maldi_quant_peak_detection: maldi_quant_peakdetection.xml comparison

comparison maldi_quant_peakdetection.xml @ 7:160538a890a6 draft default tip

planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/MALDIquant commit f1e1cd260ef2884d0ba12e2b614df3c72d0934dc

author	galaxyp
date	Sat, 04 Mar 2023 19:14:04 +0000
parents	d286ff4600dd
children

comparison

equal deleted inserted replaced

-:d286ff4600dd
+:160538a890a6
-<tool id="maldi_quant_peak_detection" name="MALDIquant peak detection" version="@VERSION@.6">
+<tool id="maldi_quant_peak_detection" name="MALDIquant peak detection" version="@VERSION@.0">
 <description>
 Peak detection, binning and filtering for mass-spectrometry imaging data
 </description>
 <macros>
 <import>maldi_macros.xml</import>
 cp '${infile.extra_files_path}/ibd' infile.ibd &&
 #elif $infile.ext == 'analyze75'
 cp '${infile.extra_files_path}/hdr' infile.hdr &&
 cp '${infile.extra_files_path}/img' infile.img &&
 cp '${infile.extra_files_path}/t2m' infile.t2m &&
-#else
-ln -s '$infile' infile.RData &&
 #end if
 cat '${maldi_quant_peak_detection}' &&
 Rscript '${maldi_quant_peak_detection}' &&
 mkdir $outfile_imzml.files_path &&
 mv ./out.imzMl "${os.path.join($outfile_imzml.files_path, 'imzml')}" | true &&
 #if $restriction_conditional.restriction == 'restrict':
 print('Reading mask region')
-## Import imzML file
+		## Import imzML file
-coordinate_matrix = as.matrix(read.delim("$restriction_conditional.coordinates_file", header = $restriction_conditional.coordinates_header, stringsAsFactors = FALSE))[,1:2]
+		coordinate_matrix = read.delim("$restriction_conditional.coordinates_file", header = $restriction_conditional.coordinates_header, stringsAsFactors = FALSE)
-coordinate_matrix = coordinate_matrix[,c($restriction_conditional.column_x, $restriction_conditional.column_y)]
+		coordinate_matrix = coordinate_matrix[,c($restriction_conditional.column_x, $restriction_conditional.column_y)]
+		#if str($centroids) == "TRUE"
-maldi_data <- importImzMl('infile.imzML',
+		    peaks <- importImzMl('infile.imzML',
-coordinates = coordinate_matrix, centroided = $centroids)
+		                 coordinates = as.matrix(coordinate_matrix), centroided = $centroids)
-pixelnames = paste("xy", coordinates(maldi_data)[,1],coordinates(maldi_data)[,2], sep="_")
+			pixelnames = paste("xy", coordinates(peaks)[,1],coordinates(peaks)[,2], sep="_")
+		#else
+			maldi_data <- importImzMl('infile.imzML',
+		                 coordinates = as.matrix(coordinate_matrix), centroided = $centroids)
+			pixelnames = paste("xy", coordinates(maldi_data)[,1],coordinates(maldi_data)[,2], sep="_")
+		#end if
 #else:
 print('Reading entire file')
 ## Import imzML file
 {
 single_peaks = createMassPeaks(peak_list[[spectra]]\$mass, peak_list[[spectra]]\$intensity, snr=peak_list[[spectra]]\$snr)
 peaks[[spectra]] = single_peaks
 }
-#else
-print('rdata file')
-loadRData <- function(fileName){
-#loads an RData file, and returns it
-load(fileName)
-get(ls()[ls() != "fileName"])
-}
-msidata = loadRData('infile.RData')
-centroided(msidata) = $centroids
-## change to correct pixelnames
-x_coords = unlist(lapply(strsplit(names(Cardinal::pixels(msidata)), ","), `[[`, 1))
-y_coords = unlist(lapply(strsplit(names(Cardinal::pixels(msidata)), ","), `[[`, 2))
-x_coordinates = gsub("x = ","",x_coords)
-y_coordinates = gsub(" y = ","",y_coords)
-pixelnames = paste0("xy_", x_coordinates, "_", y_coordinates)
-cardinal_coordinates = as.matrix(Cardinal::coord(msidata)[,1:2])
-if (centroided(msidata) == FALSE){
-## create mass spectrum object
-cardinal_mzs = Cardinal::mz(msidata)
-maldi_data = list()
-for(number_spectra in 1:ncol(msidata)){
-maldi_data[[number_spectra]] = createMassSpectrum(mass = cardinal_mzs, intensity = iData(msidata)[,number_spectra])}
-coordinates_info = cbind(cardinal_coordinates, c(1:length(maldi_data)))
-}else{
-peaks = list()
-for (spectra in 1:ncol(msidata))
-{
-single_peaks = createMassPeaks(Cardinal::mz(msidata), Cardinal::spectra(msidata)[,spectra], snr=as.numeric(rep("NA", nrow(msidata))))
-peaks[[spectra]] = single_peaks
-}
-coordinates_info = cbind(cardinal_coordinates, c(1:length(peaks)))}
 #end if
 #end if
 ## default summarized = FALSE
 summarized_spectra = FALSE
 title(main=paste("$filename"))
 ## plot input file spectrum:
 #if $centroids:
 ## Choose random spectra for QC plots
-print(length(peaks))
+random_spectra = sample(1:length(peaks), size=4, replace=length(peaks)<4)## in case dataset has less than 4 spectra, same spetra are allowed to be sampled
-random_spectra = sample(1:length(peaks), 4, replace=FALSE)
 random_spectra_name = pixelnames[random_spectra]
 par(mfrow = c(2, 2), oma=c(0,0,2,0))
 for (random_sample in random_spectra){
 plot(peaks[[random_sample]],sub="", main=paste0("spectrum ", pixelnames[random_sample]))}
 title("Input spectra", outer=TRUE, line=0)
 #else
 ## Choose random spectra for QC plots
-random_spectra = sample(1:length(maldi_data), 4, replace=FALSE)
+random_spectra = sample(1:length(maldi_data), size=4, replace=length(maldi_data)<4)## in case dataset has less than 4 spectra, same spetra are allowed to be sampled
 par(mfrow = c(2, 2), oma=c(0,0,2,0))
 for (random_sample in random_spectra){
 plot(maldi_data[[random_sample]],sub="", main=paste0("spectrum ", pixelnames[random_sample]))
 }
 title("Input spectra", outer=TRUE, line=0)
 #end if
 ## QC numbers for input file
 #if str($centroids) == "TRUE"
 pixel_number = length(peaks)
 minmz = round(min(unlist(lapply(peaks,mass))), digits=4)
 maxmz = round(max(unlist(lapply(peaks,mass))), digits=4)
 mean_features = round(length(unlist(lapply(peaks,mass)))/length(peaks), digits=2)
 medint = round(median(unlist(lapply(peaks,intensity))), digits=2)
 number_features = length(unique(unlist(lapply(peaks,mass))))
-inputdata = c(minmz, maxmz,number_features,mean_features,  medint)
+number_spectra = length(peaks)
-QC_numbers= data.frame(inputdata = c(minmz, maxmz,number_features, mean_features, medint))
+inputdata = c(minmz, maxmz,number_features,mean_features, medint, number_spectra)
+QC_numbers= data.frame(inputdata = c(minmz, maxmz,number_features, mean_features, medint, number_spectra))
 vectorofactions = "inputdata"
 #else
 pixel_number = length(maldi_data)
 minmz = round(min(unlist(lapply(maldi_data,mass))), digits=4)
 maxmz = round(max(unlist(lapply(maldi_data,mass))), digits=4)
 mean_features = round(length(unlist(lapply(maldi_data,mass)))/length(maldi_data), digits=2)
 medint = round(median(unlist(lapply(maldi_data,intensity))), digits=2)
 number_features = length(unique(unlist(lapply(maldi_data,mass))))
-inputdata = c(minmz, maxmz,number_features,mean_features,  medint)
+number_spectra = length(maldi_data)
-QC_numbers= data.frame(inputdata = c(minmz, maxmz,number_features, mean_features, medint))
+inputdata = c(minmz, maxmz,number_features,mean_features,  medint, number_spectra)
+QC_numbers= data.frame(inputdata = c(minmz, maxmz,number_features, mean_features, medint, number_spectra))
 vectorofactions = "inputdata"
 #end if
 #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)]
+annotation_input[,3] <- as.character(annotation_input[,3]) ## make sure annotations are character
 colnames(annotation_input) = c("x", "y", "annotation") ## rename annotations header to default name "annotation"
 ## merge provided annotation with coordinate information of MSI data
 colnames(coordinates_info)[3] = "pixel_index"
 merged_annotation = merge(coordinates_info, annotation_input, by=c("x", "y"), all.x=TRUE)
 merged_annotation[is.na(merged_annotation)] = "NA"
 ## order coordinate information according to pixel index to make sure that the order stays the same
 merged_annotation = merged_annotation[order(merged_annotation\$pixel_index),]
 samples = as.factor(merged_annotation\$annotation)
 ## print annotation overview into PDF output
 combine_plot = ggplot(merged_annotation, aes(x=x, y=y, fill=annotation))+
 geom_tile() +
 coord_fixed()+
 ggtitle("Spatial orientation of annotated data")+
 minmz = round(min(unlist(lapply(peaks,mass))), digits=4)
 maxmz = round(max(unlist(lapply(peaks,mass))), digits=4)
 mean_features = round(length(unlist(lapply(peaks,mass)))/length(peaks), digits=2)
 medint = round(median(unlist(lapply(peaks,intensity))), digits=2)
 number_features = length(unique(unlist(lapply(peaks,mass))))
-peaks_picked = c(minmz, maxmz,number_features,mean_features,  medint)
+number_spectra = length(peaks)
+peaks_picked = c(minmz, maxmz,number_features,mean_features,  medint, number_spectra)
 QC_numbers= cbind(QC_numbers, peaks_picked)
 vectorofactions = append(vectorofactions, "peaks_picked")
 if (length(peaks[!sapply(peaks, isEmpty)])>0){
 featureMatrix <- intensityMatrix(peaks)
 minmz = round(min(unlist(lapply(peaks,mass))), digits=4)
 maxmz = round(max(unlist(lapply(peaks,mass))), digits=4)
 mean_features = round(length(unlist(lapply(peaks,mass)))/length(peaks), digits=2)
 medint = round(median(unlist(lapply(peaks,intensity))), digits=2)
 number_features = length(unique(unlist(lapply(peaks,mass))))
-monoisotopes = c(minmz, maxmz,number_features,mean_features,  medint)
+number_spectra = length(peaks)
+monoisotopes = c(minmz, maxmz,number_features,mean_features,  medint, number_spectra)
 QC_numbers= cbind(QC_numbers, monoisotopes)
 vectorofactions = append(vectorofactions, "monoisotopes")
 if (length(peaks[!sapply(peaks, isEmpty)])>0){
 featureMatrix <- intensityMatrix(peaks)
 ## only if there are empty spectra to remove
 if (length(findEmptyMassObjects(peaks))>0)
 {
-#if $infile.ext == 'rdata'
-cardinal_coordinates = cardinal_coordinates[-findEmptyMassObjects(peaks),,drop=FALSE] ## remove coordinates of empty spectra for Cardinal RData input
-#end if
 peaks = removeEmptyMassObjects(peaks)
 pixelnames = paste("xy", coordinates(peaks)[,1],coordinates(peaks)[,2], sep="_")
 }
 #end if
 minmz = round(min(unlist(lapply(peaks,mass))), digits=4)
 maxmz = round(max(unlist(lapply(peaks,mass))), digits=4)
 mean_features = round(length(unlist(lapply(peaks,mass)))/length(peaks), digits=2)
 medint = round(median(unlist(lapply(peaks,intensity))), digits=2)
 number_features = length(unique(unlist(lapply(peaks,mass))))
-aligned = c(minmz, maxmz,number_features,mean_features,  medint)
+number_spectra = length(peaks)
+aligned = c(minmz, maxmz,number_features,mean_features,  medint, number_spectra)
 QC_numbers= cbind(QC_numbers, aligned)
 vectorofactions = append(vectorofactions, "aligned")
 if (length(peaks[!sapply(peaks, isEmpty)])>0){
 featureMatrix <- intensityMatrix(peaks)
 write.table(featureMatrix2, file="$intensity_matrix", quote = FALSE, row.names = TRUE, col.names=FALSE, sep = "\t")
 }else{print("There are no spectra with peaks left")}
 #elif str( $method.methods_conditional.method ) == 'Binning':
 print('binning')
 ##m/z binning
 peaks <- binPeaks(peaks, tolerance=$method.methods_conditional.bin_tolerance, method="$method.methods_conditional.bin_method")
 minmz = round(min(unlist(lapply(peaks,mass))), digits=4)
 maxmz = round(max(unlist(lapply(peaks,mass))), digits=4)
 mean_features = round(length(unlist(lapply(peaks,mass)))/length(peaks), digits=2)
 medint =round( median(unlist(lapply(peaks,intensity))), digits=2)
 number_features = length(unique(unlist(lapply(peaks,mass))))
-binned = c(minmz, maxmz,number_features,mean_features,  medint)
+number_spectra = length(peaks)
+binned = c(minmz, maxmz,number_features,mean_features,  medint, number_spectra)
 QC_numbers= cbind(QC_numbers, binned)
 vectorofactions = append(vectorofactions, "binned")
 if (length(peaks[!sapply(peaks, isEmpty)])>0){
 featureMatrix <- intensityMatrix(peaks)
 #elif str( $method.methods_conditional.method ) == 'Filtering':
 print('filtering')
 ##m/z filtering
-## filtering on all pixels or on pixel groups:
-#if str($method.methods_conditional.filter_annot_groups ) == 'FALSE':
+peaks <- filterPeaks(peaks
+#if str( $method.methods_conditional.filter_type.filter_params ) != 'min_Number':
-peaks <- filterPeaks(peaks,
+,minFrequency=$method.methods_conditional.filter_type.minFrequency
-minFrequency=$method.methods_conditional.minFrequency,
-minNumber=$method.methods_conditional.minNumber,
-mergeWhitelists=$method.methods_conditional.mergeWhitelists)
-#elif str( $method.methods_conditional.filter_annot_groups ) == 'TRUE':
-peaks <- filterPeaks(peaks,
-minFrequency=$method.methods_conditional.minFrequency,
-minNumber=$method.methods_conditional.minNumber,
-mergeWhitelists=$method.methods_conditional.mergeWhitelists, label = samples)
 #end if
+#if str( $method.methods_conditional.filter_type.filter_params ) != 'min_Frequency':
+,minNumber=$method.methods_conditional.filter_type.minNumber
+#end if
+## in case of group wise filtering set grouping conditions
+#if str( $method.methods_conditional.filter_groups.filter_annot_groups ) == 'yes_grouping':
+,mergeWhitelists=$method.methods_conditional.filter_groups.mergeWhitelists, label = samples
+#end if
+) ## finishes filterPeaks function
 ##QC plot and numbers
 par(mfrow = c(2, 2), oma=c(0,0,2,0))
 for (random_sample in random_spectra){
 plot(NULL, xlim=c(0,0), ylim=c(0,0), ylab="intensity", xlab="m/z")
 }
 )
 }
+## QC numbers for filtered file
 title("Filtered spectra", outer=TRUE, line=0)
 minmz = round(min(unlist(lapply(peaks,mass))), digits=4)
 maxmz = round(max(unlist(lapply(peaks,mass))), digits=4)
 mean_features = round(length(unlist(lapply(peaks,mass)))/length(peaks), digits=2)
 medint = round(median(unlist(lapply(peaks,intensity))), digits=2)
 number_features = length(unique(unlist(lapply(peaks,mass))))
-filtered = c(minmz, maxmz,number_features,mean_features,  medint)
+number_spectra = length(peaks)
+filtered = c(minmz, maxmz,number_features,mean_features,  medint, number_spectra)
 QC_numbers= cbind(QC_numbers, filtered)
 vectorofactions = append(vectorofactions, "filtered")
 if (length(peaks[!sapply(peaks, isEmpty)])>0){
 featureMatrix <- intensityMatrix(peaks)
 colnames(featureMatrix2)[1] = c("mz")
 featureMatrix2 = t(featureMatrix2)
 }else{print("There are no spectra with peaks left")
 featureMatrix2 = matrix(0, ncol=1, nrow=1)}
 write.table(featureMatrix2, file="$intensity_matrix", quote = FALSE, row.names = TRUE, col.names=FALSE, sep = "\t")
+#elif str( $method.methods_conditional.method ) == 'skip_preprocessing':
+##for now as option to filter large files
 #end if
 #end for
 if (length(peaks[!sapply(peaks, isEmpty)])>0){
 ## mass peaks output
 mass_peaks = data.frame(matrix(,ncol=3, nrow=0))
 colnames(mass_peaks) = c("snr", "mass", "intensity", "spectrum")
 write.table(mass_peaks, file="$masspeaks", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
 }else{print("There are no spectra with peaks left")}
 ## print table with QC values
-rownames(QC_numbers) = c("min m/z", "max mz", "# features", "median \n# peaks (int.>0)", "median\nintensity")
+rownames(QC_numbers) = c("min m/z", "max mz", "# features", "median \n# peaks (int.>0)", "median\nintensity", "# spectra")
 plot(0,type='n',axes=FALSE,ann=FALSE)
 grid.table(t(QC_numbers))
 dev.off()
 if (summarized_spectra == FALSE){
 #if $infile.ext == 'imzml'
 MALDIquantForeign::exportImzMl(peaks, file="out.imzMl", processed=TRUE)
 #elif $infile.ext == 'tabular'
 masspeaks_coordinates = matrix(unlist(strsplit(as.character(pixelnames), "\\_")), ncol=3, byrow=TRUE)
 ## extract x and y values and create the coordinate matrix in case tabular was input
 peaklist_coordinates = unique(cbind(as.numeric(masspeaks_coordinates[,2]), as.numeric(masspeaks_coordinates[,3])))
 exportImzMl(peaks, file="out.imzMl", processed=TRUE, coordinates=peaklist_coordinates)
-#elif $infile.ext == 'rdata'
-MALDIquantForeign::exportImzMl(peaks, file="out.imzMl", processed=TRUE, coordinates=cardinal_coordinates)
 #end if
 }
 ]]>
 </configfile>
 </configfiles>
 <inputs>
-<param name="infile" type="data" format="imzml,tabular,rdata" label="MSI data" help="Input file as imzML (composite upload), tabular peaklist or Cardinal MSImageSet saved as RData (regular upload)"/>
+<param name="infile" type="data" format="imzml,tabular" label="MSI data" help="Input file as imzML (composite upload) or tabular peaklist (regular upload)"/>
 <param name="centroids" type="boolean" label="Centroided input" help="Choose Yes if peak detection has already been done. Peak detection cannot be run again on centroided data" truevalue="TRUE" falsevalue="FALSE"/>
 <conditional name="restriction_conditional">
 <param name="restriction" type="select" label="Use only spectra of interest" help="This option only works for imzML files">
-<option value="no_restriction" selected="True">No, calculate on entire file</option>
+<option value="no_restriction" selected="True">No, use all spectra</option>
 <option value="restrict">Yes, restrict to spectra of interest</option>
 </param>
 <when value="restrict">
 <param name="coordinates_file" type="data" format="tabular" label="Tabular file with coordinates"/>
 <param name="column_x" data_ref="coordinates_file" label="Column with x values" type="data_column"/>
 <option value="Peak_detection">Peak detection</option>
 <option value="monoisotopic_peaks">Monoisotopic peaks</option>
 <option value="Align">Align Spectra (warping/phase correction)</option>
 <option value="Binning">Binning</option>
 <option value="Filtering">Filtering</option>
+<option value="skip_preprocessing">No method</option>
 </param>
 <when value="Peak_detection">
 <param name="peak_method" type="select" label="Noise estimation function">
 <option value="MAD" selected="True">MAD</option>
 <option value="SuperSmoother">SuperSmoother</option>
 <param name="tolerance" type="float" value="0.00005"
 label="Tolerance = abs(mz1 - mz2)/mz2"
 help="Maximal relative deviation of a peak position (m/z) to be considered as identical. For 50ppm use 0.00005 or 5e-5" />
 <param name="allow_nomatch" type="boolean" label="Don't throw an error when less than 2 reference m/z were found in a spectrum" truevalue="TRUE" falsevalue="FALSE"/>
 <param name="empty_nomatch" type="boolean" label="If TRUE the intensity values of MassSpectrum or MassPeaks objects with missing (NA) warping functions are set to zero" truevalue="TRUE" falsevalue="FALSE"/>
-<param name="remove_empty" type="boolean" label="Should empty spectra be removed" truevalue="TRUE" falsevalue="FALSE" help="For Cardinal RData files this step can only be performed if pixel annotations were provided"/>
+<param name="remove_empty" type="boolean" label="Should empty spectra be removed" truevalue="TRUE" falsevalue="FALSE"/>
 <conditional name="reference_for_alignment">
 <param name="align_ref" type="select" label="Reference" help="If given, samples will be aligned to reference, use internal calibrants to perform m/z calibration">
 <option value="no_reference" selected="True">no reference</option>
 <option value="yes_reference">reference from tabular file</option>
 <option value="strict" selected="True" >strict</option>
 <option value="relaxed" >relaxed</option>
 </param>
 </when>
 <when value="Filtering">
-<param name="minFrequency" type="float" value="0.25"
+<conditional name="filter_groups">
-label="Removal of all peaks which occur in less than minFrequency spectra" help="Relative threshold. The higher value from relative and absolute threshold is taken. Set one value to zero to be sure it will not be used."/>
+<param name="filter_annot_groups" type="select" label="m/z filtering parameters are applied to all spectra or groups of spectra" help="By default a single group is assumed and filtering will be done based on all pixels. To filter groups of spectra, an annotation annotation file has to be specified above">
-<param name="minNumber" type="float" value="1.0"
+<option value="no_grouping" selected="True">use single group</option>
-label="Removal of all peaks which occur in less than minNumber spectra" help="Absolute threshold. The higher value from relative and absolute threshold is taken. Set one value to zero to be sure it will not be used."/>
+<option value="yes_grouping">use spectra groups from annotation file</option>
-<param name="filter_annot_groups" type="boolean" label="Group wise filtering with pixel annotations." help="If not specified a single group is assumed or when filtering has been done group wise it will automatically be group wise when selecting filtering on all pixel" truevalue="TRUE" falsevalue="FALSE"/>
+</param>
-<param name="mergeWhitelists" type="boolean" truevalue="TRUE" falsevalue="FALSE"
+<when value="no_grouping"/>
-label="mergeWhitelists" help="Yes means that peaks that survive the filtering in one annotation group are also kept in other groups regardless if the filtering criteria are met in these groups"/>
+<when value="yes_grouping">
+			<param name="mergeWhitelists" type="boolean" truevalue="TRUE" falsevalue="FALSE"
+label="mergeWhitelists" help="Yes means that peaks that survive the filtering in one annotation group are also kept in other groups regardless if the filtering criteria are met in these groups"/>
+			</when>
+				</conditional>
+				<conditional name="filter_type">
+<param name="filter_params" type="select" label="m/z filtering parameters" help="minFrequency: Removal of all peaks which occur in less than minFrequency spectra (relative treshold). minNumber: Removal of all peaks which ocur in lass than minNumber spectra (absolute threshold). If both are set the higher value from relative and absolute threshold is taken.">
+<option value="min_Frequency" selected="True">minFrequency</option>
+<option value="min_Number">minNumber</option>
+<option value="both">both</option>
+</param>
+<when value="min_Frequency">
+	<param name="minFrequency" type="float" value="0.25" label="Removal of all peaks which occur in less than minFrequency spectra" />
+</when>
+<when value="min_Number">
+<param name="minNumber" type="float" value="1.0" label="Removal of all peaks which occur in less than minNumber spectra" />
+</when>
+<when value="both">
+	<param name="minFrequency" type="float" value="0.25" label="Removal of all peaks which occur in less than minFrequency spectra" />
+	<param name="minNumber" type="float" value="1.0" label="Removal of all peaks which occur in less than minNumber spectra" />
+</when>
+					</conditional>
 </when>
+<when value="skip_preprocessing"/>
 </conditional>
 </repeat>
 </inputs>
 <outputs>
 <data format="imzml" name="outfile_imzml" label="${tool.name} on ${on_string}: imzml">
 <repeat name="methods">
 <conditional name="methods_conditional">
 <param name="method" value="monoisotopic_peaks"/>
 <param name="tolerance" value="0.0004"/>
 <param name="size" value="3"/>
-</conditional>
+	</conditional>
 </repeat>
 <output name="plots" file="peakdetection2_QC.pdf" compare="sim_size"/>
 <output name="masspeaks" file="masspeaks2.tabular"/>
 <output name="intensity_matrix" file="int2.tabular"/>
 <output name="outfile_imzml" ftype="imzml" file="peak_detection2.imzml.txt" lines_diff="4">
 <extra_files type="file" file="peak_detection2.imzml" name="imzml" lines_diff="6"/>
 <extra_files type="file" file="peak_detection2.ibd" name="ibd" compare="sim_size"/>
 </output>
 </test>
 <test>
 <param name="infile" value="" ftype="imzml">
 <composite_data value="Example_Continuous.imzML"/>
 <composite_data value="Example_Continuous.ibd"/>
 </param>
 <conditional name="tabular_annotation">
 </conditional>
 <repeat name="methods">
 <conditional name="methods_conditional">
 <param name="method" value="Peak_detection"/>
 <param name="peak_method" value="MAD"/>
-<param name="halfWindowSize" value="1"/>
+<param name="halfWindowSize" value="10"/>
 <param name="snr" value="2"/>
+</conditional>
+</repeat>
+<repeat name="methods">
+<conditional name="methods_conditional">
+<param name="method" value="Filtering"/>
+<conditional name="filter_groups">
+<param name="filter_annot_groups" value="yes_grouping"/>
+<param name="mergeWhitelists" value="FALSE"/>
+</conditional>
+<conditional name="filter_type">
+<param name="filter_params" value="min_Frequency"/>
+<param name="minFrequency" value="0.25"/>
+</conditional>
+</conditional>
+</repeat>
+<output name="plots" file="peakdetection3_QC.pdf" compare="sim_size"/>
+<output name="intensity_matrix" file="int3.tabular"/>
+<output name="masspeaks" file="masspeaks3.tabular"/>
+<output name="outfile_imzml" ftype="imzml" file="peak_detection3.imzml.txt" lines_diff="4">
+<extra_files type="file" file="peak_detection3.imzml" name="imzml" lines_diff="6"/>
+<extra_files type="file" file="peak_detection3.ibd" name="ibd" compare="sim_size"/>
+</output>
+</test>
+<test>
+<param name="infile" value="" ftype="imzml">
+<composite_data value="Example_Processed.imzML"/>
+<composite_data value="Example_Processed.ibd"/>
+</param>
+<repeat name="methods">
+<conditional name="methods_conditional">
+<param name="method" value="Peak_detection"/>
+<param name="peak_method" value="SuperSmoother"/>
+<param name="halfWindowSize" value="5"/>
+<param name="snr" value="3"/>
 </conditional>
 </repeat>
 <repeat name="methods">
 <conditional name="methods_conditional">
 <param name="method" value="Binning"/>
 <param name="bin_tolerance" value="0.01"/>
 </conditional>
 </repeat>
-<repeat name="methods">
-<conditional name="methods_conditional">
-<param name="method" value="Filtering"/>
-<param name="minFrequency" value="0.5"/>
-<param name="minNumber" value="3"/>
-<param name="filter_annot_groups" value="TRUE"/>
-<param name="mergeWhitelists" value="FALSE"/>
-</conditional>
-</repeat>
-<output name="plots" file="peakdetection3_QC.pdf" compare="sim_size"/>
-<output name="intensity_matrix" file="intensity_matrix3.tabular"/>
-<output name="masspeaks" file="masspeaks3.tabular"/>
-<output name="outfile_imzml" ftype="imzml" file="peak_detection3.imzml.txt" lines_diff="4">
-<extra_files type="file" file="peak_detection3.imzml" name="imzml" lines_diff="6"/>
-<extra_files type="file" file="peak_detection3.ibd" name="ibd" compare="sim_size"/>
-</output>
-</test>
-<test>
-<param name="infile" value="testfile_squares.rdata" ftype="rdata"/>
-<param name="method" value="Peak_detection"/>
-<param name="peak_method" value="MAD"/>
-<param name="halfWindowSize" value="20"/>
-<param name="snr" value="2"/>
 <output name="plots" file="peakdetection4_QC.pdf" compare="sim_size"/>
-<output name="intensity_matrix" file="intensity_matrix4.tabular"/>
+<output name="intensity_matrix" file="int4.tabular"/>
 <output name="masspeaks" file="masspeaks4.tabular"/>
 <output name="outfile_imzml" ftype="imzml" file="peak_detection4.imzml.txt" lines_diff="4">
 <extra_files type="file" file="peak_detection4.imzml" name="imzml" lines_diff="6"/>
 <extra_files type="file" file="peak_detection4.ibd" name="ibd" compare="sim_size"/>
 </output>
 </test>
+<test>
+<param name="infile" value="" ftype="imzml">
+<composite_data value="preprocessing_results3.imzML"/>
+<composite_data value="preprocessing_results3.ibd"/>
+</param>
+<param name="centroids" value="TRUE"/>
+<conditional name="restriction_conditional">
+		        <param name="restriction" value="restrict"/>
+<param name="coordinates_file" value="annotations.tabular"/>
+<param name="column_x" value="1"/>
+<param name="column_y" value="2"/>
+<param name="coordinates_header" value="TRUE"/>
+</conditional>
+<repeat name="methods">
+<conditional name="methods_conditional">
+<param name="method" value="skip_preprocessing"/>
+</conditional>
+</repeat>
+<output name="plots" file="peakdetection5_QC.pdf" compare="sim_size"/>
+<output name="masspeaks" file="masspeaks5.tabular"/>
+<output name="outfile_imzml" ftype="imzml" file="peak_detection5.imzml.txt" lines_diff="4">
+<extra_files type="file" file="peak_detection5.imzml" name="imzml" lines_diff="6"/>
+<extra_files type="file" file="peak_detection5.ibd" name="ibd" compare="sim_size"/>
+</output>
+</test>
 </tests>
 <help>
 <![CDATA[
 @MADLI_QUANT_DESCRIPTION@
 **Input data**
 - MSI 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/>`_
+- imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format <https://ms-imaging.org/imzml/>`_
-- Cardinal "MSImageSet" data saved as .RData
 - MSI data as peak list (tabular file) with the columns named "snr", "mass", "intensity" and "spectrum". The spectrum has to be in the following format: xy_1_1 (for pixel coordinates x1y1). The header must have exactly the four column names.
 ::
 snr          mass      intensity   spectrum

Mercurial > repos > galaxyp > maldi_quant_peak_detection

comparison maldi_quant_peakdetection.xml @ 7:160538a890a6 draft default tip