Mercurial > repos > galaxyp > msi_preprocessing
changeset 4:42580292d381 draft
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_preprocessing commit a7be47698f53eb4f00961192327d93e8989276a7
| author | galaxyp |
|---|---|
| date | Mon, 11 Jun 2018 17:34:07 -0400 |
| parents | b9523950e79d |
| children | 2fccfd11360d |
| files | msi_preprocessing.xml test-data/Example_Continuous.ibd test-data/Example_Continuous.imzML test-data/example_continous.RData test-data/preprocessed.RData test-data/preprocessing_results1.RData test-data/preprocessing_results1.txt test-data/preprocessing_results2.RData test-data/preprocessing_results2.pdf test-data/preprocessing_results2.txt test-data/preprocessing_results3.RData test-data/preprocessing_results3.pdf test-data/preprocessing_results3.txt test-data/preprocessing_results4.RData test-data/preprocessing_results5.RData test-data/preprocessing_results5.pdf test-data/preprocessing_results5.txt |
| diffstat | 17 files changed, 791 insertions(+), 10172 deletions(-) [+] |
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--- a/msi_preprocessing.xml Mon May 28 12:38:04 2018 -0400 +++ b/msi_preprocessing.xml Mon Jun 11 17:34:07 2018 -0400 @@ -1,4 +1,4 @@ -<tool id="mass_spectrometry_imaging_preprocessing" name="MSI preprocessing" version="1.10.0.0"> +<tool id="mass_spectrometry_imaging_preprocessing" name="MSI preprocessing" version="1.10.0.1"> <description> mass spectrometry imaging preprocessing </description> @@ -6,6 +6,7 @@ <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement> <requirement type="package" version="2.2.1">r-gridextra</requirement> <requirement type="package" version="0.20-35">r-lattice</requirement> + <requirement type="package" version="3.34.9">bioconductor-limma</requirement> </requirements> <command detect_errors="exit_code"> <![CDATA[ @@ -27,9 +28,13 @@ </command> <configfiles> <configfile name="cardinal_preprocessing"><![CDATA[ + +################################# load libraries and read file ################# + library(Cardinal) library(gridExtra) library(lattice) +library(limma) #if $infile.ext == 'imzml' msidata = readImzML('infile') @@ -39,12 +44,20 @@ load('infile.RData') #end if +## function to later read RData reference files in -##################################### Preparations for QC report ############### +loadRData <- function(fileName){ +#loads an RData file, and returns it +load(fileName) +get(ls()[ls() != "fileName"]) +} + +######################### preparations for optional QC report ################# #if $outputs.outputs_select == "quality_control": ### values for QC table: + maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0)) medint = round(median(spectra(msidata)[]), digits=2) @@ -52,54 +65,50 @@ QC_numbers= data.frame(rawdata = c(maxfeatures, medianpeaks, medint, TICs)) vectorofactions = "rawdata" + ### Read tabular file with calibrant m/z: - ### Read tabular file with calibrant masses: - calibrant_list = read.delim("$outputs.calibrant_file", header = FALSE, stringsAsFactors = FALSE) + calibrant_list = read.delim("$outputs.calibrant_file", header = FALSE, stringsAsFactors = FALSE) - ### calculate how many input calibrant masses are valid: - inputcalibrants = calibrant_list[calibrant_list[,$outputs.calibrants_column]>min(mz(msidata)) & calibrant_list[,$outputs.calibrants_column]<max(mz(msidata)),$outputs.calibrants_column] - number_calibrants_in = length(calibrant_list[,$outputs.calibrants_column]) - number_calibrants_valid = length(inputcalibrants) + ### calculate how many input calibrant m/z are valid: - - + inputcalibrants = calibrant_list[calibrant_list[,$outputs.calibrants_column]>min(mz(msidata)) & calibrant_list[,$outputs.calibrants_column]<max(mz(msidata)),$outputs.calibrants_column] + number_calibrants_in = length(calibrant_list[,$outputs.calibrants_column]) + number_calibrants_valid = length(inputcalibrants) ### Quality control report - pdf("Preprocessing.pdf", fonts = "Times", pointsize = 12) plot(0,type='n',axes=FALSE,ann=FALSE) title(main=paste("Quality control during preprocessing \n", "Filename:", "$infile.display_name")) - title(main=paste0("\n\n\n\n Number valid masses in ", "$outputs.calibrant_file.display_name",": ", number_calibrants_valid, "/", number_calibrants_in)) - - + title(main=paste0("\n\n\n\n Number valid m/z in ", "$outputs.calibrant_file.display_name",": ", number_calibrants_valid, "/", number_calibrants_in)) for (calibrant in inputcalibrants) - { - - currentimage = image(msidata , mz=calibrant, strip = strip.custom(bg="lightgrey", + {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="raw") - - assign(paste("rawdata",calibrant, sep="_"), currentimage) - } - + assign(paste("rawdata",calibrant, sep="_"), currentimage)} current_plot_raw = vector(length(inputcalibrants), mode='list') - #end if ############################### Preprocessing steps ########################### +############################################################################### + #for $method in $methods: +############################### Normalization ########################### + #if str( $method.methods_conditional.preprocessing_method ) == 'Normalization': print('Normalization') ##normalization msidata = normalize(msidata, method="tic") + ############################### optional QC ########################### + #if $outputs.outputs_select == "quality_control": + ### values for QC table: maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0)) @@ -107,316 +116,390 @@ 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", + {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) - } - + assign(paste("normalized",calibrant, sep="_"), currentimage)} #end if +############################### Baseline reduction ########################### + #elif str( $method.methods_conditional.preprocessing_method ) == 'Baseline_reduction': print('Baseline_reduction') ##baseline reduction + msidata = reduceBaseline(msidata, method="median", blocks=$method.methods_conditional.blocks_baseline) + ############################### 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) 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", + {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) - } + assign(paste("baseline_rem",calibrant, sep="_"), currentimage)} + #end if +############################### Smoothing ########################### + #elif str( $method.methods_conditional.preprocessing_method ) == 'Smoothing': print('Smoothing') ## Smoothing #if str( $method.methods_conditional.methods_for_smoothing.smoothing_method) == 'gaussian': print('gaussian smoothing') + msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, sd = $method.methods_conditional.methods_for_smoothing.sd_gaussian) + #elif str( $method.methods_conditional.methods_for_smoothing.smoothing_method) == 'sgolay': print('sgolay smoothing') + msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, order = $method.methods_conditional.methods_for_smoothing.order_of_filters) #elif str($method.methods_conditional.methods_for_smoothing.smoothing_method) == 'ma': print('sgolay smoothing') + msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, coef = $method.methods_conditional.methods_for_smoothing.coefficients_ma_filter) + #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) 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", + {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) - } + assign(paste("smoothed",calibrant, sep="_"), currentimage)} + #end if +############################### Peak picking ########################### + #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_picking': print('Peak_picking') ## Peakpicking #if str( $method.methods_conditional.methods_for_picking.picking_method) == 'adaptive': print('adaptive peakpicking') + msidata = peakPick(msidata, window = $method.methods_conditional.window_picking, blocks = $method.methods_conditional.blocks_picking, method='$method.methods_conditional.methods_for_picking.picking_method', SNR=$method.methods_conditional.SNR_picking_method, spar=$method.methods_conditional.methods_for_picking.spar_picking) #elif str( $method.methods_conditional.methods_for_picking.picking_method) == 'limpic': print('limpic peakpicking') + msidata = peakPick(msidata, window = $method.methods_conditional.window_picking, blocks = $method.methods_conditional.blocks_picking, method='$method.methods_conditional.methods_for_picking.picking_method', SNR=$method.methods_conditional.SNR_picking_method, thresh=$method.methods_conditional.methods_for_picking.tresh_picking) #elif str( $method.methods_conditional.methods_for_picking.picking_method) == 'simple': print('simple peakpicking') + msidata = peakPick(msidata, window = $method.methods_conditional.window_picking, blocks = $method.methods_conditional.blocks_picking, method='$method.methods_conditional.methods_for_picking.picking_method', SNR=$method.methods_conditional.SNR_picking_method) + #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) 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", + {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) - } + assign(paste("picked",calibrant, sep="_"), currentimage)} + #end if +############################### Peak alignment ########################### + #elif str( $method.methods_conditional.preprocessing_method ) == 'Peak_alignment': print('Peak_alignment') ## Peakalignment #if str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_noref': + align_peak_reference = msidata #elif str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_table': + align_reference_table = read.delim("$method.methods_conditional.align_ref_type.align_peaks_table", header = FALSE, stringsAsFactors = FALSE) - align_reference_column = align_reference_table[,$method.methods_conditional.align_ref_type.align_mass_column] - align_peak_reference = align_reference_column[align_reference_column>=min(mz(msidata)) & align_reference_column<=max(mz(msidata))] if (length(align_peak_reference) == 0) - {align_peak_reference = 0 - } - - - -#### if length <0: align_peak_reference = 0 + {align_peak_reference = 0} + + #elif str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_msidata_ref': - - #elif str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_msidata_ref': - loadRData <- function(fileName){ - #loads an RData file, and returns it - load(fileName) - get(ls()[ls() != "fileName"]) - } align_peak_reference = loadRData('$method.methods_conditional.align_ref_type.align_peaks_msidata') + #end if #if str( $method.methods_conditional.methods_for_alignment.alignment_method) == 'diff': print('diff peakalignment') + msidata = peakAlign(msidata, method='$method.methods_conditional.methods_for_alignment.alignment_method',diff.max =$method.methods_conditional.methods_for_alignment.value_diffalignment, units = "$method.methods_conditional.methods_for_alignment.units_diffalignment", ref=align_peak_reference) #elif str( $method.methods_conditional.methods_for_alignment.alignment_method) == 'DP': print('DPpeakalignment') + msidata = peakAlign(msidata, method='$method.methods_conditional.methods_for_alignment.alignment_method',gap = $method.methods_conditional.methods_for_alignment.gap_DPalignment, ref=align_peak_reference) + #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) 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", + {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) - } + assign(paste("aligned",calibrant, sep="_"), currentimage)} + #end if +############################### Peak filtering ########################### + #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_filtering': print('Peak_filtering') + msidata = peakFilter(msidata, method='freq', freq.min = $method.methods_conditional.frequ_filtering) + ############################### 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) 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", + {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) - } + assign(paste("filtered",calibrant, sep="_"), currentimage)} + #end if +############################### Data reduction ########################### + #elif str( $method.methods_conditional.preprocessing_method) == 'Data_reduction': print('Data_reduction') #if str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'bin': print('bin reduction') + msidata = reduceDimension(msidata, method="bin", width=$method.methods_conditional.methods_for_reduction.bin_width, units="$method.methods_conditional.methods_for_reduction.bin_units", fun=$method.methods_conditional.methods_for_reduction.bin_fun) #elif str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'resample': print('resample reduction') + msidata = reduceDimension(msidata, method="resample", step=$method.methods_conditional.methods_for_reduction.resample_step) #elif str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'peaks': print('peaks reduction') #if str( $method.methods_conditional.methods_for_reduction.ref_type.reference_datatype) == 'table': + reference_table = read.delim("$method.methods_conditional.methods_for_reduction.ref_type.peaks_table", header = FALSE, stringsAsFactors = FALSE) reference_column = reference_table[,$method.methods_conditional.methods_for_reduction.ref_type.mass_column] peak_reference = reference_column[reference_column>min(mz(msidata)) & reference_column<max(mz(msidata))] #elif str( $method.methods_conditional.methods_for_reduction.ref_type.reference_datatype) == 'msidata_ref': - loadRData <- function(fileName){ - #loads an RData file, and returns it - load(fileName) - get(ls()[ls() != "fileName"]) - } + peak_reference = loadRData('$method.methods_conditional.methods_for_reduction.ref_type.peaks_msidata') + #end if + msidata = reduceDimension(msidata, method="peaks", ref=peak_reference, type="$method.methods_conditional.methods_for_reduction.peaks_type") + #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) 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", + {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) - } + assign(paste("reduced",calibrant, sep="_"), currentimage)} + #end if - #elif str( $method.methods_conditional.preprocessing_method) == 'Transformation': - print('Transformation') + ############################### Transformation ########################### + + ####elif str( $method.methods_conditional.preprocessing_method) == 'Transformation': + ###print('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)) + + ####elif str( $method.methods_conditional.transf_conditional.trans_type) == 'sqrt': + ###print('squareroot transformation') + + ###spectra(msidata) = sqrt(spectra(msidata)) + + ###end if + + ############################### optional QC ########################### + + #if $outputs.outputs_select == "quality_control": + + ### 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) - #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)) + ### 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": - #elif str( $method.methods_conditional.transf_conditional.trans_type) == 'sqrt': - print('squareroot transformation') - spectra(msidata) = sqrt(spectra(msidata)) + ### 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) - #end if + ### 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 = $outputs.plusminus_dalton, main="sample normalized") + assign(paste("sample_norm",calibrant, sep="_"), currentimage)} + + #end if + #end if #end for ###################### Outputs: RData, tabular and QC report ################### - +############################################################################### ## save as (.RData) save(msidata, file="$msidata_preprocessed") +print(paste0("Number of NAs in intensity matrix: ", sum(is.na(spectra(msidata))))) +## save output matrix #if $output_matrix: if (length(features(msidata))> 0) - { - + { ## save as intensity matrix - spectramatrix = spectra(msidata) rownames(spectramatrix) = mz(msidata) newmatrix = rbind(pixels(msidata), spectramatrix) write.table(newmatrix[2:nrow(newmatrix),], file="$matrixasoutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") - }else{ - print("file has no features left") - write.table(matrix(rownames(coord(msidata)), ncol=ncol(msidata), nrow=1), file="$matrixasoutput", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t") - } + }else{ + print("file has no features left") + write.table(matrix(rownames(coord(msidata)), ncol=ncol(msidata), nrow=1), file="$matrixasoutput", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t") + } #end if - +## save QC report #if $outputs.outputs_select == "quality_control": rownames(QC_numbers) = c("# features", "median # peaks", "median intensity", "median TIC") grid.table(t(QC_numbers)) for (calibrant in inputcalibrants) - { - imagelist = list() - + {imagelist = list() for (numberprepro in 1:length(vectorofactions)){ - - imagelist[[numberprepro]] = get(paste(vectorofactions[numberprepro],calibrant, sep="_")) - - - } - - do.call(grid.arrange,imagelist) - } - + imagelist[[numberprepro]] = get(paste(vectorofactions[numberprepro],calibrant, sep="_"))} + do.call(grid.arrange,imagelist)} dev.off() @@ -439,7 +522,8 @@ <option value="Peak_alignment">Peak alignment</option> <option value="Peak_filtering">Peak filtering</option> <option value="Data_reduction">Data reduction</option> - <option value="Transformation">Transformation</option> + <!--option value="Transformation">Transformation</option--> + </param> <when value="Normalization"/> <when value="Baseline_reduction"> @@ -506,7 +590,7 @@ <param name="value_diffalignment" type="integer" value="200" label="diff.max" help="Peaks that differ less than this value will be aligned together"/> <param name="units_diffalignment" type="select" display = "radio" optional = "False" - label="units" help="The coefficients for the moving average filter"> + label="units"> <option value="ppm" selected="True">ppm</option> <option value="Da">Da</option> </param> @@ -525,8 +609,8 @@ <when value="align_noref"/> <when value="align_table"> <param name="align_peaks_table" type="data" format="tabular" - label="Reference mz values to use for alignment - only these will be kept" help="One column with mz values (without empty cells or letters)"/> - <param name="align_mass_column" data_ref="align_peaks_table" label="Column with reference mz" type="data_column"/> + label="Reference m/z values to use for alignment - only these will be kept" help="One column with m/z values (without empty cells or letters)"/> + <param name="align_mass_column" data_ref="align_peaks_table" label="Column with reference m/z" type="data_column"/> </when> <when value="align_msidata_ref"> <param name="align_peaks_msidata" type="data" format="rdata," label="Picked and aligned Cardinal MSImageSet saved as RData"/> @@ -546,7 +630,7 @@ </param> <when value="bin"> <param name="bin_width" type="float" value="1" - label="The width of a bin in mz or ppm" help="Width must be greater than range of mz values divided by number of mz features"/> + label="The width of a bin in m/z or ppm" help="Width must be greater than range of m/z values divided by number of m/z features"/> <param name="bin_units" type="select" display="radio" label="Unit for bin"> <option value="mz" selected="True">mz</option> @@ -560,7 +644,7 @@ </when> <when value="resample"> <param name="resample_step" type="float" value="1" - label="The step size in mz" help="Step size must be greater than range of mz values divided by number of mz features"/> + label="The step size in m/z" help="Step size must be greater than range of m/z values divided by number of m/z features"/> </when> <when value="peaks"> <param name="peaks_type" type="select" display="radio" @@ -575,8 +659,8 @@ </param> <when value="table"> <param name="peaks_table" type="data" format="tabular" - label="Reference mz values to use to reduce the dimension" help="One column with mz values (without empty cells or letters, mz outside mz range are not used for filtering)"/> - <param name="mass_column" data_ref="peaks_table" label="Column with reference mz" type="data_column"/> + label="Reference m/z values to use to reduce the dimension" help="One column with m/z values (without empty cells or letters, m/z outside m/z range are not used for filtering)"/> + <param name="mass_column" data_ref="peaks_table" label="Column with reference m/z" type="data_column"/> </when> <when value="msidata_ref"> <param name="peaks_msidata" type="data" format="rdata," label="Picked and aligned Cardinal MSImageSet saved as RData"/> @@ -585,7 +669,7 @@ </when> </conditional> </when> - <when value="Transformation"> + <!--when value="Transformation"> <conditional name="transf_conditional"> <param name="trans_type" type="select" label="Choose which intensity transformation you want to apply" help="logarithm base 2 (log2) or squareroot (sqrt)"> <option value="log2" selected="True">log2</option> @@ -594,7 +678,7 @@ <when value="log2"/> <when value="sqrt"/> </conditional> - </when> + </when--> </conditional> </repeat> <conditional name="outputs"> @@ -604,21 +688,21 @@ </param> <when value="quality_control"> <param name="calibrant_file" type="data" format="tabular" - label="Provide a list of masses which will be plotted in the quality control report" - help="Use internal calibrant masses"/> - <param name="calibrants_column" data_ref="calibrant_file" label="Column with masses" type="data_column"/> - <param name="plusminus_dalton" value="0.25" type="text" label="Mass range" help="Plusminus mass window in Dalton"/> + label="Provide a list of m/z, which will be plotted in the quality control report" + help="Use internal calibrant m/z"/> + <param name="calibrants_column" data_ref="calibrant_file" label="Column with m/z" type="data_column"/> + <param name="plusminus_dalton" value="0.25" type="text" label="M/z range" help="Plusminus m/z window in Dalton"/> </when> <when value="no_quality_control"/> </conditional> <param name="output_matrix" type="boolean" display="radio" label="Intensity matrix output"/> </inputs> <outputs> - <data format="rdata" name="msidata_preprocessed" label="Preprocessed ${on_string}"/> - <data format="pdf" name="QC_plots" from_work_dir="Preprocessing.pdf" label = "QC preprocessing report on ${on_string}"> + <data format="rdata" name="msidata_preprocessed" label="$infile.display_name preprocessed"/> + <data format="pdf" name="QC_plots" from_work_dir="Preprocessing.pdf" label = "$infile.display_name preprocessed_QC"> <filter>outputs["outputs_select"] == "quality_control"</filter> </data> - <data format="tabular" name="matrixasoutput" label="Intensity matrix ${on_string}"> + <data format="tabular" name="matrixasoutput" label="$infile.display_name preprocessed_matrix"> <filter>output_matrix</filter> </data> </outputs> @@ -665,28 +749,29 @@ <param name="frequ_filtering" value="2"/> </conditional> </repeat> - <repeat name="methods"> + <!--repeat name="methods"> <conditional name="methods_conditional"> <param name="preprocessing_method" value="Transformation"/> <conditional name="transf_conditional"> <param name="trans_type" value="sqrt"/> </conditional> </conditional> - </repeat> + </repeat--> <param name="outputs_select" value="no_quality_control"/> <param name="output_matrix" value="True"/> <output name="msidata_preprocessed" file="preprocessing_results1.RData" compare="sim_size"/> <output name="matrixasoutput" file="preprocessing_results1.txt"/> </test> <test expect_num_outputs="3"> - <param name="infile" value="example_continous.RData" ftype="rdata"/> + <param name="infile" value="preprocessed.RData" ftype="rdata"/> <repeat name="methods"> <conditional name="methods_conditional"> <param name="preprocessing_method" value="Peak_picking"/> <param name="blocks_picking" value="3"/> <param name="window_picking" value="5"/> + <param name="SNR_picking_method" value="2"/> <conditional name="methods_for_picking"> - <param name="picking_method" value="simple"/> + <param name="picking_method" value="adaptive"/> </conditional> </conditional> </repeat> @@ -699,7 +784,7 @@ </conditional> </repeat> <param name="outputs_select" value="quality_control"/> - <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile2.tabular"/> + <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile1.tabular"/> <param name="calibrants_column" value="1"/> <param name="plusminus_dalton" value="0.25"/> <param name="output_matrix" value="True"/> @@ -723,9 +808,7 @@ <param name="preprocessing_method" value="Peak_picking"/> <param name="blocks_picking" value="100"/> <param name="window_picking" value="5"/> - <conditional name="methods_for_picking"> <param name="picking_method" value="limpic"/> - </conditional> </conditional> </repeat> <repeat name="methods"> @@ -779,14 +862,6 @@ </conditional> </conditional> </repeat> - <repeat name="methods"> - <conditional name="methods_conditional"> - <param name="preprocessing_method" value="Transformation"/> - <conditional name="transf_conditional"> - <param name="trans_type" value="log2"/> - </conditional> - </conditional> - </repeat> <param name="outputs_select" value="quality_control"/> <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile1.tabular"/> <param name="calibrants_column" value="1"/> @@ -802,7 +877,7 @@ Cardinal is an R package that implements statistical & computational tools for analyzing mass spectrometry imaging datasets. `More information on Cardinal <http://cardinalmsi.org//>`_ -This tool provides provides multiple Cardinal functions to preprocess mass-spectrometry imaging data. +This tool provides provides multiple Cardinal functions to preprocess mass spectrometry imaging data. Input data: 3 types of input data can be used: @@ -814,18 +889,18 @@ - Normalization: Normalization of intensities to total ion current (TIC) - Baseline reduction: Baseline reduction removes backgroundintensity generated by chemical noise (common in MALDI datasets) -- Smoothening: Smoothing of the mass peaks reduces noise and improves peak detection +- Smoothening: Smoothing of the peaks reduces noise and improves peak detection - Peak picking: relevant peaks are picked while noise-peaks are removed (needs peak alignment afterwards) -- Peak alignment: only possible after peak picking, mz inaccuracies are removed by alignment of same peaks to a common mz value +- Peak alignment: only possible after peak picking, m/z inaccuracies are removed by alignment of same peaks to a common m/z value - Peak filtering: works only on centroided data (after peak picking and alignment or data reduction with peak filtering), removes peaks that occur only in a small proportion of pixels. If not sure which cutoff to chose run qualitycontrol first and decide according to the zero value plot. - Data reduction: binning, resampling or peak filtering to reduce data -- Transformation: log2 or squareroot transformation of all intensities + Output: - imzML file, preprocessed -- optional: pdf with heatmap of masses of interest after each preprocessing step -- optional: intensity matrix as tabular file (intensities for masses in rows and pixel in columns) +- optional: pdf with heatmap of m/z of interest after each preprocessing step +- optional: intensity matrix as tabular file (intensities for m/z in rows and pixel in columns) Tip:
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