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A non-parametric regression approach for missing value imputation in microarray

Microarray experiments often generate data sets with multiple missing expression values. Estimating these missing values is very important since they affect biological applications and many multivariate statistical analyses. A limitation of the existing estimating methods is that they assume the relations between genes to be linear. However, that is not always the case. In this paper, we propose a new method based on non-parametric regression combined with nearest neighbor approach, referred to as NPRA, which can capture both linear and non-linear relations between both genes and arrays. A comparative study of the imputation methods was performed on several public datasets, and our proposed method NPRA, exhibits competitive performance when compared to other methods over various datasets and percentage of missing values.