Fig. 1.

Schematic diagram of the proposed method. For discovering driver genes from somatic mutation data, we propose a robust and sparse co-regularized NMF framework by incorporating prior information of both mRNA expression patterns and interaction network. The input data contain three parts: 1) the binary somatic mutation matrix of cancer samples and genes, 2) the mRNA expression matrix of cancer samples and genes, and 3) the interaction network of genes. The mRNA expression patterns are used to calculate the sample similarities between tumor samples, which is used as the intermediate variable. We then use NMF co-regularized by the sample similarity and gene interaction network to incorporate their prior information. Robust regularization are employed to prevent overfitting issue for the representation of samples, and sparsity-inducing penalty is also used to generate sparse representation of genes. The tested genes are scored through the maximal values in their low-dimensional representations, and the top scored genes are selected as driver candidates