Decoupling Baseline from Condition in Single-Cell Perturbation Data with SplitDGD

Decoupling the representation of normal cellular states from condition-specific perturbation responses is a central modelling problem in single-cell biology and beyond. Achieving this decoupling without deteriorating the representation’s utility for reconstruction and downstream analyses is a non-trivial trade-off. To address this, existing methods build on variational inference with soft regularisers or structural constraints on the latent, and we show that both strategies leak condition information into the invariant component or degrade the utility of the representation. We propose splitDGD, an extension of the Deep Generative Decoder framework (Schuster & Krogh, 2023) that enforces decoupling architecturally. This is obtained by constructing two separate latent blocks—an invariant one encoding condition-independent variations (e.g., of cell types) and a condition-specific one capturing the perturbation response—and by masking the latter when the perturbation is absent. Across controlled image benchmarks and single-cell perturbation datasets, in both unsupervised and label-supervised regimes, splitDGD is the only method to mitigate the leakage-vs-utility tradeoff that other models exhibit.