[BioC] Loop design -> inferring

[Gordon Smyth]

Dear Yannick,

Most clustering algorithms are invariant relative to linear 
transformations, so you can cluster with your individual array data 
without any transformation. It doesn't matter than you don't have a 
common reference, except from the point of view of interpretting the 
clustered patterns that you end up with.

(Personally I usually prefer to cluster on fitted coefficients, but 
that's another matter.)

To visualize variability, you have 10 reps of T0-T1, 10 of T1-T2, 10 
of T2-T10. Why not just do boxplots for each group. Again no need to transform.

Best wishes
Gordon

>Date: Thu, 9 Aug 2007 18:58:46 +0200
>From: Yannick Wurm <Yannick.Wurm at unil.ch>
>Subject: [BioC] Loop design -> inferring
>To: bioconductor at stat.math.ethz.ch
>
>Hello list,
>
>I am a graduate student doing some micorarray experiments on social
>behavior in ants. I've recently started using limma, and have been
>pleasantly surprised by the elegance of its implementation. This is
>my first question to the list.
>
>My experiment is a 3 point time-course that I set up as a loop design
>on our two-color cDNA arrays:
>         T0 -> T1 -> T2 ---> (back to T0)
>My 10 replicates of this are dye-balanced.
>
>My favorite contrasts are T1-T0 and T2-T0. I can get my genes
>estimated relative expression levels through either topTable or by fit
>$coefficients.
>However, I would like to visuallze relative expression levels, at the
>level of individual replicates. That way I can get visual feeling of
>how much variability there is between my replicates. And do
>clustering as if I had used a reference design.
>So for each gene I want 10 values for T1-T0, and 10 values for T2-T0.
>I could get these by simply taking the numbers from my direct
>comparisons. But it feels wrong ignoring the information provided
>indirectly about T1-T0 through (T1-T2)+(T2-T0).
>
>How would you go about this?
>
>Thanks for any tips,
>
>Yannick