Single-cell multi-omics sequencing represents an advanced technology capable of simultaneously measuring multiple omics data from the same cell. The joint clustering of single-cell multi-omics sequencing data enables a comprehensive depiction of cell states and uncovers intricate molecular mechanisms, holding immense significance in fields such as oncology, neurology, and developmental biology. However, the disparities in feature spaces across different omics layers and data noise present substantial challenges for achieving accurate clustering. To tackle these challenges, we introduce a novel clustering method for single-cell multi-omics data, termed scMSAC, which is grounded in a denoising subgraph attention autoencoder. The proposed method employs a weighted nearest neighbor graph strategy to ascertain the weights of multi-omics data, subsequently generating a similarity graph that holistically encapsulates intercellular connections through the weighted amalgamation of diverse omics perspectives. The scMSAC model captures the topological features of cells through the subgraph attention autoencoder, constructing relationships among cells. For the omics features extracted by the subgraph attention autoencoder, scMSAC incorporates an SCA (Spatial Channel Attention) mechanism for feature fusion to reduce the differences in feature spaces of different omics and achieve better clustering performance. Comparative experiments with various existing methods demonstrate that scMSAC has excellent clustering performance and performs well in detecting rare cell types and differential expression analysis. Our scMSAC model codes are freely available at https://github.com/yancy2024/scMSAC.
Wang et al. (2026) studied this question.