Wellcome Trust Sanger Institute, Hinxton, UK.
European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
Nat Methods. 2018 May;15(5):343-346. doi: 10.1038/nmeth.4636. Epub 2018 Mar 19.
Technological advances have made it possible to measure spatially resolved gene expression at high throughput. However, methods to analyze these data are not established. Here we describe SpatialDE, a statistical test to identify genes with spatial patterns of expression variation from multiplexed imaging or spatial RNA-sequencing data. SpatialDE also implements 'automatic expression histology', a spatial gene-clustering approach that enables expression-based tissue histology.
技术进步使得高吞吐量地测量空间分辨基因表达成为可能。然而，分析这些数据的方法尚未建立。在这里，我们描述了 SpatialDE，这是一种统计检验方法，用于从多重成像或空间 RNA 测序数据中识别具有空间表达变异模式的基因。SpatialDE 还实现了“自动表达组织学”，这是一种基于表达的组织学的空间基因聚类方法。
