PuSH - Publication Server of Helmholtz Zentrum München: 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.

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Li, X.* ; Hansen, J.* ; Zhao, X.J.* ; Lu, X.* ; Weigert, C. ; Häring, H.-U. ; Pedersen, B.K.* ; Plomgaard, P.* ; Lehmann, R. ; Xu, G.W.*

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. Chromatogr. B 910, 156-162 (2012)

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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.

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