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.
- Yicheng Gao
- Kejing Dong
- Qi Liu
