title: A global learning with local preservation method for microarray data imputation.
abstract: Microarray data suffer from missing values for various reasons, including insufficient resolution, image noise, and experimental errors. Because missing values can hinder downstream analysis steps that require complete data as input, it is crucial to be able to estimate the missing values. In this study, we propose a Global Learning with Local Preservation method (GL2P) for imputation of missing values in microarray data. GL2P consists of two components: a local similarity measurement module and a global weighted imputation module. The former uses a local structure preservation scheme to exploit as much information as possible from the observable data, and the latter is responsible for estimating the missing values of a target gene by considering all of its neighbors rather than a subset of them. Furthermore, GL2P imputes the missing values in ascending order according to the rate of missing data for each target gene to fully utilize previously estimated values. To validate the proposed method, we conducted extensive experiments on six benchmarked microarray datasets. We compared GL2P with eight state-of-the-art imputation methods in terms of four performance metrics. The experimental results indicate that GL2P outperforms its competitors in terms of imputation accuracy and better preserves the structure of differentially expressed genes. In addition, GL2P is less sensitive to the number of neighbors than other local learning-based imputation methods.
authors: School of Computer and Information, Hefei University of Technology, Hefei 230009, China. Electronic address: ye1991214@126.com.
authors: Ye
authors: Y
authors: Chen
authors: School of Computer and Information, Hefei University of Technology, Hefei 230009, China; School of Software, Hefei University of Technology, Hefei 230009, China. Electronic address: wangaiguo2546@163.com.
authors: Aiguo
authors: A
authors: Wang
authors: School of Computer and Information, Hefei University of Technology, Hefei 230009, China. Electronic address: ding_huitong@163.com.
authors: Huitong
authors: H
authors: Ding
authors: School of Computer and Information, Hefei University of Technology, Hefei 230009, China. Electronic address: quexia@hfut.edu.cn.
authors: Xia
authors: X
authors: Que
authors: College of Life Sciences, Lanzhou University, Lanzhou 730000, China. Electronic address: liya19890211@126.com.
authors: Yabo
authors: Y
authors: Li
authors: School of Computer and Information, Hefei University of Technology, Hefei 230009, China. Electronic address: ning.g.an@acm.org.
authors: Ning
authors: N
authors: An
authors: Department of Computing Science, Umeå University, Umeå 90187, Sweden. Electronic address: lili.jiang@cs.umu.se.
authors: Lili
authors: L
authors: Jiang
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012044:Regression Analysis
keywords: Global learning; Local preservation; Microarray data; Missing value imputation; Regression model