Deep structure integrative representation of multi-omics data for cancer subtyping

Motivation: Cancer is a heterogeneous group of diseases. Cancer subtyping is a crucial and critical step to diagnosis, prognosis and treatment. Since high-throughput sequencing technologies provide an unprecedented opportunity to rapidly collect multi-omics data for the same individuals, an urgent need in current is how to effectively represent and integrate these multi-omics data to achieve clinically meaningful cancer subtyping. Results: We propose a novel deep learning model, called Deep Structure Integrative Representation (DSIR), for cancer subtypes dentification by integrating representation and clustering multi-omics data. DSIR simultaneously captures the global structures in sparse subspace and local structures in manifold subspace from multi-omics data and constructs a consensus similarity matrix by utilizing deep neural networks. Extensive tests are performed in 12 different cancers on three levels of omics data from The Cancer Genome Atlas. The results demonstrate that DSIR obtains more significant performances than the state-of-the-art integrative methods.