A hybrid imputation approach for microarray missing value estimation

• Published in: BMC genomics Vol. 16; no. Suppl 9; p. S1
• Main Authors: Li, Huihui, Zhao, Changbo, Shao, Fengfeng, Li, Guo-Zheng, Wang, Xiao
• Format: Journal Article
• Language: English
• Published: London BioMed Central 17.08.2015
• Subjects: Algorithms; Animal Genetics and Genomics; Bayes Theorem; Biomedical and Life Sciences; Gene Expression Profiling; Humans; Least-Squares Analysis; Life Sciences; Microarrays; Microbial Genetics and Genomics; Models, Statistical; Oligonucleotide Array Sequence Analysis - methods; Plant Genetics and Genomics; Proteomics; Microarray gene expression data; large missing rate; Complement strategy; normalized root mean squared error; missing value imputation
• ISSN: 1471-2164; 1471-2164
• DOI: 10.1186/1471-2164-16-S9-S1

Background Missing data is an inevitable phenomenon in gene expression microarray experiments due to instrument failure or human error. It has a negative impact on performance of downstream analysis. Technically, most existing approaches suffer from this prevalent problem. Imputation is one of the frequently used methods for processing missing data. Actually many developments have been achieved in the research on estimating missing values. The challenging task is how to improve imputation accuracy for data with a large missing rate. Methods In this paper, induced by the thought of collaborative training, we propose a novel hybrid imputation method, called Recursive Mutual Imputation (RMI). Specifically, RMI exploits global correlation information and local structure in the data, captured by two popular methods, Bayesian Principal Component Analysis (BPCA) and Local Least Squares (LLS), respectively. Mutual strategy is implemented by sharing the estimated data sequences at each recursive process. Meanwhile, we consider the imputation sequence based on the number of missing entries in the target gene. Furthermore, a weight based integrated method is utilized in the final assembling step. Results We evaluate RMI with three state-of-art algorithms (BPCA, LLS, Iterated Local Least Squares imputation (ItrLLS)) on four publicly available microarray datasets. Experimental results clearly demonstrate that RMI significantly outperforms comparative methods in terms of Normalized Root Mean Square Error (NRMSE), especially for datasets with large missing rates and less complete genes. Conclusions It is noted that our proposed hybrid imputation approach incorporates both global and local information of microarray genes, which achieves lower NRMSE values against to any single approach only. Besides, this study highlights the need for considering the imputing sequence of missing entries for imputation methods.