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From big flow cytometry datasets to smart diagnostic strategies: The EuroFlow approach

AuthorsPedreira, C. E.; Sobral da Costa, E.; Lécrevisse, Quentin; Grigore, Georgiana Emilia; Fluxá, Rafael; Verde, Javier; Hernández, Juan; Dongen, J. J. M. van; Orfao, Alberto
Issue Date2019
CitationJournal of Immunological Methods 475: 112631 (2019)
AbstractThe 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.
DescriptionOn behalf of EuroFlow.
Publisher version (URL)https://doi.org/10.1016/j.jim.2019.07.003
Appears in Collections:(IBMCC) Artículos
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