Statistical methods for the analysis of spatial gene expression data

Abstract

In recent years, there has been rapid development of spatial gene expression and spatial transcriptomics technologies, though corresponding advances in computational and statistical tools for the analysis of the data generated from these technologies have lagged. Initial approaches often neglected to consider differences between spatial transcriptomics and its predecessors, thus leading to analyses that may not fully realize the potential of spatial transcriptomics to generate biological insights. The overall aim of my dissertation research is to develop statistical methods for the analysis of spatial gene expression data. Specifically, I present new approaches for spatial clustering and resolution enhancement of spatial transcriptomics data as well as a joint model for spatial transcriptomics and single-cell RNA sequencing data.