Causal disentanglement for single-cell representations and controllable counterfactual generation.

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 l

SUBMITTER: Gao Y 

PROVIDER: S-EPMC12287260 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature