Hi, 
I would think there is correlation in  time-course data, and two-factor model may not handle it correctly as it assumes that data are independent.  I tried with linear mixed model by "lme4" packages and the results seems good. 
Best,
Chunxuan 
> Date: Mon, 16 Dec 2013 09:12:51 -0800
> From: lianoglou.steve@gene.com
> To: rct@thompsonclan.org
> CC: bioconductor@r-project.org
> Subject: Re: [BioC] time series data edgeR?
> 
> Howdy,
> 
> On Mon, Dec 16, 2013 at 8:57 AM, Ryan <rct@thompsonclan.org> wrote:
> > Yes, edgeR can handle this case. You have a couple of options. The
> > conceptually simplest one is to treat the timepoint as a second factor and
> > fit a two-factor additive model (i.e. model.matrix(~genotype + timepoint) ).
> > Another option is to use natural splines. This is an approach that I am not
> > personally experienced with, but it has been discussed before on this list,
> > so you should be able to find an example in the archives. Personally, I
> > would recommend using the two-factor analysis initially, and only switching
> > to something more complex if you decide you need it.
> 
> To add to Ryan's advice -- I'm pretty sure there is an example of
> using splines for timecourse data in the limma user's guide. Assuming
> so, the least effort required to get a first round analysis by
> following along there would be to apply limma::voom to your count
> data, then follow the spline method from the user's guide accordingly.
> 
> HTH,
> -steve
> 
> -- 
> Steve Lianoglou
> Computational Biologist
> Genentech
> 
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