scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866917010763415552 |
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| author | Liang, Zhaokang Zhuang, Shuyang Jiao, Xiaoran Mao, Weian Chen, Hao Shen, Chunhua |
| author_facet | Liang, Zhaokang Zhuang, Shuyang Jiao, Xiaoran Mao, Weian Chen, Hao Shen, Chunhua |
| contents | This paper introduces the Single-Cell Perturbation Prediction Diffusion Model (scPPDM), the first diffusion-based framework for single-cell drug-response prediction from scRNA-seq data. scPPDM couples two condition channels, pre-perturbation state and drug with dose, in a unified latent space via non-concatenative GD-Attn. During inference, factorized classifier-free guidance exposes two interpretable controls for state preservation and drug-response strength and maps dose to guidance magnitude for tunable intensity. Evaluated on the Tahoe-100M benchmark under two stringent regimes, unseen covariate combinations (UC) and unseen drugs (UD), scPPDM sets new state-of-the-art results across log fold-change recovery, delta correlations, explained variance, and DE-overlap. Representative gains include +36.11%/+34.21% on DEG logFC-Spearman/Pearson in UD over the second-best model. This control interface enables transparent what-if analyses and dose tuning, reducing experimental burden while preserving biological specificity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_11726 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction Liang, Zhaokang Zhuang, Shuyang Jiao, Xiaoran Mao, Weian Chen, Hao Shen, Chunhua Quantitative Methods Machine Learning This paper introduces the Single-Cell Perturbation Prediction Diffusion Model (scPPDM), the first diffusion-based framework for single-cell drug-response prediction from scRNA-seq data. scPPDM couples two condition channels, pre-perturbation state and drug with dose, in a unified latent space via non-concatenative GD-Attn. During inference, factorized classifier-free guidance exposes two interpretable controls for state preservation and drug-response strength and maps dose to guidance magnitude for tunable intensity. Evaluated on the Tahoe-100M benchmark under two stringent regimes, unseen covariate combinations (UC) and unseen drugs (UD), scPPDM sets new state-of-the-art results across log fold-change recovery, delta correlations, explained variance, and DE-overlap. Representative gains include +36.11%/+34.21% on DEG logFC-Spearman/Pearson in UD over the second-best model. This control interface enables transparent what-if analyses and dose tuning, reducing experimental burden while preserving biological specificity. |
| title | scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction |
| topic | Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2510.11726 |