Dear list,
Rather than reinventing the wheel, I hope someone in the community can  
point me in the right direction.

In a pilot study, we have looked at a single xenograft over time  
(0,8,24,48 hours) with or without drug treatment (7 groups). Using  
Illumina WG6 arrays, with biological quadruplicates and controls at  
each time point we have a phenomenal amount of clean signal at 8hr  
(3200 genes q < 0.05), which tapers off at 24 and 48hr (307 and 65  
genes q < 0.05 respectively).
This study is about to grow with many more xenograft lines and more  
drugs, so from this pilot study data, we'd like to work out (1) how  
few replicates we can use, yet still get strong signal, and (2)  
whether we need the matched timepoint controls.

part (1) sounds like a leave-one-out cross validation problem, but i'd  
appreciate anyone else's advice here. I've come across the MCREstimate  
package on BioC, and was wondering if there were any others worth using?
part (2) I can probably assess by comparing DE genes treated vs  
untreated at each timepoint to the values I get comparing treated at  
each timepoint to the time 0 control. It already looks like the  
timepoint controls are very similar to the t0, but there must be a  
more elegant way to quantify this.

Here's where I started re-inventing the wheel:
by randomly selecting 3 arrays per group, repeating the limma  
analysis, and assessing the number of DE genes that remain, I recover  
~ 78% of genes with q<0.05, but with just 2 replicates I recover only  
30% of the genes. I expect that the loss of statistical power, would  
be offset somewhat by the benefits due to moderated t-stats and eBayes  
(since I have 7 groups in the model).

looking for any advice

cheers,
Mark


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Mark Cowley, PhD

Peter Wills Bioinformatics Centre
Garvan Institute of Medical Research
384 Victoria St | Darlinghurst | NSW 2010 | Australia
phone: +61 2 9295 8542 | fax: +61 2 9295 8538 | email: m.cowley@garvan.org.au 
  | web: www.garvan.org.au
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