[BioC] limma: design for paired data with multiple time points

[Jonathan Ellis]

Dear list,

I am analysing some microarrays with the limma package, and so far have
discovered no significant DEGs.  I'm unsure if this is because there
really are no DEGs or I've misunderstood the limma package.  I'm hoping
someone with more experience can tell me if my analysis is correct, or,
if not, where I've gone wrong.

I have arrays collected at three time points (t0, t14 and t56) from the
same patients, so I have a data frame that resembles:

Patient Time
1       t0
1       t14
1       t56
2       t0
2       t14
2       t56
etc.

and I'm interested in comparing t14 to t0, t56 to t0 and t56 to t14,
whilst accounting for fact that array are from the same patient.  My
analysis has been:

design <- model.matrix(~ 0 + Time + Patient)
colnames(design) <- c('t0', 't14', 't56', 'p1', 'p2', 'p3', 'p4', 'p5', 'p6')
array.weights <- arrayWeights(x.filtered, design)
fitw <- lmFit(x.filtered, design, weights = array.weights)
contrast.matrix <- makeContrasts(t14-t0, t56-t14, t56-t0, levels = design)
fit2 <- contrasts.fit(fitw, contrast.matrix)
fit2 <- eBayes(fit2)

I would be very grateful if someone can tell me if this analysis is
correct or not.

Cheers,
Jonathan