[BioC] All equal high p-values from limma topTable . What to do ?

[Giulio Di Giovanni]

First of all, thanks to everyone, James, Bjorn, Sean, Stefano etc etc, all 
your suggestions are important.

to James, I wasn't expecting a solution to my problem, only a couple of 
hints, and really your answer fully satisfies me, thanks again.

I checked normalization and QC. I used RMA background correction, robust 
spline within arrays and quantile between arrays, after some try (and 
following limma user's guide) this was the configuration showing best fit 
between channels and between arrays. Density Red and Green plot, step after 
step, looked as quite well normalized. MA plots too were not too bad.
I also received some clue from outside that already there were problems to 
find expressed genes in a previous try in the lab itself, but before to say 
it loudly I would like to be quite sure that I'm not making big mistakes 
since is my first cDNA analysis...


thanks again,


Giulio



>From: Sean Davis <sdavis2 at mail.nih.gov>
>To: Giulio Di Giovanni <perimessaggini at hotmail.com>,Bioconductor 
><bioconductor at stat.math.ethz.ch>
>Subject: Re: [BioC] All equal high p-values from limma topTable . What to 
>do ? Need help...
>Date: Thu, 23 Mar 2006 12:56:45 -0500
>
>
>
>
>On 3/23/06 11:14 AM, "Giulio Di Giovanni" <perimessaggini at hotmail.com>
>wrote:
>
> >
> > Hi I'm looking at the results of an analysis on 4 S. Cerevisiae cDNA 
>arrays,
> > (2 of these have a dye-swap).
> > I followed the example in limma user's guide (that if I'm not wrong is
> > exactly my case).
> >
> > I obtain a topTable of genes of this type
> >
> > Block Row Column ID Name M A t P.Value B
> > 4030 30 11 11 YLR162W :::::1: 1.624122241 6.560234285 4.467743568 
>0.997257765
> > -2.0502737
> > 1254 10 4 11 YDR166C SEC5:::::1: -0.56584704 5.143049435 -3.824556839 
>0.997257
> > 765 -2.61592631
> > 1113 9 4 1 YDR409W :::::1: 1.016356426 4.282139218 3.988554403 
>0.997257765 -2.
> > 683464218
> > 2115 16 10 5 YKL209C STE6:::::1: -0.540528098 7.340027942 -3.533312325 
>0.99725
> > 7765 -2.686586054
> > 5022 37 16 11 YPR070W :::::1: -0.493446684 6.765075537 -3.415141086 
>0.99725776
> > 5 -2.77744221
> > 3642 27 14 3 YOL045W :::::1: -0.678996258 5.185557216 -3.448815561 
>0.997257765
> >  -2.865258763
> > 1602 12 14 3 YNL253W :::::1: -0.542907069 6.019893924 -3.427152231 
>0.997257765
> >  -2.880360788
> > 4177 31 13 1 YNL221C POP1:::::1: -0.49777162 6.787712846 -3.274291885 
>0.997257
> > 765 -2.8886384
> >
> >
> > Where all the p-values are 0.997257765. I read in the topTable help  
>that
> > "if there is no good evidence for differential
> >      expression in the experiment, that it is quite possible for all
> >      the adjusted p-values to be large, even for all of them to be
> >      equal to one."
> >
> > I'm quite astonished ... and now ?
> > This fact implies that is not a good experiment ? Or that data were not 
>well
> > preprocessed ?
>
>Giulio,
>
>These two questions can't be answered by p-values; they should be answered
>by other means.  There are several packages for looking at array quality 
>and
>for preprocessing.
>
> > Or maybe that for that experiment there are no genes
> > significantly differently expressed ?
>
>That is a distinct possibility.  If there are not data quality issues and
>your sample size is large enough, then perhaps there are not detectible
>differences (although this doesn't mean that there ARE NOT differences, 
>just
>that you couldn't see them).
>
>Sean
>