A Modified Local Least Squares-Based Missing Value Estimation Method in Microarray Gene Expression Data

Micro array gene expression data often contains missing values normally due to various experimental reasons. However, most of the gene expression data analysis algorithms, such as clustering, classification and network design, require a complete matrix of gene array during analysis. It is therefore very important to accurately impute the missing values before applying the data analysis algorithms. In this paper, a modified Local Least Square imputation based algorithm known as NSLLSimpute has been introduced which overcomes the drawbacks of previously developed LLSimpute and SLLSimpute algorithms. The performance of NSLLSimpute algorithm is compared with the most commonly used imputation methods like K-nearest neighbor imputation (KNNimpute), Sequential K-nearest neighbor imputation (SKNNimpute), Iterative K-nearest neighbor imputation (IKNNimpute), Singular Value Decomposition (SVDimpute), Local Least Squares imputation (LLSimpute) and Sequential Local Least Squares imputation (SLLSimpute) in terms estimation accuracy using Root Mean Square error when applied on four publicly available micro array data sets over different rates of randomly introduced missing entries.