Cell data clustering method in flow cytometry based on kernel principal component analysis_Journal of Biomedical Engineering

Authors：

MA Shanshan ,  DONG Mingli , ZHANG Fan , PAN Zhikang , ZHU Lianqing

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

Corresponding author：

DONG Mingli, Email: dongml@sina.com

Keywords：

kernel principal component analysis; principal component analysis; flow cytometry; cell clustering

DOI：

10.7507/1001-5515.201604088

Video：

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The process of multi-parametric flow cytometry data analysis is complicate and time-consuming, which requires well-trained professionals to operate on. To overcome this limitation, a method for multi-parameter flow cytometry data processing based on kernel principal component analysis (KPCA) was proposed in this paper. The dimensionality of the data was reduced by nonlinear transform. After the new characteristic variables were obtained, automatical clustering can be achieved using improvedK-means algorithm. Experimental data of peripheral blood lymphocyte were processed using the principal component analysis (PCA)-based method and KPCA-based method and then the influence of different feature parameter selections was explored. The results indicate that the KPCA can be successfully applied in the multi-parameter flow cytometry data analysis for efficient and accurate cell clustering, which can improve the efficiency of flow cytometry in clinical diagnosis analysis.

Citation： MA Shanshan, DONG Mingli, ZHANG Fan, PAN Zhikang, ZHU Lianqing. Cell data clustering method in flow cytometry based on kernel principal component analysis. Journal of Biomedical Engineering, 2017, 34(1): 115-122. doi: 10.7507/1001-5515.201604088 Copy

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15.	Grégori G, Patsekin V, Rajwa B, et al. Hyperspectral cytometry at the single-cell level using a 32-channel photodetector. Cytometry A, 2012, 81(1): 35-44.
16.	Costa E S, Pedreira C E, Barrena S, et al. Automated pattern-guided principal component analysis versus expert-based immuno-phenotypic classification of hematological malignancies:a step forward in the standardization of clinical immunophenotyping. Leukemia, 2010, 24: 1927-1933.
17.	Pedreira C E, Costa E S, Lecrevisse Q, et al. Overview of clinical flow cytometry data analysis: recent advances and future challenges. Trends Biotechnol, 2013, 31(7): 415-425.
18.	唐勇波. 改进特征样本方法的 KPCA 变压器故障检测模型. 计算机工程与应用, 2014, 50(21): 4-7.
19.	刘嵩. 结合 DCT 与 KPCA 的人脸识别. 计算机工程与应用, 2012, 48(27): 186-188, 205.
20.	周鹏, 李向新, 张翼, 等. 基于主成分分析和支持向量机的睡眠分期研究. 生物医学工程学杂志, 2013, 30(6): 1176-1179.
21.	Abdi H, Williams L J. Principal component analysis. Wiley Interdisciplinary Review Computational Statics, 2010, 2(4): 433-459.
22.	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.
23.	杜卓明, 屠宏, 耿国华. KPCA 方法过程研究与应用. 计算机工程与应用, 2010, 46(7): 8-10.
24.	Hartigan J A, Wong M A. A K-means clustering algorithm. Appl Stat, 2013, 28(1): 100-108.

1. Robinson J P, Roederer M. Flow cytometry strikes Gold. Science, 2015, 350(6262): 739-740.
2. 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.
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. 王先文, 陈锋, 程智, 等. 基于偏斜 t 混合模型的流式数据自动聚类方法研究. 电子学报, 2014, 12(12): 2527-2535.
6. 王先文. 基于偏斜 t 混合模型的流式数据细胞类群自动识别算法研究. 北京: 中国人民解放军军事医学科学院, 2015.
7. Bashashati A, Brinkman R R. A survey of flow cytometry data analysis methods. Advance in Bioinformatics, 2009: 584603.
8. Aghaeepour N, Finak G, Hoos H, et al. Critical assessment of automated flow cytometry data analysis techniques. Nat Methods, 2013, 10(3): 228-238.
9. Wilkins M F, Hardy S A, Boddy L, et al. Comparison of five clustering algorithms to classify phytoplankton from flow cytometry data. Cytometry, 2001, 44(3): 210-217.
10. Chan C, Feng Feng, Ottinger J, et al. Statistical mixture modeling for cell subtype identification in flow cytometry. Cytometry A, 2008, 73(8): 693-701.
11. Zeng Q T, Pratt J P, Pak J, et al. Feature-guided clustering of multi-dimensional flow cytometry datasets. J Biomed Inform, 2007, 40(3): 325-331.
12. Zare H, Shooshtari P, Gupta A, et al. Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinformatics, 2010, 11(1): 1-16.
13. Sugar I P, Sealfon S C. Misty mountain clustering:application to fast unsupervised flow cytometry gating. BMC Bioinformatics, 2010, 11(3): 2277-2289.
14. Ge Yongchao, Sealfon S C. flowPeaks: a fast unsupervised clustering for flow cytometry data via k-means and density peak finding[J]. Bioinformatics, 2012, 28(15): 2052-2058.
15. Grégori G, Patsekin V, Rajwa B, et al. Hyperspectral cytometry at the single-cell level using a 32-channel photodetector. Cytometry A, 2012, 81(1): 35-44.
16. Costa E S, Pedreira C E, Barrena S, et al. Automated pattern-guided principal component analysis versus expert-based immuno-phenotypic classification of hematological malignancies:a step forward in the standardization of clinical immunophenotyping. Leukemia, 2010, 24: 1927-1933.
17. Pedreira C E, Costa E S, Lecrevisse Q, et al. Overview of clinical flow cytometry data analysis: recent advances and future challenges. Trends Biotechnol, 2013, 31(7): 415-425.
18. 唐勇波. 改进特征样本方法的 KPCA 变压器故障检测模型. 计算机工程与应用, 2014, 50(21): 4-7.
19. 刘嵩. 结合 DCT 与 KPCA 的人脸识别. 计算机工程与应用, 2012, 48(27): 186-188, 205.
20. 周鹏, 李向新, 张翼, 等. 基于主成分分析和支持向量机的睡眠分期研究. 生物医学工程学杂志, 2013, 30(6): 1176-1179.
21. Abdi H, Williams L J. Principal component analysis. Wiley Interdisciplinary Review Computational Statics, 2010, 2(4): 433-459.
22. 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.
23. 杜卓明, 屠宏, 耿国华. KPCA 方法过程研究与应用. 计算机工程与应用, 2010, 46(7): 8-10.
24. Hartigan J A, Wong M A. A K-means clustering algorithm. Appl Stat, 2013, 28(1): 100-108.

Journal of Biomedical Engineering

Cell data clustering method in flow cytometry based on kernel principal component analysis

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