This review will explore the integration of machine learning (ML) techniques to enhance the analysis of increasingly complex and voluminous flow cytometry data, as traditional manual methods are insufficient for handling this data. We attempt to provide a comprehensive introduction to ML in flow cytometry, detailing the transition from manual gating to computational methods and emphasizing the importance of data quality. Key ML techniques are discussed, including supervised learning methods like logistic regression, support vector machines, and neural networks, which rely on labeled data to classify disease states. Unsupervised methods, such as k-means clustering, FlowSOM, UMAP, and t-SNE, are highlighted for their ability to identify novel cell populations without predefined labels. We also delve into newer semi-supervised and weakly supervised methods, which leverage partial labeling to improve model performance. Practical aspects of implementing ML in clinical settings are addressed, including regulatory considerations, data preprocessing, model training, validation, and the importance of generalizability, and we underscore the collaborative effort required among pathologists, data scientists, and laboratory professionals to ensure robust model development and deployment. Finally, we show the transformative potential of ML in flow cytometry in uncovering new biological insights through advanced computational techniques.
本综述将探讨如何整合机器学习技术以提升对日益复杂且海量的流式细胞术数据的分析能力，因为传统人工方法已不足以处理此类数据。我们尝试全面介绍机器学习在流式细胞术中的应用，详细阐述从人工设门到计算方法的转变过程，并强调数据质量的重要性。文中讨论了关键的机器学习技术，包括依赖标记数据进行疾病状态分类的有监督学习方法（如逻辑回归、支持向量机和神经网络），以及无需预定义标记即可识别新型细胞群体的无监督方法（如k均值聚类、FlowSOM、UMAP和t-SNE）。我们还深入探讨了较新的半监督与弱监督方法，这些方法通过利用部分标记数据来提升模型性能。针对临床实践中实施机器学习的具体环节，我们探讨了监管考量、数据预处理、模型训练与验证、模型泛化能力的重要性，并强调病理学家、数据科学家和实验室专业人员需通力协作，以确保模型的稳健开发与部署。最后，我们展示了机器学习通过先进计算技术，在流式细胞术中揭示新生物学见解的变革潜力。
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