Gillespie Jessica, Pietrzak Maciej, Song Min-Ae, Chung Dongjun
Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA.
Cells. 2025 Jul 10;14(14):1060. doi: 10.3390/cells14141060.
Spatial transcriptomics combines gene expression data with spatial coordinates to allow for the discovery of detailed RNA localization, study development, investigating the tumor microenvironment, and creating a tissue atlas. A large range of spatial transcriptomics software is available, with little information on which may be better suited for particular datasets or computing environments. A review was conducted to detail the useful metrics when choosing appropriate software for spatial transcriptomics analysis. Specifically, the results from benchmarking studies that compared software across four key areas of spatial transcriptomics analysis (tissue architecture identification, spatially variable gene discovery, cell-cell communication analysis, and deconvolution) were assimilated into a single review that can serve as guidance when choosing potential spatial transcriptomics analysis software.
空间转录组学将基因表达数据与空间坐标相结合，以发现详细的RNA定位、研究发育过程、探究肿瘤微环境并创建组织图谱。目前有大量的空间转录组学软件，但对于哪些软件可能更适合特定数据集或计算环境的信息却很少。本文进行了一项综述，详细介绍了选择适合空间转录组学分析的软件时的有用指标。具体而言，将在空间转录组学分析的四个关键领域（组织结构识别、空间可变基因发现、细胞间通讯分析和解卷积）对软件进行比较的基准研究结果汇总到一篇综述中，可为选择潜在的空间转录组学分析软件提供指导。
