Merging mixture components for cell population identification in flow cytometry.
Merging mixture components for cell population identification in flow cytometry.
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DOI：
10.1155/2009/247646
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发表时间：
2009
影响因子：
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通讯作者：
Gottardo R
中科院分区：
文献类型：
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作者：
Finak G;Bashashati A;Brinkman R;Gottardo R
我们提出了一个框架，用于使用FlowClust方法合并混合组件在流式细胞仪数据中识别细胞亚群。我们表明，在我们的框架下合并算法的群集改善了模型拟合度，并比高斯混合模型或Flowclust更好地估计了不同细胞亚群的数量，尤其是对于复杂的流式细胞仪数据分布。我们的框架允许自动选择不同的单元格亚群的数量，并且我们能够识别算法失败的情况，从而使其适合在高吞吐量FCM分析管道中应用。此外，我们演示了一种简单地与现有的FlowClust框架集成并启用下游数据分析的方法，以简单的方式汇总了复杂的合并单元格亚群。我们在模拟和真实的FCM数据上演示了框架的性能。该软件可通过BioConductor项目在FlowMerge软件包中找到。
We present a framework for the identification of cell subpopulations in flow cytometry data based on merging mixture components using the flowClust methodology. We show that the cluster merging algorithm under our framework improves model fit and provides a better estimate of the number of distinct cell subpopulations than either Gaussian mixture models or flowClust, especially for complicated flow cytometry data distributions. Our framework allows the automated selection of the number of distinct cell subpopulations and we are able to identify cases where the algorithm fails, thus making it suitable for application in a high throughput FCM analysis pipeline. Furthermore, we demonstrate a method for summarizing complex merged cell subpopulations in a simple manner that integrates with the existing flowClust framework and enables downstream data analysis. We demonstrate the performance of our framework on simulated and real FCM data. The software is available in the flowMerge package through the Bioconductor project.