Dual alignment feature embedding network for multi-omics data clustering

Abstract

Multi-omics data clustering, with its capability to utilize the biological information of cross-omics to partition cells into their respective clusters, has attracted considerable attention due to its effectiveness for pathological analysis. Aside from cross-omics discrepancy, existing methods suffer from distribution differences, making it difficult to learn high-quality cross-omics consistent information. To tackle this issue, we propose a novel dual alignment feature embedding network for multi-omics data clustering (DAMIC). Specifically, we first utilize an attention-induced feature fusion mechanism to capture intra-omics specific and inter-omics structural information for more discriminative features. Moreover, we maximize the mutual information between the unified target distribution and other omics-specific assignments by simultaneously optimizing contrastive learning loss and Kullback–Leibler (KL) divergence loss. Finally, we can extract omics-invariant features with robust and rich common embeddings for multi-omics clustering. Extensive experimental results on six real-world benchmark datasets demonstrate that our approach surpasses existing state-of-the-art methods in multi-omics data clustering analysis, which provides effective pathologic analysis way for tumors such as Leukemia and Colorectal Neoplasms. The source code is available at https://github.com/YuangXiao/DAMIC.