view cluster_kegg.xml @ 34:f739a296a339

Update to Miller Lab devshed revision 09dc81dbebc5

<tool id="gd_cluster_kegg" name="Cluster KEGG" version="1.0.0">
  <description>: Group gene categories connected by shared genes</description>

  <command interpreter="python">
    #set $ensembltcolmn_arg = int(str($ensembltcolmn)) - 1
    cluster_onConnctdComps.py '--input=$input' '--input_columns=${input.dataset.metadata.columns}' '--outfile=$output' '--threshold=$threshold' '--ENSEMBLTcolmn=$ensembltcolmn_arg' '--classClmns=$classclmns'
  </command>

  <inputs>
    <param name="input" type="data" format="tabular" label="Input dataset" />
    <param name="ensembltcolmn" type="data_column" data_ref="input" numerical="false" label="Column with the ENSEMBL code in the Input dataset" />
    <param name="threshold" type="float" value="90" min="0" max="100" label="Threshold to disconnect the nodes" />
    <param name="classclmns" size="10" type="text" value="c1,c2" label="Gene category columns"/>

  </inputs>

  <outputs>
    <data name="output" format="tabular" />
  </outputs>

  <requirements>
    <requirement type="package" version="1.8.1">networkx</requirement>
  </requirements>

  <help>
**What it does**

The program builds a network of gene categories connected by shared
genes.  The edges of this network are weighted based on the number of
genes that each node shares.  The clustering coefficient, c\ :sub:`u`\ , is then calculated for each node using the formula:

.. image:: $PATH_TO_IMAGES/cluster_kegg_formula.png

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where deg(u) is the degree of u and edge weights, w\ :sub:`uv`\ ,
are normalized by the maximum weight in the network.  The cluster
coefficients are then filtered by our program based on threshold (that
could be a percentile or a value choose by the user) and all the nodes
with a cluster coefficient lower than this threshold are deleted from
the network.  Finally, the program reports each connected component as
a cluster of gene classifications.  With our program a lower number of
gene categories is obtained, but the results are easier to interpret as
they exclude genes present in many gene groups.
  </help>
</tool>