用反事实推理分离单细胞数据中的技术噪声，提升基因扰动预测效果。
CRADLE-VAE: Enhancing Single-Cell Gene Perturbation Modeling with Counterfactual Reasoning-based Artifact Disentanglement
- 基于反事实推理解耦技术伪影与真实扰动效应
- 在多个数据集上显著提升治疗效果估计与生成质量
- 适合从事单细胞药物研发与生成模型研究的学者
预测细胞对各种扰动的响应是药物发现和个性化治疗的关键，深度学习模型在此领域发挥重要作用。单细胞数据包含可能影响模型可预测性的技术伪影，带来质量控制难题。为此，我们提出CRADLE-VAE，一种针对单细胞基因扰动建模的因果生成框架，通过基于反事实推理的伪影解耦机制进行增强。训练过程中，CRADLE-VAE建模单细胞数据中技术伪影与扰动效应的潜在分布，利用反事实推理调节基线潜空间以有效解耦伪影，学习鲁棒特征并生成高质量的细胞响应数据。实验结果表明，该方法不仅提升了治疗效应估计性能，还改善了生成质量。CRADLE-VAE代码已公开于https://github.com/dmis-lab/CRADLE-VAE。
原文摘要 · Abstract (English)
Predicting cellular responses to various perturbations is a critical focus in drug discovery and personalized therapeutics, with deep learning models playing a significant role in this endeavor. Single-cell datasets contain technical artifacts that may hinder the predictability of such models, which poses quality control issues highly regarded in this area. To address this, we propose CRADLE-VAE, a causal generative framework tailored for single-cell gene perturbation modeling, enhanced with counterfactual reasoning-based artifact disentanglement. Throughout training, CRADLE-VAE models the underlying latent distribution of technical artifacts and perturbation effects present in single-cell datasets. It employs counterfactual reasoning to effectively disentangle such artifacts by modulating the latent basal spaces and learns robust features for generating cellular response data with improved quality. Experimental results demonstrate that this approach improves not only treatment effect estimation performance but also generative quality as well. The CRADLE-VAE codebase is publicly available at https://github.com/dmis-lab/CRADLE-VAE.
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