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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/11873

Title: A Bayesian Markov-Chain-Based Heteroscedastic Regression Model for the Analysis of O-18-Labeled Mass Spectra
Authors: Zhu, Qi
Issue Date: 2011
Publisher: SPRINGER
Abstract: To reduce the influence of the between-spectra variability on the results of peptide quantification, one can consider the O-18-labeling approach. Ideally, with such labeling technique, a mass shift of 4 Da of the isotopic distributions of peptides from the labeled sample is induced, which allows one to distinguish the two samples and to quantify the relative abundance of the peptides. It is worth noting, however, that the presence of small quantities of O-16 and O-17 atoms during the labeling step can cause incomplete labeling. In practice, ignoring incomplete labeling may result in the biased estimation of the relative abundance of the peptide in the compared samples. A Markov model was developed to address this issue (Zhu, Valkenborg, Burzykowski. J. Proteome Res. 9, 2669-2677, 2010). The model assumed that the peak intensities were normally distributed with heteroscedasticity using a power-of-the-mean variance funtion. Such a dependence has been observed in practice. Alternatively, we formulate the model within the Bayesian framework. This opens the possibility to further extend the model by the inclusion of random effects that can be used to capture the biological/technical variability of the peptide abundance. The operational characteristics of the model were investigated by applications to real-life mass-spectrometry data sets and a simulation study.
Notes: [Zhu, Qi] Katholieke Univ Leuven, Dept Elect Engn, ESAT SCD, B-3001 Leuven, Belgium. [Burzykowski, Tomasz] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Hasselt, Belgium.
URI: http://hdl.handle.net/1942/11873
DOI: 10.1007/s13361-010-0056-x
ISI #: 000288561400010
ISSN: 1044-0305
Category: A1
Type: Journal Contribution
Validation: ecoom, 2012
Appears in Collections: Research publications

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