Congcong Hu1, Nana Wei2,3, Jiyuan Yang1, Hua-Jun Wu2,4,5 and Xiaoqi Zheng1 1Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; 2Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Lymphoma, Peking University Cancer Hospital and Institute, Beijing 100142, China; 3The Guangxi Key Laboratory of Intelligent Precision Medicine, Guangxi Zhuang Autonomous Region, Nanning 530007, China; 4Center for Precision Medicine Multi-Omics Research, Institute of Advanced Clinical Medicine, Peking University, Beijing 100191, China; 5Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China Corresponding authors: hjwupku.edu.cn, xqzhengshsmu.edu.cn Abstract

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.

Received September 16, 2024. Accepted March 31, 2025.