Hi Bioconductors I have two data sets from two two-colour microarray experiments. In the first experiment I compared female mosquitoes from strain A with female mosquitoes from strain B. As I am now also interested in the difference between the sexes from the same strain - but the two experiments are unconnected - I thought I could do a separate channel analysis using the limma package. I followed the example in limma's user guide and inspected my results with a MA-plot for all possible comparisons (i.e., female A vs female B, male A vs. male B, female A vs. male A, and female B vs. male B). The MA- plots for the strain comparison look fine but the sex comparisons come our rather strangely. The MA-plot of "female A vs. male A" looks very similar to the one of "female B vs. male B"! It seems as if these data are highly correlated. My only explanation is that the fitted data are still highly correlated... though the correlation has been taken into account in the linear model. What went wrong? I have added the codes below. In advance, many thanks for any help!!! Pie My target file is: File Cy3 Cy5 slide 13017473 - array 1.gpr KIS.female ODU_S.female slide 13017473 - array 2.gpr ODU_S.female KIS.female slide 13009409 - array 1.gpr KIS.female ODU_S.female slide 13009409 - array 2.gpr ODU_S.female KIS.female slide 13051277 - array 2.gpr KIS.female ODU_S.female slide 13051277 - array 1.gpr ODU_S.female KIS.female slide 13017475 - array 1.gpr KIS.male ODU_S.male slide 13017475 - array 2.gpr ODU_S.male KIS.male slide 13051279 - array 2.gpr KIS.male ODU_S.male slide 13051279 - array 1.gpr ODU_S.male KIS.male slide 12784108 - array 2.gpr KIS.male ODU_S.male slide 12784108 - array 1.gpr ODU_S.male KIS.male My R code is: RG=backgroundCorrect(RG, method="normexp", offset=50) w1=modifyWeights(array(1,dim(RG)), RG$genes$Status, c("gene", "sense oligo", "ratio", "utility", "empty", "blank", "calibration"), c(0.1,0,0,0,0,0,1)) # give zero weights to spike-in spots, sense oligos and "empty" spots MA=normalizeWithinArrays(RG, method="loess", weights=w1) MA=normalizeBetweenArrays(MA, method="Aquantile") targets.sc=targetsA2C(targets) design.sc=model.matrix(~0+factortargets.sc$Target)+factortargets.sc$ channel)) colnamesdesign.sc)=c("KIS.female", "ODU_S.female", "KIS.male", "ODU_S.male", "ch") corfit=intraspotCorrelation(MA, design.sc) fit=lmscFit(MA, design.sc, correlation=corfit$consensus) cont.matrix=makeContrasts("KIS.female-KIS.male", levels=design.sc) fit2=contrasts.fit(fit, cont.matrix) plotMA(odumasi.fit2[1,], ylim=c(-4,4)) plotMA(odumasi.fit2[,2], ylim=c(-4,4)) plotMA(odumasi.fit2[,3], ylim=c(-4,4)) plotMA(odumasi.fit2[,4], ylim=c(-4,4))