msi_preprocessing: msi_preprocessing.xml comparison

comparison msi_preprocessing.xml @ 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

comparison

equal deleted inserted replaced

-:b9523950e79d
+:42580292d381
-<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>
 <requirements>
 <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[
 #if $infile.ext == 'imzml'
 ]]>
 </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')
 #elif $infile.ext == 'analyze75'
 msidata = readAnalyze('infile')
 #else
 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 $outputs.outputs_select == "quality_control":
 ### values for QC table:
 maxfeatures = length(features(msidata))
 medianpeaks = median(colSums(spectra(msidata)[]>0))
 medint = round(median(spectra(msidata)[]), digits=2)
 TICs = round(mean(colSums(spectra(msidata)[])), digits=1)
 QC_numbers= data.frame(rawdata = c(maxfeatures, medianpeaks, medint, TICs))
 vectorofactions = "rawdata"
+### Read tabular file with calibrant m/z:
-### Read tabular file with calibrant masses:
 calibrant_list = read.delim("$outputs.calibrant_file", header = FALSE, stringsAsFactors = FALSE)
-### calculate how many input calibrant masses are valid:
+### 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])
+inputcalibrants = calibrant_list[calibrant_list[,$outputs.calibrants_column]>min(mz(msidata)) & calibrant_list[,$outputs.calibrants_column]<max(mz(msidata)),$outputs.calibrants_column]
-number_calibrants_valid = length(inputcalibrants)
+number_calibrants_in = length(calibrant_list[,$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))
 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",
-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 ###########################
-#end if
 #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
-#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
+#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
-#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
+#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
-#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
+{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( $method.methods_conditional.methods_for_alignment.alignment_method) == 'diff':
 print('diff peakalignment')
 msidata = peakAlign(msidata, method='$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
-#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
-#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
+#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
-#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':
-spectra(msidata)[spectra(msidata) ==0] = NA
+####print('log2 transformation')
-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)))))
-#elif str( $method.methods_conditional.transf_conditional.trans_type) == 'sqrt':
+###spectra(msidata) = log2(spectra(msidata))
-print('squareroot transformation')
-spectra(msidata) = sqrt(spectra(msidata))
+####elif str( $method.methods_conditional.transf_conditional.trans_type) == 'sqrt':
+###print('squareroot transformation')
-#end if
+###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)
+### 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 = $outputs.plusminus_dalton, main="sample normalized")
+assign(paste("sample_norm",calibrant, sep="_"), currentimage)}
+#end if
 #end if
 #end for
 ###################### Outputs: RData, tabular and QC report ###################
+###############################################################################
 ## save as (.RData)
 save(msidata, file="$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")
 }
 #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="_"))}
-imagelist[[numberprepro]] = get(paste(vectorofactions[numberprepro],calibrant, sep="_"))
+do.call(grid.arrange,imagelist)}
-}
-do.call(grid.arrange,imagelist)
-}
 dev.off()
 #end if
 <option value="Smoothing">Peak smoothing</option>
 <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>
-<option value="Transformation">Transformation</option>
+<!--option value="Transformation">Transformation</option-->
 </param>
 <when value="Normalization"/>
 <when value="Baseline_reduction">
 <param name="blocks_baseline" type="integer" value="50"
 label="Blocks"/>
 </param>
 <when value="diff">
 <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>
 </when>
 <when value="DP">
 <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)"/>
+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 mz" type="data_column"/>
+<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"/>
 </when>
 </conditional>
 <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 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>
 <option value="ppm">ppm</option>
 </param>
 <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 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"
 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="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)"/>
+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 mz" type="data_column"/>
+<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"/>
 </when>
 </conditional>
 </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>
 <option value="sqrt">sqrt</option>
 </param>
 <when value="log2"/>
 <when value="sqrt"/>
 </conditional>
-</when>
+</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"
+label="Provide a list of m/z, which will be plotted in the quality control report"
-help="Use internal calibrant masses"/>
+help="Use internal calibrant m/z"/>
-<param name="calibrants_column" data_ref="calibrant_file" label="Column with masses" type="data_column"/>
+<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="Mass range" help="Plusminus mass window in Dalton"/>
+<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 ${on_string}"/>
+<data format="rdata" name="msidata_preprocessed" label="$infile.display_name preprocessed"/>
-<data format="pdf" name="QC_plots" from_work_dir="Preprocessing.pdf" label = "QC preprocessing report on ${on_string}">
+<data format="pdf" name="QC_plots" from_work_dir="Preprocessing.pdf" label = "$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>
 <tests>
 <test expect_num_outputs="2">
 <conditional name="methods_conditional">
 <param name="preprocessing_method" value="Peak_filtering"/>
 <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>
 <repeat name="methods">
 <conditional name="methods_conditional">
 <param name="alignment_method" value="DP"/>
 </conditional>
 </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"/>
 <output name="msidata_preprocessed" file="preprocessing_results2.RData" compare="sim_size"/>
 <output name="matrixasoutput" file="preprocessing_results2.txt" lines_diff="2"/>
 <repeat name="methods">
 <conditional name="methods_conditional">
 <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">
 <conditional name="methods_conditional">
 <param name="preprocessing_method" value="Peak_alignment"/>
 <param name="reduction_method" value="resample"/>
 <param name="step_width" value="0.1"/>
 </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"/>
 <param name="plusminus_dalton" value="0.25"/>
 <param name="output_matrix" value="True"/>
 <help>
 <![CDATA[
 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:
 - imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format <https://ms-imaging.org/wp/imzml/>`_
 - Analyze7.5 (upload hdr, img and t2m file via the "composite" function)
 Options:
 - 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: pdf with heatmap of m/z of interest after each preprocessing step
-- optional: intensity matrix as tabular file (intensities for masses in rows and pixel in columns)
+- optional: intensity matrix as tabular file (intensities for m/z in rows and pixel in columns)
 Tip:
 - Peak alignment works only after peak picking
 - Peak filtering works only on centroided data (peak picking and alignment or Data reduction peaks)

Mercurial > repos > galaxyp > msi_preprocessing

comparison msi_preprocessing.xml @ 4:42580292d381 draft