[1]雷 伟,李智伟,郭玉娟,等.人工神经网络在流式细胞术数据分析中的应用[J].医学信息,2023,36(18):74.[doi:10.3969/j.issn.1006-1959.2023.18.013]
　LEI Wei,LI Zhi-wei,GUO Yu-juan,et al.Application of Artificial Neural Networks in Flow Cytometry Data Analysis[J].Journal of Medical Information,2023,36(18):74.[doi:10.3969/j.issn.1006-1959.2023.18.013]
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人工神经网络在流式细胞术数据分析中的应用()
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医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
36卷
期数:
2023年18期
页码:
74
栏目:
论著
出版日期:
2023-09-15

文章信息/Info

Title:
Application of Artificial Neural Networks in Flow Cytometry Data Analysis
文章编号:
1006-1959（2023）18-0074-05
作者:
雷 伟; 李智伟; 郭玉娟; 等
（1.石河子大学医学院，新疆 石河子 832000；2.新疆维吾尔自治区人民医院临床检测中心，新疆 乌鲁木齐 830001）
Author(s):
LEI Wei; LI Zhi-wei; GUO Yu-juan; et al.
(1.Shihezi University School of Medicine,Shihezi 832000,Xinjiang,China;2.Clinical Testing Center of Xinjiang Uygur Autonomous Region People’s Hospital,Urumqi 830001,Xinjiang,China)
关键词:
流式细胞术; 细胞分群; 人工神经网络; 自动分析
Keywords:
Flow cytometry; Cell clustering; Artificial neural networks; Automated analysis
分类号:
R319
DOI:
10.3969/j.issn.1006-1959.2023.18.013
文献标志码:
A
摘要:
目的 探究人工神经网络（ANN）应用于流式细胞术（FCM）数据自动分析的可行性。方法 以10名健康志愿者骨髓标本的FCM数据作为数据集，在Python中构建ANN模型，模型在训练中自学习提取特征，得到最优参数。以人工设门分析结果作为金标准，采用交叉验证的方式评价ANN模型在细胞亚群分群上的效果，并与决策树和K-means模型进行结果比较。结果 ANN模型在数据集上拟合良好，模型在散点图上的分群轮廓与人工设门基本一致，能够很好的复现人工分析结果。ANN和决策树模型的分群效果优于K-means模型，分群准确率分别为0.970、0.972和0.899。结论 ANN在FCM数据自动分析中具有一定的应用价值，可以为后续研究提供参考。
Abstract:
Objective To explore the feasibility of artificial neural network (ANN) for automatic analysis of flow cytometry (FCM) data.Methods The FCM data of bone marrow samples of 10 healthy volunteers were used as a dataset to construct an ANN model in Python, and the model extracted features by self-learning during training to obtain the optimal parameters. The results of manual gate analysis were used as the gold standard, and the effect of ANN model on cell subpopulation was evaluated by cross-validation, and the results were compared with decision trees and K-means models.Results The ANN model fits well on the dataset, and the grouping profile of the model on the scatter plot was basically consistent with the manual gate, which could reproduce the manual analysis results well. The grouping effect of ANN and decision tree model was better than that of K-means model, and the grouping accuracy was 0.970, 0.972 and 0.899, respectively.Conclusion ANN has certain application value in the automatic analysis of FCM data, which can provide reference for subsequent research.

参考文献/References:

[1]Suo Y,Gu Z,Wei X.Advances of in vivo flow cytometry on cancer studies[J].Cytometry Part A,2020,97(1):15-23.[2]McKinnon KM.Flow Cytometry:An Overview[J].Curr Protoc Immunol,2018,120(1):1-11.[3]Jaye DL,Bray RA,Gebel HM,et al.Translational applications of flow cytometry in clinical practice[J].J Immunol,2012,188(10):4715-4719.[4]Pedreira CE,Costa ES,Lecrevisse Q,et al.Overview of clinical flow cytometry data analysis:recent advances and future challenges[J].Trends Biotechnol,2013,31(7):415-425.[5]Aghaeepour N,Finak G,Hoos H,et al.Critical assessment of automated flow cytometry data analysis techniques[J].Nat Methods,2013,10(3):228-238.[6]Van Gassen S,Callebaut B,Van Helden MJ,et al.FlowSOM:Using self-organizing maps for visualization and interpretation of cytometry data[J].Cytometry A,2015,87(7):636-645.[7]Ge Y,Sealfon SC.flowPeaks:a fast unsupervised clustering for flow cytometry data via K-means and density peak finding[J].Bioinformatics,2012,28(15):2052-2058.[8]Commenges D,Alkhassim C,Gottardo R,et al.cytometree:A binary tree algorithm for automatic gating in cytometry analysis[J].Cytometry A,2018,93(11):1132-1140.[9]孟晓辰,王玥,祝连庆.基于t分布邻域嵌入算法的流式数据自动分群方法[J].生物医学工程学杂志,2018,35(5):697-704.[10]马闪闪,董明利,张帆,等.基于核主成分分析的流式细胞数据分群方法研究[J].生物医学工程学杂志,2017,34(1):115-122.[11]周丽娜,苗林子,龚岩,等.人工智能辅助多参数流式细胞术诊断儿童急性B淋巴细胞白血病微小残留病[J].中华检验医学杂志,2020,43(12):1196-1204.[12]王海燕,鲁思博,孟军,等.基于人工神经网络技术辨识妇科疾病证素-证型逻辑关系[J].医学信息,2020,33(11):1-4.[13]刘微丽,张雨嫣,孟丽君,等.人工神经网络技术预测颅脑损伤患者的预后效果[J].中华重症医学电子杂志(网络版),2021,7(4):355-329.[14]Chen X,Cherian S.Acute Myeloid Leukemia Immunophenotyping by Flow Cytometric Analysis[J].Clin Lab Med,2017,37(4):753-769.[15]Hahne F,LeMeur N,Brinkman RR,et al.flowCore: a Bioconductor package for high throughput flow cytometry[J].BMC Bioinformatics,2009,10:106.[16]Nettey L,Giles AJ,Chattopadhyay PK.OMIP-050:A 28-color/30-parameter Fluorescence Flow Cytometry Panel to Enumerate and Characterize Cells Expressing a Wide Array of Immune Checkpoint Molecules[J].Cytometry A,2018,93(11):1094-1096.[17]Cheung M,Campbell JJ,Whitby L,et al.Current trends in flow cytometry automated data analysis software[J].Cytometry A,2021,99(10):1007-1021.[18]Esteva A,Kuprel B,Novoa RA,et al.Dermatologist-level classification of skin cancer with deep neural networks[J].Nature,2017,542(7639):115-118.[19]Ting DSW,Cheung CY,Lim G,et al.Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes[J].JAMA,2017,318(22):2211-2223.[20]Dong J,Wu H,Zhou D,et al.Application of Big Data and Artificial Intelligence in COVID-19 Prevention,Diagnosis,Treatment and Management Decisions in China[J].J Med Syst,2021,45(9):84.[21]章毅,郭泉,王建勇.大数据分析的神经网络方法[J].工程科学与技术,2017,49(1):9-18.

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更新日期/Last Update: 1900-01-01