BACKGROUND Modern flow cytometry technology has enabled the simultaneous analysis of multiple cell markers at the single-cell level, and it is widely used in a broad field of research. The detection of cell populations in flow cytometry data has long been dependent on "manual gating" by visual inspection. Recently, numerous software have been developed for automatic, computationally guided detection of cell populations; however, they are not designed for time-series flow cytometry data. Time-series flow cytometry data are indispensable for investigating the dynamics of cell populations that could not be elucidated by static time-point analysis. Therefore, there is a great need for tools to systematically analyze time-series flow cytometry data. RESULTS We propose a simple and efficient statistical framework, named CYBERTRACK (CYtometry-Based Estimation and Reasoning for TRACKing cell populations), to perform clustering and cell population tracking for time-series flow cytometry data. CYBERTRACK assumes that flow cytometry data are generated from a multivariate Gaussian mixture distribution with its mixture proportion at the current time dependent on that at a previous timepoint. Using simulation data, we evaluate the performance of CYBERTRACK when estimating parameters for a multivariate Gaussian mixture distribution, tracking time-dependent transitions of mixture proportions, and detecting change-points in the overall mixture proportion. The CYBERTRACK performance is validated using two real flow cytometry datasets, which demonstrate that the population dynamics detected by CYBERTRACK are consistent with our prior knowledge of lymphocyte behavior. CONCLUSIONS Our results indicate that CYBERTRACK offers better understandings of time-dependent cell population dynamics to cytometry users by systematically analyzing time-series flow cytometry data.

中文翻译：

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基于模型的细胞聚类和种群跟踪，用于时间序列流式细胞术数据。

背景技术现代流式细胞术技术使得能够在单细胞水平上同时分析多种细胞标志物，并且其被广泛地用于广泛的研究领域。流式细胞术数据中细胞群的检测长期以来一直依赖于通过目视检查的“手动门控”。最近，已经开发了许多软件，用于自动，计算机指导的细胞群检测。但是，它们不是为时间序列流式细胞术数据而设计的。时间序列流式细胞术数据对于调查无法通过静态时间点分析阐明的细胞群体动态必不可少。因此，迫切需要系统分析时间序列流式细胞仪数据的工具。结果我们提出了一个简单有效的统计框架，名为CYBERTRACK（用于跟踪细胞群体的基于细胞计数的估计和推理），以对时间序列流式细胞术数据执行聚类和细胞群体跟踪。CYBERTRACK假定流式细胞术数据是从多元高斯混合物分布生成的，其当前时间的混合物比例取决于先前时间点的比例。使用仿真数据，当估算多元高斯混合分布的参数，跟踪混合比例随时间变化的过渡以及检测整体混合比例的变化点时，我们评估CYBERTRACK的性能。使用两个真实的流式细胞术数据集验证了CYBERTRACK的性能，这表明CYBERTRACK检测到的种群动态与我们对淋巴细胞行为的先验知识相一致。