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Proceedings Paper

Gaussian graphical modeling reveals specific lipid correlations in glioblastoma cells
Author(s): Nikola S. Mueller; Jan Krumsiek; Fabian J. Theis; Christian Böhm; Anke Meyer-Bäse
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Paper Abstract

Advances in high-throughput measurements of biological specimens necessitate the development of biologically driven computational techniques. To understand the molecular level of many human diseases, such as cancer, lipid quantifications have been shown to offer an excellent opportunity to reveal disease-specific regulations. The data analysis of the cell lipidome, however, remains a challenging task and cannot be accomplished solely based on intuitive reasoning. We have developed a method to identify a lipid correlation network which is entirely disease-specific. A powerful method to correlate experimentally measured lipid levels across the various samples is a Gaussian Graphical Model (GGM), which is based on partial correlation coefficients. In contrast to regular Pearson correlations, partial correlations aim to identify only direct correlations while eliminating indirect associations. Conventional GGM calculations on the entire dataset can, however, not provide information on whether a correlation is truly disease-specific with respect to the disease samples and not a correlation of control samples. Thus, we implemented a novel differential GGM approach unraveling only the disease-specific correlations, and applied it to the lipidome of immortal Glioblastoma tumor cells. A large set of lipid species were measured by mass spectrometry in order to evaluate lipid remodeling as a result to a combination of perturbation of cells inducing programmed cell death, while the other perturbations served solely as biological controls. With the differential GGM, we were able to reveal Glioblastoma-specific lipid correlations to advance biomedical research on novel gene therapies.

Paper Details

Date Published: 4 June 2011
PDF: 8 pages
Proc. SPIE 8058, Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX, 805819 (4 June 2011); doi: 10.1117/12.884196
Show Author Affiliations
Nikola S. Mueller, Max Planck Institut of Biochemistry (Germany)
Jan Krumsiek, Helmholtz Zentrum München GmbH (Germany)
Fabian J. Theis, Helmholtz Zentrum München GmbH (Germany)
Christian Böhm, Ludwig-Maximilians-Univ. München (Germany)
Anke Meyer-Bäse, The Florida State Univ. (United States)


Published in SPIE Proceedings Vol. 8058:
Independent Component Analyses, Wavelets, Neural Networks, Biosystems, and Nanoengineering IX
Harold Szu, Editor(s)

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