Tailored strategies for the analysis of metabolomic data

Authors:
Kristen Feher;Kathrin Jürchott;Joachim Selbig
Affiliations:
Institute of Biochemistry and Biology, AG Bioinformatics, University of Potsdam, Potsdam, Germany;Institute of Biochemistry and Biology, AG Bioinformatics, University of Potsdam, Potsdam, Germany;Institute of Biochemistry and Biology, AG Bioinformatics, University of Potsdam, Potsdam, Germany and Max-Planck Institute for Molecular Plant Physiology, Potsdam, Germany
Venue:
IPCAT'12 Proceedings of the 9th international conference on Information Processing in Cells and Tissues
Year:
2012

Citing 2
Cited 0

Biclustering of Expression Data

Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Biclustering Algorithms for Biological Data Analysis: A Survey

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

Quantified Score

Hi-index	0.00

Visualization

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.