OpenMS  3.0.0
ConsensusMapNormalizer

Normalization of intensities in a set of maps using robust regression.

potential predecessor tools $ \longrightarrow $ ConsensusMapNormalizer $ \longrightarrow $ potential successor tools
FeatureLinkerUnlabeled
(or another feature grouping tool)
ProteinQuantifier
TextExporter

The tool normalizes the intensities of a set of maps (consensusXML file). The following normalization algorithms are available:

  • Robust regression: Maps are normalized pair-wise relative to the map with the most features. Given two maps, peptide featues are classified as non-outliers (ratio_threshold < intensity ratio < 1/ratio_threshold) or outliers. From the non-outliers an average intensity ratio is calculated and used for normalization.
  • Median correction: The median of all maps is set to the median of the map with the most features.
  • Quantile normalization: Performs an exact quantile normalization if the number of features is equal across all maps. Otherwise, an approximate quantile normalization using resampling is applied.

The command line parameters of this tool are:

ConsensusMapNormalizer -- Normalizes maps of one consensusXML file
Full documentation: http://www.openms.de/doxygen/nightly/html/TOPP_ConsensusMapNormalizer.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:
  ConsensusMapNormalizer <options>

Options (mandatory options marked with '*'):
  -in <file>*               Input file (valid formats: 'consensusXML')
  -out <file>*              Output file (valid formats: 'consensusXML')
                            
  -algorithm_type <type>    The normalization algorithm that is applied. 'robust_regression' scales each map 
                            by a fator computed from the ratios of non-differential background features (as
                            determined by the ratio_threshold parameter), 'quantile' performs quantile normal
                            ization, 'median' scales all maps to the same median intensity, 'median_shift'
                            shifts the median instead of scaling (WARNING: if you have regular, log-normal
                            MS data, 'median_shift' is probably the wrong choice. Use only if you know what
                            you're doing!) (default: 'robust_regression' valid: 'robust_regression', 'median'
                            , 'median_shift', 'quantile')
  -ratio_threshold <ratio>  Only for 'robust_regression': the parameter is used to distinguish between non-ou
                            tliers (ratio_threshold < intensity ratio < 1/ratio_threshold) and outliers. (def
                            ault: '0.67' min: '1.0e-03' max: '1.0')
                            
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)

INI file documentation of this tool:

Legend:
required parameter
advanced parameter
+ConsensusMapNormalizerNormalizes maps of one consensusXML file
version3.0.0-pre-nightly-2022-07-20 Version of the tool that generated this parameters file.
++1Instance '1' section for 'ConsensusMapNormalizer'
in input fileinput file*.consensusXML
out output fileoutput file*.consensusXML
algorithm_typerobust_regression The normalization algorithm that is applied. 'robust_regression' scales each map by a fator computed from the ratios of non-differential background features (as determined by the ratio_threshold parameter), 'quantile' performs quantile normalization, 'median' scales all maps to the same median intensity, 'median_shift' shifts the median instead of scaling (WARNING: if you have regular, log-normal MS data, 'median_shift' is probably the wrong choice. Use only if you know what you're doing!)robust_regression,median,median_shift,quantile
ratio_threshold0.67 Only for 'robust_regression': the parameter is used to distinguish between non-outliers (ratio_threshold < intensity ratio < 1/ratio_threshold) and outliers.1.0e-03:1.0
accession_filter Use only features with accessions (partially) matching this regular expression for computing the normalization factors. Useful, e.g., if you have known house keeping proteins in your samples. When this parameter is empty or the regular expression matches the empty string, all features are used (even those without an ID). No effect if quantile normalization is used.
description_filter Use only features with description (partially) matching this regular expression for computing the normalization factors. Useful, e.g., if you have known house keeping proteins in your samples. When this parameter is empty or the regular expression matches the empty string, all features are used (even those without an ID). No effect if quantile normalization is used.
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