scUCAF: An uncertainty-aware cross-omics alignment and fusion network for single-cell multi-omics data clustering

The development of single-cell multi-omics sequencing technologies provides new insights into cell heterogeneity. Cell clustering is a crucial step in the analysis of multi-omics data. However, existing methods often overlook variations in data quality across omics, leading to unreliable feature representations. To address this issue, we propose scUCAF, an uncertainty-aware network for multi-omics clustering. Specifically, to mitigate the impact of noise on cell feature extraction, we introduce a variational autoencoder with a negative binomial distribution. After extracting each omics feature, we propose a high-confidence cluster-guided contrastive learning method to ensure cross-omics feature consistency. Finally, an uncertainty-aware fusion and gating network dynamically integrates the omics features to mitigate biases from low-quality data and produce reliable cell representations for clustering. Clustering results on eight real single-cell multi-omics datasets demonstrate that scUCAF outperforms existing multi-omics clustering methods. We also conduct downstream analyses to validate the effectiveness of scUCAF for cell type annotation and biomarker identification in liver cancer.