多种归一化方法对miRNA微阵列数据的作用分析及比较  

Analysis and Comparison of Various Normalization Methods on Microarray Data of MiRNA

作　　者：侯丽云 张旭[1] 吴珍 HOU Li-yun;ZHANG Xu;WU Zhen(School of Mathematics and Statistic,Southwest University,Chongqing 400715,China)

机构地区：[1]西南大学数学与统计学院,重庆400715

出　　处：《西南师范大学学报（自然科学版）》2020年第5期98-102,共5页Journal of Southwest China Normal University(Natural Science Edition)

基　　金：国家自然科学基金项目(11701471);重庆市基础科学与前沿技术研究项目(cstc2017jcyjAX0476).

摘　　要：通过MA图和箱线图比较归一化前后miRNA微阵列数据分布情况的变化,用K-S检验和均方误差来评估6种归一化方法的优良性.结果显示,对于miRNA微阵列数据而言,局部加权回归方法和分位数归一化方法比其它方法效果更好,其中又以局部加权回归方法的效果最佳.Detecting the level of miRNA in cells with microarray has become a widely used technology.There are many normalization methods for microarray of miRNA.Different normalization methods have different effects on microarray data of miRNA.In this paper,six normalization methods for microarray data of Agilent platform have been studied,including global normalization,locally weighted regression method,quantile normalization,trimmed mean method,variance stabilizing normalization and scale normalization.And the distribution changes of miRNA microarray data have been presented and compared before and after normalization by drawing MA plots and box plots.The six normalization methods have also been evaluated by Kolmogorov-Smirnov statistic and mean square error.The result shows that the locally weighted regression method and quantile normalization method are better than other methods for miRNA microarray data,and the locally weighted regression method is the best.

关 键 词：miRNA微阵列数据 归一化方法 MA图 K-S检验 均方误差 

分 类 号：Q522[生物学—生物化学]