GEARS is a geometry-first generative framework that learns domain-invariant encoders and permutation-equivariant diffusion generators to reconstruct intrinsic 2D cell coordinates and distance matrices from unpaired scRNA-seq guided by ST.
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Geometry-First Generative Spatial Single-Cell Reconstruction
GEARS is a geometry-first generative framework that learns domain-invariant encoders and permutation-equivariant diffusion generators to reconstruct intrinsic 2D cell coordinates and distance matrices from unpaired scRNA-seq guided by ST.