DECANT: decoupling mechanism from context in single-cell drug perturbation representation

Motivation: Single-cell chemical perturbation profiling offers a powerful opportunity to organize drugs by shared mechanism-associated transcriptional responses, but observed transcriptional responses are entangled with contextual variation from cell identity, dose, and treatment time. As a result, models that perform well in perturbation-response prediction may still learn latent spaces dominated by context-associated structure rather than transferable drug-associated signal. We developed DECANT to learn mechanism-aligned perturbation representations that remain stable across context shifts while preserving response fidelity. Results: DECANT represents each perturbation as a matched treated–control cell set and separates a context-suppressed, mechanism-aligned perturbation representation from context-dependent response information. The resulting mechanism-aligned perturbation space is shaped to support drug-level retrieval and biological interpretation. Under a fixed drug-level unseen-compound benchmark, DECANT achieved the strongest overall response-difference profile among adapted published perturbation models and strong pseudo-bulk baselines across gene- and program-level metrics. Beyond prediction, DECANT produced embeddings that remained stable across changes in dose, cell line, and treatment time, recovered drug neighborhoods enriched for shared mechanism-family annotations, and linked these neighborhoods to interpretable downstream consequence programs. Ablation analyses showed that mechanism–context decoupling provided the main signal-separation backbone, whereas retrieval-oriented shaping was critical for organizing local representation-space geometry. These results support DECANT as a framework for learning context-robust, mechanism-aligned perturbation representations from single-cell transcriptional responses, providing a basis for mechanism-aligned perturbation analysis and representation-based compound prioritization. Availability and implementation: The DECANT web server is publicly available at http://bliulab.net/DECANT. All source code and analysis scripts are available at https://github.com/bliulab/DECANT and archived on Zenodo at https://doi.org/10.5281/zenodo.21216567.