Rapid automated analysis method of flow cytometry data
10.7644/j.issn.1674-9960.2015.10.002
- VernacularTitle:一种快速自动分析流式数据方法研究
- Author:
Xianwen WANG
;
Yinan WANG
;
Hongtao BAO
;
Zhi CHENG
;
Yaohua DU
;
Taihu WU
;
Feng CHEN
- Publication Type:Journal Article
- Keywords:
flow cytometry;
clustering analysis;
kernel density estimation;
K-means;
k-d tree;
T-lymphocyte subsets;
data interpretation,statistical
- From:
Military Medical Sciences
2015;(10):736-741
- CountryChina
- Language:Chinese
-
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 .
