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Learn computational methods for identifying spatial domains in spatial transcriptomics data through this 31-minute conference talk from the Computational Genomics Summer Institute 2024. Explore advanced techniques including BayesSpace for subspot resolution analysis, adaptive graph attention auto-encoders for deciphering spatial domains, and benchmarking approaches for spatial clustering methods. Discover how to infer allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics data, and understand interpretable deep learning approaches for mapping spatial gene expression topography. Gain insights into cutting-edge research spanning from foundational spatial transcriptomics methods published in Nature Biotechnology and Nature Communications to the latest developments in tumor spatial analysis and interpretable machine learning techniques for genomic spatial data.
