comparison qiime2/qiime_gneiss_correlation-clustering.xml @ 14:a0a8d77a991c draft

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13:887cd4ad8e16

<?xml version="1.0" ?>
<tool id="qiime_gneiss_correlation-clustering" name="qiime gneiss correlation-clustering" version="2019.7">
	<description> - Hierarchical clustering using feature correlation.</description>
	<requirements>
		<requirement type="package" version="2019.7">qiime2</requirement>
	</requirements>
	<command><![CDATA[
qiime gneiss correlation-clustering

--i-table=$itable

#if $ppseudocount:
 --p-pseudocount=$ppseudocount
#end if

--o-clustering=oclustering
;
cp oclustering.qza $oclustering
	]]></command>
	<inputs>
		<param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency] The feature table containing the samples in which the columns will be clustered.            [required]" name="itable" optional="False" type="data"/>
		<param label="--p-pseudocount: NUMBER  The value to add to zero counts in the feature table.                                [default: 0.5]" name="ppseudocount" optional="True" type="float" value="0.5"/>
	</inputs>
	<outputs>
		<data format="qza" label="${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.

clustering : Hierarchy
    A hierarchy of feature identifiers where each tip corresponds 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>
<macros>
    <import>qiime_citation.xml</import>
</macros>
<expand macro="qiime_citation"/>
</tool>

14:a0a8d77a991c

<?xml version="1.0" ?>
<tool id="qiime_gneiss_correlation-clustering" name="qiime gneiss correlation-clustering"
      version="2020.8">
  <description>Hierarchical clustering using feature correlation.</description>
  <requirements>
    <requirement type="package" version="2020.8">qiime2</requirement>
  </requirements>
  <command><![CDATA[
qiime gneiss correlation-clustering

--i-table=$itable

--p-pseudocount=$ppseudocount

--o-clustering=oclustering

#if str($examples) != 'None':
--examples=$examples
#end if

;
cp oclustering.qza $oclustering

  ]]></command>
  <inputs>
    <param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency] The feature table containing the samples in which the columns will be clustered.            [required]" name="itable" optional="False" type="data" />
    <param label="--p-pseudocount: NUMBER  The value to add to zero counts in the feature table.                                [default: 0.5]" name="ppseudocount" optional="True" type="float" value="0.5" />
    <param label="--examples: Show usage examples and exit." name="examples" optional="False" 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.

clustering : Hierarchy
    A hierarchy of feature identifiers where each tip corresponds 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>
  <macros>
    <import>qiime_citation.xml</import>
  </macros>
  <expand macro="qiime_citation"/>
</tool>