Yu Xiaoyan, Ren Yixuan, Xia Min, Shu Zhenqiu, Zhu Liehuang
School of Computer Science and Technology, Beijing Institute of Technology, Zhongguancun South Street, Haidian, Beijing, 100081, China.
Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Jingming South Road, Chenggong, Kunming, Yunnan, 650500, China.
Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf198.
Clustering is pivotal in deciphering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data. However, it suffers from several challenges in handling the high dimensionality and complexity of scRNA-seq data. Especially when employing graph neural networks (GNNs) for cell clustering, the dependencies between cells expand exponentially with the number of layers. This results in high computational complexity, negatively impacting the model's training efficiency. To address these challenges, we propose a novel approach, called decoupled GNNs, based on multi-view contrastive learning (scDeGNN), for scRNA-seq data clustering. Firstly, this method constructs two adjacency matrices to generate distinct views, and trains them using decoupled GNNs to derive the initial cell feature representations. These representations are then refined through a multilayer perceptron and a contrastive learning layer, ensuring the consistency and discriminability of the learned features. Finally, the learned representations are fused and applied to the cell clustering task. Extensive experimental results on nine real scRNA-seq datasets from various organisms and tissues show that the proposed scDeGNN method significantly outperforms other state-of-the-art scRNA-seq data clustering algorithms across multiple evaluation metrics.
聚类在解读单细胞RNA测序（scRNA-seq）数据中的细胞异质性方面至关重要。然而，它在处理scRNA-seq数据的高维度和复杂性时面临若干挑战。特别是在使用图神经网络（GNN）进行细胞聚类时，细胞之间的依赖性会随着层数呈指数级增长。这导致计算复杂度很高，对模型的训练效率产生负面影响。为了应对这些挑战，我们提出了一种基于多视图对比学习的新颖方法，称为解耦GNN（scDeGNN），用于scRNA-seq数据聚类。首先，该方法构建两个邻接矩阵以生成不同的视图，并使用解耦GNN对其进行训练，以获得初始细胞特征表示。然后，通过多层感知器和对比学习层对这些表示进行优化，确保所学习特征的一致性和可区分性。最后，将所学习的表示进行融合，并应用于细胞聚类任务。对来自各种生物体和组织的九个真实scRNA-seq数据集进行的大量实验结果表明，所提出的scDeGNN方法在多个评估指标上显著优于其他最先进的scRNA-seq数据聚类算法。
