Figure 1.

A schematic showing how DriverNet works. (a) An example of a Cytoscape visualization of a glioblastoma patient with a high-level amplification of epidermal growth factor receptor (EGFR) (shown in green) and coincident outlying expression of genes connected to EGFR in the Reactome influence graph (shown in yellow). Examples of the overrepresented pathways (by Reactome FI plug-in for Cytoscape, FDR < 0.001) from the list of genes showing outlying expression associated with the EGFR amplification are depicted at the bottom. The box plot shows the population-level expression distribution of BRAF, an interacting protein with EGFR, and where the specific case with EGFR amplification sits on that distribution (red 'x'). We note that in this case, BRAF itself is not mutated or amplified. (b) Fitted Gaussian expression distributions of three genes that interact with EGFR: FGF11, PIK3R1, and PRKACB, with each point indicating the probability density function for individual cases. For each gene, blue dots indicate cases with mutations in the gene itself and red arrows indicate cases with outlying expression with coincident EGFR amplifications. (c) Schematic representation of the DriverNet approach. Given the genomic aberration states for different patients and genes, gene expression data, and the influence graph, which captures biological pathway information, the bipartite graph shown on the right is constructed. Green nodes on the left partition of the bipartite graph correspond to aberrated genes and nodes on the right represent the outlying expression status for each patient where red indicates outlying patient-gene events from the gene expression matrix. The genes with the highest number of outlying expression events (for example, g2) are nominated as putative drivers.

Bashashati et al. Genome Biology 2012 13:R124   doi:10.1186/gb-2012-13-12-r124
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