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Explore cutting-edge statistical methodologies revolutionizing spatial transcriptomics research in this comprehensive conference talk delivered at the International Conference on Biological Sciences 2025. Delve into innovative computational approaches and statistical frameworks that enable researchers to analyze gene expression patterns within their spatial context at unprecedented resolution. Learn about novel algorithms and mathematical models designed to handle the complexity of spatially-resolved transcriptomic data, including methods for spatial clustering, differential expression analysis, and trajectory inference. Discover how these statistical innovations are overcoming traditional limitations in spatial biology, enabling more accurate identification of cell types, tissue architecture, and cellular communication networks. Examine real-world applications demonstrating how these advanced statistical techniques are being applied to understand disease mechanisms, developmental biology, and tissue organization. Gain insights into the computational challenges inherent in processing large-scale spatial transcriptomics datasets and the elegant statistical solutions being developed to address them. Understand the integration of machine learning approaches with traditional statistical methods to enhance spatial pattern recognition and biological interpretation. This presentation provides valuable perspectives for computational biologists, statisticians, and researchers working at the intersection of genomics and spatial biology.
