Vaccine Research Center, NIAID, NIH, Bethesda, MD, USA.
Cytometry A. 2011 Feb;79(2):167-74. doi: 10.1002/cyto.a.21015. Epub 2011 Jan 7.
Polychromatic flow cytometry results in complex, multivariate datasets. To date, tools for the aggregate analysis of these datasets across multiple specimens grouped by different categorical variables, such as demographic information, have not been optimized. Often, the exploration of such datasets is accomplished by visualization of patterns with pie charts or bar charts, without easy access to statistical comparisons of measurements that comprise multiple components. Here we report on algorithms and a graphical interface we developed for these purposes. In particular, we discuss thresholding necessary for accurate representation of data in pie charts, the implications for display and comparison of normalized versus unnormalized data, and the effects of averaging when samples with significant background noise are present. Finally, we define a statistic for the nonparametric comparison of complex distributions to test for difference between groups of samples based on multi-component measurements. While originally developed to support the analysis of T cell functional profiles, these techniques are amenable to a broad range of datatypes.
多色流式细胞术的结果是复杂的、多变量的数据集。迄今为止，还没有针对这些数据集的聚合分析工具，这些数据集是根据不同的分类变量（如人口统计学信息）对多个样本进行分组的。通常，通过饼图或条形图来探索这些数据集的模式，而无法轻松进行包含多个组件的测量值的统计比较。在这里，我们报告了为此目的开发的算法和图形界面。特别是，我们讨论了在饼图中准确表示数据所需的阈值，以及显示和比较归一化与非归一化数据的影响，以及在存在具有显著背景噪声的样本时平均化的影响。最后，我们定义了一个统计量，用于对复杂分布进行非参数比较，以根据多组件测量值测试样本组之间的差异。虽然这些技术最初是为了支持 T 细胞功能谱的分析而开发的，但它们适用于广泛的数据类型。
