军事医学Issue(10)：736-741,6.DOI:10.7644/j.issn.1674-9960.2015.10.002
一种快速自动分析流式数据方法研究
Rapid automated analysis method of flow cytometry data
摘要
Abstract
Objective A major component of flow cytometry data analysis involves gating , which is the process of identifying homogeneous groups of cells .As manual gating is error-prone, non-reproducible, nonstandardized, and time-consuming , we propose a time-efficient and accurate approach to automated analysis of flow cytometry data .Methods Unlike manual analysis that successively gates the data projected onto a two-dimensional filed, this approach, using the K-means clustering results , directly analyzed multidimensional flow cytometry data via a similar subpopulations-merged algorithm.In order to apply the K-means to analysis of flow cytometric data , kernel density estimation for selecting the initial number of clustering and k-d tree for optimizing efficiency were proposed .After K-means clustering , results closest to the true populations could be achieved via a two-segment line regression algorithm .Results The misclassification rate (MR) was 0.0736 and time was 2 s in Experiment One, but was 0.0805 and 1 s respectively in Experiment Two. Conclusion The approach we proposed is capable of a rapid and direct analysis of the multidimensional flow cytometry data with a lower misclassification rate compared to both nonprobabilistic and probabilistic clustering methods .关键词
流式细胞术/聚类分析/核密度估计/K-means/k-d树/T淋巴细胞亚群/数据说明,统计Key words
flow cytometry/clustering analysis/kernel density estimation/K-means/k-d tree/T-lymphocyte subsets/data interpretation ,statistical分类
生物科学引用本文复制引用
王先文,王懿男,暴洪涛,程智,杜耀华,吴太虎,陈锋..一种快速自动分析流式数据方法研究[J].军事医学,2015,(10):736-741,6.基金项目
国家科技重大专项资助项目 （）
