<?xml version="1.0" ?>
<tool id="qiime_gneiss_correlation-clustering" name="qiime gneiss correlation-clustering" version="2018.4">
	<description> - Hierarchical clustering using feature correlation.</description>
	<requirements>
		<requirement type="package" version="2018.4">qiime2</requirement>
	</requirements>
	<command>
	<![CDATA[
	qiime gneiss correlation-clustering --i-table=$itable

	#if str($cmdconfig) != 'None':
	 --cmd-config=$cmdconfig
	#end if
	 --o-clustering=oclustering;

	 cp oclustering.qza $oclustering;
	]]>
	</command>
	<inputs>
		<param format="qza,no_unzip.zip" label="--i-table: FeatureTable[Composition] The feature table containing the samples in which the columns will be clustered. [required]" name="itable" optional="False" type="data"/>
		<param label="--cmd-config: Use config file for command options" name="cmdconfig" optional="True" type="data"/>
	</inputs>
	<outputs>
		<data format="qza" label="${tool.name} on ${on_string}: clustering.qza" name="oclustering"/>
	</outputs>
	<help>
			<![CDATA[
Hierarchical clustering using feature correlation.
---------------------------------------------------

Build a bifurcating tree that represents a hierarchical clustering of
features.  The hiearchical clustering uses Ward hierarchical clustering
based on the degree of proportionality between features.

Parameters
----------
table : FeatureTable[Composition]
    The feature table containing the samples in which the columns will be
    clustered.

Returns
-------
clustering : Hierarchy
    A hierarchy of feature identifiers where each tipcorresponds to the
    feature identifiers in the table. This tree can contain tip ids that
    are not present in the table, but all feature ids in the table must be
    present in this tree.
    ]]>
	</help>
</tool>