Ignore previous message!!!!!! Re: [BioC] Large # of significant genes with SAM

[Vincent Detours]

ignore previous message, my finger slipped on the wrong key!!
Sorry! Correct reply is comming soon

Vincent

On Tue,
10 May 2005, Sean Davis wrote:

> Date: Tue, 10 May 2005 11:58:37 -0400
> From: Sean Davis <sdavis2 at mail.nih.gov>
> To: Joern Toedling <toedling at ebi.ac.uk>
> Cc: Vincent Detours <vdetours at ulb.ac.be>,
>      Bioconductor mailing list <bioconductor at stat.math.ethz.ch>
> Subject: Re: [BioC] Large # of significant genes with SAM
>
>
> On May 10, 2005, at 11:35 AM, Joern Toedling wrote:
>
> > Hi Vincent,
> >
> > I imagine such large numbers of differentially expressed genes could
> > arise for various reasons.
> > One issue could be that there are large technical or experimental
> > differences between your tumour and control samples due to scanner
> > settings or hybridisation protocols etc. I would check if after
> > normalisation such large differences between the groups are obvious by
> > using boxplots, Scatter-Plots etc. (many examples for such control
> > procedures can be found on the Bioconductor website , especially on
> > the pages containing material for courses and workshops). If so, you
> > might think about other methods for normalisation or combining the two
> > groups data in another way, if they happen to be too different.
> > Another reason for large differences could be that there might really
> > be huge biological differences between the two groups. For instance,
> > when analyzing T- versus B-lymphocytes, one usually observes large
> > percentages > 20% of differentially expressed genes, since in that
> > case we were comparing very different cell types with each other.
> > However, I would not expect such striking differences between a tumour
> > and the related physiological tissue.
>
> Vincent,
>
> Actually, having a large proportion of differentially-expressed genes
> between tumor and normal is certainly possible.  You got the same
> results with two different data sets if I read your original post
> correctly, so go back to check quality of data, statistical biases,
> etc., but it seems quite possible that your results are correct.  You
> will, of course, have to think about validation strategies, but....
>
> Sean
>

Vincent Detours, Ph.D.
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