1:number_combined), labels=levels(msidata\$annotation), las=2))

        ## 14e) Heatmap of mean intensities of annotation groups

        colnames(mean_matrix) = levels(msidata\$annotation)
        mean_matrix[is.na(mean_matrix)] = 0
            heatmap.parameters <- list(mean_matrix, 
            show_rownames = T, show_colnames = T,
            main = "Heatmap of mean intensities per annotation group")
            par(oma=c(3,0,0,0))
            print(heatmap(mean_matrix),margins = c(10, 10))


        ## 14f) PCA of mean intensities of annotation groups

        ## define annotation by colour
        annotation_colour = rainbow(length(levels(msidata\$annotation)))[as.factor(levels(msidata\$annotation))]
        ## transform and scale dataframe
        pca = prcomp(t(mean_matrix),center=FALSE,scale.=FALSE)
        ## plot single plot
        plot(pca\$x[,c(1,2)],col=annotation_colour,pch=19)
        ## plot pca with colours for max first 5 PCs
        pc_comp = ifelse(ncol(pca\$x)<5 , ncol(pca\$x), 5)
        pairs(pca\$x[,1:pc_comp],col=annotation_colour,pch=19)
        legend("bottom", horiz = TRUE, legend=levels(msidata\$annotation), col=rainbow(length(levels(msidata\$annotation))), pch=19)

    }

    ################################## VI) Mass spectra and m/z accuracy ########################
    ############################################################################

    ## find three equal m/z ranges for the average mass spectra plots: 
    third_mz_range = round(nrow(msidata_no_NA)/3,0)

    par(cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
    print(plot(msidata_no_NA, run="infile", layout=c(2,2), strip=FALSE, main= "Average spectrum"))
    print(plot(msidata_no_NA[1:third_mz_range,], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum"))
    print(plot(msidata_no_NA[third_mz_range:(2*third_mz_range),], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum"))
    print(plot(msidata_no_NA[(2*third_mz_range):nrow(msidata_no_NA),], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum"))

    ## plot one average mass spectrum for each pixel annotation group

    if (!is.null(levels(msidata\$annotation))){
        ## print legend only for less than 10 samples
        if (length(levels(msidata\$annotation)) < 10){
            key_legend = TRUE
        }else{key_legend = FALSE}
        par(mfrow = c(1,1), cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
        print(plot(msidata, run="infile", pixel.groups=msidata\$annotation, key=key_legend, col=hue_pal()(length(levels(msidata\$annotation))),superpose=TRUE, main="Average mass spectra for annotation groups"))
    }

    ## plot 4 random mass spectra
    ## find four random, not empty pixel to plot their spectra in the following plots:
    pixel_vector = sample(which(TICs != 0),4)

    par(mfrow = c(2, 2), cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
    print(plot(msidata_no_NA, pixel = pixel_vector))


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

    count = 1

            ## plotting of 4 spectra in one page
            par(oma=c(0,0,2,0))
            ## average plot

            print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", layout=c(2,2), strip=FALSE, main= "Average spectrum"))
            abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3))
            abline(v=c(maxvalue), col="red", lty=2)
            abline(v=c(mzvalue), col="green2", lty=4)
            ## average plot including points per data point
            print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", layout=FALSE, strip=FALSE, main="Average spectrum with data points"))
            points(mz(msidata_no_NA[minmasspixel1:maxmasspixel1,]), rowMeans(spectra(msidata_no_NA)[minmasspixel1:maxmasspixel1,,drop=FALSE]), col="blue", pch=20)
            ## plot of third average plot
            print(plot(msidata_no_NA[minmasspixel2:maxmasspixel2,], run="infile", layout=FALSE, strip=FALSE, main= "Average spectrum"))
            abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3))
            abline(v=c(maxvalue), col="red", lty=2)
            abline(v=c(mzvalue), col="green2", lty=4)
            ## plot of fourth average plot
            print(plot(msidata_no_NA[minmasspixel3:maxmasspixel3,], run="infile", layout=FALSE, strip=FALSE, main= "Average spectrum"))
            abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3))
            abline(v=c(maxvalue), col="red", lty=2)
            abline(v=c(mzvalue), col="green2", lty=4)
            title(paste0("theor. m/z: ", round(inputcalibrants[count,1], digits=4)), 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 pixel annotation groups

            if (!is.null(levels(msidata\$annotation))){
                if (number_combined < 10){
                    key_zoomed = TRUE
                }else{key_zoomed = FALSE}
                par(mfrow = c(1, 1))
                print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", strip=FALSE,main="Average spectrum per annotation group",

            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(-0.2,0), xpd = TRUE, bty="n", cex=0.8,legend=inputcalibrantmasses, col=mycolours[1:ncol(ppm_df)],lty=1)
             if (!is.null(levels(msidata\$annotation))){
                abline(v=abline_vector, lty = 3)}}

            ### make x-y-images for mz accuracy

            ppm_dataframe = data.frame(coord(msidata)\$x, coord(msidata)\$y, ppm_df)

11:f396c176f366

    msidata_coordinates = data.frame(coord(msidata)\$x, coord(msidata)\$y, c(1:ncol(msidata)))
    colnames(msidata_coordinates) = c("x", "y", "pixel_index")
    merged_annotation = merge(msidata_coordinates, annotation_input, by=c("x", "y"), all.x=TRUE)
    merged_annotation[is.na(merged_annotation)] = "NA"
    merged_annotation = merged_annotation[order(merged_annotation\$pixel_index),]
    msidata\$annotation = as.character(merged_annotation[,4])

#end if

###################### calculation of data properties ################################
@DATA_PROPERTIES_INRAM@


## Median intensities
medint = round(median(int_matrix), digits=2)
## Spectra multiplied with m/z (potential number of peaks)
numpeaks = as.numeric(ncol(msidata)*nrow(msidata))
## Percentage of intensities > 0
percpeaks = round(npeaks/numpeaks*100, digits=2)
## Number of empty TICs
TICs = pixelApply(msidata, sum, na.rm=TRUE)
NumemptyTIC = sum(TICs == 0)
## Median und sd TIC
medTIC = round(median(TICs), digits=1)
sdTIC = round(sd(TICs), digits=0)
## Median and sd # peaks per spectrum

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

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

    if (!is.null(unique(msidata\$annotation))){

        number_combined = length(unique(msidata\$annotation))

        position_df = data.frame(coord(msidata)\$x, coord(msidata)\$y, msidata\$annotation)
        colnames(position_df) = c("x", "y","annotation")
                print(position_df)

        combine_plot = ggplot(position_df, aes(x=x, y=y, fill=annotation))+
               geom_tile() +
               coord_fixed()+
               ggtitle("Spatial orientation of pixel annotations")+

        rm(TICcoordarray)
        gc()

    ############################### 6b) median int image ###############################

    median_int = pixelApply(msidata, median, na.rm=TRUE)

    median_coordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, median_int)
    colnames(median_coordarray) = c("x", "y", "median_int")
    print(ggplot(median_coordarray, aes(x=x, y=y, fill=median_int))+
     geom_tile() + coord_fixed() +

        rm(median_coordarray)
        gc()

    ############################### 6c) max int image ###############################

    max_int = pixelApply(msidata, max, na.rm=TRUE)

    max_coordarray=data.frame(coord(msidata)\$x, coord(msidata)\$y, max_int)
    colnames(max_coordarray) = c("x", "y", "max_int")
    print(ggplot(max_coordarray, aes(x=x, y=y, fill=max_int))+
     geom_tile() + coord_fixed() +

        print(image(pca, run="infile", col=c("black", "white"), strip=FALSE,  ylim= c(maximumy+0.2*maximumy,minimumy-1), layout=FALSE))

        par(oma=c(0,0,0,1))## margin for image legend
        print(image(pca, column = "PC1" , strip=FALSE, superpose = FALSE, main="PC1", col.regions = risk.colors(100), layout=c(2,1), ylim= c(maximumy+0.2*maximumy,minimumy-1)))
        print(image(pca, column = "PC2" , strip=FALSE, superpose = FALSE, main="PC2", col.regions = risk.colors(100), layout=FALSE,  ylim= c(maximumy+0.2*maximumy,minimumy-1)))
    	## remove pca to clean up space
        rm(pca)
        gc()

    #end if

    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)

    if (!is.null(unique(msidata\$annotation))){
        abline(v=abline_vector, lty = 3)}

    ## 9b) histogram

    hist(peaksperpixel, main="", las=1, xlab = "Number of peaks per spectrum", ylab="") 

    title(ylab="Frequency = # spectra", line=4)
    abline(v=median(peaksperpixel), col="blue")

    ## 9c) additional histogram to show contribution of annotation groups

    if (!is.null(unique(msidata\$annotation))){

        df_9 = data.frame(peaksperpixel, msidata\$annotation)
        colnames(df_9) = c("Npeaks", "annotation")
 
        hist_9 = ggplot(df_9, aes(x=Npeaks, fill=annotation)) +
        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 = 8))+

        plot_colorByDensity(pixels(msidata), TICs,  ylab = "", xlab = "", main="TIC per spectrum")
    }

    title(xlab="Spectra index", line=3)
    title(ylab = "Total ion current intensity", line=4)
    if (!is.null(unique(msidata\$annotation))){
        abline(v=abline_vector, lty = 3)}

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

    ## 10c) additional histogram to show annotation contributions
    if (!is.null(unique(msidata\$annotation))){
        df_10 = data.frame((TICs), msidata\$annotation)
        colnames(df_10) = c("TICs", "annotation")

        hist_10 = ggplot(df_10, aes(x=TICs, fill=annotation)) +
        geom_histogram()+ theme_bw()+

    ########################## 14) Intensity distribution ######################

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

    ## 14a) Median intensity over spectra
    medianint_spectra = pixelApply(msidata, median, na.rm=TRUE)
    plot(medianint_spectra, main="Median intensity per spectrum",las=1, xlab="Spectra index", ylab="")
    title(ylab="Median spectrum intensity", line=4)
    if (!is.null(unique(msidata\$annotation))){
        abline(v=abline_vector, lty = 3)}

    ## 14b) histogram: 
    hist(int_matrix, main="", xlab = "", ylab="", las=1)
    title(main="Intensity histogram", line=2)
    title(xlab="intensities")
    title(ylab="Frequency", line=4)
    abline(v=median(int_matrix)[(as.matrix(spectra(msidata))>0)], col="blue")


    ## 14c) histogram to show contribution of annotation groups

    if (!is.null(unique(msidata\$annotation))){

        df_13 = data.frame(matrix(,ncol=2, nrow=0))
        for (subsample in unique(msidata\$annotation)){
            log2_int_subsample = spectra(msidata)[,msidata\$annotation==subsample]
            df_subsample = data.frame(as.numeric(log2_int_subsample))
            df_subsample\$annotation = subsample
            df_13 = rbind(df_13, df_subsample)}
        df_13\$annotation = as.factor(df_13\$annotation)

        labs(title="Intensities per sample", x="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 = 8))+
        guides(fill=guide_legend(ncol=5,byrow=TRUE))+
        geom_vline(xintercept = median(int_matrix)[(int_matrix>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(10,4.1,5.1,2.1))

        mean_matrix = matrix(,ncol=0, nrow = nrow(msidata))
        for (subsample in unique(msidata\$annotation)){
            mean_mz_sample = rowMeans(spectra(msidata)[,msidata\$annotation==subsample],na.rm=TRUE)
            mean_matrix = cbind(mean_matrix, mean_mz_sample)}
            
        boxplot(log10(as.data.frame(mean_matrix)), ylab = "Log10 mean intensity per m/z", main="Log10 mean m/z intensities per annotation group", xaxt = "n")
        (axis(1, at = c(1:number_combined), cex.axis=0.9, labels=unique(msidata\$annotation), las=2))

        ## 14e) Heatmap of mean intensities of annotation groups

        colnames(mean_matrix) = unique(msidata\$annotation)
        mean_matrix[is.na(mean_matrix)] = 0
            heatmap.parameters <- list(mean_matrix, 
            show_rownames = T, show_colnames = T,
            main = "Heatmap of mean intensities per annotation group")
            par(oma=c(5,0,0,0))
        heatmap(mean_matrix)


        ## 14f) PCA of mean intensities of annotation groups
            par(mar=c(4.1, 4.1, 4.1, 8.5))
        ## define annotation by colour
        annotation_colour = rainbow(length(unique(msidata\$annotation)))[as.factor(unique(msidata\$annotation))]
        ## transform and scale dataframe
        pca = prcomp(t(mean_matrix),center=FALSE,scale.=FALSE)
        ## plot single plot
        plot(pca\$x[,c(1,2)],col=annotation_colour,pch=19)
        legend("topright",xpd=TRUE, bty="n", inset=c(-0.3,0), cex=0.8, legend=unique(msidata\$annotation), col=rainbow(length(unique(msidata\$annotation))), pch=19)
        ## plot pca with colours for max first 5 PCs
        pc_comp = ifelse(ncol(pca\$x)<5 , ncol(pca\$x), 5)
        pairs(pca\$x[,1:pc_comp],col=annotation_colour,pch=19)

    }

    ################################## VI) Mass spectra and m/z accuracy ########################
    ############################################################################

    ## find three equal m/z ranges for the average mass spectra plots: 
    third_mz_range = round(nrow(msidata_no_NA)/3,0)

    par(cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
    print(plot(msidata_no_NA, run="infile", layout=c(2,2), strip=FALSE, main= "Average spectrum", col="black"))
    print(plot(msidata_no_NA[1:third_mz_range,], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum", col="black"))
    print(plot(msidata_no_NA[third_mz_range:(2*third_mz_range),], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum", col="black"))
    print(plot(msidata_no_NA[(2*third_mz_range):nrow(msidata_no_NA),], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum", col="black"))

    ## plot one average mass spectrum for each pixel annotation group

    if (!is.null(unique(msidata\$annotation))){
        ## print legend only for less than 10 samples
        if (length(unique(msidata\$annotation)) < 10){
            key_legend = TRUE
        }else{key_legend = FALSE}
        par(mfrow = c(1,1), cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
        print(plot(msidata, run="infile", pixel.groups=msidata\$annotation, key=key_legend, col=hue_pal()(length(unique(msidata\$annotation))),superpose=TRUE, main="Average mass spectra for annotation groups"))
    }

    ## plot 4 random mass spectra
    ## find four random, not empty pixel to plot their spectra in the following plots:
    pixel_vector = sample(which(TICs != 0),4)

    par(mfrow = c(2, 2), cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1))
    print(plot(msidata_no_NA, pixel = pixel_vector, col="black"))


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

    count = 1

            ## plotting of 4 spectra in one page
            par(oma=c(0,0,2,0))
            ## average plot

            print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", layout=c(2,2), strip=FALSE, main= "Average spectrum", col="black"))
            abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3))
            abline(v=c(maxvalue), col="red", lty=2)
            abline(v=c(mzvalue), col="green2", lty=4)
            ## average plot including points per data point
            print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", layout=FALSE, strip=FALSE, main="Average spectrum with data points", col="black"))
            points(mz(msidata_no_NA[minmasspixel1:maxmasspixel1,]), rowMeans(spectra(msidata_no_NA)[minmasspixel1:maxmasspixel1,,drop=FALSE]), col="blue", pch=20)
            ## plot of third average plot
            print(plot(msidata_no_NA[minmasspixel2:maxmasspixel2,], run="infile", layout=FALSE, strip=FALSE, main= "Average spectrum", col="black"))
            abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3))
            abline(v=c(maxvalue), col="red", lty=2)
            abline(v=c(mzvalue), col="green2", lty=4)
            ## plot of fourth average plot
            print(plot(msidata_no_NA[minmasspixel3:maxmasspixel3,], run="infile", layout=FALSE, strip=FALSE, main= "Average spectrum", col="black"))
            abline(v=c(inputcalibrantmasses[mass] -plusminusvalues[count], inputcalibrantmasses[mass] ,inputcalibrantmasses[mass] +plusminusvalues[count]), col="blue", lty=c(3,5,3))
            abline(v=c(maxvalue), col="red", lty=2)
            abline(v=c(mzvalue), col="green2", lty=4)
            title(paste0("theor. m/z: ", round(inputcalibrants[count,1], digits=4)), 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 pixel annotation groups

            if (!is.null(unique(msidata\$annotation))){
                if (number_combined < 10){
                    key_zoomed = TRUE
                }else{key_zoomed = FALSE}
                par(mfrow = c(1, 1))
                print(plot(msidata_no_NA[minmasspixel1:maxmasspixel1,], run="infile", strip=FALSE,main="Average spectrum per annotation group",

            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(-0.2,0), xpd = TRUE, bty="n", cex=0.8,legend=inputcalibrantmasses, col=mycolours[1:ncol(ppm_df)],lty=1)
             if (!is.null(unique(msidata\$annotation))){
                abline(v=abline_vector, lty = 3)}}

            ### make x-y-images for mz accuracy

            ppm_dataframe = data.frame(coord(msidata)\$x, coord(msidata)\$y, ppm_df)