Hi, I am quite insecure if some parts of the analyis I did in Limma are really correct and I would highly appreciate if someone could take a look and give advice. My main concern is that I may not use the duplicatecorrelation, dyeeffect,arrayweights and spotweights correctly. The arrays I use are printed in duplicates with a spacing of 15000 (so 30000 features in total), and I did the imageprocessing in GenePixPro6.1. Thereby I flagged all spots close to backgroundsignal and with a rgn r2 <0.5 bad, and only 30% of my data remain unflagged. And this is what I did using Limma: > targets=readTargets("Targets_basicSat.txt") > targets SlideNumber FileName Cy3 Cy5 1 1 3096_basicSat.gpr ref A 2 2 3079_basicSat.gpr A ref 3 3 3089_basicSat.gpr ref A 4 4 3081_basicSat.gpr A ref 5 5 3071_basicSat.gpr ref B 6 6 3082_basicSat.gpr B ref 7 7 3085_basicSat.gpr ref B 8 8 8268_basicSat.gpr B ref 9 9 7829_basicSat.gpr ref C 10 10 3086_basicSat.gpr C ref 11 11 7823_basicSat.gpr ref C 12 12 7826_basicSat.gpr C ref 13 13 3090_basicSat.gpr ref D 14 14 3091_basicSat.gpr D ref 15 15 3092_basicSat.gpr ref D 16 16 7827_basicSat.gpr D ref Every other slide is a dyeswapped technical replicate and per "group" (A,B,C,D) there are 2 biological replicates. > K=read.maimages(targets$FileName, source="genepix.median", wt.fun=wtflags(0)) > types=readSpotTypes("SpottypesGAPDH.txt") > Status=controlStatus(types, K) > K$genes$Status=Status > K3=backgroundCorrect(K, method=?normexp?, offset=50) > K3=normalizeWithinArrays(K3, method="median") > K3a=normalizeBetweenArrays(K3, method="quantile") > design=modelMatrix(targets, ref="ref") > design A B C D [1,] 1 0 0 0 [2,] -1 0 0 0 [3,] 1 0 0 0 [4,] -1 0 0 0 [5,] 0 1 0 0 [6,] 0 -1 0 0 [7,] 0 1 0 0 [8,] 0 -1 0 0 [9,] 0 0 1 0 [10,] 0 0 -1 0 [11,] 0 0 1 0 [12,] 0 0 -1 0 [13,] 0 0 0 1 [14,] 0 0 0 -1 [15,] 0 0 0 1 [16,] 0 0 0 -1 Since I am expecting a non-negligible dyeeffect I created an other designmatrix and the following contrastMatrix: >design1=cbind(DyeEffect=1, design) >design.cont=makeContrasts("A", ?B?, ?A-B", levels=design1) Next I estimate the correlation of within-array-duplicates: >cor=duplicateCorrelation(K3b, design=design1, ndups=2, spacing=15000, weights=K3b$weights) My first question is: is it correct to use here the designmatrix for the dyeeffect (design1 in this case)? When fitting the linear model, I also want to use arrayweights, combined with spotweights. So I gave following commands: > aw=arrayWeights(K3b, design=design1) > w=matvec(K3b$weights, aw) Again the question: is it correct to use here the "design1"-matrix considering the dyeeffect? Then I fit the linear model: >fit=lmFit(K3b, design=design1, ndups=2, spacing=15000, cor=cor$consensus, weights=w) >fit1=contrasts.fit(fit, design.cont) >eb=eBayes(fit1) Another thing I am worried about is that taking into account the dyeeffect plus arrayweights plus spotweights might be a bit "too much"? Like in a way "overtransforming" my data? Especially since approx 70% of my data have a spotweight of zero. Might it be better to use the spotweight of 0,1 for bad spots, so that I do not loose the data completely? My apologies for this long email, I tried hard to find out the answers for myself reading the limmaguide and lots of other documents I found googleing, but still feel quite "stuck" in my analysis process. Thanks very much for any kind of help in advance! Best regards Dorthe -- Dorthe Belgardt Institute of Basic Medical Sciences Department of Physiology P.O. Box 1103 Blindern 0317 Oslo Norway