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1 <?xml version="1.0" ?>
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2 <tool id="qiime_longitudinal_maturity-index" name="qiime longitudinal maturity-index" version="2019.4">
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3 <description> - Microbial maturity index prediction.</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 longitudinal maturity-index
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9
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10 --i-table=$itable
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11 --p-state-column="$pstatecolumn"
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12 --p-group-by="$pgroupby"
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13 --p-control="$pcontrol"
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14
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15 #if str($pindividualidcolumn):
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16 --p-individual-id-column="$pindividualidcolumn"
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17 #end if
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18
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19 #if str($pestimator) != 'None':
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20 --p-estimator=$pestimator
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21 #end if
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22
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23 #if $pnestimators:
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24 --p-n-estimators=$pnestimators
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25 #end if
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26
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27 #if $ptestsize:
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28 --p-test-size=$ptestsize
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29 #end if
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30
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31 #if $pstep:
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32 --p-step=$pstep
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33 #end if
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34
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35 #if $pcv:
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36 --p-cv=$pcv
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37 #end if
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38
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39 #if str($prandomstate):
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40 --p-random-state="$prandomstate"
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41 #end if
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42
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43 #set $pnjobs = '${GALAXY_SLOTS:-4}'
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44 #if str($pnjobs):
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45 --p-n-jobs="$pnjobs"
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46 #end if
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47
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48
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49 #if $pparametertuning:
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50 --p-parameter-tuning
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51 #end if
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52
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53 #if $poptimizefeatureselection:
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54 --p-optimize-feature-selection
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55 #end if
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56
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57 #if $pstratify:
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58 --p-stratify
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59 #end if
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60
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61 #if str($pmissingsamples) != 'None':
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62 --p-missing-samples=$pmissingsamples
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63 #end if
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64
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65 #if $pfeaturecount:
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66 --p-feature-count=$pfeaturecount
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67 #end if
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68
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69
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70 #if $input_files_mmetadatafile:
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71 #def list_dict_to_string(list_dict):
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72 #set $file_list = list_dict[0]['additional_input'].__getattr__('file_name')
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73 #for d in list_dict[1:]:
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74 #set $file_list = $file_list + ' --m-metadata-file=' + d['additional_input'].__getattr__('file_name')
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75 #end for
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76 #return $file_list
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77 #end def
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78 --m-metadata-file=$list_dict_to_string($input_files_mmetadatafile)
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79 #end if
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80
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81
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82 --o-sample-estimator=osampleestimator
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83 --o-feature-importance=ofeatureimportance
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84 --o-predictions=opredictions
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85 --o-model-summary=omodelsummary
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86 --o-accuracy-results=oaccuracyresults
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87 --o-maz-scores=omazscores
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88 --o-clustermap=oclustermap
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89 --o-volatility-plots=ovolatilityplots
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90 ;
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91 cp osampleestimator.qza $osampleestimator;
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92 cp ofeatureimportance.qza $ofeatureimportance;
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93 cp opredictions.qza $opredictions;
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94 qiime tools export --input-path omodelsummary.qzv --output-path out && mkdir -p '$omodelsummary.files_path'
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95 && cp -r out/* '$omodelsummary.files_path'
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96 && mv '$omodelsummary.files_path/index.html' '$omodelsummary';
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97 qiime tools export --input-path oaccuracyresults.qzv --output-path out && mkdir -p '$oaccuracyresults.files_path'
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98 && cp -r out/* '$oaccuracyresults.files_path'
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99 && mv '$oaccuracyresults.files_path/index.html' '$oaccuracyresults';
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100 cp omazscores.qza $omazscores;
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101 qiime tools export --input-path oclustermap.qzv --output-path out && mkdir -p '$oclustermap.files_path'
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102 && cp -r out/* '$oclustermap.files_path'
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103 && mv '$oclustermap.files_path/index.html' '$oclustermap';
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104 qiime tools export --input-path ovolatilityplots.qzv --output-path out && mkdir -p '$ovolatilityplots.files_path'
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105 && cp -r out/* '$ovolatilityplots.files_path'
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106 && mv '$ovolatilityplots.files_path/index.html' '$ovolatilityplots'
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107 ]]></command>
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108 <inputs>
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109 <param format="qza,no_unzip.zip" label="--i-table: ARTIFACT FeatureTable[Frequency] Feature table containing all features that should be used for target prediction. [required]" name="itable" optional="False" type="data"/>
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110 <param label="--p-state-column: TEXT Numeric metadata column containing sampling time (state) data to use as prediction target. [required]" name="pstatecolumn" optional="False" type="text"/>
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111 <param label="--p-group-by: TEXT Categorical metadata column to use for plotting and significance testing between main treatment groups. [required]" name="pgroupby" optional="False" type="text"/>
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112 <param label="--p-control: TEXT Value of group-by to use as control group. The regression model will be trained using only control group data, and the maturity scores of other groups consequently will be assessed relative to this group. [required]" name="pcontrol" optional="False" type="text"/>
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113 <param label="--p-individual-id-column: TEXT Optional metadata column containing IDs for individual subjects. Adds individual subject (spaghetti) vectors to volatility charts if a column name is provided. [optional]" name="pindividualidcolumn" optional="True" type="text"/>
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114 <param label="--p-estimator: " name="pestimator" optional="True" type="select">
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115 <option selected="True" value="None">Selection is Optional</option>
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116 <option value="RandomForestRegressor">RandomForestRegressor</option>
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117 <option value="ExtraTreesRegressor">ExtraTreesRegressor</option>
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118 <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>
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119 <option value="AdaBoostRegressor">AdaBoostRegressor</option>
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120 <option value="ElasticNet">ElasticNet</option>
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121 <option value="Ridge">Ridge</option>
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122 <option value="Lasso">Lasso</option>
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123 <option value="KNeighborsRegressor">KNeighborsRegressor</option>
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124 <option value="LinearSVR">LinearSVR</option>
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125 <option value="SVR">SVR</option>
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126 </param>
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127 <param label="--p-n-estimators: INTEGER Range(1, None) Number of trees to grow for estimation. More trees will improve predictive accuracy up to a threshold level, but will also increase time and memory requirements. This parameter only affects ensemble estimators, such as Random Forest, AdaBoost, ExtraTrees, and GradientBoosting. [default: 100]" name="pnestimators" optional="True" type="integer" min="1" value="100"/>
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128 <param label="--p-test-size: PROPORTION Range(0.0, 1.0, inclusive_start=False) Fraction of input samples to exclude from training set and use for classifier testing. [default: 0.5]" name="ptestsize" optional="True" type="float" exclusive_start="True" min="0" max="1" value="0.5"/>
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129 <param label="--p-step: PROPORTION Range(0.0, 1.0, inclusive_start=False) If optimize-feature-selection is True, step is the percentage of features to remove at each iteration. [default: 0.05]" name="pstep" optional="True" type="float" exclusive_start="True" min="0" max="1" value="0.05"/>
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130 <param label="--p-cv: INTEGER Number of k-fold cross-validations to perform. Range(1, None) [default: 5]" name="pcv" optional="True" type="integer" min="1" value="5"/>
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131 <param label="--p-random-state: INTEGER Seed used by random number generator. [optional]" name="prandomstate" optional="True" type="integer"/>
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132 <param label="--p-parameter-tuning: --p-no-parameter-tuning Automatically tune hyperparameters using random grid search. [default: False]" name="pparametertuning" selected="False" type="boolean"/>
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133 <param label="--p-optimize-feature-selection: --p-no-optimize-feature-selection Automatically optimize input feature selection using recursive feature elimination. [default: False]" name="poptimizefeatureselection" selected="False" type="boolean"/>
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134 <param label="--p-stratify: --p-no-stratify Evenly stratify training and test data among metadata categories. If True, all values in column must match at least two samples. [default: False]" name="pstratify" selected="False" type="boolean"/>
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135 <param label="--p-missing-samples: " name="pmissingsamples" optional="True" type="select">
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136 <option selected="True" value="None">Selection is Optional</option>
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137 <option value="error">error</option>
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138 <option value="ignore">ignore</option>
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139 </param>
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140 <param label="--p-feature-count: INTEGER Range(0, None) Filter feature table to include top N most important features. Set to zero to include all features. [default: 50]" name="pfeaturecount" optional="True" type="integer" min="0" value="50"/>
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141
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142 <repeat name="input_files_mmetadatafile" optional="True" title="--m-metadata-file">
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143 <param label="--m-metadata-file: Metadata file or artifact viewable as metadata. This option may be supplied multiple times to merge metadata. [optional]" name="additional_input" type="data" format="tabular,qza,no_unzip.zip" />
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144 </repeat> </inputs>
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145
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146 <outputs>
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147 <data format="qza" label="${tool.name} on ${on_string}: sampleestimator.qza" name="osampleestimator"/>
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148 <data format="qza" label="${tool.name} on ${on_string}: featureimportance.qza" name="ofeatureimportance"/>
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149 <data format="qza" label="${tool.name} on ${on_string}: predictions.qza" name="opredictions"/>
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150 <data format="html" label="${tool.name} on ${on_string}: modelsummary.qzv" name="omodelsummary"/>
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151 <data format="html" label="${tool.name} on ${on_string}: accuracyresults.qzv" name="oaccuracyresults"/>
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152 <data format="qza" label="${tool.name} on ${on_string}: mazscores.qza" name="omazscores"/>
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153 <data format="html" label="${tool.name} on ${on_string}: clustermap.qzv" name="oclustermap"/>
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154 <data format="html" label="${tool.name} on ${on_string}: volatilityplots.qzv" name="ovolatilityplots"/>
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155 </outputs>
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156 <help><![CDATA[
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157 Microbial maturity index prediction.
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158 ####################################
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159
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160 Calculates a "microbial maturity" index from a regression model trained on
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161 feature data to predict a given continuous metadata column, e.g., to
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162 predict age as a function of microbiota composition. The model is trained
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163 on a subset of control group samples, then predicts the column value for
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164 all samples. This visualization computes maturity index z-scores to compare
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165 relative "maturity" between each group, as described in
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166 doi:10.1038/nature13421. This method can be used to predict between-group
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167 differences in relative trajectory across any type of continuous metadata
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168 gradient, e.g., intestinal microbiome development by age, microbial
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169 succession during wine fermentation, or microbial community differences
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170 along environmental gradients, as a function of two or more different
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171 "treatment" groups.
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172
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173 Parameters
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174 ----------
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175 table : FeatureTable[Frequency]
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176 Feature table containing all features that should be used for target
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177 prediction.
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178 metadata : Metadata
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179 \
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180 state_column : Str
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181 Numeric metadata column containing sampling time (state) data to use as
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182 prediction target.
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183 group_by : Str
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184 Categorical metadata column to use for plotting and significance
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185 testing between main treatment groups.
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186 control : Str
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187 Value of group_by to use as control group. The regression model will be
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188 trained using only control group data, and the maturity scores of other
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189 groups consequently will be assessed relative to this group.
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190 individual_id_column : Str, optional
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191 Optional metadata column containing IDs for individual subjects. Adds
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192 individual subject (spaghetti) vectors to volatility charts if a column
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193 name is provided.
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194 estimator : Str % Choices('RandomForestRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor', 'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR'), optional
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195 Regression model to use for prediction.
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196 n_estimators : Int % Range(1, None), optional
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197 Number of trees to grow for estimation. More trees will improve
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198 predictive accuracy up to a threshold level, but will also increase
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199 time and memory requirements. This parameter only affects ensemble
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200 estimators, such as Random Forest, AdaBoost, ExtraTrees, and
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201 GradientBoosting.
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202 test_size : Float % Range(0.0, 1.0, inclusive_start=False), optional
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203 Fraction of input samples to exclude from training set and use for
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204 classifier testing.
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205 step : Float % Range(0.0, 1.0, inclusive_start=False), optional
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206 If optimize_feature_selection is True, step is the percentage of
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207 features to remove at each iteration.
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208 cv : Int % Range(1, None), optional
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209 Number of k-fold cross-validations to perform.
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210 random_state : Int, optional
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211 Seed used by random number generator.
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212 parameter_tuning : Bool, optional
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213 Automatically tune hyperparameters using random grid search.
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214 optimize_feature_selection : Bool, optional
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215 Automatically optimize input feature selection using recursive feature
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216 elimination.
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217 stratify : Bool, optional
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218 Evenly stratify training and test data among metadata categories. If
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219 True, all values in column must match at least two samples.
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220 missing_samples : Str % Choices('error', 'ignore'), optional
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221 How to handle missing samples in metadata. "error" will fail if missing
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222 samples are detected. "ignore" will cause the feature table and
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223 metadata to be filtered, so that only samples found in both files are
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224 retained.
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225 feature_count : Int % Range(0, None), optional
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226 Filter feature table to include top N most important features. Set to
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227 zero to include all features.
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228
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229 Returns
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230 -------
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231 sample_estimator : SampleEstimator[Regressor]
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232 Trained sample estimator.
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233 feature_importance : FeatureData[Importance]
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234 Importance of each input feature to model accuracy.
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235 predictions : SampleData[RegressorPredictions]
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236 Predicted target values for each input sample.
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237 model_summary : Visualization
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238 Summarized parameter and (if enabled) feature selection information for
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239 the trained estimator.
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240 accuracy_results : Visualization
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241 Accuracy results visualization.
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242 maz_scores : SampleData[RegressorPredictions]
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243 Microbiota-for-age z-score predictions.
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244 clustermap : Visualization
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245 Heatmap of important feature abundance at each time point in each
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246 group.
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247 volatility_plots : Visualization
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248 Interactive volatility plots of MAZ and maturity scores, target
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249 (column) predictions, and the sample metadata.
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250 ]]></help>
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251 <macros>
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252 <import>qiime_citation.xml</import>
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253 </macros>
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254 <expand macro="qiime_citation"/>
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255 </tool>
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