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planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_preprocessing commit e87eea03505ab1ba067e192bbbcdc6197dc4b42e
| author | galaxyp |
|---|---|
| date | Tue, 04 Sep 2018 13:42:22 -0400 |
| parents | 4d5578b57a77 |
| children |
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<tool id="mass_spectrometry_imaging_preprocessing" name="MSI preprocessing" version="1.10.0.7"> <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="2.2.1">r-ggplot2</requirement> </requirements> <command detect_errors="exit_code"> <![CDATA[ #if $infile.ext == 'imzml' ln -s '${infile.extra_files_path}/imzml' infile.imzML && ln -s '${infile.extra_files_path}/ibd' infile.ibd && #elif $infile.ext == 'analyze75' ln -s '${infile.extra_files_path}/hdr' infile.hdr && ln -s '${infile.extra_files_path}/img' infile.img && ln -s '${infile.extra_files_path}/t2m' infile.t2m && #else ln -s '$infile' infile.RData && #end if cat '${cardinal_preprocessing}' && Rscript '${cardinal_preprocessing}' ]]> </command> <configfiles> <configfile name="cardinal_preprocessing"><![CDATA[ ################################# load libraries and read file ################# library(Cardinal) library(gridExtra) library(lattice) library(ggplot2) #if $infile.ext == 'imzml' #if str($processed_cond.processed_file) == "processed": msidata <- readImzML('infile', mass.accuracy=$processed_cond.accuracy, units.accuracy = "$processed_cond.units") #else msidata <- readImzML('infile', attach.only=TRUE) #end if #elif $infile.ext == 'analyze75' msidata = readAnalyze('infile', attach.only=TRUE) #else loadRData <- function(fileName){ load(fileName) get(ls()[ls() != "fileName"]) } msidata = loadRData('infile.RData') #end if print(paste0("Number of NA in input file: ",sum(is.na(spectra(msidata)[])))) ## function to later read RData reference files in loadRData <- function(fileName){ #loads an RData file, and returns it load(fileName) get(ls()[ls() != "fileName"]) } if (sum(spectra(msidata)[]>0, na.rm=TRUE)> 0){ ######################### preparations for QC report ################# maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE)) medint = round(median(spectra(msidata)[],na.rm=TRUE), digits=2) minmz = round(min(mz(msidata)), digits=2) maxmz = round(max(mz(msidata)), digits=2) QC_numbers= data.frame(inputdata = c(minmz, maxmz,maxfeatures, medianpeaks, medint)) vectorofactions = "inputdata" ############################### Preprocessing steps ########################### ############################################################################### #for $method in $methods: ############################### Normalization ########################### #if str( $method.methods_conditional.preprocessing_method ) == 'Normalization': print('Normalization') ##normalization msidata = normalize(msidata, method="tic") ############################### QC ########################### maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE),) medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2) minmz = round(min(mz(msidata)), digits=2) maxmz = round(max(mz(msidata)), digits=2) normalized = c(minmz, maxmz,maxfeatures, medianpeaks, medint) QC_numbers= cbind(QC_numbers, normalized) vectorofactions = append(vectorofactions, "normalized") ############################### 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, spar=$method.methods_conditional.spar_baseline) ############################### QC ########################### maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE)) medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2) minmz = round(min(mz(msidata)), digits=2) maxmz = round(max(mz(msidata)), digits=2) baseline = c(minmz, maxmz,maxfeatures, medianpeaks, medint) QC_numbers= cbind(QC_numbers, baseline) vectorofactions = append(vectorofactions, "baseline red.") ############################### 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('moving average 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 ############################### QC ########################### maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE)) medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2) minmz = round(min(mz(msidata)), digits=2) maxmz = round(max(mz(msidata)), digits=2) smoothed = c(minmz, maxmz,maxfeatures, medianpeaks, medint) QC_numbers= cbind(QC_numbers, smoothed) vectorofactions = append(vectorofactions, "smoothed") ############################### Peak picking ########################### #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_picking': print('Peak_picking') ## Peakpicking ## remove duplicated coordinates, otherwise peak picking will fail print(paste0(sum(duplicated(coord(msidata))), " duplicated coordinates were removed")) msidata <- msidata[,!duplicated(coord(msidata))] #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 ############################### QC ########################### maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE)) medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2) minmz = round(min(mz(msidata)), digits=2) maxmz = round(max(mz(msidata)), digits=2) picked = c(minmz, maxmz,maxfeatures, medianpeaks, medint) QC_numbers= cbind(QC_numbers, picked) vectorofactions = append(vectorofactions, "picked") ############################### 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} #elif str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_msidata_ref': align_peak_reference = loadRData('$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 ############################### QC ########################### maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE)) medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2) minmz = round(min(mz(msidata)), digits=2) maxmz = round(max(mz(msidata)), digits=2) aligned = c(minmz, maxmz,maxfeatures, medianpeaks, medint) QC_numbers= cbind(QC_numbers, aligned) vectorofactions = append(vectorofactions, "aligned") ############################### 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) ############################### QC ########################### maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE)) medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2) minmz = round(min(mz(msidata)), digits=2) maxmz = round(max(mz(msidata)), digits=2) filtered = c(minmz, maxmz,maxfeatures, medianpeaks, medint) QC_numbers= cbind(QC_numbers, filtered) vectorofactions = append(vectorofactions, "filtered") ############################### 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( $method.methods_conditional.methods_for_reduction.ref_type.reference_datatype) == 'msidata_ref': peak_reference = loadRData('$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 ############################### QC ########################### maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE)) medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2) minmz = round(min(mz(msidata)), digits=2) maxmz = round(max(mz(msidata)), digits=2) reduced = c(minmz, maxmz,maxfeatures, medianpeaks, medint) QC_numbers= cbind(QC_numbers, reduced) vectorofactions = append(vectorofactions, "reduced") ############################### 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( $method.methods_conditional.transf_conditional.trans_type) == 'sqrt': print('squareroot transformation') spectra(msidata)[] = sqrt(spectra(msidata)[]) #end if ############################### QC ########################### maxfeatures = length(features(msidata)) medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE)) medint = round(median(spectra(msidata)[], na.rm=TRUE), digits=2) minmz = round(min(mz(msidata)), digits=2) maxmz = round(max(mz(msidata)), digits=2) transformed = c(minmz, maxmz,maxfeatures, medianpeaks, medint) QC_numbers= cbind(QC_numbers, transformed) vectorofactions = append(vectorofactions, "transformed") #end if #end for ############# Outputs: summar matrix, RData, tabular and QC report ############# ################################################################################ ## optional summarized matrix print('Summarized matrix') ## optional annotation from tabular file to obtain groups over which to apply mean, median or sd (otherwise all pixels are considered to be sample) #if str($tabular_annotation.load_annotation) == 'yes_annotation': ## read and extract x,y,annotation information input_tabular = read.delim("$tabular_annotation.annotation_file", header = $tabular_annotation.tabular_header, stringsAsFactors = FALSE) annotation_input = input_tabular[,c($tabular_annotation.column_x, $tabular_annotation.column_y, $tabular_annotation.column_names)] colnames(annotation_input) = c("x", "y", "annotation") ## merge with coordinate information of msidata msidata_coordinates = cbind(coord(msidata)[,1:2], c(1:ncol(msidata))) colnames(msidata_coordinates)[3] = "pixel_index" merged_annotation = merge(msidata_coordinates, annotation_input, by=c("x", "y"), all.x=TRUE) merged_annotation[is.na(merged_annotation)] = "NA" merged_annotation = merged_annotation[order(merged_annotation\$pixel_index),] msidata\$annotation = as.factor(merged_annotation[,4]) #end if #if "mean" in str($summary_type).split(","): print("mean matrix") if (!is.null(levels(msidata\$annotation))){ sample_matrix = matrix(,ncol=0, nrow=nrow(msidata)) count = 1 for (subsample in levels(msidata\$annotation)){ subsample_pixels = msidata[,msidata\$annotation == subsample] subsample_calc = apply(spectra(subsample_pixels)[],1,mean, na.rm=TRUE) sample_matrix = cbind(sample_matrix, subsample_calc) count = count+1 } sample_matrix_mean = cbind(mz(msidata),sample_matrix) sample_matrix_mean = rbind(c("mz", levels(msidata\$annotation)), sample_matrix_mean) write.table(sample_matrix_mean, file="$summarized_mean", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t") }else{ full_sample_calc_mean = as.data.frame(apply(spectra(msidata)[],1,mean, na.rm=TRUE)) full_sample_calc_mean = cbind(mz(msidata),full_sample_calc_mean) full_sample_calc_mean = rbind(c("mz", "$infile.display_name"), full_sample_calc_mean) write.table(full_sample_calc_mean, file="$summarized_mean", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t") } #end if #if "median" in str($summary_type).split(","): print("median matrix") if (!is.null(levels(msidata\$annotation))){ sample_matrix = matrix(,ncol=0, nrow=nrow(msidata)) count = 1 for (subsample in levels(msidata\$annotation)){ subsample_pixels = msidata[,msidata\$annotation == subsample] ## no idea why it does not work??? NA problem?! subsample_calc = apply(spectra(subsample_pixels)[],1,median, na.rm=TRUE) sample_matrix = cbind(sample_matrix, subsample_calc) count = count+1 } sample_matrix_median = cbind(mz(msidata),sample_matrix) sample_matrix_median = rbind(c("mz", levels(msidata\$annotation)), sample_matrix_median) write.table(sample_matrix_median, file="$summarized_median", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t") }else{ full_sample_calc_median = as.data.frame(apply(spectra(msidata)[],1,median, na.rm=TRUE)) full_sample_calc_median = cbind(mz(msidata),full_sample_calc_median) full_sample_calc_median = rbind(c("mz", "$infile.display_name"), full_sample_calc_median) write.table(full_sample_calc_median, file="$summarized_median", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t") } #end if #if "sd" in str($summary_type).split(","): print("sd matrix") if (!is.null(levels(msidata\$annotation))){ sample_matrix = matrix(,ncol=0, nrow=nrow(msidata)) count = 1 for (subsample in levels(msidata\$annotation)){ subsample_pixels = msidata[,msidata\$annotation == subsample] subsample_calc = apply(spectra(subsample_pixels)[],1,sd, na.rm=TRUE) sample_matrix = cbind(sample_matrix, subsample_calc) count = count+1 } sample_matrix_sd = cbind(mz(msidata),sample_matrix) sample_matrix_sd = rbind(c("mz", levels(msidata\$annotation)), sample_matrix_sd) write.table(sample_matrix_sd, file="$summarized_sd", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t") }else{ full_sample_calc_sd = as.data.frame(apply(spectra(msidata)[],1,sd, na.rm=TRUE)) full_sample_calc_sd = cbind(mz(msidata),full_sample_calc_sd) full_sample_calc_sd = rbind(c("mz", "$infile.display_name"), full_sample_calc_sd) write.table(full_sample_calc_sd, file="$summarized_sd", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t") } #end if print(paste0("Number of NA in output file: ",sum(is.na(spectra(msidata)[])))) ## save as (.RData) save(msidata, file="$msidata_preprocessed") ## save output matrix #if $output_matrix: if (length(features(msidata))> 0 & length(pixels(msidata)) > 0){ spectramatrix = spectra(msidata)[] spectramatrix = cbind(mz(msidata),spectramatrix) newmatrix = rbind(c("mz", names(pixels(msidata))), spectramatrix) write.table(newmatrix, file="$intensity_matrix", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t") }else{ print("file has no features or pixels left") } #end if ## save QC 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:", "$infile.display_name")) rownames(QC_numbers) = c("min m/z", "max mz", "# features", "median\n# peaks", "median\nintensity") grid.table(t(QC_numbers)) #if str($tabular_annotation.load_annotation) == 'yes_annotation': ## the more annotation groups a file has the smaller will be the legend number_combined = length(levels(msidata\$annotation)) if (number_combined<20){ legend_size = 10 }else if (number_combined>20 && number_combined<40){ legend_size = 9 }else if (number_combined>40 && number_combined<60){ legend_size = 8 }else if (number_combined>60 && number_combined<100){ legend_size = 7 }else{ legend_size = 6 } position_df = cbind(coord(msidata)[,1:2], msidata\$annotation) colnames(position_df)[3] = "sample_name" combine_plot = ggplot(position_df, aes(x=x, y=y, fill=sample_name))+ geom_tile() + coord_fixed()+ ggtitle("Spatial orientation of annotated data")+ theme_bw()+ theme(plot.title = element_text(hjust = 0.5))+ theme(text=element_text(family="ArialMT", face="bold", size=12))+ theme(legend.position="bottom",legend.direction="vertical")+ theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = legend_size))+ guides(fill=guide_legend(ncol=5,byrow=TRUE)) coord_labels = aggregate(cbind(x,y)~sample_name, data=position_df, mean) coord_labels\$file_number = gsub( "_.*$", "", coord_labels\$sample_name) for(file_count in 1:nrow(coord_labels)) {combine_plot = combine_plot + annotate("text",x=coord_labels[file_count,"x"], y=coord_labels[file_count,"y"],label=toString(coord_labels[file_count,4]))} print(combine_plot) #end if dev.off() }else{ print("inputfile has no intensities > 0") } ]]></configfile> </configfiles> <inputs> <param name="infile" type="data" format="imzml,rdata,analyze75" label="MSI data as imzml, analyze7.5 or Cardinal MSImageSet saved as RData" help="load imzml and ibd file by uploading composite datatype imzml"/> <conditional name="processed_cond"> <param name="processed_file" type="select" label="Is the input file a processed imzML file "> <option value="no_processed" selected="True">not a processed imzML</option> <option value="processed">processed imzML</option> </param> <when value="no_processed"/> <when value="processed"> <param name="accuracy" type="float" value="50" label="Mass accuracy to which the m/z values will be binned" help="This should be set to the native accuracy of the mass spectrometer, if known"/> <param name="units" display="radio" type="select" label="Unit of the mass accuracy" help="either m/z or ppm"> <option value="mz" >mz</option> <option value="ppm" selected="True" >ppm</option> </param> </when> </conditional> <repeat name="methods" title="Preprocessing" min="1" max="50"> <conditional name="methods_conditional"> <param name="preprocessing_method" type="select" label="Preprocessing methods"> <option value="Normalization" selected="True">Normalization to TIC</option> <option value="Baseline_reduction">Baseline Reduction</option> <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> </param> <when value="Normalization"/> <when value="Baseline_reduction"> <param name="blocks_baseline" type="integer" value="500" label="Blocks"/> <param name="spar_baseline" type="float" value="1.0" 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="Smoothing"> <conditional name="methods_for_smoothing"> <param name="smoothing_method" type="select" label="Smoothing method"> <option value="gaussian" selected="True">gaussian</option> <option value="sgolay">Savitsky-Golay</option> <option value="ma">moving average</option> </param> <when value="gaussian"> <param name="sd_gaussian" type="float" value="2" label="The standard deviation for the Gaussian kernel (window/sd)"/> </when> <when value="sgolay"> <param name="order_of_filters" type="integer" value="3" label="The order of the smoothing filter, must be smaller than window size"/> </when> <when value="ma"> <param name="coefficients_ma_filter" type="float" value="1" label="The coefficients for the moving average filter"/> </when> </conditional> <param name="window_smoothing" type="float" value="8" label="Window size"/> </when> <when value="Peak_picking"> <param name="SNR_picking_method" type="integer" value="6" 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" help="Number of blocks in which to divide mass spectrum to calculate noise"/> <param name="window_picking" type="float" 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"> <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" 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"/> </when> <when value="simple"/> </conditional> </when> <when value="Peak_alignment"> <conditional name="methods_for_alignment"> <param name="alignment_method" type="select" label="Alignment method"> <option value="diff" selected="True">diff</option> <option value="DP">DP</option> </param> <when value="diff"> <param name="value_diffalignment" type="float" 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"> <option value="ppm" selected="True">ppm</option> <option value="Da">m/z</option> </param> </when> <when value="DP"> <param name="gap_DPalignment" type="float" value="0" 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 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"/> </when> </conditional> </when> <when value="Peak_filtering"> <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 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> <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 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="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 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"/> </when> </conditional> </when> </conditional> </when> <when value="Transformation"> <conditional name="transf_conditional"> <param name="trans_type" type="select" label="Intensity transformations" 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> </conditional> </repeat> <param name="summary_type" type="select" display="checkboxes" multiple="true" label="Summarize all pixels of a sample and calculate the mean, median or standard deviation"> <option value="mean">mean</option> <option value="median">median</option> <option value="sd">standard deviation</option> </param> <conditional name="tabular_annotation"> <param name="load_annotation" type="select" label="Use pixel annotations from tabular file to summarize pixel"> <option value="no_annotation" selected="True">summarize over all pixels</option> <option value="yes_annotation">summarize over categories from annotation file</option> </param> <when value="yes_annotation"> <param name="annotation_file" type="data" format="tabular" label="Use annotations from tabular file to summarize pixel" help="Tabular file with three columns: x values, y values and pixel annotations"/> <param name="column_x" data_ref="annotation_file" label="Column with x values" type="data_column"/> <param name="column_y" data_ref="annotation_file" label="Column with y values" type="data_column"/> <param name="column_names" data_ref="annotation_file" label="Column with pixel annotations" type="data_column"/> <param name="tabular_header" type="boolean" label="Tabular file contains a header line" truevalue="TRUE" falsevalue="FALSE"/> </when> <when value="no_annotation"/> </conditional> <param name="output_matrix" type="boolean" label="Intensity matrix output"/> </inputs> <outputs> <data format="rdata" name="msidata_preprocessed" label="$infile.display_name preprocessed"/> <data format="pdf" name="QC_overview" from_work_dir="Preprocessing.pdf" label = "$infile.display_name preprocessed_QC"/> <data format="tabular" name="summarized_mean" label="$infile.display_name preprocessed_mean"> <filter>summary_type and "mean" in summary_type</filter> </data> <data format="tabular" name="summarized_median" label="$infile.display_name preprocessed_median"> <filter>summary_type and "median" in summary_type</filter> </data> <data format="tabular" name="summarized_sd" label="$infile.display_name preprocessed_sd"> <filter>summary_type and "sd" in summary_type</filter> </data> <data format="tabular" name="intensity_matrix" label="$infile.display_name preprocessed_matrix"> <filter>output_matrix</filter> </data> </outputs> <tests> <test expect_num_outputs="3"> <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="Normalization"/> </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"/> <param name="sd_gaussian" value="4"/> </conditional> <param name="window_smoothing" value="9"/> </conditional> </repeat> <repeat name="methods"> <conditional name="methods_conditional"> <param name="preprocessing_method" value="Peak_picking"/> <conditional name="methods_for_picking"> <param name="picking_method" value="adaptive"/> </conditional> <param name="blocks_picking" value="3"/> <param name="window_picking" value="3"/> <param name="SNR_picking_method" value="3"/> </conditional> </repeat> <repeat name="methods"> <conditional name="methods_conditional"> <param name="preprocessing_method" value="Peak_alignment"/> <conditional name="methods_for_alignment"> <param name="alignment_method" value="diff"/> </conditional> </conditional> </repeat> <repeat name="methods"> <conditional name="methods_conditional"> <param name="preprocessing_method" value="Peak_filtering"/> <param name="frequ_filtering" value="2"/> </conditional> </repeat> <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> <conditional name="tabular_annotation"> <param name="load_annotation" value="no_annotation"/> </conditional> <param name="output_matrix" value="True"/> <output name="msidata_preprocessed" file="preprocessing_results1.RData" compare="sim_size"/> <output name="intensity_matrix" file="preprocessing_results1.txt"/> <output name="QC_overview" file="preprocessing_results1.pdf" compare="sim_size"/> </test> <test expect_num_outputs="4"> <param name="infile" value="123_combined.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="adaptive"/> </conditional> </conditional> </repeat> <repeat name="methods"> <conditional name="methods_conditional"> <param name="preprocessing_method" value="Peak_alignment"/> <conditional name="methods_for_alignment"> <param name="alignment_method" value="DP"/> </conditional> </conditional> </repeat> <conditional name="tabular_annotation"> <param name="load_annotation" value="yes_annotation"/> <param name="annotation_file" value="pixel_annotations.tabular"/> <param name="column_x" value="1"/> <param name="column_y" value="2"/> <param name="column_names" value="3"/> <param name="tabular_header" value="FALSE"/> </conditional> <param name="summary_type" value="median,sd"/> <output name="msidata_preprocessed" file="preprocessing_results2.RData" compare="sim_size"/> <output name="summarized_median" file="preprocessing_median2.txt" lines_diff="2"/> <output name="summarized_sd" file="preprocessing_sd2.txt" lines_diff="2"/> <output name="QC_overview" file="preprocessing_results2.pdf" compare="sim_size"/> </test> <test expect_num_outputs="3"> <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="Peak_picking"/> <param name="blocks_picking" value="100"/> <param name="window_picking" value="5"/> <param name="SNR_picking_method" value="3"/> <param name="picking_method" value="limpic"/> </conditional> </repeat> <repeat name="methods"> <conditional name="methods_conditional"> <param name="preprocessing_method" value="Peak_alignment"/> <conditional name="methods_for_alignment"> <param name="alignment_method" value="diff"/> </conditional> </conditional> </repeat> <param name="summary_type" value="mean"/> <conditional name="tabular_annotation"> <param name="load_annotation" value="no_annotation"/> </conditional> <output name="msidata_preprocessed" file="preprocessing_results3.RData" compare="sim_size"/> <output name="QC_overview" file="preprocessing_results3.pdf" compare="sim_size"/> <output name="summarized_mean" file="preprocessing_mean3.txt" lines_diff="2"/> </test> <test expect_num_outputs="3"> <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> <conditional name="tabular_annotation"> <param name="load_annotation" value="no_annotation"/> </conditional> <param name="output_matrix" value="True"/> <output name="msidata_preprocessed" file="preprocessing_results4.RData" compare="sim_size"/> <output name="intensity_matrix" file="preprocessing_results4.txt"/> <output name="QC_overview" file="preprocessing_results4.pdf" compare="sim_size"/> </test> <test expect_num_outputs="2"> <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"/> <conditional name="methods_for_reduction"> <param name="reduction_method" value="resample"/> <param name="step_width" value="0.1"/> </conditional> </conditional> </repeat> <conditional name="tabular_annotation"> <param name="load_annotation" value="no_annotation"/> </conditional> <output name="msidata_preprocessed" file="preprocessing_results5.RData" compare="sim_size"/> <output name="QC_overview" file="preprocessing_results5.pdf" compare="sim_size"/> </test> </tests> <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. Input data: 3 types of MSI 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) - Cardinal "MSImageSet" data (with variable name "msidata", saved as .RData) - Coordinates stored as decimals rather than integers will be rounded to obtain a regular pixel grid. This might lead to duplicated coordinates which will be automatically removed before peak picking. - optional: tabular file with pixel annotations: x and y values in separate columns and the corresponding annotation in a third column Options: - Normalization: Normalization of intensities to total ion current (TIC) - Baseline reduction: Baseline reduction removes background intensity generated by chemical noise (common in MALDI datasets) - Smoothing: 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, 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; when using log2 transformation zero intensities will become NA, this can lead to compatibility problems. Output: - imzML file, preprocessed - pdf with key values after each processing step, in case of loaded annotations file overview plot of pixel annotations - optional: intensity matrix as tabular file (m/z in rows and pixel in columns, filled with intensity values) - optional: summarized intensity matrix: mean, median or standard deviation for each m/z feature; in case pixel annotations are provided the intensity values are summarized for each pixel group Tip: - Peak alignment works only after peak picking - Peak filtering works only on centroided data (peak picking and alignment or Data reduction peaks) ]]> </help> <citations> <citation type="doi">10.1093/bioinformatics/btv146</citation> </citations> </tool>