comparison plotPCA.xml @ 3:eae52f5abf7b draft

planemo upload for repository https://github.com/fidelram/deepTools/tree/master/galaxy/wrapper/ commit 2e8510e4f4015f51f7726de5697ba2de9b4e2f4c

2:b15aaad44ab8

        @BINARY@
            --corData "$corData"
            --plotTitle "$plotTitle"
            --plotFile "$outFileName"
            --plotFileFormat "$outFileFormat"
]]>
    </command>
    <inputs>
        <param name="corData" format="deeptools_coverage_matrix" type="data" label="Matrix file from the multiBamSummary or multiBigwigSummary tools"/>
        <expand macro="input_image_file_format" />
        <expand macro="plotTitle" />
    </inputs>
    <outputs>
        <expand macro="output_image_file_format_not_nested" />
    </outputs>
    <tests>
        <test>
            <param name="corData" value="multiBamSummary_result2.npz" ftype="deeptools_coverage_matrix" />
            <param name="plotTitle" value="Test Plot" />
            <param name="outFileFormat" value="png" />
            <output name="outFileName" file="plotPCA_result1.png" ftpye="png" compare="sim_size" delta="4000" />
        </test>
    </tests>
    <help>
<![CDATA[

What it does
---------------

This tool takes the **default output file of ``multiBamSummary``** or ``multiBigwigSummary`` to perform a principal component analysis (PCA).

Output
-------------

The result is a panel of two plots:

1. The eigenvalues of the **top two principal components**.
2. The **Scree plot** for the top five principal components where the bars represent the amount of variability explained by the individual factors and the red line traces the amount of variability is explained by the individual components in a cumulative manner

Example plot
---------------

.. image:: $PATH_TO_IMAGES/plotPCA_annotated.png
   :width: 600
   :height: 315

===================

Background
-----------------

Principal component analysis (PCA) can be used, for example, to determine whether **samples display greater variability** between experimental conditions than between replicates of the same treatment. PCA is also useful to identify unexpected patterns, such as those caused by batch effects or outliers.
Principal components represent the directions along which the variation in the data is maximal, so that the information (e.g., read coverage values) from thousands of regions can be represented by just a few dimensions.

PCA is not necessarily meant to identify unknown groupings or clustering; it is up to the researcher to determine the experimental or technical reason underlying the principal components.

3:eae52f5abf7b

        @BINARY@
            --corData "$corData"
            --plotTitle "$plotTitle"
            --plotFile "$outFileName"
            --plotFileFormat "$outFileFormat"
            #if $outFileNameData
                --outFileNameData "$output_outFileNameData"
            #end if
]]>
    </command>
    <inputs>
        <param name="corData" format="deeptools_coverage_matrix" type="data" label="Matrix file from the multiBamSummary or multiBigwigSummary tools"/>
        <expand macro="input_image_file_format" />
        <expand macro="plotTitle" />
        <param argument="--outFileNameData" type="boolean" label="Save the matrix of PCA and eigenvalues underlying the plot."/>
    </inputs>
    <outputs>
        <expand macro="output_image_file_format_not_nested" />
        <data format="tabular" name="output_outFileNameData" label="${tool.name} on ${on_string}: PCA matrix">
            <filter>outFileNameData</filter>
        </data>
    </outputs>
    <tests>
        <test>
            <param name="corData" value="multiBamSummary_result2.npz" ftype="deeptools_coverage_matrix" />
            <param name="plotTitle" value="Test Plot" />
            <param name="outFileFormat" value="png" />
            <output name="outFileName" file="plotPCA_result1.png" ftpye="png" compare="sim_size" delta="4000" />
        </test>
        <test>
            <param name="corData" value="multiBamSummary_result2.npz" ftype="deeptools_coverage_matrix" />
            <param name="plotTitle" value="Test Plot" />
            <param name="outFileFormat" value="png" />
            <param name="outFileNameData" value="True" />
            <output name="outFileName" file="plotPCA_result2.png" ftpye="png" compare="sim_size" delta="4000" />
            <output name="output_outFileNameData" file="plotPCA_result2.tabular" ftype="tabular" />
        </test>
    </tests>
    <help>
<![CDATA[

What it does
------------

This tool takes the **default output file** of ``multiBamSummary`` or ``multiBigwigSummary`` to perform a principal component analysis (PCA).

Output
------

The result is a panel of two plots:

1. The eigenvalues of the **top two principal components**.
2. The **Scree plot** for the top five principal components where the bars represent the amount of variability explained by the individual factors and the red line traces the amount of variability is explained by the individual components in a cumulative manner

Example plot
------------

.. image:: $PATH_TO_IMAGES/plotPCA_annotated.png
   :width: 600
   :height: 315

-----

Theoretical Background
----------------------

Principal component analysis (PCA) can be used, for example, to determine whether **samples display greater variability** between experimental conditions than between replicates of the same treatment. PCA is also useful to identify unexpected patterns, such as those caused by batch effects or outliers.
Principal components represent the directions along which the variation in the data is maximal, so that the information (e.g., read coverage values) from thousands of regions can be represented by just a few dimensions.

PCA is not necessarily meant to identify unknown groupings or clustering; it is up to the researcher to determine the experimental or technical reason underlying the principal components.