[BioC] Help for analysis a Factorial experiment

[Marcelo Luiz de Laia]

My apologizes, but I am not sure if that I am doing is correct in limma package and nor if it answers my questions! I need a help.

I run an analysis whose script finding below, at the end of this message. I make it after reading the user's manual and the help in html. 
If it is possible, I would like to known this script is correct to answer the following questions:

- Which genes are up-regulated in the three times?
- Which genes are down-regulated in the three times?
This first 2 questions I have known how for to do. But, if you have a suggestions, send it me, please.
- Which are up-regulateds in the time 1 and later they do decrease in the times 2 and 3?
- Which are up-regulateds in the times 1 and 2 and later it does decrease in the time 3?
- Which are down-regulateds in the time 1 and up-regulated  in the times 2 and 3?
- Which are down-regulateds in the times 1 and 2 and up-regulated in the time 3?
I believe that these are the main questions. Would you have suggestions?

The experiment design is:

                Time
           1day 2day 3day

           Rep1 Rep1 Rep1
Un Treated Rep2 Rep2 Rep2
           Rep3 Rep3 Rep3

           Rep1 Rep1 Rep1
   Treated Rep2 Rep2 Rep2
           Rep3 Rep3 Rep3
2 treatment (treated and untreated); 3 repetitions, and 3 times.
 
My script (step-by-step)

> library(limma)
> RG <- read.maimages(files, columns=list(Rf="DataVol",Gf="CtrlVol",Rb="DataBkgd",Gb="CtrlBkgd"))
> show(RG)
> summary(RG$R)
> genes.names[1:10,]
> printer <- list(ngrid.r=4, ngrid.c=5, nspot.r=16, nspot.c=24, ndups=2, spacing=1, npins=20, start="topleft")
> printer
> MA <- normalizeWithinArrays(RG, method="none", printer)
> boxplot(MA$M~col(MA$M))
> MA <- normalizeWithinArrays(RG, printer)
> boxplot(MA$M~col(MA$M))
> MA.fa <- normalizeBetweenArrays(MA,method="scale")
> boxplot(MA.fa$M~col(MA.fa$M))
> design <- model.matrix(~ -1+factor(c(1,1,1,2,2,2,3,3,3)))
> colnames(design) <- c("time1","time2","time3")
> fit <- lmFit(MA.fa,design)
> contrast.matrix <- makeContrasts(time2-time1, time3-time2,time3-time1,levels=design)
> fit2 <- contrasts.fit(fit,contrast.matrix)
> fit3 <- eBayes(fit2)
> time2.time1 <- topTable(fit3, coef=1, adjust="fdr")
> time3.time2 <- topTable(fit3, coef=2, adjust="fdr")
> time3.time1 <- topTable(fit3, coef=3, adjust="fdr")
> clas <- classifyTests(fit3)
> vennDiagram(clas)

If someone already accomplished an analysis as this, could send me some suggestions! You would could suggest another way to analyze it, for example.

Thaks very much!

I am sorry, but the English is not my native language.

-- 
Marcelo Luiz de Laia, M.Sc.
Dep. de Tecnologia, Lab. Bioquímica e de Biologia Molecular
Universidade Estadual Paulista - UNESP
Via de Acesso Prof. Paulo Donato Castelane, Km 05
14.884-900 - Jaboticabal, SP, Brazil
PhoneFax: 16 3209-2675/2676/2677 R. 202/208/203 (trab.)
HomePhone: 16 3203 2328 - www.lbm.fcav.unesp.br - mlaia at yahoo.com