期刊
IEEE ACCESS
卷 9, 期 -, 页码 16899-16913出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3053631
关键词
Microarray data; missing value imputation; local structure; penalty
资金
- Guangdong Basic and Applied Basic Research Foundation [2020A1515011499]
This study formalizes the problem of missing values in microarray data under a regularized sparse framework and proposes local learning-based imputation models with elastic net regularization to accurately estimate missing entries in gene expression profiles. Experimental results demonstrate the superiority of elastic net over other methods in terms of statistical analysis metrics.
The existence of missing values in microarray data inevitably hinders downstream biological analyses that expect complete data as input, therefore how to effectively explore the underlying structure of data to accurately estimate missing entries remains crucial and meaningful. In this study, we formalize the problem under a regularized sparse framework and accordingly propose local learning-based imputation models to capture the relationships that are hidden in gene expression profiles towards better imputation. Specifically, in view of the simultaneous variable selection and grouping effect of the elastic net penalty, we present an elastic net regularized local least squares-based imputation method to estimate the missing entries of a target gene with its neighbors. Besides, we investigate different similarity filtering metrics to select neighbor genes and develop another four imputation methods under the framework. Furthermore, the proposed methods process the target genes in ascending order of their associated missing rates. Finally, extensive comparative experiments against other eight commonly-used methods are conducted on multiple microarray datasets having varying missing rates. Results indicate the power of sparse regularization techniques and the superiority of elastic net over its competitors in terms of statistical analysis metrics.
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