Mercurial > repos > galaxyp > cardinal_quality_report
changeset 1:b43a77bf3256 draft
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit d2f311f7fff24e54c565127c40414de708e31b3c
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--- a/macros.xml Mon Oct 01 01:00:27 2018 -0400 +++ b/macros.xml Thu Oct 25 07:20:07 2018 -0400 @@ -4,10 +4,17 @@ <xml name="requirements"> <requirements> <requirement type="package" version="@VERSION@">bioconductor-cardinal</requirement> + <requirement type="package" version="3.5.1">r-base</requirement> <yield/> </requirements> </xml> + <xml name="print_version"> + <version_command><![CDATA[ +echo $(R --version | grep version | grep -v GNU)", Cardinal version" $(R --vanilla --slave -e "library(Cardinal); cat(sessionInfo()\$otherPkgs\$Cardinal\$Version)" 2> /dev/null | grep -v -i "WARNING: ") + ]]></version_command> + </xml> + <token name="@INPUT_LINKING@"><![CDATA[ #if $infile.ext == 'imzml' ln -s '${infile.extra_files_path}/imzml' infile.imzML && @@ -33,14 +40,14 @@ #if $infile.ext == 'imzml' #if str($processed_cond.processed_file) == "processed": - msidata <- readImzML('infile', mass.accuracy=$processed_cond.accuracy, units.accuracy = "$processed_cond.units") + msidata <- readImzML('infile', mass.accuracy=$processed_cond.accuracy, units.accuracy = "$processed_cond.units", attach.only=TRUE) centroided(msidata) = $centroids #else - msidata <- readImzML('infile') + msidata <- readImzML('infile', attach.only=TRUE) centroided(msidata) = $centroids #end if #elif $infile.ext == 'analyze75' - msidata = readAnalyze('infile') + msidata = readAnalyze('infile', attach.only=TRUE) centroided(msidata) = $centroids #else msidata = loadRData('infile.RData') @@ -177,7 +184,8 @@ <param name="filename" type="text" value="" label="Title" help="Will appear in the pdf output, if nothing given it will take the dataset name"> <sanitizer invalid_char=""> <valid initial="string.ascii_letters,string.digits"> - <add value="_" /> + <add value="_"/> + <add value=" "/> </valid> </sanitizer> </param> @@ -198,12 +206,12 @@ <param name="feature_header" type="boolean" label="Tabular file contains a header line" truevalue="TRUE" falsevalue="FALSE"/> </xml> - <xml name="reading_2_column_mz_tabular"> - <param name="calibrant_file" type="data" optional="true" format="tabular" + <xml name="reading_2_column_mz_tabular" token_optional="false"> + <param name="calibrant_file" type="data" optional="@OPTIONAL@" format="tabular" label="m/z of interest (e.g. internal Calibrants)" help="one column with m/z values, optional second column with names (m/z values can also be selected as name)"/> - <param name="mz_column" data_ref="calibrant_file" label="Column with m/z values" type="data_column"/> - <param name="name_column" data_ref="calibrant_file" label="Column with name of m/z values" type="data_column"/> - <param name="calibrant_header" type="boolean" label="Tabular file contains a header line" truevalue="TRUE" falsevalue="FALSE"/> + <param name="mz_column" data_ref="calibrant_file" optional="@OPTIONAL@" label="Column with m/z values" type="data_column"/> + <param name="name_column" data_ref="calibrant_file" optional="@OPTIONAL@" label="Column with name of m/z values" type="data_column"/> + <param name="calibrant_header" type="boolean" optional="@OPTIONAL@" label="Tabular file contains a header line" truevalue="TRUE" falsevalue="FALSE"/> </xml> <xml name="reading_pixel_annotations">
--- a/quality_report.xml Mon Oct 01 01:00:27 2018 -0400 +++ b/quality_report.xml Thu Oct 25 07:20:07 2018 -0400 @@ -1,4 +1,4 @@ -<tool id="cardinal_quality_report" name="MSI Qualitycontrol" version="@VERSION@.0"> +<tool id="cardinal_quality_report" name="MSI Qualitycontrol" version="@VERSION@.1"> <description> mass spectrometry imaging QC </description> @@ -6,12 +6,12 @@ <import>macros.xml</import> </macros> <expand macro="requirements"> - <requirement type="package" version="2.2.1">r-ggplot2</requirement> <requirement type="package" version="1.1_2">r-rcolorbrewer</requirement> - <requirement type="package" version="2.2.1">r-gridextra</requirement> + <requirement type="package" version="2.3">r-gridextra</requirement> + <requirement type="package" version="3.0">r-ggplot2</requirement> <requirement type="package" version="2.23_15">r-kernsmooth</requirement> - <requirement type="package" version="0.5.0">r-scales</requirement> - <requirement type="package" version="1.0.8"> r-pheatmap</requirement> + <requirement type="package" version="1.0.0">r-scales</requirement> + <requirement type="package" version="1.0.10"> r-pheatmap</requirement> </expand> <command detect_errors="exit_code"> <![CDATA[ @@ -33,6 +33,7 @@ library(scales) library(pheatmap) + @READING_MSIDATA@ ## remove duplicated coordinates @@ -362,7 +363,7 @@ peaksperpixel = colSums(spectra(msidata)[]> 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 @@ <when value="no_annotation"/> </conditional> <expand macro="pdf_filename"/> - <expand macro="reading_2_column_mz_tabular"/> + <expand macro="reading_2_column_mz_tabular" optional="true"/> <param name="plusminus_ppm" value="200" type="float" label="ppm range" help="Will be added in both directions to input calibrant m/z"/> <param name="do_pca" type="boolean" label="PCA with 2 components"/> <repeat name="calibrantratio" title="Plot fold change of two m/z" min="0" max="10"> @@ -855,9 +872,6 @@ <conditional name="tabular_annotation"> <param name="load_annotation" value="no_annotation"/> </conditional> - <param name="calibrant_file" value="inputcalibrantfile2.txt"/> - <param name="mz_column" value="1"/> - <param name="name_column" value="2"/> <param name="filename" value="Testfile_analyze75"/> <param name="do_pca" value="True"/> <output name="QC_report" file="QC_analyze75.pdf" compare="sim_size"/> @@ -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. This corresponds to the difference between the dashed blue line and the green line in the zoomed in mass spectra.
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