Cheng Jinming, Jin Xinyi, Smyth Gordon K, Chen Yunshun
Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.
Department of Medical Biology, The University of Melbourne, Parkville, VIC, 3010, Australia.
BMC Bioinformatics. 2025 Jan 20;26(1):22. doi: 10.1186/s12859-025-06044-0.
Imaging-based spatial transcriptomics technologies allow us to explore spatial gene expression profiles at the cellular level. Cell type annotation of imaging-based spatial data is challenging due to the small gene panel, but it is a crucial step for downstream analyses. Many good reference-based cell type annotation tools have been developed for single-cell RNA sequencing and sequencing-based spatial transcriptomics data. However, the performance of the reference-based cell type annotation tools on imaging-based spatial transcriptomics data has not been well studied yet.
We compared performance of five reference-based methods (SingleR, Azimuth, RCTD, scPred and scmapCell) with the marker-gene-based manual annotation method on an imaging-based Xenium data of human breast cancer. A practical workflow has been demonstrated for preparing a high-quality single-cell RNA reference, evaluating the accuracy, and estimating the running time for reference-based cell type annotation tools.
SingleR was the best performing reference-based cell type annotation tool for the Xenium platform, being fast, accurate and easy to use, with results closely matching those of manual annotation.
基于成像的空间转录组学技术使我们能够在细胞水平上探索空间基因表达谱。由于基因面板较小，基于成像的空间数据的细胞类型注释具有挑战性，但它是下游分析的关键步骤。已经为单细胞RNA测序和基于测序的空间转录组学数据开发了许多优秀的基于参考的细胞类型注释工具。然而，基于参考的细胞类型注释工具在基于成像的空间转录组学数据上的性能尚未得到充分研究。
我们在人类乳腺癌的基于成像的Xenium数据上，将五种基于参考的方法（SingleR、Azimuth、RCTD、scPred和scmapCell）与基于标记基因的手动注释方法的性能进行了比较。展示了一个实用的工作流程，用于制备高质量的单细胞RNA参考、评估准确性以及估计基于参考的细胞类型注释工具的运行时间。
对于Xenium平台，SingleR是性能最佳的基于参考的细胞类型注释工具，它快速、准确且易于使用，结果与手动注释结果非常接近。
