comparison filtering.xml @ 2:0c4579390f73 draft

planemo upload for repository https://github.com/galaxyproteomics/tools-galaxyp/tree/master/tools/cardinal commit f127be2141cf22e269c85282d226eb16fe14a9c1

1:aac805a9d2ae

<tool id="cardinal_filtering" name="MSI filtering" version="@VERSION@.1">
    <description>tool for filtering mass spectrometry imaging data</description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements">

        @INPUT_LINKING@
        cat '${MSI_subsetting}' &&
        Rscript '${MSI_subsetting}' &&

        #if $imzml_output:
            mkdir $outfile_imzml.files_path &&
            ls -l &&
            mv ./out.imzML "${os.path.join($outfile_imzml.files_path, 'imzml')}" | true &&
            mv ./out.ibd "${os.path.join($outfile_imzml.files_path, 'ibd')}" | true &&
        #end if
        echo "imzML file:" > $outfile_imzml &&
        ls -l "$outfile_imzml.files_path" >> $outfile_imzml


    ]]>
    </command>


    <configfiles>

        minimumx = min(coord(msidata)[,1])
        maximumx = max(coord(msidata)[,1])
        ## Range y coordinates
        minimumy = min(coord(msidata)[,2])
        maximumy = max(coord(msidata)[,2])
        ## Number of intensities > 0
        npeaks= sum(spectra(msidata)[]>0, na.rm=TRUE)
        ## Spectra multiplied with m/z (potential number of peaks)
        numpeaks = ncol(spectra(msidata)[])*nrow(spectra(msidata)[])
        ## Percentage of intensities > 0
        percpeaks = round(npeaks/numpeaks*100, digits=2)
        ## Number of empty TICs
        TICs = colSums(spectra(msidata)[], na.rm=TRUE) 
        NumemptyTIC = sum(TICs == 0)
        ## median TIC
        medint = round(median(TICs), digits=2)
        ## Store features for QC plot
        featuresinfile = mz(msidata)

## Next steps will only run if there are more than 0 intensities/pixels/features in the file

if (sum(spectra(msidata)[]>0, na.rm=TRUE) > 0)
{


        ## prepare dataframe for QC of pixel distribution (will be overwritten in filtering of pixels condition)
        position_df = cbind(coord(msidata)[,1:2], rep("$infile.element_identifier", times=ncol(msidata)))
        colnames(position_df)[3] = "annotation"

    ###################################### Filtering of pixels #####################
    ################################################################################

    ############ Pixels in two columns format: x and y in different columns #############

        print("two columns")

        ## read tabular file
        input_list = read.delim("$pixels_cond.annotation_file", header = $pixels_cond.tabular_header, 
        stringsAsFactors = FALSE)
        numberpixels = nrow(input_list)
        inputpixels = input_list[,c($pixels_cond.column_x, $pixels_cond.column_y, $pixels_cond.column_names)]

        ## rewrite into x = 1, y = 1 format and filter msidata, count validpixels
        pixelvector = character()
        for (pixel in 1:nrow(inputpixels)){
            pixelvector[pixel] = paste0("x = ", inputpixels[pixel,1],", ", "y = ", inputpixels[pixel,2])}
        pixelsofinterest= pixels(msidata)[names(pixels(msidata)) %in% pixelvector]
        msidata = msidata[,pixelsofinterest]
        validpixels=ncol(msidata)

        ## in case some pixels are left print annotation plot
        colnames(inputpixels) = c("x", "y", "annotation")
        position_df = merge(coord(msidata)[,1:2], inputpixels, by=c("x", "y"), all.x=TRUE)
        colnames(position_df)[3] = "annotation"
        position_df\$annotation = factor(position_df\$annotation)


    ########### Pixels wihin x and y minima and maxima are kept ###################

    #elif str($pixels_cond.pixel_filtering) == "pixel_range":
        print("pixel range")

        numberpixels = "range"
        validpixels = "range"

        ## only filter pixels if at least one pixel will be left
        if (sum(coord(msidata)\$x <= $pixels_cond.max_x_range & coord(msidata)\$x >= $pixels_cond.min_x_range) > 0 & sum(coord(msidata)\$y <= $pixels_cond.max_y_range & coord(msidata)\$y >= $pixels_cond.min_y_range) > 0){

            msidata = msidata[, coord(msidata)\$x <= $pixels_cond.max_x_range & coord(msidata)\$x >= $pixels_cond.min_x_range]
            msidata = msidata[, coord(msidata)\$y <= $pixels_cond.max_y_range & coord(msidata)\$y >= $pixels_cond.min_y_range]
        }else{

            print("no valid pixel found")
            msidata = msidata[,0]}

        ## update position_df for filtered pixels
        position_df = cbind(coord(msidata)[,1:2], rep("$infile.element_identifier", times=ncol(msidata)))
        colnames(position_df)[3] = "annotation"
        position_df\$annotation = factor(position_df\$annotation)

    #elif str($pixels_cond.pixel_filtering) == "none":
        print("no pixel filtering")

        numberpixels = 0
        validpixels = 0

    #end if

}else{
    print("Inputfile has no intensities > 0")
}

    ################################# filtering of features ######################
    ##############################################################################

    ####################### Keep m/z from tabular file #########################

## feature filtering only when pixels/features/intensities are left

if (ncol(msidata) > 0){
    npeaks_before_filtering= sum(spectra(msidata)[]>0, na.rm=TRUE)
    if (npeaks_before_filtering > 0)
    {

        #if str($features_cond.features_filtering) == "features_list":
            print("feature list")

            ## read tabular file, define starting row, extract and count valid features
            input_features = read.delim("$mz_tabular", header = $features_cond.feature_header, stringsAsFactors = FALSE)
            extracted_features = input_features[,$features_cond.feature_column]
            numberfeatures = length(extracted_features)
            if (class(extracted_features) == "numeric"){
                ### max digits given in the input file will be used to match m/z but the maximum is 4
                   max_digits = max(nchar(sapply(strsplit(as.character(extracted_features), "\\."),`[`,2)), na.rm=TRUE)

                   if (max_digits >4)

                   }

                validfeatures = round(extracted_features, max_digits) %in% round(mz(msidata),max_digits)
                featuresofinterest = features(msidata)[round(mz(msidata), digits = max_digits) %in% round(extracted_features[validfeatures], max_digits)]
                validmz = length(unique(featuresofinterest))
            }else{
                    validmz = 0
                    featuresofinterest = 0}

            ### filter msidata for valid features
            msidata = msidata[featuresofinterest,]

        ############### features within a given range are kept #####################

        #elif str($features_cond.features_filtering) == "features_range":
            print("feature range")

            numberfeatures = "range"
            validmz = "range"

            if (sum(mz(msidata) >= $features_cond.min_mz & mz(msidata) <= $features_cond.max_mz)> 0){
                msidata = msidata[mz(msidata) >= $features_cond.min_mz & mz(msidata) <= $features_cond.max_mz,]
            }else{ 
                msidata = msidata[0,]
                print("no valid mz range")}

        ############### Remove m/z from tabular file #########################

        #elif str($features_cond.features_filtering) == "remove_features":
            print("remove features")

            ## read tabular file, define starting row, extract and count valid features
            input_features = read.delim("$mz_tabular", header = $features_cond.removal_header, stringsAsFactors = FALSE) 
            extracted_features = input_features[,$features_cond.removal_column]
            numberfeatures = length(extracted_features)
            if (class(extracted_features) == "numeric"){
                print("input is numeric")
                featuresofinterest = extracted_features
                validmz = sum(featuresofinterest <= max(mz(msidata))& featuresofinterest >= min(mz(msidata)))
            }else{featuresofinterest = 0
                    validmz = 0}

        ### Here starts removal of features: 
            plusminus = $features_cond.removal_plusminus

            mass_to_remove = numeric()
            if (sum(featuresofinterest) > 0){
                for (masses in featuresofinterest){
                    #if str($features_cond.units_removal) == "ppm": 
                        plusminus = masses * $features_cond.removal_plusminus/1000000
                    #end if 
                    current_mass = which(c(mz(msidata) <= masses + plusminus & mz(msidata) >= masses - plusminus))
                    mass_to_remove = append(mass_to_remove, current_mass)}
                msidata= msidata[-mass_to_remove, ]
            }else{print("No features were removed as they were not fitting to m/z values and/or range")}


        #elif str($features_cond.features_filtering) == "none":

            print("no feature filtering")
            validmz = 0
            numberfeatures = 0

        #end if

        ## save msidata as Rfile
        save(msidata, file="$msidata_filtered")

        ## Number of empty TICs
        TICs2 = colSums(spectra(msidata)[], na.rm=TRUE)
        ## Number of intensities > 0
        npeaks2= sum(spectra(msidata)[]>0, na.rm=TRUE)
        ## Spectra multiplied with m/z (potential number of peaks)
        numpeaks2 = ncol(spectra(msidata)[])*nrow(spectra(msidata)[])



    }else{
        print("Inputfile or file filtered for pixels has no intensities > 0")
        numberfeatures = NA
        validmz = NA
        ## Number of empty TICs
        TICs2 = 0
        npeaks2 = 0
        numpeaks2 = 0
    }
}else{
        print("Inputfile or file filtered for pixels has no pixels left")
        numberfeatures = NA
        validmz = NA
        ## Number of empty TICs
        TICs2 = 0
        npeaks2 = 0
        numpeaks2 = 0
}
    #################### QC numbers #######################


        ## Number of features (m/z)
        maxfeatures2 = length(features(msidata))
        ## Range m/z
        minmz2 = round(min(mz(msidata)), digits=2)
        maxmz2 = round(max(mz(msidata)), digits=2)
        ## Number of spectra (pixels)
        pixelcount2 = length(pixels(msidata))
        ## Range x coordinates
        minimumx2 = min(coord(msidata)[,1])
        maximumx2 = max(coord(msidata)[,1])
        ## Range y coordinates
        minimumy2 = min(coord(msidata)[,2])
        maximumy2 = max(coord(msidata)[,2])

        ## Percentage of intensities > 0
        percpeaks2 = round(npeaks2/numpeaks2*100, digits=2)
        ## Number of empty TICs
        NumemptyTIC2 = sum(TICs2 == 0)
        ## median TIC
        medint2 = round(median(TICs2), digits=2)

        properties = c("Number of m/z features",
                       "Range of m/z values",
                       "Number of pixels", 
                       "Range of x coordinates", 
                       "Range of y coordinates",
                       "Intensities > 0",
                       "Median TIC per pixel",
                       "Number of empty spectra", 
                       "pixel overview", 
                       "feature overview")

        before = c(paste0(maxfeatures), 
                   paste0(minmz, " - ", maxmz), 
                   paste0(pixelcount), 
                   paste0(minimumx, " - ", maximumx), 
                   paste0(minimumy, " - ", maximumy), 
                   paste0(percpeaks, " %"), 
                   paste0(medint),
                   paste0(NumemptyTIC), 
                   paste0("input pixels: ", numberpixels),
                   paste0("input mz: ", numberfeatures))

        filtered = c(paste0(maxfeatures2), 
                   paste0(minmz2, " - ", maxmz2), 
                   paste0(pixelcount2), 
                   paste0(minimumx2, " - ", maximumx2),  
                   paste0(minimumy2, " - ", maximumy2), 
                   paste0(percpeaks2, " %"), 
                   paste0(medint2),
                   paste0(NumemptyTIC2), 
                   paste0("valid pixels: ", validpixels),
                   paste0("valid mz: ", validmz))

        property_df = data.frame(properties, before, filtered)
print(property_df)

    ########################### PDF QC and imzml output ###########################

        pdf("filtertool_QC.pdf", fonts = "Times", pointsize = 12)
        plot(0,type='n',axes=FALSE,ann=FALSE)
        title(main=paste0("Qualitycontrol of filtering tool for file: \n\n", "$infile.display_name"))
        grid.table(property_df, rows= NULL)

## QC report with more than value-table: only when pixels/features/intensities are left
if (npeaks2 > 0)
{

    ## save msidata as imzML file, will only work if there is at least 1 m/z left
    #if $imzml_output:
        if (maxfeatures2 > 0){
        writeImzML(msidata, "out")}
    #end if


        ### visual pixel control

            levels(position_df\$annotation) = factor(paste(1:length(levels(position_df\$annotation)), levels(position_df\$annotation), sep="_"))

            pixel_image = ggplot(position_df, aes(x=x, y=y, fill=annotation))+
                   geom_tile(height = 1, width=1)+
                   coord_fixed()+
                   ggtitle("Spatial orientation of filtered pixels")+
                   theme_bw()+
                   theme(plot.title = element_text(hjust = 0.5))+
                   theme(text=element_text(family="ArialMT", face="bold", size=12))+
                   theme(legend.position="bottom",legend.direction="vertical")+
                   theme(legend.key.size = unit(0.2, "line"), legend.text = element_text(size = 6))+
                   guides(fill=guide_legend(ncol=4,byrow=TRUE))

    coord_labels = aggregate(cbind(x,y)~annotation, data=position_df, mean, na.rm=TRUE, na.action="na.pass")
    coord_labels\$file_number = 1:length(levels(position_df\$annotation))

    for(file_count in 1:nrow(coord_labels))
    {pixel_image = pixel_image + annotate("text",x=coord_labels[file_count,"x"],
    y=coord_labels[file_count,"y"],label=toString(coord_labels[file_count,4]))}

            print(pixel_image)

            ### control features which are removed
            hist(mz(msidata), xlab="m/z", main="Kept m/z values")
            #if str($features_cond.features_filtering) == "none":
                print("no difference histogram as no m/z filtering took place")
            #else:

                if (isTRUE(all.equal(featuresinfile, mz(msidata)))){
                print("No difference in m/z values before and after filtering, no histogram drawn")
                }else{
                hist(setdiff(featuresinfile, mz(msidata)), xlab="m/z", main="Removed m/z values")}
            #end if

        dev.off()


}else{
    print("Inputfile or filtered file has no intensities > 0")
    dev.off()
}
    ]]></configfile>
    </configfiles>
    <inputs>
        <expand macro="reading_msidata"/>
        <conditional name="pixels_cond">
            <param name="pixel_filtering" type="select" label="Select pixel filtering option">
                <option value="none" selected="True">none</option>
                <option value="two_columns">list of pixel coordinates (tabular file)</option>
                <option value="pixel_range">ranges for x and y (manually)</option>
            </param>
            <when value="none"/>
            <when value="two_columns">
                <expand macro="reading_pixel_annotations"/>

            </when> 
            <when value="pixel_range">
                <param name="min_x_range" type="integer" value="0" label="Minimum value for x"/>
                <param name="max_x_range" type="integer" value="100" label="Maximum value for x"/>
                <param name="min_y_range" type="integer" value="0" label="Minimum value for y"/>

        </conditional>

        <conditional name="features_cond">
            <param name="features_filtering" type="select" label="Select m/z feature filtering option">
                <option value="none" selected="True">none</option>
                <option value="features_list">keep a list of m/z (tabular file)</option>
                <option value="features_range">m/z range (manually)</option>
                <option value="remove_features">remove a list of m/z (tabular file)</option>
            </param>
            <when value="none"/>
            <when value="features_list">
                <expand macro="reading_1_column_mz_tabular" label="Tabular file with m/z features to keep"/>
            </when> 

                        <option value="ppm" selected="True">ppm</option>
                        <option value="Da">Da</option>
                </param>
            </when>
        </conditional>
        <param name="imzml_output" type="boolean" label="Output of imzML file" truevalue="TRUE" falsevalue="FALSE"/>

    </inputs>

    <outputs>
        <data format="rdata" name="msidata_filtered" label="${tool.name} on ${on_string}"/>
        <data format="pdf" name="QC_overview" from_work_dir="filtertool_QC.pdf" label = "${tool.name} on ${on_string}: QC"/>
        <data format="imzml" name="outfile_imzml" label="${tool.name} on ${on_string}: imzML">
            <filter>imzml_output</filter>
       </data>
    </outputs>
    <tests>
        <test>
            <expand macro="infile_imzml"/>
            <param name="pixel_filtering" value="pixel_range"/>
            <param name="min_x_range" value="1"/>
            <param name="max_x_range" value="20"/>
            <param name="min_y_range" value="2"/>
            <param name="max_y_range" value="2"/>
            <param name="features_filtering" value="features_range"/>
            <param name="min_mz" value="350" />
            <param name="max_mz" value="500"/>
            <output name="QC_overview" file="imzml_filtered3.pdf" compare="sim_size"/>
            <output name="msidata_filtered" file="imzml_filtered3.RData" compare="sim_size"/>
        </test>
        <test>
            <expand macro="infile_imzml"/>
            <param name="pixel_filtering" value="two_columns"/>
            <param name="annotation_file" ftype="tabular" value = "inputpixels_2column.tabular"/>
            <param name="column_x" value="1"/>
            <param name="column_y" value="3"/>
            <param name="column_names" value="2"/>
            <output name="QC_overview" file="imzml_filtered4.pdf" compare="sim_size"/>
            <output name="msidata_filtered" file="imzml_filtered4.RData" compare="sim_size"/>
            <!--imzml output test not yet working: output name="outfile_imzml" file="filtering_imzmls/summary" compare="sim_size" delta="10000">
                <extra_files type="file" name="imzml" value="filtering_imzmls/out4.imzML" compare="sim_size" delta="10000"/>
                <extra_files type="file" name="ibd" value="filtering_imzmls/out4.ibd" compare="sim_size" delta="10000"/>
            </output-->
        </test>
        <test>
            <expand macro="infile_imzml"/>
            <param name="pixel_filtering" value="pixel_range"/>
            <param name="min_x_range" value="0"/>

            <param name="max_y_range" value="20"/>
            <param name="features_filtering" value="features_list"/>
            <param name="mz_tabular" ftype="tabular" value = "featuresofinterest5.tabular"/>
            <param name="feature_column" value="1"/>
            <param name="feature_header" value="0"/>
            <output name="QC_overview" file="imzml_filtered5.pdf" compare="sim_size"/>
            <output name="msidata_filtered" file="imzml_filtered5.RData" compare="sim_size" />
        </test>
        <test>
            <expand macro="infile_analyze75"/>
            <output name="QC_overview" file="analyze75_filtered2.pdf" compare="sim_size"/>
            <output name="msidata_filtered" file="analyze_filteredoutside.RData" compare="sim_size" />
        </test>
        <test>
            <param name="infile" value="preprocessed.RData" ftype="rdata"/>
            <conditional name="outputs">
                <param name="outputs_select" value="no_quality_control"/>
            </conditional>
            <output name="msidata_filtered" file="rdata_notfiltered.RData" compare="sim_size"/>
            <output name="QC_overview" file="rdata_notfiltered.pdf" compare="sim_size" />
        </test>
    </tests>
    <help>
        <![CDATA[

This tool provides options to filter (subset) pixels and m/z features of mass spectrometry imaging data.

@MSIDATA_INPUT_DESCRIPTION@

@SPECTRA_TABULAR_INPUT_DESCRIPTION@

@MZ_TABULAR_INPUT_DESCRIPTION@

**Options**

- pixel filtering/annotation: either with a tabular file containing x and y coordinates and pixel annotations or by defining a range for x and y by hand (for the latter no annotation is possible). Pixel that are not present in the dataset are ignored. It is not possible to filter only for pixels that are not present in the dataset. 
- m/z feature filtering: m/z values for filtering should be either imported as a tabular file containing containing m/z of interest or by defining a range for the m/z values. m/z that are not present in the dataset are ignored. It is not possible to filter only for m/z that are not present in the dataset. 
- m/z feature removing: perturbing m/z features such as matrix contaminants can be removed by specifying their m/z in a tabular file, optionally with a half window size in ppm or m/z for the window in which peaks should be removed.


**Tips**

- Numeric m/z features imported via a tabular file and m/z features of the dataset are rounded to 4 decimal points (or maximum number of decimal points of input m/z) and then matched. Therefore, it is recommended to use the filtering tool only for m/z which have been extracted from the same dataset. If the m/z values are from a different dataset, the tool "Join two files on column allowing a small difference" should be used to find corresponding m/z values, which can then be used for filtering. 
- In case tabular file cannot be selected in drop-down menu: Datatype in Galaxy must be tabular otherwise file will not appear in selection window (if Galaxy auto-detection was wrong, datatype can be changed by pressing the pen button (edit attributes))


**Output**

- MSI data as .RData output (can be read with the Cardinal package in R)
- optional: MSI data as imzML file
- pdf with heatmap showing the pixels that are left after filtering and histograms of kept and removed m/z


        ]]>
    </help>

2:0c4579390f73

<tool id="cardinal_filtering" name="MSI filtering" version="@VERSION@.2">
    <description>tool for filtering mass spectrometry imaging data</description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements">

        @INPUT_LINKING@
        cat '${MSI_subsetting}' &&
        Rscript '${MSI_subsetting}' &&

        #if str($imzml_output) == "imzml_format":
            mkdir $outfile_imzml.files_path &&
            mv ./out.imzML "${os.path.join($outfile_imzml.files_path, 'imzml')}" | true &&
            mv ./out.ibd "${os.path.join($outfile_imzml.files_path, 'ibd')}" | true &&
        #end if
        echo "imzML file:" > $outfile_imzml &&
        ls -l "$outfile_imzml.files_path" >> $outfile_imzml
    ]]>
    </command>


    <configfiles>

        minimumx = min(coord(msidata)[,1])
        maximumx = max(coord(msidata)[,1])
        ## Range y coordinates
        minimumy = min(coord(msidata)[,2])
        maximumy = max(coord(msidata)[,2])
        ## Store features for QC plot
        featuresinfile = mz(msidata)

## Next steps will only run if there are more than 0 pixels/features in the file

if (ncol(msidata)>0 & nrow(msidata) >0)
{
    ###################################### Filtering of pixels #####################
    ################################################################################

    ############ Pixels in two columns format: x and y in different columns #############

        print("two columns")

        ## read tabular file
        input_list = read.delim("$pixels_cond.annotation_file", header = $pixels_cond.tabular_header, 
        stringsAsFactors = FALSE)
        inputpixels = input_list[,c($pixels_cond.column_x, $pixels_cond.column_y)]
        input_pixels = paste(inputpixels[,1], inputpixels[,2], sep="_")
        dataset_pixels = paste(coord(msidata)\$x, coord(msidata)\$y, sep="_")
        pixelsofinterest = dataset_pixels %in% input_pixels

        tryCatch(
                {
                msidata = msidata[,pixelsofinterest]
                if (ncol(msidata) == 0)
                    {
                    stop(call.=FALSE)
                    }
                },
                error=function(cond) {
                ## in case all coordinates were outside the dataset leading to zero pixels, tool is stopped to avoid continuing with wrong data
                    message("Error during pixel filtering")
                    message("Possible problems: Forgot to set 'Tabular file contains a header line' = Yes, wrong columns selected, columns with coordinates contain empty fields or letters, all coordinates were outside the range of the dataset - this can be checked with the 'MSI qualitycontrol' tool)")
                    stop(call.=FALSE)
                }
            )    

        ## QC values:
        numberpixels = nrow(input_list)
        validpixels=ncol(msidata)

    ########### Pixels wihin x and y minima and maxima are kept #################

    #elif str($pixels_cond.pixel_filtering) == "pixel_range":
        print("pixel range")

        ## QC values:
        numberpixels = "range"
        validpixels = "range"

        tryCatch(
                {
                    msidata = msidata[, coord(msidata)\$x <= $pixels_cond.max_x_range & coord(msidata)\$x >= $pixels_cond.min_x_range]
                    msidata = msidata[, coord(msidata)\$y <= $pixels_cond.max_y_range & coord(msidata)\$y >= $pixels_cond.min_y_range]
                    if (ncol(msidata) == 0)
                        {
                        stop(call.=FALSE)
                        }
                },
                error=function(cond) {
                ## in case one of the ranges was outside the dataset leading to zero pixels, tool is stopped to avoid continuing with wrong data
                    message("Error during pixel filtering")
                    message("Check that both x and y ranges were inside the dataset coordinates (can be checked with the 'MSI qualitycontrol' tool) or if any not numeric character was entered into the input fields")
                    stop(call.=FALSE)
                }
            )    

    ######################## no pixel filtering ################################

    #elif str($pixels_cond.pixel_filtering) == "none":
        print("no pixel filtering")

        ## QC values:
        numberpixels = 0
        validpixels = 0

    #end if

    ############################# QC data #####################################

    ## dataframe for QC of pixel distribution
    position_df = cbind(coord(msidata)[,1:2], rep("remaining pixels", times=ncol(msidata)))
    colnames(position_df)[3] = "annotation"
    position_df\$annotation = factor(position_df\$annotation)
    gc()

}else{
    print("Inputfile has no intensities > 0")
}

################################# filtering of features ######################
##############################################################################

####################### Keep m/z from tabular file #########################

## feature filtering only when pixels/features/intensities are left


if (ncol(msidata) > 0){
    if (nrow(msidata) > 0)
    {

        #if str($features_cond.features_filtering) == "features_list":
            print("feature list")

            ## read tabular file, define starting row, extract and count valid features
            input_features = read.delim("$mz_tabular", header = $features_cond.feature_header, stringsAsFactors = FALSE)
            extracted_features = input_features[,$features_cond.feature_column]
            numberfeatures = length(extracted_features)

            if (class(extracted_features) == "numeric"){
                ### max digits given in the input file will be used to match m/z but the maximum is 4
                   max_digits = max(nchar(sapply(strsplit(as.character(extracted_features), "\\."),`[`,2)), na.rm=TRUE)

                   if (max_digits >4)

                   }

                validfeatures = round(extracted_features, max_digits) %in% round(mz(msidata),max_digits)
                featuresofinterest = features(msidata)[round(mz(msidata), digits = max_digits) %in% round(extracted_features[validfeatures], max_digits)]
                validmz = length(unique(featuresofinterest))

            }else{
                    validmz = 0
                    featuresofinterest = 0}

            ### filter msidata for valid features

                tryCatch(
                        {
                        msidata = msidata[featuresofinterest,]
                        ## does not throw error when processed file has no features left, therefore create error to avoid continuing with wrong data
                        if (nrow(msidata) == 0)
                            {
                            stop(call.=FALSE)
                            }
                        },
                        error=function(cond) {
                        ## in case all provided m/z values were outside the m/z range
                        ## tool is stopped to avoid continuing with wrong data
                            message("Error during m/z filtering")
                            message("Possible problems: Forgot to set 'Tabular file contains a header line' = Yes, wrong columns selected, column with m/z features contains empty fields or letters, all m/z features s were outside the range of the dataset  - this can be checked with the 'MSI qualitycontrol' tool) or did not match any m/z feature of the dataset (see help section for more information on that)")
                            stop(call.=FALSE)
                        }
                    )    


        ############### features within a given range are kept #####################

        #elif str($features_cond.features_filtering) == "features_range":
            print("feature range")

            numberfeatures = "range"
            validmz = "range"

            tryCatch(
                    {
                    msidata = msidata[mz(msidata) >= $features_cond.min_mz & mz(msidata) <= $features_cond.max_mz,]
                    ## does not throw error when processed file has no features left, therefore create error to avoid continuing with wrong data
                    if (nrow(msidata) == 0)
                        {
                        stop(call.=FALSE)
                        }
                    },
                    error=function(cond) {
                    ## in case all m/z features were outside the dataset leading to zero m/z features, tool is stopped to avoid continuing with wrong data
                        message("Error during m/z filtering")
                        message("Check that the entered m/z range is inside the dataset coordinates (can be checked with the 'MSI qualitycontrol' tool) or if any not numeric character was entered into the input fields")
                        stop(call.=FALSE)
                    }
                )    

        ############### Remove m/z from tabular file #########################

        #elif str($features_cond.features_filtering) == "remove_features":
            print("remove features")

            ## read tabular file, define starting row, extract and count valid features
            input_features = read.delim("$mz_tabular", header = $features_cond.feature_header, stringsAsFactors = FALSE) 
            extracted_features = input_features[,$features_cond.feature_column]
            numberfeatures = length(extracted_features)
            if (class(extracted_features) == "numeric"){
                print("input is numeric")
                featuresofinterest = extracted_features
            }else{featuresofinterest = 0}

        ### Here starts removal of features: 
            plusminus = $features_cond.removal_plusminus

            tryCatch(
                    {
                    mass_to_remove = numeric()
                    for (masses in featuresofinterest){
                        #if str($features_cond.units_removal) == "ppm": 
                            plusminus = masses * $features_cond.removal_plusminus/1000000
                        #end if 
                        current_mass = which(c(mz(msidata) <= masses + plusminus & mz(msidata) >= masses - plusminus))
                        mass_to_remove = append(mass_to_remove, current_mass)}
                    msidata= msidata[-mass_to_remove, ]
                    validmz = numberfeatures - nrow(msidata)
                    ## does not throw error when processed file has no features left, therefore create error to avoid continuing with wrong data
                    if (nrow(msidata) == 0)
                        {
                        stop(call.=FALSE)
                        }
                    },
                    error=function(cond) {
                        message("Error during removal of m/z features")
                        stop(call.=FALSE)
                    }
                )    




        ######################## No m/z filtering ##############################

        #elif str($features_cond.features_filtering) == "none":

            print("no feature filtering")
            validmz = 0
            numberfeatures = 0

        #end if

    }else{
        print("Inputfile has no m/z features")
        numberfeatures = NA
        validmz = NA
    }
}else{
        print("Inputfile or file filtered for pixels has no pixels")
        numberfeatures = NA
        validmz = NA
}
gc()

#################### QC numbers #######################

## Number of features (m/z)
maxfeatures2 = length(features(msidata))
## Range m/z
minmz2 = round(min(mz(msidata)), digits=2)
maxmz2 = round(max(mz(msidata)), digits=2)
## Number of spectra (pixels)
pixelcount2 = length(pixels(msidata))
## Range x coordinates
minimumx2 = min(coord(msidata)[,1])
maximumx2 = max(coord(msidata)[,1])
## Range y coordinates
minimumy2 = min(coord(msidata)[,2])
maximumy2 = max(coord(msidata)[,2])

properties = c("Number of m/z features",
               "Range of m/z values",
               "Number of pixels", 
               "Range of x coordinates", 
               "Range of y coordinates",
               "pixel overview", 
               "feature overview")

before = c(paste0(maxfeatures), 
           paste0(minmz, " - ", maxmz), 
           paste0(pixelcount), 
           paste0(minimumx, " - ", maximumx), 
           paste0(minimumy, " - ", maximumy), 
           paste0("input pixels: ", numberpixels),
           paste0("input mz: ", numberfeatures))

filtered = c(paste0(maxfeatures2), 
           paste0(minmz2, " - ", maxmz2), 
           paste0(pixelcount2), 
           paste0(minimumx2, " - ", maximumx2),  
           paste0(minimumy2, " - ", maximumy2), 
           paste0("valid pixels: ", validpixels),
           paste0("valid mz: ", validmz))

property_df = data.frame(properties, before, filtered)

########################### PDF QC and MSI output ###########################

pdf("filtertool_QC.pdf", fonts = "Times", pointsize = 12)
plot(0,type='n',axes=FALSE,ann=FALSE)
title(main=paste0("Qualitycontrol of filtering tool for file: \n\n", "$infile.display_name"))
grid.table(property_df, rows= NULL)

## QC report only when pixels/features are left
if (ncol(msidata)>0 & nrow(msidata) >0)
{

    ### visual pixel control

    pixel_image = ggplot(position_df, aes(x=x, y=y, fill=annotation))+
           geom_tile(height = 1, width=1)+
           coord_fixed()+
           ggtitle("Spatial orientation of filtered pixels")+
           theme_bw()+
           theme(plot.title = element_text(hjust = 0.5))+
           theme(legend.position="bottom",legend.direction="vertical")
    print(pixel_image)

    ### plot features which are removed
    hist(mz(msidata), xlab="m/z", main="Kept m/z values")
    #if str($features_cond.features_filtering) == "none":
        print("no difference histogram as no m/z filtering took place")
    #else:
        if (isTRUE(all.equal(featuresinfile, mz(msidata)))){
        print("No difference in m/z values before and after filtering, no histogram drawn")
        }else{
        hist(setdiff(featuresinfile, mz(msidata)), xlab="m/z", main="Removed m/z values")}
    #end if

        dev.off()

    ## save msidata as imzML file, will only work if there is at least 1 m/z left

    #if str($imzml_output) == "imzml_format":
        if (maxfeatures2 > 0){
            ## make sure that coordinates are integers
            coord(msidata)\$y = as.integer(coord(msidata)\$y)
            coord(msidata)\$x = as.integer(coord(msidata)\$x)
        writeImzML(msidata, "out")}
    #elif str($imzml_output) == "rdata_format":
        ## save msidata as Rfile
        iData(msidata) = iData(msidata)[]
        save(msidata, file="$outfile_rdata")
    #end if


}else{
    print("Inputfile or filtered file has no intensities > 0")
    dev.off()
}

    ]]></configfile>
    </configfiles>
    <inputs>
        <expand macro="reading_msidata"/>
        <conditional name="pixels_cond">
            <param name="pixel_filtering" type="select" label="Select pixel filtering option">
                <option value="none" selected="True">none</option>
                <option value="two_columns">coordinates from tabular file</option>
                <option value="pixel_range">ranges for x and y (manually)</option>
            </param>
            <when value="none"/>
            <when value="two_columns">
                <param name="annotation_file" type="data" format="tabular" label="Tabular file with pixel coordinates"
                help="Tabular file with two columns: x values and y values"/>
                <param name="column_x" data_ref="annotation_file" label="Column with x values" type="data_column"/>
                <param name="column_y" data_ref="annotation_file" label="Column with y values" type="data_column"/>
                <param name="tabular_header" type="boolean" label="Tabular file contains a header line" truevalue="TRUE" falsevalue="FALSE"/>
            </when> 
            <when value="pixel_range">
                <param name="min_x_range" type="integer" value="0" label="Minimum value for x"/>
                <param name="max_x_range" type="integer" value="100" label="Maximum value for x"/>
                <param name="min_y_range" type="integer" value="0" label="Minimum value for y"/>

        </conditional>

        <conditional name="features_cond">
            <param name="features_filtering" type="select" label="Select m/z feature filtering option">
                <option value="none" selected="True">none</option>
                <option value="features_list">keep m/z (tabular file)</option>
                <option value="features_range">m/z range (manually)</option>
                <option value="remove_features">remove m/z (tabular file)</option>
            </param>
            <when value="none"/>
            <when value="features_list">
                <expand macro="reading_1_column_mz_tabular" label="Tabular file with m/z features to keep"/>
            </when> 

                        <option value="ppm" selected="True">ppm</option>
                        <option value="Da">Da</option>
                </param>
            </when>
        </conditional>
        <param name="imzml_output" type="select" display = "radio" optional = "False"
               label="Output format" help= "Choose the output format">
                <option value="imzml_format" selected="True">imzML</option>
                <option value="rdata_format">RData</option>
        </param>
    </inputs>

    <outputs>
        <data format="imzml" name="outfile_imzml" label="${tool.name} on ${on_string}: imzML">
            <filter>imzml_output =='imzml_format'</filter>
        </data>
        <data format="rdata" name="outfile_rdata" label="${tool.name} on ${on_string}: RData">
            <filter>imzml_output == 'rdata_format'</filter>
        </data>
        <data format="pdf" name="QC_overview" from_work_dir="filtertool_QC.pdf" label = "${tool.name} on ${on_string}: QC"/>
    </outputs>
    <tests>
        <test>
            <expand macro="infile_imzml"/>
            <param name="pixel_filtering" value="pixel_range"/>
            <param name="min_x_range" value="1"/>
            <param name="max_x_range" value="20"/>
            <param name="min_y_range" value="2"/>
            <param name="max_y_range" value="2"/>
            <param name="features_filtering" value="features_range"/>
            <param name="min_mz" value="350"/>
            <param name="max_mz" value="500"/>
            <param name="imzml_output" value="imzml_format"/>
            <output name="QC_overview" file="imzml_filtered3.pdf" compare="sim_size"/>
            <output name="outfile_imzml" ftype="imzml" file="out3.imzml.txt" compare="sim_size">
                <extra_files type="file" file="out3.imzml" name="imzml" lines_diff="4"/>
                <extra_files type="file" file="out3.ibd" name="ibd" compare="sim_size"/>
            </output>
        </test>
        <test>
            <expand macro="infile_imzml"/>
            <param name="pixel_filtering" value="two_columns"/>
            <param name="annotation_file" ftype="tabular" value="inputpixels_2column.tabular"/>
            <param name="column_x" value="1"/>
            <param name="column_y" value="3"/>
            <param name="imzml_output" value="imzml_format"/>
            <output name="QC_overview" file="imzml_filtered4.pdf" compare="sim_size"/>
            <output name="outfile_imzml" ftype="imzml" file="out4.imzml.txt" compare="sim_size">
                <extra_files type="file" file="out4.imzml" name="imzml" lines_diff="4"/>
                <extra_files type="file" file="out4.ibd" name="ibd" compare="sim_size"/>
            </output>
        </test>
        <test>
            <expand macro="infile_imzml"/>
            <param name="pixel_filtering" value="pixel_range"/>
            <param name="min_x_range" value="0"/>

            <param name="max_y_range" value="20"/>
            <param name="features_filtering" value="features_list"/>
            <param name="mz_tabular" ftype="tabular" value = "featuresofinterest5.tabular"/>
            <param name="feature_column" value="1"/>
            <param name="feature_header" value="0"/>
            <param name="imzml_output" value="imzml_format"/>
            <output name="QC_overview" file="imzml_filtered5.pdf" compare="sim_size"/>
            <output name="outfile_imzml" ftype="imzml" file="out5.imzml.txt" compare="sim_size">
                <extra_files type="file" file="out5.imzml" name="imzml" lines_diff="4"/>
                <extra_files type="file" file="out5.ibd" name="ibd" compare="sim_size"/>
            </output>
        </test>
        <test>
            <expand macro="infile_analyze75"/>
            <param name="imzml_output" value="imzml_format"/>
            <output name="QC_overview" file="analyze75_filtered2.pdf" compare="sim_size"/>
            <output name="outfile_imzml" ftype="imzml" file="out6.imzml.txt" compare="sim_size">
                <extra_files type="file" file="out6.imzml" name="imzml" lines_diff="4"/>
                <extra_files type="file" file="out6.ibd" name="ibd" compare="sim_size"/>
            </output>
        </test>
        <test>
            <param name="infile" value="preprocessed.RData" ftype="rdata"/>
            <conditional name="outputs">
                <param name="outputs_select" value="no_quality_control"/>
            </conditional>
            <param name="imzml_output" value="imzml_format"/>
            <output name="QC_overview" file="rdata_notfiltered.pdf" compare="sim_size" />
            <output name="outfile_imzml" ftype="imzml" file="out7.imzml.txt" compare="sim_size">
                <extra_files type="file" file="out7.imzml" name="imzml" lines_diff="4"/>
                <extra_files type="file" file="out7.ibd" name="ibd" compare="sim_size"/>
            </output>
        </test>
    </tests>
    <help>
        <![CDATA[

This tool provides options to filter (subset) pixels and m/z features of mass spectrometry imaging data.

@MSIDATA_INPUT_DESCRIPTION@

        - Optional file with pixel coordinates and annotation: 

            - Tabular file: One column with x values, one column with y values
            - The file is allowed to have any column names as header (in this case set "Tabular file contains a header line" to yes)
            - Pixel with coordinates outside the coordinates of the input file are ignored

                ::

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

@MZ_TABULAR_INPUT_DESCRIPTION@

**Options**

- Pixel filtering/annotation: 

    - Either with a tabular file containing x and y coordinates or by entering x-min, x-max, y-min, y-max manually
    - Pixel that are not present in the dataset are ignored
    - An error occurs if the input for filtering (tabular file, x-range or y-range) contains not a single coordinate that occurs in the input dataset


- m/z feature filtering: 

    - Either with a tabular file containing m/z values or by entering m/z-min and m/z-max manually
    - m/z values that are not present in the dataset are ignored
    - An error occurs if the input for filtering (tabular file or mz-range) contains not a single m/z feature that occurs in the dataset


- m/z feature removing: 

    - Perturbing m/z features such as matrix contaminants can be removed by specifying their m/z value in a tabular file, optionally with a half window size in ppm or m/z for the window in which peaks should be removed


**Tips**

- m/z feautre filtering with a tabular file: 

    - For matching the m/z features of the input dataset are rounded to the number of decimal points of the m/z values from the tabular file. In case the input had more than 4 digits m/z values of dataset and tabular file are rounded to 4 digits. 
    - Therefore, it is recommended to use the filtering tool only for m/z features which have been extracted from the same dataset. If the m/z values are from a different dataset, the tool 'Join two files on column allowing a small difference' should be used to find corresponding m/z values, which can then be used for filtering. 

- Problems to select tabular file: 

    - In case tabular file cannot be selected in drop-down menu: Datatype in Galaxy might be different from 'tabular' - datatype can be changed by pressing the pen button of the dataset (edit attributes)


**Output**

- MSI data as imzML file or .RData (can be read with the Cardinal package in R)
- pdf with heatmap showing the pixels that are left after filtering and histograms of kept and removed m/z


        ]]>
    </help>