Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

Yi, Lunzhao; Dong, Naiping; Yun, Yong‐Huan; Deng, Baichuan; Ren, Dabing; Shao, Lei; Liang, Yi‐Zeng

doi:10.1016/j.aca.2016.02.001

Cited by 247 publications

(152 citation statements)

References 212 publications

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“…In this regard, scientists must propose guidelines for metabolomics to ensure that the results from different studies are comparable. In addition, single analytical platforms limit the investigation of the chemical complexity of metabolomes . Thus, we suggest that, in future metabolomics research, scientists extend the metabolite coverage and combine data sets from various analytical platforms when they characterize their targeted biological systems.…”

Section: Discussion and Future Perspectivesmentioning

confidence: 99%

Characterizing Alzheimer's disease through metabolomics and investigating anti‐Alzheimer's disease effects of natural products

Liu

Wang

et al. 2017

Annals of the New York Academy of Sciences

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Alzheimer's disease (AD) is the most common cause of dementia in elderly people and is among the greatest healthcare challenges of the 21st century. However, the etiology and pathogenesis of AD remain poorly understood, and no curative treatments are available to slow down or stop the degenerative effects of AD. As a high-throughput approach, metabolomics is gaining significant attention in AD research, because it has a powerful potential to discover novel biomarkers, unravel new therapeutic targets for AD, and identify perturbed metabolic pathways involved in AD progression. Here, we systematically review metabolomics with regard to its recent advances and applications in the identification of potential biomarkers for early AD diagnosis and pathogenesis research. In addition, we illustrate the developments in metabolomics as an effective tool for understanding the anti-AD mechanisms of natural products. We believe that the insights from these advances can narrow the gap between metabolomics research and clinical applications of laboratory findings. Moreover, we discuss some limitations and perspectives of biomarker identification in metabolomics.

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Section: Discussion and Future Perspectivesmentioning

confidence: 99%

Characterizing Alzheimer's disease through metabolomics and investigating anti‐Alzheimer's disease effects of natural products

Liu

Wang

et al. 2017

Annals of the New York Academy of Sciences

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“…In this article, we intend to compare our algorithms with other four classification methods,, which is widely used in the high‐dimension spectral data. The first one is the NSC method; the second method is the PLSDA; the third method is SPLSDA; and the fourth method is the SVM .…”

Section: Theory and Methodsmentioning

confidence: 99%

High‐dimensional spectral data classification with nonparametric feature screening

2019

Journal of Chemometrics

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Two nonparametric feature screening methods, namely, the Kolmogorov filter and model free, marginally measure the relationship between categorical response and predictor variables without the parametrical assumption. And they can select important variables in the high‐dimensional classification data. Random forest, as a classical nonparametric method, can solve various classification problems. In this paper, we combine the two nonparametric feature screening methods with random forest to handle with spectral data classification. And then other conventional classification methods are compared with ours on three spectral datasets. The comparison results illustrated that our methods have more desirable ability about classification performance and variable selection than other methods.

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“…Typically, the general workflow involves (i) extraction of molecular features, (ii) data treatment and statistical analysis, and (iii) structure identification and confirmation. Detailed reviews on this topic are available here (Yi et al, 2016, Gorrochategui et al, 2016), and briefly discussed below in the context of HRMS data mining for interpreting the environmental chemical space in human matrices.…”

Section: High Resolution Data Extraction Features For Scaling the mentioning

confidence: 99%

“…Principle Component Analysis (PCA) was applied to distinguish polychlorinated biphenyls exposure groups (Megson et al, 2015) and Volcano plot and fold change were used to classify fluorinated compound exposure groups (Rotander et al, 2015). Statistical tools for HRMS data analysis have been reviewed elsewhere (Yi et al, 2016, Gorrochategui et al, 2016). …”

Section: High Resolution Data Extraction Features For Scaling the mentioning

confidence: 99%

Trends in the application of high-resolution mass spectrometry for human biomonitoring: An analytical primer to studying the environmental chemical space of the human exposome

Andra

Austin

Patel

et al. 2017

Environment International

130

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Global profiling of xenobiotics in human matrices in an untargeted mode is gaining attention for studying the environmental chemical space of the human exposome. Defined as the study of a comprehensive inclusion of environmental influences and associated biological responses, human exposome science is currently evolving out of the metabolomics science. In analogy to the latter, the development and applications of high resolution mass spectrometry (HRMS) has shown potential and promise to greatly expand our ability to capture the broad spectrum of environmental chemicals in exposome studies. HRMS can perform both untargeted and targeted analysis because of its capability of full- and/or tandem-mass spectrum acquisition at high mass accuracy with good sensitivity. The collected data from target, suspect and non-target screening can be used not only for the identification of environmental chemical contaminants in human matrices prospectively but also retrospectively. This review covers recent trends and advances in this field. We focus on advances and applications of HRMS in human biomonitoring studies, and data acquisition and mining. The acquired insights provide stepping stones to improve understanding of the human exposome by applying HRMS, and the challenges and prospects for future research.

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Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

Cited by 247 publications

References 212 publications

Characterizing Alzheimer's disease through metabolomics and investigating anti‐Alzheimer's disease effects of natural products

Characterizing Alzheimer's disease through metabolomics and investigating anti‐Alzheimer's disease effects of natural products

High‐dimensional spectral data classification with nonparametric feature screening

Trends in the application of high-resolution mass spectrometry for human biomonitoring: An analytical primer to studying the environmental chemical space of the human exposome

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