Abstract: Single-cell multi-omics joint clustering analysis has been extensively utilized in complex disease research.However,due to technical limitations of single-cell sequencing platforms,gene expression matrices contain substantial missing values,and inhe-rent heterogeneity across modalities pose significant challenges for clustering analysis.To solve the above problems,a single-cell multi-omics clustering method based on missing value imputation and cross-modal alignment(scCMAC) is proposed to address missing value imputation,cross-modal alignment and fusion of unpaired single-cell multi-omics data.The method uses a triple imputation strategy to dynamically localize missing values using modality-specific neighborhood structural information,enabling precise data recovery.To mitigate cross-modal heterogeneity and enhance inter-modal correlations,a bidirectional generative adversarial network is introduced for cross-modal alignment,while a graph attention network(GAT) facilitates multi-modal feature fusion.Moreover,the method performs clustering by jointly modeling cell-cell interactions and intracellular multi-omics associations.The results of comparison experiments on five single-cell multi-omics unpaired datasets show that scCMAC achieves 71.62% ACC on the PBMC dataset,which is an improvement of 2.36 percentage points over the suboptimal method.scCMAC improves the ARI to 70.48% on the SMAGE-3K dataset,which verifies its superiority in clustering tasks.

Key words: Missing value imputation, Cross-modal alignment, Cross-modal fusion, Clustering analysis, Single-cell multi-omics