Dear All,

I wonder if someone could point me towards some guidance using the package 'sizepower'.
The archives have not netted much in this regard; the vignette was very useful.

I'm doing a prospective power analysis for an array experiment with a matched pair design.

I found the function power.matched(ERO, G0, absMu1, sigmaD, n) useful, however, I have a few questions.

Two issues:
1. I don't have a good feel for the parameter ER0 - mean number of false positives.
Of course I'd like it to be as low as possible, so I simply ran scenarios with setting ER0 at numbers we'd be able to tolerate and calculate power/sample size accordingly.

2. Pardon my ignorance, but I'm also struggling with parameter G0 - anticipated number of genes in the experiment that are not differentially expressed.
At the moment, I'm assuming G0=G i.e. number of gene (probes) tested.
Does G0 need to be based on the number of gene probes on the array (~46,000)? Inspecting a similar paradigm on the same array platform on GEO reveals that only about ~12,000 gene probes are consistently detected. I'm aware that it doesn't make a huge difference (and almost none in practical terms), but I'm curious.

I've left the parameter absMu1 at 1 (as I understand it, this represents effect size, as fold change of at least 2).
My sigmaD parameter comes from a preliminary experiment: gene expression by qPCR, standard deviation of the (log2) differences between pairs.

Thanks for the help.

Cheers, michael

P.S. If I should be using a different package for the task at hand, I'd like to hear such a recommendation as well - thanks.

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> library(sizepower)
> power.matched(1, 12000, 1, 0.731, 20)
$power
[1] 0.9854905

$psi1
[1] 37.42788

> power.matched(1, 46000, 1, 0.731, 20)
$power
[1] 0.9693688

$psi1
[1] 37.42788

> 

J. Michael Salbaum, Ph.D.
Associate Professor
Pennington Biomedical Research Center
Louisiana State University System
6400 Perkins Road
Baton Rouge, LA 70808

(225) 763-2782

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