Zhang Daoliang, Li Wenrui, Sui Xinyi, Yu Na, Wang Shan, Liu Zhiping, Wang Xiaowo, Yuan Zhiyuan, Gao Rui, Zhang Wei
Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China.
MOE Key Lab of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China.
Bioinform Adv. 2025 Apr 11;5(1):vbaf084. doi: 10.1093/bioadv/vbaf084. eCollection 2025.
The rapid development of spatially resolved transcriptomics (SRT) technologies has provided unprecedented opportunities for characterizing and understanding tissue architecture. As this field continues to advance, various methods have been developed to computationally identify spatial domains within tissues. However, the performance of different algorithms on the same dataset is not always consistent. This inconsistency makes it difficult for researchers to select the most reliable results for downstream analysis.
To address this challenge, we propose a domain identification method named Space. Space measures consistency between different methods to select reliable algorithms. It then constructs a consensus matrix to integrate the outputs from multiple algorithms. We introduce similarity loss, spatial loss, and low-rank loss in Space to enhance the accuracy and optimize computational efficiency. This strategy not only resolves the inconsistent issue of clustering labels among different methods but also achieves highly reliable clustering output. Flexible interfaces are also provided for downstream analysis such as visualization, domain-specific gene analysis and trajectory inference. Testing results on multiple publicly available SRT datasets demonstrate that Space performs exceptionally well in deciphering key tissue structures and biological features.
The Space package can be easily installed through conda or mamba, and its source code is available at https://honchkrow.github.io/Space.
空间分辨转录组学（SRT）技术的快速发展为表征和理解组织结构提供了前所未有的机会。随着该领域的不断发展，已经开发了各种方法来通过计算识别组织内的空间域。然而，不同算法在同一数据集上的性能并不总是一致的。这种不一致性使得研究人员难以选择最可靠的结果进行下游分析。
为了应对这一挑战，我们提出了一种名为Space的域识别方法。Space通过衡量不同方法之间的一致性来选择可靠的算法。然后，它构建一个共识矩阵来整合多种算法的输出。我们在Space中引入了相似性损失、空间损失和低秩损失，以提高准确性并优化计算效率。该策略不仅解决了不同方法之间聚类标签不一致的问题，还实现了高度可靠的聚类输出。还为可视化、特定域基因分析和轨迹推断等下游分析提供了灵活的接口。在多个公开可用的SRT数据集上的测试结果表明，Space在解读关键组织结构和生物学特征方面表现出色。
Space软件包可以通过conda或mamba轻松安装，其源代码可在https://honchkrow.github.io/Space获取。
