[BioC] How to determine sample size for second-stage experiments

[Sean Davis]

On 1/26/06 1:20 PM, "Kort, Eric" <Eric.Kort at vai.org> wrote:

> 
> 
> Qunyuan Zhang writes....
>> Hi,
>> 
>> We just finished an initial inverstigation (50000-gene Affymetrix, 15
>> cancered people and 10 normal people). 40 genes' RNA expressional
> levels
>> were found significantly different between the two groups (by two
> sample
>> t tests, p values corrected). We are now planning a second-stage
>> experiment to validate this finding. We want to do power analysis and
>> sample size calculation, especially want to know how many peoples
> should
>> be included in the second-stage experiment.
>> 
>> Besides the function  Power.t.test(), is there any other functions in
>> any packages availabe in bioConductor for this kind of experimantal
>> design problems?
> 
> It depends how you intend to summarize your data.  Will you be
> calculating some type of summary score based on the expression of these
> 40 genes?  If so, you can easily perform a power calculation based on
> the expected means and standard deviations as estimated by your initial
> experiment.  Or will you test each of the 40 genes independently?  In
> that case, you will need to take into account some type of multiple
> comparisons adjustment (e.g. bonferroni, etc.).

Just to add a bit, with only 40 genes, it may be more cost-effective to use
PCR rather than another set of arrays.

Sean