Celcomen：用于单细胞和组织扰动建模的空间因果解耦
Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling
AI总结：
提出Celcomen框架，结合因果建模与生成式图神经网络解耦细胞内外基因调控，并预测扰动后的空间转录组反事实结果。
摘要：
Celcomen利用数学因果框架，通过生成式图神经网络解耦空间转录组学和单细胞数据中的细胞内及细胞间基因调控程序。它能够学习基因-基因相互作用，并生成扰动后的反事实空间转录组学数据，从而获取实验上无法获得的样本。我们通过模拟实验，以及在具有临床相关性的人类胶质母细胞瘤、人类胎儿脾脏和小鼠肺癌样本中，验证了其解耦、可辨识性和反事实预测能力。Celcomen为建模疾病和治疗诱导的变化提供了手段，使人们能够深入了解与人类健康相关的单细胞空间分辨组织反应。
英文摘要：
Celcomen leverages a mathematical causality framework to disentangle intra- and inter- cellular gene regulation programs in spatial transcriptomics and single-cell data through a generative graph neural network. It can learn gene-gene interactions, as well as generate post-perturbation counterfactual spatial transcriptomics, thereby offering access to experimentally inaccessible samples. We validated its disentanglement, identifiability, and counterfactual prediction capabilities through simulations and in clinically relevant human glioblastoma, human fetal spleen, and mouse lung cancer samples. Celcomen provides the means to model disease and therapy induced changes allowing for new insights into single-cell spatially resolved tissue responses relevant to human health.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
