Xiang Zhou, PhD (Professor, Department of Statistics and Data Science, Yale University)

Host: Dr. Audrey Fu

“Seeing Where Genes Act: Identifying Spatially Variable Genes from Tissues to Subcellular Scales”

Abstract

Spatial transcriptomics technologies enable the measurement of gene expression with spatial context. Detecting spatially variable genes (SVGs) is a central task in the analysis of such data. In this talk, I will present several computational methods developed by our group for the statistical detection of SVGs at multiple biological resolutions. I will first discuss SPARK, a statistical framework for rigorous identification of spatially expressed genes, and SPARK-X, a nonparametric extension designed for rapid and scalable SVG detection in large spatial transcriptomic studies. I will then introduce CELINA, which focuses on detecting cell type–specific spatially variable genes, and ELLA, which models subcellular mRNA localization to identify genes exhibiting within-cell spatial variation in high-resolution spatial transcriptomics data. Together, these methods provide a comprehensive toolkit for detecting spatial gene expression patterns at the tissue, cell-type, and subcellular levels.