Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
Genome Biol. 2023 Sep 18;24(1):209. doi: 10.1186/s13059-023-03045-1.
Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing number of published tools designed to define SVG sets currently lack benchmarking methods to accurately assess performance. This study compares results of 6 purpose-built packages for SVG identification across 9 public and 5 simulated datasets and highlights discrepancies between results. Additional tools for generation of simulated data and development of benchmarking methods are required to improve methods for identifying SVGs.
鉴定空间可变基因（SVGs）是分析空间分辨转录组学数据的关键步骤。SVGs 通过定义组织内的转录组差异提供了生物学见解，这是以前使用 RNA-seq 技术无法实现的。然而，目前用于定义 SVG 集的已发表工具数量不断增加，但缺乏准确评估性能的基准测试方法。本研究比较了 6 种专门用于 SVG 识别的工具在 9 个公共数据集和 5 个模拟数据集上的结果，并强调了结果之间的差异。需要额外的工具来生成模拟数据和开发基准测试方法，以改进识别 SVGs 的方法。
