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顶层空间:在多重成像中无监督发现多细胞空间组织结构的空间主题建模.

Junsouk Choi, Jian Kang, Veerabhadran Baladandayuthapani

    ArXiv
    |May 2, 2025
    PubMed
    概括

    我们开发了TopSpace,这是一个新的空间主题模型,用于分析组织图像. 它可以识别复杂的细胞结构,如三级淋巴细胞结构 (TLS),并预测非小细胞肺癌 (NSCLC) 患者的生存率.

    科学领域:

    • 计算生物学是一种计算生物学.
    • 空间转录组学 空间转录组学
    • 生物信息学是一种生物信息学.

    背景情况:

    • 了解组织空间结构对于疾病病理学至关重要.
    • 多复合成像揭示了细胞表型和空间分布.
    • 现有的方法与细微的细胞社区分析作斗争.

    研究的目的:

    • 开发一种新的空间主题建模框架,用于无监督发现空间组织结构.
    • 解决硬集群和基于邻近的模型的局限性.
    • 在多重成像数据中分析复杂的细胞相互作用.

    主要方法:

    • 提出了TopSpace,这是一个贝叶斯空间主题模型,将高斯过程与潜在的迪里克莱特分配集成在一起.
    • 实施了多细胞混合成员集群的框架.
    • 利用强大的不确定性量化和数据驱动的微环境数量的确定.

    主要成果:

    • 在模拟中,TopSpace准确地恢复潜伏的组织微环境和空间聚类模式.
    • 在不同空间依赖的场景中表现优于现有方法.
    • 在非小细胞肺癌 (NSCLC) 数据中确定了三级淋巴体结构 (TLS),与患者存活率相关.

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    结论:

    • TopSpace提供了一种灵活而强大的方法来分析空间组织结构.
    • 该模型成功地捕捉了复杂的细胞社区及其空间关系.
    • 由TopSpace识别的空间模式对预测患者的结果有重大影响.