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comparison qiime2-2020.8/qiime_gneiss_gradient-clustering.xml @ 0:5c352d975ef7 draft

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author florianbegusch
date Thu, 03 Sep 2020 09:33:04 +0000
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1 <?xml version="1.0" ?>
2 <tool id="qiime_gneiss_gradient-clustering" name="qiime gneiss gradient-clustering"
3 version="2020.8">
4 <description>Hierarchical clustering using gradient information.</description>
5 <requirements>
6 <requirement type="package" version="2020.8">qiime2</requirement>
7 </requirements>
8 <command><![CDATA[
9 qiime gneiss gradient-clustering
10
11 --i-table=$itable
12
13 #if str($mgradientfile) != 'None':
14 --m-gradient-file=$mgradientfile
15 #end if
16
17 #if '__ob__' in str($mgradientcolumn):
18 #set $mgradientcolumn_temp = $mgradientcolumn.replace('__ob__', '[')
19 #set $mgradientcolumn = $mgradientcolumn_temp
20 #end if
21 #if '__cb__' in str($mgradientcolumn):
22 #set $mgradientcolumn_temp = $mgradientcolumn.replace('__cb__', ']')
23 #set $mgradientcolumn = $mgradientcolumn_temp
24 #end if
25 #if 'X' in str($mgradientcolumn):
26 #set $mgradientcolumn_temp = $mgradientcolumn.replace('X', '\\')
27 #set $mgradientcolumn = $mgradientcolumn_temp
28 #end if
29 #if '__sq__' in str($mgradientcolumn):
30 #set $mgradientcolumn_temp = $mgradientcolumn.replace('__sq__', "'")
31 #set $mgradientcolumn = $mgradientcolumn_temp
32 #end if
33 #if '__db__' in str($mgradientcolumn):
34 #set $mgradientcolumn_temp = $mgradientcolumn.replace('__db__', '"')
35 #set $mgradientcolumn = $mgradientcolumn_temp
36 #end if
37
38 --m-gradient-column=$mgradientcolumn
39
40
41 #if $pignoremissingsamples:
42 --p-ignore-missing-samples
43 #end if
44
45 #if $pnoweighted:
46 --p-no-weighted
47 #end if
48
49 --o-clustering=oclustering
50
51 #if str($examples) != 'None':
52 --examples=$examples
53 #end if
54
55 ;
56 cp oclustering.qza $oclustering
57
58 ]]></command>
59 <inputs>
60 <param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency | RelativeFrequency | Composition] The feature table containing the samples in which the columns will be clustered. [required]" name="itable" optional="False" type="data" />
61 <param format="tabular,qza,no_unzip.zip" label="--m-gradient-file: METADATA" name="mgradientfile" optional="False" type="data" />
62 <param label="--m-gradient-column: COLUMN MetadataColumn[Numeric] Contains gradient values to sort the features and samples. [required]" name="mgradientcolumn" optional="False" type="text" />
63 <param label="--p-ignore-missing-samples: --p-ignore-missing-samples: / --p-no-ignore-missing-samples [default: False]" name="pignoremissingsamples" selected="False" type="boolean" />
64 <param label="--p-no-weighted: Do not specifies if abundance or presence/absence information should be used to perform the clustering. [default: True]" name="pnoweighted" selected="False" type="boolean" />
65 <param label="--examples: Show usage examples and exit." name="examples" optional="False" type="data" />
66
67 </inputs>
68
69 <outputs>
70 <data format="qza" label="${tool.name} on ${on_string}: clustering.qza" name="oclustering" />
71
72 </outputs>
73
74 <help><![CDATA[
75 Hierarchical clustering using gradient information.
76 ###############################################################
77
78 Build a bifurcating tree that represents a hierarchical clustering of
79 features. The hiearchical clustering uses Ward hierarchical clustering
80 based on the mean difference of gradients that each feature is observed in.
81 This method is primarily used to sort the table to reveal the underlying
82 block-like structures.
83
84 Parameters
85 ----------
86 table : FeatureTable[Frequency | RelativeFrequency | Composition]
87 The feature table containing the samples in which the columns will be
88 clustered.
89 gradient : MetadataColumn[Numeric]
90 Contains gradient values to sort the features and samples.
91 ignore_missing_samples : Bool, optional
92 weighted : Bool, optional
93 Specifies if abundance or presence/absence information should be used
94 to perform the clustering.
95
96 Returns
97 -------
98 clustering : Hierarchy
99 A hierarchy of feature identifiers where each tip corresponds to the
100 feature identifiers in the table. This tree can contain tip ids that
101 are not present in the table, but all feature ids in the table must be
102 present in this tree.
103 ]]></help>
104 <macros>
105 <import>qiime_citation.xml</import>
106 </macros>
107 <expand macro="qiime_citation"/>
108 </tool>