comparison qiime2-2020.8/qiime_sample-classifier_scatterplot.xml @ 20:d93d8888f0b0 draft

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20:d93d8888f0b0

<?xml version="1.0" ?>
<tool id="qiime_sample-classifier_scatterplot" name="qiime sample-classifier scatterplot"
      version="2020.8">
  <description>Make 2D scatterplot and linear regression of regressor predictions.</description>
  <requirements>
    <requirement type="package" version="2020.8">qiime2</requirement>
  </requirements>
  <command><![CDATA[
qiime sample-classifier scatterplot

--i-predictions=$ipredictions

#if str($mtruthfile) != 'None':
--m-truth-file=$mtruthfile
#end if

#if '__ob__' in str($mtruthcolumn):
  #set $mtruthcolumn_temp = $mtruthcolumn.replace('__ob__', '[')
  #set $mtruthcolumn = $mtruthcolumn_temp
#end if
#if '__cb__' in str($mtruthcolumn):
  #set $mtruthcolumn_temp = $mtruthcolumn.replace('__cb__', ']')
  #set $mtruthcolumn = $mtruthcolumn_temp
#end if
#if 'X' in str($mtruthcolumn):
  #set $mtruthcolumn_temp = $mtruthcolumn.replace('X', '\\')
  #set $mtruthcolumn = $mtruthcolumn_temp
#end if
#if '__sq__' in str($mtruthcolumn):
  #set $mtruthcolumn_temp = $mtruthcolumn.replace('__sq__', "'")
  #set $mtruthcolumn = $mtruthcolumn_temp
#end if
#if '__db__' in str($mtruthcolumn):
  #set $mtruthcolumn_temp = $mtruthcolumn.replace('__db__', '"')
  #set $mtruthcolumn = $mtruthcolumn_temp
#end if

--m-truth-column=$mtruthcolumn


#if str($pmissingsamples) != 'None':
--p-missing-samples=$pmissingsamples
#end if

--o-visualization=ovisualization

#if str($examples) != 'None':
--examples=$examples
#end if

;
cp ofeatureimportance.qza $ofeatureimportance

;
qiime tools export  ovisualization.qzv --output-path out
&& mkdir -p '$ovisualization.files_path'
&& cp -r out/* '$ovisualization.files_path'
&& mv '$ovisualization.files_path/index.html' '$ovisualization'

  ]]></command>
  <inputs>
    <param format="qza,no_unzip.zip" label="--i-predictions: ARTIFACT SampleData[RegressorPredictions] Predicted values to plot on y axis. Must be predictions of numeric data produced by a sample regressor.                                   [required]" name="ipredictions" optional="False" type="data" />
    <param label="--m-truth-file: METADATA" name="mtruthfile" optional="False" type="data" />
    <param label="--m-truth-column: COLUMN  MetadataColumn[Numeric] Metadata column (true values) to plot on x axis. [required]" name="mtruthcolumn" optional="False" type="text" />
    <param label="--p-missing-samples: " name="pmissingsamples" optional="True" type="select">
      <option selected="True" value="None">Selection is Optional</option>
      <option value="error">error</option>
      <option value="ignore">ignore</option>
    </param>
    <param label="--examples: Show usage examples and exit." name="examples" optional="False" type="data" />
    
  </inputs>

  <outputs>
    <data format="html" label="${tool.name} on ${on_string}: visualization.html" name="ovisualization" />
    
  </outputs>

  <help><![CDATA[
Make 2D scatterplot and linear regression of regressor predictions.
###############################################################

Make a 2D scatterplot and linear regression of predicted vs. true values
for a set of samples predicted using a sample regressor.

Parameters
----------
predictions : SampleData[RegressorPredictions]
    Predicted values to plot on y axis. Must be predictions of numeric data
    produced by a sample regressor.
truth : MetadataColumn[Numeric]
    Metadata column (true values) to plot on x axis.
missing_samples : Str % Choices('error', 'ignore'), optional
    How to handle missing samples in metadata. "error" will fail if missing
    samples are detected. "ignore" will cause the feature table and
    metadata to be filtered, so that only samples found in both files are
    retained.

Returns
-------
visualization : Visualization
  ]]></help>
  <macros>
    <import>qiime_citation.xml</import>
  </macros>
  <expand macro="qiime_citation"/>
</tool>