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

potential predecessor tools	→ ConsensusMapNormalizer →	potential successor tools
FeatureLinkerUnlabeled (or another feature grouping tool)		ProteinQuantifier
FeatureLinkerUnlabeled (or another feature grouping tool)		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.
RT-local median correction ('rt_local'): Every map gets a log2 offset that may vary with retention time, so that both different loading and a drift of the ion signal along the gradient are removed. The offset of a feature is the median, over the rt_local:neighbors features of the same map before it and after it in retention time, of their log2 deviation from their mean over the maps. Features quantified in at least rt_local:min_maps maps (and passing the accession and description filters) serve as references; the offsets are refined in rt_local:rounds backfitting rounds. See OpenMS::ConsensusMapNormalizerAlgorithmRTLocal for details.

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: 3.7.0-pre-nightly-2026-11-06 Nov  7 2026, 01:17:16, Revision: 05518e6
To cite OpenMS:
 + Pfeuffer, J., Bielow, C., Wein, S. et al.. OpenMS 3 enables reproducible analysis of large-scale mass spec
   trometry data. Nat Methods (2024). doi:10.1038/s41592-024-02197-7.

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!), 'rt_local' scales each map by a factor that may vary with retenti
                            on time, estimated from the features quantified in several maps (see 'rt_local:*'
                            ) (default: 'robust_regression') (valid: 'robust_regression', 'median', 'median_s
                            hift', 'quantile', 'rt_local')
  -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 (0 = all available
                             cores) (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

This section lists all parameters supported by the tool. Parameters are organized into hierarchical subsections that group related settings together. Subsections may contain further subsections or individual parameters.

Each parameter entry contains the following information:

Name The identifier used in configuration files and on the command line.
Default value The value used if the parameter is not explicitly specified.
Description A short explanation describing the purpose and behavior of the parameter.
Tags Additional metadata associated with the parameter.
Restrictions Allowed value ranges for numeric parameters or valid options for string parameters.

Parameter tags provide additional information about how a parameter is used. Some tags indicate whether a parameter is required or intended for advanced configuration, while others may be used internally by OpenMS or workflow tools.

Parameters highlighted as required must be specified for the tool to run successfully. Parameters marked as advanced allow fine-tuning of algorithm behavior and are typically not needed for standard workflows.

+ConsensusMapNormalizerNormalizes maps of one consensusXML file

version3.7.0-pre-nightly-2026-11-06 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!), 'rt_local' scales each map by a factor that may vary with retention time, estimated from the features quantified in several maps (see 'rt_local:*')robust_regression, median, median_shift, quantile, rt_local

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 (0 = all available cores)

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

+++rt_localOptions for the 'rt_local' normalization

neighbors50 Number of reference features on each side (before and after in retention time) whose median log2 deviation is a feature's offset. Fewer: follows faster drifts, but noisier offsets.1:∞

min_maps3 A consensus feature is a reference feature if it has an intensity in at least this many maps.2:∞

min_reference20 Maps with fewer reference features are not normalized.1:∞

rounds5 Backfitting rounds: each round recomputes the features' mean levels from the offset-corrected intensities and then the offsets.1:∞