Using Generalized Procrustes Analysis (GPA) for normalization of cDNA microarray data

Background: Normalization is essential in dual-labelled microarray data analysis to remove nonbiological variations and systematic biases. Many normalization methods have been used to remove such biases within slides (Global, Lowess) and across slides (Scale, Quantile and VSN). However, all these popular approaches have critical assumptions about data distribution, which is often not valid in practice. Results: In this study, we propose a novel assumption-free normalization method based on the Generalized Procrustes Analysis (GPA) algorithm. Using experimental and simulated normal microarray data and boutique array data, we systemically evaluate the ability of the GPA method in normalization compared with six other popular normalization methods including Global, Lowess, Scale, Quantile, VSN, and one boutique array-specific housekeeping gene method. The assessment of these methods is based on three different empirical criteria: across-slide variability, the Kolmogorov-Smirnov (K-S) sta...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Huiling Xiong, Dapeng Zhang, Christopher J. Martyniuk, Vance L. Trudeau, Xuhua Xia
• Keywords: BMCBI 2008, Microarray Data, Microarray Data Analysis, Normalization Methods