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 (https://cellina.readthedocs.io) - 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.

翻译：组织图反事实问题探讨的是：在改变空间邻近环境后，细胞表达将如何变化。此类问题对于预测组织中的细胞行为至关重要，但目前缺乏统一定义——现有方法或针对特定干预类型，或将细胞视为独立同分布个体。本文首先将组织图反事实形式化为一类空间干预：包括重连细胞间连接（边扰动）或修改其邻居表达（节点扰动）。随后我们提出Cellina框架（https://cellina.readthedocs.io），该框架通过监督解耦将细胞的内在状态与其空间环境分离，并以空间环境作为条件输入进行反事实预测。在涵盖结直肠癌和小鼠脑中超过250万个空间解析细胞的基准测试中，Cellina在计算机模拟图扰动、解耦能力和可扩展性方面均优于空间感知与非空间的竞争方法。此外，我们证明Cellina能以无监督方式揭示具有生物学差异的癌症子区域，并实现靶向邻居扰动模拟。