Mercurial > repos > galaxyp > msi_preprocessing
changeset 2:50ba097d03e0 draft
planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_preprocessing commit 06c2b45d8644b1d7fc01622a5c59dcbf8886d0f1
| author | galaxyp |
|---|---|
| date | Mon, 23 Apr 2018 17:19:16 -0400 |
| parents | c4abf42419c2 |
| children | b9523950e79d |
| files | msi_preprocessing.xml test-data/inputcalibrantfile1.tabular test-data/inputcalibrantfile2.tabular test-data/preprocessing_results1.RData test-data/preprocessing_results1.csv test-data/preprocessing_results1.txt test-data/preprocessing_results2.RData test-data/preprocessing_results2.csv test-data/preprocessing_results2.pdf test-data/preprocessing_results2.txt test-data/preprocessing_results3.RData test-data/preprocessing_results3.csv test-data/preprocessing_results3.pdf test-data/preprocessing_results4.RData test-data/preprocessing_results4.txt test-data/preprocessing_results5.RData test-data/preprocessing_results5.pdf test-data/preprocessing_results5.txt |
| diffstat | 18 files changed, 2408 insertions(+), 17099 deletions(-) [+] |
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--- a/msi_preprocessing.xml Fri Nov 24 18:08:19 2017 -0500 +++ b/msi_preprocessing.xml Mon Apr 23 17:19:16 2018 -0400 @@ -1,9 +1,11 @@ -<tool id="mass_spectrometry_imaging_preprocessing" name="MSI preprocessing" version="1.7.0.1"> +<tool id="mass_spectrometry_imaging_preprocessing" name="MSI preprocessing" version="1.7.0.2"> <description> mass spectrometry imaging preprocessing </description> <requirements> <requirement type="package" version="1.7.0">bioconductor-cardinal</requirement> + <requirement type="package" version="2.2.1">r-gridextra</requirement> + <requirement type="package" version="0.20-35">r-lattice</requirement> </requirements> <command detect_errors="exit_code"> <![CDATA[ @@ -26,6 +28,8 @@ <configfiles> <configfile name="cardinal_preprocessing"><![CDATA[ library(Cardinal) +library(gridExtra) +library(lattice) #if $infile.ext == 'imzml' msidata <- readMSIData('infile.imzML') @@ -36,19 +40,111 @@ #end if -maxpixel = length(pixels(msidata)) -pixelnumber = c(1:maxpixel) +##################################### Preparations for QC report ####################################################################################### + +#if $outputs.outputs_select == "quality_control": + + ### values for QC table: + maxfeatures = length(features(msidata)) + medianpeaks = median(colSums(spectra(msidata)[]>0)) + medint = round(median(spectra(msidata)[]), digits=2) + TICs = round(mean(colSums(spectra(msidata)[])), digits=1) + QC_numbers= data.frame(rawdata = c(maxfeatures, medianpeaks, medint, TICs)) + vectorofactions = "rawdata" + + + ### Read tabular file with calibrant masses: + calibrant_list = read.delim("$outputs.calibrant_file", header = FALSE, na.strings=c("","NA"), 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) + + + + + ### 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)) + + + + 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="raw") + + assign(paste("rawdata",calibrant, sep="_"), currentimage) + } + + + current_plot_raw <- vector(length(inputcalibrants), mode='list') + + +#end if + +################################################### Preprocessing steps ####################################################################################### #for $method in $methods: #if str( $method.methods_conditional.preprocessing_method ) == 'Normalization': print('Normalization') ##normalization - msidata <- normalize(msidata, pixel=pixelnumber, method="tic") + + msidata <- normalize(msidata, method="tic") + + #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) + 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 #elif str( $method.methods_conditional.preprocessing_method ) == 'Baseline_reduction': print('Baseline_reduction') ##baseline reduction - msidata = reduceBaseline(msidata, pixel=pixelnumber, method="median", blocks=$method.methods_conditional.blocks_baseline) + msidata = reduceBaseline(msidata, method="median", blocks=$method.methods_conditional.blocks_baseline) + + #if $outputs.outputs_select == "quality_control": + 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 #elif str( $method.methods_conditional.preprocessing_method ) == 'Smoothing': print('Smoothing') @@ -65,6 +161,25 @@ msidata <- smoothSignal(msidata, method="$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 + #if $outputs.outputs_select == "quality_control": + 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 #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_picking': print('Peak_picking') @@ -72,62 +187,231 @@ #if str( $method.methods_conditional.methods_for_picking.picking_method) == 'adaptive': print('adaptive peakpicking') - msidata = peakPick(msidata, pixel=pixelnumber, 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) + 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, pixel=pixelnumber, 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) + 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, pixel=pixelnumber, 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) + 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 + #if $outputs.outputs_select == "quality_control": + 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 #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, na.strings=c("","NA"), 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 + + + #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( $method.methods_conditional.methods_for_alignment.alignment_method) == 'diff': - print('diff peaklignment') - msidata = peakAlign(msidata, method='$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") + 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( $method.methods_conditional.methods_for_alignment.alignment_method) == 'DP': - print('DPpeaklignment') - msidata = peakAlign(msidata, method='$method.methods_conditional.methods_for_alignment.alignment_method',gap = $method.methods_conditional.methods_for_alignment.gap_DPalignment ) + print('DPpeakalignment') + msidata = peakAlign(msidata, method='$method.methods_conditional.methods_for_alignment.alignment_method',gap = $method.methods_conditional.methods_for_alignment.gap_DPalignment, ref=align_peak_reference) #end if + #if $outputs.outputs_select == "quality_control": + 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 + #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_filtering': print('Peak_filtering') msidata = peakFilter(msidata, method='freq', freq.min = $method.methods_conditional.frequ_filtering) + + #if $outputs.outputs_select == "quality_control": + 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 + + #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, na.strings=c("","NA"), 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 + + #if $outputs.outputs_select == "quality_control": + 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 = $outputs.plusminus_dalton, main="reduced") + + assign(paste("reduced",calibrant, sep="_"), currentimage) + } + #end if #end if #end for +####################################################### Outputs: RData, tabular and QC report ###################################################### -## save as as (.RData) + +## save as (.RData) save(msidata, file="$msidata_preprocessed") -if (length(features(msidata))> 0) -{ + +#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="$matrixasoutput", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t") + } + +#end if + -## save as intensity matrix -spectramatrix = spectra(msidata) -rownames(spectramatrix) = mz(msidata) -newmatrix = rbind(pixels(msidata), spectramatrix) -write.table(newmatrix[2:nrow(newmatrix),], file="$matrixasoutput", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t") +#if $outputs.outputs_select == "quality_control": + + rownames(QC_numbers) = c("# features", "median # peaks", "median intensity", "median TIC") + grid.table(t(QC_numbers)) -}else{ - print("file has no features left") -write.table(matrix(rownames(coord(msidata)), ncol=ncol(msidata), nrow=1), file="$matrixasoutput", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t") -} + 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 ]]></configfile> </configfiles> <inputs> <param name="infile" type="data" format="imzml,rdata,analyze75" - label="MSI rawdata as imzml or Cardinal MSImageSet saved as RData" + label="MSI rawdata as imzml, analyze7.5 or Cardinal MSImageSet saved as RData" help="load imzml and ibd file by uploading composite datatype imzml"/> <repeat name="methods" title="Preprocessing" min="1" max="50"> <conditional name="methods_conditional"> @@ -138,11 +422,13 @@ <option value="Peak_picking">Peak picking</option> <option value="Peak_alignment">Peak alignment</option> <option value="Peak_filtering">Peak filtering</option> + <option value="Data_reduction">Data reduction</option> </param> + <when value="Normalization"/> <when value="Baseline_reduction"> <param name="blocks_baseline" type="integer" value="50" - label="blocks"/> + label="Blocks"/> </when> <when value="Smoothing"> <conditional name="methods_for_smoothing"> @@ -171,25 +457,25 @@ <param name="SNR_picking_method" type="integer" value="3" label="Signal to noise ratio" help="The minimal signal to noise ratio for peaks to be considered as a valid peak."/> - <param name="blocks_picking" type="integer" value="100" label = "number of blocks" + <param name="blocks_picking" type="integer" value="100" label = "Number of blocks" help="Number of blocks in which to divide mass spectrum to calculate noise"/> - <param name="window_picking" type="integer" value="5" label= "window size" help="Window width for seeking local maxima"/> + <param name="window_picking" type="integer" value="5" label= "Window size" help="Window width for seeking local maxima"/> <conditional name="methods_for_picking"> - <param name="picking_method" type="select" label="Peak picking method" help = "only simple works for processed imzML files"> + <param name="picking_method" type="select" label="Peak picking method" help="only simple works for processed imzML files"> <option value="adaptive" selected="True">adaptive</option> <option value="limpic">limpic</option> <option value="simple">simple</option> </param> <when value="adaptive"> <param name="spar_picking" type="float" value="1.0" - label="spar value" + label="Spar value" help = "Smoothing parameter for the spline smoothing applied to the spectrum in order to decide the cutoffs for throwing away false noise spikes that might occur inside peaks"/> </when> <when value="limpic"> <param name="tresh_picking" type="float" value="0.75" - label="thresh value" help = "The thresholding quantile to use when comparing slopes in order to throw away peaks that are too flat"/> + label="thresh value" help="The thresholding quantile to use when comparing slopes in order to throw away peaks that are too flat"/> </when> <when value="simple"/> </conditional> @@ -204,27 +490,111 @@ <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" help="The coefficients for the moving average filter"> <option value="ppm" selected="True">ppm</option> <option value="Da">Da</option> </param> </when> <when value="DP"> <param name="gap_DPalignment" type="integer" value="0" - label="gap" help = "The gap penalty for the dynamic programming sequence alignment"/> + label="Gap" help="The gap penalty for the dynamic programming sequence alignment"/> + </when> + </conditional> + <conditional name="align_ref_type"> + <param name="align_reference_datatype" type="select" label="Choose reference"> + <option value="align_noref" selected="True">no reference</option> + <option value="align_table" >tabular file as reference</option> + <option value="align_msidata_ref">msidata file as reference</option> + </param> + <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"/> + </when> + <when value="align_msidata_ref"> + <param name="align_peaks_msidata" type="data" format="rdata," label="Picked and aligned Cardinal MSImageSet saved as RData"/> </when> </conditional> </when> <when value="Peak_filtering"> - <param name="frequ_filtering" type="float" value="1" - label="freq.min" help = "Peaks that occur in the dataset fewer times than this will be removed"/> + <param name="frequ_filtering" type="integer" value="1000" + label="Freq.min" help="Peaks that occur in the dataset fewer times than this will be removed. Number should be between 1 (no filtering) and number of spectra (pixel)"/> + </when> + <when value="Data_reduction"> + <conditional name="methods_for_reduction"> + <param name="reduction_method" type="select" label="Reduction method"> + <option value="bin" selected="True">bin</option> + <option value="resample">resample</option> + <option value="peaks">peaks</option> + </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/Number of mz features"/> + <param name="bin_units" type="select" display="radio" + label="Unit for bin"> + <option value="mz" selected="True">mz</option> + <option value="ppm">ppm</option> + </param> + <param name="bin_fun" type="select" display="radio" + label="Calculate sum or mean intensity for ions of the same bin"> + <option value="mean" selected="True">mean</option> + <option value="sum">sum</option> + </param> + </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/Number of mz features"/> + </when> + <when value="peaks"> + <param name="peaks_type" type="select" display="radio" + label="Should the peak height or area under the curve be taken as the intensity value?"> + <option value="height" selected="True">height</option> + <option value="area">area</option> + </param> + <conditional name="ref_type"> + <param name="reference_datatype" type="select" label="Choose reference datatype"> + <option value="table" selected="True">tabular file</option> + <option value="msidata_ref">msidata file</option> + </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"/> + </when> + <when value="msidata_ref"> + <param name="peaks_msidata" type="data" format="rdata," label="Picked and aligned Cardinal MSImageSet saved as RData"/> + </when> + </conditional> + </when> + </conditional> </when> </conditional> </repeat> + <conditional name="outputs"> + <param name="outputs_select" type="select" label="Quality control output"> + <option value="quality_control" selected="True">yes</option> + <option value="no_quality_control">no</option> + </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"/> + </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="${tool.name} on $infile.display_name"/> - <data format="tabular" name="matrixasoutput" label="${tool.name} on $infile.display_name" /> + <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}"> + <filter>outputs["outputs_select"] == "quality_control"</filter> + </data> + <data format="tabular" name="matrixasoutput" label="Intensity matrix ${on_string}"> + <filter>output_matrix</filter> + </data> </outputs> <tests> <test> @@ -239,12 +609,6 @@ </repeat> <repeat name="methods"> <conditional name="methods_conditional"> - <param name="preprocessing_method" value="Baseline_reduction" /> - <param name="blocks_baseline" value="100" /> - </conditional> - </repeat> - <repeat name="methods"> - <conditional name="methods_conditional"> <param name="preprocessing_method" value="Smoothing" /> <conditional name="methods_for_smoothing"> <param name="smoothing_method" value="gaussian" /> @@ -269,22 +633,19 @@ </conditional> </conditional> </repeat> - <!--repeat name="methods"> + <repeat name="methods"> <conditional name="methods_conditional"> <param name="preprocessing_method" value="Peak_filtering" /> + <param name="frequ_filtering" value="2"/> </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> <param name="infile" value="example_continous.RData" ftype="rdata"/> - <!--repeat name="methods"> - <conditional name="methods_conditional"> - <param name="preprocessing_method" value="Baseline_reduction" /> - <param name="blocks_baseline" value="3" /> - </conditional> - </repeat--> <repeat name="methods"> <conditional name="methods_conditional"> <param name="preprocessing_method" value="Peak_picking" /> @@ -299,8 +660,14 @@ <param name="methods_for_alignment" value="DP" /> </conditional> </repeat> + <param name="outputs_select" value="quality_control"/> + <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile2.tabular"/> + <param name="calibrants_column" value="1"/> + <param name="plusminus_dalton" value="0.25"/> + <param name="output_matrix" value="True"/> <output name="msidata_preprocessed" file="preprocessing_results2.RData" compare="sim_size" /> <output name="matrixasoutput" file="preprocessing_results2.txt" lines_diff="2" /> + <output name="QC_plots" file="preprocessing_results2.pdf" compare="sim_size"/> </test> <test> <param name="infile" value="" ftype="analyze75"> @@ -313,12 +680,6 @@ <param name="preprocessing_method" value="Normalization" /> </conditional> </repeat> - <!--repeat name="methods"> - <conditional name="methods_conditional"> - <param name="preprocessing_method" value="Baseline_reduction" /> - <param name="blocks_baseline" value="50" /> - </conditional> - </repeat--> <repeat name="methods"> <conditional name="methods_conditional"> <param name="preprocessing_method" value="Peak_picking" /> @@ -333,10 +694,55 @@ <param name="methods_for_alignment" value="diff" /> </conditional> </repeat> + <param name="outputs_select" value="quality_control"/> + <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile2.tabular"/> + <param name="calibrants_column" value="1"/> + <param name="plusminus_dalton" value="0.25"/> <output name="msidata_preprocessed" file="preprocessing_results3.RData" compare="sim_size" /> - <output name="matrixasoutput" file="preprocessing_results3.txt" /> + <output name="QC_plots" file="preprocessing_results3.pdf" compare="sim_size"/> </test> - + <test> + <param name="infile" value="" ftype="analyze75"> + <composite_data value="Analyze75.hdr"/> + <composite_data value="Analyze75.img"/> + <composite_data value="Analyze75.t2m"/> + </param> + <repeat name="methods"> + <conditional name="methods_conditional"> + <param name="preprocessing_method" value="Normalization" /> + </conditional> + </repeat> + <repeat name="methods"> + <conditional name="methods_conditional"> + <param name="preprocessing_method" value="Data_reduction" /> + <param name="bin_width" value="0.1" /> + </conditional> + </repeat> + <param name="outputs_select" value="no_quality_control"/> + <param name="output_matrix" value="True"/> + <output name="msidata_preprocessed" file="preprocessing_results4.RData" compare="sim_size" /> + <output name="matrixasoutput" file="preprocessing_results4.txt" /> + </test> + <test> + <param name="infile" value="" ftype="imzml"> + <composite_data value="Example_Continuous.imzML"/> + <composite_data value="Example_Continuous.ibd"/> + </param> + <repeat name="methods"> + <conditional name="methods_conditional"> + <param name="preprocessing_method" value="Data_reduction" /> + <param name="step_width" value="0.1" /> + </conditional> + </repeat> + <param name="outputs_select" value="quality_control"/> + <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"/> + <output name="msidata_preprocessed" file="preprocessing_results5.RData" compare="sim_size" /> + <output name="matrixasoutput" file="preprocessing_results5.txt" /> + <output name="QC_plots" file="preprocessing_results5.pdf" compare="sim_size"/> + </test> </tests> <help> <![CDATA[ @@ -348,8 +754,9 @@ - Normalization: Normalization of intensities to total ion current (TIC) - Baseline reduction: Baseline reduction removes backgroundintensity generated by chemical noise (common in MALDI datasets) - Peak picking: relevant peaks are picked while noise-peaks are removed -- Peak alignment: mz inaccuracies are removed by alignment of same peaks to a common mz value -- Peak filtering: removes peaks that occur infrequently, such as those which only occur in a small proportion of pixels +- Peak alignment: after peak picking, mz inaccuracies are removed by alignment of same peaks to a common mz value +- Peak filtering: after peak picking and alignment, removes peaks that occur infrequently, such as those which only occur 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 ]]> </help> <citations>
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/inputcalibrantfile1.tabular Mon Apr 23 17:19:16 2018 -0400 @@ -0,0 +1,3 @@ +101.5 +356.7 +555.1
--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/inputcalibrantfile2.tabular Mon Apr 23 17:19:16 2018 -0400 @@ -0,0 +1,3 @@ +869.51 mass1 +1001.62 mass2 +1023.6 mass3
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--- a/test-data/preprocessing_results1.txt Fri Nov 24 18:08:19 2017 -0500 +++ b/test-data/preprocessing_results1.txt Mon Apr 23 17:19:16 2018 -0400 @@ -1,8394 +1,12 @@ x = 1, y = 1 x = 2, y = 1 x = 3, y = 1 x = 1, y = 2 x = 2, y = 2 x = 3, y = 2 x = 1, y = 3 x = 2, y = 3 x = 3, y = 3 -100.25 0 0 0 0 0 0 0 0 0 -100.333335876465 0 0 0 0 0 0 0 0 0 -100.416664123535 0 0 0 0 0 0 0 0 0 -100.5 0 0 0 0 0 0 0 0 0 -100.583335876465 0 0 0 0 0 0 0 0 0 -100.666664123535 0 0 0 0 0 0 0 0 0 -100.75 0 0 0 0 0 0 0 0 0 -100.833335876465 0 0 0 0 0 0 0 0 0 -100.916664123535 0 0 0 0 0 0 0 0 0 -101 0 0 0 0 0 0 0 0 0 -101.083335876465 0 0 0 0 0 0 0 0 0 -101.166664123535 0 0 0 0 0 0 0 0 0 -101.25 0 0 0 0 0 0 0 0 0 -101.333335876465 0 0 0 0 0 0 0 0 0 -101.416664123535 0 0 0 0 0 0 0 0 0 -101.5 0 0 0 0 0 0 0 0 0 -101.583335876465 0 0 0 0 0 0 0 0 0 -101.666664123535 0 0 0 0 0 0 0 0 0 -101.75 0 0 0 0 0 0 0 0 0 -101.833335876465 0 0 0 0 0 0 0 0 0 -101.916664123535 0 0 0 0 0 0 0 0 0 -102 0 0 0 0 0 0 0 0 0 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