# HG changeset patch # User galaxyp # Date 1540466922 14400 # Node ID ae9ffc7ba261d3d942a8e7e8b15cc98523971847 # Parent 5f18275c250abd4f8b7dd42bd9e2a5525b4d3163 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit d2f311f7fff24e54c565127c40414de708e31b3c diff -r 5f18275c250a -r ae9ffc7ba261 macros.xml --- a/macros.xml Mon Oct 01 01:07:13 2018 -0400 +++ b/macros.xml Thu Oct 25 07:28:42 2018 -0400 @@ -4,10 +4,17 @@ bioconductor-cardinal + r-base + + /dev/null | grep -v -i "WARNING: ") + ]]> + + - + + @@ -198,12 +206,12 @@ - - + - - - + + + diff -r 5f18275c250a -r ae9ffc7ba261 quality_report.xml --- a/quality_report.xml Mon Oct 01 01:07:13 2018 -0400 +++ b/quality_report.xml Thu Oct 25 07:28:42 2018 -0400 @@ -1,4 +1,4 @@ - + mass spectrometry imaging QC @@ -6,12 +6,12 @@ macros.xml - r-ggplot2 r-rcolorbrewer - r-gridextra + r-gridextra + r-ggplot2 r-kernsmooth - r-scales - r-pheatmap + r-scales + r-pheatmap 0, na.rm=TRUE) peakscoordarray=cbind(coord(msidata)[,1:2], peaksperpixel) - print(ggplot(peakscoordarray, aes(x=x, y=y, fill=peaksperpixel), colour=colo)+ + print(ggplot(peakscoordarray, aes(x=x, y=y, fill=peaksperpixel))+ geom_tile() + coord_fixed() + ggtitle("Number of peaks per spectrum")+ theme_bw() + @@ -375,9 +376,8 @@ ############################### 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)+ + + print(ggplot(TICcoordarray, aes(x=x, y=y, fill=TICs))+ geom_tile() + coord_fixed() + ggtitle("Total Ion Chromatogram")+ theme_bw() + @@ -386,6 +386,20 @@ scale_fill_gradientn(colours = c("blue", "purple" , "red","orange") ,space = "Lab", na.value = "black", name = "TIC")) + ############################### 6b) median int image ############################### + + median_int = apply(spectra(msidata)[],2,median) + median_coordarray=cbind(coord(msidata)[,1:2], median_int) + print(ggplot(median_coordarray, aes(x=x, y=y, fill=median_int))+ + geom_tile() + coord_fixed() + + ggtitle("Median intensity per pixel")+ + 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 = "median\nintensity")) + + ############################### 7) Most abundant m/z image ################# @@ -407,6 +421,7 @@ #if $do_pca: + set.seed(1) pca = PCA(msidata, ncomp=2) par(mfrow = c(2,1)) plot(pca, col=c("black", "darkgrey"), main="PCA for two components") @@ -499,7 +514,6 @@ ########################## 12) Number of peaks per m/z ##################### peakspermz = rowSums(spectra(msidata)[] > 0, na.rm=TRUE) -print(median(peakspermz/pixelcount*100)) par(mfrow = c(2,1), mar=c(5,6,4,4.5)) ## 12a) scatterplot @@ -600,7 +614,7 @@ heatmap.parameters <- list(corr_matrix, show_rownames = T, show_colnames = T, - main = "Pearson correlation on mean intensities for each annotation group") + main = "Pearson correlation on mean intensities") do.call("pheatmap", heatmap.parameters) } @@ -639,8 +653,8 @@ for (mass in 1:length(inputcalibrantmasses)){ ### define the plot window with xmin und xmax - minmasspixel = features(msidata_no_NA, mz=inputcalibrantmasses[mass]-1) - maxmasspixel = features(msidata_no_NA, mz=inputcalibrantmasses[mass]+3) + minmasspixel = features(msidata_no_NA, mz=inputcalibrantmasses[mass]-0.5) + maxmasspixel = features(msidata_no_NA, mz=inputcalibrantmasses[mass]+1.5) ### 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_no_NA[mz(msidata_no_NA) >= inputcalibrantmasses[mass]-plusminusvalues[mass] & mz(msidata_no_NA) <= inputcalibrantmasses[mass]+plusminusvalues[mass],] @@ -662,19 +676,22 @@ ppmdifference2 = mzdifference2/inputcalibrantmasses[mass]*1000000 differencevector2[mass] = round(ppmdifference2, digits=2) + ## plotting of 4 spectra in one page par(mfrow = c(2, 2), oma=c(0,0,2,0)) + ## average plot plot(msidata_no_NA[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(3,5,3)) abline(v=c(maxvalue), col="red", lty=2) abline(v=c(mzvalue), col="green2", lty=4) - plot(msidata_no_NA[minmasspixel:maxmasspixel,], pixel = pixel1, main=paste0("Spectrum at ", rownames(coord(msidata_no_NA)[pixel1,1:2]))) - 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 + plot(msidata_no_NA[minmasspixel:maxmasspixel,], pixel = 1:length(pixelnumber), main="Average spectrum with data points") + points(mz(msidata_no_NA[minmasspixel:maxmasspixel,]), rowMeans(spectra(msidata_no_NA)[minmasspixel:maxmasspixel,]), col="blue", pch=20) + ## plot of a random pixel (1) plot(msidata_no_NA[minmasspixel:maxmasspixel,], pixel = pixel2, main= paste0("Spectrum at ", rownames(coord(msidata_no_NA)[pixel2,1:2]))) 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 a random pixel (2) plot(msidata_no_NA[minmasspixel:maxmasspixel,], pixel = pixel3, main= paste0("Spectrum at ", rownames(coord(msidata_no_NA)[pixel3,1:2]))) 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) @@ -773,12 +790,12 @@ }else{ ### plot ppm differences over pixels (spectra index) - par(mar=c(4.1, 4.1, 4.1, 7.5)) + par(mar=c(4.1, 4.1, 4.1, 8.5)) plot(0,0,type="n", ylim=c(min(ppm_df, na.rm=TRUE),max(ppm_df, na.rm=TRUE)), xlim = c(1,ncol(filtered_data)),xlab = "Spectra index", ylab = "m/z difference in ppm", main="Difference m/z with max. average intensity vs. theor. m/z\n(per spectrum)") for (each_cal in 1:ncol(ppm_df)){ lines(ppm_df[,each_cal], col=mycolours[each_cal], type="p")} - legend("topright", inset=c(-0.25,0), xpd = TRUE, bty="n", legend=inputcalibrantmasses, col=mycolours[1:ncol(ppm_df)],lty=1) + 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)}} @@ -804,7 +821,7 @@ - + @@ -855,9 +872,6 @@ - - - @@ -946,6 +960,7 @@ - (cal) Intensity heatmaps for the m/z value that is closest to the calibrant m/z. The intensities are averaged within the calibrant m/z window (ppm range). - Number of peaks per spectrum: For each spectrum the number of m/z values with intensity > 0 is calculated and plotted as heatmap. - Total ion chromatogram: For each spectrum all intensities are summed up to obtain the TIC which is plotted as heatmap. +- Median intensity: For each spectrum the median intensity is plotted as heatmap. - Most abundant m/z in each spectrum: For each spectrum the m/z value with the highest intensity is plotted. - PCA for two components: Result of a principal component analysis (PCA) for two components is given. The loading plot depicts the contribution of each m/z value and the x-y image represents the differences between the pixels. @@ -973,7 +988,7 @@ **Mass spectra and m/z accuracy** - Mass spectra over the full m/z range: First plot shows the average intensities over all spectra. The other three mass spectra are from random individual pixels (spectra). -- (cal) For each calibrant four zoomed in mass spectrum are drawn: The first shows the average intensities over all spectra and the other three are single mass spectra. The theoretical calibrant m/z (taken from the input file) is represented by the dashed blue line. The dotted blue lines show the given ppm range. The green line is the m/z value that is closest to the theoretical calibrant and the red line is the m/z with the highest average intensity in the m/z window. +- (cal) For each calibrant four zoomed in mass spectrum are drawn: The first two mass spectra show the average intensities over all spectra and the other two specra are from random individual pixels. The theoretical calibrant m/z (taken from the input file) is represented by the dashed blue line. The dotted blue lines show the given ppm range. The green line is the m/z value that is closest to the theoretical calibrant and the red line is the m/z with the highest average intensity in the m/z window. In the second average spectra plot each blue plot indicates one data point. - (annot) Average spectrum per annotation group: For each calibrant a zoomed in mass spectrum is plotted this time with the average intensities for each annotation group separately. - (cal) Difference m/z with max. average intensity vs. theor. calibrant m/z: The difference in ppm between the m/z with the highest average intensity and the theoretical m/z are plotted for each calibrant. This corresponds to the difference between the dashed blue line and the red line in the zoomed in mass spectra. - (cal) Difference closest measured m/z vs. theor. calibrant m/z: The difference in ppm between the closest m/z value and the theoretical m/z values are plotted for each calibrant. 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