彩票预测-十大彩票预测

荔园学者Colloquium第一百七十七期

讲座题目：Statistical Modeling for Spatial Transcriptomics: Methods for Deconvolution and Spatial Gene Discovery

主讲人：崔跃华 教授（密歇根州立大学)

讲座时间：2026年6月30日10::00-11:00

讲座地点：深圳大学粤海校区汇星楼三号教室

内容摘要：Spatial transcriptomics has transformed our ability to study gene expression within intact tissues, revealing how cellular organization shapes biological function. However, fully realizing its potential requires rigorous statistical and computational modeling. In this talk, I will present some of our recent method developments in modeling spatial transcriptomics data, focusing on two topics. First, I will introduce new developments in reference-free spatial deconvolution, which infers cellular composition from multicellular-resolution data under a latent Dirichlet allocation (LDA) modeling framework. Second, I will describe methods for identifying cell type-specific spatially variable genes (ctSVGs) and temporally-informed SVGs (TSVGs) using kernel mixed-effects models, enabling the discovery of context-dependent transcriptional patterns. Together, these advances demonstrate how principled statistical modeling can translate experimental complexity into meaningful biological insight.

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主讲人简介：Yuehua Cui is a Professor in the Department of Statistics and Probability at Michigan State University (MSU). His research focuses on statistical genetics and genomics, with an emphasis on gene-gene and gene-environment interactions, multi-omics data integration, causal inference, and spatial transcriptomics. He has published in leading journals such as Nature Communications, Nucleic Acids Research, Genome Research, JRSSB and Biometrics. He is an elected Fellow of the American Statistical Association (ASA) and an elected member of the International Statistical Institute (ISI). He currently serves as an Academic Editor for PLOS Genetics and PLOS Computational Biology, and an Associate Editor for several journals in statistics and computational genomics including Statistics and Probability Letters and Statistical Applications in Genetics and Molecular Biology.

欢迎师生参加！

　　　　　　　　　　　　　　　　　　　　　　　十大彩票预测

　　　　　　　　　　　　　　　　　　　　 2026年6月26日