Hi, We have two data sets from two experiments. We did an experiment seven months ago. which is three old mutant mice(oldmu) were compared with three old wild type mice(oldwt). Eighteen two-color arrays were used(including dye swap), and each mouse appeared on six different arrays to study differential expression between oldmu and oldwt. oldwt1 vs oldmu1 oldwt1 vs oldmu2 oldwt1 vs oldmu3 oldwt2 vs oldmu1 oldwt2 vs oldmu2 oldwt2 vs oldmu3 oldwt3 vs oldmu1 oldwt3 vs oldmu2 oldwt3 vs oldmu3 and corresponding dye swap arrays. Recently, we did another experiment, which is four young mutant mice(mu) were compared with four young wild type mice(wt). Eight two- color arrays were used(including dye swap), and each mouse appeared on two different arrays to study differetial expression between youngmu and youngwt. wt1 vs mu1 wt2 vs mu2 wt3 vs mu3 wt4 vs mu4 and corresponding dye swap arrays. For this experiment, we used newly printed slides which have 200 spot missing on each slide( 16k array). Now, we are interested in the differential expression between oldmu and mu, and I want to combine these two data sets and use single channel analysis to get significant gene list. I did two tests: Test1: I picked six arrays from first data set oldwt1 vs oldmu1 oldwt2 vs oldmu2 oldwt3 vs oldmu3 and corresponding dye swap arrays, and all arrays from second experiment. My target file is SlideNumber FileName Cy3 Cy5 1 1529.spot wt mu 2 1530.spot mu wt 3 1521.spot wt mu 4 1532.spot mu wt 5 1535.spot wt mu 6 1536.spot mu wt 7 1523.spot wt mu 8 1524.spot mu wt 9 1391.spot oldwt oldmu 10 1392.spot oldmu oldwt 11 1371.spot oldwt oldmu 12 1372.spot oldmu oldwt 13 1397.spot oldwt oldmu 14 1398.spot oldmu oldwt I analyzed in the following way require(limma) targets<-readTargets("Targets.txt") RG<-read.maimages(targets$FileName, source="spot",wt.fun=wtarea(100)) RG$genes<-readGAL("Mouse24052004_246.txt") RG$printer<-getLayout(RG$genes) spottypes<-readSpotTypes("spottypes2.txt") RG$genes$Status<-controlStatus(spottypes,RG$genes) RG<-backgroundCorrect(RG,method="minimum") MA<-normalizeWithinArrays(RG,method="printtiploess") MA<-normalizeBetweenArrays(MA,method="quantile") ind<-(MA$genes$Status %in% c("blank", "miss")) targets.sc<-targetsA2C(targets) design.sc<-model.matrix(~0+factortargets.sc$Target)+factortargets.sc $channel)) colnamesdesign.sc)<-c("mu","oldmu","oldwt","wt","ch") # I subset MA to get rid of missing spots, and blank(buffer) control corfit<-intraspotCorrelation(MA[!ind,],design.sc) fit <- lmscFit(MA[!ind,],design.sc,correlation=corfit$consensus) contrast.matrix<-makeContrasts(oldmu-mu,levels=design.sc) fit2<-contrasts.fit(fit,contrast.matrix) fit2<-eBayes(fit2) tab<-topTable(fit2,number=400,adjust="fdr") My questions are 1) Can I analyze like above to get significant gene list? If what I did is correct, and if I want to include all eighteen arrays from first experiment instead of six arrays, how should I modify the codes? 2) I used subset MA(get rid of missing spots) to fit the model, is it correct way to handle slide differece between two batchs of slides? Test2 Before I did Test1, I thought I was going to get error from function intraspotCorrelation(I got error from this function before for my other data sets, but not when I did my Test1. I still use limma version 1.8.8). I tried another "weird" way like the following(target file is same as that in Test1) require(limma) require(convert) targets <- readTargets("Targets.txt") RG<-read.maimages(targets$FileName, source="spot",wt.fun=wtarea(100)) RG$genes<- readGAL("Mouse24052004_246.txt") RG$printer<-getLayout(RG$genes) spottypes<-readSpotTypes("spottypes2.txt") RG$genes$Status<- controlStatus(spottypes, RG$genes) RG<-backgroundCorrect(RG,method="minimum") MA<-normalizeWithinArrays(RG, method="printtiploess") MA<-normalizeBetweenArrays(MA, method="quantile") # I convert MA to RG, then convert RG to exprSet, and then analyse like affy metrix array RG2<-RG.MA(MA) eset<-as(RG2,"exprSet") geneNames(eset)<-paste(RG2$genes$ID,RG2$genes$Symbol,sep="\t") design<-model.matrix(~ -1+factor(c(1,2,2,1,1,2,2,1,1,2,2,1,1,2,2,1,3,4,4,3,3,4,4,3,3,4,4,3))) colnames(design)<-c("youngwt","youngmu","oldwt","oldmu") ind<-(MA$genes$Status %in% c("blank", "miss")) fit<-lmFit(eset[!ind,],design) contrast.matrix<-makeContrasts(oldmu-youngmu,levels=design) fit2<-contrasts.fit(fit,contrast.matrix) fit2<-eBayes(fit2) tab<-topTable(fit2,number=400,adjust="fdr") I am just wondering whether the way I did here make sense? Thanks in advance! Ren [[alternative HTML version deleted]]