Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement

arXiv:2606.08493v1 Announce Type: cross Abstract: extit{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 extit{tissue graph counterfactuals} as a class of spatial interventions that either rewire connections between cells (extit{edge perturbation}) or modify the expression of their neighbors (extit{node perturbation}). We then introduce extit{Cellina} {renewcommand{hefootnote}{dag}footnote{https://cellina.readthedocs.io}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, extit{Cellina} outperforms spatially-informed and non-spatial competitors in tissue perturbations, disentanglement, and scalability. Additionally, we show that extit{Cellina} reveals biologically distinct cancer subdomains in an unsupervised manner and enables targeted neighbor perturbation simulations.

Source: arXiv cs.LG | 2026-06-09