Analysis

High-level analysis like PeakPicking, Quantitation, Identification, MapAlignment. More...

Collaboration diagram for Analysis:

Modules
	SignalProcessing
	Signal processing classes (noise estimation, noise filters, baseline filters)
	PeakPicking
	Classes for the transformation of raw ms data into peak data.
	FeatureFinder
	The feature detection algorithms.
	MapAlignment
	The map alignment algorithms.
	FeatureGrouping
	The feature grouping.
	Identification
	Protein and peptide identification classes.
	Clustering
	This class contains SpectraClustering classes These classes are components for clustering all kinds of data for which a distance relation, normalizable in the range of [0,1], is available. Mainly this will be data for which there is a corresponding CompareFunctor given (e.g. PeakSpectrum) that is yielding the similarity normalized in the range of [0,1] of such two elements, so it can easily converted to the needed distances.

Classes
class	FeatureDeconvolution
	An algorithm to decharge features (i.e. as found by FeatureFinder). More...
class	MetaboliteFeatureDeconvolution
	An algorithm to decharge small molecule features (i.e. as found by FeatureFinder). More...
class	PeakIntensityPredictor
	Predict peak heights of peptides based on Local Linear Map model. More...

Detailed Description

High-level analysis like PeakPicking, Quantitation, Identification, MapAlignment.