Automated Gating of flow cytometry data via robust model-based clustering
Automated Gating of flow cytometry data via robust model-based clustering
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DOI：
10.1002/cyto.a.20531
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发表时间：
2008-04-01
期刊：
影响因子：
3.7
通讯作者：
Gottardo, Raphael
中科院分区：
文献类型：
--
作者：
Lo, Kenneth;Brinkman, Ryan Remy;Gottardo, Raphael
流式细胞术的能力可以快速量化数百万个细胞的多维特征，这使得这项技术对于健康研究，医学诊断和治疗都是必不可少的。但是，缺乏与最近的高通量技术进步相似的统计和生物信息学工具阻碍了这项技术的全部潜力。我们提出了一种基于灵活的统计模型聚类方法，用于基于具有盒子型cox转换的T混合模型在流式细胞仪数据中识别细胞群体。这种方法概括了流行的高斯混合模型，以解释异常值并允许非旋转簇。我们描述了一种期望最大化（EM）算法，以同时处理参数估计和转换选择。使用两个公开可用的数据集，我们证明我们提出的方法学为模仿专家研究人员执行的模拟手动门控结果提供了足够的灵活性和鲁棒性。此外，我们提出了一项仿真研究的结果，该研究表明，与流行的混合模型相比，该新的聚类框架在鲁棒性方面可以更好地效果。所提出的聚类方法很好地适应了流式细胞仪数据的自动分析。它倾向于给出更多可再现的结果，并有助于减少手动门控分析中遇到的显着主观性和人为成本。 （c）2008国际分析细胞学学会。
The capability of flow cytometry to offer rapid quantification of multidimensional characteristics for millions of cells has made this technology indispensable for health research, medical diagnosis, and treatment. However, the lack of statistical and bioinformatics tools to parallel recent high-throughput technological advancements has hindered this technology from reaching its full potential. We propose a flexible statistical model-based clustering approach for identifying cell populations in flow cytometry data based on t-mixture models with a Box-Cox transformation. This approach generalizes the popular Gaussian mixture models to account for outliers and allow for nonelliptical clusters. We describe an Expectation-Maximization (EM) algorithm to simultaneously handle parameter estimation and transformation selection. Using two publicly available datasets, we demonstrate that our proposed methodology provides enough flexibility and robustness to mimic manual gating results performed by an expert researcher. In addition, we present results from a simulation study, which show that this new clustering framework gives better results in terms of robustness to model misspecification and estimation of the number of clusters, compared to the popular mixture models. The proposed clustering methodology is well adapted to automated analysis of flow cytometry data. It tends to give more reproducible results, and helps reduce the significant subjectivity and human time cost encountered in manual gating analysis. (C) 2008 International Society for Analytical Cytology.