[BioC] Intra variance Vs inter group variance: scared!

[Emmanuel Levy]

Dear James and Naomi,

Thanks for your suggestions.

Quality control is not exactly what I am looking for: I would like to compare
the experimental noise compared to the "biological signal".

I agree that fold change is not a great measure, and of course I use a
statisticaly
robust method for comparing the INTER variance (cyber-T). So I am
confident about
the DEGs I find. What I am more concerned about are the trues DEGs
that I do _not_
find because of the experimental noise. And, if the experimental noise
is of the same
order of magnitude as my biological signal, I guess my conclusions
would not be very meaningful. (am I right?)

So, to compare the INTRA VS. INTER, I looked at the number of genes found above
different fold change thresholds, between samples in the same or in
different groups. (I used fold change because I have only three
replicates so I can only do pairwise comparisons). Obviously this
method has important limits but it is to get an idea.

I was wondering if there was an established standart procedure to check this.

I hope I made my thoughts clearer and that you can point me to something.

Best wishes,

Emmanuel



> You should look at some quality control measures for your arrays.

> If
> all is well, then you should use a statistical measure of
> differential expression.  There are several available in
> Bioconductor.  I usually use Limma.  Others like multtest, samr or siggenes.
>
> --Naomi
>
> At 03:23 PM 2/5/2007, you wrote:
> >Dear All,
> >
> >I've got two conditions and three replicates per condition:
> >A1 A2 A3 B1 B2 B3
> >
> >To test the INTRA VS INTER group variance, I compared the fold changes
> >within group and between groups:
> >
> >length(which(A1/A2 > 5))=686
> >length(which(A1/B1 > 5))=708
> >
> >The fact that this is similar is quite scary! What do you think?
> >
> >Do you know of a package that would show somehow that the noise found above
> >should not prevent me from getting meaningful results with these data?
> >
> >Many thanks in advance for your help,
> >
> >Emmanuel
> >
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>
> Naomi S. Altman                                814-865-3791 (voice)
> Associate Professor
> Dept. of Statistics                              814-863-7114 (fax)
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>
>