Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC 27710, USA.
Cytometry A. 2010 Dec;77(12):1126-36. doi: 10.1002/cyto.a.20987.
The design of a panel to identify target cell subsets in flow cytometry can be difficult when specific markers unique to each cell subset do not exist, and a combination of parameters must be used to identify target cells of interest and exclude irrelevant events. Thus, the ability to objectively measure the contribution of a parameter or group of parameters toward target cell identification independent of any gating strategy could be very helpful for both panel design and gating strategy design. In this article, we propose a discriminative information measure evaluation (DIME) based on statistical mixture modeling; DIME is a numerical measure of the contribution of different parameters towards discriminating a target cell subset from all the others derived from the fitted posterior distribution of a Gaussian mixture model. Informally, DIME measures the "usefulness" of each parameter for identifying a target cell subset. We show how DIME provides an objective basis for inclusion or exclusion of specific parameters in a panel, and how ranked sets of such parameters can be used to optimize gating strategies. An illustrative example of the application of DIME to streamline the gating strategy for a highly standardized carboxyfluorescein succinimidyl ester (CFSE) assay is described.
当不存在特定于每个细胞亚群的独特标记物时，设计用于流式细胞术鉴定靶细胞亚群的面板可能会很困难，并且必须使用参数组合来鉴定感兴趣的靶细胞并排除不相关的事件。因此，能够客观地衡量参数或参数组对目标细胞识别的贡献，而不依赖于任何门控策略，这对于面板设计和门控策略设计都非常有帮助。在本文中，我们提出了一种基于统计混合模型的判别信息度量评估 (DIME)；DIME 是一种从高斯混合模型拟合后验分布中得出的数值度量，用于衡量不同参数对从所有其他参数中区分目标细胞亚群的贡献。非正式地说，DIME 衡量每个参数识别目标细胞亚群的“有用性”。我们展示了 DIME 如何为在面板中包含或排除特定参数提供客观依据，以及如何使用此类参数的排序集来优化门控策略。描述了一个应用 DIME 简化高度标准化的羧基荧光素琥珀酰亚胺酯 (CFSE) 测定的门控策略的示例。
