I'm sure this is an easy question, but I'm fairly new to using limma and R and I'd appreciate any help or guidance. I've run a paired limma analysis trying to look at differentially expressed genes in a pre-post experiment design. I'm trying to understand what direction my logFC values represent i.e. whether the gene is being up regulated or down regulated from pre to post or post to pre. My code is as follows:
write.exprs(e,file="expressionDataHFNIH5-7-14.txt") d<-read.table("expressionDataHFNIH5-7-14.txt", header=T, row.names=1) names(d)<-gsub(".CEL", "", gsub("X", "", names(d))) d<-round(d, 5) write.table(d, file="filepath.txt", quote=F, sep='\t') #create a design matrix #congestion 1=pre and 2=post design <- cbind(ID = c(1,1,2,2,7,7,8,8,9,9,10,10,11,11,14,14,16,16,20,20,21,21,22,22,23,23,24,24,25,25,26,26,27,27,28,28,29,29,30,30,33,33,34,34,35,35,36,36,37,37,39,39,40,40,41,41), Congestion=c(1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2)) ID<-factor(design[,1]) Congestion<-factor(design[,2], levels=c("1","2")) #create a design matrix design<-model.matrix(~ID+Congestion) fit <- lmFit(e, design) fit <- eBayes(fit) topTable(fit, coef = "Congestion2", adjust = "fdr") topt2<-topTable(fit, coef = "Congestion2", adjust.method = "fdr", n=Inf, sort.by="p") write.table(topt2,file="filepath.txt",quote=F,sep='\t'