Machine Learning of Discriminative Gate Locations for Clinical Diagnosis
Machine Learning of Discriminative Gate Locations for Clinical Diagnosis
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DOI：
10.1002/cyto.a.23906
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发表时间：
2019-11-05
期刊：
影响因子：
3.7
通讯作者：
Smyth, Padhraic
中科院分区：
文献类型：
--
作者：
Ji, Disi;Putzel, Preston;Smyth, Padhraic
高通量单细胞细胞计数技术显著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，主观和特设手动门控分析不能充分处理细胞计数数据的增加量和异质性以进行最佳诊断。先前的工作已经表明，机器学习可以有效地应用于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体的特征进行分类的情况下难以解释，要么由于使用了从门控边界导出的不准确的细胞群体特征而不理想。迄今为止，很少有人做优化门控边界和诊断精度同时进行。在这项工作中，我们描述了一种完全区分的机器学习方法，可以同时学习特征表示（例如，门控边界的坐标的组合）和分类器参数，用于优化来自细胞计数测量的临床诊断。该方法从初始选通位置开始，然后通过梯度下降来细化选通边界的位置，直到实现跨不同样本的全局优化的门的集合。学习过程受到正则化项编码领域知识的约束，鼓励算法寻求可解释的结果。我们使用模拟和真实的数据来评估所提出的方法，在门控边界的位置和诊断准确性方面产生的分类结果与通过人类专业知识产生的结果相当。(c)2019年，任作家。Cytometry Part A由Wiley Periodicals，Inc.出版。代表国际细胞计数促进协会。
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. (c) 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.