Local-Atlas Control-Anchored Flow Matching for Unpaired Single-Cell Perturbation Prediction

khan huang, Chang Li

Keywords: single-cell perturbation prediction, flow matching, optimal transport, causal invariance, foundation models for life sciences

Abstract:

Destructive single-cell perturbation assays ob-
serve control and treated populations without
true before/after pairs, so response prediction
is an unpaired conditional distribution problem
rather than paired regression. We introduce
Atlas-CFM, a local-atlas control-anchored flow-
matching formulation for this setting. The method
constructs training paths and inference rollouts
from the same observed control-derived anchor,
uses transport-aware pseudo-pairing to reduce tra-
jectory noise, and regularizes dynamics with a soft
tangent/normal vector-field decomposition. On
Norman perturbation-OOD and sciPlex3-K562
dose-extrapolation benchmarks, the largest gains
occur in the harder generalization regimes. Sup-
plementary native-space checks, distribution met-
rics, consistency studies, sensitivity analyses, and
A549 cross-context results show that training-
inference-consistent control anchoring strength-
ens perturbation-response modules in life-science
foundation-model systems.

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Submission Number: 122