[BioC] Limma topTable; fold changes look completely different to the normalized data and Limma fold change

[john herbert]

Dear all,
I have a problem with the log Fold changes calculated in Limma. I am
using protein abundance index of proteomic data
The log2 of this data is normally distributed and after log2, I use
quantile normalization

This is then the data matrix I use as input to Limma

> class(norm_ctw)
[1] "matrix"

> dim(norm_ctw)
[1] 683   9
design <- model.matrix(~ 0+factor(c(1,1,1,2,2,2,3,3,3)))
colnames(design) <- c("cam", "tumour", "wound")
fit <- lmFit(norm_ctw, design)

contrast.matrix <- makeContrasts(tumour-wound, tumour-cam, levels=design)
fit2 <- contrasts.fit(fit, contrast.matrix)
fit2 <- eBayes(fit2)

topTable(fit2, coef=1, adjust="BH")

Taking one gene as an example. NAMPT in tumour versus wound and
calculating fold change by hand of normalized data;

> norm_ctw["NAMPT",]
    cam1     cam2     cam3  tumour1  tumour2  tumour3   wound1
wound2   wound3
19.80164 19.46355 19.26075 22.75347 22.62651 22.39521 16.17398 16.60262 16.72368

In Excel, calculating log2 fold change using Average of Tumour/Average
of wound =
T1 22.75347	T2 22.62651	T3 22.39521	W1 16.17398	W2 16.60262	W3 16.72368
Tumour average = 22.59173
Wound average = 16.50009333		
Log2 Fold change = 0.453320567		

However, from TopTable....
> topTable(fit2,coef=1)
          ID     logFC  AveExpr         t      P.Value    adj.P.Val         B
431    NAMPT  6.091632 19.53349  20.16810 2.688444e-09 1.750946e-06 11.409857

>From toptable, NAMPT has an apparent log2 FC of 6 or 64 fold change
but that is impossible right??

Please can someone explain if I am using Limma wrong or how the fold
change can be massively different between "by hand" and with Limma.

Thank you very much for any advice.

John.