TALK TITLE: Applications of Graph Neural Networks in Single-Cell Data Analyses
Single-cell data analysis is widely used to reveal the heterogeneity and dynamics of tissues, organisms, and complex diseases. Graph neural networks (GNN) provides a great tool to uncover intricate biological patterns and relationships underlying large-scale, noisy single-cell data. We introduced scGNN as a hypothesis-free GNN framework for single-cell RNA-Seq analyses. It integrates three iterative multi-modal autoencoders and outperforms existing tools for gene imputation and cell clustering on four benchmark single-cell RNA-Seq datasets. We further developed RESEPT, a GNN framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics by reconstructing and segmenting a transcriptome mapped RGB image.
RESEPT can identify the tissue architecture, and represent corresponding marker genes and biological functions accurately. We also developed DeepMAPS for biological network inference from single-cell multi-omits data. By building a heterogeneous graph containing both cell and gene nodes, DeepMAPS identifies the joint embedding of all the nodes simultaneously and enables the inference of cell-type-specific biological networks. These tools provide critical insights into the underlying mechanisms driving the complex tissue heterogeneities in development and diseases.
Dong Xu is Shumaker Endowed Professor in Department of Electrical Engineering and Computer Science, with appointments in the Christopher S. Bond Life Sciences Center and the Informatics Institute at the University of Missouri-Columbia. He obtained his PhD from the University of Illinois, Urbana-Champaign in 1995 and did two years of postdoctoral work at the US National Cancer Institute. He was a Staff Scientist at Oak Ridge National Laboratory until 2003 before joining the University of Missouri, where he served as Department Chair of Computer Science during 2007-2016. His research is in computational biology and bioinformatics, including machine-learning applications in bioinformatics, protein structure prediction, post-translational modification prediction, high-throughput biological data analyses, in silico studies of plants, microbes and cancers, biological information systems, and mobile App development for healthcare. He has published more than 360 papers. He was elected to the rank of American Association for the Advancement of Science (AAAS) Fellow in 2015 and American Institute for Medical and Biological Engineering (AIMBE) Fellow in 2020.