SRSA-VAE: Self-Attention-Based Feature Learning for Single-Cell Multimodal Clustering

Clustering plays a critical role in the analysis of single-cell omics data for identifying cellular heterogeneity and uncovering biological mechanisms. However, the high dimensionality, sparsity, and multimodal nature of single-cell datasets such as single-cell RNA sequencing (scRNA-seq) and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) pose significant challenges for effective feature learning and representation learning. Traditional dimensionality reduction methods often rely on linear transformations and fail to capture complex nonlinear relationships between gene and protein expression profiles. In this work, we propose SRSA-VAE, a scalable variational autoencoder framework that integrates a residual self-attention encoder for context-aware feature learning and multimodal representation learning. The proposed model dynamically contextualizes gene and protein representations through a self-attention mechanism, enabling the encoder to capture inter-cell relationships and emphasize biologically informative signals. A scalable residual connection further stabilizes training and preserves essential input information during latent representation learning. We evaluate SRSA-VAE on five large-scale publicly available single-cell datasets, including both scRNA-seq and CITE-seq data, and compare its performance with established deep generative models. Experimental results demonstrate that SRSA-VAE consistently outperforms existing methods in Adjusted Rand Index (ARI) across benchmark datasets, with particularly strong gains on complex immune cell populations. Ablation studies further confirm the importance of the self-attention mechanism and residual connection in enhancing model stability and clustering accuracy. The proposed model offers a generalizable, robust, and scalable solution for single-cell clustering tasks.