Authors
Boyang Wu, Yuhang Liu, Yue Cheng, Xiangrong Liu, Quan Zou, Leyi Wei, Lei Xu
Published in
Bioinformatics (Oxford, England). Sep 28, 2026. Epub Sep 28, 2026.
Abstract
Accurate prediction of single-cell responses to external stimuli is pivotal for deciphering gene regulatory mechanisms and accelerating data-driven drug discovery. However, effectively capturing the complex, non-linear mapping between intrinsic cell states and external stimuli remains an open problem.
We propose scLDM, a generative framework based on latent diffusion models for predicting single-cell perturbation responses. scLDM first compresses high-dimensional gene expression into a compact latent space via a variational autoencoder, followed by a conditional diffusion process to generate post-perturbation states, explicitly guided by pre-perturbation cellular state, cell type, and perturbation type. Systematic evaluations on six datasets across diverse biological settings, spanning pharmacological stimulation, viral infection, helminth infection, genetic perturbations, and multi-species immune response contexts, demonstrate that scLDM achieves superior predictive accuracy compared to state-of-the-art methods. Furthermore, the model exhibits strong interpretability, as the learned perturbation embeddings show high functional alignment with known biological mechanisms. Overall, scLDM provides a robust and biologically consistent strategy for in silico perturbation screening.
The code is available at https://github.com/samrogers1233/scLDM and archived on Zenodo at https://doi.org/10.5281/zenodo.22658164.
Supplementary data are available at Bioinformatics online.
PMID:
42804716
Bibliographic data and abstract were imported from PubMed on 29 Sep 2026.
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