Hello all, I have some custom Agilent arrays with 3600 spots representing functional genes. In each of the RNA samples the same amount of exogenous RNA was spiked in as a control, these are the "Arab" spots in the script below. I want to compare some treatments that were never hybridized on the same slide, therefore I want to do seperate channel analysis of two channel arrays. The script below - follows from the user's guide. However, I am unsure about the most appropriate way to nomalize my data between the arrays. When I looked at the MA plots the "Arab" spots are often differentially expressed. Is there a way that I can normalize between arrays using a modified quantile, where all of the points would be normalized based on trying to obtain equal distributions of a subset of spots on the array (ie. the "Arab" control spots)? Thank you, alison >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> targets<-readTargets("EPBDIExprTargets.txt") RG<-read.maimages(targets$FileName,source='genepix') spottypes<-readSpotTypes("SpotTypesEP.txt") RGnm<-backgroundCorrect(RG,method='normexp') RGnm$genes$Status<-controlStatus(spottypes,RG) ControlSpots<-grep("Arab",RGnm$genes$Status) w<-modifyWeights(array(1,dim(RGnm)), RGnm$genes$Status, c("All","Arab"), c(1,2)) MAnmw<-normalizeWithinArrays(RGnm,weights=w) MAnmwq<-normalizeBetweenArrays(MAnmw, method="quantile") targets2<-targetsA2C(targets) u<-unique(targets2$Target) f<-factor(targets2$Target, levels=u) design<-model.matrix(~0+f) colnames(design)<-u corfit<-intraspotCorrelation(MAnmwq, design) fit<-lmscFit(MAnmwq, design, correlation=corfit$consensus) cont.matrix<-makeContrasts("PCE-TCE","PCE-cisDCE","PCE-VC", "PCE- lactate","TCE-cisDCE","TCE-VC", "TCE-lactate","cisDCE-VC", "cisDCE- lactate","VC-lactate",levels=design) fit2<-contrasts.fit(fit, cont.matrix) fit2<-eBayes(fit2) plotMA(fit2) --------------------------------------------------------- Alison Waller Ph.D alison.waller@utoronto.ca [[alternative HTML version deleted]]