scRGCL: Neighbor-Aware Graph Contrastive Learning for Robust Single-Cell Clustering

Abstract

Accurate cell type identification is a fundamental step in single-cell RNA sequencing (scRNA-seq) data analysis, providing critical insights into cellular heterogeneity at high resolution. However, the high dimensionality, zero-inflated, and long-tailed distribution of scRNA-seq data pose significant computational challenges for conventional clustering approaches. Although recent deep learning-based methods utilize contrastive learning to joint-learn representations and clustering assignments, they often overlook cluster-level information, leading to suboptimal feature extraction for downstream tasks. To address these limitations, we propose scRGCL, a single-cell clustering method that learns a regularized representation guided by contrastive learning. Specifically, scRGCL captures the cell-type-associated expression structure by clustering similar cells together while ensuring consistency. For each sample, the model performs negative sampling by selecting cells from distinct clusters, thereby ensuring semantic dissimilarity between the target cell and its negative pairs. Moreover, scRGCL introduces a neighbor-aware re-weighting strategy that increases the contribution of samples from clusters closely related to the target. This mechanism prevents cells from the same category from being mistakenly pushed apart, effectively preserving intra-cluster compactness. Extensive experiments on fourteen public datasets demonstrate that scRGCL consistently outperforms state-of-the-art methods, as evidenced by significant improvements in normalized mutual information (NMI) and adjusted rand index (ARI). Moreover, ablation studies confirm that the integration of cluster-aware negative sampling and the neighbor-aware re-weighting module is essential for achieving high-fidelity clustering. By harmonizing cell-level contrast with cluster-level guidance, scRGCL provides a robust and scalable framework that advances the precision of automated cell-type discovery in increasingly complex single-cell landscapes. Key MessagesO_LIscRGCL uses contrastive learning on a regularized representation for single-cell clustering. C_LIO_LIscRGCL outperforms four state-of-the-art methods on 15 datasets. C_LIO_LIscRGCLs cluster-aware negative sampling and the neighbor-aware re-weighting modules are essential for high-fidelity single cell clustering. C_LI