| Summary: | Spatial transcriptomics technologies are an emerging class of high-throughput sequencing methodologies for measuring gene expression at near single-cell resolution at spatiallydefined 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. The key contributions of this thesis include developing 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 extend our approach to identify cell types from spatial transcriptomics data in an unsupervised manner, drawing from our other algorithms.
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