Multiscale Processing of Mass Spectrometry Data

Timothy Randolph and Yutaka Yasui
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Timothy Randolph: University of Washington
Yutaka Yasui: Fred Hutchinson Cancer Research Center

No 1063, UW Biostatistics Working Paper Series from Berkeley Electronic Press

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.

Keywords: mass spectrometry; multiscale structures; feature extraction; wavelets (search for similar items in EconPapers)
Date: 2004-07-19
Note: oai:bepress.com:uwbiostat-1063
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