Abstract
Differences in tissues arising from a single organism are attributable, at least partially, to differing metabolic regimes. A highly topical instance of this is the Warburg effect in tumour development, whereby malignant tissue exhibits greatly altered metabolism compared to healthy tissue. To this end, we consider the emergent properties of two metabolomic datasets from a human glioma cell line (U87) and a human mesenchymal stem cell line (hMSC). Using a random matrix theory (RMT) approach, U87 is found to have a modular structure, whereas hMSC does not. The datasets are then compared using between groups comparison of principal components, and finally, a group of metabolites is found that remains highly correlated in both conditions.
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© 2012 Springer-Verlag Berlin Heidelberg
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Feher, K., Jürchott, K., Selbig, J. (2012). Tailored Strategies for the Analysis of Metabolomic Data. In: Lones, M.A., Smith, S.L., Teichmann, S., Naef, F., Walker, J.A., Trefzer, M.A. (eds) Information Processign in Cells and Tissues. IPCAT 2012. Lecture Notes in Computer Science, vol 7223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28792-3_12
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DOI: https://doi.org/10.1007/978-3-642-28792-3_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28791-6
Online ISBN: 978-3-642-28792-3
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