Mercurial > repos > galaxyp > msi_qualitycontrol
changeset 9:963c7ec00141 draft
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_qualitycontrol commit a7be47698f53eb4f00961192327d93e8989276a7
| author | galaxyp |
|---|---|
| date | Mon, 11 Jun 2018 17:34:19 -0400 |
| parents | 52ef77866de8 |
| children | 3eee933c27cf |
| files | msi_qualitycontrol.xml test-data/123_combined.RData test-data/Example_Continuous.ibd test-data/Example_Continuous.imzML test-data/QC_analyze75.pdf test-data/QC_empty_spectra.pdf test-data/QC_imzml.pdf test-data/QC_rdata.pdf |
| diffstat | 8 files changed, 750 insertions(+), 626 deletions(-) [+] |
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--- a/msi_qualitycontrol.xml Mon May 28 12:38:50 2018 -0400 +++ b/msi_qualitycontrol.xml Mon Jun 11 17:34:19 2018 -0400 @@ -1,4 +1,4 @@ -<tool id="mass_spectrometry_imaging_qc" name="MSI Qualitycontrol" version="1.10.0.0"> +<tool id="mass_spectrometry_imaging_qc" name="MSI Qualitycontrol" version="1.10.0.1"> <description> mass spectrometry imaging QC </description> @@ -28,14 +28,14 @@ <configfiles> <configfile name="cardinal_qualitycontrol_script"><![CDATA[ +################################# load libraries and read file ################# + library(Cardinal) library(ggplot2) library(RColorBrewer) library(gridExtra) library(KernSmooth) -## Read MALDI Imaging dataset - #if $infile.ext == 'imzml' msidata = readImzML('infile') #elif $infile.ext == 'analyze75' @@ -44,13 +44,11 @@ load('infile.RData') #end if - - ###################################### file properties in numbers ###################### -## Number of features (mz) +## Number of features (m/z) maxfeatures = length(features(msidata)) -## Range mz +## Range m/z minmz = round(min(mz(msidata)), digits=2) maxmz = round(max(mz(msidata)), digits=2) ## Number of spectra (pixels) @@ -67,19 +65,24 @@ medint = round(median(spectra(msidata)[]), digits=2) ## Number of intensities > 0 npeaks= sum(spectra(msidata)[]>0) -## Spectra multiplied with mz (potential number of peaks) +## 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 = median(TICs) +## 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 # TRUE or FALSE +centroidedinfo = processinginfo@centroided -## if TRUE write processinginfo if no write FALSE +## if TRUE write processinginfo if FALSE write FALSE ## normalization if (length(processinginfo@normalization) == 0) { @@ -106,60 +109,84 @@ peakpickinginfo=processinginfo@peakPicking } -### Read tabular file with masses for plots and heatmap images: + +############## Read and filter tabular file with m/z ########################### + +### reading peptide file: #if $peptide_file: input_list = read.delim("$peptide_file", header = FALSE, na.strings=c("","NA"), stringsAsFactors = FALSE) - if (ncol(input_list) == 1) - { - input_list = cbind(input_list, input_list) - } + if (ncol(input_list) == 1) + {input_list = cbind(input_list, input_list)} ## if there is just one column dublicate it to have a names column - ### calculate how many input peptide masses are valid: - inputpeptides = input_list[input_list[,1]>minmz & input_list[,1]<maxmz,] - number_peptides_in = length(input_list[,1]) - number_peptides_valid = length(inputpeptides[,1]) + ### calculate how many input peptide m/z are valid: + + inputpeptides = input_list[input_list[,1]>minmz & input_list[,1]<maxmz,] + number_peptides_in = length(input_list[,1]) + number_peptides_valid = length(inputpeptides[,1]) #else - ###inputpeptides = data.frame(0,0) + inputpeptides = as.data.frame(matrix(, nrow = 0, ncol = 2)) number_peptides_in = 0 number_peptides_valid = 0 + #end if -colnames(inputpeptides) = c("mz", "name") +colnames(inputpeptides) = c("m/z", "name") + +### reading calibrant file: #if $calibrant_file: - ### Read tabular file with calibrant masses: + calibrant_list = read.delim("$calibrant_file", header = FALSE, na.strings=c("","NA"), stringsAsFactors = FALSE) - if (ncol(calibrant_list) == 1) - { - calibrant_list = cbind(calibrant_list, calibrant_list) - } - ### calculate how many input calibrant masses are valid: - inputcalibrants = calibrant_list[calibrant_list[,1]>minmz & calibrant_list[,1]<maxmz,] - number_calibrants_in = length(calibrant_list[,1]) - number_calibrants_valid = length(inputcalibrants[,1]) + 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 -colnames(inputcalibrants) = c("mz", "name") +colnames(inputcalibrants) = c("m/z", "name") -### bind inputcalibrants and inputpeptides together, to make heatmap on both lists +### bind inputcalibrants and inputpeptides together, to make m/z heatmaps on both inputs_all = rbind(inputcalibrants[,1:2], inputpeptides[,1:2]) inputmasses = inputs_all[,1] inputnames = inputs_all[,2] +######################################## 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 -properties = c("Number of mz features", - "Range of mz values [Da]", +#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 [Da]", "Number of pixels", "Range of x coordinates", "Range of y coordinates", @@ -167,7 +194,8 @@ "Median of intensities", "Intensities > 0", "Number of zero TICs", - "Preprocessing", + "Median TIC", + "Median # peaks per spectrum", "Normalization", "Smoothing", "Baseline reduction", @@ -185,7 +213,8 @@ paste0(medint), paste0(percpeaks, " %"), paste0(NumemptyTIC), - paste0(" "), + paste0(medTIC), + paste0(medpeaks), paste0(normalizationinfo), paste0(smoothinginfo), paste0(baselinereductioninfo), @@ -197,81 +226,95 @@ property_df = data.frame(properties, values) -######################################## PDF ############################################# -########################################################################################## -########################################################################################## - -pdf("qualitycontrol.pdf", fonts = "Times", pointsize = 12) -plot(0,type='n',axes=FALSE,ann=FALSE) -#if not $filename: - #set $filename = $infile.display_name -#end if -title(main=paste("Quality control of MSI data\n\n", "Filename:", "$filename")) - -############################# I) numbers #################################### -############################################################################# grid.table(property_df, rows= NULL) -if (npeaks > 0) -{ - ############################# II) ion images ################################# + ####################### II) images in x-y grid ############################### ############################################################################## + print("x-y images") +if (npeaks > 0){ - ## function without xaxt for plots with automatic x axis + ## 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) - } + 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( "_.*$", "", coord_labels\$sample_name) + for(file_count in 1:nrow(coord_labels)) + {combine_plot = combine_plot + annotate("text",x=coord_labels[file_count,"x"], + y=coord_labels[file_count,"y"],label=toString(coord_labels[file_count,4]))} + print(combine_plot) + + ### find max pixelnumber per subsample to later draw ablines + pixel_name_df = data.frame(pixels(msidata), msidata\$combined_sample) + colnames(pixel_name_df) = c("pixel_number", "pixel_name") + last_pixel = aggregate(pixel_number~pixel_name, data = pixel_name_df, max) + pixel_vector = last_pixel[,2] + abline_vector = pixel_vector[1:length(levels(msidata\$combined_sample))-1] + print(abline_vector) + } - ############################################################################ - - ## 1) Acquisition image + ################### 1) Pixel order image ################################### pixelnumber = 1:pixelcount pixelxyarray=cbind(coord(msidata)[,1:2],pixelnumber) print(ggplot(pixelxyarray, aes(x=x, y=y, fill=pixelnumber)) + geom_tile() + coord_fixed() - + ggtitle("Order of Acquisition") + + ggtitle("Pixel order") +theme_bw() + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange"), - space = "Lab", na.value = "black", name = "Acq")) + space = "Lab", na.value = "black", name = "Acq")) - ## 2) Number of calibrants per spectrum + ################ 2) Number of calibrants per spectrum ###################### pixelmatrix = matrix(ncol=ncol(msidata), nrow=0) inputcalibrantmasses = inputcalibrants[,1] - if (length(inputcalibrantmasses) != 0) - { for (calibrantnr in 1:length(inputcalibrantmasses)) - { - calibrantmz = inputcalibrantmasses[calibrantnr] - calibrantfeaturemin = features(msidata, mz=calibrantmz-$plusminus_dalton) - calibrantfeaturemax = features(msidata, mz=calibrantmz+$plusminus_dalton) + ### find m/z range (ppm) for each calibrant and extract intensity matrix for this range + plusminusvalues = rep($plusminus_ppm/1000000, length(inputcalibrantmasses))*inputcalibrantmasses + + if (length(inputcalibrantmasses) != 0){ + for (calibrantnr in 1:length(inputcalibrantmasses)){ + calibrantmz = inputcalibrantmasses[calibrantnr] + calibrantfeaturemin = features(msidata, mz=calibrantmz-plusminusvalues[calibrantnr]) + calibrantfeaturemax = features(msidata, mz=calibrantmz+plusminusvalues[calibrantnr]) - if (calibrantfeaturemin == calibrantfeaturemax) - { - - calibrantintensity = spectra(msidata)[calibrantfeaturemin,] - + ## in case m/z range includes only 1 m/z: + if (calibrantfeaturemin == calibrantfeaturemax){ + calibrantintensity = spectra(msidata)[calibrantfeaturemin,] }else{ - - calibrantintensity = colSums(spectra(msidata)[calibrantfeaturemin:calibrantfeaturemax,] ) - + ## if m/z range includes more than 1 m/z take sum of intensities + calibrantintensity = colSums(spectra(msidata)[calibrantfeaturemin:calibrantfeaturemax,]) } - pixelmatrix = rbind(pixelmatrix, calibrantintensity) + ## for each pixel add sum of intensity in the given m/z range + pixelmatrix = rbind(pixelmatrix, calibrantintensity) } countvector= as.factor(colSums(pixelmatrix>0)) @@ -279,16 +322,15 @@ 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() + + 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 masses were not provided or outside the mass range")} + }else{print("2) The inputcalibrant m/z were not provided or outside the m/z range")} - -############# new 2b) image of foldchanges (log2 intensity ratios) between two masses in the same spectrum + ########################## 3) fold change image ########################### #if $calibrantratio: #for $foldchanges in $calibrantratio: @@ -296,98 +338,95 @@ 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 - ### find rows which contain masses: + ### 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,] - mzrowdown1 = features(msidata, mz = mass1-distance) - mzrowup1 = features(msidata, mz = mass1+distance) - mzrowdown2 = features(msidata, mz = mass2-distance) - mzrowup2 = features(msidata, mz = 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)] - ### lower and upperlimit for the plot + ### 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) - ### find mass in the given range with the highest intensity (will be plotted in red) - - if (mzrowdown1 == mzrowup1) - { - maxmass1 = mz(msidata)[ mzrowdown1] - }else{ ### for all masses in the massrange calculate mean intensity over all pixels and take mass which has highest mean - maxmassrow1 = rowMeans(spectra(msidata)[mzrowdown1:mzrowup1,]) - maxmass1 = mz(msidata)[mzrowdown1:mzrowup1][which.max(maxmassrow1)] - } - if (mzrowdown2 == mzrowup2) - { - maxmass2 = mz(msidata)[mzrowup2] - }else{ - maxmassrow2 = rowMeans(spectra(msidata)[mzrowdown2:mzrowup2,]) - maxmass2 = mz(msidata)[mzrowdown2:mzrowup2][which.max(maxmassrow2)] - } - - ### plot the part which was chosen, with chosen value in blue, distance in blue, maxmass in red, xlim fixed to 5 Da window + ### 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(3,5,3)) + 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(3,5,3)) + 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 maxmass to have two vectors, which can be divided + ### 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 - mass1vector = spectra(msidata)[features(msidata, mz = maxmass1),] - mass2vector = spectra(msidata)[features(msidata, mz = maxmass2),] - foldchange = log2(mass1vector/mass2vector) - - ratiomatrix = cbind(foldchange, coord(msidata)[,1:2]) + 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(ratiomatrix, aes(x=x, y=y, fill=foldchange), colour=colo) - + geom_tile() + coord_fixed() - + ggtitle("$label") - + theme_bw() - + theme(text=element_text(family="ArialMT", face="bold", size=12)) - + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange") - ,space = "Lab", na.value = "black", name ="FC")) + print(ggplot(fcmatrix, aes(x=x, y=y, fill=foldchange), colour=colo) + + geom_tile() + coord_fixed() + + ggtitle("$label") + + theme_bw() + + theme(text=element_text(family="ArialMT", face="bold", size=12)) + + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange") + ,space = "Lab", na.value = "black", name ="FC")) + }else{ + plot(0,type='n',axes=FALSE,ann=FALSE) + title(main=paste("At least one m/z range did not contain any intensity value > 0,\n therefore no foldchange plot could be drawn"))} + #end for #end if - ## 3) Calibrant images: + #################### 4) m/z heatmaps ####################################### par(mfrow=c(1,1), mar=c(5.1, 4.1, 4.1, 2.1), mgp=c(3, 1, 0), las=0) - if (length(inputmasses) != 0) - { for (mass in 1:length(inputmasses)) - { - image(msidata, mz=inputmasses[mass], plusminus=$plusminus_dalton, - main= paste0(inputnames[mass], " (", round(inputmasses[mass], digits = 2)," ± ", $plusminus_dalton, " Da)"), - contrast.enhance = "histogram", ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy)) + if (length(inputmasses) != 0){ + for (mass in 1:length(inputmasses)){ + image(msidata, mz=inputmasses[mass], plusminus=$plusminus_dalton, + main= paste0(inputnames[mass], " (", round(inputmasses[mass], digits = 2)," ± ", $plusminus_dalton, " Da)"), + contrast.enhance = "histogram") } - } else {print("3) The inputpeptide masses were not provided or outside the mass range")} - + } else {print("4) The input peptide and calibrant m/z were not provided or outside the m/z range")} - ## 4) Number of peaks per pixel - image + #################### 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 pixel") + + 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")) - ## 5) TIC image + ############################### 6) TIC image ############################### + TICcoordarray=cbind(coord(msidata)[,1:2], TICs) colo = colorRampPalette( c("blue", "cyan", "green", "yellow","red")) @@ -399,7 +438,7 @@ + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange") ,space = "Lab", na.value = "black", name = "TIC")) - ## 6) Most abundant mass image + ############################### 7) Most abundant m/z image ################# highestmz = apply(spectra(msidata)[],2,which.max) highestmz_matrix = cbind(coord(msidata)[,1:2],mz(msidata)[highestmz]) @@ -407,182 +446,327 @@ print(ggplot(highestmz_matrix, aes(x=x, y=y, fill=highestmzinDa)) + geom_tile() + coord_fixed() - + ggtitle("Most abundant m/z in each pixel") + + 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))) + labels = as.character(pretty(highestmz_matrix\$highestmzinDa)[c(1,3,5,7)]), + breaks = pretty(highestmz_matrix\$highestmzinDa)[c(1,3,5,7)], limits=c(min(highestmz_matrix\$highestmzinDa), max(highestmz_matrix\$highestmzinDa))) + theme(text=element_text(family="ArialMT", face="bold", size=12))) - ## which mz are highest + ## 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] - ## 7) pca image for two components + 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"),ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), strip=FALSE) - + image(pca, col=c("black", "white"), strip=FALSE) - ############################# III) properties over acquisition (spectra index)########## + ################## III) properties over spectra index ########## ############################################################################## - + print("properties over pixels") par(mfrow = c(2,1), mar=c(5,6,4,2)) - ## 8a) number of peaks per spectrum - scatterplot + ########################## 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 \n (= Acquisition time)", line=3) + title(xlab="Spectra index", line=3) title(ylab="Number of peaks", line=4) + abline(v=abline_vector, lty = 3) - ## 8b) number of peaks per spectrum - histogram + ## 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") - ## 9a) TIC per spectrum - density scatterplot - zero=0 + ## 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))+ + 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 ########################### + + ## 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 \n (= Acquisition time)", line=3) + title(xlab="Spectra index", line=3) title(ylab = "Total ion chromatogram intensity", line=4) + abline(v=abline_vector, lty = 3) - ## 9b) TIC per spectrum - histogram + ## 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") + 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") - ################################## IV) changes over mz ############################ - ################################################################################### + 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)} - ## 11) Number of peaks per mz - ## Number of peaks per mz - number across all pixel + ################################## IV) changes over m/z #################### + ############################################################################ + print("changes over m/z") + ########################## 11) Number of peaks per m/z ##################### + peakspermz = rowSums(spectra(msidata)[] > 0 ) par(mfrow = c(2,1), mar=c(5,6,4,4.5)) - ## Number of peaks per mz - scatterplot - plot_colorByDensity(mz(msidata),peakspermz, main= "Number of peaks per mz", ylab ="") - title(xlab="mz in Dalton", line=2.5) + ## 11a) 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) - # make plot smaller to fit axis and labels, add second y axis with % - ## Number of peaks per mz - histogram + ## 11b) histogram hist(peakspermz, main="", las=1, ylab="", xlab="") title(ylab = "Frequency", line=4) - title(main="Number of peaks per mz", xlab = "Number of peaks per mz", line=2) + title(main="Number of peaks per m/z", xlab = "Number of peaks per m/z", line=2) abline(v=median(peakspermz), col="blue") - - ## 12) Sum of intensities per mz + ########################## 12) Sum of intensities per m/z ################## - ## Sum of all intensities for each mz (like TIC, but for mz instead of pixel) - mzTIC = rowSums(spectra(msidata)[]) # calculate intensity sum for each mz + ## 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)) - # 12a) sum of intensities per mz - scatterplot - plot_colorByDensity(mz(msidata),mzTIC, main= "Sum of intensities per mz", ylab ="") - title(xlab="mz in Dalton", line=2.5) + ## 12a) 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) - # 12b) sum of intensities per mz - histogram + + ## 12b) histogram hist(log(mzTIC), main="", xlab = "", las=1, ylab="") - title(main="Sum of intensities per mz", line=2, ylab="") - title(xlab = "log (sum of intensities per mz)") + 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) general plots ############################ - ################################################################################### - - ## 13) Intensity distribution + ################################## V) general plots ######################## + ############################################################################ + print("general plots") + ########################## 13) Intensity distribution ###################### par(mfrow = c(2,1), mar=c(5,6,4,2)) - ## 13a) Intensity histogram: + ## 13a) 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) + + ## 13b) 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") - ## 13b) Median intensity over spectra - medianint_spectra = apply(spectra(msidata), 2, median) - plot(medianint_spectra, main="Median intensity per spectrum",las=1, xlab="Spectra index \n (= Acquisition time)", ylab="") - title(ylab="Median spectrum intensity", line=4) + ## 13c) 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") - ## 13c) Histogram on mz values - par(mfrow = c(1, 1)) - hist(mz(msidata), xlab = "mz in Dalton", main="Histogram of mz values") + 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) Mass spectra + ########################## 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)) + hist(mz(msidata), xlab = "m/z", main="Histogram of m/z values") + + ############################ 15) Mass spectra ############################## par(mfrow = c(2, 2)) + 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 =round(length(pixelnumber)/2, digits=0), main="Spectrum in middle of acquisition") - plot(msidata, pixel = highestmz_pixel, main= paste0("Spectrum at ", rownames(coord(msidata)[highestmz_pixel,1:2]))) - plot(msidata, pixel = secondhighestmz_pixel, main= paste0("Spectrum at ", rownames(coord(msidata)[secondhighestmz_pixel,1:2]))) + 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############## - ## 15) Zoomed in mass spectra for calibrants - plusminusvalue = $plusminus_dalton - x = 1 - if (length(inputcalibrantmasses) != 0) - { + count = 1 + differencevector = numeric() + differencevector2 = vector() + + if (length(inputcalibrantmasses) != 0){ + + ### calculate plusminus values in m/z for each calibrant, this is used for all following plots + plusminusvalues = rep($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) - for (calibrant in inputcalibrantmasses) - { - minmasspixel = features(msidata, mz=calibrant-1) - maxmasspixel = features(msidata, mz=calibrant+3) - par(mfrow = c(2, 2), oma=c(0,0,2,0)) - plot(msidata[minmasspixel:maxmasspixel,], pixel = 1:length(pixelnumber), main= "average spectrum") - abline(v=c(calibrant-plusminusvalue, calibrant,calibrant+plusminusvalue), col="blue", lty=c(3,5,3)) - plot(msidata[minmasspixel:maxmasspixel,], pixel =round(length(pixelnumber)/2, digits=0), main="pixel in middle of acquisition") - abline(v=c(calibrant-plusminusvalue, calibrant,calibrant+plusminusvalue), col="blue", lty=c(3,5,3)) - plot(msidata[minmasspixel:maxmasspixel,], pixel = highestmz_pixel,main= paste0("Spectrum at ", rownames(coord(msidata)[highestmz_pixel,1:2]))) - abline(v=c(calibrant-plusminusvalue, calibrant,calibrant+plusminusvalue), col="blue", lty=c(3,5,3)) - plot(msidata[minmasspixel:maxmasspixel,], pixel = secondhighestmz_pixel, main= paste0("Spectrum at ", rownames(coord(msidata)[secondhighestmz_pixel,1:2]))) - abline(v=c(calibrant-plusminusvalue, calibrant,calibrant+plusminusvalue), col="blue", lty=c(3,5,3)) - title(paste0(inputcalibrants[x,1]), outer=TRUE) - x=x+1 + ### 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)) + maxvalue = mz(filtered_data)[which.max(maxmassrow)] ### m/z with highestaverage 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 closest featurenumber which is closest to given m/z + mzvalue = mz(msidata)[mznumber] ### gives the 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(3,6,3)) + 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 } - }else{print("15) The inputcalibrant masses were not provided or outside the mass range")} + ######### 17) ppm difference input calibrant m/z and m/z with max intensity in given m/z range######### - ## 16) ppm accuracy measured vs. theoretical calibrant mass - - if (length(inputcalibrantmasses) != 0) - { par(mfrow = c(1, 1)) - differencevector = vector() - - for (mass in 1:length(inputcalibrantmasses)) - {mznumber = features(msidata, mz = inputcalibrantmasses[mass]) ### this gives the featurenumber which is closest to given mz - mzvalue = mz(msidata)[mznumber] ### gives the mz in Da which is closest to the given mz (using the featurenumber) - mzdifference = inputcalibrantmasses[mass] - mzvalue ### difference in Da: theoretical value - closest mz value - ppmdifference = mzdifference/inputcalibrantmasses[mass]*1000000 ### calculate ppm for accuracy measurement - differencevector[mass] = ppmdifference } - differencevector = round(differencevector, digits=2) - - ### plot the ppm difference theor. mz value to closest mz value: + ### 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) - diff_plot<-ggplot(data=diff_df, aes(x=calibrant_names, y=differencevector)) + geom_col() + theme_minimal() + - labs(title="Theoretical calibrant mz vs. closest measured mz", x="calibrants", y = "Difference in ppm")+ + 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. 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 ########### + + ### 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. 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) - }else{print("16) The inputcalibrant masses were not provided or outside the mass range")} + #################### 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. 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() @@ -596,24 +780,23 @@ <inputs> <param name="infile" type="data" format="imzml,rdata,analyze75" label="Inputfile as imzML, Analyze7.5 or Cardinal MSImageSet saved as RData" help="Upload composite datatype imzml (ibd+imzML) or analyze75 (hdr+img+t2m) or regular upload .RData (Cardinal MSImageSet)"/> - <param name="filename" type="text" value="" optional="true" label="Title" help="will appear in the quality report. If nothing given it will take the dataset name."/> - <param name="peptide_file" type="data" optional="true" format="tabular" label="Text file with masses and names" - help="first column m/z, second column name, tab separated file"/> + <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="Internal calibrants and names" - help="Used for plot number of calibrant per spectrum and for zoomed in mass spectra"/> - <param name="plusminus_dalton" value="0.25" type="text" label="Mass range in Dalton" help="Plusminus mass window in Dalton for calibrant and peptide plots"/> - <repeat name="calibrantratio" title="Plot fold change of two masses for each spectrum" min="0" max="10"> - <param name="mass1" value="1111" type="float" label="Mass 1" help="First mass in Dalton"/> - <param name="mass2" value="2222" type="float" label="Mass 2" help="Second mass in Dalton"/> - <param name="distance" value="0.25" type="float" label="Distance in Dalton" help="Distance in Da used to find peak maximum from input masses in both directions"/> + 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="plusminus_dalton" value="0.25" type="float" label="M/z range for m/z heatmaps (x-y grid)" help="Will be added in both directions to input calibrants and peptide m/z"/> + <param name="plusminus_ppm" value="50" type="float" label="Ppm range for accuracy and number of calibrants plots" 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> </inputs> <outputs> - <data format="pdf" name="plots" from_work_dir="qualitycontrol.pdf" label = "${tool.name} ${on_string}"/> + <data format="pdf" name="plots" from_work_dir="qualitycontrol.pdf" label = "$infile.display_name QC_report"/> </outputs> - <tests> <test> <param name="infile" value="" ftype="imzml"> @@ -623,12 +806,13 @@ <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"/> <repeat name="calibrantratio"> - <param name="mass1" value="111"/> - <param name="mass2" value="222"/> + <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 (111) / mass2 (222)"/> + <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> @@ -645,8 +829,8 @@ <output name="plots" file="QC_analyze75.pdf" compare="sim_size" delta="20000"/> </test> <test> - <param name="infile" value="preprocessed.RData" ftype="rdata"/> - <param name="plusminus_dalton" value="0"/> + <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> @@ -663,7 +847,7 @@ <![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 some Cardinal functions to create a quality control report with descriptive plots for mass-spectrometry imaging data. +This tool uses some Cardinal functions to create a quality control report with descriptive plots for mass spectrometry imaging data. Input data: 3 types of input data can be used: @@ -673,17 +857,17 @@ Options: -- masses of interest as tabular file, used to generate heatmap images -- internal calibrants as tabular file, used for the following plots: Number of calibrant per spectrum, heatmap images, mass-spectrum plot zoomed in for calibrant region, ppm accuracy -- fold change plot: draws a heatmap of the fold change of two masses (log2(intensity ratio)) +- 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 +- m/z of interest are used to generate m/z heatmaps (x-y grid) +- optional fold change plot: draws a heatmap (x-y grid) for the fold change of two m/z (log2(intensity ratio)) Output: -- pdf with numbers and descriptive plots to check the quality of the mass-spectrometry imaging data +- quality control report as pdf with key numbers and descriptive plots describing the mass spectrometry imaging data Tip: -- For additional heatmap images use the MSI ion images tool and to plot more mass spectra use the MSI massspectra tool. +- For additional m/z heatmaps use the MSI ion images tool and to plot more mass spectra use the MSI massspectra tool. ]]> </help>
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