本文關鍵詞： 流式細胞術 自動分群 核熵成分分析 K-means算法 余弦相似度　出處：《儀器儀表學報》2017年01期 　論文類型：期刊論文

【摘要】：針對多參數流式細胞數據傳統(tǒng)人工分群過程復雜、自動化程度不高等問題,提出了一種基于核熵成分分析(KECA)的自動分群方法。選取對瑞利(Renyi)熵具有最大貢獻的特征向量作為投影方向,對數據進行特征提取;設計了一種基于余弦相似度和K-means算法的分類器,并采用一種基于向量夾角的最佳聚類數確定方法,最終獲得細胞的分類標簽。對實驗獲得的淋巴細胞免疫表型分析數據進行處理,結果表明,該方法能夠實現細胞的快速、自動分群,整體分群準確率能夠達到97%以上,操作簡單便捷,提高了細胞分析的效率。
[Abstract]:In view of the traditional artificial clustering process of multi-parameter flow cell data, the process is complex and the degree of automation is not high. An automatic clustering method based on Kernel Entropy component Analysis (KECA) is proposed. The feature vector which has the greatest contribution to Rayleigh Renyi entropy is selected as the projection direction to extract the feature of the data. A classifier based on cosine similarity and K-means algorithm is designed. Finally, the classification label of cells was obtained. The experimental data of lymphocyte immunophenotypic analysis were processed. The results show that this method can realize the rapid and automatic grouping of cells, and the accuracy of overall grouping can reach more than 97%. The operation is simple and convenient, and the efficiency of cell analysis is improved.
【作者單位】： 北京信息科技大學光電測試技術北京市重點實驗室;
【基金】：教育部"長江學者與創(chuàng)新團隊"發(fā)展計劃(IRT1212) 國家重大科學儀器設備開發(fā)專項基金(2011YQ030134) 國家自然科學基金(61605010)項目資助
【分類號】：Q2-3;TP311.13
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本文編號：1508882