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

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ID: 327995
2026
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Abstract
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. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7211876151 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ren Qi, Wenjie Teng, Xin Yang, Yue Cheng, Alexey К. Shaytan, Bin Liu
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag662
URL
https://doi.org/10.1093/bioinformatics/btag662
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