Causal disentanglement for single-cell representations and controllable counterfactual generation

Area of Science:

• Computational Biology
• Genomics
• Artificial Intelligence

Background:

• Traditional black-box models for single-cell omics data lack explainability.
• Disentanglement learning aims to separate biological concepts within data.
• Need for reliable and interpretable single-cell data representations.

Purpose of the Study:

• Introduce CausCell for reliable disentangled cellular representations.
• Enhance explainability, generalizability, and controllability of single-cell omics data.
• Establish a comprehensive benchmark for single-cell disentanglement learning.

Main Methods:

• Developed CausCell, a diffusion model incorporating causal relationships.
• Applied factual information about causal links among disentangled concepts.
• Utilized quantitative evaluation scenarios for disentanglement and reconstruction.

Main Results:

• CausCell outperforms state-of-the-art methods in disentanglement and reconstruction benchmarks.
• Demonstrated CausCell's ability for controllable data generation via concept intervention.
• Showcased potential for uncovering biological insights through counterfactual generation.

Conclusions:

• CausCell provides a more reliable and interpretable approach to single-cell data analysis.
• The method advances disentanglement learning in computational biology.
• CausCell offers potential for biological discovery from complex omics datasets.