A global learning with local preservation method for microarray data imputation. (1st October 2016)

• Record Type: Journal Article
• Authors: Chen, Ye; Wang, Aiguo; Ding, Huitong; Que, Xia; Li, Yabo; An, Ning; Jiang, Lili
• Is Part Of: Computers in biology and medicine. Volume 77(2016)
• Page Start: 76
• Page End: 89
• Publication Date: 2016-10-01
• Subjects: Missing value imputation -- Microarray data -- Global learning -- Local preservation -- Regression model; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2016.08.005
• Languages: English
• ISSNs: 0010-4825

Abstract

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. Highlights: We propose a novel method for imputing missing values in microarray data. The proposed method works in a global learning with local preservation scheme. We design a strategy for automatically selecting similar genes. Experimental results demonstrate its effectiveness on real-world datasets.