[BioC] Significant dye bias using limma
johanl at biotech.kth.se
Wed Jul 20 08:28:35 CEST 2005
A way of controling your model would be to plot the genes that you find
to have high b-scores due to dye bias in RI-plots (MA-plots). If they
always tend to have, say a positive M-value, in one of the dyes
(regardless of control of treatment), say the green dye, then you
probably have some genes differentially expressed due to dye bias.
From: bioconductor-bounces at stat.math.ethz.ch
[mailto:bioconductor-bounces at stat.math.ethz.ch] On Behalf Of Mark Pinese
Sent: Wednesday, July 20, 2005 12:21 AM
To: bioconductor at stat.math.ethz.ch
Subject: [BioC] Significant dye bias using limma
I have some questions regarding whether the significant dye bias I'm
my analyses could be an artefact of my analysis method.
I've been using limma to analyse a simple design comparing treatment and
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
significant dye effects. limma reports very many significantly
(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
as technical replicates, on Compugen human 19k cDNA slides.
Is such a strong result plausible, or due to me incorrectly analysing
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,
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