Multi-omics clustering based on interpretable and discriminative features for cancer subtyping

Abstract

Recent advances in multi-omics databases have enabled biomedical researchers to explore complex cancer systems across hierarchical biological levels. Although there are numerous multi-omics clustering methods, most of them directly integrate heterogeneous features of different omics which may include redundancy or noise and lead to poor clustering results. In this paper, we propose a novel multi-omics clustering method for cancer subtyping which extracts interpretable and discriminative features from different omics before data integration. The proposed method utilizes the clinical information of each omics to supervise the process of extracting interpretable and discriminative features based on SHAP (SHapley Additive exPlanation) values. The shared nearest neighbor-based approach is then applied to calculate the similarity matrix of the extracted features. Finally, we integrate the similarity matrices of different omics and apply spectral clustering on the integrated similarity matrix to obtain the clustering result. Experimental results conducted on four different cancer datasets on three levels of omics demonstrate the superior performance of the proposed method in comparison to the existing multi-omics clustering methods.