Deep Self-Reinforced Multi-View Subspace Clustering for Cancer Subtyping

Identifying cancer subtypes is crucial for understanding disease progression. With advancements in high-throughput experimental technology, leveraging multiple types of omic data for subtype identification has become feasible. Various integrative cancer subtyping methods present a promising computational approach for identifying cancer subtypes from heterogeneous datasets. While existing integrative cancer subtyping methods have shown promising results in this task, efficiently integrating and clustering multi-omics datasets remains challenging due to high noise levels in omics data, which hinder accurate relationship capture among samples. To overcome this challenge, we propose a new deep multi-view subspace clustering model that introduces a self-reinforced learning strategy. This strategy iteratively enhances the quality of self-representation, crucial for capturing relationships among samples and for clustering. Specifically, during model training, our method is capable of learning a highly reliable self-representation by leveraging a good neighbor learning approach. This capability enables us to capture more accurate and robust relationships among samples. Subsequently, with the assistance of this highly reliable self-representation, we further develop a learnable view-graph fusion approach, which enables us to learn an accurate consensus for clustering and guides the overall model learning process. Additionally, we introduce a local graph-guided learning mechanism based on an initial graph learned from raw data. This mechanism helps prevent the model from converging to suboptimal solutions, thereby avoiding unsatisfactory and unstable results. Experimental results demonstrate that our method outperforms several state-of-the-art methods, verify the effectiveness of our approach in cancer subtype identification task.