用因果模型拆解细胞间基因调控，生成治疗后组织图谱。
Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling
- 基于图神经网络的因果框架，分离细胞内与细胞间调控机制。
- 可生成扰动后虚拟空间转录组，还原不可实验获取的样本。
- 在胶质母细胞瘤等数据中验证了建模能力，适合生物医学研究者。
Celcomen 利用数学因果框架，通过生成式图神经网络，在空间转录组和单细胞数据中解耦细胞内与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，从而获得实验无法直接获取的样本。我们在模拟数据及临床相关的人胶质母细胞瘤、人胎儿脾脏和小鼠肺癌样本中验证了其解耦、可识别性与反事实预测能力。Celcomen 为建模疾病和治疗引起的改变提供了新工具，有助于揭示与人类健康相关的单细胞空间组织响应机制。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。