Dear BioConductor list: I have a set of one color (cy3) data in which each gene was spotted duplicate in separate blocks (grids). I looked at how well the duplicated genes were correlated by using either "duplicateCorrelation" (limma) or regular "Pearson" correlation test. Both tests gave out similar values around 0.55. Duplicate spots should be highly correlated in intensities. The correlation coefficient of 5.5 is not that great as I understand. Limma has some ways to process those technical replicates, such as "print-tip loess" and incorporating estimated spatial correlation into analysis using generalized linear model. But what if I just wanted to preprocess data with something like vsn or IQR normalization without using linear model fit? Should I normalize the grids, e.g. grids 1 and 3, grids 2 and 4 (which contain corresponding duplicate spots) on each chip to make duplicate spots more correlated? Usually I just get mean values of the duplicate spots for such a small data sets (722 genes on each slide). Does anybody know a better way to handle these duplicates than using means? I will greatly appreciate if anyone could share her/his experience with me! Thanks in advance! Jianping xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx x Jianping Jin Ph.D. x x Bioinformatics scientist x x Center for bioinformatics x x 3133 Bioinformatics Building x x CB# 7104 x x University of North Carolina x x Chapel Hill, NC 27599 x x Tel: (919)843-6105 x x Fax: (919)843-3103 x x E-mail: jjin at email.unc.edu x xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx