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annotate compare_categories.xml @ 0:c1bd0c560018 draft default tip

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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">
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2
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3 <description>Analyzes statistical significance of sample groupings using
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4 distance matrices</description>
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5
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6 <macros>
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7 <import>macros.xml</import>
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8 </macros>
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9
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10 <expand macro="requirements" />
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11
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12 <command>
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13 <![CDATA[
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14 compare_categories.py
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15 --method=$method
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16 -i $input_dm
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17 -m $mapping_file
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18 -c $categories
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19 -o compare_categories_output
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20 #if $num_permutations:
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21 -n $num_permutations
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22 #end if
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23 ]]>
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24 </command>
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25
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26 <inputs>
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27 <param label="--method: the statistical method to use. Valid options:
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28 adonis, anosim, bioenv, morans_i, mrpp, permanova, permdisp,
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29 dbrda" name="method" optional="False" type="select">
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30 <option value="adonis">adonis</option>
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31 <option value="anosim">anosim</option>
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32 <option value="bioenv">bioenv</option>
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33 <option value="morans_i">morans_i</option>
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34 <option value="mrpp">mrpp</option>
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35 <option value="permanova">permanova</option>
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36 <option value="permdisp">permdisp</option>
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37 <option value="dbrda">dbrda</option>
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38 </param>
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39 <param label="-i/--input_dm: the input distance matrix. WARNING: Only
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40 symmetric, hollow distance matrices may be used as input. Asymmetric
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41 distance matrices, such as those obtained by the UniFrac Gain metric
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42 (i.e. beta_diversity.py -m unifrac_g), should not be used as input"
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43 name="input_dm" optional="False" type="data"/>
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44 <param label="-m/--mapping_file: the metadata mapping file"
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45 name="mapping_file" optional="False" type="data"/>
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46 <param label="-c/--categories: a comma-delimited list of categories from
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47 the mapping file. Note: all methods except for BIO-ENV accept just a
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48 single category. If multiple categories are provided, only the first
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49 will be used" name="categories" optional="False" type="text"/>
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50 <param default="999" label="-n/--num_permutations: the number of permutations
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51 to use when calculating statistical significance. Only applies to
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52 adonis, ANOSIM, MRPP, PERMANOVA, PERMDISP, and db-RDA. Must be greater
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53 than or equal to zero [default: 999]" name="num_permutations"
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54 optional="True" type="integer"/>
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55 </inputs>
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56
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57 <outputs>
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58 <data format="txt" from_work_dir="compare_categories_output/*.txt"
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59 name="output_dir" label="Compare_categories.txt"/>
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60 </outputs>
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61
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62 <tests>
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63 <test>
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64 </test>
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65 </tests>
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66
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67 <help><![CDATA[
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68 **What it does**
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69
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70 This script allows for the analysis of the strength and statistical
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71 significance of sample groupings using a distance matrix as the primary input.
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72 Several statistical methods are available: adonis, ANOSIM, BIO-ENV, Moran's I,
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73 MRPP, PERMANOVA, PERMDISP, and db-RDA.
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74
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75 Note: R's vegan and ape packages are used to compute many of these methods, and
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76 for the ones that are not, their implementations are based on the
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77 implementations found in those packages. It is recommended to read through the
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78 detailed descriptions provided by the authors (they are not reproduced here)
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79 and to refer to the primary literature for complete details, including the
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80 methods' assumptions. To view the documentation of a method in R, prepend a
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81 question mark before the method name. For example:
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82
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83 ?vegan::adonis
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84
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85 The following are brief descriptions of the available methods:
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86
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87 adonis - Partitions a distance matrix among sources of variation in order to
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88 describe the strength and significance that a categorical or continuous
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89 variable has in determining variation of distances. This is a nonparametric
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90 method and is nearly equivalent to db-RDA (see below) except when distance
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91 matrices constructed with semi-metric or non-metric dissimilarities are
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92 provided, which may result in negative eigenvalues. adonis is very similar to
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93 PERMANOVA, though it is more robust in that it can accept either categorical or
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94 continuous variables in the metadata mapping file, while PERMANOVA can only
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95 accept categorical variables. See vegan::adonis for more details.
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96
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97 ANOSIM - Tests whether two or more groups of samples are significantly
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98 different based on a categorical variable found in the metadata mapping file.
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99 You can specify a category in the metadata mapping file to separate
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100 samples into groups and then test whether there are significant differences
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101 between those groups. For example, you might test whether 'Control' samples are
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102 significantly different from 'Fast' samples. Since ANOSIM is nonparametric,
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103 significance is determined through permutations. See vegan::anosim for more
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104 details.
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105
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106 BIO-ENV - Finds subsets of variables whose Euclidean distances (after scaling
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107 the variables) are maximally rank-correlated with the distance matrix. For
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108 example, the distance matrix might contain UniFrac distances between
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109 communities, and the variables might be numeric environmental variables (e.g.,
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110 pH and latitude). Correlation between the community distance matrix and
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111 Euclidean environmental distance matrix is computed using Spearman's rank
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112 correlation coefficient (rho). This method will only accept categories that are
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113 numerical (continuous or discrete). This is currently the only method in the
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114 script that accepts more than one category (via -c). See vegan::bioenv for more
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115 details. This method is also known as BEST (previously called BIO-ENV) in the
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116 PRIMER-E software package.
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117
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118 Moran's I - This method uses the numerical (e.g. geographical) data supplied to
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119 identify what type of spatial configuration occurs in the samples. For example,
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120 are they dispersed, clustered, or of no distinctly noticeable configuration
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121 when compared to each other? This method will only accept a category that is
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122 numerical. See ape::Moran.I for more details.
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123
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124 MRPP - This method tests whether two or more groups of samples are
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125 significantly different based on a categorical variable found in the metadata
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126 mapping file. You can specify a category in the metadata mapping file to
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127 separate samples into groups and then test whether there are significant
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128 differences between those groups. For example, you might test whether 'Control'
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129 samples are significantly different from 'Fast' samples. Since MRPP is
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130 nonparametric, significance is determined through permutations. See
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131 vegan::mrpp for more details.
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132
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133 PERMANOVA - This method is very similar to adonis except that it only accepts a
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134 categorical variable in the metadata mapping file. It uses an ANOVA
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135 experimental design and returns a pseudo-F value and a p-value. Since PERMANOVA
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136 is nonparametric, significance is determined through permutations.
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137
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138 PERMDISP - This method analyzes the multivariate homogeneity of group
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139 dispersions (variances). In essence, it determines whether the variances of
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140 groups of samples are significantly different. The results of both parametric
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141 and nonparametric significance tests are provided in the output. This method is
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142 generally used as a companion to PERMANOVA. See vegan::betadisper for more
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143 details.
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144
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145 db-RDA - This method is very similar to adonis and will only differ if certain
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146 non-Euclidean semi- or non-metrics are used to generate the input distance
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147 matrix, and negative eigenvalues are encountered. The only difference then will
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148 be in the p-values, not the R^2 values. As part of the output, an ordination
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149 plot is also generated that shows grouping/clustering of samples based on a
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150 category in the metadata mapping file. This category is used to explain the
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151 variability between samples. Thus, the ordination output of db-RDA is similar
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152 to PCoA except that it is constrained, while PCoA is unconstrained (i.e. with
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153 db-RDA, you must specify which category should be used to explain the
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154 variability in your data). See vegan::capscale for more details.
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155
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156 For more information and examples pertaining to this script, please refer to
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157 the accompanying tutorial, which can be found at
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158 http://qiime.org/tutorials/category_comparison.html.
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159
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160
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161 At least one file will be created in the output directory specified by -o. For
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162 most methods, a single output file containing the results of the test (e.g. the
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163 effect size statistic and p-value) will be created. The format of the output
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164 files will vary between methods as some are generated by native QIIME code,
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165 while others are generated by R's vegan or ape packages. Please refer to the
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166 script description for details on how to access additional information for
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167 these methods, including what information is included in the output files.
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168
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169 db-RDA is the only exception in that two output files are created: a results
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170 text file and a PDF of the ordination plot.
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171 ]]>
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172 </help>
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173
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174 <citations>
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175 <expand macro="citations" />
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176 </citations>
c1bd0c560018 planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/qiime commit bcbe76277f3e60303faf826f8ce7f018bc663a9a-dirty
bebatut
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177 </tool>