[BioC] Significant dye bias using limma

michael watson (IAH-C) michael.watson at bbsrc.ac.uk
Thu Jul 21 10:44:27 CEST 2005

I guess the idea is that, as you have included dye-bias in your model,
you can now judge the effects of treatment with impunity.  If you hadn't
included it in your model, then any "treatment" effects you
observed/were reported *could* have been due to dye-effects.  BUT then,
you wouldn't have known your array had significant dye-effects, and
therefore you wouldn't have cared :-p 

Have you looked at the original data?  If you have technical (or
biological) replicates as dye-swaps, what do the numbers look like?  Is
there a good correlation?

-----Original Message-----
From: bioconductor-bounces at stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Mark Pinese
Sent: 21 July 2005 02:08
To: bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] Significant dye bias using limma

Will such a significant bias affect the validity of my treatment effect 
results?  In other words, can I just appreciate that dye bias is 
rampant, then ignore it and confidently extract meaningful statistics 
from my treatment vs control coefficient?


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.
>>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?

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