Microbial flow cytometry can rapidly characterize the status of microbial communities. Upon measurement, large amounts of quantitative single-cell data are generated, which need to be analyzed appropriately. Cytometric fingerprinting approaches are often used for this purpose. Traditional approaches either require a manual annotation of regions of interest, do not fully consider the multivariate characteristics of the data, or result in many community-describing variables. To address these shortcomings, we propose an automated model-based fingerprinting approach based on Gaussian mixture models, which we call PhenoGMM. The method successfully quantifies changes in microbial community structure based on flow cytometry data, which can be expressed in terms of cytometric diversity. We evaluate the performance of PhenoGMM using data sets from both synthetic and natural ecosystems and compare the method with a generic binning fingerprinting approach. PhenoGMM supports the rapid and quantitative screening of microbial community structure and dynamics.

中文翻译：

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PhenoGMM：细胞计数数据的高斯混合模型量化微生物群落结构的变化

微生物流式细胞术可以快速表征微生物群落的状态。测量时会产生大量的定量单细胞数据，需要对其进行适当的分析。细胞指纹法通常用于此目的。传统方法要么需要手动注释感兴趣的区域，没有充分考虑数据的多变量特征，要么导致许多社区描述变量。为了解决这些缺点，我们提出了一种基于高斯混合模型的基于模型的自动指纹识别方法，我们称之为 PhenoGMM。该方法基于流式细胞术数据成功量化了微生物群落结构的变化，可以用细胞学多样性来表示。我们使用来自合成和自然生态系统的数据集评估 PhenoGMM 的性能，并将该方法与通用分箱指纹方法进行比较。PhenoGMM 支持微生物群落结构和动态的快速和定量筛选。