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.
Focused on the world-wide issue of improving the accuracy of emotion recognition, this paper proposes an electroencephalogram (EEG) signal feature extraction algorithm based on wavelet packet energy entropy and auto-regressive (AR) model. The auto-regressive process can be approached to EEG signal as much as possible, and provide a wealth of spectral information with few parameters. The wavelet packet entropy reflects the spectral energy distribution of the signal in each frequency band. Combination of them gives a better reflect of the energy characteristics of EEG signals. Feature extraction and fusion are implemented based on kernel principal component analysis. Six emotional states from a public multimodal database for emotion analysis using physiological signals (DEAP) are recognized. The results show that the recognition accuracy of the proposed algorithm is more than 90%, and the highest recognition accuracy is 99.33%. It indicates that this algorithm can extract the feature of EEG emotion well, and it is a kind of effective emotion feature extraction algorithm, providing support to emotion recognition.
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.