Automatic clustering method of flow cytometry data based on -distributed stochastic neighbor embedding.
10.7507/1001-5515.201802037
- Author:
Xiaochen MENG
1
;
Yue WANG
1
;
Lianqing ZHU
2
Author Information
1. Beijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science and Technology University, Beijing 100192, P.R.China.
2. Beijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science and Technology University, Beijing 100192, P.R.China.zhulianqing@sina.com.
- Publication Type:Journal Article
- Keywords:
K-means;
biomedicine;
cell clustering;
kernel principal component analysis;
t-distributed stochastic neighbor embedding
- From:
Journal of Biomedical Engineering
2018;35(5):697-704
- CountryChina
- Language:Chinese
-
Abstract:
The traditional method of multi-parameter flow data clustering in flow cytometry is to mainly use professional software to manually set the door and circle out the target cells for analysis. The analysis process is complex and professional. Based on this, a clustering algorithm, which is based on -distributed stochastic neighbor embedding ( -SNE) algorithm for multi-parameter stream data, is proposed in the paper. In this algorithm, the Euclidean distance of sample data in high dimensional space is transformed into conditional probability to represent similarity, and the data is reduced to low dimensional space. In this paper, the stained human peripheral blood cells were treated by flow cytometry, and the processed data were derived as experimental sample data. The -SNE algorithm is compared with the kernel principal component analysis (KPCA) dimensionality reduction algorithm, and the main component data obtained by the dimensionality reduction are classified using -means algorithm. The results show that the -SNE algorithm has a good clustering effect on the cell population with asymmetric and trailing distribution, and the clustering accuracy can reach 92.55%, which may be helpful for automatic analysis of multi-color multi-parameter flow data.
