[1]摆文丽,农卫霞,李智伟,等.FCM数据细胞亚群分类和标注的自动化研究[J].医学信息,2024,37(06):78-83.[doi:10.3969/j.issn.1006-1959.2024.06.013]
　BAI Wen-li,NONG Wei-xia,LI Zhi-wei,et al.Automatic Classification and Labeling of Cell Subsets in FCM Data[J].Journal of Medical Information,2024,37(06):78-83.[doi:10.3969/j.issn.1006-1959.2024.06.013]
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FCM数据细胞亚群分类和标注的自动化研究()
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医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
37卷
期数:
2024年06期
页码:
78-83
栏目:
论著
出版日期:
2024-03-15

文章信息/Info

Title:
Automatic Classification and Labeling of Cell Subsets in FCM Data
文章编号:
1006-1959（2024）06-0078-06
作者:
摆文丽; 农卫霞; 李智伟; 等
（1.石河子大学医学院预防医学系，新疆 石河子 832000；2.石河子大学医学院第一附属医院血液风湿科，新疆 石河子 832000；3.新疆维吾尔自治区人民医院临床检验中心，新疆 乌鲁木齐 830001）
Author(s):
BAI Wen-li; NONG Wei-xia; LI Zhi-wei; et al.
(1.Department of Preventive Medicine,Shihezi University School of Medicine,Shihezi 832000,Xinjiang,China;2.Department of Hematology and Rheumatology,the First Affiliated Hospital of Shihezi University School of Medicine,Shihezi 832000,Xinjiang,China;3.Clinical Laboratory Center,Xinjiang Uygur Autonomous Region People’s Hospital,Urumqi 830001,Xinjiang,China)
关键词:
流式细胞术; 聚类分析; 亚群分类; 亚群标注; 自动化分析
Keywords:
Flow cytometry; Cluster analysis; Subpopulation classification; Subpopulation registration; Automatic analysis
分类号:
R319
DOI:
10.3969/j.issn.1006-1959.2024.06.013
文献标志码:
A
摘要:
目的 研究自动化分析方法用于流式细胞术数据分析、解决细胞亚群自动分类和标注问题的价值，以期为疾病诊断提供参考。方法 收集2021年急性白血病骨髓流式检测数据528例，通过补偿、转换以及去粘连完成流式细胞术原始数据预处理，对预处理后的数据使用无监督聚类方法进行聚类分析，利用生成的细胞亚群的中心位置，即宏细胞的分布规律来训练有监督分类模型，将亚群进一步分类，最后通过人工识别与标注，将细胞亚群标注为已知的细胞类型。结果 无监督聚类方法与有监督分类方法共同用于流式细胞术数据分析能够实现细胞亚群的自动分类与标注，且准确度达到或基本达到手工分析水平。结论 该研究提出的流式细胞术数据自动分类和标注方法，解决了目前流式细胞自动化分析存在的细胞聚类方法和病人分类方法之间不相关问题，为全程自动化提供了解决方案；且提供的临床诊断中所需的中间结果，可用于疾病诊断的质量控制。
Abstract:
Objective To study the value of automatic analysis method for flow cytometry data analysis and solving the problem of automatic classification and labeling of cell subsets, so as to provide reference for disease diagnosis.Methods The data of bone marrow flow cytometry from 528 cases of acute leukemia in 2021 were collected, and the original flow cytometry data were preprocessed by compensation, conversion and deadhesion. The preprocessed data were analyzed by unsupervised clustering method, and the supervised classification model was trained by using the central location of the generated cell subsets, namely the distribution rule of macro cells, to further classify the subsets. Finally, the cell subsets were labeled as known cell types by manual recognition and labeling.Results Unsupervised clustering method and supervised classification method could be used in flow cytometry data analysis, which can realize automatic classification and labeling of cell subsets, and the accuracy can reach or almost reach the level of manual analysis.Conclusion The method of automatic classification and labeling of flow cytometry data proposed in this study bridge the gap between cell clustering and patient classification existing in current flow cytometry automation, and provid a solution for the whole process automation. The intermediate results required for clinical diagnosis can be used for quality control of disease diagnosis.

参考文献/References:

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更新日期/Last Update: 1900-01-01