Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement

Abstract:Tissue graph counterfactuals ask what a cell would express if its spatial neighborhood were altered, a question central to predicting cell behavior in tissues. Existing methods, however, present only specific instances of such queries. 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 in-silico perturbation predictions. Across benchmarks spanning over 2.5 million spatially-resolved cells in observational and interventional screens, Cellina outperforms both spatially-informed and non-spatial competitors in in-silico perturbations, while training as fast as non-spatial methods. Moreover, we show that Cellina reveals cancer subdomains with distinct signaling programs 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:2606.08493v3 [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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