ReliST: A model-agnostic risk layer for spatial transcriptomics deconvolution

Spatial transcriptomics deconvolution maps cell-type abundance across tissue, but most outputs do not indicate which local predictions can be trusted. We developed ReliST, a model-agnostic risk layer that preserves base predictions while assigning spot-level risk from output ambiguity, local inconsistency, and reference-related evidence. We evaluated ReliST with five deconvolution models across human dorsolateral prefrontal cortex (DLPFC), mouse brain, human breast cancer, and an independent immunofluorescence/gene-protein dataset. In DLPFC, risk scores aligned with layer difficulty, marker discordance, and signature residuals. In mouse brain, ReliST provided reference-control and review signals without layer labels. In breast cancer, high-risk regions aligned with histology context, stromal and vascular shifts, immune-associated protein evidence, and abundance-adjusted protein residuals consistent with protein-supported under-calls. ReliST extends spatial deconvolution from prediction-only maps to risk-aware interpretation, supporting filtering, abstention, and targeted review rather than universal model ranking or spot-level truth assignment.