报告时间：2024年10月11日（周五）14:00-15:00
报告地点：中国人民大学明德主楼1016
报告主题：Optimal Transport Applications in Single-cell RNA Sequencing Data
报告摘要：
In this presentation, I will discuss two applications of optimal transport in the context of single-cell RNA sequencing. In the first part, we introduce Laplacian Linear Optimal Transport (LLOT), a biologically interpretable method designed to integrate single-cell and spatial transcriptomics data, enabling the reconstruction of missing information at both whole-genome and single-cell resolution. LLOT has two key features: it efficiently identifies differences between datasets and corrects platform effects through a linear mapping approach, and it adeptly manages complex spatial structures within tissues. We benchmarked LLOT against several alternative methods using real datasets, and the results consistently demonstrated its superior performance in predicting spatial gene expression and single-cell locations. In the second part, we develop a novel method based on discrete unbalanced optimal transport to model cell type developmental trajectories. This method detects biological changes in cell types and infers transitions to various states using the transport matrix. We evaluated it with single-cell RNA data from mouse embryonic fibroblasts, where it accurately identified major cell type developmental changes, validated by experimental results. Additionally, the transition probabilities between cell types revealed a high level of biological precision.
报告人简介：
Dr. Dehan Kong is an Associate Professor in statistics at the University of Toronto. His research interests focus on biomedical data science, with the goal of developing advanced statistical tools and methodologies to handle large, complex, multi-scale real-world biomedical data. He is currently an Associate Editor for the Journal of the American Statistical Association, Applications and Case Studies.
