Microarray Missing Value Imputation: A Regularized Local Learning Method

• Published in: IEEE/ACM transactions on computational biology and bioinformatics Vol. 16; no. 3; pp. 980 - 993
• Main Authors: Wang, Aiguo, Chen, Ye, An, Ning, Yang, Jing, Li, Lian, Jiang, Lili
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bayes methods; Biological analysis; Computational Biology - methods; Data models; DNA microarrays; Gene expression; Gene Expression Profiling - methods; Humans; Knowledge based systems; Learning; Learning systems; Least squares; Least-Squares Analysis; local learning; Machine Learning; Mathematical model; Microarray data; missing value imputation; Oligonucleotide Array Sequence Analysis - methods; Performance measurement; Preservation; Regularization; regularized model; similarity measurement; Teaching methods; Time series
• ISSN: 1545-5963; 1557-9964; 1557-9964
• DOI: 10.1109/TCBB.2018.2810205

Microarray experiments on gene expression inevitably generate missing values, which impedes further downstream biological analysis. Therefore, it is key to estimate the missing values accurately. Most of the existing imputation methods tend to suffer from the over-fitting problem. In this study, we propose two regularized local learning methods for microarray missing value imputation. Motivated by the grouping effect of L_{2}L2 regularization, after selecting the target gene, we train an L_{2}L2 Regularized Local Least Squares imputation model (RLLSimpute_L2) on the target gene and its neighbors to estimate the missing values of the target gene. Furthermore, RLLSimpute_L2 imputes the missing values in an ascending order based on the associated missing rate with each target gene. This contributes to fully utilizing the previously estimated values. Besides L_{2}L2, we further explore L_{1}L1 regularization and propose an L_{1}L1 Regularized Local Least Squares imputation model (RLLSimpute_L1). To evaluate their effectiveness, we conducted extensive experimental studies on six benchmark datasets covering both time series and non-time series cases. Nine state-of-the-art imputation methods are compared with RLLSimpute_L2 and RLLSimpute_L1 in terms of three performance metrics. The comparative experimental results indicate that RLLSimpute_L2 outperforms its competitors by achieving smaller imputation errors and better structure preservation of differentially expressed genes.