AI 中文总结
研究针对scRNA-seq传统注释的缺陷，采用GmGM框架联合推断细胞与基因依赖结构，通过聚类整合与功能验证，揭示了传统方法未区分的细胞亚群，提供了统一的细胞聚类与基因网络推断框架。
AI 中文摘要
传统的单细胞RNA测序（scRNA-seq）数据注释高度依赖基于标记的手动阈值划分，该方法可能掩盖细微的转录组梯度，并将功能不同的细胞状态合并为宽泛、异质的细胞群。本研究将高斯多图模型（GmGM）框架应用于10x Genomics PBMC数据集，该框架可从单一scRNA-seq数据矩阵中联合推断细胞-细胞和基因-基因的依赖结构。通过软集群集成方法整合10次独立的GMGM-Leiden聚类运行，形成稳健的共识划分，并与参考细胞类型注释进行基准测试。该策略产生了稳定的集群划分，可解析参考注释未区分的具有生物学意义的亚群。同时，对每个集群，通过跨分辨率的共识Leiden聚类从拟合模型中提取基因共表达模块，使用标准网络指标进行评估，并通过网络富集分析测试（NEAT）进行功能验证，确认了非随机富集信号。模块评分程序将网络拓扑与单个细胞、单个集群的表达特征关联起来，对GmGM的一项新扩展（在单次模型运行中恢复共享的细胞-细胞网络以及种群特异性基因网络）在CD4+ T细胞种群的案例研究中得到验证。这些结果表明，GmGM为联合细胞聚类和基因网络推断提供了统一、可重复的框架，能够揭示传统流程无法捕捉的细胞结构。
英文摘要
Conventional annotation of single-cell RNA-sequencing (scRNA-seq) data relies heavily on manual, marker-based thresholding, an approach that can obscure subtle transcriptomic gradients and collapse functionally distinct cell states into broad, heterogeneous populations. Here we apply the Gaussian multi-Graphical Model (GmGM) framework, which jointly infers cell-cell and gene-gene dependency structure from a single scRNA-seq data matrix, to a 10x Genomics PBMC dataset. Ten independent GMGM-Leiden clustering runs were integrated into a robust consensus partition using a soft cluster ensemble approach and benchmarked against reference cell-type annotations. This strategy yielded stable cluster partitions that resolve biologically meaningful sub-populations not distinguished by the reference annotation. In parallel, for each cluster, gene co-expression modules were extracted from the fitted model via consensus Leiden clustering across resolutions, evaluated using standard network metrics, and validated functionally with the Network Enrichment Analysis Test (NEAT), which confirmed non-random enrichment signal. A module-scoring procedure linked network topology to per-cell, per-cluster expression signatures, and a novel extension of GmGM, recovering a shared cell-cell network together with population-specific gene networks in a single model run, was demonstrated in a case study on the CD4+ T-cell population. These results indicate that GmGM provides a unified, reproducible framework for joint cell clustering and gene-network inference, capable of revealing cellular structure beyond that captured by conventional pipelines.
Comments9 pages, 3 figures
