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使用深度生成模型进行单细胞同位化分析.

Yasuhiro Kojima1, Shinji Mii2, Shuto Hayashi3

  • 1Laboratory of Computational Life Science, National Cancer Center Research Institute, Chuo-ku, Tokyo 104-0045, Japan; Department of Computational and Systems Biology, Medical Research Insitute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-0034, Japan; Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan.

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概括
此摘要是机器生成的。

DeepCOLOR是一种新的计算框架,通过分析单细胞协同定位网络,揭示了细胞与细胞之间的相互作用. 这种方法改善了对组织和疾病中细胞间通信的理解.

关键词:
细胞与细胞的相互作用.深度生成模型深度生成模型一个单细胞的同居化.单细胞转录组学 单细胞转录组学空间转录学 空间转录学

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科学领域:

  • 计算生物学是一种计算生物学.
  • 基因组学就是基因组学.
  • 系统生物学 系统生物学

背景情况:

  • 了解细胞与细胞之间的相互作用对于疾病和组织生物学至关重要.
  • 目前分析细胞聚合的方法掩盖了细胞间通信的分子特征.

研究的目的:

  • 引入DeepCOLOR,这是一个新的计算框架,用于在单细胞分辨率下恢复细胞间定位网络.
  • 克服依赖于预定义细胞群的现有方法的局限性.

主要方法:

  • 开发DeepCOLOR,一个深度生成模型.
  • 单细胞和空间转录组学数据的整合.
  • 在模拟和真实生物数据集中分析局部化模式.

主要成果:

  • 与模拟数据上的现有方法相比,DeepCOLOR在检测局部化种群方面表现出更高的准确性.
  • 在小鼠大脑,人类状细胞癌和SARS-CoV-2感染的人类肺组织中确定了可信的细胞与细胞相互作用候选者.
  • 揭示了单细胞和分离细胞群之间的同位化关系.

结论:

  • DeepCOLOR有效地恢复了单细胞分辨率的细胞间协同定位网络.
  • 该框架广泛适用于研究跨不同空间的细胞-细胞相互作用.
  • 通过详细的相互作用分析,DeepCOLOR增强了对疾病和组织中的生物功能的理解.