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学科主题: 物理化学
题名: 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
作者: 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
通讯作者: RainerLehmann ;  许国旺
关键词: Independent component analysis ;  Metabolomics ;  Exercise ;  Metabolic profiling ;  GC-MS
刊名: JOURNAL OF CHROMATOGRAPHY B-ANALYTICAL TECHNOLOGIES IN THE BIOMEDICAL AND LIFE SCIENCES
发表日期: 2012-12-01
DOI: 10.1016/j.jchromb.2012.06.030
卷: 910, 页:156-162
收录类别: SCI
文章类型: Article
部门归属: 18
项目归属: 1808
产权排名: 1,1
WOS标题词: Science & Technology ;  Life Sciences & Biomedicine ;  Physical Sciences
类目[WOS]: Biochemical Research Methods ;  Chemistry, Analytical
研究领域[WOS]: Biochemistry & Molecular Biology ;  Chemistry
英文摘要: In 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.
关键词[WOS]: ARABIDOPSIS-THALIANA ;  CLASSIFICATION ;  CHEMOMETRICS ;  ALGORITHMS ;  SEPARATION ;  PROFILES
语种: 英语
WOS记录号: WOS:000312174700018
Citation statistics: 
内容类型: 期刊论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/118215
Appears in Collections:中国科学院大连化学物理研究所_期刊论文

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作者单位: 1.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:
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.
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