comparison heatmap2.xml @ 9:ee1c90c1c6cc draft

planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/heatmap2 commit e3d7e01c96e49e65000f45d07e453d9f2625651a

8:81988aed29bd

<tool id="ggplot2_heatmap2" name="heatmap2" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="22.01">
    <macros>
        <token name="@TOOL_VERSION@">3.2.0</token>
        <token name="@VERSION_SUFFIX@">0</token>
    </macros>
    <requirements>
        <requirement type="package" version="@TOOL_VERSION@">r-gplots</requirement>
        <requirement type="package" version="1.1_3">r-rcolorbrewer</requirement>
    </requirements>

#if $cluster_cond.cluster == "yes":
    dendrogramtoplot <- "$cluster_cond.cluster_cols_rows"
    #if $cluster_cond.cluster_cols_rows == "both"
        reorder_cols <- TRUE
        reorder_rows <- TRUE
        layout_matrix <- rbind(c(4,3), c(2,1))
        key_margins <- list(mar=c(4,0.5,2,1))
        lheight <- c(1, 5)
        lwidth <- c(1,3)
    #elif $cluster_cond.cluster_cols_rows == "row"
        reorder_cols <- FALSE
        reorder_rows <- TRUE
        layout_matrix <- rbind(c(3,4), c(2,1))
        key_margins <- list(mar=c(3,5,1,10))
        lheight <- c(1,7)
        lwidth <- c(1,3)
    #elif $cluster_cond.cluster_cols_rows == "column"
        reorder_cols <- TRUE
        reorder_rows <- FALSE
        layout_matrix <- rbind(c(4), c(3), c(1), c(2))
        key_margins <- list(mar=c(4,0.5,2,1))
        lheight <- c(0.3, 2, 6, 1)
        lwidth <- c(1)
    #end if
    hclust_fun <- function(x) hclust(x, method='$cluster_cond.clustering')
    #if $cluster_cond.distance == 'pearson_correlation':
        dist_fun <- function(x) as.dist(1 - cor(t(x)))
    #elif $cluster_cond.distance == 'spearmann_correlation':

        </test>
        <test>
            <param name="input1" value="mtcars.txt"/>
            <param name="cluster" value="no"/>
            <param name="image_file_format" value="png"/>
            <output name="output1" file="result2.png" compare="sim_size" delta="12000" />
        </test>
        <test>
            <param name="input1" value="mtcars.txt"/>
            <param name="zscore" value="cols"/>
            <param name="cluster" value="yes"/>
            <param name="cluster_cols_rows" value="row"/>
            <param name="labels" value="rows"/>
            <param name="type" value="three"/>
            <param name="image_file_format" value="png"/>
            <output name="output1" file="result3.png" compare="sim_size"/>
        </test>
        <test>
            <param name="input1" value="mtcars.txt"/>
            <param name="cluster" value="yes"/>
            <param name="distance" value="pearson_correlation"/>

        <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" delta="12000" />
        </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" delta="12000" />
        </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" delta="12000" />
        </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.

9:ee1c90c1c6cc

<tool id="ggplot2_heatmap2" name="heatmap2" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="22.01">
    <macros>
        <token name="@TOOL_VERSION@">3.2.0</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>

#if $cluster_cond.cluster == "yes":
    dendrogramtoplot <- "$cluster_cond.cluster_cols_rows"
    #if $cluster_cond.cluster_cols_rows == "both"
        reorder_cols <- TRUE
        reorder_rows <- TRUE
        # Layout is:
        # 4 = color key           | 3 = dendrogram for columns
        # 2 = dendrogram for rows | 1 = heatmap
        layout_matrix <- rbind(c(4,3), c(2,1))
        key_margins <- list(mar=c(4,0.5,2,1))
        lheight <- c(1, 5)
        lwidth <- c(1,3)
    #elif $cluster_cond.cluster_cols_rows == "row"
        reorder_cols <- FALSE
        reorder_rows <- TRUE
        # Layout is:
        # 3 = dendrogram for columns (not plotted) + title | 4 = color key
        # 2 = dendrogram for rows                          | 1 = heatmap
        layout_matrix <- rbind(c(3,4), c(2,1))
        key_margins <- list(mar=c(3,5,1,10))
        lheight <- c(1,7)
        lwidth <- c(1,3)
    #elif $cluster_cond.cluster_cols_rows == "column"
        reorder_cols <- TRUE
        reorder_rows <- FALSE
        # Layout is:
        # nothing                               | 4 = color key
        # nothing                               | 3 = dendrogram for columns
        # 2 = dendrogram for rows (not plotted) | 1 = heatmap
        layout_matrix <- rbind(c(0, 4), c(0, 3), c(2, 1))
        key_margins <- list(mar=c(1, 5, 1, 10))
        lheight <- c(0.5, 1, 5)
        lwidth <- c(0.01, 1)
    #end if
    hclust_fun <- function(x) hclust(x, method='$cluster_cond.clustering')
    #if $cluster_cond.distance == 'pearson_correlation':
        dist_fun <- function(x) as.dist(1 - cor(t(x)))
    #elif $cluster_cond.distance == 'spearmann_correlation':

        </test>
        <test>
            <param name="input1" value="mtcars.txt"/>
            <param name="cluster" value="no"/>
            <param name="image_file_format" value="png"/>
            <output name="output1" file="result2.png" compare="image_diff" />
        </test>
        <test>
            <param name="input1" value="mtcars.txt"/>
            <param name="zscore" value="cols"/>
            <param name="cluster" value="yes"/>
            <param name="cluster_cols_rows" value="row"/>
            <param name="labels" value="rows"/>
            <param name="type" value="three"/>
            <param name="image_file_format" value="png"/>
            <output name="output1" file="result3.png" compare="image_diff"/>
        </test>
        <test>
            <param name="input1" value="mtcars.txt"/>
            <param name="cluster" value="yes"/>
            <param name="distance" value="pearson_correlation"/>

        <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="image_diff" />
        </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="image_diff" />
        </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="image_diff" />
        </test>
        <test>
            <param name="input1" value="mtcars.txt"/>
            <param name="cluster" value="yes"/>
            <param name="cluster_cols_rows" value="column"/>
            <param name="distance" value="pearson_correlation"/>
            <param name="scale" value="row"/>
            <param name="type" value="palettes"/>
            <param name="name" value="YlOrBr"/>
            <param name="image_file_format" value="png"/>
            <output name="output1" file="result7.png" compare="image_diff" />
        </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.