题 目: Manifold learning uncovers hidden structure in complex cellular state space
报告人: David van Dijk, Ph.D.
Assistant Professor of Medicine & Computer Science, Yale University
时 间: 5月18日（周一）上午10:00-11:00
地 点: Online (Zoom会议)
会议 ID：614 6126 5441
主持人: Lucas Carey
In the era of big biological data, there is a pressing need for methods that visualize, integrate and interpret high-throughput high-dimensional data to enable biological discovery. There are several major challenges in analyzing high-throughput biological data. These include the curse of (high) dimensionality, noise, sparsity, missing values, bias, and collection artifacts. In my work, I try to solve these problems using computational methods that are based on manifold learning. A manifold is a smoothly varying low-dimensional structure embedded within high-dimensional ambient measurement space. In my talk, I will present a number of recently completed and ongoing projects that utilize the manifold, implemented using graph signal processing and deep learning, to understand large biomedical datasets. First, I will present MAGIC, a data denoising and imputation method designed to ‘fix’ single-cell RNA-sequencing data. MAGIC uses data diffusion to learn the data manifold and at the same time fill in and smooth the data, thereby revealing the underlying structure of the data. I will show how MAGIC reveals a continuous phenotypic state-space in an epithelial-to-mesenchymal transition system. I will then talk about PHATE, a dimensionality reduction and visualization method specifically designed to reveal continuous progression structure. I will demonstrate how PHATE can give profound insight into a newly measured human embryonic stem cell system. Finally, I will talk about two deep learning methods that use specially designed constraints to allow for deep interpretable representations of heterogeneous systems such as gut microbiome data and single cell-data of tumor infiltrating lymphocytes.
David completed his PhD at the University of Amsterdam (with Prof. Jaap Kaandorp and Prof. Peter Sloot) and the Weizmann Institute of Science (with Prof. Eran Segal) in Computer Science. David then was a postdoctoral fellow and associate research scientist at Yale Medical School (Dept. of Genetics) and Yale Computer Science with Prof. Smita Krishnaswamy. There he developed new machine learning tools for unsupervised learning on biomedical data with an emphasis on single-cell data. David is currently an Assistant Professor in Medicine and in Computer Science at Yale, where he leads a research group in machine learning on biomedical data.
Lab website: https://www.vandijklab.org/