A framework for neighbourhood configuration to improve the KNN based imputation algorithms on microarray gene expression data

Abstract

Due to technical problems in DNA microarray experiments, a large number of entries are found missing in microarray datasets. As a consequence, the effectiveness of the analysis algorithms deteriorates. Among different imputation techniques, the weighted average based methods always generate consistent results, are algorithmically simple and very popular but they also suffer from some drawbacks. These deficiencies have been pointed out in this work, and a new framework has been suggested to overcome those. The proposed framework is embedded in the K-nearest neighbour imputation method (KNNimpute), as well as its different versions. It is based on a hybrid distance and gene transformation procedure which utilises simultaneously the advantages of Euclidean distance, mean squared residue score, and Pearson correlation coefficient to select the best possible neighbours, using pattern-based similarity. The framework is tested on well-known microarray datasets. From the experimental results, the superiority of the proposed work has been found.