Cancer classification based on microarray gene expression data using a principal component accumulation method  

作　　者：LIU JingJing CAI WenSheng SHAO XueGuang 

机构地区：[1]Research Center for Analytical Sciences,College of Chemistry,Nankai University,Tianjin 300071,China

出　　处：《Science China Chemistry》2011年第5期802-811,共10页中国科学（化学英文版）

基　　金：supported by the National Natural Science Foundation of China (20835002);International Science and Technology Cooperation Program of the Ministry of Science and Technology (MOST) of China (2008DFA32250)

摘　　要：The classification of cancer is a major research topic in bioinformatics. The nature of high dimensionality and small size associated with gene expression data,however,makes the classification quite challenging. Although principal component analysis (PCA) is of particular interest for the high-dimensional data,it may overemphasize some aspects and ignore some other important information contained in the richly complex data,because it displays only the difference in the first twoor three-dimensional PC subspaces. Based on PCA,a principal component accumulation (PCAcc) method was proposed. It employs the information contained in multiple PC subspaces and improves the class separability of cancers. The effectiveness of the present method was evaluated by four commonly used gene expression datasets,and the results show that the method performs well for cancer classification.The classification of cancer is a major research topic in bioinformatics. The nature of high dimensionality and small size associated with gene expression data,however,makes the classification quite challenging. Although principal component analysis （PCA） is of particular interest for the high-dimensional data,it may overemphasize some aspects and ignore some other important information contained in the richly complex data,because it displays only the difference in the first twoor three-dimensional PC subspaces. Based on PCA,a principal component accumulation （PCAcc） method was proposed. It employs the information contained in multiple PC subspaces and improves the class separability of cancers. The effectiveness of the present method was evaluated by four commonly used gene expression datasets,and the results show that the method performs well for cancer classification.

关 键 词：cancer classification principal component analysis principal component accumulation gene expression data 

分 类 号：O212[理学—概率论与数理统计] Q-332[理学—数学]