comparison distribution_features.xml @ 1:2e7d47c0b027 draft

"planemo upload for repository https://[e-mail]/repos/malex/secimtools"

1:2e7d47c0b027

<tool id="secimtools_distribution_features" name="Generate Distribution of Features across Samples." version="@WRAPPER_VERSION@">
    <description></description>
    <macros>
        <import>macros.xml</import>
    </macros>
    <expand macro="requirements" />
    <command detect_errors="exit_code"><![CDATA[
distribution_features.py
--input $input
--design $design
--ID $uniqID
--figure $figure
#if $group
    --group $group
#end if
    ]]></command>
    <inputs>
        <param name="input" type="data" format="tabular" label="Wide Dataset" help="Input your tab-separated wide format dataset. If not tab separated see TIP below."/>
        <param name="design" type="data" format="tabular" label="Design File" help="Input your design file (tab-separated). Note you need a 'sampleID' column. If not tab separated see TIP below."/>
        <param name="uniqID" type="text" size="30" value="" label="Unique Feature ID" help="Name of the column in your Wide Dataset that has unique identifiers."/>
        <param name="group" type="text" size="30" value="" optional="true" label="Group/Treatment [Optional]" help="Name of the column in your Design File that contains group classifications."/>
    </inputs>
    <outputs>
        <data format="pdf" name="figure" label="${tool.name} on ${on_string}: Feature Distribution"/>
    </outputs>
    <tests>
        <test>
            <param name="input"  value="ST000006_data.tsv"/>
            <param name="design" value="ST000006_design.tsv"/>
            <param name="uniqID" value="Retention_Index" />
            <param name="group"  value="White_wine_type_and_source" />
            <output name="figure" file="ST000006_distribution_features_with_group_figure.pdf" compare="sim_size" delta="10000"/>
        </test>
    </tests>
    <help><![CDATA[

@TIP_AND_WARNING@

**Tool Description**

The tool summarizes the distribution of 50 randomly selected features (rows) across all samples.
Boxplots with outliers and mean value are provided for each selected feature across all samples.
If group or treatment information is provided, boxplots are generated for samples within each group and for all samples.
If a group or treatment variable is not provided, boxplots are provided for all samples.

**NOTE:** This script works best with log transformed data.


--------------------------------------------------------------------------------

**Input**

    - Two input datasets are required.

@WIDE@

**NOTE:** The sample IDs must match the sample IDs in the Design File
(below). Extra columns will automatically be ignored.


@METADATA@

@UNIQID@

@GROUP_OPTIONAL@

--------------------------------------------------------------------------------

**Output**

A PDF file with boxplot(s) and density plot(s):

if Group/Treatment [Optional] is provided  plots will be generated for every group as well as for all samples. Otherwise, a single plot will be generated for all samples.

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