标题： 软图聚类用于单细胞RNA测序数据 显示英文标题

标题： Soft Graph Clustering for single-cell RNA Sequencing Data

摘要： 聚类分析在单细胞RNA测序(scRNA-seq)数据分析中是基础性的，用于阐明细胞的异质性和多样性。最近基于图的scRNA-seq聚类方法，特别是图神经网络(GNNs)，在解决高维、高稀疏性和频繁的丢失事件导致的细胞群体边界模糊等挑战方面有了显著提高。然而，它们依赖于从阈值相似性矩阵得出的硬图结构，这带来了以下挑战：(i) 通过应用阈值将细胞间关系简化为二进制边(0或1)，这限制了细胞间连续相似特征的捕捉，并导致显著的信息丢失。(ii) 硬图中存在显著的跨簇连接，这可能混淆依赖于图结构的GNN方法，可能导致错误的信息传播和偏差的聚类结果。为了解决这些挑战，我们引入了scSGC，一种用于单细胞RNA测序数据的软图聚类方法，旨在通过非二进制边权重更准确地表征细胞间的连续相似性，从而缓解刚性数据结构的局限性。scSGC框架包括三个核心组件：(i) 基于零膨胀负二项式(ZINB)的特征自编码器；(ii) 双通道剪切信息引导的软图嵌入模块；以及(iii) 基于最优传输的聚类优化模块。在十个数据集上的广泛实验表明，scSGC在聚类准确性、细胞类型注释和计算效率方面优于13种最先进的聚类模型。这些结果突显了其在推进scRNA-seq数据分析和加深对细胞异质性理解方面的巨大潜力。 显示英文摘要

摘要： Clustering analysis is fundamental in single-cell RNA sequencing (scRNA-seq) data analysis for elucidating cellular heterogeneity and diversity. Recent graph-based scRNA-seq clustering methods, particularly graph neural networks (GNNs), have significantly improved in tackling the challenges of high-dimension, high-sparsity, and frequent dropout events that lead to ambiguous cell population boundaries. However, their reliance on hard graph constructions derived from thresholded similarity matrices presents challenges:(i) The simplification of intercellular relationships into binary edges (0 or 1) by applying thresholds, which restricts the capture of continuous similarity features among cells and leads to significant information loss.(ii) The presence of significant inter-cluster connections within hard graphs, which can confuse GNN methods that rely heavily on graph structures, potentially causing erroneous message propagation and biased clustering outcomes. To tackle these challenges, we introduce scSGC, a Soft Graph Clustering for single-cell RNA sequencing data, which aims to more accurately characterize continuous similarities among cells through non-binary edge weights, thereby mitigating the limitations of rigid data structures. The scSGC framework comprises three core components: (i) a zero-inflated negative binomial (ZINB)-based feature autoencoder; (ii) a dual-channel cut-informed soft graph embedding module; and (iii) an optimal transport-based clustering optimization module. Extensive experiments across ten datasets demonstrate that scSGC outperforms 13 state-of-the-art clustering models in clustering accuracy, cell type annotation, and computational efficiency. These results highlight its substantial potential to advance scRNA-seq data analysis and deepen our understanding of cellular heterogeneity.