Systems and Computing Department (PESC), COPPE, Federal University of Rio de Janeiro (UFRJ), Brazil.
School of Medicine, Federal University of Rio de Janeiro (UFRJ), Brazil.
J Immunol Methods. 2019 Dec;475:112631. doi: 10.1016/j.jim.2019.07.003. Epub 2019 Jul 12.
The rise in the analytical speed of mutiparameter flow cytometers made possible by the introduction of digital instruments, has brought up the possibility to manage progressively higher number of parameters simultaneously on significantly greater numbers of individual cells. This has led to an exponential increase in the complexity and volume of flow cytometry data generated about cells present in individual samples evaluated in a single measurement. This increase demands for new developments in flow cytometry data analysis, graphical representation, and visualization and interpretation tools to address the new big data challenges, i.e. processing data files of ≥10-25 parameters per cell in samples with >5-10 million cells (= up to 250 million data points per cell sample) obtained in a few minutes. Here, we present a comprehensive review of some of the tools developed by the EuroFlow consortium for processing flow cytometric big data files in diagnostic laboratories, particularly focused on automated EuroFlow approaches for: i) identification of all cell populations coexisting in a sample (automated gating); ii) smart classification of aberrant cell populations in routine diagnostics; iii) automated reporting; together with iv) new tools developed to visualize n-dimensional data in 2-dimensional plots to support expert-guided automated data analysis. The concept of using reference data bases implemented into software programs, in combination with multivariate statistical analysis pioneered by EuroFlow, provides an innovative, highly efficient and fast approach for diagnostic screening, classification and monitoring of patients with distinct hematological and immune disorders, as well as other diseases.
多参数流式细胞仪分析速度的提高得益于数字仪器的引入,这使得同时对更多个体细胞进行更多参数的管理成为可能。这导致流式细胞术数据的复杂性和数量呈指数级增长,这些数据是关于单个测量中评估的单个样本中存在的细胞的。这种增加需要在流式细胞术数据分析、图形表示以及可视化和解释工具方面进行新的开发,以应对新的大数据挑战,即处理每个细胞≥10-25 个参数的数据文件,每个样本中有>5-1000 万个细胞(即每个细胞样本高达 2.5 亿个数据点),这些数据是在几分钟内获得的。在这里,我们全面回顾了 EuroFlow 联盟为诊断实验室开发的一些处理流式细胞术大数据文件的工具,特别是侧重于用于:i)识别样品中所有共存的细胞群体(自动门控);ii)在常规诊断中智能分类异常细胞群体;iii)自动报告;以及 iv)开发的新工具,用于在 2 维图中可视化 n 维数据,以支持专家指导的自动数据分析。将参考数据库应用于软件程序的概念,结合 EuroFlow 首创的多元统计分析,为诊断筛查、分类和监测具有不同血液学和免疫疾病以及其他疾病的患者提供了一种创新、高效和快速的方法。