Bioinformatics Advance Access originally published online on April 15, 2004
Bioinformatics 2004 20(15):2447-2454; doi:10.1093/bioinformatics/bth270
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Bioinformatics 20(15) © Oxford University Press 2004; all rights reserved.
Metabolite fingerprinting: detecting biological features by independent component analysis
1 Max Planck Institute of Molecular Plant Physiology, 14424 Potsdam, Germany and 2 Advion BioSciences Ltd, Norwich NR9 3DB UK
Received on December 16, 2003; revised on March 12, 2004; accepted on April 1, 2004
Advance Access Publication April 15, 2004
Motivation: Metabolite fingerprinting is a technology for providing information from spectra of total compositions of metabolites. Here, spectra acquisitions by microchip-based nanoflow-direct-infusion QTOF mass spectrometry, a simple and high throughput technique, is tested for its informative power. As a simple test case we are using Arabidopsis thaliana crosses. The question is how metabolite fingerprinting reflects the biological background. In many applications the classical principal component analysis (PCA) is used for detecting relevant information. Here a modern alternative is introduced—the independent component analysis (ICA). Due to its independence condition, ICA is more suitable for our questions than PCA. However, ICA has not been developed for a small number of high-dimensional samples, therefore a strategy is needed to overcome this limitation.
Results: To apply ICA successfully it is essential first to reduce the high dimension of the dataset, by using PCA. The number of principal components determines the quality of ICA significantly, therefore we propose a criterion for estimating the optimal dimension automatically. The kurtosis measure is used to order the extracted components to our interest. Applied to our A. thaliana data, ICA detects three relevant factors, two biological and one technical, and clearly outperforms the PCA.
Contact: scholz{at}mpimp-golm.mpg.de
* To whom correspondence should be addressed.
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