Causal disentanglement for single-cell representations and controllable counterfactual generation

作者：Yicheng Gao, Kejing Dong, Caihua Shan, Dongsheng Li, Qi Liu · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-025-62008-1 · 被引用次数：8 · 研究领域：Cell Image Analysis Techniques、Single-cell and spatial transcriptomics、Gene Regulatory Network Analysis

Conducting disentanglement learning on single-cell omics data offers a promising alternative to traditional black-box representation learning by separating the semantic concepts embedded in a biological process. We present CausCell, which incorporates the factual information about causal relationships among disentangled concepts within a diffusion model to generate more reliable disentangled cellular representations, with the aim of increasing the explainability, generalizability and controllability of single-cell data, including spatial-temporal omics data, relative to those of the existing black-box representation learning models. Two quantitative evaluation scenarios, i.e., disentanglement and reconstruction, are presented to conduct the first comprehensive single-cell disentanglement learning benchmark, which demonstrates that CausCell outperforms the state-of-the-art methods in both scenarios. Additionally, CausCell can implement controllable generation by intervening with the concepts of single-cell data when given a causal structure. It also has the potential to uncover biological insights by generating counterfactuals from small and noisy single-cell datasets. Single-cell omics data contain complex, entangled biological signals that challenge interpretation. Here, authors present CausCell, a causal disentanglement framework that outperforms current methods in generating explainable, generalisable, and controllable representations.