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来自多重成像数据的细胞计数的统计建模和分析.

Pierre Bost1, Ruben Casanova1, Uria Mor2

  • 1University of Zurich, Department of Quantitative Biomedicine, Zurich 8057, Switzerland; ETH Zurich, Institute for Molecular Health Sciences, Zurich 8093, Switzerland.

Cell systems
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概括

新的统计模型通过准确描述细胞分布来改善多重成像数据的分析. 这些模型增强了用于比较组织样本的统计能力,特别是在处理细胞聚合时.

关键词:
实验设计 实验设计多重成像多重成像成像统计建模 统计建模

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物统计学 生物统计学
  • 病理学 病理学 病理学

背景情况:

  • 多重成像技术允许在健康和生病组织中详细地绘制细胞的空间图.
  • 现有的统计模型不足以在不同的样本组中比较组织细胞性.
  • 准确的统计分析对于了解健康和疾病中的组织组成至关重要.

研究的目的:

  • 开发和验证用于分析多重成像数据中的细胞数分布的统计模型.
  • 为了确定增强差异丰度测试功率的统计测试.
  • 解决组织样本中高度聚合的细胞数据分析的挑战.

主要方法:

  • 开发了两种用于细胞计数分布的新型统计模型.
  • 模型的应用用于成像从淋巴结,COVID-19肺和哈希莫托病组织的质量细胞计数据.
  • 与传统的基于等级的测试相比,新测试的统计能力的比较.

主要成果:

  • 开发的模型准确地描述了细胞计数分布,将参数与视野大小以及密度和空间聚合等细胞特性联系起来.
  • 识别的统计测试显示,与基于等级的方法相比,差异性丰度测试的功率有所提高.
  • 空间聚合显著影响统计能力,需要更大的样本大小,高度聚合的细胞.

结论:

  • 拟议的统计模型为分析多重成像数据提供了一个强大的框架.
  • 引入了分层采样策略,以减少处理聚合细胞时的样本大小要求.
  • 这些进步有助于在样本组之间更强大,更有效地比较组织组成.