optimalFlow：流式细胞术门控和群体匹配的最佳转运方法
optimalFlow: Optimal-transport approach to flow cytometry gating and population matching
摘要：
由于生物学和技术原因，流式细胞术获得的数据存在明显的变异性。生物变异性是一种众所周知的现象，由不同个体的测量产生，具有不同的特征，如疾病，年龄，性别等。使用不同的测量设置，实验期间条件的变化和不同类型的流式细胞仪是变异性的一些技术原因。这种可变性来源的混合使得难以使用监督机器学习来识别细胞群体。目前的工作被认为是一个战略的组合，以促进监督门控的任务。我们提出了$optimalFlowTemplates$，基于相似性距离和$\text{Wasserstein barycenters}$，聚类细胞计数，并产生原型细胞计数为不同的群体。我们表明，监督学习，仅限于新的群体，表现优于相同的技术应用于整个集合。我们还介绍了$optimalFlowClassification$，它使用一个数据库的门控细胞计数和optimalFlowTemplates分配细胞类型的一个新的细胞计数。我们表明，这个过程可以在拟议的数据集的最先进的技术。我们的代码作为$optimalFlow$（Bioconductor R软件包）在https://bioconductor.org/packages/optimalFlow上免费提供。optimalFlowTemplates+optimalFlowClassification解决了使用监督学习的问题，同时考虑了生物和技术的可变性。我们的方法提供了一个强大的自动化门控工作流程，可以很好地处理流式细胞术数据的固有变异性。我们的主要创新是方法本身和我们应用于流式细胞术分析的最佳转运技术。
英文摘要：
Data obtained from Flow Cytometry present pronounced variability due to biological and technical reasons. Biological variability is a well-known phenomenon produced by measurements on different individuals, with different characteristics such as illness, age, sex, etc. The use of different settings for measurement, the variation of the conditions during experiments and the different types of flow cytometers are some of the technical causes of variability. This mixture of sources of variability makes the use of supervised machine learning for identification of cell populations difficult. The present work is conceived as a combination of strategies to facilitate the task of supervised gating. We propose $optimalFlowTemplates$, based on a similarity distance and $\text{Wasserstein barycenters}$, which clusters cytometries and produces prototype cytometries for the different groups. We show that supervised learning, restricted to the new groups, performs better than the same techniques applied to the whole collection. We also present $optimalFlowClassification$, which uses a database of gated cytometries and optimalFlowTemplates to assign cell types to a new cytometry. We show that this procedure can outperform state of the art techniques in the proposed datasets. Our code is freely available as $optimalFlow$ a Bioconductor R package at https://bioconductor.org/packages/optimalFlow. optimalFlowTemplates+optimalFlowClassification addresses the problem of using supervised learning while accounting for biological and technical variability. Our methodology provides a robust automated gating workflow that handles the intrinsic variability of flow cytometry data well. Our main innovation is the methodology itself and the optimal-transport techniques that we apply to flow cytometry analysis.
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