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UID:20260314T071910EDT-6211UM9RMK@132.216.98.100
DTSTAMP:20260314T111910Z
DESCRIPTION:Title: Seeing Where Genes Act: Identifying Spatially Variable G
 enes from Tissues to Subcellular Scales\n\n聽\n\nAbstract\n\nSpatial transc
 riptomics technologies enable the measurement of gene expression with spat
 ial context. Detecting spatially variable genes (SVGs) is a central task i
 n the analysis of such data.In this talk\, I will present several computat
 ional methods developed by our group for the statistical detection of SVGs
  at multiple biological resolutions. I will first discuss SPARK\, a statis
 tical framework for rigorous identification of spatially expressed genes\,
  and SPARK-X\, a nonparametric extension designed for rapid and scalable S
 VG detection in large spatial transcriptomic studies. I will then introduc
 e CELINA\, which focuses on detecting cell type鈥搒pecific spatially variabl
 e genes\, and ELLA\, which models subcellular mRNA localization to identif
 y genes exhibiting within-cell spatial variation in high-resolution spatia
 l transcriptomics data. Together\, these methods provide a comprehensive t
 oolkit for detecting spatial gene expression patterns at the tissue\, cell
 -type\, and subcellular levels.\n\nSpeaker Bio\n\nXiang Zhou is a Professo
 r in the Department of Statistics and Data Science at Yale University. He 
 received a BS in Biology from Peking University and earned both an MS in S
 tatistics and a PhD in Neurobiology from Duke University. After postdoctor
 al training and instructorship at the University of Chicago\, he joined th
 e University of Michigan in 2014\, where he rose to full Professor and hel
 d leadership roles in Precision Health and AI & Digital Health Innovation 
 before moving to Yale in 2025. Dr. Zhou is a Fellow of the American Statis
 tical Association and a recipient of the 2024 MBioFAR Award and the 2025 I
 CIBM Eminent Scholar Award. He serves on the NIH MRAA Study Section and as
  an Associate Editor for PLOS Genetics and Journal of the American Statist
 ical Association. His research focuses on genomic data science\, developin
 g statistical and machine learning methods\, including deep learning and A
 I\, for large-scale genetic and genomic data\, with applications in GWAS\,
  single-cell sequencing\, and spatial multi-omics.聽https://xiangzhou.githu
 b.io\n\n聽\n
DTSTART:20260311T193000Z
DTEND:20260311T203000Z
LOCATION:Room 1104\, 成人VR视频 College 2001\, CA\, QC\, Montreal\, H3A 1G1\, 2
 001\, avenue 成人VR视频 College
SUMMARY:Xiang Zhou (Yale University)
URL:/mathstat/channels/event/xiang-zhou-yale-universit
 y-371860
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