题目: Optimal Transport Applications in Single-cell RNA Sequencing Data
报告人：孔德含教授（多伦多大学）
时间：2024年11月1日下午16:00-17:00
地点：微格堂113
Abstract: 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.
个人简介：孔德含，多伦多大学统计学副教授，研究方向包括脑图像，函数型数据分析，因果推断，高维数据分析以及机器学习。研究成果发表在统计学国际顶级期刊JRSSB，JASA，Biometrika等，现任统计学期刊JASA副主编。
邀请人：徐礼柏