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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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在细胞计量中用于细胞类型注释的生物驱动的深度生成模型.

Quentin Blampey1, Nadège Bercovici2,3, Charles-Antoine Dutertre4

  • 1Université Paris-Saclay, CentraleSupélec, Laboratory of Mathematics and Computer Science (MICS), 3 rue Joliot Curie, 91190,Gif-sur-Yvette, France.

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概括

单细胞细胞测量注释网络Scyan自动化了细胞类型注释,用于高维细胞测量数据. 这种深度学习模型提高了准确性和速度,克服了手动封锁和细胞分析中的批量效应的局限性.

关键词:
批量效应校正正 批量效应校正单元格类型注释细胞计量 (cytometry) 是一种细胞计量方法.深度学习 (Deep Learning) 是一种深度学习.规范化流动 规范化流动

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科学领域:

  • 生物技术是生物技术.
  • 计算生物学 计算生物学
  • 免疫学 免疫学 免疫学

背景情况:

  • 细胞测量对于复杂的生物样本中单细胞表型化至关重要.
  • 手动关门,细胞注释的传统方法,受到糟糕的可重现性和批量效应敏感性.
  • 来自先进细胞计 (光谱,质量) 的高维数据挑战手动分析.

研究的目的:

  • 介绍Scyan,一个用于细胞计数据的自动化细胞类型注释网络.
  • 为了解决手动关门在可重复性,速度和高维数据处理方面的局限性.
  • 为细胞计分析提供可解释和高效的深度学习解决方案.

主要方法:

  • 开发Scyan,一个单细胞细胞计注释网络,使用深度生成模型 (规范流).
  • 将高维蛋白表达数据映射到一个生物相关的潜空间.
  • 利用先前对细胞计量面板的专家知识进行自动注释.

主要成果:

  • 在多个公共细胞计量数据集上,Scyan显著优于现有的最先进模型.
  • 与传统方法相比,该模型显示了增强的速度和可解释性.
  • 斯凯恩有效地解决了补充任务,包括批量效应校正,脱条码和细胞群体发现.

结论:

  • 斯凯恩加速和简化了细胞群体的表征,量化和细胞测量中的发现.
  • 该网络为复杂,高维的细胞计量数据提供了一个强大的,高效的替代手动门.
  • 使用Scyan的自动注释提高了可复制性,并减少了单细胞分析中的批量效应.