讲座人简介：

Prof. Yang Can is currently Dr Tai-chin Lo Associate Professor of Science, Department of Mathematics, The Hong Kong University of Science and Technology. He serves as the associate director of Big Data Bio-Intelligence Lab (BDBI) at HKUST. He is currently an associate editor of Annals of Applied Statistics. His research focuses on data science with the development of novel statistical and computational methods for large-scale data analysis, including deep generative models, graph neural networks, and adversarial domain translation. His research papers have appeared in high-impact journals and prestigious machine learning conferences, such asNature Machine Intelligence,Nature Computational Science,Nature Communications,Proceedings of the National Academy of Sciences (PNAS),Annals of Statistics,IEEE Transactions on Pattern Analysis and Machine Intelligence,The American Journal of Human Genetics, andthe International Conference on Machine Learning. Prof. Yang has also established industrial collaborations supported by the Innovation and Technology Fund of the Hong Kong Government.

讲座简介：

The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), offer valuable insights, they face limitations either in cellular resolution or transcriptome-wide profiling. To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data and ST data using deep generative models. With innovation in model and algorithm designs, SpatialScope not only enhances seq-based ST data to achieve single-cell resolution, but also accurately infers transcriptome-wide expression levels for image-based ST data. We demonstrate SpatialScope’s utility through simulation studies and real data analysis from both seq-based and image-based ST approaches. SpatialScope provides spatial characterization of tissue structures at transcriptome-wide single-cell resolution, facilitating downstream analysis, including detecting cellular communication through ligand-receptor interactions, localizing cellular subtypes, and identifying spatially differentially expressed genes. This is a joint work with Xiaomeng Wan, Jiashun Xiao, Sindy Sing Ting Tam, Mingxuan Cai, Ryohichi Sugimura, Yang Wang, Xiang Wan, Zhixiang Lin, and Angela Ruohao Wu.