Hello All,
I used the following script to find the genes that both have p.adj < 0.05
and logFC < -1 in comparisons (TCdn - TCwt and Tcestw - TCwt).
I checked one candidate gene in the normalized expression output
(affy_My_data.txt). The first two are TCdn, middle two are TCwt and the last
two are TCestw):

MKG.56.34.1_at  4.30886639379254 5.33637926862047 11.8385942887500
10.9736438003031 4.22713527087133 3.28741332008267

It seems that this gene have same logFC in two comparisons (TCdn~ TCwt and
TCets~TCwt). But the p.adj is very different: one is 0.053759(TCdn~TCwt) and
the other is 0.009286 (TCetsw~TCwt).

I am not familiar with LIMMA model and the code is simply from Biocoductor
manual, please give some advice and tell me how to interpret this
result.(How the p.adj is calculated? Why the same gene in two comparisons
have same logFC but the p.adj is so different?)

Thanks.

Yisong



library(limma)
library(affy)
targets<-readTargets("targets.txt")
#targets;
data<-ReadAffy(filenames=targets$FileName)
eset<-rma(data)
write.exprs(eset, file="affy_My_data.txt")
design<-model.matrix(~-1+factor(c(1,1,2,2,3,3)))
colnames(design)<-c("TCdn","TCwt","TCetsw")
fit<-lmFit(eset,design)
contrast.matrix<-makeContrasts(TCdn-TCwt,TCetsw-TCwt, levels=design)
fit2<-contrasts.fit(fit, contrast.matrix)
fit2<-eBayes(fit2)
write.table(topTable(fit2, coef=1, adjust="fdr", sort.by="B", number=50000),
file="limmadn_wt.xls", row.names=F, sep="\t")
write.table(topTable(fit2, coef=2, adjust="fdr", sort.by="B", number=50000),
file="limmaestw_wt.xls", row.names=F, sep="\t")

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