Causal disentanglement for single-cell representations and controllable counterfactual generation

Yicheng Gao, Kejing Dong, Caihua Shan (), Dongsheng Li () and Qi Liu ()
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Yicheng Gao: Tongji University
Kejing Dong: Tongji University
Caihua Shan: Microsoft Research Asia
Dongsheng Li: Microsoft Research Asia
Qi Liu: Tongji University

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-62008-1

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