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A novel wavelet‐based thresholding method for the pre‐processing of mass spectrometry data that accounts for heterogeneous noise
Author(s) -
Kwon Deukwoo,
Vannucci Marina,
Song Joon Jin,
Jeong Jaesik,
Pfeiffer Ruth M.
Publication year - 2008
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.200701010
Subject(s) - thresholding , noise reduction , wavelet , noise (video) , computer science , pattern recognition (psychology) , mass spectrometry , artificial intelligence , data processing , chemistry , chromatography , image (mathematics) , operating system
In recent years there has been an increased interest in using protein mass spectroscopy to discriminate diseased from healthy individuals with the aim of discovering molecular markers for disease. A crucial step before any statistical analysis is the pre‐processing of the mass spectrometry data. Statistical results are typically strongly affected by the specific pre‐processing techniques used. One important pre‐processing step is the removal of chemical and instrumental noise from the mass spectra. Wavelet denoising techniques are a standard method for denoising. Existing techniques, however, do not accommodate errors that vary across the mass spectrum, but instead assume a homogeneous error structure. In this paper we propose a novel wavelet denoising approach that deals with heterogeneous errors by incorporating a variance change point detection method in the thresholding procedure. We study our method on real and simulated mass specrometry data and show that it improves on performances of peak detection methods.