Hello, We have a microarray dataset where there seems to be significant variation caused by the centre (2 centres) the sample came from and the plate (2 plates), so I would like to basically remove these effects by including them in the linear model for any other comparison I do. I have a factor sampleType that has levels 1 to 4 and I am interested in the comparisons 1 vs 2, 1 vs 3 and 1 vs 4. Is this a suitable way to set up the design matrix: design <- model.matrix(~centre + plate + sampleType,data=data) which produces something like: (Intercept) Plate2 CentreRMH sampleType2 sampleType3 samType4 Cb016_001 1 0 0 1 0 0 Cb016_003 1 0 1 0 0 1 Cb016_004 1 0 1 0 0 1 I then look at the top table for coefficient 4 to get the result of sample type 2 vs sample type 1 Is this a reasonable way to do it? The reason I am unsure is that to test it I reduced the dataset to those samples just containing sampleType 1 and 2 and got different results, but this could be explained by different estimates for centre and plate and a different estimate produced by eBayes. Many Thanks -- ************************************************************** Daniel Brewer, Ph.D. Institute of Cancer Research Molecular Carcinogenesis Email: daniel.brewer at icr.ac.uk ************************************************************** The Institute of Cancer Research: Royal Cancer Hospital, a charitable Company Limited by Guarantee, Registered in England under Company No. 534147 with its Registered Office at 123 Old Brompton Road, London SW7 3RP. This e-mail message is confidential and for use by the a...{{dropped:2}}