Talk title: Construction of a 3D whole organism spatial atlas by joint modelling of multiple slices with deep neural networks
报告人：杨灿 香港科技大学
报告时间： 2023年12月21日 10:00am-11:30am
地点：数学楼2-2
Abstract: Spatial transcriptomics (ST) technologies are revolutionizing the way to explore the spatial architecture of tissues. Currently, ST data analysis is often restricted to a single two-dimensional (2D) tissue slice, limiting our capacity to understand biological processes that take place in 3D space. Here we present STitch3D, a unified framework that integrates multiple ST slices to reconstruct 3D cellular structures. By jointly modelling multiple slices and integrating them with single-cell RNA-sequencing data, STitch3D simultaneously identifies 3D spatial regions with coherent gene-expression levels and reveals 3D cell-type distributions. STitch3D distinguishes biological variation among slices from batch effects, and effectively borrows information across slices to assemble powerful 3D models. Through comprehensive experiments, we demonstrate STitch3D’s performance in building comprehensive 3D architectures, which allow 3D analysis in the entire tissue region or even the whole organism. The outputs of STitch3D can be used for multiple downstream tasks, enabling a comprehensive understanding of biological systems. This is a joint work with Wang Gefei, Zhao Jia, Yan Yan, Wang Yang, and Wu Angela Ruohao.
报告人简介：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 as Nature 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, and the International Conference on Machine Learning. Prof. Yang has also established industrial collaborations supported by the Innovation and Technology Fund of the Hong Kong Government.
