[BioC] Significant dye bias using limma

[Gordon K Smyth]

The fact that the dye effect is often highly significant is the reason that it is recommended to
include it in the model.

Gordon

> 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' Guide,
> 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 significantly
> 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