用收缩估计提升单细胞数据聚类精度，有效降低组内差异。
JojoSCL: Shrinkage Contrastive Learning for single-cell RNA sequence Clustering
- 基于分层贝叶斯的收缩估计，优化基因表达值以贴近簇中心。
- 在10个数据集上优于主流聚类方法，显著降低组内离散度。
- 适合生物信息学研究者，尤其关注单细胞数据精细聚类场景。
单细胞RNA测序（scRNA-seq）使我们在单个细胞层面解析基因表达成为可能，推动了对细胞过程的理解。聚类分析有助于识别细胞类型并发现内在模式，但高维度与稀疏性仍挑战现有模型。本文提出JojoSCL，一种新型自监督对比学习框架用于scRNA-seq聚类。通过引入基于分层贝叶斯估计的收缩估计器，将基因表达值向更可靠的簇中心调整，从而减少组内离散度，并利用Stein无偏风险估计（SURE）进行优化，同时改进实例级与簇级对比学习。在10个scRNA-seq数据集上的实验表明，JojoSCL持续优于主流聚类方法；鲁棒性分析与消融实验进一步验证其有效性。代码已公开于https://github.com/ziwenwang28/JojoSCL。
原文摘要 · Abstract (English)
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.
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