Spatial perturbation profiling is becoming an important tool in functional genomics because it reveals how genetic interventions reshape transcription within intact tissue contexts. However, destructive readout and limited screening capacity leave many perturbation-by-location response profiles unmeasured, motivating the task of spatial perturbation profile completion. The task is to infer the held-out response population at query locations from reported profiles of the same perturbation. Existing methods either generate responses de novo or reuse these profiles without spatial adaptation. These strategies make it difficult to preserve empirical population structure while modeling location-specific variation. Our key insight is that the reported population already defines an empirical response distribution for the target perturbation. To exploit this empirical support, we propose Reference-Anchored Dynamic Flow (RADF), which employs a Sinkhorn-balanced decoder to construct a population-valued anchor in which every reference profile has equal total contribution. Additionally, a bounded dynamic relational flow is used to recompute spatial relations from the evolving expression state and query geometry. Across diverse spatial contexts, RADF reduces macro E-distance by 70.6% compared with an existing state-of-the-art spatial method, highlighting the advantage of combining a reference-supported population anchor with bounded, location-dependent refinement. Code will be made publicly available upon acceptance.
Cai, H., Wang, H., Chen, J., Xue, Z., Sheng, X., Zhang, T.
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