Entering edit mode
@massimoacquavivaospedale-gaslinigeit-4282
Last seen 9.7 years ago
Hallo to everyone,
In have a model like the factorial design example in the limma user
guide
TS <- factor(TS, levels = c("WT.U", "WT.S", "Mu.U", "Mu.S"))
design <- model.matrix(~0 + TS)
colnames(design) <- levels(TS)
cont.matrix <- makeContrasts(
WT.SvsU = WT.S - WT.U,
Mu.SvsU = Mu.S - Mu.U,
Diff = (Mu.S - Mu.U) - (WT.S - WT.U),levels = design)
fit <- lmFit(eset, design)
fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2)
I am planning to extract the common response to the treatment between
WT
and Mu using nestedF
cont.matrix <- makeContrasts(
WT.SvsU = WT.S - WT.U,
Mu.SvsU = Mu.S - Mu.U)
fit <- lmFit(eset, design)
fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2)
results <- decideTests(fit2,method="nestedF",p=0.05)
and to extract the different response testing the interaction term
with
moderated t statistics
cont.matrix <- makeContrasts(
Diff = (Mu.S - Mu.U) - (WT.S - WT.U),levels = design)
fit <- lmFit(eset, design)
fit2 <- contrasts.fit(fit, cont.matrix)
fit2 <- eBayes(fit2)
toptable(fit2,adjust="BH")
and finally make a Venn Diagram with the results of the three
comparisons
Is this approach correct to determinate which genes respond only in
WT,
which ones only in Mu and which, among the common ones, are
differently
expressed?
I would really appreciate some help
Thank you in advance
Massimo Acquaviva
Department of Molecular Biology
Giannina Gaslini Institute, Genova Italy
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