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<tool id="openms_feature_linker_unlabeled_qt" version="0.1.0" name="Feature Linker (Unlabeled QT)">
  <description>
    Groups corresponding features from multiple maps using a QT clustering approach.
  </description>
  <macros>
    <import>macros.xml</import>
  </macros>
  <expand macro="stdio" />
  <expand macro="requires" />
  <command interpreter="python">
    openms_wrapper.py --executable 'FeatureLinkerUnlabeledQT' --config $config
  </command>
  <configfiles>
    <configfile name="config">[simple_options]
in=${",".join(map(str, $inputs))}
out=$output
#if $type.input_type == "consensusxml"
$keep_subelements
#end if
$ignore_charge
$use_identifications
algorithm!distance_RT!max_difference=$rt_max_difference
algorithm!distance_MZ!max_difference=$mz_max_difference
algorithm!distance_MZ!unit=$mz_max_difference_unit
#if $advanced.specify
algorithm!distance_RT!weight=$advanced.rt_weight
algorithm!distance_MZ!weight=$advanced.mz_weight
algorithm!distance_intensity!weight=$advanced.intensity_weight
algorithm!distance_RT!exponent=$advanced.rt_exponent
algorithm!distance_MZ!exponent=$advanced.mz_exponent
algorithm!distance_intensity!exponent=$advanced.intensity_exponent
#end if
</configfile>
  </configfiles>
  <inputs>
    <expand macro="feature_linker_input" />
    
    <expand macro="feature_linker_algorithm_options" />
  </inputs>
  <outputs>
    <data format="consensusxml" name="output" />
  </outputs>
  <help>
**What it does**

This tool provides an algorithm for grouping corresponding features in multiple runs of label-free experiments. For more details and algorithm-specific parameters (set in the INI file) see "Detailed Description" in the algorithm documentation or the INI file table below.

FeatureLinkerUnlabeled takes several feature maps (featureXML files) and stores the corresponding features in a consensus map (consensusXML file). Feature maps can be created from MS experiments (peak data) using one of the FeatureFinder TOPP tools.

Advanced users can convert the consensusXML generated by this tool to EDTA using FileConverter and plot the distribution of distances in RT (or m/z) between different input files (can be done in Excel). The distribution should be Gaussian-like with very few points beyond the tails. Points far away from the Gaussian indicate a too wide tolerance. A Gaussian with its left/right tail trimmed indicates a too narrow tolerance.

**Citation**

For the underlying tool, please cite ``Weisser et al.: An automated pipeline for high-throughput label-free quantitative proteomics (J. Proteome Res., 2013, PMID: 23391308).``

If you use this tool in Galaxy, please cite Chilton J, et al. https://bitbucket.org/galaxyp/galaxyp-toolshed-openms
  </help>
</tool>