[BioC] Differential expression in time series

[Sanjat Kanjilal]

James W. MacDonald <jmacdon at ...> writes:

> >   I have two strains (B and W) and two different time points (2 and 1 
hours). I am trying to find genes which
> respond differently:
> >   1. in B vs W, when comparing time points 1 and 2 (i.e. I compare BvsW in 
time point 1 to BvsW in time point 2)
> >   2. in time in different strains (i.e. I compare B1vsB2 versus W1vsW2) 
> >    
> >   I am getting the same results, or they should be the same?
> >   Could you comment on whether this is a right way to analyse time series?
> 
> It appears you want to test for the interaction between time and strain. 
>   Both of your points (1 and 2 above) are essentially identical. You 
> want to know what genes react differently over time in the two strains 
> (and worded differently, but meaning the same thing - what genes react 
> differently between the strains at different times). These two things 
> are verbo-algebraically the same (yup, new word. My next move is to 
> start a new Wikipedia entry describing exactly what it means ;-D)
> 
> >    
> >   My code is below.
> >    
> >    
> >   Thank you,
> >   Lev.
> >    
> >   > temp<-rma(data)
> >   > targets <- readTargets("Targets.txt")
> > 
> >>lev <- c("W.1","B.1","W.2","B.2")
> >>f <- factor(targets$Target, levels=lev)
> >>design <- model.matrix(~0+f)
> >>colnames(design) <- lev
> >>fit <- lmFit(temp, design)
> >>cont.dif <- makeContrasts(Diff.Time=(B.2-W.2)-(B.1-W.1), Diff.Strain=(B.2-
B.1)-(W.2-W.1), levels=design)
> 
> As the statements above are verbo-algebraically the same, these two 
> terms are algebraically the same.
> 
> (B.2-W.2)-(B.1-W.1) = (B.2-B.1)-(W.2-W.1)
> 
> Hence you should get the same results from each contrast. And yes, this 
> contrast does give you the interaction.
> 
> Best,
> 
> Jim
> 
Thanks for your helpful explanation Jim.

As a follow-up question:

Can the (B.2-W.2) - (B.1-W.1) contrast be thought of as comparing B @ time 2 
to B @ time 1 while 'normalizing' to the effects of the wild type (ie I'd like 
to subtract out the effects of the control group)?  If not, is there a term 
that would do that?

Thanks,
Sanjat