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DESCRIPTION:Xiang Zhou\, PhD\n\nProfessor of Statistics and Data Science| Y
 ale\n\nWHEN: Wednesday\, March 11\, 2026\, from 3:30 to 4:30 p.m.\n	WHERE: 
 Hybrid | 2001 ³ÉÈËVRÊÓƵ College Avenue\, Rm 1140\; Zoom\n	NOTE: Xiang Zhou wil
 l be presenting in-person at SPGH \n\nAbstract\n\nSpatial transcriptomics 
 technologies enable the measurement of gene expression with spatial contex
 t. Detecting spatially variable genes (SVGs) is a central task in the anal
 ysis of such data.In this talk\, I will present several computational meth
 ods developed by our group for the statistical detection of SVGs at multip
 le biological resolutions. I will first discuss SPARK\, a statistical fram
 ework for rigorous identification of spatially expressed genes\, and SPARK
 -X\, a nonparametric extension designed for rapid and scalable SVG detecti
 on 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 ex
 hibiting within-cell spatial variation in high-resolution spatial transcri
 ptomics data. Together\, these methods provide a comprehensive toolkit for
  detecting spatial gene expression patterns at the tissue\, cell-type\, an
 d subcellular levels.\n\nSpeaker Bio\n\nXiang Zhou is a Professor in the D
 epartment of Statistics and Data Science at Yale University. He received a
  BS in Biology from Peking University and earned both an MS in Statistics 
 and a PhD in Neurobiology from Duke University. After postdoctoral trainin
 g and instructorship at the University of Chicago\, he joined the Universi
 ty of Michigan in 2014\, where he rose to full Professor and held leadersh
 ip roles in Precision Health and AI & Digital Health Innovation before mov
 ing to Yale in 2025. Dr. Zhou is a Fellow of the American Statistical Asso
 ciation and a recipient of the 2024 MBioFAR Award and the 2025 ICIBM Emine
 nt Scholar Award. He serves on the NIH MRAA Study Section and as an Associ
 ate Editor for PLOS Genetics and Journal of the American Statistical Assoc
 iation. His research focuses on genomic data science\, developing statisti
 cal and machine learning methods\, including deep learning and AI\, for la
 rge-scale genetic and genomic data\, with applications in GWAS\, single-ce
 ll sequencing\, and spatial multi-omics. https://xiangzhou.github.io \n
DTSTART:20260311T193000Z
DTEND:20260311T203000Z
SUMMARY:Seeing Where Genes Act: Identifying Spatially Variable Genes from T
 issues to Subcellular Scales
URL:/epi-biostat-occh/channels/event/seeing-where-gene
 s-act-identifying-spatially-variable-genes-tissues-subcellular-scales-3714
 37
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