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相关概念视频

Flow Cytometry01:23

Flow Cytometry

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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.
In...
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相关实验视频

Updated: Jun 9, 2025

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

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使用FlowSOM优化稳定集群的参数:来自CyTOF的案例研究.

Weiyang Tao1, Anirban Sinha1, Khadir Raddassi1

  • 1Immunology Discovery Research, AbbVie Cambridge Research Center, Cambridge, MA, United States.

Frontiers in immunology
|October 24, 2024
PubMed
概括
此摘要是机器生成的。

对FlowSOM包的关键修改改进了使用CyTOF的高维细胞表型化. 优化的参数使复杂的免疫细胞数据可靠地用于疾病研究的分析.

关键词:
流量SOM 的流量.聚类集群是指聚类的聚类.通过 cyTOF,可以实现 cyTOF.一个高维数据集.参数优化的参数优化

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相关实验视频

Last Updated: Jun 9, 2025

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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科学领域:

  • 免疫学 免疫学 免疫学
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 使用CyTOF进行高维细胞表型鉴定对于了解健康和疾病至关重要.
  • 分析复杂的CyTOF数据需要先进的计算方法,例如用于自动化门的机器学习.

研究的目的:

  • 对基于机器学习的FlowSOM包进行批判性修改和优化,以实现可靠的CyTOF数据分析.
  • 为了解决错误,并提高FlowSOM管道的大数据集的可扩展性.

主要方法:

  • 修改了FlowSOM包,以修复现有的错误并提高可扩展性.
  • 执行精确的参数优化,以评估其对集群结果的影响.
  • 在大型外部免疫学数据集上验证了增强的FlowSOM管道.

主要成果:

  • 关键修改和参数优化显著提高了CyTOF数据分析的可靠性.
  • 确定了影响FlowSOM集群结果的关键参数.
  • 证明了数据特定参数调整对于准确的免疫细胞群体定义的必要性.

结论:

  • 修改后的FlowSOM管道提供了一种可靠的方法,用于使用CyTOF进行高维细胞表型化.
  • 量身定制的参数优化对于在免疫疾病中询问免疫细胞群体至关重要.
  • 这项工作增强了CyTOF在免疫学研究和疾病研究中的实用性.