RSC-based differential model with correlation removal for improving multi-omics clustering

Abstract

Multi-omics clustering plays an important role in cancer subtyping. However, the data of different kinds of omics are often related, these correlations may reduce the clustering algorithm performance. It is crucial to eliminate the unexpected redundant information caused by these correlations between different omics. We proposed RSC-based differential model with correlation removal for improving multi-omics clustering (RSC-MCR). This method first introduced RSC to calculate the pairwise correlations of all features, and decomposed it to obtain the pairwise correlations of different omics features, thus built the connection between different omics based on the pairwise correlations of different omics features. Then, to remove the redundant correlation, we designed a differential model to calculate the degree of difference between the original feature matrix and the correlation matrix which contained the most relevant information between different omics. We compared the performance of RSC-MCR with decorrelation methods on different clustering methods (CC, FCM, SNF, NMF, LRAcluster). The experimental results on five cancer datasets show the efficiency of the RSC-MCR as well as improvements over other decorrelation methods.