Multi-Omics Data Clustering via the Guidance of Highly Correlated Features

Multi-omics data clustering aims at assigning elements to their respective categories via exploiting the complementary information among multi-omics samples, with the widely application on data mining, bioinformatics, biomedicine and so on. Current multi-omics data clustering methods rely heavily on accurate sample-wise affinity estimation, which is limited by large heterogeneous gap among different sampling spaces. In this paper, we propose a novelly collaborative clustering method, which measures feature-wise matching instead of sample-wise affinity for sample assignment via the guidance of the highly matched features. The method consists of three modules. Firstly, the feature matching is learned by feature-wise correlation. Secondly, the feature matching matrix is factorized into two coded bases within yielded feature spaces. Thirdly, the sample assignment is achieved by joint sample reconstruction employing the two bases. The sample-wise assignment and feature-wise matching are optimized in a collaborative scheme with mutually reinforcing relationship. Extensive experiments demonstrate the superior performance of our proposed method compared with benchmark ones.