Multiscale Processing of Mass Spectrometry Data
Abstract
This work addresses the problem of extracting signal content from protein mass spectrometry data. A multiscale decomposition of these spectra is used to focus on local scale-based structure and provide an unambiguous definition of scale-specific features. An objective quantification of features/peaks is accompanied by an efficient method for calculating the location of features that avoids ad hoc decisions regarding signal-to-noise ratios or bandwidths. Scale-based histograms serve as spectral-density-like functions indicating the regions of high density of features in the data. These regions provide bins within which features can be quantified and compared across samples. As a preliminary step, the locations of dominant features within coarse-scale bins are used for registration of spectra. The multiscale decomposition, the scale-based feature definition, the calculation of feature locations and subsequent quantification of features is carried out by way of a translation-invariant wavelet analysis.