Abstract
The increased volume and complexity of flow cytometry (FCM) data resulting from the increased throughput greatly boosts the demand for reliable statistical methods for the analysis of multidimensional data. The Support Vector Machines (SVM) model can be used for classification recognition. However, the selection of penalty factor c and kernel parameter g in the model has a great influence on the correctness of clustering. To solve the problem of parameter optimization of the SVM model, a support vector machine algorithm of particle swarm optimization (PSO-SVM) based on adaptive mutation is proposed. Firstly, a large number of FCM data were used to carry out the experiment, and the kernel function adapted to the sample data was selected. Then the PSO algorithm of adaptive mutation was used to optimize the parameters of the SVM classifier. Finally, the cell clustering results were obtained. The method greatly improves the clustering correctness of traditional SVM. That also overcomes the shortcomings of PSO algorithm, which is easy to fall into local optimum in the iterative optimization process and has poor convergence effect in dealing with a large number of data. Compared with the traditional SVM algorithm, the experimental results show that, the correctness of the method is improved by 19.38%. Compared with the cross-validation algorithm and the PSO algorithm, the adaptive mutation PSO algorithm can also improve the correctness of FCM data clustering. The correctness of the algorithm can reach 99.79% and the time complexity is relatively lower. At the same time, the method does not need manual intervention, which promotes the research of cell group identification in biomedical detection technology.
Highlights
Flow cytometry (FCM) is a precise instrument for quantitative analysis and sorting of suspended cells and particles
That overcomes the shortcomings of Particle Swarm Optimization (PSO) algorithm, which is easy to fall into local optimum in the iterative optimization process and has poor convergence effect in dealing with a large number of data
Compared with the traditional Support Vector Machines (SVM) algorithm, the experimental results show that, the correctness of the method is improved by 19.38%
Summary
Flow cytometry (FCM) is a precise instrument for quantitative analysis and sorting of suspended cells and particles. It plays an important role in genetics, oncology, immune cell subgroup analysis and so on [1]. The test samples are usually cells stained with a variety of fluorescent dyes, such as single laser five colors, double laser four colors, three laser eight colors, etc., the latest is Beckman Kurt’s upgraded of four laser tricolor CytoFLEXS flow cytometry, the wavelength of the laser is 488 nm, 638 nm, 405 nm and 561 nm respectively [2], which greatly increases the dimension of the fluorescence signal parameter in the process of signal analysis. How to realize automation of data analysis has become a hot research topic for many experts and scholars
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