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michael watson IAH-C
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3.4k
@michael-watson-iah-c-378
Last seen 10.1 years ago
Hi
Briefly, I am using R 2.0.1 and limma 1.8.6. I have a double dye-flip
experiment with only four arrays, and my targets and design matrix is
so:
> targets
SlideName File Cy3
Cy5
1 TB_43_97 Untreated Cy3/TB_43_97/results.gpr Untreated
H202
2 TB_43_106 Untreated Cy3/TB_43_106/results.gpr Untreated
H202
3 TB_43_99 Untreated Cy5/TB_43_99/results.gpr H202
Untreated
4 TB_43_101 Untreated Cy5/TB_43_101/results 750vs630.gpr H202
Untreated
> design
[1] 1 1 -1 -1
After running:
> fit <- lmFit(MA, design)
> fit <- eBayes(fit)
> topTable(fit, adjust="fdr")
I find that EVERY single one of my p-values is 0.9999963, apart from
the
last 10 or so which are NA (as all the data for those spots is NA).
Looking at the top gene, it has values of -1.9, -2.9, 2, 1.5, which
when
flipped the right way and given to t.test show a significant result
(for
what it's worth).
So, why are all my p-values 0.9999963? Have I done something wrong
(it
is just after Christmas)? Is my data seriously screwed, or is my
limma/R seriously screwed? Has anyone seen this before?
Cheers
Mick