Multiscale processing of mass spectrometry data.

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 by defining scale-specific features. Quantification of features is accompanied by an efficient method for calculating the location of features which avoids estimation of 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 are quantified and compared across samples. As a preliminary step, the locations of prominent features within coarse-scale bins may be used for a crude registration of spectra. The multiscale decomposition, the scale-based feature definition, the calculation of feature locations, and subsequent quantification of features are carried out by way of a translation-invariant wavelet analysis.