Automatic clustering of flow cytometry data with density-based merging.
Automatic clustering of flow cytometry data with density-based merging.
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DOI：
10.1155/2009/686759
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发表时间：
2009
影响因子：
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通讯作者：
Herzenberg L
中科院分区：
文献类型：
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作者：
Walther G;Zimmerman N;Moore W;Parks D;Meehan S;Belitskaya I;Pan J;Herzenberg L
流式细胞术能够对大量细胞进行快速的单细胞检测，这使得这项技术在临床和实验室环境中无处不在，不可或缺。目前限制这项技术潜力的一个因素是缺乏分析结果数据的自动化工具。我们描述了在流式细胞术数据中自动识别细胞群的方法和软件。我们的方法将手动门控数据的顺序二维投影的范例推进到一个基于统计理论自动产生门的过程。我们的方法是非参数的，可以重现已知在流式细胞术样本中出现的非凸亚群，但不能用当前基于参数模型的方法产生。我们用小鼠脾脏和腹腔细胞的样本来说明这种方法。
The ability of flow cytometry to allow fast single cell interrogation of a large number of cells has made this technology ubiquitous and indispensable in the clinical and laboratory setting. A current limit to the potential of this technology is the lack of automated tools for analyzing the resulting data. We describe methodology and software to automatically identify cell populations in flow cytometry data. Our approach advances the paradigm of manually gating sequential two-dimensional projections of the data to a procedure that automatically produces gates based on statistical theory. Our approach is nonparametric and can reproduce nonconvex subpopulations that are known to occur in flow cytometry samples, but which cannot be produced with current parametric model-based approaches. We illustrate the methodology with a sample of mouse spleen and peritoneal cavity cells.