Iterative Clustering Analysis for Grouping Missing Data in Gene Expression Profiles

Abstract

Clustering has been used as a popular technique for finding groups of genes that show similar expression patterns under multiple experimental conditions. Because a clustering method requires a complete data matrix as an input, we must estimate the missing values using an imputation method in the preprocessing step of clustering. However, a common limitation of these conventional approach is that once the estimates of missing values are fixed in the preprocessing step, they are not changed during subsequent process of clustering. Badly estimated missing values obtained in data preprocessing are likely to deteriorate the quality and reliability of clustering results. Thus, a new clustering method is required for improving missing values during iterative clustering process.