Presenter:
Barbara Engelhardt, Ph.D.
Princeton University
Barbara Engelhardt, Ph.D.
Princeton University
Talk title:
Understanding how genetics and environment influences cells through images and gene expression data
Abstract:
A central goal of single-cell genomics is to understand how cells interact and influence each other, and how tissues grow and respond to specific interventions. In this talk, I will give two examples of how we can use machine learning approaches to begin to quantify relationships between cells. First, using pathology images and paired bulk RNA-seq data, I show how canonical correlation analysis models can be used to find image morphology that covaries with gene expression, and we use these results to identify image QTLs. Second, I describe a method for dimension reduction that allows us to augment disassociated single-cell RNA-seq data with spatial information and, conversely, expand often sparse spatial transcriptomic data to all 20,000 genes in the human genome. Using these augmented data sets, we begin to quantify how specific cellular neighbors influence each other and to predict how cells and tissues might respond to interventions.
Short Bio:
Barbara E. Engelhardt is an associate professor in the Princeton Computer Science Department. She graduated from Stanford University and received her Ph.D. from the University of California, Berkeley, advised by Professor Michael Jordan. She did postdoctoral research at the University of Chicago, working with Professor Matthew Stephens, and spent three years at Duke University as an assistant professor. Interspersed among her academic experiences, she spent two years working at the Jet Propulsion Laboratory, a summer at Google Research, a year at 23andMe, and a year at Genomics plc. Professor Engelhardt received an NSF Graduate Research Fellowship, the Google Anita Borg Memorial Scholarship, and the Walter M. Fitch Prize from the Society for Molecular Biology and Evolution. As a faculty member, she received the NIH NHGRI K99/R00 Pathway to Independence Award, a Sloan Faculty Fellowship, and an NSF CAREER Award. She is on the Scientific Advisory Boards of Freenome and Celsius Therapeutics. Professor Engelhardt’s research interests involve developing statistical models and methods for the analysis of high-dimensional biomedical data, with a goal of understanding the underlying biological mechanisms of complex phenotypes and human disease.
