Hierarchical Multi-View Graph Diffusion Weighted Model for Cancer Subtype Identification

Accurate cancer subtype identification is crucial for personalized medicine, as it enables precise diagnosis based on molecular characteristics. With the advent of large-scale multi-omics data from various resources, researchers now have unprecedented opportunities to explore cancer subtypes comprehensively. However, the inherent complexity, high dimensionality, and heterogeneity of these datasets present significant statistical and computational challenges, often leading to suboptimal clustering performance when inter-omics heterogeneity is overlooked. To address these challenges, we propose a novel method called the Hierarchical Multi-view Graph Diffusion Weighted (HMGDW) model for cancer subtype identification. Our approach begins with the generation of multiple base clusterings through random feature sampling, effectively mitigating the impact of high dimensionality. These base clusterings are subsequently integrated via a late integration strategy to yield the consensus clustering result. Then, we introduce a graph diffusion weighted mechanism that prioritizes views with the most significant contributions to the unified graph representation. Lastly, we conducted extensive experiments on both generic multi-view datasets and multi-omics cancer multi-omics datasets. The experimental results demonstrate that HMGDW consistently outperforms several state-of-the-art methods, achieving robust and accurate clustering. Additionally, a case study on the acute myeloid leukemia (AML) dataset validates the practical efficacy of our model in identifying clinically relevant subtypes.