comparison heatmap2.xml @ 6:566197475670 draft

"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/heatmap2 commit 4f9970932fa3521656d5618582ba5df4173ca66c"

5:3b37b1b1c3a7

<tool id="ggplot2_heatmap2" name="heatmap2" version="@VERSION@">
    <macros>
        <token name="@VERSION@">3.1.1</token>
    </macros>
    <requirements>
        <requirement type="package" version="@VERSION@">r-gplots</requirement>
        <requirement type="package" version="1.1_3">r-rcolorbrewer</requirement>
    </requirements>
    <command detect_errors="exit_code"><![CDATA[
cat '$script' &&
Rscript '$script'

    linput <- log10(mat_input+1)
#end if

#if $zscore_cond.zscore == "rows":
    linput <- t(apply(linput, 1, scale))
    colnames(linput) <- colnames(input)
    rownames(linput) <- input[,1]
    scale <- "none"
#elif $zscore_cond.zscore == "cols":
    linput <- apply(linput, 2, scale)
    rownames(linput) <- input[,1]

            <param name="cluster" value="yes"/>
            <param name="distance" value="pearson_correlation"/>
            <param name="type" value="two"/>
            <output name="output1" file="result1pearson.pdf" compare="sim_size"/>
        </test>
    </tests>
    <help><![CDATA[
This tool employs the heatmap.2 function from the R gplots package and will generate a heatmap of your data. If clustering is enabled, the heatmap uses the Euclidean distance method and the Complete hierarchical clustering method by default.

Input data should have row labels in the first column and column labels. For example, the row labels (the first column) should represent gene IDs and the column labels should represent sample IDs.

6:566197475670

<tool id="ggplot2_heatmap2" name="heatmap2" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@">
    <macros>
        <token name="@TOOL_VERSION@">3.1.1</token>
        <token name="@VERSION_SUFFIX@">1</token>
    </macros>
    <requirements>
        <requirement type="package" version="@TOOL_VERSION@">r-gplots</requirement>
        <requirement type="package" version="1.1_3">r-rcolorbrewer</requirement>
    </requirements>
    <command detect_errors="exit_code"><![CDATA[
cat '$script' &&
Rscript '$script'

    linput <- log10(mat_input+1)
#end if

#if $zscore_cond.zscore == "rows":
    linput <- t(apply(linput, 1, scale))
    colnames(linput) <- colnames(input)[2:ncol(input)]
    rownames(linput) <- input[,1]
    scale <- "none"
#elif $zscore_cond.zscore == "cols":
    linput <- apply(linput, 2, scale)
    rownames(linput) <- input[,1]

            <param name="cluster" value="yes"/>
            <param name="distance" value="pearson_correlation"/>
            <param name="type" value="two"/>
            <output name="output1" file="result1pearson.pdf" compare="sim_size"/>
        </test>
        <test>
            <param name="input1" value="mtcars.txt"/>
            <param name="zscore" value="rows"/>
            <param name="type" value="three"/>
            <param name="image_file_format" value="png"/>
            <output name="output1" file="result4.png" compare="sim_size"/>
        </test>
        <test>
            <param name="input1" value="mtcars.txt"/>
            <param name="cluster" value="yes"/>
            <param name="distance" value="pearson_correlation"/>
            <param name="scale" value="row"/>
            <param name="type" value="three"/>
            <param name="image_file_format" value="png"/>
            <output name="output1" file="result5.png" compare="sim_size"/>
        </test>
        <test>
            <param name="input1" value="mtcars.txt"/>
            <param name="cluster" value="yes"/>
            <param name="distance" value="spearmann_correlation"/>
            <param name="scale" value="column"/>
            <param name="type" value="three"/>
            <param name="image_file_format" value="png"/>
            <output name="output1" file="result6.png" compare="sim_size"/>
        </test>
    </tests>
    <help><![CDATA[
This tool employs the heatmap.2 function from the R gplots package and will generate a heatmap of your data. If clustering is enabled, the heatmap uses the Euclidean distance method and the Complete hierarchical clustering method by default.

Input data should have row labels in the first column and column labels. For example, the row labels (the first column) should represent gene IDs and the column labels should represent sample IDs.