The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA, USA.
Department of Mathematics, University of California Irvine, Irvine, CA, USA.
Commun Biol. 2022 Mar 10;5(1):220. doi: 10.1038/s42003-022-03175-5.
The rapid development of spatial transcriptomics (ST) techniques has allowed the measurement of transcriptional levels across many genes together with the spatial positions of cells. This has led to an explosion of interest in computational methods and techniques for harnessing both spatial and transcriptional information in analysis of ST datasets. The wide diversity of approaches in aim, methodology and technology for ST provides great challenges in dissecting cellular functions in spatial contexts. Here, we synthesize and review the key problems in analysis of ST data and methods that are currently applied, while also expanding on open questions and areas of future development.
空间转录组学（ST）技术的快速发展使得能够同时测量多个基因的转录水平和细胞的空间位置。这导致人们对计算方法和技术产生了浓厚的兴趣，这些方法和技术可以利用 ST 数据集的空间和转录信息进行分析。ST 在目标、方法和技术方面的广泛多样性在解析空间背景下的细胞功能方面带来了巨大的挑战。在这里，我们综合和回顾了目前应用的 ST 数据分析中的关键问题和方法，同时也扩展了一些开放性问题和未来发展领域。
