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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"
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3 version="2020.8">
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4 <description>Microbial maturity index prediction.</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 maturity-index
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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 --p-group-by=$pgroupby
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48
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49 --p-control=$pcontrol
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50
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51 #if '__ob__' in str($pindividualidcolumn):
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52 #set $pindividualidcolumn_temp = $pindividualidcolumn.replace('__ob__', '[')
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53 #set $pindividualidcolumn = $pindividualidcolumn_temp
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54 #end if
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55 #if '__cb__' in str($pindividualidcolumn):
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56 #set $pindividualidcolumn_temp = $pindividualidcolumn.replace('__cb__', ']')
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57 #set $pindividualidcolumn = $pindividualidcolumn_temp
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58 #end if
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59 #if 'X' in str($pindividualidcolumn):
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60 #set $pindividualidcolumn_temp = $pindividualidcolumn.replace('X', '\\')
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61 #set $pindividualidcolumn = $pindividualidcolumn_temp
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62 #end if
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63 #if '__sq__' in str($pindividualidcolumn):
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64 #set $pindividualidcolumn_temp = $pindividualidcolumn.replace('__sq__', "'")
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65 #set $pindividualidcolumn = $pindividualidcolumn_temp
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66 #end if
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67 #if '__db__' in str($pindividualidcolumn):
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68 #set $pindividualidcolumn_temp = $pindividualidcolumn.replace('__db__', '"')
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69 #set $pindividualidcolumn = $pindividualidcolumn_temp
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70 #end if
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71
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72 #if str($pindividualidcolumn):
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73 --p-individual-id-column=$pindividualidcolumn
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74 #end if
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75
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76 #if str($pestimator) != 'None':
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77 --p-estimator=$pestimator
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78 #end if
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79
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80 --p-n-estimators=$pnestimators
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81
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82 --p-test-size=$ptestsize
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83
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84 --p-step=$pstep
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85
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86 --p-cv=$pcv
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87
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88 #if str($prandomstate):
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89 --p-random-state=$prandomstate
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90 #end if
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91 --p-n-jobs=$pnjobs
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92
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93 #if $pparametertuning:
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94 --p-parameter-tuning
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95 #end if
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96
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97 #if $poptimizefeatureselection:
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98 --p-optimize-feature-selection
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99 #end if
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100
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101 #if $pstratify:
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102 --p-stratify
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103 #end if
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104
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105 #if str($pmissingsamples) != 'None':
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106 --p-missing-samples=$pmissingsamples
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107 #end if
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108
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109 --p-feature-count=$pfeaturecount
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110
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111 --o-sample-estimator=osampleestimator
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112
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113 --o-feature-importance=ofeatureimportance
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114
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115 --o-predictions=opredictions
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116
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117 --o-model-summary=omodelsummary
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118
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119 --o-accuracy-results=oaccuracyresults
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120
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121 --o-maz-scores=omazscores
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122
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123 --o-clustermap=oclustermap
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124
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125 --o-volatility-plots=ovolatilityplots
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126
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127 #if str($examples) != 'None':
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128 --examples=$examples
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129 #end if
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130
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131 ;
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132 cp omazscores.qza $omazscores
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133
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134 ;
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135 qiime tools export oclustermap.qzv --output-path out
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136 && mkdir -p '$oclustermap.files_path'
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137 && cp -r out/* '$oclustermap.files_path'
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138 && mv '$oclustermap.files_path/index.html' '$oclustermap'
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139
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140 ;
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141 qiime tools export ovolatilityplots.qzv --output-path out
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142 && mkdir -p '$ovolatilityplots.files_path'
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143 && cp -r out/* '$ovolatilityplots.files_path'
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144 && mv '$ovolatilityplots.files_path/index.html' '$ovolatilityplots'
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145
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146 ]]></command>
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147 <inputs>
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148 <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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149 <repeat name="input_files_mmetadatafile" optional="False" title="--m-metadata-file">
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150 <param format="tabular,qza,no_unzip.zip" label="--m-metadata-file: METADATA... (multiple arguments will be merged) [required]" name="additional_input" optional="False" type="data" />
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151 </repeat>
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152 <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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153 <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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154 <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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155 <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="False" type="text" />
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156 <param label="--p-estimator: " name="pestimator" optional="True" type="select">
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157 <option selected="True" value="None">Selection is Optional</option>
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158 <option value="RandomForestRegressor">RandomForestRegressor</option>
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159 <option value="ExtraTreesRegressor">ExtraTreesRegressor</option>
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160 <option value="GradientBoostingRegressor">GradientBoostingRegressor</option>
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161 <option value="AdaBoostRegressor">AdaBoostRegressor</option>
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162 <option value="ElasticNet">ElasticNet</option>
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163 <option value="Ridge">Ridge</option>
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164 <option value="Lasso">Lasso</option>
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165 <option value="KNeighborsRegressor">KNeighborsRegressor</option>
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166 <option value="LinearSVR">LinearSVR</option>
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167 <option value="SVR">SVR</option>
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168 </param>
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169 <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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170 <param exclude_min="True" 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]" max="1.0" min="0.0" name="ptestsize" optional="True" type="float" value="0.5" />
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171 <param exclude_min="True" 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]" max="1.0" min="0.0" name="pstep" optional="True" type="float" value="0.05" />
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172 <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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173 <param label="--p-random-state: INTEGER Seed used by random number generator. [optional]" name="prandomstate" optional="False" type="text" />
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174 <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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175 <param label="--p-optimize-feature-selection: --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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176 <param label="--p-stratify: --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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177 <param label="--p-missing-samples: " name="pmissingsamples" optional="True" type="select">
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178 <option selected="True" value="None">Selection is Optional</option>
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179 <option value="error">error</option>
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180 <option value="ignore">ignore</option>
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181 </param>
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182 <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]" min="0" name="pfeaturecount" optional="True" type="integer" value="50" />
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183 <param label="--examples: Show usage examples and exit." name="examples" optional="False" type="data" />
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184
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185 </inputs>
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186
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187 <outputs>
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188 <data format="qza" label="${tool.name} on ${on_string}: sampleestimator.qza" name="osampleestimator" />
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189 <data format="qza" label="${tool.name} on ${on_string}: featureimportance.qza" name="ofeatureimportance" />
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190 <data format="qza" label="${tool.name} on ${on_string}: predictions.qza" name="opredictions" />
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191 <data format="html" label="${tool.name} on ${on_string}: modelsummary.html" name="omodelsummary" />
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192 <data format="html" label="${tool.name} on ${on_string}: accuracyresults.html" name="oaccuracyresults" />
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193 <data format="qza" label="${tool.name} on ${on_string}: mazscores.qza" name="omazscores" />
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194 <data format="html" label="${tool.name} on ${on_string}: clustermap.html" name="oclustermap" />
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195 <data format="html" label="${tool.name} on ${on_string}: volatilityplots.html" name="ovolatilityplots" />
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196
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197 </outputs>
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198
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199 <help><![CDATA[
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200 Microbial maturity index prediction.
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201 ###############################################################
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202
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203 Calculates a "microbial maturity" index from a regression model trained on
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204 feature data to predict a given continuous metadata column, e.g., to
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205 predict age as a function of microbiota composition. The model is trained
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206 on a subset of control group samples, then predicts the column value for
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207 all samples. This visualization computes maturity index z-scores to compare
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208 relative "maturity" between each group, as described in
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209 doi:10.1038/nature13421. This method can be used to predict between-group
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210 differences in relative trajectory across any type of continuous metadata
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211 gradient, e.g., intestinal microbiome development by age, microbial
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212 succession during wine fermentation, or microbial community differences
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213 along environmental gradients, as a function of two or more different
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214 "treatment" groups.
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215
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216 Parameters
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217 ----------
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218 table : FeatureTable[Frequency]
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219 Feature table containing all features that should be used for target
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220 prediction.
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221 metadata : Metadata
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222 state_column : Str
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223 Numeric metadata column containing sampling time (state) data to use as
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224 prediction target.
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225 group_by : Str
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226 Categorical metadata column to use for plotting and significance
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227 testing between main treatment groups.
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228 control : Str
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229 Value of group_by to use as control group. The regression model will be
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230 trained using only control group data, and the maturity scores of other
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231 groups consequently will be assessed relative to this group.
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232 individual_id_column : Str, optional
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233 Optional metadata column containing IDs for individual subjects. Adds
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234 individual subject (spaghetti) vectors to volatility charts if a column
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235 name is provided.
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236 estimator : Str % Choices('RandomForestRegressor', 'ExtraTreesRegressor', 'GradientBoostingRegressor', 'AdaBoostRegressor', 'ElasticNet', 'Ridge', 'Lasso', 'KNeighborsRegressor', 'LinearSVR', 'SVR'), optional
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237 Regression model to use for prediction.
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238 n_estimators : Int % Range(1, None), optional
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239 Number of trees to grow for estimation. More trees will improve
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240 predictive accuracy up to a threshold level, but will also increase
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241 time and memory requirements. This parameter only affects ensemble
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242 estimators, such as Random Forest, AdaBoost, ExtraTrees, and
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243 GradientBoosting.
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244 test_size : Float % Range(0.0, 1.0, inclusive_start=False), optional
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245 Fraction of input samples to exclude from training set and use for
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246 classifier testing.
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247 step : Float % Range(0.0, 1.0, inclusive_start=False), optional
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248 If optimize_feature_selection is True, step is the percentage of
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249 features to remove at each iteration.
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250 cv : Int % Range(1, None), optional
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251 Number of k-fold cross-validations to perform.
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252 random_state : Int, optional
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253 Seed used by random number generator.
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254 n_jobs : Int, optional
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255 Number of jobs to run in parallel.
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256 parameter_tuning : Bool, optional
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257 Automatically tune hyperparameters using random grid search.
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258 optimize_feature_selection : Bool, optional
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259 Automatically optimize input feature selection using recursive feature
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260 elimination.
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261 stratify : Bool, optional
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262 Evenly stratify training and test data among metadata categories. If
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263 True, all values in column must match at least two samples.
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264 missing_samples : Str % Choices('error', 'ignore'), optional
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265 How to handle missing samples in metadata. "error" will fail if missing
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266 samples are detected. "ignore" will cause the feature table and
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267 metadata to be filtered, so that only samples found in both files are
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268 retained.
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269 feature_count : Int % Range(0, None), optional
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270 Filter feature table to include top N most important features. Set to
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271 zero to include all features.
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272
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273 Returns
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274 -------
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275 sample_estimator : SampleEstimator[Regressor]
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276 Trained sample estimator.
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277 feature_importance : FeatureData[Importance]
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278 Importance of each input feature to model accuracy.
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279 predictions : SampleData[RegressorPredictions]
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280 Predicted target values for each input sample.
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281 model_summary : Visualization
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282 Summarized parameter and (if enabled) feature selection information for
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283 the trained estimator.
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284 accuracy_results : Visualization
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285 Accuracy results visualization.
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286 maz_scores : SampleData[RegressorPredictions]
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287 Microbiota-for-age z-score predictions.
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288 clustermap : Visualization
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289 Heatmap of important feature abundance at each time point in each
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290 group.
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291 volatility_plots : Visualization
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292 Interactive volatility plots of MAZ and maturity scores, target
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293 (column) predictions, and the sample metadata.
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294 ]]></help>
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295 <macros>
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296 <import>qiime_citation.xml</import>
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297 </macros>
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298 <expand macro="qiime_citation"/>
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299 </tool> |
