OpenMS  3.0.0
FeatureLinkerUnlabeledQT

Groups corresponding features from multiple maps using a QT clustering approach.

potential predecessor tools $ \longrightarrow $ FeatureLinkerUnlabeledQT $ \longrightarrow $ potential successor tools
FeatureFinderCentroided
(or another feature detection algorithm)
ProteinQuantifier
MapAlignerPoseClustering
(or another map alignment algorithm)
TextExporter
SeedListGenerator

Reference:
Weisser et al.: An automated pipeline for high-throughput label-free quantitative proteomics (J. Proteome Res., 2013, PMID: 23391308).

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".

FeatureLinkerUnlabeledQT 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.

See also
FeatureLinkerUnlabeled FeatureLinkerLabeled

The command line parameters of this tool are:

FeatureLinkerUnlabeledQT -- Groups corresponding features from multiple maps.
Full documentation: http://www.openms.de/doxygen/nightly/html/TOPP_FeatureLinkerUnlabeledQT.html
Version: 3.0.0-pre-nightly-2022-07-20 Jul 21 2022, 00:07:28, Revision: ea0316e
To cite OpenMS:
  Rost HL, Sachsenberg T, Aiche S, Bielow C et al.. OpenMS: a flexible open-source software platform for mass spectrometry data analysis. Nat Meth. 2016; 13, 9: 741-748. doi:10.1038/nmeth.3959.

Usage:
  FeatureLinkerUnlabeledQT <options>

This tool has algorithm parameters that are not shown here! Please check the ini file for a detailed descript
ion or use the --helphelp option.

Options (mandatory options marked with '*'):
  -in <files>*        Input files separated by blanks (valid formats: 'featureXML', 'consensusXML')
  -out <file>*        Output file (valid formats: 'consensusXML')
  -design <file>      Input file containing the experimental design (valid formats: 'tsv')
                      
  -keep_subelements   For consensusXML input only: If set, the sub-features of the inputs are transferred to 
                      the output.
                      
Common TOPP options:
  -ini <file>         Use the given TOPP INI file
  -threads <n>        Sets the number of threads allowed to be used by the TOPP tool (default: '1')
  -write_ini <file>   Writes the default configuration file
  --help              Shows options
  --helphelp          Shows all options (including advanced)

The following configuration subsections are valid:
 - algorithm   Algorithm parameters section

You can write an example INI file using the '-write_ini' option.
Documentation of subsection parameters can be found in the doxygen documentation or the INIFileEditor.
For more information, please consult the online documentation for this tool:
  - http://www.openms.de/doxygen/nightly/html/TOPP_FeatureLinkerUnlabeledQT.html

INI file documentation of this tool:

Legend:
required parameter
advanced parameter
+FeatureLinkerUnlabeledQTGroups corresponding features from multiple maps.
version3.0.0-pre-nightly-2022-07-20 Version of the tool that generated this parameters file.
++1Instance '1' section for 'FeatureLinkerUnlabeledQT'
in[] input files separated by blanksinput file*.featureXML,*.consensusXML
out Output fileoutput file*.consensusXML
design input file containing the experimental designinput file*.tsv
keep_subelementsfalse For consensusXML input only: If set, the sub-features of the inputs are transferred to the output.true,false
log Name of log file (created only when specified)
debug0 Sets the debug level
threads1 Sets the number of threads allowed to be used by the TOPP tool
no_progressfalse Disables progress logging to command linetrue,false
forcefalse Overrides tool-specific checkstrue,false
testfalse Enables the test mode (needed for internal use only)true,false
+++algorithmAlgorithm parameters section
use_identificationsfalse Never link features that are annotated with different peptides (only the best hit per peptide identification is taken into account).true,false
nr_partitions100 How many partitions in m/z space should be used for the algorithm (more partitions means faster runtime and more memory efficient execution).1:∞
min_nr_diffs_per_bin50 If IDs are used: How many differences from matching IDs should be used to calculate a linking tolerance for unIDed features in an RT region. RT regions will be extended until that number is reached.5:∞
min_IDscore_forTolCalc1.0 If IDs are used: What is the minimum score of an ID to assume a reliable match for tolerance calculation. Check your current score type!
noID_penalty0.0 If IDs are used: For the normalized distances, how high should the penalty for missing IDs be? 0 = no bias, 1 = IDs inside the max tolerances always preferred (even if much further away).0.0:1.0
ignore_chargefalse false [default]: pairing requires equal charge state (or at least one unknown charge '0'); true: Pairing irrespective of charge statetrue,false
ignore_adducttrue true [default]: pairing requires equal adducts (or at least one without adduct annotation); true: Pairing irrespective of adductstrue,false
++++distance_RTDistance component based on RT differences
max_difference100.0 Never pair features with a larger RT distance (in seconds).0.0:∞
exponent1.0 Normalized RT differences ([0-1], relative to 'max_difference') are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)0.0:∞
weight1.0 Final RT distances are weighted by this factor0.0:∞
++++distance_MZDistance component based on m/z differences
max_difference0.3 Never pair features with larger m/z distance (unit defined by 'unit')0.0:∞
unitDa Unit of the 'max_difference' parameterDa,ppm
exponent2.0 Normalized ([0-1], relative to 'max_difference') m/z differences are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)0.0:∞
weight1.0 Final m/z distances are weighted by this factor0.0:∞
++++distance_intensityDistance component based on differences in relative intensity (usually relative to highest peak in the whole data set)
exponent1.0 Differences in relative intensity ([0-1]) are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)0.0:∞
weight0.0 Final intensity distances are weighted by this factor0.0:∞
log_transformdisabled Log-transform intensities? If disabled, d = |int_f2 - int_f1| / int_max. If enabled, d = |log(int_f2 + 1) - log(int_f1 + 1)| / log(int_max + 1))enabled,disabled