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comparison plotCorrelation.xml @ 13:49ac0d30a800 draft

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planemo upload for repository https://github.com/fidelram/deepTools/tree/master/galaxy/wrapper/ commit 13910e1a5ebcfc740c1bc5e38fc676592ef44f11
author bgruening
date Mon, 15 Feb 2016 10:07:12 -0500
parents cc0acb70ded3
children 714250469582
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12:82e3789b5e59 13:49ac0d30a800
1 <tool id="deeptools_plot_correlation" name="plotCorrelation" version="@WRAPPER_VERSION@.0"> 1 <tool id="deeptools_plot_correlation" name="plotCorrelation" version="@WRAPPER_VERSION@.0">
2 <description>creates a heatmap or scatterplot of correlation scores between different samples </description> 2 <description>Create a heatmap or scatterplot of correlation scores between different samples </description>
3 <macros> 3 <macros>
4 <token name="@BINARY@">plotCorrelation</token> 4 <token name="@BINARY@">plotCorrelation</token>
5 <import>deepTools_macros.xml</import> 5 <import>deepTools_macros.xml</import>
6 </macros> 6 </macros>
7 <expand macro="requirements"/> 7 <expand macro="requirements"/>
86 <output name="outFileName" file="plotCorrelation_result2.png" ftpye="png" compare="sim_size" delta="100" /> 86 <output name="outFileName" file="plotCorrelation_result2.png" ftpye="png" compare="sim_size" delta="100" />
87 </test> 87 </test>
88 </tests> 88 </tests>
89 <help> 89 <help>
90 <![CDATA[ 90 <![CDATA[
91 **What it does** 91 What it does
92 --------------
92 93
93 This tools takes a compressed matrix of scores (such as read coverages) for a number of genomic regions 94 This tools takes the default output of ``multiBamSummary`` or ``multiBigwigSummary``, and computes the pairwise correlation among samples.
94 and different samples. It can visualize the correlation among samples as scatterplots or as 95 Results can be visualized as **scatterplots** or as
95 a heatmap of correlation coefficients. Further output files are optional. 96 a **heatmap** of correlation coefficients (see below for examples).
96 The compressed input matrices are easily generated using the "multiBamSummary" and "multiBigwigSummary" tools.
97 97
98 Background
99 ------------
98 100
99 .. image:: $PATH_TO_IMAGES/QC_multiBamSummary_humanSamples.png 101 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.)
100 :alt: Heatmap of RNA Polymerase II ChIP-seq
101 102
103 We offer two different functions for the correlation computation: *Pearson* or *Spearman*.
102 104
103 You can find more details on plotCorrelation here http://deeptools.readthedocs.org/en/master/content/tools/plotCorrelation.html 105 The *Pearson method* measures the **metric differences** between samples and is therefore influenced by outliers.
106 The *Spearman method* is based on **rankings**.
104 107
108 Output
109 --------
105 110
106 **Output files**: 111 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).
107 112
108 - **diagnostic plot**: Either a scatterplot or clustered heatmap (select above) displaying the values for each pair-wise correlation, 113 Optionally, you can also obtain a table of the pairwise correlation coefficients.
109 see below for an example
110 - data matrix (optional): if you want to analyze or plot the correlation values using a different program, e.g. R, this matrix can be used
111 114
112 **Output with test dataset**: 115 .. image:: $PATH_TO_IMAGES/plotCorrelation_output.png
116 :width: 600
117 :height: 271
113 118
114 The following is the output of plotCorrelation with our test ChIP-Seq datasets. Average coverages were computed over 10kb bins for chromosome X, 119 Example plots
115 from bigwig files using multiBigwigSummary. The output was used by plotCorrelation to make a heatmap of spearman correlation between samples. 120 --------------
121
122 The following is the output of ``plotCorrelation`` with our test ChIP-Seq datasets (to be found under "Shared Data" --> "Data Library").
123
124 Average coverages were computed over 10 kb bins for chromosome X,
125 from bigWig files using ``multiBigwigSummary``. This was then used with ``plotCorrelation`` to make a heatmap of Spearman correlation coefficients.
116 126
117 .. image:: $PATH_TO_IMAGES/plotCorrelation_galaxy_bw_heatmap_output.png 127 .. image:: $PATH_TO_IMAGES/plotCorrelation_galaxy_bw_heatmap_output.png
128 :width: 600
129 :height: 518
118 130
131 The scatterplot could look like this:
132
133 .. image:: $PATH_TO_IMAGES/plotCorrelation_scatterplot_PearsonCorr_bigwigScores.png
134 :width: 600
135 :height: 600
119 136
120 ----- 137 -----
121 138
122 @REFERENCES@ 139 @REFERENCES@
123 ]]> 140 ]]>