comparison classification.xml @ 7:6f4c34f8d5ba draft

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

6:b574b84afc4d

<tool id="cardinal_classification" name="MSI classification" version="@VERSION@.3">
    <description>spatial classification of mass spectrometry imaging data</description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements">
        <requirement type="package" version="3.0">r-ggplot2</requirement>
        <requirement type="package" version="2.3">r-gridextra</requirement>
        <requirement type="package" version="0.20_35">r-lattice</requirement>
    </expand>
    <command detect_errors="exit_code">
    <![CDATA[

        @INPUT_LINKING@

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

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

@READING_MSIDATA_INRAM@


## remove duplicated coordinates
msidata <- msidata[,!duplicated(coord(msidata))]

                grid.table(summary_table5, rows= NULL)

                ### image of the best m/z
                minimumy = min(coord(msidata)[,2])
                maximumy = max(coord(msidata)[,2])
                print(image(msidata, mz = topLabels(msidata.pls)[1,1], normalize.image = "linear", contrast.enhance = "histogram",ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), smooth.image="gaussian", main="best m/z heatmap"))

                ### m/z and pixel information output
                pls_classes = data.frame(msidata.pls\$classes[[1]])
                ## pixel names and coordinates
                ## to remove potential sample names and z dimension, split at comma and take only x and y 

                ## remove msidata to clean up RAM space
                rm(msidata)
                gc()
                pls_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, pls_classes)
                colnames(pls_classes2) = c("pixel names", "x", "y","predicted condition")
                pls_toplabels = topLabels(msidata.pls, n=Inf)
                pls_toplabels[,4:6] <-round(pls_toplabels[,4:6],6)
                write.table(pls_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
                write.table(pls_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")

                ## image with predicted classes

                grid.table(summary_table5, rows= NULL)

                ### image of the best m/z
                minimumy = min(coord(msidata)[,2])
                maximumy = max(coord(msidata)[,2])
                print(image(msidata, mz = topLabels(msidata.opls)[1,1], normalize.image = "linear", contrast.enhance = "histogram",smooth.image="gaussian", ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), main="best m/z heatmap"))

                opls_classes = data.frame(msidata.opls\$classes[[1]])
                ## pixel names and coordinates
                ## to remove potential sample names and z dimension, split at comma and take only x and y
                x_coords = unlist(lapply(strsplit(names(pixels(msidata)), ","), `[[`, 1))

                ## remove msidata to clean up RAM space
                rm(msidata)
                gc()

                opls_toplabels = topLabels(msidata.opls, n=Inf)
                opls_toplabels[,4:6] <-round(opls_toplabels[,4:6],6)
                write.table(opls_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
                write.table(opls_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")

                ## image with predicted classes

                grid.table(summary_table5, rows= NULL)

                ### image of the best m/z
                minimumy = min(coord(msidata)[,2])
                maximumy = max(coord(msidata)[,2])
                print(image(msidata, mz = topLabels(msidata.ssc)[1,1], normalize.image = "linear", contrast.enhance = "histogram",smooth.image="gaussian", ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), main="best m/z heatmap"))

                ## m/z and pixel information output
                ssc_classes = data.frame(msidata.ssc\$classes[[1]])

                ## pixel names and coordinates

                rm(msidata)
                gc()

                ssc_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, ssc_classes)
                colnames(ssc_classes2) = c("pixel names", "x", "y","predicted condition")
                ssc_toplabels = topLabels(msidata.ssc, n=Inf)
                ssc_toplabels[,6:9] <-round(ssc_toplabels[,6:9],6)
                write.table(ssc_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
                write.table(ssc_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")

                ## image with predicted classes

        pixel_names = gsub(" = ", "y_", pixel_names)
        x_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,2]
        y_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,3]
        predicted_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, predicted_classes)
        colnames(predicted_classes2) = c("pixel names", "x", "y","predicted condition")
        predicted_toplabels = topLabels(prediction, n=Inf)
        if (colnames(predicted_toplabels)[4] == "coefficients"){
            predicted_toplabels[,4:6] <-round(predicted_toplabels[,4:6],5)

        }else{
            predicted_toplabels[,6:9] <-round(predicted_toplabels[,6:9],5)}

            </conditional>
            <param name="output_rdata" value="True"/>
            <output name="mzfeatures" file="features_test6.tabular"/>
            <output name="pixeloutput" file="pixels_test6.tabular"/>
            <output name="classification_images" file="test6.pdf" compare="sim_size"/>
            <output name="classification_rdata" file="test6.rdata" compare="sim_size" />
        </test>

        <test expect_num_outputs="4">
            <param name="infile" value="testfile_squares.rdata" ftype="rdata"/>
            <conditional name="type_cond">

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

7:6f4c34f8d5ba

<tool id="cardinal_classification" name="MSI classification" version="@VERSION@.0">
    <description>spatial classification of mass spectrometry imaging data</description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements">
        <requirement type="package" version="2.3">r-gridextra</requirement>
        <requirement type="package" version="3.2.1">r-ggplot2</requirement>
    </expand>
    <command detect_errors="exit_code">
    <![CDATA[

        @INPUT_LINKING@

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

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


@READING_MSIDATA@

   msidata = as(msidata, "MSImageSet") ##coercion to MSImageSet


## remove duplicated coordinates
msidata <- msidata[,!duplicated(coord(msidata))]

                grid.table(summary_table5, rows= NULL)

                ### image of the best m/z
                minimumy = min(coord(msidata)[,2])
                maximumy = max(coord(msidata)[,2])
                print(image(msidata, mz = topFeatures(msidata.pls)[1,1], normalize.image = "linear", contrast.enhance = "histogram",ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), smooth.image="gaussian", main="best m/z heatmap"))

                ### m/z and pixel information output
                pls_classes = data.frame(msidata.pls\$classes[[1]])
                ## pixel names and coordinates
                ## to remove potential sample names and z dimension, split at comma and take only x and y 

                ## remove msidata to clean up RAM space
                rm(msidata)
                gc()
                pls_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, pls_classes)
                colnames(pls_classes2) = c("pixel names", "x", "y","predicted condition")
                pls_toplabels = topFeatures(msidata.pls, n=Inf)
                pls_toplabels[,4:6] <-round(pls_toplabels[,4:6],6)
                write.table(pls_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
                write.table(pls_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")

                ## image with predicted classes

                grid.table(summary_table5, rows= NULL)

                ### image of the best m/z
                minimumy = min(coord(msidata)[,2])
                maximumy = max(coord(msidata)[,2])
                print(image(msidata, mz = topFeatures(msidata.opls)[1,1], normalize.image = "linear", contrast.enhance = "histogram",smooth.image="gaussian", ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), main="best m/z heatmap"))

                opls_classes = data.frame(msidata.opls\$classes[[1]])
                ## pixel names and coordinates
                ## to remove potential sample names and z dimension, split at comma and take only x and y
                x_coords = unlist(lapply(strsplit(names(pixels(msidata)), ","), `[[`, 1))

                ## remove msidata to clean up RAM space
                rm(msidata)
                gc()

                opls_toplabels = topFeatures(msidata.opls, n=Inf)
                opls_toplabels[,4:6] <-round(opls_toplabels[,4:6],6)
                write.table(opls_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
                write.table(opls_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")

                ## image with predicted classes

                grid.table(summary_table5, rows= NULL)

                ### image of the best m/z
                minimumy = min(coord(msidata)[,2])
                maximumy = max(coord(msidata)[,2])
                print(image(msidata, mz = topFeatures(msidata.ssc)[1,1], normalize.image = "linear", contrast.enhance = "histogram",smooth.image="gaussian", ylim= c(maximumy+0.2*maximumy,minimumy-0.2*minimumy), main="best m/z heatmap"))

                ## m/z and pixel information output
                ssc_classes = data.frame(msidata.ssc\$classes[[1]])

                ## pixel names and coordinates

                rm(msidata)
                gc()

                ssc_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, ssc_classes)
                colnames(ssc_classes2) = c("pixel names", "x", "y","predicted condition")
                ssc_toplabels = topFeatures(msidata.ssc, n=Inf)
                ssc_toplabels[,6:9] <-round(ssc_toplabels[,6:9],6)
                write.table(ssc_toplabels, file="$mzfeatures", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")
                write.table(ssc_classes2, file="$pixeloutput", quote = FALSE, row.names = FALSE, col.names=TRUE, sep = "\t")

                ## image with predicted classes

        pixel_names = gsub(" = ", "y_", pixel_names)
        x_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,2]
        y_coordinates = matrix(unlist(strsplit(pixel_names, "_")), ncol=3, byrow=TRUE)[,3]
        predicted_classes2 = data.frame(pixel_names, x_coordinates, y_coordinates, predicted_classes)
        colnames(predicted_classes2) = c("pixel names", "x", "y","predicted condition")
        predicted_toplabels = topFeatures(prediction, n=Inf)
        if (colnames(predicted_toplabels)[4] == "coefficients"){
            predicted_toplabels[,4:6] <-round(predicted_toplabels[,4:6],5)

        }else{
            predicted_toplabels[,6:9] <-round(predicted_toplabels[,6:9],5)}

            </conditional>
            <param name="output_rdata" value="True"/>
            <output name="mzfeatures" file="features_test6.tabular"/>
            <output name="pixeloutput" file="pixels_test6.tabular"/>
            <output name="classification_images" file="test6.pdf" compare="sim_size"/>
            <output name="classification_rdata" file="test6.rdata" compare="sim_size" delta="15000"/>
        </test>

        <test expect_num_outputs="4">
            <param name="infile" value="testfile_squares.rdata" ftype="rdata"/>
            <conditional name="type_cond">

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