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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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. 改進特征樣本方法的 KPCA 變壓器故障檢測模型. 計算機工程與應用, 2014, 50(21): 4-7.
19.	劉嵩. 結合 DCT 與 KPCA 的人臉識別. 計算機工程與應用, 2012, 48(27): 186-188, 205.
20.	周鵬, 李向新, 張翼, 等. 基于主成分分析和支持向量機的睡眠分期研究. 生物醫學工程學雜志, 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, 左國坤. 基于互信息與主成分分析的運動想象腦電特征選擇算法. 生物醫學工程學雜志, 2016, 33(2): 201-207.
23.	杜卓明, 屠宏, 耿國華. KPCA 方法過程研究與應用. 計算機工程與應用, 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. 張文昌, 祝連慶, 婁小平, 等. 基于灰色預測恢復算法的流式細胞儀多參數提取. 儀器儀表學報, 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, 陳鋒, 程智, 等. 基于偏斜 t 混合模型的流式數據自動聚類方法研究. 電子學報, 2014, 12(12): 2527-2535.
6. 王先文. 基于偏斜 t 混合模型的流式數據細胞類群自動識別算法研究. 北京: 中國人民解放軍軍事醫學科學院, 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. 改進特征樣本方法的 KPCA 變壓器故障檢測模型. 計算機工程與應用, 2014, 50(21): 4-7.
19. 劉嵩. 結合 DCT 與 KPCA 的人臉識別. 計算機工程與應用, 2012, 48(27): 186-188, 205.
20. 周鵬, 李向新, 張翼, 等. 基于主成分分析和支持向量機的睡眠分期研究. 生物醫學工程學雜志, 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, 左國坤. 基于互信息與主成分分析的運動想象腦電特征選擇算法. 生物醫學工程學雜志, 2016, 33(2): 201-207.
23. 杜卓明, 屠宏, 耿國華. KPCA 方法過程研究與應用. 計算機工程與應用, 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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