Quantification and deconvolution of asymmetric LC-MS peaks using the bi-Gaussian mixture model and statistical model selection

Yu, Tianwei; Peng, Hesen

doi:10.1186/1471-2105-11-559

Cited by 36 publications

(27 citation statements)

References 24 publications

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“…In the current study, we merged metabolites from HMDB 29 and features found from the example dataset of the apLCMS package 9, 20 . The data was generated using anion exchange column with formic acid gradient combined electrospray ionization (ESI) 30 .…”

Section: Methodsmentioning

confidence: 99%

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Hybrid Feature Detection and Information Accumulation Using High-Resolution LC–MS Metabolomics Data

Park

et al. 2013

J. Proteome Res.

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Feature detection is a critical step in the preprocessing of Liquid Chromatography – Mass Spectrometry (LC-MS) metabolomics data. Currently, the predominant approach is to detect features using noise filters and peak shape models based on the data at hand alone. Databases of known metabolites and historical data contain information that could help boost the sensitivity of feature detection, especially for low-concentration metabolites. However, utilizing such information in targeted feature detection may cause large number of false-positives because of the high levels of noise in LC-MS data. With high-resolution mass spectrometry such as Liquid Chromatograph – Fourier Transform Liquid Chromatography (LC-FTMS), high-confidence matching of peaks to known features is feasible. Here we describe a computational approach that serves two purposes. First it boosts feature detection sensitivity by using a hybrid procedure of both untargeted and targeted peak detection. New algorithms are designed to reduce the chance of false-positives by non-parametric local peak detection and filtering. Second, it can accumulate information on the concentration variation of metabolites over large number of samples, which can help find rare features and/or features with uncommon concentration in future studies. Information can be accumulated on features that are consistently found in real data even before their identities are found. We demonstrate the value of the approach in a proof-of-concept study. The method is implemented as part of the R package apLCMS at http://www.sph.emory.edu/apLCMS/.

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Section: Methodsmentioning

confidence: 99%

“…The apLCMS package conducts retention time deconvolution using the bi-Gaussian mixture model to separate features sharing m/z 20 . Thus the feature list from the new data may contain features with almost identical m/z values and different retention time values.…”

Section: Methodsmentioning

confidence: 99%

Hybrid Feature Detection and Information Accumulation Using High-Resolution LC–MS Metabolomics Data

Park

et al. 2013

J. Proteome Res.

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“…Other algorithms try to improve the peak detection accuracy through better modeling of chromatographic peaks. For example, a bi-Gaussian mixture model have been used for peak detection 44 . However, the fitting of the model to a particular dataset often needs to be checked as chromatographic peak characteristics vary among experiments and instruments.…”

Section: Pre-processing Of Lc-ms Datamentioning

confidence: 99%

LC-MS-based metabolomics

et al. 2012

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Metabolomics aims at identification and quantitation of small molecules involved in metabolic reactions. LC-MS has enjoyed a growing popularity as the platform for metabolomic studies due to its high throughput, soft ionization, and good coverage of metabolites. The success of LC-MS-based metabolomic study often depends on multiple experimental, analytical, and computational steps. This review presents a workflow of a typical LC-MS-based metabolomic analysis for identification and quantitation of metabolites indicative of biological/environmental perturbations. Challenges and current solutions in each step of the workflow are reviewed. The review intends to help investigators understand the challenges in metabolomic studies and to determine appropriate experimental, analytical, and computational methods to address these challenges.

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“…3), then curve-fit with a bi-Gaussian model (Yu and Peng, 2010). When compared to non-averaged data in Table 1, this averaged data showed smaller peak changes and longer decay times in all amplitude-related EEG measures with similar time to peak changes (Table 2).…”

Section: Resultsmentioning

confidence: 99%

Quantitative EEG Is an Objective, Sensitive, and Reliable Indicator of Transient Anesthetic Effects During Wada Tests

Assassi

Bazil

et al. 2015

Journal of Clinical Neurophysiology

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The intracarotid amobarbital or Wada procedure is a component of the presurgical evaluation for refractory epilepsy, during which monitoring the onset and offset of transient anesthetic effects is critical. In this study, we characterized changes of 8 QEEG measures during 26 Wada tests, which included alpha, beta, theta, and delta powers, alpha/delta power ratio (ADR), beta/delta power ratio (BDR), median amplitude-integrated EEG (aEEG) and 90% spectral edge frequency (SEF90), and correlated them with contralateral hemiplegia. We found that on the side of injection, delta and theta powers, ADR, BDR, and SEF90 peaked within 1 min after injection of 70-150 mg amobarbital or 4-7 mg methohexital. When contralateral arm strength returned to 3/5, delta power and aEEG decayed on average 24% and 19%, respectively, for amobarbital, similar to that of methohexital (27% and 18%). Since delta power resolution most closely mirrored that of the hemiplegia and aEEG had the highest signal/noise ratio (SNR), these QEEG values appear to be the best measures for decay of anesthetic effects. Increase in alpha power persisted longest, and therefore may be the best measure of late residual anesthetic effects.

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Quantification and deconvolution of asymmetric LC-MS peaks using the bi-Gaussian mixture model and statistical model selection

Cited by 36 publications

References 24 publications

Hybrid Feature Detection and Information Accumulation Using High-Resolution LC–MS Metabolomics Data

Hybrid Feature Detection and Information Accumulation Using High-Resolution LC–MS Metabolomics Data

LC-MS-based metabolomics

Quantitative EEG Is an Objective, Sensitive, and Reliable Indicator of Transient Anesthetic Effects During Wada Tests

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