临床流式细胞术数据的自动化分析：以慢性淋巴细胞白血病为例
Automated Analysis of Clinical Flow Cytometry Data: A Chronic Lymphocytic Leukemia Illustration.
作者信息
Scheuermann Richard H, Bui Jack, Wang Huan-You, Qian Yu
机构信息
Department of Informatics, J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA 92037, USA.
Department of Pathology, University of California, San Diego, Biomedical Sciences Building Room 1028, 9500 Gilman Drive, La Jolla, CA 92093-0612, USA.
出版信息
Clin Lab Med. 2017 Dec;37(4):931-944. doi: 10.1016/j.cll.2017.07.011.
Flow cytometry is used in cell-based diagnostic evaluation for blood-borne malignancies including leukemia and lymphoma. The current practice for cytometry data analysis relies on manual gating to identify cell subsets in complex mixtures, which is subjective, labor-intensive, and poorly reproducible. This article reviews recent efforts to develop, validate, and disseminate automated computational methods and pipelines for cytometry data analysis that could help overcome the limitations of manual analysis and provide for efficient and data-driven diagnostic applications. It demonstrates the performance of an optimized computational pipeline in a pilot study of chronic lymphocytic leukemia data from the authors' clinical diagnostic laboratory.
流式细胞术用于基于细胞的血液系统恶性肿瘤（包括白血病和淋巴瘤）的诊断评估。目前流式细胞术数据分析的做法依赖于人工设门来识别复杂混合物中的细胞亚群，这是主观的、劳动密集型的，且重现性差。本文综述了近期为开发、验证和推广用于流式细胞术数据分析的自动化计算方法及流程所做的努力，这些方法和流程有助于克服人工分析的局限性，并实现高效且数据驱动的诊断应用。本文展示了一个优化的计算流程在作者临床诊断实验室慢性淋巴细胞白血病数据的初步研究中的表现。
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