Multi-omics Correlation Reconstruction of Complete Graph Forms Based on the Self-expressive Learning Network for Cancer Subtype Prediction.

Abstract

Multi-omics cancer subtype prediction can identify cancer subtypes effectively and has the advantage of correlating genotype and phenotype. However, insufficient exploration of the correlation of information among different omics levels may lead to poor prediction of cancer subtypes. To address this issue, we propose a novel framework, termed Multi-Omics correlation reconstruction (MOCR), which performs reconstruction of complete graph forms based on a self-expressive learning network. Specifically, MOCR first employs autoencoders to unify the dimensions across different omics types. It then leverages parallel query and key networks (QKNets) to learn representations for each omics. These representations are passed into a correlation reconstruction module (CRModule), which computes self-expressive coefficients that jointly capture omics-self characteristics and inter-omics relationships. QKNets and CRModule form a correlative self-expressive learning network, enabling better utilization of the advantages of multi-omics. Importantly, the CRModule's complete graph reconstruction of omics correlations models each omics pair exactly once, thereby avoiding redundancy. Finally, spectral clustering is applied to derive cancer subtypes. We have evaluated our method on nine TCGA cancer datasets and three simulation datasets. The results showed that the MOCR had significant advantages in cancer subtype identification. The complete code is available at https://github.com/JerryZ09/MOCR.