Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement

Abstract:\textit{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 \textit{tissue graph counterfactuals} as a class of spatial interventions that either rewire connections between cells (\textit{edge perturbation}) or modify the expression of their neighbors (\textit{node perturbation}). We then introduce \textit{Cellina} {\renewcommand{\thefootnote}‡\footnote{this https URL}\addtocounter{footnote}{-1}}, 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, \textit{Cellina} outperforms spatially-informed and non-spatial competitors in tissue perturbations, disentanglement, and scalability. Additionally, we show that \textit{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:2606.08493v1 [q-bio.GN] for this version)

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

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