Statistics Seminar: Spatial Transcriptomics: Identifying Spatially Variable Genes
Statistics Seminar: Fall 2026
Speaker: Yu Chen, Ph.D. Student, Department of Mathematical Sciences, IU Indianapolis.
Talk time: December 8, 2026, 12:15–1:15 p.m. EST
Meeting format: In person in seminar room LD 265, IU Indianapolis.
Presentation overview
Yu Chen will discuss two statistical approaches to identifying spatially variable genes in spatial transcriptomics. These data combine gene-expression measurements with tissue locations, making it possible to study how expression changes across space. The presentation will distinguish overall spatial variation from variation within a particular cell type.
Weber and colleagues introduce nnSVG, which uses nearest-neighbor Gaussian processes to identify spatially variable genes while reducing computational cost. Shang, Wu, and Zhou introduce Celina, which uses a spatially varying coefficient model to detect cell type-specific spatial expression patterns. Together, the papers motivate a discussion of scalability, cell-type composition, and the biological questions addressed by different definitions of spatial variation.
Readings
Weber, L. M., Saha, A., Datta, A., Hansen, K. D., & Hicks, S. C. (2023). nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes. Nature Communications, 14, 4059. Read online.
Shang, L., Wu, P., & Zhou, X. (2025). Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics. Nature Communications, 16, 1059. Read online.