Adaptive multi-view information bottleneck for multi-omics data clustering

Here, we propose an Adaptive Multi-View clustering framework with the Information Bottleneck principle to solve the multi-omics data clustering task (named scAMVIB). The proposed model could learn multi-view omics representations that capture both inter-omics associations and omics-specific patterns, with the adaptive weight allocation. Specifically, multi-view data comprise two components: (i) the integrated omics feature matrix derived from the similarity network fusion strategy and (ii) omics-specific representations from distinct platforms. These inputs are processed through a multi-view information bottleneck clustering framework that leverages cross-view complementarity to enhance representations. View weights are adaptively assigned via maximum entropy regularization, proportional to their information content. The final cell partitions are obtained through sequential iterative optimization. Comprehensive experiments across multiple datasets demonstrate that scAMVIB has strong competitiveness in clustering while maintaining biological interpretability.