Spectral clustering of weighted variables on multi-omics data

In recent years, high-throughput sequencing technologies generated rich resources of diverse types of omics data.Using the integration and analysis of multi-omics datasets, we can better understand cancer etiology and treatment responses.One of the important goals of multi-omics analysis in cancer research is to classify tumors by identifying subtypes of cancer patients.However, there are limitations to applying the existing clustering methods due to high dimensionality and heterogeneity of omics data.In this paper, we propose a new clustering method based on the spectral clustering algorithm, in which different weights are assigned to each omic and gene.The proposed optimization problem is non-convex, and clustering is performed through the iterative update process.The proposed clustering method performs better than the existing methods through simulation and real data application.