Figure 1: Overview of network-based stratification (NBS).

(a) Flowchart of the approach. (b) Example illustrating smoothing of patient somatic mutation profiles over a molecular interaction network. Mutated genes are shown in yellow (patient 1) and blue (patient 2) in the context of a gene interaction network. Following smoothing, the mutational activity of a gene is a continuous value reflected in the intensity of yellow or blue; genes with high scores in both patients appear in green (dashed oval). (c) Clustering mutation profiles using non-negative matrix factorization (NMF) regularized by a network. The input data matrix (F) is decomposed into the product of two matrices: one of subtype prototypes (W) and the other of assignments of each mutation profile to the prototypes (H). The decomposition attempts to minimize the objective function shown, which includes a network influence constraint L on the subtype prototypes. k, predefined number of subtypes. (d) The final tumor subtypes are obtained from the consensus (majority) assignments of each tumor after 1,000 applications of the procedures in b and c to samples of the original data set. A darker blue color in the matrix coincides with higher co-clustering for pairs of patients.