Abstract: Gene expression data frequently suffers from missing value,which adversely affects downstream analysis.A new similarity metric method named reduced relational grade was proposed.Based on this,we presented the iterative imputation algorithm for gene expression data (RKNNimpute).Reduced relational grade is an improvement of gray relational grade.The former can achieve the same performance as the latter while greatly reducing the time complexity.RKNNimpute imputes missing value iteratively by considering the reduced relational grade as similarity metric and expanding the set of candidate genes to nearest neighbors with imputed genes,which improves the effect and performance of the imputation algorithm.We selected data sets of different kind,such as time series,non-time series and mixed,and then experimentally evaluated the proposed method.The results demonstrate that the reduced relational grade is effective and RKNNimpute outperforms common imputation algorithms.

Key words: Gene expression data,Reduced relational grade,Imputation,Iteration,Missing value