Abstract
BackgroundIt is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile analysis in bioinformatics. Although several methods have been suggested, their performances are not satisfactory for datasets with high missing percentages.ResultsThe paper explores the feasibility of doing missing value imputation with the help of gene regulatory mechanism. An imputation framework called histone acetylation information aided imputation method (HAIimpute method) is presented. It incorporates the histone acetylation information into the conventional KNN(k-nearest neighbor) and LLS(local least square) imputation algorithms for final prediction of the missing values. The experimental results indicated that the use of acetylation information can provide significant improvements in microarray imputation accuracy. The HAIimpute methods consistently improve the widely used methods such as KNN and LLS in terms of normalized root mean squared error (NRMSE). Meanwhile, the genes imputed by HAIimpute methods are more correlated with the original complete genes in terms of Pearson correlation coefficients. Furthermore, the proposed methods also outperform GOimpute, which is one of the existing related methods that use the functional similarity as the external information.ConclusionWe demonstrated that the using of histone acetylation information could greatly improve the performance of the imputation especially at high missing percentages. This idea can be generalized to various imputation methods to facilitate the performance. Moreover, with more knowledge accumulated on gene regulatory mechanism in addition to histone acetylation, the performance of our approach can be further improved and verified.
Highlights
It is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile analysis in bioinformatics
We have proposed an imputation framework, which can take advantage of the acetylation information to facilitate the imputation
The theoretical basis is that the acetylation states in chromatin provide a mechanism to straightforward coordinate the regulation of co-expressed genes
Summary
It is an important pre-processing step to accurately estimate missing values in microarray data, because complete datasets are required in numerous expression profile analysis in bioinformatics. DNA microarray technology can simultaneously measure the mRNA levels of thousands of genes under certain experiments. It gives a global overview of gene expression profiles in particular cells or tissues, so it has become one of the most prominent tools in functional genomics research. Though playing crucial roles in these studies, the existing multivariate analysis methods for expression profile data have been greatly negatively affected by the high percentage of missing values, e.g. hierarchical clustering and the support vector machine classifier [8,9]. Often disregarded, the missing value imputation is essential to minimize the detrimental effect of missing values on the microarray data analysis
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