報告人: Xiang Zhou, Ph.D., Associate Professor Department of Biostatistics, University of Michigan
時間: 2019年12月18日 （星期三）上午 10:00 -11:30
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 developed a statistical method, SPARK, 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. It relies on newly developed statistical formulas for hypothesis testing, providing effective type I error control and yielding high statistical power. With a computationally efficient algorithm based on penalized quasi-likelihood, SPARK is also scalable to data sets with tens of thousands of genes measured on tens of thousands of samples. In four published spatially resolved transcriptomic data sets, we show that SPARK can be up to ten times more powerful than existing methods, revealing new biology in the data that otherwise cannot be revealed by existing approaches.
Dr. Xiang Zhou is an Associate Professor in Department of Biostatistics at University of Michigan. 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.
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