Normal–Gamma–Bernoulli peak detection for analysis of comprehensive two-dimensional gas chromatography mass spectrometry data

Seongho Kim, Hyejeong Jang, Imhoi Koo, Joohyoung Lee and Xiang Zhang

Computational Statistics & Data Analysis, 2017, vol. 105, issue C, 96-111

Abstract: Compared to other analytical platforms, comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC×GC–MS) has much increased separation power for analysis of complex samples and thus is increasingly used in metabolomics for biomarker discovery. However, accurate peak detection remains a bottleneck for wide applications of GC×GC–MS. Therefore, the normal–exponential–Bernoulli (NEB) model is generalized by gamma distribution and a new peak detection algorithm using the Normal–Gamma–Bernoulli (NGB) model is developed. Unlike the NEB model, the NGB model has no closed-form analytical solution, hampering its practical use in peak detection. To circumvent this difficulty, three numerical approaches, which are fast Fourier transform (FFT), the first-order and the second-order delta methods (D1 and D2), are introduced. The applications to simulated data and two real GC×GC–MS data sets show that the NGB-D1 method performs the best in terms of both computational expense and peak detection performance.

Keywords: Comprehensive two-dimensional gas chromatography–mass spectrometry (GC×GC–MS); Metabolomics; Normal–Exponential–Bernoulli (NEB) model; Normal–Gamma–Bernoulli (NGB) model; Peak detection (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:105:y:2017:i:c:p:96-111

DOI: 10.1016/j.csda.2016.07.015

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