comparison plotCorrelation.xml @ 16:7c0b9268fbb9 draft

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

15:714250469582

        <test>
            <param name="corData" value="multiBamSummary_result1.npz" ftype="deeptools_coverage_matrix" />
            <param name="outFileFormat" value="png" />
            <param name="outFileCorMatrix" value="True" />
            <output name="matrix" file="plotCorrelation_result1.tabular" ftype="tabular" />
            <output name="outFileName" file="plotCorrelation_result1.png" ftype="png" compare="sim_size" delta="200" />
        </test>
        <test>
            <param name="corData" value="multiBamSummary_result1.npz" ftype="deeptools_coverage_matrix" />
            <param name="outFileFormat" value="png" />
            <param name="whatToPlot" value="scatterplot" />
            <param name="removeOutliers" value="True" />
            <param name="plotTitle" value="Test Plot" />
            <output name="outFileName" file="plotCorrelation_result2.png" ftpye="png" compare="sim_size" delta="200" />
        </test>
    </tests>
    <help>
<![CDATA[
What it does
--------------

This tools takes the default output of ``multiBamSummary`` or ``multiBigwigSummary``, and computes the pairwise correlation among samples.
Results can be visualized as **scatterplots** or as
a **heatmap** of correlation coefficients (see below for examples).

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

The result of the correlation computation is a **table of correlation coefficients** that indicates how "strong" the relationship between two samples is and it will consist of numbers between -1 and 1. (-1 indicates perfect anti-correlation, 1 perfect correlation.)

We offer two different functions for the correlation computation: *Pearson* or *Spearman*.

The *Pearson method* measures the **metric differences** between samples and is therefore influenced by outliers.
The *Spearman method* is based on **rankings**.

Output
--------

The default output is a **diagnostic plot** -- either a scatterplot or a clustered heatmap displaying the values for each pair-wise correlation (see below for example plots).

Optionally, you can also obtain a table of the pairwise correlation coefficients.

.. image:: $PATH_TO_IMAGES/plotCorrelation_output.png
   :width: 600
   :height: 271

Example plots
--------------

The following is the output of ``plotCorrelation`` with our test ChIP-Seq datasets (to be found under "Shared Data" --> "Data Library").

Average coverages were computed over 10 kb bins for chromosome X,
from bigWig files using ``multiBigwigSummary``. This was then used with ``plotCorrelation`` to make a heatmap of Spearman correlation coefficients.

.. image:: $PATH_TO_IMAGES/plotCorrelation_galaxy_bw_heatmap_output.png
   :width: 600
   :height: 518

The scatterplot could look like this:

.. image:: $PATH_TO_IMAGES/plotCorrelation_scatterplot_PearsonCorr_bigwigScores.png
   :width: 600
   :height: 600

-----

@REFERENCES@
]]>

16:7c0b9268fbb9

        <test>
            <param name="corData" value="multiBamSummary_result1.npz" ftype="deeptools_coverage_matrix" />
            <param name="outFileFormat" value="png" />
            <param name="outFileCorMatrix" value="True" />
            <output name="matrix" file="plotCorrelation_result1.tabular" ftype="tabular" />
            <output name="outFileName" file="plotCorrelation_result1.png" ftype="png" compare="sim_size" delta="300" />
        </test>
        <test>
            <param name="corData" value="multiBamSummary_result1.npz" ftype="deeptools_coverage_matrix" />
            <param name="outFileFormat" value="png" />
            <param name="whatToPlot" value="scatterplot" />
            <param name="removeOutliers" value="True" />
            <param name="plotTitle" value="Test Plot" />
            <output name="outFileName" file="plotCorrelation_result2.png" ftpye="png" compare="sim_size" delta="300" />
        </test>
    </tests>
    <help>
<![CDATA[
What it does
------------

This tools takes the default output of ``multiBamSummary`` or ``multiBigwigSummary``, and computes the pairwise correlation among samples.
Results can be visualized as **scatterplots** or as a **heatmap** of correlation coefficients (see below for examples).

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

The result of the correlation computation is a **table of correlation coefficients** that indicates how "strong" the relationship between two samples is and it will consist of numbers between -1 and 1. (-1 indicates perfect anti-correlation, 1 perfect correlation.)

We offer two different functions for the correlation computation: *Pearson* or *Spearman*.

The *Pearson method* measures the **metric differences** between samples and is therefore influenced by outliers.
The *Spearman method* is based on **rankings**.

Output
------

The default output is a **diagnostic plot** -- either a scatterplot or a clustered heatmap displaying the values for each pair-wise correlation (see below for example plots).

Optionally, you can also obtain a table of the pairwise correlation coefficients.

.. image:: $PATH_TO_IMAGES/plotCorrelation_output.png
    :width: 600
    :height: 271

Example plots
-------------

The following is the output of ``plotCorrelation`` with our test ChIP-Seq datasets (to be found under "Shared Data" --> "Data Library").

Average coverages were computed over 10 kb bins for chromosome X,
from bigWig files using ``multiBigwigSummary``. This was then used with ``plotCorrelation`` to make a heatmap of Spearman correlation coefficients.

.. image:: $PATH_TO_IMAGES/plotCorrelation_galaxy_bw_heatmap_output.png
    :width: 600
    :height: 518

The scatterplot could look like this:

.. image:: $PATH_TO_IMAGES/plotCorrelation_scatterplot_PearsonCorr_bigwigScores.png
    :width: 600
    :height: 600

-----

@REFERENCES@
]]>