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1 <?xml version="1.0" ?>
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2 <tool id="qiime_gneiss_gradient-clustering" name="qiime gneiss gradient-clustering" version="2019.4">
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3 <description> - Hierarchical clustering using gradient information.</description>
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4 <requirements>
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5 <requirement type="package" version="2019.4">qiime2</requirement>
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6 </requirements>
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7 <command><![CDATA[
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8 qiime gneiss gradient-clustering
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9
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10 --i-table=$itable
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11 --m-gradient-column="$mgradientcolumn"
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12
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13
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14 #def list_dict_to_string(list_dict):
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15 #set $file_list = list_dict[0]['additional_input'].__getattr__('file_name')
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16 #for d in list_dict[1:]:
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17 #set $file_list = $file_list + ',' + d['additional_input'].__getattr__('file_name')
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18 #end for
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19 #return $file_list
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20 #end def
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21
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22 --m-gradient-file=$list_dict_to_string($input_files_mgradientfile)
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23
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24
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25 #if $pnoweighted:
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26 --p-no-weighted
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27 #end if
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28
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29 --o-clustering=oclustering
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30 ;
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31 cp oclustering.qza $oclustering
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32 ]]></command>
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33 <inputs>
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34 <param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency | RelativeFrequency | Composition] The feature table containing the samples in which the columns will be clustered. [required]" name="itable" optional="False" type="data"/>
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35 <param label="--m-gradient-column: COLUMN MetadataColumn[Numeric] Contains gradient values to sort the features and samples. [required]" name="mgradientcolumn" optional="False" type="text"/>
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36 <param label="--p-no-weighted : Specifies if abundance or presence/absence information should be used to perform the clustering. [default: False]" name="pnoweighted" selected="False" type="boolean"/>
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37
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38 <repeat name="input_files_mgradientfile" optional="False" title="--m-gradient-file">
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39 <param label="--m-gradient-file: Metadata file or artifact viewable as metadata. This option may be supplied multiple times to merge metadata. [required]" name="additional_input" type="data" format="tabular,qza,no_unzip.zip" />
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40 </repeat>
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41
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42 </inputs>
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43 <outputs>
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44 <data format="qza" label="${tool.name} on ${on_string}: clustering.qza" name="oclustering"/>
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45 </outputs>
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46 <help><![CDATA[
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47 Hierarchical clustering using gradient information.
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48 ###################################################
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49
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50 Build a bifurcating tree that represents a hierarchical clustering of
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51 features. The hiearchical clustering uses Ward hierarchical clustering
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52 based on the mean difference of gradients that each feature is observed in.
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53 This method is primarily used to sort the table to reveal the underlying
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54 block-like structures.
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55
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56 Parameters
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57 ----------
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58 table : FeatureTable[Frequency | RelativeFrequency | Composition]
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59 The feature table containing the samples in which the columns will be
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60 clustered.
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61 gradient : MetadataColumn[Numeric]
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62 Contains gradient values to sort the features and samples.
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63 weighted : Bool, optional
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64 Specifies if abundance or presence/absence information should be used
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65 to perform the clustering.
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66
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67 Returns
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68 -------
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69 clustering : Hierarchy
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70 A hierarchy of feature identifiers where each tip corresponds to the
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71 feature identifiers in the table. This tree can contain tip ids that
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72 are not present in the table, but all feature ids in the table must be
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73 present in this tree.
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74 ]]></help>
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75 <macros>
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76 <import>qiime_citation.xml</import>
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77 </macros>
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78 <expand macro="qiime_citation"/>
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79 </tool>
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