Metabolite-based clustering and visualization of mass spectrometry data using one-dimensional self-organizing maps

Background: One of the goals of global metabolomic analysis is to identify metabolic markers that are hidden within a large background of data originating from high-throughput analytical measurements. Metabolite-based clustering is an unsupervised approach for marker identification based on grouping similar concentration profiles of putative metabolites. A major problem of this approach is that in general there is no prior information about an adequate number of clusters. Results: We present an approach for data mining on metabolite intensity profiles as obtained from mass spectrometry measurements. We propose one-dimensional self-organizing maps for metabolite-based clustering and visualization of marker candidates. In a case study on the wound response of Arabidopsis thaliana, based on metabolite profile intensities from eight different experimental conditions, we show how the clustering and visualization capabilities can be used to identify relevant groups of markers. Conclusion: O...

• Type: Journal
• Year: 2008
• Where: ALMOB
• Authors: Peter Meinicke, Thomas Lingner, Alexander Kaever, Kirstin Feussner, Cornelia Göbel, Ivo Feussner, Petr Karlovsky, Burkhard Morgenstern
• Keywords: ALMOB 2008, Metabolite-based Clustering, Metabolites, Metabolomics