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[1] "matrix"

dim(norm_ctw)

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 wound1wound2 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 B431 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.