[BioC] (no subject)

[karen]

Hi Folks,

Thanks, Fangxin, for your replies.

I still have a few questions on my linear model for the following experiment.
If someone could help, I'd be grateful.

This is an an Affymetrix time series experiment, 7 time points, 
2 genotypes, 2 replicates of each (28 arrays).

Of interest are genes that are differentially expressed
between genotypes across all but the first time state.

The design I set up with 14 treatments and 6 contrasts:

genotype1_time1, genotype1_time2....
genotype2_time1, genotype2_time2....

genotype1_time2 - genotype2_time2
genotype1_time3 - gentotype2_time3 ...

The code:

treatment.vector<-c(rep(1,2),rep(2,2),rep(3,2), rep(4,2), rep(5,2), rep(6,2), 
rep(7,2),
                            
rep(8,2),rep(9,2),rep(10,2),rep(11,2),rep(12,2),rep(13,2),rep(14,2))
treatments<-factor(treatment.vector,labels=exp.labels)
design<-model.matrix(~-1+treatments)
fit <- lmFit(Mrma, design)
contrast.matrix<-makeContrasts(KO.15min-WT.15min,
                                            KO.30min-WT.30min,
                                            KO.90min-WT.90min,
                                            KO.3hr-WT.3hr,
                                            KO.6hr-WT.6hr,
                                            KO.24hr-WT.24hr,levels=design)

fit2 <- contrasts.fit(fit, contrast.matrix) 
fit3 <- eBayes(fit2)
clas <- classifyTestsF(fit3,fstat.only=FALSE) 
FStats<-FStat(fit3)

The Questions:
1) Is this an acceptable model to use?

2) How would I report the model via an equation?
Even using contrasts, (2 main effects) will the model be written as Y=xij + e

3) Should significant FStats be significant in 1 or more contrasts, but not 
necessarily in all six?

Thank you for any help,
Karen