Comparison of microarray preprocessing methods

Data preprocessing in microarray technology is a crucial initial step before data analysis is performed. Many preprocessing methods have been proposed but none has proved to be ideal to date. Frequently, datasets are limited by laboratory constraints so that the need is for guidelines on quality and robustness, to inform further experimentation while data are yet restricted. In this paper, we compared the performance of four popular methods, namely MAS5, Li & Wong pmonly (LWPM), Li & Wong subtractMM (LWMM), and Robust Multichip Average (RMA). The comparison is based on the analysis carried out on sets of laboratory-generated data from the Bioinformatics Lab, National Institute of Cellular Biotechnology (NICB), Dublin City University, Ireland. These experiments were designed to examine the effect of Bromodeoxyuridine (5-bromo-2-deoxyuridine, BrdU) treatment in deep lamellar keratoplasty (DLKP) cells. The methodology employed is to assess dispersion across the replicates and analyze the false discovery rate. From the dispersion analysis, we found that variability is reduced more effectively by LWPM and RMA methods. From the false positive analysis, and for both parametric and nonparametric approaches, LWMM is found to perform best. Based on a complementary q-value analysis, LWMM approach again is the strongest candidate. The indications are that, while LWMM is marginally less effective than LWPM and RMA in terms of variance reduction, it has considerably improved discrimination overall.