Nature Methods ( IF 36.1 ) Pub Date : 2023-03-02 , DOI: 10.1038/s41592-023-01766-6
Ethan A G Baker 1, 2 , Denis Schapiro 1, 3, 4, 5 , Bianca Dumitrascu 1, 6, 7 , Sanja Vickovic 1, 8, 9, 10 , Aviv Regev 1, 2, 11
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As spatially resolved multiplex profiling of RNA and proteins becomes more prominent, it is increasingly important to understand the statistical power available to test specific hypotheses when designing and interpreting such experiments. Ideally, it would be possible to create an oracle that predicts sampling requirements for generalized spatial experiments. However, the unknown number of relevant spatial features and the complexity of spatial data analysis make this challenging. Here, we enumerate multiple parameters of interest that should be considered in the design of a properly powered spatial omics study. We introduce a method for tunable in silico tissue (IST) generation and use it with spatial profiling data sets to construct an exploratory computational framework for spatial power analysis. Finally, we demonstrate that our framework can be applied across diverse spatial data modalities and tissues of interest. While we demonstrate ISTs in the context of spatial power analysis, these simulated tissues have other potential use cases, including spatial method benchmarking and optimization.
中文翻译:
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用于空间组学的计算机组织生成和功率分析
随着 RNA 和蛋白质的空间分辨多重分析变得更加突出,在设计和解释此类实验时了解可用于测试特定假设的统计能力变得越来越重要。理想情况下,可以创建一个预言机来预测广义空间实验的采样要求。然而,未知数量的相关空间特征和空间数据分析的复杂性使得这具有挑战性。在这里,我们列举了在设计适当动力的空间组学研究时应该考虑的多个感兴趣的参数。我们介绍了一种用于可调谐计算机组织 (IST) 生成的方法,并将其与空间分析数据集一起使用,以构建用于空间功率分析的探索性计算框架。最后,我们证明我们的框架可以应用于不同的空间数据模式和感兴趣的组织。虽然我们在空间功率分析的背景下展示了 IST,但这些模拟组织还有其他潜在的用例,包括空间方法基准测试和优化。