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首页> 外文期刊>Clinical immunology: The official journal of the Clinical Immunology Society >Automated flow cytometric analysis across large numbers of samples and cell types
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Automated flow cytometric analysis across large numbers of samples and cell types

机译：对大量样品和细胞类型进行自动流式细胞分析

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Multi-parametric flow cytometry is a key technology for characterization of immune cell phenotypes. However, robust high-dimensional post-analytic strategies for automated data analysis in large numbers of donors are still lacking. Here, we report a computational pipeline, called FlowGM, which minimizes operator input, is insensitive to compensation settings, and can be adapted to different analytic panels. A Gaussian Mixture Model (GMM)-based approach was utilized for initial clustering, with the number of clusters determined using Bayesian Information Criterion. Meta-clustering in a reference donor permitted automated identification of 24 cell types across four panels. Cluster labels were integrated into FCS files, thus permitting comparisons to manual gating. Cell numbers and coefficient of variation (CV) were similar between FlowGM and conventional gating for lymphocyte populations, but notably FlowGM provided improved discrimination of "hard-to-gate" monocyte and dendritic cell (DC) subsets. FlowGM thus provides rapid high-dimensional analysis of cell phenotypes and is amenable to cohort studies. (C) 2015 The Authors. Published by Elsevier Inc.
机译：多参数流式细胞仪是表征免疫细胞表型的关键技术。但是，仍然缺乏用于大量捐助者的自动数据分析的强大的高维后分析策略。在这里，我们报告了一个称为FlowGM的计算管道，该管道可以最大程度地减少操作员的输入，对补偿设置不敏感，并且可以适应不同的分析面板。基于高斯混合模型（GMM）的方法用于初始聚类，使用贝叶斯信息准则确定聚类的数量。通过在参考供体中进行元聚类，可以跨四个面板自动识别24种细胞类型。群集标签已集成到FCS文件中，因此可以与手动选通进行比较。 FlowGM和常规门控的淋巴细胞数量之间的细胞数量和变异系数（CV）相似，但值得注意的是FlowGM改善了对“难门”单核细胞和树突状细胞（DC）子集的区分。因此，FlowGM提供了对细胞表型的快速高维分析，并且适合队列研究。 （C）2015作者。由Elsevier Inc.发布

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