Translational Genomics Research Institute: Identification of Pathways Enriched with Condition-Specific Statistical Dependencies Across Four Subtypes of Glioblastoma Multiforme

Evaluation of Differential DependencY (EDDY) is a statistical test for the differential dependency relationship of a set of genes between two given conditions. For each condition, possible dependency network structures are enumerated and their likelihoods are computed to represent a probability distribution of dependency networks. The difference between the probability distributions of dependency networks is computed between conditions, and its statistical significance is evaluated with random permutations of condition labels on the samples.  

For this project, the CTD2 Center at the Translational Genomics Research Institute applied EDDY to the gene expression data of glioblastoma multiforme (GBM) from The Cancer Genome Atlas (TCGA) to reveal the functional difference between the four subtypes of GBM - Proneural (PN), Neural (N), Mesenchymal (MES) and Classical (CL).  The results show that the proposed method can identify novel gene sets that could not be found with Gene Set Enrichment Analysis, which is considered a representative method of considering only differential expressions, while providing many results specific to the subtypes of GBM.

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Experimental Approaches

Gene expression data of GBM (AgilentG4502A_07; Level 3) obtained from TCGA were used for these analyses.

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If you cannot access the manuscript, or if you have additional questions, please email Gil Speyer.


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Last updated: September 25, 2017