Mercurial > repos > bgruening > hicexplorer_hicfindtads

changeset 5:4b2096acb3ab draft

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planemo upload for repository https://github.com/maxplanck-ie/HiCExplorer/tree/master/galaxy/wrapper/ commit dfa5a68cb20842407941c7ffda9ef956a0e86a04
author iuc
date Sat, 16 Dec 2017 16:25:58 -0500
parents dac89b782608
children 49899531dd07
files hicFindTADs.xml macros.xml static/images/SRR027956.svg static/images/SRR0279XX_perChr_eigenvector1.png static/images/diagnostic_plot.png static/images/hicCorrelate_pearson.png static/images/hicCorrelate_pearson_scatter.png static/images/hicCorrelate_spearman.png static/images/hicCorrelate_spearman_scatter.png static/images/hicPlotDistVsCounts_result1.png static/images/hicPlotDistVsCounts_result2.png static/images/hicQC.png static/images/master_TADs_plot.png static/images/z-score.svg
diffstat 14 files changed, 763 insertions(+), 3 deletions(-) [+]
line wrap: on
line diff
--- a/hicFindTADs.xml	Mon Nov 27 08:34:54 2017 -0500
+++ b/hicFindTADs.xml	Sat Dec 16 16:25:58 2017 -0500
@@ -111,11 +111,66 @@
         </test>
     </tests>
     <help><![CDATA[
+Calculate TADs
+==============
 
-**What it does**
+Topological associated domains (TADs) are regions on the DNA which tend to interact within the region a lot, but not outside their boundaries. More information_.
+
+Calculation
+------------
+``hicFindTADs`` computes the TAD regions in two steps: In a first step it computes a TAD-separation score based on a z-score matrix for all bins. The z-score is defined as:
+
+“The absolute value of z represents the distance between the raw score and the population mean in 
+units of the standard deviation. z is negative when the raw score is below the mean, positive when above.” 
+[Source_].
+
+.. image:: $PATH_TO_IMAGES/z-score.svg
+   :width: 150
+   
+`Source of image <https://wikimedia.org/api/rest_v1/media/math/render/svg/5ceed701c4042bb34618535c9a902ca1a937a351>`_
+
+In our case the distribution describes the counts per bin of a genomic distance. In a second step the local minima of the TAD-separation score is evaluated with respect to the surrounding bins to assign a p-value. Two multiple testing corrections can be applied to filter the results: `Bonferroni <https://en.wikipedia.org/wiki/Bonferroni_correction>`_ or the `false discovery rate <https://en.wikipedia.org/wiki/False_discovery_rate>`_.
+
+
+Input
+-----
 
-Uses the graph clustering measure "conductance" to find minimum cuts that correspond to boundaries.
+Parameters
+__________
+- contact matrix to compute the TADs on
+- minimum window length
+- maximum window length
+- step size
+- multiple testing correction
+- minimum threshold
+- minimum distance
+
+
+hicFindTADs tries to identify sensible parameters but those can be change to identify more stringent set of boundaries.
+
+Output
+------
 
+- Boundary positions as a bed file
+- Matrix with multi-scale TAD scores as a bedgraph 
+- TAD domains as a bed file
+- Boundary information plus score as gff
+- TAD information in bm file
+- Z-score matrix in h5
+
+The calulated TAD regions can be plotted with ``hicPlotTADs``.
+
+
+.. image:: $PATH_TO_IMAGES/master_TADs_plot.png
+   :width: 80 %
+
+
+
+For more information about HiCExplorer please consider our documentation on readthedocs.io_
+
+.. _readthedocs.io: http://hicexplorer.readthedocs.io/en/latest/index.html
+.. _Source: https://en.wikipedia.org/wiki/Standard_score#Calculation_from_raw_score 
+.. _information: https://en.wikipedia.org/wiki/Topologically_associating_domain_
 ]]></help>
     <expand macro="citations" />
 </tool>
--- a/macros.xml	Mon Nov 27 08:34:54 2017 -0500
+++ b/macros.xml	Sat Dec 16 16:25:58 2017 -0500
@@ -21,7 +21,7 @@
     </xml>
 
     <xml name="colormap">
-        <param argument="--colorMap" name="colormap" type="select" optional="True" label="Color map to use for the heatmap" help=" Available color map names can be found here: http://www.astro.lsa.umich.edu/~msshin/science/code/matplotlib_cm/">
+        <param argument="--colorMap" name="colormap" type="select" optional="True" label="Color map to use for the heatmap" help=" Available color map names can be found here: https://matplotlib.org/examples/color/colormaps_reference.html">
             <option value="RdYlBu">RdYlBu</option>
             <option value="Accent">Accent</option>
             <option value="Spectral">Spectral</option>
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/static/images/SRR027956.svg	Sat Dec 16 16:25:58 2017 -0500
@@ -0,0 +1,678 @@
+<?xml version="1.0" encoding="utf-8" standalone="no"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN"
+  "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<!-- Created with matplotlib (http://matplotlib.org/) -->
+<svg height="432pt" version="1.1" viewBox="0 0 504 432" width="504pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
+ <defs>
+  <style type="text/css">
+*{stroke-linecap:butt;stroke-linejoin:round;}
+  </style>
+ </defs>
+ <g id="figure_1">
+  <g id="patch_1">
+   <path d="M 0 432 
+L 504 432 
+L 504 0 
+L 0 0 
+z
+" style="fill:#ffffff;"/>
+  </g>
+  <g id="axes_1">
+   <g id="patch_2">
+    <path d="M 57.6 374.4 
+L 402.497561 374.4 
+L 402.497561 28.8 
+L 57.6 28.8 
+z
+" style="fill:#ffffff;"/>
+   </g>
+   <image height="345" id="image8c8ebbe161" transform="scale(1 -1)translate(0 -345)" width="344" x="58" xlink:href="data:image/png;base64,
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