CytOpT：用于解释流式细胞术数据的具有域自适应的最佳传输
CytOpT: Optimal Transport with Domain Adaptation for Interpreting Flow Cytometry data
摘要：
流式细胞术测量的自动分析是一个活跃的研究领域。我们介绍了一种新的算法，称为CytOpT，使用正则化的最佳运输，直接估计不同的细胞群体的比例，从生物样品的特征与流式细胞术测量。我们依赖于正则化的Wasserstein度量来比较来自不同样品的细胞计数测量，从而解释了样品中给定细胞群的可能的错误对齐（由于测量技术的技术可变性）。在这项工作中，我们依赖于一种基于Wasserstein度量的监督学习技术，该度量用于估计混合模型中类比例的最佳重新加权，该混合模型来自源分布（已知分割成细胞亚群），以拟合具有未知分割的目标分布。由于流式细胞术数据的高维性，我们使用随机算法来近似正则化的Wasserstein度量，以解决在估计代表目标分布中的细胞群体比例的最佳权重中涉及的优化问题。几个流式细胞术的数据集被用来说明CytOpT的性能，也比较现有的自动门控算法的基础上监督学习。
英文摘要：
The automated analysis of flow cytometry measurements is an active research field. We introduce a new algorithm, referred to as CytOpT, using regularized optimal transport to directly estimate the different cell population proportions from a biological sample characterized with flow cytometry measurements. We rely on the regularized Wasserstein metric to compare cytometry measurements from different samples, thus accounting for possible mis-alignment of a given cell population across sample (due to technical variability from the technology of measurements). In this work, we rely on a supervised learning technique based on the Wasserstein metric that is used to estimate an optimal re-weighting of class proportions in a mixture model from a source distribution (with known segmentation into cell sub-populations) to fit a target distribution with unknown segmentation. Due to the high-dimensionality of flow cytometry data, we use stochastic algorithms to approximate the regularized Wasserstein metric to solve the optimization problem involved in the estimation of optimal weights representing the cell population proportions in the target distribution. Several flow cytometry data sets are used to illustrate the performances of CytOpT that are also compared to those of existing algorithms for automatic gating based on supervised learning.
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