Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement

Abstract:Tissue graph counterfactuals ask how a cell's expression would change under altered spatial neighbor contexts. Such queries are central to predicting cell behavior in tissues, but lack a unified definition, with existing methods targeting specific intervention types or treating cells as i.i.d. In this work, we first formalize tissue graph counterfactuals as a class of spatial interventions that either rewire connections between cells (edge perturbation) or modify the expression of their neighbors (node perturbation). We then introduce Cellina (this https URL) - a framework that uses supervised disentanglement to decompose a cell's intrinsic state from its spatial context, using the latter as a conditioning input for counterfactual predictions. Across benchmarks spanning over 2.5 million spatially-resolved cells in colorectal cancer and mouse brain, Cellina outperforms spatially-informed and non-spatial competitors in in-silico graph perturbations, disentanglement, and scalability. Additionally, we show that Cellina reveals biologically distinct cancer subdomains in an unsupervised manner and enables targeted neighbor perturbation simulations.

Genomics (q-bio.GN); Machine Learning (cs.LG); Machine Learning (stat.ML)

arXiv:2606.08493 [q-bio.GN]

(or arXiv:2606.08493v2 [q-bio.GN] for this version)

https://doi.org/10.48550/arXiv.2606.08493 Focus to learn more arXiv-issued DOI via DataCite

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