I have a question to ask for the limma two factor design.
my experiment design is like this:
FileName Dox KnockDown
L N C
LD Y C
PN5 N C
PN5D Y KD
PN7 N C
PN7D Y KD
I have six files from Affymetrix microarray. two factors are considered: with or without adding Doxcycline (Y=yes, N=no) and control(C) and Knockdown(KD).
Is it a two factor design? from the limma guider, it says every combination of factors should be observed, but I do not have the level of N.KD (the KD is only occurred after adding Dox)
I am only interested in whether KD has effect on gene expression. How should I design the contrast matrix?
I am following the limma guider:
#differentially expressed genes by limma library(limma) targets<- data.frame(FileName=c("L","LD","PN5","PN5D","PN7","PN7D"), Dox=c("N","Y","N","Y","N","Y"), KnockDown=c("Control","Control","Control","KD","Control","KD")) targets TS<- paste(targets$Dox, targets$KnockDown, sep=".") TS TS<- factor(TS, levels=c("N.Control", "Y.Control", "Y.KD")) design<- model.matrix(~0+TS) colnames(design)<- levels(TS) fit<- lmFit(eset, design) cont.matrix<- makeContrasts( KDvsControlinY=Y.KD-Y.Control, YvsNinControl=Y.Control-N.Control, levels=design) fit2 <- contrasts.fit(fit, cont.matrix) fit2 <- eBayes(fit2) toptable<- topTable(fit2, coef=1,number=30000, sort.by="P")