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.