Unlasting: Unpaired Single-Cell Multi-Perturbation Estimation by Dual Conditional Diffusion Implicit Bridges
Journal:
arXiv
Published Date:
Jun 26, 2025
Abstract
Estimating single-cell responses across various perturbations facilitates the
identification of key genes and enhances drug screening, significantly boosting
experimental efficiency. However, single-cell sequencing is a destructive
process, making it impossible to capture the same cell's phenotype before and
after perturbation. Consequently, data collected under perturbed and
unperturbed conditions are inherently unpaired. Existing methods either attempt
to forcibly pair unpaired data using random sampling, or neglect the inherent
relationship between unperturbed and perturbed cells during the modeling. In
this work, we propose a framework based on Dual Diffusion Implicit Bridges
(DDIB) to learn the mapping between different data distributions, effectively
addressing the challenge of unpaired data. We further interpret this framework
as a form of data augmentation. We integrate gene regulatory network (GRN)
information to propagate perturbation signals in a biologically meaningful way,
and further incorporate a masking mechanism to predict silent genes, improving
the quality of generated profiles. Moreover, gene expression under the same
perturbation often varies significantly across cells, frequently exhibiting a
bimodal distribution that reflects intrinsic heterogeneity. To capture this, we
introduce a more suitable evaluation metric. We propose Unlasting, dual
conditional diffusion models that overcome the problem of unpaired single-cell
perturbation data and strengthen the model's insight into perturbations under
the guidance of the GRN, with a dedicated mask model designed to improve
generation quality by predicting silent genes. In addition, we introduce a
biologically grounded evaluation metric that better reflects the inherent
heterogeneity in single-cell responses.
