Comparison of Microarray Pre-Processing Methods K. Shakya, H. J. Ruskin, G. Kerr, M. Crane, J. Becker Dublin City University, Dublin 9, Ireland Abstract Data pre-processing in microarray technology is a crucial initial step before data analysis is performed. Many pre-processing methods have been proposed but none has proved ideal to date. Frequently, datasets are limited by laboratory constraints so that the need is for guidelines on quality and robustness, to inform further ex- perimentation while data are yet restricted. In this paper, we compared the per- formance of 4 popular methods, namely MAS5, Li & Wong pmonly (LWPM), Li & Wong subtractMM (LWMM) and RMA. The comparison is based on analysis car- ried out on sets of laboratory-generated data from the Bioinformatics Lab, Na- tional 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 vari- ability is reduced more effectively by LWPM and RMA methods. From the false positive analysis, and for both parametric and non-parametric 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 re- duction, it has considerably improved discrimination overall. 1 Introduction Microarray technology allows the monitoring of expression levels of thousands of genes, simultaneously, which in turn helps to explore gene sequence information and ultimately gene function(s). Since microarray gene expression data are charac-