Genome Biology | |
SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies | |
Xiang Zhou1  Jiaqiang Zhu1  Shiquan Sun1  | |
[1] Department of Biostatistics, University of Michigan; | |
关键词: Spatial transcriptomics; SE analysis; Covariance test; Non-parametric modeling; Slide-seq; HDST; | |
DOI : 10.1186/s13059-021-02404-0 | |
来源: DOAJ |
【 摘 要 】
Abstract 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.
【 授权许可】
Unknown