Mercurial > repos > bgruening > deeptools_plot_correlation
diff plotCorrelation.xml @ 6:7cbaaf80dced draft
planemo upload for repository https://github.com/fidelram/deepTools/tree/master/galaxy/wrapper/ commit 54a10cf268ca9a5399f13458a1b218be7891bd41
author | bgruening |
---|---|
date | Wed, 23 Dec 2015 03:57:45 -0500 |
parents | ae54e08b15d7 |
children | 44d87fb8403e |
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--- a/plotCorrelation.xml Tue Dec 22 13:45:00 2015 -0500 +++ b/plotCorrelation.xml Wed Dec 23 03:57:45 2015 -0500 @@ -1,5 +1,5 @@ <tool id="deeptools_plot_correlation" name="plotCorrelation" version="@WRAPPER_VERSION@.0"> - <description>creates a heatmap or scatterplot of correlation scores between different samples </description> + <description>creates a heatmap of correlation scores between different samples </description> <macros> <token name="@BINARY@">plotCorrelation</token> <import>deepTools_macros.xml</import> @@ -91,7 +91,7 @@ **What it does** This tools takes a compressed matrix of scores (such as read coverages) for a number of genomic regions -and different samples. It can visualize the correlation among samples as scatterplots or as +and different samples. It can visualize the correlation among samples as scatterplots or as heatmap of correlation coefficients. Further output files are optional. The compressed input matrices are easily generated using the "bamCorrelate" and "bigwigCorrelate" modules of deeptools. @@ -105,18 +105,10 @@ **Output files**: -- **diagnostic plot**: Either a scatterplot or clustered heatmap (select above) displaying the values for each pair-wise correlation, - see below for an example +- **correlation structure**: a scatterplot of all mutual correlations between all samples in matrix +- **diagnostic plot**: clustered heatmap displaying the values for each pair-wise correlation, see below for an example - 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 -**Output with test dataset**: - -Following is the output of plotCorrelation with our test ChIP-Seq datasets. Average coverages were computed over 10kb bins for chromosome X, -from bigwig files using bigwigCorrelate. The output was used by plotCorrelation to make a heatmap of spearman correlation between samples. - -.. image:: $PATH_TO_IMAGES/plotCorrelation_galaxy_bw_heatmap_output.png - - ----- @REFERENCES@