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1 <?xml version="1.0" ?>
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2 <tool id="qiime_longitudinal_feature-volatility" name="qiime longitudinal feature-volatility"
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3 version="2020.8">
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4 <description>Feature volatility analysis</description>
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5 <requirements>
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6 <requirement type="package" version="2020.8">qiime2</requirement>
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7 </requirements>
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8 <command><![CDATA[
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9 qiime longitudinal feature-volatility
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10
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11 --i-table=$itable
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12 # if $input_files_mmetadatafile:
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13 # def list_dict_to_string(list_dict):
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14 # set $file_list = list_dict[0]['additional_input'].__getattr__('file_name')
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15 # for d in list_dict[1:]:
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16 # set $file_list = $file_list + ' --m-metadata-file=' + d['additional_input'].__getattr__('file_name')
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17 # end for
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18 # return $file_list
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19 # end def
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20 --m-metadata-file=$list_dict_to_string($input_files_mmetadatafile)
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21 # end if
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22
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23 #if '__ob__' in str($pstatecolumn):
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24 #set $pstatecolumn_temp = $pstatecolumn.replace('__ob__', '[')
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25 #set $pstatecolumn = $pstatecolumn_temp
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26 #end if
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27 #if '__cb__' in str($pstatecolumn):
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28 #set $pstatecolumn_temp = $pstatecolumn.replace('__cb__', ']')
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29 #set $pstatecolumn = $pstatecolumn_temp
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30 #end if
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31 #if 'X' in str($pstatecolumn):
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32 #set $pstatecolumn_temp = $pstatecolumn.replace('X', '\\')
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33 #set $pstatecolumn = $pstatecolumn_temp
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34 #end if
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35 #if '__sq__' in str($pstatecolumn):
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36 #set $pstatecolumn_temp = $pstatecolumn.replace('__sq__', "'")
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37 #set $pstatecolumn = $pstatecolumn_temp
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38 #end if
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39 #if '__db__' in str($pstatecolumn):
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40 #set $pstatecolumn_temp = $pstatecolumn.replace('__db__', '"')
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41 #set $pstatecolumn = $pstatecolumn_temp
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42 #end if
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43
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44 --p-state-column=$pstatecolumn
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45
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46
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47 #if '__ob__' in str($pindividualidcolumn):
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48 #set $pindividualidcolumn_temp = $pindividualidcolumn.replace('__ob__', '[')
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49 #set $pindividualidcolumn = $pindividualidcolumn_temp
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50 #end if
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51 #if '__cb__' in str($pindividualidcolumn):
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52 #set $pindividualidcolumn_temp = $pindividualidcolumn.replace('__cb__', ']')
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53 #set $pindividualidcolumn = $pindividualidcolumn_temp
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54 #end if
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55 #if 'X' in str($pindividualidcolumn):
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56 #set $pindividualidcolumn_temp = $pindividualidcolumn.replace('X', '\\')
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57 #set $pindividualidcolumn = $pindividualidcolumn_temp
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58 #end if
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59 #if '__sq__' in str($pindividualidcolumn):
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60 #set $pindividualidcolumn_temp = $pindividualidcolumn.replace('__sq__', "'")
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61 #set $pindividualidcolumn = $pindividualidcolumn_temp
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62 #end if
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63 #if '__db__' in str($pindividualidcolumn):
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64 #set $pindividualidcolumn_temp = $pindividualidcolumn.replace('__db__', '"')
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65 #set $pindividualidcolumn = $pindividualidcolumn_temp
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66 #end if
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67
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68 #if str($pindividualidcolumn):
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69 --p-individual-id-column=$pindividualidcolumn
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70 #end if
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71
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72 --p-cv=$pcv
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73
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74 #if str($prandomstate):
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75 --p-random-state=$prandomstate
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76 #end if
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77 --p-n-jobs=$pnjobs
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78
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79 --p-n-estimators=$pnestimators
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80
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81 #if str($pestimator) != 'None':
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82 --p-estimator=$pestimator
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83 #end if
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84
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85 #if $pparametertuning:
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86 --p-parameter-tuning
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87 #end if
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88
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89 #if str($pmissingsamples) != 'None':
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90 --p-missing-samples=$pmissingsamples
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91 #end if
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92
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93 #if str($pimportancethreshold) != 'None':
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94 --p-importance-threshold=$pimportancethreshold
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95 #end if
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96
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97 #if str($pfeaturecount) != 'None':
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98 --p-feature-count=$pfeaturecount
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99 #end if
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100
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101 --o-filtered-table=ofilteredtable
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102
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103 --o-feature-importance=ofeatureimportance
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104
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105 --o-volatility-plot=ovolatilityplot
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106
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107 --o-accuracy-results=oaccuracyresults
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108
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109 --o-sample-estimator=osampleestimator
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110
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111 #if str($examples) != 'None':
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112 --examples=$examples
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113 #end if
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114
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115 ;
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116 cp osampleestimator.qza $osampleestimator
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117
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118 ]]></command>
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119 <inputs>
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120 <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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121 <repeat name="input_files_mmetadatafile" optional="False" title="--m-metadata-file">
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122 <param format="tabular,qza,no_unzip.zip" label="--m-metadata-file: METADATA... (multiple Sample metadata file containing arguments will be individual-id-column. merged) [required]" name="additional_input" optional="False" type="data" />
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123 </repeat>
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124 <param label="--p-state-column: TEXT Metadata containing collection time (state) values for each sample. Must contain exclusively numeric values. [required]" name="pstatecolumn" optional="False" type="text" />
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125 <param label="--p-individual-id-column: TEXT Metadata column containing IDs for individual subjects. [optional]" name="pindividualidcolumn" optional="False" type="text" />
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126 <param label="--p-cv: INTEGER Number of k-fold cross-validations to perform. Range(1, None) [default: 5]" min="1" name="pcv" optional="True" type="integer" value="5" />
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127 <param label="--p-random-state: INTEGER Seed used by random number generator. [optional]" name="prandomstate" optional="False" type="text" />
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128 <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]" min="1" name="pnestimators" optional="True" type="integer" value="100" />
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129 <param label="--p-estimator: " name="pestimator" optional="True" type="select">
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130 <option selected="True" value="None">Selection is Optional</option>
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131 <option value="RandomForestRegressor">RandomForestRegressor</option>
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132 <option value="ExtraTreesRegressor">ExtraTreesRegressor</option>
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133 <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>
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134 <option value="AdaBoostRegressor">AdaBoostRegressor</option>
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135 <option value="ElasticNet">ElasticNet</option>
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136 <option value="Ridge">Ridge</option>
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137 <option value="Lasso">Lasso</option>
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138 <option value="KNeighborsRegressor">KNeighborsRegressor</option>
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139 <option value="LinearSVR">LinearSVR</option>
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140 <option value="SVR">SVR</option>
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141 </param>
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142 <param label="--p-parameter-tuning: --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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143 <param label="--p-missing-samples: " name="pmissingsamples" optional="True" type="select">
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144 <option selected="True" value="None">Selection is Optional</option>
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145 <option value="error">error</option>
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146 <option value="ignore">ignore</option>
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147 </param>
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148 <param label="--p-importance-threshold: " name="pimportancethreshold" optional="True" type="select">
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149 <option selected="True" value="None">Selection is Optional</option>
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150 <option value="Float % Range(0">Float % Range(0</option>
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151 <option value="None">None</option>
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152 <option value="inclusive_start=False">inclusive_start=False</option>
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153 </param>
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154 <param label="--p-feature-count: " name="pfeaturecount" optional="True" type="select">
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155 <option selected="True" value="None">Selection is Optional</option>
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156 <option value="Int % Range(1">Int % Range(1</option>
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157 <option value="None">None</option>
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158 </param>
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159 <param label="--examples: Show usage examples and exit." name="examples" optional="False" type="data" />
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160
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161 </inputs>
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162
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163 <outputs>
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164 <data format="qza" label="${tool.name} on ${on_string}: filteredtable.qza" name="ofilteredtable" />
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165 <data format="qza" label="${tool.name} on ${on_string}: featureimportance.qza" name="ofeatureimportance" />
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166 <data format="html" label="${tool.name} on ${on_string}: volatilityplot.html" name="ovolatilityplot" />
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167 <data format="html" label="${tool.name} on ${on_string}: accuracyresults.html" name="oaccuracyresults" />
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168 <data format="qza" label="${tool.name} on ${on_string}: sampleestimator.qza" name="osampleestimator" />
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169
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170 </outputs>
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171
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172 <help><![CDATA[
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173 Feature volatility analysis
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174 ###############################################################
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175
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176 Identify features that are predictive of a numeric metadata column,
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177 state_column (e.g., time), and plot their relative frequencies across
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178 states using interactive feature volatility plots. A supervised learning
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179 regressor is used to identify important features and assess their ability
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180 to predict sample states. state_column will typically be a measure of time,
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181 but any numeric metadata column can be used.
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182
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183 Parameters
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184 ----------
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185 table : FeatureTable[Frequency]
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186 Feature table containing all features that should be used for target
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187 prediction.
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188 metadata : Metadata
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189 Sample metadata file containing individual_id_column.
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190 state_column : Str
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191 Metadata containing collection time (state) values for each sample.
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192 Must contain exclusively numeric values.
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193 individual_id_column : Str, optional
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194 Metadata column containing IDs for individual subjects.
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195 cv : Int % Range(1, None), optional
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196 Number of k-fold cross-validations to perform.
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197 random_state : Int, optional
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198 Seed used by random number generator.
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199 n_jobs : Int, optional
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200 Number of jobs to run in parallel.
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201 n_estimators : Int % Range(1, None), optional
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202 Number of trees to grow for estimation. More trees will improve
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203 predictive accuracy up to a threshold level, but will also increase
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204 time and memory requirements. This parameter only affects ensemble
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205 estimators, such as Random Forest, AdaBoost, ExtraTrees, and
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206 GradientBoosting.
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207 estimator : Str % Choices('RandomForestRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor', 'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR'), optional
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208 Estimator method to use for sample prediction.
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209 parameter_tuning : Bool, optional
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210 Automatically tune hyperparameters using random grid search.
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211 missing_samples : Str % Choices('error', 'ignore'), optional
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212 How to handle missing samples in metadata. "error" will fail if missing
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213 samples are detected. "ignore" will cause the feature table and
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214 metadata to be filtered, so that only samples found in both files are
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215 retained.
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216 importance_threshold : Float % Range(0, None, inclusive_start=False) | Str % Choices('q1', 'q2', 'q3'), optional
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217 Filter feature table to exclude any features with an importance score
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218 less than this threshold. Set to "q1", "q2", or "q3" to select the
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219 first, second, or third quartile of values. Set to "None" to disable
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220 this filter.
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221 feature_count : Int % Range(1, None) | Str % Choices('all'), optional
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222 Filter feature table to include top N most important features. Set to
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223 "all" to include all features.
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224
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225 Returns
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226 -------
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227 filtered_table : FeatureTable[RelativeFrequency]
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228 Feature table containing only important features.
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229 feature_importance : FeatureData[Importance]
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230 Importance of each input feature to model accuracy.
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231 volatility_plot : Visualization
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232 Interactive volatility plot visualization.
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233 accuracy_results : Visualization
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234 Accuracy results visualization.
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235 sample_estimator : SampleEstimator[Regressor]
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236 Trained sample regressor.
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237 ]]></help>
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238 <macros>
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239 <import>qiime_citation.xml</import>
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240 </macros>
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241 <expand macro="qiime_citation"/>
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242 </tool> |
