Entering edit mode
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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