Identifying replicable genes that display spatial expression patterns from different yet related spatially resolved transcriptomic studies provides stronger scientific evidence and more powerful inference. We present an empirical Bayesian method, STAREG, for identifying replicable spatially variable genes in data generated from various spatially resolved transcriptomic techniques. STAREG models the joint distribution of p-values from different studies with a mixture model and accounts for the heterogeneity of different studies. It provides effective control of the false discovery rate and has higher power by borrowing information across genes and different studies. Moreover, it provides different rankings of important spatially variable genes. With the EM algorithm in combination with pool-adjacent-violator-algorithm (PAVA), STAREG is scalable to datasets with tens of thousands of genes measured on tens of thousands of spatial spots without any tuning parameters. Analyzing three pairs of spatially resolved transcriptomic datasets using STAREG, we show that it makes biological discoveries that otherwise cannot be obtained by using existing methods.

中文翻译：

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STAREG：一种在空间转录组学研究中检测可复制空间可变基因的经验贝叶斯方法

从不同但相关的空间分辨转录组学研究中识别显示空间表达模式的可复制基因提供了更有力的科学证据和更有力的推论。我们提出了一种经验贝叶斯方法，STAREG，用于识别从各种空间分辨转录组学技术生成的数据中的可复制空间可变基因。STAREG 模拟p的联合分布- 来自具有混合模型的不同研究的值，并解释了不同研究的异质性。它提供了对错误发现率的有效控制，并通过跨基因和不同研究借用信息而具有更高的功效。此外，它提供了重要空间可变基因的不同排名。通过将 EM 算法与 pool-adjacent-violator-algorithm (PAVA) 相结合，STAREG 可扩展到具有在数万个空间点上测量的数万个基因的数据集，无需任何调整参数。使用 STAREG 分析三对空间分辨的转录组数据集，我们表明它可以做出使用现有方法无法获得的生物学发现。