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

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) statistic and the mean square error (MSE). Compared with other methods, the GPA method performs effectively and consistently better in reducing across-slide variability and removing systematic bias.The GPA method is an effective normalization approach for microarray data analysis. In particular, it is free from the statistical and biological assumptions inherent in other normalization methods that are often difficult to validate. Therefore, the GPA method has a major advantage in that it can be applied to diverse types of array sets, especially to the boutique array where the majority of genes may be differentially expressed.The cDNA microarray is a widely used high-throughput technique for gene expression profiling, especially for organisms whose genome sequences are unavailable. However, in microarray experiments, there exist many non-random variations and systematic biases, which can confound the extraction of the true fluorescence intensity signals, and thus compromise downstream data analysis and interpretation of the experimental data. Therefore, proper data normalization is required to remove these biases before accurate identification of differential gene expression [1-4].The main objective of normalization is to ensure that measured intensities within and across slides are comparable. Based on different biological or statistical assumptions about data distribution or experimental design,