High-throughput single-cell cytometry technologies have significantly improved our understanding of cellular phenotypes to support translational research and the clinical diagnosis of hematological and immunological diseases. However, subjective and ad hoc manual gating analysis does not adequately handle the increasing volume and heterogeneity of cytometry data for optimal diagnosis. Prior work has shown that machine learning can be applied to classify cytometry samples effectively. However, many of the machine learning classification results are either difficult to interpret without using characteristics of cell populations to make the classification, or suboptimal due to the use of inaccurate cell population characteristics derived from gating boundaries. To date, little has been done to optimize both the gating boundaries and the diagnostic accuracy simultaneously. In this work, we describe a fully discriminative machine learning approach that can simultaneously learn feature representations (e.g., combinations of coordinates of gating boundaries) and classifier parameters for optimizing clinical diagnosis from cytometry measurements. The approach starts from an initial gating position and then refines the position of the gating boundaries by gradient descent until a set of globally-optimized gates across different samples are achieved. The learning procedure is constrained by regularization terms encoding domain knowledge that encourage the algorithm to seek interpretable results. We evaluate the proposed approach using both simulated and real data, producing classification results on par with those generated via human expertise, in terms of both the positions of the gating boundaries and the diagnostic accuracy. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

中文翻译：

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用于临床诊断的判别门位置的机器学习。

高通量单细胞细胞术技术显着提高了我们对细胞表型的理解，以支持血液学和免疫学疾病的转化研究和临床诊断。然而，主观和临时的手动门控分析无法充分处理不断增加的细胞计数数据量和异质性以实现最佳诊断。先前的工作表明，机器学习可以应用于有效地对细胞计数样本进行分类。然而，许多机器学习分类结果要么在不使用细胞群特征进行分类的情况下难以解释，要么由于使用从门控边界得出的不准确的细胞群特征而导致次优。迄今为止，在同时优化门控边界和诊断准确性方面几乎没有采取任何措施。在这项工作中，我们描述了一种完全判别性的机器学习方法，该方法可以同时学习特征表示（例如，门控边界的坐标组合）和分类器参数，以根据细胞计数测量优化临床诊断。该方法从初始选通位置开始，然后通过梯度下降细化选通边界的位置，直到实现一组跨不同样本的全局优化的门。学习过程受到编码领域知识的正则化项的限制，这些正则化项鼓励算法寻求可解释的结果。我们使用模拟和真实数据评估所提出的方法，在门控边界的位置和诊断准确性方面产生与通过人类专业知识生成的分类结果相当的分类结果。© 2019 作者。细胞计数法 A 部分由 Wiley periodicals, Inc. 代表国际细胞计数法促进会出版。