Hi, I have the following MA experiment running on Affymetrix technology. It includes 4 condition and 3 time points for each condition as follow: FileName Targets Time Treatment Batch F21.CEL I18 18 I 3 F22.CEL I18 18 I 3 F25.CEL N18 18 N 3 F26.CEL N18 18 N 3 F23.CEL R18 18 R 3 F24.CEL R18 18 R 3 F19.CEL S18 18 S 3 F20.CEL S18 18 S 3 F13.CEL I24 24 I 2 F39.CEL I24 24 I 4 F9.CEL I24 24 I 1 F10.CEL N24 24 N 1 F14.CEL N24 24 N 2 F41.CEL N24 24 N 4 F12.CEL R24 24 R 2 F40.CEL R24 24 R 4 F8.CEL R24 24 R 1 F11.CEL S24 24 S 2 F38.CEL S24 24 S 4 F7.CEL S24 24 S 1 F43.CEL I28 28 I 4 F56.CEL I28 28 I 5 F57.CEL I28 28 I 5 F49.CEL N28 28 N 4 F60.CEL N28 28 N 5 F61.CEL N28 28 N 5 F48.CEL R28 28 R 4 F58.CEL R28 28 R 5 F59.CEL R28 28 R 5 F46.CEL S28 28 S 4 F54.CEL S28 28 S 5 F55.CEL S28 28 S 5 The replicates in this experiment are biological replicates not technical. Note that some of the replicates were prepared in a different batches. >From the attached PCA image, it seems we have here a batch effect (lines are connection among replicates), i.e., is highly contributes to the variance. Would it be correct to run duplicateCorrelation(MA, design, ndups = 1, block = targets$Batch) before lmFit() to remove the batch effect or it is not right to do so because the replicates (e.g., 28 R 4 and 28 R 5) are not technical? If it is wrong, are you suggesting in the future to double the number of chips and preparing one technical replicate in one batch and another replicate in other batch? Thanks, Ron