SelCell: Self-Certifying Cell Velocity from Destructive Snapshots

Spatial transcriptomics destroys the tissue it measures, so every cell is observed exactly once: cell velocity—where cells are going—must be inferred from a single snapshot and can never be checked against a later observation. Current practice reads RNA velocity as cell motion, an implicit assumption that real data do not always support: coherent arrows are drawn even on embryonic tissue where the motion signal is indistinguishable from a shuffled control, and existing confidence scores do not separate a method’s reliable cells from the rest. We replace the assumption with an explicit observation model that separates a cell’s intrinsic expression change from the change caused by its motion across the tissue, and invert it in closed form—training-free, compatible with any RNA-velocity backbone. Three per-cell certificates then follow analytically, each answering a different question: where to trust the recovered arrows, how much (direction-wise error bars, order-optimal for the noise they certify), and what is being measured (motion, or a wave of expression sweeping past still cells—bounded by planting synthetic waves). On real tissue the error bars are calibrated and remain valid on the hardest cells where split conformal bands collapse; ranking cells by the validity certificate is the only ranking we found that improves the kept cells. On certified tissue SELCELL outperforms the strongest baseline (CBDir 0.232 vs 0.183, non-overlapping intervals) while abstaining where no motion signal exists. Code, raw-read rebuild pipelines, and the preregistered, hash-sealed protocols are in the supplementary material. We believe this recipe—replace an unchecked assumption with an explicit observation model, then certify what its inversion returns—extends to any estimand that can never be observed twice.