Mercurial > repos > galaxyp > msi_qualitycontrol
changeset 10:3eee933c27cf draft
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_qualitycontrol commit 37da74ed68228b16efbdbde776e7c38cc06eb5d5
| author | galaxyp |
|---|---|
| date | Tue, 19 Jun 2018 18:08:15 -0400 |
| parents | 963c7ec00141 |
| children | 30d0aabb1b46 |
| files | msi_qualitycontrol.xml test-data/Example_Continuous.ibd test-data/Example_Continuous.imzML test-data/Example_Processed.ibd test-data/Example_Processed.imzML test-data/QC_analyze75.pdf test-data/QC_empty_spectra.pdf test-data/QC_imzml.pdf test-data/QC_rdata.pdf test-data/spectra_info_123_combi.txt test-data/spectra_info_imzml.txt |
| diffstat | 11 files changed, 589 insertions(+), 396 deletions(-) [+] |
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--- a/msi_qualitycontrol.xml Mon Jun 11 17:34:19 2018 -0400 +++ b/msi_qualitycontrol.xml Tue Jun 19 18:08:15 2018 -0400 @@ -1,4 +1,4 @@ -<tool id="mass_spectrometry_imaging_qc" name="MSI Qualitycontrol" version="1.10.0.1"> +<tool id="mass_spectrometry_imaging_qc" name="MSI Qualitycontrol" version="1.10.0.2"> <description> mass spectrometry imaging QC </description> @@ -8,6 +8,7 @@ <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[ @@ -35,15 +36,19 @@ library(RColorBrewer) library(gridExtra) library(KernSmooth) +library(scales) #if $infile.ext == 'imzml' - msidata = readImzML('infile') + msidata <- readImzML('infile', mass.accuracy=$accuracy, units.accuracy = "$units") #elif $infile.ext == 'analyze75' msidata = readAnalyze('infile') #else load('infile.RData') #end if +## create full matrix to make processed imzML files compatible with segmentation +iData(msidata) <- iData(msidata)[] + ###################################### file properties in numbers ###################### ## Number of features (m/z) @@ -73,7 +78,7 @@ TICs = colSums(spectra(msidata)[]) NumemptyTIC = sum(TICs == 0) ## Median TIC -medTIC = median(TICs) +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")) @@ -109,7 +114,6 @@ peakpickinginfo=processinginfo@peakPicking } - ############## Read and filter tabular file with m/z ########################### ### reading peptide file: @@ -183,10 +187,10 @@ ################# I) file properties in numbers ################################ ################################################################################ - print("properties in numbers") +print("properties in numbers") properties = c("Number of m/z features", - "Range of m/z values [Da]", + "Range of m/z values", "Number of pixels", "Range of x coordinates", "Range of y coordinates", @@ -223,16 +227,15 @@ paste0(number_peptides_valid, " / " , number_peptides_in), paste0(number_calibrants_valid, " / ", number_calibrants_in)) - property_df = data.frame(properties, values) grid.table(property_df, rows= NULL) - ####################### II) images in x-y grid ############################### - ############################################################################## - print("x-y images") +####################### II) images in x-y grid ############################### +############################################################################## +print("x-y images") + if (npeaks > 0){ - ## function for density plots plot_colorByDensity = function(x1,x2, ylim=c(min(x2),max(x2)), @@ -248,10 +251,35 @@ 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" @@ -260,37 +288,40 @@ coord_fixed()+ ggtitle("Spatial orientation of combined data")+ theme_bw()+ - theme(text=element_text(family="ArialMT", face="bold", size=15))+ + 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")+ - guides(fill=guide_legend(ncol=4,byrow=TRUE)) + 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]))} + {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 + ### 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] + 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() - + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange"), - space = "Lab", na.value = "black", name = "Acq")) + print(ggplot(pixelxyarray, aes(x=x, y=y, fill=pixelnumber))+ + geom_tile() + coord_fixed()+ + ggtitle("Pixel order") + theme_bw()+ + theme(plot.title = element_text(hjust = 0.5))+ + theme(text=element_text(family="ArialMT", face="bold", size=12))+ + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange"), + space = "Lab", na.value = "black", name = "Pixel number")) ################ 2) Number of calibrants per spectrum ###################### @@ -321,13 +352,20 @@ countdf= cbind(coord(msidata)[,1:2], countvector) mycolours = c("black","grey", "darkblue", "blue", "green" , "red", "yellow", "magenta", "olivedrab1", "lightseagreen") - print(ggplot(countdf, aes(x=x, y=y, fill=countvector)) - + geom_tile() + coord_fixed() - + ggtitle("Number of calibrants per pixel") - + theme_bw() - + theme(text=element_text(family="ArialMT", face="bold", size=12)) - + scale_fill_manual(values = mycolours[1:length(countvector)], + print(ggplot(countdf, aes(x=x, y=y, fill=countvector))+ + geom_tile() + coord_fixed() + + ggtitle("Number of calibrants per pixel") + + theme_bw() + + theme(plot.title = element_text(hjust = 0.5))+ + theme(text=element_text(family="ArialMT", face="bold", size=12))+ + scale_fill_manual(values = mycolours[1:length(countvector)], na.value = "black", name = "# calibrants")) + + ## append list for optional spectrum values output + colnames(countdf)[3] = "Number of Calibrants" + spectrum_list[[list_count]] = countdf + list_count = list_count+1 + }else{print("2) The inputcalibrant m/z were not provided or outside the m/z range")} ########################## 3) fold change image ########################### @@ -386,12 +424,13 @@ foldchange= log2(mass1vector/mass2vector) fcmatrix = cbind(foldchange, coord(msidata)[,1:2]) - print(ggplot(fcmatrix, aes(x=x, y=y, fill=foldchange), colour=colo) - + geom_tile() + coord_fixed() - + ggtitle("$label") - + theme_bw() - + theme(text=element_text(family="ArialMT", face="bold", size=12)) - + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange") + print(ggplot(fcmatrix, aes(x=x, y=y, fill=foldchange), colour=colo)+ + geom_tile() + coord_fixed()+ + ggtitle("$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) @@ -407,7 +446,7 @@ 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") + 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")} @@ -417,41 +456,54 @@ peaksperpixel = colSums(spectra(msidata)[]> 0) peakscoordarray=cbind(coord(msidata)[,1:2], peaksperpixel) - print(ggplot(peakscoordarray, aes(x=x, y=y, fill=peaksperpixel), colour=colo) - + geom_tile() + coord_fixed() - + ggtitle("Number of peaks per spectrum") - + theme_bw() - + theme(text=element_text(family="ArialMT", face="bold", size=12)) - + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange") + print(ggplot(peakscoordarray, aes(x=x, y=y, fill=peaksperpixel), colour=colo)+ + geom_tile() + coord_fixed() + + ggtitle("Number of peaks per spectrum")+ + theme_bw() + + theme(plot.title = element_text(hjust = 0.5))+ + theme(text=element_text(family="ArialMT", face="bold", size=12))+ + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange") ,space = "Lab", na.value = "black", name = "# peaks")) + ## append list for optional spectrum values output + colnames(peakscoordarray)[3] = "Number of Peaks" + spectrum_list[[list_count]] = peakscoordarray + list_count = list_count+1 + ############################### 6) TIC image ############################### TICcoordarray=cbind(coord(msidata)[,1:2], TICs) colo = colorRampPalette( c("blue", "cyan", "green", "yellow","red")) - print(ggplot(TICcoordarray, aes(x=x, y=y, fill=TICs), colour=colo) - + geom_tile() + coord_fixed() - + ggtitle("Total Ion Chromatogram") - + theme_bw() - + theme(text=element_text(family="ArialMT", face="bold", size=12)) - + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange") + print(ggplot(TICcoordarray, aes(x=x, y=y, fill=TICs), colour=colo)+ + geom_tile() + coord_fixed() + + ggtitle("Total Ion Chromatogram")+ + theme_bw() + + theme(plot.title = element_text(hjust = 0.5))+ + theme(text=element_text(family="ArialMT", face="bold", size=12))+ + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange") ,space = "Lab", na.value = "black", name = "TIC")) + ## append list for optional spectrum values output + colnames(TICcoordarray)[3] = "TIC per spectrum" + spectrum_list[[list_count]] = TICcoordarray + list_count = list_count+1 + ############################### 7) Most abundant m/z image ################# highestmz = apply(spectra(msidata)[],2,which.max) highestmz_matrix = cbind(coord(msidata)[,1:2],mz(msidata)[highestmz]) colnames(highestmz_matrix)[3] = "highestmzinDa" - print(ggplot(highestmz_matrix, aes(x=x, y=y, fill=highestmzinDa)) - + geom_tile() + coord_fixed() - + ggtitle("Most abundant m/z in each spectrum") - + theme_bw() - + scale_fill_gradientn(colours = c("blue", "purple" , "red","orange"), space = "Lab", na.value = "black", name = "m/z", + 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))) + 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]) @@ -462,12 +514,16 @@ print(head(sort(table(round(highestmz_matrix\$highestmzinDa, digits=0)), decreasing=TRUE))) + ## append list for optional spectrum values output + colnames(highestmz_matrix)[3] = "Most abundant m/z" + spectrum_list[[list_count]] = highestmz_matrix + ########################## 8) pca image for two components ################# pca = PCA(msidata, ncomp=2) par(mfrow = c(2,1)) plot(pca, col=c("black", "darkgrey"), main="PCA for two components") - image(pca, col=c("black", "white"), strip=FALSE) + image(pca, col=c("black", "white"), strip=FALSE, ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy)) ################## III) properties over spectra index ########## ############################################################################## @@ -498,7 +554,11 @@ 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)} @@ -526,7 +586,11 @@ 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)} @@ -606,6 +670,10 @@ 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) @@ -614,9 +682,11 @@ 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_mz_sample = rowMeans(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) + + boxplot(log2(mean_matrix), ylab = "log2 mean intensity per m/z", main="Mean intensities per m/z and sample", xaxt = "n") + (axis(1, at = c(1:number_combined), labels=levels(msidata\$combined_sample), cex.axis=cex_boxplot, las=2)) } ########################## 14) Histogram on m/z values ##################### @@ -671,25 +741,37 @@ 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) + 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(3,6,3)) - abline(v=c(maxvalue), col="red", lty=5) - abline(v=c(mzvalue), col="green2", lty=5) + 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(3,6,3)) - abline(v=c(maxvalue), col="red", lty=5) - abline(v=c(mzvalue), col="green2", lty=5) + 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(3,6,3)) - abline(v=c(maxvalue), col="red", lty=5) - abline(v=c(mzvalue), col="green2", lty=5) + 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######### @@ -706,7 +788,8 @@ }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")+ + 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)} @@ -720,7 +803,8 @@ 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")+ + 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) @@ -759,7 +843,7 @@ ### 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)") + 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")} @@ -771,15 +855,30 @@ dev.off() }else{ - print("inputfile has no intensities > 0") -dev.off() + print("inputfile has no intensities > 0") + dev.off() } +## tabular output of spectra values + +#if $pixel_output: + print("pixel list") + pixel_df = Reduce(function(...) merge(..., by=c("x", "y"), all=T), spectrum_list) + write.table(pixel_df, file="$pixel_tabular_output", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") +#end if + + + ]]></configfile> </configfiles> <inputs> <param name="infile" type="data" format="imzml,rdata,analyze75" label="Inputfile as imzML, Analyze7.5 or Cardinal MSImageSet saved as RData" help="Upload composite datatype imzml (ibd+imzML) or analyze75 (hdr+img+t2m) or regular upload .RData (Cardinal MSImageSet)"/> + <param name="accuracy" type="float" value="50" label="Only for processed imzML files: enter mass accuracy to which the m/z values will be binned" help="This should be set to the native accuracy of the mass spectrometer, if known"/> + <param name="units" display="radio" type="select" label="Only for processed imzML files: unit of the mass accuracy" help="either m/z or ppm"> + <option value="mz" >mz</option> + <option value="ppm" selected="True" >ppm</option> + </param> <param name="filename" type="text" value="" optional="true" label="Title" help="will appear as header in the quality report, if nothing given input dataset name is used"/> <param name="calibrant_file" type="data" optional="true" format="tabular" label="File with internal calibrants" help="first column: m/z, second column: name (optional), tabular file"/> @@ -793,16 +892,22 @@ <param name="distance" value="0.25" type="float" label="M/z range" help="Plusminus m/z window added to input m/z. In both m/z ranges the maximum intensity is used to calculate the fold change"/> <param name="filenameratioplot" type="text" optional="true" label="Title" help="Optional title for fold change plot."/> </repeat> + <param name="pixel_output" type="boolean" display="radio" label="Tabular with spectra information" help="Values for each spectrum (pixel) in x-y grid images"/> </inputs> <outputs> <data format="pdf" name="plots" from_work_dir="qualitycontrol.pdf" label = "$infile.display_name QC_report"/> + <data format="tabular" name="pixel_tabular_output" label="$infile.display_name spectra information"> + <filter>pixel_output</filter> + </data> </outputs> <tests> - <test> + <test expect_num_outputs="2"> <param name="infile" value="" ftype="imzml"> - <composite_data value="Example_Continuous.imzML" /> - <composite_data value="Example_Continuous.ibd" /> + <composite_data value="Example_Processed.imzML"/> + <composite_data value="Example_Processed.ibd"/> </param> + <param name="accuracy" value="200"/> + <param name="units" value="ppm"/> <param name="peptide_file" value="inputpeptides.txt"/> <param name="calibrant_file" value="inputcalibrantfile1.txt"/> <param name="plusminus_dalton" value="0.25"/> @@ -814,9 +919,11 @@ <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> + <test expect_num_outputs="1"> <param name="infile" value="" ftype="analyze75"> <composite_data value="Analyze75.hdr"/> <composite_data value="Analyze75.img"/> @@ -828,13 +935,15 @@ <param name="filename" value="Testfile_analyze75"/> <output name="plots" file="QC_analyze75.pdf" compare="sim_size" delta="20000"/> </test> - <test> + <test expect_num_outputs="2"> <param name="infile" value="123_combined.RData" ftype="rdata"/> <param name="plusminus_dalton" value="0.2"/> <param name="filename" value="Testfile_rdata"/> + <param name="pixel_output" value="True"/> + <output name="pixel_tabular_output" file="spectra_info_123_combi.txt"/> <output name="plots" file="QC_rdata.pdf" compare="sim_size" delta="20000"/> </test> - <test> + <test expect_num_outputs="1"> <param name="infile" value="empty_spectra.rdata" ftype="rdata"/> <param name="peptide_file" value="inputpeptides.txt"/> <param name="calibrant_file" value="inputcalibrantfile2.txt"/> @@ -864,6 +973,7 @@ Output: - quality control report as pdf with key numbers and descriptive plots describing the mass spectrometry imaging data +- optional spectra information as tabular file with numbers of calibrants (needs input calibrant file), numbers of peaks, TIC and most abundant m/z in each spectrum Tip:
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/spectra_info_123_combi.txt Tue Jun 19 18:08:15 2018 -0400 @@ -0,0 +1,13 @@ + x y Number of Peaks TIC per spectrum Most abundant m/z +1 1 1 1798 121.850390398685 152.91667175293 +2 1 2 2836 200.963327709254 153.08332824707 +3 1 3 2405 127.846644478468 153.16667175293 +4 3 1 2810 182.318354201019 153.08332824707 +5 3 2 2540 135.305841731585 328.916687011719 +6 3 3 2812 168.270181475225 153 +7 4 1 2844 161.809190448268 153 +8 4 2 2157 108.395974184216 171.25 +9 4 3 3168 243.539506603108 153.08332824707 +10 9 1 2844 161.809190448268 153 +11 9 2 2157 108.395974184216 171.25 +12 9 3 3168 243.539506603108 153.08332824707
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/spectra_info_imzml.txt Tue Jun 19 18:08:15 2018 -0400 @@ -0,0 +1,10 @@ + x y Number of Calibrants Number of Peaks TIC per spectrum Most abundant m/z +1 1 1 1 1364 121.850390398685 328.971197672656 +2 1 2 2 1961 200.963327709254 328.971197672656 +3 1 3 2 1714 127.846644478468 153.173335465757 +4 2 1 1 1986 182.318354201019 153.112078382987 +5 2 2 0 1801 135.305841731585 328.971197672656 +6 2 3 0 1968 168.270181475225 255.28235280251 +7 3 1 1 1974 161.809190448268 152.989637701451 +8 3 2 0 1505 108.395974184216 255.28235280251 +9 3 3 1 2180 243.539506603108 153.112078382987