Authors
Yu, L., Hsieh, K.-L., Chu, Y., Lan, Q., Zhao, X., Hsu, Y.-C., Wood, C. S., Rasmy, L., Pilie, P. G., Zhi, D., Zhao, Z., Jiang, X., Dai, Y.
Abstract
Single-cell perturbation profiling maps intervention-induced phenotypes, yet experiments measure only a fraction of the perturbation-context space. Learning context-dependent perturbation effects could enable response prediction beyond measured conditions. Here we introduce PerturbLDM, a latent-diffusion framework for conditional generation of single-cell transcriptional responses. Following Tahoe-100M pretraining, it outperformed leading methods across 13,942 held-out combinations of observed drugs, doses and cell lines, with higher matched-control effect correlation than an additive marginal baseline in 95.2% of conditions. The Tahoe-100M-pretrained model was further used to rank PANACEA compounds by pathway similarity, placing shared-mechanism pairs among nearest neighbours. In smaller datasets, PerturbLDM generated a mid-gestational fetal-colon state with 67% lower gene-wise error than Squidiff, retaining the balance between absorptive and BEST4/OTOP2-like epithelial programmes. In PBMCs, it captured six of seven interferon and antiviral programmes and the interferon-associated FAO-OXPHOS programme more accurately than scGen. Together, these results support conditional response generation across data scales and biological settings.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 13 Aug 2026.
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