ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2017
[Cell data clustering method in flow cytometry based on kernel principal component analysis].
Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Deciphering cellular complexity: advances and future directions in single-cell protein analysis.Frontiers in bioengineering and biotechnology · 2024Review
- [Automatic clustering method of flow cytometry data based onSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2018Article
- Article
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5 authors.
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Abstract
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 improved K-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.
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29717598PMC9935356What OpenQuestion holds
Registered trials
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