Which missing value imputation method to use in expression profiles: a comparative study and two selection schemes

Background: Gene expression data frequently contain missing values, however, most downstream analyses for microarray experiments require complete data. In the literature many methods have been proposed to estimate missing values via information of the correlation patterns within the gene expression matrix. Each method has its own advantages, but the specific conditions for which each method is preferred remains largely unclear. In this report we describe an extensive evaluation of eight current imputation methods on multiple types of microarray experiments, including time series, multiple exposures, and multiple exposures

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Guy N. Brock, John R. Shaffer, Richard E. Blakesley, Meredith J. Lotz, George C. Tseng
• Keywords: BMCBI 2008, Gene Expression, Gene Expression Matrix, Imputation Methods