Talk Title:

Enabling the robust characterization of spatial gene expression architecture in tissue sections at increased resolution using Bayesian modeling

Talk Summary:

New single-cell technologies such as single-cell RNA-seq and high-dimensional flow cytometry enable the unprecedented interrogation of single-cell phenotypes (and functions) under various biological conditions. A common statistical problem is the discovery and characterization of such cell phenotypes from single-cell data and their relationship to clinical outcomes including response to cancer immunotherapy or protection after vaccination. More recently, technological advances (e.g. Spatial Transcriptomics) have allowed for high-throughput profiling of gene expression while retaining spatial information bringing new computational challenges. During this talk, I will present some statistical methodology we have developed to analyze spatial transcriptomic data and enhance the resolution of these data bringing us closer to single-cell resolution. I will illustrate these novel approaches using simulated data and several publicly available datasets that we have recently (re)analyzed to characterize the tumor microenvironment.