scAGC: Learning Adaptive Cell Graphs with Contrastive Guidance for Single-Cell Clustering

• URL: http://arxiv.org/abs/2508.09180v1
• Date: Thu, 07 Aug 2025 10:55:52 GMT
• Title: scAGC: Learning Adaptive Cell Graphs with Contrastive Guidance for Single-Cell Clustering
• Authors: Huifa Li, Jie Fu, Xinlin Zhuang, Haolin Yang, Xinpeng Ling, Tong Cheng, Haochen xue, Imran Razzak, Zhili Chen,
• Abstract summary: We propose scAGC, a single-cell clustering method that learns adaptive cell graphs with contrastive guidance.<n> scAGC consistently outperforms other state-of-the-art methods, yielding the best NMI and ARI scores on 9 and 7 datasets, respectively.
• Score: 26.506077979458908
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate cell type annotation is a crucial step in analyzing single-cell RNA sequencing (scRNA-seq) data, which provides valuable insights into cellular heterogeneity. However, due to the high dimensionality and prevalence of zero elements in scRNA-seq data, traditional clustering methods face significant statistical and computational challenges. While some advanced methods use graph neural networks to model cell-cell relationships, they often depend on static graph structures that are sensitive to noise and fail to capture the long-tailed distribution inherent in single-cell populations.To address these limitations, we propose scAGC, a single-cell clustering method that learns adaptive cell graphs with contrastive guidance. Our approach optimizes feature representations and cell graphs simultaneously in an end-to-end manner. Specifically, we introduce a topology-adaptive graph autoencoder that leverages a differentiable Gumbel-Softmax sampling strategy to dynamically refine the graph structure during training. This adaptive mechanism mitigates the problem of a long-tailed degree distribution by promoting a more balanced neighborhood structure. To model the discrete, over-dispersed, and zero-inflated nature of scRNA-seq data, we integrate a Zero-Inflated Negative Binomial (ZINB) loss for robust feature reconstruction. Furthermore, a contrastive learning objective is incorporated to regularize the graph learning process and prevent abrupt changes in the graph topology, ensuring stability and enhancing convergence. Comprehensive experiments on 9 real scRNA-seq datasets demonstrate that scAGC consistently outperforms other state-of-the-art methods, yielding the best NMI and ARI scores on 9 and 7 datasets, respectively.Our code is available at Anonymous Github.

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