Abstract ED04-03: Interpretation of metabolomic data: Challenges and approaches

Abstract

Abstract Revealing biologically relevant information from metabolomic experiments typically involves multiple data analysis steps, which are performed on different abstraction levels: primary data analysis (peak detection, metabolite identification, etc.), data preprocessing (normalization, outlier detection, etc.), biostatistic analysis (hypothesis testing, clustering, etc.), and data integration for putting the results into a larger biological context. For many of these tasks, a large pool of well-established methods exists and in various aspects we can borrow from experience on other omics technologies. However, specificities of metabolomic data such as the usually incomplete coverage of the metabolic space also require novel concepts for data analysis and interpretation. After a brief overview over typical challenges in metabolomic data interpretation, this lecture will introduce two examples for such concepts. First, the analysis of metabolite ratios as proxies for reaction constants will be discussed. Second, the reconstruction of in vivo metabolic networks from metabolomic data and the application of these networks for data integration and interpretation will be shown. Citation Format: Gabi Kastenmüller, Jan Krumsiek, Christian Gieger, Karsten Suhre. Interpretation of metabolomic data: Challenges and approaches. [abstract]. In: Proceedings of the Twelfth Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2013 Oct 27-30; National Harbor, MD. Philadelphia (PA): AACR; Can Prev Res 2013;6(11 Suppl): Abstract nr ED04-03.