A Review on Missing Value Imputation Algorithms for Microarray Gene Expression Data

Many bioinformatics analytical tools, especially for cancer classification and prediction, require complete sets of data matrix. Having missing values in gene expression studies significantly influences the interpretation of final data. However, to most analysts' dismay, this has become a common problem and thus, relevant missing value imputation algorithms have to be developed and/or refined to address this matter. This paper intends to present a review of preferred and available missing value imputation methods for the analysis and imputation of missing values in gene expression data. Focus is placed on the abilities of algorithms in performing local or global data correlation to estimate the missing values. Approaches of the algorithms mentioned have been categorized into global approach, local approach, hybrid approach, and knowledge assisted approach. The methods presented are accompanied with suitable performance evaluation. The aim of this review is to highlight possible improvements on existing research techniques, rather than recommending new algorithms with the same functional aim.