Hello everybody,

I have 32 samples, 4 factors with 2 levels each. Each level has 2
replicates.

>str(gxexprs)
 num [1:15584, 1:32] 7.94 6.67 9.93 9.62 12.19 ...

>Group
 [1] R52VQ R52VQ R52VE R52VE R52EQ R52EQ R52EE R52EE R95VQ R95VQ R95VQ R95VE
[13] R95VE R95VE R95EQ R95EQ R95EQ R95EE R95EE R95EE R97VQ R97VQ R97VQ R97VE
[25] R97VE R97VE R97EQ R97EQ R97EQ R97EE R97EE R97EE
16 Levels: R52VQ R52VE R52EQ R52EE R95VQ R95VE R95EQ R95EE R97VQ ... R97EE


design <- model.matrix(~0+Group)
fit <- lmFit(gxexprs, design)

contrast.matrix <- makeContrasts(contrasts="R52VQ - R52VE",levels=design)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)

TTable = topTable(fit2)

global_p_val = TTable$P.Val


gxexprs = gxexprs[, 1:4]


## same code as above but the expression matrix has only the first 4
columns which represent the contrast tested above

design <- model.matrix(~0+Group)
fit <- lmFit(gxexprs, design)

contrast.matrix <- makeContrasts(contrasts="R52VQ - R52VE",levels=design)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)

TTable = topTable(fit2)

local_p_val = TTable$P.Val

local_p_val has much greater values than global_p_val even though they
represent the same comparison.

What is the explanation for this?

Can you point to some diagnostic functions that will show the difference?

Thank you,

Giovanni

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