Mercurial > repos > bgruening > deeptools_plot_correlation
diff plotCorrelation.xml @ 12:ed2767cdf4e8 draft
planemo upload for repository https://github.com/fidelram/deepTools/tree/master/galaxy/wrapper/ commit 09975f870c75347fba5c6777c9f3b442bdeeb289
| author | bgruening |
|---|---|
| date | Fri, 31 Mar 2017 09:27:20 -0400 |
| parents | cdde99ce71c8 |
| children | d40bd147adf3 |
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--- a/plotCorrelation.xml Tue Jan 24 04:57:11 2017 -0500 +++ b/plotCorrelation.xml Fri Mar 31 09:27:20 2017 -0400 @@ -1,142 +1,142 @@ -<tool id="deeptools_plot_correlation" name="plotCorrelation" version="@WRAPPER_VERSION@.0"> - <description>Create a heatmap or scatterplot of correlation scores between different samples </description> - <macros> - <token name="@BINARY@">plotCorrelation</token> - <import>deepTools_macros.xml</import> - </macros> - <expand macro="requirements"/> - <command> -<![CDATA[ - @BINARY@ - --corData "$corData" - --plotFile "$outFileName" - --corMethod "$corMethod" - --whatToPlot "$plotting_type.whatToPlot" - #if str($plotting_type.whatToPlot) == 'heatmap': - @HEATMAP_OPTIONS@ - #else: - --plotTitle '$plotting_type.plotTitle' - #end if - $skipZeros - --plotFileFormat "$outFileFormat" - $removeOutliers - #if $outFileCorMatrix: - --outFileCorMatrix "$matrix" - #end if - -]]> - </command> - <inputs> - <param name="corData" format="deeptools_coverage_matrix" type="data" label="Matrix file from the multiBamSummary tool"/> - <expand macro="corMethod" /> - - <conditional name="plotting_type" > - <param argument="--whatToPlot" type="select" label="Plotting type"> - <option value="heatmap" selected="True">Heatmap</option> - <option value="scatterplot">Scatterplot</option> - </param> - <when value="heatmap"> - <expand macro="heatmap_options" /> - </when> - <when value="scatterplot"> - <expand macro="plotTitle" /> - </when> - </conditional> - - <expand macro="skipZeros" /> - - <expand macro="input_image_file_format" /> - - <param argument="--removeOutliers" type="boolean" - truevalue="--removeOutliers" falsevalue="" label="Remove regions with very large counts" - help="If set, bins with very large counts are removed. Bins - with abnormally high reads counts artificially - increase pearson correlation; that's why, by default, - plotCorrelation tries to remove outliers using the median - absolute deviation (MAD) method applying a threshold - of 200 to only consider extremely large deviations - from the median. ENCODE blacklist page (https://sites. - google.com/site/anshulkundaje/projects/blacklists) - contains useful information about regions with - unusually high counts."/> - - <param name="outFileCorMatrix" type="boolean" label="Save the matrix of values underlying the heatmap"/> - - </inputs> - <outputs> - <expand macro="output_image_file_format_not_nested" /> - <data format="tabular" name="matrix" label="${tool.name} on ${on_string}: Correlation matrix"> - <filter>outFileCorMatrix is True</filter> - </data> - </outputs> - <tests> - <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" ftype="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:: $PATH_TO_IMAGES/plotCorrelation_scatterplot_PearsonCorr_bigwigScores.png - :width: 600 - :height: 600 - ------ - -@REFERENCES@ -]]> - </help> - <expand macro="citations" /> -</tool> +<tool id="deeptools_plot_correlation" name="plotCorrelation" version="@WRAPPER_VERSION@.0"> + <description>Create a heatmap or scatterplot of correlation scores between different samples </description> + <macros> + <token name="@BINARY@">plotCorrelation</token> + <import>deepTools_macros.xml</import> + </macros> + <expand macro="requirements"/> + <command> +<![CDATA[ + @BINARY@ + --corData "$corData" + --plotFile "$outFileName" + --corMethod "$corMethod" + --whatToPlot "$plotting_type.whatToPlot" + #if str($plotting_type.whatToPlot) == 'heatmap': + @HEATMAP_OPTIONS@ + #else: + --plotTitle '$plotting_type.plotTitle' + #end if + $skipZeros + --plotFileFormat "$outFileFormat" + $removeOutliers + #if $outFileCorMatrix: + --outFileCorMatrix "$matrix" + #end if + +]]> + </command> + <inputs> + <param name="corData" format="deeptools_coverage_matrix" type="data" label="Matrix file from the multiBamSummary tool"/> + <expand macro="corMethod" /> + + <conditional name="plotting_type" > + <param argument="--whatToPlot" type="select" label="Plotting type"> + <option value="heatmap" selected="True">Heatmap</option> + <option value="scatterplot">Scatterplot</option> + </param> + <when value="heatmap"> + <expand macro="heatmap_options" /> + </when> + <when value="scatterplot"> + <expand macro="plotTitle" /> + </when> + </conditional> + + <expand macro="skipZeros" /> + + <expand macro="input_image_file_format" /> + + <param argument="--removeOutliers" type="boolean" + truevalue="--removeOutliers" falsevalue="" label="Remove regions with very large counts" + help="If set, bins with very large counts are removed. Bins + with abnormally high reads counts artificially + increase pearson correlation; that's why, by default, + plotCorrelation tries to remove outliers using the median + absolute deviation (MAD) method applying a threshold + of 200 to only consider extremely large deviations + from the median. ENCODE blacklist page (https://sites. + google.com/site/anshulkundaje/projects/blacklists) + contains useful information about regions with + unusually high counts."/> + + <param name="outFileCorMatrix" type="boolean" label="Save the matrix of values underlying the heatmap"/> + + </inputs> + <outputs> + <expand macro="output_image_file_format_not_nested" /> + <data format="tabular" name="matrix" label="${tool.name} on ${on_string}: Correlation matrix"> + <filter>outFileCorMatrix is True</filter> + </data> + </outputs> + <tests> + <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" ftype="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@ +]]> + </help> + <expand macro="citations" /> +</tool>