Centre for Mathematical Sciences, Lund University, Lund, Sweden.
Department of Automatic Control, Lund University, Lund, Sweden.
Cytometry A. 2017 Sep;91(9):908-916. doi: 10.1002/cyto.a.23173. Epub 2017 Jul 31.
Many automated gating algorithms for flow cytometry data are based on the concept of unimodal cell populations. However, in this article, we show that criteria previously used to make decisions on unimodality cannot adequately distinguish unimodal from bimodal densities. We show that dip and bandwidth tests for unimodality, taken from the statistics literature, can do this with consistent and low error rates. These tests also have the possibility to adjust the significance level to handle the trade-off between failing to detect a second mode and seeing a second mode when there is none. The differences between the dip and bandwidth tests are elucidated using real data from the FlowCAP I challenge, also guidelines for flow cytometry data preprocessing are given. © 2017 International Society for Advancement of Cytometry.
许多流式细胞术数据的自动化门控算法都是基于单峰细胞群体的概念。然而，在本文中，我们表明，以前用于确定单峰性的标准不能充分地区分单峰和双峰密度。我们表明，来自统计学文献的单峰性的凹陷和带宽测试可以用一致的低错误率来做到这一点。这些测试还有可能调整显著性水平，以处理在未能检测到第二个模式和在不存在第二个模式时看到第二个模式之间的权衡。使用 FlowCAP I 挑战赛的真实数据阐明了凹陷和带宽测试之间的差异，并给出了用于流式细胞术数据预处理的指南。2017 年国际细胞分析协会。
