The Engelhardt Group develops statistical models and methods for high-dimensional genomic data. In particular, we study human genetic variation and its impact on genomic regulation, including gene expression and splicing, with the goal of identifying mechanisms of human disorders and diseases.
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 tissues might respond to interventions.
Julia Salzman, Assistant Professor of Biochemistry and of Biomedical Data Science, Stanford University
Pre-Seminar January 14th, 2020 at 4:00 PM in Clark S361