高密度寡核苷酸阵列的数据标准化方法  

Normalization methods for high-density oligonucleotide microarray data

作　　者：刘琦[1,2] 张引[3] 俞荣栋[1,2] 王明怡 叶修梓[3] 

机构地区：[1]浙江大学生命科学学院,浙江杭州310058 [2]浙江大学加州纳米科学研究院,浙江杭州310029 [3]浙江大学计算机科学与技术学院,浙江杭州310027 [4]谷物研究中心,Winnipeg,MB加拿大

出　　处：《浙江大学学报（工学版）》2008年第9期1653-1660,共8页Journal of Zhejiang University：Engineering Science

基　　金：浙江省教育厅科研项目资助项目研究(20050399)

摘　　要：针对微阵列数据的标准化方法进行系统阐述,对高密度寡核苷酸阵列(Affymetrix芯片)的两类主要标准化算法:全数据算法和基线算法进行了探讨,同时对其他标准化算法(复合算法、VSN算法、全局loess算法I、nvari-ant set算法等)进行了综合的论述和分析.基于标准数据集对前两类标准化算法处理的效果和效率做了对比测试,结果表明,算法在数据变异性的消除方面,对于非差异表达数据,全数据算法可以达到比较优秀的处理结果;对于差异表达数据,Quantile和Non-linear算法比较有效.在算法的耗时方面,Scale算法最优.全面考虑时间效率和标准化处理效果,Quantile算法具有一定的综合优势.Different normalization methods for the pre-process of expression data from high-density oligonucleotide microarray were discussed. The two major normalization methods, complete data method and baseline method, were reviewed and compared in details. Other normalization methods （compound method, VSN method, global Loess method, invariant set method） were also introduced. The performances of these methods in terms of variation reduction and efficiency were experimentally demonstrated on the standard data sets. Results indicated that in terms of variation reduction, all the complete data methods are suitable for non-spike-in data, whereas the quantile and non-linear methods are suitable for spike-in data. In addition, scale algorithm has the shortest running time. The quantile method is a good selection when considering both of these aspects.

关 键 词：寡核苷酸阵列 Affymetrix芯片 标准化 数据集 变异性 

分 类 号：Q332[生物学—遗传学]