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

[Emmanuel Levy]

Dear Naomi,

Thanks a lot, I guess this is what I was looking for :)
Thanks for the summary too.

Typically do you know how common it is that INTER and INTRA variations
are comparable?

Best,

Emmanuel


On 2/6/07, Naomi Altman <naomi at stat.psu.edu> wrote:
> CyberT compares the experimental noise to the biological
> signal.  Statistically significant genes are those that have signal
> higher than noise.
>
> I think that you are asking about the false detection and
> nondetection rates.  False nondetection will be high if the noise is
> high.  A rough estimate of the false nondetection rate (but not the
> contributing genes) can be made using the qvalue package.
>
> qvalue uses the p-values from cyberT to estimate FDR.  En route, it
> estimate pi-0, the percentage of genes that do NOT differentially express.
> (1-pi-0)x Ngenes = estimated number of genes that do differentially express.
>
> Subtract from this the estimated number of truly differentially
> expressed genes you have detected (1-FDR) x N significant.  You now
> have a rough estimate of how many you missed.  But realistically, the
> more noise in the data, the rougher this estimate is, too.
>
> --Naomi
>
> At 10:57 AM 2/6/2007, Emmanuel Levy wrote:
> >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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> > > >Search the archives:
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> > >
> > > Naomi S. Altman                                814-865-3791 (voice)
> > > Associate Professor
> > > Dept. of Statistics                              814-863-7114 (fax)
> > > Penn State University                         814-865-1348 (Statistics)
> > > University Park, PA 16802-2111
> > >
> > >
> >
> >_______________________________________________
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>
> Naomi S. Altman                                814-865-3791 (voice)
> Associate Professor
> Dept. of Statistics                              814-863-7114 (fax)
> Penn State University                         814-865-1348 (Statistics)
> University Park, PA 16802-2111
>
>