Understanding Genotype-Phenotype Effects in Cancer via Network Approaches
Fig 1
Illustrations of how gene networks can be used in cancer data analysis.
A) Information propagation approach: the information about mutated genes (in red) is propagated to their neighborhood through interactions, helping to identify significantly affected subnetworks. The level of redness of a node indicates how likely the gene is affected. B) Module Cover approach finds the minimum cost subnetworks so that each patient is covered by at least k mutated genes. The edges in the gene interaction network (blue edges) may be weighted based on interaction confidence or mutual exclusivity. For example, the patients covered by gene C and D are mutually exclusive. There is an edge between a gene and a patient if the gene is mutated in the patient (black edges). The figure shows an example where two modules are selected, covering each patient at least three times (k = 3). The green nodes are selected genes, and the thick edges indicate the selected interactions or gene-patient relationships.