Physics-Guided Graph Networks for Gene Expression Recovery in Spatially Overlapping Cells

Overlapping segmented cell footprints can mix gene expression and obscure source profiles in Stereo-cell sequencing. We form paired mixtures by aligning two measured PBMC chips and translating one chip map relative to the other; directional polygon overlap sets donor contributions, while true overlap edges and source profiles remain hidden at inference. We present (Physics-Guided Graph Recovery), a four-block network with a learned circle-overlap surrogate, residual backprojection, adaptive increment modulation, and graph-context correction. Identity-disjoint evaluation uses three technically matched chips. On the fixed test split, achieves the lowest Count-MAE, Log-MAE, and Rel. $L_1$ among evaluated methods; its Log-MAE is $0.00369$ versus $0.00888$ for GATv2 ($58.4\%$ lower, based on three-seed means). Under strict receiver leave-one-chip-out evaluation within this chip series, it again has the lowest three errors, with Log-MAE of $0.00801$ versus $0.01305$ for GraphSAGE. Component removals support all three modules within the evaluated stack. These results support source recovery under controlled geometric crosstalk.