view maldi_quant_peakdetection.xml @ 1:96264fce1847 draft

planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/MALDIquant commit 0825a4ccd3ebf4ca8a298326d14f3e7b25ae8415

<tool id="maldi_quant_peak_detection" name="MALDIquant peak detection" version="@VERSION@.1">
    <description>
        Peak detection, binning and filtering for mass-spectrometry imaging data
    </description>
    <macros>
        <import>maldi_macros.xml</import>
    </macros>
    <expand macro="requirements"/>
    <command detect_errors="exit_code">
    <![CDATA[
        #if $infile.ext == 'imzml'
            cp '${infile.extra_files_path}/imzml' infile.imzML &&
            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 $infile infile.RData &&
        #end if
        Rscript '${maldi_quant_peak_detection}'&&
        mkdir $outfile_imzml.files_path &&
        mv ./out.imzMl "${os.path.join($outfile_imzml.files_path, 'imzml')}" | true &&
        mv ./out.ibd "${os.path.join($outfile_imzml.files_path, 'ibd')}" | true &&
        echo "imzML file:" > $outfile_imzml &&
        ls -l "$outfile_imzml.files_path" >> $outfile_imzml
    ]]>
    </command>
    <configfiles>
        <configfile name="maldi_quant_peak_detection"><![CDATA[

@R_IMPORTS@



#if $restriction_conditional.restriction == 'restrict':

    print('Reading mask region')

    ## Import imzML file
    coordinate_matrix = as.matrix(read.delim("$restriction_conditional.coordinates_file", header = $restriction_conditional.coordinates_header, stringsAsFactors = FALSE))[,1:2]

    maldi_data <- importImzMl('infile.imzML',
                     coordinates = coordinate_matrix, centroided = $centroids)
    pixelnames = paste("xy", coordinates(maldi_data)[,1],coordinates(maldi_data)[,2], sep="_")


#else:

    print('Reading entire file')
    ## Import imzML file

    #if $infile.ext == 'imzml'
        print('imzML file')
        #if str($centroids) == "TRUE"
            peaks <- importImzMl('infile.imzML', centroided = $centroids)
            pixelnames = paste("xy", coordinates(maldi_data)[,1],coordinates(maldi_data)[,2], sep="_")
        #else
            maldi_data <- importImzMl('infile.imzML', centroided = $centroids)
            pixelnames = paste("xy", coordinates(maldi_data)[,1],coordinates(maldi_data)[,2], sep="_")
        #end if
            coordinates_info = cbind(coordinates(maldi_data)[,1:2], c(1:length(maldi_data)))

    #elif $infile.ext == 'tabular'
        print('tabular file')
        #set $centroids = "TRUE" ## will be used in some if conditions
        peak_tabular = read.delim("$infile", header = TRUE, stringsAsFactors = FALSE)
        peak_list = split(peak_tabular, f = peak_tabular\$spectrum) ## will be ordered according to spectrum
        pixelnames = unique(peak_tabular\$spectrum)

        peaks = list()
        for (spectra in 1:length(peak_list))
        {
            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
        pixelnames = gsub(", y = ", "_", names(Cardinal::pixels(msidata)))
        pixelnames = gsub(" = ", "y_", pixelnames)

        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)))}
            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
            }}
    #end if
#end if










## default summarized = FALSE
summarized_spectra = FALSE

## Quality control plots during peak detection
pdf("peaks_qc_plot.pdf", fonts = "Times", pointsize = 12)
plot(0,type='n',axes=FALSE,ann=FALSE)

## if no filename is given, name of file in Galaxy history is used
#set $filename = $infile.display_name

title(main=paste("$filename"))

## plot input file spectrum: 
#if str($centroids) == "TRUE"
        plot(peaks[[1]], main="First spectrum of input file")
#else
    avgSpectra <- averageMassSpectra(maldi_data,method="mean")
    plot(avgSpectra, main="Average spectrum of input file")
#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)
    maxfeatures = round(length(unlist(lapply(peaks,mass)))/length(peaks), digits=2)
    medint = round(median(unlist(lapply(peaks,intensity))), digits=2)
    inputdata = c(minmz, maxmz,maxfeatures,  medint)
    QC_numbers= data.frame(inputdata = c(minmz, maxmz,maxfeatures, medint))
    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)
    maxfeatures = round(length(unlist(lapply(maldi_data,mass)))/length(maldi_data), digits=2)
    medint = round(median(unlist(lapply(maldi_data,intensity))), digits=2)
    inputdata = c(minmz, maxmz,maxfeatures,  medint)
    QC_numbers= data.frame(inputdata = c(minmz, maxmz,maxfeatures, medint))
    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)]
    colnames(annotation_input) = c("x", "y", "annotation") ## rename annotations header to default name "annotation"

    ## merge 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"
    merged_annotation = merged_annotation[order(merged_annotation\$pixel_index),]
    samples = as.factor(merged_annotation\$annotation)

## print annotation overview into PDF output

        ## the more annotation groups a file has the smaller will be the legend
        number_combined = length(levels(as.factor(merged_annotation\$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
        }

        combine_plot = ggplot(merged_annotation, aes(x=x, y=y, fill=annotation))+
               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))

        print(combine_plot)

#end if


#################### Preprocessing methods #####################################

#for $method in $methods:

    #if str( $method.methods_conditional.method ) == 'Peak_detection':
        print('peak detection')
        ##peak detection

        #if $method.methods_conditional.use_annotations:
            maldi_data <- averageMassSpectra(maldi_data, labels=samples,method="mean") ## use average spectra for peak picking
            pixelnames = levels(samples)
            summarized_spectra = TRUE

        #end if

        peaks <- detectPeaks(maldi_data, method="$method.methods_conditional.peak_method",
                  halfWindowSize=$method.methods_conditional.halfWindowSize,SNR=$method.methods_conditional.snr)

        ## QC plot and numbers
        plot(peaks[[1]], main="First spectrum after peak detection")
        pixel_number = length(peaks)
        minmz = round(min(unlist(lapply(peaks,mass))), digits=4)
        maxmz = round(max(unlist(lapply(peaks,mass))), digits=4)
        maxfeatures = round(length(unlist(lapply(peaks,mass)))/length(peaks), digits=2)
        medint = round(median(unlist(lapply(peaks,intensity))), digits=2)
        peaks_picked = c(minmz, maxmz,maxfeatures, medint)
        QC_numbers= cbind(QC_numbers, peaks_picked)
        vectorofactions = append(vectorofactions, "peaks_picked")

        if (length(peaks[!sapply(peaks, isEmpty)])>0){
            #if $infile.ext == 'imzml'
                #if str($centroids) == "FALSE"
                    featureMatrix <- intensityMatrix(peaks, maldi_data)
                #end if
            #else
                featureMatrix <- intensityMatrix(peaks)
            #end if
            featureMatrix2 =cbind(pixelnames, featureMatrix)
            colnames(featureMatrix2)[1] = c("mz")
            featureMatrix2 = t(featureMatrix2)
            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 ) == 'monoisotopic_peaks':

        print('monoisotopic peaks')
        ##monoisotopic peaks

        peaks = monoisotopicPeaks(peaks, minCor=$method.methods_conditional.minCor, tolerance=$method.methods_conditional.tolerance, distance=$method.methods_conditional.distance, size=$method.methods_conditional.size)

        ## QC plot and numbers
        plot(peaks[[1]], main="First spectrum after monoisotopic peaks detection")
        minmz = round(min(unlist(lapply(peaks,mass))), digits=4)
        maxmz = round(max(unlist(lapply(peaks,mass))), digits=4)
        maxfeatures = round(length(unlist(lapply(peaks,mass)))/length(peaks), digits=2)
        medint = round(median(unlist(lapply(peaks,intensity))), digits=2)
        monoisotopes = c(minmz, maxmz,maxfeatures, medint)
        QC_numbers= cbind(QC_numbers, monoisotopes)
        vectorofactions = append(vectorofactions, "monoisotopes")

        if (length(peaks[!sapply(peaks, isEmpty)])>0){
            #if $infile.ext == 'imzml'
                #if str($centroids) == "FALSE"
                    featureMatrix <- intensityMatrix(peaks, maldi_data)
                #end if
            #else
                featureMatrix <- intensityMatrix(peaks)
            #end if
            featureMatrix2 =cbind(pixelnames, featureMatrix)
            colnames(featureMatrix2)[1] = c("mz")
            featureMatrix2 = t(featureMatrix2)
            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)

        ## QC plot and numbers
        plot(peaks[[1]], main="First spectrum after binning")
        minmz = round(min(unlist(lapply(peaks,mass))), digits=4)
        maxmz = round(max(unlist(lapply(peaks,mass))), digits=4)
        maxfeatures = round(length(unlist(lapply(peaks,mass)))/length(peaks), digits=2)
        medint =round( median(unlist(lapply(peaks,intensity))), digits=2)
        binned = c(minmz, maxmz,maxfeatures, medint)
        QC_numbers= cbind(QC_numbers, binned)
        vectorofactions = append(vectorofactions, "binned")

        if (length(peaks[!sapply(peaks, isEmpty)])>0){
            #if $infile.ext == 'imzml'
                #if str($centroids) == "FALSE"
                    featureMatrix <- intensityMatrix(peaks, maldi_data)
                #end if
                    #if str($centroids) == "TRUE"
                        featureMatrix <- intensityMatrix(peaks)
                    #end if
            #else
                featureMatrix <- intensityMatrix(peaks)
            #end if
            featureMatrix2 =cbind(pixelnames, featureMatrix)
            colnames(featureMatrix2)[1] = c("mz")
            featureMatrix2 = t(featureMatrix2)
            write.table(featureMatrix2, file="$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 ) == '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,
            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

        ##QC plot and numbers
        plot(peaks[[1]], main="First spectrum after m/z filtering")
        minmz = round(min(unlist(lapply(peaks,mass))), digits=4)
        maxmz = round(max(unlist(lapply(peaks,mass))), digits=4)
        maxfeatures = round(length(unlist(lapply(peaks,mass)))/length(peaks), digits=2)
        medint = round(median(unlist(lapply(peaks,intensity))), digits=2)
        filtered = c(minmz, maxmz,maxfeatures, medint)
        QC_numbers= cbind(QC_numbers, filtered)
        vectorofactions = append(vectorofactions, "filtered")
  
        if (length(peaks[!sapply(peaks, isEmpty)])>0){
            #if $infile.ext == 'imzml'
                #if str($centroids) == "FALSE"
                    featureMatrix <- intensityMatrix(peaks, maldi_data)
                #end if
            #else
                featureMatrix <- intensityMatrix(peaks)
            #end if
            featureMatrix2 =cbind(pixelnames, featureMatrix)
            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")
    #end if
#end for

if (length(peaks[!sapply(peaks, isEmpty)])>0){
   ## mass peaks output
    mass_peaks = data.frame(matrix(,ncol=3, nrow=0))
    for (spectrum in 1:length(peaks)){
    spectrum_df = data.frame(peaks[[spectrum]]@snr, peaks[[spectrum]]@mass, peaks[[spectrum]]@intensity)
    spectrum_df\$spectrum_id = rep(pixelnames[[spectrum]], length(peaks[[spectrum]]@mass))
    mass_peaks = rbind(mass_peaks,spectrum_df)
    }
    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\nintensity")
plot(0,type='n',axes=FALSE,ann=FALSE)
grid.table(t(QC_numbers))

dev.off()

if (summarized_spectra == FALSE){ 
    #if $infile.ext == 'imzml'
        MALDIquantForeign::exportImzMl(peaks, file="out.imzMl", processed=$export_processed)
    #elif $infile.ext == 'tabular'
        masspeaks_coordinates = matrix(unlist(strsplit(as.character(pixelnames), "\\_")), ncol=3, byrow=TRUE)
        ## extract x and y values and create the coordinate matrix in case tabular was input
        peaklist_coordinates = unique(cbind(as.numeric(masspeaks_coordinates[,2]), as.numeric(masspeaks_coordinates[,3])))
        exportImzMl(peaks, file="out.imzMl", processed=$export_processed, coordinates=peaklist_coordinates)
    #elif $infile.ext == 'rdata'
        MALDIquantForeign::exportImzMl(peaks, file="out.imzMl", processed=$export_processed, coordinates=cardinal_coordinates)
    #end if

}

    ]]>
        </configfile>
    </configfiles>
    <inputs>
        <param name="infile" type="data" format="imzml,tabular,rdata" label="Inputfile as imzML or Cardinal MSImageSet saved as RData" help="This file is in imzML or tabular format (peak list, peak detection cannot be run again) or Cardinal MSImageSet saved as RData"/>
        <param name="centroids" type="boolean" label="Is the imzML/RData data centroided (picked)" 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="Read in only spectra of interest" help="This option only works for imzML files">
                <option value="no_restriction" selected="True">Calculate on entire file</option>
                <option value="restrict">Restrict to coordinates of interest</option>
            </param>
            <when value="restrict">
                <param name="coordinates_file" type="data" format="tabular" label="Tabular file with coordinates" help="x-values in first column, y-values in second column"/>
                <param name="coordinates_header" type="boolean" label="Tabular file contains a header line" truevalue="TRUE" falsevalue="FALSE"/>
            </when>
            <when value="no_restriction"/>
        </conditional>

        <conditional name="tabular_annotation">
            <param name="load_annotation" type="select" label="Use pixel annotation from tabular file - select in peak detection or filtering step where annotation should be used">
                <option value="no_annotation" selected="True">pixels belong into one group only</option>
                <option value="yes_annotation">use pixel annotation from a tabular file</option>
            </param>
            <when value="yes_annotation">
                <param name="annotation_file" type="data" format="tabular" label="Use annotations from tabular file"
                    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>
        <repeat name="methods" title="Method" min="1">
            <conditional name="methods_conditional">
                <param name="method" type="select" label="Select a method">
                    <option value="Peak_detection">Peak detection</option>
                    <option value="monoisotopic_peaks">Keep only monoisotopic peaks</option>
                    <option value="Binning">Binning</option>
                    <option value="Filtering">Filtering</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>
                    <param name="halfWindowSize" type="integer" value="20"
                        label="Half window size"
                        help="The resulting window reaches from 
                            mass[currentIndex-halfWindowSize] to mass[currentIndex+halfWindowSize]
                            (window size is 2*halfWindowSize+1).
                            The best size differs depending on the selected smoothing method."/>
                    <param name="snr" type="integer" value="2" label="Signal-to-noise-ratio" help=""/>
                    <param name="use_annotations" type="boolean" label="Generate average mass spectra for each annotation group" help="Spectra with same annotation are summarized, no imzML export possible" truevalue="TRUE" falsevalue="FALSE"/>
                </when>
                <when value="monoisotopic_peaks">
                    <param name="minCor" type="float" value="0.95" label="minimal correlation"
                        help="double , minimal correlation between the peak pattern generated by the model and the experimental peaks in the MassPeaks object to be recognized as isotopic pattern"/>
                    <param name="tolerance" type="float" label="tolerance" value="0.0004"
                        help="double, maximal relative deviation of peaks position (mass) to be considered as isotopic distance"/>
                    <param name="distance" type="float" label="distance" value="1.00235" help="double, distance between two consecutive peaks in an isotopic pattern"/>
                    <param name="size" type="integer" label="size" value="3" help="double, size (length) of isotopic pattern, longer patterns are prefered over shorter ones"/>
                </when>
                <when value="Binning">
                    <param name="bin_tolerance" type="float" value="0.002" label="Peak binning tolerance"
                        help="After the alignment the peak positions (mass) are very similar but not identical. The binning is needed to make similar peak mass values identical."/>
                </when>
                <when value="Filtering">
                    <param name="minFrequency" type="float" value="0.25"
                        label="Removal of all peaks which occur in less than minFrequency spectra" help="It is a relative threshold. The higher value from relative and absolute threshold is taken. Set one value to zero to be sure it will not be sure."/>
                    <param name="minNumber" type="float" value="1.0"
                        label="Removal of all peaks which occur in less than minNumber spectra" help="It is an absolute threshold. The higher value from relative and absolute threshold is taken. Set one value to zero to be sure it will not be sure."/>
                    <param name="filter_annot_groups" type="boolean" label="Group wise filtering with pixel annotations. 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 name="mergeWhitelists" type="boolean" truevalue="TRUE" falsevalue="FALSE"
                        label="mergeWhitelists" help="if FALSE the filtering criteria are applied groupwise. If TRUE peaks that survive the filtering in one group (level of labels) these peaks are also kept in other groups even if their frequencies are below minFrequency"/>
                </when>
            </conditional>
        </repeat>
        <param name="export_processed" type="boolean" label="Export file as processed imzML" help="otherwise continuous imzML will be exported" checked="true" truevalue="TRUE" falsevalue="FALSE"/>
    </inputs>
    <outputs>
        <data format="imzml" name="outfile_imzml" label="$infile.display_name peaks"/>
        <data format="pdf" name="plots" from_work_dir="peaks_qc_plot.pdf" label = "$infile.display_name peakdetection QC"/>
        <data format="tabular" name="masspeaks" label="$infile.display_name mass_peaks"/>
        <data format="tabular" name="intensity_matrix" label="intensity_matrix"/>
    </outputs>
    <tests>
        <test>
            <param name="infile" value="" ftype="imzml">
                <composite_data value="Example_Continuous.imzML"/>
                <composite_data value="Example_Continuous.ibd"/>
            </param>
                <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="TRUE"/>
                </conditional>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="method" value="Peak_detection"/>
                    <param name="peak_method" value="SuperSmoother"/>
                    <param name="halfWindowSize" value="1"/>
                    <param name="snr" value="5"/>
                    <param name="use_annotations" value="TRUE"/>
                </conditional>
            </repeat>
            <output name="plots" file="peakdetection1_QC.pdf" compare="sim_size"/>
            <output name="masspeaks" file="masspeaks1.tabular"/>
            <output name="intensity_matrix" file="int1.tabular"/>
        </test>
        <test>
            <param name="infile" value="masspeaks3_forinput.tabular"/>
            <param name="centroids" value="TRUE"/>
             <repeat name="methods">
                <conditional name="methods_conditional">
                <param name="method" value="monoisotopic_peaks"/>
            </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"/>
        </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">
                    <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="TRUE"/>
                </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="snr" value="2"/>
                </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"/>
        </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/>`_
    - 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
            5.34	304.16     0.10         xy_1_1
            12.09	305        0.2          xy_1_1
            6.80	306.25     0.133        xy_1_1
            ...
            ...    


- Optional:  Tabular file with pixel coordinates to restrict reading of imzML files to coordinates of interest. The file has to contain x values in the first column and y values in the second columns. Further columns are allowed. Tabular files with any header name or no header at all are supported. 

    ::

       x_coord     y_coord 
         1            1    
         2            1    
         3            1    
        ...
        ...


- Optional: Tabular file(s) containing pixel coordinates and annotation. X and y values in separate columns and the corresponding annotation in a third column. Tabular files with any header name or no header at all are supported. The annotations can be used to summarize pixels of an imzML file which belong to the same group and detect peaks on average spectra, further steps will be done on average spectra as well and average spectra are exported. If this option was not chosen the filtering tool can use the annotations to filter for peaks within pixel groups (select "Group wise filtering").

    ::

     x_coord     y_coord    annotation
        1            1        healthy
        2            1        healthy
        3            1        disease
       ...
       ...


**Options**

- Peak detection: detection of peaks, only possible with profile mode input
- Monoisotopic peaks: detection of monoisotopic peaks
- Peak binning: After the alignment the peak positions (m/z) are very similar but not identical. The binning is needed to make similar peak m/z values identical.
- Peak filtering: Removal of less frequent peaks (either with a minimum ratio or with an absolute minimum number of spectra in which the peak has to occur)


**Output**

- centroided imzML file (processed or continuous), except for peak picking on the average of multiple spectra
- pdf with mass spectra plots after each preprocessing step
- peak list (tabular file) with the columns "snr", "mass", "intensity" and "spectrum"
- tabular file with intensity matrix (m/z in rows and spectra in columns). If the input file was imzML in profile mode the intensities before peak picking are also stored in the matrix . For all other inputs not picked values are set to NA. For peak picking on the average of multiple spectra, each spectra group is a column with mean intensities for each m/z

.. _MALDIquant: http://strimmerlab.org/software/maldiquant/

        ]]>
    </help>
    <expand macro="citation"/>
</tool>