[BioC] Significant dye bias using limma
naomi at stat.psu.edu
Sat Aug 27 16:41:21 CEST 2005
My recent problems with extremely high dye bias turned out to be due to a
defective dye batch. While it is expensive to do, if all the arrays were
done with a particular batch and an anomalous result is found, it does pay
to redo at least a couple of samples with a new set of reagents.
At 06:53 PM 7/20/2005, Gordon K Smyth wrote:
>The fact that the dye effect is often highly significant is the reason
>that it is recommended to
>include it in the model.
> > Date: Wed, 20 Jul 2005 08:21:23 +1000
> > From: Mark Pinese <z3062573 at student.unsw.edu.au>
> > Subject: [BioC] Significant dye bias using limma
> > To: bioconductor at stat.math.ethz.ch
> > Hello all,
> > I have some questions regarding whether the significant dye bias I'm
> finding in
> > my analyses could be an artefact of my analysis method.
> > I've been using limma to analyse a simple design comparing treatment
> and control
> > cases using dye swaps. As per suggestions in the recent limma Users'
> > I've added an intercept term to the design, and used it to find genes with
> > significant dye effects. limma reports very many significantly
> dye-biased genes
> > (B-values as high as 12.7, 205 genes with B > 5), and very few
> > differentially-expressed genes (highest B = 3.1).
> > I'm using three biological replicates, each hybridised to two
> dye-swapped arrays
> > as technical replicates, on Compugen human 19k cDNA slides.
> > Is such a strong result plausible, or due to me incorrectly analysing
> the data?
> > If so, what major pitfalls could I have blundered into? What sort of
> > diagnostics can I try to test how reliable the model results are?
> > Thanks for your time,
> > Mark Pinese
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Naomi S. Altman 814-865-3791 (voice)
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