Optimal transport for single-cell omics data analysis
Abstract: The recent development of high-resolution omics level technologies has reshaped modern biological research. These high-dimensional and noisy datasets are accumulating at a fast pace. Efficient and biologically meaningful algorithms are needed to extract biological insights from these raw datasets. In this talk, I will discuss using optimal transport, a powerful geometric data analysis method, to integrate multimodal omics datasets and infer cell-cell communications, a crucial process that drives the correct developments and functions of biological systems. I will also talk about a new formulation of optimal transport called supervised optimal transport inspired by these biological applications.
Biography: Zixuan Cang is an assistant professor in the department of mathematics at North Carolina State University. His research focuses on 1) utilizing mathematical tools such as topological data analysis and optimal transport paired with machine learning to extract biological insights from data such as spatial transcriptomics and 2) developing novel mathematical methods motivated by these applications. He is also interested in developing data-driven models to further study certain biological systems in detail.