Selecting interpretable features for cancer subtyping on multi-omics data

Analyzing multi-omics data is powerful in cancer research, offering essential insights for identifying distinct cancer subtypes. Due to the significant noise and redundancy in multi-omics data, effective feature extraction before data integration is crucial. However, most existing multi-omics clustering methods directly integrate different omics, which may lead to poor clustering results. In this study, we propose a novel multi-omics clustering method which selects interpretable features from different omics before data integration. The proposed method utilizes clinical information and SHAP values to extract interpretable features from different omics. Feature selection is then performed to select the most important interpretable features by grouping and ranking on the SHAP values. We construct similarity network for each omics based on the selected features, and then perform similarity network fusion to integrate the similarities across different omics. Finally, spectral clustering is applied to obtain the clustering result. We conduct experiments on five cancer datasets across three levels of omics to evaluate the proposed method. Experimental results demonstrate the superior performance of our proposed method in multi-omics clustering analysis for cancer subtyping.