765-Celcomen: Spatial Causal Perturbation Modeling

Paper Talk · Apr 8, 2026 · 24m

Celcomen is a newly developed computational framework that utilizes causal disentanglement and generative graph neural networks to analyze spatial transcriptomics data. The model is designed to separate intrinsic gene regulation within a single cell from extrinsic signals coming from the surrounding tissue environment. By establishing a mathematically identifiable causal structure, the tool can predict counterfactual outcomes, allowing researchers to simulate how tissues respond to genetic perturbations like knockouts. Scientists validated the model's accuracy using simulated data and…