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Explore advanced computational methods for analyzing spatiotemporal transcriptomics data in developmental biology through this 47-minute conference talk from the Computational Genomics Summer Institute. Learn about cutting-edge techniques for aligning developmental processes across space and time, including the DeST-OT (Developmental Spatiotemporal Optimal Transport) framework for spatiotemporal transcriptomics data alignment. Discover how Hidden-Markov optimal transport models can reveal latent trajectories in developmental time series data, enabling researchers to track cellular differentiation and tissue development patterns. Examine hierarchical refinement approaches that extend optimal transport methods beyond traditional boundaries, providing new insights into complex developmental processes. Gain understanding of how these computational tools address key challenges in developmental biology, including the integration of spatial and temporal information from single-cell and spatial transcriptomics experiments. The presentation covers recent methodological advances published in Cell Systems and presented at computational biology conferences, offering practical applications for researchers studying embryonic development, tissue morphogenesis, and cellular fate determination.
