# HG changeset patch # User galaxyp # Date 1529445934 14400 # Node ID 755d77066d4b3d0d5c70b5804914c08f7b889750 # Parent ada9dee67b5dbdcde5e8875f75169f2bba24a853 planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_preprocessing commit 37da74ed68228b16efbdbde776e7c38cc06eb5d5 diff -r ada9dee67b5d -r 755d77066d4b msi_preprocessing.xml --- a/msi_preprocessing.xml Mon Jun 11 17:32:11 2018 -0400 +++ b/msi_preprocessing.xml Tue Jun 19 18:05:34 2018 -0400 @@ -1,4 +1,4 @@ - + mass spectrometry imaging preprocessing @@ -6,7 +6,7 @@ bioconductor-cardinal r-gridextra r-lattice - bioconductor-limma + 0)) medint = round(median(spectra(msidata)[]), digits=2) TICs = round(mean(colSums(spectra(msidata)[])), digits=1) - QC_numbers= data.frame(rawdata = c(maxfeatures, medianpeaks, medint, TICs)) - vectorofactions = "rawdata" - - ### Read tabular file with calibrant m/z: - - calibrant_list = read.delim("$outputs.calibrant_file", header = FALSE, stringsAsFactors = FALSE) - - ### calculate how many input calibrant m/z are valid: - - inputcalibrants = calibrant_list[calibrant_list[,$outputs.calibrants_column]>min(mz(msidata)) & calibrant_list[,$outputs.calibrants_column]0)) medint = round(median(spectra(msidata)[]), digits=2) TICs = round(mean(colSums(spectra(msidata)[])), digits=1) normalized = c(maxfeatures, medianpeaks, medint, TICs) QC_numbers= cbind(QC_numbers, normalized) - - ### preparation for QC plots vectorofactions = append(vectorofactions, "normalized") - for (calibrant in inputcalibrants) - {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey", - par.strip.text=list(col="black", cex=.9)),lattice=TRUE, - scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="normalized") - assign(paste("normalized",calibrant, sep="_"), currentimage)} - - #end if ############################### Baseline reduction ########################### @@ -135,28 +92,15 @@ msidata = reduceBaseline(msidata, method="median", blocks=$method.methods_conditional.blocks_baseline) - ############################### optional QC ########################### + ############################### QC ########################### - #if $outputs.outputs_select == "quality_control": - - ### values for QC table: maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0)) medint = round(median(spectra(msidata)[]), digits=2) TICs = round(mean(colSums(spectra(msidata)[])), digits=1) baseline= c(maxfeatures, medianpeaks, medint, TICs) QC_numbers= cbind(QC_numbers, baseline) - - ### preparation for QC plots - vectorofactions = append(vectorofactions, "baseline_rem") - - for (calibrant in inputcalibrants) - {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey", - par.strip.text=list(col="black", cex=.9)),lattice=TRUE, - scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="baseline removed") - assign(paste("baseline_rem",calibrant, sep="_"), currentimage)} - - #end if + vectorofactions = append(vectorofactions, "baseline red.") ############################### Smoothing ########################### @@ -180,29 +124,16 @@ #end if - ############################### optional QC ########################### + ############################### QC ########################### - #if $outputs.outputs_select == "quality_control": - - ### values for QC table: maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0)) medint = round(median(spectra(msidata)[]), digits=2) TICs = round(mean(colSums(spectra(msidata)[])), digits=1) smoothed= c(maxfeatures, medianpeaks, medint, TICs) QC_numbers= cbind(QC_numbers, smoothed) - - ### preparation for QC plots vectorofactions = append(vectorofactions, "smoothed") - for (calibrant in inputcalibrants) - {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey", - par.strip.text=list(col="black", cex=.9)),lattice=TRUE, - scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="smoothed") - assign(paste("smoothed",calibrant, sep="_"), currentimage)} - - #end if - ############################### Peak picking ########################### #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_picking': @@ -226,29 +157,16 @@ #end if - ############################### optional QC ########################### + ############################### QC ########################### - #if $outputs.outputs_select == "quality_control": - - ### values for QC table: maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0)) medint = round(median(spectra(msidata)[]), digits=2) TICs = round(mean(colSums(spectra(msidata)[])), digits=1) picked= c(maxfeatures, medianpeaks, medint, TICs) QC_numbers= cbind(QC_numbers, picked) - - ### preparation for QC plots vectorofactions = append(vectorofactions, "picked") - for (calibrant in inputcalibrants) - {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey", - par.strip.text=list(col="black", cex=.9)),lattice=TRUE, - scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="picked") - assign(paste("picked",calibrant, sep="_"), currentimage)} - - #end if - ############################### Peak alignment ########################### #elif str( $method.methods_conditional.preprocessing_method ) == 'Peak_alignment': @@ -285,28 +203,16 @@ #end if - ############################### optional QC ########################### - #if $outputs.outputs_select == "quality_control": + ############################### QC ########################### - ### values for QC table: maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0)) medint = round(median(spectra(msidata)[]), digits=2) TICs = round(mean(colSums(spectra(msidata)[])), digits=1) aligned= c(maxfeatures, medianpeaks, medint, TICs) QC_numbers= cbind(QC_numbers, aligned) - - ### preparation for QC plots vectorofactions = append(vectorofactions, "aligned") - for (calibrant in inputcalibrants) - {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey", - par.strip.text=list(col="black", cex=.9)),lattice=TRUE, - scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="aligned") - assign(paste("aligned",calibrant, sep="_"), currentimage)} - - #end if - ############################### Peak filtering ########################### #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_filtering': @@ -314,29 +220,16 @@ msidata = peakFilter(msidata, method='freq', freq.min = $method.methods_conditional.frequ_filtering) - ############################### optional QC ########################### + ############################### QC ########################### - #if $outputs.outputs_select == "quality_control": - - ### values for QC table: maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0)) medint = round(median(spectra(msidata)[]), digits=2) TICs = round(mean(colSums(spectra(msidata)[])), digits=1) filtered= c(maxfeatures, medianpeaks, medint, TICs) QC_numbers= cbind(QC_numbers, filtered) - - ### preparation for QC plots vectorofactions = append(vectorofactions, "filtered") - for (calibrant in inputcalibrants) - {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey", - par.strip.text=list(col="black", cex=.9)),lattice=TRUE, - scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="filtered") - assign(paste("filtered",calibrant, sep="_"), currentimage)} - - #end if - ############################### Data reduction ########################### #elif str( $method.methods_conditional.preprocessing_method) == 'Data_reduction': @@ -368,116 +261,136 @@ #end if msidata = reduceDimension(msidata, method="peaks", ref=peak_reference, type="$method.methods_conditional.methods_for_reduction.peaks_type") - #end if + ############################### QC ########################### - ############################### optional QC ########################### - - #if $outputs.outputs_select == "quality_control": - - ### values for QC table: maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0)) medint = round(median(spectra(msidata)[]), digits=2) TICs = round(mean(colSums(spectra(msidata)[])), digits=1) reduced= c(maxfeatures, medianpeaks, medint, TICs) QC_numbers= cbind(QC_numbers, reduced) - - ### preparation for QC plots vectorofactions = append(vectorofactions, "reduced") - for (calibrant in inputcalibrants) - {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey", - par.strip.text=list(col="black", cex=.9)),lattice=TRUE, - scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="reduced") - assign(paste("reduced",calibrant, sep="_"), currentimage)} - - #end if - ############################### Transformation ########################### - ####elif str( $method.methods_conditional.preprocessing_method) == 'Transformation': - ###print('Transformation') + #elif str( $method.methods_conditional.preprocessing_method) == 'Transformation': + print('Transformation') - ####if str( $method.methods_conditional.transf_conditional.trans_type) == 'log2': - ####print('log2 transformation') + #if str( $method.methods_conditional.transf_conditional.trans_type) == 'log2': + print('log2 transformation') - ###spectra(msidata)[spectra(msidata) ==0] = NA - ###print(paste0("Number of 0 which were converted into NA:",sum(is.na(spectra(msidata))))) - ###spectra(msidata) = log2(spectra(msidata)) + spectra(msidata)[][spectra(msidata)[] ==0] = NA + print(paste0("Number of 0 which were converted into NA:",sum(is.na(spectra(msidata)[])))) + spectra(msidata)[] = log2(spectra(msidata)[]) - ####elif str( $method.methods_conditional.transf_conditional.trans_type) == 'sqrt': - ###print('squareroot transformation') + #elif str( $method.methods_conditional.transf_conditional.trans_type) == 'sqrt': + print('squareroot transformation') - ###spectra(msidata) = sqrt(spectra(msidata)) + spectra(msidata)[] = sqrt(spectra(msidata)[]) - ###end if - - ############################### optional QC ########################### + #end if - #if $outputs.outputs_select == "quality_control": + ############################### QC ########################### - ### values for QC table: maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0), na.rm=TRUE) medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2) TICs = round(mean(colSums(spectra(msidata)[]), na.rm=TRUE), digits=1) transformed= c(maxfeatures, medianpeaks, medint, TICs) QC_numbers= cbind(QC_numbers, transformed) - - ### preparation for QC plots vectorofactions = append(vectorofactions, "transformed") - for (calibrant in inputcalibrants) - {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey", - par.strip.text=list(col="black", cex=.9)),lattice=TRUE, - scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="transformed") - assign(paste("transformed",calibrant, sep="_"), currentimage)} - #end if - - ############################### optional QC ########################### - - #if $outputs.outputs_select == "quality_control": - - ### values for QC table: - maxfeatures = length(features(msidata)) - medianpeaks = median(colSums(spectra(msidata)[]>0)) - medint = round(median(spectra(msidata)[]), digits=2) - TICs = round(mean(colSums(spectra(msidata)[])), digits=1) - sample_norm= c(maxfeatures, medianpeaks, medint, TICs) - QC_numbers= cbind(QC_numbers, sample_norm) - - ### preparation for QC plots - vectorofactions = append(vectorofactions, "sample_norm") - - for (calibrant in inputcalibrants) - {currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey", - par.strip.text=list(col="black", cex=.9)),lattice=TRUE, - scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="sample normalized") - assign(paste("sample_norm",calibrant, sep="_"), currentimage)} - - #end if - - #end if #end for -###################### Outputs: RData, tabular and QC report ################### -############################################################################### +############# Outputs: summar matrix, RData, tabular and QC report ############# +################################################################################ +## optional summarized matrix + print('Summarized matrix') + +#if "mean" in str($summary_type).split(","): + print("mean matrix") + if (!is.null(levels(msidata\$combined_sample))){ + + sample_matrix = matrix(,ncol=0, nrow=nrow(msidata)) + count = 1 + for (subsample in levels(msidata\$combined_sample)){ + subsample_pixels = msidata[,msidata\$combined_sample == subsample] + subsample_calc = apply(spectra(subsample_pixels)[],1,mean, na.rm=TRUE) + sample_matrix = cbind(sample_matrix, subsample_calc) + count = count+1 + } + rownames(sample_matrix) = mz(msidata) + colnames(sample_matrix) = levels(msidata\$combined_sample) + write.table(sample_matrix, file="$summarized_output_mean", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") + }else{ + full_sample_calc = as.data.frame(apply(spectra(msidata)[],1,mean, na.rm=TRUE)) + rownames(full_sample_calc) = mz(msidata) + colnames(full_sample_calc) = "$infile.display_name" + write.table(full_sample_calc, file="$summarized_output_mean", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") + } + +#end if + +#if "median" in str($summary_type).split(","): + print("median matrix") + if (!is.null(levels(msidata\$combined_sample))){ + sample_matrix = matrix(,ncol=0, nrow=nrow(msidata)) + count = 1 + for (subsample in levels(msidata\$combined_sample)){ + subsample_pixels = msidata[,msidata\$combined_sample == subsample] + subsample_calc = apply(spectra(subsample_pixels)[],1,median, na.rm=TRUE) + sample_matrix = cbind(sample_matrix, subsample_calc) + count = count+1 + } + + rownames(sample_matrix) = mz(msidata) + colnames(sample_matrix) = levels(msidata\$combined_sample) + write.table(sample_matrix, file="$summarized_output_median", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") + }else{ + full_sample_calc = apply(spectra(msidata)[],1,median, na.rm=TRUE) + rownames(full_sample_calc) = mz(msidata) + colnames(full_sample_calc) = "$infile.display_name" + write.table(full_sample_calc, file="$summarized_output_mean", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") + } +#end if + +#if "sd" in str($summary_type).split(","): + print("sd matrix") + if (!is.null(levels(msidata\$combined_sample))){ + sample_matrix = matrix(,ncol=0, nrow=nrow(msidata)) + count = 1 + for (subsample in levels(msidata\$combined_sample)){ + subsample_pixels = msidata[,msidata\$combined_sample == subsample] + subsample_calc = apply(spectra(subsample_pixels)[],1,sd, na.rm=TRUE) + sample_matrix = cbind(sample_matrix, subsample_calc) + count = count+1 + } + + rownames(sample_matrix) = mz(msidata) + colnames(sample_matrix) = levels(msidata\$combined_sample) + write.table(sample_matrix, file="$summarized_output_sd", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") + }else{ + full_sample_calc = apply(spectra(msidata)[],1,sd, na.rm=TRUE) + rownames(full_sample_calc) = mz(msidata) + colnames(full_sample_calc) = "$infile.display_name" + write.table(full_sample_calc, file="$summarized_output_mean", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") + } +#end if ## save as (.RData) save(msidata, file="$msidata_preprocessed") -print(paste0("Number of NAs in intensity matrix: ", sum(is.na(spectra(msidata))))) +print(paste0("Number of NAs in intensity matrix: ", sum(is.na(spectra(msidata)[])))) ## save output matrix #if $output_matrix: - if (length(features(msidata))> 0) { ## save as intensity matrix - spectramatrix = spectra(msidata) + spectramatrix = spectra(msidata)[] rownames(spectramatrix) = mz(msidata) newmatrix = rbind(pixels(msidata), spectramatrix) write.table(newmatrix[2:nrow(newmatrix),], file="$matrixasoutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") @@ -490,28 +403,25 @@ #end if ## save QC report -#if $outputs.outputs_select == "quality_control": + pdf("Preprocessing.pdf", fonts = "Times", pointsize = 12) + plot(0,type='n',axes=FALSE,ann=FALSE) + title(main=paste("Quality control during preprocessing \n", "Filename:", "$infile.display_name")) rownames(QC_numbers) = c("# features", "median # peaks", "median intensity", "median TIC") grid.table(t(QC_numbers)) - - for (calibrant in inputcalibrants) - {imagelist = list() - for (numberprepro in 1:length(vectorofactions)){ - imagelist[[numberprepro]] = get(paste(vectorofactions[numberprepro],calibrant, sep="_"))} - do.call(grid.arrange,imagelist)} - dev.off() -#end if - - ]]> + + + + + @@ -522,8 +432,7 @@ - - + @@ -669,7 +578,7 @@ - + - - - - - - - - - - - - - + + + + + + - - outputs["outputs_select"] == "quality_control" + + + summary_type and "mean" in summary_type + + + summary_type and "median" in summary_type + + + summary_type and "sd" in summary_type output_matrix - + @@ -749,21 +656,21 @@ - - + + - - + + @@ -783,16 +690,13 @@ - - - - - + - + + - + @@ -819,14 +723,12 @@ - - - - + + - + @@ -843,12 +745,12 @@ - + - + @@ -862,13 +764,7 @@ - - - - - - - + @@ -899,7 +795,7 @@ Output: - imzML file, preprocessed -- optional: pdf with heatmap of m/z of interest after each preprocessing step +- pdf with key values after each processing step - optional: intensity matrix as tabular file (intensities for m/z in rows and pixel in columns) Tip: diff -r ada9dee67b5d -r 755d77066d4b test-data/123_combined.RData Binary file test-data/123_combined.RData has changed diff -r ada9dee67b5d -r 755d77066d4b test-data/preprocessed.RData Binary file test-data/preprocessed.RData has 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