Mercurial > repos > galaxyp > cardinal_quality_report
changeset 9:060440cf66f3 draft
"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit d008f6ea0f5c8435fb975a34cb99ea4d42c5ebd2"
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--- a/macros.xml Thu Apr 23 12:01:59 2020 +0000 +++ b/macros.xml Wed May 13 17:55:17 2020 +0000 @@ -32,6 +32,12 @@ <token name="@READING_MSIDATA@"><![CDATA[ ## importing MSI data files + ## read RData files (MSI and other data) independent of filename + loadRData <- function(fileName){ + load(fileName) + get(ls()[ls() != "fileName"]) + } + #if $infile.ext == 'imzml' #if str($processed_cond.processed_file) == "processed": msidata <- readImzML('infile', resolution=$processed_cond.accuracy, attach.only=TRUE, units = "$processed_cond.units") @@ -45,11 +51,6 @@ msidata = readAnalyze('infile', attach.only=TRUE) centroided(msidata) = $centroids #else - ## function to read RData files independent of filename - loadRData <- function(fileName){ - load(fileName) - get(ls()[ls() != "fileName"]) - } msidata = loadRData('infile.RData') #end if
--- a/quality_report.xml Thu Apr 23 12:01:59 2020 +0000 +++ b/quality_report.xml Wed May 13 17:55:17 2020 +0000 @@ -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> @@ -40,7 +40,7 @@ msidata = as(msidata, "MSImagingExperiment") run(msidata) = "infile" } -print(class(msidata)) + ## remove duplicated coordinates msidata <- msidata[,!duplicated(coord(msidata))] @@ -74,7 +74,7 @@ ## Percentage of intensities > 0 percpeaks = round(npeaks/numpeaks*100, digits=2) ## Number of empty TICs -TICs = colSums(spectra(msidata), na.rm=TRUE) +TICs = pixelApply(msidata, sum) NumemptyTIC = sum(TICs == 0) ## Median und sd TIC medTIC = round(median(TICs), digits=1) @@ -329,8 +329,8 @@ ### plot spatial distribution of fold change ## calculate mean intensity for each m/z over the ppm range; then calculate log2 foldchange - mass1vector = colMeans(spectra(filtered_data1), na.rm =TRUE) - mass2vector = colMeans(spectra(filtered_data2), na.rm = TRUE) + mass1vector = pixelApply(filtered_data1, mean, na.rm =TRUE) + mass2vector = pixelApply(filtered_data2, mean, na.rm = TRUE) foldchange= log2(mass1vector/mass2vector) fcmatrix = data.frame(coord(msidata)\$x, coord(msidata)\$y,foldchange) colnames(fcmatrix) = c("x", "y", "foldchange") @@ -610,7 +610,7 @@ ########################## 13) Sum of intensities per m/z ################## ## Sum of all intensities for each m/z (like TIC, but for m/z instead of pixel) - mzTIC = rowSums(spectra(msidata), na.rm=TRUE) ## calculate intensity sum for each m/z + mzTIC = featureApply(msidata, sum, na.rm=TRUE) ## calculate intensity sum for each m/z par(mfrow = c(2,1), mar=c(5,6,4,2)) ## 13a) scatterplot @@ -633,7 +633,7 @@ par(mfrow = c(2,1), mar=c(5,6,4,2)) ## 14a) Median intensity over spectra - medianint_spectra = apply(spectra(msidata), 2, median, na.rm=TRUE) + medianint_spectra = pixelApply(msidata, median) plot(medianint_spectra, main="Median intensity per spectrum",las=1, xlab="Spectra index", ylab="") title(ylab="Median spectrum intensity", line=4) if (!is.null(levels(msidata\$annotation))){ @@ -682,22 +682,30 @@ boxplot(log10(mean_matrix), ylab = "Log10 mean intensity per m/z", main="Log10 mean m/z intensities per annotation group", xaxt = "n") (axis(1, at = c(1:number_combined), labels=levels(msidata\$annotation), las=2)) - ## 14e) Heatmap of pearson correlation on mean intensities between annotation groups + ## 14e) Heatmap of mean intensities of annotation groups - corr_matrix = mean_matrix - corr_matrix[corr_matrix == 0] <- NA - colnames(corr_matrix) = levels(msidata\$annotation) + colnames(mean_matrix) = levels(msidata\$annotation) + mean_matrix[is.na(mean_matrix)] = 0 + heatmap.parameters <- list(mean_matrix, + show_rownames = T, show_colnames = T, + main = "Heatmap of mean intensities per annotation group") + par(oma=c(3,0,0,0)) + print(heatmap(mean_matrix),margins = c(10, 10)) + - ## pearson correlation is only possible if there are at least 2 groups - if (length(colnames)>1) - { - corr_matrix = cor(corr_matrix, method= "pearson",use="complete.obs") + ## 14f) PCA of mean intensities of annotation groups - heatmap.parameters <- list(corr_matrix, - show_rownames = T, show_colnames = T, - main = "Pearson correlation on mean intensities") - do.call("pheatmap", heatmap.parameters) - } + ## define annotation by colour + annotation_colour = rainbow(length(levels(msidata\$annotation)))[as.factor(levels(msidata\$annotation))] + ## transform and scale dataframe + pca = prcomp(t(mean_matrix),center=FALSE,scale.=FALSE) + ## plot single plot + plot(pca\$x[,c(1,2)],col=annotation_colour,pch=19) + ## plot pca with colours for max first 5 PCs + pc_comp = ifelse(ncol(pca\$x)<5 , ncol(pca\$x), 5) + pairs(pca\$x[,1:pc_comp],col=annotation_colour,pch=19) + legend("bottom", horiz = TRUE, legend=levels(msidata\$annotation), col=rainbow(length(levels(msidata\$annotation))), pch=19) + } ################################## VI) Mass spectra and m/z accuracy ######################## @@ -710,13 +718,13 @@ msidata_no_NA = msidata ## find three equal m/z ranges for the average mass spectra plots: - third_mz_range = nrow(msidata_no_NA)/3 + third_mz_range = round(nrow(msidata_no_NA)/3,0) par(cex.axis=1, cex.lab=1, mar=c(5.1,4.1,4.1,2.1)) print(plot(msidata_no_NA, run="infile", layout=c(2,2), strip=FALSE, main= "Average spectrum")) - print(plot(msidata_no_NA[1:third_mz_range,], run="infile", layout=FALSE, strip=FALSE, main= "Zoomed average spectrum")) - print(plot(msidata_no_NA[third_mz_range:(2*third_mz_range),], run="infile", layout=FALSE, strip=FALSE, main= "Zoomed average spectrum")) - print(plot(msidata_no_NA[(2*third_mz_range):nrow(msidata_no_NA),], run="infile", layout=FALSE, strip=FALSE, main= "Zoomed average spectrum")) + print(plot(msidata_no_NA[1:third_mz_range,], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum")) + print(plot(msidata_no_NA[third_mz_range:(2*third_mz_range),], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum")) + print(plot(msidata_no_NA[(2*third_mz_range):nrow(msidata_no_NA),], layout=FALSE, run="infile", strip=FALSE, main="Zoomed average spectrum")) ## plot one average mass spectrum for each pixel annotation group @@ -762,7 +770,7 @@ filtered_data = msidata_no_NA[mz(msidata_no_NA) >= inputcalibrantmasses[mass]-plusminusvalues[mass] & mz(msidata_no_NA) <= inputcalibrantmasses[mass]+plusminusvalues[mass],] if (nrow(filtered_data) > 0 & sum(spectra(filtered_data)) > 0){ - maxmassrow = rowMeans(spectra(filtered_data)) ## for each m/z average intensity is calculated + maxmassrow = featureApply(filtered_data, mean) ## for each m/z average intensity is calculated maxvalue = mz(filtered_data)[which.max(maxmassrow)] ### m/z with highest average 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 @@ -1115,7 +1123,8 @@ - Histogram of intensities. - (annot) Intensities per annotation group: Same histogram as before but with colours to show the contribution of each pixel annotation group. - (annot) Log10 mean intensities per m/z and annotation group: For all pixels of an annotation group the log10 mean intensity for each m/z is calculated and shown as boxplot. -- (annot) Pearson correlation between annotation groups (needs at least 2 groups) based on mean intensities and shown as heatmap. +- (annot) Heatmap of mean intensity per m/z +- (annot) PCA of mean intensity per m/z **Mass spectra and m/z accuracy**
--- a/test-data/preprocessing_results1.imzml Thu Apr 23 12:01:59 2020 +0000 +++ b/test-data/preprocessing_results1.imzml Wed May 13 17:55:17 2020 +0000 @@ -9,8 +9,8 @@ <fileContent> <cvParam cvRef="MS" accession="MS:1000579" name="MS1 spectrum" value="" /> <cvParam cvRef="MS" accession="MS:1000127" name="centroid spectrum" value="" /> - <cvParam cvRef="IMS" accession="IMS:1000080" name="universally unique identifier" value="722d404b-4188-40b4-a3b2-3764ebbdecad" /> - <cvParam cvRef="IMS" accession="IMS:1000091" name="ibd SHA-1" value="c5aded0bab57fd7317901ff7709f2395fc91152e" /> + <cvParam cvRef="IMS" accession="IMS:1000080" name="universally unique identifier" value="25fb4349-d529-4b12-8523-a6aa0306ecf4" /> + <cvParam cvRef="IMS" accession="IMS:1000091" name="ibd SHA-1" value="7f41f9f5efcb31248caefc91b7d6edce87d7f42c" /> <cvParam cvRef="IMS" accession="IMS:1000030" name="continuous" value="" /> </fileContent> </fileDescription>
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--- a/test-data/preprocessing_results2.imzml Thu Apr 23 12:01:59 2020 +0000 +++ b/test-data/preprocessing_results2.imzml Wed May 13 17:55:17 2020 +0000 @@ -9,8 +9,8 @@ <fileContent> <cvParam cvRef="MS" accession="MS:1000579" name="MS1 spectrum" value="" /> <cvParam cvRef="MS" accession="MS:1000127" name="centroid spectrum" value="" /> - <cvParam cvRef="IMS" accession="IMS:1000080" name="universally unique identifier" value="e536d1e2-427b-4c5b-9743-8210ae52a564" /> - <cvParam cvRef="IMS" accession="IMS:1000091" name="ibd SHA-1" value="7cea783e9d345946b874fc1f25d40ce90bffd2f9" /> + <cvParam cvRef="IMS" accession="IMS:1000080" name="universally unique identifier" value="fa59b434-12e0-47fe-b338-814c92daa7d6" /> + <cvParam cvRef="IMS" accession="IMS:1000091" name="ibd SHA-1" value="5764be27b51a23d8c23e4c93befb9d8e57161bd9" /> <cvParam cvRef="IMS" accession="IMS:1000030" name="continuous" value="" /> </fileContent> </fileDescription>
--- a/test-data/preprocessing_results2.imzml.txt Thu Apr 23 12:01:59 2020 +0000 +++ b/test-data/preprocessing_results2.imzml.txt Wed May 13 17:55:17 2020 +0000 @@ -1,4 +1,4 @@ imzML file: total 64 --rw-r--r-- 1 meli meli 37404 Mär 24 10:40 ibd --rw-r--r-- 1 meli meli 22796 Mär 24 10:40 imzml +-rw-r--r-- 1 meli meli 37404 Mai 10 17:38 ibd +-rw-r--r-- 1 meli meli 22796 Mai 10 17:38 imzml
--- a/test-data/preprocessing_results3.imzml Thu Apr 23 12:01:59 2020 +0000 +++ b/test-data/preprocessing_results3.imzml Wed May 13 17:55:17 2020 +0000 @@ -9,8 +9,8 @@ <fileContent> <cvParam cvRef="MS" accession="MS:1000579" name="MS1 spectrum" value="" /> <cvParam cvRef="MS" accession="MS:1000127" name="centroid spectrum" value="" /> - <cvParam cvRef="IMS" accession="IMS:1000080" name="universally unique identifier" value="df9dd256-6485-4954-8338-ddb0c760d4dc" /> - <cvParam cvRef="IMS" accession="IMS:1000091" name="ibd SHA-1" value="ea9ad44f2615ef4d24b92d3ceb38dfa8ec47a978" /> + <cvParam cvRef="IMS" accession="IMS:1000080" name="universally unique identifier" value="d11b7693-3397-4d1f-9e67-29d952701100" /> + <cvParam cvRef="IMS" accession="IMS:1000091" name="ibd SHA-1" value="94906879d47bbdcf97126d50c906dfd76e6681e4" /> <cvParam cvRef="IMS" accession="IMS:1000031" name="processed" value="" /> </fileContent> </fileDescription>
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--- a/test-data/preprocessing_results4.imzml.txt Thu Apr 23 12:01:59 2020 +0000 +++ b/test-data/preprocessing_results4.imzml.txt Wed May 13 17:55:17 2020 +0000 @@ -1,4 +1,4 @@ imzML file: total 84 --rw-r--r-- 1 meli meli 63256 Mär 24 11:35 ibd --rw-r--r-- 1 meli meli 18199 Mär 24 11:35 imzml +-rw-r--r-- 1 meli meli 63256 Mai 10 17:40 ibd +-rw-r--r-- 1 meli meli 18199 Mai 10 17:40 imzml
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