HICON: Hierarchical Technical Conditioning for Contrastive Cell Painting Batch Correction

Batch effects remain a major challenge in analyzing Cell Painting data. They introduce hierarchical and spatially structured technical variations that are entangled with perturbation-induced biological variations. Existing methods rely on global feature alignment or normalization over predefined batch groups. However, these approaches may fail to capture the heterogeneous technical variations, which can disrupt the biological signals. In this paper, we formulate Cell Painting batch correction as a conditional representation learning problem, aiming to maximize biological information given latent technical states. We propose HICON, a hierarchy-aware technical conditioning framework for contrastive biological representation learning. HICON uses a two-branch architecture to disentangle biological and technical representations. The technical branch learns a hierarchical technical embedding with a confidence-aware mechanism to capture sample-specific technical variation beyond metadata labels. The biological branch leverages perturbation replicates to define positive and negative samples and adaptively reweights the contrastive differentiation between the sampled pairs according to their inferred technical similarity, thereby emphasizing perturbation variation under comparable technical conditions. Extensive experiments across JUMP-CP, BBBC, CPG0036, and RxRx1 show that HICON achieves strong batch correction, preserves biological structure, and improves downstream biological utility.