Learning Cross-Domain Representations for Transferable Drug Perturbations on Single-Cell Transcriptional Responses

• URL: http://arxiv.org/abs/2412.19228v2
• Date: Wed, 15 Jan 2025 01:16:30 GMT
• Title: Learning Cross-Domain Representations for Transferable Drug Perturbations on Single-Cell Transcriptional Responses
• Authors: Hui Liu, Shikai Jin,
• Abstract summary: Phenotypic drug discovery has attracted widespread attention because of its potential to identify bioactive molecules. In this paper, we propose XTransferCDR, a novel generative framework designed for feature decoupling and transferable representation learning. The experimental results show that XTransferCDR achieved better performance than current state-of-the-art methods.
• Score: 3.5863110323469
• License:
• Abstract: Phenotypic drug discovery has attracted widespread attention because of its potential to identify bioactive molecules. Transcriptomic profiling provides a comprehensive reflection of phenotypic changes in cellular responses to external perturbations. In this paper, we propose XTransferCDR, a novel generative framework designed for feature decoupling and transferable representation learning across domains. Given a pair of perturbed expression profiles, our approach decouples the perturbation representations from basal states through domain separation encoders and then cross-transfers them in the latent space. The transferred representations are then used to reconstruct the corresponding perturbed expression profiles via a shared decoder. This cross-transfer constraint effectively promotes the learning of transferable drug perturbation representations. We conducted extensive evaluations of our model on multiple datasets, including single-cell transcriptional responses to drugs and single- and combinatorial genetic perturbations. The experimental results show that XTransferCDR achieved better performance than current state-of-the-art methods, showcasing its potential to advance phenotypic drug discovery.

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