comparison msi_preprocessing.xml @ 3:b9523950e79d draft

planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_preprocessing commit 1c808d60243bb1eeda0cd26cb4b0a17ab05de2c0

2:50ba097d03e0

<tool id="mass_spectrometry_imaging_preprocessing" name="MSI preprocessing" version="1.7.0.2">
    <description>
        mass spectrometry imaging preprocessing
    </description>
    <requirements>
        <requirement type="package" version="1.7.0">bioconductor-cardinal</requirement>
        <requirement type="package" version="2.2.1">r-gridextra</requirement>
        <requirement type="package" version="0.20-35">r-lattice</requirement>
    </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 '$infile' infile.RData &&
        #end if
        cat '${cardinal_preprocessing}' &&
        Rscript '${cardinal_preprocessing}'

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

#if $infile.ext == 'imzml'
    msidata <- readMSIData('infile.imzML')
#elif $infile.ext == 'analyze75'
    msidata <- readMSIData('infile.hdr')
#else
    load('infile.RData')
#end if


##################################### Preparations for QC report #######################################################################################

#if $outputs.outputs_select == "quality_control":

    ### values for QC table:
    maxfeatures = length(features(msidata))

    QC_numbers= data.frame(rawdata = c(maxfeatures, medianpeaks, medint, TICs))
    vectorofactions = "rawdata"


        ### Read tabular file with calibrant masses: 
        calibrant_list = read.delim("$outputs.calibrant_file", header = FALSE, na.strings=c("","NA"), stringsAsFactors = FALSE)

            ### calculate how many input calibrant masses are valid: 
            inputcalibrants = calibrant_list[calibrant_list[,$outputs.calibrants_column]>min(mz(msidata)) & calibrant_list[,$outputs.calibrants_column]<max(mz(msidata)),$outputs.calibrants_column]
            number_calibrants_in = length(calibrant_list[,$outputs.calibrants_column])
            number_calibrants_valid = length(inputcalibrants)

      
        assign(paste("rawdata",calibrant, sep="_"), currentimage)
    }


        current_plot_raw <- vector(length(inputcalibrants), mode='list')


#end if

################################################### Preprocessing steps #######################################################################################
#for $method in $methods:
    #if str( $method.methods_conditional.preprocessing_method ) == 'Normalization':
        print('Normalization')
        ##normalization

        msidata <- normalize(msidata, method="tic")

        #if $outputs.outputs_select == "quality_control":
            ### values for QC table:
            maxfeatures = length(features(msidata))
            medianpeaks = median(colSums(spectra(msidata)[]>0))

        print('Smoothing')
        ## Smoothing

        #if str( $method.methods_conditional.methods_for_smoothing.smoothing_method) == 'gaussian':
            print('gaussian smoothing')
            msidata <- smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, sd = $method.methods_conditional.methods_for_smoothing.sd_gaussian)
        #elif str( $method.methods_conditional.methods_for_smoothing.smoothing_method) == 'sgolay':
            print('sgolay smoothing')
            msidata <- smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, order = $method.methods_conditional.methods_for_smoothing.order_of_filters)
        #elif str($method.methods_conditional.methods_for_smoothing.smoothing_method) == 'ma':
            print('sgolay smoothing')
            msidata <- smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, coef = $method.methods_conditional.methods_for_smoothing.coefficients_ma_filter)
        #end if

        #if $outputs.outputs_select == "quality_control":
            maxfeatures = length(features(msidata))
            medianpeaks = median(colSums(spectra(msidata)[]>0))

        #if str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_noref':
            align_peak_reference = msidata

        #elif str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_table':
            align_reference_table = read.delim("$method.methods_conditional.align_ref_type.align_peaks_table", header = FALSE, na.strings=c("","NA"), stringsAsFactors = FALSE)

            align_reference_column = align_reference_table[,$method.methods_conditional.align_ref_type.align_mass_column]

            align_peak_reference = align_reference_column[align_reference_column>=min(mz(msidata)) & align_reference_column<=max(mz(msidata))]
            if (length(align_peak_reference) == 0)

        #elif str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'peaks':
            print('peaks reduction')

            #if str( $method.methods_conditional.methods_for_reduction.ref_type.reference_datatype) == 'table':
                reference_table = read.delim("$method.methods_conditional.methods_for_reduction.ref_type.peaks_table", header = FALSE, na.strings=c("","NA"), stringsAsFactors = FALSE)
                reference_column = reference_table[,$method.methods_conditional.methods_for_reduction.ref_type.mass_column]
                peak_reference = reference_column[reference_column>min(mz(msidata)) & reference_column<max(mz(msidata))]

            #elif str( $method.methods_conditional.methods_for_reduction.ref_type.reference_datatype) == 'msidata_ref':
                      loadRData <- function(fileName){

                               scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="reduced")
              
               assign(paste("reduced",calibrant, sep="_"), currentimage)
            }
        #end if
    #end if
#end for

####################################################### Outputs: RData, tabular and QC report ######################################################


## save as (.RData)
save(msidata, file="$msidata_preprocessed")

    ]]></configfile>
    </configfiles>
    <inputs>
        <param name="infile" type="data" format="imzml,rdata,analyze75"
            label="MSI rawdata as imzml, analyze7.5 or Cardinal MSImageSet saved as RData"
            help="load imzml and ibd file by uploading composite datatype imzml"/>
        <repeat name="methods" title="Preprocessing" min="1" max="50">
            <conditional name="methods_conditional">
                <param name="preprocessing_method" type="select" label="Select the preprocessing methods you want to apply">

                    <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>
                </param>

                <when value="Normalization"/>
                <when value="Baseline_reduction">
                    <param name="blocks_baseline" type="integer" value="50"
                        label="Blocks"/>
                </when>

                            <option value="resample">resample</option>
                            <option value="peaks">peaks</option>
                        </param>
                        <when value="bin">
                            <param name="bin_width" type="float" value="1"
                                   label="The width of a bin in mz or ppm" help="Width must be greater than Range of mz values/Number of mz features"/>
                            <param name="bin_units" type="select" display="radio"
                                   label="Unit for bin">
                                    <option value="mz" selected="True">mz</option>
                                    <option value="ppm">ppm</option>
                            </param>

                                    <option value="sum">sum</option>
                            </param>
                        </when>
                        <when value="resample">
                            <param name="resample_step" type="float" value="1"
                                   label="The step size in mz" help="Step size must be greater than Range of mz values/Number of mz 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>

                                <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>
            </conditional>
        </repeat>
        <conditional name="outputs">

        <data format="tabular" name="matrixasoutput" label="Intensity matrix ${on_string}">
            <filter>output_matrix</filter>
        </data>
    </outputs>
    <tests>
        <test>
            <param name="infile" value="" ftype="imzml">
                <composite_data value="Example_Continuous.imzML"/>
                <composite_data value="Example_Continuous.ibd"/>
            </param>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Normalization" />
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Smoothing" />
                    <conditional name="methods_for_smoothing">
                        <param name="smoothing_method" value="gaussian" />
                    </conditional>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_picking" />
                    <conditional name="methods_for_picking">
                        <param name="picking_method" value="adaptive" />
                    </conditional>
                    <param name="blocks_picking" value="3" />
                    <param name="window_picking" value="3" />
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_alignment" />
                    <conditional name="methods_for_alignment">
                        <param name="alignment_method" value="diff" />
                    </conditional>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_filtering" />
                    <param name="frequ_filtering" value="2"/>
                </conditional>
            </repeat>
            <param name="outputs_select" value="no_quality_control"/>
            <param name="output_matrix" value="True"/>
            <output name="msidata_preprocessed" file="preprocessing_results1.RData" compare="sim_size" />
            <output name="matrixasoutput" file="preprocessing_results1.txt" />
        </test>
        <test>
            <param name="infile" value="example_continous.RData" ftype="rdata"/>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_picking" />
                    <param name="blocks_picking" value="3" />
                    <param name="window_picking" value="5" />
                    <param name="methods_for_picking" value="simple" />
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_alignment" />
                    <param name="methods_for_alignment" value="DP" />
                </conditional>
            </repeat>
            <param name="outputs_select" value="quality_control"/>
            <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile2.tabular"/>
            <param name="calibrants_column" value="1"/>
            <param name="plusminus_dalton" value="0.25"/>
            <param name="output_matrix" value="True"/>
            <output name="msidata_preprocessed" file="preprocessing_results2.RData" compare="sim_size" />
            <output name="matrixasoutput" file="preprocessing_results2.txt" lines_diff="2" />
            <output name="QC_plots" file="preprocessing_results2.pdf" compare="sim_size"/>
        </test>
        <test>
            <param name="infile" value="" ftype="analyze75">
                <composite_data value="Analyze75.hdr"/>
                <composite_data value="Analyze75.img"/>
                <composite_data value="Analyze75.t2m"/>
            </param>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Normalization" />
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_picking" />
                    <param name="blocks_picking" value="3" />
                    <param name="window_picking" value="5" />
                    <param name="methods_for_picking" value="limpic" />
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_alignment" />
                    <param name="methods_for_alignment" value="diff" />
                </conditional>
            </repeat>
            <param name="outputs_select" value="quality_control"/>
            <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile2.tabular"/>
            <param name="calibrants_column" value="1"/>
            <param name="plusminus_dalton" value="0.25"/>
            <output name="msidata_preprocessed" file="preprocessing_results3.RData" compare="sim_size" />
            <output name="QC_plots" file="preprocessing_results3.pdf" compare="sim_size"/>
        </test>
        <test>
            <param name="infile" value="" ftype="analyze75">
                <composite_data value="Analyze75.hdr"/>
                <composite_data value="Analyze75.img"/>
                <composite_data value="Analyze75.t2m"/>
            </param>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Normalization" />
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Data_reduction" />
                    <param name="bin_width" value="0.1" />
                </conditional>
            </repeat>
            <param name="outputs_select" value="no_quality_control"/>
            <param name="output_matrix" value="True"/>
            <output name="msidata_preprocessed" file="preprocessing_results4.RData" compare="sim_size" />
            <output name="matrixasoutput" file="preprocessing_results4.txt" />
        </test>
        <test>
            <param name="infile" value="" ftype="imzml">
                <composite_data value="Example_Continuous.imzML"/>
                <composite_data value="Example_Continuous.ibd"/>
            </param>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Data_reduction" />
                    <param name="step_width" value="0.1" />
                </conditional>
            </repeat>
            <param name="outputs_select" value="quality_control"/>
            <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile1.tabular"/>
            <param name="calibrants_column" value="1"/>
            <param name="plusminus_dalton" value="0.25"/>
            <param name="output_matrix" value="True"/>
            <output name="msidata_preprocessed" file="preprocessing_results5.RData" compare="sim_size" />
            <output name="matrixasoutput" file="preprocessing_results5.txt" />
            <output name="QC_plots" file="preprocessing_results5.pdf" compare="sim_size"/>
        </test>
    </tests>
    <help>
        <![CDATA[
Cardinal is an R package that implements statistical & computational tools for analyzing mass spectrometry imaging datasets.

**Preprocessing steps:**

- Smoothening: Smoothing of the mass peaks reduces noise and improves peak detection
- Normalization: Normalization of intensities to total ion current (TIC)
- Baseline reduction: Baseline  reduction removes backgroundintensity generated by chemical noise (common in MALDI datasets)
- Peak picking: relevant peaks are picked while noise-peaks are removed
- Peak alignment: after peak picking, mz inaccuracies are removed by alignment of same peaks to a common mz value
- Peak filtering: after peak picking and alignment, removes peaks that occur infrequently, such as those which only occur in a small proportion of pixels. If not sure which cutoff to chose run qualitycontrol first and decide according to the zero value plot.
- Data reduction: binning, resampling or peak filtering to reduce data
        ]]>
    </help>
    <citations>
        <citation type="doi">10.1093/bioinformatics/btv146</citation>
    </citations>

3:b9523950e79d

<tool id="mass_spectrometry_imaging_preprocessing" name="MSI preprocessing" version="1.10.0.0">
    <description>
        mass spectrometry imaging preprocessing
    </description>
    <requirements>
        <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement>
        <requirement type="package" version="2.2.1">r-gridextra</requirement>
        <requirement type="package" version="0.20-35">r-lattice</requirement>
    </requirements>
    <command detect_errors="exit_code">
    <![CDATA[

        #if $infile.ext == 'imzml'
            ln -s '${infile.extra_files_path}/imzml' infile.imzML &&
            ln -s '${infile.extra_files_path}/ibd' infile.ibd &&
        #elif $infile.ext == 'analyze75'
            ln -s '${infile.extra_files_path}/hdr' infile.hdr &&
            ln -s '${infile.extra_files_path}/img' infile.img &&
            ln -s '${infile.extra_files_path}/t2m' infile.t2m &&
        #else
            ln -s '$infile' infile.RData &&
        #end if
        cat '${cardinal_preprocessing}' &&
        Rscript '${cardinal_preprocessing}'

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

#if $infile.ext == 'imzml'
    msidata = readImzML('infile')
#elif $infile.ext == 'analyze75'
    msidata = readAnalyze('infile')
#else
    load('infile.RData')
#end if


##################################### Preparations for QC report ###############

#if $outputs.outputs_select == "quality_control":

    ### values for QC table:
    maxfeatures = length(features(msidata))

    QC_numbers= data.frame(rawdata = c(maxfeatures, medianpeaks, medint, TICs))
    vectorofactions = "rawdata"


        ### Read tabular file with calibrant masses: 
        calibrant_list = read.delim("$outputs.calibrant_file", header = FALSE, stringsAsFactors = FALSE)

            ### calculate how many input calibrant masses are valid: 
            inputcalibrants = calibrant_list[calibrant_list[,$outputs.calibrants_column]>min(mz(msidata)) & calibrant_list[,$outputs.calibrants_column]<max(mz(msidata)),$outputs.calibrants_column]
            number_calibrants_in = length(calibrant_list[,$outputs.calibrants_column])
            number_calibrants_valid = length(inputcalibrants)

      
        assign(paste("rawdata",calibrant, sep="_"), currentimage)
    }


        current_plot_raw = vector(length(inputcalibrants), mode='list')


#end if

############################### Preprocessing steps ###########################
#for $method in $methods:

    #if str( $method.methods_conditional.preprocessing_method ) == 'Normalization':
        print('Normalization')
        ##normalization

        msidata = normalize(msidata, method="tic")

        #if $outputs.outputs_select == "quality_control":
            ### values for QC table:
            maxfeatures = length(features(msidata))
            medianpeaks = median(colSums(spectra(msidata)[]>0))

        print('Smoothing')
        ## Smoothing

        #if str( $method.methods_conditional.methods_for_smoothing.smoothing_method) == 'gaussian':
            print('gaussian smoothing')
            msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, sd = $method.methods_conditional.methods_for_smoothing.sd_gaussian)
        #elif str( $method.methods_conditional.methods_for_smoothing.smoothing_method) == 'sgolay':
            print('sgolay smoothing')
            msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, order = $method.methods_conditional.methods_for_smoothing.order_of_filters)
        #elif str($method.methods_conditional.methods_for_smoothing.smoothing_method) == 'ma':
            print('sgolay smoothing')
            msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, coef = $method.methods_conditional.methods_for_smoothing.coefficients_ma_filter)
        #end if

        #if $outputs.outputs_select == "quality_control":
            maxfeatures = length(features(msidata))
            medianpeaks = median(colSums(spectra(msidata)[]>0))

        #if str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_noref':
            align_peak_reference = msidata

        #elif str( $method.methods_conditional.align_ref_type.align_reference_datatype) == 'align_table':
            align_reference_table = read.delim("$method.methods_conditional.align_ref_type.align_peaks_table", header = FALSE, stringsAsFactors = FALSE)

            align_reference_column = align_reference_table[,$method.methods_conditional.align_ref_type.align_mass_column]

            align_peak_reference = align_reference_column[align_reference_column>=min(mz(msidata)) & align_reference_column<=max(mz(msidata))]
            if (length(align_peak_reference) == 0)

        #elif str( $method.methods_conditional.methods_for_reduction.reduction_method) == 'peaks':
            print('peaks reduction')

            #if str( $method.methods_conditional.methods_for_reduction.ref_type.reference_datatype) == 'table':
                reference_table = read.delim("$method.methods_conditional.methods_for_reduction.ref_type.peaks_table", header = FALSE, stringsAsFactors = FALSE)
                reference_column = reference_table[,$method.methods_conditional.methods_for_reduction.ref_type.mass_column]
                peak_reference = reference_column[reference_column>min(mz(msidata)) & reference_column<max(mz(msidata))]

            #elif str( $method.methods_conditional.methods_for_reduction.ref_type.reference_datatype) == 'msidata_ref':
                      loadRData <- function(fileName){

                               scales = list(draw = FALSE), plusminus = $outputs.plusminus_dalton, main="reduced")
              
               assign(paste("reduced",calibrant, sep="_"), currentimage)
            }
        #end if

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

            #if str( $method.methods_conditional.transf_conditional.trans_type) == 'log2':
                print('log2 transformation')
                spectra(msidata)[spectra(msidata) ==0] = NA
                print(paste0("Number of 0 which were converted into NA:",sum(is.na(spectra(msidata)))))
                spectra(msidata) = log2(spectra(msidata))

            #elif str( $method.methods_conditional.transf_conditional.trans_type) == 'sqrt':
                print('squareroot transformation')
                spectra(msidata) = sqrt(spectra(msidata))

            #end if
    #end if
#end for

###################### Outputs: RData, tabular and QC report ###################


## save as (.RData)
save(msidata, file="$msidata_preprocessed")

    ]]></configfile>
    </configfiles>
    <inputs>
        <param name="infile" type="data" format="imzml,rdata,danalyze75"
            label="MSI rawdata as imzml, analyze7.5 or Cardinal MSImageSet saved as RData"
            help="load imzml and ibd file by uploading composite datatype imzml"/>
        <repeat name="methods" title="Preprocessing" min="1" max="50">
            <conditional name="methods_conditional">
                <param name="preprocessing_method" type="select" label="Select the preprocessing methods you want to apply">

                    <option value="Smoothing">Peak smoothing</option>
                    <option value="Peak_picking">Peak picking</option>
                    <option value="Peak_alignment">Peak alignment</option>
                    <option value="Peak_filtering">Peak filtering</option>
                    <option value="Data_reduction">Data reduction</option>
                    <option value="Transformation">Transformation</option>
                </param>
                <when value="Normalization"/>
                <when value="Baseline_reduction">
                    <param name="blocks_baseline" type="integer" value="50"
                        label="Blocks"/>
                </when>

                            <option value="resample">resample</option>
                            <option value="peaks">peaks</option>
                        </param>
                        <when value="bin">
                            <param name="bin_width" type="float" value="1"
                                   label="The width of a bin in mz or ppm" help="Width must be greater than range of mz values divided by number of mz features"/>
                            <param name="bin_units" type="select" display="radio"
                                   label="Unit for bin">
                                    <option value="mz" selected="True">mz</option>
                                    <option value="ppm">ppm</option>
                            </param>

                                    <option value="sum">sum</option>
                            </param>
                        </when>
                        <when value="resample">
                            <param name="resample_step" type="float" value="1"
                                   label="The step size in mz" help="Step size must be greater than range of mz values divided by number of mz features"/>
                        </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>

                                <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="Choose which intensity transformation you want to apply" help="logarithm base 2 (log2) or squareroot (sqrt)">
                            <option value="log2" selected="True">log2</option>
                            <option value="sqrt">sqrt</option>
                        </param>
                            <when value="log2"/>
                            <when value="sqrt"/>
                    </conditional>
                </when>
            </conditional>
        </repeat>
        <conditional name="outputs">

        <data format="tabular" name="matrixasoutput" label="Intensity matrix ${on_string}">
            <filter>output_matrix</filter>
        </data>
    </outputs>
    <tests>
        <test expect_num_outputs="2">
            <param name="infile" value="" ftype="imzml">
                <composite_data value="Example_Continuous.imzML"/>
                <composite_data value="Example_Continuous.ibd"/>
            </param>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Normalization"/>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Smoothing"/>
                    <conditional name="methods_for_smoothing">
                        <param name="smoothing_method" value="gaussian"/>
                    </conditional>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_picking"/>
                    <conditional name="methods_for_picking">
                        <param name="picking_method" value="adaptive"/>
                    </conditional>
                    <param name="blocks_picking" value="3"/>
                    <param name="window_picking" value="3"/>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_alignment"/>
                    <conditional name="methods_for_alignment">
                        <param name="alignment_method" value="diff"/>
                    </conditional>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_filtering"/>
                    <param name="frequ_filtering" value="2"/>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Transformation"/>
                        <conditional name="transf_conditional">
                            <param name="trans_type" value="sqrt"/>
                        </conditional>
                </conditional>
            </repeat>
            <param name="outputs_select" value="no_quality_control"/>
            <param name="output_matrix" value="True"/>
            <output name="msidata_preprocessed" file="preprocessing_results1.RData" compare="sim_size"/>
            <output name="matrixasoutput" file="preprocessing_results1.txt"/>
        </test>
        <test expect_num_outputs="3">
            <param name="infile" value="example_continous.RData" ftype="rdata"/>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_picking"/>
                    <param name="blocks_picking" value="3"/>
                    <param name="window_picking" value="5"/>
                    <conditional name="methods_for_picking">
                        <param name="picking_method" value="simple"/>
                    </conditional>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_alignment"/>
                    <conditional name="methods_for_alignment">
                        <param name="alignment_method" value="DP"/>
                    </conditional>
                </conditional>
            </repeat>
            <param name="outputs_select" value="quality_control"/>
            <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile2.tabular"/>
            <param name="calibrants_column" value="1"/>
            <param name="plusminus_dalton" value="0.25"/>
            <param name="output_matrix" value="True"/>
            <output name="msidata_preprocessed" file="preprocessing_results2.RData" compare="sim_size"/>
            <output name="matrixasoutput" file="preprocessing_results2.txt" lines_diff="2"/>
            <output name="QC_plots" file="preprocessing_results2.pdf" compare="sim_size"/>
        </test>
        <test expect_num_outputs="2">
            <param name="infile" value="" ftype="analyze75">
                <composite_data value="Analyze75.hdr"/>
                <composite_data value="Analyze75.img"/>
                <composite_data value="Analyze75.t2m"/>
            </param>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Normalization"/>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_picking"/>
                    <param name="blocks_picking" value="100"/>
                    <param name="window_picking" value="5"/>
                    <conditional name="methods_for_picking">
                        <param name="picking_method" value="limpic"/>
                    </conditional>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_alignment"/>
                    <conditional name="methods_for_alignment">
                        <param name="alignment_method" value="diff"/>
                    </conditional>
                </conditional>
            </repeat>
            <param name="outputs_select" value="quality_control"/>
            <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile2.tabular"/>
            <param name="calibrants_column" value="1"/>
            <param name="plusminus_dalton" value="0.25"/>
            <output name="msidata_preprocessed" file="preprocessing_results3.RData" compare="sim_size"/>
            <output name="QC_plots" file="preprocessing_results3.pdf" compare="sim_size"/>
        </test>
        <test expect_num_outputs="2">
            <param name="infile" value="" ftype="analyze75">
                <composite_data value="Analyze75.hdr"/>
                <composite_data value="Analyze75.img"/>
                <composite_data value="Analyze75.t2m"/>
            </param>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Normalization"/>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Data_reduction"/>
                    <param name="bin_width" value="0.1"/>
                </conditional>
            </repeat>
            <param name="outputs_select" value="no_quality_control"/>
            <param name="output_matrix" value="True"/>
            <output name="msidata_preprocessed" file="preprocessing_results4.RData" compare="sim_size"/>
            <output name="matrixasoutput" file="preprocessing_results4.txt"/>
        </test>
        <test expect_num_outputs="3">
            <param name="infile" value="" ftype="imzml">
                <composite_data value="Example_Continuous.imzML"/>
                <composite_data value="Example_Continuous.ibd"/>
            </param>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Data_reduction"/>
                        <conditional name="methods_for_reduction">
                            <param name="reduction_method" value="resample"/>
                            <param name="step_width" value="0.1"/>
                        </conditional>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Transformation"/>
                        <conditional name="transf_conditional">
                            <param name="trans_type" value="log2"/>
                        </conditional>
                </conditional>
            </repeat>
            <param name="outputs_select" value="quality_control"/>
            <param name="calibrant_file" ftype="tabular" value="inputcalibrantfile1.tabular"/>
            <param name="calibrants_column" value="1"/>
            <param name="plusminus_dalton" value="0.25"/>
            <param name="output_matrix" value="True"/>
            <output name="msidata_preprocessed" file="preprocessing_results5.RData" compare="sim_size"/>
            <output name="matrixasoutput" file="preprocessing_results5.txt"/>
            <output name="QC_plots" file="preprocessing_results5.pdf" compare="sim_size"/>
        </test>
    </tests>
    <help>
        <![CDATA[

Cardinal is an R package that implements statistical & computational tools for analyzing mass spectrometry imaging datasets. `More information on Cardinal <http://cardinalmsi.org//>`_

This tool provides provides multiple Cardinal functions to preprocess mass-spectrometry imaging data. 

Input data: 3 types of input data can be used:

- imzml file (upload imzml and ibd file via the "composite" function) `Introduction to the imzml format <https://ms-imaging.org/wp/imzml/>`_
- Analyze7.5 (upload hdr, img and t2m file via the "composite" function)
- Cardinal "MSImageSet" data (with variable name "msidata", saved as .RData)

Options:

- Normalization: Normalization of intensities to total ion current (TIC)
- Baseline reduction: Baseline  reduction removes backgroundintensity generated by chemical noise (common in MALDI datasets)
- Smoothening: Smoothing of the mass peaks reduces noise and improves peak detection
- Peak picking: relevant peaks are picked while noise-peaks are removed (needs peak alignment afterwards)
- Peak alignment: only possible after peak picking, mz inaccuracies are removed by alignment of same peaks to a common mz value
- Peak filtering: works only on centroided data (after peak picking and alignment or data reduction with peak filtering), removes peaks that occur only in a small proportion of pixels. If not sure which cutoff to chose run qualitycontrol first and decide according to the zero value plot.
- Data reduction: binning, resampling or peak filtering to reduce data
- Transformation: log2 or squareroot transformation of all intensities

Output: 

- imzML file, preprocessed
- optional: pdf with heatmap of masses of interest after each preprocessing step
- optional: intensity matrix as tabular file (intensities for masses in rows and pixel in columns)

Tip: 

- Peak alignment works only after peak picking
- Peak filtering works only on centroided data (peak picking and alignment or Data reduction peaks)

        ]]>
    </help>
    <citations>
        <citation type="doi">10.1093/bioinformatics/btv146</citation>
    </citations>