董明利,马闪闪,张帆,潘志康.基于核熵成分分析的流式数据自动分群方法[J].仪器仪表学报,2017,38(1):206-211
基于核熵成分分析的流式数据自动分群方法
Auto classification method of flow cytometry data based on kernel entropy component analysis
  
DOI:
中文关键词:  流式细胞术  自动分群  核熵成分分析  K means算法  余弦相似度
英文关键词:flow cytometry  automatic clustering  kernel entropy component analysis (KECA)  K means algorithm  cosine similarity
基金项目:教育部"长江学者与创新团队"发展计划(IRT1212) 、国家重大科学仪器设备开发专项基金(2011YQ030134) 、国家自然科学基金(61605010)项目资助
作者单位
董明利 北京信息科技大学 光电测试技术北京市重点实验室北京100192 
马闪闪 北京信息科技大学 光电测试技术北京市重点实验室北京100192 
张帆 北京信息科技大学 光电测试技术北京市重点实验室北京100192 
潘志康 北京信息科技大学 光电测试技术北京市重点实验室北京100192 
AuthorInstitution
Dong Mingli Beijing Key Laboratory for Optoelectronics Measurement Technology, Beijing Information Science and Technology University, Beijing 100192, China 
Ma Shanshan Beijing Key Laboratory for Optoelectronics Measurement Technology, Beijing Information Science and Technology University, Beijing 100192, China 
Zhang Fan Beijing Key Laboratory for Optoelectronics Measurement Technology, Beijing Information Science and Technology University, Beijing 100192, China 
Pan Zhikang Beijing Key Laboratory for Optoelectronics Measurement Technology, Beijing Information Science and Technology University, Beijing 100192, China 
摘要点击次数: 687
全文下载次数: 780
中文摘要:
      针对多参数流式细胞数据传统人工分群过程复杂、自动化程度不高等问题,提出了一种基于核熵成分分析(KECA)的自动分群方法。选取对瑞利(Renyi)熵具有最大贡献的特征向量作为投影方向,对数据进行特征提取;设计了一种基于余弦相似度和K means算法的分类器,并采用一种基于向量夹角的最佳聚类数确定方法,最终获得细胞的分类标签。对实验获得的淋巴细胞免疫表型分析数据进行处理,结果表明,该方法能够实现细胞的快速、自动分群,整体分群准确率能够达到97%以上,操作简单便捷,提高了细胞分析的效率。
英文摘要:
      The traditional clustering process of multi parametric flow cytometry data analysis is complicated, non automated and time consuming. To overcome this limitation, an automatic clustering method based on kernel entropy analysis (KECA) is proposed. The feature vector with the greatest contribution to the Renyi entropy is selected as the projection direction to carry out the feature extraction. A classifier based on cosine similarity and the K means algorithm is designed to get the label of each cell, and a method for determining the optimal number of clusters based on the angle of vectors is adopted. Experimental data of peripheral blood lymphocyte is processed, and the results indicate that the proposed method can realize automatically clustering with simple operation and the overall accuracy rate of clustering can reach 97%, which can improve the efficiency of cell analysis.
查看全文  查看/发表评论  下载PDF阅读器