Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.
Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA.
Genome Biol. 2021 Jun 21;22(1):184. doi: 10.1186/s13059-021-02404-0.
Spatial transcriptomic studies are becoming increasingly common and large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies. SPARK-X not only produces effective type I error control and high power but also brings orders of magnitude computational savings. We apply SPARK-X to analyze three large datasets, one of which is only analyzable by SPARK-X. In these data, SPARK-X identifies many spatially expressed genes including those that are spatially expressed within the same cell type, revealing new biological insights.
空间转录组学研究越来越普遍和庞大，给许多分析任务带来了重要的统计和计算挑战。在这里，我们提出了 SPARK-X，这是一种用于快速有效检测大型空间转录组学研究中空间表达基因的非参数方法。SPARK-X 不仅能实现有效的第一类错误控制和高功效，还能节省数量级的计算资源。我们应用 SPARK-X 分析了三个大型数据集，其中一个数据集只能用 SPARK-X 进行分析。在这些数据中，SPARK-X 鉴定了许多空间表达基因，包括在同一细胞类型内空间表达的基因，揭示了新的生物学见解。
