[BioC] Swirl Data Results

[Rob Cribbie]

I am a newcomer to DNA microarrays and I have recently been playing 
around with the swirl dataset that is available through the bioconductor 
R package.

I have seen some results regarding the swirl data set on the internet 
that have used one-sample empirical bayes moderated t-tests and the 
results seem to come out as expected (i.e. the most differentially 
expressed genes, bmp2 and dlx3, show up as the most differentially 
expressed).

I have also tried performing paired t-tests on the data (paired since 
they share the same slide) after extracting the logR and logG values 
from the normalized M and A values, but I get very different results. 
Very few genes are differentially expressed with FDR control, and the 
top genes are no longer the bmp2 and dlx3 genes. Has anyone else tried 
doing paired t-tests on this data?

The commands I used were:

swirlnorm<-maNorm(swirl,norm="s")
M<-maM(swirlnorm)
A<-maA(swirlnorm)
logG<-(2*A-M)/2
logR<-(2*A+M)/2
newswirl<-cbind(logR[,1], logG[,1], logR[,2], logG[,2], logR[,3], 
logG[,3], logR[,4], logG[,4])
classlabel<-c(1, 0, 0, 1, 1, 0, 0, 1)
tstat<-mt.teststat(newswirl,classlabel,test="pairt", nonpara='"n")
rawp<-2*(1-pt(abs(tstat),3))
result<-mt.rawp2adjp(rawp,proc=c("Bonferroni", "BH")
resultp<-mt.reject(result$adjp,seq(0,1,0.05))$r
resultp


Rob.