Intensity normalisation of a ConsensusMap by averaging per-feature intensity ratios against a reference map. More...

#include <OpenMS/ANALYSIS/MAPMATCHING/ConsensusMapNormalizerAlgorithmThreshold.h>

Public Member Functions
	ConsensusMapNormalizerAlgorithmThreshold ()
	Default constructor.

virtual	~ConsensusMapNormalizerAlgorithmThreshold ()
	Destructor.

Static Public Member Functions
static std::vector< double >	computeCorrelation (const ConsensusMap &map, const double &ratio_threshold, const std::string &acc_filter, const std::string &desc_filter)
	Compute one normalisation factor per map.

static void	normalizeMaps (ConsensusMap &map, const std::vector< double > &ratios)
	Multiply every feature handle's intensity by its map's factor.

Private Member Functions
	ConsensusMapNormalizerAlgorithmThreshold (const ConsensusMapNormalizerAlgorithmThreshold &copyin)
	Copy construction is deliberately suppressed.

ConsensusMapNormalizerAlgorithmThreshold &	operator= (const ConsensusMapNormalizerAlgorithmThreshold &rhs)
	Assignment is deliberately suppressed.

Detailed Description

Intensity normalisation of a ConsensusMap by averaging per-feature intensity ratios against a reference map.

A stateless utility class – computeCorrelation derives one scalar normalisation factor per map, and normalizeMaps applies those factors to every feature handle in the consensus map.

The reference map is the one with the most features (largest size in the column headers). For each other map, the ratio reference_intensity / map_intensity is computed per feature that is present in both maps, then averaged after dropping ratios that fall outside the configurable ratio_threshold window (see computeCorrelation).

Copy construction and assignment are deliberately suppressed.

Constructor & Destructor Documentation

◆ ConsensusMapNormalizerAlgorithmThreshold() [1/2]

ConsensusMapNormalizerAlgorithmThreshold ( const ConsensusMapNormalizerAlgorithmThreshold & copyin )

private

Copy construction is deliberately suppressed.

◆ ConsensusMapNormalizerAlgorithmThreshold() [2/2]

ConsensusMapNormalizerAlgorithmThreshold ( )

Default constructor.

◆ ~ConsensusMapNormalizerAlgorithmThreshold()

virtual ~ConsensusMapNormalizerAlgorithmThreshold ( )

virtual

Destructor.

Member Function Documentation

◆ computeCorrelation()

static std::vector< double > computeCorrelation	(	const ConsensusMap &	map,
		const double &	ratio_threshold,
		const std::string &	acc_filter,
		const std::string &	desc_filter
	)

static

Compute one normalisation factor per map.

The reference map is the column-header entry with the largest size in map. For every other map j and every consensus feature that has a non-zero intensity in both the reference and j, the ratio reference_intensity / intensity_j is collected; ratios outside the window (ratio_threshold, 1/ratio_threshold) are discarded as outliers. The mean of the surviving ratios is returned as the factor for map j.

Features are first filtered by acc_filter (regex on protein accessions) and desc_filter (regex on protein descriptions); only features that pass both filters contribute to the ratios.

Warning: When no feature passes the filters for a given map (the surviving ratio list is empty), the function returns a vector of 1.0 of length number_of_maps and logs a warning – i.e. the result is "no normalisation".

Parameters

[in]	map	ConsensusMap providing the per-map feature handles.
[in]	ratio_threshold	Lower bound of the kept-ratio window. Use e.g. `0.5` to keep ratios in (0.5, 2.0).
[in]	acc_filter	Regex applied to accession strings; empty means "match anything".
[in]	desc_filter	Regex applied to description strings; empty means "match anything".

Returns: One normalisation factor per map (size equals map.getColumnHeaders().size()).

◆ normalizeMaps()

static void normalizeMaps	(	ConsensusMap &	map,
		const std::vector< double > &	ratios
	)

static

Multiply every feature handle's intensity by its map's factor.

Iterates every consensus feature in map and, for each sub-feature handle, multiplies its intensity by ratios[handle.getMapIndex()]. Progress is reported to the console via ProgressLogger.

Parameters

[in,out]	map	Consensus map whose feature-handle intensities are scaled in place.
[in]	ratios	Per-map factors (typically obtained from computeCorrelation). Must have at least (max-map-index + 1) entries.

◆ operator=()

ConsensusMapNormalizerAlgorithmThreshold & operator= ( const ConsensusMapNormalizerAlgorithmThreshold & rhs )