标题： JojoSCL：用于单细胞RNA序列聚类的收缩对比学习 显示英文标题

标题： JojoSCL: Shrinkage Contrastive Learning for single-cell RNA sequence Clustering

摘要： 单细胞RNA测序（scRNA-seq）通过实现单个细胞水平上的基因表达分析，彻底改变了我们对细胞过程的理解。 聚类允许识别细胞类型，并进一步发现单细胞数据中的内在模式。 然而，scRNA-seq数据的高维性和稀疏性继续挑战现有的聚类模型。 本文介绍了一种名为JojoSCL的新颖自监督对比学习框架，用于scRNA-seq聚类。 通过结合基于分层贝叶斯估计的收缩估计器，该估计器将基因表达估计调整为更可靠的聚类质心以减少簇内离散度，并使用Stein的无偏风险估计（SURE）进行优化，JojoSCL改进了实例级和簇级对比学习。 在十个scRNA-seq数据集上的实验验证了JojoSCL始终优于流行的聚类方法，并通过鲁棒性分析和消融研究进一步验证了其实用性。 JojoSCL的代码可在以下网址获取：https://github.com/ziwenwang28/JojoSCL。 显示英文摘要

摘要： Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular processes by enabling gene expression analysis at the individual cell level. Clustering allows for the identification of cell types and the further discovery of intrinsic patterns in single-cell data. However, the high dimensionality and sparsity of scRNA-seq data continue to challenge existing clustering models. In this paper, we introduce JojoSCL, a novel self-supervised contrastive learning framework for scRNA-seq clustering. By incorporating a shrinkage estimator based on hierarchical Bayesian estimation, which adjusts gene expression estimates towards more reliable cluster centroids to reduce intra-cluster dispersion, and optimized using Stein's Unbiased Risk Estimate (SURE), JojoSCL refines both instance-level and cluster-level contrastive learning. Experiments on ten scRNA-seq datasets substantiate that JojoSCL consistently outperforms prevalent clustering methods, with further validation of its practicality through robustness analysis and ablation studies. JojoSCL's code is available at: https://github.com/ziwenwang28/JojoSCL.