Deep Learning for Spatial Transcriptomics: Coverage and Resolution Enhancement
Date:
Seminar on applying deep learning to enhance coverage and resolution in spatial transcriptomics data. Presented the MuST (Multimodal Structure Transformation) framework for unified spatial transcriptomics analysis. Discussed methods for imputing missing gene expression, super-resolution reconstruction of spatial maps, and integrating imaging with sequencing data. Covered attention-based architectures that learn spatial dependencies and cell-cell communication patterns. Demonstrated applications in tumor microenvironment analysis, developmental biology, and neuroscience. Showed how physics-inspired priors and biological constraints can improve model generalization and interpretability in spatial omics analysis.