Benchmarking computational methods to identify spatially variable genes and peaks.
Benchmarking computational methods to identify spatially variable genes and peaks.
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对计算方法进行基准测试以识别空间可变基因和峰值。
DOI：
10.1101/2023.12.02.569717
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Pinello,Luca
中科院分区：
文献类型：
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作者：
Li,Zhijian;Patel,ZainM;Song,Dongyuan;Yan,Guanao;Li,JingyiJessica;Pinello,Luca
空间分辨转录组学提供了前所未有的洞察力，使能够在完整的细胞空间背景下分析基因表达，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，分析这类数据的一个基本步骤涉及识别空间可变的基因。尽管研究人员已经开发了几种计算方法来完成这项任务，但在该领域缺乏评估其表现的全面基准仍然是一个相当大的差距。在这里，我们使用由四种不同的模拟策略生成的60个模拟数据集、12个真实世界转录本和3个空间ATAC-SEQ数据集，对14种方法进行了系统的评估。我们发现，spatialDE2的性能一直优于其他基准方法，而Moran的I在不同的实验设置中取得了与之相当的性能。此外，我们的结果表明，需要更专门的算法来识别空间可变的峰值。
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. 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 present a systematic evaluation of 14 methods using 60 simulated datasets generated by four different simulation strategies, 12 real-world transcriptomics, and three spatial ATAC-seq datasets. We find that spatialDE2 consistently outperforms the other benchmarked methods, and Moran’s I achieves competitive performance in different experimental settings. Moreover, our results reveal that more specialized algorithms are needed to identify spatially variable peaks.