[BioC] LIMMA on RT-PCR data

[Ryan]

Wow, I wasn't aware that limma could be beneficial for such a small 
number of genes. That's good to know.

On Tue Nov 12 14:50:21 2013, Gordon K Smyth wrote:
> Hi Sandhya,
>
> Yes, limma should work fine on this data, although you are on the
> lower boundary in terms of number of genes.  Theoretically, the
> minimum number of genes for the empirical Bayes procedure to be
> beneficial is three. Four genes is probably the minimum from a
> practical point of view.
>
> You may already know how to use duplicateCorrelation.  If you have a
> simple before vs after paired comparison with some unmatched samples,
> then you could proceed like this:
>
>  design <- model.matrix(~treatment)
>  dupcor <- duplicateCorrelation(y, design, block=patient)
>  fit <- lmFit(y, design, block=patient, correlation=dupcor$consensus)
>  fit <- eBayes(fit)
>  topTable(fit,coef=2)
>
> Be sure to check that dupcor$consensus is greater than zero.
>
> We used this strategy to compare tumour vs normal tissue in the
> presence of unmatched samples in
>
>  http://www.ncbi.nlm.nih.gov/pubmed/17236199
>
> although that was microarray data rather than PCR.
>
> Best wishes
> Gordon
>
>
> ---------- original message ----------
> BioC] LIMMA on RT-PCR data
> Sandhya Pemmasani Kiran sandhya.p at ocimumbio.com
> Tue Nov 12 12:16:25 CET 2013
>
> Dear list,
>
> I have RT-PCR data on 4 genes and 85 samples.
> Can I use 'limma' on this small set of genes.
>
> I want to use limma rather than usual paired t-test because I have
> missing values and I don't want to miss the information available on
> paired samples..
>
> Please advise.
>
>
> Thanks,
> Sandhya
>
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