comparison spectra_plots.xml @ 2:95fc17c26ade draft

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

1:45fb4dd8400b

<tool id="cardinal_spectra_plots" name="MSI plot spectra" version="@VERSION@.1">
    <description>
        mass spectrometry imaging mass spectra plots
    </description>
    <macros>
        <import>macros.xml</import>

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

@READING_MSIDATA@

@DATA_PROPERTIES@

grid.table(property_df, rows= NULL)

## set NA to 0 otherwise plot function will not work

    ##spectra(msidata)[is.na(spectra(msidata)[])] = 0 ## in case of NA values they will be set to zero

    spectra_df = spectra(msidata)[]
    spectra_df[is.na(spectra_df)] = 0
    print(paste0("Number of NA which were converted into 0:",sum(is.na(spectra_df))))
    spectra(msidata) = spectra_df


if (npeaks > 0){

    pixeldf = data.frame(matrix(ncol = 2, nrow=0))

    ############################# single pixel ################################
    ###########################################################################

        print("single_pixel")

        #for $chosenpixel in $pixel_conditional.repeatpixel: 

            pixelname = paste0("x = ", $chosenpixel.inputx,", ", "y = ", $chosenpixel.inputy)
            pixelisvalid = as.character(pixelname %in% names(pixels(msidata)))
            pixeldf = rbind(pixeldf, cbind(pixelname, pixelisvalid))

            ############################# II) control image ####################

                if (pixelisvalid == "TRUE"){
                    print(pixelisvalid)

                    image(msidata, mz=$chosenpixel.inputmz, ylim = c(maximumy+(0.2*maximumy),minimumy-1),
                    colorkey=FALSE, plusminus = $chosenpixel.plusminusinDalton,
                    main= paste0("x= ",$chosenpixel.inputx, ", y= ", $chosenpixel.inputy))

                    ## print legend only for less than 10 samples
                    if (length(levels(msidata\$annotation)) < 10){
                        key_legend = TRUE
                    }else{key_legend = FALSE}

            spectra(msidata)[is.na(spectra(msidata)[])] = 0 ## in case of NA values they will be set to zero
            plot(msidata, pixel=1:ncol(msidata), pixel.groups=msidata\$annotation, key=key_legend, col=hue_pal()(length(levels(msidata\$annotation))),superpose=TRUE)
        }else{
            spectra(msidata)[is.na(spectra(msidata)[])] = 0 ## in case of NA values they will be set to zero
            plot(msidata, pixel=1:ncol(msidata), key=TRUE)}

        ##################### II) Sample: plot zoom-in mass spectrum ##########

        #if $pixel_conditional.zoomed_sample:
            iData(msidata) <- iData(msidata)[] ## getting back data on disk 
            #for $token in $pixel_conditional.zoomed_sample:
                print("zoomed sample pixels")

                minmasspixel = features(msidata, mz=$token.xlimmin)
                maxmasspixel = features(msidata, mz=$token.xlimmax)

                </repeat>
            </when>
        </conditional>
    </inputs>
    <outputs>
        <data format="pdf" name="plots" from_work_dir="mzplots.pdf" label="${tool.name} on ${on_string}"/>
    </outputs>
    <tests>
        <test>
            <expand macro="infile_imzml"/>
            <conditional name="pixel_conditional">

2:95fc17c26ade

<tool id="cardinal_spectra_plots" name="MSI plot spectra" version="@VERSION@.2">
    <description>
        mass spectrometry imaging mass spectra plots
    </description>
    <macros>
        <import>macros.xml</import>

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


@READING_MSIDATA@

@DATA_PROPERTIES@

grid.table(property_df, rows= NULL)

## set NA to 0 otherwise plot function will not work

    #if str($processed_cond.processed_file) == "processed":
        ##processed file needs to be converted into matrix to be able to replace NAs
        iData(msidata) <- iData(msidata)[]
        spectra(msidata)[][is.na(spectra(msidata)[])] = 0
    #else
        spectra(msidata)[is.na(spectra(msidata))] = 0
    #end if

## run only if mz and pixels are > 0

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

    pixeldf = data.frame(matrix(ncol = 2, nrow=0))

    ############################# single pixel ################################
    ###########################################################################

        print("single_pixel")

        #for $chosenpixel in $pixel_conditional.repeatpixel: 

            pixelname = paste0("x = ", $chosenpixel.inputx,", ", "y = ", $chosenpixel.inputy)



        input_pixels = paste($chosenpixel.inputx, $chosenpixel.inputy, sep="_")
        dataset_pixels = paste(coord(msidata)\$x, coord(msidata)\$y, sep="_")

        pixelisvalid = as.character(input_pixels %in% dataset_pixels)



            pixeldf = rbind(pixeldf, cbind(pixelname, pixelisvalid))

            ############################# II) control image ####################

                if (pixelisvalid == "TRUE"){

                    image(msidata, mz=$chosenpixel.inputmz, ylim = c(maximumy+(0.2*maximumy),minimumy-1),
                    colorkey=FALSE, plusminus = $chosenpixel.plusminusinDalton,
                    main= paste0("x= ",$chosenpixel.inputx, ", y= ", $chosenpixel.inputy))

                    ## print legend only for less than 10 samples
                    if (length(levels(msidata\$annotation)) < 10){
                        key_legend = TRUE
                    }else{key_legend = FALSE}

            plot(msidata, pixel=1:ncol(msidata), pixel.groups=msidata\$annotation, key=key_legend, col=hue_pal()(length(levels(msidata\$annotation))),superpose=TRUE)
        }else{
            plot(msidata, pixel=1:ncol(msidata), key=TRUE)}

        ##################### II) Sample: plot zoom-in mass spectrum ##########

        #if $pixel_conditional.zoomed_sample:
            #for $token in $pixel_conditional.zoomed_sample:
                print("zoomed sample pixels")

                minmasspixel = features(msidata, mz=$token.xlimmin)
                maxmasspixel = features(msidata, mz=$token.xlimmax)

                </repeat>
            </when>
        </conditional>
    </inputs>
    <outputs>
        <data format="pdf" name="plots" from_work_dir="mzplots.pdf" label="${tool.name} on ${on_string}:results"/>
    </outputs>
    <tests>
        <test>
            <expand macro="infile_imzml"/>
            <conditional name="pixel_conditional">