[BioC] Linear models?

[Gordon K Smyth]

Dear Hajja,

Your targets frame (in limma) needs to contain three columns: Pair (taking 
values A, A, B, B, C, C etc), Treatment, before or after (taking values 
1,2,1,2 etc) and Treatment length.

  Pair <- factor(Pair)
  Treatment <- factor(Treatment)
  design <- model.matrix(~Pair+Treatment+Treatment:Length)

The last coefficient tests when the differences in expression are 
correlated with treatment length.

Best wishes
Gordon

> Date: Tue, 12 Jul 2011 03:57:12 -0700 (PDT)
> From: khadeeja ismail <hajjja at yahoo.com>
> To: bioconductor at r-project.org
> Subject: [BioC] Linear models?
> Message-ID:
>
> Dear All,
>  
> I have a small problem that is silimar to the case below, and would really appreciate if someone could give me some ideas.
>  
> I am doing a pairwise analysis for some samples (say 8 pairs, A1, A2, B1, B2, C1, C2,...H1, H2) using linear models, to find out gene expression difference between before treatment and after treatment. But if the length of the treatment is different for every pair, how can I include the treatment length in my analysis?
>  
> If one objective of my study is to see if the difference in gene expression correlates with the treatment length, will adjusting the expression differences relative to the highest treatment length introduce a bias?
>  
> Thanks in advance,
> Hajja