Document Detail


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.
MedLine Citation:
PMID:  22809791     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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.
Authors:
Xiang Li; Jakob Hansen; Xinjie Zhao; Xin Lu; Cora Weigert; Hans-Ulrich Häring; Bente K Pedersen; Peter Plomgaard; Rainer Lehmann; Guowang Xu
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-7-1
Journal Detail:
Title:  Journal of chromatography. B, Analytical technologies in the biomedical and life sciences     Volume:  -     ISSN:  1873-376X     ISO Abbreviation:  -     Publication Date:  2012 Jul 
Date Detail:
Created Date:  2012-7-19     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101139554     Medline TA:  J Chromatogr B Analyt Technol Biomed Life Sci     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2012 Elsevier B.V. All rights reserved.
Affiliation:
CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, 16023 Dalian, China; Qinhuangdao Entry-Exit Inspection and Quarantine Bureau of P.R.C., 066004 Qinhuangdao, China.
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