SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies

Genome Biology

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