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author bebatut
date Tue, 02 Feb 2016 05:50:37 -0500
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1 <tool id="qiime_compare_categories" name="compare categories" version="1.9.1galaxy1">
2
3 <description>Analyzes statistical significance of sample groupings using
4 distance matrices</description>
5
6 <macros>
7 <import>macros.xml</import>
8 </macros>
9
10 <expand macro="requirements" />
11
12 <command>
13 <![CDATA[
14 compare_categories.py
15 --method=$method
16 -i $input_dm
17 -m $mapping_file
18 -c $categories
19 -o compare_categories_output
20 #if $num_permutations:
21 -n $num_permutations
22 #end if
23 ]]>
24 </command>
25
26 <inputs>
27 <param label="--method: the statistical method to use. Valid options:
28 adonis, anosim, bioenv, morans_i, mrpp, permanova, permdisp,
29 dbrda" name="method" optional="False" type="select">
30 <option value="adonis">adonis</option>
31 <option value="anosim">anosim</option>
32 <option value="bioenv">bioenv</option>
33 <option value="morans_i">morans_i</option>
34 <option value="mrpp">mrpp</option>
35 <option value="permanova">permanova</option>
36 <option value="permdisp">permdisp</option>
37 <option value="dbrda">dbrda</option>
38 </param>
39 <param label="-i/--input_dm: the input distance matrix. WARNING: Only
40 symmetric, hollow distance matrices may be used as input. Asymmetric
41 distance matrices, such as those obtained by the UniFrac Gain metric
42 (i.e. beta_diversity.py -m unifrac_g), should not be used as input"
43 name="input_dm" optional="False" type="data"/>
44 <param label="-m/--mapping_file: the metadata mapping file"
45 name="mapping_file" optional="False" type="data"/>
46 <param label="-c/--categories: a comma-delimited list of categories from
47 the mapping file. Note: all methods except for BIO-ENV accept just a
48 single category. If multiple categories are provided, only the first
49 will be used" name="categories" optional="False" type="text"/>
50 <param default="999" label="-n/--num_permutations: the number of permutations
51 to use when calculating statistical significance. Only applies to
52 adonis, ANOSIM, MRPP, PERMANOVA, PERMDISP, and db-RDA. Must be greater
53 than or equal to zero [default: 999]" name="num_permutations"
54 optional="True" type="integer"/>
55 </inputs>
56
57 <outputs>
58 <data format="txt" from_work_dir="compare_categories_output/*.txt"
59 name="output_dir" label="Compare_categories.txt"/>
60 </outputs>
61
62 <tests>
63 <test>
64 </test>
65 </tests>
66
67 <help><![CDATA[
68 **What it does**
69
70 This script allows for the analysis of the strength and statistical
71 significance of sample groupings using a distance matrix as the primary input.
72 Several statistical methods are available: adonis, ANOSIM, BIO-ENV, Moran's I,
73 MRPP, PERMANOVA, PERMDISP, and db-RDA.
74
75 Note: R's vegan and ape packages are used to compute many of these methods, and
76 for the ones that are not, their implementations are based on the
77 implementations found in those packages. It is recommended to read through the
78 detailed descriptions provided by the authors (they are not reproduced here)
79 and to refer to the primary literature for complete details, including the
80 methods' assumptions. To view the documentation of a method in R, prepend a
81 question mark before the method name. For example:
82
83 ?vegan::adonis
84
85 The following are brief descriptions of the available methods:
86
87 adonis - Partitions a distance matrix among sources of variation in order to
88 describe the strength and significance that a categorical or continuous
89 variable has in determining variation of distances. This is a nonparametric
90 method and is nearly equivalent to db-RDA (see below) except when distance
91 matrices constructed with semi-metric or non-metric dissimilarities are
92 provided, which may result in negative eigenvalues. adonis is very similar to
93 PERMANOVA, though it is more robust in that it can accept either categorical or
94 continuous variables in the metadata mapping file, while PERMANOVA can only
95 accept categorical variables. See vegan::adonis for more details.
96
97 ANOSIM - Tests whether two or more groups of samples are significantly
98 different based on a categorical variable found in the metadata mapping file.
99 You can specify a category in the metadata mapping file to separate
100 samples into groups and then test whether there are significant differences
101 between those groups. For example, you might test whether 'Control' samples are
102 significantly different from 'Fast' samples. Since ANOSIM is nonparametric,
103 significance is determined through permutations. See vegan::anosim for more
104 details.
105
106 BIO-ENV - Finds subsets of variables whose Euclidean distances (after scaling
107 the variables) are maximally rank-correlated with the distance matrix. For
108 example, the distance matrix might contain UniFrac distances between
109 communities, and the variables might be numeric environmental variables (e.g.,
110 pH and latitude). Correlation between the community distance matrix and
111 Euclidean environmental distance matrix is computed using Spearman's rank
112 correlation coefficient (rho). This method will only accept categories that are
113 numerical (continuous or discrete). This is currently the only method in the
114 script that accepts more than one category (via -c). See vegan::bioenv for more
115 details. This method is also known as BEST (previously called BIO-ENV) in the
116 PRIMER-E software package.
117
118 Moran's I - This method uses the numerical (e.g. geographical) data supplied to
119 identify what type of spatial configuration occurs in the samples. For example,
120 are they dispersed, clustered, or of no distinctly noticeable configuration
121 when compared to each other? This method will only accept a category that is
122 numerical. See ape::Moran.I for more details.
123
124 MRPP - This method tests whether two or more groups of samples are
125 significantly different based on a categorical variable found in the metadata
126 mapping file. You can specify a category in the metadata mapping file to
127 separate samples into groups and then test whether there are significant
128 differences between those groups. For example, you might test whether 'Control'
129 samples are significantly different from 'Fast' samples. Since MRPP is
130 nonparametric, significance is determined through permutations. See
131 vegan::mrpp for more details.
132
133 PERMANOVA - This method is very similar to adonis except that it only accepts a
134 categorical variable in the metadata mapping file. It uses an ANOVA
135 experimental design and returns a pseudo-F value and a p-value. Since PERMANOVA
136 is nonparametric, significance is determined through permutations.
137
138 PERMDISP - This method analyzes the multivariate homogeneity of group
139 dispersions (variances). In essence, it determines whether the variances of
140 groups of samples are significantly different. The results of both parametric
141 and nonparametric significance tests are provided in the output. This method is
142 generally used as a companion to PERMANOVA. See vegan::betadisper for more
143 details.
144
145 db-RDA - This method is very similar to adonis and will only differ if certain
146 non-Euclidean semi- or non-metrics are used to generate the input distance
147 matrix, and negative eigenvalues are encountered. The only difference then will
148 be in the p-values, not the R^2 values. As part of the output, an ordination
149 plot is also generated that shows grouping/clustering of samples based on a
150 category in the metadata mapping file. This category is used to explain the
151 variability between samples. Thus, the ordination output of db-RDA is similar
152 to PCoA except that it is constrained, while PCoA is unconstrained (i.e. with
153 db-RDA, you must specify which category should be used to explain the
154 variability in your data). See vegan::capscale for more details.
155
156 For more information and examples pertaining to this script, please refer to
157 the accompanying tutorial, which can be found at
158 http://qiime.org/tutorials/category_comparison.html.
159
160
161 At least one file will be created in the output directory specified by -o. For
162 most methods, a single output file containing the results of the test (e.g. the
163 effect size statistic and p-value) will be created. The format of the output
164 files will vary between methods as some are generated by native QIIME code,
165 while others are generated by R's vegan or ape packages. Please refer to the
166 script description for details on how to access additional information for
167 these methods, including what information is included in the output files.
168
169 db-RDA is the only exception in that two output files are created: a results
170 text file and a PDF of the ordination plot.
171 ]]>
172 </help>
173
174 <citations>
175 <expand macro="citations" />
176 </citations>
177 </tool>