Missing value estimation for microarray data through cluster analysis

Microarray datasets with missing values need to impute accurately before analyzing diseases. The proposed method first discretizes the samples and temporarily assigns a value in missing position of a gene by the mean value of all samples in the same class. The frequencies of each gene value in both types of samples for all genes are calculated separately and if the maximum frequency occurs for same expression value in both types, then the whole gene is entered into a subset; otherwise, each portion of the gene of respective sample type (i.e., normal or disease) is entered into two separate subsets. Thus, for each gene expression value, maximum three different clusters of genes are formed. Each gene subset is further partitioned into a stable number of clusters using proposed splitting and merging clustering algorithm that overcomes the weakness of Euclidian distance metric used in high-dimensional space. Finally, similarity between a gene with missing values and centroids of the clusters are measured and the missing values are estimated by corresponding expression values of a centroid having maximum similarity. The method is compared with various statistical, cluster-based and regression-based methods with respect to statistical and biological metrics using microarray datasets to measure its effectiveness.