comparison msi_preprocessing.xml @ 10:df8d7f6f210b draft default tip

planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/msi_preprocessing commit e87eea03505ab1ba067e192bbbcdc6197dc4b42e

9:4d5578b57a77

<tool id="mass_spectrometry_imaging_preprocessing" name="MSI preprocessing" version="1.10.0.6">
    <description>
        mass spectrometry imaging preprocessing
    </description>
    <requirements>
        <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement>

        #elif str( $method.methods_conditional.preprocessing_method ) == 'Baseline_reduction':
            print('Baseline_reduction')
            ##baseline reduction

            msidata = reduceBaseline(msidata, method="median", blocks=$method.methods_conditional.blocks_baseline)

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

                maxfeatures = length(features(msidata))
                medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))

            #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

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

            ## remove duplicated coordinates, otherwise peak picking will fail
            print(paste0(sum(duplicated(coord(msidata))), " coordinates were removed"))
            msidata <- msidata[,!duplicated(coord(msidata))]

            #if str( $method.methods_conditional.methods_for_picking.picking_method) == 'adaptive':
                print('adaptive peakpicking')

            subsample_pixels = msidata[,msidata\$annotation == subsample]
            subsample_calc = apply(spectra(subsample_pixels)[],1,mean, na.rm=TRUE)
            sample_matrix = cbind(sample_matrix, subsample_calc)
            count = count+1
            }
            rownames(sample_matrix) = mz(msidata)
            colnames(sample_matrix) = levels(msidata\$annotation)
            write.table(sample_matrix, file="$summarized_output_mean", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
        }else{
            full_sample_calc = as.data.frame(apply(spectra(msidata)[],1,mean, na.rm=TRUE))
            rownames(full_sample_calc) = mz(msidata)
            colnames(full_sample_calc) = "$infile.display_name"
            write.table(full_sample_calc, file="$summarized_output_mean", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
        }

    #end if

    #if "median" in str($summary_type).split(","):

            sample_matrix = cbind(sample_matrix, subsample_calc)
            count = count+1
            }

            rownames(sample_matrix) = mz(msidata)
            colnames(sample_matrix) = levels(msidata\$annotation)
            write.table(sample_matrix, file="$summarized_output_median", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
        }else{
            full_sample_calc = as.data.frame(apply(spectra(msidata)[],1,median, na.rm=TRUE))
            rownames(full_sample_calc) = mz(msidata)
            colnames(full_sample_calc) = "$infile.display_name"
            write.table(full_sample_calc, file="$summarized_output_median", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
        }
    #end if

    #if "sd" in str($summary_type).split(","):
        print("sd matrix")

            subsample_calc = apply(spectra(subsample_pixels)[],1,sd, na.rm=TRUE)
            sample_matrix = cbind(sample_matrix, subsample_calc)
            count = count+1
            }

            rownames(sample_matrix) = mz(msidata)
            colnames(sample_matrix) = levels(msidata\$annotation)
            write.table(sample_matrix, file="$summarized_output_sd", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
        }else{

            full_sample_calc = as.data.frame(apply(spectra(msidata)[],1,sd, na.rm=TRUE))
            rownames(full_sample_calc) = mz(msidata)
            colnames(full_sample_calc) = "$infile.display_name"
            write.table(full_sample_calc, file="$summarized_output_sd", quote = FALSE, row.names = TRUE, col.names=NA, sep = "\t")
        }
    #end if
    print(paste0("Number of NA in output file: ",sum(is.na(spectra(msidata)[]))))

    ## save as (.RData)

    #if $output_matrix:

        if (length(features(msidata))> 0 & length(pixels(msidata)) > 0){
            spectramatrix = spectra(msidata)[]
            spectramatrix = cbind(mz(msidata),spectramatrix)
            newmatrix = rbind(c("mz | spectra", names(pixels(msidata))), spectramatrix)
            write.table(newmatrix, file="$matrixasoutput", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t")
        }else{
            print("file has no features or pixels left")
        }
    #end if

                </param>
            </when>
        </conditional>
        <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="Normalization" selected="True">Normalization to TIC</option>
                    <option value="Baseline_reduction">Baseline Reduction</option>
                    <option value="Smoothing">Peak smoothing</option>
                    <option value="Peak_picking">Peak picking</option>
                    <option value="Peak_alignment">Peak alignment</option>

                    <option value="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>
                <when value="Smoothing">
                    <conditional name="methods_for_smoothing">
                        <param name="smoothing_method" type="select" label="Smoothing method">
                            <option value="gaussian" selected="True">gaussian</option>
                            <option value="sgolay">Savitsky-Golay</option>
                            <option value="ma">moving average</option>
                        </param>
                        <when value="gaussian">
                            <param name="sd_gaussian" type="float" value="4"
                                   label="The standard deviation for the Gaussian kernel (window/sd)"/>
                        </when>
                        <when value="sgolay">
                            <param name="order_of_filters" type="integer" value="3"
                                   label="The order of the smoothing filter"/>
                        </when>
                        <when value="ma">
                            <param name="coefficients_ma_filter" type="integer" value="1"
                                   label="The coefficients for the moving average filter"/>
                        </when>
                    </conditional>
                    <param name="window_smoothing" type="integer" value="9"
                                label="Window size"/>
                </when>
                <when value="Peak_picking">
                    <param name="SNR_picking_method" type="integer" value="3"
                        label="Signal to noise ratio"
                        help="The minimal signal to noise ratio for peaks to be considered as a valid peak."/>
                    <param name="blocks_picking" type="integer" value="100" label = "Number of blocks"
                        help="Number of blocks in which to divide mass spectrum to calculate noise"/>
                    <param name="window_picking" type="integer" value="5" label= "Window size" help="Window width for seeking local maxima"/>
                    <conditional name="methods_for_picking">
                        <param name="picking_method" type="select" label="Peak picking method" help="only simple works for processed imzML files">
                            <option value="adaptive" selected="True">adaptive</option>
                            <option value="limpic">limpic</option>
                            <option value="simple">simple</option>

                        <param name="alignment_method" type="select" label="Alignment method">
                            <option value="diff" selected="True">diff</option>
                            <option value="DP">DP</option>
                        </param>
                        <when value="diff">
                            <param name="value_diffalignment" type="integer" value="200"
                                   label="diff.max" help="Peaks that differ less than this value will be aligned together"/>
                            <param name="units_diffalignment" type="select" display = "radio" optional = "False"
                                   label="units">
                                    <option value="ppm" selected="True">ppm</option>
                                    <option value="Da">m/z</option>
                            </param>
                        </when>
                        <when value="DP">
                            <param name="gap_DPalignment" type="integer" value="0"
                                   label="Gap" help="The gap penalty for the dynamic programming sequence alignment"/>
                        </when>
                    </conditional>
                    <conditional name="align_ref_type">
                        <param name="align_reference_datatype" type="select" label="Choose reference">

                        </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"/>

        <param name="output_matrix" type="boolean" label="Intensity matrix output"/>
    </inputs>
    <outputs>
        <data format="rdata" name="msidata_preprocessed" label="$infile.display_name preprocessed"/>
        <data format="pdf" name="QC_overview" from_work_dir="Preprocessing.pdf" label = "$infile.display_name preprocessed_QC"/>
        <data format="tabular" name="summarized_output_mean" label="$infile.display_name mean_matrix">
            <filter>summary_type and "mean" in summary_type</filter>
        </data>
        <data format="tabular" name="summarized_output_median" label="$infile.display_name median_matrix">
            <filter>summary_type and "median" in summary_type</filter>
        </data>
        <data format="tabular" name="summarized_output_sd" label="$infile.display_name sd_matrix">
            <filter>summary_type and "sd" in summary_type</filter>
        </data>
        <data format="tabular" name="matrixasoutput" label="$infile.display_name preprocessed_matrix">
            <filter>output_matrix</filter>
        </data>
    </outputs>
    <tests>
        <test expect_num_outputs="3">

            <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="tabular_annotation">
                <param name="load_annotation" value="no_annotation"/>
            </conditional>
            <param name="output_matrix" value="True"/>
            <output name="msidata_preprocessed" file="preprocessing_results1.RData" compare="sim_size"/>
            <output name="matrixasoutput" file="preprocessing_results1.txt"/>
            <output name="QC_overview" file="preprocessing_results1.pdf" compare="sim_size"/>
        </test>
        <test expect_num_outputs="4">
            <param name="infile" value="123_combined.RData" ftype="rdata"/>
            <repeat name="methods">

                        <param name="column_names" value="3"/>
                        <param name="tabular_header" value="FALSE"/>
            </conditional>
            <param name="summary_type" value="median,sd"/>
            <output name="msidata_preprocessed" file="preprocessing_results2.RData" compare="sim_size"/>
            <output name="summarized_output_median" file="preprocessing_median2.txt" lines_diff="2"/>
            <output name="summarized_output_sd" file="preprocessing_sd2.txt" lines_diff="2"/>
            <output name="QC_overview" file="preprocessing_results2.pdf" compare="sim_size"/>
        </test>
        <test expect_num_outputs="3">
            <param name="infile" value="" ftype="analyze75">
                <composite_data value="Analyze75.hdr"/>

            <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="picking_method" value="limpic"/>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">

            <conditional name="tabular_annotation">
                <param name="load_annotation" value="no_annotation"/>
            </conditional>
            <output name="msidata_preprocessed" file="preprocessing_results3.RData" compare="sim_size"/>
            <output name="QC_overview" file="preprocessing_results3.pdf" compare="sim_size"/>
            <output name="summarized_output_mean" file="preprocessing_mean3.txt" lines_diff="2"/>
        </test>
        <test expect_num_outputs="3">
            <param name="infile" value="" ftype="analyze75">
                <composite_data value="Analyze75.hdr"/>
                <composite_data value="Analyze75.img"/>

            <conditional name="tabular_annotation">
                <param name="load_annotation" value="no_annotation"/>
            </conditional>
            <param name="output_matrix" value="True"/>
            <output name="msidata_preprocessed" file="preprocessing_results4.RData" compare="sim_size"/>
            <output name="matrixasoutput" file="preprocessing_results4.txt"/>
            <output name="QC_overview" file="preprocessing_results4.pdf" compare="sim_size"/>
        </test>
        <test expect_num_outputs="2">
            <param name="infile" value="" ftype="imzml">
                <composite_data value="Example_Continuous.imzML"/>

10:df8d7f6f210b

<tool id="mass_spectrometry_imaging_preprocessing" name="MSI preprocessing" version="1.10.0.7">
    <description>
        mass spectrometry imaging preprocessing
    </description>
    <requirements>
        <requirement type="package" version="1.10.0">bioconductor-cardinal</requirement>

        #elif str( $method.methods_conditional.preprocessing_method ) == 'Baseline_reduction':
            print('Baseline_reduction')
            ##baseline reduction

            msidata = reduceBaseline(msidata, method="median", blocks=$method.methods_conditional.blocks_baseline, spar=$method.methods_conditional.spar_baseline)

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

                maxfeatures = length(features(msidata))
                medianpeaks = median(colSums(spectra(msidata)[]>0, na.rm=TRUE))

            #elif str( $method.methods_conditional.methods_for_smoothing.smoothing_method) == 'sgolay':
                print('sgolay smoothing')

                msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, order = $method.methods_conditional.methods_for_smoothing.order_of_filters)
            #elif str($method.methods_conditional.methods_for_smoothing.smoothing_method) == 'ma':
                print('moving average smoothing')

                msidata = smoothSignal(msidata, method="$method.methods_conditional.methods_for_smoothing.smoothing_method", window=$method.methods_conditional.window_smoothing, coef = $method.methods_conditional.methods_for_smoothing.coefficients_ma_filter)

            #end if

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

            ## remove duplicated coordinates, otherwise peak picking will fail
            print(paste0(sum(duplicated(coord(msidata))), " duplicated coordinates were removed"))
            msidata <- msidata[,!duplicated(coord(msidata))]

            #if str( $method.methods_conditional.methods_for_picking.picking_method) == 'adaptive':
                print('adaptive peakpicking')

            subsample_pixels = msidata[,msidata\$annotation == subsample]
            subsample_calc = apply(spectra(subsample_pixels)[],1,mean, na.rm=TRUE)
            sample_matrix = cbind(sample_matrix, subsample_calc)
            count = count+1
            }

            sample_matrix_mean = cbind(mz(msidata),sample_matrix)
            sample_matrix_mean = rbind(c("mz", levels(msidata\$annotation)), sample_matrix_mean)
            write.table(sample_matrix_mean, file="$summarized_mean", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t")
        }else{
            full_sample_calc_mean = as.data.frame(apply(spectra(msidata)[],1,mean, na.rm=TRUE))
            full_sample_calc_mean = cbind(mz(msidata),full_sample_calc_mean)
            full_sample_calc_mean = rbind(c("mz", "$infile.display_name"), full_sample_calc_mean)
            write.table(full_sample_calc_mean, file="$summarized_mean", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t")
        }

    #end if

    #if "median" in str($summary_type).split(","):

            sample_matrix = cbind(sample_matrix, subsample_calc)
            count = count+1
            }

            sample_matrix_median = cbind(mz(msidata),sample_matrix)
            sample_matrix_median = rbind(c("mz", levels(msidata\$annotation)), sample_matrix_median)
            write.table(sample_matrix_median, file="$summarized_median", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t")
        }else{

            full_sample_calc_median = as.data.frame(apply(spectra(msidata)[],1,median, na.rm=TRUE))
            full_sample_calc_median = cbind(mz(msidata),full_sample_calc_median)
            full_sample_calc_median = rbind(c("mz", "$infile.display_name"), full_sample_calc_median)
            write.table(full_sample_calc_median, file="$summarized_median", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t")
        }
    #end if

    #if "sd" in str($summary_type).split(","):
        print("sd matrix")

            subsample_calc = apply(spectra(subsample_pixels)[],1,sd, na.rm=TRUE)
            sample_matrix = cbind(sample_matrix, subsample_calc)
            count = count+1
            }

            sample_matrix_sd = cbind(mz(msidata),sample_matrix)
            sample_matrix_sd = rbind(c("mz", levels(msidata\$annotation)), sample_matrix_sd)
            write.table(sample_matrix_sd, file="$summarized_sd", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t")
        }else{

            full_sample_calc_sd = as.data.frame(apply(spectra(msidata)[],1,sd, na.rm=TRUE))
            full_sample_calc_sd = cbind(mz(msidata),full_sample_calc_sd)
            full_sample_calc_sd = rbind(c("mz", "$infile.display_name"), full_sample_calc_sd)
            write.table(full_sample_calc_sd, file="$summarized_sd", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t")
        }
    #end if
    print(paste0("Number of NA in output file: ",sum(is.na(spectra(msidata)[]))))

    ## save as (.RData)

    #if $output_matrix:

        if (length(features(msidata))> 0 & length(pixels(msidata)) > 0){
            spectramatrix = spectra(msidata)[]
            spectramatrix = cbind(mz(msidata),spectramatrix)
            newmatrix = rbind(c("mz", names(pixels(msidata))), spectramatrix)
            write.table(newmatrix, file="$intensity_matrix", quote = FALSE, row.names = FALSE, col.names=FALSE, sep = "\t")
        }else{
            print("file has no features or pixels left")
        }
    #end if

                </param>
            </when>
        </conditional>
        <repeat name="methods" title="Preprocessing" min="1" max="50">
            <conditional name="methods_conditional">
                <param name="preprocessing_method" type="select" label="Preprocessing methods">
                    <option value="Normalization" selected="True">Normalization to TIC</option>
                    <option value="Baseline_reduction">Baseline Reduction</option>
                    <option value="Smoothing">Peak smoothing</option>
                    <option value="Peak_picking">Peak picking</option>
                    <option value="Peak_alignment">Peak alignment</option>

                    <option value="Data_reduction">Data reduction</option>
                    <option value="Transformation">Transformation</option>
                </param>
                <when value="Normalization"/>
                <when value="Baseline_reduction">
                    <param name="blocks_baseline" type="integer" value="500"
                        label="Blocks"/>
                    <param name="spar_baseline" type="float" value="1.0" label="Spar value" 
                           help = "Smoothing parameter for the spline smoothing 
                                  applied to the spectrum in order to decide the cutoffs 
                              for throwing away false noise spikes that might occur inside peaks"/>
                </when>
                <when value="Smoothing">
                    <conditional name="methods_for_smoothing">
                        <param name="smoothing_method" type="select" label="Smoothing method">
                            <option value="gaussian" selected="True">gaussian</option>
                            <option value="sgolay">Savitsky-Golay</option>
                            <option value="ma">moving average</option>
                        </param>
                        <when value="gaussian">
                            <param name="sd_gaussian" type="float" value="2"
                                   label="The standard deviation for the Gaussian kernel (window/sd)"/>
                        </when>
                        <when value="sgolay">
                            <param name="order_of_filters" type="integer" value="3"
                                   label="The order of the smoothing filter, must be smaller than window size"/>
                        </when>
                        <when value="ma">
                            <param name="coefficients_ma_filter" type="float" value="1"
                                   label="The coefficients for the moving average filter"/>
                        </when>
                    </conditional>
                    <param name="window_smoothing" type="float" value="8"
                                label="Window size"/>
                </when>
                <when value="Peak_picking">
                    <param name="SNR_picking_method" type="integer" value="6"
                        label="Signal to noise ratio"
                        help="The minimal signal to noise ratio for peaks to be considered as a valid peak."/>
                    <param name="blocks_picking" type="integer" value="100" label = "Number of blocks"
                        help="Number of blocks in which to divide mass spectrum to calculate noise"/>
                    <param name="window_picking" type="float" value="5" label= "Window size" help="Window width for seeking local maxima"/>
                    <conditional name="methods_for_picking">
                        <param name="picking_method" type="select" label="Peak picking method" help="only simple works for processed imzML files">
                            <option value="adaptive" selected="True">adaptive</option>
                            <option value="limpic">limpic</option>
                            <option value="simple">simple</option>

                        <param name="alignment_method" type="select" label="Alignment method">
                            <option value="diff" selected="True">diff</option>
                            <option value="DP">DP</option>
                        </param>
                        <when value="diff">
                            <param name="value_diffalignment" type="float" value="200"
                                   label="diff.max" help="Peaks that differ less than this value will be aligned together"/>
                            <param name="units_diffalignment" type="select" display = "radio" optional = "False"
                                   label="units">
                                    <option value="ppm" selected="True">ppm</option>
                                    <option value="Da">m/z</option>
                            </param>
                        </when>
                        <when value="DP">
                            <param name="gap_DPalignment" type="float" value="0"
                                   label="Gap" help="The gap penalty for the dynamic programming sequence alignment"/>
                        </when>
                    </conditional>
                    <conditional name="align_ref_type">
                        <param name="align_reference_datatype" type="select" label="Choose reference">

                        </when>
                    </conditional>
                </when>
                <when value="Transformation">
                    <conditional name="transf_conditional">
                        <param name="trans_type" type="select" label="Intensity transformations" help="logarithm base 2 (log2) or squareroot (sqrt)">
                            <option value="log2" selected="True">log2</option>
                            <option value="sqrt">sqrt</option>
                        </param>
                            <when value="log2"/>
                            <when value="sqrt"/>

        <param name="output_matrix" type="boolean" label="Intensity matrix output"/>
    </inputs>
    <outputs>
        <data format="rdata" name="msidata_preprocessed" label="$infile.display_name preprocessed"/>
        <data format="pdf" name="QC_overview" from_work_dir="Preprocessing.pdf" label = "$infile.display_name preprocessed_QC"/>
        <data format="tabular" name="summarized_mean" label="$infile.display_name preprocessed_mean">
            <filter>summary_type and "mean" in summary_type</filter>
        </data>
        <data format="tabular" name="summarized_median" label="$infile.display_name preprocessed_median">
            <filter>summary_type and "median" in summary_type</filter>
        </data>
        <data format="tabular" name="summarized_sd" label="$infile.display_name preprocessed_sd">
            <filter>summary_type and "sd" in summary_type</filter>
        </data>
        <data format="tabular" name="intensity_matrix" label="$infile.display_name preprocessed_matrix">
            <filter>output_matrix</filter>
        </data>
    </outputs>
    <tests>
        <test expect_num_outputs="3">

            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Smoothing"/>
                    <conditional name="methods_for_smoothing">
                        <param name="smoothing_method" value="gaussian"/>
                        <param name="sd_gaussian" value="4"/>
                    </conditional>
                        <param name="window_smoothing" value="9"/>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_picking"/>
                    <conditional name="methods_for_picking">
                        <param name="picking_method" value="adaptive"/>
                    </conditional>
                    <param name="blocks_picking" value="3"/>
                    <param name="window_picking" value="3"/>
                    <param name="SNR_picking_method" value="3"/>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_alignment"/>

            <conditional name="tabular_annotation">
                <param name="load_annotation" value="no_annotation"/>
            </conditional>
            <param name="output_matrix" value="True"/>
            <output name="msidata_preprocessed" file="preprocessing_results1.RData" compare="sim_size"/>
            <output name="intensity_matrix" file="preprocessing_results1.txt"/>
            <output name="QC_overview" file="preprocessing_results1.pdf" compare="sim_size"/>
        </test>
        <test expect_num_outputs="4">
            <param name="infile" value="123_combined.RData" ftype="rdata"/>
            <repeat name="methods">

                        <param name="column_names" value="3"/>
                        <param name="tabular_header" value="FALSE"/>
            </conditional>
            <param name="summary_type" value="median,sd"/>
            <output name="msidata_preprocessed" file="preprocessing_results2.RData" compare="sim_size"/>
            <output name="summarized_median" file="preprocessing_median2.txt" lines_diff="2"/>
            <output name="summarized_sd" file="preprocessing_sd2.txt" lines_diff="2"/>
            <output name="QC_overview" file="preprocessing_results2.pdf" compare="sim_size"/>
        </test>
        <test expect_num_outputs="3">
            <param name="infile" value="" ftype="analyze75">
                <composite_data value="Analyze75.hdr"/>

            <repeat name="methods">
                <conditional name="methods_conditional">
                    <param name="preprocessing_method" value="Peak_picking"/>
                    <param name="blocks_picking" value="100"/>
                    <param name="window_picking" value="5"/>
                    <param name="SNR_picking_method" value="3"/>
                        <param name="picking_method" value="limpic"/>
                </conditional>
            </repeat>
            <repeat name="methods">
                <conditional name="methods_conditional">

            <conditional name="tabular_annotation">
                <param name="load_annotation" value="no_annotation"/>
            </conditional>
            <output name="msidata_preprocessed" file="preprocessing_results3.RData" compare="sim_size"/>
            <output name="QC_overview" file="preprocessing_results3.pdf" compare="sim_size"/>
            <output name="summarized_mean" file="preprocessing_mean3.txt" lines_diff="2"/>
        </test>
        <test expect_num_outputs="3">
            <param name="infile" value="" ftype="analyze75">
                <composite_data value="Analyze75.hdr"/>
                <composite_data value="Analyze75.img"/>

            <conditional name="tabular_annotation">
                <param name="load_annotation" value="no_annotation"/>
            </conditional>
            <param name="output_matrix" value="True"/>
            <output name="msidata_preprocessed" file="preprocessing_results4.RData" compare="sim_size"/>
            <output name="intensity_matrix" file="preprocessing_results4.txt"/>
            <output name="QC_overview" file="preprocessing_results4.pdf" compare="sim_size"/>
        </test>
        <test expect_num_outputs="2">
            <param name="infile" value="" ftype="imzml">
                <composite_data value="Example_Continuous.imzML"/>