1176-CytoCommunity：基于图神经网络的组织细胞领域识别算法

这项研究介绍了一种名为 CytoCommunity 的新型算法，旨在通过细胞表型和空间分布来识别组织细胞邻域（TCNs）。该算法利用图神经网络（GNN）模型，实现了从细胞表型空间到空间邻域空间的直接映射，有效弥补了现有方法在跨尺度空间分析上的不足。通过对结直肠癌和乳腺癌等多种空间多组学数据的分析，CytoCommunity 展现了其在无监督和有监督学习任务中的卓越性能，能够更精准地捕捉特定病理条件下的组织结构。研究进一步揭示了高风险肿瘤中特有的粒细胞富集和肿瘤相关成纤维细胞富集区域，为理解复杂的细胞间通讯提供了新视角。此外，该工具不仅提升了空间结构识别的准确度，还通过识别具有预测价值的细胞邻域，为癌症的风险分层和预后评估提供了有力的计算支持。References:Hu Y, Rong J, Xu Y, Xie R, Peng J, Gao L, Tan K. Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes. Nat Methods. 2024 Feb;21(2):267-278. doi: 10.1038/s41592-023-02124-2. Epub 2024 Jan 8. PMID: 38191930; PMCID: PMC10864185.前往小宇宙评论区与主播互动

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1176-CytoCommunity：基于图神经网络的组织细胞领域识别算法

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