Tailored Strategies for the Analysis of Metabolomic Data

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.

[1]  Ralf Steuer,et al.  Review: On the analysis and interpretation of correlations in metabolomic data , 2006, Briefings Bioinform..

[2]  I. Johnstone.  On the distribution of the largest eigenvalue in principal components analysis , 2001 .

[3]  R. Heinrich,et al.  The Regulation of Cellular Systems , 1996, Springer US.

[4]  P. Mendes,et al.  The origin of correlations in metabolomics data , 2005, Metabolomics.

[5]  Arlindo L. Oliveira,et al.  Biclustering algorithms for biological data analysis: a survey , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[6]  R. Steuer,et al.  Metabolomic networks in plants: Transitions from pattern recognition to biological interpretation. , 2006, Bio Systems.

[7]  S. Müller,et al.  Assessing Modularity Using a Random Matrix Theory Approach , 2011, Statistical applications in genetics and molecular biology.

[8]  Jan Schellenberger,et al.  Use of Randomized Sampling for Analysis of Metabolic Networks* , 2009, Journal of Biological Chemistry.

[9]  George M. Church,et al.  Biclustering of Expression Data , 2000, ISMB.

[10]  R. F. Potthoff.  A NON-PARAMETRIC TEST OF WHETHER TWO SIMPLE REGRESSION LINES ARE PARALLEL' , 1974 .

[11]  Jürgen Kurths,et al.  Observing and Interpreting Correlations in Metabolic Networks , 2003, Bioinform..

[12]  Joachim Selbig,et al.  Comparison of metabolite profiles in U87 glioma cells and mesenchymal stem cells , 2011, Biosyst..

[13]  W. Krzanowski.  Between-Groups Comparison of Principal Components , 1979 .