Clustering of Cancer Attributed Networks via Integration of Graph Embedding and Matrix Factorization

Advances in bio-technologies enable the generation of genomic data from various platforms. The accumulated omic data provides an opportunity to exploit the underlying mechanisms of cancers, and imposes a great challenge on designing algorithms for the integration of heterogeneous genomic data. Clustering of gene interaction networks is a promising approach for revealing the structure and functions of genes. However, current algorithms are criticized for either ignoring the attributes of genes or their high complexity. To overcome these problems, we propose a novel algorithm for cancer attributed networks (called jGENMF-AN), wherein graph representation and nonnegative matrix factorization are integrated. Specifically, graph representation learning is employed to obtain the low-dimensional features by preserving topology of the attributed networks, thereby reducing the complexity of the algorithm. To address heterogeneity of the topological features and attributes of genes, nonnegative matrix factorization for graph embedding and dimension reduction for the attribute matrix are jointly learned with a smoothness strategy. The experimental results indicate that jGENMF-AN is more accurate than state-of-the-art methods in the social and cancer attributed networks. The proposed model and algorithm provide an effective strategy for the integrative analysis of genomic data.