title: Shrinkage regression-based methods for microarray missing value imputation.
abstract: BACKGROUND
  Missing values commonly occur in the microarray data, which usually contain more than 5% missing values with up to 90% of genes affected. Inaccurate missing value estimation results in reducing the power of downstream microarray data analyses. Many types of methods have been developed to estimate missing values. Among them, the regression-based methods are very popular and have been shown to perform better than the other types of methods in many testing microarray datasets.
  
  
  RESULTS
  To further improve the performances of the regression-based methods, we propose shrinkage regression-based methods. Our methods take the advantage of the correlation structure in the microarray data and select similar genes for the target gene by Pearson correlation coefficients. Besides, our methods incorporate the least squares principle, utilize a shrinkage estimation approach to adjust the coefficients of the regression model, and then use the new coefficients to estimate missing values. Simulation results show that the proposed methods provide more accurate missing value estimation in six testing microarray datasets than the existing regression-based methods do.
  
  
  CONCLUSIONS
  Imputation of missing values is a very important aspect of microarray data analyses because most of the downstream analyses require a complete dataset. Therefore, exploring accurate and efficient methods for estimating missing values has become an essential issue. Since our proposed shrinkage regression-based methods can provide accurate missing value estimation, they are competitive alternatives to the existing regression-based methods.
authors.forename: Hsiuying
authors.initials: H
authors.lastname: Wang
authors.forename: Chia-Chun
authors.initials: CC
authors.lastname: Chiu
authors.forename: Yi-Ching
authors.initials: YC
authors.lastname: Wu
authors.forename: Wei-Sheng
authors.initials: WS
authors.lastname: Wu
mesh_terms: D056808:Biostatistics / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D016018:Least-Squares Analysis; D020411:Oligonucleotide Array Sequence Analysis; D012044:Regression Analysis