Hu Congcong, Wei Nana, Yang Jiyuan, Wu Hua-Jun, Zheng Xiaoqi
Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Lymphoma, Peking University Cancer Hospital and Institute, Beijing 100142, China.
Genome Res. 2025 Jun 2;35(6):1415-1428. doi: 10.1101/gr.280031.124.
The rapid advance of spatially resolved transcriptomics technologies has yielded substantial spatial transcriptomics data. Deriving biological insights from these data poses nontrivial computational and analysis challenges, of which the most fundamental step is spatial domain detection (or spatial clustering). Although a number of tools for spatial domain detection have been proposed in recent years, their performance varies across data sets and experimental platforms. It is thus an important task to take full advantage of different tools to get a more accurate and stable result through consensus strategy. In this work, we developed STCC, a novel consensus clustering framework for spatial transcriptomics data that aggregates outcomes from state-of-the-art tools using a variety of consensus strategies, including Onehot-based, average-based, hypergraph-based, and wNMF-based methods. Comprehensive assessments on simulated and real data from distinct experimental platforms show that consensus clustering significantly improves clustering accuracy over individual methods under varied input parameters. For normal tissue samples exhibiting clear layered structure, consensus clustering by integrating multiple baseline methods leads to improved results. Conversely, when analyzing tumor samples that display scattered cell type distribution patterns, integration of a single baseline method yields satisfactory performance. For consensus strategies, average-based and hypergraph-based approaches demonstrate optimal precision and stability. Overall, STCC provides a scalable and practical solution for spatial domain detection in spatial transcriptomics data, laying a solid foundation for future research and applications in spatial transcriptomics.
空间分辨转录组学技术的迅速发展产生了大量的空间转录组学数据。从这些数据中获取生物学见解面临着重大的计算和分析挑战，其中最基本的步骤是空间域检测（或空间聚类）。尽管近年来已经提出了许多用于空间域检测的工具，但它们在不同数据集和实验平台上的性能各不相同。因此，充分利用不同工具通过共识策略获得更准确和稳定的结果是一项重要任务。在这项工作中，我们开发了STCC，这是一种用于空间转录组学数据的新型共识聚类框架，它使用多种共识策略（包括基于Onehot、基于平均、基于超图和基于加权非负矩阵分解的方法）聚合来自最先进工具的结果。对来自不同实验平台的模拟数据和真实数据的综合评估表明，在不同输入参数下，共识聚类比单个方法显著提高了聚类准确性。对于具有清晰分层结构的正常组织样本，通过整合多种基线方法进行共识聚类可得到更好的结果。相反，在分析显示细胞类型分布模式分散的肿瘤样本时，整合单一基线方法可产生令人满意的性能。对于共识策略，基于平均和基于超图的方法表现出最佳的精度和稳定性。总体而言，STCC为空间转录组学数据中的空间域检测提供了一种可扩展且实用的解决方案，为空间转录组学的未来研究和应用奠定了坚实基础。
