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这项研究引入了一种集成的工作流程,用于分析多重组织成像数据,从而实现细胞的详细空间概况. 这种用户友好的协议简化了基础生物信息学培训的研究人员的复杂计算任务.

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 图像分析 图像分析

背景情况:

  • 多复合成像允许以单细胞分辨率同时对组织中的众多生物分子进行空间分析.
  • 从这些数据中提取生物见解需要复杂的计算步骤,如细分,特征提取和空间分析.

研究的目的:

  • 为处理和分析多重组织成像数据提供一个端到端,用户友好和可定制的工作流.
  • 整合现有的计算工具,进行精简的图像分析和空间解析的单细胞分析.

主要方法:

  • 使用napari-imc进行原始数据检查和细胞映射器进行基于R的可视化.
  • 使用steinbock工具包进行预处理,细分 (监督和深度学习) 和特征提取.
  • 利用R/生物导体包和imcRtools进行下游分析,包括缩小维度,表型和细胞与细胞相互作用测试.

主要成果:

  • 展示了适用于成像质细胞计的综合工作流程,并适应其他多重成像技术.
  • 在工作流程中展示了两个不同的细胞细分方法.
  • 启用空间解析的单细胞分析,包括社区和邻里检测.

结论:

  • 提出的工作流简化了复杂的计算任务在多重组织成像分析.
  • 该协议可供具有基本生物信息学培训的研究人员访问,并且可以有效地完成.
  • 通过综合空间单细胞分析,促进更深入的生物学理解.