Multi-modal Hierarchical Clustering Network for Cancer Subtype Identification of Multi-omics Data
Abstract
Multi-modal clustering is an effective method for integrating multiple omics data in identifying and analyzing cancer diseases, enabling the unsupervised discovery of latent cluster patterns within multi-omics cancer data. However, existing multi-modal clustering methods primarily focus on single-level flat partitions, often overlooking the hierarchical subtype structures of real-world data. To address this issue, we propose a novel Multi-modal Hierarchical Clustering method for cancer Subtype identification of multi-omics data, termed Subtype-MHC. Specifically, Subtype-MHC is a hyperbolic neural network incorporating multiple Poincaré autoencoders, where the latent representations of each omic modality are mapped from the Euclidean space to the hyperbolic space, thus explicitly modeling hierarchical subtype structures during multi-omics integration process. On one hand, we leverage inter-omics complementarity by utilizing an inter-omics Poincaré reconstruction loss to capture modality-specific information within each omic, while a hyperbolic diversity loss is introduced to promote cluster separability on the Poincaré ball. On the other hand, in order to exploit cross-omics consistency, we design a self-weighted cross-omics integration loss to extract the shared hierarchies across all omic modalities. Additionally, a prototype-based weighting strategy is applied on the representation alignment, compressing task-relevant cluster information and mitigating the representation degradation caused by the quality differences of all omic modalities. Extensive experiments on ten multi-omics datasets demonstrate the hierarchical representation ability and clustering effectiveness of Subtype-MHC for cancer subtype identification.
Authors: Fangfei Lin, Jie Xu, Yazhou Ren, Junjie Chen, Irwin King, Zenglin Xu