Doctoral Thesis: Computational and statistical methods for spatial transcriptomics

Dylan Cable

Abstract:

Spatial transcriptomics technologies are an emerging class of high-throughput sequencing methodologies for measuring gene expression at near single-cell resolution at spatially-defined measurement spots across a biological tissue. We show how measuring cells in their native environment has the potential to identify spatial patterns of cell types, cell-to-cell interactions, and spatial variation in cellular behavior. However, several technical challenges necessitate the development of appropriate statistical methods, including additive mixtures of single cells, overdispersion, and technical platform effects across technologies. We develop a statistical framework accounting for these challenges to identify cell types within spatial transcriptomics datasets. We extend this approach to a general regression framework that can, accounting for multiple replicates, learn cell type-specific differential gene expression (DE) across many scenarios including DE across spatial regions and due to cell-to-cell interactions. We apply our framework to a metastatic tumor clone and discover an association between immune cell localization and an epithelial-mesenchymal transition of cancer cells. We also discuss extensions and future research.

Thesis Supervisors: Rafa Irizarry and Fei Chen

Details

• Date: Thursday, May 11
• Time: 11:00 am - 12:30 pm
• Category: Thesis Defense
• Location: 32-G882 (Hewlett Room)

Additional Location Details:

Zoom link available; contact doctoral candidate if interested.