Systematic benchmarking of computational methods to identify spatially variable genes

IF 10.1 1区 生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Zhijian Li, Zain M.Patel, Dongyuan Song, Sai Nirmayi Yasa, Robrecht Cannoodt, Guanao Yan, Jingyi Jessica Li, Luca Pinello
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引用次数: 0

Abstract

Spatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes (SVGs). Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field. Here, we systematically evaluate 14 methods using 96 spatial datasets and 6 metrics. We compare the methods regarding gene ranking and classification based on real spatial variation, statistical calibration, and computation scalability and investigate the impact of identified SVGs on downstream applications such as spatial domain detection. Finally, we explore the applicability of the methods to spatial ATAC-seq data to examine their effectiveness in identifying spatially variable peaks (SVPs). Overall, SPARK-X outperforms other benchmarked methods and Moran’s I achieves a competitive performance, representing a strong baseline for future method development. Moreover, our results reveal that most methods are poorly calibrated, and more specialized algorithms are needed to identify spatially variable peaks. Our benchmarking provides a detailed comparison of SVG detection methods and serves as a reference for both users and method developers.
系统的基准计算方法,以确定空间可变的基因
空间解析转录组学通过在完整的细胞空间背景下分析基因表达提供了前所未有的洞察力,有效地为数据解释增加了一个新的和必要的维度。为了有效地检测感兴趣的空间结构,分析这些数据的一个重要步骤是识别空间可变基因(svg)。尽管研究人员已经开发了几种计算方法来完成这项任务,但缺乏评估其性能的综合基准仍然是该领域的一个相当大的差距。在这里,我们使用96个空间数据集和6个指标系统地评估了14种方法。我们比较了基于真实空间变异、统计校准和计算可扩展性的基因排序和分类方法,并研究了识别出的svg对下游应用(如空间域检测)的影响。最后,我们探讨了该方法在空间ATAC-seq数据中的适用性,以检验其在识别空间可变峰(svp)方面的有效性。总体而言,SPARK-X优于其他基准方法,Moran的I实现了具有竞争力的性能,代表了未来方法开发的强大基线。此外,我们的研究结果表明,大多数方法的校准效果很差,需要更专门的算法来识别空间可变的峰值。我们的基准测试提供了SVG检测方法的详细比较,可以作为用户和方法开发人员的参考。
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来源期刊
Genome Biology
Genome Biology Biochemistry, Genetics and Molecular Biology-Genetics
CiteScore
21.00
自引率
3.30%
发文量
241
审稿时长
2 months
期刊介绍: Genome Biology stands as a premier platform for exceptional research across all domains of biology and biomedicine, explored through a genomic and post-genomic lens. With an impressive impact factor of 12.3 (2022),* the journal secures its position as the 3rd-ranked research journal in the Genetics and Heredity category and the 2nd-ranked research journal in the Biotechnology and Applied Microbiology category by Thomson Reuters. Notably, Genome Biology holds the distinction of being the highest-ranked open-access journal in this category. Our dedicated team of highly trained in-house Editors collaborates closely with our esteemed Editorial Board of international experts, ensuring the journal remains on the forefront of scientific advances and community standards. Regular engagement with researchers at conferences and institute visits underscores our commitment to staying abreast of the latest developments in the field.
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