Multiscale processing of mass spectrometry data.

T W Randolph; Y Yasui

doi:10.1111/j.1541-0420.2005.00504.x

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Multiscale processing of mass spectrometry data.

T W Randolph·Y Yasui

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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 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.

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Algorithms Biometry Data Interpretation, Statistical Mass Spectrometry Models, Statistical Molecular Structure Proteins Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Journal Article Research Support, N.I.H., Extramural

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T W Randolph, Y Yasui. 2006. Multiscale processing of mass spectrometry data.. https://doi.org/10.1111/j.1541-0420.2005.00504.x

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