Identifying genes that display spatial expression patterns in spatially resolved transcriptomic studies is an important first step toward characterizing the spatial transcriptomic landscape of complex tissues. Here we present a statistical method, SPARK, for identifying spatial expression patterns of genes in data generated from various spatially resolved transcriptomic techniques. SPARK directly models spatial count data through generalized linear spatial models. It relies on recently developed statistical formulas for hypothesis testing, providing effective control of type I errors and yielding high statistical power. With a computationally efficient algorithm, which is based on penalized quasi-likelihood, SPARK is also scalable to datasets with tens of thousands of genes measured on tens of thousands of samples. Analyzing four published spatially resolved transcriptomic datasets using SPARK, we show it can be up to ten times more powerful than existing methods and disclose biological discoveries that otherwise cannot be revealed by existing approaches.

识别在空间分辨转录组研究中显示空间表达模式的基因是表征复杂组织的空间转录组景观的重要的第一步。在这里，我们提出了一种统计方法 SPARK，用于识别由各种空间分辨转录组技术生成的数据中基因的空间表达模式。SPARK 通过广义线性空间模型直接对空间计数数据进行建模。它依赖于最近开发的用于假设检验的统计公式，可有效控制 I 类错误并产生高统计功效。借助基于惩罚准似然的计算高效算法，SPARK 还可以扩展到具有在数万个样本上测量的数万个基因的数据集。