Iterated local least squares microarray missing value imputation

作     者：Cai, Zhipeng Heydari, Maysam Lin, Guohui 

作者机构：Bioinformatics Research Group Department of Computing Science University of Alberta Edmonton AB T6G 2E8 Canada 

出 版 物：《Journal of Bioinformatics and Computational Biology》 (J. Bioinformatics Comput. Biol.)

年 卷 期：2006年第4卷第5期

页      面：935-957页

学科分类：0710[理学-生物学] 07[理学] 09[农学] 0703[理学-化学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Natural Sciences and Engineering Research Council of Canada, NSERC Canada Foundation for Innovation, CFI 

主　　题：Local least squares Microarray gene expression data Missing value imputation Normalized root mean squared error 

摘      要：Microarray gene expression data often contains multiple missing values due to various reasons. However, most of gene expression data analysis algorithms require complete expression data. Therefore, accurate estimation of the missing values is critical to further data analysis. In this paper, an Iterated Local Least Squares Imputation (ILLSimpute) method is proposed for estimating missing values. Two unique features of ILLSimpute method are: ILLSimpute method does not fix a common number of coherent genes for target genes for estimation purpose, but defines coherent genes as those within a distance threshold to the target genes. Secondly, in ILLSimpute method, estimated values in one iteration are used for missing value estimation in the next iteration and the method terminates after certain iterations or the imputed values converge. Experimental results on six real microarray datasets showed that ILLSimpute method performed at least as well as, and most of the time much better than, five most recent imputation methods. © 2006 Imperial College Press.