Nonparametric Modeling Enables Scalable and Robust Detection of Spatial Expression Patterns for Large Spatial Transcriptomic Studies

• Activity Number: 308 - Statistical Methods for Studying Spatial Transcriptomics, Tissue Heterogeneity, and Pleiotropy
• Type: Topic-Contributed
• Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Xiang Zhou*
• Companies: University of Michigan
• Keywords: Spatial transcriptomics; non-parametric; SPARK; spatial expression; computation; SPARK-X

Abstract

Spatial transcriptomic studies are becoming increasingly common and increasingly large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric modeling framework for rapid and effective identification of genes with spatial expression patterns 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 applied SPARK-X to analyze three large spatial transcriptomic data, one of which is only analyzable by SPARK-X. In these data, SPARK-X identified many spatially expressed genes not identifiable by existing approaches, revealing new biological insights.