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

[Naomi Altman]

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:
> > >http://news.gmane.org/gmane.science.biology.informatics.conductor
> >
> > 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