DICP OpenIR
Subject Area物理化学
Independent component analysis in non-hypothesis driven metabolomics: Improvement of pattern discovery and simplification of biological data interpretation demonstrated with plasma samples of exercising humans
Li, Xiang1,6; Hansen, Jakob2,3; Zhao, Xinjie1; Lu, Xin1; Weigert, Cora4,5; Haering, Hans-Ulrich4,5; Pedersen, Bente K.2,3; Plomgaard, Peter2,3; Lehmann, Rainer4,5; Xu, Guowang1; RainerLehmann; Xu GW(许国旺)
KeywordIndependent Component Analysis Metabolomics Exercise Metabolic Profiling Gc-ms
Source PublicationJOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES
2012-12-01
DOI10.1016/j.jchromb.2012.06.030
Volume910Pages:156-162
Indexed BySCI
SubtypeArticle
Department18
Funding Project1808
Contribution Rank1,1
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine ; Physical Sciences
WOS SubjectBiochemical Research Methods ; Chemistry, Analytical
WOS Research AreaBiochemistry & Molecular Biology ; Chemistry
WOS KeywordARABIDOPSIS-THALIANA ; CLASSIFICATION ; CHEMOMETRICS ; ALGORITHMS ; SEPARATION ; PROFILES
AbstractIn a non-hypothesis driven metabolomics approach plasma samples collected at six different time points (before, during and after an exercise bout) were analyzed by gas chromatography-time of flight mass spectrometry (GC-TOF MS). Since independent component analysis (ICA) does not need a priori information on the investigated process and moreover can separate statistically independent source signals with non-Gaussian distribution, we aimed to elucidate the analytical power of ICA for the metabolic pattern analysis and the identification of key metabolites in this exercise study. A novel approach based on descriptive statistics was established to optimize ICA model. In the GC-TOF MS data set the number of principal components after whitening and the number of independent components of ICA were optimized and systematically selected by descriptive statistics. The elucidated dominating independent components were involved in fuel metabolism, representing one of the most affected metabolic changes occurring in exercising humans. Conclusive time dependent physiological changes of the metabolic pattern under exercise conditions were detected. We conclude that after optimization ICA can successfully elucidate key metabolite pattern as well as characteristic metabolites in metabolic processes thereby simplifying the explanation of complex biological processes. Moreover. ICA is capable to study time series in complex experiments with multi-levels and multi-factors. (C) 2012 Elsevier B.V. All rights reserved.
Language英语
WOS IDWOS:000312174700018
Citation statistics
Document Type期刊论文
Identifierhttp://cas-ir.dicp.ac.cn/handle/321008/118215
Collection中国科学院大连化学物理研究所
Corresponding AuthorRainerLehmann; Xu GW(许国旺)
Affiliation1.Chinese Acad Sci, Dalian Inst Chem Phys, CAS Key Lab Separat Sci Analyt Chem, Dalian 16023, Peoples R China
2.Univ Copenhagen, Fac Hlth Sci, Rigshosp, Dept Infect Dis,Ctr Inflammat & Metab, DK-2100 Copenhagen, Denmark
3.Univ Copenhagen, Fac Hlth Sci, Rigshosp, Copenhagen Muscle Res Ctr, DK-2100 Copenhagen, Denmark
4.Univ Tubingen Hosp, Div Clin Chem & Pathobiochem, Cent Lab, D-72076 Tubingen, Germany
5.Univ Tubingen, Paul Langerhans Inst Tubingen, Helmholtz Ctr Munich, Inst Diabet Res & Metab Dis, D-72076 Tubingen, Germany
6.Qinhuangdao Entry Exit Inspect & Quarantine Bur P, Qinhuangdao 066004, Peoples R China
Recommended Citation
GB/T 7714
Li, Xiang,Hansen, Jakob,Zhao, Xinjie,et al. Independent component analysis in non-hypothesis driven metabolomics: Improvement of pattern discovery and simplification of biological data interpretation demonstrated with plasma samples of exercising humans[J]. JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES,2012,910:156-162.
APA Li, Xiang.,Hansen, Jakob.,Zhao, Xinjie.,Lu, Xin.,Weigert, Cora.,...&许国旺.(2012).Independent component analysis in non-hypothesis driven metabolomics: Improvement of pattern discovery and simplification of biological data interpretation demonstrated with plasma samples of exercising humans.JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES,910,156-162.
MLA Li, Xiang,et al."Independent component analysis in non-hypothesis driven metabolomics: Improvement of pattern discovery and simplification of biological data interpretation demonstrated with plasma samples of exercising humans".JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES 910(2012):156-162.
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