[BioC] LIMMA on RT-PCR data

[Gordon K Smyth]

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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