[BioC] Extracting expressions after a multtest procedure

[JUAN MIGUEL MARIN DIAZARAQUE]

Hello,

I was trying to reproduce just an example of differential expressions 
from FDR (false discovery rates) in ALL data (see the book 
Bioinformatics and Computational Biology solutions using R and 
Bioconductor of Gentleman et al., p. 232-234). But I am not sure to 
understand what it makes.

It is an application of multtest package to control the FDR at 0.05 level.
In brief, one can write

data(ALL)
pdat<- pData(ALL)
subset <- intersect (grep("^B", as.character(pdat$BT)),
which(pdat$mol %in% c("BCR/ABL", "NEG")))
eset <- ALL[,subset]

library(genefilter)
f1 <- pOverA(0.25,log2(100))
f2 <- function(x) (IQR(x)>0.5)
ff <- filterfun(f1,f2)
selected <- genefilter(eset,ff)
sum(selected)
esetSub <- eset[selected,]
cl <- as.numeric(esetSub$mol == "BCR/ABL")
resT <- mt.maxT(exprs(esetSub), classlabel = cl, B=10000)
ord <- order(resT$index)
rawp <- resT$rawp[ord]

res <- mt.rawp2adjp(rawp, proc="BH")
sum(res$adjp[,"BH"]<0.05)
> 102

That leaves 102 significant genes.

I would like to extract just the expressions of these 102 genes under 
the two conditions. Therefore I would expect something like several 
columns corresponding to different persons (37 and 42?) in 102 rows 
with the values of expressions and the names of these genes (as 
dimnames?). But I do not know how to handle with this task :-(
Any help?

Thank you very much

jm~

_______________________________

     Juan Miguel Marin

http://www.est.uc3m.es/jmmarin

    Dep. of Statistics
University Carlos III of Madrid
        Spain (E.U.)