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 language | English (US) |
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Pages (from-to) | 96-111 |
Number of pages | 16 |
Journal | Computational Statistics and Data Analysis |
Volume | 105 |
DOIs | |
State | Published - Jan 1 2017 |
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All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Computational Mathematics
- Computational Theory and Mathematics
- Applied Mathematics
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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 journal › Article
TY - JOUR
T1 - Normal–Gamma–Bernoulli peak detection for analysis of comprehensive two-dimensional gas chromatography mass spectrometry data
AU - Kim, Seongho
AU - Jang, Hyejeong
AU - Koo, Imhoi
AU - Lee, Joohyoung
AU - Zhang, Xiang
PY - 2017/1/1
Y1 - 2017/1/1
N2 - 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.
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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UR - http://www.scopus.com/inward/citedby.url?scp=84982171763&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2016.07.015
DO - 10.1016/j.csda.2016.07.015
M3 - Article
AN - SCOPUS:84982171763
VL - 105
SP - 96
EP - 111
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
SN - 0167-9473
ER -