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Dylan Cable, MIT – “A statistical framework for characterizing cellular behavior in spatial transcriptomics”
May 22 @ 11:00 am - 12:00 pm
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. Links:
- Software: https://github.com/dmcable/spacexr
Dylan Cable is PhD student in Computer Science at MIT specializing in developing statistical and computational methods for spatial transcriptomics data. He is advised by Fei Chen (Harvard SCRB/Broad Institute) and Rafa Irizarry (Harvard biostatistics/DFCI) and he is supported by the Hertz and NSF fellowships. Prior to MIT, Dylan completed his undergraduate degree in mathematics at Stanford, where he pursued research in probability theory and computational neuroscience. Graduating in 2023, Dylan is an incoming postdoc at Stanford/Gladstone Inst. with Barbara Engelhardt and an incoming Assistant Professor in Biostatistics at the University of Michigan in Fall 2024.
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