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

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

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)96-111
Number of pages16
JournalComputational Statistics and Data Analysis
Volume105
DOIs
StatePublished - Jan 1 2017

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Gas Chromatography
Mass Spectrometry
Gas chromatography
Mass spectrometry
Delta Method
Metabolomics
Gamma distribution
Biomarkers
Fast Fourier transform
Model
Fast Fourier transforms
Analytical Solution
Closed-form
First-order

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

Cite this

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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.",
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Normal–Gamma–Bernoulli peak detection for analysis of comprehensive two-dimensional gas chromatography mass spectrometry data. / Kim, Seongho; Jang, Hyejeong; Koo, Imhoi; Lee, Joohyoung; Zhang, Xiang.

In: Computational Statistics and Data Analysis, Vol. 105, 01.01.2017, p. 96-111.

Research output: Contribution to journalArticle

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AU - Kim, Seongho

AU - Jang, Hyejeong

AU - Koo, Imhoi

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AU - Zhang, Xiang

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AB - 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.

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