Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/14598
Title: A Markov-chain-based regression model with random effects for the analysis of 18O-labelled mass spectra
Authors: ZHU, Qi 
BURZYKOWSKI, Tomasz 
Issue Date: 2013
Publisher: TAYLOR & FRANCIS LTD
Source: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 83 (1), p. 145-157
Abstract: The enzymatic 18O-labelling is a useful technique for reducing the influence of the between-spectra variability on the results of mass-spectrometry experiments. A difficulty in applying the technique lies in the quantification of the corresponding peptides due to the possibility of an incomplete labelling, which may result in biased estimates of the relative peptide abundance. To address the problem, Zhu et al. [A Markov-chain-based heteroscedastic regression model for the analysis of high-resolution enzymatically 18O-labeled mass spectra, J. Proteome Res. 9(5) (2010), pp. 26692677] proposed a Markov-chain-based regression model with heteroscedastic residual variance, which corrects for the possible bias. In this paper, we extend the model by allowing for the estimation of the technical and/or biological variability for the mass spectra data. To this aim, we use a mixed-effects version of the model. The performance of the model is evaluated based on results of an application to real-life mass spectra data and a simulation study.
Notes: [Zhu, Qi] Katholieke Univ Leuven, Dept Elect Engn, ESAT SCD, B-3001 Heverlee, Belgium. [Burzykowski, Tomasz] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, B-3590 Diepenbeek, Belgium. qi.zhu@esat.kuleuven.be
Keywords: Computer Science, Interdisciplinary Applications; Statistics & Probability; 18O-labelling; heteroscedastic regression; Markov model; random effects of mass spectra; two-stage analysis;18O-labelling; heteroscedastic regression; Markov model; random effects of mass spectra; two-stage analysis
Document URI: http://hdl.handle.net/1942/14598
ISSN: 0094-9655
e-ISSN: 1563-5163
DOI: 10.1080/00949655.2011.620610
ISI #: 000313033800010
Category: A1
Type: Journal Contribution
Validations: ecoom 2014
Appears in Collections:Research publications

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