[BioC] Calculation of t-tests for factorial design
pascal.gellert at mpi-bn.mpg.de
Fri Sep 10 16:04:12 CEST 2010
Unfortunately I cannot find out how limma calculates t-tests for my
factorial design of micrarrays, but let's begin from the start:
I have the following microarray experiment:
Treated 2 2
Untreated 2 2
This way I can make the following comparisons:
WT Treated / WT Untreated: This shows me, what the effect of the
treatment itself is.
Knock-out Treated / Knock-out Untreated: To see what effect the
treatment has on Knock out animals.
(Knock-out Treated / Knock-out Untreated) / (WT Treated / WT Untreated):
The effect of treatment to Knock-Out animals under consideration of
changes of gene expression due to treatment in WT animals.
So my main interest lies in the last comparison. I did this with the
targets <-read.table(file="Targets.csv", sep="\t", header=T,
TS <- paste(targets$Strain, targets$Treatment, sep=".")
TS <- factor(TS, levels=c("wt.non","ko.non","wt.treat","ko.treat"))
design <- model.matrix(~0+TS)
colnames(design) <- levels(TS)
fit <- lmFit(eset, design)
cont.matrix <- makeContrasts(
fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2)
eset contains my expression values in log2 scale.
The contrast are calculated by
as I described above. For all 3 comparisons, eBayes calculates t-tests,
but for the last comparison, which values are used for the t-test?
All signals of ko.non and ko.treat compared to wt.non and wt.treat? I
think it's calculated in a completely different way, does anyone know how?
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