Lunch will be served at 11:45 AM.
Gene co-expression graphs are a rich source of information, revealing critical insights into cellular functions, states, and activities. Yet, extracting meaningful signals from these graphs presents a formidable challenge. This complexity arises due to the presence of multiple overlapping sources of heterogeneity and biological signals, as well as the pronounced inherent noise in single cell sequencing data. In this talk, we explore our latest endeavors and progress in unraveling these intricate signals in gene co-expression graphs. In this talk, we focus on the identification and interpretation of various topological structures within these graphs. We demonstrate how these structures can help in pinpointing feature genes and analyzing cellular pathway activities. We also develop novel graph encoders, and new integrative deep learning and hypothesis testing framework to explore cell heterogeneities and functions. By applying these tools, we have successfully identified novel markers of cellular senescence and driver pathways of type 1 diabetes.
Jichun Xie joined Duke in 2014. She is an Associate Professor with a joint position in the Department of Biostatistics and Bioinformatics and the Department of Mathematics at Duke. She is also the co-director of the statistical and computational division at the Center for Human Systems Immunology. Her main research area is statistical and computational genetics and genomics, focusing on developing novel computational tools to understand complex cellular heterogeneity and to characterize cells of interest, and their cellular and intercellular activities, such as cell state transitions. Her biomedical areas of focus include cancer, aging, and neurodegenerative diseases.
Department of Computer Science