[BioC] Re: limma linear model [was: (no subject)]

[kschlauc at vt.edu]

Thank you, Gordon, for your replies.

Regarding question 2), below, I would like to learn
how to write the model in its correct equation form. 
I was thinking about Yij=u+Ti+Gj+TGij+error
(T=Time, G=Genotype) 

Regarding 3) below, in the FStat help, it states
that FStat tests for all contrasts = 0.
Thus, I thought that a significant FStat would 
represent a test in clas in which all contrasts were non-zero.
classifyTestsF classifies genes which have two or more significant contrasts, correct? 
i'm sorry, then i don't understand the equivalence classifyTestsF(FStat.only=T)==FStat 

thank you .... and sorry for more questions.
    
> > 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?
> 
> You seem to be fitting a separate coefficient for each factor 
> combination, and then extracting
> contrasts of interest between the coefficients.  This is a basic 
> approach which is generally
> applicable.  So, yes.
> 
> > 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
> 
> You're just fitting a linear regression model, and then testing 
> hypotheses about the coefficients,
> so it could in principle be written as a regression equation.  But 
> do you really need to?
> 
> > 3) Should significant FStats be significant in 1 or more 
> contrasts, but not
> > necessarily in all six?
> 
> Here, as elsewhere in statistics, there is no apriori rule about 
> what must be significant.  Any
> combination is presumably possible.
> 
> Gordon
> 
> > Thank you for any help,
> > Karen
> 
> 
>