Incorporating Nonlinear Relationships in Microarray Missing Value Imputation

• Activity Number: 295
• Type: Contributed
• Date/Time: Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Hesen Peng*+ and Tianwei Yu and Wei Sun
• Companies: Emory University and Emory University and The University of North Carolina at Chapel Hill
• Keywords: high-dimensional data ; missing value imputation

Abstract

Microarray gene expression data often contain missing values. Accurate estimation of the missing values is important for down-stream data analyses that require complete data. Nonlinear relationships between gene expression levels have not been well-utilized in missing value imputatiosn. We propose an imputation scheme based on nonlinear dependencies between genes. By imulations based on real microarray data, we show that incorporating non-linear relationships could improve the accuracy of missing value imputation, both in terms of normalized root mean squared error and in terms of the preservation of the list of significant genes in statistical testing. In addition, we studied the impact of artificial dependencies introduced by data normalization on the simulation results.