[BioC] Loop design -> inferring

[Yannick Wurm]

Hello Gordon & list,

thanks for your help.
Clustering on the fitted coefficients make sense to see a gene's  
general profile. But once again, getting a feeling for a gene's  
variability is important because it gives information about both the  
gene's robustness, as well as the sample's. And if I'm looking at a  
large number of genes, it doesn't make sense to do boxplots for each.

What I did for now is just directly use my individual array data.
But I think I'd get a better estimation if I used the information  
provided by the loop. So for each comparison, I think I'll use a  
weigted average of the 2 loop directions.  Eg if I want to estimate  
differences between T0 and T1, I could do:
	ratio = 2/3 * (T1vsT0) + 1/3 * (T1vsT2 - T2vsT0)

Kind regards,
yannick

On Aug 12, 2007, at 11:41 PM, Gordon Smyth wrote:

> 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
>
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