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Abstract:
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Comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC×GC-MS) has much increased separation power for analysis of complex samples unlike other analytical platforms and so is widely used in metabolomics for biomarker discovery. Accurate peak detection still remains a bottleneck for wide applications of GC×GC-MS, however. For this reason, we develop a new peak detection algorithm using the normal-gamma-Bernoulli (NGB) model, generalizing the normal-exponential-Bernoulli (NEB) model. Compared to the NEB model, no closed-form analytical solution is available for the NGB model, preventing its practical use in peak detection. To circumvent this difficulty, we introduce three numerical approaches: fast Fourier transform (FFT), the first-order and the second-order delta methods (D1 and D2). 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.
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