Identifying genes that display spatial expression pattern in spatially resolved transcriptomic studies is an important first step towards characterizing the spatial transcriptomic landscape of complex tissues. Here, we describe two statistical methods, SPARK and SPARK-X, for identifying such spatially expressed genes in data generated from various spatially resolved transcriptomic techniques. SPARK directly models spatial count data through the generalized linear spatial models while SPARK-X models the data in a non-parametric fashion to ensure scalable computation. Both SPARK and SPARK-X provide effective type I error control and yield high statistical power. We illustrate the benefits of SPARK and SPARK-X in multiple published spatially resolved transcriptomic data sets and show that these methods can lead to new biological findings not detected by existing approaches.

Bio: Xiang Zhou is an Associate Professor of Biostatistics. He received his M.S. in Statistics and PhD in Neurobiology from Duke University in 2010, and completed a postdoctoral training in Human Genetics at the University of Chicago afterwards. He was a William H. Kruskal Instructor in the Department of Statistics at the University of Chicago before he joined the faculty at the University of Michigan in 2014. His research focuses on developing statistical methods and computational tools for genetic and genomic studies. These studies often involve large-scale and high-dimensional data; examples include genome-wide association studies and various functional genomic sequencing studies such as bulk and single cell RNA sequencing and bisulfite sequencing. By developing novel analytic methods, he seeks to extract important information from these data and to advance our understanding of the genetic basis of phenotypic variation for various human diseases and disease related quantitative traits.