通过解耦细胞状态与空间环境，实现对组织图中反事实情境的精准预测。
Querying Counterfactuals on Tissue Graphs with Supervised Disentanglement
- 利用监督解耦分离细胞内在状态与空间上下文，以条件输入预测反事实表达变化。
- 在超过250万张空间分辨细胞数据上，反事实预测、解耦效果和可扩展性均优于现有方法。
- 可无监督发现癌症亚区，并支持针对性邻居扰动模拟，适用于肿瘤微环境研究。
组织图反事实问题探讨的是：当细胞的空间邻居关系发生变化时，其表达水平将如何改变。这类查询对预测组织内细胞行为至关重要，但现有方法缺乏统一定义，多针对特定干预类型或忽略空间依赖性。本文首次将组织图反事实建模为两类空间干预：重连细胞间连接（边扰动）或修改邻居表达（节点扰动）。我们提出Cellina框架（https://cellina.readthedocs.io），通过监督解耦将细胞内在状态与其空间上下文分离，并以空间上下文作为条件输入进行反事实预测。在包含超过250万张空间分辨细胞的结直肠癌与小鼠脑数据集上，Cellina在模拟图扰动、解耦性能及可扩展性方面均超越空间感知与非空间基线模型。此外，该方法能无监督识别生物上不同的癌症亚区域，并支持定向邻居扰动仿真。
原文摘要 · Abstract (English)
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.
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