diff plotCorrelation.xml @ 5:ae54e08b15d7 draft

planemo upload for repository https://github.com/fidelram/deepTools/tree/master/galaxy/wrapper/ commit 8b9cdb8dc4a8fedd2b2286e7afab6529e49a9d22-dirty
author bgruening
date Tue, 22 Dec 2015 13:45:00 -0500
parents e556e34fc12a
children 7cbaaf80dced
line wrap: on
line diff
--- a/plotCorrelation.xml	Mon Dec 21 19:07:40 2015 -0500
+++ b/plotCorrelation.xml	Tue Dec 22 13:45:00 2015 -0500
@@ -1,5 +1,5 @@
 <tool id="deeptools_plot_correlation" name="plotCorrelation" version="@WRAPPER_VERSION@.0">
-    <description>creates a heatmap of correlation scores between different samples </description>
+    <description>creates a heatmap or scatterplot 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.
 
@@ -105,10 +105,18 @@
 
 **Output files**:
 
-- **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
+- **diagnostic plot**: Either a scatterplot or clustered heatmap (select above) 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@