comparison preprocessing.xml @ 7:44a4b31fcbf3 draft

"planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit f986c51abe33c7f622d429a3c4a79ee24b33c1f3"

6:5bf056c0354e

<tool id="cardinal_preprocessing" name="MSI preprocessing" version="2.4.0.0">
    <description>
        mass spectrometry imaging preprocessing
    </description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <requirements>
        <requirement type="package" version="2.4.0">bioconductor-cardinal</requirement>
        <requirement type="package" version="3.6.1">r-base</requirement>
        <requirement type="package" version="2.3">r-gridextra</requirement>
        <requirement type="package" version="3.2.1">r-ggplot2</requirement>
        <requirement type="package" version="0.20_38">r-lattice</requirement>
    </requirements>
    <command detect_errors="exit_code">
    <![CDATA[

        @INPUT_LINKING@
        cat '${cardinal_preprocessing}' &&

    <configfiles>
        <configfile name="cardinal_preprocessing"><![CDATA[

################################# load libraries and read file #################

library(Cardinal)
library(gridExtra)
library(lattice)
library(ggplot2)


        ## function to read RData files independent of filename
        loadRData <- function(fileName){
        load(fileName)
        get(ls()[ls() != "fileName"])
        }

        #if $infile.ext == 'imzml'
            #if str($processed_cond.processed_file) == "processed":
                msidata <- readImzML('infile', resolution=$processed_cond.accuracy, units = "$processed_cond.units")
                centroided(msidata) = $centroids
            #else
                msidata <- readImzML('infile')
                centroided(msidata) = $centroids
            #end if
        #elif $infile.ext == 'analyze75'
            msidata = readAnalyze('infile')
            centroided(msidata) = $centroids
        #else
            msidata = loadRData('infile.RData')
            msidata = as(msidata, "MSImagingExperiment")
        #end if


## remove duplicated coordinates, otherwise peak picking and log2 transformation will fail
msidata <- msidata[,!duplicated(coord(msidata)[,1:2])] 

            if (class(msidata) == "MSProcessedImagingExperiment"){
                msidata = as(msidata, "MSContinuousImagingExperiment")
            }

            msidata = normalize(msidata, method="tic")
            msidata <- process(msidata, BPPARAM=MulticoreParam())


            ############################### QC ###########################

            maxfeatures =nrow(msidata)

            if (class(msidata) == "MSProcessedImagingExperiment"){
                msidata = as(msidata, "MSContinuousImagingExperiment")
            }

            msidata = reduceBaseline(msidata, method="median", blocks=$method.methods_conditional.blocks_baseline, spar=$method.methods_conditional.spar_baseline)
            msidata <- process(msidata, BPPARAM=MulticoreParam())

            ############################### QC ###########################

            maxfeatures =nrow(msidata)
            pixelcount = ncol(msidata)

                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
            msidata <- process(msidata, BPPARAM=MulticoreParam())

            ############################### QC ###########################

            maxfeatures =nrow(msidata)
            pixelcount = ncol(msidata)

                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
            msidata <- process(msidata, BPPARAM=MulticoreParam())

            #if str($method.methods_conditional.imzml_output) == "cont_format":
                #set $continuous_format = True
            #end if

                msidata = peakAlign(msidata,tolerance =$method.methods_conditional.value_diffalignment, units = "$method.methods_conditional.units_diffalignment")

            #end if

            msidata <- process(msidata, BPPARAM=MulticoreParam())

            #if str($method.methods_conditional.imzml_output) == "cont_format":
                #set $continuous_format = True
            #end if

        #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_filtering':
            print('Peak_filtering')

            msidata = peakFilter(msidata, freq.min = $method.methods_conditional.frequ_filtering)
            msidata <- process(msidata, BPPARAM=MulticoreParam())

            ############################### QC ###########################

            maxfeatures =nrow(msidata)
            pixelcount = ncol(msidata)

            QC_numbers= cbind(QC_numbers, filtered)
            vectorofactions = append(vectorofactions, "filtered")
            print(plot(msidata, pixel=random_spectra))
            title("Spectra after filtering", outer=TRUE, line=0)

    ############################### Data reduction ###########################

        #elif str( $method.methods_conditional.preprocessing_method) == 'Data_reduction':
            print('Data_reduction')

            #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.mz_tabular", header = $method.methods_conditional.methods_for_reduction.ref_type.feature_header, stringsAsFactors = FALSE)
                    reference_column = reference_table[,$method.methods_conditional.methods_for_reduction.ref_type.feature_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

            ## coercition into new format
            msidata = as(msidata, "MSImagingExperiment")

                    <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">
                    <conditional name="methods_for_normalization">

                </when>
                <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">

                                  for throwing away false noise spikes that might occur inside peaks"/>
                        </when>
                        <when value="mad"/>
                        <when value="simple"/>
                    </conditional>
                <param name="imzml_output" type="boolean" label="imzML output in processed format" truevalue="proc_format" falsevalue="cont_format" help= "Processed imzML works only in MALDIquant tools, not yet in MSI tools (Cardinal)"/>
                </when>
                <when value="Peak_alignment">
                    <param name="value_diffalignment" type="float" value="200"
                           label="tolerance" help="Peaks that differ less than this value will be aligned together"/>
                    <param name="units_diffalignment" type="select" display="radio" optional="False" label="units">

                        <when value="align_noref"/>
                        <when value="align_table">
                            <expand macro="reading_1_column_mz_tabular" label="Tabular file with m/z features to use for alignment. Only the m/z values from the tabular file will be kept."/>
                        </when>
                    </conditional>
                <param name="imzml_output" type="boolean" label="imzML output in processed format" truevalue="proc_format" falsevalue="cont_format" help= "Processed imzML works only in MALDIquant tools, not yet in MSI tools (Cardinal)"/>
                </when>
                <when value="Peak_filtering">
                    <param name="frequ_filtering" type="float" value="0.01" max="1" min="0" label="Minimum frequency" help="Peaks that occur in the dataset in lesser proportion than this will be dropped (0.01 --> filtering for 1% of spectra)"/>
                </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"

                        </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">
                                    <expand macro="reading_1_column_mz_tabular" label="Tabular file with m/z features to extract from input file"/>
                                </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)">

                        <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>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_filtering"/>

                    <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>
            </repeat>
            <output name="QC_overview" file="preprocessing_results2.pdf" compare="sim_size"/>
            <output name="outfile_imzml" ftype="imzml" file="preprocessing_results2.imzml.txt" compare="sim_size">
                <extra_files type="file" file="preprocessing_results2.imzml" name="imzml" lines_diff="6"/>

                </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"/>
                    <conditional name="methods_for_picking">
                        <param name="picking_method" value="mad"/>
                    </conditional>
                </conditional>
                <param name="imzml_output" value="proc_format"/>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_alignment"/>
                </conditional>
            <param name="imzml_output" value="proc_format"/>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Transformation"/>
                        <conditional name="transf_conditional">
                            <param name="trans_type" value="log2"/>
                        </conditional>
                </conditional>
            </repeat>
            <output name="QC_overview" file="preprocessing_results3.pdf" compare="sim_size"/>
            <output name="outfile_imzml" ftype="imzml" file="preprocessing_results3.imzml.txt" compare="sim_size">
                <extra_files type="file" file="preprocessing_results3.imzml" name="imzml" lines_diff="6"/>

            </conditional>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Transformation"/>
                        <conditional name="transf_conditional">
                            <param name="trans_type" value="sqrt"/>
                        </conditional>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">

- 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; if no reference is given the peaks are aligned to the local maxima of the mean spectrum of the current dataset; external reference data can be used from another MSI data file or a tabular file with m/z values, but then only the m/z from the reference will be kept
- Peak filtering: removes peaks that occur only in a small proportion of pixels. If not sure which cut off to choose run quality control tool first and decide according to the number of peaks per m/z 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**

- MSI data as continuous imzML file (option to output processed imzML file only after peak picking/peak alignment; but is not yet compatible with other MSI tools) 
- pdf with key values and four random mass spectra after each processing step

        ]]>
    </help>
    <expand macro="citations"/>

7:44a4b31fcbf3

<tool id="cardinal_preprocessing" name="MSI preprocessing" version="@VERSION@.1">
    <description>
        mass spectrometry imaging preprocessing
    </description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements">
        <requirement type="package" version="2.3">r-gridextra</requirement>
        <requirement type="package" version="3.2.1">r-ggplot2</requirement>
    </expand>
    <command detect_errors="exit_code">
    <![CDATA[

        @INPUT_LINKING@
        cat '${cardinal_preprocessing}' &&

    <configfiles>
        <configfile name="cardinal_preprocessing"><![CDATA[

################################# load libraries and read file #################

## set CPU, default = 1

if (Sys.getenv("GALAXY_SLOTS")!="")
    {
        number_cpu = 1 ## default = 1
    }else{
        number_cpu = as.numeric(Sys.getenv("GALAXY_SLOTS")) ##cpu set by Galaxy
    }

library(Cardinal)
library(gridExtra)
library(ggplot2)


@READING_MSIDATA_FULLY_COMPATIBLE@


## remove duplicated coordinates, otherwise peak picking and log2 transformation will fail
msidata <- msidata[,!duplicated(coord(msidata)[,1:2])] 

            if (class(msidata) == "MSProcessedImagingExperiment"){
                msidata = as(msidata, "MSContinuousImagingExperiment")
            }

            msidata = normalize(msidata, method="tic")
            msidata <- process(msidata, BPPARAM=MulticoreParam(workers=number_cpu))


            ############################### QC ###########################

            maxfeatures =nrow(msidata)

            if (class(msidata) == "MSProcessedImagingExperiment"){
                msidata = as(msidata, "MSContinuousImagingExperiment")
            }

            msidata = reduceBaseline(msidata, method="median", blocks=$method.methods_conditional.blocks_baseline, spar=$method.methods_conditional.spar_baseline)
            msidata <- process(msidata, BPPARAM=MulticoreParam(workers=number_cpu))

            ############################### QC ###########################

            maxfeatures =nrow(msidata)
            pixelcount = ncol(msidata)

                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
            msidata <- process(msidata, BPPARAM=MulticoreParam(workers=number_cpu))

            ############################### QC ###########################

            maxfeatures =nrow(msidata)
            pixelcount = ncol(msidata)

                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
            msidata <- process(msidata, BPPARAM=MulticoreParam(workers=number_cpu))

            #if str($method.methods_conditional.imzml_output) == "cont_format":
                #set $continuous_format = True
            #end if

                msidata = peakAlign(msidata,tolerance =$method.methods_conditional.value_diffalignment, units = "$method.methods_conditional.units_diffalignment")

            #end if

            msidata <- process(msidata, BPPARAM=MulticoreParam(workers=number_cpu))

            #if str($method.methods_conditional.imzml_output) == "cont_format":
                #set $continuous_format = True
            #end if

        #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_filtering':
            print('Peak_filtering')

            msidata = peakFilter(msidata, freq.min = $method.methods_conditional.frequ_filtering)
            msidata <- process(msidata, BPPARAM=MulticoreParam(workers=number_cpu))

            ############################### QC ###########################

            maxfeatures =nrow(msidata)
            pixelcount = ncol(msidata)

            QC_numbers= cbind(QC_numbers, filtered)
            vectorofactions = append(vectorofactions, "filtered")
            print(plot(msidata, pixel=random_spectra))
            title("Spectra after filtering", outer=TRUE, line=0)

    ############################### Peak binning ###########################

        #elif str( $method.methods_conditional.preprocessing_method) == 'Peak_binning':
            print('Peak_binning')

            ## reading reference file
            reference_table = read.delim("$method.methods_conditional.mz_tabular", header = $method.methods_conditional.feature_header, stringsAsFactors = FALSE)
            reference_column = reference_table[,$method.methods_conditional.feature_column]
            peak_reference = reference_column[reference_column>min(mz(msidata)) & reference_column<max(mz(msidata))]

            msidata = peakBin(msidata, ref = peak_reference, tolerance = $method.methods_conditional.peakbin_tol, units = "$method.methods_conditional.peakbin_units", type="$method.methods_conditional.peaks_type")
            msidata <- process(msidata, BPPARAM=MulticoreParam(workers=number_cpu))

            ############################### QC ###########################

            maxfeatures =nrow(msidata)
            pixelcount = ncol(msidata)
            minmz = round(min(mz(msidata)), digits=2)
            maxmz = round(max(mz(msidata)), digits=2)
            peak_binned = c(minmz, maxmz,maxfeatures, pixelcount)
            QC_numbers= cbind(QC_numbers, peak_binned)
            vectorofactions = append(vectorofactions, "peak binned")
            print(plot(msidata, pixel=random_spectra))
            title("Spectra after peak binning", outer=TRUE, line=0)


    ############################### Data reduction ###########################

        #elif str( $method.methods_conditional.preprocessing_method) == 'Data_reduction':
            print('Data_reduction')

            #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)
            #end if

            ## coercition into new format
            msidata = as(msidata, "MSImagingExperiment")

                    <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="Peak_binning">Peak binning to reference peaks</option>
                    <option value="Data_reduction">Data reduction</option>
                    <option value="Transformation">Transformation</option>
                </param>
                <when value="Normalization">
                    <conditional name="methods_for_normalization">

                </when>
                <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">

                                  for throwing away false noise spikes that might occur inside peaks"/>
                        </when>
                        <when value="mad"/>
                        <when value="simple"/>
                    </conditional>
                <param name="imzml_output" type="boolean" label="imzML output in processed format" checked="True" truevalue="proc_format" falsevalue="cont_format"/>
                </when>
                <when value="Peak_alignment">
                    <param name="value_diffalignment" type="float" value="200"
                           label="tolerance" help="Peaks that differ less than this value will be aligned together"/>
                    <param name="units_diffalignment" type="select" display="radio" optional="False" label="units">

                        <when value="align_noref"/>
                        <when value="align_table">
                            <expand macro="reading_1_column_mz_tabular" label="Tabular file with m/z features to use for alignment. Only the m/z values from the tabular file will be kept."/>
                        </when>
                    </conditional>
                <param name="imzml_output" type="boolean" label="imzML output in processed format" checked="True" truevalue="proc_format" falsevalue="cont_format" help= "Processed imzML works only in MALDIquant tools, not yet in MSI tools (Cardinal)"/>
                </when>
                <when value="Peak_filtering">
                    <param name="frequ_filtering" type="float" value="0.01" max="1" min="0" label="Minimum frequency" help="Peaks that occur in the dataset in lesser proportion than this will be dropped (0.01 --> filtering for 1% of spectra)"/>
                </when>
                <when value="Peak_binning">
                    <expand macro="reading_1_column_mz_tabular" label="A reference to which the peaks are binned." help="Tabular file with m/z features to extract from input file"/>
                    <param name="peakbin_tol" value="NA" type="text" label="The tolerance to be used when matching the m/z features in the dataset to the reference. If this is NA, then automatically guess a resolution from the data." >
                        <sanitizer invalid_char="">
                            <valid initial="string.digits">
                                <add value="NA" />
                            </valid>
                        </sanitizer>
                    </param>
                    <param name="peakbin_units" display="radio" type="select" label="The units to use for the tolerance.">
                        <option value="mz" >mz</option>
                        <option value="ppm" selected="True" >ppm</option>
                    </param>
                    <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>
                </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>
                        </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"

                        </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>
                    </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)">

                        <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"/>
                    <param name="imzml_output" value="cont_format"/>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_alignment"/>
                    <param name="imzml_output" value="cont_format"/>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_filtering"/>

                    <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>
                    <param name="imzml_output" value="cont_format"/>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_alignment"/>
                    <param name="imzml_output" value="cont_format"/>
                </conditional>
            </repeat>
            <output name="QC_overview" file="preprocessing_results2.pdf" compare="sim_size"/>
            <output name="outfile_imzml" ftype="imzml" file="preprocessing_results2.imzml.txt" compare="sim_size">
                <extra_files type="file" file="preprocessing_results2.imzml" name="imzml" lines_diff="6"/>

                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_picking"/>
                    <param name="blocks_picking" value="20"/>
                    <param name="window_picking" value="5"/>
                    <param name="SNR_picking_method" value="2"/>
                    <conditional name="methods_for_picking">
                        <param name="picking_method" value="mad"/>
                    </conditional>
                <param name="imzml_output" value="proc_format"/>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_alignment"/>
                    <param name="imzml_output" value="proc_format"/>
                </conditional>
            </repeat>
            <output name="QC_overview" file="preprocessing_results3.pdf" compare="sim_size"/>
            <output name="outfile_imzml" ftype="imzml" file="preprocessing_results3.imzml.txt" compare="sim_size">
                <extra_files type="file" file="preprocessing_results3.imzml" name="imzml" lines_diff="6"/>

            </conditional>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Transformation"/>
                        <conditional name="transf_conditional">
                            <param name="trans_type" value="log2"/>
                        </conditional>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">

- 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; if no reference is given the peaks are aligned to the local maxima of the mean spectrum of the current dataset; external reference data can be used from another MSI data file or a tabular file with m/z values, but then only the m/z from the reference will be kept
- Peak filtering: removes peaks that occur only in a small proportion of pixels. If not sure which cut off to choose run quality control tool first and decide according to the number of peaks per m/z plot
- Peak binning: extracts peaks intensities (from a profile dataset) for a list of m/z (reference) values
- Data reduction: binning or resampling 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**

- MSI data as continuous or processed imzML file
- pdf with key values and four random mass spectra after each processing step

        ]]>
    </help>
    <expand macro="citations"/>