Evaluating spatially variable gene detection methods for spatial transcriptomics data.
> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)
书目信息
作者摘要（按来源许可复用）
Background The identification of genes that vary across spatial domains in tissues and cells is an essential step for spatial transcriptomics data analysis. Given the critical role it serves for downstream data interpretations, various methods for detecting spatially variable genes (SVGs) have been proposed. However, the lack of benchmarking complicates the selection of a suitable method. Results Here we systematically evaluate a panel of popular SVG detection methods on a large collection of spatial transcriptomics datasets, covering various tissue types, biotechnologies, and spatial resolutions. We address questions including whether different methods select a similar set of SVGs, how reliable is the reported statistical significance from each method, how accurate and robust is each method in terms of SVG detection, and how well the selected SVGs perform in downstream applications such as clustering of spatial domains. Besides these, practical considerations such as computational time and memory usage are also crucial for deciding which method to use. Conclusions Our study evaluates the performance of each method from multiple aspects and highlights the discrepancy among different methods when calling statistically significant SVGs across diverse datasets. Overall, our work provides useful considerations for choosing methods for identifying SVGs and serves as a key reference for the future development of related methods.
合规说明
本页保存的是来源文献书目信息及其在 CC-BY 许可下公开的作者摘要。除去除来源 HTML 标签和规范化空白外，摘要未作内容改写。本页不代表 GeniOmics 的医学建议；原文版权、署名和许可仍归原权利人，请通过原始记录核对最新版本、更正或撤稿状态。