Automatic clustering method of flow cytometry data based on <i>t</i>-distributed stochastic neighbor embedding _Journal of Biomedical Engineering

Authors：

MENG Xiaochen , WANG Yue ,  ZHU Lianqing

Beijing Key Laboratory for Optoelectronic Measurement Technology, Beijing Information Science and Technology University, Beijing 100192, P.R.China;

Corresponding author：

ZHU Lianqing, Email: zhulianqing@sina.com

Keywords：

biomedicine; cell clustering; t-distributed stochastic neighbor embedding; kernel principal component analysis; K-means

DOI：

10.7507/1001-5515.201802037

Video：

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Abstract Full text Figures/Tables Video References Cited by

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 t-distributed stochastic neighbor embedding (t-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. Thet-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 K-means algorithm. The results show that thet-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.

Citation： MENG Xiaochen, WANG Yue, ZHU Lianqing. Automatic clustering method of flow cytometry data based on t-distributed stochastic neighbor embedding . Journal of Biomedical Engineering, 2018, 35(5): 697-704. doi: 10.7507/1001-5515.201802037 Copy

1.	Bashashati A, Brinkman R R. A survey of flow cytometry data analysis methods. Advances in Bioinformatics, 2009, 2009: 584603.
2.	Jahn K, Buschmann V, Hille C. Simultaneous fluorescence and phosphorescence lifetime imaging microscopy in living cells. Sci Rep, 2015, 5(6262): 739-740.
3.	张文昌, 祝连庆, 娄小平, 等. 基于灰色预测恢复算法的流式细胞仪多参数提取. 仪器仪表学报, 2015, 36(7): 1660-1665.
4.	Krutzik P O, Irish J M, Nolan G P, et al. Analysis of protein phosphorylation and cellular signaling events by flow cytometry: techniques and clinical applications. Clin Immunol, 2004, 110(3): 206-221.
5.	Brie D, Klotz R, Miron S, et al. Joint analysis of flow cytometry data and fluorescence spectra as a non-negative array factorization problem. Chemometrics and Intelligent Laboratory Systems, 2014, 137(23): 21-32.
6.	Qian Yu, Wei C, Lee F H, et al. Elucidation of seventeen human peripheral blood B-cell subsets and quantification of the tetanus response using a density-based method for the automated identification of cell populations in multidimensional flow cytometry data. Cytometry B Clin Cytom, 2010, 78B(1): S69-S82.
7.	Aghaeepour N, Nikolic R, Hoos H H, et al. Rapid cell population identification in flow cytometry data. Cytometry Part A, 2011, 79A(1): 6-13.
8.	Zeng Q T, Pratt J P, Pak J, et al. Feature-guided clustering of multi-dimensional flow cytometry datasets. Journal of Biomedical Informatics, 2007, 40(3): 325-331.
9.	Sugár I P, Sealfon S C. Misty mountain clustering: application to fast unsupervised flow cytometry gating. BMC Bioinformatics, 2010, 11(1): 502.
10.	Morris C W, Autret A, Boddy L. Support vector machines for identifying organisms - a comparison with strongly partitioned radial basis function networks. Ecological Modelling, 2001, 146(1/3, SI): 57-67.
11.	Boedigheimer M J, Ferbas J. Mixture modeling approach to flow cytometry data. Cytometry Part A, 2008, 73A(5): 421-429.
12.	Pedreira C E, Costa E S, Lecrevisse Q, et al. Overview of clinical flow cytometry data analysis: recent advances and future challenges. Trends in Biotechnology, 2013, 31(7): 415-425.
13.	Ghaleb T A, Mohammed M A, Ramadan E. Automated analysis of flow cytometry data: a systematic review of recent methods//2016 2nd International Conference On Open Source Software Computing (OSSCOM), IEEE, 2016: 1-7.
14.	张雨晨. 基于改进的SVM和t-SNE高速列车走行部故障诊断. 成都: 西南交通大学, 2016.
15.	徐佳琳, 左国坤. 基于互信息与主成分分析的运动想象脑电特征选择算法. 生物医学工程学杂志, 2016, 33(2): 201-207.
16.	姜战伟, 郑近德, 潘海洋, 等. 基于多尺度时不可逆与t-SNE流形学习的滚动轴承故障诊断. 振动与冲击, 2017, 36(17): 61-68.
17.	Gu Yuhai, He Linfeng, Deng Yali, et al. A fault identification method of rotating machinery based on t-SNE. 仪器仪表学报, 2016(s1): 152-156.
18.	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. 生物医学工程学杂志, 2017, 34(1): 115-122.
19.	张婷婷, 孙群, 杨磊, 等. 基于电子鼻传感器阵列优化的甜玉米种子活力检测. 农业工程学报, 2017, 33(21): 275-281.
20.	高国琴, 李明. 基于K-means算法的温室移动机器人导航路径识别. 农业工程学报, 2014, 30(7): 25-33.
21.	Zhang Wenchang, Lou Xiaoping, Meng Xiaochen, et al. Representation method for spectrally overlapping signals in flow cytometry based on fluorescence pulse time-delay estimation. Sensors, 2016, 16(11): 1978.
22.	Zhang W, Zhu L, Lou X, et al. New method of evaluating the liquid path stability of flow cytometer// International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale. IEEE, 2016: 316-320.

1. Bashashati A, Brinkman R R. A survey of flow cytometry data analysis methods. Advances in Bioinformatics, 2009, 2009: 584603.
2. Jahn K, Buschmann V, Hille C. Simultaneous fluorescence and phosphorescence lifetime imaging microscopy in living cells. Sci Rep, 2015, 5(6262): 739-740.
3. 张文昌, 祝连庆, 娄小平, 等. 基于灰色预测恢复算法的流式细胞仪多参数提取. 仪器仪表学报, 2015, 36(7): 1660-1665.
4. Krutzik P O, Irish J M, Nolan G P, et al. Analysis of protein phosphorylation and cellular signaling events by flow cytometry: techniques and clinical applications. Clin Immunol, 2004, 110(3): 206-221.
5. Brie D, Klotz R, Miron S, et al. Joint analysis of flow cytometry data and fluorescence spectra as a non-negative array factorization problem. Chemometrics and Intelligent Laboratory Systems, 2014, 137(23): 21-32.
6. Qian Yu, Wei C, Lee F H, et al. Elucidation of seventeen human peripheral blood B-cell subsets and quantification of the tetanus response using a density-based method for the automated identification of cell populations in multidimensional flow cytometry data. Cytometry B Clin Cytom, 2010, 78B(1): S69-S82.
7. Aghaeepour N, Nikolic R, Hoos H H, et al. Rapid cell population identification in flow cytometry data. Cytometry Part A, 2011, 79A(1): 6-13.
8. Zeng Q T, Pratt J P, Pak J, et al. Feature-guided clustering of multi-dimensional flow cytometry datasets. Journal of Biomedical Informatics, 2007, 40(3): 325-331.
9. Sugár I P, Sealfon S C. Misty mountain clustering: application to fast unsupervised flow cytometry gating. BMC Bioinformatics, 2010, 11(1): 502.
10. Morris C W, Autret A, Boddy L. Support vector machines for identifying organisms - a comparison with strongly partitioned radial basis function networks. Ecological Modelling, 2001, 146(1/3, SI): 57-67.
11. Boedigheimer M J, Ferbas J. Mixture modeling approach to flow cytometry data. Cytometry Part A, 2008, 73A(5): 421-429.
12. Pedreira C E, Costa E S, Lecrevisse Q, et al. Overview of clinical flow cytometry data analysis: recent advances and future challenges. Trends in Biotechnology, 2013, 31(7): 415-425.
13. Ghaleb T A, Mohammed M A, Ramadan E. Automated analysis of flow cytometry data: a systematic review of recent methods//2016 2nd International Conference On Open Source Software Computing (OSSCOM), IEEE, 2016: 1-7.
14. 张雨晨. 基于改进的SVM和t-SNE高速列车走行部故障诊断. 成都: 西南交通大学, 2016.
15. 徐佳琳, 左国坤. 基于互信息与主成分分析的运动想象脑电特征选择算法. 生物医学工程学杂志, 2016, 33(2): 201-207.
16. 姜战伟, 郑近德, 潘海洋, 等. 基于多尺度时不可逆与t-SNE流形学习的滚动轴承故障诊断. 振动与冲击, 2017, 36(17): 61-68.
17. Gu Yuhai, He Linfeng, Deng Yali, et al. A fault identification method of rotating machinery based on t-SNE. 仪器仪表学报, 2016(s1): 152-156.
18. 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. 生物医学工程学杂志, 2017, 34(1): 115-122.
19. 张婷婷, 孙群, 杨磊, 等. 基于电子鼻传感器阵列优化的甜玉米种子活力检测. 农业工程学报, 2017, 33(21): 275-281.
20. 高国琴, 李明. 基于K-means算法的温室移动机器人导航路径识别. 农业工程学报, 2014, 30(7): 25-33.
21. Zhang Wenchang, Lou Xiaoping, Meng Xiaochen, et al. Representation method for spectrally overlapping signals in flow cytometry based on fluorescence pulse time-delay estimation. Sensors, 2016, 16(11): 1978.
22. Zhang W, Zhu L, Lou X, et al. New method of evaluating the liquid path stability of flow cytometer// International Conference on Manipulation, Manufacturing and Measurement on the Nanoscale. IEEE, 2016: 316-320.

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